[GNA] FIX CopyLayerPass for concat parent cases (#2485)
* [GNA] fix cases when layer output is used in both memory and concat parent layer * coma fixes * Issue-36189 CopyLayerPass for concat parent cases fix test * Fix test for CPU * Remove test for GPU
This commit is contained in:
Kamil Magierski
GitHub
parent
8abdc32676
commit
0e62e5e17f
@ -708,8 +708,15 @@ void InsertCopyLayerPass::run() {
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|| LayerInfo(prevIndirectLayer).isSplit()) { bInsertDelayed = true;}
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// memory usualy preceded by either activation or split, or other layers in order to have 2b precision
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for (auto && inputto : getInputTo(prevLayers[i].first->outData[prevLayers[i].second])) {
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auto current_layer = inputto.second;
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while (LayerInfo(current_layer).isNonFunctional() || LayerInfo(current_layer).isSplit()) {
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if (current_layer->outData.size() == 0) break;
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if (getInputTo(current_layer->outData[0]).size() == 0) break;
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auto new_layer = CNNNetGetNextLayerSkipCertain(current_layer, 0, 0, [](CNNLayerPtr origin){return false;}).first;
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current_layer = new_layer;
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}
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// if preceding layer is common for memory and concat
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if (LayerInfo(inputto.second).isConcat()) {
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if (LayerInfo(current_layer).isConcat()) {
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bInsertDelayed = true;
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break;
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}
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@ -0,0 +1,35 @@
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// Copyright (C) 2020 Intel Corporation
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// SPDX-License-Identifier: Apache-2.0
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#include <subgraph_tests/multiple_LSTMCell.hpp>
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#include "common_test_utils/test_constants.hpp"
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namespace SubgraphTestsDefinitions {
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namespace {
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std::vector<size_t> input_sizes = {
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80,
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32,
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64,
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25
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};
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std::vector<size_t> hidden_sizes = {
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64,
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100,
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24,
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32,
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};
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std::map<std::string, std::string> additional_config = {
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};
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} // namespace
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INSTANTIATE_TEST_CASE_P(MultipleLSTMCellTest, MultipleLSTMCellTest,
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::testing::Combine(
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::testing::Values(CommonTestUtils::DEVICE_CPU),
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::testing::Values(InferenceEngine::Precision::FP32),
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::testing::ValuesIn(input_sizes),
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::testing::ValuesIn(hidden_sizes),
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::testing::Values(additional_config)),
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MultipleLSTMCellTest::getTestCaseName);
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} // namespace SubgraphTestsDefinitions
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@ -0,0 +1,38 @@
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// Copyright (C) 2020 Intel Corporation
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// SPDX-License-Identifier: Apache-2.0
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#include <subgraph_tests/multiple_LSTMCell.hpp>
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#include "common_test_utils/test_constants.hpp"
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namespace SubgraphTestsDefinitions {
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namespace {
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std::vector<size_t> input_sizes = {
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80,
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32,
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64,
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25
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};
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std::vector<size_t> hidden_sizes = {
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64,
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100,
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24,
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32,
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};
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std::map<std::string, std::string> additional_config = {
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{"GNA_COMPACT_MODE", "NO"},
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{"GNA_DEVICE_MODE", "GNA_SW_EXACT"},
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{"GNA_SCALE_FACTOR_0", "1638.4"},
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};
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} // namespace
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INSTANTIATE_TEST_CASE_P(MultipleLSTMCellTest, MultipleLSTMCellTest,
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::testing::Combine(
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::testing::Values(CommonTestUtils::DEVICE_GNA),
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::testing::Values(InferenceEngine::Precision::FP32),
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::testing::ValuesIn(input_sizes),
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::testing::ValuesIn(hidden_sizes),
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::testing::Values(additional_config)),
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MultipleLSTMCellTest::getTestCaseName);
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} // namespace SubgraphTestsDefinitions
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@ -0,0 +1,39 @@
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// Copyright (C) 2020 Intel Corporation
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// SPDX-License-Identifier: Apache-2.0
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#pragma once
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#include "common_test_utils/test_common.hpp"
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#include "functional_test_utils/layer_test_utils.hpp"
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#include <ie_core.hpp>
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namespace SubgraphTestsDefinitions {
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typedef std::tuple<
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std::string, // Target device name
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InferenceEngine::Precision, // Network precision
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size_t, // Input size
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size_t, // Hidden size
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std::map<std::string, std::string> // Configuration
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> multipleLSTMCellParams;
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class MultipleLSTMCellTest : public LayerTestsUtils::LayerTestsCommon,
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public testing::WithParamInterface<multipleLSTMCellParams> {
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private:
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// you have to Unroll TI manually and remove memory untill ngraph supports it
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void switchToNgraphFriendlyModel();
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// since we switching models we need to generate and save weights biases and inputs in SetUp
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size_t hiddenSize;
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std::vector<float> input_bias;
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std::vector<float> input_weights;
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std::vector<float> hidden_memory_init;
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std::vector<float> cell_memory_init;
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std::vector<float> weights_vals;
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std::vector<float> weights_2_vals;
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std::vector<float> reccurrenceWeights_vals;
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std::vector<float> bias_vals;
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protected:
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void SetUp() override;
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void Run() override;
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public:
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static std::string getTestCaseName(const testing::TestParamInfo<multipleLSTMCellParams> &obj);
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};
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} // namespace SubgraphTestsDefinitions
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@ -0,0 +1,320 @@
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// Copyright (C) 2020 Intel Corporation
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// SPDX-License-Identifier: Apache-2.0
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//
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#include <tuple>
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#include <string>
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#include <vector>
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#include <memory>
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#include <functional>
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#include "ie_core.hpp"
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#include "common_test_utils/common_utils.hpp"
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#include "functional_test_utils/blob_utils.hpp"
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#include "functional_test_utils/precision_utils.hpp"
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#include "functional_test_utils/plugin_cache.hpp"
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#include "functional_test_utils/skip_tests_config.hpp"
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#include "ngraph_functions/utils/ngraph_helpers.hpp"
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#include "ngraph_functions/builders.hpp"
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#include <transformations/lstm_cell_decomposition.hpp>
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#include "subgraph_tests/multiple_LSTMCell.hpp"
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namespace SubgraphTestsDefinitions {
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std::string MultipleLSTMCellTest::getTestCaseName(const testing::TestParamInfo<multipleLSTMCellParams> &obj) {
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std::string targetDevice;
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InferenceEngine::Precision netPrecision;
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size_t inputSize;
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size_t hiddenSize;
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std::map<std::string, std::string> config;
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std::tie(targetDevice, netPrecision, inputSize, hiddenSize, config) = obj.param;
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std::ostringstream result;
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result << "netPrecision=" << netPrecision.name() << "_";
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result << "IS=" << inputSize << "_";
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result << "HS=" << hiddenSize << "_";
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result << "targetDevice=" << targetDevice;
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return result.str();
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}
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void MultipleLSTMCellTest::SetUp() {
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InferenceEngine::Precision netPrecision;
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std::map<std::string, std::string> config;
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size_t inputSize;
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std::tie(targetDevice, netPrecision, inputSize, hiddenSize, config) = this->GetParam();
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configuration.insert(config.begin(), config.end());
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auto ngPrc = FuncTestUtils::PrecisionUtils::convertIE2nGraphPrc(netPrecision);
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std::vector<size_t> input_dims { 1, inputSize };
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std::vector<size_t> squeeze_axes {0};
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std::vector<size_t> hidden_memory_dims {1, hiddenSize};
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std::vector<size_t> cell_memory_dims {1, hiddenSize};
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const int seed = 0;
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std::mt19937 gen(static_cast<float>(seed));
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auto generateFloatNumbers = [gen](std::size_t vec_len, float min, float max) mutable {
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std::vector<float> res;
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std::uniform_real_distribution<float> dist(min, max);
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for (int i = 0; i < vec_len; i++)
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res.emplace_back(static_cast<float>(dist(gen)));
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return res;
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};
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input_bias = generateFloatNumbers(inputSize, -0.25f, 0.0f);
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input_weights = generateFloatNumbers(inputSize, 0.0f, 0.15f);
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hidden_memory_init = generateFloatNumbers(hiddenSize, -0.2f, 0.2f);
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cell_memory_init = generateFloatNumbers(hiddenSize, -0.2f, 0.2f);
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weights_vals = generateFloatNumbers(4 * hiddenSize * inputSize, -0.1f, 0.1f);
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weights_2_vals = generateFloatNumbers(4 * hiddenSize * hiddenSize, -0.1f, 0.1f);
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reccurrenceWeights_vals = generateFloatNumbers(4 * hiddenSize * hiddenSize, -0.1f, 0.1f);
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bias_vals = generateFloatNumbers(4 * hiddenSize, -0.25f, 0.15f);
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auto input_parameter = ngraph::builder::makeParams(ngPrc, {input_dims});
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auto input_add_const = ngraph::builder::makeConstant(ngPrc, input_dims, input_bias);
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auto add = ngraph::builder::makeEltwise(input_parameter[0], input_add_const, ngraph::helpers::EltwiseTypes::ADD);
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auto input_mul_const = ngraph::builder::makeConstant(ngPrc, input_dims, input_weights);
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auto mul = ngraph::builder::makeEltwise(add, input_mul_const, ngraph::helpers::EltwiseTypes::MULTIPLY);
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auto unsqueeze_input_const = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{1}, squeeze_axes);
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auto unsqueeze_input = std::make_shared<ngraph::op::Unsqueeze>(mul, unsqueeze_input_const);
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auto permute_in_params = std::make_shared<ngraph::opset1::Constant>(ngraph::element::i64, ngraph::Shape{3}, ngraph::Shape{{1, 0, 2}});
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auto permute_in = std::make_shared<ngraph::opset1::Transpose>(unsqueeze_input, permute_in_params);
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auto cell_memory_constant = ngraph::builder::makeConstant<float>(ngPrc, cell_memory_dims, cell_memory_init);
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auto cell_memory_read = std::make_shared<ngraph::op::ReadValue>(cell_memory_constant, "cell_memory");
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cell_memory_read->set_friendly_name("cell_memory");
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auto hidden_memory_constant = ngraph::builder::makeConstant<float>(ngPrc, hidden_memory_dims, hidden_memory_init);
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auto hidden_memory_read = std::make_shared<ngraph::op::ReadValue>(hidden_memory_constant, "hidden_memory");
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hidden_memory_read->set_friendly_name("hidden_memory");
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// Body - inputs
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auto X = std::make_shared<ngraph::op::Parameter>(ngPrc, ngraph::Shape{1, 1, inputSize});
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auto H_t = std::make_shared<ngraph::op::Parameter>(ngPrc, ngraph::Shape{1, hiddenSize});
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auto C_t = std::make_shared<ngraph::op::Parameter>(ngPrc, ngraph::Shape{1, hiddenSize});
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// Body - layers
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auto squeeze_const = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{1}, squeeze_axes);
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auto squeeze = std::make_shared<ngraph::op::Squeeze>(X, squeeze_const);
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auto weightsNode = ngraph::builder::makeConstant<float>(ngPrc, { 4 * hiddenSize, inputSize }, weights_vals);
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auto reccurrenceWeightsNode = ngraph::builder::makeConstant<float>(ngPrc, { 4 * hiddenSize, hiddenSize }, reccurrenceWeights_vals);
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auto biasNode = ngraph::builder::makeConstant<float>(ngPrc, {4 * hiddenSize}, bias_vals);
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auto lstm = std::make_shared<ngraph::opset4::LSTMCell>(squeeze, H_t, C_t, weightsNode, reccurrenceWeightsNode, biasNode, hiddenSize);
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auto unsqueeze_const = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{1}, squeeze_axes);
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auto unsqueeze = std::make_shared<ngraph::op::Unsqueeze>(lstm->output(0), unsqueeze_const);
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// body - outputs
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auto H_o = lstm->output(0);
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auto C_o = lstm->output(1);
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auto unsqueeze_o = unsqueeze->output(0);
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auto body = std::make_shared<ngraph::Function>(ngraph::OutputVector{unsqueeze_o, H_o, C_o}, ngraph::ParameterVector {X, H_t, C_t});
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// TI construction
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auto tensor_iterator = std::make_shared<ngraph::op::TensorIterator>();
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tensor_iterator->set_body(body);
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tensor_iterator->set_invariant_input(X, permute_in);
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tensor_iterator->set_merged_input(H_t, hidden_memory_read, H_o);
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tensor_iterator->set_merged_input(C_t, cell_memory_read, C_o);
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auto out_unsqueeze = tensor_iterator->get_iter_value(unsqueeze_o, -1);
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auto out_hidden = tensor_iterator->get_iter_value(H_o, -1);
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auto out_cell = tensor_iterator->get_iter_value(C_o, -1);
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out_hidden.get_tensor().set_element_type(ngPrc);
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out_cell.get_tensor().set_element_type(ngPrc);
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auto cell_memory_write = std::make_shared<ngraph::op::Assign>(out_cell, "cell_memory");
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auto hidden_memory_write = std::make_shared<ngraph::op::Assign>(out_hidden, "hidden_memory");
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auto first_reshape_pattern = std::make_shared<ngraph::op::Constant>(ngraph::element::i64,
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ngraph::Shape{4}, std::vector<size_t>({1, 1, 1, hiddenSize}));
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auto first_reshape = std::make_shared<ngraph::op::v1::Reshape>(out_unsqueeze, first_reshape_pattern, false);
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// End of TI 1
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auto inbetween_squeeze_const = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{1}, squeeze_axes);
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auto inbetween_squeeze = std::make_shared<ngraph::op::Squeeze>(first_reshape, inbetween_squeeze_const);
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// Second TI
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auto cell_memory_2_constant = ngraph::builder::makeConstant<float>(ngPrc, cell_memory_dims, cell_memory_init);
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auto cell_memory_2_read = std::make_shared<ngraph::op::ReadValue>(cell_memory_2_constant, "cell_memory_2");
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cell_memory_2_read->set_friendly_name("cell_memory_2");
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auto hidden_memory_2_constant = ngraph::builder::makeConstant<float>(ngPrc, hidden_memory_dims, hidden_memory_init);
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auto hidden_memory_2_read = std::make_shared<ngraph::op::ReadValue>(hidden_memory_2_constant, "hidden_memory_2");
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hidden_memory_2_read->set_friendly_name("hidden_memory_2");
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// Body - inputs
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auto X_2 = std::make_shared<ngraph::op::Parameter>(ngPrc, ngraph::Shape{1, 1, hiddenSize});
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auto H_t_2 = std::make_shared<ngraph::op::Parameter>(ngPrc, ngraph::Shape{1, hiddenSize});
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auto C_t_2 = std::make_shared<ngraph::op::Parameter>(ngPrc, ngraph::Shape{1, hiddenSize});
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// Body - layers
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auto squeeze_2_const = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{1}, squeeze_axes);
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auto squeeze_2 = std::make_shared<ngraph::op::Squeeze>(X_2, squeeze_2_const);
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auto weightsNode_2 = ngraph::builder::makeConstant<float>(ngPrc, { 4 * hiddenSize, hiddenSize }, weights_2_vals);
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auto reccurrenceWeightsNode_2 = ngraph::builder::makeConstant<float>(ngPrc, { 4 * hiddenSize, hiddenSize }, reccurrenceWeights_vals);
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auto biasNode_2 = ngraph::builder::makeConstant<float>(ngPrc, {4 * hiddenSize}, bias_vals);
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auto lstm_2 = std::make_shared<ngraph::opset4::LSTMCell>(squeeze_2, H_t_2, C_t_2, weightsNode_2, reccurrenceWeightsNode_2, biasNode_2, hiddenSize);
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auto unsqueeze_2_const = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{1}, squeeze_axes);
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auto unsqueeze_2 = std::make_shared<ngraph::op::Unsqueeze>(lstm_2->output(0), unsqueeze_2_const);
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// body - outputs
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auto H_o_2 = lstm_2->output(0);
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auto C_o_2 = lstm_2->output(1);
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auto unsqueeze_o_2 = unsqueeze_2->output(0);
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auto body_2 = std::make_shared<ngraph::Function>(ngraph::OutputVector{unsqueeze_o_2, H_o_2, C_o_2}, ngraph::ParameterVector {X_2, H_t_2, C_t_2});
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// TI construction
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auto tensor_iterator_2 = std::make_shared<ngraph::op::TensorIterator>();
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tensor_iterator_2->set_body(body_2);
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tensor_iterator_2->set_invariant_input(X_2, inbetween_squeeze);
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tensor_iterator_2->set_merged_input(H_t_2, hidden_memory_2_read, H_o_2);
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tensor_iterator_2->set_merged_input(C_t_2, cell_memory_2_read, C_o_2);
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auto out_unsqueeze_2 = tensor_iterator_2->get_iter_value(unsqueeze_o_2, -1);
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auto out_hidden_2 = tensor_iterator_2->get_iter_value(H_o_2, -1);
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auto out_cell_2 = tensor_iterator_2->get_iter_value(C_o_2, -1);
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out_hidden_2.get_tensor().set_element_type(ngPrc);
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out_cell_2.get_tensor().set_element_type(ngPrc);
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auto cell_memory_2_write = std::make_shared<ngraph::op::Assign>(out_cell_2, "cell_memory_2");
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auto hidden_memory_2_write = std::make_shared<ngraph::op::Assign>(out_hidden_2, "hidden_memory_2");
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auto final_reshape_pattern = std::make_shared<ngraph::op::Constant>(ngraph::element::i64,
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ngraph::Shape{4}, std::vector<size_t>({1, 1, 1, hiddenSize}));
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auto final_reshape = std::make_shared<ngraph::op::v1::Reshape>(out_unsqueeze_2, final_reshape_pattern, false);
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cell_memory_write->add_control_dependency(cell_memory_read);
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final_reshape->add_control_dependency(cell_memory_write);
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hidden_memory_write->add_control_dependency(hidden_memory_read);
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final_reshape->add_control_dependency(hidden_memory_write);
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cell_memory_2_write->add_control_dependency(cell_memory_2_read);
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final_reshape->add_control_dependency(cell_memory_2_write);
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hidden_memory_2_write->add_control_dependency(hidden_memory_2_read);
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final_reshape->add_control_dependency(hidden_memory_2_write);
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function = std::make_shared<ngraph::Function>(final_reshape, input_parameter, "TI_with_memory");
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}
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void MultipleLSTMCellTest::switchToNgraphFriendlyModel() {
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InferenceEngine::Precision netPrecision;
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std::map<std::string, std::string> config;
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size_t inputSize;
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std::tie(targetDevice, netPrecision, inputSize, hiddenSize, config) = this->GetParam();
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auto ngPrc = FuncTestUtils::PrecisionUtils::convertIE2nGraphPrc(netPrecision);
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std::vector<size_t> input_dims { 1, inputSize };
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std::vector<size_t> squeeze_axes {0};
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std::vector<size_t> hidden_memory_dims {1, hiddenSize};
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std::vector<size_t> cell_memory_dims {1, hiddenSize};
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auto input_parameter = ngraph::builder::makeParams(ngPrc, {input_dims});
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auto input_add_const = ngraph::builder::makeConstant(ngPrc, input_dims, input_bias);
|
||||
auto add = ngraph::builder::makeEltwise(input_parameter[0], input_add_const, ngraph::helpers::EltwiseTypes::ADD);
|
||||
|
||||
auto input_mul_const = ngraph::builder::makeConstant(ngPrc, input_dims, input_weights);
|
||||
auto mul = ngraph::builder::makeEltwise(add, input_mul_const, ngraph::helpers::EltwiseTypes::MULTIPLY);
|
||||
|
||||
auto unsqueeze_input_const = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{1}, squeeze_axes);
|
||||
auto unsqueeze_input = std::make_shared<ngraph::op::Unsqueeze>(mul, unsqueeze_input_const);
|
||||
|
||||
// Body 1 - layers
|
||||
auto cell_memory_constant = ngraph::builder::makeConstant<float>(ngPrc, cell_memory_dims, cell_memory_init);
|
||||
|
||||
auto hidden_memory_constant = ngraph::builder::makeConstant<float>(ngPrc, hidden_memory_dims, hidden_memory_init);
|
||||
|
||||
auto squeeze_const = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{1}, squeeze_axes);
|
||||
auto squeeze = std::make_shared<ngraph::op::Squeeze>(unsqueeze_input, squeeze_const);
|
||||
|
||||
auto weightsNode = ngraph::builder::makeConstant<float>(ngPrc, { 4 * hiddenSize, inputSize }, weights_vals);
|
||||
auto reccurrenceWeightsNode = ngraph::builder::makeConstant<float>(ngPrc, { 4 * hiddenSize, hiddenSize }, reccurrenceWeights_vals);
|
||||
auto biasNode = ngraph::builder::makeConstant<float>(ngPrc, {4 * hiddenSize}, bias_vals);
|
||||
auto lstm = std::make_shared<ngraph::opset4::LSTMCell>(squeeze, hidden_memory_constant, cell_memory_constant, weightsNode,
|
||||
reccurrenceWeightsNode, biasNode, hiddenSize);
|
||||
|
||||
auto unsqueeze_const = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{1}, squeeze_axes);
|
||||
auto unsqueeze = std::make_shared<ngraph::op::Unsqueeze>(lstm->output(0), unsqueeze_const);
|
||||
|
||||
auto first_reshape_pattern = std::make_shared<ngraph::op::Constant>(ngraph::element::i64,
|
||||
ngraph::Shape{4}, std::vector<size_t>({1, 1, 1, hiddenSize}));
|
||||
auto first_reshape = std::make_shared<ngraph::op::v1::Reshape>(unsqueeze, first_reshape_pattern, false);
|
||||
// Body 1 - end
|
||||
|
||||
auto inbetween_squeeze_const = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{1}, squeeze_axes);
|
||||
auto inbetween_squeeze = std::make_shared<ngraph::op::Squeeze>(first_reshape, inbetween_squeeze_const);
|
||||
|
||||
// Body 2 - layers
|
||||
auto cell_memory_2_constant = ngraph::builder::makeConstant<float>(ngPrc, cell_memory_dims, cell_memory_init);
|
||||
|
||||
auto hidden_memory_2_constant = ngraph::builder::makeConstant<float>(ngPrc, hidden_memory_dims, hidden_memory_init);
|
||||
|
||||
auto squeeze_2_const = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{1}, squeeze_axes);
|
||||
auto squeeze_2 = std::make_shared<ngraph::op::Squeeze>(inbetween_squeeze, squeeze_2_const);
|
||||
|
||||
auto weightsNode_2 = ngraph::builder::makeConstant<float>(ngPrc, { 4 * hiddenSize, hiddenSize }, weights_2_vals);
|
||||
auto reccurrenceWeightsNode_2 = ngraph::builder::makeConstant<float>(ngPrc, { 4 * hiddenSize, hiddenSize }, reccurrenceWeights_vals);
|
||||
auto biasNode_2 = ngraph::builder::makeConstant<float>(ngPrc, {4 * hiddenSize}, bias_vals);
|
||||
auto lstm_2 = std::make_shared<ngraph::opset4::LSTMCell>(squeeze_2, hidden_memory_2_constant, cell_memory_2_constant, weightsNode_2,
|
||||
reccurrenceWeightsNode_2, biasNode_2, hiddenSize);
|
||||
|
||||
auto unsqueeze_2_const = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{1}, squeeze_axes);
|
||||
auto unsqueeze_2 = std::make_shared<ngraph::op::Unsqueeze>(lstm_2->output(0), unsqueeze_2_const);
|
||||
|
||||
auto final_reshape_pattern = std::make_shared<ngraph::op::Constant>(ngraph::element::i64,
|
||||
ngraph::Shape{4}, std::vector<size_t>({1, 1, 1, hiddenSize}));
|
||||
auto final_reshape = std::make_shared<ngraph::op::v1::Reshape>(unsqueeze_2, final_reshape_pattern, false);
|
||||
// Body 2 - end
|
||||
|
||||
function = std::make_shared<ngraph::Function>(final_reshape, input_parameter, "TI_unrolled_without_memory");
|
||||
}
|
||||
|
||||
void MultipleLSTMCellTest::Run() {
|
||||
SKIP_IF_CURRENT_TEST_IS_DISABLED()
|
||||
InferenceEngine::TensorDesc state_description(InferenceEngine::Precision::FP32,
|
||||
InferenceEngine::SizeVector({1, hiddenSize}),
|
||||
InferenceEngine::Layout::NC);
|
||||
LoadNetwork();
|
||||
auto states = executableNetwork.QueryState();
|
||||
for (auto& state : states) {
|
||||
auto name = state.GetName();
|
||||
if (name == "cell_memory") {
|
||||
auto blob = FuncTestUtils::createAndFillBlobWithFloatArray(state_description,
|
||||
cell_memory_init.data(), cell_memory_init.size());
|
||||
state.SetState(blob);
|
||||
} else if (name == "hidden_memory") {
|
||||
auto blob = FuncTestUtils::createAndFillBlobWithFloatArray(state_description,
|
||||
hidden_memory_init.data(), hidden_memory_init.size());
|
||||
state.SetState(blob);
|
||||
} else if (name == "cell_memory_2") {
|
||||
auto blob = FuncTestUtils::createAndFillBlobWithFloatArray(state_description,
|
||||
cell_memory_init.data(), cell_memory_init.size());
|
||||
state.SetState(blob);
|
||||
} else if (name == "hidden_memory_2") {
|
||||
auto blob = FuncTestUtils::createAndFillBlobWithFloatArray(state_description,
|
||||
hidden_memory_init.data(), hidden_memory_init.size());
|
||||
state.SetState(blob);
|
||||
} else {
|
||||
GTEST_FAIL() << "unknown memory state";
|
||||
}
|
||||
}
|
||||
Infer();
|
||||
switchToNgraphFriendlyModel();
|
||||
Validate();
|
||||
}
|
||||
|
||||
TEST_P(MultipleLSTMCellTest, CompareWithRefs) {
|
||||
Run();
|
||||
};
|
||||
} // namespace SubgraphTestsDefinitions
|
||||
Loading…
Reference in New Issue
Block a user