pre-commit: running and fixing...

thunder/executors/nvfuserex_impl.py

@@ -1948,6 +1948,7 @@ def var_mean(
 
 register_supported(PrimIDs.VAR_MEAN, var_mean, _var_mean_check)
 
+
 def _matmul_check(
     a: TensorProxy,
     b: TensorProxy,
@@ -1957,6 +1958,7 @@ def _matmul_check(
         enable_matmul = False
     return enable_matmul and is_supported_tensor(a) and is_supported_tensor(b)
 
+
 def matmul(
     a: TensorProxy,
     b: TensorProxy,
@@ -1968,6 +1970,7 @@ def matmul(
     nvb = getnv(b, fd, lc_to_nv_map)
     return fd.ops.matmul(nva, nvb)
 
+
 register_supported(PrimIDs.MATMUL, matmul, _matmul_check)
 
 

thunder/tests/test_nvfuser.py

@@ -852,15 +852,17 @@ def get_num_fusions(cfn):
     nvfuserex.set_fuel(thunder.extend.FUEL_LEVEL.UNLIMITED)
 
 
-@instantiate(dtypes=(thunder.float16, thunder.bfloat16), devicetypes=(devices.DeviceType.CUDA,), executors=(nvFuserExecutor,))
+@instantiate(
+    dtypes=(thunder.float16, thunder.bfloat16), devicetypes=(devices.DeviceType.CUDA,), executors=(nvFuserExecutor,)
+)
 def test_matmul(executor, device: str, dtype: dtypes.dtype):
     m, n, k = 128, 64, 32
     torch_dtype = ltorch.to_torch_dtype(dtype)
     a = torch.randn((m, k), dtype=torch_dtype, device=device)
     b = torch.randn((k, n), dtype=torch_dtype, device=device)
 
-    def fn(a , b):
-        return a.matmul(b);
+    def fn(a, b):
+        return a.matmul(b)
 
     compiled_func = thunder.compile(
         fn,
@@ -872,4 +874,4 @@ def fn(a , b):
     traces = thunder.last_traces(compiled_func)
     fusions = examine.get_fusions(traces[-1])
     assert len(fusions) == 1
-    assert torch.allclose(out, torch.matmul(a, b))
+    assert torch.allclose(out, torch.matmul(a, b))