v0.5.1 Release Tracker

[simon-mo]

ETA Friday -> Wednesday 07/03

• [Model] Jamba support #4115
• Support Deepseek-V2 #4650
• [Kernel][Model] logits_soft_cap for Gemma2 with flashinfer #6051
• [Bug]: Current Main Does Not Work On Python3.8 #6033
• [BugFix] Ensure worker model loop is always stopped at the right time #5987
• [Bugfix] Add explicit end_forward calls to flashinfer #6044
• [Core] Pipeline Parallel Support #4412
• [Speculative Decoding] MLPSpeculator Tensor Parallel support (1/2) #6050
• [ci][misc] fix more device count #6055 <- [Core] Dynamic image size support for VLMs #5276

• [Bugfix] set OMP_NUM_THREADS to 1 by default when using the multiproc_gpu_executor #6109

[NiuBlibing]

Could you release nightly versions for esaier testing?

[DarkLight1337]

For multi-modal support, we plan to only include new VLMs (#4986 is user-facing while #5591 is intended to be a component of other VLMs) and #5214 (which involves dev-facing changes) in this release. The other upcoming PRs such as #5276 introduce a sequence of breaking changes to users, so we will try to bundle them within a single major release (e.g. v0.6) to avoid continuously interrupting users.

[WangErXiao]

Deepseek-V2 this can be merged in v0.5.1?

[simon-mo]

Nightly is in the Q3 roadmap for CI/CD.

[sasha0552]

Hi. I haven't seen this release tracker as it's not pinned, but could #4409 be included in the release? At the moment, at least ~10 users want Pascal support in vLLM.

#4409 (comment)
#5224 (comment)
https://github.com/sasha0552/vllm-ci/stargazers

[DarkLight1337]

For multi-modal support, we plan to only include new VLMs (#4986 is user-facing while #5591 is intended to be a component of other VLMs) and #5214 (which involves dev-facing changes) in this release. The other upcoming PRs such as #5276 introduce a sequence of breaking changes to users, so we will try to bundle them within a single major release (e.g. v0.6) to avoid continuously interrupting users.

Since the release has been delayed, to avoid soft blocking other PRs from getting merged we have included those PRs in the release anyway. The expected user-facing breaking changes are:

• Simplified engine args: Image-specific arguments have been removed from all entrypoints as we found them unnecessary.
  • --image-input-type is removed; models can now support different inputs at runtime ([VLM] Remove image_input_type from VLM config #5852)
  • --image-token-id, --image-input-shape and --image-feature-size are removed; models can now infer this information from HuggingFace ([vlm] Remove vision language config. #6089)
  • --image-processor-related arguments are no longer supported as we will always use the processor identified by the same model name ([VLM] Remove image_input_type from VLM config #5852)
• Simplified interface for multimodal inputs: This affects usage of the LLM API class. On the other hand, the OpenAI-compatible server handles the conversion internally so end users remain unaffected.
  • No more repeating <image> tokens in the prompt - please follow the format documented on the HuggingFace repo ([Core] Dynamic image size support for VLMs #5276)

     # e.g. LLaVA-1.5 (llava-hf/llava-1.5-7b-hf)
     llm.generate({
    -    "prompt": "<image>" * 576 + "\nUSER: What is the content of this image?\nASSISTANT:",
    +    "prompt": "USER: <image>\nWhat is the content of this image?\nASSISTANT:",
         "multi_modal_data": multi_modal_data,
     })

  • Instead of passing ImagePixelData(pil_image), you should pass {"image": pil_image} to multimodal prompts ([VLM] Remove image_input_type from VLM config #5852)

     llm.generate({
         "prompt": prompt,
    -    "multi_modal_data": ImagePixelData(pil_image),
    +    "multi_modal_data": {"image": pil_image},
     })

  • ImagePixelData(tensor) and ImageFeatureData are no longer supported ([VLM] Remove image_input_type from VLM config #5852)
    • If you are currently using ImageFeatureData to represent multi-image inputs, please refrain from upgrading since we are going to replace it with embeddings soon (see below).
  • We will support multi-modal embeddings in an upcoming PR to be included in the next release. Expect the format to be along the lines of:

     llm.generate({
         "prompt": prompt,
    -    "multi_modal_data": ImageFeatureData(feature_tensor),
    +    "multi_modal_data": {"image": {"embeds": model.multi_modal_projector(feature_tensor)}},  # Or just pass the embeddings directly
     })
        ```

[DarkLight1337]

@simon-mo btw this thread is not pinned