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【NPU】Support npu op elementwise_div and elementwise_div_grad (#31573)

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* Support npu op elementwise_div and elementwise_div_grad

* Support npu op elementwise_div and elementwise_div_grad

* Support npu op elementwise_div and elementwise_div_grad
revert-31562-mean
oyjxer 5 years ago committed by GitHub
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commit de65486c19
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140
paddle/fluid/operators/elementwise/elementwise_div_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. */
#ifdef PADDLE_WITH_ASCEND_CL
#include <memory>
#include <string>
#include "paddle/fluid/operators/elementwise/elementwise_div_op.h"
#include "paddle/fluid/operators/npu_op_runner.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
template <typename DeviceContext, typename T>
class ElementwiseDivNPUKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* x = ctx.Input<Tensor>("X");
auto* y = ctx.Input<Tensor>("Y");
auto* out = ctx.Output<Tensor>("Out");
auto place = ctx.GetPlace();
out->mutable_data<T>(place);
auto stream =
ctx.template device_context<paddle::platform::NPUDeviceContext>()
.stream();
auto runner = NpuOpRunner("Div", {*x, *y}, {*out}, {});
runner.Run(stream);
}
};
template <typename DeviceContext, typename T>
class ElementwiseDivGradNPUKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* out = ctx.Input<Tensor>("Out");
auto* dout = ctx.Input<Tensor>(framework::GradVarName("Out"));
auto* x = ctx.Input<Tensor>("X");
auto* y = ctx.Input<Tensor>("Y");
auto* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
auto* dy = ctx.Output<Tensor>(framework::GradVarName("Y"));
auto place = ctx.GetPlace();
auto stream =
ctx.template device_context<paddle::platform::NPUDeviceContext>()
.stream();
Tensor y_power(y->type());
y_power.mutable_data<T>(y->dims(), place);
auto y_power_runner = NpuOpRunner("Power", {*y},
{y_power}, {{"power", static_cast<float>(-1)}});
y_power_runner.Run(stream);
if (dx) {
dx->mutable_data<T>(place);
Tensor tensor_zeros(x->type());
tensor_zeros.mutable_data<T>(x->dims(), place);
auto tensor_zeros_runner = NpuOpRunner("ZerosLike", {*x},
{tensor_zeros}, {});
tensor_zeros_runner.Run(stream);
Tensor x_zero(paddle::framework::proto::VarType::BOOL);
x_zero.mutable_data<bool>(x->dims(), place);
auto x_zero_runner = NpuOpRunner("Equal", {*x, tensor_zeros},
{x_zero}, {});
x_zero_runner.Run(stream);
Tensor x_nozero(paddle::framework::proto::VarType::BOOL);
x_nozero.mutable_data<bool>(x->dims(), place);
auto x_nozero_runner = NpuOpRunner("LogicalNot", {x_zero},
{x_nozero}, {});
x_nozero_runner.Run(stream);
Tensor x_nozero_f(x->type());
x_nozero_f.mutable_data<T>(x->dims(), place);
auto x_nozero_f_runner = NpuOpRunner("Cast", {x_nozero},
{x_nozero_f}, {{"dst_type", static_cast<int32_t>(0)}});
x_nozero_f_runner.Run(stream);
Tensor x_grad_w(x->type());
x_grad_w.mutable_data<T>(x->dims(), place);
auto x_grad_w_runner = NpuOpRunner("Mul", {x_nozero_f, y_power},
{x_grad_w}, {});
x_grad_w_runner.Run(stream);
auto x_grad_runner = NpuOpRunner("Mul", {x_grad_w, *dout}, {*dx}, {});
x_grad_runner.Run(stream);
}
if (dy) {
dy->mutable_data<T>(place);
Tensor y_grad_w(x->type());
y_grad_w.mutable_data<T>(y->dims(), place);
auto y_grad_w_runner = NpuOpRunner("Mul", {*out, y_power},
{y_grad_w}, {});
y_grad_w_runner.Run(stream);
auto y_grad_runner = NpuOpRunner("Mul", {y_grad_w, *dout}, {*dy}, {});
y_grad_runner.Run(stream);
}
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_NPU_KERNEL(
elementwise_div,
ops::ElementwiseDivNPUKernel<paddle::platform::NPUDeviceContext, float>,
ops::ElementwiseDivNPUKernel<paddle::platform::NPUDeviceContext,
paddle::platform::float16>);
REGISTER_OP_NPU_KERNEL(
elementwise_div_grad,
ops::ElementwiseDivGradNPUKernel<paddle::platform::NPUDeviceContext, float>,
ops::ElementwiseDivGradNPUKernel<paddle::platform::NPUDeviceContext,
paddle::platform::float16>);
#endif

171
python/paddle/fluid/tests/unittests/npu/test_elementwise_div_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 TestElementwiseDiv(OpTest):
def setUp(self):
self.set_npu()
self.op_type = "elementwise_div"
self.place = paddle.NPUPlace(0)
self.init_dtype()
np.random.seed(SEED)
x = np.random.uniform(1, 2, [11, 17]).astype(self.dtype)
y = np.random.uniform(1, 2, [11, 17]).astype(self.dtype)
out = np.divide(x, y)
self.inputs = {
'X': OpTest.np_dtype_to_fluid_dtype(x),
'Y': OpTest.np_dtype_to_fluid_dtype(y)
}
self.attrs = {}
self.outputs = {'Out': out}
def set_npu(self):
self.__class__.use_npu = True
def init_dtype(self):
self.dtype = np.float32
def test_check_output(self):
self.check_output_with_place(self.place, check_dygraph=False)
# TODO(ascendrc): Div 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 TestElementwiseDivFp16(OpTest):
def setUp(self):
self.set_npu()
self.op_type = "elementwise_div"
self.place = paddle.NPUPlace(0)
self.init_dtype()
np.random.seed(SEED)
x = np.random.uniform(1, 2, [3, 4]).astype(self.dtype)
y = np.random.uniform(1, 2, [3, 4]).astype(self.dtype)
out = np.divide(x, y)
self.inputs = {
'X': OpTest.np_dtype_to_fluid_dtype(x),
'Y': OpTest.np_dtype_to_fluid_dtype(y)
}
self.attrs = {}
self.outputs = {'Out': out}
def set_npu(self):
self.__class__.use_npu = True
self.__class__.no_need_check_grad = True
def init_dtype(self):
self.dtype = np.float16
def test_check_output(self):
self.check_output_with_place(self.place, check_dygraph=False, atol=1e-5)
@unittest.skipIf(not paddle.is_compiled_with_npu(),
"core is not compiled with NPU")
class TestElementwiseDivNet(unittest.TestCase):
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.uniform(1, 2, [32, 32]).astype('float32')
b_np = np.random.uniform(1, 2, [32, 32]).astype('float32')
c_np = np.random.uniform(1, 2, [32, 32]).astype('float32')
d_np = np.random.uniform(1, 2, [32, 32]).astype('float32')
label_np = np.random.randint(2, size=(32, 1)).astype('int64')
with paddle.static.program_guard(main_prog, startup_prog):
a = paddle.static.data(name="a", shape=[32, 32], dtype='float32')
b = paddle.static.data(name="b", shape=[32, 32], dtype='float32')
c = paddle.static.data(name="c", shape=[32, 32], dtype='float32')
d = paddle.static.data(name="d", shape=[32, 32], dtype='float32')
label = paddle.static.data(
name="label", shape=[32, 1], dtype='int64')
e = paddle.multiply(a, b)
f = paddle.multiply(c, d)
f.stop_gradient = True
g = paddle.divide(e, f)
fc_1 = fluid.layers.fc(input=g, size=128)
prediction = fluid.layers.fc(input=fc_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,
"c": c_np,
"d": d_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))
if __name__ == '__main__':
unittest.main()
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