Make Softmax kernels and operator stateless
COMPMID-3997
Change-Id: I3a3cc76d8247dd769d9a5e6e171d718ea909312c
Signed-off-by: Michalis Spyrou <michalis.spyrou@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/4986
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
diff --git a/src/runtime/NEON/functions/NEFillBorder.cpp b/src/runtime/NEON/functions/NEFillBorder.cpp
index bb57222..256aad6 100644
--- a/src/runtime/NEON/functions/NEFillBorder.cpp
+++ b/src/runtime/NEON/functions/NEFillBorder.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2016-2020 Arm Limited.
+ * Copyright (c) 2016-2021 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -29,6 +29,11 @@
namespace arm_compute
{
+NEFillBorder::NEFillBorder()
+ : _border_handler(nullptr)
+{
+}
+
void NEFillBorder::configure(ITensor *input, unsigned int border_width, BorderMode border_mode, const PixelValue &constant_border_value)
{
_border_handler = std::make_unique<NEFillBorderKernel>();
diff --git a/src/runtime/NEON/functions/NESoftmaxLayer.cpp b/src/runtime/NEON/functions/NESoftmaxLayer.cpp
index 6be34ad..3f1e43a 100644
--- a/src/runtime/NEON/functions/NESoftmaxLayer.cpp
+++ b/src/runtime/NEON/functions/NESoftmaxLayer.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017-2020 Arm Limited.
+ * Copyright (c) 2017-2021 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -22,49 +22,62 @@
* SOFTWARE.
*/
#include "arm_compute/runtime/NEON/functions/NESoftmaxLayer.h"
-
-#include "arm_compute/core/Helpers.h"
-#include "arm_compute/core/utils/misc/ShapeCalculator.h"
-#include "arm_compute/runtime/NEON/NEScheduler.h"
-#include "src/core/NEON/kernels/NEFillBorderKernel.h"
-#include "src/core/NEON/kernels/NESoftmaxLayerKernel.h"
-#include "src/core/NEON/kernels/NESoftmaxLayerKernel.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/runtime/Tensor.h"
+#include "src/core/cpu/kernels/CpuSoftmaxKernel.h"
#include "src/core/helpers/SoftmaxHelpers.h"
+#include "src/runtime/cpu/operators/CpuSoftmax.h"
namespace arm_compute
{
template <bool IS_LOG>
-NESoftmaxLayerGeneric<IS_LOG>::~NESoftmaxLayerGeneric() = default;
+struct NESoftmaxLayerGeneric<IS_LOG>::Impl
+{
+ const ITensor *src{ nullptr };
+ ITensor *dst{ nullptr };
+ Tensor max{ nullptr };
+ Tensor tmp{ nullptr };
+ Tensor input_permuted{ nullptr };
+ Tensor output_permuted{ nullptr };
+ std::unique_ptr<cpu::CpuSoftmaxGeneric<IS_LOG>> op{ nullptr };
+};
template <bool IS_LOG>
NESoftmaxLayerGeneric<IS_LOG>::NESoftmaxLayerGeneric(std::shared_ptr<IMemoryManager> memory_manager)
- : _memory_group(std::move(memory_manager)), _permute_input(), _permute_output(), _max_kernel(), _softmax_kernel(), _fill_border_kernel(), _max(), _tmp(), _input_permuted(), _output_permuted(),
- _needs_permute(false)
+ : _memory_group(std::move(memory_manager)), _impl(std::make_unique<Impl>())
{
}
template <bool IS_LOG>
+NESoftmaxLayerGeneric<IS_LOG>::NESoftmaxLayerGeneric(NESoftmaxLayerGeneric &&) = default;
+template <bool IS_LOG>
+NESoftmaxLayerGeneric<IS_LOG> &NESoftmaxLayerGeneric<IS_LOG>::operator=(NESoftmaxLayerGeneric &&) = default;
+template <bool IS_LOG>
+NESoftmaxLayerGeneric<IS_LOG>::~NESoftmaxLayerGeneric() = default;
+
+template <bool IS_LOG>
void NESoftmaxLayerGeneric<IS_LOG>::configure(ITensor *input, ITensor *output, float beta, int32_t axis)
{
- // Perform validation step
ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
- ARM_COMPUTE_ERROR_THROW_ON(NESoftmaxLayerGeneric::validate(input->info(), output->info(), beta, axis));
- const unsigned int actual_axis = static_cast<unsigned int>(wrap_around(axis, static_cast<int32_t>(input->info()->num_dimensions())));
+ _impl->src = input;
+ _impl->dst = output;
+ _impl->op = std::make_unique<cpu::CpuSoftmaxGeneric<IS_LOG>>();
+ _impl->op->configure(input->info(), output->info(), beta, axis);
- _needs_permute = actual_axis > 0;
-
- if(_needs_permute)
+ const unsigned int actual_axis = static_cast<unsigned int>(wrap_around(axis, static_cast<int32_t>(input->info()->num_dimensions())));
+ const bool needs_permute = actual_axis > 0;
+ if(needs_permute)
{
// Add to the memory manager _input_permuted
- _memory_group.manage(&_input_permuted);
-
- _permute_input.configure(input, &_input_permuted, softmax_helpers::get_permutation_vector_from_softmax_axis(actual_axis));
+ auto permute_input = std::make_unique<cpu::CpuPermute>();
+ _memory_group.manage(&_impl->input_permuted);
+ permute_input->configure(input->info(), _impl->input_permuted.info(), softmax_helpers::get_permutation_vector_from_softmax_axis(actual_axis));
}
// We want to deal with a 2D input. Either it is the permuted version of the original input (4D case)
// or it is the original input case (2D case)
- ITensor *tmp_input = (_needs_permute ? &_input_permuted : input);
+ ITensor *tmp_input = (needs_permute ? &_impl->input_permuted : input);
// Create intermediate tensors shapes
const TensorInfo input_info = tmp_input->info()->clone()->reset_padding().set_is_resizable(true);
@@ -74,80 +87,49 @@
// Init intermediate tensors
TensorShape max_sum_shape = tmp_input->info()->tensor_shape();
max_sum_shape.set(0, 1);
- _max.allocator()->init(input_info.clone()->set_tensor_shape(max_sum_shape));
- _tmp.allocator()->init(tensor_info_tmp);
+ _impl->max.allocator()->init(input_info.clone()->set_tensor_shape(max_sum_shape));
+ _impl->tmp.allocator()->init(tensor_info_tmp);
// Manage intermediate buffers
- _memory_group.manage(&_max);
- _memory_group.manage(&_tmp);
+ _memory_group.manage(&_impl->max);
+ _memory_group.manage(&_impl->tmp);
// Configure kernels
- _max_kernel = std::make_unique<NELogits1DMaxKernel>();
- _softmax_kernel = std::make_unique<NELogits1DSoftmaxKernel<IS_LOG>>();
- _max_kernel->configure(tmp_input, &_max);
- if(_needs_permute)
+ auto max_kernel = std::make_unique<cpu::kernels::CpuLogits1DMaxKernel>();
+ auto softmax_kernel = std::make_unique<cpu::kernels::CpuLogits1DSoftmaxKernel<IS_LOG>>();
+ max_kernel->configure(tmp_input->info(), _impl->max.info());
+
+ if(needs_permute)
{
+ auto permute_output = std::make_unique<cpu::CpuPermute>();
// Add to the memory manager _output_permuted
- _memory_group.manage(&_output_permuted);
+ _memory_group.manage(&_impl->output_permuted);
// The normalization kernel stores the result in a permuted output tensor
- _softmax_kernel->configure(tmp_input, &_max, &_output_permuted, beta, &_tmp);
- _input_permuted.allocator()->allocate();
+ softmax_kernel->configure(tmp_input->info(), _impl->max.info(), _impl->output_permuted.info(), beta, _impl->tmp.info());
+ _impl->input_permuted.allocator()->allocate();
// Re-permute the permuted output into the requested (4D) output
- _permute_output.configure(&_output_permuted, output, softmax_helpers::get_permutation_vector_from_softmax_axis(actual_axis));
+ permute_output->configure(_impl->output_permuted.info(), output->info(), softmax_helpers::get_permutation_vector_from_softmax_axis(actual_axis));
// Allocate the intermediate permuted tensors
- _output_permuted.allocator()->allocate();
+ _impl->output_permuted.allocator()->allocate();
}
else
{
- // Softmax 2D case
- _fill_border_kernel = std::make_unique<NEFillBorderKernel>();
- _fill_border_kernel->configure(tmp_input, _max_kernel->border_size(), BorderMode::REPLICATE);
- _softmax_kernel->configure(tmp_input, &_max, output, beta, &_tmp);
+ softmax_kernel->configure(tmp_input->info(), _impl->max.info(), output->info(), beta, _impl->tmp.info());
}
// Allocate intermediate buffers
- _max.allocator()->allocate();
- _tmp.allocator()->allocate();
+ _impl->max.allocator()->allocate();
+ _impl->tmp.allocator()->allocate();
}
template <bool IS_LOG>
Status NESoftmaxLayerGeneric<IS_LOG>::validate(const ITensorInfo *input, const ITensorInfo *output, float beta, int32_t axis)
{
- // Perform validation step
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<int32_t>(-input->num_dimensions()) || static_cast<int32_t>(input->num_dimensions()) <= axis);
-
- // Create intermediate tensor info
- DataType tmp_data_type = input->data_type();
- const 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);
- const TensorInfo tensor_info_max_sum(input->clone()->set_tensor_shape(max_sum_shape).set_data_type(tmp_data_type).set_quantization_info(input->quantization_info()).set_is_resizable(true));
- const TensorInfo dont_care;
-
- const unsigned int actual_axis = static_cast<unsigned int>(wrap_around(axis, static_cast<int32_t>(input->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(*input, permutation_vector);
- TensorInfo input_permuted(input->clone()->set_tensor_shape(permuted_shape));
- ARM_COMPUTE_RETURN_ON_ERROR(NEPermute::validate(input, &input_permuted, permutation_vector));
- TensorInfo output_permuted(output->clone()->set_tensor_shape(permuted_shape));
- ARM_COMPUTE_RETURN_ON_ERROR(NEPermute::validate(&output_permuted, output, permutation_vector));
- }
-
- ARM_COMPUTE_RETURN_ON_ERROR(NELogits1DMaxKernel::validate(input, &tensor_info_max_sum));
- ARM_COMPUTE_RETURN_ON_ERROR(NELogits1DSoftmaxKernel<IS_LOG>::validate(&tensor_info_tmp, &tensor_info_max_sum, output, beta, &dont_care));
-
+ ARM_COMPUTE_RETURN_ON_ERROR(cpu::CpuSoftmaxGeneric<IS_LOG>::validate(input, output, beta, axis));
return Status{};
}
@@ -155,23 +137,14 @@
void NESoftmaxLayerGeneric<IS_LOG>::run()
{
MemoryGroupResourceScope scope_mg(_memory_group);
-
- if(_needs_permute)
- {
- _permute_input.run();
- }
- else
- {
- NEScheduler::get().schedule(_fill_border_kernel.get(), Window::DimY);
- }
-
- NEScheduler::get().schedule(_max_kernel.get(), Window::DimY);
- NEScheduler::get().schedule(_softmax_kernel.get(), Window::DimY);
-
- if(_needs_permute)
- {
- _permute_output.run();
- }
+ ITensorPack pack;
+ pack.add_tensor(TensorType::ACL_SRC, _impl->src);
+ pack.add_tensor(TensorType::ACL_DST, _impl->dst);
+ pack.add_tensor(TensorType::ACL_INT_0, &_impl->tmp);
+ pack.add_tensor(TensorType::ACL_INT_1, &_impl->max);
+ pack.add_tensor(TensorType::ACL_INT_2, &_impl->input_permuted);
+ pack.add_tensor(TensorType::ACL_INT_3, &_impl->output_permuted);
+ _impl->op->run(pack);
}
template class NESoftmaxLayerGeneric<false>;
diff --git a/src/runtime/cpu/operators/CpuSoftmax.cpp b/src/runtime/cpu/operators/CpuSoftmax.cpp
new file mode 100644
index 0000000..0e1bcd5
--- /dev/null
+++ b/src/runtime/cpu/operators/CpuSoftmax.cpp
@@ -0,0 +1,204 @@
+/*
+ * 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/cpu/operators/CpuSoftmax.h"
+
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/utils/misc/ShapeCalculator.h"
+#include "arm_compute/runtime/NEON/NEScheduler.h"
+#include "src/core/cpu/kernels/CpuSoftmaxKernel.h"
+#include "src/core/helpers/SoftmaxHelpers.h"
+
+namespace arm_compute
+{
+namespace cpu
+{
+template <bool IS_LOG>
+CpuSoftmaxGeneric<IS_LOG>::CpuSoftmaxGeneric()
+ : _permute_input(), _permute_output(), _max_kernel(), _softmax_kernel(), _max(nullptr), _tmp(nullptr), _input_permuted(nullptr), _output_permuted(nullptr), _needs_permute(false)
+{
+}
+
+template <bool IS_LOG>
+void CpuSoftmaxGeneric<IS_LOG>::configure(const ITensorInfo *src, ITensorInfo *dst, float beta, int32_t axis)
+{
+ // Perform validation step
+ ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst);
+ ARM_COMPUTE_ERROR_THROW_ON(CpuSoftmaxGeneric::validate(src, dst, beta, axis));
+
+ const unsigned int actual_axis = static_cast<unsigned int>(wrap_around(axis, static_cast<int32_t>(src->num_dimensions())));
+
+ _needs_permute = actual_axis > 0;
+
+ if(_needs_permute)
+ {
+ _input_permuted = std::make_unique<TensorInfo>();
+ _permute_input.configure(src, _input_permuted.get(), softmax_helpers::get_permutation_vector_from_softmax_axis(actual_axis));
+ }
+
+ // We want to deal with a 2D input. Either it is the permuted version of the original input (4D case)
+ // or it is the original input case (2D case)
+ const ITensorInfo *tmp_input = (_needs_permute ? _input_permuted.get() : src);
+
+ // Create intermediate tensors shapes
+ TensorShape max_sum_shape = tmp_input->tensor_shape();
+ max_sum_shape.set(0, 1);
+ const TensorInfo input_info = tmp_input->clone()->reset_padding().set_is_resizable(true);
+ DataType tmp_data_type = is_data_type_quantized_asymmetric(tmp_input->data_type()) ? DataType::F32 : tmp_input->data_type();
+ TensorInfo tensor_info_tmp(input_info.clone()->set_data_type(tmp_data_type));
+ TensorInfo max_info(tmp_input->clone()->set_tensor_shape(max_sum_shape));
+
+ // Init intermediate tensors
+ _max = std::make_unique<TensorInfo>(max_info);
+ _tmp = std::make_unique<TensorInfo>(tensor_info_tmp);
+
+ // Configure kernels
+ auto mk = std::make_unique<kernels::CpuLogits1DMaxKernel>();
+ mk->configure(tmp_input, _max.get());
+ _max_kernel = std::move(mk);
+
+ auto sm = std::make_unique<kernels::CpuLogits1DSoftmaxKernel<IS_LOG>>();
+ if(_needs_permute)
+ {
+ _output_permuted = std::make_unique<TensorInfo>();
+
+ // The normalization kernel stores the result in a permuted output tensor
+ sm->configure(tmp_input, _max.get(), _output_permuted.get(), beta, _tmp.get());
+
+ // Re-permute the permuted output into the requested (4D) output
+ _permute_output.configure(_output_permuted.get(), dst, softmax_helpers::get_permutation_vector_from_softmax_axis(actual_axis));
+ }
+ else
+ {
+ // Softmax 2D case
+ sm->configure(tmp_input, _max.get(), dst, beta, _tmp.get());
+ }
+ _softmax_kernel = std::move(sm);
+}
+
+template <bool IS_LOG>
+Status CpuSoftmaxGeneric<IS_LOG>::validate(const ITensorInfo *src, const ITensorInfo *dst, float beta, int32_t axis)
+{
+ // Perform validation step
+ ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src, dst);
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(src->num_dimensions() > 4, "Only up to 4 dimensions are supported");
+ ARM_COMPUTE_UNUSED(beta);
+ ARM_COMPUTE_RETURN_ERROR_ON(axis < static_cast<int32_t>(-src->num_dimensions()) || static_cast<int32_t>(src->num_dimensions()) <= axis);
+
+ // Create intermediate tensor info
+ DataType tmp_data_type = src->data_type();
+ const 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);
+ const TensorInfo tensor_info_max_sum(src->clone()->set_tensor_shape(max_sum_shape).set_data_type(tmp_data_type).set_quantization_info(src->quantization_info()).set_is_resizable(true));
+ const TensorInfo dont_care;
+
+ const unsigned int actual_axis = static_cast<unsigned int>(wrap_around(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(CpuPermute::validate(src, &input_permuted, permutation_vector));
+ TensorInfo output_permuted(dst->clone()->set_tensor_shape(permuted_shape));
+ ARM_COMPUTE_RETURN_ON_ERROR(CpuPermute::validate(&output_permuted, dst, permutation_vector));
+ }
+
+ ARM_COMPUTE_RETURN_ON_ERROR(kernels::CpuLogits1DMaxKernel::validate(src, &tensor_info_max_sum));
+ ARM_COMPUTE_RETURN_ON_ERROR(kernels::CpuLogits1DSoftmaxKernel<IS_LOG>::validate(&tensor_info_tmp, &tensor_info_max_sum, dst, beta, &dont_care));
+
+ return Status{};
+}
+
+template <bool IS_LOG>
+void CpuSoftmaxGeneric<IS_LOG>::run(ITensorPack &tensors)
+{
+ ARM_COMPUTE_ERROR_ON_MSG(tensors.empty(), "No inputs provided");
+
+ ITensorPack max_pack;
+ ITensorPack softmax_pack;
+
+ if(_needs_permute)
+ {
+ ITensorPack permute_in_pack;
+ permute_in_pack.add_tensor(TensorType::ACL_SRC, tensors.get_const_tensor(ACL_SRC));
+ permute_in_pack.add_tensor(TensorType::ACL_DST, tensors.get_tensor(ACL_INT_2));
+ _permute_input.run(permute_in_pack);
+
+ max_pack.add_tensor(TensorType::ACL_SRC, tensors.get_tensor(ACL_INT_2));
+
+ softmax_pack.add_tensor(TensorType::ACL_SRC_0, tensors.get_tensor(ACL_INT_2));
+ softmax_pack.add_tensor(TensorType::ACL_SRC_1, tensors.get_tensor(ACL_INT_1));
+ softmax_pack.add_tensor(TensorType::ACL_DST_0, tensors.get_tensor(ACL_INT_3));
+ softmax_pack.add_tensor(TensorType::ACL_DST_1, tensors.get_tensor(ACL_INT_0));
+ }
+ else
+ {
+ max_pack.add_tensor(TensorType::ACL_SRC, tensors.get_const_tensor(ACL_SRC));
+ softmax_pack.add_tensor(TensorType::ACL_SRC_0, tensors.get_const_tensor(ACL_SRC));
+ softmax_pack.add_tensor(TensorType::ACL_SRC_1, tensors.get_tensor(ACL_INT_1));
+ softmax_pack.add_tensor(TensorType::ACL_DST_0, tensors.get_tensor(ACL_DST));
+ softmax_pack.add_tensor(TensorType::ACL_DST_1, tensors.get_tensor(ACL_INT_0));
+ }
+
+ max_pack.add_tensor(TensorType::ACL_DST, tensors.get_tensor(ACL_INT_1));
+
+ NEScheduler::get().schedule_op(_max_kernel.get(), Window::DimY, _max_kernel->window(), max_pack);
+ NEScheduler::get().schedule_op(_softmax_kernel.get(), Window::DimY, _softmax_kernel->window(), softmax_pack);
+
+ if(_needs_permute)
+ {
+ ITensorPack permute_out_pack;
+ permute_out_pack.add_tensor(TensorType::ACL_SRC, tensors.get_tensor(ACL_INT_3));
+ permute_out_pack.add_tensor(TensorType::ACL_DST, tensors.get_tensor(ACL_DST));
+ _permute_output.run(permute_out_pack);
+ }
+}
+
+template <bool IS_LOG>
+experimental::MemoryRequirements CpuSoftmaxGeneric<IS_LOG>::workspace() const
+{
+ experimental::MemoryRequirements req{};
+
+ req.push_back({ TensorType::ACL_INT_0, _tmp->total_size(), 0 });
+ req.push_back({ TensorType::ACL_INT_1, _max->total_size(), 0 });
+
+ if(_needs_permute)
+ {
+ req.push_back({ TensorType::ACL_INT_2, _input_permuted->total_size(), 0 });
+ req.push_back({ TensorType::ACL_INT_3, _output_permuted->total_size(), 0 });
+ }
+
+ return req;
+}
+
+template class CpuSoftmaxGeneric<false>;
+template class CpuSoftmaxGeneric<true>;
+} // namespace cpu
+} // namespace arm_compute
diff --git a/src/runtime/cpu/operators/CpuSoftmax.h b/src/runtime/cpu/operators/CpuSoftmax.h
new file mode 100644
index 0000000..9f18e0e
--- /dev/null
+++ b/src/runtime/cpu/operators/CpuSoftmax.h
@@ -0,0 +1,105 @@
+/*
+ * 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_CPU_SOFTMAX_H
+#define ARM_COMPUTE_CPU_SOFTMAX_H
+
+#include "arm_compute/core/ITensorInfo.h"
+#include "arm_compute/core/experimental/Types.h"
+#include "src/core/cpu/ICpuKernel.h"
+#include "src/runtime/cpu/ICpuOperator.h"
+#include "src/runtime/cpu/operators/CpuPermute.h"
+#include <memory>
+
+namespace arm_compute
+{
+namespace cpu
+{
+class CpuLogits1DMaxKernel;
+template <bool IS_LOG>
+class CpuLogits1DSoftmaxKernel;
+
+/** Basic function to compute a SoftmaxLayer and a Log SoftmaxLayer.
+ *
+ * Softmax is calculated by :
+ * @f[ out = exp((x - max(x)) * beta) / sum(exp((x - max(x)) * beta)) @f]
+ *
+ * Log Softmax is calculated by :
+ * @f[ out = (x - max(x) * beta) - log(\sum{e^{x - max(x) * beta}}) @f]
+ *
+ * This function runs the following function/kernels:
+ * -# If axis is not 0:
+ * -# @ref CpuPermute
+ * -# @ref kernels::CpuLogits1DMaxKernel
+ * -# @ref kernels::CpuLogits1DSoftmaxKernel
+ */
+template <bool IS_LOG = false>
+class CpuSoftmaxGeneric : public ICpuOperator
+{
+public:
+ /** Constructor */
+ CpuSoftmaxGeneric();
+ /** Set the input and output tensors.
+ *
+ * @param[in,out] src Source tensor info. Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32.
+ * last value of each row to the nearest multiple.
+ * @param[out] dst Destination tensor ifo. Data types supported: same as @p input.
+ * @param[in] beta (Optional) A scaling factor for the exponent.
+ * @param[in] axis (Optional) The dimension in which to apply the function. E.g. for input of shape 4x5x6 and
+ * axis=1, softmax will be applied to 4x6=24 vectors of size 5. Defaults to 0
+ */
+ void configure(const ITensorInfo *src, ITensorInfo *dst, float beta = 1.0f, int32_t axis = 0);
+
+ /** Static function to check if given info will lead to a valid configuration of @ref CpuSoftmax
+ *
+ * @param[in] src Source tensor info. Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32.
+ * @param[in] dst Destination tensor info. Data types supported: same as @p input
+ * @param[in] beta (Optional) A scaling factor for the exponent.
+ * @param[in] axis (Optional) The dimension in which to apply the function. E.g. for input of shape 4x5x6 and
+ * axis=1, softmax will be applied to 4x6=24 vectors of size 5. Defaults to 0
+ *
+ * @return a status
+ */
+ static Status validate(const ITensorInfo *src, const ITensorInfo *dst, float beta = 1.0f, int32_t axis = 0);
+
+ // Inherited methods overridden:
+ void run(ITensorPack &tensors) override;
+ experimental::MemoryRequirements workspace() const override;
+
+private:
+ CpuPermute _permute_input;
+ CpuPermute _permute_output;
+ std::unique_ptr<ICpuKernel> _max_kernel;
+ std::unique_ptr<ICpuKernel> _softmax_kernel;
+ std::unique_ptr<ITensorInfo> _max;
+ std::unique_ptr<ITensorInfo> _tmp;
+ std::unique_ptr<ITensorInfo> _input_permuted;
+ std::unique_ptr<ITensorInfo> _output_permuted;
+ bool _needs_permute;
+};
+using CpuSoftmax = CpuSoftmaxGeneric<false>;
+using CpuLogSoftmax = CpuSoftmaxGeneric<true>;
+
+} // namespace cpu
+} // namespace arm_compute
+#endif /* ARM_COMPUTE_CPU_SOFTMAX_H */