chore: import upstream snapshot with attribution

paddle/phi/kernels/impl/bmm_grad_kernel_impl.h

@@ -0,0 +1,106 @@
+// 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 "paddle/phi/kernels/bmm_grad_kernel.h"
+
+#include "paddle/phi/kernels/funcs/blas/blas.h"
+#include "paddle/phi/kernels/impl/matmul_grad_kernel_impl.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void MatMul(const Context& dev_ctx,
+            const DenseTensor& a,
+            bool trans_a,
+            const DenseTensor& b,
+            bool trans_b,
+            DenseTensor* out) {
+  dev_ctx.template Alloc<T>(out);
+  auto blas = funcs::GetBlas<Context, T>(dev_ctx);
+  auto mat_dim_a = funcs::CreateMatrixDescriptor(a.dims(), 0, trans_a);
+  auto mat_dim_b = funcs::CreateMatrixDescriptor(b.dims(), 0, trans_b);
+
+  blas.MatMul(a, mat_dim_a, b, mat_dim_b, T(1), out, T(0));
+}
+
+template <typename T, typename Context>
+void CalcInputGrad(const Context& dev_ctx,
+                   const DenseTensor& a,
+                   bool trans_a,
+                   const DenseTensor& b,
+                   bool trans_b,
+                   DenseTensor* out) {
+  if (out == nullptr) return;
+  MatMul<T, Context>(dev_ctx, a, trans_a, b, trans_b, out);
+}
+
+template <typename T, typename Context>
+void BmmGradKernel(const Context& dev_ctx,
+                   const DenseTensor& x,
+                   const DenseTensor& y,
+                   const DenseTensor& out_grad,
+                   DenseTensor* x_grad,
+                   DenseTensor* y_grad) {
+  if (x_grad && x_grad->numel() == 0) {
+    dev_ctx.template Alloc<T>(x_grad);
+    Full<T, Context>(dev_ctx, y.dims(), 0, y_grad);
+    return;
+  }
+  if (y_grad && y_grad->numel() == 0) {
+    dev_ctx.template Alloc<T>(y_grad);
+    Full<T, Context>(dev_ctx, x.dims(), 0, x_grad);
+    return;
+  }
+  DenseTensor x_help = x;
+  DenseTensor y_help = y;
+  DenseTensor out_grad_help = out_grad;
+  ReshapeXYOutIntoMatrixSequence(
+      &x_help, &y_help, &out_grad_help, false, false);
+
+  DDim dx_dims;
+  if (x_grad) {
+    dx_dims = x_grad->dims();
+    if (dx_dims != x_help.dims()) {
+      x_grad->Resize(x_help.dims());
+    }
+  }
+
+  DDim dy_dims;
+  if (y_grad) {
+    dy_dims = y_grad->dims();
+    if (dy_dims != y_help.dims()) {
+      y_grad->Resize(y_help.dims());
+    }
+  }
+
+  CalcInputGrad<T, Context>(
+      dev_ctx, out_grad_help, false, y_help, true, x_grad);
+  CalcInputGrad<T, Context>(
+      dev_ctx, x_help, true, out_grad_help, false, y_grad);
+
+  if (x_grad) {
+    if (dx_dims != x_help.dims()) {
+      x_grad->Resize(dx_dims);
+    }
+  }
+  if (y_grad) {
+    if (dy_dims != y_help.dims()) {
+      y_grad->Resize(dy_dims);
+    }
+  }
+}
+
+}  // namespace phi