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Merge pull request #12295 from jacquesqiao/speedup-reduce-sum-grad-op

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Speedup reduce sum grad op
distributed_test
Qiao Longfei 7 years ago committed by GitHub
parent eec412b230 273f737517
commit b41f8b9d42
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GPG Key ID: 4AEE18F83AFDEB23
4 changed files with 137 additions and 32 deletions
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19
paddle/fluid/operators/reduce_sum_op.cc
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@ -23,12 +23,13 @@ REGISTER_OP_CPU_KERNEL(
ops::ReduceKernel<paddle::platform::CPUDeviceContext, int, ops::SumFunctor>,
ops::ReduceKernel<paddle::platform::CPUDeviceContext, int64_t,
ops::SumFunctor>);
REGISTER_OP_CPU_KERNEL(reduce_sum_grad,
ops::ReduceGradKernel<paddle::platform::CPUDeviceContext,
float, ops::SumGradFunctor>,
ops::ReduceGradKernel<paddle::platform::CPUDeviceContext,
double, ops::SumGradFunctor>,
ops::ReduceGradKernel<paddle::platform::CPUDeviceContext,
int, ops::SumGradFunctor>,
ops::ReduceGradKernel<paddle::platform::CPUDeviceContext,
int64_t, ops::SumGradFunctor>);
REGISTER_OP_CPU_KERNEL(
reduce_sum_grad,
ops::ReduceSumGradKernel<paddle::platform::CPUDeviceContext, float,
ops::SumGradFunctor>,
ops::ReduceSumGradKernel<paddle::platform::CPUDeviceContext, double,
ops::SumGradFunctor>,
ops::ReduceSumGradKernel<paddle::platform::CPUDeviceContext, int,
ops::SumGradFunctor>,
ops::ReduceSumGradKernel<paddle::platform::CPUDeviceContext, int64_t,
ops::SumGradFunctor>);

60
paddle/fluid/operators/reduce_sum_op.h
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@ -14,11 +14,69 @@
#pragma once
#include <vector>
#include "paddle/fluid/operators/reduce_op.h"
namespace paddle {
namespace operators {
// use for loop to speed up Eigen broadcast. 4 timer faster then broadcast
template <typename DeviceContext, typename T, typename Functor>
class ReduceSumGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto dims = context.Attr<std::vector<int>>("dim");
if (context.GetPlace().type() == typeid(platform::CPUPlace) &&
dims.size() == 1) {
auto* input0 = context.Input<Tensor>("X");
auto* input2 = context.Input<Tensor>(framework::GradVarName("Out"));
auto* output = context.Output<Tensor>(framework::GradVarName("X"));
output->mutable_data<T>(context.GetPlace());
const auto* input2_d = input2->data<T>();
auto* output_d = output->data<T>();
// handle reduce_all
if (input2->dims().size() == 1 && input2->dims()[0] == 1) {
for (int64_t i = 0; i < framework::product(input0->dims()); ++i) {
output_d[i] = input2_d[0];
}
return;
}
// handle reduce by one dimension
int reduce_dim_index = dims[0];
if (reduce_dim_index < 0) {
reduce_dim_index += input0->dims().size();
}
auto& input_dim = input0->dims();
int64_t before_dim = 1;
for (int i = 0; i < reduce_dim_index; ++i) {
before_dim *= input_dim[i];
}
int64_t reduce_dim = input_dim[reduce_dim_index];
int64_t after_dim = 1;
for (int i = reduce_dim_index + 1; i < input_dim.size(); ++i) {
after_dim *= input_dim[i];
}
for (int64_t i = 0; i < before_dim; ++i) {
for (int64_t j = 0; j < reduce_dim; ++j) {
for (int64_t k = 0; k < after_dim; ++k) {
output_d[i * reduce_dim * after_dim + j * after_dim + k] =
input2_d[i * after_dim + k];
}
}
}
return;
}
// default use Eigen broadcast
ReduceGradKernel<DeviceContext, T, Functor> kernel;
kernel.Compute(context);
}
};
struct SumFunctor {
template <typename DeviceContext, typename X, typename Y, typename Dim>
void operator()(const DeviceContext& place, X* x, Y* y, const Dim& dim) {
@ -31,7 +89,7 @@ struct SumGradFunctor {
typename DY, typename Dim>
void operator()(const DeviceContext& place, X* x, Y* y, DX* dx, DY* dy,
const Dim& dim, int size) {
dx->device(place) = dy->broadcast(dim);
dx->device(place) = dy->eval().broadcast(dim);
}
};

4
python/paddle/fluid/layers/nn.py
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@ -2961,7 +2961,7 @@ def reduce_sum(input, dim=None, keep_dim=False, name=None):
# x is a Tensor variable with following elements:
# [[0.2, 0.3, 0.5, 0.9]
# [0.1, 0.2, 0.6, 0.7]]
# Each example is followed by the correspending output tensor.
# Each example is followed by the corresponding output tensor.
fluid.layers.reduce_sum(x) # [3.5]
fluid.layers.reduce_sum(x, dim=0) # [0.3, 0.5, 1.1, 1.6]
fluid.layers.reduce_sum(x, dim=-1) # [1.9, 1.6]
@ -2970,7 +2970,7 @@ def reduce_sum(input, dim=None, keep_dim=False, name=None):
# x is a Tensor variable with shape [2, 2, 2] and elements as below:
# [[[1, 2], [3, 4]],
# [[5, 6], [7, 8]]]
# Each example is followed by the correspending output tensor.
# Each example is followed by the corresponding output tensor.
fluid.layers.reduce_sum(x, dim=[1, 2]) # [10, 26]
fluid.layers.reduce_sum(x, dim=[0, 1]) # [16, 20]

86
python/paddle/fluid/tests/unittests/test_reduce_op.py
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@ -89,15 +89,11 @@ class TestProdOp(OpTest):
self.check_grad(['X'], 'Out')
class TestKeepDimReduce(OpTest):
class Test1DReduce(OpTest):
def setUp(self):
self.op_type = "reduce_sum"
self.inputs = {'X': np.random.random((5, 6, 10)).astype("float64")}
self.attrs = {'dim': [-2], 'keep_dim': True}
self.outputs = {
'Out':
self.inputs['X'].sum(axis=tuple(self.attrs['dim']), keepdims=True)
}
self.inputs = {'X': np.random.random(20).astype("float64")}
self.outputs = {'Out': self.inputs['X'].sum(axis=0)}
def test_check_output(self):
self.check_output()
@ -106,32 +102,82 @@ class TestKeepDimReduce(OpTest):
self.check_grad(['X'], 'Out')
class Test1DReduce(OpTest):
class Test2DReduce0(Test1DReduce):
def setUp(self):
self.op_type = "reduce_sum"
self.inputs = {'X': np.random.random(20).astype("float64")}
self.attrs = {'dim': [0]}
self.inputs = {'X': np.random.random((20, 10)).astype("float64")}
self.outputs = {'Out': self.inputs['X'].sum(axis=0)}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Out')
class Test2DReduce1(Test1DReduce):
def setUp(self):
self.op_type = "reduce_sum"
self.attrs = {'dim': [1]}
self.inputs = {'X': np.random.random((20, 10)).astype("float64")}
self.outputs = {
'Out': self.inputs['X'].sum(axis=tuple(self.attrs['dim']))
}
class TestReduceAll(OpTest):
class Test3DReduce0(Test1DReduce):
def setUp(self):
self.op_type = "reduce_sum"
self.attrs = {'dim': [1]}
self.inputs = {'X': np.random.random((5, 6, 7)).astype("float64")}
self.outputs = {
'Out': self.inputs['X'].sum(axis=tuple(self.attrs['dim']))
}
class Test3DReduce1(Test1DReduce):
def setUp(self):
self.op_type = "reduce_sum"
self.attrs = {'dim': [2]}
self.inputs = {'X': np.random.random((5, 6, 7)).astype("float64")}
self.outputs = {
'Out': self.inputs['X'].sum(axis=tuple(self.attrs['dim']))
}
class Test3DReduce2(Test1DReduce):
def setUp(self):
self.op_type = "reduce_sum"
self.attrs = {'dim': [-2]}
self.inputs = {'X': np.random.random((5, 6, 7)).astype("float64")}
self.outputs = {
'Out': self.inputs['X'].sum(axis=tuple(self.attrs['dim']))
}
class Test3DReduce3(Test1DReduce):
def setUp(self):
self.op_type = "reduce_sum"
self.attrs = {'dim': [1, 2]}
self.inputs = {'X': np.random.random((5, 6, 7)).astype("float64")}
self.outputs = {
'Out': self.inputs['X'].sum(axis=tuple(self.attrs['dim']))
}
class TestKeepDimReduce(Test1DReduce):
def setUp(self):
self.op_type = "reduce_sum"
self.inputs = {'X': np.random.random((5, 6, 10)).astype("float64")}
self.attrs = {'dim': [1], 'keep_dim': True}
self.outputs = {
'Out': self.inputs['X'].sum(axis=tuple(self.attrs['dim']),
keepdims=self.attrs['keep_dim'])
}
class TestReduceAll(Test1DReduce):
def setUp(self):
self.op_type = "reduce_sum"
self.inputs = {'X': np.random.random((5, 6, 2, 10)).astype("float64")}
self.attrs = {'reduce_all': True}
self.outputs = {'Out': self.inputs['X'].sum()}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Out')
## reduction in multi dims
class TestReduceMeanOpMultiAxises(OpTest):

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