fix expand mask for multiple batch items, make sure we pad position_ids
This commit is contained in:
Wing Lian
@@ -10,21 +10,41 @@ def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int]
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"""
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Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
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"""
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# Move the mask to the CPU
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mask = mask.cpu()
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bsz, src_len = mask.size()
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tgt_len = tgt_len if tgt_len is not None else src_len
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binary_mask = torch.where(
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mask != 0, torch.tensor(1).to(torch.int16), torch.tensor(0).to(torch.int16)
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)
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# Initialize a tensor to hold the expanded masks
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expanded_masks = torch.zeros(bsz, 1, tgt_len, src_len).to(dtype)
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zero_one_mask = torch.eq(mask, mask.t()).int() * binary_mask
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expanded_mask = zero_one_mask.unsqueeze(0).expand(bsz, 1, tgt_len, src_len)
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# For each sequence in the batch
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for i in range(bsz):
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# Get the mask for this sequence
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mask_i = mask[i].unsqueeze(0)
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inverted_mask = 1.0 - expanded_mask
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# Create a binary mask from the original mask where zeros remain zeros and all other values are set to one
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binary_mask_i = torch.where(
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mask_i != 0,
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torch.tensor(1).to(dtype).cpu(),
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torch.tensor(0).to(dtype).cpu(),
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)
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# Create a block-diagonal mask
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zero_one_mask_i = torch.eq(mask_i, mask_i.t()).int() * binary_mask_i
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# Expand the mask
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expanded_mask_i = zero_one_mask_i.unsqueeze(0).expand(1, 1, tgt_len, src_len)
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# Store the expanded mask
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expanded_masks[i] = expanded_mask_i
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inverted_mask = 1.0 - expanded_masks
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return inverted_mask.masked_fill(
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inverted_mask.to(torch.bool), torch.finfo(dtype).min
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)
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).cpu()
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def hijack_expand_mask():
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121
src/axolotl/utils/collators.py
Normal file
121
src/axolotl/utils/collators.py
Normal file
@@ -0,0 +1,121 @@
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"""
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DataCollator for axolotl to pad labels and position_ids for packed sequences
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"""
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from dataclasses import dataclass
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from typing import Any, Optional, Union
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import numpy as np
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from transformers import PreTrainedTokenizerBase
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from transformers.utils import PaddingStrategy
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@dataclass
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class DataCollatorForSeq2Seq:
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"""
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Data collator that will dynamically pad the inputs received, as well as the labels and position_ids
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Args:
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tokenizer ([`PreTrainedTokenizer`] or [`PreTrainedTokenizerFast`]):
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The tokenizer used for encoding the data.
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model ([`PreTrainedModel`]):
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The model that is being trained. If set and has the *prepare_decoder_input_ids_from_labels*, use it to
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prepare the *decoder_input_ids*
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This is useful when using *label_smoothing* to avoid calculating loss twice.
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padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `True`):
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Select a strategy to pad the returned sequences (according to the model's padding side and padding index)
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among:
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- `True` or `'longest'` (default): Pad to the longest sequence in the batch (or no padding if only a single
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sequence is provided).
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- `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
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acceptable input length for the model if that argument is not provided.
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- `False` or `'do_not_pad'`: No padding (i.e., can output a batch with sequences of different lengths).
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max_length (`int`, *optional*):
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Maximum length of the returned list and optionally padding length (see above).
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pad_to_multiple_of (`int`, *optional*):
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If set will pad the sequence to a multiple of the provided value.
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This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >=
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7.5 (Volta).
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label_pad_token_id (`int`, *optional*, defaults to -100):
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The id to use when padding the labels (-100 will be automatically ignored by PyTorch loss functions).
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return_tensors (`str`):
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The type of Tensor to return. Allowable values are "np", "pt" and "tf".
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"""
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tokenizer: PreTrainedTokenizerBase
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model: Optional[Any] = None
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padding: Union[bool, str, PaddingStrategy] = True
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max_length: Optional[int] = None
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pad_to_multiple_of: Optional[int] = None
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label_pad_token_id: int = -100
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position_pad_token_id: int = 0
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return_tensors: str = "pt"
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def __call__(self, features, return_tensors=None):
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labels = None
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if return_tensors is None:
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return_tensors = self.return_tensors
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for feature_name, pad_token_id in [
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("labels", self.label_pad_token_id),
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("position_ids", self.position_pad_token_id),
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]:
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feat = (
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[feature[feature_name] for feature in features]
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if feature_name in features[0].keys()
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else None
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)
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labels = feat if feat and feature_name == "labels" else labels
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# We have to pad the labels before calling `tokenizer.pad` as this method won't pad them and needs them of the
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# same length to return tensors.
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if feat is not None:
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max_feature_length = max(len(l) for l in feat) # noqa: E741
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if self.pad_to_multiple_of is not None:
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max_feature_length = (
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(max_feature_length + self.pad_to_multiple_of - 1)
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// self.pad_to_multiple_of
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* self.pad_to_multiple_of
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)
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padding_side = self.tokenizer.padding_side
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for feature in features:
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remainder = [pad_token_id] * (
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max_feature_length - len(feature[feature_name])
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)
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if isinstance(feature[feature_name], list):
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feature[feature_name] = (
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feature[feature_name] + remainder
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if padding_side == "right"
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else remainder + feature[feature_name]
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)
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elif padding_side == "right":
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feature[feature_name] = np.concatenate(
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[feature[feature_name], remainder]
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).astype(np.int64)
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else:
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feature[feature_name] = np.concatenate(
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[remainder, feature[feature_name]]
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).astype(np.int64)
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features = self.tokenizer.pad(
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features,
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padding=self.padding,
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max_length=self.max_length,
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pad_to_multiple_of=self.pad_to_multiple_of,
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return_tensors=return_tensors,
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)
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# prepare decoder_input_ids
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if (
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labels is not None
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and self.model is not None
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and hasattr(self.model, "prepare_decoder_input_ids_from_labels")
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):
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decoder_input_ids = self.model.prepare_decoder_input_ids_from_labels(
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labels=features["labels"]
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)
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features["decoder_input_ids"] = decoder_input_ids
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return features
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@@ -21,6 +21,7 @@ from axolotl.utils.callbacks import (
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SaveBetterTransformerModelCallback,
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SavePeftModelCallback,
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)
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from axolotl.utils.collators import DataCollatorForSeq2Seq
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from axolotl.utils.schedulers import (
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InterpolatingLogScheduler,
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get_cosine_schedule_with_quadratic_warmup,
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@@ -346,7 +347,7 @@ def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer):
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train_dataset=train_dataset,
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eval_dataset=eval_dataset,
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args=training_args,
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data_collator=transformers.DataCollatorForSeq2Seq(
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data_collator=DataCollatorForSeq2Seq(
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tokenizer,
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return_tensors="pt",
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**data_collator_kwargs,
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