Optimize CL softmax
* The new softmax implementation consists of only a single kernel.
- There are 2 versions of softmax, one for the x dimension
and one for any other dimensions.
- Softmax kernel handles both native and quantized data type.
Resolves: COMPMID-6447
Signed-off-by: Viet-Hoa Do <viet-hoa.do@arm.com>
Change-Id: I4a9ae5bc63f78aebeaa85ee48a0d102c9c245eda
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/10489
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: SiCong Li <sicong.li@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Benchmark: Arm Jenkins <bsgcomp@arm.com>
diff --git a/src/gpu/cl/operators/ClSoftmax.cpp b/src/gpu/cl/operators/ClSoftmax.cpp
index 2bec400..427f6b4 100644
--- a/src/gpu/cl/operators/ClSoftmax.cpp
+++ b/src/gpu/cl/operators/ClSoftmax.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2021 Arm Limited.
+ * Copyright (c) 2021, 2023 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -23,15 +23,14 @@
*/
#include "src/gpu/cl/operators/ClSoftmax.h"
-#include "arm_compute/core/utils/misc/ShapeCalculator.h"
+#include "arm_compute/core/experimental/Types.h"
+#include "arm_compute/core/ITensorPack.h"
+#include "arm_compute/runtime/CL/CLScheduler.h"
#include "src/common/utils/Log.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;
@@ -39,17 +38,8 @@
{
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)
+
+ClSoftmax::ClSoftmax() : _aux_mem(InternalTensorIdx::COUNT)
{
}
@@ -58,152 +48,37 @@
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())));
+ auto k = std::make_unique<kernels::ClSoftmaxKernel>();
+ k->configure(compile_context, src, dst, info);
- _needs_permute = actual_axis != 0;
+ _tmp_info = k->tmp_tensor_info();
- const ITensorInfo &tmp_input_info = _needs_permute ? _permuted_src_info : src;
- ITensorInfo &tmp_output_info = _needs_permute ? _permuted_dst_info : dst;
+ _kernel = std::move(k);
- 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{};
+ return kernels::ClSoftmaxKernel::validate(src, dst, info);
}
void ClSoftmax::run(ITensorPack &tensors)
{
- auto src = tensors.get_const_tensor(TensorType::ACL_SRC);
- auto dst = tensors.get_tensor(TensorType::ACL_DST);
+ CLAuxTensorHandler tmp(offset_int_vec(InternalTensorIdx::TMP), _tmp_info, tensors);
- 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);
+ tensors.add_tensor(TensorType::ACL_INT_0, tmp.get());
- 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);
- }
+ CLScheduler::get().enqueue_op(*_kernel, tensors, false);
}
experimental::MemoryRequirements ClSoftmax::workspace() const
{
return _aux_mem;
}
+
} // namespace opencl
} // namespace arm_compute