tarekziade HF Staff
Publish MIC usage, slim fixture and reviewed Transformers expectations
3350601 verified | # GLM-OCR: image-to-text with Transformers | |
| This page is the Transformers contract of the GLM-OCR 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 `zai-org/GLM-OCR`. The MIC runner runs the same blocks against the slim fixture and the checkpoint in [`integration.json`](../../integration.json). Lines marked `# doc-builder: hide` only matter there: they swap in the pinned checkpoint or variant and print the result record. | |
| ## Read text from an image | |
| ```py runnable:ocr | |
| import torch | |
| from PIL import Image, ImageDraw | |
| from transformers import AutoModelForImageTextToText, AutoProcessor, set_seed | |
| import json # doc-builder: hide | |
| import os # doc-builder: hide | |
| model_id = "zai-org/GLM-OCR" | |
| 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 | |
| model.eval() | |
| # Two small synthetic document images so the batch perturbation is meaningful. | |
| def make_doc(text): | |
| image = Image.new("RGB", (224, 96), "white") | |
| ImageDraw.Draw(image).text((8, 40), text, fill="black") | |
| return image | |
| images = [make_doc("INVOICE 42"), make_doc("TOTAL 7.50")] | |
| messages = [[{"role": "user", "content": [{"type": "image"}, {"type": "text", "text": "Read the text in this image."}]}] for _ in images] | |
| 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, dtype=model.dtype) | |
| with torch.no_grad(): | |
| logits = model(**inputs).logits | |
| # With right padding the last real token of each row is at its attention_mask sum - 1. | |
| last = inputs["attention_mask"].sum(dim=-1) - 1 | |
| probe_logits = logits[torch.arange(logits.shape[0]), last].float() | |
| predicted = probe_logits.argmax(dim=-1) | |
| print(processor.tokenizer.convert_ids_to_tokens(predicted)) | |
| ``` | |
| ## One training step | |
| ```py runnable:ocr:2 | |
| # Language-model labels: copy the 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()) | |
| checked = ( | |
| "model.language_model.layers.0.self_attn.q_proj.weight", | |
| "model.language_model.layers.0.mlp.down_proj.weight", | |
| "model.visual.blocks.0.attn.qkv.weight", | |
| ) | |
| result = { | |
| "runtime": { | |
| "device": model.device.type, | |
| "dtype": str(model.dtype).removeprefix("torch."), | |
| "attn_implementation": model.config._attn_implementation, | |
| }, | |
| "predicted_tokens": predicted.tolist(), | |
| "answer_logits": probe_logits[:, predicted].tolist(), | |
| "training": { | |
| "loss": loss.item(), | |
| "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}, | |
| "updated": any(not torch.equal(p.detach().cpu(), before[n]) for n, p in model.named_parameters() if n in before), | |
| }, | |
| } | |
| print(json.dumps(result)) | |
| ``` | |