Spaces:
Runtime error
Runtime error
File size: 4,062 Bytes
577d9ca |
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 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 |
from PIL import Image
import torch
import fire
from processor import MultiModalProcessor
from model.utils.kv_cache import KVCache
from model.multimodal.multimodal_model import PaliGemmaForConditionalGeneration
from load_model import load_hf_model
def move_inputs_to_device(model_inputs: dict, device: str):
model_inputs = {k: v.to(device) for k, v in model_inputs.items()}
return model_inputs
def get_model_inputs(
processor: MultiModalProcessor, prompt: str, image_file_path: str, device: str
):
image = Image.open(image_file_path)
images = [image]
prompts = [prompt]
model_inputs = processor(text=prompts, images=images)
model_inputs = move_inputs_to_device(model_inputs, device)
return model_inputs
def test_inference(
model: PaliGemmaForConditionalGeneration,
processor: MultiModalProcessor,
device: str,
prompt: str,
image_file_path: str,
max_tokens_to_generate: int,
temperature: float,
top_p: float,
do_sample: bool,
):
model_inputs = get_model_inputs(processor, prompt, image_file_path, device)
input_ids = model_inputs["input_ids"]
attention_mask = model_inputs["attention_mask"]
pixel_values = model_inputs["pixel_values"]
kv_cache = KVCache()
stop_token = processor.tokenizer.eos_token_id
generated_tokens = []
for _ in range(max_tokens_to_generate):
outputs = model(
input_ids=input_ids,
pixel_values=pixel_values,
attention_mask=attention_mask,
kv_cache=kv_cache,
)
kv_cache = outputs["kv_cache"]
next_token_logits = outputs["logits"][:, -1, :]
if do_sample:
next_token_logits = torch.softmax(next_token_logits / temperature, dim=-1)
next_token = _sample_top_p(next_token_logits, top_p)
else:
next_token = torch.argmax(next_token_logits, dim=-1, keepdim=True)
assert next_token.size() == (1, 1)
next_token = next_token.squeeze(0)
generated_tokens.append(next_token)
if next_token.item() == stop_token:
break
input_ids = next_token.unsqueeze(-1)
attention_mask = torch.cat(
[attention_mask, torch.ones((1, 1), device=input_ids.device)], dim=-1
)
generated_tokens = torch.cat(generated_tokens, dim=-1)
decoded = processor.tokenizer.decode(generated_tokens, skip_special_tokens=True)
print(decoded)
def _sample_top_p(probs: torch.Tensor, p: float):
probs_sort, probs_idx = torch.sort(probs, dim=-1, descending=True)
probs_sum = torch.cumsum(probs_sort, dim=-1)
mask = probs_sum - probs_sort > p
probs_sort[mask] = 0.0
probs_sort.div_(probs_sort.sum(dim=-1, keepdim=True))
next_token = torch.multinomial(probs_sort, num_samples=1)
next_token = torch.gather(probs_idx, -1, next_token)
return next_token
def main(
model_path: str = None,
prompt: str = None,
image_file_path: str = None,
max_tokens_to_generate: int = 100,
temperature: float = 0.8,
top_p: float = 0.9,
do_sample: bool = False,
only_cpu: bool = False,
):
device = "cpu"
if not only_cpu:
if torch.cuda.is_available():
device = "cuda"
elif torch.backends.mps.is_available():
device = "mps"
print("Device in use: ", device)
print(f"Loading model")
model, tokenizer = load_hf_model(model_path, device)
model = model.to(device).eval()
num_image_tokens = model.config.vision_config.num_image_tokens
image_size = model.config.vision_config.image_size
max_length = 512
processor = MultiModalProcessor(tokenizer, num_image_tokens, image_size, max_length)
print("Running inference")
with torch.no_grad():
test_inference(
model,
processor,
device,
prompt,
image_file_path,
max_tokens_to_generate,
temperature,
top_p,
do_sample,
)
if __name__ == "__main__":
fire.Fire(main) |