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Diffstat (limited to 'src/runtime/gpu/cl/operators/ClSoftmax.cpp')
| -rw-r--r-- | src/runtime/gpu/cl/operators/ClSoftmax.cpp | 276 |
1 files changed, 276 insertions, 0 deletions
diff --git a/src/runtime/gpu/cl/operators/ClSoftmax.cpp b/src/runtime/gpu/cl/operators/ClSoftmax.cpp new file mode 100644 index 0000000000..c3ec7cc0da --- /dev/null +++ b/src/runtime/gpu/cl/operators/ClSoftmax.cpp @@ -0,0 +1,276 @@ +/* + * 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/runtime/gpu/cl/operators/ClSoftmax.h" +#include "arm_compute/core/utils/misc/ShapeCalculator.h" +#include "src/core/gpu/cl/kernels/ClSoftmaxKernel.h" +#include "src/core/helpers/SoftmaxHelpers.h" +#include "src/runtime/gpu/cl/operators/ClPermute.h" +#include "support/Cast.h" + +namespace arm_compute +{ +namespace opencl +{ +namespace +{ +void run_permute(ClPermute *op, const ITensor *src, ITensor *dst) +{ + ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst, op); + ITensorPack pack; + pack.add_const_tensor(TensorType::ACL_SRC, src); + pack.add_tensor(TensorType::ACL_DST, dst); + op->run(pack); +} +} // namespace + +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(_internal_info[static_cast<uint32_t>(InternalTensorIdx::MAX)]), + _sum_info(_internal_info[static_cast<uint32_t>(InternalTensorIdx::SUM)]), + _tmp_info(_internal_info[static_cast<uint32_t>(InternalTensorIdx::TMP)]), + _permuted_src_info(_internal_info[static_cast<uint32_t>(InternalTensorIdx::PERMUTED_SRC)]), + _permuted_dst_info(_internal_info[static_cast<uint32_t>(InternalTensorIdx::PERMUTED_DST)]) +{ +} + +TensorType ClSoftmax::convert_internal_idx_to_tensor_type(InternalTensorIdx idx) const +{ + switch(idx) + { + case InternalTensorIdx::MAX: + return TensorType::ACL_INT_0; + case InternalTensorIdx::SUM: + return TensorType::ACL_INT_1; + case InternalTensorIdx::TMP: + return TensorType::ACL_INT_2; + case InternalTensorIdx::PERMUTED_SRC: + return TensorType::ACL_INT_3; + case InternalTensorIdx::PERMUTED_DST: + return TensorType::ACL_INT_4; + default: + ARM_COMPUTE_ERROR("invalid internal tensor index is given."); + break; + }; + return TensorType::ACL_UNKNOWN; +} + +void ClSoftmax::create_internal_tensor(TensorInfo &info, InternalTensorIdx idx) +{ + const auto tensor_idx = static_cast<uint32_t>(idx); + if(!_internal_tensor[tensor_idx]) + { + _internal_tensor[tensor_idx] = std::make_unique<CLTensor>(); + } + _internal_tensor[tensor_idx]->allocator()->init(info); +} + +void ClSoftmax::create_internal_tensor() +{ + for(uint32_t i = 0; i < static_cast<uint32_t>(InternalTensorIdx::COUNT); i++) + { + const auto tensor_idx = static_cast<InternalTensorIdx>(i); + + if(!_needs_permute && (tensor_idx == InternalTensorIdx::PERMUTED_DST || tensor_idx == InternalTensorIdx::PERMUTED_SRC)) + { + continue; + } + create_internal_tensor(_internal_info[i], static_cast<InternalTensorIdx>(i)); + } +} + +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); + } +} + +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::import_workspace_memory(ITensorPack &tensors) +{ + auto import_workspace_memory = [this, &tensors](InternalTensorIdx idx) + { + const auto workspace_idx = convert_internal_idx_to_tensor_type(idx); + auto imported_tensor = tensors.get_tensor(workspace_idx); + if(imported_tensor) + { + auto imported_memory = utils::cast::polymorphic_downcast<ICLTensor *>(imported_tensor)->cl_buffer(); + _internal_tensor[static_cast<uint32_t>(idx)].get()->allocator()->import_memory(imported_memory); + } + }; + + import_workspace_memory(InternalTensorIdx::PERMUTED_SRC); + import_workspace_memory(InternalTensorIdx::PERMUTED_DST); + import_workspace_memory(InternalTensorIdx::MAX); + import_workspace_memory(InternalTensorIdx::SUM); + import_workspace_memory(InternalTensorIdx::TMP); +} + +void ClSoftmax::run_source_permute(const ITensor *src) +{ + if(_needs_permute) + { + auto permuted_src = _internal_tensor[static_cast<uint32_t>(InternalTensorIdx::PERMUTED_SRC)].get(); + run_permute(_permute_input.get(), src, permuted_src); + } +} + +void ClSoftmax::run_destination_permute(ITensor *dst) +{ + if(_needs_permute) + { + auto permuted_dst = _internal_tensor[static_cast<uint32_t>(InternalTensorIdx::PERMUTED_DST)].get(); + run_permute(_permute_output.get(), permuted_dst, dst); + } +} + +void ClSoftmax::run_max_sum(const ITensor *src) +{ + auto max = _internal_tensor[static_cast<uint32_t>(InternalTensorIdx::MAX)].get(); + auto sum = _internal_tensor[static_cast<uint32_t>(InternalTensorIdx::SUM)].get(); + auto tmp = _internal_tensor[static_cast<uint32_t>(InternalTensorIdx::TMP)].get(); + + ARM_COMPUTE_ERROR_ON_NULLPTR(src, tmp, max, sum); + + ITensorPack sum_pack; + sum_pack.add_const_tensor(TensorType::ACL_SRC, src); + sum_pack.add_tensor(TensorType::ACL_DST, tmp); + sum_pack.add_tensor(TensorType::ACL_INT_0, max); + sum_pack.add_tensor(TensorType::ACL_INT_1, sum); + + CLScheduler::get().enqueue_op(*_max_shift_exp_sum_kernel.get(), sum_pack, false); +} + +void ClSoftmax::run_norm(ITensor *dst) +{ + auto sum = _internal_tensor[static_cast<uint32_t>(InternalTensorIdx::SUM)].get(); + auto tmp = _internal_tensor[static_cast<uint32_t>(InternalTensorIdx::TMP)].get(); + + ARM_COMPUTE_ERROR_ON_NULLPTR(tmp, sum, dst); + + ITensorPack norm_pack; + norm_pack.add_const_tensor(TensorType::ACL_SRC, tmp); + norm_pack.add_tensor(TensorType::ACL_DST, dst); + norm_pack.add_tensor(TensorType::ACL_INT_0, sum); + + CLScheduler::get().enqueue_op(*_norm_kernel.get(), norm_pack, false); +} + +void ClSoftmax::run(ITensorPack &tensors) +{ + create_internal_tensor(); + + auto src = tensors.get_const_tensor(TensorType::ACL_SRC); + auto dst = tensors.get_tensor(TensorType::ACL_DST); + + import_workspace_memory(tensors); + run_source_permute(src); + run_max_sum(!_needs_permute ? src : _internal_tensor[static_cast<uint32_t>(InternalTensorIdx::PERMUTED_SRC)].get()); + run_norm(!_needs_permute ? dst : _internal_tensor[static_cast<uint32_t>(InternalTensorIdx::PERMUTED_DST)].get()); + run_destination_permute(dst); +} + +experimental::MemoryRequirements ClSoftmax::workspace() const +{ + experimental::MemoryRequirements req{}; + + req.emplace_back(convert_internal_idx_to_tensor_type(InternalTensorIdx::SUM), _sum_info.total_size(), 0); + req.emplace_back(convert_internal_idx_to_tensor_type(InternalTensorIdx::TMP), _tmp_info.total_size(), 0); + req.emplace_back(convert_internal_idx_to_tensor_type(InternalTensorIdx::MAX), _max_info.total_size(), 0); + + if(_needs_permute) + { + req.emplace_back(convert_internal_idx_to_tensor_type(InternalTensorIdx::PERMUTED_SRC), _permuted_src_info.total_size(), 0); + req.emplace_back(convert_internal_idx_to_tensor_type(InternalTensorIdx::PERMUTED_DST), _permuted_dst_info.total_size(), 0); + } + + return req; +} +} // namespace opencl +} // namespace arm_compute
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