[PHI] Support bmm and bmm_grad in xpu

paddle/fluid/eager/utils.h

@@ -223,7 +223,7 @@ class EagerUtils {
       const std::vector<paddle::experimental::Tensor*>& out_var,
       std::vector<paddle::experimental::Tensor>* result);
 
-  // end Intermidate needed
+  // end Intermidate needed.
 
   static void CheckAndRetainGrad(const paddle::experimental::Tensor& tensor);
   static void CheckAndRetainGrad(

paddle/phi/kernels/xpu/bmm_grad_kernel.cc

@@ -0,0 +1,157 @@
+// 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.
+
+#include "paddle/phi/kernels/bmm_grad_kernel.h"
+
+#include "paddle/phi/backends/xpu/xpu_context.h"
+#include "paddle/phi/core/enforce.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/impl/matmul_grad_kernel_impl.h"
+
+// See Note [ Why still include the fluid headers? ]
+#include "paddle/fluid/platform/device/xpu/xpu_header.h"
+#include "paddle/phi/kernels/xpu/xpu_api_wrapper.h"
+namespace phi {
+
+template <typename T, typename FCT>
+static void MatMulXPUFunction(const DenseTensor& x,
+                              const DenseTensor& y,
+                              DenseTensor* out,
+                              bool trans_x,
+                              bool trans_y,
+                              xpu::Context* xpu_ctx) {
+  using XPUType = typename XPUTypeTrait<T>::Type;
+  const auto& x_dims = x.dims();
+  const auto& y_dims = y.dims();
+
+  auto mat_dim_a = phi::funcs::CreateMatrixDescriptor(
+      RowMatrixFromVector(x_dims), 0, trans_x);
+  auto mat_dim_b = phi::funcs::CreateMatrixDescriptor(
+      ColumnMatrixFromVector(y_dims), 0, trans_y);
+
+  T* data_c = out->data<T>();
+  int m = mat_dim_a.height_;
+  int n = mat_dim_b.width_;
+  int k = mat_dim_a.width_;
+  int batch_size = mat_dim_a.batch_size_;
+  // batch matmul
+  int r = xpu::fc_batched<XPUType, XPUType, XPUType, FCT>(
+      xpu_ctx,                                        // Context* ctx,
+      batch_size,                                     // int batch_size,
+      mat_dim_a.trans_,                               // bool x_trans,
+      mat_dim_b.trans_,                               // bool w_trans,
+      m,                                              // int m,
+      n,                                              // int n,
+      k,                                              // int k,
+      1.0,                                            // float alpha,
+      reinterpret_cast<const XPUType*>(x.data<T>()),  // const TX* x,
+      mat_dim_a.stride_,                              // int stride_a,
+      reinterpret_cast<const XPUType*>(y.data<T>()),  // const TW* w,
+      mat_dim_b.stride_,                              // int stride_b,
+      0.0,                                            // float beta,
+      reinterpret_cast<XPUType*>(data_c),             // TY* y,
+      m * n,                                          // int stride_c,
+      nullptr,                                        // const float* x_maxptr,
+      nullptr);                                       // const float* w_maxptr
+
+  PADDLE_ENFORCE_XDNN_SUCCESS(r, "fc_batched");
+}
+
+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);
+  xpu::Context* xpu_ctx = dev_ctx.x_context();
+  if (std::is_same<paddle::platform::float16, T>::value) {
+    MatMulXPUFunction<T, int16_t>(a, b, out, trans_a, trans_b, xpu_ctx);
+  } else {
+    if (std::getenv("XPU_PADDLE_FC_INT32") != nullptr) {
+      MatMulXPUFunction<T, int32_t>(a, b, out, trans_a, trans_b, xpu_ctx);
+    } else if (std::getenv("XPU_PADDLE_FC_LOCAL_INT16") != nullptr) {
+      MatMulXPUFunction<T, float>(a, b, out, trans_a, trans_b, xpu_ctx);
+    } else {
+      MatMulXPUFunction<T, int16_t>(a, b, out, trans_a, trans_b, xpu_ctx);
+    }
+  }
+}
+
+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) {
+  DenseTensor x_help = x;
+  DenseTensor y_help = y;
+  DenseTensor out_grad_help = out_grad;
+  ReshapeXYOutIntoMatrixSequence(
+      &x_help, &y_help, &out_grad_help, false, false);
+
+  phi::DDim dx_dims;
+  if (x_grad) {
+    dx_dims = x_grad->dims();
+    if (dx_dims != x_help.dims()) {
+      x_grad->Resize(x_help.dims());
+    }
+  }
+
+  phi::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
+
+PD_REGISTER_KERNEL(bmm_grad,
+                   XPU,
+                   ALL_LAYOUT,
+                   phi::BmmGradKernel,
+                   float,
+                   paddle::platform::float16) {}