From 78c009079654268cca9c22848e4fae9f222b100d Mon Sep 17 00:00:00 2001 From: Georgios Pinitas Date: Tue, 9 Jan 2018 17:33:11 +0000 Subject: COMPMID-754: Add validation to kernels. Adds validation method to: - CLConvolutionLayer Change-Id: I95516e20cfb71c1e603c60fc6491ac695883a856 Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/117355 Reviewed-by: Anthony Barbier Tested-by: Jenkins --- .../CL/functions/CLGEMMConvolutionLayer.cpp | 345 +++++++++++---------- 1 file changed, 184 insertions(+), 161 deletions(-) (limited to 'src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp') diff --git a/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp b/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp index 60e1bde4e2..23c3050476 100644 --- a/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp +++ b/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp @@ -27,6 +27,7 @@ #include "arm_compute/core/Size2D.h" #include "arm_compute/core/Utils.h" #include "arm_compute/core/Validate.h" +#include "arm_compute/core/utils/misc/ShapeCalculator.h" #include "arm_compute/core/utils/quantization/AsymmHelpers.h" #include "arm_compute/runtime/CL/CLScheduler.h" @@ -35,53 +36,52 @@ #include using namespace arm_compute; +using namespace arm_compute::misc::shape_calculator; CLConvolutionLayerReshapeWeights::CLConvolutionLayerReshapeWeights(std::shared_ptr memory_manager) - : _memory_group(std::move(memory_manager)), _weights_reshape_kernel(), _weights_transposed_kernel(), _weights_reshaped(), _transpose1xW(false) + : _memory_group(std::move(memory_manager)), _weights_reshape_kernel(), _weights_transposed_kernel(), _weights_reshaped() { } -void CLConvolutionLayerReshapeWeights::configure(const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, bool transpose1xW) +void CLConvolutionLayerReshapeWeights::configure(const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output) { + // Perform validation step ARM_COMPUTE_ERROR_ON_NULLPTR(weights, output); - ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 4); - - if(biases != nullptr) - { - ARM_COMPUTE_ERROR_ON(is_data_type_quantized_asymmetric(weights->info()->data_type())); - ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases); - ARM_COMPUTE_ERROR_ON(biases->info()->dimension(0) != weights->info()->dimension(3)); - ARM_COMPUTE_ERROR_ON(biases->info()->num_dimensions() > 1); - } + ARM_COMPUTE_ERROR_THROW_ON(CLConvolutionLayerReshapeWeights::validate(weights->info(), + (biases != nullptr) ? biases->info() : nullptr, + output->info())); const bool append_biases = (biases != nullptr) && !is_data_type_quantized_asymmetric(weights->info()->data_type()); - const unsigned bias_element = (append_biases) ? 1 : 0; const ICLTensor *biases_to_use = (append_biases) ? biases : nullptr; - _transpose1xW = transpose1xW; + _weights_reshape_kernel.configure(weights, biases_to_use, output); + + output->info()->set_quantization_info(weights->info()->quantization_info()); +} + +Status CLConvolutionLayerReshapeWeights::validate(const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output) +{ + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(weights); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QS8, DataType::QASYMM8, DataType::QS16, DataType::F16, DataType::F32); + ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 4); - if(transpose1xW) + if(biases != nullptr) { - // 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); - _memory_group.manage(&_weights_reshaped); - _weights_reshape_kernel.configure(weights, biases_to_use, &_weights_reshaped); - _weights_transposed_kernel.configure(&_weights_reshaped, output); - _weights_reshaped.allocator()->allocate(); + ARM_COMPUTE_RETURN_ERROR_ON(is_data_type_quantized_asymmetric(weights->data_type())); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases); + ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != weights->dimension(3)); + ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1); } - else + + if((output != nullptr) && (output->total_size() != 0)) { - _weights_reshape_kernel.configure(weights, biases_to_use, output); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, output); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(weights, output); + + CLWeightsReshapeKernel::validate(weights, biases, output); } - output->info()->set_quantization_info(weights->info()->quantization_info()); + return Status{}; } void CLConvolutionLayerReshapeWeights::run() @@ -89,99 +89,92 @@ void CLConvolutionLayerReshapeWeights::run() _memory_group.acquire(); CLScheduler::get().enqueue(_weights_reshape_kernel); - if(_transpose1xW) - { - CLScheduler::get().enqueue(_weights_transposed_kernel); - } _memory_group.release(); } CLGEMMConvolutionLayer::CLGEMMConvolutionLayer(std::shared_ptr memory_manager) - : _memory_group(memory_manager), _reshape_weights(), _im2col_kernel(), _interleave_kernel(), _mm_kernel(), _mm_gemm(memory_manager), _mm_gemmlowp(memory_manager), _gemmlowp_output_stage(), - _col2im_kernel(), _im2col_output(), _interleave_output(), _weights_reshaped(), _weights_transposed(), _gemm_output(), _tmp_output(), _are_weights_reshaped(false), _is_quantized(false), - _is_interleaved_transposed(false) + : _memory_group(memory_manager), _reshape_weights(), _im2col_kernel(), _mm_gemm(memory_manager), _mm_gemmlowp(memory_manager), _gemmlowp_output_stage(), _col2im_kernel(), _im2col_output(), + _interleave_output(), _weights_reshaped(), _weights_transposed(), _gemm_output(), _tmp_output(), _is_quantized(false) { } -void CLGEMMConvolutionLayer::configure_mm(const ICLTensor *input, const ICLTensor *weights, ICLTensor *output, bool is_interleaved_transposed, bool are_weights_reshaped) +void CLGEMMConvolutionLayer::configure_mm(const ICLTensor *input, const ICLTensor *weights, ICLTensor *output) { ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights); + ARM_COMPUTE_ERROR_THROW_ON(validate_mm(input->info(), weights->info(), output->info())); + if(_is_quantized) { - if(are_weights_reshaped) - { - ARM_COMPUTE_ERROR("Weights already reshaped are not suppported with gemmlowp"); - } - else - { - // Since we need negative offsets for computing convolution, we need to change QuantizationInfo() - // Extract and negate input and weights offset - const QuantizationInfo input_quantization_info = input->info()->quantization_info(); - const QuantizationInfo weights_quantization_info = weights->info()->quantization_info(); + // Since we need negative offsets for computing convolution, we need to change QuantizationInfo() + // Extract and negate input and weights offset + const QuantizationInfo input_quantization_info = input->info()->quantization_info(); + const QuantizationInfo weights_quantization_info = weights->info()->quantization_info(); - input->info()->set_quantization_info(QuantizationInfo(input_quantization_info.scale, -input_quantization_info.offset)); - weights->info()->set_quantization_info(QuantizationInfo(weights_quantization_info.scale, -weights_quantization_info.offset)); + input->info()->set_quantization_info(QuantizationInfo(input_quantization_info.scale, -input_quantization_info.offset)); + weights->info()->set_quantization_info(QuantizationInfo(weights_quantization_info.scale, -weights_quantization_info.offset)); - _mm_gemmlowp.configure(input, weights, output, GEMMInfo(false, false, true /* Reshape weights only for the first run*/)); + _mm_gemmlowp.configure(input, weights, output, GEMMInfo(false, false, true /* Reshape weights only for the first run*/)); - // Revert back QuantizatioInfo as input and weights could be used in other convolution layers - input->info()->set_quantization_info(input_quantization_info); - weights->info()->set_quantization_info(weights_quantization_info); - } + // Revert back QuantizatioInfo as input and weights could be used in other convolution layers + input->info()->set_quantization_info(input_quantization_info); + weights->info()->set_quantization_info(weights_quantization_info); } else { - if(are_weights_reshaped) - { - // Configure matrix multiply kernel - _mm_kernel.configure(input, weights, output, 1.f, is_interleaved_transposed); - } - else - { - // Configure matrix multiply function - _mm_gemm.configure(input, weights, nullptr, output, 1.0f, 0.0f, GEMMInfo(false, false, true /* Reshape weights only for the first run*/)); - } + // Configure matrix multiply function + _mm_gemm.configure(input, weights, nullptr, output, 1.0f, 0.0f, GEMMInfo(false, false, true /* Reshape weights only for the first run*/)); } } +Status CLGEMMConvolutionLayer::validate_mm(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *output) +{ + const bool is_quantized = is_data_type_quantized_asymmetric(input->data_type()); + + const GEMMInfo &gemm_info = GEMMInfo(false, false, true /* Reshape weights only for the first run */); + if(is_quantized) + { + // Since we need negative offsets for computing convolution, we need to change QuantizationInfo() + // Extract and negate input and weights offset + const QuantizationInfo input_quantization_info = input->quantization_info(); + const QuantizationInfo weights_quantization_info = weights->quantization_info(); + + std::unique_ptr input_qa = input->clone(); + std::unique_ptr weights_qa = weights->clone(); + input_qa->set_quantization_info(QuantizationInfo(input_quantization_info.scale, -input_quantization_info.offset)); + weights_qa->set_quantization_info(QuantizationInfo(weights_quantization_info.scale, -weights_quantization_info.offset)); + + // Perform validation step on GEMMLowp + CLGEMMLowpMatrixMultiplyCore::validate(input_qa.get(), weights_qa.get(), output, gemm_info); + } + else + { + // Perform validation step on Matrix multiply function + CLGEMM::validate(input, weights, nullptr, output, 1.0f, 0.0f, gemm_info); + } + return Status{}; +} + void CLGEMMConvolutionLayer::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info) { ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output); - ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QASYMM8, DataType::QS16, DataType::F16, DataType::F32); - ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights); - ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, weights); - ARM_COMPUTE_ERROR_ON(weights_info.are_reshaped() && CLScheduler::get().target() == GPUTarget::BIFROST); - ARM_COMPUTE_ERROR_ON(!weights_info.are_reshaped() && weights->info()->dimension(2) != input->info()->dimension(2)); - ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 4); - ARM_COMPUTE_ERROR_ON(weights_info.are_reshaped() && is_data_type_quantized_asymmetric(input->info()->data_type())); - _is_quantized = is_data_type_quantized_asymmetric(input->info()->data_type()); + ARM_COMPUTE_ERROR_THROW_ON(CLGEMMConvolutionLayer::validate(input->info(), + weights->info(), + biases != nullptr ? biases->info() : nullptr, + output->info(), + conv_info, + weights_info)); - if(biases != nullptr) - { - if(_is_quantized) - { - ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::S32); - } - else - { - ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases); - } - ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, biases); - ARM_COMPUTE_ERROR_ON(!weights_info.are_reshaped() && biases->info()->dimension(0) != weights->info()->dimension(3)); - ARM_COMPUTE_ERROR_ON(biases->info()->num_dimensions() > 1); - } + _is_quantized = is_data_type_quantized_asymmetric(input->info()->data_type()); const DataType dt = input->info()->data_type(); - // Set the GPU target for matrix multiply and im2col and col2im - _mm_kernel.set_target(CLScheduler::get().target()); + // Set the GPU target for im2col and col2im _im2col_kernel.set_target(CLScheduler::get().target()); _col2im_kernel.set_target(CLScheduler::get().target()); const bool append_bias = (biases != nullptr) && (!_is_quantized); - _are_weights_reshaped = weights_info.are_reshaped(); const unsigned bias_element = (append_bias) ? 1 : 0; const ICLTensor *biases_to_use = (append_bias) ? biases : nullptr; @@ -195,41 +188,19 @@ void CLGEMMConvolutionLayer::configure(const ICLTensor *input, const ICLTensor * 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); - // Check if its a "fully connected" convolution - const bool is_fully_connected_convolution = ((conv_w == 1) && (conv_h == 1)); - _is_interleaved_transposed = (!is_fully_connected_convolution) && (!_is_quantized) && (_are_weights_reshaped); - 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 || _is_quantized) - { - 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 - { - // _weights_reshaped will be auto configured in the kernel. - // Just append biases and do not transpose 1xW as it will be reshaped in CLGEMM - _reshape_weights.configure(weights, biases_to_use, &_weights_reshaped, false); + // _weights_reshaped will be auto configured in the kernel. + // Just append biases and do not transpose 1xW as it will be reshaped in CLGEMM + _reshape_weights.configure(weights, biases_to_use, &_weights_reshaped); - weights = &_weights_reshaped; - } + weights = &_weights_reshaped; // Create tensor to store im2col reshaped inputs const unsigned int mat_input_cols = mat_weights_rows; @@ -259,21 +230,9 @@ void CLGEMMConvolutionLayer::configure(const ICLTensor *input, const ICLTensor * // Configure im2col _im2col_kernel.configure(input, &_im2col_output, Size2D(kernel_width, kernel_height), conv_info, append_bias); - // Configure matrix multiply - if(_is_interleaved_transposed) - { - // Configure GEMMInterleave4x4. _input_interleaved_reshaped will be auto configured in the kernel - _memory_group.manage(&_interleave_output); - _interleave_kernel.configure(&_im2col_output, &_interleave_output); + // Configure GEMM + configure_mm(&_im2col_output, weights, &_gemm_output); - // Configure GEMM - configure_mm(&_interleave_output, weights, &_gemm_output, true, _are_weights_reshaped); - _interleave_output.allocator()->allocate(); - } - else - { - configure_mm(&_im2col_output, weights, &_gemm_output, false, _are_weights_reshaped); - } _im2col_output.allocator()->allocate(); // Configure output stage for quantized case @@ -299,53 +258,117 @@ void CLGEMMConvolutionLayer::configure(const ICLTensor *input, const ICLTensor * 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(); + + ARM_COMPUTE_UNUSED(weights_info); +} + +Status CLGEMMConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, + const WeightsInfo &weights_info) +{ + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights_info.are_reshaped(), "Weights already reshaped are not supported!"); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QASYMM8, DataType::QS16, DataType::F16, DataType::F32); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, weights); + ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(2) != input->dimension(2)); + ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 4); + + const bool is_quantized = is_data_type_quantized_asymmetric(input->data_type()); + const bool append_bias = (biases != nullptr) && (!is_quantized); + const unsigned bias_element = (append_bias) ? 1 : 0; + const DataType dt = input->data_type(); + + // Get convolved dimensions + unsigned int conv_w = 0; + unsigned int conv_h = 0; + + const unsigned int kernel_width = weights->dimension(0); + const unsigned int kernel_height = weights->dimension(1); + + std::tie(conv_w, conv_h) = scaled_dimensions(input->dimension(0), input->dimension(1), kernel_width, kernel_height, conv_info); + + unsigned int mat_weights_cols = weights->dimension(3); + unsigned int mat_weights_rows = weights->dimension(0) * weights->dimension(1) * weights->dimension(2) + bias_element; + + CLConvolutionLayerReshapeWeights::validate(weights, biases, nullptr); + + // Create tensor info for im2col reshaped inputs + const unsigned int mat_input_cols = mat_weights_rows; + const unsigned int mat_input_rows = conv_w * conv_h; + TensorShape shape_im2col = input->tensor_shape(); + shape_im2col.set(0, mat_input_cols); + shape_im2col.set(1, mat_input_rows); + shape_im2col.set(2, 1); + TensorInfo im2col_reshaped_info(shape_im2col, 1, dt, input->fixed_point_position()); + im2col_reshaped_info.set_quantization_info(input->quantization_info()); + CLIm2ColKernel::validate(input, &im2col_reshaped_info, Size2D(kernel_width, kernel_height), conv_info, append_bias); + + // Create GEMM output tensor + TensorShape shape_gemm = im2col_reshaped_info.tensor_shape(); + shape_gemm.set(0, mat_weights_cols); + shape_gemm.set(1, mat_input_rows); + const DataType gemm_data_type = is_quantized ? DataType::S32 : dt; + // GEMM output should be S32 for acquiring raw integer accumulator without quantized postprocessing for quantized asymmetric input. + TensorInfo info_gemm(shape_gemm, 1, gemm_data_type, input->fixed_point_position()); + info_gemm.set_quantization_info(output->quantization_info()); + + validate_mm(&im2col_reshaped_info, weights, &info_gemm); + + TensorInfo tmp_info(input->tensor_shape(), 1, DataType::QASYMM8, input->fixed_point_position()); + if(is_quantized) { - _weights_reshaped.allocator()->allocate(); + float multiplier = input->quantization_info().scale * weights->quantization_info().scale / output->quantization_info().scale; + int output_multiplier, output_shift; + quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift); + // Validate output stage for quantized case + CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint::validate(&info_gemm, biases, &tmp_info, output->quantization_info().offset); } + + // Validate Col2Im + CLCol2ImKernel::validate(is_quantized ? &tmp_info : &info_gemm, output, std::make_pair(conv_w, conv_h)); + + if(biases != nullptr) + { + if(is_quantized) + { + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::S32); + } + else + { + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases); + } + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, biases); + ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != weights->dimension(3)); + ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1); + } + + return Status{}; } void CLGEMMConvolutionLayer::run() { // Run weights reshaping (Runs once for every configure) - if(!_are_weights_reshaped) - { - _are_weights_reshaped = true; - _reshape_weights.run(); - } + _reshape_weights.run(); _memory_group.acquire(); // Run im2col CLScheduler::get().enqueue(_im2col_kernel); - // Note: _is_interleaved_transposed is true only if the weights passed to the function have been passed already reshaped - // and if we do not have QASYMM8 data type. If this flag is true, we need to run the - // gemm kernel instead of gemm function - if(_is_interleaved_transposed) + // Runs CLGEMM or CLGEMMLowpMatrixMultiplyCore functions + if(_is_quantized) { - // Run interleave4x4 kernel - CLScheduler::get().enqueue(_interleave_kernel); + // Run gemmlowp + _mm_gemmlowp.run(); - // Run matrix multiply kernel - CLScheduler::get().enqueue(_mm_kernel); + // Run output stage + _gemmlowp_output_stage.run(); } else { - // Runs CLGEMM or CLGEMMLowpMatrixMultiplyCore functions - if(_is_quantized) - { - // Run gemmlowp - _mm_gemmlowp.run(); - - // Run output stage - _gemmlowp_output_stage.run(); - } - else - { - // Run gemm - _mm_gemm.run(); - } + // Run gemm + _mm_gemm.run(); } // Reshape output matrix -- cgit v1.2.1