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0d80452 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 | #!/usr/bin/env python
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from collections import defaultdict
from collections.abc import Callable
from typing import Any
import torch
from .utils import format_big_number
_VALID_REDUCTIONS = ("none", "max", "mean", "sum")
class AverageMeter:
"""
Computes and stores the average and current value
Adapted from https://github.com/pytorch/examples/blob/main/imagenet/main.py
Args:
name: Display name of the metric.
fmt: Format string used when rendering the metric.
reduction: Cross-process reduction applied by :meth:`MetricsTracker.reduce_across_ranks`
before logging. One of ``"none"`` (per-rank value, default), ``"max"``, ``"mean"``,
or ``"sum"``. Use ``"max"`` for bottleneck-style metrics (e.g. dataloading or
update wall time) so multi-GPU runs report the slowest rank rather than rank 0.
"""
def __init__(self, name: str, fmt: str = ":f", reduction: str = "none"):
if reduction not in _VALID_REDUCTIONS:
raise ValueError(
f"Invalid reduction {reduction!r} for AverageMeter; expected one of {_VALID_REDUCTIONS}."
)
self.name = name
self.fmt = fmt
self.reduction = reduction
self.reset()
def reset(self) -> None:
self.val = 0.0
self.avg = 0.0
self.sum = 0.0
self.count = 0.0
def update(self, val: float, n: int = 1) -> None:
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
def __str__(self):
fmtstr = "{name}:{avg" + self.fmt + "}"
return fmtstr.format(**self.__dict__)
class MetricsTracker:
"""
A helper class to track and log metrics over time.
Usage pattern:
```python
# initialize, potentially with non-zero initial step (e.g. if resuming run)
metrics = {"loss": AverageMeter("loss", ":.3f")}
train_metrics = MetricsTracker(cfg, dataset, metrics, initial_step=step)
# update metrics derived from step (samples, episodes, epochs) at each training step
train_metrics.step()
# update various metrics
loss = policy.forward(batch)
train_metrics.loss = loss
# display current metrics
logging.info(train_metrics)
# export for wandb
wandb.log(train_metrics.to_dict())
# reset averages after logging
train_metrics.reset_averages()
```
"""
__keys__ = [
"_batch_size",
"_num_frames",
"_avg_samples_per_ep",
"metrics",
"steps",
"samples",
"episodes",
"epochs",
"accelerator",
]
def __init__(
self,
batch_size: int,
num_frames: int,
num_episodes: int,
metrics: dict[str, AverageMeter],
initial_step: int = 0,
accelerator: Callable | None = None,
):
self.__dict__.update(dict.fromkeys(self.__keys__))
self._batch_size = batch_size
self._num_frames = num_frames
self._avg_samples_per_ep = num_frames / num_episodes
self.metrics = metrics
self.steps = initial_step
world_size = accelerator.num_processes if accelerator else 1
# A sample is an (observation,action) pair, where observation and action
# can be on multiple timestamps. In a batch, we have `batch_size` number of samples.
self.samples = self.steps * self._batch_size * world_size
self.episodes = self.samples / self._avg_samples_per_ep
self.epochs = self.samples / self._num_frames
self.accelerator = accelerator
def __getattr__(self, name: str) -> int | dict[str, AverageMeter] | AverageMeter | Any:
if name in self.__dict__:
return self.__dict__[name]
elif name in self.metrics:
return self.metrics[name]
else:
raise AttributeError(f"'{self.__class__.__name__}' object has no attribute '{name}'")
def __setattr__(self, name: str, value: Any) -> None:
if name in self.__dict__:
super().__setattr__(name, value)
elif name in self.metrics:
self.metrics[name].update(value)
else:
raise AttributeError(f"'{self.__class__.__name__}' object has no attribute '{name}'")
def step(self) -> None:
"""
Updates metrics that depend on 'step' for one step.
"""
self.steps += 1
world_size = self.accelerator.num_processes if self.accelerator else 1
self.samples += self._batch_size * world_size
self.episodes = self.samples / self._avg_samples_per_ep
self.epochs = self.samples / self._num_frames
def reduce_across_ranks(self) -> None:
"""
Synchronises the running averages of every metric whose ``reduction`` is not ``"none"``
across all distributed processes (in-place).
This is a collective operation and MUST be invoked on every rank — typically just before
logging. With no accelerator or in single-process runs it is a no-op. Without it, metrics
reported by the main process only reflect rank 0; for bottleneck-style timings
(``dataloading_s``, ``update_s``, ...) that means the slowest worker's stall is invisible.
"""
if self.accelerator is None or self.accelerator.num_processes <= 1:
return
buckets: dict[str, list[str]] = defaultdict(list)
for name, meter in self.metrics.items():
if meter.reduction != "none":
buckets[meter.reduction].append(name)
if not buckets:
return
device = self.accelerator.device
for reduction, names in buckets.items():
tensor = torch.tensor([self.metrics[n].avg for n in names], dtype=torch.float32, device=device)
reduced = self.accelerator.reduce(tensor, reduction=reduction)
for name, value in zip(names, reduced.tolist(), strict=True):
meter = self.metrics[name]
# Preserve avg == sum / count so a later .update() on this meter accumulates
# against the cluster view, not the stale per-rank history.
meter.avg = value
meter.sum = value * meter.count
def __str__(self) -> str:
display_list = [
f"step:{format_big_number(self.steps)}",
# number of samples seen during training
f"smpl:{format_big_number(self.samples)}",
# number of episodes seen during training
f"ep:{format_big_number(self.episodes)}",
# number of time all unique samples are seen
f"epch:{self.epochs:.2f}",
*[str(m) for m in self.metrics.values()],
]
return " ".join(display_list)
def to_dict(self, use_avg: bool = True) -> dict[str, int | float]:
"""
Returns the current metric values (or averages if `use_avg=True`) as a dict.
"""
return {
"steps": self.steps,
"samples": self.samples,
"episodes": self.episodes,
"epochs": self.epochs,
**{k: m.avg if use_avg else m.val for k, m in self.metrics.items()},
}
def reset_averages(self) -> None:
"""Resets average meters."""
for m in self.metrics.values():
m.reset()
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