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【NPU】Support NPU kernel for reduce_sum op v2 (#31620)

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* add reduce_sum

* fix broadcastd

* fix test

* fix

* add unsqueeze in reduce_sum

* add template

* add unittest for keep_dim

* test reduce_all

Co-authored-by: frankwhzhang <frankwhzhang@126.com>
revert-31562-mean
pangyoki 4 years ago committed by GitHub
parent b541ca8795
commit 795b0f92d3
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109
paddle/fluid/operators/reduce_ops/reduce_sum_op_npu.cc
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/* Copyright (c) 2021 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 <memory>
#include <string>
#include "paddle/fluid/operators/npu_op_runner.h"
#include "paddle/fluid/operators/reduce_ops/reduce_op.h"
#include "paddle/fluid/operators/unsqueeze_op.h"
namespace paddle {
namespace operators {
template <typename DeviceContext, typename T>
class ReduceSumNPUKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* x = ctx.Input<framework::Tensor>("X");
auto* out = ctx.Output<framework::Tensor>("Out");
bool reduce_all = ctx.Attr<bool>("reduce_all");
bool keep_dims = ctx.Attr<bool>("keep_dim");
auto dims = ctx.Attr<std::vector<int>>("dim");
out->mutable_data<T>(ctx.GetPlace());
auto stream =
ctx.template device_context<paddle::platform::NPUDeviceContext>()
.stream();
if (reduce_all) {
std::vector<int> dim_vec;
for (int i = 0; i < x->dims().size(); i++) {
dim_vec.push_back(i);
}
auto runner = NpuOpRunner("ReduceSumD", {*x}, {*out},
{{"axes", dim_vec}, {"keep_dims", keep_dims}});
runner.Run(stream);
} else {
auto runner = NpuOpRunner("ReduceSumD", {*x}, {*out},
{{"axes", dims}, {"keep_dims", keep_dims}});
runner.Run(stream);
}
}
};
template <typename DeviceContext, typename T>
class ReduceSumGradNPUKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* x = ctx.Input<framework::Tensor>("X");
auto* out_grad =
ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
auto* x_grad = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
bool reduce_all = ctx.Attr<bool>("reduce_all");
bool keep_dims = ctx.Attr<bool>("keep_dim");
auto dims = ctx.Attr<std::vector<int>>("dim");
x_grad->mutable_data<T>(ctx.GetPlace());
auto stream =
ctx.template device_context<paddle::platform::NPUDeviceContext>()
.stream();
if (keep_dims || reduce_all) {
auto runner = NpuOpRunner("BroadcastToD", {*out_grad}, {*x_grad},
{{"shape", framework::vectorize(x->dims())}});
runner.Run(stream);
} else {
framework::DDim out_dims;
out_dims = UnsqueezeKernel<DeviceContext, T>::GetOutputShape(
dims, out_grad->dims());
Tensor out_grad_tmp(out_grad->type());
out_grad_tmp.Resize(out_dims);
out_grad_tmp.mutable_data<T>(ctx.GetPlace());
auto runner = NpuOpRunner("BroadcastToD", {out_grad_tmp}, {*x_grad},
{{"shape", framework::vectorize(x->dims())}});
runner.Run(stream);
}
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_NPU_KERNEL(
reduce_sum,
ops::ReduceSumNPUKernel<paddle::platform::NPUDeviceContext, float>,
ops::ReduceSumNPUKernel<paddle::platform::NPUDeviceContext, int>,
ops::ReduceSumNPUKernel<paddle::platform::NPUDeviceContext,
paddle::platform::float16>);
REGISTER_OP_NPU_KERNEL(
reduce_sum_grad,
ops::ReduceSumGradNPUKernel<paddle::platform::NPUDeviceContext, float>,
ops::ReduceSumGradNPUKernel<paddle::platform::NPUDeviceContext, int>,
ops::ReduceSumGradNPUKernel<paddle::platform::NPUDeviceContext,
paddle::platform::float16>);

198
python/paddle/fluid/tests/unittests/npu/test_reduce_sum_op_npu.py
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# Copyright (c) 2021 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 numpy as np
import unittest
import sys
sys.path.append("..")
from op_test import OpTest
import paddle
import paddle.fluid as fluid
paddle.enable_static()
SEED = 2021
@unittest.skipIf(not paddle.is_compiled_with_npu(),
"core is not compiled with NPU")
class TestReduceSum(OpTest):
def setUp(self):
np.random.seed(SEED)
self.set_npu()
self.place = paddle.NPUPlace(0)
self.init_op_type()
self.initTestCase()
self.use_mkldnn = False
self.attrs = {
'dim': self.axis,
'keep_dim': self.keep_dim,
'reduce_all': self.reduce_all
}
self.inputs = {'X': np.random.random(self.shape).astype("float32")}
if self.attrs['reduce_all']:
self.outputs = {'Out': self.inputs['X'].sum()}
else:
self.outputs = {
'Out': self.inputs['X'].sum(axis=self.axis,
keepdims=self.attrs['keep_dim'])
}
def set_npu(self):
self.__class__.use_npu = True
def init_dtype(self):
self.dtype = np.float32
def init_op_type(self):
self.op_type = "reduce_sum"
self.use_mkldnn = False
self.keep_dim = False
self.reduce_all = False
def initTestCase(self):
self.shape = (5, 6)
self.axis = (0, )
def test_check_output(self):
self.check_output_with_place(self.place, check_dygraph=False)
# TODO(ascendrc): Add grad test
# def test_check_grad(self):
# if self.dtype == np.float16:
# return
# self.check_grad(['X'], 'Out')
#
@unittest.skipIf(not paddle.is_compiled_with_npu(),
"core is not compiled with NPU")
class TestReduceSumNet(unittest.TestCase):
def set_reduce_sum_function(self, x):
# keep_dim = False
return paddle.fluid.layers.reduce_sum(x, dim=-1)
def _test(self, run_npu=True):
main_prog = paddle.static.Program()
startup_prog = paddle.static.Program()
main_prog.random_seed = SEED
startup_prog.random_seed = SEED
np.random.seed(SEED)
a_np = np.random.random(size=(2, 3, 4)).astype('float32')
b_np = np.random.random(size=(2, 3, 4)).astype('float32')
label_np = np.random.randint(2, size=(2, 1)).astype('int64')
with paddle.static.program_guard(main_prog, startup_prog):
a = paddle.static.data(name="a", shape=[2, 3, 4], dtype='float32')
b = paddle.static.data(name="b", shape=[2, 3, 4], dtype='float32')
label = paddle.static.data(
name="label", shape=[2, 1], dtype='int64')
z = paddle.add(a, b)
z_1 = self.set_reduce_sum_function(z)
prediction = fluid.layers.fc(input=z_1, size=2, act='softmax')
cost = fluid.layers.cross_entropy(input=prediction, label=label)
loss = fluid.layers.reduce_mean(cost)
sgd = fluid.optimizer.SGD(learning_rate=0.01)
sgd.minimize(loss)
if run_npu:
place = paddle.NPUPlace(0)
else:
place = paddle.CPUPlace()
exe = paddle.static.Executor(place)
exe.run(startup_prog)
print("Start run on {}".format(place))
for epoch in range(100):
pred_res, loss_res = exe.run(
main_prog,
feed={"a": a_np,
"b": b_np,
"label": label_np},
fetch_list=[prediction, loss])
if epoch % 10 == 0:
print("Epoch {} | Prediction[0]: {}, Loss: {}".format(
epoch, pred_res[0], loss_res))
return pred_res, loss_res
def test_npu(self):
cpu_pred, cpu_loss = self._test(False)
npu_pred, npu_loss = self._test(True)
self.assertTrue(np.allclose(npu_pred, cpu_pred))
self.assertTrue(np.allclose(npu_loss, cpu_loss))
@unittest.skipIf(not paddle.is_compiled_with_npu(),
"core is not compiled with NPU")
class TestReduceSumNet2(TestReduceSumNet):
def set_reduce_sum_function(self, x):
# keep_dim = True
return paddle.fluid.layers.reduce_sum(x, dim=-1, keep_dim=True)
@unittest.skipIf(not paddle.is_compiled_with_npu(),
"core is not compiled with NPU")
class TestReduceSumNet3(TestReduceSumNet):
def _test(self, run_npu=True):
main_prog = paddle.static.Program()
startup_prog = paddle.static.Program()
main_prog.random_seed = SEED
startup_prog.random_seed = SEED
np.random.seed(SEED)
a_np = np.random.random(size=(2, 3, 4)).astype('float32')
b_np = np.random.random(size=(2, 3, 4)).astype('float32')
with paddle.static.program_guard(main_prog, startup_prog):
a = paddle.static.data(name="a", shape=[2, 3, 4], dtype='float32')
b = paddle.static.data(name="b", shape=[2, 3, 4], dtype='float32')
z = paddle.add(a, b)
loss = fluid.layers.reduce_sum(z)
sgd = fluid.optimizer.SGD(learning_rate=0.01)
sgd.minimize(loss)
if run_npu:
place = paddle.NPUPlace(0)
else:
place = paddle.CPUPlace()
exe = paddle.static.Executor(place)
exe.run(startup_prog)
print("Start run on {}".format(place))
for epoch in range(100):
loss_res = exe.run(main_prog,
feed={"a": a_np,
"b": b_np},
fetch_list=[loss])
if epoch % 10 == 0:
print("Epoch {} | Loss: {}".format(epoch, loss_res))
return loss_res, loss_res
if __name__ == '__main__':
unittest.main()
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