compile time benchmarks for AOTDispatcher (partitioner) (#136760)

compile time benchmark for the min cut partitioner. I'm hoping that this is a reasonable benchmark because:

(1) it consists of a single input + many weights that are used sequentially
(2) contains a mix of recompute vs non-recomputed ops (matmul + sin)
(3) it is relatively simple

from running locally:
```
collecting compile time instruction count for aotdispatcher_partitioner_cpu
compile time instruction count for iteration 0 is 21764219181
compile time instruction count for iteration 1 is 12475020009
compile time instruction count for iteration 2 is 12463710140
compile time instruction count for iteration 3 is 12455676489
compile time instruction count for iteration 4 is 12451344330
```

Pull Request resolved: https://github.com/pytorch/pytorch/pull/136760
Approved by: https://github.com/ezyang
ghstack dependencies: #136759

benchmarks/dynamo/pr_time_benchmarks/benchmarks/aotdispatcher_partitioner.py

@@ -0,0 +1,46 @@
+import sys
+
+from benchmark_base import BenchmarkBase
+
+import torch
+
+
+class Benchmark(BenchmarkBase):
+    def name(self):
+        return "aotdispatcher_partitioner_cpu"
+
+    def description(self):
+        return "partitioner benchmark 1 input and 100 weights, mix of recompute and non-recompute ops"
+
+    def _prepare_once(self):
+        self.weights = [torch.randn(16, 16, requires_grad=True) for _ in range(100)]
+        self.inp = torch.randn(16, 16)
+
+    def _prepare(self):
+        torch._dynamo.reset()
+
+    def _work(self):
+        @torch.compile(backend="aot_eager_decomp_partition", fullgraph=True)
+        def f(inp, *weights):
+            x = inp
+            for w in weights:
+                x = torch.matmul(w, x).sin().sin()
+            return x
+
+        f(self.inp, *self.weights)
+
+
+def main():
+    result_path = sys.argv[1]
+    all = [
+        Benchmark(),
+    ]
+
+    for benchmark in all:
+        benchmark.enable_compile_time_instruction_count().collect_all().append_results(
+            result_path
+        )
+
+
+if __name__ == "__main__":
+    main()