Fix: add delinearization and make qlora work with fsdp2 (#2515)

* fixes for delinearization, and make qlora work with fsdp2

* Add back mistakenly removed lm_eval

* typo [skip ci]

* patch evals for torch.compile + fsdp2

* also check torch_compile w fsdp2

* lots of fixes for flex attn with llama4

* fix patch check and patch llama4 too

* attempt to make the patches stick

* use transformers 4.51.2

* update configs and README for llama4

* remove torch.compile for CI test

* cleanup any existing singletons

* set singleton cache to None instead of deleting

* use importlib reload with monkeypatch

* don't worry about transformers version, mark inputs with grads, fix regex

* make sure embeds aren't on cpu

* logging and mem improvements

* vllm version and add to docker, make sure to save processor on conversion

* fix ambiguous tensor bool check

* fix vllm to not use v1, upgrade hf transformers

* fix tests

* make flex_attn_compile_kwargs configurable, since this depends on model params

---------

Co-authored-by: Wing Lian <wing@axolotl.ai>
Co-authored-by: Salman Mohammadi <salman.mohammadi@outlook.com>

.github/workflows/main.yml

@@ -29,7 +29,7 @@ jobs:
             cuda_version: 12.4.1
             python_version: "3.11"
             pytorch: 2.6.0
-            axolotl_extras:
+            axolotl_extras: vllm
             is_latest: true
     runs-on: axolotl-gpu-runner
     steps:

examples/llama-4/README.md

@@ -1,16 +1,28 @@
 # Llama 4 by Meta AI
 
+## Flash Attention vs Flex Attention
+
+While Flash Attention to support is "enabled" for Llama-4, the upstream implementation is not correct and usage of Flex Attention is recommended.
+
 ## Available Examples
 
 ### Llama 4 Scout 17Bx16Experts (109B)
-- [Multi-Modal/Vision QLoRA w/ FSDP1](./scout-vision-qlora-fsdp.yaml)
-- [Text Single GPU (H100) QLoRA](./scout-qlora-single-h100.yaml)
-- [Text Multi GPU QLoRA w/ FSDP1](./scout-qlora-fsdp1.yaml)
 
-Our Single H100 implementation for Llama 4 Scout uses only 68.5GB VRAM for post-training with 4k context length @ 546 tokens/second. [WandB logs here](https://wandb.ai/axolotl-ai/llama4-sft/runs/zic56rhd)
+Flex Attention
+- [Text Single GPU (H100) QLoRA](./scout-qlora-single-h100-flex.yaml)
+- [Text Multi GPU QLoRA w/ FSDP2](./scout-qlora-flexattn-fsdp2.yaml)
+
+[//]: # (Flash Attention (Do not use))
+
+[//]: # (- [Multi-Modal/Vision QLoRA w/ FSDP1](./scout-vision-qlora-fsdp.yaml))
+
+[//]: # (- [Text Single GPU (H100) QLoRA](./scout-qlora-single-h100.yaml))
+
+[//]: # (- [Text Multi GPU QLoRA w/ FSDP1](./scout-qlora-fsdp1.yaml))
+
+Our Single H100 implementation for Llama 4 Scout uses only 64.5GB VRAM for post-training with 4k context length @ 519 tokens/second. [WandB logs here](https://wandb.ai/axolotl-ai/llama4-flexattn-qlora/runs/wpie7dkj)
+Multi-GPU (4xH100) for Llama 4 Scout uses 62.8GB VRAM/GPU @ 4k contenxt length @ 280tps/gpu, [WandB logs here](https://wandb.ai/axolotl-ai/llama4-flexattn-qlora/runs/2lkezdj8)
 
 ### Llama 4 Maverick 17Bx128Experts (400B)
 
-- [Text Multi GPU QLoRA w/FSDP1](./maverick-qlora-fsdp1.yaml)
-
-Our 4xH100 implementation for Llama 4 Maverick uses 79.5GB VRAM/GPU for post-training with 4k context length @ 206 tokens/second. [WandB logs here.](https://wandb.ai/axolotl-ai/llama-sft/runs/siyvwuxc?nw=nwuserwinglian)
+Coming Soon

examples/llama-4/scout-qlora-flexattn-fsdp2.yaml

@@ -0,0 +1,86 @@
+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
+
+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
+
+gradient_accumulation_steps: 1
+micro_batch_size: 2
+num_epochs: 3
+optimizer: adamw_torch_4bit
+lr_scheduler: cosine
+learning_rate: 1e-4
+
+bf16: true
+tf32: true
+
+logging_steps: 1
+flex_attention: true
+flex_attn_compile_kwargs:
+  dynamic: false
+  mode: max-autotune-no-cudagraphs
+
+warmup_steps: 10
+evals_per_epoch: 1
+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|>

examples/llama-4/scout-qlora-single-h100-flex.yaml

@@ -0,0 +1,85 @@
+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
+
+plugins:
+  - axolotl.integrations.liger.LigerPlugin
+  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
+
+liger_glu_activation: true
+liger_rms_norm: true
+liger_layer_norm: true
+cut_cross_entropy: true
+
+llama4_linearized_experts: true  # needed with custom linearized experts model
+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]+$  # optionally train the moe experts
+  # - experts.up_projs.[0-9]+$
+  # - experts.down_projs.[0-9]+$
+lora_modules_to_save:
+  # - lm_head  # needed if modifying vocabulary
+  # - embed_tokens
+
+lora_mlp_kernel: true
+lora_qkv_kernel: true
+lora_o_kernel: true
+
+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  # up to 8k will work on a single H100
+sample_packing: true
+pad_to_sequence_len: true
+
+gradient_accumulation_steps: 1
+micro_batch_size: 1
+num_epochs: 1
+optimizer: adamw_torch_4bit
+lr_scheduler: cosine
+learning_rate: 1e-4
+
+bf16: true
+tf32: true
+
+torch_compile: true
+flex_attention: true
+flex_attn_compile_kwargs:
+  dynamic: false
+  mode: max-autotune-no-cudagraphs
+
+gradient_checkpointing: offload
+gradient_checkpointing_kwargs:
+  use_reentrant: false
+
+logging_steps: 1
+warmup_steps: 20
+evals_per_epoch: 1
+saves_per_epoch: 1
+
+weight_decay: 0.0
+special_tokens:
+  pad_token: <|finetune_right_pad_id|>
+  eos_token: <|eot|>

examples/llama-4/scout-vision-qlora-fsdp2-flex.yaml

@@ -0,0 +1,89 @@
+base_model: axolotl-quants/Llama-4-Scout-17B-16E-Linearized-bnb-nf4-bf16
+model_type: Llama4ForConditionalGeneration
+processor_type: Llama4Processor
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
+
+# these 3 lines are needed for now to handle vision chat templates w images
+skip_prepare_dataset: true
+remove_unused_columns: false
+sample_packing: false
+
+sequence_len: 4096
+
+plugins:
+  - axolotl.integrations.liger.LigerPlugin
+
+liger_glu_activation: true
+liger_rms_norm: true
+liger_layer_norm: true
+
+llama4_linearized_experts: true  # use Axolotl's customized model
+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
+  - vision_adapter.mlp.fc1
+  - vision_adapter.mlp.fc2
+  # - 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: HuggingFaceH4/llava-instruct-mix-vsft
+    type: chat_template
+    split: train[:1%]
+    field_messages: messages
+
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.0
+output_dir: ./outputs/out
+
+gradient_accumulation_steps: 1
+micro_batch_size: 1
+num_epochs: 1
+optimizer: adamw_torch_4bit
+lr_scheduler: cosine
+learning_rate: 1e-4
+
+bf16: true
+tf32: true
+
+logging_steps: 1
+flex_attention: true
+flex_attn_compile_kwargs:
+  dynamic: false
+  mode: max-autotune-no-cudagraphs
+
+warmup_steps: 10
+evals_per_epoch: 1
+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

@@ -12,7 +12,7 @@ liger-kernel==0.5.6
 packaging==23.2
 
 peft==0.15.1
-transformers==4.51.1
+transformers==4.51.3
 tokenizers>=0.21.1
 accelerate==1.6.0
 datasets==3.5.0

setup.py

@@ -67,7 +67,7 @@ def parse_requirements(extras_require_map):
             if (major, minor) >= (2, 6):
                 _install_requires.pop(_install_requires.index(xformers_version))
                 _install_requires.append("xformers==0.0.29.post2")
-                extras_require_map["vllm"] = ["vllm==0.8.1"]
+                extras_require_map["vllm"] = ["vllm==0.8.3"]
             elif (major, minor) >= (2, 5):
                 _install_requires.pop(_install_requires.index(xformers_version))
                 if patch == 0:

src/axolotl/cli/delinearize_llama4.py

@@ -0,0 +1,156 @@
+"""
+CLI tool to delinearize quantized/Linearized Llama-4 models.
+"""
+
+import os
+from pathlib import Path
+from typing import Generator, Union
+
+import fire
+import torch
+from accelerate import init_empty_weights
+from dotenv import load_dotenv
+from transformers import AutoProcessor
+
+
+def iter_convert_patched_to_hf(model_state_dict, num_experts) -> Generator:
+    keys = list(model_state_dict.keys())
+    for key in keys:
+        if ".feed_forward.experts." not in key:
+            yield key, model_state_dict[key]
+        if ".feed_forward.experts.gate_projs" in key:
+            # gate gets fused with up so skip the yield on this and we'll fuse it when asking for the up
+            continue
+        if ".feed_forward.experts.up_projs" in key:
+            if ".feed_forward.experts.up_projs.0." in key:
+                # handle the re-shape and fusing of gate and up, and conversion from linear to parameter
+                prefix = key.split(".up_projs.0.")[0]
+                key = f"{prefix}.gate_up_proj"
+                # grab all the up_projs and gate_projs across all experts
+                gate_stacked = torch.stack(
+                    [
+                        model_state_dict[
+                            f"{prefix}.gate_projs.{expert_idx}.weight"
+                        ].transpose(0, 1)
+                        for expert_idx in range(num_experts)
+                    ]
+                )
+                up_stacked = torch.stack(
+                    [
+                        model_state_dict[
+                            f"{prefix}.up_projs.{expert_idx}.weight"
+                        ].transpose(0, 1)
+                        for expert_idx in range(num_experts)
+                    ]
+                )
+                gate_up_proj = torch.cat((gate_stacked, up_stacked), dim=-1)
+                del gate_stacked, up_stacked
+                yield key, gate_up_proj
+            else:
+                del model_state_dict[key]
+                continue
+        if ".feed_forward.experts.down_projs" in key:
+            if ".feed_forward.experts.down_projs.0." in key:
+                # handle the re-shape and fusing of gate and up, and conversion from linear to parameter
+                prefix = key.split(".down_projs.0.")[0]
+                key = f"{prefix}.down_proj"
+                # grab all the down_projs across all experts
+                down_stacked = torch.stack(
+                    [
+                        model_state_dict[
+                            f"{prefix}.down_projs.{expert_idx}.weight"
+                        ].transpose(0, 1)
+                        for expert_idx in range(num_experts)
+                    ]
+                )
+                yield key, down_stacked
+            else:
+                del model_state_dict[key]
+                continue
+
+
+def do_cli(model: Union[Path, str], output: Union[Path, str]) -> None:
+    """
+    Convert a patched HF format Llama4 model (with separated projections)
+    back to the original HF format (with fused projections).
+
+    Args:
+        model: Path to the patched HF model
+        output: Path to save the converted model
+    """
+    print(f"Loading model from {model}")
+    from axolotl.monkeypatch.models.llama4.modeling import (
+        patch_llama4_linearized_modeling,
+    )
+
+    unpatch_llama4 = patch_llama4_linearized_modeling()
+    from transformers import Llama4ForConditionalGeneration
+
+    model_ = Llama4ForConditionalGeneration.from_pretrained(
+        model, torch_dtype=torch.bfloat16
+    )
+    processor = AutoProcessor.from_pretrained(model)
+    processor.save_pretrained(output)
+
+    device = model_.device.type
+    if device == "cuda":
+        print(
+            f"peak memory allocated: {torch.cuda.max_memory_allocated() / 1024**2} MB"
+        )
+        print(f"peak memory reserved: {torch.cuda.max_memory_reserved() / 1024**2} MB")
+    model_config = model_.config
+    config = model_.config.get_text_config()
+
+    # Get key dimensions from the config
+    hidden_size = config.hidden_size
+    intermediate_size = config.intermediate_size
+    num_experts = config.num_local_experts
+
+    print(
+        f"Model dimensions: hidden_size={hidden_size}, intermediate_size={intermediate_size}, num_experts={num_experts}"
+    )
+
+    # Create output directory if it doesn't exist
+    os.makedirs(output, exist_ok=True)
+
+    # Get state dict
+    state_dict = model_.state_dict()
+    del model_
+
+    # Create a new state dict for the converted model
+    converted_state_dict = {}
+
+    # First, copy all keys that don't need modification
+    for key, value in iter_convert_patched_to_hf(state_dict, num_experts):
+        converted_state_dict[key] = value
+
+    del state_dict
+    if device == "cuda":
+        torch.cuda.empty_cache()
+        print("State dict converted.")
+        print(
+            f"peak memory allocated: {torch.cuda.max_memory_allocated() / 1024**2} MB"
+        )
+        print(f"peak memory reserved: {torch.cuda.max_memory_reserved() / 1024**2} MB")
+    # Ideally re-load the model import to load the converted state dict
+    # Save the converted model
+    with init_empty_weights():
+        unpatch_llama4()
+        model_ = Llama4ForConditionalGeneration(model_config)
+
+    if device == "cuda":
+        print("State dict loaded into model.")
+        print(
+            f"peak memory allocated: {torch.cuda.max_memory_allocated() / 1024**2} MB"
+        )
+        print(f"peak memory reserved: {torch.cuda.max_memory_reserved() / 1024**2} MB")
+    model_.load_state_dict(converted_state_dict, strict=False, assign=True)
+    print(f"Saving converted model to {output}...")
+    model_.save_pretrained(output)
+
+    print(f"Model successfully converted and saved to {output}")
+
+
+if __name__ == "__main__":
+    load_dotenv()
+    fire.Fire(do_cli)

src/axolotl/cli/main.py

@@ -330,6 +330,15 @@ def vllm_serve(config: str, **cli_args: VllmServeCliArgs):
     do_vllm_serve(config, cli_args)
 
 
+@cli.command()
+@click.argument("model", type=click.Path(exists=True, path_type=str))
+@click.argument("output", type=click.Path(exists=False, path_type=str))
+def delinearize_llama4(model: str, output: str) -> None:
+    from axolotl.cli.delinearize_llama4 import do_cli as do_delinearize_llama4
+
+    do_delinearize_llama4(model, output)
+
+
 cli.add_command(lm_eval)
 
 

src/axolotl/cli/merge_lora.py

@@ -40,6 +40,7 @@ def do_merge_lora(*, cfg: DictDefault) -> None:
         LOG.warning("Error raised: %s", e)
 
     model.generation_config.do_sample = True
+    model.config.use_cache = True
 
     if cfg.local_rank == 0:
         LOG.info(f"Saving merged model to: {str(Path(cfg.output_dir) / 'merged')}...")

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

@@ -165,7 +165,7 @@ def cce_forward(
 )
 def cce_forward_multimodal(
     self,
-    input_ids: torch.LongTensor | None = None,
+    input_ids: torch.LongTensor | None = None,  # type: ignore
     pixel_values: torch.FloatTensor | None = None,
     attention_mask: Optional[torch.Tensor] = None,
     position_ids: Optional[torch.LongTensor] = None,
@@ -254,7 +254,7 @@ def cce_forward_multimodal(
         )
 
     if inputs_embeds is None:
-        inputs_embeds = self.get_input_embeddings()(input_ids)
+        inputs_embeds = self.get_input_embeddings()(input_ids)  # type: ignore
 
     if pixel_values is not None:
         image_features = self.get_image_features(
@@ -263,13 +263,13 @@ def cce_forward_multimodal(
             vision_feature_select_strategy=vision_feature_select_strategy,
             image_sizes=image_sizes,
         )
-        original_inputs_embeds_shape = inputs_embeds.shape
+        original_inputs_embeds_shape = inputs_embeds.shape  # type: ignore
 
         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)
+        final_mask = special_image_mask.to(inputs_embeds.device)  # type: ignore
         inputs_embeds = inputs_embeds.view(-1, inputs_embeds.size(-1))  # type: ignore
 
         final_mask_1d = final_mask[..., 0].reshape(-1)

src/axolotl/monkeypatch/accelerate/fsdp2.py

@@ -49,7 +49,7 @@ def fsdp2_load_full_state_dict(accelerator, model: torch.nn.Module, full_sd: dic
             )
             sharded_sd[param_name] = sharded_tensor
 
-    model.load_state_dict(sharded_sd)
+    model.load_state_dict(sharded_sd, assign=True)
 
 
 def patch_accelerate_fsdp_utils():

src/axolotl/monkeypatch/attention/flex_attn.py

@@ -7,12 +7,11 @@ import torch
 import transformers
 
 
-def patch_flex_wrapper():
+def patch_flex_wrapper(**flex_attn_compile_kwargs):
     # 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 not is_torch_2_6:
         return
 
     from torch.nn.attention.flex_attention import flex_attention
@@ -32,17 +31,24 @@ def patch_flex_wrapper():
                 cls._instance = super().__new__(cls)
             return cls._instance
 
+        @classmethod
+        def del_singleton(cls):
+            cls._instance = None
+
         @torch.compiler.disable(recursive=False)
-        def __init__(self):
+        def __init__(self, training):
             """
             Initialize or update the singleton instance.
             """
-            if not self._is_flex_compiled:
+            self.training = None
+            if not self._is_flex_compiled or training != self.training:
+                # In PyTorch 2.6.0, there's a known issue with flex attention compilation which may
+                # cause errors. The suggested fix is to compile with "max-autotune-no-cudagraphs"
+                # see https://github.com/pytorch/pytorch/issues/146260 for training
+                self.training = training
                 self._compiled_flex_attention = torch.compile(
                     flex_attention,
-                    dynamic=False,
-                    mode="max-autotune-no-cudagraphs",
-                    fullgraph=True,
+                    **flex_attn_compile_kwargs,
                 )
                 self._is_flex_compiled = True
 
@@ -50,15 +56,22 @@ def patch_flex_wrapper():
             return self._compiled_flex_attention
 
     transformers.integrations.flex_attention.WrappedFlexAttention = WrappedFlexAttention
+    setattr(
+        sys.modules["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):
+    if not is_torch_2_6:
         return
 
+    from torch.nn.attention.flex_attention import (
+        _DEFAULT_SPARSE_BLOCK_SIZE as flex_default_block_size,
+    )
     from torch.nn.attention.flex_attention import (
         BlockMask,
     )
@@ -104,14 +117,16 @@ def patch_flex_make_mask():
         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)
+            attention_mask_2d,
+            value=0,
+            pad=(0, abs(total_seq_len - max(key_length, flex_default_block_size))),
         )
         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) // (
+            chunk_idxs = (document_ids.clone().fill_(1).cumsum(-1) - 1) // (
                 attention_chunk_size
             )
 
@@ -138,6 +153,18 @@ def patch_flex_make_mask():
             final_mask = causal_mask & padding_mask & document_mask
             return final_mask
 
+        def chunk_causal_mask_mod(batch_idx, head_idx, q_idx, kv_idx):
+            """
+            Combines the chunk mask with the causal mask for chunked attention.
+            """
+            chunk_mask = chunk_idxs[batch_idx, q_idx] == chunk_idxs[batch_idx, kv_idx]
+            causal_doc_mask = causal_mask_mod(batch_idx, head_idx, q_idx, kv_idx)
+            return chunk_mask & causal_doc_mask
+
+        mask_mod_maybe_combined = (
+            causal_mask_mod if attention_chunk_size is None else chunk_causal_mask_mod
+        )
+
         if offsets is not None:
             q_offset = offsets[0]
             kv_offset = offsets[1]
@@ -145,10 +172,10 @@ def patch_flex_make_mask():
             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)
+                return mask_mod_maybe_combined(batch_idx, head_idx, offset_q, offset_kv)
 
         else:
-            mask_mod = causal_mask_mod
+            mask_mod = mask_mod_maybe_combined
         return create_block_causal_mask_flex(
             mask_mod=mask_mod,
             B=batch_size,
@@ -160,11 +187,16 @@ def patch_flex_make_mask():
         )
 
     for n in tuple(sys.modules):
-        if ".modeling_" in n and "llama4" not in n:
+        if ".modeling_" 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
                 )
+                setattr(
+                    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/models/llama4/modeling.py

@@ -93,9 +93,20 @@ def patch_llama4_linearized_modeling():
     """
     from transformers.models.llama4 import modeling_llama4
 
+    old_lamma_4_text_experts = modeling_llama4.Llama4TextExperts
     modeling_llama4.Llama4TextExperts = Llama4TextExperts
     setattr(
         sys.modules["transformers.models.llama4"],
         "Llama4TextExperts",
         Llama4TextExperts,
     )
+
+    def unpatch():
+        modeling_llama4.Llama4TextExperts = old_lamma_4_text_experts
+        setattr(
+            sys.modules["transformers.models.llama4"],
+            "Llama4TextExperts",
+            old_lamma_4_text_experts,
+        )
+
+    return unpatch

src/axolotl/monkeypatch/trainer_eval_guard.py

@@ -0,0 +1,78 @@
+"""
+fix for FSDP2 evals when using torch.compile
+"""
+
+import inspect
+import logging
+
+from transformers import Trainer
+
+from axolotl.monkeypatch.utils import detab_code
+
+LOG = logging.getLogger(__name__)
+
+ORIGINAL_TRAINER_CODE = """
+    model.eval()
+"""
+
+PATCHED_TRAINER_CODE = """
+    if hasattr(model, "eval") and callable(model.eval):
+        self.model.eval()
+"""
+
+
+def get_evaluation_loop_code() -> str:
+    training_loop = inspect.getsource(Trainer.evaluation_loop)
+    return training_loop
+
+
+def check_evaluation_loop_is_patchable() -> bool:
+    eval_loop = get_evaluation_loop_code()
+    eval_loop, _ = detab_code(eval_loop)
+    return ORIGINAL_TRAINER_CODE in eval_loop
+
+
+def patch_evaluation_loop_for_fsdp2():
+    """
+    monkeypatch for fixing the eval loop for fsdp2 with torch.compile
+    """
+
+    try:
+        evaluation_loop = get_evaluation_loop_code()
+    except OSError:
+        return
+    Trainer._original_evaluation_loop = (  # pylint: disable=protected-access
+        evaluation_loop
+    )
+    evaluation_loop, _ = detab_code(evaluation_loop)
+    if ORIGINAL_TRAINER_CODE not in evaluation_loop:
+        return
+
+    evaluation_loop = evaluation_loop.replace(
+        ORIGINAL_TRAINER_CODE, PATCHED_TRAINER_CODE
+    )
+    evaluation_loop = evaluation_loop.replace(
+        "def evaluation_loop(",
+        "def _fixed_evaluation_loop(",
+        1,
+    )
+
+    # load imports necessary
+    import transformers.trainer
+
+    items_to_import = []
+    for item in dir(transformers.trainer):
+        if item in evaluation_loop:
+            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(evaluation_loop, globals())  # pylint: disable=exec-used  # nosec B102
+    LOG.info("patching _inner_training_loop for fsdp optimizer save")
+    Trainer.evaluation_loop = (  # pylint: disable=protected-access
+        _fixed_evaluation_loop  # pylint: disable=undefined-variable  # noqa: F821
+    )

src/axolotl/train.py

@@ -81,6 +81,11 @@ def setup_model_and_tokenizer(
     # Apply freezing if specified
     if cfg.unfrozen_parameters:
         freeze_layers_except(model, cfg.unfrozen_parameters)
+        if any(
+            any(embed in param for embed in ["lm_head", "embed_tokens"])
+            for param in cfg.unfrozen_parameters
+        ):
+            model.enable_input_require_grads()
 
     return model, tokenizer, peft_config, processor
 

src/axolotl/utils/freeze.py

@@ -2,13 +2,14 @@
 module to freeze/unfreeze parameters by name
 """
 
-import logging
 import re
 from typing import Callable, List, Tuple, Union
 
+from accelerate.logging import get_logger
+
 from axolotl.utils.distributed import is_main_process
 
-LOG = logging.getLogger("axolotl.utils.freeze")
+LOG = get_logger(__name__)
 
 
 def freeze_layers_except(model, regex_patterns):
@@ -184,7 +185,7 @@ class LayerNamePattern:
         """
         self.raw_pattern = pattern
         name_pattern, self.range = self._parse_pattern(pattern)
-        self.name_regex = re.compile(name_pattern.replace(".", "\\."))
+        self.name_regex = re.compile(re.sub(r"\.(?!\+)", "\\.", name_pattern))
 
     def match(self, name: str) -> bool:
         """

src/axolotl/utils/models.py

@@ -542,6 +542,17 @@ class ModelLoader:
             from axolotl.monkeypatch.accelerate.fsdp2 import patch_accelerate_fsdp_utils
 
             patch_accelerate_fsdp_utils()
+
+        if self.cfg.flex_attention:
+            from axolotl.monkeypatch.attention.flex_attn import (
+                patch_flex_make_mask,
+                patch_flex_wrapper,
+            )
+
+            flex_attn_compile_kwargs = self.cfg.flex_attn_compile_kwargs or {}
+            patch_flex_wrapper(**flex_attn_compile_kwargs)
+            patch_flex_make_mask()
+
         # patch gemma3 conditional generation forward before loading plugins
         # as it could be overridden by plugins
         if self.cfg.model_config_type == "llama4":
@@ -905,13 +916,6 @@ class ModelLoader:
             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 not self.cfg.sample_packing and self.cfg.s2_attention:

src/axolotl/utils/schemas/config.py

@@ -225,6 +225,7 @@ class AxolotlInputConfig(
     sdp_attention: bool | None = None
     s2_attention: bool | None = None
     flex_attention: bool | None = None
+    flex_attn_compile_kwargs: dict[str, Any] | None = None
     flash_attention: bool | None = None
     flash_attn_cross_entropy: bool | None = None
     flash_attn_rms_norm: bool | None = None
@@ -1276,11 +1277,14 @@ class AxolotlConfigWCapabilities(AxolotlInputConfig):
         ):
             capabilities = data.get("capabilities")
             is_fsdp = data.get("fsdp") is not None
-
-            if capabilities and capabilities.get("n_gpu", 0) > 1:
+            is_fsdp2 = (
+                data.get("fsdp_config") is not None
+                and str(data.get("fsdp_config").get("fsdp_version")) == "2"
+            )
+            if capabilities and capabilities.get("n_gpu", 0) > 1 and not is_fsdp2:
                 if is_fsdp:
                     raise ValueError(
-                        "lora_mlp_kernel, lora_qkv_kernel, and lora_o_kernel are not compatible with FSDP."
+                        "lora_mlp_kernel, lora_qkv_kernel, and lora_o_kernel are not compatible with FSDP1."
                     )
         return data
 

src/axolotl/utils/trainer.py

@@ -17,6 +17,7 @@ from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
 from transformers.utils import is_torch_bf16_gpu_available
 
 from axolotl.core.trainer_builder import HFCausalTrainerBuilder, HFRLTrainerBuilder
+from axolotl.monkeypatch.trainer_eval_guard import patch_evaluation_loop_for_fsdp2
 from axolotl.utils.distributed import reduce_and_broadcast
 from axolotl.utils.environment import check_cuda_p2p_ib_support
 from axolotl.utils.samplers import MultipackBatchSampler, get_dataset_lengths
@@ -235,7 +236,8 @@ def drop_long_seq(sample, sequence_len=2048, min_sequence_len=2):
 
 
 def process_datasets_for_packing(cfg, train_dataset, eval_dataset):
-    if cfg.model_config_type in ["mamba", "gemma3"]:
+    drop_attn_mask = cfg.model_config_type in ["mamba", "gemma3"]
+    if drop_attn_mask:
         LOG.info("dropping attention_mask column")
         train_dataset = train_dataset.remove_columns("attention_mask")
         if eval_dataset:
@@ -625,6 +627,12 @@ def setup_trainer(
         A trainer instance (either `HFRLTrainer` or `HFCausalTrainer`) configured based
             on the provided parameters.
     """
+    if (
+        cfg.torch_compile
+        and cfg.fsdp_config
+        and str(cfg.fsdp_config.fsdp_version) == "2"
+    ):
+        patch_evaluation_loop_for_fsdp2()
     if cfg.rl:
         trainer_builder = HFRLTrainerBuilder(cfg, model, tokenizer, processor)
         trainer_builder.model_ref = model_ref

tests/e2e/multigpu/solo/test_flex.py

@@ -56,11 +56,12 @@ class TestPackedFlex:
                 "num_epochs": 1,
                 "micro_batch_size": 2,
                 "gradient_accumulation_steps": 2,
+                "gradient_checkpointing": True,
                 "output_dir": temp_dir,
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_torch_fused",
                 "lr_scheduler": "cosine",
-                "max_steps": 5,
+                "max_steps": 2,
                 "use_tensorboard": True,
                 "save_strategy": "no",
             }

tests/e2e/multigpu/solo/test_grpo.py

@@ -177,6 +177,7 @@ def oai_gsm8k_transform(cfg, *args, **kwargs):
             "NCCL_P2P_LEVEL": "LOC",
             **current_env,
             "CUDA_VISIBLE_DEVICES": "1",
+            "VLLM_USE_V1": "0",
         }
         vllm_process_id = start_vllm(
             cfg.base_model,
@@ -264,6 +265,7 @@ def oai_gsm8k_transform(cfg, *args, **kwargs):
             "NCCL_P2P_LEVEL": "LOC",  # nccl can be brittle, assume P2P isn't reliable
             **current_env,
             "CUDA_VISIBLE_DEVICES": "1",
+            "VLLM_USE_V1": "0",
         }
         vllm_process_id = start_vllm(
             cfg.base_model,