| /* |
| * Copyright (c) 2017-2021 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 "src/core/gpu/cl/kernels/ClSoftmaxKernel.h" |
| #include "arm_compute/core/CL/ICLTensor.h" |
| #include "arm_compute/core/Utils.h" |
| #include "arm_compute/core/experimental/Types.h" |
| #include "arm_compute/core/utils/quantization/AsymmHelpers.h" |
| #include "src/core/CL/CLValidate.h" |
| #include "src/core/helpers/AutoConfiguration.h" |
| #include "src/core/helpers/WindowHelpers.h" |
| #include "support/Cast.h" |
| #include "support/StringSupport.h" |
| |
| namespace arm_compute |
| { |
| namespace opencl |
| { |
| namespace kernels |
| { |
| namespace |
| { |
| /** Calculates softmax parameters from the quantized input scale and scaling factor for the exponent and places them as build options. |
| * |
| * Prepares these build options: |
| * -INPUT_BETA_MULTIPLIER, INPUT_BETA_LEFT_SHIFT - quantized representation of beta multiplier. |
| * -DIFF_MIN - threshold difference between maximum value of input data and current processed value, |
| * it defines whether the value will be taken into account or not. |
| * |
| * @param[in] build_opts Build options to extend |
| * @param[in] input_scale Input scaling factor |
| * @param[in] beta Exponent scaling factor beta |
| */ |
| CLBuildOptions prepare_quantized_softmax_build_options(float input_scale, float beta) |
| { |
| // Number of integer bits in temporary fixed-point representation of current-to-max difference |
| static const int scaled_diff_int_bits = 5; |
| // Number of integer bits used in temporary fixed-point representation of exponent accumulator |
| static const int exp_accumulation_in_bits = 12; |
| |
| const double beta_multiplier = std::min( |
| 1.0 * beta * input_scale * (1 << (31 - scaled_diff_int_bits)), |
| (1LL << 31) - 1.0); |
| int input_beta_multiplier; |
| int input_beta_left_shift; |
| quantization::calculate_quantized_multiplier_greater_than_one(beta_multiplier, &input_beta_multiplier, &input_beta_left_shift); |
| |
| const double max_input_rescaled = 1.0 * ((1 << scaled_diff_int_bits) - 1) * (1LL << (31 - scaled_diff_int_bits)) / (1LL << input_beta_left_shift); |
| const int diff_min = -1.f * std::floor(max_input_rescaled); |
| |
| CLBuildOptions build_opts; |
| build_opts.add_option("-DSCALED_DIFF_INT_BITS=" + support::cpp11::to_string(scaled_diff_int_bits)); |
| build_opts.add_option("-DEXP_ACCUMULATION_INT_BITS=" + support::cpp11::to_string(exp_accumulation_in_bits)); |
| build_opts.add_option("-DINPUT_BETA_MULTIPLIER=" + support::cpp11::to_string(input_beta_multiplier)); |
| build_opts.add_option("-DINPUT_BETA_LEFT_SHIFT=" + support::cpp11::to_string(input_beta_left_shift)); |
| build_opts.add_option("-DDIFF_MIN=" + support::cpp11::to_string(diff_min)); |
| |
| return build_opts; |
| } |
| |
| Status validate_arguments_1DMaxShiftExpSum(const ITensorInfo &src, const ITensorInfo &max, const ITensorInfo &dst, const ITensorInfo &sum) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(&src); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&src, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::F16, DataType::F32); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&src, &max); |
| |
| const bool is_quantized_asymmetric = is_data_type_quantized_asymmetric(src.data_type()); |
| |
| // Checks performed when output is configured |
| if(dst.total_size() != 0) |
| { |
| if(is_quantized_asymmetric) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&dst, 1, DataType::S32); |
| } |
| else |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&src, &dst); |
| } |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(&src, &dst); |
| } |
| |
| // Checks performed when sum is configured |
| if(sum.total_size() != 0) |
| { |
| if(is_quantized_asymmetric) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&sum, 1, DataType::S32); |
| } |
| else |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&max, &sum); |
| } |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(&max, &sum); |
| } |
| |
| return Status{}; |
| } |
| |
| Status validate_arguments_1DNorm(const ITensorInfo &src, const ITensorInfo &sum, const ITensorInfo &dst, const SoftmaxKernelInfo &info) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(&src); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&src, 1, DataType::S32, DataType::F16, DataType::F32); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&src, &sum); |
| ARM_COMPUTE_RETURN_ERROR_ON(info.is_log && !is_data_type_float(info.input_data_type)); |
| |
| // Note: output should always have a scale of 1/256 and offset 0 |
| const QuantizationInfo allowed_quantization_info = get_softmax_output_quantization_info(info.input_data_type, info.is_log); |
| const bool is_quantized_asymmetric = is_data_type_quantized_asymmetric(info.input_data_type); |
| |
| // Checks performed when output is configured |
| if(dst.total_size() != 0) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(&src, &dst); |
| if(!is_quantized_asymmetric) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&src, &dst); |
| } |
| else |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&dst, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED); |
| ARM_COMPUTE_RETURN_ERROR_ON(dst.quantization_info() != allowed_quantization_info); |
| } |
| } |
| |
| return Status{}; |
| } |
| } // namespace |
| |
| /**< Grid size (obtained through auto-tuning) */ |
| const unsigned int ClLogits1DMaxShiftExpSumKernel::_grid_size = 64; |
| /**< Vector size in the serial case (obtained through auto-tuning) */ |
| const unsigned int ClLogits1DMaxShiftExpSumKernel::_serial_vector_size = 8; |
| /**< Vector size in the parallel case (obtained through auto-tuning, enables the best memory access pattern for Bifrost) .*/ |
| const unsigned int ClLogits1DMaxShiftExpSumKernel::_parallel_vector_size = 4; |
| |
| ClLogits1DMaxShiftExpSumKernel::ClLogits1DMaxShiftExpSumKernel() |
| { |
| _type = CLKernelType::ELEMENTWISE; |
| } |
| |
| void ClLogits1DMaxShiftExpSumKernel::configure(const CLCompileContext &compile_context, const ITensorInfo &src, ITensorInfo &max, ITensorInfo &dst, ITensorInfo &sum, const SoftmaxKernelInfo &info) |
| { |
| auto padding_info = get_padding_info({ &src, &max, &dst, &sum }); |
| |
| // Output auto initialization if not yet initialized |
| auto_init_if_empty(sum, src.clone()->set_tensor_shape(max.tensor_shape())); |
| auto_init_if_empty(dst, *src.clone()); |
| |
| // Perform validation step |
| ARM_COMPUTE_ERROR_THROW_ON(validate_arguments_1DMaxShiftExpSum(src, max, dst, sum)); |
| |
| const DataType dt = src.data_type(); |
| const UniformQuantizationInfo qinfo = src.quantization_info().uniform(); |
| const size_t reduction_dim_size = src.dimension(0); |
| const float beta = info.beta; |
| const auto is_signed_qasymm8 = is_data_type_quantized_asymmetric_signed(info.input_data_type); |
| const int min_value = is_signed_qasymm8 ? CL_SCHAR_MIN : 0; |
| |
| ParallelReductionInfo parallel_reduction_info = is_parallel_reduction(reduction_dim_size); |
| const unsigned int vector_size = adjust_vec_size(std::get<1>(parallel_reduction_info), reduction_dim_size); |
| |
| // Set build options |
| CLBuildOptions build_opts; |
| build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(dt)); |
| build_opts.add_option("-DMIN_VALUE=" + support::cpp11::to_string(min_value)); |
| build_opts.add_option("-DVECTOR_SIZE=" + support::cpp11::to_string(vector_size)); |
| build_opts.add_option("-DSRC_WIDTH=" + support::cpp11::to_string(reduction_dim_size)); |
| build_opts.add_option("-DVECTOR_SIZE_LEFTOVER=" + support::cpp11::to_string(reduction_dim_size % vector_size)); |
| build_opts.add_option("-DLOG_VECTOR_SIZE=" + support::cpp11::to_string(lround(log2(vector_size)))); |
| build_opts.add_option_if((reduction_dim_size % vector_size) != 0, "-DNON_MULTIPLE_OF_VECTOR_SIZE"); |
| build_opts.add_option_if(is_signed_qasymm8, "-DQASYMM8_SIGNED"); |
| build_opts.add_option_if(is_data_type_float(dt) && (beta != 1.0f), "-DBETA=" + float_to_string_with_full_precision(beta)); |
| build_opts.add_option_if(is_data_type_float(dt) && info.is_log, "-DLOG_SOFTMAX"); |
| build_opts.add_option_if(is_data_type_float(dt), "-DMINVAL=" + ((dt == DataType::F16) ? std::string("-HALF_MAX") : std::string("-FLT_MAX"))); |
| build_opts.add_options_if(is_data_type_quantized_asymmetric(dt), prepare_quantized_softmax_build_options(qinfo.scale, beta).options()); |
| |
| cl::NDRange lws_hint(cl::NullRange); |
| std::string kernel_name = std::string("softmax_layer_max_shift_exp_sum_") + (is_data_type_quantized_asymmetric(dt) ? "quantized_" : ""); |
| |
| // Configure parallel kernel if needed |
| if(std::get<0>(parallel_reduction_info)) |
| { |
| kernel_name += "parallel"; |
| bool is_grid_size_pow2 = (_grid_size != 0) && ((_grid_size & (_grid_size - 1)) == 0); |
| build_opts.add_option_if(is_grid_size_pow2 && _grid_size <= 256, "-DGRID_SIZE=" + support::cpp11::to_string(_grid_size)); |
| |
| // Handle boundary conditions. |
| const unsigned int multiple_grid_size = (reduction_dim_size / vector_size) % _grid_size; |
| build_opts.add_option_if((multiple_grid_size != 0) || ((reduction_dim_size % vector_size) != 0), "-DNON_MULTIPLE_OF_GRID_SIZE"); |
| // Setting _lws_hint in this way can also communicate grid_size to ClLogits1DMaxShiftExpSumKernel::run(). |
| // A single workgroup performs reduction in dimension 0 in the parallel case, hence lws[0]==gws[0]. |
| lws_hint = cl::NDRange(_grid_size); |
| } |
| else |
| { |
| kernel_name += "serial"; |
| } |
| |
| // Create kernel. |
| _kernel = create_kernel(compile_context, kernel_name, build_opts.options()); |
| |
| // Configure window |
| Window win = calculate_max_window(src, Steps(reduction_dim_size)); |
| IClKernel::configure_internal(win, lws_hint); |
| |
| ARM_COMPUTE_ERROR_ON(has_padding_changed(padding_info)); |
| } |
| |
| Status ClLogits1DMaxShiftExpSumKernel::validate(const ITensorInfo &src, const ITensorInfo &max, const ITensorInfo &dst, const ITensorInfo &sum) |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_1DMaxShiftExpSum(src, max, dst, sum)); |
| return Status{}; |
| } |
| |
| ClLogits1DMaxShiftExpSumKernel::ParallelReductionInfo ClLogits1DMaxShiftExpSumKernel::is_parallel_reduction(size_t size) |
| { |
| bool is_parallel_reduction = (size >= (_grid_size * _serial_vector_size)) && (_grid_size > 1); |
| unsigned int vector_size = is_parallel_reduction ? _parallel_vector_size : _serial_vector_size; |
| return std::make_tuple(is_parallel_reduction, vector_size); |
| } |
| |
| void ClLogits1DMaxShiftExpSumKernel::run_op(ITensorPack &tensors, const Window &window, ::cl::CommandQueue &queue) |
| { |
| ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); |
| ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window); |
| |
| auto src = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC)); |
| auto dst = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST)); |
| auto max = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_INT_0)); |
| auto sum = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_INT_1)); |
| |
| ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst, max, sum); |
| |
| // Collapse window in Z dimension |
| Window window_collapsed = window.collapse_if_possible(IClKernel::window(), Window::DimZ); |
| |
| // Reconfigure window in case of parallel reduction |
| ParallelReductionInfo parallel_reduction_info = is_parallel_reduction(src->info()->dimension(0)); |
| if(std::get<0>(parallel_reduction_info)) |
| { |
| // Launch grid_size parallel work items |
| window_collapsed.set(Window::DimX, Window::Dimension(0, _grid_size, 1)); |
| } |
| |
| // Get slices |
| Window slice = window_collapsed.first_slice_window_3D(); |
| do |
| { |
| unsigned int idx = 0; |
| // Set inputs |
| add_3D_tensor_argument(idx, src, slice); |
| add_3D_tensor_argument(idx, max, slice); |
| add_3D_tensor_argument(idx, dst, slice); |
| add_3D_tensor_argument(idx, sum, slice); |
| enqueue(queue, *this, slice, lws_hint()); |
| } |
| while(window_collapsed.slide_window_slice_3D(slice)); |
| } |
| |
| ClLogits1DNormKernel::ClLogits1DNormKernel() |
| { |
| _type = CLKernelType::ELEMENTWISE; |
| } |
| |
| void ClLogits1DNormKernel::configure(const CLCompileContext &compile_context, const ITensorInfo &src, const ITensorInfo &sum, ITensorInfo &dst, const SoftmaxKernelInfo &info) |
| { |
| auto padding_info = get_padding_info({ &src, &dst, &sum }); |
| |
| // Note: output should always have a scale of 1/256 and offset 0 |
| const bool is_quantized_asymmetric = is_data_type_quantized_asymmetric(info.input_data_type); |
| const DataType output_data_type = info.input_data_type; |
| const QuantizationInfo allowed_quantization_info = get_softmax_output_quantization_info(info.input_data_type, info.is_log); |
| const UniformQuantizationInfo qinfo = src.quantization_info().uniform(); |
| |
| // Output auto initialization if not yet initialized |
| auto_init_if_empty(dst, src.clone()->set_data_type(output_data_type).set_quantization_info(allowed_quantization_info)); |
| |
| // Perform validation step |
| ARM_COMPUTE_ERROR_THROW_ON(validate_arguments_1DNorm(src, sum, dst, info)); |
| |
| const auto is_signed_qasymm8 = is_data_type_quantized_asymmetric_signed(info.input_data_type); |
| const int min_value = is_signed_qasymm8 ? CL_SCHAR_MIN : 0; |
| const unsigned int vector_size = adjust_vec_size(16, src.dimension(0)); |
| |
| // Set build options |
| CLBuildOptions build_opts; |
| build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(info.input_data_type)); |
| build_opts.add_option("-DMIN_VALUE=" + support::cpp11::to_string(min_value)); |
| build_opts.add_option("-DVECTOR_SIZE=" + support::cpp11::to_string(vector_size)); |
| build_opts.add_option("-DVECTOR_SIZE_LEFTOVER=" + support::cpp11::to_string(src.dimension(0) % vector_size)); |
| build_opts.add_option_if(is_data_type_quantized_asymmetric_signed(info.input_data_type), "-DQASYMM8_SIGNED"); |
| build_opts.add_options_if(is_quantized_asymmetric, |
| prepare_quantized_softmax_build_options(qinfo.scale, info.beta).options()); |
| build_opts.add_option_if(info.is_log, "-DLOG_SOFTMAX"); |
| |
| // Create kernel |
| std::string kernel_name = std::string("softmax_layer_norm") + (is_quantized_asymmetric ? "_quantized" : ""); |
| _kernel = create_kernel(compile_context, kernel_name, build_opts.options()); |
| |
| // Configure window |
| auto win = calculate_max_window(src, Steps(vector_size)); |
| ICLKernel::configure_internal(win); |
| |
| ARM_COMPUTE_ERROR_ON(has_padding_changed(padding_info)); |
| } |
| |
| Status ClLogits1DNormKernel::validate(const ITensorInfo &src, const ITensorInfo &sum, const ITensorInfo &dst, const SoftmaxKernelInfo &info) |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_1DNorm(src, sum, dst, info)); |
| |
| return Status{}; |
| } |
| |
| void ClLogits1DNormKernel::run_op(ITensorPack &tensors, const Window &window, ::cl::CommandQueue &queue) |
| { |
| ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); |
| ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window); |
| |
| auto src = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC)); |
| auto dst = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST)); |
| auto sum = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_INT_0)); |
| |
| ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst, sum); |
| |
| Window window_collapsed = window.collapse_if_possible(ICLKernel::window(), Window::DimZ); |
| Window slice = window_collapsed.first_slice_window_3D(); |
| |
| do |
| { |
| Window sum_slice = slice; |
| sum_slice.set(Window::DimX, Window::Dimension(0, 1, 1)); |
| |
| unsigned int idx = 0; |
| // Set inputs |
| add_3D_tensor_argument(idx, src, slice); |
| add_3D_tensor_argument(idx, sum, sum_slice); |
| add_3D_tensor_argument(idx, dst, slice); |
| enqueue(queue, *this, slice, lws_hint()); |
| } |
| while(window_collapsed.slide_window_slice_3D(slice)); |
| } |
| } // namespace kernels |
| } // namespace opencl |
| } // namespace arm_compute |