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>

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|>