Add ArmoRM to RewardBench

rewardbench/models/__init__.py

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import torch
 from transformers import (
     AutoModel,
     AutoModelForCausalLM,
@@ -23,6 +24,7 @@
     pipeline,
 )
 
+from .armorm import ArmoRMPipeline
 from .beaver import BeaverCostPipeline, BeaverPipeline, LlamaForScore
 from .betterpairrm import BetterPairRMPipeline
 from .openassistant import *  # noqa
@@ -130,6 +132,14 @@
         "custom_dialogue": True,
         "model_type": "Custom Classifier",
     },
+    "RLHFlow/ArmoRM-Llama3-8B-v0.1": {
+        "model_builder": AutoModelForSequenceClassification.from_pretrained,
+        "pipeline_builder": ArmoRMPipeline,
+        "quantized": False,
+        "custom_dialogue": True,
+        "model_type": "Custom Classifier",
+        "torch_dtype": torch.bfloat16,
+    },
 }
 
 DPO_MODEL_CONFIG = {

rewardbench/models/armorm.py

@@ -0,0 +1,32 @@
+import random
+from typing import List
+
+import torch
+
+
+class ArmoRMPipeline:
+    def __init__(self, task, model, tokenizer):
+        self.task = task
+        self.model = model
+        self.tokenizer = tokenizer
+        random.seed(0)
+
+    def __call__(self, candidates_A: List[str], candidates_B: List[str], **kwargs):
+        """
+        samples: List[str]
+        """
+        device = self.model.device
+        out = []
+        with torch.no_grad():
+            for candidate_A, candidate_B in zip(candidates_A, candidates_B):
+                pair_scores = []
+                for candidate in [candidate_A, candidate_B]:
+                    input_ids = self.tokenizer.apply_chat_template(candidate, return_tensors="pt").to(device)
+                    output = self.model(input_ids)
+                    score = output.score.float().item()
+                    pair_scores.append(score)
+                if pair_scores[0] == pair_scores[1]:
+                    out.append(random.choice([True, False]))
+                else:
+                    out.append(pair_scores[0] > pair_scores[1])
+        return torch.Tensor(out).bool()

scripts/configs/eval_configs.yaml

@@ -532,3 +532,10 @@ RLHFlow/RewardModel-Mistral-7B-for-DPA-v1:
   batch_size: 4
   trust_remote_code: True
   dpo: False
+RLHFlow/ArmoRM-Llama3-8B-v0.1:
+  model: RLHFlow/ArmoRM-Llama3-8B-v0.1
+  tokenizer: RLHFlow/ArmoRM-Llama3-8B-v0.1
+  chat_template: # none for tokenizer
+  batch_size: 4
+  trust_remote_code: True
+  dpo: False

scripts/run_rm.py

@@ -35,6 +35,10 @@
 from rewardbench.constants import EXAMPLE_COUNTS, SUBSET_MAPPING
 from rewardbench.utils import calculate_scores_per_section
 
+# Enable TensorFloat32 (TF32) tensor cores on Ampere GPUs for matrix multiplications (faster than FP32)
+torch.backends.cuda.matmul.allow_tf32 = True
+torch.backends.cudnn.allow_tf32 = True
+
 # get token from HF_TOKEN env variable, but if it doesn't exist pass none
 HF_TOKEN = os.getenv("HF_TOKEN", None)
 # this is necessary to automatically log in when running this script in docker/batch beaker jobs
@@ -117,7 +121,7 @@ def main():
     model_type = config["model_type"]
     model_builder = config["model_builder"]
     pipeline_builder = config["pipeline_builder"]
-
+    torch_dtype = config.get("torch_dtype", None)
     # not included in config to make user explicitly understand they are passing this
     trust_remote_code = args.trust_remote_code
 
@@ -167,7 +171,10 @@ def main():
             "torch_dtype": torch.float16 if torch.cuda.is_available() else None,
         }
     else:
-        model_kwargs = {"device_map": {"": current_device}}
+        model_kwargs = {
+            "device_map": {"": current_device},
+            "torch_dtype": torch_dtype,
+        }
 
     model = model_builder(args.model, **model_kwargs, trust_remote_code=trust_remote_code)
     reward_pipe = pipeline_builder(