1. fix elementwise ops'bug 2. fix softmax_with_cross_entropy_op 3. add biliner_interp_op (#29448)

Co-authored-by: Nroot <root@bjhw-sys-rpm0223.bjhw.baidu.com>

cmake/external/xpu.cmake

@@ -4,7 +4,7 @@ endif()
 
 INCLUDE(ExternalProject)
 SET(XPU_PROJECT                 "extern_xpu")
-SET(XPU_URL    "https://baidu-kunlun-public.su.bcebos.com/paddle_depence/xpu_2020_11_30.tar.gz" CACHE STRING "" FORCE)
+SET(XPU_URL    "https://baidu-kunlun-public.su.bcebos.com/paddle_depence/xpu_2020_12_04.tar.gz" CACHE STRING "" FORCE)
 SET(XPU_SOURCE_DIR              "${THIRD_PARTY_PATH}/xpu")
 SET(XPU_DOWNLOAD_DIR            "${XPU_SOURCE_DIR}/src/${XPU_PROJECT}")
 SET(XPU_INSTALL_DIR             "${THIRD_PARTY_PATH}/install/xpu")

paddle/fluid/operators/elementwise/elementwise_div_op_xpu.cc

@@ -28,9 +28,10 @@ class ElementwiseDivXPUKernel : public framework::OpKernel<T> {
 };
 
 template <typename DeviceContext, typename T>
-class ElementwiseDivGradXPUKernel : public framework::OpKernel<T> {
+class ElementwiseDivGradXPUKernel : public ElemwiseGradKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
+    ElemwiseGradKernel<T>::Compute(ctx);
     XPUElementwiseGrad<T>(ctx, xpu::div_grad<T>, true);
   }
 };

paddle/fluid/operators/elementwise/elementwise_max_op_xpu.cc

@@ -29,9 +29,10 @@ class ElementwiseMaxXPUKernel : public framework::OpKernel<T> {
 };
 
 template <typename DeviceContext, typename T>
-class ElementwiseMaxGradXPUKernel : public framework::OpKernel<T> {
+class ElementwiseMaxGradXPUKernel : public ElemwiseGradKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
+    ElemwiseGradKernel<T>::Compute(ctx);
     XPUElementwiseGrad<T>(ctx, xpu::max_grad<T>, true);
   }
 };

paddle/fluid/operators/elementwise/elementwise_min_op_xpu.cc

@@ -29,9 +29,10 @@ class ElementwiseMinXPUKernel : public framework::OpKernel<T> {
 };
 
 template <typename DeviceContext, typename T>
-class ElementwiseMinGradXPUKernel : public framework::OpKernel<T> {
+class ElementwiseMinGradXPUKernel : public ElemwiseGradKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
+    ElemwiseGradKernel<T>::Compute(ctx);
     XPUElementwiseGrad<T>(ctx, xpu::min_grad<T>, true);
   }
 };

paddle/fluid/operators/elementwise/elementwise_mul_op_xpu.cc

@@ -27,9 +27,10 @@ class ElementwiseMulXPUKernel : public framework::OpKernel<T> {
 };
 // DEFINE_XPU_GRAD_KERNEL(Mul, mul, true);
 template <typename DeviceContext, typename T>
-class ElementwiseMulGradXPUKernel : public framework::OpKernel<T> {
+class ElementwiseMulGradXPUKernel : public ElemwiseGradKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
+    ElemwiseGradKernel<T>::Compute(ctx);
     XPUElementwiseGrad<T>(ctx, xpu::mul_grad<T>, true);
   }
 };

paddle/fluid/operators/interpolate_op_xpu.cc

+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+   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 <memory>
+#include <string>
+#include <vector>
+
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/operators/interpolate_op.h"
+
+#ifdef PADDLE_WITH_XPU
+
+namespace paddle {
+namespace operators {
+
+using framework::Tensor;
+using DataLayout = framework::DataLayout;
+
+inline std::vector<int> get_new_shape_xpu(
+    const std::vector<const Tensor*>& list_new_shape_tensor) {
+  // get tensor from
+  std::vector<int> vec_new_shape;
+  for (size_t i = 0; i < list_new_shape_tensor.size(); ++i) {
+    auto tensor = list_new_shape_tensor[i];
+    PADDLE_ENFORCE_EQ(
+        tensor->dims(), framework::make_ddim({1}),
+        platform::errors::InvalidArgument("shape of dim tensor should be [1]"));
+    if (platform::is_xpu_place(tensor->place())) {
+      framework::Tensor temp;
+      TensorCopySync(*tensor, platform::CPUPlace(), &temp);
+      vec_new_shape.push_back(static_cast<int32_t>(*temp.data<int32_t>()));
+    } else {
+      vec_new_shape.push_back(static_cast<int32_t>(*tensor->data<int32_t>()));
+    }
+  }
+
+  return vec_new_shape;
+}
+
+template <typename T>
+inline std::vector<T> get_new_data_from_tensor_xpu(
+    const Tensor* new_data_tensor) {
+  std::vector<T> vec_new_data;
+  auto* new_data = new_data_tensor->data<T>();
+  framework::Tensor cpu_starts_tensor;
+  if (platform::is_xpu_place(new_data_tensor->place())) {
+    TensorCopySync(*new_data_tensor, platform::CPUPlace(), &cpu_starts_tensor);
+    new_data = cpu_starts_tensor.data<T>();
+  }
+  vec_new_data = std::vector<T>(new_data, new_data + new_data_tensor->numel());
+  return vec_new_data;
+}
+
+template <typename T>
+class InterpolateXPUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* input = ctx.Input<Tensor>("X");
+    auto* output = ctx.Output<Tensor>("Out");
+
+    auto input_dims = input->dims();
+    PADDLE_ENFORCE_EQ(
+        input_dims.size(), 4,
+        platform::errors::External("XPU Interpolate kernel only support 2d"));
+
+    const std::string data_layout_str = ctx.Attr<std::string>("data_layout");
+    const DataLayout data_layout =
+        framework::StringToDataLayout(data_layout_str);
+    int n, c, in_d, in_h, in_w;
+    ExtractNCDWH(input_dims, data_layout, &n, &c, &in_d, &in_h, &in_w);
+
+    auto interp_method = ctx.Attr<std::string>("interp_method");
+    bool align_corners = ctx.Attr<bool>("align_corners");
+    int align_mode = ctx.Attr<int>("align_mode");
+
+    int out_h = ctx.Attr<int>("out_h");
+    int out_w = ctx.Attr<int>("out_w");
+
+    auto list_new_size_tensor = ctx.MultiInput<framework::Tensor>("SizeTensor");
+    if (list_new_size_tensor.size() > 0) {
+      // have size tensor
+      auto new_size = get_new_shape_xpu(list_new_size_tensor);
+      out_h = new_size[0];
+      out_w = new_size[1];
+    } else {
+      float scale;
+      auto scale_tensor = ctx.Input<Tensor>("Scale");
+      if (scale_tensor != nullptr) {
+        auto scale_data = get_new_data_from_tensor_xpu<float>(scale_tensor);
+        scale = scale_data[0];
+      } else {
+        scale = ctx.Attr<float>("scale");
+      }
+      if (scale > 0) {
+        out_h = static_cast<int>(in_h * scale);
+        out_w = static_cast<int>(in_w * scale);
+      }
+      auto out_size = ctx.Input<Tensor>("OutSize");
+      if (out_size != nullptr) {
+        auto out_size_data = get_new_data_from_tensor_xpu<int>(out_size);
+        out_h = out_size_data[0];
+        out_w = out_size_data[1];
+      }
+    }
+    PADDLE_ENFORCE_GT(out_h, 0, platform::errors::InvalidArgument(
+                                    "out_h in Attr(out_shape) of "
+                                    "Op(interpolate) "
+                                    "should be greater than 0."));
+    PADDLE_ENFORCE_GT(out_w, 0, platform::errors::InvalidArgument(
+                                    "out_w in Attr(out_shape) of "
+                                    "Op(interpolate) "
+                                    "should be greater than 0."));
+    framework::DDim dim_out;
+    if (data_layout == DataLayout::kNCHW) {
+      dim_out = {n, c, out_h, out_w};
+    } else {
+      dim_out = {n, out_h, out_w, c};
+    }
+    output->mutable_data<T>(dim_out, ctx.GetPlace());
+
+    if (in_h == out_h && in_w == out_w) {
+      framework::TensorCopy(*input, ctx.GetPlace(), output);
+      return;
+    }
+    bool nearest = "nearest" == interp_method;
+    int trans_mode = (align_corners) ? (0) : ((align_mode == 0) ? (1) : (2));
+    auto& dev_ctx = ctx.template device_context<platform::XPUDeviceContext>();
+    if (nearest) {
+      PADDLE_ENFORCE_EQ((data_layout == DataLayout::kNCHW), true,
+                        platform::errors::InvalidArgument(
+                            "XPU nearest is only support NCHW"));
+    }
+    int r = xpu::interpolate2d<float>(dev_ctx.x_context(), input->data<float>(),
+                                      output->data<float>(), n, c, in_h, in_w,
+                                      out_h, out_w, nearest, trans_mode,
+                                      (data_layout == DataLayout::kNCHW));
+    PADDLE_ENFORCE_EQ(r, XPU_SUCCESS,
+                      platform::errors::External("XPU interpolate2d kernel "
+                                                 "return wrong value[%d %s]",
+                                                 r, XPUAPIErrorMsg[r]));
+  }
+};
+
+template <typename T>
+class InterpolateGradXPUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* input_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
+    auto* output_grad = ctx.Input<Tensor>(framework::GradVarName("Out"));
+
+    auto output_grad_dims = output_grad->dims();
+
+    PADDLE_ENFORCE_EQ(output_grad_dims.size(), 4,
+                      platform::errors::External(
+                          "XPU Interpolategrad kernel only support 2d"));
+
+    auto* input = ctx.Input<Tensor>("X");
+    const std::string data_layout_str = ctx.Attr<std::string>("data_layout");
+    const DataLayout data_layout =
+        framework::StringToDataLayout(data_layout_str);
+    int n, c, in_d, in_h, in_w;
+    ExtractNCDWH(input->dims(), data_layout, &n, &c, &in_d, &in_h, &in_w);
+
+    auto interp_method = ctx.Attr<std::string>("interp_method");
+    bool align_corners = ctx.Attr<bool>("align_corners");
+    int align_mode = ctx.Attr<int>("align_mode");
+
+    int out_h = ctx.Attr<int>("out_h");
+    int out_w = ctx.Attr<int>("out_w");
+    float scale;
+    auto scale_tensor = ctx.Input<Tensor>("Scale");
+    if (scale_tensor != nullptr) {
+      auto scale_data = get_new_data_from_tensor_xpu<float>(scale_tensor);
+      scale = scale_data[0];
+    } else {
+      scale = ctx.Attr<float>("scale");
+    }
+    if (scale > 0) {
+      out_h = static_cast<int>(in_h * scale);
+      out_w = static_cast<int>(in_w * scale);
+    }
+    auto out_size = ctx.Input<Tensor>("OutSize");
+    if (out_size != nullptr) {
+      auto out_size_data = get_new_data_from_tensor_xpu<int>(out_size);
+      out_h = out_size_data[0];
+      out_w = out_size_data[1];
+    }
+    auto list_new_size_tensor = ctx.MultiInput<framework::Tensor>("SizeTensor");
+    if (list_new_size_tensor.size() > 0) {
+      // have size tensor
+      auto new_size = get_new_shape_xpu(list_new_size_tensor);
+      out_h = new_size[0];
+      out_w = new_size[1];
+    }
+
+    framework::DDim dim_grad;
+    if (data_layout == DataLayout::kNCHW) {
+      dim_grad = {n, c, in_h, in_w};
+    } else {
+      dim_grad = {n, in_h, in_w, c};
+    }
+    input_grad->mutable_data<T>(dim_grad, ctx.GetPlace());
+
+    auto& dev_ctx = ctx.template device_context<platform::XPUDeviceContext>();
+
+    int r = XPU_SUCCESS;
+    r = xpu::constant<T>(dev_ctx.x_context(), input_grad->data<T>(),
+                         input_grad->numel(), static_cast<T>(0.0));
+    PADDLE_ENFORCE_EQ(r, XPU_SUCCESS,
+                      platform::errors::External(
+                          "XPU constant in interpolate2d_grad kernel return "
+                          "wrong value[%d %s]",
+                          r, XPUAPIErrorMsg[r]));
+
+    if (in_h == out_h && in_w == out_w) {
+      framework::TensorCopy(*output_grad, ctx.GetPlace(), input_grad);
+      return;
+    }
+
+    bool nearest = "nearest" == interp_method;
+    int trans_mode = (align_corners) ? (0) : ((align_mode == 0) ? (1) : (2));
+
+    if (nearest) {
+      PADDLE_ENFORCE_EQ((data_layout == DataLayout::kNCHW), true,
+                        platform::errors::InvalidArgument(
+                            "XPU nearest is only support NCHW"));
+    }
+
+    r = xpu::interpolate2d_grad<T>(dev_ctx.x_context(), output_grad->data<T>(),
+                                   input_grad->data<T>(), n, c, in_h, in_w,
+                                   out_h, out_w, nearest, trans_mode,
+                                   (data_layout == DataLayout::kNCHW));
+    PADDLE_ENFORCE_EQ(
+        r, XPU_SUCCESS,
+        platform::errors::External("XPU interpolate2d_grad kernel return "
+                                   "wrong value[%d %s]",
+                                   r, XPUAPIErrorMsg[r]));
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+
+REGISTER_OP_XPU_KERNEL(bilinear_interp, ops::InterpolateXPUKernel<float>);
+
+REGISTER_OP_XPU_KERNEL(bilinear_interp_grad,
+                       ops::InterpolateGradXPUKernel<float>);
+#endif

paddle/fluid/operators/softmax_with_cross_entropy_op_xpu.cc

@@ -70,7 +70,8 @@ class SoftmaxWithCrossEntropyXPUKernel : public framework::OpKernel<T> {
                                      r));
     } else {
       Tensor labels_int32;
-      labels_int32.mutable_data<int32_t>(context.GetPlace(), labels->numel());
+      labels_int32.mutable_data<int32_t>(context.GetPlace(),
+                                         labels->numel() * sizeof(int32_t));
       r = xpu::cast_v2<int64_t, int32_t>(
           dev_ctx.x_context(), labels->data<int64_t>(),
           labels_int32.data<int32_t>(), labels->numel());

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

+#   Copyright (c) 2018 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 unittest
+import numpy as np
+import paddle
+import paddle.fluid.core as core
+import sys
+sys.path.append("..")
+from op_test_xpu import XPUOpTest
+import paddle.fluid as fluid
+from paddle.fluid import Program, program_guard
+import time
+
+paddle.enable_static()
+
+
+def bilinear_interp_np(input,
+                       out_h,
+                       out_w,
+                       out_size=None,
+                       actual_shape=None,
+                       align_corners=True,
+                       align_mode=0,
+                       data_layout='NCHW'):
+    """bilinear interpolation implement in shape [N, C, H, W]"""
+    if data_layout == "NHWC":
+        input = np.transpose(input, (0, 3, 1, 2))  # NHWC => NCHW
+    if out_size is not None:
+        out_h = out_size[0]
+        out_w = out_size[1]
+    if actual_shape is not None:
+        out_h = actual_shape[0]
+        out_w = actual_shape[1]
+    batch_size, channel, in_h, in_w = input.shape
+
+    ratio_h = ratio_w = 0.0
+    if out_h > 1:
+        if (align_corners):
+            ratio_h = (in_h - 1.0) / (out_h - 1.0)
+        else:
+            ratio_h = 1.0 * in_h / out_h
+    if out_w > 1:
+        if (align_corners):
+            ratio_w = (in_w - 1.0) / (out_w - 1.0)
+        else:
+            ratio_w = 1.0 * in_w / out_w
+
+    out = np.zeros((batch_size, channel, out_h, out_w))
+
+    for i in range(out_h):
+        if (align_mode == 0 and not align_corners):
+            h = int(ratio_h * (i + 0.5) - 0.5)
+        else:
+            h = int(ratio_h * i)
+
+        h = max(0, h)
+        hid = 1 if h < in_h - 1 else 0
+        if (align_mode == 0 and not align_corners):
+            idx_src_h = max(ratio_h * (i + 0.5) - 0.5, 0)
+            h1lambda = idx_src_h - h
+        else:
+            h1lambda = ratio_h * i - h
+        h2lambda = 1.0 - h1lambda
+        for j in range(out_w):
+            if (align_mode == 0 and not align_corners):
+                w = int(ratio_w * (j + 0.5) - 0.5)
+            else:
+                w = int(ratio_w * j)
+            w = max(0, w)
+            wid = 1 if w < in_w - 1 else 0
+            if (align_mode == 0 and not align_corners):
+                idx_src_w = max(ratio_w * (j + 0.5) - 0.5, 0)
+                w1lambda = idx_src_w - w
+            else:
+                w1lambda = ratio_w * j - w
+            w2lambda = 1.0 - w1lambda
+
+            out[:, :, i, j] = h2lambda*(w2lambda*input[:, :, h, w] +
+                                        w1lambda*input[:, :, h, w+wid]) + \
+                h1lambda*(w2lambda*input[:, :, h+hid, w] +
+                          w1lambda*input[:, :, h+hid, w+wid])
+
+    if data_layout == "NHWC":
+        out = np.transpose(out, (0, 2, 3, 1))  # NCHW => NHWC
+
+    return out.astype(input.dtype)
+
+
+@unittest.skipIf(not paddle.is_compiled_with_xpu(),
+                 "core is not compiled with XPU")
+class TestBilinearInterpOp(XPUOpTest):
+    def setUp(self):
+        self.use_xpu = True
+        self.out_size = None
+        self.actual_shape = None
+        self.data_layout = 'NCHW'
+        self.init_test_case()
+        self.op_type = "bilinear_interp"
+        input_np = np.random.random(self.input_shape).astype("float32")
+
+        if self.data_layout == "NCHW":
+            in_h = self.input_shape[2]
+            in_w = self.input_shape[3]
+        else:
+            in_h = self.input_shape[1]
+            in_w = self.input_shape[2]
+
+        if self.scale > 0:
+            out_h = int(in_h * self.scale)
+            out_w = int(in_w * self.scale)
+        else:
+            out_h = self.out_h
+            out_w = self.out_w
+
+        output_np = bilinear_interp_np(input_np, out_h, out_w, self.out_size,
+                                       self.actual_shape, self.align_corners,
+                                       self.align_mode, self.data_layout)
+        self.inputs = {'X': input_np}
+        if self.out_size is not None:
+            self.inputs['OutSize'] = self.out_size
+        if self.actual_shape is not None:
+            self.inputs['OutSize'] = self.actual_shape
+
+        self.attrs = {
+            'out_h': self.out_h,
+            'out_w': self.out_w,
+            'scale': self.scale,
+            'interp_method': self.interp_method,
+            'align_corners': self.align_corners,
+            'align_mode': self.align_mode,
+            'data_layout': self.data_layout
+        }
+        self.outputs = {'Out': output_np}
+
+    def test_check_output(self):
+        place = paddle.XPUPlace(0)
+        self.check_output_with_place(place)
+
+    def test_check_grad(self):
+        place = paddle.XPUPlace(0)
+        self.check_grad_with_place(place, ['X'], 'Out', in_place=True)
+
+    def init_test_case(self):
+        self.interp_method = 'bilinear'
+        self.input_shape = [2, 3, 5, 5]
+        self.out_h = 2
+        self.out_w = 2
+        self.scale = 0.
+        self.out_size = np.array([3, 3]).astype("int32")
+        self.align_corners = True
+        self.align_mode = 1
+
+
+@unittest.skipIf(not paddle.is_compiled_with_xpu(),
+                 "core is not compiled with XPU")
+class TestBilinearInterpCase1(TestBilinearInterpOp):
+    def init_test_case(self):
+        self.interp_method = 'bilinear'
+        self.input_shape = [4, 1, 7, 8]
+        self.out_h = 1
+        self.out_w = 1
+        self.scale = 0.
+        self.align_corners = True
+        self.align_mode = 1
+
+
+@unittest.skipIf(not paddle.is_compiled_with_xpu(),
+                 "core is not compiled with XPU")
+class TestBilinearInterpCase2(TestBilinearInterpOp):
+    def init_test_case(self):
+        self.interp_method = 'bilinear'
+        self.input_shape = [3, 3, 9, 6]
+        self.out_h = 12
+        self.out_w = 12
+        self.scale = 0.
+        self.align_corners = True
+        self.align_mode = 1
+
+
+@unittest.skipIf(not paddle.is_compiled_with_xpu(),
+                 "core is not compiled with XPU")
+class TestBilinearInterpCase3(TestBilinearInterpOp):
+    def init_test_case(self):
+        self.interp_method = 'bilinear'
+        self.input_shape = [1, 1, 32, 64]
+        self.out_h = 64
+        self.out_w = 32
+        self.scale = 0.
+        self.align_corners = True
+        self.align_mode = 1
+
+
+@unittest.skipIf(not paddle.is_compiled_with_xpu(),
+                 "core is not compiled with XPU")
+class TestBilinearInterpCase4(TestBilinearInterpOp):
+    def init_test_case(self):
+        self.interp_method = 'bilinear'
+        self.input_shape = [4, 1, 7, 8]
+        self.out_h = 1
+        self.out_w = 1
+        self.scale = 0.
+        self.out_size = np.array([2, 2]).astype("int32")
+        self.align_corners = True
+        self.align_mode = 1
+
+
+@unittest.skipIf(not paddle.is_compiled_with_xpu(),
+                 "core is not compiled with XPU")
+class TestBilinearInterpCase5(TestBilinearInterpOp):
+    def init_test_case(self):
+        self.interp_method = 'bilinear'
+        self.input_shape = [3, 3, 9, 6]
+        self.out_h = 12
+        self.out_w = 12
+        self.scale = 0.
+        self.out_size = np.array([11, 11]).astype("int32")
+        self.align_corners = True
+        self.align_mode = 1
+
+
+@unittest.skipIf(not paddle.is_compiled_with_xpu(),
+                 "core is not compiled with XPU")
+class TestBilinearInterpCase6(TestBilinearInterpOp):
+    def init_test_case(self):
+        self.interp_method = 'bilinear'
+        self.input_shape = [1, 1, 32, 64]
+        self.out_h = 64
+        self.out_w = 32
+        self.scale = 0.
+        self.out_size = np.array([65, 33]).astype("int32")
+        self.align_corners = True
+        self.align_mode = 1
+
+
+@unittest.skipIf(not paddle.is_compiled_with_xpu(),
+                 "core is not compiled with XPU")
+class TestBilinearInterpSame(TestBilinearInterpOp):
+    def init_test_case(self):
+        self.interp_method = 'bilinear'
+        self.input_shape = [2, 3, 32, 64]
+        self.out_h = 32
+        self.out_w = 64
+        self.scale = 0.
+        self.align_corners = True
+        self.align_mode = 1
+
+
+@unittest.skipIf(not paddle.is_compiled_with_xpu(),
+                 "core is not compiled with XPU")
+class TestBilinearInterpActualShape(TestBilinearInterpOp):
+    def init_test_case(self):
+        self.interp_method = 'bilinear'
+        self.input_shape = [3, 2, 32, 16]
+        self.out_h = 64
+        self.out_w = 32
+        self.scale = 0.
+        self.out_size = np.array([66, 40]).astype("int32")
+        self.align_corners = True
+        self.align_mode = 1
+
+
+@unittest.skipIf(not paddle.is_compiled_with_xpu(),
+                 "core is not compiled with XPU")
+class TestBilinearInterpDataLayout(TestBilinearInterpOp):
+    def init_test_case(self):
+        self.interp_method = 'bilinear'
+        self.input_shape = [2, 5, 5, 3]
+        self.out_h = 2
+        self.out_w = 2
+        self.scale = 0.
+        self.out_size = np.array([3, 3]).astype("int32")
+        self.align_corners = True
+        self.align_mode = 1
+        self.data_layout = "NHWC"
+
+
+@unittest.skipIf(not paddle.is_compiled_with_xpu(),
+                 "core is not compiled with XPU")
+class TestBilinearInterpOtherMethod1(TestBilinearInterpOp):
+    def set_align_mode(self):
+        self.align_corners = False
+        self.align_mode = 1
+
+
+@unittest.skipIf(not paddle.is_compiled_with_xpu(),
+                 "core is not compiled with XPU")
+class TestBilinearInterpWithMethod2(TestBilinearInterpOp):
+    def set_align_mode(self):
+        self.align_corners = False
+        self.align_mode = 0
+
+
+@unittest.skipIf(not paddle.is_compiled_with_xpu(),
+                 "core is not compiled with XPU")
+class TestBilinearInterpWithMethod3(TestBilinearInterpOp):
+    def set_align_mode(self):
+        self.align_corners = True
+        self.align_mode = 0
+
+
+@unittest.skipIf(not paddle.is_compiled_with_xpu(),
+                 "core is not compiled with XPU")
+class TestBilinearInterpScale1(TestBilinearInterpOp):
+    def init_test_case(self):
+        self.interp_method = 'bilinear'
+        self.input_shape = [2, 3, 5, 7]
+        self.out_h = 60
+        self.out_w = 25
+        self.scale = 2.
+        self.align_corners = True
+        self.align_mode = 1
+
+
+@unittest.skipIf(not paddle.is_compiled_with_xpu(),
+                 "core is not compiled with XPU")
+class TestBilinearInterpScale2(TestBilinearInterpOp):
+    def init_test_case(self):
+        self.interp_method = 'bilinear'
+        self.input_shape = [2, 3, 5, 7]
+        self.out_h = 60
+        self.out_w = 25
+        self.scale = 1.
+        self.align_corners = True
+        self.align_mode = 1
+
+
+@unittest.skipIf(not paddle.is_compiled_with_xpu(),
+                 "core is not compiled with XPU")
+class TestBilinearInterpScale3(TestBilinearInterpOp):
+    def init_test_case(self):
+        self.interp_method = 'bilinear'
+        self.input_shape = [2, 3, 5, 7]
+        self.out_h = 60
+        self.out_w = 25
+        self.scale = 1.5
+        self.align_corners = True
+        self.align_mode = 1
+
+
+@unittest.skipIf(not paddle.is_compiled_with_xpu(),
+                 "core is not compiled with XPU")
+class TestBilinearInterpZero(TestBilinearInterpOp):
+    def init_test_case(self):
+        self.interp_method = 'bilinear'
+        self.input_shape = [2, 3, 5, 7]
+        self.out_h = 60
+        self.out_w = 25
+        self.scale = 0.2
+        self.align_corners = False
+        self.align_mode = 0
+
+
+@unittest.skipIf(not paddle.is_compiled_with_xpu(),
+                 "core is not compiled with XPU")
+class TestBilinearInterpOp_attr_tensor(XPUOpTest):
+    def setUp(self):
+        self.out_size = None
+        self.actual_shape = None
+        self.init_test_case()
+        self.op_type = "bilinear_interp"
+        self.shape_by_1Dtensor = False
+        self.scale_by_1Dtensor = False
+        self.attrs = {
+            'interp_method': self.interp_method,
+            'align_corners': self.align_corners,
+        }
+
+        input_np = np.random.random(self.input_shape).astype("float32")
+        self.inputs = {'X': input_np}
+
+        if self.scale_by_1Dtensor:
+            self.inputs['Scale'] = np.array([self.scale]).astype("float32")
+        elif self.scale > 0:
+            out_h = int(self.input_shape[2] * self.scale)
+            out_w = int(self.input_shape[3] * self.scale)
+            self.attrs['scale'] = self.scale
+        else:
+            out_h = self.out_h
+            out_w = self.out_w
+
+        if self.shape_by_1Dtensor:
+            self.inputs['OutSize'] = self.out_size
+        elif self.out_size is not None:
+            size_tensor = []
+            for index, ele in enumerate(self.out_size):
+                size_tensor.append(("x" + str(index), np.ones(
+                    (1)).astype('int32') * ele))
+            self.inputs['SizeTensor'] = size_tensor
+
+        self.attrs['out_h'] = self.out_h
+        self.attrs['out_w'] = self.out_w
+        output_np = bilinear_interp_np(input_np, out_h, out_w, self.out_size,
+                                       self.actual_shape, self.align_corners)
+        self.outputs = {'Out': output_np}
+
+    def test_check_output(self):
+        place = paddle.XPUPlace(0)
+        self.check_output_with_place(place)
+
+    def test_check_grad(self):
+        place = paddle.XPUPlace(0)
+        self.check_grad_with_place(place, ['X'], 'Out', in_place=True)
+
+    def init_test_case(self):
+        self.interp_method = 'bilinear'
+        self.input_shape = [2, 3, 5, 5]
+        self.out_h = 3
+        self.out_w = 3
+        self.scale = 0.
+        self.out_size = [3, 3]
+        self.align_corners = True
+
+
+# out_size is a 1-D tensor
+@unittest.skipIf(not paddle.is_compiled_with_xpu(),
+                 "core is not compiled with XPU")
+class TestBilinearInterp_attr_tensor_Case1(TestBilinearInterpOp_attr_tensor):
+    def init_test_case(self):
+        self.interp_method = 'bilinear'
+        self.input_shape = [3, 3, 9, 6]
+        self.out_h = 12
+        self.out_w = 12
+        self.scale = 0.
+        self.out_size = [8, 12]
+        self.align_corners = True
+
+
+# scale is a 1-D tensor
+@unittest.skipIf(not paddle.is_compiled_with_xpu(),
+                 "core is not compiled with XPU")
+class TestBilinearInterp_attr_tensor_Case2(TestBilinearInterpOp_attr_tensor):
+    def init_test_case(self):
+        self.interp_method = 'bilinear'
+        self.input_shape = [3, 2, 32, 16]
+        self.out_h = 64
+        self.out_w = 32
+        self.scale = 0.
+        self.out_size = np.array([66, 40]).astype("int32")
+        self.align_corners = True
+        self.shape_by_1Dtensor = True
+
+
+# scale is a 1-D tensor
+@unittest.skipIf(not paddle.is_compiled_with_xpu(),
+                 "core is not compiled with XPU")
+class TestBilinearInterp_attr_tensor_Case3(TestBilinearInterpOp_attr_tensor):
+    def init_test_case(self):
+        self.interp_method = 'bilinear'
+        self.input_shape = [3, 2, 32, 16]
+        self.out_h = 64
+        self.out_w = 32
+        self.scale = 2.0
+        self.out_size = None
+        self.align_corners = True
+        self.scale_by_1Dtensor = True
+
+
+@unittest.skipIf(not paddle.is_compiled_with_xpu(),
+                 "core is not compiled with XPU")
+class TestBilinearInterpOpAPI(unittest.TestCase):
+    def test_case(self):
+        x = fluid.data(name="x", shape=[2, 3, 6, 6], dtype="float32")
+
+        dim = fluid.data(name="dim", shape=[1], dtype="int32")
+        shape_tensor = fluid.data(name="shape_tensor", shape=[2], dtype="int32")
+        actual_size = fluid.data(name="actual_size", shape=[2], dtype="int32")
+        scale_tensor = fluid.data(
+            name="scale_tensor", shape=[1], dtype="float32")
+
+        out1 = fluid.layers.resize_bilinear(x, out_shape=[12, 12])
+        out2 = fluid.layers.resize_bilinear(x, out_shape=[12, dim])
+        out3 = fluid.layers.resize_bilinear(x, out_shape=shape_tensor)
+        out4 = fluid.layers.resize_bilinear(
+            x, out_shape=[4, 4], actual_shape=actual_size)
+        out5 = fluid.layers.resize_bilinear(x, scale=scale_tensor)
+
+        x_data = np.random.random((2, 3, 6, 6)).astype("float32")
+        dim_data = np.array([12]).astype("int32")
+        shape_data = np.array([12, 12]).astype("int32")
+        actual_size_data = np.array([12, 12]).astype("int32")
+        scale_data = np.array([2.0]).astype("float32")
+
+        place = core.XPUPlace(0)
+        exe = fluid.Executor(place)
+        exe.run(fluid.default_startup_program())
+        results = exe.run(fluid.default_main_program(),
+                          feed={
+                              "x": x_data,
+                              "dim": dim_data,
+                              "shape_tensor": shape_data,
+                              "actual_size": actual_size_data,
+                              "scale_tensor": scale_data
+                          },
+                          fetch_list=[out1, out2, out3, out4, out5],
+                          return_numpy=True)
+
+        expect_res = bilinear_interp_np(
+            x_data, out_h=12, out_w=12, align_corners=True)
+        for res in results:
+            self.assertTrue(np.allclose(res, expect_res))
+
+
+if __name__ == "__main__":
+    unittest.main()

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

@@ -17,7 +17,7 @@ import sys
 sys.path.append("..")
 
 from test_softmax_op import stable_softmax
-from op_test import OpTest
+from op_test_xpu import XPUOpTest
 import paddle.fluid.core as core
 import paddle
 
@@ -45,7 +45,7 @@ def cross_entropy(softmax, label, soft_label, axis, ignore_index=-1):
     return result.reshape(label.shape)
 
 
-class TestSoftmaxWithCrossEntropyOp(OpTest):
+class TestSoftmaxWithCrossEntropyOp(XPUOpTest):
     """
     Test softmax with cross entropy operator with discreate one-hot labels.
     """