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[NPU] Support npu op `expand` (#31405)

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* [npu] support npu kernel  for `expand`
ascendrc
Meiyim 4 years ago committed by GitHub
parent 444c285202
commit 3dd992e24f
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GPG Key ID: 4AEE18F83AFDEB23
5 changed files with 305 additions and 3 deletions
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5
paddle/fluid/operators/CMakeLists.txt
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@ -156,12 +156,11 @@ cc_library(tensor_formatter SRCS tensor_formatter.cc DEPS ${OP_HEADER_DEPS})
if (WITH_PYTHON)
cc_library(py_func_op SRCS py_func_op.cc DEPS op_registry python pybind)
endif()
if (WITH_ASCEND_CL)
cc_test(lookup_table_v2_op_npu_test SRCS lookup_table_v2_op_npu_test.cc DEPS op_registry lookup_table_v2_op scope device_context enforce executor compare_op)
endif()
if (WITH_ASCEND_CL)
cc_test(range_op_npu_test SRCS range_op_npu_test.cc DEPS op_registry range_op scope device_context enforce executor)
cc_test(lookup_table_v2_op_npu_test SRCS lookup_table_v2_op_npu_test.cc DEPS op_registry lookup_table_v2_op scope device_context enforce executor compare_op)
cc_test(expand_op_npu_test SRCS expand_op_npu_test.cc DEPS op_registry expand_op scope device_context enforce executor compare_op)
endif()
set(GLOB_OP_LIB ${OP_LIBRARY} CACHE INTERNAL "Global OP library")

6
paddle/fluid/operators/expand_op.h
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@ -56,6 +56,12 @@ inline std::vector<int> get_expand_times(
TensorCopySync(*expand_tensor, platform::CPUPlace(), &cpu_expand_tensor);
expand_data = cpu_expand_tensor.data<int>();
}
#ifdef PADDLE_WITH_ASCEND_CL
if (platform::is_npu_place(expand_tensor->place())) {
TensorCopySync(*expand_tensor, platform::CPUPlace(), &cpu_expand_tensor);
expand_data = cpu_expand_tensor.data<int>();
}
#endif
#ifdef PADDLE_WITH_XPU
if (platform::is_xpu_place(expand_tensor->place())) {
TensorCopySync(*expand_tensor, platform::CPUPlace(), &cpu_expand_tensor);

82
paddle/fluid/operators/expand_op_npu.cc
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@ -0,0 +1,82 @@
/* 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 <iostream>
#include <memory>
#include <string>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/expand_op.h"
#include "paddle/fluid/operators/npu_op_runner.h"
namespace paddle {
namespace operators {
template <typename DeviceContext, typename T>
class ExpandNPUKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto rank = context.Input<Tensor>("X")->dims().size();
PADDLE_ENFORCE_GE(
rank, 1,
platform::errors::InvalidArgument(
"The number of dimensions of the input 'x' for Op(expand) "
"must be greater than or equal to 1, but the value received is %d.",
rank));
PADDLE_ENFORCE_LE(
rank, MAX_RANK_SUPPORTED,
platform::errors::InvalidArgument(
"The number of dimensions of the input 'x' for Op(expand) "
"must be less than or equal to %d, but the value received is %d.",
MAX_RANK_SUPPORTED, rank));
switch (rank) { REP_EXPAND_TEMPLATE(MAX_RANK_SUPPORTED) }
}
protected:
template <int Rank>
void Expand(const framework::ExecutionContext& context) const {
auto* in0 = context.Input<framework::LoDTensor>("X");
auto in_dims = in0->dims();
auto expand_times = get_expand_times(context);
PADDLE_ENFORCE_EQ(
static_cast<size_t>(in_dims.size()), expand_times.size(),
platform::errors::InvalidArgument(
"The number of elements (%d) of 'expand_times' for "
"Op(expand) must be equal to the number "
"of dimensions (%d) of the input.",
expand_times.size(), static_cast<size_t>(in_dims.size())));
auto* out0 = context.Output<framework::LoDTensor>("Out");
framework::DDim out_dims(in_dims);
for (size_t i = 0; i < expand_times.size(); ++i) {
out_dims[i] *= expand_times[i];
}
out0->Resize(out_dims);
out0->mutable_data<T>(context.device_context().GetPlace());
auto runner = NpuOpRunner("TileD", {*in0}, {*out0}, {{"multiples", expand_times}});
auto stream =
context.template device_context<paddle::platform::NPUDeviceContext>()
.stream();
runner.Run(stream);
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_NPU_KERNEL(
expand, ops::ExpandNPUKernel<paddle::platform::NPUDeviceContext, float>,
ops::ExpandNPUKernel<paddle::platform::NPUDeviceContext,
paddle::platform::float16>);
#endif

74
paddle/fluid/operators/expand_op_npu_test.cc
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@ -0,0 +1,74 @@
/* 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. */
#ifndef _WIN32
#include <unistd.h>
#endif
#include <iostream>
#include <string>
#include <thread> // NOLINT
#include <vector>
#include "gtest/gtest.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/framework/program_desc.h"
#include "paddle/fluid/operators/dropout_op.h"
#include "paddle/fluid/operators/math/math_function.h"
#include "paddle/fluid/string/printf.h"
namespace f = paddle::framework;
namespace p = paddle::platform;
namespace m = paddle::operators::math;
USE_OP(expand);
USE_OP_DEVICE_KERNEL(expand, NPU);
template <typename T>
void Compare(f::Scope* scope, const p::DeviceContext& ctx) {
// init
auto in = scope->Var("X");
auto expand_times = scope->Var("ExpandTimes");
auto out = scope->Var("Out");
auto in_t = in->GetMutable<f::LoDTensor>();
auto out_t = out->GetMutable<f::LoDTensor>();
auto expand_times_t = expand_times->GetMutable<f::LoDTensor>();
auto place = ctx.GetPlace();
TensorFromVector(std::vector<T>(3 * 1 * 7, 1), ctx, in_t);
TensorFromVector(std::vector<int>({1, 10, 1}), ctx, expand_times_t);
in_t->Resize(f::make_ddim({3, 1, 7}));
expand_times_t->Resize(f::make_ddim({3}));
out_t->Resize(f::make_ddim({3, 10, 7}));
out_t->mutable_data<T>(place);
f::AttributeMap attrs = {{}};
auto op = f::OpRegistry::CreateOp(
"expand", {{"X", {"X"}}, {"ExpandTimes", {"ExpandTimes"}}},
{{"Out", {"Out"}}}, attrs);
op->Run(*scope, place);
ctx.Wait();
auto out_dim = out_t->dims();
EXPECT_EQ(out_dim.at(0), 3);
EXPECT_EQ(out_dim.at(1), 10);
EXPECT_EQ(out_dim.at(2), 7);
}
TEST(expand, NPU_fp32) {
f::Scope scope;
p::NPUDeviceContext ctx(p::NPUPlace(0));
Compare<float>(&scope, ctx);
}

141
python/paddle/fluid/tests/unittests/npu/test_expand_op_npu.py
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@ -0,0 +1,141 @@
# 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 TestExpand(OpTest):
def setUp(self):
self.set_npu()
self.op_type = "expand"
self.place = paddle.NPUPlace(0)
self.init_dtype()
np.random.seed(SEED)
x = np.random.randn(3,1,7).astype(self.dtype)
out = np.tile(x, [1,10,1])
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
self.attrs = {'expand_times': [1,10,1]}
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 TestExpandV2(TestExpand):
def setUp(self):
self.set_npu()
self.op_type = "expand"
self.place = paddle.NPUPlace(0)
self.init_dtype()
np.random.seed(SEED)
x = np.random.randn(3,1,7).astype(self.dtype)
out = np.tile(x, [1,10,1])
expand_times = np.array([1,10,1]).astype(np.int32)
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x), 'ExpandTimes': OpTest.np_dtype_to_fluid_dtype(expand_times)}
self.attrs = {}
self.outputs = {'Out': out}
@unittest.skipIf(not paddle.is_compiled_with_npu(),
"core is not compiled with NPU")
class TestExpandFp16(TestExpand):
no_need_check_grad = True
def init_dtype(self):
self.dtype = np.float16
@unittest.skipIf(not paddle.is_compiled_with_npu(),
"core is not compiled with NPU")
class TestExpandNet(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, 1)).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, 1], dtype='float32')
label = paddle.static.data(
name="label", shape=[32, 1], dtype='int64')
res = paddle.fluid.layers.expand(a, [1,32])
loss = res.sum()
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)
for epoch in range(100):
loss_res = exe.run(
main_prog,
feed={"a": a_np,
"label": label_np},
fetch_list=[loss])
if epoch % 10 == 0:
print("Epoch {} | Loss: {}".format(epoch, loss))
return loss_res
def test_npu(self):
cpu_loss = self._test(False)
npu_loss = self._test(True)
self.assertTrue(np.allclose(npu_loss, cpu_loss))
if __name__ == '__main__':
unittest.main()
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