src/runtime/GLES_COMPUTE/functions/GCGEMM.cpp - ml/ComputeLibrary - Gitiles

 /*
  * Copyright (c) 2017-2018 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.
  */
 #include "arm_compute/runtime/GLES_COMPUTE/functions/GCGEMM.h"

 #include "arm_compute/core/Error.h"
 #include "arm_compute/core/GLES_COMPUTE/GCHelpers.h"
 #include "arm_compute/core/GLES_COMPUTE/IGCTensor.h"
 #include "arm_compute/core/GLES_COMPUTE/kernels/GCGEMMInterleave4x4Kernel.h"
 #include "arm_compute/core/GLES_COMPUTE/kernels/GCGEMMMatrixAdditionKernel.h"
 #include "arm_compute/core/GLES_COMPUTE/kernels/GCGEMMMatrixMultiplyKernel.h"
 #include "arm_compute/core/GLES_COMPUTE/kernels/GCGEMMTranspose1xWKernel.h"
 #include "arm_compute/core/Helpers.h"
 #include "arm_compute/core/TensorInfo.h"
 #include "arm_compute/core/Types.h"
 #include "arm_compute/core/Validate.h"
 #include "arm_compute/runtime/GLES_COMPUTE/GCScheduler.h"
 #include "arm_compute/runtime/ITensorAllocator.h"

 using namespace arm_compute;

 namespace
 {
 Status validate_arguments(const ITensorInfo *a, const ITensorInfo *b, const IGCTensor *c, const ITensorInfo *output, const float alpha, const float beta, const GEMMInfo &gemm_info = GEMMInfo())
 {
     ARM_COMPUTE_ERROR_ON_NULLPTR(a, b, output);

     ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(a, 1, DataType::F16, DataType::F32);
     ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(a, b, output);
     ARM_COMPUTE_ERROR_ON_MSG(gemm_info.is_a_reshaped(), "Matrix A already reshaped is not supported");
     ARM_COMPUTE_ERROR_ON_MSG(gemm_info.is_b_reshaped(), "Matrix B already reshaped is not supported");

     if(c != nullptr)
     {
         ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(a, c->info());
         ARM_COMPUTE_ERROR_ON_MSG(a->dimension(1) != c->info()->dimension(1), "The C matrix must have the same number of rows as the matrix A");
         ARM_COMPUTE_ERROR_ON_MSG(b->dimension(0) != c->info()->dimension(0), "The C matrix must have the same number of columns as the matrix B");
     }

     if(output->total_size() != 0)
     {
         ARM_COMPUTE_RETURN_ERROR_ON_MSG(b->dimension(0) != output->dimension(0), "The output matrix must have the same number of columns as the matrix B");
         ARM_COMPUTE_RETURN_ERROR_ON_MSG(a->dimension(1) != output->dimension(1), "The output matrix must have the same number of rows as the matrix A");
     }

     ARM_COMPUTE_RETURN_ERROR_ON_MSG(a->dimension(0) != b->dimension(1), "The product AB is defined only if the number of columns in A is equal to the number of rows in B");

     ARM_COMPUTE_UNUSED(alpha);
     ARM_COMPUTE_UNUSED(beta);
     ARM_COMPUTE_UNUSED(gemm_info);
     return Status{};
 }
 } // namespace

 GCGEMM::GCGEMM(std::shared_ptr<IMemoryManager> memory_manager)
     : _memory_group(std::move(memory_manager)), _interleave_kernel(), _transpose_kernel(), _mm_kernel(), _ma_kernel(), _tmp_a(), _tmp_b(), _original_b(nullptr), _is_interleaved_transposed(false),
       _run_addition(false), _reshape_b_only_on_first_run(false), _is_prepared(false)
 {
 }

 void GCGEMM::configure(const IGCTensor *a, const IGCTensor *b, const IGCTensor *c, IGCTensor *output, float alpha, float beta, const GEMMInfo &gemm_info)
 {
     ARM_COMPUTE_ERROR_ON_NULLPTR(a, b, output);

     // Perform validation step
     ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(a->info(), b->info(), c, output->info(), alpha, beta, gemm_info));

     // Check if we need to reshape the matrix B only on the first run
     _reshape_b_only_on_first_run = gemm_info.reshape_b_only_on_first_run();
     _is_prepared                 = false;
     _original_b                  = b;

     const IGCTensor *matrix_a = a;
     const IGCTensor *matrix_b = b;

     // Get the GPU target
     const GPUTarget gpu_target = GCScheduler::get().get_target();

     // Set the target for the kernels
     _interleave_kernel.set_target(gpu_target);
     _mm_kernel.set_target(gpu_target);

     // Arguments used by GEMMReshapeInfo
     // If we pass the matrix A and matrix B reshaped to GCGEMMMatrixMultiplyKernel, we need to pass m, n, k, mult_transpose1xW_width and mult_interleave4x4_height to GCGEMMReshapeInfo
     // in order to know how the matrices have been reshaped
     const int m                         = a->info()->dimension(1);
     const int n                         = b->info()->dimension(0);
     const int k                         = a->info()->dimension(0);
     int       mult_transpose1xW_width   = 1;
     int       mult_interleave4x4_height = 1;

     // If the input tensor has less than 16 rows, we run a special version of GEMM without reshaping the input tensors
     _is_interleaved_transposed = a->info()->dimension(1) > 16;

     if(_is_interleaved_transposed)
     {
         matrix_a = &_tmp_a;
         matrix_b = &_tmp_b;

         // Manage intermediate buffers
         _memory_group.manage(&_tmp_a);
         if(!_reshape_b_only_on_first_run)
         {
             _memory_group.manage(&_tmp_b);
         }
         // _tmp_a and _tmp_b will be auto configured in _interleave_kernel and in _transpose_kernel

         // Configure interleave kernel
         _interleave_kernel.configure(a, &_tmp_a);

         // Configure transpose kernel
         _transpose_kernel.configure(b, &_tmp_b);
     }

     _mm_kernel.configure(matrix_a, matrix_b, output, alpha, _is_interleaved_transposed, GEMMReshapeInfo(m, n, k, mult_transpose1xW_width, mult_interleave4x4_height));

     if(_is_interleaved_transposed)
     {
         // Allocate intermediate tensors
         _tmp_a.allocator()->allocate();
         if(!_reshape_b_only_on_first_run)
         {
             _tmp_b.allocator()->allocate();
         }
     }

     // Configure matrix addition kernel
     if(beta != 0 && c != nullptr)
     {
         _ma_kernel.configure(c, output, beta);
         _run_addition = true;
     }
 }

 Status GCGEMM::validate(const ITensorInfo *a, const ITensorInfo *b, const IGCTensor *c, const ITensorInfo *output, const float alpha, const float beta, const GEMMInfo &gemm_info)
 {
     ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(a, b, c, output, alpha, beta, gemm_info));
     return Status{};
 }

 void GCGEMM::run()
 {
     prepare();

     _memory_group.acquire();

     if(_is_interleaved_transposed)
     {
         // Run interleave kernel
         GCScheduler::get().dispatch(_interleave_kernel, false);

         if(!_reshape_b_only_on_first_run)
         {
             // Run transpose kernel
             GCScheduler::get().dispatch(_transpose_kernel, false);
         }

         GCScheduler::get().memory_barrier();
     }

     // Run matrix multiply kernel
     GCScheduler::get().dispatch(_mm_kernel, !_run_addition);

     // Run matrix addition kernel
     if(_run_addition)
     {
         GCScheduler::get().memory_barrier();
         GCScheduler::get().dispatch(_ma_kernel);
     }

     _memory_group.release();
 }

 void GCGEMM::prepare()
 {
     if(!_is_prepared)
     {
         if(_is_interleaved_transposed && _reshape_b_only_on_first_run)
         {
             ARM_COMPUTE_ERROR_ON(!_original_b->is_used());

             // Run transpose kernel
             _tmp_b.allocator()->allocate();
             GCScheduler::get().dispatch(_transpose_kernel, false);
             GCScheduler::get().memory_barrier();

             // Mark original weights tensor as unused
             _original_b->mark_as_unused();
         }

         _is_prepared = true;
     }
 }
	/*
	* Copyright (c) 2017-2018 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.
	*/
	#include "arm_compute/runtime/GLES_COMPUTE/functions/GCGEMM.h"

	#include "arm_compute/core/Error.h"
	#include "arm_compute/core/GLES_COMPUTE/GCHelpers.h"
	#include "arm_compute/core/GLES_COMPUTE/IGCTensor.h"
	#include "arm_compute/core/GLES_COMPUTE/kernels/GCGEMMInterleave4x4Kernel.h"
	#include "arm_compute/core/GLES_COMPUTE/kernels/GCGEMMMatrixAdditionKernel.h"
	#include "arm_compute/core/GLES_COMPUTE/kernels/GCGEMMMatrixMultiplyKernel.h"
	#include "arm_compute/core/GLES_COMPUTE/kernels/GCGEMMTranspose1xWKernel.h"
	#include "arm_compute/core/Helpers.h"
	#include "arm_compute/core/TensorInfo.h"
	#include "arm_compute/core/Types.h"
	#include "arm_compute/core/Validate.h"
	#include "arm_compute/runtime/GLES_COMPUTE/GCScheduler.h"
	#include "arm_compute/runtime/ITensorAllocator.h"

	using namespace arm_compute;

	namespace
	{
	Status validate_arguments(const ITensorInfo a, const ITensorInfo b, const IGCTensor c, const ITensorInfo output, const float alpha, const float beta, const GEMMInfo &gemm_info = GEMMInfo())
	{
	ARM_COMPUTE_ERROR_ON_NULLPTR(a, b, output);

	ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(a, 1, DataType::F16, DataType::F32);
	ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(a, b, output);
	ARM_COMPUTE_ERROR_ON_MSG(gemm_info.is_a_reshaped(), "Matrix A already reshaped is not supported");
	ARM_COMPUTE_ERROR_ON_MSG(gemm_info.is_b_reshaped(), "Matrix B already reshaped is not supported");

	if(c != nullptr)
	{
	ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(a, c->info());
	ARM_COMPUTE_ERROR_ON_MSG(a->dimension(1) != c->info()->dimension(1), "The C matrix must have the same number of rows as the matrix A");
	ARM_COMPUTE_ERROR_ON_MSG(b->dimension(0) != c->info()->dimension(0), "The C matrix must have the same number of columns as the matrix B");
	}

	if(output->total_size() != 0)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_MSG(b->dimension(0) != output->dimension(0), "The output matrix must have the same number of columns as the matrix B");
	ARM_COMPUTE_RETURN_ERROR_ON_MSG(a->dimension(1) != output->dimension(1), "The output matrix must have the same number of rows as the matrix A");
	}

	ARM_COMPUTE_RETURN_ERROR_ON_MSG(a->dimension(0) != b->dimension(1), "The product AB is defined only if the number of columns in A is equal to the number of rows in B");

	ARM_COMPUTE_UNUSED(alpha);
	ARM_COMPUTE_UNUSED(beta);
	ARM_COMPUTE_UNUSED(gemm_info);
	return Status{};
	}
	} // namespace

	GCGEMM::GCGEMM(std::shared_ptr<IMemoryManager> memory_manager)
	: _memory_group(std::move(memory_manager)), _interleave_kernel(), _transpose_kernel(), _mm_kernel(), _ma_kernel(), _tmp_a(), _tmp_b(), _original_b(nullptr), _is_interleaved_transposed(false),
	_run_addition(false), _reshape_b_only_on_first_run(false), _is_prepared(false)
	{
	}

	void GCGEMM::configure(const IGCTensor a, const IGCTensor b, const IGCTensor c, IGCTensor output, float alpha, float beta, const GEMMInfo &gemm_info)
	{
	ARM_COMPUTE_ERROR_ON_NULLPTR(a, b, output);

	// Perform validation step
	ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(a->info(), b->info(), c, output->info(), alpha, beta, gemm_info));

	// Check if we need to reshape the matrix B only on the first run
	_reshape_b_only_on_first_run = gemm_info.reshape_b_only_on_first_run();
	_is_prepared = false;
	_original_b = b;

	const IGCTensor *matrix_a = a;
	const IGCTensor *matrix_b = b;

	// Get the GPU target
	const GPUTarget gpu_target = GCScheduler::get().get_target();

	// Set the target for the kernels
	_interleave_kernel.set_target(gpu_target);
	_mm_kernel.set_target(gpu_target);

	// Arguments used by GEMMReshapeInfo
	// If we pass the matrix A and matrix B reshaped to GCGEMMMatrixMultiplyKernel, we need to pass m, n, k, mult_transpose1xW_width and mult_interleave4x4_height to GCGEMMReshapeInfo
	// in order to know how the matrices have been reshaped
	const int m = a->info()->dimension(1);
	const int n = b->info()->dimension(0);
	const int k = a->info()->dimension(0);
	int mult_transpose1xW_width = 1;
	int mult_interleave4x4_height = 1;

	// If the input tensor has less than 16 rows, we run a special version of GEMM without reshaping the input tensors
	_is_interleaved_transposed = a->info()->dimension(1) > 16;

	if(_is_interleaved_transposed)
	{
	matrix_a = &_tmp_a;
	matrix_b = &_tmp_b;

	// Manage intermediate buffers
	_memory_group.manage(&_tmp_a);
	if(!_reshape_b_only_on_first_run)
	{
	_memory_group.manage(&_tmp_b);
	}
	// _tmp_a and _tmp_b will be auto configured in _interleave_kernel and in _transpose_kernel

	// Configure interleave kernel
	_interleave_kernel.configure(a, &_tmp_a);

	// Configure transpose kernel
	_transpose_kernel.configure(b, &_tmp_b);
	}

	_mm_kernel.configure(matrix_a, matrix_b, output, alpha, _is_interleaved_transposed, GEMMReshapeInfo(m, n, k, mult_transpose1xW_width, mult_interleave4x4_height));

	if(_is_interleaved_transposed)
	{
	// Allocate intermediate tensors
	_tmp_a.allocator()->allocate();
	if(!_reshape_b_only_on_first_run)
	{
	_tmp_b.allocator()->allocate();
	}
	}

	// Configure matrix addition kernel
	if(beta != 0 && c != nullptr)
	{
	_ma_kernel.configure(c, output, beta);
	_run_addition = true;
	}
	}

	Status GCGEMM::validate(const ITensorInfo a, const ITensorInfo b, const IGCTensor c, const ITensorInfo output, const float alpha, const float beta, const GEMMInfo &gemm_info)
	{
	ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(a, b, c, output, alpha, beta, gemm_info));
	return Status{};
	}

	void GCGEMM::run()
	{
	prepare();

	_memory_group.acquire();

	if(_is_interleaved_transposed)
	{
	// Run interleave kernel
	GCScheduler::get().dispatch(_interleave_kernel, false);

	if(!_reshape_b_only_on_first_run)
	{
	// Run transpose kernel
	GCScheduler::get().dispatch(_transpose_kernel, false);
	}

	GCScheduler::get().memory_barrier();
	}

	// Run matrix multiply kernel
	GCScheduler::get().dispatch(_mm_kernel, !_run_addition);

	// Run matrix addition kernel
	if(_run_addition)
	{
	GCScheduler::get().memory_barrier();
	GCScheduler::get().dispatch(_ma_kernel);
	}

	_memory_group.release();
	}

	void GCGEMM::prepare()
	{
	if(!_is_prepared)
	{
	if(_is_interleaved_transposed && _reshape_b_only_on_first_run)
	{
	ARM_COMPUTE_ERROR_ON(!_original_b->is_used());

	// Run transpose kernel
	_tmp_b.allocator()->allocate();
	GCScheduler::get().dispatch(_transpose_kernel, false);
	GCScheduler::get().memory_barrier();

	// Mark original weights tensor as unused
	_original_b->mark_as_unused();
	}

	_is_prepared = true;
	}
	}