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from dora import DoraStatus
import pyarrow as pa
from transformers import AutoProcessor, AutoModelForVision2Seq, AwqConfig
import torch
import gc
CAMERA_WIDTH = 1280
CAMERA_HEIGHT = 720
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]
model = AutoModelForVision2Seq.from_pretrained(
"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,
},
),
trust_remote_code=True,
).to("cuda")
def reset_awq_cache(model):
"""
Simple method to reset the AWQ fused modules cache
"""
from awq.modules.fused.attn import QuantAttentionFused
for name, module in model.named_modules():
if isinstance(module, QuantAttentionFused):
module.start_pos = 0
def ask_vlm(image, instruction):
global model
prompts = [
"User:",
image,
f"{instruction}.<end_of_utterance>\n",
"Assistant:",
]
inputs = {k: torch.tensor(v).to("cuda") for k, v in PROCESSOR(prompts).items()}
generated_ids = model.generate(
**inputs,
bad_words_ids=BAD_WORDS_IDS,
max_new_tokens=25,
repetition_penalty=1.2,
)
generated_texts = PROCESSOR.batch_decode(generated_ids, skip_special_tokens=True)
reset_awq_cache(model)
gc.collect()
torch.cuda.empty_cache()
return generated_texts[0].split("\nAssistant: ")[1]
class Operator:
def __init__(self):
self.image = None
self.text = None
def on_event(
self,
dora_event,
send_output,
) -> DoraStatus:
if dora_event["type"] == "INPUT":
if dora_event["id"] == "image":
self.image = (
dora_event["value"]
.to_numpy()
.reshape((CAMERA_HEIGHT, CAMERA_WIDTH, 3))
)
elif dora_event["id"] == "text":
self.text = dora_event["value"][0].as_py()
output = ask_vlm(self.image, self.text).lower()
send_output(
"speak",
pa.array([output]),
)
"""
if "sofa" in output:
send_output(
"control",
pa.array([0.0, 0.0, 0.0, 0.0, 0.0, 50.0, 50.0]),
)
elif "back" in self.text:
send_output(
"control",
pa.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]),
)
"""
return DoraStatus.CONTINUE
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