Fix: adding magistral fsdp config, fixing not eval with test_datasets, handle mllama attention (#2789) [skip ci]

* feat: add fsdp config for magistral

* fix: add mllama self attention handling for lora kernels

* fix: no eval if val_set_size 0 despite having test_datasets

* fix: add note for cce for vlm in newer model

examples/magistral/magistral-small-fsdp-qlora.yaml

@@ -0,0 +1,72 @@
+base_model: mistralai/Magistral-Small-2506
+
+# Enable to use mistral-common tokenizer
+tokenizer_use_mistral_common: true
+
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
+
+load_in_8bit: false
+load_in_4bit: true
+
+datasets:
+  - path: fozziethebeat/alpaca_messages_2k_test
+    type: chat_template
+
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.1
+output_dir: ./outputs/lora-out
+
+adapter: qlora
+lora_model_dir:
+
+sequence_len: 2048
+sample_packing: true
+eval_sample_packing: false
+pad_to_sequence_len: true
+
+lora_r: 32
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_linear: true
+lora_target_modules:
+  - gate_proj
+  - down_proj
+  - up_proj
+  - q_proj
+  - v_proj
+  - k_proj
+  - o_proj
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 4
+micro_batch_size: 2
+num_epochs: 1
+optimizer: adamw_torch_fused
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+bf16: auto
+tf32: false
+
+gradient_checkpointing:
+resume_from_checkpoint:
+logging_steps: 1
+flash_attention: true
+
+warmup_ratio: 0.1
+evals_per_epoch: 1
+saves_per_epoch: 1
+
+fsdp:
+  - full_shard
+  - auto_wrap
+fsdp_config:
+  fsdp_state_dict_type: FULL_STATE_DICT
+  fsdp_transformer_layer_cls_to_wrap: MistralDecoderLayer
+  fsdp_activation_checkpointing: true

src/axolotl/core/builders/base.py

@@ -380,8 +380,8 @@ class TrainerBuilderBase(abc.ABC):
         )
 
         # eval_strategy and eval_steps
-        if not self.eval_dataset or self.cfg.val_set_size == 0:
+        if not self.eval_dataset and self.cfg.val_set_size == 0:
-            # do not eval if no eval_dataset or val_set_size=0
+            # do not eval if no eval_dataset and val_set_size=0
             training_args_kwargs["eval_strategy"] = "no"
         elif self.cfg.eval_steps:
             training_args_kwargs["eval_strategy"] = "steps"

src/axolotl/integrations/cut_cross_entropy/README.md

@@ -24,6 +24,14 @@ pip3 uninstall -y cut-cross-entropy && pip3 install "cut-cross-entropy[transform
 
 ## Usage
 
+**NOTE**: If you are training a VLM model, please use older version of Axolotl as upstream has applied a major VLM refactor, and our patches have not been updated yet.
+
+```bash
+git checkout 787880215b3ab32ccaf81c1b2e9588c6f3e6e764
+
+pip3 install --no-build-isolation -e .
+```
+
 ```yaml
 plugins:
   - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin

src/axolotl/monkeypatch/lora_kernels.py

@@ -145,6 +145,11 @@ def get_attention_cls_from_config(cfg: DictDefault) -> Type[nn.Module]:
 
         return Qwen2Attention
 
+    if model_type == "mllama":
+        from transformers.models.mllama.modeling_mllama import MllamaTextSelfAttention
+
+        return MllamaTextSelfAttention
+
     try:
         # Dynamically import the module and attention class
         module_path = f"transformers.models.{model_type}.modeling_{model_type}"