[Misc] Load FP8 kv-cache scaling factors from checkpoints #4893
Conversation
I'm pinging some NeuralMagic folks to see if we can get those models updated |
|
Hey y'all, I made this model as a test https://huggingface.co/nm-testing/Meta-Llama-3-8B-Instruct-FP8-KV. I haven't tested the accuracy yet but it should be sufficient for a smoke test @comaniac @tlrmchlsmth |
Thanks! I'll use this model for testing in this PR and post back soon. |
|
Output comparison (using the example prompts in the tests):
Although we cannot judge the model quality only by these simple prompts, it should verify that the kv-scale from the checkpoint is loaded correctly. |
|
Side note: Since #4907, the flash-attn is used for both prefill and decoding. However, flash-attn doesn't support FP8 input, so now when FP8 kv-cache is enabled, vLLM will enforce to use xFormers backend (which uses paged attention kernel in decoding). |
|
All done. Final note: Per offline discussion with @mgoin, we should accept the checkpoint with HuggingFace model compatible format. In other words, the kv_scale should have the weight name |
…ct#4893) The 2nd PR for vllm-project#4532. This PR supports loading FP8 kv-cache scaling factors from a FP8 checkpoint (with .kv_scale parameter).
…ct#4893) The 2nd PR for vllm-project#4532. This PR supports loading FP8 kv-cache scaling factors from a FP8 checkpoint (with .kv_scale parameter).
…ct#4893) The 2nd PR for vllm-project#4532. This PR supports loading FP8 kv-cache scaling factors from a FP8 checkpoint (with .kv_scale parameter).
|
@comaniac Could you please clarify what GPU blocks is recording in the above benchmark? It's not intuitive that the number of GPU blocks are increasing as the model weights size reduced. |
|
The GPU blocks meant the available GPU memory blocks can be used for kv-cache. |
…ct#4893) The 2nd PR for vllm-project#4532. This PR supports loading FP8 kv-cache scaling factors from a FP8 checkpoint (with .kv_scale parameter).
…ct#4893) The 2nd PR for vllm-project#4532. This PR supports loading FP8 kv-cache scaling factors from a FP8 checkpoint (with .kv_scale parameter).
The 2nd PR for #4532.
This PR supports loading FP8 kv-cache scaling factors from a FP8 checkpoint (with
.kv_scaleparameter).Specifically,
--kv-cache-dtype {auto, fp8, fp8_e4m3, fp8_e5m2}.auto=fp16 or bf16andfp8=fp8_e4m3.--quantization-param-path; otherwise kv-scale is always 1 regardlessfp8_e4m3orfp8_e5m2.e4m3) AND--kv-cache-dtype {fp8, fp8_e4m3}, kv_scale will be loaded from the checkpoint (if the field presents).Here is a simple benchmark on a single NVIDIA L4 GPU:
ignore_eosis not enabled so the actual length may be differ).@robertgshaw2-neuralmagic @tlrmchlsmth can you help update nm-testing/Meta-Llama-3-8B-Instruct-FP8 to include kv-cache scaling factors so that we could test it? Thanks!
TODO
Also cc @pcmoritz @Yard1
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,LLMclass, 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.shto 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-requiredand 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-requiredlabel 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!