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LLaMA-Factory-310P3
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follow #5115

This commit is contained in:
hiyouga 2024-08-09 18:03:00 +08:00
parent 51542cb15f
commit c87023d539
6 changed files with 35 additions and 34 deletions
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2
examples/train_full/llama3_full_predict.yaml
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@ -7,7 +7,7 @@ do_predict: true
finetuning_type: full
### dataset
eval_dataset: alpaca_en_demo
eval_dataset: identity,alpaca_en_demo
template: llama3
cutoff_len: 1024
max_samples: 50

2
src/llamafactory/data/loader.py
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@ -206,8 +206,6 @@ def get_dataset(
template = get_template_and_fix_tokenizer(tokenizer, data_args.template, data_args.tool_format)
if data_args.train_on_prompt and template.efficient_eos:
raise ValueError("Current template does not support `train_on_prompt`.")
if stage!="sft" and data_args.mask_history:
raise ValueError("`Train on the last turn only` is only valid for sft training.")
# Load tokenized dataset
if data_args.tokenized_path is not None:

18
src/llamafactory/data/processors/supervised.py
View File

@ -53,8 +53,11 @@ def _encode_supervised_example(
input_ids += [image_token_id] * getattr(processor, "image_seq_length")
labels += [IGNORE_INDEX] * getattr(processor, "image_seq_length")
encoded_pairs = template.encode_multiturn(tokenizer, messages, system, tools, mask_history)
encoded_pairs = template.encode_multiturn(tokenizer, messages, system, tools)
total_length = 1 if template.efficient_eos else 0
if mask_history:
encoded_pairs = encoded_pairs[::-1] # high priority for last turns
for turn_idx, (source_ids, target_ids) in enumerate(encoded_pairs):
if total_length >= cutoff_len:
break
@ -66,20 +69,23 @@ def _encode_supervised_example(
if train_on_prompt:
source_label = source_ids
elif turn_idx != 0 and template.efficient_eos:
elif template.efficient_eos:
source_label = [tokenizer.eos_token_id] + [IGNORE_INDEX] * (source_len - 1)
else:
source_label = [IGNORE_INDEX] * source_len
if mask_history:
target_label = target_ids if turn_idx==0 else [IGNORE_INDEX] * target_len
if mask_history and turn_idx != 0: # train on the last turn only
target_label = [IGNORE_INDEX] * target_len
else:
target_label = target_ids
if mask_history: # reversed sequences
input_ids = source_ids + target_ids + input_ids
labels = source_label + target_label + labels
else:
target_label = target_ids
input_ids += source_ids + target_ids
labels += source_label + target_label
if template.efficient_eos:
input_ids += [tokenizer.eos_token_id]
labels += [tokenizer.eos_token_id]

12
src/llamafactory/data/template.py
View File

@ -69,16 +69,12 @@ class Template:
messages: Sequence[Dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
mask_history: bool = False,
) -> List[Tuple[List[int], List[int]]]:
r"""
Returns multiple pairs of token ids representing prompts and responses respectively.
"""
encoded_messages = self._encode(tokenizer, messages, system, tools)
if not mask_history:
return [(encoded_messages[i], encoded_messages[i + 1]) for i in range(0, len(encoded_messages), 2)]
else:
return [(encoded_messages[i], encoded_messages[i + 1]) for i in range(len(encoded_messages)-2, -1, -2)]
return [(encoded_messages[i], encoded_messages[i + 1]) for i in range(0, len(encoded_messages), 2)]
def extract_tool(self, content: str) -> Union[str, List[Tuple[str, str]]]:
r"""
@ -594,10 +590,10 @@ _register_template(
format_separator=EmptyFormatter(slots=["\n"]),
format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
default_system=(
"You are an AI programming assistant, utilizing the Deepseek Coder model, "
"developed by Deepseek Company, and you only answer questions related to computer science. "
"You are an AI programming assistant, utilizing the DeepSeek Coder model, "
"developed by DeepSeek Company, and you only answer questions related to computer science. "
"For politically sensitive questions, security and privacy issues, "
"and other non-computer science questions, you will refuse to answer\n"
"and other non-computer science questions, you will refuse to answer.\n"
),
)

2
src/llamafactory/hparams/data_args.py
View File

@ -143,4 +143,4 @@ class DataArguments:
raise ValueError("`max_samples` is incompatible with `streaming`.")
if self.mask_history and self.train_on_prompt:
raise ValueError("`Train on the last turn only` does not support `train_on_prompt`.")
raise ValueError("`mask_history` is incompatible with `train_on_prompt`.")

33
src/llamafactory/hparams/parser.py
View File

@ -163,11 +163,15 @@ def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
if finetuning_args.stage != "pt" and data_args.template is None:
raise ValueError("Please specify which `template` to use.")
if finetuning_args.stage != "sft" and training_args.predict_with_generate:
raise ValueError("`predict_with_generate` cannot be set as True except SFT.")
if finetuning_args.stage != "sft":
if training_args.predict_with_generate:
raise ValueError("`predict_with_generate` cannot be set as True except SFT.")
if finetuning_args.stage != "sft" and data_args.neat_packing:
raise ValueError("`neat_packing` cannot be set as True except SFT.")
if data_args.neat_packing:
raise ValueError("`neat_packing` cannot be set as True except SFT.")
if data_args.train_on_prompt or data_args.mask_history:
raise ValueError("`train_on_prompt` or `mask_history` cannot be set as True except SFT.")
if finetuning_args.stage == "sft" and training_args.do_predict and not training_args.predict_with_generate:
raise ValueError("Please enable `predict_with_generate` to save model predictions.")
@ -175,21 +179,18 @@ def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
if finetuning_args.stage in ["rm", "ppo"] and training_args.load_best_model_at_end:
raise ValueError("RM and PPO stages do not support `load_best_model_at_end`.")
if finetuning_args.stage == "ppo" and not training_args.do_train:
raise ValueError("PPO training does not support evaluation, use the SFT stage to evaluate models.")
if finetuning_args.stage == "ppo":
if not training_args.do_train:
raise ValueError("PPO training does not support evaluation, use the SFT stage to evaluate models.")
if finetuning_args.stage == "ppo" and model_args.shift_attn:
raise ValueError("PPO training is incompatible with S^2-Attn.")
if model_args.shift_attn:
raise ValueError("PPO training is incompatible with S^2-Attn.")
if finetuning_args.stage == "ppo" and finetuning_args.reward_model_type == "lora" and model_args.use_unsloth:
raise ValueError("Unsloth does not support lora reward model.")
if finetuning_args.reward_model_type == "lora" and model_args.use_unsloth:
raise ValueError("Unsloth does not support lora reward model.")
if (
finetuning_args.stage == "ppo"
and training_args.report_to
and training_args.report_to[0] not in ["wandb", "tensorboard"]
):
raise ValueError("PPO only accepts wandb or tensorboard logger.")
if training_args.report_to and training_args.report_to[0] not in ["wandb", "tensorboard"]:
raise ValueError("PPO only accepts wandb or tensorboard logger.")
if training_args.parallel_mode == ParallelMode.NOT_DISTRIBUTED:
raise ValueError("Please launch distributed training with `llamafactory-cli` or `torchrun`.")