adding fix for wandb tags and distributed ranks

llm_rl/requirements.txt

@@ -17,6 +17,7 @@ uvicorn
 pydantic
 fastapi
 sse-starlette
+packaging
 matplotlib
 py-cpuinfo
 deepspeed

llm_rl/reward_model.sh

@@ -1,21 +1,23 @@
-python src/train_bash.py \
+CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
     --stage rm \
-    --model_name_or_path meta-llama/Llama-2-13b \
+    --model_name_or_path meta-llama/Llama-2-13b-hf \
     --do_train \
     --dataset comparison_gpt4_en \
     --template default \
     --finetuning_type lora \
     --lora_target q_proj,v_proj \
     --resume_lora_training False \
-    --checkpoint_dir ./llama-2-13b-rm \
     --output_dir ./llama-2-13b-rm \
-    --per_device_train_batch_size 2 \
-    --gradient_accumulation_steps 4 \
+    --per_device_train_batch_size 8 \
+    --gradient_accumulation_steps 8 \
     --lr_scheduler_type cosine \
     --logging_steps 10 \
     --save_steps 1000 \
     --learning_rate 1e-6 \
     --num_train_epochs 1.0 \
     --plot_loss \
     --fp16 \
-    --hf_auth_token "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"
+    --use_auth_token True \
+    --wandb_token "99caa13ec9552adf0e92e5c30021307ce3cf7fa4" \
+    --hf_auth_token "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" \
+    --deepspeed ./deepspeed_config_s2.json 

llm_rl/src/llmtuner/hparams/finetuning_args.py

@@ -1,3 +1,4 @@
+from typing import List
 import json
 from typing import Literal, Optional
 from dataclasses import asdict, dataclass, field
@@ -83,15 +84,15 @@ class FinetuningArguments:
         default=0,
         metadata={"help": "The alpha parameter to control the noise magnitude in NEFTune."}
     )
-    wandb_token: Optional[str|None] = field(
+    wandb_token: Optional[str] = field(
         default=None,
         metadata={"help": "The login api token for wandb."}
     )
-    wandb_project: Optional[str|None] = field(
+    wandb_project: Optional[str] = field(
         default=None,
         metadata={"help": "The project name for the current wandb log."}
     )
-    wandb_tags: Optional[list[str]|None] = field(
+    wandb_tags: Optional[List[str]] = field(
         default=None,
         metadata={"help": "The tag for the current wandb run."}
     )

llm_rl/src/llmtuner/tuner/core/utils.py

@@ -15,8 +15,8 @@
 logger = get_logger(__name__)
 
 def is_first_node():
-    world_rank = dist.get_rank()
-    local_rank = int(os.environ['LOCAL_RANK'])
+    world_rank = dist.get_rank() if torch.distributed.is_initialized() else 0
+    local_rank = int(os.environ['LOCAL_RANK']) if 'LOCAL_RANK' in os.environ else 0
     return world_rank == local_rank == 0
 
 def find_all_linear_modules(

llm_rl/src/llmtuner/tuner/rm/trainer.py

@@ -38,7 +38,7 @@ def compute_loss(
         See: https://github.com/huggingface/transformers/blob/v4.30.2/src/transformers/trainer.py#L3509
         """
         # Compute rewards
-        _, _, values = model(**inputs, output_hidden_states=True, return_dict=True)
+        _, _, values = model(**inputs, output_hidden_states=True, return_dict=True) # (lm_logits, loss, value)
         if values.size(0) != inputs["input_ids"].size(0): # adapt to chatglm2
             values = torch.transpose(values, 0, 1)
 

llm_rl/src/llmtuner/tuner/tune.py

@@ -24,7 +24,7 @@ def run_exp(args: Optional[Dict[str, Any]] = None, callbacks: Optional[List["Tra
     callbacks = [LogCallback()] if callbacks is None else callbacks
     if is_first_node():
         wandb.login(key=finetuning_args.wandb_token)
-        wandb.init(project=finetuning_args.wandb_project, tags=[*finetuning_args.wandb_tags])
+        wandb.init(project=finetuning_args.wandb_project, tags=[*finetuning_args.wandb_tags] if finetuning_args.wandb_tags else None)
 
     if finetuning_args.stage == "pt":
         run_pt(model_args, data_args, training_args, finetuning_args, callbacks)