126 lines
4 KiB
Python
126 lines
4 KiB
Python
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import copy
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import os
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import random
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import numpy as np
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import torch
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def enable_full_determinism(seed: int):
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"""
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Helper function for reproducible behavior during distributed training. See
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- https://pytorch.org/docs/stable/notes/randomness.html for pytorch
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"""
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# set seed first
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set_seed(seed)
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# Enable PyTorch deterministic mode. This potentially requires either the environment
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# variable 'CUDA_LAUNCH_BLOCKING' or 'CUBLAS_WORKSPACE_CONFIG' to be set,
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# depending on the CUDA version, so we set them both here
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os.environ["CUDA_LAUNCH_BLOCKING"] = "1"
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os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":16:8"
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torch.use_deterministic_algorithms(True)
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# Enable CUDNN deterministic mode
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torch.backends.cudnn.deterministic = True
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torch.backends.cudnn.benchmark = False
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def set_seed(seed: int):
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"""
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Args:
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Helper function for reproducible behavior to set the seed in `random`, `numpy`, `torch`.
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seed (`int`): The seed to set.
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"""
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random.seed(seed)
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np.random.seed(seed)
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torch.manual_seed(seed)
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torch.cuda.manual_seed_all(seed)
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# ^^ safe to call this function even if cuda is not available
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class EMAModel:
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"""
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Exponential Moving Average of models weights
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"""
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def __init__(
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self,
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model,
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update_after_step=0,
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inv_gamma=1.0,
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power=2 / 3,
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min_value=0.0,
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max_value=0.9999,
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device=None,
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):
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"""
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@crowsonkb's notes on EMA Warmup:
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If gamma=1 and power=1, implements a simple average. gamma=1, power=2/3 are good values for models you plan
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to train for a million or more steps (reaches decay factor 0.999 at 31.6K steps, 0.9999 at 1M steps),
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gamma=1, power=3/4 for models you plan to train for less (reaches decay factor 0.999 at 10K steps, 0.9999
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at 215.4k steps).
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Args:
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inv_gamma (float): Inverse multiplicative factor of EMA warmup. Default: 1.
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power (float): Exponential factor of EMA warmup. Default: 2/3.
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min_value (float): The minimum EMA decay rate. Default: 0.
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"""
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self.averaged_model = copy.deepcopy(model).eval()
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self.averaged_model.requires_grad_(False)
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self.update_after_step = update_after_step
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self.inv_gamma = inv_gamma
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self.power = power
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self.min_value = min_value
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self.max_value = max_value
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if device is not None:
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self.averaged_model = self.averaged_model.to(device=device)
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self.decay = 0.0
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self.optimization_step = 0
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def get_decay(self, optimization_step):
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"""
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Compute the decay factor for the exponential moving average.
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"""
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step = max(0, optimization_step - self.update_after_step - 1)
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value = 1 - (1 + step / self.inv_gamma) ** -self.power
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if step <= 0:
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return 0.0
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return max(self.min_value, min(value, self.max_value))
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@torch.no_grad()
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def step(self, new_model):
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ema_state_dict = {}
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ema_params = self.averaged_model.state_dict()
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self.decay = self.get_decay(self.optimization_step)
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for key, param in new_model.named_parameters():
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if isinstance(param, dict):
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continue
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try:
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ema_param = ema_params[key]
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except KeyError:
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ema_param = param.float().clone() if param.ndim == 1 else copy.deepcopy(param)
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ema_params[key] = ema_param
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if not param.requires_grad:
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ema_params[key].copy_(param.to(dtype=ema_param.dtype).data)
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ema_param = ema_params[key]
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else:
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ema_param.mul_(self.decay)
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ema_param.add_(param.data.to(dtype=ema_param.dtype), alpha=1 - self.decay)
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ema_state_dict[key] = ema_param
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for key, param in new_model.named_buffers():
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ema_state_dict[key] = param
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self.averaged_model.load_state_dict(ema_state_dict, strict=False)
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self.optimization_step += 1
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