profiler.py

# 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.

import os
import socket
import datetime
from enum import Enum
from typing import Any, Callable, Iterable, Optional, Union
from warnings import warn

import paddle
from paddle.fluid.core import (_Profiler, _ProfilerResult, ProfilerOptions,
                               TracerEventType)

from .utils import RecordEvent, wrap_optimizers
from .profiler_statistic import StatisticData, _build_table, SortedKeys


class ProfilerState(Enum):
    r"""
    Profiler state that can be specified to control profiler action.

    CLOSED: The profilers are closed.

    READY:  The profilers are open, but the data will not be recorded.
    This state is used for reducing overhead influence when profilers start.

    RECORD: The profilers are open, and the data will be recorded.

    RECORD_AND_RETURN: The profilers are open, and at the last batch of current profiler period,
    the collected data will be returned.
    """
    CLOSED = 0
    READY = 1
    RECORD = 2
    RECORD_AND_RETURN = 3  # the last step of RECORD


class ProfilerTarget(Enum):
    r"""
    Target device for profiling.

    CPU: Profile events on CPU.
    
    GPU: Profile events on GPU.
    """
    CPU = 0
    GPU = 1


def make_scheduler(*,
                   closed: int,
                   ready: int,
                   record: int,
                   repeat: int=0,
                   skip_first: int=0) -> Callable:
    r"""
    Return a scheduler function, which scheduler the state according to the setting.
    The state transform confirms to:

    .. code-block:: text

        (CLOSED)  (CLOSED)    (CLOSED)  (READY)    (RECORD,last RETURN)      (CLOSED)
        START -> skip_first -> closed -> ready    ->    record       ->      END
                                |                        |
                                |                        | (if has_repeated < repeat)
                                - - - - - - - - - - - -
        Note that repeat <= 0 means the cycle will continue until the profiler exits.

    Parameters:
        closed(int): The number of steps in state ProfilerState.CLOSED.
        ready(int):  The number of steps in state ProfilerState.READY.
        record(int): The number of steps in state ProfilerState.RECORD.
        repeat(int): The number of cycles to repeat above state transform.
        skip_first(int): The number of first steps to drop, not participate in the state transform.

    Returns:
        A scheduler function, conforms to above state transform setting.

    Examples:
        1. profiling range [2, 5]

        batch 0: closed, batch 1: ready, batch [2, 5] record

            .. code-block:: python

                import paddle.profiler as profiler
                profiler.make_scheduler(closed=1, ready=1, record=4, repeat=1)


        2. profiling range [3,6], [9,12], [15,18]...

        batch 0: skiped, batch 1: closed, batch 2: ready, batch [3,6]: record, repeat

            .. code-block:: python

                import paddle.profiler as profiler
                profiler.make_scheduler(closed=1, ready=1, record=4, skip_first=1)
    """

    def getScheduleState(step: int) -> ProfilerState:
        assert step >= 0
        if step < skip_first:  # within skip_first, just skip
            return ProfilerState.CLOSED
        step = step - skip_first
        period_steps = closed + ready + record
        has_repeated = step // period_steps
        if repeat > 0 and has_repeated >= repeat:  # the period has repeated repeat times, return CLOSED state
            return ProfilerState.CLOSED
        mod_step = step % period_steps
        if mod_step < closed:
            return ProfilerState.CLOSED
        elif mod_step >= closed and mod_step < closed + ready:
            return ProfilerState.READY
        else:
            if mod_step < period_steps - 1:
                return ProfilerState.RECORD
            else:
                return ProfilerState.RECORD_AND_RETURN
    assert closed >= 0 and ready >= 0 and record > 0 and \
             repeat >= 0 and skip_first >= 0, "Invalid profiler scheduler arguments"
    if ready == 0:
        warn("Profiler will record data after enabling profiler immediately, \
          some data collected at the beginning of profiling may be 'noisy' because of overhead."
             )
    return getScheduleState


def _default_state_scheduler(step: int):
    r"""
    A default state scheduler, keep recording from the begining of the profiler until ending.
    """
    return ProfilerState.RECORD


def export_chrome_tracing(dir_name: str,
                          worker_name: Optional[str]=None) -> Callable:
    r"""
    Return a callable, used for outputing tracing data to chrome tracing format file.
    The output file will be saved in directory 'dir_name', and file name will be set as worker_name.
    if worker_name is not set, the default name is [hostname]_[pid].

    Parameters:
        dir_name(str): Directory to save profiling data.
        worker_name(Optional[str]): Prefix of the file name saved, default is [hostname]_[pid].

    Examples:
        .. code-block:: python

            # required: gpu
            import paddle.profiler as profiler
            with profiler.Profiler(
                    targets=[profiler.ProfilerTarget.CPU, profiler.ProfilerTarget.GPU],
                    scheduler = (3, 10),
                    on_trace_ready=profiler.export_protobuf('./log')) as p:
                for iter in range(10):
                    #train()
                    p.step()
    """
    if not os.path.exists(dir_name):
        try:
            os.makedirs(dir_name, exist_ok=True)
        except Exception:
            raise RuntimeError(
                "Can not create directory '{}' for saving profiling results.".
                format(dir_name))

    def handle_fn(prof):
        nonlocal worker_name
        if not worker_name:
            worker_name = "host_{}pid_{}".format(socket.gethostname(),
                                                 str(os.getpid()))
        now = datetime.datetime.now()
        filename = '{}_time_{}.paddle_trace.json'.format(
            worker_name, now.strftime('%Y_%m_%d_%H_%M_%S_%f'))
        prof.export(os.path.join(dir_name, filename), "json")

    return handle_fn


def export_protobuf(dir_name: str, worker_name: Optional[str]=None) -> Callable:
    r"""
    Return a callable, used for outputing tracing data to protobuf file.
    The output file will be saved in directory 'dir_name', and file name will be set as worker_name.
    if worker_name is not set, the default name is [hostname]_[pid].

    Parameters:
        dir_name(str): Directory to save profiling data.
        worker_name(Optional[str]): Prefix of the file name saved, default is [hostname]_[pid].

    Examples:
        .. code-block:: python

            # required: gpu
            import paddle.profiler as profiler
            with profiler.Profiler(
                    targets=[profiler.ProfilerTarget.CPU, profiler.ProfilerTarget.GPU],
                    scheduler = (3, 10),
                    on_trace_ready = profiler.export_protobuf('./log')) as p:
                for iter in range(10):
                    #train()
                    p.step()
    """
    if not os.path.exists(dir_name):
        try:
            os.makedirs(dir_name, exist_ok=True)
        except Exception:
            raise RuntimeError(
                "Can not create directory '{}' for saving profiling results.".
                format(dir_name))

    def handle_fn(prof):
        nonlocal worker_name
        if not worker_name:
            worker_name = "host_{}pid_{}".format(socket.gethostname(),
                                                 str(os.getpid()))
        now = datetime.datetime.now()
        filename = '{}_time_{}.paddle_trace.pb'.format(
            worker_name, now.strftime('%Y_%m_%d_%H_%M_%S_%f'))
        prof.export(os.path.join(dir_name, filename), "pb")

    return handle_fn


def _get_supported_targets() -> Iterable[ProfilerTarget]:
    r"""
    Get the current supported profiler target in the system.
    """
    if _Profiler.is_cupti_supported():
        return [ProfilerTarget.CPU, ProfilerTarget.GPU]
    return [ProfilerTarget.CPU]


class Profiler:
    r"""
    Profiler context manager, user interface to manage profile process.

    Parameters:
        targets (iterable): list of tracing targets, currently supported values, ``ProfilerTarget.CPU``, ``ProfilerTarget.GPU`` .
        scheduler (callable or tuple): If it is a callable object, it takes a step number as parameter and return the corresponding ``ProfilerState``.
            If not provided, the default scheduler will keep tracing until the profiler exits. If it is a tuple, it has two values start_batch and end_batch,
            which means profiling range [start_batch, end_batch).
        on_trace_ready (callable): callable object, takes the Profiler object as parameter, which provides a way for users to do post-processing.
            This callable object will be called when ``scheduler`` returns ``ProfilerState.RECORD_AND_RETURN``.

    Examples:
        1. profiling range [2, 5)

            .. code-block:: python

                # required: gpu
                import paddle.profiler as profiler
                with profiler.Profiler(
                        targets=[profiler.ProfilerTarget.CPU, profiler.ProfilerTarget.GPU],
                        scheduler = (2, 5),
                        on_trace_ready = profiler.export_chrome_tracing('./log')) as p:
                    for iter in range(10):
                        #train()
                        p.step()

        2. profiling range [2,4], [7, 9], [11,13]

            .. code-block:: python

                # required: gpu
                import paddle.profiler as profiler
                with profiler.Profiler(
                        targets=[profiler.ProfilerTarget.CPU, profiler.ProfilerTarget.GPU],
                        scheduler = profiler.make_scheduler(closed=1, ready=1, record=3, repeat=3),
                        on_trace_ready = profiler.export_chrome_tracing('./log')) as p:
                    for iter in range(10):
                        #train()
                        p.step()

        3. Use profiler without context manager, and use default parameters

            .. code-block:: python

                # required: gpu
                import paddle.profiler as profiler
                p = profiler.Profiler()
                p.start()
                for iter in range(10):
                    #train()
                    p.step()
                p.stop()
                p.summary()

    """

    def __init__(
            self,
            *,
            targets: Optional[Iterable[ProfilerTarget]]=None,
            scheduler: Union[Callable[[int], ProfilerState], tuple, None]=None,
            on_trace_ready: Optional[Callable[..., Any]]=None):
        supported_targets = _get_supported_targets()
        if targets:
            self.targets = set(targets)
            for target in targets:
                if target not in supported_targets:
                    self.targets.remove(target)
                    warn("Profiling {} is not supported in current context.".
                         format(target))
        else:
            self.targets = supported_targets
        profileoption = ProfilerOptions()
        if ProfilerTarget.CPU in self.targets:
            profileoption.trace_switch |= 1
        if ProfilerTarget.GPU in self.targets:
            profileoption.trace_switch |= (1 << 1)
        wrap_optimizers()
        self.profiler = _Profiler.create(profileoption)
        if callable(scheduler):
            self.scheduler = scheduler
        elif isinstance(scheduler, (tuple, list)):
            assert len(scheduler) == 2 and scheduler[1] > scheduler[0]
            start_batch, end_batch = scheduler
            start_batch = max(start_batch, 0)
            if start_batch >= 1:
                self.scheduler = make_scheduler(
                    closed=max(start_batch - 1, 0),
                    ready=1,
                    record=(end_batch - start_batch),
                    repeat=1)
            else:
                self.scheduler = make_scheduler(
                    closed=0,
                    ready=0,
                    record=(end_batch - start_batch),
                    repeat=1)
        else:
            self.scheduler = _default_state_scheduler

        if on_trace_ready == None:
            self.on_trace_ready = export_chrome_tracing('./profiler_log/')
        else:
            self.on_trace_ready = on_trace_ready
        self.step_num = 0
        self.previous_state = ProfilerState.CLOSED
        self.current_state = self.scheduler(self.step_num)
        self.record_event = None
        self.profiler_result = None

    def __enter__(self):
        self.start()
        return self

    def __exit__(self, exc_type, exc_val, exc_tb):
        self.stop()

    def start(self):
        r'''
        Start profiler and enter the first profiler step(0).
        State transformed from CLOSED to self.current_state and trigger corresponding action.

        Examples:
            .. code-block:: python

                # required: gpu
                import paddle.profiler as profiler
                prof = profiler.Profiler(
                    targets=[profiler.ProfilerTarget.CPU, profiler.ProfilerTarget.GPU],
                    scheduler = (1, 9),
                    on_trace_ready = profiler.export_chrome_tracing('./log'))
                prof.start()
                for iter in range(10):
                    #train()
                    prof.step()
                prof.stop()
        '''
        # CLOSED -> self.current_state
        if self.current_state == ProfilerState.READY:
            self.profiler.prepare()
        elif self.current_state == ProfilerState.RECORD:
            self.profiler.prepare()
            self.profiler.start()
        elif self.current_state == ProfilerState.RECORD_AND_RETURN:
            self.profiler.prepare()
            self.profiler.start()
        self.record_event = RecordEvent(
            name="ProfileStep#{}".format(self.step_num),
            event_type=TracerEventType.ProfileStep)
        self.record_event.begin()

    def stop(self):
        r'''
        Stop profiler and State transformed from self.current_state to CLOSED.
        Trigger corresponding action and post-process profiler result using self.on_trace_ready if result exists.

        Examples:
            .. code-block:: python

                # required: gpu
                import paddle.profiler as profiler
                prof = profiler.Profiler(
                    targets=[profiler.ProfilerTarget.CPU, profiler.ProfilerTarget.GPU],
                    scheduler = (1, 7),
                    on_trace_ready = profiler.export_chrome_tracing('./log'))
                prof.start()
                for iter in range(10):
                    #train()
                    prof.step()
                prof.stop()
        '''
        # self.current_state -> CLOSED
        # In this situation, RECORD state is regarded as RECORD_AND_RETURN
        if self.record_event:
            self.record_event.end()
            self.record_event = None
        if self.current_state == ProfilerState.READY:
            warn(
                "Inproper Profiler state transform: READY->CLOSED, profiler will start and stop without saving data"
            )
            self.profiler.start()
            self.profiler.stop()
        if self.current_state == ProfilerState.RECORD or self.current_state == ProfilerState.RECORD_AND_RETURN:
            self.profiler_result = self.profiler.stop()
            if self.on_trace_ready:
                self.on_trace_ready(self)

    def step(self):
        r"""
        Signals the profiler that the next profiling step has started.
        Get the new ProfilerState and trigger corresponding action.

        Examples:
            .. code-block:: python

                # required: gpu
                import paddle.profiler as profiler
                prof = profiler.Profiler(
                    targets=[profiler.ProfilerTarget.CPU, profiler.ProfilerTarget.GPU],
                    scheduler = (3, 7),
                    on_trace_ready = profiler.export_chrome_tracing('./log'))

                prof.start()
                for iter in range(10):
                    #train()
                    prof.step()
                prof.stop()
        """
        if self.record_event:
            self.record_event.end()
            self.record_event = None
        self.previous_state = self.current_state
        self.step_num += 1
        self.current_state = self.scheduler(self.step_num)
        self._trigger_action()
        self.record_event = RecordEvent(
            name="ProfileStep#{}".format(self.step_num),
            event_type=TracerEventType.ProfileStep)
        self.record_event.begin()

    def _trigger_action(self):
        if self.previous_state == ProfilerState.CLOSED:
            if self.current_state == ProfilerState.READY:  # CLOSED -> READY
                self.profiler.prepare()
            if self.current_state == ProfilerState.RECORD:  # CLOSED -> RECORD
                self.profiler.prepare()
                self.profiler.start()
            if self.current_state == ProfilerState.RECORD_AND_RETURN:  # CLOSED -> RECORD_AND_RETURN
                self.profiler.prepare()
                self.profiler.start()

        elif self.previous_state == ProfilerState.READY:
            if self.current_state == ProfilerState.CLOSED:  # READY -> CLOSED
                warn(
                    "Improper schedule: READY->CLOSED, profiler will start and stop without saving data"
                )
                self.profiler.start()
                self.profiler.stop()
            if self.current_state == ProfilerState.RECORD:  # READY -> RECORD
                self.profiler.start()
            if self.current_state == ProfilerState.RECORD_AND_RETURN:  # READY -> RECORD_AND_RETURN
                self.profiler.start()

        elif self.previous_state == ProfilerState.RECORD:
            if self.current_state == ProfilerState.CLOSED:  # RECORD -> CLOSED
                warn(
                    "Improper schedule: RECORD->CLOSED, profiler will not saving data"
                )
                self.profiler.stop()

            if self.current_state == ProfilerState.READY:  # RECORD -> READY
                warn(
                    "Improper schedule: RECORD->READY, profiler will stop and re-prepare"
                )
                self.profiler.stop()
                self.profiler.prepare()
            if self.current_state == ProfilerState.RECORD_AND_RETURN:  # RECORD -> RECORD_AND_RETURN
                pass

        else:
            assert self.previous_state == ProfilerState.RECORD_AND_RETURN
            if self.current_state == ProfilerState.CLOSED:  # RECORD_AND_RETURN -> CLOSED
                self.profiler_result = self.profiler.stop()
            if self.current_state == ProfilerState.READY:  # RECORD_AND_RETURN -> READY
                self.profiler_result = self.profiler.stop()
                self.profiler.prepare()
            if self.current_state == ProfilerState.RECORD:  # RECORD_AND_RETURN -> RECORD
                self.profiler_result = self.profiler.stop()
                self.profiler.prepare()
                self.profiler.start()
            if self.current_state == ProfilerState.RECORD_AND_RETURN:  # RECORD_AND_RETURN -> RECORD_AND_RETURN
                self.profiler_result = self.profiler.stop()
                self.profiler.prepare()
                self.profiler.start()
            if self.on_trace_ready:
                self.on_trace_ready(self)

    def export(self, path="", format="json"):
        r"""
        Exports the tracing data in Chrome tracing data format.

        Examples:
            .. code-block:: python

                # required: gpu
                import paddle.profiler as profiler
                prof = profiler.Profiler(
                    targets=[profiler.ProfilerTarget.CPU, profiler.ProfilerTarget.GPU],
                    scheduler = (3, 7))
                prof.start()
                for iter in range(10):
                    #train()
                    prof.step()
                prof.stop()
                prof.export(path="./profiler_data.json", format="json")
        """
        if self.profiler_result:
            self.profiler_result.save(path, format)

    def summary(self,
                sorted_by=SortedKeys.CPUTotal,
                op_detail=True,
                thread_sep=False,
                time_unit='ms'):
        r"""
        Print the Summary table.

        Parameters:
            sorted_by(SortedKeys): how to rank the op table items.
            op_detail(bool): expand each operator detail information.
            thread_sep(bool): print op table each thread.
            time_unit(str): can be chosen form ['s', 'ms', 'us', 'ns']

        Examples:
            .. code-block:: python

                # required: gpu
                import paddle.profiler as profiler
                prof = profiler.Profiler(
                    targets=[profiler.ProfilerTarget.CPU, profiler.ProfilerTarget.GPU],
                    scheduler = (3, 7),
                    on_trace_ready = profiler.export_chrome_tracing('./log'))
                prof.start()
                for iter in range(10):
                    #train()
                    prof.step()
                prof.stop()
                prof.summary(sorted_by=profiler.SortedKeys.CPUTotal, op_detail=True, thread_sep=False, time_unit='ms')
        """
        if self.profiler_result:
            statistic_data = StatisticData(
                self.profiler_result.get_data(),
                self.profiler_result.get_extra_info())
            print(
                _build_table(
                    statistic_data,
                    sorted_by=sorted_by,
                    op_detail=op_detail,
                    thread_sep=thread_sep,
                    time_unit=time_unit))