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Diffstat (limited to 'src/core/experimental/dynamic_fusion/ClKernelBuildingImpl/components/ClDirectConvolutionKernelComponent.cpp')
| -rw-r--r-- | src/core/experimental/dynamic_fusion/ClKernelBuildingImpl/components/ClDirectConvolutionKernelComponent.cpp | 398 |
1 files changed, 398 insertions, 0 deletions
diff --git a/src/core/experimental/dynamic_fusion/ClKernelBuildingImpl/components/ClDirectConvolutionKernelComponent.cpp b/src/core/experimental/dynamic_fusion/ClKernelBuildingImpl/components/ClDirectConvolutionKernelComponent.cpp new file mode 100644 index 0000000000..f951ce3d46 --- /dev/null +++ b/src/core/experimental/dynamic_fusion/ClKernelBuildingImpl/components/ClDirectConvolutionKernelComponent.cpp @@ -0,0 +1,398 @@ +/* + * Copyright (c) 2022 Arm Limited. + * + * SPDX-License-Identifier: MIT + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to + * deal in the Software without restriction, including without limitation the + * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or + * sell copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + */ +#if defined(ENABLE_EXPERIMENTAL_DYNAMIC_FUSION) + +#include "src/core/experimental/dynamic_fusion/ClKernelBuildingImpl/components/ClDirectConvolutionKernelComponent.h" + +#include "arm_compute/core/utils/misc/ShapeCalculator.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" + +namespace arm_compute +{ +namespace experimental +{ +namespace dynamic_fusion +{ +ComponentType ClDirectConvolutionKernelComponent::get_component_type() const +{ + return ComponentType::Complex; +} + +std::set<std::string> ClDirectConvolutionKernelComponent::get_headers_list() const +{ + return std::set<std::string> { "helpers.h", "tile_helpers.h", "repeat.h" }; +} + +Window ClDirectConvolutionKernelComponent::get_window() const +{ + const auto src_info = _blueprint->impl().get_kernel_argument_info(_src.arg_id); + const auto weight_info = _blueprint->impl().get_kernel_argument_info(_weight.arg_id); + auto dst_info = _blueprint->impl().get_kernel_argument_info(_blueprint->impl().get_dst_id()); + + // Get dst shape + TensorShape output_shape = misc::shape_calculator::compute_deep_convolution_shape(*src_info, *weight_info, _desc.pad_stride_info); + + // Output auto initialization if not yet initialized + auto_init_if_empty(*dst_info, output_shape, + 1, + src_info->data_type(), + src_info->quantization_info()); + + const unsigned int vec_size = std::min(static_cast<unsigned int>(dst_info->tensor_shape()[0]), 4u); + const unsigned int num_rows = (dst_info->tensor_shape()[0] > 16) ? ((src_info->data_type() == DataType::F32) ? 2U : 4U) : 1U; + + // Create and configure kernel window + Window win = calculate_max_window(output_shape, Steps(vec_size, num_rows)); + + const size_t dim_y_collapsed = ceil_to_multiple(output_shape[1] * output_shape[2], num_rows); + win.set(Window::DimY, Window::Dimension(0, dim_y_collapsed, num_rows)); + win.set(Window::DimZ, Window::Dimension(0, output_shape.total_size_upper(3), 1)); + + return win; +} + +std::string ClDirectConvolutionKernelComponent::get_additional_macros() const +{ + return R"_()_"; // no macros +} + +std::string ClDirectConvolutionKernelComponent::get_component_code() const +{ + const auto src_info = _blueprint->impl().get_kernel_argument_info(_src.arg_id); + const auto bias_info = _blueprint->impl().get_kernel_argument_info(_bias.arg_id); + + ARM_COMPUTE_ERROR_ON_MSG(src_info->data_layout() != DataLayout::NHWC, "Only NHWC data layout is supported by this component."); + + const auto channel_idx = get_data_layout_dimension_index(src_info->data_layout(), DataLayoutDimension::CHANNEL); + const auto k0 = adjust_vec_size(is_data_type_quantized(src_info->data_type()) ? 16u : 8u, src_info->dimension(channel_idx)); + const bool leftover_loop = (src_info->dimension(channel_idx) % k0) != 0; + + std::string code = R"_( + //------------------ START KERNEL {{meta_kernel_id}} --------------------- + // IN_0(src) {{src}} + // IN_1(wei) {{weight}} + // IN_1(bia) {{bias}} + // OUT(dst, accum) {{dst}} + + const int cout = GET_SPATIAL_IDX(0, N0, PARTIAL_N0); // OFM + const int mout = GET_SPATIAL_IDX(1, M0, 0); // WIDTH x HEIGHT + const int bout = GET_SPATIAL_IDX(2, 1, 0); // BATCH SIZE IDX + + // Initialize the accumulators + TILE({{ACC_DATA_TYPE}}, M0, N0, {{dst}}); + { + // All the tensor dimensions are passed at compile time. + // In case of dynamic tensor support, the following dimensions should be passed as function argument. + #define _I{{WEI_WIDTH}} {{WEI_WIDTH}} + #define _I{{WEI_HEIGHT}} {{WEI_HEIGHT}} + #define _ISRC_WIDTH {{src}}_w + #define _ISRC_HEIGHT {{src}}_h + #define _ISRC_CHANNELS {{src}}_c + #define _IDST_WIDTH {{dst_w}} + #define _IDST_HEIGHT {{dst_h}} + #define _IDST_CHANNELS {{dst_c}} + #define _IY_MULTIPLIER (_I{{WEI_WIDTH}} * _I{{WEI_HEIGHT}}) + + // .v = access the whole vector (OpenCL vector) + // .s[x] = access the vector element at position x (scalar access) + TILE(int, M0, 1, xi); + TILE(int, M0, 1, yi); + + // Convert the linear index to coordinate + LOOP_UNROLLING(int, i, 0, 1, M0, + { + xi[i].v = ((mout + i) % _IDST_WIDTH) * {{STRIDE_X}}; + yi[i].v = ((mout + i) / _IDST_WIDTH) * {{STRIDE_Y}}; + xi[i].v -= {{PAD_LEFT}}; + yi[i].v -= {{PAD_TOP}}; + }) + + LOOP_UNROLLING(int, i, 0, 1, M0, + { + {{dst}}[i].v = 0; + }) + + uint cond = (get_global_id(0) == 0) && (get_global_id(1) == 0) && (get_global_id(2) == 0); + + for(int i = 0; i < (_I{{WEI_WIDTH}} * _I{{WEI_HEIGHT}}); ++i) + { + int ck = 0; + int xk = i % _I{{WEI_WIDTH}}; + int yk = i / _I{{WEI_WIDTH}}; + + int k = 0; + for(; k <= (_ISRC_CHANNELS - K0); k += K0) + { + TILE({{SRC_DATA_TYPE}}, M0, K0, a); + TILE({{WEI_DATA_TYPE}}, N0, K0, b); + + LOOP_UNROLLING(int, i, 0, 1, M0, + { + a[i].v = {{ZERO_VALUE}}; + }) + + // Load tile from the src tensor + T_LOAD_NHWC_INDIRECT({{SRC_DATA_TYPE}}, M0, K0, {{SRC_TENSOR_TYPE}}, {{src}}, bout, yk, xk, ck, _ISRC_WIDTH, _ISRC_HEIGHT, {{src}}_stride_y, xi, yi, a); + + // Load tile from the weights tensor + T_LOAD({{WEI_DATA_TYPE}}, N0, K0, {{WEI_TENSOR_TYPE}}, {{weight}}, ck, cout * _IY_MULTIPLIER + i, _IY_MULTIPLIER, {{weight}}_stride_y, b); + + // Compute the matrix multiplication between two tiles + T_MMUL({{SRC_DATA_TYPE}}, {{WEI_DATA_TYPE}}, {{ACC_DATA_TYPE}}, M0, N0, K0, NT, T, a, b, {{dst}}); + + ck += K0; + } + + // We voluntarily use SRC_CHANNELS rather than _DSRC_CHANNELS + // This #if directive should be removed in case of dynamic tensor support + )_"; + + if(leftover_loop) + { + code += R"_( + // Left-over accumulations + for(; k < _ISRC_CHANNELS; ++k) + { + TILE({{SRC_DATA_TYPE}}, M0, 1, a); + TILE({{WEI_DATA_TYPE}}, N0, 1, b); + + LOOP_UNROLLING(int, i, 0, 1, M0, + { + a[i].v = {{ZERO_VALUE}}; + }) + + // Load tile from the src tensor + T_LOAD_NHWC_INDIRECT({{SRC_DATA_TYPE}}, M0, 1, {{SRC_TENSOR_TYPE}}, {{src}}, bout, yk, xk, ck, _ISRC_WIDTH, _ISRC_HEIGHT, {{src}}_stride_y, xi, yi, a); + + // Load tile from the weights tensor + // The T_LOAD for the left-over elements can only use BUFFER because we load one element per iteration + T_LOAD({{WEI_DATA_TYPE}}, N0, 1, BUFFER, {{weight}}, ck, cout * _IY_MULTIPLIER + i, _IY_MULTIPLIER, {{weight}}_stride_y, b); + + // Compute the matrix multiplication between two tiles + T_MMUL({{SRC_DATA_TYPE}}, {{WEI_DATA_TYPE}}, {{ACC_DATA_TYPE}}, M0, N0, 1, NT, T, a, b, {{dst}}); + + ++ck; + } + )_"; + } + + code += R"_( + } + )_"; + + if(bias_info != nullptr) + { + code += R"_( + TILE({{BIA_DATA_TYPE}}, 1, N0, bias0); + + T_LOAD({{BIA_DATA_TYPE}}, 1, N0, BUFFER, {{bias}}, cout, 0, 1, 0, bias0); + + // c = c + bias[broadcasted] + T_ADD_BROADCAST_X({{ACC_DATA_TYPE}}, M0, N0, {{dst}}, bias0, {{dst}}); + )_"; + } + + code += R"_( + #undef _I{{WEI_WIDTH}} + #undef _I{{WEI_HEIGHT}} + #undef _ISRC_WIDTH + #undef _ISRC_HEIGHT + #undef _ISRC_CHANNELS + #undef _IDST_WIDTH + #undef _IDST_HEIGHT + #undef _IDST_CHANNELS + #undef _IY_MULTIPLIER + } + + // Workaround for the discrepancy between tiles and repeats + VEC_DATA_TYPE({{ACC_DATA_TYPE}}, N0) {{dst}}0 = {{dst}}[0].v; +#if M0 >= 2 + VEC_DATA_TYPE({{ACC_DATA_TYPE}}, N0) {{dst}}1 = {{dst}}[1].v; +#endif // M0 >= 2 +#if M0 >= 3 + VEC_DATA_TYPE({{ACC_DATA_TYPE}}, N0) {{dst}}2 = {{dst}}[2].v; +#endif // M0 >= 3 +#if M0 >= 4 + VEC_DATA_TYPE({{ACC_DATA_TYPE}}, N0) {{dst}}3 = {{dst}}[3].v; +#endif // M0 >= 4 +#if M0 >= 8 + VEC_DATA_TYPE({{ACC_DATA_TYPE}}, N0) {{dst}}4 = {{dst}}[4].v; + VEC_DATA_TYPE({{ACC_DATA_TYPE}}, N0) {{dst}}5 = {{dst}}[5].v; + VEC_DATA_TYPE({{ACC_DATA_TYPE}}, N0) {{dst}}6 = {{dst}}[6].v; + VEC_DATA_TYPE({{ACC_DATA_TYPE}}, N0) {{dst}}7 = {{dst}}[7].v; +#endif // M0 >= 8 +#if M0 == 16 + VEC_DATA_TYPE({{ACC_DATA_TYPE}}, N0) {{dst}}8 = {{dst}}[8].v; + VEC_DATA_TYPE({{ACC_DATA_TYPE}}, N0) {{dst}}9 = {{dst}}[9].v; + VEC_DATA_TYPE({{ACC_DATA_TYPE}}, N0) {{dst}}A = {{dst}}[10].v; + VEC_DATA_TYPE({{ACC_DATA_TYPE}}, N0) {{dst}}B = {{dst}}[11].v; + VEC_DATA_TYPE({{ACC_DATA_TYPE}}, N0) {{dst}}C = {{dst}}[12].v; + VEC_DATA_TYPE({{ACC_DATA_TYPE}}, N0) {{dst}}D = {{dst}}[13].v; + VEC_DATA_TYPE({{ACC_DATA_TYPE}}, N0) {{dst}}E = {{dst}}[14].v; + VEC_DATA_TYPE({{ACC_DATA_TYPE}}, N0) {{dst}}F = {{dst}}[15].v; +#endif // M0 == 16 +//------------------ END KERNEL {{meta_kernel_id}} --------------------- + )_"; + return code.c_str(); +} + +bool export_to_cl_image_support(const ITensorInfo *tensor, GPUTarget gpu_target, DataLayout data_layout) +{ + if(tensor->tensor_shape()[0] % 4 || (data_layout != DataLayout::NHWC)) + { + return false; + } + + // If not floating point + if(!is_data_type_float(tensor->data_type())) + { + return false; + } + + if(gpu_target == GPUTarget::G71 || get_arch_from_target(gpu_target) == GPUTarget::MIDGARD) + { + return false; + } + + // Check if the cl_khr_image2d_from_buffer extension is supported on the target platform + if(!image2d_from_buffer_supported(CLKernelLibrary::get().get_device())) + { + return false; + } + + // Check cl image pitch alignment + if(get_cl_image_pitch_alignment(CLKernelLibrary::get().get_device()) == 0) + { + return false; + } + + const size_t image_w = tensor->tensor_shape()[0] / 4; + const size_t image_h = tensor->tensor_shape()[1] * tensor->tensor_shape()[2] * tensor->tensor_shape()[3]; + const size_t max_image_w = CLKernelLibrary::get().get_device().getInfo<CL_DEVICE_IMAGE2D_MAX_WIDTH>(); + const size_t max_image_h = CLKernelLibrary::get().get_device().getInfo<CL_DEVICE_IMAGE2D_MAX_HEIGHT>(); + + if(image_w > max_image_w || image_h > max_image_h) + { + return false; + } + + return true; +} + +CLBuildOptions ClDirectConvolutionKernelComponent::generate_build_options() const +{ + const auto src_info = _blueprint->impl().get_kernel_argument_info(_src.arg_id); + const auto weight_info = _blueprint->impl().get_kernel_argument_info(_weight.arg_id); + const auto dst_info = _blueprint->impl().get_kernel_argument_info(_blueprint->impl().get_dst_id()); + + const unsigned int channel_idx = get_data_layout_dimension_index(src_info->data_layout(), DataLayoutDimension::CHANNEL); + const DataType data_type = src_info->data_type(); + const GPUTarget gpu_target = ICLKernel().get_target(); + + Window win = get_window(); + + const unsigned int n0 = win.x().step(); + const unsigned int m0 = win.y().step(); + const unsigned int k0 = adjust_vec_size(is_data_type_quantized(data_type) ? 16u : 8u, src_info->dimension(channel_idx)); + const unsigned int partial_store_n0 = dst_info->dimension(channel_idx) % n0; + const bool export_to_cl_image = export_to_cl_image_support(weight_info, gpu_target, src_info->data_layout()); + + // Update the padding for the weights tensor if we can export to cl_image + if(export_to_cl_image) + { + arm_compute::opencl::kernels::gemm::update_padding_for_cl_image(weight_info); + } + + CLBuildOptions build_opts{}; + build_opts.add_option("-cl-fast-relaxed-math"); + build_opts.add_option("-DIS_TILED"); + build_opts.add_option("-DN0=" + support::cpp11::to_string(n0)); + build_opts.add_option("-DM0=" + support::cpp11::to_string(m0)); + build_opts.add_option("-DK0=" + support::cpp11::to_string(k0)); + build_opts.add_option("-DPARTIAL_N0=" + support::cpp11::to_string(partial_store_n0)); + + return build_opts; +} + +ClDirectConvolutionKernelComponent::TagLUT ClDirectConvolutionKernelComponent::allocate_vars(SharedVarTable &vtable) const +{ + TagLUT lut{}; + + const auto src_info = _blueprint->impl().get_kernel_argument_info(_src.arg_id); + const auto weight_info = _blueprint->impl().get_kernel_argument_info(_weight.arg_id); + const auto bias_info = _blueprint->impl().get_kernel_argument_info(_bias.arg_id); + const auto dst_info = _blueprint->impl().get_kernel_argument_info(_blueprint->impl().get_dst_id()); + + const GPUTarget gpu_target = ICLKernel().get_target(); + const bool export_to_cl_image = export_to_cl_image_support(weight_info, gpu_target, src_info->data_layout()); + + const TensorArgType weight_type = export_to_cl_image ? TensorArgType::Tensor_4D_t_Image : TensorArgType::Tensor_4D_t_Buffer; + lut["meta_kernel_id"] = id(); + lut["src"] = vtable.add(_src, ClKernelArgRuntimeDescriptor(_src.arg_id, TensorArgType::Tensor_4D_t_Buffer), "src"); + lut["weight"] = vtable.add(_weight, ClKernelArgRuntimeDescriptor(_weight.arg_id, weight_type), "weight"); + + if(!_bias.is_empty()) // optional bias + { + lut["bias"] = vtable.add(_bias, ClKernelArgRuntimeDescriptor(_bias.arg_id, TensorArgType::Vector), "bias"); + lut["BIA_DATA_TYPE"] = get_cl_type_from_data_type(bias_info->data_type()); + } + lut["dst"] = vtable.add(_dst, ClKernelArgRuntimeDescriptor(_dst.arg_id, TensorArgType::Tensor_4D_t_Buffer), "dst"); + + // Local build options + const auto width_idx = get_data_layout_dimension_index(src_info->data_layout(), DataLayoutDimension::WIDTH); + const auto height_idx = get_data_layout_dimension_index(src_info->data_layout(), DataLayoutDimension::HEIGHT); + const auto channel_idx = get_data_layout_dimension_index(src_info->data_layout(), DataLayoutDimension::CHANNEL); + + lut["dst_w"] = dst_info->dimension(width_idx); + lut["dst_h"] = dst_info->dimension(height_idx); + lut["dst_c"] = dst_info->dimension(channel_idx); + + lut["ACC_DATA_TYPE"] = src_info->data_type(); + lut["SRC_DATA_TYPE"] = src_info->data_type(); + lut["WEI_DATA_TYPE"] = weight_info->data_type(); + + lut["SRC_TENSOR_TYPE"] = "BUFFER"; + lut["WEI_TENSOR_TYPE"] = export_to_cl_image ? "IMAGE" : "BUFFER"; + + lut["WEI_WIDTH"] = weight_info->dimension(width_idx); + lut["WEI_HEIGHT"] = weight_info->dimension(height_idx); + + lut["STRIDE_X"] = std::get<0>(_desc.pad_stride_info.stride()); + lut["STRIDE_Y"] = std::get<1>(_desc.pad_stride_info.stride()); + + lut["PAD_LEFT"] = _desc.pad_stride_info.pad_left(); + lut["PAD_TOP"] = _desc.pad_stride_info.pad_top(); + + lut["ZERO_VALUE"] = 0; + + return lut; +} +} // namespace dynamic_fusion +} // namespace experimental +} // namespace arm_compute + +#endif // defined(ENABLE_EXPERIMENTAL_DYNAMIC_FUSION)
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