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Co-authored-by: Cade Daniel <[email protected]>
Co-authored-by: Michael Goin <[email protected]>
Co-authored-by: miloice <[email protected]>
Co-authored-by: Dash Desai <[email protected]> Co-authored-by: Aurick Qiao <[email protected]> Co-authored-by: Aurick Qiao <[email protected]> Co-authored-by: Aurick Qiao <[email protected]> Co-authored-by: Cody Yu <[email protected]>
This PR improves the FP8 performance of linear layers, which had been lacking before (#4118 (comment) and #4118 (comment)). We noticed that CUBLASLt can find a better algorithm if the first dimension of the matrix is greater than 16. So this PR enlarges matrices appropriately during quantization. This improves FP8 performance and removes the performance regression vs. FP16, in many cases exceeding FP16 performance. Here are benchmarks on llama3 70b (ITL numbers for 1000 input and 50 output tokens at fixed qps and at TP 4), all FP8 measurements are for dynamic quantization: qps = 1: 24 ms (FP8, this PR), 32 ms (FP8, previous main), 26 ms (FP16) qps = 2: 26 ms (FP8, this PR), 34ms (FP8, previous main), 28 ms (FP16) qps = 4: 33 ms (FP8, this PR), 44 ms (FP8, previous main), 36 ms (FP16) qps = 6: 46 ms (FP8, this PR), 56 ms (FP8, previous main), 54 ms (FP16) qps = 8: 85 ms (FP8, this PR), 85 ms (FP8, previous main), 138 ms (FP16)
[Core][Distributed] refactor pynccl to hold multiple communicators (#4591)
Fix the docs: https://docs.vllm.ai/en/latest/models/performance.html Co-authored-by: sang <[email protected]>
…env (#4737) Storing exception frame is extremely prone to circular refernece because it contains the reference to objects. When tensorizer is not installed, it leaks llm instance because error frame has references to various modules which cause circular reference problem. I also found spec decoding has a circular reference issue, and I solved it using weakref.proxy.
Co-authored-by: Cade Daniel <[email protected]>
Pass the CUDA stream into the CUTLASS GEMMs, to avoid future issues with CUDA graphs
The 2nd PR for #4532. This PR supports loading FP8 kv-cache scaling factors from a FP8 checkpoint (with .kv_scale parameter).
Signed-off-by: Muralidhar Andoorveedu <[email protected]>
Co-authored-by: Varun Sundar Rabindranath <[email protected]> Co-authored-by: Varun Sundar Rabindranath <[email protected]>
Co-authored-by: Elisei Smirnov <[email protected]>
Co-authored-by: Michael Goin <[email protected]>
Co-authored-by: Cody Yu <[email protected]>
Co-authored-by: Lei Wen <[email protected]>
…-Small model (#4799) Co-authored-by: beagleski <[email protected]> Co-authored-by: bapatra <[email protected]> Co-authored-by: Barun Patra <[email protected]> Co-authored-by: Michael Goin <[email protected]>
Co-authored-by: rsnm2 <[email protected]> Co-authored-by: Robert Shaw <[email protected]>
Co-authored-by: Ruth Evans <[email protected]>
This PR adds Triton kernel configs for the MoE kernel for MI300X
Co-authored-by: Roger Wang <[email protected]>
Signed-off-by: pandyamarut <[email protected]>
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FILL IN THE PR DESCRIPTION HERE
FIX #xxxx (link existing issues this PR will resolve)
BEFORE SUBMITTING, PLEASE READ THE CHECKLIST BELOW AND FILL IN THE DESCRIPTION ABOVE
PR Checklist (Click to Expand)
Thank you for your contribution to vLLM! Before submitting the pull request, please ensure the PR meets the following criteria. This helps vLLM maintain the code quality and improve the efficiency of the review process.
PR Title and Classification
Only specific types of PRs will be reviewed. The PR title is prefixed appropriately to indicate the type of change. Please use one of the following:
[Bugfix]
for bug fixes.[CI/Build]
for build or continuous integration improvements.[Doc]
for documentation fixes and improvements.[Model]
for adding a new model or improving an existing model. Model name should appear in the title.[Frontend]
For changes on the vLLM frontend (e.g., OpenAI API server,LLM
class, etc.)[Kernel]
for changes affecting CUDA kernels or other compute kernels.[Core]
for changes in the core vLLM logic (e.g.,LLMEngine
,AsyncLLMEngine
,Scheduler
, etc.)[Hardware][Vendor]
for hardware-specific changes. Vendor name should appear in the prefix (e.g.,[Hardware][AMD]
).[Misc]
for PRs that do not fit the above categories. Please use this sparingly.Note: If the PR spans more than one category, please include all relevant prefixes.
Code Quality
The PR need to meet the following code quality standards:
format.sh
to format your code.docs/source/
if the PR modifies the user-facing behaviors of vLLM. It helps vLLM user understand and utilize the new features or changes.Notes for Large Changes
Please keep the changes as concise as possible. For major architectural changes (>500 LOC excluding kernel/data/config/test), we would expect a GitHub issue (RFC) discussing the technical design and justification. Otherwise, we will tag it with
rfc-required
and might not go through the PR.What to Expect for the Reviews
The goal of the vLLM team is to be a transparent reviewing machine. We would like to make the review process transparent and efficient and make sure no contributor feel confused or frustrated. However, the vLLM team is small, so we need to prioritize some PRs over others. Here is what you can expect from the review process:
action-required
label on the PR if there are changes required. The contributor should address the comments and ping the reviewer to re-review the PR.Thank You
Finally, thank you for taking the time to read these guidelines and for your interest in contributing to vLLM. Your contributions make vLLM a great tool for everyone!