423A35C7/LLaMA-Factory
1
0
Fork 0
mirror of https://github.com/hiyouga/LLaMA-Factory.git synced 2025-12-16 11:50:35 +08:00
Code Issues Packages Projects Releases Wiki Activity

fix generation bug #532

Browse Source 
Former-commit-id: be21fc83f9
This commit is contained in:
hiyouga
2023-08-17 22:21:34 +08:00
parent a46f277477
commit 623a34b16f
5 changed files with 15 additions and 46 deletions
Download Patch File Download Diff File Expand all files Collapse all files

5
src/llmtuner/tuner/ppo/trainer.py
View File

@@ -10,7 +10,7 @@ from trl import PPOTrainer
from trl.core import LengthSampler
from llmtuner.extras.logging import get_logger
from llmtuner.extras.misc import AverageMeter, count_parameters, get_logits_processor, get_stopping_criteria
from llmtuner.extras.misc import AverageMeter, count_parameters, get_logits_processor
from llmtuner.tuner.core.trainer import PeftTrainer
from llmtuner.tuner.ppo.utils import cast_layernorm_dtype, replace_model
@@ -74,10 +74,9 @@ class PPOPeftTrainer(PPOTrainer, PeftTrainer):
# Keyword arguments for `model.generate`
gen_kwargs = self.generating_args.to_dict()
gen_kwargs["eos_token_id"] = self.tokenizer.eos_token_id
gen_kwargs["eos_token_id"] = [self.tokenizer.eos_token_id] + self.tokenizer.additional_special_tokens_ids
gen_kwargs["pad_token_id"] = self.tokenizer.pad_token_id
gen_kwargs["logits_processor"] = get_logits_processor()
gen_kwargs["stopping_criteria"] = get_stopping_criteria(self.tokenizer.additional_special_tokens_ids)
length_sampler = LengthSampler(max_target_length // 2, max_target_length)
unwrapped_model: "AutoModelForCausalLMWithValueHead" = self.accelerator.unwrap_model(self.model)

12
src/llmtuner/tuner/sft/trainer.py
View File

@@ -50,9 +50,10 @@ class Seq2SeqPeftTrainer(PeftTrainer):
loss, generated_tokens, labels = super().prediction_step(
model, inputs, prediction_loss_only=prediction_loss_only, ignore_keys=ignore_keys
)
generated_tokens = (
generated_tokens[:, max(prompt_len, label_len):] if generated_tokens is not None else None
)
if generated_tokens is not None:
generated_tokens[:, :max(prompt_len, label_len)] = (
self.tokenizer.pad_token_id * torch.ones_like(generated_tokens[:, :max(prompt_len, label_len)])
)
return (loss, generated_tokens, labels)
@@ -72,10 +73,7 @@ class Seq2SeqPeftTrainer(PeftTrainer):
assert self.tokenizer.padding_side == "left", "This method only accepts left-padded tensor."
pad_token_id = self.tokenizer.pad_token_id
else:
if self.model.config.pad_token_id is not None:
pad_token_id = self.model.config.pad_token_id
else:
raise ValueError("Pad_token_id must be set in the configuration of the model.")
raise ValueError("PAD token is required.")
padded_tensor = pad_token_id * torch.ones_like(tgt_tensor)
padded_tensor[:, -src_tensor.shape[-1]:] = src_tensor # adopt left-padding

5
src/llmtuner/tuner/sft/workflow.py
View File

@@ -5,7 +5,7 @@ from transformers import DataCollatorForSeq2Seq
from llmtuner.dsets import get_dataset, preprocess_dataset, split_dataset
from llmtuner.extras.constants import IGNORE_INDEX
from llmtuner.extras.misc import get_logits_processor, get_stopping_criteria
from llmtuner.extras.misc import get_logits_processor
from llmtuner.extras.ploting import plot_loss
from llmtuner.tuner.core import load_model_and_tokenizer
from llmtuner.tuner.sft.metric import ComputeMetrics
@@ -52,10 +52,9 @@ def run_sft(
# Keyword arguments for `model.generate`
gen_kwargs = generating_args.to_dict()
gen_kwargs["eos_token_id"] = tokenizer.eos_token_id
gen_kwargs["eos_token_id"] = [tokenizer.eos_token_id] + tokenizer.additional_special_tokens_ids
gen_kwargs["pad_token_id"] = tokenizer.pad_token_id
gen_kwargs["logits_processor"] = get_logits_processor()
gen_kwargs["stopping_criteria"] = get_stopping_criteria(tokenizer.additional_special_tokens_ids)
# Training
if training_args.do_train: