promote blocksparse from prototype, make it faster

[jcaip]

This PR promotes block sparsity from prototype in torchao.

Chiefly, it ports over the triton addmm blocksparse kernels from core, and makes several performance improvements to them.

All of the numbers reported below are for an H100, with blocksize=64 and sparsity_level=0.9. The default dense baseline is 134 tok/s

1. Adds padding support to the triton kernel for dense matrices with dimension < 16, like those we run into during decoding. (214 -> 218 tok/s)
2. Changes the default num_stages parameter from 1 to 4. This has a large effect on performance, and it seemed like the default kernel autotuning either does not modify or deems this parameter to be unimportant for some reason. (218 -> 263 tok/s).
3. Adds an env_var, BSR_AUTOTUNE, that users can use if they want to do kernel autotuning on top of the default parameters. (263 -> 266 tok/s) This seems to matter more for bs=n compute bound workloads, where I see a reduction from 0.3855 to 0.3745s on bs=8192 prefill (roughly 3%)

So in total we are seeing a 1.985x speedup 🚀

I've also updated the documentation to not reference prototype - planning on updating the diagram in a subsequent PR.

Testing

I added a new test case for the padding inputs and moved the test file out of prototype.

python test/sparsity/test_sparse_api.py

Benchmarking

export CHECKPOINT_PATH=../../../checkpoints # path to checkpoints folder
export MODEL_REPO=meta-llama/Meta-Llama-3.1-8B

python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --compile_prefill --write_result benchmark_results.txt --prefill_size 8192 --profile baseline_prefill
python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --compile_prefill --write_result benchmark_results.txt --prefill_size 8192 --sparsity bsr --profile bsr_prefill
python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --compile_prefill --write_result benchmark_results.txt --profile baseline
python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --compile_prefill --write_result benchmark_results.txt --sparsity bsr --profile bsr

[pytorch-bot]

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[vkuzo]

might be good to consider getting the changes from #1690 in here since you are making a major API change, it will save you a migration in the future.

[jcaip]

ah yes that's a good idea. I'll open a subsequent PR and update all of sparsity APIs.