From b4e3e1c371d8091e86ee1c6e704057559bbe1554 Mon Sep 17 00:00:00 2001 From: Ioan-Cristian Szabo Date: Thu, 30 Nov 2017 17:17:17 +0000 Subject: COMPMID-617: Add validate support for NEON FullyConnectedLayer Change-Id: I08987022c8d4cc335c00b8af27bd3edb8fe64d3b Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/111596 Tested-by: Jenkins Reviewed-by: Alexander Gilday Reviewed-by: Anthony Barbier --- src/runtime/NEON/functions/NEFlattenLayer.cpp | 4 +- .../NEON/functions/NEFullyConnectedLayer.cpp | 212 ++++++++++++++------- src/runtime/NEON/functions/NEGEMM.cpp | 8 +- .../NEON/functions/NEGEMMConvolutionLayer.cpp | 48 ++--- src/runtime/NEON/functions/NEGEMMTranspose1xW.cpp | 6 +- src/runtime/NEON/functions/NEIm2Col.cpp | 10 +- 6 files changed, 184 insertions(+), 104 deletions(-) (limited to 'src/runtime/NEON') diff --git a/src/runtime/NEON/functions/NEFlattenLayer.cpp b/src/runtime/NEON/functions/NEFlattenLayer.cpp index 408eff5746..32edf93b63 100644 --- a/src/runtime/NEON/functions/NEFlattenLayer.cpp +++ b/src/runtime/NEON/functions/NEFlattenLayer.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017 ARM Limited. + * Copyright (c) 2017-2018 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -32,6 +32,6 @@ using namespace arm_compute; void NEFlattenLayer::configure(const ITensor *input, ITensor *output) { auto k = arm_compute::support::cpp14::make_unique(); - k->configure(input, output, Size2D(1, 1), PadStrideInfo(1, 1, 0, 0), false); + k->configure(input, output, Size2D(1, 1), PadStrideInfo(1, 1, 0, 0), false, false, true); _kernel = std::move(k); } \ No newline at end of file diff --git a/src/runtime/NEON/functions/NEFullyConnectedLayer.cpp b/src/runtime/NEON/functions/NEFullyConnectedLayer.cpp index fc04e28972..26b7271710 100644 --- a/src/runtime/NEON/functions/NEFullyConnectedLayer.cpp +++ b/src/runtime/NEON/functions/NEFullyConnectedLayer.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017 ARM Limited. + * Copyright (c) 2017-2018 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -23,15 +23,18 @@ */ #include "arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h" +#include "arm_compute/core/Helpers.h" #include "arm_compute/core/Size2D.h" #include "arm_compute/core/Validate.h" +#include "arm_compute/core/utils/misc/ShapeCalculator.h" #include "arm_compute/runtime/NEON/NEScheduler.h" #include #include -namespace arm_compute -{ +using namespace arm_compute; +using namespace arm_compute::misc::shape_calculator; + NEFullyConnectedLayerReshapeWeights::NEFullyConnectedLayerReshapeWeights(std::shared_ptr memory_manager) : _memory_group(std::move(memory_manager)), _transpose_kernel(), _transpose1xW_kernel(), _transpose_output(), _transpose_weights(false), _is_batched_fc_layer(false) { @@ -39,13 +42,10 @@ NEFullyConnectedLayerReshapeWeights::NEFullyConnectedLayerReshapeWeights(std::sh void NEFullyConnectedLayerReshapeWeights::configure(const ITensor *input, ITensor *output, bool transpose_weights, bool is_batched_fc_layer) { - ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32); - ARM_COMPUTE_ERROR_ON(input->info()->num_dimensions() > 2); - ARM_COMPUTE_ERROR_ON(output == nullptr); - ARM_COMPUTE_ERROR_ON(!transpose_weights && !is_batched_fc_layer); + ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); - const DataType data_type = input->info()->data_type(); - const int fixed_point_position = input->info()->fixed_point_position(); + // Perform validate step + ARM_COMPUTE_ERROR_THROW_ON(NEFullyConnectedLayerReshapeWeights::validate(input->info(), output->info(), transpose_weights, is_batched_fc_layer)); _transpose_weights = transpose_weights; _is_batched_fc_layer = is_batched_fc_layer; @@ -56,8 +56,7 @@ void NEFullyConnectedLayerReshapeWeights::configure(const ITensor *input, ITenso if(_is_batched_fc_layer) { // Initialize the output tensor for transpose - TensorShape shape_transposed(input->info()->dimension(1), input->info()->dimension(0)); - _transpose_output.allocator()->init(TensorInfo(shape_transposed, 1, data_type, fixed_point_position)); + _transpose_output.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(compute_transposed_shape(*input->info()))); _memory_group.manage(&_transpose_output); _transpose_kernel.configure(input, &_transpose_output); @@ -79,11 +78,39 @@ void NEFullyConnectedLayerReshapeWeights::configure(const ITensor *input, ITenso // Configure transpose 1xW kernel _transpose1xW_kernel.configure(input, output); } + } +} + +Status NEFullyConnectedLayerReshapeWeights::validate(const ITensorInfo *input, const ITensorInfo *output, bool transpose_weights, bool is_batched_fc_layer) +{ + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32); + ARM_COMPUTE_RETURN_ERROR_ON(input->num_dimensions() > 2); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(!transpose_weights && !is_batched_fc_layer, "Configuration transpose_weights=false & is_batched_fc_layer=false not supported"); + + if(transpose_weights) + { + if(is_batched_fc_layer) + { + std::unique_ptr use_output = output->clone(); + use_output->set_is_resizable(true).reset_padding().set_tensor_shape(compute_transposed_shape(*input)); + + ARM_COMPUTE_RETURN_ON_ERROR(NETransposeKernel::validate(input, use_output.get())); + ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMTranspose1xWKernel::validate(use_output.get(), output)); + } else { - ARM_COMPUTE_ERROR("Configuration transpose_weights=false & is_batched_fc_layer=false not supported"); + ARM_COMPUTE_RETURN_ON_ERROR(NETransposeKernel::validate(input, output)); + } + } + else + { + if(is_batched_fc_layer) + { + ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMTranspose1xWKernel::validate(input, output)); } } + + return Status{}; } void NEFullyConnectedLayerReshapeWeights::run() @@ -122,26 +149,25 @@ void NEFullyConnectedLayer::configure(const ITensor *input, const ITensor *weigh // Weights: flat(In) x Out // Biases: Out // Output: Out x B (B can be multi-dimensional) + ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output); - ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32); - ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights, output); - ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT_POSITION(input, weights, output); + // Perform validate step + ARM_COMPUTE_ERROR_THROW_ON(NEFullyConnectedLayer::validate(input->info(), + weights->info(), + biases != nullptr ? biases->info() : nullptr, + output->info(), + transpose_weights, + are_weights_reshaped)); - const DataType data_type = input->info()->data_type(); - const int fixed_point_position = input->info()->fixed_point_position(); - const int num_batch_dimensions = std::max(0, static_cast(output->info()->tensor_shape().num_dimensions()) - 1); - const int num_input_dimensions = input->info()->tensor_shape().num_dimensions() - num_batch_dimensions; - const size_t linear_input_size = input->info()->tensor_shape().total_size_lower(num_input_dimensions); + const int num_batch_dimensions = std::max(0, static_cast(output->info()->tensor_shape().num_dimensions()) - 1); + const int num_input_dimensions = input->info()->tensor_shape().num_dimensions() - num_batch_dimensions; + const size_t linear_input_size = input->info()->tensor_shape().total_size_lower(num_input_dimensions); _linearize_input = (input->info()->tensor_shape().x() != linear_input_size) || (num_input_dimensions > 1 && linear_input_size == 1); _are_weights_reshaped = are_weights_reshaped; _accumulate_biases = biases != nullptr; _is_batched_fc_layer = num_batch_dimensions > 0; - // Check if number of batches match - ARM_COMPUTE_ERROR_ON(input->info()->tensor_shape().total_size_upper(num_input_dimensions) != output->info()->tensor_shape().total_size_upper(1)); - ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 2); - const size_t interleave_width = 16 / input->info()->element_size(); const ITensor *weights_to_use = weights; @@ -149,65 +175,33 @@ void NEFullyConnectedLayer::configure(const ITensor *input, const ITensor *weigh { weights_to_use = &_reshape_weights_output; - TensorShape reshaped_weights_shape(weights->info()->tensor_shape()); - - // Transpose weights if the user hasn't done it - if(transpose_weights) - { - const size_t shape_x = reshaped_weights_shape.x(); - reshaped_weights_shape.set(0, reshaped_weights_shape.y()); - reshaped_weights_shape.set(1, shape_x); - } - - // If the we run multiple batches we need 1xW transpose, too. - if(_is_batched_fc_layer) - { - const float shape_x = reshaped_weights_shape.x(); - reshaped_weights_shape.set(0, reshaped_weights_shape.y() * interleave_width); - reshaped_weights_shape.set(1, static_cast(std::ceil(shape_x / interleave_width))); - } - - _reshape_weights_output.allocator()->init(TensorInfo(reshaped_weights_shape, 1, data_type, fixed_point_position)); + _reshape_weights_output.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(compute_fully_connected_reshaped_weights_shape(weights->info(), + transpose_weights, + _is_batched_fc_layer, interleave_width))); // Reshape the weights _reshape_weights_kernel.configure(weights, &_reshape_weights_output, transpose_weights, _is_batched_fc_layer); } - // Check correct shape of weights - if(_is_batched_fc_layer) - { - // Transpose + Transpose1xW - ARM_COMPUTE_ERROR_ON(weights_to_use->info()->tensor_shape().x() != linear_input_size * interleave_width); - ARM_COMPUTE_ERROR_ON(weights_to_use->info()->tensor_shape().y() != static_cast(std::ceil(static_cast(output->info()->tensor_shape().x()) / interleave_width))); - } - else - { - // Transpose - ARM_COMPUTE_ERROR_ON(weights_to_use->info()->tensor_shape().x() != output->info()->tensor_shape().x()); - ARM_COMPUTE_ERROR_ON(weights_to_use->info()->tensor_shape().y() != linear_input_size); - } - const ITensor *multiply_input = input; if(_linearize_input) { - TensorShape shape_im2col(input->info()->tensor_shape()); - shape_im2col.collapse(num_input_dimensions); - _im2col_output.allocator()->init(TensorInfo(shape_im2col, 1, data_type, fixed_point_position)); + _im2col_output.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(compute_im2col_shape(input->info(), num_input_dimensions))); // Configure im2col kernel _memory_group.manage(&_im2col_output); - _im2col_kernel.configure(input, &_im2col_output, Size2D(1, 1), PadStrideInfo(1, 1, 0, 0), false); + _im2col_kernel.configure(input, &_im2col_output, Size2D(1, 1), PadStrideInfo(1, 1, 0, 0), false, true); multiply_input = &_im2col_output; } + int m = multiply_input->info()->dimension(1); + int k = multiply_input->info()->dimension(0); + if(_is_batched_fc_layer) { - TensorShape shape_interleaved(multiply_input->info()->tensor_shape()); - shape_interleaved.set(0, shape_interleaved.x() * 4); - shape_interleaved.set(1, std::ceil(shape_interleaved.y() / 4.f)); - _interleave4x4_output.allocator()->init(TensorInfo(shape_interleaved, 1, data_type, fixed_point_position)); + _interleave4x4_output.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(compute_interleaved_shape(*multiply_input->info()))); // Configure interleave4x4 kernel _memory_group.manage(&_interleave4x4_output); @@ -217,13 +211,10 @@ void NEFullyConnectedLayer::configure(const ITensor *input, const ITensor *weigh } // Configure matrix multiply kernel - _mm_kernel.configure(multiply_input, weights_to_use, output, 1.0f); + _mm_kernel.configure(multiply_input, weights_to_use, output, 1.0f, _is_batched_fc_layer, GEMMReshapeInfo(m, 0 /* no transpose */, k)); if(_accumulate_biases) { - ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases); - ARM_COMPUTE_ERROR_ON(biases->info()->tensor_shape().x() != output->info()->tensor_shape().x()); - // Configure accumulate biases kernel _accumulate_biases_kernel.configure(output, biases); } @@ -246,6 +237,88 @@ void NEFullyConnectedLayer::configure(const ITensor *input, const ITensor *weigh } } +Status NEFullyConnectedLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, bool transpose_weights, bool are_weights_reshaped) +{ + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights, output); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT_POSITION(input, weights, output); + + const int num_batch_dimensions = std::max(0, static_cast(output->tensor_shape().num_dimensions()) - 1); + const int num_input_dimensions = input->tensor_shape().num_dimensions() - num_batch_dimensions; + const size_t linear_input_size = input->tensor_shape().total_size_lower(num_input_dimensions); + + const bool linearize_input = (input->tensor_shape().x() != linear_input_size) || (num_input_dimensions > 1 && linear_input_size == 1); + const bool accumulate_biases = biases != nullptr; + const bool is_batched_fc_layer = num_batch_dimensions > 0; + + ARM_COMPUTE_RETURN_ERROR_ON(input->tensor_shape().total_size_upper(num_input_dimensions) != output->tensor_shape().total_size_upper(1)); + ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 2); + + const size_t interleave_width = 16 / input->element_size(); + const ITensorInfo *weights_to_use = weights; + std::unique_ptr reshape_weights_output = input->clone(); + + if(!are_weights_reshaped && (transpose_weights || is_batched_fc_layer)) + { + reshape_weights_output->set_tensor_shape(compute_fully_connected_reshaped_weights_shape(weights, transpose_weights, is_batched_fc_layer, interleave_width)); + + ARM_COMPUTE_RETURN_ON_ERROR(NEFullyConnectedLayerReshapeWeights::validate(weights, reshape_weights_output.get(), transpose_weights, is_batched_fc_layer)); + + weights_to_use = reshape_weights_output.get(); + } + + // Check correct shape of weights + if(is_batched_fc_layer) + { + // Transpose + Transpose1xW + ARM_COMPUTE_RETURN_ERROR_ON(weights_to_use->tensor_shape().x() != linear_input_size * interleave_width); + ARM_COMPUTE_RETURN_ERROR_ON(weights_to_use->tensor_shape().y() != static_cast(std::ceil(static_cast(output->tensor_shape().x()) / interleave_width))); + } + else + { + // Transpose + ARM_COMPUTE_RETURN_ERROR_ON(weights_to_use->tensor_shape().x() != output->tensor_shape().x()); + ARM_COMPUTE_RETURN_ERROR_ON(weights_to_use->tensor_shape().y() != linear_input_size); + } + + const ITensorInfo *multiply_input = input; + std::unique_ptr im2col_output = input->clone(); + std::unique_ptr interleave4x4_output = input->clone(); + + if(linearize_input) + { + im2col_output->set_tensor_shape(compute_im2col_shape(input, num_input_dimensions)); + + ARM_COMPUTE_RETURN_ON_ERROR(NEIm2ColKernel::validate(input, im2col_output.get(), Size2D(1, 1), PadStrideInfo(1, 1, 0, 0), false, true)); + + multiply_input = im2col_output.get(); + } + + int m = multiply_input->dimension(1); + int k = multiply_input->dimension(0); + + if(is_batched_fc_layer) + { + interleave4x4_output->set_tensor_shape(compute_interleaved_shape(*multiply_input)); + + ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMInterleave4x4Kernel::validate(multiply_input, interleave4x4_output.get())); + + multiply_input = interleave4x4_output.get(); + } + + ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMMatrixMultiplyKernel::validate(multiply_input, weights_to_use, output, 1.0f, is_batched_fc_layer, GEMMReshapeInfo(m, 0 /* no transpose */, k))); + + if(accumulate_biases) + { + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases); + ARM_COMPUTE_RETURN_ERROR_ON(biases->tensor_shape().x() != output->tensor_shape().x()); + + ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMMatrixAccumulateBiasesKernel::validate(output, biases)); + } + + return Status{}; +} + void NEFullyConnectedLayer::run() { // Reshape of the weights (happens only once) @@ -280,4 +353,3 @@ void NEFullyConnectedLayer::run() _memory_group.release(); } -} // namespace arm_compute diff --git a/src/runtime/NEON/functions/NEGEMM.cpp b/src/runtime/NEON/functions/NEGEMM.cpp index 48a0d2af1c..05907bab07 100644 --- a/src/runtime/NEON/functions/NEGEMM.cpp +++ b/src/runtime/NEON/functions/NEGEMM.cpp @@ -120,7 +120,7 @@ void NEGEMM::configure(const ITensor *a, const ITensor *b, const ITensor *c, ITe #endif /* defined(__aarch64__) */ { // Configure the matrix multiply kernel - _mm_kernel.configure(a, b, d, alpha); + _mm_kernel.configure(a, b, d, alpha, false); } // Configure matrix addition kernel @@ -212,6 +212,10 @@ void NEGEMM::configure(const ITensor *a, const ITensor *b, const ITensor *c, ITe _memory_group.manage(&_tmp_a); _memory_group.manage(&_tmp_b); + int m = a->info()->dimension(1); + int n = b->info()->dimension(0); + int k = a->info()->dimension(0); + // Configure interleave kernel _interleave_kernel.configure(a, &_tmp_a); @@ -219,7 +223,7 @@ void NEGEMM::configure(const ITensor *a, const ITensor *b, const ITensor *c, ITe _transpose_kernel.configure(b, &_tmp_b); // Configure matrix multiplication kernel - _mm_kernel.configure(&_tmp_a, &_tmp_b, d, alpha); + _mm_kernel.configure(&_tmp_a, &_tmp_b, d, alpha, true, GEMMReshapeInfo(m, n, k)); // Allocate once the all configure methods have been called _tmp_a.allocator()->allocate(); diff --git a/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp b/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp index d0a16ef40d..a85078cf71 100644 --- a/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp +++ b/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp @@ -178,7 +178,7 @@ TensorShape get_reshaped_weights_shape_conv(const ITensorInfo *weights, bool app Status validate_and_initialize_values(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const PadStrideInfo &conv_info, const WeightsInfo &weights_info, DataType &dt, bool &append_bias, bool &are_weights_reshaped, unsigned int &kernel_width, unsigned int &kernel_height, - bool &is_fully_connected_convolution, bool &is_interleaved_transposed, bool &is_quantized, + bool &is_fully_connected_convolution, bool &is_interleaved, bool &is_quantized, unsigned int &mat_weights_cols, unsigned int &mat_weights_rows, unsigned int &conv_w, unsigned int &conv_h) { @@ -219,7 +219,7 @@ Status validate_and_initialize_values(const ITensorInfo *input, const ITensorInf // Check if its a "fully connected" convolution is_fully_connected_convolution = ((conv_w == 1) && (conv_h == 1)); - is_interleaved_transposed = (!is_fully_connected_convolution && !is_quantized); + is_interleaved = (!is_fully_connected_convolution && !is_quantized); return Status{}; } @@ -228,11 +228,11 @@ Status validate_and_initialize_values(const ITensorInfo *input, const ITensorInf NEGEMMConvolutionLayer::NEGEMMConvolutionLayer(const std::shared_ptr &memory_manager) : _memory_group(memory_manager), _input_im2col_kernel(), _input_interleave_kernel(), _reshape_weights(), _mm_kernel(), _mm_optimised_kernel(nullptr), _mm_gemmlowp(memory_manager), _gemmlowp_output_stage(), _output_col2im_kernel(), _input_im2col_reshaped(), _input_interleaved_reshaped(), _weights_reshaped(), _gemm_output(), _tmp_output(), _workspace(), _append_bias(false), - _is_fully_connected_convolution(false), _are_weights_reshaped(false), _is_quantized(false), _is_interleaved_transposed(false) + _is_fully_connected_convolution(false), _are_weights_reshaped(false), _is_quantized(false), _is_interleaved(false) { } -void NEGEMMConvolutionLayer::configure_mm(const ITensor *input, const ITensor *weights, ITensor *output) +void NEGEMMConvolutionLayer::configure_mm(const ITensor *input, const ITensor *weights, ITensor *output, bool is_interleaved, const GEMMReshapeInfo &reshape_info) { if(_is_quantized) { @@ -252,7 +252,7 @@ void NEGEMMConvolutionLayer::configure_mm(const ITensor *input, const ITensor *w } else { - _mm_kernel.configure(input, weights, output, 1.f); + _mm_kernel.configure(input, weights, output, 1.f, is_interleaved, reshape_info); } } @@ -290,7 +290,7 @@ void NEGEMMConvolutionLayer::configure(const ITensor *input, const ITensor *weig Status status = validate_and_initialize_values(input->info(), weights->info(), (biases == nullptr) ? nullptr : biases->info(), conv_info, weights_info, dt, _append_bias, _are_weights_reshaped, kernel_width, kernel_height, - _is_fully_connected_convolution, _is_interleaved_transposed, _is_quantized, + _is_fully_connected_convolution, _is_interleaved, _is_quantized, mat_weights_cols, mat_weights_rows, conv_w, conv_h); ARM_COMPUTE_ERROR_THROW_ON(status); @@ -339,9 +339,8 @@ void NEGEMMConvolutionLayer::configure(const ITensor *input, const ITensor *weig } else { - const unsigned int transpose_width = 16 / input->info()->element_size(); - mat_weights_cols = weights_info.num_kernels(); - mat_weights_rows = weights->info()->dimension(0) / transpose_width + (_append_bias ? 1 : 0); + mat_weights_cols = weights_info.num_kernels(); + mat_weights_rows = weights_info.kernel_size().first * weights_info.kernel_size().second * input->info()->dimension(2) + (_append_bias ? 1 : 0); } } else @@ -362,7 +361,7 @@ void NEGEMMConvolutionLayer::configure(const ITensor *input, const ITensor *weig // Create tensor to store the reshaped weights _weights_reshaped.allocator()->init(TensorInfo(reshaped_weights_shape, 1, dt, fixed_point_position)); - _reshape_weights.configure(weights, biases_to_use, &_weights_reshaped, _is_interleaved_transposed /* 1xW transpose */); + _reshape_weights.configure(weights, biases_to_use, &_weights_reshaped, _is_interleaved /* 1xW transpose */); weights = &_weights_reshaped; } } @@ -430,18 +429,19 @@ void NEGEMMConvolutionLayer::configure(const ITensor *input, const ITensor *weig } else { - if(_is_interleaved_transposed) + if(_is_interleaved) { // Configure GEMMInterleave4x4. _input_interleaved_reshaped will be auto configured in the kernel _input_interleave_kernel.configure(&_input_im2col_reshaped, &_input_interleaved_reshaped); // Configure GEMM - configure_mm(&_input_interleaved_reshaped, weights, &_gemm_output); + configure_mm(&_input_interleaved_reshaped, weights, &_gemm_output, _is_interleaved, GEMMReshapeInfo(_input_im2col_reshaped.info()->dimension(1), 0 /* no transpose */, + _input_im2col_reshaped.info()->dimension(0))); _input_interleaved_reshaped.allocator()->allocate(); } else { - configure_mm(&_input_im2col_reshaped, weights, &_gemm_output); + configure_mm(&_input_im2col_reshaped, weights, &_gemm_output, _is_interleaved); } } @@ -479,11 +479,13 @@ void NEGEMMConvolutionLayer::configure(const ITensor *input, const ITensor *weig Status NEGEMMConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info) { + ARM_COMPUTE_UNUSED(output); + DataType dt{}; bool append_bias{}; bool are_weights_reshaped{}; bool is_fully_connected_convolution{}; - bool is_interleaved_transposed{}; + bool is_interleaved{}; bool is_quantized{}; unsigned int kernel_width = 0; unsigned int kernel_height = 0; @@ -493,9 +495,11 @@ Status NEGEMMConvolutionLayer::validate(const ITensorInfo *input, const ITensorI unsigned int conv_h = 0; Status status = validate_and_initialize_values(input, weights, biases, conv_info, weights_info, dt, append_bias, are_weights_reshaped, kernel_width, kernel_height, - is_fully_connected_convolution, is_interleaved_transposed, is_quantized, mat_weights_cols, mat_weights_rows, + is_fully_connected_convolution, is_interleaved, is_quantized, mat_weights_cols, mat_weights_rows, conv_w, conv_h); + const Size2D kernel_weights = Size2D(kernel_width, kernel_height); + ARM_COMPUTE_RETURN_ON_ERROR(status); std::unique_ptr reshaped_weights = weights->clone(); @@ -570,7 +574,7 @@ Status NEGEMMConvolutionLayer::validate(const ITensorInfo *input, const ITensorI shape_im2col.set(1, mat_input_rows); shape_im2col.set(2, 1); TensorInfo im2_col_info = input->clone()->set_tensor_shape(shape_im2col); - ARM_COMPUTE_RETURN_ON_ERROR(NEIm2ColKernel::validate(input, &im2_col_info, Size2D(weights->dimension(0), weights->dimension(1)), conv_info, append_bias)); + ARM_COMPUTE_RETURN_ON_ERROR(NEIm2ColKernel::validate(input, &im2_col_info, kernel_weights, conv_info, append_bias, false)); // Create GEMM output tensor TensorShape shape_gemm(im2_col_info.tensor_shape()); @@ -579,24 +583,20 @@ Status NEGEMMConvolutionLayer::validate(const ITensorInfo *input, const ITensorI TensorInfo gemm_output_info = input->clone()->set_tensor_shape(shape_gemm); // Validate GEMM interleave and multiply - if(is_interleaved_transposed) + if(is_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)); TensorInfo input_interleaved_info = input->clone()->set_tensor_shape(shape_interleaved); ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMInterleave4x4Kernel::validate(&im2_col_info, &input_interleaved_info)); - ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMMatrixMultiplyKernel::validate(&input_interleaved_info, weights, &gemm_output_info)); + ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMMatrixMultiplyKernel::validate(&input_interleaved_info, weights, &gemm_output_info, 1.f, is_interleaved, GEMMReshapeInfo())); } else { - ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMMatrixMultiplyKernel::validate(&im2_col_info, weights, &gemm_output_info)); + ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMMatrixMultiplyKernel::validate(&im2_col_info, weights, &gemm_output_info, 1.f, is_interleaved, GEMMReshapeInfo())); } - ARM_COMPUTE_RETURN_ON_ERROR(NECol2ImKernel::validate(&gemm_output_info, output, Size2D(conv_w, conv_h))); - - ARM_COMPUTE_RETURN_ERROR_ON_MSG((output->dimension(0) != conv_w) || (output->dimension(1) != conv_h), "Output shape does not match the expected one"); - return Status{}; } @@ -621,7 +621,7 @@ void NEGEMMConvolutionLayer::run() } else { - if(_is_interleaved_transposed) + if(_is_interleaved) { // Run interleave NEScheduler::get().schedule(&_input_interleave_kernel, Window::DimY); diff --git a/src/runtime/NEON/functions/NEGEMMTranspose1xW.cpp b/src/runtime/NEON/functions/NEGEMMTranspose1xW.cpp index 571bf2bc74..802b94650e 100644 --- a/src/runtime/NEON/functions/NEGEMMTranspose1xW.cpp +++ b/src/runtime/NEON/functions/NEGEMMTranspose1xW.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017 ARM Limited. + * Copyright (c) 2017-2018 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -38,3 +38,7 @@ void NEGEMMTranspose1xW::configure(const ITensor *input, ITensor *output) k->configure(input, output); _kernel = std::move(k); } +Status NEGEMMTranspose1xW::validate(const ITensorInfo *input, const ITensorInfo *output) +{ + return NEGEMMTranspose1xWKernel::validate(input, output); +} diff --git a/src/runtime/NEON/functions/NEIm2Col.cpp b/src/runtime/NEON/functions/NEIm2Col.cpp index 8e90e66dcc..b962db9144 100644 --- a/src/runtime/NEON/functions/NEIm2Col.cpp +++ b/src/runtime/NEON/functions/NEIm2Col.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017 ARM Limited. + * Copyright (c) 2017-2018 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -28,14 +28,14 @@ using namespace arm_compute; -void NEIm2Col::configure(const ITensor *input, ITensor *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias) +void NEIm2Col::configure(const ITensor *input, ITensor *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, bool is_fully_connected) { auto k = arm_compute::support::cpp14::make_unique(); - k->configure(input, output, kernel_dims, conv_info, has_bias); + k->configure(input, output, kernel_dims, conv_info, has_bias, is_fully_connected); _kernel = std::move(k); } -Status NEIm2Col::validate(const ITensorInfo *input, const ITensorInfo *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias) +Status NEIm2Col::validate(const ITensorInfo *input, const ITensorInfo *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, bool is_fully_connected) { - return NEIm2ColKernel::validate(input, output, kernel_dims, conv_info, has_bias); + return NEIm2ColKernel::validate(input, output, kernel_dims, conv_info, has_bias, is_fully_connected); } -- cgit v1.2.1