remove unnecessary components

src/axolotl/core/trainer_builder.py

@@ -79,7 +79,6 @@ from axolotl.utils.chat_templates import get_chat_template_from_config
 from axolotl.utils.collators import (
     BatchSamplerDataCollatorForSeq2Seq,
     DataCollatorForSeq2Seq,
-    FlexBatchSamplerDataCollatorForSeq2Seq,
     MambaDataCollator,
     V2BatchSamplerDataCollatorForSeq2Seq,
 )
@@ -817,7 +816,6 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             Union[
                 V2BatchSamplerDataCollatorForSeq2Seq,
                 BatchSamplerDataCollatorForSeq2Seq,
-                FlexBatchSamplerDataCollatorForSeq2Seq,
                 DataCollatorForSeq2Seq,
                 DataCollatorWithFlattening,
                 RewardDataCollatorWithPadding,

src/axolotl/monkeypatch/flex_attn.py

@@ -1,146 +0,0 @@
-"""
-Taken from https://github.com/pytorch/torchtune/blob/main/torchtune/modules/attention_utils.py
-"""
-from typing import Union
-
-import torch
-from torch.nn.attention.flex_attention import BlockMask
-from torch.nn.attention.flex_attention import (
-    create_block_mask as create_block_causal_mask_flex,
-)
-
-_MaskType = Union[torch.Tensor, BlockMask]
-
-
-def create_block_causal_mask(
-    seq_lens: list[torch.Tensor], max_seq_len: int
-) -> torch.Tensor:
-    """
-    Given a batch tensor of seq lens defining the lengths of samples in each pack,
-    Construct a 2D block causal mask for each pack in the batch. For example, if
-    a single sample's seq_lens is [3, 2, 1], the mask would be::
-
-        mask = [
-            [1, 0, 0, 0, 0, 0],
-            [1, 1, 0, 0, 0, 0],
-            [1, 1, 1, 0, 0, 0],
-            [0, 0, 0, 1, 0, 0],
-            [0, 0, 0, 1, 1, 0],
-            [0, 0, 0, 0, 0, 1],
-        ]
-
-    Args:
-        seq_lens (List[torch.Tensor]): Sequence lengths of samples in each pack in the batch,
-            shape (batch_size, n), where n is the max number of sequences in a pack and can vary
-            across packs.
-
-
-    Returns:
-        Tensor: Block causal mask of shape (batch_size, max_seq_len, max_seq_len).
-    """
-    batch_block_attn_masks = []
-    batch_size = len(seq_lens)
-    for sample_idx in range(batch_size):
-        block_attn_masks = [
-            torch.trilu( # torch.tril(
-                torch.ones(seq_len, seq_len, dtype=torch.bool, device=seq_len.device)
-            )
-            for seq_len in seq_lens[sample_idx]
-        ]
-
-        """residue_len = max_seq_len - torch.sum(seq_lens[sample_idx])
-        block_attn_masks.append(
-            torch.tril(
-                torch.ones(
-                    residue_len, residue_len, dtype=torch.bool, device=seq_lens[sample_idx].device
-                )
-            )
-        )"""
-
-        batch_block_attn_masks.append(torch.block_diag(*block_attn_masks))
-
-    return torch.stack(batch_block_attn_masks)[:, None, :, :]
-
-
-def _get_document_ids_from_seq_lens(
-    seq_lens: list[torch.Tensor],
-) -> torch.Tensor:
-    """
-    Convert a batch tensor of seq lens into integer IDs denoting sample ownership.
-    For example, seq_lens = [2, 3, 1] would return [0, 0, 1, 1, 1, 2].
-
-    Args:
-        seq_lens (List[torch.Tensor]): Sequence lengths of samples in each pack in the batch,
-            shape (batch_size, n), where n is the max number of sequences in a pack and can vary
-            across packs.
-
-    Returns:
-        Tensor: Document IDs of shape (batch_size, max_seq_len).
-    """
-    batch_size = len(seq_lens)
-    batch_document_ids = []
-    for sample_idx in range(batch_size):
-        # We assume seq lens sum to max seq lens, so document_ids should be of
-        # shape (max_seq_len, )
-        document_ids = torch.cat(
-            [
-                torch.full((seq_len,), i, dtype=torch.long, device=seq_len.device)
-                for i, seq_len in enumerate(seq_lens[sample_idx])
-            ]
-        )
-        batch_document_ids.append(document_ids)
-    batch_document_ids = torch.stack(batch_document_ids)
-    return batch_document_ids
-
-
-def packed_block_causal_mask(
-    seq_lens: list[torch.Tensor], totalseqlens: list[int]
-) -> _MaskType:
-    """
-    Create a block causal document mask for a batch of packed sequences. If
-    flex attention is supported by the current hardware, block causal logic and
-    passing this into :func:`torch.nn.attention.flex_attention.create_block_mask`.
-    The resultant BlockMask is a compressed representation of the full block causal
-    mask. If on an older version, a standard 2D block causal mask is created and returned.
-
-    Args:
-        seq_lens (List[torch.Tensor]): Sequence lengths of samples in each pack in the batch,
-            shape (batch_size, n), where n is the max number of sequences in a pack and can vary
-            across packs.
-
-    Returns:
-        _MaskType: BlockMask or Tensor if torch version < 2.5.0.
-    """
-
-    document_ids = _get_document_ids_from_seq_lens(seq_lens)
-    batch_size , max_seq_len = document_ids.shape
-    document_ids = document_ids.to("cuda")
-    totalseqlens = totalseqlens.to("cuda")
-
-    # Instead of passing a tensor mask, flex attention requires a mask_mod function
-    # that determines which elements of QK^T should be included in the attention
-    # computation prior to the softmax. For sample packing, we need both the
-    # logic for both causal mask and document mask. See PyTorch's official
-    # blog post for more details: https://pytorch.org/blog/flexattention/#mask-mods
-    def mask_mod(b, h, q_idx, kv_idx):
-        """
-        Defines the logic of a block causal mask by combining both a standard causal mask
-        and a block diagonal document mask.
-
-        See :func:`~torchtune.modules.attention_utils.create_block_causal_mask`
-        for an illustration.
-        """
-        causal_mask = q_idx >= kv_idx
-        document_mask = document_ids[b, q_idx] == document_ids[b, kv_idx]
-        finite_mask = q_idx < totalseqlens[b]
-        return causal_mask & document_mask & finite_mask
-
-    return create_block_causal_mask_flex(
-        mask_mod,
-        batch_size,
-        None,
-        max_seq_len,
-        max_seq_len,
-        device="cuda",
-        BLOCK_SIZE=512,
-    )

src/axolotl/utils/collators/__init__.py

@@ -4,7 +4,6 @@ shared axolotl collators for multipack, mamba, multimodal
 from .batching import (  # noqa: F401
     BatchSamplerDataCollatorForSeq2Seq,
     DataCollatorForSeq2Seq,
-    FlexBatchSamplerDataCollatorForSeq2Seq,
     PretrainingBatchSamplerDataCollatorForSeq2Seq,
     V2BatchSamplerDataCollatorForSeq2Seq,
 )

src/axolotl/utils/collators/batching.py

@@ -3,21 +3,12 @@ DataCollator for axolotl to pad labels and position_ids for packed sequences
 """
 
 from dataclasses import dataclass
-from typing import Any, List, Optional, Union
+from typing import Any, Optional, Union
 
 import numpy as np
-import torch
 from transformers import PreTrainedTokenizerBase
 from transformers.utils import PaddingStrategy
 
-from axolotl.monkeypatch.flex_attn import (
-    create_block_causal_mask,
-    packed_block_causal_mask,
-)
-from axolotl.monkeypatch.utils import (
-    get_packed_mask_from_pos_ids,
-)
-
 
 @dataclass
 class DataCollatorForSeq2Seq:
@@ -160,42 +151,6 @@ class BatchSamplerDataCollatorForSeq2Seq(DataCollatorForSeq2Seq):
         return super().__call__(out_features, return_tensors=return_tensors)
 
 
-@dataclass
-class FlexBatchSamplerDataCollatorForSeq2Seq(DataCollatorForSeq2Seq):
-    """
-    Collator for multipack specific to Flex Attention using the BatchSampler
-    """
-
-    def __call__(self, features, return_tensors=None):
-        if not isinstance(features[0], list):
-            features = [features]
-        out_features = [{} for _ in features]
-        for i, features_ in enumerate(features):
-            for feature in features_[0].keys():
-                if feature == "length":
-                    continue
-                elif feature == "attention_mask":
-                    """arrays = [
-                        i * np.array(item[feature])
-                        for i, item in enumerate(features_)
-                        if feature in item
-                    ]
-                    out_features[i][feature] = np.concatenate(arrays)"""
-                    continue
-                else:
-                    arrays = [
-                        np.array(item[feature]) for item in features_ if feature in item
-                    ]
-                    out_features[i][feature] = np.concatenate(arrays)
-        out = super().__call__(out_features, return_tensors=return_tensors)
-
-        # collated_seq_lens, totalseqlens = get_seqlens_from_pos_ids(out["position_ids"])
-        # out["attention_mask"] = packed_block_causal_mask(collated_seq_lens, totalseqlens)
-        out["attention_mask"] = get_packed_mask_from_pos_ids(out["position_ids"])
-        # out["attention_mask"] = create_block_causal_mask(collated_seq_lens, max_seq_len)
-        return out
-
-
 @dataclass
 class V2BatchSamplerDataCollatorForSeq2Seq(DataCollatorForSeq2Seq):
     """