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Enabled generalizing build_inner_model in ComposerHFCausalLM #1450

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merged 6 commits into from
Aug 13, 2024
Merged

Enabled generalizing build_inner_model in ComposerHFCausalLM #1450

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95f8497
changes for config, model calls
gupta-abhay Aug 13, 2024
9fc85d6
changes for docstrings
gupta-abhay Aug 13, 2024
f2349d4
working changes with fixed precommits
gupta-abhay Aug 13, 2024
b5d1b0d
changes for attribute testing
gupta-abhay Aug 13, 2024
e207a02
fixing comments
gupta-abhay Aug 13, 2024
97f06be
types
gupta-abhay Aug 13, 2024
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17 changes: 10 additions & 7 deletions llmfoundry/models/hf/hf_causal_lm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -9,6 +9,7 @@
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
List,
Optional,
Expand Down Expand Up @@ -193,6 +194,8 @@ def build_inner_model(
config_overrides: Dict[str, Any],
load_in_8bit: bool,
pretrained: bool,
config_fn: Optional[Callable] = AutoConfig,
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model_fn: Optional[Callable] = AutoModelForCausalLM,
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prepare_for_fsdp: bool = False,
) -> Union[PreTrainedModel, 'PeftModel']:
"""Builds the inner model for the ComposerHFCausalLM.
Expand Down Expand Up @@ -236,7 +239,7 @@ def build_inner_model(
# the different processes. To avoid this contention, we first create the config and generation config on local rank
# zero. This will set up the transformers module cache and avoid the future contention.
if dist.get_local_rank() == 0:
AutoConfig.from_pretrained(
config_fn.from_pretrained(
pretrained_model_name_or_path,
trust_remote_code=trust_remote_code,
use_auth_token=use_auth_token,
Expand All @@ -255,7 +258,7 @@ def build_inner_model(
dist.barrier()

# Construct the Hugging Face config to use
config = AutoConfig.from_pretrained(
config = config_fn.from_pretrained(
pretrained_model_name_or_path,
trust_remote_code=trust_remote_code,
use_auth_token=use_auth_token,
Expand All @@ -280,7 +283,7 @@ def build_inner_model(
with init_empty_weights(include_buffers=False):
with warnings.catch_warnings():
warnings.simplefilter('ignore', UserWarning)
AutoModelForCausalLM.from_pretrained(
model_fn.from_pretrained(
pretrained_model_name_or_path,
trust_remote_code=trust_remote_code,
use_auth_token=use_auth_token,
Expand All @@ -290,7 +293,7 @@ def build_inner_model(
)
else:
with init_empty_weights(include_buffers=False):
AutoModelForCausalLM.from_config(
model_fn.from_config(
config,
trust_remote_code=trust_remote_code,
attn_implementation=requested_attention_implementation,
Expand All @@ -301,7 +304,7 @@ def build_inner_model(
# initialize the model on the correct device
if resolved_init_device == 'cpu':
if pretrained:
model = AutoModelForCausalLM.from_pretrained(
model = model_fn.from_pretrained(
pretrained_model_name_or_path,
trust_remote_code=trust_remote_code,
use_auth_token=use_auth_token,
Expand All @@ -310,7 +313,7 @@ def build_inner_model(
config=config,
)
else:
model = AutoModelForCausalLM.from_config(
model = model_fn.from_config(
config,
trust_remote_code=trust_remote_code,
attn_implementation=requested_attention_implementation,
Expand All @@ -321,7 +324,7 @@ def build_inner_model(
'Setting cfg.pretrained=True is not supported when init_device="meta".',
)
with init_empty_weights(include_buffers=False):
model = AutoModelForCausalLM.from_config(
model = model_fn.from_config(
config,
trust_remote_code=trust_remote_code,
attn_implementation=requested_attention_implementation,
Expand Down
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