[PT2][Optimus] fix the default alpha and beta values (#139857)

Summary:
We noticed that the default coefficient values for beta and alpha should be int 1, instead of float 1.0, which will cause error when the inputs for the add are int types.

More contex:

https://fb.workplace.com/groups/1075192433118967/permalink/1539142760057263/

Test Plan:
# local reproduce
```
buck2 run mode/opt scripts/shuaiyang:test -- --optimus --flow_id 660724017 2>&1 | tee ~/local_run_shuai_660724017.txt
```

trace link: https://www.internalfb.com/intern/perfdoctor/trace_view?filepath=tree/traces/mengluy/2024-11-05-21-18-17/trace.json.gz&bucket=gpu_traces

# E2E

before fix:
f660724017

after fix:

Differential Revision: D65521638

Pull Request resolved: #139857
Approved by: https://github.com/jackiexu1992

torch/_inductor/fx_passes/group_batch_fusion.py

@@ -41,6 +41,9 @@
 
 log = logging.getLogger(__name__)
 
+DEFAULT_BETA = 1
+DEFAULT_ALPHA = 1
+
 MIN_FUSE_SET_SIZE = 5
 MAX_FUSE_SET_SIZE = 300
 MAX_FUSE_SEARCH_DEPTH = 5
@@ -178,7 +181,8 @@ class PostGradBatchLinearFusion(BatchFusion):
     def _addmm_node_can_be_fused(self, node: torch.fx.Node) -> bool:
         # pyre-fixme[7]: Incompatible return type
         return (
-            node.kwargs.get("beta", 1.0) == 1.0 and node.kwargs.get("alpha", 1.0) == 1.0  # type: ignore[return-value]
+            node.kwargs.get("beta", DEFAULT_BETA) == DEFAULT_BETA
+            and node.kwargs.get("alpha", DEFAULT_ALPHA) == DEFAULT_ALPHA  # type: ignore[return-value]
         )
 
     def _is_input_2d(self, input: torch.fx.Node) -> bool:
@@ -303,8 +307,8 @@ def _addmm_node_can_be_fused(self, node: torch.fx.Node):
         input_shape = node.args[1].meta["val"].shape  # type: ignore[union-attr]
         weight_shape = node.args[2].meta["val"].shape  # type: ignore[union-attr]
         return (
-            node.kwargs.get("beta", 1.0) == 1.0
-            and node.kwargs.get("alpha", 1.0) == 1.0
+            node.kwargs.get("beta", DEFAULT_BETA) == DEFAULT_BETA
+            and node.kwargs.get("alpha", DEFAULT_ALPHA) == DEFAULT_ALPHA
             and len(input_shape) == 2
             and len(weight_shape) == 2
             and all(x % 2 == 0 for x in input_shape + weight_shape)
@@ -411,7 +415,7 @@ def match(self, node: torch.fx.Node):
         if CallFunctionVarArgs(self.op).match(
             node
         ) and self._pointwise_node_can_be_fused(node):
-            alpha = node.kwargs.get("alpha", 1.0)
+            alpha = node.kwargs.get("alpha", DEFAULT_ALPHA)
             rounding_mode = node.kwargs.get("rounding_mode", None)
             input, other = node.args
             shape = list(input.meta["val"].shape)  # type: ignore[union-attr]
@@ -445,7 +449,7 @@ def match(self, node: torch.fx.Node):
 
     def fuse(self, graph: torch.fx.GraphModule, subset: List[torch.fx.Node]):
         batch_inputs, batch_others = [], []
-        alpha = subset[0].kwargs.get("alpha", 1.0)
+        alpha = subset[0].kwargs.get("alpha", DEFAULT_ALPHA)
         batch_inputs_meta, batch_others_meta = [], []
 
         for node in subset: