[Doc] V1 user guide

docs/source/getting_started/v1_user_guide.md

@@ -0,0 +1,132 @@
+# vLLM V1 User Guide
+
+## Why vLLM V1?
+
+vLLM V0 successfully supported a wide range of models and hardware, but as new features were developed independently, the system grew increasingly complex. This complexity made it harder to integrate new capabilities and introduced technical debt, revealing the need for a more streamlined and unified design.
+
+Building on V0’s success, vLLM V1 retains the stable and proven components from V0
+(such as the models, GPU kernels, and utilities). At the same time, it significantly
+re-architects the core systems, covering the scheduler, KV cache manager, worker,
+sampler, and API server, to provide a cohesive, maintainable framework that better
+accommodates continued growth and innovation.
+
+Specifically, V1 aims to:
+
+- Provide a **simple, modular, and easy-to-hack codebase**.
+- Ensure **high performance** with near-zero CPU overhead.
+- **Combine key optimizations** into a unified architecture.
+- Require **zero configs** by enabling features/optimizations by default.
+
+For more details, check out the vLLM V1 blog post [vLLM V1: A Major
+Upgrade to vLLM’s Core Architecture](https://blog.vllm.ai/2025/01/27/v1-alpha-release.html) (published Jan 27, 2025).
+
+This living user guide outlines a few known **important changes and limitations** introduced by vLLM V1. The team has been working actively to bring V1 as the default engine, therefore this guide will be updated constantly as more features get supported on vLLM V1.
+
+### Feature / Model Supports Overview
+
+| Feature / Model                           | Status                                                                           |
+|-------------------------------------------|-----------------------------------------------------------------------------------|
+| **Logprobs Calculation**                  | <nobr>🟢 Functional</nobr>                                                        |
+| **Prompt Logprobs with Prefix Caching**   | <nobr>🟡 Planned ([RFC #13414](https://github.com/vllm-project/vllm/issues/13414))</nobr>  |
+| **LoRA**                                  | <nobr>🟢 Functional ([PR #13096](https://github.com/vllm-project/vllm/pull/13096))</nobr>   |
+| **Spec Decode**                           | <nobr>🚧 WIP ([PR #13933](https://github.com/vllm-project/vllm/pull/13933))</nobr>          |
+| **FP8 KV Cache**                          | <nobr>🟡 Planned</nobr>                                                           |
+| **Structured Generation Fallback**        | <nobr>🔴 Deprecated</nobr>                                                        |
+| **best_of**                               | <nobr>🔴 Deprecated ([RFC #13361](https://github.com/vllm-project/vllm/issues/13361))</nobr>|
+| **Per-Request Logits Processors**         | <nobr>🔴 Deprecated ([RFC #13360](https://github.com/vllm-project/vllm/pull/13360))</nobr>  |
+| **GPU <> CPU KV Cache Swapping**          | <nobr>🔴 Deprecated</nobr>                                                        |
+| **Embedding Models**                      | <nobr>🟡 Planned</nobr>                                                           |
+| **Mamba Models**                          | <nobr>🟡 Planned</nobr>                                                           |
+| **Encoder-Decoder Models**                | <nobr>🟡 Planned</nobr>                                                           |
+
+- **🚀 Optimized**: Nearly fully optimized, with no further work currently planned.
+- **🟢 Functional**: Fully operational, with ongoing optimizations.  
+- **🚧 WIP**: Under active development.  
+- **🟡 Planned**: Scheduled for future implementation (some may have open PRs/RFCs).  
+- **🔴 Deprecated**: Not planned for v1 unless there is strong demand.
+
+### Semantic Changes and Deprecated Features
+
+#### Logprobs
+
+vLLM V1 supports logprobs and prompt logprobs. However, there are some important semantic
+differences compared to V0:
+
+**Logprobs Calculation**
+
+Logprobs in V1 are now returned immediately once computed from the model’s raw output (i.e.
+before applying any logits post-processing such as temperature scaling or penalty
+adjustments). As a result, the returned logprobs do not reflect the final adjusted
+probabilities used during sampling.
+
+Support for logprobs with post-sampling adjustments is in progress and will be added in future updates.
+
+**Prompt Logprobs with Prefix Caching**
+
+Currently prompt logprobs are only supported when prefix caching is turned off via `--no-enable-prefix-caching`. In a future release, prompt logprobs will be compatible with prefix caching, but a recomputation will be triggered to recover the full prompt logprobs even upon a prefix cache hit. See details in [RFC #13414](https://github.com/vllm-project/vllm/issues/13414).
+
+#### Deprecated Features
+
+As part of the major architectural rework in vLLM V1, several legacy features have been deprecated.
+
+**Sampling features**
+
+- **best_of**: This feature has been deprecated due to limited usage. See details at [RFC #13361](https://github.com/vllm-project/vllm/issues/13361).
+- **Per-Request Logits Processors**: In V0, users could pass custom
+  processing functions to adjust logits on a per-request basis. In vLLM V1, this
+  feature has been deprecated. Instead, the design is moving toward supporting **global logits
+  processors**, a feature the team is actively working on for future releases. See details at [RFC #13360](https://github.com/vllm-project/vllm/pull/13360).
+
+**KV Cache features**
+
+- **GPU <> CPU KV Cache Swapping**: with the new simplified core architecture, vLLM V1 no longer requires KV cache swapping
+to handle request preemptions.
+
+### Feature & Model Support in Progress
+
+Although we have re-implemented and partially optimized many features and models from V0 in vLLM V1, optimization work is still ongoing for some, and others remain unsupported.
+
+#### Features to be Optimized
+
+These features are already supported in vLLM V1, but their optimization is still
+in progress.
+
+- **LoRA**: LoRA is functionally working on vLLM V1 but its performance is
+  inferior to that of V0. The team is actively working on improving its
+  performance
+(e.g., see [PR #13096](https://github.com/vllm-project/vllm/pull/13096)).
+
+- **Spec Decode**: Currently, only ngram-based spec decode is supported in V1. There
+  will be follow-up work to support other types of spec decode (e.g., see [PR #13933](https://github.com/vllm-project/vllm/pull/13933)). We will prioritize the support for Eagle, MTP compared to draft model based spec decode.
+
+#### Unsupported Features
+
+- **FP8 KV Cache**: While vLLM V1 introduces new FP8 kernels for model weight quantization, support for an FP8 key–value cache is not yet available. Users must continue using FP16 (or other supported precisions) for the KV cache.
+
+- **Structured Generation Fallback**: For structured output tasks, V1 currently
+  supports only the `xgrammar:no_fallback` mode, meaning that it will error out if the output schema is unsupported by xgrammar.
+  Details about the structured generation can be found
+  [here](https://docs.vllm.ai/en/latest/features/structured_outputs.html).
+
+#### Unsupported Models
+
+vLLM V1 currently excludes model architectures with the `SupportsV0Only` protocol,
+and the majority fall into the following categories. V1 support for these models will be added eventually.
+
+**Embedding Models**  
+vLLM V1 does not yet include a `PoolingModelRunner` to support embedding/pooling
+  models (e.g, `XLMRobertaModel`).
+
+**Mamba Models**  
+Models using selective state-space mechanisms (instead of standard transformer attention)
+are not yet supported (e.g., `MambaForCausalLM`, `JambaForCausalLM`).
+
+**Encoder-Decoder Models**  
+vLLM V1 is currently optimized for decoder-only transformers. Models requiring
+  cross-attention between separate encoder and decoder are not yet supported (e.g., `BartForConditionalGeneration`, `MllamaForConditionalGeneration`).
+
+For a complete list of supported models, see the [list of supported models](https://docs.vllm.ai/en/latest/models/supported_models.html).
+
+## FAQ
+
+TODO

docs/source/index.md

@@ -67,6 +67,8 @@ getting_started/quickstart
 getting_started/examples/examples_index
 getting_started/troubleshooting
 getting_started/faq
+getting_started/v1_user_guide
+
 :::
 
 % What does vLLM support?