From 164b65d3c8f61f1d6d404fb484c1998a20a2cbda Mon Sep 17 00:00:00 2001 From: Michele Di Giorgio Date: Fri, 13 Apr 2018 14:28:08 +0100 Subject: COMPMID-1043: Rework GCGEMMMatrixMultiplyKernel interface and allow auto initialization of the tensors This patch also: - removes support for already reshaped weights in GCConvolutionLayer - makes GCConvolutionLayer similar to CLGEMMConvolutionLayer - enables usage of the GCGEMM function in GCConvolution instead of calling the GEMM kernels directly Change-Id: I3e4a64335555e86e18585d38d8fda4bfdb44e265 Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/127696 Tested-by: Jenkins Reviewed-by: Anthony Barbier --- src/runtime/GLES_COMPUTE/functions/GCGEMM.cpp | 105 +++++++++++++++++--------- 1 file changed, 70 insertions(+), 35 deletions(-) (limited to 'src/runtime/GLES_COMPUTE/functions/GCGEMM.cpp') diff --git a/src/runtime/GLES_COMPUTE/functions/GCGEMM.cpp b/src/runtime/GLES_COMPUTE/functions/GCGEMM.cpp index 9c8568a329..0a75a38c50 100644 --- a/src/runtime/GLES_COMPUTE/functions/GCGEMM.cpp +++ b/src/runtime/GLES_COMPUTE/functions/GCGEMM.cpp @@ -40,62 +40,82 @@ using namespace arm_compute; using namespace arm_compute::gles_compute; -GCGEMM::GCGEMM(std::shared_ptr memory_manager) - : _memory_group(std::move(memory_manager)), _interleave_kernel(), _transpose_kernel(), _mm_kernel(), _ma_kernel(), _tmp_a(), _tmp_b(), _is_interleaved_transposed(false), _run_addition(false) +namespace { -} - -void GCGEMM::configure(const IGCTensor *a, const IGCTensor *b, const IGCTensor *c, IGCTensor *output, float alpha, float beta, const GEMMInfo &gemm_info) +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_DATA_TYPE_CHANNEL_NOT_IN(a, 1, DataType::F32); + 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"); - ARM_COMPUTE_ERROR_ON_MSG(gemm_info.reshape_b_only_on_first_run(), "Reshape matrix B only on first run is not supported"); - ARM_COMPUTE_UNUSED(gemm_info); if(c != nullptr) { - ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(a, c); - ARM_COMPUTE_ERROR_ON_MSG(a->info()->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->info()->dimension(0) != c->info()->dimension(0), "The C matrix must have the same number of columns as the matrix C"); - ARM_COMPUTE_ERROR_ON_MSG(c->info()->dimension(0) != output->info()->dimension(0), "The C matrix must have the same number of rows as the output matrix"); - ARM_COMPUTE_ERROR_ON_MSG(c->info()->dimension(1) != output->info()->dimension(1), "The C matrix must have the same number of columns as the output matrix"); + 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"); } - ARM_COMPUTE_ERROR_ON_MSG(a->info()->dimension(0) != b->info()->dimension(1), "The product AB is defined only if the number of columns in A is equal to the number of rows in 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"); + } - // 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; + 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 memory_manager) + : _memory_group(std::move(memory_manager)), _interleave_kernel(), _transpose_kernel(), _mm_kernel(), _ma_kernel(), _tmp_a(), _tmp_b(), _is_interleaved_transposed(false), _run_addition(false), + _is_first_run(true), _reshape_b_only_on_first_run(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(); const IGCTensor *matrix_a = a; const IGCTensor *matrix_b = b; + // 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; - TensorShape shape_tmp_a = a->info()->tensor_shape(); - TensorShape shape_tmp_b = b->info()->tensor_shape(); - - shape_tmp_a.set(0, a->info()->dimension(0) * 4); - shape_tmp_a.set(1, std::ceil(a->info()->dimension(1) / 4.0f)); - - const unsigned int transpose_w = max_gc_vector_width / data_size_from_type(b->info()->data_type()); - shape_tmp_b.set(0, b->info()->dimension(1) * transpose_w); - shape_tmp_b.set(1, std::ceil(b->info()->dimension(0) / static_cast(transpose_w))); - - TensorInfo info_a(shape_tmp_a, 1, a->info()->data_type(), a->info()->fixed_point_position()); - _tmp_a.allocator()->init(info_a); + // Manage intermediate buffers _memory_group.manage(&_tmp_a); - - TensorInfo info_b(shape_tmp_b, 1, b->info()->data_type(), b->info()->fixed_point_position()); - _tmp_b.allocator()->init(info_b); - if(!gemm_info.reshape_b_only_on_first_run()) + 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); @@ -104,7 +124,7 @@ void GCGEMM::configure(const IGCTensor *a, const IGCTensor *b, const IGCTensor * _transpose_kernel.configure(b, &_tmp_b); } - _mm_kernel.configure(matrix_a, matrix_b, output, alpha, _is_interleaved_transposed); + _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) { @@ -121,6 +141,12 @@ void GCGEMM::configure(const IGCTensor *a, const IGCTensor *b, const IGCTensor * } } +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() { _memory_group.acquire(); @@ -129,8 +155,17 @@ void GCGEMM::run() // Run interleave kernel GCScheduler::get().dispatch(_interleave_kernel, false); - // Run transpose kernel - GCScheduler::get().dispatch(_transpose_kernel, false); + if(_is_first_run) + { + // Run transpose kernel + GCScheduler::get().dispatch(_transpose_kernel, false); + _is_first_run = false; + } + else if(!_reshape_b_only_on_first_run) + { + // Run transpose kernel + GCScheduler::get().dispatch(_transpose_kernel, false); + } GCScheduler::get().memory_barrier(); } -- cgit v1.2.1