Support value range for tensors with default df

forge/test/operators/pytorch/eltwise_binary/test_binary.py

@@ -134,6 +134,10 @@ def verify(
         if value_range is None:
             value_range = ValueRanges.SMALL
 
+        # Old behavior when dev_data_format was not set
+        if dev_data_format is None:
+            value_range = ValueRanges.SMALL_POSITIVE
+
         operator = getattr(torch, test_vector.operator)
 
         kwargs = test_vector.kwargs if test_vector.kwargs else {}

forge/test/operators/pytorch/eltwise_unary/test_unary.py

@@ -115,7 +115,8 @@ def verify(
             math_fidelity=test_vector.math_fidelity,
             pcc=test_vector.pcc,
             warm_reset=warm_reset,
-            value_range=ValueRanges.SMALL,
+            # Old behavior when dev_data_format was not set
+            value_range=ValueRanges.SMALL if test_vector.dev_data_format is not None else ValueRanges.SMALL_POSITIVE,
         )
 
 

forge/test/operators/pytorch/matmul/test_matmul.py

@@ -136,7 +136,8 @@ def verify(
         input_source_flag=input_source_flag,
         dev_data_format=dev_data_format,
         math_fidelity=math_fidelity,
-        value_range=ValueRanges.SMALL,
+        # Old behavior when dev_data_format was not set
+        value_range=ValueRanges.SMALL if dev_data_format is not None else ValueRanges.SMALL_POSITIVE,
     )
 
 

forge/test/operators/pytorch/nn/test_softmax.py

@@ -161,7 +161,8 @@ def verify(
         input_source_flag=input_source_flag,
         dev_data_format=dev_data_format,
         math_fidelity=math_fidelity,
-        value_range=ValueRanges.SMALL,
+        # Old behavior when dev_data_format was not set
+        value_range=ValueRanges.SMALL if dev_data_format is not None else ValueRanges.SMALL_POSITIVE,
     )
 
 

forge/test/operators/utils/compat.py

@@ -122,6 +122,12 @@ def get_value_range(
     ) -> OperatorParameterTypes.RangeValue:
         """Returns a range for the given data format and dtype"""
 
+        # When dev_data_format is None, the range should be the same as Float32/float32
+        if dev_data_format is None:
+            dev_data_format = forge.DataFormat.Float32
+        if dtype is None:
+            dtype = torch.float32
+
         if dev_data_format in cls.data_format_ranges:
             data_format_ranges = cls.data_format_ranges[dev_data_format]
         else:
@@ -196,7 +202,13 @@ def get_random_torch_input(
     ) -> List[torch.Tensor]:
 
         if dtype is None:
-            return torch.rand(input_shape, generator=generator)
+            input = torch.rand(input_shape, generator=generator)
+            range = cls.get_value_range(dev_data_format=dev_data_format, dtype=dtype, value_range=value_range)
+            min, max = range
+            if min == 0 and max == 1:
+                # No need to scale the input, just return it
+                return input
+            return cls.move_from_small_to_big_value_range(input, min, max)
         elif dtype in cls.DTypes.floats:
             input = torch.rand(input_shape, dtype=dtype, generator=generator)
             range = cls.get_value_range(dev_data_format=dev_data_format, dtype=dtype, value_range=value_range)