File size: 2,156 Bytes
920fd91 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 | import os, torch
from accelerate import Accelerator
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
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")
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)
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")
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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