huangyanpei/VLA
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Init

Browse Source
This commit is contained in:
Starrick
2025-09-07 14:59:17 +08:00
commit 24dbdbd24b
40 changed files with 10754 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files
wall_x/utils/__init__.py
View File
wall_x/utils/constant.py
Executable
+191
View File
@@ -0,0 +1,191 @@
action_statistic_dof = {
"x2_normal": {
# water flowers
"follow_left_arm_joint_cur": {
"min": [-3.7121],
"delta": [7.6008],
},
"follow_right_arm_joint_cur": {
"min": [-3.6176],
"delta": [8.5015],
},
"follow_left_ee_cartesian_pos": {"min": [-0.036, -0.3241, -0.1245], "delta": [0.4389, 0.557, 0.479]},
"follow_left_ee_rotation": {"min": [-1.2373, -0.1929, -1.5182], "delta": [2.2009, 1.5669, 2.0936]},
"follow_left_gripper": {"min": [-0.1196], "delta": [4.5226]},
"follow_right_ee_cartesian_pos": {"min": [-0.0326, -0.2273, -0.1377], "delta": [0.4574, 0.5704, 0.4743]},
"follow_right_ee_rotation": {"min": [-1.2201, -0.2611, -0.7427], "delta": [2.6623, 1.6622, 2.4186]},
"follow_right_gripper": {"min": [-0.1208], "delta": [4.5261]},
"height": {"min": [-0.0001], "delta": [0.5051]},
"head_actions": {"min": [-1.5000, -1.4167], "delta": [2.5000, 1.8879]},
"base_velocity": {"min": [-0.0359, -0.084, -0.0162], "delta": [0.1539, 0.1848, 0.0322]},
},
"DobbE": {
"follow_right_ee_cartesian_pos": {"min": [-0.6107, -0.3272, -0.4282], "delta": [1.2629, 1.5297, 0.8349]},
"follow_right_ee_rotation": {"min": [-1.7378, -1.4597, -1.8712], "delta": [2.7031, 2.8182, 3.5921]},
"follow_right_gripper": {"min": [0.0], "delta": [0.9983]},
},
"RH20T": {
"follow_right_ee_cartesian_pos": {"min": [0.3646, -0.2722, 0.0066], "delta": [0.3813, 0.5973, 0.3277]},
"follow_right_ee_rotation": {"min": [-1.8716, -0.4398, -3.1414], "delta": [3.4145, 1.0225, 6.2828]},
"follow_right_gripper": {"min": [0.0], "delta": [95.0]},
},
"agibotworld_alpha": {
"follow_left_ee_cartesian_pos": {"min": [0.4954, 0.0166, 0.1729], "delta": [0.3336, 0.5123, 0.9189]},
"follow_left_ee_rotation": {"min": [-3.1064, -1.2629, -3.1238], "delta": [6.2127, 2.5923, 6.2496]},
"follow_left_gripper": {"min": [34.6222], "delta": [86.1921]},
"follow_right_ee_cartesian_pos": {"min": [0.4615, -0.5975, 0.1638], "delta": [0.3823, 0.5577, 0.8873]},
"follow_right_ee_rotation": {"min": [-3.0891, -1.0739, -2.5091], "delta": [6.1707, 2.3074, 3.8533]},
"follow_right_gripper": {"min": [34.6222], "delta": [85.7635]},
"height": {"min": [0.0], "delta": [0.4535]},
"head_actions": {"min": [-0.1746, 0.0523], "delta": [0.2444, 0.4713]},
},
"austin_buds": {
"follow_right_ee_cartesian_pos": {"min": [0.3496, -0.2855, 0.0105], "delta": [0.3748, 0.492, 0.3116]},
"follow_right_ee_rotation": {"min": [-3.1405, -0.151, -0.0737], "delta": [6.2813, 0.3218, 0.1536]},
"follow_right_gripper": {"min": [0.0076], "delta": [0.0724]},
},
"austin_sailor": {
"follow_right_ee_cartesian_pos": {"min": [0.387, -0.3165, 0.0244], "delta": [0.2999, 0.5252, 0.2308]},
"follow_right_ee_rotation": {"min": [-3.1402, -0.1618, -1.5918], "delta": [6.2804, 0.337, 2.9478]},
"follow_right_gripper": {"min": [0.0005], "delta": [0.0773]},
},
"austin_sirius": {
"follow_right_ee_cartesian_pos": {"min": [0.0, -0.1182, 0.0], "delta": [0.5329, 0.3812, 0.2723]},
"follow_right_ee_rotation": {"min": [-3.1407, -0.1243, -1.7434], "delta": [6.2823, 0.1975, 1.8073]},
"follow_right_gripper": {"min": [0.0334], "delta": [0.046]},
},
"bc_z": {
"follow_right_ee_cartesian_pos": {"min": [-0.3883, -0.1116, 0.6113], "delta": [0.7199, 0.4288, 0.3709]},
"follow_right_ee_rotation": {"min": [-1.056, -1.0587, -2.6295], "delta": [1.9142, 1.9455, 4.8064]},
"follow_right_gripper": {"min": [0.2], "delta": [0.8]},
},
"berkeley_autolab_ur5": {
"follow_right_ee_cartesian_pos": {"min": [0.3018, -0.2129, -0.1888], "delta": [0.3121, 0.52, 0.3107]},
"follow_right_ee_rotation": {"min": [-3.1396, -0.2278, 1.1413], "delta": [6.279, 0.454, 0.9841]},
"follow_right_gripper": {"min": [0.0], "delta": [1.0]},
},
"berkeley_cable_routing": {
"follow_right_ee_cartesian_pos": {"min": [0.4617, -0.28, 0.03], "delta": [0.1838, 0.5665, 0.1272]},
"follow_right_ee_rotation": {"min": [-3.1413, -0.0299, -0.7665], "delta": [6.2826, 0.0692, 3.322]},
},
"berkeley_fanuc_manipulation": {
"follow_right_ee_cartesian_pos": {"min": [0.3718, -0.4072, 0.0184], "delta": [0.3483, 0.7201, 0.5229]},
"follow_right_ee_rotation": {"min": [-3.1399, -1.0166, -1.6988], "delta": [6.2802, 1.4498, 3.2074]},
"follow_right_gripper": {"min": [0.0], "delta": [1.0]},
},
"bridge_data_v2": {
"follow_right_ee_cartesian_pos": {"min": [0.1498, -0.2178, -0.0901], "delta": [0.3012, 0.469, 0.298]},
"follow_right_ee_rotation": {"min": [-0.3279, -0.6105, -1.0578], "delta": [0.7378, 1.0353, 2.2552]},
"follow_right_gripper": {"min": [0.0692], "delta": [0.9426]},
},
"dlr_edan_shared_control": {
"follow_right_ee_cartesian_pos": {"min": [-0.8387, 0.1473, -0.3934], "delta": [0.6579, 0.6025, 1.1566]},
"follow_right_ee_rotation": {"min": [-3.1217, -1.5197, -2.2516], "delta": [6.2505, 1.5594, 4.2831]},
"follow_right_gripper": {"min": [0.0], "delta": [1.0]},
},
"droid": {
"follow_right_ee_cartesian_pos": {"min": [0.2667, -0.4396, -0.0472], "delta": [0.5159, 0.8806, 0.8331]},
"follow_right_ee_rotation": {"min": [-3.1374, -1.216, -2.1741], "delta": [6.2749, 2.1075, 4.2259]},
"follow_right_gripper": {"min": [0.0], "delta": [0.9912]},
},
"fmb": {
"follow_right_ee_cartesian_pos": {"min": [0.3554, -0.2844, 0.0354], "delta": [0.336, 0.4961, 0.2943]},
"follow_right_ee_rotation": {"min": [-3.1404, -0.9302, -0.0599], "delta": [6.2807, 1.724, 1.8284]},
"follow_right_gripper": {"min": [0.0], "delta": [1.0]},
},
"fractal": {
"follow_right_ee_cartesian_pos": {"min": [0.3242, -0.2836, 0.1405], "delta": [0.5518, 0.4963, 0.9328]},
"follow_right_ee_rotation": {"min": [-3.1308, -0.2421, -2.9685], "delta": [6.2609, 1.7343, 5.819]},
"follow_right_gripper": {"min": [0.0], "delta": [1.0]},
},
"furniture_bench": {
"follow_right_ee_cartesian_pos": {"min": [0.3691, -0.181, 0.0058], "delta": [0.2962, 0.3582, 0.1775]},
"follow_right_ee_rotation": {"min": [-3.1394, -0.6121, -1.9958], "delta": [6.2786, 1.6114, 3.7748]},
"follow_right_gripper": {"min": [0.0035], "delta": [0.0762]},
},
"jaco_play": {
"follow_right_ee_cartesian_pos": {"min": [-0.3787, -0.6294, 0.1682], "delta": [0.5898, 0.3587, 0.2183]},
"follow_right_ee_rotation": {"min": [0.9792, -0.0668, -0.0498], "delta": [0.0175, 0.1277, 0.0686]},
"follow_right_gripper": {"min": [0.0791], "delta": [0.1033]},
},
"nyu_rot": {
"follow_right_ee_cartesian_pos": {"min": [0.25, -1.0, -0.2], "delta": [0.75, 2.0, 1.2]},
"follow_right_ee_rotation": {"min": [-3.1416, -3.1416, 6.2831], "delta": [9.4248, 4.1416, 0.0]},
"follow_right_gripper": {"min": [0.0], "delta": [1.0]},
},
"stanford_hydra": {
"follow_right_ee_cartesian_pos": {"min": [0.2068, -0.274, 0.1317], "delta": [0.4929, 0.4981, 0.4588]},
"follow_right_ee_rotation": {"min": [-3.1321, -0.7496, -3.0269], "delta": [6.2658, 1.5176, 5.8261]},
"follow_right_gripper": {"min": [0.0], "delta": [0.0811]},
},
"stanford_kuka_multimodal": {
"follow_right_ee_cartesian_pos": {"min": [0.4781, -0.0659, 0.3424], "delta": [0.0868, 0.0864, 0.1863]},
"follow_right_ee_rotation": {"min": [-3.136, -0.0521, -3.1413], "delta": [6.2727, 0.1109, 6.2825]},
"follow_right_gripper": {"min": [-0.4713], "delta": [0.9485]},
},
"taco_play": {
"follow_right_ee_cartesian_pos": {"min": [0.1375, -0.4291, 0.2052], "delta": [0.5327, 1.0237, 0.3913]},
"follow_right_ee_rotation": {"min": [-3.1391, -0.6946, -1.2808], "delta": [6.2784, 0.8196, 3.0856]},
"follow_right_gripper": {"min": [0.0001], "delta": [0.0806]},
},
"utaustin_mutex": {
"follow_right_ee_cartesian_pos": {"min": [0.3213, -0.4734, 0.0141], "delta": [0.2108, 0.8471, 0.5644]},
"follow_right_ee_rotation": {"min": [-3.1404, -0.2202, -1.5489], "delta": [6.2805, 0.582, 1.9282]},
"follow_right_gripper": {"min": [0.0019], "delta": [0.0738]},
},
"viola": {
"follow_right_ee_cartesian_pos": {"min": [0.4011, -0.2521, 0.0103], "delta": [0.2444, 0.4305, 0.4355]},
"follow_right_ee_rotation": {"min": [-3.1403, -0.2737, -1.8626], "delta": [6.2804, 0.4901, 2.0618]},
"follow_right_gripper": {"min": [0.0002], "delta": [0.0773]},
},
"kuka": {
"follow_right_ee_cartesian_pos": {
"min": [0.3914, -0.4901, 0.0175],
"delta": [0.3339, 0.8357, 0.9064],
},
"follow_right_ee_rotation": {
"min": [-3.1416, -0.9903, -3.1416],
"delta": [6.2832, 2.2421, 6.2832],
},
"follow_right_gripper": {
"min": [0.0000],
"delta": [1.0000],
},
},
"UMI-biarm": {
"follow_left_ee_cartesian_pos": {
"min": [-0.2917, -0.4926, 0.0063],
"delta": [0.9028, 0.8168, 0.3473],
},
"follow_left_ee_rotation": {
"min": [-2.5309, -1.5706, -1.3309],
"delta": [0.8758, 2.3315, 1.7076],
},
"follow_left_gripper": {
"min": [0.0029],
"delta": [0.0812],
},
"follow_right_ee_cartesian_pos": {
"min": [-0.0023, -0.5191, -0.0358],
"delta": [0.7474, 0.8668, 0.351],
},
"follow_right_ee_rotation": {
"min": [-2.4945, -2.0149, -0.8088],
"delta": [1.0941, 3.2628, 2.0018],
},
"follow_right_gripper": {
"min": [0.0019],
"delta": [0.0814],
},
},
"agibotworld_beta": {
"follow_left_ee_cartesian_pos": {"min": [0.4954, 0.0166, 0.1729], "delta": [0.3336, 0.5123, 0.9189]},
"follow_left_ee_rotation": {"min": [-3.1064, -1.2629, -3.1238], "delta": [6.2127, 2.5923, 6.2496]},
"follow_left_gripper": {"min": [34.6222], "delta": [86.1921]},
"follow_right_ee_cartesian_pos": {"min": [0.4615, -0.5975, 0.1638], "delta": [0.3823, 0.5577, 0.8873]},
"follow_right_ee_rotation": {"min": [-3.0891, -1.0739, -2.5091], "delta": [6.1707, 2.3074, 3.8533]},
"follow_right_gripper": {"min": [34.6222], "delta": [85.7635]},
"height": {"min": [0.0], "delta": [0.4535]},
"head_actions": {"min": [-0.1746, 0.0523], "delta": [0.2444, 0.4713]},
},
}
wall_x/utils/timers.py
+483
View File
@@ -0,0 +1,483 @@
import time
import torch
from torch.cuda import nvtx
from abc import ABC, abstractmethod
from typing import List
def _is_distributed():
return torch.distributed.is_available() and torch.distributed.is_initialized()
def _get_world_size():
if _is_distributed():
return torch.distributed.get_world_size()
return 1
def _get_rank():
if _is_distributed():
return torch.distributed.get_rank()
return 0
def _barrier(group=None):
if _is_distributed():
torch.distributed.barrier(group=group)
if torch.distributed.is_available():
try:
dist_all_gather_func = torch.distributed.all_gather_into_tensor
except AttributeError:
dist_all_gather_func = torch.distributed.all_gather
else:
dist_all_gather_func = None
class TimerBase(ABC):
"""Timer base class."""
def __init__(self, name):
self.name = name
@abstractmethod
def start(self, barrier=False):
"""Start the timer.
Args:
barrier (bool, optional): Synchronizes ranks before starting. Defaults to False.
"""
pass
@abstractmethod
def stop(self, barrier=False):
"""Stop the timer.
Args:
barrier (bool, optional): Synchronizes ranks before stopping. Defaults to False.
"""
pass
@abstractmethod
def reset(self):
"""Reset timer."""
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.
"""
pass
class DummyTimer(TimerBase):
"""Dummy Timer."""
def __init__(self):
super().__init__('dummy timer')
def start(self, barrier=False, nvtx_push=False):
return
def stop(self, barrier=False, nvtx_pop=False):
return
def reset(self):
return
def elapsed(self, reset=True, barrier=False):
raise Exception(
'dummy timer should not be used to calculate elapsed time, '
'check if timer\'s log_level <= self._log_level.'
)
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.'
)
class Timer(TimerBase):
"""
Timer class with ability to start/stop.
Comment on using `barrier`: If this flag is passed, then all
the caller processes will wait till all reach the timing routine.
It is up to the user to make sure all the ranks in `barrier_group`
call it otherwise, it will result in a hang.
Comment on `barrier_group`: By default it is set to None which
in torch distributed land, it will result in the global communicator.
"""
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):
"""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)
if torch.cuda.is_available():
torch.cuda.synchronize()
self._start_time = time.time()
self._started = True
if nvtx_push:
nvtx.range_push("{}".format(self.name))
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'
if barrier:
_barrier(group=self._barrier_group)
if torch.cuda.is_available() and sync:
torch.cuda.synchronize()
elapsed = time.time() - self._start_time
self._elapsed += elapsed
self._active_time += elapsed
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
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 (
log_option in allowed_log_options
), 'input log option {} is invalid. It must be one of {}'.format(
log_option, allowed_log_options
)
self._log_option = log_option
self._timers = {}
self._log_levels = {}
self._dummy_timer = DummyTimer()
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], (
'input log level {} does not match already existing '
'log level {} for {} timer'.format(log_level, self._log_levels[name], name)
)
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 (
log_level <= self._max_log_level
), '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.
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 measurments
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()
if torch.cuda.is_available():
device = torch.cuda.current_device()
else:
device = torch.device('cpu')
rank_name_to_time = torch.zeros(
(world_size, len(names)), dtype=torch.float, device=device
)
for i, name in enumerate(names):
if name in self._timers:
rank_name_to_time[rank, i] = self._timers[name].elapsed(reset=reset)
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:
print(f"Warning: all_gather failed: {e}. Using single rank timing.")
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,
rank_to_time.max().item() / normalizer,
)
return name_to_min_max_time
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)
if not name_to_min_max_time:
return None
world_size = _get_world_size()
if world_size == 1:
output_string = 'time (ms):'
for name in name_to_min_max_time:
_, max_time = name_to_min_max_time[name]
output_string += '\n {}: {:.2f}'.format((name + ' ').ljust(48, '.'), max_time)
else:
if max_only:
output_string = 'max time across ranks (ms):'
else:
output_string = '(min, max) time across ranks (ms):'
for name in name_to_min_max_time:
min_time, max_time = name_to_min_max_time[name]
if max_only:
output_string += '\n {}: {:.2f}'.format((name + ' ').ljust(48, '.'), max_time)
else:
output_string += '\n {}: ({:.2f}, {:.2f})'.format(
(name + ' ').ljust(48, '.'), min_time, max_time
)
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()
output_string = 'times across ranks (ms):'
no_reported_timing = True
for i, name in enumerate(names):
not_yet_found = True
for rank in range(world_size):
if rank_name_to_time[rank, i] > 0:
no_reported_timing = False
if not_yet_found:
not_yet_found = False
output_string += '\n {}:'.format(name)
if world_size == 1:
output_string += '\n {:.2f}'.format(
rank_name_to_time[rank, i] / normalizer
)
else:
output_string += '\n rank {:2d}: {:.2f}'.format(
rank, rank_name_to_time[rank, i] / normalizer
)
if no_reported_timing:
return None
return output_string
def get_all_timers_string(
self,
names: List[str] = None,
normalizer: float = 1.0,
reset: bool = True,
barrier: bool = False,
):
"""Returns the output string with logged timer values according to configured options.
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.
"""
if names == None: # get all registered timers
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
output_string = self._get_global_min_max_time_string(
names, reset, barrier, normalizer / 1000.0, max_only
)
elif self._log_option == 'all':
output_string = self._get_all_ranks_time_string(
names, reset, barrier, normalizer / 1000.0
)
else:
raise Exception('unknown timing log option {}'.format(self._log_option))
return output_string
def log(
self,
names: List[str],
rank: int = None,
normalizer: float = 1.0,
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.
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.
"""
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)
def write(
self,
names: List[str],
writer,
iteration: int,
normalizer: float = 1.0,
reset: bool = True,
barrier: bool = False,
):
"""Write timers to a tensorboard writer.
Note that we only report maximum time across ranks to tensorboard.
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.
"""
# 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)
if writer is not None:
for name in name_to_min_max_time:
_, max_time = name_to_min_max_time[name]
writer.add_scalar(name + '-time', max_time, iteration)