tarekziade HF Staff
Publish MIC usage, slim fixture and reviewed Transformers expectations
4680f62 verified | # LUKE: masked language modeling with entity awareness | |
| This page is the Transformers contract of the LUKE 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 `studio-ousia/luke-base`. 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 word | |
| ```py runnable:luke_fill_mask | |
| import torch | |
| from transformers import AutoModelForMaskedLM, AutoTokenizer, set_seed | |
| import json # doc-builder: hide | |
| import os # doc-builder: hide | |
| model_id = "studio-ousia/luke-base" | |
| 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 | |
| tokenizer = AutoTokenizer.from_pretrained(model_id) | |
| model = AutoModelForMaskedLM.from_pretrained(model_id).to(device) | |
| if variant: # doc-builder: hide | |
| del model # doc-builder: hide | |
| model = AutoModelForMaskedLM.from_pretrained(model_id, **variant).to(device) # doc-builder: hide | |
| countries = ["France", "Germany", "Italy", "the United Kingdom"] | |
| prompts = [f"The capital of {country} is {tokenizer.mask_token}." for country in countries] | |
| inputs = tokenizer(prompts, return_tensors="pt", padding=True).to(device) | |
| # LUKE expects entity inputs; use a single dummy entity per example | |
| batch_size = inputs["input_ids"].shape[0] | |
| entity_ids = torch.zeros((batch_size, 1), dtype=torch.long, device=device) | |
| entity_attention_mask = torch.ones((batch_size, 1), dtype=torch.long, device=device) | |
| entity_token_type_ids = torch.zeros((batch_size, 1), dtype=torch.long, device=device) | |
| entity_position_ids = torch.zeros((batch_size, 1, 1), dtype=torch.long, device=device) | |
| with torch.no_grad(): | |
| outputs = model( | |
| **inputs, | |
| entity_ids=entity_ids, | |
| entity_attention_mask=entity_attention_mask, | |
| entity_token_type_ids=entity_token_type_ids, | |
| entity_position_ids=entity_position_ids, | |
| ) | |
| logits = outputs.logits | |
| entity_logits = outputs.entity_logits | |
| masked = inputs["input_ids"] == tokenizer.mask_token_id | |
| predicted = logits[masked].argmax(dim=-1) | |
| print(tokenizer.convert_ids_to_tokens(predicted)) | |
| ``` | |
| ## One training step | |
| ```py runnable:luke_fill_mask:2 | |
| answers = ["paris", "berlin", "rome", "london"] | |
| answer_ids = tokenizer.convert_tokens_to_ids(answers) | |
| labels = torch.full_like(inputs["input_ids"], -100) | |
| labels[masked] = torch.tensor(answer_ids, device=device) | |
| model.train() | |
| optimizer = torch.optim.SGD(model.parameters(), lr=0.01) | |
| loss = model( | |
| **inputs, | |
| entity_ids=entity_ids, | |
| entity_attention_mask=entity_attention_mask, | |
| entity_token_type_ids=entity_token_type_ids, | |
| entity_position_ids=entity_position_ids, | |
| 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 = ( | |
| "luke.encoder.layer.0.attention.self.query.weight", | |
| "luke.encoder.layer.0.intermediate.dense.weight", | |
| "lm_head.dense.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": logits[masked][:, answer_ids].float().tolist(), | |
| "entity_logits": entity_logits[:, 0, :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)) | |
| ``` | |