wehub-resource-sync
87 lines
3.3 KiB
C++
87 lines
3.3 KiB
C++
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#pragma once
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#include <vector>
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#include "paddle/phi/backends/dynload/warpctc.h"
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#include "paddle/phi/core/dense_tensor.h"
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#include "paddle/phi/kernels/empty_kernel.h"
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#include "paddle/phi/kernels/funcs/eigen/common.h"
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#include "paddle/phi/kernels/funcs/math_function.h"
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#include "paddle/phi/kernels/funcs/sequence_padding.h"
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#include "paddle/phi/kernels/funcs/sequence_scale.h"
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#include "paddle/utils/optional.h"
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namespace phi {
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template <typename T, typename Context>
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void WarpctcGradKernel(const Context& dev_ctx,
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const DenseTensor& logits UNUSED,
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const optional<DenseTensor>& logits_length,
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const DenseTensor& warpctcgrad,
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const DenseTensor& loss_grad,
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int blank UNUSED,
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bool norm_by_times,
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DenseTensor* logits_grad) {
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dev_ctx.template Alloc<T>(logits_grad);
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if (logits_length.is_initialized()) {
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int64_t max_seq_length = warpctcgrad.dims()[0]; // Tmax
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int64_t num_sequences = warpctcgrad.dims()[1]; // B
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int64_t seq_width = warpctcgrad.dims()[2]; // D
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// B
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auto logits_len_e = EigenTensor<int64_t, 1>::From(*logits_length);
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// (B, 1)
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auto loss_grad_e = EigenTensor<T, 2>::From(loss_grad);
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// (T, B, D)
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auto warpctcgrad_e = EigenTensor<T, 3>::From(warpctcgrad);
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auto logits_grad_e = EigenTensor<T, 3>::From(*logits_grad);
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Eigen::DSizes<int64_t, 3> grad_shape(1, num_sequences, 1);
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Eigen::DSizes<int64_t, 3> bcast(max_seq_length, 1, seq_width);
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auto logits_g =
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warpctcgrad_e * loss_grad_e.reshape(grad_shape).broadcast(bcast).eval();
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auto* place = dev_ctx.eigen_device();
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if (norm_by_times) {
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auto scales = logits_len_e.cast<T>()
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.inverse()
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.reshape(grad_shape)
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.broadcast(bcast)
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.eval();
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logits_grad_e.device(*place) = logits_g * scales;
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} else {
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logits_grad_e.device(*place) = logits_g;
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}
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} else {
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funcs::UnpaddingDenseTensorFunctor<Context, T>()(dev_ctx,
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warpctcgrad,
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logits_grad,
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-1,
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0,
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norm_by_times,
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funcs::kLengthBatchWidth);
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const T* loss_grad_data = loss_grad.data<T>();
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funcs::ScaleDenseTensorFunctor<Context, T>()(
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dev_ctx, loss_grad_data, logits_grad);
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}
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}
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} // namespace phi
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