From cbf39c63a6eb89a2c80b2338afc374081803d79d Mon Sep 17 00:00:00 2001 From: Georgios Pinitas Date: Mon, 10 Sep 2018 15:07:45 +0100 Subject: COMPMID-1566: Add broadcast to CLArithmeticSubtraction Change-Id: I05d21f9a92013ecfd1128d12cf1561cfd6e5c5e9 Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/147983 Tested-by: bsgcomp Reviewed-by: Anthony Barbier --- src/core/CL/CLKernelLibrary.cpp | 1 + src/core/CL/cl_kernels/arithmetic_op_quantized.cl | 74 ++++++++- src/core/CL/kernels/CLArithmeticAdditionKernel.cpp | 2 +- .../CL/kernels/CLArithmeticSubtractionKernel.cpp | 176 +++++++++++++++------ .../NEON/kernels/NEArithmeticAdditionKernel.cpp | 2 +- .../NEON/kernels/NEArithmeticSubtractionKernel.cpp | 171 +++++++++++--------- 6 files changed, 293 insertions(+), 133 deletions(-) (limited to 'src/core') diff --git a/src/core/CL/CLKernelLibrary.cpp b/src/core/CL/CLKernelLibrary.cpp index dfc41da09f..8a309ec757 100644 --- a/src/core/CL/CLKernelLibrary.cpp +++ b/src/core/CL/CLKernelLibrary.cpp @@ -152,6 +152,7 @@ const std::map CLKernelLibrary::_kernel_program_map = { "arithmetic_add_quantized", "arithmetic_op_quantized.cl" }, { "arithmetic_add", "arithmetic_op.cl" }, { "arithmetic_sub", "arithmetic_op.cl" }, + { "arithmetic_sub_quantized", "arithmetic_op_quantized.cl" }, { "arithmetic_div", "arithmetic_op.cl" }, { "batch_to_space_nchw", "batch_to_space.cl" }, { "batch_to_space_static_nchw", "batch_to_space.cl" }, diff --git a/src/core/CL/cl_kernels/arithmetic_op_quantized.cl b/src/core/CL/cl_kernels/arithmetic_op_quantized.cl index 082317ba11..5f31c56250 100644 --- a/src/core/CL/cl_kernels/arithmetic_op_quantized.cl +++ b/src/core/CL/cl_kernels/arithmetic_op_quantized.cl @@ -31,12 +31,16 @@ #define SUB(x, y) (x) - (y) #endif /* SATURATE */ -#if defined(OFFSET_IN1) - +#if defined(OFFSET_IN1) && defined(OFFSET_IN2) && defined(OFFSET_OUT) && defined(SCALE_IN1) && defined(SCALE_IN2) && defined(SCALE_OUT) /** This function adds two tensors. * - * @attention The quantization offset must be passed at compile time using -DOFFSET_IN1, i.e. -DOFFSET_IN1=10 - * @attention To perform saturating operation -DSATURATE has to be passed to the compiler otherwise wrapping policy will be used. + * @note The quantization offset of the first operand must be passed at compile time using -DOFFSET_IN1, i.e. -DOFFSET_IN1=10 + * @note The quantization offset of the second operand must be passed at compile time using -DOFFSET_IN2, i.e. -DOFFSET_IN2=10 + * @note The quantization offset of the output must be passed at compile time using -DOFFSET_OUT, i.e. -DOFFSET_OUT=10 + * @note The quantization scale of the first operand must be passed at compile time using -DSCALE_IN1, i.e. -DSCALE_IN1=10 + * @note The quantization scale of the second operand must be passed at compile time using -DSCALE_IN2, i.e. -DSCALE_IN2=10 + * @note The quantization scale of the output must be passed at compile time using -DSCALE_OUT, i.e. -DSCALE_OUT=10 + * @note To perform saturating operation -DSATURATE has to be passed to the compiler otherwise wrapping policy will be used. * * @param[in] in1_ptr Pointer to the source tensor. Supported data types: QASYMM8 * @param[in] in1_stride_x Stride of the source tensor in X dimension (in bytes) @@ -87,4 +91,64 @@ __kernel void arithmetic_add_quantized( // Store result vstore16(res, 0, (__global uchar *)out.ptr); } -#endif /* defined(OFFSET) */ + +/** This function subtracts two tensors. + * + * @note The quantization offset of the first operand must be passed at compile time using -DOFFSET_IN1, i.e. -DOFFSET_IN1=10 + * @note The quantization offset of the second operand must be passed at compile time using -DOFFSET_IN2, i.e. -DOFFSET_IN2=10 + * @note The quantization offset of the output must be passed at compile time using -DOFFSET_OUT, i.e. -DOFFSET_OUT=10 + * @note The quantization scale of the first operand must be passed at compile time using -DSCALE_IN1, i.e. -DSCALE_IN1=10 + * @note The quantization scale of the second operand must be passed at compile time using -DSCALE_IN2, i.e. -DSCALE_IN2=10 + * @note The quantization scale of the output must be passed at compile time using -DSCALE_OUT, i.e. -DSCALE_OUT=10 + * @note To perform saturating operation -DSATURATE has to be passed to the compiler otherwise wrapping policy will be used. + * + * @param[in] in1_ptr Pointer to the source tensor. Supported data types: QASYMM8 + * @param[in] in1_stride_x Stride of the source tensor in X dimension (in bytes) + * @param[in] in1_step_x in1_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] in1_stride_y Stride of the source tensor in Y dimension (in bytes) + * @param[in] in1_step_y in1_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] in1_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] in1_step_z in1_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] in1_offset_first_element_in_bytes The offset of the first element in the source tensor + * @param[in] in2_ptr Pointer to the source tensor. Supported data types: same as @p in1_ptr + * @param[in] in2_stride_x Stride of the source tensor in X dimension (in bytes) + * @param[in] in2_step_x in2_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] in2_stride_y Stride of the source tensor in Y dimension (in bytes) + * @param[in] in2_step_y in2_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] in2_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] in2_step_z in2_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] in2_offset_first_element_in_bytes The offset of the first element in the source tensor + * @param[out] out_ptr Pointer to the destination tensor. Supported data types: same as @p in1_ptr + * @param[in] out_stride_x Stride of the destination tensor in X dimension (in bytes) + * @param[in] out_step_x out_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] out_stride_y Stride of the destination tensor in Y dimension (in bytes) + * @param[in] out_step_y out_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] out_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] out_step_z out_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] out_offset_first_element_in_bytes The offset of the first element in the destination tensor + */ +__kernel void arithmetic_sub_quantized( + TENSOR3D_DECLARATION(in1), + TENSOR3D_DECLARATION(in2), + TENSOR3D_DECLARATION(out)) +{ + // Get pixels pointer + Tensor3D in1 = CONVERT_TO_TENSOR3D_STRUCT(in1); + Tensor3D in2 = CONVERT_TO_TENSOR3D_STRUCT(in2); + Tensor3D out = CONVERT_TO_TENSOR3D_STRUCT(out); + + int16 in_a = CONVERT(vload16(0, (__global uchar *)in1.ptr), int16); + int16 in_b = CONVERT(vload16(0, (__global uchar *)in2.ptr), int16); + + in_a = SUB(in_a, (int16)((int)OFFSET_IN1)); + in_b = SUB(in_b, (int16)((int)OFFSET_IN2)); + + const float16 in1f32 = convert_float16(in_a) * (float16)((float)SCALE_IN1); + const float16 in2f32 = convert_float16(in_b) * (float16)((float)SCALE_IN2); + const float16 qresf32 = (in1f32 - in2f32) / ((float16)(float)SCALE_OUT) + ((float16)((float16)OFFSET_OUT)); + const uchar16 res = convert_uchar16_sat(convert_int16_rte(qresf32)); + + // Store result + vstore16(res, 0, (__global uchar *)out.ptr); +} +#endif /* defined(OFFSET_IN1) && defined(OFFSET_IN2) && defined(OFFSET_OUT) && defined(SCALE_IN1) && defined(SCALE_IN2) && defined(SCALE_OUT) */ \ No newline at end of file diff --git a/src/core/CL/kernels/CLArithmeticAdditionKernel.cpp b/src/core/CL/kernels/CLArithmeticAdditionKernel.cpp index 2372d458cf..de14f00856 100644 --- a/src/core/CL/kernels/CLArithmeticAdditionKernel.cpp +++ b/src/core/CL/kernels/CLArithmeticAdditionKernel.cpp @@ -159,7 +159,7 @@ void CLArithmeticAdditionKernel::configure(const ICLTensor *input1, const ICLTen Status CLArithmeticAdditionKernel::validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, ConvertPolicy policy) { - ARM_COMPUTE_ERROR_ON_NULLPTR(input1, input2, output); + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input1, input2, output); ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(*input1, *input2, *output, policy)); ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(*input1->clone(), *input2->clone(), *output->clone()).first); diff --git a/src/core/CL/kernels/CLArithmeticSubtractionKernel.cpp b/src/core/CL/kernels/CLArithmeticSubtractionKernel.cpp index 299ac553e9..95d201104d 100644 --- a/src/core/CL/kernels/CLArithmeticSubtractionKernel.cpp +++ b/src/core/CL/kernels/CLArithmeticSubtractionKernel.cpp @@ -36,45 +36,82 @@ #include #include -using namespace arm_compute; - +namespace arm_compute +{ namespace { -Status validate_arguments(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, ConvertPolicy policy) +constexpr unsigned int num_elems_processed_per_iteration = 16; + +Status validate_arguments(const ITensorInfo &input1, const ITensorInfo &input2, const ITensorInfo &output, ConvertPolicy policy) { ARM_COMPUTE_UNUSED(policy); - ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input1); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input1, 1, DataType::U8, DataType::S16, DataType::F16, DataType::F32); - ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input2); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input2, 1, DataType::U8, DataType::S16, DataType::F16, DataType::F32); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input1, input2); + ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(&input1); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input1, 1, DataType::U8, DataType::QASYMM8, DataType::S16, DataType::F16, DataType::F32); + ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(&input2); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input2, 1, DataType::U8, DataType::QASYMM8, DataType::S16, DataType::F16, DataType::F32); + const bool is_qasymm = is_data_type_quantized_asymmetric(input1.data_type()) || is_data_type_quantized_asymmetric(input2.data_type()); + if(is_qasymm) + { + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&input1, &input2); + } + + const TensorShape out_shape = TensorShape::broadcast_shape(input1.tensor_shape(), input2.tensor_shape()); + + ARM_COMPUTE_RETURN_ERROR_ON_MSG(out_shape.total_size() == 0, "Inputs are not broadcast compatible"); // Validate in case of configured output - if((output != nullptr) && (output->total_size() != 0)) + if(output.total_size() > 0) { - ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(output); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8, DataType::S16, DataType::F16, DataType::F32); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->data_type() == DataType::U8 && (input1->data_type() != DataType::U8 || input2->data_type() != DataType::U8), + ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(&output); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&output, 1, DataType::U8, DataType::QASYMM8, DataType::S16, DataType::F16, DataType::F32); + ARM_COMPUTE_RETURN_ERROR_ON_MSG((output.data_type() == DataType::U8) && ((input1.data_type() != DataType::U8) || (input2.data_type() != DataType::U8)), "Output can only be U8 if both inputs are U8"); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input1, output); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(detail::have_different_dimensions(out_shape, output.tensor_shape(), 0), + "Wrong shape for output"); + if(is_qasymm) + { + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&input1, &output); + } } return Status{}; } -std::pair validate_and_configure_window(ITensorInfo *input1, ITensorInfo *input2, ITensorInfo *output) +std::pair validate_and_configure_window(ITensorInfo &input1, ITensorInfo &input2, ITensorInfo &output) { - constexpr unsigned int num_elems_processed_per_iteration = 16; + const std::pair broadcast_pair = ITensorInfo::broadcast_shape_and_valid_region(input1, input2); + const TensorShape &out_shape = broadcast_pair.first; + const ValidRegion &valid_region = broadcast_pair.second; - Window win = calculate_max_window(*input1, Steps(num_elems_processed_per_iteration)); - AccessWindowHorizontal input1_access(input1, 0, num_elems_processed_per_iteration); - AccessWindowHorizontal input2_access(input2, 0, num_elems_processed_per_iteration); - AccessWindowHorizontal output_access(output, 0, num_elems_processed_per_iteration); + // Auto initialize output if not initialized + { + set_shape_if_empty(output, out_shape); - bool window_changed = update_window_and_padding(win, input1_access, input2_access, output_access); + if(input1.data_type() == DataType::S16 || input2.data_type() == DataType::S16) + { + set_format_if_unknown(output, Format::S16); + } + else if(input1.data_type() == DataType::F16 && input2.data_type() == DataType::F16) + { + set_format_if_unknown(output, Format::F16); + } + else if(input1.data_type() == DataType::F32 || input2.data_type() == DataType::F32) + { + set_format_if_unknown(output, Format::F32); + } + } + + Window win = calculate_max_window(valid_region, Steps(num_elems_processed_per_iteration)); + Window win_input1 = win.broadcast_if_dimension_le_one(input1); + Window win_input2 = win.broadcast_if_dimension_le_one(input2); - ValidRegion valid_region = intersect_valid_regions(input1->valid_region(), - input2->valid_region()); + AccessWindowHorizontal input1_access(&input1, 0, num_elems_processed_per_iteration); + AccessWindowHorizontal input2_access(&input2, 0, num_elems_processed_per_iteration); + AccessWindowHorizontal output_access(&output, 0, num_elems_processed_per_iteration); + + bool window_changed = update_window_and_padding(win_input1, input1_access) + || update_window_and_padding(win_input2, input2_access) + || update_window_and_padding(win, output_access); output_access.set_valid_region(win, valid_region); @@ -91,22 +128,11 @@ CLArithmeticSubtractionKernel::CLArithmeticSubtractionKernel() void CLArithmeticSubtractionKernel::configure(const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, ConvertPolicy policy) { ARM_COMPUTE_ERROR_ON_NULLPTR(input1, input2, output); + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(*input1->info(), *input2->info(), *output->info(), policy)); - // Auto initialize output if not initialized - { - set_shape_if_empty(*output->info(), input1->info()->tensor_shape()); - - if(input1->info()->data_type() == DataType::S16 || input2->info()->data_type() == DataType::S16) - { - set_format_if_unknown(*output->info(), Format::S16); - } - else if(input1->info()->data_type() == DataType::F32 || input2->info()->data_type() == DataType::F32) - { - set_format_if_unknown(*output->info(), Format::F32); - } - } - - ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input1->info(), input2->info(), output->info(), policy)); + // Configure kernel window + auto win_config = validate_and_configure_window(*input1->info(), *input2->info(), *output->info()); + ARM_COMPUTE_ERROR_THROW_ON(win_config.first); _input1 = input1; _input2 = input2; @@ -114,26 +140,39 @@ void CLArithmeticSubtractionKernel::configure(const ICLTensor *input1, const ICL bool has_float_out = is_data_type_float(output->info()->data_type()); + // Setup kernel + std::string kernel_name = "arithmetic_sub"; + // Set kernel build options - std::set build_opts; - build_opts.emplace((policy == ConvertPolicy::WRAP || has_float_out) ? "-DWRAP" : "-DSATURATE"); - build_opts.emplace("-DDATA_TYPE_IN1=" + get_cl_type_from_data_type(input1->info()->data_type())); - build_opts.emplace("-DDATA_TYPE_IN2=" + get_cl_type_from_data_type(input2->info()->data_type())); - build_opts.emplace("-DDATA_TYPE_OUT=" + get_cl_type_from_data_type(output->info()->data_type())); + CLBuildOptions build_opts; + build_opts.add_option_if_else(policy == ConvertPolicy::WRAP || has_float_out, "-DWRAP", "-DSATURATE"); + build_opts.add_option("-DDATA_TYPE_IN1=" + get_cl_type_from_data_type(input1->info()->data_type())); + build_opts.add_option("-DDATA_TYPE_IN2=" + get_cl_type_from_data_type(input2->info()->data_type())); + build_opts.add_option("-DDATA_TYPE_OUT=" + get_cl_type_from_data_type(output->info()->data_type())); + if(is_data_type_quantized_asymmetric(input1->info()->data_type())) + { + build_opts.add_option("-DOFFSET_IN1=" + support::cpp11::to_string(input1->info()->quantization_info().offset)); + build_opts.add_option("-DOFFSET_IN2=" + support::cpp11::to_string(input2->info()->quantization_info().offset)); + build_opts.add_option("-DOFFSET_OUT=" + support::cpp11::to_string(output->info()->quantization_info().offset)); + build_opts.add_option("-DSCALE_IN1=" + support::cpp11::to_string(input1->info()->quantization_info().scale)); + build_opts.add_option("-DSCALE_IN2=" + support::cpp11::to_string(input2->info()->quantization_info().scale)); + build_opts.add_option("-DSCALE_OUT=" + support::cpp11::to_string(output->info()->quantization_info().scale)); + kernel_name += "_quantized"; + } // Create kernel - _kernel = static_cast(CLKernelLibrary::get().create_kernel("arithmetic_sub", build_opts)); + _kernel = static_cast(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options())); // Configure kernel window - auto win_config = validate_and_configure_window(input1->info(), input2->info(), output->info()); - ARM_COMPUTE_ERROR_THROW_ON(win_config.first); ICLKernel::configure_internal(win_config.second); } Status CLArithmeticSubtractionKernel::validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, ConvertPolicy policy) { - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input1, input2, output, policy)); - ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input1->clone().get(), input2->clone().get(), output->clone().get()).first); + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input1, input2, output); + + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(*input1, *input2, *output, policy)); + ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(*input1->clone(), *input2->clone(), *output->clone()).first); return Status{}; } @@ -143,16 +182,51 @@ void CLArithmeticSubtractionKernel::run(const Window &window, cl::CommandQueue & ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window); - Window collapsed = window.collapse_if_possible(ICLKernel::window(), Window::DimZ); - Window slice = collapsed.first_slice_window_3D(); + const TensorShape &in_shape1 = _input1->info()->tensor_shape(); + const TensorShape &in_shape2 = _input2->info()->tensor_shape(); + const TensorShape &out_shape = _output->info()->tensor_shape(); + + // Collapse only if broadcast dimensions is less than 2, or in case of no broadcasting + bool can_collapse = true; + if(std::min(in_shape1.total_size(), in_shape2.total_size()) > 1) + { + can_collapse = (std::min(in_shape1.num_dimensions(), in_shape2.num_dimensions()) > Window::DimZ); + for(size_t d = Window::DimZ; can_collapse && (d < out_shape.num_dimensions()); d++) + { + can_collapse = (in_shape1[d] == in_shape2[d]); + } + } + + bool has_collapsed = false; + Window collapsed = can_collapse ? window.collapse_if_possible(ICLKernel::window(), Window::DimZ, &has_collapsed) : window; + + const TensorShape &in_shape1_collapsed = has_collapsed ? in_shape1.collapsed_from(Window::DimZ) : in_shape1; + const TensorShape &in_shape2_collapsed = has_collapsed ? in_shape2.collapsed_from(Window::DimZ) : in_shape2; + + Window slice = collapsed.first_slice_window_3D(); + Window slice_input1 = slice.broadcast_if_dimension_le_one(in_shape1_collapsed); + Window slice_input2 = slice.broadcast_if_dimension_le_one(in_shape2_collapsed); do { unsigned int idx = 0; - add_3D_tensor_argument(idx, _input1, slice); - add_3D_tensor_argument(idx, _input2, slice); + + add_3D_tensor_argument(idx, _input1, slice_input1); + add_3D_tensor_argument(idx, _input2, slice_input2); add_3D_tensor_argument(idx, _output, slice); + enqueue(queue, *this, slice); + + collapsed.slide_window_slice_3D(slice_input1); + collapsed.slide_window_slice_3D(slice_input2); } while(collapsed.slide_window_slice_3D(slice)); } + +BorderSize CLArithmeticSubtractionKernel::border_size() const +{ + const unsigned int replicateSize = _output->info()->dimension(0) - std::min(_input1->info()->dimension(0), _input2->info()->dimension(0)); + const unsigned int border = std::min(num_elems_processed_per_iteration - 1U, replicateSize); + return BorderSize(0, border, 0, 0); +} +} // namespace arm_compute \ No newline at end of file diff --git a/src/core/NEON/kernels/NEArithmeticAdditionKernel.cpp b/src/core/NEON/kernels/NEArithmeticAdditionKernel.cpp index a6102b159f..169554f87a 100644 --- a/src/core/NEON/kernels/NEArithmeticAdditionKernel.cpp +++ b/src/core/NEON/kernels/NEArithmeticAdditionKernel.cpp @@ -456,7 +456,7 @@ void NEArithmeticAdditionKernel::configure(const ITensor *input1, const ITensor Status NEArithmeticAdditionKernel::validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, ConvertPolicy policy) { - ARM_COMPUTE_ERROR_ON_NULLPTR(input1, input2, output); + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input1, input2, output); ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(*input1, *input2, *output, policy)); ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(*input1->clone(), *input2->clone(), *output->clone()).first); diff --git a/src/core/NEON/kernels/NEArithmeticSubtractionKernel.cpp b/src/core/NEON/kernels/NEArithmeticSubtractionKernel.cpp index 3c76548b0a..ff8fb84958 100644 --- a/src/core/NEON/kernels/NEArithmeticSubtractionKernel.cpp +++ b/src/core/NEON/kernels/NEArithmeticSubtractionKernel.cpp @@ -46,10 +46,12 @@ class Coordinates; namespace { +constexpr unsigned int num_elems_processed_per_iteration = 16; + void sub_wrap_U8_U8_U8(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window) { - Iterator input1(in1, window); - Iterator input2(in2, window); + Iterator input1(in1, window.broadcast_if_dimension_le_one(in1->info()->tensor_shape())); + Iterator input2(in2, window.broadcast_if_dimension_le_one(in2->info()->tensor_shape())); Iterator output(out, window); execute_window_loop(window, [&](const Coordinates & id) @@ -64,8 +66,8 @@ void sub_wrap_U8_U8_U8(const ITensor *in1, const ITensor *in2, ITensor *out, con void sub_saturate_U8_U8_U8(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window) { - Iterator input1(in1, window); - Iterator input2(in2, window); + Iterator input1(in1, window.broadcast_if_dimension_le_one(in1->info()->tensor_shape())); + Iterator input2(in2, window.broadcast_if_dimension_le_one(in2->info()->tensor_shape())); Iterator output(out, window); execute_window_loop(window, [&](const Coordinates & id) @@ -80,8 +82,8 @@ void sub_saturate_U8_U8_U8(const ITensor *in1, const ITensor *in2, ITensor *out, void sub_wrap_S16_S16_S16(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window) { - Iterator input1(in1, window); - Iterator input2(in2, window); + Iterator input1(in1, window.broadcast_if_dimension_le_one(in1->info()->tensor_shape())); + Iterator input2(in2, window.broadcast_if_dimension_le_one(in2->info()->tensor_shape())); Iterator output(out, window); execute_window_loop(window, [&](const Coordinates & id) @@ -104,8 +106,8 @@ void sub_wrap_S16_S16_S16(const ITensor *in1, const ITensor *in2, ITensor *out, void sub_saturate_S16_S16_S16(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window) { - Iterator input1(in1, window); - Iterator input2(in2, window); + Iterator input1(in1, window.broadcast_if_dimension_le_one(in1->info()->tensor_shape())); + Iterator input2(in2, window.broadcast_if_dimension_le_one(in2->info()->tensor_shape())); Iterator output(out, window); execute_window_loop(window, [&](const Coordinates & id) @@ -144,8 +146,8 @@ inline float16x8x2_t vsub2q_f16(const float16x8x2_t &a, const float16x8x2_t &b) void sub_F16_F16_F16(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window) { #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC - Iterator input1(in1, window); - Iterator input2(in2, window); + Iterator input1(in1, window.broadcast_if_dimension_le_one(in1->info()->tensor_shape())); + Iterator input2(in2, window.broadcast_if_dimension_le_one(in2->info()->tensor_shape())); Iterator output(out, window); execute_window_loop(window, [&](const Coordinates & id) @@ -167,8 +169,8 @@ void sub_F16_F16_F16(const ITensor *in1, const ITensor *in2, ITensor *out, const void sub_F32_F32_F32(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window) { - Iterator input1(in1, window); - Iterator input2(in2, window); + Iterator input1(in1, window.broadcast_if_dimension_le_one(in1->info()->tensor_shape())); + Iterator input2(in2, window.broadcast_if_dimension_le_one(in2->info()->tensor_shape())); Iterator output(out, window); execute_window_loop(window, [&](const Coordinates & id) @@ -192,8 +194,8 @@ void sub_F32_F32_F32(const ITensor *in1, const ITensor *in2, ITensor *out, const } void sub_wrap_S16_U8_S16(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window) { - Iterator input1(in1, window); - Iterator input2(in2, window); + Iterator input1(in1, window.broadcast_if_dimension_le_one(in1->info()->tensor_shape())); + Iterator input2(in2, window.broadcast_if_dimension_le_one(in2->info()->tensor_shape())); Iterator output(out, window); execute_window_loop(window, [&](const Coordinates & id) @@ -213,8 +215,8 @@ void sub_wrap_S16_U8_S16(const ITensor *in1, const ITensor *in2, ITensor *out, c void sub_saturate_S16_U8_S16(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window) { - Iterator input1(in1, window); - Iterator input2(in2, window); + Iterator input1(in1, window.broadcast_if_dimension_le_one(in1->info()->tensor_shape())); + Iterator input2(in2, window.broadcast_if_dimension_le_one(in2->info()->tensor_shape())); Iterator output(out, window); execute_window_loop(window, [&](const Coordinates & id) @@ -234,8 +236,8 @@ void sub_saturate_S16_U8_S16(const ITensor *in1, const ITensor *in2, ITensor *ou void sub_wrap_U8_S16_S16(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window) { - Iterator input1(in1, window); - Iterator input2(in2, window); + Iterator input1(in1, window.broadcast_if_dimension_le_one(in1->info()->tensor_shape())); + Iterator input2(in2, window.broadcast_if_dimension_le_one(in2->info()->tensor_shape())); Iterator output(out, window); execute_window_loop(window, [&](const Coordinates & id) @@ -255,8 +257,8 @@ void sub_wrap_U8_S16_S16(const ITensor *in1, const ITensor *in2, ITensor *out, c void sub_saturate_U8_S16_S16(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window) { - Iterator input1(in1, window); - Iterator input2(in2, window); + Iterator input1(in1, window.broadcast_if_dimension_le_one(in1->info()->tensor_shape())); + Iterator input2(in2, window.broadcast_if_dimension_le_one(in2->info()->tensor_shape())); Iterator output(out, window); execute_window_loop(window, [&](const Coordinates & id) @@ -276,8 +278,8 @@ void sub_saturate_U8_S16_S16(const ITensor *in1, const ITensor *in2, ITensor *ou void sub_wrap_U8_U8_S16(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window) { - Iterator input1(in1, window); - Iterator input2(in2, window); + Iterator input1(in1, window.broadcast_if_dimension_le_one(in1->info()->tensor_shape())); + Iterator input2(in2, window.broadcast_if_dimension_le_one(in2->info()->tensor_shape())); Iterator output(out, window); execute_window_loop(window, [&](const Coordinates & id) @@ -298,8 +300,8 @@ void sub_wrap_U8_U8_S16(const ITensor *in1, const ITensor *in2, ITensor *out, co void sub_saturate_U8_U8_S16(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window) { - Iterator input1(in1, window); - Iterator input2(in2, window); + Iterator input1(in1, window.broadcast_if_dimension_le_one(in1->info()->tensor_shape())); + Iterator input2(in2, window.broadcast_if_dimension_le_one(in2->info()->tensor_shape())); Iterator output(out, window); execute_window_loop(window, [&](const Coordinates & id) @@ -318,43 +320,71 @@ void sub_saturate_U8_U8_S16(const ITensor *in1, const ITensor *in2, ITensor *out input1, input2, output); } -inline Status validate_arguments(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, ConvertPolicy policy) +inline Status validate_arguments(const ITensorInfo &input1, const ITensorInfo &input2, const ITensorInfo &output, ConvertPolicy policy) { ARM_COMPUTE_UNUSED(policy); - ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(input1); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input1, input2, output); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input1, 1, DataType::U8, DataType::S16, DataType::F16, DataType::F32); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input2, 1, DataType::U8, DataType::S16, DataType::F16, DataType::F32); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8, DataType::S16, DataType::F16, DataType::F32); - - ARM_COMPUTE_RETURN_ERROR_ON_MSG( - !(input1->data_type() == DataType::U8 && input2->data_type() == DataType::U8 && output->data_type() == DataType::U8) - && !(input1->data_type() == DataType::U8 && input2->data_type() == DataType::U8 && output->data_type() == DataType::S16) - && !(input1->data_type() == DataType::U8 && input2->data_type() == DataType::S16 && output->data_type() == DataType::S16) - && !(input1->data_type() == DataType::S16 && input2->data_type() == DataType::U8 && output->data_type() == DataType::S16) - && !(input1->data_type() == DataType::S16 && input2->data_type() == DataType::S16 && output->data_type() == DataType::S16) - && !(input1->data_type() == DataType::F32 && input2->data_type() == DataType::F32 && output->data_type() == DataType::F32) - && !(input1->data_type() == DataType::F16 && input2->data_type() == DataType::F16 && output->data_type() == DataType::F16), - "You called subtract with the wrong image formats"); + ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(&input1); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input1, 1, DataType::U8, DataType::S16, DataType::F16, DataType::F32); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input2, 1, DataType::U8, DataType::S16, DataType::F16, DataType::F32); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&output, 1, DataType::U8, DataType::S16, DataType::F16, DataType::F32); + + const TensorShape out_shape = TensorShape::broadcast_shape(input1.tensor_shape(), input2.tensor_shape()); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(out_shape.total_size() == 0, "Inputs are not broadcast compatible"); + // Validate in case of configured output + if(output.total_size() > 0) + { + ARM_COMPUTE_RETURN_ERROR_ON_MSG( + !(input1.data_type() == DataType::U8 && input2.data_type() == DataType::U8 && output.data_type() == DataType::U8) + && !(input1.data_type() == DataType::U8 && input2.data_type() == DataType::U8 && output.data_type() == DataType::S16) + && !(input1.data_type() == DataType::U8 && input2.data_type() == DataType::S16 && output.data_type() == DataType::S16) + && !(input1.data_type() == DataType::S16 && input2.data_type() == DataType::U8 && output.data_type() == DataType::S16) + && !(input1.data_type() == DataType::S16 && input2.data_type() == DataType::S16 && output.data_type() == DataType::S16) + && !(input1.data_type() == DataType::F32 && input2.data_type() == DataType::F32 && output.data_type() == DataType::F32) + && !(input1.data_type() == DataType::F16 && input2.data_type() == DataType::F16 && output.data_type() == DataType::F16), + "You called subtract with the wrong image formats"); + + ARM_COMPUTE_RETURN_ERROR_ON_MSG(detail::have_different_dimensions(out_shape, output.tensor_shape(), 0), + "Wrong shape for output"); + } return Status{}; } -inline std::pair validate_and_configure_window(ITensorInfo *input1, ITensorInfo *input2, ITensorInfo *output) +inline std::pair validate_and_configure_window(ITensorInfo &input1, ITensorInfo &input2, ITensorInfo &output) { - constexpr unsigned int num_elems_processed_per_iteration = 16; + const std::pair broadcast_pair = ITensorInfo::broadcast_shape_and_valid_region(input1, input2); + const TensorShape &out_shape = broadcast_pair.first; + const ValidRegion &valid_region = broadcast_pair.second; - // Configure kernel window - Window win = calculate_max_window(*input1, Steps(num_elems_processed_per_iteration)); - AccessWindowHorizontal output_access(output, 0, num_elems_processed_per_iteration); + // Auto initialize output if not initialized + { + set_shape_if_empty(output, out_shape); - bool window_changed = update_window_and_padding(win, - AccessWindowHorizontal(input1, 0, num_elems_processed_per_iteration), - AccessWindowHorizontal(input2, 0, num_elems_processed_per_iteration), - output_access); + if(input1.data_type() == DataType::S16 || input2.data_type() == DataType::S16) + { + set_format_if_unknown(output, Format::S16); + } + else if(input1.data_type() == DataType::F16 && input2.data_type() == DataType::F16) + { + set_format_if_unknown(output, Format::F16); + } + else if(input1.data_type() == DataType::F32 || input2.data_type() == DataType::F32) + { + set_format_if_unknown(output, Format::F32); + } + } + + Window win = calculate_max_window(valid_region, Steps(num_elems_processed_per_iteration)); + Window win_input1 = win.broadcast_if_dimension_le_one(input1); + Window win_input2 = win.broadcast_if_dimension_le_one(input2); - ValidRegion valid_region = intersect_valid_regions(input1->valid_region(), - input2->valid_region()); + AccessWindowHorizontal input1_access(&input1, 0, num_elems_processed_per_iteration); + AccessWindowHorizontal input2_access(&input2, 0, num_elems_processed_per_iteration); + AccessWindowHorizontal output_access(&output, 0, num_elems_processed_per_iteration); + + bool window_changed = update_window_and_padding(win_input1, input1_access) + || update_window_and_padding(win_input2, input2_access) + || update_window_and_padding(win, output_access); output_access.set_valid_region(win, valid_region); @@ -371,26 +401,11 @@ NEArithmeticSubtractionKernel::NEArithmeticSubtractionKernel() void NEArithmeticSubtractionKernel::configure(const ITensor *input1, const ITensor *input2, ITensor *output, ConvertPolicy policy) { ARM_COMPUTE_ERROR_ON_NULLPTR(input1, input2, output); + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(*input1->info(), *input2->info(), *output->info(), policy)); - // Auto initialize output if not initialized - { - set_shape_if_empty(*output->info(), input1->info()->tensor_shape()); - - if(input1->info()->data_type() == DataType::S16 || input2->info()->data_type() == DataType::S16) - { - set_format_if_unknown(*output->info(), Format::S16); - } - else if(input1->info()->data_type() == DataType::F16 || input2->info()->data_type() == DataType::F16) - { - set_format_if_unknown(*output->info(), Format::F16); - } - else if(input1->info()->data_type() == DataType::F32 || input2->info()->data_type() == DataType::F32) - { - set_format_if_unknown(*output->info(), Format::F32); - } - } - - ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input1->info(), input2->info(), output->info(), policy)); + // Configure kernel window + auto win_config = validate_and_configure_window(*input1->info(), *input2->info(), *output->info()); + ARM_COMPUTE_ERROR_THROW_ON(win_config.first); static std::map map_function = { @@ -427,16 +442,15 @@ void NEArithmeticSubtractionKernel::configure(const ITensor *input1, const ITens _func = it->second; } - // Configure kernel window - auto win_config = validate_and_configure_window(input1->info(), input2->info(), output->info()); - ARM_COMPUTE_ERROR_THROW_ON(win_config.first); INEKernel::configure(win_config.second); } Status NEArithmeticSubtractionKernel::validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, ConvertPolicy policy) { - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input1, input2, output, policy)); - ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input1->clone().get(), input2->clone().get(), output->clone().get()).first); + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input1, input2, output); + + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(*input1, *input2, *output, policy)); + ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(*input1->clone(), *input2->clone(), *output->clone()).first); return Status{}; } @@ -450,3 +464,10 @@ void NEArithmeticSubtractionKernel::run(const Window &window, const ThreadInfo & (*_func)(_input1, _input2, _output, window); } + +BorderSize NEArithmeticSubtractionKernel::border_size() const +{ + const unsigned int replicateSize = _output->info()->dimension(0) - std::min(_input1->info()->dimension(0), _input2->info()->dimension(0)); + const unsigned int border = std::min(num_elems_processed_per_iteration - 1U, replicateSize); + return BorderSize(0, border, 0, 0); +} \ No newline at end of file -- cgit v1.2.1