tarekziade HF Staff
Publish MIC usage, slim fixture and reviewed Transformers expectations
de4a0b8 verified | # SwitchTransformers: text-to-text generation with Transformers | |
| This page is the Transformers contract of the SwitchTransformers 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/switch-base-8`. The MIC runner runs the same blocks against the slim fixture and the 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. | |
| ## Generate a batch | |
| ```py runnable:generate | |
| import torch | |
| from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, set_seed | |
| import json # doc-builder: hide | |
| import os # doc-builder: hide | |
| model_id = "google/switch-base-8" | |
| 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 = AutoModelForSeq2SeqLM.from_pretrained(model_id).to(device) | |
| if variant: # doc-builder: hide | |
| model = AutoModelForSeq2SeqLM.from_pretrained(model_id, **variant).to(device) | |
| sentences = [ | |
| "A <extra_id_0> walks into a bar and orders a <extra_id_1> with a pinch of <extra_id_2>.", | |
| "The <extra_id_0> is the best place to <extra_id_1> in town.", | |
| ] | |
| inputs = tokenizer(sentences, return_tensors="pt", padding=True).to(device) | |
| generated = model.generate(**inputs, max_new_tokens=12, do_sample=False) | |
| print(tokenizer.batch_decode(generated, skip_special_tokens=True)) | |
| ``` | |
| ## Decode a batch of token ids | |
| `decode` accepts a batch and returns one string per sequence, cleaning up the spaces the tokenizer puts before punctuation. | |
| ```py runnable:generate:3 | |
| punctuated = ["Yes , it is .", "Hello , world ! How are you ?"] | |
| roundtrip = tokenizer.decode(tokenizer(punctuated).input_ids, skip_special_tokens=True) | |
| print(roundtrip) | |
| ``` | |
| ## One training step | |
| ```py runnable:generate:3 | |
| targets = [ | |
| "A man walks into a bar and orders a beer with a pinch of salt.", | |
| "The park is the best place to relax in town.", | |
| ] | |
| labels = tokenizer(text_target=targets, return_tensors="pt", padding=True).input_ids.to(device) | |
| labels[labels == tokenizer.pad_token_id] = -100 | |
| with torch.no_grad(): | |
| # Teacher-forced log-probabilities of the reference translations. | |
| logprobs = model(**inputs, labels=labels).logits.float().log_softmax(-1) | |
| label_logprobs = logprobs.gather(-1, labels.clamp(min=0)[..., None])[..., 0].masked_fill(labels == -100, 0.0) | |
| model.train() | |
| optimizer = torch.optim.SGD(model.parameters(), lr=0.01) | |
| loss = model(**inputs, labels=labels).loss | |
| loss.backward() | |
| before = {name: p.detach().cpu().clone() for name, 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.1.EncDecAttention.q.weight", | |
| "encoder.block.1.layer.1.mlp.experts.expert_0.wi.weight", | |
| "decoder.block.1.layer.2.mlp.experts.expert_0.wi.weight", | |
| ) | |
| result = { | |
| "runtime": { | |
| "device": model.device.type, | |
| "dtype": str(model.dtype).removeprefix("torch."), | |
| "attn_implementation": model.config._attn_implementation, | |
| }, | |
| "generated_tokens": generated.tolist(), | |
| "generated_text": tokenizer.batch_decode(generated, skip_special_tokens=True), | |
| "label_logprobs": label_logprobs.tolist(), | |
| "decoded_roundtrip": roundtrip, | |
| "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)) | |
| ``` | |