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[DRAFT] Add support gradient cache for Reranker #148

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[DRAFT] Add support gradient cache for Reranker #148

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859c9e6
gc reranker
sigridjineth Aug 24, 2024
f29930c
reranker arguments
sigridjineth Aug 24, 2024
3d92da6
fix: training arguments
sigridjineth Aug 24, 2024
0127d00
fix: arguments
sigridjineth Aug 24, 2024
52616cd
fix: trainer
sigridjineth Aug 24, 2024
cd09b57
fix: trainer
sigridjineth Aug 24, 2024
6d11105
parser
sigridjineth Aug 24, 2024
e98bbf3
fix: paserr
sigridjineth Aug 24, 2024
851f01b
fix: parser
sigridjineth Aug 24, 2024
bb5d87c
fix: parser
sigridjineth Aug 24, 2024
70d65ab
fix: trainer
sigridjineth Aug 24, 2024
bfb8e5a
fix: trust_remote_code
sigridjineth Aug 24, 2024
ca4b04b
hotfix: gradient_checkpointing_enable
sigridjineth Aug 24, 2024
a79b647
fix: forward method
sigridjineth Aug 24, 2024
78ff580
fix: prediction step
sigridjineth Aug 24, 2024
43a642d
fix: trainer
sigridjineth Aug 24, 2024
0380e4e
fix: ddp
sigridjineth Aug 24, 2024
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hotfix: gradient_checkpointing_enable
  • Loading branch information
@sigridjineth
sigridjineth committed Aug 24, 2024
commit ca4b04b7fb40e4d30df16d12d6ee9c9261091209
23 changes: 14 additions & 9 deletions src/tevatron/reranker/modeling.py
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Original file line number Diff line number Diff line change
Expand Up @@ -9,7 +9,6 @@
from transformers import TrainingArguments
from peft import LoraConfig, PeftModel, TaskType, get_peft_model


from tevatron.reranker.arguments import ModelArguments

import logging
Expand All @@ -22,6 +21,7 @@ class RerankerOutput(ModelOutput):
loss: Optional[Tensor] = None
scores: Optional[Tensor] = None


class RerankerModel(nn.Module):
TRANSFORMER_CLS = AutoModelForSequenceClassification

Expand Down Expand Up @@ -49,17 +49,18 @@ def forward(self, pair: Dict[str, Tensor] = None):
grouped_logits = ranker_logits.view(self.train_batch_size, -1)
loss = self.cross_entropy(grouped_logits, self.target_label)
return RerankerOutput(
loss = loss,
scores = ranker_logits
loss=loss,
scores=ranker_logits
)

return RerankerOutput(
loss = None,
scores = ranker_logits
loss=None,
scores=ranker_logits
)

def gradient_checkpointing_enable(self, **kwargs):
self.hf_model.base_model.model.gradient_checkpointing_enable(**kwargs)
return False
# self.hf_model.base_model.model.gradient_checkpointing_enable(**kwargs)

@classmethod
def build(
Expand All @@ -79,7 +80,9 @@ def build(
base_model.enable_input_require_grads()
if model_args.lora_name_or_path:
lora_config = LoraConfig.from_pretrained(model_args.lora_name_or_path, **hf_kwargs)
lora_model = PeftModel.from_pretrained(base_model, model_args.lora_name_or_path, torch_dtype=torch.bfloat16, attn_implementation="flash_attention_2")
lora_model = PeftModel.from_pretrained(base_model, model_args.lora_name_or_path,
torch_dtype=torch.bfloat16,
attn_implementation="flash_attention_2")
else:
lora_config = LoraConfig(
base_model_name_or_path=model_args.model_name_or_path,
Expand Down Expand Up @@ -107,7 +110,9 @@ def load(cls,
model_name_or_path: str,
lora_name_or_path: str = None,
**hf_kwargs):
base_model = cls.TRANSFORMER_CLS.from_pretrained(model_name_or_path, num_labels=1, **hf_kwargs, torch_dtype=torch.bfloat16, attn_implementation="flash_attention_2")
base_model = cls.TRANSFORMER_CLS.from_pretrained(model_name_or_path, num_labels=1, **hf_kwargs,
torch_dtype=torch.bfloat16,
attn_implementation="flash_attention_2")
if base_model.config.pad_token_id is None:
base_model.config.pad_token_id = 0
if lora_name_or_path:
Expand Down