2748 lines
121 KiB
C++
2748 lines
121 KiB
C++
/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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==============================================================================*/
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// Unit test for TFLite LSTM op.
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//
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// TODO(alanchiao): add unit test with invalid input dimensions for this and its
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// variants.
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#include <stdint.h>
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#include <utility>
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#include <vector>
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#include <gmock/gmock.h>
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#include <gtest/gtest.h>
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#include "flatbuffers/flatbuffers.h" // from @flatbuffers
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#include "tensorflow/lite/interpreter.h"
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#include "tensorflow/lite/kernels/test_util.h"
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#include "tensorflow/lite/schema/schema_generated.h"
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namespace tflite {
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namespace {
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using ::testing::ElementsAreArray;
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class LSTMOpModel : public SingleOpModel {
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public:
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LSTMOpModel(int n_batch, int n_input, int n_cell, int n_output, bool use_cifg,
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bool use_peephole, bool use_projection_weights,
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bool use_projection_bias, const TensorType weight_type,
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bool model_has_legacy_20_inputs, bool is_layer_norm,
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bool asymmetric_quantize_inputs)
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: n_input_(n_input),
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n_output_(n_output),
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n_batch_(n_batch),
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weight_type_(weight_type) {
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input_ = AddInput({TensorType_FLOAT32, {n_batch, n_input}});
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if (use_cifg) {
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input_to_input_weights_ = AddNullInput();
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} else {
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input_to_input_weights_ = AddInput({weight_type, {n_cell, n_input}});
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}
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input_to_forget_weights_ = AddInput({weight_type, {n_cell, n_input}});
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input_to_cell_weights_ = AddInput({weight_type, {n_cell, n_input}});
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input_to_output_weights_ = AddInput({weight_type, {n_cell, n_input}});
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if (use_cifg) {
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recurrent_to_input_weights_ = AddNullInput();
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} else {
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recurrent_to_input_weights_ = AddInput({weight_type, {n_cell, n_output}});
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}
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recurrent_to_forget_weights_ = AddInput({weight_type, {n_cell, n_output}});
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recurrent_to_cell_weights_ = AddInput({weight_type, {n_cell, n_output}});
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recurrent_to_output_weights_ = AddInput({weight_type, {n_cell, n_output}});
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if (use_peephole) {
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if (use_cifg) {
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cell_to_input_weights_ = AddNullInput();
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} else {
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cell_to_input_weights_ = AddInput({weight_type, {n_cell}});
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}
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cell_to_forget_weights_ = AddInput({weight_type, {n_cell}});
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cell_to_output_weights_ = AddInput({weight_type, {n_cell}});
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} else {
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cell_to_input_weights_ = AddNullInput();
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cell_to_forget_weights_ = AddNullInput();
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cell_to_output_weights_ = AddNullInput();
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}
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if (use_cifg) {
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input_gate_bias_ = AddNullInput();
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} else {
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input_gate_bias_ = AddInput({TensorType_FLOAT32, {n_cell}});
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}
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forget_gate_bias_ = AddInput({TensorType_FLOAT32, {n_cell}});
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cell_gate_bias_ = AddInput({TensorType_FLOAT32, {n_cell}});
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output_gate_bias_ = AddInput({TensorType_FLOAT32, {n_cell}});
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if (use_projection_weights) {
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projection_weights_ = AddInput({weight_type, {n_output, n_cell}});
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} else {
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projection_weights_ = AddNullInput();
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}
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if (use_projection_bias) {
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CHECK(use_projection_weights);
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projection_bias_ = AddInput({TensorType_FLOAT32, {n_output}});
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} else {
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projection_bias_ = AddNullInput();
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}
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// Adding the 2 state tensors.
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AddVariableInput({TensorType_FLOAT32, {n_batch, n_output}});
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AddVariableInput({TensorType_FLOAT32, {n_batch, n_cell}});
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// Layer norm weights.
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if (!model_has_legacy_20_inputs) {
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if (is_layer_norm) {
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if (use_cifg) {
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input_layer_norm_coefficients_ = AddNullInput();
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} else {
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input_layer_norm_coefficients_ =
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AddInput({TensorType_FLOAT32, {n_cell}});
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}
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forget_layer_norm_coefficients_ =
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AddInput({TensorType_FLOAT32, {n_cell}});
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cell_layer_norm_coefficients_ =
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AddInput({TensorType_FLOAT32, {n_cell}});
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output_layer_norm_coefficients_ =
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AddInput({TensorType_FLOAT32, {n_cell}});
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} else {
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input_layer_norm_coefficients_ = AddNullInput();
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forget_layer_norm_coefficients_ = AddNullInput();
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cell_layer_norm_coefficients_ = AddNullInput();
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output_layer_norm_coefficients_ = AddNullInput();
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}
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}
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output_ = AddOutput({TensorType_FLOAT32, {n_batch, n_output}});
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// TODO(b/161825581): Add tests where cell_clip and/or proj_clip is not the
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// default 0.
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SetBuiltinOp(
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BuiltinOperator_LSTM, BuiltinOptions_LSTMOptions,
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CreateLSTMOptions(builder_, ActivationFunctionType_TANH,
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/*cell_clip=*/0.0f, /*proj_clip=*/0.0f,
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LSTMKernelType_FULL, asymmetric_quantize_inputs)
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.Union());
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// Input shapes are already set up, no need to pass them again.
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BuildInterpreter(/*input_shapes=*/{}, /*num_threads=*/-1,
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/*allow_fp32_relax_to_fp16=*/false,
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/*apply_delegate=*/false);
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}
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void SetInputToInputWeights(const std::vector<float>& f) {
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SetWeights(input_to_input_weights_, f);
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}
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void SetInputToForgetWeights(const std::vector<float>& f) {
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SetWeights(input_to_forget_weights_, f);
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}
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void SetInputToCellWeights(const std::vector<float>& f) {
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SetWeights(input_to_cell_weights_, f);
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}
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void SetInputToOutputWeights(const std::vector<float>& f) {
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SetWeights(input_to_output_weights_, f);
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}
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void SetRecurrentToInputWeights(const std::vector<float>& f) {
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SetWeights(recurrent_to_input_weights_, f);
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}
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void SetRecurrentToForgetWeights(const std::vector<float>& f) {
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SetWeights(recurrent_to_forget_weights_, f);
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}
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void SetRecurrentToCellWeights(const std::vector<float>& f) {
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SetWeights(recurrent_to_cell_weights_, f);
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}
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void SetRecurrentToOutputWeights(const std::vector<float>& f) {
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SetWeights(recurrent_to_output_weights_, f);
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}
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void SetCellToInputWeights(const std::vector<float>& f) {
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SetWeights(cell_to_input_weights_, f);
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}
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void SetCellToForgetWeights(const std::vector<float>& f) {
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SetWeights(cell_to_forget_weights_, f);
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}
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void SetCellToOutputWeights(const std::vector<float>& f) {
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SetWeights(cell_to_output_weights_, f);
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}
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void SetInputLayerNormCoefficients(const std::vector<float>& f) {
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PopulateTensor(input_layer_norm_coefficients_, f);
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}
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void SetForgetLayerNormCoefficients(const std::vector<float>& f) {
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PopulateTensor(forget_layer_norm_coefficients_, f);
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}
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void SetCellLayerNormCoefficients(const std::vector<float>& f) {
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PopulateTensor(cell_layer_norm_coefficients_, f);
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}
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void SetOutputLayerNormCoefficients(const std::vector<float>& f) {
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PopulateTensor(output_layer_norm_coefficients_, f);
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}
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void SetInputGateBias(const std::vector<float>& f) {
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PopulateTensor(input_gate_bias_, f);
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}
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void SetForgetGateBias(const std::vector<float>& f) {
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PopulateTensor(forget_gate_bias_, f);
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}
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void SetCellBias(const std::vector<float>& f) {
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PopulateTensor(cell_gate_bias_, f);
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}
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void SetOutputGateBias(const std::vector<float>& f) {
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PopulateTensor(output_gate_bias_, f);
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}
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void SetProjectionWeights(const std::vector<float>& f) {
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SetWeights(projection_weights_, f);
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}
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void SetProjectionBias(const std::vector<float>& f) {
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PopulateTensor(projection_bias_, f);
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}
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void SetInput(int offset, const float* begin, const float* end) {
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SingleOpModel::PopulateTensor(input_, offset, const_cast<float*>(begin),
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const_cast<float*>(end));
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}
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std::vector<float> GetOutput() { return ExtractVector<float>(output_); }
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int num_inputs() { return n_input_; }
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int num_outputs() { return n_output_; }
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int num_batches() { return n_batch_; }
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protected:
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int input_;
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int input_to_input_weights_;
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int input_to_forget_weights_;
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int input_to_cell_weights_;
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int input_to_output_weights_;
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int recurrent_to_input_weights_;
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int recurrent_to_forget_weights_;
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int recurrent_to_cell_weights_;
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int recurrent_to_output_weights_;
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int cell_to_input_weights_;
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int cell_to_forget_weights_;
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int cell_to_output_weights_;
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int input_layer_norm_coefficients_ = kTfLiteOptionalTensor;
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int forget_layer_norm_coefficients_ = kTfLiteOptionalTensor;
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int cell_layer_norm_coefficients_ = kTfLiteOptionalTensor;
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int output_layer_norm_coefficients_ = kTfLiteOptionalTensor;
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int input_gate_bias_;
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int forget_gate_bias_;
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int cell_gate_bias_;
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int output_gate_bias_;
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int projection_weights_;
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int projection_bias_;
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int output_;
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int n_input_;
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int n_output_;
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int n_batch_;
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private:
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void PopulateTensor(int index, const std::vector<float>& data) {
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// Nothing to do if tensor is an optional input or if data vector is empty.
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if ((index == kTfLiteOptionalTensor) || data.empty()) return;
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SingleOpModel::PopulateTensor(index, data);
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}
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void SetWeights(int index, const std::vector<float>& data) {
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if (data.empty()) return;
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if (index == kTfLiteOptionalTensor) return;
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switch (weight_type_) {
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case TensorType_FLOAT32:
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PopulateTensor(index, data);
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break;
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case TensorType_UINT8:
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SymmetricQuantizeAndPopulate(index, data);
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break;
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case TensorType_INT8:
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SignedSymmetricQuantizeAndPopulate(index, data);
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break;
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default:
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GTEST_FAIL() << "Type not supported: " << weight_type_;
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break;
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}
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}
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const TensorType weight_type_;
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};
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// Parameters:
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// std::get<0>(GetParam()) => weight_type
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// std::get<1>(GetParam()) => model_has_legacy_20_inputs
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// std::get<2>(GetParam()) => asymmetric_quantize_inputs
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class LstmOpTest
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: public ::testing::TestWithParam<std::tuple<TensorType, bool, bool>> {
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protected:
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// Weights of the LSTM model. Some are optional.
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std::vector<float> input_to_input_weights_;
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std::vector<float> input_to_cell_weights_;
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std::vector<float> input_to_forget_weights_;
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std::vector<float> input_to_output_weights_;
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std::vector<float> input_gate_bias_;
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std::vector<float> cell_gate_bias_;
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std::vector<float> forget_gate_bias_;
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std::vector<float> output_gate_bias_;
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std::vector<float> recurrent_to_input_weights_;
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std::vector<float> recurrent_to_cell_weights_;
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std::vector<float> recurrent_to_forget_weights_;
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std::vector<float> recurrent_to_output_weights_;
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std::vector<float> cell_to_input_weights_;
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std::vector<float> cell_to_forget_weights_;
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std::vector<float> cell_to_output_weights_;
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std::vector<float> projection_weights_;
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std::vector<float> input_layer_norm_coefficients_;
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std::vector<float> forget_layer_norm_coefficients_;
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std::vector<float> cell_layer_norm_coefficients_;
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std::vector<float> output_layer_norm_coefficients_;
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// LSTM input is stored as num_steps * num_batch * num_inputs vector.
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std::vector<std::vector<std::vector<float>>> lstm_input_;
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// LSTM output is stored as num_steps * num_batch * num_outputs vector.
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std::vector<std::vector<std::vector<float>>> lstm_golden_output_;
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// Compares output up to tolerance to the result of the lstm given the input.
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void VerifyGoldens(LSTMOpModel* lstm, float tolerance) {
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// The delegate, if used, needs to know the scales and zero-points of
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// quantized tensors, which are computed dynamically when weights are set,
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// so weights have to be set before applying the delegate.
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SetAllWeightsAndBiases(lstm);
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lstm->ApplyDelegate();
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const int num_inputs = lstm->num_inputs();
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const int num_outputs = lstm->num_outputs();
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const int num_batches = lstm->num_batches();
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ASSERT_EQ(lstm_input_.size(), lstm_golden_output_.size());
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const int num_steps = lstm_input_.size();
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for (int i = 0; i < num_steps; ++i) {
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ASSERT_EQ(num_batches, lstm_input_[i].size());
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for (int b = 0; b < num_batches; ++b) {
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ASSERT_EQ(num_inputs, lstm_input_[i][b].size());
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const float* batch_start = lstm_input_[i][b].data();
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const float* batch_end = batch_start + num_inputs;
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lstm->SetInput(b * num_inputs, batch_start, batch_end);
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}
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lstm->Invoke();
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std::vector<float> expected;
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ASSERT_EQ(num_batches, lstm_golden_output_[i].size());
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for (int b = 0; b < num_batches; ++b) {
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ASSERT_EQ(num_outputs, lstm_golden_output_[i][b].size());
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const float* batch_start = lstm_golden_output_[i][b].data();
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const float* batch_end = batch_start + num_outputs;
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expected.insert(expected.end(), batch_start, batch_end);
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}
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EXPECT_THAT(lstm->GetOutput(),
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ElementsAreArray(ArrayFloatNear(expected, tolerance)));
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}
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}
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// Sets all weights and biases that have been defined by test. The test can
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// define only a subset of all those vectors, and only the ones that have been
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// defined will be set.
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void SetAllWeightsAndBiases(LSTMOpModel* lstm) {
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lstm->SetInputToInputWeights(input_to_input_weights_);
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lstm->SetInputToCellWeights(input_to_cell_weights_);
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lstm->SetInputToForgetWeights(input_to_forget_weights_);
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lstm->SetInputToOutputWeights(input_to_output_weights_);
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lstm->SetInputGateBias(input_gate_bias_);
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lstm->SetCellBias(cell_gate_bias_);
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lstm->SetForgetGateBias(forget_gate_bias_);
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lstm->SetOutputGateBias(output_gate_bias_);
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lstm->SetRecurrentToInputWeights(recurrent_to_input_weights_);
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lstm->SetRecurrentToCellWeights(recurrent_to_cell_weights_);
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lstm->SetRecurrentToForgetWeights(recurrent_to_forget_weights_);
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lstm->SetRecurrentToOutputWeights(recurrent_to_output_weights_);
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lstm->SetCellToInputWeights(cell_to_input_weights_);
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lstm->SetCellToForgetWeights(cell_to_forget_weights_);
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lstm->SetCellToOutputWeights(cell_to_output_weights_);
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lstm->SetProjectionWeights(projection_weights_);
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lstm->SetInputLayerNormCoefficients(input_layer_norm_coefficients_);
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lstm->SetForgetLayerNormCoefficients(forget_layer_norm_coefficients_);
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lstm->SetCellLayerNormCoefficients(cell_layer_norm_coefficients_);
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lstm->SetOutputLayerNormCoefficients(output_layer_norm_coefficients_);
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}
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};
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TEST_P(LstmOpTest, NoCifg_NoPeephole_NoProjection_NoLayerNorm) {
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const int n_batch = 1;
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const int n_input = 2;
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// n_cell and n_output have the same size when there is no projection.
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const int n_cell = 4;
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const int n_output = 4;
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TensorType weight_type;
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bool model_has_legacy_20_inputs;
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bool asymmetric_quantize_inputs;
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std::tie(weight_type, model_has_legacy_20_inputs,
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asymmetric_quantize_inputs) = GetParam();
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// TODO(b/158205028): Fix this test if using NN-API.
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if (SingleOpModel::GetForceUseNnapi() && weight_type == TensorType_UINT8) {
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return;
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}
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input_to_input_weights_ = {-0.45018822, -0.02338299, -0.0870589, -0.34550029,
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0.04266912, -0.15680569, -0.34856534, 0.43890524};
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input_to_cell_weights_ = {-0.50013041, 0.1370284, 0.11810488, 0.2013163,
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-0.20583314, 0.44344562, 0.22077113, -0.29909778};
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input_to_forget_weights_ = {0.09701663, 0.20334584, -0.50592935,
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-0.31343272, -0.40032279, 0.44781327,
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0.01387155, -0.35593212};
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input_to_output_weights_ = {-0.25065863, -0.28290087, 0.04613829, 0.40525138,
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0.44272184, 0.03897077, -0.1556896, 0.19487578};
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input_gate_bias_ = {0., 0., 0., 0.};
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cell_gate_bias_ = {0., 0., 0., 0.};
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forget_gate_bias_ = {1., 1., 1., 1.};
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output_gate_bias_ = {0., 0., 0., 0.};
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recurrent_to_input_weights_ = {
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-0.0063535, -0.2042388, 0.31454784, -0.35746509,
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0.28902304, 0.08183324, -0.16555229, 0.02286911,
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-0.13566875, 0.03034258, 0.48091322, -0.12528998,
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0.24077177, -0.51332325, -0.33502164, 0.10629296};
|
|
|
|
recurrent_to_cell_weights_ = {
|
|
-0.3407414, 0.24443203, -0.2078532, 0.26320225,
|
|
0.05695659, -0.00123841, -0.4744786, -0.35869038,
|
|
-0.06418842, -0.13502428, -0.501764, 0.22830659,
|
|
-0.46367589, 0.26016325, -0.03894562, -0.16368064};
|
|
|
|
recurrent_to_forget_weights_ = {
|
|
-0.48684245, -0.06655136, 0.42224967, 0.2112639,
|
|
0.27654213, 0.20864892, -0.07646349, 0.45877004,
|
|
0.00141793, -0.14609534, 0.36447752, 0.09196436,
|
|
0.28053468, 0.01560611, -0.20127171, -0.01140004};
|
|
|
|
recurrent_to_output_weights_ = {
|
|
0.43385774, -0.17194885, 0.2718237, 0.09215671,
|
|
0.24107647, -0.39835793, 0.18212086, 0.01301402,
|
|
0.48572797, -0.50656658, 0.20047462, -0.20607421,
|
|
-0.51818722, -0.15390486, 0.0468148, 0.39922136};
|
|
|
|
// num_steps * num_batch * num_inputs
|
|
lstm_input_ = {{{2., 3.}}, {{3., 4.}}, {{1., 1.}}};
|
|
// num_steps * num_batch * num_outputs
|
|
lstm_golden_output_ = {{{-0.02973187, 0.1229473, 0.20885126, -0.15358765}},
|
|
{{-0.03716109, 0.12507336, 0.41193449, -0.20860538}},
|
|
{{-0.15053082, 0.09120187, 0.24278517, -0.12222792}}};
|
|
|
|
LSTMOpModel lstm(n_batch, n_input, n_cell, n_output,
|
|
/*use_cifg=*/false, /*use_peephole=*/false,
|
|
/*use_projection_weights=*/false,
|
|
/*use_projection_bias=*/false, weight_type,
|
|
model_has_legacy_20_inputs,
|
|
/*is_layer_norm=*/false, asymmetric_quantize_inputs);
|
|
|
|
static const auto* tolerance_per_type =
|
|
new std::map<TensorType, float>{{TensorType_FLOAT32, 0.00001f},
|
|
{TensorType_UINT8, 0.0157651f},
|
|
{TensorType_INT8, 0.0157651f}};
|
|
VerifyGoldens(&lstm, tolerance_per_type->at(weight_type));
|
|
}
|
|
|
|
TEST_P(LstmOpTest, Cifg_Peephole_NoProjection_NoLayerNorm) {
|
|
const int n_batch = 1;
|
|
const int n_input = 2;
|
|
// n_cell and n_output have the same size when there is no projection.
|
|
const int n_cell = 4;
|
|
const int n_output = 4;
|
|
|
|
TensorType weight_type;
|
|
bool model_has_legacy_20_inputs;
|
|
bool asymmetric_quantize_inputs;
|
|
std::tie(weight_type, model_has_legacy_20_inputs,
|
|
asymmetric_quantize_inputs) = GetParam();
|
|
|
|
// TODO(b/158205028): Fix this test if using NN-API.
|
|
if (SingleOpModel::GetForceUseNnapi() && weight_type == TensorType_UINT8) {
|
|
return;
|
|
}
|
|
|
|
input_to_cell_weights_ = {-0.49770179, -0.27711356, -0.09624726, 0.05100781,
|
|
0.04717243, 0.48944736, -0.38535351, -0.17212132};
|
|
|
|
input_to_forget_weights_ = {-0.55291498, -0.42866567, 0.13056988, -0.3633365,
|
|
-0.22755712, 0.28253698, 0.24407166, 0.33826375};
|
|
|
|
input_to_output_weights_ = {0.10725588, -0.02335852, -0.55932593,
|
|
-0.09426838, -0.44257352, 0.54939759,
|
|
0.01533556, 0.42751634};
|
|
cell_gate_bias_ = {0., 0., 0., 0.};
|
|
forget_gate_bias_ = {1., 1., 1., 1.};
|
|
output_gate_bias_ = {0., 0., 0., 0.};
|
|
|
|
recurrent_to_cell_weights_ = {
|
|
0.54066205, -0.32668582, -0.43562764, -0.56094903,
|
|
0.42957711, 0.01841056, -0.32764608, -0.33027974,
|
|
-0.10826075, 0.20675004, 0.19069612, -0.03026325,
|
|
-0.54532051, 0.33003211, 0.44901288, 0.21193194};
|
|
|
|
recurrent_to_forget_weights_ = {
|
|
-0.13832897, -0.0515101, -0.2359007, -0.16661474,
|
|
-0.14340827, 0.36986142, 0.23414481, 0.55899,
|
|
0.10798943, -0.41174671, 0.17751795, -0.34484994,
|
|
-0.35874045, -0.11352962, 0.27268326, 0.54058349};
|
|
|
|
recurrent_to_output_weights_ = {
|
|
0.41613156, 0.42610586, -0.16495961, -0.5663873,
|
|
0.30579174, -0.05115908, -0.33941799, 0.23364776,
|
|
0.11178309, 0.09481031, -0.26424935, 0.46261835,
|
|
0.50248802, 0.26114327, -0.43736315, 0.33149987};
|
|
|
|
cell_to_forget_weights_ = {0.47485286, -0.51955009, -0.24458408, 0.31544167};
|
|
cell_to_output_weights_ = {-0.17135078, 0.82760304, 0.85573703, -0.77109635};
|
|
|
|
lstm_input_ = {{{2., 3.}}, {{3., 4.}}, {{1., 1.}}};
|
|
lstm_golden_output_ = {{{-0.36444446, -0.00352185, 0.12886585, -0.05163646}},
|
|
{{-0.42312205, -0.01218222, 0.24201041, -0.08124574}},
|
|
{{-0.358325, -0.04621704, 0.21641694, -0.06471302}}};
|
|
|
|
LSTMOpModel lstm(n_batch, n_input, n_cell, n_output,
|
|
/*use_cifg=*/true, /*use_peephole=*/true,
|
|
/*use_projection_weights=*/false,
|
|
/*use_projection_bias=*/false, weight_type,
|
|
model_has_legacy_20_inputs, /*is_layer_norm=*/false,
|
|
asymmetric_quantize_inputs);
|
|
|
|
static const auto* tolerance_per_type =
|
|
new std::map<TensorType, float>{{TensorType_FLOAT32, 0.00001f},
|
|
{TensorType_UINT8, 0.03573f},
|
|
{TensorType_INT8, 0.03573f}};
|
|
VerifyGoldens(&lstm, tolerance_per_type->at(weight_type));
|
|
}
|
|
|
|
TEST_P(LstmOpTest, NoCifg_Peephole_Projection_NoLayerNorm) {
|
|
const int n_batch = 2;
|
|
const int n_input = 5;
|
|
const int n_cell = 20;
|
|
const int n_output = 16;
|
|
|
|
TensorType weight_type;
|
|
bool model_has_legacy_20_inputs;
|
|
bool asymmetric_quantize_inputs;
|
|
std::tie(weight_type, model_has_legacy_20_inputs,
|
|
asymmetric_quantize_inputs) = GetParam();
|
|
|
|
// TODO(b/158205028): Fix this test if using NN-API.
|
|
if (SingleOpModel::GetForceUseNnapi() && weight_type == TensorType_UINT8) {
|
|
return;
|
|
}
|
|
|
|
input_to_input_weights_ = {
|
|
0.021393683, 0.06124551, 0.046905167, -0.014657677, -0.03149463,
|
|
0.09171803, 0.14647801, 0.10797193, -0.0057968358, 0.0019193048,
|
|
-0.2726754, 0.10154029, -0.018539885, 0.080349885, -0.10262385,
|
|
-0.022599787, -0.09121155, -0.008675967, -0.045206103, -0.0821282,
|
|
-0.008045952, 0.015478081, 0.055217247, 0.038719587, 0.044153627,
|
|
-0.06453243, 0.05031825, -0.046935108, -0.008164439, 0.014574226,
|
|
-0.1671009, -0.15519552, -0.16819797, -0.13971269, -0.11953059,
|
|
0.25005487, -0.22790983, 0.009855087, -0.028140958, -0.11200698,
|
|
0.11295408, -0.0035217577, 0.054485075, 0.05184695, 0.064711206,
|
|
0.10989193, 0.11674786, 0.03490607, 0.07727357, 0.11390585,
|
|
-0.1863375, -0.1034451, -0.13945189, -0.049401227, -0.18767063,
|
|
0.042483903, 0.14233552, 0.13832581, 0.18350165, 0.14545603,
|
|
-0.028545704, 0.024939531, 0.050929718, 0.0076203286, -0.0029723682,
|
|
-0.042484224, -0.11827596, -0.09171104, -0.10808628, -0.16327988,
|
|
-0.2273378, -0.0993647, -0.017155107, 0.0023917493, 0.049272764,
|
|
0.0038534778, 0.054764505, 0.089753784, 0.06947234, 0.08014476,
|
|
-0.04544234, -0.0497073, -0.07135631, -0.048929106, -0.004042012,
|
|
-0.009284026, 0.018042054, 0.0036860977, -0.07427302, -0.11434604,
|
|
-0.018995456, 0.031487543, 0.012834908, 0.019977754, 0.044256654,
|
|
-0.39292613, -0.18519334, -0.11651281, -0.06809892, 0.011373677};
|
|
|
|
input_to_forget_weights_ = {
|
|
-0.0018401089, -0.004852237, 0.03698424, 0.014181704, 0.028273236,
|
|
-0.016726194, -0.05249759, -0.10204261, 0.00861066, -0.040979505,
|
|
-0.009899187, 0.01923892, -0.028177269, -0.08535103, -0.14585495,
|
|
0.10662567, -0.01909731, -0.017883534, -0.0047269356, -0.045103323,
|
|
0.0030784295, 0.076784775, 0.07463696, 0.094531395, 0.0814421,
|
|
-0.12257899, -0.033945758, -0.031303465, 0.045630626, 0.06843887,
|
|
-0.13492945, -0.012480007, -0.0811829, -0.07224499, -0.09628791,
|
|
0.045100946, 0.0012300825, 0.013964662, 0.099372394, 0.02543059,
|
|
0.06958324, 0.034257296, 0.0482646, 0.06267997, 0.052625068,
|
|
0.12784666, 0.07077897, 0.025725935, 0.04165009, 0.07241905,
|
|
0.018668644, -0.037377294, -0.06277783, -0.08833636, -0.040120605,
|
|
-0.011405586, -0.007808335, -0.010301386, -0.005102167, 0.027717464,
|
|
0.05483423, 0.11449111, 0.11289652, 0.10939839, 0.13396506,
|
|
-0.08402166, -0.01901462, -0.044678304, -0.07720565, 0.014350063,
|
|
-0.11757958, -0.0652038, -0.08185733, -0.076754324, -0.092614375,
|
|
0.10405491, 0.052960336, 0.035755895, 0.035839386, -0.012540553,
|
|
0.036881298, 0.02913376, 0.03420159, 0.05448447, -0.054523353,
|
|
0.02582715, 0.02327355, -0.011857179, -0.0011980024, -0.034641717,
|
|
-0.026125094, -0.17582615, -0.15923657, -0.27486774, -0.0006143371,
|
|
0.0001771948, -8.470171e-05, 0.02651807, 0.045790765, 0.06956496};
|
|
|
|
input_to_cell_weights_ = {
|
|
-0.04580283, -0.09549462, -0.032418985, -0.06454633, -0.043528453,
|
|
0.043018587, -0.049152344, -0.12418144, -0.078985475, -0.07596889,
|
|
0.019484362, -0.11434962, -0.0074034138, -0.06314844, -0.092981495,
|
|
0.0062155537, -0.025034338, -0.0028890965, 0.048929527, 0.06235075,
|
|
0.10665918, -0.032036792, -0.08505916, -0.10843358, -0.13002433,
|
|
-0.036816437, -0.02130134, -0.016518239, 0.0047691227, -0.0025825808,
|
|
0.066017866, 0.029991534, -0.10652836, -0.1037554, -0.13056071,
|
|
-0.03266643, -0.033702414, -0.006473424, -0.04611692, 0.014419339,
|
|
-0.025174323, 0.0396852, 0.081777506, 0.06157468, 0.10210095,
|
|
-0.009658194, 0.046511717, 0.03603906, 0.0069369148, 0.015960095,
|
|
-0.06507666, 0.09551598, 0.053568836, 0.06408714, 0.12835667,
|
|
-0.008714329, -0.20211966, -0.12093674, 0.029450472, 0.2849013,
|
|
-0.029227901, 0.1164364, -0.08560263, 0.09941786, -0.036999565,
|
|
-0.028842626, -0.0033637602, -0.017012902, -0.09720865, -0.11193351,
|
|
-0.029155117, -0.017936034, -0.009768936, -0.04223324, -0.036159635,
|
|
0.06505112, -0.021742892, -0.023377212, -0.07221364, -0.06430552,
|
|
0.05453865, 0.091149814, 0.06387331, 0.007518393, 0.055960953,
|
|
0.069779344, 0.046411168, 0.10509911, 0.07463894, 0.0075130584,
|
|
0.012850982, 0.04555431, 0.056955688, 0.06555285, 0.050801456,
|
|
-0.009862683, 0.00826772, -0.026555609, -0.0073611983, -0.0014897042};
|
|
|
|
input_to_output_weights_ = {
|
|
-0.0998932, -0.07201956, -0.052803773, -0.15629593, -0.15001918,
|
|
-0.07650751, 0.02359855, -0.075155355, -0.08037709, -0.15093534,
|
|
0.029517552, -0.04751393, 0.010350531, -0.02664851, -0.016839722,
|
|
-0.023121163, 0.0077019283, 0.012851257, -0.05040649, -0.0129761,
|
|
-0.021737747, -0.038305793, -0.06870586, -0.01481247, -0.001285394,
|
|
0.10124236, 0.083122835, 0.053313006, -0.062235646, -0.075637154,
|
|
-0.027833903, 0.029774971, 0.1130802, 0.09218906, 0.09506135,
|
|
-0.086665764, -0.037162706, -0.038880914, -0.035832845, -0.014481564,
|
|
-0.09825003, -0.12048569, -0.097665586, -0.05287633, -0.0964047,
|
|
-0.11366429, 0.035777505, 0.13568819, 0.052451383, 0.050649304,
|
|
0.05798951, -0.021852335, -0.099848844, 0.014740475, -0.078897946,
|
|
0.04974699, 0.014160473, 0.06973932, 0.04964942, 0.033364646,
|
|
0.08190124, 0.025535367, 0.050893165, 0.048514254, 0.06945813,
|
|
-0.078907564, -0.06707616, -0.11844508, -0.09986688, -0.07509403,
|
|
0.06263226, 0.14925587, 0.20188436, 0.12098451, 0.14639415,
|
|
0.0015017595, -0.014267382, -0.03417257, 0.012711468, 0.0028300495,
|
|
-0.024758482, -0.05098548, -0.0821182, 0.014225672, 0.021544158,
|
|
0.08949725, 0.07505268, -0.0020780868, 0.04908258, 0.06476295,
|
|
-0.022907063, 0.027562456, 0.040185735, 0.019567577, -0.015598739,
|
|
-0.049097303, -0.017121866, -0.083368234, -0.02332002, -0.0840956};
|
|
|
|
input_gate_bias_ = {0.02234832, 0.14757581, 0.18176508, 0.10380666,
|
|
0.053110216, -0.06928846, -0.13942584, -0.11816189,
|
|
0.19483899, 0.03652339, -0.10250295, 0.036714908,
|
|
-0.18426876, 0.036065217, 0.21810818, 0.02383196,
|
|
-0.043370757, 0.08690144, -0.04444982, 0.00030581196};
|
|
|
|
forget_gate_bias_ = {0.035185695, -0.042891346, -0.03032477, 0.23027696,
|
|
0.11098921, 0.15378423, 0.09263801, 0.09790885,
|
|
0.09508917, 0.061199076, 0.07665568, -0.015443159,
|
|
-0.03499149, 0.046190713, 0.08895977, 0.10899629,
|
|
0.40694186, 0.06030037, 0.012413437, -0.06108739};
|
|
|
|
cell_gate_bias_ = {-0.024379363, 0.0055531194, 0.23377132, 0.033463873,
|
|
-0.1483596, -0.10639995, -0.091433935, 0.058573797,
|
|
-0.06809782, -0.07889636, -0.043246906, -0.09829136,
|
|
-0.4279842, 0.034901652, 0.18797937, 0.0075234566,
|
|
0.016178843, 0.1749513, 0.13975595, 0.92058027};
|
|
|
|
output_gate_bias_ = {0.046159424, -0.0012809046, 0.03563469, 0.12648113,
|
|
0.027195795, 0.35373217, -0.018957434, 0.008907322,
|
|
-0.0762701, 0.12018895, 0.04216877, 0.0022856654,
|
|
0.040952638, 0.3147856, 0.08225149, -0.057416286,
|
|
-0.14995944, -0.008040261, 0.13208859, 0.029760877};
|
|
|
|
recurrent_to_input_weights_ = {
|
|
-0.001374326, -0.078856036, 0.10672688, 0.029162422,
|
|
-0.11585556, 0.02557986, -0.13446963, -0.035785314,
|
|
-0.01244275, 0.025961924, -0.02337298, -0.044228926,
|
|
-0.055839065, -0.046598054, -0.010546039, -0.06900766,
|
|
0.027239809, 0.022582639, -0.013296484, -0.05459212,
|
|
0.08981, -0.045407712, 0.08682226, -0.06867011,
|
|
-0.14390695, -0.02916037, 0.000996957, 0.091420636,
|
|
0.14283475, -0.07390571, -0.06402044, 0.062524505,
|
|
-0.093129106, 0.04860203, -0.08364217, -0.08119002,
|
|
0.009352075, 0.22920375, 0.0016303885, 0.11583097,
|
|
-0.13732095, 0.012405723, -0.07551853, 0.06343048,
|
|
0.12162708, -0.031923793, -0.014335606, 0.01790974,
|
|
-0.10650317, -0.0724401, 0.08554849, -0.05727212,
|
|
0.06556731, -0.042729504, -0.043227166, 0.011683251,
|
|
-0.013082158, -0.029302018, -0.010899579, -0.062036745,
|
|
-0.022509435, -0.00964907, -0.01567329, 0.04260106,
|
|
-0.07787477, -0.11576462, 0.017356863, 0.048673786,
|
|
-0.017577527, -0.05527947, -0.082487635, -0.040137455,
|
|
-0.10820036, -0.04666372, 0.022746278, -0.07851417,
|
|
0.01068115, 0.032956902, 0.022433773, 0.0026891115,
|
|
0.08944216, -0.0685835, 0.010513544, 0.07228705,
|
|
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|
|
-0.0039728214, -0.08683098, -0.08116779, -0.014675607, -0.037924774,
|
|
-0.023314456, -0.007401714, -0.09255757, 0.029460307, -0.08829125,
|
|
-0.005139627, -0.08989442, -0.0555066, 0.13596267, -0.025062224,
|
|
-0.048351806, -0.03850004, 0.07266485, -0.022414139, 0.05940088,
|
|
0.075114764, 0.09597592, -0.010211725, -0.0049794707, -0.011523867,
|
|
-0.025980417, 0.072999895, 0.11091378, -0.081685916, 0.014416728,
|
|
0.043229222, 0.034178585, -0.07530371, 0.035837382, -0.085607,
|
|
-0.007721233, -0.03287832, -0.043848954, -0.06404588, -0.06632928,
|
|
-0.073643476, 0.008214239, -0.045984086, 0.039764922, 0.03474462,
|
|
0.060612556, -0.080590084, 0.049127717, 0.04151091, -0.030063879,
|
|
0.008801774, -0.023021035, -0.019558564, 0.05158114, -0.010947698,
|
|
-0.011825728, 0.0075720972, 0.0699727, -0.0039981045, 0.069350146,
|
|
0.08799282, 0.016156472, 0.035502106, 0.11695009, 0.006217345,
|
|
0.13392477, -0.037875112, 0.025745004, 0.08940699, -0.00924166,
|
|
0.0046702605, -0.036598757, -0.08811812, 0.10522024, -0.032441203,
|
|
0.008176899, -0.04454919, 0.07058152, 0.0067963637, 0.039206743,
|
|
0.03259838, 0.03725492, -0.09515802, 0.013326398, -0.052055415,
|
|
-0.025676316, 0.03198509, -0.015951829, -0.058556724, 0.036879618,
|
|
0.043357447, 0.028362012, -0.05908629, 0.0059240665, -0.04995891,
|
|
-0.019187413, 0.0276265, -0.01628143, 0.0025863599, 0.08800015,
|
|
0.035250366, -0.022165963, -0.07328642, -0.009415526, -0.07455109,
|
|
0.11690406, 0.0363299, 0.07411125, 0.042103454, -0.009660886,
|
|
0.019076364, 0.018299393, -0.046004917, 0.08891175, 0.0431396,
|
|
-0.026327137, -0.051502608, 0.08979574, -0.051670972, 0.04940282,
|
|
-0.07491107, -0.021240504, 0.022596184, -0.034280192, 0.060163025,
|
|
-0.058211457, -0.051837247, -0.01349775, -0.04639988, -0.035936575,
|
|
-0.011681591, 0.064818054, 0.0073146066, -0.021745546, -0.043124277,
|
|
-0.06471268, -0.07053354, -0.029321948, -0.05330136, 0.016933719,
|
|
-0.053782392, 0.13747959, -0.1361751, -0.11569455, 0.0033329215,
|
|
0.05693899, -0.053219706, 0.063698, 0.07977434, -0.07924483,
|
|
0.06936997, 0.0034815092, -0.007305279, -0.037325785, -0.07251102,
|
|
-0.033633437, -0.08677009, 0.091591336, -0.14165086, 0.021752775,
|
|
0.019683983, 0.0011612234, -0.058154266, 0.049996935, 0.0288841,
|
|
-0.0024567875, -0.14345716, 0.010955264, -0.10234828, 0.1183656,
|
|
-0.0010731248, -0.023590032, -0.072285876, -0.0724771, -0.026382286,
|
|
-0.0014920527, 0.042667855, 0.0018776858, 0.02986552, 0.009814309,
|
|
0.0733756, 0.12289186, 0.018043943, -0.0458958, 0.049412545,
|
|
0.033632483, 0.05495232, 0.036686596, -0.013781798, -0.010036754,
|
|
0.02576849, -0.08307328, 0.010112348, 0.042521734, -0.05869831,
|
|
-0.071689695, 0.03876447, -0.13275425, -0.0352966, -0.023077697,
|
|
0.10285965, 0.084736146, 0.15568255, -0.00040734606, 0.027835453,
|
|
-0.10292561, -0.032401145, 0.10053256, -0.026142767, -0.08271222,
|
|
-0.0030240538, -0.016368777, 0.1070414, 0.042672627, 0.013456989,
|
|
-0.0437609, -0.022309763, 0.11576483, 0.04108048, 0.061026827,
|
|
-0.0190714, -0.0869359, 0.037901703, 0.0610107, 0.07202949,
|
|
0.01675338, 0.086139716, -0.08795751, -0.014898893, -0.023771819,
|
|
-0.01965048, 0.007955471, -0.043740474, 0.03346837, -0.10549954,
|
|
0.090567775, 0.042013682, -0.03176985, 0.12569028, -0.02421228,
|
|
-0.029526481, 0.023851605, 0.031539805, 0.05292009, -0.02344001,
|
|
-0.07811758, -0.08834428, 0.10094801, 0.16594367, -0.06861939,
|
|
-0.021256343, -0.041093912, -0.06669611, 0.035498552, 0.021757556,
|
|
-0.09302526, -0.015403468, -0.06614931, -0.051798206, -0.013874718,
|
|
0.03630673, 0.010412845, -0.08077351, 0.046185967, 0.0035662893,
|
|
0.03541868, -0.094149634, -0.034814864, 0.003128424, -0.020674974,
|
|
-0.03944324, -0.008110165, -0.11113267, 0.08484226, 0.043586485,
|
|
0.040582247, 0.0968012, -0.065249965, -0.028036479, 0.0050708856,
|
|
0.0017462453, 0.0326779, 0.041296225, 0.09164146, -0.047743853,
|
|
-0.015952192, -0.034451712, 0.084197424, -0.05347844, -0.11768019,
|
|
0.085926116, -0.08251791, -0.045081906, 0.0948852, 0.068401024,
|
|
0.024856757, 0.06978981, -0.057309967, -0.012775832, -0.0032452994,
|
|
0.01977615, -0.041040014, -0.024264973, 0.063464895, 0.05431621,
|
|
};
|
|
|
|
cell_to_input_weights_ = {
|
|
0.040369894, 0.030746894, 0.24704495, 0.018586371, -0.037586458,
|
|
-0.15312155, -0.11812848, -0.11465643, 0.20259799, 0.11418174,
|
|
-0.10116027, -0.011334949, 0.12411352, -0.076769054, -0.052169047,
|
|
0.21198851, -0.38871562, -0.09061183, -0.09683246, -0.21929175};
|
|
|
|
cell_to_forget_weights_ = {
|
|
-0.01998659, -0.15568835, -0.24248174, -0.012770197, 0.041331276,
|
|
-0.072311886, -0.052123554, -0.0066330447, -0.043891653, 0.036225766,
|
|
-0.047248036, 0.021479502, 0.033189066, 0.11952997, -0.020432774,
|
|
0.64658105, -0.06650122, -0.03467612, 0.095340036, 0.23647355};
|
|
|
|
cell_to_output_weights_ = {0.08286371, -0.08261836, -0.51210177, 0.002913762,
|
|
0.17764764, -0.5495371, -0.08460716, -0.24552552,
|
|
0.030037103, 0.04123544, -0.11940523, 0.007358328,
|
|
0.1890978, 0.4833202, -0.34441817, 0.36312827,
|
|
-0.26375428, 0.1457655, -0.19724406, 0.15548733};
|
|
|
|
projection_weights_ = {
|
|
-0.009802181, 0.09401916, 0.0717386, -0.13895074, 0.09641832,
|
|
0.060420845, 0.08539281, 0.054285463, 0.061395317, 0.034448683,
|
|
-0.042991187, 0.019801661, -0.16840284, -0.015726732, -0.23041931,
|
|
-0.024478018, -0.10959692, -0.013875541, 0.18600968, -0.061274476,
|
|
0.0138165, -0.08160894, -0.07661644, 0.032372914, 0.16169067,
|
|
0.22465782, -0.03993472, -0.004017731, 0.08633481, -0.28869787,
|
|
0.08682067, 0.17240396, 0.014975425, 0.056431185, 0.031037588,
|
|
0.16702051, 0.0077946745, 0.15140012, 0.29405436, 0.120285,
|
|
-0.188994, -0.027265169, 0.043389652, -0.022061434, 0.014777949,
|
|
-0.20203483, 0.094781205, 0.19100232, 0.13987629, -0.036132768,
|
|
-0.06426278, -0.05108664, 0.13221376, 0.009441198, -0.16715929,
|
|
0.15859416, -0.040437475, 0.050779544, -0.022187516, 0.012166504,
|
|
0.027685808, -0.07675938, -0.0055694645, -0.09444123, 0.0046453946,
|
|
0.050794356, 0.10770313, -0.20790008, -0.07149004, -0.11425117,
|
|
0.008225835, -0.035802525, 0.14374903, 0.15262283, 0.048710253,
|
|
0.1847461, -0.007487823, 0.11000021, -0.09542012, 0.22619456,
|
|
-0.029149994, 0.08527916, 0.009043713, 0.0042746216, 0.016261552,
|
|
0.022461696, 0.12689082, -0.043589946, -0.12035478, -0.08361797,
|
|
-0.050666027, -0.1248618, -0.1275799, -0.071875185, 0.07377272,
|
|
0.09944291, -0.18897448, -0.1593054, -0.06526116, -0.040107165,
|
|
-0.004618631, -0.067624845, -0.007576253, 0.10727444, 0.041546922,
|
|
-0.20424393, 0.06907816, 0.050412357, 0.00724631, 0.039827548,
|
|
0.12449835, 0.10747581, 0.13708383, 0.09134148, -0.12617786,
|
|
-0.06428341, 0.09956831, 0.1208086, -0.14676677, -0.0727722,
|
|
0.1126304, 0.010139365, 0.015571211, -0.038128063, 0.022913318,
|
|
-0.042050496, 0.16842307, -0.060597885, 0.10531834, -0.06411776,
|
|
-0.07451711, -0.03410368, -0.13393489, 0.06534304, 0.003620307,
|
|
0.04490757, 0.05970546, 0.05197996, 0.02839995, 0.10434969,
|
|
-0.013699693, -0.028353551, -0.07260381, 0.047201227, -0.024575593,
|
|
-0.036445823, 0.07155557, 0.009672501, -0.02328883, 0.009533515,
|
|
-0.03606021, -0.07421458, -0.028082801, -0.2678904, -0.13221288,
|
|
0.18419984, -0.13012612, -0.014588381, -0.035059117, -0.04824723,
|
|
0.07830115, -0.056184657, 0.03277091, 0.025466874, 0.14494097,
|
|
-0.12522776, -0.098633975, -0.10766018, -0.08317623, 0.08594209,
|
|
0.07749552, 0.039474737, 0.1776665, -0.07409566, -0.0477268,
|
|
0.29323658, 0.10801441, 0.1154011, 0.013952499, 0.10739139,
|
|
0.10708251, -0.051456142, 0.0074137426, -0.10430189, 0.10034707,
|
|
0.045594677, 0.0635285, -0.0715442, -0.089667566, -0.10811871,
|
|
0.00026344223, 0.08298446, -0.009525053, 0.006585689, -0.24567553,
|
|
-0.09450807, 0.09648481, 0.026996298, -0.06419476, -0.04752702,
|
|
-0.11063944, -0.23441927, -0.17608605, -0.052156363, 0.067035615,
|
|
0.19271925, -0.0032889997, -0.043264326, 0.09663576, -0.057112187,
|
|
-0.10100678, 0.0628376, 0.04447668, 0.017961001, -0.10094388,
|
|
-0.10190601, 0.18335468, 0.10494553, -0.052095775, -0.0026118709,
|
|
0.10539724, -0.04383912, -0.042349473, 0.08438151, -0.1947263,
|
|
0.02251204, 0.11216432, -0.10307853, 0.17351969, -0.039091777,
|
|
0.08066188, -0.00561982, 0.12633002, 0.11335965, -0.0088127935,
|
|
-0.019777594, 0.06864014, -0.059751723, 0.016233567, -0.06894641,
|
|
-0.28651384, -0.004228674, 0.019708522, -0.16305895, -0.07468996,
|
|
-0.0855457, 0.099339016, -0.07580735, -0.13775392, 0.08434318,
|
|
0.08330512, -0.12131499, 0.031935584, 0.09180414, -0.08876437,
|
|
-0.08049874, 0.008753825, 0.03498998, 0.030215185, 0.03907079,
|
|
0.089751154, 0.029194152, -0.03337423, -0.019092513, 0.04331237,
|
|
0.04299654, -0.036394123, -0.12915532, 0.09793732, 0.07512415,
|
|
-0.11319543, -0.032502122, 0.15661901, 0.07671967, -0.005491124,
|
|
-0.19379048, -0.218606, 0.21448623, 0.017840758, 0.1416943,
|
|
-0.07051762, 0.19488361, 0.02664691, -0.18104725, -0.09334311,
|
|
0.15026465, -0.15493552, -0.057762887, -0.11604192, -0.262013,
|
|
-0.01391798, 0.012185008, 0.11156489, -0.07483202, 0.06693364,
|
|
-0.26151478, 0.046425626, 0.036540434, -0.16435726, 0.17338543,
|
|
-0.21401681, -0.11385144, -0.08283257, -0.069031075, 0.030635102,
|
|
0.010969227, 0.11109743, 0.010919218, 0.027526086, 0.13519906,
|
|
0.01891392, -0.046839405, -0.040167913, 0.017953383, -0.09700955,
|
|
0.0061885654, -0.07000971, 0.026893595, -0.038844477, 0.14543656};
|
|
|
|
lstm_input_ = {// Step 1
|
|
{{0.787926, 0.151646, 0.071352, 0.118426, 0.458058},
|
|
{0.295743, 0.544053, 0.690064, 0.858138, 0.497181}},
|
|
// Step 2
|
|
{{0.596268, 0.998386, 0.568695, 0.864524, 0.571277},
|
|
{0.642421, 0.524260, 0.134799, 0.003639, 0.162482}},
|
|
// Step 3
|
|
{{0.073204, 0.296072, 0.743333, 0.069199, 0.045348},
|
|
{0.640394, 0.930399, 0.050782, 0.432485, 0.988078}},
|
|
// Step 4
|
|
{{0.867394, 0.291279, 0.013714, 0.482521, 0.626339},
|
|
{0.082922, 0.563329, 0.865614, 0.333232, 0.259916}}};
|
|
|
|
lstm_golden_output_ = {
|
|
{{-0.00396806, 0.029352, -0.00279226, 0.0159977, -0.00835576, -0.0211779,
|
|
0.0283512, -0.0114597, 0.00907307, -0.0244004, -0.0152191, -0.0259063,
|
|
0.00914318, 0.00415118, 0.017147, 0.0134203},
|
|
{-0.013869, 0.0287268, -0.00334693, 0.00733398, -0.0287926, -0.0186926,
|
|
0.0193662, -0.0115437, 0.00422612, -0.0345232, 0.00223253, -0.00957321,
|
|
0.0210624, 0.013331, 0.0150954, 0.02168}},
|
|
|
|
{{-0.0166936, 0.0381209, 0.000889694, 0.0143363, -0.0328911, -0.0234288,
|
|
0.0333051, -0.012229, 0.0110322, -0.0457725, -0.000832209, -0.0202817,
|
|
0.0327257, 0.0121308, 0.0155969, 0.0312091},
|
|
{-0.0141913, 0.0322082, 0.00227024, 0.0260507, -0.0188721, -0.0296489,
|
|
0.0399134, -0.0160509, 0.0116039, -0.0447318, -0.0150515, -0.0277406,
|
|
0.0316596, 0.0118233, 0.0214762, 0.0293641}},
|
|
|
|
{{-0.0213783, 0.0350169, 0.000324794, 0.0276012, -0.0263374, -0.0371449,
|
|
0.0446149, -0.0205474, 0.0103729, -0.0576349, -0.0150052, -0.0292043,
|
|
0.0376827, 0.0136115, 0.0243435, 0.0354492},
|
|
{-0.0204549, 0.0450315, -0.00117378, 0.0167673, -0.0375007, -0.0238314,
|
|
0.038784, -0.0174034, 0.0131743, -0.0506589, -0.0048447, -0.0240239,
|
|
0.0325789, 0.00790065, 0.0220157, 0.0333314}},
|
|
|
|
{{-0.0189322, 0.0464512, -0.00251373, 0.0225745, -0.0308346, -0.0317124,
|
|
0.0460407, -0.0189395, 0.0149363, -0.0530162, -0.0150767, -0.0340193,
|
|
0.0286833, 0.00824207, 0.0264887, 0.0305169},
|
|
{-0.0264787, 0.0387855, -0.000764675, 0.0217599, -0.037537, -0.0335206,
|
|
0.0431679, -0.0211424, 0.010203, -0.062785, -0.00832363, -0.025181,
|
|
0.0412031, 0.0118723, 0.0239643, 0.0394009}}};
|
|
|
|
LSTMOpModel lstm(n_batch, n_input, n_cell, n_output,
|
|
/*use_cifg=*/false, /*use_peephole=*/true,
|
|
/*use_projection_weights=*/true,
|
|
/*use_projection_bias=*/false, weight_type,
|
|
model_has_legacy_20_inputs, /*is_layer_norm=*/false,
|
|
asymmetric_quantize_inputs);
|
|
|
|
static const auto* tolerance_per_type = new std::map<TensorType, float>{
|
|
{TensorType_FLOAT32, 0.00001f},
|
|
{TensorType_UINT8, 0.00467f},
|
|
{TensorType_INT8, 0.0015f},
|
|
};
|
|
VerifyGoldens(&lstm, tolerance_per_type->at(weight_type));
|
|
}
|
|
|
|
TEST_P(LstmOpTest, NoCifg_Peephole_Projection_LayerNorm) {
|
|
const int n_batch = 2;
|
|
const int n_input = 5;
|
|
const int n_cell = 4;
|
|
const int n_output = 3;
|
|
|
|
TensorType weight_type;
|
|
// Layer normalization needs 24 inputs.
|
|
bool asymmetric_quantize_inputs;
|
|
std::tie(weight_type, std::ignore, asymmetric_quantize_inputs) = GetParam();
|
|
|
|
// TODO(b/158205028): Fix this test if using NN-API.
|
|
if (SingleOpModel::GetForceUseNnapi() && weight_type == TensorType_UINT8) {
|
|
return;
|
|
}
|
|
|
|
input_to_input_weights_ = {0.5, 0.6, 0.7, -0.8, -0.9, 0.1, 0.2,
|
|
0.3, -0.4, 0.5, -0.8, 0.7, -0.6, 0.5,
|
|
-0.4, -0.5, -0.4, -0.3, -0.2, -0.1};
|
|
|
|
input_to_forget_weights_ = {-0.6, -0.1, 0.3, 0.2, 0.9, -0.5, -0.2,
|
|
-0.4, 0.3, -0.8, -0.4, 0.3, -0.5, -0.4,
|
|
-0.6, 0.3, -0.4, -0.6, -0.5, -0.5};
|
|
|
|
input_to_cell_weights_ = {-0.4, -0.3, -0.2, -0.1, -0.5, 0.5, -0.2,
|
|
-0.3, -0.2, -0.6, 0.6, -0.1, -0.4, -0.3,
|
|
-0.7, 0.7, -0.9, -0.5, 0.8, 0.6};
|
|
|
|
input_to_output_weights_ = {-0.8, -0.4, -0.2, -0.9, -0.1, -0.7, 0.3,
|
|
-0.3, -0.8, -0.2, 0.6, -0.2, 0.4, -0.7,
|
|
-0.3, -0.5, 0.1, 0.5, -0.6, -0.4};
|
|
|
|
input_gate_bias_ = {0.03, 0.15, 0.22, 0.38};
|
|
|
|
forget_gate_bias_ = {0.1, -0.3, -0.2, 0.1};
|
|
|
|
cell_gate_bias_ = {-0.05, 0.72, 0.25, 0.08};
|
|
|
|
output_gate_bias_ = {0.05, -0.01, 0.2, 0.1};
|
|
|
|
recurrent_to_input_weights_ = {-0.2, -0.3, 0.4, 0.1, -0.5, 0.9,
|
|
-0.2, -0.3, -0.7, 0.05, -0.2, -0.6};
|
|
|
|
recurrent_to_cell_weights_ = {-0.3, 0.2, 0.1, -0.3, 0.8, -0.08,
|
|
-0.2, 0.3, 0.8, -0.6, -0.1, 0.2};
|
|
|
|
recurrent_to_forget_weights_ = {-0.5, -0.3, -0.5, -0.2, 0.6, 0.4,
|
|
0.9, 0.3, -0.1, 0.2, 0.5, 0.2};
|
|
|
|
recurrent_to_output_weights_ = {0.3, -0.1, 0.1, -0.2, -0.5, -0.7,
|
|
-0.2, -0.6, -0.1, -0.4, -0.7, -0.2};
|
|
|
|
cell_to_input_weights_ = {0.05, 0.1, 0.25, 0.15};
|
|
|
|
cell_to_forget_weights_ = {-0.02, -0.15, -0.25, -0.03};
|
|
|
|
cell_to_output_weights_ = {0.1, -0.1, -0.5, 0.05};
|
|
|
|
input_layer_norm_coefficients_ = {0.1, 0.2, 0.3, 0.5};
|
|
forget_layer_norm_coefficients_ = {0.2, 0.2, 0.4, 0.3};
|
|
cell_layer_norm_coefficients_ = {0.7, 0.2, 0.3, 0.8};
|
|
output_layer_norm_coefficients_ = {0.6, 0.2, 0.2, 0.5};
|
|
|
|
projection_weights_ = {-0.1, 0.2, 0.01, -0.2, 0.1, 0.5,
|
|
0.3, 0.08, 0.07, 0.2, -0.4, 0.2};
|
|
|
|
lstm_input_ = {
|
|
{{0.7, 0.8, 0.1, 0.2, 0.3}, {0.3, 0.2, 0.9, 0.8, 0.1}},
|
|
|
|
{{0.8, 0.1, 0.2, 0.4, 0.5}, {0.1, 0.5, 0.2, 0.4, 0.2}},
|
|
|
|
{{0.2, 0.7, 0.7, 0.1, 0.7}, {0.6, 0.9, 0.2, 0.5, 0.7}},
|
|
};
|
|
|
|
lstm_golden_output_ = {
|
|
{{0.0244077, 0.128027, -0.00170918}, {-0.00692428, 0.0848741, 0.063445}},
|
|
|
|
{{0.0137642, 0.140751, 0.0395835}, {-0.00403912, 0.139963, 0.072681}},
|
|
|
|
{{-0.00459231, 0.155278, 0.0837377}, {0.00752706, 0.161903, 0.0561371}}};
|
|
|
|
LSTMOpModel lstm(n_batch, n_input, n_cell, n_output,
|
|
/*use_cifg=*/false, /*use_peephole=*/true,
|
|
/*use_projection_weights=*/true,
|
|
/*use_projection_bias=*/false, weight_type,
|
|
/*model_has_legacy_20_inputs=*/false,
|
|
/*is_layer_norm=*/true, asymmetric_quantize_inputs);
|
|
|
|
static const auto* tolerance_per_type =
|
|
new std::map<TensorType, float>{{TensorType_FLOAT32, 0.00001f},
|
|
{TensorType_UINT8, 0.0010907f},
|
|
{TensorType_INT8, 0.00106f}};
|
|
VerifyGoldens(&lstm, tolerance_per_type->at(weight_type));
|
|
}
|
|
|
|
TEST_P(LstmOpTest, Cifg_Peephole_Projection_LayerNorm) {
|
|
const int n_batch = 2;
|
|
const int n_input = 5;
|
|
const int n_cell = 4;
|
|
const int n_output = 3;
|
|
|
|
TensorType weight_type;
|
|
// Layer normalization needs 24 inputs.
|
|
bool asymmetric_quantize_inputs;
|
|
std::tie(weight_type, std::ignore, asymmetric_quantize_inputs) = GetParam();
|
|
|
|
// TODO(b/158205028): Fix this test if using NN-API.
|
|
if (SingleOpModel::GetForceUseNnapi() && weight_type == TensorType_UINT8) {
|
|
return;
|
|
}
|
|
|
|
input_to_forget_weights_ = {-0.6, -0.1, 0.3, 0.2, 0.9, -0.5, -0.2,
|
|
-0.4, 0.3, -0.8, -0.4, 0.3, -0.5, -0.4,
|
|
-0.6, 0.3, -0.4, -0.6, -0.5, -0.5};
|
|
input_to_cell_weights_ = {-0.4, -0.3, -0.2, -0.1, -0.5, 0.5, -0.2,
|
|
-0.3, -0.2, -0.6, 0.6, -0.1, -0.4, -0.3,
|
|
-0.7, 0.7, -0.9, -0.5, 0.8, 0.6};
|
|
input_to_output_weights_ = {-0.8, -0.4, -0.2, -0.9, -0.1, -0.7, 0.3,
|
|
-0.3, -0.8, -0.2, 0.6, -0.2, 0.4, -0.7,
|
|
-0.3, -0.5, 0.1, 0.5, -0.6, -0.4};
|
|
|
|
forget_gate_bias_ = {0.1, -0.3, -0.2, 0.1};
|
|
cell_gate_bias_ = {-0.05, 0.72, 0.25, 0.08};
|
|
output_gate_bias_ = {0.05, -0.01, 0.2, 0.1};
|
|
|
|
recurrent_to_cell_weights_ = {-0.3, 0.2, 0.1, -0.3, 0.8, -0.08,
|
|
-0.2, 0.3, 0.8, -0.6, -0.1, 0.2};
|
|
recurrent_to_forget_weights_ = {-0.5, -0.3, -0.5, -0.2, 0.6, 0.4,
|
|
0.9, 0.3, -0.1, 0.2, 0.5, 0.2};
|
|
recurrent_to_output_weights_ = {0.3, -0.1, 0.1, -0.2, -0.5, -0.7,
|
|
-0.2, -0.6, -0.1, -0.4, -0.7, -0.2};
|
|
|
|
cell_to_forget_weights_ = {-0.02, -0.15, -0.25, -0.03};
|
|
cell_to_output_weights_ = {0.1, -0.1, -0.5, 0.05};
|
|
|
|
forget_layer_norm_coefficients_ = {0.2, 0.2, 0.4, 0.3};
|
|
cell_layer_norm_coefficients_ = {0.7, 0.2, 0.3, 0.8};
|
|
output_layer_norm_coefficients_ = {0.6, 0.2, 0.2, 0.5};
|
|
projection_weights_ = {-0.1, 0.2, 0.01, -0.2, 0.1, 0.5,
|
|
0.3, 0.08, 0.07, 0.2, -0.4, 0.2};
|
|
|
|
lstm_input_ = {{{0.7, 0.8, 0.1, 0.2, 0.3}, {0.3, 0.2, 0.9, 0.8, 0.1}},
|
|
|
|
{{0.8, 0.1, 0.2, 0.4, 0.5}, {0.1, 0.5, 0.2, 0.4, 0.2}},
|
|
|
|
{{0.2, 0.7, 0.7, 0.1, 0.7}, {0.6, 0.9, 0.2, 0.5, 0.7}}};
|
|
lstm_golden_output_ = {{{0.02129706, 0.140816242, 0.0112733059},
|
|
{-0.0226350538, 0.0916948169, 0.0769175813}},
|
|
|
|
{{0.0132302344, 0.152308047, 0.0346313119},
|
|
{-0.0269966982, 0.149707705, 0.094149217}},
|
|
|
|
{{-0.0123688057, 0.165790111, 0.0893077999},
|
|
{-0.0103429332, 0.173016444, 0.0720508844}}};
|
|
|
|
LSTMOpModel lstm(n_batch, n_input, n_cell, n_output,
|
|
/*use_cifg=*/true, /*use_peephole=*/true,
|
|
/*use_projection_weights=*/true,
|
|
/*use_projection_bias=*/false, weight_type,
|
|
/*model_has_legacy_20_inputs=*/false,
|
|
/*is_layer_norm=*/true, asymmetric_quantize_inputs);
|
|
|
|
static const auto* tolerance_per_type =
|
|
new std::map<TensorType, float>{{TensorType_FLOAT32, 0.00001f},
|
|
{TensorType_UINT8, 0.000971057f},
|
|
{TensorType_INT8, 0.001f}};
|
|
VerifyGoldens(&lstm, tolerance_per_type->at(weight_type));
|
|
}
|
|
|
|
class LSTMIntegerOpModel : public SingleOpModel {
|
|
public:
|
|
LSTMIntegerOpModel(int n_batch, int n_input, int n_cell, int n_output,
|
|
bool use_cifg, bool use_peephole,
|
|
bool use_projection_weights, bool use_projection_bias,
|
|
bool use_layer_norm, bool use_8x8_8_implementation,
|
|
const std::vector<std::pair<float, float>>& ranges,
|
|
const std::vector<std::pair<float, int>>& intermediates)
|
|
: n_input_(n_input), n_output_(n_output) {
|
|
input_ = AddInput({TensorType_INT8,
|
|
{n_batch, n_input},
|
|
ranges[0].first,
|
|
ranges[0].second});
|
|
|
|
if (use_cifg) {
|
|
input_to_input_weights_ = AddNullInput();
|
|
} else {
|
|
input_to_input_weights_ = AddInput({TensorType_INT8,
|
|
{n_cell, n_input},
|
|
ranges[1].first,
|
|
ranges[1].second});
|
|
}
|
|
input_to_forget_weights_ = AddInput({TensorType_INT8,
|
|
{n_cell, n_input},
|
|
ranges[2].first,
|
|
ranges[2].second});
|
|
input_to_cell_weights_ = AddInput({TensorType_INT8,
|
|
{n_cell, n_input},
|
|
ranges[3].first,
|
|
ranges[3].second});
|
|
input_to_output_weights_ = AddInput({TensorType_INT8,
|
|
{n_cell, n_input},
|
|
ranges[4].first,
|
|
ranges[4].second});
|
|
|
|
if (use_cifg) {
|
|
recurrent_to_input_weights_ = AddNullInput();
|
|
} else {
|
|
recurrent_to_input_weights_ = AddInput({TensorType_INT8,
|
|
{n_cell, n_output},
|
|
ranges[5].first,
|
|
ranges[5].second});
|
|
}
|
|
recurrent_to_forget_weights_ = AddInput({TensorType_INT8,
|
|
{n_cell, n_output},
|
|
ranges[6].first,
|
|
ranges[6].second});
|
|
recurrent_to_cell_weights_ = AddInput({TensorType_INT8,
|
|
{n_cell, n_output},
|
|
ranges[7].first,
|
|
ranges[7].second});
|
|
recurrent_to_output_weights_ = AddInput({TensorType_INT8,
|
|
{n_cell, n_output},
|
|
ranges[8].first,
|
|
ranges[8].second});
|
|
|
|
if (use_peephole) {
|
|
if (use_cifg) {
|
|
cell_to_input_weights_ = AddNullInput();
|
|
} else {
|
|
cell_to_input_weights_ = AddInput(
|
|
{TensorType_INT16, {n_cell}, ranges[9].first, ranges[9].second});
|
|
}
|
|
cell_to_forget_weights_ = AddInput(
|
|
{TensorType_INT16, {n_cell}, ranges[10].first, ranges[10].second});
|
|
cell_to_output_weights_ = AddInput(
|
|
{TensorType_INT16, {n_cell}, ranges[11].first, ranges[11].second});
|
|
} else {
|
|
cell_to_input_weights_ = AddNullInput();
|
|
cell_to_forget_weights_ = AddNullInput();
|
|
cell_to_output_weights_ = AddNullInput();
|
|
}
|
|
|
|
if (use_cifg) {
|
|
input_gate_bias_ = AddNullInput();
|
|
} else {
|
|
input_gate_bias_ = AddInput(
|
|
{TensorType_INT32, {n_cell}, ranges[12].first, ranges[12].second});
|
|
}
|
|
forget_gate_bias_ = AddInput(
|
|
{TensorType_INT32, {n_cell}, ranges[13].first, ranges[13].second});
|
|
cell_gate_bias_ = AddInput(
|
|
{TensorType_INT32, {n_cell}, ranges[14].first, ranges[14].second});
|
|
output_gate_bias_ = AddInput(
|
|
{TensorType_INT32, {n_cell}, ranges[15].first, ranges[15].second});
|
|
|
|
if (use_projection_weights) {
|
|
projection_weights_ = AddInput({TensorType_INT8,
|
|
{n_output, n_cell},
|
|
ranges[16].first,
|
|
ranges[16].second});
|
|
} else {
|
|
projection_weights_ = AddNullInput();
|
|
}
|
|
if (use_projection_bias) {
|
|
CHECK(use_projection_weights);
|
|
projection_bias_ = AddInput(
|
|
{TensorType_INT32, {n_output}, ranges[17].first, ranges[17].second});
|
|
} else {
|
|
projection_bias_ = AddNullInput();
|
|
}
|
|
|
|
// Adding the 2 state tensors.
|
|
AddVariableInput({TensorType_INT16,
|
|
{n_batch, n_output},
|
|
ranges[18].first,
|
|
ranges[18].second});
|
|
AddVariableInput({TensorType_INT16,
|
|
{n_batch, n_cell},
|
|
ranges[19].first,
|
|
ranges[19].second});
|
|
|
|
// Layer norm weights.
|
|
if (use_layer_norm) {
|
|
if (use_cifg) {
|
|
input_layer_norm_coefficients_ = AddNullInput();
|
|
} else {
|
|
input_layer_norm_coefficients_ = AddInput(
|
|
{TensorType_INT16, {n_cell}, ranges[20].first, ranges[20].second});
|
|
}
|
|
forget_layer_norm_coefficients_ = AddInput(
|
|
{TensorType_INT16, {n_cell}, ranges[21].first, ranges[21].second});
|
|
cell_layer_norm_coefficients_ = AddInput(
|
|
{TensorType_INT16, {n_cell}, ranges[22].first, ranges[22].second});
|
|
output_layer_norm_coefficients_ = AddInput(
|
|
{TensorType_INT16, {n_cell}, ranges[23].first, ranges[23].second});
|
|
}
|
|
|
|
if (use_8x8_8_implementation) {
|
|
EXPECT_EQ(intermediates.size(), 12);
|
|
} else {
|
|
EXPECT_EQ(intermediates.size(), 5);
|
|
}
|
|
for (int i = 0; i < intermediates.size(); ++i) {
|
|
AddIntermediate(TensorType_INT16, {intermediates[i].first},
|
|
{intermediates[i].second});
|
|
}
|
|
|
|
output_ = AddOutput({TensorType_INT8,
|
|
{n_batch, n_output},
|
|
ranges[24].first,
|
|
ranges[24].second});
|
|
|
|
// TODO(b/161825581): Add tests where cell_clip and/or proj_clip is not the
|
|
// default 0.
|
|
SetBuiltinOp(
|
|
BuiltinOperator_LSTM, BuiltinOptions_LSTMOptions,
|
|
CreateLSTMOptions(builder_, ActivationFunctionType_TANH).Union());
|
|
|
|
BuildInterpreter(/*input_shapes=*/{}, /*num_threads=*/-1,
|
|
/*allow_fp32_relax_to_fp16=*/false,
|
|
/*apply_delegate=*/true, /*allocate_and_delegate=*/false);
|
|
}
|
|
|
|
void PerformAllocateAndDelegate() { AllocateAndDelegate(true); }
|
|
|
|
void SetInputToInputWeights(const std::vector<float>& f) {
|
|
QuantizeAndPopulate<int8_t>(input_to_input_weights_, f);
|
|
}
|
|
|
|
void SetInputToForgetWeights(const std::vector<float>& f) {
|
|
QuantizeAndPopulate<int8_t>(input_to_forget_weights_, f);
|
|
}
|
|
|
|
void SetInputToCellWeights(const std::vector<float>& f) {
|
|
QuantizeAndPopulate<int8_t>(input_to_cell_weights_, f);
|
|
}
|
|
|
|
void SetInputToOutputWeights(const std::vector<float>& f) {
|
|
QuantizeAndPopulate<int8_t>(input_to_output_weights_, f);
|
|
}
|
|
|
|
void SetRecurrentToInputWeights(const std::vector<float>& f) {
|
|
QuantizeAndPopulate<int8_t>(recurrent_to_input_weights_, f);
|
|
}
|
|
|
|
void SetRecurrentToForgetWeights(const std::vector<float>& f) {
|
|
QuantizeAndPopulate<int8_t>(recurrent_to_forget_weights_, f);
|
|
}
|
|
|
|
void SetRecurrentToCellWeights(const std::vector<float>& f) {
|
|
QuantizeAndPopulate<int8_t>(recurrent_to_cell_weights_, f);
|
|
}
|
|
|
|
void SetRecurrentToOutputWeights(const std::vector<float>& f) {
|
|
QuantizeAndPopulate<int8_t>(recurrent_to_output_weights_, f);
|
|
}
|
|
|
|
void SetCellToInputWeights(const std::vector<float>& f) {
|
|
QuantizeAndPopulate<int16_t>(cell_to_input_weights_, f);
|
|
}
|
|
|
|
void SetCellToForgetWeights(const std::vector<float>& f) {
|
|
QuantizeAndPopulate<int16_t>(cell_to_forget_weights_, f);
|
|
}
|
|
|
|
void SetCellToOutputWeights(const std::vector<float>& f) {
|
|
QuantizeAndPopulate<int16_t>(cell_to_output_weights_, f);
|
|
}
|
|
|
|
void SetInputLayerNormCoefficients(const std::vector<float>& f) {
|
|
QuantizeAndPopulate<int16_t>(input_layer_norm_coefficients_, f);
|
|
}
|
|
|
|
void SetForgetLayerNormCoefficients(const std::vector<float>& f) {
|
|
QuantizeAndPopulate<int16_t>(forget_layer_norm_coefficients_, f);
|
|
}
|
|
|
|
void SetCellLayerNormCoefficients(const std::vector<float>& f) {
|
|
QuantizeAndPopulate<int16_t>(cell_layer_norm_coefficients_, f);
|
|
}
|
|
|
|
void SetOutputLayerNormCoefficients(const std::vector<float>& f) {
|
|
QuantizeAndPopulate<int16_t>(output_layer_norm_coefficients_, f);
|
|
}
|
|
|
|
void SetInputGateBias(const std::vector<float>& f) {
|
|
QuantizeAndPopulate<int32_t>(input_gate_bias_, f);
|
|
}
|
|
|
|
void SetForgetGateBias(const std::vector<float>& f) {
|
|
QuantizeAndPopulate<int32_t>(forget_gate_bias_, f);
|
|
}
|
|
|
|
void SetCellBias(const std::vector<float>& f) {
|
|
QuantizeAndPopulate<int32_t>(cell_gate_bias_, f);
|
|
}
|
|
|
|
void SetOutputGateBias(const std::vector<float>& f) {
|
|
QuantizeAndPopulate<int32_t>(output_gate_bias_, f);
|
|
}
|
|
|
|
void SetProjectionWeights(const std::vector<float>& f) {
|
|
QuantizeAndPopulate<int8_t>(projection_weights_, f);
|
|
}
|
|
|
|
void SetProjectionBias(const std::vector<float>& f) {
|
|
QuantizeAndPopulate<int32_t>(projection_bias_, f);
|
|
}
|
|
|
|
void SetInput(const std::vector<float>& f) {
|
|
QuantizeAndPopulate<int8_t>(input_, f);
|
|
}
|
|
|
|
std::vector<int8_t> GetOutput() { return ExtractVector<int8_t>(output_); }
|
|
|
|
int num_inputs() { return n_input_; }
|
|
int num_outputs() { return n_output_; }
|
|
|
|
protected:
|
|
int input_;
|
|
int input_to_input_weights_;
|
|
int input_to_forget_weights_;
|
|
int input_to_cell_weights_;
|
|
int input_to_output_weights_;
|
|
|
|
int recurrent_to_input_weights_;
|
|
int recurrent_to_forget_weights_;
|
|
int recurrent_to_cell_weights_;
|
|
int recurrent_to_output_weights_;
|
|
|
|
int cell_to_input_weights_;
|
|
int cell_to_forget_weights_;
|
|
int cell_to_output_weights_;
|
|
|
|
int input_layer_norm_coefficients_;
|
|
int forget_layer_norm_coefficients_;
|
|
int cell_layer_norm_coefficients_;
|
|
int output_layer_norm_coefficients_;
|
|
|
|
int input_gate_bias_;
|
|
int forget_gate_bias_;
|
|
int cell_gate_bias_;
|
|
int output_gate_bias_;
|
|
|
|
int projection_weights_;
|
|
int projection_bias_;
|
|
|
|
int output_;
|
|
|
|
int n_input_;
|
|
int n_output_;
|
|
};
|
|
|
|
TEST(IntegerLstmOpTest, NoCifg_NoPeephole_Projection_LayerNorm) {
|
|
// Hyper parameters.
|
|
const int n_batch = 2;
|
|
const int n_input = 5;
|
|
const int n_cell = 4;
|
|
const int n_output = 3;
|
|
|
|
// Model related weights.
|
|
const std::vector<float> input_to_input_weights = {
|
|
0.5, 0.6, 0.7, -0.8, -0.9, 0.1, 0.2, 0.3, -0.4, 0.5,
|
|
-0.8, 0.7, -0.6, 0.5, -0.4, -0.5, -0.4, -0.3, -0.2, -0.1};
|
|
|
|
const std::vector<float> input_to_forget_weights = {
|
|
-0.6, -0.1, 0.3, 0.2, 0.9, -0.5, -0.2, -0.4, 0.3, -0.8,
|
|
-0.4, 0.3, -0.5, -0.4, -0.6, 0.3, -0.4, -0.6, -0.5, -0.5};
|
|
|
|
const std::vector<float> input_to_cell_weights = {
|
|
-0.4, -0.3, -0.2, -0.1, -0.5, 0.5, -0.2, -0.3, -0.2, -0.6,
|
|
0.6, -0.1, -0.4, -0.3, -0.7, 0.7, -0.9, -0.5, 0.8, 0.6};
|
|
|
|
const std::vector<float> input_to_output_weights = {
|
|
-0.8, -0.4, -0.2, -0.9, -0.1, -0.7, 0.3, -0.3, -0.8, -0.2,
|
|
0.6, -0.2, 0.4, -0.7, -0.3, -0.5, 0.1, 0.5, -0.6, -0.4};
|
|
|
|
const std::vector<float> input_gate_bias = {0.03, 0.15, 0.22, 0.38};
|
|
|
|
const std::vector<float> forget_gate_bias = {0.1, -0.3, -0.2, 0.1};
|
|
|
|
const std::vector<float> cell_gate_bias = {-0.05, 0.72, 0.25, 0.08};
|
|
|
|
const std::vector<float> output_gate_bias = {0.05, -0.01, 0.2, 0.1};
|
|
|
|
const std::vector<float> recurrent_to_input_weights = {
|
|
-0.2, -0.3, 0.4, 0.1, -0.5, 0.9, -0.2, -0.3, -0.7, 0.05, -0.2, -0.6};
|
|
|
|
const std::vector<float> recurrent_to_cell_weights = {
|
|
-0.3, 0.2, 0.1, -0.3, 0.8, -0.08, -0.2, 0.3, 0.8, -0.6, -0.1, 0.2};
|
|
|
|
const std::vector<float> recurrent_to_forget_weights = {
|
|
-0.5, -0.3, -0.5, -0.2, 0.6, 0.4, 0.9, 0.3, -0.1, 0.2, 0.5, 0.2};
|
|
|
|
const std::vector<float> recurrent_to_output_weights = {
|
|
0.3, -0.1, 0.1, -0.2, -0.5, -0.7, -0.2, -0.6, -0.1, -0.4, -0.7, -0.2};
|
|
|
|
const std::vector<float> input_layer_norm_coefficients = {0.1, 0.2, 0.3, 0.5};
|
|
const std::vector<float> forget_layer_norm_coefficients = {0.2, 0.2, 0.4,
|
|
0.3};
|
|
const std::vector<float> cell_layer_norm_coefficients = {0.7, 0.2, 0.3, 0.8};
|
|
const std::vector<float> output_layer_norm_coefficients = {0.6, 0.2, 0.2,
|
|
0.5};
|
|
|
|
const std::vector<float> projection_weights = {
|
|
-0.1, 0.2, 0.01, -0.2, 0.1, 0.5, 0.3, 0.08, 0.07, 0.2, -0.4, 0.2};
|
|
|
|
// Input ranges.
|
|
const std::vector<std::pair<float, float>> ranges = {
|
|
{-1.0, 127.0 / 128}, // input tensor
|
|
{-1.0, 1.0}, // input_to_input_weight tensor
|
|
{-1.0, 1.0}, // input_to_forget_weight tensor
|
|
{-1.0, 1.0}, // input_to_cell_weight tensor
|
|
{-1.0, 1.0}, // input_to_output_weight tensor
|
|
|
|
{-1.0, 1.0}, // recurrent_to_input_weight tensor
|
|
{-1.0, 1.0}, // recurrent_to_forget_weight tensor
|
|
{-1.0, 1.0}, // recurrent_to_cell_weight tensor
|
|
{-1.0, 1.0}, // recurrent_to_output_weight tensor
|
|
|
|
{-1, 1}, // cell_to_input_weight tensor
|
|
{-1, 1}, // cell_to_forget_weight tensor
|
|
{-1, 1}, // cell_to_output_weight tensor
|
|
|
|
{-100, 100}, // input_gate_bias tensor
|
|
{-100, 100}, // forget_gate_bias tensor
|
|
{-100, 100}, // cell_gate_bias tensor
|
|
{-100, 100}, // output_gate_bias tensor
|
|
|
|
{-0.5, 0.5}, // projection_weight tensor
|
|
{-1, 1}, // projection_bias tensor
|
|
|
|
{-1.0, 32767.0 / 32768}, // output_state tensor
|
|
{-1, 1}, // cell_state tensor
|
|
|
|
{-1.00001, 1.0}, // input_layer_norm_coefficient tensor
|
|
{-1.00001, 1.0}, // forget_layer_norm_coefficient tensor
|
|
{-1.00001, 1.0}, // cell_layer_norm_coefficient tensor
|
|
{-1.00001, 1.0}, // output_layer_norm_coefficient tensor
|
|
// Output scale is the same as output_state scale and only output_state
|
|
// scale is used in the op, so this is only provided for clarity.
|
|
{-1.0, 32767.0 / 32768}, // output tensor.
|
|
};
|
|
|
|
// The scale and zero point of intermediate tensors.
|
|
std::vector<std::pair<float, int>> intermediates = {
|
|
{0.007059, 0}, {0.007812, 0}, {0.007059, 0}, {0.007812, 0}, {0.007, 0}};
|
|
|
|
// Create model.
|
|
LSTMIntegerOpModel lstm(n_batch, n_input, n_cell, n_output,
|
|
/*use_cifg=*/false, /*use_peephole=*/false,
|
|
/*use_projection_weights=*/true,
|
|
/*use_projection_bias=*/false,
|
|
/*use_layer_norm=*/true,
|
|
/*use_8x8_8_implementation=*/false, ranges,
|
|
intermediates);
|
|
// Do allocate.
|
|
lstm.PerformAllocateAndDelegate();
|
|
|
|
// Set weights.
|
|
lstm.SetInputToInputWeights(input_to_input_weights);
|
|
lstm.SetInputToCellWeights(input_to_cell_weights);
|
|
lstm.SetInputToForgetWeights(input_to_forget_weights);
|
|
lstm.SetInputToOutputWeights(input_to_output_weights);
|
|
|
|
lstm.SetInputGateBias(input_gate_bias);
|
|
lstm.SetCellBias(cell_gate_bias);
|
|
lstm.SetForgetGateBias(forget_gate_bias);
|
|
lstm.SetOutputGateBias(output_gate_bias);
|
|
|
|
lstm.SetRecurrentToInputWeights(recurrent_to_input_weights);
|
|
lstm.SetRecurrentToCellWeights(recurrent_to_cell_weights);
|
|
lstm.SetRecurrentToForgetWeights(recurrent_to_forget_weights);
|
|
lstm.SetRecurrentToOutputWeights(recurrent_to_output_weights);
|
|
|
|
lstm.SetProjectionWeights(projection_weights);
|
|
|
|
lstm.SetInputLayerNormCoefficients(input_layer_norm_coefficients);
|
|
lstm.SetForgetLayerNormCoefficients(forget_layer_norm_coefficients);
|
|
lstm.SetCellLayerNormCoefficients(cell_layer_norm_coefficients);
|
|
lstm.SetOutputLayerNormCoefficients(output_layer_norm_coefficients);
|
|
|
|
// Model inputs. sequence -batch - input
|
|
const std::vector<std::vector<float>> lstm_input = {
|
|
{
|
|
0.7, 0.8, 0.1, 0.2, 0.3, //
|
|
0.8, 0.1, 0.2, 0.4, 0.5, //
|
|
},
|
|
{
|
|
0.2, 0.7, 0.7, 0.1, 0.7, //
|
|
0.3, 0.2, 0.9, 0.8, 0.1, //
|
|
},
|
|
{
|
|
0.7, 0.8, 0.1, 0.2, 0.3, //
|
|
0.3, 0.2, 0.9, 0.8, 0.1, //
|
|
},
|
|
};
|
|
|
|
// Expected outputs.
|
|
const std::vector<std::vector<int8_t>> expected_output = {
|
|
{127, 127, -108, -67, 127, 127},
|
|
{-128, 127, 127, -128, 127, 127},
|
|
{127, 127, 127, -128, 127, 127},
|
|
};
|
|
|
|
// Invoke and verify the result.
|
|
const int input_sequence_size = lstm_input.size();
|
|
EXPECT_GT(input_sequence_size, 0);
|
|
for (int i = 0; i < input_sequence_size; ++i) {
|
|
lstm.SetInput(lstm_input[i]);
|
|
lstm.Invoke();
|
|
EXPECT_THAT(lstm.GetOutput(), ElementsAreArray(expected_output[i]));
|
|
}
|
|
}
|
|
|
|
TEST(IntegerLstmOpTest, NoCifg_Peephole_Projection_LayerNorm) {
|
|
// Hyper parameters.
|
|
const int n_batch = 2;
|
|
const int n_input = 5;
|
|
const int n_cell = 4;
|
|
const int n_output = 3;
|
|
|
|
// Model related weights.
|
|
const std::vector<float> input_to_input_weights = {
|
|
0.5, 0.6, 0.7, -0.8, -0.9, 0.1, 0.2, 0.3, -0.4, 0.5,
|
|
-0.8, 0.7, -0.6, 0.5, -0.4, -0.5, -0.4, -0.3, -0.2, -0.1};
|
|
|
|
const std::vector<float> input_to_forget_weights = {
|
|
-0.6, -0.1, 0.3, 0.2, 0.9, -0.5, -0.2, -0.4, 0.3, -0.8,
|
|
-0.4, 0.3, -0.5, -0.4, -0.6, 0.3, -0.4, -0.6, -0.5, -0.5};
|
|
|
|
const std::vector<float> input_to_cell_weights = {
|
|
-0.4, -0.3, -0.2, -0.1, -0.5, 0.5, -0.2, -0.3, -0.2, -0.6,
|
|
0.6, -0.1, -0.4, -0.3, -0.7, 0.7, -0.9, -0.5, 0.8, 0.6};
|
|
|
|
const std::vector<float> input_to_output_weights = {
|
|
-0.8, -0.4, -0.2, -0.9, -0.1, -0.7, 0.3, -0.3, -0.8, -0.2,
|
|
0.6, -0.2, 0.4, -0.7, -0.3, -0.5, 0.1, 0.5, -0.6, -0.4};
|
|
|
|
const std::vector<float> input_gate_bias = {0.03, 0.15, 0.22, 0.38};
|
|
|
|
const std::vector<float> forget_gate_bias = {0.1, -0.3, -0.2, 0.1};
|
|
|
|
const std::vector<float> cell_gate_bias = {-0.05, 0.72, 0.25, 0.08};
|
|
|
|
const std::vector<float> output_gate_bias = {0.05, -0.01, 0.2, 0.1};
|
|
|
|
const std::vector<float> recurrent_to_input_weights = {
|
|
-0.2, -0.3, 0.4, 0.1, -0.5, 0.9, -0.2, -0.3, -0.7, 0.05, -0.2, -0.6};
|
|
|
|
const std::vector<float> recurrent_to_cell_weights = {
|
|
-0.3, 0.2, 0.1, -0.3, 0.8, -0.08, -0.2, 0.3, 0.8, -0.6, -0.1, 0.2};
|
|
|
|
const std::vector<float> recurrent_to_forget_weights = {
|
|
-0.5, -0.3, -0.5, -0.2, 0.6, 0.4, 0.9, 0.3, -0.1, 0.2, 0.5, 0.2};
|
|
|
|
const std::vector<float> recurrent_to_output_weights = {
|
|
0.3, -0.1, 0.1, -0.2, -0.5, -0.7, -0.2, -0.6, -0.1, -0.4, -0.7, -0.2};
|
|
|
|
const std::vector<float> cell_to_input_weights = {0.3, -0.1, 0.1, -0.2};
|
|
|
|
const std::vector<float> cell_to_forget_weights = {0.2, -0.1, 0.1, -0.2};
|
|
|
|
const std::vector<float> cell_to_output_weights = {0.3, -0.1, 0.1, -0.3};
|
|
|
|
const std::vector<float> input_layer_norm_coefficients = {0.1, 0.2, 0.3, 0.5};
|
|
const std::vector<float> forget_layer_norm_coefficients = {0.2, 0.2, 0.4,
|
|
0.3};
|
|
const std::vector<float> cell_layer_norm_coefficients = {0.7, 0.2, 0.3, 0.8};
|
|
const std::vector<float> output_layer_norm_coefficients = {0.6, 0.2, 0.2,
|
|
0.5};
|
|
|
|
const std::vector<float> projection_weights = {
|
|
-0.1, 0.2, 0.01, -0.2, 0.1, 0.5, 0.3, 0.08, 0.07, 0.2, -0.4, 0.2};
|
|
|
|
// Input ranges.
|
|
const std::vector<std::pair<float, float>> ranges = {
|
|
{-1.0, 127.0 / 128}, // input tensor
|
|
{-1.0, 1.0}, // input_to_input_weight tensor
|
|
{-1.0, 1.0}, // input_to_forget_weight tensor
|
|
{-1.0, 1.0}, // input_to_cell_weight tensor
|
|
{-1.0, 1.0}, // input_to_output_weight tensor
|
|
|
|
{-1.0, 1.0}, // recurrent_to_input_weight tensor
|
|
{-0.9, 0.9}, // recurrent_to_forget_weight tensor
|
|
{-1.0, 1.0}, // recurrent_to_cell_weight tensor
|
|
{-1.0, 1.0}, // recurrent_to_output_weight tensor
|
|
|
|
{-0.3, 0.3}, // cell_to_input_weight tensor
|
|
{-0.3, 0.3}, // cell_to_forget_weight tensor
|
|
{-0.3, 0.3}, // cell_to_output_weight tensor
|
|
|
|
{-100, 100}, // input_gate_bias tensor
|
|
{-100, 80}, // forget_gate_bias tensor
|
|
{-100, 100}, // cell_gate_bias tensor
|
|
{-100, 100}, // output_gate_bias tensor
|
|
|
|
{-0.5, 0.5}, // projection_weight tensor
|
|
{-1, 1}, // projection_bias tensor
|
|
|
|
{-1.0, 32767.0 / 32768}, // output_state tensor
|
|
{-1, 1}, // cell_state tensor
|
|
|
|
{-0.5, 0.5}, // input_layer_norm_coefficient tensor
|
|
{-0.5, 0.5}, // forget_layer_norm_coefficient tensor
|
|
{-1.0, 1.0}, // cell_layer_norm_coefficient tensor
|
|
{-1.0, 1.0}, // output_layer_norm_coefficient tensor
|
|
// Output scale is the same as output_state scale and only output_state
|
|
// scale is used in the op, so this is only provided for clarity.
|
|
{-1.0, 32767.0 / 32768}, // output tensor.
|
|
};
|
|
|
|
// The scale and zero point of intermediate tensors.
|
|
std::vector<std::pair<float, int>> intermediates = {
|
|
{0.007059, 0}, {0.007812, 0}, {0.007059, 0}, {0.007812, 0}, {0.007, 0}};
|
|
|
|
// Create model.
|
|
LSTMIntegerOpModel lstm(n_batch, n_input, n_cell, n_output,
|
|
/*use_cifg=*/false, /*use_peephole=*/true,
|
|
/*use_projection_weights=*/true,
|
|
/*use_projection_bias=*/false,
|
|
/*use_layer_norm=*/true,
|
|
/*use_8x8_8_implementation=*/false, ranges,
|
|
intermediates);
|
|
|
|
// Do allocate.
|
|
lstm.PerformAllocateAndDelegate();
|
|
|
|
// Set weights.
|
|
lstm.SetInputToInputWeights(input_to_input_weights);
|
|
lstm.SetInputToCellWeights(input_to_cell_weights);
|
|
lstm.SetInputToForgetWeights(input_to_forget_weights);
|
|
lstm.SetInputToOutputWeights(input_to_output_weights);
|
|
|
|
lstm.SetInputGateBias(input_gate_bias);
|
|
lstm.SetCellBias(cell_gate_bias);
|
|
lstm.SetForgetGateBias(forget_gate_bias);
|
|
lstm.SetOutputGateBias(output_gate_bias);
|
|
|
|
lstm.SetRecurrentToInputWeights(recurrent_to_input_weights);
|
|
lstm.SetRecurrentToCellWeights(recurrent_to_cell_weights);
|
|
lstm.SetRecurrentToForgetWeights(recurrent_to_forget_weights);
|
|
lstm.SetRecurrentToOutputWeights(recurrent_to_output_weights);
|
|
|
|
lstm.SetCellToInputWeights(cell_to_input_weights);
|
|
lstm.SetCellToForgetWeights(cell_to_forget_weights);
|
|
lstm.SetCellToOutputWeights(cell_to_output_weights);
|
|
|
|
lstm.SetProjectionWeights(projection_weights);
|
|
|
|
lstm.SetInputLayerNormCoefficients(input_layer_norm_coefficients);
|
|
lstm.SetForgetLayerNormCoefficients(forget_layer_norm_coefficients);
|
|
lstm.SetCellLayerNormCoefficients(cell_layer_norm_coefficients);
|
|
lstm.SetOutputLayerNormCoefficients(output_layer_norm_coefficients);
|
|
|
|
// Model inputs. sequence -batch - input
|
|
const std::vector<std::vector<float>> lstm_input = {
|
|
{
|
|
0.7, 0.8, 0.1, 0.2, 0.3, //
|
|
0.8, 0.1, 0.2, 0.4, 0.5, //
|
|
},
|
|
{
|
|
0.2, 0.7, 0.7, 0.1, 0.7, //
|
|
0.3, 0.2, 0.9, 0.8, 0.1, //
|
|
},
|
|
{
|
|
0.7, 0.8, 0.1, 0.2, 0.3, //
|
|
0.3, 0.2, 0.9, 0.8, 0.1, //
|
|
},
|
|
};
|
|
|
|
// Expected outputs.
|
|
const std::vector<std::vector<int8_t>> expected_output = {
|
|
{127, 127, -16, -21, 127, 127},
|
|
{23, 127, 127, -128, 127, 127},
|
|
{127, 127, 127, -128, 127, 127},
|
|
};
|
|
|
|
// Invoke and verify the result.
|
|
const int input_sequence_size = lstm_input.size();
|
|
EXPECT_GT(input_sequence_size, 0);
|
|
for (int i = 0; i < input_sequence_size; ++i) {
|
|
lstm.SetInput(lstm_input[i]);
|
|
lstm.Invoke();
|
|
EXPECT_THAT(lstm.GetOutput(), ElementsAreArray(expected_output[i]));
|
|
}
|
|
}
|
|
|
|
TEST(IntegerLstmOpTest, Cifg_NoPeephole_Projection_LayerNorm_8x8_8) {
|
|
// Hyper parameters.
|
|
const int n_batch = 2;
|
|
const int n_input = 5;
|
|
const int n_cell = 4;
|
|
const int n_output = 3;
|
|
|
|
// Model related weights.
|
|
const std::vector<float> input_to_input_weights = {
|
|
0.5, 0.6, 0.7, -0.8, -0.9, 0.1, 0.2, 0.3, -0.4, 0.5,
|
|
-0.8, 0.7, -0.6, 0.5, -0.4, -0.5, -0.4, -0.3, -0.2, -0.1};
|
|
|
|
const std::vector<float> input_to_forget_weights = {
|
|
-0.6, -0.1, 0.3, 0.2, 0.9, -0.5, -0.2, -0.4, 0.3, -0.8,
|
|
-0.4, 0.3, -0.5, -0.4, -0.6, 0.3, -0.4, -0.6, -0.5, -0.5};
|
|
|
|
const std::vector<float> input_to_cell_weights = {
|
|
-0.4, -0.3, -0.2, -0.1, -0.5, 0.5, -0.2, -0.3, -0.2, -0.6,
|
|
0.6, -0.1, -0.4, -0.3, -0.7, 0.7, -0.9, -0.5, 0.8, 0.6};
|
|
|
|
const std::vector<float> input_to_output_weights = {
|
|
-0.8, -0.4, -0.2, -0.9, -0.1, -0.7, 0.3, -0.3, -0.8, -0.2,
|
|
0.6, -0.2, 0.4, -0.7, -0.3, -0.5, 0.1, 0.5, -0.6, -0.4};
|
|
|
|
const std::vector<float> input_gate_bias = {0.03, 0.15, 0.22, 0.38};
|
|
|
|
const std::vector<float> forget_gate_bias = {0.1, -0.3, -0.2, 0.1};
|
|
|
|
const std::vector<float> cell_gate_bias = {-0.05, 0.72, 0.25, 0.08};
|
|
|
|
const std::vector<float> output_gate_bias = {0.05, -0.01, 0.2, 0.1};
|
|
|
|
const std::vector<float> recurrent_to_input_weights = {
|
|
-0.2, -0.3, 0.4, 0.1, -0.5, 0.9, -0.2, -0.3, -0.7, 0.05, -0.2, -0.6};
|
|
|
|
const std::vector<float> recurrent_to_cell_weights = {
|
|
-0.3, 0.2, 0.1, -0.3, 0.8, -0.08, -0.2, 0.3, 0.8, -0.6, -0.1, 0.2};
|
|
|
|
const std::vector<float> recurrent_to_forget_weights = {
|
|
-0.5, -0.3, -0.5, -0.2, 0.6, 0.4, 0.9, 0.3, -0.1, 0.2, 0.5, 0.2};
|
|
|
|
const std::vector<float> recurrent_to_output_weights = {
|
|
0.3, -0.1, 0.1, -0.2, -0.5, -0.7, -0.2, -0.6, -0.1, -0.4, -0.7, -0.2};
|
|
|
|
const std::vector<float> input_layer_norm_coefficients = {0.1, 0.2, 0.3, 0.5};
|
|
const std::vector<float> forget_layer_norm_coefficients = {0.2, 0.2, 0.4,
|
|
0.3};
|
|
const std::vector<float> cell_layer_norm_coefficients = {0.7, 0.2, 0.3, 0.8};
|
|
const std::vector<float> output_layer_norm_coefficients = {0.6, 0.2, 0.2,
|
|
0.5};
|
|
|
|
const std::vector<float> projection_weights = {
|
|
-0.1, 0.2, 0.01, -0.2, 0.1, 0.5, 0.3, 0.08, 0.07, 0.2, -0.4, 0.2};
|
|
const std::vector<float> projection_bias = {0.1, 0.3, 0.5};
|
|
|
|
// Input ranges.
|
|
const std::vector<std::pair<float, float>> ranges = {
|
|
{-1.0, 127.0 / 128}, // input tensor
|
|
{-1.0, 1.0}, // input_to_input_weight tensor
|
|
{-1.0, 1.0}, // input_to_forget_weight tensor
|
|
{-1.0, 1.0}, // input_to_cell_weight tensor
|
|
{-1.0, 1.0}, // input_to_output_weight tensor
|
|
|
|
{-1.0, 1.0}, // recurrent_to_input_weight tensor
|
|
{-1.0, 1.0}, // recurrent_to_forget_weight tensor
|
|
{-1.0, 1.0}, // recurrent_to_cell_weight tensor
|
|
{-1.0, 1.0}, // recurrent_to_output_weight tensor
|
|
|
|
{-1, 1}, // cell_to_input_weight tensor
|
|
{-1, 1}, // cell_to_forget_weight tensor
|
|
{-1, 1}, // cell_to_output_weight tensor
|
|
|
|
{-100, 100}, // input_gate_bias tensor
|
|
{-100, 100}, // forget_gate_bias tensor
|
|
{-100, 100}, // cell_gate_bias tensor
|
|
{-100, 100}, // output_gate_bias tensor
|
|
|
|
{-0.5, 0.5}, // projection_weight tensor
|
|
{-1, 1}, // projection_bias tensor
|
|
|
|
{-1.0, 32767.0 / 32768}, // output_state tensor
|
|
{-1.0, 32767.0 / 32768}, // cell_state tensor
|
|
|
|
{-1.00001, 1.0}, // input_layer_norm_coefficient tensor
|
|
{-1.00001, 1.0}, // forget_layer_norm_coefficient tensor
|
|
{-1.00001, 1.0}, // cell_layer_norm_coefficient tensor
|
|
{-1.00001, 1.0}, // output_layer_norm_coefficient tensor
|
|
// Output scale is the same as output_state scale and only output_state
|
|
// scale is used in the op, so this is only provided for clarity.
|
|
{-1.0, 32767.0 / 32768}, // output tensor.
|
|
};
|
|
|
|
// The scale and zero point of intermediate tensors.
|
|
std::vector<std::pair<float, int>> intermediates = {
|
|
{0.007059, 0}, {0.007812, 0}, {0.007059, 0}, {0.007812, 0},
|
|
{0.007, 0}, {0.007059, 0}, {0.007, 0}, {0.007, 0},
|
|
{0.007059, 0}, {0.007, 0}, {0.007, 0}, {0.3, 0}};
|
|
|
|
// Create model.
|
|
LSTMIntegerOpModel lstm(n_batch, n_input, n_cell, n_output,
|
|
/*use_cifg=*/true, /*use_peephole=*/false,
|
|
/*use_projection_weights=*/true,
|
|
/*use_projection_bias=*/true,
|
|
/*use_layer_norm=*/true,
|
|
/*use_8x8_8_implementation=*/true, ranges,
|
|
intermediates);
|
|
|
|
// Do allocate.
|
|
lstm.PerformAllocateAndDelegate();
|
|
|
|
// Set weights.
|
|
// lstm.SetInputToInputWeights(input_to_input_weights);
|
|
lstm.SetInputToCellWeights(input_to_cell_weights);
|
|
lstm.SetInputToForgetWeights(input_to_forget_weights);
|
|
lstm.SetInputToOutputWeights(input_to_output_weights);
|
|
|
|
// lstm.SetInputGateBias(input_gate_bias);
|
|
lstm.SetCellBias(cell_gate_bias);
|
|
lstm.SetForgetGateBias(forget_gate_bias);
|
|
lstm.SetOutputGateBias(output_gate_bias);
|
|
|
|
// lstm.SetRecurrentToInputWeights(recurrent_to_input_weights);
|
|
lstm.SetRecurrentToCellWeights(recurrent_to_cell_weights);
|
|
lstm.SetRecurrentToForgetWeights(recurrent_to_forget_weights);
|
|
lstm.SetRecurrentToOutputWeights(recurrent_to_output_weights);
|
|
|
|
lstm.SetProjectionWeights(projection_weights);
|
|
lstm.SetProjectionBias(projection_bias);
|
|
|
|
// lstm.SetInputLayerNormCoefficients(input_layer_norm_coefficients);
|
|
lstm.SetForgetLayerNormCoefficients(forget_layer_norm_coefficients);
|
|
lstm.SetCellLayerNormCoefficients(cell_layer_norm_coefficients);
|
|
lstm.SetOutputLayerNormCoefficients(output_layer_norm_coefficients);
|
|
|
|
// Model inputs. sequence -batch - input
|
|
const std::vector<std::vector<float>> lstm_input = {
|
|
{
|
|
0.7, 0.8, 0.1, 0.2, 0.3, //
|
|
0.8, 0.1, 0.2, 0.4, 0.5, //
|
|
},
|
|
{
|
|
0.2, 0.7, 0.7, 0.1, 0.7, //
|
|
0.3, 0.2, 0.9, 0.8, 0.1, //
|
|
},
|
|
{
|
|
0.7, 0.8, 0.1, 0.2, 0.3, //
|
|
0.3, 0.2, 0.9, 0.8, 0.1, //
|
|
},
|
|
};
|
|
|
|
// Expected outputs.
|
|
const std::vector<std::vector<int8_t>> expected_output = {
|
|
{127, 127, 127, 127, 127, 127},
|
|
{127, 127, 127, 127, 127, 127},
|
|
{127, 127, 127, 127, 127, 127},
|
|
};
|
|
|
|
// Invoke and verify the result.
|
|
const int input_sequence_size = lstm_input.size();
|
|
EXPECT_GT(input_sequence_size, 0);
|
|
for (int i = 0; i < input_sequence_size; ++i) {
|
|
lstm.SetInput(lstm_input[i]);
|
|
lstm.Invoke();
|
|
EXPECT_THAT(lstm.GetOutput(), ElementsAreArray(expected_output[i]));
|
|
}
|
|
}
|
|
|
|
#ifdef GTEST_HAS_DEATH_TEST
|
|
TEST(LstmOpTest, InvalidTypes) {
|
|
const int n_batch = 1;
|
|
const int n_input = 2;
|
|
const int n_cell = 4;
|
|
const int n_output = 4;
|
|
|
|
EXPECT_DEATH(LSTMOpModel lstm(n_batch, n_input, n_cell, n_output,
|
|
/*use_cifg=*/false, /*use_peephole=*/false,
|
|
/*use_projection_weights=*/false,
|
|
/*use_projection_bias=*/false,
|
|
/*weight_type=*/TensorType_INT32,
|
|
/*model_has_legacy_20_inputs=*/true,
|
|
/*is_layer_norm=*/false,
|
|
/*asymmetric_quantize_inputs=*/false),
|
|
"");
|
|
|
|
EXPECT_DEATH(LSTMOpModel lstm(n_batch, n_input, n_cell, n_output,
|
|
/*use_cifg=*/false, /*use_peephole=*/false,
|
|
/*use_projection_weights=*/false,
|
|
/*use_projection_bias=*/false,
|
|
/*weight_type=*/TensorType_COMPLEX64,
|
|
/*model_has_legacy_20_inputs=*/true,
|
|
/*is_layer_norm=*/false,
|
|
/*asymmetric_quantize_inputs=*/false),
|
|
"");
|
|
}
|
|
#endif
|
|
|
|
class HybridSparseLSTMOpModel : public ::tflite::SingleOpModel {
|
|
public:
|
|
HybridSparseLSTMOpModel(
|
|
int n_batch, int n_input, int n_cell, int n_output, bool use_cifg,
|
|
bool use_peephole, bool use_projection_weights, bool use_projection_bias,
|
|
float cell_clip, float proj_clip,
|
|
const std::vector<std::vector<int>>& input_shapes,
|
|
const TensorData& input_weights_td,
|
|
const std::vector<float>& input_to_input_weights,
|
|
const std::vector<float>& input_to_forget_weights,
|
|
const std::vector<float>& input_to_cell_weights,
|
|
const std::vector<float>& input_to_output_weights,
|
|
const TensorData& recurrent_weights_td,
|
|
const std::vector<float>& recurrent_to_input_weights,
|
|
const std::vector<float>& recurrent_to_forget_weights,
|
|
const std::vector<float>& recurrent_to_cell_weights,
|
|
const std::vector<float>& recurrent_to_output_weights,
|
|
const ::tflite::TensorType& weight_type = ::tflite::TensorType_INT8)
|
|
: n_batch_(n_batch),
|
|
n_input_(n_input),
|
|
n_cell_(n_cell),
|
|
n_output_(n_output) {
|
|
input_ = AddInput(::tflite::TensorType_FLOAT32);
|
|
|
|
if (use_cifg) {
|
|
input_to_input_weights_ = AddNullInput();
|
|
} else {
|
|
input_to_input_weights_ =
|
|
AddConstSparseInput(input_weights_td, input_to_input_weights, true);
|
|
}
|
|
|
|
input_to_forget_weights_ =
|
|
AddConstSparseInput(input_weights_td, input_to_forget_weights, true);
|
|
|
|
input_to_cell_weights_ =
|
|
AddConstSparseInput(input_weights_td, input_to_cell_weights, true);
|
|
|
|
input_to_output_weights_ =
|
|
AddConstSparseInput(input_weights_td, input_to_output_weights, true);
|
|
|
|
if (use_cifg) {
|
|
recurrent_to_input_weights_ = AddNullInput();
|
|
} else {
|
|
recurrent_to_input_weights_ = AddConstSparseInput(
|
|
recurrent_weights_td, recurrent_to_input_weights, true);
|
|
}
|
|
|
|
recurrent_to_forget_weights_ = AddConstSparseInput(
|
|
recurrent_weights_td, recurrent_to_forget_weights, true);
|
|
recurrent_to_cell_weights_ = AddConstSparseInput(
|
|
recurrent_weights_td, recurrent_to_cell_weights, true);
|
|
recurrent_to_output_weights_ = AddConstSparseInput(
|
|
recurrent_weights_td, recurrent_to_output_weights, true);
|
|
|
|
if (use_peephole) {
|
|
if (use_cifg) {
|
|
cell_to_input_weights_ = AddNullInput();
|
|
} else {
|
|
cell_to_input_weights_ = AddInput(weight_type);
|
|
}
|
|
cell_to_forget_weights_ = AddInput(weight_type);
|
|
cell_to_output_weights_ = AddInput(weight_type);
|
|
} else {
|
|
cell_to_input_weights_ = AddNullInput();
|
|
cell_to_forget_weights_ = AddNullInput();
|
|
cell_to_output_weights_ = AddNullInput();
|
|
}
|
|
|
|
if (use_cifg) {
|
|
input_gate_bias_ = AddNullInput();
|
|
} else {
|
|
input_gate_bias_ = AddInput(::tflite::TensorType_FLOAT32);
|
|
}
|
|
forget_gate_bias_ = AddInput(::tflite::TensorType_FLOAT32);
|
|
cell_bias_ = AddInput(::tflite::TensorType_FLOAT32);
|
|
output_gate_bias_ = AddInput(::tflite::TensorType_FLOAT32);
|
|
|
|
if (use_projection_weights) {
|
|
projection_weights_ = AddInput(weight_type);
|
|
if (use_projection_bias) {
|
|
projection_bias_ = AddInput(::tflite::TensorType_FLOAT32);
|
|
} else {
|
|
projection_bias_ = AddNullInput();
|
|
}
|
|
} else {
|
|
projection_weights_ = AddNullInput();
|
|
projection_bias_ = AddNullInput();
|
|
}
|
|
|
|
// Adding the 2 state tensors.
|
|
output_state_ = AddVariableInput(::tflite::TensorData{
|
|
::tflite::TensorType_FLOAT32, {n_output_ * n_batch_}});
|
|
cell_state_ = AddVariableInput(::tflite::TensorData{
|
|
::tflite::TensorType_FLOAT32, {n_cell_ * n_batch_}});
|
|
|
|
if (use_cifg) {
|
|
input_layer_norm_weights_ = AddNullInput();
|
|
} else {
|
|
input_layer_norm_weights_ = AddInput(::tflite::TensorType_FLOAT32);
|
|
}
|
|
forget_layer_norm_weights_ = AddInput(::tflite::TensorType_FLOAT32);
|
|
cell_layer_norm_weights_ = AddInput(::tflite::TensorType_FLOAT32);
|
|
output_layer_norm_weights_ = AddInput(::tflite::TensorType_FLOAT32);
|
|
|
|
output_ = AddOutput(::tflite::TensorType_FLOAT32);
|
|
|
|
SetBuiltinOp(
|
|
BuiltinOperator_LSTM, BuiltinOptions_LSTMOptions,
|
|
CreateLSTMOptions(builder_, ActivationFunctionType_TANH, cell_clip,
|
|
proj_clip, LSTMKernelType_FULL, false)
|
|
.Union());
|
|
BuildInterpreter(input_shapes);
|
|
}
|
|
|
|
void SetCellToInputWeights(std::vector<float> f) {
|
|
SignedSymmetricQuantizeAndPopulate(cell_to_input_weights_, f);
|
|
}
|
|
|
|
void SetCellToForgetWeights(std::vector<float> f) {
|
|
SignedSymmetricQuantizeAndPopulate(cell_to_forget_weights_, f);
|
|
}
|
|
|
|
void SetCellToOutputWeights(std::vector<float> f) {
|
|
SignedSymmetricQuantizeAndPopulate(cell_to_output_weights_, f);
|
|
}
|
|
|
|
void SetInputLayerNormWeights(std::vector<float> f) {
|
|
PopulateTensor(input_layer_norm_weights_, f);
|
|
}
|
|
|
|
void SetForgetLayerNormWeights(std::vector<float> f) {
|
|
PopulateTensor(forget_layer_norm_weights_, f);
|
|
}
|
|
|
|
void SetCellLayerNormWeights(std::vector<float> f) {
|
|
PopulateTensor(cell_layer_norm_weights_, f);
|
|
}
|
|
|
|
void SetOutputLayerNormWeights(std::vector<float> f) {
|
|
PopulateTensor(output_layer_norm_weights_, f);
|
|
}
|
|
|
|
void SetInputGateBias(std::vector<float> f) {
|
|
PopulateTensor(input_gate_bias_, f);
|
|
}
|
|
|
|
void SetForgetGateBias(std::vector<float> f) {
|
|
PopulateTensor(forget_gate_bias_, f);
|
|
}
|
|
|
|
void SetCellBias(std::vector<float> f) { PopulateTensor(cell_bias_, f); }
|
|
|
|
void SetOutputGateBias(std::vector<float> f) {
|
|
PopulateTensor(output_gate_bias_, f);
|
|
}
|
|
|
|
void SetProjectionWeights(std::vector<float> f) {
|
|
SignedSymmetricQuantizeAndPopulate(projection_weights_, f);
|
|
}
|
|
|
|
void SetProjectionBias(std::vector<float> f) {
|
|
PopulateTensor(projection_bias_, f);
|
|
}
|
|
|
|
void SetInput(int offset, const float* begin, const float* end) {
|
|
PopulateTensor(input_, offset, const_cast<float*>(begin),
|
|
const_cast<float*>(end));
|
|
}
|
|
|
|
std::vector<float> GetOutput() { return ExtractVector<float>(output_); }
|
|
|
|
int num_inputs() { return n_input_; }
|
|
int num_outputs() { return n_output_; }
|
|
int num_cells() { return n_cell_; }
|
|
int num_batches() { return n_batch_; }
|
|
|
|
protected:
|
|
int input_;
|
|
int input_to_input_weights_;
|
|
int input_to_forget_weights_;
|
|
int input_to_cell_weights_;
|
|
int input_to_output_weights_;
|
|
|
|
int recurrent_to_input_weights_;
|
|
int recurrent_to_forget_weights_;
|
|
int recurrent_to_cell_weights_;
|
|
int recurrent_to_output_weights_;
|
|
|
|
int cell_to_input_weights_;
|
|
int cell_to_forget_weights_;
|
|
int cell_to_output_weights_;
|
|
|
|
int input_layer_norm_weights_;
|
|
int forget_layer_norm_weights_;
|
|
int cell_layer_norm_weights_;
|
|
int output_layer_norm_weights_;
|
|
|
|
int input_gate_bias_;
|
|
int forget_gate_bias_;
|
|
int cell_bias_;
|
|
int output_gate_bias_;
|
|
|
|
int projection_weights_;
|
|
int projection_bias_;
|
|
|
|
int output_state_;
|
|
int cell_state_;
|
|
|
|
int output_;
|
|
|
|
int n_batch_;
|
|
int n_input_;
|
|
int n_cell_;
|
|
int n_output_;
|
|
};
|
|
|
|
class BaseSparseLstmTest : public ::testing::Test {
|
|
protected:
|
|
// Weights of the Sparse Layer Norm LSTM model. Some are optional.
|
|
std::vector<float> input_to_input_weights_;
|
|
std::vector<float> input_to_cell_weights_;
|
|
std::vector<float> input_to_forget_weights_;
|
|
std::vector<float> input_to_output_weights_;
|
|
std::vector<float> input_gate_bias_;
|
|
std::vector<float> cell_gate_bias_;
|
|
std::vector<float> forget_gate_bias_;
|
|
std::vector<float> output_gate_bias_;
|
|
std::vector<float> recurrent_to_input_weights_;
|
|
std::vector<float> recurrent_to_cell_weights_;
|
|
std::vector<float> recurrent_to_forget_weights_;
|
|
std::vector<float> recurrent_to_output_weights_;
|
|
std::vector<float> cell_to_input_weights_;
|
|
std::vector<float> cell_to_forget_weights_;
|
|
std::vector<float> cell_to_output_weights_;
|
|
std::vector<float> input_layer_norm_weights_;
|
|
std::vector<float> forget_layer_norm_weights_;
|
|
std::vector<float> cell_layer_norm_weights_;
|
|
std::vector<float> output_layer_norm_weights_;
|
|
std::vector<float> projection_weights_;
|
|
|
|
std::vector<int> input_to_input_weights_size_;
|
|
std::vector<int> input_to_cell_weights_size_;
|
|
std::vector<int> input_to_forget_weights_size_;
|
|
std::vector<int> input_to_output_weights_size_;
|
|
std::vector<int> recurrent_to_input_weights_size_;
|
|
std::vector<int> recurrent_to_cell_weights_size_;
|
|
std::vector<int> recurrent_to_forget_weights_size_;
|
|
std::vector<int> recurrent_to_output_weights_size_;
|
|
|
|
int n_batch_;
|
|
int n_input_;
|
|
int n_cell_;
|
|
int n_output_;
|
|
float cell_clip_;
|
|
float proj_clip_;
|
|
|
|
// Layer Norm LSTM input is stored as num_batch x num_inputs vector.
|
|
std::vector<std::vector<float>> sparse_layer_norm_lstm_input_;
|
|
|
|
// Compares output up to tolerance to the result of the layer_norm_lstm given
|
|
// the input.
|
|
void VerifyGoldens(const std::vector<std::vector<float>>& input,
|
|
const std::vector<std::vector<float>>& output,
|
|
HybridSparseLSTMOpModel* sparse_layer_norm_lstm,
|
|
float tolerance = 1e-5) {
|
|
const int num_batches = input.size();
|
|
EXPECT_GT(num_batches, 0);
|
|
const int num_inputs = sparse_layer_norm_lstm->num_inputs();
|
|
EXPECT_GT(num_inputs, 0);
|
|
const int input_sequence_size = input[0].size() / num_inputs;
|
|
EXPECT_GT(input_sequence_size, 0);
|
|
for (int i = 0; i < input_sequence_size; ++i) {
|
|
for (int b = 0; b < num_batches; ++b) {
|
|
const float* batch_start = input[b].data() + i * num_inputs;
|
|
const float* batch_end = batch_start + num_inputs;
|
|
|
|
sparse_layer_norm_lstm->SetInput(
|
|
b * sparse_layer_norm_lstm->num_inputs(), batch_start, batch_end);
|
|
}
|
|
|
|
sparse_layer_norm_lstm->Invoke();
|
|
|
|
const int num_outputs = sparse_layer_norm_lstm->num_outputs();
|
|
std::vector<float> expected;
|
|
for (int b = 0; b < num_batches; ++b) {
|
|
const float* golden_start_batch = output[b].data() + i * num_outputs;
|
|
const float* golden_end_batch = golden_start_batch + num_outputs;
|
|
expected.insert(expected.end(), golden_start_batch, golden_end_batch);
|
|
}
|
|
EXPECT_THAT(
|
|
sparse_layer_norm_lstm->GetOutput(),
|
|
ElementsAreArray(::tflite::ArrayFloatNear(expected, tolerance)));
|
|
}
|
|
}
|
|
};
|
|
|
|
class NoCifgPeepholeProjectionNoClippingSparseLstmTest
|
|
: public BaseSparseLstmTest {
|
|
void SetUp() override {
|
|
n_batch_ = 2;
|
|
n_input_ = 48;
|
|
n_cell_ = 4;
|
|
n_output_ = 16;
|
|
cell_clip_ = 0.0;
|
|
proj_clip_ = 0.0;
|
|
|
|
/* clang-format off */
|
|
input_to_input_weights_ = {
|
|
/* 1st row */
|
|
1.1, 2.2, 3.3, 4.4, 5.5, 6.6, 7.7, 8.8, 9.9, 10.1, 11.11, 12.12, 13.13,
|
|
14.14, 15.15, 16.16, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
|
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 33.33, 34.34, 35.35, 36.36, 37.37, 38.38,
|
|
39.39, 40.40, 41.41, 42.42, 43.43, 44.44, 0.0, 0.0, 0.0, 0.0,
|
|
/* 2nd row */
|
|
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
|
0.0, -17.17, -18.18, -19.19, -20.2, -21.21, -22.22, -23.23, -24.24,
|
|
-25.25, -26.26, -27.27, -28.28, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
|
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
|
/* 3rd row */
|
|
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
|
0.0, 17.17, -18.18, 19.19, -20.2, 21.21, -22.22, 23.23, -24.24, 25.25,
|
|
-26.26, 27.27, -28.28, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
|
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
|
/* 4th row */
|
|
-1.1, 2.2, -3.3, 4.4, -5.5, 6.6, -7.7, 8.8, -9.9, 10.1, -11.11, 12.12,
|
|
-13.13, 14.14, -15.15, 16.16, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
|
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -33.33, 34.34, -35.35, 36.36, -37.37,
|
|
38.38, -39.39, 40.40, -41.41, 42.42, -43.43, 44.44, 0.0, 0.0, 0.0, 0};
|
|
input_to_input_weights_size_ = {4, 48};
|
|
|
|
input_to_forget_weights_ = {
|
|
/* 1st row */
|
|
1.1, 2.2, 3.3, 4.4, 5.5, 6.6, 7.7, 8.8, 9.9, 10.1, 11.11, 12.12, 13.13,
|
|
14.14, 15.15, 16.16, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
|
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 33.33, 34.34, 35.35, 36.36, 37.37, 38.38,
|
|
39.39, 40.40, 41.41, 42.42, 43.43, 44.44, 0.0, 0.0, 0.0, 0.0,
|
|
/* 2nd row */
|
|
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
|
0.0, -17.17, -18.18, -19.19, -20.2, -21.21, -22.22, -23.23, -24.24,
|
|
-25.25, -26.26, -27.27, -28.28, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
|
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
|
/* 3rd row */
|
|
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
|
0.0, 17.17, -18.18, 19.19, -20.2, 21.21, -22.22, 23.23, -24.24, 25.25,
|
|
-26.26, 27.27, -28.28, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
|
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
|
/* 4th row */
|
|
-1.1, 2.2, -3.3, 4.4, -5.5, 6.6, -7.7, 8.8, -9.9, 10.1, -11.11, 12.12,
|
|
-13.13, 14.14, -15.15, 16.16, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
|
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -33.33, 34.34, -35.35, 36.36, -37.37,
|
|
38.38, -39.39, 40.40, -41.41, 42.42, -43.43, 44.44, 0.0, 0.0, 0.0, 0};
|
|
input_to_forget_weights_size_ = {4, 48};
|
|
|
|
input_to_cell_weights_ = {
|
|
/* 1st row */
|
|
1.1, 2.2, 3.3, 4.4, 5.5, 6.6, 7.7, 8.8, 9.9, 10.1, 11.11, 12.12, 13.13,
|
|
14.14, 15.15, 16.16, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
|
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 33.33, 34.34, 35.35, 36.36, 37.37, 38.38,
|
|
39.39, 40.40, 41.41, 42.42, 43.43, 44.44, 0.0, 0.0, 0.0, 0.0,
|
|
/* 2nd row */
|
|
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
|
0.0, -17.17, -18.18, -19.19, -20.2, -21.21, -22.22, -23.23, -24.24,
|
|
-25.25, -26.26, -27.27, -28.28, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
|
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
|
/* 3rd row */
|
|
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
|
0.0, 17.17, -18.18, 19.19, -20.2, 21.21, -22.22, 23.23, -24.24, 25.25,
|
|
-26.26, 27.27, -28.28, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
|
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
|
/* 4th row */
|
|
-1.1, 2.2, -3.3, 4.4, -5.5, 6.6, -7.7, 8.8, -9.9, 10.1, -11.11, 12.12,
|
|
-13.13, 14.14, -15.15, 16.16, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
|
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -33.33, 34.34, -35.35, 36.36, -37.37,
|
|
38.38, -39.39, 40.40, -41.41, 42.42, -43.43, 44.44, 0.0, 0.0, 0.0, 0};
|
|
input_to_cell_weights_size_ = {4, 48};
|
|
|
|
input_to_output_weights_ = {
|
|
/* 1st row */
|
|
1.1, 2.2, 3.3, 4.4, 5.5, 6.6, 7.7, 8.8, 9.9, 10.1, 11.11, 12.12, 13.13,
|
|
14.14, 15.15, 16.16, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
|
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 33.33, 34.34, 35.35, 36.36, 37.37, 38.38,
|
|
39.39, 40.40, 41.41, 42.42, 43.43, 44.44, 0.0, 0.0, 0.0, 0.0,
|
|
/* 2nd row */
|
|
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
|
0.0, -17.17, -18.18, -19.19, -20.2, -21.21, -22.22, -23.23, -24.24,
|
|
-25.25, -26.26, -27.27, -28.28, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
|
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
|
/* 3rd row */
|
|
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
|
0.0, 17.17, -18.18, 19.19, -20.2, 21.21, -22.22, 23.23, -24.24, 25.25,
|
|
-26.26, 27.27, -28.28, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
|
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
|
/* 4th row */
|
|
-1.1, 2.2, -3.3, 4.4, -5.5, 6.6, -7.7, 8.8, -9.9, 10.1, -11.11, 12.12,
|
|
-13.13, 14.14, -15.15, 16.16, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
|
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -33.33, 34.34, -35.35, 36.36, -37.37,
|
|
38.38, -39.39, 40.40, -41.41, 42.42, -43.43, 44.44, 0.0, 0.0, 0.0, 0};
|
|
input_to_output_weights_size_ = {4, 48};
|
|
|
|
input_gate_bias_ = {0.03, 0.15, 0.22, 0.38};
|
|
|
|
forget_gate_bias_ = {0.1, -0.3, -0.2, 0.1};
|
|
|
|
cell_gate_bias_ = {-0.05, 0.72, 0.25, 0.08};
|
|
|
|
output_gate_bias_ = {0.05, -0.01, 0.2, 0.1};
|
|
|
|
recurrent_to_input_weights_ = {
|
|
-0.2, -0.3, 0.4, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
|
0.0, 0.0, // 1st row
|
|
0.1, -0.5, 0.9, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
|
0.0, 0.0, // 2nd row
|
|
-0.2, -0.3, -0.7, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
|
0.0, 0.0, // 3rd row
|
|
0.05, -0.2, -0.6, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
|
0.0, 0.0, // 4th row
|
|
};
|
|
recurrent_to_input_weights_size_ = {4, 16};
|
|
|
|
recurrent_to_cell_weights_ = {
|
|
-0.3, 0.2, 0.1, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
|
0.0, 0.0, // 1st row
|
|
-0.3, 0.8, -0.08, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
|
0.0, 0.0, // 2nd row
|
|
-0.2, 0.3, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
|
0.0, 0.0, // 3rd row
|
|
-0.6, -0.1, 0.2, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
|
0.0, 0.0, // 4th row
|
|
};
|
|
recurrent_to_cell_weights_size_ = {4, 16};
|
|
|
|
recurrent_to_forget_weights_ = {
|
|
-0.5, -0.3, -0.5, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
|
0.0, 0.0, // 1st row
|
|
-0.2, 0.6, 0.4, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
|
0.0, 0.0, // 2nd row
|
|
0.9, 0.3, -0.1, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
|
0.0, 0.0, // 3rd row
|
|
0.2, 0.5, 0.2, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
|
0.0, 0.0, // 4th row
|
|
};
|
|
recurrent_to_forget_weights_size_ = {4, 16};
|
|
|
|
recurrent_to_output_weights_ = {
|
|
0.3, -0.1, 0.1, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
|
0.0, 0.0, // 1st row
|
|
-0.2, -0.5, -0.7, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
|
0.0, 0.0, // 2nd row
|
|
-0.2, -0.6, -0.1, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
|
0.0, 0.0, // 3rd row
|
|
-0.4, -0.7, -0.2, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
|
0.0, 0.0, // 4th row
|
|
};
|
|
recurrent_to_output_weights_size_ = {4, 16};
|
|
|
|
cell_to_input_weights_ = {0.05, 0.1, 0.25, 0.15};
|
|
|
|
cell_to_forget_weights_ = {-0.02, -0.15, -0.25, -0.03};
|
|
|
|
cell_to_output_weights_ = {0.1, -0.1, -0.5, 0.05};
|
|
|
|
input_layer_norm_weights_ = {0.1, 0.2, 0.3, 0.5};
|
|
forget_layer_norm_weights_ = {0.2, 0.2, 0.4, 0.3};
|
|
cell_layer_norm_weights_ = {0.7, 0.2, 0.3, 0.8};
|
|
output_layer_norm_weights_ = {0.6, 0.2, 0.2, 0.5};
|
|
|
|
projection_weights_ = {
|
|
-0.1, 0.2, 0.01, -0.2, // 1st row
|
|
0.1, 0.5, 0.3, 0.08, // 2nd row
|
|
0.07, 0.2, -0.4, 0.2, // 3rd row
|
|
0.0, 0.0, 0.0, 0.0, // 4th row
|
|
0.0, 0.0, 0.0, 0.0, // 5th row
|
|
0.0, 0.0, 0.0, 0.0, // 6th row
|
|
0.0, 0.0, 0.0, 0.0, // 7th row
|
|
0.0, 0.0, 0.0, 0.0, // 8th row
|
|
0.0, 0.0, 0.0, 0.0, // 9th row
|
|
0.0, 0.0, 0.0, 0.0, // 10th row
|
|
0.0, 0.0, 0.0, 0.0, // 11th row
|
|
0.0, 0.0, 0.0, 0.0, // 12th row
|
|
0.0, 0.0, 0.0, 0.0, // 13th row
|
|
0.0, 0.0, 0.0, 0.0, // 14th row
|
|
0.0, 0.0, 0.0, 0.0, // 15th row
|
|
0.0, 0.0, 0.0, 0.0, // 16th row
|
|
0.0, 0.0, 0.0, 0.0, // 17th row
|
|
0.0, 0.0, 0.0, 0.0, // 18th row
|
|
};
|
|
|
|
sparse_layer_norm_lstm_input_ = {
|
|
// Batch0: 2 (input_sequence_size) * 45 (n_input_)
|
|
{
|
|
1.0, -1.0, 1.0, -1.0, 1.0, -1.0, 1.0, -1.0, 1.0, -1.0, 1.0, -1.0, 1.0,
|
|
-1.0, 1.0, -1.0, 1.0, -1.0, 1.0, -1.0, 1.0, -1.0, 1.0, -1.0, 1.0, -1.0,
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|
1.0, -1.0, 1.0, -1.0, 1.0, -1.0, 1.0, -1.0, 1.0, -1.0, 1.0, -1.0, 1.0,
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-1.0, 1.0, -1.0, 1.0, -1.0, 1.0, -1.0, 1.0, -1.0, // seq 0
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|
2.5, 0.0, -2.1, 0.0, 3.0, 0.0, -1.3, 0.0, 1.3, 0.0, -1.1, 0.0, 2.0, 0.0,
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|
-1.7, 0.0, 1.9, 0.0, -1.5, 0.0, 0.5, 0.0, -0.7, 0.0, 0.8, 0.0, -0.3,
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|
0.0, 2.8, 0.0, -2.8, 0.0, 1.1, -2.3, 1.9, -1.9, 2.1, -0.5, 2.4, -0.1,
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1.0, -2.5, 0.7, -1.9, 0.2, 0.1, 0.2, 0.3, // seq 1
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|
},
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|
// Batch1: 2 (input_sequence_size) * 45 (n_input_)
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|
{
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|
1.0, -1.0, 1.0, -1.0, 1.0, -1.0, 1.0, -1.0, 1.0, -1.0, 1.0, -1.0, 1.0,
|
|
-1.0, 1.0, -1.0, 1.0, -1.0, 1.0, -1.0, 1.0, -1.0, 1.0, -1.0, 1.0, -1.0,
|
|
1.0, -1.0, 1.0, -1.0, 1.0, -1.0, 1.0, -1.0, 1.0, -1.0, 1.0, -1.0, 1.0,
|
|
-1.0, 1.0, -1.0, 1.0, -1.0, 1.0, -1.0, 1.0, -1.0, // seq 0
|
|
2.5, 0.0, -2.1, 0.0, 3.0, 0.0, -1.3, 0.0, 1.3, 0.0, -1.1, 0.0, 2.0, 0.0,
|
|
-1.7, 0.0, 1.9, 0.0, -1.5, 0.0, 0.5, 0.0, -0.7, 0.0, 0.8, 0.0, -0.3,
|
|
0.0, 2.8, 0.0, -2.8, 0.0, 1.1, -2.3, 1.9, -1.9, 2.1, -0.5, 2.4, -0.1,
|
|
1.0, -2.5, 0.7, -1.9, 0.2, -1.0, 1.0, -1.0, // seq 1
|
|
},
|
|
};
|
|
/* clang-format on */
|
|
}
|
|
};
|
|
|
|
TEST_F(NoCifgPeepholeProjectionNoClippingSparseLstmTest,
|
|
HybridSparseLstmBlackBoxTest) {
|
|
TensorData input_weight = {};
|
|
input_weight.type = TensorType_FLOAT32;
|
|
input_weight.shape = {4, 48};
|
|
input_weight.traversal_order = {0, 1, 2};
|
|
input_weight.format = {kTfLiteDimDense, kTfLiteDimSparseCSR};
|
|
input_weight.block_map = {1};
|
|
input_weight.block_size = {16};
|
|
TensorData recurrent_weight = {};
|
|
recurrent_weight.type = TensorType_FLOAT32;
|
|
recurrent_weight.shape = {4, 16};
|
|
recurrent_weight.traversal_order = {0, 1, 2};
|
|
recurrent_weight.format = {kTfLiteDimDense, kTfLiteDimSparseCSR};
|
|
recurrent_weight.block_map = {1};
|
|
recurrent_weight.block_size = {16};
|
|
HybridSparseLSTMOpModel sparse_layer_norm_lstm(
|
|
n_batch_, n_input_, n_cell_, n_output_,
|
|
/*use_cifg=*/false, /*use_peephole=*/true,
|
|
/*use_projection_weights=*/true,
|
|
/*use_projection_bias=*/false, cell_clip_, proj_clip_,
|
|
{
|
|
{n_batch_, n_input_}, // input tensor
|
|
|
|
{input_to_input_weights_size_},
|
|
{input_to_forget_weights_size_},
|
|
{input_to_cell_weights_size_},
|
|
{input_to_output_weights_size_},
|
|
|
|
{recurrent_to_input_weights_size_},
|
|
{recurrent_to_forget_weights_size_},
|
|
{recurrent_to_cell_weights_size_},
|
|
{recurrent_to_output_weights_size_},
|
|
|
|
{n_cell_}, // cell_to_input_weight tensor
|
|
{n_cell_}, // cell_to_forget_weight tensor
|
|
{n_cell_}, // cell_to_output_weight tensor
|
|
|
|
{n_cell_}, // input_gate_bias tensor
|
|
{n_cell_}, // forget_gate_bias tensor
|
|
{n_cell_}, // cell_bias tensor
|
|
{n_cell_}, // output_gate_bias tensor
|
|
|
|
{n_output_, n_cell_}, // projection_weight tensor
|
|
{0}, // projection_bias tensor
|
|
|
|
{n_output_ * n_batch_}, // output_state tensor
|
|
{n_cell_ * n_batch_}, // cell_state tensor
|
|
|
|
{n_cell_}, // input_layer_norm_weight tensor
|
|
{n_cell_}, // forget_layer_norm_weight tensor
|
|
{n_cell_}, // cell_layer_norm_weight tensor
|
|
{n_cell_}, // output_layer_norm_weight tensor
|
|
},
|
|
input_weight, input_to_input_weights_, input_to_forget_weights_,
|
|
input_to_cell_weights_, input_to_output_weights_, recurrent_weight,
|
|
recurrent_to_input_weights_, recurrent_to_forget_weights_,
|
|
recurrent_to_cell_weights_, recurrent_to_output_weights_);
|
|
|
|
sparse_layer_norm_lstm.SetInputGateBias(input_gate_bias_);
|
|
sparse_layer_norm_lstm.SetCellBias(cell_gate_bias_);
|
|
sparse_layer_norm_lstm.SetForgetGateBias(forget_gate_bias_);
|
|
sparse_layer_norm_lstm.SetOutputGateBias(output_gate_bias_);
|
|
|
|
sparse_layer_norm_lstm.SetCellToInputWeights(cell_to_input_weights_);
|
|
sparse_layer_norm_lstm.SetCellToForgetWeights(cell_to_forget_weights_);
|
|
sparse_layer_norm_lstm.SetCellToOutputWeights(cell_to_output_weights_);
|
|
|
|
sparse_layer_norm_lstm.SetInputLayerNormWeights(input_layer_norm_weights_);
|
|
sparse_layer_norm_lstm.SetForgetLayerNormWeights(forget_layer_norm_weights_);
|
|
sparse_layer_norm_lstm.SetCellLayerNormWeights(cell_layer_norm_weights_);
|
|
sparse_layer_norm_lstm.SetOutputLayerNormWeights(output_layer_norm_weights_);
|
|
|
|
sparse_layer_norm_lstm.SetProjectionWeights(projection_weights_);
|
|
|
|
/* clang-format off */
|
|
const std::vector<std::vector<float>> sparse_layer_norm_lstm_golden_output = {
|
|
{
|
|
// Batch0: 2 (input_sequence_size) * 3 (n_output_)
|
|
0.0550758, 0.138464, -0.0628034, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
|
0.0, 0.0, 0.0, 0.0, 0.0,
|
|
0.069672, 0.195428, -0.0605584, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
|
0.0, 0.0, 0.0, 0.0, 0.0,
|
|
},
|
|
{
|
|
// Batch1: 3 (input_sequence_size) * 3 (n_output_)
|
|
0.0550758, 0.138464, -0.0628034, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
|
0.0, 0.0, 0.0, 0.0, 0.0,
|
|
0.069672, 0.195428, -0.0605584, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
|
0.0, 0.0, 0.0, 0.0, 0.0,
|
|
}};
|
|
/* clang-format on */
|
|
|
|
VerifyGoldens(sparse_layer_norm_lstm_input_,
|
|
sparse_layer_norm_lstm_golden_output, &sparse_layer_norm_lstm);
|
|
}
|
|
|
|
// Test parameter controls asymmetric_quantize_inputs in LSTMOpModel.
|
|
INSTANTIATE_TEST_SUITE_P(
|
|
Parameterized, LstmOpTest,
|
|
::testing::Combine(::testing::Values(TensorType_FLOAT32, TensorType_UINT8,
|
|
TensorType_INT8),
|
|
::testing::Bool(), ::testing::Bool()));
|
|
|
|
} // namespace
|
|
} // namespace tflite
|