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# Copyright 2024 OpenAccess AI Collective and the LlamaFactory team.
#
# This code is inspired by the OpenAccess AI Collective's axolotl library.
# https://github.com/OpenAccess-AI-Collective/axolotl/blob/main/src/axolotl/monkeypatch/utils.py
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from dataclasses import dataclass
from typing import Any, Dict, Literal, Sequence
import torch
from transformers import DataCollatorForSeq2Seq
def prepare_4d_attention_mask(attention_mask_with_indices: "torch.Tensor", dtype: "torch.dtype") -> "torch.Tensor":
r"""
Expands the attention mask with indices from (batch_size, seq_len) to (batch_size, 1, seq_len, seq_len),
while handles packed sequences and transforms the mask to lower triangular form to prevent future peeking.
e.g.
```
[[1, 1, 2, 2, 2, 0]]
```
->
```
[
[
[
[o, x, x, x, x, x],
[o, o, x, x, x, x],
[x, x, o, x, x, x],
[x, x, o, o, x, x],
[x, x, o, o, o, x],
[x, x, x, x, x, x],
]
]
]
```
where `o` equals to `0.0`, `x` equals to `min_dtype`.
"""
bsz, seq_len = attention_mask_with_indices.size()
min_dtype = torch.finfo(dtype).min
expanded_mask = attention_mask_with_indices[:, None, None, :].expand(bsz, 1, seq_len, seq_len)
# Create a binary mask from the original mask where zeros remain zeros and all other values are set to one
padding_mask = torch.where(expanded_mask != 0, 1, 0)
# Create a block-diagonal mask.
attention_mask_4d = torch.eq(expanded_mask, expanded_mask.transpose(-1, -2)).int() * padding_mask
# Use the lower triangular mask to zero out the upper triangular part
attention_mask_4d *= torch.tril(torch.ones((seq_len, seq_len), dtype=torch.long))
# Invert the attention mask.
attention_mask_4d = torch.where(attention_mask_4d != 0, torch.tensor(0, dtype=dtype), min_dtype)
return attention_mask_4d
@dataclass
class SFTDataCollatorWith4DAttentionMask(DataCollatorForSeq2Seq):
r"""
Data collator for 4d attention mask.
"""
block_diag_attn: bool = False
attn_implementation: Literal["eager", "sdpa", "flash_attention_2"] = "eager"
compute_dtype: "torch.dtype" = torch.float32
def __call__(self, features: Sequence[Dict[str, Any]]) -> Dict[str, "torch.Tensor"]:
features = super().__call__(features)
if self.block_diag_attn and self.attn_implementation != "flash_attention_2":
features["attention_mask"] = prepare_4d_attention_mask(features["attention_mask"], self.compute_dtype)
return features
@dataclass
class PairwiseDataCollatorWithPadding(DataCollatorForSeq2Seq):
r"""
Data collator for pairwise data.
"""
def __call__(self, features: Sequence[Dict[str, Any]]) -> Dict[str, "torch.Tensor"]:
r"""
Pads batched data to the longest sequence in the batch.
We generate 2 * n examples where the first n examples represent chosen examples and
the last n examples represent rejected examples.
"""
concatenated_features = []
for key in ("chosen", "rejected"):
for feature in features:
target_feature = {
"input_ids": feature["{}_input_ids".format(key)],
"attention_mask": feature["{}_attention_mask".format(key)],
"labels": feature["{}_labels".format(key)],
}
if "pixel_values" in feature:
target_feature["pixel_values"] = feature["pixel_values"]
if "{}_token_type_ids".format(key) in feature:
target_feature["token_type_ids"] = feature["{}_token_type_ids".format(key)]
concatenated_features.append(target_feature)
return super().__call__(concatenated_features)
@dataclass
class KTODataCollatorWithPadding(DataCollatorForSeq2Seq):
r"""
Data collator for KTO data.
"""
def __call__(self, features: Sequence[Dict[str, Any]]) -> Dict[str, "torch.Tensor"]:
target_features = []
kl_features = []
kto_tags = []
for feature in features:
target_feature = {
"input_ids": feature["input_ids"],
"attention_mask": feature["attention_mask"],
"labels": feature["labels"],
}
kl_feature = {
"input_ids": feature["kl_input_ids"],
"attention_mask": feature["kl_attention_mask"],
"labels": feature["kl_labels"],
}
if "pixel_values" in feature:
target_feature["pixel_values"] = feature["pixel_values"]
if "token_type_ids" in feature:
target_feature["token_type_ids"] = feature["token_type_ids"]
kl_feature["token_type_ids"] = feature["kl_token_type_ids"]
target_features.append(target_feature)
kl_features.append(kl_feature)
kto_tags.append(feature["kto_tags"])
batch = super().__call__(target_features)
kl_batch = super().__call__(kl_features)
batch["kl_input_ids"] = kl_batch["input_ids"]
batch["kl_attention_mask"] = kl_batch["attention_mask"]
batch["kl_labels"] = kl_batch["labels"]
if "token_type_ids" in batch:
batch["kl_token_type_ids"] = kl_batch["token_type_ids"]
batch["kto_tags"] = torch.tensor(kto_tags)
return batch
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