test commit powershell

test_float16_compatibility.py

@@ -1,96 +0,0 @@
-#!/usr/bin/env python3
-"""
-Test script for float16 compatibility with pre-quantized model
-"""
-
-import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer
-import logging
-
-# Set up logging
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-def test_float16_compatibility():
-    """Test float16 compatibility with pre-quantized model"""
-    
-    model_id = "Tonic/petite-elle-L-aime-3-sft"
-    device = "cuda" if torch.cuda.is_available() else "cpu"
-    
-    logger.info(f"Testing float16 compatibility on device: {device}")
-    
-    # Test both float32 and float16
-    dtypes_to_test = []
-    
-    if device == "cuda":
-        dtypes_to_test = [torch.float32, torch.float16]
-    else:
-        dtypes_to_test = [torch.float32]  # Only test float32 on CPU
-    
-    for dtype in dtypes_to_test:
-        logger.info(f"\nTesting with dtype: {dtype}")
-        
-        try:
-            # Load tokenizer
-            tokenizer = AutoTokenizer.from_pretrained(model_id)
-            if tokenizer.pad_token_id is None:
-                tokenizer.pad_token_id = tokenizer.eos_token_id
-            
-            # Load model with specific dtype
-            model_kwargs = {
-                "device_map": "auto" if device == "cuda" else "cpu",
-                "torch_dtype": dtype,
-                "trust_remote_code": True,
-                "low_cpu_mem_usage": True,
-            }
-            
-            logger.info(f"Loading model with {dtype}...")
-            model = AutoModelForCausalLM.from_pretrained(model_id, **model_kwargs)
-            
-            # Test generation
-            test_prompt = "Bonjour, comment allez-vous?"
-            inputs = tokenizer(test_prompt, return_tensors="pt")
-            
-            if device == "cuda":
-                inputs = {k: v.cuda() for k, v in inputs.items()}
-            
-            logger.info("Generating response...")
-            with torch.no_grad():
-                output_ids = model.generate(
-                    inputs['input_ids'],
-                    max_new_tokens=50,
-                    temperature=0.7,
-                    top_p=0.95,
-                    do_sample=True,
-                    attention_mask=inputs['attention_mask'],
-                    pad_token_id=tokenizer.eos_token_id,
-                    eos_token_id=tokenizer.eos_token_id,
-                    cache_implementation="static"
-                )
-            
-            response = tokenizer.decode(output_ids[0], skip_special_tokens=True)
-            assistant_response = response[len(test_prompt):].strip()
-            
-            logger.info(f"✅ {dtype} test successful!")
-            logger.info(f"Input: {test_prompt}")
-            logger.info(f"Output: {assistant_response}")
-            
-            # Check memory usage
-            if device == "cuda":
-                memory_used = torch.cuda.memory_allocated() / 1024**3
-                logger.info(f"GPU Memory used: {memory_used:.2f} GB")
-            
-            # Check model dtype
-            logger.info(f"Model dtype: {model.dtype}")
-            
-            # Clean up
-            del model
-            torch.cuda.empty_cache() if device == "cuda" else None
-            
-        except Exception as e:
-            logger.error(f"❌ {dtype} test failed: {e}")
-            import traceback
-            traceback.print_exc()
-
-if __name__ == "__main__":
-    test_float16_compatibility()