Upload scripts/eval_mbpp_hf.py with huggingface_hub

scripts/eval_mbpp_hf.py

@@ -5,6 +5,8 @@
 """
 MBPP Evaluation: Base Devstral vs Fine-tuned Alizee-Coder
 Runs on HF Jobs with GPU support
+
+FIXED: Extract expected function name from test cases and include in prompt
 """
 
 import os
@@ -20,6 +22,7 @@ from huggingface_hub import HfApi
 print("=" * 60)
 print("EVALUATION: Devstral-Small vs Alizee-Coder-Devstral")
 print("Benchmark: MBPP (Mostly Basic Python Problems)")
+print("VERSION: Fixed function name extraction")
 print("=" * 60)
 
 # Configuration
@@ -78,6 +81,31 @@ def load_model(model_name, adapter_name=None):
     model.eval()
     return model, tokenizer
 
+def extract_function_name(test_list):
+    """Extract expected function name from test cases"""
+    if not test_list:
+        return None
+
+    # Try to find function call in first test case
+    # Pattern: assert function_name(...) or function_name(...)
+    test = test_list[0]
+
+    # Match: assert func_name( or just func_name(
+    patterns = [
+        r'assert\s+(\w+)\s*\(',  # assert func_name(
+        r'^\s*(\w+)\s*\(',       # func_name( at start
+    ]
+
+    for pattern in patterns:
+        match = re.search(pattern, test)
+        if match:
+            func_name = match.group(1)
+            # Skip common non-function names
+            if func_name not in ['assert', 'print', 'len', 'str', 'int', 'float', 'list', 'dict', 'set', 'tuple']:
+                return func_name
+
+    return None
+
 def extract_python_code(text):
     """Extract Python code from model output"""
     # Try ```python blocks
@@ -94,10 +122,13 @@ def extract_python_code(text):
 
     return text.strip()
 
-def generate_completion_base(model, tokenizer, prompt):
+def generate_completion_base(model, tokenizer, prompt, func_name=None):
     """Generate code completion for BASE model (direct completion)"""
-    # For base model, use a simple completion format
-    code_prompt = f"# Python function\n{prompt}\n\ndef"
+    # Include expected function name in prompt if available
+    if func_name:
+        code_prompt = f"# Python function\n# Task: {prompt}\n# Function name: {func_name}\n\ndef {func_name}("
+    else:
+        code_prompt = f"# Python function\n{prompt}\n\ndef"
 
     inputs = tokenizer(code_prompt, return_tensors="pt", truncation=True, max_length=2048).to(model.device)
 
@@ -114,7 +145,10 @@ def generate_completion_base(model, tokenizer, prompt):
     completion = tokenizer.decode(outputs[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True)
 
     # Reconstruct the function
-    full_code = "def" + completion
+    if func_name:
+        full_code = f"def {func_name}(" + completion
+    else:
+        full_code = "def" + completion
 
     # Stop at function boundary
     stop_tokens = ["\ndef ", "\nclass ", "\nif __name__", "\n\n\n"]
@@ -124,9 +158,13 @@ def generate_completion_base(model, tokenizer, prompt):
 
     return full_code
 
-def generate_completion_finetuned(model, tokenizer, prompt):
+def generate_completion_finetuned(model, tokenizer, prompt, func_name=None):
     """Generate code completion for FINE-TUNED model (Instruct format)"""
-    instruct_prompt = f"<s>[INST] Solve this programming problem with detailed reasoning:\n\n{prompt}\n[/INST]"
+    # Include expected function name in prompt
+    if func_name:
+        instruct_prompt = f"<s>[INST] Solve this programming problem with detailed reasoning:\n\n{prompt}\n\nIMPORTANT: The function MUST be named `{func_name}`.\n[/INST]"
+    else:
+        instruct_prompt = f"<s>[INST] Solve this programming problem with detailed reasoning:\n\n{prompt}\n[/INST]"
 
     inputs = tokenizer(instruct_prompt, return_tensors="pt", truncation=True, max_length=2048).to(model.device)
 
@@ -143,6 +181,14 @@ def generate_completion_finetuned(model, tokenizer, prompt):
     full_response = tokenizer.decode(outputs[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True)
     code = extract_python_code(full_response)
 
+    # If function name was specified but model used different name, try to rename
+    if func_name and code:
+        # Find the actual function name in generated code
+        match = re.search(r'def\s+(\w+)\s*\(', code)
+        if match and match.group(1) != func_name:
+            # Replace the function name
+            code = re.sub(r'def\s+' + re.escape(match.group(1)) + r'\s*\(', f'def {func_name}(', code)
+
     return code
 
 def evaluate_model(model, tokenizer, dataset, model_name, is_finetuned=False):
@@ -153,18 +199,23 @@ def evaluate_model(model, tokenizer, dataset, model_name, is_finetuned=False):
     for i, problem in enumerate(tqdm(dataset, desc=f"Generating ({model_name})")):
         task_id = problem.get("task_id", i)
         prompt = problem["prompt"]  # Natural language description
+        test_list = problem.get("test_list", [])
+
+        # Extract expected function name from test cases
+        func_name = extract_function_name(test_list)
 
         try:
             if is_finetuned:
-                completion = generate_completion_finetuned(model, tokenizer, prompt)
+                completion = generate_completion_finetuned(model, tokenizer, prompt, func_name)
             else:
-                completion = generate_completion_base(model, tokenizer, prompt)
+                completion = generate_completion_base(model, tokenizer, prompt, func_name)
 
             samples.append({
                 "task_id": task_id,
                 "prompt": prompt[:200],
                 "completion": completion,
-                "test_list": problem.get("test_list", []),
+                "test_list": test_list,
+                "expected_func": func_name,
                 "model": model_name
             })
         except Exception as e:
@@ -173,7 +224,8 @@ def evaluate_model(model, tokenizer, dataset, model_name, is_finetuned=False):
                 "task_id": task_id,
                 "prompt": prompt[:200],
                 "completion": "# Error during generation",
-                "test_list": problem.get("test_list", []),
+                "test_list": test_list,
+                "expected_func": func_name,
                 "model": model_name
             })