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feat: adapt to InternTrain and InternEvo respectively (#121)
Adapt to InternTrain and InternEvo respectively.
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| Original file line number | Diff line number | Diff line change |
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| @@ -1,170 +1,6 @@ | ||
| # Copyright (c) 2024, DeepLink. | ||
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| import torch | ||
| from torch.optim.optimizer import Optimizer | ||
| from typing import List | ||
| import deeplink_ext.cpp_extensions as ext | ||
| from deeplink_ext.interntrain_ops.adamw import AdamW | ||
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| assert hasattr(ext, "adamw") | ||
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| __all__ = ["AdamW"] | ||
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| def fused_adamw( | ||
| params: List[torch.Tensor], | ||
| grads: List[torch.Tensor], | ||
| exp_avgs: List[torch.Tensor], | ||
| exp_avg_sqs: List[torch.Tensor], | ||
| max_exp_avg_sqs: List[torch.Tensor], | ||
| step: int, | ||
| *, | ||
| amsgrad: bool, | ||
| beta1: float, | ||
| beta2: float, | ||
| lr: float, | ||
| weight_decay: float, | ||
| eps: float, | ||
| maximize: bool, | ||
| ): | ||
| r"""Functional API that performs AdamW algorithm computation. | ||
| See :class:`~torch.optim.AdamW` for details. | ||
| """ | ||
| if maximize is True: | ||
| raise RuntimeError( | ||
| "Deeplink Adamw with fused=True does not support maximize=True!" | ||
| ) | ||
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| for i, param in enumerate(params): | ||
| grad = grads[i] | ||
| exp_avg = exp_avgs[i] | ||
| exp_avg_sq = exp_avg_sqs[i] | ||
| if amsgrad and len(max_exp_avg_sqs): | ||
| max_exp_avg_sq = max_exp_avg_sqs[i] | ||
| else: | ||
| max_exp_avg_sq = None | ||
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| ext.adamw( | ||
| param, | ||
| exp_avg, | ||
| exp_avg_sq, | ||
| max_exp_avg_sq, | ||
| grad, | ||
| lr, | ||
| beta1, | ||
| beta2, | ||
| eps, | ||
| weight_decay, | ||
| step, | ||
| amsgrad, | ||
| ) | ||
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| class AdamW(Optimizer): | ||
| def __init__( | ||
| self, | ||
| params, | ||
| lr=1e-3, | ||
| betas=(0.9, 0.999), | ||
| eps=1e-8, | ||
| weight_decay=1e-2, | ||
| amsgrad=False, | ||
| *, | ||
| maximize: bool = False, | ||
| ): | ||
| if not 0.0 <= lr: | ||
| raise ValueError("Invalid learning rate: {}".format(lr)) | ||
| if not 0.0 <= eps: | ||
| raise ValueError("Invalid epsilon value: {}".format(eps)) | ||
| if not 0.0 <= betas[0] < 1.0: | ||
| raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0])) | ||
| if not 0.0 <= betas[1] < 1.0: | ||
| raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1])) | ||
| if not 0.0 <= weight_decay: | ||
| raise ValueError("Invalid weight_decay value: {}".format(weight_decay)) | ||
| defaults = dict( | ||
| lr=lr, | ||
| betas=betas, | ||
| eps=eps, | ||
| weight_decay=weight_decay, | ||
| amsgrad=amsgrad, | ||
| maximize=maximize, | ||
| ) | ||
| super(AdamW, self).__init__(params, defaults) | ||
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| def __setstate__(self, state): | ||
| super(AdamW, self).__setstate__(state) | ||
| for group in self.param_groups: | ||
| group.setdefault("amsgrad", False) | ||
| group.setdefault("maximize", False) | ||
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| @torch.no_grad() | ||
| def step(self, closure=None): | ||
| loss = None | ||
| if closure is not None: | ||
| with torch.enable_grad(): | ||
| loss = closure() | ||
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| for group in self.param_groups: | ||
| params_with_grad = [] | ||
| grads = [] | ||
| exp_avgs = [] | ||
| exp_avg_sqs = [] | ||
| max_exp_avg_sqs = [] | ||
| amsgrad = group["amsgrad"] | ||
| beta1, beta2 = group["betas"] | ||
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| if "step" in group: | ||
| group["step"] += 1 | ||
| else: | ||
| group["step"] = 1 | ||
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| for p in group["params"]: | ||
| if p.grad is None: | ||
| continue | ||
| params_with_grad.append(p) | ||
| if p.grad.is_sparse: | ||
| raise RuntimeError("AdamW does not support sparse gradients") | ||
| grads.append(p.grad) | ||
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| state = self.state[p] | ||
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| # State initialization | ||
| if len(state) == 0: | ||
| # Exponential moving average of gradient values | ||
| state["exp_avg"] = torch.zeros_like( | ||
| p, memory_format=torch.preserve_format | ||
| ) | ||
| # Exponential moving average of squared gradient values | ||
| state["exp_avg_sq"] = torch.zeros_like( | ||
| p, memory_format=torch.preserve_format | ||
| ) | ||
| if amsgrad: | ||
| # Maintains max of all exp. moving avg. of sq. grad. values | ||
| state["max_exp_avg_sq"] = torch.zeros_like( | ||
| p, memory_format=torch.preserve_format | ||
| ) | ||
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| exp_avgs.append(state["exp_avg"]) | ||
| exp_avg_sqs.append(state["exp_avg_sq"]) | ||
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| if amsgrad: | ||
| max_exp_avg_sqs.append(state["max_exp_avg_sq"]) | ||
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| fused_adamw( | ||
| params_with_grad, | ||
| grads, | ||
| exp_avgs, | ||
| exp_avg_sqs, | ||
| max_exp_avg_sqs, | ||
| group["step"], | ||
| amsgrad=amsgrad, | ||
| beta1=beta1, | ||
| beta2=beta2, | ||
| lr=group["lr"], | ||
| weight_decay=group["weight_decay"], | ||
| eps=group["eps"], | ||
| maximize=group["maximize"], | ||
| ) | ||
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| return loss |
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