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diff --git a/unsupported/Eigen/CXX11/src/Tensor/README.md b/unsupported/Eigen/CXX11/src/Tensor/README.md new file mode 100644 index 000000000..02146527b --- /dev/null +++ b/unsupported/Eigen/CXX11/src/Tensor/README.md @@ -0,0 +1,1757 @@ +# Eigen Tensors + +Tensors are multidimensional arrays of elements. Elements are typically scalars, +but more complex types such as strings are also supported. + +[TOC] + +## Tensor Classes + +You can manipulate a tensor with one of the following classes. They all are in +the namespace ```::Eigen.``` + + +### Class Tensor<data_type, rank> + +This is the class to use to create a tensor and allocate memory for it. The +class is templatized with the tensor datatype, such as float or int, and the +tensor rank. The rank is the number of dimensions, for example rank 2 is a +matrix. + +Tensors of this class are resizable. For example, if you assign a tensor of a +different size to a Tensor, that tensor is resized to match its new value. + +#### Constructor Tensor<data_type, rank>(size0, size1, ...) + +Constructor for a Tensor. The constructor must be passed ```rank``` integers +indicating the sizes of the instance along each of the the ```rank``` +dimensions. + + // Create a tensor of rank 3 of sizes 2, 3, 4. This tensor owns + // memory to hold 24 floating point values (24 = 2 x 3 x 4). + Tensor<float, 3> t_3d(2, 3, 4); + + // Resize t_3d by assigning a tensor of different sizes, but same rank. + t_3d = Tensor<float, 3>(3, 4, 3); + +#### Constructor Tensor<data_type, rank>(size_array) + +Constructor where the sizes for the constructor are specified as an array of +values instead of an explicitly list of parameters. The array type to use is +```Eigen::array<Eigen::Index>```. The array can be constructed automatically +from an initializer list. + + // Create a tensor of strings of rank 2 with sizes 5, 7. + Tensor<string, 2> t_2d({5, 7}); + + +### Class TensorFixedSize<data_type, Sizes<size0, size1, ...>> + +Class to use for tensors of fixed size, where the size is known at compile +time. Fixed sized tensors can provide very fast computations because all their +dimensions are known by the compiler. FixedSize tensors are not resizable. + +If the total number of elements in a fixed size tensor is small enough the +tensor data is held onto the stack and does not cause heap allocation and free. + + // Create a 4 x 3 tensor of floats. + TensorFixedSize<float, Sizes<4, 3>> t_4x3; + +### Class TensorMap<Tensor<data_type, rank>> + +This is the class to use to create a tensor on top of memory allocated and +owned by another part of your code. It allows to view any piece of allocated +memory as a Tensor. Instances of this class do not own the memory where the +data are stored. + +A TensorMap is not resizable because it does not own the memory where its data +are stored. + +#### Constructor TensorMap<Tensor<data_type, rank>>(data, size0, size1, ...) + +Constructor for a Tensor. The constructor must be passed a pointer to the +storage for the data, and "rank" size attributes. The storage has to be +large enough to hold all the data. + + // Map a tensor of ints on top of stack-allocated storage. + int storage[128]; // 2 x 4 x 2 x 8 = 128 + TensorMap<int, 4> t_4d(storage, 2, 4, 2, 8); + + // The same storage can be viewed as a different tensor. + // You can also pass the sizes as an array. + TensorMap<int, 2> t_2d(storage, 16, 8); + + // You can also map fixed-size tensors. Here we get a 1d view of + // the 2d fixed-size tensor. + Tensor<float, Sizes<4, 5>> t_4x3; + TensorMap<float, 1> t_12(t_4x3, 12); + + +#### Class TensorRef + +See Assigning to a TensorRef below. + +## Accessing Tensor Elements + +#### <data_type> tensor(index0, index1...) + +Return the element at position ```(index0, index1...)``` in tensor +```tensor```. You must pass as many parameters as the rank of ```tensor```. +The expression can be used as an l-value to set the value of the element at the +specified position. The value returned is of the datatype of the tensor. + + // Set the value of the element at position (0, 1, 0); + Tensor<float, 3> t_3d(2, 3, 4); + t_3d(0, 1, 0) = 12.0f; + + // Initialize all elements to random values. + for (int i = 0; i < 2; ++i) { + for (int j = 0; j < 3; ++j) { + for (int k = 0; k < 4; ++k) { + t_3d(i, j, k) = ...some random value...; + } + } + } + + // Print elements of a tensor. + for (int i = 0; i < 2; ++i) { + LOG(INFO) << t_3d(i, 0, 0); + } + + +## TensorLayout + +The tensor library supports 2 layouts: ```ColMajor``` (the default) and +```RowMajor```. Only the default column major layout is currently fully +supported, and it is therefore not recommended to attempt to use the row major +layout at the moment. + +The layout of a tensor is optionally specified as part of its type. If not +specified explicitly column major is assumed. + + Tensor<float, 3, ColMajor> col_major; // equivalent to Tensor<float, 3> + TensorMap<Tensor<float, 3, RowMajor> > row_major(data, ...); + +All the arguments to an expression must use the same layout. Attempting to mix +different layouts will result in a compilation error. + +It is possible to change the layout of a tensor or an expression using the +```swap_layout()``` method. Note that this will also reverse the order of the +dimensions. + + Tensor<float, 2, ColMajor> col_major(2, 4); + Tensor<float, 2, RowMajor> row_major(2, 4); + + Tensor<float, 2> col_major_result = col_major; // ok, layouts match + Tensor<float, 2> col_major_result = row_major; // will not compile + + // Simple layout swap + col_major_result = row_major.swap_layout(); + eigen_assert(col_major_result.dimension(0) == 4); + eigen_assert(col_major_result.dimension(1) == 2); + + // Swap the layout and preserve the order of the dimensions + array<int, 2> shuffle(1, 0); + col_major_result = row_major.swap_layout().shuffle(shuffle); + eigen_assert(col_major_result.dimension(0) == 2); + eigen_assert(col_major_result.dimension(1) == 4); + + +## Tensor Operations + +The Eigen Tensor library provides a vast library of operations on Tensors: +numerical operations such as addition and multiplication, geometry operations +such as slicing and shuffling, etc. These operations are available as methods +of the Tensor classes, and in some cases as operator overloads. For example +the following code computes the elementwise addition of two tensors: + + Tensor<float, 3> t1(2, 3, 4); + ...set some values in t1... + Tensor<float, 3> t2(2, 3, 4); + ...set some values in t2... + // Set t3 to the element wise sum of t1 and t2 + Tensor<float, 3> t3 = t1 + t2; + +While the code above looks easy enough, it is important to understand that the +expression ```t1 + t2``` is not actually adding the values of the tensors. The +expression instead constructs a "tensor operator" object of the class +TensorCwiseBinaryOp<scalar_sum>, which has references to the tensors +```t1``` and ```t2```. This is a small C++ object that knows how to add +```t1``` and ```t2```. It is only when the value of the expression is assigned +to the tensor ```t3``` that the addition is actually performed. Technically, +this happens through the overloading of ```operator=()``` in the Tensor class. + +This mechanism for computing tensor expressions allows for lazy evaluation and +optimizations which are what make the tensor library very fast. + +Of course, the tensor operators do nest, and the expression ```t1 + t2 * +0.3f``` is actually represented with the (approximate) tree of operators: + + TensorCwiseBinaryOp<scalar_sum>(t1, TensorCwiseUnaryOp<scalar_mul>(t2, 0.3f)) + + +### Tensor Operations and C++ "auto" + +Because Tensor operations create tensor operators, the C++ ```auto``` keyword +does not have its intuitive meaning. Consider these 2 lines of code: + + Tensor<float, 3> t3 = t1 + t2; + auto t4 = t1 + t2; + +In the first line we allocate the tensor ```t3``` and it will contain the +result of the addition of ```t1``` and ```t2```. In the second line, ```t4``` +is actually the tree of tensor operators that will compute the addition of +```t1``` and ```t2```. In fact, ```t4``` is *not* a tensor and you cannot get +the values of its elements: + + Tensor<float, 3> t3 = t1 + t2; + cout << t3(0, 0, 0); // OK prints the value of t1(0, 0, 0) + t2(0, 0, 0) + + auto t4 = t1 + t2; + cout << t4(0, 0, 0); // Compilation error! + +When you use ```auto``` you do not get a Tensor as a result but instead a +non-evaluated expression. So only use ```auto``` to delay evaluation. + +Unfortunately, there is no single underlying concrete type for holding +non-evaluated expressions, hence you have to use auto in the case when you do +want to hold non-evaluated expressions. + +When you need the results of set of tensor computations you have to assign the +result to a Tensor that will be capable of holding onto them. This can be +either a normal Tensor, a fixed size Tensor, or a TensorMap on an existing +piece of memory. All the following will work: + + auto t4 = t1 + t2; + + Tensor<float, 3> result = t4; // Could also be: result(t4); + cout << result(0, 0, 0); + + TensorMap<float, 4> result(<a float* with enough space>, <size0>, ...) = t4; + cout << result(0, 0, 0); + + TensorFixedSize<float, Sizes<size0, ...>> result = t4; + cout << result(0, 0, 0); + +Until you need the results, you can keep the operation around, and even reuse +it for additional operations. As long as you keep the expression as an +operation, no computation is performed. + + // One way to compute exp((t1 + t2) * 0.2f); + auto t3 = t1 + t2; + auto t4 = t3 * 0.2f; + auto t5 = t4.exp(); + Tensor<float, 3> result = t5; + + // Another way, exactly as efficient as the previous one: + Tensor<float, 3> result = ((t1 + t2) * 0.2f).exp(); + +### Controlling When Expression are Evaluated + +There are several ways to control when expressions are evaluated: + +* Assignment to a Tensor, TensorFixedSize, or TensorMap. +* Use of the eval() method. +* Assignment to a TensorRef. + +#### Assigning to a Tensor, TensorFixedSize, or TensorMap. + +The most common way to evaluate an expression is to assign it to a Tensor. In +the example below, the ```auto``` declarations make the intermediate values +"Operations", not Tensors, and do not cause the expressions to be evaluated. +The assignment to the Tensor ```result``` causes the evaluation of all the +operations. + + auto t3 = t1 + t2; // t3 is an Operation. + auto t4 = t3 * 0.2f; // t4 is an Operation. + auto t5 = t4.exp(); // t5 is an Operation. + Tensor<float, 3> result = t5; // The operations are evaluated. + +If you know the ranks and sizes of the Operation value you can assign the +Operation to a TensorFixedSize instead of a Tensor, which is a bit more +efficient. + + // We know that the result is a 4x4x2 tensor! + TensorFixedSize<float, 4, 4, 2> result = t5; + +Simiarly, assigning an expression to a TensorMap causes its evaluation. Like +tensors of type TensorFixedSize, TensorMaps cannot be resized so they have to +have the rank and sizes of the expression that are assigned to them. + +#### Calling eval(). + +When you compute large composite expressions, you sometimes want to tell Eigen +that an intermediate value in the expression tree is worth evaluating ahead of +time. This is done by inserting a call to the ```eval()``` method of the +expression Operation. + + // The previous example could have been written: + Tensor<float, 3> result = ((t1 + t2) * 0.2f).exp(); + + // If you want to compute (t1 + t2) once ahead of time you can write: + Tensor<float, 3> result = ((t1 + t2).eval() * 0.2f).exp(); + +Semantically, calling ```eval()``` is equivalent to materializing the value of +the expression in a temporary Tensor of the right size. The code above in +effect does: + + // .eval() knows the size! + TensorFixedSize<float, 4, 4, 2> tmp = t1 + t2; + Tensor<float, 3> result = (tmp * 0.2f).exp(); + +Note that the return value of ```eval()``` is itself an Operation, so the +following code does not do what you may think: + + // Here t3 is an evaluation Operation. t3 has not been evaluated yet. + auto t3 = (t1 + t2).eval(); + + // You can use t3 in another expression. Still no evaluation. + auto t4 = (t3 * 0.2f).exp(); + + // The value is evaluated when you assign the Operation to a Tensor, using + // an intermediate tensor to represent t3.x + Tensor<float, 3> result = t4; + +While in the examples above calling ```eval()``` does not make a difference in +performance, in other cases it can make a huge difference. In the expression +below the ```broadcast()``` expression causes the ```X.maximum()``` expression +to be evaluated many times: + + Tensor<...> X ...; + Tensor<...> Y = ((X - X.maximum(depth_dim).reshape(dims2d).broadcast(bcast)) + * beta).exp(); + +Inserting a call to ```eval()``` between the ```maximum()``` and +```reshape()``` calls guarantees that maximum() is only computed once and +greatly speeds-up execution: + + Tensor<...> Y = + ((X - X.maximum(depth_dim).eval().reshape(dims2d).broadcast(bcast)) + * beta).exp(); + +In the other example below, the tensor ```Y``` is both used in the expression +and its assignment. This is an aliasing problem and if the evaluation is not +done in the right order Y will be updated incrementally during the evaluation +resulting in bogus results: + + Tensor<...> Y ...; + Y = Y / (Y.sum(depth_dim).reshape(dims2d).broadcast(bcast)); + +Inserting a call to ```eval()``` between the ```sum()``` and ```reshape()``` +expressions ensures that the sum is computed before any updates to ```Y``` are +done. + + Y = Y / (Y.sum(depth_dim).eval().reshape(dims2d).broadcast(bcast)); + +Note that an eval around the full right hand side expression is not needed +because the generated has to compute the i-th value of the right hand side +before assigning it to the left hand side. + +However, if you were assigning the expression value to a shuffle of ```Y``` +then you would need to force an eval for correctness by adding an ```eval()``` +call for the right hand side: + + Y.shuffle(...) = + (Y / (Y.sum(depth_dim).eval().reshape(dims2d).broadcast(bcast))).eval(); + + +#### Assigning to a TensorRef. + +If you need to access only a few elements from the value of an expression you +can avoid materializing the value in a full tensor by using a TensorRef. + +A TensorRef is a small wrapper class for any Eigen Operation. It provides +overloads for the ```()``` operator that let you access individual values in +the expression. TensorRef is convenient, because the Operation themselves do +not provide a way to access individual elements. + + // Create a TensorRef for the expression. The expression is not + // evaluated yet. + TensorRef<Tensor<float, 3> > ref = ((t1 + t2) * 0.2f).exp(); + + // Use "ref" to access individual elements. The expression is evaluated + // on the fly. + float at_0 = ref(0, 0, 0); + cout << ref(0, 1, 0); + +Only use TensorRef when you need a subset of the values of the expression. +TensorRef only computes the values you access. However note that if you are +going to access all the values it will be much faster to materialize the +results in a Tensor first. + +In some cases, if the full Tensor result would be very large, you may save +memory by accessing it as a TensorRef. But not always. So don't count on it. + + +### Controlling How Expressions Are Evaluated + +The tensor library provides several implementations of the various operations +such as contractions and convolutions. The implementations are optimized for +different environments: single threaded on CPU, multi threaded on CPU, or on a +GPU using cuda. Additional implementations may be added later. + +You can choose which implementation to use with the ```device()``` call. If +you do not choose an implementation explicitly the default implementation that +uses a single thread on the CPU is used. + +The default implementation has been optimized for recent Intel CPUs, taking +advantage of SSE, AVX, and FMA instructions. Work is ongoing to tune the +library on ARM CPUs. Note that you need to pass compiler-dependent flags +to enable the use of SSE, AVX, and other instructions. + +For example, the following code adds two tensors using the default +single-threaded CPU implementation: + + Tensor<float, 2> a(30, 40); + Tensor<float, 2> b(30, 40); + Tensor<float, 2> c = a + b; + +To choose a different implementation you have to insert a ```device()``` call +before the assignment of the result. For technical C++ reasons this requires +that the Tensor for the result be declared on its own. This means that you +have to know the size of the result. + + Eigen::Tensor<float, 2> c(30, 40); + c.device(...) = a + b; + +The call to ```device()``` must be the last call on the left of the operator=. + +You must pass to the ```device()``` call an Eigen device object. There are +presently three devices you can use: DefaultDevice, ThreadPoolDevice and +GpuDevice. + + +#### Evaluating With the DefaultDevice + +This is exactly the same as not inserting a ```device()``` call. + + DefaultDevice my_device; + c.device(my_device) = a + b; + +#### Evaluating with a Thread Pool + + // Create the Eigen ThreadPoolDevice. + Eigen::ThreadPoolDevice my_device(4 /* number of threads to use */); + + // Now just use the device when evaluating expressions. + Eigen::Tensor<float, 2> c(30, 50); + c.device(my_device) = a.contract(b, dot_product_dims); + + +#### Evaluating On GPU + +This is presently a bit more complicated than just using a thread pool device. +You need to create a GPU device but you also need to explicitly allocate the +memory for tensors with cuda. + + +## API Reference + +### Datatypes + +In the documentation of the tensor methods and Operation we mention datatypes +that are tensor-type specific: + +#### <Tensor-Type>::Dimensions + +Acts like an array of ints. Has an ```int size``` attribute, and can be +indexed like an array to access individual values. Used to represent the +dimensions of a tensor. See ```dimensions()```. + +#### <Tensor-Type>::Index + +Acts like an ```int```. Used for indexing tensors along their dimensions. See +```operator()```, ```dimension()```, and ```size()```. + +#### <Tensor-Type>::Scalar + +Represents the datatype of individual tensor elements. For example, for a +```Tensor<float>```, ```Scalar``` is the type ```float```. See +```setConstant()```. + +#### <Operation> + +We use this pseudo type to indicate that a tensor Operation is returned by a +method. We indicate in the text the type and dimensions of the tensor that the +Operation returns after evaluation. + +The Operation will have to be evaluated, for example by assigning it to a +tensor, before you can access the values of the resulting tensor. You can also +access the values through a TensorRef. + + +## Built-in Tensor Methods + +These are usual C++ methods that act on tensors immediately. They are not +Operations which provide delayed evaluation of their results. Unless specified +otherwise, all the methods listed below are available on all tensor classes: +Tensor, TensorFixedSize, and TensorMap. + +## Metadata + +### int NumDimensions + +Constant value indicating the number of dimensions of a Tensor. This is also +known as the tensor "rank". + + Eigen::Tensor<float, 2> a(3, 4); + cout << "Dims " << a.NumDimensions; + => Dims 2 + +### Dimensions dimensions() + +Returns an array-like object representing the dimensions of the tensor. +The actual type of the dimensions() result is <Tensor-Type>::Dimensions. + + Eigen::Tensor<float, 2> a(3, 4); + const Eigen::Tensor<float, 2>::Dimensions& d = a.dimensions(); + cout << "Dim size: " << d.size << ", dim 0: " << d[0] + << ", dim 1: " << d[1]; + => Dim size: 2, dim 0: 3, dim 1: 4 + +If you use a C++11 compiler, you can use ```auto``` to simplify the code: + + const auto& d = a.dimensions(); + cout << "Dim size: " << d.size << ", dim 0: " << d[0] + << ", dim 1: " << d[1]; + => Dim size: 2, dim 0: 3, dim 1: 4 + +### Index dimension(Index n) + +Returns the n-th dimension of the tensor. The actual type of the +```dimension()``` result is ```<Tensor-Type>::Index```, but you can +always use it like an int. + + Eigen::Tensor<float, 2> a(3, 4); + int dim1 = a.dimension(1); + cout << "Dim 1: " << dim1; + => Dim 1: 4 + +### Index size() + +Returns the total number of elements in the tensor. This is the product of all +the tensor dimensions. The actual type of the ```size()``` result is +```<Tensor-Type>::Index```, but you can always use it like an int. + + Eigen::Tensor<float, 2> a(3, 4); + cout << "Size: " << a.size(); + => Size: 12 + + +### Getting Dimensions From An Operation + +A few operations provide ```dimensions()``` directly, +e.g. ```TensorReslicingOp```. Most operations defer calculating dimensions +until the operation is being evaluated. If you need access to the dimensions +of a deferred operation, you can wrap it in a TensorRef (see Assigning to a +TensorRef above), which provides ```dimensions()``` and ```dimension()``` as +above. + +TensorRef can also wrap the plain Tensor types, so this is a useful idiom in +templated contexts where the underlying object could be either a raw Tensor +or some deferred operation (e.g. a slice of a Tensor). In this case, the +template code can wrap the object in a TensorRef and reason about its +dimensionality while remaining agnostic to the underlying type. + + +## Constructors + +### Tensor + +Creates a tensor of the specified size. The number of arguments must be equal +to the rank of the tensor. The content of the tensor is not initialized. + + Eigen::Tensor<float, 2> a(3, 4); + cout << "NumRows: " << a.dimension(0) << " NumCols: " << a.dimension(1) << endl; + => NumRows: 3 NumCols: 4 + +### TensorFixedSize + +Creates a tensor of the specified size. The number of arguments in the Size<> +template parameter determines the rank of the tensor. The content of the tensor +is not initialized. + + Eigen::TensorFixedSize<float, Size<3, 4>> a; + cout << "Rank: " << a.rank() << endl; + => Rank: 2 + cout << "NumRows: " << a.dimension(0) << " NumCols: " << a.dimension(1) << endl; + => NumRows: 3 NumCols: 4 + +### TensorMap + +Creates a tensor mapping an existing array of data. The data must not be freed +until the TensorMap is discarded, and the size of the data must be large enough +to accomodate of the coefficients of the tensor. + + float data[] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}; + Eigen::TensorMap<float, 2> a(data, 3, 4); + cout << "NumRows: " << a.dimension(0) << " NumCols: " << a.dimension(1) << endl; + => NumRows: 3 NumCols: 4 + cout << "a(1, 2): " << a(1, 2) << endl; + => a(1, 2): 9 + + +## Contents Initialization + +When a new Tensor or a new TensorFixedSize are created, memory is allocated to +hold all the tensor elements, but the memory is not initialized. Similarly, +when a new TensorMap is created on top of non-initialized memory the memory its +contents are not initialized. + +You can use one of the methods below to initialize the tensor memory. These +have an immediate effect on the tensor and return the tensor itself as a +result. These are not tensor Operations which delay evaluation. + +### <Tensor-Type> setConstant(const Scalar& val) + +Sets all elements of the tensor to the constant value ```val```. ```Scalar``` +is the type of data stored in the tensor. You can pass any value that is +convertible to that type. + +Returns the tensor itself in case you want to chain another call. + + a.setConstant(12.3f); + cout << "Constant: " << endl << a << endl << endl; + => + Constant: + 12.3 12.3 12.3 12.3 + 12.3 12.3 12.3 12.3 + 12.3 12.3 12.3 12.3 + +Note that ```setConstant()``` can be used on any tensor where the element type +has a copy constructor and an ```operator=()```: + + Eigen::Tensor<string, 2> a(2, 3); + a.setConstant("yolo"); + cout << "String tensor: " << endl << a << endl << endl; + => + String tensor: + yolo yolo yolo + yolo yolo yolo + + +### <Tensor-Type> setZero() + +Fills the tensor with zeros. Equivalent to ```setConstant(Scalar(0))```. +Returns the tensor itself in case you want to chain another call. + + a.setZero(); + cout << "Zeros: " << endl << a << endl << endl; + => + Zeros: + 0 0 0 0 + 0 0 0 0 + 0 0 0 0 + + +### <Tensor-Type> setValues({..initializer_list}) + +Fills the tensor with explicit values specified in a std::initializer_list. +The type of the initializer list depends on the type and rank of the tensor. + +If the tensor has rank N, the initializer list must be nested N times. The +most deeply nested lists must contains P scalars of the Tensor type where P is +the size of the last dimension of the Tensor. + +For example, for a ```TensorFixedSize<float, 2, 3>``` the initializer list must +contains 2 lists of 3 floats each. + +```setValues()``` returns the tensor itself in case you want to chain another +call. + + Eigen::Tensor<float, 2> a(2, 3); + a.setValues({{0.0f, 1.0f, 2.0f}, {3.0f, 4.0f, 5.0f}}); + cout << "a" << endl << a << endl << endl; + => + a + 0 1 2 + 3 4 5 + +If a list is too short, the corresponding elements of the tensor will not be +changed. This is valid at each level of nesting. For example the following +code only sets the values of the first row of the tensor. + + Eigen::Tensor<int, 2> a(2, 3); + a.setConstant(1000); + a.setValues({{10, 20, 30}}); + cout << "a" << endl << a << endl << endl; + => + a + 10 20 30 + 1000 1000 1000 + +### <Tensor-Type> setRandom() + +Fills the tensor with random values. Returns the tensor itself in case you +want to chain another call. + + a.setRandom(); + cout << "Random: " << endl << a << endl << endl; + => + Random: + 0.680375 0.59688 -0.329554 0.10794 + -0.211234 0.823295 0.536459 -0.0452059 + 0.566198 -0.604897 -0.444451 0.257742 + +You can customize ```setRandom()``` by providing your own random number +generator as a template argument: + + a.setRandom<MyRandomGenerator>(); + +Here, ```MyRandomGenerator``` must be a struct with the following member +functions, where Scalar and Index are the same as ```<Tensor-Type>::Scalar``` +and ```<Tensor-Type>::Index```. + +See ```struct UniformRandomGenerator``` in TensorFunctors.h for an example. + + // Custom number generator for use with setRandom(). + struct MyRandomGenerator { + // Default and copy constructors. Both are needed + MyRandomGenerator() { } + MyRandomGenerator(const MyRandomGenerator& ) { } + + // Return a random value to be used. "element_location" is the + // location of the entry to set in the tensor, it can typically + // be ignored. + Scalar operator()(Eigen::DenseIndex element_location, + Eigen::DenseIndex /*unused*/ = 0) const { + return <randomly generated value of type T>; + } + + // Same as above but generates several numbers at a time. + typename internal::packet_traits<Scalar>::type packetOp( + Eigen::DenseIndex packet_location, Eigen::DenseIndex /*unused*/ = 0) const { + return <a packet of randomly generated values>; + } + }; + +You can also use one of the 2 random number generators that are part of the +tensor library: +* UniformRandomGenerator +* NormalRandomGenerator + + +## Data Access + +The Tensor, TensorFixedSize, and TensorRef classes provide the following +accessors to access the tensor coefficients: + + const Scalar& operator()(const array<Index, NumIndices>& indices) + const Scalar& operator()(Index firstIndex, IndexTypes... otherIndices) + Scalar& operator()(const array<Index, NumIndices>& indices) + Scalar& operator()(Index firstIndex, IndexTypes... otherIndices) + +The number of indices must be equal to the rank of the tensor. Moreover, these +accessors are not available on tensor expressions. In order to access the +values of a tensor expression, the expression must either be evaluated or +wrapped in a TensorRef. + + +### Scalar* data() and const Scalar* data() const + +Returns a pointer to the storage for the tensor. The pointer is const if the +tensor was const. This allows direct access to the data. The layout of the +data depends on the tensor layout: RowMajor or ColMajor. + +This access is usually only needed for special cases, for example when mixing +Eigen Tensor code with other libraries. + +Scalar is the type of data stored in the tensor. + + Eigen::Tensor<float, 2> a(3, 4); + float* a_data = a.data(); + a_data[0] = 123.45f; + cout << "a(0, 0): " << a(0, 0); + => a(0, 0): 123.45 + + +## Tensor Operations + +All the methods documented below return non evaluated tensor ```Operations```. +These can be chained: you can apply another Tensor Operation to the value +returned by the method. + +The chain of Operation is evaluated lazily, typically when it is assigned to a +tensor. See "Controlling when Expression are Evaluated" for more details about +their evaluation. + +### <Operation> constant(const Scalar& val) + +Returns a tensor of the same type and dimensions as the original tensor but +where all elements have the value ```val```. + +This is useful, for example, when you want to add or subtract a constant from a +tensor, or multiply every element of a tensor by a scalar. + + Eigen::Tensor<float, 2> a(2, 3); + a.setConstant(1.0f); + Eigen::Tensor<float, 2> b = a + a.constant(2.0f); + Eigen::Tensor<float, 2> c = b * b.constant(0.2f); + cout << "a" << endl << a << endl << endl; + cout << "b" << endl << b << endl << endl; + cout << "c" << endl << c << endl << endl; + => + a + 1 1 1 + 1 1 1 + + b + 3 3 3 + 3 3 3 + + c + 0.6 0.6 0.6 + 0.6 0.6 0.6 + +### <Operation> random() + +Returns a tensor of the same type and dimensions as the current tensor +but where all elements have random values. + +This is for example useful to add random values to an existing tensor. +The generation of random values can be customized in the same manner +as for ```setRandom()```. + + Eigen::Tensor<float, 2> a(2, 3); + a.setConstant(1.0f); + Eigen::Tensor<float, 2> b = a + a.random(); + cout << "a" << endl << a << endl << endl; + cout << "b" << endl << b << endl << endl; + => + a + 1 1 1 + 1 1 1 + + b + 1.68038 1.5662 1.82329 + 0.788766 1.59688 0.395103 + + +## Unary Element Wise Operations + +All these operations take a single input tensor as argument and return a tensor +of the same type and dimensions as the tensor to which they are applied. The +requested operations are applied to each element independently. + +### <Operation> operator-() + +Returns a tensor of the same type and dimensions as the original tensor +containing the opposite values of the original tensor. + + Eigen::Tensor<float, 2> a(2, 3); + a.setConstant(1.0f); + Eigen::Tensor<float, 2> b = -a; + cout << "a" << endl << a << endl << endl; + cout << "b" << endl << b << endl << endl; + => + a + 1 1 1 + 1 1 1 + + b + -1 -1 -1 + -1 -1 -1 + +### <Operation> sqrt() + +Returns a tensor of the same type and dimensions as the original tensor +containing the square roots of the original tensor. + +### <Operation> rsqrt() + +Returns a tensor of the same type and dimensions as the original tensor +containing the inverse square roots of the original tensor. + +### <Operation> square() + +Returns a tensor of the same type and dimensions as the original tensor +containing the squares of the original tensor values. + +### <Operation> inverse() + +Returns a tensor of the same type and dimensions as the original tensor +containing the inverse of the original tensor values. + +### <Operation> exp() + +Returns a tensor of the same type and dimensions as the original tensor +containing the exponential of the original tensor. + +### <Operation> log() + +Returns a tensor of the same type and dimensions as the original tensor +containing the natural logarithms of the original tensor. + +### <Operation> abs() + +Returns a tensor of the same type and dimensions as the original tensor +containing the absolute values of the original tensor. + +### <Operation> pow(Scalar exponent) + +Returns a tensor of the same type and dimensions as the original tensor +containing the coefficients of the original tensor to the power of the +exponent. + +The type of the exponent, Scalar, is always the same as the type of the +tensor coefficients. For example, only integer exponents can be used in +conjuntion with tensors of integer values. + +You can use cast() to lift this restriction. For example this computes +cubic roots of an int Tensor: + + Eigen::Tensor<int, 2> a(2, 3); + a.setValues({{0, 1, 8}, {27, 64, 125}}); + Eigen::Tensor<double, 2> b = a.cast<double>().pow(1.0 / 3.0); + cout << "a" << endl << a << endl << endl; + cout << "b" << endl << b << endl << endl; + => + a + 0 1 8 + 27 64 125 + + b + 0 1 2 + 3 4 5 + +### <Operation> operator * (Scalar scale) + +Multiplies all the coefficients of the input tensor by the provided scale. + +### <Operation> cwiseMax(Scalar threshold) +TODO + +### <Operation> cwiseMin(Scalar threshold) +TODO + +### <Operation> unaryExpr(const CustomUnaryOp& func) +TODO + + +## Binary Element Wise Operations + +These operations take two input tensors as arguments. The 2 input tensors should +be of the same type and dimensions. The result is a tensor of the same +dimensions as the tensors to which they are applied, and unless otherwise +specified it is also of the same type. The requested operations are applied to +each pair of elements independently. + +### <Operation> operator+(const OtherDerived& other) + +Returns a tensor of the same type and dimensions as the input tensors +containing the coefficient wise sums of the inputs. + +### <Operation> operator-(const OtherDerived& other) + +Returns a tensor of the same type and dimensions as the input tensors +containing the coefficient wise differences of the inputs. + +### <Operation> operator*(const OtherDerived& other) + +Returns a tensor of the same type and dimensions as the input tensors +containing the coefficient wise products of the inputs. + +### <Operation> operator/(const OtherDerived& other) + +Returns a tensor of the same type and dimensions as the input tensors +containing the coefficient wise quotients of the inputs. + +This operator is not supported for integer types. + +### <Operation> cwiseMax(const OtherDerived& other) + +Returns a tensor of the same type and dimensions as the input tensors +containing the coefficient wise maximums of the inputs. + +### <Operation> cwiseMin(const OtherDerived& other) + +Returns a tensor of the same type and dimensions as the input tensors +containing the coefficient wise mimimums of the inputs. + +### <Operation> Logical operators + +The following logical operators are supported as well: + +* operator&&(const OtherDerived& other) +* operator||(const OtherDerived& other) +* operator<(const OtherDerived& other) +* operator<=(const OtherDerived& other) +* operator>(const OtherDerived& other) +* operator>=(const OtherDerived& other) +* operator==(const OtherDerived& other) +* operator!=(const OtherDerived& other) + +They all return a tensor of boolean values. + + +## Selection (select(const ThenDerived& thenTensor, const ElseDerived& elseTensor) + +Selection is a coefficient-wise ternary operator that is the tensor equivalent +to the if-then-else operation. + + Tensor<bool, 3> if = ...; + Tensor<float, 3> then = ...; + Tensor<float, 3> else = ...; + Tensor<float, 3> result = if.select(then, else); + +The 3 arguments must be of the same dimensions, which will also be the dimension +of the result. The 'if' tensor must be of type boolean, the 'then' and the +'else' tensor must be of the same type, which will also be the type of the +result. + +Each coefficient in the result is equal to the corresponding coefficient in the +'then' tensor if the corresponding value in the 'if' tensor is true. If not, the +resulting coefficient will come from the 'else' tensor. + + +## Contraction + +Tensor *contractions* are a generalization of the matrix product to the +multidimensional case. + + // Create 2 matrices using tensors of rank 2 + Eigen::Tensor<int, 2> a(2, 3); + a.setValues({{1, 2, 3}, {6, 5, 4}}); + Eigen::Tensor<int, 2> b(3, 2); + a.setValues({{1, 2}, {4, 5}, {5, 6}}); + + // Compute the traditional matrix product + array<IndexPair<int>, 1> product_dims = { IndexPair(1, 0) }; + Eigen::Tensor<int, 2> AB = a.contract(b, product_dims); + + // Compute the product of the transpose of the matrices + array<IndexPair<int>, 1> transpose_product_dims = { IndexPair(0, 1) }; + Eigen::Tensor<int, 2> AtBt = a.contract(b, transposed_product_dims); + + +## Reduction Operations + +A *Reduction* operation returns a tensor with fewer dimensions than the +original tensor. The values in the returned tensor are computed by applying a +*reduction operator* to slices of values from the original tensor. You specify +the dimensions along which the slices are made. + +The Eigen Tensor library provides a set of predefined reduction operators such +as ```maximum()``` and ```sum()``` and lets you define additional operators by +implementing a few methods from a reductor template. + +### Reduction Dimensions + +All reduction operations take a single parameter of type +```<TensorType>::Dimensions``` which can always be specified as an array of +ints. These are called the "reduction dimensions." The values are the indices +of the dimensions of the input tensor over which the reduction is done. The +parameter can have at most as many element as the rank of the input tensor; +each element must be less than the tensor rank, as it indicates one of the +dimensions to reduce. + +Each dimension of the input tensor should occur at most once in the reduction +dimensions as the implementation does not remove duplicates. + +The order of the values in the reduction dimensions does not affect the +results, but the code may execute faster if you list the dimensions in +increasing order. + +Example: Reduction along one dimension. + + // Create a tensor of 2 dimensions + Eigen::Tensor<int, 2> a(2, 3); + a.setValues({{1, 2, 3}, {6, 5, 4}}); + // Reduce it along the second dimension (1)... + Eigen::array<int, 1> dims({1 /* dimension to reduce */}); + // ...using the "maximum" operator. + // The result is a tensor with one dimension. The size of + // that dimension is the same as the first (non-reduced) dimension of a. + Eigen::Tensor<int, 1> b = a.maximum(dims); + cout << "a" << endl << a << endl << endl; + cout << "b" << endl << b << endl << endl; + => + a + 1 2 3 + 6 5 4 + + b + 3 + 6 + +Example: Reduction along two dimensions. + + Eigen::Tensor<float, 3, Eigen::ColMajor> a(2, 3, 4); + a.setValues({{{0.0f, 1.0f, 2.0f, 3.0f}, + {7.0f, 6.0f, 5.0f, 4.0f}, + {8.0f, 9.0f, 10.0f, 11.0f}}, + {{12.0f, 13.0f, 14.0f, 15.0f}, + {19.0f, 18.0f, 17.0f, 16.0f}, + {20.0f, 21.0f, 22.0f, 23.0f}}}); + // The tensor a has 3 dimensions. We reduce along the + // first 2, resulting in a tensor with a single dimension + // of size 4 (the last dimension of a.) + // Note that we pass the array of reduction dimensions + // directly to the maximum() call. + Eigen::Tensor<float, 1, Eigen::ColMajor> b = + a.maximum(Eigen::array<int, 2>({0, 1})); + cout << "b" << endl << b << endl << endl; + => + b + 20 + 21 + 22 + 23 + +#### Reduction along all dimensions + +As a special case, if you pass no parameter to a reduction operation the +original tensor is reduced along *all* its dimensions. The result is a +scalar, represented as a zero-dimension tensor. + + Eigen::Tensor<float, 3> a(2, 3, 4); + a.setValues({{{0.0f, 1.0f, 2.0f, 3.0f}, + {7.0f, 6.0f, 5.0f, 4.0f}, + {8.0f, 9.0f, 10.0f, 11.0f}}, + {{12.0f, 13.0f, 14.0f, 15.0f}, + {19.0f, 18.0f, 17.0f, 16.0f}, + {20.0f, 21.0f, 22.0f, 23.0f}}}); + // Reduce along all dimensions using the sum() operator. + Eigen::Tensor<float, 0> b = a.sum(); + cout << "b" << endl << b << endl << endl; + => + b + 276 + + +### <Operation> sum(const Dimensions& new_dims) +### <Operation> sum() + +Reduce a tensor using the sum() operator. The resulting values +are the sum of the reduced values. + +### <Operation> mean(const Dimensions& new_dims) +### <Operation> mean() + +Reduce a tensor using the mean() operator. The resulting values +are the mean of the reduced values. + +### <Operation> maximum(const Dimensions& new_dims) +### <Operation> maximum() + +Reduce a tensor using the maximum() operator. The resulting values are the +largest of the reduced values. + +### <Operation> minimum(const Dimensions& new_dims) +### <Operation> minimum() + +Reduce a tensor using the minimum() operator. The resulting values +are the smallest of the reduced values. + +### <Operation> prod(const Dimensions& new_dims) +### <Operation> prod() + +Reduce a tensor using the prod() operator. The resulting values +are the product of the reduced values. + +### <Operation> all(const Dimensions& new_dims) +### <Operation> all() +Reduce a tensor using the all() operator. Casts tensor to bool and then checks +whether all elements are true. Runs through all elements rather than +short-circuiting, so may be significantly inefficient. + +### <Operation> any(const Dimensions& new_dims) +### <Operation> any() +Reduce a tensor using the any() operator. Casts tensor to bool and then checks +whether any element is true. Runs through all elements rather than +short-circuiting, so may be significantly inefficient. + + +### <Operation> reduce(const Dimensions& new_dims, const Reducer& reducer) + +Reduce a tensor using a user-defined reduction operator. See ```SumReducer``` +in TensorFunctors.h for information on how to implement a reduction operator. + + +## Scan Operations + +A *Scan* operation returns a tensor with the same dimensions as the original +tensor. The operation performs an inclusive scan along the specified +axis, which means it computes a running total along the axis for a given +reduction operation. +If the reduction operation corresponds to summation, then this computes the +prefix sum of the tensor along the given axis. + +Example: +dd a comment to this line + + // Create a tensor of 2 dimensions + Eigen::Tensor<int, 2> a(2, 3); + a.setValues({{1, 2, 3}, {4, 5, 6}}); + // Scan it along the second dimension (1) using summation + Eigen::Tensor<int, 2> b = a.cumsum(1); + // The result is a tensor with the same size as the input + cout << "a" << endl << a << endl << endl; + cout << "b" << endl << b << endl << endl; + => + a + 1 2 3 + 6 5 4 + + b + 1 3 6 + 4 9 15 + +### <Operation> cumsum(const Index& axis) + +Perform a scan by summing consecutive entries. + +### <Operation> cumprod(const Index& axis) + +Perform a scan by multiplying consecutive entries. + + +## Convolutions + +### <Operation> convolve(const Kernel& kernel, const Dimensions& dims) + +Returns a tensor that is the output of the convolution of the input tensor with the kernel, +along the specified dimensions of the input tensor. The dimension size for dimensions of the output tensor +which were part of the convolution will be reduced by the formula: +output_dim_size = input_dim_size - kernel_dim_size + 1 (requires: input_dim_size >= kernel_dim_size). +The dimension sizes for dimensions that were not part of the convolution will remain the same. +Performance of the convolution can depend on the length of the stride(s) of the input tensor dimension(s) along which the +convolution is computed (the first dimension has the shortest stride for ColMajor, whereas RowMajor's shortest stride is +for the last dimension). + + // Compute convolution along the second and third dimension. + Tensor<float, 4, DataLayout> input(3, 3, 7, 11); + Tensor<float, 2, DataLayout> kernel(2, 2); + Tensor<float, 4, DataLayout> output(3, 2, 6, 11); + input.setRandom(); + kernel.setRandom(); + + Eigen::array<ptrdiff_t, 2> dims({1, 2}); // Specify second and third dimension for convolution. + output = input.convolve(kernel, dims); + + for (int i = 0; i < 3; ++i) { + for (int j = 0; j < 2; ++j) { + for (int k = 0; k < 6; ++k) { + for (int l = 0; l < 11; ++l) { + const float result = output(i,j,k,l); + const float expected = input(i,j+0,k+0,l) * kernel(0,0) + + input(i,j+1,k+0,l) * kernel(1,0) + + input(i,j+0,k+1,l) * kernel(0,1) + + input(i,j+1,k+1,l) * kernel(1,1); + VERIFY_IS_APPROX(result, expected); + } + } + } + } + + +## Geometrical Operations + +These operations return a Tensor with different dimensions than the original +Tensor. They can be used to access slices of tensors, see them with different +dimensions, or pad tensors with additional data. + +### <Operation> reshape(const Dimensions& new_dims) + +Returns a view of the input tensor that has been reshaped to the specified +new dimensions. The argument new_dims is an array of Index values. The +rank of the resulting tensor is equal to the number of elements in new_dims. + +The product of all the sizes in the new dimension array must be equal to +the number of elements in the input tensor. + + // Increase the rank of the input tensor by introducing a new dimension + // of size 1. + Tensor<float, 2> input(7, 11); + array<int, 3> three_dims{{7, 11, 1}}; + Tensor<float, 3> result = input.reshape(three_dims); + + // Decrease the rank of the input tensor by merging 2 dimensions; + array<int, 1> one_dim{{7 * 11}}; + Tensor<float, 1> result = input.reshape(one_dim); + +This operation does not move any data in the input tensor, so the resulting +contents of a reshaped Tensor depend on the data layout of the original Tensor. + +For example this is what happens when you ```reshape()``` a 2D ColMajor tensor +to one dimension: + + Eigen::Tensor<float, 2, Eigen::ColMajor> a(2, 3); + a.setValues({{0.0f, 100.0f, 200.0f}, {300.0f, 400.0f, 500.0f}}); + Eigen::array<Eigen::DenseIndex, 1> one_dim({3 * 2}); + Eigen::Tensor<float, 1, Eigen::ColMajor> b = a.reshape(one_dim); + cout << "b" << endl << b << endl; + => + b + 0 + 300 + 100 + 400 + 200 + 500 + +This is what happens when the 2D Tensor is RowMajor: + + Eigen::Tensor<float, 2, Eigen::RowMajor> a(2, 3); + a.setValues({{0.0f, 100.0f, 200.0f}, {300.0f, 400.0f, 500.0f}}); + Eigen::array<Eigen::DenseIndex, 1> one_dim({3 * 2}); + Eigen::Tensor<float, 1, Eigen::RowMajor> b = a.reshape(one_dim); + cout << "b" << endl << b << endl; + => + b + 0 + 100 + 200 + 300 + 400 + 500 + +The reshape operation is a lvalue. In other words, it can be used on the left +side of the assignment operator. + +The previous example can be rewritten as follow: + + Eigen::Tensor<float, 2, Eigen::ColMajor> a(2, 3); + a.setValues({{0.0f, 100.0f, 200.0f}, {300.0f, 400.0f, 500.0f}}); + Eigen::array<Eigen::DenseIndex, 2> two_dim({2, 3}); + Eigen::Tensor<float, 1, Eigen::ColMajor> b; + b.reshape(two_dim) = a; + cout << "b" << endl << b << endl; + => + b + 0 + 300 + 100 + 400 + 200 + 500 + +Note that "b" itself was not reshaped but that instead the assignment is done to +the reshape view of b. + + +### <Operation> shuffle(const Shuffle& shuffle) + +Returns a copy of the input tensor whose dimensions have been +reordered according to the specified permutation. The argument shuffle +is an array of Index values. Its size is the rank of the input +tensor. It must contain a permutation of 0, 1, ..., rank - 1. The i-th +dimension of the output tensor equals to the size of the shuffle[i]-th +dimension of the input tensor. For example: + + // Shuffle all dimensions to the left by 1. + Tensor<float, 3> input(20, 30, 50); + // ... set some values in input. + Tensor<float, 3> output = input.shuffle({1, 2, 0}) + + eigen_assert(output.dimension(0) == 30); + eigen_assert(output.dimension(1) == 50); + eigen_assert(output.dimension(2) == 20); + +Indices into the output tensor are shuffled accordingly to formulate +indices into the input tensor. For example, one can assert in the above +code snippet that: + + eigen_assert(output(3, 7, 11) == input(11, 3, 7)); + +In general, one can assert that + + eigen_assert(output(..., indices[shuffle[i]], ...) == + input(..., indices[i], ...)) + +The shuffle operation results in a lvalue, which means that it can be assigned +to. In other words, it can be used on the left side of the assignment operator. + +Let's rewrite the previous example to take advantage of this feature: + + // Shuffle all dimensions to the left by 1. + Tensor<float, 3> input(20, 30, 50); + // ... set some values in input. + Tensor<float, 3> output(30, 50, 20); + output.shuffle({2, 0, 1}) = input; + + +### <Operation> stride(const Strides& strides) + +Returns a view of the input tensor that strides (skips stride-1 +elements) along each of the dimensions. The argument strides is an +array of Index values. The dimensions of the resulting tensor are +ceil(input_dimensions[i] / strides[i]). + +For example this is what happens when you ```stride()``` a 2D tensor: + + Eigen::Tensor<int, 2> a(4, 3); + a.setValues({{0, 100, 200}, {300, 400, 500}, {600, 700, 800}, {900, 1000, 1100}}); + Eigen::array<Eigen::DenseIndex, 2> strides({3, 2}); + Eigen::Tensor<int, 2> b = a.stride(strides); + cout << "b" << endl << b << endl; + => + b + 0 200 + 900 1100 + +It is possible to assign a tensor to a stride: + Tensor<float, 3> input(20, 30, 50); + // ... set some values in input. + Tensor<float, 3> output(40, 90, 200); + output.stride({2, 3, 4}) = input; + + +### <Operation> slice(const StartIndices& offsets, const Sizes& extents) + +Returns a sub-tensor of the given tensor. For each dimension i, the slice is +made of the coefficients stored between offset[i] and offset[i] + extents[i] in +the input tensor. + + Eigen::Tensor<int, 2> a(4, 3); + a.setValues({{0, 100, 200}, {300, 400, 500}, + {600, 700, 800}, {900, 1000, 1100}}); + Eigen::array<int, 2> offsets = {1, 0}; + Eigen::array<int, 2> extents = {2, 2}; + Eigen::Tensor<int, 1> slice = a.slice(offsets, extents); + cout << "a" << endl << a << endl; + => + a + 0 100 200 + 300 400 500 + 600 700 800 + 900 1000 1100 + cout << "slice" << endl << slice << endl; + => + slice + 300 400 + 600 700 + + +### <Operation> chip(const Index offset, const Index dim) + +A chip is a special kind of slice. It is the subtensor at the given offset in +the dimension dim. The returned tensor has one fewer dimension than the input +tensor: the dimension dim is removed. + +For example, a matrix chip would be either a row or a column of the input +matrix. + + Eigen::Tensor<int, 2> a(4, 3); + a.setValues({{0, 100, 200}, {300, 400, 500}, + {600, 700, 800}, {900, 1000, 1100}}); + Eigen::Tensor<int, 1> row_3 = a.chip(2, 0); + Eigen::Tensor<int, 1> col_2 = a.chip(1, 1); + cout << "a" << endl << a << endl; + => + a + 0 100 200 + 300 400 500 + 600 700 800 + 900 1000 1100 + cout << "row_3" << endl << row_3 << endl; + => + row_3 + 600 700 800 + cout << "col_2" << endl << col_2 << endl; + => + col_2 + 100 400 700 1000 + +It is possible to assign values to a tensor chip since the chip operation is a +lvalue. For example: + + Eigen::Tensor<int, 1> a(3); + a.setValues({{100, 200, 300}}); + Eigen::Tensor<int, 2> b(2, 3); + b.setZero(); + b.chip(0, 0) = a; + cout << "a" << endl << a << endl; + => + a + 100 + 200 + 300 + cout << "b" << endl << b << endl; + => + b + 100 200 300 + 0 0 0 + + +### <Operation> reverse(const ReverseDimensions& reverse) + +Returns a view of the input tensor that reverses the order of the coefficients +along a subset of the dimensions. The argument reverse is an array of boolean +values that indicates whether or not the order of the coefficients should be +reversed along each of the dimensions. This operation preserves the dimensions +of the input tensor. + +For example this is what happens when you ```reverse()``` the first dimension +of a 2D tensor: + + Eigen::Tensor<int, 2> a(4, 3); + a.setValues({{0, 100, 200}, {300, 400, 500}, + {600, 700, 800}, {900, 1000, 1100}}); + Eigen::array<bool, 2> reverse({true, false}); + Eigen::Tensor<int, 2> b = a.reverse(reverse); + cout << "a" << endl << a << endl << "b" << endl << b << endl; + => + a + 0 100 200 + 300 400 500 + 600 700 800 + 900 1000 1100 + b + 900 1000 1100 + 600 700 800 + 300 400 500 + 0 100 200 + + +### <Operation> broadcast(const Broadcast& broadcast) + +Returns a view of the input tensor in which the input is replicated one to many +times. +The broadcast argument specifies how many copies of the input tensor need to be +made in each of the dimensions. + + Eigen::Tensor<int, 2> a(2, 3); + a.setValues({{0, 100, 200}, {300, 400, 500}}); + Eigen::array<int, 2> bcast({3, 2}); + Eigen::Tensor<int, 2> b = a.broadcast(bcast); + cout << "a" << endl << a << endl << "b" << endl << b << endl; + => + a + 0 100 200 + 300 400 500 + b + 0 100 200 0 100 200 + 300 400 500 300 400 500 + 0 100 200 0 100 200 + 300 400 500 300 400 500 + 0 100 200 0 100 200 + 300 400 500 300 400 500 + +### <Operation> concatenate(const OtherDerived& other, Axis axis) + +TODO + +### <Operation> pad(const PaddingDimensions& padding) + +Returns a view of the input tensor in which the input is padded with zeros. + + Eigen::Tensor<int, 2> a(2, 3); + a.setValues({{0, 100, 200}, {300, 400, 500}}); + Eigen::array<pair<int, int>, 2> paddings; + paddings[0] = make_pair(0, 1); + paddings[1] = make_pair(2, 3); + Eigen::Tensor<int, 2> b = a.pad(paddings); + cout << "a" << endl << a << endl << "b" << endl << b << endl; + => + a + 0 100 200 + 300 400 500 + b + 0 0 0 0 + 0 0 0 0 + 0 100 200 0 + 300 400 500 0 + 0 0 0 0 + 0 0 0 0 + 0 0 0 0 + + +### <Operation> extract_patches(const PatchDims& patch_dims) + +Returns a tensor of coefficient patches extracted from the input tensor, where +each patch is of dimension specified by 'patch_dims'. The returned tensor has +one greater dimension than the input tensor, which is used to index each patch. +The patch index in the output tensor depends on the data layout of the input +tensor: the patch index is the last dimension ColMajor layout, and the first +dimension in RowMajor layout. + +For example, given the following input tensor: + + Eigen::Tensor<float, 2, DataLayout> tensor(3,4); + tensor.setValues({{0.0f, 1.0f, 2.0f, 3.0f}, + {4.0f, 5.0f, 6.0f, 7.0f}, + {8.0f, 9.0f, 10.0f, 11.0f}}); + + cout << "tensor: " << endl << tensor << endl; +=> +tensor: + 0 1 2 3 + 4 5 6 7 + 8 9 10 11 + +Six 2x2 patches can be extracted and indexed using the following code: + + Eigen::Tensor<float, 3, DataLayout> patch; + Eigen::array<ptrdiff_t, 2> patch_dims; + patch_dims[0] = 2; + patch_dims[1] = 2; + patch = tensor.extract_patches(patch_dims); + for (int k = 0; k < 6; ++k) { + cout << "patch index: " << k << endl; + for (int i = 0; i < 2; ++i) { + for (int j = 0; j < 2; ++j) { + if (DataLayout == ColMajor) { + cout << patch(i, j, k) << " "; + } else { + cout << patch(k, i, j) << " "; + } + } + cout << endl; + } + } + +This code results in the following output when the data layout is ColMajor: + +patch index: 0 +0 1 +4 5 +patch index: 1 +4 5 +8 9 +patch index: 2 +1 2 +5 6 +patch index: 3 +5 6 +9 10 +patch index: 4 +2 3 +6 7 +patch index: 5 +6 7 +10 11 + +This code results in the following output when the data layout is RowMajor: +(NOTE: the set of patches is the same as in ColMajor, but are indexed differently). + +patch index: 0 +0 1 +4 5 +patch index: 1 +1 2 +5 6 +patch index: 2 +2 3 +6 7 +patch index: 3 +4 5 +8 9 +patch index: 4 +5 6 +9 10 +patch index: 5 +6 7 +10 11 + +### <Operation> extract_image_patches(const Index patch_rows, const Index patch_cols, + const Index row_stride, const Index col_stride, + const PaddingType padding_type) + +Returns a tensor of coefficient image patches extracted from the input tensor, +which is expected to have dimensions ordered as follows (depending on the data +layout of the input tensor, and the number of additional dimensions 'N'): + +*) ColMajor +1st dimension: channels (of size d) +2nd dimension: rows (of size r) +3rd dimension: columns (of size c) +4th-Nth dimension: time (for video) or batch (for bulk processing). + +*) RowMajor (reverse order of ColMajor) +1st-Nth dimension: time (for video) or batch (for bulk processing). +N+1'th dimension: columns (of size c) +N+2'th dimension: rows (of size r) +N+3'th dimension: channels (of size d) + +The returned tensor has one greater dimension than the input tensor, which is +used to index each patch. The patch index in the output tensor depends on the +data layout of the input tensor: the patch index is the 4'th dimension in +ColMajor layout, and the 4'th from the last dimension in RowMajor layout. + +For example, given the following input tensor with the following dimension +sizes: + *) depth: 2 + *) rows: 3 + *) columns: 5 + *) batch: 7 + + Tensor<float, 4> tensor(2,3,5,7); + Tensor<float, 4, RowMajor> tensor_row_major = tensor.swap_layout(); + +2x2 image patches can be extracted and indexed using the following code: + +*) 2D patch: ColMajor (patch indexed by second-to-last dimension) + Tensor<float, 5> twod_patch; + twod_patch = tensor.extract_image_patches<2, 2>(); + // twod_patch.dimension(0) == 2 + // twod_patch.dimension(1) == 2 + // twod_patch.dimension(2) == 2 + // twod_patch.dimension(3) == 3*5 + // twod_patch.dimension(4) == 7 + +*) 2D patch: RowMajor (patch indexed by the second dimension) + Tensor<float, 5, RowMajor> twod_patch_row_major; + twod_patch_row_major = tensor_row_major.extract_image_patches<2, 2>(); + // twod_patch_row_major.dimension(0) == 7 + // twod_patch_row_major.dimension(1) == 3*5 + // twod_patch_row_major.dimension(2) == 2 + // twod_patch_row_major.dimension(3) == 2 + // twod_patch_row_major.dimension(4) == 2 + +## Special Operations + +### <Operation> cast<T>() + +Returns a tensor of type T with the same dimensions as the original tensor. +The returned tensor contains the values of the original tensor converted to +type T. + + Eigen::Tensor<float, 2> a(2, 3); + Eigen::Tensor<int, 2> b = a.cast<int>(); + +This can be useful for example if you need to do element-wise division of +Tensors of integers. This is not currently supported by the Tensor library +but you can easily cast the tensors to floats to do the division: + + Eigen::Tensor<int, 2> a(2, 3); + a.setValues({{0, 1, 2}, {3, 4, 5}}); + Eigen::Tensor<int, 2> b = + (a.cast<float>() / a.constant(2).cast<float>()).cast<int>(); + cout << "a" << endl << a << endl << endl; + cout << "b" << endl << b << endl << endl; + => + a + 0 1 2 + 3 4 5 + + b + 0 0 1 + 1 2 2 + + +### <Operation> eval() + +TODO + + +## Representation of scalar values + +Scalar values are often represented by tensors of size 1 and rank 1. It would be +more logical and user friendly to use tensors of rank 0 instead. For example +Tensor<T, N>::maximum() currently returns a Tensor<T, 1>. Similarly, the inner +product of 2 1d tensors (through contractions) returns a 1d tensor. In the +future these operations might be updated to return 0d tensors instead. + +## Limitations + +* The number of tensor dimensions is currently limited to 250 when using a + compiler that supports cxx11. It is limited to only 5 for older compilers. +* The IndexList class requires a cxx11 compliant compiler. You can use an + array of indices instead if you don't have access to a modern compiler. +* On GPUs only floating point values are properly tested and optimized for. +* Complex and integer values are known to be broken on GPUs. If you try to use + them you'll most likely end up triggering a static assertion failure such as + EIGEN_STATIC_ASSERT(packetSize > 1, YOU_MADE_A_PROGRAMMING_MISTAKE) + + diff --git a/unsupported/Eigen/CXX11/src/Tensor/Tensor.h b/unsupported/Eigen/CXX11/src/Tensor/Tensor.h new file mode 100644 index 000000000..1940a9692 --- /dev/null +++ b/unsupported/Eigen/CXX11/src/Tensor/Tensor.h @@ -0,0 +1,527 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> +// Copyright (C) 2013 Christian Seiler <christian@iwakd.de> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_CXX11_TENSOR_TENSOR_H +#define EIGEN_CXX11_TENSOR_TENSOR_H + +namespace Eigen { + +/** \class Tensor + * \ingroup CXX11_Tensor_Module + * + * \brief The tensor class. + * + * The %Tensor class is the work-horse for all \em dense tensors within Eigen. + * + * The %Tensor class encompasses only dynamic-size objects so far. + * + * The first two template parameters are required: + * \tparam Scalar_ \anchor tensor_tparam_scalar Numeric type, e.g. float, double, int or std::complex<float>. + * User defined scalar types are supported as well (see \ref user_defined_scalars "here"). + * \tparam NumIndices_ Number of indices (i.e. rank of the tensor) + * + * The remaining template parameters are optional -- in most cases you don't have to worry about them. + * \tparam Options_ \anchor tensor_tparam_options A combination of either \b #RowMajor or \b #ColMajor, and of either + * \b #AutoAlign or \b #DontAlign. + * The former controls \ref TopicStorageOrders "storage order", and defaults to column-major. The latter controls alignment, which is required + * for vectorization. It defaults to aligning tensors. Note that tensors currently do not support any operations that profit from vectorization. + * Support for such operations (i.e. adding two tensors etc.) is planned. + * + * You can access elements of tensors using normal subscripting: + * + * \code + * Eigen::Tensor<double, 4> t(10, 10, 10, 10); + * t(0, 1, 2, 3) = 42.0; + * \endcode + * + * This class can be extended with the help of the plugin mechanism described on the page + * \ref TopicCustomizingEigen by defining the preprocessor symbol \c EIGEN_TENSOR_PLUGIN. + * + * <i><b>Some notes:</b></i> + * + * <dl> + * <dt><b>Relation to other parts of Eigen:</b></dt> + * <dd>The midterm developement goal for this class is to have a similar hierarchy as Eigen uses for matrices, so that + * taking blocks or using tensors in expressions is easily possible, including an interface with the vector/matrix code + * by providing .asMatrix() and .asVector() (or similar) methods for rank 2 and 1 tensors. However, currently, the %Tensor + * class does not provide any of these features and is only available as a stand-alone class that just allows for + * coefficient access. Also, when fixed-size tensors are implemented, the number of template arguments is likely to + * change dramatically.</dd> + * </dl> + * + * \ref TopicStorageOrders + */ + +template<typename Scalar_, int NumIndices_, int Options_, typename IndexType_> +class Tensor : public TensorBase<Tensor<Scalar_, NumIndices_, Options_, IndexType_> > +{ + public: + typedef Tensor<Scalar_, NumIndices_, Options_, IndexType_> Self; + typedef TensorBase<Tensor<Scalar_, NumIndices_, Options_, IndexType_> > Base; + typedef typename Eigen::internal::nested<Self>::type Nested; + typedef typename internal::traits<Self>::StorageKind StorageKind; + typedef typename internal::traits<Self>::Index Index; + typedef Scalar_ Scalar; + typedef typename NumTraits<Scalar>::Real RealScalar; + typedef typename Base::CoeffReturnType CoeffReturnType; + + enum { + IsAligned = bool(EIGEN_MAX_ALIGN_BYTES>0) & !(Options_&DontAlign), + Layout = Options_ & RowMajor ? RowMajor : ColMajor, + CoordAccess = true, + RawAccess = true + }; + + static const int Options = Options_; + static const int NumIndices = NumIndices_; + typedef DSizes<Index, NumIndices_> Dimensions; + + protected: + TensorStorage<Scalar, Dimensions, Options> m_storage; + +#ifdef EIGEN_HAS_SFINAE + template<typename CustomIndices> + struct isOfNormalIndex{ + static const bool is_array = internal::is_base_of<array<Index, NumIndices>, CustomIndices>::value; + static const bool is_int = NumTraits<CustomIndices>::IsInteger; + static const bool value = is_array | is_int; + }; +#endif + + public: + // Metadata + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index rank() const { return NumIndices; } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index dimension(std::size_t n) const { return m_storage.dimensions()[n]; } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_storage.dimensions(); } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index size() const { return m_storage.size(); } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar *data() { return m_storage.data(); } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar *data() const { return m_storage.data(); } + + // This makes EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED + // work, because that uses base().coeffRef() - and we don't yet + // implement a similar class hierarchy + inline Self& base() { return *this; } + inline const Self& base() const { return *this; } + +#if EIGEN_HAS_VARIADIC_TEMPLATES + template<typename... IndexTypes> + EIGEN_DEVICE_FUNC inline const Scalar& coeff(Index firstIndex, Index secondIndex, IndexTypes... otherIndices) const + { + // The number of indices used to access a tensor coefficient must be equal to the rank of the tensor. + EIGEN_STATIC_ASSERT(sizeof...(otherIndices) + 2 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE) + return coeff(array<Index, NumIndices>{{firstIndex, secondIndex, otherIndices...}}); + } +#endif + + // normal indices + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar& coeff(const array<Index, NumIndices>& indices) const + { + eigen_internal_assert(checkIndexRange(indices)); + return m_storage.data()[linearizedIndex(indices)]; + } + + // custom indices +#ifdef EIGEN_HAS_SFINAE + template<typename CustomIndices, + EIGEN_SFINAE_ENABLE_IF( !(isOfNormalIndex<CustomIndices>::value) ) + > + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar& coeff(CustomIndices& indices) const + { + return coeff(internal::customIndices2Array<Index,NumIndices>(indices)); + } +#endif + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar& coeff() const + { + EIGEN_STATIC_ASSERT(NumIndices == 0, YOU_MADE_A_PROGRAMMING_MISTAKE); + return m_storage.data()[0]; + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar& coeff(Index index) const + { + eigen_internal_assert(index >= 0 && index < size()); + return m_storage.data()[index]; + } + +#if EIGEN_HAS_VARIADIC_TEMPLATES + template<typename... IndexTypes> + inline Scalar& coeffRef(Index firstIndex, Index secondIndex, IndexTypes... otherIndices) + { + // The number of indices used to access a tensor coefficient must be equal to the rank of the tensor. + EIGEN_STATIC_ASSERT(sizeof...(otherIndices) + 2 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE) + return coeffRef(array<Index, NumIndices>{{firstIndex, secondIndex, otherIndices...}}); + } +#endif + + // normal indices + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef(const array<Index, NumIndices>& indices) + { + eigen_internal_assert(checkIndexRange(indices)); + return m_storage.data()[linearizedIndex(indices)]; + } + + // custom indices +#ifdef EIGEN_HAS_SFINAE + template<typename CustomIndices, + EIGEN_SFINAE_ENABLE_IF( !(isOfNormalIndex<CustomIndices>::value) ) + > + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef(CustomIndices& indices) + { + return coeffRef(internal::customIndices2Array<Index,NumIndices>(indices)); + } +#endif + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef() + { + EIGEN_STATIC_ASSERT(NumIndices == 0, YOU_MADE_A_PROGRAMMING_MISTAKE); + return m_storage.data()[0]; + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef(Index index) + { + eigen_internal_assert(index >= 0 && index < size()); + return m_storage.data()[index]; + } + +#if EIGEN_HAS_VARIADIC_TEMPLATES + template<typename... IndexTypes> + inline const Scalar& operator()(Index firstIndex, Index secondIndex, IndexTypes... otherIndices) const + { + // The number of indices used to access a tensor coefficient must be equal to the rank of the tensor. + EIGEN_STATIC_ASSERT(sizeof...(otherIndices) + 2 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE) + return this->operator()(array<Index, NumIndices>{{firstIndex, secondIndex, otherIndices...}}); + } +#else + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const Scalar& operator()(Index i0, Index i1) const + { + return coeff(array<Index, 2>(i0, i1)); + } + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const Scalar& operator()(Index i0, Index i1, Index i2) const + { + return coeff(array<Index, 3>(i0, i1, i2)); + } + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const Scalar& operator()(Index i0, Index i1, Index i2, Index i3) const + { + return coeff(array<Index, 4>(i0, i1, i2, i3)); + } + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const Scalar& operator()(Index i0, Index i1, Index i2, Index i3, Index i4) const + { + return coeff(array<Index, 5>(i0, i1, i2, i3, i4)); + } +#endif + + // custom indices +#ifdef EIGEN_HAS_SFINAE + template<typename CustomIndices, + EIGEN_SFINAE_ENABLE_IF( !(isOfNormalIndex<CustomIndices>::value) ) + > + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar& operator()(CustomIndices& indices) const + { + return coeff(internal::customIndices2Array<Index,NumIndices>(indices)); + } +#endif + + // normal indices + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar& operator()(const array<Index, NumIndices>& indices) const + { + return coeff(indices); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar& operator()(Index index) const + { + eigen_internal_assert(index >= 0 && index < size()); + return coeff(index); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar& operator()() const + { + EIGEN_STATIC_ASSERT(NumIndices == 0, YOU_MADE_A_PROGRAMMING_MISTAKE); + return coeff(); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar& operator[](Index index) const + { + // The bracket operator is only for vectors, use the parenthesis operator instead. + EIGEN_STATIC_ASSERT(NumIndices == 1, YOU_MADE_A_PROGRAMMING_MISTAKE); + return coeff(index); + } + +#if EIGEN_HAS_VARIADIC_TEMPLATES + template<typename... IndexTypes> + inline Scalar& operator()(Index firstIndex, Index secondIndex, IndexTypes... otherIndices) + { + // The number of indices used to access a tensor coefficient must be equal to the rank of the tensor. + EIGEN_STATIC_ASSERT(sizeof...(otherIndices) + 2 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE) + return operator()(array<Index, NumIndices>{{firstIndex, secondIndex, otherIndices...}}); + } +#else + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Scalar& operator()(Index i0, Index i1) + { + return coeffRef(array<Index, 2>(i0, i1)); + } + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Scalar& operator()(Index i0, Index i1, Index i2) + { + return coeffRef(array<Index, 3>(i0, i1, i2)); + } + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Scalar& operator()(Index i0, Index i1, Index i2, Index i3) + { + return coeffRef(array<Index, 4>(i0, i1, i2, i3)); + } + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Scalar& operator()(Index i0, Index i1, Index i2, Index i3, Index i4) + { + return coeffRef(array<Index, 5>(i0, i1, i2, i3, i4)); + } +#endif + + // normal indices + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& operator()(const array<Index, NumIndices>& indices) + { + return coeffRef(indices); + } + + // custom indices +#ifdef EIGEN_HAS_SFINAE + template<typename CustomIndices, + EIGEN_SFINAE_ENABLE_IF( !(isOfNormalIndex<CustomIndices>::value) ) + > + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& operator()(CustomIndices& indices) + { + return coeffRef(internal::customIndices2Array<Index,NumIndices>(indices)); + } +#endif + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& operator()(Index index) + { + eigen_assert(index >= 0 && index < size()); + return coeffRef(index); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& operator()() + { + EIGEN_STATIC_ASSERT(NumIndices == 0, YOU_MADE_A_PROGRAMMING_MISTAKE); + return coeffRef(); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& operator[](Index index) + { + // The bracket operator is only for vectors, use the parenthesis operator instead + EIGEN_STATIC_ASSERT(NumIndices == 1, YOU_MADE_A_PROGRAMMING_MISTAKE) + return coeffRef(index); + } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Tensor() + : m_storage() + { + } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Tensor(const Self& other) + : m_storage(other.m_storage) + { + } + +#if EIGEN_HAS_VARIADIC_TEMPLATES + template<typename... IndexTypes> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Tensor(Index firstDimension, IndexTypes... otherDimensions) + : m_storage(firstDimension, otherDimensions...) + { + // The number of dimensions used to construct a tensor must be equal to the rank of the tensor. + EIGEN_STATIC_ASSERT(sizeof...(otherDimensions) + 1 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE) + } +#else + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE explicit Tensor(Index dim1) + : m_storage(dim1, array<Index, 1>(dim1)) + { + EIGEN_STATIC_ASSERT(1 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE) + } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Tensor(Index dim1, Index dim2) + : m_storage(dim1*dim2, array<Index, 2>(dim1, dim2)) + { + EIGEN_STATIC_ASSERT(2 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE) + } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Tensor(Index dim1, Index dim2, Index dim3) + : m_storage(dim1*dim2*dim3, array<Index, 3>(dim1, dim2, dim3)) + { + EIGEN_STATIC_ASSERT(3 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE) + } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Tensor(Index dim1, Index dim2, Index dim3, Index dim4) + : m_storage(dim1*dim2*dim3*dim4, array<Index, 4>(dim1, dim2, dim3, dim4)) + { + EIGEN_STATIC_ASSERT(4 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE) + } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Tensor(Index dim1, Index dim2, Index dim3, Index dim4, Index dim5) + : m_storage(dim1*dim2*dim3*dim4*dim5, array<Index, 5>(dim1, dim2, dim3, dim4, dim5)) + { + EIGEN_STATIC_ASSERT(5 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE) + } +#endif + + /** Normal Dimension */ + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE explicit Tensor(const array<Index, NumIndices>& dimensions) + : m_storage(internal::array_prod(dimensions), dimensions) + { + EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED + } + + template<typename OtherDerived> + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Tensor(const TensorBase<OtherDerived, ReadOnlyAccessors>& other) + { + typedef TensorAssignOp<Tensor, const OtherDerived> Assign; + Assign assign(*this, other.derived()); + resize(TensorEvaluator<const Assign, DefaultDevice>(assign, DefaultDevice()).dimensions()); + internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice()); + } + template<typename OtherDerived> + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Tensor(const TensorBase<OtherDerived, WriteAccessors>& other) + { + typedef TensorAssignOp<Tensor, const OtherDerived> Assign; + Assign assign(*this, other.derived()); + resize(TensorEvaluator<const Assign, DefaultDevice>(assign, DefaultDevice()).dimensions()); + internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice()); + } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Tensor& operator=(const Tensor& other) + { + typedef TensorAssignOp<Tensor, const Tensor> Assign; + Assign assign(*this, other); + resize(TensorEvaluator<const Assign, DefaultDevice>(assign, DefaultDevice()).dimensions()); + internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice()); + return *this; + } + template<typename OtherDerived> + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Tensor& operator=(const OtherDerived& other) + { + typedef TensorAssignOp<Tensor, const OtherDerived> Assign; + Assign assign(*this, other); + resize(TensorEvaluator<const Assign, DefaultDevice>(assign, DefaultDevice()).dimensions()); + internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice()); + return *this; + } + +#if EIGEN_HAS_VARIADIC_TEMPLATES + template<typename... IndexTypes> EIGEN_DEVICE_FUNC + void resize(Index firstDimension, IndexTypes... otherDimensions) + { + // The number of dimensions used to resize a tensor must be equal to the rank of the tensor. + EIGEN_STATIC_ASSERT(sizeof...(otherDimensions) + 1 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE) + resize(array<Index, NumIndices>{{firstDimension, otherDimensions...}}); + } +#endif + + /** Normal Dimension */ + EIGEN_DEVICE_FUNC void resize(const array<Index, NumIndices>& dimensions) + { + int i; + Index size = Index(1); + for (i = 0; i < NumIndices; i++) { + internal::check_rows_cols_for_overflow<Dynamic>::run(size, dimensions[i]); + size *= dimensions[i]; + } + #ifdef EIGEN_INITIALIZE_COEFFS + bool size_changed = size != this->size(); + m_storage.resize(size, dimensions); + if(size_changed) EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED + #else + m_storage.resize(size, dimensions); + #endif + } + + // Why this overload, DSizes is derived from array ??? // + EIGEN_DEVICE_FUNC void resize(const DSizes<Index, NumIndices>& dimensions) { + array<Index, NumIndices> dims; + for (int i = 0; i < NumIndices; ++i) { + dims[i] = dimensions[i]; + } + resize(dims); + } + + EIGEN_DEVICE_FUNC + void resize() + { + EIGEN_STATIC_ASSERT(NumIndices == 0, YOU_MADE_A_PROGRAMMING_MISTAKE); + // Nothing to do: rank 0 tensors have fixed size + } + + /** Custom Dimension */ +#ifdef EIGEN_HAS_SFINAE + template<typename CustomDimension, + EIGEN_SFINAE_ENABLE_IF( !(isOfNormalIndex<CustomDimension>::value) ) + > + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void resize(CustomDimension& dimensions) + { + resize(internal::customIndices2Array<Index,NumIndices>(dimensions)); + } +#endif + +#ifndef EIGEN_EMULATE_CXX11_META_H + template <typename std::ptrdiff_t... Indices> + EIGEN_DEVICE_FUNC + void resize(const Sizes<Indices...>& dimensions) { + array<Index, NumIndices> dims; + for (int i = 0; i < NumIndices; ++i) { + dims[i] = static_cast<Index>(dimensions[i]); + } + resize(dims); + } +#else + template <std::size_t V1, std::size_t V2, std::size_t V3, std::size_t V4, std::size_t V5> + EIGEN_DEVICE_FUNC + void resize(const Sizes<V1, V2, V3, V4, V5>& dimensions) { + array<Index, NumIndices> dims; + for (int i = 0; i < NumIndices; ++i) { + dims[i] = static_cast<Index>(dimensions[i]); + } + resize(dims); + } +#endif + + protected: + + bool checkIndexRange(const array<Index, NumIndices>& indices) const + { + using internal::array_apply_and_reduce; + using internal::array_zip_and_reduce; + using internal::greater_equal_zero_op; + using internal::logical_and_op; + using internal::lesser_op; + + return + // check whether the indices are all >= 0 + array_apply_and_reduce<logical_and_op, greater_equal_zero_op>(indices) && + // check whether the indices fit in the dimensions + array_zip_and_reduce<logical_and_op, lesser_op>(indices, m_storage.dimensions()); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index linearizedIndex(const array<Index, NumIndices>& indices) const + { + if (Options&RowMajor) { + return m_storage.dimensions().IndexOfRowMajor(indices); + } else { + return m_storage.dimensions().IndexOfColMajor(indices); + } + } +}; + +} // end namespace Eigen + +#endif // EIGEN_CXX11_TENSOR_TENSOR_H diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorArgMax.h b/unsupported/Eigen/CXX11/src/Tensor/TensorArgMax.h new file mode 100644 index 000000000..d06f40cd8 --- /dev/null +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorArgMax.h @@ -0,0 +1,299 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2015 Eugene Brevdo <ebrevdo@gmail.com> +// Benoit Steiner <benoit.steiner.goog@gmail.com> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_CXX11_TENSOR_TENSOR_ARG_MAX_H +#define EIGEN_CXX11_TENSOR_TENSOR_ARG_MAX_H + +namespace Eigen { +namespace internal { + +/** \class TensorIndexTuple + * \ingroup CXX11_Tensor_Module + * + * \brief Tensor + Index Tuple class. + * + * + */ +template<typename XprType> +struct traits<TensorIndexTupleOp<XprType> > : public traits<XprType> +{ + typedef traits<XprType> XprTraits; + typedef typename XprTraits::StorageKind StorageKind; + typedef typename XprTraits::Index Index; + typedef Tuple<Index, typename XprTraits::Scalar> Scalar; + typedef typename XprType::Nested Nested; + typedef typename remove_reference<Nested>::type _Nested; + static const int NumDimensions = XprTraits::NumDimensions; + static const int Layout = XprTraits::Layout; +}; + +template<typename XprType> +struct eval<TensorIndexTupleOp<XprType>, Eigen::Dense> +{ + typedef const TensorIndexTupleOp<XprType>& type; +}; + +template<typename XprType> +struct nested<TensorIndexTupleOp<XprType>, 1, + typename eval<TensorIndexTupleOp<XprType> >::type> +{ + typedef TensorIndexTupleOp<XprType> type; +}; + +} // end namespace internal + +template<typename XprType> +class TensorIndexTupleOp : public TensorBase<TensorIndexTupleOp<XprType>, ReadOnlyAccessors> +{ + public: + typedef typename Eigen::internal::traits<TensorIndexTupleOp>::Scalar Scalar; + typedef typename Eigen::NumTraits<Scalar>::Real RealScalar; + typedef typename Eigen::internal::nested<TensorIndexTupleOp>::type Nested; + typedef typename Eigen::internal::traits<TensorIndexTupleOp>::StorageKind StorageKind; + typedef typename Eigen::internal::traits<TensorIndexTupleOp>::Index Index; + typedef Tuple<Index, typename XprType::CoeffReturnType> CoeffReturnType; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorIndexTupleOp(const XprType& expr) + : m_xpr(expr) {} + + EIGEN_DEVICE_FUNC + const typename internal::remove_all<typename XprType::Nested>::type& + expression() const { return m_xpr; } + + protected: + typename XprType::Nested m_xpr; +}; + +// Eval as rvalue +template<typename ArgType, typename Device> +struct TensorEvaluator<const TensorIndexTupleOp<ArgType>, Device> +{ + typedef TensorIndexTupleOp<ArgType> XprType; + typedef typename XprType::Index Index; + typedef typename XprType::Scalar Scalar; + typedef typename XprType::CoeffReturnType CoeffReturnType; + + typedef typename TensorEvaluator<ArgType, Device>::Dimensions Dimensions; + static const int NumDims = internal::array_size<Dimensions>::value; + + enum { + IsAligned = /*TensorEvaluator<ArgType, Device>::IsAligned*/ false, + PacketAccess = /*TensorEvaluator<ArgType, Device>::PacketAccess*/ false, + BlockAccess = false, + Layout = TensorEvaluator<ArgType, Device>::Layout, + CoordAccess = false, // to be implemented + RawAccess = false + }; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) + : m_impl(op.expression(), device) { } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { + return m_impl.dimensions(); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* /*data*/) { + m_impl.evalSubExprsIfNeeded(NULL); + return true; + } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() { + m_impl.cleanup(); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const + { + return CoeffReturnType(index, m_impl.coeff(index)); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost + costPerCoeff(bool vectorized) const { + return m_impl.costPerCoeff(vectorized) + TensorOpCost(0, 0, 1); + } + + EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; } + + protected: + TensorEvaluator<ArgType, Device> m_impl; +}; + +namespace internal { + +/** \class TensorTupleIndex + * \ingroup CXX11_Tensor_Module + * + * \brief Converts to Tensor<Tuple<Index, Scalar> > and reduces to Tensor<Index>. + * + */ +template<typename ReduceOp, typename Dims, typename XprType> +struct traits<TensorTupleReducerOp<ReduceOp, Dims, XprType> > : public traits<XprType> +{ + typedef traits<XprType> XprTraits; + typedef typename XprTraits::StorageKind StorageKind; + typedef typename XprTraits::Index Index; + typedef Index Scalar; + typedef typename XprType::Nested Nested; + typedef typename remove_reference<Nested>::type _Nested; + static const int NumDimensions = XprTraits::NumDimensions - array_size<Dims>::value; + static const int Layout = XprTraits::Layout; +}; + +template<typename ReduceOp, typename Dims, typename XprType> +struct eval<TensorTupleReducerOp<ReduceOp, Dims, XprType>, Eigen::Dense> +{ + typedef const TensorTupleReducerOp<ReduceOp, Dims, XprType>& type; +}; + +template<typename ReduceOp, typename Dims, typename XprType> +struct nested<TensorTupleReducerOp<ReduceOp, Dims, XprType>, 1, + typename eval<TensorTupleReducerOp<ReduceOp, Dims, XprType> >::type> +{ + typedef TensorTupleReducerOp<ReduceOp, Dims, XprType> type; +}; + +} // end namespace internal + +template<typename ReduceOp, typename Dims, typename XprType> +class TensorTupleReducerOp : public TensorBase<TensorTupleReducerOp<ReduceOp, Dims, XprType>, ReadOnlyAccessors> +{ + public: + typedef typename Eigen::internal::traits<TensorTupleReducerOp>::Scalar Scalar; + typedef typename Eigen::NumTraits<Scalar>::Real RealScalar; + typedef typename Eigen::internal::nested<TensorTupleReducerOp>::type Nested; + typedef typename Eigen::internal::traits<TensorTupleReducerOp>::StorageKind StorageKind; + typedef typename Eigen::internal::traits<TensorTupleReducerOp>::Index Index; + typedef Index CoeffReturnType; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorTupleReducerOp(const XprType& expr, + const ReduceOp& reduce_op, + const int return_dim, + const Dims& reduce_dims) + : m_xpr(expr), m_reduce_op(reduce_op), m_return_dim(return_dim), m_reduce_dims(reduce_dims) {} + + EIGEN_DEVICE_FUNC + const typename internal::remove_all<typename XprType::Nested>::type& + expression() const { return m_xpr; } + + EIGEN_DEVICE_FUNC + const ReduceOp& reduce_op() const { return m_reduce_op; } + + EIGEN_DEVICE_FUNC + const Dims& reduce_dims() const { return m_reduce_dims; } + + EIGEN_DEVICE_FUNC + int return_dim() const { return m_return_dim; } + + protected: + typename XprType::Nested m_xpr; + const ReduceOp m_reduce_op; + const int m_return_dim; + const Dims m_reduce_dims; +}; + +// Eval as rvalue +template<typename ReduceOp, typename Dims, typename ArgType, typename Device> +struct TensorEvaluator<const TensorTupleReducerOp<ReduceOp, Dims, ArgType>, Device> +{ + typedef TensorTupleReducerOp<ReduceOp, Dims, ArgType> XprType; + typedef typename XprType::Index Index; + typedef typename XprType::Scalar Scalar; + typedef typename XprType::CoeffReturnType CoeffReturnType; + typedef typename TensorIndexTupleOp<ArgType>::CoeffReturnType TupleType; + typedef typename TensorEvaluator<const TensorReductionOp<ReduceOp, Dims, const TensorIndexTupleOp<ArgType> >, Device>::Dimensions Dimensions; + typedef typename TensorEvaluator<const TensorIndexTupleOp<ArgType> , Device>::Dimensions InputDimensions; + static const int NumDims = internal::array_size<InputDimensions>::value; + typedef array<Index, NumDims> StrideDims; + + enum { + IsAligned = /*TensorEvaluator<ArgType, Device>::IsAligned*/ false, + PacketAccess = /*TensorEvaluator<ArgType, Device>::PacketAccess*/ false, + BlockAccess = false, + Layout = TensorEvaluator<const TensorReductionOp<ReduceOp, Dims, const TensorIndexTupleOp<ArgType> >, Device>::Layout, + CoordAccess = false, // to be implemented + RawAccess = false + }; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) + : m_orig_impl(op.expression(), device), + m_impl(op.expression().index_tuples().reduce(op.reduce_dims(), op.reduce_op()), device), + m_return_dim(op.return_dim()) { + + gen_strides(m_orig_impl.dimensions(), m_strides); + if (Layout == static_cast<int>(ColMajor)) { + const Index total_size = internal::array_prod(m_orig_impl.dimensions()); + m_stride_mod = (m_return_dim < NumDims - 1) ? m_strides[m_return_dim + 1] : total_size; + } else { + const Index total_size = internal::array_prod(m_orig_impl.dimensions()); + m_stride_mod = (m_return_dim > 0) ? m_strides[m_return_dim - 1] : total_size; + } + m_stride_div = m_strides[m_return_dim]; + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { + return m_impl.dimensions(); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* /*data*/) { + m_impl.evalSubExprsIfNeeded(NULL); + return true; + } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() { + m_impl.cleanup(); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const { + const TupleType v = m_impl.coeff(index); + return (m_return_dim < 0) ? v.first : (v.first % m_stride_mod) / m_stride_div; + } + + EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost + costPerCoeff(bool vectorized) const { + const double compute_cost = 1.0 + + (m_return_dim < 0 ? 0.0 : (TensorOpCost::ModCost<Index>() + TensorOpCost::DivCost<Index>())); + return m_orig_impl.costPerCoeff(vectorized) + + m_impl.costPerCoeff(vectorized) + TensorOpCost(0, 0, compute_cost); + } + + private: + EIGEN_DEVICE_FUNC void gen_strides(const InputDimensions& dims, StrideDims& strides) { + if (m_return_dim < 0) { + return; // Won't be using the strides. + } + eigen_assert(m_return_dim < NumDims && + "Asking to convert index to a dimension outside of the rank"); + + // Calculate m_stride_div and m_stride_mod, which are used to + // calculate the value of an index w.r.t. the m_return_dim. + if (Layout == static_cast<int>(ColMajor)) { + strides[0] = 1; + for (int i = 1; i < NumDims; ++i) { + strides[i] = strides[i-1] * dims[i-1]; + } + } else { + strides[NumDims-1] = 1; + for (int i = NumDims - 2; i >= 0; --i) { + strides[i] = strides[i+1] * dims[i+1]; + } + } + } + + protected: + TensorEvaluator<const TensorIndexTupleOp<ArgType>, Device> m_orig_impl; + TensorEvaluator<const TensorReductionOp<ReduceOp, Dims, const TensorIndexTupleOp<ArgType> >, Device> m_impl; + const int m_return_dim; + StrideDims m_strides; + Index m_stride_mod; + Index m_stride_div; +}; + +} // end namespace Eigen + +#endif // EIGEN_CXX11_TENSOR_TENSOR_ARG_MAX_H diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorAssign.h b/unsupported/Eigen/CXX11/src/Tensor/TensorAssign.h new file mode 100644 index 000000000..166be200c --- /dev/null +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorAssign.h @@ -0,0 +1,181 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_CXX11_TENSOR_TENSOR_ASSIGN_H +#define EIGEN_CXX11_TENSOR_TENSOR_ASSIGN_H + +namespace Eigen { + +/** \class TensorAssign + * \ingroup CXX11_Tensor_Module + * + * \brief The tensor assignment class. + * + * This class is represents the assignment of the values resulting from the evaluation of + * the rhs expression to the memory locations denoted by the lhs expression. + */ +namespace internal { +template<typename LhsXprType, typename RhsXprType> +struct traits<TensorAssignOp<LhsXprType, RhsXprType> > +{ + typedef typename LhsXprType::Scalar Scalar; + typedef typename traits<LhsXprType>::StorageKind StorageKind; + typedef typename promote_index_type<typename traits<LhsXprType>::Index, + typename traits<RhsXprType>::Index>::type Index; + typedef typename LhsXprType::Nested LhsNested; + typedef typename RhsXprType::Nested RhsNested; + typedef typename remove_reference<LhsNested>::type _LhsNested; + typedef typename remove_reference<RhsNested>::type _RhsNested; + static const std::size_t NumDimensions = internal::traits<LhsXprType>::NumDimensions; + static const int Layout = internal::traits<LhsXprType>::Layout; + + enum { + Flags = 0 + }; +}; + +template<typename LhsXprType, typename RhsXprType> +struct eval<TensorAssignOp<LhsXprType, RhsXprType>, Eigen::Dense> +{ + typedef const TensorAssignOp<LhsXprType, RhsXprType>& type; +}; + +template<typename LhsXprType, typename RhsXprType> +struct nested<TensorAssignOp<LhsXprType, RhsXprType>, 1, typename eval<TensorAssignOp<LhsXprType, RhsXprType> >::type> +{ + typedef TensorAssignOp<LhsXprType, RhsXprType> type; +}; + +} // end namespace internal + + + +template<typename LhsXprType, typename RhsXprType> +class TensorAssignOp : public TensorBase<TensorAssignOp<LhsXprType, RhsXprType> > +{ + public: + typedef typename Eigen::internal::traits<TensorAssignOp>::Scalar Scalar; + typedef typename Eigen::NumTraits<Scalar>::Real RealScalar; + typedef typename LhsXprType::CoeffReturnType CoeffReturnType; + typedef typename Eigen::internal::nested<TensorAssignOp>::type Nested; + typedef typename Eigen::internal::traits<TensorAssignOp>::StorageKind StorageKind; + typedef typename Eigen::internal::traits<TensorAssignOp>::Index Index; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorAssignOp(LhsXprType& lhs, const RhsXprType& rhs) + : m_lhs_xpr(lhs), m_rhs_xpr(rhs) {} + + /** \returns the nested expressions */ + EIGEN_DEVICE_FUNC + typename internal::remove_all<typename LhsXprType::Nested>::type& + lhsExpression() const { return *((typename internal::remove_all<typename LhsXprType::Nested>::type*)&m_lhs_xpr); } + + EIGEN_DEVICE_FUNC + const typename internal::remove_all<typename RhsXprType::Nested>::type& + rhsExpression() const { return m_rhs_xpr; } + + protected: + typename internal::remove_all<typename LhsXprType::Nested>::type& m_lhs_xpr; + const typename internal::remove_all<typename RhsXprType::Nested>::type& m_rhs_xpr; +}; + + +template<typename LeftArgType, typename RightArgType, typename Device> +struct TensorEvaluator<const TensorAssignOp<LeftArgType, RightArgType>, Device> +{ + typedef TensorAssignOp<LeftArgType, RightArgType> XprType; + typedef typename XprType::Index Index; + typedef typename XprType::Scalar Scalar; + typedef typename XprType::CoeffReturnType CoeffReturnType; + typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType; + typedef typename TensorEvaluator<RightArgType, Device>::Dimensions Dimensions; + static const int PacketSize = internal::unpacket_traits<PacketReturnType>::size; + + enum { + IsAligned = TensorEvaluator<LeftArgType, Device>::IsAligned & TensorEvaluator<RightArgType, Device>::IsAligned, + PacketAccess = TensorEvaluator<LeftArgType, Device>::PacketAccess & TensorEvaluator<RightArgType, Device>::PacketAccess, + Layout = TensorEvaluator<LeftArgType, Device>::Layout, + RawAccess = TensorEvaluator<LeftArgType, Device>::RawAccess + }; + + EIGEN_DEVICE_FUNC TensorEvaluator(const XprType& op, const Device& device) : + m_leftImpl(op.lhsExpression(), device), + m_rightImpl(op.rhsExpression(), device) + { + EIGEN_STATIC_ASSERT((static_cast<int>(TensorEvaluator<LeftArgType, Device>::Layout) == static_cast<int>(TensorEvaluator<RightArgType, Device>::Layout)), YOU_MADE_A_PROGRAMMING_MISTAKE); + } + + EIGEN_DEVICE_FUNC const Dimensions& dimensions() const + { + // The dimensions of the lhs and the rhs tensors should be equal to prevent + // overflows and ensure the result is fully initialized. + // TODO: use left impl instead if right impl dimensions are known at compile time. + return m_rightImpl.dimensions(); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar*) { + eigen_assert(dimensions_match(m_leftImpl.dimensions(), m_rightImpl.dimensions())); + m_leftImpl.evalSubExprsIfNeeded(NULL); + // If the lhs provides raw access to its storage area (i.e. if m_leftImpl.data() returns a non + // null value), attempt to evaluate the rhs expression in place. Returns true iff in place + // evaluation isn't supported and the caller still needs to manually assign the values generated + // by the rhs to the lhs. + return m_rightImpl.evalSubExprsIfNeeded(m_leftImpl.data()); + } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() { + m_leftImpl.cleanup(); + m_rightImpl.cleanup(); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalScalar(Index i) { + m_leftImpl.coeffRef(i) = m_rightImpl.coeff(i); + } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalPacket(Index i) { + const int LhsStoreMode = TensorEvaluator<LeftArgType, Device>::IsAligned ? Aligned : Unaligned; + const int RhsLoadMode = TensorEvaluator<RightArgType, Device>::IsAligned ? Aligned : Unaligned; + m_leftImpl.template writePacket<LhsStoreMode>(i, m_rightImpl.template packet<RhsLoadMode>(i)); + } + EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index index) const + { + return m_leftImpl.coeff(index); + } + template<int LoadMode> + EIGEN_DEVICE_FUNC PacketReturnType packet(Index index) const + { + return m_leftImpl.template packet<LoadMode>(index); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost + costPerCoeff(bool vectorized) const { + // We assume that evalPacket or evalScalar is called to perform the + // assignment and account for the cost of the write here, but reduce left + // cost by one load because we are using m_leftImpl.coeffRef. + TensorOpCost left = m_leftImpl.costPerCoeff(vectorized); + return m_rightImpl.costPerCoeff(vectorized) + + TensorOpCost( + numext::maxi(0.0, left.bytes_loaded() - sizeof(CoeffReturnType)), + left.bytes_stored(), left.compute_cycles()) + + TensorOpCost(0, sizeof(CoeffReturnType), 0, vectorized, PacketSize); + } + + /// required by sycl in order to extract the accessor + const TensorEvaluator<LeftArgType, Device>& left_impl() const { return m_leftImpl; } + /// required by sycl in order to extract the accessor + const TensorEvaluator<RightArgType, Device>& right_impl() const { return m_rightImpl; } + + EIGEN_DEVICE_FUNC CoeffReturnType* data() const { return m_leftImpl.data(); } + + private: + TensorEvaluator<LeftArgType, Device> m_leftImpl; + TensorEvaluator<RightArgType, Device> m_rightImpl; +}; + +} + + +#endif // EIGEN_CXX11_TENSOR_TENSOR_ASSIGN_H diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorBase.h b/unsupported/Eigen/CXX11/src/Tensor/TensorBase.h new file mode 100644 index 000000000..7a45a5cf4 --- /dev/null +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorBase.h @@ -0,0 +1,1010 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_CXX11_TENSOR_TENSOR_BASE_H +#define EIGEN_CXX11_TENSOR_TENSOR_BASE_H + +// clang-format off + +namespace Eigen { + +/** \class TensorBase + * \ingroup CXX11_Tensor_Module + * + * \brief The tensor base class. + * + * This class is the common parent of the Tensor and TensorMap class, thus + * making it possible to use either class interchangably in expressions. + */ + +template<typename Derived> +class TensorBase<Derived, ReadOnlyAccessors> +{ + public: + typedef internal::traits<Derived> DerivedTraits; + typedef typename DerivedTraits::Scalar Scalar; + typedef typename DerivedTraits::Index Index; + typedef typename internal::remove_const<Scalar>::type CoeffReturnType; + static const int NumDimensions = DerivedTraits::NumDimensions; + + // Generic nullary operation support. + template <typename CustomNullaryOp> EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const TensorCwiseNullaryOp<CustomNullaryOp, const Derived> + nullaryExpr(const CustomNullaryOp& func) const { + return TensorCwiseNullaryOp<CustomNullaryOp, const Derived>(derived(), func); + } + + // Coefficient-wise nullary operators + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const TensorCwiseNullaryOp<internal::scalar_constant_op<Scalar>, const Derived> + constant(const Scalar& value) const { + return nullaryExpr(internal::scalar_constant_op<Scalar>(value)); + } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const TensorCwiseNullaryOp<internal::UniformRandomGenerator<Scalar>, const Derived> + random() const { + return nullaryExpr(internal::UniformRandomGenerator<Scalar>()); + } + template <typename RandomGenerator> EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const TensorCwiseNullaryOp<RandomGenerator, const Derived> + random(const RandomGenerator& gen = RandomGenerator()) const { + return nullaryExpr(gen); + } + + // Tensor generation + template <typename Generator> EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const TensorGeneratorOp<Generator, const Derived> + generate(const Generator& generator) const { + return TensorGeneratorOp<Generator, const Derived>(derived(), generator); + } + + // Generic unary operation support. + template <typename CustomUnaryOp> EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<CustomUnaryOp, const Derived> + unaryExpr(const CustomUnaryOp& func) const { + return TensorCwiseUnaryOp<CustomUnaryOp, const Derived>(derived(), func); + } + + // Coefficient-wise unary operators + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_opposite_op<Scalar>, const Derived> + operator-() const { + return unaryExpr(internal::scalar_opposite_op<Scalar>()); + } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_sqrt_op<Scalar>, const Derived> + sqrt() const { + return unaryExpr(internal::scalar_sqrt_op<Scalar>()); + } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_sign_op<Scalar>, const Derived> + sign() const { + return unaryExpr(internal::scalar_sign_op<Scalar>()); + } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_rsqrt_op<Scalar>, const Derived> + rsqrt() const { + return unaryExpr(internal::scalar_rsqrt_op<Scalar>()); + } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_square_op<Scalar>, const Derived> + square() const { + return unaryExpr(internal::scalar_square_op<Scalar>()); + } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_cube_op<Scalar>, const Derived> + cube() const { + return unaryExpr(internal::scalar_cube_op<Scalar>()); + } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_inverse_op<Scalar>, const Derived> + inverse() const { + return unaryExpr(internal::scalar_inverse_op<Scalar>()); + } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_tanh_op<Scalar>, const Derived> + tanh() const { + return unaryExpr(internal::scalar_tanh_op<Scalar>()); + } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_lgamma_op<Scalar>, const Derived> + lgamma() const { + return unaryExpr(internal::scalar_lgamma_op<Scalar>()); + } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_digamma_op<Scalar>, const Derived> + digamma() const { + return unaryExpr(internal::scalar_digamma_op<Scalar>()); + } + + // igamma(a = this, x = other) + template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const TensorCwiseBinaryOp<internal::scalar_igamma_op<Scalar>, const Derived, const OtherDerived> + igamma(const OtherDerived& other) const { + return binaryExpr(other.derived(), internal::scalar_igamma_op<Scalar>()); + } + + // igammac(a = this, x = other) + template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const TensorCwiseBinaryOp<internal::scalar_igammac_op<Scalar>, const Derived, const OtherDerived> + igammac(const OtherDerived& other) const { + return binaryExpr(other.derived(), internal::scalar_igammac_op<Scalar>()); + } + + // zeta(x = this, q = other) + template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const TensorCwiseBinaryOp<internal::scalar_zeta_op<Scalar>, const Derived, const OtherDerived> + zeta(const OtherDerived& other) const { + return binaryExpr(other.derived(), internal::scalar_zeta_op<Scalar>()); + } + + // polygamma(n = this, x = other) + template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const TensorCwiseBinaryOp<internal::scalar_polygamma_op<Scalar>, const Derived, const OtherDerived> + polygamma(const OtherDerived& other) const { + return binaryExpr(other.derived(), internal::scalar_polygamma_op<Scalar>()); + } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_erf_op<Scalar>, const Derived> + erf() const { + return unaryExpr(internal::scalar_erf_op<Scalar>()); + } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_erfc_op<Scalar>, const Derived> + erfc() const { + return unaryExpr(internal::scalar_erfc_op<Scalar>()); + } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_sigmoid_op<Scalar>, const Derived> + sigmoid() const { + return unaryExpr(internal::scalar_sigmoid_op<Scalar>()); + } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_exp_op<Scalar>, const Derived> + exp() const { + return unaryExpr(internal::scalar_exp_op<Scalar>()); + } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_log_op<Scalar>, const Derived> + log() const { + return unaryExpr(internal::scalar_log_op<Scalar>()); + } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_log1p_op<Scalar>, const Derived> + log1p() const { + return unaryExpr(internal::scalar_log1p_op<Scalar>()); + } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_abs_op<Scalar>, const Derived> + abs() const { + return unaryExpr(internal::scalar_abs_op<Scalar>()); + } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_conjugate_op<Scalar>, const Derived> + conjugate() const { + return unaryExpr(internal::scalar_conjugate_op<Scalar>()); + } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::bind2nd_op<internal::scalar_pow_op<Scalar,Scalar> >, const Derived> + pow(Scalar exponent) const { + return unaryExpr(internal::bind2nd_op<internal::scalar_pow_op<Scalar,Scalar> >(exponent)); + } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_real_op<Scalar>, const Derived> + real() const { + return unaryExpr(internal::scalar_real_op<Scalar>()); + } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_imag_op<Scalar>, const Derived> + imag() const { + return unaryExpr(internal::scalar_imag_op<Scalar>()); + } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::bind2nd_op<internal::scalar_sum_op<Scalar,Scalar> >, const Derived> + operator+ (Scalar rhs) const { + return unaryExpr(internal::bind2nd_op<internal::scalar_sum_op<Scalar,Scalar> >(rhs)); + } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE friend + const TensorCwiseUnaryOp<internal::bind1st_op<internal::scalar_sum_op<Scalar> >, const Derived> + operator+ (Scalar lhs, const Derived& rhs) { + return rhs.unaryExpr(internal::bind1st_op<internal::scalar_sum_op<Scalar> >(lhs)); + } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::bind2nd_op<internal::scalar_difference_op<Scalar,Scalar> >, const Derived> + operator- (Scalar rhs) const { + EIGEN_STATIC_ASSERT((NumTraits<Scalar>::IsSigned || internal::is_same<Scalar, const std::complex<float> >::value), YOU_MADE_A_PROGRAMMING_MISTAKE); + return unaryExpr(internal::bind2nd_op<internal::scalar_difference_op<Scalar,Scalar> >(rhs)); + } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE friend + const TensorCwiseUnaryOp<internal::bind1st_op<internal::scalar_difference_op<Scalar> >, const Derived> + operator- (Scalar lhs, const Derived& rhs) { + return rhs.unaryExpr(internal::bind1st_op<internal::scalar_difference_op<Scalar> >(lhs)); + } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::bind2nd_op<internal::scalar_product_op<Scalar,Scalar> >, const Derived> + operator* (Scalar rhs) const { + return unaryExpr(internal::bind2nd_op<internal::scalar_product_op<Scalar,Scalar> >(rhs)); + } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE friend + const TensorCwiseUnaryOp<internal::bind1st_op<internal::scalar_product_op<Scalar> >, const Derived> + operator* (Scalar lhs, const Derived& rhs) { + return rhs.unaryExpr(internal::bind1st_op<internal::scalar_product_op<Scalar> >(lhs)); + } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::bind2nd_op<internal::scalar_quotient_op<Scalar,Scalar> >, const Derived> + operator/ (Scalar rhs) const { + return unaryExpr(internal::bind2nd_op<internal::scalar_quotient_op<Scalar,Scalar> >(rhs)); + } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE friend + const TensorCwiseUnaryOp<internal::bind1st_op<internal::scalar_quotient_op<Scalar> >, const Derived> + operator/ (Scalar lhs, const Derived& rhs) { + return rhs.unaryExpr(internal::bind1st_op<internal::scalar_quotient_op<Scalar> >(lhs)); + } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_mod_op<Scalar>, const Derived> + operator% (Scalar rhs) const { + EIGEN_STATIC_ASSERT(NumTraits<Scalar>::IsInteger, YOU_MADE_A_PROGRAMMING_MISTAKE_TRY_MOD); + return unaryExpr(internal::scalar_mod_op<Scalar>(rhs)); + } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const TensorCwiseBinaryOp<internal::scalar_max_op<Scalar>, const Derived, const TensorCwiseNullaryOp<internal::scalar_constant_op<Scalar>, const Derived> > + cwiseMax(Scalar threshold) const { + return cwiseMax(constant(threshold)); + } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const TensorCwiseBinaryOp<internal::scalar_min_op<Scalar>, const Derived, const TensorCwiseNullaryOp<internal::scalar_constant_op<Scalar>, const Derived> > + cwiseMin(Scalar threshold) const { + return cwiseMin(constant(threshold)); + } + + template <typename NewType> EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const TensorConversionOp<NewType, const Derived> + cast() const { + return TensorConversionOp<NewType, const Derived>(derived()); + } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_round_op<Scalar>, const Derived> + round() const { + return unaryExpr(internal::scalar_round_op<Scalar>()); + } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_ceil_op<Scalar>, const Derived> + ceil() const { + return unaryExpr(internal::scalar_ceil_op<Scalar>()); + } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_floor_op<Scalar>, const Derived> + floor() const { + return unaryExpr(internal::scalar_floor_op<Scalar>()); + } + + // Generic binary operation support. + template <typename CustomBinaryOp, typename OtherDerived> EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const TensorCwiseBinaryOp<CustomBinaryOp, const Derived, const OtherDerived> + binaryExpr(const OtherDerived& other, const CustomBinaryOp& func) const { + return TensorCwiseBinaryOp<CustomBinaryOp, const Derived, const OtherDerived>(derived(), other, func); + } + + // Coefficient-wise binary operators. + template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const TensorCwiseBinaryOp<internal::scalar_sum_op<Scalar>, const Derived, const OtherDerived> + operator+(const OtherDerived& other) const { + return binaryExpr(other.derived(), internal::scalar_sum_op<Scalar>()); + } + + template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const TensorCwiseBinaryOp<internal::scalar_difference_op<Scalar>, const Derived, const OtherDerived> + operator-(const OtherDerived& other) const { + return binaryExpr(other.derived(), internal::scalar_difference_op<Scalar>()); + } + + template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const TensorCwiseBinaryOp<internal::scalar_product_op<Scalar>, const Derived, const OtherDerived> + operator*(const OtherDerived& other) const { + return binaryExpr(other.derived(), internal::scalar_product_op<Scalar>()); + } + + template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const TensorCwiseBinaryOp<internal::scalar_quotient_op<Scalar>, const Derived, const OtherDerived> + operator/(const OtherDerived& other) const { + return binaryExpr(other.derived(), internal::scalar_quotient_op<Scalar>()); + } + + template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const TensorCwiseBinaryOp<internal::scalar_max_op<Scalar>, const Derived, const OtherDerived> + cwiseMax(const OtherDerived& other) const { + return binaryExpr(other.derived(), internal::scalar_max_op<Scalar>()); + } + + template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const TensorCwiseBinaryOp<internal::scalar_min_op<Scalar>, const Derived, const OtherDerived> + cwiseMin(const OtherDerived& other) const { + return binaryExpr(other.derived(), internal::scalar_min_op<Scalar>()); + } + + template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const TensorCwiseBinaryOp<internal::scalar_boolean_and_op, const Derived, const OtherDerived> + operator&&(const OtherDerived& other) const { + return binaryExpr(other.derived(), internal::scalar_boolean_and_op()); + } + + template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const TensorCwiseBinaryOp<internal::scalar_boolean_or_op, const Derived, const OtherDerived> + operator||(const OtherDerived& other) const { + return binaryExpr(other.derived(), internal::scalar_boolean_or_op()); + } + + template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const TensorCwiseBinaryOp<internal::scalar_boolean_xor_op, const Derived, const OtherDerived> + operator^(const OtherDerived& other) const { + return binaryExpr(other.derived(), internal::scalar_boolean_xor_op()); + } + + // Comparisons and tests. + template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const TensorCwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_LT>, const Derived, const OtherDerived> + operator<(const OtherDerived& other) const { + return binaryExpr(other.derived(), internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_LT>()); + } + template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const TensorCwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_LE>, const Derived, const OtherDerived> + operator<=(const OtherDerived& other) const { + return binaryExpr(other.derived(), internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_LE>()); + } + template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const TensorCwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_GT>, const Derived, const OtherDerived> + operator>(const OtherDerived& other) const { + return binaryExpr(other.derived(), internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_GT>()); + } + template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const TensorCwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_GE>, const Derived, const OtherDerived> + operator>=(const OtherDerived& other) const { + return binaryExpr(other.derived(), internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_GE>()); + } + + template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const TensorCwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_EQ>, const Derived, const OtherDerived> + operator==(const OtherDerived& other) const { + return binaryExpr(other.derived(), internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_EQ>()); + } + + template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const TensorCwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_NEQ>, const Derived, const OtherDerived> + operator!=(const OtherDerived& other) const { + return binaryExpr(other.derived(), internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_NEQ>()); + } + + // comparisons and tests for Scalars + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const TensorCwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_LT>, const Derived, const TensorCwiseNullaryOp<internal::scalar_constant_op<Scalar>, const Derived> > + operator<(Scalar threshold) const { + return operator<(constant(threshold)); + } + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const TensorCwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_LE>, const Derived, const TensorCwiseNullaryOp<internal::scalar_constant_op<Scalar>, const Derived> > + operator<=(Scalar threshold) const { + return operator<=(constant(threshold)); + } + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const TensorCwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_GT>, const Derived, const TensorCwiseNullaryOp<internal::scalar_constant_op<Scalar>, const Derived> > + operator>(Scalar threshold) const { + return operator>(constant(threshold)); + } + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const TensorCwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_GE>, const Derived, const TensorCwiseNullaryOp<internal::scalar_constant_op<Scalar>, const Derived> > + operator>=(Scalar threshold) const { + return operator>=(constant(threshold)); + } + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const TensorCwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_EQ>, const Derived, const TensorCwiseNullaryOp<internal::scalar_constant_op<Scalar>, const Derived> > + operator==(Scalar threshold) const { + return operator==(constant(threshold)); + } + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const TensorCwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_NEQ>, const Derived, const TensorCwiseNullaryOp<internal::scalar_constant_op<Scalar>, const Derived> > + operator!=(Scalar threshold) const { + return operator!=(constant(threshold)); + } + + // Checks + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_isnan_op<Scalar>, const Derived> + (isnan)() const { + return unaryExpr(internal::scalar_isnan_op<Scalar>()); + } + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_isinf_op<Scalar>, const Derived> + (isinf)() const { + return unaryExpr(internal::scalar_isinf_op<Scalar>()); + } + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_isfinite_op<Scalar>, const Derived> + (isfinite)() const { + return unaryExpr(internal::scalar_isfinite_op<Scalar>()); + } + + // Coefficient-wise ternary operators. + template<typename ThenDerived, typename ElseDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const TensorSelectOp<const Derived, const ThenDerived, const ElseDerived> + select(const ThenDerived& thenTensor, const ElseDerived& elseTensor) const { + return TensorSelectOp<const Derived, const ThenDerived, const ElseDerived>(derived(), thenTensor.derived(), elseTensor.derived()); + } + + // Contractions. + typedef Eigen::IndexPair<Index> DimensionPair; + + template<typename OtherDerived, typename Dimensions> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const TensorContractionOp<const Dimensions, const Derived, const OtherDerived> + contract(const OtherDerived& other, const Dimensions& dims) const { + return TensorContractionOp<const Dimensions, const Derived, const OtherDerived>(derived(), other.derived(), dims); + } + + // Convolutions. + template<typename KernelDerived, typename Dimensions> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const TensorConvolutionOp<const Dimensions, const Derived, const KernelDerived> + convolve(const KernelDerived& kernel, const Dimensions& dims) const { + return TensorConvolutionOp<const Dimensions, const Derived, const KernelDerived>(derived(), kernel.derived(), dims); + } + + // Fourier transforms + template <int FFTDataType, int FFTDirection, typename FFT> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const TensorFFTOp<const FFT, const Derived, FFTDataType, FFTDirection> + fft(const FFT& fft) const { + return TensorFFTOp<const FFT, const Derived, FFTDataType, FFTDirection>(derived(), fft); + } + + // Scan. + typedef TensorScanOp<internal::SumReducer<CoeffReturnType>, const Derived> TensorScanSumOp; + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const TensorScanSumOp + cumsum(const Index& axis, bool exclusive = false) const { + return TensorScanSumOp(derived(), axis, exclusive); + } + + typedef TensorScanOp<internal::ProdReducer<CoeffReturnType>, const Derived> TensorScanProdOp; + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const TensorScanProdOp + cumprod(const Index& axis, bool exclusive = false) const { + return TensorScanProdOp(derived(), axis, exclusive); + } + + template <typename Reducer> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const TensorScanOp<Reducer, const Derived> + scan(const Index& axis, const Reducer& reducer, bool exclusive = false) const { + return TensorScanOp<Reducer, const Derived>(derived(), axis, exclusive, reducer); + } + + // Reductions. + template <typename Dims> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const TensorReductionOp<internal::SumReducer<CoeffReturnType>, const Dims, const Derived> + sum(const Dims& dims) const { + return TensorReductionOp<internal::SumReducer<CoeffReturnType>, const Dims, const Derived>(derived(), dims, internal::SumReducer<CoeffReturnType>()); + } + + const TensorReductionOp<internal::SumReducer<CoeffReturnType>, const DimensionList<Index, NumDimensions>, const Derived> + sum() const { + DimensionList<Index, NumDimensions> in_dims; + return TensorReductionOp<internal::SumReducer<CoeffReturnType>, const DimensionList<Index, NumDimensions>, const Derived>(derived(), in_dims, internal::SumReducer<CoeffReturnType>()); + } + + template <typename Dims> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const TensorReductionOp<internal::MeanReducer<CoeffReturnType>, const Dims, const Derived> + mean(const Dims& dims) const { + return TensorReductionOp<internal::MeanReducer<CoeffReturnType>, const Dims, const Derived>(derived(), dims, internal::MeanReducer<CoeffReturnType>()); + } + + const TensorReductionOp<internal::MeanReducer<CoeffReturnType>, const DimensionList<Index, NumDimensions>, const Derived> + mean() const { + DimensionList<Index, NumDimensions> in_dims; + return TensorReductionOp<internal::MeanReducer<CoeffReturnType>, const DimensionList<Index, NumDimensions>, const Derived>(derived(), in_dims, internal::MeanReducer<CoeffReturnType>()); + } + + template <typename Dims> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const TensorReductionOp<internal::ProdReducer<CoeffReturnType>, const Dims, const Derived> + prod(const Dims& dims) const { + return TensorReductionOp<internal::ProdReducer<CoeffReturnType>, const Dims, const Derived>(derived(), dims, internal::ProdReducer<CoeffReturnType>()); + } + + const TensorReductionOp<internal::ProdReducer<CoeffReturnType>, const DimensionList<Index, NumDimensions>, const Derived> + prod() const { + DimensionList<Index, NumDimensions> in_dims; + return TensorReductionOp<internal::ProdReducer<CoeffReturnType>, const DimensionList<Index, NumDimensions>, const Derived>(derived(), in_dims, internal::ProdReducer<CoeffReturnType>()); + } + + template <typename Dims> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const TensorReductionOp<internal::MaxReducer<CoeffReturnType>, const Dims, const Derived> + maximum(const Dims& dims) const { + return TensorReductionOp<internal::MaxReducer<CoeffReturnType>, const Dims, const Derived>(derived(), dims, internal::MaxReducer<CoeffReturnType>()); + } + + const TensorReductionOp<internal::MaxReducer<CoeffReturnType>, const DimensionList<Index, NumDimensions>, const Derived> + maximum() const { + DimensionList<Index, NumDimensions> in_dims; + return TensorReductionOp<internal::MaxReducer<CoeffReturnType>, const DimensionList<Index, NumDimensions>, const Derived>(derived(), in_dims, internal::MaxReducer<CoeffReturnType>()); + } + + template <typename Dims> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const TensorReductionOp<internal::MinReducer<CoeffReturnType>, const Dims, const Derived> + minimum(const Dims& dims) const { + return TensorReductionOp<internal::MinReducer<CoeffReturnType>, const Dims, const Derived>(derived(), dims, internal::MinReducer<CoeffReturnType>()); + } + + const TensorReductionOp<internal::MinReducer<CoeffReturnType>, const DimensionList<Index, NumDimensions>, const Derived> + minimum() const { + DimensionList<Index, NumDimensions> in_dims; + return TensorReductionOp<internal::MinReducer<CoeffReturnType>, const DimensionList<Index, NumDimensions>, const Derived>(derived(), in_dims, internal::MinReducer<CoeffReturnType>()); + } + + template <typename Dims> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const TensorReductionOp<internal::AndReducer, const Dims, const TensorConversionOp<bool, const Derived> > + all(const Dims& dims) const { + return cast<bool>().reduce(dims, internal::AndReducer()); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const TensorReductionOp<internal::AndReducer, const DimensionList<Index, NumDimensions>, const TensorConversionOp<bool, const Derived> > + all() const { + DimensionList<Index, NumDimensions> in_dims; + return cast<bool>().reduce(in_dims, internal::AndReducer()); + } + + template <typename Dims> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const TensorReductionOp<internal::OrReducer, const Dims, const TensorConversionOp<bool, const Derived> > + any(const Dims& dims) const { + return cast<bool>().reduce(dims, internal::OrReducer()); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const TensorReductionOp<internal::OrReducer, const DimensionList<Index, NumDimensions>, const TensorConversionOp<bool, const Derived> > + any() const { + DimensionList<Index, NumDimensions> in_dims; + return cast<bool>().reduce(in_dims, internal::OrReducer()); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const TensorTupleReducerOp< + internal::ArgMaxTupleReducer<Tuple<Index, CoeffReturnType> >, + const array<Index, NumDimensions>, const Derived> + argmax() const { + array<Index, NumDimensions> in_dims; + for (int d = 0; d < NumDimensions; ++d) in_dims[d] = d; + return TensorTupleReducerOp< + internal::ArgMaxTupleReducer<Tuple<Index, CoeffReturnType> >, + const array<Index, NumDimensions>, + const Derived>(derived(), internal::ArgMaxTupleReducer<Tuple<Index, CoeffReturnType> >(), -1, in_dims); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const TensorTupleReducerOp< + internal::ArgMinTupleReducer<Tuple<Index, CoeffReturnType> >, + const array<Index, NumDimensions>, const Derived> + argmin() const { + array<Index, NumDimensions> in_dims; + for (int d = 0; d < NumDimensions; ++d) in_dims[d] = d; + return TensorTupleReducerOp< + internal::ArgMinTupleReducer<Tuple<Index, CoeffReturnType> >, + const array<Index, NumDimensions>, + const Derived>(derived(), internal::ArgMinTupleReducer<Tuple<Index, CoeffReturnType> >(), -1, in_dims); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const TensorTupleReducerOp< + internal::ArgMaxTupleReducer<Tuple<Index, CoeffReturnType> >, + const array<Index, 1>, const Derived> + argmax(const int return_dim) const { + array<Index, 1> in_dims; + in_dims[0] = return_dim; + return TensorTupleReducerOp< + internal::ArgMaxTupleReducer<Tuple<Index, CoeffReturnType> >, + const array<Index, 1>, + const Derived>(derived(), internal::ArgMaxTupleReducer<Tuple<Index, CoeffReturnType> >(), return_dim, in_dims); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const TensorTupleReducerOp< + internal::ArgMinTupleReducer<Tuple<Index, CoeffReturnType> >, + const array<Index, 1>, const Derived> + argmin(const int return_dim) const { + array<Index, 1> in_dims; + in_dims[0] = return_dim; + return TensorTupleReducerOp< + internal::ArgMinTupleReducer<Tuple<Index, CoeffReturnType> >, + const array<Index, 1>, + const Derived>(derived(), internal::ArgMinTupleReducer<Tuple<Index, CoeffReturnType> >(), return_dim, in_dims); + } + + template <typename Reducer, typename Dims> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const TensorReductionOp<Reducer, const Dims, const Derived> + reduce(const Dims& dims, const Reducer& reducer) const { + return TensorReductionOp<Reducer, const Dims, const Derived>(derived(), dims, reducer); + } + + template <typename Broadcast> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const TensorBroadcastingOp<const Broadcast, const Derived> + broadcast(const Broadcast& broadcast) const { + return TensorBroadcastingOp<const Broadcast, const Derived>(derived(), broadcast); + } + + template <typename Axis, typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const TensorConcatenationOp<Axis, const Derived, const OtherDerived> + concatenate(const OtherDerived& other, Axis axis) const { + return TensorConcatenationOp<Axis, const Derived, const OtherDerived>(derived(), other.derived(), axis); + } + + template <typename PatchDims> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const TensorPatchOp<const PatchDims, const Derived> + extract_patches(const PatchDims& patch_dims) const { + return TensorPatchOp<const PatchDims, const Derived>(derived(), patch_dims); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const TensorImagePatchOp<Dynamic, Dynamic, const Derived> + extract_image_patches(const Index patch_rows = 1, const Index patch_cols = 1, + const Index row_stride = 1, const Index col_stride = 1, + const Index in_row_stride = 1, const Index in_col_stride = 1, + const PaddingType padding_type = PADDING_SAME, const Scalar padding_value = Scalar(0)) const { + return TensorImagePatchOp<Dynamic, Dynamic, const Derived>(derived(), patch_rows, patch_cols, row_stride, col_stride, + in_row_stride, in_col_stride, 1, 1, padding_type, padding_value); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const TensorImagePatchOp<Dynamic, Dynamic, const Derived> + extract_image_patches(const Index patch_rows, const Index patch_cols, + const Index row_stride, const Index col_stride, + const Index in_row_stride, const Index in_col_stride, + const Index row_inflate_stride, const Index col_inflate_stride, + const Index padding_top, const Index padding_bottom, + const Index padding_left,const Index padding_right, + const Scalar padding_value) const { + return TensorImagePatchOp<Dynamic, Dynamic, const Derived>(derived(), patch_rows, patch_cols, row_stride, col_stride, + in_row_stride, in_col_stride, row_inflate_stride, col_inflate_stride, + padding_top, padding_bottom, padding_left, padding_right, padding_value); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const TensorVolumePatchOp<Dynamic, Dynamic, Dynamic, const Derived> + extract_volume_patches(const Index patch_planes, const Index patch_rows, const Index patch_cols, + const Index plane_stride = 1, const Index row_stride = 1, const Index col_stride = 1, + const PaddingType padding_type = PADDING_SAME, const Scalar padding_value = Scalar(0)) const { + return TensorVolumePatchOp<Dynamic, Dynamic, Dynamic, const Derived>(derived(), patch_planes, patch_rows, patch_cols, plane_stride, row_stride, col_stride, 1, 1, 1, 1, 1, 1, padding_type, padding_value); + } + + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const TensorVolumePatchOp<Dynamic, Dynamic, Dynamic, const Derived> + extract_volume_patches(const Index patch_planes, const Index patch_rows, const Index patch_cols, + const Index plane_stride, const Index row_stride, const Index col_stride, + const Index plane_inflate_stride, const Index row_inflate_stride, const Index col_inflate_stride, + const Index padding_top_z, const Index padding_bottom_z, + const Index padding_top, const Index padding_bottom, + const Index padding_left, const Index padding_right, const Scalar padding_value = Scalar(0)) const { + return TensorVolumePatchOp<Dynamic, Dynamic, Dynamic, const Derived>(derived(), patch_planes, patch_rows, patch_cols, plane_stride, row_stride, col_stride, 1, 1, 1, plane_inflate_stride, row_inflate_stride, col_inflate_stride, padding_top_z, padding_bottom_z, padding_top, padding_bottom, padding_left, padding_right, padding_value); + } + + // Morphing operators. + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const TensorLayoutSwapOp<const Derived> + swap_layout() const { + return TensorLayoutSwapOp<const Derived>(derived()); + } + template <typename NewDimensions> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const TensorReshapingOp<const NewDimensions, const Derived> + reshape(const NewDimensions& newDimensions) const { + return TensorReshapingOp<const NewDimensions, const Derived>(derived(), newDimensions); + } + template <typename StartIndices, typename Sizes> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const TensorSlicingOp<const StartIndices, const Sizes, const Derived> + slice(const StartIndices& startIndices, const Sizes& sizes) const { + return TensorSlicingOp<const StartIndices, const Sizes, const Derived>(derived(), startIndices, sizes); + } + template <typename StartIndices, typename StopIndices, typename Strides> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const TensorStridingSlicingOp<const StartIndices, const StopIndices, const Strides, const Derived> + stridedSlice(const StartIndices& startIndices, const StopIndices& stopIndices, const Strides& strides) const { + return TensorStridingSlicingOp<const StartIndices, const StopIndices, const Strides, + const Derived>(derived(), startIndices, stopIndices, strides); + } + template <Index DimId> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const TensorChippingOp<DimId, const Derived> + chip(const Index offset) const { + return TensorChippingOp<DimId, const Derived>(derived(), offset, DimId); + } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const TensorChippingOp<Dynamic, const Derived> + chip(const Index offset, const Index dim) const { + return TensorChippingOp<Dynamic, const Derived>(derived(), offset, dim); + } + template <typename ReverseDimensions> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const TensorReverseOp<const ReverseDimensions, const Derived> + reverse(const ReverseDimensions& rev) const { + return TensorReverseOp<const ReverseDimensions, const Derived>(derived(), rev); + } + template <typename PaddingDimensions> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const TensorPaddingOp<const PaddingDimensions, const Derived> + pad(const PaddingDimensions& padding) const { + return TensorPaddingOp<const PaddingDimensions, const Derived>(derived(), padding, internal::scalar_cast_op<int, Scalar>()(0)); + } + template <typename PaddingDimensions> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const TensorPaddingOp<const PaddingDimensions, const Derived> + pad(const PaddingDimensions& padding, const Scalar padding_value) const { + return TensorPaddingOp<const PaddingDimensions, const Derived>(derived(), padding, padding_value); + } + template <typename Shuffle> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const TensorShufflingOp<const Shuffle, const Derived> + shuffle(const Shuffle& shuffle) const { + return TensorShufflingOp<const Shuffle, const Derived>(derived(), shuffle); + } + template <typename Strides> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const TensorStridingOp<const Strides, const Derived> + stride(const Strides& strides) const { + return TensorStridingOp<const Strides, const Derived>(derived(), strides); + } + template <typename Strides> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const TensorInflationOp<const Strides, const Derived> + inflate(const Strides& strides) const { + return TensorInflationOp<const Strides, const Derived>(derived(), strides); + } + + // Returns a tensor containing index/value tuples + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const TensorIndexTupleOp<const Derived> + index_tuples() const { + return TensorIndexTupleOp<const Derived>(derived()); + } + + // Support for custom unary and binary operations + template <typename CustomUnaryFunc> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const TensorCustomUnaryOp<const CustomUnaryFunc, const Derived> customOp(const CustomUnaryFunc& op) const { + return TensorCustomUnaryOp<const CustomUnaryFunc, const Derived>(derived(), op); + } + template <typename OtherDerived, typename CustomBinaryFunc> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const TensorCustomBinaryOp<const CustomBinaryFunc, const Derived, const OtherDerived> customOp(const OtherDerived& other, const CustomBinaryFunc& op) const { + return TensorCustomBinaryOp<const CustomBinaryFunc, const Derived, const OtherDerived>(derived(), other, op); + } + + // Force the evaluation of the expression. + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const TensorForcedEvalOp<const Derived> eval() const { + return TensorForcedEvalOp<const Derived>(derived()); + } + + protected: + template <typename Scalar, int NumIndices, int Options, typename IndexType> friend class Tensor; + template <typename Scalar, typename Dimensions, int Option, typename IndexTypes> friend class TensorFixedSize; + template <typename OtherDerived, int AccessLevel> friend class TensorBase; + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const Derived& derived() const { return *static_cast<const Derived*>(this); } +}; + +template<typename Derived, int AccessLevel = internal::accessors_level<Derived>::value> +class TensorBase : public TensorBase<Derived, ReadOnlyAccessors> { + public: + typedef internal::traits<Derived> DerivedTraits; + typedef typename DerivedTraits::Scalar Scalar; + typedef typename DerivedTraits::Index Index; + typedef Scalar CoeffReturnType; + static const int NumDimensions = DerivedTraits::NumDimensions; + + template <typename Scalar, int NumIndices, int Options, typename IndexType> friend class Tensor; + template <typename Scalar, typename Dimensions, int Option, typename IndexTypes> friend class TensorFixedSize; + template <typename OtherDerived, int OtherAccessLevel> friend class TensorBase; + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Derived& setZero() { + return setConstant(Scalar(0)); + } + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Derived& setConstant(const Scalar& val) { + return derived() = this->constant(val); + } + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Derived& setRandom() { + return derived() = this->random(); + } + template <typename RandomGenerator> EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Derived& setRandom() { + return derived() = this->template random<RandomGenerator>(); + } + +#if EIGEN_HAS_VARIADIC_TEMPLATES + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Derived& setValues( + const typename internal::Initializer<Derived, NumDimensions>::InitList& vals) { + TensorEvaluator<Derived, DefaultDevice> eval(derived(), DefaultDevice()); + internal::initialize_tensor<Derived, NumDimensions>(eval, vals); + return derived(); + } +#endif // EIGEN_HAS_VARIADIC_TEMPLATES + + template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + Derived& operator+=(const OtherDerived& other) { + return derived() = derived() + other.derived(); + } + template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + Derived& operator-=(const OtherDerived& other) { + return derived() = derived() - other.derived(); + } + template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + Derived& operator*=(const OtherDerived& other) { + return derived() = derived() * other.derived(); + } + template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + Derived& operator/=(const OtherDerived& other) { + return derived() = derived() / other.derived(); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const TensorLayoutSwapOp<const Derived> + swap_layout() const { + return TensorLayoutSwapOp<const Derived>(derived()); + } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + TensorLayoutSwapOp<Derived> + swap_layout() { + return TensorLayoutSwapOp<Derived>(derived()); + } + + template <typename Axis, typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const TensorConcatenationOp<const Axis, const Derived, const OtherDerived> + concatenate(const OtherDerived& other, const Axis& axis) const { + return TensorConcatenationOp<const Axis, const Derived, const OtherDerived>(derived(), other, axis); + } + template <typename Axis, typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + TensorConcatenationOp<const Axis, Derived, OtherDerived> + concatenate(const OtherDerived& other, const Axis& axis) { + return TensorConcatenationOp<const Axis, Derived, OtherDerived>(derived(), other, axis); + } + + template <typename NewDimensions> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const TensorReshapingOp<const NewDimensions, const Derived> + reshape(const NewDimensions& newDimensions) const { + return TensorReshapingOp<const NewDimensions, const Derived>(derived(), newDimensions); + } + template <typename NewDimensions> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + TensorReshapingOp<const NewDimensions, Derived> + reshape(const NewDimensions& newDimensions) { + return TensorReshapingOp<const NewDimensions, Derived>(derived(), newDimensions); + } + + template <typename StartIndices, typename Sizes> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const TensorSlicingOp<const StartIndices, const Sizes, const Derived> + slice(const StartIndices& startIndices, const Sizes& sizes) const { + return TensorSlicingOp<const StartIndices, const Sizes, const Derived>(derived(), startIndices, sizes); + } + template <typename StartIndices, typename Sizes> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + TensorSlicingOp<const StartIndices, const Sizes, Derived> + slice(const StartIndices& startIndices, const Sizes& sizes) { + return TensorSlicingOp<const StartIndices, const Sizes, Derived>(derived(), startIndices, sizes); + } + + template <typename StartIndices, typename StopIndices, typename Strides> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const TensorStridingSlicingOp<const StartIndices, const StopIndices, const Strides, const Derived> + stridedSlice(const StartIndices& startIndices, const StopIndices& stopIndices, const Strides& strides) const { + return TensorStridingSlicingOp<const StartIndices, const StopIndices, const Strides, + const Derived>(derived(), startIndices, stopIndices, strides); + } + template <typename StartIndices, typename StopIndices, typename Strides> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + TensorStridingSlicingOp<const StartIndices, const StopIndices, const Strides, Derived> + stridedSlice(const StartIndices& startIndices, const StopIndices& stopIndices, const Strides& strides) { + return TensorStridingSlicingOp<const StartIndices, const StopIndices, const Strides, + Derived>(derived(), startIndices, stopIndices, strides); + } + + template <DenseIndex DimId> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const TensorChippingOp<DimId, const Derived> + chip(const Index offset) const { + return TensorChippingOp<DimId, const Derived>(derived(), offset, DimId); + } + template <Index DimId> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + TensorChippingOp<DimId, Derived> + chip(const Index offset) { + return TensorChippingOp<DimId, Derived>(derived(), offset, DimId); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const TensorChippingOp<Dynamic, const Derived> + chip(const Index offset, const Index dim) const { + return TensorChippingOp<Dynamic, const Derived>(derived(), offset, dim); + } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + TensorChippingOp<Dynamic, Derived> + chip(const Index offset, const Index dim) { + return TensorChippingOp<Dynamic, Derived>(derived(), offset, dim); + } + + template <typename ReverseDimensions> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const TensorReverseOp<const ReverseDimensions, const Derived> + reverse(const ReverseDimensions& rev) const { + return TensorReverseOp<const ReverseDimensions, const Derived>(derived(), rev); + } + template <typename ReverseDimensions> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + TensorReverseOp<const ReverseDimensions, Derived> + reverse(const ReverseDimensions& rev) { + return TensorReverseOp<const ReverseDimensions, Derived>(derived(), rev); + } + + template <typename Shuffle> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const TensorShufflingOp<const Shuffle, const Derived> + shuffle(const Shuffle& shuffle) const { + return TensorShufflingOp<const Shuffle, const Derived>(derived(), shuffle); + } + template <typename Shuffle> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + TensorShufflingOp<const Shuffle, Derived> + shuffle(const Shuffle& shuffle) { + return TensorShufflingOp<const Shuffle, Derived>(derived(), shuffle); + } + + template <typename Strides> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const TensorStridingOp<const Strides, const Derived> + stride(const Strides& strides) const { + return TensorStridingOp<const Strides, const Derived>(derived(), strides); + } + template <typename Strides> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + TensorStridingOp<const Strides, Derived> + stride(const Strides& strides) { + return TensorStridingOp<const Strides, Derived>(derived(), strides); + } + + // Select the device on which to evaluate the expression. + template <typename DeviceType> + TensorDevice<Derived, DeviceType> device(const DeviceType& device) { + return TensorDevice<Derived, DeviceType>(device, derived()); + } + + protected: + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Derived& derived() { return *static_cast<Derived*>(this); } + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const Derived& derived() const { return *static_cast<const Derived*>(this); } +}; + +} // end namespace Eigen + +#endif // EIGEN_CXX11_TENSOR_TENSOR_BASE_H diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorBroadcasting.h b/unsupported/Eigen/CXX11/src/Tensor/TensorBroadcasting.h new file mode 100644 index 000000000..4cfe300eb --- /dev/null +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorBroadcasting.h @@ -0,0 +1,392 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_CXX11_TENSOR_TENSOR_BROADCASTING_H +#define EIGEN_CXX11_TENSOR_TENSOR_BROADCASTING_H + +namespace Eigen { + +/** \class TensorBroadcasting + * \ingroup CXX11_Tensor_Module + * + * \brief Tensor broadcasting class. + * + * + */ +namespace internal { +template<typename Broadcast, typename XprType> +struct traits<TensorBroadcastingOp<Broadcast, XprType> > : public traits<XprType> +{ + typedef typename XprType::Scalar Scalar; + typedef traits<XprType> XprTraits; + typedef typename XprTraits::StorageKind StorageKind; + typedef typename XprTraits::Index Index; + typedef typename XprType::Nested Nested; + typedef typename remove_reference<Nested>::type _Nested; + static const int NumDimensions = XprTraits::NumDimensions; + static const int Layout = XprTraits::Layout; +}; + +template<typename Broadcast, typename XprType> +struct eval<TensorBroadcastingOp<Broadcast, XprType>, Eigen::Dense> +{ + typedef const TensorBroadcastingOp<Broadcast, XprType>& type; +}; + +template<typename Broadcast, typename XprType> +struct nested<TensorBroadcastingOp<Broadcast, XprType>, 1, typename eval<TensorBroadcastingOp<Broadcast, XprType> >::type> +{ + typedef TensorBroadcastingOp<Broadcast, XprType> type; +}; + +template <typename Dims> +struct is_input_scalar { + static const bool value = false; +}; +template <> +struct is_input_scalar<Sizes<> > { + static const bool value = true; +}; +#ifndef EIGEN_EMULATE_CXX11_META_H +template <typename std::size_t... Indices> +struct is_input_scalar<Sizes<Indices...> > { + static const bool value = (Sizes<Indices...>::total_size == 1); +}; +#endif + +} // end namespace internal + + + +template<typename Broadcast, typename XprType> +class TensorBroadcastingOp : public TensorBase<TensorBroadcastingOp<Broadcast, XprType>, ReadOnlyAccessors> +{ + public: + typedef typename Eigen::internal::traits<TensorBroadcastingOp>::Scalar Scalar; + typedef typename Eigen::NumTraits<Scalar>::Real RealScalar; + typedef typename XprType::CoeffReturnType CoeffReturnType; + typedef typename Eigen::internal::nested<TensorBroadcastingOp>::type Nested; + typedef typename Eigen::internal::traits<TensorBroadcastingOp>::StorageKind StorageKind; + typedef typename Eigen::internal::traits<TensorBroadcastingOp>::Index Index; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBroadcastingOp(const XprType& expr, const Broadcast& broadcast) + : m_xpr(expr), m_broadcast(broadcast) {} + + EIGEN_DEVICE_FUNC + const Broadcast& broadcast() const { return m_broadcast; } + + EIGEN_DEVICE_FUNC + const typename internal::remove_all<typename XprType::Nested>::type& + expression() const { return m_xpr; } + + protected: + typename XprType::Nested m_xpr; + const Broadcast m_broadcast; +}; + + +// Eval as rvalue +template<typename Broadcast, typename ArgType, typename Device> +struct TensorEvaluator<const TensorBroadcastingOp<Broadcast, ArgType>, Device> +{ + typedef TensorBroadcastingOp<Broadcast, ArgType> XprType; + typedef typename XprType::Index Index; + static const int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value; + typedef DSizes<Index, NumDims> Dimensions; + typedef typename XprType::Scalar Scalar; + typedef typename TensorEvaluator<ArgType, Device>::Dimensions InputDimensions; + typedef typename XprType::CoeffReturnType CoeffReturnType; + typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType; + static const int PacketSize = internal::unpacket_traits<PacketReturnType>::size; + + enum { + IsAligned = true, + PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess, + Layout = TensorEvaluator<ArgType, Device>::Layout, + RawAccess = false + }; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) + : m_broadcast(op.broadcast()),m_impl(op.expression(), device) + { + // The broadcasting op doesn't change the rank of the tensor. One can't broadcast a scalar + // and store the result in a scalar. Instead one should reshape the scalar into a a N-D + // tensor with N >= 1 of 1 element first and then broadcast. + EIGEN_STATIC_ASSERT((NumDims > 0), YOU_MADE_A_PROGRAMMING_MISTAKE); + const InputDimensions& input_dims = m_impl.dimensions(); + const Broadcast& broadcast = op.broadcast(); + for (int i = 0; i < NumDims; ++i) { + eigen_assert(input_dims[i] > 0); + m_dimensions[i] = input_dims[i] * broadcast[i]; + } + + if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { + m_inputStrides[0] = 1; + m_outputStrides[0] = 1; + for (int i = 1; i < NumDims; ++i) { + m_inputStrides[i] = m_inputStrides[i-1] * input_dims[i-1]; + m_outputStrides[i] = m_outputStrides[i-1] * m_dimensions[i-1]; + } + } else { + m_inputStrides[NumDims-1] = 1; + m_outputStrides[NumDims-1] = 1; + for (int i = NumDims-2; i >= 0; --i) { + m_inputStrides[i] = m_inputStrides[i+1] * input_dims[i+1]; + m_outputStrides[i] = m_outputStrides[i+1] * m_dimensions[i+1]; + } + } + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* /*data*/) { + m_impl.evalSubExprsIfNeeded(NULL); + return true; + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() { + m_impl.cleanup(); + } + + EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE CoeffReturnType coeff(Index index) const + { + if (internal::is_input_scalar<typename internal::remove_all<InputDimensions>::type>::value) { + return m_impl.coeff(0); + } + + if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { + return coeffColMajor(index); + } else { + return coeffRowMajor(index); + } + } + + // TODO: attempt to speed this up. The integer divisions and modulo are slow + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeffColMajor(Index index) const + { + Index inputIndex = 0; + for (int i = NumDims - 1; i > 0; --i) { + const Index idx = index / m_outputStrides[i]; + if (internal::index_statically_eq<Broadcast>(i, 1)) { + eigen_assert(idx < m_impl.dimensions()[i]); + inputIndex += idx * m_inputStrides[i]; + } else { + if (internal::index_statically_eq<InputDimensions>(i, 1)) { + eigen_assert(idx % m_impl.dimensions()[i] == 0); + } else { + inputIndex += (idx % m_impl.dimensions()[i]) * m_inputStrides[i]; + } + } + index -= idx * m_outputStrides[i]; + } + if (internal::index_statically_eq<Broadcast>(0, 1)) { + eigen_assert(index < m_impl.dimensions()[0]); + inputIndex += index; + } else { + if (internal::index_statically_eq<InputDimensions>(0, 1)) { + eigen_assert(index % m_impl.dimensions()[0] == 0); + } else { + inputIndex += (index % m_impl.dimensions()[0]); + } + } + return m_impl.coeff(inputIndex); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeffRowMajor(Index index) const + { + Index inputIndex = 0; + for (int i = 0; i < NumDims - 1; ++i) { + const Index idx = index / m_outputStrides[i]; + if (internal::index_statically_eq<Broadcast>(i, 1)) { + eigen_assert(idx < m_impl.dimensions()[i]); + inputIndex += idx * m_inputStrides[i]; + } else { + if (internal::index_statically_eq<InputDimensions>(i, 1)) { + eigen_assert(idx % m_impl.dimensions()[i] == 0); + } else { + inputIndex += (idx % m_impl.dimensions()[i]) * m_inputStrides[i]; + } + } + index -= idx * m_outputStrides[i]; + } + if (internal::index_statically_eq<Broadcast>(NumDims-1, 1)) { + eigen_assert(index < m_impl.dimensions()[NumDims-1]); + inputIndex += index; + } else { + if (internal::index_statically_eq<InputDimensions>(NumDims-1, 1)) { + eigen_assert(index % m_impl.dimensions()[NumDims-1] == 0); + } else { + inputIndex += (index % m_impl.dimensions()[NumDims-1]); + } + } + return m_impl.coeff(inputIndex); + } + + template<int LoadMode> + EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE PacketReturnType packet(Index index) const + { + if (internal::is_input_scalar<typename internal::remove_all<InputDimensions>::type>::value) { + return internal::pset1<PacketReturnType>(m_impl.coeff(0)); + } + + if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { + return packetColMajor<LoadMode>(index); + } else { + return packetRowMajor<LoadMode>(index); + } + } + + // Ignore the LoadMode and always use unaligned loads since we can't guarantee + // the alignment at compile time. + template<int LoadMode> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetColMajor(Index index) const + { + EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE) + eigen_assert(index+PacketSize-1 < dimensions().TotalSize()); + + const Index originalIndex = index; + + Index inputIndex = 0; + for (int i = NumDims - 1; i > 0; --i) { + const Index idx = index / m_outputStrides[i]; + if (internal::index_statically_eq<Broadcast>(i, 1)) { + eigen_assert(idx < m_impl.dimensions()[i]); + inputIndex += idx * m_inputStrides[i]; + } else { + if (internal::index_statically_eq<InputDimensions>(i, 1)) { + eigen_assert(idx % m_impl.dimensions()[i] == 0); + } else { + inputIndex += (idx % m_impl.dimensions()[i]) * m_inputStrides[i]; + } + } + index -= idx * m_outputStrides[i]; + } + Index innermostLoc; + if (internal::index_statically_eq<Broadcast>(0, 1)) { + eigen_assert(index < m_impl.dimensions()[0]); + innermostLoc = index; + } else { + if (internal::index_statically_eq<InputDimensions>(0, 1)) { + eigen_assert(index % m_impl.dimensions()[0] == 0); + innermostLoc = 0; + } else { + innermostLoc = index % m_impl.dimensions()[0]; + } + } + inputIndex += innermostLoc; + + // Todo: this could be extended to the second dimension if we're not + // broadcasting alongside the first dimension, and so on. + if (innermostLoc + PacketSize <= m_impl.dimensions()[0]) { + return m_impl.template packet<Unaligned>(inputIndex); + } else { + EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize]; + values[0] = m_impl.coeff(inputIndex); + for (int i = 1; i < PacketSize; ++i) { + values[i] = coeffColMajor(originalIndex+i); + } + PacketReturnType rslt = internal::pload<PacketReturnType>(values); + return rslt; + } + } + + template<int LoadMode> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetRowMajor(Index index) const + { + EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE) + eigen_assert(index+PacketSize-1 < dimensions().TotalSize()); + + const Index originalIndex = index; + + Index inputIndex = 0; + for (int i = 0; i < NumDims - 1; ++i) { + const Index idx = index / m_outputStrides[i]; + if (internal::index_statically_eq<Broadcast>(i, 1)) { + eigen_assert(idx < m_impl.dimensions()[i]); + inputIndex += idx * m_inputStrides[i]; + } else { + if (internal::index_statically_eq<InputDimensions>(i, 1)) { + eigen_assert(idx % m_impl.dimensions()[i] == 0); + } else { + inputIndex += (idx % m_impl.dimensions()[i]) * m_inputStrides[i]; + } + } + index -= idx * m_outputStrides[i]; + } + Index innermostLoc; + if (internal::index_statically_eq<Broadcast>(NumDims-1, 1)) { + eigen_assert(index < m_impl.dimensions()[NumDims-1]); + innermostLoc = index; + } else { + if (internal::index_statically_eq<InputDimensions>(NumDims-1, 1)) { + eigen_assert(index % m_impl.dimensions()[NumDims-1] == 0); + innermostLoc = 0; + } else { + innermostLoc = index % m_impl.dimensions()[NumDims-1]; + } + } + inputIndex += innermostLoc; + + // Todo: this could be extended to the second dimension if we're not + // broadcasting alongside the first dimension, and so on. + if (innermostLoc + PacketSize <= m_impl.dimensions()[NumDims-1]) { + return m_impl.template packet<Unaligned>(inputIndex); + } else { + EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize]; + values[0] = m_impl.coeff(inputIndex); + for (int i = 1; i < PacketSize; ++i) { + values[i] = coeffRowMajor(originalIndex+i); + } + PacketReturnType rslt = internal::pload<PacketReturnType>(values); + return rslt; + } + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost + costPerCoeff(bool vectorized) const { + double compute_cost = TensorOpCost::AddCost<Index>(); + if (NumDims > 0) { + for (int i = NumDims - 1; i > 0; --i) { + compute_cost += TensorOpCost::DivCost<Index>(); + if (internal::index_statically_eq<Broadcast>(i, 1)) { + compute_cost += + TensorOpCost::MulCost<Index>() + TensorOpCost::AddCost<Index>(); + } else { + if (!internal::index_statically_eq<InputDimensions>(i, 1)) { + compute_cost += TensorOpCost::MulCost<Index>() + + TensorOpCost::ModCost<Index>() + + TensorOpCost::AddCost<Index>(); + } + } + compute_cost += + TensorOpCost::MulCost<Index>() + TensorOpCost::AddCost<Index>(); + } + } + return m_impl.costPerCoeff(vectorized) + + TensorOpCost(0, 0, compute_cost, vectorized, PacketSize); + } + + EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; } + + const TensorEvaluator<ArgType, Device>& impl() const { return m_impl; } + + Broadcast functor() const { return m_broadcast; } + + protected: + const Broadcast m_broadcast; + Dimensions m_dimensions; + array<Index, NumDims> m_outputStrides; + array<Index, NumDims> m_inputStrides; + TensorEvaluator<ArgType, Device> m_impl; +}; + + +} // end namespace Eigen + +#endif // EIGEN_CXX11_TENSOR_TENSOR_BROADCASTING_H diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorChipping.h b/unsupported/Eigen/CXX11/src/Tensor/TensorChipping.h new file mode 100644 index 000000000..1ba7ef170 --- /dev/null +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorChipping.h @@ -0,0 +1,384 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_CXX11_TENSOR_TENSOR_CHIPPING_H +#define EIGEN_CXX11_TENSOR_TENSOR_CHIPPING_H + +namespace Eigen { + +/** \class TensorKChippingReshaping + * \ingroup CXX11_Tensor_Module + * + * \brief A chip is a thin slice, corresponding to a column or a row in a 2-d tensor. + * + * + */ + +namespace internal { +template<DenseIndex DimId, typename XprType> +struct traits<TensorChippingOp<DimId, XprType> > : public traits<XprType> +{ + typedef typename XprType::Scalar Scalar; + typedef traits<XprType> XprTraits; + typedef typename XprTraits::StorageKind StorageKind; + typedef typename XprTraits::Index Index; + typedef typename XprType::Nested Nested; + typedef typename remove_reference<Nested>::type _Nested; + static const int NumDimensions = XprTraits::NumDimensions - 1; + static const int Layout = XprTraits::Layout; +}; + +template<DenseIndex DimId, typename XprType> +struct eval<TensorChippingOp<DimId, XprType>, Eigen::Dense> +{ + typedef const TensorChippingOp<DimId, XprType>& type; +}; + +template<DenseIndex DimId, typename XprType> +struct nested<TensorChippingOp<DimId, XprType>, 1, typename eval<TensorChippingOp<DimId, XprType> >::type> +{ + typedef TensorChippingOp<DimId, XprType> type; +}; + +template <DenseIndex DimId> +struct DimensionId +{ + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DimensionId(DenseIndex dim) { + eigen_assert(dim == DimId); + } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DenseIndex actualDim() const { + return DimId; + } +}; +template <> +struct DimensionId<Dynamic> +{ + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DimensionId(DenseIndex dim) : actual_dim(dim) { + eigen_assert(dim >= 0); + } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DenseIndex actualDim() const { + return actual_dim; + } + private: + const DenseIndex actual_dim; +}; + + +} // end namespace internal + + + +template<DenseIndex DimId, typename XprType> +class TensorChippingOp : public TensorBase<TensorChippingOp<DimId, XprType> > +{ + public: + typedef typename Eigen::internal::traits<TensorChippingOp>::Scalar Scalar; + typedef typename Eigen::NumTraits<Scalar>::Real RealScalar; + typedef typename XprType::CoeffReturnType CoeffReturnType; + typedef typename Eigen::internal::nested<TensorChippingOp>::type Nested; + typedef typename Eigen::internal::traits<TensorChippingOp>::StorageKind StorageKind; + typedef typename Eigen::internal::traits<TensorChippingOp>::Index Index; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorChippingOp(const XprType& expr, const Index offset, const Index dim) + : m_xpr(expr), m_offset(offset), m_dim(dim) { + } + + EIGEN_DEVICE_FUNC + const Index offset() const { return m_offset; } + EIGEN_DEVICE_FUNC + const Index dim() const { return m_dim.actualDim(); } + + EIGEN_DEVICE_FUNC + const typename internal::remove_all<typename XprType::Nested>::type& + expression() const { return m_xpr; } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE TensorChippingOp& operator = (const TensorChippingOp& other) + { + typedef TensorAssignOp<TensorChippingOp, const TensorChippingOp> Assign; + Assign assign(*this, other); + internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice()); + return *this; + } + + template<typename OtherDerived> + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE TensorChippingOp& operator = (const OtherDerived& other) + { + typedef TensorAssignOp<TensorChippingOp, const OtherDerived> Assign; + Assign assign(*this, other); + internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice()); + return *this; + } + + protected: + typename XprType::Nested m_xpr; + const Index m_offset; + const internal::DimensionId<DimId> m_dim; +}; + + +// Eval as rvalue +template<DenseIndex DimId, typename ArgType, typename Device> +struct TensorEvaluator<const TensorChippingOp<DimId, ArgType>, Device> +{ + typedef TensorChippingOp<DimId, ArgType> XprType; + static const int NumInputDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value; + static const int NumDims = NumInputDims-1; + typedef typename XprType::Index Index; + typedef DSizes<Index, NumDims> Dimensions; + typedef typename XprType::Scalar Scalar; + typedef typename XprType::CoeffReturnType CoeffReturnType; + typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType; + static const int PacketSize = internal::unpacket_traits<PacketReturnType>::size; + + + enum { + // Alignment can't be guaranteed at compile time since it depends on the + // slice offsets. + IsAligned = false, + PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess, + Layout = TensorEvaluator<ArgType, Device>::Layout, + CoordAccess = false, // to be implemented + RawAccess = false + }; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) + : m_impl(op.expression(), device), m_dim(op.dim()), m_device(device) + { + EIGEN_STATIC_ASSERT((NumInputDims >= 1), YOU_MADE_A_PROGRAMMING_MISTAKE); + eigen_assert(NumInputDims > m_dim.actualDim()); + + const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions(); + eigen_assert(op.offset() < input_dims[m_dim.actualDim()]); + + int j = 0; + for (int i = 0; i < NumInputDims; ++i) { + if (i != m_dim.actualDim()) { + m_dimensions[j] = input_dims[i]; + ++j; + } + } + + m_stride = 1; + m_inputStride = 1; + if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { + for (int i = 0; i < m_dim.actualDim(); ++i) { + m_stride *= input_dims[i]; + m_inputStride *= input_dims[i]; + } + } else { + for (int i = NumInputDims-1; i > m_dim.actualDim(); --i) { + m_stride *= input_dims[i]; + m_inputStride *= input_dims[i]; + } + } + m_inputStride *= input_dims[m_dim.actualDim()]; + m_inputOffset = m_stride * op.offset(); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* /*data*/) { + m_impl.evalSubExprsIfNeeded(NULL); + return true; + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() { + m_impl.cleanup(); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const + { + return m_impl.coeff(srcCoeff(index)); + } + + template<int LoadMode> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const + { + EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE) + eigen_assert(index+PacketSize-1 < dimensions().TotalSize()); + + if ((static_cast<int>(Layout) == static_cast<int>(ColMajor) && m_dim.actualDim() == 0) || + (static_cast<int>(Layout) == static_cast<int>(RowMajor) && m_dim.actualDim() == NumInputDims-1)) { + // m_stride is equal to 1, so let's avoid the integer division. + eigen_assert(m_stride == 1); + Index inputIndex = index * m_inputStride + m_inputOffset; + EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize]; + for (int i = 0; i < PacketSize; ++i) { + values[i] = m_impl.coeff(inputIndex); + inputIndex += m_inputStride; + } + PacketReturnType rslt = internal::pload<PacketReturnType>(values); + return rslt; + } else if ((static_cast<int>(Layout) == static_cast<int>(ColMajor) && m_dim.actualDim() == NumInputDims - 1) || + (static_cast<int>(Layout) == static_cast<int>(RowMajor) && m_dim.actualDim() == 0)) { + // m_stride is aways greater than index, so let's avoid the integer division. + eigen_assert(m_stride > index); + return m_impl.template packet<LoadMode>(index + m_inputOffset); + } else { + const Index idx = index / m_stride; + const Index rem = index - idx * m_stride; + if (rem + PacketSize <= m_stride) { + Index inputIndex = idx * m_inputStride + m_inputOffset + rem; + return m_impl.template packet<LoadMode>(inputIndex); + } else { + // Cross the stride boundary. Fallback to slow path. + EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize]; + for (int i = 0; i < PacketSize; ++i) { + values[i] = coeff(index); + ++index; + } + PacketReturnType rslt = internal::pload<PacketReturnType>(values); + return rslt; + } + } + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost + costPerCoeff(bool vectorized) const { + double cost = 0; + if ((static_cast<int>(Layout) == static_cast<int>(ColMajor) && + m_dim.actualDim() == 0) || + (static_cast<int>(Layout) == static_cast<int>(RowMajor) && + m_dim.actualDim() == NumInputDims - 1)) { + cost += TensorOpCost::MulCost<Index>() + TensorOpCost::AddCost<Index>(); + } else if ((static_cast<int>(Layout) == static_cast<int>(ColMajor) && + m_dim.actualDim() == NumInputDims - 1) || + (static_cast<int>(Layout) == static_cast<int>(RowMajor) && + m_dim.actualDim() == 0)) { + cost += TensorOpCost::AddCost<Index>(); + } else { + cost += 3 * TensorOpCost::MulCost<Index>() + TensorOpCost::DivCost<Index>() + + 3 * TensorOpCost::AddCost<Index>(); + } + + return m_impl.costPerCoeff(vectorized) + + TensorOpCost(0, 0, cost, vectorized, PacketSize); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType* data() const { + CoeffReturnType* result = const_cast<CoeffReturnType*>(m_impl.data()); + if (((static_cast<int>(Layout) == static_cast<int>(ColMajor) && m_dim.actualDim() == NumDims) || + (static_cast<int>(Layout) == static_cast<int>(RowMajor) && m_dim.actualDim() == 0)) && + result) { + return result + m_inputOffset; + } else { + return NULL; + } + } + + protected: + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index srcCoeff(Index index) const + { + Index inputIndex; + if ((static_cast<int>(Layout) == static_cast<int>(ColMajor) && m_dim.actualDim() == 0) || + (static_cast<int>(Layout) == static_cast<int>(RowMajor) && m_dim.actualDim() == NumInputDims-1)) { + // m_stride is equal to 1, so let's avoid the integer division. + eigen_assert(m_stride == 1); + inputIndex = index * m_inputStride + m_inputOffset; + } else if ((static_cast<int>(Layout) == static_cast<int>(ColMajor) && m_dim.actualDim() == NumInputDims-1) || + (static_cast<int>(Layout) == static_cast<int>(RowMajor) && m_dim.actualDim() == 0)) { + // m_stride is aways greater than index, so let's avoid the integer division. + eigen_assert(m_stride > index); + inputIndex = index + m_inputOffset; + } else { + const Index idx = index / m_stride; + inputIndex = idx * m_inputStride + m_inputOffset; + index -= idx * m_stride; + inputIndex += index; + } + return inputIndex; + } + + Dimensions m_dimensions; + Index m_stride; + Index m_inputOffset; + Index m_inputStride; + TensorEvaluator<ArgType, Device> m_impl; + const internal::DimensionId<DimId> m_dim; + const Device& m_device; +}; + + +// Eval as lvalue +template<DenseIndex DimId, typename ArgType, typename Device> +struct TensorEvaluator<TensorChippingOp<DimId, ArgType>, Device> + : public TensorEvaluator<const TensorChippingOp<DimId, ArgType>, Device> +{ + typedef TensorEvaluator<const TensorChippingOp<DimId, ArgType>, Device> Base; + typedef TensorChippingOp<DimId, ArgType> XprType; + static const int NumInputDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value; + static const int NumDims = NumInputDims-1; + typedef typename XprType::Index Index; + typedef DSizes<Index, NumDims> Dimensions; + typedef typename XprType::Scalar Scalar; + typedef typename XprType::CoeffReturnType CoeffReturnType; + typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType; + static const int PacketSize = internal::unpacket_traits<PacketReturnType>::size; + + enum { + IsAligned = false, + PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess, + RawAccess = false + }; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) + : Base(op, device) + { } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType& coeffRef(Index index) + { + return this->m_impl.coeffRef(this->srcCoeff(index)); + } + + template <int StoreMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + void writePacket(Index index, const PacketReturnType& x) + { + EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE) + + if ((static_cast<int>(this->Layout) == static_cast<int>(ColMajor) && this->m_dim.actualDim() == 0) || + (static_cast<int>(this->Layout) == static_cast<int>(RowMajor) && this->m_dim.actualDim() == NumInputDims-1)) { + // m_stride is equal to 1, so let's avoid the integer division. + eigen_assert(this->m_stride == 1); + EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize]; + internal::pstore<CoeffReturnType, PacketReturnType>(values, x); + Index inputIndex = index * this->m_inputStride + this->m_inputOffset; + for (int i = 0; i < PacketSize; ++i) { + this->m_impl.coeffRef(inputIndex) = values[i]; + inputIndex += this->m_inputStride; + } + } else if ((static_cast<int>(this->Layout) == static_cast<int>(ColMajor) && this->m_dim.actualDim() == NumInputDims-1) || + (static_cast<int>(this->Layout) == static_cast<int>(RowMajor) && this->m_dim.actualDim() == 0)) { + // m_stride is aways greater than index, so let's avoid the integer division. + eigen_assert(this->m_stride > index); + this->m_impl.template writePacket<StoreMode>(index + this->m_inputOffset, x); + } else { + const Index idx = index / this->m_stride; + const Index rem = index - idx * this->m_stride; + if (rem + PacketSize <= this->m_stride) { + const Index inputIndex = idx * this->m_inputStride + this->m_inputOffset + rem; + this->m_impl.template writePacket<StoreMode>(inputIndex, x); + } else { + // Cross stride boundary. Fallback to slow path. + EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize]; + internal::pstore<CoeffReturnType, PacketReturnType>(values, x); + for (int i = 0; i < PacketSize; ++i) { + this->coeffRef(index) = values[i]; + ++index; + } + } + } + } +}; + + +} // end namespace Eigen + +#endif // EIGEN_CXX11_TENSOR_TENSOR_CHIPPING_H diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorConcatenation.h b/unsupported/Eigen/CXX11/src/Tensor/TensorConcatenation.h new file mode 100644 index 000000000..59bf90d93 --- /dev/null +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorConcatenation.h @@ -0,0 +1,361 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_CXX11_TENSOR_TENSOR_CONCATENATION_H +#define EIGEN_CXX11_TENSOR_TENSOR_CONCATENATION_H + +namespace Eigen { + +/** \class TensorConcatenationOp + * \ingroup CXX11_Tensor_Module + * + * \brief Tensor concatenation class. + * + * + */ +namespace internal { +template<typename Axis, typename LhsXprType, typename RhsXprType> +struct traits<TensorConcatenationOp<Axis, LhsXprType, RhsXprType> > +{ + // Type promotion to handle the case where the types of the lhs and the rhs are different. + typedef typename promote_storage_type<typename LhsXprType::Scalar, + typename RhsXprType::Scalar>::ret Scalar; + typedef typename promote_storage_type<typename traits<LhsXprType>::StorageKind, + typename traits<RhsXprType>::StorageKind>::ret StorageKind; + typedef typename promote_index_type<typename traits<LhsXprType>::Index, + typename traits<RhsXprType>::Index>::type Index; + typedef typename LhsXprType::Nested LhsNested; + typedef typename RhsXprType::Nested RhsNested; + typedef typename remove_reference<LhsNested>::type _LhsNested; + typedef typename remove_reference<RhsNested>::type _RhsNested; + static const int NumDimensions = traits<LhsXprType>::NumDimensions; + static const int Layout = traits<LhsXprType>::Layout; + enum { Flags = 0 }; +}; + +template<typename Axis, typename LhsXprType, typename RhsXprType> +struct eval<TensorConcatenationOp<Axis, LhsXprType, RhsXprType>, Eigen::Dense> +{ + typedef const TensorConcatenationOp<Axis, LhsXprType, RhsXprType>& type; +}; + +template<typename Axis, typename LhsXprType, typename RhsXprType> +struct nested<TensorConcatenationOp<Axis, LhsXprType, RhsXprType>, 1, typename eval<TensorConcatenationOp<Axis, LhsXprType, RhsXprType> >::type> +{ + typedef TensorConcatenationOp<Axis, LhsXprType, RhsXprType> type; +}; + +} // end namespace internal + + +template<typename Axis, typename LhsXprType, typename RhsXprType> +class TensorConcatenationOp : public TensorBase<TensorConcatenationOp<Axis, LhsXprType, RhsXprType>, WriteAccessors> +{ + public: + typedef typename internal::traits<TensorConcatenationOp>::Scalar Scalar; + typedef typename internal::traits<TensorConcatenationOp>::StorageKind StorageKind; + typedef typename internal::traits<TensorConcatenationOp>::Index Index; + typedef typename internal::nested<TensorConcatenationOp>::type Nested; + typedef typename internal::promote_storage_type<typename LhsXprType::CoeffReturnType, + typename RhsXprType::CoeffReturnType>::ret CoeffReturnType; + typedef typename NumTraits<Scalar>::Real RealScalar; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorConcatenationOp(const LhsXprType& lhs, const RhsXprType& rhs, Axis axis) + : m_lhs_xpr(lhs), m_rhs_xpr(rhs), m_axis(axis) {} + + EIGEN_DEVICE_FUNC + const typename internal::remove_all<typename LhsXprType::Nested>::type& + lhsExpression() const { return m_lhs_xpr; } + + EIGEN_DEVICE_FUNC + const typename internal::remove_all<typename RhsXprType::Nested>::type& + rhsExpression() const { return m_rhs_xpr; } + + EIGEN_DEVICE_FUNC const Axis& axis() const { return m_axis; } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE TensorConcatenationOp& operator = (const TensorConcatenationOp& other) + { + typedef TensorAssignOp<TensorConcatenationOp, const TensorConcatenationOp> Assign; + Assign assign(*this, other); + internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice()); + return *this; + } + + template<typename OtherDerived> + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE TensorConcatenationOp& operator = (const OtherDerived& other) + { + typedef TensorAssignOp<TensorConcatenationOp, const OtherDerived> Assign; + Assign assign(*this, other); + internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice()); + return *this; + } + + protected: + typename LhsXprType::Nested m_lhs_xpr; + typename RhsXprType::Nested m_rhs_xpr; + const Axis m_axis; +}; + + +// Eval as rvalue +template<typename Axis, typename LeftArgType, typename RightArgType, typename Device> +struct TensorEvaluator<const TensorConcatenationOp<Axis, LeftArgType, RightArgType>, Device> +{ + typedef TensorConcatenationOp<Axis, LeftArgType, RightArgType> XprType; + typedef typename XprType::Index Index; + static const int NumDims = internal::array_size<typename TensorEvaluator<LeftArgType, Device>::Dimensions>::value; + static const int RightNumDims = internal::array_size<typename TensorEvaluator<RightArgType, Device>::Dimensions>::value; + typedef DSizes<Index, NumDims> Dimensions; + typedef typename XprType::Scalar Scalar; + typedef typename XprType::CoeffReturnType CoeffReturnType; + typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType; + enum { + IsAligned = false, + PacketAccess = TensorEvaluator<LeftArgType, Device>::PacketAccess & TensorEvaluator<RightArgType, Device>::PacketAccess, + Layout = TensorEvaluator<LeftArgType, Device>::Layout, + RawAccess = false + }; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) + : m_leftImpl(op.lhsExpression(), device), m_rightImpl(op.rhsExpression(), device), m_axis(op.axis()) + { + EIGEN_STATIC_ASSERT((static_cast<int>(TensorEvaluator<LeftArgType, Device>::Layout) == static_cast<int>(TensorEvaluator<RightArgType, Device>::Layout) || NumDims == 1), YOU_MADE_A_PROGRAMMING_MISTAKE); + EIGEN_STATIC_ASSERT((NumDims == RightNumDims), YOU_MADE_A_PROGRAMMING_MISTAKE); + EIGEN_STATIC_ASSERT((NumDims > 0), YOU_MADE_A_PROGRAMMING_MISTAKE); + + eigen_assert(0 <= m_axis && m_axis < NumDims); + const Dimensions& lhs_dims = m_leftImpl.dimensions(); + const Dimensions& rhs_dims = m_rightImpl.dimensions(); + { + int i = 0; + for (; i < m_axis; ++i) { + eigen_assert(lhs_dims[i] > 0); + eigen_assert(lhs_dims[i] == rhs_dims[i]); + m_dimensions[i] = lhs_dims[i]; + } + eigen_assert(lhs_dims[i] > 0); // Now i == m_axis. + eigen_assert(rhs_dims[i] > 0); + m_dimensions[i] = lhs_dims[i] + rhs_dims[i]; + for (++i; i < NumDims; ++i) { + eigen_assert(lhs_dims[i] > 0); + eigen_assert(lhs_dims[i] == rhs_dims[i]); + m_dimensions[i] = lhs_dims[i]; + } + } + + if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { + m_leftStrides[0] = 1; + m_rightStrides[0] = 1; + m_outputStrides[0] = 1; + + for (int j = 1; j < NumDims; ++j) { + m_leftStrides[j] = m_leftStrides[j-1] * lhs_dims[j-1]; + m_rightStrides[j] = m_rightStrides[j-1] * rhs_dims[j-1]; + m_outputStrides[j] = m_outputStrides[j-1] * m_dimensions[j-1]; + } + } else { + m_leftStrides[NumDims - 1] = 1; + m_rightStrides[NumDims - 1] = 1; + m_outputStrides[NumDims - 1] = 1; + + for (int j = NumDims - 2; j >= 0; --j) { + m_leftStrides[j] = m_leftStrides[j+1] * lhs_dims[j+1]; + m_rightStrides[j] = m_rightStrides[j+1] * rhs_dims[j+1]; + m_outputStrides[j] = m_outputStrides[j+1] * m_dimensions[j+1]; + } + } + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; } + + // TODO(phli): Add short-circuit memcpy evaluation if underlying data are linear? + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* /*data*/) + { + m_leftImpl.evalSubExprsIfNeeded(NULL); + m_rightImpl.evalSubExprsIfNeeded(NULL); + return true; + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() + { + m_leftImpl.cleanup(); + m_rightImpl.cleanup(); + } + + // TODO(phli): attempt to speed this up. The integer divisions and modulo are slow. + // See CL/76180724 comments for more ideas. + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const + { + // Collect dimension-wise indices (subs). + array<Index, NumDims> subs; + if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { + for (int i = NumDims - 1; i > 0; --i) { + subs[i] = index / m_outputStrides[i]; + index -= subs[i] * m_outputStrides[i]; + } + subs[0] = index; + } else { + for (int i = 0; i < NumDims - 1; ++i) { + subs[i] = index / m_outputStrides[i]; + index -= subs[i] * m_outputStrides[i]; + } + subs[NumDims - 1] = index; + } + + const Dimensions& left_dims = m_leftImpl.dimensions(); + if (subs[m_axis] < left_dims[m_axis]) { + Index left_index; + if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { + left_index = subs[0]; + for (int i = 1; i < NumDims; ++i) { + left_index += (subs[i] % left_dims[i]) * m_leftStrides[i]; + } + } else { + left_index = subs[NumDims - 1]; + for (int i = NumDims - 2; i >= 0; --i) { + left_index += (subs[i] % left_dims[i]) * m_leftStrides[i]; + } + } + return m_leftImpl.coeff(left_index); + } else { + subs[m_axis] -= left_dims[m_axis]; + const Dimensions& right_dims = m_rightImpl.dimensions(); + Index right_index; + if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { + right_index = subs[0]; + for (int i = 1; i < NumDims; ++i) { + right_index += (subs[i] % right_dims[i]) * m_rightStrides[i]; + } + } else { + right_index = subs[NumDims - 1]; + for (int i = NumDims - 2; i >= 0; --i) { + right_index += (subs[i] % right_dims[i]) * m_rightStrides[i]; + } + } + return m_rightImpl.coeff(right_index); + } + } + + // TODO(phli): Add a real vectorization. + template<int LoadMode> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const + { + const int packetSize = internal::unpacket_traits<PacketReturnType>::size; + EIGEN_STATIC_ASSERT((packetSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE) + eigen_assert(index + packetSize - 1 < dimensions().TotalSize()); + + EIGEN_ALIGN_MAX CoeffReturnType values[packetSize]; + for (int i = 0; i < packetSize; ++i) { + values[i] = coeff(index+i); + } + PacketReturnType rslt = internal::pload<PacketReturnType>(values); + return rslt; + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost + costPerCoeff(bool vectorized) const { + const double compute_cost = NumDims * (2 * TensorOpCost::AddCost<Index>() + + 2 * TensorOpCost::MulCost<Index>() + + TensorOpCost::DivCost<Index>() + + TensorOpCost::ModCost<Index>()); + const double lhs_size = m_leftImpl.dimensions().TotalSize(); + const double rhs_size = m_rightImpl.dimensions().TotalSize(); + return (lhs_size / (lhs_size + rhs_size)) * + m_leftImpl.costPerCoeff(vectorized) + + (rhs_size / (lhs_size + rhs_size)) * + m_rightImpl.costPerCoeff(vectorized) + + TensorOpCost(0, 0, compute_cost); + } + + EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; } + + protected: + Dimensions m_dimensions; + array<Index, NumDims> m_outputStrides; + array<Index, NumDims> m_leftStrides; + array<Index, NumDims> m_rightStrides; + TensorEvaluator<LeftArgType, Device> m_leftImpl; + TensorEvaluator<RightArgType, Device> m_rightImpl; + const Axis m_axis; +}; + +// Eval as lvalue +template<typename Axis, typename LeftArgType, typename RightArgType, typename Device> + struct TensorEvaluator<TensorConcatenationOp<Axis, LeftArgType, RightArgType>, Device> + : public TensorEvaluator<const TensorConcatenationOp<Axis, LeftArgType, RightArgType>, Device> +{ + typedef TensorEvaluator<const TensorConcatenationOp<Axis, LeftArgType, RightArgType>, Device> Base; + typedef TensorConcatenationOp<Axis, LeftArgType, RightArgType> XprType; + typedef typename Base::Dimensions Dimensions; + enum { + IsAligned = false, + PacketAccess = TensorEvaluator<LeftArgType, Device>::PacketAccess & TensorEvaluator<RightArgType, Device>::PacketAccess, + Layout = TensorEvaluator<LeftArgType, Device>::Layout, + RawAccess = false + }; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(XprType& op, const Device& device) + : Base(op, device) + { + EIGEN_STATIC_ASSERT((static_cast<int>(Layout) == static_cast<int>(ColMajor)), YOU_MADE_A_PROGRAMMING_MISTAKE); + } + + typedef typename XprType::Index Index; + typedef typename XprType::Scalar Scalar; + typedef typename XprType::CoeffReturnType CoeffReturnType; + typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType& coeffRef(Index index) + { + // Collect dimension-wise indices (subs). + array<Index, Base::NumDims> subs; + for (int i = Base::NumDims - 1; i > 0; --i) { + subs[i] = index / this->m_outputStrides[i]; + index -= subs[i] * this->m_outputStrides[i]; + } + subs[0] = index; + + const Dimensions& left_dims = this->m_leftImpl.dimensions(); + if (subs[this->m_axis] < left_dims[this->m_axis]) { + Index left_index = subs[0]; + for (int i = 1; i < Base::NumDims; ++i) { + left_index += (subs[i] % left_dims[i]) * this->m_leftStrides[i]; + } + return this->m_leftImpl.coeffRef(left_index); + } else { + subs[this->m_axis] -= left_dims[this->m_axis]; + const Dimensions& right_dims = this->m_rightImpl.dimensions(); + Index right_index = subs[0]; + for (int i = 1; i < Base::NumDims; ++i) { + right_index += (subs[i] % right_dims[i]) * this->m_rightStrides[i]; + } + return this->m_rightImpl.coeffRef(right_index); + } + } + + template <int StoreMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + void writePacket(Index index, const PacketReturnType& x) + { + const int packetSize = internal::unpacket_traits<PacketReturnType>::size; + EIGEN_STATIC_ASSERT((packetSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE) + eigen_assert(index + packetSize - 1 < this->dimensions().TotalSize()); + + EIGEN_ALIGN_MAX CoeffReturnType values[packetSize]; + internal::pstore<CoeffReturnType, PacketReturnType>(values, x); + for (int i = 0; i < packetSize; ++i) { + coeffRef(index+i) = values[i]; + } + } +}; + +} // end namespace Eigen + +#endif // EIGEN_CXX11_TENSOR_TENSOR_CONCATENATION_H diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorContraction.h b/unsupported/Eigen/CXX11/src/Tensor/TensorContraction.h new file mode 100644 index 000000000..20b29e5fd --- /dev/null +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorContraction.h @@ -0,0 +1,628 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_H +#define EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_H + +namespace Eigen { + +/** \class TensorContraction + * \ingroup CXX11_Tensor_Module + * + * \brief Tensor contraction class. + * + * + */ +namespace internal { + +template<typename Dimensions, typename LhsXprType, typename RhsXprType> +struct traits<TensorContractionOp<Dimensions, LhsXprType, RhsXprType> > +{ + // Type promotion to handle the case where the types of the lhs and the rhs are different. + typedef typename gebp_traits<typename remove_const<typename LhsXprType::Scalar>::type, + typename remove_const<typename RhsXprType::Scalar>::type>::ResScalar Scalar; + + typedef typename promote_storage_type<typename traits<LhsXprType>::StorageKind, + typename traits<RhsXprType>::StorageKind>::ret StorageKind; + typedef typename promote_index_type<typename traits<LhsXprType>::Index, + typename traits<RhsXprType>::Index>::type Index; + typedef typename LhsXprType::Nested LhsNested; + typedef typename RhsXprType::Nested RhsNested; + typedef typename remove_reference<LhsNested>::type _LhsNested; + typedef typename remove_reference<RhsNested>::type _RhsNested; + + // From NumDims below. + static const int NumDimensions = traits<RhsXprType>::NumDimensions + traits<RhsXprType>::NumDimensions - 2 * array_size<Dimensions>::value; + static const int Layout = traits<LhsXprType>::Layout; + + enum { + Flags = 0 + }; +}; + +template<typename Dimensions, typename LhsXprType, typename RhsXprType> +struct eval<TensorContractionOp<Dimensions, LhsXprType, RhsXprType>, Eigen::Dense> +{ + typedef const TensorContractionOp<Dimensions, LhsXprType, RhsXprType>& type; +}; + +template<typename Dimensions, typename LhsXprType, typename RhsXprType> +struct nested<TensorContractionOp<Dimensions, LhsXprType, RhsXprType>, 1, typename eval<TensorContractionOp<Dimensions, LhsXprType, RhsXprType> >::type> +{ + typedef TensorContractionOp<Dimensions, LhsXprType, RhsXprType> type; +}; + +template<typename Indices_, typename LeftArgType_, typename RightArgType_, typename Device_> +struct traits<TensorEvaluator<const TensorContractionOp<Indices_, LeftArgType_, RightArgType_>, Device_> > { + typedef Indices_ Indices; + typedef LeftArgType_ LeftArgType; + typedef RightArgType_ RightArgType; + typedef Device_ Device; + + // From NumDims below. + static const int NumDimensions = traits<LeftArgType_>::NumDimensions + traits<RightArgType_>::NumDimensions - 2 * array_size<Indices_>::value; +}; + +} // end namespace internal + +template<typename Indices, typename LhsXprType, typename RhsXprType> +class TensorContractionOp : public TensorBase<TensorContractionOp<Indices, LhsXprType, RhsXprType>, ReadOnlyAccessors> +{ + public: + typedef typename Eigen::internal::traits<TensorContractionOp>::Scalar Scalar; + typedef typename internal::gebp_traits<typename LhsXprType::CoeffReturnType, + typename RhsXprType::CoeffReturnType>::ResScalar CoeffReturnType; + typedef typename Eigen::internal::nested<TensorContractionOp>::type Nested; + typedef typename Eigen::internal::traits<TensorContractionOp>::StorageKind StorageKind; + typedef typename Eigen::internal::traits<TensorContractionOp>::Index Index; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorContractionOp( + const LhsXprType& lhs, const RhsXprType& rhs, const Indices& dims) + : m_lhs_xpr(lhs), m_rhs_xpr(rhs), m_indices(dims) {} + + EIGEN_DEVICE_FUNC + const Indices& indices() const { return m_indices; } + + /** \returns the nested expressions */ + EIGEN_DEVICE_FUNC + const typename internal::remove_all<typename LhsXprType::Nested>::type& + lhsExpression() const { return m_lhs_xpr; } + + EIGEN_DEVICE_FUNC + const typename internal::remove_all<typename RhsXprType::Nested>::type& + rhsExpression() const { return m_rhs_xpr; } + + protected: + typename LhsXprType::Nested m_lhs_xpr; + typename RhsXprType::Nested m_rhs_xpr; + const Indices m_indices; +}; + + +template<typename Derived> +struct TensorContractionEvaluatorBase +{ + typedef typename internal::traits<Derived>::Indices Indices; + typedef typename internal::traits<Derived>::LeftArgType LeftArgType; + typedef typename internal::traits<Derived>::RightArgType RightArgType; + typedef typename internal::traits<Derived>::Device Device; + + typedef TensorContractionOp<Indices, LeftArgType, RightArgType> XprType; + typedef typename internal::remove_const<typename XprType::Scalar>::type Scalar; + typedef typename XprType::Index Index; + typedef typename XprType::CoeffReturnType CoeffReturnType; + typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType; + + enum { + IsAligned = true, + PacketAccess = (internal::unpacket_traits<PacketReturnType>::size > 1), + Layout = TensorEvaluator<LeftArgType, Device>::Layout, + CoordAccess = false, // to be implemented + RawAccess = true + }; + + // Most of the code is assuming that both input tensors are ColMajor. If the + // inputs are RowMajor, we will "cheat" by swapping the LHS and RHS: + // If we want to compute A * B = C, where A is LHS and B is RHS, the code + // will pretend B is LHS and A is RHS. + typedef typename internal::conditional< + static_cast<int>(Layout) == static_cast<int>(ColMajor), LeftArgType, RightArgType>::type EvalLeftArgType; + typedef typename internal::conditional< + static_cast<int>(Layout) == static_cast<int>(ColMajor), RightArgType, LeftArgType>::type EvalRightArgType; + + static const int LDims = + internal::array_size<typename TensorEvaluator<EvalLeftArgType, Device>::Dimensions>::value; + static const int RDims = + internal::array_size<typename TensorEvaluator<EvalRightArgType, Device>::Dimensions>::value; + static const int ContractDims = internal::array_size<Indices>::value; + static const int NumDims = LDims + RDims - 2 * ContractDims; + + typedef array<Index, ContractDims> contract_t; + typedef array<Index, LDims - ContractDims> left_nocontract_t; + typedef array<Index, RDims - ContractDims> right_nocontract_t; + + typedef DSizes<Index, NumDims> Dimensions; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + TensorContractionEvaluatorBase(const XprType& op, const Device& device) + : m_leftImpl(choose(Cond<static_cast<int>(Layout) == static_cast<int>(ColMajor)>(), + op.lhsExpression(), op.rhsExpression()), device), + m_rightImpl(choose(Cond<static_cast<int>(Layout) == static_cast<int>(ColMajor)>(), + op.rhsExpression(), op.lhsExpression()), device), + m_device(device), + m_result(NULL) { + EIGEN_STATIC_ASSERT((static_cast<int>(TensorEvaluator<LeftArgType, Device>::Layout) == + static_cast<int>(TensorEvaluator<RightArgType, Device>::Layout)), + YOU_MADE_A_PROGRAMMING_MISTAKE); + + + DSizes<Index, LDims> eval_left_dims; + DSizes<Index, RDims> eval_right_dims; + array<IndexPair<Index>, ContractDims> eval_op_indices; + if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { + // For ColMajor, we keep using the existing dimensions + for (int i = 0; i < LDims; i++) { + eval_left_dims[i] = m_leftImpl.dimensions()[i]; + } + for (int i = 0; i < RDims; i++) { + eval_right_dims[i] = m_rightImpl.dimensions()[i]; + } + // We keep the pairs of contracting indices. + for (int i = 0; i < ContractDims; i++) { + eval_op_indices[i].first = op.indices()[i].first; + eval_op_indices[i].second = op.indices()[i].second; + } + } else { + // For RowMajor, we need to reverse the existing dimensions + for (int i = 0; i < LDims; i++) { + eval_left_dims[i] = m_leftImpl.dimensions()[LDims - i - 1]; + } + for (int i = 0; i < RDims; i++) { + eval_right_dims[i] = m_rightImpl.dimensions()[RDims - i - 1]; + } + // We need to flip all the pairs of contracting indices as well as + // reversing the dimensions. + for (int i = 0; i < ContractDims; i++) { + eval_op_indices[i].first = LDims - 1 - op.indices()[ContractDims - 1 - i].second; + eval_op_indices[i].second = RDims - 1 - op.indices()[ContractDims - 1 - i].first; + } + } + + // Check for duplicate axes and make sure the first index in eval_op_indices + // is increasing. Using O(n^2) sorting is OK since ContractDims is small + for (int i = 0; i < ContractDims; i++) { + for (int j = i + 1; j < ContractDims; j++) { + eigen_assert(eval_op_indices[j].first != eval_op_indices[i].first && + eval_op_indices[j].second != eval_op_indices[i].second && + "contraction axes should be unique"); + if (eval_op_indices[j].first < eval_op_indices[i].first) { + numext::swap(eval_op_indices[j], eval_op_indices[i]); + } + } + } + + array<Index, LDims> lhs_strides; + lhs_strides[0] = 1; + for (int i = 0; i < LDims-1; ++i) { + lhs_strides[i+1] = lhs_strides[i] * eval_left_dims[i]; + } + + array<Index, RDims> rhs_strides; + rhs_strides[0] = 1; + for (int i = 0; i < RDims-1; ++i) { + rhs_strides[i+1] = rhs_strides[i] * eval_right_dims[i]; + } + + if (m_i_strides.size() > 0) m_i_strides[0] = 1; + if (m_j_strides.size() > 0) m_j_strides[0] = 1; + if (m_k_strides.size() > 0) m_k_strides[0] = 1; + + m_i_size = 1; + m_j_size = 1; + m_k_size = 1; + + // To compute the dimension, we simply concatenate the non-contracting + // dimensions of the left and then the right tensor. Additionally, we also + // compute the strides corresponding to the left non-contracting + // dimensions and right non-contracting dimensions. + m_lhs_inner_dim_contiguous = true; + int dim_idx = 0; + unsigned int nocontract_idx = 0; + + for (int i = 0; i < LDims; i++) { + // find if we are contracting on index i of left tensor + bool contracting = false; + for (int j = 0; j < ContractDims; j++) { + if (eval_op_indices[j].first == i) { + contracting = true; + break; + } + } + if (!contracting) { + // add dimension size to output dimensions + m_dimensions[dim_idx] = eval_left_dims[i]; + m_left_nocontract_strides[nocontract_idx] = lhs_strides[i]; + if (dim_idx != i) { + m_lhs_inner_dim_contiguous = false; + } + if (nocontract_idx+1 < internal::array_size<left_nocontract_t>::value) { + m_i_strides[nocontract_idx+1] = + m_i_strides[nocontract_idx] * eval_left_dims[i]; + } else { + m_i_size = m_i_strides[nocontract_idx] * eval_left_dims[i]; + } + dim_idx++; + nocontract_idx++; + } + } + + nocontract_idx = 0; + for (int i = 0; i < RDims; i++) { + bool contracting = false; + // find if we are contracting on index i of right tensor + for (int j = 0; j < ContractDims; j++) { + if (eval_op_indices[j].second == i) { + contracting = true; + break; + } + } + if (!contracting) { + m_dimensions[dim_idx] = eval_right_dims[i]; + if (nocontract_idx+1 < internal::array_size<right_nocontract_t>::value) { + m_j_strides[nocontract_idx+1] = + m_j_strides[nocontract_idx] * eval_right_dims[i]; + } else { + m_j_size = m_j_strides[nocontract_idx] * eval_right_dims[i]; + } + m_right_nocontract_strides[nocontract_idx] = rhs_strides[i]; + dim_idx++; + nocontract_idx++; + } + } + + // Now compute the strides corresponding to the contracting dimensions. We + // assumed above that non-contracting axes are represented in the same order + // in the matrix as they are in the tensor. This is not the case for + // contracting axes. As the contracting axes must be of the same size in + // each tensor, we'll only look at the first tensor here. + m_rhs_inner_dim_contiguous = true; + m_rhs_inner_dim_reordered = false; + for (int i = 0; i < ContractDims; i++) { + Index left = eval_op_indices[i].first; + Index right = eval_op_indices[i].second; + + Index size = eval_left_dims[left]; + eigen_assert(size == eval_right_dims[right] && + "Contraction axes must be same size"); + + if (i+1 < static_cast<int>(internal::array_size<contract_t>::value)) { + m_k_strides[i+1] = m_k_strides[i] * size; + } else { + m_k_size = m_k_strides[i] * size; + } + m_left_contracting_strides[i] = lhs_strides[left]; + m_right_contracting_strides[i] = rhs_strides[right]; + + if (i > 0 && right < eval_op_indices[i-1].second) { + m_rhs_inner_dim_reordered = true; + } + if (right != i) { + m_rhs_inner_dim_contiguous = false; + } + } + + // If the layout is RowMajor, we need to reverse the m_dimensions + if (static_cast<int>(Layout) == static_cast<int>(RowMajor)) { + for (int i = 0, j = NumDims - 1; i < j; i++, j--) { + numext::swap(m_dimensions[i], m_dimensions[j]); + } + } + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* data) { + m_leftImpl.evalSubExprsIfNeeded(NULL); + m_rightImpl.evalSubExprsIfNeeded(NULL); + if (data) { + evalTo(data); + return false; + } else { + m_result = static_cast<Scalar *>(m_device.allocate(dimensions().TotalSize() * sizeof(Scalar))); + evalTo(m_result); + return true; + } + } + + EIGEN_DEVICE_FUNC void evalTo(Scalar* buffer) const { + if (this->m_lhs_inner_dim_contiguous) { + if (this->m_rhs_inner_dim_contiguous) { + if (this->m_rhs_inner_dim_reordered) { + static_cast<const Derived*>(this)->template evalProduct<true, true, true, Unaligned>(buffer); + } + else { + static_cast<const Derived*>(this)->template evalProduct<true, true, false, Unaligned>(buffer); + } + } + else { + if (this->m_rhs_inner_dim_reordered) { + static_cast<const Derived*>(this)->template evalProduct<true, false, true, Unaligned>(buffer); + } + else { + static_cast<const Derived*>(this)->template evalProduct<true, false, false, Unaligned>(buffer); + } + } + } + else { + if (this->m_rhs_inner_dim_contiguous) { + if (this->m_rhs_inner_dim_reordered) { + static_cast<const Derived*>(this)->template evalProduct<false, true, true, Unaligned>(buffer); + } + else { + static_cast<const Derived*>(this)->template evalProduct<false, true, false, Unaligned>(buffer); + } + } + else { + if (this->m_rhs_inner_dim_reordered) { + static_cast<const Derived*>(this)->template evalProduct<false, false, true, Unaligned>(buffer); + } + else { + static_cast<const Derived*>(this)->template evalProduct<false, false, false, Unaligned>(buffer); + } + } + } + } + + template <bool lhs_inner_dim_contiguous, bool rhs_inner_dim_contiguous, bool rhs_inner_dim_reordered, int Alignment> + EIGEN_DEVICE_FUNC void evalGemv(Scalar* buffer) const { + const Index rows = m_i_size; + const Index cols = m_k_size; + + typedef typename internal::remove_const<typename EvalLeftArgType::Scalar>::type LhsScalar; + typedef typename internal::remove_const<typename EvalRightArgType::Scalar>::type RhsScalar; + typedef TensorEvaluator<EvalLeftArgType, Device> LeftEvaluator; + typedef TensorEvaluator<EvalRightArgType, Device> RightEvaluator; + const Index lhs_packet_size = internal::unpacket_traits<typename LeftEvaluator::PacketReturnType>::size; + const Index rhs_packet_size = internal::unpacket_traits<typename RightEvaluator::PacketReturnType>::size; + const int lhs_alignment = LeftEvaluator::IsAligned ? Aligned : Unaligned; + const int rhs_alignment = RightEvaluator::IsAligned ? Aligned : Unaligned; + typedef internal::TensorContractionInputMapper<LhsScalar, Index, internal::Lhs, + LeftEvaluator, left_nocontract_t, + contract_t, lhs_packet_size, + lhs_inner_dim_contiguous, + false, lhs_alignment> LhsMapper; + + typedef internal::TensorContractionInputMapper<RhsScalar, Index, internal::Rhs, + RightEvaluator, right_nocontract_t, + contract_t, rhs_packet_size, + rhs_inner_dim_contiguous, + rhs_inner_dim_reordered, rhs_alignment> RhsMapper; + + LhsMapper lhs(m_leftImpl, m_left_nocontract_strides, m_i_strides, + m_left_contracting_strides, m_k_strides); + RhsMapper rhs(m_rightImpl, m_right_nocontract_strides, m_j_strides, + m_right_contracting_strides, m_k_strides); + + const Scalar alpha(1); + const Index resIncr(1); + + // zero out the result buffer (which must be of size at least rows * sizeof(Scalar) + m_device.memset(buffer, 0, rows * sizeof(Scalar)); + + internal::general_matrix_vector_product<Index,LhsScalar,LhsMapper,ColMajor,false,RhsScalar,RhsMapper,false>::run( + rows, cols, lhs, rhs, + buffer, resIncr, alpha); + } + + template <bool lhs_inner_dim_contiguous, bool rhs_inner_dim_contiguous, bool rhs_inner_dim_reordered, int Alignment> + EIGEN_DEVICE_FUNC void evalGemm(Scalar* buffer) const { + // columns in left side, rows in right side + const Index k = this->m_k_size; + + // rows in left side + const Index m = this->m_i_size; + + // columns in right side + const Index n = this->m_j_size; + + // zero out the result buffer (which must be of size at least m * n * sizeof(Scalar) + this->m_device.memset(buffer, 0, m * n * sizeof(Scalar)); + + // define mr, nr, and all of my data mapper types + typedef typename internal::remove_const<typename EvalLeftArgType::Scalar>::type LhsScalar; + typedef typename internal::remove_const<typename EvalRightArgType::Scalar>::type RhsScalar; + typedef typename internal::gebp_traits<LhsScalar, RhsScalar> Traits; + + const Index nr = Traits::nr; + const Index mr = Traits::mr; + + typedef TensorEvaluator<EvalLeftArgType, Device> LeftEvaluator; + typedef TensorEvaluator<EvalRightArgType, Device> RightEvaluator; + + const Index lhs_packet_size = internal::unpacket_traits<typename LeftEvaluator::PacketReturnType>::size; + const Index rhs_packet_size = internal::unpacket_traits<typename RightEvaluator::PacketReturnType>::size; + + typedef internal::TensorContractionInputMapper<LhsScalar, Index, internal::Lhs, + LeftEvaluator, left_nocontract_t, + contract_t, lhs_packet_size, + lhs_inner_dim_contiguous, + false, Unaligned> LhsMapper; + + typedef internal::TensorContractionInputMapper<RhsScalar, Index, internal::Rhs, + RightEvaluator, right_nocontract_t, + contract_t, rhs_packet_size, + rhs_inner_dim_contiguous, + rhs_inner_dim_reordered, Unaligned> RhsMapper; + + typedef internal::blas_data_mapper<Scalar, Index, ColMajor> OutputMapper; + + // Declare GEBP packing and kernel structs + internal::gemm_pack_lhs<LhsScalar, Index, typename LhsMapper::SubMapper, mr, Traits::LhsProgress, ColMajor> pack_lhs; + internal::gemm_pack_rhs<RhsScalar, Index, typename RhsMapper::SubMapper, nr, ColMajor> pack_rhs; + + internal::gebp_kernel<LhsScalar, RhsScalar, Index, OutputMapper, mr, nr, false, false> gebp; + + // initialize data mappers + LhsMapper lhs(this->m_leftImpl, this->m_left_nocontract_strides, this->m_i_strides, + this->m_left_contracting_strides, this->m_k_strides); + + RhsMapper rhs(this->m_rightImpl, this->m_right_nocontract_strides, this->m_j_strides, + this->m_right_contracting_strides, this->m_k_strides); + + OutputMapper output(buffer, m); + + // Sizes of the blocks to load in cache. See the Goto paper for details. + internal::TensorContractionBlocking<LhsMapper, RhsMapper, Index, internal::ShardByCol> blocking(k, m, n, 1); + const Index kc = blocking.kc(); + const Index mc = numext::mini(m, blocking.mc()); + const Index nc = numext::mini(n, blocking.nc()); + const Index sizeA = mc * kc; + const Index sizeB = kc * nc; + + LhsScalar* blockA = static_cast<LhsScalar *>(this->m_device.allocate(sizeA * sizeof(LhsScalar))); + RhsScalar* blockB = static_cast<RhsScalar *>(this->m_device.allocate(sizeB * sizeof(RhsScalar))); + + for(Index i2=0; i2<m; i2+=mc) + { + const Index actual_mc = numext::mini(i2+mc,m)-i2; + for (Index k2 = 0; k2 < k; k2 += kc) { + // make sure we don't overshoot right edge of left matrix, then pack vertical panel + const Index actual_kc = numext::mini(k2 + kc, k) - k2; + pack_lhs(blockA, lhs.getSubMapper(i2, k2), actual_kc, actual_mc, 0, 0); + + // series of horizontal blocks + for (Index j2 = 0; j2 < n; j2 += nc) { + // make sure we don't overshoot right edge of right matrix, then pack block + const Index actual_nc = numext::mini(j2 + nc, n) - j2; + pack_rhs(blockB, rhs.getSubMapper(k2, j2), actual_kc, actual_nc, 0, 0); + + // call gebp (matrix kernel) + // The parameters here are copied from Eigen's GEMM implementation + gebp(output.getSubMapper(i2, j2), blockA, blockB, actual_mc, actual_kc, actual_nc, Scalar(1), -1, -1, 0, 0); + } + } + } + + this->m_device.deallocate(blockA); + this->m_device.deallocate(blockB); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() { + m_leftImpl.cleanup(); + m_rightImpl.cleanup(); + + if (m_result != NULL) { + m_device.deallocate(m_result); + m_result = NULL; + } + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const { + return m_result[index]; + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool) const { + return TensorOpCost(sizeof(CoeffReturnType), 0, 0); + } + + template<int LoadMode> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const { + return internal::ploadt<PacketReturnType, LoadMode>(m_result + index); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar* data() const { return m_result; } + + protected: + // Prevent assignment + TensorContractionEvaluatorBase& operator = (const TensorContractionEvaluatorBase&); + Dimensions m_dimensions; + + contract_t m_k_strides; + contract_t m_left_contracting_strides; + contract_t m_right_contracting_strides; + + bool m_lhs_inner_dim_contiguous; + bool m_rhs_inner_dim_contiguous; + bool m_rhs_inner_dim_reordered; + + left_nocontract_t m_i_strides; + right_nocontract_t m_j_strides; + left_nocontract_t m_left_nocontract_strides; + right_nocontract_t m_right_nocontract_strides; + + Index m_i_size; + Index m_j_size; + Index m_k_size; + + TensorEvaluator<EvalLeftArgType, Device> m_leftImpl; + TensorEvaluator<EvalRightArgType, Device> m_rightImpl; + const Device& m_device; + Scalar* m_result; +}; + + +// evaluator for default device +template<typename Indices, typename LeftArgType, typename RightArgType, typename Device> +struct TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType>, Device> : + public TensorContractionEvaluatorBase< + TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType>, Device> > { + typedef TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType>, Device> Self; + typedef TensorContractionEvaluatorBase<Self> Base; + + typedef TensorContractionOp<Indices, LeftArgType, RightArgType> XprType; + typedef typename internal::remove_const<typename XprType::Scalar>::type Scalar; + typedef typename XprType::Index Index; + typedef typename XprType::CoeffReturnType CoeffReturnType; + typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType; + + enum { + Layout = TensorEvaluator<LeftArgType, Device>::Layout + }; + + // Most of the code is assuming that both input tensors are ColMajor. If the + // inputs are RowMajor, we will "cheat" by swapping the LHS and RHS: + // If we want to compute A * B = C, where A is LHS and B is RHS, the code + // will pretend B is LHS and A is RHS. + typedef typename internal::conditional< + static_cast<int>(Layout) == static_cast<int>(ColMajor), LeftArgType, RightArgType>::type EvalLeftArgType; + typedef typename internal::conditional< + static_cast<int>(Layout) == static_cast<int>(ColMajor), RightArgType, LeftArgType>::type EvalRightArgType; + + static const int LDims = + internal::array_size<typename TensorEvaluator<EvalLeftArgType, Device>::Dimensions>::value; + static const int RDims = + internal::array_size<typename TensorEvaluator<EvalRightArgType, Device>::Dimensions>::value; + static const int ContractDims = internal::array_size<Indices>::value; + + typedef array<Index, ContractDims> contract_t; + typedef array<Index, LDims - ContractDims> left_nocontract_t; + typedef array<Index, RDims - ContractDims> right_nocontract_t; + + static const int NumDims = LDims + RDims - 2 * ContractDims; + + // Could we use NumDimensions here? + typedef DSizes<Index, NumDims> Dimensions; + + EIGEN_DEVICE_FUNC TensorEvaluator(const XprType& op, const Device& device) : + Base(op, device) { } + + template <bool lhs_inner_dim_contiguous, bool rhs_inner_dim_contiguous, bool rhs_inner_dim_reordered, int Alignment> + EIGEN_DEVICE_FUNC void evalProduct(Scalar* buffer) const { + if (this->m_j_size == 1) { + this->template evalGemv<lhs_inner_dim_contiguous, rhs_inner_dim_contiguous, rhs_inner_dim_reordered, Alignment>(buffer); + return; + } + + this->template evalGemm<lhs_inner_dim_contiguous, rhs_inner_dim_contiguous, rhs_inner_dim_reordered, Alignment>(buffer); + } +}; + +} // end namespace Eigen + +#endif // EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_H diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorContractionBlocking.h b/unsupported/Eigen/CXX11/src/Tensor/TensorContractionBlocking.h new file mode 100644 index 000000000..5cf7b4f71 --- /dev/null +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorContractionBlocking.h @@ -0,0 +1,56 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_BLOCKING_H +#define EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_BLOCKING_H + + +namespace Eigen { +namespace internal { + +enum { + ShardByRow = 0, + ShardByCol = 1 +}; + + +// Default Blocking Strategy +template <typename LhsMapper, typename RhsMapper, typename Index, int ShardingType=ShardByCol> +class TensorContractionBlocking { + public: + + typedef typename LhsMapper::Scalar LhsScalar; + typedef typename RhsMapper::Scalar RhsScalar; + + EIGEN_DEVICE_FUNC TensorContractionBlocking(Index k, Index m, Index n, Index num_threads = 1) : + kc_(k), mc_(m), nc_(n) + { + if (ShardingType == ShardByCol) { + computeProductBlockingSizes<LhsScalar, RhsScalar, 1>(kc_, mc_, nc_, num_threads); + } + else { + computeProductBlockingSizes<LhsScalar, RhsScalar, 1>(kc_, nc_, mc_, num_threads); + } + } + + EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Index kc() const { return kc_; } + EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Index mc() const { return mc_; } + EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Index nc() const { return nc_; } + + private: + Index kc_; + Index mc_; + Index nc_; +}; + + +} // end namespace internal +} // end namespace Eigen + +#endif // EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_BLOCKING_H diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorContractionCuda.h b/unsupported/Eigen/CXX11/src/Tensor/TensorContractionCuda.h new file mode 100644 index 000000000..d65dbb40f --- /dev/null +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorContractionCuda.h @@ -0,0 +1,1391 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2014-2015 Benoit Steiner <benoit.steiner.goog@gmail.com> +// Copyright (C) 2015 Navdeep Jaitly <ndjaitly@google.com> +// Copyright (C) 2014 Eric Martin <eric@ericmart.in> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_CUDA_H +#define EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_CUDA_H + +#if defined(EIGEN_USE_GPU) && defined(__CUDACC__) + +namespace Eigen { + +template<typename Scalar, typename Index, typename LhsMapper, + typename RhsMapper, typename OutputMapper, bool needs_edge_check> +__device__ EIGEN_STRONG_INLINE void +EigenContractionKernelInternal(const LhsMapper lhs, const RhsMapper rhs, + const OutputMapper output, Scalar* lhs_shmem, Scalar* rhs_shmem, + const Index m_size, const Index n_size, const Index k_size) { + + const Index m_block_idx = blockIdx.x; + const Index n_block_idx = blockIdx.y; + + const Index base_m = 64 * m_block_idx; + const Index base_n = 64 * n_block_idx; + + // declare and initialize 64 registers for output 8x8 block + + // prefetch registers + Scalar lhs_pf0; + Scalar lhs_pf1; + Scalar lhs_pf2; + Scalar lhs_pf3; + Scalar lhs_pf4; + Scalar lhs_pf5; + Scalar lhs_pf6; + Scalar lhs_pf7; + + Scalar rhs_pf0; + Scalar rhs_pf1; + Scalar rhs_pf2; + Scalar rhs_pf3; + Scalar rhs_pf4; + Scalar rhs_pf5; + Scalar rhs_pf6; + Scalar rhs_pf7; + + // shared memory is formatted + // (contract idx in block, nocontract idx in block, block idx) + // where block idx is column major. This transposition limits the number of + // bank conflicts when reading the LHS. The core idea is that since the contracting + // index is shared by both sides, then the contracting index should be in threadIdx.x. + + // On the LHS, we pad each row inside of each block with an extra element. This makes + // each block 8 rows of 9 elements, which is 72 elements. This gives no bank conflicts + // on writes and very few 2-way conflicts on reads. There is an 8x8 grid of these blocks. + + // On the RHS we just add 8 padding elements to the end of each block. This gives no bank + // conflicts on writes and also none on reads. + + // storage indices + const Index lhs_store_idx_base = threadIdx.y * 72 + threadIdx.x * 9 + threadIdx.z; + const Index rhs_store_idx_base = threadIdx.y * 72 + threadIdx.z * 8 + threadIdx.x; + + const Index lhs_store_idx_0 = lhs_store_idx_base + 576 * 0; + const Index lhs_store_idx_1 = lhs_store_idx_base + 576 * 1; + const Index lhs_store_idx_2 = lhs_store_idx_base + 576 * 2; + const Index lhs_store_idx_3 = lhs_store_idx_base + 576 * 3; + const Index lhs_store_idx_4 = lhs_store_idx_base + 576 * 4; + const Index lhs_store_idx_5 = lhs_store_idx_base + 576 * 5; + const Index lhs_store_idx_6 = lhs_store_idx_base + 576 * 6; + const Index lhs_store_idx_7 = lhs_store_idx_base + 576 * 7; + + const Index rhs_store_idx_0 = rhs_store_idx_base + 576 * 0; + const Index rhs_store_idx_1 = rhs_store_idx_base + 576 * 1; + const Index rhs_store_idx_2 = rhs_store_idx_base + 576 * 2; + const Index rhs_store_idx_3 = rhs_store_idx_base + 576 * 3; + const Index rhs_store_idx_4 = rhs_store_idx_base + 576 * 4; + const Index rhs_store_idx_5 = rhs_store_idx_base + 576 * 5; + const Index rhs_store_idx_6 = rhs_store_idx_base + 576 * 6; + const Index rhs_store_idx_7 = rhs_store_idx_base + 576 * 7; + + // in the loading code, the following variables are important: + // threadIdx.x: the vertical position in an 8x8 block + // threadIdx.y: the vertical index of the 8x8 block in the grid + // threadIdx.z: the horizontal position in an 8x8 block + // k: the horizontal index of the 8x8 block in the grid + // + // The k parameter is implicit (it was the loop counter for a loop that went + // from 0 to <8, but now that loop is unrolled in the below code. + + const Index load_idx_vert = threadIdx.x + 8 * threadIdx.y; + const Index lhs_vert = base_m + load_idx_vert; + +#define prefetchIntoRegisters(base_k) \ + { \ + lhs_pf0 = conv(0); \ + lhs_pf1 = conv(0); \ + lhs_pf2 = conv(0); \ + lhs_pf3 = conv(0); \ + lhs_pf4 = conv(0); \ + lhs_pf5 = conv(0); \ + lhs_pf6 = conv(0); \ + lhs_pf7 = conv(0); \ + \ + rhs_pf0 = conv(0); \ + rhs_pf1 = conv(0); \ + rhs_pf2 = conv(0); \ + rhs_pf3 = conv(0); \ + rhs_pf4 = conv(0); \ + rhs_pf5 = conv(0); \ + rhs_pf6 = conv(0); \ + rhs_pf7 = conv(0); \ + \ + if (!needs_edge_check || lhs_vert < m_size) { \ + const Index lhs_horiz_0 = base_k + threadIdx.z + 0 * 8; \ + const Index lhs_horiz_1 = base_k + threadIdx.z + 1 * 8; \ + const Index lhs_horiz_2 = base_k + threadIdx.z + 2 * 8; \ + const Index lhs_horiz_3 = base_k + threadIdx.z + 3 * 8; \ + const Index lhs_horiz_4 = base_k + threadIdx.z + 4 * 8; \ + const Index lhs_horiz_5 = base_k + threadIdx.z + 5 * 8; \ + const Index lhs_horiz_6 = base_k + threadIdx.z + 6 * 8; \ + const Index lhs_horiz_7 = base_k + threadIdx.z + 7 * 8; \ + \ + if (!needs_edge_check || lhs_horiz_7 < k_size) { \ + lhs_pf0 = lhs(lhs_vert, lhs_horiz_0); \ + lhs_pf1 = lhs(lhs_vert, lhs_horiz_1); \ + lhs_pf2 = lhs(lhs_vert, lhs_horiz_2); \ + lhs_pf3 = lhs(lhs_vert, lhs_horiz_3); \ + lhs_pf4 = lhs(lhs_vert, lhs_horiz_4); \ + lhs_pf5 = lhs(lhs_vert, lhs_horiz_5); \ + lhs_pf6 = lhs(lhs_vert, lhs_horiz_6); \ + lhs_pf7 = lhs(lhs_vert, lhs_horiz_7); \ + } else if (lhs_horiz_6 < k_size) { \ + lhs_pf0 = lhs(lhs_vert, lhs_horiz_0); \ + lhs_pf1 = lhs(lhs_vert, lhs_horiz_1); \ + lhs_pf2 = lhs(lhs_vert, lhs_horiz_2); \ + lhs_pf3 = lhs(lhs_vert, lhs_horiz_3); \ + lhs_pf4 = lhs(lhs_vert, lhs_horiz_4); \ + lhs_pf5 = lhs(lhs_vert, lhs_horiz_5); \ + lhs_pf6 = lhs(lhs_vert, lhs_horiz_6); \ + } else if (lhs_horiz_5 < k_size) { \ + lhs_pf0 = lhs(lhs_vert, lhs_horiz_0); \ + lhs_pf1 = lhs(lhs_vert, lhs_horiz_1); \ + lhs_pf2 = lhs(lhs_vert, lhs_horiz_2); \ + lhs_pf3 = lhs(lhs_vert, lhs_horiz_3); \ + lhs_pf4 = lhs(lhs_vert, lhs_horiz_4); \ + lhs_pf5 = lhs(lhs_vert, lhs_horiz_5); \ + } else if (lhs_horiz_4 < k_size) { \ + lhs_pf0 = lhs(lhs_vert, lhs_horiz_0); \ + lhs_pf1 = lhs(lhs_vert, lhs_horiz_1); \ + lhs_pf2 = lhs(lhs_vert, lhs_horiz_2); \ + lhs_pf3 = lhs(lhs_vert, lhs_horiz_3); \ + lhs_pf4 = lhs(lhs_vert, lhs_horiz_4); \ + } else if (lhs_horiz_3 < k_size) { \ + lhs_pf0 = lhs(lhs_vert, lhs_horiz_0); \ + lhs_pf1 = lhs(lhs_vert, lhs_horiz_1); \ + lhs_pf2 = lhs(lhs_vert, lhs_horiz_2); \ + lhs_pf3 = lhs(lhs_vert, lhs_horiz_3); \ + } else if (lhs_horiz_2 < k_size) { \ + lhs_pf0 = lhs(lhs_vert, lhs_horiz_0); \ + lhs_pf1 = lhs(lhs_vert, lhs_horiz_1); \ + lhs_pf2 = lhs(lhs_vert, lhs_horiz_2); \ + } else if (lhs_horiz_1 < k_size) { \ + lhs_pf0 = lhs(lhs_vert, lhs_horiz_0); \ + lhs_pf1 = lhs(lhs_vert, lhs_horiz_1); \ + } else if (lhs_horiz_0 < k_size) { \ + lhs_pf0 = lhs(lhs_vert, lhs_horiz_0); \ + } \ + } \ + \ + const Index rhs_vert = base_k + load_idx_vert; \ + if (!needs_edge_check || rhs_vert < k_size) { \ + const Index rhs_horiz_0 = base_n + threadIdx.z + 0 * 8; \ + const Index rhs_horiz_1 = base_n + threadIdx.z + 1 * 8; \ + const Index rhs_horiz_2 = base_n + threadIdx.z + 2 * 8; \ + const Index rhs_horiz_3 = base_n + threadIdx.z + 3 * 8; \ + const Index rhs_horiz_4 = base_n + threadIdx.z + 4 * 8; \ + const Index rhs_horiz_5 = base_n + threadIdx.z + 5 * 8; \ + const Index rhs_horiz_6 = base_n + threadIdx.z + 6 * 8; \ + const Index rhs_horiz_7 = base_n + threadIdx.z + 7 * 8; \ + \ + if (rhs_horiz_7 < n_size) { \ + rhs_pf0 = rhs(rhs_vert, rhs_horiz_0); \ + rhs_pf1 = rhs(rhs_vert, rhs_horiz_1); \ + rhs_pf2 = rhs(rhs_vert, rhs_horiz_2); \ + rhs_pf3 = rhs(rhs_vert, rhs_horiz_3); \ + rhs_pf4 = rhs(rhs_vert, rhs_horiz_4); \ + rhs_pf5 = rhs(rhs_vert, rhs_horiz_5); \ + rhs_pf6 = rhs(rhs_vert, rhs_horiz_6); \ + rhs_pf7 = rhs(rhs_vert, rhs_horiz_7); \ + } else if (rhs_horiz_6 < n_size) { \ + rhs_pf0 = rhs(rhs_vert, rhs_horiz_0); \ + rhs_pf1 = rhs(rhs_vert, rhs_horiz_1); \ + rhs_pf2 = rhs(rhs_vert, rhs_horiz_2); \ + rhs_pf3 = rhs(rhs_vert, rhs_horiz_3); \ + rhs_pf4 = rhs(rhs_vert, rhs_horiz_4); \ + rhs_pf5 = rhs(rhs_vert, rhs_horiz_5); \ + rhs_pf6 = rhs(rhs_vert, rhs_horiz_6); \ + } else if (rhs_horiz_5 < n_size) { \ + rhs_pf0 = rhs(rhs_vert, rhs_horiz_0); \ + rhs_pf1 = rhs(rhs_vert, rhs_horiz_1); \ + rhs_pf2 = rhs(rhs_vert, rhs_horiz_2); \ + rhs_pf3 = rhs(rhs_vert, rhs_horiz_3); \ + rhs_pf4 = rhs(rhs_vert, rhs_horiz_4); \ + rhs_pf5 = rhs(rhs_vert, rhs_horiz_5); \ + } else if (rhs_horiz_4 < n_size) { \ + rhs_pf0 = rhs(rhs_vert, rhs_horiz_0); \ + rhs_pf1 = rhs(rhs_vert, rhs_horiz_1); \ + rhs_pf2 = rhs(rhs_vert, rhs_horiz_2); \ + rhs_pf3 = rhs(rhs_vert, rhs_horiz_3); \ + rhs_pf4 = rhs(rhs_vert, rhs_horiz_4); \ + } else if (rhs_horiz_3 < n_size) { \ + rhs_pf0 = rhs(rhs_vert, rhs_horiz_0); \ + rhs_pf1 = rhs(rhs_vert, rhs_horiz_1); \ + rhs_pf2 = rhs(rhs_vert, rhs_horiz_2); \ + rhs_pf3 = rhs(rhs_vert, rhs_horiz_3); \ + } else if (rhs_horiz_2 < n_size) { \ + rhs_pf0 = rhs(rhs_vert, rhs_horiz_0); \ + rhs_pf1 = rhs(rhs_vert, rhs_horiz_1); \ + rhs_pf2 = rhs(rhs_vert, rhs_horiz_2); \ + } else if (rhs_horiz_1 < n_size) { \ + rhs_pf0 = rhs(rhs_vert, rhs_horiz_0); \ + rhs_pf1 = rhs(rhs_vert, rhs_horiz_1); \ + } else if (rhs_horiz_0 < n_size) { \ + rhs_pf0 = rhs(rhs_vert, rhs_horiz_0); \ + } \ + } \ + } \ + +#define writeRegToShmem(_) \ + lhs_shmem[lhs_store_idx_0] = lhs_pf0; \ + rhs_shmem[rhs_store_idx_0] = rhs_pf0; \ + \ + lhs_shmem[lhs_store_idx_1] = lhs_pf1; \ + rhs_shmem[rhs_store_idx_1] = rhs_pf1; \ + \ + lhs_shmem[lhs_store_idx_2] = lhs_pf2; \ + rhs_shmem[rhs_store_idx_2] = rhs_pf2; \ + \ + lhs_shmem[lhs_store_idx_3] = lhs_pf3; \ + rhs_shmem[rhs_store_idx_3] = rhs_pf3; \ + \ + lhs_shmem[lhs_store_idx_4] = lhs_pf4; \ + rhs_shmem[rhs_store_idx_4] = rhs_pf4; \ + \ + lhs_shmem[lhs_store_idx_5] = lhs_pf5; \ + rhs_shmem[rhs_store_idx_5] = rhs_pf5; \ + \ + lhs_shmem[lhs_store_idx_6] = lhs_pf6; \ + rhs_shmem[rhs_store_idx_6] = rhs_pf6; \ + \ + lhs_shmem[lhs_store_idx_7] = lhs_pf7; \ + rhs_shmem[rhs_store_idx_7] = rhs_pf7; \ + + // declare and initialize result array +#define res(i, j) _res_##i##j +#define initResultRow(i) \ + Scalar res(i, 0) = conv(0); \ + Scalar res(i, 1) = conv(0); \ + Scalar res(i, 2) = conv(0); \ + Scalar res(i, 3) = conv(0); \ + Scalar res(i, 4) = conv(0); \ + Scalar res(i, 5) = conv(0); \ + Scalar res(i, 6) = conv(0); \ + Scalar res(i, 7) = conv(0); \ + + internal::scalar_cast_op<int, Scalar> conv; + initResultRow(0); + initResultRow(1); + initResultRow(2); + initResultRow(3); + initResultRow(4); + initResultRow(5); + initResultRow(6); + initResultRow(7); +#undef initResultRow + + for (Index base_k = 0; base_k < k_size; base_k += 64) { + // wait for previous iteration to finish with shmem. Despite common sense, + // the code is a bit faster with this here then at bottom of loop + __syncthreads(); + + prefetchIntoRegisters(base_k); + writeRegToShmem(); + + #undef prefetchIntoRegisters + #undef writeRegToShmem + + // wait for shared mem packing to be done before starting computation + __syncthreads(); + + // compute 8x8 matrix product by outer product. This involves packing one column + // of LHS and one row of RHS into registers (takes 16 registers). + +#define lcol(i) _lcol##i + Scalar lcol(0); + Scalar lcol(1); + Scalar lcol(2); + Scalar lcol(3); + Scalar lcol(4); + Scalar lcol(5); + Scalar lcol(6); + Scalar lcol(7); + +#define rrow(j) _rrow##j + Scalar rrow(0); + Scalar rrow(1); + Scalar rrow(2); + Scalar rrow(3); + Scalar rrow(4); + Scalar rrow(5); + Scalar rrow(6); + Scalar rrow(7); + + // Now x corresponds to k, y to m, and z to n + const Scalar* lhs_block = &lhs_shmem[threadIdx.x + 9 * threadIdx.y]; + const Scalar* rhs_block = &rhs_shmem[threadIdx.x + 8 * threadIdx.z]; + +#define lhs_element(i, j) lhs_block[72 * ((i) + 8 * (j))] +#define rhs_element(i, j) rhs_block[72 * ((i) + 8 * (j))] + +#define loadData(i, j) \ + lcol(0) = lhs_element(0, j); \ + rrow(0) = rhs_element(i, 0); \ + lcol(1) = lhs_element(1, j); \ + rrow(1) = rhs_element(i, 1); \ + lcol(2) = lhs_element(2, j); \ + rrow(2) = rhs_element(i, 2); \ + lcol(3) = lhs_element(3, j); \ + rrow(3) = rhs_element(i, 3); \ + lcol(4) = lhs_element(4, j); \ + rrow(4) = rhs_element(i, 4); \ + lcol(5) = lhs_element(5, j); \ + rrow(5) = rhs_element(i, 5); \ + lcol(6) = lhs_element(6, j); \ + rrow(6) = rhs_element(i, 6); \ + lcol(7) = lhs_element(7, j); \ + rrow(7) = rhs_element(i, 7); \ + +#define computeCol(j) \ + res(0, j) += lcol(0) * rrow(j); \ + res(1, j) += lcol(1) * rrow(j); \ + res(2, j) += lcol(2) * rrow(j); \ + res(3, j) += lcol(3) * rrow(j); \ + res(4, j) += lcol(4) * rrow(j); \ + res(5, j) += lcol(5) * rrow(j); \ + res(6, j) += lcol(6) * rrow(j); \ + res(7, j) += lcol(7) * rrow(j); \ + +#define computePass(i) \ + loadData(i, i); \ + \ + computeCol(0); \ + computeCol(1); \ + computeCol(2); \ + computeCol(3); \ + computeCol(4); \ + computeCol(5); \ + computeCol(6); \ + computeCol(7); \ + + computePass(0); + computePass(1); + computePass(2); + computePass(3); + computePass(4); + computePass(5); + computePass(6); + computePass(7); + +#undef lcol +#undef rrow +#undef lhs_element +#undef rhs_element +#undef loadData +#undef computeCol +#undef computePass + } // end loop over k + + // we've now iterated over all of the large (ie width 64) k blocks and + // accumulated results in registers. At this point thread (x, y, z) contains + // the sum across all big k blocks of the product of little k block of index (x, y) + // with block of index (y, z). To compute the final output, we need to reduce + // the 8 threads over y by summation. +#define shuffleInc(i, j, mask) res(i, j) += __shfl_xor(res(i, j), mask) + +#define reduceRow(i, mask) \ + shuffleInc(i, 0, mask); \ + shuffleInc(i, 1, mask); \ + shuffleInc(i, 2, mask); \ + shuffleInc(i, 3, mask); \ + shuffleInc(i, 4, mask); \ + shuffleInc(i, 5, mask); \ + shuffleInc(i, 6, mask); \ + shuffleInc(i, 7, mask); \ + +#define reduceMatrix(mask) \ + reduceRow(0, mask); \ + reduceRow(1, mask); \ + reduceRow(2, mask); \ + reduceRow(3, mask); \ + reduceRow(4, mask); \ + reduceRow(5, mask); \ + reduceRow(6, mask); \ + reduceRow(7, mask); \ + + // actually perform the reduction, now each thread of index (_, y, z) + // contains the correct values in its registers that belong in the output + // block + reduceMatrix(1); + reduceMatrix(2); + reduceMatrix(4); + +#undef shuffleInc +#undef reduceRow +#undef reduceMatrix + + // now we need to copy the 64 values into main memory. We can't split work + // among threads because all variables are in registers. There's 2 ways + // to do this: + // (1) have 1 thread do 64 writes from registers into global memory + // (2) have 1 thread do 64 writes into shared memory, and then 8 threads + // each do 8 writes into global memory. We can just overwrite the shared + // memory from the problem we just solved. + // (2) is slightly faster than (1) due to less branching and more ILP + + // TODO: won't yield much gain, but could just use currently unused shared mem + // and then we won't have to sync + // wait for shared mem to be out of use + __syncthreads(); + +#define writeResultShmem(i, j) \ + lhs_shmem[i + 8 * threadIdx.y + 64 * threadIdx.z + 512 * j] = res(i, j); \ + +#define writeRow(i) \ + writeResultShmem(i, 0); \ + writeResultShmem(i, 1); \ + writeResultShmem(i, 2); \ + writeResultShmem(i, 3); \ + writeResultShmem(i, 4); \ + writeResultShmem(i, 5); \ + writeResultShmem(i, 6); \ + writeResultShmem(i, 7); \ + + if (threadIdx.x == 0) { + writeRow(0); + writeRow(1); + writeRow(2); + writeRow(3); + writeRow(4); + writeRow(5); + writeRow(6); + writeRow(7); + } +#undef writeResultShmem +#undef writeRow + + const int max_i_write = numext::mini((int)((m_size - base_m - threadIdx.y + 7) / 8), 8); + const int max_j_write = numext::mini((int)((n_size - base_n - threadIdx.z + 7) / 8), 8); + + if (threadIdx.x < max_i_write) { + if (max_j_write == 8) { + // TODO: can i trade bank conflicts for coalesced writes? + Scalar val0 = lhs_shmem[threadIdx.x + 8 * threadIdx.y + 64 * threadIdx.z + 512 * 0]; + Scalar val1 = lhs_shmem[threadIdx.x + 8 * threadIdx.y + 64 * threadIdx.z + 512 * 1]; + Scalar val2 = lhs_shmem[threadIdx.x + 8 * threadIdx.y + 64 * threadIdx.z + 512 * 2]; + Scalar val3 = lhs_shmem[threadIdx.x + 8 * threadIdx.y + 64 * threadIdx.z + 512 * 3]; + Scalar val4 = lhs_shmem[threadIdx.x + 8 * threadIdx.y + 64 * threadIdx.z + 512 * 4]; + Scalar val5 = lhs_shmem[threadIdx.x + 8 * threadIdx.y + 64 * threadIdx.z + 512 * 5]; + Scalar val6 = lhs_shmem[threadIdx.x + 8 * threadIdx.y + 64 * threadIdx.z + 512 * 6]; + Scalar val7 = lhs_shmem[threadIdx.x + 8 * threadIdx.y + 64 * threadIdx.z + 512 * 7]; + + output(base_m + threadIdx.y + 8 * threadIdx.x, base_n + threadIdx.z + 8 * 0) = val0; + output(base_m + threadIdx.y + 8 * threadIdx.x, base_n + threadIdx.z + 8 * 1) = val1; + output(base_m + threadIdx.y + 8 * threadIdx.x, base_n + threadIdx.z + 8 * 2) = val2; + output(base_m + threadIdx.y + 8 * threadIdx.x, base_n + threadIdx.z + 8 * 3) = val3; + output(base_m + threadIdx.y + 8 * threadIdx.x, base_n + threadIdx.z + 8 * 4) = val4; + output(base_m + threadIdx.y + 8 * threadIdx.x, base_n + threadIdx.z + 8 * 5) = val5; + output(base_m + threadIdx.y + 8 * threadIdx.x, base_n + threadIdx.z + 8 * 6) = val6; + output(base_m + threadIdx.y + 8 * threadIdx.x, base_n + threadIdx.z + 8 * 7) = val7; + } else { +#pragma unroll 7 + for (int j = 0; j < max_j_write; j++) { + Scalar val = lhs_shmem[threadIdx.x + 8 * threadIdx.y + 64 * threadIdx.z + 512 * j]; + output(base_m + threadIdx.y + 8 * threadIdx.x, base_n + threadIdx.z + 8 * j) = val; + } + } + } +#undef res +} + + +template<typename Scalar, typename Index, typename LhsMapper, + typename RhsMapper, typename OutputMapper> +__global__ void +__launch_bounds__(512) +EigenContractionKernel(const LhsMapper lhs, const RhsMapper rhs, + const OutputMapper output, + const Index m_size, const Index n_size, const Index k_size) { + __shared__ Scalar lhs_shmem[72 * 64]; + __shared__ Scalar rhs_shmem[72 * 64]; + + const Index m_block_idx = blockIdx.x; + const Index n_block_idx = blockIdx.y; + + const Index base_m = 64 * m_block_idx; + const Index base_n = 64 * n_block_idx; + + if (base_m + 63 < m_size && base_n + 63 < n_size) { + EigenContractionKernelInternal<Scalar, Index, LhsMapper, RhsMapper, OutputMapper, false>(lhs, rhs, output, lhs_shmem, rhs_shmem, m_size, n_size, k_size); + } else { + EigenContractionKernelInternal<Scalar, Index, LhsMapper, RhsMapper, OutputMapper, true>(lhs, rhs, output, lhs_shmem, rhs_shmem, m_size, n_size, k_size); + } +} + + +template<typename Index, typename LhsMapper, + typename RhsMapper, typename OutputMapper, bool CHECK_LHS_BOUNDARY, + bool CHECK_RHS_BOUNDARY> +__device__ EIGEN_STRONG_INLINE void +EigenFloatContractionKernelInternal16x16(const LhsMapper lhs, const RhsMapper rhs, + const OutputMapper output, float2 lhs_shmem2[][16], + float2 rhs_shmem2[][8], const Index m_size, + const Index n_size, const Index k_size, + const Index base_m, const Index base_n) { + typedef float Scalar; + + // prefetch registers + float4 lhs_pf0, rhs_pf0; + + float4 results[4]; + for (int i=0; i < 4; i++) { + results[i].x = results[i].y = results[i].z = results[i].w = 0; + } + + +#define prefetch_lhs(reg, row, col) \ + if (!CHECK_LHS_BOUNDARY) { \ + if (col < k_size) { \ + reg =lhs.loadPacket<Unaligned>(row, col); \ + } \ + } else { \ + if (col < k_size) { \ + if (row + 3 < m_size) { \ + reg =lhs.loadPacket<Unaligned>(row, col); \ + } else if (row + 2 < m_size) { \ + reg.x =lhs(row + 0, col); \ + reg.y =lhs(row + 1, col); \ + reg.z =lhs(row + 2, col); \ + } else if (row + 1 < m_size) { \ + reg.x =lhs(row + 0, col); \ + reg.y =lhs(row + 1, col); \ + } else if (row < m_size) { \ + reg.x =lhs(row + 0, col); \ + } \ + } \ + } \ + + + Index lhs_vert = base_m+threadIdx.x*4; + + for (Index k = 0; k < k_size; k += 16) { + lhs_pf0 = internal::pset1<float4>(0); + rhs_pf0 = internal::pset1<float4>(0); + + Index lhs_horiz = threadIdx.y+k; + prefetch_lhs(lhs_pf0, lhs_vert, lhs_horiz) + + Index rhs_vert = k+(threadIdx.x%4)*4; + Index rhs_horiz0 = (threadIdx.x>>2)+threadIdx.y*4+base_n; + + if (!CHECK_RHS_BOUNDARY) { + if ((rhs_vert + 3) < k_size) { + // just CHECK_RHS_BOUNDARY + rhs_pf0 = rhs.loadPacket<Unaligned>(rhs_vert, rhs_horiz0); + } else if (rhs_vert + 2 < k_size) { + // just CHECK_RHS_BOUNDARY + rhs_pf0.x = rhs(rhs_vert, rhs_horiz0); + rhs_pf0.y = rhs(rhs_vert + 1, rhs_horiz0); + rhs_pf0.z = rhs(rhs_vert + 2, rhs_horiz0); + } else if (rhs_vert + 1 < k_size) { + rhs_pf0.x = rhs(rhs_vert, rhs_horiz0); + rhs_pf0.y = rhs(rhs_vert + 1, rhs_horiz0); + } else if (rhs_vert < k_size) { + rhs_pf0.x = rhs(rhs_vert, rhs_horiz0); + } + } else { + if (rhs_horiz0 < n_size) { + if ((rhs_vert + 3) < k_size) { + rhs_pf0 = rhs.loadPacket<Unaligned>(rhs_vert, rhs_horiz0); + } else if ((rhs_vert + 2) < k_size) { + rhs_pf0.x = rhs(rhs_vert, rhs_horiz0); + rhs_pf0.y = rhs(rhs_vert + 1, rhs_horiz0); + rhs_pf0.z = rhs(rhs_vert + 2, rhs_horiz0); + } else if ((rhs_vert + 1) < k_size) { + rhs_pf0.x = rhs(rhs_vert, rhs_horiz0); + rhs_pf0.y = rhs(rhs_vert + 1, rhs_horiz0); + } else if (rhs_vert < k_size) { + rhs_pf0.x = rhs(rhs_vert, rhs_horiz0); + } + } + } + float x1, x2 ; + // the following can be a bitwise operation..... some day. + if((threadIdx.x%8) < 4) { + x1 = rhs_pf0.y; + x2 = rhs_pf0.w; + } else { + x1 = rhs_pf0.x; + x2 = rhs_pf0.z; + } + x1 = __shfl_xor(x1, 4); + x2 = __shfl_xor(x2, 4); + if((threadIdx.x%8) < 4) { + rhs_pf0.y = x1; + rhs_pf0.w = x2; + } else { + rhs_pf0.x = x1; + rhs_pf0.z = x2; + } + + // We have 64 features. + // Row 0 -> times (0, 4, 8, 12, 1, 5, 9, 13) for features 0, 1. + // Row 1 -> times (0, 4, 8, 12, 1, 5, 9, 13) for features 2, 3. + // ... + // Row 31 -> times (0, 4, 8, 12, 1, 5, 9, 13) for features 62, 63 + // Row 32 -> times (2, 6, 10, 14, 3, 7, 11, 15) for features 0, 1 + // ... + rhs_shmem2[(threadIdx.x>>3)+ threadIdx.y*2][threadIdx.x%8] = make_float2(rhs_pf0.x, rhs_pf0.y); + rhs_shmem2[(threadIdx.x>>3)+ threadIdx.y*2+32][threadIdx.x%8] = make_float2(rhs_pf0.z, rhs_pf0.w); + + // Row 0 (time 0) -> features (0, 1), (4, 5), .. (28, 29), (32, 33), .. (60, 61) + // Row 1 (time 1) -> features (0, 1), (4, 5), .. (28, 29), (32, 33), .. (60, 61) + // ... + // Row 15 (time 15) -> features (0, 1), (4, 5), .. (28, 29), (32, 33), .. (60, 61) + // Row 16 (time 0) -> features (2, 3), (6, 7), .. (30, 31), (34, 35), .. (62, 63) + // ... + + lhs_shmem2[threadIdx.y][threadIdx.x] = make_float2(lhs_pf0.x, lhs_pf0.y); + lhs_shmem2[threadIdx.y+16][threadIdx.x] = make_float2(lhs_pf0.z, lhs_pf0.w); + + +#define add_vals(fl1, fl2, fr1, fr2)\ + results[0].x += fl1.x * fr1.x;\ + results[0].y += fl1.y * fr1.x;\ + results[0].z += fl2.x * fr1.x;\ + results[0].w += fl2.y * fr1.x;\ +\ + results[1].x += fl1.x * fr1.y;\ + results[1].y += fl1.y * fr1.y;\ + results[1].z += fl2.x * fr1.y;\ + results[1].w += fl2.y * fr1.y;\ +\ + results[2].x += fl1.x * fr2.x;\ + results[2].y += fl1.y * fr2.x;\ + results[2].z += fl2.x * fr2.x;\ + results[2].w += fl2.y * fr2.x;\ +\ + results[3].x += fl1.x * fr2.y;\ + results[3].y += fl1.y * fr2.y;\ + results[3].z += fl2.x * fr2.y;\ + results[3].w += fl2.y * fr2.y;\ + + __syncthreads(); + + // Do the multiplies. + #pragma unroll + for (int koff = 0; koff < 16; koff ++) { + // 32 x threads. + float2 fl1 = lhs_shmem2[koff][threadIdx.x]; + float2 fl2 = lhs_shmem2[koff + 16][threadIdx.x]; + + int start_feature = threadIdx.y * 4; + float2 fr1 = rhs_shmem2[(start_feature>>1) + 32*((koff%4)/2)][koff/4 + (koff%2)*4]; + float2 fr2 = rhs_shmem2[(start_feature>>1) + 1 + 32*((koff%4)/2)][koff/4 + (koff%2)*4]; + + add_vals(fl1, fl2, fr1, fr2) + } + __syncthreads(); + } + +#undef prefetch_lhs +#undef add_vals + + Index horiz_base = threadIdx.y*4+base_n; + if (!CHECK_LHS_BOUNDARY && !CHECK_RHS_BOUNDARY) { + for (int i = 0; i < 4; i++) { + output(lhs_vert, horiz_base + i) = results[i].x; + output(lhs_vert + 1, horiz_base + i) = results[i].y; + output(lhs_vert + 2, horiz_base + i) = results[i].z; + output(lhs_vert + 3, horiz_base + i) = results[i].w; + } + } else if (!CHECK_RHS_BOUNDARY) { + // CHECK LHS + if (lhs_vert + 3 < m_size) { + for (int i = 0; i < 4; i++) { + output(lhs_vert, horiz_base + i) = results[i].x; + output(lhs_vert + 1, horiz_base + i) = results[i].y; + output(lhs_vert + 2, horiz_base + i) = results[i].z; + output(lhs_vert + 3, horiz_base + i) = results[i].w; + } + } else if (lhs_vert + 2 < m_size) { + for (int i = 0; i < 4; i++) { + output(lhs_vert, horiz_base + i) = results[i].x; + output(lhs_vert + 1, horiz_base + i) = results[i].y; + output(lhs_vert + 2, horiz_base + i) = results[i].z; + } + } else if (lhs_vert + 1 < m_size) { + for (int i = 0; i < 4; i++) { + output(lhs_vert, horiz_base + i) = results[i].x; + output(lhs_vert + 1, horiz_base + i) = results[i].y; + } + } else if (lhs_vert < m_size) { + for (int i = 0; i < 4; i++) { + output(lhs_vert, horiz_base + i) = results[i].x; + } + } + } else if (!CHECK_LHS_BOUNDARY) { + // CHECK RHS + /* + int ncols_rem = fminf(n_size- horiz_base, 4); + for (int i = 0; i < ncols_rem; i++) { + output(lhs_vert, horiz_base + i) = results[i].x; + output(lhs_vert + 1, horiz_base + i) = results[i].y; + output(lhs_vert + 2, horiz_base + i) = results[i].z; + output(lhs_vert + 3, horiz_base + i) = results[i].w; + }*/ + for (int i = 0; i < 4; i++) { + if (horiz_base+i < n_size) { + output(lhs_vert, horiz_base + i) = results[i].x; + output(lhs_vert + 1, horiz_base + i) = results[i].y; + output(lhs_vert + 2, horiz_base + i) = results[i].z; + output(lhs_vert + 3, horiz_base + i) = results[i].w; + } + } + } else { + // CHECK both boundaries. + for (int i = 0; i < 4; i++) { + if (horiz_base+i < n_size) { + if (lhs_vert < m_size) + output(lhs_vert, horiz_base + i) = results[i].x; + if (lhs_vert + 1 < m_size) + output(lhs_vert + 1, horiz_base + i) = results[i].y; + if (lhs_vert + 2 < m_size) + output(lhs_vert + 2, horiz_base + i) = results[i].z; + if (lhs_vert + 3 < m_size) + output(lhs_vert + 3, horiz_base + i) = results[i].w; + } + } + } +} + + +template<typename Index, typename LhsMapper, + typename RhsMapper, typename OutputMapper, bool CHECK_LHS_BOUNDARY, + bool CHECK_RHS_BOUNDARY> +__device__ EIGEN_STRONG_INLINE void +EigenFloatContractionKernelInternal(const LhsMapper lhs, const RhsMapper rhs, + const OutputMapper output, float2 lhs_shmem2[][32], + float2 rhs_shmem2[][8], const Index m_size, + const Index n_size, const Index k_size, + const Index base_m, const Index base_n) { + typedef float Scalar; + + // prefetch registers + float4 lhs_pf0, lhs_pf1, lhs_pf2, lhs_pf3; + float4 rhs_pf0, rhs_pf1; + + float4 results[8]; + for (int i=0; i < 8; i++) { + results[i].x = results[i].y = results[i].z = results[i].w = 0; + } + + + Index lhs_vert = base_m+threadIdx.x*4+(threadIdx.y%4)*32; + for (Index k = 0; k < k_size; k += 32) { + lhs_pf0 = internal::pset1<float4>(0); + lhs_pf1 = internal::pset1<float4>(0); + lhs_pf2 = internal::pset1<float4>(0); + lhs_pf3 = internal::pset1<float4>(0); + + rhs_pf0 = internal::pset1<float4>(0); + rhs_pf1 = internal::pset1<float4>(0); + + if (!CHECK_LHS_BOUNDARY) { + if ((threadIdx.y/4+k+24) < k_size) { + lhs_pf0 =lhs.loadPacket<Unaligned>(lhs_vert, (threadIdx.y/4+k)); + lhs_pf1 =lhs.loadPacket<Unaligned>(lhs_vert, (threadIdx.y/4+k+8)); + lhs_pf2 =lhs.loadPacket<Unaligned>(lhs_vert, (threadIdx.y/4+k+16)); + lhs_pf3 =lhs.loadPacket<Unaligned>(lhs_vert, (threadIdx.y/4+k+24)); + } else if ((threadIdx.y/4+k+16) < k_size) { + lhs_pf0 =lhs.loadPacket<Unaligned>(lhs_vert, (threadIdx.y/4+k)); + lhs_pf1 =lhs.loadPacket<Unaligned>(lhs_vert, (threadIdx.y/4+k+8)); + lhs_pf2 =lhs.loadPacket<Unaligned>(lhs_vert, (threadIdx.y/4+k+16)); + } else if ((threadIdx.y/4+k+8) < k_size) { + lhs_pf0 =lhs.loadPacket<Unaligned>(lhs_vert, (threadIdx.y/4+k)); + lhs_pf1 =lhs.loadPacket<Unaligned>(lhs_vert, (threadIdx.y/4+k+8)); + } else if ((threadIdx.y/4+k) < k_size) { + lhs_pf0 =lhs.loadPacket<Unaligned>(lhs_vert, (threadIdx.y/4+k)); + } + } else { + // just CHECK_LHS_BOUNDARY + if (lhs_vert + 3 < m_size) { + if ((threadIdx.y/4+k+24) < k_size) { + lhs_pf0 =lhs.loadPacket<Unaligned>(lhs_vert, (threadIdx.y/4+k)); + lhs_pf1 =lhs.loadPacket<Unaligned>(lhs_vert, (threadIdx.y/4+k+8)); + lhs_pf2 =lhs.loadPacket<Unaligned>(lhs_vert, (threadIdx.y/4+k+16)); + lhs_pf3 =lhs.loadPacket<Unaligned>(lhs_vert, (threadIdx.y/4+k+24)); + } else if ((threadIdx.y/4+k+16) < k_size) { + lhs_pf0 =lhs.loadPacket<Unaligned>(lhs_vert, (threadIdx.y/4+k)); + lhs_pf1 =lhs.loadPacket<Unaligned>(lhs_vert, (threadIdx.y/4+k+8)); + lhs_pf2 =lhs.loadPacket<Unaligned>(lhs_vert, (threadIdx.y/4+k+16)); + } else if ((threadIdx.y/4+k+8) < k_size) { + lhs_pf0 =lhs.loadPacket<Unaligned>(lhs_vert, (threadIdx.y/4+k)); + lhs_pf1 =lhs.loadPacket<Unaligned>(lhs_vert, (threadIdx.y/4+k+8)); + } else if ((threadIdx.y/4+k) < k_size) { + lhs_pf0 =lhs.loadPacket<Unaligned>(lhs_vert, (threadIdx.y/4+k)); + } + } else if (lhs_vert + 2 < m_size) { + if ((threadIdx.y/4+k+24) < k_size) { + lhs_pf0.x =lhs(lhs_vert + 0, (threadIdx.y/4+k)); + lhs_pf0.y =lhs(lhs_vert + 1, (threadIdx.y/4+k)); + lhs_pf0.z =lhs(lhs_vert + 2, (threadIdx.y/4+k)); + lhs_pf1.x =lhs(lhs_vert + 0, (threadIdx.y/4+k+8)); + lhs_pf1.y =lhs(lhs_vert + 1, (threadIdx.y/4+k+8)); + lhs_pf1.z =lhs(lhs_vert + 2, (threadIdx.y/4+k+8)); + lhs_pf2.x =lhs(lhs_vert + 0, (threadIdx.y/4+k+16)); + lhs_pf2.y =lhs(lhs_vert + 1, (threadIdx.y/4+k+16)); + lhs_pf2.z =lhs(lhs_vert + 2, (threadIdx.y/4+k+16)); + lhs_pf3.x =lhs(lhs_vert + 0, (threadIdx.y/4+k+24)); + lhs_pf3.y =lhs(lhs_vert + 1, (threadIdx.y/4+k+24)); + lhs_pf3.z =lhs(lhs_vert + 2, (threadIdx.y/4+k+24)); + } else if ((threadIdx.y/4+k+16) < k_size) { + lhs_pf0.x =lhs(lhs_vert + 0, (threadIdx.y/4+k)); + lhs_pf0.y =lhs(lhs_vert + 1, (threadIdx.y/4+k)); + lhs_pf0.z =lhs(lhs_vert + 2, (threadIdx.y/4+k)); + lhs_pf1.x =lhs(lhs_vert + 0, (threadIdx.y/4+k+8)); + lhs_pf1.y =lhs(lhs_vert + 1, (threadIdx.y/4+k+8)); + lhs_pf1.z =lhs(lhs_vert + 2, (threadIdx.y/4+k+8)); + lhs_pf2.x =lhs(lhs_vert + 0, (threadIdx.y/4+k+16)); + lhs_pf2.y =lhs(lhs_vert + 1, (threadIdx.y/4+k+16)); + lhs_pf2.z =lhs(lhs_vert + 2, (threadIdx.y/4+k+16)); + } else if ((threadIdx.y/4+k+8) < k_size) { + lhs_pf0.x =lhs(lhs_vert + 0, (threadIdx.y/4+k)); + lhs_pf0.y =lhs(lhs_vert + 1, (threadIdx.y/4+k)); + lhs_pf0.z =lhs(lhs_vert + 2, (threadIdx.y/4+k)); + lhs_pf1.x =lhs(lhs_vert + 0, (threadIdx.y/4+k+8)); + lhs_pf1.y =lhs(lhs_vert + 1, (threadIdx.y/4+k+8)); + lhs_pf1.z =lhs(lhs_vert + 2, (threadIdx.y/4+k+8)); + } else if ((threadIdx.y/4+k) < k_size) { + lhs_pf0.x =lhs(lhs_vert + 0, (threadIdx.y/4+k)); + lhs_pf0.y =lhs(lhs_vert + 1, (threadIdx.y/4+k)); + lhs_pf0.z =lhs(lhs_vert + 2, (threadIdx.y/4+k)); + } + } else if (lhs_vert + 1 < m_size) { + if ((threadIdx.y/4+k+24) < k_size) { + lhs_pf0.x =lhs(lhs_vert + 0, (threadIdx.y/4+k)); + lhs_pf0.y =lhs(lhs_vert + 1, (threadIdx.y/4+k)); + lhs_pf1.x =lhs(lhs_vert + 0, (threadIdx.y/4+k+8)); + lhs_pf1.y =lhs(lhs_vert + 1, (threadIdx.y/4+k+8)); + lhs_pf2.x =lhs(lhs_vert + 0, (threadIdx.y/4+k+16)); + lhs_pf2.y =lhs(lhs_vert + 1, (threadIdx.y/4+k+16)); + lhs_pf3.x =lhs(lhs_vert + 0, (threadIdx.y/4+k+24)); + lhs_pf3.y =lhs(lhs_vert + 1, (threadIdx.y/4+k+24)); + } else if ((threadIdx.y/4+k+16) < k_size) { + lhs_pf0.x =lhs(lhs_vert + 0, (threadIdx.y/4+k)); + lhs_pf0.y =lhs(lhs_vert + 1, (threadIdx.y/4+k)); + lhs_pf1.x =lhs(lhs_vert + 0, (threadIdx.y/4+k+8)); + lhs_pf1.y =lhs(lhs_vert + 1, (threadIdx.y/4+k+8)); + lhs_pf2.x =lhs(lhs_vert + 0, (threadIdx.y/4+k+16)); + lhs_pf2.y =lhs(lhs_vert + 1, (threadIdx.y/4+k+16)); + } else if ((threadIdx.y/4+k+8) < k_size) { + lhs_pf0.x =lhs(lhs_vert + 0, (threadIdx.y/4+k)); + lhs_pf0.y =lhs(lhs_vert + 1, (threadIdx.y/4+k)); + lhs_pf1.x =lhs(lhs_vert + 0, (threadIdx.y/4+k+8)); + lhs_pf1.y =lhs(lhs_vert + 1, (threadIdx.y/4+k+8)); + } else if ((threadIdx.y/4+k) < k_size) { + lhs_pf0.x =lhs(lhs_vert + 0, (threadIdx.y/4+k)); + lhs_pf0.y =lhs(lhs_vert + 1, (threadIdx.y/4+k)); + } + } else if (lhs_vert < m_size) { + if ((threadIdx.y/4+k+24) < k_size) { + lhs_pf0.x =lhs(lhs_vert + 0, (threadIdx.y/4+k)); + lhs_pf1.x =lhs(lhs_vert + 0, (threadIdx.y/4+k+8)); + lhs_pf2.x =lhs(lhs_vert + 0, (threadIdx.y/4+k+16)); + lhs_pf3.x =lhs(lhs_vert + 0, (threadIdx.y/4+k+24)); + } else if ((threadIdx.y/4+k+16) < k_size) { + lhs_pf0.x =lhs(lhs_vert + 0, (threadIdx.y/4+k)); + lhs_pf1.x =lhs(lhs_vert + 0, (threadIdx.y/4+k+8)); + lhs_pf2.x =lhs(lhs_vert + 0, (threadIdx.y/4+k+16)); + } else if ((threadIdx.y/4+k+8) < k_size) { + lhs_pf0.x =lhs(lhs_vert + 0, (threadIdx.y/4+k)); + lhs_pf1.x =lhs(lhs_vert + 0, (threadIdx.y/4+k+8)); + } else if ((threadIdx.y/4+k) < k_size) { + lhs_pf0.x =lhs(lhs_vert + 0, (threadIdx.y/4+k)); + } + } + } + __syncthreads(); + Index rhs_vert = k+threadIdx.x*4; + Index rhs_horiz0 = threadIdx.y*2+base_n; + Index rhs_horiz1 = threadIdx.y*2+1+base_n; + if (!CHECK_RHS_BOUNDARY) { + if ((rhs_vert + 3) < k_size) { + // just CHECK_RHS_BOUNDARY + rhs_pf0 = rhs.loadPacket<Unaligned>(rhs_vert, rhs_horiz0); + rhs_pf1 = rhs.loadPacket<Unaligned>(rhs_vert, rhs_horiz1); + } else if (rhs_vert + 2 < k_size) { + // just CHECK_RHS_BOUNDARY + rhs_pf0.x = rhs(rhs_vert, rhs_horiz0); + rhs_pf0.y = rhs(rhs_vert + 1, rhs_horiz0); + rhs_pf0.z = rhs(rhs_vert + 2, rhs_horiz0); + rhs_pf1.x = rhs(rhs_vert, rhs_horiz1); + rhs_pf1.y = rhs(rhs_vert + 1, rhs_horiz1); + rhs_pf1.z = rhs(rhs_vert + 2, rhs_horiz1); + } else if (rhs_vert + 1 < k_size) { + rhs_pf0.x = rhs(rhs_vert, rhs_horiz0); + rhs_pf0.y = rhs(rhs_vert + 1, rhs_horiz0); + rhs_pf1.x = rhs(rhs_vert, rhs_horiz1); + rhs_pf1.y = rhs(rhs_vert + 1, rhs_horiz1); + } else if (rhs_vert < k_size) { + rhs_pf0.x = rhs(rhs_vert, rhs_horiz0); + rhs_pf1.x = rhs(rhs_vert, rhs_horiz1); + } + } else { + if (rhs_horiz1 < n_size) { + if ((rhs_vert + 3) < k_size) { + // just CHECK_RHS_BOUNDARY + rhs_pf0 = rhs.loadPacket<Unaligned>(rhs_vert, rhs_horiz0); + rhs_pf1 = rhs.loadPacket<Unaligned>(rhs_vert, rhs_horiz1); + } else if (rhs_vert + 2 < k_size) { + // just CHECK_RHS_BOUNDARY + rhs_pf0.x = rhs(rhs_vert, rhs_horiz0); + rhs_pf0.y = rhs(rhs_vert + 1, rhs_horiz0); + rhs_pf0.z = rhs(rhs_vert + 2, rhs_horiz0); + rhs_pf1.x = rhs(rhs_vert, rhs_horiz1); + rhs_pf1.y = rhs(rhs_vert + 1, rhs_horiz1); + rhs_pf1.z = rhs(rhs_vert + 2, rhs_horiz1); + } else if (k+threadIdx.x*4 + 1 < k_size) { + rhs_pf0.x = rhs(rhs_vert, rhs_horiz0); + rhs_pf0.y = rhs(rhs_vert + 1, rhs_horiz0); + rhs_pf1.x = rhs(rhs_vert, rhs_horiz1); + rhs_pf1.y = rhs(rhs_vert + 1, rhs_horiz1); + } else if (k+threadIdx.x*4 < k_size) { + rhs_pf0.x = rhs(rhs_vert, rhs_horiz0); + rhs_pf1.x = rhs(rhs_vert, rhs_horiz1); + } + } else if (rhs_horiz0 < n_size) { + if ((rhs_vert + 3) < k_size) { + // just CHECK_RHS_BOUNDARY + rhs_pf0 = rhs.loadPacket<Unaligned>(rhs_vert, rhs_horiz0); + } else if ((rhs_vert + 2) < k_size) { + // just CHECK_RHS_BOUNDARY + rhs_pf0.x = rhs(rhs_vert, rhs_horiz0); + rhs_pf0.y = rhs(rhs_vert + 1, rhs_horiz0); + rhs_pf0.z = rhs(rhs_vert + 2, rhs_horiz0); + } else if ((rhs_vert + 1) < k_size) { + rhs_pf0.x = rhs(rhs_vert, rhs_horiz0); + rhs_pf0.y = rhs(rhs_vert + 1, rhs_horiz0); + } else if (rhs_vert < k_size) { + rhs_pf0.x = rhs(rhs_vert, rhs_horiz0); + } + } + } + __syncthreads(); + // Loaded. Do computation + // Row 0 -> times (0, 4, 8, .. 28) for features 0, 1. + // Row 1 -> times (0, 4, 8, .. 28) for features 2, 3. + // .. + // Row 31 -> times (0, 4, 8, .. 28) for features 62, 63 + rhs_shmem2[threadIdx.y][threadIdx.x] = make_float2(rhs_pf0.x, rhs_pf1.x); + // Row 32 -> times (1, 5, 9, .. 29) for features 0, 1. + // Row 33 -> times (1, 5, 9, .. 29) for features 2, 3. + // .. + rhs_shmem2[threadIdx.y+32][threadIdx.x] = make_float2(rhs_pf0.y, rhs_pf1.y); + // Row 64 -> times (2, 6, 10, .. 30) for features 0, 1. + // Row 65 -> times (2, 6, 10, .. 30) for features 2, 3. + rhs_shmem2[threadIdx.y+64][threadIdx.x] = make_float2(rhs_pf0.z, rhs_pf1.z); + // Row 96 -> times (3, 7, 11, .. 31) for features 0, 1. + // Row 97 -> times (3, 7, 11, .. 31) for features 2, 3. + rhs_shmem2[threadIdx.y+96][threadIdx.x] = make_float2(rhs_pf0.w, rhs_pf1.w); + + // LHS. + // Row 0 (time 0) -> features (0, 1), (4, 5), .. (28, 29), (32, 33), .. (60, 61) .. (124, 125) + // Row 1 (time 1) -> features (0, 1), (4, 5), .. (28, 29), (32, 33), .. (60, 61) .. (124, 125) + // ... + // Row 8 (time 0) -> features (2, 3), (6, 7), .. (30, 31), (34, 35), .. (62, 63) .. (126, 127) + // Row 15 (time 7) -> features (2, 3), (6, 7), .. (30, 31), (34, 35), .. (62, 63) .. (126, 127) + + +#define add_vals(a_feat1, a_feat2, f1, f2, f3, f4)\ + results[0].x += a_feat1.x * f1.x;\ + results[1].x += a_feat1.x * f1.y;\ + results[2].x += a_feat1.x * f2.x;\ + results[3].x += a_feat1.x * f2.y;\ + results[4].x += a_feat1.x * f3.x;\ + results[5].x += a_feat1.x * f3.y;\ + results[6].x += a_feat1.x * f4.x;\ + results[7].x += a_feat1.x * f4.y;\ +\ + results[0].y += a_feat1.y * f1.x;\ + results[1].y += a_feat1.y * f1.y;\ + results[2].y += a_feat1.y * f2.x;\ + results[3].y += a_feat1.y * f2.y;\ + results[4].y += a_feat1.y * f3.x;\ + results[5].y += a_feat1.y * f3.y;\ + results[6].y += a_feat1.y * f4.x;\ + results[7].y += a_feat1.y * f4.y;\ +\ + results[0].z += a_feat2.x * f1.x;\ + results[1].z += a_feat2.x * f1.y;\ + results[2].z += a_feat2.x * f2.x;\ + results[3].z += a_feat2.x * f2.y;\ + results[4].z += a_feat2.x * f3.x;\ + results[5].z += a_feat2.x * f3.y;\ + results[6].z += a_feat2.x * f4.x;\ + results[7].z += a_feat2.x * f4.y;\ +\ + results[0].w += a_feat2.y * f1.x;\ + results[1].w += a_feat2.y * f1.y;\ + results[2].w += a_feat2.y * f2.x;\ + results[3].w += a_feat2.y * f2.y;\ + results[4].w += a_feat2.y * f3.x;\ + results[5].w += a_feat2.y * f3.y;\ + results[6].w += a_feat2.y * f4.x;\ + results[7].w += a_feat2.y * f4.y;\ + + lhs_shmem2[threadIdx.y/4][threadIdx.x+(threadIdx.y%4)*8] = make_float2(lhs_pf0.x, lhs_pf0.y); + lhs_shmem2[threadIdx.y/4+8][threadIdx.x+(threadIdx.y%4)*8] = make_float2(lhs_pf1.x, lhs_pf1.y); + lhs_shmem2[threadIdx.y/4+16][threadIdx.x+(threadIdx.y%4)*8] = make_float2(lhs_pf2.x, lhs_pf2.y); + lhs_shmem2[threadIdx.y/4+24][threadIdx.x+(threadIdx.y%4)*8] = make_float2(lhs_pf3.x, lhs_pf3.y); + + lhs_shmem2[threadIdx.y/4 + 32][threadIdx.x+(threadIdx.y%4)*8] = make_float2(lhs_pf0.z, lhs_pf0.w); + lhs_shmem2[threadIdx.y/4 + 40][threadIdx.x+(threadIdx.y%4)*8] = make_float2(lhs_pf1.z, lhs_pf1.w); + lhs_shmem2[threadIdx.y/4 + 48][threadIdx.x+(threadIdx.y%4)*8] = make_float2(lhs_pf2.z, lhs_pf2.w); + lhs_shmem2[threadIdx.y/4 + 56][threadIdx.x+(threadIdx.y%4)*8] = make_float2(lhs_pf3.z, lhs_pf3.w); + + __syncthreads(); + + // Do the multiplies. + #pragma unroll + for (int koff = 0; koff < 32; koff ++) { + float2 a3 = lhs_shmem2[koff][threadIdx.x + (threadIdx.y % 4) * 8]; + float2 a4 = lhs_shmem2[koff + 32][threadIdx.x + (threadIdx.y % 4) * 8]; + + // first feature is at (threadIdx.y/4) * 8 last is at start + 8. + int start_feature = (threadIdx.y / 4) * 8; + + float2 br1 = rhs_shmem2[start_feature/2 + (koff % 4) * 32][koff/4]; + float2 br2 = rhs_shmem2[start_feature/2 + 1 + (koff % 4) * 32][koff/4]; + float2 br3 = rhs_shmem2[start_feature/2 + 2 + (koff % 4) * 32][koff/4]; + float2 br4 = rhs_shmem2[start_feature/2 + 3 + (koff % 4) * 32][koff/4]; + + add_vals(a3, a4, br1, br2, br3, br4) + } + __syncthreads(); + } // end loop over k + + + __syncthreads(); + Index horiz_base = (threadIdx.y/4)*8+base_n; + if (!CHECK_LHS_BOUNDARY && !CHECK_RHS_BOUNDARY) { + for (int i = 0; i < 8; i++) { + output(lhs_vert, horiz_base + i) = results[i].x; + output(lhs_vert + 1, horiz_base + i) = results[i].y; + output(lhs_vert + 2, horiz_base + i) = results[i].z; + output(lhs_vert + 3, horiz_base + i) = results[i].w; + } + } else if (!CHECK_RHS_BOUNDARY) { + if (lhs_vert + 3 < m_size) { + for (int i = 0; i < 8; i++) { + output(lhs_vert, horiz_base + i) = results[i].x; + output(lhs_vert + 1, horiz_base + i) = results[i].y; + output(lhs_vert + 2, horiz_base + i) = results[i].z; + output(lhs_vert + 3, horiz_base + i) = results[i].w; + } + } else if (lhs_vert + 2 < m_size) { + for (int i = 0; i < 8; i++) { + output(lhs_vert, horiz_base + i) = results[i].x; + output(lhs_vert + 1, horiz_base + i) = results[i].y; + output(lhs_vert + 2, horiz_base + i) = results[i].z; + } + } else if (lhs_vert + 1 < m_size) { + for (int i = 0; i < 8; i++) { + output(lhs_vert, horiz_base + i) = results[i].x; + output(lhs_vert + 1, horiz_base + i) = results[i].y; + } + } else if (lhs_vert < m_size) { + for (int i = 0; i < 8; i++) { + output(lhs_vert, horiz_base + i) = results[i].x; + } + } + } else if (!CHECK_LHS_BOUNDARY) { + // CHECK BOUNDARY_B + for (int i = 0; i < 8; i++) { + if (horiz_base + i < n_size) { + output(lhs_vert, horiz_base + i) = results[i].x; + output(lhs_vert + 1, horiz_base + i) = results[i].y; + output(lhs_vert + 2, horiz_base + i) = results[i].z; + output(lhs_vert + 3, horiz_base + i) = results[i].w; + } + } + } else { + // CHECK both boundaries. + for (int i = 0; i < 8; i++) { + if (horiz_base + i < n_size) { + if (lhs_vert < m_size) + output(lhs_vert, horiz_base + i) = results[i].x; + if (lhs_vert + 1 < m_size) + output(lhs_vert + 1, horiz_base + i) = results[i].y; + if (lhs_vert + 2 < m_size) + output(lhs_vert + 2, horiz_base + i) = results[i].z; + if (lhs_vert + 3 < m_size) + output(lhs_vert + 3, horiz_base + i) = results[i].w; + } + } + } +} + + +template<typename Index, typename LhsMapper, + typename RhsMapper, typename OutputMapper> +__global__ void +__launch_bounds__(256) +EigenFloatContractionKernel(const LhsMapper lhs, const RhsMapper rhs, + const OutputMapper output, + const Index m_size, const Index n_size, const Index k_size) { + __shared__ float2 lhs_shmem[64*32]; + __shared__ float2 rhs_shmem[128*8]; + + typedef float2 LHS_MEM[64][32]; + typedef float2 RHS_MEM[128][8]; + + typedef float2 LHS_MEM16x16[32][16]; + typedef float2 RHS_MEM16x16[64][8]; + + const Index m_block_idx = blockIdx.x; + const Index n_block_idx = blockIdx.y; + + const Index base_m = 128 * m_block_idx; + const Index base_n = 64 * n_block_idx; + + bool check_rhs = (base_n + 63) >= n_size; + bool check_lhs128 = (base_m + 127) >= m_size; + + if (!check_rhs) { + if (!check_lhs128) { + // >= 128 rows left + EigenFloatContractionKernelInternal<Index, LhsMapper, RhsMapper, OutputMapper, false, false>( + lhs, rhs, output, *((LHS_MEM *) lhs_shmem), *((RHS_MEM *) rhs_shmem), m_size, n_size, k_size, base_m, base_n); + } else { + EigenFloatContractionKernelInternal<Index, LhsMapper, RhsMapper, OutputMapper, true, false>( + lhs, rhs, output, *((LHS_MEM *) lhs_shmem), *((RHS_MEM *) rhs_shmem), m_size, n_size, k_size, base_m, base_n); + } + } else { + if (!check_lhs128) { + // >= 128 rows left + EigenFloatContractionKernelInternal<Index, LhsMapper, RhsMapper, OutputMapper, false, true>( + lhs, rhs, output, *((LHS_MEM *) lhs_shmem), *((RHS_MEM *) rhs_shmem), m_size, n_size, k_size, base_m, base_n); + } else { + EigenFloatContractionKernelInternal<Index, LhsMapper, RhsMapper, OutputMapper, true, true>( + lhs, rhs, output, *((LHS_MEM *) lhs_shmem), *((RHS_MEM *) rhs_shmem), m_size, n_size, k_size, base_m, base_n); + } + } +} + +template<typename Index, typename LhsMapper, + typename RhsMapper, typename OutputMapper> +__global__ void +__launch_bounds__(256) +EigenFloatContractionKernel16x16(const LhsMapper lhs, const RhsMapper rhs, + const OutputMapper output, + const Index m_size, const Index n_size, const Index k_size) { + __shared__ float2 lhs_shmem[32][16]; + __shared__ float2 rhs_shmem[64][8]; + + const Index m_block_idx = blockIdx.x; + const Index n_block_idx = blockIdx.y; + + const Index base_m = 64 * m_block_idx; + const Index base_n = 64 * n_block_idx; + + if (base_m + 63 < m_size) { + if (base_n + 63 < n_size) { + EigenFloatContractionKernelInternal16x16<Index, LhsMapper, RhsMapper, OutputMapper, false, false>(lhs, rhs, output, lhs_shmem, rhs_shmem, m_size, n_size, k_size, base_m, base_n); + } else { + EigenFloatContractionKernelInternal16x16<Index, LhsMapper, RhsMapper, OutputMapper, false, true>(lhs, rhs, output, lhs_shmem, rhs_shmem, m_size, n_size, k_size, base_m, base_n); + } + } else { + if (base_n + 63 < n_size) { + EigenFloatContractionKernelInternal16x16<Index, LhsMapper, RhsMapper, OutputMapper, true, false>(lhs, rhs, output, lhs_shmem, rhs_shmem, m_size, n_size, k_size, base_m, base_n); + } else { + EigenFloatContractionKernelInternal16x16<Index, LhsMapper, RhsMapper, OutputMapper, true, true>(lhs, rhs, output, lhs_shmem, rhs_shmem, m_size, n_size, k_size, base_m, base_n); + } + } +} + + +template<typename Indices, typename LeftArgType, typename RightArgType> +struct TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType>, GpuDevice> : + public TensorContractionEvaluatorBase<TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType>, GpuDevice> > { + + typedef GpuDevice Device; + + typedef TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType>, Device> Self; + typedef TensorContractionEvaluatorBase<Self> Base; + + typedef TensorContractionOp<Indices, LeftArgType, RightArgType> XprType; + typedef typename internal::remove_const<typename XprType::Scalar>::type Scalar; + typedef typename XprType::Index Index; + typedef typename XprType::CoeffReturnType CoeffReturnType; + typedef typename PacketType<CoeffReturnType, GpuDevice>::type PacketReturnType; + + enum { + Layout = TensorEvaluator<LeftArgType, Device>::Layout, + }; + + // Most of the code is assuming that both input tensors are ColMajor. If the + // inputs are RowMajor, we will "cheat" by swapping the LHS and RHS: + // If we want to compute A * B = C, where A is LHS and B is RHS, the code + // will pretend B is LHS and A is RHS. + typedef typename internal::conditional< + static_cast<int>(Layout) == static_cast<int>(ColMajor), LeftArgType, RightArgType>::type EvalLeftArgType; + typedef typename internal::conditional< + static_cast<int>(Layout) == static_cast<int>(ColMajor), RightArgType, LeftArgType>::type EvalRightArgType; + + static const int LDims = + internal::array_size<typename TensorEvaluator<EvalLeftArgType, Device>::Dimensions>::value; + static const int RDims = + internal::array_size<typename TensorEvaluator<EvalRightArgType, Device>::Dimensions>::value; + static const int ContractDims = internal::array_size<Indices>::value; + + typedef array<Index, LDims> left_dim_mapper_t; + typedef array<Index, RDims> right_dim_mapper_t; + + typedef array<Index, ContractDims> contract_t; + typedef array<Index, LDims - ContractDims> left_nocontract_t; + typedef array<Index, RDims - ContractDims> right_nocontract_t; + + static const int NumDims = LDims + RDims - 2 * ContractDims; + + typedef DSizes<Index, NumDims> Dimensions; + + // typedefs needed in evalTo + typedef typename internal::remove_const<typename EvalLeftArgType::Scalar>::type LhsScalar; + typedef typename internal::remove_const<typename EvalRightArgType::Scalar>::type RhsScalar; + + typedef TensorEvaluator<EvalLeftArgType, Device> LeftEvaluator; + typedef TensorEvaluator<EvalRightArgType, Device> RightEvaluator; + + typedef typename LeftEvaluator::Dimensions LeftDimensions; + typedef typename RightEvaluator::Dimensions RightDimensions; + + EIGEN_DEVICE_FUNC TensorEvaluator(const XprType& op, const Device& device) : + Base(op, device) {} + + // We need to redefine this method to make nvcc happy + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* data) { + this->m_leftImpl.evalSubExprsIfNeeded(NULL); + this->m_rightImpl.evalSubExprsIfNeeded(NULL); + if (data) { + evalTo(data); + return false; + } else { + this->m_result = static_cast<Scalar *>(this->m_device.allocate(this->dimensions().TotalSize() * sizeof(Scalar))); + evalTo(this->m_result); + return true; + } + } + + void evalTo(Scalar* buffer) const { + if (this->m_lhs_inner_dim_contiguous) { + if (this->m_rhs_inner_dim_contiguous) { + if (this->m_rhs_inner_dim_reordered) { + evalTyped<true, true, true, Unaligned>(buffer); + } + else { + evalTyped<true, true, false, Unaligned>(buffer); + } + } + else { + if (this->m_rhs_inner_dim_reordered) { + evalTyped<true, false, true, Unaligned>(buffer); + } + else { + evalTyped<true, false, false, Unaligned>(buffer); + } + } + } + else { + if (this->m_rhs_inner_dim_contiguous) { + if (this->m_rhs_inner_dim_reordered) { + evalTyped<false, true, true, Unaligned>(buffer); + } + else { + evalTyped<false, true, false, Unaligned>(buffer); + } + } + else { + if (this->m_rhs_inner_dim_reordered) { + evalTyped<false, false, true, Unaligned>(buffer); + } + else { + evalTyped<false, false, false, Unaligned>(buffer); + } + } + } + } + + template <typename LhsScalar, typename RhsScalar, typename Index, typename LhsMapper, typename RhsMapper, typename OutputMapper> struct LaunchKernels { + static void Run(const LhsMapper& lhs, const RhsMapper& rhs, const OutputMapper& output, Index m, Index n, Index k, const GpuDevice& device) { + const Index m_blocks = (m + 63) / 64; + const Index n_blocks = (n + 63) / 64; + const dim3 num_blocks(m_blocks, n_blocks, 1); + const dim3 block_size(8, 8, 8); + LAUNCH_CUDA_KERNEL((EigenContractionKernel<Scalar, Index, LhsMapper, RhsMapper, OutputMapper>), num_blocks, block_size, 0, device, lhs, rhs, output, m, n, k); + } + }; + + template <typename Index, typename LhsMapper, typename RhsMapper, typename OutputMapper> struct LaunchKernels<float, float, Index, LhsMapper, RhsMapper, OutputMapper> { + static void Run(const LhsMapper& lhs, const RhsMapper& rhs, const OutputMapper& output, Index m, Index n, Index k, const GpuDevice& device) { + if (m < 768 || n < 768) { + const Index m_blocks = (m + 63) / 64; + const Index n_blocks = (n + 63) / 64; + const dim3 num_blocks(m_blocks, n_blocks, 1); + const dim3 block_size(16, 16, 1); + LAUNCH_CUDA_KERNEL((EigenFloatContractionKernel16x16<Index, LhsMapper, RhsMapper, OutputMapper>), num_blocks, block_size, 0, device, lhs, rhs, output, m, n, k); + } else { + const Index m_blocks = (m + 127) / 128; + const Index n_blocks = (n + 63) / 64; + const dim3 num_blocks(m_blocks, n_blocks, 1); + const dim3 block_size(8, 32, 1); + LAUNCH_CUDA_KERNEL((EigenFloatContractionKernel<Index, LhsMapper, RhsMapper, OutputMapper>), num_blocks, block_size, 0, device, lhs, rhs, output, m, n, k); + } + } + }; + + template <bool lhs_inner_dim_contiguous, bool rhs_inner_dim_contiguous, bool rhs_inner_dim_reordered, int Alignment> + void evalTyped(Scalar* buffer) const { + // columns in left side, rows in right side + const Index k = this->m_k_size; + EIGEN_UNUSED_VARIABLE(k) + + // rows in left side + const Index m = this->m_i_size; + + // columns in right side + const Index n = this->m_j_size; + + // zero out the result buffer (which must be of size at least m * n * sizeof(Scalar) + this->m_device.memset(buffer, 0, m * n * sizeof(Scalar)); + + typedef internal::TensorContractionInputMapper<LhsScalar, Index, internal::Lhs, + LeftEvaluator, left_nocontract_t, + contract_t, 4, + lhs_inner_dim_contiguous, + false, Unaligned> LhsMapper; + + typedef internal::TensorContractionInputMapper<RhsScalar, Index, internal::Rhs, + RightEvaluator, right_nocontract_t, + contract_t, 4, + rhs_inner_dim_contiguous, + rhs_inner_dim_reordered, Unaligned> RhsMapper; + + typedef internal::blas_data_mapper<Scalar, Index, ColMajor> OutputMapper; + + + // initialize data mappers + LhsMapper lhs(this->m_leftImpl, this->m_left_nocontract_strides, this->m_i_strides, + this->m_left_contracting_strides, this->m_k_strides); + + RhsMapper rhs(this->m_rightImpl, this->m_right_nocontract_strides, this->m_j_strides, + this->m_right_contracting_strides, this->m_k_strides); + + OutputMapper output(buffer, m); + + setCudaSharedMemConfig(cudaSharedMemBankSizeEightByte); + LaunchKernels<LhsScalar, RhsScalar, Index, LhsMapper, RhsMapper, OutputMapper>::Run(lhs, rhs, output, m, n, k, this->m_device); + } +}; + +} // end namespace Eigen + +#endif // EIGEN_USE_GPU and __CUDACC__ +#endif // EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_CUDA_H diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorContractionMapper.h b/unsupported/Eigen/CXX11/src/Tensor/TensorContractionMapper.h new file mode 100644 index 000000000..9b2cb3ff6 --- /dev/null +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorContractionMapper.h @@ -0,0 +1,467 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_MAPPER_H +#define EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_MAPPER_H + +namespace Eigen { + +namespace internal { + +enum { + Rhs = 0, + Lhs = 1 +}; + +/* + * Implementation of the Eigen blas_data_mapper class for tensors. + */ + +template <typename Tensor, bool HasRawAccess> struct CoeffLoader { + enum { + DirectOffsets = false + }; + + EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE CoeffLoader(const Tensor& tensor) : m_tensor(tensor) { } + + EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void offsetBuffer(typename Tensor::Index) { + eigen_assert(false && "unsupported"); + } + + EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE typename Tensor::Scalar coeff(typename Tensor::Index index) const { return m_tensor.coeff(index); } + + template<int LoadMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + typename Tensor::PacketReturnType packet(typename Tensor::Index index) const + { + return m_tensor.template packet<LoadMode>(index); + } + + + private: + const Tensor m_tensor; +}; + +template <typename Tensor> struct CoeffLoader<Tensor, true> { + enum { + DirectOffsets = true + }; + + EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE CoeffLoader(const Tensor& tensor) : m_data(tensor.data()) {} + + EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void offsetBuffer(typename Tensor::Index offset) { + m_data += offset; + } + + EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE typename Tensor::Scalar coeff(typename Tensor::Index index) const { return loadConstant(m_data+index); } + + template<int LoadMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + typename Tensor::PacketReturnType packet(typename Tensor::Index index) const + { + return internal::ploadt_ro<typename Tensor::PacketReturnType, LoadMode>(m_data + index); + } + private: + typedef typename Tensor::Scalar Scalar; + const Scalar* m_data; +}; + +template<typename Scalar, typename Index, int side, + typename Tensor, + typename nocontract_t, typename contract_t, + int packet_size, bool inner_dim_contiguous, int Alignment> +class SimpleTensorContractionMapper { + public: + EIGEN_DEVICE_FUNC + SimpleTensorContractionMapper(const Tensor& tensor, + const nocontract_t& nocontract_strides, + const nocontract_t& ij_strides, + const contract_t& contract_strides, + const contract_t& k_strides) : + m_tensor(tensor), + m_nocontract_strides(nocontract_strides), + m_ij_strides(ij_strides), + m_contract_strides(contract_strides), + m_k_strides(k_strides) { } + + enum { + DirectOffsets = CoeffLoader<Tensor, Tensor::RawAccess>::DirectOffsets + }; + + EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void offsetBuffer(typename Tensor::Index offset) { + m_tensor.offsetBuffer(offset); + } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE void prefetch(Index /*i*/) { } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Scalar operator()(Index row) const { + // column major assumption + return operator()(row, 0); + } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Scalar operator()(Index row, Index col) const { + return m_tensor.coeff(computeIndex(row, col)); + } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Index computeIndex(Index row, Index col) const { + const bool left = (side == Lhs); + Index nocontract_val = left ? row : col; + Index linidx = 0; + for (int i = static_cast<int>(array_size<nocontract_t>::value) - 1; i > 0; i--) { + const Index idx = nocontract_val / m_ij_strides[i]; + linidx += idx * m_nocontract_strides[i]; + nocontract_val -= idx * m_ij_strides[i]; + } + if (array_size<typename Tensor::Dimensions>::value > array_size<contract_t>::value) { + if (side == Lhs && inner_dim_contiguous) { + eigen_assert(m_nocontract_strides[0] == 1); + linidx += nocontract_val; + } else { + linidx += nocontract_val * m_nocontract_strides[0]; + } + } + + Index contract_val = left ? col : row; + if(array_size<contract_t>::value > 0) { + for (int i = static_cast<int>(array_size<contract_t>::value) - 1; i > 0; i--) { + const Index idx = contract_val / m_k_strides[i]; + linidx += idx * m_contract_strides[i]; + contract_val -= idx * m_k_strides[i]; + } + + if (side == Rhs && inner_dim_contiguous) { + eigen_assert(m_contract_strides[0] == 1); + linidx += contract_val; + } else { + linidx += contract_val * m_contract_strides[0]; + } + } + + return linidx; + } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE IndexPair<Index> computeIndexPair(Index row, Index col, const Index distance) const { + const bool left = (side == Lhs); + Index nocontract_val[2] = {left ? row : col, left ? row + distance : col}; + Index linidx[2] = {0, 0}; + if (array_size<typename Tensor::Dimensions>::value > array_size<contract_t>::value) { + for (int i = static_cast<int>(array_size<nocontract_t>::value) - 1; i > 0; i--) { + const Index idx0 = nocontract_val[0] / m_ij_strides[i]; + const Index idx1 = nocontract_val[1] / m_ij_strides[i]; + linidx[0] += idx0 * m_nocontract_strides[i]; + linidx[1] += idx1 * m_nocontract_strides[i]; + nocontract_val[0] -= idx0 * m_ij_strides[i]; + nocontract_val[1] -= idx1 * m_ij_strides[i]; + } + if (side == Lhs && inner_dim_contiguous) { + eigen_assert(m_nocontract_strides[0] == 1); + linidx[0] += nocontract_val[0]; + linidx[1] += nocontract_val[1]; + } else { + linidx[0] += nocontract_val[0] * m_nocontract_strides[0]; + linidx[1] += nocontract_val[1] * m_nocontract_strides[0]; + } + } + + Index contract_val[2] = {left ? col : row, left ? col : row + distance}; + if (array_size<contract_t>::value> 0) { + for (int i = static_cast<int>(array_size<contract_t>::value) - 1; i > 0; i--) { + const Index idx0 = contract_val[0] / m_k_strides[i]; + const Index idx1 = contract_val[1] / m_k_strides[i]; + linidx[0] += idx0 * m_contract_strides[i]; + linidx[1] += idx1 * m_contract_strides[i]; + contract_val[0] -= idx0 * m_k_strides[i]; + contract_val[1] -= idx1 * m_k_strides[i]; + } + + if (side == Rhs && inner_dim_contiguous) { + eigen_assert(m_contract_strides[0] == 1); + linidx[0] += contract_val[0]; + linidx[1] += contract_val[1]; + } else { + linidx[0] += contract_val[0] * m_contract_strides[0]; + linidx[1] += contract_val[1] * m_contract_strides[0]; + } + } + return IndexPair<Index>(linidx[0], linidx[1]); + } + + EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Index firstAligned(Index size) const { + // Only claim alignment when we can compute the actual stride (ie when we're + // dealing with the lhs with inner_dim_contiguous. This is because the + // matrix-vector product relies on the stride when dealing with aligned inputs. + return (Alignment == Aligned) && (side == Lhs) && inner_dim_contiguous ? 0 : size; + } + EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Index stride() const { + return ((side == Lhs) && inner_dim_contiguous && array_size<contract_t>::value > 0) ? m_contract_strides[0] : 1; + } + + protected: + CoeffLoader<Tensor, Tensor::RawAccess> m_tensor; + const nocontract_t m_nocontract_strides; + const nocontract_t m_ij_strides; + const contract_t m_contract_strides; + const contract_t m_k_strides; +}; + + +template<typename Scalar, typename Index, int side, + typename Tensor, + typename nocontract_t, typename contract_t, + int packet_size, bool inner_dim_contiguous, + bool inner_dim_reordered, int Alignment> +class BaseTensorContractionMapper : public SimpleTensorContractionMapper<Scalar, Index, side, Tensor, nocontract_t, contract_t, packet_size, inner_dim_contiguous, Alignment> +{ + public: + typedef SimpleTensorContractionMapper<Scalar, Index, side, Tensor, nocontract_t, contract_t, packet_size, inner_dim_contiguous, Alignment> ParentMapper; + + EIGEN_DEVICE_FUNC + BaseTensorContractionMapper(const Tensor& tensor, + const nocontract_t& nocontract_strides, + const nocontract_t& ij_strides, + const contract_t& contract_strides, + const contract_t& k_strides) : + ParentMapper(tensor, nocontract_strides, ij_strides, contract_strides, k_strides) { } + + typedef typename Tensor::PacketReturnType Packet; + typedef typename unpacket_traits<Packet>::half HalfPacket; + + template <int AlignmentType> + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Packet loadPacket(Index i, Index j) const { + // whole method makes column major assumption + + // don't need to add offsets for now (because operator handles that) + // current code assumes packet size must be a multiple of 2 + EIGEN_STATIC_ASSERT(packet_size % 2 == 0, YOU_MADE_A_PROGRAMMING_MISTAKE); + + if (Tensor::PacketAccess && inner_dim_contiguous && !inner_dim_reordered) { + const Index index = this->computeIndex(i, j); + eigen_assert(this->computeIndex(i+packet_size-1, j) == index + packet_size-1); + return this->m_tensor.template packet<AlignmentType>(index); + } + + const IndexPair<Index> indexPair = this->computeIndexPair(i, j, packet_size - 1); + const Index first = indexPair.first; + const Index last = indexPair.second; + + // We can always do optimized packet reads from left hand side right now, because + // the vertical matrix dimension on the left hand side is never contracting. + // On the right hand side we need to check if the contracting dimensions may have + // been shuffled first. + if (Tensor::PacketAccess && + (side == Lhs || internal::array_size<contract_t>::value <= 1 || !inner_dim_reordered) && + (last - first) == (packet_size - 1)) { + + return this->m_tensor.template packet<AlignmentType>(first); + } + + EIGEN_ALIGN_MAX Scalar data[packet_size]; + + data[0] = this->m_tensor.coeff(first); + for (Index k = 1; k < packet_size - 1; k += 2) { + const IndexPair<Index> internal_pair = this->computeIndexPair(i + k, j, 1); + data[k] = this->m_tensor.coeff(internal_pair.first); + data[k + 1] = this->m_tensor.coeff(internal_pair.second); + } + data[packet_size - 1] = this->m_tensor.coeff(last); + + return pload<Packet>(data); + } + + template <int AlignmentType> + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE HalfPacket loadHalfPacket(Index i, Index j) const { + // whole method makes column major assumption + + // don't need to add offsets for now (because operator handles that) + const Index half_packet_size = unpacket_traits<HalfPacket>::size; + if (half_packet_size == packet_size) { + return loadPacket<AlignmentType>(i, j); + } + EIGEN_ALIGN_MAX Scalar data[half_packet_size]; + for (Index k = 0; k < half_packet_size; k++) { + data[k] = operator()(i + k, j); + } + return pload<HalfPacket>(data); + } +}; + + +template<typename Scalar, typename Index, int side, + typename Tensor, + typename nocontract_t, typename contract_t, + bool inner_dim_contiguous, + bool inner_dim_reordered, int Alignment> +class BaseTensorContractionMapper<Scalar, Index, side, Tensor, nocontract_t, contract_t, 1, inner_dim_contiguous, inner_dim_reordered, Alignment> : public SimpleTensorContractionMapper<Scalar, Index, side, Tensor, nocontract_t, contract_t, 1, inner_dim_contiguous, Alignment> +{ + public: + typedef SimpleTensorContractionMapper<Scalar, Index, side, Tensor, nocontract_t, contract_t, 1, inner_dim_contiguous, Alignment> ParentMapper; + + EIGEN_DEVICE_FUNC + BaseTensorContractionMapper(const Tensor& tensor, + const nocontract_t& nocontract_strides, + const nocontract_t& ij_strides, + const contract_t& contract_strides, + const contract_t& k_strides) : + ParentMapper(tensor, nocontract_strides, ij_strides, contract_strides, k_strides) { } + + typedef typename Tensor::PacketReturnType Packet; + template <int> EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Packet loadPacket(Index i, Index j) const { + EIGEN_ALIGN_MAX Scalar data[1]; + data[0] = this->m_tensor.coeff(this->computeIndex(i, j)); + return pload<typename Tensor::PacketReturnType>(data); + } + template <int> EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Packet loadHalfPacket(Index i, Index j) const { + return loadPacket(i, j); + } +}; + + +template<typename Scalar, typename Index, int side, + typename Tensor, + typename nocontract_t, typename contract_t, + int packet_size, + bool inner_dim_contiguous, bool inner_dim_reordered, int Alignment> +class TensorContractionSubMapper { + public: + typedef typename Tensor::PacketReturnType Packet; + typedef typename unpacket_traits<Packet>::half HalfPacket; + + typedef BaseTensorContractionMapper<Scalar, Index, side, Tensor, nocontract_t, contract_t, packet_size, inner_dim_contiguous, inner_dim_reordered, Alignment> ParentMapper; + typedef TensorContractionSubMapper<Scalar, Index, side, Tensor, nocontract_t, contract_t, packet_size, inner_dim_contiguous, inner_dim_reordered, Alignment> Self; + typedef Self LinearMapper; + + enum { + // We can use direct offsets iff the parent mapper supports then and we can compute the strides. + // TODO: we should also enable direct offsets for the Rhs case. + UseDirectOffsets = ParentMapper::DirectOffsets && (side == Lhs) && inner_dim_contiguous && (array_size<contract_t>::value > 0) + }; + + EIGEN_DEVICE_FUNC TensorContractionSubMapper(const ParentMapper& base_mapper, Index vert_offset, Index horiz_offset) + : m_base_mapper(base_mapper), m_vert_offset(vert_offset), m_horiz_offset(horiz_offset) { + // Bake the offsets into the buffer used by the base mapper whenever possible. This avoids the need to recompute + // this offset every time we attempt to access a coefficient. + if (UseDirectOffsets) { + Index stride = m_base_mapper.stride(); + m_base_mapper.offsetBuffer(vert_offset + horiz_offset * stride); + } + } + + EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Scalar operator()(Index i) const { + if (UseDirectOffsets) { + return m_base_mapper(i, 0); + } + return m_base_mapper(i + m_vert_offset, m_horiz_offset); + } + EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Scalar operator()(Index i, Index j) const { + if (UseDirectOffsets) { + return m_base_mapper(i, j); + } + return m_base_mapper(i + m_vert_offset, j + m_horiz_offset); + } + + EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet loadPacket(Index i) const { + if (UseDirectOffsets) { + return m_base_mapper.template loadPacket<Alignment>(i, 0); + } + return m_base_mapper.template loadPacket<Alignment>(i + m_vert_offset, m_horiz_offset); + } + EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet loadPacket(Index i, Index j) const { + if (UseDirectOffsets) { + return m_base_mapper.template loadPacket<Alignment>(i, j); + } + return m_base_mapper.template loadPacket<Alignment>(i + m_vert_offset, j + m_horiz_offset); + } + + EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE HalfPacket loadHalfPacket(Index i) const { + if (UseDirectOffsets) { + return m_base_mapper.template loadHalfPacket<Alignment>(i, 0); + } + return m_base_mapper.template loadHalfPacket<Alignment>(i + m_vert_offset, m_horiz_offset); + } + + EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void storePacket(Index i, Packet p) const { + if (UseDirectOffsets) { + m_base_mapper.storePacket(i, 0, p); + } + m_base_mapper.storePacket(i + m_vert_offset, m_horiz_offset, p); + } + + EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE LinearMapper getLinearMapper(Index i, Index j) const { + if (UseDirectOffsets) { + return LinearMapper(m_base_mapper, i, j); + } + return LinearMapper(m_base_mapper, i + m_vert_offset, j + m_horiz_offset); + } + + template <typename PacketT, int AlignmentType> + EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE PacketT load(Index i) const { + EIGEN_STATIC_ASSERT((internal::is_same<PacketT, Packet>::value), YOU_MADE_A_PROGRAMMING_MISTAKE); + const int ActualAlignment = (AlignmentType == Aligned) && (Alignment == Aligned) ? Aligned : Unaligned; + if (UseDirectOffsets) { + return m_base_mapper.template loadPacket<ActualAlignment>(i, 0); + } + return m_base_mapper.template loadPacket<ActualAlignment>(i + m_vert_offset, m_horiz_offset); + } + + template <typename Packet> + EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool aligned(Index) const { + return false; + } + + private: + ParentMapper m_base_mapper; + const Index m_vert_offset; + const Index m_horiz_offset; +}; + + +template<typename Scalar_, typename Index, int side, + typename Tensor, + typename nocontract_t, typename contract_t, + int packet_size, + bool inner_dim_contiguous, bool inner_dim_reordered, int Alignment> +class TensorContractionInputMapper + : public BaseTensorContractionMapper<Scalar_, Index, side, Tensor, nocontract_t, contract_t, packet_size, inner_dim_contiguous, inner_dim_reordered, Alignment> { + + public: + typedef Scalar_ Scalar; + typedef BaseTensorContractionMapper<Scalar, Index, side, Tensor, nocontract_t, contract_t, packet_size, inner_dim_contiguous, inner_dim_reordered, Alignment> Base; + typedef TensorContractionSubMapper<Scalar, Index, side, Tensor, nocontract_t, contract_t, packet_size, inner_dim_contiguous, inner_dim_reordered, Alignment> SubMapper; + typedef SubMapper VectorMapper; + + EIGEN_DEVICE_FUNC TensorContractionInputMapper(const Tensor& tensor, + const nocontract_t& nocontract_strides, + const nocontract_t& ij_strides, + const contract_t& contract_strides, + const contract_t& k_strides) + : Base(tensor, nocontract_strides, ij_strides, contract_strides, k_strides) { } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE SubMapper getSubMapper(Index i, Index j) const { + return SubMapper(*this, i, j); + } + + EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE VectorMapper getVectorMapper(Index i, Index j) const { + return VectorMapper(*this, i, j); + } +}; + + + +} // end namespace internal +} // end namespace Eigen + +#endif // EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_MAPPER_H diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorContractionThreadPool.h b/unsupported/Eigen/CXX11/src/Tensor/TensorContractionThreadPool.h new file mode 100644 index 000000000..ee16cde9b --- /dev/null +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorContractionThreadPool.h @@ -0,0 +1,1052 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_THREAD_POOL_H +#define EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_THREAD_POOL_H + +// evaluator for thread pool device +#ifdef EIGEN_USE_THREADS + +namespace Eigen { + +#ifdef EIGEN_USE_SIMPLE_THREAD_POOL +namespace internal { + +template<typename LhsScalar, typename LhsMapper, typename Index> +struct packLhsArg { + LhsScalar* blockA; + const LhsMapper& lhs; + const Index m_start; + const Index k_start; + const Index mc; + const Index kc; +}; + +template<typename LhsScalar, typename RhsScalar, typename RhsMapper, typename OutputMapper, typename Index> +struct packRhsAndKernelArg { + const MaxSizeVector<LhsScalar*>* blockAs; + RhsScalar* blockB; + const RhsMapper& rhs; + OutputMapper& output; + const Index m; + const Index k; + const Index n; + const Index mc; + const Index kc; + const Index nc; + const Index num_threads; + const Index num_blockAs; + const Index max_m; + const Index k_block_idx; + const Index m_block_idx; + const Index n_block_idx; + const Index m_blocks; + const Index n_blocks; + MaxSizeVector<Notification*>* kernel_notifications; + const MaxSizeVector<Notification*>* lhs_notifications; + const bool need_to_pack; +}; + +} // end namespace internal +#endif // EIGEN_USE_SIMPLE_THREAD_POOL + +template<typename Indices, typename LeftArgType, typename RightArgType> +struct TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType>, ThreadPoolDevice> : + public TensorContractionEvaluatorBase<TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType>, ThreadPoolDevice> > { + + typedef ThreadPoolDevice Device; + + typedef TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType>, Device> Self; + typedef TensorContractionEvaluatorBase<Self> Base; + + typedef TensorContractionOp<Indices, LeftArgType, RightArgType> XprType; + typedef typename internal::remove_const<typename XprType::Scalar>::type Scalar; + typedef typename XprType::Index Index; + typedef typename XprType::CoeffReturnType CoeffReturnType; + typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType; + + enum { + Layout = TensorEvaluator<LeftArgType, Device>::Layout, + }; + + // Most of the code is assuming that both input tensors are ColMajor. If the + // inputs are RowMajor, we will "cheat" by swapping the LHS and RHS: + // If we want to compute A * B = C, where A is LHS and B is RHS, the code + // will pretend B is LHS and A is RHS. + typedef typename internal::conditional< + static_cast<int>(Layout) == static_cast<int>(ColMajor), LeftArgType, RightArgType>::type EvalLeftArgType; + typedef typename internal::conditional< + static_cast<int>(Layout) == static_cast<int>(ColMajor), RightArgType, LeftArgType>::type EvalRightArgType; + + static const int LDims = + internal::array_size<typename TensorEvaluator<EvalLeftArgType, Device>::Dimensions>::value; + static const int RDims = + internal::array_size<typename TensorEvaluator<EvalRightArgType, Device>::Dimensions>::value; + static const int ContractDims = internal::array_size<Indices>::value; + + typedef array<Index, LDims> left_dim_mapper_t; + typedef array<Index, RDims> right_dim_mapper_t; + + typedef array<Index, ContractDims> contract_t; + typedef array<Index, LDims - ContractDims> left_nocontract_t; + typedef array<Index, RDims - ContractDims> right_nocontract_t; + + static const int NumDims = LDims + RDims - 2 * ContractDims; + + typedef DSizes<Index, NumDims> Dimensions; + + // typedefs needed in evalTo + typedef typename internal::remove_const<typename EvalLeftArgType::Scalar>::type LhsScalar; + typedef typename internal::remove_const<typename EvalRightArgType::Scalar>::type RhsScalar; + typedef typename internal::gebp_traits<LhsScalar, RhsScalar> Traits; + + typedef TensorEvaluator<EvalLeftArgType, Device> LeftEvaluator; + typedef TensorEvaluator<EvalRightArgType, Device> RightEvaluator; + + TensorEvaluator(const XprType& op, const Device& device) : + Base(op, device) {} + +#ifndef EIGEN_USE_SIMPLE_THREAD_POOL + template <bool lhs_inner_dim_contiguous, bool rhs_inner_dim_contiguous, + bool rhs_inner_dim_reordered, int Alignment> + void evalProduct(Scalar* buffer) const { + typedef + typename internal::remove_const<typename EvalLeftArgType::Scalar>::type + LhsScalar; + typedef + typename internal::remove_const<typename EvalRightArgType::Scalar>::type + RhsScalar; + typedef typename internal::gebp_traits<LhsScalar, RhsScalar> Traits; + typedef TensorEvaluator<EvalLeftArgType, Device> LeftEvaluator; + typedef TensorEvaluator<EvalRightArgType, Device> RightEvaluator; + typedef internal::TensorContractionInputMapper< + LhsScalar, Index, internal::Lhs, LeftEvaluator, left_nocontract_t, + contract_t, internal::packet_traits<LhsScalar>::size, + lhs_inner_dim_contiguous, false, Unaligned> + LhsMapper; + typedef internal::TensorContractionInputMapper< + RhsScalar, Index, internal::Rhs, RightEvaluator, right_nocontract_t, + contract_t, internal::packet_traits<RhsScalar>::size, + rhs_inner_dim_contiguous, rhs_inner_dim_reordered, Unaligned> + RhsMapper; + typedef internal::blas_data_mapper<Scalar, Index, ColMajor> OutputMapper; + typedef internal::gemm_pack_lhs<LhsScalar, Index, + typename LhsMapper::SubMapper, Traits::mr, + Traits::LhsProgress, ColMajor> + LhsPacker; + typedef internal::gemm_pack_rhs< + RhsScalar, Index, typename RhsMapper::SubMapper, Traits::nr, ColMajor> + RhsPacker; + typedef internal::gebp_kernel<LhsScalar, RhsScalar, Index, OutputMapper, + Traits::mr, Traits::nr, false, false> + GebpKernel; + + const Index m = this->m_i_size; + const Index n = this->m_j_size; + const Index k = this->m_k_size; + if (m == 0 || n == 0 || k == 0) return; + + // Compute a set of algorithm parameters: + // - kernel block sizes (bm, bn, bk) + // - task grain sizes (number of kernels executed per task: gm, gn) + // - number of threads + // - sharding by row/column + // - parallel packing or first lhs then rhs + // and some derived parameters: + // - number of tasks (nm, nn, nk) + // - number of kernels (nm0, nn0) + // Unfortunately, all these parameters are tightly interdependent. + // So in some cases we first compute approximate values, then compute other + // values based on these approximations and then refine the approximations. + + // There are lots of heuristics here. There is some reasoning behind them, + // but ultimately they are just tuned on contraction benchmarks for + // different input configurations, thread counts and instruction sets. + // So feel free to question any of them. + + // Compute whether we want to shard by row or by column. + // This is a first approximation, it will be refined later. Since we don't + // know number of threads yet we use 2, because what's we are most + // interested in at this point is whether it makes sense to use + // parallelization at all or not. + bool shard_by_col = shardByCol(m, n, 2); + + // First approximation of kernel blocking sizes. + // Again, we don't know number of threads yet, so we use 2. + Index bm, bn, bk; + if (shard_by_col) { + internal::TensorContractionBlocking<LhsMapper, RhsMapper, Index, + internal::ShardByCol> + blocking(k, m, n, 2); + bm = blocking.mc(); + bn = blocking.nc(); + bk = blocking.kc(); + } else { + internal::TensorContractionBlocking<LhsMapper, RhsMapper, Index, + internal::ShardByRow> + blocking(k, m, n, 2); + bm = blocking.mc(); + bn = blocking.nc(); + bk = blocking.kc(); + } + + // Compute optimal number of threads. + // Note: we use bk instead of k here because we are interested in amount of + // _parallelizable_ computations, and computations are not parallelizable + // across k dimension. + const TensorOpCost cost = + contractionCost(m, n, bm, bn, bk, shard_by_col, false); + int num_threads = TensorCostModel<ThreadPoolDevice>::numThreads( + static_cast<double>(n) * m, cost, this->m_device.numThreads()); + + // TODO(dvyukov): this is a stop-gap to prevent regressions while the cost + // model is not tuned. Remove this when the cost model is tuned. + if (n == 1) num_threads = 1; + + if (num_threads == 1) { + // The single-threaded algorithm should be faster in this case. + if (n == 1) + this->template evalGemv<lhs_inner_dim_contiguous, + rhs_inner_dim_contiguous, + rhs_inner_dim_reordered, Alignment>(buffer); + else + this->template evalGemm<lhs_inner_dim_contiguous, + rhs_inner_dim_contiguous, + rhs_inner_dim_reordered, Alignment>(buffer); + return; + } + + // Now that we know number of threads, recalculate sharding and blocking. + shard_by_col = shardByCol(m, n, num_threads); + if (shard_by_col) { + internal::TensorContractionBlocking<LhsMapper, RhsMapper, Index, + internal::ShardByCol> + blocking(k, m, n, num_threads); + bm = blocking.mc(); + bn = blocking.nc(); + bk = blocking.kc(); + } else { + internal::TensorContractionBlocking<LhsMapper, RhsMapper, Index, + internal::ShardByRow> + blocking(k, m, n, num_threads); + bm = blocking.mc(); + bn = blocking.nc(); + bk = blocking.kc(); + } + + // Number of kernels for each dimension. + Index nm0 = divup(m, bm); + Index nn0 = divup(n, bn); + Index nk = divup(k, bk); + + // Calculate task grain size (number of kernels executed per task). + // This task size coarsening serves two purposes: + // 1. It reduces per-task overheads including synchronization overheads. + // 2. It allows to use caches better (reuse the same packed rhs in several + // consecutive kernels). + Index gm = 1; + Index gn = 1; + // If we are sharding by column, then we prefer to reduce rows first. + if (shard_by_col) { + gm = coarsenM(m, n, bm, bn, bk, gn, num_threads, shard_by_col); + gn = coarsenN(m, n, bm, bn, bk, gm, num_threads, shard_by_col); + } else { + gn = coarsenN(m, n, bm, bn, bk, gm, num_threads, shard_by_col); + gm = coarsenM(m, n, bm, bn, bk, gn, num_threads, shard_by_col); + } + // Number of tasks in each dimension. + Index nm = divup(nm0, gm); + Index nn = divup(nn0, gn); + + // Last by not least, decide whether we want to issue both lhs and rhs + // packing in parallel; or issue lhs packing first, and then issue rhs + // packing when lhs packing completes (for !shard_by_col lhs and rhs are + // swapped). Parallel packing allows more parallelism (for both packing and + // kernels), while sequential packing provides better locality (once + // a thread finishes rhs packing it proceed to kernels with that rhs). + // First, we are interested in parallel packing if there are few tasks. + bool parallel_pack = num_threads >= nm * nn; + // Also do parallel packing if all data fits into L2$. + if (m * bk * Index(sizeof(LhsScalar)) + n * bk * Index(sizeof(RhsScalar)) <= + l2CacheSize() * num_threads) + parallel_pack = true; + // But don't do it if we will use each rhs only once. Locality seems to be + // more important in this case. + if ((shard_by_col ? nm : nn) == 1) parallel_pack = false; + + LhsMapper lhs(this->m_leftImpl, this->m_left_nocontract_strides, + this->m_i_strides, this->m_left_contracting_strides, + this->m_k_strides); + + RhsMapper rhs(this->m_rightImpl, this->m_right_nocontract_strides, + this->m_j_strides, this->m_right_contracting_strides, + this->m_k_strides); + + Context<LhsPacker, RhsPacker, GebpKernel, LhsMapper, RhsMapper, + OutputMapper>(this->m_device, num_threads, lhs, rhs, buffer, m, n, + k, bm, bn, bk, nm, nn, nk, gm, gn, nm0, nn0, + shard_by_col, parallel_pack) + .run(); + } + + // Context coordinates a single parallel gemm operation. + template <typename LhsPacker, typename RhsPacker, typename GebpKernel, + typename LhsMapper, typename RhsMapper, typename OutputMapper> + class Context { + public: + Context(const Device& device, int num_threads, LhsMapper& lhs, + RhsMapper& rhs, Scalar* buffer, Index tm, Index tn, Index tk, Index bm, + Index bn, Index bk, Index nm, Index nn, Index nk, Index gm, + Index gn, Index nm0, Index nn0, bool shard_by_col, + bool parallel_pack) + : device_(device), + lhs_(lhs), + rhs_(rhs), + buffer_(buffer), + output_(buffer, tm), + num_threads_(num_threads), + shard_by_col_(shard_by_col), + parallel_pack_(parallel_pack), + m_(tm), + n_(tn), + k_(tk), + bm_(bm), + bn_(bn), + bk_(bk), + nm_(nm), + nn_(nn), + nk_(nk), + gm_(gm), + gn_(gn), + nm0_(nm0), + nn0_(nn0) + { + for (Index x = 0; x < P; x++) { + // Normal number of notifications for k slice switch is + // nm_ + nn_ + nm_ * nn_. However, first P - 1 slices will receive only + // nm_ + nn_ notifications, because they will not receive notifications + // from preceeding kernels. + state_switch_[x] = + x == 0 + ? 1 + : (parallel_pack_ ? nn_ + nm_ : (shard_by_col_ ? nn_ : nm_)) + + (x == P - 1 ? nm_ * nn_ : 0); + state_packing_ready_[x] = + parallel_pack_ ? 0 : (shard_by_col_ ? nm_ : nn_); + state_kernel_[x] = new std::atomic<uint8_t>*[nm_]; + for (Index m = 0; m < nm_; m++) { + state_kernel_[x][m] = new std::atomic<uint8_t>[nn_]; + // Kernels generally receive 3 notifications (previous kernel + 2 + // packing), but the first slice won't get notifications from previous + // kernels. + for (Index n = 0; n < nn_; n++) + state_kernel_[x][m][n].store( + (x == 0 ? 0 : 1) + (parallel_pack_ ? 2 : 1), + std::memory_order_relaxed); + } + } + + // Allocate memory for packed rhs/lhs matrices. + size_t align = numext::maxi(EIGEN_MAX_ALIGN_BYTES, 1); + size_t lhs_size = + divup<size_t>(bm_ * bk_ * sizeof(LhsScalar), align) * align; + size_t rhs_size = + divup<size_t>(bn_ * bk_ * sizeof(RhsScalar), align) * align; + packed_mem_ = static_cast<char*>(internal::aligned_malloc( + (nm0_ * lhs_size + nn0_ * rhs_size) * std::min<size_t>(nk_, P - 1))); + char* mem = static_cast<char*>(packed_mem_); + for (Index x = 0; x < numext::mini<Index>(nk_, P - 1); x++) { + packed_lhs_[x].resize(nm0_); + for (Index m = 0; m < nm0_; m++) { + packed_lhs_[x][m] = reinterpret_cast<LhsScalar*>(mem); + mem += lhs_size; + } + packed_rhs_[x].resize(nn0_); + for (Index n = 0; n < nn0_; n++) { + packed_rhs_[x][n] = reinterpret_cast<RhsScalar*>(mem); + mem += rhs_size; + } + } + } + + ~Context() { + for (Index x = 0; x < P; x++) { + for (Index m = 0; m < nm_; m++) delete[] state_kernel_[x][m]; + delete[] state_kernel_[x]; + } + internal::aligned_free(packed_mem_); + } + + void run() { + // Kick off packing of the first slice. + signal_switch(0, 1); + // Wait for overall completion. + // TODO(dvyukov): this wait can lead to deadlock. + // If nthreads contractions are concurrently submitted from worker + // threads, this wait will block all worker threads and the system will + // deadlock. + done_.Wait(); + } + + private: + Notification done_; + const Device& device_; + LhsMapper& lhs_; + RhsMapper& rhs_; + Scalar* const buffer_; + OutputMapper output_; + const int num_threads_; + const bool shard_by_col_; + const bool parallel_pack_; + // Matrix sizes. + const Index m_; + const Index n_; + const Index k_; + // Block sizes. + const Index bm_; + const Index bn_; + const Index bk_; + // Number of tasks. + const Index nm_; + const Index nn_; + const Index nk_; + // Task grain sizes (number of kernels executed per task). + const Index gm_; + const Index gn_; + // Number of blocks (this is different from ni_/nn_ because of task size + // coarsening). + const Index nm0_; + const Index nn0_; + + // Parallelization strategy. + // + // Blocks related to the same k block can run in parallel because they write + // to different output blocks. So we parallelize within k slices, this + // gives us parallelism level of m x n. Before we can start any kernels + // related to k-th slice, we need to issue m lhs packing tasks and n rhs + // packing tasks. + // + // However, there is a bottleneck when we are finishing kernels for k-th + // slice (at the very end there is only 1 runnable kernel). To mitigate this + // bottleneck we allow kernels from k-th and k+1-th slices to run in + // parallel. Note that (m, n, k) and (m, n, k+1) kernels write to the same + // output block, so they must not run in parallel. + // + // This gives us the following dependency graph. + // On each k slice we have m x n kernel tasks, m lhs paking tasks and n rhs + // packing tasks. + // Kernel (m, n, k) can start when: + // - kernel (m, n, k-1) has finished + // - lhs packing (m, k) has finished + // - rhs packing (n, k) has finished + // Lhs/rhs packing can start when: + // - all k-1 packing has finished (artificially imposed to limit amount of + // parallel packing) + // + // On top of that we limit runnable tasks to two consecutive k slices. + // This is done to limit amount of memory we need for packed lhs/rhs + // (for each k slice we need m*bk + n*bk memory in packed_lhs_/packed_rhs_). + // + // state_switch_ tracks when we are ready to switch to the next k slice. + // state_kernel_[m][n] tracks when we are ready to kick off kernel (m, n). + // These variable are rolling over 3 consecutive k slices: first two we are + // actively executing + one to track completion of kernels in the second + // slice. + static const Index P = 3; + void* packed_mem_; + std::vector<LhsScalar*> packed_lhs_[P - 1]; + std::vector<RhsScalar*> packed_rhs_[P - 1]; + std::atomic<uint8_t>** state_kernel_[P]; + // state_switch_ is frequently modified by worker threads, while other + // fields are read-only after constructor. Let's move it to a separate cache + // line to reduce cache-coherency traffic. + char pad_[128]; + std::atomic<Index> state_packing_ready_[P]; + std::atomic<Index> state_switch_[P]; + + void pack_lhs(Index m, Index k) { + const Index mend = m * gm_ + gm(m); + for (Index m1 = m * gm_; m1 < mend; m1++) + LhsPacker()(packed_lhs_[k % (P - 1)][m1], + lhs_.getSubMapper(m1 * bm_, k * bk_), bk(k), bm(m1)); + + if (!parallel_pack_ && shard_by_col_) { + signal_packing(k); + } else { + signal_switch(k + 1); + for (Index n = nn_ - 1; n >= 0; n--) signal_kernel(m, n, k, n == 0); + } + } + + void pack_rhs(Index n, Index k) { + const Index nend = n * gn_ + gn(n); + for (Index n1 = n * gn_; n1 < nend; n1++) { + if (k == 0) { + // Zero the output memory in parallel. + // On 10000x2x10000 mm zeroing can easily take half of time. + // Zero (bn x m) row. Safe to do here because all kernels that will + // write to this memory depend on completion of this task. + // Note: don't call device_.memset() here. device_.memset() blocks on + // thread pool worker thread, which can lead to underutilization and + // deadlocks. + memset(buffer_ + n1 * bn_ * m_, 0, bn(n1) * m_ * sizeof(Scalar)); + } + RhsPacker()(packed_rhs_[k % (P - 1)][n1], + rhs_.getSubMapper(k * bk_, n1 * bn_), bk(k), bn(n1)); + } + + if (parallel_pack_ || shard_by_col_) { + signal_switch(k + 1); + for (Index m = nm_ - 1; m >= 0; m--) signal_kernel(m, n, k, m == 0); + } else { + signal_packing(k); + } + } + + void kernel(Index m, Index n, Index k) { + // Note: order of iteration matters here. Iteration over m is innermost + // because we want to reuse the same packed rhs in consequetive tasks + // (rhs fits into L2$ while lhs only into L3$). + const Index nend = n * gn_ + gn(n); + const Index mend = m * gm_ + gm(m); + if (shard_by_col_) { + for (Index n1 = n * gn_; n1 < nend; n1++) { + for (Index m1 = m * gm_; m1 < mend; m1++) + GebpKernel()(output_.getSubMapper(m1 * bm_, n1 * bn_), + packed_lhs_[k % (P - 1)][m1], + packed_rhs_[k % (P - 1)][n1], bm(m1), bk(k), bn(n1), + Scalar(1), -1, -1, 0, 0); + } + } else { + for (Index m1 = m * gm_; m1 < mend; m1++) + for (Index n1 = n * gn_; n1 < nend; n1++) { + GebpKernel()(output_.getSubMapper(m1 * bm_, n1 * bn_), + packed_lhs_[k % (P - 1)][m1], + packed_rhs_[k % (P - 1)][n1], bm(m1), bk(k), bn(n1), + Scalar(1), -1, -1, 0, 0); + } + } + signal_kernel(m, n, k + 1, false); + signal_switch(k + 2); + } + + void signal_packing(Index k) { + eigen_assert(!parallel_pack_); + Index s = state_packing_ready_[k % P].fetch_sub(1); + eigen_assert(s > 0); + if (s != 1) return; + state_packing_ready_[k % P] = shard_by_col_ ? nm_ : nn_; + enqueue_packing(k, shard_by_col_); + } + + void signal_kernel(Index m, Index n, Index k, bool sync) { + std::atomic<uint8_t>* state = &state_kernel_[k % P][m][n]; + Index s = state->load(); + eigen_assert(s > 0); + if (s != 1 && state->fetch_sub(1) != 1) return; + state->store(parallel_pack_ ? 3 : 2, std::memory_order_relaxed); + if (sync) + kernel(m, n, k); + else + device_.enqueueNoNotification([=]() { kernel(m, n, k); }); + } + + void signal_switch(Index k, Index v = 1) { + Index s = state_switch_[k % P].fetch_sub(v); + eigen_assert(s >= v); + if (s != v) return; + + // Ready to switch to the next k slice. + // Reset counter for the next iteration. + state_switch_[k % P] = + (parallel_pack_ ? nm_ + nn_ : (shard_by_col_ ? nn_ : nm_)) + + nm_ * nn_; + if (k < nk_) { + // Issue lhs/rhs packing. Their completion will in turn kick off + // kernels. + if (parallel_pack_) { + enqueue_packing(k, !shard_by_col_); + enqueue_packing(k, shard_by_col_); + } else if (shard_by_col_) { + enqueue_packing(k, false); + } else { + enqueue_packing(k, true); + } + + // Termination handling. + // Because kernel completion signals k + 2 switch, we need to finish nk + // + 2 slices without issuing any tasks on nk + 1 slice. So here we + // pretend that all nk + 1 packing tasks just finish instantly; so that + // nk + 2 switch only waits for completion of nk kernels. + } else if (k == nk_) { + signal_switch(k + 1, + parallel_pack_ ? nm_ + nn_ : (shard_by_col_ ? nn_ : nm_)); + } else { + done_.Notify(); + } + } + + // Enqueue all rhs/lhs packing for k-th slice. + void enqueue_packing(Index k, bool rhs) { + enqueue_packing_helper(0, rhs ? nn_ : nm_, k, rhs); + } + + void enqueue_packing_helper(Index start, Index end, Index k, bool rhs) { + if (end - start == 1) { + if (rhs) + pack_rhs(start, k); + else + pack_lhs(start, k); + } else { + Index mid = (start + end) / 2; + device_.enqueueNoNotification( + [=]() { enqueue_packing_helper(mid, end, k, rhs); }); + device_.enqueueNoNotification( + [=]() { enqueue_packing_helper(start, mid, k, rhs); }); + } + } + + // Block sizes with accounting for potentially incomplete last block. + Index bm(Index m) const { return m + 1 < nm0_ ? bm_ : m_ + bm_ - bm_ * nm0_; } + Index bn(Index n) const { return n + 1 < nn0_ ? bn_ : n_ + bn_ - bn_ * nn0_; } + Index bk(Index k) const { return k + 1 < nk_ ? bk_ : k_ + bk_ - bk_ * nk_; } + // Task grain sizes accounting for potentially incomplete last task. + Index gm(Index m) const { return m + 1 < nm_ ? gm_ : nm0_ + gm_ - gm_ * nm_; } + Index gn(Index n) const { return n + 1 < nn_ ? gn_ : nn0_ + gn_ - gn_ * nn_; } + + Context(const Context&) = delete; + void operator=(const Context&) = delete; + }; + + // Decide whether we want to shard m x n contraction by columns or by rows. + static bool shardByCol(Index m, Index n, Index num_threads) { + // Note: we are comparing both n and m against Traits::nr, it is not + // a mistake. We are trying to figure out how both n and m will fit into + // the main sharding dimension. + + // Sharding by column is the default + // ... unless there is enough data for vectorization over rows + if (m / num_threads >= Traits::nr && + // and not enough data for vectorization over columns + (n / num_threads < Traits::nr || + // ... or barely enough data for vectorization over columns, + // but it is not evenly dividable across threads + (n / num_threads < 4 * Traits::nr && + (n % (num_threads * Traits::nr)) != 0 && + // ... and it is evenly dividable across threads for rows + ((m % (num_threads * Traits::nr)) == 0 || + // .. or it is not evenly dividable for both dimensions but + // there is much more data over rows so that corner effects are + // mitigated. + (m / n >= 6))))) + return false; + // Wait, or if matrices are just substantially prolonged over the other + // dimension. + if (n / num_threads < 16 * Traits::nr && m > n * 32) return false; + return true; + } + + Index coarsenM(Index m, Index n, Index bm, Index bn, Index bk, Index gn, + int num_threads, bool shard_by_col) const { + Index gm = 1; + Index gm1 = 1; + Index nm0 = divup(m, bm); + Index nm1 = nm0; + for (;;) { + // Find the next candidate for m grain size. It needs to result in + // different number of blocks. E.g. if we have 10 kernels, we want to try + // 5 and 10, but not 6, 7, 8 and 9. + while (gm1 <= nm0 && nm1 == divup(nm0, gm1)) gm1++; + if (gm1 > nm0) break; + // Check the candidate. + int res = checkGrain(m, n, bm, bn, bk, gm1, gn, gm, gn, num_threads, + shard_by_col); + if (res < 0) break; + nm1 = divup(nm0, gm1); + if (res == 0) continue; + // Commit new grain size. + gm = gm1; + } + return gm; + } + + Index coarsenN(Index m, Index n, Index bm, Index bn, Index bk, Index gm, + int num_threads, bool shard_by_col) const { + Index gn = 1; + Index gn1 = 1; + Index nn0 = divup(n, bn); + Index nn1 = nn0; + for (;;) { + while (gn1 <= nn0 && nn1 == divup(nn0, gn1)) gn1++; + if (gn1 > nn0) break; + int res = checkGrain(m, n, bm, bn, bk, gm, gn1, gm, gn, num_threads, + shard_by_col); + if (res < 0) break; + nn1 = divup(nn0, gn1); + if (res == 0) continue; + gn = gn1; + } + return gn; + } + + // checkGrain checks whether grain (gm, gn) is suitable and is better than + // (oldgm, oldgn). + int checkGrain(Index m, Index n, Index bm, Index bn, Index bk, Index gm, + Index gn, Index oldgm, Index oldgn, int num_threads, + bool shard_by_col) const { + const TensorOpCost cost = + contractionCost(bm * gm, bn * gn, bm, bn, bk, shard_by_col, true); + double taskSize = TensorCostModel<ThreadPoolDevice>::taskSize( + static_cast<double>(bm) * gm * bn * gn, cost); + // If the task is too small, then we agree on it regardless of anything + // else. Otherwise synchronization overheads will dominate. + if (taskSize < 1) return 1; + // If it is too large, then we reject it and all larger tasks. + if (taskSize > 2) return -1; + // Now we are in presumably good task size range. + // The main deciding factor here is parallelism. Consider that we have 12 + // kernels and 4 threads. Grains of 2, 3 and 4 all yield good task sizes. + // But 2/4 yield 6/3 tasks, which gives us parallelism of 0.75 (at most 3/4 + // of cores will be busy). While grain size 3 gives us 4 tasks, which gives + // us parallelism of 1 (we can load all cores). + Index nm0 = divup(m, bm); + Index nn0 = divup(n, bn); + Index new_tasks = divup(nm0, gm) * divup(nn0, gn); + double new_parallelism = static_cast<double>(new_tasks) / + (divup<int>(new_tasks, num_threads) * num_threads); + Index old_tasks = divup(nm0, oldgm) * divup(nn0, oldgn); + double old_parallelism = static_cast<double>(old_tasks) / + (divup<int>(old_tasks, num_threads) * num_threads); + if (new_parallelism > old_parallelism || new_parallelism == 1) return 1; + return 0; + } + +#else // EIGEN_USE_SIMPLE_THREAD_POOL + + template <bool lhs_inner_dim_contiguous, bool rhs_inner_dim_contiguous, bool rhs_inner_dim_reordered, int Alignment> + void evalProduct(Scalar* buffer) const { + if (this->m_j_size == 1) { + this->template evalGemv<lhs_inner_dim_contiguous, rhs_inner_dim_contiguous, rhs_inner_dim_reordered, Alignment>(buffer); + return; + } + + evalGemm<lhs_inner_dim_contiguous, rhs_inner_dim_contiguous, rhs_inner_dim_reordered, Alignment>(buffer); + } + + template <bool lhs_inner_dim_contiguous, bool rhs_inner_dim_contiguous, bool rhs_inner_dim_reordered, int Alignment> + void evalGemm(Scalar* buffer) const { + // columns in left side, rows in right side + const Index k = this->m_k_size; + + // rows in left side + const Index m = this->m_i_size; + + // columns in right side + const Index n = this->m_j_size; + + // zero out the result buffer (which must be of size at least m * n * sizeof(Scalar) + this->m_device.memset(buffer, 0, m * n * sizeof(Scalar)); + + + const int lhs_packet_size = internal::unpacket_traits<typename LeftEvaluator::PacketReturnType>::size; + const int rhs_packet_size = internal::unpacket_traits<typename RightEvaluator::PacketReturnType>::size; + + typedef internal::TensorContractionInputMapper<LhsScalar, Index, internal::Lhs, + LeftEvaluator, left_nocontract_t, + contract_t, lhs_packet_size, + lhs_inner_dim_contiguous, + false, Unaligned> LhsMapper; + + typedef internal::TensorContractionInputMapper<RhsScalar, Index, internal::Rhs, + RightEvaluator, right_nocontract_t, + contract_t, rhs_packet_size, + rhs_inner_dim_contiguous, + rhs_inner_dim_reordered, Unaligned> RhsMapper; + + typedef internal::blas_data_mapper<Scalar, Index, ColMajor> OutputMapper; + + // TODO: packing could be faster sometimes if we supported row major tensor mappers + typedef internal::gemm_pack_lhs<LhsScalar, Index, typename LhsMapper::SubMapper, Traits::mr, + Traits::LhsProgress, ColMajor> LhsPacker; + typedef internal::gemm_pack_rhs<RhsScalar, Index, typename RhsMapper::SubMapper, Traits::nr, ColMajor> RhsPacker; + + // TODO: replace false, false with conjugate values? + typedef internal::gebp_kernel<LhsScalar, RhsScalar, Index, OutputMapper, + Traits::mr, Traits::nr, false, false> GebpKernel; + + typedef internal::packLhsArg<LhsScalar, LhsMapper, Index> packLArg; + typedef internal::packRhsAndKernelArg<LhsScalar, RhsScalar, RhsMapper, OutputMapper, Index> packRKArg; + + // initialize data mappers + LhsMapper lhs(this->m_leftImpl, this->m_left_nocontract_strides, this->m_i_strides, + this->m_left_contracting_strides, this->m_k_strides); + + RhsMapper rhs(this->m_rightImpl, this->m_right_nocontract_strides, this->m_j_strides, + this->m_right_contracting_strides, this->m_k_strides); + + OutputMapper output(buffer, m); + + // compute block sizes (which depend on number of threads) + const Index num_threads = this->m_device.numThreads(); + internal::TensorContractionBlocking<LhsMapper, RhsMapper, Index, internal::ShardByCol> blocking(k, m, n, num_threads); + Index mc = blocking.mc(); + Index nc = blocking.nc(); + Index kc = blocking.kc(); + eigen_assert(mc <= m); + eigen_assert(nc <= n); + eigen_assert(kc <= k); + +#define CEIL_DIV(a, b) (((a) + (b) - 1) / (b)) + const Index k_blocks = CEIL_DIV(k, kc); + const Index n_blocks = CEIL_DIV(n, nc); + const Index m_blocks = CEIL_DIV(m, mc); + const Index sizeA = mc * kc; + const Index sizeB = kc * nc; + + /* cout << "m: " << m << " n: " << n << " k: " << k << endl; + cout << "mc: " << mc << " nc: " << nc << " kc: " << kc << endl; + cout << "m_blocks: " << m_blocks << " n_blocks: " << n_blocks << " k_blocks: " << k_blocks << endl; + cout << "num threads: " << num_threads << endl; + */ + + // note: m_device.allocate should return 16 byte aligned pointers, but if blockA and blockB + // aren't 16 byte aligned segfaults will happen due to SIMD instructions + // note: You can get away with allocating just a single blockA and offsets and meet the + // the alignment requirements with the assumption that + // (Traits::mr * sizeof(ResScalar)) % 16 == 0 + const Index numBlockAs = numext::mini(num_threads, m_blocks); + MaxSizeVector<LhsScalar *> blockAs(num_threads); + for (int i = 0; i < num_threads; i++) { + blockAs.push_back(static_cast<LhsScalar *>(this->m_device.allocate(sizeA * sizeof(LhsScalar)))); + } + + // To circumvent alignment issues, I'm just going to separately allocate the memory for each thread + // TODO: is this too much memory to allocate? This simplifies coding a lot, but is wasteful. + // Other options: (1) reuse memory when a thread finishes. con: tricky + // (2) allocate block B memory in each thread. con: overhead + MaxSizeVector<RhsScalar *> blockBs(n_blocks); + for (int i = 0; i < n_blocks; i++) { + blockBs.push_back(static_cast<RhsScalar *>(this->m_device.allocate(sizeB * sizeof(RhsScalar)))); + } + + // lhs_notifications starts with all null Notifications + MaxSizeVector<Notification*> lhs_notifications(num_threads, nullptr); + + // this should really be numBlockAs * n_blocks; + const Index num_kernel_notifications = num_threads * n_blocks; + MaxSizeVector<Notification*> kernel_notifications(num_kernel_notifications, + nullptr); + + for (Index k_block_idx = 0; k_block_idx < k_blocks; k_block_idx++) { + const Index k_start = k_block_idx * kc; + // make sure we don't overshoot right edge of left matrix + const Index actual_kc = numext::mini(k_start + kc, k) - k_start; + + for (Index m_block_idx = 0; m_block_idx < m_blocks; m_block_idx += numBlockAs) { + const Index num_blocks = numext::mini(m_blocks-m_block_idx, numBlockAs); + + for (Index mt_block_idx = m_block_idx; mt_block_idx < m_block_idx+num_blocks; mt_block_idx++) { + const Index m_start = mt_block_idx * mc; + const Index actual_mc = numext::mini(m_start + mc, m) - m_start; + eigen_assert(actual_mc > 0); + + Index blockAId = (k_block_idx * m_blocks + mt_block_idx) % num_threads; + + for (int i = 0; i < n_blocks; ++i) { + Index notification_id = (blockAId * n_blocks + i); + // Wait for any current kernels using this slot to complete + // before using it. + if (kernel_notifications[notification_id]) { + wait_until_ready(kernel_notifications[notification_id]); + delete kernel_notifications[notification_id]; + } + kernel_notifications[notification_id] = new Notification(); + } + const packLArg arg = { + blockAs[blockAId], // blockA + lhs, // lhs + m_start, // m + k_start, // k + actual_mc, // mc + actual_kc, // kc + }; + + // Delete any existing notification since we may be + // replacing it. The algorithm should ensure that there are + // no existing waiters on this notification. + delete lhs_notifications[blockAId]; + lhs_notifications[blockAId] = + this->m_device.enqueue(&Self::packLhs<packLArg, LhsPacker>, arg); + } + + // now start kernels. + const Index m_base_start = m_block_idx * mc; + const bool need_to_pack = m_block_idx == 0; + + for (Index n_block_idx = 0; n_block_idx < n_blocks; n_block_idx++) { + const Index n_start = n_block_idx * nc; + const Index actual_nc = numext::mini(n_start + nc, n) - n_start; + + // first make sure the previous kernels are all done before overwriting rhs. Also wait if + // we're going to start new k. In both cases need_to_pack is true. + if (need_to_pack) { + for (Index i = num_blocks; i < num_threads; ++i) { + Index blockAId = (k_block_idx * m_blocks + i + m_block_idx) % num_threads; + Index future_id = (blockAId * n_blocks + n_block_idx); + wait_until_ready(kernel_notifications[future_id]); + } + } + + packRKArg arg = { + &blockAs, // blockA + blockBs[n_block_idx], // blockB + rhs, // rhs + output, // output + m_base_start, // m + k_start, // k + n_start, // n + mc, // mc + actual_kc, // kc + actual_nc, // nc + num_threads, + numBlockAs, + m, + k_block_idx, + m_block_idx, + n_block_idx, // n_block_idx + m_blocks, // m_blocks + n_blocks, // n_blocks + &kernel_notifications, // kernel notifications + &lhs_notifications, // lhs notifications + need_to_pack, // need_to_pack + }; + + // We asynchronously kick off this function, which ends up + // notifying the appropriate kernel_notifications objects, + // which this thread waits on before exiting. + this->m_device.enqueueNoNotification(&Self::packRhsAndKernel<packRKArg, RhsPacker, GebpKernel>, arg); + } + } + } + + // Make sure all the kernels are done. + for (size_t i = 0; i < kernel_notifications.size(); ++i) { + wait_until_ready(kernel_notifications[i]); + delete kernel_notifications[i]; + } + + // No need to wait for lhs notifications since they should have + // already been waited on. Just clean them up. + for (size_t i = 0; i < lhs_notifications.size(); ++i) { + delete lhs_notifications[i]; + } + + // deallocate all of the memory for both A and B's + for (size_t i = 0; i < blockAs.size(); i++) { + this->m_device.deallocate(blockAs[i]); + } + for (size_t i = 0; i < blockBs.size(); i++) { + this->m_device.deallocate(blockBs[i]); + } + +#undef CEIL_DIV + } + + /* + * Packs a LHS block of size (mt, kc) starting at lhs(m, k). Before packing + * the LHS block, check that all of the kernels that worked on the same + * mt_block_idx in the previous m_block are done. + */ + template <typename packLArg, typename LhsPacker> + static void packLhs(const packLArg arg) { + // perform actual packing + LhsPacker pack_lhs; + pack_lhs(arg.blockA, arg.lhs.getSubMapper(arg.m_start, arg.k_start), arg.kc, arg.mc); + } + + /* + * Packs a RHS block of size (kc, nc) starting at (k, n) after checking that + * all kernels in the previous block are done. + * Then for each LHS future, we wait on the future and then call GEBP + * on the area packed by the future (which starts at + * blockA + future_idx * mt * kc) on the LHS and with the full packed + * RHS block. + * The output of this GEBP is written to output(m + i * mt, n). + */ + template <typename packRKArg, typename RhsPacker, typename GebpKernel> + static void packRhsAndKernel(packRKArg arg) { + if (arg.need_to_pack) { + RhsPacker pack_rhs; + pack_rhs(arg.blockB, arg.rhs.getSubMapper(arg.k, arg.n), arg.kc, arg.nc); + } + + GebpKernel gebp; + for (Index mt_block_idx = 0; mt_block_idx < arg.num_blockAs; mt_block_idx++) { + const Index m_base_start = arg.m + arg.mc*mt_block_idx; + if (m_base_start < arg.max_m) { + Index blockAId = (arg.k_block_idx * arg.m_blocks + mt_block_idx + arg.m_block_idx) % arg.num_threads; + wait_until_ready((*arg.lhs_notifications)[blockAId]); + const Index actual_mc = numext::mini(m_base_start + arg.mc, arg.max_m) - m_base_start; + gebp(arg.output.getSubMapper(m_base_start, arg.n), + (*arg.blockAs)[blockAId], arg.blockB, + actual_mc, arg.kc, arg.nc, Scalar(1), -1, -1, 0, 0); + + // Notify that the kernel is done. + const Index set_idx = blockAId * arg.n_blocks + arg.n_block_idx; + (*arg.kernel_notifications)[set_idx]->Notify(); + } + } + } +#endif // EIGEN_USE_SIMPLE_THREAD_POOL + + TensorOpCost contractionCost(Index m, Index n, Index bm, Index bn, Index bk, + bool shard_by_col, bool prepacked) const { + const int packed_size = std::min<int>(PacketType<LhsScalar, Device>::size, + PacketType<RhsScalar, Device>::size); + const int output_packet_size = internal::unpacket_traits<PacketReturnType>::size; + const double kd = static_cast<double>(bk); + // Peak VFMA bandwidth is 0.5. However if we have not enough data for + // vectorization bandwidth drops. The 4.0 and 2.0 bandwidth is determined + // experimentally. + double computeBandwidth = bk == 1 ? 4.0 : + (shard_by_col ? bn : bm) < Traits::nr || + (shard_by_col ? bm : bn) < Traits::mr ? 2.0 : 0.5; +#ifndef EIGEN_VECTORIZE_FMA + // Bandwidth of all of VFMA/MULPS/ADDPS is 0.5 on latest Intel processors. + // However for MULPS/ADDPS we have dependent sequence of 2 such instructions, + // so overall bandwidth is 1.0. + if (computeBandwidth == 0.5) computeBandwidth = 1.0; +#endif + // Computations. + TensorOpCost cost = TensorOpCost(0, 0, kd * computeBandwidth, true, packed_size); + // Output stores. + cost += TensorOpCost(0, sizeof(CoeffReturnType), 0, true, output_packet_size); + if (prepacked) { + // Packing and kernels are executed in different tasks. When we calculate + // task grain size we look only at kernel cost assuming that kernel + // is more expensive than packing. + return cost; + } + // Lhs/rhs loads + computations. + TensorOpCost lhsCost = this->m_leftImpl.costPerCoeff(true) * (kd / n); + TensorOpCost rhsCost = this->m_rightImpl.costPerCoeff(true) * (kd / m); + // Lhs packing memory cost does not contribute considerably to overall + // execution time because lhs is prefetched early and accessed sequentially. + if (shard_by_col) + lhsCost.dropMemoryCost(); + else + rhsCost.dropMemoryCost(); + return cost + lhsCost + rhsCost; + } +}; + +} // end namespace Eigen + +#endif // EIGEN_USE_THREADS +#endif // EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_THREAD_POOL_H diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorConversion.h b/unsupported/Eigen/CXX11/src/Tensor/TensorConversion.h new file mode 100644 index 000000000..860a6949a --- /dev/null +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorConversion.h @@ -0,0 +1,279 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2015 Benoit Steiner <benoit.steiner.goog@gmail.com> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_CXX11_TENSOR_TENSOR_CONVERSION_H +#define EIGEN_CXX11_TENSOR_TENSOR_CONVERSION_H + +namespace Eigen { + +/** \class TensorConversionOp + * \ingroup CXX11_Tensor_Module + * + * \brief Tensor conversion class. This class makes it possible to vectorize + * type casting operations when the number of scalars per packet in the source + * and the destination type differ + */ +namespace internal { +template<typename TargetType, typename XprType> +struct traits<TensorConversionOp<TargetType, XprType> > +{ + // Type promotion to handle the case where the types of the lhs and the rhs are different. + typedef TargetType Scalar; + typedef typename traits<XprType>::StorageKind StorageKind; + typedef typename traits<XprType>::Index Index; + typedef typename XprType::Nested Nested; + typedef typename remove_reference<Nested>::type _Nested; + static const int NumDimensions = traits<XprType>::NumDimensions; + static const int Layout = traits<XprType>::Layout; + enum { Flags = 0 }; +}; + +template<typename TargetType, typename XprType> +struct eval<TensorConversionOp<TargetType, XprType>, Eigen::Dense> +{ + typedef const TensorConversionOp<TargetType, XprType>& type; +}; + +template<typename TargetType, typename XprType> +struct nested<TensorConversionOp<TargetType, XprType>, 1, typename eval<TensorConversionOp<TargetType, XprType> >::type> +{ + typedef TensorConversionOp<TargetType, XprType> type; +}; + +} // end namespace internal + + +template <typename TensorEvaluator, typename SrcPacket, typename TgtPacket, int SrcCoeffRatio, int TgtCoeffRatio> +struct PacketConverter { + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + PacketConverter(const TensorEvaluator& impl) + : m_impl(impl) {} + + template<int LoadMode, typename Index> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TgtPacket packet(Index index) const { + return internal::pcast<SrcPacket, TgtPacket>(m_impl.template packet<LoadMode>(index)); + } + + private: + const TensorEvaluator& m_impl; +}; + + +template <typename TensorEvaluator, typename SrcPacket, typename TgtPacket> +struct PacketConverter<TensorEvaluator, SrcPacket, TgtPacket, 2, 1> { + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + PacketConverter(const TensorEvaluator& impl) + : m_impl(impl) {} + + template<int LoadMode, typename Index> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TgtPacket packet(Index index) const { + const int SrcPacketSize = internal::unpacket_traits<SrcPacket>::size; + + SrcPacket src1 = m_impl.template packet<LoadMode>(index); + SrcPacket src2 = m_impl.template packet<LoadMode>(index + SrcPacketSize); + TgtPacket result = internal::pcast<SrcPacket, TgtPacket>(src1, src2); + return result; + } + + private: + const TensorEvaluator& m_impl; +}; + +template <typename TensorEvaluator, typename SrcPacket, typename TgtPacket> +struct PacketConverter<TensorEvaluator, SrcPacket, TgtPacket, 4, 1> { + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + PacketConverter(const TensorEvaluator& impl) + : m_impl(impl) {} + + template<int LoadMode, typename Index> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TgtPacket packet(Index index) const { + const int SrcPacketSize = internal::unpacket_traits<SrcPacket>::size; + + SrcPacket src1 = m_impl.template packet<LoadMode>(index); + SrcPacket src2 = m_impl.template packet<LoadMode>(index + SrcPacketSize); + SrcPacket src3 = m_impl.template packet<LoadMode>(index + 2 * SrcPacketSize); + SrcPacket src4 = m_impl.template packet<LoadMode>(index + 3 * SrcPacketSize); + TgtPacket result = internal::pcast<SrcPacket, TgtPacket>(src1, src2, src3, src4); + return result; + } + + private: + const TensorEvaluator& m_impl; +}; + +template <typename TensorEvaluator, typename SrcPacket, typename TgtPacket> +struct PacketConverter<TensorEvaluator, SrcPacket, TgtPacket, 1, 2> { + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + PacketConverter(const TensorEvaluator& impl) + : m_impl(impl), m_maxIndex(impl.dimensions().TotalSize()) {} + + template<int LoadMode, typename Index> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TgtPacket packet(Index index) const { + const int SrcPacketSize = internal::unpacket_traits<SrcPacket>::size; + // Only call m_impl.packet() when we have direct access to the underlying data. This + // ensures that we don't compute the subexpression twice. We may however load some + // coefficients twice, but in practice this doesn't negatively impact performance. + if (m_impl.data() && (index + SrcPacketSize < m_maxIndex)) { + // Force unaligned memory loads since we can't ensure alignment anymore + return internal::pcast<SrcPacket, TgtPacket>(m_impl.template packet<Unaligned>(index)); + } else { + const int TgtPacketSize = internal::unpacket_traits<TgtPacket>::size; + typedef typename internal::unpacket_traits<SrcPacket>::type SrcType; + typedef typename internal::unpacket_traits<TgtPacket>::type TgtType; + internal::scalar_cast_op<SrcType, TgtType> converter; + EIGEN_ALIGN_MAX typename internal::unpacket_traits<TgtPacket>::type values[TgtPacketSize]; + for (int i = 0; i < TgtPacketSize; ++i) { + values[i] = converter(m_impl.coeff(index+i)); + } + TgtPacket rslt = internal::pload<TgtPacket>(values); + return rslt; + } + } + + private: + const TensorEvaluator& m_impl; + const typename TensorEvaluator::Index m_maxIndex; +}; + +template<typename TargetType, typename XprType> +class TensorConversionOp : public TensorBase<TensorConversionOp<TargetType, XprType>, ReadOnlyAccessors> +{ + public: + typedef typename internal::traits<TensorConversionOp>::Scalar Scalar; + typedef typename internal::traits<TensorConversionOp>::StorageKind StorageKind; + typedef typename internal::traits<TensorConversionOp>::Index Index; + typedef typename internal::nested<TensorConversionOp>::type Nested; + typedef Scalar CoeffReturnType; + typedef typename NumTraits<Scalar>::Real RealScalar; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorConversionOp(const XprType& xpr) + : m_xpr(xpr) {} + + EIGEN_DEVICE_FUNC + const typename internal::remove_all<typename XprType::Nested>::type& + expression() const { return m_xpr; } + + protected: + typename XprType::Nested m_xpr; +}; + +template <bool SameType, typename Eval, typename Scalar> struct ConversionSubExprEval { + static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool run(Eval& impl, Scalar*) { + impl.evalSubExprsIfNeeded(NULL); + return true; + } +}; + +template <typename Eval, typename Scalar> struct ConversionSubExprEval<true, Eval, Scalar> { + static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool run(Eval& impl, Scalar* data) { + return impl.evalSubExprsIfNeeded(data); + } +}; + + +// Eval as rvalue +template<typename TargetType, typename ArgType, typename Device> +struct TensorEvaluator<const TensorConversionOp<TargetType, ArgType>, Device> +{ + typedef TensorConversionOp<TargetType, ArgType> XprType; + typedef typename XprType::Index Index; + typedef typename TensorEvaluator<ArgType, Device>::Dimensions Dimensions; + typedef TargetType Scalar; + typedef TargetType CoeffReturnType; + typedef typename internal::remove_all<typename internal::traits<ArgType>::Scalar>::type SrcType; + typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType; + typedef typename PacketType<SrcType, Device>::type PacketSourceType; + static const int PacketSize = internal::unpacket_traits<PacketReturnType>::size; + + enum { + IsAligned = false, + PacketAccess = true, + Layout = TensorEvaluator<ArgType, Device>::Layout, + RawAccess = false + }; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) + : m_impl(op.expression(), device) + { + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_impl.dimensions(); } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* data) + { + return ConversionSubExprEval<internal::is_same<TargetType, SrcType>::value, TensorEvaluator<ArgType, Device>, Scalar>::run(m_impl, data); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() + { + m_impl.cleanup(); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const + { + internal::scalar_cast_op<SrcType, TargetType> converter; + return converter(m_impl.coeff(index)); + } + + template<int LoadMode> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const + { + const bool Vectorizable = TensorEvaluator<ArgType, Device>::PacketAccess & + internal::type_casting_traits<SrcType, TargetType>::VectorizedCast; + return PacketConv<LoadMode, Vectorizable>::run(m_impl, index); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost + costPerCoeff(bool vectorized) const { + const double cast_cost = TensorOpCost::CastCost<SrcType, TargetType>(); + if (vectorized) { + const double SrcCoeffRatio = + internal::type_casting_traits<SrcType, TargetType>::SrcCoeffRatio; + const double TgtCoeffRatio = + internal::type_casting_traits<SrcType, TargetType>::TgtCoeffRatio; + return m_impl.costPerCoeff(vectorized) * (SrcCoeffRatio / PacketSize) + + TensorOpCost(0, 0, TgtCoeffRatio * (cast_cost / PacketSize)); + } else { + return m_impl.costPerCoeff(vectorized) + TensorOpCost(0, 0, cast_cost); + } + } + + EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; } + + protected: + template <int LoadMode, bool ActuallyVectorize> + struct PacketConv { + static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType run(const TensorEvaluator<ArgType, Device>& impl, Index index) { + internal::scalar_cast_op<SrcType, TargetType> converter; + EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize]; + for (int i = 0; i < PacketSize; ++i) { + values[i] = converter(impl.coeff(index+i)); + } + PacketReturnType rslt = internal::pload<PacketReturnType>(values); + return rslt; + } + }; + + template <int LoadMode> + struct PacketConv<LoadMode, true> { + static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType run(const TensorEvaluator<ArgType, Device>& impl, Index index) { + const int SrcCoeffRatio = internal::type_casting_traits<SrcType, TargetType>::SrcCoeffRatio; + const int TgtCoeffRatio = internal::type_casting_traits<SrcType, TargetType>::TgtCoeffRatio; + PacketConverter<TensorEvaluator<ArgType, Device>, PacketSourceType, PacketReturnType, + SrcCoeffRatio, TgtCoeffRatio> converter(impl); + return converter.template packet<LoadMode>(index); + } + }; + + TensorEvaluator<ArgType, Device> m_impl; +}; + +} // end namespace Eigen + +#endif // EIGEN_CXX11_TENSOR_TENSOR_CONVERSION_H diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorConvolution.h b/unsupported/Eigen/CXX11/src/Tensor/TensorConvolution.h new file mode 100644 index 000000000..abdf742c6 --- /dev/null +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorConvolution.h @@ -0,0 +1,1104 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_CXX11_TENSOR_TENSOR_CONVOLUTION_H +#define EIGEN_CXX11_TENSOR_TENSOR_CONVOLUTION_H + +namespace Eigen { + +/** \class TensorConvolution + * \ingroup CXX11_Tensor_Module + * + * \brief Tensor convolution class. + * + * + */ +namespace internal { + +template <typename Index, typename InputDims, int NumKernelDims, int Layout> +class IndexMapper { + public: + IndexMapper(const InputDims& input_dims, const array<Index, NumKernelDims>& kernel_dims, + const array<Index, NumKernelDims>& indices) { + + array<Index, NumDims> dimensions = input_dims; + for (int i = 0; i < NumKernelDims; ++i) { + const Index index = indices[i]; + const Index input_dim = input_dims[index]; + const Index kernel_dim = kernel_dims[i]; + const Index result_dim = input_dim - kernel_dim + 1; + dimensions[index] = result_dim; + } + + array<Index, NumDims> inputStrides; + array<Index, NumDims> outputStrides; + if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { + inputStrides[0] = 1; + outputStrides[0] = 1; + for (int i = 1; i < NumDims; ++i) { + inputStrides[i] = inputStrides[i-1] * input_dims[i-1]; + outputStrides[i] = outputStrides[i-1] * dimensions[i-1]; + } + } else { + inputStrides[NumDims - 1] = 1; + outputStrides[NumDims - 1] = 1; + for (int i = static_cast<int>(NumDims) - 2; i >= 0; --i) { + inputStrides[i] = inputStrides[i + 1] * input_dims[i + 1]; + outputStrides[i] = outputStrides[i + 1] * dimensions[i + 1]; + } + } + + array<Index, NumDims> cudaInputDimensions; + array<Index, NumDims> cudaOutputDimensions; + array<Index, NumDims> tmp = dimensions; + array<Index, NumDims> ordering; + const size_t offset = static_cast<int>(Layout) == static_cast<int>(ColMajor) + ? 0 + : NumDims - NumKernelDims; + for (int i = 0; i < NumKernelDims; ++i) { + const Index index = i + offset; + ordering[index] = indices[i]; + tmp[indices[i]] = -1; + cudaInputDimensions[index] = input_dims[indices[i]]; + cudaOutputDimensions[index] = dimensions[indices[i]]; + } + + int written = static_cast<int>(Layout) == static_cast<int>(ColMajor) + ? NumKernelDims + : 0; + for (int i = 0; i < NumDims; ++i) { + if (tmp[i] >= 0) { + ordering[written] = i; + cudaInputDimensions[written] = input_dims[i]; + cudaOutputDimensions[written] = dimensions[i]; + ++written; + } + } + + for (int i = 0; i < NumDims; ++i) { + m_inputStrides[i] = inputStrides[ordering[i]]; + m_outputStrides[i] = outputStrides[ordering[i]]; + } + + if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { + for (int i = 0; i < NumDims; ++i) { + if (i > NumKernelDims) { + m_cudaInputStrides[i] = + m_cudaInputStrides[i - 1] * cudaInputDimensions[i - 1]; + m_cudaOutputStrides[i] = + m_cudaOutputStrides[i - 1] * cudaOutputDimensions[i - 1]; + } else { + m_cudaInputStrides[i] = 1; + m_cudaOutputStrides[i] = 1; + } + } + } else { + for (int i = NumDims - 1; i >= 0; --i) { + if (i + 1 < offset) { + m_cudaInputStrides[i] = + m_cudaInputStrides[i + 1] * cudaInputDimensions[i + 1]; + m_cudaOutputStrides[i] = + m_cudaOutputStrides[i + 1] * cudaOutputDimensions[i + 1]; + } else { + m_cudaInputStrides[i] = 1; + m_cudaOutputStrides[i] = 1; + } + } + } + } + + EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Index mapCudaInputPlaneToTensorInputOffset(Index p) const { + Index inputIndex = 0; + if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { + for (int d = NumDims - 1; d > NumKernelDims; --d) { + const Index idx = p / m_cudaInputStrides[d]; + inputIndex += idx * m_inputStrides[d]; + p -= idx * m_cudaInputStrides[d]; + } + inputIndex += p * m_inputStrides[NumKernelDims]; + } else { + std::ptrdiff_t limit = 0; + if (NumKernelDims < NumDims) { + limit = NumDims - NumKernelDims - 1; + } + for (int d = 0; d < limit; ++d) { + const Index idx = p / m_cudaInputStrides[d]; + inputIndex += idx * m_inputStrides[d]; + p -= idx * m_cudaInputStrides[d]; + } + inputIndex += p * m_inputStrides[limit]; + } + return inputIndex; + } + + EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Index mapCudaOutputPlaneToTensorOutputOffset(Index p) const { + Index outputIndex = 0; + if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { + for (int d = NumDims - 1; d > NumKernelDims; --d) { + const Index idx = p / m_cudaOutputStrides[d]; + outputIndex += idx * m_outputStrides[d]; + p -= idx * m_cudaOutputStrides[d]; + } + outputIndex += p * m_outputStrides[NumKernelDims]; + } else { + std::ptrdiff_t limit = 0; + if (NumKernelDims < NumDims) { + limit = NumDims - NumKernelDims - 1; + } + for (int d = 0; d < limit; ++d) { + const Index idx = p / m_cudaOutputStrides[d]; + outputIndex += idx * m_outputStrides[d]; + p -= idx * m_cudaOutputStrides[d]; + } + outputIndex += p * m_outputStrides[limit]; + } + return outputIndex; + } + + EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Index mapCudaInputKernelToTensorInputOffset(Index i) const { + const size_t offset = static_cast<int>(Layout) == static_cast<int>(ColMajor) + ? 0 + : NumDims - NumKernelDims; + return i * m_inputStrides[offset]; + } + + EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Index mapCudaOutputKernelToTensorOutputOffset(Index i) const { + const size_t offset = static_cast<int>(Layout) == static_cast<int>(ColMajor) + ? 0 + : NumDims - NumKernelDims; + return i * m_outputStrides[offset]; + } + + EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Index mapCudaInputKernelToTensorInputOffset(Index i, Index j) const { + const size_t offset = static_cast<int>(Layout) == static_cast<int>(ColMajor) + ? 0 + : NumDims - NumKernelDims; + return i * m_inputStrides[offset] + j * m_inputStrides[offset + 1]; + } + + EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Index mapCudaOutputKernelToTensorOutputOffset(Index i, Index j) const { + const size_t offset = static_cast<int>(Layout) == static_cast<int>(ColMajor) + ? 0 + : NumDims - NumKernelDims; + return i * m_outputStrides[offset] + j * m_outputStrides[offset + 1]; + } + + EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Index mapCudaInputKernelToTensorInputOffset(Index i, Index j, Index k) const { + const size_t offset = static_cast<int>(Layout) == static_cast<int>(ColMajor) + ? 0 + : NumDims - NumKernelDims; + return i * m_inputStrides[offset] + j * m_inputStrides[offset + 1] + + k * m_inputStrides[offset + 2]; + } + + EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Index mapCudaOutputKernelToTensorOutputOffset(Index i, Index j, Index k) const { + const size_t offset = static_cast<int>(Layout) == static_cast<int>(ColMajor) + ? 0 + : NumDims - NumKernelDims; + return i * m_outputStrides[offset] + j * m_outputStrides[offset + 1] + + k * m_outputStrides[offset + 2]; + } + + private: + static const int NumDims = internal::array_size<InputDims>::value; + array<Index, NumDims> m_inputStrides; + array<Index, NumDims> m_outputStrides; + array<Index, NumDims> m_cudaInputStrides; + array<Index, NumDims> m_cudaOutputStrides; +}; + + + +template<typename Dimensions, typename InputXprType, typename KernelXprType> +struct traits<TensorConvolutionOp<Dimensions, InputXprType, KernelXprType> > +{ + // Type promotion to handle the case where the types of the lhs and the rhs are different. + typedef typename promote_storage_type<typename InputXprType::Scalar, + typename KernelXprType::Scalar>::ret Scalar; + typedef typename promote_storage_type<typename traits<InputXprType>::StorageKind, + typename traits<KernelXprType>::StorageKind>::ret StorageKind; + typedef typename promote_index_type<typename traits<InputXprType>::Index, + typename traits<KernelXprType>::Index>::type Index; + typedef typename InputXprType::Nested LhsNested; + typedef typename KernelXprType::Nested RhsNested; + typedef typename remove_reference<LhsNested>::type _LhsNested; + typedef typename remove_reference<RhsNested>::type _RhsNested; + static const int NumDimensions = traits<InputXprType>::NumDimensions; + static const int Layout = traits<InputXprType>::Layout; + + enum { + Flags = 0 + }; +}; + +template<typename Dimensions, typename InputXprType, typename KernelXprType> +struct eval<TensorConvolutionOp<Dimensions, InputXprType, KernelXprType>, Eigen::Dense> +{ + typedef const TensorConvolutionOp<Dimensions, InputXprType, KernelXprType>& type; +}; + +template<typename Dimensions, typename InputXprType, typename KernelXprType> +struct nested<TensorConvolutionOp<Dimensions, InputXprType, KernelXprType>, 1, typename eval<TensorConvolutionOp<Dimensions, InputXprType, KernelXprType> >::type> +{ + typedef TensorConvolutionOp<Dimensions, InputXprType, KernelXprType> type; +}; + +} // end namespace internal + + + +template<typename Indices, typename InputXprType, typename KernelXprType> +class TensorConvolutionOp : public TensorBase<TensorConvolutionOp<Indices, InputXprType, KernelXprType>, ReadOnlyAccessors> +{ + public: + typedef typename Eigen::internal::traits<TensorConvolutionOp>::Scalar Scalar; + typedef typename Eigen::NumTraits<Scalar>::Real RealScalar; + typedef typename internal::promote_storage_type<typename InputXprType::CoeffReturnType, + typename KernelXprType::CoeffReturnType>::ret CoeffReturnType; + typedef typename Eigen::internal::nested<TensorConvolutionOp>::type Nested; + typedef typename Eigen::internal::traits<TensorConvolutionOp>::StorageKind StorageKind; + typedef typename Eigen::internal::traits<TensorConvolutionOp>::Index Index; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorConvolutionOp(const InputXprType& input, const KernelXprType& kernel, const Indices& dims) + : m_input_xpr(input), m_kernel_xpr(kernel), m_indices(dims) {} + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const Indices& indices() const { return m_indices; } + + /** \returns the nested expressions */ + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const typename internal::remove_all<typename InputXprType::Nested>::type& + inputExpression() const { return m_input_xpr; } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const typename internal::remove_all<typename KernelXprType::Nested>::type& + kernelExpression() const { return m_kernel_xpr; } + + protected: + typename InputXprType::Nested m_input_xpr; + typename KernelXprType::Nested m_kernel_xpr; + const Indices m_indices; +}; + + +template<typename Indices, typename InputArgType, typename KernelArgType, typename Device> +struct TensorEvaluator<const TensorConvolutionOp<Indices, InputArgType, KernelArgType>, Device> +{ + typedef TensorConvolutionOp<Indices, InputArgType, KernelArgType> XprType; + + static const int NumDims = internal::array_size<typename TensorEvaluator<InputArgType, Device>::Dimensions>::value; + static const int NumKernelDims = internal::array_size<Indices>::value; + typedef typename XprType::Index Index; + typedef DSizes<Index, NumDims> Dimensions; + + typedef typename XprType::Scalar Scalar; + typedef typename XprType::CoeffReturnType CoeffReturnType; + typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType; + static const int PacketSize = internal::unpacket_traits<PacketReturnType>::size; + + enum { + IsAligned = TensorEvaluator<InputArgType, Device>::IsAligned & TensorEvaluator<KernelArgType, Device>::IsAligned, + PacketAccess = TensorEvaluator<InputArgType, Device>::PacketAccess & TensorEvaluator<KernelArgType, Device>::PacketAccess, + Layout = TensorEvaluator<InputArgType, Device>::Layout, + CoordAccess = false, // to be implemented + RawAccess = false + }; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) + : m_inputImpl(op.inputExpression(), device), m_kernelImpl(op.kernelExpression(), device), m_kernelArg(op.kernelExpression()), m_kernel(NULL), m_local_kernel(false), m_device(device) + { + EIGEN_STATIC_ASSERT((static_cast<int>(TensorEvaluator<InputArgType, Device>::Layout) == static_cast<int>(TensorEvaluator<KernelArgType, Device>::Layout)), YOU_MADE_A_PROGRAMMING_MISTAKE); + + const typename TensorEvaluator<InputArgType, Device>::Dimensions& input_dims = m_inputImpl.dimensions(); + const typename TensorEvaluator<KernelArgType, Device>::Dimensions& kernel_dims = m_kernelImpl.dimensions(); + + if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { + m_inputStride[0] = 1; + for (int i = 1; i < NumDims; ++i) { + m_inputStride[i] = m_inputStride[i - 1] * input_dims[i - 1]; + } + } else { + m_inputStride[NumDims - 1] = 1; + for (int i = NumDims - 2; i >= 0; --i) { + m_inputStride[i] = m_inputStride[i + 1] * input_dims[i + 1]; + } + } + + m_dimensions = m_inputImpl.dimensions(); + if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { + for (int i = 0; i < NumKernelDims; ++i) { + const Index index = op.indices()[i]; + const Index input_dim = input_dims[index]; + const Index kernel_dim = kernel_dims[i]; + const Index result_dim = input_dim - kernel_dim + 1; + m_dimensions[index] = result_dim; + if (i > 0) { + m_kernelStride[i] = m_kernelStride[i - 1] * kernel_dims[i - 1]; + } else { + m_kernelStride[0] = 1; + } + m_indexStride[i] = m_inputStride[index]; + } + + m_outputStride[0] = 1; + for (int i = 1; i < NumDims; ++i) { + m_outputStride[i] = m_outputStride[i - 1] * m_dimensions[i - 1]; + } + } else { + for (int i = NumKernelDims - 1; i >= 0; --i) { + const Index index = op.indices()[i]; + const Index input_dim = input_dims[index]; + const Index kernel_dim = kernel_dims[i]; + const Index result_dim = input_dim - kernel_dim + 1; + m_dimensions[index] = result_dim; + if (i < NumKernelDims - 1) { + m_kernelStride[i] = m_kernelStride[i + 1] * kernel_dims[i + 1]; + } else { + m_kernelStride[NumKernelDims - 1] = 1; + } + m_indexStride[i] = m_inputStride[index]; + } + + m_outputStride[NumDims - 1] = 1; + for (int i = NumDims - 2; i >= 0; --i) { + m_outputStride[i] = m_outputStride[i + 1] * m_dimensions[i + 1]; + } + } + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar*) { + m_inputImpl.evalSubExprsIfNeeded(NULL); + preloadKernel(); + return true; + } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() { + m_inputImpl.cleanup(); + if (m_local_kernel) { + m_device.deallocate((void*)m_kernel); + m_local_kernel = false; + } + m_kernel = NULL; + } + + void evalTo(typename XprType::Scalar* buffer) { + evalSubExprsIfNeeded(NULL); + for (int i = 0; i < dimensions().TotalSize(); ++i) { + buffer[i] += coeff(i); + } + cleanup(); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const + { + CoeffReturnType result = CoeffReturnType(0); + convolve(firstInput(index), 0, NumKernelDims-1, result); + return result; + } + + template<int LoadMode> + EIGEN_DEVICE_FUNC PacketReturnType packet(const Index index) const + { + Index indices[2] = {index, index+PacketSize-1}; + Index startInputs[2] = {0, 0}; + if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { + for (int i = NumDims - 1; i > 0; --i) { + const Index idx0 = indices[0] / m_outputStride[i]; + const Index idx1 = indices[1] / m_outputStride[i]; + startInputs[0] += idx0 * m_inputStride[i]; + startInputs[1] += idx1 * m_inputStride[i]; + indices[0] -= idx0 * m_outputStride[i]; + indices[1] -= idx1 * m_outputStride[i]; + } + } else { + for (int i = 0; i < NumDims - 1; ++i) { + const Index idx0 = indices[0] / m_outputStride[i]; + const Index idx1 = indices[1] / m_outputStride[i]; + startInputs[0] += idx0 * m_inputStride[i]; + startInputs[1] += idx1 * m_inputStride[i]; + indices[0] -= idx0 * m_outputStride[i]; + indices[1] -= idx1 * m_outputStride[i]; + } + } + startInputs[0] += indices[0]; + startInputs[1] += indices[1]; + + if (startInputs[1]-startInputs[0] == PacketSize-1) { + PacketReturnType result = internal::pset1<PacketReturnType>(0); + convolvePacket(startInputs[0], 0, NumKernelDims-1, result); + return result; + } else { + EIGEN_ALIGN_MAX Scalar data[PacketSize]; + data[0] = Scalar(0); + convolve(startInputs[0], 0, NumKernelDims-1, data[0]); + for (int i = 1; i < PacketSize-1; ++i) { + data[i] = Scalar(0); + convolve(firstInput(index+i), 0, NumKernelDims-1, data[i]); + } + data[PacketSize-1] = Scalar(0); + convolve(startInputs[1], 0, NumKernelDims-1, data[PacketSize-1]); + return internal::pload<PacketReturnType>(data); + } + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost + costPerCoeff(bool vectorized) const { + const double kernel_size = m_kernelImpl.dimensions().TotalSize(); + // We ignore the use of fused multiply-add. + const double convolve_compute_cost = + TensorOpCost::AddCost<Scalar>() + TensorOpCost::MulCost<Scalar>(); + const double firstIndex_compute_cost = + NumDims * + (2 * TensorOpCost::AddCost<Index>() + 2 * TensorOpCost::MulCost<Index>() + + TensorOpCost::DivCost<Index>()); + return TensorOpCost(0, 0, firstIndex_compute_cost, vectorized, PacketSize) + + kernel_size * (m_inputImpl.costPerCoeff(vectorized) + + m_kernelImpl.costPerCoeff(vectorized) + + TensorOpCost(0, 0, convolve_compute_cost, vectorized, + PacketSize)); + } + + EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; } + + private: + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index firstInput(Index index) const { + Index startInput = 0; + if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { + for (int i = NumDims - 1; i > 0; --i) { + const Index idx = index / m_outputStride[i]; + startInput += idx * m_inputStride[i]; + index -= idx * m_outputStride[i]; + } + } else { + for (int i = 0; i < NumDims - 1; ++i) { + const Index idx = index / m_outputStride[i]; + startInput += idx * m_inputStride[i]; + index -= idx * m_outputStride[i]; + } + } + startInput += index; + return startInput; + } + + EIGEN_DEVICE_FUNC void convolve(Index firstIndex, Index firstKernel, int DimIndex, CoeffReturnType& accum) const { + for (int j = 0; j < m_kernelImpl.dimensions()[DimIndex]; ++j) { + const Index input = firstIndex + j * m_indexStride[DimIndex]; + const Index kernel = firstKernel + j * m_kernelStride[DimIndex]; + if (DimIndex > 0) { + convolve(input, kernel, DimIndex-1, accum); + } else { + accum += m_inputImpl.coeff(input) * m_kernel[kernel]; + } + } + } + + template <typename Packet> + EIGEN_DEVICE_FUNC void convolvePacket(Index firstIndex, Index firstKernel, int DimIndex, Packet& accum) const { + for (int j = 0; j < m_kernelImpl.dimensions()[DimIndex]; ++j) { + const Index input = firstIndex + j * m_indexStride[DimIndex]; + const Index kernel = firstKernel + j * m_kernelStride[DimIndex]; + if (DimIndex > 0) { + convolvePacket(input, kernel, DimIndex-1, accum); + } else { + accum = internal::pmadd<Packet>(m_inputImpl.template packet<Unaligned>(input), internal::pset1<Packet>(m_kernel[kernel]), accum); + } + } + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void preloadKernel() { + // Don't make a local copy of the kernel unless we have to (i.e. it's an + // expression that needs to be evaluated) + const Scalar* in_place = m_kernelImpl.data(); + if (in_place) { + m_kernel = in_place; + m_local_kernel = false; + } else { + size_t kernel_sz = m_kernelImpl.dimensions().TotalSize() * sizeof(Scalar); + Scalar* local = (Scalar*)m_device.allocate(kernel_sz); + typedef TensorEvalToOp<const KernelArgType> EvalTo; + EvalTo evalToTmp(local, m_kernelArg); + const bool PacketAccess = internal::IsVectorizable<Device, KernelArgType>::value; + internal::TensorExecutor<const EvalTo, Device, PacketAccess>::run(evalToTmp, m_device); + + m_kernel = local; + m_local_kernel = true; + } + } + + array<Index, NumDims> m_inputStride; + array<Index, NumDims> m_outputStride; + + array<Index, NumKernelDims> m_indexStride; + array<Index, NumKernelDims> m_kernelStride; + TensorEvaluator<InputArgType, Device> m_inputImpl; + TensorEvaluator<KernelArgType, Device> m_kernelImpl; + Dimensions m_dimensions; + + KernelArgType m_kernelArg; + const Scalar* m_kernel; + bool m_local_kernel; + const Device& m_device; +}; + + + + +// Use an optimized implementation of the evaluation code for GPUs whenever possible. +#if defined(EIGEN_USE_GPU) && defined(__CUDACC__) + +template <int StaticKernelSize> +struct GetKernelSize { + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE int operator() (const int /*kernelSize*/) const { + return StaticKernelSize; + } +}; +template <> +struct GetKernelSize<Dynamic> { + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE int operator() (const int kernelSize) const { + return kernelSize; + } +}; + +template <typename InputEvaluator, typename Index, typename InputDims, + int StaticKernelSize> +__global__ void EigenConvolutionKernel1D( + InputEvaluator eval, + const internal::IndexMapper<Index, InputDims, 1, InputEvaluator::Layout> + indexMapper, + const float* __restrict kernel, const int numPlanes, const int numX, + const int maxX, const int kernelSize, float* buffer) { + extern __shared__ float s[]; + + const int first_x = blockIdx.x * maxX; + const int last_x = (first_x + maxX < numX ? first_x + maxX : numX) - 1; + const int num_x_input = last_x - first_x + GetKernelSize<StaticKernelSize>()(kernelSize); + const int num_x_output = last_x - first_x + 1; + + const int first_plane = blockIdx.y * blockDim.y; + const int plane_stride = blockDim.y * gridDim.y; + + for (int p = first_plane + threadIdx.y; p < numPlanes; p += plane_stride) { + // Load inputs to shared memory + const int plane_input_offset = indexMapper.mapCudaInputPlaneToTensorInputOffset(p); + const int plane_kernel_offset = threadIdx.y * num_x_input; + #pragma unroll + for (int i = threadIdx.x; i < num_x_input; i += blockDim.x) { + const int tensor_index = plane_input_offset + indexMapper.mapCudaInputKernelToTensorInputOffset(i+first_x); + s[i + plane_kernel_offset] = eval.coeff(tensor_index); + } + + __syncthreads(); + + // Compute the convolution + const int plane_output_offset = indexMapper.mapCudaOutputPlaneToTensorOutputOffset(p); + + #pragma unroll + for (int i = threadIdx.x; i < num_x_output; i += blockDim.x) { + const int kernel_offset = plane_kernel_offset + i; + float result = 0.0f; + #pragma unroll + for (int k = 0; k < GetKernelSize<StaticKernelSize>()(kernelSize); ++k) { + result += s[k + kernel_offset] * kernel[k]; + } + const int tensor_index = plane_output_offset + indexMapper.mapCudaOutputKernelToTensorOutputOffset(i+first_x); + buffer[tensor_index] = result; + } + __syncthreads(); + } +}; + +template <typename InputEvaluator, typename Index, typename InputDims, + int StaticKernelSizeX, int StaticKernelSizeY> +__global__ void EigenConvolutionKernel2D( + InputEvaluator eval, + const internal::IndexMapper<Index, InputDims, 2, InputEvaluator::Layout> + indexMapper, + const float* __restrict kernel, const int numPlanes, const int numX, + const int maxX, const int numY, const int maxY, const int kernelSizeX, + const int kernelSizeY, float* buffer) { + extern __shared__ float s[]; + + const int first_x = blockIdx.x * maxX; + const int last_x = (first_x + maxX < numX ? first_x + maxX : numX) - 1; + const int num_x_input = last_x - first_x + GetKernelSize<StaticKernelSizeX>()(kernelSizeX); + const int num_x_output = last_x - first_x + 1; + + const int first_y = blockIdx.y * maxY; + const int last_y = (first_y + maxY < numY ? first_y + maxY : numY) - 1; + const int num_y_input = last_y - first_y + GetKernelSize<StaticKernelSizeY>()(kernelSizeY); + const int num_y_output = last_y - first_y + 1; + + const int first_plane = blockIdx.z * blockDim.z; + const int plane_stride = blockDim.z * gridDim.z; + + for (int p = first_plane + threadIdx.z; p < numPlanes; p += plane_stride) { + + const int plane_input_offset = indexMapper.mapCudaInputPlaneToTensorInputOffset(p); + const int plane_kernel_offset = threadIdx.z * num_y_input; + + // Load inputs to shared memory + #pragma unroll + for (int j = threadIdx.y; j < num_y_input; j += blockDim.y) { + const int input_offset = num_x_input * (j + plane_kernel_offset); + #pragma unroll + for (int i = threadIdx.x; i < num_x_input; i += blockDim.x) { + const int tensor_index = plane_input_offset + indexMapper.mapCudaInputKernelToTensorInputOffset(i+first_x, j+first_y); + s[i + input_offset] = eval.coeff(tensor_index); + } + } + + __syncthreads(); + + // Convolution + const int plane_output_offset = indexMapper.mapCudaOutputPlaneToTensorOutputOffset(p); + + #pragma unroll + for (int j = threadIdx.y; j < num_y_output; j += blockDim.y) { + #pragma unroll + for (int i = threadIdx.x; i < num_x_output; i += blockDim.x) { + float result = 0.0f; + #pragma unroll + for (int l = 0; l < GetKernelSize<StaticKernelSizeY>()(kernelSizeY); ++l) { + const int kernel_offset = kernelSizeX * l; + const int input_offset = i + num_x_input * (j + l + plane_kernel_offset); + #pragma unroll + for (int k = 0; k < GetKernelSize<StaticKernelSizeX>()(kernelSizeX); ++k) { + result += s[k + input_offset] * kernel[k + kernel_offset]; + } + } + const int tensor_index = plane_output_offset + indexMapper.mapCudaOutputKernelToTensorOutputOffset(i+first_x, j+first_y); + buffer[tensor_index] = result; + } + } + + __syncthreads(); + } +}; + +template <typename InputEvaluator, typename Index, typename InputDims> +__global__ void EigenConvolutionKernel3D( + InputEvaluator eval, + const internal::IndexMapper<Index, InputDims, 3, InputEvaluator::Layout> + indexMapper, + const float* __restrict kernel, const size_t numPlanes, const size_t numX, + const size_t maxX, const size_t numY, const size_t maxY, const size_t numZ, + const size_t maxZ, const size_t kernelSizeX, const size_t kernelSizeY, + const size_t kernelSizeZ, float* buffer) { + extern __shared__ float s[]; + + // Load inputs to shared memory + const int first_x = blockIdx.x * maxX; + const int last_x = (first_x + maxX < numX ? first_x + maxX : numX) - 1; + const int num_x_input = last_x - first_x + kernelSizeX; + + const int first_y = blockIdx.y * maxY; + const int last_y = (first_y + maxY < numY ? first_y + maxY : numY) - 1; + const int num_y_input = last_y - first_y + kernelSizeY; + + const int first_z = blockIdx.z * maxZ; + const int last_z = (first_z + maxZ < numZ ? first_z + maxZ : numZ) - 1; + const int num_z_input = last_z - first_z + kernelSizeZ; + + for (int p = 0; p < numPlanes; ++p) { + + const int plane_input_offset = indexMapper.mapCudaInputPlaneToTensorInputOffset(p); + const int plane_kernel_offset = 0; + + for (int k = threadIdx.z; k < num_z_input; k += blockDim.z) { + for (int j = threadIdx.y; j < num_y_input; j += blockDim.y) { + for (int i = threadIdx.x; i < num_x_input; i += blockDim.x) { + const int tensor_index = plane_input_offset + indexMapper.mapCudaInputKernelToTensorInputOffset(i+first_x, j+first_y, k+first_z); + s[i + num_x_input * (j + num_y_input * (k + plane_kernel_offset))] = eval.coeff(tensor_index); + } + } + } + + __syncthreads(); + + // Convolution + const int num_z_output = last_z - first_z + 1; + const int num_y_output = last_y - first_y + 1; + const int num_x_output = last_x - first_x + 1; + const int plane_output_offset = indexMapper.mapCudaOutputPlaneToTensorOutputOffset(p); + + for (int k = threadIdx.z; k < num_z_output; k += blockDim.z) { + for (int j = threadIdx.y; j < num_y_output; j += blockDim.y) { + for (int i = threadIdx.x; i < num_x_output; i += blockDim.x) { + float result = 0.0f; + for (int n = 0; n < kernelSizeZ; ++n) { + for (int m = 0; m < kernelSizeY; ++m) { + for (int l = 0; l < kernelSizeX; ++l) { + result += s[i + l + num_x_input * (j + m + num_y_input * (k + n + plane_kernel_offset))] * kernel[l + kernelSizeX * (m + kernelSizeY * n)]; + } + } + } + const int tensor_index = plane_output_offset + indexMapper.mapCudaOutputKernelToTensorOutputOffset(i+first_x, j+first_y, k+first_z); + buffer[tensor_index] = result; + } + } + } + __syncthreads(); + } +}; + + + +template<typename Indices, typename InputArgType, typename KernelArgType> +struct TensorEvaluator<const TensorConvolutionOp<Indices, InputArgType, KernelArgType>, GpuDevice> +{ + typedef TensorConvolutionOp<Indices, InputArgType, KernelArgType> XprType; + + static const int NumDims = internal::array_size<typename TensorEvaluator<InputArgType, GpuDevice>::Dimensions>::value; + static const int NumKernelDims = internal::array_size<Indices>::value; + typedef typename XprType::Index Index; + typedef DSizes<Index, NumDims> Dimensions; + typedef typename TensorEvaluator<KernelArgType, GpuDevice>::Dimensions KernelDimensions; + + enum { + IsAligned = TensorEvaluator<InputArgType, GpuDevice>::IsAligned & TensorEvaluator<KernelArgType, GpuDevice>::IsAligned, + PacketAccess = false, + Layout = TensorEvaluator<InputArgType, GpuDevice>::Layout, + CoordAccess = false, // to be implemented + RawAccess = false + }; + + EIGEN_DEVICE_FUNC TensorEvaluator(const XprType& op, const GpuDevice& device) + : m_inputImpl(op.inputExpression(), device), m_kernelArg(op.kernelExpression()), m_kernelImpl(op.kernelExpression(), device), m_indices(op.indices()), m_buf(NULL), m_kernel(NULL), m_local_kernel(false), m_device(device) + { + EIGEN_STATIC_ASSERT((static_cast<int>(TensorEvaluator<InputArgType, GpuDevice>::Layout) == static_cast<int>(TensorEvaluator<KernelArgType, GpuDevice>::Layout)), YOU_MADE_A_PROGRAMMING_MISTAKE); + + const typename TensorEvaluator<InputArgType, GpuDevice>::Dimensions& input_dims = m_inputImpl.dimensions(); + const typename TensorEvaluator<KernelArgType, GpuDevice>::Dimensions& kernel_dims = m_kernelImpl.dimensions(); + + m_dimensions = m_inputImpl.dimensions(); + for (int i = 0; i < NumKernelDims; ++i) { + const Index index = op.indices()[i]; + const Index input_dim = input_dims[index]; + const Index kernel_dim = kernel_dims[i]; + const Index result_dim = input_dim - kernel_dim + 1; + m_dimensions[index] = result_dim; + } + } + + typedef typename XprType::CoeffReturnType CoeffReturnType; + typedef typename PacketType<CoeffReturnType, GpuDevice>::type PacketReturnType; + typedef typename InputArgType::Scalar Scalar; + static const int PacketSize = internal::unpacket_traits<PacketReturnType>::size; + + EIGEN_DEVICE_FUNC const Dimensions& dimensions() const { return m_dimensions; } + + EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* data) { + preloadKernel(); + m_inputImpl.evalSubExprsIfNeeded(NULL); + if (data) { + executeEval(data); + return false; + } else { + m_buf = (Scalar*)m_device.allocate(dimensions().TotalSize() * sizeof(Scalar)); + executeEval(m_buf); + return true; + } + } + + EIGEN_STRONG_INLINE void cleanup() { + m_inputImpl.cleanup(); + if (m_buf) { + m_device.deallocate(m_buf); + m_buf = NULL; + } + if (m_local_kernel) { + m_device.deallocate((void*)m_kernel); + m_local_kernel = false; + } + m_kernel = NULL; + } + + EIGEN_STRONG_INLINE void preloadKernel() { + // Don't make a local copy of the kernel unless we have to (i.e. it's an + // expression that needs to be evaluated) + const Scalar* in_place = m_kernelImpl.data(); + if (in_place) { + m_kernel = in_place; + m_local_kernel = false; + } else { + size_t kernel_sz = m_kernelImpl.dimensions().TotalSize() * sizeof(Scalar); + Scalar* local = (Scalar*)m_device.allocate(kernel_sz); + typedef TensorEvalToOp<const KernelArgType> EvalTo; + EvalTo evalToTmp(local, m_kernelArg); + const bool PacketAccess = internal::IsVectorizable<GpuDevice, KernelArgType>::value; + internal::TensorExecutor<const EvalTo, GpuDevice, PacketAccess>::run(evalToTmp, m_device); + + m_kernel = local; + m_local_kernel = true; + } + } + + static unsigned int ceil(unsigned int num, unsigned int denom) { + const unsigned int rounded_toward_zero = num / denom; + if (num > rounded_toward_zero * denom) { + return rounded_toward_zero + 1; + } + return rounded_toward_zero; + } + + void executeEval(Scalar* data) const { + typedef typename TensorEvaluator<InputArgType, GpuDevice>::Dimensions InputDims; + + const int maxSharedMem = m_device.sharedMemPerBlock(); + const int maxThreadsPerBlock = m_device.maxCudaThreadsPerBlock(); + const int maxBlocksPerProcessor = m_device.maxCudaThreadsPerMultiProcessor() / maxThreadsPerBlock; + const int numMultiProcessors = m_device.getNumCudaMultiProcessors(); + const int warpSize = 32; + + switch (NumKernelDims) { + case 1: { + const int kernel_size = m_kernelImpl.dimensions().TotalSize(); + + const int numX = dimensions()[m_indices[0]]; + const int numP = dimensions().TotalSize() / numX; + int maxX; + dim3 block_size; + + const int single_stride_dim = + static_cast<int>(Layout) == static_cast<int>(ColMajor) + ? 0 + : m_inputImpl.dimensions().rank() - 1; + if (m_indices[0] == single_stride_dim) { + // Maximum the reuse + const int inner_dim = ((maxSharedMem / (sizeof(Scalar)) - kernel_size + 1 + 31) / 32) * 32; + maxX = numext::mini<int>(inner_dim, numX); + const int maxP = numext::mini<int>(maxSharedMem / ((kernel_size - 1 + maxX) * sizeof(Scalar)), numP); + block_size.x = numext::mini(maxThreadsPerBlock, maxX); + block_size.y = numext::mini<int>(maxThreadsPerBlock / block_size.x, maxP); + } + else { + // Read as much as possible alongside the inner most dimension, that is the plane + const int inner_dim = maxSharedMem / ((warpSize + kernel_size) * sizeof(Scalar)); + const int maxP = numext::mini<int>(inner_dim, numP); + maxX = numext::mini<int>(maxSharedMem / (inner_dim * sizeof(Scalar)) - kernel_size + 1, numX); + + block_size.x = numext::mini(warpSize, maxX); + block_size.y = numext::mini<int>(maxThreadsPerBlock/block_size.x, maxP); + } + + const int shared_mem = block_size.y * (maxX + kernel_size - 1) * sizeof(Scalar); + assert(shared_mem <= maxSharedMem); + + const int num_x_blocks = ceil(numX, maxX); + const int blocksPerProcessor = numext::mini(maxBlocksPerProcessor, maxSharedMem / shared_mem); + const int num_y_blocks = ceil(numMultiProcessors * blocksPerProcessor, num_x_blocks); + + dim3 num_blocks(num_x_blocks, numext::mini<int>(num_y_blocks, ceil(numP, block_size.y))); + + + //cout << "launching 1D kernel with block_size.x: " << block_size.x << " block_size.y: " << block_size.y << " num_blocks.x: " << num_blocks.x << " num_blocks.y: " << num_blocks.y << " maxX: " << maxX << " shared_mem: " << shared_mem << " in stream " << m_device.stream() << endl; + + const array<Index, 1> indices(m_indices[0]); + const array<Index, 1> kernel_dims(m_kernelImpl.dimensions()[0]); + internal::IndexMapper<Index, InputDims, 1, Layout> indexMapper( + m_inputImpl.dimensions(), kernel_dims, indices); + switch(kernel_size) { + case 4: { + LAUNCH_CUDA_KERNEL((EigenConvolutionKernel1D<TensorEvaluator<InputArgType, GpuDevice>, Index, InputDims, 4>), num_blocks, block_size, shared_mem, m_device, m_inputImpl, indexMapper, m_kernel, numP, numX, maxX, 4, data); + break; + } + case 7: { + LAUNCH_CUDA_KERNEL((EigenConvolutionKernel1D<TensorEvaluator<InputArgType, GpuDevice>, Index, InputDims, 7>), num_blocks, block_size, shared_mem, m_device, m_inputImpl, indexMapper, m_kernel, numP, numX, maxX, 7, data); + break; + } + default: { + LAUNCH_CUDA_KERNEL((EigenConvolutionKernel1D<TensorEvaluator<InputArgType, GpuDevice>, Index, InputDims, Dynamic>), num_blocks, block_size, shared_mem, m_device, m_inputImpl, indexMapper, m_kernel, numP, numX, maxX, kernel_size, data); + } + } + break; + } + + case 2: { + const int idxX = + static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 0 : 1; + const int idxY = + static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 1 : 0; + const int kernel_size_x = m_kernelImpl.dimensions()[idxX]; + const int kernel_size_y = m_kernelImpl.dimensions()[idxY]; + + const int numX = dimensions()[m_indices[idxX]]; + const int numY = dimensions()[m_indices[idxY]]; + const int numP = dimensions().TotalSize() / (numX*numY); + + const float scaling_factor = sqrtf(static_cast<float>(maxSharedMem) / (sizeof(Scalar) * kernel_size_y * kernel_size_x)); + + // Snap maxX to warp size + int inner_dim = ((static_cast<int>(scaling_factor * kernel_size_x) - kernel_size_x + 1 + 32) / 32) * 32; + const int maxX = numext::mini<int>(inner_dim, numX); + const int maxY = numext::mini<int>(maxSharedMem / (sizeof(Scalar) * (maxX + kernel_size_x - 1)) - kernel_size_y + 1, numY); + const int maxP = numext::mini<int>(maxSharedMem / ((kernel_size_x - 1 + maxX) * (kernel_size_y - 1 + maxY) * sizeof(Scalar)), numP); + + dim3 block_size; + block_size.x = numext::mini(1024, maxX); + block_size.y = numext::mini<int>(1024/block_size.x, maxY); + block_size.z = numext::mini<int>(1024/(block_size.x*block_size.y), maxP); + + const int shared_mem = block_size.z * (maxX + kernel_size_x - 1) * (maxY + kernel_size_y - 1) * sizeof(Scalar); + assert(shared_mem <= maxSharedMem); + + const int num_x_blocks = ceil(numX, maxX); + const int num_y_blocks = ceil(numY, maxY); + const int blocksPerProcessor = numext::mini(maxBlocksPerProcessor, maxSharedMem / shared_mem); + const int num_z_blocks = ceil(numMultiProcessors * blocksPerProcessor, num_x_blocks * num_y_blocks); + + dim3 num_blocks(num_x_blocks, num_y_blocks, numext::mini<int>(num_z_blocks, ceil(numP, block_size.z))); + + + //cout << "launching 2D kernel with block_size.x: " << block_size.x << " block_size.y: " << block_size.y << " block_size.z: " << block_size.z << " num_blocks.x: " << num_blocks.x << " num_blocks.y: " << num_blocks.y << " num_blocks.z: " << num_blocks.z << " maxX: " << maxX << " maxY: " << maxY << " maxP: " << maxP << " shared_mem: " << shared_mem << " in stream " << m_device.stream() << endl; + + const array<Index, 2> indices(m_indices[idxX], m_indices[idxY]); + const array<Index, 2> kernel_dims(m_kernelImpl.dimensions()[idxX], + m_kernelImpl.dimensions()[idxY]); + internal::IndexMapper<Index, InputDims, 2, Layout> indexMapper( + m_inputImpl.dimensions(), kernel_dims, indices); + switch (kernel_size_x) { + case 4: { + switch (kernel_size_y) { + case 7: { + LAUNCH_CUDA_KERNEL((EigenConvolutionKernel2D<TensorEvaluator<InputArgType, GpuDevice>, Index, InputDims, 4, 7>), num_blocks, block_size, shared_mem, m_device, m_inputImpl, indexMapper, m_kernel, numP, numX, maxX, numY, maxY, 4, 7, data); + break; + } + default: { + LAUNCH_CUDA_KERNEL((EigenConvolutionKernel2D<TensorEvaluator<InputArgType, GpuDevice>, Index, InputDims, 4, Dynamic>), num_blocks, block_size, shared_mem, m_device, m_inputImpl, indexMapper, m_kernel, numP, numX, maxX, numY, maxY, 4, kernel_size_y, data); + break; + } + } + break; + } + case 7: { + switch (kernel_size_y) { + case 4: { + LAUNCH_CUDA_KERNEL((EigenConvolutionKernel2D<TensorEvaluator<InputArgType, GpuDevice>, Index, InputDims, 7, 4>), num_blocks, block_size, shared_mem, m_device, m_inputImpl, indexMapper, m_kernel, numP, numX, maxX, numY, maxY, 7, 4, data); + break; + } + default: { + LAUNCH_CUDA_KERNEL((EigenConvolutionKernel2D<TensorEvaluator<InputArgType, GpuDevice>, Index, InputDims, 7, Dynamic>), num_blocks, block_size, shared_mem, m_device, m_inputImpl, indexMapper, m_kernel, numP, numX, maxX, numY, maxY, 7, kernel_size_y, data); + break; + } + } + break; + } + default: { + LAUNCH_CUDA_KERNEL((EigenConvolutionKernel2D<TensorEvaluator<InputArgType, GpuDevice>, Index, InputDims, Dynamic, Dynamic>), num_blocks, block_size, shared_mem, m_device, m_inputImpl, indexMapper, m_kernel, numP, numX, maxX, numY, maxY, kernel_size_x, kernel_size_y, data); + break; + } + } + break; + } + + case 3: { + const int idxX = + static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 0 : 2; + const int idxY = + static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 1 : 1; + const int idxZ = + static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 2 : 0; + + const int kernel_size_x = m_kernelImpl.dimensions()[idxX]; + const int kernel_size_y = m_kernelImpl.dimensions()[idxY]; + const int kernel_size_z = m_kernelImpl.dimensions()[idxZ]; + + const int numX = dimensions()[m_indices[idxX]]; + const int numY = dimensions()[m_indices[idxY]]; + const int numZ = dimensions()[m_indices[idxZ]]; + const int numP = dimensions().TotalSize() / (numX*numY*numZ); + + const int maxX = numext::mini<int>(128, numext::mini<int>(maxSharedMem / (sizeof(Scalar) * kernel_size_y * kernel_size_z) - kernel_size_x + 1, numX)); + const int maxY = numext::mini<int>(128, numext::mini<int>(maxSharedMem / (sizeof(Scalar) * (maxX + kernel_size_x - 1) * kernel_size_z) - kernel_size_y + 1, numY)); + const int maxZ = numext::mini<int>(128, numext::mini<int>(maxSharedMem / (sizeof(Scalar) * (maxX + kernel_size_x - 1) * (maxY + kernel_size_y - 1)) - kernel_size_z + 1, numZ)); + + dim3 block_size; + block_size.x = numext::mini(32, maxX); + block_size.y = numext::mini(32, maxY); + block_size.z = numext::mini<int>(1024/(block_size.x*block_size.y), maxZ); + dim3 num_blocks(ceil(numX, maxX), ceil(numY, maxY), ceil(numZ, maxZ)); + + const int shared_mem = (maxX + kernel_size_x - 1) * (maxY + kernel_size_y - 1) * (maxZ + kernel_size_z - 1) * sizeof(Scalar); + assert(shared_mem <= maxSharedMem); + + //cout << "launching 3D kernel with block_size.x: " << block_size.x << " block_size.y: " << block_size.y << " block_size.z: " << block_size.z << " num_blocks.x: " << num_blocks.x << " num_blocks.y: " << num_blocks.y << " num_blocks.z: " << num_blocks.z << " shared_mem: " << shared_mem << " in stream " << m_device.stream() << endl; + const array<Index, 3> indices(m_indices[idxX], m_indices[idxY], + m_indices[idxZ]); + const array<Index, 3> kernel_dims(m_kernelImpl.dimensions()[idxX], + m_kernelImpl.dimensions()[idxY], + m_kernelImpl.dimensions()[idxZ]); + internal::IndexMapper<Index, InputDims, 3, Layout> indexMapper( + m_inputImpl.dimensions(), kernel_dims, indices); + + LAUNCH_CUDA_KERNEL((EigenConvolutionKernel3D<TensorEvaluator<InputArgType, GpuDevice>, Index, InputDims>), num_blocks, block_size, shared_mem, m_device, m_inputImpl, indexMapper, m_kernel, numP, numX, maxX, numY, maxY, numZ, maxZ, kernel_size_x, kernel_size_y, kernel_size_z, data); + break; + } + + default: { + EIGEN_STATIC_ASSERT((NumKernelDims >= 1 && NumKernelDims <= 3), THIS_METHOD_IS_ONLY_FOR_OBJECTS_OF_A_SPECIFIC_SIZE); + } + } + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const + { + eigen_assert(m_buf); + eigen_assert(index < m_dimensions.TotalSize()); + return m_buf[index]; + } + + template<int LoadMode> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(const Index index) const + { + eigen_assert(m_buf); + eigen_assert(index < m_dimensions.TotalSize()); + return internal::ploadt<PacketReturnType, LoadMode>(m_buf+index); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost + costPerCoeff(bool vectorized) const { + // TODO(rmlarsen): FIXME: For now, this is just a copy of the CPU cost + // model. + const double kernel_size = m_kernelImpl.dimensions().TotalSize(); + // We ignore the use of fused multiply-add. + const double convolve_compute_cost = + TensorOpCost::AddCost<Scalar>() + TensorOpCost::MulCost<Scalar>(); + const double firstIndex_compute_cost = + NumDims * + (2 * TensorOpCost::AddCost<Index>() + 2 * TensorOpCost::MulCost<Index>() + + TensorOpCost::DivCost<Index>()); + return TensorOpCost(0, 0, firstIndex_compute_cost, vectorized, PacketSize) + + kernel_size * (m_inputImpl.costPerCoeff(vectorized) + + m_kernelImpl.costPerCoeff(vectorized) + + TensorOpCost(0, 0, convolve_compute_cost, vectorized, + PacketSize)); + } + + private: + // No assignment (copies are needed by the kernels) + TensorEvaluator& operator = (const TensorEvaluator&); + + TensorEvaluator<InputArgType, GpuDevice> m_inputImpl; + TensorEvaluator<KernelArgType, GpuDevice> m_kernelImpl; + KernelArgType m_kernelArg; + Indices m_indices; + Dimensions m_dimensions; + Scalar* m_buf; + const Scalar* m_kernel; + bool m_local_kernel; + + const GpuDevice& m_device; +}; +#endif + + +} // end namespace Eigen + +#endif // EIGEN_CXX11_TENSOR_TENSOR_CONVOLUTION_H diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorCostModel.h b/unsupported/Eigen/CXX11/src/Tensor/TensorCostModel.h new file mode 100644 index 000000000..83c449cf1 --- /dev/null +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorCostModel.h @@ -0,0 +1,212 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2016 Rasmus Munk Larsen <rmlarsen@google.com> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_CXX11_TENSOR_TENSOR_COST_MODEL_H +#define EIGEN_CXX11_TENSOR_TENSOR_COST_MODEL_H + +namespace Eigen { + +/** \class TensorEvaluator + * \ingroup CXX11_Tensor_Module + * + * \brief A cost model used to limit the number of threads used for evaluating + * tensor expression. + * + */ + +// Class storing the cost of evaluating a tensor expression in terms of the +// estimated number of operand bytes loads, bytes stored, and compute cycles. +class TensorOpCost { + public: + // TODO(rmlarsen): Fix the scalar op costs in Eigen proper. Even a simple + // model based on minimal reciprocal throughput numbers from Intel or + // Agner Fog's tables would be better than what is there now. + template <typename ArgType> + static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE int MulCost() { + return internal::functor_traits< + internal::scalar_product_op<ArgType, ArgType> >::Cost; + } + template <typename ArgType> + static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE int AddCost() { + return internal::functor_traits<internal::scalar_sum_op<ArgType> >::Cost; + } + template <typename ArgType> + static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE int DivCost() { + return internal::functor_traits< + internal::scalar_quotient_op<ArgType, ArgType> >::Cost; + } + template <typename ArgType> + static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE int ModCost() { + return internal::functor_traits<internal::scalar_mod_op<ArgType> >::Cost; + } + template <typename SrcType, typename TargetType> + static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE int CastCost() { + return internal::functor_traits< + internal::scalar_cast_op<SrcType, TargetType> >::Cost; + } + + EIGEN_DEVICE_FUNC + TensorOpCost() : bytes_loaded_(0), bytes_stored_(0), compute_cycles_(0) {} + EIGEN_DEVICE_FUNC + TensorOpCost(double bytes_loaded, double bytes_stored, double compute_cycles) + : bytes_loaded_(bytes_loaded), + bytes_stored_(bytes_stored), + compute_cycles_(compute_cycles) {} + + EIGEN_DEVICE_FUNC + TensorOpCost(double bytes_loaded, double bytes_stored, double compute_cycles, + bool vectorized, double packet_size) + : bytes_loaded_(bytes_loaded), + bytes_stored_(bytes_stored), + compute_cycles_(vectorized ? compute_cycles / packet_size + : compute_cycles) { + eigen_assert(bytes_loaded >= 0 && (numext::isfinite)(bytes_loaded)); + eigen_assert(bytes_stored >= 0 && (numext::isfinite)(bytes_stored)); + eigen_assert(compute_cycles >= 0 && (numext::isfinite)(compute_cycles)); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double bytes_loaded() const { + return bytes_loaded_; + } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double bytes_stored() const { + return bytes_stored_; + } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double compute_cycles() const { + return compute_cycles_; + } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double total_cost( + double load_cost, double store_cost, double compute_cost) const { + return load_cost * bytes_loaded_ + store_cost * bytes_stored_ + + compute_cost * compute_cycles_; + } + + // Drop memory access component. Intended for cases when memory accesses are + // sequential or are completely masked by computations. + EIGEN_DEVICE_FUNC void dropMemoryCost() { + bytes_loaded_ = 0; + bytes_stored_ = 0; + } + + // TODO(rmlarsen): Define min in terms of total cost, not elementwise. + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost cwiseMin( + const TensorOpCost& rhs) const { + double bytes_loaded = numext::mini(bytes_loaded_, rhs.bytes_loaded()); + double bytes_stored = numext::mini(bytes_stored_, rhs.bytes_stored()); + double compute_cycles = numext::mini(compute_cycles_, rhs.compute_cycles()); + return TensorOpCost(bytes_loaded, bytes_stored, compute_cycles); + } + + // TODO(rmlarsen): Define max in terms of total cost, not elementwise. + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost cwiseMax( + const TensorOpCost& rhs) const { + double bytes_loaded = numext::maxi(bytes_loaded_, rhs.bytes_loaded()); + double bytes_stored = numext::maxi(bytes_stored_, rhs.bytes_stored()); + double compute_cycles = numext::maxi(compute_cycles_, rhs.compute_cycles()); + return TensorOpCost(bytes_loaded, bytes_stored, compute_cycles); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost& operator+=( + const TensorOpCost& rhs) { + bytes_loaded_ += rhs.bytes_loaded(); + bytes_stored_ += rhs.bytes_stored(); + compute_cycles_ += rhs.compute_cycles(); + return *this; + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost& operator*=(double rhs) { + bytes_loaded_ *= rhs; + bytes_stored_ *= rhs; + compute_cycles_ *= rhs; + return *this; + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE friend TensorOpCost operator+( + TensorOpCost lhs, const TensorOpCost& rhs) { + lhs += rhs; + return lhs; + } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE friend TensorOpCost operator*( + TensorOpCost lhs, double rhs) { + lhs *= rhs; + return lhs; + } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE friend TensorOpCost operator*( + double lhs, TensorOpCost rhs) { + rhs *= lhs; + return rhs; + } + + friend std::ostream& operator<<(std::ostream& os, const TensorOpCost& tc) { + return os << "[bytes_loaded = " << tc.bytes_loaded() + << ", bytes_stored = " << tc.bytes_stored() + << ", compute_cycles = " << tc.compute_cycles() << "]"; + } + + private: + double bytes_loaded_; + double bytes_stored_; + double compute_cycles_; +}; + +// TODO(rmlarsen): Implement a policy that chooses an "optimal" number of theads +// in [1:max_threads] instead of just switching multi-threading off for small +// work units. +template <typename Device> +class TensorCostModel { + public: + // Scaling from Eigen compute cost to device cycles. + static const int kDeviceCyclesPerComputeCycle = 1; + + // Costs in device cycles. + static const int kStartupCycles = 100000; + static const int kPerThreadCycles = 100000; + static const int kTaskSize = 40000; + + // Returns the number of threads in [1:max_threads] to use for + // evaluating an expression with the given output size and cost per + // coefficient. + static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE int numThreads( + double output_size, const TensorOpCost& cost_per_coeff, int max_threads) { + double cost = totalCost(output_size, cost_per_coeff); + int threads = (cost - kStartupCycles) / kPerThreadCycles + 0.9; + return numext::mini(max_threads, numext::maxi(1, threads)); + } + + // taskSize assesses parallel task size. + // Value of 1.0 means ideal parallel task size. Values < 1.0 mean that task + // granularity needs to be increased to mitigate parallelization overheads. + static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double taskSize( + double output_size, const TensorOpCost& cost_per_coeff) { + return totalCost(output_size, cost_per_coeff) / kTaskSize; + } + + private: + static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double totalCost( + double output_size, const TensorOpCost& cost_per_coeff) { + // Cost of memory fetches from L2 cache. 64 is typical cache line size. + // 11 is L2 cache latency on Haswell. + // We don't know whether data is in L1, L2 or L3. But we are most interested + // in single-threaded computational time around 100us-10ms (smaller time + // is too small for parallelization, larger time is not intersting + // either because we are probably using all available threads already). + // And for the target time range, L2 seems to be what matters. Data set + // fitting into L1 is too small to take noticeable time. Data set fitting + // only into L3 presumably will take more than 10ms to load and process. + const double kLoadCycles = 1.0 / 64 * 11; + const double kStoreCycles = 1.0 / 64 * 11; + // Scaling from Eigen compute cost to device cycles. + return output_size * + cost_per_coeff.total_cost(kLoadCycles, kStoreCycles, + kDeviceCyclesPerComputeCycle); + } +}; + +} // namespace Eigen + +#endif // EIGEN_CXX11_TENSOR_TENSOR_COST_MODEL_H diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorCustomOp.h b/unsupported/Eigen/CXX11/src/Tensor/TensorCustomOp.h new file mode 100644 index 000000000..e020d076f --- /dev/null +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorCustomOp.h @@ -0,0 +1,313 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_CXX11_TENSOR_TENSOR_CUSTOM_OP_H +#define EIGEN_CXX11_TENSOR_TENSOR_CUSTOM_OP_H + +namespace Eigen { + +/** \class TensorCustomUnaryOp + * \ingroup CXX11_Tensor_Module + * + * \brief Tensor custom class. + * + * + */ +namespace internal { +template<typename CustomUnaryFunc, typename XprType> +struct traits<TensorCustomUnaryOp<CustomUnaryFunc, XprType> > +{ + typedef typename XprType::Scalar Scalar; + typedef typename XprType::StorageKind StorageKind; + typedef typename XprType::Index Index; + typedef typename XprType::Nested Nested; + typedef typename remove_reference<Nested>::type _Nested; + static const int NumDimensions = traits<XprType>::NumDimensions; + static const int Layout = traits<XprType>::Layout; +}; + +template<typename CustomUnaryFunc, typename XprType> +struct eval<TensorCustomUnaryOp<CustomUnaryFunc, XprType>, Eigen::Dense> +{ + typedef const TensorCustomUnaryOp<CustomUnaryFunc, XprType>& type; +}; + +template<typename CustomUnaryFunc, typename XprType> +struct nested<TensorCustomUnaryOp<CustomUnaryFunc, XprType> > +{ + typedef TensorCustomUnaryOp<CustomUnaryFunc, XprType> type; +}; + +} // end namespace internal + + + +template<typename CustomUnaryFunc, typename XprType> +class TensorCustomUnaryOp : public TensorBase<TensorCustomUnaryOp<CustomUnaryFunc, XprType>, ReadOnlyAccessors> +{ + public: + typedef typename internal::traits<TensorCustomUnaryOp>::Scalar Scalar; + typedef typename Eigen::NumTraits<Scalar>::Real RealScalar; + typedef typename XprType::CoeffReturnType CoeffReturnType; + typedef typename internal::nested<TensorCustomUnaryOp>::type Nested; + typedef typename internal::traits<TensorCustomUnaryOp>::StorageKind StorageKind; + typedef typename internal::traits<TensorCustomUnaryOp>::Index Index; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorCustomUnaryOp(const XprType& expr, const CustomUnaryFunc& func) + : m_expr(expr), m_func(func) {} + + EIGEN_DEVICE_FUNC + const CustomUnaryFunc& func() const { return m_func; } + + EIGEN_DEVICE_FUNC + const typename internal::remove_all<typename XprType::Nested>::type& + expression() const { return m_expr; } + + protected: + typename XprType::Nested m_expr; + const CustomUnaryFunc m_func; +}; + + +// Eval as rvalue +template<typename CustomUnaryFunc, typename XprType, typename Device> +struct TensorEvaluator<const TensorCustomUnaryOp<CustomUnaryFunc, XprType>, Device> +{ + typedef TensorCustomUnaryOp<CustomUnaryFunc, XprType> ArgType; + typedef typename internal::traits<ArgType>::Index Index; + static const int NumDims = internal::traits<ArgType>::NumDimensions; + typedef DSizes<Index, NumDims> Dimensions; + typedef typename internal::remove_const<typename ArgType::Scalar>::type Scalar; + typedef typename internal::remove_const<typename XprType::CoeffReturnType>::type CoeffReturnType; + typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType; + static const int PacketSize = internal::unpacket_traits<PacketReturnType>::size; + + enum { + IsAligned = false, + PacketAccess = (internal::packet_traits<Scalar>::size > 1), + BlockAccess = false, + Layout = TensorEvaluator<XprType, Device>::Layout, + CoordAccess = false, // to be implemented + RawAccess = false + }; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const ArgType& op, const Device& device) + : m_op(op), m_device(device), m_result(NULL) + { + m_dimensions = op.func().dimensions(op.expression()); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(CoeffReturnType* data) { + if (data) { + evalTo(data); + return false; + } else { + m_result = static_cast<CoeffReturnType*>( + m_device.allocate(dimensions().TotalSize() * sizeof(Scalar))); + evalTo(m_result); + return true; + } + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() { + if (m_result != NULL) { + m_device.deallocate(m_result); + m_result = NULL; + } + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const { + return m_result[index]; + } + + template<int LoadMode> + EIGEN_DEVICE_FUNC PacketReturnType packet(Index index) const { + return internal::ploadt<PacketReturnType, LoadMode>(m_result + index); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const { + // TODO(rmlarsen): Extend CustomOp API to return its cost estimate. + return TensorOpCost(sizeof(CoeffReturnType), 0, 0, vectorized, PacketSize); + } + + EIGEN_DEVICE_FUNC CoeffReturnType* data() const { return m_result; } + + protected: + EIGEN_DEVICE_FUNC void evalTo(Scalar* data) { + TensorMap<Tensor<CoeffReturnType, NumDims, Layout, Index> > result( + data, m_dimensions); + m_op.func().eval(m_op.expression(), result, m_device); + } + + Dimensions m_dimensions; + const ArgType m_op; + const Device& m_device; + CoeffReturnType* m_result; +}; + + + +/** \class TensorCustomBinaryOp + * \ingroup CXX11_Tensor_Module + * + * \brief Tensor custom class. + * + * + */ +namespace internal { +template<typename CustomBinaryFunc, typename LhsXprType, typename RhsXprType> +struct traits<TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType> > +{ + typedef typename internal::promote_storage_type<typename LhsXprType::Scalar, + typename RhsXprType::Scalar>::ret Scalar; + typedef typename internal::promote_storage_type<typename LhsXprType::CoeffReturnType, + typename RhsXprType::CoeffReturnType>::ret CoeffReturnType; + typedef typename promote_storage_type<typename traits<LhsXprType>::StorageKind, + typename traits<RhsXprType>::StorageKind>::ret StorageKind; + typedef typename promote_index_type<typename traits<LhsXprType>::Index, + typename traits<RhsXprType>::Index>::type Index; + typedef typename LhsXprType::Nested LhsNested; + typedef typename RhsXprType::Nested RhsNested; + typedef typename remove_reference<LhsNested>::type _LhsNested; + typedef typename remove_reference<RhsNested>::type _RhsNested; + static const int NumDimensions = traits<LhsXprType>::NumDimensions; + static const int Layout = traits<LhsXprType>::Layout; +}; + +template<typename CustomBinaryFunc, typename LhsXprType, typename RhsXprType> +struct eval<TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType>, Eigen::Dense> +{ + typedef const TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType>& type; +}; + +template<typename CustomBinaryFunc, typename LhsXprType, typename RhsXprType> +struct nested<TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType> > +{ + typedef TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType> type; +}; + +} // end namespace internal + + + +template<typename CustomBinaryFunc, typename LhsXprType, typename RhsXprType> +class TensorCustomBinaryOp : public TensorBase<TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType>, ReadOnlyAccessors> +{ + public: + typedef typename internal::traits<TensorCustomBinaryOp>::Scalar Scalar; + typedef typename Eigen::NumTraits<Scalar>::Real RealScalar; + typedef typename internal::traits<TensorCustomBinaryOp>::CoeffReturnType CoeffReturnType; + typedef typename internal::nested<TensorCustomBinaryOp>::type Nested; + typedef typename internal::traits<TensorCustomBinaryOp>::StorageKind StorageKind; + typedef typename internal::traits<TensorCustomBinaryOp>::Index Index; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorCustomBinaryOp(const LhsXprType& lhs, const RhsXprType& rhs, const CustomBinaryFunc& func) + + : m_lhs_xpr(lhs), m_rhs_xpr(rhs), m_func(func) {} + + EIGEN_DEVICE_FUNC + const CustomBinaryFunc& func() const { return m_func; } + + EIGEN_DEVICE_FUNC + const typename internal::remove_all<typename LhsXprType::Nested>::type& + lhsExpression() const { return m_lhs_xpr; } + + EIGEN_DEVICE_FUNC + const typename internal::remove_all<typename RhsXprType::Nested>::type& + rhsExpression() const { return m_rhs_xpr; } + + protected: + typename LhsXprType::Nested m_lhs_xpr; + typename RhsXprType::Nested m_rhs_xpr; + const CustomBinaryFunc m_func; +}; + + +// Eval as rvalue +template<typename CustomBinaryFunc, typename LhsXprType, typename RhsXprType, typename Device> +struct TensorEvaluator<const TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType>, Device> +{ + typedef TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType> XprType; + typedef typename internal::traits<XprType>::Index Index; + static const int NumDims = internal::traits<XprType>::NumDimensions; + typedef DSizes<Index, NumDims> Dimensions; + typedef typename XprType::Scalar Scalar; + typedef typename internal::remove_const<typename XprType::CoeffReturnType>::type CoeffReturnType; + typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType; + static const int PacketSize = internal::unpacket_traits<PacketReturnType>::size; + + enum { + IsAligned = false, + PacketAccess = (internal::packet_traits<Scalar>::size > 1), + BlockAccess = false, + Layout = TensorEvaluator<LhsXprType, Device>::Layout, + CoordAccess = false, // to be implemented + RawAccess = false + }; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) + : m_op(op), m_device(device), m_result(NULL) + { + m_dimensions = op.func().dimensions(op.lhsExpression(), op.rhsExpression()); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(CoeffReturnType* data) { + if (data) { + evalTo(data); + return false; + } else { + m_result = static_cast<Scalar *>(m_device.allocate(dimensions().TotalSize() * sizeof(Scalar))); + evalTo(m_result); + return true; + } + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() { + if (m_result != NULL) { + m_device.deallocate(m_result); + m_result = NULL; + } + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const { + return m_result[index]; + } + + template<int LoadMode> + EIGEN_DEVICE_FUNC PacketReturnType packet(Index index) const { + return internal::ploadt<PacketReturnType, LoadMode>(m_result + index); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const { + // TODO(rmlarsen): Extend CustomOp API to return its cost estimate. + return TensorOpCost(sizeof(CoeffReturnType), 0, 0, vectorized, PacketSize); + } + + EIGEN_DEVICE_FUNC CoeffReturnType* data() const { return m_result; } + + protected: + EIGEN_DEVICE_FUNC void evalTo(Scalar* data) { + TensorMap<Tensor<Scalar, NumDims, Layout> > result(data, m_dimensions); + m_op.func().eval(m_op.lhsExpression(), m_op.rhsExpression(), result, m_device); + } + + Dimensions m_dimensions; + const XprType m_op; + const Device& m_device; + CoeffReturnType* m_result; +}; + + +} // end namespace Eigen + +#endif // EIGEN_CXX11_TENSOR_TENSOR_CUSTOM_OP_H diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorDevice.h b/unsupported/Eigen/CXX11/src/Tensor/TensorDevice.h new file mode 100644 index 000000000..29e50a3b2 --- /dev/null +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorDevice.h @@ -0,0 +1,68 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_CXX11_TENSOR_TENSOR_DEVICE_H +#define EIGEN_CXX11_TENSOR_TENSOR_DEVICE_H + +namespace Eigen { + +/** \class TensorDevice + * \ingroup CXX11_Tensor_Module + * + * \brief Pseudo expression providing an operator = that will evaluate its argument + * on the specified computing 'device' (GPU, thread pool, ...) + * + * Example: + * C.device(EIGEN_GPU) = A + B; + * + * Todo: operator *= and /=. + */ + +template <typename ExpressionType, typename DeviceType> class TensorDevice { + public: + TensorDevice(const DeviceType& device, ExpressionType& expression) : m_device(device), m_expression(expression) {} + + template<typename OtherDerived> + EIGEN_STRONG_INLINE TensorDevice& operator=(const OtherDerived& other) { + typedef TensorAssignOp<ExpressionType, const OtherDerived> Assign; + Assign assign(m_expression, other); + internal::TensorExecutor<const Assign, DeviceType>::run(assign, m_device); + return *this; + } + + template<typename OtherDerived> + EIGEN_STRONG_INLINE TensorDevice& operator+=(const OtherDerived& other) { + typedef typename OtherDerived::Scalar Scalar; + typedef TensorCwiseBinaryOp<internal::scalar_sum_op<Scalar>, const ExpressionType, const OtherDerived> Sum; + Sum sum(m_expression, other); + typedef TensorAssignOp<ExpressionType, const Sum> Assign; + Assign assign(m_expression, sum); + internal::TensorExecutor<const Assign, DeviceType>::run(assign, m_device); + return *this; + } + + template<typename OtherDerived> + EIGEN_STRONG_INLINE TensorDevice& operator-=(const OtherDerived& other) { + typedef typename OtherDerived::Scalar Scalar; + typedef TensorCwiseBinaryOp<internal::scalar_difference_op<Scalar>, const ExpressionType, const OtherDerived> Difference; + Difference difference(m_expression, other); + typedef TensorAssignOp<ExpressionType, const Difference> Assign; + Assign assign(m_expression, difference); + internal::TensorExecutor<const Assign, DeviceType>::run(assign, m_device); + return *this; + } + + protected: + const DeviceType& m_device; + ExpressionType& m_expression; +}; + +} // end namespace Eigen + +#endif // EIGEN_CXX11_TENSOR_TENSOR_DEVICE_H diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceCuda.h b/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceCuda.h new file mode 100644 index 000000000..4f5767bc7 --- /dev/null +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceCuda.h @@ -0,0 +1,337 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#if defined(EIGEN_USE_GPU) && !defined(EIGEN_CXX11_TENSOR_TENSOR_DEVICE_CUDA_H) +#define EIGEN_CXX11_TENSOR_TENSOR_DEVICE_CUDA_H + +namespace Eigen { + +static const int kCudaScratchSize = 1024; + +// This defines an interface that GPUDevice can take to use +// CUDA streams underneath. +class StreamInterface { + public: + virtual ~StreamInterface() {} + + virtual const cudaStream_t& stream() const = 0; + virtual const cudaDeviceProp& deviceProperties() const = 0; + + // Allocate memory on the actual device where the computation will run + virtual void* allocate(size_t num_bytes) const = 0; + virtual void deallocate(void* buffer) const = 0; + + // Return a scratchpad buffer of size 1k + virtual void* scratchpad() const = 0; + + // Return a semaphore. The semaphore is initially initialized to 0, and + // each kernel using it is responsible for resetting to 0 upon completion + // to maintain the invariant that the semaphore is always equal to 0 upon + // each kernel start. + virtual unsigned int* semaphore() const = 0; +}; + +static cudaDeviceProp* m_deviceProperties; +static bool m_devicePropInitialized = false; + +static void initializeDeviceProp() { + if (!m_devicePropInitialized) { + // Attempts to ensure proper behavior in the case of multiple threads + // calling this function simultaneously. This would be trivial to + // implement if we could use std::mutex, but unfortunately mutex don't + // compile with nvcc, so we resort to atomics and thread fences instead. + // Note that if the caller uses a compiler that doesn't support c++11 we + // can't ensure that the initialization is thread safe. +#if __cplusplus >= 201103L + static std::atomic<bool> first(true); + if (first.exchange(false)) { +#else + static bool first = true; + if (first) { + first = false; +#endif + // We're the first thread to reach this point. + int num_devices; + cudaError_t status = cudaGetDeviceCount(&num_devices); + if (status != cudaSuccess) { + std::cerr << "Failed to get the number of CUDA devices: " + << cudaGetErrorString(status) + << std::endl; + assert(status == cudaSuccess); + } + m_deviceProperties = new cudaDeviceProp[num_devices]; + for (int i = 0; i < num_devices; ++i) { + status = cudaGetDeviceProperties(&m_deviceProperties[i], i); + if (status != cudaSuccess) { + std::cerr << "Failed to initialize CUDA device #" + << i + << ": " + << cudaGetErrorString(status) + << std::endl; + assert(status == cudaSuccess); + } + } + +#if __cplusplus >= 201103L + std::atomic_thread_fence(std::memory_order_release); +#endif + m_devicePropInitialized = true; + } else { + // Wait for the other thread to inititialize the properties. + while (!m_devicePropInitialized) { +#if __cplusplus >= 201103L + std::atomic_thread_fence(std::memory_order_acquire); +#endif + sleep(1); + } + } + } +} + +static const cudaStream_t default_stream = cudaStreamDefault; + +class CudaStreamDevice : public StreamInterface { + public: + // Use the default stream on the current device + CudaStreamDevice() : stream_(&default_stream), scratch_(NULL), semaphore_(NULL) { + cudaGetDevice(&device_); + initializeDeviceProp(); + } + // Use the default stream on the specified device + CudaStreamDevice(int device) : stream_(&default_stream), device_(device), scratch_(NULL), semaphore_(NULL) { + initializeDeviceProp(); + } + // Use the specified stream. Note that it's the + // caller responsibility to ensure that the stream can run on + // the specified device. If no device is specified the code + // assumes that the stream is associated to the current gpu device. + CudaStreamDevice(const cudaStream_t* stream, int device = -1) + : stream_(stream), device_(device), scratch_(NULL), semaphore_(NULL) { + if (device < 0) { + cudaGetDevice(&device_); + } else { + int num_devices; + cudaError_t err = cudaGetDeviceCount(&num_devices); + EIGEN_UNUSED_VARIABLE(err) + assert(err == cudaSuccess); + assert(device < num_devices); + device_ = device; + } + initializeDeviceProp(); + } + + virtual ~CudaStreamDevice() { + if (scratch_) { + deallocate(scratch_); + } + } + + const cudaStream_t& stream() const { return *stream_; } + const cudaDeviceProp& deviceProperties() const { + return m_deviceProperties[device_]; + } + virtual void* allocate(size_t num_bytes) const { + cudaError_t err = cudaSetDevice(device_); + EIGEN_UNUSED_VARIABLE(err) + assert(err == cudaSuccess); + void* result; + err = cudaMalloc(&result, num_bytes); + assert(err == cudaSuccess); + assert(result != NULL); + return result; + } + virtual void deallocate(void* buffer) const { + cudaError_t err = cudaSetDevice(device_); + EIGEN_UNUSED_VARIABLE(err) + assert(err == cudaSuccess); + assert(buffer != NULL); + err = cudaFree(buffer); + assert(err == cudaSuccess); + } + + virtual void* scratchpad() const { + if (scratch_ == NULL) { + scratch_ = allocate(kCudaScratchSize + sizeof(unsigned int)); + } + return scratch_; + } + + virtual unsigned int* semaphore() const { + if (semaphore_ == NULL) { + char* scratch = static_cast<char*>(scratchpad()) + kCudaScratchSize; + semaphore_ = reinterpret_cast<unsigned int*>(scratch); + cudaError_t err = cudaMemsetAsync(semaphore_, 0, sizeof(unsigned int), *stream_); + EIGEN_UNUSED_VARIABLE(err) + assert(err == cudaSuccess); + } + return semaphore_; + } + + private: + const cudaStream_t* stream_; + int device_; + mutable void* scratch_; + mutable unsigned int* semaphore_; +}; + +struct GpuDevice { + // The StreamInterface is not owned: the caller is + // responsible for its initialization and eventual destruction. + explicit GpuDevice(const StreamInterface* stream) : stream_(stream), max_blocks_(INT_MAX) { + eigen_assert(stream); + } + explicit GpuDevice(const StreamInterface* stream, int num_blocks) : stream_(stream), max_blocks_(num_blocks) { + eigen_assert(stream); + } + // TODO(bsteiner): This is an internal API, we should not expose it. + EIGEN_STRONG_INLINE const cudaStream_t& stream() const { + return stream_->stream(); + } + + EIGEN_STRONG_INLINE void* allocate(size_t num_bytes) const { + return stream_->allocate(num_bytes); + } + + EIGEN_STRONG_INLINE void deallocate(void* buffer) const { + stream_->deallocate(buffer); + } + + EIGEN_STRONG_INLINE void* scratchpad() const { + return stream_->scratchpad(); + } + + EIGEN_STRONG_INLINE unsigned int* semaphore() const { + return stream_->semaphore(); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void memcpy(void* dst, const void* src, size_t n) const { +#ifndef __CUDA_ARCH__ + cudaError_t err = cudaMemcpyAsync(dst, src, n, cudaMemcpyDeviceToDevice, + stream_->stream()); + EIGEN_UNUSED_VARIABLE(err) + assert(err == cudaSuccess); +#else + eigen_assert(false && "The default device should be used instead to generate kernel code"); +#endif + } + + EIGEN_STRONG_INLINE void memcpyHostToDevice(void* dst, const void* src, size_t n) const { + cudaError_t err = + cudaMemcpyAsync(dst, src, n, cudaMemcpyHostToDevice, stream_->stream()); + EIGEN_UNUSED_VARIABLE(err) + assert(err == cudaSuccess); + } + + EIGEN_STRONG_INLINE void memcpyDeviceToHost(void* dst, const void* src, size_t n) const { + cudaError_t err = + cudaMemcpyAsync(dst, src, n, cudaMemcpyDeviceToHost, stream_->stream()); + EIGEN_UNUSED_VARIABLE(err) + assert(err == cudaSuccess); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void memset(void* buffer, int c, size_t n) const { +#ifndef __CUDA_ARCH__ + cudaError_t err = cudaMemsetAsync(buffer, c, n, stream_->stream()); + EIGEN_UNUSED_VARIABLE(err) + assert(err == cudaSuccess); +#else + eigen_assert(false && "The default device should be used instead to generate kernel code"); +#endif + } + + EIGEN_STRONG_INLINE size_t numThreads() const { + // FIXME + return 32; + } + + EIGEN_STRONG_INLINE size_t firstLevelCacheSize() const { + // FIXME + return 48*1024; + } + + EIGEN_STRONG_INLINE size_t lastLevelCacheSize() const { + // We won't try to take advantage of the l2 cache for the time being, and + // there is no l3 cache on cuda devices. + return firstLevelCacheSize(); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void synchronize() const { +#if defined(__CUDACC__) && !defined(__CUDA_ARCH__) + cudaError_t err = cudaStreamSynchronize(stream_->stream()); + if (err != cudaSuccess) { + std::cerr << "Error detected in CUDA stream: " + << cudaGetErrorString(err) + << std::endl; + assert(err == cudaSuccess); + } +#else + assert(false && "The default device should be used instead to generate kernel code"); +#endif + } + + EIGEN_STRONG_INLINE int getNumCudaMultiProcessors() const { + return stream_->deviceProperties().multiProcessorCount; + } + EIGEN_STRONG_INLINE int maxCudaThreadsPerBlock() const { + return stream_->deviceProperties().maxThreadsPerBlock; + } + EIGEN_STRONG_INLINE int maxCudaThreadsPerMultiProcessor() const { + return stream_->deviceProperties().maxThreadsPerMultiProcessor; + } + EIGEN_STRONG_INLINE int sharedMemPerBlock() const { + return stream_->deviceProperties().sharedMemPerBlock; + } + EIGEN_STRONG_INLINE int majorDeviceVersion() const { + return stream_->deviceProperties().major; + } + EIGEN_STRONG_INLINE int minorDeviceVersion() const { + return stream_->deviceProperties().minor; + } + + EIGEN_STRONG_INLINE int maxBlocks() const { + return max_blocks_; + } + + // This function checks if the CUDA runtime recorded an error for the + // underlying stream device. + inline bool ok() const { +#ifdef __CUDACC__ + cudaError_t error = cudaStreamQuery(stream_->stream()); + return (error == cudaSuccess) || (error == cudaErrorNotReady); +#else + return false; +#endif + } + + private: + const StreamInterface* stream_; + int max_blocks_; +}; + +#define LAUNCH_CUDA_KERNEL(kernel, gridsize, blocksize, sharedmem, device, ...) \ + (kernel) <<< (gridsize), (blocksize), (sharedmem), (device).stream() >>> (__VA_ARGS__); \ + assert(cudaGetLastError() == cudaSuccess); + + +// FIXME: Should be device and kernel specific. +#ifdef __CUDACC__ +static EIGEN_DEVICE_FUNC inline void setCudaSharedMemConfig(cudaSharedMemConfig config) { +#ifndef __CUDA_ARCH__ + cudaError_t status = cudaDeviceSetSharedMemConfig(config); + EIGEN_UNUSED_VARIABLE(status) + assert(status == cudaSuccess); +#else + EIGEN_UNUSED_VARIABLE(config) +#endif +} +#endif + +} // end namespace Eigen + +#endif // EIGEN_CXX11_TENSOR_TENSOR_DEVICE_CUDA_H diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceDefault.h b/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceDefault.h new file mode 100644 index 000000000..9d141395b --- /dev/null +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceDefault.h @@ -0,0 +1,81 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_CXX11_TENSOR_TENSOR_DEVICE_DEFAULT_H +#define EIGEN_CXX11_TENSOR_TENSOR_DEVICE_DEFAULT_H + + +namespace Eigen { + +// Default device for the machine (typically a single cpu core) +struct DefaultDevice { + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void* allocate(size_t num_bytes) const { + return internal::aligned_malloc(num_bytes); + } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void deallocate(void* buffer) const { + internal::aligned_free(buffer); + } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void memcpy(void* dst, const void* src, size_t n) const { + ::memcpy(dst, src, n); + } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void memcpyHostToDevice(void* dst, const void* src, size_t n) const { + memcpy(dst, src, n); + } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void memcpyDeviceToHost(void* dst, const void* src, size_t n) const { + memcpy(dst, src, n); + } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void memset(void* buffer, int c, size_t n) const { + ::memset(buffer, c, n); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE size_t numThreads() const { +#ifndef __CUDA_ARCH__ + // Running on the host CPU + return 1; +#else + // Running on a CUDA device + return 32; +#endif + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE size_t firstLevelCacheSize() const { +#ifndef __CUDA_ARCH__ + // Running on the host CPU + return l1CacheSize(); +#else + // Running on a CUDA device, return the amount of shared memory available. + return 48*1024; +#endif + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE size_t lastLevelCacheSize() const { +#ifndef __CUDA_ARCH__ + // Running single threaded on the host CPU + return l3CacheSize(); +#else + // Running on a CUDA device + return firstLevelCacheSize(); +#endif + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE int majorDeviceVersion() const { +#ifndef __CUDA_ARCH__ + // Running single threaded on the host CPU + // Should return an enum that encodes the ISA supported by the CPU + return 1; +#else + // Running on a CUDA device + return __CUDA_ARCH__ / 100; +#endif + } +}; + +} // namespace Eigen + +#endif // EIGEN_CXX11_TENSOR_TENSOR_DEVICE_DEFAULT_H diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceSycl.h b/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceSycl.h new file mode 100644 index 000000000..7c039890e --- /dev/null +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceSycl.h @@ -0,0 +1,122 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Mehdi Goli Codeplay Software Ltd. +// Ralph Potter Codeplay Software Ltd. +// Luke Iwanski Codeplay Software Ltd. +// Contact: <eigen@codeplay.com> +// Copyright (C) 2016 Benoit Steiner <benoit.steiner.goog@gmail.com> + +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#if defined(EIGEN_USE_SYCL) && !defined(EIGEN_CXX11_TENSOR_TENSOR_DEVICE_SYCL_H) +#define EIGEN_CXX11_TENSOR_TENSOR_DEVICE_SYCL_H + +namespace Eigen { +struct SyclDevice { + /// class members + /// sycl queue + mutable cl::sycl::queue m_queue; + /// std::map is the container used to make sure that we create only one buffer + /// per pointer. The lifespan of the buffer now depends on the lifespan of SyclDevice. + /// If a non-read-only pointer is needed to be accessed on the host we should manually deallocate it. + mutable std::map<const void *, std::shared_ptr<void>> buffer_map; + /// creating device by using selector + template<typename dev_Selector> SyclDevice(dev_Selector s) + : +#ifdef EIGEN_EXCEPTIONS + m_queue(cl::sycl::queue(s, [=](cl::sycl::exception_list l) { + for (const auto& e : l) { + try { + std::rethrow_exception(e); + } catch (cl::sycl::exception e) { + std::cout << e.what() << std::endl; + } + } + })) +#else + m_queue(cl::sycl::queue(s)) +#endif + {} + // destructor + ~SyclDevice() { deallocate_all(); } + + template <typename T> void deallocate(T *p) const { + auto it = buffer_map.find(p); + if (it != buffer_map.end()) { + buffer_map.erase(it); + internal::aligned_free(p); + } + } + void deallocate_all() const { + std::map<const void *, std::shared_ptr<void>>::iterator it=buffer_map.begin(); + while (it!=buffer_map.end()) { + auto p=it->first; + buffer_map.erase(it); + internal::aligned_free(const_cast<void*>(p)); + it=buffer_map.begin(); + } + buffer_map.clear(); + } + + /// creation of sycl accessor for a buffer. This function first tries to find + /// the buffer in the buffer_map. If found it gets the accessor from it, if not, + ///the function then adds an entry by creating a sycl buffer for that particular pointer. + template <cl::sycl::access::mode AcMd, typename T> inline cl::sycl::accessor<T, 1, AcMd, cl::sycl::access::target::global_buffer> + get_sycl_accessor(size_t num_bytes, cl::sycl::handler &cgh, const T * ptr) const { + return (get_sycl_buffer<T>(num_bytes, ptr)->template get_access<AcMd, cl::sycl::access::target::global_buffer>(cgh)); + } + + template<typename T> inline std::pair<std::map<const void *, std::shared_ptr<void>>::iterator,bool> add_sycl_buffer(const T *ptr, size_t num_bytes) const { + using Type = cl::sycl::buffer<T, 1>; + std::pair<std::map<const void *, std::shared_ptr<void>>::iterator,bool> ret = buffer_map.insert(std::pair<const void *, std::shared_ptr<void>>(ptr, std::shared_ptr<void>(new Type(cl::sycl::range<1>(num_bytes)), + [](void *dataMem) { delete static_cast<Type*>(dataMem); }))); + (static_cast<Type*>(buffer_map.at(ptr).get()))->set_final_data(nullptr); + return ret; + } + + template <typename T> inline cl::sycl::buffer<T, 1>* get_sycl_buffer(size_t num_bytes,const T * ptr) const { + return static_cast<cl::sycl::buffer<T, 1>*>(add_sycl_buffer(ptr, num_bytes).first->second.get()); + } + + /// allocating memory on the cpu + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void *allocate(size_t) const { + return internal::aligned_malloc(8); + } + + // some runtime conditions that can be applied here + bool isDeviceSuitable() const { return true; } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void memcpy(void *dst, const void *src, size_t n) const { + ::memcpy(dst, src, n); + } + + template<typename T> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void memcpyHostToDevice(T *dst, const T *src, size_t n) const { + auto host_acc= (static_cast<cl::sycl::buffer<T, 1>*>(add_sycl_buffer(dst, n).first->second.get()))-> template get_access<cl::sycl::access::mode::discard_write, cl::sycl::access::target::host_buffer>(); + memcpy(host_acc.get_pointer(), src, n); + } + /// whith the current implementation of sycl, the data is copied twice from device to host. This will be fixed soon. + template<typename T> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void memcpyDeviceToHost(T *dst, const T *src, size_t n) const { + auto it = buffer_map.find(src); + if (it != buffer_map.end()) { + auto host_acc= (static_cast<cl::sycl::buffer<T, 1>*>(it->second.get()))-> template get_access<cl::sycl::access::mode::read, cl::sycl::access::target::host_buffer>(); + memcpy(dst,host_acc.get_pointer(), n); + } else{ + eigen_assert("no device memory found. The memory might be destroyed before creation"); + } + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void memset(void *buffer, int c, size_t n) const { + ::memset(buffer, c, n); + } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE int majorDeviceVersion() const { + return 1; + } +}; + +} // end namespace Eigen + +#endif // EIGEN_CXX11_TENSOR_TENSOR_DEVICE_SYCL_H diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceThreadPool.h b/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceThreadPool.h new file mode 100644 index 000000000..069680a11 --- /dev/null +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceThreadPool.h @@ -0,0 +1,279 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#if defined(EIGEN_USE_THREADS) && !defined(EIGEN_CXX11_TENSOR_TENSOR_DEVICE_THREAD_POOL_H) +#define EIGEN_CXX11_TENSOR_TENSOR_DEVICE_THREAD_POOL_H + +namespace Eigen { + +// Use the SimpleThreadPool by default. We'll switch to the new non blocking +// thread pool later. +#ifndef EIGEN_USE_SIMPLE_THREAD_POOL +template <typename Env> using ThreadPoolTempl = NonBlockingThreadPoolTempl<Env>; +typedef NonBlockingThreadPool ThreadPool; +#else +template <typename Env> using ThreadPoolTempl = SimpleThreadPoolTempl<Env>; +typedef SimpleThreadPool ThreadPool; +#endif + + +// Barrier is an object that allows one or more threads to wait until +// Notify has been called a specified number of times. +class Barrier { + public: + Barrier(unsigned int count) : state_(count << 1), notified_(false) { + eigen_assert(((count << 1) >> 1) == count); + } + ~Barrier() { + eigen_assert((state_>>1) == 0); + } + + void Notify() { + unsigned int v = state_.fetch_sub(2, std::memory_order_acq_rel) - 2; + if (v != 1) { + eigen_assert(((v + 2) & ~1) != 0); + return; // either count has not dropped to 0, or waiter is not waiting + } + std::unique_lock<std::mutex> l(mu_); + eigen_assert(!notified_); + notified_ = true; + cv_.notify_all(); + } + + void Wait() { + unsigned int v = state_.fetch_or(1, std::memory_order_acq_rel); + if ((v >> 1) == 0) return; + std::unique_lock<std::mutex> l(mu_); + while (!notified_) { + cv_.wait(l); + } + } + + private: + std::mutex mu_; + std::condition_variable cv_; + std::atomic<unsigned int> state_; // low bit is waiter flag + bool notified_; +}; + + +// Notification is an object that allows a user to to wait for another +// thread to signal a notification that an event has occurred. +// +// Multiple threads can wait on the same Notification object, +// but only one caller must call Notify() on the object. +struct Notification : Barrier { + Notification() : Barrier(1) {}; +}; + + +// Runs an arbitrary function and then calls Notify() on the passed in +// Notification. +template <typename Function, typename... Args> struct FunctionWrapperWithNotification +{ + static void run(Notification* n, Function f, Args... args) { + f(args...); + if (n) { + n->Notify(); + } + } +}; + +template <typename Function, typename... Args> struct FunctionWrapperWithBarrier +{ + static void run(Barrier* b, Function f, Args... args) { + f(args...); + if (b) { + b->Notify(); + } + } +}; + +template <typename SyncType> +static EIGEN_STRONG_INLINE void wait_until_ready(SyncType* n) { + if (n) { + n->Wait(); + } +} + + +// Build a thread pool device on top the an existing pool of threads. +struct ThreadPoolDevice { + // The ownership of the thread pool remains with the caller. + ThreadPoolDevice(ThreadPoolInterface* pool, int num_cores) : pool_(pool), num_threads_(num_cores) { } + + EIGEN_STRONG_INLINE void* allocate(size_t num_bytes) const { + return internal::aligned_malloc(num_bytes); + } + + EIGEN_STRONG_INLINE void deallocate(void* buffer) const { + internal::aligned_free(buffer); + } + + EIGEN_STRONG_INLINE void memcpy(void* dst, const void* src, size_t n) const { + ::memcpy(dst, src, n); + } + EIGEN_STRONG_INLINE void memcpyHostToDevice(void* dst, const void* src, size_t n) const { + memcpy(dst, src, n); + } + EIGEN_STRONG_INLINE void memcpyDeviceToHost(void* dst, const void* src, size_t n) const { + memcpy(dst, src, n); + } + + EIGEN_STRONG_INLINE void memset(void* buffer, int c, size_t n) const { + ::memset(buffer, c, n); + } + + EIGEN_STRONG_INLINE int numThreads() const { + return num_threads_; + } + + EIGEN_STRONG_INLINE size_t firstLevelCacheSize() const { + return l1CacheSize(); + } + + EIGEN_STRONG_INLINE size_t lastLevelCacheSize() const { + // The l3 cache size is shared between all the cores. + return l3CacheSize() / num_threads_; + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE int majorDeviceVersion() const { + // Should return an enum that encodes the ISA supported by the CPU + return 1; + } + + template <class Function, class... Args> + EIGEN_STRONG_INLINE Notification* enqueue(Function&& f, Args&&... args) const { + Notification* n = new Notification(); + pool_->Schedule(std::bind(&FunctionWrapperWithNotification<Function, Args...>::run, n, f, args...)); + return n; + } + + template <class Function, class... Args> + EIGEN_STRONG_INLINE void enqueue_with_barrier(Barrier* b, + Function&& f, + Args&&... args) const { + pool_->Schedule(std::bind( + &FunctionWrapperWithBarrier<Function, Args...>::run, b, f, args...)); + } + + template <class Function, class... Args> + EIGEN_STRONG_INLINE void enqueueNoNotification(Function&& f, Args&&... args) const { + pool_->Schedule(std::bind(f, args...)); + } + + // Returns a logical thread index between 0 and pool_->NumThreads() - 1 if + // called from one of the threads in pool_. Returns -1 otherwise. + EIGEN_STRONG_INLINE int currentThreadId() const { + return pool_->CurrentThreadId(); + } + + // parallelFor executes f with [0, n) arguments in parallel and waits for + // completion. F accepts a half-open interval [first, last). + // Block size is choosen based on the iteration cost and resulting parallel + // efficiency. If block_align is not nullptr, it is called to round up the + // block size. + void parallelFor(Index n, const TensorOpCost& cost, + std::function<Index(Index)> block_align, + std::function<void(Index, Index)> f) const { + typedef TensorCostModel<ThreadPoolDevice> CostModel; + if (n <= 1 || numThreads() == 1 || + CostModel::numThreads(n, cost, static_cast<int>(numThreads())) == 1) { + f(0, n); + return; + } + + // Calculate block size based on (1) the iteration cost and (2) parallel + // efficiency. We want blocks to be not too small to mitigate + // parallelization overheads; not too large to mitigate tail + // effect and potential load imbalance and we also want number + // of blocks to be evenly dividable across threads. + + double block_size_f = 1.0 / CostModel::taskSize(1, cost); + Index block_size = numext::mini(n, numext::maxi<Index>(1, block_size_f)); + const Index max_block_size = + numext::mini(n, numext::maxi<Index>(1, 2 * block_size_f)); + if (block_align) { + Index new_block_size = block_align(block_size); + eigen_assert(new_block_size >= block_size); + block_size = numext::mini(n, new_block_size); + } + Index block_count = divup(n, block_size); + // Calculate parallel efficiency as fraction of total CPU time used for + // computations: + double max_efficiency = + static_cast<double>(block_count) / + (divup<int>(block_count, numThreads()) * numThreads()); + // Now try to increase block size up to max_block_size as long as it + // doesn't decrease parallel efficiency. + for (Index prev_block_count = block_count; prev_block_count > 1;) { + // This is the next block size that divides size into a smaller number + // of blocks than the current block_size. + Index coarser_block_size = divup(n, prev_block_count - 1); + if (block_align) { + Index new_block_size = block_align(coarser_block_size); + eigen_assert(new_block_size >= coarser_block_size); + coarser_block_size = numext::mini(n, new_block_size); + } + if (coarser_block_size > max_block_size) { + break; // Reached max block size. Stop. + } + // Recalculate parallel efficiency. + const Index coarser_block_count = divup(n, coarser_block_size); + eigen_assert(coarser_block_count < prev_block_count); + prev_block_count = coarser_block_count; + const double coarser_efficiency = + static_cast<double>(coarser_block_count) / + (divup<int>(coarser_block_count, numThreads()) * numThreads()); + if (coarser_efficiency + 0.01 >= max_efficiency) { + // Taking it. + block_size = coarser_block_size; + block_count = coarser_block_count; + if (max_efficiency < coarser_efficiency) { + max_efficiency = coarser_efficiency; + } + } + } + + // Recursively divide size into halves until we reach block_size. + // Division code rounds mid to block_size, so we are guaranteed to get + // block_count leaves that do actual computations. + Barrier barrier(static_cast<unsigned int>(block_count)); + std::function<void(Index, Index)> handleRange; + handleRange = [=, &handleRange, &barrier, &f](Index first, Index last) { + if (last - first <= block_size) { + // Single block or less, execute directly. + f(first, last); + barrier.Notify(); + return; + } + // Split into halves and submit to the pool. + Index mid = first + divup((last - first) / 2, block_size) * block_size; + pool_->Schedule([=, &handleRange]() { handleRange(mid, last); }); + pool_->Schedule([=, &handleRange]() { handleRange(first, mid); }); + }; + handleRange(0, n); + barrier.Wait(); + } + + // Convenience wrapper for parallelFor that does not align blocks. + void parallelFor(Index n, const TensorOpCost& cost, + std::function<void(Index, Index)> f) const { + parallelFor(n, cost, nullptr, std::move(f)); + } + + private: + ThreadPoolInterface* pool_; + int num_threads_; +}; + + +} // end namespace Eigen + +#endif // EIGEN_CXX11_TENSOR_TENSOR_DEVICE_THREAD_POOL_H diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorDimensionList.h b/unsupported/Eigen/CXX11/src/Tensor/TensorDimensionList.h new file mode 100644 index 000000000..1a30e45fb --- /dev/null +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorDimensionList.h @@ -0,0 +1,236 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2015 Benoit Steiner <benoit.steiner.goog@gmail.com> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_CXX11_TENSOR_TENSOR_DIMENSION_LIST_H +#define EIGEN_CXX11_TENSOR_TENSOR_DIMENSION_LIST_H + +namespace Eigen { + +/** \internal + * + * \class TensorDimensionList + * \ingroup CXX11_Tensor_Module + * + * \brief Special case of tensor index list used to list all the dimensions of a tensor of rank n. + * + * \sa Tensor + */ + +template <typename Index, std::size_t Rank> struct DimensionList { + EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE + const Index operator[] (const Index i) const { return i; } +}; + +namespace internal { + +template<typename Index, std::size_t Rank> struct array_size<DimensionList<Index, Rank> > { + static const size_t value = Rank; +}; +template<typename Index, std::size_t Rank> struct array_size<const DimensionList<Index, Rank> > { + static const size_t value = Rank; +}; + +template<DenseIndex n, typename Index, std::size_t Rank> const Index array_get(DimensionList<Index, Rank>&) { + return n; +} +template<DenseIndex n, typename Index, std::size_t Rank> const Index array_get(const DimensionList<Index, Rank>&) { + return n; +} + + +#if EIGEN_HAS_CONSTEXPR +template <typename Index, std::size_t Rank> +struct index_known_statically_impl<DimensionList<Index, Rank> > { + EIGEN_DEVICE_FUNC static constexpr bool run(const DenseIndex) { + return true; + } +}; +template <typename Index, std::size_t Rank> +struct index_known_statically_impl<const DimensionList<Index, Rank> > { + EIGEN_DEVICE_FUNC static constexpr bool run(const DenseIndex) { + return true; + } +}; + +template <typename Index, std::size_t Rank> +struct all_indices_known_statically_impl<DimensionList<Index, Rank> > { + EIGEN_DEVICE_FUNC static constexpr bool run() { + return true; + } +}; +template <typename Index, std::size_t Rank> +struct all_indices_known_statically_impl<const DimensionList<Index, Rank> > { + EIGEN_DEVICE_FUNC static constexpr bool run() { + return true; + } +}; + +template <typename Index, std::size_t Rank> +struct indices_statically_known_to_increase_impl<DimensionList<Index, Rank> > { + EIGEN_DEVICE_FUNC static constexpr bool run() { + return true; + } +}; +template <typename Index, std::size_t Rank> +struct indices_statically_known_to_increase_impl<const DimensionList<Index, Rank> > { + EIGEN_DEVICE_FUNC static constexpr bool run() { + return true; + } +}; + +template <typename Index, std::size_t Rank> +struct index_statically_eq_impl<DimensionList<Index, Rank> > { + static constexpr bool run(const DenseIndex i, const DenseIndex value) { + return i == value; + } +}; +template <typename Index, std::size_t Rank> +struct index_statically_eq_impl<const DimensionList<Index, Rank> > { + EIGEN_DEVICE_FUNC static constexpr bool run(const DenseIndex i, const DenseIndex value) { + return i == value; + } +}; + +template <typename Index, std::size_t Rank> +struct index_statically_ne_impl<DimensionList<Index, Rank> > { + EIGEN_DEVICE_FUNC static constexpr bool run(const DenseIndex i, const DenseIndex value) { + return i != value; + } +}; +template <typename Index, std::size_t Rank> +struct index_statically_ne_impl<const DimensionList<Index, Rank> > { + static constexpr bool run(const DenseIndex i, const DenseIndex value) { + return i != value; + } +}; + +template <typename Index, std::size_t Rank> +struct index_statically_gt_impl<DimensionList<Index, Rank> > { + EIGEN_DEVICE_FUNC static constexpr bool run(const DenseIndex i, const DenseIndex value) { + return i > value; + } +}; +template <typename Index, std::size_t Rank> +struct index_statically_gt_impl<const DimensionList<Index, Rank> > { + EIGEN_DEVICE_FUNC static constexpr bool run(const DenseIndex i, const DenseIndex value) { + return i > value; + } +}; + +template <typename Index, std::size_t Rank> +struct index_statically_lt_impl<DimensionList<Index, Rank> > { + EIGEN_DEVICE_FUNC static constexpr bool run(const DenseIndex i, const DenseIndex value) { + return i < value; + } +}; +template <typename Index, std::size_t Rank> +struct index_statically_lt_impl<const DimensionList<Index, Rank> > { + EIGEN_DEVICE_FUNC static constexpr bool run(const DenseIndex i, const DenseIndex value) { + return i < value; + } +}; + +#else +template <typename Index, std::size_t Rank> +struct index_known_statically_impl<DimensionList<Index, Rank> > { + EIGEN_DEVICE_FUNC static EIGEN_ALWAYS_INLINE bool run(const DenseIndex) { + return true; + } +}; +template <typename Index, std::size_t Rank> +struct index_known_statically_impl<const DimensionList<Index, Rank> > { + EIGEN_DEVICE_FUNC static EIGEN_ALWAYS_INLINE bool run(const DenseIndex) { + return true; + } +}; + +template <typename Index, std::size_t Rank> +struct all_indices_known_statically_impl<DimensionList<Index, Rank> > { + EIGEN_DEVICE_FUNC static EIGEN_ALWAYS_INLINE bool run() { + return true; + } +}; +template <typename Index, std::size_t Rank> +struct all_indices_known_statically_impl<const DimensionList<Index, Rank> > { + EIGEN_DEVICE_FUNC static EIGEN_ALWAYS_INLINE bool run() { + return true; + } +}; + +template <typename Index, std::size_t Rank> +struct indices_statically_known_to_increase_impl<DimensionList<Index, Rank> > { + static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool run() { + return true; + } +}; +template <typename Index, std::size_t Rank> +struct indices_statically_known_to_increase_impl<const DimensionList<Index, Rank> > { + static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool run() { + return true; + } +}; + +template <typename Index, std::size_t Rank> +struct index_statically_eq_impl<DimensionList<Index, Rank> > { + static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool run(const DenseIndex, const DenseIndex) { + return false; + } +}; +template <typename Index, std::size_t Rank> +struct index_statically_eq_impl<const DimensionList<Index, Rank> > { + static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool run(const DenseIndex, const DenseIndex) { + return false; + } +}; + +template <typename Index, std::size_t Rank> +struct index_statically_ne_impl<DimensionList<Index, Rank> > { + static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool run(const DenseIndex, const DenseIndex){ + return false; + } +}; +template <typename Index, std::size_t Rank> +struct index_statically_ne_impl<const DimensionList<Index, Rank> > { + static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool run(const DenseIndex, const DenseIndex) { + return false; + } +}; + +template <typename Index, std::size_t Rank> +struct index_statically_gt_impl<DimensionList<Index, Rank> > { + static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool run(const DenseIndex, const DenseIndex) { + return false; + } +}; +template <typename Index, std::size_t Rank> +struct index_statically_gt_impl<const DimensionList<Index, Rank> > { + static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool run(const DenseIndex, const DenseIndex) { + return false; + } +}; + +template <typename Index, std::size_t Rank> +struct index_statically_lt_impl<DimensionList<Index, Rank> > { + static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool run(const DenseIndex, const DenseIndex) { + return false; + } +}; +template <typename Index, std::size_t Rank> +struct index_statically_lt_impl<const DimensionList<Index, Rank> > { + static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool run(const DenseIndex, const DenseIndex) { + return false; + } +}; +#endif + +} // end namespace internal +} // end namespace Eigen + + +#endif // EIGEN_CXX11_TENSOR_TENSOR_DIMENSION_LIST_H diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorDimensions.h b/unsupported/Eigen/CXX11/src/Tensor/TensorDimensions.h new file mode 100644 index 000000000..b24cdebf1 --- /dev/null +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorDimensions.h @@ -0,0 +1,428 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_CXX11_TENSOR_TENSOR_DIMENSIONS_H +#define EIGEN_CXX11_TENSOR_TENSOR_DIMENSIONS_H + + +namespace Eigen { + +/** \internal + * + * \class TensorDimensions + * \ingroup CXX11_Tensor_Module + * + * \brief Set of classes used to encode and store the dimensions of a Tensor. + * + * The Sizes class encodes as part of the type the number of dimensions and the + * sizes corresponding to each dimension. It uses no storage space since it is + * entirely known at compile time. + * The DSizes class is its dynamic sibling: the number of dimensions is known + * at compile time but the sizes are set during execution. + * + * \sa Tensor + */ + +// Boilerplate code +namespace internal { + +template<std::size_t n, typename Dimension> struct dget { + static const std::size_t value = get<n, Dimension>::value; +}; + + +template<typename Index, std::size_t NumIndices, std::size_t n, bool RowMajor> +struct fixed_size_tensor_index_linearization_helper +{ + template <typename Dimensions> EIGEN_DEVICE_FUNC + static inline Index run(array<Index, NumIndices> const& indices, + const Dimensions& dimensions) + { + return array_get<RowMajor ? n - 1 : (NumIndices - n)>(indices) + + dget<RowMajor ? n - 1 : (NumIndices - n), Dimensions>::value * + fixed_size_tensor_index_linearization_helper<Index, NumIndices, n - 1, RowMajor>::run(indices, dimensions); + } +}; + +template<typename Index, std::size_t NumIndices, bool RowMajor> +struct fixed_size_tensor_index_linearization_helper<Index, NumIndices, 0, RowMajor> +{ + template <typename Dimensions> EIGEN_DEVICE_FUNC + static inline Index run(array<Index, NumIndices> const&, const Dimensions&) + { + return 0; + } +}; + +template<typename Index, std::size_t n> +struct fixed_size_tensor_index_extraction_helper +{ + template <typename Dimensions> EIGEN_DEVICE_FUNC + static inline Index run(const Index index, + const Dimensions& dimensions) + { + const Index mult = (index == n-1) ? 1 : 0; + return array_get<n-1>(dimensions) * mult + + fixed_size_tensor_index_extraction_helper<Index, n - 1>::run(index, dimensions); + } +}; + +template<typename Index> +struct fixed_size_tensor_index_extraction_helper<Index, 0> +{ + template <typename Dimensions> EIGEN_DEVICE_FUNC + static inline Index run(const Index, + const Dimensions&) + { + return 0; + } + }; + +} // end namespace internal + + +// Fixed size +#ifndef EIGEN_EMULATE_CXX11_META_H +template <typename std::ptrdiff_t... Indices> +struct Sizes : internal::numeric_list<std::ptrdiff_t, Indices...> { + typedef internal::numeric_list<std::ptrdiff_t, Indices...> Base; + static const std::ptrdiff_t total_size = internal::arg_prod(Indices...); + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::ptrdiff_t rank() const { + return Base::count; + } + + static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::ptrdiff_t TotalSize() { + return internal::arg_prod(Indices...); + } + + EIGEN_DEVICE_FUNC Sizes() { } + template <typename DenseIndex> + explicit EIGEN_DEVICE_FUNC Sizes(const array<DenseIndex, Base::count>& /*indices*/) { + // todo: add assertion + } +#if EIGEN_HAS_VARIADIC_TEMPLATES + template <typename... DenseIndex> EIGEN_DEVICE_FUNC Sizes(DenseIndex...) { } + explicit EIGEN_DEVICE_FUNC Sizes(std::initializer_list<std::ptrdiff_t> /*l*/) { + // todo: add assertion + } +#endif + + template <typename T> Sizes& operator = (const T& /*other*/) { + // add assertion failure if the size of other is different + return *this; + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::ptrdiff_t operator[] (const std::size_t index) const { + return internal::fixed_size_tensor_index_extraction_helper<std::ptrdiff_t, Base::count>::run(index, *this); + } + + template <typename DenseIndex> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + size_t IndexOfColMajor(const array<DenseIndex, Base::count>& indices) const { + return internal::fixed_size_tensor_index_linearization_helper<DenseIndex, Base::count, Base::count, false>::run(indices, *static_cast<const Base*>(this)); + } + template <typename DenseIndex> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + size_t IndexOfRowMajor(const array<DenseIndex, Base::count>& indices) const { + return internal::fixed_size_tensor_index_linearization_helper<DenseIndex, Base::count, Base::count, true>::run(indices, *static_cast<const Base*>(this)); + } +}; + +namespace internal { +template <typename std::ptrdiff_t... Indices> +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::ptrdiff_t array_prod(const Sizes<Indices...>&) { + return Sizes<Indices...>::total_size; +} +} + +#else + +template <std::size_t n> +struct non_zero_size { + typedef internal::type2val<std::size_t, n> type; +}; +template <> +struct non_zero_size<0> { + typedef internal::null_type type; +}; + +template <std::size_t V1=0, std::size_t V2=0, std::size_t V3=0, std::size_t V4=0, std::size_t V5=0> struct Sizes { + typedef typename internal::make_type_list<typename non_zero_size<V1>::type, typename non_zero_size<V2>::type, typename non_zero_size<V3>::type, typename non_zero_size<V4>::type, typename non_zero_size<V5>::type >::type Base; + static const size_t count = Base::count; + static const std::size_t total_size = internal::arg_prod<Base>::value; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE size_t rank() const { + return count; + } + + static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE size_t TotalSize() { + return internal::arg_prod<Base>::value; + } + + Sizes() { } + template <typename DenseIndex> + explicit Sizes(const array<DenseIndex, Base::count>& /*indices*/) { + // todo: add assertion + } + template <typename T> Sizes& operator = (const T& /*other*/) { + // add assertion failure if the size of other is different + return *this; + } + +#if EIGEN_HAS_VARIADIC_TEMPLATES + template <typename... DenseIndex> Sizes(DenseIndex... /*indices*/) { } + explicit Sizes(std::initializer_list<std::size_t>) { + // todo: add assertion + } +#else + EIGEN_DEVICE_FUNC explicit Sizes(const DenseIndex) { + } + EIGEN_DEVICE_FUNC Sizes(const DenseIndex, const DenseIndex) { + } + EIGEN_DEVICE_FUNC Sizes(const DenseIndex, const DenseIndex, const DenseIndex) { + } + EIGEN_DEVICE_FUNC Sizes(const DenseIndex, const DenseIndex, const DenseIndex, const DenseIndex) { + } + EIGEN_DEVICE_FUNC Sizes(const DenseIndex, const DenseIndex, const DenseIndex, const DenseIndex, const DenseIndex) { + } +#endif + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DenseIndex operator[] (const int index) const { + switch (index) { + case 0: + return internal::get<0, Base>::value; + case 1: + return internal::get<1, Base>::value; + case 2: + return internal::get<2, Base>::value; + case 3: + return internal::get<3, Base>::value; + case 4: + return internal::get<4, Base>::value; + default: + eigen_assert(false && "index overflow"); + return static_cast<DenseIndex>(-1); + } + } + + template <typename DenseIndex> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + size_t IndexOfColMajor(const array<DenseIndex, Base::count>& indices) const { + return internal::fixed_size_tensor_index_linearization_helper<DenseIndex, Base::count, Base::count, false>::run(indices, *reinterpret_cast<const Base*>(this)); + } + template <typename DenseIndex> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + size_t IndexOfRowMajor(const array<DenseIndex, Base::count>& indices) const { + return internal::fixed_size_tensor_index_linearization_helper<DenseIndex, Base::count, Base::count, true>::run(indices, *reinterpret_cast<const Base*>(this)); + } +}; + +namespace internal { +template <std::size_t V1, std::size_t V2, std::size_t V3, std::size_t V4, std::size_t V5> +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::size_t array_prod(const Sizes<V1, V2, V3, V4, V5>&) { + return Sizes<V1, V2, V3, V4, V5>::total_size; +} +} + +#endif + +// Boilerplate +namespace internal { +template<typename Index, std::size_t NumIndices, std::size_t n, bool RowMajor> +struct tensor_index_linearization_helper +{ + static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + Index run(array<Index, NumIndices> const& indices, array<Index, NumIndices> const& dimensions) + { + return array_get<RowMajor ? n : (NumIndices - n - 1)>(indices) + + array_get<RowMajor ? n : (NumIndices - n - 1)>(dimensions) * + tensor_index_linearization_helper<Index, NumIndices, n - 1, RowMajor>::run(indices, dimensions); + } +}; + +template<typename Index, std::size_t NumIndices, bool RowMajor> +struct tensor_index_linearization_helper<Index, NumIndices, 0, RowMajor> +{ + static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + Index run(array<Index, NumIndices> const& indices, array<Index, NumIndices> const&) + { + return array_get<RowMajor ? 0 : NumIndices - 1>(indices); + } +}; +} // end namespace internal + + + +// Dynamic size +template <typename DenseIndex, int NumDims> +struct DSizes : array<DenseIndex, NumDims> { + typedef array<DenseIndex, NumDims> Base; + static const int count = NumDims; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE size_t rank() const { + return NumDims; + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DenseIndex TotalSize() const { + return (NumDims == 0) ? 1 : internal::array_prod(*static_cast<const Base*>(this)); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DSizes() { + for (int i = 0 ; i < NumDims; ++i) { + (*this)[i] = 0; + } + } + EIGEN_DEVICE_FUNC explicit DSizes(const array<DenseIndex, NumDims>& a) : Base(a) { } + + EIGEN_DEVICE_FUNC explicit DSizes(const DenseIndex i0) { + eigen_assert(NumDims == 1); + (*this)[0] = i0; + } + +#if EIGEN_HAS_VARIADIC_TEMPLATES + template<typename... IndexTypes> EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE explicit DSizes(DenseIndex firstDimension, DenseIndex secondDimension, IndexTypes... otherDimensions) : Base({{firstDimension, secondDimension, otherDimensions...}}) { + EIGEN_STATIC_ASSERT(sizeof...(otherDimensions) + 2 == NumDims, YOU_MADE_A_PROGRAMMING_MISTAKE) + } +#else + EIGEN_DEVICE_FUNC DSizes(const DenseIndex i0, const DenseIndex i1) { + eigen_assert(NumDims == 2); + (*this)[0] = i0; + (*this)[1] = i1; + } + EIGEN_DEVICE_FUNC DSizes(const DenseIndex i0, const DenseIndex i1, const DenseIndex i2) { + eigen_assert(NumDims == 3); + (*this)[0] = i0; + (*this)[1] = i1; + (*this)[2] = i2; + } + EIGEN_DEVICE_FUNC DSizes(const DenseIndex i0, const DenseIndex i1, const DenseIndex i2, const DenseIndex i3) { + eigen_assert(NumDims == 4); + (*this)[0] = i0; + (*this)[1] = i1; + (*this)[2] = i2; + (*this)[3] = i3; + } + EIGEN_DEVICE_FUNC DSizes(const DenseIndex i0, const DenseIndex i1, const DenseIndex i2, const DenseIndex i3, const DenseIndex i4) { + eigen_assert(NumDims == 5); + (*this)[0] = i0; + (*this)[1] = i1; + (*this)[2] = i2; + (*this)[3] = i3; + (*this)[4] = i4; + } +#endif + + EIGEN_DEVICE_FUNC DSizes& operator = (const array<DenseIndex, NumDims>& other) { + *static_cast<Base*>(this) = other; + return *this; + } + + // A constexpr would be so much better here + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DenseIndex IndexOfColMajor(const array<DenseIndex, NumDims>& indices) const { + return internal::tensor_index_linearization_helper<DenseIndex, NumDims, NumDims - 1, false>::run(indices, *static_cast<const Base*>(this)); + } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DenseIndex IndexOfRowMajor(const array<DenseIndex, NumDims>& indices) const { + return internal::tensor_index_linearization_helper<DenseIndex, NumDims, NumDims - 1, true>::run(indices, *static_cast<const Base*>(this)); + } +}; + + + + +// Boilerplate +namespace internal { +template<typename Index, std::size_t NumIndices, std::size_t n, bool RowMajor> +struct tensor_vsize_index_linearization_helper +{ + static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + Index run(array<Index, NumIndices> const& indices, std::vector<DenseIndex> const& dimensions) + { + return array_get<RowMajor ? n : (NumIndices - n - 1)>(indices) + + array_get<RowMajor ? n : (NumIndices - n - 1)>(dimensions) * + tensor_vsize_index_linearization_helper<Index, NumIndices, n - 1, RowMajor>::run(indices, dimensions); + } +}; + +template<typename Index, std::size_t NumIndices, bool RowMajor> +struct tensor_vsize_index_linearization_helper<Index, NumIndices, 0, RowMajor> +{ + static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + Index run(array<Index, NumIndices> const& indices, std::vector<DenseIndex> const&) + { + return array_get<RowMajor ? 0 : NumIndices - 1>(indices); + } +}; +} // end namespace internal + + +namespace internal { + +template <typename DenseIndex, int NumDims> struct array_size<const DSizes<DenseIndex, NumDims> > { + static const size_t value = NumDims; +}; +template <typename DenseIndex, int NumDims> struct array_size<DSizes<DenseIndex, NumDims> > { + static const size_t value = NumDims; +}; +#ifndef EIGEN_EMULATE_CXX11_META_H +template <typename std::ptrdiff_t... Indices> struct array_size<const Sizes<Indices...> > { +static const std::ptrdiff_t value = Sizes<Indices...>::count; +}; +template <typename std::ptrdiff_t... Indices> struct array_size<Sizes<Indices...> > { +static const std::ptrdiff_t value = Sizes<Indices...>::count; +}; +template <std::ptrdiff_t n, typename std::ptrdiff_t... Indices> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::ptrdiff_t array_get(const Sizes<Indices...>&) { + return get<n, internal::numeric_list<std::size_t, Indices...> >::value; +} +template <std::ptrdiff_t n> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::ptrdiff_t array_get(const Sizes<>&) { + eigen_assert(false && "should never be called"); + return -1; +} +#else +template <std::size_t V1, std::size_t V2, std::size_t V3, std::size_t V4, std::size_t V5> struct array_size<const Sizes<V1,V2,V3,V4,V5> > { + static const size_t value = Sizes<V1,V2,V3,V4,V5>::count; +}; +template <std::size_t V1, std::size_t V2, std::size_t V3, std::size_t V4, std::size_t V5> struct array_size<Sizes<V1,V2,V3,V4,V5> > { + static const size_t value = Sizes<V1,V2,V3,V4,V5>::count; +}; +template <std::size_t n, std::size_t V1, std::size_t V2, std::size_t V3, std::size_t V4, std::size_t V5> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::size_t array_get(const Sizes<V1,V2,V3,V4,V5>&) { + return get<n, typename Sizes<V1,V2,V3,V4,V5>::Base>::value; +} + +#endif + + +template <typename Dims1, typename Dims2, size_t n, size_t m> +struct sizes_match_below_dim { + static EIGEN_DEVICE_FUNC inline bool run(Dims1&, Dims2&) { + return false; + } +}; +template <typename Dims1, typename Dims2, size_t n> +struct sizes_match_below_dim<Dims1, Dims2, n, n> { + static EIGEN_DEVICE_FUNC inline bool run(Dims1& dims1, Dims2& dims2) { + return (array_get<n-1>(dims1) == array_get<n-1>(dims2)) & + sizes_match_below_dim<Dims1, Dims2, n-1, n-1>::run(dims1, dims2); + } +}; +template <typename Dims1, typename Dims2> +struct sizes_match_below_dim<Dims1, Dims2, 0, 0> { + static EIGEN_DEVICE_FUNC inline bool run(Dims1&, Dims2&) { + return true; + } +}; + +} // end namespace internal + + +template <typename Dims1, typename Dims2> +EIGEN_DEVICE_FUNC bool dimensions_match(Dims1& dims1, Dims2& dims2) { + return internal::sizes_match_below_dim<Dims1, Dims2, internal::array_size<Dims1>::value, internal::array_size<Dims2>::value>::run(dims1, dims2); +} + +} // end namespace Eigen + +#endif // EIGEN_CXX11_TENSOR_TENSOR_DIMENSIONS_H diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorEvalTo.h b/unsupported/Eigen/CXX11/src/Tensor/TensorEvalTo.h new file mode 100644 index 000000000..06987132b --- /dev/null +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorEvalTo.h @@ -0,0 +1,181 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_CXX11_TENSOR_TENSOR_EVAL_TO_H +#define EIGEN_CXX11_TENSOR_TENSOR_EVAL_TO_H + +namespace Eigen { + +/** \class TensorForcedEval + * \ingroup CXX11_Tensor_Module + * + * \brief Tensor reshaping class. + * + * + */ +namespace internal { +template<typename XprType, template <class> class MakePointer_> +struct traits<TensorEvalToOp<XprType, MakePointer_> > +{ + // Type promotion to handle the case where the types of the lhs and the rhs are different. + typedef typename XprType::Scalar Scalar; + typedef traits<XprType> XprTraits; + typedef typename XprTraits::StorageKind StorageKind; + typedef typename XprTraits::Index Index; + typedef typename XprType::Nested Nested; + typedef typename remove_reference<Nested>::type _Nested; + static const int NumDimensions = XprTraits::NumDimensions; + static const int Layout = XprTraits::Layout; + + enum { + Flags = 0 + }; + template <class T> + struct MakePointer { + // Intermediate typedef to workaround MSVC issue. + typedef MakePointer_<T> MakePointerT; + typedef typename MakePointerT::Type Type; + }; +}; + +template<typename XprType, template <class> class MakePointer_> +struct eval<TensorEvalToOp<XprType, MakePointer_>, Eigen::Dense> +{ + typedef const TensorEvalToOp<XprType, MakePointer_>& type; +}; + +template<typename XprType, template <class> class MakePointer_> +struct nested<TensorEvalToOp<XprType, MakePointer_>, 1, typename eval<TensorEvalToOp<XprType, MakePointer_> >::type> +{ + typedef TensorEvalToOp<XprType, MakePointer_> type; +}; + +} // end namespace internal + + + + +template<typename XprType, template <class> class MakePointer_> +class TensorEvalToOp : public TensorBase<TensorEvalToOp<XprType, MakePointer_>, ReadOnlyAccessors> +{ + public: + typedef typename Eigen::internal::traits<TensorEvalToOp>::Scalar Scalar; + typedef typename Eigen::NumTraits<Scalar>::Real RealScalar; + typedef typename internal::remove_const<typename XprType::CoeffReturnType>::type CoeffReturnType; + typedef typename MakePointer_<CoeffReturnType>::Type PointerType; + typedef typename Eigen::internal::nested<TensorEvalToOp>::type Nested; + typedef typename Eigen::internal::traits<TensorEvalToOp>::StorageKind StorageKind; + typedef typename Eigen::internal::traits<TensorEvalToOp>::Index Index; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvalToOp(PointerType buffer, const XprType& expr) + : m_xpr(expr), m_buffer(buffer) {} + + EIGEN_DEVICE_FUNC + const typename internal::remove_all<typename XprType::Nested>::type& + expression() const { return m_xpr; } + + EIGEN_DEVICE_FUNC PointerType buffer() const { return m_buffer; } + + protected: + typename XprType::Nested m_xpr; + PointerType m_buffer; +}; + + + +template<typename ArgType, typename Device, template <class> class MakePointer_> +struct TensorEvaluator<const TensorEvalToOp<ArgType, MakePointer_>, Device> +{ + typedef TensorEvalToOp<ArgType, MakePointer_> XprType; + typedef typename ArgType::Scalar Scalar; + typedef typename TensorEvaluator<ArgType, Device>::Dimensions Dimensions; + typedef typename XprType::Index Index; + typedef typename internal::remove_const<typename XprType::CoeffReturnType>::type CoeffReturnType; + typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType; + static const int PacketSize = internal::unpacket_traits<PacketReturnType>::size; + + enum { + IsAligned = TensorEvaluator<ArgType, Device>::IsAligned, + PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess, + Layout = TensorEvaluator<ArgType, Device>::Layout, + CoordAccess = false, // to be implemented + RawAccess = true + }; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) + : m_impl(op.expression(), device), m_device(device), + m_buffer(op.buffer()), m_op(op), m_expression(op.expression()) + { } + + // Used for accessor extraction in SYCL Managed TensorMap: + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const XprType& op() const { + return m_op; + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ~TensorEvaluator() { + } + + typedef typename internal::traits<const TensorEvalToOp<ArgType, MakePointer_> >::template MakePointer<CoeffReturnType>::Type DevicePointer; + EIGEN_DEVICE_FUNC const Dimensions& dimensions() const { return m_impl.dimensions(); } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(DevicePointer scalar) { + EIGEN_UNUSED_VARIABLE(scalar); + eigen_assert(scalar == NULL); + return m_impl.evalSubExprsIfNeeded(m_buffer); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalScalar(Index i) { + m_buffer[i] = m_impl.coeff(i); + } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalPacket(Index i) { + internal::pstoret<CoeffReturnType, PacketReturnType, Aligned>(m_buffer + i, m_impl.template packet<TensorEvaluator<ArgType, Device>::IsAligned ? Aligned : Unaligned>(i)); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() { + m_impl.cleanup(); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const + { + return m_buffer[index]; + } + + template<int LoadMode> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const + { + return internal::ploadt<PacketReturnType, LoadMode>(m_buffer + index); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const { + // We assume that evalPacket or evalScalar is called to perform the + // assignment and account for the cost of the write here. + return m_impl.costPerCoeff(vectorized) + + TensorOpCost(0, sizeof(CoeffReturnType), 0, vectorized, PacketSize); + } + + EIGEN_DEVICE_FUNC DevicePointer data() const { return m_buffer; } + ArgType expression() const { return m_expression; } + + /// required by sycl in order to extract the accessor + const TensorEvaluator<ArgType, Device>& impl() const { return m_impl; } + /// added for sycl in order to construct the buffer from the sycl device + const Device& device() const{return m_device;} + + private: + TensorEvaluator<ArgType, Device> m_impl; + const Device& m_device; + DevicePointer m_buffer; + const XprType& m_op; + const ArgType m_expression; +}; + + +} // end namespace Eigen + +#endif // EIGEN_CXX11_TENSOR_TENSOR_EVAL_TO_H diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorEvaluator.h b/unsupported/Eigen/CXX11/src/Tensor/TensorEvaluator.h new file mode 100644 index 000000000..834ce07df --- /dev/null +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorEvaluator.h @@ -0,0 +1,633 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_CXX11_TENSOR_TENSOR_EVALUATOR_H +#define EIGEN_CXX11_TENSOR_TENSOR_EVALUATOR_H + +namespace Eigen { + +/** \class TensorEvaluator + * \ingroup CXX11_Tensor_Module + * + * \brief The tensor evaluator classes. + * + * These classes are responsible for the evaluation of the tensor expression. + * + * TODO: add support for more types of expressions, in particular expressions + * leading to lvalues (slicing, reshaping, etc...) + */ + +// Generic evaluator +template<typename Derived, typename Device> +struct TensorEvaluator +{ + typedef typename Derived::Index Index; + typedef typename Derived::Scalar Scalar; + typedef typename Derived::Scalar CoeffReturnType; + typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType; + typedef typename Derived::Dimensions Dimensions; + + // NumDimensions is -1 for variable dim tensors + static const int NumCoords = internal::traits<Derived>::NumDimensions > 0 ? + internal::traits<Derived>::NumDimensions : 0; + + enum { + IsAligned = Derived::IsAligned, + PacketAccess = (internal::unpacket_traits<PacketReturnType>::size > 1), + Layout = Derived::Layout, + CoordAccess = NumCoords > 0, + RawAccess = true + }; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const Derived& m, const Device& device) + : m_data(const_cast<typename internal::traits<Derived>::template MakePointer<Scalar>::Type>(m.data())), m_dims(m.dimensions()), m_device(device), m_impl(m) + { } + + // Used for accessor extraction in SYCL Managed TensorMap: + const Derived& derived() const { return m_impl; } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dims; } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(CoeffReturnType* dest) { + if (dest) { + m_device.memcpy((void*)dest, m_data, sizeof(Scalar) * m_dims.TotalSize()); + return false; + } + return true; + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() { } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const { + eigen_assert(m_data); + return m_data[index]; + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef(Index index) { + eigen_assert(m_data); + return m_data[index]; + } + + template<int LoadMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + PacketReturnType packet(Index index) const + { + return internal::ploadt<PacketReturnType, LoadMode>(m_data + index); + } + + template <int StoreMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + void writePacket(Index index, const PacketReturnType& x) + { + return internal::pstoret<Scalar, PacketReturnType, StoreMode>(m_data + index, x); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(const array<DenseIndex, NumCoords>& coords) const { + eigen_assert(m_data); + if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { + return m_data[m_dims.IndexOfColMajor(coords)]; + } else { + return m_data[m_dims.IndexOfRowMajor(coords)]; + } + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef(const array<DenseIndex, NumCoords>& coords) { + eigen_assert(m_data); + if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { + return m_data[m_dims.IndexOfColMajor(coords)]; + } else { + return m_data[m_dims.IndexOfRowMajor(coords)]; + } + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const { + return TensorOpCost(sizeof(CoeffReturnType), 0, 0, vectorized, + internal::unpacket_traits<PacketReturnType>::size); + } + + EIGEN_DEVICE_FUNC typename internal::traits<Derived>::template MakePointer<Scalar>::Type data() const { return m_data; } + + /// required by sycl in order to construct sycl buffer from raw pointer + const Device& device() const{return m_device;} + + protected: + typename internal::traits<Derived>::template MakePointer<Scalar>::Type m_data; + Dimensions m_dims; + const Device& m_device; + const Derived& m_impl; +}; + +namespace { +template <typename T> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE +T loadConstant(const T* address) { + return *address; +} +// Use the texture cache on CUDA devices whenever possible +#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 350 +template <> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE +float loadConstant(const float* address) { + return __ldg(address); +} +template <> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE +double loadConstant(const double* address) { + return __ldg(address); +} +template <> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE +Eigen::half loadConstant(const Eigen::half* address) { + return Eigen::half(half_impl::raw_uint16_to_half(__ldg(&address->x))); +} +#endif +} + + +// Default evaluator for rvalues +template<typename Derived, typename Device> +struct TensorEvaluator<const Derived, Device> +{ + typedef typename Derived::Index Index; + typedef typename Derived::Scalar Scalar; + typedef typename Derived::Scalar CoeffReturnType; + typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType; + typedef typename Derived::Dimensions Dimensions; + + // NumDimensions is -1 for variable dim tensors + static const int NumCoords = internal::traits<Derived>::NumDimensions > 0 ? + internal::traits<Derived>::NumDimensions : 0; + + enum { + IsAligned = Derived::IsAligned, + PacketAccess = (internal::unpacket_traits<PacketReturnType>::size > 1), + Layout = Derived::Layout, + CoordAccess = NumCoords > 0, + RawAccess = true + }; + + // Used for accessor extraction in SYCL Managed TensorMap: + const Derived& derived() const { return m_impl; } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const Derived& m, const Device& device) + : m_data(m.data()), m_dims(m.dimensions()), m_device(device), m_impl(m) + { } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dims; } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(CoeffReturnType* data) { + if (!NumTraits<typename internal::remove_const<Scalar>::type>::RequireInitialization && data) { + m_device.memcpy((void*)data, m_data, m_dims.TotalSize() * sizeof(Scalar)); + return false; + } + return true; + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() { } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const { + eigen_assert(m_data); + return loadConstant(m_data+index); + } + + template<int LoadMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + PacketReturnType packet(Index index) const + { + return internal::ploadt_ro<PacketReturnType, LoadMode>(m_data + index); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(const array<DenseIndex, NumCoords>& coords) const { + eigen_assert(m_data); + const Index index = (static_cast<int>(Layout) == static_cast<int>(ColMajor)) ? m_dims.IndexOfColMajor(coords) + : m_dims.IndexOfRowMajor(coords); + return loadConstant(m_data+index); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const { + return TensorOpCost(sizeof(CoeffReturnType), 0, 0, vectorized, + internal::unpacket_traits<PacketReturnType>::size); + } + + EIGEN_DEVICE_FUNC typename internal::traits<Derived>::template MakePointer<const Scalar>::Type data() const { return m_data; } + + /// added for sycl in order to construct the buffer from the sycl device + const Device& device() const{return m_device;} + + protected: + typename internal::traits<Derived>::template MakePointer<const Scalar>::Type m_data; + Dimensions m_dims; + const Device& m_device; + const Derived& m_impl; +}; + + + + +// -------------------- CwiseNullaryOp -------------------- + +template<typename NullaryOp, typename ArgType, typename Device> +struct TensorEvaluator<const TensorCwiseNullaryOp<NullaryOp, ArgType>, Device> +{ + typedef TensorCwiseNullaryOp<NullaryOp, ArgType> XprType; + + enum { + IsAligned = true, + PacketAccess = internal::functor_traits<NullaryOp>::PacketAccess, + Layout = TensorEvaluator<ArgType, Device>::Layout, + CoordAccess = false, // to be implemented + RawAccess = false + }; + + EIGEN_DEVICE_FUNC + TensorEvaluator(const XprType& op, const Device& device) + : m_functor(op.functor()), m_argImpl(op.nestedExpression(), device), m_wrapper() + { } + + typedef typename XprType::Index Index; + typedef typename XprType::Scalar Scalar; + typedef typename internal::traits<XprType>::Scalar CoeffReturnType; + typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType; + static const int PacketSize = internal::unpacket_traits<PacketReturnType>::size; + typedef typename TensorEvaluator<ArgType, Device>::Dimensions Dimensions; + + EIGEN_DEVICE_FUNC const Dimensions& dimensions() const { return m_argImpl.dimensions(); } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(CoeffReturnType*) { return true; } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() { } + + EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index index) const + { + return m_wrapper(m_functor, index); + } + + template<int LoadMode> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const + { + return m_wrapper.template packetOp<PacketReturnType, Index>(m_functor, index); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost + costPerCoeff(bool vectorized) const { + return TensorOpCost(sizeof(CoeffReturnType), 0, 0, vectorized, + internal::unpacket_traits<PacketReturnType>::size); + } + + EIGEN_DEVICE_FUNC CoeffReturnType* data() const { return NULL; } + + /// required by sycl in order to extract the accessor + const TensorEvaluator<ArgType, Device>& impl() const { return m_argImpl; } + /// required by sycl in order to extract the accessor + NullaryOp functor() const { return m_functor; } + + + private: + const NullaryOp m_functor; + TensorEvaluator<ArgType, Device> m_argImpl; + const internal::nullary_wrapper<CoeffReturnType,NullaryOp> m_wrapper; +}; + + + +// -------------------- CwiseUnaryOp -------------------- + +template<typename UnaryOp, typename ArgType, typename Device> +struct TensorEvaluator<const TensorCwiseUnaryOp<UnaryOp, ArgType>, Device> +{ + typedef TensorCwiseUnaryOp<UnaryOp, ArgType> XprType; + + enum { + IsAligned = TensorEvaluator<ArgType, Device>::IsAligned, + PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess & internal::functor_traits<UnaryOp>::PacketAccess, + Layout = TensorEvaluator<ArgType, Device>::Layout, + CoordAccess = false, // to be implemented + RawAccess = false + }; + + EIGEN_DEVICE_FUNC TensorEvaluator(const XprType& op, const Device& device) + : m_functor(op.functor()), + m_argImpl(op.nestedExpression(), device) + { } + + typedef typename XprType::Index Index; + typedef typename XprType::Scalar Scalar; + typedef typename internal::traits<XprType>::Scalar CoeffReturnType; + typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType; + static const int PacketSize = internal::unpacket_traits<PacketReturnType>::size; + typedef typename TensorEvaluator<ArgType, Device>::Dimensions Dimensions; + + EIGEN_DEVICE_FUNC const Dimensions& dimensions() const { return m_argImpl.dimensions(); } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar*) { + m_argImpl.evalSubExprsIfNeeded(NULL); + return true; + } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() { + m_argImpl.cleanup(); + } + + EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index index) const + { + return m_functor(m_argImpl.coeff(index)); + } + + template<int LoadMode> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const + { + return m_functor.packetOp(m_argImpl.template packet<LoadMode>(index)); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const { + const double functor_cost = internal::functor_traits<UnaryOp>::Cost; + return m_argImpl.costPerCoeff(vectorized) + + TensorOpCost(0, 0, functor_cost, vectorized, PacketSize); + } + + EIGEN_DEVICE_FUNC CoeffReturnType* data() const { return NULL; } + + /// required by sycl in order to extract the accessor + const TensorEvaluator<ArgType, Device> & impl() const { return m_argImpl; } + /// added for sycl in order to construct the buffer from sycl device + UnaryOp functor() const { return m_functor; } + + + private: + const UnaryOp m_functor; + TensorEvaluator<ArgType, Device> m_argImpl; +}; + + +// -------------------- CwiseBinaryOp -------------------- + +template<typename BinaryOp, typename LeftArgType, typename RightArgType, typename Device> +struct TensorEvaluator<const TensorCwiseBinaryOp<BinaryOp, LeftArgType, RightArgType>, Device> +{ + typedef TensorCwiseBinaryOp<BinaryOp, LeftArgType, RightArgType> XprType; + + enum { + IsAligned = TensorEvaluator<LeftArgType, Device>::IsAligned & TensorEvaluator<RightArgType, Device>::IsAligned, + PacketAccess = TensorEvaluator<LeftArgType, Device>::PacketAccess & TensorEvaluator<RightArgType, Device>::PacketAccess & + internal::functor_traits<BinaryOp>::PacketAccess, + Layout = TensorEvaluator<LeftArgType, Device>::Layout, + CoordAccess = false, // to be implemented + RawAccess = false + }; + + EIGEN_DEVICE_FUNC TensorEvaluator(const XprType& op, const Device& device) + : m_functor(op.functor()), + m_leftImpl(op.lhsExpression(), device), + m_rightImpl(op.rhsExpression(), device) + { + EIGEN_STATIC_ASSERT((static_cast<int>(TensorEvaluator<LeftArgType, Device>::Layout) == static_cast<int>(TensorEvaluator<RightArgType, Device>::Layout) || internal::traits<XprType>::NumDimensions <= 1), YOU_MADE_A_PROGRAMMING_MISTAKE); + eigen_assert(dimensions_match(m_leftImpl.dimensions(), m_rightImpl.dimensions())); + } + + typedef typename XprType::Index Index; + typedef typename XprType::Scalar Scalar; + typedef typename internal::traits<XprType>::Scalar CoeffReturnType; + typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType; + static const int PacketSize = internal::unpacket_traits<PacketReturnType>::size; + typedef typename TensorEvaluator<LeftArgType, Device>::Dimensions Dimensions; + + EIGEN_DEVICE_FUNC const Dimensions& dimensions() const + { + // TODO: use right impl instead if right impl dimensions are known at compile time. + return m_leftImpl.dimensions(); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(CoeffReturnType*) { + m_leftImpl.evalSubExprsIfNeeded(NULL); + m_rightImpl.evalSubExprsIfNeeded(NULL); + return true; + } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() { + m_leftImpl.cleanup(); + m_rightImpl.cleanup(); + } + + EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index index) const + { + return m_functor(m_leftImpl.coeff(index), m_rightImpl.coeff(index)); + } + template<int LoadMode> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const + { + return m_functor.packetOp(m_leftImpl.template packet<LoadMode>(index), m_rightImpl.template packet<LoadMode>(index)); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost + costPerCoeff(bool vectorized) const { + const double functor_cost = internal::functor_traits<BinaryOp>::Cost; + return m_leftImpl.costPerCoeff(vectorized) + + m_rightImpl.costPerCoeff(vectorized) + + TensorOpCost(0, 0, functor_cost, vectorized, PacketSize); + } + + EIGEN_DEVICE_FUNC CoeffReturnType* data() const { return NULL; } + /// required by sycl in order to extract the accessor + const TensorEvaluator<LeftArgType, Device>& left_impl() const { return m_leftImpl; } + /// required by sycl in order to extract the accessor + const TensorEvaluator<RightArgType, Device>& right_impl() const { return m_rightImpl; } + /// required by sycl in order to extract the accessor + BinaryOp functor() const { return m_functor; } + + private: + const BinaryOp m_functor; + TensorEvaluator<LeftArgType, Device> m_leftImpl; + TensorEvaluator<RightArgType, Device> m_rightImpl; +}; + +// -------------------- CwiseTernaryOp -------------------- + +template<typename TernaryOp, typename Arg1Type, typename Arg2Type, typename Arg3Type, typename Device> +struct TensorEvaluator<const TensorCwiseTernaryOp<TernaryOp, Arg1Type, Arg2Type, Arg3Type>, Device> +{ + typedef TensorCwiseTernaryOp<TernaryOp, Arg1Type, Arg2Type, Arg3Type> XprType; + + enum { + IsAligned = TensorEvaluator<Arg1Type, Device>::IsAligned & TensorEvaluator<Arg2Type, Device>::IsAligned & TensorEvaluator<Arg3Type, Device>::IsAligned, + PacketAccess = TensorEvaluator<Arg1Type, Device>::PacketAccess & TensorEvaluator<Arg2Type, Device>::PacketAccess & TensorEvaluator<Arg3Type, Device>::PacketAccess & + internal::functor_traits<TernaryOp>::PacketAccess, + Layout = TensorEvaluator<Arg1Type, Device>::Layout, + CoordAccess = false, // to be implemented + RawAccess = false + }; + + EIGEN_DEVICE_FUNC TensorEvaluator(const XprType& op, const Device& device) + : m_functor(op.functor()), + m_arg1Impl(op.arg1Expression(), device), + m_arg2Impl(op.arg2Expression(), device), + m_arg3Impl(op.arg3Expression(), device) + { + EIGEN_STATIC_ASSERT((static_cast<int>(TensorEvaluator<Arg1Type, Device>::Layout) == static_cast<int>(TensorEvaluator<Arg3Type, Device>::Layout) || internal::traits<XprType>::NumDimensions <= 1), YOU_MADE_A_PROGRAMMING_MISTAKE); + + EIGEN_STATIC_ASSERT((internal::is_same<typename internal::traits<Arg1Type>::StorageKind, + typename internal::traits<Arg2Type>::StorageKind>::value), + STORAGE_KIND_MUST_MATCH) + EIGEN_STATIC_ASSERT((internal::is_same<typename internal::traits<Arg1Type>::StorageKind, + typename internal::traits<Arg3Type>::StorageKind>::value), + STORAGE_KIND_MUST_MATCH) + EIGEN_STATIC_ASSERT((internal::is_same<typename internal::traits<Arg1Type>::Index, + typename internal::traits<Arg2Type>::Index>::value), + STORAGE_INDEX_MUST_MATCH) + EIGEN_STATIC_ASSERT((internal::is_same<typename internal::traits<Arg1Type>::Index, + typename internal::traits<Arg3Type>::Index>::value), + STORAGE_INDEX_MUST_MATCH) + + eigen_assert(dimensions_match(m_arg1Impl.dimensions(), m_arg2Impl.dimensions()) && dimensions_match(m_arg1Impl.dimensions(), m_arg3Impl.dimensions())); + } + + typedef typename XprType::Index Index; + typedef typename XprType::Scalar Scalar; + typedef typename internal::traits<XprType>::Scalar CoeffReturnType; + typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType; + static const int PacketSize = internal::unpacket_traits<PacketReturnType>::size; + typedef typename TensorEvaluator<Arg1Type, Device>::Dimensions Dimensions; + + EIGEN_DEVICE_FUNC const Dimensions& dimensions() const + { + // TODO: use arg2 or arg3 dimensions if they are known at compile time. + return m_arg1Impl.dimensions(); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(CoeffReturnType*) { + m_arg1Impl.evalSubExprsIfNeeded(NULL); + m_arg2Impl.evalSubExprsIfNeeded(NULL); + m_arg3Impl.evalSubExprsIfNeeded(NULL); + return true; + } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() { + m_arg1Impl.cleanup(); + m_arg2Impl.cleanup(); + m_arg3Impl.cleanup(); + } + + EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index index) const + { + return m_functor(m_arg1Impl.coeff(index), m_arg2Impl.coeff(index), m_arg3Impl.coeff(index)); + } + template<int LoadMode> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const + { + return m_functor.packetOp(m_arg1Impl.template packet<LoadMode>(index), + m_arg2Impl.template packet<LoadMode>(index), + m_arg3Impl.template packet<LoadMode>(index)); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost + costPerCoeff(bool vectorized) const { + const double functor_cost = internal::functor_traits<TernaryOp>::Cost; + return m_arg1Impl.costPerCoeff(vectorized) + + m_arg2Impl.costPerCoeff(vectorized) + + m_arg3Impl.costPerCoeff(vectorized) + + TensorOpCost(0, 0, functor_cost, vectorized, PacketSize); + } + + EIGEN_DEVICE_FUNC CoeffReturnType* data() const { return NULL; } + + /// required by sycl in order to extract the accessor + const TensorEvaluator<Arg1Type, Device> & arg1Impl() const { return m_arg1Impl; } + /// required by sycl in order to extract the accessor + const TensorEvaluator<Arg2Type, Device>& arg2Impl() const { return m_arg2Impl; } + /// required by sycl in order to extract the accessor + const TensorEvaluator<Arg3Type, Device>& arg3Impl() const { return m_arg3Impl; } + + private: + const TernaryOp m_functor; + TensorEvaluator<Arg1Type, Device> m_arg1Impl; + TensorEvaluator<Arg2Type, Device> m_arg2Impl; + TensorEvaluator<Arg3Type, Device> m_arg3Impl; +}; + + +// -------------------- SelectOp -------------------- + +template<typename IfArgType, typename ThenArgType, typename ElseArgType, typename Device> +struct TensorEvaluator<const TensorSelectOp<IfArgType, ThenArgType, ElseArgType>, Device> +{ + typedef TensorSelectOp<IfArgType, ThenArgType, ElseArgType> XprType; + typedef typename XprType::Scalar Scalar; + + enum { + IsAligned = TensorEvaluator<ThenArgType, Device>::IsAligned & TensorEvaluator<ElseArgType, Device>::IsAligned, + PacketAccess = TensorEvaluator<ThenArgType, Device>::PacketAccess & TensorEvaluator<ElseArgType, Device>::PacketAccess & + internal::packet_traits<Scalar>::HasBlend, + Layout = TensorEvaluator<IfArgType, Device>::Layout, + CoordAccess = false, // to be implemented + RawAccess = false + }; + + EIGEN_DEVICE_FUNC TensorEvaluator(const XprType& op, const Device& device) + : m_condImpl(op.ifExpression(), device), + m_thenImpl(op.thenExpression(), device), + m_elseImpl(op.elseExpression(), device) + { + EIGEN_STATIC_ASSERT((static_cast<int>(TensorEvaluator<IfArgType, Device>::Layout) == static_cast<int>(TensorEvaluator<ThenArgType, Device>::Layout)), YOU_MADE_A_PROGRAMMING_MISTAKE); + EIGEN_STATIC_ASSERT((static_cast<int>(TensorEvaluator<IfArgType, Device>::Layout) == static_cast<int>(TensorEvaluator<ElseArgType, Device>::Layout)), YOU_MADE_A_PROGRAMMING_MISTAKE); + eigen_assert(dimensions_match(m_condImpl.dimensions(), m_thenImpl.dimensions())); + eigen_assert(dimensions_match(m_thenImpl.dimensions(), m_elseImpl.dimensions())); + } + + typedef typename XprType::Index Index; + typedef typename internal::traits<XprType>::Scalar CoeffReturnType; + typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType; + static const int PacketSize = internal::unpacket_traits<PacketReturnType>::size; + typedef typename TensorEvaluator<IfArgType, Device>::Dimensions Dimensions; + + EIGEN_DEVICE_FUNC const Dimensions& dimensions() const + { + // TODO: use then or else impl instead if they happen to be known at compile time. + return m_condImpl.dimensions(); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(CoeffReturnType*) { + m_condImpl.evalSubExprsIfNeeded(NULL); + m_thenImpl.evalSubExprsIfNeeded(NULL); + m_elseImpl.evalSubExprsIfNeeded(NULL); + return true; + } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() { + m_condImpl.cleanup(); + m_thenImpl.cleanup(); + m_elseImpl.cleanup(); + } + + EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index index) const + { + return m_condImpl.coeff(index) ? m_thenImpl.coeff(index) : m_elseImpl.coeff(index); + } + template<int LoadMode> + EIGEN_DEVICE_FUNC PacketReturnType packet(Index index) const + { + internal::Selector<PacketSize> select; + for (Index i = 0; i < PacketSize; ++i) { + select.select[i] = m_condImpl.coeff(index+i); + } + return internal::pblend(select, + m_thenImpl.template packet<LoadMode>(index), + m_elseImpl.template packet<LoadMode>(index)); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost + costPerCoeff(bool vectorized) const { + return m_condImpl.costPerCoeff(vectorized) + + m_thenImpl.costPerCoeff(vectorized) + .cwiseMax(m_elseImpl.costPerCoeff(vectorized)); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType* data() const { return NULL; } + /// required by sycl in order to extract the accessor + const TensorEvaluator<IfArgType, Device> & cond_impl() const { return m_condImpl; } + /// required by sycl in order to extract the accessor + const TensorEvaluator<ThenArgType, Device>& then_impl() const { return m_thenImpl; } + /// required by sycl in order to extract the accessor + const TensorEvaluator<ElseArgType, Device>& else_impl() const { return m_elseImpl; } + + private: + TensorEvaluator<IfArgType, Device> m_condImpl; + TensorEvaluator<ThenArgType, Device> m_thenImpl; + TensorEvaluator<ElseArgType, Device> m_elseImpl; +}; + + +} // end namespace Eigen + +#endif // EIGEN_CXX11_TENSOR_TENSOR_EVALUATOR_H diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorExecutor.h b/unsupported/Eigen/CXX11/src/Tensor/TensorExecutor.h new file mode 100644 index 000000000..f01d77c0a --- /dev/null +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorExecutor.h @@ -0,0 +1,288 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_CXX11_TENSOR_TENSOR_EXECUTOR_H +#define EIGEN_CXX11_TENSOR_TENSOR_EXECUTOR_H + +namespace Eigen { + +/** \class TensorExecutor + * \ingroup CXX11_Tensor_Module + * + * \brief The tensor executor class. + * + * This class is responsible for launch the evaluation of the expression on + * the specified computing device. + */ +namespace internal { + +// Default strategy: the expression is evaluated with a single cpu thread. +template<typename Expression, typename Device, bool Vectorizable> +class TensorExecutor +{ + public: + typedef typename Expression::Index Index; + EIGEN_DEVICE_FUNC + static inline void run(const Expression& expr, const Device& device = Device()) + { + TensorEvaluator<Expression, Device> evaluator(expr, device); + const bool needs_assign = evaluator.evalSubExprsIfNeeded(NULL); + if (needs_assign) + { + const Index size = array_prod(evaluator.dimensions()); + for (Index i = 0; i < size; ++i) { + evaluator.evalScalar(i); + } + } + evaluator.cleanup(); + } +}; + + +template<typename Expression> +class TensorExecutor<Expression, DefaultDevice, true> +{ + public: + typedef typename Expression::Index Index; + EIGEN_DEVICE_FUNC + static inline void run(const Expression& expr, const DefaultDevice& device = DefaultDevice()) + { + TensorEvaluator<Expression, DefaultDevice> evaluator(expr, device); + const bool needs_assign = evaluator.evalSubExprsIfNeeded(NULL); + if (needs_assign) + { + const Index size = array_prod(evaluator.dimensions()); + const int PacketSize = unpacket_traits<typename TensorEvaluator<Expression, DefaultDevice>::PacketReturnType>::size; + // Give the compiler a strong hint to unroll the loop. But don't insist + // on unrolling, because if the function is expensive the compiler should not + // unroll the loop at the expense of inlining. + const Index UnrolledSize = (size / (4 * PacketSize)) * 4 * PacketSize; + for (Index i = 0; i < UnrolledSize; i += 4*PacketSize) { + for (Index j = 0; j < 4; j++) { + evaluator.evalPacket(i + j * PacketSize); + } + } + const Index VectorizedSize = (size / PacketSize) * PacketSize; + for (Index i = UnrolledSize; i < VectorizedSize; i += PacketSize) { + evaluator.evalPacket(i); + } + for (Index i = VectorizedSize; i < size; ++i) { + evaluator.evalScalar(i); + } + } + evaluator.cleanup(); + } +}; + + + +// Multicore strategy: the index space is partitioned and each partition is executed on a single core +#ifdef EIGEN_USE_THREADS +template <typename Evaluator, typename Index, bool Vectorizable> +struct EvalRange { + static void run(Evaluator* evaluator_in, const Index first, const Index last) { + Evaluator evaluator = *evaluator_in; + eigen_assert(last >= first); + for (Index i = first; i < last; ++i) { + evaluator.evalScalar(i); + } + } + + static Index alignBlockSize(Index size) { + return size; + } +}; + +template <typename Evaluator, typename Index> +struct EvalRange<Evaluator, Index, true> { + static const int PacketSize = unpacket_traits<typename Evaluator::PacketReturnType>::size; + + static void run(Evaluator* evaluator_in, const Index first, const Index last) { + Evaluator evaluator = *evaluator_in; + eigen_assert(last >= first); + Index i = first; + if (last - first >= PacketSize) { + eigen_assert(first % PacketSize == 0); + Index last_chunk_offset = last - 4 * PacketSize; + // Give the compiler a strong hint to unroll the loop. But don't insist + // on unrolling, because if the function is expensive the compiler should not + // unroll the loop at the expense of inlining. + for (; i <= last_chunk_offset; i += 4*PacketSize) { + for (Index j = 0; j < 4; j++) { + evaluator.evalPacket(i + j * PacketSize); + } + } + last_chunk_offset = last - PacketSize; + for (; i <= last_chunk_offset; i += PacketSize) { + evaluator.evalPacket(i); + } + } + for (; i < last; ++i) { + evaluator.evalScalar(i); + } + } + + static Index alignBlockSize(Index size) { + // Align block size to packet size and account for unrolling in run above. + if (size >= 16 * PacketSize) { + return (size + 4 * PacketSize - 1) & ~(4 * PacketSize - 1); + } + // Aligning to 4 * PacketSize would increase block size by more than 25%. + return (size + PacketSize - 1) & ~(PacketSize - 1); + } +}; + +template <typename Expression, bool Vectorizable> +class TensorExecutor<Expression, ThreadPoolDevice, Vectorizable> { + public: + typedef typename Expression::Index Index; + static inline void run(const Expression& expr, const ThreadPoolDevice& device) + { + typedef TensorEvaluator<Expression, ThreadPoolDevice> Evaluator; + Evaluator evaluator(expr, device); + const bool needs_assign = evaluator.evalSubExprsIfNeeded(NULL); + if (needs_assign) + { + const Index size = array_prod(evaluator.dimensions()); +#if !defined(EIGEN_USE_SIMPLE_THREAD_POOL) + device.parallelFor(size, evaluator.costPerCoeff(Vectorizable), + EvalRange<Evaluator, Index, Vectorizable>::alignBlockSize, + [&evaluator](Index first, Index last) { + EvalRange<Evaluator, Index, Vectorizable>::run(&evaluator, first, last); + }); +#else + size_t num_threads = device.numThreads(); + if (num_threads > 1) { + num_threads = TensorCostModel<ThreadPoolDevice>::numThreads( + size, evaluator.costPerCoeff(Vectorizable), num_threads); + } + if (num_threads == 1) { + EvalRange<Evaluator, Index, Vectorizable>::run(&evaluator, 0, size); + } else { + const Index PacketSize = Vectorizable ? unpacket_traits<typename Evaluator::PacketReturnType>::size : 1; + Index blocksz = std::ceil<Index>(static_cast<float>(size)/num_threads) + PacketSize - 1; + const Index blocksize = numext::maxi<Index>(PacketSize, (blocksz - (blocksz % PacketSize))); + const Index numblocks = size / blocksize; + + Barrier barrier(numblocks); + for (int i = 0; i < numblocks; ++i) { + device.enqueue_with_barrier( + &barrier, &EvalRange<Evaluator, Index, Vectorizable>::run, + &evaluator, i * blocksize, (i + 1) * blocksize); + } + if (numblocks * blocksize < size) { + EvalRange<Evaluator, Index, Vectorizable>::run( + &evaluator, numblocks * blocksize, size); + } + barrier.Wait(); + } +#endif // defined(!EIGEN_USE_SIMPLE_THREAD_POOL) + } + evaluator.cleanup(); + } +}; +#endif // EIGEN_USE_THREADS + + +// GPU: the evaluation of the expression is offloaded to a GPU. +#if defined(EIGEN_USE_GPU) + +template <typename Expression, bool Vectorizable> +class TensorExecutor<Expression, GpuDevice, Vectorizable> { + public: + typedef typename Expression::Index Index; + static void run(const Expression& expr, const GpuDevice& device); +}; + + +#if defined(__CUDACC__) +template <typename Evaluator, typename Index, bool Vectorizable> +struct EigenMetaKernelEval { + static __device__ EIGEN_ALWAYS_INLINE + void run(Evaluator& eval, Index first, Index last, Index step_size) { + for (Index i = first; i < last; i += step_size) { + eval.evalScalar(i); + } + } +}; + +template <typename Evaluator, typename Index> +struct EigenMetaKernelEval<Evaluator, Index, true> { + static __device__ EIGEN_ALWAYS_INLINE + void run(Evaluator& eval, Index first, Index last, Index step_size) { + const Index PacketSize = unpacket_traits<typename Evaluator::PacketReturnType>::size; + const Index vectorized_size = (last / PacketSize) * PacketSize; + const Index vectorized_step_size = step_size * PacketSize; + + // Use the vector path + for (Index i = first * PacketSize; i < vectorized_size; + i += vectorized_step_size) { + eval.evalPacket(i); + } + for (Index i = vectorized_size + first; i < last; i += step_size) { + eval.evalScalar(i); + } + } +}; + +template <typename Evaluator, typename Index> +__global__ void +__launch_bounds__(1024) +EigenMetaKernel(Evaluator eval, Index size) { + + const Index first_index = blockIdx.x * blockDim.x + threadIdx.x; + const Index step_size = blockDim.x * gridDim.x; + + const bool vectorizable = Evaluator::PacketAccess & Evaluator::IsAligned; + EigenMetaKernelEval<Evaluator, Index, vectorizable>::run(eval, first_index, size, step_size); +} + +/*static*/ +template <typename Expression, bool Vectorizable> +inline void TensorExecutor<Expression, GpuDevice, Vectorizable>::run( + const Expression& expr, const GpuDevice& device) { + TensorEvaluator<Expression, GpuDevice> evaluator(expr, device); + const bool needs_assign = evaluator.evalSubExprsIfNeeded(NULL); + if (needs_assign) { + const int block_size = device.maxCudaThreadsPerBlock(); + const int max_blocks = device.getNumCudaMultiProcessors() * + device.maxCudaThreadsPerMultiProcessor() / block_size; + const Index size = array_prod(evaluator.dimensions()); + // Create a least one block to ensure we won't crash when tensorflow calls with tensors of size 0. + const int num_blocks = numext::maxi<int>(numext::mini<int>(max_blocks, divup<int>(size, block_size)), 1); + + LAUNCH_CUDA_KERNEL( + (EigenMetaKernel<TensorEvaluator<Expression, GpuDevice>, Index>), + num_blocks, block_size, 0, device, evaluator, size); + } + evaluator.cleanup(); +} + +#endif // __CUDACC__ +#endif // EIGEN_USE_GPU + +// SYCL Executor policy +#ifdef EIGEN_USE_SYCL + +template <typename Expression, bool Vectorizable> +class TensorExecutor<Expression, SyclDevice, Vectorizable> { +public: + static inline void run(const Expression &expr, const SyclDevice &device) { + // call TensorSYCL module + TensorSycl::run(expr, device); + } +}; + +#endif + +} // end namespace internal + +} // end namespace Eigen + +#endif // EIGEN_CXX11_TENSOR_TENSOR_EXECUTOR_H diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorExpr.h b/unsupported/Eigen/CXX11/src/Tensor/TensorExpr.h new file mode 100644 index 000000000..85dfc7a69 --- /dev/null +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorExpr.h @@ -0,0 +1,371 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_CXX11_TENSOR_TENSOR_EXPR_H +#define EIGEN_CXX11_TENSOR_TENSOR_EXPR_H + +namespace Eigen { + +/** \class TensorExpr + * \ingroup CXX11_Tensor_Module + * + * \brief Tensor expression classes. + * + * The TensorCwiseNullaryOp class applies a nullary operators to an expression. + * This is typically used to generate constants. + * + * The TensorCwiseUnaryOp class represents an expression where a unary operator + * (e.g. cwiseSqrt) is applied to an expression. + * + * The TensorCwiseBinaryOp class represents an expression where a binary + * operator (e.g. addition) is applied to a lhs and a rhs expression. + * + */ +namespace internal { +template<typename NullaryOp, typename XprType> +struct traits<TensorCwiseNullaryOp<NullaryOp, XprType> > + : traits<XprType> +{ + typedef traits<XprType> XprTraits; + typedef typename XprType::Scalar Scalar; + typedef typename XprType::Nested XprTypeNested; + typedef typename remove_reference<XprTypeNested>::type _XprTypeNested; + static const int NumDimensions = XprTraits::NumDimensions; + static const int Layout = XprTraits::Layout; + + enum { + Flags = 0 + }; +}; + +} // end namespace internal + + + +template<typename NullaryOp, typename XprType> +class TensorCwiseNullaryOp : public TensorBase<TensorCwiseNullaryOp<NullaryOp, XprType>, ReadOnlyAccessors> +{ + public: + typedef typename Eigen::internal::traits<TensorCwiseNullaryOp>::Scalar Scalar; + typedef typename Eigen::NumTraits<Scalar>::Real RealScalar; + typedef typename XprType::CoeffReturnType CoeffReturnType; + typedef TensorCwiseNullaryOp<NullaryOp, XprType> Nested; + typedef typename Eigen::internal::traits<TensorCwiseNullaryOp>::StorageKind StorageKind; + typedef typename Eigen::internal::traits<TensorCwiseNullaryOp>::Index Index; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorCwiseNullaryOp(const XprType& xpr, const NullaryOp& func = NullaryOp()) + : m_xpr(xpr), m_functor(func) {} + + EIGEN_DEVICE_FUNC + const typename internal::remove_all<typename XprType::Nested>::type& + nestedExpression() const { return m_xpr; } + + EIGEN_DEVICE_FUNC + const NullaryOp& functor() const { return m_functor; } + + protected: + typename XprType::Nested m_xpr; + const NullaryOp m_functor; +}; + + + +namespace internal { +template<typename UnaryOp, typename XprType> +struct traits<TensorCwiseUnaryOp<UnaryOp, XprType> > + : traits<XprType> +{ + // TODO(phli): Add InputScalar, InputPacket. Check references to + // current Scalar/Packet to see if the intent is Input or Output. + typedef typename result_of<UnaryOp(typename XprType::Scalar)>::type Scalar; + typedef traits<XprType> XprTraits; + typedef typename XprType::Nested XprTypeNested; + typedef typename remove_reference<XprTypeNested>::type _XprTypeNested; + static const int NumDimensions = XprTraits::NumDimensions; + static const int Layout = XprTraits::Layout; +}; + +template<typename UnaryOp, typename XprType> +struct eval<TensorCwiseUnaryOp<UnaryOp, XprType>, Eigen::Dense> +{ + typedef const TensorCwiseUnaryOp<UnaryOp, XprType>& type; +}; + +template<typename UnaryOp, typename XprType> +struct nested<TensorCwiseUnaryOp<UnaryOp, XprType>, 1, typename eval<TensorCwiseUnaryOp<UnaryOp, XprType> >::type> +{ + typedef TensorCwiseUnaryOp<UnaryOp, XprType> type; +}; + +} // end namespace internal + + + +template<typename UnaryOp, typename XprType> +class TensorCwiseUnaryOp : public TensorBase<TensorCwiseUnaryOp<UnaryOp, XprType>, ReadOnlyAccessors> +{ + public: + // TODO(phli): Add InputScalar, InputPacket. Check references to + // current Scalar/Packet to see if the intent is Input or Output. + typedef typename Eigen::internal::traits<TensorCwiseUnaryOp>::Scalar Scalar; + typedef typename Eigen::NumTraits<Scalar>::Real RealScalar; + typedef Scalar CoeffReturnType; + typedef typename Eigen::internal::nested<TensorCwiseUnaryOp>::type Nested; + typedef typename Eigen::internal::traits<TensorCwiseUnaryOp>::StorageKind StorageKind; + typedef typename Eigen::internal::traits<TensorCwiseUnaryOp>::Index Index; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorCwiseUnaryOp(const XprType& xpr, const UnaryOp& func = UnaryOp()) + : m_xpr(xpr), m_functor(func) {} + + EIGEN_DEVICE_FUNC + const UnaryOp& functor() const { return m_functor; } + + /** \returns the nested expression */ + EIGEN_DEVICE_FUNC + const typename internal::remove_all<typename XprType::Nested>::type& + nestedExpression() const { return m_xpr; } + + protected: + typename XprType::Nested m_xpr; + const UnaryOp m_functor; +}; + + +namespace internal { +template<typename BinaryOp, typename LhsXprType, typename RhsXprType> +struct traits<TensorCwiseBinaryOp<BinaryOp, LhsXprType, RhsXprType> > +{ + // Type promotion to handle the case where the types of the lhs and the rhs + // are different. + // TODO(phli): Add Lhs/RhsScalar, Lhs/RhsPacket. Check references to + // current Scalar/Packet to see if the intent is Inputs or Output. + typedef typename result_of< + BinaryOp(typename LhsXprType::Scalar, + typename RhsXprType::Scalar)>::type Scalar; + typedef traits<LhsXprType> XprTraits; + typedef typename promote_storage_type< + typename traits<LhsXprType>::StorageKind, + typename traits<RhsXprType>::StorageKind>::ret StorageKind; + typedef typename promote_index_type< + typename traits<LhsXprType>::Index, + typename traits<RhsXprType>::Index>::type Index; + typedef typename LhsXprType::Nested LhsNested; + typedef typename RhsXprType::Nested RhsNested; + typedef typename remove_reference<LhsNested>::type _LhsNested; + typedef typename remove_reference<RhsNested>::type _RhsNested; + static const int NumDimensions = XprTraits::NumDimensions; + static const int Layout = XprTraits::Layout; + + enum { + Flags = 0 + }; +}; + +template<typename BinaryOp, typename LhsXprType, typename RhsXprType> +struct eval<TensorCwiseBinaryOp<BinaryOp, LhsXprType, RhsXprType>, Eigen::Dense> +{ + typedef const TensorCwiseBinaryOp<BinaryOp, LhsXprType, RhsXprType>& type; +}; + +template<typename BinaryOp, typename LhsXprType, typename RhsXprType> +struct nested<TensorCwiseBinaryOp<BinaryOp, LhsXprType, RhsXprType>, 1, typename eval<TensorCwiseBinaryOp<BinaryOp, LhsXprType, RhsXprType> >::type> +{ + typedef TensorCwiseBinaryOp<BinaryOp, LhsXprType, RhsXprType> type; +}; + +} // end namespace internal + + + +template<typename BinaryOp, typename LhsXprType, typename RhsXprType> +class TensorCwiseBinaryOp : public TensorBase<TensorCwiseBinaryOp<BinaryOp, LhsXprType, RhsXprType>, ReadOnlyAccessors> +{ + public: + // TODO(phli): Add Lhs/RhsScalar, Lhs/RhsPacket. Check references to + // current Scalar/Packet to see if the intent is Inputs or Output. + typedef typename Eigen::internal::traits<TensorCwiseBinaryOp>::Scalar Scalar; + typedef typename Eigen::NumTraits<Scalar>::Real RealScalar; + typedef Scalar CoeffReturnType; + typedef typename Eigen::internal::nested<TensorCwiseBinaryOp>::type Nested; + typedef typename Eigen::internal::traits<TensorCwiseBinaryOp>::StorageKind StorageKind; + typedef typename Eigen::internal::traits<TensorCwiseBinaryOp>::Index Index; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorCwiseBinaryOp(const LhsXprType& lhs, const RhsXprType& rhs, const BinaryOp& func = BinaryOp()) + : m_lhs_xpr(lhs), m_rhs_xpr(rhs), m_functor(func) {} + + EIGEN_DEVICE_FUNC + const BinaryOp& functor() const { return m_functor; } + + /** \returns the nested expressions */ + EIGEN_DEVICE_FUNC + const typename internal::remove_all<typename LhsXprType::Nested>::type& + lhsExpression() const { return m_lhs_xpr; } + + EIGEN_DEVICE_FUNC + const typename internal::remove_all<typename RhsXprType::Nested>::type& + rhsExpression() const { return m_rhs_xpr; } + + protected: + typename LhsXprType::Nested m_lhs_xpr; + typename RhsXprType::Nested m_rhs_xpr; + const BinaryOp m_functor; +}; + + +namespace internal { +template<typename TernaryOp, typename Arg1XprType, typename Arg2XprType, typename Arg3XprType> +struct traits<TensorCwiseTernaryOp<TernaryOp, Arg1XprType, Arg2XprType, Arg3XprType> > +{ + // Type promotion to handle the case where the types of the args are different. + typedef typename result_of< + TernaryOp(typename Arg1XprType::Scalar, + typename Arg2XprType::Scalar, + typename Arg3XprType::Scalar)>::type Scalar; + typedef traits<Arg1XprType> XprTraits; + typedef typename traits<Arg1XprType>::StorageKind StorageKind; + typedef typename traits<Arg1XprType>::Index Index; + typedef typename Arg1XprType::Nested Arg1Nested; + typedef typename Arg2XprType::Nested Arg2Nested; + typedef typename Arg3XprType::Nested Arg3Nested; + typedef typename remove_reference<Arg1Nested>::type _Arg1Nested; + typedef typename remove_reference<Arg2Nested>::type _Arg2Nested; + typedef typename remove_reference<Arg3Nested>::type _Arg3Nested; + static const int NumDimensions = XprTraits::NumDimensions; + static const int Layout = XprTraits::Layout; + + enum { + Flags = 0 + }; +}; + +template<typename TernaryOp, typename Arg1XprType, typename Arg2XprType, typename Arg3XprType> +struct eval<TensorCwiseTernaryOp<TernaryOp, Arg1XprType, Arg2XprType, Arg3XprType>, Eigen::Dense> +{ + typedef const TensorCwiseTernaryOp<TernaryOp, Arg1XprType, Arg2XprType, Arg3XprType>& type; +}; + +template<typename TernaryOp, typename Arg1XprType, typename Arg2XprType, typename Arg3XprType> +struct nested<TensorCwiseTernaryOp<TernaryOp, Arg1XprType, Arg2XprType, Arg3XprType>, 1, typename eval<TensorCwiseTernaryOp<TernaryOp, Arg1XprType, Arg2XprType, Arg3XprType> >::type> +{ + typedef TensorCwiseTernaryOp<TernaryOp, Arg1XprType, Arg2XprType, Arg3XprType> type; +}; + +} // end namespace internal + + + +template<typename TernaryOp, typename Arg1XprType, typename Arg2XprType, typename Arg3XprType> +class TensorCwiseTernaryOp : public TensorBase<TensorCwiseTernaryOp<TernaryOp, Arg1XprType, Arg2XprType, Arg3XprType>, ReadOnlyAccessors> +{ + public: + typedef typename Eigen::internal::traits<TensorCwiseTernaryOp>::Scalar Scalar; + typedef typename Eigen::NumTraits<Scalar>::Real RealScalar; + typedef Scalar CoeffReturnType; + typedef typename Eigen::internal::nested<TensorCwiseTernaryOp>::type Nested; + typedef typename Eigen::internal::traits<TensorCwiseTernaryOp>::StorageKind StorageKind; + typedef typename Eigen::internal::traits<TensorCwiseTernaryOp>::Index Index; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorCwiseTernaryOp(const Arg1XprType& arg1, const Arg2XprType& arg2, const Arg3XprType& arg3, const TernaryOp& func = TernaryOp()) + : m_arg1_xpr(arg1), m_arg2_xpr(arg2), m_arg3_xpr(arg3), m_functor(func) {} + + EIGEN_DEVICE_FUNC + const TernaryOp& functor() const { return m_functor; } + + /** \returns the nested expressions */ + EIGEN_DEVICE_FUNC + const typename internal::remove_all<typename Arg1XprType::Nested>::type& + arg1Expression() const { return m_arg1_xpr; } + + EIGEN_DEVICE_FUNC + const typename internal::remove_all<typename Arg2XprType::Nested>::type& + arg2Expression() const { return m_arg2_xpr; } + + EIGEN_DEVICE_FUNC + const typename internal::remove_all<typename Arg3XprType::Nested>::type& + arg3Expression() const { return m_arg3_xpr; } + + protected: + typename Arg1XprType::Nested m_arg1_xpr; + typename Arg2XprType::Nested m_arg2_xpr; + typename Arg3XprType::Nested m_arg3_xpr; + const TernaryOp m_functor; +}; + + +namespace internal { +template<typename IfXprType, typename ThenXprType, typename ElseXprType> +struct traits<TensorSelectOp<IfXprType, ThenXprType, ElseXprType> > + : traits<ThenXprType> +{ + typedef typename traits<ThenXprType>::Scalar Scalar; + typedef traits<ThenXprType> XprTraits; + typedef typename promote_storage_type<typename traits<ThenXprType>::StorageKind, + typename traits<ElseXprType>::StorageKind>::ret StorageKind; + typedef typename promote_index_type<typename traits<ElseXprType>::Index, + typename traits<ThenXprType>::Index>::type Index; + typedef typename IfXprType::Nested IfNested; + typedef typename ThenXprType::Nested ThenNested; + typedef typename ElseXprType::Nested ElseNested; + static const int NumDimensions = XprTraits::NumDimensions; + static const int Layout = XprTraits::Layout; +}; + +template<typename IfXprType, typename ThenXprType, typename ElseXprType> +struct eval<TensorSelectOp<IfXprType, ThenXprType, ElseXprType>, Eigen::Dense> +{ + typedef const TensorSelectOp<IfXprType, ThenXprType, ElseXprType>& type; +}; + +template<typename IfXprType, typename ThenXprType, typename ElseXprType> +struct nested<TensorSelectOp<IfXprType, ThenXprType, ElseXprType>, 1, typename eval<TensorSelectOp<IfXprType, ThenXprType, ElseXprType> >::type> +{ + typedef TensorSelectOp<IfXprType, ThenXprType, ElseXprType> type; +}; + +} // end namespace internal + + +template<typename IfXprType, typename ThenXprType, typename ElseXprType> +class TensorSelectOp : public TensorBase<TensorSelectOp<IfXprType, ThenXprType, ElseXprType>, ReadOnlyAccessors> +{ + public: + typedef typename Eigen::internal::traits<TensorSelectOp>::Scalar Scalar; + typedef typename Eigen::NumTraits<Scalar>::Real RealScalar; + typedef typename internal::promote_storage_type<typename ThenXprType::CoeffReturnType, + typename ElseXprType::CoeffReturnType>::ret CoeffReturnType; + typedef typename Eigen::internal::nested<TensorSelectOp>::type Nested; + typedef typename Eigen::internal::traits<TensorSelectOp>::StorageKind StorageKind; + typedef typename Eigen::internal::traits<TensorSelectOp>::Index Index; + + EIGEN_DEVICE_FUNC + TensorSelectOp(const IfXprType& a_condition, + const ThenXprType& a_then, + const ElseXprType& a_else) + : m_condition(a_condition), m_then(a_then), m_else(a_else) + { } + + EIGEN_DEVICE_FUNC + const IfXprType& ifExpression() const { return m_condition; } + + EIGEN_DEVICE_FUNC + const ThenXprType& thenExpression() const { return m_then; } + + EIGEN_DEVICE_FUNC + const ElseXprType& elseExpression() const { return m_else; } + + protected: + typename IfXprType::Nested m_condition; + typename ThenXprType::Nested m_then; + typename ElseXprType::Nested m_else; +}; + + +} // end namespace Eigen + +#endif // EIGEN_CXX11_TENSOR_TENSOR_EXPR_H diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorFFT.h b/unsupported/Eigen/CXX11/src/Tensor/TensorFFT.h new file mode 100644 index 000000000..08eb5595a --- /dev/null +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorFFT.h @@ -0,0 +1,651 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2015 Jianwei Cui <thucjw@gmail.com> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_CXX11_TENSOR_TENSOR_FFT_H +#define EIGEN_CXX11_TENSOR_TENSOR_FFT_H + +// This code requires the ability to initialize arrays of constant +// values directly inside a class. +#if __cplusplus >= 201103L || EIGEN_COMP_MSVC >= 1900 + +namespace Eigen { + +/** \class TensorFFT + * \ingroup CXX11_Tensor_Module + * + * \brief Tensor FFT class. + * + * TODO: + * Vectorize the Cooley Tukey and the Bluestein algorithm + * Add support for multithreaded evaluation + * Improve the performance on GPU + */ + +template <bool NeedUprade> struct MakeComplex { + template <typename T> + EIGEN_DEVICE_FUNC + T operator() (const T& val) const { return val; } +}; + +template <> struct MakeComplex<true> { + template <typename T> + EIGEN_DEVICE_FUNC + std::complex<T> operator() (const T& val) const { return std::complex<T>(val, 0); } +}; + +template <> struct MakeComplex<false> { + template <typename T> + EIGEN_DEVICE_FUNC + std::complex<T> operator() (const std::complex<T>& val) const { return val; } +}; + +template <int ResultType> struct PartOf { + template <typename T> T operator() (const T& val) const { return val; } +}; + +template <> struct PartOf<RealPart> { + template <typename T> T operator() (const std::complex<T>& val) const { return val.real(); } +}; + +template <> struct PartOf<ImagPart> { + template <typename T> T operator() (const std::complex<T>& val) const { return val.imag(); } +}; + +namespace internal { +template <typename FFT, typename XprType, int FFTResultType, int FFTDir> +struct traits<TensorFFTOp<FFT, XprType, FFTResultType, FFTDir> > : public traits<XprType> { + typedef traits<XprType> XprTraits; + typedef typename NumTraits<typename XprTraits::Scalar>::Real RealScalar; + typedef typename std::complex<RealScalar> ComplexScalar; + typedef typename XprTraits::Scalar InputScalar; + typedef typename conditional<FFTResultType == RealPart || FFTResultType == ImagPart, RealScalar, ComplexScalar>::type OutputScalar; + typedef typename XprTraits::StorageKind StorageKind; + typedef typename XprTraits::Index Index; + typedef typename XprType::Nested Nested; + typedef typename remove_reference<Nested>::type _Nested; + static const int NumDimensions = XprTraits::NumDimensions; + static const int Layout = XprTraits::Layout; +}; + +template <typename FFT, typename XprType, int FFTResultType, int FFTDirection> +struct eval<TensorFFTOp<FFT, XprType, FFTResultType, FFTDirection>, Eigen::Dense> { + typedef const TensorFFTOp<FFT, XprType, FFTResultType, FFTDirection>& type; +}; + +template <typename FFT, typename XprType, int FFTResultType, int FFTDirection> +struct nested<TensorFFTOp<FFT, XprType, FFTResultType, FFTDirection>, 1, typename eval<TensorFFTOp<FFT, XprType, FFTResultType, FFTDirection> >::type> { + typedef TensorFFTOp<FFT, XprType, FFTResultType, FFTDirection> type; +}; + +} // end namespace internal + +template <typename FFT, typename XprType, int FFTResultType, int FFTDir> +class TensorFFTOp : public TensorBase<TensorFFTOp<FFT, XprType, FFTResultType, FFTDir>, ReadOnlyAccessors> { + public: + typedef typename Eigen::internal::traits<TensorFFTOp>::Scalar Scalar; + typedef typename Eigen::NumTraits<Scalar>::Real RealScalar; + typedef typename std::complex<RealScalar> ComplexScalar; + typedef typename internal::conditional<FFTResultType == RealPart || FFTResultType == ImagPart, RealScalar, ComplexScalar>::type OutputScalar; + typedef OutputScalar CoeffReturnType; + typedef typename Eigen::internal::nested<TensorFFTOp>::type Nested; + typedef typename Eigen::internal::traits<TensorFFTOp>::StorageKind StorageKind; + typedef typename Eigen::internal::traits<TensorFFTOp>::Index Index; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorFFTOp(const XprType& expr, const FFT& fft) + : m_xpr(expr), m_fft(fft) {} + + EIGEN_DEVICE_FUNC + const FFT& fft() const { return m_fft; } + + EIGEN_DEVICE_FUNC + const typename internal::remove_all<typename XprType::Nested>::type& expression() const { + return m_xpr; + } + + protected: + typename XprType::Nested m_xpr; + const FFT m_fft; +}; + +// Eval as rvalue +template <typename FFT, typename ArgType, typename Device, int FFTResultType, int FFTDir> +struct TensorEvaluator<const TensorFFTOp<FFT, ArgType, FFTResultType, FFTDir>, Device> { + typedef TensorFFTOp<FFT, ArgType, FFTResultType, FFTDir> XprType; + typedef typename XprType::Index Index; + static const int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value; + typedef DSizes<Index, NumDims> Dimensions; + typedef typename XprType::Scalar Scalar; + typedef typename Eigen::NumTraits<Scalar>::Real RealScalar; + typedef typename std::complex<RealScalar> ComplexScalar; + typedef typename TensorEvaluator<ArgType, Device>::Dimensions InputDimensions; + typedef internal::traits<XprType> XprTraits; + typedef typename XprTraits::Scalar InputScalar; + typedef typename internal::conditional<FFTResultType == RealPart || FFTResultType == ImagPart, RealScalar, ComplexScalar>::type OutputScalar; + typedef OutputScalar CoeffReturnType; + typedef typename PacketType<OutputScalar, Device>::type PacketReturnType; + static const int PacketSize = internal::unpacket_traits<PacketReturnType>::size; + + enum { + IsAligned = false, + PacketAccess = true, + BlockAccess = false, + Layout = TensorEvaluator<ArgType, Device>::Layout, + CoordAccess = false, + RawAccess = false + }; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) : m_fft(op.fft()), m_impl(op.expression(), device), m_data(NULL), m_device(device) { + const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions(); + for (int i = 0; i < NumDims; ++i) { + eigen_assert(input_dims[i] > 0); + m_dimensions[i] = input_dims[i]; + } + + if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { + m_strides[0] = 1; + for (int i = 1; i < NumDims; ++i) { + m_strides[i] = m_strides[i - 1] * m_dimensions[i - 1]; + } + } else { + m_strides[NumDims - 1] = 1; + for (int i = NumDims - 2; i >= 0; --i) { + m_strides[i] = m_strides[i + 1] * m_dimensions[i + 1]; + } + } + m_size = m_dimensions.TotalSize(); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { + return m_dimensions; + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(OutputScalar* data) { + m_impl.evalSubExprsIfNeeded(NULL); + if (data) { + evalToBuf(data); + return false; + } else { + m_data = (CoeffReturnType*)m_device.allocate(sizeof(CoeffReturnType) * m_size); + evalToBuf(m_data); + return true; + } + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() { + if (m_data) { + m_device.deallocate(m_data); + m_data = NULL; + } + m_impl.cleanup(); + } + + EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE CoeffReturnType coeff(Index index) const { + return m_data[index]; + } + + template <int LoadMode> + EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE PacketReturnType + packet(Index index) const { + return internal::ploadt<PacketReturnType, LoadMode>(m_data + index); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost + costPerCoeff(bool vectorized) const { + return TensorOpCost(sizeof(CoeffReturnType), 0, 0, vectorized, PacketSize); + } + + EIGEN_DEVICE_FUNC Scalar* data() const { return m_data; } + + + private: + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalToBuf(OutputScalar* data) { + const bool write_to_out = internal::is_same<OutputScalar, ComplexScalar>::value; + ComplexScalar* buf = write_to_out ? (ComplexScalar*)data : (ComplexScalar*)m_device.allocate(sizeof(ComplexScalar) * m_size); + + for (Index i = 0; i < m_size; ++i) { + buf[i] = MakeComplex<internal::is_same<InputScalar, RealScalar>::value>()(m_impl.coeff(i)); + } + + for (size_t i = 0; i < m_fft.size(); ++i) { + Index dim = m_fft[i]; + eigen_assert(dim >= 0 && dim < NumDims); + Index line_len = m_dimensions[dim]; + eigen_assert(line_len >= 1); + ComplexScalar* line_buf = (ComplexScalar*)m_device.allocate(sizeof(ComplexScalar) * line_len); + const bool is_power_of_two = isPowerOfTwo(line_len); + const Index good_composite = is_power_of_two ? 0 : findGoodComposite(line_len); + const Index log_len = is_power_of_two ? getLog2(line_len) : getLog2(good_composite); + + ComplexScalar* a = is_power_of_two ? NULL : (ComplexScalar*)m_device.allocate(sizeof(ComplexScalar) * good_composite); + ComplexScalar* b = is_power_of_two ? NULL : (ComplexScalar*)m_device.allocate(sizeof(ComplexScalar) * good_composite); + ComplexScalar* pos_j_base_powered = is_power_of_two ? NULL : (ComplexScalar*)m_device.allocate(sizeof(ComplexScalar) * (line_len + 1)); + if (!is_power_of_two) { + // Compute twiddle factors + // t_n = exp(sqrt(-1) * pi * n^2 / line_len) + // for n = 0, 1,..., line_len-1. + // For n > 2 we use the recurrence t_n = t_{n-1}^2 / t_{n-2} * t_1^2 + pos_j_base_powered[0] = ComplexScalar(1, 0); + if (line_len > 1) { + const RealScalar pi_over_len(EIGEN_PI / line_len); + const ComplexScalar pos_j_base = ComplexScalar( + std::cos(pi_over_len), std::sin(pi_over_len)); + pos_j_base_powered[1] = pos_j_base; + if (line_len > 2) { + const ComplexScalar pos_j_base_sq = pos_j_base * pos_j_base; + for (int j = 2; j < line_len + 1; ++j) { + pos_j_base_powered[j] = pos_j_base_powered[j - 1] * + pos_j_base_powered[j - 1] / + pos_j_base_powered[j - 2] * pos_j_base_sq; + } + } + } + } + + for (Index partial_index = 0; partial_index < m_size / line_len; ++partial_index) { + const Index base_offset = getBaseOffsetFromIndex(partial_index, dim); + + // get data into line_buf + const Index stride = m_strides[dim]; + if (stride == 1) { + memcpy(line_buf, &buf[base_offset], line_len*sizeof(ComplexScalar)); + } else { + Index offset = base_offset; + for (int j = 0; j < line_len; ++j, offset += stride) { + line_buf[j] = buf[offset]; + } + } + + // processs the line + if (is_power_of_two) { + processDataLineCooleyTukey(line_buf, line_len, log_len); + } + else { + processDataLineBluestein(line_buf, line_len, good_composite, log_len, a, b, pos_j_base_powered); + } + + // write back + if (FFTDir == FFT_FORWARD && stride == 1) { + memcpy(&buf[base_offset], line_buf, line_len*sizeof(ComplexScalar)); + } else { + Index offset = base_offset; + const ComplexScalar div_factor = ComplexScalar(1.0 / line_len, 0); + for (int j = 0; j < line_len; ++j, offset += stride) { + buf[offset] = (FFTDir == FFT_FORWARD) ? line_buf[j] : line_buf[j] * div_factor; + } + } + } + m_device.deallocate(line_buf); + if (!is_power_of_two) { + m_device.deallocate(a); + m_device.deallocate(b); + m_device.deallocate(pos_j_base_powered); + } + } + + if(!write_to_out) { + for (Index i = 0; i < m_size; ++i) { + data[i] = PartOf<FFTResultType>()(buf[i]); + } + m_device.deallocate(buf); + } + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE static bool isPowerOfTwo(Index x) { + eigen_assert(x > 0); + return !(x & (x - 1)); + } + + // The composite number for padding, used in Bluestein's FFT algorithm + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE static Index findGoodComposite(Index n) { + Index i = 2; + while (i < 2 * n - 1) i *= 2; + return i; + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE static Index getLog2(Index m) { + Index log2m = 0; + while (m >>= 1) log2m++; + return log2m; + } + + // Call Cooley Tukey algorithm directly, data length must be power of 2 + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void processDataLineCooleyTukey(ComplexScalar* line_buf, Index line_len, Index log_len) { + eigen_assert(isPowerOfTwo(line_len)); + scramble_FFT(line_buf, line_len); + compute_1D_Butterfly<FFTDir>(line_buf, line_len, log_len); + } + + // Call Bluestein's FFT algorithm, m is a good composite number greater than (2 * n - 1), used as the padding length + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void processDataLineBluestein(ComplexScalar* line_buf, Index line_len, Index good_composite, Index log_len, ComplexScalar* a, ComplexScalar* b, const ComplexScalar* pos_j_base_powered) { + Index n = line_len; + Index m = good_composite; + ComplexScalar* data = line_buf; + + for (Index i = 0; i < n; ++i) { + if(FFTDir == FFT_FORWARD) { + a[i] = data[i] * numext::conj(pos_j_base_powered[i]); + } + else { + a[i] = data[i] * pos_j_base_powered[i]; + } + } + for (Index i = n; i < m; ++i) { + a[i] = ComplexScalar(0, 0); + } + + for (Index i = 0; i < n; ++i) { + if(FFTDir == FFT_FORWARD) { + b[i] = pos_j_base_powered[i]; + } + else { + b[i] = numext::conj(pos_j_base_powered[i]); + } + } + for (Index i = n; i < m - n; ++i) { + b[i] = ComplexScalar(0, 0); + } + for (Index i = m - n; i < m; ++i) { + if(FFTDir == FFT_FORWARD) { + b[i] = pos_j_base_powered[m-i]; + } + else { + b[i] = numext::conj(pos_j_base_powered[m-i]); + } + } + + scramble_FFT(a, m); + compute_1D_Butterfly<FFT_FORWARD>(a, m, log_len); + + scramble_FFT(b, m); + compute_1D_Butterfly<FFT_FORWARD>(b, m, log_len); + + for (Index i = 0; i < m; ++i) { + a[i] *= b[i]; + } + + scramble_FFT(a, m); + compute_1D_Butterfly<FFT_REVERSE>(a, m, log_len); + + //Do the scaling after ifft + for (Index i = 0; i < m; ++i) { + a[i] /= m; + } + + for (Index i = 0; i < n; ++i) { + if(FFTDir == FFT_FORWARD) { + data[i] = a[i] * numext::conj(pos_j_base_powered[i]); + } + else { + data[i] = a[i] * pos_j_base_powered[i]; + } + } + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE static void scramble_FFT(ComplexScalar* data, Index n) { + eigen_assert(isPowerOfTwo(n)); + Index j = 1; + for (Index i = 1; i < n; ++i){ + if (j > i) { + std::swap(data[j-1], data[i-1]); + } + Index m = n >> 1; + while (m >= 2 && j > m) { + j -= m; + m >>= 1; + } + j += m; + } + } + + template <int Dir> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void butterfly_2(ComplexScalar* data) { + ComplexScalar tmp = data[1]; + data[1] = data[0] - data[1]; + data[0] += tmp; + } + + template <int Dir> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void butterfly_4(ComplexScalar* data) { + ComplexScalar tmp[4]; + tmp[0] = data[0] + data[1]; + tmp[1] = data[0] - data[1]; + tmp[2] = data[2] + data[3]; + if (Dir == FFT_FORWARD) { + tmp[3] = ComplexScalar(0.0, -1.0) * (data[2] - data[3]); + } else { + tmp[3] = ComplexScalar(0.0, 1.0) * (data[2] - data[3]); + } + data[0] = tmp[0] + tmp[2]; + data[1] = tmp[1] + tmp[3]; + data[2] = tmp[0] - tmp[2]; + data[3] = tmp[1] - tmp[3]; + } + + template <int Dir> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void butterfly_8(ComplexScalar* data) { + ComplexScalar tmp_1[8]; + ComplexScalar tmp_2[8]; + + tmp_1[0] = data[0] + data[1]; + tmp_1[1] = data[0] - data[1]; + tmp_1[2] = data[2] + data[3]; + if (Dir == FFT_FORWARD) { + tmp_1[3] = (data[2] - data[3]) * ComplexScalar(0, -1); + } else { + tmp_1[3] = (data[2] - data[3]) * ComplexScalar(0, 1); + } + tmp_1[4] = data[4] + data[5]; + tmp_1[5] = data[4] - data[5]; + tmp_1[6] = data[6] + data[7]; + if (Dir == FFT_FORWARD) { + tmp_1[7] = (data[6] - data[7]) * ComplexScalar(0, -1); + } else { + tmp_1[7] = (data[6] - data[7]) * ComplexScalar(0, 1); + } + tmp_2[0] = tmp_1[0] + tmp_1[2]; + tmp_2[1] = tmp_1[1] + tmp_1[3]; + tmp_2[2] = tmp_1[0] - tmp_1[2]; + tmp_2[3] = tmp_1[1] - tmp_1[3]; + tmp_2[4] = tmp_1[4] + tmp_1[6]; +// SQRT2DIV2 = sqrt(2)/2 +#define SQRT2DIV2 0.7071067811865476 + if (Dir == FFT_FORWARD) { + tmp_2[5] = (tmp_1[5] + tmp_1[7]) * ComplexScalar(SQRT2DIV2, -SQRT2DIV2); + tmp_2[6] = (tmp_1[4] - tmp_1[6]) * ComplexScalar(0, -1); + tmp_2[7] = (tmp_1[5] - tmp_1[7]) * ComplexScalar(-SQRT2DIV2, -SQRT2DIV2); + } else { + tmp_2[5] = (tmp_1[5] + tmp_1[7]) * ComplexScalar(SQRT2DIV2, SQRT2DIV2); + tmp_2[6] = (tmp_1[4] - tmp_1[6]) * ComplexScalar(0, 1); + tmp_2[7] = (tmp_1[5] - tmp_1[7]) * ComplexScalar(-SQRT2DIV2, SQRT2DIV2); + } + data[0] = tmp_2[0] + tmp_2[4]; + data[1] = tmp_2[1] + tmp_2[5]; + data[2] = tmp_2[2] + tmp_2[6]; + data[3] = tmp_2[3] + tmp_2[7]; + data[4] = tmp_2[0] - tmp_2[4]; + data[5] = tmp_2[1] - tmp_2[5]; + data[6] = tmp_2[2] - tmp_2[6]; + data[7] = tmp_2[3] - tmp_2[7]; + } + + template <int Dir> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void butterfly_1D_merge( + ComplexScalar* data, Index n, Index n_power_of_2) { + // Original code: + // RealScalar wtemp = std::sin(M_PI/n); + // RealScalar wpi = -std::sin(2 * M_PI/n); + const RealScalar wtemp = m_sin_PI_div_n_LUT[n_power_of_2]; + const RealScalar wpi = (Dir == FFT_FORWARD) + ? m_minus_sin_2_PI_div_n_LUT[n_power_of_2] + : -m_minus_sin_2_PI_div_n_LUT[n_power_of_2]; + + const ComplexScalar wp(wtemp, wpi); + const ComplexScalar wp_one = wp + ComplexScalar(1, 0); + const ComplexScalar wp_one_2 = wp_one * wp_one; + const ComplexScalar wp_one_3 = wp_one_2 * wp_one; + const ComplexScalar wp_one_4 = wp_one_3 * wp_one; + const Index n2 = n / 2; + ComplexScalar w(1.0, 0.0); + for (Index i = 0; i < n2; i += 4) { + ComplexScalar temp0(data[i + n2] * w); + ComplexScalar temp1(data[i + 1 + n2] * w * wp_one); + ComplexScalar temp2(data[i + 2 + n2] * w * wp_one_2); + ComplexScalar temp3(data[i + 3 + n2] * w * wp_one_3); + w = w * wp_one_4; + + data[i + n2] = data[i] - temp0; + data[i] += temp0; + + data[i + 1 + n2] = data[i + 1] - temp1; + data[i + 1] += temp1; + + data[i + 2 + n2] = data[i + 2] - temp2; + data[i + 2] += temp2; + + data[i + 3 + n2] = data[i + 3] - temp3; + data[i + 3] += temp3; + } + } + + template <int Dir> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void compute_1D_Butterfly( + ComplexScalar* data, Index n, Index n_power_of_2) { + eigen_assert(isPowerOfTwo(n)); + if (n > 8) { + compute_1D_Butterfly<Dir>(data, n / 2, n_power_of_2 - 1); + compute_1D_Butterfly<Dir>(data + n / 2, n / 2, n_power_of_2 - 1); + butterfly_1D_merge<Dir>(data, n, n_power_of_2); + } else if (n == 8) { + butterfly_8<Dir>(data); + } else if (n == 4) { + butterfly_4<Dir>(data); + } else if (n == 2) { + butterfly_2<Dir>(data); + } + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index getBaseOffsetFromIndex(Index index, Index omitted_dim) const { + Index result = 0; + + if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { + for (int i = NumDims - 1; i > omitted_dim; --i) { + const Index partial_m_stride = m_strides[i] / m_dimensions[omitted_dim]; + const Index idx = index / partial_m_stride; + index -= idx * partial_m_stride; + result += idx * m_strides[i]; + } + result += index; + } + else { + for (Index i = 0; i < omitted_dim; ++i) { + const Index partial_m_stride = m_strides[i] / m_dimensions[omitted_dim]; + const Index idx = index / partial_m_stride; + index -= idx * partial_m_stride; + result += idx * m_strides[i]; + } + result += index; + } + // Value of index_coords[omitted_dim] is not determined to this step + return result; + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index getIndexFromOffset(Index base, Index omitted_dim, Index offset) const { + Index result = base + offset * m_strides[omitted_dim] ; + return result; + } + + protected: + Index m_size; + const FFT& m_fft; + Dimensions m_dimensions; + array<Index, NumDims> m_strides; + TensorEvaluator<ArgType, Device> m_impl; + CoeffReturnType* m_data; + const Device& m_device; + + // This will support a maximum FFT size of 2^32 for each dimension + // m_sin_PI_div_n_LUT[i] = (-2) * std::sin(M_PI / std::pow(2,i)) ^ 2; + const RealScalar m_sin_PI_div_n_LUT[32] = { + RealScalar(0.0), + RealScalar(-2), + RealScalar(-0.999999999999999), + RealScalar(-0.292893218813453), + RealScalar(-0.0761204674887130), + RealScalar(-0.0192147195967696), + RealScalar(-0.00481527332780311), + RealScalar(-0.00120454379482761), + RealScalar(-3.01181303795779e-04), + RealScalar(-7.52981608554592e-05), + RealScalar(-1.88247173988574e-05), + RealScalar(-4.70619042382852e-06), + RealScalar(-1.17654829809007e-06), + RealScalar(-2.94137117780840e-07), + RealScalar(-7.35342821488550e-08), + RealScalar(-1.83835707061916e-08), + RealScalar(-4.59589268710903e-09), + RealScalar(-1.14897317243732e-09), + RealScalar(-2.87243293150586e-10), + RealScalar( -7.18108232902250e-11), + RealScalar(-1.79527058227174e-11), + RealScalar(-4.48817645568941e-12), + RealScalar(-1.12204411392298e-12), + RealScalar(-2.80511028480785e-13), + RealScalar(-7.01277571201985e-14), + RealScalar(-1.75319392800498e-14), + RealScalar(-4.38298482001247e-15), + RealScalar(-1.09574620500312e-15), + RealScalar(-2.73936551250781e-16), + RealScalar(-6.84841378126949e-17), + RealScalar(-1.71210344531737e-17), + RealScalar(-4.28025861329343e-18) + }; + + // m_minus_sin_2_PI_div_n_LUT[i] = -std::sin(2 * M_PI / std::pow(2,i)); + const RealScalar m_minus_sin_2_PI_div_n_LUT[32] = { + RealScalar(0.0), + RealScalar(0.0), + RealScalar(-1.00000000000000e+00), + RealScalar(-7.07106781186547e-01), + RealScalar(-3.82683432365090e-01), + RealScalar(-1.95090322016128e-01), + RealScalar(-9.80171403295606e-02), + RealScalar(-4.90676743274180e-02), + RealScalar(-2.45412285229123e-02), + RealScalar(-1.22715382857199e-02), + RealScalar(-6.13588464915448e-03), + RealScalar(-3.06795676296598e-03), + RealScalar(-1.53398018628477e-03), + RealScalar(-7.66990318742704e-04), + RealScalar(-3.83495187571396e-04), + RealScalar(-1.91747597310703e-04), + RealScalar(-9.58737990959773e-05), + RealScalar(-4.79368996030669e-05), + RealScalar(-2.39684498084182e-05), + RealScalar(-1.19842249050697e-05), + RealScalar(-5.99211245264243e-06), + RealScalar(-2.99605622633466e-06), + RealScalar(-1.49802811316901e-06), + RealScalar(-7.49014056584716e-07), + RealScalar(-3.74507028292384e-07), + RealScalar(-1.87253514146195e-07), + RealScalar(-9.36267570730981e-08), + RealScalar(-4.68133785365491e-08), + RealScalar(-2.34066892682746e-08), + RealScalar(-1.17033446341373e-08), + RealScalar(-5.85167231706864e-09), + RealScalar(-2.92583615853432e-09) + }; +}; + +} // end namespace Eigen + +#endif // EIGEN_HAS_CONSTEXPR + + +#endif // EIGEN_CXX11_TENSOR_TENSOR_FFT_H diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorFixedSize.h b/unsupported/Eigen/CXX11/src/Tensor/TensorFixedSize.h new file mode 100644 index 000000000..fcee5f60d --- /dev/null +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorFixedSize.h @@ -0,0 +1,389 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_CXX11_TENSOR_TENSOR_FIXED_SIZE_H +#define EIGEN_CXX11_TENSOR_TENSOR_FIXED_SIZE_H + +namespace Eigen { + +/** \class TensorFixedSize + * \ingroup CXX11_Tensor_Module + * + * \brief The fixed sized version of the tensor class. + * + * The fixed sized equivalent of + * Eigen::Tensor<float, 3> t(3, 5, 7); + * is + * Eigen::TensorFixedSize<float, Size<3,5,7>> t; + */ + +template<typename Scalar_, typename Dimensions_, int Options_, typename IndexType> +class TensorFixedSize : public TensorBase<TensorFixedSize<Scalar_, Dimensions_, Options_, IndexType> > +{ + public: + typedef TensorFixedSize<Scalar_, Dimensions_, Options_, IndexType> Self; + typedef TensorBase<TensorFixedSize<Scalar_, Dimensions_, Options_, IndexType> > Base; + typedef typename Eigen::internal::nested<Self>::type Nested; + typedef typename internal::traits<Self>::StorageKind StorageKind; + typedef typename internal::traits<Self>::Index Index; + typedef Scalar_ Scalar; + typedef typename NumTraits<Scalar>::Real RealScalar; + typedef typename Base::CoeffReturnType CoeffReturnType; + + static const int Options = Options_; + + enum { + IsAligned = bool(EIGEN_MAX_ALIGN_BYTES>0), + Layout = Options_ & RowMajor ? RowMajor : ColMajor, + CoordAccess = true, + RawAccess = true + }; + + typedef Dimensions_ Dimensions; + static const std::size_t NumIndices = Dimensions::count; + + protected: + TensorStorage<Scalar, Dimensions, Options> m_storage; + + public: + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index rank() const { return NumIndices; } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index dimension(std::size_t n) const { return m_storage.dimensions()[n]; } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_storage.dimensions(); } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index size() const { return m_storage.size(); } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar *data() { return m_storage.data(); } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar *data() const { return m_storage.data(); } + + // This makes EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED + // work, because that uses base().coeffRef() - and we don't yet + // implement a similar class hierarchy + inline Self& base() { return *this; } + inline const Self& base() const { return *this; } + +#if EIGEN_HAS_VARIADIC_TEMPLATES + template<typename... IndexTypes> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar& coeff(Index firstIndex, IndexTypes... otherIndices) const + { + // The number of indices used to access a tensor coefficient must be equal to the rank of the tensor. + EIGEN_STATIC_ASSERT(sizeof...(otherIndices) + 1 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE) + return coeff(array<Index, NumIndices>{{firstIndex, otherIndices...}}); + } +#endif + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const Scalar& coeff(const array<Index, NumIndices>& indices) const + { + eigen_internal_assert(checkIndexRange(indices)); + return m_storage.data()[linearizedIndex(indices)]; + } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const Scalar& coeff(Index index) const + { + eigen_internal_assert(index >= 0 && index < size()); + return m_storage.data()[index]; + } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const Scalar& coeff() const + { + EIGEN_STATIC_ASSERT(NumIndices == 0, YOU_MADE_A_PROGRAMMING_MISTAKE); + return m_storage.data()[0]; + } + + +#if EIGEN_HAS_VARIADIC_TEMPLATES + template<typename... IndexTypes> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef(Index firstIndex, IndexTypes... otherIndices) + { + // The number of indices used to access a tensor coefficient must be equal to the rank of the tensor. + EIGEN_STATIC_ASSERT(sizeof...(otherIndices) + 1 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE) + return coeffRef(array<Index, NumIndices>{{firstIndex, otherIndices...}}); + } +#endif + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Scalar& coeffRef(const array<Index, NumIndices>& indices) + { + eigen_internal_assert(checkIndexRange(indices)); + return m_storage.data()[linearizedIndex(indices)]; + } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Scalar& coeffRef(Index index) + { + eigen_internal_assert(index >= 0 && index < size()); + return m_storage.data()[index]; + } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Scalar& coeffRef() + { + EIGEN_STATIC_ASSERT(NumIndices == 0, YOU_MADE_A_PROGRAMMING_MISTAKE); + return m_storage.data()[0]; + } + +#if EIGEN_HAS_VARIADIC_TEMPLATES + template<typename... IndexTypes> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar& operator()(Index firstIndex, IndexTypes... otherIndices) const + { + // The number of indices used to access a tensor coefficient must be equal to the rank of the tensor. + EIGEN_STATIC_ASSERT(sizeof...(otherIndices) + 1 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE) + return this->operator()(array<Index, NumIndices>{{firstIndex, otherIndices...}}); + } +#else + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const Scalar& operator()(Index i0, Index i1) const + { + if (Options&RowMajor) { + const Index index = i1 + i0 * m_storage.dimensions()[1]; + return m_storage.data()[index]; + } else { + const Index index = i0 + i1 * m_storage.dimensions()[0]; + return m_storage.data()[index]; + } + } + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const Scalar& operator()(Index i0, Index i1, Index i2) const + { + if (Options&RowMajor) { + const Index index = i2 + m_storage.dimensions()[2] * (i1 + m_storage.dimensions()[1] * i0); + return m_storage.data()[index]; + } else { + const Index index = i0 + m_storage.dimensions()[0] * (i1 + m_storage.dimensions()[1] * i2); + return m_storage.data()[index]; + } + } + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const Scalar& operator()(Index i0, Index i1, Index i2, Index i3) const + { + if (Options&RowMajor) { + const Index index = i3 + m_storage.dimensions()[3] * (i2 + m_storage.dimensions()[2] * (i1 + m_storage.dimensions()[1] * i0)); + return m_storage.data()[index]; + } else { + const Index index = i0 + m_storage.dimensions()[0] * (i1 + m_storage.dimensions()[1] * (i2 + m_storage.dimensions()[2] * i3)); + return m_storage.data()[index]; + } + } + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const Scalar& operator()(Index i0, Index i1, Index i2, Index i3, Index i4) const + { + if (Options&RowMajor) { + const Index index = i4 + m_storage.dimensions()[4] * (i3 + m_storage.dimensions()[3] * (i2 + m_storage.dimensions()[2] * (i1 + m_storage.dimensions()[1] * i0))); + return m_storage.data()[index]; + } else { + const Index index = i0 + m_storage.dimensions()[0] * (i1 + m_storage.dimensions()[1] * (i2 + m_storage.dimensions()[2] * (i3 + m_storage.dimensions()[3] * i4))); + return m_storage.data()[index]; + } + } +#endif + + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const Scalar& operator()(const array<Index, NumIndices>& indices) const + { + eigen_assert(checkIndexRange(indices)); + return coeff(indices); + } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const Scalar& operator()(Index index) const + { + eigen_internal_assert(index >= 0 && index < size()); + return coeff(index); + } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const Scalar& operator()() const + { + EIGEN_STATIC_ASSERT(NumIndices == 0, YOU_MADE_A_PROGRAMMING_MISTAKE); + return coeff(); + } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const Scalar& operator[](Index index) const + { + // The bracket operator is only for vectors, use the parenthesis operator instead. + EIGEN_STATIC_ASSERT(NumIndices == 1, YOU_MADE_A_PROGRAMMING_MISTAKE); + return coeff(index); + } + +#if EIGEN_HAS_VARIADIC_TEMPLATES + template<typename... IndexTypes> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& operator()(Index firstIndex, IndexTypes... otherIndices) + { + // The number of indices used to access a tensor coefficient must be equal to the rank of the tensor. + EIGEN_STATIC_ASSERT(sizeof...(otherIndices) + 1 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE) + return operator()(array<Index, NumIndices>{{firstIndex, otherIndices...}}); + } +#else + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Scalar& operator()(Index i0, Index i1) + { + if (Options&RowMajor) { + const Index index = i1 + i0 * m_storage.dimensions()[1]; + return m_storage.data()[index]; + } else { + const Index index = i0 + i1 * m_storage.dimensions()[0]; + return m_storage.data()[index]; + } + } + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Scalar& operator()(Index i0, Index i1, Index i2) + { + if (Options&RowMajor) { + const Index index = i2 + m_storage.dimensions()[2] * (i1 + m_storage.dimensions()[1] * i0); + return m_storage.data()[index]; + } else { + const Index index = i0 + m_storage.dimensions()[0] * (i1 + m_storage.dimensions()[1] * i2); + return m_storage.data()[index]; + } + } + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Scalar& operator()(Index i0, Index i1, Index i2, Index i3) + { + if (Options&RowMajor) { + const Index index = i3 + m_storage.dimensions()[3] * (i2 + m_storage.dimensions()[2] * (i1 + m_storage.dimensions()[1] * i0)); + return m_storage.data()[index]; + } else { + const Index index = i0 + m_storage.dimensions()[0] * (i1 + m_storage.dimensions()[1] * (i2 + m_storage.dimensions()[2] * i3)); + return m_storage.data()[index]; + } + } + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Scalar& operator()(Index i0, Index i1, Index i2, Index i3, Index i4) + { + if (Options&RowMajor) { + const Index index = i4 + m_storage.dimensions()[4] * (i3 + m_storage.dimensions()[3] * (i2 + m_storage.dimensions()[2] * (i1 + m_storage.dimensions()[1] * i0))); + return m_storage.data()[index]; + } else { + const Index index = i0 + m_storage.dimensions()[0] * (i1 + m_storage.dimensions()[1] * (i2 + m_storage.dimensions()[2] * (i3 + m_storage.dimensions()[3] * i4))); + return m_storage.data()[index]; + } + } +#endif + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Scalar& operator()(const array<Index, NumIndices>& indices) + { + eigen_assert(checkIndexRange(indices)); + return coeffRef(indices); + } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Scalar& operator()(Index index) + { + eigen_assert(index >= 0 && index < size()); + return coeffRef(index); + } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Scalar& operator()() + { + EIGEN_STATIC_ASSERT(NumIndices == 0, YOU_MADE_A_PROGRAMMING_MISTAKE); + return coeffRef(); + } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Scalar& operator[](Index index) + { + // The bracket operator is only for vectors, use the parenthesis operator instead + EIGEN_STATIC_ASSERT(NumIndices == 1, YOU_MADE_A_PROGRAMMING_MISTAKE) + return coeffRef(index); + } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE TensorFixedSize() + : m_storage() + { + } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE TensorFixedSize(const Self& other) + : m_storage(other.m_storage) + { + } + +#if EIGEN_HAS_RVALUE_REFERENCES + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorFixedSize(Self&& other) + : m_storage(other.m_storage) + { + } +#endif + + template<typename OtherDerived> + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE TensorFixedSize(const TensorBase<OtherDerived, ReadOnlyAccessors>& other) + { + typedef TensorAssignOp<TensorFixedSize, const OtherDerived> Assign; + Assign assign(*this, other.derived()); + internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice()); + } + template<typename OtherDerived> + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE TensorFixedSize(const TensorBase<OtherDerived, WriteAccessors>& other) + { + typedef TensorAssignOp<TensorFixedSize, const OtherDerived> Assign; + Assign assign(*this, other.derived()); + internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice()); + } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE TensorFixedSize& operator=(const TensorFixedSize& other) + { + // FIXME: check that the dimensions of other match the dimensions of *this. + // Unfortunately this isn't possible yet when the rhs is an expression. + typedef TensorAssignOp<Self, const TensorFixedSize> Assign; + Assign assign(*this, other); + internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice()); + return *this; + } + template<typename OtherDerived> + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE TensorFixedSize& operator=(const OtherDerived& other) + { + // FIXME: check that the dimensions of other match the dimensions of *this. + // Unfortunately this isn't possible yet when the rhs is an expression. + typedef TensorAssignOp<Self, const OtherDerived> Assign; + Assign assign(*this, other); + internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice()); + return *this; + } + + protected: + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE bool checkIndexRange(const array<Index, NumIndices>& /*indices*/) const + { + using internal::array_apply_and_reduce; + using internal::array_zip_and_reduce; + using internal::greater_equal_zero_op; + using internal::logical_and_op; + using internal::lesser_op; + + return true; + // check whether the indices are all >= 0 + /* array_apply_and_reduce<logical_and_op, greater_equal_zero_op>(indices) && + // check whether the indices fit in the dimensions + array_zip_and_reduce<logical_and_op, lesser_op>(indices, m_storage.dimensions());*/ + } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Index linearizedIndex(const array<Index, NumIndices>& indices) const + { + if (Options&RowMajor) { + return m_storage.dimensions().IndexOfRowMajor(indices); + } else { + return m_storage.dimensions().IndexOfColMajor(indices); + } + } +}; + + +} // end namespace Eigen + +#endif // EIGEN_CXX11_TENSOR_TENSOR_FIXED_SIZE_H diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorForcedEval.h b/unsupported/Eigen/CXX11/src/Tensor/TensorForcedEval.h new file mode 100644 index 000000000..bbd5eb374 --- /dev/null +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorForcedEval.h @@ -0,0 +1,167 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_CXX11_TENSOR_TENSOR_FORCED_EVAL_H +#define EIGEN_CXX11_TENSOR_TENSOR_FORCED_EVAL_H + +namespace Eigen { + +/** \class TensorForcedEval + * \ingroup CXX11_Tensor_Module + * + * \brief Tensor reshaping class. + * + * + */ +/// template <class> class MakePointer_ is added to convert the host pointer to the device pointer. +/// It is added due to the fact that for our device compiler T* is not allowed. +/// If we wanted to use the same Evaluator functions we have to convert that type to our pointer T. +/// This is done through our MakePointer_ class. By default the Type in the MakePointer_<T> is T* . +/// Therefore, by adding the default value, we managed to convert the type and it does not break any +/// existing code as its default value is T*. +namespace internal { +template<typename XprType, template <class> class MakePointer_> +struct traits<TensorForcedEvalOp<XprType, MakePointer_> > +{ + // Type promotion to handle the case where the types of the lhs and the rhs are different. + typedef typename XprType::Scalar Scalar; + typedef traits<XprType> XprTraits; + typedef typename traits<XprType>::StorageKind StorageKind; + typedef typename traits<XprType>::Index Index; + typedef typename XprType::Nested Nested; + typedef typename remove_reference<Nested>::type _Nested; + static const int NumDimensions = XprTraits::NumDimensions; + static const int Layout = XprTraits::Layout; + + enum { + Flags = 0 + }; + template <class T> struct MakePointer { + // Intermediate typedef to workaround MSVC issue. + typedef MakePointer_<T> MakePointerT; + typedef typename MakePointerT::Type Type; + }; +}; + +template<typename XprType, template <class> class MakePointer_> +struct eval<TensorForcedEvalOp<XprType, MakePointer_>, Eigen::Dense> +{ + typedef const TensorForcedEvalOp<XprType, MakePointer_>& type; +}; + +template<typename XprType, template <class> class MakePointer_> +struct nested<TensorForcedEvalOp<XprType, MakePointer_>, 1, typename eval<TensorForcedEvalOp<XprType, MakePointer_> >::type> +{ + typedef TensorForcedEvalOp<XprType, MakePointer_> type; +}; + +} // end namespace internal + + + +template<typename XprType, template <class> class MakePointer_> +class TensorForcedEvalOp : public TensorBase<TensorForcedEvalOp<XprType, MakePointer_>, ReadOnlyAccessors> +{ + public: + typedef typename Eigen::internal::traits<TensorForcedEvalOp>::Scalar Scalar; + typedef typename Eigen::NumTraits<Scalar>::Real RealScalar; + typedef typename internal::remove_const<typename XprType::CoeffReturnType>::type CoeffReturnType; + typedef typename Eigen::internal::nested<TensorForcedEvalOp>::type Nested; + typedef typename Eigen::internal::traits<TensorForcedEvalOp>::StorageKind StorageKind; + typedef typename Eigen::internal::traits<TensorForcedEvalOp>::Index Index; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorForcedEvalOp(const XprType& expr) + : m_xpr(expr) {} + + EIGEN_DEVICE_FUNC + const typename internal::remove_all<typename XprType::Nested>::type& + expression() const { return m_xpr; } + + protected: + typename XprType::Nested m_xpr; +}; + + +template<typename ArgType, typename Device, template <class> class MakePointer_> +struct TensorEvaluator<const TensorForcedEvalOp<ArgType, MakePointer_>, Device> +{ + typedef TensorForcedEvalOp<ArgType, MakePointer_> XprType; + typedef typename ArgType::Scalar Scalar; + typedef typename TensorEvaluator<ArgType, Device>::Dimensions Dimensions; + typedef typename XprType::Index Index; + typedef typename XprType::CoeffReturnType CoeffReturnType; + typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType; + static const int PacketSize = internal::unpacket_traits<PacketReturnType>::size; + + enum { + IsAligned = true, + PacketAccess = (PacketSize > 1), + Layout = TensorEvaluator<ArgType, Device>::Layout, + RawAccess = true + }; + + EIGEN_DEVICE_FUNC TensorEvaluator(const XprType& op, const Device& device) + /// op_ is used for sycl + : m_impl(op.expression(), device), m_op(op.expression()), m_device(device), m_buffer(NULL) + { } + + EIGEN_DEVICE_FUNC const Dimensions& dimensions() const { return m_impl.dimensions(); } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(CoeffReturnType*) { + const Index numValues = internal::array_prod(m_impl.dimensions()); + m_buffer = (CoeffReturnType*)m_device.allocate(numValues * sizeof(CoeffReturnType)); + // Should initialize the memory in case we're dealing with non POD types. + if (NumTraits<CoeffReturnType>::RequireInitialization) { + for (Index i = 0; i < numValues; ++i) { + new(m_buffer+i) CoeffReturnType(); + } + } + typedef TensorEvalToOp< const typename internal::remove_const<ArgType>::type > EvalTo; + EvalTo evalToTmp(m_buffer, m_op); + const bool PacketAccess = internal::IsVectorizable<Device, const ArgType>::value; + internal::TensorExecutor<const EvalTo, typename internal::remove_const<Device>::type, PacketAccess>::run(evalToTmp, m_device); + return true; + } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() { + m_device.deallocate(m_buffer); + m_buffer = NULL; + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const + { + return m_buffer[index]; + } + + template<int LoadMode> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const + { + return internal::ploadt<PacketReturnType, LoadMode>(m_buffer + index); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const { + return TensorOpCost(sizeof(CoeffReturnType), 0, 0, vectorized, PacketSize); + } + + EIGEN_DEVICE_FUNC typename MakePointer<Scalar>::Type data() const { return m_buffer; } + + /// required by sycl in order to extract the sycl accessor + const TensorEvaluator<ArgType, Device>& impl() { return m_impl; } + /// used by sycl in order to build the sycl buffer + const Device& device() const{return m_device;} + private: + TensorEvaluator<ArgType, Device> m_impl; + const ArgType m_op; + const Device& m_device; + typename MakePointer<CoeffReturnType>::Type m_buffer; +}; + + +} // end namespace Eigen + +#endif // EIGEN_CXX11_TENSOR_TENSOR_FORCED_EVAL_H diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorForwardDeclarations.h b/unsupported/Eigen/CXX11/src/Tensor/TensorForwardDeclarations.h new file mode 100644 index 000000000..52b803d7f --- /dev/null +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorForwardDeclarations.h @@ -0,0 +1,109 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_CXX11_TENSOR_TENSOR_FORWARD_DECLARATIONS_H +#define EIGEN_CXX11_TENSOR_TENSOR_FORWARD_DECLARATIONS_H + +namespace Eigen { + +// MakePointer class is used as a container of the adress space of the pointer +// on the host and on the device. From the host side it generates the T* pointer +// and when EIGEN_USE_SYCL is used it construct a buffer with a map_allocator to +// T* m_data on the host. It is always called on the device. +// Specialisation of MakePointer class for creating the sycl buffer with +// map_allocator. +template<typename T> struct MakePointer { + typedef T* Type; +}; + +template<typename PlainObjectType, int Options_ = Unaligned, template <class> class MakePointer_ = MakePointer> class TensorMap; +template<typename Scalar_, int NumIndices_, int Options_ = 0, typename IndexType = DenseIndex> class Tensor; +template<typename Scalar_, typename Dimensions, int Options_ = 0, typename IndexType = DenseIndex> class TensorFixedSize; +template<typename PlainObjectType> class TensorRef; +template<typename Derived, int AccessLevel> class TensorBase; + +template<typename NullaryOp, typename PlainObjectType> class TensorCwiseNullaryOp; +template<typename UnaryOp, typename XprType> class TensorCwiseUnaryOp; +template<typename BinaryOp, typename LeftXprType, typename RightXprType> class TensorCwiseBinaryOp; +template<typename TernaryOp, typename Arg1XprType, typename Arg2XprType, typename Arg3XprType> class TensorCwiseTernaryOp; +template<typename IfXprType, typename ThenXprType, typename ElseXprType> class TensorSelectOp; +template<typename Op, typename Dims, typename XprType, template <class> class MakePointer_ = MakePointer > class TensorReductionOp; +template<typename XprType> class TensorIndexTupleOp; +template<typename ReduceOp, typename Dims, typename XprType> class TensorTupleReducerOp; +template<typename Axis, typename LeftXprType, typename RightXprType> class TensorConcatenationOp; +template<typename Dimensions, typename LeftXprType, typename RightXprType> class TensorContractionOp; +template<typename TargetType, typename XprType> class TensorConversionOp; +template<typename Dimensions, typename InputXprType, typename KernelXprType> class TensorConvolutionOp; +template<typename FFT, typename XprType, int FFTDataType, int FFTDirection> class TensorFFTOp; +template<typename PatchDim, typename XprType> class TensorPatchOp; +template<DenseIndex Rows, DenseIndex Cols, typename XprType> class TensorImagePatchOp; +template<DenseIndex Planes, DenseIndex Rows, DenseIndex Cols, typename XprType> class TensorVolumePatchOp; +template<typename Broadcast, typename XprType> class TensorBroadcastingOp; +template<DenseIndex DimId, typename XprType> class TensorChippingOp; +template<typename NewDimensions, typename XprType> class TensorReshapingOp; +template<typename XprType> class TensorLayoutSwapOp; +template<typename StartIndices, typename Sizes, typename XprType> class TensorSlicingOp; +template<typename ReverseDimensions, typename XprType> class TensorReverseOp; +template<typename PaddingDimensions, typename XprType> class TensorPaddingOp; +template<typename Shuffle, typename XprType> class TensorShufflingOp; +template<typename Strides, typename XprType> class TensorStridingOp; +template<typename StartIndices, typename StopIndices, typename Strides, typename XprType> class TensorStridingSlicingOp; +template<typename Strides, typename XprType> class TensorInflationOp; +template<typename Generator, typename XprType> class TensorGeneratorOp; +template<typename LeftXprType, typename RightXprType> class TensorAssignOp; +template<typename Op, typename XprType> class TensorScanOp; + +template<typename CustomUnaryFunc, typename XprType> class TensorCustomUnaryOp; +template<typename CustomBinaryFunc, typename LhsXprType, typename RhsXprType> class TensorCustomBinaryOp; + +template<typename XprType, template <class> class MakePointer_ = MakePointer> class TensorEvalToOp; +template<typename XprType, template <class> class MakePointer_ = MakePointer> class TensorForcedEvalOp; + +template<typename ExpressionType, typename DeviceType> class TensorDevice; +template<typename Derived, typename Device> struct TensorEvaluator; + +struct DefaultDevice; +struct ThreadPoolDevice; +struct GpuDevice; +struct SyclDevice; + +enum FFTResultType { + RealPart = 0, + ImagPart = 1, + BothParts = 2 +}; + +enum FFTDirection { + FFT_FORWARD = 0, + FFT_REVERSE = 1 +}; + + +namespace internal { + +template <typename Device, typename Expression> +struct IsVectorizable { + static const bool value = TensorEvaluator<Expression, Device>::PacketAccess; +}; + +template <typename Expression> +struct IsVectorizable<GpuDevice, Expression> { + static const bool value = TensorEvaluator<Expression, GpuDevice>::PacketAccess && + TensorEvaluator<Expression, GpuDevice>::IsAligned; +}; + +template <typename Expression, typename Device, + bool Vectorizable = IsVectorizable<Device, Expression>::value> +class TensorExecutor; + +} // end namespace internal + +} // end namespace Eigen + +#endif // EIGEN_CXX11_TENSOR_TENSOR_FORWARD_DECLARATIONS_H diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorFunctors.h b/unsupported/Eigen/CXX11/src/Tensor/TensorFunctors.h new file mode 100644 index 000000000..d73f6dc68 --- /dev/null +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorFunctors.h @@ -0,0 +1,489 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_CXX11_TENSOR_TENSOR_FUNCTORS_H +#define EIGEN_CXX11_TENSOR_TENSOR_FUNCTORS_H + +namespace Eigen { +namespace internal { + + +/** \internal + * \brief Template functor to compute the modulo between an array and a scalar. + */ +template <typename Scalar> +struct scalar_mod_op { + EIGEN_DEVICE_FUNC scalar_mod_op(const Scalar& divisor) : m_divisor(divisor) {} + EIGEN_DEVICE_FUNC inline Scalar operator() (const Scalar& a) const { return a % m_divisor; } + const Scalar m_divisor; +}; +template <typename Scalar> +struct functor_traits<scalar_mod_op<Scalar> > +{ enum { Cost = scalar_div_cost<Scalar,false>::value, PacketAccess = false }; }; + + +/** \internal + * \brief Template functor to compute the modulo between 2 arrays. + */ +template <typename Scalar> +struct scalar_mod2_op { + EIGEN_EMPTY_STRUCT_CTOR(scalar_mod2_op); + EIGEN_DEVICE_FUNC inline Scalar operator() (const Scalar& a, const Scalar& b) const { return a % b; } +}; +template <typename Scalar> +struct functor_traits<scalar_mod2_op<Scalar> > +{ enum { Cost = scalar_div_cost<Scalar,false>::value, PacketAccess = false }; }; + +template <typename Scalar> +struct scalar_fmod_op { + EIGEN_EMPTY_STRUCT_CTOR(scalar_fmod_op); + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar + operator()(const Scalar& a, const Scalar& b) const { + return numext::fmod(a, b); + } +}; +template <typename Scalar> +struct functor_traits<scalar_fmod_op<Scalar> > { + enum { Cost = 13, // Reciprocal throughput of FPREM on Haswell. + PacketAccess = false }; +}; + + +/** \internal + * \brief Template functor to compute the sigmoid of a scalar + * \sa class CwiseUnaryOp, ArrayBase::sigmoid() + */ +template <typename T> +struct scalar_sigmoid_op { + EIGEN_EMPTY_STRUCT_CTOR(scalar_sigmoid_op) + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T operator()(const T& x) const { + const T one = T(1); + return one / (one + numext::exp(-x)); + } + + template <typename Packet> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + Packet packetOp(const Packet& x) const { + const Packet one = pset1<Packet>(T(1)); + return pdiv(one, padd(one, pexp(pnegate(x)))); + } +}; + +template <typename T> +struct functor_traits<scalar_sigmoid_op<T> > { + enum { + Cost = NumTraits<T>::AddCost * 2 + NumTraits<T>::MulCost * 6, + PacketAccess = packet_traits<T>::HasAdd && packet_traits<T>::HasDiv && + packet_traits<T>::HasNegate && packet_traits<T>::HasExp + }; +}; + + +template<typename Reducer, typename Device> +struct reducer_traits { + enum { + Cost = 1, + PacketAccess = false + }; +}; + +// Standard reduction functors +template <typename T> struct SumReducer +{ + static const bool PacketAccess = packet_traits<T>::HasAdd; + static const bool IsStateful = false; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const T t, T* accum) const { + internal::scalar_sum_op<T> sum_op; + *accum = sum_op(*accum, t); + } + template <typename Packet> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reducePacket(const Packet& p, Packet* accum) const { + (*accum) = padd<Packet>(*accum, p); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T initialize() const { + internal::scalar_cast_op<int, T> conv; + return conv(0); + } + template <typename Packet> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet initializePacket() const { + return pset1<Packet>(initialize()); + } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalize(const T accum) const { + return accum; + } + template <typename Packet> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet finalizePacket(const Packet& vaccum) const { + return vaccum; + } + template <typename Packet> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalizeBoth(const T saccum, const Packet& vaccum) const { + internal::scalar_sum_op<T> sum_op; + return sum_op(saccum, predux(vaccum)); + } +}; + +template <typename T, typename Device> +struct reducer_traits<SumReducer<T>, Device> { + enum { + Cost = NumTraits<T>::AddCost, + PacketAccess = PacketType<T, Device>::HasAdd + }; +}; + + +template <typename T> struct MeanReducer +{ + static const bool PacketAccess = packet_traits<T>::HasAdd && !NumTraits<T>::IsInteger; + static const bool IsStateful = true; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + MeanReducer() : scalarCount_(0), packetCount_(0) { } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const T t, T* accum) { + internal::scalar_sum_op<T> sum_op; + *accum = sum_op(*accum, t); + scalarCount_++; + } + template <typename Packet> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reducePacket(const Packet& p, Packet* accum) { + (*accum) = padd<Packet>(*accum, p); + packetCount_++; + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T initialize() const { + internal::scalar_cast_op<int, T> conv; + return conv(0); + } + template <typename Packet> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet initializePacket() const { + return pset1<Packet>(initialize()); + } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalize(const T accum) const { + return accum / scalarCount_; + } + template <typename Packet> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet finalizePacket(const Packet& vaccum) const { + return pdiv(vaccum, pset1<Packet>(packetCount_)); + } + template <typename Packet> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalizeBoth(const T saccum, const Packet& vaccum) const { + internal::scalar_sum_op<T> sum_op; + return sum_op(saccum, predux(vaccum)) / (scalarCount_ + packetCount_ * unpacket_traits<Packet>::size); + } + + protected: + DenseIndex scalarCount_; + DenseIndex packetCount_; +}; + +template <typename T, typename Device> +struct reducer_traits<MeanReducer<T>, Device> { + enum { + Cost = NumTraits<T>::AddCost, + PacketAccess = PacketType<T, Device>::HasAdd + }; +}; + + +template <typename T, bool IsMax = true, bool IsInteger = true> +struct MinMaxBottomValue { + EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE T bottom_value() { + return Eigen::NumTraits<T>::lowest(); + } +}; +template <typename T> +struct MinMaxBottomValue<T, true, false> { + EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE T bottom_value() { + return -Eigen::NumTraits<T>::infinity(); + } +}; +template <typename T> +struct MinMaxBottomValue<T, false, true> { + EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE T bottom_value() { + return Eigen::NumTraits<T>::highest(); + } +}; +template <typename T> +struct MinMaxBottomValue<T, false, false> { + EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE T bottom_value() { + return Eigen::NumTraits<T>::infinity(); + } +}; + + +template <typename T> struct MaxReducer +{ + static const bool PacketAccess = packet_traits<T>::HasMax; + static const bool IsStateful = false; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const T t, T* accum) const { + if (t > *accum) { *accum = t; } + } + template <typename Packet> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reducePacket(const Packet& p, Packet* accum) const { + (*accum) = pmax<Packet>(*accum, p); + } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T initialize() const { + return MinMaxBottomValue<T, true, Eigen::NumTraits<T>::IsInteger>::bottom_value(); + } + template <typename Packet> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet initializePacket() const { + return pset1<Packet>(initialize()); + } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalize(const T accum) const { + return accum; + } + template <typename Packet> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet finalizePacket(const Packet& vaccum) const { + return vaccum; + } + template <typename Packet> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalizeBoth(const T saccum, const Packet& vaccum) const { + return numext::maxi(saccum, predux_max(vaccum)); + } +}; + +template <typename T, typename Device> +struct reducer_traits<MaxReducer<T>, Device> { + enum { + Cost = NumTraits<T>::AddCost, + PacketAccess = PacketType<T, Device>::HasMax + }; +}; + + +template <typename T> struct MinReducer +{ + static const bool PacketAccess = packet_traits<T>::HasMin; + static const bool IsStateful = false; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const T t, T* accum) const { + if (t < *accum) { *accum = t; } + } + template <typename Packet> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reducePacket(const Packet& p, Packet* accum) const { + (*accum) = pmin<Packet>(*accum, p); + } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T initialize() const { + return MinMaxBottomValue<T, false, Eigen::NumTraits<T>::IsInteger>::bottom_value(); + } + template <typename Packet> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet initializePacket() const { + return pset1<Packet>(initialize()); + } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalize(const T accum) const { + return accum; + } + template <typename Packet> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet finalizePacket(const Packet& vaccum) const { + return vaccum; + } + template <typename Packet> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalizeBoth(const T saccum, const Packet& vaccum) const { + return numext::mini(saccum, predux_min(vaccum)); + } +}; + +template <typename T, typename Device> +struct reducer_traits<MinReducer<T>, Device> { + enum { + Cost = NumTraits<T>::AddCost, + PacketAccess = PacketType<T, Device>::HasMin + }; +}; + + +template <typename T> struct ProdReducer +{ + static const bool PacketAccess = packet_traits<T>::HasMul; + static const bool IsStateful = false; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const T t, T* accum) const { + internal::scalar_product_op<T> prod_op; + (*accum) = prod_op(*accum, t); + } + template <typename Packet> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reducePacket(const Packet& p, Packet* accum) const { + (*accum) = pmul<Packet>(*accum, p); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T initialize() const { + internal::scalar_cast_op<int, T> conv; + return conv(1); + } + template <typename Packet> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet initializePacket() const { + return pset1<Packet>(initialize()); + } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalize(const T accum) const { + return accum; + } + template <typename Packet> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet finalizePacket(const Packet& vaccum) const { + return vaccum; + } + template <typename Packet> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalizeBoth(const T saccum, const Packet& vaccum) const { + internal::scalar_product_op<T> prod_op; + return prod_op(saccum, predux_mul(vaccum)); + } +}; + +template <typename T, typename Device> +struct reducer_traits<ProdReducer<T>, Device> { + enum { + Cost = NumTraits<T>::MulCost, + PacketAccess = PacketType<T, Device>::HasMul + }; +}; + + +struct AndReducer +{ + static const bool PacketAccess = false; + static const bool IsStateful = false; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(bool t, bool* accum) const { + *accum = *accum && t; + } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool initialize() const { + return true; + } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool finalize(bool accum) const { + return accum; + } +}; + +template <typename Device> +struct reducer_traits<AndReducer, Device> { + enum { + Cost = 1, + PacketAccess = false + }; +}; + + +struct OrReducer { + static const bool PacketAccess = false; + static const bool IsStateful = false; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(bool t, bool* accum) const { + *accum = *accum || t; + } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool initialize() const { + return false; + } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool finalize(bool accum) const { + return accum; + } +}; + +template <typename Device> +struct reducer_traits<OrReducer, Device> { + enum { + Cost = 1, + PacketAccess = false + }; +}; + + +// Argmin/Argmax reducers +template <typename T> struct ArgMaxTupleReducer +{ + static const bool PacketAccess = false; + static const bool IsStateful = false; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const T t, T* accum) const { + if (t.second > accum->second) { *accum = t; } + } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T initialize() const { + return T(0, NumTraits<typename T::second_type>::lowest()); + } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalize(const T& accum) const { + return accum; + } +}; + +template <typename T, typename Device> +struct reducer_traits<ArgMaxTupleReducer<T>, Device> { + enum { + Cost = NumTraits<T>::AddCost, + PacketAccess = false + }; +}; + + +template <typename T> struct ArgMinTupleReducer +{ + static const bool PacketAccess = false; + static const bool IsStateful = false; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const T& t, T* accum) const { + if (t.second < accum->second) { *accum = t; } + } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T initialize() const { + return T(0, NumTraits<typename T::second_type>::highest()); + } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalize(const T& accum) const { + return accum; + } +}; + +template <typename T, typename Device> +struct reducer_traits<ArgMinTupleReducer<T>, Device> { + enum { + Cost = NumTraits<T>::AddCost, + PacketAccess = false + }; +}; + + +template <typename T, typename Index, size_t NumDims> +class GaussianGenerator { + public: + static const bool PacketAccess = false; + + EIGEN_DEVICE_FUNC GaussianGenerator(const array<T, NumDims>& means, + const array<T, NumDims>& std_devs) + : m_means(means) + { + for (size_t i = 0; i < NumDims; ++i) { + m_two_sigmas[i] = std_devs[i] * std_devs[i] * 2; + } + } + + EIGEN_DEVICE_FUNC T operator()(const array<Index, NumDims>& coordinates) const { + T tmp = T(0); + for (size_t i = 0; i < NumDims; ++i) { + T offset = coordinates[i] - m_means[i]; + tmp += offset * offset / m_two_sigmas[i]; + } + return numext::exp(-tmp); + } + + private: + array<T, NumDims> m_means; + array<T, NumDims> m_two_sigmas; +}; + +template <typename T, typename Index, size_t NumDims> +struct functor_traits<GaussianGenerator<T, Index, NumDims> > { + enum { + Cost = NumDims * (2 * NumTraits<T>::AddCost + NumTraits<T>::MulCost + + functor_traits<scalar_quotient_op<T, T> >::Cost) + + functor_traits<scalar_exp_op<T> >::Cost, + PacketAccess = GaussianGenerator<T, Index, NumDims>::PacketAccess + }; +}; + +} // end namespace internal +} // end namespace Eigen + +#endif // EIGEN_CXX11_TENSOR_TENSOR_FUNCTORS_H diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorGenerator.h b/unsupported/Eigen/CXX11/src/Tensor/TensorGenerator.h new file mode 100644 index 000000000..eb1d4934e --- /dev/null +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorGenerator.h @@ -0,0 +1,185 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2015 Benoit Steiner <benoit.steiner.goog@gmail.com> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_CXX11_TENSOR_TENSOR_GENERATOR_H +#define EIGEN_CXX11_TENSOR_TENSOR_GENERATOR_H + +namespace Eigen { + +/** \class TensorGenerator + * \ingroup CXX11_Tensor_Module + * + * \brief Tensor generator class. + * + * + */ +namespace internal { +template<typename Generator, typename XprType> +struct traits<TensorGeneratorOp<Generator, XprType> > : public traits<XprType> +{ + typedef typename XprType::Scalar Scalar; + typedef traits<XprType> XprTraits; + typedef typename XprTraits::StorageKind StorageKind; + typedef typename XprTraits::Index Index; + typedef typename XprType::Nested Nested; + typedef typename remove_reference<Nested>::type _Nested; + static const int NumDimensions = XprTraits::NumDimensions; + static const int Layout = XprTraits::Layout; +}; + +template<typename Generator, typename XprType> +struct eval<TensorGeneratorOp<Generator, XprType>, Eigen::Dense> +{ + typedef const TensorGeneratorOp<Generator, XprType>& type; +}; + +template<typename Generator, typename XprType> +struct nested<TensorGeneratorOp<Generator, XprType>, 1, typename eval<TensorGeneratorOp<Generator, XprType> >::type> +{ + typedef TensorGeneratorOp<Generator, XprType> type; +}; + +} // end namespace internal + + + +template<typename Generator, typename XprType> +class TensorGeneratorOp : public TensorBase<TensorGeneratorOp<Generator, XprType>, ReadOnlyAccessors> +{ + public: + typedef typename Eigen::internal::traits<TensorGeneratorOp>::Scalar Scalar; + typedef typename Eigen::NumTraits<Scalar>::Real RealScalar; + typedef typename XprType::CoeffReturnType CoeffReturnType; + typedef typename Eigen::internal::nested<TensorGeneratorOp>::type Nested; + typedef typename Eigen::internal::traits<TensorGeneratorOp>::StorageKind StorageKind; + typedef typename Eigen::internal::traits<TensorGeneratorOp>::Index Index; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorGeneratorOp(const XprType& expr, const Generator& generator) + : m_xpr(expr), m_generator(generator) {} + + EIGEN_DEVICE_FUNC + const Generator& generator() const { return m_generator; } + + EIGEN_DEVICE_FUNC + const typename internal::remove_all<typename XprType::Nested>::type& + expression() const { return m_xpr; } + + protected: + typename XprType::Nested m_xpr; + const Generator m_generator; +}; + + +// Eval as rvalue +template<typename Generator, typename ArgType, typename Device> +struct TensorEvaluator<const TensorGeneratorOp<Generator, ArgType>, Device> +{ + typedef TensorGeneratorOp<Generator, ArgType> XprType; + typedef typename XprType::Index Index; + typedef typename TensorEvaluator<ArgType, Device>::Dimensions Dimensions; + static const int NumDims = internal::array_size<Dimensions>::value; + typedef typename XprType::Scalar Scalar; + typedef typename XprType::CoeffReturnType CoeffReturnType; + typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType; + enum { + IsAligned = false, + PacketAccess = (internal::unpacket_traits<PacketReturnType>::size > 1), + BlockAccess = false, + Layout = TensorEvaluator<ArgType, Device>::Layout, + CoordAccess = false, // to be implemented + RawAccess = false + }; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) + : m_generator(op.generator()) + { + TensorEvaluator<ArgType, Device> impl(op.expression(), device); + m_dimensions = impl.dimensions(); + + if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { + m_strides[0] = 1; + for (int i = 1; i < NumDims; ++i) { + m_strides[i] = m_strides[i - 1] * m_dimensions[i - 1]; + } + } else { + m_strides[NumDims - 1] = 1; + for (int i = NumDims - 2; i >= 0; --i) { + m_strides[i] = m_strides[i + 1] * m_dimensions[i + 1]; + } + } + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* /*data*/) { + return true; + } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() { + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const + { + array<Index, NumDims> coords; + extract_coordinates(index, coords); + return m_generator(coords); + } + + template<int LoadMode> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const + { + const int packetSize = internal::unpacket_traits<PacketReturnType>::size; + EIGEN_STATIC_ASSERT((packetSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE) + eigen_assert(index+packetSize-1 < dimensions().TotalSize()); + + EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[packetSize]; + for (int i = 0; i < packetSize; ++i) { + values[i] = coeff(index+i); + } + PacketReturnType rslt = internal::pload<PacketReturnType>(values); + return rslt; + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost + costPerCoeff(bool) const { + // TODO(rmlarsen): This is just a placeholder. Define interface to make + // generators return their cost. + return TensorOpCost(0, 0, TensorOpCost::AddCost<Scalar>() + + TensorOpCost::MulCost<Scalar>()); + } + + EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; } + + protected: + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + void extract_coordinates(Index index, array<Index, NumDims>& coords) const { + if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { + for (int i = NumDims - 1; i > 0; --i) { + const Index idx = index / m_strides[i]; + index -= idx * m_strides[i]; + coords[i] = idx; + } + coords[0] = index; + } else { + for (int i = 0; i < NumDims - 1; ++i) { + const Index idx = index / m_strides[i]; + index -= idx * m_strides[i]; + coords[i] = idx; + } + coords[NumDims-1] = index; + } + } + + Dimensions m_dimensions; + array<Index, NumDims> m_strides; + Generator m_generator; +}; + +} // end namespace Eigen + +#endif // EIGEN_CXX11_TENSOR_TENSOR_GENERATOR_H diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorGlobalFunctions.h b/unsupported/Eigen/CXX11/src/Tensor/TensorGlobalFunctions.h new file mode 100644 index 000000000..665b861cf --- /dev/null +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorGlobalFunctions.h @@ -0,0 +1,33 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2016 Eugene Brevdo <ebrevdo@gmail.com> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_CXX11_TENSOR_TENSOR_GLOBAL_FUNCTIONS_H +#define EIGEN_CXX11_TENSOR_TENSOR_GLOBAL_FUNCTIONS_H + +namespace Eigen { + +/** \cpp11 \returns an expression of the coefficient-wise betainc(\a x, \a a, \a b) to the given tensors. + * + * This function computes the regularized incomplete beta function (integral). + * + */ +template <typename ADerived, typename BDerived, typename XDerived> +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const + TensorCwiseTernaryOp<internal::scalar_betainc_op<typename XDerived::Scalar>, + const ADerived, const BDerived, const XDerived> + betainc(const ADerived& a, const BDerived& b, const XDerived& x) { + return TensorCwiseTernaryOp< + internal::scalar_betainc_op<typename XDerived::Scalar>, const ADerived, + const BDerived, const XDerived>( + a, b, x, internal::scalar_betainc_op<typename XDerived::Scalar>()); +} + +} // end namespace Eigen + +#endif // EIGEN_CXX11_TENSOR_TENSOR_GLOBAL_FUNCTIONS_H diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorIO.h b/unsupported/Eigen/CXX11/src/Tensor/TensorIO.h new file mode 100644 index 000000000..a901c5dd4 --- /dev/null +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorIO.h @@ -0,0 +1,79 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_CXX11_TENSOR_TENSOR_IO_H +#define EIGEN_CXX11_TENSOR_TENSOR_IO_H + +namespace Eigen { + +namespace internal { + +// Print the tensor as a 2d matrix +template <typename Tensor, int Rank> +struct TensorPrinter { + static void run (std::ostream& os, const Tensor& tensor) { + typedef typename internal::remove_const<typename Tensor::Scalar>::type Scalar; + typedef typename Tensor::Index Index; + const Index total_size = internal::array_prod(tensor.dimensions()); + if (total_size > 0) { + const Index first_dim = Eigen::internal::array_get<0>(tensor.dimensions()); + static const int layout = Tensor::Layout; + Map<const Array<Scalar, Dynamic, Dynamic, layout> > matrix(const_cast<Scalar*>(tensor.data()), first_dim, total_size/first_dim); + os << matrix; + } + } +}; + + +// Print the tensor as a vector +template <typename Tensor> +struct TensorPrinter<Tensor, 1> { + static void run (std::ostream& os, const Tensor& tensor) { + typedef typename internal::remove_const<typename Tensor::Scalar>::type Scalar; + typedef typename Tensor::Index Index; + const Index total_size = internal::array_prod(tensor.dimensions()); + if (total_size > 0) { + Map<const Array<Scalar, Dynamic, 1> > array(const_cast<Scalar*>(tensor.data()), total_size); + os << array; + } + } +}; + + +// Print the tensor as a scalar +template <typename Tensor> +struct TensorPrinter<Tensor, 0> { + static void run (std::ostream& os, const Tensor& tensor) { + os << tensor.coeff(0); + } +}; +} + +template <typename T> +std::ostream& operator << (std::ostream& os, const TensorBase<T, ReadOnlyAccessors>& expr) { + typedef TensorEvaluator<const TensorForcedEvalOp<const T>, DefaultDevice> Evaluator; + typedef typename Evaluator::Dimensions Dimensions; + + // Evaluate the expression if needed + TensorForcedEvalOp<const T> eval = expr.eval(); + Evaluator tensor(eval, DefaultDevice()); + tensor.evalSubExprsIfNeeded(NULL); + + // Print the result + static const int rank = internal::array_size<Dimensions>::value; + internal::TensorPrinter<Evaluator, rank>::run(os, tensor); + + // Cleanup. + tensor.cleanup(); + return os; +} + +} // end namespace Eigen + +#endif // EIGEN_CXX11_TENSOR_TENSOR_IO_H diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorImagePatch.h b/unsupported/Eigen/CXX11/src/Tensor/TensorImagePatch.h new file mode 100644 index 000000000..566856ed2 --- /dev/null +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorImagePatch.h @@ -0,0 +1,509 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_CXX11_TENSOR_TENSOR_IMAGE_PATCH_H +#define EIGEN_CXX11_TENSOR_TENSOR_IMAGE_PATCH_H + +namespace Eigen { + +/** \class TensorImagePatch + * \ingroup CXX11_Tensor_Module + * + * \brief Patch extraction specialized for image processing. + * This assumes that the input has a least 3 dimensions ordered as follow: + * 1st dimension: channels (of size d) + * 2nd dimension: rows (of size r) + * 3rd dimension: columns (of size c) + * There can be additional dimensions such as time (for video) or batch (for + * bulk processing after the first 3. + * Calling the image patch code with patch_rows and patch_cols is equivalent + * to calling the regular patch extraction code with parameters d, patch_rows, + * patch_cols, and 1 for all the additional dimensions. + */ +namespace internal { +template<DenseIndex Rows, DenseIndex Cols, typename XprType> +struct traits<TensorImagePatchOp<Rows, Cols, XprType> > : public traits<XprType> +{ + typedef typename internal::remove_const<typename XprType::Scalar>::type Scalar; + typedef traits<XprType> XprTraits; + typedef typename XprTraits::StorageKind StorageKind; + typedef typename XprTraits::Index Index; + typedef typename XprType::Nested Nested; + typedef typename remove_reference<Nested>::type _Nested; + static const int NumDimensions = XprTraits::NumDimensions + 1; + static const int Layout = XprTraits::Layout; +}; + +template<DenseIndex Rows, DenseIndex Cols, typename XprType> +struct eval<TensorImagePatchOp<Rows, Cols, XprType>, Eigen::Dense> +{ + typedef const TensorImagePatchOp<Rows, Cols, XprType>& type; +}; + +template<DenseIndex Rows, DenseIndex Cols, typename XprType> +struct nested<TensorImagePatchOp<Rows, Cols, XprType>, 1, typename eval<TensorImagePatchOp<Rows, Cols, XprType> >::type> +{ + typedef TensorImagePatchOp<Rows, Cols, XprType> type; +}; + +} // end namespace internal + +template<DenseIndex Rows, DenseIndex Cols, typename XprType> +class TensorImagePatchOp : public TensorBase<TensorImagePatchOp<Rows, Cols, XprType>, ReadOnlyAccessors> +{ + public: + typedef typename Eigen::internal::traits<TensorImagePatchOp>::Scalar Scalar; + typedef typename Eigen::NumTraits<Scalar>::Real RealScalar; + typedef typename XprType::CoeffReturnType CoeffReturnType; + typedef typename Eigen::internal::nested<TensorImagePatchOp>::type Nested; + typedef typename Eigen::internal::traits<TensorImagePatchOp>::StorageKind StorageKind; + typedef typename Eigen::internal::traits<TensorImagePatchOp>::Index Index; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorImagePatchOp(const XprType& expr, DenseIndex patch_rows, DenseIndex patch_cols, + DenseIndex row_strides, DenseIndex col_strides, + DenseIndex in_row_strides, DenseIndex in_col_strides, + DenseIndex row_inflate_strides, DenseIndex col_inflate_strides, + PaddingType padding_type, Scalar padding_value) + : m_xpr(expr), m_patch_rows(patch_rows), m_patch_cols(patch_cols), + m_row_strides(row_strides), m_col_strides(col_strides), + m_in_row_strides(in_row_strides), m_in_col_strides(in_col_strides), + m_row_inflate_strides(row_inflate_strides), m_col_inflate_strides(col_inflate_strides), + m_padding_explicit(false), m_padding_top(0), m_padding_bottom(0), m_padding_left(0), m_padding_right(0), + m_padding_type(padding_type), m_padding_value(padding_value) {} + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorImagePatchOp(const XprType& expr, DenseIndex patch_rows, DenseIndex patch_cols, + DenseIndex row_strides, DenseIndex col_strides, + DenseIndex in_row_strides, DenseIndex in_col_strides, + DenseIndex row_inflate_strides, DenseIndex col_inflate_strides, + DenseIndex padding_top, DenseIndex padding_bottom, + DenseIndex padding_left, DenseIndex padding_right, + Scalar padding_value) + : m_xpr(expr), m_patch_rows(patch_rows), m_patch_cols(patch_cols), + m_row_strides(row_strides), m_col_strides(col_strides), + m_in_row_strides(in_row_strides), m_in_col_strides(in_col_strides), + m_row_inflate_strides(row_inflate_strides), m_col_inflate_strides(col_inflate_strides), + m_padding_explicit(true), m_padding_top(padding_top), m_padding_bottom(padding_bottom), + m_padding_left(padding_left), m_padding_right(padding_right), + m_padding_type(PADDING_VALID), m_padding_value(padding_value) {} + + EIGEN_DEVICE_FUNC + DenseIndex patch_rows() const { return m_patch_rows; } + EIGEN_DEVICE_FUNC + DenseIndex patch_cols() const { return m_patch_cols; } + EIGEN_DEVICE_FUNC + DenseIndex row_strides() const { return m_row_strides; } + EIGEN_DEVICE_FUNC + DenseIndex col_strides() const { return m_col_strides; } + EIGEN_DEVICE_FUNC + DenseIndex in_row_strides() const { return m_in_row_strides; } + EIGEN_DEVICE_FUNC + DenseIndex in_col_strides() const { return m_in_col_strides; } + EIGEN_DEVICE_FUNC + DenseIndex row_inflate_strides() const { return m_row_inflate_strides; } + EIGEN_DEVICE_FUNC + DenseIndex col_inflate_strides() const { return m_col_inflate_strides; } + EIGEN_DEVICE_FUNC + bool padding_explicit() const { return m_padding_explicit; } + EIGEN_DEVICE_FUNC + DenseIndex padding_top() const { return m_padding_top; } + EIGEN_DEVICE_FUNC + DenseIndex padding_bottom() const { return m_padding_bottom; } + EIGEN_DEVICE_FUNC + DenseIndex padding_left() const { return m_padding_left; } + EIGEN_DEVICE_FUNC + DenseIndex padding_right() const { return m_padding_right; } + EIGEN_DEVICE_FUNC + PaddingType padding_type() const { return m_padding_type; } + EIGEN_DEVICE_FUNC + Scalar padding_value() const { return m_padding_value; } + + EIGEN_DEVICE_FUNC + const typename internal::remove_all<typename XprType::Nested>::type& + expression() const { return m_xpr; } + + protected: + typename XprType::Nested m_xpr; + const DenseIndex m_patch_rows; + const DenseIndex m_patch_cols; + const DenseIndex m_row_strides; + const DenseIndex m_col_strides; + const DenseIndex m_in_row_strides; + const DenseIndex m_in_col_strides; + const DenseIndex m_row_inflate_strides; + const DenseIndex m_col_inflate_strides; + const bool m_padding_explicit; + const DenseIndex m_padding_top; + const DenseIndex m_padding_bottom; + const DenseIndex m_padding_left; + const DenseIndex m_padding_right; + const PaddingType m_padding_type; + const Scalar m_padding_value; +}; + +// Eval as rvalue +template<DenseIndex Rows, DenseIndex Cols, typename ArgType, typename Device> +struct TensorEvaluator<const TensorImagePatchOp<Rows, Cols, ArgType>, Device> +{ + typedef TensorImagePatchOp<Rows, Cols, ArgType> XprType; + typedef typename XprType::Index Index; + static const int NumInputDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value; + static const int NumDims = NumInputDims + 1; + typedef DSizes<Index, NumDims> Dimensions; + typedef typename internal::remove_const<typename XprType::Scalar>::type Scalar; + typedef TensorEvaluator<const TensorImagePatchOp<Rows, Cols, ArgType>, + Device> Self; + typedef TensorEvaluator<ArgType, Device> Impl; + typedef typename XprType::CoeffReturnType CoeffReturnType; + typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType; + static const int PacketSize = internal::unpacket_traits<PacketReturnType>::size; + + enum { + IsAligned = false, + PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess, + Layout = TensorEvaluator<ArgType, Device>::Layout, + CoordAccess = false, + RawAccess = false + }; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) + : m_impl(op.expression(), device) + { + EIGEN_STATIC_ASSERT((NumDims >= 4), YOU_MADE_A_PROGRAMMING_MISTAKE); + + m_paddingValue = op.padding_value(); + + const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions(); + + // Caches a few variables. + if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { + m_inputDepth = input_dims[0]; + m_inputRows = input_dims[1]; + m_inputCols = input_dims[2]; + } else { + m_inputDepth = input_dims[NumInputDims-1]; + m_inputRows = input_dims[NumInputDims-2]; + m_inputCols = input_dims[NumInputDims-3]; + } + + m_row_strides = op.row_strides(); + m_col_strides = op.col_strides(); + + // Input strides and effective input/patch size + m_in_row_strides = op.in_row_strides(); + m_in_col_strides = op.in_col_strides(); + m_row_inflate_strides = op.row_inflate_strides(); + m_col_inflate_strides = op.col_inflate_strides(); + // The "effective" input rows and input cols are the input rows and cols + // after inflating them with zeros. + // For examples, a 2x3 matrix with row_inflate_strides and + // col_inflate_strides of 2 comes from: + // A B C + // D E F + // + // to a matrix is 3 x 5: + // + // A . B . C + // . . . . . + // D . E . F + + m_input_rows_eff = (m_inputRows - 1) * m_row_inflate_strides + 1; + m_input_cols_eff = (m_inputCols - 1) * m_col_inflate_strides + 1; + m_patch_rows_eff = op.patch_rows() + (op.patch_rows() - 1) * (m_in_row_strides - 1); + m_patch_cols_eff = op.patch_cols() + (op.patch_cols() - 1) * (m_in_col_strides - 1); + + if (op.padding_explicit()) { + m_outputRows = numext::ceil((m_input_rows_eff + op.padding_top() + op.padding_bottom() - m_patch_rows_eff + 1.f) / static_cast<float>(m_row_strides)); + m_outputCols = numext::ceil((m_input_cols_eff + op.padding_left() + op.padding_right() - m_patch_cols_eff + 1.f) / static_cast<float>(m_col_strides)); + m_rowPaddingTop = op.padding_top(); + m_colPaddingLeft = op.padding_left(); + } else { + // Computing padding from the type + switch (op.padding_type()) { + case PADDING_VALID: + m_outputRows = numext::ceil((m_input_rows_eff - m_patch_rows_eff + 1.f) / static_cast<float>(m_row_strides)); + m_outputCols = numext::ceil((m_input_cols_eff - m_patch_cols_eff + 1.f) / static_cast<float>(m_col_strides)); + // Calculate the padding + m_rowPaddingTop = numext::maxi<Index>(0, ((m_outputRows - 1) * m_row_strides + m_patch_rows_eff - m_input_rows_eff) / 2); + m_colPaddingLeft = numext::maxi<Index>(0, ((m_outputCols - 1) * m_col_strides + m_patch_cols_eff - m_input_cols_eff) / 2); + break; + case PADDING_SAME: + m_outputRows = numext::ceil(m_input_rows_eff / static_cast<float>(m_row_strides)); + m_outputCols = numext::ceil(m_input_cols_eff / static_cast<float>(m_col_strides)); + // Calculate the padding + m_rowPaddingTop = ((m_outputRows - 1) * m_row_strides + m_patch_rows_eff - m_input_rows_eff) / 2; + m_colPaddingLeft = ((m_outputCols - 1) * m_col_strides + m_patch_cols_eff - m_input_cols_eff) / 2; + break; + default: + eigen_assert(false && "unexpected padding"); + } + } + eigen_assert(m_outputRows > 0); + eigen_assert(m_outputCols > 0); + + // Dimensions for result of extraction. + if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { + // ColMajor + // 0: depth + // 1: patch_rows + // 2: patch_cols + // 3: number of patches + // 4 and beyond: anything else (such as batch). + m_dimensions[0] = input_dims[0]; + m_dimensions[1] = op.patch_rows(); + m_dimensions[2] = op.patch_cols(); + m_dimensions[3] = m_outputRows * m_outputCols; + for (int i = 4; i < NumDims; ++i) { + m_dimensions[i] = input_dims[i-1]; + } + } else { + // RowMajor + // NumDims-1: depth + // NumDims-2: patch_rows + // NumDims-3: patch_cols + // NumDims-4: number of patches + // NumDims-5 and beyond: anything else (such as batch). + m_dimensions[NumDims-1] = input_dims[NumInputDims-1]; + m_dimensions[NumDims-2] = op.patch_rows(); + m_dimensions[NumDims-3] = op.patch_cols(); + m_dimensions[NumDims-4] = m_outputRows * m_outputCols; + for (int i = NumDims-5; i >= 0; --i) { + m_dimensions[i] = input_dims[i]; + } + } + + // Strides for moving the patch in various dimensions. + if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { + m_colStride = m_dimensions[1]; + m_patchStride = m_colStride * m_dimensions[2] * m_dimensions[0]; + m_otherStride = m_patchStride * m_dimensions[3]; + } else { + m_colStride = m_dimensions[NumDims-2]; + m_patchStride = m_colStride * m_dimensions[NumDims-3] * m_dimensions[NumDims-1]; + m_otherStride = m_patchStride * m_dimensions[NumDims-4]; + } + + // Strides for navigating through the input tensor. + m_rowInputStride = m_inputDepth; + m_colInputStride = m_inputDepth * m_inputRows; + m_patchInputStride = m_inputDepth * m_inputRows * m_inputCols; + + // Fast representations of different variables. + m_fastOtherStride = internal::TensorIntDivisor<Index>(m_otherStride); + m_fastPatchStride = internal::TensorIntDivisor<Index>(m_patchStride); + m_fastColStride = internal::TensorIntDivisor<Index>(m_colStride); + m_fastInflateRowStride = internal::TensorIntDivisor<Index>(m_row_inflate_strides); + m_fastInflateColStride = internal::TensorIntDivisor<Index>(m_col_inflate_strides); + m_fastInputColsEff = internal::TensorIntDivisor<Index>(m_input_cols_eff); + + // Number of patches in the width dimension. + m_fastOutputRows = internal::TensorIntDivisor<Index>(m_outputRows); + if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { + m_fastOutputDepth = internal::TensorIntDivisor<Index>(m_dimensions[0]); + } else { + m_fastOutputDepth = internal::TensorIntDivisor<Index>(m_dimensions[NumDims-1]); + } + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* /*data*/) { + m_impl.evalSubExprsIfNeeded(NULL); + return true; + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() { + m_impl.cleanup(); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const + { + // Patch index corresponding to the passed in index. + const Index patchIndex = index / m_fastPatchStride; + // Find the offset of the element wrt the location of the first element. + const Index patchOffset = (index - patchIndex * m_patchStride) / m_fastOutputDepth; + + // Other ways to index this element. + const Index otherIndex = (NumDims == 4) ? 0 : index / m_fastOtherStride; + const Index patch2DIndex = (NumDims == 4) ? patchIndex : (index - otherIndex * m_otherStride) / m_fastPatchStride; + + // Calculate col index in the input original tensor. + const Index colIndex = patch2DIndex / m_fastOutputRows; + const Index colOffset = patchOffset / m_fastColStride; + const Index inputCol = colIndex * m_col_strides + colOffset * m_in_col_strides - m_colPaddingLeft; + const Index origInputCol = (m_col_inflate_strides == 1) ? inputCol : ((inputCol >= 0) ? (inputCol / m_fastInflateColStride) : 0); + if (inputCol < 0 || inputCol >= m_input_cols_eff || + ((m_col_inflate_strides != 1) && (inputCol != origInputCol * m_col_inflate_strides))) { + return Scalar(m_paddingValue); + } + + // Calculate row index in the original input tensor. + const Index rowIndex = patch2DIndex - colIndex * m_outputRows; + const Index rowOffset = patchOffset - colOffset * m_colStride; + const Index inputRow = rowIndex * m_row_strides + rowOffset * m_in_row_strides - m_rowPaddingTop; + const Index origInputRow = (m_row_inflate_strides == 1) ? inputRow : ((inputRow >= 0) ? (inputRow / m_fastInflateRowStride) : 0); + if (inputRow < 0 || inputRow >= m_input_rows_eff || + ((m_row_inflate_strides != 1) && (inputRow != origInputRow * m_row_inflate_strides))) { + return Scalar(m_paddingValue); + } + + const int depth_index = static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 0 : NumDims - 1; + const Index depth = index - (index / m_fastOutputDepth) * m_dimensions[depth_index]; + + const Index inputIndex = depth + origInputRow * m_rowInputStride + origInputCol * m_colInputStride + otherIndex * m_patchInputStride; + return m_impl.coeff(inputIndex); + } + + template<int LoadMode> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const + { + EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE) + eigen_assert(index+PacketSize-1 < dimensions().TotalSize()); + + if (m_in_row_strides != 1 || m_in_col_strides != 1 || m_row_inflate_strides != 1 || m_col_inflate_strides != 1) { + return packetWithPossibleZero(index); + } + + const Index indices[2] = {index, index + PacketSize - 1}; + const Index patchIndex = indices[0] / m_fastPatchStride; + if (patchIndex != indices[1] / m_fastPatchStride) { + return packetWithPossibleZero(index); + } + const Index otherIndex = (NumDims == 4) ? 0 : indices[0] / m_fastOtherStride; + eigen_assert(otherIndex == indices[1] / m_fastOtherStride); + + // Find the offset of the element wrt the location of the first element. + const Index patchOffsets[2] = {(indices[0] - patchIndex * m_patchStride) / m_fastOutputDepth, + (indices[1] - patchIndex * m_patchStride) / m_fastOutputDepth}; + + const Index patch2DIndex = (NumDims == 4) ? patchIndex : (indices[0] - otherIndex * m_otherStride) / m_fastPatchStride; + eigen_assert(patch2DIndex == (indices[1] - otherIndex * m_otherStride) / m_fastPatchStride); + + const Index colIndex = patch2DIndex / m_fastOutputRows; + const Index colOffsets[2] = {patchOffsets[0] / m_fastColStride, patchOffsets[1] / m_fastColStride}; + + // Calculate col indices in the original input tensor. + const Index inputCols[2] = {colIndex * m_col_strides + colOffsets[0] - + m_colPaddingLeft, colIndex * m_col_strides + colOffsets[1] - m_colPaddingLeft}; + if (inputCols[1] < 0 || inputCols[0] >= m_inputCols) { + return internal::pset1<PacketReturnType>(Scalar(m_paddingValue)); + } + + if (inputCols[0] == inputCols[1]) { + const Index rowIndex = patch2DIndex - colIndex * m_outputRows; + const Index rowOffsets[2] = {patchOffsets[0] - colOffsets[0]*m_colStride, patchOffsets[1] - colOffsets[1]*m_colStride}; + eigen_assert(rowOffsets[0] <= rowOffsets[1]); + // Calculate col indices in the original input tensor. + const Index inputRows[2] = {rowIndex * m_row_strides + rowOffsets[0] - + m_rowPaddingTop, rowIndex * m_row_strides + rowOffsets[1] - m_rowPaddingTop}; + + if (inputRows[1] < 0 || inputRows[0] >= m_inputRows) { + return internal::pset1<PacketReturnType>(Scalar(m_paddingValue)); + } + + if (inputRows[0] >= 0 && inputRows[1] < m_inputRows) { + // no padding + const int depth_index = static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 0 : NumDims - 1; + const Index depth = index - (index / m_fastOutputDepth) * m_dimensions[depth_index]; + const Index inputIndex = depth + inputRows[0] * m_rowInputStride + inputCols[0] * m_colInputStride + otherIndex * m_patchInputStride; + return m_impl.template packet<Unaligned>(inputIndex); + } + } + + return packetWithPossibleZero(index); + } + + EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; } + + const TensorEvaluator<ArgType, Device>& impl() const { return m_impl; } + + Index rowPaddingTop() const { return m_rowPaddingTop; } + Index colPaddingLeft() const { return m_colPaddingLeft; } + Index outputRows() const { return m_outputRows; } + Index outputCols() const { return m_outputCols; } + Index userRowStride() const { return m_row_strides; } + Index userColStride() const { return m_col_strides; } + Index userInRowStride() const { return m_in_row_strides; } + Index userInColStride() const { return m_in_col_strides; } + Index rowInflateStride() const { return m_row_inflate_strides; } + Index colInflateStride() const { return m_col_inflate_strides; } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost + costPerCoeff(bool vectorized) const { + // We conservatively estimate the cost for the code path where the computed + // index is inside the original image and + // TensorEvaluator<ArgType, Device>::CoordAccess is false. + const double compute_cost = 3 * TensorOpCost::DivCost<Index>() + + 6 * TensorOpCost::MulCost<Index>() + + 8 * TensorOpCost::MulCost<Index>(); + return m_impl.costPerCoeff(vectorized) + + TensorOpCost(0, 0, compute_cost, vectorized, PacketSize); + } + + protected: + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetWithPossibleZero(Index index) const + { + EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize]; + for (int i = 0; i < PacketSize; ++i) { + values[i] = coeff(index+i); + } + PacketReturnType rslt = internal::pload<PacketReturnType>(values); + return rslt; + } + + Dimensions m_dimensions; + + Index m_otherStride; + Index m_patchStride; + Index m_colStride; + Index m_row_strides; + Index m_col_strides; + + Index m_in_row_strides; + Index m_in_col_strides; + Index m_row_inflate_strides; + Index m_col_inflate_strides; + + Index m_input_rows_eff; + Index m_input_cols_eff; + Index m_patch_rows_eff; + Index m_patch_cols_eff; + + internal::TensorIntDivisor<Index> m_fastOtherStride; + internal::TensorIntDivisor<Index> m_fastPatchStride; + internal::TensorIntDivisor<Index> m_fastColStride; + internal::TensorIntDivisor<Index> m_fastInflateRowStride; + internal::TensorIntDivisor<Index> m_fastInflateColStride; + internal::TensorIntDivisor<Index> m_fastInputColsEff; + + Index m_rowInputStride; + Index m_colInputStride; + Index m_patchInputStride; + + Index m_inputDepth; + Index m_inputRows; + Index m_inputCols; + + Index m_outputRows; + Index m_outputCols; + + Index m_rowPaddingTop; + Index m_colPaddingLeft; + + internal::TensorIntDivisor<Index> m_fastOutputRows; + internal::TensorIntDivisor<Index> m_fastOutputDepth; + + Scalar m_paddingValue; + + TensorEvaluator<ArgType, Device> m_impl; +}; + + +} // end namespace Eigen + +#endif // EIGEN_CXX11_TENSOR_TENSOR_IMAGE_PATCH_H diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorIndexList.h b/unsupported/Eigen/CXX11/src/Tensor/TensorIndexList.h new file mode 100644 index 000000000..3209fecd3 --- /dev/null +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorIndexList.h @@ -0,0 +1,725 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_CXX11_TENSOR_TENSOR_INDEX_LIST_H +#define EIGEN_CXX11_TENSOR_TENSOR_INDEX_LIST_H + + +#if EIGEN_HAS_CONSTEXPR && EIGEN_HAS_VARIADIC_TEMPLATES + +#define EIGEN_HAS_INDEX_LIST + +namespace Eigen { + +/** \internal + * + * \class TensorIndexList + * \ingroup CXX11_Tensor_Module + * + * \brief Set of classes used to encode a set of Tensor dimensions/indices. + * + * The indices in the list can be known at compile time or at runtime. A mix + * of static and dynamic indices can also be provided if needed. The tensor + * code will attempt to take advantage of the indices that are known at + * compile time to optimize the code it generates. + * + * This functionality requires a c++11 compliant compiler. If your compiler + * is older you need to use arrays of indices instead. + * + * Several examples are provided in the cxx11_tensor_index_list.cpp file. + * + * \sa Tensor + */ + +template <DenseIndex n> +struct type2index { + static const DenseIndex value = n; + EIGEN_DEVICE_FUNC constexpr operator DenseIndex() const { return n; } + EIGEN_DEVICE_FUNC void set(DenseIndex val) { + eigen_assert(val == n); + } +}; + +// This can be used with IndexPairList to get compile-time constant pairs, +// such as IndexPairList<type2indexpair<1,2>, type2indexpair<3,4>>(). +template <DenseIndex f, DenseIndex s> +struct type2indexpair { + static const DenseIndex first = f; + static const DenseIndex second = s; + + constexpr EIGEN_DEVICE_FUNC operator IndexPair<DenseIndex>() const { + return IndexPair<DenseIndex>(f, s); + } + + EIGEN_DEVICE_FUNC void set(const IndexPair<DenseIndex>& val) { + eigen_assert(val.first == f); + eigen_assert(val.second == s); + } +}; + + +template<DenseIndex n> struct NumTraits<type2index<n> > +{ + typedef DenseIndex Real; + enum { + IsComplex = 0, + RequireInitialization = false, + ReadCost = 1, + AddCost = 1, + MulCost = 1 + }; + + EIGEN_DEVICE_FUNC static inline Real epsilon() { return 0; } + EIGEN_DEVICE_FUNC static inline Real dummy_precision() { return 0; } + EIGEN_DEVICE_FUNC static inline Real highest() { return n; } + EIGEN_DEVICE_FUNC static inline Real lowest() { return n; } +}; + +namespace internal { +template <typename T> +EIGEN_DEVICE_FUNC void update_value(T& val, DenseIndex new_val) { + val = new_val; +} +template <DenseIndex n> +EIGEN_DEVICE_FUNC void update_value(type2index<n>& val, DenseIndex new_val) { + val.set(new_val); +} + +template <typename T> +EIGEN_DEVICE_FUNC void update_value(T& val, IndexPair<DenseIndex> new_val) { + val = new_val; +} +template <DenseIndex f, DenseIndex s> +EIGEN_DEVICE_FUNC void update_value(type2indexpair<f, s>& val, IndexPair<DenseIndex> new_val) { + val.set(new_val); +} + + +template <typename T> +struct is_compile_time_constant { + static constexpr bool value = false; +}; + +template <DenseIndex idx> +struct is_compile_time_constant<type2index<idx> > { + static constexpr bool value = true; +}; +template <DenseIndex idx> +struct is_compile_time_constant<const type2index<idx> > { + static constexpr bool value = true; +}; +template <DenseIndex idx> +struct is_compile_time_constant<type2index<idx>& > { + static constexpr bool value = true; +}; +template <DenseIndex idx> +struct is_compile_time_constant<const type2index<idx>& > { + static constexpr bool value = true; +}; + +template <DenseIndex f, DenseIndex s> +struct is_compile_time_constant<type2indexpair<f, s> > { + static constexpr bool value = true; +}; +template <DenseIndex f, DenseIndex s> +struct is_compile_time_constant<const type2indexpair<f, s> > { + static constexpr bool value = true; +}; +template <DenseIndex f, DenseIndex s> +struct is_compile_time_constant<type2indexpair<f, s>& > { + static constexpr bool value = true; +}; +template <DenseIndex f, DenseIndex s> +struct is_compile_time_constant<const type2indexpair<f, s>& > { + static constexpr bool value = true; +}; + + +template<typename... T> +struct IndexTuple; + +template<typename T, typename... O> +struct IndexTuple<T, O...> { + EIGEN_DEVICE_FUNC constexpr IndexTuple() : head(), others() { } + EIGEN_DEVICE_FUNC constexpr IndexTuple(const T& v, const O... o) : head(v), others(o...) { } + + constexpr static int count = 1 + sizeof...(O); + T head; + IndexTuple<O...> others; + typedef T Head; + typedef IndexTuple<O...> Other; +}; + +template<typename T> + struct IndexTuple<T> { + EIGEN_DEVICE_FUNC constexpr IndexTuple() : head() { } + EIGEN_DEVICE_FUNC constexpr IndexTuple(const T& v) : head(v) { } + + constexpr static int count = 1; + T head; + typedef T Head; +}; + + +template<int N, typename... T> +struct IndexTupleExtractor; + +template<int N, typename T, typename... O> +struct IndexTupleExtractor<N, T, O...> { + + typedef typename IndexTupleExtractor<N-1, O...>::ValType ValType; + + EIGEN_DEVICE_FUNC static constexpr ValType& get_val(IndexTuple<T, O...>& val) { + return IndexTupleExtractor<N-1, O...>::get_val(val.others); + } + + EIGEN_DEVICE_FUNC static constexpr const ValType& get_val(const IndexTuple<T, O...>& val) { + return IndexTupleExtractor<N-1, O...>::get_val(val.others); + } + template <typename V> + EIGEN_DEVICE_FUNC static void set_val(IndexTuple<T, O...>& val, V& new_val) { + IndexTupleExtractor<N-1, O...>::set_val(val.others, new_val); + } + +}; + +template<typename T, typename... O> + struct IndexTupleExtractor<0, T, O...> { + + typedef T ValType; + + EIGEN_DEVICE_FUNC static constexpr ValType& get_val(IndexTuple<T, O...>& val) { + return val.head; + } + EIGEN_DEVICE_FUNC static constexpr const ValType& get_val(const IndexTuple<T, O...>& val) { + return val.head; + } + template <typename V> + EIGEN_DEVICE_FUNC static void set_val(IndexTuple<T, O...>& val, V& new_val) { + val.head = new_val; + } +}; + + + +template <int N, typename T, typename... O> +EIGEN_DEVICE_FUNC constexpr typename IndexTupleExtractor<N, T, O...>::ValType& array_get(IndexTuple<T, O...>& tuple) { + return IndexTupleExtractor<N, T, O...>::get_val(tuple); +} +template <int N, typename T, typename... O> +EIGEN_DEVICE_FUNC constexpr const typename IndexTupleExtractor<N, T, O...>::ValType& array_get(const IndexTuple<T, O...>& tuple) { + return IndexTupleExtractor<N, T, O...>::get_val(tuple); +} +template <typename T, typename... O> + struct array_size<IndexTuple<T, O...> > { + static const size_t value = IndexTuple<T, O...>::count; +}; +template <typename T, typename... O> + struct array_size<const IndexTuple<T, O...> > { + static const size_t value = IndexTuple<T, O...>::count; +}; + + + + +template <DenseIndex Idx, typename ValueT> +struct tuple_coeff { + template <typename... T> + EIGEN_DEVICE_FUNC static constexpr ValueT get(const DenseIndex i, const IndexTuple<T...>& t) { + // return array_get<Idx>(t) * (i == Idx) + tuple_coeff<Idx-1>::get(i, t) * (i != Idx); + return (i == Idx ? array_get<Idx>(t) : tuple_coeff<Idx-1, ValueT>::get(i, t)); + } + template <typename... T> + EIGEN_DEVICE_FUNC static void set(const DenseIndex i, IndexTuple<T...>& t, const ValueT& value) { + if (i == Idx) { + update_value(array_get<Idx>(t), value); + } else { + tuple_coeff<Idx-1, ValueT>::set(i, t, value); + } + } + + template <typename... T> + EIGEN_DEVICE_FUNC static constexpr bool value_known_statically(const DenseIndex i, const IndexTuple<T...>& t) { + return ((i == Idx) & is_compile_time_constant<typename IndexTupleExtractor<Idx, T...>::ValType>::value) || + tuple_coeff<Idx-1, ValueT>::value_known_statically(i, t); + } + + template <typename... T> + EIGEN_DEVICE_FUNC static constexpr bool values_up_to_known_statically(const IndexTuple<T...>& t) { + return is_compile_time_constant<typename IndexTupleExtractor<Idx, T...>::ValType>::value && + tuple_coeff<Idx-1, ValueT>::values_up_to_known_statically(t); + } + + template <typename... T> + EIGEN_DEVICE_FUNC static constexpr bool values_up_to_statically_known_to_increase(const IndexTuple<T...>& t) { + return is_compile_time_constant<typename IndexTupleExtractor<Idx, T...>::ValType>::value && + is_compile_time_constant<typename IndexTupleExtractor<Idx, T...>::ValType>::value && + array_get<Idx>(t) > array_get<Idx-1>(t) && + tuple_coeff<Idx-1, ValueT>::values_up_to_statically_known_to_increase(t); + } +}; + +template <typename ValueT> +struct tuple_coeff<0, ValueT> { + template <typename... T> + EIGEN_DEVICE_FUNC static constexpr ValueT get(const DenseIndex /*i*/, const IndexTuple<T...>& t) { + // eigen_assert (i == 0); // gcc fails to compile assertions in constexpr + return array_get<0>(t)/* * (i == 0)*/; + } + template <typename... T> + EIGEN_DEVICE_FUNC static void set(const DenseIndex i, IndexTuple<T...>& t, const ValueT value) { + eigen_assert (i == 0); + update_value(array_get<0>(t), value); + } + template <typename... T> + EIGEN_DEVICE_FUNC static constexpr bool value_known_statically(const DenseIndex i, const IndexTuple<T...>&) { + return is_compile_time_constant<typename IndexTupleExtractor<0, T...>::ValType>::value & (i == 0); + } + + template <typename... T> + EIGEN_DEVICE_FUNC static constexpr bool values_up_to_known_statically(const IndexTuple<T...>&) { + return is_compile_time_constant<typename IndexTupleExtractor<0, T...>::ValType>::value; + } + + template <typename... T> + EIGEN_DEVICE_FUNC static constexpr bool values_up_to_statically_known_to_increase(const IndexTuple<T...>&) { + return true; + } +}; +} // namespace internal + + + +template<typename FirstType, typename... OtherTypes> +struct IndexList : internal::IndexTuple<FirstType, OtherTypes...> { + EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC constexpr DenseIndex operator[] (const DenseIndex i) const { + return internal::tuple_coeff<internal::array_size<internal::IndexTuple<FirstType, OtherTypes...> >::value-1, DenseIndex>::get(i, *this); + } + EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC constexpr DenseIndex get(const DenseIndex i) const { + return internal::tuple_coeff<internal::array_size<internal::IndexTuple<FirstType, OtherTypes...> >::value-1, DenseIndex>::get(i, *this); + } + EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC void set(const DenseIndex i, const DenseIndex value) { + return internal::tuple_coeff<internal::array_size<internal::IndexTuple<FirstType, OtherTypes...> >::value-1, DenseIndex>::set(i, *this, value); + } + + EIGEN_DEVICE_FUNC constexpr IndexList(const internal::IndexTuple<FirstType, OtherTypes...>& other) : internal::IndexTuple<FirstType, OtherTypes...>(other) { } + EIGEN_DEVICE_FUNC constexpr IndexList(FirstType& first, OtherTypes... other) : internal::IndexTuple<FirstType, OtherTypes...>(first, other...) { } + EIGEN_DEVICE_FUNC constexpr IndexList() : internal::IndexTuple<FirstType, OtherTypes...>() { } + + EIGEN_DEVICE_FUNC constexpr bool value_known_statically(const DenseIndex i) const { + return internal::tuple_coeff<internal::array_size<internal::IndexTuple<FirstType, OtherTypes...> >::value-1, DenseIndex>::value_known_statically(i, *this); + } + EIGEN_DEVICE_FUNC constexpr bool all_values_known_statically() const { + return internal::tuple_coeff<internal::array_size<internal::IndexTuple<FirstType, OtherTypes...> >::value-1, DenseIndex>::values_up_to_known_statically(*this); + } + + EIGEN_DEVICE_FUNC constexpr bool values_statically_known_to_increase() const { + return internal::tuple_coeff<internal::array_size<internal::IndexTuple<FirstType, OtherTypes...> >::value-1, DenseIndex>::values_up_to_statically_known_to_increase(*this); + } +}; + + +template<typename FirstType, typename... OtherTypes> +constexpr IndexList<FirstType, OtherTypes...> make_index_list(FirstType val1, OtherTypes... other_vals) { + return IndexList<FirstType, OtherTypes...>(val1, other_vals...); +} + + +template<typename FirstType, typename... OtherTypes> +struct IndexPairList : internal::IndexTuple<FirstType, OtherTypes...> { + EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC constexpr IndexPair<DenseIndex> operator[] (const DenseIndex i) const { + return internal::tuple_coeff<internal::array_size<internal::IndexTuple<FirstType, OtherTypes...> >::value-1, IndexPair<DenseIndex>>::get(i, *this); + } + EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC void set(const DenseIndex i, const IndexPair<DenseIndex> value) { + return internal::tuple_coeff<internal::array_size<internal::IndexTuple<FirstType, OtherTypes...>>::value-1, IndexPair<DenseIndex> >::set(i, *this, value); + } + + EIGEN_DEVICE_FUNC constexpr IndexPairList(const internal::IndexTuple<FirstType, OtherTypes...>& other) : internal::IndexTuple<FirstType, OtherTypes...>(other) { } + EIGEN_DEVICE_FUNC constexpr IndexPairList() : internal::IndexTuple<FirstType, OtherTypes...>() { } + + EIGEN_DEVICE_FUNC constexpr bool value_known_statically(const DenseIndex i) const { + return internal::tuple_coeff<internal::array_size<internal::IndexTuple<FirstType, OtherTypes...> >::value-1, DenseIndex>::value_known_statically(i, *this); + } +}; + +namespace internal { + +template<typename FirstType, typename... OtherTypes> size_t array_prod(const IndexList<FirstType, OtherTypes...>& sizes) { + size_t result = 1; + for (int i = 0; i < array_size<IndexList<FirstType, OtherTypes...> >::value; ++i) { + result *= sizes[i]; + } + return result; +} + +template<typename FirstType, typename... OtherTypes> struct array_size<IndexList<FirstType, OtherTypes...> > { + static const size_t value = array_size<IndexTuple<FirstType, OtherTypes...> >::value; +}; +template<typename FirstType, typename... OtherTypes> struct array_size<const IndexList<FirstType, OtherTypes...> > { + static const size_t value = array_size<IndexTuple<FirstType, OtherTypes...> >::value; +}; + +template<typename FirstType, typename... OtherTypes> struct array_size<IndexPairList<FirstType, OtherTypes...> > { + static const size_t value = std::tuple_size<std::tuple<FirstType, OtherTypes...> >::value; +}; +template<typename FirstType, typename... OtherTypes> struct array_size<const IndexPairList<FirstType, OtherTypes...> > { + static const size_t value = std::tuple_size<std::tuple<FirstType, OtherTypes...> >::value; +}; + +template<DenseIndex N, typename FirstType, typename... OtherTypes> EIGEN_DEVICE_FUNC constexpr DenseIndex array_get(IndexList<FirstType, OtherTypes...>& a) { + return IndexTupleExtractor<N, FirstType, OtherTypes...>::get_val(a); +} +template<DenseIndex N, typename FirstType, typename... OtherTypes> EIGEN_DEVICE_FUNC constexpr DenseIndex array_get(const IndexList<FirstType, OtherTypes...>& a) { + return IndexTupleExtractor<N, FirstType, OtherTypes...>::get_val(a); +} + +template <typename T> +struct index_known_statically_impl { + EIGEN_DEVICE_FUNC static constexpr bool run(const DenseIndex) { + return false; + } +}; + +template <typename FirstType, typename... OtherTypes> +struct index_known_statically_impl<IndexList<FirstType, OtherTypes...> > { + EIGEN_DEVICE_FUNC static constexpr bool run(const DenseIndex i) { + return IndexList<FirstType, OtherTypes...>().value_known_statically(i); + } +}; + +template <typename FirstType, typename... OtherTypes> +struct index_known_statically_impl<const IndexList<FirstType, OtherTypes...> > { + EIGEN_DEVICE_FUNC static constexpr bool run(const DenseIndex i) { + return IndexList<FirstType, OtherTypes...>().value_known_statically(i); + } +}; + + +template <typename T> +struct all_indices_known_statically_impl { + static constexpr bool run() { + return false; + } +}; + +template <typename FirstType, typename... OtherTypes> +struct all_indices_known_statically_impl<IndexList<FirstType, OtherTypes...> > { + EIGEN_DEVICE_FUNC static constexpr bool run() { + return IndexList<FirstType, OtherTypes...>().all_values_known_statically(); + } +}; + +template <typename FirstType, typename... OtherTypes> +struct all_indices_known_statically_impl<const IndexList<FirstType, OtherTypes...> > { + EIGEN_DEVICE_FUNC static constexpr bool run() { + return IndexList<FirstType, OtherTypes...>().all_values_known_statically(); + } +}; + + +template <typename T> +struct indices_statically_known_to_increase_impl { + EIGEN_DEVICE_FUNC static constexpr bool run() { + return false; + } +}; + +template <typename FirstType, typename... OtherTypes> + struct indices_statically_known_to_increase_impl<IndexList<FirstType, OtherTypes...> > { + EIGEN_DEVICE_FUNC static constexpr bool run() { + return Eigen::IndexList<FirstType, OtherTypes...>().values_statically_known_to_increase(); + } +}; + +template <typename FirstType, typename... OtherTypes> + struct indices_statically_known_to_increase_impl<const IndexList<FirstType, OtherTypes...> > { + EIGEN_DEVICE_FUNC static constexpr bool run() { + return Eigen::IndexList<FirstType, OtherTypes...>().values_statically_known_to_increase(); + } +}; + + +template <typename Tx> +struct index_statically_eq_impl { + EIGEN_DEVICE_FUNC static constexpr bool run(DenseIndex, DenseIndex) { + return false; + } +}; + +template <typename FirstType, typename... OtherTypes> +struct index_statically_eq_impl<IndexList<FirstType, OtherTypes...> > { + EIGEN_DEVICE_FUNC static constexpr bool run(const DenseIndex i, const DenseIndex value) { + return IndexList<FirstType, OtherTypes...>().value_known_statically(i) & + (IndexList<FirstType, OtherTypes...>().get(i) == value); + } +}; + +template <typename FirstType, typename... OtherTypes> +struct index_statically_eq_impl<const IndexList<FirstType, OtherTypes...> > { + EIGEN_DEVICE_FUNC static constexpr bool run(const DenseIndex i, const DenseIndex value) { + return IndexList<FirstType, OtherTypes...>().value_known_statically(i) & + (IndexList<FirstType, OtherTypes...>().get(i) == value); + } +}; + + +template <typename T> +struct index_statically_ne_impl { + EIGEN_DEVICE_FUNC static constexpr bool run(DenseIndex, DenseIndex) { + return false; + } +}; + +template <typename FirstType, typename... OtherTypes> +struct index_statically_ne_impl<IndexList<FirstType, OtherTypes...> > { + EIGEN_DEVICE_FUNC static constexpr bool run(const DenseIndex i, const DenseIndex value) { + return IndexList<FirstType, OtherTypes...>().value_known_statically(i) & + (IndexList<FirstType, OtherTypes...>().get(i) != value); + } +}; + +template <typename FirstType, typename... OtherTypes> +struct index_statically_ne_impl<const IndexList<FirstType, OtherTypes...> > { + EIGEN_DEVICE_FUNC static constexpr bool run(const DenseIndex i, const DenseIndex value) { + return IndexList<FirstType, OtherTypes...>().value_known_statically(i) & + (IndexList<FirstType, OtherTypes...>().get(i) != value); + } +}; + + +template <typename T> +struct index_statically_gt_impl { + EIGEN_DEVICE_FUNC static constexpr bool run(DenseIndex, DenseIndex) { + return false; + } +}; + +template <typename FirstType, typename... OtherTypes> +struct index_statically_gt_impl<IndexList<FirstType, OtherTypes...> > { + EIGEN_DEVICE_FUNC static constexpr bool run(const DenseIndex i, const DenseIndex value) { + return IndexList<FirstType, OtherTypes...>().value_known_statically(i) & + (IndexList<FirstType, OtherTypes...>().get(i) > value); + } +}; + +template <typename FirstType, typename... OtherTypes> +struct index_statically_gt_impl<const IndexList<FirstType, OtherTypes...> > { + EIGEN_DEVICE_FUNC static constexpr bool run(const DenseIndex i, const DenseIndex value) { + return IndexList<FirstType, OtherTypes...>().value_known_statically(i) & + (IndexList<FirstType, OtherTypes...>().get(i) > value); + } +}; + + + +template <typename T> +struct index_statically_lt_impl { + EIGEN_DEVICE_FUNC static constexpr bool run(DenseIndex, DenseIndex) { + return false; + } +}; + +template <typename FirstType, typename... OtherTypes> +struct index_statically_lt_impl<IndexList<FirstType, OtherTypes...> > { + EIGEN_DEVICE_FUNC static constexpr bool run(const DenseIndex i, const DenseIndex value) { + return IndexList<FirstType, OtherTypes...>().value_known_statically(i) & + (IndexList<FirstType, OtherTypes...>().get(i) < value); + } +}; + +template <typename FirstType, typename... OtherTypes> +struct index_statically_lt_impl<const IndexList<FirstType, OtherTypes...> > { + EIGEN_DEVICE_FUNC static constexpr bool run(const DenseIndex i, const DenseIndex value) { + return IndexList<FirstType, OtherTypes...>().value_known_statically(i) & + (IndexList<FirstType, OtherTypes...>().get(i) < value); + } +}; + + + +template <typename Tx> +struct index_pair_first_statically_eq_impl { + EIGEN_DEVICE_FUNC static constexpr bool run(DenseIndex, DenseIndex) { + return false; + } +}; + +template <typename FirstType, typename... OtherTypes> +struct index_pair_first_statically_eq_impl<IndexPairList<FirstType, OtherTypes...> > { + EIGEN_DEVICE_FUNC static constexpr bool run(const DenseIndex i, const DenseIndex value) { + return IndexPairList<FirstType, OtherTypes...>().value_known_statically(i) & + (IndexPairList<FirstType, OtherTypes...>().operator[](i).first == value); + } +}; + +template <typename FirstType, typename... OtherTypes> +struct index_pair_first_statically_eq_impl<const IndexPairList<FirstType, OtherTypes...> > { + EIGEN_DEVICE_FUNC static constexpr bool run(const DenseIndex i, const DenseIndex value) { + return IndexPairList<FirstType, OtherTypes...>().value_known_statically(i) & + (IndexPairList<FirstType, OtherTypes...>().operator[](i).first == value); + } +}; + + + +template <typename Tx> +struct index_pair_second_statically_eq_impl { + EIGEN_DEVICE_FUNC static constexpr bool run(DenseIndex, DenseIndex) { + return false; + } +}; + +template <typename FirstType, typename... OtherTypes> +struct index_pair_second_statically_eq_impl<IndexPairList<FirstType, OtherTypes...> > { + EIGEN_DEVICE_FUNC static constexpr bool run(const DenseIndex i, const DenseIndex value) { + return IndexPairList<FirstType, OtherTypes...>().value_known_statically(i) & + (IndexPairList<FirstType, OtherTypes...>().operator[](i).second == value); + } +}; + +template <typename FirstType, typename... OtherTypes> +struct index_pair_second_statically_eq_impl<const IndexPairList<FirstType, OtherTypes...> > { + EIGEN_DEVICE_FUNC static constexpr bool run(const DenseIndex i, const DenseIndex value) { + return IndexPairList<FirstType, OtherTypes...>().value_known_statically(i) & + (IndexPairList<FirstType, OtherTypes...>().operator[](i).second == value); + } +}; + + +} // end namespace internal +} // end namespace Eigen + +#else + +namespace Eigen { +namespace internal { + +template <typename T> +struct index_known_statically_impl { + static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool run(const DenseIndex) { + return false; + } +}; + +template <typename T> +struct all_indices_known_statically_impl { + static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool run() { + return false; + } +}; + +template <typename T> +struct indices_statically_known_to_increase_impl { + static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool run() { + return false; + } +}; + +template <typename T> +struct index_statically_eq_impl { + static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool run(DenseIndex, DenseIndex) { + return false; + } +}; + +template <typename T> +struct index_statically_ne_impl { + static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool run(DenseIndex, DenseIndex) { + return false; + } +}; + +template <typename T> +struct index_statically_gt_impl { + static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool run(DenseIndex, DenseIndex) { + return false; + } +}; + +template <typename T> +struct index_statically_lt_impl { + static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool run(DenseIndex, DenseIndex) { + return false; + } +}; + +template <typename Tx> +struct index_pair_first_statically_eq_impl { + static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool run(DenseIndex, DenseIndex) { + return false; + } +}; + +template <typename Tx> +struct index_pair_second_statically_eq_impl { + static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool run(DenseIndex, DenseIndex) { + return false; + } +}; + + + +} // end namespace internal +} // end namespace Eigen + +#endif + + +namespace Eigen { +namespace internal { +template <typename T> +static EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR bool index_known_statically(DenseIndex i) { + return index_known_statically_impl<T>::run(i); +} + +template <typename T> +static EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR bool all_indices_known_statically() { + return all_indices_known_statically_impl<T>::run(); +} + +template <typename T> +static EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR bool indices_statically_known_to_increase() { + return indices_statically_known_to_increase_impl<T>::run(); +} + +template <typename T> +static EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR bool index_statically_eq(DenseIndex i, DenseIndex value) { + return index_statically_eq_impl<T>::run(i, value); +} + +template <typename T> +static EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR bool index_statically_ne(DenseIndex i, DenseIndex value) { + return index_statically_ne_impl<T>::run(i, value); +} + +template <typename T> +static EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR bool index_statically_gt(DenseIndex i, DenseIndex value) { + return index_statically_gt_impl<T>::run(i, value); +} + +template <typename T> +static EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR bool index_statically_lt(DenseIndex i, DenseIndex value) { + return index_statically_lt_impl<T>::run(i, value); +} + +template <typename T> +static EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR bool index_pair_first_statically_eq(DenseIndex i, DenseIndex value) { + return index_pair_first_statically_eq_impl<T>::run(i, value); +} + +template <typename T> +static EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR bool index_pair_second_statically_eq(DenseIndex i, DenseIndex value) { + return index_pair_second_statically_eq_impl<T>::run(i, value); +} + +} // end namespace internal +} // end namespace Eigen + + +#endif // EIGEN_CXX11_TENSOR_TENSOR_INDEX_LIST_H diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorInflation.h b/unsupported/Eigen/CXX11/src/Tensor/TensorInflation.h new file mode 100644 index 000000000..f391fb9ee --- /dev/null +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorInflation.h @@ -0,0 +1,229 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2015 Ke Yang <yangke@gmail.com> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_CXX11_TENSOR_TENSOR_INFLATION_H +#define EIGEN_CXX11_TENSOR_TENSOR_INFLATION_H + +namespace Eigen { + +/** \class TensorInflation + * \ingroup CXX11_Tensor_Module + * + * \brief Tensor inflation class. + * + * + */ +namespace internal { +template<typename Strides, typename XprType> +struct traits<TensorInflationOp<Strides, XprType> > : public traits<XprType> +{ + typedef typename XprType::Scalar Scalar; + typedef traits<XprType> XprTraits; + typedef typename XprTraits::StorageKind StorageKind; + typedef typename XprTraits::Index Index; + typedef typename XprType::Nested Nested; + typedef typename remove_reference<Nested>::type _Nested; + static const int NumDimensions = XprTraits::NumDimensions; + static const int Layout = XprTraits::Layout; +}; + +template<typename Strides, typename XprType> +struct eval<TensorInflationOp<Strides, XprType>, Eigen::Dense> +{ + typedef const TensorInflationOp<Strides, XprType>& type; +}; + +template<typename Strides, typename XprType> +struct nested<TensorInflationOp<Strides, XprType>, 1, typename eval<TensorInflationOp<Strides, XprType> >::type> +{ + typedef TensorInflationOp<Strides, XprType> type; +}; + +} // end namespace internal + +template<typename Strides, typename XprType> +class TensorInflationOp : public TensorBase<TensorInflationOp<Strides, XprType>, ReadOnlyAccessors> +{ + public: + typedef typename Eigen::internal::traits<TensorInflationOp>::Scalar Scalar; + typedef typename Eigen::NumTraits<Scalar>::Real RealScalar; + typedef typename XprType::CoeffReturnType CoeffReturnType; + typedef typename Eigen::internal::nested<TensorInflationOp>::type Nested; + typedef typename Eigen::internal::traits<TensorInflationOp>::StorageKind StorageKind; + typedef typename Eigen::internal::traits<TensorInflationOp>::Index Index; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorInflationOp(const XprType& expr, const Strides& strides) + : m_xpr(expr), m_strides(strides) {} + + EIGEN_DEVICE_FUNC + const Strides& strides() const { return m_strides; } + + EIGEN_DEVICE_FUNC + const typename internal::remove_all<typename XprType::Nested>::type& + expression() const { return m_xpr; } + + protected: + typename XprType::Nested m_xpr; + const Strides m_strides; +}; + +// Eval as rvalue +template<typename Strides, typename ArgType, typename Device> +struct TensorEvaluator<const TensorInflationOp<Strides, ArgType>, Device> +{ + typedef TensorInflationOp<Strides, ArgType> XprType; + typedef typename XprType::Index Index; + static const int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value; + typedef DSizes<Index, NumDims> Dimensions; + typedef typename XprType::Scalar Scalar; + typedef typename XprType::CoeffReturnType CoeffReturnType; + typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType; + static const int PacketSize = internal::unpacket_traits<PacketReturnType>::size; + + enum { + IsAligned = /*TensorEvaluator<ArgType, Device>::IsAligned*/ false, + PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess, + BlockAccess = false, + Layout = TensorEvaluator<ArgType, Device>::Layout, + CoordAccess = false, // to be implemented + RawAccess = false + }; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) + : m_impl(op.expression(), device), m_strides(op.strides()) + { + m_dimensions = m_impl.dimensions(); + // Expand each dimension to the inflated dimension. + for (int i = 0; i < NumDims; ++i) { + m_dimensions[i] = (m_dimensions[i] - 1) * op.strides()[i] + 1; + } + + // Remember the strides for fast division. + for (int i = 0; i < NumDims; ++i) { + m_fastStrides[i] = internal::TensorIntDivisor<Index>(m_strides[i]); + } + + const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions(); + if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { + m_outputStrides[0] = 1; + m_inputStrides[0] = 1; + for (int i = 1; i < NumDims; ++i) { + m_outputStrides[i] = m_outputStrides[i-1] * m_dimensions[i-1]; + m_inputStrides[i] = m_inputStrides[i-1] * input_dims[i-1]; + } + } else { // RowMajor + m_outputStrides[NumDims-1] = 1; + m_inputStrides[NumDims-1] = 1; + for (int i = NumDims - 2; i >= 0; --i) { + m_outputStrides[i] = m_outputStrides[i+1] * m_dimensions[i+1]; + m_inputStrides[i] = m_inputStrides[i+1] * input_dims[i+1]; + } + } + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* /*data*/) { + m_impl.evalSubExprsIfNeeded(NULL); + return true; + } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() { + m_impl.cleanup(); + } + + // Computes the input index given the output index. Returns true if the output + // index doesn't fall into a hole. + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool getInputIndex(Index index, Index* inputIndex) const + { + eigen_assert(index < dimensions().TotalSize()); + *inputIndex = 0; + if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { + for (int i = NumDims - 1; i > 0; --i) { + const Index idx = index / m_outputStrides[i]; + if (idx != idx / m_fastStrides[i] * m_strides[i]) { + return false; + } + *inputIndex += idx / m_strides[i] * m_inputStrides[i]; + index -= idx * m_outputStrides[i]; + } + if (index != index / m_fastStrides[0] * m_strides[0]) { + return false; + } + *inputIndex += index / m_strides[0]; + return true; + } else { + for (int i = 0; i < NumDims - 1; ++i) { + const Index idx = index / m_outputStrides[i]; + if (idx != idx / m_fastStrides[i] * m_strides[i]) { + return false; + } + *inputIndex += idx / m_strides[i] * m_inputStrides[i]; + index -= idx * m_outputStrides[i]; + } + if (index != index / m_fastStrides[NumDims-1] * m_strides[NumDims-1]) { + return false; + } + *inputIndex += index / m_strides[NumDims - 1]; + } + return true; + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const + { + Index inputIndex = 0; + if (getInputIndex(index, &inputIndex)) { + return m_impl.coeff(inputIndex); + } else { + return Scalar(0); + } + } + + // TODO(yangke): optimize this function so that we can detect and produce + // all-zero packets + template<int LoadMode> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const + { + EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE) + eigen_assert(index+PacketSize-1 < dimensions().TotalSize()); + + EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize]; + for (int i = 0; i < PacketSize; ++i) { + values[i] = coeff(index+i); + } + PacketReturnType rslt = internal::pload<PacketReturnType>(values); + return rslt; + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const { + const double compute_cost = NumDims * (3 * TensorOpCost::DivCost<Index>() + + 3 * TensorOpCost::MulCost<Index>() + + 2 * TensorOpCost::AddCost<Index>()); + const double input_size = m_impl.dimensions().TotalSize(); + const double output_size = m_dimensions.TotalSize(); + if (output_size == 0) + return TensorOpCost(); + return m_impl.costPerCoeff(vectorized) + + TensorOpCost(sizeof(CoeffReturnType) * input_size / output_size, 0, + compute_cost, vectorized, PacketSize); + } + + EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; } + + protected: + Dimensions m_dimensions; + array<Index, NumDims> m_outputStrides; + array<Index, NumDims> m_inputStrides; + TensorEvaluator<ArgType, Device> m_impl; + const Strides m_strides; + array<internal::TensorIntDivisor<Index>, NumDims> m_fastStrides; +}; + +} // end namespace Eigen + +#endif // EIGEN_CXX11_TENSOR_TENSOR_INFLATION_H diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorInitializer.h b/unsupported/Eigen/CXX11/src/Tensor/TensorInitializer.h new file mode 100644 index 000000000..33edc49e3 --- /dev/null +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorInitializer.h @@ -0,0 +1,82 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_CXX11_TENSOR_TENSOR_INITIALIZER_H +#define EIGEN_CXX11_TENSOR_TENSOR_INITIALIZER_H + +#if EIGEN_HAS_VARIADIC_TEMPLATES + +#include <initializer_list> + +namespace Eigen { + +/** \class TensorInitializer + * \ingroup CXX11_Tensor_Module + * + * \brief Helper template to initialize Tensors from std::initializer_lists. + */ +namespace internal { + +template <typename Derived, int N> +struct Initializer { + typedef std::initializer_list< + typename Initializer<Derived, N - 1>::InitList> InitList; + + static void run(TensorEvaluator<Derived, DefaultDevice>& tensor, + Eigen::array<typename traits<Derived>::Index, traits<Derived>::NumDimensions>* indices, + const InitList& vals) { + int i = 0; + for (auto v : vals) { + (*indices)[traits<Derived>::NumDimensions - N] = i++; + Initializer<Derived, N - 1>::run(tensor, indices, v); + } + } +}; + +template <typename Derived> +struct Initializer<Derived, 1> { + typedef std::initializer_list<typename traits<Derived>::Scalar> InitList; + + static void run(TensorEvaluator<Derived, DefaultDevice>& tensor, + Eigen::array<typename traits<Derived>::Index, traits<Derived>::NumDimensions>* indices, + const InitList& vals) { + int i = 0; + // There is likely a faster way to do that than iterating. + for (auto v : vals) { + (*indices)[traits<Derived>::NumDimensions - 1] = i++; + tensor.coeffRef(*indices) = v; + } + } +}; + +template <typename Derived> +struct Initializer<Derived, 0> { + typedef typename traits<Derived>::Scalar InitList; + + static void run(TensorEvaluator<Derived, DefaultDevice>& tensor, + Eigen::array<typename traits<Derived>::Index, traits<Derived>::NumDimensions>*, + const InitList& v) { + tensor.coeffRef(0) = v; + } +}; + + +template <typename Derived, int N> +void initialize_tensor(TensorEvaluator<Derived, DefaultDevice>& tensor, + const typename Initializer<Derived, traits<Derived>::NumDimensions>::InitList& vals) { + Eigen::array<typename traits<Derived>::Index, traits<Derived>::NumDimensions> indices; + Initializer<Derived, traits<Derived>::NumDimensions>::run(tensor, &indices, vals); +} + +} // namespace internal +} // namespace Eigen + +#endif // EIGEN_HAS_VARIADIC_TEMPLATES + +#endif // EIGEN_CXX11_TENSOR_TENSOR_INITIALIZER_H diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorIntDiv.h b/unsupported/Eigen/CXX11/src/Tensor/TensorIntDiv.h new file mode 100644 index 000000000..ede3939c2 --- /dev/null +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorIntDiv.h @@ -0,0 +1,253 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_CXX11_TENSOR_TENSOR_INTDIV_H +#define EIGEN_CXX11_TENSOR_TENSOR_INTDIV_H + + +namespace Eigen { + +/** \internal + * + * \class TensorIntDiv + * \ingroup CXX11_Tensor_Module + * + * \brief Fast integer division by a constant. + * + * See the paper from Granlund and Montgomery for explanation. + * (at http://dx.doi.org/10.1145/773473.178249) + * + * \sa Tensor + */ + +namespace internal { + +namespace { + + // Note: result is undefined if val == 0 + template <typename T> + EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE + typename internal::enable_if<sizeof(T)==4,int>::type count_leading_zeros(const T val) + { +#ifdef __CUDA_ARCH__ + return __clz(val); +#elif EIGEN_COMP_MSVC + unsigned long index; + _BitScanReverse(&index, val); + return 31 - index; +#else + EIGEN_STATIC_ASSERT(sizeof(unsigned long long) == 8, YOU_MADE_A_PROGRAMMING_MISTAKE); + return __builtin_clz(static_cast<uint32_t>(val)); +#endif + } + + template <typename T> + EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE + typename internal::enable_if<sizeof(T)==8,int>::type count_leading_zeros(const T val) + { +#ifdef __CUDA_ARCH__ + return __clzll(val); +#elif EIGEN_COMP_MSVC && EIGEN_ARCH_x86_64 + unsigned long index; + _BitScanReverse64(&index, val); + return 63 - index; +#elif EIGEN_COMP_MSVC + // MSVC's _BitScanReverse64 is not available for 32bits builds. + unsigned int lo = (unsigned int)(val&0xffffffff); + unsigned int hi = (unsigned int)((val>>32)&0xffffffff); + int n; + if(hi==0) + n = 32 + count_leading_zeros<unsigned int>(lo); + else + n = count_leading_zeros<unsigned int>(hi); + return n; +#else + EIGEN_STATIC_ASSERT(sizeof(unsigned long long) == 8, YOU_MADE_A_PROGRAMMING_MISTAKE); + return __builtin_clzll(static_cast<uint64_t>(val)); +#endif + } + + template <typename T> + struct UnsignedTraits { + typedef typename conditional<sizeof(T) == 8, uint64_t, uint32_t>::type type; + }; + + template <typename T> + struct DividerTraits { + typedef typename UnsignedTraits<T>::type type; + static const int N = sizeof(T) * 8; + }; + + template <typename T> + EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE uint32_t muluh(const uint32_t a, const T b) { +#if defined(__CUDA_ARCH__) + return __umulhi(a, b); +#else + return (static_cast<uint64_t>(a) * b) >> 32; +#endif + } + + template <typename T> + EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE uint64_t muluh(const uint64_t a, const T b) { +#if defined(__CUDA_ARCH__) + return __umul64hi(a, b); +#elif defined(__SIZEOF_INT128__) + __uint128_t v = static_cast<__uint128_t>(a) * static_cast<__uint128_t>(b); + return static_cast<uint64_t>(v >> 64); +#else + return (TensorUInt128<static_val<0>, uint64_t>(a) * TensorUInt128<static_val<0>, uint64_t>(b)).upper(); +#endif + } + + template <int N, typename T> + struct DividerHelper { + static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE uint32_t computeMultiplier(const int log_div, const T divider) { + EIGEN_STATIC_ASSERT(N == 32, YOU_MADE_A_PROGRAMMING_MISTAKE); + return static_cast<uint32_t>((static_cast<uint64_t>(1) << (N+log_div)) / divider - (static_cast<uint64_t>(1) << N) + 1); + } + }; + + template <typename T> + struct DividerHelper<64, T> { + static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE uint64_t computeMultiplier(const int log_div, const T divider) { +#if defined(__SIZEOF_INT128__) && !defined(__CUDA_ARCH__) + return static_cast<uint64_t>((static_cast<__uint128_t>(1) << (64+log_div)) / static_cast<__uint128_t>(divider) - (static_cast<__uint128_t>(1) << 64) + 1); +#else + const uint64_t shift = 1ULL << log_div; + TensorUInt128<uint64_t, uint64_t> result = TensorUInt128<uint64_t, static_val<0> >(shift, 0) / TensorUInt128<static_val<0>, uint64_t>(divider) + - TensorUInt128<static_val<1>, static_val<0> >(1, 0) + + TensorUInt128<static_val<0>, static_val<1> >(1); + return static_cast<uint64_t>(result); +#endif + } + }; +} + + +template <typename T, bool div_gt_one = false> +struct TensorIntDivisor { + public: + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorIntDivisor() { + multiplier = 0; + shift1 = 0; + shift2 = 0; + } + + // Must have 0 < divider < 2^31. This is relaxed to + // 0 < divider < 2^63 when using 64-bit indices on platforms that support + // the __uint128_t type. + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorIntDivisor(const T divider) { + const int N = DividerTraits<T>::N; + eigen_assert(static_cast<typename UnsignedTraits<T>::type>(divider) < NumTraits<UnsignedType>::highest()/2); + eigen_assert(divider > 0); + + // fast ln2 + const int leading_zeros = count_leading_zeros(static_cast<UnsignedType>(divider)); + int log_div = N - leading_zeros; + // if divider is a power of two then log_div is 1 more than it should be. + if ((static_cast<typename UnsignedTraits<T>::type>(1) << (log_div-1)) == static_cast<typename UnsignedTraits<T>::type>(divider)) + log_div--; + + multiplier = DividerHelper<N, T>::computeMultiplier(log_div, divider); + shift1 = log_div > 1 ? 1 : log_div; + shift2 = log_div > 1 ? log_div-1 : 0; + } + + // Must have 0 <= numerator. On platforms that dont support the __uint128_t + // type numerator should also be less than 2^32-1. + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T divide(const T numerator) const { + eigen_assert(static_cast<typename UnsignedTraits<T>::type>(numerator) < NumTraits<UnsignedType>::highest()/2); + //eigen_assert(numerator >= 0); // this is implicitly asserted by the line above + + UnsignedType t1 = muluh(multiplier, numerator); + UnsignedType t = (static_cast<UnsignedType>(numerator) - t1) >> shift1; + return (t1 + t) >> shift2; + } + + private: + typedef typename DividerTraits<T>::type UnsignedType; + UnsignedType multiplier; + int32_t shift1; + int32_t shift2; +}; + + +// Optimized version for signed 32 bit integers. +// Derived from Hacker's Delight. +// Only works for divisors strictly greater than one +template <> +class TensorIntDivisor<int32_t, true> { + public: + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorIntDivisor() { + magic = 0; + shift = 0; + } + // Must have 2 <= divider + EIGEN_DEVICE_FUNC TensorIntDivisor(int32_t divider) { + eigen_assert(divider >= 2); + calcMagic(divider); + } + + EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE int divide(const int32_t n) const { +#ifdef __CUDA_ARCH__ + return (__umulhi(magic, n) >> shift); +#else + uint64_t v = static_cast<uint64_t>(magic) * static_cast<uint64_t>(n); + return (static_cast<uint32_t>(v >> 32) >> shift); +#endif + } + +private: + // Compute the magic numbers. See Hacker's Delight section 10 for an in + // depth explanation. + EIGEN_DEVICE_FUNC void calcMagic(int32_t d) { + const unsigned two31 = 0x80000000; // 2**31. + unsigned ad = d; + unsigned t = two31 + (ad >> 31); + unsigned anc = t - 1 - t%ad; // Absolute value of nc. + int p = 31; // Init. p. + unsigned q1 = two31/anc; // Init. q1 = 2**p/|nc|. + unsigned r1 = two31 - q1*anc; // Init. r1 = rem(2**p, |nc|). + unsigned q2 = two31/ad; // Init. q2 = 2**p/|d|. + unsigned r2 = two31 - q2*ad; // Init. r2 = rem(2**p, |d|). + unsigned delta = 0; + do { + p = p + 1; + q1 = 2*q1; // Update q1 = 2**p/|nc|. + r1 = 2*r1; // Update r1 = rem(2**p, |nc|). + if (r1 >= anc) { // (Must be an unsigned + q1 = q1 + 1; // comparison here). + r1 = r1 - anc;} + q2 = 2*q2; // Update q2 = 2**p/|d|. + r2 = 2*r2; // Update r2 = rem(2**p, |d|). + if (r2 >= ad) { // (Must be an unsigned + q2 = q2 + 1; // comparison here). + r2 = r2 - ad;} + delta = ad - r2; + } while (q1 < delta || (q1 == delta && r1 == 0)); + + magic = (unsigned)(q2 + 1); + shift = p - 32; + } + + uint32_t magic; + int32_t shift; +}; + + +template <typename T, bool div_gt_one> +static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T operator / (const T& numerator, const TensorIntDivisor<T, div_gt_one>& divisor) { + return divisor.divide(numerator); +} + + +} // end namespace internal +} // end namespace Eigen + +#endif // EIGEN_CXX11_TENSOR_TENSOR_INTDIV_H diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorLayoutSwap.h b/unsupported/Eigen/CXX11/src/Tensor/TensorLayoutSwap.h new file mode 100644 index 000000000..cd0109ef4 --- /dev/null +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorLayoutSwap.h @@ -0,0 +1,209 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_CXX11_TENSOR_TENSOR_LAYOUT_SWAP_H +#define EIGEN_CXX11_TENSOR_TENSOR_LAYOUT_SWAP_H + +namespace Eigen { + +/** \class TensorLayoutSwap + * \ingroup CXX11_Tensor_Module + * + * \brief Swap the layout from col-major to row-major, or row-major + * to col-major, and invert the order of the dimensions. + * + * Beware: the dimensions are reversed by this operation. If you want to + * preserve the ordering of the dimensions, you need to combine this + * operation with a shuffle. + * + * \example: + * Tensor<float, 2, ColMajor> input(2, 4); + * Tensor<float, 2, RowMajor> output = input.swap_layout(); + * eigen_assert(output.dimension(0) == 4); + * eigen_assert(output.dimension(1) == 2); + * + * array<int, 2> shuffle(1, 0); + * output = input.swap_layout().shuffle(shuffle); + * eigen_assert(output.dimension(0) == 2); + * eigen_assert(output.dimension(1) == 4); + * + */ +namespace internal { +template<typename XprType> +struct traits<TensorLayoutSwapOp<XprType> > : public traits<XprType> +{ + typedef typename XprType::Scalar Scalar; + typedef traits<XprType> XprTraits; + typedef typename XprTraits::StorageKind StorageKind; + typedef typename XprTraits::Index Index; + typedef typename XprType::Nested Nested; + typedef typename remove_reference<Nested>::type _Nested; + static const int NumDimensions = traits<XprType>::NumDimensions; + static const int Layout = (traits<XprType>::Layout == ColMajor) ? RowMajor : ColMajor; +}; + +template<typename XprType> +struct eval<TensorLayoutSwapOp<XprType>, Eigen::Dense> +{ + typedef const TensorLayoutSwapOp<XprType>& type; +}; + +template<typename XprType> +struct nested<TensorLayoutSwapOp<XprType>, 1, typename eval<TensorLayoutSwapOp<XprType> >::type> +{ + typedef TensorLayoutSwapOp<XprType> type; +}; + +} // end namespace internal + + + +template<typename XprType> +class TensorLayoutSwapOp : public TensorBase<TensorLayoutSwapOp<XprType>, WriteAccessors> +{ + public: + typedef typename Eigen::internal::traits<TensorLayoutSwapOp>::Scalar Scalar; + typedef typename Eigen::NumTraits<Scalar>::Real RealScalar; + typedef typename internal::remove_const<typename XprType::CoeffReturnType>::type CoeffReturnType; + typedef typename Eigen::internal::nested<TensorLayoutSwapOp>::type Nested; + typedef typename Eigen::internal::traits<TensorLayoutSwapOp>::StorageKind StorageKind; + typedef typename Eigen::internal::traits<TensorLayoutSwapOp>::Index Index; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorLayoutSwapOp(const XprType& expr) + : m_xpr(expr) {} + + EIGEN_DEVICE_FUNC + const typename internal::remove_all<typename XprType::Nested>::type& + expression() const { return m_xpr; } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE TensorLayoutSwapOp& operator = (const TensorLayoutSwapOp& other) + { + typedef TensorAssignOp<TensorLayoutSwapOp, const TensorLayoutSwapOp> Assign; + Assign assign(*this, other); + internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice()); + return *this; + } + + template<typename OtherDerived> + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE TensorLayoutSwapOp& operator = (const OtherDerived& other) + { + typedef TensorAssignOp<TensorLayoutSwapOp, const OtherDerived> Assign; + Assign assign(*this, other); + internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice()); + return *this; + } + + protected: + typename XprType::Nested m_xpr; +}; + + +// Eval as rvalue +template<typename ArgType, typename Device> +struct TensorEvaluator<const TensorLayoutSwapOp<ArgType>, Device> +{ + typedef TensorLayoutSwapOp<ArgType> XprType; + typedef typename XprType::Index Index; + static const int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value; + typedef DSizes<Index, NumDims> Dimensions; + + enum { + IsAligned = TensorEvaluator<ArgType, Device>::IsAligned, + PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess, + Layout = (static_cast<int>(TensorEvaluator<ArgType, Device>::Layout) == static_cast<int>(ColMajor)) ? RowMajor : ColMajor, + CoordAccess = false, // to be implemented + RawAccess = TensorEvaluator<ArgType, Device>::RawAccess + }; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) + : m_impl(op.expression(), device) + { + for(int i = 0; i < NumDims; ++i) { + m_dimensions[i] = m_impl.dimensions()[NumDims-1-i]; + } + } + + typedef typename XprType::Scalar Scalar; + typedef typename XprType::CoeffReturnType CoeffReturnType; + typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(CoeffReturnType* data) { + return m_impl.evalSubExprsIfNeeded(data); + } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() { + m_impl.cleanup(); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const + { + return m_impl.coeff(index); + } + + template<int LoadMode> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const + { + return m_impl.template packet<LoadMode>(index); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const { + return m_impl.costPerCoeff(vectorized); + } + + EIGEN_DEVICE_FUNC Scalar* data() const { return m_impl.data(); } + + const TensorEvaluator<ArgType, Device>& impl() const { return m_impl; } + + protected: + TensorEvaluator<ArgType, Device> m_impl; + Dimensions m_dimensions; +}; + + +// Eval as lvalue +template<typename ArgType, typename Device> + struct TensorEvaluator<TensorLayoutSwapOp<ArgType>, Device> + : public TensorEvaluator<const TensorLayoutSwapOp<ArgType>, Device> +{ + typedef TensorEvaluator<const TensorLayoutSwapOp<ArgType>, Device> Base; + typedef TensorLayoutSwapOp<ArgType> XprType; + + enum { + IsAligned = TensorEvaluator<ArgType, Device>::IsAligned, + PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess, + Layout = (static_cast<int>(TensorEvaluator<ArgType, Device>::Layout) == static_cast<int>(ColMajor)) ? RowMajor : ColMajor, + CoordAccess = false // to be implemented + }; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) + : Base(op, device) + { } + + typedef typename XprType::Index Index; + typedef typename XprType::Scalar Scalar; + typedef typename XprType::CoeffReturnType CoeffReturnType; + typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType& coeffRef(Index index) + { + return this->m_impl.coeffRef(index); + } + template <int StoreMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + void writePacket(Index index, const PacketReturnType& x) + { + this->m_impl.template writePacket<StoreMode>(index, x); + } +}; + +} // end namespace Eigen + +#endif // EIGEN_CXX11_TENSOR_TENSOR_LAYOUT_SWAP_H diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorMacros.h b/unsupported/Eigen/CXX11/src/Tensor/TensorMacros.h new file mode 100644 index 000000000..ee0078bbc --- /dev/null +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorMacros.h @@ -0,0 +1,54 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2015 Benoit Steiner <benoit.steiner.goog@gmail.com> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_CXX11_TENSOR_TENSOR_META_MACROS_H +#define EIGEN_CXX11_TENSOR_TENSOR_META_MACROS_H + + +/** use this macro in sfinae selection in templated functions + * + * template<typename T, + * typename std::enable_if< isBanana<T>::value , int >::type = 0 + * > + * void foo(){} + * + * becomes => + * + * template<typename TopoType, + * SFINAE_ENABLE_IF( isBanana<T>::value ) + * > + * void foo(){} + */ + +// SFINAE requires variadic templates +#ifndef __CUDACC__ +#if EIGEN_HAS_VARIADIC_TEMPLATES + // SFINAE doesn't work for gcc <= 4.7 + #ifdef EIGEN_COMP_GNUC + #if EIGEN_GNUC_AT_LEAST(4,8) + #define EIGEN_HAS_SFINAE + #endif + #else + #define EIGEN_HAS_SFINAE + #endif +#endif +#endif + +#define EIGEN_SFINAE_ENABLE_IF( __condition__ ) \ + typename internal::enable_if< ( __condition__ ) , int >::type = 0 + + +#if EIGEN_HAS_CONSTEXPR +#define EIGEN_CONSTEXPR constexpr +#else +#define EIGEN_CONSTEXPR +#endif + + +#endif diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorMap.h b/unsupported/Eigen/CXX11/src/Tensor/TensorMap.h new file mode 100644 index 000000000..a8e55757e --- /dev/null +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorMap.h @@ -0,0 +1,321 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_CXX11_TENSOR_TENSOR_MAP_H +#define EIGEN_CXX11_TENSOR_TENSOR_MAP_H + +namespace Eigen { + +/** \class TensorMap + * \ingroup CXX11_Tensor_Module + * + * \brief A tensor expression mapping an existing array of data. + * + */ +/// template <class> class MakePointer_ is added to convert the host pointer to the device pointer. +/// It is added due to the fact that for our device compiler T* is not allowed. +/// If we wanted to use the same Evaluator functions we have to convert that type to our pointer T. +/// This is done through our MakePointer_ class. By default the Type in the MakePointer_<T> is T* . +/// Therefore, by adding the default value, we managed to convert the type and it does not break any +/// existing code as its default value is T*. +template<typename PlainObjectType, int Options_, template <class> class MakePointer_> class TensorMap : public TensorBase<TensorMap<PlainObjectType, Options_, MakePointer_> > +{ + public: + typedef TensorMap<PlainObjectType, Options_, MakePointer_> Self; + typedef typename PlainObjectType::Base Base; + typedef typename Eigen::internal::nested<Self>::type Nested; + typedef typename internal::traits<PlainObjectType>::StorageKind StorageKind; + typedef typename internal::traits<PlainObjectType>::Index Index; + typedef typename internal::traits<PlainObjectType>::Scalar Scalar; + typedef typename NumTraits<Scalar>::Real RealScalar; + typedef typename Base::CoeffReturnType CoeffReturnType; + + /* typedef typename internal::conditional< + bool(internal::is_lvalue<PlainObjectType>::value), + Scalar *, + const Scalar *>::type + PointerType;*/ + typedef typename MakePointer_<Scalar>::Type PointerType; + typedef PointerType PointerArgType; + + static const int Options = Options_; + + static const Index NumIndices = PlainObjectType::NumIndices; + typedef typename PlainObjectType::Dimensions Dimensions; + + enum { + IsAligned = ((int(Options_)&Aligned)==Aligned), + Layout = PlainObjectType::Layout, + CoordAccess = true, + RawAccess = true + }; + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE TensorMap(PointerArgType dataPtr) : m_data(dataPtr), m_dimensions() { + // The number of dimensions used to construct a tensor must be equal to the rank of the tensor. + EIGEN_STATIC_ASSERT((0 == NumIndices || NumIndices == Dynamic), YOU_MADE_A_PROGRAMMING_MISTAKE) + } + +#if EIGEN_HAS_VARIADIC_TEMPLATES + template<typename... IndexTypes> EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE TensorMap(PointerArgType dataPtr, Index firstDimension, IndexTypes... otherDimensions) : m_data(dataPtr), m_dimensions(firstDimension, otherDimensions...) { + // The number of dimensions used to construct a tensor must be equal to the rank of the tensor. + EIGEN_STATIC_ASSERT((sizeof...(otherDimensions) + 1 == NumIndices || NumIndices == Dynamic), YOU_MADE_A_PROGRAMMING_MISTAKE) + } +#else + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE TensorMap(PointerArgType dataPtr, Index firstDimension) : m_data(dataPtr), m_dimensions(firstDimension) { + // The number of dimensions used to construct a tensor must be equal to the rank of the tensor. + EIGEN_STATIC_ASSERT((1 == NumIndices || NumIndices == Dynamic), YOU_MADE_A_PROGRAMMING_MISTAKE) + } + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE TensorMap(PointerArgType dataPtr, Index dim1, Index dim2) : m_data(dataPtr), m_dimensions(dim1, dim2) { + EIGEN_STATIC_ASSERT(2 == NumIndices || NumIndices == Dynamic, YOU_MADE_A_PROGRAMMING_MISTAKE) + } + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE TensorMap(PointerArgType dataPtr, Index dim1, Index dim2, Index dim3) : m_data(dataPtr), m_dimensions(dim1, dim2, dim3) { + EIGEN_STATIC_ASSERT(3 == NumIndices || NumIndices == Dynamic, YOU_MADE_A_PROGRAMMING_MISTAKE) + } + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE TensorMap(PointerArgType dataPtr, Index dim1, Index dim2, Index dim3, Index dim4) : m_data(dataPtr), m_dimensions(dim1, dim2, dim3, dim4) { + EIGEN_STATIC_ASSERT(4 == NumIndices || NumIndices == Dynamic, YOU_MADE_A_PROGRAMMING_MISTAKE) + } + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE TensorMap(PointerArgType dataPtr, Index dim1, Index dim2, Index dim3, Index dim4, Index dim5) : m_data(dataPtr), m_dimensions(dim1, dim2, dim3, dim4, dim5) { + EIGEN_STATIC_ASSERT(5 == NumIndices || NumIndices == Dynamic, YOU_MADE_A_PROGRAMMING_MISTAKE) + } +#endif + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorMap(PointerArgType dataPtr, const array<Index, NumIndices>& dimensions) + : m_data(dataPtr), m_dimensions(dimensions) + { } + + template <typename Dimensions> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorMap(PointerArgType dataPtr, const Dimensions& dimensions) + : m_data(dataPtr), m_dimensions(dimensions) + { } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorMap(PlainObjectType& tensor) + : m_data(tensor.data()), m_dimensions(tensor.dimensions()) + { } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Index rank() const { return m_dimensions.rank(); } + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Index dimension(Index n) const { return m_dimensions[n]; } + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; } + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Index size() const { return m_dimensions.TotalSize(); } + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE PointerType data() { return m_data; } + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const PointerType data() const { return m_data; } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const Scalar& operator()(const array<Index, NumIndices>& indices) const + { + // eigen_assert(checkIndexRange(indices)); + if (PlainObjectType::Options&RowMajor) { + const Index index = m_dimensions.IndexOfRowMajor(indices); + return m_data[index]; + } else { + const Index index = m_dimensions.IndexOfColMajor(indices); + return m_data[index]; + } + } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const Scalar& operator()() const + { + EIGEN_STATIC_ASSERT(NumIndices == 0, YOU_MADE_A_PROGRAMMING_MISTAKE) + return m_data[0]; + } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const Scalar& operator()(Index index) const + { + eigen_internal_assert(index >= 0 && index < size()); + return m_data[index]; + } + +#if EIGEN_HAS_VARIADIC_TEMPLATES + template<typename... IndexTypes> EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const Scalar& operator()(Index firstIndex, Index secondIndex, IndexTypes... otherIndices) const + { + EIGEN_STATIC_ASSERT(sizeof...(otherIndices) + 2 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE) + if (PlainObjectType::Options&RowMajor) { + const Index index = m_dimensions.IndexOfRowMajor(array<Index, NumIndices>{{firstIndex, secondIndex, otherIndices...}}); + return m_data[index]; + } else { + const Index index = m_dimensions.IndexOfColMajor(array<Index, NumIndices>{{firstIndex, secondIndex, otherIndices...}}); + return m_data[index]; + } + } +#else + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const Scalar& operator()(Index i0, Index i1) const + { + if (PlainObjectType::Options&RowMajor) { + const Index index = i1 + i0 * m_dimensions[1]; + return m_data[index]; + } else { + const Index index = i0 + i1 * m_dimensions[0]; + return m_data[index]; + } + } + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const Scalar& operator()(Index i0, Index i1, Index i2) const + { + if (PlainObjectType::Options&RowMajor) { + const Index index = i2 + m_dimensions[2] * (i1 + m_dimensions[1] * i0); + return m_data[index]; + } else { + const Index index = i0 + m_dimensions[0] * (i1 + m_dimensions[1] * i2); + return m_data[index]; + } + } + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const Scalar& operator()(Index i0, Index i1, Index i2, Index i3) const + { + if (PlainObjectType::Options&RowMajor) { + const Index index = i3 + m_dimensions[3] * (i2 + m_dimensions[2] * (i1 + m_dimensions[1] * i0)); + return m_data[index]; + } else { + const Index index = i0 + m_dimensions[0] * (i1 + m_dimensions[1] * (i2 + m_dimensions[2] * i3)); + return m_data[index]; + } + } + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const Scalar& operator()(Index i0, Index i1, Index i2, Index i3, Index i4) const + { + if (PlainObjectType::Options&RowMajor) { + const Index index = i4 + m_dimensions[4] * (i3 + m_dimensions[3] * (i2 + m_dimensions[2] * (i1 + m_dimensions[1] * i0))); + return m_data[index]; + } else { + const Index index = i0 + m_dimensions[0] * (i1 + m_dimensions[1] * (i2 + m_dimensions[2] * (i3 + m_dimensions[3] * i4))); + return m_data[index]; + } + } +#endif + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Scalar& operator()(const array<Index, NumIndices>& indices) + { + // eigen_assert(checkIndexRange(indices)); + if (PlainObjectType::Options&RowMajor) { + const Index index = m_dimensions.IndexOfRowMajor(indices); + return m_data[index]; + } else { + const Index index = m_dimensions.IndexOfColMajor(indices); + return m_data[index]; + } + } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Scalar& operator()() + { + EIGEN_STATIC_ASSERT(NumIndices == 0, YOU_MADE_A_PROGRAMMING_MISTAKE) + return m_data[0]; + } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Scalar& operator()(Index index) + { + eigen_internal_assert(index >= 0 && index < size()); + return m_data[index]; + } + +#if EIGEN_HAS_VARIADIC_TEMPLATES + template<typename... IndexTypes> EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Scalar& operator()(Index firstIndex, Index secondIndex, IndexTypes... otherIndices) + { + static_assert(sizeof...(otherIndices) + 2 == NumIndices || NumIndices == Dynamic, "Number of indices used to access a tensor coefficient must be equal to the rank of the tensor."); + const std::size_t NumDims = sizeof...(otherIndices) + 2; + if (PlainObjectType::Options&RowMajor) { + const Index index = m_dimensions.IndexOfRowMajor(array<Index, NumDims>{{firstIndex, secondIndex, otherIndices...}}); + return m_data[index]; + } else { + const Index index = m_dimensions.IndexOfColMajor(array<Index, NumDims>{{firstIndex, secondIndex, otherIndices...}}); + return m_data[index]; + } + } +#else + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Scalar& operator()(Index i0, Index i1) + { + if (PlainObjectType::Options&RowMajor) { + const Index index = i1 + i0 * m_dimensions[1]; + return m_data[index]; + } else { + const Index index = i0 + i1 * m_dimensions[0]; + return m_data[index]; + } + } + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Scalar& operator()(Index i0, Index i1, Index i2) + { + if (PlainObjectType::Options&RowMajor) { + const Index index = i2 + m_dimensions[2] * (i1 + m_dimensions[1] * i0); + return m_data[index]; + } else { + const Index index = i0 + m_dimensions[0] * (i1 + m_dimensions[1] * i2); + return m_data[index]; + } + } + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Scalar& operator()(Index i0, Index i1, Index i2, Index i3) + { + if (PlainObjectType::Options&RowMajor) { + const Index index = i3 + m_dimensions[3] * (i2 + m_dimensions[2] * (i1 + m_dimensions[1] * i0)); + return m_data[index]; + } else { + const Index index = i0 + m_dimensions[0] * (i1 + m_dimensions[1] * (i2 + m_dimensions[2] * i3)); + return m_data[index]; + } + } + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Scalar& operator()(Index i0, Index i1, Index i2, Index i3, Index i4) + { + if (PlainObjectType::Options&RowMajor) { + const Index index = i4 + m_dimensions[4] * (i3 + m_dimensions[3] * (i2 + m_dimensions[2] * (i1 + m_dimensions[1] * i0))); + return m_data[index]; + } else { + const Index index = i0 + m_dimensions[0] * (i1 + m_dimensions[1] * (i2 + m_dimensions[2] * (i3 + m_dimensions[3] * i4))); + return m_data[index]; + } + } +#endif + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Self& operator=(const Self& other) + { + typedef TensorAssignOp<Self, const Self> Assign; + Assign assign(*this, other); + internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice()); + return *this; + } + + template<typename OtherDerived> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + Self& operator=(const OtherDerived& other) + { + typedef TensorAssignOp<Self, const OtherDerived> Assign; + Assign assign(*this, other); + internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice()); + return *this; + } + + private: + typename MakePointer_<Scalar>::Type m_data; + Dimensions m_dimensions; +}; + +} // end namespace Eigen + +#endif // EIGEN_CXX11_TENSOR_TENSOR_MAP_H diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorMeta.h b/unsupported/Eigen/CXX11/src/Tensor/TensorMeta.h new file mode 100644 index 000000000..615559d44 --- /dev/null +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorMeta.h @@ -0,0 +1,218 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2015 Benoit Steiner <benoit.steiner.goog@gmail.com> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_CXX11_TENSOR_TENSOR_META_H +#define EIGEN_CXX11_TENSOR_TENSOR_META_H + +namespace Eigen { + +template<bool cond> struct Cond {}; + +template<typename T1, typename T2> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE +const T1& choose(Cond<true>, const T1& first, const T2&) { + return first; +} + +template<typename T1, typename T2> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE +const T2& choose(Cond<false>, const T1&, const T2& second) { + return second; +} + + +template <typename T, typename X, typename Y> +EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE +T divup(const X x, const Y y) { + return static_cast<T>((x + y - 1) / y); +} + +template <typename T> +EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE +T divup(const T x, const T y) { + return static_cast<T>((x + y - 1) / y); +} + +template <size_t n> struct max_n_1 { + static const size_t size = n; +}; +template <> struct max_n_1<0> { + static const size_t size = 1; +}; + + +// Default packet types +template <typename Scalar, typename Device> +struct PacketType : internal::packet_traits<Scalar> { + typedef typename internal::packet_traits<Scalar>::type type; +}; + +// For CUDA packet types when using a GpuDevice +#if defined(EIGEN_USE_GPU) && defined(__CUDACC__) && defined(EIGEN_HAS_CUDA_FP16) +template <> +struct PacketType<half, GpuDevice> { + typedef half2 type; + static const int size = 2; + enum { + HasAdd = 1, + HasSub = 1, + HasMul = 1, + HasNegate = 1, + HasAbs = 1, + HasArg = 0, + HasAbs2 = 0, + HasMin = 1, + HasMax = 1, + HasConj = 0, + HasSetLinear = 0, + HasBlend = 0, + + HasDiv = 1, + HasSqrt = 1, + HasRsqrt = 1, + HasExp = 1, + HasLog = 1, + HasLog1p = 0, + HasLog10 = 0, + HasPow = 1, + }; +}; +#endif + +#if defined(EIGEN_USE_SYCL) +template <typename T> + struct PacketType<T, SyclDevice> { + typedef T type; + static const int size = 1; + enum { + HasAdd = 0, + HasSub = 0, + HasMul = 0, + HasNegate = 0, + HasAbs = 0, + HasArg = 0, + HasAbs2 = 0, + HasMin = 0, + HasMax = 0, + HasConj = 0, + HasSetLinear = 0, + HasBlend = 0 + }; +}; +#endif + + +// Tuple mimics std::pair but works on e.g. nvcc. +template <typename U, typename V> struct Tuple { + public: + U first; + V second; + + typedef U first_type; + typedef V second_type; + + EIGEN_CONSTEXPR EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + Tuple() : first(), second() {} + + EIGEN_CONSTEXPR EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + Tuple(const U& f, const V& s) : first(f), second(s) {} + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + Tuple& operator= (const Tuple& rhs) { + if (&rhs == this) return *this; + first = rhs.first; + second = rhs.second; + return *this; + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + void swap(Tuple& rhs) { + using numext::swap; + swap(first, rhs.first); + swap(second, rhs.second); + } +}; + +template <typename U, typename V> +EIGEN_CONSTEXPR EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE +bool operator==(const Tuple<U, V>& x, const Tuple<U, V>& y) { + return (x.first == y.first && x.second == y.second); +} + +template <typename U, typename V> +EIGEN_CONSTEXPR EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE +bool operator!=(const Tuple<U, V>& x, const Tuple<U, V>& y) { + return !(x == y); +} + + +// Can't use std::pairs on cuda devices +template <typename Idx> struct IndexPair { + EIGEN_CONSTEXPR EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE IndexPair() : first(0), second(0) {} + EIGEN_CONSTEXPR EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE IndexPair(Idx f, Idx s) : first(f), second(s) {} + + EIGEN_DEVICE_FUNC void set(IndexPair<Idx> val) { + first = val.first; + second = val.second; + } + + Idx first; + Idx second; +}; + + +#ifdef EIGEN_HAS_SFINAE +namespace internal { + + template<typename IndexType, Index... Is> + EIGEN_CONSTEXPR EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + array<Index, sizeof...(Is)> customIndices2Array(IndexType& idx, numeric_list<Index, Is...>) { + return { idx[Is]... }; + } + template<typename IndexType> + EIGEN_CONSTEXPR EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + array<Index, 0> customIndices2Array(IndexType&, numeric_list<Index>) { + return array<Index, 0>(); + } + + /** Make an array (for index/dimensions) out of a custom index */ + template<typename Index, std::size_t NumIndices, typename IndexType> + EIGEN_CONSTEXPR EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + array<Index, NumIndices> customIndices2Array(IndexType& idx) { + return customIndices2Array(idx, typename gen_numeric_list<Index, NumIndices>::type{}); + } + + + template <typename B, typename D> + struct is_base_of + { + + typedef char (&yes)[1]; + typedef char (&no)[2]; + + template <typename BB, typename DD> + struct Host + { + operator BB*() const; + operator DD*(); + }; + + template<typename T> + static yes check(D*, T); + static no check(B*, int); + + static const bool value = sizeof(check(Host<B,D>(), int())) == sizeof(yes); + }; + +} +#endif + + + +} // namespace Eigen + +#endif // EIGEN_CXX11_TENSOR_TENSOR_META_H diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorMorphing.h b/unsupported/Eigen/CXX11/src/Tensor/TensorMorphing.h new file mode 100644 index 000000000..d34f1e328 --- /dev/null +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorMorphing.h @@ -0,0 +1,888 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_CXX11_TENSOR_TENSOR_MORPHING_H +#define EIGEN_CXX11_TENSOR_TENSOR_MORPHING_H + +namespace Eigen { + +/** \class TensorReshaping + * \ingroup CXX11_Tensor_Module + * + * \brief Tensor reshaping class. + * + * + */ +namespace internal { +template<typename NewDimensions, typename XprType> +struct traits<TensorReshapingOp<NewDimensions, XprType> > : public traits<XprType> +{ + typedef typename XprType::Scalar Scalar; + typedef traits<XprType> XprTraits; + typedef typename XprTraits::StorageKind StorageKind; + typedef typename XprTraits::Index Index; + typedef typename XprType::Nested Nested; + typedef typename remove_reference<Nested>::type _Nested; + static const int NumDimensions = array_size<NewDimensions>::value; + static const int Layout = XprTraits::Layout; +}; + +template<typename NewDimensions, typename XprType> +struct eval<TensorReshapingOp<NewDimensions, XprType>, Eigen::Dense> +{ + typedef const TensorReshapingOp<NewDimensions, XprType>& type; +}; + +template<typename NewDimensions, typename XprType> +struct nested<TensorReshapingOp<NewDimensions, XprType>, 1, typename eval<TensorReshapingOp<NewDimensions, XprType> >::type> +{ + typedef TensorReshapingOp<NewDimensions, XprType> type; +}; + +} // end namespace internal + + + +template<typename NewDimensions, typename XprType> +class TensorReshapingOp : public TensorBase<TensorReshapingOp<NewDimensions, XprType>, WriteAccessors> +{ + public: + typedef typename Eigen::internal::traits<TensorReshapingOp>::Scalar Scalar; + typedef typename internal::remove_const<typename XprType::CoeffReturnType>::type CoeffReturnType; + typedef typename Eigen::internal::nested<TensorReshapingOp>::type Nested; + typedef typename Eigen::internal::traits<TensorReshapingOp>::StorageKind StorageKind; + typedef typename Eigen::internal::traits<TensorReshapingOp>::Index Index; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorReshapingOp(const XprType& expr, const NewDimensions& dims) + : m_xpr(expr), m_dims(dims) {} + + EIGEN_DEVICE_FUNC + const NewDimensions& dimensions() const { return m_dims; } + + EIGEN_DEVICE_FUNC + const typename internal::remove_all<typename XprType::Nested>::type& + expression() const { return m_xpr; } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE TensorReshapingOp& operator = (const TensorReshapingOp& other) + { + typedef TensorAssignOp<TensorReshapingOp, const TensorReshapingOp> Assign; + Assign assign(*this, other); + internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice()); + return *this; + } + + template<typename OtherDerived> + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE TensorReshapingOp& operator = (const OtherDerived& other) + { + typedef TensorAssignOp<TensorReshapingOp, const OtherDerived> Assign; + Assign assign(*this, other); + internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice()); + return *this; + } + + protected: + typename XprType::Nested m_xpr; + const NewDimensions m_dims; +}; + + +// Eval as rvalue +template<typename NewDimensions, typename ArgType, typename Device> +struct TensorEvaluator<const TensorReshapingOp<NewDimensions, ArgType>, Device> +{ + typedef TensorReshapingOp<NewDimensions, ArgType> XprType; + typedef NewDimensions Dimensions; + + enum { + IsAligned = TensorEvaluator<ArgType, Device>::IsAligned, + PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess, + Layout = TensorEvaluator<ArgType, Device>::Layout, + CoordAccess = false, // to be implemented + RawAccess = TensorEvaluator<ArgType, Device>::RawAccess + }; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) + : m_impl(op.expression(), device), m_dimensions(op.dimensions()) + { + // The total size of the reshaped tensor must be equal to the total size + // of the input tensor. + eigen_assert(internal::array_prod(m_impl.dimensions()) == internal::array_prod(op.dimensions())); + } + + typedef typename XprType::Index Index; + typedef typename XprType::Scalar Scalar; + typedef typename XprType::CoeffReturnType CoeffReturnType; + typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(CoeffReturnType* data) { + return m_impl.evalSubExprsIfNeeded(data); + } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() { + m_impl.cleanup(); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const + { + return m_impl.coeff(index); + } + + template<int LoadMode> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const + { + return m_impl.template packet<LoadMode>(index); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const { + return m_impl.costPerCoeff(vectorized); + } + + EIGEN_DEVICE_FUNC Scalar* data() const { return const_cast<Scalar*>(m_impl.data()); } + + EIGEN_DEVICE_FUNC const TensorEvaluator<ArgType, Device>& impl() const { return m_impl; } + + protected: + TensorEvaluator<ArgType, Device> m_impl; + NewDimensions m_dimensions; +}; + + +// Eval as lvalue +template<typename NewDimensions, typename ArgType, typename Device> + struct TensorEvaluator<TensorReshapingOp<NewDimensions, ArgType>, Device> + : public TensorEvaluator<const TensorReshapingOp<NewDimensions, ArgType>, Device> + +{ + typedef TensorEvaluator<const TensorReshapingOp<NewDimensions, ArgType>, Device> Base; + typedef TensorReshapingOp<NewDimensions, ArgType> XprType; + typedef NewDimensions Dimensions; + + enum { + IsAligned = TensorEvaluator<ArgType, Device>::IsAligned, + PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess, + Layout = TensorEvaluator<ArgType, Device>::Layout, + CoordAccess = false, // to be implemented + RawAccess = TensorEvaluator<ArgType, Device>::RawAccess + }; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) + : Base(op, device) + { } + + typedef typename XprType::Index Index; + typedef typename XprType::Scalar Scalar; + typedef typename XprType::CoeffReturnType CoeffReturnType; + typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType& coeffRef(Index index) + { + return this->m_impl.coeffRef(index); + } + template <int StoreMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + void writePacket(Index index, const PacketReturnType& x) + { + this->m_impl.template writePacket<StoreMode>(index, x); + } +}; + + +/** \class TensorSlicing + * \ingroup CXX11_Tensor_Module + * + * \brief Tensor slicing class. + * + * + */ +namespace internal { +template<typename StartIndices, typename Sizes, typename XprType> +struct traits<TensorSlicingOp<StartIndices, Sizes, XprType> > : public traits<XprType> +{ + typedef typename XprType::Scalar Scalar; + typedef traits<XprType> XprTraits; + typedef typename XprTraits::StorageKind StorageKind; + typedef typename XprTraits::Index Index; + typedef typename XprType::Nested Nested; + typedef typename remove_reference<Nested>::type _Nested; + static const int NumDimensions = array_size<StartIndices>::value; + static const int Layout = XprTraits::Layout; +}; + +template<typename StartIndices, typename Sizes, typename XprType> +struct eval<TensorSlicingOp<StartIndices, Sizes, XprType>, Eigen::Dense> +{ + typedef const TensorSlicingOp<StartIndices, Sizes, XprType>& type; +}; + +template<typename StartIndices, typename Sizes, typename XprType> +struct nested<TensorSlicingOp<StartIndices, Sizes, XprType>, 1, typename eval<TensorSlicingOp<StartIndices, Sizes, XprType> >::type> +{ + typedef TensorSlicingOp<StartIndices, Sizes, XprType> type; +}; + +} // end namespace internal + + + +template<typename StartIndices, typename Sizes, typename XprType> +class TensorSlicingOp : public TensorBase<TensorSlicingOp<StartIndices, Sizes, XprType> > +{ + public: + typedef typename Eigen::internal::traits<TensorSlicingOp>::Scalar Scalar; + typedef typename XprType::CoeffReturnType CoeffReturnType; + typedef typename Eigen::internal::nested<TensorSlicingOp>::type Nested; + typedef typename Eigen::internal::traits<TensorSlicingOp>::StorageKind StorageKind; + typedef typename Eigen::internal::traits<TensorSlicingOp>::Index Index; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorSlicingOp(const XprType& expr, const StartIndices& indices, const Sizes& sizes) + : m_xpr(expr), m_indices(indices), m_sizes(sizes) {} + + EIGEN_DEVICE_FUNC + const StartIndices& startIndices() const { return m_indices; } + EIGEN_DEVICE_FUNC + const Sizes& sizes() const { return m_sizes; } + + EIGEN_DEVICE_FUNC + const typename internal::remove_all<typename XprType::Nested>::type& + expression() const { return m_xpr; } + + template<typename OtherDerived> + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE TensorSlicingOp& operator = (const OtherDerived& other) + { + typedef TensorAssignOp<TensorSlicingOp, const OtherDerived> Assign; + Assign assign(*this, other); + internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice()); + return *this; + } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE TensorSlicingOp& operator = (const TensorSlicingOp& other) + { + typedef TensorAssignOp<TensorSlicingOp, const TensorSlicingOp> Assign; + Assign assign(*this, other); + internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice()); + return *this; + } + + + protected: + typename XprType::Nested m_xpr; + const StartIndices m_indices; + const Sizes m_sizes; +}; + + +// Fixme: figure out the exact threshold +namespace { +template <typename Index, typename Device> struct MemcpyTriggerForSlicing { + EIGEN_DEVICE_FUNC MemcpyTriggerForSlicing(const Device& device) : threshold_(2 * device.numThreads()) { } + EIGEN_DEVICE_FUNC bool operator ()(Index val) const { return val > threshold_; } + + private: + Index threshold_; +}; + +// It is very expensive to start the memcpy kernel on GPU: we therefore only +// use it for large copies. +#ifdef EIGEN_USE_GPU +template <typename Index> struct MemcpyTriggerForSlicing<Index, GpuDevice> { + EIGEN_DEVICE_FUNC MemcpyTriggerForSlicing(const GpuDevice&) { } + EIGEN_DEVICE_FUNC bool operator ()(Index val) const { return val > 4*1024*1024; } +}; +#endif +} + +// Eval as rvalue +template<typename StartIndices, typename Sizes, typename ArgType, typename Device> +struct TensorEvaluator<const TensorSlicingOp<StartIndices, Sizes, ArgType>, Device> +{ + typedef TensorSlicingOp<StartIndices, Sizes, ArgType> XprType; + static const int NumDims = internal::array_size<Sizes>::value; + + enum { + // Alignment can't be guaranteed at compile time since it depends on the + // slice offsets and sizes. + IsAligned = /*TensorEvaluator<ArgType, Device>::IsAligned*/false, + PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess, + Layout = TensorEvaluator<ArgType, Device>::Layout, + CoordAccess = false, + RawAccess = false + }; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) + : m_impl(op.expression(), device), m_device(device), m_dimensions(op.sizes()), m_offsets(op.startIndices()) + { + for (std::size_t i = 0; i < internal::array_size<Dimensions>::value; ++i) { + eigen_assert(m_impl.dimensions()[i] >= op.sizes()[i] + op.startIndices()[i]); + } + + const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions(); + const Sizes& output_dims = op.sizes(); + if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { + m_inputStrides[0] = 1; + for (int i = 1; i < NumDims; ++i) { + m_inputStrides[i] = m_inputStrides[i-1] * input_dims[i-1]; + } + + // Don't initialize m_fastOutputStrides[0] since it won't ever be accessed. + m_outputStrides[0] = 1; + for (int i = 1; i < NumDims; ++i) { + m_outputStrides[i] = m_outputStrides[i-1] * output_dims[i-1]; + m_fastOutputStrides[i] = internal::TensorIntDivisor<Index>(m_outputStrides[i]); + } + } else { + m_inputStrides[NumDims-1] = 1; + for (int i = NumDims - 2; i >= 0; --i) { + m_inputStrides[i] = m_inputStrides[i+1] * input_dims[i+1]; + } + + // Don't initialize m_fastOutputStrides[NumDims-1] since it won't ever be accessed. + m_outputStrides[NumDims-1] = 1; + for (int i = NumDims - 2; i >= 0; --i) { + m_outputStrides[i] = m_outputStrides[i+1] * output_dims[i+1]; + m_fastOutputStrides[i] = internal::TensorIntDivisor<Index>(m_outputStrides[i]); + } + } + } + + typedef typename XprType::Index Index; + typedef typename XprType::Scalar Scalar; + typedef typename XprType::CoeffReturnType CoeffReturnType; + typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType; + typedef Sizes Dimensions; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; } + + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(CoeffReturnType* data) { + m_impl.evalSubExprsIfNeeded(NULL); + if (!NumTraits<typename internal::remove_const<Scalar>::type>::RequireInitialization && data && m_impl.data()) { + Index contiguous_values = 1; + if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { + for (int i = 0; i < NumDims; ++i) { + contiguous_values *= dimensions()[i]; + if (dimensions()[i] != m_impl.dimensions()[i]) { + break; + } + } + } else { + for (int i = NumDims-1; i >= 0; --i) { + contiguous_values *= dimensions()[i]; + if (dimensions()[i] != m_impl.dimensions()[i]) { + break; + } + } + } + // Use memcpy if it's going to be faster than using the regular evaluation. + const MemcpyTriggerForSlicing<Index, Device> trigger(m_device); + if (trigger(contiguous_values)) { + Scalar* src = (Scalar*)m_impl.data(); + for (int i = 0; i < internal::array_prod(dimensions()); i += contiguous_values) { + Index offset = srcCoeff(i); + m_device.memcpy((void*)(data+i), src+offset, contiguous_values * sizeof(Scalar)); + } + return false; + } + } + return true; + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() { + m_impl.cleanup(); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const + { + return m_impl.coeff(srcCoeff(index)); + } + + template<int LoadMode> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const + { + const int packetSize = internal::unpacket_traits<PacketReturnType>::size; + EIGEN_STATIC_ASSERT((packetSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE) + eigen_assert(index+packetSize-1 < internal::array_prod(dimensions())); + + Index inputIndices[] = {0, 0}; + Index indices[] = {index, index + packetSize - 1}; + if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { + for (int i = NumDims - 1; i > 0; --i) { + const Index idx0 = indices[0] / m_fastOutputStrides[i]; + const Index idx1 = indices[1] / m_fastOutputStrides[i]; + inputIndices[0] += (idx0 + m_offsets[i]) * m_inputStrides[i]; + inputIndices[1] += (idx1 + m_offsets[i]) * m_inputStrides[i]; + indices[0] -= idx0 * m_outputStrides[i]; + indices[1] -= idx1 * m_outputStrides[i]; + } + inputIndices[0] += (indices[0] + m_offsets[0]); + inputIndices[1] += (indices[1] + m_offsets[0]); + } else { + for (int i = 0; i < NumDims - 1; ++i) { + const Index idx0 = indices[0] / m_fastOutputStrides[i]; + const Index idx1 = indices[1] / m_fastOutputStrides[i]; + inputIndices[0] += (idx0 + m_offsets[i]) * m_inputStrides[i]; + inputIndices[1] += (idx1 + m_offsets[i]) * m_inputStrides[i]; + indices[0] -= idx0 * m_outputStrides[i]; + indices[1] -= idx1 * m_outputStrides[i]; + } + inputIndices[0] += (indices[0] + m_offsets[NumDims-1]); + inputIndices[1] += (indices[1] + m_offsets[NumDims-1]); + } + if (inputIndices[1] - inputIndices[0] == packetSize - 1) { + PacketReturnType rslt = m_impl.template packet<Unaligned>(inputIndices[0]); + return rslt; + } + else { + EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[packetSize]; + values[0] = m_impl.coeff(inputIndices[0]); + values[packetSize-1] = m_impl.coeff(inputIndices[1]); + for (int i = 1; i < packetSize-1; ++i) { + values[i] = coeff(index+i); + } + PacketReturnType rslt = internal::pload<PacketReturnType>(values); + return rslt; + } + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const { + return m_impl.costPerCoeff(vectorized) + TensorOpCost(0, 0, NumDims); + } + + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar* data() const { + Scalar* result = m_impl.data(); + if (result) { + Index offset = 0; + if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { + for (int i = 0; i < NumDims; ++i) { + if (m_dimensions[i] != m_impl.dimensions()[i]) { + offset += m_offsets[i] * m_inputStrides[i]; + for (int j = i+1; j < NumDims; ++j) { + if (m_dimensions[j] > 1) { + return NULL; + } + offset += m_offsets[j] * m_inputStrides[j]; + } + break; + } + } + } else { + for (int i = NumDims - 1; i >= 0; --i) { + if (m_dimensions[i] != m_impl.dimensions()[i]) { + offset += m_offsets[i] * m_inputStrides[i]; + for (int j = i-1; j >= 0; --j) { + if (m_dimensions[j] > 1) { + return NULL; + } + offset += m_offsets[j] * m_inputStrides[j]; + } + break; + } + } + } + return result + offset; + } + return NULL; + } + + protected: + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index srcCoeff(Index index) const + { + Index inputIndex = 0; + if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { + for (int i = NumDims - 1; i > 0; --i) { + const Index idx = index / m_fastOutputStrides[i]; + inputIndex += (idx + m_offsets[i]) * m_inputStrides[i]; + index -= idx * m_outputStrides[i]; + } + inputIndex += (index + m_offsets[0]); + } else { + for (int i = 0; i < NumDims - 1; ++i) { + const Index idx = index / m_fastOutputStrides[i]; + inputIndex += (idx + m_offsets[i]) * m_inputStrides[i]; + index -= idx * m_outputStrides[i]; + } + inputIndex += (index + m_offsets[NumDims-1]); + } + return inputIndex; + } + + array<Index, NumDims> m_outputStrides; + array<internal::TensorIntDivisor<Index>, NumDims> m_fastOutputStrides; + array<Index, NumDims> m_inputStrides; + TensorEvaluator<ArgType, Device> m_impl; + const Device& m_device; + Dimensions m_dimensions; + const StartIndices m_offsets; +}; + + +// Eval as lvalue +template<typename StartIndices, typename Sizes, typename ArgType, typename Device> +struct TensorEvaluator<TensorSlicingOp<StartIndices, Sizes, ArgType>, Device> + : public TensorEvaluator<const TensorSlicingOp<StartIndices, Sizes, ArgType>, Device> +{ + typedef TensorEvaluator<const TensorSlicingOp<StartIndices, Sizes, ArgType>, Device> Base; + typedef TensorSlicingOp<StartIndices, Sizes, ArgType> XprType; + static const int NumDims = internal::array_size<Sizes>::value; + + enum { + IsAligned = /*TensorEvaluator<ArgType, Device>::IsAligned*/false, + PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess, + Layout = TensorEvaluator<ArgType, Device>::Layout, + CoordAccess = false, + RawAccess = false + }; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) + : Base(op, device) + { } + + typedef typename XprType::Index Index; + typedef typename XprType::Scalar Scalar; + typedef typename XprType::CoeffReturnType CoeffReturnType; + typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType; + typedef Sizes Dimensions; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType& coeffRef(Index index) + { + return this->m_impl.coeffRef(this->srcCoeff(index)); + } + + template <int StoreMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + void writePacket(Index index, const PacketReturnType& x) + { + const int packetSize = internal::unpacket_traits<PacketReturnType>::size; + Index inputIndices[] = {0, 0}; + Index indices[] = {index, index + packetSize - 1}; + if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { + for (int i = NumDims - 1; i > 0; --i) { + const Index idx0 = indices[0] / this->m_fastOutputStrides[i]; + const Index idx1 = indices[1] / this->m_fastOutputStrides[i]; + inputIndices[0] += (idx0 + this->m_offsets[i]) * this->m_inputStrides[i]; + inputIndices[1] += (idx1 + this->m_offsets[i]) * this->m_inputStrides[i]; + indices[0] -= idx0 * this->m_outputStrides[i]; + indices[1] -= idx1 * this->m_outputStrides[i]; + } + inputIndices[0] += (indices[0] + this->m_offsets[0]); + inputIndices[1] += (indices[1] + this->m_offsets[0]); + } else { + for (int i = 0; i < NumDims - 1; ++i) { + const Index idx0 = indices[0] / this->m_fastOutputStrides[i]; + const Index idx1 = indices[1] / this->m_fastOutputStrides[i]; + inputIndices[0] += (idx0 + this->m_offsets[i]) * this->m_inputStrides[i]; + inputIndices[1] += (idx1 + this->m_offsets[i]) * this->m_inputStrides[i]; + indices[0] -= idx0 * this->m_outputStrides[i]; + indices[1] -= idx1 * this->m_outputStrides[i]; + } + inputIndices[0] += (indices[0] + this->m_offsets[NumDims-1]); + inputIndices[1] += (indices[1] + this->m_offsets[NumDims-1]); + } + if (inputIndices[1] - inputIndices[0] == packetSize - 1) { + this->m_impl.template writePacket<StoreMode>(inputIndices[0], x); + } + else { + EIGEN_ALIGN_MAX CoeffReturnType values[packetSize]; + internal::pstore<CoeffReturnType, PacketReturnType>(values, x); + this->m_impl.coeffRef(inputIndices[0]) = values[0]; + this->m_impl.coeffRef(inputIndices[1]) = values[packetSize-1]; + for (int i = 1; i < packetSize-1; ++i) { + this->coeffRef(index+i) = values[i]; + } + } + } +}; + + + +namespace internal { +template<typename StartIndices, typename StopIndices, typename Strides, typename XprType> +struct traits<TensorStridingSlicingOp<StartIndices, StopIndices, Strides, XprType> > : public traits<XprType> +{ + typedef typename XprType::Scalar Scalar; + typedef traits<XprType> XprTraits; + typedef typename XprTraits::StorageKind StorageKind; + typedef typename XprTraits::Index Index; + typedef typename XprType::Nested Nested; + typedef typename remove_reference<Nested>::type _Nested; + static const int NumDimensions = array_size<StartIndices>::value; + static const int Layout = XprTraits::Layout; +}; + +template<typename StartIndices, typename StopIndices, typename Strides, typename XprType> +struct eval<TensorStridingSlicingOp<StartIndices, StopIndices, Strides, XprType>, Eigen::Dense> +{ + typedef const TensorStridingSlicingOp<StartIndices, StopIndices, Strides, XprType>& type; +}; + +template<typename StartIndices, typename StopIndices, typename Strides, typename XprType> +struct nested<TensorStridingSlicingOp<StartIndices, StopIndices, Strides, XprType>, 1, typename eval<TensorStridingSlicingOp<StartIndices, StopIndices, Strides, XprType> >::type> +{ + typedef TensorStridingSlicingOp<StartIndices, StopIndices, Strides, XprType> type; +}; + +} // end namespace internal + + +template<typename StartIndices, typename StopIndices, typename Strides, typename XprType> +class TensorStridingSlicingOp : public TensorBase<TensorStridingSlicingOp<StartIndices, StopIndices, Strides, XprType> > +{ + public: + typedef typename internal::traits<TensorStridingSlicingOp>::Scalar Scalar; + typedef typename XprType::CoeffReturnType CoeffReturnType; + typedef typename internal::nested<TensorStridingSlicingOp>::type Nested; + typedef typename internal::traits<TensorStridingSlicingOp>::StorageKind StorageKind; + typedef typename internal::traits<TensorStridingSlicingOp>::Index Index; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorStridingSlicingOp( + const XprType& expr, const StartIndices& startIndices, + const StopIndices& stopIndices, const Strides& strides) + : m_xpr(expr), m_startIndices(startIndices), m_stopIndices(stopIndices), + m_strides(strides) {} + + EIGEN_DEVICE_FUNC + const StartIndices& startIndices() const { return m_startIndices; } + EIGEN_DEVICE_FUNC + const StartIndices& stopIndices() const { return m_stopIndices; } + EIGEN_DEVICE_FUNC + const StartIndices& strides() const { return m_strides; } + + EIGEN_DEVICE_FUNC + const typename internal::remove_all<typename XprType::Nested>::type& + expression() const { return m_xpr; } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE TensorStridingSlicingOp& operator = (const TensorStridingSlicingOp& other) + { + typedef TensorAssignOp<TensorStridingSlicingOp, const TensorStridingSlicingOp> Assign; + Assign assign(*this, other); + internal::TensorExecutor<const Assign, DefaultDevice>::run( + assign, DefaultDevice()); + return *this; + } + + template<typename OtherDerived> + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE TensorStridingSlicingOp& operator = (const OtherDerived& other) + { + typedef TensorAssignOp<TensorStridingSlicingOp, const OtherDerived> Assign; + Assign assign(*this, other); + internal::TensorExecutor<const Assign, DefaultDevice>::run( + assign, DefaultDevice()); + return *this; + } + + protected: + typename XprType::Nested m_xpr; + const StartIndices m_startIndices; + const StopIndices m_stopIndices; + const Strides m_strides; +}; + +// Eval as rvalue +template<typename StartIndices, typename StopIndices, typename Strides, typename ArgType, typename Device> +struct TensorEvaluator<const TensorStridingSlicingOp<StartIndices, StopIndices, Strides, ArgType>, Device> +{ + typedef TensorStridingSlicingOp<StartIndices, StopIndices, Strides, ArgType> XprType; + static const int NumDims = internal::array_size<Strides>::value; + + enum { + // Alignment can't be guaranteed at compile time since it depends on the + // slice offsets and sizes. + IsAligned = false, + PacketAccess = false, + BlockAccess = false, + Layout = TensorEvaluator<ArgType, Device>::Layout, + RawAccess = false + }; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) + : m_impl(op.expression(), device), m_device(device), m_strides(op.strides()) + { + // Handle degenerate intervals by gracefully clamping and allowing m_dimensions to be zero + DSizes<Index,NumDims> startIndicesClamped, stopIndicesClamped; + for (size_t i = 0; i < internal::array_size<Dimensions>::value; ++i) { + eigen_assert(m_strides[i] != 0 && "0 stride is invalid"); + if(m_strides[i]>0){ + startIndicesClamped[i] = clamp(op.startIndices()[i], 0, m_impl.dimensions()[i]); + stopIndicesClamped[i] = clamp(op.stopIndices()[i], 0, m_impl.dimensions()[i]); + }else{ + /* implies m_strides[i]<0 by assert */ + startIndicesClamped[i] = clamp(op.startIndices()[i], -1, m_impl.dimensions()[i] - 1); + stopIndicesClamped[i] = clamp(op.stopIndices()[i], -1, m_impl.dimensions()[i] - 1); + } + m_startIndices[i] = startIndicesClamped[i]; + } + + const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions(); + + // check for degenerate intervals and compute output tensor shape + bool degenerate = false;; + for(int i = 0; i < NumDims; i++){ + Index interval = stopIndicesClamped[i] - startIndicesClamped[i]; + if(interval == 0 || ((interval<0) != (m_strides[i]<0))){ + m_dimensions[i] = 0; + degenerate = true; + }else{ + m_dimensions[i] = interval / m_strides[i] + + (interval % m_strides[i] != 0 ? 1 : 0); + eigen_assert(m_dimensions[i] >= 0); + } + } + Strides output_dims = m_dimensions; + + if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { + m_inputStrides[0] = m_strides[0]; + m_offsets[0] = startIndicesClamped[0]; + Index previousDimProduct = 1; + for (int i = 1; i < NumDims; ++i) { + previousDimProduct *= input_dims[i-1]; + m_inputStrides[i] = previousDimProduct * m_strides[i]; + m_offsets[i] = startIndicesClamped[i] * previousDimProduct; + } + + // Don't initialize m_fastOutputStrides[0] since it won't ever be accessed. + m_outputStrides[0] = 1; + for (int i = 1; i < NumDims; ++i) { + m_outputStrides[i] = m_outputStrides[i-1] * output_dims[i-1]; + // NOTE: if tensor is degenerate, we send 1 to prevent TensorIntDivisor constructor crash + m_fastOutputStrides[i] = internal::TensorIntDivisor<Index>(degenerate ? 1 : m_outputStrides[i]); + } + } else { + m_inputStrides[NumDims-1] = m_strides[NumDims-1]; + m_offsets[NumDims-1] = startIndicesClamped[NumDims-1]; + Index previousDimProduct = 1; + for (int i = NumDims - 2; i >= 0; --i) { + previousDimProduct *= input_dims[i+1]; + m_inputStrides[i] = previousDimProduct * m_strides[i]; + m_offsets[i] = startIndicesClamped[i] * previousDimProduct; + } + + m_outputStrides[NumDims-1] = 1; + for (int i = NumDims - 2; i >= 0; --i) { + m_outputStrides[i] = m_outputStrides[i+1] * output_dims[i+1]; + // NOTE: if tensor is degenerate, we send 1 to prevent TensorIntDivisor constructor crash + m_fastOutputStrides[i] = internal::TensorIntDivisor<Index>(degenerate ? 1 : m_outputStrides[i]); + } + } + m_block_total_size_max = numext::maxi(static_cast<std::size_t>(1), + device.lastLevelCacheSize() / + sizeof(Scalar)); + } + + typedef typename XprType::Index Index; + typedef typename XprType::Scalar Scalar; + typedef typename internal::remove_const<Scalar>::type ScalarNonConst; + typedef typename XprType::CoeffReturnType CoeffReturnType; + typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType; + typedef Strides Dimensions; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; } + + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(CoeffReturnType*) { + m_impl.evalSubExprsIfNeeded(NULL); + return true; + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() { + m_impl.cleanup(); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const + { + return m_impl.coeff(srcCoeff(index)); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const { + return m_impl.costPerCoeff(vectorized) + TensorOpCost(0, 0, NumDims); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar* data() const { + return NULL; + } + + protected: + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index srcCoeff(Index index) const + { + Index inputIndex = 0; + if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { + for (int i = NumDims - 1; i >= 0; --i) { + const Index idx = index / m_fastOutputStrides[i]; + inputIndex += idx * m_inputStrides[i] + m_offsets[i]; + index -= idx * m_outputStrides[i]; + } + } else { + for (int i = 0; i < NumDims; ++i) { + const Index idx = index / m_fastOutputStrides[i]; + inputIndex += idx * m_inputStrides[i] + m_offsets[i]; + index -= idx * m_outputStrides[i]; + } + } + return inputIndex; + } + + static EIGEN_STRONG_INLINE Index clamp(Index value, Index min, Index max) { + return numext::maxi(min, numext::mini(max,value)); + } + + array<Index, NumDims> m_outputStrides; + array<internal::TensorIntDivisor<Index>, NumDims> m_fastOutputStrides; + array<Index, NumDims> m_inputStrides; + TensorEvaluator<ArgType, Device> m_impl; + const Device& m_device; + DSizes<Index, NumDims> m_startIndices; // clamped startIndices + DSizes<Index, NumDims> m_dimensions; + DSizes<Index, NumDims> m_offsets; // offset in a flattened shape + const Strides m_strides; + std::size_t m_block_total_size_max; +}; + +// Eval as lvalue +template<typename StartIndices, typename StopIndices, typename Strides, typename ArgType, typename Device> +struct TensorEvaluator<TensorStridingSlicingOp<StartIndices, StopIndices, Strides, ArgType>, Device> + : public TensorEvaluator<const TensorStridingSlicingOp<StartIndices, StopIndices, Strides, ArgType>, Device> +{ + typedef TensorEvaluator<const TensorStridingSlicingOp<StartIndices, StopIndices, Strides, ArgType>, Device> Base; + typedef TensorStridingSlicingOp<StartIndices, StopIndices, Strides, ArgType> XprType; + static const int NumDims = internal::array_size<Strides>::value; + + enum { + IsAligned = false, + PacketAccess = false, + BlockAccess = false, + Layout = TensorEvaluator<ArgType, Device>::Layout, + CoordAccess = TensorEvaluator<ArgType, Device>::CoordAccess, + RawAccess = false + }; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) + : Base(op, device) + { } + + typedef typename XprType::Index Index; + typedef typename XprType::Scalar Scalar; + typedef typename internal::remove_const<Scalar>::type ScalarNonConst; + typedef typename XprType::CoeffReturnType CoeffReturnType; + typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType; + typedef Strides Dimensions; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType& coeffRef(Index index) + { + return this->m_impl.coeffRef(this->srcCoeff(index)); + } +}; + + +} // end namespace Eigen + +#endif // EIGEN_CXX11_TENSOR_TENSOR_MORPHING_H diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorPadding.h b/unsupported/Eigen/CXX11/src/Tensor/TensorPadding.h new file mode 100644 index 000000000..647bcf108 --- /dev/null +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorPadding.h @@ -0,0 +1,397 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_CXX11_TENSOR_TENSOR_PADDING_H +#define EIGEN_CXX11_TENSOR_TENSOR_PADDING_H + +namespace Eigen { + +/** \class TensorPadding + * \ingroup CXX11_Tensor_Module + * + * \brief Tensor padding class. + * At the moment only padding with a constant value is supported. + * + */ +namespace internal { +template<typename PaddingDimensions, typename XprType> +struct traits<TensorPaddingOp<PaddingDimensions, XprType> > : public traits<XprType> +{ + typedef typename XprType::Scalar Scalar; + typedef traits<XprType> XprTraits; + typedef typename XprTraits::StorageKind StorageKind; + typedef typename XprTraits::Index Index; + typedef typename XprType::Nested Nested; + typedef typename remove_reference<Nested>::type _Nested; + static const int NumDimensions = XprTraits::NumDimensions; + static const int Layout = XprTraits::Layout; +}; + +template<typename PaddingDimensions, typename XprType> +struct eval<TensorPaddingOp<PaddingDimensions, XprType>, Eigen::Dense> +{ + typedef const TensorPaddingOp<PaddingDimensions, XprType>& type; +}; + +template<typename PaddingDimensions, typename XprType> +struct nested<TensorPaddingOp<PaddingDimensions, XprType>, 1, typename eval<TensorPaddingOp<PaddingDimensions, XprType> >::type> +{ + typedef TensorPaddingOp<PaddingDimensions, XprType> type; +}; + +} // end namespace internal + + + +template<typename PaddingDimensions, typename XprType> +class TensorPaddingOp : public TensorBase<TensorPaddingOp<PaddingDimensions, XprType>, ReadOnlyAccessors> +{ + public: + typedef typename Eigen::internal::traits<TensorPaddingOp>::Scalar Scalar; + typedef typename Eigen::NumTraits<Scalar>::Real RealScalar; + typedef typename XprType::CoeffReturnType CoeffReturnType; + typedef typename Eigen::internal::nested<TensorPaddingOp>::type Nested; + typedef typename Eigen::internal::traits<TensorPaddingOp>::StorageKind StorageKind; + typedef typename Eigen::internal::traits<TensorPaddingOp>::Index Index; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorPaddingOp(const XprType& expr, const PaddingDimensions& padding_dims, const Scalar padding_value) + : m_xpr(expr), m_padding_dims(padding_dims), m_padding_value(padding_value) {} + + EIGEN_DEVICE_FUNC + const PaddingDimensions& padding() const { return m_padding_dims; } + EIGEN_DEVICE_FUNC + Scalar padding_value() const { return m_padding_value; } + + EIGEN_DEVICE_FUNC + const typename internal::remove_all<typename XprType::Nested>::type& + expression() const { return m_xpr; } + + protected: + typename XprType::Nested m_xpr; + const PaddingDimensions m_padding_dims; + const Scalar m_padding_value; +}; + + +// Eval as rvalue +template<typename PaddingDimensions, typename ArgType, typename Device> +struct TensorEvaluator<const TensorPaddingOp<PaddingDimensions, ArgType>, Device> +{ + typedef TensorPaddingOp<PaddingDimensions, ArgType> XprType; + typedef typename XprType::Index Index; + static const int NumDims = internal::array_size<PaddingDimensions>::value; + typedef DSizes<Index, NumDims> Dimensions; + typedef typename XprType::Scalar Scalar; + typedef typename XprType::CoeffReturnType CoeffReturnType; + typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType; + static const int PacketSize = internal::unpacket_traits<PacketReturnType>::size; + + enum { + IsAligned = true, + PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess, + Layout = TensorEvaluator<ArgType, Device>::Layout, + CoordAccess = true, + RawAccess = false + }; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) + : m_impl(op.expression(), device), m_padding(op.padding()), m_paddingValue(op.padding_value()) + { + // The padding op doesn't change the rank of the tensor. Directly padding a scalar would lead + // to a vector, which doesn't make sense. Instead one should reshape the scalar into a vector + // of 1 element first and then pad. + EIGEN_STATIC_ASSERT((NumDims > 0), YOU_MADE_A_PROGRAMMING_MISTAKE); + + // Compute dimensions + m_dimensions = m_impl.dimensions(); + for (int i = 0; i < NumDims; ++i) { + m_dimensions[i] += m_padding[i].first + m_padding[i].second; + } + const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions(); + if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { + m_inputStrides[0] = 1; + m_outputStrides[0] = 1; + for (int i = 1; i < NumDims; ++i) { + m_inputStrides[i] = m_inputStrides[i-1] * input_dims[i-1]; + m_outputStrides[i] = m_outputStrides[i-1] * m_dimensions[i-1]; + } + m_outputStrides[NumDims] = m_outputStrides[NumDims-1] * m_dimensions[NumDims-1]; + } else { + m_inputStrides[NumDims - 1] = 1; + m_outputStrides[NumDims] = 1; + for (int i = NumDims - 2; i >= 0; --i) { + m_inputStrides[i] = m_inputStrides[i+1] * input_dims[i+1]; + m_outputStrides[i+1] = m_outputStrides[i+2] * m_dimensions[i+1]; + } + m_outputStrides[0] = m_outputStrides[1] * m_dimensions[0]; + } + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar*) { + m_impl.evalSubExprsIfNeeded(NULL); + return true; + } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() { + m_impl.cleanup(); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const + { + eigen_assert(index < dimensions().TotalSize()); + Index inputIndex = 0; + if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { + for (int i = NumDims - 1; i > 0; --i) { + const Index idx = index / m_outputStrides[i]; + if (isPaddingAtIndexForDim(idx, i)) { + return m_paddingValue; + } + inputIndex += (idx - m_padding[i].first) * m_inputStrides[i]; + index -= idx * m_outputStrides[i]; + } + if (isPaddingAtIndexForDim(index, 0)) { + return m_paddingValue; + } + inputIndex += (index - m_padding[0].first); + } else { + for (int i = 0; i < NumDims - 1; ++i) { + const Index idx = index / m_outputStrides[i+1]; + if (isPaddingAtIndexForDim(idx, i)) { + return m_paddingValue; + } + inputIndex += (idx - m_padding[i].first) * m_inputStrides[i]; + index -= idx * m_outputStrides[i+1]; + } + if (isPaddingAtIndexForDim(index, NumDims-1)) { + return m_paddingValue; + } + inputIndex += (index - m_padding[NumDims-1].first); + } + return m_impl.coeff(inputIndex); + } + + template<int LoadMode> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const + { + if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { + return packetColMajor(index); + } + return packetRowMajor(index); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const { + TensorOpCost cost = m_impl.costPerCoeff(vectorized); + if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { + for (int i = 0; i < NumDims; ++i) + updateCostPerDimension(cost, i, i == 0); + } else { + for (int i = NumDims - 1; i >= 0; --i) + updateCostPerDimension(cost, i, i == NumDims - 1); + } + return cost; + } + + EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; } + + private: + EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool isPaddingAtIndexForDim( + Index index, int dim_index) const { +#if defined(EIGEN_HAS_INDEX_LIST) + return (!internal::index_pair_first_statically_eq<PaddingDimensions>(dim_index, 0) && + index < m_padding[dim_index].first) || + (!internal::index_pair_second_statically_eq<PaddingDimensions>(dim_index, 0) && + index >= m_dimensions[dim_index] - m_padding[dim_index].second); +#else + return (index < m_padding[dim_index].first) || + (index >= m_dimensions[dim_index] - m_padding[dim_index].second); +#endif + } + + EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool isLeftPaddingCompileTimeZero( + int dim_index) const { +#if defined(EIGEN_HAS_INDEX_LIST) + return internal::index_pair_first_statically_eq<PaddingDimensions>(dim_index, 0); +#else + EIGEN_UNUSED_VARIABLE(dim_index); + return false; +#endif + } + + EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool isRightPaddingCompileTimeZero( + int dim_index) const { +#if defined(EIGEN_HAS_INDEX_LIST) + return internal::index_pair_second_statically_eq<PaddingDimensions>(dim_index, 0); +#else + EIGEN_UNUSED_VARIABLE(dim_index); + return false; +#endif + } + + + void updateCostPerDimension(TensorOpCost& cost, int i, bool first) const { + const double in = static_cast<double>(m_impl.dimensions()[i]); + const double out = in + m_padding[i].first + m_padding[i].second; + if (out == 0) + return; + const double reduction = in / out; + cost *= reduction; + if (first) { + cost += TensorOpCost(0, 0, 2 * TensorOpCost::AddCost<Index>() + + reduction * (1 * TensorOpCost::AddCost<Index>())); + } else { + cost += TensorOpCost(0, 0, 2 * TensorOpCost::AddCost<Index>() + + 2 * TensorOpCost::MulCost<Index>() + + reduction * (2 * TensorOpCost::MulCost<Index>() + + 1 * TensorOpCost::DivCost<Index>())); + } + } + + protected: + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetColMajor(Index index) const + { + EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE) + eigen_assert(index+PacketSize-1 < dimensions().TotalSize()); + + const Index initialIndex = index; + Index inputIndex = 0; + for (int i = NumDims - 1; i > 0; --i) { + const Index first = index; + const Index last = index + PacketSize - 1; + const Index lastPaddedLeft = m_padding[i].first * m_outputStrides[i]; + const Index firstPaddedRight = (m_dimensions[i] - m_padding[i].second) * m_outputStrides[i]; + const Index lastPaddedRight = m_outputStrides[i+1]; + + if (!isLeftPaddingCompileTimeZero(i) && last < lastPaddedLeft) { + // all the coefficient are in the padding zone. + return internal::pset1<PacketReturnType>(m_paddingValue); + } + else if (!isRightPaddingCompileTimeZero(i) && first >= firstPaddedRight && last < lastPaddedRight) { + // all the coefficient are in the padding zone. + return internal::pset1<PacketReturnType>(m_paddingValue); + } + else if ((isLeftPaddingCompileTimeZero(i) && isRightPaddingCompileTimeZero(i)) || (first >= lastPaddedLeft && last < firstPaddedRight)) { + // all the coefficient are between the 2 padding zones. + const Index idx = index / m_outputStrides[i]; + inputIndex += (idx - m_padding[i].first) * m_inputStrides[i]; + index -= idx * m_outputStrides[i]; + } + else { + // Every other case + return packetWithPossibleZero(initialIndex); + } + } + + const Index last = index + PacketSize - 1; + const Index first = index; + const Index lastPaddedLeft = m_padding[0].first; + const Index firstPaddedRight = (m_dimensions[0] - m_padding[0].second); + const Index lastPaddedRight = m_outputStrides[1]; + + if (!isLeftPaddingCompileTimeZero(0) && last < lastPaddedLeft) { + // all the coefficient are in the padding zone. + return internal::pset1<PacketReturnType>(m_paddingValue); + } + else if (!isRightPaddingCompileTimeZero(0) && first >= firstPaddedRight && last < lastPaddedRight) { + // all the coefficient are in the padding zone. + return internal::pset1<PacketReturnType>(m_paddingValue); + } + else if ((isLeftPaddingCompileTimeZero(0) && isRightPaddingCompileTimeZero(0)) || (first >= lastPaddedLeft && last < firstPaddedRight)) { + // all the coefficient are between the 2 padding zones. + inputIndex += (index - m_padding[0].first); + return m_impl.template packet<Unaligned>(inputIndex); + } + // Every other case + return packetWithPossibleZero(initialIndex); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetRowMajor(Index index) const + { + EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE) + eigen_assert(index+PacketSize-1 < dimensions().TotalSize()); + + const Index initialIndex = index; + Index inputIndex = 0; + + for (int i = 0; i < NumDims - 1; ++i) { + const Index first = index; + const Index last = index + PacketSize - 1; + const Index lastPaddedLeft = m_padding[i].first * m_outputStrides[i+1]; + const Index firstPaddedRight = (m_dimensions[i] - m_padding[i].second) * m_outputStrides[i+1]; + const Index lastPaddedRight = m_outputStrides[i]; + + if (!isLeftPaddingCompileTimeZero(i) && last < lastPaddedLeft) { + // all the coefficient are in the padding zone. + return internal::pset1<PacketReturnType>(m_paddingValue); + } + else if (!isRightPaddingCompileTimeZero(i) && first >= firstPaddedRight && last < lastPaddedRight) { + // all the coefficient are in the padding zone. + return internal::pset1<PacketReturnType>(m_paddingValue); + } + else if ((isLeftPaddingCompileTimeZero(i) && isRightPaddingCompileTimeZero(i)) || (first >= lastPaddedLeft && last < firstPaddedRight)) { + // all the coefficient are between the 2 padding zones. + const Index idx = index / m_outputStrides[i+1]; + inputIndex += (idx - m_padding[i].first) * m_inputStrides[i]; + index -= idx * m_outputStrides[i+1]; + } + else { + // Every other case + return packetWithPossibleZero(initialIndex); + } + } + + const Index last = index + PacketSize - 1; + const Index first = index; + const Index lastPaddedLeft = m_padding[NumDims-1].first; + const Index firstPaddedRight = (m_dimensions[NumDims-1] - m_padding[NumDims-1].second); + const Index lastPaddedRight = m_outputStrides[NumDims-1]; + + if (!isLeftPaddingCompileTimeZero(NumDims-1) && last < lastPaddedLeft) { + // all the coefficient are in the padding zone. + return internal::pset1<PacketReturnType>(m_paddingValue); + } + else if (!isRightPaddingCompileTimeZero(NumDims-1) && first >= firstPaddedRight && last < lastPaddedRight) { + // all the coefficient are in the padding zone. + return internal::pset1<PacketReturnType>(m_paddingValue); + } + else if ((isLeftPaddingCompileTimeZero(NumDims-1) && isRightPaddingCompileTimeZero(NumDims-1)) || (first >= lastPaddedLeft && last < firstPaddedRight)) { + // all the coefficient are between the 2 padding zones. + inputIndex += (index - m_padding[NumDims-1].first); + return m_impl.template packet<Unaligned>(inputIndex); + } + // Every other case + return packetWithPossibleZero(initialIndex); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetWithPossibleZero(Index index) const + { + EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize]; + for (int i = 0; i < PacketSize; ++i) { + values[i] = coeff(index+i); + } + PacketReturnType rslt = internal::pload<PacketReturnType>(values); + return rslt; + } + + Dimensions m_dimensions; + array<Index, NumDims+1> m_outputStrides; + array<Index, NumDims> m_inputStrides; + TensorEvaluator<ArgType, Device> m_impl; + PaddingDimensions m_padding; + + Scalar m_paddingValue; +}; + + + + +} // end namespace Eigen + +#endif // EIGEN_CXX11_TENSOR_TENSOR_PADDING_H diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorPatch.h b/unsupported/Eigen/CXX11/src/Tensor/TensorPatch.h new file mode 100644 index 000000000..886a254f6 --- /dev/null +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorPatch.h @@ -0,0 +1,269 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_CXX11_TENSOR_TENSOR_PATCH_H +#define EIGEN_CXX11_TENSOR_TENSOR_PATCH_H + +namespace Eigen { + +/** \class TensorPatch + * \ingroup CXX11_Tensor_Module + * + * \brief Tensor patch class. + * + * + */ +namespace internal { +template<typename PatchDim, typename XprType> +struct traits<TensorPatchOp<PatchDim, XprType> > : public traits<XprType> +{ + typedef typename XprType::Scalar Scalar; + typedef traits<XprType> XprTraits; + typedef typename XprTraits::StorageKind StorageKind; + typedef typename XprTraits::Index Index; + typedef typename XprType::Nested Nested; + typedef typename remove_reference<Nested>::type _Nested; + static const int NumDimensions = XprTraits::NumDimensions + 1; + static const int Layout = XprTraits::Layout; +}; + +template<typename PatchDim, typename XprType> +struct eval<TensorPatchOp<PatchDim, XprType>, Eigen::Dense> +{ + typedef const TensorPatchOp<PatchDim, XprType>& type; +}; + +template<typename PatchDim, typename XprType> +struct nested<TensorPatchOp<PatchDim, XprType>, 1, typename eval<TensorPatchOp<PatchDim, XprType> >::type> +{ + typedef TensorPatchOp<PatchDim, XprType> type; +}; + +} // end namespace internal + + + +template<typename PatchDim, typename XprType> +class TensorPatchOp : public TensorBase<TensorPatchOp<PatchDim, XprType>, ReadOnlyAccessors> +{ + public: + typedef typename Eigen::internal::traits<TensorPatchOp>::Scalar Scalar; + typedef typename Eigen::NumTraits<Scalar>::Real RealScalar; + typedef typename XprType::CoeffReturnType CoeffReturnType; + typedef typename Eigen::internal::nested<TensorPatchOp>::type Nested; + typedef typename Eigen::internal::traits<TensorPatchOp>::StorageKind StorageKind; + typedef typename Eigen::internal::traits<TensorPatchOp>::Index Index; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorPatchOp(const XprType& expr, const PatchDim& patch_dims) + : m_xpr(expr), m_patch_dims(patch_dims) {} + + EIGEN_DEVICE_FUNC + const PatchDim& patch_dims() const { return m_patch_dims; } + + EIGEN_DEVICE_FUNC + const typename internal::remove_all<typename XprType::Nested>::type& + expression() const { return m_xpr; } + + protected: + typename XprType::Nested m_xpr; + const PatchDim m_patch_dims; +}; + + +// Eval as rvalue +template<typename PatchDim, typename ArgType, typename Device> +struct TensorEvaluator<const TensorPatchOp<PatchDim, ArgType>, Device> +{ + typedef TensorPatchOp<PatchDim, ArgType> XprType; + typedef typename XprType::Index Index; + static const int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value + 1; + typedef DSizes<Index, NumDims> Dimensions; + typedef typename XprType::Scalar Scalar; + typedef typename XprType::CoeffReturnType CoeffReturnType; + typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType; + static const int PacketSize = internal::unpacket_traits<PacketReturnType>::size; + + + enum { + IsAligned = false, + PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess, + Layout = TensorEvaluator<ArgType, Device>::Layout, + CoordAccess = false, + RawAccess = false + }; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) + : m_impl(op.expression(), device) + { + Index num_patches = 1; + const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions(); + const PatchDim& patch_dims = op.patch_dims(); + if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { + for (int i = 0; i < NumDims-1; ++i) { + m_dimensions[i] = patch_dims[i]; + num_patches *= (input_dims[i] - patch_dims[i] + 1); + } + m_dimensions[NumDims-1] = num_patches; + + m_inputStrides[0] = 1; + m_patchStrides[0] = 1; + for (int i = 1; i < NumDims-1; ++i) { + m_inputStrides[i] = m_inputStrides[i-1] * input_dims[i-1]; + m_patchStrides[i] = m_patchStrides[i-1] * (input_dims[i-1] - patch_dims[i-1] + 1); + } + m_outputStrides[0] = 1; + for (int i = 1; i < NumDims; ++i) { + m_outputStrides[i] = m_outputStrides[i-1] * m_dimensions[i-1]; + } + } else { + for (int i = 0; i < NumDims-1; ++i) { + m_dimensions[i+1] = patch_dims[i]; + num_patches *= (input_dims[i] - patch_dims[i] + 1); + } + m_dimensions[0] = num_patches; + + m_inputStrides[NumDims-2] = 1; + m_patchStrides[NumDims-2] = 1; + for (int i = NumDims-3; i >= 0; --i) { + m_inputStrides[i] = m_inputStrides[i+1] * input_dims[i+1]; + m_patchStrides[i] = m_patchStrides[i+1] * (input_dims[i+1] - patch_dims[i+1] + 1); + } + m_outputStrides[NumDims-1] = 1; + for (int i = NumDims-2; i >= 0; --i) { + m_outputStrides[i] = m_outputStrides[i+1] * m_dimensions[i+1]; + } + } + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* /*data*/) { + m_impl.evalSubExprsIfNeeded(NULL); + return true; + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() { + m_impl.cleanup(); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const + { + Index output_stride_index = (static_cast<int>(Layout) == static_cast<int>(ColMajor)) ? NumDims - 1 : 0; + // Find the location of the first element of the patch. + Index patchIndex = index / m_outputStrides[output_stride_index]; + // Find the offset of the element wrt the location of the first element. + Index patchOffset = index - patchIndex * m_outputStrides[output_stride_index]; + Index inputIndex = 0; + if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { + for (int i = NumDims - 2; i > 0; --i) { + const Index patchIdx = patchIndex / m_patchStrides[i]; + patchIndex -= patchIdx * m_patchStrides[i]; + const Index offsetIdx = patchOffset / m_outputStrides[i]; + patchOffset -= offsetIdx * m_outputStrides[i]; + inputIndex += (patchIdx + offsetIdx) * m_inputStrides[i]; + } + } else { + for (int i = 0; i < NumDims - 2; ++i) { + const Index patchIdx = patchIndex / m_patchStrides[i]; + patchIndex -= patchIdx * m_patchStrides[i]; + const Index offsetIdx = patchOffset / m_outputStrides[i+1]; + patchOffset -= offsetIdx * m_outputStrides[i+1]; + inputIndex += (patchIdx + offsetIdx) * m_inputStrides[i]; + } + } + inputIndex += (patchIndex + patchOffset); + return m_impl.coeff(inputIndex); + } + + template<int LoadMode> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const + { + EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE) + eigen_assert(index+PacketSize-1 < dimensions().TotalSize()); + + Index output_stride_index = (static_cast<int>(Layout) == static_cast<int>(ColMajor)) ? NumDims - 1 : 0; + Index indices[2] = {index, index + PacketSize - 1}; + Index patchIndices[2] = {indices[0] / m_outputStrides[output_stride_index], + indices[1] / m_outputStrides[output_stride_index]}; + Index patchOffsets[2] = {indices[0] - patchIndices[0] * m_outputStrides[output_stride_index], + indices[1] - patchIndices[1] * m_outputStrides[output_stride_index]}; + + Index inputIndices[2] = {0, 0}; + if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { + for (int i = NumDims - 2; i > 0; --i) { + const Index patchIdx[2] = {patchIndices[0] / m_patchStrides[i], + patchIndices[1] / m_patchStrides[i]}; + patchIndices[0] -= patchIdx[0] * m_patchStrides[i]; + patchIndices[1] -= patchIdx[1] * m_patchStrides[i]; + + const Index offsetIdx[2] = {patchOffsets[0] / m_outputStrides[i], + patchOffsets[1] / m_outputStrides[i]}; + patchOffsets[0] -= offsetIdx[0] * m_outputStrides[i]; + patchOffsets[1] -= offsetIdx[1] * m_outputStrides[i]; + + inputIndices[0] += (patchIdx[0] + offsetIdx[0]) * m_inputStrides[i]; + inputIndices[1] += (patchIdx[1] + offsetIdx[1]) * m_inputStrides[i]; + } + } else { + for (int i = 0; i < NumDims - 2; ++i) { + const Index patchIdx[2] = {patchIndices[0] / m_patchStrides[i], + patchIndices[1] / m_patchStrides[i]}; + patchIndices[0] -= patchIdx[0] * m_patchStrides[i]; + patchIndices[1] -= patchIdx[1] * m_patchStrides[i]; + + const Index offsetIdx[2] = {patchOffsets[0] / m_outputStrides[i+1], + patchOffsets[1] / m_outputStrides[i+1]}; + patchOffsets[0] -= offsetIdx[0] * m_outputStrides[i+1]; + patchOffsets[1] -= offsetIdx[1] * m_outputStrides[i+1]; + + inputIndices[0] += (patchIdx[0] + offsetIdx[0]) * m_inputStrides[i]; + inputIndices[1] += (patchIdx[1] + offsetIdx[1]) * m_inputStrides[i]; + } + } + inputIndices[0] += (patchIndices[0] + patchOffsets[0]); + inputIndices[1] += (patchIndices[1] + patchOffsets[1]); + + if (inputIndices[1] - inputIndices[0] == PacketSize - 1) { + PacketReturnType rslt = m_impl.template packet<Unaligned>(inputIndices[0]); + return rslt; + } + else { + EIGEN_ALIGN_MAX CoeffReturnType values[PacketSize]; + values[0] = m_impl.coeff(inputIndices[0]); + values[PacketSize-1] = m_impl.coeff(inputIndices[1]); + for (int i = 1; i < PacketSize-1; ++i) { + values[i] = coeff(index+i); + } + PacketReturnType rslt = internal::pload<PacketReturnType>(values); + return rslt; + } + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const { + const double compute_cost = NumDims * (TensorOpCost::DivCost<Index>() + + TensorOpCost::MulCost<Index>() + + 2 * TensorOpCost::AddCost<Index>()); + return m_impl.costPerCoeff(vectorized) + + TensorOpCost(0, 0, compute_cost, vectorized, PacketSize); + } + + EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; } + + protected: + Dimensions m_dimensions; + array<Index, NumDims> m_outputStrides; + array<Index, NumDims-1> m_inputStrides; + array<Index, NumDims-1> m_patchStrides; + + TensorEvaluator<ArgType, Device> m_impl; +}; + +} // end namespace Eigen + +#endif // EIGEN_CXX11_TENSOR_TENSOR_PATCH_H diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorRandom.h b/unsupported/Eigen/CXX11/src/Tensor/TensorRandom.h new file mode 100644 index 000000000..1655a813e --- /dev/null +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorRandom.h @@ -0,0 +1,276 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2016 Benoit Steiner <benoit.steiner.goog@gmail.com> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_CXX11_TENSOR_TENSOR_RANDOM_H +#define EIGEN_CXX11_TENSOR_TENSOR_RANDOM_H + +namespace Eigen { +namespace internal { + +namespace { + +EIGEN_DEVICE_FUNC uint64_t get_random_seed() { +#ifdef __CUDA_ARCH__ + // We don't support 3d kernels since we currently only use 1 and + // 2d kernels. + assert(threadIdx.z == 0); + return clock64() + + blockIdx.x * blockDim.x + threadIdx.x + + gridDim.x * blockDim.x * (blockIdx.y * blockDim.y + threadIdx.y); + +#elif defined _WIN32 + // Use the current time as a baseline. + SYSTEMTIME st; + GetSystemTime(&st); + int time = st.wSecond + 1000 * st.wMilliseconds; + // Mix in a random number to make sure that we get different seeds if + // we try to generate seeds faster than the clock resolution. + // We need 2 random values since the generator only generate 16 bits at + // a time (https://msdn.microsoft.com/en-us/library/398ax69y.aspx) + int rnd1 = ::rand(); + int rnd2 = ::rand(); + uint64_t rnd = (rnd1 | rnd2 << 16) ^ time; + return rnd; + +#elif defined __APPLE__ + // Same approach as for win32, except that the random number generator + // is better (// https://developer.apple.com/legacy/library/documentation/Darwin/Reference/ManPages/man3/random.3.html#//apple_ref/doc/man/3/random). + uint64_t rnd = ::random() ^ mach_absolute_time(); + return rnd; + +#else + // Augment the current time with pseudo random number generation + // to ensure that we get different seeds if we try to generate seeds + // faster than the clock resolution. + timespec ts; + clock_gettime(CLOCK_REALTIME, &ts); + uint64_t rnd = ::random() ^ ts.tv_nsec; + return rnd; +#endif +} + +static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE unsigned PCG_XSH_RS_generator(uint64_t* state) { + // TODO: Unify with the implementation in the non blocking thread pool. + uint64_t current = *state; + // Update the internal state + *state = current * 6364136223846793005ULL + 0xda3e39cb94b95bdbULL; + // Generate the random output (using the PCG-XSH-RS scheme) + return static_cast<unsigned>((current ^ (current >> 22)) >> (22 + (current >> 61))); +} + +static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE uint64_t PCG_XSH_RS_state(uint64_t seed) { + seed = seed ? seed : get_random_seed(); + return seed * 6364136223846793005ULL + 0xda3e39cb94b95bdbULL; +} + +} // namespace + + +template <typename T> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE +T RandomToTypeUniform(uint64_t* state) { + unsigned rnd = PCG_XSH_RS_generator(state); + return static_cast<T>(rnd); +} + + +template <> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE +Eigen::half RandomToTypeUniform<Eigen::half>(uint64_t* state) { + Eigen::half result; + // Generate 10 random bits for the mantissa + unsigned rnd = PCG_XSH_RS_generator(state); + result.x = static_cast<uint16_t>(rnd & 0x3ffu); + // Set the exponent + result.x |= (static_cast<uint16_t>(15) << 10); + // Return the final result + return result - Eigen::half(1.0f); +} + + +template <> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE +float RandomToTypeUniform<float>(uint64_t* state) { + typedef union { + uint32_t raw; + float fp; + } internal; + internal result; + // Generate 23 random bits for the mantissa mantissa + const unsigned rnd = PCG_XSH_RS_generator(state); + result.raw = rnd & 0x7fffffu; + // Set the exponent + result.raw |= (static_cast<uint32_t>(127) << 23); + // Return the final result + return result.fp - 1.0f; +} + +template <> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE +double RandomToTypeUniform<double>(uint64_t* state) { + typedef union { + uint64_t raw; + double dp; + } internal; + internal result; + result.raw = 0; + // Generate 52 random bits for the mantissa + // First generate the upper 20 bits + unsigned rnd1 = PCG_XSH_RS_generator(state) & 0xfffffu; + // The generate the lower 32 bits + unsigned rnd2 = PCG_XSH_RS_generator(state); + result.raw = (static_cast<uint64_t>(rnd1) << 32) | rnd2; + // Set the exponent + result.raw |= (static_cast<uint64_t>(1023) << 52); + // Return the final result + return result.dp - 1.0; +} + +template <> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE +std::complex<float> RandomToTypeUniform<std::complex<float> >(uint64_t* state) { + return std::complex<float>(RandomToTypeUniform<float>(state), + RandomToTypeUniform<float>(state)); +} +template <> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE +std::complex<double> RandomToTypeUniform<std::complex<double> >(uint64_t* state) { + return std::complex<double>(RandomToTypeUniform<double>(state), + RandomToTypeUniform<double>(state)); +} + +template <typename T> class UniformRandomGenerator { + public: + static const bool PacketAccess = true; + + // Uses the given "seed" if non-zero, otherwise uses a random seed. + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE UniformRandomGenerator( + uint64_t seed = 0) { + m_state = PCG_XSH_RS_state(seed); + } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE UniformRandomGenerator( + const UniformRandomGenerator& other) { + m_state = other.m_state; + } + + template<typename Index> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + T operator()(Index i) const { + uint64_t local_state = m_state + i; + T result = RandomToTypeUniform<T>(&local_state); + m_state = local_state; + return result; + } + + template<typename Packet, typename Index> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + Packet packetOp(Index i) const { + const int packetSize = internal::unpacket_traits<Packet>::size; + EIGEN_ALIGN_MAX T values[packetSize]; + uint64_t local_state = m_state + i; + for (int j = 0; j < packetSize; ++j) { + values[j] = RandomToTypeUniform<T>(&local_state); + } + m_state = local_state; + return internal::pload<Packet>(values); + } + + private: + mutable uint64_t m_state; +}; + +template <typename Scalar> +struct functor_traits<UniformRandomGenerator<Scalar> > { + enum { + // Rough estimate for floating point, multiplied by ceil(sizeof(T) / sizeof(float)). + Cost = 12 * NumTraits<Scalar>::AddCost * + ((sizeof(Scalar) + sizeof(float) - 1) / sizeof(float)), + PacketAccess = UniformRandomGenerator<Scalar>::PacketAccess + }; +}; + + + +template <typename T> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE +T RandomToTypeNormal(uint64_t* state) { + // Use the ratio of uniform method to generate numbers following a normal + // distribution. See for example Numerical Recipes chapter 7.3.9 for the + // details. + T u, v, q; + do { + u = RandomToTypeUniform<T>(state); + v = T(1.7156) * (RandomToTypeUniform<T>(state) - T(0.5)); + const T x = u - T(0.449871); + const T y = numext::abs(v) + T(0.386595); + q = x*x + y * (T(0.196)*y - T(0.25472)*x); + } while (q > T(0.27597) && + (q > T(0.27846) || v*v > T(-4) * numext::log(u) * u*u)); + + return v/u; +} + +template <> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE +std::complex<float> RandomToTypeNormal<std::complex<float> >(uint64_t* state) { + return std::complex<float>(RandomToTypeNormal<float>(state), + RandomToTypeNormal<float>(state)); +} +template <> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE +std::complex<double> RandomToTypeNormal<std::complex<double> >(uint64_t* state) { + return std::complex<double>(RandomToTypeNormal<double>(state), + RandomToTypeNormal<double>(state)); +} + + +template <typename T> class NormalRandomGenerator { + public: + static const bool PacketAccess = true; + + // Uses the given "seed" if non-zero, otherwise uses a random seed. + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE NormalRandomGenerator(uint64_t seed = 0) { + m_state = PCG_XSH_RS_state(seed); + } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE NormalRandomGenerator( + const NormalRandomGenerator& other) { + m_state = other.m_state; + } + + template<typename Index> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + T operator()(Index i) const { + uint64_t local_state = m_state + i; + T result = RandomToTypeNormal<T>(&local_state); + m_state = local_state; + return result; + } + + template<typename Packet, typename Index> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + Packet packetOp(Index i) const { + const int packetSize = internal::unpacket_traits<Packet>::size; + EIGEN_ALIGN_MAX T values[packetSize]; + uint64_t local_state = m_state + i; + for (int j = 0; j < packetSize; ++j) { + values[j] = RandomToTypeNormal<T>(&local_state); + } + m_state = local_state; + return internal::pload<Packet>(values); + } + + private: + mutable uint64_t m_state; +}; + + +template <typename Scalar> +struct functor_traits<NormalRandomGenerator<Scalar> > { + enum { + // On average, we need to generate about 3 random numbers + // 15 mul, 8 add, 1.5 logs + Cost = 3 * functor_traits<UniformRandomGenerator<Scalar> >::Cost + + 15 * NumTraits<Scalar>::AddCost + 8 * NumTraits<Scalar>::AddCost + + 3 * functor_traits<scalar_log_op<Scalar> >::Cost / 2, + PacketAccess = NormalRandomGenerator<Scalar>::PacketAccess + }; +}; + + +} // end namespace internal +} // end namespace Eigen + +#endif // EIGEN_CXX11_TENSOR_TENSOR_RANDOM_H diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorReduction.h b/unsupported/Eigen/CXX11/src/Tensor/TensorReduction.h new file mode 100644 index 000000000..41d0d0022 --- /dev/null +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorReduction.h @@ -0,0 +1,781 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> +// Copyright (C) 2016 Mehdi Goli, Codeplay Software Ltd <eigen@codeplay.com> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_CXX11_TENSOR_TENSOR_REDUCTION_H +#define EIGEN_CXX11_TENSOR_TENSOR_REDUCTION_H + +namespace Eigen { + +/** \class TensorReduction + * \ingroup CXX11_Tensor_Module + * + * \brief Tensor reduction class. + * + */ + +namespace internal { + template<typename Op, typename Dims, typename XprType,template <class> class MakePointer_ > + struct traits<TensorReductionOp<Op, Dims, XprType, MakePointer_> > + : traits<XprType> +{ + typedef traits<XprType> XprTraits; + typedef typename XprTraits::Scalar Scalar; + typedef typename XprTraits::StorageKind StorageKind; + typedef typename XprTraits::Index Index; + typedef typename XprType::Nested Nested; + static const int NumDimensions = XprTraits::NumDimensions - array_size<Dims>::value; + static const int Layout = XprTraits::Layout; + + template <class T> struct MakePointer { + // Intermediate typedef to workaround MSVC issue. + typedef MakePointer_<T> MakePointerT; + typedef typename MakePointerT::Type Type; + }; +}; + +template<typename Op, typename Dims, typename XprType, template <class> class MakePointer_> +struct eval<TensorReductionOp<Op, Dims, XprType, MakePointer_>, Eigen::Dense> +{ + typedef const TensorReductionOp<Op, Dims, XprType, MakePointer_>& type; +}; + +template<typename Op, typename Dims, typename XprType, template <class> class MakePointer_> +struct nested<TensorReductionOp<Op, Dims, XprType, MakePointer_>, 1, typename eval<TensorReductionOp<Op, Dims, XprType, MakePointer_> >::type> +{ + typedef TensorReductionOp<Op, Dims, XprType, MakePointer_> type; +}; + + +template <typename OutputDims> struct DimInitializer { + template <typename InputDims, typename ReducedDims> EIGEN_DEVICE_FUNC + static void run(const InputDims& input_dims, + const array<bool, internal::array_size<InputDims>::value>& reduced, + OutputDims* output_dims, ReducedDims* reduced_dims) { + const int NumInputDims = internal::array_size<InputDims>::value; + int outputIndex = 0; + int reduceIndex = 0; + for (int i = 0; i < NumInputDims; ++i) { + if (reduced[i]) { + (*reduced_dims)[reduceIndex] = input_dims[i]; + ++reduceIndex; + } else { + (*output_dims)[outputIndex] = input_dims[i]; + ++outputIndex; + } + } + } +}; + +template <> struct DimInitializer<Sizes<> > { + template <typename InputDims, typename Index, size_t Rank> EIGEN_DEVICE_FUNC + static void run(const InputDims& input_dims, const array<bool, Rank>&, + Sizes<>*, array<Index, Rank>* reduced_dims) { + const int NumInputDims = internal::array_size<InputDims>::value; + for (int i = 0; i < NumInputDims; ++i) { + (*reduced_dims)[i] = input_dims[i]; + } + } +}; + + +template <typename ReducedDims, int NumTensorDims, int Layout> +struct are_inner_most_dims { + static const bool value = false; +}; +template <typename ReducedDims, int NumTensorDims, int Layout> +struct preserve_inner_most_dims { + static const bool value = false; +}; + +#if EIGEN_HAS_CONSTEXPR && EIGEN_HAS_VARIADIC_TEMPLATES +template <typename ReducedDims, int NumTensorDims> +struct are_inner_most_dims<ReducedDims, NumTensorDims, ColMajor>{ + static const bool tmp1 = indices_statically_known_to_increase<ReducedDims>(); + static const bool tmp2 = index_statically_eq<ReducedDims>(0, 0); + static const bool tmp3 = index_statically_eq<ReducedDims>(array_size<ReducedDims>::value-1, array_size<ReducedDims>::value-1); + static const bool value = tmp1 & tmp2 & tmp3; +}; +template <typename ReducedDims, int NumTensorDims> +struct are_inner_most_dims<ReducedDims, NumTensorDims, RowMajor>{ + static const bool tmp1 = indices_statically_known_to_increase<ReducedDims>(); + static const bool tmp2 = index_statically_eq<ReducedDims>(0, NumTensorDims - array_size<ReducedDims>::value); + static const bool tmp3 = index_statically_eq<ReducedDims>(array_size<ReducedDims>::value - 1, NumTensorDims - 1); + static const bool value = tmp1 & tmp2 & tmp3; + +}; +template <typename ReducedDims, int NumTensorDims> +struct preserve_inner_most_dims<ReducedDims, NumTensorDims, ColMajor>{ + static const bool tmp1 = indices_statically_known_to_increase<ReducedDims>(); + static const bool tmp2 = index_statically_gt<ReducedDims>(0, 0); + static const bool value = tmp1 & tmp2; + +}; +template <typename ReducedDims, int NumTensorDims> +struct preserve_inner_most_dims<ReducedDims, NumTensorDims, RowMajor>{ + static const bool tmp1 = indices_statically_known_to_increase<ReducedDims>(); + static const bool tmp2 = index_statically_lt<ReducedDims>(array_size<ReducedDims>::value - 1, NumTensorDims - 1); + static const bool value = tmp1 & tmp2; +}; +#endif + + +template <int DimIndex, typename Self, typename Op> +struct GenericDimReducer { + static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const Self& self, typename Self::Index firstIndex, Op& reducer, typename Self::CoeffReturnType* accum) { + EIGEN_STATIC_ASSERT((DimIndex > 0), YOU_MADE_A_PROGRAMMING_MISTAKE); + for (int j = 0; j < self.m_reducedDims[DimIndex]; ++j) { + const typename Self::Index input = firstIndex + j * self.m_reducedStrides[DimIndex]; + GenericDimReducer<DimIndex-1, Self, Op>::reduce(self, input, reducer, accum); + } + } +}; +template <typename Self, typename Op> +struct GenericDimReducer<0, Self, Op> { + static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const Self& self, typename Self::Index firstIndex, Op& reducer, typename Self::CoeffReturnType* accum) { + for (int j = 0; j < self.m_reducedDims[0]; ++j) { + const typename Self::Index input = firstIndex + j * self.m_reducedStrides[0]; + reducer.reduce(self.m_impl.coeff(input), accum); + } + } +}; +template <typename Self, typename Op> +struct GenericDimReducer<-1, Self, Op> { + static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const Self& self, typename Self::Index index, Op& reducer, typename Self::CoeffReturnType* accum) { + reducer.reduce(self.m_impl.coeff(index), accum); + } +}; + +template <typename Self, typename Op, bool Vectorizable = (Self::InputPacketAccess & Op::PacketAccess)> +struct InnerMostDimReducer { + static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename Self::CoeffReturnType reduce(const Self& self, typename Self::Index firstIndex, typename Self::Index numValuesToReduce, Op& reducer) { + typename Self::CoeffReturnType accum = reducer.initialize(); + for (typename Self::Index j = 0; j < numValuesToReduce; ++j) { + reducer.reduce(self.m_impl.coeff(firstIndex + j), &accum); + } + return reducer.finalize(accum); + } +}; + +template <typename Self, typename Op> +struct InnerMostDimReducer<Self, Op, true> { + static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename Self::CoeffReturnType reduce(const Self& self, typename Self::Index firstIndex, typename Self::Index numValuesToReduce, Op& reducer) { + const int packetSize = internal::unpacket_traits<typename Self::PacketReturnType>::size; + const typename Self::Index VectorizedSize = (numValuesToReduce / packetSize) * packetSize; + typename Self::PacketReturnType p = reducer.template initializePacket<typename Self::PacketReturnType>(); + for (typename Self::Index j = 0; j < VectorizedSize; j += packetSize) { + reducer.reducePacket(self.m_impl.template packet<Unaligned>(firstIndex + j), &p); + } + typename Self::CoeffReturnType accum = reducer.initialize(); + for (typename Self::Index j = VectorizedSize; j < numValuesToReduce; ++j) { + reducer.reduce(self.m_impl.coeff(firstIndex + j), &accum); + } + return reducer.finalizeBoth(accum, p); + } +}; + +template <int DimIndex, typename Self, typename Op, bool vectorizable = (Self::InputPacketAccess & Op::PacketAccess)> +struct InnerMostDimPreserver { + static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const Self&, typename Self::Index, Op&, typename Self::PacketReturnType*) { + eigen_assert(false && "should never be called"); + } +}; + +template <int DimIndex, typename Self, typename Op> +struct InnerMostDimPreserver<DimIndex, Self, Op, true> { + static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const Self& self, typename Self::Index firstIndex, Op& reducer, typename Self::PacketReturnType* accum) { + EIGEN_STATIC_ASSERT((DimIndex > 0), YOU_MADE_A_PROGRAMMING_MISTAKE); + for (typename Self::Index j = 0; j < self.m_reducedDims[DimIndex]; ++j) { + const typename Self::Index input = firstIndex + j * self.m_reducedStrides[DimIndex]; + InnerMostDimPreserver<DimIndex-1, Self, Op>::reduce(self, input, reducer, accum); + } + } +}; + +template <typename Self, typename Op> +struct InnerMostDimPreserver<0, Self, Op, true> { + static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const Self& self, typename Self::Index firstIndex, Op& reducer, typename Self::PacketReturnType* accum) { + for (typename Self::Index j = 0; j < self.m_reducedDims[0]; ++j) { + const typename Self::Index input = firstIndex + j * self.m_reducedStrides[0]; + reducer.reducePacket(self.m_impl.template packet<Unaligned>(input), accum); + } + } +}; +template <typename Self, typename Op> +struct InnerMostDimPreserver<-1, Self, Op, true> { + static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const Self&, typename Self::Index, Op&, typename Self::PacketReturnType*) { + eigen_assert(false && "should never be called"); + } +}; + +// Default full reducer +template <typename Self, typename Op, typename Device, bool Vectorizable = (Self::InputPacketAccess & Op::PacketAccess)> +struct FullReducer { + static const bool HasOptimizedImplementation = false; + + static EIGEN_DEVICE_FUNC void run(const Self& self, Op& reducer, const Device&, typename Self::CoeffReturnType* output) { + const typename Self::Index num_coeffs = array_prod(self.m_impl.dimensions()); + *output = InnerMostDimReducer<Self, Op, Vectorizable>::reduce(self, 0, num_coeffs, reducer); + } +}; + + +#ifdef EIGEN_USE_THREADS +// Multithreaded full reducers +template <typename Self, typename Op, + bool Vectorizable = (Self::InputPacketAccess & Op::PacketAccess)> +struct FullReducerShard { + static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(const Self& self, typename Self::Index firstIndex, + typename Self::Index numValuesToReduce, Op& reducer, + typename Self::CoeffReturnType* output) { + *output = InnerMostDimReducer<Self, Op, Vectorizable>::reduce( + self, firstIndex, numValuesToReduce, reducer); + } +}; + +// Multithreaded full reducer +template <typename Self, typename Op, bool Vectorizable> +struct FullReducer<Self, Op, ThreadPoolDevice, Vectorizable> { + static const bool HasOptimizedImplementation = !Op::IsStateful; + static const int PacketSize = + unpacket_traits<typename Self::PacketReturnType>::size; + + // launch one reducer per thread and accumulate the result. + static void run(const Self& self, Op& reducer, const ThreadPoolDevice& device, + typename Self::CoeffReturnType* output) { + typedef typename Self::Index Index; + const Index num_coeffs = array_prod(self.m_impl.dimensions()); + if (num_coeffs == 0) { + *output = reducer.finalize(reducer.initialize()); + return; + } + const TensorOpCost cost = + self.m_impl.costPerCoeff(Vectorizable) + + TensorOpCost(0, 0, internal::functor_traits<Op>::Cost, Vectorizable, + PacketSize); + const int num_threads = TensorCostModel<ThreadPoolDevice>::numThreads( + num_coeffs, cost, device.numThreads()); + if (num_threads == 1) { + *output = + InnerMostDimReducer<Self, Op, Vectorizable>::reduce(self, 0, num_coeffs, reducer); + return; + } + const Index blocksize = + std::floor<Index>(static_cast<float>(num_coeffs) / num_threads); + const Index numblocks = blocksize > 0 ? num_coeffs / blocksize : 0; + eigen_assert(num_coeffs >= numblocks * blocksize); + + Barrier barrier(internal::convert_index<unsigned int>(numblocks)); + MaxSizeVector<typename Self::CoeffReturnType> shards(numblocks, reducer.initialize()); + for (Index i = 0; i < numblocks; ++i) { + device.enqueue_with_barrier(&barrier, &FullReducerShard<Self, Op, Vectorizable>::run, + self, i * blocksize, blocksize, reducer, + &shards[i]); + } + typename Self::CoeffReturnType finalShard; + if (numblocks * blocksize < num_coeffs) { + finalShard = InnerMostDimReducer<Self, Op, Vectorizable>::reduce( + self, numblocks * blocksize, num_coeffs - numblocks * blocksize, + reducer); + } else { + finalShard = reducer.initialize(); + } + barrier.Wait(); + + for (Index i = 0; i < numblocks; ++i) { + reducer.reduce(shards[i], &finalShard); + } + *output = reducer.finalize(finalShard); + } +}; + +#endif + + +// Default inner reducer +template <typename Self, typename Op, typename Device> +struct InnerReducer { + static const bool HasOptimizedImplementation = false; + + EIGEN_DEVICE_FUNC static bool run(const Self&, Op&, const Device&, typename Self::CoeffReturnType*, typename Self::Index, typename Self::Index) { + eigen_assert(false && "Not implemented"); + return true; + } +}; + +// Default outer reducer +template <typename Self, typename Op, typename Device> +struct OuterReducer { + static const bool HasOptimizedImplementation = false; + + EIGEN_DEVICE_FUNC static bool run(const Self&, Op&, const Device&, typename Self::CoeffReturnType*, typename Self::Index, typename Self::Index) { + eigen_assert(false && "Not implemented"); + return true; + } +}; + + +#if defined(EIGEN_USE_GPU) && defined(__CUDACC__) +template <int B, int N, typename S, typename R, typename I> +__global__ void FullReductionKernel(R, const S, I, typename S::CoeffReturnType*, unsigned int*); + + +#ifdef EIGEN_HAS_CUDA_FP16 +template <typename S, typename R, typename I> +__global__ void ReductionInitFullReduxKernelHalfFloat(R, const S, I, half2*); +template <int B, int N, typename S, typename R, typename I> +__global__ void FullReductionKernelHalfFloat(R, const S, I, half*, half2*); +template <int NPT, typename S, typename R, typename I> +__global__ void InnerReductionKernelHalfFloat(R, const S, I, I, half*); + +#endif + +template <int NPT, typename S, typename R, typename I> +__global__ void InnerReductionKernel(R, const S, I, I, typename S::CoeffReturnType*); + +template <int NPT, typename S, typename R, typename I> +__global__ void OuterReductionKernel(R, const S, I, I, typename S::CoeffReturnType*); +#endif + +} // end namespace internal + + +template <typename Op, typename Dims, typename XprType, template <class> class MakePointer_> +class TensorReductionOp : public TensorBase<TensorReductionOp<Op, Dims, XprType, MakePointer_>, ReadOnlyAccessors> { + public: + typedef typename Eigen::internal::traits<TensorReductionOp>::Scalar Scalar; + typedef typename Eigen::NumTraits<Scalar>::Real RealScalar; + typedef typename internal::remove_const<typename XprType::CoeffReturnType>::type CoeffReturnType; + typedef typename Eigen::internal::nested<TensorReductionOp>::type Nested; + typedef typename Eigen::internal::traits<TensorReductionOp>::StorageKind StorageKind; + typedef typename Eigen::internal::traits<TensorReductionOp>::Index Index; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + TensorReductionOp(const XprType& expr, const Dims& dims) : m_expr(expr), m_dims(dims) + { } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + TensorReductionOp(const XprType& expr, const Dims& dims, const Op& reducer) : m_expr(expr), m_dims(dims), m_reducer(reducer) + { } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const XprType& expression() const { return m_expr; } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const Dims& dims() const { return m_dims; } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const Op& reducer() const { return m_reducer; } + + protected: + typename XprType::Nested m_expr; + const Dims m_dims; + const Op m_reducer; +}; + + +// Eval as rvalue +template<typename Op, typename Dims, typename ArgType, template <class> class MakePointer_, typename Device> +struct TensorEvaluator<const TensorReductionOp<Op, Dims, ArgType, MakePointer_>, Device> +{ + typedef TensorReductionOp<Op, Dims, ArgType, MakePointer_> XprType; + typedef typename XprType::Index Index; + typedef ArgType ChildType; + typedef typename TensorEvaluator<ArgType, Device>::Dimensions InputDimensions; + static const int NumInputDims = internal::array_size<InputDimensions>::value; + static const int NumReducedDims = internal::array_size<Dims>::value; + static const int NumOutputDims = NumInputDims - NumReducedDims; + typedef typename internal::conditional<NumOutputDims==0, Sizes<>, DSizes<Index, NumOutputDims> >::type Dimensions; + typedef typename XprType::Scalar Scalar; + typedef TensorEvaluator<const TensorReductionOp<Op, Dims, ArgType, MakePointer_>, Device> Self; + static const bool InputPacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess; + typedef typename internal::remove_const<typename XprType::CoeffReturnType>::type CoeffReturnType; + typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType; + static const int PacketSize = internal::unpacket_traits<PacketReturnType>::size; + + enum { + IsAligned = false, + PacketAccess = Self::InputPacketAccess && Op::PacketAccess, + Layout = TensorEvaluator<ArgType, Device>::Layout, + CoordAccess = false, // to be implemented + RawAccess = false + }; + + static const bool ReducingInnerMostDims = internal::are_inner_most_dims<Dims, NumInputDims, Layout>::value; + static const bool PreservingInnerMostDims = internal::preserve_inner_most_dims<Dims, NumInputDims, Layout>::value; + static const bool RunningFullReduction = (NumOutputDims==0); + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) + : m_impl(op.expression(), device), m_reducer(op.reducer()), m_result(NULL), m_device(device), m_xpr_dims(op.dims()) + { + EIGEN_STATIC_ASSERT((NumInputDims >= NumReducedDims), YOU_MADE_A_PROGRAMMING_MISTAKE); + EIGEN_STATIC_ASSERT((!ReducingInnerMostDims | !PreservingInnerMostDims | (NumReducedDims == NumInputDims)), + YOU_MADE_A_PROGRAMMING_MISTAKE); + + // Build the bitmap indicating if an input dimension is reduced or not. + for (int i = 0; i < NumInputDims; ++i) { + m_reduced[i] = false; + } + for (int i = 0; i < NumReducedDims; ++i) { + eigen_assert(op.dims()[i] >= 0); + eigen_assert(op.dims()[i] < NumInputDims); + m_reduced[op.dims()[i]] = true; + } + + const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions(); + internal::DimInitializer<Dimensions>::run(input_dims, m_reduced, &m_dimensions, &m_reducedDims); + + // Precompute output strides. + if (NumOutputDims > 0) { + if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { + m_outputStrides[0] = 1; + for (int i = 1; i < NumOutputDims; ++i) { + m_outputStrides[i] = m_outputStrides[i - 1] * m_dimensions[i - 1]; + } + } else { + m_outputStrides.back() = 1; + for (int i = NumOutputDims - 2; i >= 0; --i) { + m_outputStrides[i] = m_outputStrides[i + 1] * m_dimensions[i + 1]; + } + } + } + + // Precompute input strides. + if (NumInputDims > 0) { + array<Index, NumInputDims> input_strides; + if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { + input_strides[0] = 1; + for (int i = 1; i < NumInputDims; ++i) { + input_strides[i] = input_strides[i-1] * input_dims[i-1]; + } + } else { + input_strides.back() = 1; + for (int i = NumInputDims - 2; i >= 0; --i) { + input_strides[i] = input_strides[i + 1] * input_dims[i + 1]; + } + } + + int outputIndex = 0; + int reduceIndex = 0; + for (int i = 0; i < NumInputDims; ++i) { + if (m_reduced[i]) { + m_reducedStrides[reduceIndex] = input_strides[i]; + ++reduceIndex; + } else { + m_preservedStrides[outputIndex] = input_strides[i]; + ++outputIndex; + } + } + } + + // Special case for full reductions + if (NumOutputDims == 0) { + m_preservedStrides[0] = internal::array_prod(input_dims); + } + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; } + + EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool evalSubExprsIfNeeded(typename MakePointer_<CoeffReturnType>::Type data) { + m_impl.evalSubExprsIfNeeded(NULL); + + // Use the FullReducer if possible. + if ((RunningFullReduction && RunningOnSycl) ||(RunningFullReduction && + internal::FullReducer<Self, Op, Device>::HasOptimizedImplementation && + ((RunningOnGPU && (m_device.majorDeviceVersion() >= 3)) || + !RunningOnGPU))) { + bool need_assign = false; + if (!data) { + m_result = static_cast<CoeffReturnType*>(m_device.allocate(sizeof(CoeffReturnType))); + data = m_result; + need_assign = true; + } + Op reducer(m_reducer); + internal::FullReducer<Self, Op, Device>::run(*this, reducer, m_device, data); + return need_assign; + } + else if(RunningOnSycl){ + const Index num_values_to_reduce = internal::array_prod(m_reducedDims); + const Index num_coeffs_to_preserve = internal::array_prod(m_dimensions); + if (!data) { + data = static_cast<CoeffReturnType*>(m_device.allocate(sizeof(CoeffReturnType) * num_coeffs_to_preserve)); + m_result = data; + } + Op reducer(m_reducer); + internal::InnerReducer<Self, Op, Device>::run(*this, reducer, m_device, data, num_values_to_reduce, num_coeffs_to_preserve); + return (m_result != NULL); + } + + // Attempt to use an optimized reduction. + else if (RunningOnGPU && (m_device.majorDeviceVersion() >= 3)) { + bool reducing_inner_dims = true; + for (int i = 0; i < NumReducedDims; ++i) { + if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { + reducing_inner_dims &= m_reduced[i]; + } else { + reducing_inner_dims &= m_reduced[NumInputDims - 1 - i]; + } + } + if (internal::InnerReducer<Self, Op, Device>::HasOptimizedImplementation && + (reducing_inner_dims || ReducingInnerMostDims)) { + const Index num_values_to_reduce = internal::array_prod(m_reducedDims); + const Index num_coeffs_to_preserve = internal::array_prod(m_dimensions); + if (!data) { + if (num_coeffs_to_preserve < 1024 && num_values_to_reduce > num_coeffs_to_preserve && num_values_to_reduce > 128) { + data = static_cast<CoeffReturnType*>(m_device.allocate(sizeof(CoeffReturnType) * num_coeffs_to_preserve)); + m_result = data; + } + else { + return true; + } + } + Op reducer(m_reducer); + if (internal::InnerReducer<Self, Op, Device>::run(*this, reducer, m_device, data, num_values_to_reduce, num_coeffs_to_preserve)) { + if (m_result) { + m_device.deallocate(m_result); + m_result = NULL; + } + return true; + } else { + return (m_result != NULL); + } + } + + bool preserving_inner_dims = true; + for (int i = 0; i < NumReducedDims; ++i) { + if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { + preserving_inner_dims &= m_reduced[NumInputDims - 1 - i]; + } else { + preserving_inner_dims &= m_reduced[i]; + } + } + if (internal::OuterReducer<Self, Op, Device>::HasOptimizedImplementation && + preserving_inner_dims) { + const Index num_values_to_reduce = internal::array_prod(m_reducedDims); + const Index num_coeffs_to_preserve = internal::array_prod(m_dimensions); + if (!data) { + if (num_coeffs_to_preserve < 1024 && num_values_to_reduce > num_coeffs_to_preserve && num_values_to_reduce > 32) { + data = static_cast<CoeffReturnType*>(m_device.allocate(sizeof(CoeffReturnType) * num_coeffs_to_preserve)); + m_result = data; + } + else { + return true; + } + } + Op reducer(m_reducer); + if (internal::OuterReducer<Self, Op, Device>::run(*this, reducer, m_device, data, num_values_to_reduce, num_coeffs_to_preserve)) { + if (m_result) { + m_device.deallocate(m_result); + m_result = NULL; + } + return true; + } else { + return (m_result != NULL); + } + } + } + return true; + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() { + m_impl.cleanup(); + if (m_result) { + m_device.deallocate(m_result); + m_result = NULL; + } + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const + { + if ((RunningOnSycl || RunningFullReduction || RunningOnGPU) && m_result) { + return *(m_result + index); + } + Op reducer(m_reducer); + if (ReducingInnerMostDims || RunningFullReduction) { + const Index num_values_to_reduce = + (static_cast<int>(Layout) == static_cast<int>(ColMajor)) ? m_preservedStrides[0] : m_preservedStrides[NumPreservedStrides - 1]; + return internal::InnerMostDimReducer<Self, Op>::reduce(*this, firstInput(index), + num_values_to_reduce, reducer); + } else { + typename Self::CoeffReturnType accum = reducer.initialize(); + internal::GenericDimReducer<NumReducedDims-1, Self, Op>::reduce(*this, firstInput(index), reducer, &accum); + return reducer.finalize(accum); + } + } + + // TODO(bsteiner): provide a more efficient implementation. + template<int LoadMode> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const + { + EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE) + eigen_assert(index + PacketSize - 1 < Index(internal::array_prod(dimensions()))); + + if (RunningOnGPU && m_result) { + return internal::pload<PacketReturnType>(m_result + index); + } + + EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize]; + if (ReducingInnerMostDims) { + const Index num_values_to_reduce = + (static_cast<int>(Layout) == static_cast<int>(ColMajor)) ? m_preservedStrides[0] : m_preservedStrides[NumPreservedStrides - 1]; + const Index firstIndex = firstInput(index); + for (Index i = 0; i < PacketSize; ++i) { + Op reducer(m_reducer); + values[i] = internal::InnerMostDimReducer<Self, Op>::reduce(*this, firstIndex + i * num_values_to_reduce, + num_values_to_reduce, reducer); + } + } else if (PreservingInnerMostDims) { + const Index firstIndex = firstInput(index); + const int innermost_dim = (static_cast<int>(Layout) == static_cast<int>(ColMajor)) ? 0 : NumOutputDims - 1; + // TBD: extend this the the n innermost dimensions that we preserve. + if (((firstIndex % m_dimensions[innermost_dim]) + PacketSize - 1) < m_dimensions[innermost_dim]) { + Op reducer(m_reducer); + typename Self::PacketReturnType accum = reducer.template initializePacket<typename Self::PacketReturnType>(); + internal::InnerMostDimPreserver<NumReducedDims-1, Self, Op>::reduce(*this, firstIndex, reducer, &accum); + return reducer.finalizePacket(accum); + } else { + for (int i = 0; i < PacketSize; ++i) { + values[i] = coeff(index + i); + } + } + } else { + for (int i = 0; i < PacketSize; ++i) { + values[i] = coeff(index + i); + } + } + PacketReturnType rslt = internal::pload<PacketReturnType>(values); + return rslt; + } + + // Must be called after evalSubExprsIfNeeded(). + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const { + if (RunningFullReduction && m_result) { + return TensorOpCost(sizeof(CoeffReturnType), 0, 0, vectorized, PacketSize); + } else { + const Index num_values_to_reduce = internal::array_prod(m_reducedDims); + const double compute_cost = num_values_to_reduce * internal::functor_traits<Op>::Cost; + return m_impl.costPerCoeff(vectorized) * num_values_to_reduce + + TensorOpCost(0, 0, compute_cost, vectorized, PacketSize); + } + } + + EIGEN_DEVICE_FUNC typename MakePointer_<Scalar>::Type data() const { return m_result; } + /// required by sycl in order to extract the accessor + const TensorEvaluator<ArgType, Device>& impl() const { return m_impl; } + /// added for sycl in order to construct the buffer from the sycl device + const Device& device() const{return m_device;} + /// added for sycl in order to re-construct the reduction eval on the device for the sub-kernel + const Dims& xprDims() const {return m_xpr_dims;} + + + private: + template <int, typename, typename> friend struct internal::GenericDimReducer; + template <typename, typename, bool> friend struct internal::InnerMostDimReducer; + template <int, typename, typename, bool> friend struct internal::InnerMostDimPreserver; + template <typename S, typename O, typename D, bool V> friend struct internal::FullReducer; +#ifdef EIGEN_USE_THREADS + template <typename S, typename O, bool V> friend struct internal::FullReducerShard; +#endif +#if defined(EIGEN_USE_GPU) && defined(__CUDACC__) + template <int B, int N, typename S, typename R, typename I> friend void internal::FullReductionKernel(R, const S, I, typename S::CoeffReturnType*, unsigned int*); +#ifdef EIGEN_HAS_CUDA_FP16 + template <typename S, typename R, typename I> friend void internal::ReductionInitFullReduxKernelHalfFloat(R, const S, I, half2*); + template <int B, int N, typename S, typename R, typename I> friend void internal::FullReductionKernelHalfFloat(R, const S, I, half*, half2*); + template <int NPT, typename S, typename R, typename I> friend void internal::InnerReductionKernelHalfFloat(R, const S, I, I, half*); +#endif + template <int NPT, typename S, typename R, typename I> friend void internal::InnerReductionKernel(R, const S, I, I, typename S::CoeffReturnType*); + + template <int NPT, typename S, typename R, typename I> friend void internal::OuterReductionKernel(R, const S, I, I, typename S::CoeffReturnType*); +#endif + + template <typename S, typename O, typename D> friend struct internal::InnerReducer; + + // Returns the Index in the input tensor of the first value that needs to be + // used to compute the reduction at output index "index". + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index firstInput(Index index) const { + if (ReducingInnerMostDims) { + if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { + return index * m_preservedStrides[0]; + } else { + return index * m_preservedStrides[NumPreservedStrides - 1]; + } + } + // TBD: optimize the case where we preserve the innermost dimensions. + Index startInput = 0; + if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { + for (int i = NumOutputDims - 1; i > 0; --i) { + // This is index_i in the output tensor. + const Index idx = index / m_outputStrides[i]; + startInput += idx * m_preservedStrides[i]; + index -= idx * m_outputStrides[i]; + } + if (PreservingInnerMostDims) { + eigen_assert(m_preservedStrides[0] == 1); + startInput += index; + } else { + startInput += index * m_preservedStrides[0]; + } + } else { + for (int i = 0; i < NumOutputDims - 1; ++i) { + // This is index_i in the output tensor. + const Index idx = index / m_outputStrides[i]; + startInput += idx * m_preservedStrides[i]; + index -= idx * m_outputStrides[i]; + } + if (PreservingInnerMostDims) { + eigen_assert(m_preservedStrides[NumPreservedStrides - 1] == 1); + startInput += index; + } else { + startInput += index * m_preservedStrides[NumPreservedStrides - 1]; + } + } + return startInput; + } + + // Bitmap indicating if an input dimension is reduced or not. + array<bool, NumInputDims> m_reduced; + // Dimensions of the output of the operation. + Dimensions m_dimensions; + // Precomputed strides for the output tensor. + array<Index, NumOutputDims> m_outputStrides; + // Subset of strides of the input tensor for the non-reduced dimensions. + // Indexed by output dimensions. + static const int NumPreservedStrides = max_n_1<NumOutputDims>::size; + array<Index, NumPreservedStrides> m_preservedStrides; + + // Subset of strides of the input tensor for the reduced dimensions. + // Indexed by reduced dimensions. + array<Index, NumReducedDims> m_reducedStrides; + // Size of the input dimensions that are reduced. + // Indexed by reduced dimensions. + array<Index, NumReducedDims> m_reducedDims; + + // Evaluator for the input expression. + TensorEvaluator<ArgType, Device> m_impl; + + // Operation to apply for computing the reduction. + Op m_reducer; + + // For full reductions +#if defined(EIGEN_USE_GPU) && defined(__CUDACC__) + static const bool RunningOnGPU = internal::is_same<Device, Eigen::GpuDevice>::value; + static const bool RunningOnSycl = false; +#elif defined(EIGEN_USE_SYCL) +static const bool RunningOnSycl = internal::is_same<typename internal::remove_all<Device>::type, Eigen::SyclDevice>::value; +static const bool RunningOnGPU = false; +#else + static const bool RunningOnGPU = false; + static const bool RunningOnSycl = false; +#endif + typename MakePointer_<CoeffReturnType>::Type m_result; + + const Device& m_device; + const Dims& m_xpr_dims; +}; + +} // end namespace Eigen + +#endif // EIGEN_CXX11_TENSOR_TENSOR_REDUCTION_H diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorReductionCuda.h b/unsupported/Eigen/CXX11/src/Tensor/TensorReductionCuda.h new file mode 100644 index 000000000..65638b6a8 --- /dev/null +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorReductionCuda.h @@ -0,0 +1,750 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_CXX11_TENSOR_TENSOR_REDUCTION_CUDA_H +#define EIGEN_CXX11_TENSOR_TENSOR_REDUCTION_CUDA_H + +namespace Eigen { +namespace internal { + + +#if defined(EIGEN_USE_GPU) && defined(__CUDACC__) +// Full reducers for GPU, don't vectorize for now + +// Reducer function that enables multiple cuda thread to safely accumulate at the same +// output address. It basically reads the current value of the output variable, and +// attempts to update it with the new value. If in the meantime another cuda thread +// updated the content of the output address it will try again. +template <typename T, typename R> +__device__ EIGEN_ALWAYS_INLINE void atomicReduce(T* output, T accum, R& reducer) { +#if __CUDA_ARCH__ >= 300 + if (sizeof(T) == 4) + { + unsigned int oldval = *reinterpret_cast<unsigned int*>(output); + unsigned int newval = oldval; + reducer.reduce(accum, reinterpret_cast<T*>(&newval)); + if (newval == oldval) { + return; + } + unsigned int readback; + while ((readback = atomicCAS((unsigned int*)output, oldval, newval)) != oldval) { + oldval = readback; + newval = oldval; + reducer.reduce(accum, reinterpret_cast<T*>(&newval)); + if (newval == oldval) { + return; + } + } + } + else if (sizeof(T) == 8) { + unsigned long long oldval = *reinterpret_cast<unsigned long long*>(output); + unsigned long long newval = oldval; + reducer.reduce(accum, reinterpret_cast<T*>(&newval)); + if (newval == oldval) { + return; + } + unsigned long long readback; + while ((readback = atomicCAS((unsigned long long*)output, oldval, newval)) != oldval) { + oldval = readback; + newval = oldval; + reducer.reduce(accum, reinterpret_cast<T*>(&newval)); + if (newval == oldval) { + return; + } + } + } + else { + assert(0 && "Wordsize not supported"); + } +#else + assert(0 && "Shouldn't be called on unsupported device"); +#endif +} + +// We extend atomicExch to support extra data types +template <typename Type> +__device__ inline Type atomicExchCustom(Type* address, Type val) { + return atomicExch(address, val); +} + +template <> +__device__ inline double atomicExchCustom(double* address, double val) { + unsigned long long int* address_as_ull = reinterpret_cast<unsigned long long int*>(address); + return __longlong_as_double(atomicExch(address_as_ull, __double_as_longlong(val))); +} + +#ifdef EIGEN_HAS_CUDA_FP16 +template <template <typename T> class R> +__device__ inline void atomicReduce(half2* output, half2 accum, R<half>& reducer) { + unsigned int oldval = *reinterpret_cast<unsigned int*>(output); + unsigned int newval = oldval; + reducer.reducePacket(accum, reinterpret_cast<half2*>(&newval)); + if (newval == oldval) { + return; + } + unsigned int readback; + while ((readback = atomicCAS((unsigned int*)output, oldval, newval)) != oldval) { + oldval = readback; + newval = oldval; + reducer.reducePacket(accum, reinterpret_cast<half2*>(&newval)); + if (newval == oldval) { + return; + } + } +} +#endif + +template <> +__device__ inline void atomicReduce(float* output, float accum, SumReducer<float>&) { +#if __CUDA_ARCH__ >= 300 + atomicAdd(output, accum); +#else + assert(0 && "Shouldn't be called on unsupported device"); +#endif +} + + +template <typename CoeffType, typename Index> +__global__ void ReductionInitKernel(const CoeffType val, Index num_preserved_coeffs, CoeffType* output) { + const Index thread_id = blockIdx.x * blockDim.x + threadIdx.x; + const Index num_threads = blockDim.x * gridDim.x; + for (Index i = thread_id; i < num_preserved_coeffs; i += num_threads) { + output[i] = val; + } +} + + +template <int BlockSize, int NumPerThread, typename Self, + typename Reducer, typename Index> +__global__ void FullReductionKernel(Reducer reducer, const Self input, Index num_coeffs, + typename Self::CoeffReturnType* output, unsigned int* semaphore) { +#if __CUDA_ARCH__ >= 300 + // Initialize the output value + const Index first_index = blockIdx.x * BlockSize * NumPerThread + threadIdx.x; + if (gridDim.x == 1) { + if (first_index == 0) { + *output = reducer.initialize(); + } + } + else { + if (threadIdx.x == 0) { + unsigned int block = atomicCAS(semaphore, 0u, 1u); + if (block == 0) { + // We're the first block to run, initialize the output value + atomicExchCustom(output, reducer.initialize()); + __threadfence(); + atomicExch(semaphore, 2u); + } + else { + // Wait for the first block to initialize the output value. + // Use atomicCAS here to ensure that the reads aren't cached + unsigned int val; + do { + val = atomicCAS(semaphore, 2u, 2u); + } + while (val < 2u); + } + } + } + + __syncthreads(); + + eigen_assert(gridDim.x == 1 || *semaphore >= 2u); + + typename Self::CoeffReturnType accum = reducer.initialize(); + Index max_iter = numext::mini<Index>(num_coeffs - first_index, NumPerThread*BlockSize); + for (Index i = 0; i < max_iter; i+=BlockSize) { + const Index index = first_index + i; + eigen_assert(index < num_coeffs); + typename Self::CoeffReturnType val = input.m_impl.coeff(index); + reducer.reduce(val, &accum); + } + +#pragma unroll + for (int offset = warpSize/2; offset > 0; offset /= 2) { + reducer.reduce(__shfl_down(accum, offset, warpSize), &accum); + } + + if ((threadIdx.x & (warpSize - 1)) == 0) { + atomicReduce(output, accum, reducer); + } + + if (gridDim.x > 1 && threadIdx.x == 0) { + // Let the last block reset the semaphore + atomicInc(semaphore, gridDim.x + 1); + } +#else + assert(0 && "Shouldn't be called on unsupported device"); +#endif +} + + +#ifdef EIGEN_HAS_CUDA_FP16 +template <typename Self, + typename Reducer, typename Index> +__global__ void ReductionInitFullReduxKernelHalfFloat(Reducer reducer, const Self input, Index num_coeffs, half2* scratch) { + eigen_assert(blockDim.x == 1); + eigen_assert(gridDim.x == 1); + if (num_coeffs % 2 != 0) { + half last = input.m_impl.coeff(num_coeffs-1); + *scratch = __halves2half2(last, reducer.initialize()); + } else { + *scratch = reducer.template initializePacket<half2>(); + } +} + +template <typename Self, + typename Reducer, typename Index> +__global__ void ReductionInitKernelHalfFloat(Reducer reducer, const Self input, Index num_coeffs, half* output) { + const Index thread_id = blockIdx.x * blockDim.x + threadIdx.x; + const Index num_threads = blockDim.x * gridDim.x; + const Index num_packets = num_coeffs / 2; + for (Index i = thread_id; i < num_packets; i += num_threads) { + ((half2*)output)[i] = reducer.template initializePacket<half2>(); + } + + if (thread_id == 0 && num_coeffs % 2 != 0) { + output[num_coeffs-1] = reducer.initialize(); + } +} + +template <int BlockSize, int NumPerThread, typename Self, + typename Reducer, typename Index> +__global__ void FullReductionKernelHalfFloat(Reducer reducer, const Self input, Index num_coeffs, + half* output, half2* scratch) { + eigen_assert(NumPerThread % 2 == 0); + + const Index first_index = blockIdx.x * BlockSize * NumPerThread + 2*threadIdx.x; + + // Initialize the output value if it wasn't initialized by the ReductionInitKernel + if (gridDim.x == 1 && first_index == 0) { + if (num_coeffs % 2 != 0) { + half last = input.m_impl.coeff(num_coeffs-1); + *scratch = __halves2half2(last, reducer.initialize()); + } else { + *scratch = reducer.template initializePacket<half2>(); + } + __syncthreads(); + } + + half2 accum = reducer.template initializePacket<half2>(); + const Index max_iter = numext::mini<Index>((num_coeffs - first_index) / 2, NumPerThread*BlockSize / 2); + for (Index i = 0; i < max_iter; i += BlockSize) { + const Index index = first_index + 2*i; + eigen_assert(index + 1 < num_coeffs); + half2 val = input.m_impl.template packet<Unaligned>(index); + reducer.reducePacket(val, &accum); + } + +#pragma unroll + for (int offset = warpSize/2; offset > 0; offset /= 2) { + reducer.reducePacket(__shfl_down(accum, offset, warpSize), &accum); + } + + if ((threadIdx.x & (warpSize - 1)) == 0) { + atomicReduce(scratch, accum, reducer); + } + + __syncthreads(); + + if (gridDim.x == 1 && first_index == 0) { + half tmp = __low2half(*scratch); + reducer.reduce(__high2half(*scratch), &tmp); + *output = tmp; + } +} + +template <typename Op> +__global__ void ReductionCleanupKernelHalfFloat(Op& reducer, half* output, half2* scratch) { + eigen_assert(threadIdx.x == 1); + half tmp = __low2half(*scratch); + reducer.reduce(__high2half(*scratch), &tmp); + *output = tmp; +} + +#endif + +template <typename Self, typename Op, typename OutputType, bool PacketAccess, typename Enabled = void> +struct FullReductionLauncher { + static void run(const Self&, Op&, const GpuDevice&, OutputType*, typename Self::Index) { + assert(false && "Should only be called on doubles, floats and half floats"); + } +}; + +// Specialization for float and double +template <typename Self, typename Op, typename OutputType, bool PacketAccess> +struct FullReductionLauncher< + Self, Op, OutputType, PacketAccess, + typename internal::enable_if< + internal::is_same<float, OutputType>::value || + internal::is_same<double, OutputType>::value, + void>::type> { + static void run(const Self& self, Op& reducer, const GpuDevice& device, OutputType* output, typename Self::Index num_coeffs) { + typedef typename Self::Index Index; + typedef typename Self::CoeffReturnType Scalar; + const int block_size = 256; + const int num_per_thread = 128; + const int num_blocks = divup<int>(num_coeffs, block_size * num_per_thread); + + unsigned int* semaphore = NULL; + if (num_blocks > 1) { + semaphore = device.semaphore(); + } + + LAUNCH_CUDA_KERNEL((FullReductionKernel<block_size, num_per_thread, Self, Op, Index>), + num_blocks, block_size, 0, device, reducer, self, num_coeffs, output, semaphore); + } +}; + +#ifdef EIGEN_HAS_CUDA_FP16 +template <typename Self, typename Op> +struct FullReductionLauncher<Self, Op, Eigen::half, false> { + static void run(const Self&, Op&, const GpuDevice&, half*, typename Self::Index) { + assert(false && "Should not be called since there is no packet accessor"); + } +}; + +template <typename Self, typename Op> +struct FullReductionLauncher<Self, Op, Eigen::half, true> { + static void run(const Self& self, Op& reducer, const GpuDevice& device, half* output, typename Self::Index num_coeffs) { + typedef typename Self::Index Index; + + const int block_size = 256; + const int num_per_thread = 128; + const int num_blocks = divup<int>(num_coeffs, block_size * num_per_thread); + half2* scratch = static_cast<half2*>(device.scratchpad()); + + if (num_blocks > 1) { + // We initialize the output and the scrathpad outside the reduction kernel when we can't be sure that there + // won't be a race conditions between multiple thread blocks. + LAUNCH_CUDA_KERNEL((ReductionInitFullReduxKernelHalfFloat<Self, Op, Index>), + 1, 1, 0, device, reducer, self, num_coeffs, scratch); + } + + LAUNCH_CUDA_KERNEL((FullReductionKernelHalfFloat<block_size, num_per_thread, Self, Op, Index>), + num_blocks, block_size, 0, device, reducer, self, num_coeffs, output, scratch); + + if (num_blocks > 1) { + LAUNCH_CUDA_KERNEL((ReductionCleanupKernelHalfFloat<Op>), + 1, 1, 0, device, reducer, output, scratch); + } + } +}; +#endif + + +template <typename Self, typename Op, bool Vectorizable> +struct FullReducer<Self, Op, GpuDevice, Vectorizable> { + // Unfortunately nvidia doesn't support well exotic types such as complex, + // so reduce the scope of the optimized version of the code to the simple cases + // of doubles, floats and half floats +#ifdef EIGEN_HAS_CUDA_FP16 + static const bool HasOptimizedImplementation = !Op::IsStateful && + (internal::is_same<typename Self::CoeffReturnType, float>::value || + internal::is_same<typename Self::CoeffReturnType, double>::value || + (internal::is_same<typename Self::CoeffReturnType, Eigen::half>::value && reducer_traits<Op, GpuDevice>::PacketAccess)); +#else + static const bool HasOptimizedImplementation = !Op::IsStateful && + (internal::is_same<typename Self::CoeffReturnType, float>::value || + internal::is_same<typename Self::CoeffReturnType, double>::value); +#endif + + template <typename OutputType> + static void run(const Self& self, Op& reducer, const GpuDevice& device, OutputType* output) { + assert(HasOptimizedImplementation && "Should only be called on doubles, floats or half floats"); + const Index num_coeffs = array_prod(self.m_impl.dimensions()); + // Don't crash when we're called with an input tensor of size 0. + if (num_coeffs == 0) { + return; + } + + FullReductionLauncher<Self, Op, OutputType, reducer_traits<Op, GpuDevice>::PacketAccess>::run(self, reducer, device, output, num_coeffs); + } +}; + + +template <int NumPerThread, typename Self, + typename Reducer, typename Index> +__global__ void InnerReductionKernel(Reducer reducer, const Self input, Index num_coeffs_to_reduce, Index num_preserved_coeffs, + typename Self::CoeffReturnType* output) { +#if __CUDA_ARCH__ >= 300 + typedef typename Self::CoeffReturnType Type; + eigen_assert(blockDim.y == 1); + eigen_assert(blockDim.z == 1); + eigen_assert(gridDim.y == 1); + eigen_assert(gridDim.z == 1); + + const int unroll_times = 16; + eigen_assert(NumPerThread % unroll_times == 0); + + const Index input_col_blocks = divup<Index>(num_coeffs_to_reduce, blockDim.x * NumPerThread); + const Index num_input_blocks = input_col_blocks * num_preserved_coeffs; + + const Index num_threads = blockDim.x * gridDim.x; + const Index thread_id = blockIdx.x * blockDim.x + threadIdx.x; + + // Initialize the output values if they weren't initialized by the ReductionInitKernel + if (gridDim.x == 1) { + for (Index i = thread_id; i < num_preserved_coeffs; i += num_threads) { + output[i] = reducer.initialize(); + } + __syncthreads(); + } + + for (Index i = blockIdx.x; i < num_input_blocks; i += gridDim.x) { + const Index row = i / input_col_blocks; + + if (row < num_preserved_coeffs) { + const Index col_block = i % input_col_blocks; + const Index col_begin = col_block * blockDim.x * NumPerThread + threadIdx.x; + + Type reduced_val = reducer.initialize(); + + for (Index j = 0; j < NumPerThread; j += unroll_times) { + const Index last_col = col_begin + blockDim.x * (j + unroll_times - 1); + if (last_col >= num_coeffs_to_reduce) { + for (Index col = col_begin + blockDim.x * j; col < num_coeffs_to_reduce; col += blockDim.x) { + const Type val = input.m_impl.coeff(row * num_coeffs_to_reduce + col); + reducer.reduce(val, &reduced_val); + } + break; + } else { + // Faster version of the loop with no branches after unrolling. +#pragma unroll + for (int k = 0; k < unroll_times; ++k) { + const Index col = col_begin + blockDim.x * (j + k); + reducer.reduce(input.m_impl.coeff(row * num_coeffs_to_reduce + col), &reduced_val); + } + } + } + +#pragma unroll + for (int offset = warpSize/2; offset > 0; offset /= 2) { + reducer.reduce(__shfl_down(reduced_val, offset), &reduced_val); + } + + if ((threadIdx.x & (warpSize - 1)) == 0) { + atomicReduce(&(output[row]), reduced_val, reducer); + } + } + } +#else + assert(0 && "Shouldn't be called on unsupported device"); +#endif +} + +#ifdef EIGEN_HAS_CUDA_FP16 + +template <int NumPerThread, typename Self, + typename Reducer, typename Index> +__global__ void InnerReductionKernelHalfFloat(Reducer reducer, const Self input, Index num_coeffs_to_reduce, Index num_preserved_coeffs, + half* output) { + eigen_assert(blockDim.y == 1); + eigen_assert(blockDim.z == 1); + eigen_assert(gridDim.y == 1); + eigen_assert(gridDim.z == 1); + + const int unroll_times = 16; + eigen_assert(NumPerThread % unroll_times == 0); + eigen_assert(unroll_times % 2 == 0); + + const Index input_col_blocks = divup<Index>(num_coeffs_to_reduce, blockDim.x * NumPerThread * 2); + const Index num_input_blocks = divup<Index>(input_col_blocks * num_preserved_coeffs, 2); + + const Index num_threads = blockDim.x * gridDim.x; + const Index thread_id = blockIdx.x * blockDim.x + threadIdx.x; + + // Initialize the output values if they weren't initialized by the ReductionInitKernel + if (gridDim.x == 1) { + Index i = 2*thread_id; + for (; i + 1 < num_preserved_coeffs; i += 2*num_threads) { + half* loc = output + i; + *((half2*)loc) = reducer.template initializePacket<half2>(); + } + if (i < num_preserved_coeffs) { + output[i] = reducer.initialize(); + } + __syncthreads(); + } + + for (Index i = blockIdx.x; i < num_input_blocks; i += gridDim.x) { + const Index row = 2 * (i / input_col_blocks); + + if (row + 1 < num_preserved_coeffs) { + const Index col_block = i % input_col_blocks; + const Index col_begin = 2 * (col_block * blockDim.x * NumPerThread + threadIdx.x); + + half2 reduced_val1 = reducer.template initializePacket<half2>(); + half2 reduced_val2 = reducer.template initializePacket<half2>(); + + for (Index j = 0; j < NumPerThread; j += unroll_times) { + const Index last_col = col_begin + blockDim.x * (j + unroll_times - 1) * 2; + if (last_col >= num_coeffs_to_reduce) { + Index col = col_begin + blockDim.x * j; + for (; col + 1 < num_coeffs_to_reduce; col += blockDim.x) { + const half2 val1 = input.m_impl.template packet<Unaligned>(row * num_coeffs_to_reduce + col); + reducer.reducePacket(val1, &reduced_val1); + const half2 val2 = input.m_impl.template packet<Unaligned>((row+1) * num_coeffs_to_reduce + col); + reducer.reducePacket(val2, &reduced_val2); + } + if (col < num_coeffs_to_reduce) { + // Peel; + const half last1 = input.m_impl.coeff(row * num_coeffs_to_reduce + col); + const half2 val1 = __halves2half2(last1, reducer.initialize()); + reducer.reducePacket(val1, &reduced_val1); + const half last2 = input.m_impl.coeff((row+1) * num_coeffs_to_reduce + col); + const half2 val2 = __halves2half2(last2, reducer.initialize()); + reducer.reducePacket(val2, &reduced_val2); + } + break; + } else { + // Faster version of the loop with no branches after unrolling. +#pragma unroll + for (int k = 0; k < unroll_times; ++k) { + const Index col = col_begin + blockDim.x * (j + k) * 2; + reducer.reducePacket(input.m_impl.template packet<Unaligned>(row * num_coeffs_to_reduce + col), &reduced_val1); + reducer.reducePacket(input.m_impl.template packet<Unaligned>((row + 1)* num_coeffs_to_reduce + col), &reduced_val2); + } + } + } + +#pragma unroll + for (int offset = warpSize/2; offset > 0; offset /= 2) { + reducer.reducePacket(__shfl_down(reduced_val1, offset, warpSize), &reduced_val1); + reducer.reducePacket(__shfl_down(reduced_val2, offset, warpSize), &reduced_val2); + } + + half val1 = __low2half(reduced_val1); + reducer.reduce(__high2half(reduced_val1), &val1); + half val2 = __low2half(reduced_val2); + reducer.reduce(__high2half(reduced_val2), &val2); + half2 val = __halves2half2(val1, val2); + + if ((threadIdx.x & (warpSize - 1)) == 0) { + half* loc = output + row; + atomicReduce((half2*)loc, val, reducer); + } + } + } +} + +#endif + +template <typename Self, typename Op, typename OutputType, bool PacketAccess, typename Enabled = void> +struct InnerReductionLauncher { + static EIGEN_DEVICE_FUNC bool run(const Self&, Op&, const GpuDevice&, OutputType*, typename Self::Index, typename Self::Index) { + assert(false && "Should only be called to reduce doubles, floats and half floats on a gpu device"); + return true; + } +}; + +// Specialization for float and double +template <typename Self, typename Op, typename OutputType, bool PacketAccess> +struct InnerReductionLauncher< + Self, Op, OutputType, PacketAccess, + typename internal::enable_if< + internal::is_same<float, OutputType>::value || + internal::is_same<double, OutputType>::value, + void>::type> { + static bool run(const Self& self, Op& reducer, const GpuDevice& device, OutputType* output, typename Self::Index num_coeffs_to_reduce, typename Self::Index num_preserved_vals) { + typedef typename Self::Index Index; + + const Index num_coeffs = num_coeffs_to_reduce * num_preserved_vals; + const int block_size = 256; + const int num_per_thread = 128; + const int dyn_blocks = divup<int>(num_coeffs, block_size * num_per_thread); + const int max_blocks = device.getNumCudaMultiProcessors() * + device.maxCudaThreadsPerMultiProcessor() / block_size; + const int num_blocks = numext::mini<int>(max_blocks, dyn_blocks); + + if (num_blocks > 1) { + // We initialize the outputs outside the reduction kernel when we can't be sure that there + // won't be a race conditions between multiple thread blocks. + const int dyn_blocks = divup<int>(num_preserved_vals, 1024); + const int max_blocks = device.getNumCudaMultiProcessors() * + device.maxCudaThreadsPerMultiProcessor() / 1024; + const int num_blocks = numext::mini<int>(max_blocks, dyn_blocks); + LAUNCH_CUDA_KERNEL((ReductionInitKernel<OutputType, Index>), + num_blocks, 1024, 0, device, reducer.initialize(), + num_preserved_vals, output); + } + + LAUNCH_CUDA_KERNEL((InnerReductionKernel<num_per_thread, Self, Op, Index>), + num_blocks, block_size, 0, device, reducer, self, num_coeffs_to_reduce, num_preserved_vals, output); + + return false; + } +}; + +#ifdef EIGEN_HAS_CUDA_FP16 +template <typename Self, typename Op> +struct InnerReductionLauncher<Self, Op, Eigen::half, false> { + static bool run(const Self&, Op&, const GpuDevice&, half*, typename Self::Index, typename Self::Index) { + assert(false && "Should not be called since there is no packet accessor"); + return true; + } +}; + +template <typename Self, typename Op> +struct InnerReductionLauncher<Self, Op, Eigen::half, true> { + static bool run(const Self& self, Op& reducer, const GpuDevice& device, half* output, typename Self::Index num_coeffs_to_reduce, typename Self::Index num_preserved_vals) { + typedef typename Self::Index Index; + + if (num_preserved_vals % 2 != 0) { + // Not supported yet, revert to the slower code path + return true; + } + + const Index num_coeffs = num_coeffs_to_reduce * num_preserved_vals; + const int block_size = /*256*/128; + const int num_per_thread = /*128*/64; + const int dyn_blocks = divup<int>(num_coeffs, block_size * num_per_thread); + const int max_blocks = device.getNumCudaMultiProcessors() * + device.maxCudaThreadsPerMultiProcessor() / block_size; + const int num_blocks = numext::mini<int>(max_blocks, dyn_blocks); + + if (num_blocks > 1) { + // We initialize the outputs outside the reduction kernel when we can't be sure that there + // won't be a race conditions between multiple thread blocks. + const int dyn_blocks = divup<int>(num_preserved_vals, 1024); + const int max_blocks = device.getNumCudaMultiProcessors() * + device.maxCudaThreadsPerMultiProcessor() / 1024; + const int num_blocks = numext::mini<int>(max_blocks, dyn_blocks); + LAUNCH_CUDA_KERNEL((ReductionInitKernelHalfFloat<Self, Op, Index>), + 1, 1, 0, device, reducer, self, num_preserved_vals, output); + } + + LAUNCH_CUDA_KERNEL((InnerReductionKernelHalfFloat<num_per_thread, Self, Op, Index>), + num_blocks, block_size, 0, device, reducer, self, num_coeffs_to_reduce, num_preserved_vals, output); + + return false; + } +}; +#endif + + +template <typename Self, typename Op> +struct InnerReducer<Self, Op, GpuDevice> { + // Unfortunately nvidia doesn't support well exotic types such as complex, + // so reduce the scope of the optimized version of the code to the simple case + // of floats and half floats. +#ifdef EIGEN_HAS_CUDA_FP16 + static const bool HasOptimizedImplementation = !Op::IsStateful && + (internal::is_same<typename Self::CoeffReturnType, float>::value || + internal::is_same<typename Self::CoeffReturnType, double>::value || + (internal::is_same<typename Self::CoeffReturnType, Eigen::half>::value && reducer_traits<Op, GpuDevice>::PacketAccess)); +#else + static const bool HasOptimizedImplementation = !Op::IsStateful && + (internal::is_same<typename Self::CoeffReturnType, float>::value || + internal::is_same<typename Self::CoeffReturnType, double>::value); +#endif + + template <typename OutputType> + static bool run(const Self& self, Op& reducer, const GpuDevice& device, OutputType* output, typename Self::Index num_coeffs_to_reduce, typename Self::Index num_preserved_vals) { + assert(HasOptimizedImplementation && "Should only be called on doubles, floats or half floats"); + const Index num_coeffs = array_prod(self.m_impl.dimensions()); + // Don't crash when we're called with an input tensor of size 0. + if (num_coeffs == 0) { + return true; + } + // It's faster to use the usual code. + if (num_coeffs_to_reduce <= 128) { + return true; + } + + return InnerReductionLauncher<Self, Op, OutputType, reducer_traits<Op, GpuDevice>::PacketAccess>::run(self, reducer, device, output, num_coeffs_to_reduce, num_preserved_vals); + } +}; + +template <int NumPerThread, typename Self, + typename Reducer, typename Index> +__global__ void OuterReductionKernel(Reducer reducer, const Self input, Index num_coeffs_to_reduce, Index num_preserved_coeffs, + typename Self::CoeffReturnType* output) { + const Index num_threads = blockDim.x * gridDim.x; + const Index thread_id = blockIdx.x * blockDim.x + threadIdx.x; + // Initialize the output values if they weren't initialized by the ReductionInitKernel + if (gridDim.x == 1) { + for (Index i = thread_id; i < num_preserved_coeffs; i += num_threads) { + output[i] = reducer.initialize(); + } + __syncthreads(); + } + + // Do the reduction. + const Index max_iter = num_preserved_coeffs * divup<Index>(num_coeffs_to_reduce, NumPerThread); + for (Index i = thread_id; i < max_iter; i += num_threads) { + const Index input_col = i % num_preserved_coeffs; + const Index input_row = (i / num_preserved_coeffs) * NumPerThread; + typename Self::CoeffReturnType reduced_val = reducer.initialize(); + const Index max_row = numext::mini(input_row + NumPerThread, num_coeffs_to_reduce); + for (Index j = input_row; j < max_row; j++) { + typename Self::CoeffReturnType val = input.m_impl.coeff(j * num_preserved_coeffs + input_col); + reducer.reduce(val, &reduced_val); + } + atomicReduce(&(output[input_col]), reduced_val, reducer); + } +} + + +template <typename Self, typename Op> +struct OuterReducer<Self, Op, GpuDevice> { + // Unfortunately nvidia doesn't support well exotic types such as complex, + // so reduce the scope of the optimized version of the code to the simple case + // of floats. + static const bool HasOptimizedImplementation = !Op::IsStateful && + (internal::is_same<typename Self::CoeffReturnType, float>::value || + internal::is_same<typename Self::CoeffReturnType, double>::value); + template <typename Device, typename OutputType> + static EIGEN_DEVICE_FUNC bool run(const Self&, Op&, const Device&, OutputType*, typename Self::Index, typename Self::Index) { + assert(false && "Should only be called to reduce doubles or floats on a gpu device"); + return true; + } + + static bool run(const Self& self, Op& reducer, const GpuDevice& device, float* output, typename Self::Index num_coeffs_to_reduce, typename Self::Index num_preserved_vals) { + typedef typename Self::Index Index; + + // It's faster to use the usual code. + if (num_coeffs_to_reduce <= 32) { + return true; + } + + const Index num_coeffs = num_coeffs_to_reduce * num_preserved_vals; + const int block_size = 256; + const int num_per_thread = 16; + const int dyn_blocks = divup<int>(num_coeffs, block_size * num_per_thread); + const int max_blocks = device.getNumCudaMultiProcessors() * + device.maxCudaThreadsPerMultiProcessor() / block_size; + const int num_blocks = numext::mini<int>(max_blocks, dyn_blocks); + + if (num_blocks > 1) { + // We initialize the outputs in the reduction kernel itself when we don't have to worry + // about race conditions between multiple thread blocks. + const int dyn_blocks = divup<int>(num_preserved_vals, 1024); + const int max_blocks = device.getNumCudaMultiProcessors() * + device.maxCudaThreadsPerMultiProcessor() / 1024; + const int num_blocks = numext::mini<int>(max_blocks, dyn_blocks); + LAUNCH_CUDA_KERNEL((ReductionInitKernel<float, Index>), + num_blocks, 1024, 0, device, reducer.initialize(), + num_preserved_vals, output); + } + + LAUNCH_CUDA_KERNEL((OuterReductionKernel<num_per_thread, Self, Op, Index>), + num_blocks, block_size, 0, device, reducer, self, num_coeffs_to_reduce, num_preserved_vals, output); + + return false; + } +}; + +#endif + + +} // end namespace internal +} // end namespace Eigen + +#endif // EIGEN_CXX11_TENSOR_TENSOR_REDUCTION_CUDA_H diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorReductionSycl.h b/unsupported/Eigen/CXX11/src/Tensor/TensorReductionSycl.h new file mode 100644 index 000000000..3daecb045 --- /dev/null +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorReductionSycl.h @@ -0,0 +1,242 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Mehdi Goli Codeplay Software Ltd. +// Ralph Potter Codeplay Software Ltd. +// Luke Iwanski Codeplay Software Ltd. +// Contact: <eigen@codeplay.com> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +/***************************************************************** + * TensorSyclPlaceHolderExpr.h + * + * \brief: + * This is the specialisation of the placeholder expression based on the + * operation type + * +*****************************************************************/ + +#ifndef UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSOR_REDUCTION_SYCL_HPP +#define UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSOR_REDUCTION_SYCL_HPP + +namespace Eigen { +namespace internal { + +template<typename CoeffReturnType, typename KernelName> struct syclGenericBufferReducer{ +template<typename BufferTOut, typename BufferTIn> +static void run(BufferTOut* bufOut, BufferTIn& bufI, const Eigen::SyclDevice& dev, size_t length, size_t local){ + do { + auto f = [length, local, bufOut, &bufI](cl::sycl::handler& h) mutable { + cl::sycl::nd_range<1> r{cl::sycl::range<1>{std::max(length, local)}, + cl::sycl::range<1>{std::min(length, local)}}; + /* Two accessors are used: one to the buffer that is being reduced, + * and a second to local memory, used to store intermediate data. */ + auto aI = + bufI.template get_access<cl::sycl::access::mode::read_write>(h); + auto aOut = + bufOut->template get_access<cl::sycl::access::mode::discard_write>(h); + cl::sycl::accessor<CoeffReturnType, 1, cl::sycl::access::mode::read_write, + cl::sycl::access::target::local> + scratch(cl::sycl::range<1>(local), h); + + /* The parallel_for invocation chosen is the variant with an nd_item + * parameter, since the code requires barriers for correctness. */ + h.parallel_for<KernelName>( + r, [aOut, aI, scratch, local, length](cl::sycl::nd_item<1> id) { + size_t globalid = id.get_global(0); + size_t localid = id.get_local(0); + /* All threads collectively read from global memory into local. + * The barrier ensures all threads' IO is resolved before + * execution continues (strictly speaking, all threads within + * a single work-group - there is no co-ordination between + * work-groups, only work-items). */ + if (globalid < length) { + scratch[localid] = aI[globalid]; + } + id.barrier(cl::sycl::access::fence_space::local_space); + + /* Apply the reduction operation between the current local + * id and the one on the other half of the vector. */ + if (globalid < length) { + int min = (length < local) ? length : local; + for (size_t offset = min / 2; offset > 0; offset /= 2) { + if (localid < offset) { + scratch[localid] += scratch[localid + offset]; + } + id.barrier(cl::sycl::access::fence_space::local_space); + } + /* The final result will be stored in local id 0. */ + if (localid == 0) { + aI[id.get_group(0)] = scratch[localid]; + if((length<=local) && globalid ==0){ + aOut[globalid]=scratch[localid]; + } + } + } + }); + }; + dev.m_queue.submit(f); + dev.m_queue.throw_asynchronous(); + + /* At this point, you could queue::wait_and_throw() to ensure that + * errors are caught quickly. However, this would likely impact + * performance negatively. */ + length = length / local; + + } while (length > 1); + + + +} + +}; + +/// For now let's start with a full reducer +/// Self is useless here because in expression construction we are going to treat reduction as a leafnode. +/// we want to take reduction child and then build a construction and apply the full reducer function on it. Fullreducre applies the +/// reduction operation on the child of the reduction. once it is done the reduction is an empty shell and can be thrown away and treated as +// a leafNode. +template <typename Self, typename Op, bool Vectorizable> +struct FullReducer<Self, Op, const Eigen::SyclDevice, Vectorizable> { + + typedef typename Self::CoeffReturnType CoeffReturnType; + static const bool HasOptimizedImplementation = false; + + static void run(const Self& self, Op& reducer, const Eigen::SyclDevice& dev, CoeffReturnType* output) { + typedef const typename Self::ChildType HostExpr; /// this is the child of reduction + typedef typename TensorSycl::internal::createPlaceHolderExpression<HostExpr>::Type PlaceHolderExpr; + auto functors = TensorSycl::internal::extractFunctors(self.impl()); + int red_factor =256; /// initial reduction. If the size is less than red_factor we only creates one thread. + size_t inputSize =self.impl().dimensions().TotalSize(); + size_t rng = inputSize/red_factor; // the total number of thread initially is half the size of the input + size_t remaining = inputSize% red_factor; + if(rng ==0) { + red_factor=1; + }; + size_t tileSize =dev.m_queue.get_device(). template get_info<cl::sycl::info::device::max_work_group_size>()/2; + size_t GRange=std::max((size_t )1, rng); + + // convert global range to power of 2 for redecution + GRange--; + GRange |= GRange >> 1; + GRange |= GRange >> 2; + GRange |= GRange >> 4; + GRange |= GRange >> 8; + GRange |= GRange >> 16; +#if __x86_64__ || __ppc64__ || _WIN64 + GRange |= GRange >> 32; +#endif + GRange++; + size_t outTileSize = tileSize; + /// if the shared memory is less than the GRange, we set shared_mem size to the TotalSize and in this case one kernel would be created for recursion to reduce all to one. + if (GRange < outTileSize) outTileSize=GRange; + // getting final out buffer at the moment the created buffer is true because there is no need for assign + auto out_buffer =dev.template get_sycl_buffer<typename Eigen::internal::remove_all<CoeffReturnType>::type>(self.dimensions().TotalSize(), output); + /// creating the shared memory for calculating reduction. + /// This one is used to collect all the reduced value of shared memory as we dont have global barrier on GPU. Once it is saved we can + /// recursively apply reduction on it in order to reduce the whole. + auto temp_global_buffer =cl::sycl::buffer<CoeffReturnType, 1>(cl::sycl::range<1>(GRange)); + typedef typename Eigen::internal::remove_all<decltype(self.xprDims())>::type Dims; + Dims dims= self.xprDims(); + Op functor = reducer; + dev.m_queue.submit([&](cl::sycl::handler &cgh) { + // create a tuple of accessors from Evaluator + auto tuple_of_accessors = TensorSycl::internal::createTupleOfAccessors(cgh, self.impl()); + auto tmp_global_accessor = temp_global_buffer. template get_access<cl::sycl::access::mode::read_write, cl::sycl::access::target::global_buffer>(cgh); + + cgh.parallel_for<PlaceHolderExpr>( cl::sycl::nd_range<1>(cl::sycl::range<1>(GRange), cl::sycl::range<1>(outTileSize)), [=](cl::sycl::nd_item<1> itemID) { + typedef typename TensorSycl::internal::ConvertToDeviceExpression<const HostExpr>::Type DevExpr; + auto device_expr = TensorSycl::internal::createDeviceExpression<DevExpr, PlaceHolderExpr>(functors, tuple_of_accessors); + /// reduction cannot be captured automatically through our device conversion recursion. The reason is that reduction has two behaviour + /// the first behaviour is when it is used as a root to lauch the sub-kernel. The second one is when it is treated as a leafnode to pass the + /// calculated result to its parent kernel. While the latter is automatically detected through our device expression generator. The former is created here. + const auto device_self_expr= TensorReductionOp<Op, Dims, decltype(device_expr.expr) ,MakeGlobalPointer>(device_expr.expr, dims, functor); + /// This is the evaluator for device_self_expr. This is exactly similar to the self which has been passed to run function. The difference is + /// the device_evaluator is detectable and recognisable on the device. + auto device_self_evaluator = Eigen::TensorEvaluator<decltype(device_self_expr), Eigen::DefaultDevice>(device_self_expr, Eigen::DefaultDevice()); + /// const cast added as a naive solution to solve the qualifier drop error + auto globalid=itemID.get_global_linear_id(); + + if(globalid<rng) + tmp_global_accessor.get_pointer()[globalid]=InnerMostDimReducer<decltype(device_self_evaluator), Op, false>::reduce(device_self_evaluator, red_factor*globalid, red_factor, const_cast<Op&>(functor)); + else + tmp_global_accessor.get_pointer()[globalid]=static_cast<CoeffReturnType>(0); + + if(remaining!=0 && globalid==0 ) + // this will add the rest of input buffer when the input size is not devidable to red_factor. + tmp_global_accessor.get_pointer()[globalid]+=InnerMostDimReducer<decltype(device_self_evaluator), Op, false>::reduce(device_self_evaluator, red_factor*(rng), remaining, const_cast<Op&>(functor)); + }); + }); + dev.m_queue.throw_asynchronous(); + +/// This is used to recursively reduce the tmp value to an element of 1; + syclGenericBufferReducer<CoeffReturnType,HostExpr>::run(out_buffer, temp_global_buffer,dev, GRange, outTileSize); + } + +}; + +template <typename Self, typename Op> +struct InnerReducer<Self, Op, const Eigen::SyclDevice> { + + typedef typename Self::CoeffReturnType CoeffReturnType; + static const bool HasOptimizedImplementation = false; + + static bool run(const Self& self, Op& reducer, const Eigen::SyclDevice& dev, CoeffReturnType* output, typename Self::Index , typename Self::Index num_coeffs_to_preserve) { + typedef const typename Self::ChildType HostExpr; /// this is the child of reduction + typedef typename TensorSycl::internal::createPlaceHolderExpression<HostExpr>::Type PlaceHolderExpr; + auto functors = TensorSycl::internal::extractFunctors(self.impl()); + + size_t tileSize =dev.m_queue.get_device(). template get_info<cl::sycl::info::device::max_work_group_size>()/2; + + size_t GRange=num_coeffs_to_preserve; + if (tileSize>GRange) tileSize=GRange; + else if(GRange>tileSize){ + size_t xMode = GRange % tileSize; + if (xMode != 0) GRange += (tileSize - xMode); + } + // getting final out buffer at the moment the created buffer is true because there is no need for assign + /// creating the shared memory for calculating reduction. + /// This one is used to collect all the reduced value of shared memory as we dont have global barrier on GPU. Once it is saved we can + /// recursively apply reduction on it in order to reduce the whole. + typedef typename Eigen::internal::remove_all<decltype(self.xprDims())>::type Dims; + Dims dims= self.xprDims(); + Op functor = reducer; + + dev.m_queue.submit([&](cl::sycl::handler &cgh) { + // create a tuple of accessors from Evaluator + auto tuple_of_accessors = TensorSycl::internal::createTupleOfAccessors(cgh, self.impl()); + auto output_accessor = dev.template get_sycl_accessor<cl::sycl::access::mode::discard_write>(num_coeffs_to_preserve,cgh, output); + + cgh.parallel_for<Self>( cl::sycl::nd_range<1>(cl::sycl::range<1>(GRange), cl::sycl::range<1>(tileSize)), [=](cl::sycl::nd_item<1> itemID) { + typedef typename TensorSycl::internal::ConvertToDeviceExpression<const HostExpr>::Type DevExpr; + auto device_expr = TensorSycl::internal::createDeviceExpression<DevExpr, PlaceHolderExpr>(functors, tuple_of_accessors); + /// reduction cannot be captured automatically through our device conversion recursion. The reason is that reduction has two behaviour + /// the first behaviour is when it is used as a root to lauch the sub-kernel. The second one is when it is treated as a leafnode to pass the + /// calculated result to its parent kernel. While the latter is automatically detected through our device expression generator. The former is created here. + const auto device_self_expr= TensorReductionOp<Op, Dims, decltype(device_expr.expr) ,MakeGlobalPointer>(device_expr.expr, dims, functor); + /// This is the evaluator for device_self_expr. This is exactly similar to the self which has been passed to run function. The difference is + /// the device_evaluator is detectable and recognisable on the device. + typedef Eigen::TensorEvaluator<decltype(device_self_expr), Eigen::DefaultDevice> DeiceSelf; + auto device_self_evaluator = Eigen::TensorEvaluator<decltype(device_self_expr), Eigen::DefaultDevice>(device_self_expr, Eigen::DefaultDevice()); + /// const cast added as a naive solution to solve the qualifier drop error + auto globalid=itemID.get_global_linear_id(); + if (globalid< static_cast<size_t>(num_coeffs_to_preserve)) { + typename DeiceSelf::CoeffReturnType accum = functor.initialize(); + GenericDimReducer<DeiceSelf::NumReducedDims-1, DeiceSelf, Op>::reduce(device_self_evaluator, device_self_evaluator.firstInput(globalid),const_cast<Op&>(functor), &accum); + functor.finalize(accum); + output_accessor.get_pointer()[globalid]= accum; + } + }); + }); + dev.m_queue.throw_asynchronous(); + return false; + } +}; + +} // end namespace internal +} // namespace Eigen + +#endif // UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSOR_REDUCTION_SYCL_HPP diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorRef.h b/unsupported/Eigen/CXX11/src/Tensor/TensorRef.h new file mode 100644 index 000000000..99245f778 --- /dev/null +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorRef.h @@ -0,0 +1,429 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_CXX11_TENSOR_TENSOR_REF_H +#define EIGEN_CXX11_TENSOR_TENSOR_REF_H + +namespace Eigen { + +namespace internal { + +template <typename Dimensions, typename Scalar> +class TensorLazyBaseEvaluator { + public: + TensorLazyBaseEvaluator() : m_refcount(0) { } + virtual ~TensorLazyBaseEvaluator() { } + + EIGEN_DEVICE_FUNC virtual const Dimensions& dimensions() const = 0; + EIGEN_DEVICE_FUNC virtual const Scalar* data() const = 0; + + EIGEN_DEVICE_FUNC virtual const Scalar coeff(DenseIndex index) const = 0; + EIGEN_DEVICE_FUNC virtual Scalar& coeffRef(DenseIndex index) = 0; + + void incrRefCount() { ++m_refcount; } + void decrRefCount() { --m_refcount; } + int refCount() const { return m_refcount; } + + private: + // No copy, no assigment; + TensorLazyBaseEvaluator(const TensorLazyBaseEvaluator& other); + TensorLazyBaseEvaluator& operator = (const TensorLazyBaseEvaluator& other); + + int m_refcount; +}; + + +template <typename Dimensions, typename Expr, typename Device> +class TensorLazyEvaluatorReadOnly : public TensorLazyBaseEvaluator<Dimensions, typename TensorEvaluator<Expr, Device>::Scalar> { + public: + // typedef typename TensorEvaluator<Expr, Device>::Dimensions Dimensions; + typedef typename TensorEvaluator<Expr, Device>::Scalar Scalar; + + TensorLazyEvaluatorReadOnly(const Expr& expr, const Device& device) : m_impl(expr, device), m_dummy(Scalar(0)) { + m_dims = m_impl.dimensions(); + m_impl.evalSubExprsIfNeeded(NULL); + } + virtual ~TensorLazyEvaluatorReadOnly() { + m_impl.cleanup(); + } + + EIGEN_DEVICE_FUNC virtual const Dimensions& dimensions() const { + return m_dims; + } + EIGEN_DEVICE_FUNC virtual const Scalar* data() const { + return m_impl.data(); + } + + EIGEN_DEVICE_FUNC virtual const Scalar coeff(DenseIndex index) const { + return m_impl.coeff(index); + } + EIGEN_DEVICE_FUNC virtual Scalar& coeffRef(DenseIndex /*index*/) { + eigen_assert(false && "can't reference the coefficient of a rvalue"); + return m_dummy; + }; + + protected: + TensorEvaluator<Expr, Device> m_impl; + Dimensions m_dims; + Scalar m_dummy; +}; + +template <typename Dimensions, typename Expr, typename Device> +class TensorLazyEvaluatorWritable : public TensorLazyEvaluatorReadOnly<Dimensions, Expr, Device> { + public: + typedef TensorLazyEvaluatorReadOnly<Dimensions, Expr, Device> Base; + typedef typename Base::Scalar Scalar; + + TensorLazyEvaluatorWritable(const Expr& expr, const Device& device) : Base(expr, device) { + } + virtual ~TensorLazyEvaluatorWritable() { + } + + EIGEN_DEVICE_FUNC virtual Scalar& coeffRef(DenseIndex index) { + return this->m_impl.coeffRef(index); + } +}; + +template <typename Dimensions, typename Expr, typename Device> +class TensorLazyEvaluator : public internal::conditional<bool(internal::is_lvalue<Expr>::value), + TensorLazyEvaluatorWritable<Dimensions, Expr, Device>, + TensorLazyEvaluatorReadOnly<Dimensions, const Expr, Device> >::type { + public: + typedef typename internal::conditional<bool(internal::is_lvalue<Expr>::value), + TensorLazyEvaluatorWritable<Dimensions, Expr, Device>, + TensorLazyEvaluatorReadOnly<Dimensions, const Expr, Device> >::type Base; + typedef typename Base::Scalar Scalar; + + TensorLazyEvaluator(const Expr& expr, const Device& device) : Base(expr, device) { + } + virtual ~TensorLazyEvaluator() { + } +}; + +} // namespace internal + + +/** \class TensorRef + * \ingroup CXX11_Tensor_Module + * + * \brief A reference to a tensor expression + * The expression will be evaluated lazily (as much as possible). + * + */ +template<typename PlainObjectType> class TensorRef : public TensorBase<TensorRef<PlainObjectType> > +{ + public: + typedef TensorRef<PlainObjectType> Self; + typedef typename PlainObjectType::Base Base; + typedef typename Eigen::internal::nested<Self>::type Nested; + typedef typename internal::traits<PlainObjectType>::StorageKind StorageKind; + typedef typename internal::traits<PlainObjectType>::Index Index; + typedef typename internal::traits<PlainObjectType>::Scalar Scalar; + typedef typename NumTraits<Scalar>::Real RealScalar; + typedef typename Base::CoeffReturnType CoeffReturnType; + typedef Scalar* PointerType; + typedef PointerType PointerArgType; + + static const Index NumIndices = PlainObjectType::NumIndices; + typedef typename PlainObjectType::Dimensions Dimensions; + + enum { + IsAligned = false, + PacketAccess = false, + Layout = PlainObjectType::Layout, + CoordAccess = false, // to be implemented + RawAccess = false + }; + + EIGEN_STRONG_INLINE TensorRef() : m_evaluator(NULL) { + } + + template <typename Expression> + EIGEN_STRONG_INLINE TensorRef(const Expression& expr) : m_evaluator(new internal::TensorLazyEvaluator<Dimensions, Expression, DefaultDevice>(expr, DefaultDevice())) { + m_evaluator->incrRefCount(); + } + + template <typename Expression> + EIGEN_STRONG_INLINE TensorRef& operator = (const Expression& expr) { + unrefEvaluator(); + m_evaluator = new internal::TensorLazyEvaluator<Dimensions, Expression, DefaultDevice>(expr, DefaultDevice()); + m_evaluator->incrRefCount(); + return *this; + } + + ~TensorRef() { + unrefEvaluator(); + } + + TensorRef(const TensorRef& other) : m_evaluator(other.m_evaluator) { + eigen_assert(m_evaluator->refCount() > 0); + m_evaluator->incrRefCount(); + } + + TensorRef& operator = (const TensorRef& other) { + if (this != &other) { + unrefEvaluator(); + m_evaluator = other.m_evaluator; + eigen_assert(m_evaluator->refCount() > 0); + m_evaluator->incrRefCount(); + } + return *this; + } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Index rank() const { return m_evaluator->dimensions().size(); } + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Index dimension(Index n) const { return m_evaluator->dimensions()[n]; } + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_evaluator->dimensions(); } + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Index size() const { return m_evaluator->dimensions().TotalSize(); } + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const Scalar* data() const { return m_evaluator->data(); } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const Scalar operator()(Index index) const + { + return m_evaluator->coeff(index); + } + +#if EIGEN_HAS_VARIADIC_TEMPLATES + template<typename... IndexTypes> EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const Scalar operator()(Index firstIndex, IndexTypes... otherIndices) const + { + const std::size_t num_indices = (sizeof...(otherIndices) + 1); + const array<Index, num_indices> indices{{firstIndex, otherIndices...}}; + return coeff(indices); + } + template<typename... IndexTypes> EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Scalar& coeffRef(Index firstIndex, IndexTypes... otherIndices) + { + const std::size_t num_indices = (sizeof...(otherIndices) + 1); + const array<Index, num_indices> indices{{firstIndex, otherIndices...}}; + return coeffRef(indices); + } +#else + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const Scalar operator()(Index i0, Index i1) const + { + array<Index, 2> indices; + indices[0] = i0; + indices[1] = i1; + return coeff(indices); + } + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const Scalar operator()(Index i0, Index i1, Index i2) const + { + array<Index, 3> indices; + indices[0] = i0; + indices[1] = i1; + indices[2] = i2; + return coeff(indices); + } + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const Scalar operator()(Index i0, Index i1, Index i2, Index i3) const + { + array<Index, 4> indices; + indices[0] = i0; + indices[1] = i1; + indices[2] = i2; + indices[3] = i3; + return coeff(indices); + } + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const Scalar operator()(Index i0, Index i1, Index i2, Index i3, Index i4) const + { + array<Index, 5> indices; + indices[0] = i0; + indices[1] = i1; + indices[2] = i2; + indices[3] = i3; + indices[4] = i4; + return coeff(indices); + } + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Scalar& coeffRef(Index i0, Index i1) + { + array<Index, 2> indices; + indices[0] = i0; + indices[1] = i1; + return coeffRef(indices); + } + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Scalar& coeffRef(Index i0, Index i1, Index i2) + { + array<Index, 3> indices; + indices[0] = i0; + indices[1] = i1; + indices[2] = i2; + return coeffRef(indices); + } + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Scalar& operator()(Index i0, Index i1, Index i2, Index i3) + { + array<Index, 4> indices; + indices[0] = i0; + indices[1] = i1; + indices[2] = i2; + indices[3] = i3; + return coeffRef(indices); + } + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Scalar& coeffRef(Index i0, Index i1, Index i2, Index i3, Index i4) + { + array<Index, 5> indices; + indices[0] = i0; + indices[1] = i1; + indices[2] = i2; + indices[3] = i3; + indices[4] = i4; + return coeffRef(indices); + } +#endif + + template <std::size_t NumIndices> EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const Scalar coeff(const array<Index, NumIndices>& indices) const + { + const Dimensions& dims = this->dimensions(); + Index index = 0; + if (PlainObjectType::Options & RowMajor) { + index += indices[0]; + for (size_t i = 1; i < NumIndices; ++i) { + index = index * dims[i] + indices[i]; + } + } else { + index += indices[NumIndices-1]; + for (int i = NumIndices-2; i >= 0; --i) { + index = index * dims[i] + indices[i]; + } + } + return m_evaluator->coeff(index); + } + template <std::size_t NumIndices> EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Scalar& coeffRef(const array<Index, NumIndices>& indices) + { + const Dimensions& dims = this->dimensions(); + Index index = 0; + if (PlainObjectType::Options & RowMajor) { + index += indices[0]; + for (size_t i = 1; i < NumIndices; ++i) { + index = index * dims[i] + indices[i]; + } + } else { + index += indices[NumIndices-1]; + for (int i = NumIndices-2; i >= 0; --i) { + index = index * dims[i] + indices[i]; + } + } + return m_evaluator->coeffRef(index); + } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const Scalar coeff(Index index) const + { + return m_evaluator->coeff(index); + } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE Scalar& coeffRef(Index index) + { + return m_evaluator->coeffRef(index); + } + + private: + EIGEN_STRONG_INLINE void unrefEvaluator() { + if (m_evaluator) { + m_evaluator->decrRefCount(); + if (m_evaluator->refCount() == 0) { + delete m_evaluator; + } + } + } + + internal::TensorLazyBaseEvaluator<Dimensions, Scalar>* m_evaluator; +}; + + +// evaluator for rvalues +template<typename Derived, typename Device> +struct TensorEvaluator<const TensorRef<Derived>, Device> +{ + typedef typename Derived::Index Index; + typedef typename Derived::Scalar Scalar; + typedef typename Derived::Scalar CoeffReturnType; + typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType; + typedef typename Derived::Dimensions Dimensions; + + enum { + IsAligned = false, + PacketAccess = false, + Layout = TensorRef<Derived>::Layout, + CoordAccess = false, // to be implemented + RawAccess = false + }; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const TensorRef<Derived>& m, const Device&) + : m_ref(m) + { } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_ref.dimensions(); } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar*) { + return true; + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() { } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const { + return m_ref.coeff(index); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef(Index index) { + return m_ref.coeffRef(index); + } + + EIGEN_DEVICE_FUNC Scalar* data() const { return m_ref.data(); } + + protected: + TensorRef<Derived> m_ref; +}; + + +// evaluator for lvalues +template<typename Derived, typename Device> +struct TensorEvaluator<TensorRef<Derived>, Device> : public TensorEvaluator<const TensorRef<Derived>, Device> +{ + typedef typename Derived::Index Index; + typedef typename Derived::Scalar Scalar; + typedef typename Derived::Scalar CoeffReturnType; + typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType; + typedef typename Derived::Dimensions Dimensions; + + typedef TensorEvaluator<const TensorRef<Derived>, Device> Base; + + enum { + IsAligned = false, + PacketAccess = false, + RawAccess = false + }; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(TensorRef<Derived>& m, const Device& d) : Base(m, d) + { } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef(Index index) { + return this->m_ref.coeffRef(index); + } +}; + + + +} // end namespace Eigen + +#endif // EIGEN_CXX11_TENSOR_TENSOR_REF_H diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorReverse.h b/unsupported/Eigen/CXX11/src/Tensor/TensorReverse.h new file mode 100644 index 000000000..14e392e36 --- /dev/null +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorReverse.h @@ -0,0 +1,288 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2014 Navdeep Jaitly <ndjaitly@google.com> +// Benoit Steiner <benoit.steiner.goog@gmail.com> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_CXX11_TENSOR_TENSOR_REVERSE_H +#define EIGEN_CXX11_TENSOR_TENSOR_REVERSE_H +namespace Eigen { + +/** \class TensorReverse + * \ingroup CXX11_Tensor_Module + * + * \brief Tensor reverse elements class. + * + */ +namespace internal { +template<typename ReverseDimensions, typename XprType> +struct traits<TensorReverseOp<ReverseDimensions, + XprType> > : public traits<XprType> +{ + typedef typename XprType::Scalar Scalar; + typedef traits<XprType> XprTraits; + typedef typename XprTraits::StorageKind StorageKind; + typedef typename XprTraits::Index Index; + typedef typename XprType::Nested Nested; + typedef typename remove_reference<Nested>::type _Nested; + static const int NumDimensions = XprTraits::NumDimensions; + static const int Layout = XprTraits::Layout; +}; + +template<typename ReverseDimensions, typename XprType> +struct eval<TensorReverseOp<ReverseDimensions, XprType>, Eigen::Dense> +{ + typedef const TensorReverseOp<ReverseDimensions, XprType>& type; +}; + +template<typename ReverseDimensions, typename XprType> +struct nested<TensorReverseOp<ReverseDimensions, XprType>, 1, + typename eval<TensorReverseOp<ReverseDimensions, XprType> >::type> +{ + typedef TensorReverseOp<ReverseDimensions, XprType> type; +}; + +} // end namespace internal + +template<typename ReverseDimensions, typename XprType> +class TensorReverseOp : public TensorBase<TensorReverseOp<ReverseDimensions, + XprType>, WriteAccessors> +{ + public: + typedef typename Eigen::internal::traits<TensorReverseOp>::Scalar Scalar; + typedef typename Eigen::NumTraits<Scalar>::Real RealScalar; + typedef typename XprType::CoeffReturnType CoeffReturnType; + typedef typename Eigen::internal::nested<TensorReverseOp>::type Nested; + typedef typename Eigen::internal::traits<TensorReverseOp>::StorageKind + StorageKind; + typedef typename Eigen::internal::traits<TensorReverseOp>::Index Index; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorReverseOp( + const XprType& expr, const ReverseDimensions& reverse_dims) + : m_xpr(expr), m_reverse_dims(reverse_dims) { } + + EIGEN_DEVICE_FUNC + const ReverseDimensions& reverse() const { return m_reverse_dims; } + + EIGEN_DEVICE_FUNC + const typename internal::remove_all<typename XprType::Nested>::type& + expression() const { return m_xpr; } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE TensorReverseOp& operator = (const TensorReverseOp& other) + { + typedef TensorAssignOp<TensorReverseOp, const TensorReverseOp> Assign; + Assign assign(*this, other); + internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice()); + return *this; + } + + template<typename OtherDerived> + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE TensorReverseOp& operator = (const OtherDerived& other) + { + typedef TensorAssignOp<TensorReverseOp, const OtherDerived> Assign; + Assign assign(*this, other); + internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice()); + return *this; + } + + protected: + typename XprType::Nested m_xpr; + const ReverseDimensions m_reverse_dims; +}; + +// Eval as rvalue +template<typename ReverseDimensions, typename ArgType, typename Device> +struct TensorEvaluator<const TensorReverseOp<ReverseDimensions, ArgType>, Device> +{ + typedef TensorReverseOp<ReverseDimensions, ArgType> XprType; + typedef typename XprType::Index Index; + static const int NumDims = internal::array_size<ReverseDimensions>::value; + typedef DSizes<Index, NumDims> Dimensions; + typedef typename XprType::Scalar Scalar; + typedef typename XprType::CoeffReturnType CoeffReturnType; + typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType; + static const int PacketSize = internal::unpacket_traits<PacketReturnType>::size; + + enum { + IsAligned = false, + PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess, + Layout = TensorEvaluator<ArgType, Device>::Layout, + CoordAccess = false, // to be implemented + RawAccess = false + }; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, + const Device& device) + : m_impl(op.expression(), device), m_reverse(op.reverse()) + { + // Reversing a scalar isn't supported yet. It would be a no-op anyway. + EIGEN_STATIC_ASSERT((NumDims > 0), YOU_MADE_A_PROGRAMMING_MISTAKE); + + // Compute strides + m_dimensions = m_impl.dimensions(); + if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { + m_strides[0] = 1; + for (int i = 1; i < NumDims; ++i) { + m_strides[i] = m_strides[i-1] * m_dimensions[i-1]; + } + } else { + m_strides[NumDims-1] = 1; + for (int i = NumDims - 2; i >= 0; --i) { + m_strides[i] = m_strides[i+1] * m_dimensions[i+1]; + } + } + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const Dimensions& dimensions() const { return m_dimensions; } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar*) { + m_impl.evalSubExprsIfNeeded(NULL); + return true; + } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() { + m_impl.cleanup(); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index reverseIndex( + Index index) const { + eigen_assert(index < dimensions().TotalSize()); + Index inputIndex = 0; + if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { + for (int i = NumDims - 1; i > 0; --i) { + Index idx = index / m_strides[i]; + index -= idx * m_strides[i]; + if (m_reverse[i]) { + idx = m_dimensions[i] - idx - 1; + } + inputIndex += idx * m_strides[i] ; + } + if (m_reverse[0]) { + inputIndex += (m_dimensions[0] - index - 1); + } else { + inputIndex += index; + } + } else { + for (int i = 0; i < NumDims - 1; ++i) { + Index idx = index / m_strides[i]; + index -= idx * m_strides[i]; + if (m_reverse[i]) { + idx = m_dimensions[i] - idx - 1; + } + inputIndex += idx * m_strides[i] ; + } + if (m_reverse[NumDims-1]) { + inputIndex += (m_dimensions[NumDims-1] - index - 1); + } else { + inputIndex += index; + } + } + return inputIndex; + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff( + Index index) const { + return m_impl.coeff(reverseIndex(index)); + } + + template<int LoadMode> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + PacketReturnType packet(Index index) const + { + EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE) + eigen_assert(index+PacketSize-1 < dimensions().TotalSize()); + + // TODO(ndjaitly): write a better packing routine that uses + // local structure. + EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type + values[PacketSize]; + for (int i = 0; i < PacketSize; ++i) { + values[i] = coeff(index+i); + } + PacketReturnType rslt = internal::pload<PacketReturnType>(values); + return rslt; + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const { + double compute_cost = NumDims * (2 * TensorOpCost::AddCost<Index>() + + 2 * TensorOpCost::MulCost<Index>() + + TensorOpCost::DivCost<Index>()); + for (int i = 0; i < NumDims; ++i) { + if (m_reverse[i]) { + compute_cost += 2 * TensorOpCost::AddCost<Index>(); + } + } + return m_impl.costPerCoeff(vectorized) + + TensorOpCost(0, 0, compute_cost, false /* vectorized */, PacketSize); + } + + EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; } + + protected: + Dimensions m_dimensions; + array<Index, NumDims> m_strides; + TensorEvaluator<ArgType, Device> m_impl; + ReverseDimensions m_reverse; +}; + +// Eval as lvalue + +template <typename ReverseDimensions, typename ArgType, typename Device> +struct TensorEvaluator<TensorReverseOp<ReverseDimensions, ArgType>, Device> + : public TensorEvaluator<const TensorReverseOp<ReverseDimensions, ArgType>, + Device> { + typedef TensorEvaluator<const TensorReverseOp<ReverseDimensions, ArgType>, + Device> Base; + typedef TensorReverseOp<ReverseDimensions, ArgType> XprType; + typedef typename XprType::Index Index; + static const int NumDims = internal::array_size<ReverseDimensions>::value; + typedef DSizes<Index, NumDims> Dimensions; + + enum { + IsAligned = false, + PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess, + Layout = TensorEvaluator<ArgType, Device>::Layout, + CoordAccess = false, // to be implemented + RawAccess = false + }; + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, + const Device& device) + : Base(op, device) {} + + typedef typename XprType::Scalar Scalar; + typedef typename XprType::CoeffReturnType CoeffReturnType; + typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType; + static const int PacketSize = internal::unpacket_traits<PacketReturnType>::size; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const Dimensions& dimensions() const { return this->m_dimensions; } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef(Index index) { + return this->m_impl.coeffRef(this->reverseIndex(index)); + } + + template <int StoreMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + void writePacket(Index index, const PacketReturnType& x) { + EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE) + eigen_assert(index+PacketSize-1 < dimensions().TotalSize()); + + // This code is pilfered from TensorMorphing.h + EIGEN_ALIGN_MAX CoeffReturnType values[PacketSize]; + internal::pstore<CoeffReturnType, PacketReturnType>(values, x); + for (int i = 0; i < PacketSize; ++i) { + this->coeffRef(index+i) = values[i]; + } + } + +}; + + +} // end namespace Eigen + +#endif // EIGEN_CXX11_TENSOR_TENSOR_REVERSE_H diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorScan.h b/unsupported/Eigen/CXX11/src/Tensor/TensorScan.h new file mode 100644 index 000000000..8501466ce --- /dev/null +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorScan.h @@ -0,0 +1,287 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2016 Igor Babuschkin <igor@babuschk.in> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_CXX11_TENSOR_TENSOR_SCAN_H +#define EIGEN_CXX11_TENSOR_TENSOR_SCAN_H + +namespace Eigen { + +namespace internal { + +template <typename Op, typename XprType> +struct traits<TensorScanOp<Op, XprType> > + : public traits<XprType> { + typedef typename XprType::Scalar Scalar; + typedef traits<XprType> XprTraits; + typedef typename XprTraits::StorageKind StorageKind; + typedef typename XprType::Nested Nested; + typedef typename remove_reference<Nested>::type _Nested; + static const int NumDimensions = XprTraits::NumDimensions; + static const int Layout = XprTraits::Layout; +}; + +template<typename Op, typename XprType> +struct eval<TensorScanOp<Op, XprType>, Eigen::Dense> +{ + typedef const TensorScanOp<Op, XprType>& type; +}; + +template<typename Op, typename XprType> +struct nested<TensorScanOp<Op, XprType>, 1, + typename eval<TensorScanOp<Op, XprType> >::type> +{ + typedef TensorScanOp<Op, XprType> type; +}; +} // end namespace internal + +/** \class TensorScan + * \ingroup CXX11_Tensor_Module + * + * \brief Tensor scan class. + */ +template <typename Op, typename XprType> +class TensorScanOp + : public TensorBase<TensorScanOp<Op, XprType>, ReadOnlyAccessors> { +public: + typedef typename Eigen::internal::traits<TensorScanOp>::Scalar Scalar; + typedef typename Eigen::NumTraits<Scalar>::Real RealScalar; + typedef typename XprType::CoeffReturnType CoeffReturnType; + typedef typename Eigen::internal::nested<TensorScanOp>::type Nested; + typedef typename Eigen::internal::traits<TensorScanOp>::StorageKind StorageKind; + typedef typename Eigen::internal::traits<TensorScanOp>::Index Index; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorScanOp( + const XprType& expr, const Index& axis, bool exclusive = false, const Op& op = Op()) + : m_expr(expr), m_axis(axis), m_accumulator(op), m_exclusive(exclusive) {} + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const Index axis() const { return m_axis; } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const XprType& expression() const { return m_expr; } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const Op accumulator() const { return m_accumulator; } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + bool exclusive() const { return m_exclusive; } + +protected: + typename XprType::Nested m_expr; + const Index m_axis; + const Op m_accumulator; + const bool m_exclusive; +}; + +template <typename Self, typename Reducer, typename Device> +struct ScanLauncher; + +// Eval as rvalue +template <typename Op, typename ArgType, typename Device> +struct TensorEvaluator<const TensorScanOp<Op, ArgType>, Device> { + + typedef TensorScanOp<Op, ArgType> XprType; + typedef typename XprType::Index Index; + static const int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value; + typedef DSizes<Index, NumDims> Dimensions; + typedef typename internal::remove_const<typename XprType::Scalar>::type Scalar; + typedef typename XprType::CoeffReturnType CoeffReturnType; + typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType; + typedef TensorEvaluator<const TensorScanOp<Op, ArgType>, Device> Self; + + enum { + IsAligned = false, + PacketAccess = (internal::unpacket_traits<PacketReturnType>::size > 1), + BlockAccess = false, + Layout = TensorEvaluator<ArgType, Device>::Layout, + CoordAccess = false, + RawAccess = true + }; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, + const Device& device) + : m_impl(op.expression(), device), + m_device(device), + m_exclusive(op.exclusive()), + m_accumulator(op.accumulator()), + m_size(m_impl.dimensions()[op.axis()]), + m_stride(1), + m_output(NULL) { + + // Accumulating a scalar isn't supported. + EIGEN_STATIC_ASSERT((NumDims > 0), YOU_MADE_A_PROGRAMMING_MISTAKE); + eigen_assert(op.axis() >= 0 && op.axis() < NumDims); + + // Compute stride of scan axis + const Dimensions& dims = m_impl.dimensions(); + if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { + for (int i = 0; i < op.axis(); ++i) { + m_stride = m_stride * dims[i]; + } + } else { + for (int i = NumDims - 1; i > op.axis(); --i) { + m_stride = m_stride * dims[i]; + } + } + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { + return m_impl.dimensions(); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Index& stride() const { + return m_stride; + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Index& size() const { + return m_size; + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Op& accumulator() const { + return m_accumulator; + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool exclusive() const { + return m_exclusive; + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const TensorEvaluator<ArgType, Device>& inner() const { + return m_impl; + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Device& device() const { + return m_device; + } + + EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* data) { + m_impl.evalSubExprsIfNeeded(NULL); + ScanLauncher<Self, Op, Device> launcher; + if (data) { + launcher(*this, data); + return false; + } + + const Index total_size = internal::array_prod(dimensions()); + m_output = static_cast<CoeffReturnType*>(m_device.allocate(total_size * sizeof(Scalar))); + launcher(*this, m_output); + return true; + } + + template<int LoadMode> + EIGEN_DEVICE_FUNC PacketReturnType packet(Index index) const { + return internal::ploadt<PacketReturnType, LoadMode>(m_output + index); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType* data() const + { + return m_output; + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const + { + return m_output[index]; + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool) const { + return TensorOpCost(sizeof(CoeffReturnType), 0, 0); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() { + if (m_output != NULL) { + m_device.deallocate(m_output); + m_output = NULL; + } + m_impl.cleanup(); + } + +protected: + TensorEvaluator<ArgType, Device> m_impl; + const Device& m_device; + const bool m_exclusive; + Op m_accumulator; + const Index m_size; + Index m_stride; + CoeffReturnType* m_output; +}; + +// CPU implementation of scan +// TODO(ibab) This single-threaded implementation should be parallelized, +// at least by running multiple scans at the same time. +template <typename Self, typename Reducer, typename Device> +struct ScanLauncher { + void operator()(Self& self, typename Self::CoeffReturnType *data) { + Index total_size = internal::array_prod(self.dimensions()); + + // We fix the index along the scan axis to 0 and perform a + // scan per remaining entry. The iteration is split into two nested + // loops to avoid an integer division by keeping track of each idx1 and idx2. + for (Index idx1 = 0; idx1 < total_size; idx1 += self.stride() * self.size()) { + for (Index idx2 = 0; idx2 < self.stride(); idx2++) { + // Calculate the starting offset for the scan + Index offset = idx1 + idx2; + + // Compute the scan along the axis, starting at the calculated offset + typename Self::CoeffReturnType accum = self.accumulator().initialize(); + for (Index idx3 = 0; idx3 < self.size(); idx3++) { + Index curr = offset + idx3 * self.stride(); + + if (self.exclusive()) { + data[curr] = self.accumulator().finalize(accum); + self.accumulator().reduce(self.inner().coeff(curr), &accum); + } else { + self.accumulator().reduce(self.inner().coeff(curr), &accum); + data[curr] = self.accumulator().finalize(accum); + } + } + } + } + } +}; + +#if defined(EIGEN_USE_GPU) && defined(__CUDACC__) + +// GPU implementation of scan +// TODO(ibab) This placeholder implementation performs multiple scans in +// parallel, but it would be better to use a parallel scan algorithm and +// optimize memory access. +template <typename Self, typename Reducer> +__global__ void ScanKernel(Self self, Index total_size, typename Self::CoeffReturnType* data) { + // Compute offset as in the CPU version + Index val = threadIdx.x + blockIdx.x * blockDim.x; + Index offset = (val / self.stride()) * self.stride() * self.size() + val % self.stride(); + + if (offset + (self.size() - 1) * self.stride() < total_size) { + // Compute the scan along the axis, starting at the calculated offset + typename Self::CoeffReturnType accum = self.accumulator().initialize(); + for (Index idx = 0; idx < self.size(); idx++) { + Index curr = offset + idx * self.stride(); + if (self.exclusive()) { + data[curr] = self.accumulator().finalize(accum); + self.accumulator().reduce(self.inner().coeff(curr), &accum); + } else { + self.accumulator().reduce(self.inner().coeff(curr), &accum); + data[curr] = self.accumulator().finalize(accum); + } + } + } + __syncthreads(); + +} + +template <typename Self, typename Reducer> +struct ScanLauncher<Self, Reducer, GpuDevice> { + void operator()(const Self& self, typename Self::CoeffReturnType* data) { + Index total_size = internal::array_prod(self.dimensions()); + Index num_blocks = (total_size / self.size() + 63) / 64; + Index block_size = 64; + LAUNCH_CUDA_KERNEL((ScanKernel<Self, Reducer>), num_blocks, block_size, 0, self.device(), self, total_size, data); + } +}; +#endif // EIGEN_USE_GPU && __CUDACC__ + +} // end namespace Eigen + +#endif // EIGEN_CXX11_TENSOR_TENSOR_SCAN_H diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorShuffling.h b/unsupported/Eigen/CXX11/src/Tensor/TensorShuffling.h new file mode 100644 index 000000000..113c060e3 --- /dev/null +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorShuffling.h @@ -0,0 +1,264 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_CXX11_TENSOR_TENSOR_SHUFFLING_H +#define EIGEN_CXX11_TENSOR_TENSOR_SHUFFLING_H + +namespace Eigen { + +/** \class TensorShuffling + * \ingroup CXX11_Tensor_Module + * + * \brief Tensor shuffling class. + * + * + */ +namespace internal { +template<typename Shuffle, typename XprType> +struct traits<TensorShufflingOp<Shuffle, XprType> > : public traits<XprType> +{ + typedef typename XprType::Scalar Scalar; + typedef traits<XprType> XprTraits; + typedef typename XprTraits::StorageKind StorageKind; + typedef typename XprTraits::Index Index; + typedef typename XprType::Nested Nested; + typedef typename remove_reference<Nested>::type _Nested; + static const int NumDimensions = XprTraits::NumDimensions; + static const int Layout = XprTraits::Layout; +}; + +template<typename Shuffle, typename XprType> +struct eval<TensorShufflingOp<Shuffle, XprType>, Eigen::Dense> +{ + typedef const TensorShufflingOp<Shuffle, XprType>& type; +}; + +template<typename Shuffle, typename XprType> +struct nested<TensorShufflingOp<Shuffle, XprType>, 1, typename eval<TensorShufflingOp<Shuffle, XprType> >::type> +{ + typedef TensorShufflingOp<Shuffle, XprType> type; +}; + +} // end namespace internal + + + +template<typename Shuffle, typename XprType> +class TensorShufflingOp : public TensorBase<TensorShufflingOp<Shuffle, XprType> > +{ + public: + typedef typename Eigen::internal::traits<TensorShufflingOp>::Scalar Scalar; + typedef typename Eigen::NumTraits<Scalar>::Real RealScalar; + typedef typename XprType::CoeffReturnType CoeffReturnType; + typedef typename Eigen::internal::nested<TensorShufflingOp>::type Nested; + typedef typename Eigen::internal::traits<TensorShufflingOp>::StorageKind StorageKind; + typedef typename Eigen::internal::traits<TensorShufflingOp>::Index Index; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorShufflingOp(const XprType& expr, const Shuffle& shuffle) + : m_xpr(expr), m_shuffle(shuffle) {} + + EIGEN_DEVICE_FUNC + const Shuffle& shufflePermutation() const { return m_shuffle; } + + EIGEN_DEVICE_FUNC + const typename internal::remove_all<typename XprType::Nested>::type& + expression() const { return m_xpr; } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE TensorShufflingOp& operator = (const TensorShufflingOp& other) + { + typedef TensorAssignOp<TensorShufflingOp, const TensorShufflingOp> Assign; + Assign assign(*this, other); + internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice()); + return *this; + } + + template<typename OtherDerived> + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE TensorShufflingOp& operator = (const OtherDerived& other) + { + typedef TensorAssignOp<TensorShufflingOp, const OtherDerived> Assign; + Assign assign(*this, other); + internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice()); + return *this; + } + + protected: + typename XprType::Nested m_xpr; + const Shuffle m_shuffle; +}; + + +// Eval as rvalue +template<typename Shuffle, typename ArgType, typename Device> +struct TensorEvaluator<const TensorShufflingOp<Shuffle, ArgType>, Device> +{ + typedef TensorShufflingOp<Shuffle, ArgType> XprType; + typedef typename XprType::Index Index; + static const int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value; + typedef DSizes<Index, NumDims> Dimensions; + typedef typename XprType::Scalar Scalar; + typedef typename XprType::CoeffReturnType CoeffReturnType; + typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType; + static const int PacketSize = internal::unpacket_traits<PacketReturnType>::size; + + enum { + IsAligned = false, + PacketAccess = (internal::packet_traits<Scalar>::size > 1), + Layout = TensorEvaluator<ArgType, Device>::Layout, + CoordAccess = false, // to be implemented + RawAccess = false + }; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) + : m_impl(op.expression(), device) + { + const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions(); + const Shuffle& shuffle = op.shufflePermutation(); + for (int i = 0; i < NumDims; ++i) { + m_dimensions[i] = input_dims[shuffle[i]]; + } + + array<Index, NumDims> inputStrides; + + if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { + inputStrides[0] = 1; + m_outputStrides[0] = 1; + for (int i = 1; i < NumDims; ++i) { + inputStrides[i] = inputStrides[i - 1] * input_dims[i - 1]; + m_outputStrides[i] = m_outputStrides[i - 1] * m_dimensions[i - 1]; + } + } else { + inputStrides[NumDims - 1] = 1; + m_outputStrides[NumDims - 1] = 1; + for (int i = NumDims - 2; i >= 0; --i) { + inputStrides[i] = inputStrides[i + 1] * input_dims[i + 1]; + m_outputStrides[i] = m_outputStrides[i + 1] * m_dimensions[i + 1]; + } + } + + for (int i = 0; i < NumDims; ++i) { + m_inputStrides[i] = inputStrides[shuffle[i]]; + } + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* /*data*/) { + m_impl.evalSubExprsIfNeeded(NULL); + return true; + } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() { + m_impl.cleanup(); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const + { + return m_impl.coeff(srcCoeff(index)); + } + + template<int LoadMode> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const + { + EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE) + eigen_assert(index+PacketSize-1 < dimensions().TotalSize()); + + EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize]; + for (int i = 0; i < PacketSize; ++i) { + values[i] = coeff(index+i); + } + PacketReturnType rslt = internal::pload<PacketReturnType>(values); + return rslt; + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const { + const double compute_cost = NumDims * (2 * TensorOpCost::AddCost<Index>() + + 2 * TensorOpCost::MulCost<Index>() + + TensorOpCost::DivCost<Index>()); + return m_impl.costPerCoeff(vectorized) + + TensorOpCost(0, 0, compute_cost, false /* vectorized */, PacketSize); + } + + EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; } + + protected: + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index srcCoeff(Index index) const { + Index inputIndex = 0; + if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { + for (int i = NumDims - 1; i > 0; --i) { + const Index idx = index / m_outputStrides[i]; + inputIndex += idx * m_inputStrides[i]; + index -= idx * m_outputStrides[i]; + } + return inputIndex + index * m_inputStrides[0]; + } else { + for (int i = 0; i < NumDims - 1; ++i) { + const Index idx = index / m_outputStrides[i]; + inputIndex += idx * m_inputStrides[i]; + index -= idx * m_outputStrides[i]; + } + return inputIndex + index * m_inputStrides[NumDims - 1]; + } + } + + Dimensions m_dimensions; + array<Index, NumDims> m_outputStrides; + array<Index, NumDims> m_inputStrides; + TensorEvaluator<ArgType, Device> m_impl; +}; + + +// Eval as lvalue +template<typename Shuffle, typename ArgType, typename Device> +struct TensorEvaluator<TensorShufflingOp<Shuffle, ArgType>, Device> + : public TensorEvaluator<const TensorShufflingOp<Shuffle, ArgType>, Device> +{ + typedef TensorEvaluator<const TensorShufflingOp<Shuffle, ArgType>, Device> Base; + + typedef TensorShufflingOp<Shuffle, ArgType> XprType; + typedef typename XprType::Index Index; + static const int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value; + typedef DSizes<Index, NumDims> Dimensions; + typedef typename XprType::Scalar Scalar; + typedef typename XprType::CoeffReturnType CoeffReturnType; + typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType; + static const int PacketSize = internal::unpacket_traits<PacketReturnType>::size; + + enum { + IsAligned = false, + PacketAccess = (internal::packet_traits<Scalar>::size > 1), + RawAccess = false + }; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) + : Base(op, device) + { } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType& coeffRef(Index index) + { + return this->m_impl.coeffRef(this->srcCoeff(index)); + } + + template <int StoreMode> EIGEN_STRONG_INLINE + void writePacket(Index index, const PacketReturnType& x) + { + EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE) + + EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize]; + internal::pstore<CoeffReturnType, PacketReturnType>(values, x); + for (int i = 0; i < PacketSize; ++i) { + this->coeffRef(index+i) = values[i]; + } + } +}; + + +} // end namespace Eigen + +#endif // EIGEN_CXX11_TENSOR_TENSOR_SHUFFLING_H diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorStorage.h b/unsupported/Eigen/CXX11/src/Tensor/TensorStorage.h new file mode 100644 index 000000000..2854a4a17 --- /dev/null +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorStorage.h @@ -0,0 +1,146 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2013 Christian Seiler <christian@iwakd.de> +// Copyright (C) 2014-2015 Benoit Steiner <benoit.steiner.goog@gmail.com> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_CXX11_TENSOR_TENSORSTORAGE_H +#define EIGEN_CXX11_TENSOR_TENSORSTORAGE_H + +#ifdef EIGEN_TENSOR_STORAGE_CTOR_PLUGIN + #define EIGEN_INTERNAL_TENSOR_STORAGE_CTOR_PLUGIN EIGEN_TENSOR_STORAGE_CTOR_PLUGIN; +#else + #define EIGEN_INTERNAL_TENSOR_STORAGE_CTOR_PLUGIN +#endif + +namespace Eigen { + +/** \internal + * + * \class TensorStorage + * \ingroup CXX11_Tensor_Module + * + * \brief Stores the data of a tensor + * + * This class stores the data of fixed-size, dynamic-size or mixed tensors + * in a way as compact as possible. + * + * \sa Tensor + */ +template<typename T, typename Dimensions, int Options_> class TensorStorage; + + +// Pure fixed-size storage +template<typename T, int Options_, typename FixedDimensions> +class TensorStorage<T, FixedDimensions, Options_> +{ + private: + static const std::size_t Size = FixedDimensions::total_size; + + // Allocate an array of size at least one to prevent compiler warnings. + static const std::size_t MinSize = max_n_1<Size>::size; + EIGEN_ALIGN_MAX T m_data[MinSize]; + + FixedDimensions m_dimensions; + + public: + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE TensorStorage() { + } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE T *data() { return m_data; } + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const T *data() const { return m_data; } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const FixedDimensions& dimensions() const { return m_dimensions; } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE DenseIndex size() const { return m_dimensions.TotalSize(); } +}; + + +// pure dynamic +template<typename T, int Options_, typename IndexType, int NumIndices_> +class TensorStorage<T, DSizes<IndexType, NumIndices_>, Options_> +{ + public: + typedef IndexType Index; + typedef DSizes<IndexType, NumIndices_> Dimensions; + typedef TensorStorage<T, DSizes<IndexType, NumIndices_>, Options_> Self; + + EIGEN_DEVICE_FUNC TensorStorage() : m_data(0), m_dimensions() { + if (NumIndices_ == 0) { + m_data = internal::conditional_aligned_new_auto<T,(Options_&DontAlign)==0>(1); + } + } + EIGEN_DEVICE_FUNC TensorStorage(internal::constructor_without_unaligned_array_assert) + : m_data(0), m_dimensions(internal::template repeat<NumIndices_, Index>(0)) {} + EIGEN_DEVICE_FUNC TensorStorage(Index size, const array<Index, NumIndices_>& dimensions) + : m_data(internal::conditional_aligned_new_auto<T,(Options_&DontAlign)==0>(size)), m_dimensions(dimensions) + { EIGEN_INTERNAL_TENSOR_STORAGE_CTOR_PLUGIN } + +#if EIGEN_HAS_VARIADIC_TEMPLATES + template <typename... DenseIndex> + EIGEN_DEVICE_FUNC TensorStorage(DenseIndex... indices) : m_dimensions(indices...) { + m_data = internal::conditional_aligned_new_auto<T,(Options_&DontAlign)==0>(internal::array_prod(m_dimensions)); + } +#endif + + EIGEN_DEVICE_FUNC TensorStorage(const Self& other) + : m_data(internal::conditional_aligned_new_auto<T,(Options_&DontAlign)==0>(internal::array_prod(other.m_dimensions))) + , m_dimensions(other.m_dimensions) + { + internal::smart_copy(other.m_data, other.m_data+internal::array_prod(other.m_dimensions), m_data); + } + EIGEN_DEVICE_FUNC Self& operator=(const Self& other) + { + if (this != &other) { + Self tmp(other); + this->swap(tmp); + } + return *this; + } + + EIGEN_DEVICE_FUNC ~TensorStorage() { internal::conditional_aligned_delete_auto<T,(Options_&DontAlign)==0>(m_data, internal::array_prod(m_dimensions)); } + EIGEN_DEVICE_FUNC void swap(Self& other) + { numext::swap(m_data,other.m_data); numext::swap(m_dimensions,other.m_dimensions); } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const {return m_dimensions;} + + EIGEN_DEVICE_FUNC void resize(Index size, const array<Index, NumIndices_>& nbDimensions) + { + const Index currentSz = internal::array_prod(m_dimensions); + if(size != currentSz) + { + internal::conditional_aligned_delete_auto<T,(Options_&DontAlign)==0>(m_data, currentSz); + if (size) + m_data = internal::conditional_aligned_new_auto<T,(Options_&DontAlign)==0>(size); + else if (NumIndices_ == 0) { + m_data = internal::conditional_aligned_new_auto<T,(Options_&DontAlign)==0>(1); + } + else + m_data = 0; + EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({}) + } + m_dimensions = nbDimensions; + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T *data() { return m_data; } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const T *data() const { return m_data; } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index size() const { return m_dimensions.TotalSize(); } + + private: + T *m_data; + Dimensions m_dimensions; +}; + +} // end namespace Eigen + +#endif // EIGEN_CXX11_TENSOR_TENSORSTORAGE_H diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorStriding.h b/unsupported/Eigen/CXX11/src/Tensor/TensorStriding.h new file mode 100644 index 000000000..6c35bfdb6 --- /dev/null +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorStriding.h @@ -0,0 +1,338 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_CXX11_TENSOR_TENSOR_STRIDING_H +#define EIGEN_CXX11_TENSOR_TENSOR_STRIDING_H + +namespace Eigen { + +/** \class TensorStriding + * \ingroup CXX11_Tensor_Module + * + * \brief Tensor striding class. + * + * + */ +namespace internal { +template<typename Strides, typename XprType> +struct traits<TensorStridingOp<Strides, XprType> > : public traits<XprType> +{ + typedef typename XprType::Scalar Scalar; + typedef traits<XprType> XprTraits; + typedef typename XprTraits::StorageKind StorageKind; + typedef typename XprTraits::Index Index; + typedef typename XprType::Nested Nested; + typedef typename remove_reference<Nested>::type _Nested; + static const int NumDimensions = XprTraits::NumDimensions; + static const int Layout = XprTraits::Layout; +}; + +template<typename Strides, typename XprType> +struct eval<TensorStridingOp<Strides, XprType>, Eigen::Dense> +{ + typedef const TensorStridingOp<Strides, XprType>& type; +}; + +template<typename Strides, typename XprType> +struct nested<TensorStridingOp<Strides, XprType>, 1, typename eval<TensorStridingOp<Strides, XprType> >::type> +{ + typedef TensorStridingOp<Strides, XprType> type; +}; + +} // end namespace internal + + + +template<typename Strides, typename XprType> +class TensorStridingOp : public TensorBase<TensorStridingOp<Strides, XprType> > +{ + public: + typedef typename Eigen::internal::traits<TensorStridingOp>::Scalar Scalar; + typedef typename Eigen::NumTraits<Scalar>::Real RealScalar; + typedef typename XprType::CoeffReturnType CoeffReturnType; + typedef typename Eigen::internal::nested<TensorStridingOp>::type Nested; + typedef typename Eigen::internal::traits<TensorStridingOp>::StorageKind StorageKind; + typedef typename Eigen::internal::traits<TensorStridingOp>::Index Index; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorStridingOp(const XprType& expr, const Strides& dims) + : m_xpr(expr), m_dims(dims) {} + + EIGEN_DEVICE_FUNC + const Strides& strides() const { return m_dims; } + + EIGEN_DEVICE_FUNC + const typename internal::remove_all<typename XprType::Nested>::type& + expression() const { return m_xpr; } + + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE TensorStridingOp& operator = (const TensorStridingOp& other) + { + typedef TensorAssignOp<TensorStridingOp, const TensorStridingOp> Assign; + Assign assign(*this, other); + internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice()); + return *this; + } + + template<typename OtherDerived> + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE TensorStridingOp& operator = (const OtherDerived& other) + { + typedef TensorAssignOp<TensorStridingOp, const OtherDerived> Assign; + Assign assign(*this, other); + internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice()); + return *this; + } + + protected: + typename XprType::Nested m_xpr; + const Strides m_dims; +}; + + +// Eval as rvalue +template<typename Strides, typename ArgType, typename Device> +struct TensorEvaluator<const TensorStridingOp<Strides, ArgType>, Device> +{ + typedef TensorStridingOp<Strides, ArgType> XprType; + typedef typename XprType::Index Index; + static const int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value; + typedef DSizes<Index, NumDims> Dimensions; + typedef typename XprType::Scalar Scalar; + typedef typename XprType::CoeffReturnType CoeffReturnType; + typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType; + static const int PacketSize = internal::unpacket_traits<PacketReturnType>::size; + + enum { + IsAligned = /*TensorEvaluator<ArgType, Device>::IsAligned*/false, + PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess, + Layout = TensorEvaluator<ArgType, Device>::Layout, + CoordAccess = false, // to be implemented + RawAccess = false + }; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) + : m_impl(op.expression(), device) + { + m_dimensions = m_impl.dimensions(); + for (int i = 0; i < NumDims; ++i) { + m_dimensions[i] = ceilf(static_cast<float>(m_dimensions[i]) / op.strides()[i]); + } + + const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions(); + if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { + m_outputStrides[0] = 1; + m_inputStrides[0] = 1; + for (int i = 1; i < NumDims; ++i) { + m_outputStrides[i] = m_outputStrides[i-1] * m_dimensions[i-1]; + m_inputStrides[i] = m_inputStrides[i-1] * input_dims[i-1]; + m_inputStrides[i-1] *= op.strides()[i-1]; + } + m_inputStrides[NumDims-1] *= op.strides()[NumDims-1]; + } else { // RowMajor + m_outputStrides[NumDims-1] = 1; + m_inputStrides[NumDims-1] = 1; + for (int i = NumDims - 2; i >= 0; --i) { + m_outputStrides[i] = m_outputStrides[i+1] * m_dimensions[i+1]; + m_inputStrides[i] = m_inputStrides[i+1] * input_dims[i+1]; + m_inputStrides[i+1] *= op.strides()[i+1]; + } + m_inputStrides[0] *= op.strides()[0]; + } + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* /*data*/) { + m_impl.evalSubExprsIfNeeded(NULL); + return true; + } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() { + m_impl.cleanup(); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const + { + return m_impl.coeff(srcCoeff(index)); + } + + template<int LoadMode> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const + { + EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE) + eigen_assert(index+PacketSize-1 < dimensions().TotalSize()); + + Index inputIndices[] = {0, 0}; + Index indices[] = {index, index + PacketSize - 1}; + if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { + for (int i = NumDims - 1; i > 0; --i) { + const Index idx0 = indices[0] / m_outputStrides[i]; + const Index idx1 = indices[1] / m_outputStrides[i]; + inputIndices[0] += idx0 * m_inputStrides[i]; + inputIndices[1] += idx1 * m_inputStrides[i]; + indices[0] -= idx0 * m_outputStrides[i]; + indices[1] -= idx1 * m_outputStrides[i]; + } + inputIndices[0] += indices[0] * m_inputStrides[0]; + inputIndices[1] += indices[1] * m_inputStrides[0]; + } else { // RowMajor + for (int i = 0; i < NumDims - 1; ++i) { + const Index idx0 = indices[0] / m_outputStrides[i]; + const Index idx1 = indices[1] / m_outputStrides[i]; + inputIndices[0] += idx0 * m_inputStrides[i]; + inputIndices[1] += idx1 * m_inputStrides[i]; + indices[0] -= idx0 * m_outputStrides[i]; + indices[1] -= idx1 * m_outputStrides[i]; + } + inputIndices[0] += indices[0] * m_inputStrides[NumDims-1]; + inputIndices[1] += indices[1] * m_inputStrides[NumDims-1]; + } + if (inputIndices[1] - inputIndices[0] == PacketSize - 1) { + PacketReturnType rslt = m_impl.template packet<Unaligned>(inputIndices[0]); + return rslt; + } + else { + EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize]; + values[0] = m_impl.coeff(inputIndices[0]); + values[PacketSize-1] = m_impl.coeff(inputIndices[1]); + for (int i = 1; i < PacketSize-1; ++i) { + values[i] = coeff(index+i); + } + PacketReturnType rslt = internal::pload<PacketReturnType>(values); + return rslt; + } + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const { + double compute_cost = (NumDims - 1) * (TensorOpCost::AddCost<Index>() + + TensorOpCost::MulCost<Index>() + + TensorOpCost::DivCost<Index>()) + + TensorOpCost::MulCost<Index>(); + if (vectorized) { + compute_cost *= 2; // packet() computes two indices + } + const int innerDim = (static_cast<int>(Layout) == static_cast<int>(ColMajor)) ? 0 : (NumDims - 1); + return m_impl.costPerCoeff(vectorized && m_inputStrides[innerDim] == 1) + + // Computation is not vectorized per se, but it is done once per packet. + TensorOpCost(0, 0, compute_cost, vectorized, PacketSize); + } + + EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; } + + protected: + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index srcCoeff(Index index) const + { + Index inputIndex = 0; + if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { + for (int i = NumDims - 1; i > 0; --i) { + const Index idx = index / m_outputStrides[i]; + inputIndex += idx * m_inputStrides[i]; + index -= idx * m_outputStrides[i]; + } + inputIndex += index * m_inputStrides[0]; + } else { // RowMajor + for (int i = 0; i < NumDims - 1; ++i) { + const Index idx = index / m_outputStrides[i]; + inputIndex += idx * m_inputStrides[i]; + index -= idx * m_outputStrides[i]; + } + inputIndex += index * m_inputStrides[NumDims-1]; + } + return inputIndex; + } + + Dimensions m_dimensions; + array<Index, NumDims> m_outputStrides; + array<Index, NumDims> m_inputStrides; + TensorEvaluator<ArgType, Device> m_impl; +}; + + +// Eval as lvalue +template<typename Strides, typename ArgType, typename Device> +struct TensorEvaluator<TensorStridingOp<Strides, ArgType>, Device> + : public TensorEvaluator<const TensorStridingOp<Strides, ArgType>, Device> +{ + typedef TensorStridingOp<Strides, ArgType> XprType; + typedef TensorEvaluator<const XprType, Device> Base; + // typedef typename XprType::Index Index; + static const int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value; + // typedef DSizes<Index, NumDims> Dimensions; + + enum { + IsAligned = /*TensorEvaluator<ArgType, Device>::IsAligned*/false, + PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess, + Layout = TensorEvaluator<ArgType, Device>::Layout, + CoordAccess = false, // to be implemented + RawAccess = false + }; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) + : Base(op, device) { } + + typedef typename XprType::Index Index; + typedef typename XprType::Scalar Scalar; + typedef typename XprType::CoeffReturnType CoeffReturnType; + typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType; + static const int PacketSize = internal::unpacket_traits<PacketReturnType>::size; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef(Index index) + { + return this->m_impl.coeffRef(this->srcCoeff(index)); + } + + template <int StoreMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + void writePacket(Index index, const PacketReturnType& x) + { + EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE) + eigen_assert(index+PacketSize-1 < this->dimensions().TotalSize()); + + Index inputIndices[] = {0, 0}; + Index indices[] = {index, index + PacketSize - 1}; + if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { + for (int i = NumDims - 1; i > 0; --i) { + const Index idx0 = indices[0] / this->m_outputStrides[i]; + const Index idx1 = indices[1] / this->m_outputStrides[i]; + inputIndices[0] += idx0 * this->m_inputStrides[i]; + inputIndices[1] += idx1 * this->m_inputStrides[i]; + indices[0] -= idx0 * this->m_outputStrides[i]; + indices[1] -= idx1 * this->m_outputStrides[i]; + } + inputIndices[0] += indices[0] * this->m_inputStrides[0]; + inputIndices[1] += indices[1] * this->m_inputStrides[0]; + } else { // RowMajor + for (int i = 0; i < NumDims - 1; ++i) { + const Index idx0 = indices[0] / this->m_outputStrides[i]; + const Index idx1 = indices[1] / this->m_outputStrides[i]; + inputIndices[0] += idx0 * this->m_inputStrides[i]; + inputIndices[1] += idx1 * this->m_inputStrides[i]; + indices[0] -= idx0 * this->m_outputStrides[i]; + indices[1] -= idx1 * this->m_outputStrides[i]; + } + inputIndices[0] += indices[0] * this->m_inputStrides[NumDims-1]; + inputIndices[1] += indices[1] * this->m_inputStrides[NumDims-1]; + } + if (inputIndices[1] - inputIndices[0] == PacketSize - 1) { + this->m_impl.template writePacket<Unaligned>(inputIndices[0], x); + } + else { + EIGEN_ALIGN_MAX Scalar values[PacketSize]; + internal::pstore<Scalar, PacketReturnType>(values, x); + this->m_impl.coeffRef(inputIndices[0]) = values[0]; + this->m_impl.coeffRef(inputIndices[1]) = values[PacketSize-1]; + for (int i = 1; i < PacketSize-1; ++i) { + this->coeffRef(index+i) = values[i]; + } + } + } +}; + + +} // end namespace Eigen + +#endif // EIGEN_CXX11_TENSOR_TENSOR_STRIDING_H diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorSycl.h b/unsupported/Eigen/CXX11/src/Tensor/TensorSycl.h new file mode 100644 index 000000000..bb8800d45 --- /dev/null +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorSycl.h @@ -0,0 +1,82 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Mehdi Goli Codeplay Software Ltd. +// Ralph Potter Codeplay Software Ltd. +// Luke Iwanski Codeplay Software Ltd. +// Contact: eigen@codeplay.com +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +// General include header of SYCL target for Tensor Module +#ifndef UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSORSYCL_H +#define UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSORSYCL_H + +#ifdef EIGEN_USE_SYCL + +// global pointer to set different attribute state for a class +template <class T> +struct MakeGlobalPointer { + typedef typename cl::sycl::global_ptr<T>::pointer_t Type; +}; + +// global pointer to set different attribute state for a class +template <class T> +struct MakeLocalPointer { + typedef typename cl::sycl::local_ptr<T>::pointer_t Type; +}; + + +namespace Eigen { +namespace TensorSycl { +namespace internal { + +/// This struct is used for special expression nodes with no operations (for example assign and selectOP). + struct NoOP; + +template<bool IsConst, typename T> struct GetType{ + typedef const T Type; +}; +template<typename T> struct GetType<false, T>{ + typedef T Type; +}; + +} +} +} + +// tuple construction +#include "TensorSyclTuple.h" + +// counting number of leaf at compile time +#include "TensorSyclLeafCount.h" + +// The index PlaceHolder takes the actual expression and replaces the actual +// data on it with the place holder. It uses the same pre-order expression tree +// traverse as the leaf count in order to give the right access number to each +// node in the expression +#include "TensorSyclPlaceHolderExpr.h" + +// creation of an accessor tuple from a tuple of SYCL buffers +#include "TensorSyclExtractAccessor.h" + +// this is used to change the address space type in tensor map for GPU +#include "TensorSyclConvertToDeviceExpression.h" + +// this is used to extract the functors +#include "TensorSyclExtractFunctors.h" + +// this is used to create tensormap on the device +// this is used to construct the expression on the device +#include "TensorSyclExprConstructor.h" + +/// this is used for extracting tensor reduction +#include "TensorReductionSycl.h" + +// kernel execution using fusion +#include "TensorSyclRun.h" + +#endif // end of EIGEN_USE_SYCL +#endif // UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSORSYCL_H diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorSyclConvertToDeviceExpression.h b/unsupported/Eigen/CXX11/src/Tensor/TensorSyclConvertToDeviceExpression.h new file mode 100644 index 000000000..8729c86ee --- /dev/null +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorSyclConvertToDeviceExpression.h @@ -0,0 +1,121 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Mehdi Goli Codeplay Software Ltd. +// Ralph Potter Codeplay Software Ltd. +// Luke Iwanski Codeplay Software Ltd. +// Contact: <eigen@codeplay.com> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +/***************************************************************** + * TensorSyclConvertToDeviceExpression.h + * + * \brief: + * Conversion from host pointer to device pointer + * inside leaf nodes of the expression. + * +*****************************************************************/ + +#ifndef UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSORSYCL_CONVERT_TO_DEVICE_EXPRESSION_HPP +#define UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSORSYCL_CONVERT_TO_DEVICE_EXPRESSION_HPP + +namespace Eigen { +namespace TensorSycl { +namespace internal { + +/// \struct ConvertToDeviceExpression +/// \brief This struct is used to convert the MakePointer in the host expression +/// to the MakeGlobalPointer for the device expression. For the leafNodes +/// containing the pointer. This is due to the fact that the address space of +/// the pointer T* is different on the host and the device. +template <typename Expr> +struct ConvertToDeviceExpression; + +template<template<class...> class NonOpCategory, bool IsConst, typename... Args> +struct NonOpConversion{ + typedef typename GetType<IsConst, NonOpCategory<typename ConvertToDeviceExpression<Args>::Type...> >::Type Type; +}; + + +template<template<class, template <class> class > class NonOpCategory, bool IsConst, typename Args> +struct DeviceConvertor{ + typedef typename GetType<IsConst, NonOpCategory<typename ConvertToDeviceExpression<Args>::Type, MakeGlobalPointer> >::Type Type; +}; + +/// specialisation of the \ref ConvertToDeviceExpression struct when the node +/// type is TensorMap +#define TENSORMAPCONVERT(CVQual)\ +template <typename Scalar_, int Options_, int Options2_, int NumIndices_, typename IndexType_, template <class> class MakePointer_>\ +struct ConvertToDeviceExpression<CVQual TensorMap<Tensor<Scalar_, NumIndices_, Options_, IndexType_>, Options2_, MakePointer_> > {\ + typedef CVQual TensorMap<Tensor<Scalar_, NumIndices_, Options_, IndexType_>, Options2_, MakeGlobalPointer> Type;\ +}; + +TENSORMAPCONVERT(const) +TENSORMAPCONVERT() +#undef TENSORMAPCONVERT + +/// specialisation of the \ref ConvertToDeviceExpression struct when the node +/// type is TensorCwiseNullaryOp, TensorCwiseUnaryOp, TensorCwiseBinaryOp, TensorCwiseTernaryOp, TensorBroadcastingOp +#define CATEGORYCONVERT(CVQual)\ +template <template<class, class...> class Category, typename OP, typename... subExprs>\ +struct ConvertToDeviceExpression<CVQual Category<OP, subExprs...> > {\ + typedef CVQual Category<OP, typename ConvertToDeviceExpression<subExprs>::Type... > Type;\ +}; +CATEGORYCONVERT(const) +CATEGORYCONVERT() +#undef CATEGORYCONVERT + + +/// specialisation of the \ref ConvertToDeviceExpression struct when the node +/// type is TensorCwiseSelectOp +#define SELECTOPCONVERT(CVQual, Res)\ +template <typename IfExpr, typename ThenExpr, typename ElseExpr>\ +struct ConvertToDeviceExpression<CVQual TensorSelectOp<IfExpr, ThenExpr, ElseExpr> >\ +: NonOpConversion<TensorSelectOp, Res, IfExpr, ThenExpr, ElseExpr> {}; +SELECTOPCONVERT(const, true) +SELECTOPCONVERT(, false) +#undef SELECTOPCONVERT + +/// specialisation of the \ref ConvertToDeviceExpression struct when the node +/// type is const AssingOP +#define ASSIGNCONVERT(CVQual, Res)\ +template <typename LHSExpr, typename RHSExpr>\ +struct ConvertToDeviceExpression<CVQual TensorAssignOp<LHSExpr, RHSExpr> >\ +: NonOpConversion<TensorAssignOp, Res, LHSExpr, RHSExpr>{}; + +ASSIGNCONVERT(const, true) +ASSIGNCONVERT(, false) +#undef ASSIGNCONVERT + +/// specialisation of the \ref ConvertToDeviceExpression struct when the node +/// type is either TensorForcedEvalOp or TensorEvalToOp +#define KERNELBROKERCONVERT(CVQual, Res, ExprNode)\ +template <typename Expr>\ +struct ConvertToDeviceExpression<CVQual ExprNode<Expr> > \ +: DeviceConvertor<ExprNode, Res, Expr>{}; + +KERNELBROKERCONVERT(const, true, TensorForcedEvalOp) +KERNELBROKERCONVERT(, false, TensorForcedEvalOp) +KERNELBROKERCONVERT(const, true, TensorEvalToOp) +KERNELBROKERCONVERT(, false, TensorEvalToOp) +#undef KERNELBROKERCONVERT + +/// specialisation of the \ref ConvertToDeviceExpression struct when the node type is TensorReductionOp +#define KERNELBROKERCONVERTREDUCTION(CVQual)\ +template <typename OP, typename Dim, typename subExpr, template <class> class MakePointer_>\ +struct ConvertToDeviceExpression<CVQual TensorReductionOp<OP, Dim, subExpr, MakePointer_> > {\ + typedef CVQual TensorReductionOp<OP, Dim, typename ConvertToDeviceExpression<subExpr>::Type, MakeGlobalPointer> Type;\ +}; + +KERNELBROKERCONVERTREDUCTION(const) +KERNELBROKERCONVERTREDUCTION() +#undef KERNELBROKERCONVERTREDUCTION + +} // namespace internal +} // namespace TensorSycl +} // namespace Eigen + +#endif // UNSUPPORTED_EIGEN_CXX1 diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorSyclExprConstructor.h b/unsupported/Eigen/CXX11/src/Tensor/TensorSyclExprConstructor.h new file mode 100644 index 000000000..7ed3a3a56 --- /dev/null +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorSyclExprConstructor.h @@ -0,0 +1,239 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Mehdi Goli Codeplay Software Ltd. +// Ralph Potter Codeplay Software Ltd. +// Luke Iwanski Codeplay Software Ltd. +// Contact: <eigen@codeplay.com> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +/***************************************************************** + * TensorSyclExprConstructor.h + * + * \brief: + * This file re-create an expression on the SYCL device in order + * to use the original tensor evaluator. + * +*****************************************************************/ + +#ifndef UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSORSYCL_EXPR_CONSTRUCTOR_HPP +#define UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSORSYCL_EXPR_CONSTRUCTOR_HPP + +namespace Eigen { +namespace TensorSycl { +namespace internal { +/// this class is used by EvalToOp in order to create an lhs expression which is +/// a pointer from an accessor on device-only buffer +template <typename PtrType, size_t N, typename... Params> +struct EvalToLHSConstructor { + PtrType expr; + EvalToLHSConstructor(const utility::tuple::Tuple<Params...> &t): expr((&(*(utility::tuple::get<N>(t).get_pointer())))) {} +}; + +/// \struct ExprConstructor is used to reconstruct the expression on the device and +/// recreate the expression with MakeGlobalPointer containing the device address +/// space for the TensorMap pointers used in eval function. +/// It receives the original expression type, the functor of the node, the tuple +/// of accessors, and the device expression type to re-instantiate the +/// expression tree for the device +template <typename OrigExpr, typename IndexExpr, typename... Params> +struct ExprConstructor; + +/// specialisation of the \ref ExprConstructor struct when the node type is +/// TensorMap +#define TENSORMAP(CVQual)\ +template <typename Scalar_, int Options_, int Options2_, int Options3_, int NumIndices_, typename IndexType_,\ +template <class> class MakePointer_, size_t N, typename... Params>\ +struct ExprConstructor< CVQual TensorMap<Tensor<Scalar_, NumIndices_, Options_, IndexType_>, Options2_, MakeGlobalPointer>,\ +CVQual PlaceHolder<CVQual TensorMap<Tensor<Scalar_, NumIndices_, Options_, IndexType_>, Options3_, MakePointer_>, N>, Params...>{\ + typedef CVQual TensorMap<Tensor<Scalar_, NumIndices_, Options_, IndexType_>, Options2_, MakeGlobalPointer> Type;\ + Type expr;\ + template <typename FuncDetector>\ + ExprConstructor(FuncDetector &fd, const utility::tuple::Tuple<Params...> &t)\ + : expr(Type((&(*(utility::tuple::get<N>(t).get_pointer()))), fd.dimensions())) {}\ +}; + +TENSORMAP(const) +TENSORMAP() +#undef TENSORMAP + +#define UNARYCATEGORY(CVQual)\ +template <template<class, class> class UnaryCategory, typename OP, typename OrigRHSExpr, typename RHSExpr, typename... Params>\ +struct ExprConstructor<CVQual UnaryCategory<OP, OrigRHSExpr>, CVQual UnaryCategory<OP, RHSExpr>, Params...> {\ + typedef ExprConstructor<OrigRHSExpr, RHSExpr, Params...> my_type;\ + my_type rhsExpr;\ + typedef CVQual UnaryCategory<OP, typename my_type::Type> Type;\ + Type expr;\ + template <typename FuncDetector>\ + ExprConstructor(FuncDetector &funcD, const utility::tuple::Tuple<Params...> &t)\ + : rhsExpr(funcD.rhsExpr, t), expr(rhsExpr.expr, funcD.func) {}\ +}; + +UNARYCATEGORY(const) +UNARYCATEGORY() +#undef UNARYCATEGORY + +/// specialisation of the \ref ExprConstructor struct when the node type is +/// TensorBinaryOp +#define BINARYCATEGORY(CVQual)\ +template <template<class, class, class> class BinaryCategory, typename OP, typename OrigLHSExpr, typename OrigRHSExpr, typename LHSExpr,\ +typename RHSExpr, typename... Params>\ +struct ExprConstructor<CVQual BinaryCategory<OP, OrigLHSExpr, OrigRHSExpr>, CVQual BinaryCategory<OP, LHSExpr, RHSExpr>, Params...> {\ + typedef ExprConstructor<OrigLHSExpr, LHSExpr, Params...> my_left_type;\ + typedef ExprConstructor<OrigRHSExpr, RHSExpr, Params...> my_right_type;\ + typedef CVQual BinaryCategory<OP, typename my_left_type::Type, typename my_right_type::Type> Type;\ + my_left_type lhsExpr;\ + my_right_type rhsExpr;\ + Type expr;\ + template <typename FuncDetector>\ + ExprConstructor(FuncDetector &funcD, const utility::tuple::Tuple<Params...> &t)\ + : lhsExpr(funcD.lhsExpr, t),rhsExpr(funcD.rhsExpr, t), expr(lhsExpr.expr, rhsExpr.expr, funcD.func) {}\ +}; + +BINARYCATEGORY(const) +BINARYCATEGORY() +#undef BINARYCATEGORY + +/// specialisation of the \ref ExprConstructor struct when the node type is +/// TensorCwiseTernaryOp +#define TERNARYCATEGORY(CVQual)\ +template <template <class, class, class, class> class TernaryCategory, typename OP, typename OrigArg1Expr, typename OrigArg2Expr,typename OrigArg3Expr,\ +typename Arg1Expr, typename Arg2Expr, typename Arg3Expr, typename... Params>\ +struct ExprConstructor<CVQual TernaryCategory<OP, OrigArg1Expr, OrigArg2Expr, OrigArg3Expr>, CVQual TernaryCategory<OP, Arg1Expr, Arg2Expr, Arg3Expr>, Params...> {\ + typedef ExprConstructor<OrigArg1Expr, Arg1Expr, Params...> my_arg1_type;\ + typedef ExprConstructor<OrigArg2Expr, Arg2Expr, Params...> my_arg2_type;\ + typedef ExprConstructor<OrigArg3Expr, Arg3Expr, Params...> my_arg3_type;\ + typedef CVQual TernaryCategory<OP, typename my_arg1_type::Type, typename my_arg2_type::Type, typename my_arg3_type::Type> Type;\ + my_arg1_type arg1Expr;\ + my_arg2_type arg2Expr;\ + my_arg3_type arg3Expr;\ + Type expr;\ + template <typename FuncDetector>\ + ExprConstructor(FuncDetector &funcD,const utility::tuple::Tuple<Params...> &t)\ + : arg1Expr(funcD.arg1Expr, t), arg2Expr(funcD.arg2Expr, t), arg3Expr(funcD.arg3Expr, t), expr(arg1Expr.expr, arg2Expr.expr, arg3Expr.expr, funcD.func) {}\ +}; + +TERNARYCATEGORY(const) +TERNARYCATEGORY() +#undef TERNARYCATEGORY + +/// specialisation of the \ref ExprConstructor struct when the node type is +/// TensorCwiseSelectOp +#define SELECTOP(CVQual)\ +template <typename OrigIfExpr, typename OrigThenExpr, typename OrigElseExpr, typename IfExpr, typename ThenExpr, typename ElseExpr, typename... Params>\ +struct ExprConstructor< CVQual TensorSelectOp<OrigIfExpr, OrigThenExpr, OrigElseExpr>, CVQual TensorSelectOp<IfExpr, ThenExpr, ElseExpr>, Params...> {\ + typedef ExprConstructor<OrigIfExpr, IfExpr, Params...> my_if_type;\ + typedef ExprConstructor<OrigThenExpr, ThenExpr, Params...> my_then_type;\ + typedef ExprConstructor<OrigElseExpr, ElseExpr, Params...> my_else_type;\ + typedef CVQual TensorSelectOp<typename my_if_type::Type, typename my_then_type::Type, typename my_else_type::Type> Type;\ + my_if_type ifExpr;\ + my_then_type thenExpr;\ + my_else_type elseExpr;\ + Type expr;\ + template <typename FuncDetector>\ + ExprConstructor(FuncDetector &funcD, const utility::tuple::Tuple<Params...> &t)\ + : ifExpr(funcD.ifExpr, t), thenExpr(funcD.thenExpr, t), elseExpr(funcD.elseExpr, t), expr(ifExpr.expr, thenExpr.expr, elseExpr.expr) {}\ +}; + +SELECTOP(const) +SELECTOP() +#undef SELECTOP + +/// specialisation of the \ref ExprConstructor struct when the node type is +/// const TensorAssignOp +#define ASSIGN(CVQual)\ +template <typename OrigLHSExpr, typename OrigRHSExpr, typename LHSExpr, typename RHSExpr, typename... Params>\ +struct ExprConstructor<CVQual TensorAssignOp<OrigLHSExpr, OrigRHSExpr>, CVQual TensorAssignOp<LHSExpr, RHSExpr>, Params...> {\ + typedef ExprConstructor<OrigLHSExpr, LHSExpr, Params...> my_left_type;\ + typedef ExprConstructor<OrigRHSExpr, RHSExpr, Params...> my_right_type;\ + typedef CVQual TensorAssignOp<typename my_left_type::Type, typename my_right_type::Type> Type;\ + my_left_type lhsExpr;\ + my_right_type rhsExpr;\ + Type expr;\ + template <typename FuncDetector>\ + ExprConstructor(FuncDetector &funcD, const utility::tuple::Tuple<Params...> &t)\ + : lhsExpr(funcD.lhsExpr, t), rhsExpr(funcD.rhsExpr, t), expr(lhsExpr.expr, rhsExpr.expr) {}\ + }; + + ASSIGN(const) + ASSIGN() + #undef ASSIGN +/// specialisation of the \ref ExprConstructor struct when the node type is +/// TensorEvalToOp +#define EVALTO(CVQual)\ +template <typename OrigExpr, typename Expr, typename... Params>\ +struct ExprConstructor<CVQual TensorEvalToOp<OrigExpr, MakeGlobalPointer>, CVQual TensorEvalToOp<Expr>, Params...> {\ + typedef ExprConstructor<OrigExpr, Expr, Params...> my_expr_type;\ + typedef typename TensorEvalToOp<OrigExpr, MakeGlobalPointer>::PointerType my_buffer_type;\ + typedef CVQual TensorEvalToOp<typename my_expr_type::Type, MakeGlobalPointer> Type;\ + my_expr_type nestedExpression;\ + EvalToLHSConstructor<my_buffer_type, 0, Params...> buffer;\ + Type expr;\ + template <typename FuncDetector>\ + ExprConstructor(FuncDetector &funcD, const utility::tuple::Tuple<Params...> &t)\ + : nestedExpression(funcD.rhsExpr, t), buffer(t), expr(buffer.expr, nestedExpression.expr) {}\ +}; + +EVALTO(const) +EVALTO() +#undef EVALTO + +/// specialisation of the \ref ExprConstructor struct when the node type is +/// TensorForcedEvalOp +#define FORCEDEVAL(CVQual)\ +template <typename OrigExpr, typename DevExpr, size_t N, typename... Params>\ +struct ExprConstructor<CVQual TensorForcedEvalOp<OrigExpr, MakeGlobalPointer>,\ +CVQual PlaceHolder<CVQual TensorForcedEvalOp<DevExpr>, N>, Params...> {\ + typedef CVQual TensorMap<Tensor<typename TensorForcedEvalOp<DevExpr, MakeGlobalPointer>::Scalar,\ + TensorForcedEvalOp<DevExpr, MakeGlobalPointer>::NumDimensions, 0, typename TensorForcedEvalOp<DevExpr>::Index>, 0, MakeGlobalPointer> Type;\ + Type expr;\ + template <typename FuncDetector>\ + ExprConstructor(FuncDetector &fd, const utility::tuple::Tuple<Params...> &t)\ + : expr(Type((&(*(utility::tuple::get<N>(t).get_pointer()))), fd.dimensions())) {}\ +}; + +FORCEDEVAL(const) +FORCEDEVAL() +#undef FORCEDEVAL + +template <bool Conds, size_t X , size_t Y > struct ValueCondition { + static const size_t Res =X; +}; +template<size_t X, size_t Y> struct ValueCondition<false, X , Y> { + static const size_t Res =Y; +}; + +/// specialisation of the \ref ExprConstructor struct when the node type is TensorReductionOp +#define SYCLREDUCTIONEXPR(CVQual)\ +template <typename OP, typename Dim, typename OrigExpr, typename DevExpr, size_t N, typename... Params>\ +struct ExprConstructor<CVQual TensorReductionOp<OP, Dim, OrigExpr, MakeGlobalPointer>,\ +CVQual PlaceHolder<CVQual TensorReductionOp<OP, Dim, DevExpr>, N>, Params...> {\ + static const size_t NumIndices= ValueCondition< TensorReductionOp<OP, Dim, DevExpr, MakeGlobalPointer>::NumDimensions==0, 1, TensorReductionOp<OP, Dim, DevExpr, MakeGlobalPointer>::NumDimensions >::Res;\ + typedef CVQual TensorMap<Tensor<typename TensorReductionOp<OP, Dim, DevExpr, MakeGlobalPointer>::Scalar,\ + NumIndices, 0, typename TensorReductionOp<OP, Dim, DevExpr>::Index>, 0, MakeGlobalPointer> Type;\ + Type expr;\ + template <typename FuncDetector>\ + ExprConstructor(FuncDetector &fd, const utility::tuple::Tuple<Params...> &t)\ + : expr(Type((&(*(utility::tuple::get<N>(t).get_pointer()))), fd.dimensions())) {}\ +}; + +SYCLREDUCTIONEXPR(const) +SYCLREDUCTIONEXPR() +#undef SYCLREDUCTIONEXPR + +/// template deduction for \ref ExprConstructor struct +template <typename OrigExpr, typename IndexExpr, typename FuncD, typename... Params> +auto createDeviceExpression(FuncD &funcD, const utility::tuple::Tuple<Params...> &t) + -> decltype(ExprConstructor<OrigExpr, IndexExpr, Params...>(funcD, t)) { + return ExprConstructor<OrigExpr, IndexExpr, Params...>(funcD, t); +} + +} /// namespace TensorSycl +} /// namespace internal +} /// namespace Eigen + + +#endif // UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSORSYCL_EXPR_CONSTRUCTOR_HPP diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorSyclExtractAccessor.h b/unsupported/Eigen/CXX11/src/Tensor/TensorSyclExtractAccessor.h new file mode 100644 index 000000000..b1da6858e --- /dev/null +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorSyclExtractAccessor.h @@ -0,0 +1,204 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Mehdi Goli Codeplay Software Ltd. +// Ralph Potter Codeplay Software Ltd. +// Luke Iwanski Codeplay Software Ltd. +// Contact: <eigen@codeplay.com> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +/***************************************************************** + * TensorSyclExtractAccessor.h + * + * \brief: + * ExtractAccessor takes Expression placeHolder expression and the tuple of sycl + * buffers as an input. Using pre-order tree traversal, ExtractAccessor + * recursively calls itself for its children in the expression tree. The + * leaf node in the PlaceHolder expression is nothing but a container preserving + * the order of the actual data in the tuple of sycl buffer. By invoking the + * extract accessor for the PlaceHolder<N>, an accessor is created for the Nth + * buffer in the tuple of buffers. This accessor is then added as an Nth + * element in the tuple of accessors. In this case we preserve the order of data + * in the expression tree. + * + * This is the specialisation of extract accessor method for different operation + * type in the PlaceHolder expression. + * +*****************************************************************/ + +#ifndef UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSORSYCL_EXTRACT_ACCESSOR_HPP +#define UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSORSYCL_EXTRACT_ACCESSOR_HPP + +namespace Eigen { +namespace TensorSycl { +namespace internal { +/// \struct ExtractAccessor: Extract Accessor Class is used to extract the +/// accessor from a buffer. +/// Depending on the type of the leaf node we can get a read accessor or a +/// read_write accessor +template <typename Evaluator> +struct ExtractAccessor; + +struct AccessorConstructor{ + template<typename Arg> static inline auto getTuple(cl::sycl::handler& cgh, Arg eval) + -> decltype(ExtractAccessor<Arg>::getTuple(cgh, eval)) { + return ExtractAccessor<Arg>::getTuple(cgh, eval); + } + + template<typename Arg1, typename Arg2> static inline auto getTuple(cl::sycl::handler& cgh, Arg1 eval1, Arg2 eval2) + -> decltype(utility::tuple::append(ExtractAccessor<Arg1>::getTuple(cgh, eval1), ExtractAccessor<Arg2>::getTuple(cgh, eval2))) { + return utility::tuple::append(ExtractAccessor<Arg1>::getTuple(cgh, eval1), ExtractAccessor<Arg2>::getTuple(cgh, eval2)); + } + template<typename Arg1, typename Arg2, typename Arg3> static inline auto getTuple(cl::sycl::handler& cgh, Arg1 eval1 , Arg2 eval2 , Arg3 eval3) + -> decltype(utility::tuple::append(ExtractAccessor<Arg1>::getTuple(cgh, eval1),utility::tuple::append(ExtractAccessor<Arg2>::getTuple(cgh, eval2), ExtractAccessor<Arg3>::getTuple(cgh, eval3)))) { + return utility::tuple::append(ExtractAccessor<Arg1>::getTuple(cgh, eval1),utility::tuple::append(ExtractAccessor<Arg2>::getTuple(cgh, eval2), ExtractAccessor<Arg3>::getTuple(cgh, eval3))); + } + template< cl::sycl::access::mode AcM, typename Arg> static inline auto getAccessor(cl::sycl::handler& cgh, Arg eval) + -> decltype(utility::tuple::make_tuple( eval.device().template get_sycl_accessor<AcM, + typename Eigen::internal::remove_all<typename Arg::CoeffReturnType>::type>(eval.dimensions().TotalSize(), cgh,eval.data()))){ + return utility::tuple::make_tuple(eval.device().template get_sycl_accessor<AcM, typename Eigen::internal::remove_all<typename Arg::CoeffReturnType>::type>(eval.dimensions().TotalSize(), cgh,eval.data())); + } +}; + +/// specialisation of the \ref ExtractAccessor struct when the node type is +/// const TensorCwiseNullaryOp, const TensorCwiseUnaryOp and const TensorBroadcastingOp +template <template<class, class> class UnaryCategory, typename OP, typename RHSExpr, typename Dev> +struct ExtractAccessor<TensorEvaluator<const UnaryCategory<OP, RHSExpr>, Dev> > { + static inline auto getTuple(cl::sycl::handler& cgh, const TensorEvaluator<const UnaryCategory<OP, RHSExpr>, Dev> eval) + -> decltype(AccessorConstructor::getTuple(cgh, eval.impl())){ + return AccessorConstructor::getTuple(cgh, eval.impl()); + } +}; + +/// specialisation of the \ref ExtractAccessor struct when the node type is TensorCwiseNullaryOp, TensorCwiseUnaryOp and TensorBroadcastingOp +template <template<class, class> class UnaryCategory, typename OP, typename RHSExpr, typename Dev> +struct ExtractAccessor<TensorEvaluator<UnaryCategory<OP, RHSExpr>, Dev> > +: ExtractAccessor<TensorEvaluator<const UnaryCategory<OP, RHSExpr>, Dev> > {}; + +/// specialisation of the \ref ExtractAccessor struct when the node type is const TensorCwiseBinaryOp +template <template<class, class, class> class BinaryCategory, typename OP, typename LHSExpr, typename RHSExpr, typename Dev> +struct ExtractAccessor<TensorEvaluator<const BinaryCategory<OP, LHSExpr, RHSExpr>, Dev> > { + static inline auto getTuple(cl::sycl::handler& cgh, const TensorEvaluator<const BinaryCategory<OP, LHSExpr, RHSExpr>, Dev> eval) + -> decltype(AccessorConstructor::getTuple(cgh, eval.left_impl(), eval.right_impl())){ + return AccessorConstructor::getTuple(cgh, eval.left_impl(), eval.right_impl()); + } +}; +/// specialisation of the \ref ExtractAccessor struct when the node type is TensorCwiseBinaryOp +template <template<class, class, class> class BinaryCategory, typename OP, typename LHSExpr, typename RHSExpr, typename Dev> +struct ExtractAccessor<TensorEvaluator<BinaryCategory<OP, LHSExpr, RHSExpr>, Dev> > +: ExtractAccessor<TensorEvaluator<const BinaryCategory<OP, LHSExpr, RHSExpr>, Dev> >{}; + +/// specialisation of the \ref ExtractAccessor struct when the node type is +/// const TensorCwiseTernaryOp +template <template<class, class, class, class> class TernaryCategory, typename OP, typename Arg1Expr, typename Arg2Expr, typename Arg3Expr, typename Dev> +struct ExtractAccessor<TensorEvaluator<const TernaryCategory<OP, Arg1Expr, Arg2Expr, Arg3Expr>, Dev> > { + static inline auto getTuple(cl::sycl::handler& cgh, const TensorEvaluator<const TernaryCategory<OP, Arg1Expr, Arg2Expr, Arg3Expr>, Dev> eval) + -> decltype(AccessorConstructor::getTuple(cgh, eval.arg1Impl(), eval.arg2Impl(), eval.arg3Impl())){ + return AccessorConstructor::getTuple(cgh, eval.arg1Impl(), eval.arg2Impl(), eval.arg3Impl()); + } +}; + +/// specialisation of the \ref ExtractAccessor struct when the node type is TensorCwiseTernaryOp +template <template<class, class, class, class> class TernaryCategory, typename OP, typename Arg1Expr, typename Arg2Expr, typename Arg3Expr, typename Dev> +struct ExtractAccessor<TensorEvaluator<TernaryCategory<OP, Arg1Expr, Arg2Expr, Arg3Expr>, Dev> > +: ExtractAccessor<TensorEvaluator<const TernaryCategory<OP, Arg1Expr, Arg2Expr, Arg3Expr>, Dev> >{}; + +/// specialisation of the \ref ExtractAccessor struct when the node type is +/// const TensorCwiseSelectOp. This is a special case where there is no OP +template <typename IfExpr, typename ThenExpr, typename ElseExpr, typename Dev> +struct ExtractAccessor<TensorEvaluator<const TensorSelectOp<IfExpr, ThenExpr, ElseExpr>, Dev> > { + static inline auto getTuple(cl::sycl::handler& cgh, const TensorEvaluator<const TensorSelectOp<IfExpr, ThenExpr, ElseExpr>, Dev> eval) + -> decltype(AccessorConstructor::getTuple(cgh, eval.cond_impl(), eval.then_impl(), eval.else_impl())){ + return AccessorConstructor::getTuple(cgh, eval.cond_impl(), eval.then_impl(), eval.else_impl()); + } +}; + +/// specialisation of the \ref ExtractAccessor struct when the node type is +/// TensorCwiseSelectOp. This is a special case where there is no OP +template <typename IfExpr, typename ThenExpr, typename ElseExpr, typename Dev> +struct ExtractAccessor<TensorEvaluator<TensorSelectOp<IfExpr, ThenExpr, ElseExpr>, Dev> > +: ExtractAccessor<TensorEvaluator<const TensorSelectOp<IfExpr, ThenExpr, ElseExpr>, Dev> >{}; + +/// specialisation of the \ref ExtractAccessor struct when the node type is const TensorAssignOp +template <typename LHSExpr, typename RHSExpr, typename Dev> +struct ExtractAccessor<TensorEvaluator<const TensorAssignOp<LHSExpr, RHSExpr>, Dev> > { + static inline auto getTuple(cl::sycl::handler& cgh, const TensorEvaluator<const TensorAssignOp<LHSExpr, RHSExpr>, Dev> eval) + -> decltype(AccessorConstructor::getTuple(cgh, eval.left_impl(), eval.right_impl())){ + return AccessorConstructor::getTuple(cgh, eval.left_impl(), eval.right_impl()); + } +}; + +/// specialisation of the \ref ExtractAccessor struct when the node type is TensorAssignOp +template <typename LHSExpr, typename RHSExpr, typename Dev> +struct ExtractAccessor<TensorEvaluator<TensorAssignOp<LHSExpr, RHSExpr>, Dev> > +: ExtractAccessor<TensorEvaluator<const TensorAssignOp<LHSExpr, RHSExpr>, Dev> >{}; + +/// specialisation of the \ref ExtractAccessor struct when the node type is const TensorMap +#define TENSORMAPEXPR(CVQual, ACCType)\ +template <typename PlainObjectType, int Options_, typename Dev>\ +struct ExtractAccessor<TensorEvaluator<CVQual TensorMap<PlainObjectType, Options_>, Dev> > {\ + static inline auto getTuple(cl::sycl::handler& cgh,const TensorEvaluator<CVQual TensorMap<PlainObjectType, Options_>, Dev> eval)\ + -> decltype(AccessorConstructor::template getAccessor<ACCType>(cgh, eval)){\ + return AccessorConstructor::template getAccessor<ACCType>(cgh, eval);\ + }\ +}; +TENSORMAPEXPR(const, cl::sycl::access::mode::read) +TENSORMAPEXPR(, cl::sycl::access::mode::read_write) +#undef TENSORMAPEXPR + +/// specialisation of the \ref ExtractAccessor struct when the node type is const TensorForcedEvalOp +template <typename Expr, typename Dev> +struct ExtractAccessor<TensorEvaluator<const TensorForcedEvalOp<Expr>, Dev> > { + static inline auto getTuple(cl::sycl::handler& cgh, const TensorEvaluator<const TensorForcedEvalOp<Expr>, Dev> eval) + -> decltype(AccessorConstructor::template getAccessor<cl::sycl::access::mode::read>(cgh, eval)){ + return AccessorConstructor::template getAccessor<cl::sycl::access::mode::read>(cgh, eval); + } +}; + +/// specialisation of the \ref ExtractAccessor struct when the node type is TensorForcedEvalOp +template <typename Expr, typename Dev> +struct ExtractAccessor<TensorEvaluator<TensorForcedEvalOp<Expr>, Dev> > +: ExtractAccessor<TensorEvaluator<const TensorForcedEvalOp<Expr>, Dev> >{}; + +/// specialisation of the \ref ExtractAccessor struct when the node type is const TensorEvalToOp +template <typename Expr, typename Dev> +struct ExtractAccessor<TensorEvaluator<const TensorEvalToOp<Expr>, Dev> > { + static inline auto getTuple(cl::sycl::handler& cgh,const TensorEvaluator<const TensorEvalToOp<Expr>, Dev> eval) + -> decltype(utility::tuple::append(AccessorConstructor::template getAccessor<cl::sycl::access::mode::write>(cgh, eval), AccessorConstructor::getTuple(cgh, eval.impl()))){ + return utility::tuple::append(AccessorConstructor::template getAccessor<cl::sycl::access::mode::write>(cgh, eval), AccessorConstructor::getTuple(cgh, eval.impl())); + } +}; + +/// specialisation of the \ref ExtractAccessor struct when the node type is TensorEvalToOp +template <typename Expr, typename Dev> +struct ExtractAccessor<TensorEvaluator<TensorEvalToOp<Expr>, Dev> > +: ExtractAccessor<TensorEvaluator<const TensorEvalToOp<Expr>, Dev> >{}; + +/// specialisation of the \ref ExtractAccessor struct when the node type is const TensorReductionOp +template <typename OP, typename Dim, typename Expr, typename Dev> +struct ExtractAccessor<TensorEvaluator<const TensorReductionOp<OP, Dim, Expr>, Dev> > { + static inline auto getTuple(cl::sycl::handler& cgh, const TensorEvaluator<const TensorReductionOp<OP, Dim, Expr>, Dev> eval) + -> decltype(AccessorConstructor::template getAccessor<cl::sycl::access::mode::read>(cgh, eval)){ + return AccessorConstructor::template getAccessor<cl::sycl::access::mode::read>(cgh, eval); + } +}; + +/// specialisation of the \ref ExtractAccessor struct when the node type is TensorReductionOp +template <typename OP, typename Dim, typename Expr, typename Dev> +struct ExtractAccessor<TensorEvaluator<TensorReductionOp<OP, Dim, Expr>, Dev> > +: ExtractAccessor<TensorEvaluator<const TensorReductionOp<OP, Dim, Expr>, Dev> >{}; + +/// template deduction for \ref ExtractAccessor +template <typename Evaluator> +auto createTupleOfAccessors(cl::sycl::handler& cgh, const Evaluator& expr) +-> decltype(ExtractAccessor<Evaluator>::getTuple(cgh, expr)) { + return ExtractAccessor<Evaluator>::getTuple(cgh, expr); +} + +} /// namespace TensorSycl +} /// namespace internal +} /// namespace Eigen +#endif // UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSORSYCL_EXTRACT_ACCESSOR_HPP diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorSyclExtractFunctors.h b/unsupported/Eigen/CXX11/src/Tensor/TensorSyclExtractFunctors.h new file mode 100644 index 000000000..427125343 --- /dev/null +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorSyclExtractFunctors.h @@ -0,0 +1,177 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Mehdi Goli Codeplay Software Ltd. +// Ralph Potter Codeplay Software Ltd. +// Luke Iwanski Codeplay Software Ltd. +// Contact: <eigen@codeplay.com> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +/***************************************************************** + * TensorSyclextractFunctors.h + * + * \brief: + * Used to extract all the functors allocated to each node of the expression +*tree. + * +*****************************************************************/ + +#ifndef UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSORSYCL_EXTRACT_FUNCTORS_HPP +#define UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSORSYCL_EXTRACT_FUNCTORS_HPP + +namespace Eigen { +namespace TensorSycl { +namespace internal { +/// \struct FunctorExtractor: This struct is used to extract the functors +/// constructed on +/// the host-side, to pack them and reuse them in reconstruction of the +/// expression on the device. +/// We have to do that as in Eigen the functors are not stateless so we cannot +/// re-instantiate them on the device. +/// We have to pass instantiated functors to the device. +// This struct is used for leafNode (TensorMap) and nodes behaving like leafNode (TensorForcedEval). +template <typename Evaluator> struct FunctorExtractor{ + typedef typename Evaluator::Dimensions Dimensions; + const Dimensions m_dimensions; + const Dimensions& dimensions() const { return m_dimensions; } + FunctorExtractor(const Evaluator& expr) + : m_dimensions(expr.dimensions()) {} + +}; + +/// specialisation of the \ref FunctorExtractor struct when the node type is +/// const TensorCwiseNullaryOp, const TensorCwiseUnaryOp, and const TensorBroadcastingOp +template <template <class, class> class UnaryCategory, typename OP, typename RHSExpr, typename Dev> +struct FunctorExtractor<TensorEvaluator<const UnaryCategory<OP, RHSExpr>, Dev> > { + FunctorExtractor<TensorEvaluator<RHSExpr, Dev> > rhsExpr; + OP func; + FunctorExtractor(const TensorEvaluator<const UnaryCategory<OP, RHSExpr>, Dev>& expr) + : rhsExpr(expr.impl()), func(expr.functor()) {} +}; +/// specialisation of the \ref FunctorExtractor struct when the node type is +/// TensorCwiseNullaryOp, TensorCwiseUnaryOp, and TensorBroadcastingOp +template <template <class, class> class UnaryCategory, typename OP, typename RHSExpr, typename Dev> +struct FunctorExtractor<TensorEvaluator<UnaryCategory<OP, RHSExpr>, Dev> > +: FunctorExtractor<TensorEvaluator<const UnaryCategory<OP, RHSExpr>, Dev> >{}; + +/// specialisation of the \ref FunctorExtractor struct when the node type is +/// const TensorCwiseBinaryOp +template <template<class, class, class> class BinaryCategory, typename OP, typename LHSExpr, typename RHSExpr, typename Dev> +struct FunctorExtractor<TensorEvaluator<const BinaryCategory<OP, LHSExpr, RHSExpr>, Dev> > { + FunctorExtractor<TensorEvaluator<LHSExpr, Dev> > lhsExpr; + FunctorExtractor<TensorEvaluator<RHSExpr, Dev> > rhsExpr; + OP func; + FunctorExtractor(const TensorEvaluator<const BinaryCategory<OP, LHSExpr, RHSExpr>, Dev>& expr) + : lhsExpr(expr.left_impl()),rhsExpr(expr.right_impl()),func(expr.functor()) {} +}; + +/// specialisation of the \ref FunctorExtractor struct when the node type is +/// const TensorCwiseBinaryOp +template <template <class, class, class> class BinaryCategory, typename OP, typename LHSExpr, typename RHSExpr, typename Dev> +struct FunctorExtractor<TensorEvaluator<BinaryCategory<OP, LHSExpr, RHSExpr>, Dev> > +: FunctorExtractor<TensorEvaluator<const BinaryCategory<OP, LHSExpr, RHSExpr>, Dev> >{}; + +/// specialisation of the \ref FunctorExtractor struct when the node type is +/// const TensorCwiseTernaryOp +template <template <class, class, class, class> class TernaryCategory, typename OP, typename Arg1Expr, typename Arg2Expr, typename Arg3Expr,typename Dev> +struct FunctorExtractor<TensorEvaluator<const TernaryCategory<OP, Arg1Expr, Arg2Expr, Arg3Expr>, Dev> > { + FunctorExtractor<TensorEvaluator<Arg1Expr, Dev> > arg1Expr; + FunctorExtractor<TensorEvaluator<Arg2Expr, Dev> > arg2Expr; + FunctorExtractor<TensorEvaluator<Arg3Expr, Dev> > arg3Expr; + OP func; + FunctorExtractor(const TensorEvaluator<const TernaryCategory<OP, Arg1Expr, Arg2Expr, Arg3Expr>, Dev>& expr) + : arg1Expr(expr.arg1Impl()), arg2Expr(expr.arg2Impl()), arg3Expr(expr.arg3Impl()), func(expr.functor()) {} +}; + +/// specialisation of the \ref FunctorExtractor struct when the node type is +/// TensorCwiseTernaryOp +template <template <class, class, class, class> class TernaryCategory, typename OP, typename Arg1Expr, typename Arg2Expr, typename Arg3Expr, typename Dev> +struct FunctorExtractor<TensorEvaluator< TernaryCategory<OP, Arg1Expr, Arg2Expr, Arg3Expr>, Dev> > +:FunctorExtractor<TensorEvaluator<const TernaryCategory<OP, Arg1Expr, Arg2Expr, Arg3Expr>, Dev> >{}; + +/// specialisation of the \ref FunctorExtractor struct when the node type is +/// const TensorCwiseSelectOp. This is an specialisation without OP so it has to be separated. +template <typename IfExpr, typename ThenExpr, typename ElseExpr, typename Dev> +struct FunctorExtractor< TensorEvaluator<const TensorSelectOp<IfExpr, ThenExpr, ElseExpr>, Dev> > { + FunctorExtractor<TensorEvaluator<IfExpr, Dev> > ifExpr; + FunctorExtractor<TensorEvaluator<ThenExpr, Dev> > thenExpr; + FunctorExtractor<TensorEvaluator<ElseExpr, Dev> > elseExpr; + FunctorExtractor(const TensorEvaluator<const TensorSelectOp<IfExpr, ThenExpr, ElseExpr>, Dev>& expr) + : ifExpr(expr.cond_impl()), thenExpr(expr.then_impl()), elseExpr(expr.else_impl()) {} +}; + +/// specialisation of the \ref FunctorExtractor struct when the node type is +/// TensorCwiseSelectOp. This is an specialisation without OP so it has to be separated +template <typename IfExpr, typename ThenExpr, typename ElseExpr, typename Dev> +struct FunctorExtractor<TensorEvaluator<TensorSelectOp<IfExpr, ThenExpr, ElseExpr>, Dev> > +:FunctorExtractor< TensorEvaluator<const TensorSelectOp<IfExpr, ThenExpr, ElseExpr>, Dev> > {}; + +/// specialisation of the \ref FunctorExtractor struct when the node type is +/// const TensorAssignOp. This is an specialisation without OP so it has to be separated. +template <typename LHSExpr, typename RHSExpr, typename Dev> +struct FunctorExtractor<TensorEvaluator<const TensorAssignOp<LHSExpr, RHSExpr>, Dev> > { + FunctorExtractor<TensorEvaluator<LHSExpr, Dev> > lhsExpr; + FunctorExtractor<TensorEvaluator<RHSExpr, Dev> > rhsExpr; + FunctorExtractor(const TensorEvaluator<const TensorAssignOp<LHSExpr, RHSExpr>, Dev>& expr) + : lhsExpr(expr.left_impl()), rhsExpr(expr.right_impl()) {} +}; + +/// specialisation of the \ref FunctorExtractor struct when the node type is +/// TensorAssignOp. This is an specialisation without OP so it has to be separated. +template <typename LHSExpr, typename RHSExpr, typename Dev> +struct FunctorExtractor<TensorEvaluator<TensorAssignOp<LHSExpr, RHSExpr>, Dev> > +:FunctorExtractor<TensorEvaluator<const TensorAssignOp<LHSExpr, RHSExpr>, Dev> >{}; + + +/// specialisation of the \ref FunctorExtractor struct when the node type is +/// const TensorEvalToOp, This is an specialisation without OP so it has to be separated. +template <typename RHSExpr, typename Dev> +struct FunctorExtractor<TensorEvaluator<const TensorEvalToOp<RHSExpr>, Dev> > { + FunctorExtractor<TensorEvaluator<RHSExpr, Dev> > rhsExpr; + FunctorExtractor(const TensorEvaluator<const TensorEvalToOp<RHSExpr>, Dev>& expr) + : rhsExpr(expr.impl()) {} +}; + +/// specialisation of the \ref FunctorExtractor struct when the node type is +/// TensorEvalToOp. This is a specialisation without OP so it has to be separated. +template <typename RHSExpr, typename Dev> +struct FunctorExtractor<TensorEvaluator<TensorEvalToOp<RHSExpr>, Dev> > +: FunctorExtractor<TensorEvaluator<const TensorEvalToOp<RHSExpr>, Dev> > {}; + +template<typename Dim, size_t NumOutputDim> struct DimConstr { +template<typename InDim> + static inline Dim getDim(InDim dims ) {return dims;} +}; + +template<typename Dim> struct DimConstr<Dim, 0> { + template<typename InDim> + static inline Dim getDim(InDim dims ) {return Dim(dims.TotalSize());} +}; + +template<typename Op, typename Dims, typename ArgType, template <class> class MakePointer_, typename Device> +struct FunctorExtractor<TensorEvaluator<const TensorReductionOp<Op, Dims, ArgType, MakePointer_>, Device>>{ + typedef TensorEvaluator<const TensorReductionOp<Op, Dims, ArgType, MakePointer_>, Device> Evaluator; + typedef typename Eigen::internal::conditional<Evaluator::NumOutputDims==0, DSizes<typename Evaluator::Index, 1>, typename Evaluator::Dimensions >::type Dimensions; + const Dimensions m_dimensions; + const Dimensions& dimensions() const { return m_dimensions; } + FunctorExtractor(const TensorEvaluator<const TensorReductionOp<Op, Dims, ArgType, MakePointer_>, Device>& expr) + : m_dimensions(DimConstr<Dimensions, Evaluator::NumOutputDims>::getDim(expr.dimensions())) {} +}; + + +template<typename Op, typename Dims, typename ArgType, template <class> class MakePointer_, typename Device> +struct FunctorExtractor<TensorEvaluator<TensorReductionOp<Op, Dims, ArgType, MakePointer_>, Device>> +: FunctorExtractor<TensorEvaluator<const TensorReductionOp<Op, Dims, ArgType, MakePointer_>, Device>>{}; +/// template deduction function for FunctorExtractor +template <typename Evaluator> +auto inline extractFunctors(const Evaluator& evaluator)-> FunctorExtractor<Evaluator> { + return FunctorExtractor<Evaluator>(evaluator); +} +} // namespace internal +} // namespace TensorSycl +} // namespace Eigen + +#endif // UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSORSYCL_EXTRACT_FUNCTORS_HPP diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorSyclLeafCount.h b/unsupported/Eigen/CXX11/src/Tensor/TensorSyclLeafCount.h new file mode 100644 index 000000000..25d1fac9b --- /dev/null +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorSyclLeafCount.h @@ -0,0 +1,114 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Mehdi Goli Codeplay Software Ltd. +// Ralph Potter Codeplay Software Ltd. +// Luke Iwanski Codeplay Software Ltd. +// Contact: <eigen@codeplay.com> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +/***************************************************************** + * TensorSyclLeafCount.h + * + * \brief: + * The leaf count used the pre-order expression tree traverse in order to name + * count the number of leaf nodes in the expression + * +*****************************************************************/ + +#ifndef UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSORSYCL_LEAF_COUNT_HPP +#define UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSORSYCL_LEAF_COUNT_HPP + +namespace Eigen { +namespace TensorSycl { +namespace internal { +/// \brief LeafCount used to counting terminal nodes. The total number of +/// leaf nodes is used by MakePlaceHolderExprHelper to find the order +/// of the leaf node in a expression tree at compile time. +template <typename Expr> +struct LeafCount; + +template<typename... Args> struct CategoryCount; + +template<> struct CategoryCount<> +{ + static const size_t Count =0; +}; + +template<typename Arg, typename... Args> +struct CategoryCount<Arg,Args...>{ + static const size_t Count = LeafCount<Arg>::Count + CategoryCount<Args...>::Count; +}; + +/// specialisation of the \ref LeafCount struct when the node type is const TensorMap +template <typename PlainObjectType, int Options_, template <class> class MakePointer_> +struct LeafCount<const TensorMap<PlainObjectType, Options_, MakePointer_> > { + static const size_t Count =1; +}; + +/// specialisation of the \ref LeafCount struct when the node type is TensorMap +template <typename PlainObjectType, int Options_, template <class> class MakePointer_> +struct LeafCount<TensorMap<PlainObjectType, Options_, MakePointer_> > :LeafCount<const TensorMap<PlainObjectType, Options_, MakePointer_> >{}; + +// const TensorCwiseUnaryOp, const TensorCwiseNullaryOp, const TensorCwiseBinaryOp, const TensorCwiseTernaryOp, and Const TensorBroadcastingOp +template <template <class, class...> class CategoryExpr, typename OP, typename... RHSExpr> +struct LeafCount<const CategoryExpr<OP, RHSExpr...> >: CategoryCount<RHSExpr...> {}; +// TensorCwiseUnaryOp, TensorCwiseNullaryOp, TensorCwiseBinaryOp, TensorCwiseTernaryOp, and TensorBroadcastingOp +template <template <class, class...> class CategoryExpr, typename OP, typename... RHSExpr> +struct LeafCount<CategoryExpr<OP, RHSExpr...> > :LeafCount<const CategoryExpr<OP, RHSExpr...> >{}; + +/// specialisation of the \ref LeafCount struct when the node type is const TensorSelectOp is an exception +template <typename IfExpr, typename ThenExpr, typename ElseExpr> +struct LeafCount<const TensorSelectOp<IfExpr, ThenExpr, ElseExpr> > : CategoryCount<IfExpr, ThenExpr, ElseExpr> {}; +/// specialisation of the \ref LeafCount struct when the node type is TensorSelectOp +template <typename IfExpr, typename ThenExpr, typename ElseExpr> +struct LeafCount<TensorSelectOp<IfExpr, ThenExpr, ElseExpr> >: LeafCount<const TensorSelectOp<IfExpr, ThenExpr, ElseExpr> > {}; + + +/// specialisation of the \ref LeafCount struct when the node type is const TensorAssignOp +template <typename LHSExpr, typename RHSExpr> +struct LeafCount<const TensorAssignOp<LHSExpr, RHSExpr> >: CategoryCount<LHSExpr,RHSExpr> {}; + +/// specialisation of the \ref LeafCount struct when the node type is +/// TensorAssignOp is an exception. It is not the same as Unary +template <typename LHSExpr, typename RHSExpr> +struct LeafCount<TensorAssignOp<LHSExpr, RHSExpr> > :LeafCount<const TensorAssignOp<LHSExpr, RHSExpr> >{}; + +/// specialisation of the \ref LeafCount struct when the node type is const TensorForcedEvalOp +template <typename Expr> +struct LeafCount<const TensorForcedEvalOp<Expr> > { + static const size_t Count =1; +}; + +/// specialisation of the \ref LeafCount struct when the node type is TensorForcedEvalOp +template <typename Expr> +struct LeafCount<TensorForcedEvalOp<Expr> >: LeafCount<const TensorForcedEvalOp<Expr> > {}; + +/// specialisation of the \ref LeafCount struct when the node type is const TensorEvalToOp +template <typename Expr> +struct LeafCount<const TensorEvalToOp<Expr> > { + static const size_t Count = 1 + CategoryCount<Expr>::Count; +}; + +/// specialisation of the \ref LeafCount struct when the node type is const TensorReductionOp +template <typename OP, typename Dim, typename Expr> +struct LeafCount<const TensorReductionOp<OP, Dim, Expr> > { + static const size_t Count =1; +}; + +/// specialisation of the \ref LeafCount struct when the node type is TensorReductionOp +template <typename OP, typename Dim, typename Expr> +struct LeafCount<TensorReductionOp<OP, Dim, Expr> >: LeafCount<const TensorReductionOp<OP, Dim, Expr> >{}; + +/// specialisation of the \ref LeafCount struct when the node type is TensorEvalToOp +template <typename Expr> +struct LeafCount<TensorEvalToOp<Expr> >: LeafCount<const TensorEvalToOp<Expr> >{}; + +} /// namespace TensorSycl +} /// namespace internal +} /// namespace Eigen + +#endif // UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSORSYCL_LEAF_COUNT_HPP diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorSyclPlaceHolderExpr.h b/unsupported/Eigen/CXX11/src/Tensor/TensorSyclPlaceHolderExpr.h new file mode 100644 index 000000000..d4c250c6d --- /dev/null +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorSyclPlaceHolderExpr.h @@ -0,0 +1,181 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Mehdi Goli Codeplay Software Ltd. +// Ralph Potter Codeplay Software Ltd. +// Luke Iwanski Codeplay Software Ltd. +// Contact: <eigen@codeplay.com> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +/***************************************************************** + * TensorSyclPlaceHolderExpr.h + * + * \brief: + * This is the specialisation of the placeholder expression based on the + * operation type + * +*****************************************************************/ + +#ifndef UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSORSYCL_PLACEHOLDER_EXPR_HPP +#define UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSORSYCL_PLACEHOLDER_EXPR_HPP + +namespace Eigen { +namespace TensorSycl { +namespace internal { + +/// \struct PlaceHolder +/// \brief PlaceHolder is used to replace the \ref TensorMap in the expression +/// tree. +/// PlaceHolder contains the order of the leaf node in the expression tree. +template <typename Scalar, size_t N> +struct PlaceHolder { + static constexpr size_t I = N; + typedef Scalar Type; +}; + +/// \sttruct PlaceHolderExpression +/// \brief it is used to create the PlaceHolder expression. The PlaceHolder +/// expression is a copy of expression type in which the TensorMap of the has +/// been replaced with PlaceHolder. +template <typename Expr, size_t N> +struct PlaceHolderExpression; + +template<size_t N, typename... Args> +struct CalculateIndex; + +template<size_t N, typename Arg> +struct CalculateIndex<N, Arg>{ + typedef typename PlaceHolderExpression<Arg, N>::Type ArgType; + typedef utility::tuple::Tuple<ArgType> ArgsTuple; +}; + +template<size_t N, typename Arg1, typename Arg2> +struct CalculateIndex<N, Arg1, Arg2>{ + static const size_t Arg2LeafCount = LeafCount<Arg2>::Count; + typedef typename PlaceHolderExpression<Arg1, N - Arg2LeafCount>::Type Arg1Type; + typedef typename PlaceHolderExpression<Arg2, N>::Type Arg2Type; + typedef utility::tuple::Tuple<Arg1Type, Arg2Type> ArgsTuple; +}; + +template<size_t N, typename Arg1, typename Arg2, typename Arg3> +struct CalculateIndex<N, Arg1, Arg2, Arg3> { + static const size_t Arg3LeafCount = LeafCount<Arg3>::Count; + static const size_t Arg2LeafCount = LeafCount<Arg2>::Count; + typedef typename PlaceHolderExpression<Arg1, N - Arg3LeafCount - Arg2LeafCount>::Type Arg1Type; + typedef typename PlaceHolderExpression<Arg2, N - Arg3LeafCount>::Type Arg2Type; + typedef typename PlaceHolderExpression<Arg3, N>::Type Arg3Type; + typedef utility::tuple::Tuple<Arg1Type, Arg2Type, Arg3Type> ArgsTuple; +}; + +template<template<class...> class Category , class OP, class TPL> +struct CategoryHelper; + +template<template<class...> class Category , class OP, class ...T > +struct CategoryHelper<Category, OP, utility::tuple::Tuple<T...> > { + typedef Category<OP, T... > Type; +}; + +template<template<class...> class Category , class ...T > +struct CategoryHelper<Category, NoOP, utility::tuple::Tuple<T...> > { + typedef Category<T... > Type; +}; + +/// specialisation of the \ref PlaceHolderExpression when the node is +/// TensorCwiseNullaryOp, TensorCwiseUnaryOp, TensorBroadcastingOp, TensorCwiseBinaryOp, TensorCwiseTernaryOp +#define OPEXPRCATEGORY(CVQual)\ +template <template <class, class... > class Category, typename OP, typename... SubExpr, size_t N>\ +struct PlaceHolderExpression<CVQual Category<OP, SubExpr...>, N>{\ + typedef CVQual typename CategoryHelper<Category, OP, typename CalculateIndex<N, SubExpr...>::ArgsTuple>::Type Type;\ +}; + +OPEXPRCATEGORY(const) +OPEXPRCATEGORY() +#undef OPEXPRCATEGORY + +/// specialisation of the \ref PlaceHolderExpression when the node is +/// TensorCwiseSelectOp +#define SELECTEXPR(CVQual)\ +template <typename IfExpr, typename ThenExpr, typename ElseExpr, size_t N>\ +struct PlaceHolderExpression<CVQual TensorSelectOp<IfExpr, ThenExpr, ElseExpr>, N> {\ + typedef CVQual typename CategoryHelper<TensorSelectOp, NoOP, typename CalculateIndex<N, IfExpr, ThenExpr, ElseExpr>::ArgsTuple>::Type Type;\ +}; + +SELECTEXPR(const) +SELECTEXPR() +#undef SELECTEXPR + +/// specialisation of the \ref PlaceHolderExpression when the node is +/// TensorAssignOp +#define ASSIGNEXPR(CVQual)\ +template <typename LHSExpr, typename RHSExpr, size_t N>\ +struct PlaceHolderExpression<CVQual TensorAssignOp<LHSExpr, RHSExpr>, N> {\ + typedef CVQual typename CategoryHelper<TensorAssignOp, NoOP, typename CalculateIndex<N, LHSExpr, RHSExpr>::ArgsTuple>::Type Type;\ +}; + +ASSIGNEXPR(const) +ASSIGNEXPR() +#undef ASSIGNEXPR + +/// specialisation of the \ref PlaceHolderExpression when the node is +/// TensorMap +#define TENSORMAPEXPR(CVQual)\ +template <typename Scalar_, int Options_, int Options2_, int NumIndices_, typename IndexType_, template <class> class MakePointer_, size_t N>\ +struct PlaceHolderExpression< CVQual TensorMap< Tensor<Scalar_, NumIndices_, Options_, IndexType_>, Options2_, MakePointer_>, N> {\ + typedef CVQual PlaceHolder<CVQual TensorMap<Tensor<Scalar_, NumIndices_, Options_, IndexType_>, Options2_, MakePointer_>, N> Type;\ +}; + +TENSORMAPEXPR(const) +TENSORMAPEXPR() +#undef TENSORMAPEXPR + +/// specialisation of the \ref PlaceHolderExpression when the node is +/// TensorForcedEvalOp +#define FORCEDEVAL(CVQual)\ +template <typename Expr, size_t N>\ +struct PlaceHolderExpression<CVQual TensorForcedEvalOp<Expr>, N> {\ + typedef CVQual PlaceHolder<CVQual TensorForcedEvalOp<Expr>, N> Type;\ +}; + +FORCEDEVAL(const) +FORCEDEVAL() +#undef FORCEDEVAL + +/// specialisation of the \ref PlaceHolderExpression when the node is +/// TensorEvalToOp +#define EVALTO(CVQual)\ +template <typename Expr, size_t N>\ +struct PlaceHolderExpression<CVQual TensorEvalToOp<Expr>, N> {\ + typedef CVQual TensorEvalToOp<typename CalculateIndex <N, Expr>::ArgType> Type;\ +}; + +EVALTO(const) +EVALTO() +#undef EVALTO + + +/// specialisation of the \ref PlaceHolderExpression when the node is +/// TensorReductionOp +#define SYCLREDUCTION(CVQual)\ +template <typename OP, typename Dims, typename Expr, size_t N>\ +struct PlaceHolderExpression<CVQual TensorReductionOp<OP, Dims, Expr>, N>{\ + typedef CVQual PlaceHolder<CVQual TensorReductionOp<OP, Dims,Expr>, N> Type;\ +}; +SYCLREDUCTION(const) +SYCLREDUCTION() +#undef SYCLREDUCTION + +/// template deduction for \ref PlaceHolderExpression struct +template <typename Expr> +struct createPlaceHolderExpression { + static const size_t TotalLeaves = LeafCount<Expr>::Count; + typedef typename PlaceHolderExpression<Expr, TotalLeaves - 1>::Type Type; +}; + +} // internal +} // TensorSycl +} // namespace Eigen + +#endif // UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSORSYCL_PLACEHOLDER_EXPR_HPP diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorSyclRun.h b/unsupported/Eigen/CXX11/src/Tensor/TensorSyclRun.h new file mode 100644 index 000000000..7914b6fad --- /dev/null +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorSyclRun.h @@ -0,0 +1,70 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Mehdi Goli Codeplay Software Ltd. +// Ralph Potter Codeplay Software Ltd. +// Luke Iwanski Codeplay Software Ltd. +// Cummins Chris PhD student at The University of Edinburgh. +// Contact: <eigen@codeplay.com> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +/***************************************************************** + * TensorSyclRun.h + * + * \brief: + * Schedule_kernel invoke an specialised version of kernel struct. The + * specialisation is based on the data dimension in sycl buffer + * +*****************************************************************/ + +#ifndef UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSORSYCL_SYCLRUN_HPP +#define UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSORSYCL_SYCLRUN_HPP + +namespace Eigen { +namespace TensorSycl { +/// The run function in tensor sycl convert the expression tree to a buffer +/// based expression tree; +/// creates the expression tree for the device with accessor to buffers; +/// construct the kernel and submit it to the sycl queue. +template <typename Expr, typename Dev> +void run(Expr &expr, Dev &dev) { + Eigen::TensorEvaluator<Expr, Dev> evaluator(expr, dev); + const bool needs_assign = evaluator.evalSubExprsIfNeeded(NULL); + if (needs_assign) { + typedef typename internal::createPlaceHolderExpression<Expr>::Type PlaceHolderExpr; + auto functors = internal::extractFunctors(evaluator); + + size_t tileSize =dev.m_queue.get_device(). template get_info<cl::sycl::info::device::max_work_group_size>()/2; + dev.m_queue.submit([&](cl::sycl::handler &cgh) { + + // create a tuple of accessors from Evaluator + auto tuple_of_accessors = internal::createTupleOfAccessors<decltype(evaluator)>(cgh, evaluator); + const auto range = utility::tuple::get<0>(tuple_of_accessors).get_range()[0]; + size_t GRange=range; + if (tileSize>GRange) tileSize=GRange; + else if(GRange>tileSize){ + size_t xMode = GRange % tileSize; + if (xMode != 0) GRange += (tileSize - xMode); + } + // run the kernel + cgh.parallel_for<PlaceHolderExpr>( cl::sycl::nd_range<1>(cl::sycl::range<1>(GRange), cl::sycl::range<1>(tileSize)), [=](cl::sycl::nd_item<1> itemID) { + typedef typename internal::ConvertToDeviceExpression<Expr>::Type DevExpr; + auto device_expr =internal::createDeviceExpression<DevExpr, PlaceHolderExpr>(functors, tuple_of_accessors); + auto device_evaluator = Eigen::TensorEvaluator<decltype(device_expr.expr), Eigen::DefaultDevice>(device_expr.expr, Eigen::DefaultDevice()); + if (itemID.get_global_linear_id() < range) { + device_evaluator.evalScalar(static_cast<int>(itemID.get_global_linear_id())); + } + }); + }); + dev.m_queue.throw_asynchronous(); + } + + evaluator.cleanup(); +} +} // namespace TensorSycl +} // namespace Eigen + +#endif // UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSORSYCL_SYCLRUN_HPP diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorSyclTuple.h b/unsupported/Eigen/CXX11/src/Tensor/TensorSyclTuple.h new file mode 100644 index 000000000..063b027e8 --- /dev/null +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorSyclTuple.h @@ -0,0 +1,234 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Mehdi Goli Codeplay Software Ltd. +// Ralph Potter Codeplay Software Ltd. +// Luke Iwanski Codeplay Software Ltd. +// Contact: <eigen@codeplay.com> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +/***************************************************************** + * TensroSyclTuple.h + * + * \brief: + * Minimal implementation of std::tuple that can be used inside a SYCL kernel. + * +*****************************************************************/ + +#ifndef UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSORSYCL_TUPLE_HPP +#define UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSORSYCL_TUPLE_HPP +namespace utility { +namespace tuple { +/// \struct StaticIf +/// \brief The StaticIf struct is used to statically choose the type based on the +/// condition. +template <bool, typename T = void> struct StaticIf; +/// \brief specialisation of the \ref StaticIf when the condition is true +template <typename T> +struct StaticIf<true, T> { + typedef T type; +}; + +/// \struct Tuple +/// \brief is a fixed-size collection of heterogeneous values +/// \ztparam Ts... - the types of the elements that the tuple stores. +/// Empty list is supported. +template <class... Ts> +struct Tuple {}; + +/// \brief specialisation of the \ref Tuple class when the tuple has at least +/// one element. +/// \tparam T : the type of the first element in the tuple. +/// \tparam Ts... the rest of the elements in the tuple. Ts... can be empty. +template <class T, class... Ts> +struct Tuple<T, Ts...> { + Tuple(T t, Ts... ts) : head(t), tail(ts...) {} + T head; + Tuple<Ts...> tail; +}; + +///\ struct ElemTypeHolder +/// \brief ElemTypeHolder class is used to specify the types of the +/// elements inside the tuple +/// \tparam size_t the number of elements inside the tuple +/// \tparam class the tuple class +template <size_t, class> +struct ElemTypeHolder; + +/// \brief specialisation of the \ref ElemTypeHolder class when the number of +/// elements inside the tuple is 1 +template <class T, class... Ts> +struct ElemTypeHolder<0, Tuple<T, Ts...> > { + typedef T type; +}; + +/// \brief specialisation of the \ref ElemTypeHolder class when the number of +/// elements inside the tuple is bigger than 1. It recursively calls itself to +/// detect the type of each element in the tuple +/// \tparam T : the type of the first element in the tuple. +/// \tparam Ts... the rest of the elements in the tuple. Ts... can be empty. +/// \tparam K is the Kth element in the tuple +template <size_t k, class T, class... Ts> +struct ElemTypeHolder<k, Tuple<T, Ts...> > { + typedef typename ElemTypeHolder<k - 1, Tuple<Ts...> >::type type; +}; + +/// get +/// \brief Extracts the first element from the tuple. +/// K=0 represents the first element of the tuple. The tuple cannot be empty. +/// \tparam Ts... are the type of the elements in the tuple. +/// \param t is the tuple whose contents to extract +/// \return typename ElemTypeHolder<0, Tuple<Ts...> >::type &>::type + +#define TERMINATE_CONDS_TUPLE_GET(CVQual) \ +template <size_t k, class... Ts> \ +typename StaticIf<k == 0, CVQual typename ElemTypeHolder<0, Tuple<Ts...> >::type &>::type \ +get(CVQual Tuple<Ts...> &t) { \ + static_assert(sizeof...(Ts)!=0, "The requseted value is bigger than the size of the tuple"); \ + return t.head; \ +} + +TERMINATE_CONDS_TUPLE_GET(const) +TERMINATE_CONDS_TUPLE_GET() +#undef TERMINATE_CONDS_TUPLE_GET +/// get +/// \brief Extracts the Kth element from the tuple. +///\tparam K is an integer value in [0,sizeof...(Types)). +/// \tparam T is the (sizeof...(Types) -(K+1)) element in the tuple +/// \tparam Ts... are the type of the elements in the tuple. +/// \param t is the tuple whose contents to extract +/// \return typename ElemTypeHolder<K, Tuple<Ts...> >::type &>::type +#define RECURSIVE_TUPLE_GET(CVQual) \ +template <size_t k, class T, class... Ts> \ +typename StaticIf<k != 0, CVQual typename ElemTypeHolder<k, Tuple<T, Ts...> >::type &>::type \ +get(CVQual Tuple<T, Ts...> &t) { \ + return utility::tuple::get<k - 1>(t.tail); \ +} +RECURSIVE_TUPLE_GET(const) +RECURSIVE_TUPLE_GET() +#undef RECURSIVE_TUPLE_GET + +/// make_tuple +/// \brief Creates a tuple object, deducing the target type from the types of +/// arguments. +/// \tparam Args the type of the arguments to construct the tuple from +/// \param args zero or more arguments to construct the tuple from +/// \return Tuple<Args...> +template <typename... Args> +Tuple<Args...> make_tuple(Args... args) { + return Tuple<Args...>(args...); +} + +/// size +/// \brief Provides access to the number of elements in a tuple as a +/// compile-time constant expression. +/// \tparam Args the type of the arguments to construct the tuple from +/// \return size_t +template <typename... Args> +static constexpr size_t size(Tuple<Args...> &) { + return sizeof...(Args); +} + +/// \struct IndexList +/// \brief Creates a list of index from the elements in the tuple +/// \tparam Is... a list of index from [0 to sizeof...(tuple elements)) +template <size_t... Is> +struct IndexList {}; + +/// \struct RangeBuilder +/// \brief Collects internal details for generating index ranges [MIN, MAX) +/// Declare primary template for index range builder +/// \tparam MIN is the starting index in the tuple +/// \tparam N represents sizeof..(elemens)- sizeof...(Is) +/// \tparam Is... are the list of generated index so far +template <size_t MIN, size_t N, size_t... Is> +struct RangeBuilder; + +/// \brief base Step: Specialisation of the \ref RangeBuilder when the +/// MIN==MAX. In this case the Is... is [0 to sizeof...(tuple elements)) +/// \tparam MIN is the starting index of the tuple +/// \tparam Is is [0 to sizeof...(tuple elements)) +template <size_t MIN, size_t... Is> +struct RangeBuilder<MIN, MIN, Is...> { + typedef IndexList<Is...> type; +}; + +/// Induction step: Specialisation of the RangeBuilder class when N!=MIN +/// in this case we are recursively subtracting N by one and adding one +/// index to Is... list until MIN==N +/// \tparam MIN is the starting index in the tuple +/// \tparam N represents sizeof..(elemens)- sizeof...(Is) +/// \tparam Is... are the list of generated index so far +template <size_t MIN, size_t N, size_t... Is> +struct RangeBuilder : public RangeBuilder<MIN, N - 1, N - 1, Is...> {}; + +/// \brief IndexRange that returns a [MIN, MAX) index range +/// \tparam MIN is the starting index in the tuple +/// \tparam MAX is the size of the tuple +template <size_t MIN, size_t MAX> +struct IndexRange: RangeBuilder<MIN, MAX>::type {}; + +/// append_base +/// \brief unpacking the elements of the input tuple t and creating a new tuple +/// by adding element a at the end of it. +///\tparam Args... the type of the elements inside the tuple t +/// \tparam T the type of the new element going to be added at the end of tuple +/// \tparam I... is the list of index from [0 to sizeof...(t)) +/// \param t the tuple on which we want to append a. +/// \param a the new elements going to be added to the tuple +/// \return Tuple<Args..., T> +template <typename... Args, typename T, size_t... I> +Tuple<Args..., T> append_base(Tuple<Args...> t, T a,IndexList<I...>) { + return utility::tuple::make_tuple(get<I>(t)..., a); +} + +/// append +/// \brief the deduction function for \ref append_base that automatically +/// generate the \ref IndexRange +///\tparam Args... the type of the elements inside the tuple t +/// \tparam T the type of the new element going to be added at the end of tuple +/// \param t the tuple on which we want to append a. +/// \param a the new elements going to be added to the tuple +/// \return Tuple<Args..., T> +template <typename... Args, typename T> +Tuple<Args..., T> append(Tuple<Args...> t, T a) { + return utility::tuple::append_base(t, a, IndexRange<0, sizeof...(Args)>()); +} + +/// append_base +/// \brief This is a specialisation of \ref append_base when we want to +/// concatenate +/// tuple t2 at the end of the tuple t1. Here we unpack both tuples, generate the +/// IndexRange for each of them and create an output tuple T that contains both +/// elements of t1 and t2. +///\tparam Args1... the type of the elements inside the tuple t1 +///\tparam Args2... the type of the elements inside the tuple t2 +/// \tparam I1... is the list of index from [0 to sizeof...(t1)) +/// \tparam I2... is the list of index from [0 to sizeof...(t2)) +/// \param t1 is the tuple on which we want to append t2. +/// \param t2 is the tuple that is going to be added on t1. +/// \return Tuple<Args1..., Args2...> +template <typename... Args1, typename... Args2, size_t... I1, size_t... I2> +Tuple<Args1..., Args2...> append_base(Tuple<Args1...> t1, Tuple<Args2...> t2, IndexList<I1...>, IndexList<I2...>) { + return utility::tuple::make_tuple(get<I1>(t1)...,get<I2>(t2)...); +} + +/// append +/// \brief deduction function for \ref append_base when we are appending tuple +/// t1 by tuple t2. In this case the \ref IndexRange for both tuple are +/// automatically generated. +///\tparam Args1... the type of the elements inside the tuple t1 +///\tparam Args2... the type of the elements inside the tuple t2 +/// \param t1 is the tuple on which we want to append t2. +/// \param t2 is the tuple that is going to be added on t1. +/// \return Tuple<Args1..., Args2...> +template <typename... Args1, typename... Args2> +Tuple<Args1..., Args2...> append(Tuple<Args1...> t1,Tuple<Args2...> t2) { + return utility::tuple::append_base(t1, t2, IndexRange<0, sizeof...(Args1)>(), IndexRange<0, sizeof...(Args2)>()); +} +} // tuple +} // utility +#endif // UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSORSYCL_TUPLE_HPP diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorTraits.h b/unsupported/Eigen/CXX11/src/Tensor/TensorTraits.h new file mode 100644 index 000000000..ffcf8b00f --- /dev/null +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorTraits.h @@ -0,0 +1,272 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_CXX11_TENSOR_TENSOR_TRAITS_H +#define EIGEN_CXX11_TENSOR_TENSOR_TRAITS_H + +namespace Eigen { +namespace internal { + + +template<typename Scalar, int Options> +class compute_tensor_flags +{ + enum { + is_dynamic_size_storage = 1, + + is_aligned = + ( + ((Options&DontAlign)==0) && ( +#if EIGEN_MAX_STATIC_ALIGN_BYTES>0 + (!is_dynamic_size_storage) +#else + 0 +#endif + | +#if EIGEN_MAX_ALIGN_BYTES>0 + is_dynamic_size_storage +#else + 0 +#endif + ) + ), + packet_access_bit = packet_traits<Scalar>::Vectorizable && is_aligned ? PacketAccessBit : 0 + }; + + public: + enum { ret = packet_access_bit }; +}; + + +template<typename Scalar_, int NumIndices_, int Options_, typename IndexType_> +struct traits<Tensor<Scalar_, NumIndices_, Options_, IndexType_> > +{ + typedef Scalar_ Scalar; + typedef Dense StorageKind; + typedef IndexType_ Index; + static const int NumDimensions = NumIndices_; + static const int Layout = Options_ & RowMajor ? RowMajor : ColMajor; + enum { + Options = Options_, + Flags = compute_tensor_flags<Scalar_, Options_>::ret | (is_const<Scalar_>::value ? 0 : LvalueBit) + }; + template <typename T> struct MakePointer { + typedef T* Type; + }; +}; + + +template<typename Scalar_, typename Dimensions, int Options_, typename IndexType_> +struct traits<TensorFixedSize<Scalar_, Dimensions, Options_, IndexType_> > +{ + typedef Scalar_ Scalar; + typedef Dense StorageKind; + typedef IndexType_ Index; + static const int NumDimensions = array_size<Dimensions>::value; + static const int Layout = Options_ & RowMajor ? RowMajor : ColMajor; + enum { + Options = Options_, + Flags = compute_tensor_flags<Scalar_, Options_>::ret | (is_const<Scalar_>::value ? 0: LvalueBit) + }; + template <typename T> struct MakePointer { + typedef T* Type; + }; +}; + + +template<typename PlainObjectType, int Options_, template <class> class MakePointer_> +struct traits<TensorMap<PlainObjectType, Options_, MakePointer_> > + : public traits<PlainObjectType> +{ + typedef traits<PlainObjectType> BaseTraits; + typedef typename BaseTraits::Scalar Scalar; + typedef typename BaseTraits::StorageKind StorageKind; + typedef typename BaseTraits::Index Index; + static const int NumDimensions = BaseTraits::NumDimensions; + static const int Layout = BaseTraits::Layout; + enum { + Options = Options_, + Flags = BaseTraits::Flags + }; + template <class T> struct MakePointer { + // Intermediate typedef to workaround MSVC issue. + typedef MakePointer_<T> MakePointerT; + typedef typename MakePointerT::Type Type; + }; +}; + +template<typename PlainObjectType> +struct traits<TensorRef<PlainObjectType> > + : public traits<PlainObjectType> +{ + typedef traits<PlainObjectType> BaseTraits; + typedef typename BaseTraits::Scalar Scalar; + typedef typename BaseTraits::StorageKind StorageKind; + typedef typename BaseTraits::Index Index; + static const int NumDimensions = BaseTraits::NumDimensions; + static const int Layout = BaseTraits::Layout; + enum { + Options = BaseTraits::Options, + Flags = BaseTraits::Flags + }; +}; + + +template<typename _Scalar, int NumIndices_, int Options, typename IndexType_> +struct eval<Tensor<_Scalar, NumIndices_, Options, IndexType_>, Eigen::Dense> +{ + typedef const Tensor<_Scalar, NumIndices_, Options, IndexType_>& type; +}; + +template<typename _Scalar, int NumIndices_, int Options, typename IndexType_> +struct eval<const Tensor<_Scalar, NumIndices_, Options, IndexType_>, Eigen::Dense> +{ + typedef const Tensor<_Scalar, NumIndices_, Options, IndexType_>& type; +}; + +template<typename Scalar_, typename Dimensions, int Options, typename IndexType_> +struct eval<TensorFixedSize<Scalar_, Dimensions, Options, IndexType_>, Eigen::Dense> +{ + typedef const TensorFixedSize<Scalar_, Dimensions, Options, IndexType_>& type; +}; + +template<typename Scalar_, typename Dimensions, int Options, typename IndexType_> +struct eval<const TensorFixedSize<Scalar_, Dimensions, Options, IndexType_>, Eigen::Dense> +{ + typedef const TensorFixedSize<Scalar_, Dimensions, Options, IndexType_>& type; +}; + +template<typename PlainObjectType, int Options, template <class> class MakePointer> +struct eval<TensorMap<PlainObjectType, Options, MakePointer>, Eigen::Dense> +{ + typedef const TensorMap<PlainObjectType, Options, MakePointer>& type; +}; + +template<typename PlainObjectType, int Options, template <class> class MakePointer> +struct eval<const TensorMap<PlainObjectType, Options, MakePointer>, Eigen::Dense> +{ + typedef const TensorMap<PlainObjectType, Options, MakePointer>& type; +}; + +template<typename PlainObjectType> +struct eval<TensorRef<PlainObjectType>, Eigen::Dense> +{ + typedef const TensorRef<PlainObjectType>& type; +}; + +template<typename PlainObjectType> +struct eval<const TensorRef<PlainObjectType>, Eigen::Dense> +{ + typedef const TensorRef<PlainObjectType>& type; +}; + +// TODO nested<> does not exist anymore in Eigen/Core, and it thus has to be removed in favor of ref_selector. +template<typename T, int n=1, typename PlainObject = void> struct nested +{ + typedef typename ref_selector<T>::type type; +}; + +template <typename Scalar_, int NumIndices_, int Options_, typename IndexType_> +struct nested<Tensor<Scalar_, NumIndices_, Options_, IndexType_> > +{ + typedef const Tensor<Scalar_, NumIndices_, Options_, IndexType_>& type; +}; + +template <typename Scalar_, int NumIndices_, int Options_, typename IndexType_> +struct nested<const Tensor<Scalar_, NumIndices_, Options_, IndexType_> > +{ + typedef const Tensor<Scalar_, NumIndices_, Options_, IndexType_>& type; +}; + +template <typename Scalar_, typename Dimensions, int Options, typename IndexType_> +struct nested<TensorFixedSize<Scalar_, Dimensions, Options, IndexType_> > +{ + typedef const TensorFixedSize<Scalar_, Dimensions, Options, IndexType_>& type; +}; + +template <typename Scalar_, typename Dimensions, int Options, typename IndexType_> +struct nested<const TensorFixedSize<Scalar_, Dimensions, Options, IndexType_> > +{ + typedef const TensorFixedSize<Scalar_, Dimensions, Options, IndexType_>& type; +}; + + +template <typename PlainObjectType, int Options, template <class> class MakePointer> +struct nested<TensorMap<PlainObjectType, Options, MakePointer> > +{ + typedef const TensorMap<PlainObjectType, Options, MakePointer>& type; +}; + +template <typename PlainObjectType, int Options, template <class> class MakePointer> +struct nested<const TensorMap<PlainObjectType, Options, MakePointer> > +{ + typedef const TensorMap<PlainObjectType, Options, MakePointer>& type; +}; + +template <typename PlainObjectType> +struct nested<TensorRef<PlainObjectType> > +{ + typedef const TensorRef<PlainObjectType>& type; +}; + +template <typename PlainObjectType> +struct nested<const TensorRef<PlainObjectType> > +{ + typedef const TensorRef<PlainObjectType>& type; +}; + +} // end namespace internal + +// Convolutional layers take in an input tensor of shape (D, R, C, B), or (D, C, +// R, B), and convolve it with a set of filters, which can also be presented as +// a tensor (D, K, K, M), where M is the number of filters, K is the filter +// size, and each 3-dimensional tensor of size (D, K, K) is a filter. For +// simplicity we assume that we always use square filters (which is usually the +// case in images), hence the two Ks in the tensor dimension. It also takes in +// a few additional parameters: +// Stride (S): The convolution stride is the offset between locations where we +// apply the filters. A larger stride means that the output will be +// spatially smaller. +// Padding (P): The padding we apply to the input tensor along the R and C +// dimensions. This is usually used to make sure that the spatial +// dimensions of the output matches our intention. +// +// Two types of padding are often used: +// SAME: The pad value is computed so that the output will have size +// R/S and C/S. +// VALID: no padding is carried out. +// When we do padding, the padded values at the padded locations are usually +// zero. +// +// The output dimensions for convolution, when given all the parameters above, +// are as follows: +// When Padding = SAME: the output size is (B, R', C', M), where +// R' = ceil(float(R) / float(S)) +// C' = ceil(float(C) / float(S)) +// where ceil is the ceiling function. The input tensor is padded with 0 as +// needed. The number of padded rows and columns are computed as: +// Pr = ((R' - 1) * S + K - R) / 2 +// Pc = ((C' - 1) * S + K - C) / 2 +// when the stride is 1, we have the simplified case R'=R, C'=C, Pr=Pc=(K-1)/2. +// This is where SAME comes from - the output has the same size as the input has. +// When Padding = VALID: the output size is computed as +// R' = ceil(float(R - K + 1) / float(S)) +// C' = ceil(float(C - K + 1) / float(S)) +// and the number of padded rows and columns are computed in the same way as in +// the SAME case. +// When the stride is 1, we have the simplified case R'=R-K+1, C'=C-K+1, Pr=0, +// Pc=0. +typedef enum { + PADDING_VALID = 1, + PADDING_SAME = 2 +} PaddingType; + +} // end namespace Eigen + +#endif // EIGEN_CXX11_TENSOR_TENSOR_TRAITS_H diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorUInt128.h b/unsupported/Eigen/CXX11/src/Tensor/TensorUInt128.h new file mode 100644 index 000000000..3523e7c94 --- /dev/null +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorUInt128.h @@ -0,0 +1,248 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2015 Benoit Steiner <benoit.steiner.goog@gmail.com> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_CXX11_TENSOR_TENSOR_UINT128_H +#define EIGEN_CXX11_TENSOR_TENSOR_UINT128_H + +namespace Eigen { +namespace internal { + + +template <uint64_t n> +struct static_val { + static const uint64_t value = n; + EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE operator uint64_t() const { return n; } + + EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE static_val() { } + + template <typename T> + EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE static_val(const T& v) { + eigen_assert(v == n); + } +}; + + +template <typename HIGH = uint64_t, typename LOW = uint64_t> +struct TensorUInt128 +{ + HIGH high; + LOW low; + + template<typename OTHER_HIGH, typename OTHER_LOW> + EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE + TensorUInt128(const TensorUInt128<OTHER_HIGH, OTHER_LOW>& other) : high(other.high), low(other.low) { + EIGEN_STATIC_ASSERT(sizeof(OTHER_HIGH) <= sizeof(HIGH), YOU_MADE_A_PROGRAMMING_MISTAKE); + EIGEN_STATIC_ASSERT(sizeof(OTHER_LOW) <= sizeof(LOW), YOU_MADE_A_PROGRAMMING_MISTAKE); + } + + template<typename OTHER_HIGH, typename OTHER_LOW> + EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE + TensorUInt128& operator = (const TensorUInt128<OTHER_HIGH, OTHER_LOW>& other) { + EIGEN_STATIC_ASSERT(sizeof(OTHER_HIGH) <= sizeof(HIGH), YOU_MADE_A_PROGRAMMING_MISTAKE); + EIGEN_STATIC_ASSERT(sizeof(OTHER_LOW) <= sizeof(LOW), YOU_MADE_A_PROGRAMMING_MISTAKE); + high = other.high; + low = other.low; + return *this; + } + + template<typename T> + EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE + explicit TensorUInt128(const T& x) : high(0), low(x) { + eigen_assert((static_cast<typename conditional<sizeof(T) == 8, uint64_t, uint32_t>::type>(x) <= NumTraits<uint64_t>::highest())); + eigen_assert(x >= 0); + } + + EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE + TensorUInt128(HIGH y, LOW x) : high(y), low(x) { } + + EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE operator LOW() const { + return low; + } + EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE LOW lower() const { + return low; + } + EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE HIGH upper() const { + return high; + } +}; + + +template <typename HL, typename LL, typename HR, typename LR> +EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE +bool operator == (const TensorUInt128<HL, LL>& lhs, const TensorUInt128<HR, LR>& rhs) +{ + return (lhs.high == rhs.high) & (lhs.low == rhs.low); +} + +template <typename HL, typename LL, typename HR, typename LR> +EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE +bool operator != (const TensorUInt128<HL, LL>& lhs, const TensorUInt128<HR, LR>& rhs) +{ + return (lhs.high != rhs.high) | (lhs.low != rhs.low); +} + +template <typename HL, typename LL, typename HR, typename LR> +EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE +bool operator >= (const TensorUInt128<HL, LL>& lhs, const TensorUInt128<HR, LR>& rhs) +{ + if (lhs.high != rhs.high) { + return lhs.high > rhs.high; + } + return lhs.low >= rhs.low; +} + +template <typename HL, typename LL, typename HR, typename LR> +EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE +bool operator < (const TensorUInt128<HL, LL>& lhs, const TensorUInt128<HR, LR>& rhs) +{ + if (lhs.high != rhs.high) { + return lhs.high < rhs.high; + } + return lhs.low < rhs.low; +} + +template <typename HL, typename LL, typename HR, typename LR> +EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE +TensorUInt128<uint64_t, uint64_t> operator + (const TensorUInt128<HL, LL>& lhs, const TensorUInt128<HR, LR>& rhs) +{ + TensorUInt128<uint64_t, uint64_t> result(lhs.high + rhs.high, lhs.low + rhs.low); + if (result.low < rhs.low) { + result.high += 1; + } + return result; +} + +template <typename HL, typename LL, typename HR, typename LR> +EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE +TensorUInt128<uint64_t, uint64_t> operator - (const TensorUInt128<HL, LL>& lhs, const TensorUInt128<HR, LR>& rhs) +{ + TensorUInt128<uint64_t, uint64_t> result(lhs.high - rhs.high, lhs.low - rhs.low); + if (result.low > lhs.low) { + result.high -= 1; + } + return result; +} + + +template <typename HL, typename LL, typename HR, typename LR> +static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE +TensorUInt128<uint64_t, uint64_t> operator * (const TensorUInt128<HL, LL>& lhs, const TensorUInt128<HR, LR>& rhs) +{ + // Split each 128-bit integer into 4 32-bit integers, and then do the + // multiplications by hand as follow: + // lhs a b c d + // rhs e f g h + // ----------- + // ah bh ch dh + // bg cg dg + // cf df + // de + // The result is stored in 2 64bit integers, high and low. + + const uint64_t LOW = 0x00000000FFFFFFFFLL; + const uint64_t HIGH = 0xFFFFFFFF00000000LL; + + uint64_t d = lhs.low & LOW; + uint64_t c = (lhs.low & HIGH) >> 32LL; + uint64_t b = lhs.high & LOW; + uint64_t a = (lhs.high & HIGH) >> 32LL; + + uint64_t h = rhs.low & LOW; + uint64_t g = (rhs.low & HIGH) >> 32LL; + uint64_t f = rhs.high & LOW; + uint64_t e = (rhs.high & HIGH) >> 32LL; + + // Compute the low 32 bits of low + uint64_t acc = d * h; + uint64_t low = acc & LOW; + // Compute the high 32 bits of low. Add a carry every time we wrap around + acc >>= 32LL; + uint64_t carry = 0; + uint64_t acc2 = acc + c * h; + if (acc2 < acc) { + carry++; + } + acc = acc2 + d * g; + if (acc < acc2) { + carry++; + } + low |= (acc << 32LL); + + // Carry forward the high bits of acc to initiate the computation of the + // low 32 bits of high + acc2 = (acc >> 32LL) | (carry << 32LL); + carry = 0; + + acc = acc2 + b * h; + if (acc < acc2) { + carry++; + } + acc2 = acc + c * g; + if (acc2 < acc) { + carry++; + } + acc = acc2 + d * f; + if (acc < acc2) { + carry++; + } + uint64_t high = acc & LOW; + + // Start to compute the high 32 bits of high. + acc2 = (acc >> 32LL) | (carry << 32LL); + + acc = acc2 + a * h; + acc2 = acc + b * g; + acc = acc2 + c * f; + acc2 = acc + d * e; + high |= (acc2 << 32LL); + + return TensorUInt128<uint64_t, uint64_t>(high, low); +} + +template <typename HL, typename LL, typename HR, typename LR> +static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE +TensorUInt128<uint64_t, uint64_t> operator / (const TensorUInt128<HL, LL>& lhs, const TensorUInt128<HR, LR>& rhs) +{ + if (rhs == TensorUInt128<static_val<0>, static_val<1> >(1)) { + return TensorUInt128<uint64_t, uint64_t>(lhs.high, lhs.low); + } else if (lhs < rhs) { + return TensorUInt128<uint64_t, uint64_t>(0); + } else { + // calculate the biggest power of 2 times rhs that's less than or equal to lhs + TensorUInt128<uint64_t, uint64_t> power2(1); + TensorUInt128<uint64_t, uint64_t> d(rhs); + TensorUInt128<uint64_t, uint64_t> tmp(lhs - d); + while (lhs >= d) { + tmp = tmp - d; + d = d + d; + power2 = power2 + power2; + } + + tmp = TensorUInt128<uint64_t, uint64_t>(lhs.high, lhs.low); + TensorUInt128<uint64_t, uint64_t> result(0); + while (power2 != TensorUInt128<static_val<0>, static_val<0> >(0)) { + if (tmp >= d) { + tmp = tmp - d; + result = result + power2; + } + // Shift right + power2 = TensorUInt128<uint64_t, uint64_t>(power2.high >> 1, (power2.low >> 1) | (power2.high << 63)); + d = TensorUInt128<uint64_t, uint64_t>(d.high >> 1, (d.low >> 1) | (d.high << 63)); + } + + return result; + } +} + + +} // namespace internal +} // namespace Eigen + + +#endif // EIGEN_CXX11_TENSOR_TENSOR_UINT128_H diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorVolumePatch.h b/unsupported/Eigen/CXX11/src/Tensor/TensorVolumePatch.h new file mode 100644 index 000000000..0ca2cac84 --- /dev/null +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorVolumePatch.h @@ -0,0 +1,608 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. + +#ifndef EIGEN_CXX11_TENSOR_TENSOR_VOLUME_PATCH_H +#define EIGEN_CXX11_TENSOR_TENSOR_VOLUME_PATCH_H + +namespace Eigen { + +/** \class TensorVolumePatch + * \ingroup CXX11_Tensor_Module + * + * \brief Patch extraction specialized for processing of volumetric data. + * This assumes that the input has a least 4 dimensions ordered as follows: + * - channels + * - planes + * - rows + * - columns + * - (optional) additional dimensions such as time or batch size. + * Calling the volume patch code with patch_planes, patch_rows, and patch_cols + * is equivalent to calling the regular patch extraction code with parameters + * d, patch_planes, patch_rows, patch_cols, and 1 for all the additional + * dimensions. + */ +namespace internal { +template<DenseIndex Planes, DenseIndex Rows, DenseIndex Cols, typename XprType> +struct traits<TensorVolumePatchOp<Planes, Rows, Cols, XprType> > : public traits<XprType> +{ + typedef typename internal::remove_const<typename XprType::Scalar>::type Scalar; + typedef traits<XprType> XprTraits; + typedef typename XprTraits::StorageKind StorageKind; + typedef typename XprTraits::Index Index; + typedef typename XprType::Nested Nested; + typedef typename remove_reference<Nested>::type _Nested; + static const int NumDimensions = XprTraits::NumDimensions + 1; + static const int Layout = XprTraits::Layout; +}; + +template<DenseIndex Planes, DenseIndex Rows, DenseIndex Cols, typename XprType> +struct eval<TensorVolumePatchOp<Planes, Rows, Cols, XprType>, Eigen::Dense> +{ + typedef const TensorVolumePatchOp<Planes, Rows, Cols, XprType>& type; +}; + +template<DenseIndex Planes, DenseIndex Rows, DenseIndex Cols, typename XprType> +struct nested<TensorVolumePatchOp<Planes, Rows, Cols, XprType>, 1, typename eval<TensorVolumePatchOp<Planes, Rows, Cols, XprType> >::type> +{ + typedef TensorVolumePatchOp<Planes, Rows, Cols, XprType> type; +}; + +} // end namespace internal + +template<DenseIndex Planes, DenseIndex Rows, DenseIndex Cols, typename XprType> +class TensorVolumePatchOp : public TensorBase<TensorVolumePatchOp<Planes, Rows, Cols, XprType>, ReadOnlyAccessors> +{ + public: + typedef typename Eigen::internal::traits<TensorVolumePatchOp>::Scalar Scalar; + typedef typename Eigen::NumTraits<Scalar>::Real RealScalar; + typedef typename XprType::CoeffReturnType CoeffReturnType; + typedef typename Eigen::internal::nested<TensorVolumePatchOp>::type Nested; + typedef typename Eigen::internal::traits<TensorVolumePatchOp>::StorageKind StorageKind; + typedef typename Eigen::internal::traits<TensorVolumePatchOp>::Index Index; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorVolumePatchOp(const XprType& expr, DenseIndex patch_planes, DenseIndex patch_rows, DenseIndex patch_cols, + DenseIndex plane_strides, DenseIndex row_strides, DenseIndex col_strides, + DenseIndex in_plane_strides, DenseIndex in_row_strides, DenseIndex in_col_strides, + DenseIndex plane_inflate_strides, DenseIndex row_inflate_strides, DenseIndex col_inflate_strides, + PaddingType padding_type, Scalar padding_value) + : m_xpr(expr), m_patch_planes(patch_planes), m_patch_rows(patch_rows), m_patch_cols(patch_cols), + m_plane_strides(plane_strides), m_row_strides(row_strides), m_col_strides(col_strides), + m_in_plane_strides(in_plane_strides), m_in_row_strides(in_row_strides), m_in_col_strides(in_col_strides), + m_plane_inflate_strides(plane_inflate_strides), m_row_inflate_strides(row_inflate_strides), m_col_inflate_strides(col_inflate_strides), + m_padding_explicit(false), m_padding_top_z(0), m_padding_bottom_z(0), m_padding_top(0), m_padding_bottom(0), m_padding_left(0), m_padding_right(0), + m_padding_type(padding_type), m_padding_value(padding_value) {} + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorVolumePatchOp(const XprType& expr, DenseIndex patch_planes, DenseIndex patch_rows, DenseIndex patch_cols, + DenseIndex plane_strides, DenseIndex row_strides, DenseIndex col_strides, + DenseIndex in_plane_strides, DenseIndex in_row_strides, DenseIndex in_col_strides, + DenseIndex plane_inflate_strides, DenseIndex row_inflate_strides, DenseIndex col_inflate_strides, + DenseIndex padding_top_z, DenseIndex padding_bottom_z, + DenseIndex padding_top, DenseIndex padding_bottom, + DenseIndex padding_left, DenseIndex padding_right, + Scalar padding_value) + : m_xpr(expr), m_patch_planes(patch_planes), m_patch_rows(patch_rows), m_patch_cols(patch_cols), + m_plane_strides(plane_strides), m_row_strides(row_strides), m_col_strides(col_strides), + m_in_plane_strides(in_plane_strides), m_in_row_strides(in_row_strides), m_in_col_strides(in_col_strides), + m_plane_inflate_strides(plane_inflate_strides), m_row_inflate_strides(row_inflate_strides), m_col_inflate_strides(col_inflate_strides), + m_padding_explicit(true), m_padding_top_z(padding_top_z), m_padding_bottom_z(padding_bottom_z), m_padding_top(padding_top), m_padding_bottom(padding_bottom), + m_padding_left(padding_left), m_padding_right(padding_right), + m_padding_type(PADDING_VALID), m_padding_value(padding_value) {} + + EIGEN_DEVICE_FUNC + DenseIndex patch_planes() const { return m_patch_planes; } + EIGEN_DEVICE_FUNC + DenseIndex patch_rows() const { return m_patch_rows; } + EIGEN_DEVICE_FUNC + DenseIndex patch_cols() const { return m_patch_cols; } + EIGEN_DEVICE_FUNC + DenseIndex plane_strides() const { return m_plane_strides; } + EIGEN_DEVICE_FUNC + DenseIndex row_strides() const { return m_row_strides; } + EIGEN_DEVICE_FUNC + DenseIndex col_strides() const { return m_col_strides; } + EIGEN_DEVICE_FUNC + DenseIndex in_plane_strides() const { return m_in_plane_strides; } + EIGEN_DEVICE_FUNC + DenseIndex in_row_strides() const { return m_in_row_strides; } + EIGEN_DEVICE_FUNC + DenseIndex in_col_strides() const { return m_in_col_strides; } + EIGEN_DEVICE_FUNC + DenseIndex plane_inflate_strides() const { return m_plane_inflate_strides; } + EIGEN_DEVICE_FUNC + DenseIndex row_inflate_strides() const { return m_row_inflate_strides; } + EIGEN_DEVICE_FUNC + DenseIndex col_inflate_strides() const { return m_col_inflate_strides; } + EIGEN_DEVICE_FUNC + bool padding_explicit() const { return m_padding_explicit; } + EIGEN_DEVICE_FUNC + DenseIndex padding_top_z() const { return m_padding_top_z; } + EIGEN_DEVICE_FUNC + DenseIndex padding_bottom_z() const { return m_padding_bottom_z; } + EIGEN_DEVICE_FUNC + DenseIndex padding_top() const { return m_padding_top; } + EIGEN_DEVICE_FUNC + DenseIndex padding_bottom() const { return m_padding_bottom; } + EIGEN_DEVICE_FUNC + DenseIndex padding_left() const { return m_padding_left; } + EIGEN_DEVICE_FUNC + DenseIndex padding_right() const { return m_padding_right; } + EIGEN_DEVICE_FUNC + PaddingType padding_type() const { return m_padding_type; } + EIGEN_DEVICE_FUNC + Scalar padding_value() const { return m_padding_value; } + + EIGEN_DEVICE_FUNC + const typename internal::remove_all<typename XprType::Nested>::type& + expression() const { return m_xpr; } + + protected: + typename XprType::Nested m_xpr; + const DenseIndex m_patch_planes; + const DenseIndex m_patch_rows; + const DenseIndex m_patch_cols; + const DenseIndex m_plane_strides; + const DenseIndex m_row_strides; + const DenseIndex m_col_strides; + const DenseIndex m_in_plane_strides; + const DenseIndex m_in_row_strides; + const DenseIndex m_in_col_strides; + const DenseIndex m_plane_inflate_strides; + const DenseIndex m_row_inflate_strides; + const DenseIndex m_col_inflate_strides; + const bool m_padding_explicit; + const DenseIndex m_padding_top_z; + const DenseIndex m_padding_bottom_z; + const DenseIndex m_padding_top; + const DenseIndex m_padding_bottom; + const DenseIndex m_padding_left; + const DenseIndex m_padding_right; + const PaddingType m_padding_type; + const Scalar m_padding_value; +}; + + +// Eval as rvalue +template<DenseIndex Planes, DenseIndex Rows, DenseIndex Cols, typename ArgType, typename Device> +struct TensorEvaluator<const TensorVolumePatchOp<Planes, Rows, Cols, ArgType>, Device> +{ + typedef TensorVolumePatchOp<Planes, Rows, Cols, ArgType> XprType; + typedef typename XprType::Index Index; + static const int NumInputDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value; + static const int NumDims = NumInputDims + 1; + typedef DSizes<Index, NumDims> Dimensions; + typedef typename internal::remove_const<typename XprType::Scalar>::type Scalar; + typedef typename XprType::CoeffReturnType CoeffReturnType; + typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType; + static const int PacketSize = internal::unpacket_traits<PacketReturnType>::size; + + enum { + IsAligned = false, + PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess, + BlockAccess = false, + Layout = TensorEvaluator<ArgType, Device>::Layout, + CoordAccess = false, + RawAccess = false + }; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) + : m_impl(op.expression(), device) + { + EIGEN_STATIC_ASSERT((NumDims >= 5), YOU_MADE_A_PROGRAMMING_MISTAKE); + + m_paddingValue = op.padding_value(); + + const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions(); + + // Cache a few variables. + if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { + m_inputDepth = input_dims[0]; + m_inputPlanes = input_dims[1]; + m_inputRows = input_dims[2]; + m_inputCols = input_dims[3]; + } else { + m_inputDepth = input_dims[NumInputDims-1]; + m_inputPlanes = input_dims[NumInputDims-2]; + m_inputRows = input_dims[NumInputDims-3]; + m_inputCols = input_dims[NumInputDims-4]; + } + + m_plane_strides = op.plane_strides(); + m_row_strides = op.row_strides(); + m_col_strides = op.col_strides(); + + // Input strides and effective input/patch size + m_in_plane_strides = op.in_plane_strides(); + m_in_row_strides = op.in_row_strides(); + m_in_col_strides = op.in_col_strides(); + m_plane_inflate_strides = op.plane_inflate_strides(); + m_row_inflate_strides = op.row_inflate_strides(); + m_col_inflate_strides = op.col_inflate_strides(); + + // The "effective" spatial size after inflating data with zeros. + m_input_planes_eff = (m_inputPlanes - 1) * m_plane_inflate_strides + 1; + m_input_rows_eff = (m_inputRows - 1) * m_row_inflate_strides + 1; + m_input_cols_eff = (m_inputCols - 1) * m_col_inflate_strides + 1; + m_patch_planes_eff = op.patch_planes() + (op.patch_planes() - 1) * (m_in_plane_strides - 1); + m_patch_rows_eff = op.patch_rows() + (op.patch_rows() - 1) * (m_in_row_strides - 1); + m_patch_cols_eff = op.patch_cols() + (op.patch_cols() - 1) * (m_in_col_strides - 1); + + if (op.padding_explicit()) { + m_outputPlanes = numext::ceil((m_input_planes_eff + op.padding_top_z() + op.padding_bottom_z() - m_patch_planes_eff + 1.f) / static_cast<float>(m_plane_strides)); + m_outputRows = numext::ceil((m_input_rows_eff + op.padding_top() + op.padding_bottom() - m_patch_rows_eff + 1.f) / static_cast<float>(m_row_strides)); + m_outputCols = numext::ceil((m_input_cols_eff + op.padding_left() + op.padding_right() - m_patch_cols_eff + 1.f) / static_cast<float>(m_col_strides)); + m_planePaddingTop = op.padding_top_z(); + m_rowPaddingTop = op.padding_top(); + m_colPaddingLeft = op.padding_left(); + } else { + // Computing padding from the type + switch (op.padding_type()) { + case PADDING_VALID: + m_outputPlanes = numext::ceil((m_input_planes_eff - m_patch_planes_eff + 1.f) / static_cast<float>(m_plane_strides)); + m_outputRows = numext::ceil((m_input_rows_eff - m_patch_rows_eff + 1.f) / static_cast<float>(m_row_strides)); + m_outputCols = numext::ceil((m_input_cols_eff - m_patch_cols_eff + 1.f) / static_cast<float>(m_col_strides)); + m_planePaddingTop = 0; + m_rowPaddingTop = 0; + m_colPaddingLeft = 0; + break; + case PADDING_SAME: { + m_outputPlanes = numext::ceil(m_input_planes_eff / static_cast<float>(m_plane_strides)); + m_outputRows = numext::ceil(m_input_rows_eff / static_cast<float>(m_row_strides)); + m_outputCols = numext::ceil(m_input_cols_eff / static_cast<float>(m_col_strides)); + const Index dz = m_outputPlanes * m_plane_strides + m_patch_planes_eff - 1 - m_input_planes_eff; + const Index dy = m_outputRows * m_row_strides + m_patch_rows_eff - 1 - m_input_rows_eff; + const Index dx = m_outputCols * m_col_strides + m_patch_cols_eff - 1 - m_input_cols_eff; + m_planePaddingTop = dz - dz / 2; + m_rowPaddingTop = dy - dy / 2; + m_colPaddingLeft = dx - dx / 2; + break; + } + default: + eigen_assert(false && "unexpected padding"); + } + } + eigen_assert(m_outputRows > 0); + eigen_assert(m_outputCols > 0); + eigen_assert(m_outputPlanes > 0); + + // Dimensions for result of extraction. + if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { + // ColMajor + // 0: depth + // 1: patch_planes + // 2: patch_rows + // 3: patch_cols + // 4: number of patches + // 5 and beyond: anything else (such as batch). + m_dimensions[0] = input_dims[0]; + m_dimensions[1] = op.patch_planes(); + m_dimensions[2] = op.patch_rows(); + m_dimensions[3] = op.patch_cols(); + m_dimensions[4] = m_outputPlanes * m_outputRows * m_outputCols; + for (int i = 5; i < NumDims; ++i) { + m_dimensions[i] = input_dims[i-1]; + } + } else { + // RowMajor + // NumDims-1: depth + // NumDims-2: patch_planes + // NumDims-3: patch_rows + // NumDims-4: patch_cols + // NumDims-5: number of patches + // NumDims-6 and beyond: anything else (such as batch). + m_dimensions[NumDims-1] = input_dims[NumInputDims-1]; + m_dimensions[NumDims-2] = op.patch_planes(); + m_dimensions[NumDims-3] = op.patch_rows(); + m_dimensions[NumDims-4] = op.patch_cols(); + m_dimensions[NumDims-5] = m_outputPlanes * m_outputRows * m_outputCols; + for (int i = NumDims-6; i >= 0; --i) { + m_dimensions[i] = input_dims[i]; + } + } + + // Strides for the output tensor. + if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { + m_rowStride = m_dimensions[1]; + m_colStride = m_dimensions[2] * m_rowStride; + m_patchStride = m_colStride * m_dimensions[3] * m_dimensions[0]; + m_otherStride = m_patchStride * m_dimensions[4]; + } else { + m_rowStride = m_dimensions[NumDims-2]; + m_colStride = m_dimensions[NumDims-3] * m_rowStride; + m_patchStride = m_colStride * m_dimensions[NumDims-4] * m_dimensions[NumDims-1]; + m_otherStride = m_patchStride * m_dimensions[NumDims-5]; + } + + // Strides for navigating through the input tensor. + m_planeInputStride = m_inputDepth; + m_rowInputStride = m_inputDepth * m_inputPlanes; + m_colInputStride = m_inputDepth * m_inputRows * m_inputPlanes; + m_otherInputStride = m_inputDepth * m_inputRows * m_inputCols * m_inputPlanes; + + m_outputPlanesRows = m_outputPlanes * m_outputRows; + + // Fast representations of different variables. + m_fastOtherStride = internal::TensorIntDivisor<Index>(m_otherStride); + m_fastPatchStride = internal::TensorIntDivisor<Index>(m_patchStride); + m_fastColStride = internal::TensorIntDivisor<Index>(m_colStride); + m_fastRowStride = internal::TensorIntDivisor<Index>(m_rowStride); + m_fastInputRowStride = internal::TensorIntDivisor<Index>(m_row_inflate_strides); + m_fastInputColStride = internal::TensorIntDivisor<Index>(m_col_inflate_strides); + m_fastInputPlaneStride = internal::TensorIntDivisor<Index>(m_plane_inflate_strides); + m_fastInputColsEff = internal::TensorIntDivisor<Index>(m_input_cols_eff); + m_fastOutputPlanes = internal::TensorIntDivisor<Index>(m_outputPlanes); + m_fastOutputPlanesRows = internal::TensorIntDivisor<Index>(m_outputPlanesRows); + + if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { + m_fastOutputDepth = internal::TensorIntDivisor<Index>(m_dimensions[0]); + } else { + m_fastOutputDepth = internal::TensorIntDivisor<Index>(m_dimensions[NumDims-1]); + } + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* /*data*/) { + m_impl.evalSubExprsIfNeeded(NULL); + return true; + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() { + m_impl.cleanup(); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const + { + // Patch index corresponding to the passed in index. + const Index patchIndex = index / m_fastPatchStride; + + // Spatial offset within the patch. This has to be translated into 3D + // coordinates within the patch. + const Index patchOffset = (index - patchIndex * m_patchStride) / m_fastOutputDepth; + + // Batch, etc. + const Index otherIndex = (NumDims == 5) ? 0 : index / m_fastOtherStride; + const Index patch3DIndex = (NumDims == 5) ? patchIndex : (index - otherIndex * m_otherStride) / m_fastPatchStride; + + // Calculate column index in the input original tensor. + const Index colIndex = patch3DIndex / m_fastOutputPlanesRows; + const Index colOffset = patchOffset / m_fastColStride; + const Index inputCol = colIndex * m_col_strides + colOffset * m_in_col_strides - m_colPaddingLeft; + const Index origInputCol = (m_col_inflate_strides == 1) ? inputCol : ((inputCol >= 0) ? (inputCol / m_fastInputColStride) : 0); + if (inputCol < 0 || inputCol >= m_input_cols_eff || + ((m_col_inflate_strides != 1) && (inputCol != origInputCol * m_col_inflate_strides))) { + return Scalar(m_paddingValue); + } + + // Calculate row index in the original input tensor. + const Index rowIndex = (patch3DIndex - colIndex * m_outputPlanesRows) / m_fastOutputPlanes; + const Index rowOffset = (patchOffset - colOffset * m_colStride) / m_fastRowStride; + const Index inputRow = rowIndex * m_row_strides + rowOffset * m_in_row_strides - m_rowPaddingTop; + const Index origInputRow = (m_row_inflate_strides == 1) ? inputRow : ((inputRow >= 0) ? (inputRow / m_fastInputRowStride) : 0); + if (inputRow < 0 || inputRow >= m_input_rows_eff || + ((m_row_inflate_strides != 1) && (inputRow != origInputRow * m_row_inflate_strides))) { + return Scalar(m_paddingValue); + } + + // Calculate plane index in the original input tensor. + const Index planeIndex = (patch3DIndex - m_outputPlanes * (colIndex * m_outputRows + rowIndex)); + const Index planeOffset = patchOffset - colOffset * m_colStride - rowOffset * m_rowStride; + const Index inputPlane = planeIndex * m_plane_strides + planeOffset * m_in_plane_strides - m_planePaddingTop; + const Index origInputPlane = (m_plane_inflate_strides == 1) ? inputPlane : ((inputPlane >= 0) ? (inputPlane / m_fastInputPlaneStride) : 0); + if (inputPlane < 0 || inputPlane >= m_input_planes_eff || + ((m_plane_inflate_strides != 1) && (inputPlane != origInputPlane * m_plane_inflate_strides))) { + return Scalar(m_paddingValue); + } + + const int depth_index = static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 0 : NumDims - 1; + const Index depth = index - (index / m_fastOutputDepth) * m_dimensions[depth_index]; + + const Index inputIndex = depth + + origInputRow * m_rowInputStride + + origInputCol * m_colInputStride + + origInputPlane * m_planeInputStride + + otherIndex * m_otherInputStride; + + return m_impl.coeff(inputIndex); + } + + template<int LoadMode> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const + { + EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE) + eigen_assert(index+PacketSize-1 < dimensions().TotalSize()); + + if (m_in_row_strides != 1 || m_in_col_strides != 1 || m_row_inflate_strides != 1 || m_col_inflate_strides != 1 || + m_in_plane_strides != 1 || m_plane_inflate_strides != 1) { + return packetWithPossibleZero(index); + } + + const Index indices[2] = {index, index + PacketSize - 1}; + const Index patchIndex = indices[0] / m_fastPatchStride; + if (patchIndex != indices[1] / m_fastPatchStride) { + return packetWithPossibleZero(index); + } + const Index otherIndex = (NumDims == 5) ? 0 : indices[0] / m_fastOtherStride; + eigen_assert(otherIndex == indices[1] / m_fastOtherStride); + + // Find the offset of the element wrt the location of the first element. + const Index patchOffsets[2] = {(indices[0] - patchIndex * m_patchStride) / m_fastOutputDepth, + (indices[1] - patchIndex * m_patchStride) / m_fastOutputDepth}; + + const Index patch3DIndex = (NumDims == 5) ? patchIndex : (indices[0] - otherIndex * m_otherStride) / m_fastPatchStride; + eigen_assert(patch3DIndex == (indices[1] - otherIndex * m_otherStride) / m_fastPatchStride); + + const Index colIndex = patch3DIndex / m_fastOutputPlanesRows; + const Index colOffsets[2] = { + patchOffsets[0] / m_fastColStride, + patchOffsets[1] / m_fastColStride}; + + // Calculate col indices in the original input tensor. + const Index inputCols[2] = { + colIndex * m_col_strides + colOffsets[0] - m_colPaddingLeft, + colIndex * m_col_strides + colOffsets[1] - m_colPaddingLeft}; + if (inputCols[1] < 0 || inputCols[0] >= m_inputCols) { + return internal::pset1<PacketReturnType>(Scalar(m_paddingValue)); + } + + if (inputCols[0] != inputCols[1]) { + return packetWithPossibleZero(index); + } + + const Index rowIndex = (patch3DIndex - colIndex * m_outputPlanesRows) / m_fastOutputPlanes; + const Index rowOffsets[2] = { + (patchOffsets[0] - colOffsets[0] * m_colStride) / m_fastRowStride, + (patchOffsets[1] - colOffsets[1] * m_colStride) / m_fastRowStride}; + eigen_assert(rowOffsets[0] <= rowOffsets[1]); + // Calculate col indices in the original input tensor. + const Index inputRows[2] = { + rowIndex * m_row_strides + rowOffsets[0] - m_rowPaddingTop, + rowIndex * m_row_strides + rowOffsets[1] - m_rowPaddingTop}; + + if (inputRows[1] < 0 || inputRows[0] >= m_inputRows) { + return internal::pset1<PacketReturnType>(Scalar(m_paddingValue)); + } + + if (inputRows[0] != inputRows[1]) { + return packetWithPossibleZero(index); + } + + const Index planeIndex = (patch3DIndex - m_outputPlanes * (colIndex * m_outputRows + rowIndex)); + const Index planeOffsets[2] = { + patchOffsets[0] - colOffsets[0] * m_colStride - rowOffsets[0] * m_rowStride, + patchOffsets[1] - colOffsets[1] * m_colStride - rowOffsets[1] * m_rowStride}; + eigen_assert(planeOffsets[0] <= planeOffsets[1]); + const Index inputPlanes[2] = { + planeIndex * m_plane_strides + planeOffsets[0] - m_planePaddingTop, + planeIndex * m_plane_strides + planeOffsets[1] - m_planePaddingTop}; + + if (inputPlanes[1] < 0 || inputPlanes[0] >= m_inputPlanes) { + return internal::pset1<PacketReturnType>(Scalar(m_paddingValue)); + } + + if (inputPlanes[0] >= 0 && inputPlanes[1] < m_inputPlanes) { + // no padding + const int depth_index = static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 0 : NumDims - 1; + const Index depth = index - (index / m_fastOutputDepth) * m_dimensions[depth_index]; + const Index inputIndex = depth + + inputRows[0] * m_rowInputStride + + inputCols[0] * m_colInputStride + + m_planeInputStride * inputPlanes[0] + + otherIndex * m_otherInputStride; + return m_impl.template packet<Unaligned>(inputIndex); + } + + return packetWithPossibleZero(index); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost + costPerCoeff(bool vectorized) const { + const double compute_cost = + 10 * TensorOpCost::DivCost<Index>() + 21 * TensorOpCost::MulCost<Index>() + + 8 * TensorOpCost::AddCost<Index>(); + return TensorOpCost(0, 0, compute_cost, vectorized, PacketSize); + } + + EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; } + + const TensorEvaluator<ArgType, Device>& impl() const { return m_impl; } + + Index planePaddingTop() const { return m_planePaddingTop; } + Index rowPaddingTop() const { return m_rowPaddingTop; } + Index colPaddingLeft() const { return m_colPaddingLeft; } + Index outputPlanes() const { return m_outputPlanes; } + Index outputRows() const { return m_outputRows; } + Index outputCols() const { return m_outputCols; } + Index userPlaneStride() const { return m_plane_strides; } + Index userRowStride() const { return m_row_strides; } + Index userColStride() const { return m_col_strides; } + Index userInPlaneStride() const { return m_in_plane_strides; } + Index userInRowStride() const { return m_in_row_strides; } + Index userInColStride() const { return m_in_col_strides; } + Index planeInflateStride() const { return m_plane_inflate_strides; } + Index rowInflateStride() const { return m_row_inflate_strides; } + Index colInflateStride() const { return m_col_inflate_strides; } + + protected: + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetWithPossibleZero(Index index) const + { + EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize]; + for (int i = 0; i < PacketSize; ++i) { + values[i] = coeff(index+i); + } + PacketReturnType rslt = internal::pload<PacketReturnType>(values); + return rslt; + } + + Dimensions m_dimensions; + + // Parameters passed to the costructor. + Index m_plane_strides; + Index m_row_strides; + Index m_col_strides; + + Index m_outputPlanes; + Index m_outputRows; + Index m_outputCols; + + Index m_planePaddingTop; + Index m_rowPaddingTop; + Index m_colPaddingLeft; + + Index m_in_plane_strides; + Index m_in_row_strides; + Index m_in_col_strides; + + Index m_plane_inflate_strides; + Index m_row_inflate_strides; + Index m_col_inflate_strides; + + // Cached input size. + Index m_inputDepth; + Index m_inputPlanes; + Index m_inputRows; + Index m_inputCols; + + // Other cached variables. + Index m_outputPlanesRows; + + // Effective input/patch post-inflation size. + Index m_input_planes_eff; + Index m_input_rows_eff; + Index m_input_cols_eff; + Index m_patch_planes_eff; + Index m_patch_rows_eff; + Index m_patch_cols_eff; + + // Strides for the output tensor. + Index m_otherStride; + Index m_patchStride; + Index m_rowStride; + Index m_colStride; + + // Strides for the input tensor. + Index m_planeInputStride; + Index m_rowInputStride; + Index m_colInputStride; + Index m_otherInputStride; + + internal::TensorIntDivisor<Index> m_fastOtherStride; + internal::TensorIntDivisor<Index> m_fastPatchStride; + internal::TensorIntDivisor<Index> m_fastColStride; + internal::TensorIntDivisor<Index> m_fastRowStride; + internal::TensorIntDivisor<Index> m_fastInputPlaneStride; + internal::TensorIntDivisor<Index> m_fastInputRowStride; + internal::TensorIntDivisor<Index> m_fastInputColStride; + internal::TensorIntDivisor<Index> m_fastInputColsEff; + internal::TensorIntDivisor<Index> m_fastOutputPlanesRows; + internal::TensorIntDivisor<Index> m_fastOutputPlanes; + internal::TensorIntDivisor<Index> m_fastOutputDepth; + + Scalar m_paddingValue; + + TensorEvaluator<ArgType, Device> m_impl; +}; + + +} // end namespace Eigen + +#endif // EIGEN_CXX11_TENSOR_TENSOR_VOLUME_PATCH_H |