tarekziade HF Staff
Publish MIC usage, slim fixture and reviewed Transformers expectations
6c70b7e verified | # Perceiver: masked language modeling with Transformers | |
| This page is the Transformers contract of the Perceiver 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 `deepmind/language-perceiver`. 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 swap in the pinned checkpoint or variant and print the result record. | |
| ## Predict the masked bytes | |
| ```py runnable:masked_lm | |
| import torch | |
| from transformers import PerceiverTokenizer, PerceiverForMaskedLM, set_seed | |
| import json # doc-builder: hide | |
| import os # doc-builder: hide | |
| model_id = "deepmind/language-perceiver" | |
| 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) | |
| tokenizer = PerceiverTokenizer.from_pretrained(model_id) | |
| model = PerceiverForMaskedLM.from_pretrained(model_id).to(device) | |
| if variant: # doc-builder: hide | |
| del model # doc-builder: hide | |
| model = PerceiverForMaskedLM.from_pretrained(model_id, **variant).to(device) # doc-builder: hide | |
| # Two short sentences; the tokenizer maps each character to its byte value. | |
| # The decoder predicts a fixed number of positions, so pad to that length. | |
| texts = ["The cat sat on the mat.", "A dog ran in the park."] | |
| encoding = tokenizer(texts, padding="max_length", max_length=model.config.max_position_embeddings, return_tensors="pt") | |
| input_ids = encoding["input_ids"].to(device) | |
| attention_mask = encoding["attention_mask"].to(device) | |
| original_ids = input_ids.clone() | |
| # Mask "mat" in the first sentence: characters 19-21, shifted by one for the leading [CLS] token | |
| start = texts[0].index("mat") + 1 | |
| input_ids[0, start : start + 3] = tokenizer.mask_token_id | |
| with torch.no_grad(): | |
| outputs = model(inputs=input_ids, attention_mask=attention_mask) | |
| logits = outputs.logits | |
| # Compact probe: first 5 logits at each masked position | |
| masked = input_ids == tokenizer.mask_token_id | |
| masked_logits = logits[masked] | |
| print(masked_logits[:, :5].tolist()) | |
| ``` | |
| ## One training step | |
| ```py runnable:masked_lm:2 | |
| # Labels: the original bytes; ignore pad positions with -100 | |
| labels = original_ids.clone() | |
| labels[attention_mask == 0] = -100 | |
| model.train() | |
| optimizer = torch.optim.SGD(model.parameters(), lr=0.01) | |
| loss = model(inputs=input_ids, attention_mask=attention_mask, 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 = ("perceiver.input_preprocessor.embeddings.weight", "perceiver.encoder.self_attends.0.attention.self.query.weight") | |
| result = { | |
| "runtime": { | |
| "device": model.device.type, | |
| "dtype": str(model.dtype).removeprefix("torch."), | |
| "attn_implementation": model.config._attn_implementation, | |
| }, | |
| "masked_logits": masked_logits[:, :5].float().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)) | |
| ``` | |