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# Copyright 2024 Musab Gultekin and the LlamaFactory team.
#
# This code is based on the Musab Gultekin's functionary library.
# https://github.com/MeetKai/functionary/blob/main/functionary/train/packing/monkey_patch_packing.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.
#
# MIT License
#
# Copyright (c) 2023 Musab Gultekin
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
from typing import TYPE_CHECKING, Tuple
import torch
import torch.nn.functional as F
import transformers.models
from transformers.utils.versions import require_version
from ...extras.constants import SUPPORTED_CLASS_FOR_BLOCK_DIAG_ATTN
from ...extras.logging import get_logger
if TYPE_CHECKING:
from transformers import PretrainedConfig
from ...hparams import ModelArguments
logger = get_logger(__name__)
def get_seqlens_in_batch(attention_mask: "torch.Tensor") -> "torch.Tensor":
r"""
Gets the sequnce lengths in the current batch.
e.g.
```python
# input
[
[1, 1, 2, 2, 2, 0],
[1, 2, 2, 3, 3, 3],
]
# output
[2, 3, 1, 2, 3]
```
"""
bsz = attention_mask.size(0)
dtype, device = attention_mask.dtype, attention_mask.device
max_num = torch.max(attention_mask).item()
counts: "torch.Tensor" = torch.zeros((bsz, max_num), dtype=dtype, device=device)
for i in range(max_num):
counts[:, i] = torch.sum(attention_mask == (i + 1), dim=-1)
counts = counts.flatten()
seqlens = counts[counts.nonzero().squeeze(dim=-1)]
return seqlens
def get_unpad_data(attention_mask: "torch.Tensor") -> Tuple["torch.Tensor", "torch.Tensor", int]:
r"""
Prepares the indices and seqlens for flash attn varlen function.
Returns:
indices: indices of non-masked tokens from the flattened sequence.
cu_seqlens: the cumulative sequence lengths in the current batch, always starts from 0.
max_seqlen_in_batch: the largest seqlen in the current batch.
e.g.
```python
# input
[
[1, 1, 2, 2, 2, 0],
[1, 2, 2, 3, 3, 3],
]
# output
[0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11]
[0, 2, 5, 6, 8, 11]
3
```
"""
seqlens_in_batch = get_seqlens_in_batch(attention_mask)
indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
max_seqlen_in_batch = seqlens_in_batch.max().item()
cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0))
return indices, cu_seqlens, max_seqlen_in_batch
def _patch_for_block_diag_attn(model_type: str) -> None:
require_version("transformers>=4.41.2,<=4.42.4", "To fix: pip install transformers>=4.41.2,<=4.42.4")
if model_type == "cohere":
transformers.models.cohere.modeling_cohere._get_unpad_data = get_unpad_data
elif model_type == "falcon":
transformers.models.falcon.modeling_falcon._get_unpad_data = get_unpad_data
elif model_type == "gemma":
transformers.models.gemma.modeling_gemma._get_unpad_data = get_unpad_data
elif model_type == "gemma2":
transformers.models.gemma2.modeling_gemma2._get_unpad_data = get_unpad_data
elif model_type == "llama":
transformers.models.llama.modeling_llama._get_unpad_data = get_unpad_data
elif model_type == "mistral":
transformers.models.mistral.modeling_mistral._get_unpad_data = get_unpad_data
elif model_type == "phi":
transformers.models.phi.modeling_phi._get_unpad_data = get_unpad_data
elif model_type == "phi3":
transformers.models.phi3.modeling_phi3._get_unpad_data = get_unpad_data
elif model_type == "qwen2":
transformers.models.qwen2.modeling_qwen2._get_unpad_data = get_unpad_data
elif model_type == "starcoder2":
transformers.models.starcoder2.modeling_starcoder2._get_unpad_data = get_unpad_data
def configure_packing(config: "PretrainedConfig", model_args: "ModelArguments", is_trainable: bool) -> None:
if not is_trainable or not model_args.block_diag_attn:
return
model_type = getattr(config, "model_type", None)
if model_type in SUPPORTED_CLASS_FOR_BLOCK_DIAG_ATTN:
_patch_for_block_diag_attn(model_type)
logger.info("Using block diagonal attention for sequence packing without cross-attention.")
else:
raise ValueError("Current model does not support block diagonal attention.")
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