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from .vision_encoder import VisionEncoder
from .configuration_moondream import MoondreamConfig
from transformers import PreTrainedModel
from .modeling_phi import PhiForCausalLM
from .configuration_moondream import PhiConfig
class Moondream(PreTrainedModel):
config_class = MoondreamConfig
_supports_flash_attn_2 = True
def __init__(self, config):
super().__init__(config)
self.vision_encoder = VisionEncoder(
use_flash_attn=config._attn_implementation == "flash_attention_2"
)
if type(config.text_config) == dict:
phi_config = PhiConfig(
**config.text_config, attn_implementation=config._attn_implementation
)
else:
phi_config = config.text_config
self.text_model = PhiForCausalLM(phi_config)
@property
def device(self):
return self.text_model.device
def encode_image(self, image):
with torch.no_grad():
return self.vision_encoder(image)
def input_embeds(self, prompt, image_embeds, tokenizer):
def _tokenize(txt):
return tokenizer(
txt, return_tensors="pt", add_special_tokens=False
).input_ids.to(self.device)
text_emb = self.text_model.get_input_embeddings()
# Add BOS token
embeds = []
embeds.append(
text_emb((torch.tensor([[tokenizer.bos_token_id]], device=self.device)))
)
if "<image>" not in prompt:
embeds.append(text_emb(_tokenize(prompt)))
else:
assert prompt.count("<image>") == 1
before, after = prompt.split("<image>")
if len(before) > 0:
embeds.append(text_emb(_tokenize(before)))
embeds.append(image_embeds.to(self.device))
if len(after) > 0:
embeds.append(text_emb(_tokenize(after)))
return torch.cat(embeds, dim=1)
def generate(
self,
image_embeds,
prompt,
tokenizer,
max_new_tokens=128,
**kwargs,
):
generate_config = {
"eos_token_id": tokenizer.eos_token_id,
"bos_token_id": tokenizer.bos_token_id,
"pad_token_id": tokenizer.bos_token_id,
"max_new_tokens": max_new_tokens,
**kwargs,
}
with torch.no_grad():
inputs_embeds = self.input_embeds(prompt, image_embeds, tokenizer)
output_ids = self.text_model.generate(
inputs_embeds=inputs_embeds, **generate_config
)
return tokenizer.batch_decode(output_ids, skip_special_tokens=True)
def answer_question(
self,
image_embeds,
question,
tokenizer,
chat_history="",
result_queue=None,
**kwargs,
):
prompt = f"<image>\n\n{chat_history}Question: {question}\n\nAnswer:"
answer = self.generate(
image_embeds,
prompt,
tokenizer=tokenizer,
max_new_tokens=512,
**kwargs,
)[0]
cleaned_answer = answer.strip()
# Use the result_queue to pass the result if it is provided
if result_queue:
result_queue.put(cleaned_answer)
else:
return cleaned_answer
def batch_answer(
self,
images,
prompts,
tokenizer,
**kwargs,
):
image_embeds = self.encode_image(images)
templated_prompts = [
f"<image>\n\nQuestion: {prompt}\n\nAnswer:" for prompt in prompts
]
prompt_embs = [
self.input_embeds(prompt, image_embed.unsqueeze(0), tokenizer)[0]
for prompt, image_embed in zip(templated_prompts, image_embeds)
]
bos_emb = prompt_embs[0][0]
max_len = max([p.shape[0] for p in prompt_embs])
inputs_embeds = torch.cat(
[
torch.cat([bos_emb.repeat(max_len - p.shape[0], 1), p]).unsqueeze(0)
for p in prompt_embs
],
dim=0,
)
attention_mask = torch.cat(
[
torch.cat(
[
torch.zeros(
1,
max_len - p.shape[0],
device=self.device,
dtype=torch.long,
),
torch.ones(1, p.shape[0], device=self.device, dtype=torch.long),
],
dim=1,
)
for p in prompt_embs
],
dim=0,
)
generate_config = {
"eos_token_id": tokenizer.eos_token_id,
"bos_token_id": tokenizer.bos_token_id,
"pad_token_id": tokenizer.bos_token_id,
"max_new_tokens": 512,
**kwargs,
}
with torch.no_grad():
output_ids = self.text_model.generate(
inputs_embeds=inputs_embeds,
attention_mask=attention_mask,
**generate_config,
)
return [
x.strip()
for x in tokenizer.batch_decode(output_ids, skip_special_tokens=True)
]
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