modifications to dataclasses to support fast kernels on full finetuning

Signed-off-by: 1000850000 user <aaron.chew1@ibm.com>

tuning/config/acceleration_configs/acceleration_framework_config.py

@@ -92,7 +92,7 @@ class AccelerationFrameworkConfig:
     fast_kernels: Annotated[
         FastKernelsConfig,
         ConfigAnnotation(
-            path="peft.quantization",
+            path="training",
             key="fused_ops_and_kernels",
             experimental=True,
             required_packages=["foak"],
@@ -127,6 +127,15 @@ def _verify_configured_dataclasses(self):
                 self.padding_free is not None
             ), "`--multipack` is currently only supported with `--padding_free`"
 
+        # Check that fused lora must be activated with either auto_gptq or bitsandbytes
+        if self.fused_lora is not None:
+            assert (
+                self.bitsandbytes is not None or self.auto_gptq is not None
+            ), "`--fused_lora` must be accompanied by a quantized base layer"\
+                " `--auto_gptq` or `--bitsandbytes`."
+
+
+
     @staticmethod
     def from_dataclasses(*dataclasses: Type):
         "Convert one or many FMS config dataclasses to a monolithic AccelerationConfig"

tuning/config/acceleration_configs/fused_ops_and_kernels.py

@@ -54,18 +54,11 @@ class FastKernelsConfig(List):
     fast_loss: bool = False
 
     # fast rms norm triton kernels
-    fast_rsm_layernorm: bool = False
+    fast_rms_layernorm: bool = False
 
     # fast RoPE embedding triton kernels
     fast_rope_embeddings: bool = False
 
-    def __post_init__(self):
-
-        if not self.fast_loss == self.fast_rsm_layernorm == self.fast_rope_embeddings:
-            raise ValueError(
-                "fast_loss, fast_rms_layernorm and fast_rope_embedding must be enabled "
-                "together. This restriction may be relaxed in the future."
-            )
 
 
 @dataclass
@@ -77,14 +70,6 @@ class FusedOpsAndKernelsConfig:
     # fast kernels
     fast_kernels: FastKernelsConfig = None
 
-    def __post_init__(self):
-        if (self.fused_lora is not None and self.fast_kernels is None) or (
-            self.fused_lora is None and self.fast_kernels is not None
-        ):
-            raise ValueError(
-                "fused lora and fast_kernels must be used together. "
-                "This restriction may be relaxed in the future."
-            )
 
         # ensure nested dataclasses initialized
         ensure_nested_dataclasses_initialized(self)