Add Llama4 maverick examples

* fix: liger swiglu for llama4

* feat: add liger to deepseek v3

* fix: unpack not found

* fix: spelling

* fix: comment out deepseek v3

* fix: retest deepseek

* fix: map glu

* fix: patch model forward

* chore: add temp code to save

* fix: remove deepseek to move into separate PR

cicd/multigpu.py

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

examples/llama-4/README.md

@@ -0,0 +1,16 @@
+# Llama 4 by Meta AI
+
+## 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)
+
+### 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)

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

@@ -1,13 +1,20 @@
-base_model: meta-llama/Llama-4-Scout-17B-16E
+base_model: axolotl-quants/Llama-4-Maverick-17B-128E-Linearized-bnb-nf4-bf16
 model_type: Llama4ForConditionalGeneration
-  # Automatically upload checkpoint and final model to HF
+# Automatically upload checkpoint and final model to HF
-  # hub_model_id: username/custom_model_name
+# hub_model_id: username/custom_model_name
 
 strict: false
 
-  # torch_compile: true
+plugins:
+  - axolotl.integrations.liger.LigerPlugin
 
-adapter: lora
+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:
@@ -15,9 +22,15 @@ lora_target_modules:
   - 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
+# - lm_head
-  - embed_tokens
+# - embed_tokens
 
 chat_template: llama4
 datasets:
@@ -40,36 +53,37 @@ pad_to_sequence_len: true
 gradient_accumulation_steps: 1
 micro_batch_size: 1
 num_epochs: 1
-optimizer: adamw_torch_8bit
+optimizer: adamw_torch_fused
 lr_scheduler: cosine
-learning_rate: 2e-5
+learning_rate: 1e-4
 
 bf16: true
 tf32: true
 
-# gradient_checkpointing: true
-# gradient_checkpointing_kwargs:
-#   use_reentrant: false
 logging_steps: 1
 flash_attention: true
 
-warmup_steps: 100
+gradient_checkpointing: offload
-evals_per_epoch: 2
+gradient_checkpointing_kwargs:
+  use_reentrant: false
+
+warmup_steps: 20
+evals_per_epoch: 1
 saves_per_epoch: 1
 weight_decay: 0.0
 fsdp:
   - auto_wrap
   - full_shard
 fsdp_config:
-  fsdp_version: 2
+  fsdp_transformer_layer_cls_to_wrap: Llama4TextDecoderLayer
-  fsdp_offload_params: false
+  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_transformer_layer_cls_to_wrap: Llama4TextDecoderLayer
+  fsdp_state_dict_type: FULL_STATE_DICT
-  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.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
+
+strict: false
+
+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
+
+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
+
+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_4bit
+lr_scheduler: cosine
+learning_rate: 1e-4
+
+bf16: true
+tf32: true
+
+logging_steps: 1
+flash_attention: true
+
+gradient_checkpointing: offload
+gradient_checkpointing_kwargs:
+  use_reentrant: false
+
+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-fsdp.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
+
+strict: false
+
+# 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: 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.txt

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

src/axolotl/integrations/liger/__init__.py

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

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

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