Skip to content

Commit

Permalink
[NPU] Support npu op log, log_grad, sqrt, sqrt_grad, square, tanh and…
Browse files Browse the repository at this point in the history
… tanh_grad (#31600)
  • Loading branch information
oyjxer authored Mar 15, 2021
1 parent de65486 commit 47860ce
Show file tree
Hide file tree
Showing 5 changed files with 825 additions and 3 deletions.
212 changes: 209 additions & 3 deletions paddle/fluid/operators/activation_op_npu.cc
Original file line number Diff line number Diff line change
Expand Up @@ -143,23 +143,186 @@ class ReluGradNPUKernel : public framework::OpKernel<T> {
runner.Run(stream);
}
};

template <typename DeviceContext, typename T>
class SqrtNPUKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* x = ctx.Input<Tensor>("X");

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("Sqrt", {*x}, {*out}, {});
runner.Run(stream);
}
};

template <typename DeviceContext, typename T>
class SqrtGradNPUKernel : 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* dx = ctx.Output<Tensor>(framework::GradVarName("X"));

auto place = ctx.GetPlace();

dx->mutable_data<T>(place);

auto stream =
ctx.template device_context<paddle::platform::NPUDeviceContext>()
.stream();

auto dx_runner = NpuOpRunner("SqrtGrad", {*out, *dout}, {*dx}, {});
dx_runner.Run(stream);
}
};

template <typename DeviceContext, typename T>
class LogNPUKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* x = ctx.Input<Tensor>("X");

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();

Tensor one(x->type());
one.mutable_data<T>(x->dims(), place);
auto one_runner = NpuOpRunner("OnesLike", {*x}, {one}, {});
one_runner.Run(stream);

Tensor sub(x->type());
sub.mutable_data<T>(x->dims(), place);
auto sub_runner = NpuOpRunner("Sub", {*x, one}, {sub}, {});
sub_runner.Run(stream);

auto out_runner = NpuOpRunner("Log1p", {sub}, {*out}, {});
out_runner.Run(stream);
}
};

template <typename DeviceContext, typename T>
class LogGradNPUKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* dout = ctx.Input<Tensor>(framework::GradVarName("Out"));
auto* x = ctx.Input<Tensor>("X");

auto* dx = ctx.Output<Tensor>(framework::GradVarName("X"));

auto place = ctx.GetPlace();

dx->mutable_data<T>(place);

auto stream =
ctx.template device_context<paddle::platform::NPUDeviceContext>()
.stream();
auto runner = NpuOpRunner("DivNoNan", {*dout, *x}, {*dx}, {});
runner.Run(stream);
}
};

template <typename DeviceContext, typename T>
class TanhNPUKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* x = ctx.Input<Tensor>("X");

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("Tanh", {*x}, {*out}, {});
runner.Run(stream);
}
};

template <typename DeviceContext, typename T>
class TanhGradNPUKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* dout = ctx.Input<Tensor>(framework::GradVarName("Out"));
auto* out = ctx.Input<Tensor>("Out");

auto* dx = ctx.Output<Tensor>(framework::GradVarName("X"));

auto place = ctx.GetPlace();

dx->mutable_data<T>(place);

auto stream =
ctx.template device_context<paddle::platform::NPUDeviceContext>()
.stream();

auto dx_runner = NpuOpRunner("TanhGrad", {*out, *dout}, {*dx}, {});
dx_runner.Run(stream);
}
};

template <typename DeviceContext, typename T>
class SquareNPUKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* x = ctx.Input<Tensor>("X");

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("Square", {*x}, {*out}, {});
runner.Run(stream);
}
};

} // namespace operators
} // namespace paddle

namespace ops = paddle::operators;

REGISTER_OP_NPU_KERNEL(
pow, ops::PowNPUKernel<paddle::platform::NPUDeviceContext, float>,
pow,
ops::PowNPUKernel<paddle::platform::NPUDeviceContext, float>,
ops::PowNPUKernel<paddle::platform::NPUDeviceContext,
paddle::platform::float16>);

REGISTER_OP_NPU_KERNEL(
pow_grad, ops::PowGradNPUKernel<paddle::platform::NPUDeviceContext, float>,
pow_grad,
ops::PowGradNPUKernel<paddle::platform::NPUDeviceContext, float>,
ops::PowGradNPUKernel<paddle::platform::NPUDeviceContext,
paddle::platform::float16>);

REGISTER_OP_NPU_KERNEL(
relu, ops::ReluNPUKernel<paddle::platform::NPUDeviceContext, float>,
relu,
ops::ReluNPUKernel<paddle::platform::NPUDeviceContext, float>,
ops::ReluNPUKernel<paddle::platform::NPUDeviceContext,
paddle::platform::float16>);

Expand All @@ -168,3 +331,46 @@ REGISTER_OP_NPU_KERNEL(
ops::ReluGradNPUKernel<paddle::platform::NPUDeviceContext, float>,
ops::ReluGradNPUKernel<paddle::platform::NPUDeviceContext,
paddle::platform::float16>);

REGISTER_OP_NPU_KERNEL(
sqrt,
ops::SqrtNPUKernel<paddle::platform::NPUDeviceContext, float>,
ops::SqrtNPUKernel<paddle::platform::NPUDeviceContext,
paddle::platform::float16>);

REGISTER_OP_NPU_KERNEL(
sqrt_grad,
ops::SqrtGradNPUKernel<paddle::platform::NPUDeviceContext, float>,
ops::SqrtGradNPUKernel<paddle::platform::NPUDeviceContext,
paddle::platform::float16>);

REGISTER_OP_NPU_KERNEL(
log,
ops::LogNPUKernel<paddle::platform::NPUDeviceContext, float>,
ops::LogNPUKernel<paddle::platform::NPUDeviceContext,
paddle::platform::float16>);

REGISTER_OP_NPU_KERNEL(
log_grad,
ops::LogGradNPUKernel<paddle::platform::NPUDeviceContext, float>,
ops::LogGradNPUKernel<paddle::platform::NPUDeviceContext,
paddle::platform::float16>);


REGISTER_OP_NPU_KERNEL(
tanh,
ops::TanhNPUKernel<paddle::platform::NPUDeviceContext, float>,
ops::TanhNPUKernel<paddle::platform::NPUDeviceContext,
paddle::platform::float16>);

REGISTER_OP_NPU_KERNEL(
tanh_grad,
ops::TanhGradNPUKernel<paddle::platform::NPUDeviceContext, float>,
ops::TanhGradNPUKernel<paddle::platform::NPUDeviceContext,
paddle::platform::float16>);

REGISTER_OP_NPU_KERNEL(
square,
ops::SquareNPUKernel<paddle::platform::NPUDeviceContext, float>,
ops::SquareNPUKernel<paddle::platform::NPUDeviceContext,
paddle::platform::float16>);
154 changes: 154 additions & 0 deletions python/paddle/fluid/tests/unittests/npu/test_log_op_npu.py
Original file line number Diff line number Diff line change
@@ -0,0 +1,154 @@
# 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 TestLog(OpTest):
def setUp(self):
self.set_npu()
self.op_type = "log"
self.place = paddle.NPUPlace(0)

self.init_dtype()
np.random.seed(SEED)
x = np.random.uniform(1, 2, [11, 17]).astype(self.dtype)
out = np.log(x)

self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
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): 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 TestLogFp16(OpTest):
def setUp(self):
self.set_npu()
self.op_type = "log"
self.place = paddle.NPUPlace(0)

self.init_dtype()
np.random.seed(SEED)
x = np.random.uniform(1, 2, [3, 4]).astype(self.dtype)
out = np.log(x)

self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
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 TestLogNet(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.random(size=(32, 32)).astype('float32')
b_np = np.random.random(size=(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')
label = paddle.static.data(
name="label", shape=[32, 1], dtype='int64')

c = paddle.multiply(a, b)
d = paddle.log(c)

fc_1 = fluid.layers.fc(input=d, 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,
"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, atol=1e-4))
self.assertTrue(np.allclose(npu_loss, cpu_loss, atol=1e-4))


if __name__ == '__main__':
unittest.main()
Loading

0 comments on commit 47860ce

Please sign in to comment.