| import io, os | |
| import torch | |
| from huggingface_hub import hf_hub_download | |
| _CACHE = {} | |
| def _wikitext(split): | |
| if split in _CACHE: | |
| return _CACHE[split] | |
| import pandas as pd | |
| fn = hf_hub_download("Salesforce/wikitext", repo_type="dataset", | |
| filename=f"wikitext-2-raw-v1/{split}-00000-of-00001.parquet") | |
| txt = "\n\n".join(pd.read_parquet(fn)["text"].tolist()) | |
| _CACHE[split] = txt | |
| return txt | |
| def calib_batches(tok, nsamples, seqlen, seed=0): | |
| ids = tok(_wikitext("train"), return_tensors="pt").input_ids | |
| g = torch.Generator().manual_seed(seed) | |
| out = [] | |
| for _ in range(nsamples): | |
| i = torch.randint(0, ids.shape[1] - seqlen - 1, (1,), generator=g).item() | |
| out.append(ids[:, i:i + seqlen]) | |
| return out | |
| def test_tokens(tok, seqlen): | |
| ids = tok(_wikitext("test"), return_tensors="pt").input_ids | |
| n = ids.shape[1] // seqlen | |
| return [ids[:, i * seqlen:(i + 1) * seqlen] for i in range(n)] | |