| # GPT-2: next-token prediction with Transformers |
|
|
| This page is the Transformers contract of the GPT-2 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 `openai-community/gpt2`. 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 next token |
|
|
| ```py runnable:next_token |
| import torch |
| from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed |
| import json # doc-builder: hide |
| import os # doc-builder: hide |
| |
| model_id = "openai-community/gpt2" |
| 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 = AutoTokenizer.from_pretrained(model_id) |
| if tokenizer.pad_token_id is None: |
| tokenizer.pad_token = tokenizer.eos_token |
| model = AutoModelForCausalLM.from_pretrained(model_id).to(device) |
| if variant: # doc-builder: hide |
| model = AutoModelForCausalLM.from_pretrained(model_id, **variant).to(device) |
| |
| prompts = ["The quick brown fox", "Once upon a time"] |
| inputs = tokenizer(prompts, return_tensors="pt", padding=True).to(device) |
| |
| model.eval() |
| with torch.no_grad(): |
| logits = model(input_ids=inputs["input_ids"], attention_mask=inputs["attention_mask"]).logits |
| |
| last_positions = inputs["attention_mask"].sum(dim=-1) - 1 |
| last_logits = logits[torch.arange(logits.size(0)), last_positions] |
| predicted = last_logits.argmax(dim=-1) |
| print(tokenizer.convert_ids_to_tokens(predicted)) |
| ``` |
|
|
| ## One training step |
|
|
| ```py runnable:next_token:2 |
| labels = inputs["input_ids"].clone() |
| labels[inputs["attention_mask"] == 0] = -100 |
| |
| model.train() |
| optimizer = torch.optim.SGD(model.parameters(), lr=0.01) |
| loss = model(input_ids=inputs["input_ids"], attention_mask=inputs["attention_mask"], labels=labels).loss |
| loss.backward() |
| before = {name: p.detach().clone() for name, p in model.named_parameters()} # 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 = ("transformer.h.0.attn.c_attn.weight", "transformer.h.0.mlp.c_fc.weight") |
| top_k = 5 |
| top_logits = last_logits.topk(top_k, dim=-1).values.float().tolist() |
| result = { |
| "runtime": { |
| "device": model.device.type, |
| "dtype": str(next(model.parameters()).dtype).removeprefix("torch."), |
| "attn_implementation": model.config._attn_implementation, |
| }, |
| "predicted_tokens": predicted.tolist(), |
| "predicted_text": tokenizer.convert_ids_to_tokens(predicted), |
| "next_token_logits": top_logits, |
| "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, before[n]) for n, p in named.items()), |
| }, |
| } |
| print(json.dumps(result)) |
| ``` |
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|
