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/*
* Copyright (c) 2017-2019 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();
MemoryGroupResourceScope scope_mg(_memory_group);
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);
}
}
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;
}
}
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