tarekziade HF Staff
Publish MIC usage, slim fixture and reviewed Transformers expectations
1d300e4 verified | # BART: encoder-decoder forward with Transformers | |
| This page is the Transformers contract of the BART 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 `facebook/bart-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. | |
| ## Run the encoder-decoder | |
| ```py runnable:bart_forward | |
| import torch | |
| from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, set_seed | |
| import json # doc-builder: hide | |
| import os # doc-builder: hide | |
| model_id = "facebook/bart-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 and dropout draw random numbers | |
| tokenizer = AutoTokenizer.from_pretrained(model_id) | |
| model = AutoModelForSeq2SeqLM.from_pretrained(model_id).to(device) | |
| if variant: # doc-builder: hide | |
| del model # doc-builder: hide | |
| model = AutoModelForSeq2SeqLM.from_pretrained(model_id, **variant).to(device) # doc-builder: hide | |
| model.eval() | |
| sentences = [ | |
| "The quick brown fox jumps over the lazy dog.", | |
| "Transformers are useful for many natural language tasks.", | |
| ] | |
| inputs = tokenizer(sentences, return_tensors="pt", padding=True).to(device) | |
| # BART is an encoder-decoder: give the decoder a short target prefix. | |
| decoder_input_ids = torch.full((len(sentences), 1), model.config.decoder_start_token_id, dtype=torch.long, device=device) | |
| with torch.no_grad(): | |
| outputs = model(**inputs, decoder_input_ids=decoder_input_ids) | |
| encoder_shape = list(outputs.encoder_last_hidden_state.shape) | |
| # Probe a few decoder logits rather than the whole vocabulary. | |
| probe = outputs.logits[0, 0, :5].float().tolist() | |
| print(encoder_shape) | |
| print(probe) | |
| ``` | |
| ## One training step | |
| ```py runnable:bart_forward:2 | |
| # Teacher forcing: the model shifts the labels right to build the decoder inputs. | |
| labels = inputs["input_ids"].clone() | |
| labels[labels == tokenizer.pad_token_id] = -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.encoder.layers.0.self_attn.q_proj.weight", | |
| "model.decoder.layers.0.self_attn.q_proj.weight", | |
| "model.shared.weight", | |
| ) | |
| result = { | |
| "runtime": { | |
| "device": model.device.type, | |
| "dtype": str(model.dtype).removeprefix("torch."), | |
| "attn_implementation": model.config._attn_implementation, | |
| }, | |
| "encoder_last_hidden_shape": encoder_shape, | |
| "decoder_logits_probe": probe, | |
| "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)) | |
| ``` | |