1 files changed, 204 insertions, 0 deletions
diff --git a/src/runtime/cpu/operators/CpuSoftmax.cpp b/src/runtime/cpu/operators/CpuSoftmax.cpp
new file mode 100644
index 0000000000..0e1bcd5c69
--- /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