tarekziade HF Staff
Publish MIC usage, slim fixture and reviewed Transformers expectations
fb07290 verified | # ConvBERT: base encoder with Transformers | |
| This page is the Transformers contract of the ConvBERT 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 `YituTech/conv-bert-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. | |
| `YituTech/conv-bert-base` is a pretraining-only checkpoint: it ships `ConvBertModel` (no task head, no tokenizer), so this page runs the encoder and trains a small linear probe on its pooled hidden states. | |
| ## Encode a batch | |
| ```py runnable:convbert | |
| import torch | |
| from transformers import AutoModel, AutoTokenizer, set_seed | |
| import json # doc-builder: hide | |
| import os # doc-builder: hide | |
| model_id = "YituTech/conv-bert-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 = AutoModel.from_pretrained(model_id).to(device) | |
| if variant: # doc-builder: hide | |
| del model # doc-builder: hide | |
| model = AutoModel.from_pretrained(model_id, **variant).to(device) # doc-builder: hide | |
| model.eval() | |
| sentences = [ | |
| "The quick brown fox jumps over the lazy dog.", | |
| "ConvBERT mixes convolution with self-attention in every encoder layer.", | |
| ] | |
| inputs = tokenizer(sentences, return_tensors="pt", padding=True).to(device) | |
| with torch.no_grad(): | |
| outputs = model(**inputs) | |
| hidden = outputs.last_hidden_state | |
| pooled = hidden[:, 0] # first token of each row is the [CLS] summary | |
| print(hidden.shape, pooled.shape) | |
| ``` | |
| ## One training step | |
| ```py runnable:convbert:2 | |
| # A linear probe on pooled hidden states is the only trainable part: the checkpoint has no task head. | |
| probe = torch.nn.Linear(model.config.hidden_size, 2).to(device=device, dtype=model.dtype) | |
| model.train() | |
| optimizer = torch.optim.SGD(list(model.parameters()) + list(probe.parameters()), lr=0.01) | |
| labels = torch.tensor([0, 1], device=device) | |
| outputs = model(**inputs) | |
| logits = probe(outputs.last_hidden_state[:, 0]) | |
| loss = torch.nn.functional.cross_entropy(logits, labels) | |
| 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 = ( | |
| "encoder.layer.0.attention.self.query.weight", | |
| "encoder.layer.0.attention.self.key_conv_attn_layer.depthwise.weight", | |
| "encoder.layer.0.intermediate.dense.weight", | |
| ) | |
| result = { | |
| "runtime": { | |
| "device": model.device.type, | |
| "dtype": str(model.dtype).removeprefix("torch."), | |
| "attn_implementation": model.config._attn_implementation, | |
| }, | |
| "hidden_probe": hidden[0, :3, :2].float().tolist(), | |
| "pooled_probe": pooled[0, :4].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)) | |
| ``` | |