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Fix bug w.r.t. using gradient_checkpoint without tuning embed_tokens and Fix typo of template in langchain_qa.py #175

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Aug 28, 2023
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Fix bug w.r.t. using gradient_checkpoint without tuning embed_tokens and Fix typo of template in langchain_qa.py #175

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48d3018
Update langchain_qa.py
Qznan Aug 23, 2023
2dc69e8
Fix bug w.r.t. using gradient_checkpoint without tuning embed_tokens
Qznan Aug 23, 2023
3b6c634
Fix bug w.r.t. using gradient_checkpoint without tuning embed_tokens
Qznan Aug 23, 2023
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2 changes: 1 addition & 1 deletion scripts/langchain/langchain_qa.py
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Original file line number Diff line number Diff line change
Expand Up @@ -37,7 +37,7 @@
"现在还有一些文字，（如果有需要）你可以根据它们完善现有的回答。"
"\n\n"
"{context_str}\n"
"\\nn"
"\n\n"
"请根据新的文段，进一步完善你的回答。"
" [/INST]"
)
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11 changes: 11 additions & 0 deletions scripts/training/run_clm_pt_with_peft.py
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Expand Up @@ -575,6 +575,17 @@ def group_texts(examples):
lora_dropout=lora_dropout,
modules_to_save=modules_to_save)
model = get_peft_model(model, peft_config)

if training_args.gradient_checkpointing and \
(not model.modules_to_save or 'embed_tokens' not in model.modules_to_save):
# enable requires_grad to avoid exception during backward pass when using gradient_checkpoint without tuning embed.
if hasattr(model.base_model, "enable_input_require_grads"):
model.base_model.enable_input_require_grads()
elif hasattr(model.base_model, "get_input_embeddings"):
def make_inputs_require_grad(_module, _input, _output):
_output.requires_grad_(True)
model.base_model.get_input_embeddings().register_forward_hook(make_inputs_require_grad)

model.print_trainable_parameters()
old_state_dict = model.state_dict
model.state_dict = (
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10 changes: 10 additions & 0 deletions scripts/training/run_clm_sft_with_peft.py
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Original file line number Diff line number Diff line change
Expand Up @@ -382,6 +382,16 @@ def main():
modules_to_save=modules_to_save)
model = get_peft_model(model, peft_config)

if training_args.gradient_checkpointing and \
(not model.modules_to_save or 'embed_tokens' not in model.modules_to_save):
# enable requires_grad to avoid exception during backward pass when using gradient_checkpoint without tuning embed.
if hasattr(model.base_model, "enable_input_require_grads"):
model.base_model.enable_input_require_grads()
elif hasattr(model.base_model, "get_input_embeddings"):
def make_inputs_require_grad(_module, _input, _output):
_output.requires_grad_(True)
model.base_model.get_input_embeddings().register_forward_hook(make_inputs_require_grad)

#model.base_model.tie_weights()
model.print_trainable_parameters()
logger.info(f"model.modules_to_save: {model.modules_to_save}")
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