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from pathlib import Path
import json
import copy
from typing import Iterable, Any
from contextlib import contextmanager
import torch
def save_args(basepath: Path, args, extra={}):
info = {"args": vars(args)}
info["args"].update(extra)
with open(basepath.joinpath("args.json"), "w") as f:
json.dump(info, f, indent=4)
class AverageMeter:
avg: Any
def __init__(self, name=None):
self.name = name
self.reset()
def reset(self):
self.sum = self.count = self.avg = 0
def update(self, val, n=1):
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
# Adapted from torch-ema https://github.com/fadel/pytorch_ema/blob/master/torch_ema/ema.py#L14
class EMAModel:
"""
Exponential Moving Average of models weights
"""
def __init__(
self,
parameters: Iterable[torch.nn.Parameter],
update_after_step: int = 0,
inv_gamma: float = 1.0,
power: float = 2 / 3,
min_value: float = 0.0,
max_value: float = 0.9999,
):
"""
@crowsonkb's notes on EMA Warmup:
If gamma=1 and power=1, implements a simple average. gamma=1, power=2/3 are good values for models you plan
to train for a million or more steps (reaches decay factor 0.999 at 31.6K steps, 0.9999 at 1M steps),
gamma=1, power=3/4 for models you plan to train for less (reaches decay factor 0.999 at 10K steps, 0.9999
at 215.4k steps).
Args:
inv_gamma (float): Inverse multiplicative factor of EMA warmup. Default: 1.
power (float): Exponential factor of EMA warmup. Default: 2/3.
min_value (float): The minimum EMA decay rate. Default: 0.
"""
parameters = list(parameters)
self.shadow_params = [p.clone().detach() for p in parameters]
self.collected_params = None
self.update_after_step = update_after_step
self.inv_gamma = inv_gamma
self.power = power
self.min_value = min_value
self.max_value = max_value
self.decay = 0.0
self.optimization_step = 0
def get_decay(self, optimization_step: int):
"""
Compute the decay factor for the exponential moving average.
"""
step = max(0, optimization_step - self.update_after_step - 1)
value = 1 - (1 + step / self.inv_gamma) ** -self.power
if step <= 0:
return 0.0
return max(self.min_value, min(value, self.max_value))
@torch.no_grad()
def step(self, parameters):
parameters = list(parameters)
self.optimization_step += 1
# Compute the decay factor for the exponential moving average.
self.decay = self.get_decay(self.optimization_step)
for s_param, param in zip(self.shadow_params, parameters):
if param.requires_grad:
s_param.mul_(self.decay)
s_param.add_(param.data, alpha=1 - self.decay)
else:
s_param.copy_(param)
torch.cuda.empty_cache()
def copy_to(self, parameters: Iterable[torch.nn.Parameter]) -> None:
"""
Copy current averaged parameters into given collection of parameters.
Args:
parameters: Iterable of `torch.nn.Parameter`; the parameters to be
updated with the stored moving averages. If `None`, the
parameters with which this `ExponentialMovingAverage` was
initialized will be used.
"""
parameters = list(parameters)
for s_param, param in zip(self.shadow_params, parameters):
param.data.copy_(s_param.data)
def to(self, device=None, dtype=None) -> None:
r"""Move internal buffers of the ExponentialMovingAverage to `device`.
Args:
device: like `device` argument to `torch.Tensor.to`
"""
# .to() on the tensors handles None correctly
self.shadow_params = [
p.to(device=device, dtype=dtype) if p.is_floating_point() else p.to(device=device)
for p in self.shadow_params
]
def state_dict(self) -> dict:
r"""
Returns the state of the ExponentialMovingAverage as a dict.
This method is used by accelerate during checkpointing to save the ema state dict.
"""
# Following PyTorch conventions, references to tensors are returned:
# "returns a reference to the state and not its copy!" -
# https://pytorch.org/tutorials/beginner/saving_loading_models.html#what-is-a-state-dict
return {
"decay": self.decay,
"optimization_step": self.optimization_step,
"shadow_params": self.shadow_params,
"collected_params": self.collected_params,
}
def load_state_dict(self, state_dict: dict) -> None:
r"""
Loads the ExponentialMovingAverage state.
This method is used by accelerate during checkpointing to save the ema state dict.
Args:
state_dict (dict): EMA state. Should be an object returned
from a call to :meth:`state_dict`.
"""
# deepcopy, to be consistent with module API
state_dict = copy.deepcopy(state_dict)
self.decay = state_dict["decay"]
if self.decay < 0.0 or self.decay > 1.0:
raise ValueError("Decay must be between 0 and 1")
self.optimization_step = state_dict["optimization_step"]
if not isinstance(self.optimization_step, int):
raise ValueError("Invalid optimization_step")
self.shadow_params = state_dict["shadow_params"]
if not isinstance(self.shadow_params, list):
raise ValueError("shadow_params must be a list")
if not all(isinstance(p, torch.Tensor) for p in self.shadow_params):
raise ValueError("shadow_params must all be Tensors")
self.collected_params = state_dict["collected_params"]
if self.collected_params is not None:
if not isinstance(self.collected_params, list):
raise ValueError("collected_params must be a list")
if not all(isinstance(p, torch.Tensor) for p in self.collected_params):
raise ValueError("collected_params must all be Tensors")
if len(self.collected_params) != len(self.shadow_params):
raise ValueError("collected_params and shadow_params must have the same length")
@contextmanager
def apply_temporary(self, parameters: Iterable[torch.nn.Parameter]):
parameters = list(parameters)
original_params = [p.clone() for p in parameters]
self.copy_to(parameters)
try:
yield
finally:
for o_param, param in zip(original_params, parameters):
param.data.copy_(o_param.data)
del original_params
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