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 --- .../GLES_COMPUTE/functions/GCConvolutionLayer.cpp | 180 ++++++--------------- src/runtime/GLES_COMPUTE/functions/GCGEMM.cpp | 105 ++++++++---- 2 files changed, 122 insertions(+), 163 deletions(-) (limited to 'src/runtime/GLES_COMPUTE') diff --git a/src/runtime/GLES_COMPUTE/functions/GCConvolutionLayer.cpp b/src/runtime/GLES_COMPUTE/functions/GCConvolutionLayer.cpp index b1c8665216..dc73eb85e6 100644 --- a/src/runtime/GLES_COMPUTE/functions/GCConvolutionLayer.cpp +++ b/src/runtime/GLES_COMPUTE/functions/GCConvolutionLayer.cpp @@ -37,14 +37,14 @@ using namespace arm_compute; GCConvolutionLayerReshapeWeights::GCConvolutionLayerReshapeWeights() - : _weights_reshape_kernel(), _weights_transposed_kernel(), _weights_reshaped(), _transpose1xW(false) + : _weights_reshape_kernel(), _weights_reshaped() { } -void GCConvolutionLayerReshapeWeights::configure(const IGCTensor *weights, const IGCTensor *biases, IGCTensor *output, bool transpose1xW) +void GCConvolutionLayerReshapeWeights::configure(const IGCTensor *weights, const IGCTensor *biases, IGCTensor *output) { + ARM_COMPUTE_ERROR_ON_NULLPTR(weights, output); ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::F16, DataType::F32); - ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(weights, output); ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 4); if(biases != nullptr) @@ -56,75 +56,62 @@ void GCConvolutionLayerReshapeWeights::configure(const IGCTensor *weights, const } const bool append_biases = (biases != nullptr) && !is_data_type_quantized_asymmetric(weights->info()->data_type()); - const unsigned bias_element = (append_biases) ? 1 : 0; const IGCTensor *biases_to_use = (append_biases) ? biases : nullptr; - _transpose1xW = transpose1xW; - - if(transpose1xW) - { - // Create tensor to store the reshaped weights - const unsigned int mat_weights_cols = weights->info()->dimension(3); - const unsigned int mat_weights_rows = weights->info()->dimension(0) * weights->info()->dimension(1) * weights->info()->dimension(2) + bias_element; - TensorShape shape_wr(mat_weights_cols, mat_weights_rows); - const DataType dt = weights->info()->data_type(); - const int fixed_point_position = weights->info()->fixed_point_position(); - TensorInfo info_wr(shape_wr, 1, dt, fixed_point_position); - - _weights_reshaped.allocator()->init(info_wr); - _weights_reshape_kernel.configure(weights, biases_to_use, &_weights_reshaped); - _weights_transposed_kernel.configure(&_weights_reshaped, output); - _weights_reshaped.allocator()->allocate(); - } - else - { - _weights_reshape_kernel.configure(weights, biases_to_use, output); - } + _weights_reshape_kernel.configure(weights, biases_to_use, output); } void GCConvolutionLayerReshapeWeights::run() { GCScheduler::get().dispatch(_weights_reshape_kernel); - if(_transpose1xW) - { - GCScheduler::get().dispatch(_weights_transposed_kernel); - } } GCConvolutionLayer::GCConvolutionLayer(std::shared_ptr memory_manager) - : _memory_group(std::move(memory_manager)), _reshape_weights(), _input_im2col_kernel(), _input_interleave_kernel(), _mm_kernel(), _output_col2im_kernel(), _fill_border(), _activationlayer_function(), - _input_im2col_reshaped(), _input_interleaved_reshaped(), _weights_reshaped(), _weights_transposed(), _gemm_output(), _tmp_output(), _append_bias(false), _is_fully_connected_convolution(false), - _are_weights_reshaped(false), _is_activationlayer_enabled(false) + : _memory_group(std::move(memory_manager)), _reshape_weights(), _input_im2col_kernel(), _mm_gemm(), _output_col2im_kernel(), _fill_border(), _activationlayer_function(), _original_weights(nullptr), + _input_im2col_reshaped(), _input_interleaved_reshaped(), _weights_reshaped(), _weights_transposed(), _gemm_output(), _tmp_output(), _is_first_run(true), _is_activationlayer_enabled(false) { } -void GCConvolutionLayer::configure_mm(const IGCTensor *input, const IGCTensor *weights, IGCTensor *output, bool is_interleaved_transposed) +void GCConvolutionLayer::configure_mm(const IGCTensor *input, const IGCTensor *weights, IGCTensor *output) { - _mm_kernel.configure(input, weights, output, 1.f, is_interleaved_transposed); + ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights); + ARM_COMPUTE_ERROR_THROW_ON(validate_mm(input->info(), weights->info(), output->info())); + + _mm_gemm.configure(input, weights, nullptr, output, 1.f, 0.0f, GEMMInfo(false, false, true /* Reshape weights only for the first run */)); +} + +Status GCConvolutionLayer::validate_mm(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *output) +{ + // Perform validation step on Matrix multiply function + GCGEMM::validate(input, weights, nullptr, output, 1.0f, 0.0f, GEMMInfo(false, false, true /* Reshape weights only for the first run */)); + return Status{}; } void GCConvolutionLayer::configure(const IGCTensor *input, const IGCTensor *weights, const IGCTensor *biases, IGCTensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info, const Size2D &dilation, const ActivationLayerInfo &act_info) { + ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights); ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32); ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights); - ARM_COMPUTE_ERROR_ON(!weights_info.are_reshaped() && weights->info()->dimension(2) != input->info()->dimension(2)); + ARM_COMPUTE_ERROR_ON_MSG(weights_info.are_reshaped(), "Weights already reshaped are not supported!"); + ARM_COMPUTE_ERROR_ON(weights->info()->dimension(2) != input->info()->dimension(2)); ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 4); + _is_first_run = true; + _original_weights = weights; + if(biases != nullptr) { ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases); - ARM_COMPUTE_ERROR_ON(!weights_info.are_reshaped() && biases->info()->dimension(0) != weights->info()->dimension(3)); + ARM_COMPUTE_ERROR_ON(biases->info()->dimension(0) != weights->info()->dimension(3)); ARM_COMPUTE_ERROR_ON(biases->info()->num_dimensions() > 1); } const DataType dt = input->info()->data_type(); - _append_bias = (biases != nullptr); - _are_weights_reshaped = weights_info.are_reshaped(); - - const unsigned bias_element = (_append_bias) ? 1 : 0; - const IGCTensor *biases_to_use = (_append_bias) ? biases : nullptr; + const bool append_bias = (biases != nullptr); + const unsigned bias_element = (append_bias) ? 1 : 0; + const IGCTensor *biases_to_use = (append_bias) ? biases : nullptr; // Get parameters from conv_info unsigned int stride_x = 0; @@ -135,57 +122,19 @@ void GCConvolutionLayer::configure(const IGCTensor *input, const IGCTensor *weig unsigned int conv_w = 0; unsigned int conv_h = 0; - const unsigned int kernel_width = (_are_weights_reshaped) ? weights_info.kernel_size().first : weights->info()->dimension(0); - const unsigned int kernel_height = (_are_weights_reshaped) ? weights_info.kernel_size().second : weights->info()->dimension(1); + const unsigned int kernel_width = weights->info()->dimension(0); + const unsigned int kernel_height = weights->info()->dimension(1); std::tie(conv_w, conv_h) = scaled_dimensions(input->info()->dimension(0), input->info()->dimension(1), kernel_width, kernel_height, conv_info, dilation); - // Check if its a "fully connected" convolution - _is_fully_connected_convolution = ((conv_w == 1) && (conv_h == 1)); - const bool run_interleaved = (!_is_fully_connected_convolution); - unsigned int mat_weights_cols = weights->info()->dimension(3); unsigned int mat_weights_rows = weights->info()->dimension(0) * weights->info()->dimension(1) * weights->info()->dimension(2) + bias_element; - // Reshape weights if needed - if(_are_weights_reshaped) - { - if(_is_fully_connected_convolution) - { - mat_weights_cols = weights->info()->dimension(0); - mat_weights_rows = weights->info()->dimension(1); - } - else - { - mat_weights_cols = weights_info.num_kernels(); - const unsigned int quarter_reshaped_cols = weights->info()->dimension(0) / 4; - mat_weights_rows = quarter_reshaped_cols + bias_element; - } - } - else - { - if(_is_fully_connected_convolution) - { - // Create tensor to store the reshaped weights - int num_elems_read_per_iteration_x = 1; - if(dt == DataType::F16) - { - num_elems_read_per_iteration_x = 2; - } - TensorShape shape_wr((ceil_to_multiple(mat_weights_cols, num_elems_read_per_iteration_x)), mat_weights_rows); - _weights_reshaped.allocator()->init(weights->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_wr)); - _reshape_weights.configure(weights, biases_to_use, &_weights_reshaped, false /* 1xW transpose */); - } - else - { - // Create tensor to store transposed weights - const float transpose_width = 16.0f / input->info()->element_size(); - TensorShape shape_wt(mat_weights_rows * static_cast(transpose_width), static_cast(std::ceil(mat_weights_cols / transpose_width))); - _weights_reshaped.allocator()->init(weights->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_wt)); - _reshape_weights.configure(weights, biases_to_use, &_weights_reshaped, true /* 1xW transpose */); - } - weights = &_weights_reshaped; - } + // _weights_reshaped will be auto configured in the kernel. + // Just append biases and do not transpose 1xW as it will be reshaped in GCGEMM + _reshape_weights.configure(weights, biases_to_use, &_weights_reshaped); + + weights = &_weights_reshaped; // Create tensor to store im2col reshaped inputs const unsigned int mat_input_cols = mat_weights_rows; @@ -200,19 +149,6 @@ void GCConvolutionLayer::configure(const IGCTensor *input, const IGCTensor *weig _input_im2col_reshaped.allocator()->init(im2col_reshaped_info); _memory_group.manage(&_input_im2col_reshaped); - // Create tensor (interleave) to prepare input tensor for GEMM - if(run_interleaved) - { - TensorShape shape_interleaved = shape_im2col; - shape_interleaved.set(0, shape_interleaved.x() * 4); - shape_interleaved.set(1, std::ceil(shape_interleaved.y() / 4.f)); - - // FIXME: input->clone() doesn't work with subtensors for grouped convolutions. - TensorInfo interleaved_info(shape_interleaved, 1, dt, input->info()->fixed_point_position()); - _input_interleaved_reshaped.allocator()->init(interleaved_info); - _memory_group.manage(&_input_interleaved_reshaped); - } - // Create GEMM output tensor TensorShape shape_gemm = _input_im2col_reshaped.info()->tensor_shape(); shape_gemm.set(0, mat_weights_cols); @@ -224,26 +160,18 @@ void GCConvolutionLayer::configure(const IGCTensor *input, const IGCTensor *weig _gemm_output.allocator()->init(info_gemm); _memory_group.manage(&_gemm_output); - // Configure kernels if(dt == DataType::F16) { BorderSize border_size = BorderSize(conv_info.pad_top(), conv_info.pad_right(), conv_info.pad_bottom(), conv_info.pad_left()); input->info()->extend_padding(border_size); _fill_border.configure(input, border_size, BorderMode::CONSTANT, PixelValue(0)); // for PAD of im2col fp16: consider it as border } - _input_im2col_kernel.configure(input, &_input_im2col_reshaped, Size2D(kernel_width, kernel_height), conv_info, _append_bias, dilation); + // Configure im2col + _input_im2col_kernel.configure(input, &_input_im2col_reshaped, Size2D(kernel_width, kernel_height), conv_info, append_bias, dilation); + + // Configure GEMM + configure_mm(&_input_im2col_reshaped, weights, &_gemm_output); - // Configure matrix multiply - if(run_interleaved) - { - _input_interleave_kernel.configure(&_input_im2col_reshaped, &_input_interleaved_reshaped); - configure_mm(&_input_interleaved_reshaped, weights, &_gemm_output); - _input_interleaved_reshaped.allocator()->allocate(); - } - else - { - configure_mm(&_input_im2col_reshaped, weights, &_gemm_output, false); - } _input_im2col_reshaped.allocator()->allocate(); // Configure Col2Im @@ -253,10 +181,7 @@ void GCConvolutionLayer::configure(const IGCTensor *input, const IGCTensor *weig ARM_COMPUTE_ERROR_ON_MSG((output->info()->dimension(0) != conv_w) || (output->info()->dimension(1) != conv_h), "Output shape does not match the expected one"); // Allocate intermediate tensor - if(!_are_weights_reshaped) - { - _weights_reshaped.allocator()->allocate(); - } + _weights_reshaped.allocator()->allocate(); //Configure Activation Layer _is_activationlayer_enabled = act_info.enabled(); @@ -265,15 +190,22 @@ void GCConvolutionLayer::configure(const IGCTensor *input, const IGCTensor *weig { _activationlayer_function.configure(output, nullptr, act_info); } + + ARM_COMPUTE_UNUSED(weights_info); } void GCConvolutionLayer::run() { // Run weights reshaping (Runs once for every configure) - if(!_are_weights_reshaped) + if(_is_first_run) { - _are_weights_reshaped = true; + ARM_COMPUTE_ERROR_ON(!_original_weights->is_used()); + _reshape_weights.run(); + _is_first_run = false; + + // Mark original weights tensor as unused + _original_weights->mark_as_unused(); } _memory_group.acquire(); @@ -283,16 +215,8 @@ void GCConvolutionLayer::run() GCScheduler::get().memory_barrier(); GCScheduler::get().dispatch(_input_im2col_kernel); - if(!_is_fully_connected_convolution) - { - GCScheduler::get().memory_barrier(); - // Run interleave4x4 - GCScheduler::get().dispatch(_input_interleave_kernel); - } - - GCScheduler::get().memory_barrier(); - // Runs matrix multiply on reshaped matrices - GCScheduler::get().dispatch(_mm_kernel); + // Run gemm on reshaped matrices + _mm_gemm.run(); GCScheduler::get().memory_barrier(); // Reshape output matrix 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