introduce auto_set to simplify benchmarking the backward path of operators (#23276)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23276
This diff introduces a new feature to simplify benchmarking the backward path of ops. Here is an example:
```
...
self.input_one = torch.rand(M, N, K, requires_grad=self.auto_set())
self.input_two = torch.rand(M, N, K, requires_grad=self.auto_set())
...
```
In this way, the benchmark will generate three different test cases.
1. input_one requires grad
2. input_two requires grad
3. both inputs require grad
Here is a sample output:
```
# Benchmarking PyTorch: add
# Mode: Eager
# Name: add_M1_N8_K8_bwdall
# Input: M: 1, N: 8, K: 8
Backward Execution Time (us) : 863.744
# Benchmarking PyTorch: add
# Mode: Eager
# Name: add_M1_N8_K8_bwd1
# Input: M: 1, N: 8, K: 8
Backward Execution Time (us) : 727.915
# Benchmarking PyTorch: add
# Mode: Eager
# Name: add_M1_N8_K8_bwd2
# Input: M: 1, N: 8, K: 8
Backward Execution Time (us) : 687.626
```
Reviewed By: zheng-xq
Differential Revision: D16450355
fbshipit-source-id: 50ae0916e81c3ff9f0c482ed6d386319eb15b305
pytorch
3c986dff - introduce auto_set to simplify benchmarking the backward path of operators (#23276)
