Enable half/bfloat16 dtypes

facto/inputgen/argument/gen.py

@@ -8,13 +8,13 @@
 from dataclasses import dataclass
 from typing import Optional, Tuple
 
+import facto.utils.dtypes as dt
 import torch
 from facto.inputgen.argument.engine import MetaArg
 from facto.inputgen.utils.config import Condition, TensorConfig
 from facto.inputgen.utils.random_manager import seeded_random_manager
 from facto.inputgen.variable.gen import VariableGenerator
 from facto.inputgen.variable.space import VariableSpace
-from torch.testing._internal.common_dtype import floating_types, integral_types
 
 
 FLOAT_RESOLUTION = 8
@@ -218,10 +218,10 @@ def get_random_tensor(self, size, dtype, high=None, low=None) -> torch.Tensor:
                     low=0, high=2, size=size, dtype=dtype, generator=torch_rng
                 )
 
-        if dtype in integral_types():
+        if dtype in dt._int:
             low = math.ceil(low)
             high = math.floor(high) + 1
-        elif dtype in floating_types():
+        elif dtype in dt._floating:
             low = math.ceil(FLOAT_RESOLUTION * low)
             high = math.floor(FLOAT_RESOLUTION * high) + 1
         else:
@@ -263,9 +263,9 @@ def get_random_tensor(self, size, dtype, high=None, low=None) -> torch.Tensor:
                 )
             t = torch.where(t == 0, pos, t)
 
-        if dtype in integral_types():
+        if dtype in dt._int:
             return t
-        if dtype in floating_types():
+        if dtype in dt._floating:
             return t / FLOAT_RESOLUTION
         raise ValueError(f"Unsupported Dtype: {dtype}")
 

facto/inputgen/attribute/engine.py

@@ -12,7 +12,11 @@
 from facto.inputgen.attribute.solve import AttributeSolver
 from facto.inputgen.specs.model import Constraint
 from facto.inputgen.variable.gen import VariableGenerator
-from facto.inputgen.variable.type import ScalarDtype, sort_values_of_type
+from facto.inputgen.variable.type import (
+    ScalarDtype,
+    sort_values_of_type,
+    SUPPORTED_TENSOR_DTYPES,
+)
 
 
 class AttributeEngine(AttributeSolver):
@@ -33,7 +37,7 @@ def gen(self, focus: Attribute, *args):
             num = 2
         elif self.attribute == focus:
             if self.attribute == Attribute.DTYPE:
-                num = 8
+                num = len(SUPPORTED_TENSOR_DTYPES)
             else:
                 num = 6
         else:

facto/inputgen/variable/type.py

@@ -35,12 +35,12 @@ def __str__(self):
     torch.int64,
     torch.float32,
     torch.float64,
+    torch.float16,
+    torch.bfloat16,
     # The following types are not supported yet, but we should support them soon:
-    # torch.float16,
     # torch.complex32,
     # torch.complex64,
     # torch.complex128,
-    # torch.bfloat16,
 ]
 
 

facto/specdb/db.py

@@ -6,8 +6,8 @@
 
 import math
 
-import facto.specdb.dtypes as dt
 import facto.specdb.function as fn
+import facto.utils.dtypes as dt
 import torch
 from facto.inputgen.argument.type import ArgType
 from facto.inputgen.specs.model import (

facto/specdb/dtypes.py → facto/utils/dtypes.py

@@ -8,13 +8,12 @@
 
 _int = [torch.uint8, torch.int8, torch.short, torch.int, torch.long]
 _int_and_bool = [torch.bool] + _int
-_floating = [torch.float, torch.double]
+_floating = [torch.float16, torch.bfloat16, torch.float, torch.double]
 _real = _int + _floating
 _real_and_bool = [torch.bool] + _int + _floating
-_floating_and_half = [torch.half] + _floating
 _complex = [torch.chalf, torch.cfloat, torch.cdouble]
 _quant = [torch.qint8, torch.quint8, torch.qint32, torch.quint4x2, torch.quint2x4]
-_all = [torch.bool] + _int + _floating_and_half + _complex + _quant
+_all = _real_and_bool + _complex + _quant
 
 
 def can_cast_from(t):

test/specdb/test_specdb_cpu.py

@@ -22,6 +22,10 @@ def test_all_ops_cpu(self):
             "split_with_sizes_copy.default",
         ]
 
+        # "cdist" not implemented for 'Half' on CPU
+        # "pdist" not implemented for 'Half' on CPU
+        skip_ops += ["_cdist_forward.default", "_pdist_forward.default"]
+
         self._run_all_ops(skip_ops=skip_ops)
 
 

test/specdb/test_specdb_mps.py

@@ -51,6 +51,9 @@ def test_all_ops_mps(self):
             "var.dim",
         ]
 
+        # ConvTranspose 3D with BF16 or FP16 types is not supported on MPS
+        skip_ops += ["convolution.default"]
+
         config = TensorConfig(device="mps", disallow_dtypes=[torch.float64])
         self._run_all_ops(config=config, skip_ops=skip_ops)