File size: 2,425 Bytes
d5049a2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 | from __future__ import annotations
import argparse
import os
import torch
from transformers import AutoConfig
from .common import (
apply_chat_template,
build_messages,
load_rows,
move_to_device,
normalized_row,
tokenizer_fingerprint,
)
from .modeling import load_base_model, load_processor
def main() -> None:
parser = argparse.ArgumentParser(description="Qwen3.5 VLM environment preflight")
parser.add_argument("--model", required=True)
parser.add_argument("--data-dir", required=True)
parser.add_argument("--split", default="train")
parser.add_argument("--num-views", type=int, default=1)
parser.add_argument("--max-length", type=int, default=2048)
parser.add_argument("--load-model", action="store_true")
parser.add_argument("--attn-implementation", default="sdpa")
args = parser.parse_args()
if not os.path.isdir(args.model):
raise SystemExit(f"Local model directory does not exist: {args.model}")
config = AutoConfig.from_pretrained(
args.model, trust_remote_code=True, local_files_only=True
)
if not hasattr(config, "vision_config"):
raise SystemExit(
f"{args.model} is not recognized as a multimodal model (no vision_config)"
)
processor = load_processor(args.model)
fingerprint = tokenizer_fingerprint(processor.tokenizer)
row = normalized_row(load_rows(args.data_dir, args.split)[0])
batch = apply_chat_template(
processor,
build_messages(row["question"], row["image_paths"], args.num_views),
add_generation_prompt=True,
max_length=args.max_length,
)
print(f"model_type={getattr(config, 'model_type', 'unknown')}")
print(f"tokenizer_size={len(processor.tokenizer)}")
print(f"tokenizer_sha256={fingerprint}")
print(f"prompt_tokens={batch['input_ids'].shape[1]}")
print(f"batch_keys={sorted(batch)}")
if args.load_model:
if not torch.cuda.is_available():
raise SystemExit("--load-model requested but CUDA is unavailable")
model = load_base_model(
args.model, attn_implementation=args.attn_implementation
).cuda().eval()
with torch.inference_mode():
outputs = model(**move_to_device(batch, torch.device("cuda")))
print(f"forward_logits_shape={tuple(outputs.logits.shape)}")
print("PREFLIGHT_OK")
if __name__ == "__main__":
main()
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