| import os, torch |
| from accelerate import Accelerator |
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|
|
| class ModelLogger: |
| def __init__(self, output_path, remove_prefix_in_ckpt=None, state_dict_converter=lambda x:x): |
| self.output_path = output_path |
| self.remove_prefix_in_ckpt = remove_prefix_in_ckpt |
| self.state_dict_converter = state_dict_converter |
| self.num_steps = 0 |
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|
| def on_step_end(self, accelerator: Accelerator, model: torch.nn.Module, save_steps=None): |
| self.num_steps += 1 |
| if save_steps is not None and self.num_steps % save_steps == 0: |
| self.save_model(accelerator, model, f"step-{self.num_steps}.safetensors") |
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|
|
| def on_epoch_end(self, accelerator: Accelerator, model: torch.nn.Module, epoch_id): |
| accelerator.wait_for_everyone() |
| if accelerator.is_main_process: |
| state_dict = accelerator.get_state_dict(model) |
| state_dict = accelerator.unwrap_model(model).export_trainable_state_dict(state_dict, remove_prefix=self.remove_prefix_in_ckpt) |
| state_dict = self.state_dict_converter(state_dict) |
| os.makedirs(self.output_path, exist_ok=True) |
| path = os.path.join(self.output_path, f"epoch-{epoch_id}.safetensors") |
| accelerator.save(state_dict, path, safe_serialization=True) |
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|
|
| def on_training_end(self, accelerator: Accelerator, model: torch.nn.Module, save_steps=None): |
| if save_steps is not None and self.num_steps % save_steps != 0: |
| self.save_model(accelerator, model, f"step-{self.num_steps}.safetensors") |
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|
|
| def save_model(self, accelerator: Accelerator, model: torch.nn.Module, file_name): |
| accelerator.wait_for_everyone() |
| if accelerator.is_main_process: |
| state_dict = accelerator.get_state_dict(model) |
| state_dict = accelerator.unwrap_model(model).export_trainable_state_dict(state_dict, remove_prefix=self.remove_prefix_in_ckpt) |
| state_dict = self.state_dict_converter(state_dict) |
| os.makedirs(self.output_path, exist_ok=True) |
| path = os.path.join(self.output_path, file_name) |
| accelerator.save(state_dict, path, safe_serialization=True) |
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|
