build(deps): bump vllm from 0.5.3.post1 to 0.5.5 in /requirements

[dependabot]

Bumps vllm from 0.5.3.post1 to 0.5.5.

Release notes

Sourced from vllm's releases.

v0.5.5

Highlights

Performance Update

• We introduced a new mode that schedule multiple GPU steps in advance, reducing CPU overhead (#7000, #7387, #7452, #7703). Initial result shows 20% improvements in QPS for a single GPU running 8B and 30B models. You can set --num-scheduler-steps 8 as a parameter to the API server (via vllm serve) or AsyncLLMEngine. We are working on expanding the coverage to LLM class and aiming to turning it on by default
• Various enhancements:
  • Use flashinfer sampling kernel when avaiable, leading to 7% decoding throughput speedup (#7137)
  • Reduce Python allocations, leading to 24% throughput speedup (#7162, 7364)
  • Improvements to the zeromq based decoupled frontend (#7570, #7716, #7484)

Model Support

• Support Jamba 1.5 (#7415, #7601, #6739)
• Support for the first audio model UltravoxModel (#7615, #7446)
• Improvements to vision models:
  • Support image embeddings as input (#6613)
  • Support SigLIP encoder and alternative decoders for LLaVA models (#7153)
• Support loading GGUF model (#5191) with tensor parallelism (#7520)
• Progress in encoder decoder models: support for serving encoder/decoder models (#7258), and architecture for cross-attention (#4942)

Hardware Support

• AMD: Add fp8 Linear Layer for rocm (#7210)
• Enhancements to TPU support: load time W8A16 quantization (#7005), optimized rope (#7635), and support multi-host inference (#7457).
• Intel: various refactoring for worker, executor, and model runner (#7686, #7712)

Others

• Optimize prefix caching performance (#7193)
• Speculative decoding
  • Use target model max length as default for draft model (#7706)
  • EAGLE Implementation with Top-1 proposer (#6830)
• Entrypoints
  • A new chat method in the LLM class (#5049)
  • Support embeddings in the run_batch API (#7132)
  • Support prompt_logprobs in Chat Completion (#7453)
• Quantizations
  • Expand MoE weight loading + Add Fused Marlin MoE Kernel (#7527)
  • Machete - Hopper Optimized Mixed Precision Linear Kernel (#7174)
• torch.compile: register custom ops for kernels (#7591, #7594, #7536)

What's Changed

• [ci][frontend] deduplicate tests by @​youkaichao in vllm-project/vllm#7101
• [Doc] [SpecDecode] Update MLPSpeculator documentation by @​tdoublep in vllm-project/vllm#7100
• [Bugfix] Specify device when loading LoRA and embedding tensors by @​jischein in vllm-project/vllm#7129
• [MISC] Use non-blocking transfer in prepare_input by @​comaniac in vllm-project/vllm#7172
• [Core] Support loading GGUF model by @​Isotr0py in vllm-project/vllm#5191
• [Build] Add initial conditional testing spec by @​simon-mo in vllm-project/vllm#6841
• [LoRA] Relax LoRA condition by @​jeejeelee in vllm-project/vllm#7146
• [Model] Support SigLIP encoder and alternative decoders for LLaVA models by @​DarkLight1337 in vllm-project/vllm#7153
• [BugFix] Fix DeepSeek remote code by @​dsikka in vllm-project/vllm#7178
• [ BugFix ] Fix ZMQ when VLLM_PORT is set by @​robertgshaw2-neuralmagic in vllm-project/vllm#7205
• [Bugfix] add gguf dependency by @​kpapis in vllm-project/vllm#7198

... (truncated)

Commits

• 09c7792 Bump version to v0.5.5 (#7823)
• f1df5db [Misc] Update marlin to use vLLMParameters (#7803)
• 35ee2ad [github][misc] promote asking llm first (#7809)
• e25fee5 [BugFix] Fix server crash on empty prompt (#7746)
• faeddb5 [misc] Add Torch profiler support for CPU-only devices (#7806)
• fc5ebbd [Hardware][Intel GPU] refactor xpu_model_runner for tp (#7712)
• c01a6cb [Ray backend] Better error when pg topology is bad. (#7584)
• b903e1b [Frontend] error suppression cleanup (#7786)
• a152246 [Misc] fix typo in triton import warning (#7794)
• 666ad0a [ci] Cleanup & refactor Dockerfile to pass different Python versions and scca...
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[oyilmaz-nvidia]

Any reason to bump vllm to 0.5.5? This will probably break all of our vllm export tests btw. Can you test the steps here? https://docs.nvidia.com/nemo-framework/user-guide/latest/deployment/llm/optimized/vllm.html