Update create_dynamic_map to always return a float32 tensor (#1521)

bitsandbytes/functional.py

@@ -389,14 +389,14 @@ def create_dynamic_map(signed=True, max_exponent_bits=7, total_bits=8):
             if signed
             else 2 ** (i + non_sign_bits - max_exponent_bits + 1) + 1,
         )
-        boundaries = torch.linspace(0.1, 1, fraction_items)
+        boundaries = torch.linspace(0.1, 1, fraction_items, dtype=torch.float32)
         means = (boundaries[:-1] + boundaries[1:]) / 2.0
         data += ((10 ** (-(max_exponent_bits - 1) + i)) * means).tolist()
         if signed:
             data += (-(10 ** (-(max_exponent_bits - 1) + i)) * means).tolist()
 
     if additional_items > 0:
-        boundaries = torch.linspace(0.1, 1, additional_items + 1)
+        boundaries = torch.linspace(0.1, 1, additional_items + 1, dtype=torch.float32)
         means = (boundaries[:-1] + boundaries[1:]) / 2.0
         data += ((10 ** (-(max_exponent_bits - 1) + i)) * means).tolist()
         if signed:
@@ -412,7 +412,7 @@ def create_dynamic_map(signed=True, max_exponent_bits=7, total_bits=8):
         data.append(0)
 
     data.sort()
-    return torch.tensor(data)
+    return torch.tensor(data, dtype=torch.float32)
 
 
 def create_quantile_map(A, total_bits=8):