| from dora import DoraStatus |
| import pyarrow as pa |
| from transformers import AutoProcessor, AutoModelForVision2Seq, AwqConfig |
| import torch |
| import time |
|
|
| CAMERA_WIDTH = 960 |
| CAMERA_HEIGHT = 540 |
| 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) |
| 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 |
|
|
