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未验证 提交 0279486b 编写于 作者: P pangyoki 提交者： GitHub
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【NPU】Support npu kernel for update_loss_scaling op (#31830) 

* add update_loss_scaling_npu NPU kernel

* change TensorFromVec to Memset
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paddle/fluid/operators/amp/update_loss_scaling_op_npu.cc 0 → 100644
浏览文件 @ 0279486b
/* 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 "paddle/fluid/operators/amp/update_loss_scaling_op.h"
#include <cmath>
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/npu_op_runner.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
template <typename T>
void Update(const platform::NPUDeviceContext& ctx,
const std::vector<bool> found_inf_vec,
const Tensor* pre_loss_scaling_tensor, const Tensor* good_in_tensor,
const Tensor* bad_in_tensor, const int incr_every_n_steps,
const int decr_every_n_nan_or_inf, const float incr_ratio,
const float decr_ratio, Tensor* updated_loss_scaling_tensor,
Tensor* good_out_tensor, Tensor* bad_out_tensor) {
auto place = ctx.GetPlace();
auto stream = ctx.stream();
if (found_inf_vec[0]) {
// good_out_data = 0
auto g = good_out_tensor->mutable_data<int>(place);
platform::NPUMemsetAsync(static_cast<void*>(g), 0,
good_out_tensor->numel() * sizeof(int), stream);
// bad_out_data = bad_in_data + 1
Tensor factor_tensor(bad_out_tensor->type());
factor_tensor.mutable_data<int>({1}, place);
TensorFromVector(std::vector<int>{1}, ctx, &factor_tensor);
auto runner_p2 = NpuOpRunner("Add", {*bad_in_tensor, factor_tensor},
{*bad_out_tensor}, {});
runner_p2.Run(stream);
std::vector<int> bad_out_data;
TensorToVector(*bad_out_tensor, ctx, &bad_out_data);
if (bad_out_data[0] == decr_every_n_nan_or_inf) {
auto runner_p3 = NpuOpRunner("Power", {*pre_loss_scaling_tensor},
{*updated_loss_scaling_tensor},
{{"power", static_cast<float>(1)},
{"scale", decr_ratio},
{"shift", static_cast<float>(0)}});
runner_p3.Run(stream);
std::vector<T> new_loss_scaling;
TensorToVector(*updated_loss_scaling_tensor, ctx, &new_loss_scaling);
if (new_loss_scaling[0] < static_cast<T>(1)) {
// updated_loss_scaling_data = 1
auto runner_p4 = NpuOpRunner("Power", {*pre_loss_scaling_tensor},
{*updated_loss_scaling_tensor},
{{"power", static_cast<float>(1)},
{"scale", static_cast<float>(0)},
{"shift", static_cast<float>(1)}});
runner_p4.Run(stream);
}
// bad_out_data = 0
auto b = bad_out_tensor->mutable_data<int>(place);
platform::NPUMemsetAsync(static_cast<void*>(b), 0,
bad_out_tensor->numel() * sizeof(int), stream);
}
} else {
// bad_out_data = 0
auto b = bad_out_tensor->mutable_data<int>(place);
platform::NPUMemsetAsync(static_cast<void*>(b), 0,
bad_out_tensor->numel() * sizeof(int), stream);
// good_out_data = good_in_data + 1
Tensor factor_tensor(good_out_tensor->type());
factor_tensor.mutable_data<int>({1}, place);
TensorFromVector(std::vector<int>{1}, ctx, &factor_tensor);
auto runner_p2 = NpuOpRunner("Add", {*good_in_tensor, factor_tensor},
{*good_out_tensor}, {});
runner_p2.Run(stream);
std::vector<int> good_out_data;
TensorToVector(*good_out_tensor, ctx, &good_out_data);
if (good_out_data[0] == incr_every_n_steps) {
auto runner_p3 = NpuOpRunner("Power", {*pre_loss_scaling_tensor},
{*updated_loss_scaling_tensor},
{{"power", static_cast<float>(1)},
{"scale", incr_ratio},
{"shift", static_cast<float>(0)}});
runner_p3.Run(stream);
std::vector<T> new_loss_scaling;
TensorToVector(*updated_loss_scaling_tensor, ctx, &new_loss_scaling);
if (!std::isfinite(new_loss_scaling[0])) {
// updated_loss_scaling_data = pre_loss_scaling_data
auto runner_p4 = NpuOpRunner("Power", {*pre_loss_scaling_tensor},
{*updated_loss_scaling_tensor},
{{"power", static_cast<float>(1)},
{"scale", static_cast<float>(1)},
{"shift", static_cast<float>(0)}});
runner_p4.Run(stream);
}
// good_out_data = 0
auto g = good_out_tensor->mutable_data<int>(place);
platform::NPUMemsetAsync(static_cast<void*>(g), 0,
good_out_tensor->numel() * sizeof(int), stream);
}
}
}
template <typename T>
class UpdateLossScalingFunctor<platform::NPUDeviceContext, T> {
public:
void operator()(const platform::NPUDeviceContext& dev_ctx,
const std::vector<bool> found_inf_vec,
const Tensor* pre_loss_scaling_tensor,
const Tensor* good_in_tensor, const Tensor* bad_in_tensor,
const int incr_every_n_steps,
const int decr_every_n_nan_or_inf, const float incr_ratio,
const float decr_ratio, Tensor* updated_loss_scaling_tensor,
Tensor* good_out_tensor, Tensor* bad_out_tensor) const {
Update<T>(dev_ctx, found_inf_vec, pre_loss_scaling_tensor, good_in_tensor,
bad_in_tensor, incr_every_n_steps, decr_every_n_nan_or_inf,
incr_ratio, decr_ratio, updated_loss_scaling_tensor,
good_out_tensor, bad_out_tensor);
}
};
template <typename T>
class LazyZerosNPU {
public:
void operator()(const platform::NPUDeviceContext& dev_ctx,
const std::vector<bool> found_inf_vec,
const std::vector<const framework::Tensor*>& xs,
const std::vector<framework::Tensor*>& outs) const {
for (size_t i = 0; i < xs.size(); ++i) {
auto* out = outs[i];
if (found_inf_vec[0]) {
VLOG(1) << "-- UpdateLossScaling: Find infinite grads. --";
auto place = dev_ctx.GetPlace();
auto stream = dev_ctx.stream();
auto g = out->mutable_data<int>(place);
platform::NPUMemsetAsync(static_cast<void*>(g), 0,
out->numel() * sizeof(int), stream);
}
}
}
};
template <typename DeviceContext, typename T>
class UpdateLossScalingNPUKernel : public framework::OpKernel<T> {
using MPDType = typename details::MPTypeTrait<T>::Type;
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto& dev_ctx = ctx.template device_context<DeviceContext>();
const auto xs = ctx.MultiInput<framework::Tensor>("X");
auto outs = ctx.MultiOutput<framework::Tensor>("Out");
const auto* found_inf = ctx.Input<Tensor>("FoundInfinite");
PADDLE_ENFORCE_EQ(found_inf->numel(), 1,
platform::errors::InvalidArgument(
"FoundInfinite must has only one element."));
std::vector<bool> found_inf_vec;
TensorToVector(*found_inf, ctx.device_context(), &found_inf_vec);
LazyZerosNPU<T>{}(dev_ctx, found_inf_vec, xs, outs);
const bool stop_update = ctx.Attr<bool>("stop_update");
if (stop_update) {
return;
}
const auto* pre_loss_scaling = ctx.Input<Tensor>("PrevLossScaling");
const auto* good_in = ctx.Input<Tensor>("InGoodSteps");
const auto* bad_in = ctx.Input<Tensor>("InBadSteps");
auto* updated_loss_scaling = ctx.Output<Tensor>("LossScaling");
auto* good_out = ctx.Output<Tensor>("OutGoodSteps");
auto* bad_out = ctx.Output<Tensor>("OutBadSteps");
updated_loss_scaling->mutable_data<MPDType>(dev_ctx.GetPlace());
good_out->mutable_data<int>(dev_ctx.GetPlace());
bad_out->mutable_data<int>(dev_ctx.GetPlace());
const int incr_every_n_steps = ctx.Attr<int>("incr_every_n_steps");
const int decr_every_n_nan_or_inf =
ctx.Attr<int>("decr_every_n_nan_or_inf");
const float incr_ratio = ctx.Attr<float>("incr_ratio");
const float decr_ratio = ctx.Attr<float>("decr_ratio");
UpdateLossScalingFunctor<DeviceContext, MPDType>{}(
dev_ctx, found_inf_vec, pre_loss_scaling, good_in, bad_in,
incr_every_n_steps, decr_every_n_nan_or_inf, incr_ratio, decr_ratio,
updated_loss_scaling, good_out, bad_out);
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_NPU_KERNEL(
update_loss_scaling,
ops::UpdateLossScalingNPUKernel<paddle::platform::NPUDeviceContext, float>,
ops::UpdateLossScalingNPUKernel<paddle::platform::NPUDeviceContext,
double>);
python/paddle/fluid/tests/unittests/npu/test_update_loss_scaling_op_npu.py 0 → 100644
浏览文件 @ 0279486b
# 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.
import unittest
import numpy as np
import sys
sys.path.append("..")
from op_test import OpTest
import paddle
import paddle.fluid as fluid
import paddle.fluid.contrib.mixed_precision.amp_nn as amp_nn
paddle.enable_static()
SEED = 2021
@unittest.skipIf(not paddle.is_compiled_with_npu(),
"core is not compiled with NPU")
class TestUpdateLossScalingOp(OpTest):
def setUp(self):
self.set_npu()
self.op_type = "update_loss_scaling"
self.place = paddle.NPUPlace(0)
self.init()
found_inf = np.array([False], dtype=np.bool)
x = np.random.random((1024, 1024)).astype(self.dtype)
self.inputs = {
'X': [('x0', x)],
'FoundInfinite': found_inf,
'PrevLossScaling': self.prev_loss_scaling,
'InGoodSteps': self.num_good_steps,
'InBadSteps': self.num_bad_steps
}
self.outputs = {
'Out': [('out0', x)],
'LossScaling': self.prev_loss_scaling * self.incr_ratio,
'OutGoodSteps': self.zero_steps,
'OutBadSteps': self.zero_steps
}
def set_npu(self):
self.__class__.use_npu = True
def init(self):
self.incr_ratio = 2.0
self.decr_ratio = 0.8
self.dtype = np.float32
self.prev_loss_scaling = np.array([2048]).astype(self.dtype)
self.num_good_steps = np.array([999], dtype=np.int32)
self.num_bad_steps = np.array([1], dtype=np.int32)
self.zero_steps = np.array([0], dtype=np.int32)
self.attrs = {
'incr_every_n_steps': 1000,
'decr_every_n_nan_or_inf': 2,
'incr_ratio': self.incr_ratio,
'decr_ratio': self.decr_ratio,
}
def test_check_output(self):
self.check_output_with_place(
self.place, check_dygraph=False, no_check_set=['Out'])
class TestUpdateLossScalingOpBad(TestUpdateLossScalingOp):
def setUp(self):
self.set_npu()
self.op_type = "update_loss_scaling"
self.place = paddle.NPUPlace(0)
self.init()
found_inf = np.array([True], dtype=np.bool)
x = np.random.random((1024, 1024)).astype(self.dtype)
i = np.random.randint(0, 1024, 1)
j = np.random.randint(0, 1024, 1)
x[i[0]][j[0]] = np.inf
self.inputs = {
'X': [('x0', x)],
'FoundInfinite': found_inf,
'PrevLossScaling': self.prev_loss_scaling,
'InGoodSteps': self.num_good_steps,
'InBadSteps': self.num_bad_steps
}
self.outputs = {
'Out': [('out0', np.zeros_like(x))],
'LossScaling': self.prev_loss_scaling * self.decr_ratio,
'OutGoodSteps': self.zero_steps,
'OutBadSteps': self.zero_steps
}
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 TestUpdateLossScalingLayer(unittest.TestCase):
def loss_scaling_check(self, use_npu=True, scope=fluid.Scope()):
a = fluid.data(name="a", shape=[1024, 1024], dtype='float32')
b = fluid.data(name="b", shape=[512, 128], dtype='float32')
x = [a, b]
found_inf = fluid.data(name="found_inf", shape=[1], dtype='bool')
prev_loss_scaling = fluid.data(
name="prev_loss_scaling", shape=[1], dtype='float32')
num_good_steps = fluid.data(
name="num_good_steps", shape=[1], dtype='int32')
num_bad_steps = fluid.data(
name="num_bad_steps", shape=[1], dtype='int32')
a_v = np.random.random([1024, 1024]).astype('float32')
b_v = np.random.random([512, 128]).astype('float32')
found_inf_v = np.array([False]).astype('bool')
prev_loss_scaling_v = np.array([2048]).astype('float32')
num_good_steps_v = np.array([999], dtype=np.int32)
num_bad_steps_v = np.array([1], dtype=np.int32)
incr_every_n_steps = 1000
decr_every_n_nan_or_inf = 2
incr_ratio = 2
decr_ratio = 0.8
result = amp_nn.update_loss_scaling(
x,
found_inf,
prev_loss_scaling,
num_good_steps,
num_bad_steps,
incr_every_n_steps,
decr_every_n_nan_or_inf,
incr_ratio,
decr_ratio,
name="update_loss_scaling")
place = paddle.NPUPlace(0) if use_npu else fluid.CPUPlace()
exe = fluid.Executor(place)
with fluid.scope_guard(scope):
exe.run(fluid.default_startup_program())
result_v = exe.run(feed={
'a': a_v,
'b': b_v,
'found_inf': found_inf_v,
'prev_loss_scaling': prev_loss_scaling_v,
'num_good_steps': num_good_steps_v,
'num_bad_steps': num_bad_steps_v
},
fetch_list=[
result, x, found_inf, prev_loss_scaling,
num_good_steps, num_bad_steps
])
assert np.array_equal(result_v[0], a_v)
assert np.array_equal(result_v[1], b_v)
assert np.array_equal(result_v[0], result_v[2])
assert np.array_equal(result_v[1], result_v[3])
assert np.array_equal(result_v[4], found_inf_v)
assert np.array_equal(result_v[5], prev_loss_scaling_v * incr_ratio)
assert np.array_equal(result_v[6], np.zeros_like(num_good_steps_v))
assert np.array_equal(result_v[7], np.zeros_like(num_bad_steps_v))
def loss_scaling_check_inf(self, use_npu=True, scope=fluid.Scope()):
a = fluid.data(name="a", shape=[1024, 1024], dtype='float32')
b = fluid.data(name="b", shape=[512, 128], dtype='float32')
x = [a, b]
found_inf = fluid.data(name="found_inf", shape=[1], dtype='bool')
prev_loss_scaling = fluid.data(
name="prev_loss_scaling", shape=[1], dtype='float32')
num_good_steps = fluid.data(
name="num_good_steps", shape=[1], dtype='int32')
num_bad_steps = fluid.data(
name="num_bad_steps", shape=[1], dtype='int32')
a_v = np.random.random([1024, 1024]).astype('float32')
b_v = np.random.random([512, 128]).astype('float32')
i = np.random.randint(0, 1024, 1)
j = np.random.randint(0, 1024, 1)
a_v[i[0]][j[0]] = np.inf
found_inf_v = np.array([True]).astype('bool')
prev_loss_scaling_v = np.array([2048]).astype('float32')
num_good_steps_v = np.array([999], dtype=np.int32)
num_bad_steps_v = np.array([1], dtype=np.int32)
incr_every_n_steps = 1000
decr_every_n_nan_or_inf = 2
incr_ratio = 2
decr_ratio = 0.8
result = amp_nn.update_loss_scaling(
x,
found_inf,
prev_loss_scaling,
num_good_steps,
num_bad_steps,
incr_every_n_steps,
decr_every_n_nan_or_inf,
incr_ratio,
decr_ratio,
name="update_loss_scaling")
place = paddle.NPUPlace(0) if use_npu else fluid.CPUPlace()
exe = fluid.Executor(place)
with fluid.scope_guard(scope):
exe.run(fluid.default_startup_program())
result_v = exe.run(feed={
'a': a_v,
'b': b_v,
'found_inf': found_inf_v,
'prev_loss_scaling': prev_loss_scaling_v,
'num_good_steps': num_good_steps_v,
'num_bad_steps': num_bad_steps_v
},
fetch_list=[
result, x, found_inf, prev_loss_scaling,
num_good_steps, num_bad_steps
])
assert np.array_equal(result_v[0], np.zeros_like(a_v))
assert np.array_equal(result_v[1], np.zeros_like(b_v))
assert np.array_equal(result_v[2], np.zeros_like(a_v))
assert np.array_equal(result_v[3], np.zeros_like(b_v))
assert np.array_equal(result_v[4], found_inf_v)
assert np.array_equal(result_v[5], prev_loss_scaling_v * decr_ratio)
assert np.array_equal(result_v[6], np.zeros_like(num_good_steps_v))
assert np.array_equal(result_v[7], np.zeros_like(num_bad_steps_v))
def test_loss_scaling_cpu(self):
main = fluid.Program()
startup = fluid.Program()
with fluid.unique_name.guard():
with fluid.program_guard(main, startup):
self.loss_scaling_check(use_npu=False)
def test_loss_scaling_cpu_inf(self):
main = fluid.Program()
startup = fluid.Program()
with fluid.unique_name.guard():
with fluid.program_guard(main, startup):
self.loss_scaling_check_inf(use_npu=False)
def test_loss_scaling_npu(self):
main = fluid.Program()
startup = fluid.Program()
with fluid.unique_name.guard():
with fluid.program_guard(main, startup):
self.loss_scaling_check(use_npu=True)
def test_loss_scaling_npu_inf(self):
main = fluid.Program()
startup = fluid.Program()
with fluid.unique_name.guard():
with fluid.program_guard(main, startup):
self.loss_scaling_check_inf(use_npu=True)
if __name__ == '__main__':
unittest.main()
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