fix lora target

src/llmtuner/chat/stream_chat.py

@@ -1,7 +1,7 @@
 import torch
 from typing import Any, Dict, Generator, List, Optional, Tuple
 from threading import Thread
-from transformers import TextIteratorStreamer
+from transformers import GenerationConfig, TextIteratorStreamer
 
 from llmtuner.extras.misc import dispatch_model, get_logits_processor
 from llmtuner.extras.template import get_template_and_fix_tokenizer
@@ -40,26 +40,30 @@ class ChatModel:
         max_length = input_kwargs.pop("max_length", None)
         max_new_tokens = input_kwargs.pop("max_new_tokens", None)
 
-        gen_kwargs = self.generating_args.to_dict()
-        gen_kwargs.update(dict(
-            input_ids=input_ids,
-            do_sample=do_sample if do_sample is not None else gen_kwargs["do_sample"],
-            temperature=temperature or gen_kwargs["temperature"],
-            top_p=top_p or gen_kwargs["top_p"],
-            top_k=top_k or gen_kwargs["top_k"],
-            repetition_penalty=repetition_penalty or gen_kwargs["repetition_penalty"],
+        generating_args = self.generating_args.to_dict()
+        generating_args.update(dict(
+            do_sample=do_sample if do_sample is not None else generating_args["do_sample"],
+            temperature=temperature or generating_args["temperature"],
+            top_p=top_p or generating_args["top_p"],
+            top_k=top_k or generating_args["top_k"],
+            repetition_penalty=repetition_penalty or generating_args["repetition_penalty"],
             eos_token_id=[self.tokenizer.eos_token_id] + self.tokenizer.additional_special_tokens_ids,
-            pad_token_id=self.tokenizer.pad_token_id,
-            logits_processor=get_logits_processor()
+            pad_token_id=self.tokenizer.pad_token_id
         ))
 
         if max_length:
-            gen_kwargs.pop("max_new_tokens", None)
-            gen_kwargs["max_length"] = max_length
+            generating_args.pop("max_new_tokens", None)
+            generating_args["max_length"] = max_length
 
         if max_new_tokens:
-            gen_kwargs.pop("max_length", None)
-            gen_kwargs["max_new_tokens"] = max_new_tokens
+            generating_args.pop("max_length", None)
+            generating_args["max_new_tokens"] = max_new_tokens
+
+        gen_kwargs = dict(
+            inputs=input_ids,
+            generation_config=GenerationConfig(**generating_args),
+            logits_processor=get_logits_processor()
+        )
 
         return gen_kwargs, prompt_length
 

src/llmtuner/dsets/preprocess.py

@@ -74,7 +74,7 @@ def preprocess_dataset(
                 if len(input_ids) + len(source_ids) + len(target_ids) > max_length:
                     break
 
-                if turn_idx != 0 and template.efficient_eos: # used in baichuan, qwen and gpt2 models
+                if turn_idx != 0 and template.efficient_eos:
                     source_mask = [tokenizer.eos_token_id] + [IGNORE_INDEX] * (len(source_ids) - 1)
                 else:
                     source_mask = [IGNORE_INDEX] * len(source_ids)
@@ -104,6 +104,9 @@ def preprocess_dataset(
             if len(target_ids) > data_args.max_target_length:
                 target_ids = target_ids[:data_args.max_target_length]
 
+            if template.efficient_eos:
+                target_ids += [tokenizer.eos_token_id]
+
             model_inputs["input_ids"].append(source_ids)
             model_inputs["attention_mask"].append([1] * len(source_ids))
             model_inputs["labels"].append(target_ids)
@@ -124,6 +127,10 @@ def preprocess_dataset(
             if len(rejected_ids) > data_args.max_target_length:
                 rejected_ids = rejected_ids[:data_args.max_target_length]
 
+            if template.efficient_eos:
+                chosen_ids += [tokenizer.eos_token_id]
+                rejected_ids += [tokenizer.eos_token_id]
+
             model_inputs["prompt_ids"].append(prompt_ids)
             model_inputs["chosen_ids"].append(chosen_ids)
             model_inputs["rejected_ids"].append(rejected_ids)

src/llmtuner/extras/template.py

@@ -77,13 +77,13 @@ class Template:
     ) -> Tuple[List[int], List[int]]:
         if tokenizer.bos_token_id is not None and getattr(tokenizer, "add_bos_token", True):
             bos_ids = [tokenizer.bos_token_id]
-        else: # baichuan, qwen and gpt2 models has no bos token
+        else: # baichuan, qwen and gpt2 models have no bos token
             bos_ids = []
 
         if tokenizer.eos_token_id is None:
             raise ValueError("EOS token is required.")
 
-        if self.efficient_eos: # used in baichuan, qwen and gpt2 models
+        if self.efficient_eos: # used in baichuan, qwen, chatglm, etc.
             eos_ids = []
         else:
             eos_ids = [tokenizer.eos_token_id]

src/llmtuner/tuner/core/adapter.py

@@ -82,7 +82,7 @@ def init_adapter(
                 model = PeftModel.from_pretrained(model, latest_checkpoint, is_trainable=is_trainable)
 
         if is_trainable and latest_checkpoint is None: # create new lora weights while training
-            if len(finetuning_args.lora_target) == 1 and finetuning_args.lora_target == "all":
+            if len(finetuning_args.lora_target) == 1 and finetuning_args.lora_target[0] == "all":
                 target_modules = find_all_linear_modules(model, model_args.quantization_bit)
             else:
                 target_modules = finetuning_args.lora_target

src/llmtuner/tuner/core/utils.py

@@ -20,7 +20,7 @@ def find_all_linear_modules(
 
     module_names = set()
     for name, module in model.named_modules():
-        if isinstance(module, linear_cls):
+        if output_layer_name not in name and isinstance(module, linear_cls):
             module_names.add(name.split(".")[-1])
 
     if output_layer_name in module_names:

src/llmtuner/tuner/ppo/trainer.py

@@ -2,9 +2,9 @@ import os
 import math
 import torch
 from tqdm import tqdm
-from typing import TYPE_CHECKING, Callable, Dict, List, Optional, Tuple
+from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple
 
-from transformers import TrainerState, TrainerControl
+from transformers import GenerationConfig, TrainerState, TrainerControl
 
 from trl import PPOTrainer
 from trl.core import LengthSampler, PPODecorators, logprobs_from_logits
@@ -78,10 +78,11 @@ class PPOPeftTrainer(PPOTrainer, PeftTrainer):
             logger.info(f"  Number of trainable parameters = {count_parameters(self.model)[0]}")
 
         # Keyword arguments for `model.generate`
-        gen_kwargs = self.generating_args.to_dict()
-        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()
+        generating_args = self.generating_args.to_dict()
+        generating_args.update(dict(
+            eos_token_id=[self.tokenizer.eos_token_id] + self.tokenizer.additional_special_tokens_ids,
+            pad_token_id=self.tokenizer.pad_token_id
+        ))
 
         length_sampler = LengthSampler(max_target_length // 2, max_target_length)
         unwrapped_model: "AutoModelForCausalLMWithValueHead" = self.accelerator.unwrap_model(self.model)
@@ -103,7 +104,7 @@ class PPOPeftTrainer(PPOTrainer, PeftTrainer):
             self.model.eval()
 
             # Get inputs
-            queries, responses = self.get_inputs(batch, length_sampler, **gen_kwargs)
+            queries, responses = self.get_inputs(batch, length_sampler, generating_args)
             self.tokenizer.padding_side = "right" # change padding side
             rewards = self.get_rewards(queries, responses, unwrapped_model)
 
@@ -152,32 +153,36 @@ class PPOPeftTrainer(PPOTrainer, PeftTrainer):
     def get_inputs(
         self,
         batch: Dict[str, torch.Tensor],
-        length_sampler: Optional[Callable] = None,
-        **generation_kwargs
+        length_sampler: Callable,
+        generating_args: Dict[str, Any]
     ) -> Tuple[List[torch.Tensor], List[torch.Tensor]]:
         r"""
         Generates model's responses given queries.
         """
-        if length_sampler is not None:
-            generation_kwargs["max_new_tokens"] = length_sampler()
+        generating_args["max_new_tokens"] = length_sampler()
+        gen_kwargs = dict(
+            generation_config=GenerationConfig(**generating_args),
+            logits_processor=get_logits_processor(),
+            **batch
+        )
 
+        input_ids = batch["input_ids"]
         unwrapped_model: "AutoModelForCausalLMWithValueHead" = self.accelerator.unwrap_model(self.model)
-        response: torch.Tensor = unwrapped_model.generate(**batch, **generation_kwargs)
-
-        # Temporary hack to ensure the generation config is not initialized for each iteration of the evaluation loop
-        # Inspired by: https://github.com/huggingface/transformers/blob/v4.28.1/src/transformers/trainer_seq2seq.py#L273
-        if unwrapped_model.pretrained_model.generation_config._from_model_config:
-            unwrapped_model.pretrained_model.generation_config._from_model_config = False
+        response: torch.Tensor = unwrapped_model.generate(**gen_kwargs)
+        query, response = input_ids.detach().cpu(), response[:, input_ids.size(-1):].detach().cpu()
 
         queries, responses = [], []
-        query, response = batch["input_ids"].detach().cpu(), response[:, batch["input_ids"].size(-1):].detach().cpu()
         for i in range(len(query)):
             query_length = (query[i] != self.tokenizer.pad_token_id).nonzero()[0]
             response_index = (response[i] != self.tokenizer.pad_token_id).nonzero()
+
             if len(response_index) == 0:
                 response_length = 1 # allow empty response
+            elif self.tokenizer.pad_token_id == self.tokenizer.eos_token_id:
+                response_length = response_index[-1] + 2 # save the EOS token
             else:
                 response_length = response_index[-1] + 1
+
             queries.append(query[i, query_length:]) # remove padding from left
             responses.append(response[i, :response_length]) # remove padding from right
 
@@ -204,7 +209,7 @@ class PPOPeftTrainer(PPOTrainer, PeftTrainer):
 
         rewards = []
         for i in range(values.size(0)):
-            end_index = batch["attention_mask"][i].nonzero()[-1]
+            end_index = batch["attention_mask"][i].nonzero()[-1] # use the score on the EOS token
             rewards.append(values[i, end_index].float().detach().cpu()) # use fp32 type
 
         replace_model(unwrapped_model, target="default")

src/llmtuner/tuner/rm/trainer.py

@@ -69,13 +69,17 @@ class PairwisePeftTrainer(PeftTrainer):
             assert div_index > 0
             chosen_trunc_rewards = chosen_rewards[i, div_index:end_index]
             rejected_trunc_rewards = rejected_rewards[i, div_index:end_index]
-            chosen_scores.append(chosen_trunc_rewards[-1]) # use the end score for inference
-            rejected_scores.append(rejected_trunc_rewards[-1])
+            if return_outputs: # use the score on the EOS token for inference
+                chosen_scores.append(chosen_rewards[i, chosen_length-1])
+                rejected_scores.append(rejected_rewards[i, rejected_length-1])
             loss += -torch.nn.functional.logsigmoid(chosen_trunc_rewards - rejected_trunc_rewards).mean()
 
         loss = loss / batch_size
-        chosen_scores, rejected_scores = torch.stack(chosen_scores), torch.stack(rejected_scores)
-        return (loss, [loss, chosen_scores, rejected_scores]) if return_outputs else loss
+        if return_outputs:
+            chosen_scores, rejected_scores = torch.stack(chosen_scores), torch.stack(rejected_scores)
+            return loss, [loss, chosen_scores, rejected_scores]
+
+        return loss
 
     def save_predictions(
         self,