From 201e0fee596dafcf9c869a550fae29779aad2394 Mon Sep 17 00:00:00 2001 From: Sang-Hoon Park Date: Wed, 27 Jan 2021 13:14:56 +0000 Subject: Make Softmax kernels on OpenCL stateless * ClSoftmaxKernel and ClSoftmax are created. * ClSoftmaxKernel is now state-less and ClSoftmax handles the internal tensors required for computation. * add_const_tensor() is added to TensorPack not only to have symmetric interface but also to benefit from implicit conversion. Implements: COMPMID-3998 Change-Id: I4f823121777be24260fd12b2cd71a6ff718c4eed Signed-off-by: Sang-Hoon Park Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/5087 Reviewed-by: Georgios Pinitas Tested-by: Arm Jenkins Comments-Addressed: Arm Jenkins --- src/runtime/CL/functions/CLSoftmaxLayer.cpp | 172 ++++++----------- src/runtime/gpu/cl/operators/ClSoftmax.cpp | 276 ++++++++++++++++++++++++++++ src/runtime/gpu/cl/operators/ClSoftmax.h | 119 ++++++++++++ 3 files changed, 452 insertions(+), 115 deletions(-) create mode 100644 src/runtime/gpu/cl/operators/ClSoftmax.cpp create mode 100644 src/runtime/gpu/cl/operators/ClSoftmax.h (limited to 'src/runtime') diff --git a/src/runtime/CL/functions/CLSoftmaxLayer.cpp b/src/runtime/CL/functions/CLSoftmaxLayer.cpp index 93e63dd779..938a10a7c0 100644 --- a/src/runtime/CL/functions/CLSoftmaxLayer.cpp +++ b/src/runtime/CL/functions/CLSoftmaxLayer.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017-2020 Arm Limited. + * Copyright (c) 2017-2021 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -22,34 +22,35 @@ * SOFTWARE. */ #include "arm_compute/runtime/CL/functions/CLSoftmaxLayer.h" - #include "arm_compute/core/CL/CLHelpers.h" +#include "arm_compute/core/CL/CLKernelLibrary.h" #include "arm_compute/core/Helpers.h" +#include "arm_compute/core/KernelDescriptors.h" #include "arm_compute/core/Types.h" #include "arm_compute/core/Utils.h" -#include "arm_compute/core/utils/misc/ShapeCalculator.h" -#include "arm_compute/runtime/CL/CLScheduler.h" -#include "src/core/CL/ICLKernel.h" -#include "src/core/CL/kernels/CLFillBorderKernel.h" -#include "src/core/CL/kernels/CLSoftmaxLayerKernel.h" -#include "src/core/helpers/SoftmaxHelpers.h" +#include "src/core/gpu/cl/kernels/ClSoftmaxKernel.h" +#include "src/runtime/gpu/cl/operators/ClPermute.h" +#include "src/runtime/gpu/cl/operators/ClSoftmax.h" namespace arm_compute { +using OperatorType = opencl::ClSoftmax; + +template +struct CLSoftmaxLayerGeneric::Impl +{ + const ICLTensor *src{ nullptr }; + ICLTensor *dst{ nullptr }; + std::unique_ptr op{ nullptr }; + MemoryGroup memory_group{}; + std::vector>> workspace_tensors{}; +}; + template CLSoftmaxLayerGeneric::CLSoftmaxLayerGeneric(std::shared_ptr memory_manager) - : _memory_group(std::move(memory_manager)), - _permute_input(), - _permute_output(), - _max_shift_exp_sum_kernel(std::make_unique()), - _norm_kernel(std::make_unique()), - _max(), - _sum(), - _tmp(), - _input_permuted(), - _output_permuted(), - _needs_permute() + : _impl(std::make_unique()) { + _impl->memory_group = MemoryGroup(std::move(memory_manager)); } template @@ -64,118 +65,59 @@ void CLSoftmaxLayerGeneric::configure(const ICLTensor *input, ICLTensor template void CLSoftmaxLayerGeneric::configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, float beta, int32_t axis) { - // Perform validation step - ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); - ARM_COMPUTE_ERROR_THROW_ON(CLSoftmaxLayerGeneric::validate(input->info(), output->info(), beta, axis)); - - const size_t actual_axis = static_cast(wrap_around(axis, static_cast(input->info()->num_dimensions()))); + _impl->src = input; + _impl->dst = output; + _impl->op = std::make_unique(); - _needs_permute = actual_axis != 0; - ICLTensor *tmp_output = output; - const ICLTensor *tmp_input = _needs_permute ? &_input_permuted : input; - if(_needs_permute) - { - _memory_group.manage(&_input_permuted); - _memory_group.manage(&_output_permuted); - _permute_input.configure(compile_context, input, &_input_permuted, softmax_helpers::get_permutation_vector_from_softmax_axis(actual_axis)); - tmp_output = &_output_permuted; - } - - // Create intermediate tensors - DataType tmp_data_type = is_data_type_quantized_asymmetric(tmp_input->info()->data_type()) ? DataType::S32 : tmp_input->info()->data_type(); - TensorInfo tensor_info_tmp(tmp_input->info()->clone()->set_data_type(tmp_data_type)); - _tmp.allocator()->init(tensor_info_tmp); - TensorShape max_sum_shape = tmp_input->info()->tensor_shape(); - max_sum_shape.set(0, 1); - _max.allocator()->init(tmp_input->info()->clone()->set_tensor_shape(max_sum_shape)); - _sum.allocator()->init(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()); - - // Manage intermediate buffers - _memory_group.manage(&_tmp); - _memory_group.manage(&_max); - _memory_group.manage(&_sum); - - SoftmaxKernelInfo softmax_info; - softmax_info.beta = beta; - softmax_info.is_log = IS_LOG; - softmax_info.input_data_type = tmp_input->info()->data_type(); - - // Configure kernels - _max_shift_exp_sum_kernel->configure(compile_context, tmp_input, &_max, &_tmp, &_sum, softmax_info); - _norm_kernel->configure(compile_context, &_tmp, &_sum, tmp_output, softmax_info); - - // Allocate intermediate buffers - _tmp.allocator()->allocate(); - _max.allocator()->allocate(); - _sum.allocator()->allocate(); - if(_needs_permute) - { - _permute_output.configure(compile_context, &_output_permuted, output, softmax_helpers::get_permutation_vector_from_softmax_axis(actual_axis)); - _input_permuted.allocator()->allocate(); - _output_permuted.allocator()->allocate(); - } + SoftmaxKernelInfo softmax_info{ beta, IS_LOG, input->info()->data_type(), axis }; + _impl->op->configure(compile_context, *input->info(), *output->info(), softmax_info); } template Status CLSoftmaxLayerGeneric::validate(const ITensorInfo *input, const ITensorInfo *output, float beta, int32_t axis) { - ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->num_dimensions() > 4, "Only up to 4 dimensions are supported"); - ARM_COMPUTE_UNUSED(beta); - ARM_COMPUTE_RETURN_ERROR_ON(axis < static_cast(-input->num_dimensions()) || static_cast(input->num_dimensions()) <= axis); - - const size_t actual_axis = static_cast(wrap_around(axis, static_cast(input->num_dimensions()))); - const bool needs_permute = actual_axis != 0; - if(needs_permute) + SoftmaxKernelInfo softmax_info{ beta, IS_LOG, input->data_type(), axis }; + return OperatorType::validate(*input, *output, softmax_info); +} + +template +void CLSoftmaxLayerGeneric::allocate_workspace() +{ + const auto memory_requirements = _impl->op->workspace(); + std::for_each(memory_requirements.begin(), memory_requirements.end(), [this](const experimental::MemoryInfo & memory_info) { - 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(*input, permutation_vector); - TensorInfo input_permuted(input->clone()->set_tensor_shape(permuted_shape)); - ARM_COMPUTE_RETURN_ON_ERROR(CLPermute::validate(input, &input_permuted, permutation_vector)); - TensorInfo output_permuted(output->clone()->set_tensor_shape(permuted_shape)); - ARM_COMPUTE_RETURN_ON_ERROR(CLPermute::validate(&output_permuted, output, permutation_vector)); - } - - // Create intermediate tensor info - DataType tmp_data_type = is_data_type_quantized_asymmetric(input->data_type()) ? DataType::S32 : input->data_type(); - TensorInfo tensor_info_tmp(input->clone()->set_data_type(tmp_data_type).set_is_resizable(true)); - - TensorShape max_sum_shape = input->tensor_shape(); - max_sum_shape.set(0, 1); - TensorInfo tensor_info_max(input->clone()->set_tensor_shape(max_sum_shape).set_is_resizable(true)); - TensorInfo tensor_info_sum(input->clone()->set_tensor_shape(max_sum_shape).set_data_type(tmp_data_type).set_quantization_info(QuantizationInfo()).set_is_resizable(true)); - - SoftmaxKernelInfo softmax_info; - softmax_info.beta = beta; - softmax_info.is_log = IS_LOG; - softmax_info.input_data_type = input->data_type(); - - ARM_COMPUTE_RETURN_ON_ERROR(CLLogits1DMaxShiftExpSumKernel::validate(input, &tensor_info_max, &tensor_info_tmp, &tensor_info_sum)); - ARM_COMPUTE_RETURN_ON_ERROR(CLLogits1DNormKernel::validate(&tensor_info_tmp, &tensor_info_sum, output, softmax_info)); - - return Status{}; + auto tensor_info = TensorInfo{ TensorShape(memory_info.size), 1, DataType::U8 }; + _impl->workspace_tensors.emplace_back(memory_info.type, std::make_unique()); + auto tensor = _impl->workspace_tensors.back().second.get(); + ARM_COMPUTE_ERROR_ON_NULLPTR(tensor); + _impl->memory_group.manage(tensor); + tensor->allocator()->init(tensor_info); + tensor->allocator()->allocate(); + }); } template void CLSoftmaxLayerGeneric::run() { - MemoryGroupResourceScope scope_mg(_memory_group); + allocate_workspace(); - if(_needs_permute) - { - _permute_input.run(); - } + // Acquire all the temporaries + MemoryGroupResourceScope scope_mg(_impl->memory_group); + + ARM_COMPUTE_ERROR_ON_NULLPTR(_impl->src, _impl->dst); - CLScheduler::get().enqueue(*_max_shift_exp_sum_kernel, false); - CLScheduler::get().enqueue(*_norm_kernel, !_needs_permute); + ITensorPack pack; + pack.add_tensor(TensorType::ACL_SRC, _impl->src); + pack.add_tensor(TensorType::ACL_DST, _impl->dst); - if(_needs_permute) + std::for_each(_impl->workspace_tensors.begin(), _impl->workspace_tensors.end(), [&pack](std::pair> &wt) { - _permute_output.run(); - } + auto tensor = wt.second.get(); + ARM_COMPUTE_ERROR_ON_NULLPTR(tensor); + pack.add_tensor(wt.first, tensor); + }); + + _impl->op->run(pack); } template class CLSoftmaxLayerGeneric; 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()), + _permute_output(std::make_unique()), + _max_shift_exp_sum_kernel(std::make_unique()), + _norm_kernel(std::make_unique()), + _max_info(_internal_info[static_cast(InternalTensorIdx::MAX)]), + _sum_info(_internal_info[static_cast(InternalTensorIdx::SUM)]), + _tmp_info(_internal_info[static_cast(InternalTensorIdx::TMP)]), + _permuted_src_info(_internal_info[static_cast(InternalTensorIdx::PERMUTED_SRC)]), + _permuted_dst_info(_internal_info[static_cast(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(idx); + if(!_internal_tensor[tensor_idx]) + { + _internal_tensor[tensor_idx] = std::make_unique(); + } + _internal_tensor[tensor_idx]->allocator()->init(info); +} + +void ClSoftmax::create_internal_tensor() +{ + for(uint32_t i = 0; i < static_cast(InternalTensorIdx::COUNT); i++) + { + const auto tensor_idx = static_cast(i); + + if(!_needs_permute && (tensor_idx == InternalTensorIdx::PERMUTED_DST || tensor_idx == InternalTensorIdx::PERMUTED_SRC)) + { + continue; + } + create_internal_tensor(_internal_info[i], static_cast(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(wrap_around(info.axis, static_cast(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(-src.num_dimensions()) || static_cast(src.num_dimensions()) <= info.axis); + + const size_t actual_axis = static_cast(wrap_around(info.axis, static_cast(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(imported_tensor)->cl_buffer(); + _internal_tensor[static_cast(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(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(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(InternalTensorIdx::MAX)].get(); + auto sum = _internal_tensor[static_cast(InternalTensorIdx::SUM)].get(); + auto tmp = _internal_tensor[static_cast(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(InternalTensorIdx::SUM)].get(); + auto tmp = _internal_tensor[static_cast(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(InternalTensorIdx::PERMUTED_SRC)].get()); + run_norm(!_needs_permute ? dst : _internal_tensor[static_cast(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 \ No newline at end of file diff --git a/src/runtime/gpu/cl/operators/ClSoftmax.h b/src/runtime/gpu/cl/operators/ClSoftmax.h new file mode 100644 index 0000000000..e38b7c595a --- /dev/null +++ b/src/runtime/gpu/cl/operators/ClSoftmax.h @@ -0,0 +1,119 @@ +/* + * 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. + */ +#ifndef ARM_COMPUTE_CL_SOFTMAX_H +#define ARM_COMPUTE_CL_SOFTMAX_H + +#include "arm_compute/runtime/CL/CLTensor.h" +#include "src/core/gpu/cl/ClCompileContext.h" +#include "src/runtime/gpu/cl/IClOperator.h" + +namespace arm_compute +{ +struct SoftmaxKernelInfo; + +namespace opencl +{ +class ClPermute; +namespace kernels +{ +class ClLogits1DMaxShiftExpSumKernel; +class ClLogits1DNormKernel; +} // namespace kernels +class ClSoftmax : public IClOperator +{ +public: + /** Constructor */ + ClSoftmax(); + /** Configure the operator + * + * @param[in] compile_context The compile context to be used. + * @param[in] src Source tensor info. Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32 for Softmax and F16/F32 for Log Softmax + * @param[out] dst Destination tensor info. Data types supported: same as @p src + * @param[in] info Contains information consumed by kernels for softmax described in @ref SoftmaxKernelInfo. + * + */ + void configure(const CLCompileContext &compile_context, const ITensorInfo &src, ITensorInfo &dst, const SoftmaxKernelInfo &info); + /** Static function to check if the given info will lead to a valid configuration + * + * @param[in] src Source tensor info. Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32 for Softmax and F16/F32 for Log Softmax + * @param[out] dst Destination tensor info. Data types supported: same as @p src + * @param[in] info Contains information consumed by kernels for softmax described in @ref SoftmaxKernelInfo. + * + */ + static Status validate(const ITensorInfo &src, const ITensorInfo &dst, const SoftmaxKernelInfo &info); + // Inherited methods overridden: + void run(ITensorPack &tensors) override; + experimental::MemoryRequirements workspace() const override; + +private: + enum class InternalTensorIdx + { + MAX = 0, + SUM, + TMP, + PERMUTED_SRC, + PERMUTED_DST, + COUNT + }; + + /** Create a single internal tensor + * + * @param[in] info The information used to create a tensor + * @param[in] idx The index within the internal array the created tensor will be held + */ + void create_internal_tensor(TensorInfo &info, InternalTensorIdx idx); + /** Create all required internal tensors */ + void create_internal_tensor(); + /** Function to convert from internal tensor index to @ref TensorType used externally */ + TensorType convert_internal_idx_to_tensor_type(InternalTensorIdx idx) const; + /** Function to import workspace memory allocated by the caller into internal tensor instances */ + void import_workspace_memory(ITensorPack &tensors); + /** Function to permute the given source tensor when permutation is required */ + void run_source_permute(const ITensor *src); + /** Function to permute the intemediate tensor to the final destination tensor when permutation is required */ + void run_destination_permute(ITensor *dst); + /** Function to run @ref arm_compute::opencl::kernels::ClLogits1DMaxShiftExpSumKernel */ + void run_max_sum(const ITensor *src); + /** Function to run @ref kernels::ClLogits1DNormKernel */ + void run_norm(ITensor *dst); + + std::unique_ptr _permute_input; + std::unique_ptr _permute_output; + std::unique_ptr _max_shift_exp_sum_kernel; + std::unique_ptr _norm_kernel; + bool _needs_permute{ false }; + + std::array(InternalTensorIdx::COUNT)> _internal_info{}; + std::array, static_cast(InternalTensorIdx::COUNT)> _internal_tensor{}; + + TensorInfo &_max_info; + TensorInfo &_sum_info; + TensorInfo &_tmp_info; + TensorInfo &_permuted_src_info; + TensorInfo &_permuted_dst_info; +}; + +} // opencl +} // arm_compute +#endif /* ARM_COMPUTE_CL_SOFTMAX_H */ \ No newline at end of file -- cgit v1.2.1