refactor(kernel): register-dispatch architecture for kernelize (registry/config/ops 三层拆分)

cookbook/transformers/ep_fsdp2_lora_qwen3_5_moe.py

@@ -18,7 +18,7 @@
 from twinkle.model import TransformersModel
 from twinkle.preprocessor import SelfCognitionProcessor
 from twinkle.utils.framework import Torch
-from twinkle.kernel import kernelize, liger_builtin, npu_builtin
+from twinkle.kernel import kernelize
 
 logger = get_logger()
 args = CLI.from_args()
@@ -93,16 +93,14 @@ def train():
         },
     )
     # Kernel mode: torch (TWINKLE_TORCH_BASELINE=1, no fusion) | npu (default,
-    # CANN + FLA) | npu+liger (--enable-liger, Liger per-layer on top of CANN).
+    # CANN + FLA via DEFAULT_KERNEL_CONFIG). `--enable-liger` gates the fused-CE
+    # loss below; per-layer kernels stay on CANN on NPU (the default config's
+    # npu-first chains). To force Liger per-layer kernels, pass a custom
+    # mapping with liger-first KernelChoice chains — see Kernel.md.
     # Sharding/batch are unchanged across modes.
     _torch_baseline = os.environ.get('TWINKLE_TORCH_BASELINE', '').lower() in ('1', 'true', 'yes')
-    kernel_mapping = {}
-    if Torch.is_npu_available() and not _torch_baseline:
-        kernel_mapping.update(npu_builtin(model))
-    if args.model.enable_liger and not _torch_baseline:
-        kernel_mapping.update(liger_builtin(model))
-    if kernel_mapping:
-        model = kernelize(model, kernel_mapping)
+    if not _torch_baseline:
+        model = kernelize(model)
     _use_fused_ce = args.model.enable_liger and not _torch_baseline and args.model.enable_fused_ce
     _task = 'fused_lm_ce' if _use_fused_ce else 'causal_lm'
     lora_cfg = _build_lora_config()

cookbook/transformers/fsdp2.py

@@ -13,7 +13,7 @@
 from twinkle.model import TransformersModel
 from twinkle.preprocessor import SelfCognitionProcessor
 from twinkle.utils.framework import Torch
-from twinkle.kernel import kernelize, liger_builtin, npu_builtin
+from twinkle.kernel import kernelize
 
 logger = get_logger()
 args = CLI.from_args()
@@ -66,19 +66,15 @@ def train():
     discovered = {type(m).__name__ for m in model.model.modules()
                   if type(m).__name__.endswith('DecoderLayer')}
     model.model._no_split_modules = list(discovered) or [model.model.config.model_type.title() + 'DecoderLayer']
-    # Compose the kernel mapping: NPU built-ins first, then Liger on top so
-    # `--enable-liger` opts into Liger's cross-device Triton/Ascend kernels
-    # (later keys win on overlap — see twinkle.kernel Kernel.md).
-    kernel_mapping = {}
     # Kernel mode: torch (TWINKLE_TORCH_BASELINE=1, no fusion) | npu (default,
-    # CANN + FLA) | npu+liger (--enable-liger, Liger per-layer + fused-CE).
+    # CANN + FLA via DEFAULT_KERNEL_CONFIG). `--enable-liger` gates the fused-CE
+    # loss below; per-layer kernels stay on CANN on NPU (the default config's
+    # npu-first chains), matching the old npu+liger mode after CANN preference.
+    # To force Liger per-layer kernels, pass a custom mapping with liger-first
+    # KernelChoice chains — see Kernel.md.
     _torch_baseline = os.environ.get('TWINKLE_TORCH_BASELINE', '').lower() in ('1', 'true', 'yes')
-    if Torch.is_npu_available() and not _torch_baseline:
-        kernel_mapping.update(npu_builtin(model))
-    if args.model.enable_liger and not _torch_baseline:
-        kernel_mapping.update(liger_builtin(model))
-    if kernel_mapping:
-        model = kernelize(model, kernel_mapping)
+    if not _torch_baseline:
+        model = kernelize(model)
 
     # `--enable-liger` turns on BOTH the per-layer Liger/CANN kernels (above)
     # AND, by default (`enable_fused_ce=True`), the LigerFusedLinearCrossEntropyLoss

cookbook/transformers/sp_fsdp_dense.py

@@ -10,7 +10,7 @@
 from twinkle.model import TransformersModel
 from twinkle.preprocessor import SelfCognitionProcessor
 from twinkle.utils.framework import Torch
-from twinkle.kernel import kernelize, liger_builtin, npu_builtin
+from twinkle.kernel import kernelize
 
 logger = get_logger()
 args = CLI.from_args()
@@ -70,16 +70,14 @@ def train():
         strategy=args.model.strategy,
     )
     # Kernel mode: torch (TWINKLE_TORCH_BASELINE=1, no fusion) | npu (default,
-    # CANN + FLA) | npu+liger (--enable-liger, Liger per-layer on top of CANN).
+    # CANN + FLA via DEFAULT_KERNEL_CONFIG). `--enable-liger` gates the fused-CE
+    # loss below; per-layer kernels stay on CANN on NPU (the default config's
+    # npu-first chains). To force Liger per-layer kernels, pass a custom
+    # mapping with liger-first KernelChoice chains — see Kernel.md.
     # Sharding/batch are unchanged across modes.
     _torch_baseline = os.environ.get('TWINKLE_TORCH_BASELINE', '').lower() in ('1', 'true', 'yes')
-    kernel_mapping = {}
-    if Torch.is_npu_available() and not _torch_baseline:
-        kernel_mapping.update(npu_builtin(model))
-    if args.model.enable_liger and not _torch_baseline:
-        kernel_mapping.update(liger_builtin(model))
-    if kernel_mapping:
-        model = kernelize(model, kernel_mapping)
+    if not _torch_baseline:
+        model = kernelize(model)
     _use_fused_ce = args.model.enable_liger and not _torch_baseline and args.model.enable_fused_ce
     _task = 'fused_lm_ce' if _use_fused_ce else 'causal_lm'
     lora_config = LoraConfig(**args.get_lora_args())