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[Draft] Revisit categorical crossentropy
ONE-DCO-1.0-Signed-off-by: ragmani <[email protected]>
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| /* | ||
| * Copyright (c) 2024 Samsung Electronics Co., Ltd. All Rights Reserved | ||
| * Copyright 2016 The TensorFlow Authors. All Rights Reserved. | ||
| * | ||
| * Licensed under the Apache License, Version 2.0 (the "License"); | ||
| * you may not use this file except in compliance with the License. | ||
| * You may obtain a copy of the License at | ||
| * | ||
| * http://www.apache.org/licenses/LICENSE-2.0 | ||
| * | ||
| * Unless required by applicable law or agreed to in writing, software | ||
| * distributed under the License is distributed on an "AS IS" BASIS, | ||
| * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| * See the License for the specific language governing permissions and | ||
| * limitations under the License. | ||
| */ | ||
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| #ifndef __NNFW_CKER_EIGEN_XENT_OPS_H__ | ||
| #define __NNFW_CKER_EIGEN_XENT_OPS_H__ | ||
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| // From tensorflow/core/kernels/xent_op.cc | ||
| #define EIGEN_USE_THREADS | ||
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| #include "unsupported/Eigen/CXX11/Tensor" | ||
| #include "cker/operation/Helper/Tensor.h" | ||
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| // From tensorflow/core/kernels/xent_op.h | ||
| namespace nnfw | ||
| { | ||
| namespace cker | ||
| { | ||
| namespace xent_ops | ||
| { | ||
| namespace functor | ||
| { | ||
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| // Functor used by XentOp to do the computations. | ||
| template <typename Device, typename T> struct XentFunctor | ||
| { | ||
| // Computes Cross Entropy loss and backprop. | ||
| // | ||
| // logits: batch_size, num_classes. | ||
| // labels: batch_size, num_classes. | ||
| // scratch: temporary tensor, dims: batch_size, 1 | ||
| // loss: output tensor for the loss, dims: batch_size. | ||
| // backprop: output tensor for the backprop, dims: batch_size, num_classes. | ||
| void operator()(const Device &d, const Eigen::DSizes<Eigen::DenseIndex, 2> &shape, | ||
| const Eigen::array<Eigen::DenseIndex, 2> &logits_bcast, | ||
| const Eigen::array<Eigen::DenseIndex, 2> &labels_bcast, | ||
| typename TTypes<T>::ConstMatrix logits, typename TTypes<T>::ConstMatrix labels, | ||
| typename TTypes<T>::Matrix scratch, typename TTypes<T>::Vec loss, | ||
| typename TTypes<T>::Matrix backprop); | ||
| }; | ||
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| } // namespace functor | ||
| } // namespace xent_ops | ||
| } // namespace cker | ||
| } // namespace nnfw | ||
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| // From tensorflow/core/kernels/xent_op.cc | ||
| namespace nnfw | ||
| { | ||
| namespace cker | ||
| { | ||
| namespace xent_ops | ||
| { | ||
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| // Enable CPUDevice only for xent_ops | ||
| using CPUDevice = Eigen::ThreadPoolDevice; | ||
| using Index = Eigen::Index; | ||
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| // Partial specialization for a CPUDevice, that uses the Eigen implementation | ||
| // from XentEigenImpl. | ||
| namespace functor | ||
| { | ||
| template <typename Device, typename T> struct XentFunctorBase | ||
| { | ||
| void operator()(const Device &d, const Eigen::DSizes<Eigen::DenseIndex, 2> &shape, | ||
| const Eigen::array<Eigen::DenseIndex, 2> &logits_bcast, | ||
| const Eigen::array<Eigen::DenseIndex, 2> &labels_bcast, | ||
| typename TTypes<T>::ConstMatrix logits, typename TTypes<T>::ConstMatrix labels, | ||
| typename TTypes<T>::Matrix scratch, typename TTypes<T>::Vec loss, | ||
| typename TTypes<T>::Matrix backprop) | ||
| { | ||
| T *scratch_ptr = scratch.data(); | ||
| T *backprop_ptr = backprop.data(); | ||
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| T *loss_ptr = loss.data(); | ||
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| int row_size = shape[1]; | ||
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| if (shape[0] > 0) | ||
| { | ||
| backprop.device(d) = logits.broadcast(logits_bcast); | ||
| scratch.device(d) = labels.broadcast(labels_bcast); | ||
| auto reductionWorker = [&](int64_t begin, int64_t end) -> void { | ||
| for (int i = begin; i < end; i++) | ||
| { | ||
| T *this_backprop = backprop_ptr + (i * row_size); | ||
| T *this_logits = backprop_ptr + (i * row_size); | ||
| T *this_labels = scratch_ptr + (i * row_size); | ||
| T max_logits = this_logits[0]; | ||
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| // calculating max_logits | ||
| for (int j = 1; j < row_size; j++) | ||
| { | ||
| max_logits = std::max(max_logits, this_logits[j]); | ||
| } | ||
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| T sum = T(0); | ||
| T loss_sum = T(0); | ||
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| for (int j = 0; j < row_size; j++) | ||
| { | ||
| // Note that if input is reused than this_logits and this_backprop | ||
| // is same buffer, so after this calculation this_logits should no | ||
| // longer be trusted | ||
| this_backprop[j] = this_logits[j] - max_logits; | ||
| sum = sum + exp(this_backprop[j]); | ||
| } | ||
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| // loss calculation | ||
| T log_sum = log(sum); | ||
| for (int j = 0; j < row_size; j++) | ||
| { | ||
| loss_sum += this_labels[j] * (log_sum - this_backprop[j]); | ||
| this_backprop[j] = (exp(this_backprop[j]) / sum) - this_labels[j]; | ||
| } | ||
| loss_ptr[i] = loss_sum; | ||
| } | ||
| }; | ||
| const int64_t compute_cycles = 50 * row_size; | ||
| const int64_t input_bytes = sizeof(T) * row_size; | ||
| const int64_t output_bytes = sizeof(T) * row_size; | ||
| const Eigen::TensorOpCost cost(input_bytes, output_bytes, compute_cycles); | ||
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| d.parallelFor(shape[0], cost, reductionWorker); | ||
| } | ||
| } | ||
| }; | ||
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| template <typename T> struct XentFunctor<CPUDevice, T> : XentFunctorBase<CPUDevice, T> | ||
| { | ||
| }; | ||
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| } // namespace functor | ||
| } // namespace xent_ops | ||
| } // namespace cker | ||
| } // namespace nnfw | ||
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| #endif // __NNFW_CKER_EIGEN_XENT_OPS_H__ |
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