-
Notifications
You must be signed in to change notification settings - Fork 843
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Update submodules as well since MPS changes were needed
- Loading branch information
1 parent
b755ebd
commit 4e56a20
Showing
26 changed files
with
5,785 additions
and
187 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Binary file not shown.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,106 @@ | ||
| import math | ||
| import torch | ||
| from transformers import Adafactor | ||
|
|
||
| @torch.no_grad() | ||
| def adafactor_step_param(self, p, group): | ||
| if p.grad is None: | ||
| return | ||
| grad = p.grad | ||
| if grad.dtype in {torch.float16, torch.bfloat16}: | ||
| grad = grad.float() | ||
| if grad.is_sparse: | ||
| raise RuntimeError("Adafactor does not support sparse gradients.") | ||
|
|
||
| state = self.state[p] | ||
| grad_shape = grad.shape | ||
|
|
||
| factored, use_first_moment = Adafactor._get_options(group, grad_shape) | ||
| # State Initialization | ||
| if len(state) == 0: | ||
| state["step"] = 0 | ||
|
|
||
| if use_first_moment: | ||
| # Exponential moving average of gradient values | ||
| state["exp_avg"] = torch.zeros_like(grad) | ||
| if factored: | ||
| state["exp_avg_sq_row"] = torch.zeros(grad_shape[:-1]).to(grad) | ||
| state["exp_avg_sq_col"] = torch.zeros(grad_shape[:-2] + grad_shape[-1:]).to(grad) | ||
| else: | ||
| state["exp_avg_sq"] = torch.zeros_like(grad) | ||
|
|
||
| state["RMS"] = 0 | ||
| else: | ||
| if use_first_moment: | ||
| state["exp_avg"] = state["exp_avg"].to(grad) | ||
| if factored: | ||
| state["exp_avg_sq_row"] = state["exp_avg_sq_row"].to(grad) | ||
| state["exp_avg_sq_col"] = state["exp_avg_sq_col"].to(grad) | ||
| else: | ||
| state["exp_avg_sq"] = state["exp_avg_sq"].to(grad) | ||
|
|
||
| p_data_fp32 = p | ||
| if p.dtype in {torch.float16, torch.bfloat16}: | ||
| p_data_fp32 = p_data_fp32.float() | ||
|
|
||
| state["step"] += 1 | ||
| state["RMS"] = Adafactor._rms(p_data_fp32) | ||
| lr = Adafactor._get_lr(group, state) | ||
|
|
||
| beta2t = 1.0 - math.pow(state["step"], group["decay_rate"]) | ||
| update = (grad ** 2) + group["eps"][0] | ||
| if factored: | ||
| exp_avg_sq_row = state["exp_avg_sq_row"] | ||
| exp_avg_sq_col = state["exp_avg_sq_col"] | ||
|
|
||
| exp_avg_sq_row.mul_(beta2t).add_(update.mean(dim=-1), alpha=(1.0 - beta2t)) | ||
| exp_avg_sq_col.mul_(beta2t).add_(update.mean(dim=-2), alpha=(1.0 - beta2t)) | ||
|
|
||
| # Approximation of exponential moving average of square of gradient | ||
| update = Adafactor._approx_sq_grad(exp_avg_sq_row, exp_avg_sq_col) | ||
| update.mul_(grad) | ||
| else: | ||
| exp_avg_sq = state["exp_avg_sq"] | ||
|
|
||
| exp_avg_sq.mul_(beta2t).add_(update, alpha=(1.0 - beta2t)) | ||
| update = exp_avg_sq.rsqrt().mul_(grad) | ||
|
|
||
| update.div_((Adafactor._rms(update) / group["clip_threshold"]).clamp_(min=1.0)) | ||
| update.mul_(lr) | ||
|
|
||
| if use_first_moment: | ||
| exp_avg = state["exp_avg"] | ||
| exp_avg.mul_(group["beta1"]).add_(update, alpha=(1 - group["beta1"])) | ||
| update = exp_avg | ||
|
|
||
| if group["weight_decay"] != 0: | ||
| p_data_fp32.add_(p_data_fp32, alpha=(-group["weight_decay"] * lr)) | ||
|
|
||
| p_data_fp32.add_(-update) | ||
|
|
||
| if p.dtype in {torch.float16, torch.bfloat16}: | ||
| p.copy_(p_data_fp32) | ||
|
|
||
|
|
||
| @torch.no_grad() | ||
| def adafactor_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"]: | ||
| adafactor_step_param(self, p, group) | ||
|
|
||
| return loss | ||
|
|
||
| def patch_adafactor_fused(optimizer: Adafactor): | ||
| optimizer.step_param = adafactor_step_param.__get__(optimizer) | ||
| optimizer.step = adafactor_step.__get__(optimizer) |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Oops, something went wrong.