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Diffstat (limited to 'src/gpu/cl/operators/ClSoftmax.cpp')
| -rw-r--r-- | src/gpu/cl/operators/ClSoftmax.cpp | 186 |
1 files changed, 186 insertions, 0 deletions
diff --git a/src/gpu/cl/operators/ClSoftmax.cpp b/src/gpu/cl/operators/ClSoftmax.cpp new file mode 100644 index 0000000000..6b728f5354 --- /dev/null +++ b/src/gpu/cl/operators/ClSoftmax.cpp @@ -0,0 +1,186 @@ +/* + * 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" + +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)); + + 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
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