tarekziade HF Staff
Publish MIC usage, slim fixture and reviewed Transformers expectations
5d21eb3 verified | # MuseGlimmer: image-conditioned text generation with Transformers | |
| This page is the Transformers contract of the MuseGlimmer MIC and also a usage example. [doc-builder](https://github.com/huggingface/doc-builder/blob/main/docs/runnable-code-blocks.md) renders the `runnable` blocks below as documentation, and its pytest plugin runs them as written against `optimumintel/tiny-random-muse-glimmer`. The MIC runner runs the same blocks against each slim fixture and each checkpoint in [`integration.json`](../../integration.json). Lines marked `# doc-builder: hide` only matter there: they pin the checkpoint or variant and print the result record. | |
| ## Describe two images | |
| ```py runnable:describe | |
| import json # doc-builder: hide | |
| import os # doc-builder: hide | |
| import numpy as np | |
| import torch | |
| from transformers import AutoModelForImageTextToText, AutoProcessor, set_seed | |
| model_id = "optimum-intel-internal-testing/tiny-random-muse-glimmer" | |
| model_id = os.environ.get("MIC_MODEL", model_id) # doc-builder: hide | |
| variant = json.loads(os.environ.get("MIC_MODEL_KWARGS", "{}")) # doc-builder: hide | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| set_seed(0) # seeds torch, NumPy, and Python: loading, dropout, and data augmentation draw random numbers | |
| processor = AutoProcessor.from_pretrained(model_id) | |
| model = AutoModelForImageTextToText.from_pretrained(model_id).to(device) | |
| if variant: # doc-builder: hide | |
| del model # doc-builder: hide | |
| model = AutoModelForImageTextToText.from_pretrained(model_id, **variant).to(device) # doc-builder: hide | |
| # Two synthetic images that differ in colour, not only in texture. | |
| images = [ | |
| np.full((56, 56, 3), (200, 40, 40), dtype=np.uint8), | |
| np.full((56, 56, 3), (40, 40, 200), dtype=np.uint8), | |
| ] | |
| messages = [ | |
| [ | |
| {"role": "user", "content": [ | |
| {"type": "image", "image": images[0]}, | |
| {"type": "text", "text": "What colour is this image?"}, | |
| ]}, | |
| ], | |
| [ | |
| {"role": "user", "content": [ | |
| {"type": "image", "image": images[1]}, | |
| {"type": "text", "text": "What colour is this image?"}, | |
| ]}, | |
| ], | |
| ] | |
| texts = [ | |
| processor.apply_chat_template(m, add_generation_prompt=True, tokenize=False) | |
| for m in messages | |
| ] | |
| inputs = processor(text=texts, images=images, padding=True, return_tensors="pt") | |
| inputs = inputs.to(device) | |
| if "pixel_values" in inputs: | |
| inputs["pixel_values"] = inputs["pixel_values"].to(dtype=model.dtype) | |
| model.eval() | |
| with torch.no_grad(): | |
| outputs = model(**inputs) | |
| # Probe the last position of each sequence (left padding puts the real last token at -1). | |
| probe_logits = outputs.logits[:, -1, :] | |
| predicted = probe_logits.argmax(dim=-1) | |
| print(processor.batch_decode(predicted.unsqueeze(-1), skip_special_tokens=True)) | |
| ``` | |
| ## One training step | |
| ```py runnable:describe:2 | |
| # Causal-LM labels: copy input_ids and mask padded positions. | |
| labels = inputs["input_ids"].clone() | |
| labels[inputs["attention_mask"] == 0] = -100 | |
| model.train() | |
| optimizer = torch.optim.SGD([p for p in model.parameters() if p.requires_grad], lr=0.01) | |
| loss = model(**inputs, labels=labels).loss | |
| loss.backward() | |
| before = {n: p.detach().cpu().clone() for n, p in model.named_parameters() if p.requires_grad} # doc-builder: hide | |
| optimizer.step() | |
| print(f"loss: {loss.item():.4f}") | |
| if os.environ.get("MIC_RUNNER"): # doc-builder: hide | |
| named = dict(model.named_parameters()) # doc-builder: hide | |
| checked = ( # doc-builder: hide | |
| "model.language_model.layers.0.self_attn.q_proj.weight", # doc-builder: hide | |
| "model.vision_adapter.fc1.weight", # doc-builder: hide | |
| ) # doc-builder: hide | |
| result = { # doc-builder: hide | |
| "runtime": { # doc-builder: hide | |
| "device": model.device.type, # doc-builder: hide | |
| "dtype": str(model.dtype).removeprefix("torch."), # doc-builder: hide | |
| "attn_implementation": model.config._attn_implementation, # doc-builder: hide | |
| }, # doc-builder: hide | |
| "predicted_tokens": predicted.tolist(), # doc-builder: hide | |
| "predicted_text": processor.batch_decode(predicted.unsqueeze(-1), skip_special_tokens=True), # doc-builder: hide | |
| "answer_logits": probe_logits[:, predicted].float().tolist(), # doc-builder: hide | |
| "training": { # doc-builder: hide | |
| "loss": loss.item(), # doc-builder: hide | |
| "gradients": {n: bool(named[n].grad is not None and torch.count_nonzero(named[n].grad) > 0 and torch.isfinite(named[n].grad).all()) for n in checked}, # doc-builder: hide | |
| "updated": any(not torch.equal(p.detach().cpu(), before[n]) for n, p in model.named_parameters() if n in before), # doc-builder: hide | |
| }, # doc-builder: hide | |
| } # doc-builder: hide | |
| print(json.dumps(result)) # doc-builder: hide | |
| ``` | |