未验证 提交 2ff18e53 编写于 作者: J JesseyXujin 提交者: GitHub

add expand_as op, test=develop (#20565)

* add expand_as op, test=develop

* add expand_as op,test=develop

* add expand_as op,test=develop

* add nn.py, test=develop

* delele paddle_enforce, test=develop
上级 5c41805d
...@@ -243,6 +243,7 @@ paddle.fluid.layers.sequence_enumerate (ArgSpec(args=['input', 'win_size', 'pad_ ...@@ -243,6 +243,7 @@ paddle.fluid.layers.sequence_enumerate (ArgSpec(args=['input', 'win_size', 'pad_
paddle.fluid.layers.unique (ArgSpec(args=['x', 'dtype'], varargs=None, keywords=None, defaults=('int32',)), ('document', 'cab0b06e5683875f12f0efc62fa230a9')) paddle.fluid.layers.unique (ArgSpec(args=['x', 'dtype'], varargs=None, keywords=None, defaults=('int32',)), ('document', 'cab0b06e5683875f12f0efc62fa230a9'))
paddle.fluid.layers.unique_with_counts (ArgSpec(args=['x', 'dtype'], varargs=None, keywords=None, defaults=('int32',)), ('document', '4496682f302007019e458a2f30d8a7c3')) paddle.fluid.layers.unique_with_counts (ArgSpec(args=['x', 'dtype'], varargs=None, keywords=None, defaults=('int32',)), ('document', '4496682f302007019e458a2f30d8a7c3'))
paddle.fluid.layers.expand (ArgSpec(args=['x', 'expand_times', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'e93a1b102ab64b247c1b774e60d4c0d0')) paddle.fluid.layers.expand (ArgSpec(args=['x', 'expand_times', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'e93a1b102ab64b247c1b774e60d4c0d0'))
paddle.fluid.layers.expand_as (ArgSpec(args=['x', 'target_tensor', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'ca6b29aa6987776628a0b33f6dcaaaa6'))
paddle.fluid.layers.sequence_concat (ArgSpec(args=['input', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'f47f9d207ac60b6f294087bcb1b64ae8')) paddle.fluid.layers.sequence_concat (ArgSpec(args=['input', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'f47f9d207ac60b6f294087bcb1b64ae8'))
paddle.fluid.layers.scale (ArgSpec(args=['x', 'scale', 'bias', 'bias_after_scale', 'act', 'name'], varargs=None, keywords=None, defaults=(1.0, 0.0, True, None, None)), ('document', 'a33547d41970fa3c59e6b2f21fe5f76d')) paddle.fluid.layers.scale (ArgSpec(args=['x', 'scale', 'bias', 'bias_after_scale', 'act', 'name'], varargs=None, keywords=None, defaults=(1.0, 0.0, True, None, None)), ('document', 'a33547d41970fa3c59e6b2f21fe5f76d'))
paddle.fluid.layers.elementwise_add (ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)), ('document', '0c9c260e7738165a099f6a76da0b7814')) paddle.fluid.layers.elementwise_add (ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)), ('document', '0c9c260e7738165a099f6a76da0b7814'))
......
/* Copyright (c) 2019 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/expand_as_op.h"
#include <memory>
#include <vector>
namespace paddle {
namespace operators {
using framework::Tensor;
class ExpandAsOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE_EQ(ctx->HasInput("X"), true);
PADDLE_ENFORCE_EQ(ctx->HasInput("target_tensor"), true);
PADDLE_ENFORCE_EQ(ctx->HasOutput("Out"), true);
auto x_dims = ctx->GetInputDim("X");
auto target_tensor_dims = ctx->GetInputDim("target_tensor");
PADDLE_ENFORCE_EQ(static_cast<size_t>(x_dims.size()),
target_tensor_dims.size(),
"The rank of input(target_tensor) must be equal "
"to the rank of Input(X).");
PADDLE_ENFORCE_LE(x_dims.size(), 6,
"The rank of Input(X) must not be greater than 6.");
std::vector<int64_t> out_shape(x_dims.size());
ctx->SetOutputDim("Out", framework::make_ddim(out_shape));
}
};
class ExpandAsOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X",
"(Tensor, default Tensor<float>). A tensor with rank in [1, 6]."
"X is the input to be expanded.");
AddOutput("Out",
"(Tensor, default Tensor<float>). A tensor with rank in [1, 6]."
"The rank of Output(Out) have the same with Input(X). "
"After expanding, size of each dimension of Output(Out) is equal "
"to size of the corresponding dimension of Input(X) multiplying "
"the corresponding value given by Attr(expand_times).");
AddInput("target_tensor", "Expand tensor's shape for each dimension.");
AddComment(R"DOC(
Expand as operator tiles the input by given times number. You should set times
number for each dimension by providing tensor 'expend_tensor'. The rank of X
should be in [1, 6]. Please note that size of 'expend_tensor' must be the same
with X's rank. Following is a using case:
Input(X) is a 3-D tensor with shape [2, 3, 1]:
[
[[1], [2], [3]],
[[4], [5], [6]]
]
target_tensors'shape: [2, 6, 2]
Output(Out) is a 3-D tensor with shape [2, 6, 2]:
[
[[1, 1], [2, 2], [3, 3], [1, 1], [2, 2], [3, 3]],
[[4, 4], [5, 5], [6, 6], [4, 4], [5, 5], [6, 6]]
]
)DOC");
}
};
class ExpandAsGradOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE_EQ(ctx->HasInput("X"), true);
PADDLE_ENFORCE_EQ(ctx->HasInput(framework::GradVarName("Out")), true);
auto x_dims = ctx->GetInputDim("X");
auto x_grad_name = framework::GradVarName("X");
if (ctx->HasOutput(x_grad_name)) {
ctx->SetOutputDim(x_grad_name, x_dims);
}
}
};
class ExpandAsGradOpDescMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDesc> Apply() const override {
std::unique_ptr<framework::OpDesc> op(new framework::OpDesc());
op->SetType("expand_as_grad");
op->SetInput("X", Input("X"));
op->SetInput("target_tensor", Input("target_tensor"));
op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
op->SetAttrMap(Attrs());
return op;
}
};
// DECLARE_NO_NEED_BUFFER_VARS_INFERENCE(ExpandGradNoNeedBufVarsInferer, "X");
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(expand_as, ops::ExpandAsOp, ops::ExpandAsOpMaker,
ops::ExpandAsGradOpDescMaker);
REGISTER_OPERATOR(expand_as_grad, ops::ExpandAsGradOp);
REGISTER_OP_CPU_KERNEL(
expand_as, ops::ExpandAsKernel<paddle::platform::CPUDeviceContext, float>,
ops::ExpandAsKernel<paddle::platform::CPUDeviceContext, double>,
ops::ExpandAsKernel<paddle::platform::CPUDeviceContext, int>,
ops::ExpandAsKernel<paddle::platform::CPUDeviceContext, bool>);
REGISTER_OP_CPU_KERNEL(
expand_as_grad,
ops::ExpandAsGradKernel<paddle::platform::CPUDeviceContext, float>,
ops::ExpandAsGradKernel<paddle::platform::CPUDeviceContext, double>);
/* Copyright (c) 2019 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/expand_as_op.h"
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(
expand_as, ops::ExpandAsKernel<paddle::platform::CUDADeviceContext, float>,
ops::ExpandAsKernel<paddle::platform::CUDADeviceContext, double>,
ops::ExpandAsKernel<paddle::platform::CUDADeviceContext, int>,
ops::ExpandAsKernel<paddle::platform::CUDADeviceContext, bool>);
REGISTER_OP_CUDA_KERNEL(
expand_as_grad,
ops::ExpandAsGradKernel<paddle::platform::CUDADeviceContext, float>,
ops::ExpandAsGradKernel<paddle::platform::CUDADeviceContext, double>);
/* Copyright (c) 2019 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/div.hpp>
#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"
#define MAX_RANK_SUPPORTED 6
#define EXPAND_AS_TEMPLATE(z, n, data) \
case n + 1: { \
ExpandAs<n + 1>(context); \
break; \
}
#define REP_EXPAND_AS_TEMPLATE(n) BOOST_PP_REPEAT(n, EXPAND_AS_TEMPLATE, ~)
#define COND(n) \
BOOST_PP_GREATER_EQUAL(BOOST_PP_DIV(n, MAX_RANK_SUPPORTED), \
BOOST_PP_MOD(n, MAX_RANK_SUPPORTED))
#define EXPAND_AS_GRAD_CASE(n) \
case n: { \
ExpandAsBackward<n>(context, reshape_dims_vec, reduce_dims_vec); \
break; \
}
#define EXPAND_AS_GRAD_TEMPLATE(z, n, data) \
BOOST_PP_IF(COND(n), EXPAND_AS_GRAD_CASE(n), )
#define REP_EXPAND_AS_GRAD_TEMPLATE(n) \
BOOST_PP_REPEAT(n, EXPAND_AS_GRAD_TEMPLATE, ~)
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
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 ExpandAsKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto rank = context.Input<Tensor>("X")->dims().size();
switch (rank) {
REP_EXPAND_AS_TEMPLATE(MAX_RANK_SUPPORTED)
default:
PADDLE_THROW("Only support tensor with rank being between 1 and 6.");
}
}
protected:
template <int Rank>
void ExpandAs(const framework::ExecutionContext& context) const {
auto* in0 = context.Input<Tensor>("X");
auto in_dims = in0->dims();
auto* target_tensor = context.Input<Tensor>("target_tensor");
auto* out0 = context.Output<Tensor>("Out");
Eigen::DSizes<int, Rank> bcast_dims;
int bcast_dims_remainder = 0;
auto x_dims = in0->dims();
auto y_dims = target_tensor->dims();
for (int i = 0; i < y_dims.size(); ++i) {
PADDLE_ENFORCE_NE(x_dims[i], 0, "X(input) should not have 0 dim");
bcast_dims[i] = y_dims[i] / x_dims[i];
bcast_dims_remainder += y_dims[i] % x_dims[i];
}
PADDLE_ENFORCE_EQ(bcast_dims_remainder, 0,
"X(input) could not be broadcast together with remapped "
"shape(expand tensor's shape)");
framework::DDim out_dims(in_dims);
for (size_t i = 0; i < bcast_dims.size(); ++i) {
out_dims[i] *= bcast_dims[i];
}
out0->Resize(out_dims);
auto x = EigenTensor<T, Rank>::From(*in0);
out0->mutable_data<T>(context.GetPlace());
auto y = EigenTensor<T, Rank>::From(*out0);
auto& place =
*context.template device_context<DeviceContext>().eigen_device();
y.device(place) = x.broadcast(bcast_dims);
}
};
template <typename DeviceContext, typename T>
class ExpandAsGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* in0 = context.Input<Tensor>("X");
auto* target_tensor = context.Input<Tensor>("target_tensor");
auto x_dims = in0->dims();
auto y_dims = target_tensor->dims();
std::vector<int> bcast_dims;
for (int i = 0; i < y_dims.size(); ++i) {
bcast_dims.push_back(y_dims[i] / x_dims[i]);
}
std::vector<int> reshape_dims_vec;
std::vector<int> reduce_dims_vec;
for (size_t i = 0; i < bcast_dims.size(); ++i) {
if (bcast_dims[i] == 1) {
reshape_dims_vec.push_back(x_dims[i]);
} else {
if (x_dims[i] == 1) {
reduce_dims_vec.push_back(reshape_dims_vec.size());
reshape_dims_vec.push_back(bcast_dims[i]);
} else {
reduce_dims_vec.push_back(reshape_dims_vec.size());
reshape_dims_vec.push_back(bcast_dims[i]);
reshape_dims_vec.push_back(x_dims[i]);
}
}
}
int dims = reshape_dims_vec.size() * MAX_RANK_SUPPORTED +
reduce_dims_vec.size() - MAX_RANK_SUPPORTED - 1;
// no need reduce, just copy
if (reduce_dims_vec.size() == 0) {
auto* in0 = context.Input<Tensor>(framework::GradVarName("Out"));
auto* out0 = context.Output<Tensor>(framework::GradVarName("X"));
out0->mutable_data<T>(context.GetPlace());
framework::TensorCopy(*in0, context.GetPlace(), context.device_context(),
out0);
} else {
switch (dims) {
REP_EXPAND_AS_GRAD_TEMPLATE(72)
default:
PADDLE_THROW("Only support tensor with rank being between 1 and 6.");
}
}
}
protected:
template <int Dims>
void ExpandAsBackward(const framework::ExecutionContext& context,
const std::vector<int>& reshape_dims_vec,
const std::vector<int>& reduce_dims_vec) const {
size_t reshape_size = Dims / MAX_RANK_SUPPORTED + 1;
size_t reduce_size = Dims % MAX_RANK_SUPPORTED + 1;
PADDLE_ENFORCE_EQ(reshape_size, reshape_dims_vec.size(),
"Inconsistent size between template Dims and "
"reshape dimensions.");
PADDLE_ENFORCE_EQ(reduce_size, reduce_dims_vec.size(),
"Inconsistent size between template Dims and "
"reduce dimensions.");
auto* in0 = context.Input<Tensor>(framework::GradVarName("Out"));
auto* out0 = context.Output<Tensor>(framework::GradVarName("X"));
out0->mutable_data<T>(context.GetPlace());
auto x_grad = EigenVector<T>::Flatten(*out0);
Eigen::DSizes<int, Dims / MAX_RANK_SUPPORTED + 1> reshape_dims;
for (size_t i = 0; i < reshape_size; ++i) {
reshape_dims[i] = reshape_dims_vec[i];
}
Eigen::DSizes<int, Dims % MAX_RANK_SUPPORTED + 1> reduce_dims;
for (size_t i = 0; i < reduce_size; ++i) {
reduce_dims[i] = reduce_dims_vec[i];
}
auto out_grad = EigenVector<T>::Flatten(*in0);
x_grad.device(
*context.template device_context<DeviceContext>().eigen_device()) =
out_grad.reshape(reshape_dims)
.sum(reduce_dims)
.reshape(x_grad.dimensions());
}
};
} // namespace operators
} // namespace paddle
...@@ -157,6 +157,7 @@ __all__ = [ ...@@ -157,6 +157,7 @@ __all__ = [
'unique', 'unique',
'unique_with_counts', 'unique_with_counts',
'expand', 'expand',
'expand_as',
'sequence_concat', 'sequence_concat',
'scale', 'scale',
'elementwise_add', 'elementwise_add',
...@@ -12864,6 +12865,76 @@ def expand(x, expand_times, name=None): ...@@ -12864,6 +12865,76 @@ def expand(x, expand_times, name=None):
return out return out
def expand_as(x, target_tensor, name=None):
"""
expand_as operator tiles to the input by given expand tensor. You should set expand tensor
for each dimension by providing tensor 'target_tensor'. The rank of X
should be in [1, 6]. Please note that size of 'target_tensor' must be the same
with X's rank. Following is a using case:
.. code-block:: text
Input(X) is a 3-D tensor with shape [2, 3, 1]:
[
[[1], [2], [3]],
[[4], [5], [6]]
]
target_tensor's shape: [2, 6, 2]
Output(Out) is a 3-D tensor with shape [2, 6, 2]:
[
[[1, 1], [2, 2], [3, 3], [1, 1], [2, 2], [3, 3]],
[[4, 4], [5, 5], [6, 6], [4, 4], [5, 5], [6, 6]]
]
Args:
x (Variable): A Tensor with dtype float64, float32, int32.
A tensor with rank in [1, 6].
target_tensor (Variable): A Tensor with dtype float64, float32, int32.
target_tensor for expanding to Input(X). Only use target_tensor'shape.
Returns:
Variable: A Tensor with dtype float64, float32, int32.
After expanding, size of each dimension of Output(Out) is equal to the size
of the corresponding dimension of target_tensor multiplying the corresponding
value given by target_tensor.
Examples:
.. code-block:: python
import paddle.fluid as fluid
import numpy as np
data = fluid.layers.data(name="data", shape=[-1,10], dtype='float64')
target_tensor = fluid.layers.data(
name="target_tensor", shape=[-1,20], dtype='float64')
result = fluid.layers.expand_as(x=data, target_tensor=target_tensor)
use_cuda = False
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
x = np.random.rand(3,10)
y = np.random.rand(3,20)
output= exe.run(feed={"data":x,"target_tensor":y},fetch_list=[result.name])
print(output[0].shape)
#(3,20)
"""
helper = LayerHelper('expand_as', input=x, **locals())
dtype = helper.input_dtype(input_param_name='x')
out = helper.create_variable_for_type_inference(dtype)
inputs = {'X': x, 'target_tensor': target_tensor}
helper.append_op(type='expand_as', inputs=inputs, outputs={'Out': out})
return out
from paddle.fluid.framework import convert_np_dtype_to_dtype_ from paddle.fluid.framework import convert_np_dtype_to_dtype_
......
# Copyright (c) 2019 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
import paddle.fluid as fluid
def bcast(x, target_tensor):
x_dims = x.shape
y_dims = target_tensor.shape
bcast_dims = []
for i in range(len(x_dims)):
bcast_dims.append(int(y_dims[i] / x_dims[i]))
bcast_dims = np.array(bcast_dims).astype("int64")
return bcast_dims
class TestExpandAsOpRank1(OpTest):
def setUp(self):
self.op_type = "expand_as"
x = np.random.rand(12).astype("float64")
target_tensor = np.random.rand(24).astype("float64")
self.inputs = {'X': x, 'target_tensor': target_tensor}
self.attrs = {}
bcast_dims = bcast(x, target_tensor)
output = np.tile(self.inputs['X'], bcast_dims)
self.outputs = {'Out': output}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Out')
class TestExpandAsOpRank2(OpTest):
def setUp(self):
self.op_type = "expand_as"
x = np.random.rand(2, 3).astype("float64")
target_tensor = np.random.rand(4, 6).astype("float64")
self.inputs = {'X': x, 'target_tensor': target_tensor}
self.attrs = {}
bcast_dims = bcast(x, target_tensor)
output = np.tile(self.inputs['X'], bcast_dims)
self.outputs = {'Out': output}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Out')
class TestExpandAsOpRank3(OpTest):
def setUp(self):
self.op_type = "expand_as"
x = np.random.rand(2, 3, 3).astype("float64")
target_tensor = np.random.rand(4, 6, 6).astype("float64")
self.inputs = {'X': x, 'target_tensor': target_tensor}
self.attrs = {}
bcast_dims = bcast(x, target_tensor)
output = np.tile(self.inputs['X'], bcast_dims)
self.outputs = {'Out': output}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Out')
class TestExpandAsOpRank4(OpTest):
def setUp(self):
self.op_type = "expand_as"
x = np.random.rand(1, 1, 3, 16).astype("float64")
target_tensor = np.random.rand(4, 6, 6, 32).astype("float64")
self.inputs = {'X': x, 'target_tensor': target_tensor}
self.attrs = {}
bcast_dims = bcast(x, target_tensor)
output = np.tile(self.inputs['X'], bcast_dims)
self.outputs = {'Out': output}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Out')
# Test python API
class TestExpandAPI(OpTest):
def test_api(self):
input1 = np.random.random([12, 14]).astype("float32")
input2 = np.random.random([48, 14]).astype("float32")
x = fluid.layers.data(
name='x', shape=[12, 14], append_batch_size=False, dtype="float32")
y = fluid.layers.data(
name='target_tensor',
shape=[48, 14],
append_batch_size=False,
dtype="float32")
out_1 = fluid.layers.expand_as(x, target_tensor=y)
exe = fluid.Executor(place=fluid.CPUPlace())
res_1 = exe.run(fluid.default_main_program(),
feed={"x": input1,
"target_tensor": input2},
fetch_list=[out_1])
assert np.array_equal(res_1[0], np.tile(input1, (4, 1)))
if __name__ == "__main__":
unittest.main()
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