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add mv op(c++, python, unit test) (#27024)

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revert-27520-disable_pr
furnace 5 years ago committed by GitHub
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125
paddle/fluid/operators/mv_op.cc
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/* 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/mv_op.h"
namespace paddle {
namespace operators {
class MVOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X", "The matrix input of mv op");
AddInput("Vec", "The vector input of mv op");
AddOutput("Out", "The output of mv op");
AddComment(R"DOC(
MV Operator.
This operator is used to perform matrix vector multiplication
of the input tensors `X` and `Vec`.
)DOC");
}
};
class MVOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(framework::InferShapeContext *context) const override {
OP_INOUT_CHECK(context->HasInput("X"), "Input", "X", "mv");
OP_INOUT_CHECK(context->HasInput("Vec"), "Input", "Vec", "mv");
OP_INOUT_CHECK(context->HasOutput("Out"), "Output", "Out", "mv");
auto dim_x = context->GetInputDim("X");
auto dim_y = context->GetInputDim("Vec");
PADDLE_ENFORCE_EQ(
dim_x.size(), 2,
platform::errors::InvalidArgument(
"The rank of input X should be 2, but is %d", dim_x.size()));
PADDLE_ENFORCE_EQ(
dim_y.size(), 1,
platform::errors::InvalidArgument(
"The rank of input Vec should be 1, but is %d", dim_y.size()));
PADDLE_ENFORCE_EQ(dim_x[1] == dim_y[0], true,
platform::errors::InvalidArgument(
"The length of input X' second dim should equal the "
"length of input Vec,"
" but X[%d, %d], Vec[%d]",
dim_x[0], dim_x[1], dim_y[0]));
framework::DDim dim_out = framework::make_ddim({dim_x[0]});
context->SetOutputDim("Out", dim_out);
context->ShareLoD("X", /*->*/ "Out");
}
};
template <typename T>
class MVOpGradMaker : public framework::SingleGradOpMaker<T> {
public:
using framework::SingleGradOpMaker<T>::SingleGradOpMaker;
protected:
void Apply(GradOpPtr<T> retv) const override {
retv->SetType("mv_grad");
retv->SetInput("X", this->Input("X"));
retv->SetInput("Vec", this->Input("Vec"));
retv->SetInput(framework::GradVarName("Out"), this->OutputGrad("Out"));
retv->SetOutput(framework::GradVarName("X"), this->InputGrad("X"));
retv->SetOutput(framework::GradVarName("Vec"), this->InputGrad("Vec"));
}
};
class MVOpGrad : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(framework::InferShapeContext *context) const override {
OP_INOUT_CHECK(context->HasInput("X"), "Input", "X", "mv");
OP_INOUT_CHECK(context->HasInput("Vec"), "Input", "Vec", "mv");
OP_INOUT_CHECK(context->HasInput(framework::GradVarName("Out")), "Input",
"Out@GRAD", "mv");
auto x_dims = context->GetInputDim("X");
auto vec_dims = context->GetInputDim("Vec");
auto x_grad_name = framework::GradVarName("X");
auto vec_grad_name = framework::GradVarName("Vec");
if (context->HasOutput(x_grad_name)) {
context->SetOutputDim(x_grad_name, x_dims);
}
if (context->HasOutput(vec_grad_name)) {
context->SetOutputDim(vec_grad_name, vec_dims);
}
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
namespace plat = paddle::platform;
REGISTER_OPERATOR(mv, ops::MVOp, ops::MVOpMaker,
ops::MVOpGradMaker<paddle::framework::OpDesc>,
ops::MVOpGradMaker<paddle::imperative::OpBase>);
REGISTER_OPERATOR(mv_grad, ops::MVOpGrad);
REGISTER_OP_CPU_KERNEL(
mv, ops::MVKernel<paddle::platform::CPUDeviceContext, float>,
ops::MVKernel<paddle::platform::CPUDeviceContext, double>);
REGISTER_OP_CPU_KERNEL(
mv_grad, ops::MVGradKernel<paddle::platform::CPUDeviceContext, float>,
ops::MVGradKernel<paddle::platform::CPUDeviceContext, double>);

95
paddle/fluid/operators/mv_op.cu
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/* 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/mv_op.h"
#include "paddle/fluid/platform/gpu_launch_param_config.h"
namespace paddle {
namespace operators {
template <typename T>
__global__ void MVGradCUDAKernel(const int m, const int n, const T *dout,
const T *vec, T *dx) {
int idx = blockDim.x * blockIdx.x + threadIdx.x;
for (; idx < m * n; idx += blockDim.x * gridDim.x) {
int i = idx / n;
int j = idx % n;
dx[idx] = dout[i] * vec[j];
}
}
// Using dimensional constraints on matrix multiplication, it is
// straight-forward to check the following table for when X and Y
// are both matrices.
//
// dX = | dOut Vec^T
// dVec = | X^T dOut
template <typename T>
class MVGradKernel<platform::CUDADeviceContext, T>
: public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &context) const override {
auto *x = context.Input<framework::Tensor>("X");
auto *vec = context.Input<framework::Tensor>("Vec");
auto *dout =
context.Input<framework::Tensor>(framework::GradVarName("Out"));
auto *dx = context.Output<framework::Tensor>(framework::GradVarName("X"));
auto *dvec =
context.Output<framework::Tensor>(framework::GradVarName("Vec"));
auto dim_x = x->dims();
int m = dim_x[0];
int n = dim_x[1];
dx->Resize(framework::make_ddim({m * n}));
// get data ptr
const T *x_data = x->data<T>();
const T *vec_data = vec->data<T>();
const T *dout_data = dout->data<T>();
T *dx_data = dx->mutable_data<T>(context.GetPlace());
T *dvec_data = dvec->mutable_data<T>(context.GetPlace());
auto &dev_ctx =
context.template device_context<platform::CUDADeviceContext>();
auto blas = math::GetBlas<platform::CUDADeviceContext, T>(dev_ctx);
// calculate dx
auto stream = context.cuda_device_context().stream();
auto config = GetGpuLaunchConfig1D(dev_ctx, m * n);
MVGradCUDAKernel<
T><<<config.block_per_grid.x, config.thread_per_block.x, 0, stream>>>(
m, n, dout_data, vec_data, dx_data);
dx->Resize(framework::make_ddim({m, n}));
// calculate dvec
blas.GEMV(true, dim_x[0], dim_x[1], static_cast<T>(1), x_data, dout_data,
static_cast<T>(0), dvec_data);
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
namespace plat = paddle::platform;
REGISTER_OP_CUDA_KERNEL(
mv, ops::MVKernel<paddle::platform::CUDADeviceContext, float>,
ops::MVKernel<paddle::platform::CUDADeviceContext, double>);
REGISTER_OP_CUDA_KERNEL(
mv_grad, ops::MVGradKernel<paddle::platform::CUDADeviceContext, float>,
ops::MVGradKernel<paddle::platform::CUDADeviceContext, double>);

105
paddle/fluid/operators/mv_op.h
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/* 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 <algorithm>
#include <utility>
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/math/blas.h"
#ifdef PADDLE_WITH_MKLDNN
#include "paddle/fluid/platform/mkldnn_helper.h"
#endif
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
template <typename DeviceContext, typename T>
class MVKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &context) const override {
auto *x = context.Input<framework::Tensor>("X");
auto *vec = context.Input<framework::Tensor>("Vec");
auto *out = context.Output<framework::Tensor>("Out");
auto dim_x = x->dims();
// get data ptr
const T *x_data = x->data<T>();
const T *vec_data = vec->data<T>();
T *out_data = out->mutable_data<T>(context.GetPlace());
auto &dev_ctx = context.template device_context<DeviceContext>();
auto blas = math::GetBlas<DeviceContext, T>(dev_ctx);
blas.GEMV(false, dim_x[0], dim_x[1], static_cast<T>(1), x_data, vec_data,
static_cast<T>(0), out_data);
}
};
// Using dimensional constraints on matrix multiplication, it is
// straight-forward to check the following table for when X and Y
// are both matrices.
//
// dX = | dOut vec^T
// dVec = | X^T dOut
template <typename DeviceContext, typename T>
class MVGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &context) const override {
auto *x = context.Input<framework::Tensor>("X");
auto *vec = context.Input<framework::Tensor>("Vec");
auto *dout =
context.Input<framework::Tensor>(framework::GradVarName("Out"));
auto *dx = context.Output<framework::Tensor>(framework::GradVarName("X"));
auto *dvec =
context.Output<framework::Tensor>(framework::GradVarName("Vec"));
auto dim_x = x->dims();
int m = dim_x[0];
int n = dim_x[1];
dx->Resize(framework::make_ddim({m * n}));
// get data ptr
const T *x_data = x->data<T>();
const T *vec_data = vec->data<T>();
const T *dout_data = dout->data<T>();
T *dx_data = dx->mutable_data<T>(context.GetPlace());
T *dvec_data = dvec->mutable_data<T>(context.GetPlace());
auto &dev_ctx = context.template device_context<DeviceContext>();
auto blas = math::GetBlas<DeviceContext, T>(dev_ctx);
// calculate dx
for (int i = 0; i < m; ++i) {
for (int j = 0; j < n; ++j)
dx_data[i * n + j] = dout_data[i] * vec_data[j];
}
dx->Resize(framework::make_ddim({m, n}));
// calculate dvec
blas.GEMV(true, dim_x[0], dim_x[1], static_cast<T>(1), x_data, dout_data,
static_cast<T>(0), dvec_data);
}
};
} // namespace operators
} // namespace paddle

1
python/paddle/__init__.py
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@ -90,6 +90,7 @@ from .tensor.linalg import cholesky #DEFINE_ALIAS
# from .tensor.linalg import tensordot #DEFINE_ALIAS
from .tensor.linalg import bmm #DEFINE_ALIAS
from .tensor.linalg import histogram #DEFINE_ALIAS
from .tensor.linalg import mv #DEFINE_ALIAS
from .tensor.logic import equal #DEFINE_ALIAS
from .tensor.logic import greater_equal #DEFINE_ALIAS
from .tensor.logic import greater_than #DEFINE_ALIAS

94
python/paddle/fluid/tests/unittests/test_mv_op.py
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#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
import paddle
import paddle.fluid as fluid
import paddle.fluid.layers as layers
import paddle.fluid.core as core
from op_test import OpTest
class TestMVOp(OpTest):
def setUp(self):
self.op_type = "mv"
self.init_config()
self.inputs = {'X': self.x, 'Vec': self.vec}
self.outputs = {'Out': np.dot(self.x, self.vec)}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X', 'Vec'], 'Out')
def init_config(self):
self.x = np.random.random((5, 100)).astype("float64")
self.vec = np.random.random((100)).astype("float64")
class TestMVAPI(unittest.TestCase):
def test_dygraph_api_out(self):
paddle.disable_static()
self.x_data = np.random.random((5, 100)).astype("float64")
self.x = paddle.to_tensor(self.x_data)
self.vec_data = np.random.random((100)).astype("float64")
self.vec = paddle.to_tensor(self.vec_data)
z = paddle.mv(self.x, self.vec)
np_z = z.numpy()
z_expected = np.array(np.dot(self.x_data, self.vec_data))
self.assertTrue(np.allclose(np_z, z_expected))
paddle.enable_static()
def test_static_graph(self):
paddle.enable_static()
self.input_x = np.random.rand(5, 100).astype("float64")
self.input_vec = np.random.rand(100).astype("float64")
data_x = paddle.static.data("x", shape=[5, 100], dtype="float64")
data_vec = paddle.static.data("vec", shape=[100], dtype="float64")
result_vec = paddle.mv(data_x, data_vec)
self.place = paddle.CPUPlace()
exe = paddle.static.Executor(self.place)
res, = exe.run(feed={"x": self.input_x,
"vec": self.input_vec},
fetch_list=[result_vec])
z_expected = np.array(np.dot(self.input_x, self.input_vec))
self.assertTrue(np.allclose(res, z_expected))
class TestMVError(unittest.TestCase):
def test_input(self):
def test_shape():
paddle.enable_static()
self.input_x = np.random.rand(5, 100).astype("float64")
self.input_vec = np.random.rand(100).astype("float64")
data_x = paddle.static.data("x", shape=[5, 100], dtype="float64")
data_vec = paddle.static.data(
"vec", shape=[100, 2], dtype="float64")
result_vec = paddle.mv(data_x, data_vec)
self.assertRaises(ValueError, test_shape)
if __name__ == '__main__':
unittest.main()

1
python/paddle/tensor/__init__.py
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@ -56,6 +56,7 @@ from .linalg import cholesky #DEFINE_ALIAS
# from .linalg import tensordot #DEFINE_ALIAS
from .linalg import bmm #DEFINE_ALIAS
from .linalg import histogram #DEFINE_ALIAS
from .linalg import mv #DEFINE_ALIAS
from .logic import equal #DEFINE_ALIAS
from .logic import greater_equal #DEFINE_ALIAS
from .logic import greater_than #DEFINE_ALIAS

64
python/paddle/tensor/linalg.py
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@ -32,7 +32,8 @@ __all__ = [
'cholesky',
# 'tensordot',
'bmm',
'histogram'
'histogram',
'mv'
]
@ -920,3 +921,64 @@ def histogram(input, bins=100, min=0, max=0):
'min': min,
'max': max})
return out
def mv(x, vec, name=None):
"""
Performs a matrix-vector product of the matrix x and the vector vec.
Args:
x (Variable): A tensor with shape :math:`[M, N]` , The data type of the input Tensor x
should be one of float32, float64.
vec (Variable): A tensor with shape :math:`[N]` , The data type of the input Tensor x
should be one of float32, float64.
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:
Tensor: The tensor which is producted by x and vec.
Examples:
.. code-block:: python
# x: [M, N], vec: [N]
# paddle.mv(x, vec) # out: [M]
import numpy as np
import paddle
paddle.disable_static()
x_data = np.array([[2, 1, 3], [3, 0, 1]]).astype("float64")
x = paddle.to_tensor(x_data)
vec_data = np.array([3, 5, 1])
vec = paddle.to_tensor(vec_data).astype("float64")
out = paddle.mv(x, vec)
paddle.enable_static()
"""
if in_dygraph_mode():
out = core.ops.mv(x, vec)
return out
def __check_input(x, vec):
var_names = {'x': x, 'vec': vec}
for name, val in var_names.items():
check_variable_and_dtype(val, name, ['float32', 'float64'], 'mv')
x_shape = list(x.shape)
vec_shape = list(vec.shape)
if len(x_shape) != 2:
raise ValueError(
"x should be 2-dimensional. But received x's dimention: {}".
format(x_shape))
if len(vec_shape) != 1:
raise ValueError(
"vec should be 1-dimensional. But received vec's dimention: {}".
format(vec_shape))
__check_input(x, vec)
helper = LayerHelper('mv', **locals())
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type='mv', inputs={'X': x,
'Vec': vec}, outputs={'Out': out})
return out

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