| from dora import DoraStatus | |
| import pyarrow as pa | |
| from transformers import AutoProcessor, AutoModelForVision2Seq, AwqConfig | |
| import torch | |
| import time | |
| import awq_ext | |
| CAMERA_WIDTH = 960 | |
| CAMERA_HEIGHT = 540 | |
| PROCESSOR = AutoProcessor.from_pretrained("/home/peiji/idefics2-8b-AWQ") | |
| 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( | |
| "/home/peiji/idefics2-8b-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) | |
| return generated_texts[0].split("\nAssistant: ")[1] | |
| class Operator: | |
| def __init__(self): | |
| self.state = "person" | |
| self.last_output = False | |
| def on_event( | |
| self, | |
| dora_event, | |
| send_output, | |
| ) -> DoraStatus: | |
| if dora_event["type"] == "INPUT": | |
| image = ( | |
| dora_event["value"].to_numpy().reshape((CAMERA_HEIGHT, CAMERA_WIDTH, 3)) | |
| ) | |
| if self.state == "person": | |
| output = ask_vlm(image, "Can you read the note?").lower() | |
| print(output, flush=True) | |
| if "coffee" in output or "tea" in output or "water" in output: | |
| send_output( | |
| "control", | |
| pa.array([-3.0, 0.0, 0.0, 0.8, 0.0, 10.0, 180.0]), | |
| ) | |
| send_output( | |
| "speak", | |
| pa.array([output + ". Going to the kitchen."]), | |
| ) | |
| time.sleep(10) | |
| self.state = "coffee" | |
| self.last_output = False | |
| elif not self.last_output: | |
| self.last_output = True | |
| send_output( | |
| "speak", | |
| pa.array([output]), | |
| ) | |
| time.sleep(4) | |
| elif self.state == "coffee": | |
| output = ask_vlm(image, "Is there a person with a hands up?").lower() | |
| print(output, flush=True) | |
| if "yes" in output: | |
| send_output( | |
| "speak", | |
| pa.array([output + ". Going to the office."]), | |
| ) | |
| send_output( | |
| "control", | |
| pa.array([2.0, 0.0, 0.0, 0.8, 0.0, 10.0, 0.0]), | |
| ) | |
| time.sleep(10) | |
| self.state = "person" | |
| self.last_output = False | |
| elif not self.last_output: | |
| self.last_output = True | |
| send_output( | |
| "speak", | |
| pa.array([output]), | |
| ) | |
| time.sleep(4) | |
| return DoraStatus.CONTINUE | |