fix RM save model

README.md

@@ -128,6 +128,7 @@ Note: please update `data/dataset_info.json` to use your custom dataset. About t
 ### Dependence Installation (optional)
 
 ```bash
+git lfs install
 git clone https://github.com/hiyouga/LLaMA-Efficient-Tuning.git
 conda create -n llama_etuning python=3.10
 conda activate llama_etuning

README_zh.md

@@ -128,6 +128,7 @@ huggingface-cli login
 ### 环境搭建（可跳过）
 
 ```bash
+git lfs install
 git clone https://github.com/hiyouga/LLaMA-Efficient-Tuning.git
 conda create -n llama_etuning python=3.10
 conda activate llama_etuning

src/llmtuner/extras/logging.py

@@ -16,8 +16,16 @@ class LoggerHandler(logging.Handler):
         self.log += "\n\n"
 
 
-def get_logger(name: str) -> logging.Logger:
+def reset_logging():
+    r"""
+    Removes basic config of root logger
+    """
+    root = logging.getLogger()
+    list(map(root.removeHandler, root.handlers))
+    list(map(root.removeFilter, root.filters))
 
+
+def get_logger(name: str) -> logging.Logger:
     formatter = logging.Formatter(
         fmt="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
         datefmt="%m/%d/%Y %H:%M:%S"

src/llmtuner/tuner/core/loader.py

@@ -15,7 +15,7 @@ from transformers.modeling_utils import PretrainedConfig, PreTrainedModel
 from transformers.tokenization_utils import PreTrainedTokenizerBase
 from trl import AutoModelForCausalLMWithValueHead
 
-from llmtuner.extras.logging import get_logger
+from llmtuner.extras.logging import reset_logging, get_logger
 from llmtuner.extras.misc import count_parameters, prepare_model_for_training
 from llmtuner.extras.save_and_load import load_valuehead_params
 from llmtuner.hparams import FinetuningArguments
@@ -95,7 +95,10 @@ def load_model_and_tokenizer(
         is_mergeable = False
         logger.info("Quantizing model to {} bit.".format(model_args.quantization_bit))
 
-    if model_args.quantization_bit is not None or os.environ.get("LOCAL_RANK") is not None:
+    if (
+        model_args.quantization_bit is not None
+        or (os.environ.get('LOCAL_RANK') is not None and not is_deepspeed_zero3_enabled())
+    ):
         config_kwargs["device_map"] = {"": int(os.environ.get("LOCAL_RANK", "0"))}
 
     if model_args.checkpoint_dir is not None and finetuning_args.finetuning_type == "full":
@@ -126,6 +129,7 @@ def load_model_and_tokenizer(
 
     if stage == "rm" or stage == "ppo": # add value head
         model = AutoModelForCausalLMWithValueHead.from_pretrained(model)
+        reset_logging()
 
         if stage == "rm" and model_args.checkpoint_dir is not None: # load valuehead weights to evaluate reward model
             logger.warning("Only the last checkpoint containing valuehead will be loaded as the valuehead.")

src/llmtuner/tuner/core/parser.py

@@ -85,6 +85,9 @@ def get_train_args(
     assert training_args.evaluation_strategy == "no" or (not data_args.streaming), \
         "Streaming mode does not support evaluation currently."
 
+    assert not (general_args.stage == "ppo" and data_args.streaming), \
+        "Streaming mode does not suppport PPO training currently."
+
     if model_args.checkpoint_dir is not None:
         if finetuning_args.finetuning_type != "lora":
             assert len(model_args.checkpoint_dir) == 1, "Only LoRA tuning accepts multiple checkpoints."
@@ -107,8 +110,8 @@ def get_train_args(
         training_args.ddp_find_unused_parameters = False
 
     if data_args.max_samples is not None and data_args.streaming:
-        logger.warning("`max_samples` is incompatible with `streaming`. Disabling streaming mode.")
+        logger.warning("`max_samples` is incompatible with `streaming`. Disabling max_samples.")
-        data_args.streaming = False
+        data_args.max_samples = None
 
     if data_args.dev_ratio > 1e-6 and data_args.streaming:
         logger.warning("`dev_ratio` is incompatible with `streaming`. Disabling development set.")

src/llmtuner/tuner/core/trainer.py

@@ -47,20 +47,19 @@ class PeftTrainer(Seq2SeqTrainer):
         logger.info(f"Saving model checkpoint to {output_dir}")
 
         model = unwrap_model(self.model)
-        state_dict = state_dict or get_state_dict(model)
 
         if isinstance(model, PreTrainedModelWrapper):
-            model_params, v_head_params = {}, {}
+            # Custom state dict: https://github.com/lvwerra/trl/blob/v0.4.7/trl/models/modeling_value_head.py#L200
-            for name in state_dict.keys():
+            model_state_dict = state_dict or model.state_dict()
-                if name.startswith("pretrained_model."):
+            v_head_state_dict = {
-                    model_params[name.replace("pretrained_model.", "")] = state_dict[name]
+                name.replace("v_head.", ""): model_state_dict[name].cpu().clone().detach()
-                elif name.startswith("v_head."):
+                for name in model_state_dict.keys() if name.startswith("v_head.")
-                    v_head_params[name.replace("v_head.", "")] = state_dict[name]
+            }
 
-            torch.save(v_head_params, os.path.join(output_dir, VALUE_HEAD_FILE_NAME))
+            torch.save(v_head_state_dict, os.path.join(output_dir, VALUE_HEAD_FILE_NAME))
-            state_dict = model_params
             model = model.pretrained_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)

src/llmtuner/tuner/ppo/trainer.py

@@ -11,7 +11,7 @@ from trl import PPOTrainer
 from trl.core import LengthSampler
 
 from llmtuner.extras.logging import get_logger
-from llmtuner.extras.misc import AverageMeter, get_logits_processor
+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
@@ -29,6 +29,7 @@ class PPOPeftTrainer(PPOTrainer, PeftTrainer):
     r"""
     Inherits PPOTrainer.
     """
+
     def __init__(
         self,
         training_args: "Seq2SeqTrainingArguments",
@@ -70,7 +71,7 @@ class PPOPeftTrainer(PPOTrainer, PeftTrainer):
             logger.info(f"  Total train batch size (w. parallel, distributed & accumulation) = {total_train_batch_size}")
             logger.info(f"  Gradient Accumulation steps = {self.args.gradient_accumulation_steps}")
             logger.info(f"  Total optimization steps = {max_steps}")
-            logger.info(f"  Number of trainable parameters = {sum(p.numel() for p in self.model.parameters() if p.requires_grad)}")
+            logger.info(f"  Number of trainable parameters = {count_parameters(self.model)[0]}")
 
         # Keyword arguments for `model.generate`
         gen_kwargs = {