tensorflow/lite/delegates/gpu/cl/kernels/fully_connected_test.cc - platform/external/tensorflow - Git at Google

 /* Copyright 2019 The TensorFlow Authors. All Rights Reserved.

 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
 You may obtain a copy of the License at

     http://www.apache.org/licenses/LICENSE-2.0

 Unless required by applicable law or agreed to in writing, software
 distributed under the License is distributed on an "AS IS" BASIS,
 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/

 #include "tensorflow/lite/delegates/gpu/cl/kernels/fully_connected.h"

 #include <vector>

 #include <gmock/gmock.h>
 #include <gtest/gtest.h>
 #include "tensorflow/lite/delegates/gpu/cl/kernels/cl_test.h"
 #include "tensorflow/lite/delegates/gpu/cl/precision.h"
 #include "tensorflow/lite/delegates/gpu/common/operations.h"
 #include "tensorflow/lite/delegates/gpu/common/status.h"

 using ::testing::ElementsAreArray;
 using ::testing::FloatNear;
 using ::testing::Pointwise;

 namespace tflite {
 namespace gpu {
 namespace cl {
 namespace {

 TEST_F(OpenCLOperationTest, FullyConnected) {
   TensorFloat32 src_tensor;
   src_tensor.shape = BHWC(1, 1, 1, 4);
   src_tensor.data = {0.0f, 1.0f, 2.0f, 3.0f};

   FullyConnectedAttributes attr;
   attr.weights.shape = OHWI(2, 1, 1, 4);
   attr.weights.data = {0.0f, 1.0f, 2.0f, 3.0f,  //
                        4.0f, 5.0f, 6.0f, 7.0f};
   attr.bias.shape = Linear(2);
   attr.bias.data = {0.5f, -0.5f};

   for (auto storage : env_.GetSupportedStorages()) {
     for (auto precision : env_.GetSupportedPrecisions()) {
       const float eps = precision == CalculationsPrecision::F32 ? 1e-6f : 1e-3f;
       OperationDef op_def;
       op_def.precision = precision;
       auto data_type = DeduceDataTypeFromPrecision(precision);
       op_def.src_tensors.push_back({data_type, storage, Layout::HWC});
       op_def.dst_tensors.push_back({data_type, storage, Layout::HWC});
       TensorFloat32 dst_tensor;
       FullyConnected operation =
           CreateFullyConnected(creation_context_.GetDeviceInfo(), op_def, attr);
       ASSERT_OK(ExecuteGPUOperation(src_tensor, creation_context_, &operation,
                                     BHWC(1, 1, 1, 2), &dst_tensor));
       EXPECT_THAT(dst_tensor.data, Pointwise(FloatNear(eps), {14.5f, 37.5f}));
     }
   }
 }

 TEST_F(OpenCLOperationTest, FullyConnectedLarge) {
   TensorFloat32 src_tensor;
   src_tensor.shape = BHWC(1, 1, 1, 8);
   src_tensor.data = {0.0f, 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f};

   FullyConnectedAttributes attr;
   attr.weights.shape = OHWI(12, 1, 1, 8);
   attr.weights.data = {
       0.0f,  1.0f,  2.0f,  3.0f,  4.0f,  5.0f,  6.0f,  7.0f,   //
       8.0f,  9.0f,  10.0f, 11.0f, 12.0f, 13.0f, 14.0f, 15.0f,  //
       16.0f, 17.0f, 18.0f, 19.0f, 20.0f, 21.0f, 22.0f, 23.0f,  //
       24.0f, 25.0f, 26.0f, 27.0f, 28.0f, 29.0f, 30.0f, 31.0f,  //
       32.0f, 33.0f, 34.0f, 35.0f, 36.0f, 37.0f, 38.0f, 39.0f,  //
       40.0f, 41.0f, 42.0f, 43.0f, 44.0f, 45.0f, 46.0f, 47.0f,  //
       48.0f, 49.0f, 50.0f, 51.0f, 52.0f, 53.0f, 54.0f, 55.0f,  //
       56.0f, 57.0f, 58.0f, 59.0f, 60.0f, 61.0f, 62.0f, 63.0f,  //
       64.0f, 65.0f, 66.0f, 67.0f, 68.0f, 69.0f, 70.0f, 71.0f,  //
       72.0f, 73.0f, 74.0f, 75.0f, 76.0f, 77.0f, 78.0f, 79.0f,  //
       80.0f, 81.0f, 82.0f, 83.0f, 84.0f, 85.0f, 86.0f, 87.0f,  //
       88.0f, 89.0f, 90.0f, 91.0f, 92.0f, 93.0f, 94.0f, 95.0f,  //
   };
   attr.bias.shape = Linear(12);
   attr.bias.data = {-0.6f, -0.5f, -0.4f, -0.3f, -0.2f, -0.1f,
                     0.1f,  0.2f,  0.3f,  0.4f,  0.5f,  0.6f};

   for (auto storage : env_.GetSupportedStorages()) {
     for (auto precision : env_.GetSupportedPrecisions()) {
       const float eps = precision == CalculationsPrecision::F32 ? 0.0f : 0.601f;
       OperationDef op_def;
       op_def.precision = precision;
       auto data_type = DeduceDataTypeFromPrecision(precision);
       op_def.src_tensors.push_back({data_type, storage, Layout::HWC});
       op_def.dst_tensors.push_back({data_type, storage, Layout::HWC});
       TensorFloat32 dst_tensor;
       FullyConnected operation =
           CreateFullyConnected(creation_context_.GetDeviceInfo(), op_def, attr);
       ASSERT_OK(ExecuteGPUOperation(src_tensor, creation_context_, &operation,
                                     BHWC(1, 1, 1, 12), &dst_tensor));
       EXPECT_THAT(
           dst_tensor.data,
           Pointwise(FloatNear(eps),
                     {139.4f, 363.5f, 587.6f, 811.7f, 1035.8f, 1259.9f, 1484.1f,
                      1708.2f, 1932.3f, 2156.4f, 2380.5f, 2604.6f}));
     }
   }
 }

 TEST_F(OpenCLOperationTest, FullyConnectedExtraLarge) {
   static const int kInputSize = 1024;
   static const int kOutputSize = 1024;
   TensorFloat32 src_tensor;
   src_tensor.shape = BHWC(1, 1, 1, kInputSize);
   src_tensor.data.assign(kInputSize, 1.1f);

   FullyConnectedAttributes attr;
   attr.weights.shape = OHWI(1024, 1, 1, kInputSize);
   attr.weights.data.assign(kOutputSize * kInputSize, 2.2f);
   attr.bias.shape = Linear(kOutputSize);
   attr.bias.data.assign(kOutputSize, 3.3f);

   std::vector<float> expected(kOutputSize, 2481.38f);

   for (auto storage : env_.GetSupportedStorages()) {
     for (auto precision : env_.GetSupportedPrecisions()) {
       float eps;
       switch (precision) {
         case CalculationsPrecision::F32:
           eps = 1.23e-3f;
           break;
         case CalculationsPrecision::F32_F16:
           eps = 1.38f;
           break;
         case CalculationsPrecision::F16:
           eps = 3.38f;
           break;
       }
       OperationDef op_def;
       op_def.precision = precision;
       auto data_type = DeduceDataTypeFromPrecision(precision);
       op_def.src_tensors.push_back({data_type, storage, Layout::HWC});
       op_def.dst_tensors.push_back({data_type, storage, Layout::HWC});
       TensorFloat32 dst_tensor;
       FullyConnected operation =
           CreateFullyConnected(creation_context_.GetDeviceInfo(), op_def, attr);
       ASSERT_OK(ExecuteGPUOperation(src_tensor, creation_context_, &operation,
                                     BHWC(1, 1, 1, kOutputSize), &dst_tensor));
       EXPECT_THAT(dst_tensor.data, Pointwise(FloatNear(eps), expected));
     }
   }
 }

 TEST_F(OpenCLOperationTest, RearrageWeights) {
   tflite::gpu::Tensor<OHWI, DataType::FLOAT32> weights;
   weights.shape = OHWI(8, 1, 1, 8);
   weights.data = {0.0,  1.0,  2.0,  3.0,  4.0,  5.0,  6.0,  7.0,  10.0, 11.0,
                   12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 20.0, 21.0, 22.0, 23.0,
                   24.0, 25.0, 26.0, 27.0, 30.0, 31.0, 32.0, 33.0, 34.0, 35.0,
                   36.0, 37.0, 40.0, 41.0, 42.0, 43.0, 44.0, 45.0, 46.0, 47.0,
                   50.0, 51.0, 52.0, 53.0, 54.0, 55.0, 56.0, 57.0, 60.0, 61.0,
                   62.0, 63.0, 64.0, 65.0, 66.0, 67.0, 70.0, 71.0, 72.0, 73.0,
                   74.0, 75.0, 76.0, 77.0};

   std::vector<float> expected_rearranged_data = {
       0.0,  1.0,  2.0,  3.0,  10.0, 11.0, 12.0, 13.0, 20.0, 21.0, 22.0,
       23.0, 30.0, 31.0, 32.0, 33.0, 40.0, 41.0, 42.0, 43.0, 50.0, 51.0,
       52.0, 53.0, 60.0, 61.0, 62.0, 63.0, 70.0, 71.0, 72.0, 73.0, 4.0,
       5.0,  6.0,  7.0,  14.0, 15.0, 16.0, 17.0, 24.0, 25.0, 26.0, 27.0,
       34.0, 35.0, 36.0, 37.0, 44.0, 45.0, 46.0, 47.0, 54.0, 55.0, 56.0,
       57.0, 64.0, 65.0, 66.0, 67.0, 74.0, 75.0, 76.0, 77.0,
   };

   std::vector<float> data(8 * 8);
   float4* data_ptr = static_cast<float4*>(static_cast<void*>(data.data()));
   RearrangeFCWeightsToIOO4I4(weights, absl::MakeSpan(data_ptr, 8 * 8 / 4));

   EXPECT_THAT(data, ElementsAreArray(expected_rearranged_data));
 }

 TEST_F(OpenCLOperationTest, RearrageWeightsWhenPaddingIsRequired) {
   tflite::gpu::Tensor<OHWI, DataType::FLOAT32> weights;
   weights.shape = OHWI(7, 1, 1, 7);
   weights.data = {
       0.0,  1.0,  2.0,  3.0,  4.0,  5.0,  6.0,  10.0, 11.0, 12.0,
       13.0, 14.0, 15.0, 16.0, 20.0, 21.0, 22.0, 23.0, 24.0, 25.0,
       26.0, 30.0, 31.0, 32.0, 33.0, 34.0, 35.0, 36.0, 40.0, 41.0,
       42.0, 43.0, 44.0, 45.0, 46.0, 50.0, 51.0, 52.0, 53.0, 54.0,
       55.0, 56.0, 60.0, 61.0, 62.0, 63.0, 64.0, 65.0, 66.0,
   };

   std::vector<float> expected_rearranged_data = {
       0.0,  1.0,  2.0,  3.0,  10.0, 11.0, 12.0, 13.0, 20.0, 21.0, 22.0,
       23.0, 30.0, 31.0, 32.0, 33.0, 40.0, 41.0, 42.0, 43.0, 50.0, 51.0,
       52.0, 53.0, 60.0, 61.0, 62.0, 63.0, 0.0,  0.0,  0.0,  0.0,  4.0,
       5.0,  6.0,  0.0,  14.0, 15.0, 16.0, 0.0,  24.0, 25.0, 26.0, 0.0,
       34.0, 35.0, 36.0, 0.0,  44.0, 45.0, 46.0, 0.0,  54.0, 55.0, 56.0,
       0.0,  64.0, 65.0, 66.0, 0.0,  0.0,  0.0,  0.0,  0.0,
   };

   std::vector<float> data(8 * 8);
   float4* data_ptr = static_cast<float4*>(static_cast<void*>(data.data()));
   RearrangeFCWeightsToIOO4I4(weights, absl::MakeSpan(data_ptr, 8 * 8 / 4));

   EXPECT_THAT(data, ElementsAreArray(expected_rearranged_data));
 }

 }  // namespace
 }  // namespace cl
 }  // namespace gpu
 }  // namespace tflite
	/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.

	Licensed under the Apache License, Version 2.0 (the "License");
	you may not use this file except in compliance with the License.
	You may obtain a copy of the License at

	http://www.apache.org/licenses/LICENSE-2.0

	Unless required by applicable law or agreed to in writing, software
	distributed under the License is distributed on an "AS IS" BASIS,
	WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	See the License for the specific language governing permissions and
	limitations under the License.
	==============================================================================*/

	#include "tensorflow/lite/delegates/gpu/cl/kernels/fully_connected.h"

	#include <vector>

	#include <gmock/gmock.h>
	#include <gtest/gtest.h>
	#include "tensorflow/lite/delegates/gpu/cl/kernels/cl_test.h"
	#include "tensorflow/lite/delegates/gpu/cl/precision.h"
	#include "tensorflow/lite/delegates/gpu/common/operations.h"
	#include "tensorflow/lite/delegates/gpu/common/status.h"

	using ::testing::ElementsAreArray;
	using ::testing::FloatNear;
	using ::testing::Pointwise;

	namespace tflite {
	namespace gpu {
	namespace cl {
	namespace {

	TEST_F(OpenCLOperationTest, FullyConnected) {
	TensorFloat32 src_tensor;
	src_tensor.shape = BHWC(1, 1, 1, 4);
	src_tensor.data = {0.0f, 1.0f, 2.0f, 3.0f};

	FullyConnectedAttributes attr;
	attr.weights.shape = OHWI(2, 1, 1, 4);
	attr.weights.data = {0.0f, 1.0f, 2.0f, 3.0f, //
	4.0f, 5.0f, 6.0f, 7.0f};
	attr.bias.shape = Linear(2);
	attr.bias.data = {0.5f, -0.5f};

	for (auto storage : env_.GetSupportedStorages()) {
	for (auto precision : env_.GetSupportedPrecisions()) {
	const float eps = precision == CalculationsPrecision::F32 ? 1e-6f : 1e-3f;
	OperationDef op_def;
	op_def.precision = precision;
	auto data_type = DeduceDataTypeFromPrecision(precision);
	op_def.src_tensors.push_back({data_type, storage, Layout::HWC});
	op_def.dst_tensors.push_back({data_type, storage, Layout::HWC});
	TensorFloat32 dst_tensor;
	FullyConnected operation =
	CreateFullyConnected(creation_context_.GetDeviceInfo(), op_def, attr);
	ASSERT_OK(ExecuteGPUOperation(src_tensor, creation_context_, &operation,
	BHWC(1, 1, 1, 2), &dst_tensor));
	EXPECT_THAT(dst_tensor.data, Pointwise(FloatNear(eps), {14.5f, 37.5f}));
	}
	}
	}

	TEST_F(OpenCLOperationTest, FullyConnectedLarge) {
	TensorFloat32 src_tensor;
	src_tensor.shape = BHWC(1, 1, 1, 8);
	src_tensor.data = {0.0f, 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f};

	FullyConnectedAttributes attr;
	attr.weights.shape = OHWI(12, 1, 1, 8);
	attr.weights.data = {
	0.0f, 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, //
	8.0f, 9.0f, 10.0f, 11.0f, 12.0f, 13.0f, 14.0f, 15.0f, //
	16.0f, 17.0f, 18.0f, 19.0f, 20.0f, 21.0f, 22.0f, 23.0f, //
	24.0f, 25.0f, 26.0f, 27.0f, 28.0f, 29.0f, 30.0f, 31.0f, //
	32.0f, 33.0f, 34.0f, 35.0f, 36.0f, 37.0f, 38.0f, 39.0f, //
	40.0f, 41.0f, 42.0f, 43.0f, 44.0f, 45.0f, 46.0f, 47.0f, //
	48.0f, 49.0f, 50.0f, 51.0f, 52.0f, 53.0f, 54.0f, 55.0f, //
	56.0f, 57.0f, 58.0f, 59.0f, 60.0f, 61.0f, 62.0f, 63.0f, //
	64.0f, 65.0f, 66.0f, 67.0f, 68.0f, 69.0f, 70.0f, 71.0f, //
	72.0f, 73.0f, 74.0f, 75.0f, 76.0f, 77.0f, 78.0f, 79.0f, //
	80.0f, 81.0f, 82.0f, 83.0f, 84.0f, 85.0f, 86.0f, 87.0f, //
	88.0f, 89.0f, 90.0f, 91.0f, 92.0f, 93.0f, 94.0f, 95.0f, //
	};
	attr.bias.shape = Linear(12);
	attr.bias.data = {-0.6f, -0.5f, -0.4f, -0.3f, -0.2f, -0.1f,
	0.1f, 0.2f, 0.3f, 0.4f, 0.5f, 0.6f};

	for (auto storage : env_.GetSupportedStorages()) {
	for (auto precision : env_.GetSupportedPrecisions()) {
	const float eps = precision == CalculationsPrecision::F32 ? 0.0f : 0.601f;
	OperationDef op_def;
	op_def.precision = precision;
	auto data_type = DeduceDataTypeFromPrecision(precision);
	op_def.src_tensors.push_back({data_type, storage, Layout::HWC});
	op_def.dst_tensors.push_back({data_type, storage, Layout::HWC});
	TensorFloat32 dst_tensor;
	FullyConnected operation =
	CreateFullyConnected(creation_context_.GetDeviceInfo(), op_def, attr);
	ASSERT_OK(ExecuteGPUOperation(src_tensor, creation_context_, &operation,
	BHWC(1, 1, 1, 12), &dst_tensor));
	EXPECT_THAT(
	dst_tensor.data,
	Pointwise(FloatNear(eps),
	{139.4f, 363.5f, 587.6f, 811.7f, 1035.8f, 1259.9f, 1484.1f,
	1708.2f, 1932.3f, 2156.4f, 2380.5f, 2604.6f}));
	}
	}
	}

	TEST_F(OpenCLOperationTest, FullyConnectedExtraLarge) {
	static const int kInputSize = 1024;
	static const int kOutputSize = 1024;
	TensorFloat32 src_tensor;
	src_tensor.shape = BHWC(1, 1, 1, kInputSize);
	src_tensor.data.assign(kInputSize, 1.1f);

	FullyConnectedAttributes attr;
	attr.weights.shape = OHWI(1024, 1, 1, kInputSize);
	attr.weights.data.assign(kOutputSize * kInputSize, 2.2f);
	attr.bias.shape = Linear(kOutputSize);
	attr.bias.data.assign(kOutputSize, 3.3f);

	std::vector<float> expected(kOutputSize, 2481.38f);

	for (auto storage : env_.GetSupportedStorages()) {
	for (auto precision : env_.GetSupportedPrecisions()) {
	float eps;
	switch (precision) {
	case CalculationsPrecision::F32:
	eps = 1.23e-3f;
	break;
	case CalculationsPrecision::F32_F16:
	eps = 1.38f;
	break;
	case CalculationsPrecision::F16:
	eps = 3.38f;
	break;
	}
	OperationDef op_def;
	op_def.precision = precision;
	auto data_type = DeduceDataTypeFromPrecision(precision);
	op_def.src_tensors.push_back({data_type, storage, Layout::HWC});
	op_def.dst_tensors.push_back({data_type, storage, Layout::HWC});
	TensorFloat32 dst_tensor;
	FullyConnected operation =
	CreateFullyConnected(creation_context_.GetDeviceInfo(), op_def, attr);
	ASSERT_OK(ExecuteGPUOperation(src_tensor, creation_context_, &operation,
	BHWC(1, 1, 1, kOutputSize), &dst_tensor));
	EXPECT_THAT(dst_tensor.data, Pointwise(FloatNear(eps), expected));
	}
	}
	}

	TEST_F(OpenCLOperationTest, RearrageWeights) {
	tflite::gpu::Tensor<OHWI, DataType::FLOAT32> weights;
	weights.shape = OHWI(8, 1, 1, 8);
	weights.data = {0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 10.0, 11.0,
	12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 20.0, 21.0, 22.0, 23.0,
	24.0, 25.0, 26.0, 27.0, 30.0, 31.0, 32.0, 33.0, 34.0, 35.0,
	36.0, 37.0, 40.0, 41.0, 42.0, 43.0, 44.0, 45.0, 46.0, 47.0,
	50.0, 51.0, 52.0, 53.0, 54.0, 55.0, 56.0, 57.0, 60.0, 61.0,
	62.0, 63.0, 64.0, 65.0, 66.0, 67.0, 70.0, 71.0, 72.0, 73.0,
	74.0, 75.0, 76.0, 77.0};

	std::vector<float> expected_rearranged_data = {
	0.0, 1.0, 2.0, 3.0, 10.0, 11.0, 12.0, 13.0, 20.0, 21.0, 22.0,
	23.0, 30.0, 31.0, 32.0, 33.0, 40.0, 41.0, 42.0, 43.0, 50.0, 51.0,
	52.0, 53.0, 60.0, 61.0, 62.0, 63.0, 70.0, 71.0, 72.0, 73.0, 4.0,
	5.0, 6.0, 7.0, 14.0, 15.0, 16.0, 17.0, 24.0, 25.0, 26.0, 27.0,
	34.0, 35.0, 36.0, 37.0, 44.0, 45.0, 46.0, 47.0, 54.0, 55.0, 56.0,
	57.0, 64.0, 65.0, 66.0, 67.0, 74.0, 75.0, 76.0, 77.0,
	};

	std::vector<float> data(8 * 8);
	float4* data_ptr = static_cast<float4>(static_cast<void>(data.data()));
	RearrangeFCWeightsToIOO4I4(weights, absl::MakeSpan(data_ptr, 8 * 8 / 4));

	EXPECT_THAT(data, ElementsAreArray(expected_rearranged_data));
	}

	TEST_F(OpenCLOperationTest, RearrageWeightsWhenPaddingIsRequired) {
	tflite::gpu::Tensor<OHWI, DataType::FLOAT32> weights;
	weights.shape = OHWI(7, 1, 1, 7);
	weights.data = {
	0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 10.0, 11.0, 12.0,
	13.0, 14.0, 15.0, 16.0, 20.0, 21.0, 22.0, 23.0, 24.0, 25.0,
	26.0, 30.0, 31.0, 32.0, 33.0, 34.0, 35.0, 36.0, 40.0, 41.0,
	42.0, 43.0, 44.0, 45.0, 46.0, 50.0, 51.0, 52.0, 53.0, 54.0,
	55.0, 56.0, 60.0, 61.0, 62.0, 63.0, 64.0, 65.0, 66.0,
	};

	std::vector<float> expected_rearranged_data = {
	0.0, 1.0, 2.0, 3.0, 10.0, 11.0, 12.0, 13.0, 20.0, 21.0, 22.0,
	23.0, 30.0, 31.0, 32.0, 33.0, 40.0, 41.0, 42.0, 43.0, 50.0, 51.0,
	52.0, 53.0, 60.0, 61.0, 62.0, 63.0, 0.0, 0.0, 0.0, 0.0, 4.0,
	5.0, 6.0, 0.0, 14.0, 15.0, 16.0, 0.0, 24.0, 25.0, 26.0, 0.0,
	34.0, 35.0, 36.0, 0.0, 44.0, 45.0, 46.0, 0.0, 54.0, 55.0, 56.0,
	0.0, 64.0, 65.0, 66.0, 0.0, 0.0, 0.0, 0.0, 0.0,
	};

	std::vector<float> data(8 * 8);
	float4* data_ptr = static_cast<float4>(static_cast<void>(data.data()));
	RearrangeFCWeightsToIOO4I4(weights, absl::MakeSpan(data_ptr, 8 * 8 / 4));

	EXPECT_THAT(data, ElementsAreArray(expected_rearranged_data));
	}

	} // namespace
	} // namespace cl
	} // namespace gpu
	} // namespace tflite