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author | Miao Wang <miaowang@google.com> | 2018-03-23 14:24:44 -0700 |
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committer | Miao Wang <miaowang@google.com> | 2018-03-23 14:24:44 -0700 |
commit | 6c79676c3e1ce1fae7ca151d116a846f3f68f59c (patch) | |
tree | 5374170b45a510be77e0b7f90c84b8be7ebb7c0f | |
parent | 18b73567d561fd5a5f0067ebbc54ca1dd2c2a1ab (diff) | |
download | tensorflow-pie-qpr1-s2-release.tar.gz |
Port in minimal changes to support BroadcastSub and BroadcastDivandroid-9.0.0_r47android-9.0.0_r46android-9.0.0_r45android-9.0.0_r44android-9.0.0_r43android-9.0.0_r42android-9.0.0_r41android-9.0.0_r40android-9.0.0_r39android-9.0.0_r38android-9.0.0_r37android-9.0.0_r36android-9.0.0_r35android-9.0.0_r34android-9.0.0_r33android-9.0.0_r32android-9.0.0_r31android-9.0.0_r30android-9.0.0_r22android-9.0.0_r21android-9.0.0_r20android-9.0.0_r19android-9.0.0_r16android-9.0.0_r12android-9.0.0_r11pie-qpr3-s1-releasepie-qpr3-releasepie-qpr3-b-releasepie-qpr2-releasepie-qpr1-s3-releasepie-qpr1-s2-releasepie-qpr1-s1-releasepie-qpr1-releasepie-dr1-releasepie-dr1-devpie-devpie-b4s4-releasepie-b4s4-dev
Bug: 73661777
Test: mm
Test: NeuralNetworksTests
Change-Id: I4edfa9e39ef0a28c03de9f725d8eeef716cf88cd
-rw-r--r-- | tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h | 91 |
1 files changed, 91 insertions, 0 deletions
diff --git a/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h b/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h index d4f031e130f..132c9e1e5f0 100644 --- a/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h +++ b/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h @@ -1928,6 +1928,51 @@ inline void Div(const float* input1_data, const Dims<4>& input1_dims, } } +// TODO(jiawen): We can implement BroadcastDiv on buffers of arbitrary +// dimensionality if the runtime code does a single loop over one dimension +// that handles broadcasting as the base case. The code generator would then +// generate max(D1, D2) nested for loops. +// TODO(benoitjacob): BroadcastDiv is intentionally duplicated from +// reference_ops.h. Once an optimized version is implemented and NdArrayDesc<T> +// is no longer referenced in this file, move NdArrayDesc<T> from types.h to +// reference_ops.h. +template <typename T> +void BroadcastDiv(const T* input1_data, const Dims<4>& input1_dims, + const T* input2_data, const Dims<4>& input2_dims, + T output_activation_min, T output_activation_max, + T* output_data, const Dims<4>& output_dims) { + gemmlowp::ScopedProfilingLabel label("BroadcastDiv"); + + NdArrayDesc<4> desc1; + NdArrayDesc<4> desc2; + NdArrayDescsForElementwiseBroadcast(input1_dims, input2_dims, &desc1, &desc2); + + // In Tensorflow, the dimensions are canonically named (batch_number, row, + // col, channel), with extents (batches, height, width, depth), with the + // trailing dimension changing most rapidly (channels has the smallest stride, + // typically 1 element). + // + // In generated C code, we store arrays with the dimensions reversed. The + // first dimension has smallest stride. + // + // We name our variables by their Tensorflow convention, but generate C code + // nesting loops such that the innermost loop has the smallest stride for the + // best cache behavior. + for (int b = 0; b < ArraySize(output_dims, 3); ++b) { + for (int y = 0; y < ArraySize(output_dims, 2); ++y) { + for (int x = 0; x < ArraySize(output_dims, 1); ++x) { + for (int c = 0; c < ArraySize(output_dims, 0); ++c) { + output_data[Offset(output_dims, c, x, y, b)] = + ActivationFunctionWithMinMax( + input1_data[SubscriptToIndex(desc1, c, x, y, b)] / + input2_data[SubscriptToIndex(desc2, c, x, y, b)], + output_activation_min, output_activation_max); + } + } + } + } +} + // TODO(aselle): This is not actually optimized yet. inline void Sub(const float* input1_data, const Dims<4>& input1_dims, const float* input2_data, const Dims<4>& input2_dims, @@ -1955,6 +2000,52 @@ inline void Sub(const float* input1_data, const Dims<4>& input1_dims, } } } + +// TODO(jiawen): We can implement BroadcastSub on buffers of arbitrary +// dimensionality if the runtime code does a single loop over one dimension +// that handles broadcasting as the base case. The code generator would then +// generate max(D1, D2) nested for loops. +// TODO(benoitjacob): BroadcastSub is intentionally duplicated from +// reference_ops.h. Once an optimized version is implemented and NdArrayDesc<T> +// is no longer referenced in this file, move NdArrayDesc<T> from types.h to +// reference_ops.h. +template <typename T> +void BroadcastSub(const T* input1_data, const Dims<4>& input1_dims, + const T* input2_data, const Dims<4>& input2_dims, + T output_activation_min, T output_activation_max, + T* output_data, const Dims<4>& output_dims) { + gemmlowp::ScopedProfilingLabel label("BroadcastSub"); + + NdArrayDesc<4> desc1; + NdArrayDesc<4> desc2; + NdArrayDescsForElementwiseBroadcast(input1_dims, input2_dims, &desc1, &desc2); + + // In Tensorflow, the dimensions are canonically named (batch_number, row, + // col, channel), with extents (batches, height, width, depth), with the + // trailing dimension changing most rapidly (channels has the smallest stride, + // typically 1 element). + // + // In generated C code, we store arrays with the dimensions reversed. The + // first dimension has smallest stride. + // + // We name our variables by their Tensorflow convention, but generate C code + // nesting loops such that the innermost loop has the smallest stride for the + // best cache behavior. + for (int b = 0; b < ArraySize(output_dims, 3); ++b) { + for (int y = 0; y < ArraySize(output_dims, 2); ++y) { + for (int x = 0; x < ArraySize(output_dims, 1); ++x) { + for (int c = 0; c < ArraySize(output_dims, 0); ++c) { + output_data[Offset(output_dims, c, x, y, b)] = + ActivationFunctionWithMinMax( + input1_data[SubscriptToIndex(desc1, c, x, y, b)] - + input2_data[SubscriptToIndex(desc2, c, x, y, b)], + output_activation_min, output_activation_max); + } + } + } + } +} + template <FusedActivationFunctionType Ac, typename Scalar> void Concatenation(int concat_dim, const Scalar* const* input_data, const Dims<4>* const* input_dims, int inputs_count, |