Add meshgrid op (#23736)

* test=develop fix api doc error

paddle/fluid/operators/meshgrid_op.cc

+// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle/fluid/operators/meshgrid_op.h"
+
+#include <memory>
+#include <string>
+#include <vector>
+
+namespace paddle {
+namespace operators {
+
+using framework::Tensor;
+
+class MeshgridOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    PADDLE_ENFORCE_GE(
+        ctx->Inputs("X").size(), 1UL,
+        platform::errors::InvalidArgument("Input(X) should not be empty."));
+    PADDLE_ENFORCE_GE(
+        ctx->Outputs("Out").size(), 1UL,
+        platform::errors::InvalidArgument("Output(Out) should not be empty."));
+
+    auto inputs_dims = ctx->GetInputsDim("X");
+    const size_t inputs_num = inputs_dims.size();
+    auto outs_names = ctx->Outputs("Out");
+    const size_t outputs_num = outs_names.size();
+
+    auto out_shape = std::vector<int>(inputs_num);
+
+    for (size_t i = 0; i < inputs_num; i++) {
+      out_shape[i] = inputs_dims[i][0];
+    }
+    auto out_dims = framework::make_ddim(std::vector<int>(out_shape));
+    std::vector<framework::DDim> outs_dims(outputs_num, out_dims);
+    ctx->SetOutputsDim("Out", outs_dims);
+  }
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    auto inputs = ctx.MultiInput<Tensor>("X");
+    auto input_data_type = framework::proto::VarType::Type(0);
+    bool flag = 0;
+    for (auto* input : inputs) {
+      if (input->IsInitialized() && input->numel() > 0) {
+        input_data_type = input->type();
+        flag = 1;
+        break;
+      }
+    }
+    if (flag == 0) {
+      PADDLE_THROW(platform::errors::InvalidArgument(
+          "All Inputs of Meshgrid OP are Empty!"));
+    }
+
+    return framework::OpKernelType(input_data_type, ctx.GetPlace());
+  }
+};
+
+class MeshgridOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  void Make() override {
+    AddInput("X", "(Tensor, default Tensor<float>).").AsDuplicable();
+    AddOutput("Out", "(Tensor, default Tensor<float>.)").AsDuplicable();
+
+    AddComment(R"DOC(
+Meshgrid Operator.
+Take: N tensors, each of which can be either scalr or 1-dimensional vector, and create
+N-dimensional grids.
+
+Args:
+  tensors (list of tensor): if the input k tensors has (N1,), (N2,),..., (Nk,), then 
+  the output tensors are all of size (N1, N2, ...., Nk).
+
+Example::
+>>> x = fluid.data(name='x', shape=[10], dtype='float64')
+>>> y = fluid.data(name='y', shape=[20], dtype='float64')
+>>> grid_x, grid_y = fluid.layers.meshgrid([x, y])
+>>> grid_x.shape
+(10,20)
+>>> grid_y.shape
+(10,20)
+)DOC");
+  }
+};
+
+class MeshgridGradOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    PADDLE_ENFORCE_GT(ctx->Inputs(framework::GradVarName("Out")).size(), 1,
+                      platform::errors::InvalidArgument(
+                          "Number of Inputs(Out@Grad) must be larger than 1"));
+    ctx->SetOutputsDim(framework::GradVarName("X"), ctx->GetInputsDim("X"));
+  }
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    return framework::OpKernelType(OperatorWithKernel::IndicateVarDataType(
+                                       ctx, framework::GradVarName("Out")),
+                                   ctx.device_context());
+  }
+};
+
+template <typename T>
+class MeshgridGradOpMaker : public framework::SingleGradOpMaker<T> {
+ public:
+  using framework::SingleGradOpMaker<T>::SingleGradOpMaker;
+
+ protected:
+  void Apply(GradOpPtr<T> op) const override {
+    op->SetType("meshgrid_grad");
+    op->SetInput("X", this->Input("X"));
+    op->SetInput(framework::GradVarName("Out"), this->OutputGrad("Out"));
+    op->SetOutput(framework::GradVarName("X"), this->InputGrad("X", false));
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OPERATOR(meshgrid, ops::MeshgridOp, ops::MeshgridOpMaker,
+                  ops::MeshgridGradOpMaker<paddle::framework::OpDesc>,
+                  ops::MeshgridGradOpMaker<paddle::imperative::OpBase>);
+REGISTER_OPERATOR(meshgrid_grad, ops::MeshgridGradOp);
+REGISTER_OP_CPU_KERNEL(
+    meshgrid, ops::MeshgridKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::MeshgridKernel<paddle::platform::CPUDeviceContext, double>,
+    ops::MeshgridKernel<paddle::platform::CPUDeviceContext, int>,
+    ops::MeshgridKernel<paddle::platform::CPUDeviceContext, int64_t>);
+
+REGISTER_OP_CPU_KERNEL(
+    meshgrid_grad,
+    ops::MeshgridGradKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::MeshgridGradKernel<paddle::platform::CPUDeviceContext, int64_t>,
+    ops::MeshgridGradKernel<paddle::platform::CPUDeviceContext, int>,
+    ops::MeshgridGradKernel<paddle::platform::CPUDeviceContext, double>);

paddle/fluid/operators/meshgrid_op.cu

+// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle/fluid/operators/meshgrid_op.h"
+
+namespace ops = paddle::operators;
+REGISTER_OP_CUDA_KERNEL(
+    meshgrid, ops::MeshgridKernel<paddle::platform::CUDADeviceContext, float>,
+    ops::MeshgridKernel<paddle::platform::CUDADeviceContext, double>,
+    ops::MeshgridKernel<paddle::platform::CUDADeviceContext, int>,
+    ops::MeshgridKernel<paddle::platform::CUDADeviceContext, int64_t>,
+    ops::MeshgridKernel<paddle::platform::CUDADeviceContext, bool>);
+REGISTER_OP_CUDA_KERNEL(
+    meshgrid_grad,
+    ops::MeshgridGradKernel<paddle::platform::CUDADeviceContext, float>,
+    ops::MeshgridGradKernel<paddle::platform::CUDADeviceContext, double>,
+    ops::MeshgridGradKernel<paddle::platform::CUDADeviceContext, int>,
+    ops::MeshgridGradKernel<paddle::platform::CUDADeviceContext, int64_t>);

paddle/fluid/operators/meshgrid_op.h

+// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+
+#include <vector>
+
+#include <boost/preprocessor/arithmetic/mod.hpp>
+#include <boost/preprocessor/comparison/greater.hpp>
+#include <boost/preprocessor/comparison/greater_equal.hpp>
+#include <boost/preprocessor/control/if.hpp>
+#include <boost/preprocessor/repetition/repeat.hpp>
+
+#include "paddle/fluid/framework/eigen.h"
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/framework/operator.h"
+#include "paddle/fluid/platform/errors.h"
+
+#define MAX_RANK_SUPPORTED 6
+
+#define MESHGRID_TEMPLATE(z, n, data) \
+  case n + 1: {                       \
+    MeshgridForward<n + 1>(context);  \
+    break;                            \
+  }
+#define REP_MESHGRID_TEMPLATE(n) BOOST_PP_REPEAT(n, MESHGRID_TEMPLATE, ~)
+#define COND(n) BOOST_PP_GREATER_EQUAL(n, BOOST_PP_MOD(n, MAX_RANK_SUPPORTED))
+
+#define MESHGRID_GRAD_CASE(n)     \
+  case n: {                       \
+    MeshgridBackward<n>(context); \
+    break;                        \
+  }
+#define MESHGRID_GRAD_TEMPLATE(z, n, data) \
+  BOOST_PP_IF(COND(n), MESHGRID_GRAD_CASE(n), )
+#define REP_MESHGRID_GRAD_TEMPLATE(n) \
+  BOOST_PP_REPEAT(n, MESHGRID_GRAD_TEMPLATE, ~)
+
+namespace paddle {
+namespace operators {
+
+template <typename T, int MajorType = Eigen::RowMajor,
+          typename IndexType = Eigen::DenseIndex>
+using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
+template <typename T, int MajorType = Eigen::RowMajor,
+          typename IndexType = Eigen::DenseIndex>
+using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
+template <typename T, size_t D, int MajorType = Eigen::RowMajor,
+          typename IndexType = Eigen::DenseIndex>
+using EigenTensor = framework::EigenTensor<T, D, MajorType, IndexType>;
+
+template <typename DeviceContext, typename T>
+class MeshgridKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto ins = context.MultiInput<framework::Tensor>("X");
+    auto rank = ins.size();
+    switch (rank) {
+      REP_MESHGRID_TEMPLATE(MAX_RANK_SUPPORTED)
+      default:
+        PADDLE_THROW(platform::errors::InvalidArgument(
+            "Only support tensor nums between 1 and 6."));
+    }
+  }
+
+ protected:
+  template <int Rank>
+  void MeshgridForward(const framework::ExecutionContext& context) const {
+    auto ins = context.MultiInput<framework::Tensor>("X");
+    auto outs = context.MultiOutput<framework::Tensor>("Out");
+    PADDLE_ENFORCE_EQ(
+        ins.size() > 1, true,
+        platform::errors::InvalidArgument("expect at least 2 input tensors"));
+
+    int64_t size = ins.size();
+    std::vector<int64_t> shape(size);
+
+    for (int64_t i = 0; i < size; i++) {
+      switch (ins[i]->dims().size()) {
+        case 0:
+          shape[i] = 1;
+          break;
+        case 1:
+          shape[i] = ins[i]->dims()[0];
+          break;
+        default:
+          PADDLE_THROW(platform::errors::InvalidArgument(
+              "Expected scalar or 1D tensor in the tensor list but got tensor "
+              "%d: ",
+              i));
+      }
+    }
+
+    for (int64_t i = 0; i < size; i++) {
+      std::vector<int64_t> view_shape(size, 1);
+      view_shape[i] = shape[i];
+
+      framework::Tensor reshape_ins_tensor;
+      TensorCopy(*ins[i], context.GetPlace(), context.device_context(),
+                 &reshape_ins_tensor);
+      framework::DDim out_dims_reshape = framework::make_ddim(view_shape);
+      reshape_ins_tensor.Resize(out_dims_reshape);
+      framework::DDim out_dims = framework::make_ddim(shape);
+
+      Eigen::DSizes<int, Rank> bcast_dims;
+      for (int64_t j = 0; j < size; j++) {
+        bcast_dims[j] = shape[j];
+      }
+      bcast_dims[i] = 1;
+
+      outs[i]->Resize(out_dims);
+      auto x = EigenTensor<T, Rank>::From(reshape_ins_tensor);
+      outs[i]->mutable_data<T>(context.GetPlace());
+      auto y = EigenTensor<T, Rank>::From(*outs[i]);
+      auto& place =
+          *context.template device_context<DeviceContext>().eigen_device();
+      y.device(place) = x.broadcast(bcast_dims);
+    }
+  }
+};
+
+template <typename DeviceContext, typename T>
+class MeshgridGradKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto out_grad =
+        context.MultiInput<framework::Tensor>(framework::GradVarName("Out"));
+    int n = out_grad.size();
+    switch (n) {
+      REP_MESHGRID_GRAD_TEMPLATE(MAX_RANK_SUPPORTED)
+      default:
+        PADDLE_THROW(platform::errors::InvalidArgument(
+            "only support tensor nums being between 1 and 6."));
+    }
+  }
+
+ protected:
+  template <int Rank>
+  void MeshgridBackward(const framework::ExecutionContext& context) const {
+    auto out_grad =
+        context.MultiInput<framework::Tensor>(framework::GradVarName("Out"));
+    auto ins = context.MultiInput<framework::Tensor>("X");
+    auto outs =
+        context.MultiOutput<framework::Tensor>(framework::GradVarName("X"));
+
+    int n = out_grad.size();
+    auto out_dims = out_grad[0]->dims();
+
+    for (int i = 0; i < n; i++) {
+      outs[i]->mutable_data<T>(context.GetPlace());
+      auto out_grad_tmp = EigenVector<T>::Flatten(*out_grad[i]);
+      auto in_grad = EigenVector<T>::Flatten(*outs[i]);
+
+      std::vector<int> reduce_dims_vec;
+      std::vector<int> reshape_dims_vec;
+      for (int j = 0; j < n; j++) {
+        reduce_dims_vec.push_back(reshape_dims_vec.size());
+        if (j == i) {
+          reshape_dims_vec.push_back(1);
+          reshape_dims_vec.push_back(out_dims[j]);
+        } else {
+          reshape_dims_vec.push_back(out_dims[j]);
+          reshape_dims_vec.push_back(1);
+        }
+      }
+
+      Eigen::DSizes<int, Rank> reduce_dims;
+      for (int k = 0; k < n; k++) {
+        reduce_dims[k] = reduce_dims_vec[k];
+      }
+
+      Eigen::DSizes<int, Rank * 2> reshape_dims;
+      for (int k = 0; k < n * 2; k++) {
+        reshape_dims[k] = reshape_dims_vec[k];
+      }
+
+      auto tensor_reduce_tmp =
+          out_grad_tmp.reshape(reshape_dims).sum(reduce_dims);
+      auto& place =
+          *context.template device_context<DeviceContext>().eigen_device();
+      in_grad.device(place) = tensor_reduce_tmp.reshape(in_grad.dimensions());
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

python/paddle/__init__.py

@@ -59,7 +59,7 @@ from .tensor.creation import full  #DEFINE_ALIAS
 from .tensor.creation import full_like  #DEFINE_ALIAS
 # from .tensor.creation import triu   #DEFINE_ALIAS
 # from .tensor.creation import tril   #DEFINE_ALIAS
-# from .tensor.creation import meshgrid   #DEFINE_ALIAS
+from .tensor.creation import meshgrid  #DEFINE_ALIAS
 # from .tensor.stat import mean   #DEFINE_ALIAS
 # from .tensor.stat import reduce_mean   #DEFINE_ALIAS
 # from .tensor.stat import std   #DEFINE_ALIAS

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

+#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import print_function
+
+import unittest
+import numpy as np
+from op_test import OpTest, skip_check_grad_ci
+import paddle.fluid as fluid
+import paddle
+from paddle.fluid import compiler, Program, program_guard, core
+
+
+class TestMeshgridOp(OpTest):
+    def setUp(self):
+        self.op_type = "meshgrid"
+        self.dtype = self.get_dtype()
+        ins, outs = self.init_test_data()
+        self.inputs = {'X': [('x%d' % i, ins[i]) for i in range(len(ins))]}
+        self.outputs = {
+            'Out': [('out%d' % i, outs[i]) for i in range(len(outs))]
+        }
+
+    def get_dtype(self):
+        return "float64"
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(['x0'], ['out0'])
+        self.check_grad(['x1'], ['out1'])
+
+    def init_test_data(self):
+        self.shape = self.get_x_shape()
+        ins = []
+        outs = []
+        for i in range(len(self.shape)):
+            ins.append(np.random.random((self.shape[i], )).astype(self.dtype))
+
+        for i in range(len(self.shape)):
+            out_reshape = [1] * len(self.shape)
+            out_reshape[i] = self.shape[i]
+            out_temp = np.reshape(ins[i], out_reshape)
+            outs.append(np.broadcast_to(out_temp, self.shape))
+        return ins, outs
+
+    def get_x_shape(self):
+        return [100, 200]
+
+
+class TestMeshgridOp2(TestMeshgridOp):
+    def get_x_shape(self):
+        return [100, 300]
+
+
+class TestMeshgridOp3(unittest.TestCase):
+    def test_api(self):
+        x = fluid.data(shape=[100], dtype='int32', name='x')
+        y = fluid.data(shape=[200], dtype='int32', name='y')
+
+        input_1 = np.random.randint(0, 100, [100, ]).astype('int32')
+        input_2 = np.random.randint(0, 100, [200, ]).astype('int32')
+
+        out_1 = np.reshape(input_1, [100, 1])
+        out_1 = np.broadcast_to(out_1, [100, 200])
+        out_2 = np.reshape(input_2, [1, 200])
+        out_2 = np.broadcast_to(out_2, [100, 200])
+
+        exe = fluid.Executor(place=fluid.CPUPlace())
+        grid_x, grid_y = paddle.tensor.meshgrid([x, y])
+        res_1, res_2 = exe.run(fluid.default_main_program(),
+                               feed={'x': input_1,
+                                     'y': input_2},
+                               fetch_list=[grid_x, grid_y])
+
+        assert np.array_equal(res_1, out_1)
+        assert np.array_equal(res_2, out_2)
+
+
+class TestMeshgridOp4(unittest.TestCase):
+    def test_errors(self):
+        with program_guard(Program(), Program()):
+
+            def test_input_type():
+                x = fluid.data(shape=[200], dtype='float32', name='x2')
+                paddle.tensor.meshgrid(x)
+
+        self.assertRaises(TypeError, test_input_type)
+
+
+class TestMeshgridOp5(unittest.TestCase):
+    def test_api_with_dygraph(self):
+        input_3 = np.random.randint(0, 100, [100, ]).astype('int32')
+        input_4 = np.random.randint(0, 100, [200, ]).astype('int32')
+
+        with fluid.dygraph.guard():
+            tensor_3 = fluid.dygraph.to_variable(input_3)
+            tensor_4 = fluid.dygraph.to_variable(input_4)
+            res_3, res_4 = paddle.tensor.meshgrid([tensor_3, tensor_4])
+
+            assert np.array_equal(res_3.shape, [100, 200])
+            assert np.array_equal(res_4.shape, [100, 200])
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/tensor/__init__.py

@@ -39,7 +39,7 @@ from .creation import full  # DEFINE_ALIAS
 from .creation import full_like  #DEFINE_ALIAS
 from .creation import triu  #DEFINE_ALIAS
 from .creation import tril  #DEFINE_ALIAS
-# from .creation import meshgrid   #DEFINE_ALIAS
+from .creation import meshgrid  #DEFINE_ALIAS
 # from .stat import mean   #DEFINE_ALIAS
 # from .stat import reduce_mean   #DEFINE_ALIAS
 # from .stat import std   #DEFINE_ALIAS

python/paddle/tensor/creation.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 from __future__ import print_function
-from ..fluid.framework import Variable
+from ..fluid.framework import Variable, in_dygraph_mode
 from ..fluid.initializer import Constant
 from ..fluid.layers import core
 from ..fluid.layer_helper import LayerHelper
@@ -43,7 +43,7 @@ __all__ = [
     'full_like',
     'triu',
     'tril',
-    #            'meshgrid',
+    'meshgrid',
 ]
 
 
@@ -723,3 +723,85 @@ def triu(input, diagonal=0, name=None):
     """
 
     return _tril_triu_op(LayerHelper('triu', **locals()))
+
+
+def meshgrid(input, name=None):
+    """
+    This op takes a list of N tensors as input, each of which is 1-dimensional 
+    vector, and creates N-dimensional grids.
+    
+    Args:
+        input(Variable) : tensors (list of tensor): the shapes of input k tensors are (N1,), 
+            (N2,),..., (Nk,). Support data types: ``float64``, ``float32``, ``int32``, ``int64``.
+        name (str, optional): The default value is None. Normally there is no need for
+            user to set this property. For more information, please refer to :ref:`api_guide_Name`.
+ 
+    Returns:
+         Variable: k tensors. The shape of each tensor is (N1, N2, ..., Nk)
+
+    Examples:
+      .. code-block:: python
+
+          import paddle
+          import paddle.fluid as fluid
+          import numpy as np
+
+          x = fluid.data(name='x', shape=[100], dtype='int32')
+          y = fluid.data(name='y', shape=[200], dtype='int32')
+
+          input_1 = np.random.randint(0, 100, [100, ]).astype('int32')
+          input_2 = np.random.randint(0, 100, [200, ]).astype('int32')
+
+          exe = fluid.Executor(place=fluid.CPUPlace())
+          grid_x, grid_y = paddle.tensor.meshgrid([x, y])
+          res_1, res_2 = exe.run(fluid.default_main_program(),
+                                 feed={'x': input_1,
+                                       'y': input_2},
+                                 fetch_list=[grid_x, grid_y])
+     
+          #the shape of res_1 is (100, 200)
+          #the shape of res_2 is (100, 200)
+
+      .. code-block:: python
+
+          #example 2: in dygraph mode
+
+          import paddle
+          import paddle.fluid as fluid
+          import numpy as np
+
+          input_3 = np.random.randint(0, 100, [100, ]).astype('int32')
+          input_4 = np.random.randint(0, 100, [200, ]).astype('int32')
+          with fluid.dygraph.guard():
+              tensor_3 = fluid.dygraph.to_variable(input_3)
+              tensor_4 = fluid.dygraph.to_variable(input_4)
+              grid_x, grid_y = paddle.tensor.meshgrid([tensor_3, tensor_4])
+
+          #the shape of grid_x is (100, 200)
+          #the shape of grid_y is (100, 200)
+
+    """
+
+    if in_dygraph_mode():
+        num = len(input)
+        out = core.ops.meshgrid(input, num)
+        return out
+
+    helper = LayerHelper('meshgrid', **locals())
+
+    if not isinstance(input, list):
+        raise TypeError("The type of input in meshgrid should be list.")
+
+    for id, input_ in enumerate(input):
+        check_dtype(input_.dtype, 'create data type',
+                    ['float16', 'float32', 'float64', 'int32', 'int64'],
+                    'meshgrid')
+
+    num = len(input)
+    out = [
+        helper.create_variable_for_type_inference(dtype=input[i].dtype)
+        for i in range(num)
+    ]
+    helper.append_op(type='meshgrid', inputs={'X': input}, outputs={'Out': out})
+
+    return out