change to right-padding, update reward score #803

src/llmtuner/extras/template.py

@@ -214,10 +214,7 @@ def get_template_and_fix_tokenizer(
         logger.info("Add eos token: {}".format(tokenizer.eos_token))
 
     if tokenizer.pad_token_id is None:
-        if tokenizer.unk_token_id is not None:
-            tokenizer.pad_token = tokenizer.unk_token
-        else:
-            tokenizer.pad_token = tokenizer.eos_token
+        tokenizer.pad_token = tokenizer.eos_token
         logger.info("Add pad token: {}".format(tokenizer.pad_token))
 
     tokenizer.add_special_tokens(

src/llmtuner/hparams/data_args.py

@@ -26,7 +26,8 @@ class DataArguments:
     r"""
     Arguments pertaining to what data we are going to input our model for training and evaluation.
     """
-    template: str = field(
+    template: Optional[str] = field(
+        default=None,
         metadata={"help": "Which template to use for constructing prompts in training and inference."}
     )
     dataset: Optional[str] = field(
@@ -46,7 +47,7 @@ class DataArguments:
         metadata={"help": "Enable streaming mode."}
     )
     buffer_size: Optional[int] = field(
-        default=16384,
+        default=1024,
         metadata={"help": "Size of the buffer to randomly sample examples from in streaming mode."}
     )
     mix_strategy: Optional[Literal["concat", "interleave_under", "interleave_over"]] = field(

src/llmtuner/hparams/model_args.py

@@ -27,10 +27,6 @@ class ModelArguments:
         default="main",
         metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}
     )
-    padding_side: Optional[Literal["left", "right"]] = field(
-        default="left",
-        metadata={"help": "The side on which the model should have padding applied."}
-    )
     quantization_bit: Optional[int] = field(
         default=None,
         metadata={"help": "The number of bits to quantize the model."}

src/llmtuner/tuner/core/loader.py

@@ -68,7 +68,7 @@ def load_model_and_tokenizer(
     tokenizer = AutoTokenizer.from_pretrained(
         model_args.model_name_or_path,
         use_fast=model_args.use_fast_tokenizer,
-        padding_side=model_args.padding_side,
+        padding_side="right", # training with left-padded tensors in fp16 precision may cause overflow
         **config_kwargs
     )
 

src/llmtuner/tuner/core/parser.py

@@ -96,6 +96,9 @@ def get_train_args(
     # Check arguments (do not check finetuning_args since it may be loaded from checkpoints)
     data_args.init_for_training()
 
+    if general_args.stage != "pt" and data_args.template is None:
+        raise ValueError("Please specify which `template` to use.")
+
     if general_args.stage != "sft" and training_args.predict_with_generate:
         raise ValueError("`predict_with_generate` cannot be set as True except SFT.")
 
@@ -221,6 +224,9 @@ def get_infer_args(
 ]:
     model_args, data_args, finetuning_args, generating_args = parse_infer_args(args)
 
+    if data_args.template is None:
+        raise ValueError("Please specify which `template` to use.")
+
     if model_args.quantization_bit is not None and finetuning_args.finetuning_type != "lora":
         raise ValueError("Quantization is only compatible with the LoRA method.")
 

src/llmtuner/tuner/core/trainer.py

@@ -44,26 +44,37 @@ class PeftModelMixin:
         output_dir = output_dir if output_dir is not None else self.args.output_dir
         os.makedirs(output_dir, exist_ok=True)
         logger.info(f"Saving model checkpoint to {output_dir}")
+        model = self.model
+        model_unwrapped = unwrap_model(model)
 
-        model = unwrap_model(self.model)
-        if isinstance(model, PreTrainedModelWrapper):
-            # Custom state dict: https://github.com/lvwerra/trl/blob/v0.4.7/trl/models/modeling_value_head.py#L200
+        if isinstance(model_unwrapped, PreTrainedModelWrapper):
+            # Custom state dict: https://github.com/lvwerra/trl/blob/v0.7.1/trl/models/modeling_value_head.py#L200
             model_state_dict = state_dict or model.state_dict()
             v_head_state_dict = {
                 name.replace("v_head.", ""): model_state_dict[name].cpu().clone().detach()
                 for name in model_state_dict.keys() if name.startswith("v_head.")
             }
-
             torch.save(v_head_state_dict, os.path.join(output_dir, VALUE_HEAD_FILE_NAME))
-            model = model.pretrained_model
+            model = model_unwrapped.pretrained_model
+            model_unwrapped = unwrap_model(model)
 
         state_dict = state_dict or get_state_dict(model)
-        if isinstance(model, (PeftModel, PreTrainedModel)):
-            model.config.use_cache = True
-            model.save_pretrained(output_dir, state_dict=state_dict, safe_serialization=self.args.save_safetensors)
-            model.config.use_cache = False
+        if not isinstance(model, (PeftModel, PreTrainedModel)):
+            if isinstance(model_unwrapped, (PeftModel, PreTrainedModel)):
+                model_unwrapped.config.use_cache = True
+                model_unwrapped.save_pretrained(
+                    output_dir, state_dict=state_dict, safe_serialization=self.args.save_safetensors
+                )
+                model_unwrapped.config.use_cache = False
+            else:
+                logger.info("Trainer.model is not a `PreTrainedModel`, only saving its state dict.")
+                torch.save(state_dict, os.path.join(output_dir, WEIGHTS_NAME))
         else:
-            torch.save(state_dict, os.path.join(output_dir, WEIGHTS_NAME))
+            model.config.use_cache = True
+            model.save_pretrained(
+                output_dir, state_dict=state_dict, safe_serialization=self.args.save_safetensors
+            )
+            model.config.use_cache = False
 
         if self.finetuning_args.finetuning_type == "full" and self.tokenizer is not None:
             try:

src/llmtuner/tuner/ppo/trainer.py

@@ -102,6 +102,7 @@ class PPOPeftTrainer(PPOTrainer, PeftTrainer):
 
             # Get inputs
             queries, responses = self.get_inputs(batch, length_sampler, **gen_kwargs)
+            self.tokenizer.padding_side = "right" # change padding side
             rewards = self.get_rewards(queries, responses, unwrapped_model)
 
             # Cast to training mode
@@ -110,6 +111,7 @@ class PPOPeftTrainer(PPOTrainer, PeftTrainer):
 
             # Run PPO step
             stats = self.step(queries, responses, rewards)
+            self.tokenizer.padding_side = "left" # restore padding side
             loss_meter.update(stats["ppo/loss/total"], n=len(rewards))
             reward_meter.update(torch.stack(rewards).mean().item(), n=len(rewards))
 
@@ -169,7 +171,11 @@ class PPOPeftTrainer(PPOTrainer, PeftTrainer):
         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_length = (response[i] != self.tokenizer.pad_token_id).nonzero()[-1] + 1
+            response_index = (response[i] != self.tokenizer.pad_token_id).nonzero()
+            if len(response_index) == 0:
+                response_length = 1 # allow empty response
+            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
 
@@ -194,7 +200,11 @@ class PPOPeftTrainer(PPOTrainer, PeftTrainer):
         if values.size(0) != batch["input_ids"].size(0): # adapt to chatglm2
             values = torch.transpose(values, 0, 1)
 
-        rewards = [reward for reward in values[:, -1].float().detach().cpu()] # use fp32 type
+        rewards = []
+        for i in range(values.size(0)):
+            end_index = batch["attention_mask"][i].nonzero()[-1]
+            rewards.append(values[i, end_index].float().detach().cpu()) # use fp32 type
+
         replace_model(unwrapped_model, target="default")
         return rewards
 
@@ -241,7 +251,7 @@ class PPOPeftTrainer(PPOTrainer, PeftTrainer):
 
             for j in range(len(query_batch)):
                 start = len(query_batch[j]) - 1
-                if attention_mask[j, 0] == 0:  # offset left padding
+                if attention_mask[j, 0] == 0: # offset left padding
                     start += attention_mask[j, :].nonzero()[0]
                 end = start + len(response_batch[j])
 

src/llmtuner/tuner/ppo/workflow.py

@@ -4,7 +4,7 @@ import math
 from trl import PPOConfig
 from torch.optim import AdamW
 from typing import TYPE_CHECKING, Optional, List
-from transformers import DataCollatorForSeq2Seq
+from transformers import DataCollatorWithPadding
 from transformers.optimization import get_scheduler
 
 from llmtuner.dsets import get_dataset, preprocess_dataset
@@ -28,7 +28,9 @@ def run_ppo(
     dataset = get_dataset(model_args, data_args)
     model, tokenizer = load_model_and_tokenizer(model_args, finetuning_args, training_args.do_train, stage="ppo")
     dataset = preprocess_dataset(dataset, tokenizer, data_args, training_args, stage="ppo")
-    data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, label_pad_token_id=tokenizer.pad_token_id)
+
+    tokenizer.padding_side = "left" # use left-padding in generation while using right-padding in training
+    data_collator = DataCollatorWithPadding(tokenizer=tokenizer)
 
     ppo_config = PPOConfig(
         model_name=model_args.model_name_or_path,

src/llmtuner/tuner/rm/trainer.py

@@ -32,21 +32,50 @@ class PairwisePeftTrainer(PeftTrainer):
         r"""
         Computes pairwise loss. The first n examples are chosen and the last n examples are rejected.
 
-        We use score on the EOS token to represent reward of the whole sentence.
-
-        Subclass and override to inject custom behavior. It should not be directly used by external scripts.
-
-        Note that the first element will be removed from the output tuple.
+        Subclass and override to inject custom behavior.
 
+        Note that the first element will be removed from the output tuple. 
         See: https://github.com/huggingface/transformers/blob/v4.30.2/src/transformers/trainer.py#L3509
         """
-        batch_size = inputs["input_ids"].size(0) // 2
+        # Compute rewards
         _, _, values = model(**inputs, output_hidden_states=True, return_dict=True)
         if values.size(0) != inputs["input_ids"].size(0): # adapt to chatglm2
             values = torch.transpose(values, 0, 1)
-        r_accept, r_reject = values[:, -1].split(batch_size, dim=0)
-        loss = -torch.log(torch.sigmoid(r_accept - r_reject)).mean()
-        return (loss, [loss, r_accept, r_reject]) if return_outputs else loss
+
+        # Split the inputs and rewards into two parts, chosen and rejected
+        batch_size = inputs["input_ids"].size(0) // 2
+        chosen_input_ids, rejected_input_ids = inputs["input_ids"][:batch_size], inputs["input_ids"][batch_size:]
+        chosen_attn_mask, rejected_attn_mask = (
+            inputs["attention_mask"][:batch_size], inputs["attention_mask"][batch_size:]
+        )
+        chosen_rewards, rejected_rewards = values[:batch_size], values[batch_size:]
+        chosen_scores, rejected_scores = [], []
+
+        # Compute pairwise loss. Only backprop on the different tokens before padding
+        # Inspired by: https://github.com/CarperAI/trlx/blob/main/examples/summarize_rlhf/reward_model/reward_model.py
+        loss = 0
+        for i in range(batch_size):
+            chosen_length = chosen_attn_mask[i].nonzero()[-1] + 1
+            rejected_length = rejected_attn_mask[i].nonzero()[-1] + 1
+            check_divergence = (chosen_input_ids[i] != rejected_input_ids[i]).nonzero()
+
+            if len(check_divergence) == 0:
+                end_index = chosen_length
+                div_index = end_index - 1
+            else:
+                end_index = max(chosen_length, rejected_length)
+                div_index = check_divergence[0]
+
+            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])
+            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
 
     def save_predictions(
         self,
@@ -63,10 +92,10 @@ class PairwisePeftTrainer(PeftTrainer):
         output_prediction_file = os.path.join(self.args.output_dir, "generated_predictions.jsonl")
         logger.info(f"Saving prediction results to {output_prediction_file}")
 
-        acc_scores, rej_scores = predict_results.predictions
+        chosen_scores, rejected_scores = predict_results.predictions
 
         with open(output_prediction_file, "w", encoding="utf-8") as writer:
             res: List[str] = []
-            for acc_score, rej_score in zip(acc_scores, rej_scores):
-                res.append(json.dumps({"accept": round(float(acc_score), 2), "reject": round(float(rej_score), 2)}))
+            for c_score, r_score in zip(chosen_scores, rejected_scores):
+                res.append(json.dumps({"chosen": round(float(c_score), 2), "rejected": round(float(r_score), 2)}))
             writer.write("\n".join(res))

src/llmtuner/tuner/rm/workflow.py

@@ -1,5 +1,4 @@
 # Inspired by:
-# https://github.com/lvwerra/trl/blob/main/examples/summarization/scripts/reward_summarization.py
 # https://github.com/CarperAI/trlx/blob/main/examples/summarize_rlhf/reward_model/train_reward_model_gptj.py
 
 from typing import TYPE_CHECKING, Optional, List

src/llmtuner/tuner/sft/trainer.py

@@ -50,10 +50,9 @@ class Seq2SeqPeftTrainer(PeftTrainer):
         loss, generated_tokens, labels = super().prediction_step(
             model, inputs, prediction_loss_only=prediction_loss_only, ignore_keys=ignore_keys
         )
-        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)])
-            )
+        generated_tokens = (
+            generated_tokens[:, max(prompt_len, label_len):] if generated_tokens is not None else None
+        )
 
         return loss, generated_tokens, labels
 

src/llmtuner/tuner/sft/workflow.py

@@ -27,6 +27,10 @@ def run_sft(
     dataset = get_dataset(model_args, data_args)
     model, tokenizer = load_model_and_tokenizer(model_args, finetuning_args, training_args.do_train, stage="sft")
     dataset = preprocess_dataset(dataset, tokenizer, data_args, training_args, stage="sft")
+
+    if training_args.predict_with_generate:
+        tokenizer.padding_side = "left" # use left-padding in generation
+
     data_collator = DataCollatorForSeq2Seq(
         tokenizer=tokenizer,
         label_pad_token_id=IGNORE_INDEX if data_args.ignore_pad_token_for_loss else tokenizer.pad_token_id

src/llmtuner/webui/components/train.py

@@ -56,7 +56,6 @@ def create_train_tab(top_elems: Dict[str, "Component"], runner: "Runner") -> Dic
             save_steps = gr.Slider(value=100, minimum=10, maximum=5000, step=10)
             warmup_steps = gr.Slider(value=0, minimum=0, maximum=5000, step=1)
             compute_type = gr.Radio(choices=["fp16", "bf16"], value="fp16")
-            padding_side = gr.Radio(choices=["left", "right"], value="left")
 
     with gr.Accordion(label="LoRA config", open=False) as lora_tab:
         with gr.Row():
@@ -122,7 +121,6 @@ def create_train_tab(top_elems: Dict[str, "Component"], runner: "Runner") -> Dic
         save_steps,
         warmup_steps,
         compute_type,
-        padding_side,
         lora_rank,
         lora_dropout,
         lora_target,
@@ -168,7 +166,6 @@ def create_train_tab(top_elems: Dict[str, "Component"], runner: "Runner") -> Dic
         save_steps=save_steps,
         warmup_steps=warmup_steps,
         compute_type=compute_type,
-        padding_side=padding_side,
         lora_tab=lora_tab,
         lora_rank=lora_rank,
         lora_dropout=lora_dropout,

src/llmtuner/webui/locales.py

@@ -287,16 +287,6 @@ LOCALES = {
             "info": "是否启用 FP16 或 BF16 混合精度训练。"
         }
     },
-    "padding_side": {
-        "en": {
-            "label": "Padding side",
-            "info": "The side on which the model should have padding applied."
-        },
-        "zh": {
-            "label": "填充位置",
-            "info": "使用左填充或右填充。"
-        }
-    },
     "lora_tab": {
         "en": {
             "label": "LoRA configurations"

src/llmtuner/webui/runner.py

@@ -87,7 +87,6 @@ class Runner:
         save_steps: int,
         warmup_steps: int,
         compute_type: str,
-        padding_side: str,
         lora_rank: int,
         lora_dropout: float,
         lora_target: str,
@@ -129,7 +128,6 @@ class Runner:
             logging_steps=logging_steps,
             save_steps=save_steps,
             warmup_steps=warmup_steps,
-            padding_side=padding_side,
             lora_rank=lora_rank,
             lora_dropout=lora_dropout,
             lora_target=lora_target or DEFAULT_MODULE.get(model_name.split("-")[0], "q_proj,v_proj"),
@@ -142,7 +140,6 @@ class Runner:
 
         if args["stage"] == "ppo":
             args["reward_model"] = reward_model
-            args["padding_side"] = "left"
             val_size = 0
 
         if args["stage"] == "dpo":