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Update Wall-X to 1.1.0 (#104)

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This commit is contained in:
Starrick Liu
2026-06-15 11:40:00 +08:00
committed by GitHub
parent e23a586846
commit 72834e7de5
200 changed files with 33916 additions and 16771 deletions
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wall_x/utils/timers.py
+39 -178
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@@ -1,12 +1,15 @@
import logging
import os
import time
from torch.cuda import nvtx
from abc import ABC, abstractmethod
from contextlib import nullcontext
from functools import wraps
from typing import List
import torch
from functools import wraps
from contextlib import nullcontext
import os
from torch.cuda import nvtx
logger = logging.getLogger(__name__)
ENABLE_PERFORMANCE_TIMING = (
os.environ.get("ENABLE_PERFORMANCE_TIMING", "True").lower() == "true"
@@ -32,23 +35,14 @@ class ScopeTimerContext:
torch.cuda.synchronize()
end_time = time.perf_counter()
cost_ms = (end_time - self.start_time) * 1e3
print(f"\033[92m{self.msg} took {cost_ms:.3f} ms to execute\033[0m")
logger.info("%s took %.3f ms to execute", self.msg, cost_ms)
ScopeTimer = ScopeTimerContext if ENABLE_PERFORMANCE_TIMING else nullcontext
def timer(func, msg=None):
"""
Decorator to measure function execution time.
Args:
func: Function to be timed
Returns:
Wrapped function with timing functionality
"""
"""Decorator to measure function execution time."""
if msg is None:
msg = func.__name__
else:
@@ -58,44 +52,36 @@ def timer(func, msg=None):
def wrapper(*args, **kwargs):
with ScopeTimer(msg):
result = func(*args, **kwargs)
return result
return wrapper
# Helper functions to check for distributed environment
def _is_distributed():
"""Checks if the current environment is set up for distributed training."""
return torch.distributed.is_available() and torch.distributed.is_initialized()
def _get_world_size():
"""Safely retrieves the world size (number of processes)."""
if _is_distributed():
return torch.distributed.get_world_size()
return 1
def _get_rank():
"""Safely retrieves the rank of the current process."""
if _is_distributed():
return torch.distributed.get_rank()
return 0
def _barrier(group=None):
"""Safely executes a distributed barrier to synchronize processes."""
if _is_distributed():
torch.distributed.barrier(group=group)
# Dynamically set the all_gather function
if torch.distributed.is_available():
try:
dist_all_gather_func = torch.distributed.all_gather_into_tensor
except AttributeError:
# Fallback to standard all_gather if all_gather_into_tensor is missing
dist_all_gather_func = torch.distributed.all_gather
else:
dist_all_gather_func = None
@@ -109,51 +95,35 @@ class TimerBase(ABC):
@abstractmethod
def start(self, barrier=False):
"""Start the timer.
Args:
barrier (bool, optional): Synchronizes ranks before starting. Defaults to False.
"""
"""Start the timer, optionally syncing all ranks with a barrier first."""
pass
@abstractmethod
def stop(self, barrier=False):
"""Stop the timer.
Args:
barrier (bool, optional): Synchronizes ranks before stopping. Defaults to False.
"""
"""Stop the timer, optionally syncing all ranks with a barrier first."""
pass
@abstractmethod
def reset(self):
"""Reset timer."""
"""Reset accumulated elapsed time to zero."""
pass
@abstractmethod
def elapsed(self, reset=True, barrier=False):
"""Calculates the elapsed time and restarts timer.
Args:
reset (bool, optional): Resets timer before restarting. Defaults to True.
barrier (bool, optional): Synchronizes ranks before stopping. Defaults to False.
Returns:
float: Elapsed time.
"""
"""Return accumulated elapsed time in seconds; reset if reset=True."""
pass
class DummyTimer(TimerBase):
"""Dummy Timer."""
"""Dummy Timer - no-op placeholder used when log level exceeds threshold."""
def __init__(self):
super().__init__("dummy timer")
def start(self, barrier=False, nvtx_push=False):
def start(self, barrier=False, nvtx_push=False, sync=False, **kwargs):
return
def stop(self, barrier=False, nvtx_pop=False):
def stop(self, barrier=False, sync=False, **kwargs):
return
def reset(self):
@@ -166,9 +136,6 @@ class DummyTimer(TimerBase):
)
def active_time(self):
"""Returns the cumulative duration the timer has been active.
Note: Not supported for DummyTimer.
"""
raise Exception(
"active timer should not be used to calculate elapsed time, "
"check if timer's log_level <= self._log_level."
@@ -188,34 +155,18 @@ class Timer(TimerBase):
"""
def __init__(self, name):
"""Initialize Timer.
Args:
name (str): Name of the timer.
"""
super().__init__(name)
self._elapsed = 0.0
self._active_time = 0.0
self._started = False
# Note that None will default to the global process group
self._barrier_group = None
self._start_time = time.time()
self.nvtx = False
def set_barrier_group(self, barrier_group):
"""Sets barrier group.
Args:
barrier_group (ProcessGroup): Torch ProcessGroup for barrier.
"""
self._barrier_group = barrier_group
def start(self, barrier=False, nvtx_push=False, sync=False):
"""Start the timer.
Args:
barrier (bool, optional): Synchronizes ranks before starting. Defaults to False.
"""
assert not self._started, "timer has already been started"
if barrier:
_barrier(group=self._barrier_group)
@@ -228,11 +179,6 @@ class Timer(TimerBase):
self.nvtx = True
def stop(self, barrier=False, sync=False):
"""Stop the timer.
Args:
barrier (bool, optional): Synchronizes ranks before stopping. Defaults to False.
"""
if self.nvtx:
nvtx.range_pop()
assert self._started, "timer is not started"
@@ -246,37 +192,21 @@ class Timer(TimerBase):
self._started = False
def reset(self):
"""Reset timer."""
# Don't reset _active_time
self._elapsed = 0.0
self._started = False
def elapsed(self, reset=True, barrier=False):
"""Calculates the elapsed time and restarts timer.
Args:
reset (bool, optional): Resets timer before restarting. Defaults to True.
barrier (bool, optional): Synchronizes ranks before stopping. Defaults to False.
Returns:
float: Elapsed time.
"""
_started = self._started
# If the timing in progress, end it first.
if self._started:
self.stop(barrier=barrier)
# Get the elapsed time.
_elapsed = self._elapsed
# Reset the elapsed time
if reset:
self.reset()
# If timing was in progress, set it back.
if _started:
self.start(barrier=barrier)
return _elapsed
def active_time(self):
"""Calculates the cumulative duration for which the timer has been active"""
return self._active_time
@@ -284,13 +214,6 @@ class Timers:
"""Class for a group of Timers."""
def __init__(self, log_level, log_option):
"""Initialize group of timers.
Args:
log_level (int): Log level to control what timers are enabled.
log_option (str): Setting for logging statistics over ranks for all the timers.
Allowed: ['max', 'minmax', 'all'].
"""
self._log_level = log_level
allowed_log_options = set(["max", "minmax", "all"])
assert (
@@ -305,9 +228,6 @@ class Timers:
self._max_log_level = 2
def __call__(self, name, log_level=None):
"""Call timer with name and log level."""
# If the timer has already been set, then check if the log-level
# is provided, it matches the one that the timer was created with.
if name in self._timers:
if log_level is not None:
assert log_level == self._log_levels[name], (
@@ -317,8 +237,6 @@ class Timers:
)
)
return self._timers[name]
# If timer does not exist and no log level is provided,
# set it to the max log level which is 2.
if log_level is None:
log_level = self._max_log_level
assert (
@@ -326,38 +244,19 @@ class Timers:
), "log level {} is larger than max supported log level {}".format(
log_level, self._max_log_level
)
# Now if the input log level is larger than the one set for
# the timers class, just ignore it and return a dummy timer.
if log_level > self._log_level:
return self._dummy_timer
# Otherwise, initalize the timer and set the level.
self._timers[name] = Timer(name)
self._log_levels[name] = log_level
return self._timers[name]
def _get_elapsed_time_all_ranks(self, names, reset, barrier):
"""Returns elapsed times of timers in names.
For single-node/single-GPU cases, directly returns the time for the current rank.
For distributed cases, maintains the existing all_gather logic.
Args:
names (List[str]): list of timer names
reset (bool): reset the timer after recording the elapsed time
barrier (bool): if set, do a global barrier before time measurements
Returns:
torch.tensor: Tensor of size [world_size, len(names)] with times in float.
"""
# First make sure all the callers are in sync.
if barrier:
_barrier()
world_size = _get_world_size()
rank = _get_rank()
# Create device tensor
if torch.cuda.is_available():
device = torch.cuda.current_device()
else:
@@ -367,33 +266,26 @@ class Timers:
(world_size, len(names)), dtype=torch.float, device=device
)
# Fill timing data for the current rank
for i, name in enumerate(names):
if name in self._timers:
rank_name_to_time[rank, i] = self._timers[name].elapsed(reset=reset)
# Return directly for single-node; perform all_gather for distributed setup
if world_size > 1 and _is_distributed() and dist_all_gather_func is not None:
try:
dist_all_gather_func(
rank_name_to_time.view(-1), rank_name_to_time[rank, :].view(-1)
)
except Exception as e:
# If all_gather fails, print a warning and proceed with single rank timing
print(f"Warning: all_gather failed: {e}. Using single rank timing.")
logger.warning("all_gather failed: %s. Using single rank timing.", e)
return rank_name_to_time
def _get_global_min_max_time(self, names, reset, barrier, normalizer):
"""Report only min and max times across all ranks."""
rank_name_to_time = self._get_elapsed_time_all_ranks(names, reset, barrier)
name_to_min_max_time = {}
for i, name in enumerate(names):
rank_to_time = rank_name_to_time[:, i]
# filter out the ones we did not have any timings for
rank_to_time = rank_to_time[rank_to_time > 0.0]
# If the timer exists:
if rank_to_time.numel() > 0:
name_to_min_max_time[name] = (
rank_to_time.min().item() / normalizer,
@@ -404,7 +296,6 @@ class Timers:
def _get_global_min_max_time_string(
self, names, reset, barrier, normalizer, max_only
):
"""Report strings for max/minmax times across all ranks."""
name_to_min_max_time = self._get_global_min_max_time(
names, reset, barrier, normalizer
)
@@ -413,17 +304,13 @@ class Timers:
world_size = _get_world_size()
if world_size == 1:
# Simplified output for single-node setup
output_string = "time (ms):"
for name in name_to_min_max_time:
_, max_time = name_to_min_max_time[
name
] # min and max are identical for a single rank
_, max_time = name_to_min_max_time[name]
output_string += "\n {}: {:.2f}".format(
(name + " ").ljust(48, "."), max_time
)
else:
# Maintain original output format for multi-node setup
if max_only:
output_string = "max time across ranks (ms):"
else:
@@ -441,7 +328,6 @@ class Timers:
return output_string
def _get_all_ranks_time_string(self, names, reset, barrier, normalizer):
"""Report times across all ranks."""
rank_name_to_time = self._get_elapsed_time_all_ranks(names, reset, barrier)
world_size = _get_world_size()
@@ -474,32 +360,20 @@ class Timers:
reset: bool = True,
barrier: bool = False,
):
"""Returns the output string with logged timer values according to configured options.
"""Return a formatted timing string for the given timer names.
Args:
names (List[str]): Names of the timers to log. If None, all registered timers are
fetched. Defaults to None.
normalizer (float, optional): Normalizes the timer values by the factor.
Defaults to 1.0.
reset (bool, optional): Whether to reset timer values after logging. Defaults to True.
barrier (bool, optional): Whether to do a global barrier before time measurments.
Defaults to False.
Raises:
Exception: Raises if log option is invalid.
Returns:
str: Formatted string with the timer values.
names: Timers to include; defaults to all registered timers.
normalizer: Divide raw seconds by this value (e.g. 1000 for ms output).
reset: Reset each timer after reading its elapsed time.
barrier: Synchronize across ranks before gathering times.
"""
if names is None: # get all registered timers
if names is None:
names = list(self._timers.keys())
assert normalizer > 0.0
if self._log_option in ["max", "minmax"]:
max_only = False
if self._log_option == "max":
max_only = True
max_only = self._log_option == "max"
output_string = self._get_global_min_max_time_string(
names, reset, barrier, normalizer / 1000.0, max_only
)
@@ -519,30 +393,23 @@ class Timers:
reset: bool = True,
barrier: bool = False,
):
"""logs the timers passed in names to stdout. Example usage is to log average per step
value for timer 'foo', this function can be called with normalizer factor set to logging
interval.
"""Print timing results for the given names to stdout on one rank.
Args:
names (List[str]): Names of the timers to log.
rank (int, optional): logs the timers to a specific rank. If set to None, logs to the
last rank. Defaults to None.
normalizer (float, optional): Normalizes the timer values by the factor.
Defaults to 1.0.
reset (bool, optional): Whether to reset timer values after logging. Defaults to True.
barrier (bool, optional): Whether to do a global barrier before time measurments.
Defaults to False.
names: Timer names to log.
rank: Rank that prints; defaults to the last rank (world_size - 1).
normalizer: Divide raw seconds by this value before printing.
reset: Reset each timer after reading.
barrier: Synchronize across ranks first.
"""
output_string = self.get_all_timers_string(names, normalizer, reset, barrier)
# If no input rank is provided, log on last rank.
world_size = _get_world_size()
current_rank = _get_rank()
if rank is None:
rank = world_size - 1
if rank == current_rank and output_string is not None:
print(output_string, flush=True)
logger.info("%s", output_string)
def write(
self,
@@ -553,22 +420,16 @@ class Timers:
reset: bool = True,
barrier: bool = False,
):
"""Write timers to a tensorboard writer.
Note that we only report maximum time across ranks to tensorboard.
"""Write per-timer max times as TensorBoard scalars.
Args:
names (List[str]): Names of the timers to log.
writer (SummaryWriter): Tensorboard SummaryWriter object
iteration (int): Current iteration.
normalizer (float, optional): Normalizes the timer values by the factor.
Defaults to 1.0.
reset (bool, optional): Whether to reset timer values after logging. Defaults to True.
barrier (bool, optional): Whether to do a global barrier before time measurments.
Defaults to False.
names: Timer names to write.
writer: TensorBoard SummaryWriter instance.
iteration: Global step value for the scalar.
normalizer: Divide raw seconds by this value.
reset: Reset each timer after reading.
barrier: Synchronize across ranks first.
"""
# currently when using add_scalars,
# torch.utils.add_scalars makes each timer its own run, which
# polutes the runs list, so we just add each as a scalar
assert normalizer > 0.0
name_to_min_max_time = self._get_global_min_max_time(
names, reset, barrier, normalizer