diff options
Diffstat (limited to 'src/core/CL/kernels/CLDepthwiseConvolutionLayerNativeKernel.cpp')
| -rw-r--r-- | src/core/CL/kernels/CLDepthwiseConvolutionLayerNativeKernel.cpp | 418 |
1 files changed, 290 insertions, 128 deletions
diff --git a/src/core/CL/kernels/CLDepthwiseConvolutionLayerNativeKernel.cpp b/src/core/CL/kernels/CLDepthwiseConvolutionLayerNativeKernel.cpp index fcfa7f878d..b95abe795f 100644 --- a/src/core/CL/kernels/CLDepthwiseConvolutionLayerNativeKernel.cpp +++ b/src/core/CL/kernels/CLDepthwiseConvolutionLayerNativeKernel.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2019-2021 Arm Limited. + * Copyright (c) 2019-2023 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -28,47 +28,94 @@ #include "arm_compute/core/CL/ICLTensor.h" #include "arm_compute/core/Helpers.h" #include "arm_compute/core/TensorInfo.h" -#include "arm_compute/core/Utils.h" +#include "arm_compute/core/utils/ActivationFunctionUtils.h" +#include "arm_compute/core/utils/helpers/AdjustVecSize.h" #include "arm_compute/core/utils/misc/ShapeCalculator.h" #include "arm_compute/core/utils/quantization/AsymmHelpers.h" +#include "arm_compute/core/utils/StringUtils.h" + +#include "src/core/CL/CLUtils.h" #include "src/core/CL/CLValidate.h" #include "src/core/CL/ICLKernel.h" #include "src/core/helpers/AutoConfiguration.h" #include "src/core/helpers/WindowHelpers.h" +#include "src/gpu/cl/kernels/gemm/ClGemmHelpers.h" #include "support/StringSupport.h" namespace arm_compute { namespace { -Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const DWCWeightsKernelInfo &dwc_weights_info, - const DWCKernelInfo &dwc_info, const PadStrideInfo &conv_info, unsigned int depth_multiplier, const Size2D &dilation, - const ITensorInfo *output_multipliers, const ITensorInfo *output_shifts) +Status validate_arguments(const ITensorInfo *input, + const ITensorInfo *weights, + const ITensorInfo *biases, + const ITensorInfo *output, + const DWCComputeKernelInfo &dwc_info, + const ConvolutionInfo &conv_info, + const ITensorInfo *output_multipliers, + const ITensorInfo *output_shifts) { ARM_COMPUTE_UNUSED(dwc_info); - ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output); + bool in_place = false; + if (output == nullptr || output == input) + { + in_place = true; + output = input; + } + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights); ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input); ARM_COMPUTE_RETURN_ERROR_ON_DATA_LAYOUT_NOT_IN(input, DataLayout::NHWC); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::F16, DataType::F32); - ARM_COMPUTE_RETURN_ERROR_ON(depth_multiplier > 1 && dwc_weights_info.n0 != 1); - ARM_COMPUTE_RETURN_ERROR_ON(conv_info.stride().first < 1); - ARM_COMPUTE_RETURN_ERROR_ON(conv_info.stride().second < 1); - ARM_COMPUTE_RETURN_ERROR_ON((dilation.x() < 1) || (dilation.y() < 1)); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, + DataType::F16, DataType::F32); + ARM_COMPUTE_RETURN_ERROR_ON(conv_info.pad_stride_info.stride().first > 1 && dwc_info.m0 != 1); + ARM_COMPUTE_RETURN_ERROR_ON(conv_info.dilation.x() > 1 && dwc_info.m0 != 1); + ARM_COMPUTE_RETURN_ERROR_ON((dwc_info.export_input_to_cl_image == true)); + ARM_COMPUTE_RETURN_ERROR_ON_MSG((dwc_info.export_weights_to_cl_image == true) && + (export_to_cl_image(weights) == false), + "Weights cannot be exported to cl_image!"); + ARM_COMPUTE_RETURN_ERROR_ON((dwc_info.export_weights_to_cl_image == true) && ((dwc_info.n0 % 4) != 0)); + ARM_COMPUTE_RETURN_ERROR_ON(conv_info.pad_stride_info.stride().first < 1); + ARM_COMPUTE_RETURN_ERROR_ON(conv_info.pad_stride_info.stride().second < 1); + ARM_COMPUTE_RETURN_ERROR_ON((conv_info.dilation.x() < 1) || (conv_info.dilation.y() < 1)); const size_t idx_c = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::CHANNEL); ARM_COMPUTE_UNUSED(idx_c); - ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(idx_c) != (input->dimension(idx_c) * depth_multiplier)); + ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(idx_c) != (input->dimension(idx_c) * conv_info.depth_multiplier)); + + // In place restrictions + if (in_place) + { + const int weights_width_idx = + get_data_layout_dimension_index(weights->data_layout(), DataLayoutDimension::WIDTH); + const int weights_height_idx = + get_data_layout_dimension_index(weights->data_layout(), DataLayoutDimension::HEIGHT); + ARM_COMPUTE_RETURN_ERROR_ON(weights->tensor_shape()[weights_width_idx] != 1U || + weights->tensor_shape()[weights_height_idx] != 1U); + ARM_COMPUTE_RETURN_ERROR_ON(conv_info.depth_multiplier != 1U); + ARM_COMPUTE_RETURN_ERROR_ON(conv_info.pad_stride_info.stride() != std::make_pair(1U, 1U)); + ARM_COMPUTE_RETURN_ERROR_ON(conv_info.dilation != Size2D(1U, 1U)); + ARM_COMPUTE_RETURN_ERROR_ON( + conv_info.pad_stride_info + .has_padding()); // Note that in princple padding can be supported with in_place but we choose not to support it + } - const ConvolutionInfo info{ conv_info, depth_multiplier, ActivationLayerInfo(), dilation }; - const TensorShape output_shape = arm_compute::misc::shape_calculator::compute_depthwise_convolution_shape(*input, *weights, info); + const ConvolutionInfo info{conv_info.pad_stride_info, conv_info.depth_multiplier, ActivationLayerInfo(), + conv_info.dilation}; + const TensorShape output_shape = + arm_compute::misc::shape_calculator::compute_depthwise_convolution_shape(*input, *weights, conv_info); + + if (conv_info.depth_multiplier > 1 && dwc_info.n0 > 1) + { + ARM_COMPUTE_RETURN_ERROR_ON((conv_info.depth_multiplier % dwc_info.n0) != 0); + } const bool is_quantized = is_data_type_quantized(input->data_type()); - if(biases != nullptr) + if (biases != nullptr) { ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != output_shape[idx_c]); ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1); - if(is_quantized) + if (is_quantized) { ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::S32); } @@ -78,7 +125,7 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, } } - if(is_quantized) + if (is_quantized) { ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(output_multipliers, output_shifts); ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output_multipliers, 1, DataType::S32); @@ -86,7 +133,7 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, ARM_COMPUTE_RETURN_ERROR_ON(output_multipliers->num_dimensions() > 1); ARM_COMPUTE_RETURN_ERROR_ON(output_shifts->num_dimensions() > 1); - if(is_data_type_quantized_per_channel(weights->data_type())) + if (is_data_type_quantized_per_channel(weights->data_type())) { ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QSYMM8_PER_CHANNEL); ARM_COMPUTE_RETURN_ERROR_ON(output_shape[idx_c] != output_multipliers->dimension(0)); @@ -104,22 +151,24 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights); } - if(output->total_size() != 0) + if (output->total_size() != 0) { ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), output_shape); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); } - if(is_data_type_quantized(input->data_type())) + if (is_data_type_quantized(input->data_type())) { const UniformQuantizationInfo iq_info = input->quantization_info().uniform(); const UniformQuantizationInfo wq_info = weights->quantization_info().uniform(); - const UniformQuantizationInfo oq_info = (output->total_size() != 0) ? output->quantization_info().uniform() : iq_info; + const UniformQuantizationInfo oq_info = + (output->total_size() != 0) ? output->quantization_info().uniform() : iq_info; float multiplier = iq_info.scale * wq_info.scale / oq_info.scale; int output_multiplier = 0; int output_shift = 0; - ARM_COMPUTE_RETURN_ON_ERROR(quantization::calculate_quantized_multiplier(multiplier, &output_multiplier, &output_shift)); + ARM_COMPUTE_RETURN_ON_ERROR( + quantization::calculate_quantized_multiplier(multiplier, &output_multiplier, &output_shift)); } return Status{}; @@ -134,111 +183,194 @@ CLDepthwiseConvolutionLayerNativeKernel::CLDepthwiseConvolutionLayerNativeKernel _depth_multiplier(1), _output_multipliers(nullptr), _output_shifts(nullptr), + _export_input_to_cl_image(false), + _export_weights_to_cl_image(false), _is_quantized(false) { + _type = CLKernelType::DEPTHWISE; } -void CLDepthwiseConvolutionLayerNativeKernel::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const DWCWeightsKernelInfo &dwc_weights_info, - const DWCKernelInfo &dwc_info, const PadStrideInfo &conv_info, unsigned int depth_multiplier, const Size2D &dilation, - const ICLTensor *output_multipliers, const ICLTensor *output_shifts) +void CLDepthwiseConvolutionLayerNativeKernel::configure(ICLTensor *input, + const ICLTensor *weights, + const ICLTensor *biases, + ICLTensor *output, + const DWCComputeKernelInfo &dwc_info, + const ConvolutionInfo &conv_info, + const ICLTensor *output_multipliers, + const ICLTensor *output_shifts) { - configure(CLKernelLibrary::get().get_compile_context(), input, weights, biases, output, dwc_weights_info, dwc_info, conv_info, depth_multiplier, dilation, output_multipliers, output_shifts); + configure(CLKernelLibrary::get().get_compile_context(), input, weights, biases, output, dwc_info, conv_info, + output_multipliers, output_shifts); } -void CLDepthwiseConvolutionLayerNativeKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, - const DWCWeightsKernelInfo &dwc_weights_info, - const DWCKernelInfo &dwc_info, const PadStrideInfo &conv_info, unsigned int depth_multiplier, const Size2D &dilation, - const ICLTensor *output_multipliers, const ICLTensor *output_shifts) +void CLDepthwiseConvolutionLayerNativeKernel::configure(const CLCompileContext &compile_context, + ICLTensor *input, + const ICLTensor *weights, + const ICLTensor *biases, + ICLTensor *output, + const DWCComputeKernelInfo &dwc_info, + const ConvolutionInfo &conv_info, + const ICLTensor *output_multipliers, + const ICLTensor *output_shifts) { - ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output); - ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), weights->info(), (biases != nullptr) ? biases->info() : nullptr, output->info(), - dwc_weights_info, dwc_info, conv_info, depth_multiplier, dilation, - (output_multipliers != nullptr) ? output_multipliers->info() : nullptr, (output_shifts != nullptr) ? output_shifts->info() : nullptr)); + ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights); + if (output == nullptr) + { + // In-place + output = input; + } + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments( + input->info(), weights->info(), (biases != nullptr) ? biases->info() : nullptr, output->info(), dwc_info, + conv_info, (output_multipliers != nullptr) ? output_multipliers->info() : nullptr, + (output_shifts != nullptr) ? output_shifts->info() : nullptr)); + + auto padding_info = get_padding_info({input, output}); + + const TensorShape output_shape = arm_compute::misc::shape_calculator::compute_depthwise_convolution_shape( + *(input->info()), *(weights->info()), conv_info); + auto_init_if_empty(*(output->info()), input->info() + ->clone() + ->set_tensor_shape(output_shape) + .set_quantization_info(output->info()->quantization_info())); + + _input = input; + _output = output; + _weights = weights; + _biases = biases; + _depth_multiplier = conv_info.depth_multiplier; + _output_multipliers = output_multipliers; + _output_shifts = output_shifts; + _export_input_to_cl_image = dwc_info.export_input_to_cl_image; + _export_weights_to_cl_image = dwc_info.export_weights_to_cl_image; + _is_quantized = is_data_type_quantized(input->info()->data_type()); + + const unsigned int n0 = adjust_vec_size(dwc_info.n0, output->info()->dimension(0)); + const unsigned int m0 = std::min(dwc_info.m0, (unsigned int)output->info()->dimension(1)); + std::string kernel_name = ""; - auto padding_info = get_padding_info({ input, output }); + CLBuildOptions build_opts; - const ConvolutionInfo info{ conv_info, depth_multiplier, ActivationLayerInfo(), dilation }; - const TensorShape output_shape = arm_compute::misc::shape_calculator::compute_depthwise_convolution_shape(*(input->info()), *(weights->info()), info); - auto_init_if_empty(*(output->info()), input->info()->clone()->set_tensor_shape(output_shape).set_quantization_info(output->info()->quantization_info())); + // Update the padding for the input/weights tensor if we can export to cl_image + if (_export_input_to_cl_image) + { + arm_compute::opencl::kernels::gemm::update_padding_for_cl_image(input->info()); + } - _input = input; - _output = output; - _weights = weights; - _biases = biases; - _depth_multiplier = depth_multiplier; - _output_multipliers = output_multipliers; - _output_shifts = output_shifts; - _is_quantized = is_data_type_quantized(input->info()->data_type()); + if (_export_weights_to_cl_image) + { + arm_compute::opencl::kernels::gemm::update_padding_for_cl_image(weights->info()); + } - const unsigned int n0 = adjust_vec_size(dwc_weights_info.n0, input->info()->dimension(0)); + // Conditions of -cl-fast-relaxed-math causing accuracy issues can be traced from COMPMID-5324 + const GPUTarget gpu_target = get_target(); + const auto act_function = conv_info.act_info.activation(); + const auto dst_data_type = _output->info()->data_type(); - CLBuildOptions build_opts; - build_opts.add_option_if(_biases != nullptr, "-DHAS_BIAS"); - build_opts.add_option_if(_input->info()->tensor_shape().total_size_upper(3) > 1, "-DDST_DEPTH=" + support::cpp11::to_string(static_cast<int>(_output->info()->dimension(2)))); - build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(_input->info()->data_type())); - build_opts.add_option("-DACTIVATION_TYPE=" + lower_string(string_from_activation_func(dwc_info.activation_info.activation()))); - build_opts.add_option("-DDEPTH_MULTIPLIER=" + support::cpp11::to_string(depth_multiplier)); + if ((gpu_target != GPUTarget::G71 && (gpu_target & GPUTarget::GPU_ARCH_MASK) == GPUTarget::BIFROST) && + (act_function == ActivationLayerInfo::ActivationFunction::BOUNDED_RELU || + act_function == ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU) && + (dst_data_type == DataType::F32 || dst_data_type == DataType::F16)) + { + // -cl-fast-relaxed-math also sets -cl-finite-math-only and -cl-unsafe-math-optimizations + // to disable -cl-finite-math-only, we only include -cl-unsafe-math-optimizations + build_opts.add_option("-cl-unsafe-math-optimizations"); + } + else + { + build_opts.add_option("-cl-fast-relaxed-math"); + } + + build_opts.add_option("-DACTIVATION_TYPE=" + lower_string(string_from_activation_func(act_function))); + build_opts.add_option("-DDEPTH_MULTIPLIER=" + support::cpp11::to_string(conv_info.depth_multiplier)); + build_opts.add_option_if_else(_export_input_to_cl_image, "-DSRC_TENSOR_TYPE=IMAGE", "-DSRC_TENSOR_TYPE=BUFFER"); + // Note: SRC_DATA_TYPE must have the same data type of WEI_DATA_TYPE. In quantized, we could + // have a case where the data types for the activation and weights are different. However, since the implementation + // only works when both have same data type, we have to change the offset to take into account this aspect + build_opts.add_option("-DSRC_DATA_TYPE=" + get_cl_type_from_data_type(_input->info()->data_type())); + build_opts.add_option("-DDST_TENSOR_TYPE=BUFFER"); + build_opts.add_option("-DDST_DATA_TYPE=" + get_cl_type_from_data_type(dst_data_type)); + build_opts.add_option_if_else(_export_weights_to_cl_image, "-DWEI_TENSOR_TYPE=IMAGE", "-DWEI_TENSOR_TYPE=BUFFER"); + build_opts.add_option("-DSRC_WIDTH=" + support::cpp11::to_string(_input->info()->dimension(1))); + build_opts.add_option("-DSRC_HEIGHT=" + support::cpp11::to_string(_input->info()->dimension(2))); + build_opts.add_option("-DDST_WIDTH=" + support::cpp11::to_string(_output->info()->dimension(1))); + build_opts.add_option("-DDST_HEIGHT=" + support::cpp11::to_string(_output->info()->dimension(2))); + build_opts.add_option("-DWEI_WIDTH=" + support::cpp11::to_string(_weights->info()->dimension(1))); + build_opts.add_option("-DWEI_HEIGHT=" + support::cpp11::to_string(_weights->info()->dimension(2))); + build_opts.add_option("-DWEI_DATA_TYPE=" + get_cl_type_from_data_type(_weights->info()->data_type())); + build_opts.add_option("-DPAD_TOP=" + support::cpp11::to_string(conv_info.pad_stride_info.pad_top())); + build_opts.add_option("-DPAD_LEFT=" + support::cpp11::to_string(conv_info.pad_stride_info.pad_left())); + build_opts.add_option("-DSTRIDE_X=" + support::cpp11::to_string(conv_info.pad_stride_info.stride().first)); + build_opts.add_option("-DSTRIDE_Y=" + support::cpp11::to_string(conv_info.pad_stride_info.stride().second)); + build_opts.add_option("-DDILATION_X=" + support::cpp11::to_string(conv_info.dilation.x())); + build_opts.add_option("-DDILATION_Y=" + support::cpp11::to_string(conv_info.dilation.y())); build_opts.add_option("-DN0=" + support::cpp11::to_string(n0)); - build_opts.add_option("-DSRC_DIM1=" + support::cpp11::to_string(_input->info()->dimension(1))); - build_opts.add_option("-DSRC_DIM2=" + support::cpp11::to_string(_input->info()->dimension(2))); - build_opts.add_option("-DKERNEL_WIDTH=" + support::cpp11::to_string(weights->info()->dimension(1))); - build_opts.add_option("-DKERNEL_HEIGHT=" + support::cpp11::to_string(weights->info()->dimension(2))); - build_opts.add_option("-DCONV_PAD_TOP=" + support::cpp11::to_string(conv_info.pad_top())); - build_opts.add_option("-DCONV_PAD_LEFT=" + support::cpp11::to_string(conv_info.pad_left())); - build_opts.add_option("-DCONV_STRIDE_X=" + support::cpp11::to_string(conv_info.stride().first)); - build_opts.add_option("-DCONV_STRIDE_Y=" + support::cpp11::to_string(conv_info.stride().second)); - build_opts.add_option("-DDILATION_X=" + support::cpp11::to_string(dilation.x())); - build_opts.add_option("-DDILATION_Y=" + support::cpp11::to_string(dilation.y())); - build_opts.add_option("-DVEC_SIZE_LEFTOVER=" + support::cpp11::to_string(_input->info()->dimension(0) % n0)); - - std::string kernel_name = (_is_quantized) ? "dwc_MxN_native_quantized8_nhwc" : "dwc_MxN_native_fp_nhwc"; - - if(_is_quantized) + build_opts.add_option("-DM0=" + support::cpp11::to_string(m0)); + build_opts.add_option("-DM0_A=" + support::cpp11::to_string(_weights->info()->dimension(1) + m0 - 1)); + build_opts.add_option_if_else(conv_info.depth_multiplier > 1, "-DN0_A=1", + "-DN0_A=" + support::cpp11::to_string(n0)); + build_opts.add_option("-DPARTIAL_N0=" + support::cpp11::to_string(_output->info()->dimension(0) % n0)); + build_opts.add_option_if(_input->info()->num_dimensions() > 3, "-DBATCHED_EXECUTION"); + + // Force unroll with pragma when any of the following values exceed the maximum number of manual unroll + set_unroll_with_pragma(build_opts, {static_cast<int>(_weights->info()->dimension(1) + m0 - 1), + static_cast<int>(_weights->info()->dimension(1)), + static_cast<int>(_weights->info()->dimension(2))}); + + if (biases != nullptr) { - const UniformQuantizationInfo iq_info = _input->info()->quantization_info().uniform(); - const UniformQuantizationInfo wq_info = _weights->info()->quantization_info().uniform(); - const UniformQuantizationInfo oq_info = _output->info()->quantization_info().uniform(); + build_opts.add_option(std::string("-DHAS_BIAS")); + build_opts.add_option( + std::string("-DBIA_DATA_TYPE=" + get_cl_type_from_data_type(biases->info()->data_type()))); + } - build_opts.add_option("-DINPUT_OFFSET=" + support::cpp11::to_string(-iq_info.offset)); - build_opts.add_option("-DWEIGHTS_OFFSET=" + support::cpp11::to_string(-wq_info.offset)); - build_opts.add_option("-DOUTPUT_OFFSET=" + support::cpp11::to_string(oq_info.offset)); - build_opts.add_option_if(is_data_type_quantized_per_channel(weights->info()->data_type()), "-DPER_CHANNEL_QUANTIZATION"); + if (_is_quantized) + { + kernel_name = "dwc_native_quantized_nhwc"; + const UniformQuantizationInfo iqinfo = input->info()->quantization_info().uniform(); + const UniformQuantizationInfo wqinfo = weights->info()->quantization_info().uniform(); + const UniformQuantizationInfo oqinfo = output->info()->quantization_info().uniform(); - // Compute non-per-channel multiplier and shift anyway to make OpenCL kernel simpler - float multiplier = iq_info.scale * wq_info.scale / oq_info.scale; + PixelValue zero_value = PixelValue(0, input->info()->data_type(), input->info()->quantization_info()); + int zero_value_s32; + zero_value.get(zero_value_s32); + + float multiplier = iqinfo.scale * wqinfo.scale / oqinfo.scale; int output_multiplier = 0; int output_shift = 0; quantization::calculate_quantized_multiplier(multiplier, &output_multiplier, &output_shift); - build_opts.add_option("-DOUTPUT_MULTIPLIER=" + support::cpp11::to_string(output_multiplier)); - build_opts.add_option("-DOUTPUT_SHIFT=" + support::cpp11::to_string(output_shift)); - - if(dwc_info.activation_info.enabled()) - { - int a_val{}; - int b_val{}; - std::tie(b_val, a_val) = get_quantized_activation_min_max(dwc_info.activation_info, input->info()->data_type(), oq_info); - - const int o1 = oq_info.offset; - - build_opts.add_option("-DA_VAL=" + support::cpp11::to_string(a_val)); - build_opts.add_option("-DB_VAL=" + support::cpp11::to_string(b_val)); - build_opts.add_option("-DCONST_0=" + support::cpp11::to_string(o1)); - - const float s1 = iq_info.scale; - build_opts.add_option("-DS1_VAL=" + float_to_string_with_full_precision(s1)); - build_opts.add_option("-DO1_VAL=" + support::cpp11::to_string(o1)); - } - - build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type())); - build_opts.add_option("-DWEIGHTS_TYPE=" + get_cl_type_from_data_type(weights->info()->data_type())); + build_opts.add_option("-DDST_MULTIPLIER=" + support::cpp11::to_string(output_multiplier)); + build_opts.add_option("-DDST_SHIFT=" + support::cpp11::to_string(output_shift)); + build_opts.add_option("-DSRC_OFFSET=" + support::cpp11::to_string(-iqinfo.offset)); + build_opts.add_option("-DWEI_OFFSET=" + support::cpp11::to_string(-wqinfo.offset)); + build_opts.add_option("-DDST_OFFSET=" + support::cpp11::to_string(oqinfo.offset)); + build_opts.add_option("-DZERO_VALUE=" + support::cpp11::to_string(zero_value_s32)); + build_opts.add_option("-DACC_DATA_TYPE=" + get_cl_type_from_data_type(DataType::S32)); + build_opts.add_option("-DDST_MULTIPLIERS_DATA_TYPE=" + + get_cl_type_from_data_type(_output_multipliers->info()->data_type())); + build_opts.add_option("-DDST_SHIFTS_DATA_TYPE=" + + get_cl_type_from_data_type(_output_shifts->info()->data_type())); + build_opts.add_option_if_else(weights->info()->data_type() == DataType::QSYMM8_PER_CHANNEL, + "-DQUANTIZATION_TYPE=PER_CHANNEL", "-DQUANTIZATION_TYPE=PER_TENSOR"); + // Note: We expect the input and output tensors to always adopt a per-tensor quantization approach + int a_val{}; + int b_val{}; + std::tie(b_val, a_val) = + get_quantized_activation_min_max(conv_info.act_info, input->info()->data_type(), oqinfo); + + build_opts.add_option_if(conv_info.act_info.enabled(), "-DA_VAL=" + support::cpp11::to_string(a_val)); + build_opts.add_option_if(conv_info.act_info.enabled(), "-DB_VAL=" + support::cpp11::to_string(b_val)); } else { - build_opts.add_option_if(dwc_info.activation_info.enabled(), "-DA_VAL=" + float_to_string_with_full_precision(dwc_info.activation_info.a())); - build_opts.add_option_if(dwc_info.activation_info.enabled(), "-DB_VAL=" + float_to_string_with_full_precision(dwc_info.activation_info.b())); + kernel_name = "dwc_native_fp_nhwc"; + build_opts.add_option("-DACC_DATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type())); + build_opts.add_option_if(conv_info.act_info.enabled(), + "-DA_VAL=" + float_to_string_with_full_precision(conv_info.act_info.a())); + build_opts.add_option_if(conv_info.act_info.enabled(), + "-DB_VAL=" + float_to_string_with_full_precision(conv_info.act_info.b())); } - Window win = calculate_max_window(*(output->info()), Steps(n0)); + Window win = calculate_max_window(*(output->info()), Steps(n0, m0)); ICLKernel::configure_internal(win); _kernel = create_kernel(compile_context, kernel_name, build_opts.options()); @@ -263,11 +395,17 @@ void CLDepthwiseConvolutionLayerNativeKernel::configure(const CLCompileContext & _config_id += string_from_data_type(input->info()->data_type()); } -Status CLDepthwiseConvolutionLayerNativeKernel::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, - const DWCWeightsKernelInfo &dwc_weights_info, const DWCKernelInfo &dwc_info, const PadStrideInfo &conv_info, - unsigned int depth_multiplier, const Size2D &dilation, const ITensorInfo *output_multipliers, const ITensorInfo *output_shifts) +Status CLDepthwiseConvolutionLayerNativeKernel::validate(const ITensorInfo *input, + const ITensorInfo *weights, + const ITensorInfo *biases, + const ITensorInfo *output, + const DWCComputeKernelInfo &dwc_info, + const ConvolutionInfo &conv_info, + const ITensorInfo *output_multipliers, + const ITensorInfo *output_shifts) { - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, weights, biases, output, dwc_weights_info, dwc_info, conv_info, depth_multiplier, dilation, output_multipliers, output_shifts)); + ARM_COMPUTE_RETURN_ON_ERROR( + validate_arguments(input, weights, biases, output, dwc_info, conv_info, output_multipliers, output_shifts)); return Status{}; } @@ -278,37 +416,61 @@ void CLDepthwiseConvolutionLayerNativeKernel::run(const Window &window, cl::Comm // Collapse window Window window_collapsed = window.collapse(ICLKernel::window(), Window::DimZ); - Window slice_in = window.first_slice_window_4D(); - Window slice_out = window_collapsed.first_slice_window_4D(); - if(_depth_multiplier != 1) - { - ARM_COMPUTE_ERROR_ON(slice_out.x().step() != 1); - slice_out.set(Window::DimX, Window::Dimension(0, _input->info()->tensor_shape()[0], 1)); - } + Window slice = window_collapsed.first_slice_window_4D(); - unsigned int idx = 2 * num_arguments_per_4D_tensor() + num_arguments_per_3D_tensor(); + cl::Image2D input_cl_image; + cl::Image2D weights_cl_image; - // Set output multipliers in case of quantized data type - if(_is_quantized) + if (_export_input_to_cl_image || _export_weights_to_cl_image) { - add_1D_tensor_argument(idx, _output_multipliers, slice_in); - add_1D_tensor_argument(idx, _output_shifts, slice_in); + // Export cl_buffer to cl_image + if (_export_input_to_cl_image) + { + const size_t image_w = _input->info()->dimension(0) / 4; + const size_t image_h = + _input->info()->dimension(1) * _input->info()->dimension(2) * _input->info()->dimension(3); + const TensorShape shape2d(image_w, image_h); + const size_t image_row_pitch = _input->info()->strides_in_bytes()[1]; + input_cl_image = + create_image2d_from_buffer(CLKernelLibrary::get().context(), _input->cl_buffer(), shape2d, + _input->info()->data_type(), image_row_pitch, CLImage2DType::ReadOnly); + } + + if (_export_weights_to_cl_image) + { + const size_t image_w = _weights->info()->dimension(0) / 4; + const size_t image_h = + _weights->info()->dimension(1) * _weights->info()->dimension(2) * _weights->info()->dimension(3); + const TensorShape shape2d(image_w, image_h); + const size_t image_row_pitch = _weights->info()->strides_in_bytes()[1]; + weights_cl_image = + create_image2d_from_buffer(CLKernelLibrary::get().context(), _weights->cl_buffer(), shape2d, + _weights->info()->data_type(), image_row_pitch, CLImage2DType::ReadOnly); + } } - if(_biases != nullptr) + unsigned int idx = 0; + if (_export_input_to_cl_image) { - add_1D_tensor_argument(idx, _biases, slice_in); + _kernel.setArg(idx++, input_cl_image); } - - do + add_4d_tensor_nhwc_argument(idx, _input); + add_4d_tensor_nhwc_argument(idx, _output); + if (_export_weights_to_cl_image) + { + _kernel.setArg(idx++, weights_cl_image); + } + add_4d_tensor_nhwc_argument(idx, _weights); + if (_is_quantized) + { + add_1D_tensor_argument(idx, _output_multipliers, slice); + add_1D_tensor_argument(idx, _output_shifts, slice); + } + if (_biases != nullptr) { - idx = 0; - add_4D_tensor_argument(idx, _input, slice_in); - add_4D_tensor_argument(idx, _output, slice_out); - add_3D_tensor_argument(idx, _weights, slice_out); - enqueue(queue, *this, slice_out, lws_hint()); + add_1D_tensor_argument(idx, _biases, slice); } - while(window_collapsed.slide_window_slice_4D(slice_out) && window.slide_window_slice_4D(slice_in)); + enqueue(queue, *this, slice, lws_hint()); } } // namespace arm_compute |