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/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