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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]
class Interval:
def __init__(self, start, end):
self.start = start
self.end = end
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,
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.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,
)
)
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]
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]
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)
|