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#!/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()
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