feat: LoRA kernel support for bias, dropout, dora, embeddings (#3528) [skip ci]

* feat: LoRA kernel support for bias, dropout, dora, embeddings

* chore: lint

* chore: lint

* address PR feedback, add regression tests, add fsdp2 tests for lora kernels

* update tests for new sigs

* update tests now that bias and dropout are supported

tests/e2e/multigpu/test_fsdp2_lora_kernels.py

@@ -0,0 +1,120 @@
+"""Test LoRA kernels under FSDP2 multi-GPU training.
+
+Verifies that lora_qkv_kernel, lora_o_kernel, lora_mlp_kernel, and
+lora_embedding_kernel work correctly with FSDP2 sharding, including
+with bias, dropout, and DoRA enabled.
+"""
+
+from pathlib import Path
+
+import yaml
+from accelerate.test_utils import execute_subprocess_async
+from transformers.testing_utils import get_torch_dist_unique_port
+
+from axolotl.utils.dict import DictDefault
+
+from tests.e2e.utils import require_torch_2_7_0
+
+AXOLOTL_ROOT = Path(__file__).parent.parent.parent.parent
+
+
+def _run_training(temp_dir, cfg):
+    """Write config and launch multi-GPU training."""
+    Path(temp_dir).mkdir(parents=True, exist_ok=True)
+    with open(Path(temp_dir) / "config.yaml", "w", encoding="utf-8") as fout:
+        fout.write(yaml.dump(cfg.to_dict(), Dumper=yaml.Dumper))
+
+    execute_subprocess_async(
+        [
+            "axolotl",
+            "train",
+            str(Path(temp_dir) / "config.yaml"),
+            "--num-processes",
+            "2",
+            "--main-process-port",
+            f"{get_torch_dist_unique_port()}",
+        ]
+    )
+
+
+def _base_lora_fsdp2_config(temp_dir, **overrides):
+    """Base config for LoRA + FSDP2 + kernel tests."""
+    cfg = {
+        "base_model": "Qwen/Qwen3-0.6B",
+        "sequence_len": 512,
+        "val_set_size": 0.0,
+        "datasets": [
+            {
+                "path": "tatsu-lab/alpaca",
+                "type": "alpaca",
+                "split": "train[:1%]",
+            },
+        ],
+        "adapter": "lora",
+        "lora_r": 8,
+        "lora_alpha": 16,
+        "lora_target_linear": True,
+        "num_epochs": 1,
+        "max_steps": 3,
+        "micro_batch_size": 1,
+        "gradient_accumulation_steps": 1,
+        "output_dir": temp_dir,
+        "learning_rate": 1e-4,
+        "optimizer": "adamw_torch_fused",
+        "lr_scheduler": "cosine",
+        "flash_attention": True,
+        "bf16": True,
+        "fsdp_version": 2,
+        "fsdp_config": {
+            "offload_params": False,
+            "cpu_ram_efficient_loading": False,
+            "transformer_layer_cls_to_wrap": "Qwen3DecoderLayer",
+            "state_dict_type": "FULL_STATE_DICT",
+            "auto_wrap_policy": "TRANSFORMER_BASED_WRAP",
+            "reshard_after_forward": True,
+        },
+        # Enable all LoRA kernels
+        "lora_mlp_kernel": True,
+        "lora_qkv_kernel": True,
+        "lora_o_kernel": True,
+        "lora_embedding_kernel": True,
+        "save_safetensors": True,
+    }
+    cfg.update(overrides)
+    return DictDefault(cfg)
+
+
+class TestFSDP2LoRAKernels:
+    """Test LoRA kernels under FSDP2."""
+
+    @require_torch_2_7_0
+    def test_lora_kernels_basic(self, temp_dir):
+        """Basic LoRA + kernels + FSDP2: no dropout, no bias, no DoRA."""
+        cfg = _base_lora_fsdp2_config(temp_dir)
+        _run_training(temp_dir, cfg)
+        assert (Path(temp_dir) / "adapter_model.safetensors").exists()
+
+    @require_torch_2_7_0
+    def test_lora_kernels_with_dropout(self, temp_dir):
+        """LoRA kernels + dropout + FSDP2."""
+        cfg = _base_lora_fsdp2_config(temp_dir, lora_dropout=0.1)
+        _run_training(temp_dir, cfg)
+        assert (Path(temp_dir) / "adapter_model.safetensors").exists()
+
+    @require_torch_2_7_0
+    def test_lora_kernels_with_dora(self, temp_dir):
+        """LoRA kernels + DoRA + FSDP2."""
+        cfg = _base_lora_fsdp2_config(temp_dir, peft_use_dora=True)
+        _run_training(temp_dir, cfg)
+        assert (Path(temp_dir) / "adapter_model.safetensors").exists()
+
+    @require_torch_2_7_0
+    def test_lora_kernels_with_dora_and_dropout(self, temp_dir):
+        """LoRA kernels + DoRA + dropout + FSDP2."""
+        cfg = _base_lora_fsdp2_config(
+            temp_dir,
+            peft_use_dora=True,
+            lora_dropout=0.05,
+        )
+        _run_training(temp_dir, cfg)
+        assert (Path(temp_dir) / "adapter_model.safetensors").exists()