| import torch |
| from transformers import PreTrainedTokenizerBase |
| from typing import Dict, List, Any |
|
|
| class DynamicPaddingDataCollater: |
| def __init__(self, tokenizer: PreTrainedTokenizerBase): |
|
|
| self.tokenizer = tokenizer |
|
|
| if tokenizer.pad_token_id is None: |
| print("Warning: Tokenizer does not have a pad_token_id. Using 0 for input_ids and attention_mask padding.") |
| self.padding_value_input = 0 |
| else: |
| self.padding_value_input = tokenizer.pad_token_id |
|
|
| |
| self.padding_value_label = tokenizer.pad_token_id |
|
|
| def __call__(self, features: List[Dict[str, Any]]) -> Dict[str, torch.Tensor]: |
|
|
| processed_features = [] |
| for feature in features: |
| input_ids = feature["input_ids"] |
| completion_mask = feature["completion_mask"] |
|
|
| prompt_ids = [token for token, is_completion in zip(input_ids, completion_mask) if not is_completion] |
|
|
| label_ids = [token for token, is_completion in zip(input_ids, completion_mask) if is_completion] |
|
|
| processed_features.append({ |
| "prompt_ids": prompt_ids, |
| "label_ids": label_ids, |
|
|
| "original": feature |
| }) |
|
|
| max_prompt_len = max(len(f["prompt_ids"]) for f in processed_features) |
| max_label_len = max(len(f["label_ids"]) for f in processed_features) |
|
|
| padded_prompt_ids = [] |
| padded_input_attention_mask = [] |
| padded_label_ids = [] |
| padded_labels_attention_mask = [] |
|
|
| for feature in processed_features: |
|
|
| prompt_ids = feature["prompt_ids"] |
| label_ids = feature["label_ids"] |
| |
|
|
| num_input_pads = max_prompt_len - len(prompt_ids) |
| padded_prompt_ids.append([self.padding_value_input] * num_input_pads + prompt_ids) |
|
|
| input_attention_mask = [1] * len(prompt_ids) |
| num_input_mask_pads = max_prompt_len - len(input_attention_mask) |
| padded_input_attention_mask.append([0] * num_input_mask_pads + input_attention_mask) |
|
|
| num_label_pads = max_label_len - len(label_ids) |
| padded_label_ids.append(label_ids + [self.padding_value_label] * num_label_pads) |
| |
| labels_attention_mask = [1] * len(label_ids) |
| num_label_mask_pads = max_label_len - len(labels_attention_mask) |
| padded_labels_attention_mask.append(labels_attention_mask + [0] * num_label_mask_pads) |
| |
| batch = { |
| "prompt_ids": torch.tensor(padded_prompt_ids, dtype=torch.long), |
| "prompt_attention_mask": torch.tensor(padded_input_attention_mask, dtype=torch.long), |
| "label_ids": torch.tensor(padded_label_ids, dtype=torch.long), |
| "label_attention_mask": torch.tensor(padded_labels_attention_mask, dtype=torch.long), |
| } |
| |
| batch["raw_samples"] = [f["original"] for f in processed_features] |
|
|
| return batch |
|
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| |
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|
