Upload test_exact_training_format.py with huggingface_hub

test_exact_training_format.py

@@ -0,0 +1,105 @@
+#!/usr/bin/env python3
+"""
+Test with EXACT training format to see if model generates correctly
+"""
+
+import json
+import sys
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).parent / "scripts" / "inference"))
+
+from inference_codellama import load_local_model
+import torch
+from transformers import AutoTokenizer
+
+def main():
+    script_dir = Path(__file__).parent
+    model_path = script_dir / "training-outputs" / "codellama-fifo-v1"
+    base_model_path = script_dir / "models" / "base-models" / "CodeLlama-7B-Instruct"
+    train_dataset = script_dir / "datasets" / "processed" / "split" / "train.jsonl"
+    
+    print("=" * 80)
+    print("🧪 TESTING WITH EXACT TRAINING FORMAT")
+    print("=" * 80)
+    
+    # Load sample
+    with open(train_dataset, 'r') as f:
+        sample = json.loads(f.readline())
+    
+    instruction = sample["instruction"]
+    expected_response = sample["response"]
+    
+    print(f"\n📝 Instruction ({len(instruction)} chars):")
+    print(instruction[:300] + "...")
+    
+    print(f"\n🎯 Expected Response ({len(expected_response)} chars):")
+    print(expected_response[:300] + "...")
+    
+    # Load model
+    print("\n📦 Loading model...")
+    model, tokenizer = load_local_model(
+        str(model_path),
+        str(base_model_path) if base_model_path.exists() else None
+    )
+    
+    # EXACT training format: instruction + EOS (model continues)
+    prompt = f"{instruction}{tokenizer.eos_token}"
+    
+    print(f"\n🔍 Prompt format (EXACT training format):")
+    print(f"   Format: instruction + EOS")
+    print(f"   Length: {len(prompt)} chars")
+    print()
+    
+    inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=1536).to(model.device)
+    
+    print(f"📊 Tokenized: {inputs['input_ids'].shape[1]} tokens")
+    print(f"\n🤖 Generating with temperature 0.1...")
+    print("=" * 80)
+    
+    with torch.no_grad():
+        outputs = model.generate(
+            **inputs,
+            max_new_tokens=1000,
+            temperature=0.1,
+            do_sample=False,  # Greedy decoding
+            repetition_penalty=1.2,
+            pad_token_id=tokenizer.pad_token_id if tokenizer.pad_token_id else tokenizer.eos_token_id,
+            eos_token_id=tokenizer.eos_token_id,
+        )
+    
+    # Decode only new tokens
+    input_length = inputs['input_ids'].shape[1]
+    generated_ids = outputs[0][input_length:]
+    generated_text = tokenizer.decode(generated_ids, skip_special_tokens=False)
+    
+    if generated_text.endswith(tokenizer.eos_token):
+        generated_text = generated_text[:-len(tokenizer.eos_token)].rstrip()
+    
+    print("\n" + "=" * 80)
+    print("✅ GENERATED OUTPUT:")
+    print("=" * 80)
+    print(generated_text)
+    print("=" * 80)
+    
+    # Check if it's code
+    has_module = "module" in generated_text.lower()
+    has_endmodule = "endmodule" in generated_text.lower()
+    has_verilog = "verilog" in generated_text.lower() or "```" in generated_text
+    
+    print(f"\n📊 Analysis:")
+    print(f"   Contains 'module': {has_module}")
+    print(f"   Contains 'endmodule': {has_endmodule}")
+    print(f"   Contains 'verilog': {has_verilog}")
+    print(f"   Length: {len(generated_text)} chars")
+    
+    if has_module and has_endmodule:
+        print(f"   ✅ STATUS: Generated Verilog code!")
+    elif has_module:
+        print(f"   ⚠️  STATUS: Partial code")
+    else:
+        print(f"   ❌ STATUS: Not generating code")
+
+if __name__ == "__main__":
+    main()
+