diff options
Diffstat (limited to 'src/core')
| -rw-r--r-- | src/core/CL/CLKernelLibrary.cpp | 1 | ||||
| -rw-r--r-- | src/core/CL/cl_kernels/depthwise_convolution.cl | 151 | ||||
| -rw-r--r-- | src/core/CL/cl_kernels/helpers.h | 1 | ||||
| -rw-r--r-- | src/core/CL/kernels/CLDepthwiseConvolutionLayerNativeKernel.cpp | 225 |
4 files changed, 376 insertions, 2 deletions
diff --git a/src/core/CL/CLKernelLibrary.cpp b/src/core/CL/CLKernelLibrary.cpp index 4f017b792b..2f748de53e 100644 --- a/src/core/CL/CLKernelLibrary.cpp +++ b/src/core/CL/CLKernelLibrary.cpp @@ -221,6 +221,7 @@ const std::map<std::string, std::string> CLKernelLibrary::_kernel_program_map = { "depthwise_convolution_3x3_f16", "depthwise_convolution.cl" }, { "depthwise_convolution_3x3_nhwc", "depthwise_convolution.cl" }, { "depthwise_convolution_3x3_nhwc_stride1", "depthwise_convolution.cl" }, + { "dwc_MxN_native_fp_nhwc", "depthwise_convolution.cl" }, { "dwc_3x3_native_qasymm8_nchw", "depthwise_convolution_quantized.cl" }, { "dwc_3x3_native_qasymm8_dot8_nchw", "depthwise_convolution_quantized.cl" }, { "dwc_3x3_reshaped_qasymm8_nhwc", "depthwise_convolution_quantized.cl" }, diff --git a/src/core/CL/cl_kernels/depthwise_convolution.cl b/src/core/CL/cl_kernels/depthwise_convolution.cl index fb4a0fc157..1b2f5cccaa 100644 --- a/src/core/CL/cl_kernels/depthwise_convolution.cl +++ b/src/core/CL/cl_kernels/depthwise_convolution.cl @@ -21,7 +21,6 @@ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ - #include "helpers.h" #include "activation_float_helpers.h" @@ -1479,7 +1478,7 @@ __kernel void depthwise_convolution_3x3_stridex2_stridey2_bifrost_f16( //3x3 Convolution of elements starting in 0th row pixels0 = convolution_3x3_dilation_stridex2_stridey2_bifrost_f16(src_addr, src.stride_x, src.stride_y, 0, weights_addr, weights_stride_y); //3x3 Convolution of elements starting in 2nd row - pixels1 = convolution_3x3_dilation_stridex2_stridey2_bifrost_f16(src_addr, src.stride_x, src.stride_y, 2, weights_addr, weights_stride_y); + pixels1 = convolution_3x3_dilation_stridex2_stridey2_bifrost_f16(src_addr, src.stride_x, src.stride_y, 2, weights_addr, weights_stride_y); #endif /* DILATION_X==1 && DILATION_Y==1 */ #ifdef HAS_BIAS @@ -1492,6 +1491,153 @@ __kernel void depthwise_convolution_3x3_stridex2_stridey2_bifrost_f16( } #endif // defined(ARM_COMPUTE_OPENCL_FP16_ENABLED) && defined(DEPTH_MULTIPLIER) && defined(DST_CHANNELS) && defined(IS_F16) +#if defined(SRC_DIM1) && defined(SRC_DIM2) && defined(KERNEL_WIDTH) && defined(KERNEL_HEIGHT) && defined(N0) && defined(DATA_TYPE) && defined(DILATION_X) && defined(DILATION_Y) && defined(CONV_STRIDE_X) && defined(CONV_STRIDE_Y) && defined(CONV_PAD_LEFT) && defined(CONV_PAD_TOP) +/** This function computes the depthwise convolution for NHWC data layout. This kernel assumes that the weights tensor is NOT reshaped + * + * @note Datatype should be given as a preprocessor argument using -DDATA_TYPE=type. e.g. -DDATA_TYPE=float + * @note The number of elements processed must be passed at compile time using -DN0 (e.g. -DN0=2) + * @note The depth multiplier must be passed at compile time using -DDEPTH_MULTIPLIER (e.g. -DDEPTH_MULTIPLIER=1) + * @note The first dimension of the input tensor must be passed at compile time using -DSRC_DIM1 (e.g. -DSRC_DIM1=112) + * @note The second dimension of the input tensor must be passed at compile time using -DSRC_DIM2 (e.g. -DSRC_DIM2=80) + * @note The kernel width must be passed at compile time using -DKERNEL_WIDTH (e.g. -DKERNEL_WIDTH=5) + * @note The kernel height must be passed at compile time using -DKERNEL_HEIGHT (e.g. -DKERNEL_HEIGHT=5) + * @note The convolution pad top must be passed at compile time using -DCONV_PAD_TOP (e.g. -DCONV_PAD_TOP=1) + * @note The convolution pad top must be passed at compile time using -DCONV_PAD_LEFT (e.g. -DCONV_PAD_LEFT=1) + * @note The convolution stride along the width must be passed at compile time using -DCONV_STRIDE_X (e.g. -DCONV_STRIDE_Y=X) + * @note The convolution stride along the height must be passed at compile time using -DCONV_STRIDE_Y (e.g. -DCONV_STRIDE_Y=1) + * @note It is possible to select the activation function to apply using -DACTIVATION_TYPE e.g. -DACTIVATION_TYPE=relu + * @note A, B variables required by some activation functions are set using -DA_VAL= and -DB_VAL= respectively + * + * @param[in] src_ptr Pointer to the source tensor. Supported data types: F16/F32 + * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes) + * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes) + * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] src_step_z src_stride_y * number of elements along Z processed per workitem(in bytes) + * @param[in] src_stride_w Stride of the source tensor in W dimension (in bytes) + * @param[in] src_step_w src_stride_w * number of elements along W processed per workitem(in bytes) + * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor + * @param[in] dst_ptr Pointer to the destination tensor. Supported data types: same as src_ptr + * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes) + * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes) + * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes) + * @param[in] dst_step_z dst_stride_z * number of elements along Y processed per workitem(in bytes) + * @param[in] dst_stride_w Stride of the destination tensor in W dimension (in bytes) + * @param[in] dst_step_w dst_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 + * @param[in] weights_ptr Pointer to the weights tensor. Supported data types: F16/F32 + * @param[in] weights_stride_x Stride of the weights tensor in X dimension (in bytes) + * @param[in] weights_step_x weights_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] weights_stride_y Stride of the weights tensor in Y dimension (in bytes) + * @param[in] weights_step_y weights_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] weights_stride_z Stride of the weights tensor in Z dimension (in bytes) + * @param[in] weights_step_z weights_stride_z * number of elements along Y processed per workitem(in bytes) + * @param[in] weights_offset_first_element_in_bytes The offset of the first element in the weights tensor + * @param[in] biases_ptr (Optional) Pointer to the biases vector. Supported data types: same as src_ptr + * @param[in] biases_stride_x (Optional) Stride of the biases vector in X dimension (in bytes) + * @param[in] biases_step_x (Optional) biases_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] biases_offset_first_element_in_bytes (Optional) The offset of the first element in the biases vector + */ +__kernel void dwc_MxN_native_fp_nhwc( + TENSOR4D_DECLARATION(src), + TENSOR4D_DECLARATION(dst), + TENSOR3D_DECLARATION(weights), +#if defined(HAS_BIAS) + VECTOR_DECLARATION(biases) +#endif // defined(HAS_BIAS) +) +{ + int x = get_global_id(0); // channels + int y = get_global_id(1); // spatial coordinate x +#if defined(DST_DEPTH) + int z = get_global_id(2) % (int)DST_DEPTH; // spatial coordinate y + int b = get_global_id(2) / (int)DST_DEPTH; // batch +#else // defined(DST_DEPTH) + int z = get_global_id(2); // spatial coordinate y +#endif // defined(DST_DEPTH) + + __global uchar *s_addr = src_ptr + src_offset_first_element_in_bytes + + x * sizeof(DATA_TYPE) * (int)N0; + + __global uchar *d_addr = dst_ptr + dst_offset_first_element_in_bytes + + x * sizeof(DATA_TYPE) * (int)DEPTH_MULTIPLIER * (int)N0 + + y * dst_stride_y + + z * dst_stride_z; + + __global uchar *w_addr = weights_ptr + weights_offset_first_element_in_bytes + + x * sizeof(DATA_TYPE) * (int)DEPTH_MULTIPLIER * (int)N0; + +#if defined(HAS_BIAS) + __global uchar *b_addr = biases_ptr + biases_offset_first_element_in_bytes + + x * sizeof(DATA_TYPE) * (int)DEPTH_MULTIPLIER * (int)N0; +#endif // defined(HAS_BIAS) + +#if defined(DST_DEPTH) + s_addr += b * src_stride_w; + d_addr += b * dst_stride_w; +#endif // defined(DST_DEPTH) + + for(int d = 0; d < (int)DEPTH_MULTIPLIER; ++d) + { + // Each work-item computes N0x1x1 elements + VEC_DATA_TYPE(DATA_TYPE, N0) + res = 0; + + int x_coord = y * CONV_STRIDE_X - (int)CONV_PAD_LEFT; + int y_coord = z * CONV_STRIDE_Y - (int)CONV_PAD_TOP; + + for(int yk = 0; yk < KERNEL_HEIGHT; ++yk) + { + if(y_coord >= 0 && y_coord < SRC_DIM2) + { + int x_coord_tmp = x_coord; + + for(int xk = 0; xk < KERNEL_WIDTH; ++xk) + { + if(x_coord_tmp >= 0 && x_coord_tmp < SRC_DIM1) + { + int s_offset = x_coord_tmp * (int)src_stride_y + y_coord * (int)src_stride_z; + int w_offset = xk * weights_stride_y + yk * weights_stride_z; + + // Load input and weights values + VEC_DATA_TYPE(DATA_TYPE, N0) + i = VLOAD(N0)(0, (__global DATA_TYPE *)(s_addr + s_offset)); + VEC_DATA_TYPE(DATA_TYPE, N0) + w = VLOAD(N0)(0, (__global DATA_TYPE *)(w_addr + w_offset)); + +#if GPU_ARCH == GPU_ARCH_MIDGARD + res += i * w; +#else // GPU_ARCH == GPU_ARCH_MIDGARD + res = fma(i, w, res); +#endif // GPU_ARCH == GPU_ARCH_MIDGARD + } + x_coord_tmp += DILATION_X; + } + } + y_coord += DILATION_Y; + } + +#if defined(HAS_BIAS) + res += VLOAD(N0)(0, (__global DATA_TYPE *)(b_addr)); +#endif // defined(HAS_BIAS) + + res = ACTIVATION(ACTIVATION_TYPE, DATA_TYPE, res, A_VAL, B_VAL); + + VSTORE(N0) + (res, 0, (__global DATA_TYPE *)(d_addr)); + + w_addr += sizeof(DATA_TYPE); + d_addr += sizeof(DATA_TYPE); +#if defined(HAS_BIAS) + b_addr += sizeof(DATA_TYPE); +#endif // defined(HAS_BIAS) + } +} +#endif // defined(SRC_DIM1) && defined(SRC_DIM2) && defined(KERNEL_WIDTH) && defined(KERNEL_HEIGHT) && defiend(N0) && defined(DATA_TYPE) && defined(DILATION_X) && defined(DILATION_Y) && defined(CONV_STRIDE_X) && defined(CONV_STRIDE_Y) && defined(CONV_PAD_LEFT) && defined(CONV_PAD_TOP) + #if defined(VEC_SIZE) && defined(SRC_DIM_2) && defined(CONV_PAD_TOP) && defined(CONV_PAD_LEFT) && defined(DATA_TYPE) #if DATA_TYPE != float || DATA_TYPE != half @@ -1501,6 +1647,7 @@ __kernel void depthwise_convolution_3x3_stridex2_stridey2_bifrost_f16( #define VEC_FLOAT VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE) #if defined(CONV_STRIDE_X) && defined(CONV_STRIDE_Y) + /** This function computes the depthwise convolution for NHWC data layout when the stride along the width or height is not 1. * * @note Datatype should be given as a preprocessor argument using -DDATA_TYPE=type. e.g. -DDATA_TYPE=float diff --git a/src/core/CL/cl_kernels/helpers.h b/src/core/CL/cl_kernels/helpers.h index 756c906e66..f501077a40 100644 --- a/src/core/CL/cl_kernels/helpers.h +++ b/src/core/CL/cl_kernels/helpers.h @@ -68,6 +68,7 @@ #define double1 double #define vload1(OFFSET, PTR) *(OFFSET + PTR) +#define vstore1(DATA, OFFSET, PTR) *(OFFSET + PTR) = DATA #define VEC_DATA_TYPE_STR(type, size) type##size #define VEC_DATA_TYPE(type, size) VEC_DATA_TYPE_STR(type, size) diff --git a/src/core/CL/kernels/CLDepthwiseConvolutionLayerNativeKernel.cpp b/src/core/CL/kernels/CLDepthwiseConvolutionLayerNativeKernel.cpp new file mode 100644 index 0000000000..b34c261a40 --- /dev/null +++ b/src/core/CL/kernels/CLDepthwiseConvolutionLayerNativeKernel.cpp @@ -0,0 +1,225 @@ +/* + * Copyright (c) 2019 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. + */ +#include "arm_compute/core/CL/kernels/CLDepthwiseConvolutionLayerNativeKernel.h" + +#include "arm_compute/core/IAccessWindow.h" +#include "arm_compute/core/CL/CLHelpers.h" +#include "arm_compute/core/CL/CLKernelLibrary.h" +#include "arm_compute/core/CL/CLValidate.h" +#include "arm_compute/core/CL/ICLKernel.h" +#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/Utils.h" +#include "arm_compute/core/utils/misc/ShapeCalculator.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) +{ + 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::F16, DataType::F32); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights); + 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)); + 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)); + + if(biases != nullptr) + { + ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != weights->dimension(0)); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases); + ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1); + } + + if(output->total_size() != 0) + { + const TensorShape output_shape = arm_compute::misc::shape_calculator::compute_depthwise_convolution_shape(*input, *weights, conv_info, depth_multiplier, dilation); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), output_shape); + } + + return Status{}; +} + +std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *weights, ITensorInfo *bias, ITensorInfo *output, const DWCWeightsKernelInfo &dwc_weights_info, + const DWCKernelInfo &dwc_info, const PadStrideInfo &conv_info, unsigned int depth_multiplier, const Size2D &dilation) +{ + // Get convolved dimensions + const TensorShape output_shape = arm_compute::misc::shape_calculator::compute_depthwise_convolution_shape(*input, *weights, conv_info, depth_multiplier, dilation); + + auto_init_if_empty(*output, input->clone()->set_tensor_shape(output_shape).set_quantization_info(output->quantization_info())); + + const unsigned int n0 = dwc_weights_info.n0; + + // Configure kernel window + Window win = calculate_max_window(*output, Steps(n0)); + + // The following access windows are only valid in case of NHWC and because n0 must unit in case depth_multiplier > 1 + AccessWindowHorizontal input_access(input, 0, n0); + AccessWindowHorizontal weights_access(weights, 0, n0); + AccessWindowHorizontal output_access(output, 0, n0); + + bool window_changed = false; + + if(bias != nullptr) + { + AccessWindowHorizontal bias_access(bias, 0, n0); + window_changed = update_window_and_padding(win, input_access, weights_access, bias_access, output_access); + } + else + { + window_changed = update_window_and_padding(win, input_access, weights_access, output_access); + } + + output_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape())); + + Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; + return std::make_pair(err, win); +} +} // namespace + +CLDepthwiseConvolutionLayerNativeKernel::CLDepthwiseConvolutionLayerNativeKernel() + : _input(nullptr), _weights(nullptr), _biases(nullptr), _output(nullptr), _depth_multiplier(1) +{ +} + +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) +{ + 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)); + + auto win_config = validate_and_configure_window(input->info(), weights->info(), biases != nullptr ? biases->info() : nullptr, output->info(), dwc_weights_info, dwc_info, conv_info, depth_multiplier, + dilation); + ARM_COMPUTE_ERROR_THROW_ON(win_config.first); + + _input = input; + _output = output; + _weights = weights; + _biases = biases; + _depth_multiplier = depth_multiplier; + + const size_t idx_w = get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::WIDTH); + const size_t idx_h = get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::HEIGHT); + const size_t weights_width = weights->info()->dimension(idx_w); + const size_t weights_height = weights->info()->dimension(idx_h); + + 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)); + build_opts.add_option("-DN0=" + support::cpp11::to_string(dwc_weights_info.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_width)); + build_opts.add_option("-DKERNEL_HEIGHT=" + support::cpp11::to_string(weights_height)); + 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_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())); + + std::string kernel_name("dwc_MxN_native_fp_nhwc"); + + ICLKernel::configure_internal(win_config.second); + _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options())); + + // Set config_id for enabling LWS tuning + _config_id = kernel_name; + _config_id += "_"; + _config_id += support::cpp11::to_string(input->info()->dimension(0)); + _config_id += "_"; + _config_id += support::cpp11::to_string(input->info()->dimension(1)); + _config_id += "_"; + _config_id += support::cpp11::to_string(input->info()->dimension(2)); + _config_id += "_"; + _config_id += support::cpp11::to_string(output->info()->dimension(0)); + _config_id += "_"; + _config_id += support::cpp11::to_string(output->info()->dimension(1)); + _config_id += "_"; + _config_id += support::cpp11::to_string(output->info()->dimension(2)); + _config_id += "_"; + _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) +{ + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, weights, biases, output, dwc_weights_info, dwc_info, conv_info, depth_multiplier, dilation)); + ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), weights->clone().get(), + biases != nullptr ? biases->clone().get() : nullptr, + output->clone().get(), dwc_weights_info, dwc_info, conv_info, depth_multiplier, dilation) + .first); + + return Status{}; +} + +void CLDepthwiseConvolutionLayerNativeKernel::run(const Window &window, cl::CommandQueue &queue) +{ + ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); + ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window); + + // 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)); + } + + if(_biases != nullptr) + { + unsigned int idx = 2 * num_arguments_per_4D_tensor() + num_arguments_per_3D_tensor(); + add_1D_tensor_argument(idx, _biases, slice_in); + } + + do + { + unsigned int 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()); + } + while(window_collapsed.slide_window_slice_4D(slice_out) && window.slide_window_slice_4D(slice_in)); +} +} // namespace arm_compute |