Tighten type hints for tensor arithmetic (#135392)

Fixes #124015

Pull Request resolved: https://github.com/pytorch/pytorch/pull/135392
Approved by: https://github.com/ezyang

tools/pyi/gen_pyi.py

@@ -177,14 +177,18 @@ blocklist = [
     "copy_",
 ]
 
-binary_ops = (
+shift_ops = (
+    "lshift",
+    "rshift",
+    "ilshift",
+    "irshift",  # inplace ops
+)
+arithmetic_ops = (
     "add",
     "sub",
     "mul",
     "div",
     "pow",
-    "lshift",
-    "rshift",
     "mod",
     "truediv",
     "matmul",
@@ -195,24 +199,26 @@ binary_ops = (
     "rtruediv",
     "rfloordiv",
     "rpow",  # reverse arithmetic
+    "iadd",
+    "idiv",
+    "imul",
+    "isub",
+    "ifloordiv",
+    "imod",  # inplace ops
+)
+logic_ops = (
     "and",
     "or",
     "xor",
     "rand",
     "ror",
-    "rxor",  # logic
+    "rxor",  # reverse logic
-    "iadd",
     "iand",
-    "idiv",
-    "ilshift",
-    "imul",
     "ior",
-    "irshift",
+    "ixor",  # inplace ops
-    "isub",
-    "ixor",
-    "ifloordiv",
-    "imod",  # inplace ops
 )
+binary_ops = shift_ops + arithmetic_ops + logic_ops
+
 symmetric_comparison_ops = ("eq", "ne")
 asymmetric_comparison_ops = ("ge", "gt", "lt", "le")
 comparison_ops = symmetric_comparison_ops + asymmetric_comparison_ops
@@ -232,14 +238,24 @@ def sig_for_ops(opname: str) -> list[str]:
     assert opname.endswith("__") and opname.startswith("__"), f"Unexpected op {opname}"
 
     name = opname[2:-2]
-    if name in binary_ops:
+    if name in arithmetic_ops:
-        return [f"def {opname}(self, other: Any) -> Tensor: ..."]
+        return [
-    elif name in comparison_ops:
+            f"def {opname}(self, other: Union[Tensor, Number, _complex]) -> Tensor: ..."
-        sig = f"def {opname}(self, other: Any) -> Tensor: ..."
+        ]
-        if name in symmetric_comparison_ops:
+    elif name in logic_ops:
+        return [f"def {opname}(self, other: Union[Tensor, _bool]) -> Tensor: ..."]
+    elif name in shift_ops:
+        return [f"def {opname}(self, other: Union[Tensor, _int]) -> Tensor: ..."]
+    elif name in symmetric_comparison_ops:
+        return [
             # unsafe override https://github.com/python/mypy/issues/5704
-            sig += "  # type: ignore[override]"
+            f"def {opname}(self, other: Union[Tensor, Number, _complex]) -> Tensor: ...  # type: ignore[override]",
-        return [sig]
+            f"def {opname}(self, other: Any) -> _bool: ...",
+        ]
+    elif name in asymmetric_comparison_ops:
+        return [
+            f"def {opname}(self, other: Union[Tensor, Number, _complex]) -> Tensor: ..."
+        ]
     elif name in unary_ops:
         return [f"def {opname}(self) -> Tensor: ..."]
     elif name in to_py_type_ops:

torch/_decomp/decompositions.py

@@ -2291,7 +2291,8 @@ def native_batch_norm_backward(
     mean = save_mean_cast
     invstd = save_invstd_cast
     if train:
-        assert save_mean_cast is not None and save_invstd_cast is not None
+        assert mean is not None and invstd is not None
+
     else:
         assert running_mean_cast is not None and running_var_cast is not None
         mean = running_mean_cast

torch/_inductor/fx_passes/efficient_conv_bn_eval.py

@@ -33,6 +33,7 @@ def efficient_conv_bn_eval(
     """
 
     assert bn.running_var is not None
+    assert bn.running_mean is not None
 
     # These lines of code are designed to deal with various cases
     # like bn without affine transform, and conv without bias

torch/ao/quantization/_equalize.py

@@ -128,8 +128,7 @@ def cross_layer_equalization(module1, module2, output_axis=0, input_axis=1):
             "module type not supported:", type(module1), " ", type(module2)
         )
 
-    conv1_has_bias = has_bias(module1)
+    bias = get_module_bias(module1) if has_bias(module1) else None
-    bias = None
 
     weight1 = get_module_weight(module1)
     weight2 = get_module_weight(module2)
@@ -140,9 +139,6 @@ def cross_layer_equalization(module1, module2, output_axis=0, input_axis=1):
         number input channels of second arg"
         )
 
-    if conv1_has_bias:
-        bias = get_module_bias(module1)
-
     weight1_range = channel_range(weight1, output_axis)
     weight2_range = channel_range(weight2, input_axis)
 
@@ -151,7 +147,7 @@ def cross_layer_equalization(module1, module2, output_axis=0, input_axis=1):
     scaling_factors = torch.sqrt(weight1_range / weight2_range)
     inverse_scaling_factors = torch.reciprocal(scaling_factors)
 
-    if conv1_has_bias:
+    if bias is not None:
         bias = bias * inverse_scaling_factors
 
     # formatting the scaling (1D) tensors to be applied on the given argument tensors
@@ -168,7 +164,7 @@ def cross_layer_equalization(module1, module2, output_axis=0, input_axis=1):
     weight2 = weight2 * scaling_factors
 
     set_module_weight(module1, weight1)
-    if conv1_has_bias:
+    if bias is not None:
         set_module_bias(module1, bias)
     set_module_weight(module2, weight2)