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[NPU] Support npu kernel for amp_check_finite_and_unscale_npu op (#31457)

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* Support npu kernel for amp_check_finite_and_unscale_npu op

* support EnforceNotMet exception

* fix exception bug

* modify python unittest

* precommit

* update c++ unittest

* fix review

* fix review
revert-31562-mean
xiayanming 5 years ago committed by GitHub
parent d746197398
commit 3bf8a34c69
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4 changed files with 377 additions and 0 deletions
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4
paddle/fluid/operators/amp/CMakeLists.txt
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@ -4,3 +4,7 @@ if(WITH_UNITY_BUILD)
include(unity_build_rule.cmake)
endif()
register_operators()
if(WITH_ASCEND_CL)
cc_test(check_finite_and_unscale_op_npu_test SRCS check_finite_and_unscale_op_npu_test.cc DEPS op_registry check_finite_and_unscale_op scope device_context enforce executor)
endif()

119
paddle/fluid/operators/amp/check_finite_and_unscale_op_npu.cc
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@ -0,0 +1,119 @@
/* 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. */
#include <memory>
#include <string>
#include "paddle/fluid/framework/tensor_util.h"
#include "paddle/fluid/operators/amp/check_finite_and_unscale_op.h"
#include "paddle/fluid/operators/npu_op_runner.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
template <typename T>
class CheckFiniteAndUnscaleNPUKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const {
const auto xs = ctx.MultiInput<framework::Tensor>("X");
const auto* scale = ctx.Input<framework::Tensor>("Scale");
auto outs = ctx.MultiOutput<framework::Tensor>("Out");
auto* found_inf = ctx.Output<framework::Tensor>("FoundInfinite");
found_inf->mutable_data<bool>(ctx.GetPlace());
bool found_inf_data = false;
auto stream =
ctx.template device_context<paddle::platform::NPUDeviceContext>()
.stream();
// step1: inverse scale(RealDiv)
Tensor const_tensor;
const_tensor.mutable_data<T>({1}, ctx.GetPlace());
TensorFromVector(std::vector<T>{static_cast<T>(1.0)}, ctx.device_context(),
&const_tensor);
ctx.template device_context<paddle::platform::NPUDeviceContext>().Wait();
// Inverse(1.0/scale)
Tensor* tmp_inverse_out = const_cast<Tensor*>(scale);
Tensor inverse_out(scale->type());
inverse_out.Resize(scale->dims());
inverse_out.mutable_data<T>(ctx.GetPlace());
auto runner_inverse =
NpuOpRunner("Div", {const_tensor, *scale}, {inverse_out}, {});
runner_inverse.Run(stream);
tmp_inverse_out = &inverse_out;
size_t x_size = xs.size();
for (size_t i = 0; i < x_size; ++i) {
found_inf_data = true;
const auto* x = xs[i];
auto* out = outs[i];
out->mutable_data<T>(ctx.GetPlace());
// step2: CheckNumerics
// CheckNumerics runs on the Ascend AI CPU, which delivers poor
// performance.
Tensor check_xout(x->type());
check_xout.Resize(x->dims());
check_xout.mutable_data<T>(ctx.GetPlace());
try {
auto runner_checknumerics =
NpuOpRunner("CheckNumerics", {*x}, {check_xout},
{{"message", std::string("check_nan_and_inf")}});
runner_checknumerics.Run(stream);
} catch (platform::EnforceNotMet& exception) {
LOG(WARNING) << "[check_nan_and_inf] detected contains NaN or INF!!!";
found_inf_data = true;
} catch (...) {
LOG(WARNING) << "[check_nan_and_inf] detected contains NaN or INF!!!";
found_inf_data = true;
}
if (!found_inf_data) {
// MatMul
auto runner_matmul =
NpuOpRunner("Mul", {*x, *tmp_inverse_out}, {*out}, {});
runner_matmul.Run(stream);
} else {
// ZerosLike
auto runner_zeroslike = NpuOpRunner("ZerosLike", {*x}, {*out}, {});
runner_zeroslike.Run(stream);
} // end if
} // end for
// set found_inf to true
if (found_inf_data) {
Tensor found_inf_tensor;
found_inf_tensor.Resize({1});
bool* is_found_inf =
found_inf_tensor.mutable_data<bool>(paddle::platform::CPUPlace());
*is_found_inf = true;
framework::TensorCopySync(found_inf_tensor, ctx.GetPlace(), found_inf);
}
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
namespace plat = paddle::platform;
REGISTER_OP_NPU_KERNEL(check_finite_and_unscale,
ops::CheckFiniteAndUnscaleNPUKernel<float>,
ops::CheckFiniteAndUnscaleNPUKernel<plat::float16>);

131
paddle/fluid/operators/amp/check_finite_and_unscale_op_npu_test.cc
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@ -0,0 +1,131 @@
/* 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 <algorithm>
#include <cstdlib>
#include <memory>
#include <random>
#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/math/math_function.h"
#include "paddle/fluid/platform/enforce.h"
namespace f = paddle::framework;
namespace p = paddle::platform;
namespace m = paddle::operators::math;
using Tensor = paddle::framework::Tensor;
USE_OP(check_finite_and_unscale);
USE_OP_DEVICE_KERNEL(check_finite_and_unscale, NPU);
struct InputVars {
std::string name;
f::LoDTensor *tensor;
};
template <typename T>
void Compare(f::Scope *scope, const p::DeviceContext &ctx) {
const f::DDim dims = f::make_ddim({2, 2});
auto place = ctx.GetPlace();
// init input
std::vector<InputVars> input_names = {
{"x", scope->Var("x")->GetMutable<f::LoDTensor>()},
{"x1", scope->Var("x1")->GetMutable<f::LoDTensor>()}};
auto *scale = scope->Var("scale")->GetMutable<f::LoDTensor>();
// init output
auto *out = scope->Var("out")->GetMutable<f::LoDTensor>();
auto *out1 = scope->Var("out1")->GetMutable<f::LoDTensor>();
auto *found_inf = scope->Var("found_inf")->GetMutable<f::LoDTensor>();
// Initialize input data
const int num_inputs = input_names.size();
size_t numel = static_cast<size_t>(f::product(dims));
for (int i = 0; i < num_inputs; ++i) {
std::vector<T> init_xs;
for (size_t j = 0; j < numel; ++j) {
if (j == 0) {
init_xs.push_back(static_cast<T>(NAN));
} else {
init_xs.push_back(static_cast<T>(j + 1));
}
}
f::TensorFromVector(init_xs, ctx, input_names[i].tensor);
input_names[i].tensor->Resize(dims);
}
f::TensorFromVector(std::vector<T>{static_cast<T>(0.5)}, ctx, scale);
ctx.Wait();
// run
f::AttributeMap attrs;
auto op = f::OpRegistry::CreateOp(
"check_finite_and_unscale", {{"X", {"x", "x1"}}, {"Scale", {"scale"}}},
{{"Out", {"out", "out1"}}, {"FoundInfinite", {"found_inf"}}}, attrs);
op->Run(*scope, place);
ctx.Wait();
// out0
std::vector<T> out_vec;
f::TensorToVector(*out, ctx, &out_vec);
EXPECT_EQ(out_vec.size(), static_cast<size_t>(4));
for (size_t j = 0; j < out_vec.size(); ++j) {
VLOG(3) << "out_vec[" << j << "]:" << out_vec[j];
}
ctx.Wait();
// out0
std::vector<T> out1_vec;
f::TensorToVector(*out1, ctx, &out1_vec);
EXPECT_EQ(out1_vec.size(), static_cast<size_t>(4));
for (size_t j = 0; j < out1_vec.size(); ++j) {
VLOG(3) << "out1_vec[" << j << "]:" << out1_vec[j];
}
ctx.Wait();
// out found_inf
Tensor found_inf_tensor;
found_inf_tensor.Resize({1});
bool *is_finite_data =
found_inf_tensor.mutable_data<bool>(paddle::platform::CPUPlace());
f::TensorCopy(*found_inf, place, &found_inf_tensor);
EXPECT_FALSE(*is_finite_data);
ctx.Wait();
}
TEST(check_finite_and_unscale, NPU_fp32) {
f::Scope scope;
p::NPUDeviceContext ctx(p::NPUPlace(0));
Compare<float>(&scope, ctx);
}
TEST(check_finite_and_unscale, NPU_fp16) {
f::Scope scope;
p::NPUDeviceContext ctx(p::NPUPlace(0));
Compare<p::float16>(&scope, ctx);
}

123
python/paddle/fluid/tests/unittests/npu/test_amp_check_finite_and_scale_op_npu.py
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@ -0,0 +1,123 @@
# 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.
import unittest
import numpy as np
from op_test import OpTest, skip_check_grad_ci
import paddle
import paddle.fluid as fluid
paddle.enable_static()
@unittest.skipIf(not paddle.is_compiled_with_npu(),
"core is not compiled with NPU")
class TestCheckFiniteAndUnscaleOp(OpTest):
def setUp(self):
self.set_npu()
self.op_type = "check_finite_and_unscale"
self.place = paddle.NPUPlace(0)
self.init_dtype()
x = np.random.random((1024, 1024)).astype(self.dtype)
scale = np.random.random((1)).astype(self.dtype)
self.inputs = {'X': [('x0', x)], 'Scale': scale}
self.outputs = {
'FoundInfinite': np.array([0]),
'Out': [('out0', x / scale)],
}
def set_npu(self):
self.__class__.use_npu = True
def init_kernel_type(self):
self.use_mkldnn = False
def init_dtype(self):
self.dtype = np.float32
def test_check_output(self):
self.check_output_with_place(self.place, check_dygraph=False)
@unittest.skipIf(not paddle.is_compiled_with_npu(),
"core is not compiled with NPU")
class TestCheckFiniteAndUnscaleOpWithNan(OpTest):
def setUp(self):
self.set_npu()
self.op_type = "check_finite_and_unscale"
self.place = paddle.NPUPlace(0)
self.init_dtype()
x = np.random.random((1024, 1024)).astype(self.dtype)
x[128][128] = np.nan
scale = np.random.random((1)).astype(self.dtype)
self.inputs = {'X': [('x0', x)], 'Scale': scale}
self.outputs = {
'FoundInfinite': np.array([1]),
'Out': [('out0', x)],
}
def set_npu(self):
self.__class__.use_npu = True
def init_kernel_type(self):
self.use_mkldnn = False
def init_dtype(self):
self.dtype = np.float32
def test_check_output(self):
# When input contains nan, do not check the output,
# since the output may be nondeterministic and will be discarded.
self.check_output_with_place(
self.place, check_dygraph=False, no_check_set=['Out'])
@unittest.skipIf(not paddle.is_compiled_with_npu(),
"core is not compiled with NPU")
class TestCheckFiniteAndUnscaleOpWithInf(OpTest):
def setUp(self):
self.set_npu()
self.op_type = "check_finite_and_unscale"
self.place = paddle.NPUPlace(0)
self.init_dtype()
x = np.random.random((1024, 1024)).astype(self.dtype)
x[128][128] = np.inf
scale = np.random.random((1)).astype(self.dtype)
self.inputs = {'X': [('x0', x)], 'Scale': scale}
self.outputs = {
'FoundInfinite': np.array([1]),
'Out': [('out0', x)],
}
def set_npu(self):
self.__class__.use_npu = True
def init_kernel_type(self):
self.use_mkldnn = False
def init_dtype(self):
self.dtype = np.float32
def test_check_output(self):
# When input contains inf, do not check the output,
# since the output may be nondeterministic and will be discarded.
self.check_output_with_place(
self.place, check_dygraph=False, no_check_set=['Out'])
if __name__ == '__main__':
unittest.main()
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