chore: import upstream snapshot with attribution

2026-07-13 12:40:42 +08:00
commit e25996e7db
15472 changed files with 3536181 additions and 0 deletions
@@ -0,0 +1,86 @@
+// Copyright (c) 2022 PaddlePaddle 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.
+
+#pragma once
+
+#include <vector>
+
+#include "paddle/phi/backends/dynload/warpctc.h"
+#include "paddle/phi/core/dense_tensor.h"
+#include "paddle/phi/kernels/empty_kernel.h"
+#include "paddle/phi/kernels/funcs/eigen/common.h"
+#include "paddle/phi/kernels/funcs/math_function.h"
+#include "paddle/phi/kernels/funcs/sequence_padding.h"
+#include "paddle/phi/kernels/funcs/sequence_scale.h"
+#include "paddle/utils/optional.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void WarpctcGradKernel(const Context& dev_ctx,
+                       const DenseTensor& logits UNUSED,
+                       const optional<DenseTensor>& logits_length,
+                       const DenseTensor& warpctcgrad,
+                       const DenseTensor& loss_grad,
+                       int blank UNUSED,
+                       bool norm_by_times,
+                       DenseTensor* logits_grad) {
+  dev_ctx.template Alloc<T>(logits_grad);
+
+  if (logits_length.is_initialized()) {
+    int64_t max_seq_length = warpctcgrad.dims()[0];  // Tmax
+    int64_t num_sequences = warpctcgrad.dims()[1];   // B
+    int64_t seq_width = warpctcgrad.dims()[2];       // D
+
+    // B
+    auto logits_len_e = EigenTensor<int64_t, 1>::From(*logits_length);
+    // (B, 1)
+    auto loss_grad_e = EigenTensor<T, 2>::From(loss_grad);
+    // (T, B, D)
+    auto warpctcgrad_e = EigenTensor<T, 3>::From(warpctcgrad);
+
+    auto logits_grad_e = EigenTensor<T, 3>::From(*logits_grad);
+
+    Eigen::DSizes<int64_t, 3> grad_shape(1, num_sequences, 1);
+    Eigen::DSizes<int64_t, 3> bcast(max_seq_length, 1, seq_width);
+    auto logits_g =
+        warpctcgrad_e * loss_grad_e.reshape(grad_shape).broadcast(bcast).eval();
+
+    auto* place = dev_ctx.eigen_device();
+    if (norm_by_times) {
+      auto scales = logits_len_e.cast<T>()
+                        .inverse()
+                        .reshape(grad_shape)
+                        .broadcast(bcast)
+                        .eval();
+      logits_grad_e.device(*place) = logits_g * scales;
+    } else {
+      logits_grad_e.device(*place) = logits_g;
+    }
+  } else {
+    funcs::UnpaddingDenseTensorFunctor<Context, T>()(dev_ctx,
+                                                     warpctcgrad,
+                                                     logits_grad,
+                                                     -1,
+                                                     0,
+                                                     norm_by_times,
+                                                     funcs::kLengthBatchWidth);
+
+    const T* loss_grad_data = loss_grad.data<T>();
+    funcs::ScaleDenseTensorFunctor<Context, T>()(
+        dev_ctx, loss_grad_data, logits_grad);
+  }
+}
+
+}  // namespace phi