GitLab

[Misc] Add per channel support for static activation quantization; update w8a8 schemes to share base classes (#5650)

[Misc] Add channel-wise quantization support for w8a8 dynamic per token activation quantization (#5542)

[Speculative Decoding 1/2 ] Add typical acceptance sampling as one of the sampling techniques in the verifier (#5131)

Co-authored-by: Jiang Li <jiang1.li@intel.com>
Co-authored-by: Abhilash Majumder <abhilash.majumder@intel.com>
Co-authored-by: Abhilash Majumder <30946547+abhilash1910@users.noreply.github.com>

Co-authored-by: mgoin <michael@neuralmagic.com>
Co-authored-by: Dipika Sikka <dipikasikka1@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>

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>

Co-authored-by: Michael Goin <michael@neuralmagic.com>

Bug description:
With torch 2.4.0.dev20240603+cu121,
cutlass_fp8_supported outputs False, and the (capability, version) before the comparison is (90, 11111111112)

This PR fixes the support check for FP8 CUTLASS ( cutlass_fp8_supported) which was introduced in https://github.com/vllm-project/vllm/pull/5183.

Co-authored-by: Varun Sundar Rabindranath <varunsundar08@gmail.com>
Co-authored-by: Varun Sundar Rabindranath <varun@neuralmagic.com>

Switching from torch._scaled_mm to vLLM's cutlass fp8 kernels when supported as we are seeing 5-15% improvement in e2e performance on neuralmagic/Meta-Llama-3-8B-Instruct-FP8

see https://docs.google.com/spreadsheets/d/1GiAnmzyGHgZ6zL_LDSTm35Bdrt4A8AaFEurDlISYYA4/ for some quick e2e benchmarks and #5144 for comparisons across different GEMM sizes.

This PR enables the fused topk_softmax kernel used in moe layer for HIP