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import requests
import torch
from PIL import Image
from io import BytesIO
from transformers import AutoProcessor, AutoModelForVision2Seq, AwqConfig
import awq_ext
MODE = "quantized"
DEVICE = "cuda"
PROCESSOR = AutoProcessor.from_pretrained("HuggingFaceM4/idefics2-tfrm-compatible")
BAD_WORDS_IDS = PROCESSOR.tokenizer(
["<image>", "<fake_token_around_image>"], add_special_tokens=False
).input_ids
EOS_WORDS_IDS = PROCESSOR.tokenizer(
"<end_of_utterance>", add_special_tokens=False
).input_ids + [PROCESSOR.tokenizer.eos_token_id]
# Load model
if MODE == "regular":
model = AutoModelForVision2Seq.from_pretrained(
"HuggingFaceM4/idefics2-tfrm-compatible",
torch_dtype=torch.float16,
trust_remote_code=True,
_attn_implementation="flash_attention_2",
revision="3dc93be345d64fb6b1c550a233fe87ddb36f183d",
).to(DEVICE)
elif MODE == "quantized":
quant_path = "HuggingFaceM4/idefics2-tfrm-compatible-AWQ"
model = AutoModelForVision2Seq.from_pretrained(
quant_path, trust_remote_code=True
).to(DEVICE)
elif MODE == "fused_quantized":
quant_path = "HuggingFaceM4/idefics2-tfrm-compatible-AWQ"
quantization_config = AwqConfig(
bits=4,
fuse_max_seq_len=4096,
modules_to_fuse={
"attention": ["q_proj", "k_proj", "v_proj", "o_proj"],
"mlp": ["gate_proj", "up_proj", "down_proj"],
"layernorm": ["input_layernorm", "post_attention_layernorm", "norm"],
"use_alibi": False,
"num_attention_heads": 32,
"num_key_value_heads": 8,
"hidden_size": 4096,
},
)
model = AutoModelForVision2Seq.from_pretrained(
quant_path,
quantization_config=quantization_config,
trust_remote_code=True,
).to(DEVICE)
else:
raise ValueError("Unknown mode")
def download_image(url):
try:
# Send a GET request to the URL to download the image
response = requests.get(url)
# Check if the request was successful (status code 200)
if response.status_code == 200:
# Open the image using PIL
image = Image.open(BytesIO(response.content))
# Return the PIL image object
return image
else:
print(f"Failed to download image. Status code: {response.status_code}")
return None
except Exception as e:
print(f"An error occurred: {e}")
return None
# Create inputs
image1 = download_image(
"https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
)
def ask_vlm(image, instruction):
prompts = [
"User:",
image,
f"{instruction}.<end_of_utterance>\n",
"Assistant:",
]
inputs = PROCESSOR(prompts)
inputs = {k: torch.tensor(v).to(DEVICE) for k, v in inputs.items()}
generated_ids = model.generate(
**inputs,
bad_words_ids=BAD_WORDS_IDS,
max_new_tokens=100,
)
generated_texts = PROCESSOR.batch_decode(generated_ids, skip_special_tokens=True)
return generated_texts
import time
now = time.time()
print(ask_vlm(image1, "What is this?")[0].split("\nAssistant: ")[1])
print("resp:", time.time() - now)
import time
now = time.time()
print(ask_vlm(image1, "What is this?")[0].split("\nAssistant: ")[1])
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