add cast/concat/assign xpu op (#27911) (#28050)

* addd

* add cast_op_xpu, test=kunlun

* fix bug for cast_op_xpu,test=kunlun

* add concat_op_xpu, test=kunlun

* slove conflicts, test=kunlun

* fix bug,test=kunlun

* add assign_op_xpu, test=kunlun

* fix bug,test=kunlun

* test=kunlun;test=develop

* fix concat bug,test=kunlun

* fix check_dygraph set in test_concat_op_xpu.py,test=kunlun

* fix error message,test=kunlun
Co-authored-by: Nmapingshuo <mps2012@yeah.net>
Co-authored-by: Nmapingshuo <mps2012@yeah.net>

paddle/fluid/operators/assign_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/assign_op.h"
+
+#include <string>
+
+namespace paddle {
+namespace framework {
+class OpDesc;
+class Variable;
+}  // namespace framework
+namespace imperative {
+class OpBase;
+}  // namespace imperative
+namespace platform {
+struct CPUPlace;
+struct CUDAPlace;
+struct float16;
+}  // namespace platform
+}  // namespace paddle
+
+namespace paddle {
+namespace operators {
+
+class AssignOp : public framework::OperatorWithKernel {
+ public:
+  AssignOp(const std::string &type, const framework::VariableNameMap &inputs,
+           const framework::VariableNameMap &outputs,
+           const framework::AttributeMap &attrs)
+      : OperatorWithKernel(type, inputs, outputs, attrs) {}
+
+  void InferShape(framework::InferShapeContext *ctx) const override {
+    if (ctx->HasInput("X")) {
+      auto type = ctx->GetInputsVarType("X")[0];
+      if (type == framework::proto::VarType::SELECTED_ROWS ||
+          type == framework::proto::VarType::LOD_TENSOR) {
+        ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
+        if (type == framework::proto::VarType::LOD_TENSOR) {
+          ctx->ShareLoD("X", /*->*/ "Out");
+        }
+      } else if (type == framework::proto::VarType::LOD_TENSOR_ARRAY) {
+        if (ctx->IsRuntime()) {
+          // The runtime output shape is determined in kernel.
+          return;
+        } else {
+          ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
+        }
+      }
+    }
+  }
+
+ protected:
+  framework::OpKernelType GetKernelTypeForVar(
+      const std::string &var_name, const framework::Tensor &tensor,
+      const framework::OpKernelType &expected_kernel_type) const override {
+    return framework::OpKernelType(expected_kernel_type.data_type_,
+                                   expected_kernel_type.place_,
+                                   tensor.layout());
+  }
+
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext &ctx) const override {
+    const framework::Variable *var = ctx.InputVar("X");
+    if (var->IsType<framework::LoDTensorArray>()) {
+      auto t_arr = var->Get<framework::LoDTensorArray>();
+      // NOTE(liym27): Support an empty tensor array as Input.
+      // And set the kernel type is float.
+      if (t_arr.size() == 0) {
+        return framework::OpKernelType(framework::proto::VarType::FP32,
+                                       ctx.device_context());
+      }
+    }
+
+    return framework::OpKernelType(
+        OperatorWithKernel::IndicateVarDataType(ctx, "X"),
+        ctx.device_context());
+  }
+};
+
+class AssignInferVarType : public framework::VarTypeInference {
+ public:
+  void operator()(framework::InferVarTypeContext *ctx) const override {
+    ctx->SyncTypeAndDataType("X", "Out");
+  }
+};
+
+class AssignKernel {
+ public:
+  void operator()(const framework::ExecutionContext &ctx) const {
+    auto *x = ctx.InputVar("X");
+    if (x == nullptr) {
+      return;
+    }
+    PADDLE_ENFORCE_EQ(
+        ctx.HasOutput("Out"), true,
+        platform::errors::NotFound("Output(Out) of assign_op is not found."));
+    auto *out = ctx.OutputVar("Out");
+    platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
+    auto &dev_ctx = *pool.Get(ctx.GetPlace());
+
+    framework::VisitVarType(*x, AssignFunctor(out, dev_ctx));
+  }
+};
+
+class AssignOpProtoMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  void Make() override {
+    AddInput("X",
+             "(LoDTensor, SelectedRows or LoDTensorArray) The input variable "
+             "could be LoDTensor, SelectedRows or LoDTensorArray.")
+        .AsDispensable();
+    AddOutput("Out",
+              "(LoDTensor, SelectedRows or LoDTensorArray) The type of output "
+              "is the same as input X.");
+    AddComment(R"DOC(Assign Operator
+
+Out = X,  when type in [LoDTensor/SelectedRows/LoDTensorArray]
+raise error if the type is not listed above.
+)DOC");
+  }
+};
+
+template <typename T>
+class AssignGradMaker : public framework::SingleGradOpMaker<T> {
+ public:
+  using framework::SingleGradOpMaker<T>::SingleGradOpMaker;
+
+ protected:
+  void Apply(GradOpPtr<T> op) const override {
+    op->SetType("assign");
+    op->SetInput("X", this->OutputGrad("Out"));
+    op->SetOutput("Out", this->InputGrad("X"));
+  }
+};
+
+DECLARE_INPLACE_OP_INFERER(AssignOpInplaceInferer, {"X", "Out"});
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+namespace plat = paddle::platform;
+
+REGISTER_OP_XPU_KERNEL_FUNCTOR(assign, float, ops::AssignKernel, double,
+                               ops::AssignKernel, int, ops::AssignKernel,
+                               int64_t, ops::AssignKernel, bool,
+                               ops::AssignKernel);
+#endif

paddle/fluid/operators/cast_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/cast_op.h"
+#include <memory>
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/platform/float16.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename DeviceContext, typename InT>
+class CastXPUKernel : public framework::OpKernel<InT> {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto* in = context.Input<framework::Tensor>("X");
+    auto* out = context.Output<framework::Tensor>("Out");
+    auto in_type = static_cast<framework::proto::VarType::Type>(
+        context.Attr<int>("in_dtype"));
+    auto out_type = static_cast<framework::proto::VarType::Type>(
+        context.Attr<int>("out_dtype"));
+    auto* in_data = in->data<InT>();
+    auto numel = in->numel();
+    auto& dev_ctx = context.template device_context<DeviceContext>();
+    int r = -1;
+    if (out_type == framework::proto::VarType::FP32) {
+      auto* out_data = out->mutable_data<float>(context.GetPlace());
+      r = xpu::cast<InT, float>(dev_ctx.x_context(), in_data, out_data, numel);
+    } else if (out_type == framework::proto::VarType::INT32) {
+      auto* out_data = out->mutable_data<int>(context.GetPlace());
+      r = xpu::cast<InT, int>(dev_ctx.x_context(), in_data, out_data, numel);
+    } else if (out_type == framework::proto::VarType::INT64) {
+      auto* out_data = out->mutable_data<int64_t>(context.GetPlace());
+      r = xpu::cast<InT, int64_t>(dev_ctx.x_context(), in_data, out_data,
+                                  numel);
+    } else {
+      PADDLE_THROW(platform::errors::Unavailable("Not supported cast %d -> %d",
+                                                 in_type, out_type));
+    }
+    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));
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP_XPU_KERNEL(
+    cast, ops::CastXPUKernel<paddle::platform::XPUDeviceContext, int>,
+    ops::CastXPUKernel<paddle::platform::XPUDeviceContext, float>,
+    ops::CastXPUKernel<paddle::platform::XPUDeviceContext, int64_t>);
+#endif

paddle/fluid/operators/concat_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. */
+
+#include "paddle/fluid/operators/concat_op.h"
+
+#include <memory>
+#include <string>
+#include <vector>
+
+#ifdef PADDLE_WITH_MKLDNN
+#include <paddle/fluid/platform/mkldnn_helper.h>
+#endif
+
+#ifdef PADDLE_WITH_XPU
+
+namespace paddle {
+namespace operators {
+using Tensor = framework::Tensor;
+
+template <typename DeviceContext, typename T>
+class ConcatXPUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto ins = ctx.MultiInput<framework::Tensor>("X");
+    framework::Tensor* out = ctx.Output<framework::Tensor>("Out");
+    int axis = ctx.Attr<int>("axis");
+    PADDLE_ENFORCE_NE(ins[0], nullptr, platform::errors::InvalidArgument(
+                                           "The input should not be null."));
+    PADDLE_ENFORCE_NE(ctx.HasInput("AxisTensor"), true,
+                      platform::errors::InvalidArgument(
+                          "XPU donot surpport AxisTensor for now"));
+    axis = ComputeAxis(static_cast<int64_t>(axis),
+                       static_cast<int64_t>(ins[0]->dims().size()));
+    PADDLE_ENFORCE_GE(
+        axis, 0, platform::errors::InvalidArgument("concat: axis shoud >= 0!"));
+    PADDLE_ENFORCE_LT(axis, ins[0]->dims().size(),
+                      platform::errors::InvalidArgument(
+                          "concat: axis shoud < ins[0]->dims()!"));
+    auto place = ctx.GetPlace();
+    out->mutable_data<T>(place);
+    std::vector<int> choose_idx;
+    int n = 0;
+    for (unsigned int i = 0; i < ins.size(); ++i) {
+      if (ins[i] && ins[i]->numel() > 0) {
+        choose_idx.push_back(i);
+        n++;
+      }
+    }
+    PADDLE_ENFORCE_LE(n, 8, platform::errors::InvalidArgument(
+                                "XPU only surpport at most 8 tensors for now"));
+    PADDLE_ENFORCE_GT(
+        n, 0, platform::errors::InvalidArgument("No tensor need concat?"));
+    int h = 1;
+    int w_except_axis = 1;
+    for (int i = 0; i < axis; ++i) {
+      h *= (ins[choose_idx[0]]->dims())[i];
+    }
+    for (int i = axis + 1; i < ins[0]->dims().size(); ++i) {
+      w_except_axis *= (ins[choose_idx[0]]->dims())[i];
+    }
+    for (int i = 1; i < n; ++i) {
+      int hh = 1;
+      int ww = 1;
+      for (int j = 0; j < axis; ++j) {
+        hh *= (ins[choose_idx[i]]->dims())[j];
+      }
+      for (int j = axis + 1; j < ins[i]->dims().size(); ++j) {
+        ww *= (ins[choose_idx[i]]->dims())[j];
+      }
+      PADDLE_ENFORCE_EQ(hh, h, platform::errors::InvalidArgument(
+                                   "concat: h should be eual!"));
+      PADDLE_ENFORCE_EQ(ww, w_except_axis,
+                        platform::errors::InvalidArgument(
+                            "concat: w should be eual except for axis!"));
+    }
+    auto& dev_ctx = ctx.template device_context<DeviceContext>();
+    std::unique_ptr<int[]> in_w_host(new int[n]);
+    std::unique_ptr<const float* []> ptrs(new const float*[n]);
+    for (int i = 0; i < n; ++i) {
+      ptrs[i] = ins[choose_idx[i]]->data<T>();
+      in_w_host[i] = w_except_axis * (ins[choose_idx[i]]->dims())[axis];
+    }
+    int r =
+        xpu::concat<float>(dev_ctx.x_context(), h, (const int*)in_w_host.get(),
+                           n, (const float**)ptrs.get(), out->data<T>());
+    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));
+  }
+};
+template <typename DeviceContext, typename T>
+class ConcatGradXPUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const {
+    auto* out_grad =
+        ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
+    auto ins = ctx.MultiInput<framework::LoDTensor>("X");
+    auto out_var_names = ctx.OutputNames(framework::GradVarName("X"));
+    auto outs =
+        ctx.MultiOutput<framework::LoDTensor>(framework::GradVarName("X"));
+    {
+      auto dx = outs;
+      auto x = ins;
+      for (size_t i = 0; i < dx.size(); ++i) {
+        if (dx[i] != nullptr) {
+          dx[i]->set_lod(x[i]->lod());
+        }
+      }
+    }
+    PADDLE_ENFORCE_NE(ins[0], nullptr, platform::errors::InvalidArgument(
+                                           "The input should not be null."));
+    auto axis = ctx.Attr<int>("axis");
+    if (ctx.HasInput("AxisTensor")) {
+      auto* axis_tensor = ctx.Input<framework::Tensor>("AxisTensor");
+      axis = GetDataFromTensor<int>(axis_tensor)[0];
+    }
+    axis = ComputeAxis(static_cast<int64_t>(axis),
+                       static_cast<int64_t>(ins[0]->dims().size()));
+    // get output tensor that the name is not kEmptyVarName
+    std::vector<framework::Tensor*> outputs;
+    for (size_t j = 0; j < outs.size(); ++j) {
+      if (out_var_names[j] != framework::kEmptyVarName &&
+          outs[j]->numel() != 0UL) {
+        outs[j]->mutable_data<T>(ctx.GetPlace());
+        outputs.push_back(outs[j]);
+      } else {
+        outputs.push_back(nullptr);
+      }
+    }
+    PADDLE_ENFORCE_GE(axis, 0, platform::errors::InvalidArgument(
+                                   "concat_grad: axis shoud >= 0!"));
+    PADDLE_ENFORCE_LT(axis, out_grad->dims().size(),
+                      platform::errors::InvalidArgument(
+                          "concat_grad: axis shoud < ins[0]->dims()!"));
+    auto out_grad_stride = framework::stride_numel(out_grad->dims());
+    int n = outputs.size();
+    PADDLE_ENFORCE_LE(n, 16,
+                      platform::errors::InvalidArgument(
+                          "XPU only surpport at most 16 tensors for now"));
+    int h = out_grad_stride[0] / out_grad_stride[axis];
+    auto& dev_ctx = ctx.template device_context<DeviceContext>();
+    std::unique_ptr<int[]> in_w_host(new int[n]);
+    std::unique_ptr<float* []> ptrs(new float*[n]);
+    for (int i = 0; i < n; ++i) {
+      auto out_stride = framework::stride_numel(outputs[i]->dims());
+      ptrs[i] = outputs[i]->data<T>();
+      in_w_host[i] = out_stride[axis];
+    }
+    int r = xpu::concat_grad(dev_ctx.x_context(), h, in_w_host.get(), n,
+                             reinterpret_cast<float**>(ptrs.get()),
+                             out_grad->data<T>());
+    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));
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP_XPU_KERNEL(
+    concat, ops::ConcatXPUKernel<paddle::platform::XPUDeviceContext, float>);
+REGISTER_OP_XPU_KERNEL(
+    concat_grad,
+    ops::ConcatGradXPUKernel<paddle::platform::XPUDeviceContext, float>);
+
+#endif

python/paddle/fluid/tests/unittests/test_concat_op.py

@@ -253,7 +253,8 @@ class TestConcatAPI(unittest.TestCase):
         assert np.array_equal(res_3, np.concatenate((input_2, input_3), axis=1))
 
     def test_api(self):
-        x_1 = paddle.fluid.data(shape=[None, 1, 4, 5], dtype='int32', name='x_1')
+        x_1 = paddle.fluid.data(
+            shape=[None, 1, 4, 5], dtype='int32', name='x_1')
         paddle.concat([x_1, x_1], 0)
 
         input_2 = np.random.random([2, 1, 4, 5]).astype("int32")

python/paddle/fluid/tests/unittests/xpu/test_assign_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 sys
+
+sys.path.append("..")
+import op_test
+import numpy as np
+import unittest
+import paddle
+import paddle.fluid.core as core
+from paddle.fluid.op import Operator
+import paddle.fluid as fluid
+from paddle.fluid import compiler, Program, program_guard
+from paddle.fluid.backward import append_backward
+
+
+class TestAssignOp(op_test.OpTest):
+    def setUp(self):
+        self.op_type = "assign"
+        x = np.random.random(size=(100, 10)).astype('float32')
+        self.inputs = {'X': x}
+        self.outputs = {'Out': x}
+
+    def test_forward(self):
+        if paddle.is_compiled_with_xpu():
+            place = paddle.XPUPlace(0)
+            self.check_output_with_place(place)
+
+    def test_backward(self):
+        if paddle.is_compiled_with_xpu():
+            place = paddle.XPUPlace(0)
+            self.check_grad_with_place(place, ['X'], 'Out')
+
+
+class TestAssignOpWithLoDTensorArray(unittest.TestCase):
+    def test_assign_LoDTensorArray(self):
+        main_program = Program()
+        startup_program = Program()
+        with program_guard(main_program):
+            x = fluid.data(name='x', shape=[100, 10], dtype='float32')
+            x.stop_gradient = False
+            y = fluid.layers.fill_constant(
+                shape=[100, 10], dtype='float32', value=1)
+            z = fluid.layers.elementwise_add(x=x, y=y)
+            i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=0)
+            init_array = fluid.layers.array_write(x=z, i=i)
+            array = fluid.layers.assign(init_array)
+            sums = fluid.layers.array_read(array=init_array, i=i)
+            mean = fluid.layers.mean(sums)
+            append_backward(mean)
+
+        place = fluid.CUDAPlace(0) if core.is_compiled_with_cuda(
+        ) else fluid.CPUPlace()
+        exe = fluid.Executor(place)
+        feed_x = np.random.random(size=(100, 10)).astype('float32')
+        ones = np.ones((100, 10)).astype('float32')
+        feed_add = feed_x + ones
+        res = exe.run(main_program,
+                      feed={'x': feed_x},
+                      fetch_list=[sums.name, x.grad_name])
+        self.assertTrue(np.allclose(res[0], feed_add))
+        self.assertTrue(np.allclose(res[1], ones / 1000.0))
+
+
+class TestAssignOpError(unittest.TestCase):
+    def test_errors(self):
+        with program_guard(Program(), Program()):
+            # The type of input must be Variable or numpy.ndarray.
+            x1 = fluid.create_lod_tensor(
+                np.array([[-1]]), [[1]], fluid.XPUPlace(0))
+            self.assertRaises(TypeError, fluid.layers.assign, x1)
+            # When the type of input is Variable, the dtype of input must be float16, float32, float64, int32, int64, bool.
+            x3 = fluid.layers.data(name='x3', shape=[4], dtype="uint8")
+            self.assertRaises(TypeError, fluid.layers.assign, x3)
+            # When the type of input is numpy.ndarray, the dtype of input must be float32, int32.
+            x4 = np.array([[2.5, 2.5]], dtype='float64')
+            self.assertRaises(TypeError, fluid.layers.assign, x4)
+            x5 = np.array([[2.5, 2.5]], dtype='uint8')
+            self.assertRaises(TypeError, fluid.layers.assign, x5)
+
+
+if __name__ == '__main__':
+    paddle.enable_static()
+    unittest.main()

python/paddle/fluid/tests/unittests/xpu/test_cast_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 sys
+
+sys.path.append("..")
+import op_test
+import unittest
+import numpy as np
+import paddle
+import paddle.fluid.core as core
+import paddle.fluid as fluid
+from paddle.fluid import compiler, Program, program_guard
+
+
+class TestCastOp1(op_test.OpTest):
+    def setUp(self):
+        ipt = np.random.random(size=[10, 10])
+        self.inputs = {'X': ipt.astype('float32')}
+        self.outputs = {'Out': ipt.astype('float32')}
+        self.attrs = {
+            'in_dtype': int(core.VarDesc.VarType.FP32),
+            'out_dtype': int(core.VarDesc.VarType.FP32)
+        }
+        self.op_type = 'cast'
+
+    def test_check_output(self):
+        if paddle.is_compiled_with_xpu():
+            place = paddle.XPUPlace(0)
+            self.check_output_with_place(place)
+
+    def test_grad(self):
+        if paddle.is_compiled_with_xpu():
+            place = paddle.XPUPlace(0)
+            self.check_grad_with_place(place, ['X'], ['Out'])
+
+
+class TestCastOp2(op_test.OpTest):
+    def setUp(self):
+        ipt = np.random.random(size=[10, 10])
+        self.inputs = {'X': ipt.astype('float32')}
+        self.outputs = {'Out': ipt.astype('float32')}
+        self.attrs = {
+            'in_dtype': int(core.VarDesc.VarType.FP32),
+            'out_dtype': int(core.VarDesc.VarType.FP32)
+        }
+        self.op_type = 'cast'
+
+    def test_check_output(self):
+        #self.check_output(atol=1e-3)
+        if paddle.is_compiled_with_xpu():
+            place = paddle.XPUPlace(0)
+            self.check_output_with_place(place, atol=1e-3)
+
+
+class TestCastOp3(op_test.OpTest):
+    def setUp(self):
+        ipt = np.random.random(size=[10, 10])
+        self.inputs = {'X': ipt.astype('float32')}
+        self.outputs = {'Out': ipt.astype('float32')}
+        self.attrs = {
+            'in_dtype': int(core.VarDesc.VarType.FP32),
+            'out_dtype': int(core.VarDesc.VarType.FP32)
+        }
+        self.op_type = 'cast'
+
+    def test_check_output(self):
+        #self.check_output(atol=1e-3)
+        if paddle.is_compiled_with_xpu():
+            place = paddle.XPUPlace(0)
+            self.check_output_with_place(place, atol=1e-3)
+
+
+class TestCastOpError(unittest.TestCase):
+    def test_errors(self):
+        with program_guard(Program(), Program()):
+            # The input type of cast_op must be Variable.
+            x1 = fluid.create_lod_tensor(
+                np.array([[-1]]), [[1]], fluid.XPUPlace(0))
+            self.assertRaises(TypeError, fluid.layers.cast, x1, 'int32')
+            # The input dtype of cast_op must be float32, int32, int64.
+            x2 = fluid.layers.data(name='x2', shape=[4], dtype='int16')
+            self.assertRaises(TypeError, fluid.layers.cast, x2, 'int32')
+
+            def test_dtype_type():
+                x4 = fluid.layers.data(name='x4', shape=[4], dtype='int32')
+                output = fluid.layers.cast(x=x4, dtype='int16')
+
+            self.assertRaises(TypeError, test_dtype_type)
+
+
+if __name__ == '__main__':
+    paddle.enable_static()
+    unittest.main()

python/paddle/fluid/tests/unittests/xpu/test_concat_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 sys
+
+sys.path.append("..")
+import unittest
+import numpy as np
+from op_test import OpTest, skip_check_grad_ci
+import paddle.fluid as fluid
+from paddle.fluid import compiler, Program, program_guard, core
+import paddle
+
+
+class TestConcatOp(OpTest):
+    def setUp(self):
+        self.op_type = "concat"
+        self.dtype = self.get_dtype()
+        self.init_test_data()
+        self.inputs = {'X': [('x0', self.x0), ('x1', self.x1), ('x2', self.x2)]}
+        self.attrs = {'axis': self.axis}
+        if self.axis < 0:
+            self.actual_axis = self.axis + len(self.x0.shape)
+            self.actual_axis = self.actual_axis if self.actual_axis > 0 else 0
+        else:
+            self.actual_axis = self.axis
+
+        self.outputs = {
+            'Out': np.concatenate(
+                (self.x0, self.x1, self.x2), axis=self.actual_axis)
+        }
+
+    def get_dtype(self):
+        return "float64"
+
+    def test_check_output(self):
+        if paddle.is_compiled_with_xpu():
+            place = paddle.XPUPlace(0)
+            self.check_output_with_place(place)
+
+    def test_check_grad(self):
+        if paddle.is_compiled_with_xpu():
+            place = paddle.XPUPlace(0)
+            self.check_grad_with_place(place, ['x0'], 'Out')
+            self.check_grad_with_place(place, ['x1'], 'Out')
+            self.check_grad_with_place(place, ['x2'], 'Out')
+
+    def init_test_data(self):
+        self.x0 = np.random.random((5, 1, 4, 5)).astype(self.dtype)
+        self.x1 = np.random.random((5, 2, 4, 5)).astype(self.dtype)
+        self.x2 = np.random.random((5, 3, 4, 5)).astype(self.dtype)
+        self.axis = 1
+
+
+class TestConcatOp2(TestConcatOp):
+    def init_test_data(self):
+        self.x0 = np.random.random((2, 3, 4, 5)).astype(self.dtype)
+        self.x1 = np.random.random((2, 3, 4, 5)).astype(self.dtype)
+        self.x2 = np.random.random((2, 3, 4, 5)).astype(self.dtype)
+        self.axis = 1
+
+
+@skip_check_grad_ci(
+    reason="The function 'check_grad' for large inputs is too slow.")
+class TestConcatOp3(TestConcatOp):
+    def init_test_data(self):
+        self.x0 = np.random.random((1, 256, 170, 256)).astype(self.dtype)
+        self.x1 = np.random.random((1, 128, 170, 256)).astype(self.dtype)
+        self.x2 = np.random.random((1, 128, 170, 256)).astype(self.dtype)
+        self.axis = 1
+
+    def test_check_grad(self):
+        pass
+
+
+@skip_check_grad_ci(
+    reason="This test will meet fetch error when there is a null grad. The detailed information is in PR#17015."
+)
+class TestConcatOp4(TestConcatOp):
+    def init_test_data(self):
+        self.x0 = np.random.random((2, 3, 4, 5)).astype(self.dtype)
+        self.x1 = np.random.random((2, 3, 4, 5)).astype(self.dtype)
+        self.x2 = np.random.random((0, 3, 4, 5)).astype(self.dtype)
+        self.axis = 0
+
+    def test_check_grad(self):
+        pass
+
+
+class TestConcatOp5(TestConcatOp):
+    def init_test_data(self):
+        self.x0 = np.random.random((5, 1, 4, 5)).astype(self.dtype)
+        self.x1 = np.random.random((5, 2, 4, 5)).astype(self.dtype)
+        self.x2 = np.random.random((5, 3, 4, 5)).astype(self.dtype)
+        self.axis = -3
+
+
+class TestConcatOp6(TestConcatOp):
+    def setUp(self):
+        self.op_type = "concat"
+        self.dtype = self.get_dtype()
+        self.init_test_data()
+        self.lod = [[20, 80]]
+        self.out_lod = [[20, 80, 20, 80, 20, 80]]
+        self.inputs = {
+            'X': [('x0', (self.x0, self.lod)), ('x1', (self.x1, self.lod)),
+                  ('x2', (self.x2, self.lod))]
+        }
+        self.attrs = {'axis': self.axis}
+        if self.axis < 0:
+            self.actual_axis = self.axis + len(self.x0.shape)
+            self.actual_axis = self.actual_axis if self.actual_axis > 0 else 0
+        else:
+            self.actual_axis = self.axis
+        out = np.concatenate((self.x0, self.x1, self.x2), axis=self.actual_axis)
+        self.outputs = {'Out': (out, self.out_lod)}
+
+    def test_check_output(self):
+        if paddle.is_compiled_with_xpu():
+            place = paddle.XPUPlace(0)
+            self.check_output_with_place(place)
+
+    def test_check_grad(self):
+        if paddle.is_compiled_with_xpu():
+            place = paddle.XPUPlace(0)
+            self.check_grad_with_place(place, ['x0'], 'Out')
+            self.check_grad_with_place(place, ['x1'], 'Out')
+            self.check_grad_with_place(place, ['x2'], 'Out')
+
+    def init_test_data(self):
+        self.x0 = np.random.random([100]).astype(self.dtype)
+        self.x1 = np.random.random([100]).astype(self.dtype)
+        self.x2 = np.random.random([100]).astype(self.dtype)
+        self.axis = 0
+
+
+class TestConcatOpError(unittest.TestCase):
+    def test_errors(self):
+        with program_guard(Program(), Program()):
+            # The input type of concat_op should be list.
+            x1 = fluid.layers.data(shape=[4], dtype='int32', name='x1')
+            fluid.layers.concat(x1)
+            # The item in input must be Variable.
+            x2 = fluid.create_lod_tensor(
+                np.array([[-1]]), [[1]], fluid.CPUPlace())
+            x3 = fluid.create_lod_tensor(
+                np.array([[-1]]), [[1]], fluid.CPUPlace())
+            self.assertRaises(TypeError, fluid.layers.concat, [x2])
+            # The input dtype of concat_op must be float16, float32, float64, int32, int64.
+            x4 = fluid.layers.data(shape=[4], dtype='uint8', name='x4')
+            x5 = fluid.layers.data(shape=[4], dtype='uint8', name='x5')
+            self.assertRaises(TypeError, fluid.layers.concat, [x4, x5])
+            x6 = fluid.layers.data(shape=[4], dtype='float16', name='x6')
+            x7 = fluid.layers.data(shape=[4], dtype='float16', name='x7')
+            x8 = fluid.layers.data(shape=[4], dtype='float32', name='x8')
+            fluid.layers.concat([x6, x7])
+
+            # The type of axis in concat_op should be int or Variable.
+            def test_axis_type():
+                fluid.layers.concat([x6, x7], 3.2)
+
+            self.assertRaises(TypeError, test_axis_type)
+
+            def test_input_same_dtype():
+                fluid.layers.concat([x7, x8])
+
+            self.assertRaises(TypeError, test_input_same_dtype)
+
+
+class TestConcatAPI(unittest.TestCase):
+    def test_fluid_api(self):
+        x_1 = fluid.data(shape=[None, 1, 4, 5], dtype='float32', name='x_1')
+        fluid.layers.concat([x_1, x_1], 0)
+
+        input_2 = np.random.random([2, 1, 4, 5]).astype("float32")
+        input_3 = np.random.random([2, 2, 4, 5]).astype("float32")
+        x_2 = fluid.data(shape=[2, 1, 4, 5], dtype='float32', name='x_2')
+        x_3 = fluid.data(shape=[2, 2, 4, 5], dtype='float32', name='x_3')
+        positive_1_int32 = fluid.layers.fill_constant([1], "float32", 1)
+        positive_1_int64 = fluid.layers.fill_constant([1], "float32", 1)
+        out_1 = fluid.layers.concat(input=[x_2, x_3], axis=1)
+        out_2 = fluid.layers.concat(input=[x_2, x_3], axis=1)
+        out_3 = fluid.layers.concat(input=[x_2, x_3], axis=1)
+
+        exe = fluid.Executor(place=fluid.XPUPlace(0))
+        [res_1, res_2, res_3] = exe.run(
+            fluid.default_main_program(),
+            feed={"x_1": input_2,
+                  "x_2": input_2,
+                  "x_3": input_3},
+            fetch_list=[out_1, out_2, out_3])
+        assert np.array_equal(res_1, np.concatenate((input_2, input_3), axis=1))
+        assert np.array_equal(res_2, np.concatenate((input_2, input_3), axis=1))
+        assert np.array_equal(res_3, np.concatenate((input_2, input_3), axis=1))
+
+    def test_errors(self):
+        with program_guard(Program(), Program()):
+            # The item in input must be Variable.
+            x2 = fluid.create_lod_tensor(
+                np.array([[-1]]), [[1]], fluid.XPUPlace(0))
+            x3 = fluid.create_lod_tensor(
+                np.array([[-1]]), [[1]], fluid.XPUPlace(0))
+            self.assertRaises(TypeError, paddle.concat, [x2])
+            # The input dtype of concat_op must be float32.
+            x4 = fluid.data(shape=[4], dtype='uint8', name='x4')
+            x5 = fluid.data(shape=[4], dtype='uint8', name='x5')
+            self.assertRaises(TypeError, fluid.layers.concat, [x4, x5])
+
+            # The type of axis in concat_op should be int or Variable.
+            x6 = fluid.layers.data(shape=[4], dtype='float16', name='x6')
+            x7 = fluid.layers.data(shape=[4], dtype='float16', name='x7')
+            x8 = fluid.layers.data(shape=[4], dtype='float32', name='x8')
+
+            def test_axis_type():
+                paddle.concat([x6, x7], 3.2)
+
+            self.assertRaises(TypeError, test_axis_type)
+
+            def test_input_same_dtype():
+                paddle.concat([x7, x8])
+
+            self.assertRaises(TypeError, test_input_same_dtype)
+
+
+if __name__ == '__main__':
+    paddle.enable_static()
+    unittest.main()