Add unique identifer to bmm thread_mm functions (#145303)

Summary:
The bmm template generates code like this

```
template<bool accum>
void cpp_fused_bmm_66_micro_gemm(...) {
    ...
}

void single_thread_mm() {
    ...
    cpp_fused_bmm_66_micro_gemm(...)
    ...
}

void threaded_mm() {
    ...
    cpp_fused_bmm_66_micro_gemm(...)
    ...
}

void cpp_fused_bmm_66(...)
{
    ...
    single_thread_mm(...);
    ...
    threaded_mm(...);
    ...
}
```

The generated  `fused_bmm` and `fused_bmm_microgemm` functions both have unique identifiers added to their names, but the `single_threaded_mm` and `threaded_mm` do not.

This diff adds unique identifies to those generated functions as well. The identifier is based on the kernel name. So for the example above we would generate a bmm template name like `cpp_fused_bmm_66_single_thread_mm()`.

Differential Revision: D68364772

Pull Request resolved: https://github.com/pytorch/pytorch/pull/145303
Approved by: https://github.com/leslie-fang-intel, https://github.com/frost-intel, https://github.com/hl475

test/inductor/test_cpu_select_algorithm.py

@@ -2264,6 +2264,41 @@ class TestSelectAlgorithm(BaseTestSelectAlgorithm):
         self.assertEqual(counters["inductor"]["select_algorithm_autotune"], 1)
         self.assertEqual(counters["inductor"]["cpp_epilogue_fusion_counter"], 1)
 
+    @patches
+    @torch.no_grad
+    @dtypes(torch.float)
+    def test_aoti_bmm_unique_identifiers(self, dtype):
+        try:
+            try:
+                from . import test_aot_inductor_utils
+            except ImportError:
+                import test_aot_inductor_utils
+        except Exception:
+            # skip this UT if import failed
+            return
+
+        class M(torch.nn.Module):
+            def __init__(self):
+                super().__init__()
+
+            def forward(self, x, w):
+                y = x @ w
+                return y @ w
+
+        counters.clear()
+        x = torch.randn(3, 64, 64).to(dtype=dtype)
+        w = torch.randn(3, 64, 64).to(dtype=dtype)
+        mod = M().to(dtype=dtype).eval()
+        with verify(dtype) as (atol, rtol), torch.no_grad():
+            expected = mod(x, w)
+            actual = test_aot_inductor_utils.AOTIRunnerUtil.run(
+                "cpu",
+                mod,
+                (x, w),
+            )
+            self.assertEqual(actual, expected, atol=atol, rtol=rtol)
+        self.assertEqual(counters["inductor"]["select_algorithm_autotune"], 2)
+
 
 @dynamo_config.patch({"dynamic_shapes": True, "assume_static_by_default": False})
 class _DynamicShapesTestBase(BaseTestSelectAlgorithm):