未验证 提交 0a8ebb0d 编写于 作者: C cucuzg 提交者: GitHub

add clip_by_norm on kunlun, *test=kunlun (#30862) (#31331)

上级 ff4612a3
...@@ -13,7 +13,7 @@ if(NOT XPU_SDK_ROOT) ...@@ -13,7 +13,7 @@ if(NOT XPU_SDK_ROOT)
elseif(WITH_SUNWAY) elseif(WITH_SUNWAY)
SET(XPU_URL "https://baidu-kunlun-public.su.bcebos.com/paddle_depence/sunway/xpu_2021_01_13.tar.gz" CACHE STRING "" FORCE) SET(XPU_URL "https://baidu-kunlun-public.su.bcebos.com/paddle_depence/sunway/xpu_2021_01_13.tar.gz" CACHE STRING "" FORCE)
else() else()
SET(XPU_URL "https://baidu-kunlun-public.su.bcebos.com/paddle_depence/xpu_2021_01_13.tar.gz" CACHE STRING "" FORCE) SET(XPU_URL "https://baidu-kunlun-public.su.bcebos.com/paddle_depence/xpu_2021_02_03.tar.gz" CACHE STRING "" FORCE)
endif() endif()
SET(XPU_SOURCE_DIR "${THIRD_PARTY_PATH}/xpu") SET(XPU_SOURCE_DIR "${THIRD_PARTY_PATH}/xpu")
......
/* Copyright (c) 2016 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_XPU
#include "paddle/fluid/operators/clip_by_norm_op.h"
#include <vector>
namespace paddle {
namespace operators {
template <typename DeviceContext, typename T>
class XPUClipByNormKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto max_norm = context.Attr<T>("max_norm");
auto in_var = context.InputVar("X");
Tensor* output = nullptr;
const Tensor* input = nullptr;
if (in_var->IsType<framework::LoDTensor>()) {
input = context.Input<Tensor>("X");
output = context.Output<Tensor>("Out");
output->mutable_data<T>(context.GetPlace());
} else {
PADDLE_THROW(platform::errors::InvalidArgument(
"Invalid input variable type, only support LodTensor"
"type, but got type is %s.",
framework::ToTypeName(in_var->Type())));
}
PADDLE_ENFORCE_NOT_NULL(input,
platform::errors::InvalidArgument(
"Input(X) of ClipByNormOp should not be null. "
"Please check if it is created correctly."));
auto& dev_ctx = context.template device_context<DeviceContext>();
const auto& x_dims = input->dims();
std::vector<int> xshape(x_dims.size());
std::vector<int> rdims(x_dims.size());
for (int i = 0; i < x_dims.size(); i++) {
xshape[i] = x_dims[i];
rdims[i] = i;
}
int r = xpu::clip_by_norm<T>(dev_ctx.x_context(), input->data<T>(),
output->data<T>(), max_norm, xshape, rdims);
PADDLE_ENFORCE_EQ(
r, XPU_SUCCESS,
platform::errors::External("XPU API(clip_by_norm) return "
"wrong value[%d], please check whether "
"Baidu Kunlun Card is properly installed.",
r));
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_XPU_KERNEL(
clip_by_norm,
ops::XPUClipByNormKernel<paddle::platform::XPUDeviceContext, float>);
#endif // PADDLE_WITH_XPU
# 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 sys
sys.path.append("..")
import unittest
import numpy as np
import paddle.fluid.core as core
import paddle.fluid as fluid
from op_test_xpu import OpTest, XPUOpTest
import paddle
from paddle.fluid import Program, program_guard
class TestXPUClipByNormOp(XPUOpTest):
def setUp(self):
self.op_type = "clip_by_norm"
self.dtype = np.float32
self.use_xpu = True
self.max_relative_error = 0.006
self.initTestCase()
input = np.random.random(self.shape).astype("float32")
input[np.abs(input) < self.max_relative_error] = 0.5
self.inputs = {'X': input, }
self.attrs = {}
self.attrs['max_norm'] = self.max_norm
norm = np.sqrt(np.sum(np.square(input)))
if norm > self.max_norm:
output = self.max_norm * input / norm
else:
output = input
self.outputs = {'Out': output}
def test_check_output(self):
if paddle.is_compiled_with_xpu():
paddle.enable_static()
place = paddle.XPUPlace(0)
self.check_output_with_place(place)
def initTestCase(self):
self.shape = (100, )
self.max_norm = 1.0
class TestCase1(TestXPUClipByNormOp):
def initTestCase(self):
self.shape = (100, )
self.max_norm = 1e20
class TestCase2(TestXPUClipByNormOp):
def initTestCase(self):
self.shape = (16, 16)
self.max_norm = 0.1
class TestCase3(TestXPUClipByNormOp):
def initTestCase(self):
self.shape = (4, 8, 16)
self.max_norm = 1.0
if __name__ == "__main__":
unittest.main()
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