1043 lines
40 KiB
Python
1043 lines
40 KiB
Python
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from collections import defaultdict
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from typing import Any, Dict, List, Optional
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from warnings import warn
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import torch
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import torch.cuda
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from torch._C import _get_privateuse1_backend_name
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from torch._C._profiler import _ExperimentalConfig
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from torch.autograd import (
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_disable_profiler,
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_enable_profiler,
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_kineto_step,
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_prepare_profiler,
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_ProfilerResult,
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_supported_activities,
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DeviceType,
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kineto_available,
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ProfilerActivity,
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ProfilerConfig,
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ProfilerState,
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)
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from torch.autograd.profiler_util import (
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_filter_name,
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_filter_stack_entry,
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_rewrite_name,
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EventList,
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FunctionEvent,
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MEMORY_EVENT_NAME,
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MemRecordsAcc,
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OUT_OF_MEMORY_EVENT_NAME,
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)
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from torch.futures import Future
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__all__ = [
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"profile",
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"record_function",
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"emit_itt",
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"emit_nvtx",
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"load_nvprof",
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"EnforceUnique",
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"parse_nvprof_trace",
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"KinetoStepTracker",
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"EventList",
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"FunctionEvent",
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"MemRecordsAcc",
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]
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try:
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# Available in Python >= 3.2
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from contextlib import ContextDecorator as _ContextDecorator
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except ImportError:
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import functools
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class _ContextDecorator: # type: ignore[no-redef]
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def __enter__(self):
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raise NotImplementedError
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def __exit__(self, exc_type, exc_val, exc_tb):
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raise NotImplementedError
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def __call__(self, func):
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@functools.wraps(func)
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def wrapped(*args, **kwargs):
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with self:
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return func(*args, **kwargs)
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return wrapped
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# global python state - whether profiler is currently enabled
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# useful for fast python checks to reduce latency
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_is_profiler_enabled: bool = False
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def _set_is_profiler_enabled(enable: bool):
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global _is_profiler_enabled
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_is_profiler_enabled = enable
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def _run_on_profiler_start():
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_set_is_profiler_enabled(True)
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def _run_on_profiler_stop():
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_set_is_profiler_enabled(False)
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class profile:
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"""Context manager that manages autograd profiler state and holds a summary of results.
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Under the hood it just records events of functions being executed in C++ and
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exposes those events to Python. You can wrap any code into it and it will
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only report runtime of PyTorch functions.
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Note: profiler is thread local and is automatically propagated into the async tasks
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Args:
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enabled (bool, optional): Setting this to False makes this context manager a no-op.
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use_cuda (bool, optional): Enables timing of CUDA events as well using the cudaEvent API.
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Adds approximately 4us of overhead to each tensor operation.
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record_shapes (bool, optional): If shapes recording is set, information
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about input dimensions will be collected. This allows one to see which
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dimensions have been used under the hood and further group by them
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using prof.key_averages(group_by_input_shape=True). Please note that
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shape recording might skew your profiling data. It is recommended to
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use separate runs with and without shape recording to validate the timing.
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Most likely the skew will be negligible for bottom most events (in a case
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of nested function calls). But for higher level functions the total
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self cpu time might be artificially increased because of the shape
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collection.
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with_flops (bool, optional): If with_flops is set, the profiler will estimate
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the FLOPs (floating point operations) value using the operator's input shape.
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This allows one to estimate the hardware performance. Currently,
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this option only works for the matrix multiplication and 2D convolution operators.
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profile_memory (bool, optional): track tensor memory allocation/deallocation.
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with_stack (bool, optional): record source information (file and line number) for the ops.
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with_modules (bool): record module hierarchy (including function names)
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corresponding to the callstack of the op. e.g. If module A's forward call's
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module B's forward which contains an aten::add op,
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then aten::add's module hierarchy is A.B
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Note that this support exist, at the moment, only for TorchScript models
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and not eager mode models.
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use_kineto (bool, optional): experimental, enable profiling with Kineto profiler.
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use_cpu (bool, optional): profile CPU events; setting to ``False`` requires
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``use_kineto=True`` and can be used to lower the overhead for GPU-only profiling.
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experimental_config (_ExperimentalConfig) : A set of experimental options
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used by profiler libraries like Kineto. Note, backward compatibility is not guaranteed.
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.. warning:
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Enabling memory profiling or source attribution incurs additional profiler
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overhead
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.. warning:
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This context managers should not be called recursively, i.e. no nested
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instances are allowed
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.. warning:
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Due to some CUDA multiprocessing limitations (multiprocessing-cuda-note_),
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one cannot use the profiler with ``use_cuda = True`` to benchmark
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DataLoaders with ``num_workers > 0``. If you wish to benchmark data loading,
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please use ``use_cuda = False`` or ``num_workers = 0``.
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Example:
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>>> # xdoctest: +SKIP
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>>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD_PROFILER)
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>>> x = torch.randn((1, 1), requires_grad=True)
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>>> with torch.autograd.profiler.profile() as prof:
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>>> for _ in range(100): # any normal python code, really!
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>>> y = x ** 2
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>>> y.backward()
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>>> # NOTE: some columns were removed for brevity
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>>> print(prof.key_averages().table(sort_by="self_cpu_time_total"))
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----------------------------------- --------------- --------------- ---------------
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Name Self CPU total CPU time avg Number of Calls
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----------------------------------- --------------- --------------- ---------------
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mul 32.048ms 32.048ms 200
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pow 27.041ms 27.041ms 200
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PowBackward0 9.727ms 55.483ms 100
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torch::autograd::AccumulateGrad 9.148ms 9.148ms 100
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torch::autograd::GraphRoot 691.816us 691.816us 100
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----------------------------------- --------------- --------------- ---------------
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"""
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def __init__(
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self,
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enabled=True,
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*,
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use_cuda=False,
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use_device=None,
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record_shapes=False,
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with_flops=False,
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profile_memory=False,
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with_stack=False,
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with_modules=False,
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use_kineto=False,
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use_cpu=True,
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use_mtia=False,
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experimental_config=None,
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):
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self.enabled: bool = enabled
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if not self.enabled:
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return
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self.use_cuda = use_cuda
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self.use_device: Optional[str] = (
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use_device if use_device != "privateuseone" else None
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)
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self.function_events: Optional[EventList] = None
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self.entered = False
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self.record_shapes = record_shapes
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self.with_flops = with_flops
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self.record_shapes |= self.with_flops
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self.profile_memory = profile_memory
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self.with_stack = with_stack
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self.with_modules = with_modules
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self.use_cpu = use_cpu
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self.use_mtia = use_mtia
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if experimental_config is None:
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experimental_config = _ExperimentalConfig()
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self.experimental_config = experimental_config
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self.kineto_results: Optional[_ProfilerResult] = None
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if not self.use_cpu:
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assert (
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use_kineto
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), "Device-only events supported only with Kineto (use_kineto=True)"
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if self.use_device == "cuda":
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self.use_device = None
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self.use_cuda = True
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if self.use_device and self.use_device != _get_privateuse1_backend_name():
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warn(f"{self.use_device} doesn't support profile.")
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self.use_device = None
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if self.use_cuda and not torch.cuda.is_available():
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warn("CUDA is not available, disabling CUDA profiling")
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self.use_cuda = False
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self.kineto_activities = set()
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if self.use_cpu:
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self.kineto_activities.add(ProfilerActivity.CPU)
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if self.use_mtia:
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self.kineto_activities.add(ProfilerActivity.MTIA)
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self.profiler_kind = ProfilerState.KINETO
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if self.use_cuda:
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if not use_kineto or ProfilerActivity.CUDA not in _supported_activities():
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assert self.use_cpu, "Legacy CUDA profiling requires use_cpu=True"
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self.profiler_kind = ProfilerState.KINETO_GPU_FALLBACK
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else:
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self.kineto_activities.add(ProfilerActivity.CUDA)
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if self.use_device:
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if (
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not use_kineto
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or ProfilerActivity.PrivateUse1 not in _supported_activities()
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):
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assert (
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self.use_cpu
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), "Legacy custombackend profiling requires use_cpu=True"
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self.profiler_kind = ProfilerState.KINETO_PRIVATEUSE1_FALLBACK
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else:
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self.kineto_activities.add(ProfilerActivity.PrivateUse1)
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self.profiler_kind = ProfilerState.KINETO_PRIVATEUSE1
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assert (
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len(self.kineto_activities) > 0
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), "No activities specified for the profiler"
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def config(self):
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return ProfilerConfig(
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self.profiler_kind,
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self.record_shapes,
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self.profile_memory,
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self.with_stack,
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self.with_flops,
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self.with_modules,
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self.experimental_config,
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)
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def __enter__(self):
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if not self.enabled:
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return
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if self.entered:
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raise RuntimeError("Profiler context manager is not reentrant")
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self._prepare_trace()
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self._start_trace()
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return self
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def _prepare_trace(self):
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self.entered = True
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_prepare_profiler(self.config(), self.kineto_activities)
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def _start_trace(self):
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self.entered = True
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_run_on_profiler_start()
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_enable_profiler(self.config(), self.kineto_activities)
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def __exit__(self, exc_type, exc_val, exc_tb):
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if not self.enabled:
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return
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if self.use_cuda:
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torch.cuda.synchronize()
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self.kineto_results = _disable_profiler()
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_run_on_profiler_stop()
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parsed_results = self._parse_kineto_results(self.kineto_results)
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self.function_events = EventList(
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parsed_results,
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use_cuda=self.use_cuda,
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use_device=self.use_device,
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profile_memory=self.profile_memory,
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with_flops=self.with_flops,
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)
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self.function_events._build_tree()
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return False
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def __repr__(self):
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if self.function_events is None:
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return "<unfinished torch.autograd.profile>"
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return repr(self.function_events)
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def __str__(self):
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if self.function_events is None:
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return "<unfinished torch.autograd.profile>"
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return str(self.function_events)
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def _check_finish(self):
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if self.function_events is None:
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raise RuntimeError("Profiler didn't finish running")
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|
|
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def table(
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self,
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sort_by=None,
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row_limit=100,
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max_src_column_width=75,
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max_name_column_width=55,
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max_shapes_column_width=80,
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header=None,
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top_level_events_only=False,
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):
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self._check_finish()
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assert self.function_events is not None
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return self.function_events.table(
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sort_by=sort_by,
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row_limit=row_limit,
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max_src_column_width=max_src_column_width,
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max_name_column_width=max_name_column_width,
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max_shapes_column_width=max_shapes_column_width,
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header=header,
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top_level_events_only=top_level_events_only,
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)
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table.__doc__ = EventList.table.__doc__
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|
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||
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def export_chrome_trace(self, path):
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self._check_finish()
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if kineto_available():
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self.kineto_results.save(path) # type: ignore[union-attr]
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else:
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return self.function_events.export_chrome_trace(path) # type: ignore[union-attr]
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export_chrome_trace.__doc__ = EventList.export_chrome_trace.__doc__
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def export_stacks(self, path: str, metric: str = "self_cpu_time_total"):
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self._check_finish()
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assert self.function_events is not None, "Expected profiling results"
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assert self.with_stack, "export_stacks() requires with_stack=True"
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return self.function_events.export_stacks(path, metric)
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def key_averages(self, group_by_input_shape=False, group_by_stack_n=0):
|
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self._check_finish()
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||
|
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assert self.function_events is not None, "Expected profiling results"
|
||
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return self.function_events.key_averages(group_by_input_shape, group_by_stack_n)
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key_averages.__doc__ = EventList.key_averages.__doc__
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def total_average(self):
|
||
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self._check_finish()
|
||
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assert self.function_events is not None, "Expected profiling results"
|
||
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return self.function_events.total_average()
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||
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|
|
||
|
|
total_average.__doc__ = EventList.total_average.__doc__
|
||
|
|
|
||
|
|
@property
|
||
|
|
def self_cpu_time_total(self):
|
||
|
|
"""Returns total time spent on CPU.
|
||
|
|
|
||
|
|
The total time is a sum of all self times across all the events.
|
||
|
|
"""
|
||
|
|
self._check_finish()
|
||
|
|
assert self.function_events is not None
|
||
|
|
return self.function_events.self_cpu_time_total
|
||
|
|
|
||
|
|
def _parse_kineto_results(self, result: _ProfilerResult):
|
||
|
|
# result.events() has most of the events - PyTorch op-level and device-level events
|
||
|
|
|
||
|
|
trace_start_us = result.trace_start_us()
|
||
|
|
mem_records = [
|
||
|
|
[evt, False] for evt in result.events() if evt.name() == MEMORY_EVENT_NAME
|
||
|
|
]
|
||
|
|
oom_records = [
|
||
|
|
evt for evt in result.events() if evt.name() == OUT_OF_MEMORY_EVENT_NAME
|
||
|
|
]
|
||
|
|
mem_records_acc = MemRecordsAcc(mem_records)
|
||
|
|
|
||
|
|
def _cpu_memory_usage(mem_record):
|
||
|
|
return (
|
||
|
|
mem_record.nbytes()
|
||
|
|
if mem_record.device_type()
|
||
|
|
in [DeviceType.CPU, DeviceType.MKLDNN, DeviceType.IDEEP]
|
||
|
|
else 0
|
||
|
|
)
|
||
|
|
|
||
|
|
def _cuda_memory_usage(mem_record):
|
||
|
|
return (
|
||
|
|
mem_record.nbytes()
|
||
|
|
if mem_record.device_type() in [DeviceType.CUDA, DeviceType.HIP]
|
||
|
|
else 0
|
||
|
|
)
|
||
|
|
|
||
|
|
def _privateuse1_memory_usage(mem_record):
|
||
|
|
return (
|
||
|
|
mem_record.nbytes()
|
||
|
|
if mem_record.device_type() in [DeviceType.PrivateUse1]
|
||
|
|
else 0
|
||
|
|
)
|
||
|
|
|
||
|
|
# Create and return FunctionEvent list
|
||
|
|
function_events = []
|
||
|
|
device_corr_map: Dict[int, List[FunctionEvent]] = {}
|
||
|
|
max_evt_id = 0
|
||
|
|
for kineto_event in result.events():
|
||
|
|
if _filter_name(kineto_event.name()):
|
||
|
|
continue
|
||
|
|
rel_start_us = kineto_event.start_us() - trace_start_us
|
||
|
|
rel_end_us = rel_start_us + kineto_event.duration_us()
|
||
|
|
abs_end_us = kineto_event.start_us() + kineto_event.duration_us()
|
||
|
|
|
||
|
|
cpu_memory_usage = 0
|
||
|
|
cuda_memory_usage = 0
|
||
|
|
privateuse1_memory_usage = 0
|
||
|
|
if kineto_event.device_type() == DeviceType.CPU:
|
||
|
|
# find the corresponding memory allocation events
|
||
|
|
for mem_record in mem_records_acc.in_interval(
|
||
|
|
kineto_event.start_us(), abs_end_us
|
||
|
|
):
|
||
|
|
cpu_memory_usage += _cpu_memory_usage(mem_record[0])
|
||
|
|
cuda_memory_usage += _cuda_memory_usage(mem_record[0])
|
||
|
|
privateuse1_memory_usage += _privateuse1_memory_usage(mem_record[0])
|
||
|
|
mem_record[1] = True
|
||
|
|
|
||
|
|
is_async = kineto_event.is_async() or (
|
||
|
|
kineto_event.start_thread_id() != kineto_event.end_thread_id()
|
||
|
|
)
|
||
|
|
|
||
|
|
fe = FunctionEvent(
|
||
|
|
id=kineto_event.correlation_id(),
|
||
|
|
name=_rewrite_name(name=kineto_event.name(), with_wildcard=True),
|
||
|
|
trace_name=_rewrite_name(name=kineto_event.name(), with_wildcard=False),
|
||
|
|
thread=kineto_event.start_thread_id(),
|
||
|
|
start_us=rel_start_us,
|
||
|
|
end_us=rel_end_us,
|
||
|
|
fwd_thread=kineto_event.fwd_thread_id(),
|
||
|
|
input_shapes=kineto_event.shapes(),
|
||
|
|
concrete_inputs=kineto_event.concrete_inputs(),
|
||
|
|
stack=[
|
||
|
|
entry
|
||
|
|
for entry in kineto_event.stack()
|
||
|
|
if _filter_stack_entry(entry)
|
||
|
|
],
|
||
|
|
scope=kineto_event.scope(),
|
||
|
|
use_device=self.use_device,
|
||
|
|
cpu_memory_usage=cpu_memory_usage,
|
||
|
|
cuda_memory_usage=cuda_memory_usage,
|
||
|
|
privateuse1_memory_usage=privateuse1_memory_usage,
|
||
|
|
is_async=is_async,
|
||
|
|
sequence_nr=kineto_event.sequence_nr(),
|
||
|
|
device_type=kineto_event.device_type(),
|
||
|
|
device_index=kineto_event.device_index(),
|
||
|
|
flops=kineto_event.flops(),
|
||
|
|
)
|
||
|
|
max_evt_id = max(max_evt_id, fe.id)
|
||
|
|
if fe.device_type == DeviceType.CPU and not fe.is_async:
|
||
|
|
if self.use_device:
|
||
|
|
privateuse1_time = kineto_event.privateuse1_elapsed_us()
|
||
|
|
if privateuse1_time > 0:
|
||
|
|
fe.append_kernel(fe.name, fe.device_index, privateuse1_time)
|
||
|
|
fe.is_legacy = True
|
||
|
|
else:
|
||
|
|
# Check if we have CUDA time as a fallback
|
||
|
|
cuda_time = kineto_event.cuda_elapsed_us()
|
||
|
|
if cuda_time > 0:
|
||
|
|
fe.append_kernel(fe.name, fe.device_index, cuda_time)
|
||
|
|
fe.is_legacy = True
|
||
|
|
function_events.append(fe)
|
||
|
|
corr_id = kineto_event.linked_correlation_id()
|
||
|
|
if corr_id > 0:
|
||
|
|
if corr_id not in device_corr_map:
|
||
|
|
device_corr_map[corr_id] = []
|
||
|
|
device_corr_map[corr_id].append(fe)
|
||
|
|
|
||
|
|
# associate CUDA kernels and CUDA runtime (CPU) with CPU events
|
||
|
|
for fe in function_events:
|
||
|
|
if (
|
||
|
|
fe.device_type == DeviceType.CPU
|
||
|
|
and not fe.is_async
|
||
|
|
and fe.id in device_corr_map
|
||
|
|
):
|
||
|
|
for f_evt in device_corr_map[fe.id]:
|
||
|
|
if f_evt.device_type == DeviceType.CUDA:
|
||
|
|
fe.append_kernel(
|
||
|
|
f_evt.name,
|
||
|
|
f_evt.device_index,
|
||
|
|
f_evt.time_range.end - f_evt.time_range.start,
|
||
|
|
)
|
||
|
|
elif f_evt.device_type == DeviceType.CPU:
|
||
|
|
# make sure that 'thread' of a CPU Kineto (e.g. CUDA Runtime) event is associated
|
||
|
|
# with the 'thread' of the corresponding linked PyTorch event to properly track
|
||
|
|
# parents and children
|
||
|
|
f_evt.thread = fe.thread
|
||
|
|
|
||
|
|
def createFunctionEventForMemoryEvents(evt):
|
||
|
|
rel_start_us = evt.start_us() - trace_start_us
|
||
|
|
fe = FunctionEvent(
|
||
|
|
id=max_evt_id,
|
||
|
|
name=evt.name(),
|
||
|
|
trace_name=None, # not outputting in the trace
|
||
|
|
thread=evt.start_thread_id(),
|
||
|
|
start_us=rel_start_us,
|
||
|
|
end_us=rel_start_us, # no duration
|
||
|
|
fwd_thread=evt.start_thread_id(),
|
||
|
|
input_shapes=[],
|
||
|
|
stack=[],
|
||
|
|
scope=0, # RecordScope::FUNCTION
|
||
|
|
use_device=self.use_device,
|
||
|
|
cpu_memory_usage=_cpu_memory_usage(evt),
|
||
|
|
cuda_memory_usage=_cuda_memory_usage(evt),
|
||
|
|
privateuse1_memory_usage=_privateuse1_memory_usage(evt),
|
||
|
|
is_async=False,
|
||
|
|
sequence_nr=-1,
|
||
|
|
device_type=DeviceType.CPU,
|
||
|
|
device_index=0,
|
||
|
|
)
|
||
|
|
return fe
|
||
|
|
|
||
|
|
# output top-level memory events
|
||
|
|
for mem_record in mem_records:
|
||
|
|
if not mem_record[1]:
|
||
|
|
max_evt_id += 1
|
||
|
|
fe = createFunctionEventForMemoryEvents(mem_record[0])
|
||
|
|
function_events.append(fe)
|
||
|
|
|
||
|
|
for oom_record in oom_records:
|
||
|
|
max_evt_id += 1
|
||
|
|
fe = createFunctionEventForMemoryEvents(oom_record)
|
||
|
|
function_events.append(fe)
|
||
|
|
|
||
|
|
function_events.sort(
|
||
|
|
key=lambda evt: [evt.time_range.start, -evt.time_range.end]
|
||
|
|
)
|
||
|
|
return function_events
|
||
|
|
|
||
|
|
|
||
|
|
class record_function(_ContextDecorator):
|
||
|
|
"""Context manager/function decorator that adds a label to a code block/function when running autograd profiler.
|
||
|
|
|
||
|
|
It is useful when tracing the code profile.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
name (str): Label assigned to the block of code.
|
||
|
|
node_id (int): ID of node, for distributed profiling. Unset in
|
||
|
|
non-distributed cases.
|
||
|
|
|
||
|
|
Example:
|
||
|
|
>>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD_PROFILER)
|
||
|
|
>>> x = torch.randn((1, 1), requires_grad=True)
|
||
|
|
>>> with torch.autograd.profiler.profile() as prof:
|
||
|
|
... y = x ** 2
|
||
|
|
... with torch.autograd.profiler.record_function("label-z"): # label the block
|
||
|
|
... z = y ** 3
|
||
|
|
... y.backward()
|
||
|
|
...
|
||
|
|
>>> # xdoctest: +IGNORE_WANT
|
||
|
|
>>> # NOTE: some columns were removed for brevity
|
||
|
|
>>> print(prof.key_averages().table(sort_by="self_cpu_time_total"))
|
||
|
|
----------------------------------- --------------- --------------- ---------------
|
||
|
|
Name Self CPU total % CPU time avg Number of Calls
|
||
|
|
----------------------------------- --------------- --------------- ---------------
|
||
|
|
pow 60.77% 47.470us 3
|
||
|
|
mul 21.73% 25.465us 2
|
||
|
|
PowBackward0 12.03% 121.891us 1
|
||
|
|
torch::autograd::AccumulateGrad 2.70% 6.324us 1
|
||
|
|
label-z 2.13% 12.421us 1
|
||
|
|
torch::autograd::GraphRoot 0.64% 1.503us 1
|
||
|
|
----------------------------------- --------------- --------------- ---------------
|
||
|
|
Self CPU time total: 234.344us
|
||
|
|
CUDA time total: 0.000us
|
||
|
|
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(self, name: str, args: Optional[str] = None):
|
||
|
|
self.name: str = name
|
||
|
|
self.args: Optional[str] = args
|
||
|
|
# Whether or not we should run record function's end callbacks when exiting.
|
||
|
|
self.run_callbacks_on_exit: bool = True
|
||
|
|
# TODO: TorchScript ignores standard type annotation here
|
||
|
|
# self.record: Optional["torch.classes.profiler._RecordFunction"] = None
|
||
|
|
self.record = torch.jit.annotate(
|
||
|
|
Optional["torch.classes.profiler._RecordFunction"], None
|
||
|
|
)
|
||
|
|
|
||
|
|
def __enter__(self):
|
||
|
|
self.record = torch.ops.profiler._record_function_enter_new(
|
||
|
|
self.name, self.args
|
||
|
|
)
|
||
|
|
return self
|
||
|
|
|
||
|
|
def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any):
|
||
|
|
if not self.run_callbacks_on_exit:
|
||
|
|
return
|
||
|
|
|
||
|
|
# Local variable is needed by TorchScript to refine Optional[T] to T
|
||
|
|
record = self.record
|
||
|
|
assert record is not None
|
||
|
|
|
||
|
|
# TODO: Too slow with __torch_function__ handling enabled
|
||
|
|
# See https://github.com/pytorch/pytorch/issues/76410
|
||
|
|
if not torch.jit.is_scripting():
|
||
|
|
with torch._C.DisableTorchFunctionSubclass():
|
||
|
|
torch.ops.profiler._record_function_exit._RecordFunction(record)
|
||
|
|
else:
|
||
|
|
torch.ops.profiler._record_function_exit(record)
|
||
|
|
|
||
|
|
def _call_end_callbacks_on_future(self, fut: Future[Any]) -> Future[Any]:
|
||
|
|
"""Use for profiling async calls that return a future.
|
||
|
|
|
||
|
|
Calling this function will extend recording beyond this scope, until the future is
|
||
|
|
satisfied. It is useful for profiling the end to end time of asynchronous calls.
|
||
|
|
This function should only be called once to attach the callback onto the future, and
|
||
|
|
will throw if called multiple times.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
fut: (torch._C.Future): future for which to schedule
|
||
|
|
callback for.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
A future that completes with the value of the passed in future when
|
||
|
|
the profiling callbacks have ran.
|
||
|
|
|
||
|
|
"""
|
||
|
|
# Throw if we have already attached a callback onto the future.
|
||
|
|
if not self.run_callbacks_on_exit:
|
||
|
|
raise RuntimeError("_call_end_callbacks_on_future can only be called once.")
|
||
|
|
|
||
|
|
# We are scheduling to run this RecordFunction's end callbacks when the
|
||
|
|
# passed in future completes, so don't run end callbacks on exit.
|
||
|
|
self.run_callbacks_on_exit = False
|
||
|
|
|
||
|
|
# Local variable is needed by TorchScript to refine Optional[T] to T
|
||
|
|
record = self.record
|
||
|
|
assert record is not None
|
||
|
|
|
||
|
|
# TODO: Too slow with __torch_function__ handling enabled
|
||
|
|
# See https://github.com/pytorch/pytorch/issues/76410
|
||
|
|
if not torch.jit.is_scripting():
|
||
|
|
with torch._C.DisableTorchFunctionSubclass():
|
||
|
|
profiled_future = (
|
||
|
|
torch.ops.profiler._call_end_callbacks_on_jit_fut._RecordFunction(
|
||
|
|
record, fut
|
||
|
|
)
|
||
|
|
)
|
||
|
|
else:
|
||
|
|
profiled_future = torch.ops.profiler._call_end_callbacks_on_jit_fut(
|
||
|
|
record, fut
|
||
|
|
)
|
||
|
|
return profiled_future
|
||
|
|
|
||
|
|
|
||
|
|
class emit_itt:
|
||
|
|
"""Context manager that makes every autograd operation emit an ITT range.
|
||
|
|
|
||
|
|
It is useful when running the program under Intel(R) VTune Profiler::
|
||
|
|
|
||
|
|
vtune <--vtune-flags> <regular command here>
|
||
|
|
|
||
|
|
The Instrumentation and Tracing Technology (ITT) API enables your application to generate and
|
||
|
|
control the collection of trace data during its execution across different Intel tools.
|
||
|
|
This context manager is to annotate Intel(R) VTune Profiling trace. With help of this context manager,
|
||
|
|
you will be able to see labled ranges in Intel(R) VTune Profiler GUI.
|
||
|
|
|
||
|
|
.. warning:
|
||
|
|
This context manager should not be called recursively, i.e. at most one
|
||
|
|
instance should be enabled at any given time.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
enabled (bool, optional): Setting ``enabled=False`` makes this context manager a no-op.
|
||
|
|
Default: ``True``.
|
||
|
|
record_shapes (bool, optional): If ``record_shapes=True``, the itt range wrapping
|
||
|
|
each autograd op will append information about the sizes of Tensor arguments received
|
||
|
|
by that op, in the following format:
|
||
|
|
``[[arg0.size(0), arg0.size(1), ...], [arg1.size(0), arg1.size(1), ...], ...]``
|
||
|
|
Non-tensor arguments will be represented by ``[]``.
|
||
|
|
Arguments will be listed in the order they are received by the backend op.
|
||
|
|
Please note that this order may not match the order in which those arguments were passed
|
||
|
|
on the Python side. Also note that shape recording may increase the overhead of itt range creation.
|
||
|
|
Default: ``False``
|
||
|
|
|
||
|
|
Example:
|
||
|
|
>>> # xdoctest: +SKIP("Undefined variables")
|
||
|
|
>>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD_PROFILER)
|
||
|
|
>>> with torch.autograd.profiler.emit_itt():
|
||
|
|
... model(x)
|
||
|
|
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(self, enabled=True, record_shapes=False):
|
||
|
|
self.enabled = enabled
|
||
|
|
self.entered = False
|
||
|
|
self.record_shapes = record_shapes
|
||
|
|
|
||
|
|
def __enter__(self):
|
||
|
|
if not self.enabled:
|
||
|
|
return
|
||
|
|
if self.entered:
|
||
|
|
raise RuntimeError("ITT annotation context manager is not reentrant")
|
||
|
|
self.entered = True
|
||
|
|
_run_on_profiler_start()
|
||
|
|
_enable_profiler(
|
||
|
|
ProfilerConfig(
|
||
|
|
ProfilerState.ITT,
|
||
|
|
self.record_shapes,
|
||
|
|
False,
|
||
|
|
False,
|
||
|
|
False,
|
||
|
|
False,
|
||
|
|
_ExperimentalConfig(),
|
||
|
|
),
|
||
|
|
set(),
|
||
|
|
)
|
||
|
|
return self
|
||
|
|
|
||
|
|
def __exit__(self, exc_type, exc_val, exc_tb):
|
||
|
|
if not self.enabled:
|
||
|
|
return
|
||
|
|
_disable_profiler()
|
||
|
|
_run_on_profiler_stop()
|
||
|
|
return False
|
||
|
|
|
||
|
|
|
||
|
|
class emit_nvtx:
|
||
|
|
"""Context manager that makes every autograd operation emit an NVTX range.
|
||
|
|
|
||
|
|
It is useful when running the program under nvprof::
|
||
|
|
|
||
|
|
nvprof --profile-from-start off -o trace_name.prof -- <regular command here>
|
||
|
|
|
||
|
|
Unfortunately, there's no way to force nvprof to flush the data it collected
|
||
|
|
to disk, so for CUDA profiling one has to use this context manager to annotate
|
||
|
|
nvprof traces and wait for the process to exit before inspecting them.
|
||
|
|
Then, either NVIDIA Visual Profiler (nvvp) can be used to visualize the timeline, or
|
||
|
|
:func:`torch.autograd.profiler.load_nvprof` can load the results for inspection
|
||
|
|
e.g. in Python REPL.
|
||
|
|
|
||
|
|
.. warning:
|
||
|
|
This context manager should not be called recursively, i.e. at most one
|
||
|
|
instance should be enabled at any given time.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
enabled (bool, optional): Setting ``enabled=False`` makes this context manager a no-op.
|
||
|
|
Default: ``True``.
|
||
|
|
record_shapes (bool, optional): If ``record_shapes=True``, the nvtx range wrapping
|
||
|
|
each autograd op will append information about the sizes of Tensor arguments received
|
||
|
|
by that op, in the following format:
|
||
|
|
``[[arg0.size(0), arg0.size(1), ...], [arg1.size(0), arg1.size(1), ...], ...]``
|
||
|
|
Non-tensor arguments will be represented by ``[]``.
|
||
|
|
Arguments will be listed in the order they are received by the backend op.
|
||
|
|
Please note that this order may not match the order in which those arguments were passed
|
||
|
|
on the Python side. Also note that shape recording may increase the overhead of nvtx range creation.
|
||
|
|
Default: ``False``
|
||
|
|
|
||
|
|
Example:
|
||
|
|
>>> # xdoctest: +SKIP("undefined variables")
|
||
|
|
>>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD_PROFILER)
|
||
|
|
>>> with torch.cuda.profiler.profile():
|
||
|
|
... model(x) # Warmup CUDA memory allocator and profiler
|
||
|
|
... with torch.autograd.profiler.emit_nvtx():
|
||
|
|
... model(x)
|
||
|
|
|
||
|
|
**Forward-backward correlation**
|
||
|
|
|
||
|
|
When viewing a profile created using :class:`emit_nvtx` in the Nvidia Visual Profiler,
|
||
|
|
correlating each backward-pass op with the corresponding forward-pass op can be difficult.
|
||
|
|
To ease this task, :class:`emit_nvtx` appends sequence number information to the ranges it
|
||
|
|
generates.
|
||
|
|
|
||
|
|
During the forward pass, each function range is decorated with ``seq=<N>``. ``seq`` is a running
|
||
|
|
counter, incremented each time a new backward Function object is created and stashed for backward.
|
||
|
|
Thus, the ``seq=<N>`` annotation associated with each forward function range tells you that
|
||
|
|
if a backward Function object is created by this forward function,
|
||
|
|
the backward object will receive sequence number N.
|
||
|
|
During the backward pass, the top-level range wrapping each C++ backward Function's
|
||
|
|
``apply()`` call is decorated with ``stashed seq=<M>``. ``M`` is the sequence number that
|
||
|
|
the backward object was created with. By comparing ``stashed seq`` numbers in backward with ``seq``
|
||
|
|
numbers in forward, you can track down which forward op created each backward Function.
|
||
|
|
|
||
|
|
Any functions executed during the backward pass are also decorated with ``seq=<N>``. During
|
||
|
|
default backward (with ``create_graph=False``) this information is irrelevant, and in fact,
|
||
|
|
``N`` may simply be 0 for all such functions. Only the top-level ranges associated with
|
||
|
|
backward Function objects' ``apply()`` methods are useful, as a way to correlate these Function
|
||
|
|
objects with the earlier forward pass.
|
||
|
|
|
||
|
|
**Double-backward**
|
||
|
|
|
||
|
|
If, on the other hand, a backward pass with ``create_graph=True`` is underway (in other words,
|
||
|
|
if you are setting up for a double-backward), each function's execution during backward
|
||
|
|
is given a nonzero, useful ``seq=<N>``. Those functions may themselves create Function objects
|
||
|
|
to be executed later during double-backward, just as the original functions in the forward pass did.
|
||
|
|
The relationship between backward and double-backward is conceptually the same as the relationship
|
||
|
|
between forward and backward: The functions still emit current-sequence-number-tagged ranges,
|
||
|
|
the Function objects they create still stash those sequence numbers, and during the eventual
|
||
|
|
double-backward, the Function objects' ``apply()`` ranges are still tagged with ``stashed seq``
|
||
|
|
numbers, which can be compared to `seq` numbers from the backward pass.
|
||
|
|
|
||
|
|
.. warning:
|
||
|
|
The sequence number is thread-local, and some forward functions don't create an associated
|
||
|
|
backward Function object (instead delegating that to sub-functions further down the call chain).
|
||
|
|
For these reasons, the correspondence of stashed sequence numbers in
|
||
|
|
backward Function ``apply()`` ranges with `seq` numbers in forward-pass ranges is
|
||
|
|
not guaranteed to be 1 to 1. The sequence numbers alone may not be enough to fully
|
||
|
|
disambiguate which forward function created which
|
||
|
|
backward Function object. You may need to make a judgment based on analytic knowledge of what
|
||
|
|
the expected correspondence should be.
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(self, enabled=True, record_shapes=False):
|
||
|
|
self.enabled = enabled
|
||
|
|
self.entered = False
|
||
|
|
self.record_shapes = record_shapes
|
||
|
|
|
||
|
|
def __enter__(self):
|
||
|
|
if not self.enabled:
|
||
|
|
return
|
||
|
|
if self.entered:
|
||
|
|
raise RuntimeError("NVTX annotation context manager is not reentrant")
|
||
|
|
self.entered = True
|
||
|
|
torch.cuda.synchronize()
|
||
|
|
_run_on_profiler_start()
|
||
|
|
_enable_profiler(
|
||
|
|
ProfilerConfig(
|
||
|
|
ProfilerState.NVTX,
|
||
|
|
self.record_shapes,
|
||
|
|
False,
|
||
|
|
False,
|
||
|
|
False,
|
||
|
|
False,
|
||
|
|
_ExperimentalConfig(),
|
||
|
|
),
|
||
|
|
set(),
|
||
|
|
)
|
||
|
|
return self
|
||
|
|
|
||
|
|
def __exit__(self, exc_type, exc_val, exc_tb):
|
||
|
|
if not self.enabled:
|
||
|
|
return
|
||
|
|
torch.cuda.synchronize()
|
||
|
|
_disable_profiler()
|
||
|
|
_run_on_profiler_stop()
|
||
|
|
return False
|
||
|
|
|
||
|
|
|
||
|
|
def load_nvprof(path):
|
||
|
|
"""Open an nvprof trace file and parses autograd annotations.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
path (str): path to nvprof trace
|
||
|
|
"""
|
||
|
|
return EventList(parse_nvprof_trace(path))
|
||
|
|
|
||
|
|
|
||
|
|
class EnforceUnique:
|
||
|
|
"""Raises an error if a key is seen more than once."""
|
||
|
|
|
||
|
|
def __init__(self):
|
||
|
|
self.seen = set()
|
||
|
|
|
||
|
|
def see(self, *key):
|
||
|
|
r"""
|
||
|
|
Observe a key and raise an error if it is seen multiple times.
|
||
|
|
"""
|
||
|
|
if key in self.seen:
|
||
|
|
raise RuntimeError("duplicate key: " + str(key))
|
||
|
|
self.seen.add(key)
|
||
|
|
|
||
|
|
|
||
|
|
def parse_nvprof_trace(path):
|
||
|
|
import sqlite3
|
||
|
|
|
||
|
|
conn = sqlite3.connect(path)
|
||
|
|
conn.row_factory = sqlite3.Row
|
||
|
|
|
||
|
|
# Parse strings table
|
||
|
|
strings = {}
|
||
|
|
for r in conn.execute("SELECT _id_ as id, value FROM StringTable"):
|
||
|
|
strings[r["id"]] = torch._C._demangle(r["value"])
|
||
|
|
|
||
|
|
# First, find all functions and create FunctionEvents for them
|
||
|
|
marker_query = """
|
||
|
|
SELECT
|
||
|
|
start.id AS marker_id, start.name, start.timestamp AS start_time, end.timestamp AS end_time
|
||
|
|
FROM
|
||
|
|
CUPTI_ACTIVITY_KIND_MARKER AS start INNER JOIN CUPTI_ACTIVITY_KIND_MARKER AS end
|
||
|
|
ON start.id = end.id
|
||
|
|
WHERE
|
||
|
|
start.name != 0 AND end.name = 0
|
||
|
|
"""
|
||
|
|
functions = []
|
||
|
|
functions_map = {}
|
||
|
|
unique = EnforceUnique()
|
||
|
|
for row in conn.execute(marker_query):
|
||
|
|
unique.see(row["marker_id"])
|
||
|
|
evt = FunctionEvent(
|
||
|
|
id=row["marker_id"],
|
||
|
|
node_id=0, # missing a node_id when calling FunctionEvent. This is just to ensure
|
||
|
|
# that pytorch doesn't crash when creating a FunctionEvent() object
|
||
|
|
name=strings[row["name"]],
|
||
|
|
start_us=row["start_time"],
|
||
|
|
end_us=row["end_time"],
|
||
|
|
thread=0,
|
||
|
|
) # TODO: find in sqlite database
|
||
|
|
functions.append(evt)
|
||
|
|
functions_map[evt.id] = evt
|
||
|
|
|
||
|
|
# Now, correlate all kernels with FunctionEvents
|
||
|
|
kernel_query = """
|
||
|
|
SELECT
|
||
|
|
start.id AS marker_id, start.name, start.timestamp, end.timestamp,
|
||
|
|
runtime._id_ AS runtime_id, runtime.cbid, runtime.start AS runtime_start, runtime.end AS runtime_end,
|
||
|
|
kernel.start AS kernel_start, kernel.end AS kernel_end, kernel.name AS kernel_name
|
||
|
|
FROM
|
||
|
|
CUPTI_ACTIVITY_KIND_MARKER AS start
|
||
|
|
INNER JOIN CUPTI_ACTIVITY_KIND_MARKER AS end
|
||
|
|
ON start.id = end.id
|
||
|
|
INNER JOIN CUPTI_ACTIVITY_KIND_RUNTIME as runtime
|
||
|
|
ON (start.timestamp < runtime.start AND runtime.end < end.timestamp)
|
||
|
|
INNER JOIN CUPTI_ACTIVITY_KIND_CONCURRENT_KERNEL AS kernel
|
||
|
|
ON kernel.correlationId = runtime.correlationId
|
||
|
|
"""
|
||
|
|
unique = EnforceUnique()
|
||
|
|
for row in conn.execute(kernel_query):
|
||
|
|
unique.see(row["marker_id"], row["runtime_id"])
|
||
|
|
# 211 is cudaKernelLaunch for cuda >= 9.2
|
||
|
|
assert row["cbid"] == 211
|
||
|
|
evt = functions_map[row["marker_id"]]
|
||
|
|
evt.append_kernel(
|
||
|
|
row["kernel_name"], 0, row["kernel_end"] - row["kernel_start"]
|
||
|
|
)
|
||
|
|
|
||
|
|
functions.sort(key=lambda evt: evt.time_range.start)
|
||
|
|
return functions
|
||
|
|
|
||
|
|
|
||
|
|
class KinetoStepTracker:
|
||
|
|
"""Provides an abstraction for incrementing the step count globally.
|
||
|
|
|
||
|
|
Previously, we only had one place to mark that a step() has occurred
|
||
|
|
in the program via pytorch profiler step(). We will now add step hooks
|
||
|
|
in the Optimizer class https://github.com/pytorch/pytorch/issues/88446
|
||
|
|
|
||
|
|
- This could mean programs that already call profiler.step() every
|
||
|
|
iteration can end up double incrementing step count.
|
||
|
|
- If a model uses multiple optimizers we can also have double or more
|
||
|
|
counting of the step.
|
||
|
|
|
||
|
|
We fix this by adding a layer of abstraction before calling step()
|
||
|
|
to the kineto library. The idea is to maintain steps per requester in a dict:
|
||
|
|
|
||
|
|
.. code-block::
|
||
|
|
|
||
|
|
{
|
||
|
|
"ProfilerStep": 100, # triggered by profiler step() call
|
||
|
|
"Optimizer1Step": 100, # Optimizer 1 or 2 are just examples, could be SGD, Adam etc
|
||
|
|
"Optimizer2Step": 100,
|
||
|
|
}
|
||
|
|
|
||
|
|
To figure out the global step count just take the max of dict values (100).
|
||
|
|
|
||
|
|
If one of the count increments the max will go up.
|
||
|
|
|
||
|
|
.. code-block::
|
||
|
|
|
||
|
|
{
|
||
|
|
"ProfilerStep": 100,
|
||
|
|
"Optimizer1Step": 101, # Optimizer1 got incremented first say
|
||
|
|
"Optimizer2Step": 100,
|
||
|
|
}
|
||
|
|
|
||
|
|
Then global step count is 101
|
||
|
|
We only call the kineto step() function when global count increments.
|
||
|
|
|
||
|
|
NOTE: Please do not use the KinetoStepTracker in modules beside the Optimizer
|
||
|
|
for now. The result could be incorrect increments of the step count.
|
||
|
|
"""
|
||
|
|
|
||
|
|
_current_step = 0
|
||
|
|
_step_dict: Dict[str, int] = defaultdict(int)
|
||
|
|
|
||
|
|
@classmethod
|
||
|
|
def init_step_count(cls, requester: str):
|
||
|
|
r"""
|
||
|
|
Initialize for a given requester.
|
||
|
|
"""
|
||
|
|
cls._step_dict[requester] = cls._current_step
|
||
|
|
|
||
|
|
@classmethod
|
||
|
|
def erase_step_count(cls, requester: str) -> bool:
|
||
|
|
r"""
|
||
|
|
Remove a given requester.
|
||
|
|
"""
|
||
|
|
return cls._step_dict.pop(requester, None) is not None
|
||
|
|
|
||
|
|
@classmethod
|
||
|
|
def increment_step(cls, requester: str) -> int:
|
||
|
|
"""Increments the step count for the requester.
|
||
|
|
|
||
|
|
Additionally if the max over all step counts has incremented then
|
||
|
|
trigger the _kineto_step() returns global step count
|
||
|
|
"""
|
||
|
|
if requester not in cls._step_dict:
|
||
|
|
cls.init_step_count(requester)
|
||
|
|
cls._step_dict[requester] += 1
|
||
|
|
|
||
|
|
new_step = max(cls._step_dict.values())
|
||
|
|
if new_step > cls._current_step:
|
||
|
|
delta = new_step - cls._current_step
|
||
|
|
if delta > 1:
|
||
|
|
warn(
|
||
|
|
"Profiler step count has increased more than 1 - "
|
||
|
|
f"current_step = {cls._current_step} step dict = {cls._step_dict}"
|
||
|
|
)
|
||
|
|
for _ in range(0, delta):
|
||
|
|
_kineto_step()
|
||
|
|
cls._current_step = new_step
|
||
|
|
return cls._current_step
|
||
|
|
|
||
|
|
@classmethod
|
||
|
|
def current_step(cls) -> int:
|
||
|
|
r"""
|
||
|
|
Get the latest step for any requester
|
||
|
|
"""
|
||
|
|
return cls._current_step
|