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#
# Copyright (C) 2020 The Android Open Source Project
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# Model: given n, produces [fib(n), fib(n + 1)].
#
# fib = [1, 1]
# i = 1
# while i < n:
# fib = matmul(fib, [0 1;
# 1 1])
# i = i + 1
FibType = ["TENSOR_FLOAT32", [1, 2]]
FibTypeQuant8 = ["TENSOR_QUANT8_ASYMM", 1.0, 0]
FibTypeQuant8Signed = ["TENSOR_QUANT8_ASYMM_SIGNED", 1.0, 0]
CounterType = ["TENSOR_INT32", [1]]
BoolType = ["TENSOR_BOOL8", [1]]
def MakeConditionModel():
fib = Input("fib", FibType)
i = Input("i", CounterType)
n = Input("n", CounterType)
out = Output("out", BoolType)
model = Model()
model.IdentifyInputs(fib, i, n)
model.IdentifyOutputs(out)
model.Operation("LESS", i, n).To(out)
quant8 = DataTypeConverter().Identify({
fib: FibTypeQuant8,
})
quant8_signed = DataTypeConverter().Identify({
fib: FibTypeQuant8Signed,
})
return SubgraphReference("cond", model), quant8, quant8_signed
def MakeBodyModel():
fib = Input("fib", FibType)
i = Input("i", CounterType)
n = Input("n", CounterType)
fib_out = Output("fib_out", FibType)
i_out = Output("i_out", CounterType)
matrix = Parameter("matrix", ["TENSOR_FLOAT32", [2, 2]], [0, 1, 1, 1])
zero_bias = Parameter("zero_bias", ["TENSOR_FLOAT32", [2, 1]], [0, 0])
model = Model()
model.IdentifyInputs(fib, i, n)
model.IdentifyOutputs(fib_out, i_out)
model.Operation("ADD", i, [1], 0).To(i_out)
model.Operation("FULLY_CONNECTED", fib, matrix, zero_bias, 0).To(fib_out)
quant8 = DataTypeConverter().Identify({
fib: FibTypeQuant8,
matrix: FibTypeQuant8,
zero_bias: ["TENSOR_INT32", 1.0, 0],
fib_out: FibTypeQuant8,
})
quant8_signed = DataTypeConverter().Identify({
fib: FibTypeQuant8Signed,
matrix: FibTypeQuant8Signed,
zero_bias: ["TENSOR_INT32", 1.0, 0],
fib_out: FibTypeQuant8Signed,
})
return SubgraphReference("body", model), quant8, quant8_signed
def Test(n_data, fib_data, add_unused_output=False):
n = Input("n", CounterType)
fib_out = Output("fib_out", FibType)
cond, cond_quant8, cond_quant8_signed = MakeConditionModel()
body, body_quant8, body_quant8_signed = MakeBodyModel()
fib_init = Parameter("fib_init", FibType, [1, 1])
i_init = [1]
outputs = [fib_out]
if add_unused_output:
i_out = Internal("i_out", CounterType) # Unused.
outputs.append(i_out)
model = Model().Operation("WHILE", cond, body, fib_init, i_init, n).To(outputs)
quant8 = DataTypeConverter().Identify({
fib_init: FibTypeQuant8,
fib_out: FibTypeQuant8,
cond: cond_quant8,
body: body_quant8,
})
quant8_signed = DataTypeConverter().Identify({
fib_init: FibTypeQuant8Signed,
fib_out: FibTypeQuant8Signed,
cond: cond_quant8_signed,
body: body_quant8_signed,
})
name = "n_{}".format(n_data)
if add_unused_output:
name += "_unused_output"
example = Example({n: [n_data], fib_out: fib_data}, name=name)
example.AddVariations("relaxed", "float16", quant8, quant8_signed)
example.AddVariations(AllOutputsAsInternalCoverter())
for use_shm_for_weights in [False, True]:
Configuration.use_shm_for_weights = use_shm_for_weights
# Fibonacci numbers: 1 1 2 3 5 8
Test(n_data=1, fib_data=[1, 1], add_unused_output=True)
Test(n_data=2, fib_data=[1, 2], add_unused_output=True)
Test(n_data=3, fib_data=[2, 3], add_unused_output=True)
Test(n_data=4, fib_data=[3, 5])
Test(n_data=5, fib_data=[5, 8])
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