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- 20 Jul, 2024 1 commit
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zhuwenwen authored
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- 18 Jul, 2024 1 commit
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zhuwenwen authored
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zhuwenwen authored
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- 12 Jul, 2024 1 commit
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zhuwenwen authored
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- 09 Jul, 2024 1 commit
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huangwb authored
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- 08 Jul, 2024 1 commit
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zhuwenwen authored
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zhuwenwen authored
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- 28 Jun, 2024 1 commit
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zhuwenwen authored
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- 20 Jun, 2024 1 commit
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zhuwenwen authored
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- 11 Jun, 2024 9 commits
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Nick Hill authored
Co-authored-by:Antoni Baum <antoni.baum@protonmail.com>
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youkaichao authored
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sasha0552 authored
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Ali Panahi authored
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Junichi Sato authored
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Woosuk Kwon authored
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Cyrus Leung authored
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maor-ps authored
Co-authored-by:DarkLight1337 <tlleungac@connect.ust.hk>
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Nick Hill authored
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- 10 Jun, 2024 8 commits
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Simon Mo authored
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Cyrus Leung authored
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Itay Etelis authored
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Cyrus Leung authored
Co-authored-by:Roger Wang <ywang@roblox.com>
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Cyrus Leung authored
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youkaichao authored
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Roger Wang authored
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Dipika Sikka authored
Co-authored-by:Michael Goin <michael@neuralmagic.com>
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- 09 Jun, 2024 3 commits
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Bla_ckB authored
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bnellnm authored
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youkaichao authored
[Core][CUDA Graph] add output buffer for cudagraph to reduce memory footprint (#5074)
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- 08 Jun, 2024 3 commits
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Michael Goin authored
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Hongxia Yang authored
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Cheng Li authored
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.
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- 07 Jun, 2024 6 commits
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Calvinn Ng authored
Co-authored-by:team <calvinn.ng@ahrefs.com>
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Roger Wang authored
Co-authored-by: -
Dipika Sikka authored
Co-authored-by:
Varun Sundar Rabindranath <varunsundar08@gmail.com> Co-authored-by:
Varun Sundar Rabindranath <varun@neuralmagic.com>
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limingshu authored
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Antoni Baum authored
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Tyler Michael Smith authored
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.
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