-
Notifications
You must be signed in to change notification settings - Fork 126
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Merge pull request #6 from TJ-Solergibert/nanosets_and_datatrove
Supporting datatrove tokenized documents with Nanosets
- Loading branch information
Showing
11 changed files
with
309 additions
and
235 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,80 @@ | ||
| import dataclasses | ||
| from typing import Dict, List, Union | ||
|
|
||
| import numpy as np | ||
| import torch | ||
| from nanotron import distributed as dist | ||
| from nanotron.parallel.context import ParallelContext | ||
| from nanotron.parallel.pipeline_parallel.tensor_pointer import TensorPointer | ||
|
|
||
|
|
||
| @dataclasses.dataclass | ||
| class NanosetDataCollatorForCLM: | ||
| """ | ||
| Data collator used for causal language modeling with Nanosets dataset. | ||
| - input_pp_rank: Discards last input id token | ||
| - output_pp_rank: Discards first label id token | ||
| - other pp ranks: Don't have data. Instead, we use `TensorPointer` to point to the rank having the data. | ||
| """ | ||
|
|
||
| sequence_length: int | ||
| input_pp_rank: int | ||
| output_pp_rank: int | ||
| parallel_context: ParallelContext | ||
|
|
||
| def __call__(self, examples: List[Dict[str, List[np.ndarray]]]) -> Dict[str, Union[torch.Tensor, TensorPointer]]: | ||
| # Process the case when current rank doesn't require data. We return `TensorPointer` that points to ranks having the data. | ||
| current_pp_rank = dist.get_rank(self.parallel_context.pp_pg) | ||
| if current_pp_rank not in [ | ||
| self.input_pp_rank, | ||
| self.output_pp_rank, | ||
| ]: | ||
| assert all(len(example) == 0 for example in examples) | ||
| return { | ||
| "input_ids": TensorPointer(group_rank=self.input_pp_rank), | ||
| "input_mask": TensorPointer(group_rank=self.input_pp_rank), | ||
| "label_ids": TensorPointer(group_rank=self.output_pp_rank), | ||
| "label_mask": TensorPointer(group_rank=self.output_pp_rank), | ||
| } | ||
|
|
||
| # Make sure we load only what's necessary, ie we only load a `input_ids` column. | ||
| assert all(list(example.keys()) == ["input_ids"] for example in examples) | ||
|
|
||
| # TODO @nouamanetazi: Is it better to have examples as np.array or torch.Tensor? | ||
| input_ids = torch.vstack([examples[i]["input_ids"] for i in range(len(examples))]) # (b, s) | ||
| batch_size, expanded_input_length = input_ids.shape | ||
|
|
||
| result: Dict[str, Union[torch.LongTensor, TensorPointer]] = {} | ||
|
|
||
| result["input_ids"] = TensorPointer(group_rank=self.input_pp_rank) | ||
| result["input_mask"] = TensorPointer(group_rank=self.input_pp_rank) | ||
| result["label_ids"] = TensorPointer(group_rank=self.output_pp_rank) | ||
| result["label_mask"] = TensorPointer(group_rank=self.output_pp_rank) | ||
|
|
||
| assert ( | ||
| expanded_input_length == self.sequence_length + 1 | ||
| ), f"Samples should be of length {self.sequence_length + 1} (seq_len+1), but got {expanded_input_length}" | ||
|
|
||
| # Process inputs: last token is the label | ||
| if current_pp_rank == self.input_pp_rank: | ||
| result["input_ids"] = input_ids[:, :-1] | ||
| result["input_mask"] = torch.ones((batch_size, self.sequence_length), dtype=torch.bool) | ||
|
|
||
| # Process labels: shift them to the left | ||
| if current_pp_rank == self.output_pp_rank: | ||
| result["label_ids"] = input_ids[:, 1:] | ||
| result["label_mask"] = torch.ones((batch_size, self.sequence_length), dtype=torch.bool) | ||
|
|
||
| if isinstance(result["input_ids"], torch.Tensor) and result["input_ids"].shape[-1] != self.sequence_length: | ||
| raise ValueError( | ||
| f"`labels` are incorrectly preprocessed. `labels` length is {result['input_ids'].shape[-1]}, but should be" | ||
| f" {self.sequence_length}." | ||
| ) | ||
| if isinstance(result["label_ids"], torch.Tensor) and result["label_ids"].shape[-1] != self.sequence_length: | ||
| raise ValueError( | ||
| f"`labels` are incorrectly preprocessed. `labels` length is {result['label_ids'].shape[-1]}, but should be" | ||
| f" {self.sequence_length}." | ||
| ) | ||
|
|
||
| return result |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Oops, something went wrong.