| import torch |
|
|
|
|
| from transformers import AutoProcessor, AutoModelForVision2Seq, AwqConfig |
|
|
| import numpy as np |
| import pyttsx3 |
|
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|
|
| START_TO_COUCH = np.array([[0.5, 0], [0.5, 0.5]]).ravel() |
| COUCH_TO_KITCHEN = np.array([[0.5, -0.5], [1.0, -1.0]]).ravel() |
| KITCHEN_TO_START = np.array([[0.5, -0.5], [0, 0]]).ravel() |
|
|
| engine = pyttsx3.init("espeak") |
| voices = engine.getProperty("voices") |
| engine.setProperty("voice", voices[3].id) |
|
|
|
|
| def speak(text): |
| print(f"said {text}", flush=True) |
| engine.say(text) |
| engine.runAndWait() |
|
|
|
|
| speak("hello") |
|
|
| MODE = "fused_quantized" |
| DEVICE = "cuda" |
| 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] |
|
|
| |
| if MODE == "regular": |
| model = AutoModelForVision2Seq.from_pretrained( |
| "HuggingFaceM4/idefics2-tfrm-compatible", |
| torch_dtype=torch.float16, |
| trust_remote_code=True, |
| _attn_implementation="flash_attention_2", |
| revision="3dc93be345d64fb6b1c550a233fe87ddb36f183d", |
| ).to(DEVICE) |
| elif MODE == "quantized": |
| quant_path = "HuggingFaceM4/idefics2-tfrm-compatible-AWQ" |
| model = AutoModelForVision2Seq.from_pretrained( |
| quant_path, trust_remote_code=True |
| ).to(DEVICE) |
| elif MODE == "fused_quantized": |
| quant_path = "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, |
| }, |
| ) |
| model = AutoModelForVision2Seq.from_pretrained( |
| quant_path, quantization_config=quantization_config, trust_remote_code=True |
| ).to(DEVICE) |
| else: |
| raise ValueError("Unknown mode") |
|
|
|
|
| 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): |
| prompts = [ |
| "User:", |
| image, |
| f"{instruction}.<end_of_utterance>\n", |
| "Assistant:", |
| ] |
| speak(instruction) |
| inputs = PROCESSOR(prompts) |
| inputs = {k: torch.tensor(v).to(DEVICE) for k, v in inputs.items()} |
|
|
| generated_ids = model.generate( |
| **inputs, bad_words_ids=BAD_WORDS_IDS, max_new_tokens=50 |
| ) |
| generated_texts = PROCESSOR.batch_decode(generated_ids, skip_special_tokens=True) |
|
|
| text = generated_texts[0].split("\nAssistant: ")[1] |
| reset_awq_cache(model) |
| speak(text) |
| return text |
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