use yet-another-deepspeed branch from transformers#37324

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

@@ -24,13 +24,6 @@ jobs:
       fail-fast: false
       matrix:
         include:
-          - cuda: 124
-            cuda_version: 12.4.1
-            python_version: "3.11"
-            pytorch: 2.6.0
-            axolotl_extras: vllm
-            num_gpus: 2
-            nightly_build: "true"
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
@@ -45,6 +38,13 @@ jobs:
             axolotl_extras: vllm
             num_gpus: 2
             nightly_build: "true"
+          - cuda: 124
+            cuda_version: 12.4.1
+            python_version: "3.11"
+            pytorch: 2.6.0
+            axolotl_extras: vllm
+            num_gpus: 2
+            nightly_build: "true"
     runs-on: [self-hosted, modal]
     timeout-minutes: 120
     steps:

.github/workflows/tests.yml

@@ -211,7 +211,7 @@ jobs:
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
-            pytorch: 2.6.0
+            pytorch: 2.5.1
             num_gpus: 1
             axolotl_extras: vllm
     steps:
@@ -258,7 +258,7 @@ jobs:
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
-            pytorch: 2.5.1
+            pytorch: 2.6.0
             num_gpus: 1
             axolotl_extras: vllm
     steps:

examples/llama4/scout-lora.yaml

@@ -1,75 +0,0 @@
-base_model: meta-llama/Llama-4-Scout-17B-16E
-model_type: Llama4ForConditionalGeneration
-  # Automatically upload checkpoint and final model to HF
-  # hub_model_id: username/custom_model_name
-
-strict: false
-
-  # torch_compile: true
-
-adapter: lora
-lora_r: 32
-lora_alpha: 64
-lora_target_modules:
-  - self_attn.q_proj
-  - self_attn.k_proj
-  - self_attn.v_proj
-  - self_attn.o_proj
-lora_modules_to_save:
-  - lm_head
-  - embed_tokens
-
-chat_template: llama4
-datasets:
-  - path: mlabonne/FineTome-100k
-    type: chat_template
-    split: train[:20%]
-    field_messages: conversations
-    message_property_mappings:
-      role: from
-      content: value
-
-dataset_prepared_path: last_run_prepared
-val_set_size: 0.0
-output_dir: ./outputs/out
-
-sequence_len: 4096
-sample_packing: true
-pad_to_sequence_len: true
-
-gradient_accumulation_steps: 1
-micro_batch_size: 1
-num_epochs: 1
-optimizer: adamw_torch_8bit
-lr_scheduler: cosine
-learning_rate: 2e-5
-
-bf16: true
-tf32: true
-
-# gradient_checkpointing: true
-# gradient_checkpointing_kwargs:
-#   use_reentrant: false
-logging_steps: 1
-flash_attention: true
-
-warmup_steps: 100
-evals_per_epoch: 2
-saves_per_epoch: 1
-weight_decay: 0.0
-fsdp:
-  - auto_wrap
-  - full_shard
-fsdp_config:
-  fsdp_version: 2
-  fsdp_offload_params: false
-  fsdp_cpu_ram_efficient_loading: true
-  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
-  fsdp_transformer_layer_cls_to_wrap: Llama4TextDecoderLayer
-  fsdp_state_dict_type: SHARDED_STATE_DICT
-  fsdp_sharding_strategy: FULL_SHARD
-  fsdp_reshard_after_forward: true
-  fsdp_activation_checkpointing: true
-special_tokens:
-  pad_token: <|finetune_right_pad_id|>
-  eos_token: <|eot|>

requirements.txt

@@ -6,19 +6,18 @@ triton>=3.0.0
 mamba-ssm==1.2.0.post1
 xformers>=0.0.23.post1
 autoawq==0.2.7.post3
-liger-kernel==0.5.6
+liger-kernel==0.5.5
 # END section
 
 packaging==23.2
 
-peft==0.15.1
+peft==0.15.0
-transformers==4.51.0
+transformers @ git+https://github.com/huggingface/transformers.git@yet-another-deepspeed
 tokenizers>=0.21.1
 accelerate==1.6.0
 datasets==3.5.0
-deepspeed>=0.15.4
+deepspeed==0.15.4
-trl==0.16.1
+trl==0.16.0
-hf_xet==1.0.0
 
 optimum==1.16.2
 hf_transfer

src/axolotl/core/trainers/base.py

@@ -562,19 +562,6 @@ class AxolotlTrainer(
 
         return res
 
-    def additional_accelerator_args(
-        self, fp8=None, **kwargs
-    ):  # pylint: disable=unused-argument
-        ret_kwargs = {}
-        if fp8:
-            from accelerate.utils import AORecipeKwargs
-
-            ret_kwargs["mixed_precision"] = "fp8"
-            ret_kwargs["kwargs_handlers"] = [AORecipeKwargs()]
-            os.environ["ACCELERATE_MIXED_PRECISION"] = "fp8"
-
-        return ret_kwargs
-
     def log(self, logs: dict[str, float], start_time: float | None = None) -> None:
         """
         Log `logs` on the various objects watching training, including stored metrics.

src/axolotl/integrations/liger/__init__.py

@@ -27,7 +27,6 @@ from axolotl.integrations.base import BasePlugin
 
 from ...utils.distributed import zero_only
 from .args import LigerArgs  # pylint: disable=unused-import. # noqa: F401
-from .utils import patch_with_compile_disable
 
 LOG = logging.getLogger("axolotl.integrations.liger")
 
@@ -41,18 +40,6 @@ class LigerPlugin(BasePlugin):
         return "axolotl.integrations.liger.LigerArgs"
 
     def pre_model_load(self, cfg):
-        if cfg.torch_compile:
-            # torch compile will unnecessarily attempt to optimize the triton kernel unless explicitly disabled
-            import liger_kernel.ops.fused_linear_cross_entropy
-
-            patch_with_compile_disable(
-                liger_kernel.ops.fused_linear_cross_entropy,
-                "fused_linear_cross_entropy_forward",
-            )
-            patch_with_compile_disable(
-                liger_kernel.ops.fused_linear_cross_entropy,
-                "fused_linear_cross_entropy_backward",
-            )
         from liger_kernel.transformers.cross_entropy import LigerCrossEntropyLoss
         from liger_kernel.transformers.functional import liger_cross_entropy
         from liger_kernel.transformers.geglu import LigerGEGLUMLP
@@ -173,17 +160,5 @@ class LigerPlugin(BasePlugin):
                 raise NotImplementedError(
                     "Fused linear cross entropy is not yet supported for Gemma3."
                 )
-        elif cfg.model_config_type == "llama4":
-            from axolotl.integrations.liger.models.llama4 import (
-                apply_liger_kernel_to_llama4,
-            )
-
-            apply_liger_kernel_to_llama4(
-                cross_entropy=cfg.liger_cross_entropy,
-                fused_linear_cross_entropy=cfg.liger_fused_linear_cross_entropy,
-                glu_activation=cfg.liger_glu_activation,
-                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}")

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

@@ -1,171 +0,0 @@
-"""
-Liger FLCE for llama4
-"""
-
-import sys
-from typing import List, Optional, Tuple, Union
-
-import torch
-from liger_kernel.transformers.model.loss_utils import LigerForCausalLMLoss
-from transformers.modeling_outputs import CausalLMOutputWithPast
-
-
-def lce_forward(
-    self,
-    input_ids: torch.LongTensor = None,
-    attention_mask: Optional[torch.Tensor] = None,
-    position_ids: Optional[torch.LongTensor] = None,
-    past_key_values: Optional[
-        Union["Cache", List[torch.FloatTensor]]  # noqa: F821
-    ] = None,
-    inputs_embeds: Optional[torch.FloatTensor] = None,
-    labels: Optional[torch.LongTensor] = None,
-    use_cache: Optional[bool] = None,
-    output_attentions: Optional[bool] = None,
-    output_hidden_states: Optional[bool] = None,
-    return_dict: Optional[bool] = None,
-    cache_position: Optional[torch.LongTensor] = None,
-    logits_to_keep: Union[int, torch.Tensor] = 0,
-    **loss_kwargs,
-) -> Union[Tuple, CausalLMOutputWithPast]:
-    r"""
-    Args:
-        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
-            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
-            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
-
-        logits_to_keep (`int` or `torch.Tensor`, *optional*):
-            If an `int`, compute logits for the last `logits_to_keep` tokens. If `0`, calculate logits for all
-            `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
-            token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
-            If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension.
-            This is useful when using packed tensor format (single dimension for batch and sequence length).
-
-    Returns:
-    """
-
-    # pylint: disable=duplicate-code
-    output_attentions = (
-        output_attentions
-        if output_attentions is not None
-        else self.config.output_attentions
-    )
-    output_hidden_states = (
-        output_hidden_states
-        if output_hidden_states is not None
-        else self.config.output_hidden_states
-    )
-    return_dict = (
-        return_dict if return_dict is not None else self.config.use_return_dict
-    )
-
-    # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
-    outputs = self.model(
-        input_ids=input_ids,
-        attention_mask=attention_mask,
-        position_ids=position_ids,
-        past_key_values=past_key_values,
-        inputs_embeds=inputs_embeds,
-        use_cache=use_cache,
-        output_attentions=output_attentions,
-        output_hidden_states=output_hidden_states,
-        return_dict=return_dict,
-        cache_position=cache_position,
-    )
-
-    hidden_states = outputs[0]
-
-    if hasattr(self.config, "pretraining_tp") and self.config.pretraining_tp > 1:
-        raise Exception(  # pylint: disable=broad-exception-raised
-            "Liger Kernel does not support pretraining_tp!!"
-        )
-
-    logits = None
-    loss = None
-    # if in training mode, don't materialize logits
-    if self.training and (labels is not None):
-        loss = LigerForCausalLMLoss(
-            hidden_states=hidden_states,
-            lm_head_weight=self.lm_head.weight,
-            labels=labels,
-            hidden_size=self.config.hidden_size,
-            **loss_kwargs,
-        )
-
-    else:  # if in inference mode materialize logits
-        slice_indices = (
-            slice(-logits_to_keep, None)
-            if isinstance(logits_to_keep, int)
-            else logits_to_keep
-        )
-        logits = self.lm_head(hidden_states[:, slice_indices, :])
-        if labels is not None:
-            loss = self.loss_function(
-                logits=logits,
-                labels=labels,
-                vocab_size=self.config.vocab_size,
-                **loss_kwargs,
-            )
-
-    if not return_dict:
-        output = (logits,) + outputs[1:]
-        return (loss,) + output if loss is not None else output
-
-    return CausalLMOutputWithPast(
-        loss=loss,
-        logits=logits,
-        past_key_values=outputs.past_key_values,
-        hidden_states=outputs.hidden_states,
-        attentions=outputs.attentions,
-    )
-
-
-def apply_liger_kernel_to_llama4(
-    cross_entropy: bool = False,
-    fused_linear_cross_entropy: bool = False,
-    rms_norm: bool = False,
-    glu_activation: bool = False,
-    layer_norm: bool = False,
-    **kwargs,  # pylint: disable=unused-argument
-) -> None:
-    """
-    Apply Liger kernels to replace original implementation in HuggingFace Llama models (2 and 3)
-
-    Args:
-        cross_entropy (bool): Whether to apply Liger's cross entropy loss. Default is False.
-        fused_linear_cross_entropy (bool):
-            Whether to apply Liger's fused linear cross entropy loss. Default is False.
-            `cross_entropy` and `fused_linear_cross_entropy` cannot both be False.
-            If `fused_linear_cross_entropy` is True, the logits will not be materialized but more memory efficient.
-        rms_norm (bool): Whether to apply Liger's RMSNorm. Default is False.
-        glu_activation (bool): Whether to apply Liger's SwiGLU MLP. Default is False.
-        layer_norm (bool): Whether to apply Liger's LayerNorm. Default is False.
-    """
-
-    import transformers.models.llama4.modeling_llama4  # noqa: F401  # pylint: disable=unused-import
-    from liger_kernel.transformers.functional import liger_cross_entropy
-    from liger_kernel.transformers.layer_norm import LigerLayerNorm
-    from liger_kernel.transformers.rms_norm import LigerRMSNorm
-    from liger_kernel.transformers.swiglu import LigerSwiGLUMLP
-
-    assert not (
-        cross_entropy and fused_linear_cross_entropy
-    ), "cross_entropy and fused_linear_cross_entropy cannot both be True."
-
-    modeling_llama4 = sys.modules["transformers.models.llama4.modeling_llama4"]
-
-    if rms_norm:
-        modeling_llama4.Llama4TextRMSNorm = LigerRMSNorm
-    if glu_activation:
-        modeling_llama4.Llama4TextMLP = LigerSwiGLUMLP
-    if layer_norm:
-        modeling_llama4.nn.LayerNorm = LigerLayerNorm
-
-    if cross_entropy:
-        from transformers.loss.loss_utils import nn
-
-        nn.functional.cross_entropy = liger_cross_entropy
-
-    if fused_linear_cross_entropy:
-        modeling_llama4.Llama4ForCausalLM.forward = lce_forward

src/axolotl/integrations/liger/utils.py

@@ -1,29 +0,0 @@
-"""
-utils to patch liger kernel ops to disable torch.compile
-"""
-
-from functools import wraps
-
-import torch
-
-
-def patch_with_compile_disable(module, function_name):
-    """
-    Patch a function in a module by wrapping it with torch.compile.disable
-
-    Args:
-        module: The module containing the function to patch
-        function_name: The name of the function to patch
-    """
-    original_function = getattr(module, function_name)
-
-    @wraps(original_function)
-    @torch.compiler.disable
-    def wrapped_function(*args, **kwargs):
-        return original_function(*args, **kwargs)
-
-    # Replace the original function with the wrapped one
-    setattr(module, function_name, wrapped_function)
-
-    # Return the original function in case you need to restore it later
-    return original_function

src/axolotl/monkeypatch/attention/flex_attn.py

@@ -1,171 +1,48 @@
 """Flex attention monkey patch"""
 
-import sys
-from typing import Optional, Tuple, Union
-
 import torch
 import transformers
 
 
-def patch_flex_wrapper():
+def patch_flex():
-    # 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):
+    if is_torch_2_6 and is_transformers_below_4_51:
-        return
+        from torch.nn.attention.flex_attention import flex_attention
 
-    from torch.nn.attention.flex_attention import flex_attention
+        class WrappedFlexAttention:
-
-    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.
+            We are doing a singleton class so that flex attention is compiled once when it's first called.
             """
-            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):
+            _instance = None
-            return self._compiled_flex_attention
+            _is_flex_compiled = False
+            _compiled_flex_attention = None
 
-    transformers.integrations.flex_attention.WrappedFlexAttention = WrappedFlexAttention
+            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 patch_flex_make_mask():
+            def __call__(self):
-    is_torch_2_6 = torch.__version__.startswith("2.6")
+                return self._compiled_flex_attention
-    is_transformers_eq_4_51 = transformers.__version__ == "4.51.0"
 
-    if not (is_torch_2_6 and is_transformers_eq_4_51):
+        transformers.integrations.flex_attention.WrappedFlexAttention = (
-        return
+            WrappedFlexAttention
-
-    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/monkeypatch/trainer_accelerator_args.py

@@ -1,80 +0,0 @@
-"""
-allow adding additional kwargs to Accelerator init
-"""
-
-import inspect
-import logging
-
-from transformers import Trainer
-
-from axolotl.monkeypatch.utils import detab_code
-
-LOG = logging.getLogger(__name__)
-
-ORIGINAL_TRAINER_CODE = """
-    # create accelerator object
-    self.accelerator = Accelerator(**args)
-"""
-
-PATCHED_TRAINER_CODE = """
-    if hasattr(self, "additional_accelerator_args"):
-        additional_args = self.additional_accelerator_args(fp8=True, **args)
-        if additional_args:
-            args.update(additional_args)
-
-    # create accelerator object
-    self.accelerator = Accelerator(**args)
-"""
-
-
-def get_create_accelerate_code() -> str:
-    training_loop = inspect.getsource(Trainer.create_accelerator_and_postprocess)
-    return training_loop
-
-
-def check_create_accelerate_code_is_patchable() -> bool:
-    create_code = get_create_accelerate_code()
-    create_code, _ = detab_code(create_code)
-    return ORIGINAL_TRAINER_CODE in create_code
-
-
-def patch_create_accelerate_code_for_fp8():
-    """
-    monkeypatch create_accelerator_and_postprocess so it checks for additional kwargs
-    """
-
-    try:
-        create_code = get_create_accelerate_code()
-    except OSError:
-        return
-    Trainer._original_create_accelerator_and_postprocess = (  # pylint: disable=protected-access
-        create_code
-    )
-    create_code, _ = detab_code(create_code)
-    if ORIGINAL_TRAINER_CODE not in create_code:
-        return
-
-    create_code = create_code.replace(ORIGINAL_TRAINER_CODE, PATCHED_TRAINER_CODE)
-    create_code = create_code.replace(
-        "def create_accelerator_and_postprocess(",
-        "def fixed_create_accelerator_and_postprocess(",
-        1,
-    )
-
-    # load imports necessary
-    import transformers.trainer
-
-    items_to_import = []
-    for item in dir(transformers.trainer):
-        if item in create_code:
-            items_to_import.append(item)
-
-    exec(  # pylint: disable=exec-used  # nosec B102
-        "from transformers.trainer import ("
-        + ", ".join(x for x in items_to_import)
-        + ")",
-        globals(),
-    )
-    exec(create_code, globals())  # pylint: disable=exec-used  # nosec B102
-    LOG.info("patching create_accelerator_and_postprocess to allow for overrides")
-    Trainer.create_accelerator_and_postprocess = fixed_create_accelerator_and_postprocess  # pylint: disable=protected-access  # pylint: disable=undefined-variable  # noqa: F821

src/axolotl/train.py

@@ -217,7 +217,7 @@ def save_trained_model(
 
     # Handle FSDP state dict type
     state_dict_type = "FULL_STATE_DICT"
-    if trainer.is_fsdp_enabled and str(cfg.fsdp_config.fsdp_version) != "2":
+    if trainer.is_fsdp_enabled:
         if cfg.fsdp_final_state_dict_type:
             state_dict_type = cfg.fsdp_final_state_dict_type
         trainer.accelerator.state.fsdp_plugin.set_state_dict_type(state_dict_type)

src/axolotl/utils/models.py

@@ -557,14 +557,6 @@ class ModelLoader:
         plugin_manager = PluginManager.get_instance()
         plugin_manager.pre_model_load(self.cfg)
 
-        # monkey patch to allow additional Accelerator init kwargs
-        if self.cfg.fp8:
-            from axolotl.monkeypatch.trainer_accelerator_args import (
-                patch_create_accelerate_code_for_fp8,
-            )
-
-            patch_create_accelerate_code_for_fp8()
-
         if self.cfg.adapter:
             from axolotl.monkeypatch.transformers_fa_utils import (
                 patch_fa_peft_integration,
@@ -897,13 +889,9 @@ class ModelLoader:
             self.model_config._attn_implementation = (  # pylint: disable=protected-access
                 "flex_attention"
             )
-            from axolotl.monkeypatch.attention.flex_attn import (
+            from axolotl.monkeypatch.attention.flex_attn import patch_flex
-                patch_flex_make_mask,
-                patch_flex_wrapper,
-            )
 
-            patch_flex_wrapper()
+            patch_flex()
-            patch_flex_make_mask()
 
         elif self.cfg.flash_attention:
             if not self.cfg.sample_packing and self.cfg.s2_attention:
@@ -996,11 +984,10 @@ class ModelLoader:
             )
             skip_move_to_device = True
         elif (
-            self.model_config.model_type in ["llama", "llama4"]
+            self.model_config.model_type == "llama"
             and not self.cfg.trust_remote_code
             and not self.cfg.gptq
         ):
-            # TODO do we need to open this up for all models?
             if self.cfg.fsdp and self.cfg.fsdp_config.fsdp_cpu_ram_efficient_loading:
                 skip_move_to_device = True
                 if "device_map" in self.model_kwargs:

src/axolotl/utils/schemas/config.py

@@ -169,7 +169,6 @@ class AxolotlInputConfig(
 
     bf16: Literal["auto"] | bool | None = "auto"
     fp16: bool | None = None
-    fp8: bool | None = None
     bfloat16: bool | None = None  # for non-AMP cases
     float16: bool | None = None  # for non-AMP cases
     tf32: bool | None = None
@@ -465,10 +464,9 @@ class AxolotlInputConfig(
             data.get("sample_packing")
             and not data.get("flash_attention")
             and not data.get("sdp_attention")
-            and not data.get("flex_attention")
         ):
             LOG.warning(
-                "sample_packing without flash, sdp or flex attention does not handle cross sample decontamination."
+                "sample_packing without flash_attention or sdp_attention does not handle cross-attention."
             )
 
         return data
@@ -952,23 +950,10 @@ class AxolotlInputConfig(
             and "8bit" in data.get("optimizer", "")
             and data.get("fsdp_config")
             and data["fsdp_config"].get("fsdp_offload_params")
-            and str(data["fsdp_config"].get("fsdp_version")) != "2"
         ):
             raise ValueError(
                 f"FSDP Offload not compatible with {data.get('optimizer')}"
             )
-        if (
-            data.get("fsdp")
-            and "8bit" in data.get("optimizer", "")
-            and data.get("fsdp_config")
-            and str(data["fsdp_config"].get("fsdp_version")) == "2"
-        ):
-            if data.get("optimizer", "") in ["adamw_8bit", "adamw_bnb_8bit"]:
-                # CUDA ops errors with bnb 8bit optimizer + FSDP2
-                raise ValueError(
-                    f"FSDP2 not compatible with {data.get('optimizer')}, use `adamw_torch_8bit` instead"
-                )
-
         return data
 
     @model_validator(mode="before")

src/axolotl/utils/schemas/enums.py

@@ -26,7 +26,6 @@ class ChatTemplate(str, Enum):
     gemma = "gemma"  # pylint: disable=invalid-name
     cohere = "cohere"  # pylint: disable=invalid-name
     llama3 = "llama3"  # pylint: disable=invalid-name
-    llama4 = "llama4"  # pylint: disable=invalid-name
     llama3_2_vision = "llama3_2_vision"  # pylint: disable=invalid-name
     phi_3 = "phi_3"  # pylint: disable=invalid-name
     phi_35 = "phi_35"  # pylint: disable=invalid-name

src/axolotl/utils/trainer.py

@@ -538,8 +538,6 @@ def setup_deepspeed_env(cfg, stage=None):
 
 def setup_fsdp_envs(cfg):
     os.environ["ACCELERATE_USE_FSDP"] = "true"
-    if str(cfg.fsdp_config.fsdp_version) == "2":
-        os.environ["FSDP_VERSION"] = "2"
     if cfg.fsdp_config.fsdp_activation_checkpointing:
         os.environ["FSDP_ACTIVATION_CHECKPOINTING"] = "true"
     if cfg.fsdp_config.fsdp_offload_params:
@@ -558,10 +556,6 @@ def setup_fsdp_envs(cfg):
         os.environ["FSDP_TRANSFORMER_CLS_TO_WRAP"] = (
             cfg.fsdp_config.fsdp_transformer_layer_cls_to_wrap
         )
-    if cfg.fsdp_config.fsdp_reshard_after_forward is not None:
-        os.environ["FSDP_RESHARD_AFTER_FORWARD"] = (
-            "true" if cfg.fsdp_config.fsdp_reshard_after_forward else "false"
-        )
 
 
 def prepare_optim_env(cfg):
@@ -582,9 +576,7 @@ def prepare_optim_env(cfg):
 
     setup_torch_compile_env(cfg)
 
-    if cfg.fp8:
+    if (cfg.bf16 == "auto" and is_torch_bf16_gpu_available()) or cfg.bf16 is True:
-        os.environ["ACCELERATE_MIXED_PRECISION"] = "fp8"
-    elif (cfg.bf16 == "auto" and is_torch_bf16_gpu_available()) or cfg.bf16 is True:
         os.environ["ACCELERATE_MIXED_PRECISION"] = "bf16"
     elif cfg.fp16:
         os.environ["ACCELERATE_MIXED_PRECISION"] = "fp16"

tests/e2e/multigpu/test_llama.py

@@ -7,16 +7,14 @@ import os
 from pathlib import Path
 
 import pytest
-import transformers
 import yaml
 from accelerate.test_utils import execute_subprocess_async
 from huggingface_hub import snapshot_download
-from packaging import version
 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
 
 LOG = logging.getLogger("axolotl.tests.e2e.multigpu")
 os.environ["WANDB_DISABLED"] = "true"
@@ -30,10 +28,6 @@ def download_model():
     snapshot_download("HuggingFaceTB/SmolLM2-135M")
 
 
-def transformers_version_eq(required_version):
-    return version.parse(transformers.__version__) == version.parse(required_version)
-
-
 class TestMultiGPULlama:
     """
     Test case for Llama models using LoRA
@@ -114,7 +108,7 @@ class TestMultiGPULlama:
                 "lora_alpha": 16,
                 "lora_dropout": 0.05,
                 "lora_target_linear": True,
-                "val_set_size": 0.05,
+                "val_set_size": 0.01,
                 "special_tokens": {
                     "pad_token": "<|endoftext|>",
                 },
@@ -122,7 +116,6 @@ class TestMultiGPULlama:
                     {
                         "path": "tatsu-lab/alpaca",
                         "type": "alpaca",
-                        "split": "train[:20%]",
                     },
                 ],
                 "num_epochs": 1,
@@ -457,86 +450,6 @@ class TestMultiGPULlama:
             temp_dir + "/runs", "train/train_loss", 2.3, "Train Loss is too high"
         )
 
-    @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
-    ):
-        # 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,
-            }
-        )
-        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"
-        )
-
     def test_fsdp_qlora_prequant_packed(self, temp_dir):
         # pylint: disable=duplicate-code
         cfg = DictDefault(
@@ -617,12 +530,6 @@ 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],
@@ -852,9 +759,6 @@ class TestMultiGPULlama:
             temp_dir + "/runs", "train/train_loss", 2.3, "Train Loss is too high"
         )
 
-    @pytest.mark.skip(
-        reason="fix untrained tokens brittle with lots of edge cases in latest transformers"
-    )
     def test_fix_untrained_tokens(self, temp_dir):
         # pylint: disable=duplicate-code
         cfg = DictDefault(
@@ -893,7 +797,7 @@ class TestMultiGPULlama:
                 "sample_packing": True,
                 "bf16": True,
                 "save_safetensors": True,
-                # "deepspeed": str(AXOLOTL_ROOT / "deepspeed_configs/zero1.json"),
+                "deepspeed": str(AXOLOTL_ROOT / "deepspeed_configs/zero3_bf16.json"),
                 "use_tensorboard": True,
             }
         )

tests/e2e/multigpu/test_ray.py

@@ -31,7 +31,7 @@ class TestMultiGPURay:
         cfg = DictDefault(
             {
                 "base_model": "HuggingFaceTB/SmolLM2-135M",
-                "sequence_len": 1024,
+                "sequence_len": 2048,
                 "adapter": "lora",
                 "lora_r": 8,
                 "lora_alpha": 16,
@@ -94,8 +94,8 @@ class TestMultiGPURay:
                 "base_model": "HuggingFaceTB/SmolLM2-135M",
                 "sample_packing": True,
                 "pad_to_sequence_len": True,
-                "sequence_len": 1024,
+                "sequence_len": 2048,
-                "val_set_size": 0.01,
+                "val_set_size": 0.05,
                 "special_tokens": {
                     "pad_token": "<|endoftext|>",
                 },