chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
@@ -0,0 +1,280 @@
|
||||
# Copyright (c) 2022 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 annotations
|
||||
|
||||
import functools
|
||||
import logging
|
||||
import sys
|
||||
from contextlib import ContextDecorator, contextmanager
|
||||
from typing import TYPE_CHECKING
|
||||
from warnings import warn
|
||||
|
||||
from paddle.base import core
|
||||
from paddle.base.core import TracerEventType, _RecordEvent
|
||||
|
||||
if TYPE_CHECKING:
|
||||
import types
|
||||
|
||||
from typing_extensions import Self
|
||||
|
||||
from paddle.base.core import _ProfilerResult
|
||||
|
||||
_is_profiler_used = False
|
||||
_has_optimizer_wrapped = False
|
||||
|
||||
_AllowedEventTypeList = [
|
||||
TracerEventType.Dataloader,
|
||||
TracerEventType.ProfileStep,
|
||||
TracerEventType.Forward,
|
||||
TracerEventType.Backward,
|
||||
TracerEventType.Optimization,
|
||||
TracerEventType.PythonOp,
|
||||
TracerEventType.PythonUserDefined,
|
||||
]
|
||||
|
||||
|
||||
class RecordEvent(ContextDecorator):
|
||||
r"""
|
||||
Interface for recording a time range by user defined.
|
||||
|
||||
Args:
|
||||
name (str): Name of the record event.
|
||||
event_type (TracerEventType, optional): Optional, default value is
|
||||
`TracerEventType.PythonUserDefined`. It is reserved for internal
|
||||
purpose, and it is better not to specify this parameter.
|
||||
|
||||
Examples:
|
||||
.. code-block:: pycon
|
||||
:name: code-example1
|
||||
|
||||
>>> import paddle
|
||||
>>> import paddle.profiler as profiler
|
||||
>>> # method1: using context manager
|
||||
>>> paddle.seed(2023)
|
||||
>>> with profiler.RecordEvent("record_add"):
|
||||
... data1 = paddle.randn(shape=[3])
|
||||
... data2 = paddle.randn(shape=[3])
|
||||
... result = data1 + data2
|
||||
>>> # method2: call begin() and end()
|
||||
>>> record_event = profiler.RecordEvent("record_add")
|
||||
>>> record_event.begin()
|
||||
>>> data1 = paddle.randn(shape=[3])
|
||||
>>> data2 = paddle.randn(shape=[3])
|
||||
>>> result = data1 + data2
|
||||
>>> record_event.end()
|
||||
|
||||
Note:
|
||||
RecordEvent will take effect only when :ref:`Profiler <api_paddle_profiler_Profiler>` is on and at the state of `RECORD`.
|
||||
"""
|
||||
|
||||
name: str
|
||||
event_type: TracerEventType
|
||||
event: _RecordEvent | None
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
name: str,
|
||||
event_type: TracerEventType = TracerEventType.PythonUserDefined,
|
||||
) -> None:
|
||||
self.name = name
|
||||
self.event_type = event_type
|
||||
self.event = None
|
||||
|
||||
def __enter__(self) -> Self:
|
||||
self.begin()
|
||||
return self
|
||||
|
||||
def __exit__(
|
||||
self,
|
||||
exc_type: type[BaseException] | None,
|
||||
exc_value: BaseException | None,
|
||||
traceback: types.TracebackType | None,
|
||||
) -> None:
|
||||
self.end()
|
||||
|
||||
def begin(self) -> None:
|
||||
r"""
|
||||
Record the time of beginning.
|
||||
|
||||
Examples:
|
||||
|
||||
.. code-block:: pycon
|
||||
:name: code-example2
|
||||
|
||||
>>> import paddle
|
||||
>>> import paddle.profiler as profiler
|
||||
>>> record_event = profiler.RecordEvent("record_sub")
|
||||
>>> record_event.begin()
|
||||
>>> paddle.seed(2023)
|
||||
>>> data1 = paddle.randn(shape=[3])
|
||||
>>> data2 = paddle.randn(shape=[3])
|
||||
>>> result = data1 - data2
|
||||
>>> record_event.end()
|
||||
"""
|
||||
if not _is_profiler_used:
|
||||
return
|
||||
if self.event_type not in _AllowedEventTypeList:
|
||||
warn(
|
||||
"Only TracerEvent Type in [{}, {}, {}, {}, {}, {},{}]\
|
||||
can be recorded.".format(*_AllowedEventTypeList)
|
||||
)
|
||||
self.event = None
|
||||
else:
|
||||
self.event = _RecordEvent(self.name, self.event_type)
|
||||
|
||||
def end(self) -> None:
|
||||
r"""
|
||||
Record the time of ending.
|
||||
|
||||
Examples:
|
||||
|
||||
.. code-block:: pycon
|
||||
:name: code-example3
|
||||
|
||||
>>> import paddle
|
||||
>>> import paddle.profiler as profiler
|
||||
>>> record_event = profiler.RecordEvent("record_mul")
|
||||
>>> record_event.begin()
|
||||
>>> paddle.seed(2023)
|
||||
>>> data1 = paddle.randn(shape=[3])
|
||||
>>> data2 = paddle.randn(shape=[3])
|
||||
>>> result = data1 * data2
|
||||
>>> record_event.end()
|
||||
"""
|
||||
if self.event:
|
||||
self.event.end()
|
||||
|
||||
|
||||
def load_profiler_result(filename: str) -> _ProfilerResult:
|
||||
r"""
|
||||
Load dumped profiler data back to memory.
|
||||
|
||||
Args:
|
||||
filename(str): Name of the exported protobuf file of profiler data.
|
||||
|
||||
Returns:
|
||||
``ProfilerResult`` object, which stores profiling data.
|
||||
|
||||
Examples:
|
||||
.. code-block:: pycon
|
||||
|
||||
>>> # doctest: +REQUIRES(env:GPU)
|
||||
>>> import paddle.profiler as profiler
|
||||
>>> import paddle
|
||||
>>> paddle.device.set_device('gpu')
|
||||
>>> with profiler.Profiler(
|
||||
... targets=[profiler.ProfilerTarget.CPU, profiler.ProfilerTarget.GPU],
|
||||
... scheduler=(3, 10),
|
||||
... ) as p:
|
||||
... for iter in range(10):
|
||||
... # train()
|
||||
... p.step()
|
||||
>>> p.export('test_export_protobuf.pb', format='pb')
|
||||
>>> profiler_result = profiler.load_profiler_result('test_export_protobuf.pb')
|
||||
"""
|
||||
return core.load_profiler_result(filename)
|
||||
|
||||
|
||||
def in_profiler_mode():
|
||||
return _is_profiler_used
|
||||
|
||||
|
||||
def wrap_optimizers():
|
||||
def optimizer_wrapper(func):
|
||||
@functools.wraps(func)
|
||||
def wrapper(*args, **kwargs):
|
||||
if in_profiler_mode():
|
||||
with RecordEvent(
|
||||
'Optimization Step', event_type=TracerEventType.Optimization
|
||||
):
|
||||
return func(*args, **kwargs)
|
||||
else:
|
||||
return func(*args, **kwargs)
|
||||
|
||||
return wrapper
|
||||
|
||||
global _has_optimizer_wrapped
|
||||
if _has_optimizer_wrapped:
|
||||
return
|
||||
from paddle import optimizer
|
||||
|
||||
for classname in optimizer.__all__:
|
||||
if classname != 'Optimizer':
|
||||
classobject = getattr(optimizer, classname)
|
||||
if getattr(classobject, 'step', None) is not None:
|
||||
classobject.step = optimizer_wrapper(classobject.step)
|
||||
_has_optimizer_wrapped = True
|
||||
|
||||
|
||||
@contextmanager
|
||||
def _nvprof_range(iter_id, start, end, exit_after_prof=True):
|
||||
"""
|
||||
A range profiler interface (not public yet).
|
||||
Examples:
|
||||
.. code-block:: pycon
|
||||
|
||||
>>> import paddle
|
||||
>>> model = Model()
|
||||
>>> for i in range(max_iter):
|
||||
... with paddle.profiler.utils._nvprof_range(i, 10, 20):
|
||||
... out = model(in)
|
||||
"""
|
||||
if start >= end:
|
||||
yield
|
||||
return
|
||||
|
||||
try:
|
||||
if iter_id == start:
|
||||
core.nvprof_start()
|
||||
core.nvprof_enable_record_event()
|
||||
if iter_id >= start:
|
||||
core.nvprof_nvtx_push(str(iter_id))
|
||||
yield
|
||||
finally:
|
||||
if iter_id < end:
|
||||
core.nvprof_nvtx_pop()
|
||||
if iter_id == end - 1:
|
||||
core.nvprof_stop()
|
||||
if exit_after_prof:
|
||||
sys.exit()
|
||||
|
||||
|
||||
@contextmanager
|
||||
def job_schedule_profiler_range(iter_id, start, end, exit_after_prof=True):
|
||||
if start >= end:
|
||||
yield False
|
||||
return
|
||||
|
||||
try:
|
||||
if iter_id >= start and iter_id < end:
|
||||
yield True
|
||||
else:
|
||||
yield False
|
||||
finally:
|
||||
if iter_id == end - 1:
|
||||
if exit_after_prof:
|
||||
sys.exit()
|
||||
|
||||
|
||||
def switch_job_schedule_profiler(
|
||||
model, iter_id, start, end, exit_after_prof=True
|
||||
):
|
||||
logging.info(
|
||||
f"Schedule Profiler start at step {start} and end at step {end}"
|
||||
)
|
||||
with job_schedule_profiler_range(
|
||||
iter_id, start, end, exit_after_prof
|
||||
) as status:
|
||||
model._engine.enable_job_schedule_profiler = status
|
||||
Reference in New Issue
Block a user