wehub-resource-sync
281 lines
8.2 KiB
Python
281 lines
8.2 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import annotations
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import functools
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import logging
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import sys
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from contextlib import ContextDecorator, contextmanager
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from typing import TYPE_CHECKING
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from warnings import warn
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from paddle.base import core
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from paddle.base.core import TracerEventType, _RecordEvent
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if TYPE_CHECKING:
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import types
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from typing_extensions import Self
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from paddle.base.core import _ProfilerResult
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_is_profiler_used = False
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_has_optimizer_wrapped = False
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_AllowedEventTypeList = [
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TracerEventType.Dataloader,
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TracerEventType.ProfileStep,
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TracerEventType.Forward,
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TracerEventType.Backward,
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TracerEventType.Optimization,
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TracerEventType.PythonOp,
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TracerEventType.PythonUserDefined,
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]
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class RecordEvent(ContextDecorator):
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r"""
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Interface for recording a time range by user defined.
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Args:
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name (str): Name of the record event.
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event_type (TracerEventType, optional): Optional, default value is
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`TracerEventType.PythonUserDefined`. It is reserved for internal
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purpose, and it is better not to specify this parameter.
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Examples:
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.. code-block:: pycon
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:name: code-example1
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>>> import paddle
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>>> import paddle.profiler as profiler
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>>> # method1: using context manager
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>>> paddle.seed(2023)
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>>> with profiler.RecordEvent("record_add"):
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... data1 = paddle.randn(shape=[3])
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... data2 = paddle.randn(shape=[3])
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... result = data1 + data2
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>>> # method2: call begin() and end()
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>>> record_event = profiler.RecordEvent("record_add")
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>>> record_event.begin()
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>>> data1 = paddle.randn(shape=[3])
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>>> data2 = paddle.randn(shape=[3])
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>>> result = data1 + data2
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>>> record_event.end()
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Note:
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RecordEvent will take effect only when :ref:`Profiler <api_paddle_profiler_Profiler>` is on and at the state of `RECORD`.
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"""
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name: str
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event_type: TracerEventType
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event: _RecordEvent | None
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def __init__(
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self,
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name: str,
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event_type: TracerEventType = TracerEventType.PythonUserDefined,
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) -> None:
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self.name = name
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self.event_type = event_type
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self.event = None
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def __enter__(self) -> Self:
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self.begin()
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return self
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def __exit__(
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self,
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exc_type: type[BaseException] | None,
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exc_value: BaseException | None,
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traceback: types.TracebackType | None,
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) -> None:
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self.end()
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def begin(self) -> None:
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r"""
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Record the time of beginning.
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Examples:
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.. code-block:: pycon
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:name: code-example2
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>>> import paddle
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>>> import paddle.profiler as profiler
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>>> record_event = profiler.RecordEvent("record_sub")
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>>> record_event.begin()
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>>> paddle.seed(2023)
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>>> data1 = paddle.randn(shape=[3])
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>>> data2 = paddle.randn(shape=[3])
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>>> result = data1 - data2
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>>> record_event.end()
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"""
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if not _is_profiler_used:
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return
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if self.event_type not in _AllowedEventTypeList:
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warn(
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"Only TracerEvent Type in [{}, {}, {}, {}, {}, {},{}]\
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can be recorded.".format(*_AllowedEventTypeList)
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)
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self.event = None
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else:
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self.event = _RecordEvent(self.name, self.event_type)
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def end(self) -> None:
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r"""
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Record the time of ending.
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Examples:
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.. code-block:: pycon
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:name: code-example3
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>>> import paddle
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>>> import paddle.profiler as profiler
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>>> record_event = profiler.RecordEvent("record_mul")
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>>> record_event.begin()
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>>> paddle.seed(2023)
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>>> data1 = paddle.randn(shape=[3])
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>>> data2 = paddle.randn(shape=[3])
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>>> result = data1 * data2
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>>> record_event.end()
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"""
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if self.event:
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self.event.end()
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def load_profiler_result(filename: str) -> _ProfilerResult:
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r"""
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Load dumped profiler data back to memory.
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Args:
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filename(str): Name of the exported protobuf file of profiler data.
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Returns:
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``ProfilerResult`` object, which stores profiling data.
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Examples:
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.. code-block:: pycon
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>>> # doctest: +REQUIRES(env:GPU)
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>>> import paddle.profiler as profiler
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>>> import paddle
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>>> paddle.device.set_device('gpu')
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>>> with profiler.Profiler(
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... targets=[profiler.ProfilerTarget.CPU, profiler.ProfilerTarget.GPU],
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... scheduler=(3, 10),
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... ) as p:
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... for iter in range(10):
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... # train()
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... p.step()
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>>> p.export('test_export_protobuf.pb', format='pb')
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>>> profiler_result = profiler.load_profiler_result('test_export_protobuf.pb')
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"""
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return core.load_profiler_result(filename)
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def in_profiler_mode():
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return _is_profiler_used
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def wrap_optimizers():
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def optimizer_wrapper(func):
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@functools.wraps(func)
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def wrapper(*args, **kwargs):
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if in_profiler_mode():
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with RecordEvent(
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'Optimization Step', event_type=TracerEventType.Optimization
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):
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return func(*args, **kwargs)
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else:
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return func(*args, **kwargs)
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return wrapper
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global _has_optimizer_wrapped
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if _has_optimizer_wrapped:
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return
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from paddle import optimizer
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for classname in optimizer.__all__:
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if classname != 'Optimizer':
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classobject = getattr(optimizer, classname)
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if getattr(classobject, 'step', None) is not None:
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classobject.step = optimizer_wrapper(classobject.step)
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_has_optimizer_wrapped = True
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@contextmanager
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def _nvprof_range(iter_id, start, end, exit_after_prof=True):
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"""
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A range profiler interface (not public yet).
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Examples:
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.. code-block:: pycon
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>>> import paddle
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>>> model = Model()
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>>> for i in range(max_iter):
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... with paddle.profiler.utils._nvprof_range(i, 10, 20):
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... out = model(in)
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"""
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if start >= end:
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yield
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return
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try:
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if iter_id == start:
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core.nvprof_start()
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core.nvprof_enable_record_event()
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if iter_id >= start:
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core.nvprof_nvtx_push(str(iter_id))
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yield
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finally:
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if iter_id < end:
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core.nvprof_nvtx_pop()
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if iter_id == end - 1:
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core.nvprof_stop()
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if exit_after_prof:
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sys.exit()
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@contextmanager
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def job_schedule_profiler_range(iter_id, start, end, exit_after_prof=True):
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if start >= end:
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yield False
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return
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try:
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if iter_id >= start and iter_id < end:
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yield True
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else:
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yield False
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finally:
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if iter_id == end - 1:
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if exit_after_prof:
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sys.exit()
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def switch_job_schedule_profiler(
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model, iter_id, start, end, exit_after_prof=True
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):
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logging.info(
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f"Schedule Profiler start at step {start} and end at step {end}"
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)
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with job_schedule_profiler_range(
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iter_id, start, end, exit_after_prof
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) as status:
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model._engine.enable_job_schedule_profiler = status
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