smae code

.gitignore

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+# Byte-compiled / optimized / DLL files
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+*.py[cod]
+*$py.class
+
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+__pypackages__/
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+.DS_Store

smae/README.md

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+# Starbucks Masked Autoencoder
+
+```bash
+python pretrain_bert.py \
+--output_dir checkpoints/bert-base-uncased-fineweb100bt-smae \
+--save_steps 10000 \
+--bf16 \
+--per_device_train_batch_size 32 \
+--gradient_accumulation_steps 2 \
+--learning_rate 1e-4 \
+--lr_scheduler_type cosine \
+--weight_decay 0.001 \
+--warmup_ratio 0.05 \
+--num_train_epochs 1 \
+--logging_steps 100 \
+--mlm_probability 0.2 \
+--decoder_mlm_probability 0.4 \
+--report_to wandb \
+--matryoshka_pretraining True \
+--mae_pretraining True \
+--run_name pretrain-bert-mae-matryoshka-fineweb100bt-starbucks \
+--dataloader_num_workers 16 \
+--num_processes 32 \
+--save_safetensors False \
+--log_level info \
+--logging_nan_inf_filter False
+```

smae/data.py

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+from transformers import BatchEncoding, PreTrainedTokenizer, DataCollatorForWholeWordMask, BertTokenizer, BertTokenizerFast
+from transformers.data.data_collator import _torch_collate_batch
+import warnings
+from torch.utils.data import Dataset
+from datasets import load_dataset
+import random
+import torch
+from dataclasses import dataclass
+from typing import Any, Callable, Dict, List, NewType, Optional, Tuple, Union
+
+
+class MLMDataset(Dataset):
+    def __init__(self, tokenizer: PreTrainedTokenizer, num_processes=16):
+        self.tokenizer = tokenizer
+        self.max_length = self.tokenizer.model_max_length - self.tokenizer.num_special_tokens_to_add(pair=False)
+        self.corpus = load_dataset(
+            'HuggingFaceFW/fineweb',  # hard code for now
+            'sample-100BT',
+            split='train',
+            num_proc=num_processes,
+        )
+
+    def __len__(self):
+        return len(self.corpus)
+
+    def __getitem__(self, item) -> BatchEncoding:
+        text = self.corpus[item]['text']
+
+        # if the text is too long, truncate it randomly
+        tokens = self.tokenizer.tokenize(text)
+
+        if len(tokens) > self.max_length:
+            trunc = len(tokens) - self.max_length
+            trunc_left = random.randint(0, trunc)
+            trunc_right = trunc - trunc_left
+
+            truncated = tokens[trunc_left:]
+            if trunc_right > 0:
+                truncated = truncated[:-trunc_right]
+            text = self.tokenizer.convert_tokens_to_string(truncated)
+
+        tokenized_text = self.tokenizer(text,
+                                        return_special_tokens_mask=False,
+                                        return_token_type_ids=False,
+                                        truncation=True)
+        return tokenized_text
+
+@dataclass
+class DataCollatorForWholeWordMaskWithAttentionMask(DataCollatorForWholeWordMask):
+    def torch_call(self, examples: List[Union[List[int], Any, Dict[str, Any]]]) -> Dict[str, Any]:
+        output = super().torch_call(examples)
+        batch = self.tokenizer.pad(
+            examples,
+            padding=True,
+            return_attention_mask=True,
+            pad_to_multiple_of=self.pad_to_multiple_of,
+            return_tensors='pt',
+        )
+        output['attention_mask'] = batch['attention_mask']
+        return output
+
+
+
+@dataclass
+class MaeDataCollatorForWholeWordMask(DataCollatorForWholeWordMask):
+    encoder_mlm_probability: float = 0.3
+    decoder_mlm_probability: float = 0.5
+
+    def __post_init__(self):
+        super(MaeDataCollatorForWholeWordMask, self).__post_init__()
+
+        from transformers import BertTokenizer, BertTokenizerFast
+        from transformers import RobertaTokenizer, RobertaTokenizerFast
+        if isinstance(self.tokenizer, (BertTokenizer, BertTokenizerFast)):
+            self.whole_word_cand_indexes = self._whole_word_cand_indexes_bert
+        elif isinstance(self.tokenizer, (RobertaTokenizer, RobertaTokenizerFast)):
+            self.whole_word_cand_indexes = self._whole_word_cand_indexes_roberta
+        else:
+            raise NotImplementedError(f'{type(self.tokenizer)} collator not supported yet')
+
+        self.specials = self.tokenizer.all_special_tokens
+
+    def _whole_word_cand_indexes_bert(self, input_tokens: List[str]):
+        cand_indexes = []
+        for (i, token) in enumerate(input_tokens):
+            if token in self.specials:
+                continue
+
+            if len(cand_indexes) >= 1 and token.startswith("##"):
+                cand_indexes[-1].append(i)
+            else:
+                cand_indexes.append([i])
+        return cand_indexes
+
+    def _whole_word_cand_indexes_roberta(self, input_tokens: List[str]):
+        cand_indexes = []
+        for (i, token) in enumerate(input_tokens):
+            if token in self.specials:
+                raise ValueError('We expect only raw input for roberta for current implementation')
+
+            if i == 0:
+                cand_indexes.append([0])
+            elif not token.startswith('\u0120'):
+                cand_indexes[-1].append(i)
+            else:
+                cand_indexes.append([i])
+        return cand_indexes
+
+    def _whole_word_mask(self, input_tokens: List[str], max_predictions=512):
+        """
+        Get 0/1 labels for masked tokens with whole word mask proxy
+        """
+
+        cand_indexes = self._whole_word_cand_indexes_bert(input_tokens)
+
+        random.shuffle(cand_indexes)
+        encoder_num_to_predict = min(max_predictions,
+                                     max(1, int(round(len(input_tokens) * self.encoder_mlm_probability))))
+        decoder_num_to_predict = min(max_predictions,
+                                     max(1, int(round(len(input_tokens) * self.decoder_mlm_probability))))
+
+        masked_lms = []
+        encoder_masked_lms = []
+        decoder_masked_lms = []
+        covered_indexes = set()
+        for index_set in cand_indexes:
+            if len(masked_lms) >= max(encoder_num_to_predict, decoder_num_to_predict):
+                break
+            # If adding a whole-word mask would exceed the maximum number of
+            # predictions, then just skip this candidate.
+            if len(masked_lms) + len(index_set) > max(encoder_num_to_predict, decoder_num_to_predict):
+                continue
+            is_any_index_covered = False
+            for index in index_set:
+                if index in covered_indexes:
+                    is_any_index_covered = True
+                    break
+            if is_any_index_covered:
+                continue
+
+            encoder_add = True if len(encoder_masked_lms) + len(index_set) <= encoder_num_to_predict else False
+            decoder_add = True if len(decoder_masked_lms) + len(index_set) <= decoder_num_to_predict else False
+
+            for index in index_set:
+                covered_indexes.add(index)
+                if encoder_add and decoder_add:
+                    encoder_masked_lms.append(index)
+                if decoder_add:
+                    decoder_masked_lms.append(index)
+                masked_lms.append(index)
+
+        assert len(covered_indexes) == len(masked_lms)
+        encoder_mask_labels = [1 if i in encoder_masked_lms else 0 for i in range(len(input_tokens))]
+        decoder_mask_labels = [1 if i in decoder_masked_lms else 0 for i in range(len(input_tokens))]
+        return encoder_mask_labels, decoder_mask_labels
+
+    def __call__(self, examples, return_tensors=None):
+        batch = self.tokenizer.pad(
+            examples,
+            padding=True,
+            return_attention_mask=True,
+            pad_to_multiple_of=self.pad_to_multiple_of,
+            return_tensors='pt',
+        )
+
+        input_ids = [e["input_ids"] for e in examples]
+
+        encoder_mlm_masks = []
+        decoder_mlm_masks = []
+
+        for e in input_ids:
+            tokens = []
+            for tid in e:
+                tokens.append(self.tokenizer._convert_id_to_token(tid))
+
+            encoder_mlm_mask, decoder_mlm_mask = self._whole_word_mask(tokens, self.tokenizer.model_max_length)
+            encoder_mlm_masks.append(encoder_mlm_mask)
+            decoder_mlm_masks.append(decoder_mlm_mask)
+
+        encoder_mlm_masks = _torch_collate_batch(encoder_mlm_masks, self.tokenizer, pad_to_multiple_of=self.pad_to_multiple_of)
+        decoder_mlm_masks = _torch_collate_batch(decoder_mlm_masks, self.tokenizer, pad_to_multiple_of=self.pad_to_multiple_of)
+
+
+        encoder_inputs, encoder_labels = self.torch_mask_tokens(
+            batch['input_ids'].clone(),
+            encoder_mlm_masks.clone()
+        )
+
+        decoder_inputs, decoder_labels = self.torch_mask_tokens(
+            batch['input_ids'].clone(),
+            decoder_mlm_masks.clone()
+        )
+
+        output = {
+            "input_ids": encoder_inputs,
+            "labels": encoder_labels,
+            "decoder_input_ids": decoder_inputs,
+            "decoder_labels": decoder_labels,
+            "attention_mask": batch['attention_mask'],
+        }
+
+        return output