migrate add operator to the new interface (#21152)

Summary:
Pull Request resolved: pytorch/pytorch#21152

Migrate existing add benchmark to use the new op front-end

Reviewed By: zheng-xq

Differential Revision: D15325524

fbshipit-source-id: 34e969e1bd289913d881c476711bce9f8ac18a29

benchmarks/operator_benchmark/ops/c2/add_test.py

@@ -0,0 +1,49 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+from __future__ import unicode_literals
+
+import operator_benchmark as op_bench
+from caffe2.python import core 
+
+
+"""Microbenchmarks for element-wise Add operator. Supports both Caffe2/PyTorch."""
+
+# Configs for C2 add operator 
+add_long_configs = op_bench.cross_product_configs(
+    M=[8, 64, 128],
+    N=range(2, 10, 3),
+    K=[2 ** x for x in range(0, 3)], 
+    tags=["long"]
+)
+
+
+add_short_configs = op_bench.config_list(
+    attrs=[
+        [8, 16, 32],
+        [16, 16, 64],
+        [64, 64, 128],
+    ],
+    attr_names=["M", "N", "K"], 
+    tags=["short"], 
+)
+
+class AddBenchmark(op_bench.Caffe2BenchmarkBase):
+    def init(self, M, N, K): 
+        self.input_one = self.tensor(M, N, K) 
+        self.input_two = self.tensor(M, N, K) 
+        self.output = self.tensor(M, N, K)
+        self.set_module_name("add")
+
+    def forward(self):
+        op = core.CreateOperator(
+            "Add", [self.input_one, self.input_two], self.output, **self.args 
+        )
+        return op
+
+
+op_bench.generate_c2_test(add_long_configs + add_short_configs, AddBenchmark)
+
+
+if __name__ == "__main__":
+    op_bench.benchmark_runner.main()

benchmarks/operator_benchmark/ops/pt/add_test.py

@@ -0,0 +1,45 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+from __future__ import unicode_literals
+
+import operator_benchmark as op_bench
+import torch
+
+"""Microbenchmarks for element-wise Add operator. Supports both Caffe2/PyTorch."""
+
+# Configs for PT add operator 
+add_long_configs = op_bench.cross_product_configs(
+    M=[8, 64, 128],
+    N=range(2, 10, 3),
+    K=[2 ** x for x in range(0, 3)], 
+    tags=["long"]
+)
+
+
+add_short_configs = op_bench.config_list(
+    attrs=[
+        [8, 16, 32],
+        [16, 16, 64],
+        [64, 64, 128],
+    ],
+    attr_names=["M", "N", "K"], 
+    tags=["short"], 
+)
+
+
+class AddBenchmark(op_bench.TorchBenchmarkBase):
+    def init(self, M, N, K): 
+        self.input_one = torch.rand(M, N, K)
+        self.input_two = torch.rand(M, N, K)
+        self.set_module_name("add")
+
+    def forward(self):
+        return torch.add(self.input_one, self.input_two)
+
+
+op_bench.generate_pt_test(add_long_configs + add_short_configs, AddBenchmark)
+
+
+if __name__ == "__main__":
+    op_bench.benchmark_runner.main()