src/core/experimental/dynamic_fusion/WorkloadImpl/ClKernelGraph.cpp - ml/ComputeLibrary - Gitiles

 /*
  * Copyright (c) 2022 Arm Limited.
  *
  * SPDX-License-Identifier: MIT
  *
  * Permission is hereby granted, free of charge, to any person obtaining a copy
  * of this software and associated documentation files (the "Software"), to
  * deal in the Software without restriction, including without limitation the
  * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
  * sell copies of the Software, and to permit persons to whom the Software is
  * furnished to do so, subject to the following conditions:
  *
  * The above copyright notice and this permission notice shall be included in all
  * copies or substantial portions of the Software.
  *
  * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
  * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
  * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
  * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
  * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
  * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  * SOFTWARE.
  */
 #ifndef ENABLE_EXPERIMENTAL_DYNAMIC_FUSION
 #error "This experimental feature must be enabled with -DENABLE_EXPERIMENTAL_DYNAMIC_FUSION"
 #endif /* ENABLE_EXPERIMENTAL_DYNAMIC_FUSION */
 #include "arm_compute/core/utils/misc/ShapeCalculator.h"

 #include "src/core/CL/CLValidate.h"
 #include "src/core/experimental/dynamic_fusion/ClKernelBuildingAPI.h"
 #include "src/core/experimental/dynamic_fusion/WorkloadImpl/ClKernelGraph.h"

 #include "support/Cast.h"

 namespace arm_compute
 {
 namespace experimental
 {
 namespace dynamic_fusion
 {
 Status ClDirectConv2dKernel::generate(ClKernelBlueprint &bp) const
 {
     const auto input  = _tensors.get_const_tensor(TensorType::ACL_SRC_0);
     const auto weight = _tensors.get_const_tensor(TensorType::ACL_SRC_1);
     const auto bias   = _tensors.get_const_tensor(TensorType::ACL_SRC_2);
     const auto dst    = _tensors.get_const_tensor(TensorType::ACL_DST_0);
     ARM_COMPUTE_ERROR_ON_NULLPTR(input, weight, dst);
     ArgumentID input_id;
     add_tensor(bp, input->desc, input_id, input->id);
     ArgumentID weight_id;
     add_tensor(bp, weight->desc, weight_id, weight->id);
     ArgumentID bias_id = g_arg_placeholder;
     if(bias != nullptr)
     {
         add_tensor(bp, bias->desc, bias_id, bias->id);
     }
     ArgumentID dst_id;
     add_tensor(bp, dst->desc, dst_id, dst->id);

     add_kcomp_direct_conv2d(bp, desc, input_id, weight_id, bias_id, dst_id);
     return Status{};
 }
 Status ClDirectConv2dKernel::validate(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const ClDirectConv2dKernelDescriptor &conv2d_desc)
 {
     // 1. Check validity
     ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src, weights, dst);
     // Matching data type
     ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, weights);
     ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, dst);
     if(biases != nullptr)
     {
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, biases);
     }

     // Matching data layout
     ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(src, weights);
     ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(src, dst);
     if(biases != nullptr)
     {
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(src, biases);
     }

     // All tensor infos are initialized
     ARM_COMPUTE_RETURN_ERROR_ON(src->tensor_shape().total_size() == 0);
     ARM_COMPUTE_RETURN_ERROR_ON(weights->tensor_shape().total_size() == 0);
     ARM_COMPUTE_RETURN_ERROR_ON(dst->tensor_shape().total_size() == 0);
     if(biases != nullptr)
     {
         ARM_COMPUTE_RETURN_ERROR_ON(biases->tensor_shape().total_size() == 0);
     }
     // Device requirements are met
     ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(src);
     // weights shape is correct
     const DataLayout data_layout = src->data_layout();
     const int        channel_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL);
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights->dimension(channel_idx) != src->dimension(channel_idx), "Weights feature map dimension should match the respective src's one");
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights->num_dimensions() > 4, "Weights can be at most 4 dimensional");

     // dst shape is correct
     PadStrideInfo legacy_pad_stride(conv2d_desc.conv2d.stride.x(), conv2d_desc.conv2d.stride.y(), conv2d_desc.conv2d.pad.left, conv2d_desc.conv2d.pad.right, conv2d_desc.conv2d.pad.top,
                                     conv2d_desc.conv2d.pad.bottom, DimensionRoundingType{});
     ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(dst->tensor_shape(),
                                                        misc::shape_calculator::compute_deep_convolution_shape(*src, *weights, legacy_pad_stride));

     // biases shape is correct
     if(biases != nullptr)
     {
         ARM_COMPUTE_RETURN_ERROR_ON_MSG(biases->dimension(0) != weights->dimension(3),
                                         "Biases size and number of dst feature maps should match");
         ARM_COMPUTE_RETURN_ERROR_ON_MSG(biases->num_dimensions() > 1,
                                         "Biases should be one dimensional");
     }

     // 2. Check support level
     // Data type
     ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F16, DataType::F32);
     // Data layout
     ARM_COMPUTE_RETURN_ERROR_ON_DATA_LAYOUT_NOT_IN(src, DataLayout::NHWC);

     return Status{};
 }

 bool ClDirectConv2dKernel::operator==(const ClKernel &other) const
 {
     const auto converted = *utils::cast::polymorphic_downcast<const ClDirectConv2dKernel *>(&other);
     return config() == other.config() && tensors() == other.tensors() && desc == converted.desc;
 }

 Status ClAddKernel::generate(ClKernelBlueprint &bp) const
 {
     const auto lhs = _tensors.get_const_tensor(TensorType::ACL_SRC_0);
     const auto rhs = _tensors.get_const_tensor(TensorType::ACL_SRC_1);
     const auto dst = _tensors.get_const_tensor(TensorType::ACL_DST_0);
     ARM_COMPUTE_ERROR_ON_NULLPTR(lhs, rhs, dst);
     ArgumentID lhs_id;
     add_tensor(bp, lhs->desc, lhs_id, lhs->id);
     ArgumentID rhs_id;
     add_tensor(bp, rhs->desc, rhs_id, rhs->id);
     ArgumentID dst_id;
     add_tensor(bp, dst->desc, dst_id, dst->id);

     add_kcomp_eltwise_add(bp, desc, lhs_id, rhs_id, dst_id);
     return Status{};
 }

 Status ClAddKernel::validate(const ITensorInfo *lhs, const ITensorInfo *rhs, const ITensorInfo *dst)
 {
     // 1. Check validity
     ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(lhs, rhs, dst);

     // Matching data type
     ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(lhs, rhs);
     ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(lhs, dst);

     // Matching data layout
     ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(lhs, rhs);
     ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(lhs, dst);

     // All tensor infos are initialized
     ARM_COMPUTE_RETURN_ERROR_ON(lhs->tensor_shape().total_size() == 0);
     ARM_COMPUTE_RETURN_ERROR_ON(rhs->tensor_shape().total_size() == 0);
     ARM_COMPUTE_RETURN_ERROR_ON(dst->tensor_shape().total_size() == 0);

     // Device requirements are met
     ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(lhs);

     const bool in_place      = (lhs == dst) || (rhs == dst);
     const bool src0_in_place = in_place && (lhs == dst);

     // dst shape is correct
     const TensorShape out_shape = TensorShape::broadcast_shape(lhs->tensor_shape(), rhs->tensor_shape());
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(out_shape.total_size() == 0, "Inputs are not broadcast compatible");
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(detail::have_different_dimensions(out_shape, dst->tensor_shape(), 0), "Wrong shape for dst");
     if(in_place)
     {
         ARM_COMPUTE_RETURN_ERROR_ON_MSG(detail::have_different_dimensions(out_shape, src0_in_place ? lhs->tensor_shape() : rhs->tensor_shape(), 0),
                                         "Wrong shape for dst, cannot do in_place calculation");
     }

     // 2. Check support level

     // Data type
     ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(lhs, 1, DataType::F32, DataType::F16);

     // Data layout
     ARM_COMPUTE_RETURN_ERROR_ON_DATA_LAYOUT_NOT_IN(lhs, DataLayout::NHWC);

     return Status{};
 }

 bool ClAddKernel::operator==(const ClKernel &other) const
 {
     const auto converted = *utils::cast::polymorphic_downcast<const ClAddKernel *>(&other);
     return config() == other.config() && tensors() == other.tensors() && desc == converted.desc;
 }

 std::vector<const ClKernel *> traverse(const ClKernelGraph &graph)
 {
     std::vector<const ClKernel *> kernels;
     const auto                    sorted = graph.graph.topological_sort();
     for(const auto &pack : sorted.second)
     {
         kernels.push_back(graph.kernels.at(pack.op).get());
     }
     return kernels;
 }
 std::vector<ClKernel *> traverse(ClKernelGraph &graph)
 {
     std::vector<ClKernel *> kernels;
     const auto              sorted = graph.graph.topological_sort();
     for(const auto &pack : sorted.second)
     {
         kernels.push_back(graph.kernels.at(pack.op).get());
     }
     return kernels;
 }
 } // namespace dynamic_fusion
 } // namespace experimental
 } // namespace arm_compute
	/*
	* Copyright (c) 2022 Arm Limited.
	*
	* SPDX-License-Identifier: MIT
	*
	* Permission is hereby granted, free of charge, to any person obtaining a copy
	* of this software and associated documentation files (the "Software"), to
	* deal in the Software without restriction, including without limitation the
	* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
	* sell copies of the Software, and to permit persons to whom the Software is
	* furnished to do so, subject to the following conditions:
	*
	* The above copyright notice and this permission notice shall be included in all
	* copies or substantial portions of the Software.
	*
	* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
	* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
	* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
	* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
	* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
	* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
	* SOFTWARE.
	*/
	#ifndef ENABLE_EXPERIMENTAL_DYNAMIC_FUSION
	#error "This experimental feature must be enabled with -DENABLE_EXPERIMENTAL_DYNAMIC_FUSION"
	#endif /* ENABLE_EXPERIMENTAL_DYNAMIC_FUSION */
	#include "arm_compute/core/utils/misc/ShapeCalculator.h"

	#include "src/core/CL/CLValidate.h"
	#include "src/core/experimental/dynamic_fusion/ClKernelBuildingAPI.h"
	#include "src/core/experimental/dynamic_fusion/WorkloadImpl/ClKernelGraph.h"

	#include "support/Cast.h"

	namespace arm_compute
	{
	namespace experimental
	{
	namespace dynamic_fusion
	{
	Status ClDirectConv2dKernel::generate(ClKernelBlueprint &bp) const
	{
	const auto input = _tensors.get_const_tensor(TensorType::ACL_SRC_0);
	const auto weight = _tensors.get_const_tensor(TensorType::ACL_SRC_1);
	const auto bias = _tensors.get_const_tensor(TensorType::ACL_SRC_2);
	const auto dst = _tensors.get_const_tensor(TensorType::ACL_DST_0);
	ARM_COMPUTE_ERROR_ON_NULLPTR(input, weight, dst);
	ArgumentID input_id;
	add_tensor(bp, input->desc, input_id, input->id);
	ArgumentID weight_id;
	add_tensor(bp, weight->desc, weight_id, weight->id);
	ArgumentID bias_id = g_arg_placeholder;
	if(bias != nullptr)
	{
	add_tensor(bp, bias->desc, bias_id, bias->id);
	}
	ArgumentID dst_id;
	add_tensor(bp, dst->desc, dst_id, dst->id);

	add_kcomp_direct_conv2d(bp, desc, input_id, weight_id, bias_id, dst_id);
	return Status{};
	}
	Status ClDirectConv2dKernel::validate(const ITensorInfo src, const ITensorInfo weights, const ITensorInfo biases, const ITensorInfo dst, const ClDirectConv2dKernelDescriptor &conv2d_desc)
	{
	// 1. Check validity
	ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src, weights, dst);
	// Matching data type
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, weights);
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, dst);
	if(biases != nullptr)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, biases);
	}

	// Matching data layout
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(src, weights);
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(src, dst);
	if(biases != nullptr)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(src, biases);
	}

	// All tensor infos are initialized
	ARM_COMPUTE_RETURN_ERROR_ON(src->tensor_shape().total_size() == 0);
	ARM_COMPUTE_RETURN_ERROR_ON(weights->tensor_shape().total_size() == 0);
	ARM_COMPUTE_RETURN_ERROR_ON(dst->tensor_shape().total_size() == 0);
	if(biases != nullptr)
	{
	ARM_COMPUTE_RETURN_ERROR_ON(biases->tensor_shape().total_size() == 0);
	}
	// Device requirements are met
	ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(src);
	// weights shape is correct
	const DataLayout data_layout = src->data_layout();
	const int channel_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL);
	ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights->dimension(channel_idx) != src->dimension(channel_idx), "Weights feature map dimension should match the respective src's one");
	ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights->num_dimensions() > 4, "Weights can be at most 4 dimensional");

	// dst shape is correct
	PadStrideInfo legacy_pad_stride(conv2d_desc.conv2d.stride.x(), conv2d_desc.conv2d.stride.y(), conv2d_desc.conv2d.pad.left, conv2d_desc.conv2d.pad.right, conv2d_desc.conv2d.pad.top,
	conv2d_desc.conv2d.pad.bottom, DimensionRoundingType{});
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(dst->tensor_shape(),
	misc::shape_calculator::compute_deep_convolution_shape(src, weights, legacy_pad_stride));

	// biases shape is correct
	if(biases != nullptr)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_MSG(biases->dimension(0) != weights->dimension(3),
	"Biases size and number of dst feature maps should match");
	ARM_COMPUTE_RETURN_ERROR_ON_MSG(biases->num_dimensions() > 1,
	"Biases should be one dimensional");
	}

	// 2. Check support level
	// Data type
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F16, DataType::F32);
	// Data layout
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_LAYOUT_NOT_IN(src, DataLayout::NHWC);

	return Status{};
	}

	bool ClDirectConv2dKernel::operator==(const ClKernel &other) const
	{
	const auto converted = utils::cast::polymorphic_downcast<const ClDirectConv2dKernel >(&other);
	return config() == other.config() && tensors() == other.tensors() && desc == converted.desc;
	}

	Status ClAddKernel::generate(ClKernelBlueprint &bp) const
	{
	const auto lhs = _tensors.get_const_tensor(TensorType::ACL_SRC_0);
	const auto rhs = _tensors.get_const_tensor(TensorType::ACL_SRC_1);
	const auto dst = _tensors.get_const_tensor(TensorType::ACL_DST_0);
	ARM_COMPUTE_ERROR_ON_NULLPTR(lhs, rhs, dst);
	ArgumentID lhs_id;
	add_tensor(bp, lhs->desc, lhs_id, lhs->id);
	ArgumentID rhs_id;
	add_tensor(bp, rhs->desc, rhs_id, rhs->id);
	ArgumentID dst_id;
	add_tensor(bp, dst->desc, dst_id, dst->id);

	add_kcomp_eltwise_add(bp, desc, lhs_id, rhs_id, dst_id);
	return Status{};
	}

	Status ClAddKernel::validate(const ITensorInfo lhs, const ITensorInfo rhs, const ITensorInfo *dst)
	{
	// 1. Check validity
	ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(lhs, rhs, dst);

	// Matching data type
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(lhs, rhs);
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(lhs, dst);

	// Matching data layout
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(lhs, rhs);
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(lhs, dst);

	// All tensor infos are initialized
	ARM_COMPUTE_RETURN_ERROR_ON(lhs->tensor_shape().total_size() == 0);
	ARM_COMPUTE_RETURN_ERROR_ON(rhs->tensor_shape().total_size() == 0);
	ARM_COMPUTE_RETURN_ERROR_ON(dst->tensor_shape().total_size() == 0);

	// Device requirements are met
	ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(lhs);

	const bool in_place = (lhs == dst) \|\| (rhs == dst);
	const bool src0_in_place = in_place && (lhs == dst);

	// dst shape is correct
	const TensorShape out_shape = TensorShape::broadcast_shape(lhs->tensor_shape(), rhs->tensor_shape());
	ARM_COMPUTE_RETURN_ERROR_ON_MSG(out_shape.total_size() == 0, "Inputs are not broadcast compatible");
	ARM_COMPUTE_RETURN_ERROR_ON_MSG(detail::have_different_dimensions(out_shape, dst->tensor_shape(), 0), "Wrong shape for dst");
	if(in_place)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_MSG(detail::have_different_dimensions(out_shape, src0_in_place ? lhs->tensor_shape() : rhs->tensor_shape(), 0),
	"Wrong shape for dst, cannot do in_place calculation");
	}

	// 2. Check support level

	// Data type
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(lhs, 1, DataType::F32, DataType::F16);

	// Data layout
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_LAYOUT_NOT_IN(lhs, DataLayout::NHWC);

	return Status{};
	}

	bool ClAddKernel::operator==(const ClKernel &other) const
	{
	const auto converted = utils::cast::polymorphic_downcast<const ClAddKernel >(&other);
	return config() == other.config() && tensors() == other.tensors() && desc == converted.desc;
	}

	std::vector<const ClKernel *> traverse(const ClKernelGraph &graph)
	{
	std::vector<const ClKernel *> kernels;
	const auto sorted = graph.graph.topological_sort();
	for(const auto &pack : sorted.second)
	{
	kernels.push_back(graph.kernels.at(pack.op).get());
	}
	return kernels;
	}
	std::vector<ClKernel *> traverse(ClKernelGraph &graph)
	{
	std::vector<ClKernel *> kernels;
	const auto sorted = graph.graph.topological_sort();
	for(const auto &pack : sorted.second)
	{
	kernels.push_back(graph.kernels.at(pack.op).get());
	}
	return kernels;
	}
	} // namespace dynamic_fusion
	} // namespace experimental
	} // namespace arm_compute