Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23442
Replace the argument name from `operator` to `operators` which can take a list of operators to test.
Reviewed By: hl475
Differential Revision: D16520779
fbshipit-source-id: 94284a87c64471793e319f5bd3143f89b9a192bb
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21211
There are cases where the `init` method used to create inputs can exit with error. When this happens, that specific input should be skipped.
Reviewed By: zheng-xq
Differential Revision: D15466410
fbshipit-source-id: 55e86764b2ec56f7730349ff1df6e50efc0239d7
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21209
This diff introduces a new interface to add a list of operators. Here are the steps to add ops using this interface:
- create op_list:
```unary_ops_list = op_bench.op_list(
attr_names=["op_name", "op_function"],
attrs=[
["abs", torch.abs],
["abs_", torch.abs_],
],
)
```
- create a bench class:
```
class UnaryOpBenchmark(op_bench.TorchBenchmarkBase):
def init(self, M, N, op_function):
self.input_one = torch.rand(M, N)
self.op_func = op_function
def forward(self):
return self.op_func(self.input_one)
```
- 3. register those ops
``` op_bench.generate_pt_tests_from_list(unary_ops_list, unary_ops_configs, UnaryOpBenchmark)
```
Reviewed By: zheng-xq
Differential Revision: D15514188
fbshipit-source-id: f09b359cab8175eeb8d51b3ad7bbbcfbc9f6430f
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21147
This diff introduces a new interface to add PT/C2 operators to the benchmark suite.
The following steps are needed to add a new operator:
1. Specify the input shapes, args to an operator in configs
2. Create a PT/C2 benchmark class which includes ```init``` (create tensors), ```forward``` (specify the operator to be tested.), and ```backward```(gradient of an op.) methods
3. call generate_pt_test/generate_c2_test to create test cases based on configs
Reviewed By: zheng-xq
Differential Revision: D15250380
fbshipit-source-id: 1025a7cf60d2427baa0f3f716455946d3d3e6a27
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19450
We want to make each operator benchmark as a separate binary. The previous way to run the benchmark is by collecting all operators into a single binary, it is unnecessary when we want to filter a specific operator. This diff aims to resolve that issue.
Reviewed By: ilia-cher
Differential Revision: D14808159
fbshipit-source-id: 43cd25b219c6e358d0cd2a61463b34596bf3bfac
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19433
For operator benchmark project, we need to cover a lot of operators, so the interface for adding operators needs to be very clean and simple. This diff is implementing a new interface to add op.
Here is the logic to add new operator to the benchmark:
```
long_config = {}
short_config = {}
map_func
add_test(
[long_config, short_config],
map_func,
[caffe2 op]
[pt op]
)
```
Reviewed By: zheng-xq
Differential Revision: D14791191
fbshipit-source-id: ac6738507cf1b9d6013dc8e546a2022a9b177f05
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18740
Test utilities for writing Caffe2/PyTorch performance microbenchmarks. Brief description of the file structure
* benchmark_core.py : core utiltiites for running microbenchmark tests
* benchmark_caffe2.py : Caffe2 specific benchmark utilitites
* benchmark_pytorch.py: PyTorch specific benchmark utilities
* benchmark_runner.py : Main function. Currently it can run the microbenchmark tests in a stand-alone mode. The next step is to have this integrate with AI-PEP.
The utilities are located at https://github.com/pytorch/pytorch/tree/master/test to have access to both Caffe2/PyTorch Python's frontend.
Include two operator microbenchmarks; support both Caffe2/PyTorch:
* MatMul
* Add
Reference: PyTorch benchmarks : https://github.com/pytorch/benchmark/tree/master/timing/python. In this work, we start with two example binary operators MatMul and Add, but eventually we should to cover unary operators like in the PyTorch benchmark repo.
Reviewed By: zheng-xq
Differential Revision: D13887111
fbshipit-source-id: b7a56b95448c9ec3e674b0de0ffb96af4439bfce
