support roi_align & affine_channel for kunlun (#29561)

* support roi_align & affine_channel for kunlun

* minor

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_12_07_cdfbf0c.tar.gz" CACHE STRING "" FORCE)
+SET(XPU_URL    "https://baidu-kunlun-public.su.bcebos.com/paddle_depence/xpu_2020_12_11.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/affine_channel_op_xpu.cc

+/* Copyright (c) 2016 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.
+Indicesou 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 <unordered_map>
+#include <vector>
+#include "paddle/fluid/framework/data_layout.h"
+#include "paddle/fluid/framework/eigen.h"
+#include "paddle/fluid/framework/op_registry.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename DeviceContext, typename T>
+class AffineChannelXPUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* x = ctx.Input<framework::Tensor>("X");
+    auto* scale = ctx.Input<framework::Tensor>("Scale");
+    auto* bias = ctx.Input<framework::Tensor>("Bias");
+
+    auto* y = ctx.Output<framework::Tensor>("Out");
+    y->mutable_data<T>(ctx.GetPlace());
+
+    const framework::DataLayout layout =
+        framework::StringToDataLayout(ctx.Attr<std::string>("data_layout"));
+
+    auto dims = x->dims();
+    int N = dims[0];
+    int C = layout == framework::DataLayout::kNCHW ? dims[1]
+                                                   : dims[dims.size() - 1];
+    int HxW = x->numel() / N / C;
+
+    auto* scale_d = scale->data<T>();
+    auto* bias_d = bias->data<T>();
+
+    auto* x_d = x->data<T>();
+    auto* y_d = y->data<T>();
+    auto& dev_ctx = ctx.template device_context<DeviceContext>();
+    std::vector<int> x_shape;
+    std::vector<int> b_shape;
+    if (layout == framework::DataLayout::kNCHW) {
+      x_shape.push_back(N);
+      x_shape.push_back(C);
+      x_shape.push_back(HxW);
+      b_shape.push_back(1);
+      b_shape.push_back(C);
+      b_shape.push_back(1);
+    } else {
+      x_shape.push_back(N * HxW);
+      x_shape.push_back(C);
+      b_shape.push_back(1);
+      b_shape.push_back(C);
+    }
+    int r = 0;
+    r = xpu::broadcast_mul(dev_ctx.x_context(), x_d, scale_d, y_d, x_shape,
+                           b_shape);
+    PADDLE_ENFORCE_EQ(r, xpu::Error_t::SUCCESS,
+                      platform::errors::External(
+                          "The broadcast_mul XPU OP return wrong value[%d %s]",
+                          r, XPUAPIErrorMsg[r]));
+    r = xpu::broadcast_add(dev_ctx.x_context(), y_d, bias_d, y_d, x_shape,
+                           b_shape);
+    PADDLE_ENFORCE_EQ(r, xpu::Error_t::SUCCESS,
+                      platform::errors::External(
+                          "The broadcast_add XPU OP return wrong value[%d %s]",
+                          r, XPUAPIErrorMsg[r]));
+  }
+};
+
+template <typename DeviceContext, typename T>
+class AffineChannelGradXPUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* x = ctx.Input<framework::Tensor>("X");
+    auto* scale = ctx.Input<framework::Tensor>("Scale");
+    auto* dy = ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
+
+    auto* dx = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
+    auto* dscale =
+        ctx.Output<framework::Tensor>(framework::GradVarName("Scale"));
+    auto* dbias = ctx.Output<framework::Tensor>(framework::GradVarName("Bias"));
+
+    const framework::DataLayout layout =
+        framework::StringToDataLayout(ctx.Attr<std::string>("data_layout"));
+
+    auto dims = x->dims();
+    int N = dims[0];
+    int C = layout == framework::DataLayout::kNCHW ? dims[1]
+                                                   : dims[dims.size() - 1];
+    int HxW = x->numel() / N / C;
+
+    auto* dy_d = dy->data<T>();
+    auto* scale_d = scale->data<T>();
+
+    T* dx_d = dx ? dx->mutable_data<T>(ctx.GetPlace()) : nullptr;
+    T* dscale_d = dscale ? dscale->mutable_data<T>(ctx.GetPlace()) : nullptr;
+    T* dbias_d = dbias ? dbias->mutable_data<T>(ctx.GetPlace()) : nullptr;
+
+    auto& dev_ctx = ctx.template device_context<DeviceContext>();
+    std::vector<int> x_shape;
+    std::vector<int> b_shape;
+    std::vector<int> rdims;
+    if (layout == framework::DataLayout::kNCHW) {
+      x_shape.push_back(N);
+      x_shape.push_back(C);
+      x_shape.push_back(HxW);
+      b_shape.push_back(1);
+      b_shape.push_back(C);
+      b_shape.push_back(1);
+      rdims.push_back(0);
+      rdims.push_back(2);
+    } else {
+      x_shape.push_back(N * HxW);
+      x_shape.push_back(C);
+      b_shape.push_back(1);
+      b_shape.push_back(C);
+      rdims.push_back(0);
+    }
+
+    int r = 0;
+    if (dscale_d && dbias_d) {
+      r = xpu::reduce_sum<T>(dev_ctx.x_context(), dy_d, dbias_d, x_shape,
+                             rdims);
+      PADDLE_ENFORCE_EQ(r, xpu::Error_t::SUCCESS,
+                        platform::errors::External(
+                            "The reduce_sum XPU OP return wrong value[%d %s]",
+                            r, XPUAPIErrorMsg[r]));
+      T* tmp = nullptr;
+      r = xpu_malloc(reinterpret_cast<void**>(&tmp), dy->numel() * sizeof(T));
+      PADDLE_ENFORCE_EQ(r, xpu::Error_t::SUCCESS,
+                        platform::errors::External("no enough memory in xpu"));
+
+      r = xpu::mul<T>(dev_ctx.x_context(), dy_d, x->data<T>(), tmp,
+                      dy->numel());
+      PADDLE_ENFORCE_EQ(
+          r, xpu::Error_t::SUCCESS,
+          platform::errors::External("The mul XPU OP return wrong value[%d %s]",
+                                     r, XPUAPIErrorMsg[r]));
+      r = xpu::reduce_sum<T>(dev_ctx.x_context(), tmp, dscale_d, x_shape,
+                             rdims);
+      PADDLE_ENFORCE_EQ(r, xpu::Error_t::SUCCESS,
+                        platform::errors::External(
+                            "The reduce_sum XPU OP return wrong value[%d %s]",
+                            r, XPUAPIErrorMsg[r]));
+      if (dev_ctx.x_context()->xpu_stream) {
+        dev_ctx.Wait();
+      }
+      xpu_free(tmp);
+    }
+    if (dx_d) {
+      r = xpu::broadcast_mul(dev_ctx.x_context(), dy_d, scale_d, dx_d, x_shape,
+                             b_shape);
+      PADDLE_ENFORCE_EQ(
+          r, xpu::Error_t::SUCCESS,
+          platform::errors::External(
+              "The broadcast_mul XPU OP return wrong value[%d %s]", r,
+              XPUAPIErrorMsg[r]));
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+using XPU = paddle::platform::XPUDeviceContext;
+
+REGISTER_OP_XPU_KERNEL(affine_channel, ops::AffineChannelXPUKernel<XPU, float>);
+REGISTER_OP_XPU_KERNEL(affine_channel_grad,
+                       ops::AffineChannelGradXPUKernel<XPU, float>);
+
+#endif

paddle/fluid/operators/roi_align_op_xpu.cc

@@ -24,89 +24,202 @@ template <typename DeviceContext, typename T>
 class XPUROIAlignOpKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
-    auto* in = ctx.Input<framework::Tensor>("X");
-    auto* rois = ctx.Input<framework::LoDTensor>("ROIs");
-    auto* out = ctx.Output<framework::Tensor>("Out");
+    auto* in = ctx.Input<Tensor>("X");
+    auto* rois = ctx.Input<LoDTensor>("ROIs");
+    auto* out = ctx.Output<Tensor>("Out");
+
     auto pooled_height = ctx.Attr<int>("pooled_height");
     auto pooled_width = ctx.Attr<int>("pooled_width");
     auto spatial_scale = ctx.Attr<float>("spatial_scale");
     auto sampling_ratio = ctx.Attr<int>("sampling_ratio");
-    auto& dev_ctx = ctx.template device_context<DeviceContext>();
+
     auto in_dims = in->dims();
     int batch_size = in_dims[0];
     int channels = in_dims[1];
     int height = in_dims[2];
     int width = in_dims[3];
+
     int rois_num = rois->dims()[0];
-    const T* input_data = in->data<T>();
 
-    framework::Tensor _roi_batch_list;
-    _roi_batch_list.Resize({rois_num});
-    int* rois_lod = _roi_batch_list.mutable_data<int>(ctx.GetPlace());
-    int rois_batch_size = 1;
+    if (rois_num == 0) return;
+
+    Tensor roi_batch_id_list;
+    roi_batch_id_list.Resize({rois_num});
+    auto cplace = platform::CPUPlace();
+    int* roi_batch_id_data = roi_batch_id_list.mutable_data<int>(cplace);
+    auto& dev_ctx = ctx.template device_context<DeviceContext>();
+    auto xplace = BOOST_GET_CONST(platform::XPUPlace, ctx.GetPlace());
+    int rois_batch_size = 0;
+    int* cpu_lod = nullptr;
     if (ctx.HasInput("RoisNum")) {
-      auto* rois_num_t = ctx.Input<framework::Tensor>("RoisNum");
+      auto* rois_num_t = ctx.Input<Tensor>("RoisNum");
       rois_batch_size = rois_num_t->numel();
       PADDLE_ENFORCE_EQ(
           rois_batch_size, batch_size,
           platform::errors::InvalidArgument(
-              "The batch size of rois and the batch size of images "
-              " must be the same. But received the batch size of rois is %d, "
-              "and the batch size of images is %d",
+              "The rois_batch_size and imgs "
+              "batch_size must be the same. But received rois_batch_size = %d, "
+              "batch_size = %d",
               rois_batch_size, batch_size));
-      auto* rois_num_data = rois_num_t->data<int>();
-      rois_lod[0] = 0;
-      for (int n = 0; n < rois_batch_size; ++n) {
-        rois_lod[n + 1] = rois_lod[n] + rois_num_data[n];
+
+      std::vector<int> rois_num_list(rois_batch_size);
+      memory::Copy(cplace, rois_num_list.data(), xplace,
+                   rois_num_t->data<int>(), sizeof(int) * rois_batch_size);
+      cpu_lod = new int[rois_batch_size + 1];
+      cpu_lod[0] = 0;
+      for (int i = 0; i < rois_batch_size; i++) {
+        cpu_lod[i + 1] = cpu_lod[i] + rois_num_list[i];
       }
     } else {
-      auto _rois_lod = rois->lod().back();
-      rois_batch_size = _rois_lod.size() - 1;
-      for (int n = 0; n < static_cast<int>(_rois_lod.size()); ++n) {
-        rois_lod[n] = _rois_lod[n];
-      }
+      auto lod = rois->lod();
+      PADDLE_ENFORCE_EQ(
+          lod.empty(), false,
+          platform::errors::InvalidArgument("Input(ROIs) in ROIAlignOp does "
+                                            "not contain LoD information."));
+      auto rois_lod = lod.back();
+      rois_batch_size = rois_lod.size() - 1;
       PADDLE_ENFORCE_EQ(
           rois_batch_size, batch_size,
           platform::errors::InvalidArgument(
-              "The rois_batch_size and imgs batch_size of roi_align_xpu OP "
-              "must "
-              "be the same. But received rois_batch_size %d , batch_size %d",
+              "The batch size of rois and batch size "
+              "of images must be the same. But received rois batch size = %d, "
+              "and images batch size = %d",
               rois_batch_size, batch_size));
+      int rois_num_with_lod = rois_lod[rois_batch_size];
+      PADDLE_ENFORCE_EQ(
+          rois_num, rois_num_with_lod,
+          platform::errors::InvalidArgument(
+              "The actual number of rois and the number of rois "
+              "provided from Input(RoIsLoD) in RoIAlign must be the same."
+              " But received actual number of rois is %d, and the number "
+              "of rois from RoIsLoD is %d",
+              rois_num, rois_num_with_lod));
+      for (int n = 0; n < rois_batch_size; ++n) {
+        for (size_t i = rois_lod[n]; i < rois_lod[n + 1]; ++i) {
+          roi_batch_id_data[i] = n;
+        }
+      }
+      cpu_lod = new int[rois_batch_size + 1];
+      for (int i = 0; i < rois_batch_size + 1; i++) {
+        cpu_lod[i] = rois_lod[i];
+      }
     }
-    int rois_num_with_lod = rois_lod[rois_batch_size];
-    PADDLE_ENFORCE_EQ(
-        rois_num, rois_num_with_lod,
-        platform::errors::InvalidArgument(
-            "The rois_num from input and lod of roi_align_xpu OP must be the "
-            "same. But received input rois_num %d , input lod %d",
-            rois_num, rois_num_with_lod));
-    T* output_data = out->mutable_data<T>(ctx.GetPlace());
-    const T* rois_data = rois->data<T>();
-    for (int n = 0; n < rois_batch_size; n++) {
-      int cur_batch_rois_num = rois_lod[n + 1] - rois_lod[n];
-      if (cur_batch_rois_num != 0) {
-        int r = xpu::roi_align(
-            dev_ctx.x_context(), input_data + n * channels * height * width,
-            rois_data + rois_lod[n] * 4, cur_batch_rois_num, channels, height,
-            width, pooled_height, pooled_width, sampling_ratio, spatial_scale,
-            output_data +
-                rois_lod[n] * channels * pooled_height * pooled_width);
-        PADDLE_ENFORCE_EQ(
-            r, xpu::Error_t::SUCCESS,
-            platform::errors::External(
-                "The roi_align XPU OP return wrong value[%d], please check "
-                "where Baidu Kunlun Card is properly installed.",
-                r));
+
+    int* roi_id_data = nullptr;
+    int r = xpu_malloc(reinterpret_cast<void**>(&roi_id_data),
+                       (rois_batch_size + 1) * sizeof(int));
+    PADDLE_ENFORCE_EQ(r, xpu::Error_t::SUCCESS,
+                      platform::errors::External("no enough memory in xpu"));
+    memory::Copy(xplace, roi_id_data, cplace, cpu_lod,
+                 (rois_batch_size + 1) * sizeof(int));
+    delete[] cpu_lod;
+    r = xpu::roi_align<T, int>(
+        dev_ctx.x_context(), in->data<T>(),
+        out->mutable_data<T>(ctx.GetPlace()), rois->data<T>(), roi_id_data,
+        batch_size, channels, height, width, out->dims()[0], pooled_height,
+        pooled_width, spatial_scale, sampling_ratio, true);
+    PADDLE_ENFORCE_EQ(r, xpu::Error_t::SUCCESS,
+                      platform::errors::External(
+                          "The roi_align XPU OP return wrong value[%d %s]", r,
+                          XPUAPIErrorMsg[r]));
+    if (dev_ctx.x_context()->xpu_stream) {
+      dev_ctx.Wait();
+    }
+    xpu_free(roi_id_data);
+  }
+};
+
+template <typename DeviceContext, typename T>
+class XPUROIAlignGradOpKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* in = ctx.Input<Tensor>("X");
+    auto* rois = ctx.Input<LoDTensor>("ROIs");
+
+    auto* out_grad = ctx.Input<Tensor>(framework::GradVarName("Out"));
+    auto* in_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
+
+    auto pooled_height = ctx.Attr<int>("pooled_height");
+    auto pooled_width = ctx.Attr<int>("pooled_width");
+    auto spatial_scale = ctx.Attr<float>("spatial_scale");
+    auto sampling_ratio = ctx.Attr<int>("sampling_ratio");
+
+    int rois_num = rois->dims()[0];
+    int channels = in->dims()[1];
+    int height = in->dims()[2];
+    int width = in->dims()[3];
+
+    if (!in_grad) {
+      return;
+    }
+    Tensor roi_batch_id_list;
+    roi_batch_id_list.Resize({rois_num});
+    auto cplace = platform::CPUPlace();
+
+    auto& dev_ctx = ctx.template device_context<DeviceContext>();
+    auto xplace = BOOST_GET_CONST(platform::XPUPlace, ctx.GetPlace());
+
+    int rois_batch_size = 0;
+    int* cpu_lod = nullptr;
+    if (ctx.HasInput("RoisNum")) {
+      auto* rois_num_t = ctx.Input<Tensor>("RoisNum");
+      rois_batch_size = rois_num_t->numel();
+      std::vector<int> rois_num_list(rois_batch_size);
+      memory::Copy(cplace, rois_num_list.data(), xplace,
+                   rois_num_t->data<int>(), sizeof(int) * rois_batch_size);
+      cpu_lod = new int[rois_batch_size + 1];
+      cpu_lod[0] = 0;
+      for (int i = 0; i < rois_batch_size; i++) {
+        cpu_lod[i + 1] = cpu_lod[i] + rois_num_list[i];
+      }
+    } else {
+      auto rois_lod = rois->lod().back();
+      rois_batch_size = rois_lod.size() - 1;
+      cpu_lod = new int[rois_batch_size + 1];
+      for (int i = 0; i < rois_batch_size + 1; i++) {
+        cpu_lod[i] = rois_lod[i];
       }
     }
+    int* roi_id_data = nullptr;
+    int r = xpu_malloc(reinterpret_cast<void**>(&roi_id_data),
+                       (rois_batch_size + 1) * sizeof(int));
+    PADDLE_ENFORCE_EQ(r, xpu::Error_t::SUCCESS,
+                      platform::errors::External("no enough memory in xpu"));
+    memory::Copy(xplace, roi_id_data, cplace, cpu_lod,
+                 (rois_batch_size + 1) * sizeof(int));
+    in_grad->mutable_data<T>(ctx.GetPlace());
+
+    int output_grad_size = out_grad->numel();
+
+    delete[] cpu_lod;
+    if (output_grad_size > 0) {
+      r = xpu::roi_align_grad<T, int>(
+          dev_ctx.x_context(), out_grad->data<T>(), in_grad->data<T>(),
+          rois->data<T>(), roi_id_data, in->dims()[0], channels, height, width,
+          out_grad->dims()[0], pooled_height, pooled_width, spatial_scale,
+          sampling_ratio, true);
+      PADDLE_ENFORCE_EQ(
+          r, xpu::Error_t::SUCCESS,
+          platform::errors::External(
+              "The roi_align_grad XPU OP return wrong value[%d %s]", r,
+              XPUAPIErrorMsg[r]));
+    }
+    if (dev_ctx.x_context()->xpu_stream) {
+      dev_ctx.Wait();
+    }
+    xpu_free(roi_id_data);
   }
 };
 
 }  // namespace operators
 }  // namespace paddle
+
 namespace ops = paddle::operators;
 REGISTER_OP_XPU_KERNEL(
     roi_align,
     ops::XPUROIAlignOpKernel<paddle::platform::XPUDeviceContext, float>);
+REGISTER_OP_XPU_KERNEL(
+    roi_align_grad,
+    ops::XPUROIAlignGradOpKernel<paddle::platform::XPUDeviceContext, float>);
 
 #endif

python/paddle/fluid/tests/unittests/xpu/test_affine_channel_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.
+"""
+Unit testing for affine_channel_op
+"""
+
+from __future__ import print_function
+
+import sys
+sys.path.append("..")
+
+import unittest
+import numpy as np
+from op_test_xpu import XPUOpTest
+import paddle
+import paddle.fluid.core as core
+import paddle.fluid as fluid
+
+
+def affine_channel(x, scale, bias, layout):
+    C = x.shape[1] if layout == 'NCHW' else x.shape[-1]
+    if len(x.shape) == 4:
+        new_shape = (1, C, 1, 1) if layout == 'NCHW' else (1, 1, 1, C)
+    else:
+        new_shape = (1, C)
+    scale = scale.reshape(new_shape)
+    bias = bias.reshape(new_shape)
+    return x * scale + bias
+
+
+class TestAffineChannelOp(XPUOpTest):
+    def setUp(self):
+        self.op_type = "affine_channel"
+        self.init_test_case()
+
+        x = np.random.random(self.shape).astype("float32")
+        scale = np.random.random(self.C).astype("float32")
+        bias = np.random.random(self.C).astype("float32")
+
+        y = affine_channel(x, scale, bias, self.layout)
+
+        self.inputs = {'X': x, 'Scale': scale, 'Bias': bias}
+        self.attrs = {'data_layout': self.layout}
+        self.outputs = {'Out': y}
+
+    def test_check_output(self):
+        if core.is_compiled_with_xpu():
+            paddle.enable_static()
+            place = paddle.XPUPlace(0)
+            self.check_output_with_place(place)
+
+    def test_check_grad(self):
+        if core.is_compiled_with_xpu():
+            paddle.enable_static()
+            place = paddle.XPUPlace(0)
+            self.check_grad_with_place(place, ['X', 'Scale', 'Bias'], 'Out')
+
+    def test_check_grad_stopgrad_dx(self):
+        if core.is_compiled_with_xpu():
+            paddle.enable_static()
+            place = paddle.XPUPlace(0)
+            self.check_grad_with_place(
+                place, ['Scale', 'Bias'], 'Out', no_grad_set=set('X'))
+
+    def test_check_grad_stopgrad_dscale_dbias(self):
+        if core.is_compiled_with_xpu():
+            paddle.enable_static()
+            place = paddle.XPUPlace(0)
+            self.check_grad_with_place(
+                place, ['X'], 'Out', no_grad_set=set(['Scale', 'Bias']))
+
+    def init_test_case(self):
+        self.shape = [2, 100, 3, 3]
+        self.C = 100
+        self.layout = 'NCHW'
+
+
+class TestAffineChannelOpError(unittest.TestCase):
+    def test_errors(self):
+        with fluid.program_guard(fluid.Program()):
+
+            def test_x_type():
+                input_data = np.random.random(2, 1, 2, 2).astype("float32")
+                fluid.layers.affine_channel(input_data)
+
+            self.assertRaises(TypeError, test_x_type)
+
+            def test_x_dtype():
+                x2 = fluid.layers.data(
+                    name='x2', shape=[None, 1, 2, 2], dtype='int32')
+                fluid.layers.affine_channel(x2)
+
+            self.assertRaises(TypeError, test_x_dtype)
+
+            def test_scale_type():
+                x3 = fluid.layers.data(
+                    name='x3', shape=[None, 1, 2, 2], dtype='float32')
+                fluid.layers.affine_channel(x3, scale=1)
+
+            self.assertRaises(TypeError, test_scale_type)
+
+            def test_bias_type():
+                x4 = fluid.layers.data(
+                    name='x4', shape=[None, 1, 2, 2], dtype='float32')
+                fluid.layers.affine_channel(x4, bias=1)
+
+            self.assertRaises(TypeError, test_bias_type)
+
+
+class TestAffineChannelNHWC(TestAffineChannelOp):
+    def init_test_case(self):
+        self.shape = [2, 3, 3, 100]
+        self.C = 100
+        self.layout = 'NHWC'
+
+    def test_check_grad_stopgrad_dx(self):
+        return
+
+    def test_check_grad_stopgrad_dscale_dbias(self):
+        return
+
+
+class TestAffineChannel2D(TestAffineChannelOp):
+    def init_test_case(self):
+        self.shape = [2, 100]
+        self.C = 100
+        self.layout = 'NCHW'
+
+    def test_check_grad_stopgrad_dx(self):
+        return
+
+    def test_check_grad_stopgrad_dscale_dbias(self):
+        return
+
+
+if __name__ == '__main__':
+    unittest.main()

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

@@ -20,13 +20,13 @@ import math
 import numpy as np
 import paddle.fluid.core as core
 from op_test import OpTest, skip_check_grad_ci
+from op_test_xpu import XPUOpTest
 import paddle
 import paddle.fluid as fluid
 from paddle.fluid import Program, program_guard
 
 
-@skip_check_grad_ci(reason="There is no grad kernel for roi_align_xpu kernel.")
-class TestROIAlignOp(OpTest):
+class TestROIAlignOp(XPUOpTest):
     def set_data(self):
         self.init_test_case()
         self.make_rois()
@@ -59,16 +59,16 @@ class TestROIAlignOp(OpTest):
         self.pooled_width = 2
         self.sampling_ratio = -1
 
-        self.x = np.random.random(self.x_dim).astype('float64')
+        self.x = np.random.random(self.x_dim).astype('float32')
 
     def pre_calc(self, x_i, roi_xmin, roi_ymin, roi_bin_grid_h, roi_bin_grid_w,
                  bin_size_h, bin_size_w):
         count = roi_bin_grid_h * roi_bin_grid_w
         bilinear_pos = np.zeros(
             [self.channels, self.pooled_height, self.pooled_width, count, 4],
-            np.float64)
+            np.float32)
         bilinear_w = np.zeros(
-            [self.pooled_height, self.pooled_width, count, 4], np.float64)
+            [self.pooled_height, self.pooled_width, count, 4], np.float32)
         for ph in range(self.pooled_width):
             for pw in range(self.pooled_height):
                 c = 0
@@ -118,7 +118,7 @@ class TestROIAlignOp(OpTest):
     def calc_roi_align(self):
         self.out_data = np.zeros(
             (self.rois_num, self.channels, self.pooled_height,
-             self.pooled_width)).astype('float64')
+             self.pooled_width)).astype('float32')
 
         for i in range(self.rois_num):
             roi = self.rois[i]
@@ -166,7 +166,7 @@ class TestROIAlignOp(OpTest):
                 roi = [bno, x1, y1, x2, y2]
                 rois.append(roi)
         self.rois_num = len(rois)
-        self.rois = np.array(rois).astype("float64")
+        self.rois = np.array(rois).astype("float32")
 
     def setUp(self):
         self.op_type = "roi_align"
@@ -178,6 +178,12 @@ class TestROIAlignOp(OpTest):
             place = paddle.XPUPlace(0)
             self.check_output_with_place(place)
 
+    def test_check_grad(self):
+        if core.is_compiled_with_xpu():
+            paddle.enable_static()
+            place = paddle.XPUPlace(0)
+            self.check_grad_with_place(place, {'X'}, 'Out')
+
 
 class TestROIAlignInLodOp(TestROIAlignOp):
     def set_data(self):