support kunlun matmul_v2 (#27910)

*test=kunlun

paddle/fluid/operators/matmul_v2_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 "paddle/fluid/operators/matmul_v2_op.h"
+#include <string>
+#include <vector>
+
+namespace paddle {
+namespace operators {
+
+template <typename T>
+void MatMulXPUFunction(const Tensor* X, const Tensor* Y,
+                       const std::vector<std::int64_t>& x_dims,
+                       const std::vector<std::int64_t>& y_dims, Tensor* Out,
+                       bool trans_x, bool trans_y,
+                       const paddle::framework::ExecutionContext& ctx) {
+  const int x_ndim = x_dims.size();
+  const int y_ndim = y_dims.size();
+
+  auto& dev_ctx =
+      ctx.template device_context<paddle::platform::XPUDeviceContext>();
+
+  // currently only support x_ndim == y_dim and non-broadcast case
+  PADDLE_ENFORCE_EQ(x_ndim, y_ndim, platform::errors::InvalidArgument(
+                                        "Shape mistake in matmul_v2_op"));
+  for (int i = 0; i < x_ndim - 2; i++) {
+    PADDLE_ENFORCE_EQ(
+        x_dims.data()[i], y_dims.data()[i],
+        platform::errors::InvalidArgument("Shape mistake in matmul_v2_op"));
+  }
+
+  int ret = 0;
+  if (x_ndim == 1 && y_ndim == 1) {
+    PADDLE_ENFORCE_EQ(X->numel(), Y->numel(),
+                      platform::errors::InvalidArgument(
+                          "X's numbers is not equal to Y's numbers,"
+                          "when X/Y's dims =1"));
+    VLOG(3) << "MatMul's case 1";
+    Out->Resize({1});
+    Out->mutable_data<T>(ctx.GetPlace());
+    ret = baidu::xpu::api::fc_int16(dev_ctx.x_context(), false, false, 1, 1,
+                                    X->numel(), 1.0f, X->data<T>(),
+                                    Y->data<T>(), 0.0f, Out->data<T>());
+    PADDLE_ENFORCE_EQ(
+        ret, XPU_SUCCESS,
+        platform::errors::External(
+            "XPU API return wrong value[%d] in matmul_v2, please check whether "
+            "Baidu Kunlun Card is properly installed.",
+            ret));
+    return;
+  }
+
+  if (x_ndim == 1) {
+    const int N = X->numel();
+    if (trans_y) {
+      PADDLE_ENFORCE_EQ(
+          y_dims[y_ndim - 1], N,
+          platform::errors::InvalidArgument("Input(Y) has error dim."));
+    } else {
+      PADDLE_ENFORCE_EQ(
+          y_dims[y_ndim - 2], N,
+          platform::errors::InvalidArgument("Input(Y) has error dim."));
+    }
+    std::vector<std::int64_t> out_dims(y_ndim - 1);
+    if (trans_y) {
+      std::copy_n(y_dims.cbegin(), y_ndim - 1, out_dims.begin());
+    } else {
+      std::copy_n(y_dims.cbegin(), y_ndim - 2, out_dims.begin());
+      out_dims.back() = y_dims.back();
+    }
+    Out->Resize(framework::make_ddim(out_dims));
+    Out->mutable_data<T>(ctx.GetPlace());
+    if (trans_y) {
+      const int M = Y->numel() / N;
+      VLOG(3) << "MatMul's case 2";
+      ret = baidu::xpu::api::fc_int16(dev_ctx.x_context(), false, true, 1, M, N,
+                                      1.0f, X->data<T>(), Y->data<T>(), 0.0f,
+                                      Out->data<T>());
+      PADDLE_ENFORCE_EQ(
+          ret, XPU_SUCCESS,
+          platform::errors::External("XPU API return wrong value[%d] in "
+                                     "matmul_v2, please check whether "
+                                     "Baidu Kunlun Card is properly installed.",
+                                     ret));
+    } else {
+      const int M = y_dims[y_ndim - 1];
+      const int batch_size = Y->numel() / (M * N);
+      for (int i = 0; i < batch_size; i++) {
+        ret = baidu::xpu::api::fc_int16(
+            dev_ctx.x_context(), false, false, 1, M, N, 1.0f, X->data<T>(),
+            Y->data<T>() + i * M * N, 0.0f, Out->data<T>() + i * M);
+        PADDLE_ENFORCE_EQ(ret, XPU_SUCCESS,
+                          platform::errors::External(
+                              "XPU API return wrong value[%d] in matmul_v2, "
+                              "please check whether "
+                              "Baidu Kunlun Card is properly installed.",
+                              ret));
+      }
+    }
+    return;
+  }
+
+  if (y_ndim == 1) {
+    const int N = Y->numel();
+    if (trans_x) {
+      PADDLE_ENFORCE_EQ(
+          x_dims[x_ndim - 2], N,
+          platform::errors::InvalidArgument("Input(X) has error dim."));
+    } else {
+      PADDLE_ENFORCE_EQ(
+          x_dims[x_ndim - 1], N,
+          platform::errors::InvalidArgument("Input(X) has error dim."));
+    }
+    std::vector<std::int64_t> out_dims(x_ndim - 1);
+    if (trans_x) {
+      std::copy_n(x_dims.cbegin(), x_ndim - 2, out_dims.begin());
+      out_dims.back() = x_dims.back();
+    } else {
+      std::copy_n(x_dims.cbegin(), x_ndim - 1, out_dims.begin());
+    }
+    Out->Resize(framework::make_ddim(out_dims));
+    Out->mutable_data<T>(ctx.GetPlace());
+
+    if (trans_x) {
+      const int M = x_dims[x_ndim - 1];
+      const int batch_size = X->numel() / (M * N);
+      for (int i = 0; i < batch_size; i++) {
+        ret = baidu::xpu::api::fc_int16(dev_ctx.x_context(), true, false, M, 1,
+                                        N, 1.0f, X->data<T>() + i * M * N,
+                                        Y->data<T>(), 0.0f,
+                                        Out->data<T>() + i * M);
+        PADDLE_ENFORCE_EQ(ret, XPU_SUCCESS,
+                          platform::errors::External(
+                              "XPU API return wrong value[%d] in matmul_v2, "
+                              "please check whether "
+                              "Baidu Kunlun Card is properly installed.",
+                              ret));
+      }
+    } else {
+      const int M = X->numel() / N;
+      VLOG(3) << "MatMul's case 7";
+      ret = baidu::xpu::api::fc_int16(dev_ctx.x_context(), false, false, M, 1,
+                                      N, 1.0f, X->data<T>(), Y->data<T>(), 0.0f,
+                                      Out->data<T>());
+      PADDLE_ENFORCE_EQ(
+          ret, XPU_SUCCESS,
+          platform::errors::External("XPU API return wrong value[%d] in "
+                                     "matmul_v2, please check whether "
+                                     "Baidu Kunlun Card is properly installed.",
+                                     ret));
+    }
+    return;
+  }
+
+  const int M = trans_x ? x_dims[x_ndim - 1] : x_dims[x_ndim - 2];
+  const int K = trans_x ? x_dims[x_ndim - 2] : x_dims[x_ndim - 1];
+  if (trans_y) {
+    PADDLE_ENFORCE_EQ(y_dims[y_ndim - 1], K, platform::errors::InvalidArgument(
+                                                 "Input(X) has error dim."));
+  } else {
+    PADDLE_ENFORCE_EQ(y_dims[y_ndim - 2], K, platform::errors::InvalidArgument(
+                                                 "Input(X) has error dim."));
+  }
+  const int N = trans_y ? y_dims[y_ndim - 2] : y_dims[y_ndim - 1];
+  const int ndim = (std::max)(x_ndim, y_ndim);
+  std::vector<std::int64_t> out_broadcast_dims(ndim);
+  int batch_size = 1;
+  for (int i = 0; i < ndim - 2; i++) {
+    PADDLE_ENFORCE_EQ(
+        x_dims.data()[i], y_dims.data()[i],
+        platform::errors::InvalidArgument("Shape mistake in matmul_v2_op"));
+    out_broadcast_dims[i] = x_dims.data()[i];
+    batch_size *= x_dims.data()[i];
+  }
+
+  out_broadcast_dims[ndim - 2] = M;
+  out_broadcast_dims[ndim - 1] = N;
+
+  Out->Resize(framework::make_ddim(out_broadcast_dims));
+  Out->mutable_data<T>(ctx.GetPlace());
+  ret = baidu::xpu::api::batched_gemm_int16(
+      dev_ctx.x_context(), trans_x, trans_y, batch_size, M, N, K, 1.0f,
+      X->data<T>(), Y->data<T>(), Out->data<T>(), nullptr, nullptr);
+  PADDLE_ENFORCE_EQ(
+      ret, XPU_SUCCESS,
+      platform::errors::External(
+          "XPU API return wrong value[%d] in matmul_v2, please check whether "
+          "Baidu Kunlun Card is properly installed.",
+          ret));
+}
+
+template <typename T>
+class MatMulV2XPUKernel : 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");
+    bool trans_x = ctx.Attr<bool>("trans_x");
+    bool trans_y = ctx.Attr<bool>("trans_y");
+    MatMulXPUFunction<T>(X, Y, vectorize(X->dims()), vectorize(Y->dims()), Out,
+                         trans_x, trans_y, ctx);
+  }
+};
+
+template <typename T>
+class MatMulV2XPUGradKernel : public framework::OpKernel<T> {
+ public:
+  void MatMul(const framework::ExecutionContext& context,
+              const framework::Tensor& a, bool trans_a,
+              const framework::Tensor& b, bool trans_b,
+              framework::Tensor* out) const {
+    out->mutable_data<T>(context.GetPlace());
+    MatMulXPUFunction<T>(&a, &b, vectorize(a.dims()), vectorize(b.dims()), out,
+                         trans_a, trans_b, context);
+  }
+
+  void CalcInputGrad(const framework::ExecutionContext& context,
+                     const framework::Tensor& a, bool trans_a,
+                     bool is_fold_init_dims_a, const framework::Tensor& b,
+                     bool trans_b, bool is_fold_init_dims_b,
+                     framework::Tensor* out) const {
+    if (out == nullptr) return;
+    bool need_combine = (a.dims().size() == 3 || b.dims().size() == 3) &&
+                        out->dims().size() == 2;
+    if (!need_combine) {
+      MatMul(context, a, trans_a, b, trans_b, out);
+    } else {
+      // currently not support this case
+    }
+  }
+
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    bool transpose_x = ctx.Attr<bool>("trans_x");
+    bool transpose_y = ctx.Attr<bool>("trans_y");
+
+    auto x = *ctx.Input<framework::Tensor>("X");
+    auto y = *ctx.Input<framework::Tensor>("Y");
+    auto dout = *ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
+
+    // get dims
+    std::vector<std::int64_t> x_dims = vectorize(x.dims());
+    std::vector<std::int64_t> y_dims = vectorize(y.dims());
+    std::vector<std::int64_t> dout_dims = vectorize(dout.dims());
+
+    int x_ndim = x_dims.size();
+    int y_ndim = y_dims.size();
+
+    auto* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
+    auto* dy = ctx.Output<Tensor>(framework::GradVarName("Y"));
+
+    auto& dev_ctx =
+        ctx.template device_context<paddle::platform::XPUDeviceContext>();
+    // Case1 : x's or y's dim = 1
+    int ret = 0;
+    if (x_ndim == 1 && y_ndim == 1) {
+      if (dx) {
+        dx->mutable_data<T>(ctx.GetPlace());
+        ret = baidu::xpu::api::fc_int16(dev_ctx.x_context(), false, false,
+                                        dx->numel(), 1, 1, 1.0f, y.data<T>(),
+                                        dout.data<T>(), 0.0f, dx->data<T>());
+        PADDLE_ENFORCE_EQ(ret, XPU_SUCCESS,
+                          platform::errors::External(
+                              "XPU API return wrong value[%d] in "
+                              "matmul_v2_grad, please check whether "
+                              "Baidu Kunlun Card is properly installed.",
+                              ret));
+      }
+      if (dy) {
+        dy->mutable_data<T>(ctx.GetPlace());
+        ret = baidu::xpu::api::fc_int16(dev_ctx.x_context(), false, false,
+                                        dy->numel(), 1, 1, 1.0f, x.data<T>(),
+                                        dout.data<T>(), 0.0f, dy->data<T>());
+        PADDLE_ENFORCE_EQ(ret, XPU_SUCCESS,
+                          platform::errors::External(
+                              "XPU API return wrong value[%d] in "
+                              "matmul_v2_grad, please check whether "
+                              "Baidu Kunlun Card is properly installed.",
+                              ret));
+      }
+      return;
+    }
+
+    bool is_broadcast = true;
+    if (x_ndim <= 2 || y_ndim <= 2) {
+      is_broadcast = false;
+    } else if (x_ndim != y_ndim) {
+      is_broadcast = true;
+    } else {
+      is_broadcast = !std::equal(x_dims.cbegin(), x_dims.cbegin() + x_ndim - 2,
+                                 y_dims.cbegin());
+    }
+
+    // currently only support non-broadcast case
+    PADDLE_ENFORCE_EQ(
+        is_broadcast, false,
+        platform::errors::InvalidArgument("Shape mistake in matmul_v2_op"));
+
+    // Case2: no broadcast or no batch size, it aims to speed and it is same as
+    // matmul in old version.
+    if (!is_broadcast) {
+      ReshapeXYOutIntoMatrixSequence(&x, &y, &dout, transpose_x, transpose_y);
+      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());
+        }
+      }
+      if (transpose_x && transpose_y) {
+        CalcInputGrad(ctx, y, true, true, dout, true, false, dx);
+        CalcInputGrad(ctx, dout, true, true, x, true, false, dy);
+      } else if (transpose_x) {
+        CalcInputGrad(ctx, y, false, false, dout, true, false, dx);
+        CalcInputGrad(ctx, x, false, false, dout, false, true, dy);
+      } else if (transpose_y) {
+        CalcInputGrad(ctx, dout, false, false, y, false, true, dx);
+        CalcInputGrad(ctx, dout, true, true, x, false, true, dy);
+      } else {
+        CalcInputGrad(ctx, dout, false, false, y, true, false, dx);
+        CalcInputGrad(ctx, 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;
+
+REGISTER_OP_XPU_KERNEL(matmul_v2, ops::MatMulV2XPUKernel<float>);
+REGISTER_OP_XPU_KERNEL(matmul_v2_grad, ops::MatMulV2XPUGradKernel<float>);
+
+#endif

python/paddle/fluid/tests/unittests/xpu/test_matmul_v2_op_xpu.py

+#   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.
+
+from __future__ import print_function
+
+import unittest
+import numpy as np
+import sys
+sys.path.append("..")
+from op_test import OpTest
+import paddle.fluid.core as core
+
+import paddle
+import paddle.fluid as fluid
+import paddle.fluid.framework as framework
+
+
+def reference_matmul(X, Y, transpose_X=False, transpose_Y=False):
+    """Reference forward implementation using np.matmul."""
+    # np.matmul does not support the transpose flags, so we manually
+    # transpose X and Y appropriately.
+    if transpose_X:
+        if X.ndim == 1:
+            X = X.reshape((X.size, ))
+        elif X.ndim == 2:
+            X = X.T
+        else:
+            dim = [i for i in range(len(X.shape))]
+            dim[-1], dim[len(X.shape) - 2] = dim[len(X.shape) - 2], dim[-1]
+            X = np.transpose(X, tuple(dim))
+    if transpose_Y:
+        if Y.ndim == 1:
+            Y = Y.reshape((Y.size, ))
+        else:
+            dim = [i for i in range(len(Y.shape))]
+            dim[-1], dim[len(Y.shape) - 2] = dim[len(Y.shape) - 2], dim[-1]
+            Y = np.transpose(Y, tuple(dim))
+
+    Out = np.matmul(X, Y)
+    if not Out.shape:
+        # We do not support 0-dimensional Tensors (scalars). So where
+        # np.matmul outputs a scalar, we must convert to a Tensor of
+        # shape (1, ) instead.
+        # Everywhere else, we are compatible with np.matmul.
+        Out = np.array([Out], dtype="float64")
+    return Out
+
+
+@unittest.skipIf(not paddle.is_compiled_with_xpu(),
+                 "core is not compiled with XPU")
+class TestMatMulV2Op(OpTest):
+    """
+    case 1
+    """
+
+    def config(self):
+        self.x_shape = (100, )
+        self.y_shape = (100, )
+        self.trans_x = False
+        self.trans_y = False
+
+    def init_kernel_type(self):
+        self.dtype = "float32"
+
+    def setUp(self):
+        self.init_kernel_type()
+        self.config()
+        self.op_type = "matmul_v2"
+        self.use_xpu = True
+        x = np.random.random(self.x_shape).astype(self.dtype)
+        y = np.random.random(self.y_shape).astype(self.dtype)
+        # -0.1 ~ 0.1
+        x = -0.1 + 0.2 * x
+        y = -0.1 + 0.2 * y
+        result = reference_matmul(x, y, self.trans_x, self.trans_y)
+        result = result.astype(self.dtype)
+        self.inputs = {
+            'X': x,
+            'Y': y,
+        }
+        self.attrs = {'trans_x': self.trans_x, 'trans_y': self.trans_y}
+        self.outputs = {'Out': result}
+
+    def test_check_output(self):
+        place = paddle.XPUPlace(0)
+        self.check_output_with_place(place, atol=0.01)
+
+    def test_check_grad(self):
+        place = paddle.XPUPlace(0)
+        self.check_grad_with_place(
+            place, ['X', 'Y'], 'Out', max_relative_error=0.1)
+
+
+'''
+@unittest.skipIf(not paddle.is_compiled_with_xpu(),
+                 "core is not compiled with XPU")
+class TestMatMuklOp2(TestMatMulV2Op):
+    """
+    case 2
+    """
+
+    def config(self):
+        self.x_shape = (100, )
+        self.y_shape = (1, 3, 2, 100)
+        self.trans_x = False
+        self.trans_y = True
+
+
+@unittest.skipIf(not paddle.is_compiled_with_xpu(),
+                 "core is not compiled with XPU")
+class TestMatMuklOp3(TestMatMulV2Op):
+    """
+    case 3
+    """
+
+    def config(self):
+        self.x_shape = (100, )
+        self.y_shape = (1, 1, 100, 2)
+        self.trans_x = False
+        self.trans_y = False
+
+
+@unittest.skipIf(not paddle.is_compiled_with_xpu(),
+                 "core is not compiled with XPU")
+class TestMatMuklOp4(TestMatMulV2Op):
+    """
+    case 4
+    """
+
+    def config(self):
+        self.x_shape = (100, )
+        self.y_shape = (1, 2, 100, 2)
+        self.trans_x = False
+        self.trans_y = False
+
+@unittest.skipIf(not paddle.is_compiled_with_xpu(),
+                 "core is not compiled with XPU")
+class TestMatMuklOp5(TestMatMulV2Op):
+    """
+    case 5
+    """
+
+    def config(self):
+        self.x_shape = (1, 1, 100, 1)
+        self.y_shape = (100, )
+        self.trans_x = True
+        self.trans_y = False
+
+
+@unittest.skipIf(not paddle.is_compiled_with_xpu(),
+                 "core is not compiled with XPU")
+class TestMatMuklOp6(TestMatMulV2Op):
+    """
+    case 6
+    """
+
+    def config(self):
+        self.x_shape = (1, 2, 100, 1)
+        self.y_shape = (100, )
+        self.trans_x = True
+        self.trans_y = False
+
+
+@unittest.skipIf(not paddle.is_compiled_with_xpu(),
+                 "core is not compiled with XPU")
+class TestMatMuklOp7(TestMatMulV2Op):
+    """
+    case 7
+    """
+
+    def config(self):
+        self.x_shape = (1, 2, 1, 100)
+        self.y_shape = (100, )
+        self.trans_x = False
+        self.trans_y = False
+'''
+
+
+@unittest.skipIf(not paddle.is_compiled_with_xpu(),
+                 "core is not compiled with XPU")
+class TestMatMuklOp8(TestMatMulV2Op):
+    """
+    case 8
+    """
+
+    def config(self):
+        self.x_shape = (1, 1, 2, 100)
+        self.y_shape = (1, 1, 100, 2)
+        self.trans_x = False
+        self.trans_y = False
+
+
+@unittest.skipIf(not paddle.is_compiled_with_xpu(),
+                 "core is not compiled with XPU")
+class TestMatMuklOp13(TestMatMulV2Op):
+    """
+    case 13
+    """
+
+    def config(self):
+        self.x_shape = (2, 2, 2, 50)
+        self.y_shape = (2, 2, 2, 50)
+        self.trans_x = True
+        self.trans_y = False
+
+
+'''
+@unittest.skipIf(not paddle.is_compiled_with_xpu(),
+                 "core is not compiled with XPU")
+class TestMatMuklOp16(TestMatMulV2Op):
+    """
+    case 16 : to check the gradient for special case
+    """
+
+    def config(self):
+        self.x_shape = (100)
+        self.y_shape = (1, 2, 2, 100, 2)
+        self.trans_x = False
+        self.trans_y = False
+
+
+@unittest.skipIf(not paddle.is_compiled_with_xpu(),
+                 "core is not compiled with XPU")
+class TestMatMuklOp17(TestMatMulV2Op):
+    """
+    case 17 : to check the gradient for special case
+    """
+
+    def config(self):
+        self.x_shape = (2, 1, 100)
+        self.y_shape = (100)
+        self.trans_x = False
+        self.trans_y = False
+'''
+
+
+@unittest.skipIf(not paddle.is_compiled_with_xpu(),
+                 "core is not compiled with XPU")
+class TestMatMulV2API(unittest.TestCase):
+    def setUp(self):
+        self.places = [fluid.CPUPlace()]
+        self.places.append(fluid.XPUPlace(0))
+
+    def check_static_result(self, place):
+        with fluid.program_guard(fluid.Program(), fluid.Program()):
+            input_x = fluid.data(name="input_x", shape=[4, 3], dtype="float32")
+            input_y = fluid.data(name="input_y", shape=[3, 4], dtype="float32")
+
+            result = paddle.matmul(input_x, input_y)
+
+            x_np = np.random.random([4, 3]).astype("float32")
+            y_np = np.random.random([3, 4]).astype("float32")
+
+            exe = fluid.Executor(place)
+            fetches = exe.run(fluid.default_main_program(),
+                              feed={"input_x": x_np,
+                                    "input_y": y_np},
+                              fetch_list=[result])
+
+    def test_static(self):
+        for place in self.places:
+            self.check_static_result(place=place)
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/fluid/tests/unittests/xpu/test_mul_op_xpu.py

@@ -28,6 +28,8 @@ import time
 paddle.enable_static()
 
 
+@unittest.skipIf(not paddle.is_compiled_with_xpu(),
+                 "core is not compiled with XPU")
 class TestMulOpError(unittest.TestCase):
     def test_errors(self):
         with program_guard(Program(), Program()):
@@ -43,6 +45,8 @@ class TestMulOpError(unittest.TestCase):
             self.assertRaises(TypeError, fluid.layers.mul, x3, x4)
 
 
+@unittest.skipIf(not paddle.is_compiled_with_xpu(),
+                 "core is not compiled with XPU")
 class TestXPUMulOp1(OpTest):
     def setUp(self):
         self.op_type = "mul"
@@ -67,18 +71,23 @@ class TestXPUMulOp1(OpTest):
         pass
 
     def test_check_output(self):
-        self.check_output()
+        place = paddle.XPUPlace(0)
+        self.check_output_with_place(place, atol=0.01)
 
     def test_check_grad_normal(self):
-        self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.1)
+        place = paddle.XPUPlace(0)
+        self.check_grad_with_place(
+            place, ['X', 'Y'], 'Out', max_relative_error=0.1)
 
     def test_check_grad_ingore_x(self):
-        self.check_grad(
-            ['Y'], 'Out', max_relative_error=0.1, no_grad_set=set('X'))
+        place = paddle.XPUPlace(0)
+        self.check_grad_with_place(
+            place, ['Y'], 'Out', max_relative_error=0.1, no_grad_set=set("X"))
 
     def test_check_grad_ignore_y(self):
-        self.check_grad(
-            ['X'], 'Out', max_relative_error=0.1, no_grad_set=set('Y'))
+        place = paddle.XPUPlace(0)
+        self.check_grad_with_place(
+            place, ['X'], 'Out', max_relative_error=0.1, no_grad_set=set('Y'))
 
 
 @unittest.skipIf(not paddle.is_compiled_with_xpu(),