cherry-pick kunlun PR: 29458, 29539 (#29583)

* support mobilenet for kunlun (#29458)

* add xpu ops for training transformer in kunlun (#29539)

* 1.fix matmul bug 2. add one hot

* add xpu error msg
Co-authored-by: Nprocr <procrboo@gmail.com>
Co-authored-by: Ntaixiurong <taixiurong@126.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_12_04.tar.gz" CACHE STRING "" FORCE)
+SET(XPU_URL    "https://baidu-kunlun-public.su.bcebos.com/paddle_depence/xpu_2020_12_07_cdfbf0c.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/activation_op_xpu.cc

@@ -61,13 +61,38 @@ void xpu_activation_forward(const framework::ExecutionContext &ctx,
   const T *x_data = x->data<T>();
   T *y_data = y->mutable_data<T>(ctx.GetPlace());
   int r = 0;
-  if (xpu::Activation_t::ACT_POW == type.type) {
-    type.pow_factor = ctx.Attr<float>("factor");
-  }
   auto xpu_context = ctx.device_context<DeviceContext>().x_context();
-  r = xpu::activation_forward(xpu_context, type, x->numel(),
-                              reinterpret_cast<const float *>(x_data),
-                              reinterpret_cast<float *>(y_data));
+
+  switch (type.type) {
+    case xpu::Activation_t::HARD_SWISH: {
+      float threshold = ctx.Attr<float>("threshold");
+      float scale = ctx.Attr<float>("scale");
+      float offset = ctx.Attr<float>("offset");
+      PADDLE_ENFORCE_EQ(threshold, 6.0f,
+                        platform::errors::External(
+                            "Not support threshold [%f] in XPU", threshold));
+      PADDLE_ENFORCE_EQ(
+          scale, 6.0f,
+          platform::errors::External("Not support scale [%f] in XPU", scale));
+      PADDLE_ENFORCE_EQ(
+          offset, 3.0f,
+          platform::errors::External("Not support offset [%f] in XPU", offset));
+
+      r = xpu::hard_swish(xpu_context, reinterpret_cast<const float *>(x_data),
+                          reinterpret_cast<float *>(y_data), x->numel());
+      break;
+    }
+    case xpu::Activation_t::ACT_POW: {
+      type.pow_factor = ctx.Attr<float>("factor");
+    }
+    default: {
+      r = xpu::activation_forward(xpu_context, type, x->numel(),
+                                  reinterpret_cast<const float *>(x_data),
+                                  reinterpret_cast<float *>(y_data));
+      break;
+    }
+  }
+
   PADDLE_ENFORCE_EQ(r, XPU_SUCCESS,
                     platform::errors::External(
                         "XPU API return wrong value[%d], please check whether "
@@ -90,12 +115,40 @@ void xpu_activation_backward(const framework::ExecutionContext &ctx,
   if (y != nullptr) y_data = y->data<T>();
   if (dOut != nullptr) y_grad = dOut->data<T>();
   T *x_grad = dX->mutable_data<T>(ctx.GetPlace());
+  int r = 0;
   auto xpu_context = ctx.device_context<DeviceContext>().x_context();
-  int r = xpu::activation_backward(xpu_context, type, dX->numel(),
+
+  switch (type.type) {
+    case xpu::Activation_t::HARD_SWISH: {
+      float threshold = ctx.Attr<float>("threshold");
+      float scale = ctx.Attr<float>("scale");
+      float offset = ctx.Attr<float>("offset");
+      PADDLE_ENFORCE_EQ(threshold, 6.0f,
+                        platform::errors::External(
+                            "Not support threshold [%f] in XPU", threshold));
+      PADDLE_ENFORCE_EQ(
+          scale, 6.0f,
+          platform::errors::External("Not support scale [%f] in XPU", scale));
+      PADDLE_ENFORCE_EQ(
+          offset, 3.0f,
+          platform::errors::External("Not support offset [%f] in XPU", offset));
+      r = xpu::hard_swish_grad(xpu_context,
+                               reinterpret_cast<const float *>(x_data),
+                               reinterpret_cast<const float *>(y_data),
+                               reinterpret_cast<const float *>(y_grad),
+                               reinterpret_cast<float *>(x_grad), dX->numel());
+      break;
+    }
+    default: {
+      r = xpu::activation_backward(xpu_context, type, dX->numel(),
                                    reinterpret_cast<const float *>(x_data),
                                    reinterpret_cast<const float *>(y_data),
                                    reinterpret_cast<const float *>(y_grad),
                                    reinterpret_cast<float *>(x_grad));
+      break;
+    }
+  }
+
   PADDLE_ENFORCE_EQ(r, XPU_SUCCESS,
                     platform::errors::External(
                         "XPU API return wrong value[%d], please check whether "
@@ -132,6 +185,8 @@ using XPULogFunctor = XPUActivationFunc<T, xpu::Activation_t::LOG>;
 template <typename T>
 using XPUSquareFunctor = XPUActivationFunc<T, xpu::Activation_t::SQUARE>;
 template <typename T>
+using XPUHardSwishFunctor = XPUActivationFunc<T, xpu::Activation_t::HARD_SWISH>;
+template <typename T>
 using XPUSuareGradFunctor = XPUActivationGradFunc<T, xpu::Activation_t::SQUARE>;
 template <typename T>
 using XPUReluGradFunctor = XPUActivationGradFunc<T, xpu::Activation_t::RELU>;
@@ -147,6 +202,9 @@ using XPUSqrtFunctor = XPUActivationFunc<T, xpu::Activation_t::SQRT>;
 template <typename T>
 using XPUSqrtGradFunctor = XPUActivationGradFunc<T, xpu::Activation_t::SQRT>;
 template <typename T>
+using XPUHardSwishGradFunctor =
+    XPUActivationGradFunc<T, xpu::Activation_t::HARD_SWISH>;
+template <typename T>
 using XPUACTPowFunctor = XPUActivationFunc<T, xpu::Activation_t::ACT_POW>;
 template <typename T>
 using XPUABSFunctor = XPUActivationFunc<T, xpu::Activation_t::ABS>;
@@ -169,6 +227,8 @@ REGISTER_ACTIVATION_XPU_KERNEL(sigmoid, XPUSigmoidFunctor,
 REGISTER_ACTIVATION_XPU_KERNEL(gelu, XPUGeluFunctor, XPUGeluGradFunctor)
 REGISTER_ACTIVATION_XPU_KERNEL(sqrt, XPUSqrtFunctor, XPUSqrtGradFunctor)
 REGISTER_ACTIVATION_XPU_KERNEL(square, XPUSquareFunctor, XPUSuareGradFunctor)
+REGISTER_ACTIVATION_XPU_KERNEL(hard_swish, XPUHardSwishFunctor,
+                               XPUHardSwishGradFunctor)
 REGISTER_OP_XPU_KERNEL(log,
                        ops::XPUActivationKernel<ops::XPULogFunctor<float>>);
 REGISTER_OP_XPU_KERNEL(pow,

paddle/fluid/operators/controlflow/logical_op_xpu.h

+/* 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.
+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
+#ifdef PADDLE_WITH_XPU
+#include <algorithm>
+#include <string>
+#include <vector>
+
+#include "paddle/fluid/framework/op_registry.h"
+#include "xpu/refactor/math.h"
+
+namespace paddle {
+
+namespace operators {
+typedef enum { XPU_OR, XPU_AND } XpuLogicalType;
+
+std::string XpuLogicalType2Str(XpuLogicalType ty) {
+  switch (ty) {
+    case XpuLogicalType::XPU_OR:
+      return std::string("logical or");
+    case XpuLogicalType::XPU_AND:
+      return std::string("logical and");
+    default:
+      return std::string("unknown type");
+  }
+  return std::string("unknown");
+}
+
+template <XpuLogicalType xpu_type, typename T>
+class BinaryLogicalOpXPUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto* x = context.Input<framework::Tensor>("X");
+    auto* y = context.Input<framework::Tensor>("Y");
+    auto* out = context.Output<framework::Tensor>("Out");
+    T* out_ptr = out->mutable_data<T>(context.GetPlace());
+    const T* x_ptr = x->data<T>();
+    const T* y_ptr = y->data<T>();
+    auto& dev_ctx =
+        context.template device_context<paddle::platform::XPUDeviceContext>();
+    framework::Tensor broadcast_x;
+    framework::Tensor broadcast_y;
+    bool need_broad_cast = false;
+    if (x->numel() != out->numel()) {
+      // x need broadcast
+      T* broadcast_x_ptr =
+          broadcast_x.mutable_data<T>(context.GetPlace(), out->numel());
+      auto& out_dim = out->dims();
+      auto& x_dim = x->dims();
+      int dims = out_dim.size();
+      std::vector<int> bcast_xdims;
+      std::vector<int> bcast_ydims;
+      for (int i = 0; i < dims; ++i) {
+        if (out_dim[i] == x_dim[i]) {
+          bcast_xdims.push_back(x_dim[i]);
+          bcast_ydims.push_back(x_dim[i]);
+          continue;
+        }
+        bcast_xdims.push_back(1);
+        bcast_xdims.push_back(x_dim[i]);
+        bcast_ydims.push_back(out_dim[i] / x_dim[i]);
+        bcast_ydims.push_back(x_dim[i]);
+      }
+
+      int ret = xpu::broadcast<int8_t>(
+          dev_ctx.x_context(), reinterpret_cast<const int8_t*> x_ptr,
+          reinterpret_cast<int8_t*> broadcast_x_ptr, bcast_xdims, bcast_ydims);
+      PADDLE_ENFORCE_EQ(ret, XPU_SUCCESS,
+                        platform::errors::External(
+                            "XPU broadcast kernel return wrong value[%d %s]",
+                            ret, XPUAPIErrorMsg[ret]));
+      x_ptr = (const T*)broadcast_x_ptr;
+      need_broad_cast = true;
+    }
+    if (y->numel() != out->numel()) {
+      // y need broadcast
+      T* broadcast_y_ptr =
+          broadcast_y.mutable_data<T>(context.GetPlace(), out->numel());
+      auto& out_dim = out->dims();
+      auto& y_dim = y->dims();
+      int dims = out_dim.size();
+      std::vector<int> bcast_xdims;
+      std::vector<int> bcast_ydims;
+      for (int i = 0; i < dims; ++i) {
+        if (out_dim[i] == y_dim[i]) {
+          bcast_xdims.push_back(y_dim[i]);
+          bcast_ydims.push_back(y_dim[i]);
+          continue;
+        }
+        bcast_xdims.push_back(1);
+        bcast_xdims.push_back(y_dim[i]);
+        bcast_ydims.push_back(out_dim[i] / y_dim[i]);
+        bcast_ydims.push_back(y_dim[i]);
+      }
+
+      int ret = xpu::broadcast<int8_t>(
+          dev_ctx.x_context(), reinterpret_cast<const int8_t*> y_ptr,
+          reinterpret_cast<int8_t*> broadcast_y_ptr, bcast_xdims, bcast_ydims);
+      PADDLE_ENFORCE_EQ(ret, XPU_SUCCESS,
+                        platform::errors::External(
+                            "XPU broadcast kernel return wrong value[%d %s]",
+                            ret, XPUAPIErrorMsg[ret]));
+      y_ptr = (const T*)broadcast_y_ptr;
+      need_broad_cast = true;
+    }
+
+    // logical kernel
+    int ret = XPU_SUCCESS;
+    switch (xpu_type) {
+      case XpuLogicalType::XPU_OR:
+        ret = xpu::logical_or<bool>(dev_ctx.x_context(), x_ptr, y_ptr, out_ptr,
+                                    out->numel());
+        break;
+      case XpuLogicalType::XPU_AND:
+        ret = xpu::logical_and<bool>(dev_ctx.x_context(), x_ptr, y_ptr, out_ptr,
+                                     out->numel());
+      default:
+        LOG(ERROR) << "xpu not support logical xpu type = "
+                   << XpuLogicalType2Str(xpu_type);
+        break;
+    }
+    PADDLE_ENFORCE_EQ(
+        ret, XPU_SUCCESS,
+        platform::errors::External("XPU API return wrong value[%d %s] in "
+                                   "op_name[%s].",
+                                   ret, XPUAPIErrorMsg[ret],
+                                   XpuLogicalType2Str(xpu_type)));
+
+    if (need_broad_cast && dev_ctx.x_context()->xpu_stream != nullptr) {
+      xpu_wait();
+    }
+  }
+};
+
+template <typename T>
+class UnaryLogicalOpXPUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto* x = context.Input<framework::Tensor>("X");
+    auto* out = context.Output<framework::Tensor>("Out");
+    if (x->numel() == 0) {
+      return;
+    }
+    out->mutable_data<T>(context.GetPlace());
+    auto& dev_ctx =
+        context.template device_context<paddle::platform::XPUDeviceContext>();
+    int ret = xpu::logical_not<bool>(dev_ctx.x_context(), x->data<T>(),
+                                     out->data<T>(), x->numel());
+    PADDLE_ENFORCE_EQ(
+        ret, XPU_SUCCESS,
+        platform::errors::External("XPU API return wrong value[%d %s].", ret,
+                                   XPUAPIErrorMsg[ret]));
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+#endif

paddle/fluid/operators/controlflow/logicaland_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.
+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/controlflow/logical_op_xpu.h"
+namespace ops = paddle::operators;
+REGISTER_OP_XPU_KERNEL(
+    logical_and,
+    ops::BinaryLogicalOpXPUKernel<ops::XpuLogicalType::XPU_AND, bool>);
+#endif

paddle/fluid/operators/controlflow/logicalnot_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.
+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/controlflow/logical_op_xpu.h"
+namespace ops = paddle::operators;
+REGISTER_OP_XPU_KERNEL(logicalnot, ops::UnaryLogicalOpXPUKernel<bool>);
+#endif

paddle/fluid/operators/controlflow/logicalor_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.
+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/controlflow/logical_op_xpu.h"
+
+namespace ops = paddle::operators;
+REGISTER_OP_XPU_KERNEL(
+    logical_or,
+    ops::BinaryLogicalOpXPUKernel<ops::XpuLogicalType::XPU_OR, bool>);
+#endif

paddle/fluid/operators/matmul_op_xpu.cc

@@ -17,6 +17,7 @@ limitations under the License. */
 #include <algorithm>
 #include <utility>
 #include <vector>
+
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/operators/math/blas.h"
 
@@ -120,30 +121,40 @@ class MatMulXPUKernel : public framework::OpKernel<T> {
 
     auto &dev_ctx = context.template device_context<DeviceContext>();
     float *data_c = out->data<T>();
-    if (mat_dim_a.batch_size_ == 0 || mat_dim_a.batch_size_ == 1) {
-      int r =
-          xpu::fc_int16(dev_ctx.x_context(), mat_dim_a.trans_, mat_dim_b.trans_,
-                        mat_dim_a.height_, mat_dim_b.width_, mat_dim_a.width_,
-                        alpha, x->data<T>(), y->data<T>(), 0.0f, data_c);
-      PADDLE_ENFORCE_EQ(
-          r, XPU_SUCCESS,
-          platform::errors::External(
-              "XPU API return wrong value[%d], please check whether "
-              "Baidu Kunlun Card is properly installed.",
-              r));
+    int m = mat_dim_a.height_;
+    int n = mat_dim_b.width_;
+    int k = mat_dim_a.width_;
+    int ldx = mat_dim_a.trans_ ? m : k;
+    int ldy = mat_dim_b.trans_ ? k : n;
+    int ldout = n;
+    int batch_size = mat_dim_a.batch_size_;
+    if (batch_size == 0 || batch_size == 1) {
+      int r = xpu::fc_fusion<float, float, float, int16_t>(
+          dev_ctx.x_context(), x->data<T>(), y->data<T>(), data_c, m, n, k,
+          mat_dim_a.trans_, mat_dim_b.trans_, nullptr, nullptr, nullptr, ldx,
+          ldy, ldout, alpha, 0, nullptr, xpu::Activation_t::LINEAR);
+      PADDLE_ENFORCE_EQ(r, XPU_SUCCESS,
+                        platform::errors::External(
+                            "XPU fc_fusion kernel return wrong value[%d %s]", r,
+                            XPUAPIErrorMsg[r]));
     } else {
       // batch matmul
-      int r = xpu::batched_gemm_int16(dev_ctx.x_context(), mat_dim_a.trans_,
-                                      mat_dim_b.trans_, mat_dim_a.batch_size_,
-                                      mat_dim_a.height_, mat_dim_b.width_,
-                                      mat_dim_a.width_, alpha, x->data<T>(),
-                                      y->data<T>(), data_c, nullptr, nullptr);
-      PADDLE_ENFORCE_EQ(
-          r, XPU_SUCCESS,
-          platform::errors::External(
-              "XPU API return wrong value[%d], please check whether "
-              "Baidu Kunlun Card is properly installed.",
-              r));
+      int x_stride = mat_dim_a.stride_;
+      int y_stride = mat_dim_b.stride_;
+      int out_stride = m * n;
+      for (int i = 0; i < batch_size; ++i) {
+        const float *x_data = x->data<T>() + x_stride * i;
+        const float *y_data = y->data<T>() + y_stride * i;
+        float *out_data = data_c + out_stride * i;
+        int r = xpu::fc_fusion<float, float, float, int16_t>(
+            dev_ctx.x_context(), x_data, y_data, out_data, m, n, k,
+            mat_dim_a.trans_, mat_dim_b.trans_, nullptr, nullptr, nullptr, ldx,
+            ldy, ldout, alpha, 0, nullptr, xpu::Activation_t::LINEAR);
+        PADDLE_ENFORCE_EQ(r, XPU_SUCCESS,
+                          platform::errors::External(
+                              "XPU fc_fusion kernel return wrong value[%d %s]",
+                              r, XPUAPIErrorMsg[r]));
+      }
     }
   }
 };
@@ -171,9 +182,8 @@ static framework::Tensor XPUFoldHeadAndLastDims(
                          in_shape_host.data(), axis_host.data(), /*ndims=*/3);
   PADDLE_ENFORCE_EQ(r, XPU_SUCCESS,
                     platform::errors::External(
-                        "XPU API return wrong value[%d], please check whether "
-                        "Baidu Kunlun Card is properly installed.",
-                        r));
+                        "XPU transpose kernel return wrong value[%d %s]", r,
+                        XPUAPIErrorMsg[r]));
   output.Resize({in_dims[1], in_dims[0] * in_dims[2]});
 
   return output;
@@ -224,30 +234,41 @@ class MatMulGradXPUKernel : public framework::OpKernel<T> {
 
     auto &dev_ctx = context.template device_context<DeviceContext>();
     float *data_c = out->data<T>();
-    if (mat_dim_a.batch_size_ == 0 || mat_dim_a.batch_size_ == 1) {
-      int r =
-          xpu::fc_int16(dev_ctx.x_context(), mat_dim_a.trans_, mat_dim_b.trans_,
-                        mat_dim_a.height_, mat_dim_b.width_, mat_dim_a.width_,
-                        alpha, a.data<T>(), b.data<T>(), 0.0f, data_c);
-      PADDLE_ENFORCE_EQ(
-          r, XPU_SUCCESS,
-          platform::errors::External(
-              "XPU API return wrong value[%d], please check whether "
-              "Baidu Kunlun Card is properly installed.",
-              r));
+
+    int m = mat_dim_a.height_;
+    int n = mat_dim_b.width_;
+    int k = mat_dim_a.width_;
+    int ldx = mat_dim_a.trans_ ? m : k;
+    int ldy = mat_dim_b.trans_ ? k : n;
+    int ldout = n;
+    int batch_size = mat_dim_a.batch_size_;
+    if (batch_size == 0 || batch_size == 1) {
+      int r = xpu::fc_fusion<float, float, float, int16_t>(
+          dev_ctx.x_context(), a.data<T>(), b.data<T>(), data_c, m, n, k,
+          mat_dim_a.trans_, mat_dim_b.trans_, nullptr, nullptr, nullptr, ldx,
+          ldy, ldout, alpha, 0, nullptr, xpu::Activation_t::LINEAR);
+      PADDLE_ENFORCE_EQ(r, XPU_SUCCESS,
+                        platform::errors::External(
+                            "XPU fc_fusion kernel return wrong value[%d %s]", r,
+                            XPUAPIErrorMsg[r]));
     } else {
       // batch matmul
-      int r = xpu::batched_gemm_int16(dev_ctx.x_context(), mat_dim_a.trans_,
-                                      mat_dim_b.trans_, mat_dim_a.batch_size_,
-                                      mat_dim_a.height_, mat_dim_b.width_,
-                                      mat_dim_a.width_, alpha, a.data<T>(),
-                                      b.data<T>(), data_c, nullptr, nullptr);
-      PADDLE_ENFORCE_EQ(
-          r, XPU_SUCCESS,
-          platform::errors::External(
-              "XPU API return wrong value[%d], please check whether "
-              "Baidu Kunlun Card is properly installed.",
-              r));
+      int x_stride = mat_dim_a.stride_;
+      int y_stride = mat_dim_b.stride_;
+      int out_stride = m * n;
+      for (int i = 0; i < batch_size; ++i) {
+        const float *x_data = a.data<T>() + x_stride * i;
+        const float *y_data = b.data<T>() + y_stride * i;
+        float *out_data = data_c + out_stride * i;
+        int r = xpu::fc_fusion<float, float, float, int16_t>(
+            dev_ctx.x_context(), x_data, y_data, out_data, m, n, k,
+            mat_dim_a.trans_, mat_dim_b.trans_, nullptr, nullptr, nullptr, ldx,
+            ldy, ldout, alpha, 0, nullptr, xpu::Activation_t::LINEAR);
+        PADDLE_ENFORCE_EQ(r, XPU_SUCCESS,
+                          platform::errors::External(
+                              "XPU fc_fusion kernel return wrong value[%d %s]",
+                              r, XPUAPIErrorMsg[r]));
+      }
     }
   }
 

paddle/fluid/operators/one_hot_op_xpu.cc

+//   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.
+
+#ifdef PADDLE_WITH_XPU
+#include <string>
+#include <vector>
+
+#include "paddle/fluid/framework/framework.pb.h"
+#include "paddle/fluid/operators/one_hot_op.h"
+
+namespace paddle {
+namespace operators {
+
+using LoDTensor = framework::LoDTensor;
+using Tensor = framework::Tensor;
+
+template <typename DeviceContext, typename T>
+class OneHotXPUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto* in = context.Input<LoDTensor>("X");
+    auto* out = context.Output<LoDTensor>("Out");
+    int depth = context.Attr<int>("depth");
+    if (context.HasInput("depth_tensor")) {
+      auto* depth_tensor = context.Input<Tensor>("depth_tensor");
+      auto* depth_data = depth_tensor->data<int32_t>();
+      if (depth_tensor->place() == platform::XPUPlace()) {
+        xpu_memcpy(static_cast<void*>(&depth),
+                   static_cast<const void*>(depth_data), sizeof(int32_t),
+                   XPU_DEVICE_TO_HOST);
+      } else {
+        depth = depth_data[0];
+      }
+      auto in_dims = in->dims();
+      framework::DDim out_dims(in_dims);
+      out_dims[out_dims.size() - 1] = depth;
+      out->Resize(out_dims);
+    }
+
+    auto& dev_ctx = context.template device_context<DeviceContext>();
+    int len = in->numel();
+    int ret = xpu::one_hot<T>(dev_ctx.x_context(), in->data<T>(),
+                              out->mutable_data<float>(context.GetPlace()), len,
+                              depth);
+
+    PADDLE_ENFORCE_EQ(ret, XPU_SUCCESS,
+                      platform::errors::External(
+                          "XPU one_hot kernel return wrong value[%d %s]", ret,
+                          XPUAPIErrorMsg[ret]));
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP_XPU_KERNEL(
+    one_hot, ops::OneHotXPUKernel<paddle::platform::XPUDeviceContext, int>,
+    ops::OneHotXPUKernel<paddle::platform::XPUDeviceContext, int64_t>);
+#endif

paddle/fluid/platform/xpu_header.h

@@ -21,6 +21,7 @@
 
 #include "paddle/fluid/platform/errors.h"
 #include "xpu/api.h"
+#include "xpu/refactor/fusion.h"
 #include "xpu/refactor/math.h"
 #include "xpu/refactor/nn.h"
 #include "xpu/runtime.h"

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

@@ -20,6 +20,7 @@ import unittest
 import numpy as np
 import paddle.fluid.core as core
 from op_test import OpTest
+from op_test_xpu import XPUOpTest
 from scipy.special import expit, erf
 import paddle
 import paddle.fluid as fluid
@@ -30,7 +31,7 @@ from paddle.fluid import compiler, Program, program_guard
 
 @unittest.skipIf(not paddle.is_compiled_with_xpu(),
                  "core is not compiled with XPU")
-class TestXPUActivation(OpTest):
+class TestXPUActivation(XPUOpTest):
     def setUp(self):
         self.op_type = "exp"
         self.init_dtype()
@@ -166,6 +167,33 @@ def gelu(x, approximate):
     return y_ref.astype(x.dtype)
 
 
+@unittest.skipIf(not paddle.is_compiled_with_xpu(),
+                 "core is not compiled with XPU")
+class TestXPUHardSwish(TestXPUActivation):
+    def setUp(self):
+        self.op_type = "hard_swish"
+        self.init_dtype()
+        x = np.random.uniform(-1, 1, [11, 17]).astype(self.dtype)
+        offset = 3.0
+        threshold = 6.0
+        scale = 6.0
+        out = hard_swish(x, offset, threshold, scale)
+
+        self.inputs = {'X': x}
+        self.outputs = {'Out': out}
+        self.attrs = {'use_xpu': True}
+
+    def test_check_grad(self):
+        if paddle.is_compiled_with_xpu():
+            place = paddle.XPUPlace(0)
+            self.check_grad_with_place(place, ['X'], 'Out')
+
+
+def hard_swish(x, offset, threshold, scale):
+    y_ref = np.minimum(threshold, np.maximum(0, x + offset)) * x / scale
+    return y_ref.astype(x.dtype)
+
+
 @unittest.skipIf(not paddle.is_compiled_with_xpu(),
                  "core is not compiled with XPU")
 class TestXPULog(TestXPUActivation):

python/paddle/fluid/tests/unittests/xpu/test_logical_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
+from __future__ import print_function
+import unittest
+import numpy as np
+import sys
+sys.path.append("..")
+from paddle.fluid.op import Operator
+import paddle.fluid.core as core
+import paddle.fluid as fluid
+import paddle
+from op_test_xpu import XPUOpTest
+from paddle.static import Program, program_guard
+
+TEST_META_OP_DATA = [{
+    'op_str': 'logical_and',
+    'binary_op': True
+}, {
+    'op_str': 'logical_or',
+    'binary_op': True
+}, {
+    'op_str': 'logical_not',
+    'binary_op': False
+}]
+
+TEST_META_SHAPE_DATA = {
+    'XDimLargerThanYDim1': {
+        'x_shape': [2, 3, 4, 5],
+        'y_shape': [4, 5]
+    },
+    'XDimLargerThanYDim2': {
+        'x_shape': [2, 3, 4, 5],
+        'y_shape': [4, 1]
+    },
+    'XDimLargerThanYDim3': {
+        'x_shape': [2, 3, 4, 5],
+        'y_shape': [1, 4, 1]
+    },
+    'XDimLargerThanYDim4': {
+        'x_shape': [2, 3, 4, 5],
+        'y_shape': [3, 4, 1]
+    },
+    'XDimLargerThanYDim5': {
+        'x_shape': [2, 3, 1, 5],
+        'y_shape': [3, 1, 1]
+    },
+    'XDimLessThanYDim1': {
+        'x_shape': [4, 1],
+        'y_shape': [2, 3, 4, 5]
+    },
+    'XDimLessThanYDim2': {
+        'x_shape': [1, 4, 1],
+        'y_shape': [2, 3, 4, 5]
+    },
+    'XDimLessThanYDim3': {
+        'x_shape': [3, 4, 1],
+        'y_shape': [2, 3, 4, 5]
+    },
+    'XDimLessThanYDim4': {
+        'x_shape': [3, 1, 1],
+        'y_shape': [2, 3, 1, 5]
+    },
+    'XDimLessThanYDim5': {
+        'x_shape': [4, 5],
+        'y_shape': [2, 3, 4, 5]
+    },
+    'Axis1InLargerDim': {
+        'x_shape': [1, 4, 5],
+        'y_shape': [2, 3, 1, 5]
+    },
+    'EqualDim1': {
+        'x_shape': [10, 7],
+        'y_shape': [10, 7]
+    },
+    'EqualDim2': {
+        'x_shape': [1, 1, 4, 5],
+        'y_shape': [2, 3, 1, 5]
+    }
+}
+
+TEST_META_WRONG_SHAPE_DATA = {
+    'ErrorDim1': {
+        'x_shape': [2, 3, 4, 5],
+        'y_shape': [3, 4]
+    },
+    'ErrorDim2': {
+        'x_shape': [2, 3, 4, 5],
+        'y_shape': [4, 3]
+    }
+}
+
+
+def run_static_xpu(x_np, y_np, op_str, binary_op=True):
+    paddle.enable_static()
+    startup_program = fluid.Program()
+    main_program = fluid.Program()
+    place = paddle.XPUPlace(0)
+    exe = fluid.Executor(place)
+    with fluid.program_guard(main_program, startup_program):
+        x = paddle.static.data(name='x', shape=x_np.shape, dtype='bool')
+        op = getattr(paddle, op_str)
+        feed_list = {'x': x_np}
+        if not binary_op:
+            res = op(x)
+        else:
+            y = paddle.static.data(name='y', shape=y_np.shape, dtype='bool')
+            feed_list['y'] = y_np
+            res = op(x, y)
+        exe.run(startup_program)
+        static_result = exe.run(main_program, feed=feed_list, fetch_list=[res])
+    return static_result
+
+
+def run_dygraph_xpu(x_np, y_np, op_str, binary_op=True):
+    place = paddle.XPUPlace(0)
+    paddle.disable_static(place)
+    op = getattr(paddle, op_str)
+    x = paddle.to_tensor(x_np)
+    if not binary_op:
+        dygraph_result = op(x)
+    else:
+        y = paddle.to_tensor(y_np)
+        dygraph_result = op(x, y)
+    return dygraph_result
+
+
+def np_data_generator(np_shape, *args, **kwargs):
+    return np.random.choice(a=[True, False], size=np_shape).astype(bool)
+
+
+def test_xpu(unit_test, test_error=False):
+    for op_data in TEST_META_OP_DATA:
+        meta_data = dict(op_data)
+        np_op = getattr(np, meta_data['op_str'])
+        META_DATA = dict(TEST_META_SHAPE_DATA)
+        if test_error:
+            META_DATA = dict(TEST_META_WRONG_SHAPE_DATA)
+        for shape_data in META_DATA.values():
+            meta_data['x_np'] = np_data_generator(shape_data['x_shape'])
+            meta_data['y_np'] = np_data_generator(shape_data['y_shape'])
+            if meta_data['binary_op'] and test_error:
+                # catch C++ Exception
+                unit_test.assertRaises(BaseException, run_static_xpu,
+                                       **meta_data)
+                continue
+            static_result = run_static_xpu(**meta_data)
+            dygraph_result = run_dygraph_xpu(**meta_data)
+            if meta_data['binary_op']:
+                np_result = np_op(meta_data['x_np'], meta_data['y_np'])
+            else:
+                np_result = np_op(meta_data['x_np'])
+            unit_test.assertTrue((static_result == np_result).all())
+            unit_test.assertTrue((dygraph_result.numpy() == np_result).all())
+
+
+def test_type_error(unit_test, type_str_map):
+    def check_type(op_str, x, y, binary_op):
+        op = getattr(paddle, op_str)
+        error_type = TypeError
+        if isinstance(x, np.ndarray):
+            x = paddle.to_tensor(x)
+            y = paddle.to_tensor(y)
+            error_type = BaseException
+        if binary_op:
+            if type_str_map['x'] != 'bool' or type_str_map['y'] != 'bool':
+                unit_test.assertRaises(error_type, op, x=x, y=y)
+            if not fluid.in_dygraph_mode():
+                unit_test.assertRaises(error_type, op, x=x, y=y, out=1)
+        else:
+            if type_str_map['x'] != 'bool':
+                unit_test.assertRaises(error_type, op, x=x)
+            if not fluid.in_dygraph_mode():
+                unit_test.assertRaises(error_type, op, x=x, out=1)
+
+    place = paddle.XPUPlace(0)
+
+    for op_data in TEST_META_OP_DATA:
+        meta_data = dict(op_data)
+        binary_op = meta_data['binary_op']
+
+        paddle.disable_static(place)
+        x = np.random.choice(a=[0, 1], size=[10]).astype(type_str_map['x'])
+        y = np.random.choice(a=[0, 1], size=[10]).astype(type_str_map['y'])
+        check_type(meta_data['op_str'], x, y, binary_op)
+
+        paddle.enable_static()
+        startup_program = paddle.static.Program()
+        main_program = paddle.static.Program()
+        with paddle.static.program_guard(main_program, startup_program):
+            x = paddle.static.data(
+                name='x', shape=[10], dtype=type_str_map['x'])
+            y = paddle.static.data(
+                name='y', shape=[10], dtype=type_str_map['y'])
+            check_type(meta_data['op_str'], x, y, binary_op)
+
+
+def type_map_factory():
+    x_type_list = ['float32', 'float64', 'int32', 'int64', 'bool']
+    y_type_list = ['float32', 'float64', 'int32', 'int64', 'bool']
+    return [{
+        'x': x_type,
+        'y': y_type
+    } for x_type in x_type_list for y_type in y_type_list]
+
+
+@unittest.skipIf(not paddle.is_compiled_with_xpu(),
+                 "core is not compiled with XPU")
+class TestXPU(unittest.TestCase):
+    def test(self):
+        test_xpu(self, True)
+
+    def test_error(self):
+        test_xpu(self, True)
+
+    def test_type_error(self):
+        type_map_list = type_map_factory()
+        for type_map in type_map_list:
+            test_type_error(self, type_map)
+
+
+if __name__ == '__main__':
+    unittest.main()

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

@@ -19,11 +19,13 @@ sys.path.append("..")
 import paddle.fluid.core as core
 import unittest
 import numpy as np
-from op_test import OpTest
+from op_test_xpu import XPUOpTest
 import paddle
 import paddle.fluid as fluid
 from paddle.fluid import Program, program_guard
 
+paddle.enable_static()
+
 
 def generate_compatible_shapes(dim_X, dim_Y, transpose_X, transpose_Y):
     BATCH_SIZE = 2
@@ -92,7 +94,9 @@ def reference_matmul(X, Y, transpose_X=False, transpose_Y=False):
 
 class Generator(object):
     def setUp(self):
+        self.use_xpu = True
         self.op_type = "matmul"
+        # self.init_test_case()
         X = np.random.random(self.shape_X).astype("float32")
         Y = np.random.random(self.shape_Y).astype("float32")
         Out = reference_matmul(X, Y, self.transpose_X, self.transpose_Y)
@@ -104,7 +108,7 @@ class Generator(object):
         self.outputs = {'Out': Out}
 
     def test_check_output(self):
-        self.check_output()
+
         if paddle.is_compiled_with_xpu() and len(self.inputs['X'].shape) == len(
                 self.inputs['Y'].shape) and self.inputs['X'].shape[
                     0] == self.inputs['Y'].shape[0]:
@@ -112,7 +116,7 @@ class Generator(object):
             self.check_output_with_place(place, atol=1e-3)
 
     def test_check_grad_normal(self):
-        self.check_grad(['X', 'Y'], 'Out', max_relative_error=1e-3)
+
         if paddle.is_compiled_with_xpu() and len(self.inputs['X'].shape) == len(
                 self.inputs['Y'].shape) and self.inputs['X'].shape[
                     0] == self.inputs['Y'].shape[0]:
@@ -121,8 +125,7 @@ class Generator(object):
                 place, ['X', 'Y'], 'Out', max_relative_error=5e-2)
 
     def test_check_grad_ignore_x(self):
-        self.check_grad(
-            ['Y'], 'Out', max_relative_error=1e-3, no_grad_set=set("X"))
+
         if paddle.is_compiled_with_xpu() and len(self.inputs['X'].shape) == len(
                 self.inputs['Y'].shape) and self.inputs['X'].shape[
                     0] == self.inputs['Y'].shape[0]:
@@ -134,8 +137,7 @@ class Generator(object):
                 no_grad_set=set("X"))
 
     def test_check_grad_ignore_y(self):
-        self.check_grad(
-            ['X'], 'Out', max_relative_error=1e-3, no_grad_set=set('Y'))
+
         if paddle.is_compiled_with_xpu() and len(self.inputs['X'].shape) == len(
                 self.inputs['Y'].shape) and self.inputs['X'].shape[
                     0] == self.inputs['Y'].shape[0]:
@@ -192,7 +194,7 @@ def test_negative_dims_program(obj):
                 for idx in range(len(Ref.shape)):
                     if output.shape[idx] != -1:
                         obj.assertEqual(Ref.shape[idx], output.shape[idx])
-                exe = fluid.Executor(fluid.CPUPlace())
+                exe = fluid.Executor(fluid.XPUPlace(0))
                 res, = exe.run(fluid.default_main_program(),
                                feed={'x': X,
                                      'y': Y},
@@ -221,7 +223,7 @@ def inject_test(dim_x, dim_y, trans_x, trans_y):
         dim_x, dim_y, trans_x, trans_y))
     shape_x, shape_y = generate_compatible_shapes(dim_x, dim_y, trans_x,
                                                   trans_y)
-    globals()[test_name] = type(test_name, (Generator, OpTest), {
+    globals()[test_name] = type(test_name, (Generator, XPUOpTest), {
         'shape_X': shape_x,
         'shape_Y': shape_y,
         'transpose_X': trans_x,
@@ -231,10 +233,11 @@ def inject_test(dim_x, dim_y, trans_x, trans_y):
 
 for dim_X in (1, 2, 3):
     for dim_Y in (1, 2, 3):
-        for transose_x in (False, True):
-            for transose_y in (False, True):
-                inject_test(dim_X, dim_Y, transose_x, transose_y)
-                api_test(dim_X, dim_Y, transose_x, transose_y)
+        transose_x = False
+        transose_y = False
+        if dim_X == 3 and dim_Y == 3:
+            inject_test(dim_X, dim_Y, transose_x, transose_y)
+            api_test(dim_X, dim_Y, transose_x, transose_y)
 
 
 # Test case n-dim
@@ -267,7 +270,7 @@ for dim in [4]:
                     dim, dim, transpose_X, transpose_Y))
             shape_X, shape_Y = generate_compatible_shapes(dim, transpose_X,
                                                           transpose_Y)
-            globals()[test_name] = type(test_name, (Generator, OpTest), {
+            globals()[test_name] = type(test_name, (Generator, XPUOpTest), {
                 'shape_X': shape_X,
                 'shape_Y': shape_Y,
                 'transpose_X': transpose_X,
@@ -282,7 +285,7 @@ class API_TestMm(unittest.TestCase):
             y = fluid.data(name='y', shape=[2], dtype='float64')
             res = fluid.data(name="output", shape=[1], dtype="float64")
             result = paddle.mm(x, y)
-            exe = fluid.Executor(fluid.CPUPlace())
+            exe = fluid.Executor(fluid.XPUPlace(0))
             data1 = np.random.rand(2)
             data2 = np.random.rand(2)
             np_res = exe.run(feed={'x': data1, 'y': data2}, fetch_list=[result])
@@ -296,7 +299,7 @@ class API_TestMm(unittest.TestCase):
             {}\n{}, check diff!".format(np_res, expected_result))
 
     def test_dygraph_without_out(self):
-        device = fluid.CPUPlace()
+        device = fluid.XPUPlace(0)
         with fluid.dygraph.guard(device):
             input_array1 = np.random.rand(3, 4).astype("float64")
             input_array2 = np.random.rand(4, 3).astype("float64")
@@ -309,7 +312,7 @@ class API_TestMm(unittest.TestCase):
 
 class Test_API_Matmul(unittest.TestCase):
     def test_dygraph_without_out(self):
-        device = fluid.CPUPlace()
+        device = fluid.XPUPlace(0)
         with fluid.dygraph.guard(device):
             input_array1 = np.random.rand(3, 4).astype("float64")
             input_array2 = np.random.rand(4, 3).astype("float64")

python/paddle/fluid/tests/unittests/xpu/test_one_hot_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 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()
+
+
+@unittest.skipIf(not paddle.is_compiled_with_xpu(),
+                 "core is not compiled with XPU")
+class TestOneHotOp(XPUOpTest):
+    def setUp(self):
+        self.use_xpu = True
+        self.op_type = 'one_hot'
+        depth = 10
+        depth_np = np.array(10).astype('int32')
+        x_lod = [[4, 1, 3, 3]]
+        x = [np.random.randint(0, depth - 1) for i in range(sum(x_lod[0]))]
+        x = np.array(x).astype('int32').reshape([sum(x_lod[0]), 1])
+
+        out = np.zeros(shape=(np.product(x.shape[:-1]),
+                              depth)).astype('float32')
+
+        for i in range(np.product(x.shape)):
+            out[i, x[i]] = 1.0
+
+        self.inputs = {'X': (x, x_lod), 'depth_tensor': depth_np}
+        self.attrs = {'dtype': int(core.VarDesc.VarType.FP32)}
+        self.outputs = {'Out': (out, x_lod)}
+
+    def test_check_output(self):
+        place = paddle.XPUPlace(0)
+        self.check_output_with_place(place, check_dygraph=False)
+
+
+@unittest.skipIf(not paddle.is_compiled_with_xpu(),
+                 "core is not compiled with XPU")
+class TestOneHotOp_attr(XPUOpTest):
+    def setUp(self):
+        self.op_type = 'one_hot'
+        depth = 10
+        x_lod = [[4, 1, 3, 3]]
+        x = [np.random.randint(0, depth - 1) for i in range(sum(x_lod[0]))]
+        x = np.array(x).astype('int32').reshape([sum(x_lod[0]), 1])
+
+        out = np.zeros(shape=(np.product(x.shape[:-1]),
+                              depth)).astype('float32')
+
+        for i in range(np.product(x.shape)):
+            out[i, x[i]] = 1.0
+
+        self.inputs = {'X': (x, x_lod)}
+        self.attrs = {'dtype': int(core.VarDesc.VarType.FP32), 'depth': depth}
+        self.outputs = {'Out': (out, x_lod)}
+
+    def test_check_output(self):
+        place = paddle.XPUPlace(0)
+        self.check_output_with_place(place, check_dygraph=False)
+
+
+@unittest.skipIf(not paddle.is_compiled_with_xpu(),
+                 "core is not compiled with XPU")
+class TestOneHotOp_default_dtype(XPUOpTest):
+    def setUp(self):
+        self.op_type = 'one_hot'
+        depth = 10
+        depth_np = np.array(10).astype('int32')
+        x_lod = [[4, 1, 3, 3]]
+        x = [np.random.randint(0, depth - 1) for i in range(sum(x_lod[0]))]
+        x = np.array(x).astype('int32').reshape([sum(x_lod[0]), 1])
+
+        out = np.zeros(shape=(np.product(x.shape[:-1]),
+                              depth)).astype('float32')
+
+        for i in range(np.product(x.shape)):
+            out[i, x[i]] = 1.0
+
+        self.inputs = {'X': (x, x_lod), 'depth_tensor': depth_np}
+        self.attrs = {}
+        self.outputs = {'Out': (out, x_lod)}
+
+    def test_check_output(self):
+        place = paddle.XPUPlace(0)
+        self.check_output_with_place(place, check_dygraph=False)
+
+
+@unittest.skipIf(not paddle.is_compiled_with_xpu(),
+                 "core is not compiled with XPU")
+class TestOneHotOp_default_dtype_attr(XPUOpTest):
+    def setUp(self):
+        self.op_type = 'one_hot'
+        depth = 10
+        x_lod = [[4, 1, 3, 3]]
+        x = [np.random.randint(0, depth - 1) for i in range(sum(x_lod[0]))]
+        x = np.array(x).astype('int32').reshape([sum(x_lod[0]), 1])
+
+        out = np.zeros(shape=(np.product(x.shape[:-1]),
+                              depth)).astype('float32')
+
+        for i in range(np.product(x.shape)):
+            out[i, x[i]] = 1.0
+
+        self.inputs = {'X': (x, x_lod)}
+        self.attrs = {'depth': depth}
+        self.outputs = {'Out': (out, x_lod)}
+
+    def test_check_output(self):
+        place = paddle.XPUPlace(0)
+        self.check_output_with_place(place, check_dygraph=False)
+
+
+@unittest.skipIf(not paddle.is_compiled_with_xpu(),
+                 "core is not compiled with XPU")
+class TestOneHotOp_out_of_range(XPUOpTest):
+    def setUp(self):
+        self.op_type = 'one_hot'
+        depth = 10
+        x_lod = [[4, 1, 3, 3]]
+        x = [np.random.choice([-1, depth]) for i in range(sum(x_lod[0]))]
+        x = np.array(x).astype('int32').reshape([sum(x_lod[0]), 1])
+
+        out = np.zeros(shape=(np.product(x.shape[:-1]),
+                              depth)).astype('float32')
+
+        self.inputs = {'X': (x, x_lod)}
+        self.attrs = {'depth': depth, 'allow_out_of_range': True}
+        self.outputs = {'Out': (out, x_lod)}
+
+    def test_check_output(self):
+        place = paddle.XPUPlace(0)
+        self.check_output_with_place(place, check_dygraph=False)
+
+
+@unittest.skipIf(not paddle.is_compiled_with_xpu(),
+                 "core is not compiled with XPU")
+class TestOneHotOpError(unittest.TestCase):
+    def test_errors(self):
+        with program_guard(Program(), Program()):
+            # the input must be Variable
+            in_w = np.random.random((4, 1)).astype("int32")
+            self.assertRaises(TypeError, fluid.layers.one_hot, in_w)
+            # the input must be int32 or int 64
+            in_w2 = fluid.layers.data(
+                name="in_w2",
+                shape=[4, 1],
+                append_batch_size=False,
+                dtype="float32")
+            self.assertRaises(TypeError, fluid.layers.one_hot, in_w2)
+            # the depth must be int, long or Variable
+            in_r = fluid.layers.data(
+                name="in_r",
+                shape=[4, 1],
+                append_batch_size=False,
+                dtype="int32")
+            depth_w = np.array([4])
+            self.assertRaises(TypeError, fluid.layers.one_hot, in_r, 4.1)
+            self.assertRaises(TypeError, fluid.layers.one_hot, in_r, depth_w)
+
+
+if __name__ == '__main__':
+    paddle.enable_static()
+    unittest.main()