Fix Linear4bit warnings/test for compute dtype

bitsandbytes/nn/modules.py

@@ -455,14 +455,14 @@ class Linear4bit(nn.Linear):
             self.compute_dtype = x.dtype
         elif x.dtype == torch.float16:
             # we take the compoute dtype passed into the layer
-            if self.compute_dtype == torch.float32 and (x.numel() == x.shape[-1]):
+            if self.compute_dtype in [None, torch.float32] and (x.numel() == x.shape[-1]):
                 # single batch inference with input torch.float16 and compute_dtype float32 -> slow inference when it could be fast
                 # warn the user about this
                 warnings.warn(
                     "Input type into Linear4bit is torch.float16, but bnb_4bit_compute_dtype=torch.float32 (default). This will lead to slow inference.",
                 )
                 warnings.filterwarnings("ignore", message=".*inference.")
-            if self.compute_dtype == torch.float32 and (x.numel() != x.shape[-1]):
+            if self.compute_dtype in [None, torch.float32] and (x.numel() != x.shape[-1]):
                 warnings.warn(
                     "Input type into Linear4bit is torch.float16, but bnb_4bit_compute_dtype=torch.float32 (default). This will lead to slow inference or training speed.",
                 )

tests/test_modules.py

@@ -440,31 +440,23 @@ def test_4bit_linear_warnings(device):
     dim1 = 64
 
     with pytest.warns(UserWarning, match=r"inference or training"):
-        net = nn.Sequential(
-            *[bnb.nn.Linear4bit(dim1, dim1, quant_type="nf4", compute_dtype=torch.float32) for i in range(10)]
-        )
+        net = nn.Sequential(*[bnb.nn.Linear4bit(dim1, dim1, quant_type="nf4") for i in range(10)])
         net = net.to(device)
         inp = torch.rand(10, dim1, device=device, dtype=torch.float16)
         net(inp)
     with pytest.warns(UserWarning, match=r"inference."):
-        net = nn.Sequential(
-            *[bnb.nn.Linear4bit(dim1, dim1, quant_type="nf4", compute_dtype=torch.float32) for i in range(10)]
-        )
+        net = nn.Sequential(*[bnb.nn.Linear4bit(dim1, dim1, quant_type="nf4") for i in range(10)])
         net = net.to(device)
         inp = torch.rand(1, dim1, device=device, dtype=torch.float16)
         net(inp)
 
     with pytest.warns(UserWarning) as record:
-        net = nn.Sequential(
-            *[bnb.nn.Linear4bit(dim1, dim1, quant_type="nf4", compute_dtype=torch.float32) for i in range(10)]
-        )
+        net = nn.Sequential(*[bnb.nn.Linear4bit(dim1, dim1, quant_type="nf4") for i in range(10)])
         net = net.to(device)
         inp = torch.rand(10, dim1, device=device, dtype=torch.float16)
         net(inp)
 
-        net = nn.Sequential(
-            *[bnb.nn.Linear4bit(dim1, dim1, quant_type="nf4", compute_dtype=torch.float32) for i in range(10)]
-        )
+        net = nn.Sequential(*[bnb.nn.Linear4bit(dim1, dim1, quant_type="nf4") for i in range(10)])
         net = net.to(device)
         inp = torch.rand(1, dim1, device=device, dtype=torch.float16)
         net(inp)