0.8.1 version

* llama4 support for linearized experts

* clean up fsdp2 sharding to prevent hang

* add yaml config

* cleanup example [skip ci]

examples/llama-4/scout-qlora-fsdp1.yaml

@@ -0,0 +1,93 @@
+base_model: axolotl-quants/Llama-4-Scout-17B-16E-Linearized-bnb-nf4-bf16
+model_type: Llama4ForConditionalGeneration
+  # Automatically upload checkpoint and final model to HF
+  # hub_model_id: username/custom_model_name
+
+strict: false
+
+# torch_compile: true
+plugins:
+  - axolotl.integrations.liger.LigerPlugin
+
+liger_glu_activation: true
+liger_rms_norm: true
+liger_layer_norm: true
+
+llama4_linearized_experts: true
+load_in_4bit: true
+adapter: qlora
+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
+  - shared_expert.gate_proj
+  - shared_expert.up_proj
+  - shared_expert.down_proj
+    # - experts.gate_projs.[0-9]+$
+    # - experts.up_projs.[0-9]+$
+    # - experts.down_projs.[0-9]+$
+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
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 1
+micro_batch_size: 1
+num_epochs: 1
+optimizer: adamw_torch_fused
+lr_scheduler: cosine
+learning_rate: 2e-5
+
+bf16: true
+tf32: true
+
+logging_steps: 1
+flash_attention: true
+
+warmup_steps: 100
+evals_per_epoch: 1
+saves_per_epoch: 1
+weight_decay: 0.0
+fsdp:
+  - auto_wrap
+  - full_shard
+fsdp_config:
+  fsdp_transformer_layer_cls_to_wrap: Llama4TextDecoderLayer
+  fsdp_limit_all_gathers: true
+  fsdp_sync_module_states: true
+  fsdp_offload_params: true
+  fsdp_use_orig_params: false
+  fsdp_cpu_ram_efficient_loading: true
+  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
+  fsdp_state_dict_type: FULL_STATE_DICT
+  fsdp_sharding_strategy: FULL_SHARD
+  fsdp_activation_checkpointing: true
+special_tokens:
+  pad_token: <|finetune_right_pad_id|>
+  eos_token: <|eot|>

requirements-dev.txt

@@ -4,3 +4,5 @@ mypy
 types-requests
 quartodoc
 jupyter
+blobfile
+tiktoken

src/axolotl/__init__.py

@@ -4,4 +4,4 @@ import pkgutil
 
 __path__ = pkgutil.extend_path(__path__, __name__)  # Make this a namespace package
 
-__version__ = "0.8.0"
+__version__ = "0.8.1"

src/axolotl/integrations/cut_cross_entropy/README.md

@@ -32,6 +32,9 @@ cut_cross_entropy: true
 ## Supported Models
 
 - llama
+- llama4_text
+- llama4
+- mllama
 - phi3
 - gemma
 - gemma2

src/axolotl/integrations/cut_cross_entropy/monkeypatch/llama4.py

@@ -0,0 +1,414 @@
+"""Llama4 CCE patch. Adapted from transformers 4.51.0."""
+
+# pylint: disable=duplicate-code
+
+from types import MethodType
+from typing import Optional, Tuple, Union
+
+import torch
+import transformers
+from cut_cross_entropy.transformers.utils import (
+    PatchOptions,
+    TransformersModelT,
+    apply_lce,
+)
+from torch import nn
+from transformers.cache_utils import Cache
+from transformers.modeling_outputs import CausalLMOutputWithPast
+from transformers.models.llama4.modeling_llama4 import (
+    _CONFIG_FOR_DOC,
+    LLAMA4_INPUTS_DOCSTRING,
+    Llama4CausalLMOutputWithPast,
+)
+from transformers.utils import (
+    add_start_docstrings_to_model_forward,
+    replace_return_docstrings,
+)
+
+_PATCH_OPTS: PatchOptions | None = None
+
+
+@add_start_docstrings_to_model_forward(LLAMA4_INPUTS_DOCSTRING)
+@replace_return_docstrings(
+    output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
+)
+def cce_forward(
+    self,
+    input_ids: torch.LongTensor | None = None,
+    attention_mask: Optional[torch.Tensor] = None,
+    position_ids: Optional[torch.LongTensor] = None,
+    past_key_values: Optional[Union[Cache, list[torch.FloatTensor]]] = 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,
+    defer_logits_calculation: bool = False,
+    **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).
+
+        defer_logits_calculation (`bool`, *optional*, defaults to `False`):
+            If `True`, defer logits calculation to the ConditionalGeneration forward. This is used to avoid the
+            memory overhead of calculating logits using regular lm_head forward pass and to use CCE.
+
+    Returns:
+
+    Example:
+
+    ```python
+    >>> from transformers import AutoTokenizer, Llama4ForCausalLM
+
+    >>> model = Llama4ForCausalLM.from_pretrained("meta-llama4/Llama4-2-7b-hf")
+    >>> tokenizer = AutoTokenizer.from_pretrained("meta-llama4/Llama4-2-7b-hf")
+
+    >>> prompt = "Hey, are you conscious? Can you talk to me?"
+    >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+    >>> # Generate
+    >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+    >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+    "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+    ```"""
+    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,
+        **kwargs,
+    )
+
+    hidden_states = outputs[0]
+    loss = None
+    logits = None
+
+    # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
+    slice_indices = (
+        slice(-logits_to_keep, None)
+        if isinstance(logits_to_keep, int)
+        else logits_to_keep
+    )
+    if _PATCH_OPTS is not None and _PATCH_OPTS.use_lce(labels, self.training):
+        assert labels is not None
+        loss = apply_lce(
+            hidden_states[:, slice_indices, :],
+            self.lm_head.weight,
+            labels,
+            _PATCH_OPTS,
+            **kwargs,
+        )
+    elif _PATCH_OPTS is not None and defer_logits_calculation:
+        # defer logits calculation to the ConditionalGeneration forward
+        logits = hidden_states[:, slice_indices, :]
+    else:
+        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,
+                **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,
+    )
+
+
+@replace_return_docstrings(
+    output_type=Llama4CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
+)
+def cce_forward_multimodal(
+    self,
+    input_ids: torch.LongTensor | None = None,
+    pixel_values: torch.FloatTensor | None = None,
+    attention_mask: Optional[torch.Tensor] = None,
+    position_ids: Optional[torch.LongTensor] = None,
+    past_key_values: Optional[list[torch.FloatTensor]] = None,
+    inputs_embeds: Optional[torch.FloatTensor] = None,
+    vision_feature_layer: Optional[Union[int, list[int]]] = None,
+    vision_feature_select_strategy: Optional[str] = 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,
+    image_sizes: torch.Tensor | None = None,
+    **lm_kwargs,
+) -> Union[Tuple, Llama4CausalLMOutputWithPast]:
+    r"""
+        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:
+
+    Example:
+
+    ```python
+    >>> from PIL import Image
+    >>> import requests
+    >>> from transformers import AutoProcessor, LlavaForConditionalGeneration
+
+    >>> model = LlavaForConditionalGeneration.from_pretrained("llava-hf/llava-1.5-7b-hf")
+    >>> processor = AutoProcessor.from_pretrained("llava-hf/llava-1.5-7b-hf")
+
+    >>> prompt = "USER: <image>\nWhat's the content of the image? ASSISTANT:"
+    >>> url = "https://www.ilankelman.org/stopsigns/australia.jpg"
+    >>> image = Image.open(requests.get(url, stream=True).raw)
+
+    >>> inputs = processor(images=image, text=prompt, return_tensors="pt")
+
+    >>> # Generate
+    >>> generate_ids = model.generate(**inputs, max_new_tokens=15)
+    >>> processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+    "USER:  \nWhat's the content of the image? ASSISTANT: The image features a busy city street with a stop sign prominently displayed"
+    ```"""
+
+    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
+    )
+    vision_feature_layer = (
+        vision_feature_layer
+        if vision_feature_layer is not None
+        else self.config.vision_config.vision_feature_layer
+    )
+    vision_feature_select_strategy = (
+        vision_feature_select_strategy
+        if vision_feature_select_strategy is not None
+        else self.config.vision_config.vision_feature_select_strategy
+    )
+
+    if (input_ids is None) ^ (inputs_embeds is not None):
+        raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+    if pixel_values is not None and inputs_embeds is not None:
+        raise ValueError(
+            "You cannot specify both pixel_values and inputs_embeds at the same time, and must specify either one"
+        )
+
+    if inputs_embeds is None:
+        inputs_embeds = self.get_input_embeddings()(input_ids)
+
+    if pixel_values is not None:
+        image_features = self.get_image_features(
+            pixel_values=pixel_values,
+            vision_feature_layer=vision_feature_layer,
+            vision_feature_select_strategy=vision_feature_select_strategy,
+            image_sizes=image_sizes,
+        )
+        original_inputs_embeds_shape = inputs_embeds.shape
+
+        vision_flat = image_features.view(-1, image_features.size(-1))
+        projected_vision_flat = self.multi_modal_projector(vision_flat)
+
+        special_image_mask = (input_ids == self.config.image_token_index).unsqueeze(-1)
+        final_mask = special_image_mask.to(inputs_embeds.device)
+        inputs_embeds = inputs_embeds.view(-1, inputs_embeds.size(-1))  # type: ignore
+
+        final_mask_1d = final_mask[..., 0].reshape(-1)
+        num_tokens_to_fill = final_mask_1d.sum()
+
+        if num_tokens_to_fill != projected_vision_flat.size(0):
+            raise ValueError(
+                f"Mismatch: final_mask wants {num_tokens_to_fill} embeddings, "
+                f"but multi_modal_projector returned {projected_vision_flat.size(0)}"
+            )
+
+        expanded_mask = final_mask_1d.unsqueeze(-1).expand(-1, inputs_embeds.size(-1))
+        inputs_embeds = inputs_embeds.masked_scatter(
+            expanded_mask, projected_vision_flat
+        )  # type: ignore
+        inputs_embeds = inputs_embeds.view(original_inputs_embeds_shape)  # type: ignore
+
+    outputs = self.language_model(
+        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,
+        logits_to_keep=logits_to_keep,
+        defer_logits_calculation=True,  # enable deferred logits calculation
+        **lm_kwargs,
+    )
+
+    hidden_states = outputs[0]
+    loss = None
+    logits = None
+
+    if _PATCH_OPTS is not None and _PATCH_OPTS.use_lce(labels, self.training):
+        assert labels is not None
+        # TODO: check if need to handle attention_mask
+        loss = apply_lce(
+            hidden_states,
+            self.language_model.lm_head.weight,
+            labels,
+            _PATCH_OPTS,
+            **lm_kwargs,
+        )
+    else:
+        logits = hidden_states
+        if labels is not None:
+            # Shift so that tokens < n predict n
+            if attention_mask is not None:
+                # we use the input attention mask to shift the logits and labels, because it is 2D.
+                # we also crop attn mask in case it is longer, which happens in PrefixTuning with peft
+                shift_attention_mask = attention_mask[:, -(logits.shape[1] - 1) :].to(
+                    logits.device
+                )
+                shift_logits = logits[..., :-1, :][
+                    shift_attention_mask.to(logits.device) != 0
+                ].contiguous()
+                shift_labels = labels[..., 1:][
+                    shift_attention_mask.to(labels.device) != 0
+                ].contiguous()
+            else:
+                shift_logits = logits[..., :-1, :].contiguous()
+                shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = nn.CrossEntropyLoss()
+            loss = loss_fct(
+                shift_logits.view(-1, shift_logits.size(-1)),
+                shift_labels.view(-1).to(shift_logits.device),
+            )
+
+    if not return_dict:
+        output = (logits,) + outputs[1:]
+        return (loss,) + output if loss is not None else output
+
+    return Llama4CausalLMOutputWithPast(
+        loss=loss,
+        logits=logits,  # type: ignore  # TODO: check if need to create dummy logits
+        past_key_values=outputs.past_key_values,
+        hidden_states=outputs.hidden_states,
+        attentions=outputs.attentions,
+        image_hidden_states=image_features if pixel_values is not None else None,
+    )
+
+
+def patch_llama4_text(
+    maybe_model: TransformersModelT | str | transformers.PretrainedConfig,
+    patch_options: PatchOptions,
+) -> TransformersModelT | None:
+    global _PATCH_OPTS  # pylint: disable=global-statement
+    from transformers.models.llama4 import modeling_llama4
+
+    _PATCH_OPTS = patch_options
+
+    if isinstance(maybe_model, transformers.PreTrainedModel):
+        assert isinstance(
+            maybe_model, modeling_llama4.Llama4ForCausalLM
+        ), f"Expected a Llama4ForCausalLM model. Got {type(maybe_model)}."
+        maybe_model.forward = MethodType(cce_forward, maybe_model)
+
+        return maybe_model
+
+    setattr(
+        modeling_llama4.Llama4ForCausalLM,
+        "forward",
+        cce_forward,
+    )
+    return None
+
+
+def patch_llama4(
+    maybe_model: TransformersModelT | str | transformers.PretrainedConfig,
+    patch_options: PatchOptions,
+) -> TransformersModelT | None:
+
+    global _PATCH_OPTS  # pylint: disable=global-statement
+    from transformers.models.llama4 import modeling_llama4
+
+    _PATCH_OPTS = patch_options
+
+    if isinstance(maybe_model, transformers.PreTrainedModel):
+        assert isinstance(
+            maybe_model, modeling_llama4.Llama4ForConditionalGeneration
+        ), f"Expected a Llama4ForConditionalGeneration model. Got {type(maybe_model)}."
+        maybe_model.forward = MethodType(cce_forward_multimodal, maybe_model)
+
+        # patch the language model
+        maybe_model.language_model.forward = MethodType(
+            cce_forward, maybe_model.language_model
+        )
+        return maybe_model
+
+    setattr(
+        modeling_llama4.Llama4ForConditionalGeneration,
+        "forward",
+        cce_forward_multimodal,
+    )
+
+    # patch the causal language model
+    setattr(modeling_llama4.Llama4ForCausalLM, "forward", cce_forward)
+    return None

src/axolotl/integrations/cut_cross_entropy/monkeypatch/patch.py

@@ -20,6 +20,10 @@ from axolotl.integrations.cut_cross_entropy.monkeypatch.gemma3 import (
     patch_gemma3,
     patch_gemma3_text,
 )
+from axolotl.integrations.cut_cross_entropy.monkeypatch.llama4 import (
+    patch_llama4,
+    patch_llama4_text,
+)
 from axolotl.integrations.cut_cross_entropy.monkeypatch.mistral3 import (
     patch_mistral,
     patch_mistral3,
@@ -28,6 +32,8 @@ from axolotl.integrations.cut_cross_entropy.monkeypatch.mllama import patch_mlla
 
 CUT_CROSS_ENTROPY_MODEL_MAPPING = {
     "llama": patch_llama,
+    "llama4": patch_llama4,
+    "llama4_text": patch_llama4_text,
     "mllama": patch_mllama,
     "phi3": patch_phi3,
     "gemma": patch_gemma,
@@ -60,7 +66,14 @@ def cce_patch(
         raise ValueError(f"Unknown {impl=}")
 
     if isinstance(model_type_or_model, transformers.PreTrainedModel):
-        model_type = model_type_or_model.config.model_type
+        if hasattr(model_type_or_model, "config"):
+            model_type = getattr(
+                getattr(model_type_or_model, "config", None), "model_type", None
+            )
+        else:
+            raise ValueError(
+                "model_type_or_model is a PreTrainedModel but does not have a config attribute"
+            )
     elif isinstance(model_type_or_model, transformers.PretrainedConfig):
         model_type = model_type_or_model.model_type
     else:

src/axolotl/monkeypatch/accelerate/fsdp2.py

@@ -0,0 +1,63 @@
+"""
+monkeypatch for accelerate fsdp2 fix when modifying ordereddict during interation
+"""
+
+import logging
+import sys
+
+import torch
+
+LOG = logging.getLogger(__name__)
+
+
+def fsdp2_load_full_state_dict(accelerator, model: torch.nn.Module, full_sd: dict):
+    """
+    Loads the full state dict (could be only on rank 0) into the sharded model. This is done by broadcasting the
+    parameters from rank 0 to all other ranks. This function modifies the model in-place.
+
+    Args:
+        accelerator (`Accelerator`): The accelerator instance
+        model (`torch.nn.Module`): The model to load the state dict into
+        full_sd (`dict`): The full state dict to load, can only be on rank 0
+    """
+    import torch.distributed as dist
+    from torch.distributed.tensor import distribute_tensor
+
+    LOG.info("Broadcasting full state dict to all ranks...")
+    sharded_sd = model.state_dict()
+    param_names = sorted(sharded_sd.keys())
+    for param_name in param_names:
+        mesh = sharded_sd[param_name].device_mesh
+        if accelerator.is_main_process:
+            # Use the corresponding tensor from full_sd (assuming the key exists in full_sd)
+            full_param = full_sd[param_name].detach().cuda()
+            dist.broadcast(full_param, src=0, group=mesh.get_group())
+            sharded_tensor = distribute_tensor(
+                full_param, mesh, sharded_sd[param_name].placements
+            )
+            sharded_sd[param_name] = sharded_tensor
+        else:
+            # Prepare a tensor of matching shape and dtype
+            full_tensor = torch.empty(
+                sharded_sd[param_name].size(),
+                device="cuda",
+                dtype=sharded_sd[param_name].dtype,
+            )
+            dist.broadcast(full_tensor, src=0, group=mesh.get_group())
+            sharded_tensor = distribute_tensor(
+                full_tensor, mesh, sharded_sd[param_name].placements
+            )
+            sharded_sd[param_name] = sharded_tensor
+
+    model.load_state_dict(sharded_sd)
+
+
+def patch_accelerate_fsdp_utils():
+    from accelerate.utils import fsdp_utils
+
+    fsdp_utils.fsdp2_load_full_state_dict = fsdp2_load_full_state_dict
+    setattr(
+        sys.modules["accelerate.utils.fsdp_utils"],
+        "fsdp2_load_full_state_dict",
+        fsdp2_load_full_state_dict,
+    )

src/axolotl/monkeypatch/lora_kernels.py

@@ -4,7 +4,7 @@ import importlib
 import inspect
 import logging
 import types
-from typing import Type
+from typing import Generator, Tuple, Type
 
 import torch
 from accelerate.logging import get_logger
@@ -200,6 +200,46 @@ def patch_self_attn_lora(cfg: DictDefault):
     )
 
 
+def find_self_attn_in_layer(
+    layer: nn.Module,
+) -> Generator[Tuple[nn.Module], None, None]:
+    # general case of most models
+    if hasattr(layer, "self_attn"):
+        if all(
+            hasattr(layer.self_attn, proj)
+            for proj in ["q_proj", "k_proj", "v_proj", "o_proj"]
+        ):
+            yield layer.self_attn
+
+
+def find_mlp_in_layer(
+    layer: nn.Module,
+) -> Generator[Tuple[nn.Module, nn.Module, nn.Module, nn.Module], None, None]:
+    # general case of most models
+    if hasattr(layer, "mlp"):
+        if all(
+            hasattr(layer.mlp, proj) for proj in ["gate_proj", "up_proj", "down_proj"]
+        ):
+            yield layer.mlp.gate_proj, layer.mlp.up_proj, layer.mlp.down_proj, layer.mlp
+    # llama4 linearized experts
+    if hasattr(layer, "feedforward") and hasattr(layer.feedforward, "shared_expert"):
+        mlp = layer.feedforward.shared_expert
+        yield mlp.gate_proj, mlp.up_proj, mlp.down_proj, mlp
+    if hasattr(layer, "feedforward") and hasattr(layer.feedforward, "experts"):
+        if all(
+            hasattr(layer.feedforward.experts, proj)
+            for proj in ["gate_projs", "up_projs", "down_projs"]
+        ):
+            for gate_proj, up_proj, down_proj in zip(
+                layer.feedforward.experts.gate_projs,
+                layer.feedforward.experts.up_projs,
+                layer.feedforward.experts.down_projs,
+            ):
+                yield gate_proj, up_proj, down_proj, FakeMLP(
+                    gate_proj, up_proj, down_proj
+                )
+
+
 def apply_lora_kernel_patches(
     model: PeftModelForCausalLM, cfg: DictDefault
 ) -> PeftModelForCausalLM:
@@ -286,74 +326,82 @@ def apply_lora_kernel_patches(
     for layer in layers:
         # Add QKV, O fallback implementations to start
         # These will be overwritten later (if some conditions apply)
-        layer.self_attn.apply_qkv = types.MethodType(
-            original_apply_qkv, layer.self_attn
-        )
-        layer.self_attn.apply_o = types.MethodType(original_apply_o, layer.self_attn)
+        for self_attn in find_self_attn_in_layer(layer):
+            self_attn.apply_qkv = types.MethodType(original_apply_qkv, self_attn)
+            self_attn.apply_o = types.MethodType(original_apply_o, self_attn)
 
-        if cfg.lora_mlp_kernel:
-            # MLP patching
-            gate_proj = layer.mlp.gate_proj
-            up_proj = layer.mlp.up_proj
-            down_proj = layer.mlp.down_proj
+            if cfg.lora_qkv_kernel:
+                # Query, key, value patching
+                layer_modules = [
+                    getattr(self_attn, linear_proj)
+                    for linear_proj in ["q_proj", "k_proj", "v_proj"]
+                ]
+                can_patch_qkv = all(
+                    hasattr(module, "lora_A")
+                    and getattr(module, "base_layer", module).bias is None
+                    and len(getattr(module, "lora_magnitude_vector", []) or []) == 0
+                    for module in layer_modules
+                )
 
-            can_patch_mlp = all(
-                hasattr(proj, "lora_A")
-                and getattr(proj, "base_layer", proj).bias is None
-                and len(getattr(proj, "lora_magnitude_vector", []) or []) == 0
-                for proj in (gate_proj, up_proj, down_proj)
-            )
+                if can_patch_qkv:
+                    # Add optimized implementation
+                    self_attn.apply_qkv = types.MethodType(apply_lora_qkv, self_attn)
+                else:
+                    LOG.warning_once(
+                        "Cannot patch some attention QKV projections - requires LoRA adapters with no bias"
+                    )
+            if cfg.lora_o_kernel:
+                # Output patching
+                layer_modules = [
+                    getattr(self_attn, linear_proj) for linear_proj in ["o_proj"]
+                ]
+                can_patch_o = all(
+                    hasattr(module, "lora_A")
+                    and getattr(module, "base_layer", module).bias is None
+                    and len(getattr(module, "lora_magnitude_vector", []) or []) == 0
+                    for module in layer_modules
+                )
 
-            if can_patch_mlp:
-                apply_fn = APPLY_FN_MAPPING[activation]
-                layer.mlp.forward = types.MethodType(apply_fn, layer.mlp)
-            else:
-                LOG.warning_once(
-                    "Cannot patch some MLP layers - requires LoRA adapters with no bias"
+                if can_patch_o:
+                    self_attn.apply_o = types.MethodType(apply_lora_o, self_attn)
+                else:
+                    LOG.warning_once(
+                        "Cannot patch some attention output projection - requires LoRA adapters with no bias"
+                    )
+        for gate_proj, up_proj, down_proj, mlp in find_mlp_in_layer(layer):
+            if cfg.lora_mlp_kernel:
+                # MLP patching
+                can_patch_mlp = all(
+                    hasattr(proj, "lora_A")
+                    and getattr(proj, "base_layer", proj).bias is None
+                    and len(getattr(proj, "lora_magnitude_vector", []) or []) == 0
+                    for proj in (gate_proj, up_proj, down_proj)
                 )
-        if cfg.lora_qkv_kernel:
-            # Query, key, value patching
-            layer_modules = [
-                getattr(layer.self_attn, linear_proj)
-                for linear_proj in ["q_proj", "k_proj", "v_proj"]
-            ]
-            can_patch_qkv = all(
-                hasattr(module, "lora_A")
-                and getattr(module, "base_layer", module).bias is None
-                and len(getattr(module, "lora_magnitude_vector", []) or []) == 0
-                for module in layer_modules
-            )
 
-            if can_patch_qkv:
-                # Add optimized implementation
-                layer.self_attn.apply_qkv = types.MethodType(
-                    apply_lora_qkv, layer.self_attn
-                )
-            else:
-                LOG.warning_once(
-                    "Cannot patch some attention QKV projections - requires LoRA adapters with no bias"
-                )
-        if cfg.lora_o_kernel:
-            # Output patching
-            layer_modules = [
-                getattr(layer.self_attn, linear_proj) for linear_proj in ["o_proj"]
-            ]
-            can_patch_o = all(
-                hasattr(module, "lora_A")
-                and getattr(module, "base_layer", module).bias is None
-                and len(getattr(module, "lora_magnitude_vector", []) or []) == 0
-                for module in layer_modules
-            )
-
-            if can_patch_o:
-                layer.self_attn.apply_o = types.MethodType(
-                    apply_lora_o, layer.self_attn
-                )
-            else:
-                LOG.warning_once(
-                    "Cannot patch some attention output projection - requires LoRA adapters with no bias"
-                )
+                if can_patch_mlp:
+                    apply_fn = APPLY_FN_MAPPING[activation]
+                    layer.mlp.forward = types.MethodType(apply_fn, mlp)
+                else:
+                    LOG.warning_once(
+                        "Cannot patch some MLP layers - requires LoRA adapters with no bias"
+                    )
 
     LOG.setLevel(original_level)
 
     return model
+
+
+class FakeMLP(nn.Module):
+    """
+    placeholder MLP for triton patching
+    """
+
+    gate_proj: nn.Linear
+    up_proj: nn.Linear
+    down_proj: nn.Linear
+
+    def __init__(self, gate_proj, up_proj, down_proj):
+        super().__init__()
+        self.gate_proj = gate_proj
+        self.up_proj = up_proj
+        self.down_proj = down_proj

src/axolotl/monkeypatch/models/llama4/modeling.py

@@ -0,0 +1,101 @@
+"""
+Modified Llama-4 text experts modeling for linearized experts for improved LoRA support
+"""
+
+import sys
+
+import torch
+from torch import nn
+from transformers import Llama4Config
+from transformers.activations import ACT2FN
+
+
+class Llama4TextExperts(nn.Module):
+    """
+    Modified Llama-4 text experts modeling for linearized experts
+    """
+
+    def __init__(self, config: Llama4Config):
+        super().__init__()
+        self.num_experts = config.num_local_experts
+        self.intermediate_size = config.intermediate_size
+        self.hidden_size = config.hidden_size
+        self.expert_dim = self.intermediate_size
+
+        # Replace fused gate_up_proj with separate Linear modules
+        self.gate_projs = nn.ModuleList(
+            [
+                nn.Linear(self.hidden_size, self.expert_dim, bias=False)
+                for _ in range(self.num_experts)
+            ]
+        )
+
+        self.up_projs = nn.ModuleList(
+            [
+                nn.Linear(self.hidden_size, self.expert_dim, bias=False)
+                for _ in range(self.num_experts)
+            ]
+        )
+
+        # Replace down_proj Parameter with Linear modules
+        self.down_projs = nn.ModuleList(
+            [
+                nn.Linear(self.expert_dim, self.hidden_size, bias=False)
+                for _ in range(self.num_experts)
+            ]
+        )
+
+        self.act_fn = ACT2FN[config.hidden_act]
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        """
+        Forward method using separate Linear layers for each expert.
+
+        Args:
+            hidden_states (torch.Tensor): (num_experts * batch_size, hidden_size)
+                The input should be organized by expert
+
+        Returns:
+            torch.Tensor: (num_experts * batch_size, hidden_size)
+        """
+        # Reshape to separate by expert
+        hidden_states = hidden_states.view(self.num_experts, -1, self.hidden_size)
+        # batch_size_per_expert = hidden_states.size(1)
+
+        # Initialize output tensor
+        next_states = torch.zeros_like(hidden_states)
+
+        # Process each expert separately
+        for i in range(self.num_experts):
+            # Get input for this expert
+            expert_input = hidden_states[
+                i
+            ]  # Shape: (batch_size_per_expert, hidden_size)
+
+            # Apply gate and up projections
+            gate = self.gate_projs[i](
+                expert_input
+            )  # Shape: (batch_size_per_expert, expert_dim)
+            up = self.up_projs[i](
+                expert_input
+            )  # Shape: (batch_size_per_expert, expert_dim)
+
+            # Apply activation and down projection
+            next_states[i] = self.down_projs[i](up * self.act_fn(gate))
+
+        # Flatten back to original shape
+        return next_states.view(-1, self.hidden_size)
+
+
+def patch_llama4_linearized_modeling():
+    """
+    Patch Llama4TextExperts to use separate Linear layers for each expert.
+    """
+    from transformers.models.llama4 import modeling_llama4
+
+    modeling_llama4.Llama4TextExperts = Llama4TextExperts
+    setattr(
+        sys.modules["transformers.models.llama4"],
+        "Llama4TextExperts",
+        Llama4TextExperts,
+    )

src/axolotl/utils/models.py

@@ -544,8 +544,20 @@ class ModelLoader:
         self.auto_model_loader = AutoModelForCausalLM  # pylint: disable=invalid-name
 
     def apply_patches(self) -> None:
+        if self.cfg.fsdp_config and str(self.cfg.fsdp_config.fsdp_version) == "2":
+            from axolotl.monkeypatch.accelerate.fsdp2 import patch_accelerate_fsdp_utils
+
+            patch_accelerate_fsdp_utils()
         # patch gemma3 conditional generation forward before loading plugins
         # as it could be overridden by plugins
+        if self.cfg.model_config_type == "llama4":
+            if self.cfg.llama4_linearized_experts:
+                from axolotl.monkeypatch.models.llama4.modeling import (
+                    patch_llama4_linearized_modeling,
+                )
+
+                patch_llama4_linearized_modeling()
+
         if self.cfg.model_config_type == "gemma3":
             from axolotl.monkeypatch.gemma3 import (
                 patch_gemma3conditionalgeneration_forward,

src/axolotl/utils/schemas/config.py

@@ -245,6 +245,8 @@ class AxolotlInputConfig(
     lora_qkv_kernel: bool | None = None
     lora_o_kernel: bool | None = None
 
+    llama4_linearized_experts: bool | None = None
+
     deepspeed: str | dict[str, Any] | None = None
     fsdp: list[str] | None = None
     fsdp_config: dict[str, Any] | None = None