diff options
Diffstat (limited to 'src')
7 files changed, 177 insertions, 129 deletions
diff --git a/src/core/CL/cl_kernels/gemmlowp.cl b/src/core/CL/cl_kernels/gemmlowp.cl index e52f1ea486..e8124e7aa8 100644 --- a/src/core/CL/cl_kernels/gemmlowp.cl +++ b/src/core/CL/cl_kernels/gemmlowp.cl @@ -2222,17 +2222,29 @@ __kernel void gemmlowp_output_stage_quantize_down(TENSOR3D_DECLARATION(src), * @param[in] dst_step_y dst_gx_stride_y * number of elements along Y processed per workitem(in bytes) * @param[in] dst_stride_z Stride of the source tensor in Z dimension (in bytes) * @param[in] dst_step_z src_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] dst_stride_w Stride of the source tensor in W dimension (in bytes) + * @param[in] dst_step_w src_stride_w * number of elements along W processed per workitem(in bytes) * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor */ __kernel void gemmlowp_output_stage_quantize_down_fixedpoint(TENSOR3D_DECLARATION(src), #if defined(ADD_BIAS) VECTOR_DECLARATION(biases), #endif // defined(ADD_BIAS) +#if defined(DST_HEIGHT) + TENSOR4D_DECLARATION(dst)) +#else // defined(DST_HEIGHT) TENSOR3D_DECLARATION(dst)) +#endif // defined(DST_HEIGHT) { // Compute source and destination addresses Tensor3D src = CONVERT_TO_TENSOR3D_STRUCT(src); +#if defined(DST_HEIGHT) + Tensor4D dst = CONVERT_TO_TENSOR4D_STRUCT_NO_STEP(dst, 1); + dst.ptr += get_global_id(0) * dst_step_x + (get_global_id(1) % DST_HEIGHT) * dst_step_y + (get_global_id(1) / DST_HEIGHT) * dst_step_z + get_global_id(2) * dst_step_w; +#else // defined(DST_HEIGHT) Tensor3D dst = CONVERT_TO_TENSOR3D_STRUCT(dst); +#endif // defined(DST_HEIGHT) + #if defined(ADD_BIAS) Vector biases = CONVERT_TO_VECTOR_STRUCT(biases); #endif // defined(ADD_BIAS) diff --git a/src/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.cpp b/src/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.cpp index 875e26d6cb..d403d67173 100644 --- a/src/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.cpp +++ b/src/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.cpp @@ -27,9 +27,12 @@ #include "arm_compute/core/CL/ICLTensor.h" #include "arm_compute/core/Error.h" #include "arm_compute/core/Helpers.h" +#include "arm_compute/core/TensorInfo.h" #include "arm_compute/core/Types.h" #include "arm_compute/core/Validate.h" #include "arm_compute/core/Window.h" +#include "arm_compute/core/utils/misc/ShapeCalculator.h" + #include "support/ToolchainSupport.h" using namespace arm_compute; @@ -38,7 +41,8 @@ namespace arm_compute { namespace { -Status validate_arguments(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min, int max) +Status validate_arguments(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, + int min, int max, unsigned int output_3d_depth) { ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::S32); ARM_COMPUTE_RETURN_ERROR_ON(max > 255); @@ -54,8 +58,10 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *bias, con if(output->total_size() != 0) { + const TensorShape output_shape = arm_compute::misc::shape_calculator::compute_output_stage_shape(*input, output_3d_depth, true); + const TensorInfo tensor_info_output = output->clone()->set_tensor_shape(output_shape); ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QASYMM8); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &tensor_info_output); } return Status{}; @@ -66,7 +72,7 @@ std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITen constexpr unsigned int num_elems_processed_per_iteration = 16; // Configure kernel window - Window win = calculate_max_window(*output, Steps(num_elems_processed_per_iteration)); + Window win = calculate_max_window(*input, Steps(num_elems_processed_per_iteration)); AccessWindowHorizontal input_access(input, 0, num_elems_processed_per_iteration); @@ -75,8 +81,9 @@ std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITen if(output->total_size() != 0) { + Window win_out = calculate_max_window(*output, Steps(num_elems_processed_per_iteration)); AccessWindowHorizontal output_result_access(output, 0, num_elems_processed_per_iteration); - window_changed = window_changed || update_window_and_padding(win, output_result_access); + window_changed = window_changed || update_window_and_padding(win_out, output_result_access); output_result_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape())); } @@ -96,14 +103,15 @@ class Coordinates; } // namespace arm_compute CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel() - : _input(nullptr), _bias(nullptr), _output(nullptr) + : _input(nullptr), _bias(nullptr), _output(nullptr), _reinterpret_as_3d(false) { } -Status CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min, int max) +Status CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, + int min, int max, unsigned int output_3d_depth) { ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, bias, output, min, max)); + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, bias, output, min, max, output_3d_depth)); ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), (bias != nullptr) ? bias->clone().get() : nullptr, output->clone().get()) @@ -112,24 +120,24 @@ Status CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::validate(const return Status{}; } -void CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::configure(const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, int result_fixedpoint_multiplier, int result_shift, - int result_offset_after_shift, int min, int max) +void CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::configure(const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, + int result_fixedpoint_multiplier, int result_shift, int result_offset_after_shift, + int min, int max, unsigned int output_3d_depth) { // Perform validate step ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); // Output auto inizialitation if not yet initialized - auto_init_if_empty(*output->info(), input->info()->clone()->set_data_type(DataType::QASYMM8)); + const TensorShape output_shape = arm_compute::misc::shape_calculator::compute_output_stage_shape(*input->info(), output_3d_depth, true); + auto_init_if_empty(*output->info(), input->info()->clone()->set_data_type(DataType::QASYMM8).set_tensor_shape(output_shape)); - ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), - (bias != nullptr) ? bias->info() : nullptr, - output->info(), - min, - max)); + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), (bias != nullptr) ? bias->info() : nullptr, output->info(), + min, max, output_3d_depth)); - _input = input; - _bias = bias; - _output = output; + _input = input; + _bias = bias; + _output = output; + _reinterpret_as_3d = output_3d_depth > 1; // Set the arguments to pass at compile time CLBuildOptions build_opts; @@ -139,6 +147,7 @@ void CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::configure(const build_opts.add_option_if((min != 0) && (min != max), "-DMIN_BOUND=" + support::cpp11::to_string(min)); build_opts.add_option_if((max != 255) && (min != max), "-DMAX_BOUND=" + support::cpp11::to_string(max)); build_opts.add_option_if(bias != nullptr, "-DADD_BIAS"); + build_opts.add_option_if(_reinterpret_as_3d, "-DDST_HEIGHT=" + support::cpp11::to_string(input->info()->tensor_shape().y() / output_3d_depth)); // Create kernel _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("gemmlowp_output_stage_quantize_down_fixedpoint", build_opts.options())); @@ -154,9 +163,11 @@ void CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::run(const Window ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window); + // Create input window Window collapsed = window.collapse_if_possible(ICLKernel::window(), Window::DimZ); Window slice = collapsed.first_slice_window_3D(); + // Setup bias slice unsigned int idx1 = num_arguments_per_3D_tensor(); if(_bias != nullptr) { @@ -166,12 +177,32 @@ void CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::run(const Window add_1D_tensor_argument(idx1, _bias, biases_slice); } - do + if(_reinterpret_as_3d) + { + // Create output window + Window window_out; + window_out.use_tensor_dimensions(_output->info()->tensor_shape()); + Window collapsed_out = window_out.collapse_if_possible(window_out, 3); + Window slice_out = collapsed.first_slice_window_4D(); + + do + { + unsigned int idx = 0; + add_3D_tensor_argument(idx, _input, slice); + add_4D_tensor_argument(idx1, _output, slice_out); + enqueue(queue, *this, slice); + } + while(collapsed.slide_window_slice_3D(slice) && collapsed_out.slide_window_slice_4D(slice_out)); + } + else { - unsigned int idx = 0; - add_3D_tensor_argument(idx, _input, slice); - add_3D_tensor_argument(idx1, _output, slice); - enqueue(queue, *this, slice); + do + { + unsigned int idx = 0; + add_3D_tensor_argument(idx, _input, slice); + add_3D_tensor_argument(idx1, _output, slice); + enqueue(queue, *this, slice); + } + while(collapsed.slide_window_slice_3D(slice)); } - while(collapsed.slide_window_slice_3D(slice)); } diff --git a/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.cpp b/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.cpp index 0196bacdcf..7cd50cc5a0 100644 --- a/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.cpp +++ b/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.cpp @@ -28,6 +28,7 @@ #include "arm_compute/core/Helpers.h" #include "arm_compute/core/ITensor.h" #include "arm_compute/core/NEON/NEAsymm.h" +#include "arm_compute/core/TensorInfo.h" #include "arm_compute/core/Types.h" #include "arm_compute/core/Utils.h" #include "arm_compute/core/Validate.h" @@ -43,9 +44,8 @@ using namespace arm_compute; namespace { Status validate_arguments(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, - int min, int max, unsigned int gemm_3d_depth) + int min, int max, unsigned int output_3d_depth) { - ARM_COMPUTE_UNUSED(gemm_3d_depth); ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::S32); ARM_COMPUTE_RETURN_ERROR_ON(max > 255); ARM_COMPUTE_RETURN_ERROR_ON(min < 0 || min > max); @@ -60,21 +60,10 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *bias, con if(output->total_size() != 0) { - const TensorShape ref_shape = output->tensor_shape(); - const TensorShape output_shape = arm_compute::misc::shape_calculator::compute_output_stage_shape(*input, gemm_3d_depth); - // Check in case of mismatching dimensions when permuting, usually in case of 1x1xC input shapes - if(output_shape.num_dimensions() != ref_shape.num_dimensions() && ref_shape.num_dimensions() < 4) - { - for(unsigned int i = output_shape.num_dimensions(); i < ref_shape.num_dimensions(); ++i) - { - ARM_COMPUTE_RETURN_ERROR_ON(ref_shape[i] != 1); - } - } - else - { - ARM_COMPUTE_RETURN_ERROR_ON(output->tensor_shape() != output_shape); - } + const TensorShape output_shape = arm_compute::misc::shape_calculator::compute_output_stage_shape(*input, output_3d_depth); + const TensorInfo tensor_info_output = output->clone()->set_tensor_shape(output_shape); ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QASYMM8); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &tensor_info_output); } return Status{}; @@ -160,7 +149,7 @@ void NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::run(const Window const int window_step_x = 16; const auto window_start_x = static_cast<int>(window.x().start()); const auto window_end_x = static_cast<int>(window.x().end()); - const unsigned int gemm_3d_height = _input->info()->tensor_shape().y() / _gemm_3d_depth; + const unsigned int gemm_3d_height = _input->info()->tensor_shape().y() / _output_3d_depth; Window win_collapsed = window.collapse_if_possible(window, Window::DimZ); win_collapsed.set(Window::DimX, Window::Dimension(0, 1, 1)); @@ -177,7 +166,7 @@ void NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::run(const Window { // Calculate output coordinates Coordinates out_coords = id; - if(_gemm_3d_depth != 1) + if(_output_3d_depth != 1) { out_coords.set(Window::DimY, id.y() % gemm_3d_height); out_coords.set(Window::DimZ, id.y() / gemm_3d_height); @@ -240,10 +229,10 @@ void NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::run(const Window { // Calculate output coordinates Coordinates out_coords = id; - if(_gemm_3d_depth != 1) + if(_output_3d_depth != 1) { - out_coords.set(Window::DimY, id.y() % _gemm_3d_depth); - out_coords.set(Window::DimZ, id.y() / _gemm_3d_depth); + out_coords.set(Window::DimY, id.y() % _output_3d_depth); + out_coords.set(Window::DimZ, id.y() / _output_3d_depth); out_coords.set(3, id.z()); } uint8_t *out_ptr = _output->ptr_to_element(out_coords); @@ -279,22 +268,22 @@ void NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::run(const Window } NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel() - : _func(nullptr), _input(nullptr), _bias(nullptr), _output(nullptr), _result_fixedpoint_multiplier(0), _result_shift(0), _result_offset_after_shift(0), _min(0), _max(0), _gemm_3d_depth(1) + : _func(nullptr), _input(nullptr), _bias(nullptr), _output(nullptr), _result_fixedpoint_multiplier(0), _result_shift(0), _result_offset_after_shift(0), _min(0), _max(0), _output_3d_depth(1) { } void NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::configure(const ITensor *input, const ITensor *bias, ITensor *output, int result_fixedpoint_multiplier, int result_shift, - int result_offset_after_shift, int min, int max, unsigned int gemm_3d_depth) + int result_offset_after_shift, int min, int max, unsigned int output_3d_depth) { // Perform validate step ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); // Output auto inizialitation if not yet initialized - const TensorShape output_shape = arm_compute::misc::shape_calculator::compute_output_stage_shape(*input->info(), gemm_3d_depth); + const TensorShape output_shape = arm_compute::misc::shape_calculator::compute_output_stage_shape(*input->info(), output_3d_depth); auto_init_if_empty(*output->info(), input->info()->clone()->set_data_type(DataType::QASYMM8).set_tensor_shape(output_shape)); ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), (bias != nullptr) ? bias->info() : nullptr, output->info(), - min, max, gemm_3d_depth)); + min, max, output_3d_depth)); _input = input; _bias = bias; @@ -304,7 +293,7 @@ void NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::configure(const _result_offset_after_shift = result_offset_after_shift; _min = min; _max = max; - _gemm_3d_depth = gemm_3d_depth; + _output_3d_depth = output_3d_depth; // Configure kernel window auto win_config = validate_and_configure_window(input->info(), (bias != nullptr) ? bias->info() : nullptr, output->info()); @@ -316,10 +305,10 @@ void NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::configure(const _func = is_bounded_relu ? &NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::run<true> : &NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::run<false>; } -Status NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min, int max, unsigned int gemm_3d_depth) +Status NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min, int max, unsigned int output_3d_depth) { ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, bias, output, min, max, gemm_3d_depth)); + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, bias, output, min, max, output_3d_depth)); ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), (bias != nullptr) ? bias->clone().get() : nullptr, output->clone().get()) diff --git a/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp b/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp index bd5e969921..f41a12ae48 100644 --- a/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp +++ b/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp @@ -92,8 +92,8 @@ void CLConvolutionLayerReshapeWeights::run() CLGEMMConvolutionLayer::CLGEMMConvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager) : _memory_group(memory_manager), _reshape_weights(), _im2col_kernel(), _mm_gemm(memory_manager), _mm_gemmlowp(memory_manager), _gemmlowp_output_stage(), _col2im_kernel(), _activationlayer_function(), - _add_bias_kernel(), _reshape_layer(), _original_weights(nullptr), _im2col_output(), _weights_reshaped(), _gemm_output(), _tmp_output(), _data_layout(DataLayout::NCHW), _append_bias(false), - _skip_im2col(false), _is_quantized(false), _is_activationlayer_enabled(false), _is_prepared(false) + _add_bias_kernel(), _original_weights(nullptr), _im2col_output(), _weights_reshaped(), _gemm_output(), _tmp_output(), _data_layout(DataLayout::NCHW), _append_bias(false), _skip_im2col(false), + _skip_col2im(false), _is_quantized(false), _is_activationlayer_enabled(false), _is_prepared(false) { } @@ -102,6 +102,9 @@ void CLGEMMConvolutionLayer::configure_mm(const ICLTensor *input, const ICLTenso ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights); ARM_COMPUTE_ERROR_THROW_ON(validate_mm(input->info(), weights->info(), output->info(), gemm_3d_depth, _skip_im2col)); + const GEMMInfo &gemm_info = GEMMInfo(false, false, true /* Reshape weights only for the first run */, + gemm_3d_depth, _skip_im2col /* Reinterpret the input as 3D if im2col is skipped */); + if(_is_quantized) { // Since we need negative offsets for computing convolution, we need to change QuantizationInfo() @@ -112,7 +115,7 @@ void CLGEMMConvolutionLayer::configure_mm(const ICLTensor *input, const ICLTenso 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, gemm_info); // Revert back QuantizatioInfo as input and weights could be used in other convolution layers input->info()->set_quantization_info(input_quantization_info); @@ -121,8 +124,7 @@ void CLGEMMConvolutionLayer::configure_mm(const ICLTensor *input, const ICLTenso 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*/, gemm_3d_depth, - _skip_im2col /* Reinterpret the input as 3D if im2col is skipped */)); + _mm_gemm.configure(input, weights, nullptr, output, 1.0f, 0.0f, gemm_info); } } @@ -130,10 +132,11 @@ Status CLGEMMConvolutionLayer::validate_mm(const ITensorInfo *input, const ITens { 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 */, + gemm_3d_depth, skip_im2col /* Reinterpret the input as 3D if im2col is skipped */); + if(is_quantized) { - const GEMMInfo &gemm_info = GEMMInfo(false, false, true /* Reshape weights only for the first run */); - // 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(); @@ -149,8 +152,6 @@ Status CLGEMMConvolutionLayer::validate_mm(const ITensorInfo *input, const ITens } else { - const GEMMInfo &gemm_info = GEMMInfo(false, false, true /* Reshape weights only for the first run */, gemm_3d_depth, skip_im2col /* Reinterpret the input as 3D if im2col is skipped */); - // Perform validation step on Matrix multiply function return CLGEMM::validate(input, weights, nullptr, output, 1.0f, 0.0f, gemm_info); } @@ -175,6 +176,7 @@ void CLGEMMConvolutionLayer::configure(const ICLTensor *input, const ICLTensor * const DataLayout data_layout = input->info()->data_layout(); const int idx_width = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); const int idx_height = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT); + const int idx_channel = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL); const int idx_kernels = get_data_layout_dimension_index(data_layout, DataLayoutDimension::BATCHES); const unsigned int kernel_width = weights->info()->dimension(idx_width); @@ -184,14 +186,14 @@ void CLGEMMConvolutionLayer::configure(const ICLTensor *input, const ICLTensor * _original_weights = weights; _is_quantized = is_data_type_quantized_asymmetric(input->info()->data_type()); _data_layout = data_layout; - _skip_im2col = (data_layout == DataLayout::NHWC && kernel_width == 1 && kernel_height == 1 && conv_info.stride().first == 1 && conv_info.stride().second == 1) && !_is_quantized; + _skip_im2col = (data_layout == DataLayout::NHWC && kernel_width == 1 && kernel_height == 1 && conv_info.stride().first == 1 && conv_info.stride().second == 1); + _skip_col2im = data_layout == DataLayout::NHWC; _append_bias = (biases != nullptr) && (!_is_quantized); // Set the GPU target for im2col and col2im _im2col_kernel.set_target(CLScheduler::get().target()); _col2im_kernel.set_target(CLScheduler::get().target()); - bool is_nhwc = _data_layout == DataLayout::NHWC; const ICLTensor *gemm_input_to_use = input; ICLTensor *gemm_output_to_use = output; ICLTensor *gemm_output_staged_to_use = output; @@ -241,18 +243,27 @@ void CLGEMMConvolutionLayer::configure(const ICLTensor *input, const ICLTensor * } // Create GEMM output tensor - if(!is_nhwc || _is_quantized) + if(!_skip_col2im || _is_quantized) { - // Calculate GEMM output shape - TensorShape shape_gemm = _im2col_output.info()->tensor_shape(); - shape_gemm.set(0, mat_weights_cols); - shape_gemm.set(1, conv_w * conv_h); - + TensorShape shape_gemm; + if(_skip_col2im) + { + shape_gemm = input->info()->tensor_shape(); + shape_gemm.set(idx_width, conv_w); + shape_gemm.set(idx_height, conv_h); + shape_gemm.set(idx_channel, mat_weights_cols); + } + else + { + shape_gemm = _im2col_output.info()->tensor_shape(); + shape_gemm.set(0, mat_weights_cols); + shape_gemm.set(1, conv_w * conv_h); + } // GEMM output should be S32 for acquiring raw integer accumulator without quantized postprocessing for quantized asymmetric input. const DataType gemm_data_type = _is_quantized ? DataType::S32 : data_type; // FIXME: input->clone() doesn't work with subtensors for grouped convolutions. TensorInfo info_gemm(shape_gemm, 1, gemm_data_type); - info_gemm.set_quantization_info(output->info()->quantization_info()); + info_gemm.set_quantization_info(output->info()->quantization_info()).set_data_layout(input->info()->data_layout()); _gemm_output.allocator()->init(info_gemm); _memory_group.manage(&_gemm_output); @@ -277,30 +288,29 @@ void CLGEMMConvolutionLayer::configure(const ICLTensor *input, const ICLTensor * int output_multiplier, output_shift; quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift); - _memory_group.manage(&_tmp_output); - gemm_output_staged_to_use = &_tmp_output; + if(!_skip_col2im) + { + _memory_group.manage(&_tmp_output); + gemm_output_staged_to_use = &_tmp_output; + } _gemmlowp_output_stage.configure(gemm_output_to_use, biases, gemm_output_staged_to_use, output_multiplier, output_shift, output_quant_info.offset); } - if(!is_nhwc || _is_quantized) + if(!_skip_col2im) { - if(input->info()->data_layout() == DataLayout::NCHW) - { - // Configure and tune Col2Im - _col2im_kernel.configure(_is_quantized ? gemm_output_staged_to_use : gemm_output_to_use, output, Size2D(conv_w, conv_h), num_groups); - CLScheduler::get().tune_kernel_static(_col2im_kernel); - } - else - { - // Configure reshape layer - _reshape_layer.configure(_is_quantized ? gemm_output_staged_to_use : gemm_output_to_use, output); - } + // Configure and tune Col2Im + _col2im_kernel.configure(_is_quantized ? gemm_output_staged_to_use : gemm_output_to_use, output, Size2D(conv_w, conv_h), num_groups); + CLScheduler::get().tune_kernel_static(_col2im_kernel); } - if(!is_nhwc || _is_quantized) + if(!_skip_col2im) { _tmp_output.allocator()->allocate(); + } + + if(!_skip_col2im || _is_quantized) + { _gemm_output.allocator()->allocate(); } @@ -346,10 +356,10 @@ Status CLGEMMConvolutionLayer::validate(const ITensorInfo *input, const ITensorI const ITensorInfo *gemm_output_staged_to_use = output; const ITensorInfo *weights_to_use = weights; - const bool is_nhwc = data_layout == DataLayout::NHWC; const bool is_quantized = is_data_type_quantized_asymmetric(data_type); - const bool skip_im2col = (data_layout == DataLayout::NHWC && kernel_width == 1 && kernel_height == 1 && conv_info.stride().first == 1 && conv_info.stride().second == 1) && !is_quantized; const bool append_bias = (biases != nullptr) && (!is_quantized); + const bool skip_im2col = (data_layout == DataLayout::NHWC && kernel_width == 1 && kernel_height == 1 && conv_info.stride().first == 1 && conv_info.stride().second == 1); + const bool skip_col2im = data_layout == DataLayout::NHWC; ARM_COMPUTE_RETURN_ERROR_ON((weights->dimension(idx_channel) * num_groups) != input->dimension(idx_channel)); ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 4); @@ -411,19 +421,30 @@ Status CLGEMMConvolutionLayer::validate(const ITensorInfo *input, const ITensorI } // Create GEMM output tensor - if(!is_nhwc || is_quantized) + if(!skip_col2im || is_quantized) { - TensorShape shape_gemm = gemm_input_to_use->tensor_shape(); - shape_gemm.set(0, mat_weights_cols); - shape_gemm.set(1, conv_w * conv_h); const DataType gemm_data_type = is_quantized ? DataType::S32 : data_type; + TensorShape shape_gemm; + if(skip_col2im) + { + shape_gemm = input->tensor_shape(); + shape_gemm.set(idx_width, conv_w); + shape_gemm.set(idx_height, conv_h); + shape_gemm.set(idx_channel, mat_weights_cols); + } + else + { + shape_gemm = gemm_input_to_use->tensor_shape(); + shape_gemm.set(0, mat_weights_cols); + shape_gemm.set(1, conv_w * conv_h); + } // GEMM output should be S32 for acquiring raw integer accumulator without quantized postprocessing for quantized asymmetric input. info_gemm = TensorInfo(shape_gemm, 1, gemm_data_type); - info_gemm.set_quantization_info(output->quantization_info()); + info_gemm.set_quantization_info(output->quantization_info()).set_data_layout(input->data_layout()); gemm_output_to_use = &info_gemm; } - ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(gemm_input_to_use, weights_to_use, gemm_output_to_use, (data_layout == DataLayout::NHWC) ? conv_h : 1, skip_im2col)); + ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(gemm_input_to_use, weights_to_use, gemm_output_to_use, skip_col2im ? conv_h : 1, skip_im2col)); if(is_quantized) { @@ -431,23 +452,22 @@ Status CLGEMMConvolutionLayer::validate(const ITensorInfo *input, const ITensorI int output_multiplier, output_shift; quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift); - tmp_info = TensorInfo(gemm_output_to_use->tensor_shape(), 1, DataType::QASYMM8); - tmp_info.set_quantization_info(output->quantization_info()); - gemm_output_staged_to_use = &tmp_info; + if(!skip_col2im) + { + tmp_info = TensorInfo(gemm_output_to_use->tensor_shape(), 1, DataType::QASYMM8); + tmp_info.set_quantization_info(output->quantization_info()); + gemm_output_staged_to_use = &tmp_info; + } // Validate output stage for quantized case - CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint::validate(gemm_output_to_use, biases, gemm_output_staged_to_use, output->quantization_info().offset); + CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint::validate(gemm_output_to_use, biases, gemm_output_staged_to_use); } // Validate Col2Im - if(!is_nhwc || is_quantized) + if(!skip_col2im) { - if(input->data_layout() == DataLayout::NCHW) - { - ARM_COMPUTE_RETURN_ON_ERROR(CLCol2ImKernel::validate(is_quantized ? gemm_output_staged_to_use : gemm_output_to_use, - output, - Size2D(conv_w, conv_h), num_groups)); - } + ARM_COMPUTE_RETURN_ON_ERROR(CLCol2ImKernel::validate(is_quantized ? gemm_output_staged_to_use : gemm_output_to_use, output, + Size2D(conv_w, conv_h), num_groups)); } //Validate Activation Layer @@ -492,16 +512,9 @@ void CLGEMMConvolutionLayer::run() } // Reshape output matrix - if(_data_layout == DataLayout::NCHW || _is_quantized) + if(!_skip_col2im) { - if(_data_layout == DataLayout::NCHW) - { - CLScheduler::get().enqueue(_col2im_kernel, false); - } - else - { - _reshape_layer.run(); - } + CLScheduler::get().enqueue(_col2im_kernel, false); } //Run Activation Layer if enabled diff --git a/src/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.cpp b/src/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.cpp index 1d6f343cb2..62e7ee7ce6 100644 --- a/src/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.cpp +++ b/src/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.cpp @@ -108,7 +108,7 @@ void CLGEMMLowpMatrixMultiplyCore::configure(const ICLTensor *a, const ICLTensor // If we pass the matrix A and matrix B reshaped to CLGEMMMatrixMultiplyKernel, we need to pass m, n, k, mult_transpose1xW_width and mult_interleave4x4_height to CLGEMMReshapeInfo // in order to know how the matrices have been reshaped bool reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d(); - const int m = a->info()->dimension(1); + const int m = reinterpret_input_as_3d ? (a->info()->dimension(1) * a->info()->dimension(2)) : a->info()->dimension(1); const int n = b->info()->dimension(0); const int k = a->info()->dimension(0); const int depth_output_gemm3d = gemm_info.depth_output_gemm3d(); @@ -206,12 +206,12 @@ Status CLGEMMLowpMatrixMultiplyCore::validate(const ITensorInfo *a, const ITenso int32_t a_offset = a->quantization_info().offset; int32_t b_offset = b->quantization_info().offset; - const int m = a->dimension(1); + bool reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d(); + const int m = reinterpret_input_as_3d ? (a->dimension(1) * a->dimension(2)) : a->dimension(1); const int n = b->dimension(0); const int k = a->dimension(0); constexpr int mult_transpose1xW_width = 1; constexpr int mult_interleave4x4_height = 1; - bool reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d(); const int depth_output_gemm3d = gemm_info.depth_output_gemm3d(); bool reshape_matrices = is_interleaved_transposed(m, n, k, gemm_info.reshape_b_only_on_first_run(), CLScheduler::get().target()); diff --git a/src/runtime/CL/functions/CLGEMMLowpOutputStage.cpp b/src/runtime/CL/functions/CLGEMMLowpOutputStage.cpp index 16d8678386..b18d23fac9 100644 --- a/src/runtime/CL/functions/CLGEMMLowpOutputStage.cpp +++ b/src/runtime/CL/functions/CLGEMMLowpOutputStage.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017 ARM Limited. + * Copyright (c) 2017-2018 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -28,8 +28,8 @@ #include "arm_compute/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleKernel.h" #include "support/ToolchainSupport.h" -using namespace arm_compute; - +namespace arm_compute +{ void CLGEMMLowpQuantizeDownInt32ToUint8Scale::configure(const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, int result_offset, int result_mult_int, int result_shift, int min, int max) { auto k = arm_compute::support::cpp14::make_unique<CLGEMMLowpQuantizeDownInt32ToUint8ScaleKernel>(); @@ -42,15 +42,18 @@ Status CLGEMMLowpQuantizeDownInt32ToUint8Scale::validate(const ITensorInfo *inpu return CLGEMMLowpQuantizeDownInt32ToUint8ScaleKernel::validate(input, bias, output, min, max); } -void CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint::configure(const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, int result_fixedpoint_multiplier, int result_shift, - int result_offset_after_shift, int min, int max) +void CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint::configure(const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, + int result_fixedpoint_multiplier, int result_shift, int result_offset_after_shift, + int min, int max, unsigned int output_3d_depth) { auto k = arm_compute::support::cpp14::make_unique<CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel>(); - k->configure(input, bias, output, result_fixedpoint_multiplier, result_shift, result_offset_after_shift, min, max); + k->configure(input, bias, output, result_fixedpoint_multiplier, result_shift, result_offset_after_shift, min, max, output_3d_depth); _kernel = std::move(k); } -Status CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min, int max) +Status CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, + int min, int max, unsigned int output_3d_depth) { - return CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::validate(input, bias, output, min, max); -}
\ No newline at end of file + return CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::validate(input, bias, output, min, max, output_3d_depth); +} +} // namespace arm_compute
\ No newline at end of file diff --git a/src/runtime/NEON/functions/NEGEMMLowpOutputStage.cpp b/src/runtime/NEON/functions/NEGEMMLowpOutputStage.cpp index cb7004992b..d270a77fc2 100644 --- a/src/runtime/NEON/functions/NEGEMMLowpOutputStage.cpp +++ b/src/runtime/NEON/functions/NEGEMMLowpOutputStage.cpp @@ -43,14 +43,14 @@ Status NEGEMMLowpQuantizeDownInt32ToUint8Scale::validate(const ITensorInfo *inpu } void NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint::configure(const ITensor *input, const ITensor *bias, ITensor *output, int result_fixedpoint_multiplier, int result_shift, - int result_offset_after_shift, int min, int max, unsigned int gemm_3d_depth) + int result_offset_after_shift, int min, int max, unsigned int output_3d_depth) { auto k = arm_compute::support::cpp14::make_unique<NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel>(); - k->configure(input, bias, output, result_fixedpoint_multiplier, result_shift, result_offset_after_shift, min, max, gemm_3d_depth); + k->configure(input, bias, output, result_fixedpoint_multiplier, result_shift, result_offset_after_shift, min, max, output_3d_depth); _kernel = std::move(k); } -Status NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min, int max, unsigned int gemm_3d_depth) +Status NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min, int max, unsigned int output_3d_depth) { - return NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::validate(input, bias, output, min, max, gemm_3d_depth); + return NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::validate(input, bias, output, min, max, output_3d_depth); }
\ No newline at end of file |