未验证 提交 74ce0397 编写于 作者: P pangyoki 提交者: GitHub

Add uniform_random XPU kernel (#27846)

* support uniform_random op on Baidu Kunlun

* change dtype of attr shape from int to int64_t

* kunlun ci, test=kunlun

* new version, test=kunlun

* change boost_get to BOOST_GET_CONST

* change boost_get to BOOST_GET_CONST, test=kunlun

* use Generator to generate random number and optimize format

* run Kunlun CI, test=kunlun

* add TODO, test=kunlun
上级 abf4d52a
/* 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. */
#ifdef PADDLE_WITH_XPU
#include "paddle/fluid/operators/uniform_random_op.h"
#include <string>
#include "paddle/fluid/framework/generator.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/operator.h"
namespace paddle {
namespace operators {
template <typename T>
class XPUUniformRandomKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &ctx) const override {
framework::Tensor *tensor = nullptr;
auto out_var = ctx.OutputVar("Out");
if (out_var->IsType<framework::LoDTensor>()) {
tensor = out_var->GetMutable<framework::LoDTensor>();
} else if (out_var->IsType<framework::SelectedRows>()) {
auto shape = ctx.Attr<std::vector<int64_t>>("shape");
auto *selected_rows = out_var->GetMutable<framework::SelectedRows>();
tensor = selected_rows->mutable_value();
tensor->Resize(framework::make_ddim(shape));
selected_rows->mutable_rows()->reserve(shape[0]);
} else {
PADDLE_THROW(platform::errors::InvalidArgument(
"Expected type of Output(out) in uniform_random_op must be "
"LoDTensor, "
"SelectedRows. But got unsupport type: %s.",
framework::ToTypeName(out_var->Type())));
}
T *data = tensor->mutable_data<T>(ctx.GetPlace());
int64_t size = tensor->numel();
std::uniform_real_distribution<T> dist(
static_cast<T>(ctx.Attr<float>("min")),
static_cast<T>(ctx.Attr<float>("max")));
unsigned int seed = static_cast<unsigned int>(ctx.Attr<int>("seed"));
// TODO(pangyoki): implement GetXPURandomEngine to set different seeds on
// corresponding XPU device.
auto engine = framework::GetCPURandomEngine(seed);
std::unique_ptr<T[]> data_cpu(new T[size]);
for (int64_t i = 0; i < size; ++i) {
data_cpu[i] = dist(*engine);
}
memory::Copy(BOOST_GET_CONST(platform::XPUPlace, ctx.GetPlace()), data,
platform::CPUPlace(), reinterpret_cast<void *>(data_cpu.get()),
size * sizeof(T));
}
};
} // namespace operators
} // namespace paddle
REGISTER_OP_XPU_KERNEL(uniform_random,
paddle::operators::XPUUniformRandomKernel<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 subprocess
import unittest
import numpy as np
from op_test import OpTest
import paddle
import paddle.fluid.core as core
from paddle.fluid.op import Operator
import paddle.fluid as fluid
from paddle.fluid import Program, program_guard
from test_uniform_random_op import TestUniformRandomOp, TestUniformRandomOpSelectedRows
paddle.enable_static()
class TestXPUUniformRandomOp(TestUniformRandomOp):
def test_check_output(self):
if paddle.is_compiled_with_xpu():
place = paddle.XPUPlace(0)
outs = self.calc_output(place)
outs = [np.array(out) for out in outs]
outs.sort(key=len)
self.verify_output(outs)
class TestXPUUniformRandomOpSelectedRows(TestUniformRandomOpSelectedRows):
def test_check_output(self):
if paddle.is_compiled_with_xpu():
place = paddle.XPUPlace(0)
self.check_with_place(place)
if __name__ == "__main__":
unittest.main()
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