| /* |
| * Copyright (c) 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/gpu/cl/operators/ClSoftmax.h" |
| #include "arm_compute/core/utils/misc/ShapeCalculator.h" |
| #include "src/core/helpers/MemoryHelpers.h" |
| #include "src/core/helpers/SoftmaxHelpers.h" |
| #include "src/gpu/cl/kernels/ClSoftmaxKernel.h" |
| #include "src/gpu/cl/operators/ClPermute.h" |
| #include "src/gpu/cl/utils/ClAuxTensorHandler.h" |
| #include "support/Cast.h" |
| |
| #include "src/common/utils/Log.h" |
| |
| using namespace arm_compute::experimental; |
| |
| namespace arm_compute |
| { |
| namespace opencl |
| { |
| ClSoftmax::ClSoftmax() |
| : _permute_input(std::make_unique<ClPermute>()), |
| _permute_output(std::make_unique<ClPermute>()), |
| _max_shift_exp_sum_kernel(std::make_unique<kernels::ClLogits1DMaxShiftExpSumKernel>()), |
| _norm_kernel(std::make_unique<kernels::ClLogits1DNormKernel>()), |
| _max_info(), |
| _sum_info(), |
| _tmp_info(), |
| _permuted_src_info(), |
| _permuted_dst_info(), |
| _aux_mem(InternalTensorIdx::COUNT) |
| { |
| } |
| |
| void ClSoftmax::configure(const CLCompileContext &compile_context, const ITensorInfo &src, ITensorInfo &dst, const SoftmaxKernelInfo &info) |
| { |
| ARM_COMPUTE_ERROR_THROW_ON(validate(src, dst, info)); |
| ARM_COMPUTE_LOG_PARAMS(src, dst, info); |
| |
| const size_t actual_axis = static_cast<size_t>(wrap_around(info.axis, static_cast<int32_t>(src.num_dimensions()))); |
| |
| _needs_permute = actual_axis != 0; |
| |
| const ITensorInfo &tmp_input_info = _needs_permute ? _permuted_src_info : src; |
| ITensorInfo &tmp_output_info = _needs_permute ? _permuted_dst_info : dst; |
| |
| if(_needs_permute) |
| { |
| const auto perm_info = softmax_helpers::get_permutation_vector_from_softmax_axis(actual_axis); |
| _permute_input->configure(compile_context, &src, &_permuted_src_info, perm_info); |
| } |
| |
| DataType tmp_data_type = is_data_type_quantized_asymmetric(tmp_input_info.data_type()) ? DataType::S32 : tmp_input_info.data_type(); |
| _tmp_info = tmp_input_info.clone()->set_data_type(tmp_data_type); |
| |
| TensorShape max_sum_shape = tmp_input_info.tensor_shape(); |
| _max_info = tmp_input_info.clone()->set_tensor_shape(max_sum_shape); |
| _sum_info = tmp_input_info.clone()->set_tensor_shape(max_sum_shape).set_data_type(tmp_data_type); |
| |
| // Set GPU target to kernels |
| _max_shift_exp_sum_kernel->set_target(CLScheduler::get().target()); |
| |
| _max_shift_exp_sum_kernel->configure(compile_context, tmp_input_info, _max_info, _tmp_info, _sum_info, info); |
| _norm_kernel->configure(compile_context, _tmp_info, _sum_info, tmp_output_info, info); |
| |
| if(_needs_permute) |
| { |
| const auto perm_info = softmax_helpers::get_permutation_vector_from_softmax_axis(actual_axis); |
| _permute_output->configure(compile_context, &_permuted_dst_info, &dst, perm_info); |
| } |
| |
| _aux_mem[InternalTensorIdx::SUM] = MemoryInfo(offset_int_vec(InternalTensorIdx::SUM), MemoryLifetime::Temporary, _sum_info.total_size()); |
| _aux_mem[InternalTensorIdx::TMP] = MemoryInfo(offset_int_vec(InternalTensorIdx::TMP), MemoryLifetime::Temporary, _tmp_info.total_size()); |
| _aux_mem[InternalTensorIdx::MAX] = MemoryInfo(offset_int_vec(InternalTensorIdx::MAX), MemoryLifetime::Temporary, _max_info.total_size()); |
| |
| _aux_mem[InternalTensorIdx::PERMUTED_SRC] = MemoryInfo(offset_int_vec(InternalTensorIdx::PERMUTED_SRC), MemoryLifetime::Temporary, _permuted_src_info.total_size()); |
| _aux_mem[InternalTensorIdx::PERMUTED_DST] = MemoryInfo(offset_int_vec(InternalTensorIdx::PERMUTED_DST), MemoryLifetime::Temporary, _permuted_dst_info.total_size()); |
| } |
| |
| Status ClSoftmax::validate(const ITensorInfo &src, const ITensorInfo &dst, const SoftmaxKernelInfo &info) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(src.num_dimensions() > 4, "Only up to 4 dimensions are supported"); |
| ARM_COMPUTE_UNUSED(info.beta); |
| ARM_COMPUTE_RETURN_ERROR_ON(info.axis < static_cast<int32_t>(-src.num_dimensions()) || static_cast<int32_t>(src.num_dimensions()) <= info.axis); |
| |
| const size_t actual_axis = static_cast<size_t>(wrap_around(info.axis, static_cast<int32_t>(src.num_dimensions()))); |
| const bool needs_permute = actual_axis != 0; |
| if(needs_permute) |
| { |
| const PermutationVector permutation_vector = softmax_helpers::get_permutation_vector_from_softmax_axis(actual_axis); |
| const TensorShape permuted_shape = misc::shape_calculator::compute_permutation_output_shape(src, permutation_vector); |
| TensorInfo input_permuted(src.clone()->set_tensor_shape(permuted_shape)); |
| ARM_COMPUTE_RETURN_ON_ERROR(ClPermute::validate(&src, &input_permuted, permutation_vector)); |
| TensorInfo output_permuted(dst.clone()->set_tensor_shape(permuted_shape)); |
| ARM_COMPUTE_RETURN_ON_ERROR(ClPermute::validate(&output_permuted, &dst, permutation_vector)); |
| } |
| |
| // Create intermediate tensor info |
| DataType tmp_data_type = is_data_type_quantized_asymmetric(src.data_type()) ? DataType::S32 : src.data_type(); |
| TensorInfo tensor_info_tmp(src.clone()->set_data_type(tmp_data_type).set_is_resizable(true)); |
| |
| TensorShape max_sum_shape = src.tensor_shape(); |
| max_sum_shape.set(0, 1); |
| TensorInfo tensor_info_max(src.clone()->set_tensor_shape(max_sum_shape).set_is_resizable(true)); |
| TensorInfo tensor_info_sum(src.clone()->set_tensor_shape(max_sum_shape).set_data_type(tmp_data_type).set_quantization_info(QuantizationInfo()).set_is_resizable(true)); |
| |
| ARM_COMPUTE_RETURN_ON_ERROR(kernels::ClLogits1DMaxShiftExpSumKernel::validate(src, tensor_info_max, tensor_info_tmp, tensor_info_sum)); |
| ARM_COMPUTE_RETURN_ON_ERROR(kernels::ClLogits1DNormKernel::validate(tensor_info_tmp, tensor_info_sum, dst, info)); |
| |
| return Status{}; |
| } |
| |
| void ClSoftmax::run(ITensorPack &tensors) |
| { |
| auto src = tensors.get_const_tensor(TensorType::ACL_SRC); |
| auto dst = tensors.get_tensor(TensorType::ACL_DST); |
| |
| CLAuxTensorHandler sum(offset_int_vec(InternalTensorIdx::SUM), _sum_info, tensors, false); |
| CLAuxTensorHandler tmp(offset_int_vec(InternalTensorIdx::TMP), _tmp_info, tensors, false); |
| CLAuxTensorHandler max(offset_int_vec(InternalTensorIdx::MAX), _max_info, tensors, false); |
| |
| CLAuxTensorHandler permuted_src(offset_int_vec(InternalTensorIdx::PERMUTED_SRC), _permuted_src_info, tensors, false); |
| CLAuxTensorHandler permuted_dst(offset_int_vec(InternalTensorIdx::PERMUTED_DST), _permuted_dst_info, tensors, false); |
| |
| if(_needs_permute) |
| { |
| ITensorPack pack; |
| pack.add_const_tensor(TensorType::ACL_SRC, src); |
| pack.add_tensor(TensorType::ACL_DST, permuted_src.get()); |
| _permute_input.get()->run(pack); |
| } |
| |
| ITensorPack sum_pack; |
| ITensorPack norm_pack; |
| if(_needs_permute) |
| { |
| sum_pack.add_const_tensor(TensorType::ACL_SRC, permuted_src.get()); |
| norm_pack.add_tensor(TensorType::ACL_DST, permuted_dst.get()); |
| } |
| else |
| { |
| sum_pack.add_const_tensor(TensorType::ACL_SRC, src); |
| norm_pack.add_tensor(TensorType::ACL_DST, dst); |
| } |
| sum_pack.add_tensor(TensorType::ACL_DST, tmp.get()); |
| sum_pack.add_tensor(TensorType::ACL_INT_0, max.get()); |
| sum_pack.add_tensor(TensorType::ACL_INT_1, sum.get()); |
| |
| norm_pack.add_const_tensor(TensorType::ACL_SRC, tmp.get()); |
| norm_pack.add_tensor(TensorType::ACL_INT_0, sum.get()); |
| |
| CLScheduler::get().enqueue_op(*_max_shift_exp_sum_kernel.get(), sum_pack, false); |
| CLScheduler::get().enqueue_op(*_norm_kernel.get(), norm_pack, false); |
| |
| if(_needs_permute) |
| { |
| ITensorPack pack; |
| pack.add_const_tensor(TensorType::ACL_SRC, permuted_dst.get()); |
| pack.add_tensor(TensorType::ACL_DST, dst); |
| _permute_output.get()->run(pack); |
| } |
| } |
| |
| experimental::MemoryRequirements ClSoftmax::workspace() const |
| { |
| return _aux_mem; |
| } |
| } // namespace opencl |
| } // namespace arm_compute |