Source code for fairseq.optim.adam

# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.

import logging
import math
from collections import Collection
from dataclasses import dataclass, field
from typing import List

import torch
import torch.distributed as dist
import torch.optim
from fairseq.dataclass import FairseqDataclass
from fairseq.optim import FairseqOptimizer, register_optimizer
from fairseq.optim.fused_adam import get_fused_adam_class
from omegaconf import II, DictConfig


logger = logging.getLogger(__name__)


@dataclass
class FairseqAdamConfig(FairseqDataclass):
    adam_betas: str = field(
        default="(0.9, 0.999)", metadata={"help": "betas for Adam optimizer"}
    )
    adam_eps: float = field(
        default=1e-8, metadata={"help": "epsilon for Adam optimizer"}
    )
    weight_decay: float = field(default=0.0, metadata={"help": "weight decay"})
    use_old_adam: bool = field(
        default=False, metadata={"help": "Use fairseq.optim.adam.Adam"}
    )
    # TODO common vars below in parent
    tpu: bool = II("common.tpu")
    lr: List[float] = II("optimization.lr")


[docs]@register_optimizer("adam", dataclass=FairseqAdamConfig) class FairseqAdam(FairseqOptimizer): """Adam optimizer for fairseq. Important note: this optimizer corresponds to the "AdamW" variant of Adam in its weight decay behavior. As such, it is most closely analogous to torch.optim.AdamW from PyTorch. """ def __init__(self, cfg: DictConfig, params): super().__init__(cfg) fused_adam_cls = get_fused_adam_class() use_fused_adam = ( not getattr(cfg, "use_old_adam", False) and fused_adam_cls is not None and torch.cuda.is_available() ) if getattr(cfg, "tpu", False): # on TPUs we use the Adam defined here, since it # automatically casts gradients to FP32 self._optimizer = Adam(params, **self.optimizer_config) elif use_fused_adam: logger.info("using FusedAdam") self._optimizer = fused_adam_cls(params, **self.optimizer_config) else: self._optimizer = Adam(params, **self.optimizer_config) @property def optimizer_config(self): """ Return a kwarg dictionary that will be used to override optimizer args stored in checkpoints. This allows us to load a checkpoint and resume training using a different set of optimizer args, e.g., with a different learning rate. """ return { "lr": self.cfg.lr[0] if isinstance(self.cfg.lr, Collection) else self.cfg.lr, "betas": eval(self.cfg.adam_betas), "eps": self.cfg.adam_eps, "weight_decay": self.cfg.weight_decay, }
[docs] def average_params(self): """Reduce Params is only used during BMUF distributed training.""" state_dict = self.optimizer.state_dict() total_gpus = float(dist.get_world_size()) for _, value in state_dict["state"].items(): value["exp_avg"] /= total_gpus value["exp_avg_sq"] /= total_gpus dist.all_reduce(value["exp_avg"], op=dist.ReduceOp.SUM) dist.all_reduce(value["exp_avg_sq"], op=dist.ReduceOp.SUM)
class Adam(torch.optim.Optimizer): """Implements Adam algorithm. This implementation is modified from torch.optim.Adam based on: `Fixed Weight Decay Regularization in Adam` (see https://arxiv.org/abs/1711.05101) It has been proposed in `Adam: A Method for Stochastic Optimization`_. Arguments: params (iterable): iterable of parameters to optimize or dicts defining parameter groups lr (float, optional): learning rate (default: 1e-3) betas (Tuple[float, float], optional): coefficients used for computing running averages of gradient and its square (default: (0.9, 0.999)) eps (float, optional): term added to the denominator to improve numerical stability (default: 1e-8) weight_decay (float, optional): weight decay (L2 penalty) (default: 0) amsgrad (boolean, optional): whether to use the AMSGrad variant of this algorithm from the paper `On the Convergence of Adam and Beyond`_ .. _Adam\: A Method for Stochastic Optimization: https://arxiv.org/abs/1412.6980 .. _On the Convergence of Adam and Beyond: https://openreview.net/forum?id=ryQu7f-RZ """ def __init__( self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0, amsgrad=False, ): defaults = dict( lr=lr, betas=betas, eps=eps, weight_decay=weight_decay, amsgrad=amsgrad ) super(Adam, self).__init__(params, defaults) @property def supports_memory_efficient_fp16(self): return True @property def supports_flat_params(self): return True def step(self, closure=None): """Performs a single optimization step. Arguments: closure (callable, optional): A closure that reevaluates the model and returns the loss. """ loss = None if closure is not None: loss = closure() for group in self.param_groups: for p in group["params"]: if p.grad is None: continue grad = p.grad.data if grad.dtype in {torch.float16, torch.bfloat16}: grad = grad.float() if grad.is_sparse: raise RuntimeError( "Adam does not support sparse gradients, please consider SparseAdam instead" ) amsgrad = group.get("amsgrad", False) p_data_fp32 = p.data if p.data.dtype in {torch.float16, torch.bfloat16}: p_data_fp32 = p_data_fp32.float() state = self.state[p] # State initialization if len(state) == 0: state["step"] = 0 # Exponential moving average of gradient values state["exp_avg"] = torch.zeros_like(p_data_fp32) # Exponential moving average of squared gradient values state["exp_avg_sq"] = torch.zeros_like(p_data_fp32) if amsgrad: # Maintains max of all exp. moving avg. of sq. grad. values state["max_exp_avg_sq"] = torch.zeros_like(p_data_fp32) else: state["exp_avg"] = state["exp_avg"].to(p_data_fp32) state["exp_avg_sq"] = state["exp_avg_sq"].to(p_data_fp32) if amsgrad: state["max_exp_avg_sq"] = state["max_exp_avg_sq"].to( p_data_fp32 ) exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"] if amsgrad: max_exp_avg_sq = state["max_exp_avg_sq"] beta1, beta2 = group["betas"] state["step"] += 1 # Decay the first and second moment running average coefficient exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1) exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2) if amsgrad: # Maintains the maximum of all 2nd moment running avg. till now torch.max(max_exp_avg_sq, exp_avg_sq, out=max_exp_avg_sq) # Use the max. for normalizing running avg. of gradient denom = max_exp_avg_sq.sqrt().add_(group["eps"]) else: denom = exp_avg_sq.sqrt().add_(group["eps"]) bias_correction1 = 1 - beta1 ** state["step"] bias_correction2 = 1 - beta2 ** state["step"] step_size = group["lr"] * math.sqrt(bias_correction2) / bias_correction1 if group["weight_decay"] != 0: p_data_fp32.add_( p_data_fp32, alpha=-group["weight_decay"] * group["lr"] ) p_data_fp32.addcdiv_(exp_avg, denom, value=-step_size) if p.data.dtype in {torch.float16, torch.bfloat16}: p.data.copy_(p_data_fp32) return loss