chore: import upstream snapshot with attribution

paddle/phi/kernels/impl/warprnnt_grad_kernel_impl.h

@@ -0,0 +1,58 @@
+// 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/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"
+
+namespace phi {
+
+template <typename T, typename Context>
+void WarprnntGradKernel(const Context& dev_ctx,
+                        const DenseTensor& input UNUSED,
+                        const DenseTensor& input_lengths UNUSED,
+                        const DenseTensor& warprnntgrad,
+                        const DenseTensor& loss_grad,
+                        int blank UNUSED,
+                        float fastemit_lambda UNUSED,
+                        DenseTensor* input_grad) {
+  dev_ctx.template Alloc<T>(input_grad);
+
+  int64_t B = warprnntgrad.dims()[0];
+  int64_t Tmax = warprnntgrad.dims()[1];
+  int64_t Umax = warprnntgrad.dims()[2];
+  int64_t D = warprnntgrad.dims()[3];
+
+  // (B,)
+  auto loss_grad_e = EigenTensor<T, 1>::From(loss_grad);
+
+  // (B, T, U, D)
+  auto warprnntgrad_e = EigenTensor<T, 4>::From(warprnntgrad);
+  auto acts_grad_e = EigenTensor<T, 4>::From(*input_grad);
+
+  Eigen::DSizes<int64_t, 4> grad_shape(B, 1, 1, 1);
+  Eigen::DSizes<int64_t, 4> bcast(1, Tmax, Umax, D);
+  auto acts_g =
+      warprnntgrad_e * loss_grad_e.reshape(grad_shape).broadcast(bcast).eval();
+
+  auto* place = dev_ctx.eigen_device();
+  acts_grad_e.device(*place) = acts_g;
+}
+
+}  // namespace phi