push result

openr/infer.slurm

@@ -21,7 +21,7 @@
 #SBATCH --gres=gpu:8                 # Request M GPU per node
 #SBATCH --gres-flags=enforce-binding # CPU-GPU Affinity
 #SBATCH --qos=gpu-normal           # Request QOS Type
-#SBATCH --constraint="L40"
+#SBATCH --constraint="L40"|"L40S"
 
 ###
 ### The system will alloc 8 or 16 cores per gpu by default.
@@ -80,10 +80,18 @@ ulimit -u 20000
 cd /nfs_global/S/shiwenxuan/prm/openr
 export PYTHONPATH=$(pwd)
 
-bash reason/llm_service/create_service_qwen2.5_math_vllm_gold_prm_speed.sh --acc 0.3 --policy_model_name Qwen2.5-Math-1.5B-Instruct
-sleep 100s
-bash scripts/eval/beam_search.sh --acc 0.3 --policy_model_name Qwen2.5-Math-1.5B-Instruct
+# bash reason/llm_service/create_service_qwen2.5_math_vllm_gold_prm_speed.sh --acc 1.0 --policy_model_name Qwen2.5-Math-1.5B-Instruct
+# sleep 100s
+# bash scripts/eval/beam_search.sh --acc 1.0 --policy_model_name Qwen2.5-Math-1.5B-Instruct --num_sequence 1 --tree_max_width 4
 
+for acc in $(seq 0.3 0.1 1.0); do
+    echo "当前循环，ACC=${acc}"
+    bash reason/llm_service/create_service_qwen2.5_math_vllm_gold_prm_speed.sh --acc ${acc} --policy_model_name Qwen2.5-Math-1.5B-Instruct
+    sleep 100s
+
+    bash scripts/eval/beam_search.sh --acc ${acc} --policy_model_name Qwen2.5-Math-1.5B-Instruct --num_sequence 2 --tree_max_width 8
+    tmux kill-session -t FastChat
+done
 # sleep 6h
 
 #- End

openr/reason/guided_search/tree.py

@@ -431,9 +431,9 @@ class SearchTree:
             self.root = root
 
         simulate_env_copy = simulate_env.copy()
-        print(simulate_env_copy.config["max_actions"])
+        # print(simulate_env_copy.config["max_actions"])
         simulate_env_copy.config["max_actions"] = int(simulate_env_copy.config["max_actions"] / beam_size)
-        print(simulate_env_copy.config["max_actions"])
+        # print(simulate_env_copy.config["max_actions"])
         # end_nodes, top_k_nodes = [], [(-root._initial_value, root, simulate_env.copy())]
         end_nodes, top_k_nodes = [], [(-root._initial_value, root, simulate_env_copy)]
         k = beam_size

openr/scripts/eval/beam_search.sh

@@ -4,10 +4,15 @@ echo "RAY TEMP DIR is $RAY_TEMP_DIR"
 POLICY_MODEL_NAME=Qwen2.5-Math-1.5B-Instruct
 ACC=0.5
 
+NUM_SEQUENCE=1       
+TREE_MAX_WIDTH=4     
+
 while [[ "$#" -gt 0 ]]; do
     case $1 in
         --acc) ACC="$2"; shift ;;
         --policy_model_name) POLICY_MODEL_NAME="$2"; shift ;;
+        --num_sequence) NUM_SEQUENCE="$2"; shift ;;
+        --tree_max_width) TREE_MAX_WIDTH="$2"; shift ;;
         *) echo "Unknown parameter passed: $1"; exit 1 ;;
     esac
     shift
@@ -15,7 +20,7 @@ done
 
 
 VALUE_MODEL_NAME=${POLICY_MODEL_NAME}_RM
-SAVE_DIR="/nfs_global/S/shiwenxuan/prm/openr/results/${POLICY_MODEL_NAME}/${ACC}"
+SAVE_DIR="/nfs_global/S/shiwenxuan/prm/openr/results/${POLICY_MODEL_NAME}/${NUM_SEQUENCE}/${TREE_MAX_WIDTH}/${ACC}"
 
 echo "POLICY_MODEL_NAME is $POLICY_MODEL_NAME"
 echo "VALUE_MODEL_NAME is $VALUE_MODEL_NAME"
@@ -26,8 +31,8 @@ python reason/evaluation/evaluate.py \
     --task_name MATH \
     --temperature 0.7 \
     --max_new_tokens 2048 \
-    --num_sequence 2 \
-    --tree_max_width 4 \
+    --num_sequence $NUM_SEQUENCE \
+    --tree_max_width $TREE_MAX_WIDTH \
     --tree_max_depth 50 \
     --save_dir $SAVE_DIR \
     --method beam_search \

result.md

 ### experiment
-
 - policy model: Qwen2.5-Math-1.5B-Instruct
 - reward model: Qwen2.5-Math-1.5B-Instruct
-- method: beam search
-- - ACC:1.0 result:[{"majority_vote": 0.828, "total_completion_tokens": 2544.756}]
-- - ACC:0.9 result:[{"majority_vote": 0.798, "total_completion_tokens": 2576.35}]
-- - ACC:0.8 result:[{"majority_vote": 0.794, "total_completion_tokens": 2497.672}]
-- - ACC:0.7 result:[{"majority_vote": 0.782, "total_completion_tokens": 2502.832}]
-- - ACC:0.6 result:[{"majority_vote": 0.76, "total_completion_tokens": 2491.27}]
-- - ACC:0.5 result:[{'majority_vote': 0.724, 'total_completion_tokens': 2400.16}]
-- - ACC:0.4 result:[{'majority_vote': 0.75, 'total_completion_tokens': 2418.876}]
-- - ACC:0.3 result:[{'majority_vote': 0.748, 'total_completion_tokens': 2463.39}]
-
+- method: beam search (num_seq=2 tree_width=4)
+- task: Math500
+- - ACC:0.3 [{"majority_vote": 0.708, "prm_min_max": 0.71, "prm_min_vote": 0.71, "prm_last_max": 0.708, "prm_last_vote": 0.708, "total_completion_tokens": 2196.464}]
+- - ACC:0.4 [{"majority_vote": 0.726, "prm_min_max": 0.726, "prm_min_vote": 0.726, "prm_last_max": 0.722, "prm_last_vote": 0.722, "total_completion_tokens": 2175.506}]
+- - ACC:0.5 [{"majority_vote": 0.738, "prm_min_max": 0.738, "prm_min_vote": 0.738, "prm_last_max": 0.742, "prm_last_vote": 0.742, "total_completion_tokens": 2234.252}]
+- - ACC:0.6 [{"majority_vote": 0.746, "prm_min_max": 0.746, "prm_min_vote": 0.746, "prm_last_max": 0.752, "prm_last_vote": 0.752, "total_completion_tokens": 2178.612}]
+- - ACC:0.7 [{"majority_vote": 0.774, "prm_min_max": 0.776, "prm_min_vote": 0.776, "prm_last_max": 0.78, "prm_last_vote": 0.78, "total_completion_tokens": 2251.602}]
+- - ACC:0.8 [{"majority_vote": 0.804, "prm_min_max": 0.804, "prm_min_vote": 0.804, "prm_last_max": 0.812, "prm_last_vote": 0.812, "total_completion_tokens": 2251.908}]
+- - ACC:0.9 [{"majority_vote": 0.838, "prm_min_max": 0.84, "prm_min_vote": 0.84, "prm_last_max": 0.852, "prm_last_vote": 0.852, "total_completion_tokens": 2265.382}]
+- - ACC:1.0 [{"majority_vote": 0.838, "prm_min_max": 0.838, "prm_min_vote": 0.838, "prm_last_max": 0.838, "prm_last_vote": 0.838, "total_completion_tokens": 2276.126}]