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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,80 @@ | ||
| import dataclasses | ||
| from typing import Dict, List, Union | ||
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| 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 | ||
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| @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. | ||
| """ | ||
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| sequence_length: int | ||
| input_pp_rank: int | ||
| output_pp_rank: int | ||
| parallel_context: ParallelContext | ||
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| 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), | ||
| } | ||
|
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| # 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) | ||
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| # 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 | ||
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| result: Dict[str, Union[torch.LongTensor, TensorPointer]] = {} | ||
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| 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) | ||
|
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||
| 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}" | ||
|
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| # 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) | ||
|
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| # 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) | ||
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| 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}." | ||
| ) | ||
|
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||
| return result |
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