Revert "[inductor] Do variance calculation in opmath type (pytorch#11…

…5181)"

This reverts commit 42390a0.

Reverted pytorch#115181 on behalf of https://github.com/atalman due to OSSCI oncall, broke periodic tests ([comment](pytorch#115181 (comment)))

test/inductor/test_torchinductor.py

@@ -1125,19 +1125,6 @@ def fn(a):
         self.common(fn, ((torch.rand((10, 3, 352, 352), dtype=torch.float32),)))
         self.common(fn, ((torch.rand((14923), dtype=torch.float32),)))
 
-    def test_multilayer_var_lowp(self):
-        if self.device == "cpu" and IS_MACOS and not IS_X86:
-            atol, rtol = 1e-5, 5e-3
-        else:
-            atol, rtol = None, None
-
-        def fn(a):
-            return torch.var(a)
-
-        run_test = functools.partial(self.common, atol=atol, rtol=rtol)
-        run_test(fn, (torch.rand((16, 16, 352, 352), dtype=torch.float16),))
-        run_test(fn, (torch.rand((14923), dtype=torch.float16),))
-
     def test_embedding_bag_byte_unpack(self):
         if self.device != "cpu":
             raise unittest.SkipTest("No CUDA implementation (it returns empty)")

torch/_inductor/lowering.py

@@ -23,7 +23,6 @@
     dtype_to_type,
     elementwise_dtypes,
     ELEMENTWISE_TYPE_PROMOTION_KIND,
-    get_computation_dtype,
     is_boolean_dtype,
     is_float_dtype,
     is_integer_dtype,
@@ -4575,7 +4574,7 @@ def var_mean_sum_(x, axis, correction, keepdim, return_mean):
     denom = ExpandView.create(denom, list(sum_result.get_size()))
     x_var = div(sum_result, denom)
     if not return_mean:
-        return (x_var,)
+        return x_var
 
     x_mean = x_mean if keepdim else squeeze(x_mean, axis)
     return x_var, x_mean
@@ -4637,39 +4636,29 @@ def scale_fn(data):
     if return_mean:
         mean.realize()
         return var, mean
-    return (var,)
-
-
-def var_mean_helper_(x, *, axis, correction, keepdim, return_mean):
-    out_dtype = x.get_dtype()
-    compute_dtype = get_computation_dtype(out_dtype)
-    x = to_dtype(x, compute_dtype, copy=False)
-    kwargs = dict(
-        x=x,
-        axis=axis,
-        correction=correction,
-        keepdim=keepdim,
-        return_mean=return_mean,
-    )
-    output = (
-        var_mean_sum_(**kwargs)
-        if use_two_step_variance(x, axis=axis, keepdim=keepdim)
-        else var_mean_welford_(**kwargs)
-    )
-    output = tuple(to_dtype(x, out_dtype, copy=False) for x in output)
-    return output[0] if not return_mean else output
+    return var
 
 
 @register_lowering([aten.var, prims.var])
 def var_(x, axis=None, *, correction=None, keepdim=False):
-    return var_mean_helper_(
+    if use_two_step_variance(x, axis=axis, keepdim=keepdim):
+        return var_mean_sum_(
+            x, axis=axis, correction=correction, keepdim=keepdim, return_mean=False
+        )
+
+    return var_mean_welford_(
         x, axis=axis, correction=correction, keepdim=keepdim, return_mean=False
     )
 
 
 @register_lowering(aten.var_mean)
 def var_mean(x, axis=None, *, correction=None, keepdim=False):
-    return var_mean_helper_(
+    if use_two_step_variance(x, axis=axis, keepdim=keepdim):
+        return var_mean_sum_(
+            x, axis=axis, correction=correction, keepdim=keepdim, return_mean=True
+        )
+
+    return var_mean_welford_(
         x, axis=axis, correction=correction, keepdim=keepdim, return_mean=True
     )