From 47d39dc615d1dee2482bc84699802165a9778ac8 Mon Sep 17 00:00:00 2001 From: Georgios Pinitas Date: Mon, 11 Mar 2019 14:03:23 +0000 Subject: COMPMID-1975: Update depthwise convolution. Change-Id: Iad58672be35710a7ec2e918653d6d529709387e8 Signed-off-by: Georgios Pinitas Reviewed-on: https://review.mlplatform.org/c/898 Tested-by: Arm Jenkins Reviewed-by: Giuseppe Rossini Comments-Addressed: Arm Jenkins Reviewed-by: Gian Marco Iodice --- .../NEON/functions/NEDepthwiseConvolutionLayer.cpp | 314 +++++++++++++-------- 1 file changed, 199 insertions(+), 115 deletions(-) (limited to 'src/runtime/NEON/functions/NEDepthwiseConvolutionLayer.cpp') diff --git a/src/runtime/NEON/functions/NEDepthwiseConvolutionLayer.cpp b/src/runtime/NEON/functions/NEDepthwiseConvolutionLayer.cpp index f0fd4cf256..5db94a67c0 100644 --- a/src/runtime/NEON/functions/NEDepthwiseConvolutionLayer.cpp +++ b/src/runtime/NEON/functions/NEDepthwiseConvolutionLayer.cpp @@ -31,112 +31,78 @@ #include "arm_compute/runtime/NEON/NEScheduler.h" #include "support/ToolchainSupport.h" -using namespace arm_compute; +#include "arm_compute/core/utils/misc/InfoHelpers.h" + using namespace arm_compute::misc; using namespace arm_compute::misc::shape_calculator; -NEDepthwiseConvolutionLayer3x3::NEDepthwiseConvolutionLayer3x3() - : _dwc_kernel(), _output_stage_kernel(), _border_handler(), _permute_input(), _permute_weights(), _permute_output(), _activationlayer_function(), _accumulator(), _permuted_input(), - _permuted_weights(), _permuted_output(), _has_bias(false), _is_quantized(false), _is_optimized(false), _are_weights_reshaped(false), _is_nchw(true), _is_first_run(true), _permute(false), - _is_activationlayer_enabled(false) +namespace arm_compute +{ +NEDepthwiseConvolutionLayer3x3::NEDepthwiseConvolutionLayer3x3(std::shared_ptr memory_manager) + : _memory_group(memory_manager), _dwc_kernel(), _dwc_optimized_func(memory_manager), _output_stage_kernel(), _border_handler(), _permute_input(), _permute_weights(), _permute_output(), + _activationlayer_function(), _accumulator(), _permuted_input(), _permuted_weights(), _permuted_output(), _original_weights(nullptr), _has_bias(false), _is_quantized(false), _is_optimized(false), + _is_nchw(true), _permute(false), _is_activationlayer_enabled(false), _is_prepared(false) { } -void NEDepthwiseConvolutionLayer3x3::configure(ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, - unsigned int depth_multiplier, const ActivationLayerInfo &act_info) +void NEDepthwiseConvolutionLayer3x3::configure_generic(ITensor *input, + const ITensor *weights, + const ITensor *biases, + ITensor *output, + const PadStrideInfo &conv_info, + unsigned int depth_multiplier, + const ActivationLayerInfo &act_info) { - ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32); - ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights); + ARM_COMPUTE_UNUSED(act_info); PixelValue zero_value(0.f); - _is_quantized = is_data_type_quantized_asymmetric(input->info()->data_type()); - _has_bias = biases != nullptr; - _is_optimized = NEDepthwiseConvolutionLayer3x3Kernel::is_optimized_execution_possible(input->info()->tensor_shape(), - conv_info, - input->info()->data_type(), - depth_multiplier, - input->info()->data_layout()); - _are_weights_reshaped = false; - _is_nchw = input->info()->data_layout() == DataLayout::NCHW; - _permute = _is_optimized == _is_nchw; - // Initialize the intermediate accumulator tensor in case of quantized input if(_is_quantized) { TensorShape accum_shape = output->info()->tensor_shape(); DataLayout accum_layout = output->info()->data_layout(); - if(!_is_optimized && !_is_nchw) + if(!_is_nchw) { permute(accum_shape, PermutationVector(1U, 2U, 0U)); accum_layout = DataLayout::NCHW; } + _memory_group.manage(&_accumulator); _accumulator.allocator()->init(TensorInfo(accum_shape, 1, DataType::S32, output->info()->quantization_info())); _accumulator.info()->set_data_layout(accum_layout); zero_value = PixelValue(static_cast(input->info()->quantization_info().offset)); } - if(_is_optimized) + if(!_is_nchw) { - ITensor *optimized_output = (_is_quantized) ? &_accumulator : output; - if(_is_nchw) - { - // Configure the function to transform the input tensor from NCHW -> NHWC - _permute_input.configure(input, &_permuted_input, PermutationVector(2U, 0U, 1U)); - _permuted_input.info()->set_data_layout(DataLayout::NHWC); + _memory_group.manage(&_permuted_input); + _memory_group.manage(&_permuted_output); - // Configure the function to transform the weights tensor from IHW -> HWI - _permute_weights.configure(weights, &_permuted_weights, PermutationVector(2U, 0U, 1U)); - _permuted_weights.info()->set_data_layout(DataLayout::NHWC); + // Configure the function to transform the input tensor from NHWC -> NCHW + _permute_input.configure(input, &_permuted_input, PermutationVector(1U, 2U, 0U)); + _permuted_input.info()->set_data_layout(DataLayout::NCHW); - // Configure optimized depthwise - _dwc_kernel.configure(&_permuted_input, &_permuted_weights, &_permuted_output, conv_info, depth_multiplier, DataLayout::NHWC); + // Configure the function to transform the weights tensor from HWI -> IHW + _permute_weights.configure(weights, &_permuted_weights, PermutationVector(1U, 2U, 0U)); + _permuted_weights.info()->set_data_layout(DataLayout::NCHW); - // Configure the function to transform the convoluted output to ACL's native ordering format NCHW - _permuted_output.info()->set_data_layout(DataLayout::NHWC); - _permute_output.configure(&_permuted_output, optimized_output, PermutationVector(1U, 2U, 0U)); + // Configure optimized depthwise + _dwc_kernel.configure(&_permuted_input, &_permuted_weights, (_is_quantized) ? &_accumulator : &_permuted_output, conv_info, depth_multiplier); - // Allocate tensors - _permuted_input.allocator()->allocate(); - _permuted_weights.allocator()->allocate(); - _permuted_output.allocator()->allocate(); - } - else - { - _dwc_kernel.configure(input, weights, optimized_output, conv_info, depth_multiplier, DataLayout::NHWC); - } + // Configure border handler + _border_handler.configure(&_permuted_input, _dwc_kernel.border_size(), BorderMode::CONSTANT, zero_value); + + // Allocate tensors + _permuted_input.allocator()->allocate(); } else { - if(!_is_nchw) - { - // Configure the function to transform the input tensor from NHWC -> NCHW - _permute_input.configure(input, &_permuted_input, PermutationVector(1U, 2U, 0U)); - _permuted_input.info()->set_data_layout(DataLayout::NCHW); - - // Configure the function to transform the weights tensor from HWI -> IHW - _permute_weights.configure(weights, &_permuted_weights, PermutationVector(1U, 2U, 0U)); - _permuted_weights.info()->set_data_layout(DataLayout::NCHW); - - // Configure optimized depthwise - _dwc_kernel.configure(&_permuted_input, &_permuted_weights, (_is_quantized) ? &_accumulator : &_permuted_output, conv_info, depth_multiplier); - - // Configure border handler - _border_handler.configure(&_permuted_input, _dwc_kernel.border_size(), BorderMode::CONSTANT, zero_value); + // Configure depthwise convolution kernel + _dwc_kernel.configure(input, weights, (_is_quantized) ? &_accumulator : output, conv_info, depth_multiplier); - // Allocate tensors - _permuted_input.allocator()->allocate(); - _permuted_weights.allocator()->allocate(); - } - else - { - // Configure depthwise convolution kernel - _dwc_kernel.configure(input, weights, (_is_quantized) ? &_accumulator : output, conv_info, depth_multiplier); - - // Configure border handler - _border_handler.configure(input, _dwc_kernel.border_size(), BorderMode::CONSTANT, zero_value); - } + // Configure border handler + _border_handler.configure(input, _dwc_kernel.border_size(), BorderMode::CONSTANT, zero_value); } // Configure biases accumulation @@ -147,32 +113,116 @@ void NEDepthwiseConvolutionLayer3x3::configure(ITensor *input, const ITensor *we float multiplier = input->info()->quantization_info().scale * weights->info()->quantization_info().scale / output_quant_info.scale; int output_multiplier, output_shift; quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift); - _output_stage_kernel.configure(&_accumulator, biases, (_is_nchw || _is_optimized) ? output : &_permuted_output, output_multiplier, output_shift, output_quant_info.offset); + _output_stage_kernel.configure(&_accumulator, biases, _is_nchw ? output : &_permuted_output, output_multiplier, output_shift, output_quant_info.offset); _accumulator.allocator()->allocate(); } else if(_has_bias) { - _output_stage_kernel.configure((_is_nchw || _is_optimized) ? output : &_permuted_output, biases); + _output_stage_kernel.configure(_is_nchw ? output : &_permuted_output, biases); } - if(!_is_optimized && !_is_nchw) + // Permute output + if(!_is_nchw) { // Configure the function to transform the convoluted output to NHWC _permute_output.configure(&_permuted_output, output, PermutationVector(2U, 0U, 1U)); _permuted_output.allocator()->allocate(); } +} + +void NEDepthwiseConvolutionLayer3x3::configure_optimized(const ITensor *input, + const ITensor *weights, + const ITensor *biases, + ITensor *output, + const PadStrideInfo &conv_info, + unsigned int depth_multiplier, + const ActivationLayerInfo &act_info) +{ + ActivationLayerInfo act_info_to_use = ActivationLayerInfo(); + const bool is_relu = arm_compute::utils::info_helpers::is_relu(act_info); + const bool is_relu6 = arm_compute::utils::info_helpers::is_relu6(act_info); + _is_activationlayer_enabled = act_info.enabled() && !(is_relu || is_relu6); + if(!_is_activationlayer_enabled) + { + act_info_to_use = act_info; + } + + if(_is_nchw) + { + _memory_group.manage(&_permuted_input); + _memory_group.manage(&_permuted_output); + + // Configure the function to transform the input tensor from NCHW -> NHWC + _permute_input.configure(input, &_permuted_input, PermutationVector(2U, 0U, 1U)); + _permuted_input.info()->set_data_layout(DataLayout::NHWC); + + // Configure the function to transform the weights tensor from IHW -> HWI + _permute_weights.configure(weights, &_permuted_weights, PermutationVector(2U, 0U, 1U)); + _permuted_weights.info()->set_data_layout(DataLayout::NHWC); + + // Configure optimized depthwise + _dwc_optimized_func.configure(&_permuted_input, &_permuted_weights, biases, &_permuted_output, conv_info, depth_multiplier, act_info_to_use); + + // Configure the function to transform the convoluted output to ACL's native ordering format NCHW + _permuted_output.info()->set_data_layout(DataLayout::NHWC); + _permute_output.configure(&_permuted_output, output, PermutationVector(1U, 2U, 0U)); + + // Allocate tensors + _permuted_input.allocator()->allocate(); + _permuted_output.allocator()->allocate(); + } + else + { + _dwc_optimized_func.configure(input, weights, biases, output, conv_info, depth_multiplier, act_info_to_use); + } +} - //Configure Activation Layer +void NEDepthwiseConvolutionLayer3x3::configure(ITensor *input, + const ITensor *weights, + const ITensor *biases, + ITensor *output, const PadStrideInfo &conv_info, + unsigned int depth_multiplier, + const ActivationLayerInfo &act_info) +{ + ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32); + ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights); + + _original_weights = weights; + _is_quantized = is_data_type_quantized_asymmetric(input->info()->data_type()); + _has_bias = biases != nullptr; + _is_optimized = NEDepthwiseConvolutionAssemblyDispatch::is_optimized_supported(input->info(), + weights->info(), + conv_info, + depth_multiplier); + _is_nchw = input->info()->data_layout() == DataLayout::NCHW; + _permute = _is_optimized == _is_nchw; + _is_prepared = false; _is_activationlayer_enabled = act_info.enabled(); + // Configure appropriate pipeline + if(_is_optimized) + { + configure_optimized(input, weights, biases, output, conv_info, depth_multiplier, act_info); + } + else + { + configure_generic(input, weights, biases, output, conv_info, depth_multiplier, act_info); + } + + // Configure activation if(_is_activationlayer_enabled) { _activationlayer_function.configure(output, nullptr, act_info); } } -Status NEDepthwiseConvolutionLayer3x3::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, - unsigned int depth_multiplier, const ActivationLayerInfo &act_info) +Status NEDepthwiseConvolutionLayer3x3::validate(const ITensorInfo *input, + const ITensorInfo *weights, + const ITensorInfo *biases, + const ITensorInfo *output, + const PadStrideInfo &conv_info, + unsigned int depth_multiplier, + const ActivationLayerInfo &act_info) { ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output); ARM_COMPUTE_RETURN_ERROR_ON(input->data_layout() == DataLayout::UNKNOWN); @@ -184,14 +234,20 @@ Status NEDepthwiseConvolutionLayer3x3::validate(const ITensorInfo *input, const ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != weights->dimension(channel_idx)); } - const bool is_quantized = is_data_type_quantized_asymmetric(input->data_type()); - TensorInfo accumulator = TensorInfo(output->clone()->set_is_resizable(true).reset_padding().set_data_type(DataType::S32)); - - ARM_COMPUTE_RETURN_ON_ERROR(NEDepthwiseConvolutionLayer3x3Kernel::validate(input, weights, is_quantized ? &accumulator : output, conv_info, depth_multiplier)); + if(!NEDepthwiseConvolutionAssemblyDispatch::is_optimized_supported(input, weights, conv_info, depth_multiplier)) + { + const bool is_quantized = is_data_type_quantized_asymmetric(input->data_type()); + TensorInfo accumulator = TensorInfo(output->clone()->set_is_resizable(true).reset_padding().set_data_type(DataType::S32)); + ARM_COMPUTE_RETURN_ON_ERROR(NEDepthwiseConvolutionLayer3x3Kernel::validate(input, weights, is_quantized ? &accumulator : output, conv_info, depth_multiplier)); - if(is_quantized) + if(is_quantized) + { + ARM_COMPUTE_RETURN_ON_ERROR(NEDirectConvolutionLayerOutputStageKernel::validate(&accumulator, biases, output)); + } + } + else { - ARM_COMPUTE_RETURN_ON_ERROR(NEDirectConvolutionLayerOutputStageKernel::validate(&accumulator, biases, output)); + ARM_COMPUTE_RETURN_ON_ERROR(NEDepthwiseConvolutionAssemblyDispatch::validate(input, weights, biases, output, conv_info, depth_multiplier)); } //Validate Activation Layer @@ -203,59 +259,86 @@ Status NEDepthwiseConvolutionLayer3x3::validate(const ITensorInfo *input, const return Status{}; } -void NEDepthwiseConvolutionLayer3x3::run() +void NEDepthwiseConvolutionLayer3x3::run_generic() { - if(_is_first_run && _is_optimized) - { - _is_first_run = false; - // Create convolver (deferred) - _dwc_kernel.generate_convolver(); - } + // Fill border + NEScheduler::get().schedule(&_border_handler, Window::DimX); - // Permute weights - if(_permute) - { - if(!_are_weights_reshaped) - { - _are_weights_reshaped = true; - _permute_weights.run(); - } + // Execute depthwise convolution + NEScheduler::get().schedule(&_dwc_kernel, Window::DimX); - _permute_input.run(); + // Add biases + if(_has_bias || _is_quantized) + { + NEScheduler::get().schedule(&_output_stage_kernel, Window::DimX); } - // Handle input - if(!_is_optimized) + // Permute output + if(!_is_nchw) { - // Fill border - NEScheduler::get().schedule(&_border_handler, Window::DimX); + _permute_output.run(); } +} - // Execute depthwise convolution - NEScheduler::get().schedule(&_dwc_kernel, Window::DimX); +void NEDepthwiseConvolutionLayer3x3::run_optimized() +{ + // Run assembly function + _dwc_optimized_func.run(); // Permute output - if(_is_optimized && _is_nchw) + if(_is_nchw) { _permute_output.run(); } +} - // Add biases - if(_has_bias || _is_quantized) - { - NEScheduler::get().schedule(&_output_stage_kernel, Window::DimX); - } +void NEDepthwiseConvolutionLayer3x3::run() +{ + prepare(); - // Permute output - if(!_is_optimized && !_is_nchw) + _memory_group.acquire(); + + // Permute input + if(_permute) { - _permute_output.run(); + _permute_input.run(); } + _is_optimized ? run_optimized() : run_generic(); + + // Run activation if(_is_activationlayer_enabled) { _activationlayer_function.run(); } + + _memory_group.release(); +} + +void NEDepthwiseConvolutionLayer3x3::prepare() +{ + if(!_is_prepared) + { + // Permute weights + if(_permute) + { + _permuted_weights.allocator()->allocate(); + _permute_weights.run(); + _original_weights->mark_as_unused(); + } + + // Prepare optimized function + if(_is_optimized) + { + _dwc_optimized_func.prepare(); + if(!_permuted_weights.is_used()) + { + _permuted_weights.allocator()->free(); + } + } + + _is_prepared = true; + } } NEDepthwiseConvolutionLayer::NEDepthwiseConvolutionLayer() @@ -542,3 +625,4 @@ void NEDepthwiseConvolutionLayer::prepare() _is_prepared = true; } } +} // namespace arm_compute -- cgit v1.2.1