add deterministic generation to random matrix

Signed-off-by: Kyle Sayers <kylesayrs@gmail.com>

Author: kylesayrs

src/compressed_tensors/transform/factory/base.py

@@ -49,6 +49,7 @@ class TransformFactory(RegistryMixin, ABC):
     def __init__(self, name: str, scheme: TransformScheme, seed: int = 42):
         self.name = name
         self.scheme = scheme
+        self.generator = torch.Generator().manual_seed(seed)
         self.seed = seed
 
     @classmethod

src/compressed_tensors/transform/factory/hadamard.py

@@ -40,7 +40,6 @@ class HadamardFactory(TransformFactory):
 
     def __init__(self, name: str, scheme: TransformScheme, seed: int = 42):
         super().__init__(name, scheme, seed)
-        self.generator = torch.Generator(device="cpu").manual_seed(seed)
         self.weights = ParameterizedDefaultDict(self._create_weight)
 
     def create_transform(self, module: Module, args: TransformArgs):

src/compressed_tensors/transform/factory/matrix_multiply.py

@@ -60,7 +60,9 @@ def create_transform(self, module: Module, args: TransformArgs):
         return RandomMatrixTransform(weight, args)
 
     def _create_weight(self, size: int, dtype: dtype, device: device) -> Parameter:
-        data = torch.rand((size, size), dtype=dtype, device=device)
+        data = torch.rand(
+            (size, size), generator=self.generator, dtype=dtype, device=device
+        )
         return Parameter(data, requires_grad=self.scheme.requires_grad)
 
     def _create_inverse(self, weight: Parameter) -> Parameter: