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support mllm hf inference

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Former-commit-id: 2c7c01282acd7ddabbb17ce3246b8dae4bc4b8cf
This commit is contained in:
hiyouga
2024-04-26 05:34:58 +08:00
parent 10a6c395bb
commit 23b881bff1
23 changed files with 128 additions and 49 deletions
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5
src/llmtuner/train/dpo/workflow.py
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@@ -24,8 +24,9 @@ def run_dpo(
finetuning_args: "FinetuningArguments",
callbacks: Optional[List["TrainerCallback"]] = None,
):
tokenizer = load_tokenizer(model_args)
dataset = get_dataset(tokenizer, model_args, data_args, training_args, stage="rm")
tokenizer_module = load_tokenizer(model_args)
tokenizer = tokenizer_module["tokenizer"]
dataset = get_dataset(model_args, data_args, training_args, stage="rm", **tokenizer_module)
model = load_model(tokenizer, model_args, finetuning_args, training_args.do_train)
data_collator = PairwiseDataCollatorWithPadding(

5
src/llmtuner/train/orpo/workflow.py
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@@ -24,8 +24,9 @@ def run_orpo(
finetuning_args: "FinetuningArguments",
callbacks: Optional[List["TrainerCallback"]] = None,
):
tokenizer = load_tokenizer(model_args)
dataset = get_dataset(tokenizer, model_args, data_args, training_args, stage="rm")
tokenizer_module = load_tokenizer(model_args)
tokenizer = tokenizer_module["tokenizer"]
dataset = get_dataset(model_args, data_args, training_args, stage="rm", **tokenizer_module)
model = load_model(tokenizer, model_args, finetuning_args, training_args.do_train)
data_collator = PairwiseDataCollatorWithPadding(

5
src/llmtuner/train/ppo/workflow.py
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@@ -27,8 +27,9 @@ def run_ppo(
generating_args: "GeneratingArguments",
callbacks: Optional[List["TrainerCallback"]] = None,
):
tokenizer = load_tokenizer(model_args)
dataset = get_dataset(tokenizer, model_args, data_args, training_args, stage="ppo")
tokenizer_module = load_tokenizer(model_args)
tokenizer = tokenizer_module["tokenizer"]
dataset = get_dataset(model_args, data_args, training_args, stage="ppo", **tokenizer_module)
model = load_model(tokenizer, model_args, finetuning_args, training_args.do_train, add_valuehead=True)
tokenizer.padding_side = "left" # use left-padding in generation while using right-padding in training

5
src/llmtuner/train/pt/workflow.py
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@@ -25,8 +25,9 @@ def run_pt(
finetuning_args: "FinetuningArguments",
callbacks: Optional[List["TrainerCallback"]] = None,
):
tokenizer = load_tokenizer(model_args)
dataset = get_dataset(tokenizer, model_args, data_args, training_args, stage="pt")
tokenizer_module = load_tokenizer(model_args)
tokenizer = tokenizer_module["tokenizer"]
dataset = get_dataset(model_args, data_args, training_args, stage="pt", **tokenizer_module)
model = load_model(tokenizer, model_args, finetuning_args, training_args.do_train)
data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)

5
src/llmtuner/train/rm/workflow.py
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@@ -25,8 +25,9 @@ def run_rm(
finetuning_args: "FinetuningArguments",
callbacks: Optional[List["TrainerCallback"]] = None,
):
tokenizer = load_tokenizer(model_args)
dataset = get_dataset(tokenizer, model_args, data_args, training_args, stage="rm")
tokenizer_module = load_tokenizer(model_args)
tokenizer = tokenizer_module["tokenizer"]
dataset = get_dataset(model_args, data_args, training_args, stage="rm", **tokenizer_module)
model = load_model(tokenizer, model_args, finetuning_args, training_args.do_train, add_valuehead=True)
data_collator = PairwiseDataCollatorWithPadding(tokenizer, pad_to_multiple_of=8)

4
src/llmtuner/train/sft/workflow.py
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@@ -29,9 +29,9 @@ def run_sft(
callbacks: Optional[List["TrainerCallback"]] = None,
):
tokenizer_module = load_tokenizer(model_args)
dataset = get_dataset(model_args, data_args, training_args, stage="sft", **tokenizer_module)
tokenizer = tokenizer_module["tokenizer"]
model = load_model(tokenizer, model_args, finetuning_args, is_trainable=training_args.do_train)
dataset = get_dataset(model_args, data_args, training_args, stage="sft", **tokenizer_module)
model = load_model(tokenizer, model_args, finetuning_args, training_args.do_train)
if training_args.predict_with_generate:
tokenizer.padding_side = "left" # use left-padding in generation

2
src/llmtuner/train/tuner.py
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@@ -52,7 +52,7 @@ def export_model(args: Optional[Dict[str, Any]] = None):
if model_args.adapter_name_or_path is not None and model_args.export_quantization_bit is not None:
raise ValueError("Please merge adapters before quantizing the model.")
tokenizer = load_tokenizer(model_args)
tokenizer = load_tokenizer(model_args)["tokenizer"]
get_template_and_fix_tokenizer(tokenizer, data_args.template)
model = load_model(tokenizer, model_args, finetuning_args) # must after fixing tokenizer to resize vocab

6
src/llmtuner/train/utils.py
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@@ -91,7 +91,7 @@ def create_ref_model(
)
ref_model_args = ModelArguments(**ref_model_args_dict)
ref_finetuning_args = FinetuningArguments(finetuning_type="lora")
tokenizer = load_tokenizer(ref_model_args)
tokenizer = load_tokenizer(ref_model_args)["tokenizer"]
ref_model = load_model(
tokenizer, ref_model_args, ref_finetuning_args, is_trainable=False, add_valuehead=add_valuehead
)
@@ -100,7 +100,7 @@ def create_ref_model(
if finetuning_args.finetuning_type == "lora":
ref_model = None
else:
tokenizer = load_tokenizer(model_args)
tokenizer = load_tokenizer(model_args)["tokenizer"]
ref_model = load_model(
tokenizer, model_args, finetuning_args, is_trainable=False, add_valuehead=add_valuehead
)
@@ -147,7 +147,7 @@ def create_reward_model(
)
reward_model_args = ModelArguments(**reward_model_args_dict)
reward_finetuning_args = FinetuningArguments(finetuning_type="lora")
tokenizer = load_tokenizer(reward_model_args)
tokenizer = load_tokenizer(reward_model_args)["tokenizer"]
reward_model = load_model(
tokenizer, reward_model_args, reward_finetuning_args, is_trainable=False, add_valuehead=True
)