[Fix] Adding missing f prefixes to formatted strings [2/N] (#164066)

As stated in the title.

* #164068
* #164067
* __->__ #164066
* #164065

Pull Request resolved: https://github.com/pytorch/pytorch/pull/164066
Approved by: https://github.com/Skylion007

torch/ao/quantization/pt2e/_affine_quantization.py

@@ -285,10 +285,10 @@ def _choose_qparams_affine(
         max_val = torch.amax(input, dim=reduction_dims, keepdim=False)
     else:
         assert min_val is not None and max_val is not None, (
-            "Need to provide `min_val` and `max_val` when `input` is None, got: {min_val, max_val}"
+            f"Need to provide `min_val` and `max_val` when `input` is None, got: {min_val, max_val}"
         )
         assert min_val.dtype == max_val.dtype, (
-            "Expecting `min_val` and `max_val` to have the same dtype, got: {min_val.dtype, max_val.dtype}"
+            f"Expecting `min_val` and `max_val` to have the same dtype, got: {min_val.dtype, max_val.dtype}"
         )
 
         if scale_dtype is None:

torch/autograd/gradcheck.py

@@ -453,7 +453,7 @@ def _prepare_input(
 def _check_outputs_same_dtype_and_shape(output1, output2, eps, idx=None) -> None:
     # Check that the returned outputs don't have different dtype or shape when you
     # perturb the input
-    on_index = "on index {idx} " if idx is not None else ""
+    on_index = f"on index {idx} " if idx is not None else ""
     assert output1.shape == output2.shape, (
         f"Expected `func` to return outputs with the same shape"
         f" when inputs are perturbed {on_index}by {eps}, but got:"

torch/distributed/_symmetric_memory/__init__.py

@@ -1421,7 +1421,7 @@ def _maybe_convert_scalar_types_to_dtypes(
         if scalar_type is None:
             dtypes.append(scalar_type)
         elif scalar_type not in _SCALAR_TYPE_TO_DTYPE:
-            raise ValueError("Unrecognized scalar type {scalar_type}")
+            raise ValueError(f"Unrecognized scalar type {scalar_type}")
         else:
             dtypes.append(_SCALAR_TYPE_TO_DTYPE[scalar_type])
     return dtypes

torch/distributed/fsdp/_optim_utils.py

@@ -351,7 +351,7 @@ def _broadcast_state(
         if isinstance(state, torch.Tensor):
             assert state.dim() == 0, (
                 "For non-zero ranks, a tensor state should have zero dimension, "
-                "but got the state with shape {state.shape()}."
+                f"but got the state with shape {state.shape}."
             )
             return state
         elif not isinstance(state, _PosDimTensorInfo):

torch/distributed/pipelining/schedules.py

@@ -1941,7 +1941,7 @@ class _PipelineScheduleRuntime(PipelineScheduleMulti):
                             stage_idx,
                             mb_index,
                         ) not in fwd_recv_ops, (
-                            "Recv twice for {stage_idx=} {mb_index=} without executing forward"
+                            f"Recv twice for {stage_idx=} {mb_index=} without executing forward"
                         )
                         fwd_recv_ops[(stage_idx, mb_index)] = _batch_p2p(
                             stage.get_fwd_recv_ops(mb_index)

torch/fx/experimental/symbolic_shapes.py

@@ -1648,7 +1648,7 @@ def constrain_range(
     if max < min:
         raise ValueError(
             "Maximum value to constrain_as_size can't be less than the specified min value, "
-            "received min={min} and max={max}"
+            f"received min={min} and max={max}"
         )
 
     if isinstance(a, int):
@@ -4095,7 +4095,7 @@ class ShapeEnv:
         if max < min:
             raise ValueError(
                 "Maximum value to constrain_as_size can't be less than the specified min value, "
-                "received min={min} and max={max}"
+                f"received min={min} and max={max}"
             )
 
         self.constrain_symbol_range(

torch/masked/maskedtensor/_ops_refs.py

@@ -416,7 +416,7 @@ def where(func, *args, **kwargs):
         args, kwargs, f"__torch_dispatch__, {func}", len_args=3, len_kwargs=0
     )
     if not torch.is_tensor(args[0]):
-        raise ValueError("__torch_dispatch__, {func}: expected args[0] to be a tensor")
+        raise ValueError(f"__torch_dispatch__, {func}: expected args[0] to be a tensor")
     mx = args[1]
     my = args[2]
     if not is_masked_tensor(mx):

torch/utils/_config_module.py

@@ -404,7 +404,7 @@ class ConfigModule(ModuleType):
         try:
             module = importlib.import_module(module_name)
         except ImportError as e:
-            raise AttributeError("config alias {alias} does not exist") from e
+            raise AttributeError(f"config alias {alias} does not exist") from e
         return module, constant_name
 
     def _get_alias_val(self, entry: _ConfigEntry) -> Any:

torch/utils/_cpp_embed_headers.py

@@ -53,6 +53,6 @@ if __name__ == "__main__":
     import sys
 
     if len(sys.argv) < 2:
-        print("Usage:\n {sys.argv[0]} filename")
+        print(f"Usage:\n {sys.argv[0]} filename")
         sys.exit(1)
     print(embed_headers(sys.argv[1]))