Source code for torch.autograd.profiler

import itertools
from typing import Any
import torch
from torch.autograd import DeviceType
from torch.futures import Future

from collections import defaultdict, namedtuple
from operator import attrgetter

from typing import Dict, List, Tuple, Optional

import math

    # Available in Python >= 3.2
    from contextlib import ContextDecorator
except ImportError:
    import functools

    class ContextDecorator(object):  # type: ignore[no-redef]

        def __enter__(self):
            raise NotImplementedError

        def __exit__(self, exc_type, exc_val, exc_tb):
            raise NotImplementedError

        def __call__(self, func):
            def wrapped(*args, **kwargs):
                with self:
                    return func(*args, **kwargs)

            return wrapped

class EventList(list):
    """A list of Events (for pretty printing)"""
    def __init__(self, *args, **kwargs):
        use_cuda = kwargs.pop('use_cuda', True)
        profile_memory = kwargs.pop('profile_memory', False)
        with_flops = kwargs.pop('with_flops', False)
        super(EventList, self).__init__(*args, **kwargs)
        self._use_cuda = use_cuda
        self._profile_memory = profile_memory
        self._tree_built = False
        self._with_flops = with_flops

    def _build_tree(self):
        self._tree_built = True

    def __str__(self):
        return self.table()

    def _remove_dup_nodes(self):
        while True:
            to_delete = []
            for idx in range(len(self)):
                if (self[idx].cpu_parent is not None and
                        self[idx] == self[idx].name and
                        len(self[idx].cpu_parent.cpu_children) == 1):
                    self[idx].cpu_parent.cpu_children = self[idx].cpu_children
                    self[idx].cpu_parent.kernels = self[idx].kernels  # lift kernels up
                    for ch in self[idx].cpu_children:
                        ch.cpu_parent = self[idx].cpu_parent
            if len(to_delete) == 0:
            new_evts = [ev for ind, ev in enumerate(self) if ind not in to_delete]

    def _populate_cpu_children(self):
        """Populates child events into each underlying FunctionEvent object.
        One event is a child of another if [s1, e1) is inside [s2, e2). Where
        s1 and e1 would be start and end of the child event's interval. And
        s2 and e2 start and end of the parent event's interval

        Example: In event list [[0, 10], [1, 3], [3, 4]] would have make [0, 10]
        be a parent of two other intervals.

        If for any reason two intervals intersect only partially, this function
        will not record a parent child relationship between then.

        # Some events can be async (i.e. start and end on different threads),
        # since it's generally undefined how to attribute children ranges to
        # async ranges, we do not use them when calculating nested ranges and stats
        sync_events = [evt for evt in self if not evt.is_async and evt.device_type == DeviceType.CPU]
        events = sorted(
        # Group by both thread and node_id, so that events that happen to have
        # the same thread_id but are from different nodes aren't incorrectly
        # grouped together.
        threads = itertools.groupby(
            events, key=lambda event: (event.thread, event.node_id)

        # For each thread we keep a stack of current nested parents.
        # We maintain the invariant that each interval is a subset of all other
        # intervals lower in the stack.
        # First we sort the intervals by their start time. Then we iterate over them.
        # Every time we see a new interval we remove several parents from
        # the top until we restore the invariant. Then parent child relationship
        # if recorded if the stack is not empty.
        # Finally we add new interval to the list
        # Algorithm has O(N * log(N)) complexity where N is number of
        # intervals
        for thread_id, thread_events in threads:
            thread_events_ = sorted(
                key=lambda event: [event.time_range.start, -event.time_range.end],
            current_events: List[FunctionEvent] = []
            cur_end = 0
            for event in thread_events_:
                while len(current_events) > 0:
                    parent = current_events[-1]
                    if event.time_range.start >= parent.time_range.end or \
                            event.time_range.end > parent.time_range.end:
                        # this can't be a parent
                        assert (
                            event.cpu_parent is None
                        ), "There is already a CPU parent event for {}".format(


    def _set_backward_stacktraces(self):
        def bw_parent(evt):
            if evt is None:
                return None
            elif evt.scope == 1:  # BACKWARD_FUNCTION
                return evt
                return bw_parent(evt.cpu_parent)

        fwd_stacks = {}
        for evt in self:
            if bw_parent(evt) is None and evt.stack is not None:
                t = (evt.sequence_nr, evt.thread)
                if t not in fwd_stacks:
                    fwd_stacks[t] = evt.stack

        for evt in self:
            p = bw_parent(evt)
            if p is not None:
                assert p.fwd_thread is not None
                t = (p.sequence_nr, p.fwd_thread)
                if t in fwd_stacks:
                    evt.stack = fwd_stacks[t]
                    evt.stack = []

    def self_cpu_time_total(self):
        return sum([event.self_cpu_time_total for event in self])

    def table(self, sort_by=None, row_limit=100, max_src_column_width=75, header=None, top_level_events_only=False):
        """Prints an EventList as a nicely formatted table.

            sort_by (str, optional): Attribute used to sort entries. By default
                they are printed in the same order as they were registered.
                Valid keys include: ``cpu_time``, ``cuda_time``, ``cpu_time_total``,
                ``cuda_time_total``, ``cpu_memory_usage``, ``cuda_memory_usage``,
                ``self_cpu_memory_usage``, ``self_cuda_memory_usage``, ``count``.
            top_level_events_only(bool, optional): Boolean flag to determine the
                selection of events to display. If true, the profiler will only
                display events at top level like top-level invocation of python
                `lstm`, python `add` or other functions, nested events like low-level
                cpu/cuda ops events are omitted for profiler result readability.

            A string containing the table.
        return build_table(

    def export_chrome_trace(self, path):
        """Exports an EventList as a Chrome tracing tools file.

        The checkpoint can be later loaded and inspected under ``chrome://tracing`` URL.

            path (str): Path where the trace will be written.
        import os
        with open(path, 'w') as f:
            chrome_events = []
            next_id = 0
            # Use file IO over using json.dump since JSON dumping is very slow and
            # this technique is proven to give a 4x speedup.
            for evt in self:
                if evt.trace_name is None:
                    '{"name": "%s", '
                    '"ph": "X", '
                    '"ts": %s, '
                    '"dur": %s, '
                    '"tid": %s, '
                    '"pid": "CPU functions", '
                    '"args": {}}, '
                    % (
                        if not evt.is_remote
                        else f'" node_id:{evt.node_id}, thread_id:{evt.thread} "',
                for k in evt.kernels:
                    # 's' and 'f' draw Flow arrows from
                    # the CPU launch to the GPU kernel
                    f.write('{"name": "%s", '
                            '"ph": "s", '
                            '"ts": %s, '
                            '"tid": %s, '
                            '"pid": "CPU functions", '
                            '"id": %s, '
                            '"cat": "cpu_to_cuda", '
                            '"args": {}}, ' % (evt.trace_name, evt.time_range.start,
                                               evt.thread, next_id))
                    f.write('{"name": "%s", '
                            '"ph": "f", '
                            '"ts": %s, '
                            '"tid": %s, '
                            '"pid": "CUDA functions", '
                            '"id": %s, '
                            '"cat": "cpu_to_cuda", '
                            '"args": {}}, ' % (, k.interval.start, k.device, next_id))
                    f.write('{"name": "%s", '
                            '"ph": "X", '
                            '"ts": %s, '
                            '"dur": %s, '
                            '"tid": %s, '
                            '"pid": "CUDA functions", '
                            '"args": {}}, ' % (, k.interval.start,
                                               k.interval.elapsed_us(), k.device))
                    next_id += 1

            # remove trailing whitespace and comma
   - 2, os.SEEK_SET)

    def supported_export_stacks_metrics(self):
        return ["self_cpu_time_total", "self_cuda_time_total"]

    def export_stacks(self, path: str, metric: str):
        if metric not in self.supported_export_stacks_metrics():
            raise ValueError("metric should be one of: " + str(self.supported_export_stacks_metrics()))
        translate_table = str.maketrans(" ;\t\n", "____")
        with open(path, 'w') as f:
            for evt in self:
                if evt.stack and len(evt.stack) > 0:
                    metric_value = getattr(evt, metric)
                    if int(metric_value) > 0:
                        stack_str = ""
                        for entry in reversed(evt.stack):
                            stack_str += entry.translate(translate_table)
                            stack_str += ";"
                        stack_str = stack_str[:-1] + " " + str(int(metric_value))
                        f.write(stack_str + "\n")

    def key_averages(self, group_by_input_shapes=False, group_by_stack_n=0):
        """Averages all function events over their keys.

            group_by_input_shapes: group entries by
            (event name, input shapes) rather than just event name.
            This is useful to see which input shapes contribute to the runtime
            the most and may help with size-specific optimizations or
            choosing the best candidates for quantization (aka fitting a roof line)

            group_by_stack_n: group by top n stack trace entries

            An EventList containing FunctionEventAvg objects.
        assert self._tree_built
        stats: Dict[Tuple[str, ...], FunctionEventAvg] = defaultdict(FunctionEventAvg)

        def get_key(event, group_by_input_shapes, group_by_stack_n) -> Tuple[str, ...]:
            key = [str(event.key), str(event.node_id), str(event.device_type), str(event.is_legacy)]
            if group_by_input_shapes:
            if group_by_stack_n > 0:
                key += event.stack[:group_by_stack_n]
            return tuple(key)
        for evt in self:
            stats[get_key(evt, group_by_input_shapes, group_by_stack_n)].add(evt)

        avg_list = EventList(
        for evt in avg_list:
            evt.stack = evt.stack[:group_by_stack_n]
            if not group_by_input_shapes:
                evt.input_shapes = ""
        return avg_list

    def total_average(self):
        """Averages all events.

            A FunctionEventAvg object.
        total_stat = FunctionEventAvg()
        for evt in self:
            total_stat += evt
            total_stat.key = None
        total_stat.key = 'Total'
        return total_stat

[docs]class profile(object): """Context manager that manages autograd profiler state and holds a summary of results. Under the hood it just records events of functions being executed in C++ and exposes those events to Python. You can wrap any code into it and it will only report runtime of PyTorch functions. Note: profiler is thread local and is automatically propagated into the async tasks Args: enabled (bool, optional): Setting this to False makes this context manager a no-op. use_cuda (bool, optional): Enables timing of CUDA events as well using the cudaEvent API. Adds approximately 4us of overhead to each tensor operation. record_shapes (bool, optional): If shapes recording is set, information about input dimensions will be collected. This allows one to see which dimensions have been used under the hood and further group by them using prof.key_averages(group_by_input_shape=True). Please note that shape recording might skew your profiling data. It is recommended to use separate runs with and without shape recording to validate the timing. Most likely the skew will be negligible for bottom most events (in a case of nested function calls). But for higher level functions the total self cpu time might be artificially increased because of the shape collection. with_flops (bool, optional): If with_flops is set, the profiler will estimate the FLOPS (floating pointer operations per second) value using the operator's input shape and total time. This allows one to estimate the hardware performance. Currently, this option only works for the matrix multiplication and 2D convolution operators. profile_memory (bool, optional): track tensor memory allocation/deallocation. with_stack (bool, optional): record source information (file and line number) for the ops. use_kineto (bool, optional): experimental, enable profiling with Kineto profiler. use_cpu (bool, optional): profile CPU events; setting to ``False`` requires ``use_kineto=True`` and can be used to lower the overhead for GPU-only profiling. .. warning: Enabling memory profiling or source attribution incurs additional profiler overhead .. warning: This context managers should not be called recursively, i.e. no nested instances are allowed .. warning: Due to some CUDA multiprocessing limitations (multiprocessing-cuda-note_), one cannot use the profiler with ``use_cuda = True`` to benchmark DataLoaders with ``num_workers > 0``. If you wish to benchmark data loading, please use ``use_cuda = False`` or ``num_workers = 0``. Example: >>> x = torch.randn((1, 1), requires_grad=True) >>> with torch.autograd.profiler.profile() as prof: >>> for _ in range(100): # any normal python code, really! >>> y = x ** 2 >> y.backward() >>> # 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 ----------------------------------- --------------- --------------- --------------- mul 32.048ms 32.048ms 200 pow 27.041ms 27.041ms 200 PowBackward0 9.727ms 55.483ms 100 torch::autograd::AccumulateGrad 9.148ms 9.148ms 100 torch::autograd::GraphRoot 691.816us 691.816us 100 ----------------------------------- --------------- --------------- --------------- """ def __init__( self, enabled=True, *, use_cuda=False, record_shapes=False, with_flops=False, profile_memory=False, with_stack=False, use_kineto=False, use_cpu=True): self.enabled: bool = enabled if not self.enabled: return self.use_cuda = use_cuda self.function_events = None self.entered = False self.record_shapes = record_shapes self.with_flops = with_flops self.record_shapes |= self.with_flops self.profile_memory = profile_memory self.with_stack = with_stack self.use_cpu = use_cpu self.kineto_results = None if not self.use_cpu: assert use_kineto, \ "Device-only events supported only with Kineto (use_kineto=True)" self.profiler_kind = None self.kineto_activities = set() if use_kineto: self.profiler_kind = torch.autograd.ProfilerState.KINETO if self.use_cpu: self.kineto_activities.add(torch.autograd.ProfilerActivity.CPU) if self.use_cuda: self.kineto_activities.add( # uses CUPTI torch.autograd.ProfilerActivity.CUDA) assert len(self.kineto_activities) > 0, \ "No activities specified for Kineto profiler" elif self.use_cuda: # legacy CUDA mode self.profiler_kind = torch.autograd.ProfilerState.CUDA else: self.profiler_kind = torch.autograd.ProfilerState.CPU if self.profiler_kind == torch.autograd.ProfilerState.KINETO: assert ( torch.autograd.kineto_available() ), """Requested Kineto profiling but Kineto is not available, make sure PyTorch is built with USE_KINETO=1""" def config(self): assert self.profiler_kind is not None return torch.autograd.ProfilerConfig( self.profiler_kind, self.record_shapes, self.profile_memory, self.with_stack, self.with_flops) def __enter__(self): if not self.enabled: return if self.entered: raise RuntimeError("profiler context manager is not reentrant") self.entered = True if self.kineto_activities: torch.autograd._prepare_profiler(self.config(), self.kineto_activities) torch.autograd._enable_profiler(self.config(), self.kineto_activities) else: torch.autograd._enable_profiler_legacy(self.config()) return self def _prepare_kineto_trace(self): assert self.kineto_activities self.entered = True torch.autograd._prepare_profiler(self.config(), self.kineto_activities) def _start_kineto_trace(self): assert self.kineto_activities torch.autograd._enable_profiler(self.config(), self.kineto_activities) def __exit__(self, exc_type, exc_val, exc_tb): if not self.enabled: return if self.kineto_activities: self.kineto_results = torch.autograd._disable_profiler() parsed_results = parse_kineto_results(self.kineto_results) else: records = torch.autograd._disable_profiler_legacy() parsed_results = parse_legacy_records(records) self.function_events = EventList( parsed_results, use_cuda=self.use_cuda, profile_memory=self.profile_memory, with_flops=self.with_flops) self.function_events._build_tree() return False def __repr__(self): if self.function_events is None: return '<unfinished torch.autograd.profile>' return repr(self.function_events) def __str__(self): if self.function_events is None: return '<unfinished torch.autograd.profile>' return str(self.function_events) def _check_finish(self): if self.function_events is None: raise RuntimeError("can't export a trace that didn't finish running")
[docs] def table(self, sort_by=None, row_limit=100, max_src_column_width=75, header=None, top_level_events_only=False): self._check_finish() assert self.function_events is not None return self.function_events.table( sort_by=sort_by, row_limit=row_limit, max_src_column_width=max_src_column_width, header=header, top_level_events_only=top_level_events_only )
table.__doc__ = EventList.table.__doc__
[docs] def export_chrome_trace(self, path): self._check_finish() if self.kineto_results is not None: else: assert self.function_events is not None return self.function_events.export_chrome_trace(path)
export_chrome_trace.__doc__ = EventList.export_chrome_trace.__doc__ def export_stacks(self, path: str, metric: str = "self_cpu_time_total"): self._check_finish() assert self.function_events is not None, "Expected profiling results" assert self.with_stack, "export_stacks() requires with_stack=True" return self.function_events.export_stacks(path, metric)
[docs] def key_averages(self, group_by_input_shape=False, group_by_stack_n=0): self._check_finish() assert self.function_events is not None, "Expected profiling results" return self.function_events.key_averages(group_by_input_shape, group_by_stack_n)
key_averages.__doc__ = EventList.key_averages.__doc__
[docs] def total_average(self): self._check_finish() assert self.function_events is not None, "Expected profiling results" return self.function_events.total_average()
total_average.__doc__ = EventList.total_average.__doc__ @property def self_cpu_time_total(self): """ Returns total time spent on CPU obtained as 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
class record_function(ContextDecorator): """Context manager/function decorator that adds a label to a block of Python code (or 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: >>> 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() ... >>> # 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): str = name # Whether or not we should run record function's end callbacks when exiting. self.run_callbacks_on_exit: bool = True # Stores underlying RecordFunction as a tensor. TODO: move to custom # class ( self.handle: torch.Tensor = torch.zeros(1) def __enter__(self): self.handle = torch.ops.profiler._record_function_enter( return self def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any): if self.run_callbacks_on_exit: torch.ops.profiler._record_function_exit(self.handle) def _call_end_callbacks_on_future(self, fut: Future[Any]) -> Future[Any]: """ _call_end_callbacks_on_future is meant to be used 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 profiled_future = torch.ops.profiler._call_end_callbacks_on_jit_fut(self.handle, fut) return profiled_future
[docs]class emit_nvtx(object): """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 -- <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, default=True): Setting ``enabled=False`` makes this context manager a no-op. Default: ``True``. record_shapes (bool, optional, default=False): 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. Example: >>> 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() torch.autograd._enable_profiler_legacy( torch.autograd.ProfilerConfig( torch.autograd.ProfilerState.NVTX, self.record_shapes, False, False, False) ) return self def __exit__(self, exc_type, exc_val, exc_tb): if not self.enabled: return torch.cuda.synchronize() torch.autograd._disable_profiler_legacy() return False
[docs]def load_nvprof(path): """Opens an nvprof trace file and parses autograd annotations. Args: path (str): path to nvprof trace """ return EventList(parse_nvprof_trace(path))
################################################################################ # FunctionEvent def format_time(time_us): """Defines how to format time in FunctionEvent""" US_IN_SECOND = 1000.0 * 1000.0 US_IN_MS = 1000.0 if time_us >= US_IN_SECOND: return '{:.3f}s'.format(time_us / US_IN_SECOND) if time_us >= US_IN_MS: return '{:.3f}ms'.format(time_us / US_IN_MS) return '{:.3f}us'.format(time_us) def format_time_share(time_us, total_time_us): """Defines how to format time in FunctionEvent""" if total_time_us == 0: assert time_us == 0, "Expected time_us == 0 but got {}".format(time_us) return "NaN" return '{:.2f}%'.format(time_us * 100.0 / total_time_us) def format_memory(nbytes): """Returns a formatted memory size string""" KB = 1024 MB = 1024 * KB GB = 1024 * MB if (abs(nbytes) >= GB): return '{:.2f} Gb'.format(nbytes * 1.0 / GB) elif (abs(nbytes) >= MB): return '{:.2f} Mb'.format(nbytes * 1.0 / MB) elif (abs(nbytes) >= KB): return '{:.2f} Kb'.format(nbytes * 1.0 / KB) else: return str(nbytes) + ' b' def attr_formatter(name): return property(lambda self: format_time(getattr(self, name))) class FormattedTimesMixin(object): """Helpers for FunctionEvent and FunctionEventAvg. The subclass should define `*_time_total` and `count` attributes. """ cpu_time_str = attr_formatter('cpu_time') cuda_time_str = attr_formatter('cuda_time') cpu_time_total_str = attr_formatter('cpu_time_total') cuda_time_total_str = attr_formatter('cuda_time_total') self_cpu_time_total_str = attr_formatter('self_cpu_time_total') self_cuda_time_total_str = attr_formatter('self_cuda_time_total') @property def cpu_time(self): return 0.0 if self.count == 0 else 1.0 * self.cpu_time_total / self.count # type: ignore @property def cuda_time(self): return 0.0 if self.count == 0 else 1.0 * self.cuda_time_total / self.count # type: ignore class Interval(object): def __init__(self, start, end): self.start = start self.end = end def elapsed_us(self): return self.end - self.start Kernel = namedtuple('Kernel', ['name', 'device', 'interval']) class FunctionEvent(FormattedTimesMixin): """Profiling information about a single function.""" def __init__( self, id, name, thread, start_us, end_us, fwd_thread=None, input_shapes=None, stack=None, scope=0, cpu_memory_usage=0, cuda_memory_usage=0, is_async=False, is_remote=False, sequence_nr=-1, node_id=-1, device_type=DeviceType.CPU, device_index=0, is_legacy=False, flops=None, trace_name=None): int = id self.node_id: int = node_id str = name self.trace_name: str = trace_name self.time_range: Interval = Interval(start_us, end_us) self.thread: int = thread self.fwd_thread: Optional[int] = fwd_thread self.kernels: List[Kernel] = [] self.count: int = 1 self.cpu_children: List[FunctionEvent] = [] self.cpu_parent: Optional[FunctionEvent] = None self.input_shapes: Tuple[int, ...] = input_shapes self.stack: List = stack self.scope: int = scope self.cpu_memory_usage: int = cpu_memory_usage self.cuda_memory_usage: int = cuda_memory_usage self.is_async: bool = is_async self.is_remote: bool = is_remote self.sequence_nr: int = sequence_nr self.device_type: DeviceType = device_type self.device_index: int = device_index self.is_legacy: bool = is_legacy self.flops: Optional[float] = flops def append_kernel(self, name, device, start, end): assert self.device_type == DeviceType.CPU self.kernels.append(Kernel(name, device, Interval(start, end))) def append_cpu_child(self, child): """Append a CPU child of type FunctionEvent. One is supposed to append only direct children to the event to have correct self cpu time being reported. """ assert(self.device_type == DeviceType.CPU) assert(isinstance(child, FunctionEvent)) assert(child.device_type == DeviceType.CPU) self.cpu_children.append(child) def set_cpu_parent(self, parent): """Set the immediate CPU parent of type FunctionEvent One profiling FunctionEvent should have only one CPU parent such that the child's range interval is completely inside the parent's. We use this connection to determine the event is from top-level op or not. """ assert(self.device_type == DeviceType.CPU) assert(isinstance(parent, FunctionEvent)) assert(parent.device_type == DeviceType.CPU) self.cpu_parent = parent # Note: async events don't have children, are not used when computing 'self' # metrics of other events, have only total cpu time @property def self_cpu_memory_usage(self): if self.is_async or self.device_type != DeviceType.CPU: return 0 return self.cpu_memory_usage - sum( [child.cpu_memory_usage for child in self.cpu_children] ) @property def self_cuda_memory_usage(self): if self.is_async or self.device_type != DeviceType.CPU: return 0 return self.cuda_memory_usage - sum( [child.cuda_memory_usage for child in self.cpu_children] ) @property def self_cpu_time_total(self): if self.is_async or self.device_type != DeviceType.CPU: return 0 return self.cpu_time_total - sum( [child.cpu_time_total for child in self.cpu_children] ) @property def cuda_time_total(self): if self.is_async: return 0 if self.device_type == DeviceType.CPU: if not self.is_legacy: # account for the kernels in the children ops return (sum(kinfo.interval.elapsed_us() for kinfo in self.kernels) + sum(ch.cuda_time_total for ch in self.cpu_children)) else: # each legacy cpu events has a single (fake) kernel return sum(kinfo.interval.elapsed_us() for kinfo in self.kernels) else: assert self.device_type == DeviceType.CUDA return self.time_range.elapsed_us() @property def self_cuda_time_total(self): if self.is_async: return 0 if self.device_type == DeviceType.CPU: return self.cuda_time_total - \ sum([child.cuda_time_total for child in self.cpu_children]) else: assert(self.device_type == DeviceType.CUDA) return self.cuda_time_total @property def cpu_time_total(self): if self.device_type == DeviceType.CPU: return self.time_range.elapsed_us() else: return 0 @property def key(self): return def __repr__(self): return ( '<FunctionEvent id={} name={} device_type={} node_id={} cpu_time={} start_us={} end_us={} ' 'cpu_children={} cuda_time={} name={} thread={} input_shapes={} ' 'cpu_memory_usage={} cuda_memory_usage={} is_async={} is_remote={} seq_nr={} is_legacy={}>'.format(,, self.device_type, self.node_id, self.cpu_time_str, self.time_range.start, self.time_range.end, str([ for child in self.cpu_children]), self.cuda_time_str,, self.thread, str(self.input_shapes), self.cpu_memory_usage, self.cuda_memory_usage, self.is_async, self.is_remote, self.sequence_nr, self.is_legacy, ) ) class FunctionEventAvg(FormattedTimesMixin): """Used to average stats over multiple FunctionEvent objects.""" def __init__(self): self.key: Optional[str] = None self.count: int = 0 self.node_id: int = 0 self.is_async: bool = False self.is_remote: bool = False self.cpu_time_total: int = 0 self.cuda_time_total: int = 0 self.self_cpu_time_total: int = 0 self.self_cuda_time_total: int = 0 self.input_shapes: Optional[List[List[int]]] = None self.stack: Optional[List] = None self.scope: Optional[int] = None self.cpu_memory_usage: int = 0 self.cuda_memory_usage: int = 0 self.self_cpu_memory_usage: int = 0 self.self_cuda_memory_usage: int = 0 self.cpu_children: Optional[List[FunctionEvent]] = None self.cpu_parent: Optional[FunctionEvent] = None self.device_type: DeviceType = DeviceType.CPU self.is_legacy: bool = False self.flops: float = 0.0 def add(self, other): if self.key is None: # First function being recorded as part of FunctionEventAvg, propagate # fields. self.key = other.key self.node_id = other.node_id self.is_async = other.is_async self.is_remote = other.is_remote self.cpu_parent = other.cpu_parent self.cpu_children = other.cpu_children self.input_shapes = other.input_shapes self.stack = other.stack self.scope = other.scope self.device_type = other.device_type self.is_legacy = other.is_legacy assert isinstance(other, (FunctionEvent, FunctionEventAvg)) assert other.key == self.key self.cpu_time_total += other.cpu_time_total self.cuda_time_total += other.cuda_time_total self.self_cpu_time_total += other.self_cpu_time_total self.self_cuda_time_total += other.self_cuda_time_total self.cpu_memory_usage += other.cpu_memory_usage self.cuda_memory_usage += other.cuda_memory_usage self.self_cpu_memory_usage += other.self_cpu_memory_usage self.self_cuda_memory_usage += other.self_cuda_memory_usage self.count += other.count if self.flops is None: self.flops = other.flops elif other.flops is not None: self.flops += other.flops return self def __iadd__(self, other): return self.add(other) def __repr__(self): return ( '<FunctionEventAvg key={} self_cpu_time={} cpu_time={} ' ' self_cuda_time={} cuda_time={} input_shapes={} ' 'cpu_memory_usage={} cuda_memory_usage={}>'.format( self.key, self.self_cpu_time_total_str, self.cpu_time_str, self.self_cuda_time_total_str, self.cuda_time_str, str(self.input_shapes), self.cpu_memory_usage, self.cuda_memory_usage, ) ) ################################################################################ # Utilities class StringTable(defaultdict): def __missing__(self, key): # manage cases like 't' (demangled to 'unsigned short') separately, # for now simply check the length to avoid unexpected results for # the short sequences self[key] = torch._C._demangle(key) if len(key) > 1 else key return self[key] def filter_stack_entry(entry): filtered_entries = [ ("autograd/__init__", "_make_grads"), ("autograd/__init__", "backward"), ("torch/tensor", "backward"), ("_internal/common_utils", "prof_callable"), ("_internal/common_utils", "prof_func_call"), ("_internal/common_utils", "prof_meth_call"), ] return all([not (f[0] in entry and f[1] in entry) for f in filtered_entries]) def filter_name(name): # ignoring the following utility ops filtered_out_names = [ "profiler::_record_function_enter", "profiler::_record_function_exit", "aten::is_leaf", "aten::output_nr", "aten::_version", ] return name in filtered_out_names # Demangles and optionally rewrites the provided event name, # with_wildcard - whether to replace certain numbered event names # with a wildcard name to aggregate them together in the profiler table # output def rewrite_name(name, with_wildcard=False): string_table = StringTable() name = string_table[name] if with_wildcard: if name.startswith("ProfilerStep#"): name = "ProfilerStep*" return name # Parsing of kineto profiler events def parse_kineto_results(result): # has most of the events - PyTorch op-level and device-level events # result.legacy_events() has events not yet ported to kineto # (e.g. start/stop marks, tensor memory allocator events) # First, find __start_profile mark to get the absolute time of the start of the trace; # save memory allocation records start_record = None mem_records = [] for record in itertools.chain(*result.legacy_events()): if record.kind() == 'mark' and == '__start_profile': assert start_record is None start_record = record if record.kind() == 'memory_alloc': mem_records.append([record, False]) assert start_record is not None, "Invalid profiler output, __start_profile is missing" # Create and return FunctionEvent list function_events = [] cuda_corr_map: Dict[int, List[FunctionEvent]] = {} for kineto_event in if filter_name( continue rel_start_us = kineto_event.start_us() - start_record.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 if kineto_event.device_type() == DeviceType.CPU: # find the corresponding memory allocation events for mem_record in mem_records: if (mem_record[0].start_us() >= kineto_event.start_us() and mem_record[0].start_us() <= abs_end_us): cpu_memory_usage += mem_record[0].cpu_memory_usage() cuda_memory_usage += mem_record[0].cuda_memory_usage() mem_record[1] = True is_async = kineto_event.start_thread_id() != kineto_event.end_thread_id() fe = FunctionEvent( id=kineto_event.correlation_id(), name=rewrite_name(, with_wildcard=True), trace_name=rewrite_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(), stack=[entry for entry in kineto_event.stack() if filter_stack_entry(entry)], scope=kineto_event.scope(), cpu_memory_usage=cpu_memory_usage, cuda_memory_usage=cuda_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(), ) function_events.append(fe) corr_id = kineto_event.linked_correlation_id() if corr_id > 0: if corr_id not in cuda_corr_map: cuda_corr_map[corr_id] = [] cuda_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 in cuda_corr_map): for f_evt in cuda_corr_map[]: if f_evt.device_type == DeviceType.CUDA: fe.append_kernel(, f_evt.device_index, f_evt.time_range.start, f_evt.time_range.end) 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 # output top-level memory events for mem_record in mem_records: if not mem_record[1]: fe = FunctionEvent( id=mem_record[0].handle(), name="[memory]", trace_name=None, # not outputting in the trace thread=mem_record[0].thread_id(), start_us=mem_record[0].start_us(), end_us=mem_record[0].start_us(), # no duration fwd_thread=mem_record[0].fwd_thread_id(), input_shapes=[], stack=[], scope=mem_record[0].scope(), cpu_memory_usage=mem_record[0].cpu_memory_usage(), cuda_memory_usage=mem_record[0].cuda_memory_usage(), is_async=False, sequence_nr=-1, device_type=DeviceType.CPU, device_index=0, ) function_events.append(fe) function_events.sort(key=lambda evt: [evt.time_range.start, -evt.time_range.end]) return function_events # Parsing of legacy profiler events def parse_legacy_records(thread_records): def get_record_key(record): """ Returns a tuple to be used by parse_legacy_records for correlating start and end records. """ return (record.handle(), record.node_id()) next_id = 0 start_record = None cuda_records = {} functions = [] record_stack = [] # cuda start events and the overall profiler start event don't happen # at exactly the same time because we need to record an event on each device # and each record takes ~4us. So we adjust here by the difference # adding the difference in CPU time between the profiler start event # and the CPU time of the cuda start event for the device def adjusted_time(cuda_record, cuda_records_map): assert cuda_record.device() != -1 assert start_record is not None cuda_time_0 = cuda_records_map[(cuda_record.node_id(), cuda_record.device())] return cuda_time_0.cuda_elapsed_us(cuda_record) + start_record.cpu_elapsed_us(cuda_time_0) # '__start_profile' is not guaranteed to be first, so we must find it here for record in itertools.chain(*thread_records): name = if start_record is None and name == '__start_profile': start_record = record elif '__cuda_start_event' in name: # N.B.: Each CUDA device has its own __cuda_start_event. assert record.device() != -1 # key for cuda_records is (node_id, device) in case of multiple nodes # having the same device cuda_records[(record.node_id(), record.device())] = record assert start_record is not None and not start_record.is_remote() for thread_record_list in thread_records: # accumulated memory allocations per handle cpu_memory_allocs = {} cuda_memory_allocs = {} # ranges per handle range_starts = {} filtered_handles = set() prev_record = None for record in thread_record_list: record_key = get_record_key(record) if (filter_name( or record_key in filtered_handles): filtered_handles.add(record_key) continue if record.kind() == 'push': # workaround to reduce double logging from operator # wrappers and redispatch if prev_record is not None: duplicate = ( == and prev_record.kind() == record.kind() and prev_record.node_id() == record.node_id() ) if duplicate: filtered_handles.add(record_key) continue range_starts[record_key] = record cpu_memory_allocs[record_key] = 0 cuda_memory_allocs[record_key] = 0 elif record.kind() == 'pop': assert ( record_key in range_starts ), """Expected record with key {} to exist in range_starts. This means that the pop event did not have a corresponding push.""".format( record_key ) start = range_starts[record_key] cpu_memory_usage = cpu_memory_allocs[record_key] cuda_memory_usage = cuda_memory_allocs[record_key] is_async = start.thread_id() != record.thread_id() is_remote_event = record.is_remote() start_flops = start.flops() fe = FunctionEvent( id=record.handle(), node_id=record.node_id(), name=rewrite_name(, with_wildcard=True), trace_name=rewrite_name(, with_wildcard=False), thread=start.thread_id(), start_us=start_record.cpu_elapsed_us(start), end_us=start_record.cpu_elapsed_us(record), fwd_thread=start.fwd_thread_id(), input_shapes=start.shapes(), stack=[entry for entry in start.stack() if filter_stack_entry(entry)], scope=start.scope(), cpu_memory_usage=cpu_memory_usage, cuda_memory_usage=cuda_memory_usage, is_async=is_async, is_remote=is_remote_event, sequence_nr=start.sequence_nr(), device_type=DeviceType.CPU, is_legacy=True, flops=start_flops, ) # note: async events have only cpu total time if not is_async and start.has_cuda(): cuda_start = adjusted_time(start, cuda_records) cuda_end = adjusted_time(record, cuda_records) if (cuda_end - cuda_start) > 0: fe.append_kernel(, start.device(), cuda_start, cuda_end) functions.append(fe) del range_starts[record_key] del cpu_memory_allocs[record_key] del cuda_memory_allocs[record_key] elif record.kind() == 'memory_alloc': num_open_handles_cpu = len(cpu_memory_allocs) num_open_handles_cuda = len(cuda_memory_allocs) assert num_open_handles_cpu == num_open_handles_cuda for handle in cpu_memory_allocs.keys(): cpu_memory_allocs[handle] += record.cpu_memory_usage() for handle in cuda_memory_allocs.keys(): cuda_memory_allocs[handle] += record.cuda_memory_usage() if num_open_handles_cpu == 0: # output event as a top-level memory event fe = FunctionEvent( id=0, name="[memory]", trace_name=None, thread=0, start_us=0, end_us=0, stack=[], cpu_memory_usage=record.cpu_memory_usage(), cuda_memory_usage=record.cuda_memory_usage(), is_legacy=True, ) functions.append(fe) prev_record = record # Sort functions by start time then by end time ascending. # This ensures that--in the case of nested events which # have the same start time (which may happen due to the # granularity of the given clock tick)--we always show # the outermost nested call first. This adds stability # in how FunctionEvents appear functions.sort(key=lambda evt: [evt.time_range.start, -evt.time_range.end]) return functions ################################################################################ # CUDA checkpoints class EnforceUnique(object): """Raises an error if a key is seen more than once.""" def __init__(self): self.seen = set() def see(self, *key): 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 AS marker_id,, 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 = WHERE != 0 AND = 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 # Now, correlate all kernels with FunctionEvents kernel_query = """ SELECT AS marker_id,, 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, AS kernel_name FROM CUPTI_ACTIVITY_KIND_MARKER AS start INNER JOIN CUPTI_ACTIVITY_KIND_MARKER AS end ON = 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; 13 is for older cuda versions assert (row['cbid'] == 211) or (row['cbid'] == 13) evt = functions_map[row['marker_id']] evt.append_kernel(row['kernel_name'], 0, row['kernel_start'], row['kernel_end']) functions.sort(key=lambda evt: evt.time_range.start) return functions ################################################################################ # Pretty printer def build_table( events, sort_by=None, header=None, row_limit=100, max_src_column_width=75, with_flops=False, profile_memory=False, top_level_events_only=False): """Prints a summary of events (which can be a list of FunctionEvent or FunctionEventAvg).""" if len(events) == 0: return "" has_cuda_time = any([event.self_cuda_time_total > 0 for event in events]) has_cuda_mem = any([event.self_cuda_memory_usage > 0 for event in events]) has_input_shapes = any( [(event.input_shapes is not None and len(event.input_shapes) > 0) for event in events]) if sort_by is not None: events = EventList(sorted( events, key=lambda evt: getattr(evt, sort_by), reverse=True ), use_cuda=has_cuda_time, profile_memory=profile_memory, with_flops=with_flops) MAX_NAME_COLUMN_WIDTH = 55 name_column_width = max([len(evt.key) for evt in events]) + 4 name_column_width = min(name_column_width, MAX_NAME_COLUMN_WIDTH) DEFAULT_COLUMN_WIDTH = 12 shapes_column_width = max([len(str(evt.input_shapes)) for evt in events]) + 4 shapes_column_width = min(shapes_column_width, 45) flops_column_width = DEFAULT_COLUMN_WIDTH src_column_width = None stacks = [] for evt in events: if evt.stack is not None and len(evt.stack) > 0: stacks.append(evt.stack) has_stack = len(stacks) > 0 if has_stack: src_column_width = max([max([len(entry) for entry in stack]) for stack in stacks]) + 4 src_column_width = min(src_column_width, max_src_column_width) headers = [ 'Name', 'Self CPU %', 'Self CPU', 'CPU total %', 'CPU total', 'CPU time avg', ] if has_cuda_time: headers.extend([ 'Self CUDA', 'Self CUDA %', 'CUDA total', 'CUDA time avg', ]) if profile_memory: headers.extend([ 'CPU Mem', 'Self CPU Mem', ]) if has_cuda_mem: headers.extend([ 'CUDA Mem', 'Self CUDA Mem', ]) headers.append( '# of Calls' ) # Only append Node ID if any event has a valid (>= 0) Node ID append_node_id = any([evt.node_id != -1 for evt in events]) if append_node_id: headers.append('Node ID') # Have to use a list because nonlocal is Py3 only... SPACING_SIZE = 2 row_format_lst = [""] header_sep_lst = [""] line_length_lst = [-SPACING_SIZE] MAX_STACK_ENTRY = 5 def add_column(padding, text_dir='>'): row_format_lst[0] += '{: ' + text_dir + str(padding) + '}' + (' ' * SPACING_SIZE) header_sep_lst[0] += '-' * padding + (' ' * SPACING_SIZE) line_length_lst[0] += padding + SPACING_SIZE def auto_scale_flops(flops): flop_headers = [ 'FLOPS', 'KFLOPS', 'MFLOPS', 'GFLOPS', 'TFLOPS', 'PFLOPS', ] assert flops > 0 log_flops = max(0, min(math.log10(flops) / 3, float(len(flop_headers) - 1))) assert log_flops >= 0 and log_flops < len(flop_headers) return (pow(10, (math.floor(log_flops) * -3.0)), flop_headers[int(log_flops)]) add_column(name_column_width) for _ in headers[1:]: add_column(DEFAULT_COLUMN_WIDTH) if has_input_shapes: headers.append('Input Shapes') add_column(shapes_column_width) if has_stack: headers.append('Source Location') add_column(src_column_width, text_dir='<') if with_flops: # Auto-scaling of flops header US_IN_SECOND = 1000.0 * 1000.0 # cpu_time_total is in us raw_flops = [] for evt in events: if evt.flops > 0: if evt.cuda_time_total != 0: evt.flops = float(evt.flops) / evt.cuda_time_total * US_IN_SECOND else: evt.flops = float(evt.flops) / evt.cpu_time_total * US_IN_SECOND raw_flops.append(evt.flops) if len(raw_flops) != 0: (flops_scale, flops_header) = auto_scale_flops(min(raw_flops)) headers.append(flops_header) add_column(flops_column_width) else: with_flops = False # can't find any valid flops row_format = row_format_lst[0] header_sep = header_sep_lst[0] line_length = line_length_lst[0] add_column = None # type: ignore # Have to use a list because nonlocal is Py3 only... result = [] def append(s): result.append(s) result.append('\n') # Yes, newline after the end as well sum_self_cpu_time_total = sum([event.self_cpu_time_total for event in events]) sum_self_cuda_time_total = 0 for evt in events: if evt.device_type == DeviceType.CPU: # in legacy profiler, kernel info is stored in cpu events if evt.is_legacy: sum_self_cuda_time_total += evt.self_cuda_time_total elif evt.device_type == DeviceType.CUDA: # in kineto profiler, there're events with the correct device type (e.g. CUDA) sum_self_cuda_time_total += evt.self_cuda_time_total # Actual printing if header is not None: append('=' * line_length) append(header) if top_level_events_only: append('=' * line_length) append('This report only display top-level ops statistics') append(header_sep) append(row_format.format(*headers)) append(header_sep) def trim_path(path, src_column_width): if len(path) > src_column_width: offset = len(path) - src_column_width path = path[offset:] if len(path) > 3: path = "..." + path[3:] return path event_limit = 0 for evt in events: if event_limit == row_limit: break if top_level_events_only and evt.cpu_parent is not None: continue else: event_limit += 1 name = evt.key if len(name) >= MAX_NAME_COLUMN_WIDTH - 3: name = name[:(MAX_NAME_COLUMN_WIDTH - 3)] + "..." row_values = [ name, # Self CPU total %, 0 for async events. format_time_share(evt.self_cpu_time_total, sum_self_cpu_time_total), evt.self_cpu_time_total_str, # Self CPU total # CPU total %, 0 for async events. format_time_share(evt.cpu_time_total, sum_self_cpu_time_total) if not evt.is_async else 0, evt.cpu_time_total_str, # CPU total evt.cpu_time_str, # CPU time avg ] if has_cuda_time: row_values.extend([ evt.self_cuda_time_total_str, # CUDA time total % format_time_share(evt.self_cuda_time_total, sum_self_cuda_time_total), evt.cuda_time_total_str, evt.cuda_time_str, # Cuda time avg ]) if profile_memory: row_values.extend([ # CPU Mem Total format_memory(evt.cpu_memory_usage), # Self CPU Mem Total format_memory(evt.self_cpu_memory_usage), ]) if has_cuda_mem: row_values.extend([ # CUDA Mem Total format_memory(evt.cuda_memory_usage), # Self CUDA Mem Total format_memory(evt.self_cuda_memory_usage), ]) row_values.append( evt.count, # Number of calls ) if append_node_id: row_values.append(evt.node_id) if has_input_shapes: row_values.append(str(evt.input_shapes)[:shapes_column_width]) if with_flops: if evt.flops <= 0.0: row_values.append("--") else: row_values.append('{0:8.3f}'.format(evt.flops * flops_scale)) if has_stack: src_field = "" if len(evt.stack) > 0: src_field = trim_path(evt.stack[0], src_column_width) row_values.append(src_field) append(row_format.format(*row_values)) if has_stack: empty_headers = [""] * (len(headers) - 1) for entry in evt.stack[1:MAX_STACK_ENTRY]: append(row_format.format(*(empty_headers + [trim_path(entry, src_column_width)]))) empty_headers.append("") append(row_format.format(*empty_headers)) append(header_sep) append("Self CPU time total: {}".format(format_time(sum_self_cpu_time_total))) if has_cuda_time: append("Self CUDA time total: {}".format(format_time(sum_self_cuda_time_total))) return ''.join(result)


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