Merge branch 'main' into flx_attn_support

fixes silly mistake

.github/workflows/multi-gpu-e2e.yml

@@ -4,10 +4,6 @@ on:
   pull_request:
     paths:
       - 'tests/e2e/multigpu/*.py'
-      - 'requirements.txt'
-      - 'setup.py'
-      - 'pyproject.toml'
-      - '.github/workflows/multi-gpu-e2e.yml'
   workflow_dispatch:
   schedule:
     - cron: '0 0 * * 1,4'  # Runs at 00:00 UTC every monday & thursday

cicd/multigpu.py

@@ -37,11 +37,15 @@ temp_dir = tempfile.mkdtemp()
 with open(pathlib.Path(temp_dir) / "Dockerfile", "w", encoding="utf-8") as f:
     f.write(dockerfile_contents)
 
-cicd_image = Image.from_dockerfile(
+cicd_image = (
-    pathlib.Path(temp_dir) / "Dockerfile",
+    Image.from_dockerfile(
-    force_build=True,
+        pathlib.Path(temp_dir) / "Dockerfile",
-    gpu="A10G",
+        force_build=True,
-).env(df_args)
+        gpu="A10G",
+    )
+    .env(df_args)
+    .pip_install("fastapi==0.110.0", "pydantic==2.6.3")
+)
 
 app = App("Axolotl CI/CD", secrets=[])
 

src/axolotl/cli/cloud/modal_.py

@@ -123,6 +123,8 @@ class ModalCloud(Cloud):
         if env := self.get_env():
             image = image.env(env)
 
+        image = image.pip_install("fastapi==0.110.0", "pydantic==2.6.3")
+
         return image
 
     def get_secrets(self):

src/axolotl/core/trainer_builder.py

@@ -59,7 +59,6 @@ from axolotl.core.training_args import (
     AxolotlTrainingArguments,
 )
 from axolotl.integrations.base import PluginManager
-from axolotl.monkeypatch.attention.sequence_parallel import USPRingAttnType, get_extract_fn
 from axolotl.monkeypatch.multipack import SUPPORTED_MULTIPACK_MODEL_TYPES
 from axolotl.monkeypatch.relora import ReLoRACallback
 from axolotl.utils import is_comet_available, is_mlflow_available
@@ -747,11 +746,6 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             # A100 is best at 64, while others at 8. Let's use the larger so we don't have to check
             # https://docs.nvidia.com/deeplearning/performance/dl-performance-matrix-multiplication/index.html
             data_collator_kwargs["pad_to_multiple_of"] = 64
-        if self.cfg.sp_ulysses_degree:
-            data_collator_kwargs["sp_extract_fn"] = get_extract_fn(
-                USPRingAttnType.ZIGZAG,
-                sp_ulysses_degree=self.cfg.sp_ulysses_degree
-            )
 
         if self.cfg.reward_model:
             data_collator_kwargs["max_length"] = self.cfg.sequence_len
@@ -837,7 +831,9 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             if "max_length" in kwargs:
                 kwargs.pop("max_length")
         elif use_batch_sampler_collator:
-            if self.cfg.model_config_type in SUPPORTED_MULTIPACK_MODEL_TYPES:
+            if self.cfg.flex_attention is True:
+                collator = V2BatchSamplerDataCollatorForSeq2Seq
+            elif self.cfg.model_config_type in SUPPORTED_MULTIPACK_MODEL_TYPES:
                 collator = V2BatchSamplerDataCollatorForSeq2Seq
             elif (
                 self.cfg.model_config_type in ["llama"]

src/axolotl/core/training_args.py

@@ -206,16 +206,6 @@ class AxolotlTrainingMixins:
         },
     )
 
-    sp_ulysses_degree: Optional[int] = field(
-        default=None,
-        metadata={"help": "Ulysses parallelism for hybrid sequence parallel long context attn"},
-    )
-
-    sp_ring_degree: Optional[int] = field(
-        default=None,
-        metadata={"help": "Ring attention parallelism for sequence parallel long context attn"},
-    )
-
 
 @dataclass
 class AxolotlTrainingArguments(AxolotlTrainingMixins, TrainingArguments):

src/axolotl/monkeypatch/attention/sequence_parallel/__init__.py

@@ -1,45 +0,0 @@
-from enum import Enum
-from functools import partial
-
-from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS
-from yunchang import set_seq_parallel_pg, EXTRACT_FUNC_DICT
-
-from axolotl.utils.distributed import get_world_size, get_rank
-
-
-class USPRingAttnType(Enum):
-    BASIC = "basic"
-    ZIGZAG = "zigzag"
-    STRIPE = "stripe"
-
-def apply_usp_attn_patch(ring_impl_type: USPRingAttnType):
-    from axolotl.monkeypatch.attention.sequence_parallel.usp import build_usp_fa_forward
-
-    fa_forward = build_usp_fa_forward(ring_impl_type)
-    ALL_ATTENTION_FUNCTIONS["flash_attention_2"] = fa_forward
-
-def get_extract_fn(ring_impl_type: USPRingAttnType, sp_ulysses_degree: int):
-    fn = EXTRACT_FUNC_DICT["basic"]
-    if ring_impl_type.value in EXTRACT_FUNC_DICT.keys():
-        fn = EXTRACT_FUNC_DICT[ring_impl_type.value]
-
-    # map bad key upstream
-    elif ring_impl_type == USPRingAttnType.STRIPE:
-        fn = EXTRACT_FUNC_DICT["strip"]
-
-    world_size = get_world_size()
-    rd = world_size // sp_ulysses_degree
-
-    return partial(fn, rank=get_rank(), world_size=world_size, rd=rd, ud=sp_ulysses_degree)
-
-def set_usp_parallel_group(sp_ulysses_degree):
-    """
-    setup distributed parallel group for USP attention
-    make sure this gets called before building any USP attention modules
-    :param sp_ulysses_degree:
-    :return:
-    """
-    world_size = get_world_size()
-    rank = get_rank()
-    sp_ring_degree = world_size // sp_ulysses_degree
-    set_seq_parallel_pg(sp_ulysses_degree, sp_ring_degree, rank, world_size)

src/axolotl/monkeypatch/attention/sequence_parallel/usp.py

@@ -1,36 +0,0 @@
-from enum import Enum
-from typing import Optional, Tuple, Callable
-
-import torch
-from yunchang import LongContextAttention
-
-from axolotl.monkeypatch.attention.sequence_parallel import USPRingAttnType
-
-
-def build_usp_fa_forward(ring_impl_type: USPRingAttnType) -> Callable:
-    usp_attn = LongContextAttention(ring_impl_type.value)
-
-    def flash_attention_forward(
-            module: torch.nn.Module,  # pylint: disable=unused-argument
-            query: torch.Tensor,
-            key: torch.Tensor,
-            value: torch.Tensor,
-            attention_mask: Optional[torch.Tensor],  # pylint: disable=unused-argument
-            dropout: float = 0.0,
-            scaling: Optional[float] = None,
-            sliding_window: Optional[int] = None,  # pylint: disable=unused-argument
-            softcap: Optional[float] = None,
-            **kwargs,  # pylint: disable=unused-argument
-    ) -> Tuple[torch.Tensor, None]:
-        attn_output = usp_attn(
-            query,
-            key,
-            value,
-            dropout_p=dropout,
-            softmax_scale=scaling,
-            causal=True,
-            softcap=softcap,
-        )
-        return attn_output, None
-
-    return flash_attention_forward

src/axolotl/monkeypatch/utils.py

@@ -95,6 +95,103 @@ def get_cu_seqlens(attn_mask):
     return torch.stack(results).to(dtype=torch.int32), torch.stack(max_seq_lens)
 
 
+def get_packed_mask_from_pos_ids(position_ids):
+    if len(position_ids.shape) == 1:
+        position_ids = position_ids.unsqueeze(0)
+
+    device = position_ids.device
+    results = []
+
+    for i, row in enumerate(position_ids):
+        # Count the number of consecutive zeros from the right side
+        padding_length = (row == 0).int().flip(dims=[0]).cumprod(dim=0).sum().item()
+
+        # Adjust the row to exclude padding
+        adjusted_row = row[:-padding_length] if padding_length else row.clone()
+
+        # Find where the position resets to 0 (indicating a new sequence)
+        seq_starts = torch.cat(
+            [
+                torch.tensor([True], dtype=torch.bool, device=device),
+                adjusted_row[1:] == 0,
+            ]
+        )
+        # Get the indices where the sequence starts
+        start_indices = torch.cat(
+            [
+                torch.nonzero(seq_starts).unbind(dim=1)[0],
+                torch.tensor([len(adjusted_row)], dtype=torch.int32, device=device),
+            ]
+        )
+        # Calculate the sequence lengths
+        seq_lengths = start_indices[1:] - start_indices[:-1]
+        # Append the padding length to the sequence lengths
+        doc_mask = torch.ones(len(row), dtype=torch.int32, device=device)
+        for i, seq_len in enumerate(seq_lengths):
+            start_id = start_indices[i]
+            doc_mask[start_id : start_id + seq_len] = (
+                (i+1) * doc_mask[start_id : start_id + seq_len]
+            )
+        if padding_length:
+            doc_mask[len(adjusted_row) :] = 0 * doc_mask[len(adjusted_row) :]
+
+        results.append(doc_mask)
+
+    return torch.stack(results)
+
+
+def get_seqlens_from_pos_ids(position_ids):
+    """generate a sequence length set using pos ids for doc mask creation in flex attention"""
+    if len(position_ids.shape) == 1:
+        position_ids = position_ids.unsqueeze(0)
+    max_seq_len = position_ids.shape[1]
+
+    device = position_ids.device
+    results = []
+    totalseqlens = []
+
+    for row in position_ids:
+        # Count the number of consecutive zeros from the right side
+        padding_length = (row == 0).int().flip(dims=[0]).cumprod(dim=0).sum().item()
+
+        # Adjust the row to exclude padding
+        adjusted_row = row[:-padding_length] if padding_length else row.clone()
+
+        # Find where the position resets to 0 (indicating a new sequence)
+        seq_starts = torch.cat(
+            [
+                torch.tensor([True], dtype=torch.bool, device=device),
+                adjusted_row[1:] == 0,
+            ]
+        )
+        # Get the indices where the sequence starts
+        start_indices = torch.cat(
+            [
+                torch.nonzero(seq_starts).unbind(dim=1)[0],
+                torch.tensor([len(adjusted_row)], dtype=torch.int32, device=device),
+            ]
+        )
+        # Calculate the sequence lengths
+        seq_lengths = start_indices[1:] - start_indices[:-1]
+        # Append the padding length to the sequence lengths
+        if padding_length:
+            seq_lengths = torch.cat(
+                [
+                    seq_lengths,
+                    torch.tensor(
+                        [len(row) - torch.sum(seq_lengths)],
+                        dtype=torch.int32,
+                        device=device,
+                    ),
+                ]
+            )
+
+        results.append(seq_lengths)
+        totalseqlens.append(len(adjusted_row))
+
+    return results, torch.tensor(totalseqlens, dtype=torch.int32, device=device)
+
+
 def get_cu_seqlens_from_pos_ids(position_ids):
     """generate a cumulative sequence length mask for flash attention using pos ids"""
     if len(position_ids.shape) == 1:
@@ -176,7 +273,10 @@ def mask_2d_to_4d(
     when they attend to each other within that sequence.
     This expansion transforms the mask to lower triangular form to prevent future peeking.
     """
-    bsz, src_len = mask.size()
+
+    if len(mask.size()) == 4:
+        return mask
+    bsz, src_len = int(mask.size()[0]), int(mask.size()[1])
     tgt_len = tgt_len if tgt_len is not None else src_len
 
     mask = mask.unsqueeze(1).unsqueeze(2)

src/axolotl/utils/collators/batching.py

@@ -3,7 +3,7 @@ DataCollator for axolotl to pad labels and position_ids for packed sequences
 """
 
 from dataclasses import dataclass
-from typing import Any, Optional, Union, Callable
+from typing import Any, Optional, Union
 
 import numpy as np
 from transformers import PreTrainedTokenizerBase
@@ -53,7 +53,6 @@ class DataCollatorForSeq2Seq:
     label_pad_token_id: int = -100
     position_pad_token_id: int = 0
     return_tensors: str = "pt"
-    sp_extract_fn: Optional[Callable] = None
 
     def __call__(self, features, return_tensors=None):
         labels = None
@@ -122,10 +121,6 @@ class DataCollatorForSeq2Seq:
 
         return features
 
-    def seq_parallel_split(self, features):
-        if self.sp_extract_fn:
-            pass
-        return features
 
 @dataclass
 class BatchSamplerDataCollatorForSeq2Seq(DataCollatorForSeq2Seq):

src/axolotl/utils/config/models/input/v0_4_1/__init__.py

@@ -823,6 +823,7 @@ class AxolotlInputConfig(
     xformers_attention: Optional[bool] = None
     sdp_attention: Optional[bool] = None
     s2_attention: Optional[bool] = None
+    flex_attention: Optional[bool] = None
     flash_attention: Optional[bool] = None
     flash_attn_cross_entropy: Optional[bool] = None
     flash_attn_rms_norm: Optional[bool] = None
@@ -832,8 +833,6 @@ class AxolotlInputConfig(
 
     eager_attention: Optional[bool] = None
 
-    sp_ulysses_degree: Optional[int] = None
-
     unsloth_cross_entropy_loss: Optional[bool] = None
     unsloth_lora_mlp: Optional[bool] = None
     unsloth_lora_qkv: Optional[bool] = None
@@ -1791,6 +1790,26 @@ class AxolotlConfigWCapabilities(AxolotlInputConfig):
                 )
         return data
 
+    @model_validator(mode="before")
+    @classmethod
+    def check_flex_torch_version(cls, data):
+        if (data.get("flex_attention") is not None) and (
+            data.get("flex_attention") is True
+        ):
+            env_capabilities = data.get("env_capabilities", {})
+            torch_version = env_capabilities.get("torch_version")
+
+            if torch_version is None:
+                import torch
+
+                torch_version = str(torch.__version__).split("+", maxsplit=1)[0]
+
+            if version.parse(torch_version) < version.parse("2.5.1"):
+                raise ValueError(
+                    "Flex attention is not supported on torch version < 2.5.1"
+                )
+        return data
+
     @model_validator(mode="before")
     @classmethod
     def check_torch_compile_auto(cls, data):

src/axolotl/utils/distributed.py

@@ -86,12 +86,6 @@ def get_world_size():
     return int(os.getenv("WORLD_SIZE", "1"))
 
 
-def get_rank():
-    if not is_distributed():
-        return 0
-    return dist.get_rank()
-
-
 @contextmanager
 def zero_only():
     """

src/axolotl/utils/models.py

@@ -403,7 +403,7 @@ class ModelLoader:
 
         if (
             self.cfg.model_config_type in SUPPORTED_MULTIPACK_MODEL_TYPES
-            and self.cfg.flash_attention
+            and (self.cfg.flash_attention or self.cfg.flex_attention)
             and self.cfg.sample_packing
         ):
             if "auto_map" in self.model_config:
@@ -707,7 +707,13 @@ class ModelLoader:
         """
         sample packing uses custom FA2 patch
         """
-        if self.cfg.flash_attention:
+
+        if self.cfg.flex_attention:
+            self.model_kwargs["attn_implementation"] = "flex_attention"
+            self.model_config._attn_implementation = (  # pylint: disable=protected-access
+                "flex_attention"
+            )
+        elif self.cfg.flash_attention:
             if not self.cfg.sample_packing and self.cfg.s2_attention:
                 pass
             self.model_kwargs["attn_implementation"] = "flash_attention_2"
@@ -1113,7 +1119,7 @@ class ModelLoader:
         should_convert = (
             # LlamaRMSNorm layers are in fp32 after kbit_training or full finetune, so we need to
             # convert them back to fp16/bf16 for flash-attn compatibility.
-            ((needs_fa2_dtype or self.cfg.flash_attention) and not qlora_fsdp)
+            ((needs_fa2_dtype or self.cfg.flash_attention or self.cfg.flex_attention) and not qlora_fsdp)
             or self.cfg.cut_cross_entropy  # Cut cross entropy requires embedding layers to be in fp16/bf16 for backward pass
         )
 

src/axolotl/utils/trainer.py

@@ -346,7 +346,7 @@ def process_datasets_for_packing(cfg, train_dataset, eval_dataset):
                     load_from_cache_file=not cfg.is_preprocess,
                     desc="Add position_id column (PoSE)",
                 )
-    elif cfg.sample_packing or cfg.sp_ulysses_degree:
+    elif cfg.sample_packing:
         drop_long_kwargs = {}
         if filter_map_kwargs:
             drop_long_kwargs["desc"] = "Add position_id column (Sample Packing)"