diff options
Diffstat (limited to 'src/runtime/NEON/functions/NEFullyConnectedLayer.cpp')
-rw-r--r-- | src/runtime/NEON/functions/NEFullyConnectedLayer.cpp | 212 |
1 files changed, 142 insertions, 70 deletions
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 <algorithm> #include <cmath> -namespace arm_compute -{ +using namespace arm_compute; +using namespace arm_compute::misc::shape_calculator; + NEFullyConnectedLayerReshapeWeights::NEFullyConnectedLayerReshapeWeights(std::shared_ptr<IMemoryManager> 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<ITensorInfo> 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<int>(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<int>(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<unsigned int>(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<unsigned int>(std::ceil(static_cast<float>(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<int>(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<ITensorInfo> 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<unsigned int>(std::ceil(static_cast<float>(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<ITensorInfo> im2col_output = input->clone(); + std::unique_ptr<ITensorInfo> 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 |