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Co-authored-by: Philipp Moritz <pcmoritz@gmail.com>

Co-authored-by: Michael Goin <michael@neuralmagic.com>
Co-authored-by: youkaichao <youkaichao@gmail.com>
Co-authored-by: zifeitong <zifei.tong@parasail.io>
Co-authored-by: Robert Shaw <114415538+robertgshaw2-neuralmagic@users.noreply.github.com>

Co-authored-by: Roger Wang <ywang@roblox.com>

Co-authored-by: Jianan Gu <jianan.gu@intel.com>

Tune Qwen2-57B-A14B configs based on #4921

Throughput Performance
command: python benchmarks/benchmark_throughput.py --model=Qwen/Qwen2-57B-A14B-Instruct --input-len 1000 --output-len 50 -tp 2

A100 GPU

benchmark	no config	w/ PR
tp=2	10.53 requests/s, 11058.17 tokens/s	12.47 requests/s, 13088.57 tokens/s
tp=4	17.77 requests/s, 18662.95 tokens/s	20.20 requests/s, 21212.32 tokens/s

Co-authored-by: Robert Shaw <114415538+robertgshaw2-neuralmagic@users.noreply.github.com>

Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
Co-authored-by: Roger Wang <ywang@roblox.com>

Signed-off-by: kevin <kevin@anyscale.com>

[Core][Distributed] add coordinator to reduce code duplication in tp and pp (#5293)

Signed-off-by: Travis Johnson <tsjohnso@us.ibm.com>
Co-authored-by: Sanger Steel <sangersteel@gmail.com>
Co-authored-by: Roger Wang <ywang@roblox.com>

Inspired by #5146, this PR improves FP8 quantize kernel by vectorizing data transfer to better utilize memory bandwidth. Microbenchmark shows that this improved kernel can achieve 1.0x-1.5x speedup (especially when hidden size is large).

In details, we applied 3 optimizations:

- Use inverted scale so that most divisions are changed to multiplications.
- Unroll the loop by 4 times to improve ILP.
- Use vectorized 4 to transfer data between HBM and SRAM.

Signed-off-by: kevin <kevin@anyscale.com>

Revert "[CI/Build] Add `is_quant_method_supported` to control quantization test configurations" (#5463)