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Source code for torch.autograd.profiler_util

import bisect
import itertools
import math

from collections import defaultdict, namedtuple
from operator import attrgetter

from typing import Any, Dict, List, Optional, Tuple

import torch
from torch.autograd import DeviceType

__all__ = [
    "EventList",
    "FormattedTimesMixin",
    "Interval",
    "Kernel",
    "FunctionEvent",
    "FunctionEventAvg",
    "StringTable",
    "MemRecordsAcc",
]


class EventList(list):
    """A list of Events (for pretty printing)."""

    def __init__(self, *args, **kwargs):
        use_cuda = kwargs.pop("use_cuda", True)
        use_device = kwargs.pop("use_device", None)
        profile_memory = kwargs.pop("profile_memory", False)
        with_flops = kwargs.pop("with_flops", False)
        super().__init__(*args, **kwargs)
        self._use_cuda = use_cuda
        self._use_device = use_device
        self._profile_memory = profile_memory
        self._tree_built = False
        self._with_flops = with_flops

    def _build_tree(self):
        self._populate_cpu_children()
        self._remove_dup_nodes()
        self._set_backward_stacktraces()
        self._tree_built = True

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

    def _remove_dup_nodes(self):
        while True:
            to_delete = set()
            for idx in range(len(self)):
                if (
                    self[idx].cpu_parent is not None
                    and self[idx].cpu_parent.name == 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
                    to_delete.add(idx)
            if len(to_delete) == 0:
                break
            new_evts = [ev for ind, ev in enumerate(self) if ind not in to_delete]
            self.clear()
            self.extend(new_evts)

    def _populate_cpu_children(self):
        """Populate 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(
            sync_events,
            key=attrgetter("thread"),
        )
        # 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(
                thread_events,
                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
                        current_events.pop()
                    else:
                        parent.append_cpu_child(event)
                        assert (
                            event.cpu_parent is None
                        ), f"There is already a CPU parent event for {event.key}"
                        event.set_cpu_parent(parent)
                        break

                current_events.append(event)

    def _set_backward_stacktraces(self):
        def bw_parent(evt):
            if evt is None:
                return None
            elif evt.scope == 1:  # BACKWARD_FUNCTION
                return evt
            else:
                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]
                else:
                    evt.stack = []

    @property
    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,
        max_name_column_width=55,
        max_shapes_column_width=80,
        header=None,
        top_level_events_only=False,
    ):
        """Print an EventList as a nicely formatted table.

        Args:
            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.

        Returns:
            A string containing the table.
        """
        return _build_table(
            self,
            sort_by=sort_by,
            row_limit=row_limit,
            max_src_column_width=max_src_column_width,
            max_name_column_width=max_name_column_width,
            max_shapes_column_width=max_shapes_column_width,
            header=header,
            profile_memory=self._profile_memory,
            with_flops=self._with_flops,
            top_level_events_only=top_level_events_only,
        )

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

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

        Args:
            path (str): Path where the trace will be written.
        """
        import os

        device_name = "cuda" if not self._use_device else self._use_device
        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.
            f.write("[")
            for evt in self:
                if evt.trace_name is None:
                    continue
                f.write(
                    '{{"name": "{}", '
                    '"ph": "X", '
                    '"ts": {}, '
                    '"dur": {}, '
                    '"tid": {}, '
                    '"pid": "CPU functions", '
                    '"args": {{}}}}, '.format(
                        evt.trace_name,
                        evt.time_range.start,
                        evt.time_range.elapsed_us(),
                        evt.thread
                        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(
                        f'{{"name": "{evt.trace_name}", '
                        '"ph": "s", '
                        f'"ts": {evt.time_range.start}, '
                        f'"tid": {evt.thread}, '
                        '"pid": "CPU functions", '
                        f'"id": {next_id}, '
                        f'"cat": "cpu_to_{device_name}", '
                        '"args": {}}, '
                    )
                    # Note: use torch.profiler to get device kernel trace
                    next_id += 1
            if len(self) > 0:
                # remove trailing whitespace and comma
                f.seek(f.tell() - 2, os.SEEK_SET)
                f.truncate()
            f.write("]")

    def supported_export_stacks_metrics(self):
        return [
            "self_cpu_time_total",
            "self_cuda_time_total",
            "self_privateuse1_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.

        Args:
            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

        Returns:
            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:
                key.append(str(event.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(
            stats.values(),
            use_cuda=self._use_cuda,
            use_device=self._use_device,
            profile_memory=self._profile_memory,
            with_flops=self._with_flops,
        )
        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.

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


def _format_time(time_us):
    """Define 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 f"{time_us / US_IN_SECOND:.3f}s"
    if time_us >= US_IN_MS:
        return f"{time_us / US_IN_MS:.3f}ms"
    return f"{time_us:.3f}us"


def _format_time_share(time_us, total_time_us):
    """Define how to format time in FunctionEvent."""
    if total_time_us == 0:
        assert time_us == 0, f"Expected time_us == 0 but got {time_us}"
        return "NaN"
    return f"{time_us * 100.0 / total_time_us:.2f}%"


def _format_memory(nbytes):
    """Return a formatted memory size string."""
    KB = 1024
    MB = 1024 * KB
    GB = 1024 * MB
    if abs(nbytes) >= GB:
        return f"{nbytes * 1.0 / GB:.2f} Gb"
    elif abs(nbytes) >= MB:
        return f"{nbytes * 1.0 / MB:.2f} Mb"
    elif abs(nbytes) >= KB:
        return f"{nbytes * 1.0 / KB:.2f} Kb"
    else:
        return str(nbytes) + " b"


def _attr_formatter(name):
    return property(lambda self: _format_time(getattr(self, name)))


class FormattedTimesMixin:
    """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")
    privateuse1_time_str = _attr_formatter("privateuse1_time")
    cpu_time_total_str = _attr_formatter("cpu_time_total")
    cuda_time_total_str = _attr_formatter("cuda_time_total")
    privateuse1_time_total_str = _attr_formatter("privateuse1_time_total")
    self_cpu_time_total_str = _attr_formatter("self_cpu_time_total")
    self_cuda_time_total_str = _attr_formatter("self_cuda_time_total")
    self_privateuse1_time_total_str = _attr_formatter("self_privateuse1_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[attr-defined]

    @property
    def cuda_time(self):
        return 0.0 if self.count == 0 else 1.0 * self.cuda_time_total / self.count  # type: ignore[attr-defined]

    @property
    def privateuse1_time(self):
        return 0.0 if self.count == 0 else 1.0 * self.privateuse1_time_total / self.count  # type: ignore[attr-defined]


[docs]class Interval: def __init__(self, start, end): self.start = start self.end = end
[docs] def elapsed_us(self): r""" Returns the length of the interval """ return self.end - self.start
Kernel = namedtuple("Kernel", ["name", "device", "duration"]) 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, use_device=None, cpu_memory_usage=0, cuda_memory_usage=0, privateuse1_memory_usage=0, is_async=False, is_remote=False, sequence_nr=-1, node_id=-1, device_type=DeviceType.CPU, device_index=0, device_resource_id=None, is_legacy=False, flops=None, trace_name=None, concrete_inputs=None, ): self.id: int = id self.node_id: int = node_id self.name: 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.concrete_inputs: List[Any] = concrete_inputs self.stack: List = stack self.scope: int = scope self.use_device: Optional[str] = use_device self.cpu_memory_usage: int = cpu_memory_usage self.cuda_memory_usage: int = cuda_memory_usage self.privateuse1_memory_usage: int = privateuse1_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.device_resource_id: int = ( thread if device_resource_id is None else device_resource_id ) self.is_legacy: bool = is_legacy self.flops: Optional[int] = flops def append_kernel(self, name, device, duration): assert self.device_type == DeviceType.CPU self.kernels.append(Kernel(name, device, duration)) 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_privateuse1_memory_usage(self): if self.is_async or self.device_type != DeviceType.CPU: return 0 return self.privateuse1_memory_usage - sum( [child.privateuse1_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 or self.use_device: return 0 if self.device_type == DeviceType.CPU: if not self.is_legacy: # account for the kernels in the children ops return sum(kinfo.duration 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.duration 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 or self.use_device: 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 self_privateuse1_time_total(self): if self.is_async or not self.use_device: return 0 if self.device_type == DeviceType.CPU: return self.privateuse1_time_total - sum( [child.privateuse1_time_total for child in self.cpu_children] ) else: assert self.device_type == DeviceType.CUDA return self.privateuse1_time_total @property def privateuse1_time_total(self): if self.is_async or not self.use_device: return 0 if self.device_type == DeviceType.CPU: if not self.is_legacy: # account for the kernels in the children ops return sum(kinfo.duration for kinfo in self.kernels) + sum( ch.privateuse1_time_total for ch in self.cpu_children ) else: # each legacy cpu events has a single (fake) kernel return sum(kinfo.duration for kinfo in self.kernels) else: assert self.device_type == DeviceType.PrivateUse1 return self.time_range.elapsed_us() @property def key(self): return self.name def __repr__(self): device_name = "cuda" if not self.use_device else self.use_device device_time = ( self.cuda_time_str if not self.use_device else self.privateuse1_time_str ) device_memory_usage = ( self.cuda_memory_usage if not self.use_device else self.privateuse1_memory_usage ) return ( "<FunctionEvent id={} name={} device_type={} node_id={} cpu_time={} start_us={} end_us={} " "cpu_children={} {}_time={} name={} thread={} input_shapes={} " "cpu_memory_usage={} {}_memory_usage={} is_async={} is_remote={} seq_nr={} is_legacy={}>".format( self.id, self.name, self.device_type, self.node_id, self.cpu_time_str, self.time_range.start, self.time_range.end, str([child.id for child in self.cpu_children]), device_name, device_time, self.name, self.thread, str(self.input_shapes), self.cpu_memory_usage, device_name, device_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.use_device: Optional[str] = None self.cpu_time_total: int = 0 self.cuda_time_total: int = 0 self.privateuse1_time_total: int = 0 self.self_cpu_time_total: int = 0 self.self_cuda_time_total: int = 0 self.self_privateuse1_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.privateuse1_memory_usage: int = 0 self.self_cpu_memory_usage: int = 0 self.self_cuda_memory_usage: int = 0 self.self_privateuse1_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: int = 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 self.use_device = other.use_device 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.privateuse1_time_total += other.privateuse1_time_total self.self_cpu_time_total += other.self_cpu_time_total self.self_cuda_time_total += other.self_cuda_time_total self.self_privateuse1_time_total += other.self_privateuse1_time_total self.cpu_memory_usage += other.cpu_memory_usage self.cuda_memory_usage += other.cuda_memory_usage self.privateuse1_memory_usage += other.privateuse1_memory_usage self.self_cpu_memory_usage += other.self_cpu_memory_usage self.self_cuda_memory_usage += other.self_cuda_memory_usage self.self_privateuse1_memory_usage += other.self_privateuse1_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): device_name = "cuda" if not self.use_device else self.use_device self_device_time = ( self.self_cuda_time_total_str if not self.use_device else self.self_privateuse1_time_total_str ) device_time = ( self.cuda_time_str if not self.use_device else self.privateuse1_time_str ) device_memory = ( self.cuda_memory_usage if not self.use_device else self.privateuse1_memory_usage ) return ( "<FunctionEventAvg key={} self_cpu_time={} cpu_time={} " " self_{}_time={} {}_time={} input_shapes={} " "cpu_memory_usage={} {}_memory_usage={}>".format( self.key, self.self_cpu_time_total_str, self.cpu_time_str, device_name, self_device_time, device_name, device_time, str(self.input_shapes), self.cpu_memory_usage, device_name, device_memory, ) )
[docs]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]
[docs]class MemRecordsAcc: """Acceleration structure for accessing mem_records in interval.""" def __init__(self, mem_records): self._mem_records = mem_records self._start_uses: List[int] = [] self._indices: List[int] = [] if len(mem_records) > 0: tmp = sorted([(r[0].start_us(), i) for i, r in enumerate(mem_records)]) self._start_uses, self._indices = zip(*tmp) # type: ignore[assignment]
[docs] def in_interval(self, start_us, end_us): r""" Return all records in the given interval """ start_idx = bisect.bisect_left(self._start_uses, start_us) end_idx = bisect.bisect_right(self._start_uses, end_us) for i in range(start_idx, end_idx): yield self._mem_records[self._indices[i]]
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) MEMORY_EVENT_NAME = "[memory]" OUT_OF_MEMORY_EVENT_NAME = "[OutOfMemory]" def _filter_name(name): # ignoring the following utility ops filtered_out_names = [ MEMORY_EVENT_NAME, # used only for the top-level memory events OUT_OF_MEMORY_EVENT_NAME, "profiler::_record_function_enter", "profiler::_record_function_enter_new", "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 def _build_table( events, sort_by=None, header=None, row_limit=100, max_src_column_width=75, max_name_column_width=55, max_shapes_column_width=80, with_flops=False, profile_memory=False, top_level_events_only=False, ): """Print 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_privateuse1_time = any( event.self_privateuse1_time_total > 0 for event in events ) has_privateuse1_mem = any( event.self_privateuse1_memory_usage > 0 for event in events ) use_device = events[0].use_device if not use_device and (has_privateuse1_mem or has_privateuse1_time): raise RuntimeError( "use_device is None, but there is private device performance data." ) 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, use_device=use_device, profile_memory=profile_memory, with_flops=with_flops, ) name_column_width = max([len(evt.key) for evt in events]) + 4 if max_name_column_width is not None: name_column_width = min(name_column_width, max_name_column_width) shapes_column_width = max([len(str(evt.input_shapes)) for evt in events]) + 4 if max_shapes_column_width is not None: shapes_column_width = min(shapes_column_width, max_shapes_column_width) DEFAULT_COLUMN_WIDTH = 12 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 ) if max_src_column_width is not None: 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 has_privateuse1_time: privateuse1 = use_device.upper() headers.extend( [ f"Self {privateuse1}", f"Self {privateuse1} %", f"{privateuse1} total", f"{privateuse1} time avg", ] ) if profile_memory: headers.extend( [ "CPU Mem", "Self CPU Mem", ] ) if has_cuda_mem: headers.extend( [ "CUDA Mem", "Self CUDA Mem", ] ) if has_privateuse1_mem: privateuse1 = use_device.upper() headers.extend( [ f"{privateuse1} Mem", f"Self {privateuse1} 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 raw_flops = [] for evt in events: if evt.flops > 0: raw_flops.append(evt.flops) if len(raw_flops) != 0: (flops_scale, flops_header) = auto_scale_flops(min(raw_flops)) headers.append(f"Total {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[assignment] # 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 sum_self_privateuse1_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: if not use_device: sum_self_cuda_time_total += evt.self_cuda_time_total else: sum_self_privateuse1_time_total += evt.self_privateuse1_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 elif evt.device_type == DeviceType.PrivateUse1: sum_self_privateuse1_time_total += evt.self_privateuse1_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 max_name_column_width is not None and 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 has_privateuse1_time: row_values.extend( [ evt.self_privateuse1_time_total_str, # PrivateUse1 time total % _format_time_share( evt.self_privateuse1_time_total, sum_self_privateuse1_time_total ), evt.privateuse1_time_total_str, evt.privateuse1_time_str, # PrivateUse1 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), ] ) if has_privateuse1_mem: row_values.extend( [ # PrivateUse1 Mem Total _format_memory(evt.privateuse1_memory_usage), # Self PrivateUse1 Mem Total _format_memory(evt.self_privateuse1_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: row_values.append("--") else: row_values.append(f"{evt.flops * flops_scale:8.3f}") # type: ignore[possibly-undefined] 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(f"Self CPU time total: {_format_time(sum_self_cpu_time_total)}") if has_cuda_time: append(f"Self CUDA time total: {_format_time(sum_self_cuda_time_total)}") if has_privateuse1_time: append( f"Self {use_device.upper()} time total: {_format_time(sum_self_privateuse1_time_total)}" ) return "".join(result)

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