tarekziade HF Staff
Publish MIC usage, slim fixture and reviewed Transformers expectations
daf3b50 verified | # MT5: seq2seq generation with Transformers | |
| This page is the Transformers contract of the MT5 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 `google/mt5-small`. 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. | |
| ## Translate a batch | |
| ```py runnable:mt5 | |
| import torch | |
| from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, set_seed | |
| import json # doc-builder: hide | |
| import os # doc-builder: hide | |
| model_id = "google/mt5-small" | |
| 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 generation 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 | |
| # MT5 is multilingual; the "translate English to German:" prefix selects the task. | |
| prompts = [ | |
| "translate English to German: The house is wonderful.", | |
| "translate English to German: I like to read books.", | |
| ] | |
| inputs = tokenizer(prompts, return_tensors="pt", padding=True).to(device) | |
| model.eval() | |
| with torch.no_grad(): | |
| generated = model.generate(**inputs, max_new_tokens=8) | |
| logits = model(**inputs, decoder_input_ids=generated).logits | |
| decoded = tokenizer.batch_decode(generated, skip_special_tokens=True) | |
| print(decoded) | |
| # Probe a few logits at the first generated position of each sequence. | |
| probe = logits[:, 0, :5].float().tolist() | |
| ``` | |
| ## One training step | |
| ```py runnable:mt5:2 | |
| # Teacher forcing: the labels are the tokenized German references. | |
| references = ["Das Haus ist wunderbar.", "Ich lese gerne Bücher."] | |
| labels = tokenizer(references, return_tensors="pt", padding=True).to(device)["input_ids"] | |
| 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 = ( | |
| "encoder.block.0.layer.0.SelfAttention.q.weight", | |
| "decoder.block.0.layer.0.SelfAttention.q.weight", | |
| "decoder.block.0.layer.1.EncDecAttention.q.weight", | |
| ) | |
| result = { | |
| "runtime": { | |
| "device": model.device.type, | |
| "dtype": str(model.dtype).removeprefix("torch."), | |
| "attn_implementation": model.config._attn_implementation, | |
| }, | |
| "decoded_text": decoded, | |
| "probe_logits": 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)) | |
| ``` | |