# 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 datetime import importlib import json import os import socket from enum import Enum from typing import TYPE_CHECKING, Callable, Literal from warnings import warn import paddle from paddle.base.core import ( ProfilerOptions, TracerEventType, _Profiler, disable_memory_recorder, disable_op_info_recorder, enable_memory_recorder, enable_op_info_recorder, ) from paddle.profiler import utils from .profiler_statistic import ( SortedKeys, StatisticData, _build_table, gen_layer_flops, ) from .timer import benchmark from .utils import RecordEvent, wrap_optimizers if TYPE_CHECKING: from collections.abc import Iterable from types import TracebackType from typing_extensions import Self from paddle.base.core import _ProfilerResult class SummaryView(Enum): r""" SummaryView define the summary view of different contents. - **SummaryView.DeviceView** : The device summary view. - **SummaryView.OverView** : The overview summary view. - **SummaryView.ModelView** : The model summary view. - **SummaryView.DistributedView** : The distributed summary view. - **SummaryView.KernelView** : The kernel summary view. - **SummaryView.OperatorView** : The operator summary view. - **SummaryView.MemoryView** : The memory summary view. - **SummaryView.MemoryManipulationView** : The memory manipulation summary view. - **SummaryView.UDFView** : The user defined summary view. """ DeviceView = 0 OverView = 1 ModelView = 2 DistributedView = 3 KernelView = 4 OperatorView = 5 MemoryView = 6 MemoryManipulationView = 7 UDFView = 8 class ProfilerState(Enum): r""" ProfilerState is used to present the state of :ref:`Profiler ` . The meaning of each ProfilerState is as following - **ProfilerState.CLOSED** : The profiler is closed, and no profiling data will be recorded. - **ProfilerState.READY** : The profiler is open, but the data will not be recorded. This state is used for reducing overhead influence when profiler starts. - **ProfilerState.RECORD** : The profiler is open, and the data will be recorded. - **ProfilerState.RECORD_AND_RETURN** : The profiler is open, and this state stands for the last batch of "RECORD" state in current profiling period. The collected data will be returned in this state. """ CLOSED = 0 READY = 1 RECORD = 2 RECORD_AND_RETURN = 3 # the last step of RECORD class ProfilerTarget(Enum): r""" ProfilerTarget is used to specify target device for :ref:`Profiler ` . Only CPU, GPU and XPU are supported currently. The meaning of each ProfilerState is as following - **ProfilerTarget.CPU** : Profile events on CPU. - **ProfilerTarget.GPU** : Profile events on GPU. - **ProfilerTarget.XPU** : Profile events on XPU. """ CPU = 0 GPU = 1 XPU = 2 CUSTOM_DEVICE = 3 def make_scheduler( *, closed: int, ready: int, record: int, repeat: int = 0, skip_first: int = 0, ) -> Callable[[int], ProfilerState]: r""" Return a scheduler function, which scheduler the :ref:`ProfilerState ` 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. Args: 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, and the state in last step will be set as ProfilerState.RECORD_AND_RETURN. repeat(int, optional): The number of cycles to repeat above state transform. Default value is 0, which means it will repeat this cycle until profiler exits. skip_first(int, optional): The number of first steps to drop, not participate in the state transform, and at ProfilerState.CLOSED state. Default value is 0. Returns: A scheduler function, conforms to above state transform setting. The function will takes one parameter `step_num`, and returns corresponding ProfilerState. Examples: 1. profiling range [2, 5]. Assume batch 0: closed, batch 1: ready, batch [2, 5] record. .. code-block:: python :name: code-example1 >>> 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]. Assume batch 0: skipped, batch 1: closed, batch 2: ready, batch [3,6]: record, repeat. .. code-block:: python :name: code-example2 >>> 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 beginning of the profiler until ending. """ return ProfilerState.RECORD def export_chrome_tracing( dir_name: str, worker_name: str | None = None ) -> Callable[[Profiler], None]: r""" Return a callable, used for outputting 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]`. Args: dir_name(str): Directory to save profiling data. worker_name(str, optional): Prefix of the file name saved, default is `[hostname]_[pid]`. Returns: A callable, which takes a Profiler object as parameter and calls its export method to save data to chrome tracing format file. Examples: The return value can be used as parameter ``on_trace_ready`` in :ref:`Profiler ` . .. code-block:: python >>> # 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), ... on_trace_ready=profiler.export_chrome_tracing('./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( f"Can not create directory '{dir_name}' for saving profiling results." ) def handle_fn(prof): nonlocal worker_name if not worker_name: worker_name = f"host_{socket.gethostname()}pid_{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: str | None = None ) -> Callable[[Profiler], None]: r""" Return a callable, used for outputting 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]`. Args: dir_name(str): Directory to save profiling data. worker_name(str, optional): Prefix of the file name saved, default is `[hostname]_[pid]`. Returns: A callable, which takes a Profiler object as parameter and calls its export method to save data to protobuf file. Examples: The return value can be used as parameter ``on_trace_ready`` in :ref:`Profiler ` . .. code-block:: python >>> # 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), ... 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( f"Can not create directory '{dir_name}' for saving profiling results." ) def handle_fn(prof): nonlocal worker_name if not worker_name: worker_name = f"host_{socket.gethostname()}pid_{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, ProfilerTarget.CUSTOM_DEVICE, ] if _Profiler.is_cnpapi_supported(): return [ ProfilerTarget.CPU, ProfilerTarget.CUSTOM_DEVICE, ] if _Profiler.is_xpti_supported(): return [ ProfilerTarget.CPU, ProfilerTarget.XPU, ProfilerTarget.CUSTOM_DEVICE, ] return [ProfilerTarget.CPU, ProfilerTarget.CUSTOM_DEVICE] class Profiler: r""" Profiler context manager, user interface to manage profiling process to start, stop, export profiling data and print summary table. Args: targets (list, optional): specify target devices to profile, and all existing and supported devices will be chosen by default. Currently supported values, :ref:`ProfilerTarget.CPU ` , :ref:`ProfilerTarget.GPU ` and :ref:`ProfilerTarget.XPU ` . scheduler (Callable|tuple, optional): If it is a callable object, it takes a step number as parameter and return the corresponding :ref:`ProfilerState `. This callable object can be generated by :ref:`make_scheduler ` function. If not provided (None), 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, optional): Callable object, serves as callback function, and 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``. The default value is :ref:`export_chrome_tracing `. timer_only (bool, optional): If it is True, the cost of Dataloader and every step of the model will be count without profiling. Otherwise, the model will be timed and profiled. Default: False. record_shapes (bool, optional): If it is True, collect op's input shape information. Default: False. profile_memory (bool, optional): If it is True, collect tensor memory allocation and release information. Default: False. custom_device_types (list, optional): If targets contain profiler.ProfilerTarget.CUSTOM_DEVICE, custom_device_types select the custom device type for profiling. The default value represents all custom devices will be selected. with_flops (bool, optional): If it is True, the flops of the op will be calculated. Default: False. Examples: 1. profiling range [2, 5). .. code-block:: python :name: code-example1 >>> # 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 = (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 :name: code-example2 >>> # 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 = 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 :name: code-example3 >>> # doctest: +REQUIRES(env:GPU) >>> import paddle.profiler as profiler >>> import paddle >>> paddle.device.set_device('gpu') >>> p = profiler.Profiler() >>> p.start() >>> for iter in range(10): ... #train() ... p.step() >>> p.stop() >>> p.summary() 4. Use profiler to get throughput and cost of the model. .. code-block:: python :name: code-example-timer1 >>> import paddle >>> import paddle.profiler as profiler >>> class RandomDataset(paddle.io.Dataset): # type: ignore[type-arg] ... def __init__(self, num_samples): ... self.num_samples = num_samples ... def __getitem__(self, idx): ... image = paddle.rand(shape=[100], dtype='float32') ... label = paddle.randint(0, 10, shape=[1], dtype='int64') ... return image, label ... def __len__(self): ... return self.num_samples >>> class SimpleNet(paddle.nn.Layer): ... def __init__(self): ... super().__init__() ... self.fc = paddle.nn.Linear(100, 10) ... def forward(self, image, label=None): ... return self.fc(image) >>> dataset = RandomDataset(20 * 4) >>> simple_net = SimpleNet() >>> opt = paddle.optimizer.SGD(learning_rate=1e-3, parameters=simple_net.parameters()) >>> BATCH_SIZE = 4 >>> loader = paddle.io.DataLoader( ... dataset, ... batch_size=BATCH_SIZE) >>> p = profiler.Profiler(timer_only=True) >>> p.start() >>> for i, (image, label) in enumerate(loader()): ... out = simple_net(image) ... loss = paddle.nn.functional.cross_entropy(out, label) ... avg_loss = paddle.mean(loss) ... avg_loss.backward() ... opt.minimize(avg_loss) ... simple_net.clear_gradients() ... p.step(num_samples=BATCH_SIZE) ... if i % 10 == 0: ... step_info = p.step_info(unit='images') ... print("Iter {}: {}".format(i, step_info)) ... # The average statistics for 10 steps between the last and this call will be ... # printed when the "step_info" is called at 10 iteration intervals. ... # The values you get may be different from the following. ... # Iter 0: reader_cost: 0.51946 s batch_cost: 0.66077 s ips: 6.054 images/s ... # Iter 10: reader_cost: 0.00014 s batch_cost: 0.00441 s ips: 907.009 images/s >>> p.stop() >>> # The performance summary will be automatically printed when the "stop" is called. >>> # Reader Ratio: 2.658% >>> # Time Unit: s, IPS Unit: images/s >>> # | | avg | max | min | >>> # | reader_cost | 0.00011 | 0.00013 | 0.00007 | >>> # | batch_cost | 0.00405 | 0.00434 | 0.00326 | >>> # | ips | 1086.42904 | 1227.30604 | 959.92796 | """ targets: Iterable[ProfilerTarget] profiler: _Profiler scheduler: Callable[[int], ProfilerState] on_trace_ready: Callable[[Profiler], None] step_num: int previous_state: ProfilerState current_state: ProfilerState record_event: RecordEvent profiler_result: _ProfilerResult timer_only: bool record_shapes: bool profile_memory: bool with_flops: bool emit_nvtx: bool def __init__( self, *, targets: Iterable[ProfilerTarget] | None = None, scheduler: ( Callable[[int], ProfilerState] | tuple[int, int] | None ) = None, on_trace_ready: Callable[[Profiler], None] | None = None, record_shapes: bool = False, profile_memory: bool = False, timer_only: bool = False, emit_nvtx: bool = False, custom_device_types: list[str] = [], with_flops: bool = False, ) -> 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( f"Profiling {target} is not supported in current context." ) 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 if ProfilerTarget.XPU in self.targets: profileoption.trace_switch |= 1 << 2 if ProfilerTarget.CUSTOM_DEVICE in self.targets: profileoption.trace_switch |= 1 << 3 if not custom_device_types: custom_device_types = paddle.device.get_all_custom_device_type() wrap_optimizers() self.profiler = _Profiler.create(profileoption, custom_device_types) 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 is 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 self.timer_only = timer_only self.record_shapes = record_shapes self.profile_memory = profile_memory self.with_flops = with_flops self.emit_nvtx = emit_nvtx def __enter__(self) -> Self: self.start() return self def __exit__( self, exc_type: type[BaseException] | None, exc_val: BaseException | None, exc_tb: TracebackType | None, ) -> None: self.stop() def start(self) -> None: 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 :name: code-example4 >>> # doctest: +REQUIRES(env:GPU) >>> import paddle.profiler as profiler >>> import paddle >>> paddle.device.set_device('gpu') >>> 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() ''' # Timing only without profiling. benchmark().begin() if not self.timer_only or self.emit_nvtx: utils._is_profiler_used = True if self.timer_only: return if self.record_shapes or self.with_flops: enable_op_info_recorder() if self.profile_memory: enable_memory_recorder() # 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=f"ProfileStep#{self.step_num}", event_type=TracerEventType.ProfileStep, ) self.record_event.begin() def stop(self) -> None: 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 :name: code-example5 >>> # doctest: +REQUIRES(env:GPU) >>> import paddle.profiler as profiler >>> import paddle >>> paddle.device.set_device('gpu') >>> 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() ''' benchmark().end() if self.timer_only: return if self.record_shapes or self.with_flops: disable_op_info_recorder() if self.profile_memory: disable_memory_recorder() # 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( "Improper 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) utils._is_profiler_used = False def step(self, num_samples: int | None = None) -> None: r""" Signals the profiler that the next profiling step has started. Get the new ProfilerState and trigger corresponding action. Args: num_samples (int|None, optional): Specifies the batch size of every step of the model that is used to compute throughput when `timer_only` is True. Default: None. Examples: .. code-block:: python :name: code-example6 >>> # doctest: +REQUIRES(env:GPU) >>> import paddle.profiler as profiler >>> import paddle >>> paddle.device.set_device('gpu') >>> 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() """ benchmark().step(num_samples) if self.timer_only: return 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=f"ProfileStep#{self.step_num}", event_type=TracerEventType.ProfileStep, ) self.record_event.begin() def step_info(self, unit: str | None = None) -> str: r""" Get statistics for current step. If the function is called at certain iteration intervals, the result is the average of all steps between the previous call and this call. Statistics are as follows: 1. reader_cost: the cost of loading data measured in seconds. 2. batch_cost: the cost of step measured in seconds. 3. ips(Instance Per Second): the throughput of the model measured in `samples/s` or others depends on the `unit`. When `num_samples` of `step()` is None, it is measured in `steps/s`. Args: unit (string, optional): The unit of input data is only used When `num_samples` of `step()` is specified as a number. For example, when it is `images`, the unit of throughput is `images/s`. Default: None, the unit of throughput is `samples/s`. Returns: string: A string representing the statistic. Examples: .. code-block:: python :name: code-example-timer2 >>> import paddle.profiler as profiler >>> prof = profiler.Profiler(timer_only=True) >>> prof.start() >>> for iter in range(20): ... #train() ... prof.step() ... if iter % 10 == 0: ... print("Iter {}: {}".format(iter, prof.step_info())) ... # The example does not call the DataLoader, so there is no "reader_cost". ... # Iter 0: batch_cost: 0.00001 s ips: 86216.623 steps/s ... # Iter 10: batch_cost: 0.00001 s ips: 103645.034 steps/s >>> prof.stop() >>> # Time Unit: s, IPS Unit: steps/s >>> # | | avg | max | min | >>> # | batch_cost | 0.00000 | 0.00002 | 0.00000 | >>> # | ips | 267846.19437 | 712030.38727 | 45134.16662 | """ if unit is None: unit = 'samples' return benchmark().step_info(unit) 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: str = "", format: str = "json") -> None: r""" Exports the tracing data to file. Args: path(str): file path of the output. format(str, optional): output format, can be chosen from ['json', 'pb'], 'json' for chrome tracing and 'pb' for protobuf, default value is 'json'. Examples: .. code-block:: python :name: code-example7 >>> # doctest: +REQUIRES(env:GPU) >>> import paddle >>> paddle.device.set_device('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 = SortedKeys.CPUTotal, op_detail: bool = True, thread_sep: bool = False, time_unit: Literal['s', 'ms', 'us', 'ns'] = 'ms', views: SummaryView | list[SummaryView] | None = None, ) -> None: r""" Print the Summary table. Currently support overview, model, distributed, operator, memory manipulation and user-defined summary. Args: sorted_by( :ref:`SortedKeys ` , optional): how to rank the op table items, default value is SortedKeys.CPUTotal. op_detail(bool, optional): expand each operator detail information, default value is True. thread_sep(bool, optional): print op table each thread, default value is False. time_unit(str, optional): time unit for display, can be chosen from ['s', 'ms', 'us', 'ns'], default value is 'ms'. views(SummaryView|list[SummaryView], optional): summary tables to print, default to None means all views to be printed. Examples: .. code-block:: python >>> # doctest: +REQUIRES(env:GPU) >>> import paddle >>> paddle.device.set_device('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 isinstance(views, SummaryView): views = [views] 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, views=views, ) ) if self.with_flops: self._print_flops() def _print_flops(self, repeat=1): if not self.with_flops: print('ERROR: with_flops disabled.') return print(" Flops Profiler Begin ".center(100, "-")) print(gen_layer_flops(self.profiler_result.get_data(), repeat)) print("- Flops Profiler End -".center(100, "-")) def get_profiler(config_path): try: with open(config_path, 'r') as filehandle: config_dict = json.load(filehandle) except Exception as e: print(f'Load config file for profiler error: {e}') print('Use default parameters instead.') return Profiler() translated_config_dict = {} if "targets" in config_dict: try: translated_config_dict['targets'] = [] for target in config_dict['targets']: if target.lower() == "cpu": translated_config_dict['targets'].append(ProfilerTarget.CPU) elif target.lower() == 'gpu': translated_config_dict['targets'].append(ProfilerTarget.GPU) except: print('Set targets parameter error, use default parameter instead.') translated_config_dict['targets'] = None if "scheduler" in config_dict: try: if isinstance(config_dict['scheduler'], dict): for key, value in config_dict['scheduler'].items(): module_path = value['module'] use_direct = value['use_direct'] module = importlib.import_module(module_path) method = getattr(module, key) if not use_direct: translated_config_dict['scheduler'] = method( *value['args'], **value['kwargs'] ) else: translated_config_dict['scheduler'] = method else: translated_config_dict['scheduler'] = [ config_dict['scheduler'][0], config_dict['scheduler'][1], ] except: print( 'Set scheduler parameter error, use default parameter instead.' ) translated_config_dict['scheduler'] = None if "on_trace_ready" in config_dict: try: if isinstance(config_dict['on_trace_ready'], dict): for key, value in config_dict['on_trace_ready'].items(): module_path = value['module'] use_direct = value['use_direct'] module = importlib.import_module(module_path) method = getattr(module, key) if not use_direct: translated_config_dict['on_trace_ready'] = method( *value['args'], **value['kwargs'] ) else: translated_config_dict['on_trace_ready'] = method except: print( 'Set on_trace_ready parameter error, use default parameter instead.' ) translated_config_dict['on_trace_ready'] = None if "timer_only" in config_dict: if isinstance(config_dict['timer_only'], bool): translated_config_dict['timer_only'] = config_dict['timer_only'] else: print( 'Set timer_only parameter error, use default parameter instead.' ) return Profiler(**translated_config_dict)