From 0ea924afd594e057b5f7ca69598ea1ea92ad5843 Mon Sep 17 00:00:00 2001 From: root <23239305+b-chu@users.noreply.github.com> Date: Wed, 5 Jun 2024 21:28:42 +0000 Subject: [PATCH] Add curriculum learning callback --- .../callbacks/curriculum_learning_callback.py | 246 +++++++++++++----- llmfoundry/data/text_data.py | 2 +- llmfoundry/utils/__init__.py | 9 +- llmfoundry/utils/builders.py | 40 +-- llmfoundry/utils/callback_builder.py | 36 +++ llmfoundry/utils/config_dist_utils.py | 81 ++++++ llmfoundry/utils/config_utils.py | 79 ------ scripts/eval/eval.py | 4 +- scripts/train/train.py | 4 +- .../inference/test_convert_composer_to_hf.py | 3 +- tests/callbacks/test_async_eval_callback.py | 2 +- .../test_curriculum_learning_callback.py | 2 +- .../test_mbmoe_tok_per_expert_callback.py | 2 +- tests/utils/test_builders.py | 2 +- 14 files changed, 320 insertions(+), 192 deletions(-) create mode 100644 llmfoundry/utils/callback_builder.py create mode 100644 llmfoundry/utils/config_dist_utils.py diff --git a/llmfoundry/callbacks/curriculum_learning_callback.py b/llmfoundry/callbacks/curriculum_learning_callback.py index 961bf1cae1..43a194b04e 100644 --- a/llmfoundry/callbacks/curriculum_learning_callback.py +++ b/llmfoundry/callbacks/curriculum_learning_callback.py @@ -8,22 +8,28 @@ """ import logging -from typing import Any, Dict +import copy +from typing import Any -from composer.core import State -from composer.loggers import Logger +from composer import DataSpec +from composer.core import State, TimeUnit, ensure_time +from composer.loggers import Logger, MosaicMLLogger +from composer.trainer.trainer import _get_initial_device_train_microbatch_size from streaming import StreamingDataset +from streaming.base.util import clean_stale_shared_memory from torch.utils.data import DataLoader +from llmfoundry.data.dataloader import build_dataloader from llmfoundry.interfaces import CallbackWithConfig -from llmfoundry.utils.warnings import experimental_class +from llmfoundry.utils.builders import build_tokenizer +from llmfoundry.utils.config_utils import calculate_batch_size_info +from llmfoundry.utils.exceptions import BaseContextualError, TrainDataLoaderLocation log = logging.getLogger(__name__) __all__ = ['CurriculumLearning'] -@experimental_class('CurriculumLearning callback') class CurriculumLearning(CallbackWithConfig): """Starts an epoch with a different dataset when resuming from a checkpoint. @@ -34,20 +40,179 @@ class CurriculumLearning(CallbackWithConfig): dataset_index (int): The index of the dataset currently being used. """ - def __init__(self, train_config: Dict, dataset_index: int): - self.dataset_index = dataset_index - self.saved_dataset_index = 0 - self.all_dataset_configs = [] - self.current_dataset_state = {} - # The current dataset config is resolved and passed in train.py - self.current_dataset_config = train_config['train_loader'] + def __init__( + self, train_config: dict[str, Any], duration: str | int | TimeUnit, + schedule: list[dict[str, Any]] + ): + non_positive_error = ValueError('The duration must be positive.') + unit_error = ValueError( + 'Schedules can only be defined in terms of epochs or tokens.' + ) + + # Ensure all duration values are positive + # Ensure all duration units are in epochs or tokens + self._duration = ensure_time(duration, TimeUnit.EPOCH) + if self._duration.value <= 0: + raise non_positive_error + if self._duration.unit != TimeUnit.EPOCH and self._duration.unit != TimeUnit.TOKEN: + raise unit_error + + self._schedule = schedule + for datamix in self._schedule: + assert 'duration' in datamix, 'Each datamix must have a duration.' + datamix['duration'] = ensure_time( + datamix['duration'], TimeUnit.EPOCH + ) + if datamix['duration'].value <= 0: + raise non_positive_error + if datamix['duration'].unit != TimeUnit.EPOCH and datamix[ + 'duration'].unit != TimeUnit.TOKEN: + raise unit_error + assert 'train_loader' in datamix, 'Each datamix must have a train_loader.' + + self._schedule_index = -1 + + # Copied from llmfoundry/utils/config_utils.py + self.device_train_batch_size, _, _ = calculate_batch_size_info( + train_config['global_train_batch_size'], + train_config['device_train_microbatch_size'], + data_replication_degree=1, + ) + + # Copied from scripts/train/train.py + tokenizer_name = train_config['tokenizer']['name'] + tokenizer_kwargs = train_config['tokenizer'].get('kwargs', {}) + self.tokenizer = build_tokenizer(tokenizer_name, tokenizer_kwargs) def before_load(self, state: State, logger: Logger): del logger - # Save the current dataset state so we can restore it correctly - # if we are resuming with a new dataset. - train_loader = state.train_dataloader + # Ensure all duration units are the same as max_duration + units_match = True + if self._duration.unit != state.max_duration.unit: + units_match = False + for datamix in self._schedule: + if datamix['duration'].unit != state.max_duration.unit: + units_match = False + if not units_match: + raise ValueError(( + 'All durations in the schedule must have the same units as ' + 'the max_duration.' + )) + + # Ensure schedule duration is greater than max_duration + schedule_duration = self._duration + for datamix in self._schedule: + schedule_duration += datamix['duration'] + if schedule_duration < state.max_duration: + raise ValueError(( + 'The sum of all durations in the schedule must be greater than ' + 'or equal to the max_duration.' + )) + + self._validate_dataloader(state.train_dataloader) + + def after_load(self, state: State, logger: Logger): + del logger + + self._validate_dataloader(state.train_dataloader) + + # Check if adding a new datamix to a run that didn't use this callback + if self._schedule_index == -1 and state.timestamp >= self._duration: + self._schedule_index = 0 + state.timestamp = state.timestamp.to_next_iteration() + # If checkpoint was saved before iteration was incremented, we need to increment it now + elif (( + self._schedule[self._schedule_index]['duration'].unit + == TimeUnit.TOKEN and state.timestamp.token_in_iteration + >= self._schedule[self._schedule_index]['duration'].value + ) or ( + self._schedule[self._schedule_index]['duration'].unit + == TimeUnit.EPOCH and state.timestamp.epoch_in_iteration + >= self._schedule[self._schedule_index]['duration'].value + )): + log.warning(( + 'The CurriculumLearning callback has detected that the previous run did not correctly ' + 'increment the iteration.' + )) + self._schedule_index += 1 + state.timestamp = state.timestamp.to_next_iteration() + + def iteration_start(self, state: State, logger: Logger): + # Reset and initialize state train dataloader + log.warning( + 'trainer._train_data_spec should be updated whenever the dataloader is updated' + ) + + # Swap the dataset if starting a new iteration that's not the original datamix + if self._schedule_index >= 0: + clean_stale_shared_memory() + data_spec = self._build_train_loader( + train_loader_config=copy.deepcopy( + self._schedule[self._schedule_index] + )['train_loader'], + logger=logger, + ) + state.set_dataloader( + dataloader=data_spec.dataloader, + dataloader_label='train', + ) + # state.train_dataloader = state.dataloader + state.device_train_microbatch_size = _get_initial_device_train_microbatch_size( + state.device_train_microbatch_size, + state.auto_microbatching, + state.train_dataloader, + ) + self._validate_dataloader(state.train_dataloader) + + # Set the length of the new iteration + if self._schedule_index == -1: + state._iteration_length = self._duration + else: + state._iteration_length = self._schedule[self._schedule_index + ]['duration'] + + def iteration_end(self, state: State, logger: Logger): + del state, logger # unused + + self._schedule_index += 1 + + def state_dict(self): + return { + 'duration': self._duration, + 'schedule': self._schedule, + 'schedule_index': self._schedule_index, + } + + def load_state_dict(self, state: dict[str, Any]): + # Ensure that the schedule has not changed on already trained datamixes + assert self._duration == state['duration'] + for idx in range(state['schedule_index'] + 1): + assert self._schedule[idx] == state['schedule'][idx] + + self._schedule_index = state['schedule_index'] + + def _build_train_loader( + self, train_loader_config: dict[str, Any], logger: Logger + ) -> DataSpec: + # Copied from scripts/train/train.py + log.info( + f'Building train loader in CurriculumLearning callback for dataset {self._schedule_index}' + ) + try: + return build_dataloader( + train_loader_config, + self.tokenizer, + self.device_train_batch_size, + ) + except BaseContextualError as e: + for destination in logger.destinations: + if isinstance(destination, MosaicMLLogger): + e.location = TrainDataLoaderLocation + destination.log_exception(e) + raise e + + def _validate_dataloader(self, train_loader: Any): # Check if we are using a DataLoader and StreamingDataset if not isinstance(train_loader, DataLoader): raise ValueError( @@ -61,54 +226,3 @@ def before_load(self, state: State, logger: Logger): f'because it requires loading and saving dataset state. ', f'Instead, got a dataset of type {type(dataset)}', ) - assert isinstance(dataset, StreamingDataset) - # Save the current dataset state so we can restore it if needed. - self.current_dataset_state = dataset.state_dict( # type: ignore - num_samples=0, from_beginning=False) - - def after_load(self, state: State, logger: Logger): - del logger - - # As saved_dataset_index is loaded from state_dict, this only runs when - # a user explicitly increments the dataset_index and not on any other - # resumption, including autoresume. - train_loader = state._train_dataloader - assert isinstance( - train_loader, - DataLoader, - ), 'CurriculumLearning callback requires a DataLoader.' - dataset = train_loader.dataset - assert isinstance( - dataset, - StreamingDataset, - ), 'CurriculumLearning callback requires a StreamingDataset.' - if self.saved_dataset_index < self.dataset_index: - # Ignore the dataset state that was read in from the checkpoint, and - # replace with the new dataset state. This preserves resumption info. - if self.current_dataset_state['epoch'] < 0: - # Make sure the epoch in the loaded state dict is not negative. - # Since `__iter__` has not yet been called on the dataset, the - # epoch index in the dataset will still be -1. We need to ensure - # that we set the epoch correctly to 0 in this case. - self.current_dataset_state['epoch'] = 0 - dataset.load_state_dict( # type: ignore - self.current_dataset_state) - # Start a new epoch since we are using a new dataset. - # This will also reset the sample_in_epoch written to checkpoint, - # making sure that subsequent resumptions proceed correctly. - state.timestamp = state.timestamp.to_next_epoch() - # Append the new dataset config to the list of all dataset configs. - self.all_dataset_configs.append(self.current_dataset_config) - elif self.dataset_index == 0 and len(self.all_dataset_configs) == 0: - # Make sure to track our current dataset config if we are just starting training. - self.all_dataset_configs.append(self.current_dataset_config) - - def state_dict(self): - return { - 'dataset_index': self.dataset_index, - 'all_dataset_configs': self.all_dataset_configs, - } - - def load_state_dict(self, state: Dict[str, Any]): - self.saved_dataset_index = state.get('dataset_index', 0) - self.all_dataset_configs = state.get('all_dataset_configs', []) diff --git a/llmfoundry/data/text_data.py b/llmfoundry/data/text_data.py index 86d5edbaf4..db64a397e5 100644 --- a/llmfoundry/data/text_data.py +++ b/llmfoundry/data/text_data.py @@ -289,7 +289,7 @@ def get_sequence_id_from_batch( return torch.cat([left_zeros, cumulative_sep[:, :-1]], dim=1) -def build_streams(streams: Optional[Dict[str, Any]] = None,): +def build_streams(streams: Optional[Dict[str, Any]] = None): streams_dict = streams # build streams streams_ret = [] diff --git a/llmfoundry/utils/__init__.py b/llmfoundry/utils/__init__.py index dd43efcdd7..1752e024e7 100644 --- a/llmfoundry/utils/__init__.py +++ b/llmfoundry/utils/__init__.py @@ -2,9 +2,10 @@ # SPDX-License-Identifier: Apache-2.0 from llmfoundry.utils.builders import ( + add_metrics_to_eval_loaders, build_algorithm, - build_callback, build_composer_model, + build_eval_loaders, build_evaluators, build_icl_data_and_gauntlet, build_icl_evaluators, @@ -12,18 +13,18 @@ build_metric, build_optimizer, build_scheduler, - build_tokenizer, ) +from llmfoundry.utils.callback_builder import build_callback from llmfoundry.utils.checkpoint_conversion_helpers import ( convert_and_save_ft_weights, get_hf_tokenizer_from_composer_state_dict, load_tokenizer, ) +from llmfoundry.utils.config_dist_utils import process_init_device from llmfoundry.utils.config_utils import ( calculate_batch_size_info, log_config, pop_config, - process_init_device, update_batch_size_info, ) from llmfoundry.utils.data_prep_utils import ( @@ -60,8 +61,10 @@ ) __all__ = [ + 'add_metrics_to_eval_loaders', 'build_algorithm', 'build_callback', + 'build_eval_loaders', 'build_evaluators', 'build_icl_data_and_gauntlet', 'build_icl_evaluators', diff --git a/llmfoundry/utils/builders.py b/llmfoundry/utils/builders.py index 73eb026d98..9f27a9923e 100644 --- a/llmfoundry/utils/builders.py +++ b/llmfoundry/utils/builders.py @@ -19,7 +19,7 @@ ) import torch -from composer.core import Algorithm, Callback, Evaluator +from composer.core import Algorithm, Evaluator from composer.loggers import LoggerDestination from composer.models import ComposerModel from composer.optim.scheduler import ComposerScheduler @@ -31,7 +31,7 @@ from transformers import AutoTokenizer, PreTrainedTokenizerBase from llmfoundry import registry -from llmfoundry.callbacks import EvalGauntlet +from llmfoundry.callbacks.eval_gauntlet_callback import EvalGauntlet from llmfoundry.data.dataloader import build_dataloader from llmfoundry.eval.datasets.in_context_learning_evaluation import \ get_icl_task_dataloader @@ -43,7 +43,6 @@ __all__ = [ 'build_algorithm', - 'build_callback', 'build_evaluators', 'build_icl_data_and_gauntlet', 'build_icl_evaluators', @@ -234,33 +233,6 @@ def build_composer_model( return model -def build_callback( - name: str, - kwargs: Optional[Dict[str, Any]] = None, - train_config: Any = None, -) -> Callback: - """Builds a callback from the registry.""" - registry_to_use = registry.callbacks - if name in registry.callbacks_with_config: - if kwargs is None: - kwargs = {} - if 'train_config' in kwargs: - raise ValueError( - f'`train_config` is a reserved keyword for callbacks with config. Please remove it from the kwargs.', - ) - kwargs['train_config'] = train_config - registry_to_use = registry.callbacks_with_config - - return construct_from_registry( - name=name, - registry=registry_to_use, - partial_function=True, - pre_validation_function=Callback, - post_validation_function=None, - kwargs=kwargs, - ) - - def build_logger( name: str, kwargs: Optional[Dict[str, Any]] = None, @@ -455,8 +427,8 @@ def build_tokenizer( signal_file_path = f'.node_{dist.get_node_rank()}_local_rank0_completed_tokenizer_setup' - if dist.is_available() and dist.is_initialized( - ) and dist.get_world_size() > 1: + if dist.is_available() and dist.is_initialized() and dist.get_world_size( + ) > 1: # Make sure the tokenizer files are downloaded and cached first by local rank 0 with dist.local_rank_zero_download_and_wait(signal_file_path): pass @@ -482,8 +454,8 @@ def build_tokenizer( f'The tokenizer {tokenizer_name} must have an eos_token.', ) - if dist.is_available() and dist.is_initialized( - ) and dist.get_world_size() > 1: + if dist.is_available() and dist.is_initialized() and dist.get_world_size( + ) > 1: if dist.get_local_rank() == 0: with open(signal_file_path, 'wb') as f: f.write(b'local_rank0_completed_tokenizer_setup') diff --git a/llmfoundry/utils/callback_builder.py b/llmfoundry/utils/callback_builder.py new file mode 100644 index 0000000000..b0387c6a9f --- /dev/null +++ b/llmfoundry/utils/callback_builder.py @@ -0,0 +1,36 @@ +# Copyright 2022 MosaicML LLM Foundry authors +# SPDX-License-Identifier: Apache-2.0 + +from typing import Any, Optional + +from composer.core import Callback + +from llmfoundry import registry +from llmfoundry.utils.registry_utils import construct_from_registry + + +def build_callback( + name: str, + kwargs: Optional[dict[str, Any]] = None, + train_config: Any = None, +) -> Callback: + """Builds a callback from the registry.""" + registry_to_use = registry.callbacks + if name in registry.callbacks_with_config: + if kwargs is None: + kwargs = {} + if 'train_config' in kwargs: + raise ValueError( + f'`train_config` is a reserved keyword for callbacks with config. Please remove it from the kwargs.', + ) + kwargs['train_config'] = train_config + registry_to_use = registry.callbacks_with_config + + return construct_from_registry( + name=name, + registry=registry_to_use, + partial_function=True, + pre_validation_function=Callback, + post_validation_function=None, + kwargs=kwargs, + ) diff --git a/llmfoundry/utils/config_dist_utils.py b/llmfoundry/utils/config_dist_utils.py new file mode 100644 index 0000000000..f75821aae8 --- /dev/null +++ b/llmfoundry/utils/config_dist_utils.py @@ -0,0 +1,81 @@ +import contextlib +import warnings +from typing import Any, Mapping, Optional + +from llmfoundry.layers_registry import ffns_with_megablocks +from llmfoundry.models.utils import init_empty_weights + +__all__ = ['process_init_device'] + + +def process_init_device(model_cfg: dict[str, Any], fsdp_config: Optional[dict]): + # Restrict model init_device to 'meta' and 'cpu', + # using 'cuda' vs. 'cuda:id' is tricky and can lead to common user errors + # when multiple GPUs are available. + # Also 'meta' is only valid when using FSDP + init_context = contextlib.nullcontext() + if 'init_device' in model_cfg: + assert model_cfg['init_device'] in ['meta', 'cpu', 'mixed'] + if fsdp_config is None and model_cfg['init_device'] == 'meta': + warnings.warn( + "Using `cfg.model.init_device='meta'` is only valid when using FSDP! " +\ + "Reverting to `cfg.model.init_device='cpu'`.") + model_cfg['init_device'] = 'cpu' + if model_cfg['init_device'] == 'meta': + init_context = init_empty_weights() + if model_cfg['init_device'] == 'mixed': + if fsdp_config is None: + raise NotImplementedError( + 'Using init_device `mixed` is only supported with FSDP. ' + + 'Please add a FSDP config.', + ) + # Always set `sync_module_states` to True for mixed initialization + if not fsdp_config.get('sync_module_states', False): + warnings.warn(( + 'Setting `sync_module_states = True` for FSDP. This is required ' + 'when using mixed initialization.' + )) + fsdp_config['sync_module_states'] = True + + # Set defaults for mixed initialization + fsdp_config.setdefault('use_orig_params', False) + fsdp_config.setdefault('load_monolith_rank0_only', True) + + # Set ffn_config.device_mesh to fsdp_config.device_mesh + if fsdp_config is not None and 'device_mesh' in fsdp_config and 'ffn_config' in model_cfg and model_cfg[ + 'ffn_config'].get('ffn_type', None) in ffns_with_megablocks: + # Raise ValueError if not using device mesh with MoE expert parallelism + if fsdp_config['device_mesh'] is None and model_cfg['ffn_config'].get( + 'moe_world_size', + 1, + ) > 1: + raise ValueError( + 'device_mesh must be specified in fsdp_config when using MoE with moe_world_size > 1.', + ) + model_cfg['ffn_config']['device_mesh'] = fsdp_config['device_mesh'] + + # No mixed precision needed for weights when they're already 16 bits + master_dtype = model_cfg.get('master_weights_dtype') + small_dtypes = ( + 'bf16', + 'fp16', + 'float16', + 'bfloat16', + 'amp_fp16', + 'amp_bf16', + ) + if fsdp_config and master_dtype in small_dtypes: + reduce_dtype = None + buffer_dtype = None + mixed_precision = fsdp_config.get('mixed_precision') + if isinstance(mixed_precision, Mapping): + reduce_dtype = mixed_precision.get('reduce_dtype') + buffer_dtype = mixed_precision.get('buffer_dtype') + fsdp_config['mixed_precision'] = { + 'param_dtype': None, + 'reduce_dtype': reduce_dtype, + 'buffer_dtype': buffer_dtype, + 'keep_low_precision_grads': True, + } + + return init_context diff --git a/llmfoundry/utils/config_utils.py b/llmfoundry/utils/config_utils.py index 5c1ec9114a..250a59bba9 100644 --- a/llmfoundry/utils/config_utils.py +++ b/llmfoundry/utils/config_utils.py @@ -1,7 +1,6 @@ # Copyright 2022 MosaicML LLM Foundry authors # SPDX-License-Identifier: Apache-2.0 -import contextlib import copy import logging import math @@ -14,7 +13,6 @@ Dict, List, Literal, - Mapping, Optional, Set, Tuple, @@ -28,16 +26,12 @@ from omegaconf import OmegaConf as om from transformers import PretrainedConfig -from llmfoundry.layers_registry import ffns_with_megablocks -from llmfoundry.models.utils import init_empty_weights - log = logging.getLogger(__name__) __all__ = [ 'pop_config', 'calculate_batch_size_info', 'update_batch_size_info', - 'process_init_device', 'log_config', 'log_dataset_uri', ] @@ -423,79 +417,6 @@ def update_batch_size_info(cfg: Dict[str, Any]) -> Dict[str, Any]: return cfg -def process_init_device(model_cfg: Dict[str, Any], fsdp_config: Optional[Dict]): - # Restrict model init_device to 'meta' and 'cpu', - # using 'cuda' vs. 'cuda:id' is tricky and can lead to common user errors - # when multiple GPUs are available. - # Also 'meta' is only valid when using FSDP - init_context = contextlib.nullcontext() - if 'init_device' in model_cfg: - assert model_cfg['init_device'] in ['meta', 'cpu', 'mixed'] - if fsdp_config is None and model_cfg['init_device'] == 'meta': - warnings.warn( - "Using `cfg.model.init_device='meta'` is only valid when using FSDP! " +\ - "Reverting to `cfg.model.init_device='cpu'`.") - model_cfg['init_device'] = 'cpu' - if model_cfg['init_device'] == 'meta': - init_context = init_empty_weights() - if model_cfg['init_device'] == 'mixed': - if fsdp_config is None: - raise NotImplementedError( - 'Using init_device `mixed` is only supported with FSDP. ' + - 'Please add a FSDP config.', - ) - # Always set `sync_module_states` to True for mixed initialization - if not fsdp_config.get('sync_module_states', False): - warnings.warn(( - 'Setting `sync_module_states = True` for FSDP. This is required ' - 'when using mixed initialization.' - )) - fsdp_config['sync_module_states'] = True - - # Set defaults for mixed initialization - fsdp_config.setdefault('use_orig_params', False) - fsdp_config.setdefault('load_monolith_rank0_only', True) - - # Set ffn_config.device_mesh to fsdp_config.device_mesh - if fsdp_config is not None and 'device_mesh' in fsdp_config and 'ffn_config' in model_cfg and model_cfg[ - 'ffn_config'].get('ffn_type', None) in ffns_with_megablocks: - # Raise ValueError if not using device mesh with MoE expert parallelism - if fsdp_config['device_mesh'] is None and model_cfg['ffn_config'].get( - 'moe_world_size', - 1, - ) > 1: - raise ValueError( - 'device_mesh must be specified in fsdp_config when using MoE with moe_world_size > 1.', - ) - model_cfg['ffn_config']['device_mesh'] = fsdp_config['device_mesh'] - - # No mixed precision needed for weights when they're already 16 bits - master_dtype = model_cfg.get('master_weights_dtype') - small_dtypes = ( - 'bf16', - 'fp16', - 'float16', - 'bfloat16', - 'amp_fp16', - 'amp_bf16', - ) - if fsdp_config and master_dtype in small_dtypes: - reduce_dtype = None - buffer_dtype = None - mixed_precision = fsdp_config.get('mixed_precision') - if isinstance(mixed_precision, Mapping): - reduce_dtype = mixed_precision.get('reduce_dtype') - buffer_dtype = mixed_precision.get('buffer_dtype') - fsdp_config['mixed_precision'] = { - 'param_dtype': None, - 'reduce_dtype': reduce_dtype, - 'buffer_dtype': buffer_dtype, - 'keep_low_precision_grads': True, - } - - return init_context - - def log_config(cfg: Dict[str, Any]) -> None: """Logs the current config and updates the wandb and mlflow configs. diff --git a/scripts/eval/eval.py b/scripts/eval/eval.py index 8a0a5c104f..7deb5c4397 100644 --- a/scripts/eval/eval.py +++ b/scripts/eval/eval.py @@ -26,18 +26,18 @@ install() from llmfoundry.utils.builders import ( add_metrics_to_eval_loaders, - build_callback, build_composer_model, build_evaluators, build_logger, build_tokenizer, ) +from llmfoundry.utils.callback_builder import build_callback +from llmfoundry.utils.config_dist_utils import process_init_device from llmfoundry.utils.config_utils import ( EVAL_CONFIG_KEYS, EvalConfig, log_config, make_dataclass_and_log_config, - process_init_device, ) from llmfoundry.utils.registry_utils import import_file diff --git a/scripts/train/train.py b/scripts/train/train.py index f2a70b526d..bf55d69a8b 100644 --- a/scripts/train/train.py +++ b/scripts/train/train.py @@ -34,7 +34,6 @@ from llmfoundry.utils.builders import ( add_metrics_to_eval_loaders, build_algorithm, - build_callback, build_composer_model, build_evaluators, build_logger, @@ -42,6 +41,8 @@ build_scheduler, build_tokenizer, ) +from llmfoundry.utils.callback_builder import build_callback +from llmfoundry.utils.config_dist_utils import process_init_device from llmfoundry.utils.config_utils import ( TRAIN_CONFIG_KEYS, TrainConfig, @@ -49,7 +50,6 @@ log_dataset_uri, make_dataclass_and_log_config, pop_config, - process_init_device, update_batch_size_info, ) from llmfoundry.utils.exceptions import ( diff --git a/tests/a_scripts/inference/test_convert_composer_to_hf.py b/tests/a_scripts/inference/test_convert_composer_to_hf.py index 0577e13a1f..e4bd1dbe97 100644 --- a/tests/a_scripts/inference/test_convert_composer_to_hf.py +++ b/tests/a_scripts/inference/test_convert_composer_to_hf.py @@ -33,7 +33,8 @@ build_optimizer, build_tokenizer, ) -from llmfoundry.utils.config_utils import process_init_device, to_dict_container +from llmfoundry.utils.config_dist_utils import process_init_device +from llmfoundry.utils.config_utils import to_dict_container from scripts.inference.convert_composer_to_hf import convert_composer_to_hf from tests.data_utils import make_tiny_ft_dataset diff --git a/tests/callbacks/test_async_eval_callback.py b/tests/callbacks/test_async_eval_callback.py index e0794df08f..d3974003a3 100644 --- a/tests/callbacks/test_async_eval_callback.py +++ b/tests/callbacks/test_async_eval_callback.py @@ -15,7 +15,7 @@ validate_eval_run_config, validate_interval, ) -from llmfoundry.utils.builders import build_callback +from llmfoundry.utils.callback_builder import build_callback from mcli import Run, RunConfig, RunStatus RUN_NAME = 'foo_bar-1234' diff --git a/tests/callbacks/test_curriculum_learning_callback.py b/tests/callbacks/test_curriculum_learning_callback.py index bbdbf3d691..5549b90947 100644 --- a/tests/callbacks/test_curriculum_learning_callback.py +++ b/tests/callbacks/test_curriculum_learning_callback.py @@ -1,7 +1,7 @@ # Copyright 2024 MosaicML LLM Foundry authors # SPDX-License-Identifier: Apache-2.0 -from llmfoundry.utils.builders import build_callback +from llmfoundry.utils.callback_builder import build_callback def test_curriculum_learning_callback_builds(): diff --git a/tests/callbacks/test_mbmoe_tok_per_expert_callback.py b/tests/callbacks/test_mbmoe_tok_per_expert_callback.py index aff9f1724b..29a960e539 100644 --- a/tests/callbacks/test_mbmoe_tok_per_expert_callback.py +++ b/tests/callbacks/test_mbmoe_tok_per_expert_callback.py @@ -1,7 +1,7 @@ # Copyright 2024 MosaicML LLM Foundry authors # SPDX-License-Identifier: Apache-2.0 -from llmfoundry.utils.builders import build_callback +from llmfoundry.utils.callback_builder import build_callback def test_mbmoe_tok_per_expert_builds(): diff --git a/tests/utils/test_builders.py b/tests/utils/test_builders.py index dfcb5b327c..22fa61e6d2 100644 --- a/tests/utils/test_builders.py +++ b/tests/utils/test_builders.py @@ -19,13 +19,13 @@ from llmfoundry.tokenizers.tiktoken import TiktokenTokenizerWrapper from llmfoundry.utils.builders import ( add_metrics_to_eval_loaders, - build_callback, build_eval_loaders, build_evaluators, build_logger, build_optimizer, build_tokenizer, ) +from llmfoundry.utils.callback_builder import build_callback @pytest.mark.parametrize(