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Add a callback to write huggingface checkpoints during the training r…
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| Original file line number | Diff line number | Diff line change |
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| # Copyright 2022 MosaicML LLM Foundry authors | ||
| # SPDX-License-Identifier: Apache-2.0 | ||
|
|
||
| import contextlib | ||
| import json | ||
| import logging | ||
| import os | ||
| import tempfile | ||
| from pathlib import Path | ||
| from typing import Optional, Union | ||
|
|
||
| import torch | ||
| from composer.callbacks.utils import create_interval_scheduler | ||
| from composer.core import Callback, Event, State, Time | ||
| from composer.core.state import fsdp_state_dict_type_context | ||
| from composer.loggers import Logger | ||
| from composer.loggers.remote_uploader_downloader import RemoteUploaderDownloader | ||
| from composer.models import HuggingFaceModel | ||
| from composer.utils import dist, format_name_with_dist_and_time, parse_uri | ||
| from transformers import PreTrainedTokenizerBase | ||
|
|
||
| from llmfoundry.models.mpt import MPTConfig, MPTForCausalLM | ||
| from llmfoundry.utils.huggingface_hub_utils import \ | ||
| edit_files_for_hf_compatibility | ||
|
|
||
| log = logging.getLogger(__name__) | ||
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|
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| class HuggingFaceCheckpointer(Callback): | ||
| """Save a huggingface formatted checkpoint during training. | ||
| Args: | ||
| save_folder (str): Top level folder to save checkpoints to (can be a URI). It is likely that | ||
| this would be the same as your save_folder. | ||
| save_interval: Union[str, int, Time]: The interval describing how often checkpoints should be | ||
| saved. If an integer, it will be assumed to be in :attr:`.TimeUnit.EPOCH`. | ||
| Otherwise, the unit must be either :attr:`.TimeUnit.EPOCH`, :attr:`.TimeUnit.BATCH`, | ||
| :attr:`.TimeUnit.TOKEN`, or :attr:`.TimeUnit.SAMPLE`. | ||
| huggingface_folder_name (str): Folder to save each checkpoint under (can be a format string). Default is ``ba{batch}``. | ||
| precision: The precision to save the model in. Default is ``float32``. Options are ``bfloat16``, ``float16``, or ``float32``. | ||
| overwrite (bool): Whether to overwrite previous checkpoints. | ||
| """ | ||
|
|
||
| def __init__( | ||
| self, | ||
| save_folder: str, | ||
| save_interval: Union[str, int, Time], | ||
| huggingface_folder_name: str = 'ba{batch}', | ||
| precision: str = 'fp32', | ||
| overwrite: bool = False, | ||
| ): | ||
| self.backend, self.bucket_name, self.save_dir_format_str = parse_uri( | ||
| save_folder) | ||
| self.overwrite = overwrite | ||
| self.precision = precision | ||
| self.dtype = { | ||
| 'float32': torch.float32, | ||
| 'float16': torch.float16, | ||
| 'bfloat16': torch.bfloat16, | ||
| }[precision] | ||
| self.huggingface_folder_name_fstr = os.path.join( | ||
| 'huggingface', huggingface_folder_name) | ||
| self.check_interval = create_interval_scheduler( | ||
| save_interval, include_end_of_training=True) | ||
| self.upload_to_object_store = (self.backend != '') | ||
| if self.upload_to_object_store: | ||
| self.remote_ud = RemoteUploaderDownloader( | ||
| bucket_uri=f'{self.backend}://{self.bucket_name}', | ||
| num_concurrent_uploads=4) | ||
| else: | ||
| self.remote_ud = None | ||
|
|
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| self.last_checkpoint_batch: Optional[Time] = None | ||
|
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| def run_event(self, event: Event, state: State, logger: Logger) -> None: | ||
| # The interval scheduler handles only returning True for the appropriate events | ||
| if state.get_elapsed_duration() is not None and self.check_interval( | ||
| state, | ||
| event) and self.last_checkpoint_batch != state.timestamp.batch: | ||
| self._save_checkpoint(state, logger) | ||
| elif event == Event.INIT: | ||
| if not isinstance(state.model, HuggingFaceModel): | ||
| raise ValueError( | ||
| f'`HuggingFaceCheckpointer` is only compatible with `HuggingFaceModel`s. ' | ||
| + f'Got {type(state.model)} instead.') | ||
| if self.upload_to_object_store and self.remote_ud is not None: | ||
| self.remote_ud.init(state, logger) | ||
| state.callbacks.append(self.remote_ud) | ||
|
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| def _save_checkpoint(self, state: State, logger: Logger): | ||
| del logger # unused | ||
|
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| self.last_checkpoint_batch = state.timestamp.batch | ||
|
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| log.info('Saving HuggingFace formatted checkpoint') | ||
|
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| from transformers.models.auto.configuration_auto import CONFIG_MAPPING | ||
| CONFIG_MAPPING._extra_content['mpt'] = MPTConfig | ||
| MPTConfig.register_for_auto_class() | ||
| MPTForCausalLM.register_for_auto_class('AutoModelForCausalLM') | ||
|
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| assert isinstance(state.model, HuggingFaceModel) | ||
|
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| save_dir = format_name_with_dist_and_time( | ||
| str( | ||
| Path(self.save_dir_format_str) / | ||
| self.huggingface_folder_name_fstr), state.run_name, | ||
| state.timestamp) | ||
| dir_context_mgr = tempfile.TemporaryDirectory( | ||
| ) if self.upload_to_object_store else contextlib.nullcontext( | ||
| enter_result=save_dir) | ||
|
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| with dir_context_mgr as temp_save_dir: | ||
| assert isinstance(temp_save_dir, | ||
| str) # pyright doesn't know about enter_result | ||
|
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| with fsdp_state_dict_type_context(state.model.model, | ||
| state_dict_type='full'): | ||
| state_dict = state.model.model.state_dict() | ||
|
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||
| # convert the state dict to the requested precision | ||
| for k, v in state_dict.items(): | ||
| if isinstance(v, torch.Tensor): | ||
| state_dict[k] = v.to(dtype=self.dtype) | ||
|
|
||
| if dist.get_global_rank() == 0: | ||
| # We raise above if the model is not a HuggingFaceModel, so this assert is safe | ||
| assert hasattr(state.model.model, 'save_pretrained') | ||
| state.model.model.save_pretrained(temp_save_dir, | ||
| state_dict=state_dict) | ||
|
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||
| if state.model.tokenizer is not None: | ||
| assert isinstance(state.model.tokenizer, | ||
| PreTrainedTokenizerBase) | ||
| state.model.tokenizer.save_pretrained(temp_save_dir) | ||
|
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| # Only need to edit files for MPT because it has custom code | ||
| if state.model.model.config.model_type == 'mpt': | ||
| edit_files_for_hf_compatibility(temp_save_dir) | ||
|
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| with open(os.path.join(temp_save_dir, 'config.json'), 'r') as f: | ||
| edited_config = json.load(f) | ||
|
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| if state.model.model.config.model_type == 'mpt': | ||
| edited_config['attn_config']['attn_impl'] = 'torch' | ||
| edited_config['init_device'] = 'cpu' | ||
|
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| edited_config['torch_dtype'] = self.precision | ||
| with open(os.path.join(temp_save_dir, 'config.json'), 'w') as f: | ||
| json.dump(edited_config, f, indent=4) | ||
|
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||
| if self.upload_to_object_store: | ||
| assert self.remote_ud is not None | ||
| # TODO change to log after other pr | ||
| log.info( | ||
| f'Uploading HuggingFace formatted checkpoint to {self.backend}://{self.bucket_name}/{save_dir}' | ||
| ) | ||
| for filename in os.listdir(temp_save_dir): | ||
| self.remote_ud.upload_file( | ||
| state=state, | ||
| remote_file_name=os.path.join(save_dir, filename), | ||
| file_path=Path(os.path.join(temp_save_dir, | ||
| filename)), | ||
| overwrite=self.overwrite, | ||
| ) | ||
|
|
||
| dist.barrier() |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,2 @@ | ||
| # Copyright 2022 MosaicML LLM Foundry authors | ||
| # SPDX-License-Identifier: Apache-2.0 |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,67 @@ | ||
| # Copyright 2022 MosaicML LLM Foundry authors | ||
| # SPDX-License-Identifier: Apache-2.0 | ||
|
|
||
| import json | ||
| import os | ||
| from typing import Optional | ||
|
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||
|
|
||
| def make_tiny_ft_dataset( | ||
| path: str, | ||
| size: int = 4, | ||
| add_bad_data_dropped: bool = False, | ||
| add_bad_data_error: bool = False, | ||
| add_just_bos_eos_pad: bool = False, | ||
| pad_token: Optional[str] = None, | ||
| start_token: Optional[str] = None, | ||
| end_token: Optional[str] = None, | ||
| ): | ||
| good_sample = {'prompt': 'hello', 'response': 'goodbye'} | ||
| samples = [good_sample] * size | ||
| if add_bad_data_dropped: | ||
| if pad_token is None: | ||
| raise ValueError( | ||
| 'pad_token, start_token, and end_token must be specified if add_bad_data is True' | ||
| ) | ||
| # empty prompt | ||
| samples.append({'prompt': '', 'response': 'goodbye'}) | ||
| # empty response | ||
| samples.append({'prompt': 'hello', 'response': ''}) | ||
| # response just pad | ||
| samples.append({'prompt': 'hello', 'response': pad_token}) | ||
| # response just pad multiple times | ||
| samples.append({'prompt': 'hello', 'response': pad_token * 3}) | ||
|
|
||
| if add_bad_data_error: | ||
| # prompt just None | ||
| samples.append({ | ||
| 'prompt': None, | ||
| 'response': 'goodbye' | ||
| }) # type: ignore (intentional test) | ||
| # response just None | ||
| samples.append({ | ||
| 'prompt': 'hello', | ||
| 'response': None | ||
| }) # type: ignore (intentional test) | ||
|
|
||
| if add_just_bos_eos_pad: | ||
| if pad_token is None or start_token is None or end_token is None: | ||
| raise ValueError( | ||
| 'pad_token, start_token, and end_token must be specified if add_just_bos_eos is True' | ||
| ) | ||
| # prompt just start | ||
| samples.append({'prompt': start_token, 'response': 'goodbye'}) | ||
| # response just start | ||
| samples.append({'prompt': 'hello', 'response': start_token}) | ||
| # prompt just end | ||
| samples.append({'prompt': end_token, 'response': 'goodbye'}) | ||
| # response just end | ||
| samples.append({'prompt': 'hello', 'response': end_token}) | ||
| # prompt just pad | ||
| samples.append({'prompt': pad_token, 'response': 'goodbye'}) | ||
|
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||
| os.makedirs(os.path.dirname(path), exist_ok=True) | ||
| with open(path, 'w') as _f: | ||
| for sample in samples: | ||
| _f.write(json.dumps(sample)) | ||
| _f.write('\n') |
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