Update dper3 to use torch.nan_to_num and nan_to_num_ (pytorch#46873)

Summary:
Pull Request resolved: pytorch#46873

OSS:
Add op benchmark for torch.nan_to_num and torch.nan_to_num_

Test Plan:
OSS:
`buck run mode/opt caffe2/benchmarks/operator_benchmark/pt:nan_to_num_test`

Reviewed By: qizzzh, houseroad

Differential Revision: D24521835

fbshipit-source-id: 1fd50a99e5329ffec2d470525ce6976d39424958

benchmarks/operator_benchmark/pt/nan_to_num_test.py

@@ -0,0 +1,59 @@
+import operator_benchmark as op_bench
+import torch
+import math
+
+
+"""Microbenchmarks for torch.nan_to_num / nan_to_num_ operators"""
+
+# Configs for PT torch.nan_to_num / nan_to_num_ operators
+nan_to_num_long_configs = op_bench.cross_product_configs(
+    M=[32, 64, 128],
+    N=range(32, 128, 32),
+    dtype=[torch.float, torch.double],
+    op=["nan_to_num", "nan_to_num_"],
+    replace_inf=[True, False],
+    tags=["long"],
+)
+
+
+nan_to_num_short_configs = op_bench.cross_product_configs(
+    M=[16, 64],
+    N=[64, 64],
+    dtype=[torch.float, torch.double],
+    op=["nan_to_num", "nan_to_num_"],
+    replace_inf=[True, False],
+    tags=["short"],
+)
+
+
+class ReplaceNaNBenchmark(op_bench.TorchBenchmarkBase):
+    def init(self, M, N, dtype, op, replace_inf):
+        self.input = torch.randn(M, N, dtype=dtype)
+        self.input[0][0] = float("nan")
+        self.op = op
+        self.replace_inf = replace_inf
+        self.set_module_name("nan_to_num")
+
+    def forward(self):
+        # compare inplace
+        if self.op == "nan_to_num":
+            if self.replace_inf:
+                output = torch.nan_to_num(self.input, nan=1.0)
+            else:
+                output = torch.nan_to_num(self.input, nan=1.0, posinf=math.inf, neginf=-math.inf)
+        else:
+            if self.replace_inf:
+                output = torch.nan_to_num_(self.input, nan=1.0)
+            else:
+                output = torch.nan_to_num_(self.input, nan=1.0, posinf=math.inf, neginf=-math.inf)
+        return output
+
+
+op_bench.generate_pt_test(
+    nan_to_num_long_configs + nan_to_num_short_configs,
+    ReplaceNaNBenchmark,
+)
+
+
+if __name__ == "__main__":
+    op_bench.benchmark_runner.main()