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#!/usr/bin/env python3
"""
Test script for FastVLM-7B model loading and configuration
"""
import asyncio
import sys
import os
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
# Add backend to path
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
def check_dependencies():
"""Check if all required dependencies are installed"""
print("Checking dependencies...")
deps = {
"torch": None,
"transformers": None,
"sentencepiece": None,
"einops": None,
"accelerate": None
}
for dep in deps:
try:
module = __import__(dep)
deps[dep] = getattr(module, "__version__", "installed")
print(f"✓ {dep}: {deps[dep]}")
except ImportError:
print(f"✗ {dep}: NOT INSTALLED")
deps[dep] = None
return all(v is not None for v in deps.values())
def check_hardware():
"""Check hardware capabilities"""
print("\nHardware check:")
if torch.cuda.is_available():
print(f"✓ CUDA available: {torch.cuda.get_device_name(0)}")
print(f" Memory: {torch.cuda.get_device_properties(0).total_memory / 1e9:.2f} GB")
elif torch.backends.mps.is_available():
print("✓ Apple Silicon MPS available")
# Get system memory
import subprocess
result = subprocess.run(['sysctl', 'hw.memsize'], capture_output=True, text=True)
if result.returncode == 0:
mem_bytes = int(result.stdout.split()[1])
print(f" System Memory: {mem_bytes / 1e9:.2f} GB")
else:
print("✓ CPU mode")
import psutil
print(f" Available Memory: {psutil.virtual_memory().available / 1e9:.2f} GB")
async def test_fastvlm_loading():
"""Test loading FastVLM-7B model"""
print("\n" + "="*50)
print("Testing FastVLM-7B Model Loading")
print("="*50)
model_name = "apple/FastVLM-7B"
try:
print(f"\n1. Loading tokenizer from {model_name}...")
tokenizer = AutoTokenizer.from_pretrained(
model_name,
trust_remote_code=True,
use_fast=True
)
print(" ✓ Tokenizer loaded successfully")
print(f" Tokenizer class: {tokenizer.__class__.__name__}")
print(f" Vocab size: {tokenizer.vocab_size}")
# Check for IMAGE_TOKEN_INDEX
IMAGE_TOKEN_INDEX = -200
if hasattr(tokenizer, 'IMAGE_TOKEN_INDEX'):
print(f" IMAGE_TOKEN_INDEX: {tokenizer.IMAGE_TOKEN_INDEX}")
else:
print(f" Setting IMAGE_TOKEN_INDEX to {IMAGE_TOKEN_INDEX}")
tokenizer.IMAGE_TOKEN_INDEX = IMAGE_TOKEN_INDEX
print("\n2. Attempting to load model...")
print(" Note: This requires ~14GB RAM for full precision")
# Determine device
if torch.cuda.is_available():
device = "cuda"
dtype = torch.float16
elif torch.backends.mps.is_available():
device = "mps"
dtype = torch.float16
else:
device = "cpu"
dtype = torch.float32
print(f" Device: {device}")
print(f" Dtype: {dtype}")
# Try loading with minimal memory usage
print(" Loading with low_cpu_mem_usage=True...")
model = AutoModelForCausalLM.from_pretrained(
model_name,
trust_remote_code=True,
torch_dtype=dtype,
low_cpu_mem_usage=True
)
print(" ✓ Model loaded successfully!")
# Count parameters
total_params = sum(p.numel() for p in model.parameters())
print(f" Parameters: {total_params / 1e9:.2f}B")
# Move to device
print(f"\n3. Moving model to {device}...")
model = model.to(device)
model.eval()
print(" ✓ Model ready for inference")
# Test a simple generation
print("\n4. Testing generation...")
test_prompt = "Hello, this is a test of"
inputs = tokenizer(test_prompt, return_tensors="pt").to(device)
with torch.no_grad():
outputs = model.generate(
**inputs,
max_new_tokens=10,
temperature=0.7,
do_sample=True
)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(f" Input: {test_prompt}")
print(f" Output: {response}")
print("\n✓ FastVLM-7B is working correctly!")
return True
except ImportError as e:
print(f"\n✗ Import Error: {e}")
if "trust_remote_code" in str(e):
print("\nSolution: The model requires trust_remote_code=True")
print("This is already set in the code, but the model files may need to be re-downloaded.")
return False
except RuntimeError as e:
if "out of memory" in str(e).lower():
print(f"\n✗ Out of Memory Error")
print("\nSolutions:")
print("1. Use the quantized version:")
print(" model_name = 'apple/FastVLM-7B-int4'")
print("2. Use a smaller variant:")
print(" model_name = 'apple/FastVLM-1.5B'")
print("3. Enable 8-bit quantization (requires bitsandbytes)")
print("4. Increase system RAM or use a GPU")
else:
print(f"\n✗ Runtime Error: {e}")
return False
except Exception as e:
print(f"\n✗ Error: {e}")
print(f" Error type: {type(e).__name__}")
import traceback
traceback.print_exc()
return False
async def test_alternative_models():
"""Test alternative model options if FastVLM-7B fails"""
print("\n" + "="*50)
print("Alternative Model Options")
print("="*50)
alternatives = [
("apple/FastVLM-1.5B", "Smaller FastVLM variant (1.5B params)"),
("apple/FastVLM-7B-int4", "Quantized FastVLM for lower memory"),
("apple/FastVLM-0.5B", "Smallest FastVLM variant (0.5B params)")
]
for model_name, description in alternatives:
print(f"\n• {model_name}")
print(f" {description}")
try:
# Just check if the model card exists
from transformers import AutoConfig
config = AutoConfig.from_pretrained(model_name, trust_remote_code=True)
print(f" ✓ Model available")
except Exception as e:
print(f" ✗ Not accessible: {str(e)[:50]}...")
async def main():
"""Main test function"""
print("FastVLM-7B Integration Test")
print("="*50)
# Check dependencies
if not check_dependencies():
print("\n❌ Missing dependencies. Please install all requirements.")
return
# Check hardware
check_hardware()
# Test FastVLM loading
success = await test_fastvlm_loading()
if not success:
# Show alternatives
await test_alternative_models()
print("\n" + "="*50)
print("Recommendations:")
print("="*50)
print("\n1. If memory is limited, use FastVLM-1.5B or FastVLM-0.5B")
print("2. For Apple Silicon, ensure you have enough RAM (16GB+ recommended)")
print("3. Consider using the quantized version (FastVLM-7B-int4)")
print("4. Make sure transformers >= 4.40.0 is installed")
if __name__ == "__main__":
asyncio.run(main()) |