File size: 3,770 Bytes
6c70b7e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 | # 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))
```
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