Add LM-Evaluation-Harness integration with unified adapter pattern

.gitignore

@@ -214,6 +214,7 @@ __marimo__/
 
 # personal files
 *technical_architecture.md
-*PLAN.md
+*test_outputs/
+*AGENTS.md
 *personal_experimentation/
 *uv.lock

config/lm_eval_test_config.yaml

@@ -0,0 +1,10 @@
+model: hf
+model_args: pretrained=gpt2,dtype=float32
+tasks:
+  - hellaswag
+batch_size: 2
+num_fewshot: 0
+output_dir: test_outputs
+limit: 3
+device: cpu
+seed: 42

eval_converters/common/utils.py

@@ -1,4 +1,10 @@
-from schema.eval_types import Family, HfSplit
+from schema.eval_types import (
+    BitPrecision, 
+    Family, 
+    HfSplit, 
+    QuantizationMethod, 
+    QuantizationType)
+from transformers import AutoConfig
 
 def detect_family(model_name: str) -> Family:
     """Return the Family enum if any of its values is a substring of model_name."""
@@ -25,4 +31,77 @@ def detect_hf_split(split_str: str) -> HfSplit:
     elif "train" in s:
         return HfSplit.train
     else:
-        return HfSplit.validation
+        return HfSplit.validation
+
+def infer_quantization_from_model_name(model_name_or_path: str) -> tuple[BitPrecision, QuantizationMethod, QuantizationType]:
+    pass
+
+def infer_quantization_from_model_config(model_name_or_path: str) -> tuple[BitPrecision, QuantizationMethod, QuantizationType]:
+    pass
+
+def infer_quantization(model_name_or_path: str) -> tuple[BitPrecision, QuantizationMethod, QuantizationType]:
+    try:
+        cfg = AutoConfig.from_pretrained(model_name_or_path)
+    except Exception as e:
+        return BitPrecision.none, QuantizationMethod.none, QuantizationType.none
+
+    qcfg = getattr(cfg, 'quantization_config', None)
+    if not qcfg:
+        return BitPrecision.none, QuantizationMethod.none, QuantizationType.none
+
+    bits = int(qcfg.get("bits") or qcfg.get("weight_bits") or qcfg.get("q_bits"))
+
+    if bits == 8:
+        precision = BitPrecision.int8
+    elif bits == 4:
+        precision = BitPrecision.int4
+    elif bits == 16:
+        precision = BitPrecision.float16
+    elif bits == 32:
+        precision = BitPrecision.float32
+    else:
+        precision = BitPrecision.none
+
+    method_key = str(qcfg.get("quant_method") or "").lower()
+
+    method_map = {
+        "gptq": QuantizationMethod.gptq,
+        "awq": QuantizationMethod.awq,
+    }
+
+    type_map = {
+        "gptq": QuantizationType.static,
+        "awq": QuantizationType.static,
+        "bitsandbytes": QuantizationType.dynamic,
+        "quanto": QuantizationType.static,
+        "hqq": QuantizationType.static,
+        "torchao": QuantizationType.static,
+    }
+
+    qmethod = method_map.get(method_key, QuantizationMethod.none)
+    qtype = type_map.get(method_key, QuantizationType.none)
+    return precision, qmethod, qtype
+
+def extract_context_window_from_config(model):
+    try:
+        config = AutoConfig.from_pretrained(model)
+
+        priority_fields = [
+            "max_position_embeddings",
+            "n_positions",
+            "seq_len",
+            "seq_length",
+            "n_ctx",
+            "sliding_window"
+        ]
+
+        context_window = next((getattr(config, f) for f in priority_fields if hasattr(config, f)), None)
+        if context_window is None:
+            context_window = 1
+
+    except Exception as e:
+        print(f"Error getting context window: {e}")
+        context_window = 1
+
+    finally:
+        return context_window

eval_converters/helm/adapter.py

@@ -17,57 +17,14 @@
 from schema import SCHEMA_VERSION
 
 from eval_converters.common.adapter import BaseEvaluationAdapter, AdapterMetadata, SupportedLibrary
-from eval_converters.common.utils import detect_family, detect_hf_split
+from eval_converters.common.utils import detect_family, detect_hf_split, infer_quantization, extract_context_window_from_config
 from .utils import detect_prompt_class, get_adapter_class_from_method_string
 
 from transformers import AutoConfig
 
 # run this just once in your process to initialize the registry
 register_builtin_configs_from_helm_package()
 
-def infer_quantization(model_name_or_path: str):
-	"""
-	Returns (BitPrecision, Method) enums for the given HF model.
-	"""
-	try:
-		cfg = AutoConfig.from_pretrained(model_name_or_path)
-	except Exception as e:
-		raise ValueError(
-			f"Failed to load model config for {model_name_or_path}: {e} \n"
-			"This may happen if you are using a HELM model name instead of HuggingFace model name in the adapter_spec.model field."
-			"For example, HELM uses 'meta/llama-3.1-8b-instruct' while HuggingFace uses meta-llama/llama-3.1-8b-instruct' \n"
-			"Please verify the model name and try again."
-		)
-	qcfg = getattr(cfg, "quantization_config", None)
-
-	if qcfg is None:
-		return BitPrecision.none, Method.None_
-
-	bits = int(qcfg.get("bits") or qcfg.get("weight_bits") or qcfg.get("q_bits"))
-
-	if bits == 8:
-		precision = BitPrecision.int8
-	elif bits == 4:
-		precision = BitPrecision.int4
-	elif bits == 16:
-		precision = BitPrecision.float16
-	elif bits == 32:
-		precision = BitPrecision.float32
-	else:
-		precision = BitPrecision.none
-
-	method_key = qcfg.get("quant_method") or ""
-	method_map = {
-		"gptq": Method.static,
-		"awq": Method.static,
-		"bitsandbytes": Method.dynamic,
-		"quanto": Method.static,
-		"hqq": Method.static,
-		"torchao": Method.static,
-	}
-
-	method = method_map.get(method_key, Method.None_)
-	return precision, method
 
 class HELMAdapter(BaseEvaluationAdapter):
 	"""
@@ -148,33 +105,14 @@ def transform_from_directory(self, dir_path):
 		)
 
 		# 1.2. Configuration
-		# HELM does not provide context window size, try loading it from model config, else set to 1
-		try:
-			# try getting context window from model deployment
-			deployment = get_model_deployment(adapter_spec.model_deployment)
-			if deployment and deployment.max_sequence_length is not None:
-				context_window = deployment.max_sequence_length
-
-			# if not available, try loading it from model config
-			else:
-				config = AutoConfig.from_pretrained(adapter_spec.model)
-
-				priority_fields = [
-					"max_position_embeddings",
-					"n_positions",
-					"seq_len",
-					"seq_length",
-					"n_ctx",
-					"sliding_window"
-				]
-
-				context_window = next((getattr(config, f) for f in priority_fields if hasattr(config, f)), None)
-				if context_window is None:
-					context_window = 1
-
-		except Exception as e:
-			print(f"Error getting context window: {e}")
-			context_window = 1
+		# HELM does not provide context window size, try loading it from model deployment, else set to 1
+		deployment = get_model_deployment(adapter_spec.model_deployment)
+		if deployment and deployment.max_sequence_length is not None:
+			context_window = deployment.max_sequence_length
+
+		# if not available, try loading it from model config, else set to 1
+		else:
+			context_window = extract_context_window_from_config(adapter_spec.model)
 
 		configuration = Configuration(
 			context_window=context_window,
@@ -336,33 +274,14 @@ def _transform_single(self, raw_data, base_dir=None):
 		)
 
 		# 1.2. Configuration
-		# HELM does not provide context window size, try loading it from model config, else set to 1
-		try:
-			# try getting context window from model deployment
-			deployment = get_model_deployment(adapter_spec.model_deployment)
-			if deployment and deployment.max_sequence_length is not None:
-				context_window = deployment.max_sequence_length
-
-			# if not available, try loading it from model config
-			else:
-				config = AutoConfig.from_pretrained(adapter_spec.model)
-
-				priority_fields = [
-					"max_position_embeddings",
-					"n_positions",
-					"seq_len",
-					"seq_length",
-					"n_ctx",
-					"sliding_window"
-				]
-
-				context_window = next((getattr(config, f) for f in priority_fields if hasattr(config, f)), None)
-				if context_window is None:
-					context_window = 1
-
-		except Exception as e:
-			print(f"Error getting context window: {e}")
-			context_window = 1
+		# HELM does not provide context window size, try loading it from model deployment
+		deployment = get_model_deployment(adapter_spec.model_deployment)
+		if deployment and deployment.max_sequence_length is not None:
+			context_window = deployment.max_sequence_length
+
+		# if not available, try loading it from model config, else set to 1
+		else:
+			context_window = extract_context_window_from_config(adapter_spec.model)
 
 		configuration = Configuration(
 			context_window=context_window,

eval_converters/helm/utils.py

@@ -59,4 +59,4 @@ def get_adapter_class_from_method_string(method_str: str) -> type[Adapter]:
         if key in method_str:
             return mapping[key]
 
-    raise ValueError(f"Unknown adapter method string: {method_str}")
+    raise ValueError(f"Unknown adapter method string: {method_str}")