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/*
* 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/MemoryHelpers.h"
#include "src/core/helpers/SoftmaxHelpers.h"
#include "src/runtime/gpu/cl/operators/ClPermute.h"
#include "src/runtime/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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