Add device to TensorConfig (#34)

Summary:
Pull Request resolved: #34

imported-using-ghimport

Test Plan:
Imported from OSS

Rollback Plan:

Reviewed By: digantdesai

Differential Revision: D80468304

Pulled By: manuelcandales

fbshipit-source-id: bbd928b6797ec45fee04a15161640c284611e1e6

Author: manuelcandales

examples/config_example.py

@@ -53,12 +53,17 @@ def generate_inputs():
     spec = SpecDictDB["add.Tensor"]
 
     config = TensorConfig(
+        device="mps",
         empty=False,
         transposed=False,
         permuted=True,
         strided=True,
     ).set_probability(0.7)
 
+    print("Generating inputs with the following config:")
+    print("\tdevice: ", config.device)
+    print("\tconditions: ", config.conditions)
+
     generator = ArgumentTupleGenerator(spec, config=config)
     for ix, tup in enumerate(generator.gen()):
         posargs, inkwargs, outargs = tup
@@ -72,7 +77,8 @@ def test_add_op():
     for posargs, inkwargs, outargs in generate_inputs():
         try:
             op(*posargs, **inkwargs, **outargs)
-        except Exception:
+        except Exception as e:
+            print(f"Failed with error: {e}")
             return False
     return True
 

facto/inputgen/argument/gen.py

@@ -112,11 +112,13 @@ def __init__(
         dtype: Optional[torch.dtype],
         structure: Tuple,
         space: VariableSpace,
+        device: str = "cpu",
         transformation: Optional[TensorTransformation] = None,
     ):
         self.dtype = dtype
         self.structure = structure
         self.space = space
+        self.device = device
         self.transformation = transformation
 
     def gen(self):
@@ -141,6 +143,7 @@ def gen(self):
         tensor = self.get_random_tensor(
             size=underlying_shape, dtype=self.dtype, high=max_val, low=min_val
         )
+        tensor = tensor.to(self.device)
 
         # Apply transformations as instructed
         tensor = self._apply_transformation(tensor)
@@ -192,15 +195,17 @@ def _apply_noncontiguity(self, tensor):
 
         return tensor[indices]
 
-    def get_random_tensor(self, size, dtype, high=None, low=None):
+    def get_random_tensor(self, size, dtype, high=None, low=None) -> torch.Tensor:
         torch_rng = seeded_random_manager.get_torch()
 
         if low is None and high is None:
             low = -100
             high = 100
         elif low is None:
+            assert high is not None
             low = high - 100
         elif high is None:
+            assert low is not None
             high = low + 100
         size = tuple(size)
         if dtype == torch.bool:
@@ -262,6 +267,7 @@ def get_random_tensor(self, size, dtype, high=None, low=None):
             return t
         if dtype in floating_types():
             return t / FLOAT_RESOLUTION
+        raise ValueError(f"Unsupported Dtype: {dtype}")
 
 
 class ArgumentGenerator:
@@ -270,6 +276,8 @@ def __init__(self, meta: MetaArg, config=None):
         self.config = config
 
     def gen(self):
+        device = "cpu" if self.config is None else self.config.device
+
         if self.meta.optional:
             return None
         elif self.meta.argtype.is_tensor():
@@ -284,6 +292,7 @@ def gen(self):
                 dtype=self.meta.dtype,
                 structure=self.meta.structure,
                 space=self.meta.value,
+                device=device,
                 transformation=transformation,
             ).gen()
         elif self.meta.argtype.is_tensor_list():
@@ -302,6 +311,7 @@ def gen(self):
                     dtype=self.meta.dtype[i],
                     structure=self.meta.structure[i],
                     space=self.meta.value,
+                    device=device,
                     transformation=transformation,
                 ).gen()
                 tensors.append(tensor)

facto/inputgen/utils/config.py

@@ -15,7 +15,8 @@ class Condition(str, Enum):
 
 
 class TensorConfig:
-    def __init__(self, **conditions):
+    def __init__(self, device="cpu", **conditions):
+        self.device = device
         self.conditions = {condition: False for condition in Condition}
         for condition, value in conditions.items():
             if condition in self.conditions: