raising value error

* fix: liger swiglu for llama4

* feat: add liger to deepseek v3

* fix: unpack not found

* fix: spelling

* fix: comment out deepseek v3

* fix: retest deepseek

* fix: map glu

* fix: patch model forward

* chore: add temp code to save

* fix: remove deepseek to move into separate PR

cicd/multigpu.py

@@ -68,7 +68,7 @@ def run_cmd(cmd: str, run_folder: str):
 @app.function(
     image=cicd_image,
     gpu=GPU_CONFIG,
-    timeout=60 * 60,
+    timeout=90 * 60,
     cpu=8.0,
     memory=131072 * N_GPUS,
     volumes=VOLUME_CONFIG,

requirements.txt

@@ -12,7 +12,7 @@ liger-kernel==0.5.6
 packaging==23.2
 
 peft==0.15.1
-transformers==4.51.0
+transformers==4.51.1
 tokenizers>=0.21.1
 accelerate==1.6.0
 datasets==3.5.0

src/axolotl/integrations/liger/__init__.py

@@ -185,5 +185,7 @@ class LigerPlugin(BasePlugin):
                 rms_norm=cfg.liger_rms_norm,
                 layer_norm=cfg.liger_layer_norm,
             )
-        elif cfg.model_config_type in ["deepseek_v3"]:
-            raise ValueError(f"Unsupported model config type: {cfg.model_config_type}")
+        else:
+            logging.warning(
+                f"Unsupported model config type: {cfg.model_config_type}. Liger not applied."
+            )

src/axolotl/integrations/liger/models/llama4.py

@@ -3,6 +3,7 @@ Liger FLCE for llama4
 """
 
 import sys
+from copy import deepcopy
 from typing import List, Optional, Tuple, Union
 
 import torch
@@ -158,7 +159,16 @@ def apply_liger_kernel_to_llama4(
     if rms_norm:
         modeling_llama4.Llama4TextRMSNorm = LigerRMSNorm
     if glu_activation:
-        modeling_llama4.Llama4TextMLP = LigerSwiGLUMLP
+
+        def _liger_swiglu_mlp_wrapper(config, intermediate_size=None, **kwargs):
+            "Accepts intermediate_size to pass to LigerSwiGLUMLP"
+            # clone config to avoid modifying the original
+            config = deepcopy(config)
+            if intermediate_size:
+                setattr(config, "intermediate_size", intermediate_size)
+            return LigerSwiGLUMLP(config, **kwargs)
+
+        modeling_llama4.Llama4TextMLP = _liger_swiglu_mlp_wrapper
     if layer_norm:
         modeling_llama4.nn.LayerNorm = LigerLayerNorm
 

src/axolotl/monkeypatch/attention/flex_attn.py

@@ -1,171 +0,0 @@
-"""Flex attention monkey patch"""
-
-import sys
-from typing import Optional, Tuple, Union
-
-import torch
-import transformers
-
-
-def patch_flex_wrapper():
-    # TODO remove this patch when transformers#37285 is merged and in a release
-    is_torch_2_6 = torch.__version__.startswith("2.6")
-    is_transformers_below_4_51 = transformers.__version__ < "4.51.0"
-
-    if not (is_torch_2_6 and is_transformers_below_4_51):
-        return
-
-    from torch.nn.attention.flex_attention import flex_attention
-
-    class WrappedFlexAttention:
-        """
-        We are doing a singleton class so that flex attention is compiled once when it's first called.
-        """
-
-        _instance = None
-        _is_flex_compiled = False
-        _compiled_flex_attention = None
-
-        def __new__(cls, *args, **kwargs):
-            if cls._instance is None:
-                # Create a new instance if one doesn't already exist
-                cls._instance = super().__new__(cls)
-            return cls._instance
-
-        @torch.compiler.disable(recursive=False)
-        def __init__(self):
-            """
-            Initialize or update the singleton instance.
-            """
-            if not self._is_flex_compiled:
-                self._compiled_flex_attention = torch.compile(
-                    flex_attention,
-                    dynamic=False,
-                    mode="max-autotune-no-cudagraphs",
-                    fullgraph=True,
-                )
-                self._is_flex_compiled = True
-
-        def __call__(self):
-            return self._compiled_flex_attention
-
-    transformers.integrations.flex_attention.WrappedFlexAttention = WrappedFlexAttention
-
-
-def patch_flex_make_mask():
-    is_torch_2_6 = torch.__version__.startswith("2.6")
-    is_transformers_eq_4_51 = transformers.__version__ == "4.51.0"
-
-    if not (is_torch_2_6 and is_transformers_eq_4_51):
-        return
-
-    from torch.nn.attention.flex_attention import (
-        BlockMask,
-    )
-    from torch.nn.attention.flex_attention import (
-        create_block_mask as create_block_causal_mask_flex,
-    )
-
-    Offset = Union[torch.Tensor, int]
-
-    def patched_make_flex_block_causal_mask(
-        attention_mask_2d: torch.Tensor,
-        attention_chunk_size: Optional[int] = None,
-        query_length=None,
-        key_length=None,
-        offsets: Optional[Tuple[Offset, Offset]] = None,
-    ) -> "BlockMask":
-        """
-        Create a block causal document mask for a batch of sequences, both packed and unpacked.
-        Create Block causal logic and passing it into :func:`torch.nn.attention.flex_attention.create_block_mask`.
-        The resultant BlockMask is a compressed representation of the full block causal
-        mask. BlockMask is essential for performant computation of flex attention.
-        See: https://pytorch.org/blog/flexattention/
-
-        Args:
-            attention_mask_2d (torch.Tensor): Attention mask for packed and padded sequences
-            of shape (batch_size, total_seq_len). e.g.
-
-            For unpacked sequence:
-            [[1, 1, 1, 1, 0, 0, 0],
-             [1, 1, 1, 1, 1, 0, 0]]
-
-            For packed sequence:
-            [[1, 1, 1, 2, 2, 2, 0],
-             [1, 1, 2, 2, 2, 3, 3]]
-
-        Returns:
-            BlockMask
-        """
-
-        batch_size, total_seq_len = attention_mask_2d.shape
-        if not key_length:
-            key_length = total_seq_len
-        if not query_length:
-            query_length = total_seq_len
-        attention_mask_2d = torch.nn.functional.pad(
-            attention_mask_2d, value=0, pad=(0, key_length)
-        )
-        device = attention_mask_2d.device
-        document_ids = attention_mask_2d.clone()
-
-        if attention_chunk_size is not None:
-            # we create an arange, then we just // by chunk size to get [0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3]
-            document_ids = (document_ids.fill_(1).cumsum(-1) - 1) // (
-                attention_chunk_size
-            )
-
-        # 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 causal_mask_mod(
-            batch_idx, head_idx, q_idx, kv_idx
-        ):  # pylint: disable=unused-argument
-            """
-            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  # not valid when decoding
-            document_mask = (
-                document_ids[batch_idx, q_idx] == document_ids[batch_idx, kv_idx]
-            )
-            padding_mask = attention_mask_2d[batch_idx, q_idx] > 0
-            final_mask = causal_mask & padding_mask & document_mask
-            return final_mask
-
-        if offsets is not None:
-            q_offset = offsets[0]
-            kv_offset = offsets[1]
-
-            def mask_mod(batch_idx, head_idx, q_idx, kv_idx):
-                offset_q = q_idx + q_offset
-                offset_kv = kv_idx + kv_offset
-                return causal_mask_mod(batch_idx, head_idx, offset_q, offset_kv)
-
-        else:
-            mask_mod = causal_mask_mod
-        return create_block_causal_mask_flex(
-            mask_mod=mask_mod,
-            B=batch_size,
-            H=None,  # attention head
-            Q_LEN=query_length,
-            KV_LEN=key_length,
-            device=device,
-            _compile=True,
-        )
-
-    for n in tuple(sys.modules):
-        if ".modeling_" in n and "llama4" not in n:
-            if hasattr(sys.modules[n], "make_flex_block_causal_mask"):
-                sys.modules[n].make_flex_block_causal_mask = (
-                    patched_make_flex_block_causal_mask
-                )
-
-    transformers.integrations.flex_attention.make_flex_block_causal_mask = (
-        patched_make_flex_block_causal_mask
-    )

src/axolotl/utils/models.py

@@ -906,20 +906,7 @@ class ModelLoader:
         """
         sample packing uses custom FA2 patch
         """
-        if self.cfg.flex_attention:
-            self.model_kwargs["attn_implementation"] = "flex_attention"
-            self.model_config._attn_implementation = (  # pylint: disable=protected-access
-                "flex_attention"
-            )
-            from axolotl.monkeypatch.attention.flex_attn import (
-                patch_flex_make_mask,
-                patch_flex_wrapper,
-            )
-
-            patch_flex_wrapper()
-            patch_flex_make_mask()
-
-        elif self.cfg.flash_attention:
+        if self.cfg.flash_attention:
             if not self.cfg.sample_packing and self.cfg.s2_attention:
                 pass
             self.model_kwargs["attn_implementation"] = "flash_attention_2"

src/axolotl/utils/schemas/config.py

@@ -1316,8 +1316,29 @@ class AxolotlConfigWCapabilities(AxolotlInputConfig):
 
             if version.parse(torch_version) < version.parse("2.6.0"):
                 raise ValueError(
-                    "Flex attention is not supported on torch version < 2.6.0"
+                    "Flex attention is not supported on torch version < 2.6.0."
                 )
+            if version.parse(torch_version) < version.parse("2.7.0"):
+                LOG.warning(
+                    f"You are currently using torch version {torch_version}. "
+                    "We recommend using the latest version of torch for flex attention. "
+                    "You may encounter unexpected issues with flex attention on older versions of torch. "
+                    "Please upgrade to the latest stable, or nightly version of torch. "
+                )
+
+            transformers_version = env_capabilities.get("transformers_version")
+            if transformers_version is None:
+                import transformers
+
+                transformers_version = str(transformers.__version__).split(
+                    "+", maxsplit=1
+                )[0]
+
+            if version.parse(transformers_version) < version.parse("4.45.1"):
+                raise ValueError(
+                    "Transformers version < 4.45.1 is not supported with flex attention. "
+                )
+
         return data
 
     @model_validator(mode="before")

tests/e2e/multigpu/test_llama.py

@@ -16,7 +16,7 @@ from transformers.testing_utils import get_torch_dist_unique_port
 
 from axolotl.utils.dict import DictDefault
 
-from tests.e2e.utils import check_tensorboard, require_torch_2_6_0
+from tests.e2e.utils import check_tensorboard, require_torch_2_6_0, require_torch_2_7_0
 
 LOG = logging.getLogger("axolotl.tests.e2e.multigpu")
 os.environ["WANDB_DISABLED"] = "true"
@@ -458,17 +458,11 @@ class TestMultiGPULlama:
         )
 
     @require_torch_2_6_0
-    @pytest.mark.parametrize(
-        "attention_backend",
-        ["flash", "flex"],
-    )
     @pytest.mark.parametrize(
         "fsdp_reshard_after_forward",
         [True, False],
     )
-    def test_fsdp2_packed(
-        self, temp_dir, attention_backend, fsdp_reshard_after_forward
-    ):
+    def test_fsdp2_packed_flash(self, temp_dir, fsdp_reshard_after_forward):
         # pylint: disable=duplicate-code
         cfg = DictDefault(
             {
@@ -509,13 +503,79 @@ class TestMultiGPULlama:
                     "fsdp_reshard_after_forward": fsdp_reshard_after_forward,
                 },
                 "use_tensorboard": True,
+                "flash_attention": True,
             }
         )
-        if attention_backend == "flash":
-            cfg.flash_attention = True
-        elif attention_backend == "flex":
-            cfg.flex_attention = True
+        # write cfg to yaml file
+        Path(temp_dir).mkdir(parents=True, exist_ok=True)
+        with open(Path(temp_dir) / "config.yaml", "w", encoding="utf-8") as fout:
+            fout.write(yaml.dump(cfg.to_dict(), Dumper=yaml.Dumper))
 
+        execute_subprocess_async(
+            [
+                "axolotl",
+                "train",
+                str(Path(temp_dir) / "config.yaml"),
+                "--num-processes",
+                "2",
+                "--main-process-port",
+                f"{get_torch_dist_unique_port()}",
+            ]
+        )
+
+        check_tensorboard(
+            temp_dir + "/runs", "train/train_loss", 2.1, "Train Loss is too high"
+        )
+
+    @require_torch_2_7_0
+    @pytest.mark.parametrize(
+        "fsdp_reshard_after_forward",
+        [True, False],
+    )
+    def test_fsdp2_packed_flex(self, temp_dir, fsdp_reshard_after_forward):
+        # pylint: disable=duplicate-code
+        cfg = DictDefault(
+            {
+                "base_model": "HuggingFaceTB/SmolLM2-135M",
+                "sample_packing": True,
+                "pad_to_sequence_len": True,
+                "sequence_len": 2048,
+                "val_set_size": 0.05,
+                "special_tokens": {
+                    "pad_token": "<|endoftext|>",
+                },
+                "datasets": [
+                    {
+                        "path": "tatsu-lab/alpaca",
+                        "type": "alpaca",
+                    },
+                ],
+                "num_epochs": 1,
+                "max_steps": 2,
+                "micro_batch_size": 4,
+                "gradient_accumulation_steps": 2,
+                "gradient_checkpointing": True,
+                "output_dir": temp_dir,
+                "learning_rate": 0.00001,
+                "optimizer": "adamw_torch_8bit",
+                "lr_scheduler": "cosine",
+                "fsdp": [
+                    "auto_wrap",
+                ],
+                "fsdp_config": {
+                    "fsdp_version": 2,
+                    # "fsdp_forward_prefetch": True,  # not yet implemented in accelerate
+                    "fsdp_offload_params": False,
+                    "fsdp_cpu_ram_efficient_loading": False,
+                    "fsdp_transformer_layer_cls_to_wrap": "LlamaDecoderLayer",
+                    "fsdp_state_dict_type": "SHARDED_STATE_DICT",
+                    "fsdp_auto_wrap_policy": "TRANSFORMER_BASED_WRAP",
+                    "fsdp_reshard_after_forward": fsdp_reshard_after_forward,
+                },
+                "use_tensorboard": True,
+                "flex_attention": True,
+            }
+        )
         # write cfg to yaml file
         Path(temp_dir).mkdir(parents=True, exist_ok=True)
         with open(Path(temp_dir) / "config.yaml", "w", encoding="utf-8") as fout:
@@ -617,12 +677,6 @@ class TestMultiGPULlama:
             temp_dir + "/runs", "train/train_loss", 2.3, "Train Loss is too high"
         )
 
-    # TODO: remove skip once deepspeed regression is fixed
-    # see https://github.com/huggingface/transformers/pull/37324
-    @pytest.mark.skipif(
-        transformers_version_eq("4.51.0"),
-        reason="zero3 is not supported with transformers==4.51.0",
-    )
     @pytest.mark.parametrize(
         "gradient_accumulation_steps",
         [1, 2],

tests/e2e/solo/test_flex.py

@@ -14,7 +14,7 @@ from axolotl.train import train
 from axolotl.utils.config import normalize_config, validate_config
 from axolotl.utils.dict import DictDefault
 
-from ..utils import check_tensorboard, require_torch_2_6_0, with_temp_dir
+from ..utils import check_tensorboard, require_torch_2_7_0, with_temp_dir
 
 LOG = logging.getLogger("axolotl.tests.e2e")
 os.environ["WANDB_DISABLED"] = "true"
@@ -25,7 +25,7 @@ class TestPackedFlex(unittest.TestCase):
     Test case for Packed training of llama models
     """
 
-    @require_torch_2_6_0
+    @require_torch_2_7_0
     @with_temp_dir
     def test_loss_llama(self, temp_dir):
         # pylint: disable=duplicate-code

tests/e2e/utils.py

@@ -33,6 +33,18 @@ def with_temp_dir(test_func):
     return wrapper
 
 
+def require_torch_2_7_0(test_case):
+    """
+    Decorator marking a test that requires torch >= 2.7.0
+    """
+
+    def is_min_2_7_0():
+        torch_version = version.parse(torch.__version__)
+        return torch_version >= version.parse("2.7.0")
+
+    return unittest.skipUnless(is_min_2_7_0(), "test requires torch>=2.7.0")(test_case)
+
+
 def most_recent_subdir(path):
     base_path = Path(path)
     subdirectories = [d for d in base_path.iterdir() if d.is_dir()]