[ghstack-poisoned]

Recently I feel it's a bit painful to run benchmark scripts on my dev environment. E.g., the command below
```
 python benchmarks/dynamo/huggingface.py --backend inductor --amp --performance --only YituTechConvBert --training
```
took about 2 minutes to run. It may take even longer for some other models.

The command is slow since it
- need do dynamo work
- verify the model on CPU
- run perf tests
- compile all the graphs

However, often times I only need to debug inductor specific logic like loop ordering and fusion. A lot of the things the script is done are useless for me. Also I only need test one graph at a time (e.g. check fwd graph first and when I'm done, continue to check bwd graph) rather than compiling all the graphs.

The graph replayer add a `save_args` decorator to compile_fx_inner function. When `config.save_args` is true, it will pickle all the arguments to `comple_fx_inner` to the file system.  Later on, we can call `load_args_and_run_compile_fx_inner("/tmp/inductor_saved_args/compile_fx_inner_0.pkl")` to replay the graph and compile it with inductor.

Replaying the fwd graph took around 60 seconds (maybe this can be further reduced but this is already 2x speedup for dev efficiency) , and it only took around 20 seconds to reach `Scheduler.__init__` method.

I also checked `TORCH_COMPILE_DEBUG` flag that already exists. The most similar part of `TORCH_COMPILE_DEBUG` is it can save a graph and it's arguments and later on rerun it. But the difference here is, rather than run the model, we want to call inductor API to compile the model (without even going thru dynamo or aot-autograd).


[ghstack-poisoned]

Recently I feel it's a bit painful to run benchmark scripts on my dev environment. E.g., the command below
```
 python benchmarks/dynamo/huggingface.py --backend inductor --amp --performance --only YituTechConvBert --training
```
took about 2 minutes to run. It may take even longer for some other models.

The command is slow since it
- need do dynamo work
- verify the model on CPU
- run perf tests
- compile all the graphs

However, often times I only need to debug inductor specific logic like loop ordering and fusion. A lot of the things the script is done are useless for me. Also I only need test one graph at a time (e.g. check fwd graph first and when I'm done, continue to check bwd graph) rather than compiling all the graphs.

The graph replayer add a `save_args` decorator to compile_fx_inner function. When `config.save_args` is true, it will pickle all the arguments to `comple_fx_inner` to the file system.  Later on, we can call `load_args_and_run_compile_fx_inner("/tmp/inductor_saved_args/compile_fx_inner_0.pkl")` to replay the graph and compile it with inductor.

Replaying the fwd graph took around 60 seconds (maybe this can be further reduced but this is already 2x speedup for dev efficiency) , and it only took around 20 seconds to reach `Scheduler.__init__` method.

I also checked `TORCH_COMPILE_DEBUG` flag that already exists. The most similar part of `TORCH_COMPILE_DEBUG` is it can save a graph and it's arguments and later on rerun it. But the difference here is, rather than run the model, we want to call inductor API to compile the model (without even going thru dynamo or aot-autograd).


[ghstack-poisoned]

Recently I feel it's a bit painful to run benchmark scripts on my dev environment. E.g., the command below
```
 python benchmarks/dynamo/huggingface.py --backend inductor --amp --performance --only YituTechConvBert --training
```
took about 2 minutes to run. It may take even longer for some other models.

The command is slow since it
- need do dynamo work
- verify the model on CPU
- run perf tests
- compile all the graphs

However, often times I only need to debug inductor specific logic like loop ordering and fusion. A lot of the things the script is done are useless for me. Also I only need test one graph at a time (e.g. check fwd graph first and when I'm done, continue to check bwd graph) rather than compiling all the graphs.

The graph replayer add a `save_args` decorator to compile_fx_inner function. When `config.save_args` is true, it will pickle all the arguments to `comple_fx_inner` to the file system.  Later on, we can call `load_args_and_run_compile_fx_inner("/tmp/inductor_saved_args/compile_fx_inner_0.pkl")` to replay the graph and compile it with inductor.

Replaying the fwd graph took around 60 seconds (maybe this can be further reduced but this is already 2x speedup for dev efficiency) , and it only took around 20 seconds to reach `Scheduler.__init__` method.

I also checked `TORCH_COMPILE_DEBUG` flag that already exists. The most similar part of `TORCH_COMPILE_DEBUG` is it can save a graph and it's arguments and later on rerun it. But the difference here is, rather than run the model, we want to call inductor API to compile the model (without even going thru dynamo or aot-autograd).


[ghstack-poisoned]

Recently I feel it's a bit painful to run benchmark scripts on my dev environment. E.g., the command below
```
 python benchmarks/dynamo/huggingface.py --backend inductor --amp --performance --only YituTechConvBert --training
```
took about 2 minutes to run. It may take even longer for some other models.

The command is slow since it
- need do dynamo work
- verify the model on CPU
- run perf tests
- compile all the graphs

However, often times I only need to debug inductor specific logic like loop ordering and fusion. A lot of the things the script is done are useless for me. Also I only need test one graph at a time (e.g. check fwd graph first and when I'm done, continue to check bwd graph) rather than compiling all the graphs.

The graph replayer add a `save_args` decorator to compile_fx_inner function. When `config.save_args` is true, it will pickle all the arguments to `comple_fx_inner` to the file system.  Later on, we can call `load_args_and_run_compile_fx_inner("/tmp/inductor_saved_args/compile_fx_inner_0.pkl")` to replay the graph and compile it with inductor.

Replaying the fwd graph took around 60 seconds (maybe this can be further reduced but this is already 2x speedup for dev efficiency) , and it only took around 20 seconds to reach `Scheduler.__init__` method.

I also checked `TORCH_COMPILE_DEBUG` flag that already exists. The most similar part of `TORCH_COMPILE_DEBUG` is it can save a graph and it's arguments and later on rerun it. But the difference here is, rather than run the model, we want to call inductor API to compile the model (without even going thru dynamo or aot-autograd).


[ghstack-poisoned]