[Cherry-pick] Support bmm and bmm_grad in xpu (#45887) (#46132)

* [PHI] Support bmm and bmm_grad in xpu (#45887)

* support bmm and bmm_grad in xpu

* add error removal

* test=kunlun

* refactor code for better structure

* test=kunlun

* add fp16 kernel for bmm

* test=kunlun

* test=kunlun

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/fluid/operators/bmm_op_xpu.cc

-//   Copyright (c) 2020 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.
-
-#ifdef PADDLE_WITH_XPU
-
-#include <string>
-#include <vector>
-
-#include "paddle/fluid/operators/matmul_v2_op.h"
-#include "paddle/fluid/operators/xpu_api_wrapper.h"
-#include "paddle/fluid/platform/device/device_wrapper.h"
-
-namespace paddle {
-namespace operators {
-
-template <typename T, typename FCT>
-static void MatMulXPUFunction(const Tensor* x,
-                              const Tensor* y,
-                              Tensor* out,
-                              bool trans_x,
-                              bool trans_y,
-                              const paddle::framework::ExecutionContext& ctx) {
-  using XPUType = typename XPUTypeTrait<T>::Type;
-  const auto& x_dims = x->dims();
-  const auto& y_dims = y->dims();
-  auto& dev_ctx =
-      ctx.template device_context<paddle::platform::XPUDeviceContext>();
-
-  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>(
-      dev_ctx.x_context(),                             // 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>
-class BmmXPUKernel : public framework::OpKernel<T> {
- public:
-  void Compute(const paddle::framework::ExecutionContext& ctx) const override {
-    auto* x = ctx.Input<Tensor>("X");
-    auto* y = ctx.Input<Tensor>("Y");
-    auto* out = ctx.Output<Tensor>("Out");
-    out->mutable_data<T>(ctx.GetPlace());
-
-    if (x->numel() == 0 || y->numel() == 0) {
-      return;
-    }
-    bool trans_x = false;
-    bool trans_y = false;
-
-    auto x_dims = x->dims();
-    auto y_dims = y->dims();
-
-    PADDLE_ENFORCE_EQ(x_dims.size(),
-                      3,
-                      platform::errors::InvalidArgument(
-                          "Input(X) of BmmOp must be 3-dimensional in BmmOp, "
-                          "but received X's shape: [%s].",
-                          x_dims));
-    PADDLE_ENFORCE_EQ(y_dims.size(),
-                      3,
-                      platform::errors::InvalidArgument(
-                          "Input(Y) of BmmOp must be 3-dimensional in BmmOp, "
-                          "but received Y's shape: [%s].",
-                          y_dims));
-    PADDLE_ENFORCE_EQ(
-        x_dims[0],
-        y_dims[0],
-        platform::errors::InvalidArgument(
-            "Input(X) and Input(Y) must have the same batch size in BmmOp, "
-            "but received X's batch size: [%s],"
-            "Y's batch size [%s]",
-            x_dims[0],
-            y_dims[0]));
-    PADDLE_ENFORCE_EQ(
-        x_dims[2],
-        y_dims[1],
-        platform::errors::InvalidArgument(
-            "Input(X)'s width must be equal with Input(Y)'s height in BmmOp,"
-            "but receive X's width: [%s],"
-            "Y's height: [%s].",
-            x_dims[2],
-            y_dims[1]));
-
-    if (std::is_same<paddle::platform::float16, T>::value) {
-      MatMulXPUFunction<T, int16_t>(x, y, out, trans_x, trans_y, ctx);
-    } else {
-      if (std::getenv("XPU_PADDLE_FC_INT32") != nullptr) {
-        MatMulXPUFunction<T, int32_t>(x, y, out, trans_x, trans_y, ctx);
-      } else if (std::getenv("XPU_PADDLE_FC_LOCAL_INT16") != nullptr) {
-        MatMulXPUFunction<T, float>(x, y, out, trans_x, trans_y, ctx);
-      } else {
-        MatMulXPUFunction<T, int16_t>(x, y, out, trans_x, trans_y, ctx);
-      }
-    }
-  }
-};
-
-template <typename T>
-class BmmXPUGradKernel : public framework::OpKernel<T> {
- public:
-  void MatMul(const framework::ExecutionContext& ctx,
-              const framework::Tensor& a,
-              bool trans_a,
-              const framework::Tensor& b,
-              bool trans_b,
-              framework::Tensor* out) const {
-    out->mutable_data<T>(ctx.GetPlace());
-    if (std::is_same<paddle::platform::float16, T>::value) {
-      MatMulXPUFunction<T, int16_t>(&a, &b, out, trans_a, trans_b, ctx);
-    } else {
-      if (std::getenv("XPU_PADDLE_FC_INT32") != nullptr) {
-        MatMulXPUFunction<T, int32_t>(&a, &b, out, trans_a, trans_b, ctx);
-      } else if (std::getenv("XPU_PADDLE_FC_LOCAL_INT16") != nullptr) {
-        MatMulXPUFunction<T, float>(&a, &b, out, trans_a, trans_b, ctx);
-      } else {
-        MatMulXPUFunction<T, int16_t>(&a, &b, out, trans_a, trans_b, ctx);
-      }
-    }
-  }
-
-  void CalcInputGrad(const framework::ExecutionContext& context,
-                     const framework::Tensor& a,
-                     bool trans_a,
-                     const framework::Tensor& b,
-                     bool trans_b,
-                     framework::Tensor* out) const {
-    if (out == nullptr) return;
-    MatMul(context, a, trans_a, b, trans_b, out);
-  }
-
-  void Compute(const framework::ExecutionContext& context) const override {
-    auto x = *context.Input<framework::Tensor>("X");
-    auto y = *context.Input<framework::Tensor>("Y");
-    auto dout =
-        *context.Input<framework::Tensor>(framework::GradVarName("Out"));
-    auto* dx = context.Output<framework::Tensor>(framework::GradVarName("X"));
-    auto* dy = context.Output<framework::Tensor>(framework::GradVarName("Y"));
-    ReshapeXYOutIntoMatrixSequence(&x, &y, &dout, false, false);
-
-    framework::DDim dx_dims;
-    if (dx) {
-      dx_dims = dx->dims();
-      if (dx_dims != x.dims()) {
-        dx->Resize(x.dims());
-      }
-    }
-
-    framework::DDim dy_dims;
-    if (dy) {
-      dy_dims = dy->dims();
-      if (dy_dims != y.dims()) {
-        dy->Resize(y.dims());
-      }
-    }
-
-    CalcInputGrad(context, dout, false, y, true, dx);
-    CalcInputGrad(context, x, true, dout, false, dy);
-
-    // CalcInputGrad(context, dout, false, false, y, true, false, dx);
-    // CalcInputGrad(context, x, true, true, dout, false, true, dy);
-
-    if (dx) {
-      if (dx_dims != x.dims()) {
-        dx->Resize(dx_dims);
-      }
-    }
-
-    if (dy) {
-      if (dy_dims != y.dims()) {
-        dy->Resize(dy_dims);
-      }
-    }
-  }
-};
-
-}  // namespace operators
-}  // namespace paddle
-
-namespace ops = paddle::operators;
-namespace plat = paddle::platform;
-REGISTER_OP_XPU_KERNEL(bmm,
-                       ops::BmmXPUKernel<float>,
-                       ops::BmmXPUKernel<plat::float16>);
-REGISTER_OP_XPU_KERNEL(bmm_grad,
-                       ops::BmmXPUGradKernel<float>,
-                       ops::BmmXPUGradKernel<plat::float16>);
-
-#endif

paddle/fluid/pybind/eager_functions.cc

@@ -903,7 +903,7 @@ static PyObject* eager_api_to_uva_tensor(PyObject* self,
         "Input object type error or incompatible array data type. "
         "tensor.set() supports array with bool, float16, float32, "
         "float64, int8, int16, int32, int64,"
-        "please check your input or input array data type."));
+        "please check your input or input array data type.."));
   }
 
   return ToPyObject(*(new_tensor.get()));

paddle/phi/kernels/xpu/bmm_grad_kernel.cc

+// 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/kernels/xpu/bmm_xpu_utils.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);
+  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) {}

paddle/phi/kernels/xpu/bmm_kernel.cc

+// 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_kernel.h"
+#include "paddle/phi/kernels/xpu/bmm_xpu_utils.h"
+namespace phi {
+template <typename T, typename Context>
+void BmmKernel(const Context& dev_ctx,
+               const DenseTensor& x,
+               const DenseTensor& y,
+               DenseTensor* out) {
+  dev_ctx.template Alloc<T>(out);
+  if (x.numel() == 0 || y.numel() == 0) {
+    return;
+  }
+  bool trans_x = false;
+  bool trans_y = false;
+
+  auto x_dims = x.dims();
+  auto y_dims = y.dims();
+
+  PADDLE_ENFORCE_EQ(x_dims.size(),
+                    3,
+                    phi::errors::InvalidArgument(
+                        "Input(X) of BmmOp must be 3-dimensional in BmmOp, "
+                        "but received X's shape: [%s]",
+                        x_dims));
+  PADDLE_ENFORCE_EQ(y_dims.size(),
+                    3,
+                    phi::errors::InvalidArgument(
+                        "Input(Y) of BmmOp must be 3-dimensional in BmmOp, "
+                        "but received Y's shape: [%s].",
+                        y_dims));
+  PADDLE_ENFORCE_EQ(
+      x_dims[0],
+      y_dims[0],
+      phi::errors::InvalidArgument(
+          "Input(X) and Input(Y) must have the same batch size in BmmOp, "
+          "but received X's batch size: [%s],"
+          "Y's batch size [%s]",
+          x_dims[0],
+          y_dims[0]));
+  PADDLE_ENFORCE_EQ(
+      x_dims[2],
+      y_dims[1],
+      phi::errors::InvalidArgument(
+          "Input(X)'s width must be equal with Input(Y)'s height in BmmOp,"
+          "but receive X's width: [%s],"
+          "Y's height: [%s].",
+          x_dims[2],
+          y_dims[1]));
+
+  xpu::Context* xpu_ctx = dev_ctx.x_context();
+  if (std::is_same<paddle::platform::float16, T>::value) {
+    MatMulXPUFunction<T, int16_t>(x, y, out, trans_x, trans_y, xpu_ctx);
+  } else {
+    if (std::getenv("XPU_PADDLE_FC_INT32") != nullptr) {
+      MatMulXPUFunction<T, int32_t>(x, y, out, trans_x, trans_y, xpu_ctx);
+    } else if (std::getenv("XPU_PADDLE_FC_LOCAL_INT16") != nullptr) {
+      MatMulXPUFunction<T, float>(x, y, out, trans_x, trans_y, xpu_ctx);
+    } else {
+      MatMulXPUFunction<T, int16_t>(x, y, out, trans_x, trans_y, xpu_ctx);
+    }
+  }
+}
+}  // namespace phi
+
+PD_REGISTER_KERNEL(
+    bmm, XPU, ALL_LAYOUT, phi::BmmKernel, float, paddle::platform::float16) {}

paddle/phi/kernels/xpu/bmm_xpu_utils.h

+// 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/backends/xpu/enforce_xpu.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/impl/matmul_grad_kernel_impl.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");
+}
+}  // namespace phi