pytorch

962b3f78 - [inductor] run all kernel benchmarks individually in a compiled module (#95845)

[inductor] run all kernel benchmarks individually in a compiled module (#95845) This is a follow up for PR #95506 to run all the triton kernels in a compiled module individually as suggested by Horace. Here are the steps: 1. Run the model as usual with a benchmark script and with TORCHINDUCTOR_BENCHMARK_KERNEL enabled. e.g. ``` TORCHINDUCTOR_BENCHMARK_KERNEL=1 python benchmarks/dynamo/torchbench.py --backend inductor --amp --performance --dashboard --only resnet18 --disable-cudagraphs --training ``` 2. From the output we will see 3 lines like ``` Compiled module path: /tmp/torchinductor_shunting/rs/crsuc6zrt3y6lktz33jjqgpkuahya56xj6sentyiz7iv4pjud43j.py ``` That's because we have one graph module for fwd/bwd/optitimizer respectively. Each graph module will have one such output corresponding to the compiled module. 3. We can run the compiled module directly. Without any extra arguments, we just maintain the previous behavior to run the call function -- which just does what the original graph module does but in a more efficient way. But if we add the '-k' argument, we will run benchmark for each individual kernels in the file. ``` python /tmp/torchinductor_shunting/rs/crsuc6zrt3y6lktz33jjqgpkuahya56xj6sentyiz7iv4pjud43j.py -k ``` Example output: <img width="430" alt="Screenshot 2023-03-01 at 4 51 06 PM" src="https://user-images.githubusercontent.com/52589240/222302996-814a85be-472b-463c-9e85-39d2c9d20e1a.png"> Note: I use the first 10 characters of the hash to identify each kernel since 1. hash is easier to get in the code :) 2. name like `triton__3` only makes sense within a compiled module, but a hash can make sense even without specifying the compiled module (assuming we have enough bytes for the hash) If we found a triton kernel with hash like c226iuf2wi having poor performance, we can look it up in the original compiled module file. It works since we comment each compiled triton kernel with the full hash. Pull Request resolved: https://github.com/pytorch/pytorch/pull/95845 Approved by: https://github.com/Chillee