Allow `markdownlint` to run locally (#36398)

Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>

Allow `markdownlint` to run locally (#36398)
Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
a0f44bb6 · Harry Mellor · GitHub · fde4771b · a0f44bb6 · a0f44bb6
Unverified Commit a0f44bb6 authored Mar 09, 2026 by Harry Mellor Committed by GitHub Mar 08, 2026
20 changed files
--- a/.github/mergify.yml
+++ b/.github/mergify.yml
@@ -38,15 +38,13 @@ pull_request_rules:
        > [!TIP]
        > <details>
-        > <summary>Is <code>mypy</code> or <code>markdownlint</code> failing?</summary>
+        > <summary>Is <code>mypy</code> failing?</summary>
        > <br/>
-        > <code>mypy</code> and <code>markdownlint</code> are run differently in CI. If the failure is related to either of these checks, please use the following commands to run them locally:
+        > <code>mypy</code> is run differently in CI. If the failure is related to this check, please use the following command to run it locally:
        >
        > ```bash
        > # For mypy (substitute "3.10" with the failing version if needed)
        > pre-commit run --hook-stage manual mypy-3.10
-        > # For markdownlint
-        > pre-commit run --hook-stage manual markdownlint
        > ```
        > </details>

--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -24,12 +24,12 @@ repos:
    exclude: 'csrc/(moe/topk_softmax_kernels.cu|quantization/gguf/(ggml-common.h|dequantize.cuh|vecdotq.cuh|mmq.cuh|mmvq.cuh))|vllm/third_party/.*'
    types_or: [c++, cuda]
    args: [--style=file, --verbose]
- repo: https://github.com/igorshubovych/markdownlint-cli
+- repo: https://github.com/DavidAnson/markdownlint-cli2
-  rev: v0.45.0
+  rev: v0.21.0
  hooks:
-  - id: markdownlint
+  - id: markdownlint-cli2
-    exclude: '.*\.inc\.md'
+    language_version: lts
-    stages: [manual] # Only run in CI
+    args: [--fix]
 - repo: https://github.com/rhysd/actionlint
  rev: v1.7.7
  hooks:

--- a/benchmarks/attention_benchmarks/README.md
+++ b/benchmarks/attention_benchmarks/README.md
@@ -187,7 +187,7 @@ python benchmark.py \
 ## Hardware Requirements
 | Backend | Hardware |
-|---------|----------|
+| ------- | -------- |
 | Flash/Triton/FlashInfer | Any CUDA GPU |
 | CUTLASS MLA | Blackwell (SM100+) |
 | FlashAttn MLA | Hopper (SM90+) |

--- a/benchmarks/auto_tune/README.md
+++ b/benchmarks/auto_tune/README.md
@@ -41,7 +41,7 @@ MODEL=meta-llama/Llama-3.3-70B-Instruct SYSTEM=TPU TP=8 DOWNLOAD_DIR='' INPUT_LE
 | --- | --- | --- |
 | `BASE` | **Required.** The absolute path to the parent directory of your vLLM repository directory. | `"$HOME"` |
 | `MODEL` | **Required.** The Hugging Face model identifier to be served by vllm. | `"meta-llama/Llama-3.1-8B-Instruct"` |
-| `SYSTEM`| **Required.** The hardware you are running on. Choices: `TPU` or `GPU`. (For other systems, it might not support saving profiles) | `"TPU"` |
+| `SYSTEM` | **Required.** The hardware you are running on. Choices: `TPU` or `GPU`. (For other systems, it might not support saving profiles) | `"TPU"` |
 | `TP` | **Required.** The tensor-parallelism size. | `1` |
 | `DOWNLOAD_DIR` | **Required.** Directory to download and load model weights from. | `""` (default download path) |
 | `INPUT_LEN` | **Required.** Request input length. | `4000` |

--- a/docs/benchmarking/cli.md
+++ b/docs/benchmarking/cli.md
@@ -18,7 +18,7 @@ th {
 </style>
 | Dataset | Online | Offline | Data Path |
-|---------|--------|---------|-----------|
+| ------- | ------ | ------- | --------- |
 | ShareGPT | ✅ | ✅ | `wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json` |
 | ShareGPT4V (Image) | ✅ | ✅ | `wget https://huggingface.co/datasets/Lin-Chen/ShareGPT4V/resolve/main/sharegpt4v_instruct_gpt4-vision_cap100k.json`<br>Note that the images need to be downloaded separately. For example, to download COCO's 2017 Train images:<br>`wget http://images.cocodataset.org/zips/train2017.zip` |
 | ShareGPT4Video (Video) | ✅ | ✅ | `git clone https://huggingface.co/datasets/ShareGPT4Video/ShareGPT4Video` |
@@ -383,14 +383,14 @@ The `--burstiness` parameter mathematically controls request arrival patterns us
 Load Pattern Recommendations by Use Case:
-| Use Case           | Burstiness   | Request Rate    | Max Concurrency | Description                                               |
+| Use Case           | Burstiness   | Request Rate    | Max Concurrency | Description                                                                        |
-| ---                | ---          | ---             | ---             | ---                                                       |
+| ---                | ---          | ---             | ---             | ---                                                                                |
 | Maximum Throughput | N/A          | Infinite        | Limited         | **Most common**: Simulates load balancer/gateway limits with unlimited user demand |
-| Realistic Testing  | 1.0          | Moderate (5-20) | Infinite        | Natural Poisson traffic patterns for baseline performance |
+| Realistic Testing  | 1.0          | Moderate (5-20) | Infinite        | Natural Poisson traffic patterns for baseline performance                          |
-| Stress Testing     | 0.1-0.5      | High (20-100)   | Infinite        | Challenging burst patterns to test resilience             |
+| Stress Testing     | 0.1-0.5      | High (20-100)   | Infinite        | Challenging burst patterns to test resilience                                      |
-| Latency Profiling  | 2.0-5.0      | Low (1-10)      | Infinite        | Uniform load for consistent timing analysis               |
+| Latency Profiling  | 2.0-5.0      | Low (1-10)      | Infinite        | Uniform load for consistent timing analysis                                        |
-| Capacity Planning  | 1.0          | Variable        | Limited         | Test resource limits with realistic constraints           |
+| Capacity Planning  | 1.0          | Variable        | Limited         | Test resource limits with realistic constraints                                    |
-| SLA Validation     | 1.0          | Target rate     | SLA limit       | Production-like constraints for compliance testing        |
+| SLA Validation     | 1.0          | Target rate     | SLA limit       | Production-like constraints for compliance testing                                 |
 These load patterns help evaluate different aspects of your vLLM deployment, from basic performance characteristics to resilience under challenging traffic conditions.
@@ -941,7 +941,7 @@ Benchmark per-stage latency of the multimodal (MM) input processor pipeline, inc
 The benchmark measures the following stages for each request:
 | Stage | Description |
-|-------|-------------|
+| ----- | ----------- |
 | `get_mm_hashes_secs` | Time spent hashing multimodal inputs |
 | `get_cache_missing_items_secs` | Time spent looking up the processor cache |
 | `apply_hf_processor_secs` | Time spent in the HuggingFace processor |

--- a/docs/benchmarking/dashboard.md
+++ b/docs/benchmarking/dashboard.md
@@ -60,12 +60,12 @@ Here is an example using the script to compare result_a and result_b with max co
 ***Output Tput (tok/s) — Model : [ meta-llama/Llama-3.1-8B-Instruct ] , Dataset Name : [ random ] , Input Len : [ 2048.0 ] , Output Len : [ 2048.0 ]***
-|    | # of max concurrency | qps  | results_a/benchmark_results.json | results_b/benchmark_results.json | perf_ratio        |
+| | # of max concurrency | qps | results_a/benchmark_results.json | results_b/benchmark_results.json | perf_ratio |
-|----|------|-----|-----------|----------|----------|
+| | -------------------- | --- | -------------------------------- | -------------------------------- | ---------- |
-| 0  | 12 | inf | 24.98   | 186.03 |  7.45 |
+| 0 | 12 | inf | 24.98 | 186.03 |  7.45 |
-| 1  | 16 | inf|  25.49  | 246.92 | 9.69 |
+| 1 | 16 | inf |  25.49 | 246.92 | 9.69 |
-| 2  | 24 | inf| 27.74  | 293.34 |  10.57 |
+| 2 | 24 | inf | 27.74 | 293.34 |  10.57 |
-| 3  | 32 | inf| 28.61  |306.69 | 10.72 |
+| 3 | 32 | inf | 28.61 |306.69 | 10.72 |
 ***compare-json-results.py – Command-Line Parameters***  

--- a/docs/cli/bench/mm_processor.md
+++ b/docs/cli/bench/mm_processor.md
@@ -29,7 +29,7 @@ vllm bench mm-processor \
 ## Measured Stages
 | Stage | Description |
-|-------|-------------|
+| ----- | ----------- |
 | `get_mm_hashes_secs` | Time spent hashing multimodal inputs |
 | `get_cache_missing_items_secs` | Time spent looking up the processor cache |
 | `apply_hf_processor_secs` | Time spent in the HuggingFace processor |

--- a/docs/cli/json_tip.inc.md
+++ b/docs/cli/json_tip.inc.md
+<!-- markdownlint-disable MD041 -->
 When passing JSON CLI arguments, the following sets of arguments are equivalent:
 - `--json-arg '{"key1": "value1", "key2": {"key3": "value2"}}'`
@@ -6,4 +7,4 @@ When passing JSON CLI arguments, the following sets of arguments are equivalent:
 Additionally, list elements can be passed individually using `+`:
 - `--json-arg '{"key4": ["value3", "value4", "value5"]}'`
 - `--json-arg.key4+ value3 --json-arg.key4+='value4,value5'`
\ No newline at end of file
--- a/docs/configuration/optimization.md
+++ b/docs/configuration/optimization.md
@@ -293,7 +293,7 @@ llm = LLM(
 Based on the configuration, the content of the multi-modal caches on `P0` and `P1` are as follows:
 | mm_processor_cache_type | Cache Type | `P0` Cache | `P1` Engine Cache | `P1` Worker Cache | Max. Memory |
-|-------------------|-------------|------------|------------|-------------|-------------|
+| ----------------- | ----------- | ---------- | ---------- | ----------- | ----------- |
 | lru | Processor Caching | K + V | N/A | N/A | `mm_processor_cache_gb * data_parallel_size` |
 | lru | Key-Replicated Caching | K | K + V | N/A | `mm_processor_cache_gb * api_server_count` |
 | shm | Shared Memory Caching | K | N/A | V | `mm_processor_cache_gb * api_server_count` |

--- a/docs/contributing/README.md
+++ b/docs/contributing/README.md
@@ -94,7 +94,6 @@ vLLM's `pre-commit` hooks will now run automatically every time you commit.
    Some `pre-commit` hooks only run in CI. If you need to, you can run them locally with:
    ```bash
-    pre-commit run --hook-stage manual markdownlint
    pre-commit run --hook-stage manual mypy-3.10
    ```

--- a/docs/contributing/ci/update_pytorch_version.md
+++ b/docs/contributing/ci/update_pytorch_version.md
@@ -66,12 +66,12 @@ This complicates the process as we cannot use the out-of-the-box
 - Important indexes at the moment include:
 | Platform | `--extra-index-url` |
-|----------|-----------------|
+| -------- | ------------------- |
-| CUDA 12.8| [https://download.pytorch.org/whl/cu128](https://download.pytorch.org/whl/cu128)|
+| CUDA 12.8 | [https://download.pytorch.org/whl/cu128](https://download.pytorch.org/whl/cu128) |
-| CPU      | [https://download.pytorch.org/whl/cpu](https://download.pytorch.org/whl/cpu)|
+| CPU | [https://download.pytorch.org/whl/cpu](https://download.pytorch.org/whl/cpu) |
 | ROCm 6.2 | [https://download.pytorch.org/whl/rocm6.2.4](https://download.pytorch.org/whl/rocm6.2.4) |
 | ROCm 6.3 | [https://download.pytorch.org/whl/rocm6.3](https://download.pytorch.org/whl/rocm6.3) |
-| XPU      | [https://download.pytorch.org/whl/xpu](https://download.pytorch.org/whl/xpu) |
+| XPU | [https://download.pytorch.org/whl/xpu](https://download.pytorch.org/whl/xpu) |
 - Update the below files to match the CUDA version from step 1. This makes sure that the release vLLM wheel is tested on CI.
    - `.buildkite/release-pipeline.yaml`

--- a/docs/contributing/deprecation_policy.md
+++ b/docs/contributing/deprecation_policy.md
@@ -66,7 +66,7 @@ stages will be removed.
 Assume a feature is deprecated in `v0.9.0`.
 | Release       | Status                                                                                          |
-|---------------|-------------------------------------------------------------------------------------------------|
+| ------------- | ----------------------------------------------------------------------------------------------- |
 | `v0.9.0`      | Feature is deprecated with clear removal version listed.                                        |
 | `v0.10.0`     | Feature is now off by default, throws an error when used, and can be re-enabled for legacy use. |
 | `v0.11.0`     | Feature is removed.                                                                             |

--- a/docs/deployment/frameworks/helm.md
+++ b/docs/deployment/frameworks/helm.md
@@ -49,7 +49,7 @@ chart **including persistent volumes** and deletes the release.
 The following table describes configurable parameters of the chart in `values.yaml`:
 | Key | Type | Default | Description |
-|-----|------|---------|-------------|
+| --- | ---- | ------- | ----------- |
 | autoscaling | object | {"enabled":false,"maxReplicas":100,"minReplicas":1,"targetCPUUtilizationPercentage":80} | Autoscaling configuration |
 | autoscaling.enabled | bool | false | Enable autoscaling |
 | autoscaling.maxReplicas | int | 100 | Maximum replicas |

--- a/docs/deployment/integrations/kuberay.md
+++ b/docs/deployment/integrations/kuberay.md
@@ -6,7 +6,7 @@ A Ray cluster can be declared in YAML, and the operator then handles pod schedul
 ## Why KubeRay instead of manual scripts?
 | Feature | Manual scripts | KubeRay |
-|---------|-----------------------------------------------------------|---------|
+| ------- | --------------------------------------------------------- | ------- |
 | Cluster bootstrap | Manually SSH into every node and run a script | One command to create or update the whole cluster: `kubectl apply -f cluster.yaml` |
 | Autoscaling | Manual | Automatically patches CRDs for adjusting cluster size |
 | Upgrades | Tear down & re-create manually | Blue/green deployment updates supported |

--- a/docs/design/arch_overview.md
+++ b/docs/design/arch_overview.md
@@ -119,7 +119,7 @@ The code can be found in [vllm/v1/engine/coordinator.py](../../vllm/v1/engine/co
 For a deployment with `N` GPUs, `TP` tensor parallel size, `DP` data parallel size, and `A` API server count:
 | Process Type | Count | Notes |
-|---|---|---|
+| - | - | - |
 | API Server | `A` (default `DP`) | Handles HTTP requests and input processing |
 | Engine Core | `DP` (default 1) | Scheduler and KV cache management |
 | GPU Worker | `N` (= `DP x PP x TP`) | One per GPU, executes model forward passes |

--- a/docs/design/attention_backends.md
+++ b/docs/design/attention_backends.md
@@ -101,7 +101,7 @@ Priority is **1 = highest** (tried first).
 **Blackwell (SM 10.x):**
 | Priority | Backend |
-|----------|---------|
+| -------- | ------- |
 | 1 | `FLASHINFER` |
 | 2 | `FLASH_ATTN` |
 | 3 | `TRITON_ATTN` |
@@ -110,7 +110,7 @@ Priority is **1 = highest** (tried first).
 **Ampere/Hopper (SM 8.x-9.x):**
 | Priority | Backend |
-|----------|---------|
+| -------- | ------- |
 | 1 | `FLASH_ATTN` |
 | 2 | `FLASHINFER` |
 | 3 | `TRITON_ATTN` |
@@ -121,7 +121,7 @@ Priority is **1 = highest** (tried first).
 **Blackwell (SM 10.x):**
 | Priority | Backend |
-|----------|---------|
+| -------- | ------- |
 | 1 | `FLASHINFER_MLA` |
 | 2 | `CUTLASS_MLA` |
 | 3 | `FLASH_ATTN_MLA` |
@@ -133,7 +133,7 @@ Priority is **1 = highest** (tried first).
 **Ampere/Hopper (SM 8.x-9.x):**
 | Priority | Backend |
-|----------|---------|
+| -------- | ------- |
 | 1 | `FLASH_ATTN_MLA` |
 | 2 | `FLASHMLA` |
 | 3 | `FLASHINFER_MLA` |
@@ -145,7 +145,7 @@ Priority is **1 = highest** (tried first).
 ## Legend
 | Column | Description |
-|--------|-------------|
+| ------ | ----------- |
 | **Dtypes** | Supported model data types (fp16, bf16, fp32) |
 | **KV Dtypes** | Supported KV cache data types (`auto`, `fp8`, `fp8_e4m3`, etc.) |
 | **Block Sizes** | Supported KV cache block sizes (%N means multiples of N) |
@@ -162,20 +162,20 @@ Priority is **1 = highest** (tried first).
 ## Standard Attention (MHA, MQA, GQA) Backends
 | Backend | Version | Dtypes | KV Dtypes | Block Sizes | Head Sizes | Sink | MM Prefix | DCP | Attention Types | Compute Cap. |
-|---------|---------|--------|-----------|-------------|------------|------|-----------|-----|-----------------|--------------|
+| ------- | ------- | ------ | --------- | ----------- | ---------- | ---- | --------- | --- | --------------- | ------------ |
-| `CPU_ATTN` |  | fp16, bf16, fp32 | `auto` | Any | 32, 64, 80, 96, 112, 128, 160, 192, 224, 256 | ❌ | ❌ | ❌ | All | N/A |
+| `CPU_ATTN` | | fp16, bf16, fp32 | `auto` | Any | 32, 64, 80, 96, 112, 128, 160, 192, 224, 256 | ❌ | ❌ | ❌ | All | N/A |
 | `FLASHINFER` | Native† | fp16, bf16 | `auto`, `bfloat16`, `fp8`, `fp8_e4m3`, `fp8_e5m2` | 16, 32, 64 | 64, 128, 256 | ❌ | ❌ | ✅ | Decoder | 7.x-9.x |
 | `FLASHINFER` | TRTLLM† | fp16, bf16 | `auto`, `bfloat16`, `fp8`, `fp8_e4m3`, `fp8_e5m2` | 16, 32, 64 | 64, 128, 256 | ✅ | ❌ | ✅ | Decoder | 10.x |
 | `FLASH_ATTN` | FA2* | fp16, bf16 | `auto`, `bfloat16` | %16 | Any | ❌ | ❌ | ✅ | All | ≥8.0 |
 | `FLASH_ATTN` | FA3* | fp16, bf16 | `auto`, `bfloat16`, `fp8`, `fp8_e4m3`, `fp8_e5m2` | %16 | Any | ✅ | ❌ | ✅ | All | 9.x |
 | `FLASH_ATTN` | FA4* | fp16, bf16 | `auto`, `bfloat16` | %16 | Any | ❌ | ❌ | ✅ | All | ≥10.0 |
-| `FLASH_ATTN_DIFFKV` |  | fp16, bf16 | `auto` | Any | Any | ❌ | ❌ | ✅ | Decoder | Any |
+| `FLASH_ATTN_DIFFKV` | | fp16, bf16 | `auto` | Any | Any | ❌ | ❌ | ✅ | Decoder | Any |
-| `FLEX_ATTENTION` |  | fp16, bf16, fp32 | `auto`, `bfloat16` | Any | Any | ❌ | ✅ | ❌ | Decoder, Encoder Only | Any |
+| `FLEX_ATTENTION` | | fp16, bf16, fp32 | `auto`, `bfloat16` | Any | Any | ❌ | ✅ | ❌ | Decoder, Encoder Only | Any |
-| `ROCM_AITER_FA` |  | fp16, bf16 | `auto`, `bfloat16`, `fp8`, `fp8_e4m3`, `fp8_e5m2` | 16, 32 | 64, 128, 256 | ❌ | ❌ | ❌ | Decoder, Enc-Dec | N/A |
+| `ROCM_AITER_FA` | | fp16, bf16 | `auto`, `bfloat16`, `fp8`, `fp8_e4m3`, `fp8_e5m2` | 16, 32 | 64, 128, 256 | ❌ | ❌ | ❌ | Decoder, Enc-Dec | N/A |
-| `ROCM_AITER_UNIFIED_ATTN` |  | fp16, bf16 | `auto` | %16 | Any | ✅ | ✅ | ❌ | All | N/A |
+| `ROCM_AITER_UNIFIED_ATTN` | | fp16, bf16 | `auto` | %16 | Any | ✅ | ✅ | ❌ | All | N/A |
-| `ROCM_ATTN` |  | fp16, bf16, fp32 | `auto`, `bfloat16`, `fp8`, `fp8_e4m3`, `fp8_e5m2` | 16, 32, 544 | 32, 64, 80, 96, 128, 160, 192, 224, 256 | ✅ | ✅ | ❌ | All | N/A |
+| `ROCM_ATTN` | | fp16, bf16, fp32 | `auto`, `bfloat16`, `fp8`, `fp8_e4m3`, `fp8_e5m2` | 16, 32, 544 | 32, 64, 80, 96, 128, 160, 192, 224, 256 | ✅ | ✅ | ❌ | All | N/A |
-| `TREE_ATTN` |  | fp16, bf16 | `auto` | %16 | 32, 64, 96, 128, 160, 192, 224, 256 | ❌ | ❌ | ❌ | Decoder | Any |
+| `TREE_ATTN` | | fp16, bf16 | `auto` | %16 | 32, 64, 96, 128, 160, 192, 224, 256 | ❌ | ❌ | ❌ | Decoder | Any |
-| `TRITON_ATTN` |  | fp16, bf16, fp32 | `auto`, `bfloat16`, `fp8`, `fp8_e4m3`, `fp8_e5m2` | %16 | Any | ✅ | ✅ | ❌ | All | Any |
+| `TRITON_ATTN` | | fp16, bf16, fp32 | `auto`, `bfloat16`, `fp8`, `fp8_e4m3`, `fp8_e5m2` | %16 | Any | ✅ | ✅ | ❌ | All | Any |
 > **†** FlashInfer uses TRTLLM attention on Blackwell (SM100), which supports sinks. Disable via `--attention-config.use_trtllm_attention=0`.
 >
@@ -191,10 +191,10 @@ The prefill backend is selected at runtime based on hardware and
 configuration.
 | Backend | Description | Compute Cap. | Enable | Disable | Notes |
-|---------|-------------|--------------|--------|---------|-------|
+| ------- | ----------- | ------------ | ------ | ------- | ----- |
 | TRT-LLM Ragged‡ | TensorRT-LLM ragged attention | 10.x | Default on SM100 | `-ac.use_trtllm_ragged_deepseek_prefill=0` | DeepSeek R1 dims only |
 | FlashInfer | FlashInfer CUTLASS backend | 10.x | `-ac.disable_flashinfer_prefill=0` | `-ac.disable_flashinfer_prefill=1` | DeepSeek R1 dims only |
-| cuDNN | cuDNN-based attention | 10.x | `-ac.use_cudnn_prefill=1` | `-ac.use_cudnn_prefill=0` |  |
+| cuDNN | cuDNN-based attention | 10.x | `-ac.use_cudnn_prefill=1` | `-ac.use_cudnn_prefill=0` | |
 | FlashAttention | FlashAttention varlen (FA2/FA3) | Any | Default fallback | Use other backends | FA3 on SM90, FA2 otherwise |
 > **‡** TRT-LLM Ragged is the default on Blackwell (SM100).
@@ -203,7 +203,7 @@ configuration.
 ### Decode Backends
 | Backend | Dtypes | KV Dtypes | Block Sizes | Head Sizes | Sink | Sparse | MM Prefix | DCP | Attention Types | Compute Cap. |
-|---------|--------|-----------|-------------|------------|------|--------|-----------|-----|-----------------|--------------|
+| ------- | ------ | --------- | ----------- | ---------- | ---- | ------ | --------- | --- | --------------- | ------------ |
 | `CUTLASS_MLA` | fp16, bf16 | `auto`, `bfloat16`, `fp8`, `fp8_e4m3` | 128 | Any | ❌ | ❌ | ❌ | ✅ | Decoder | 10.x |
 | `FLASHINFER_MLA` | fp16, bf16 | `auto`, `bfloat16`, `fp8`, `fp8_e4m3` | 32, 64 | Any | ❌ | ❌ | ❌ | ❌ | Decoder | 10.x |
 | `FLASHINFER_MLA_SPARSE` | fp16, bf16 | `auto`, `bfloat16`, `fp8`, `fp8_e4m3` | 32, 64 | 576 | ❌ | ✅ | ❌ | ❌ | Decoder | 10.x |

--- a/docs/design/cuda_graphs.md
+++ b/docs/design/cuda_graphs.md
@@ -174,18 +174,18 @@ Suppose we have hybrid attention backends (e.g., in mamba mixer models). In that
 The following table lists backends that support full CUDA Graphs at the time of writing.
 | Attention Backend | cudagraph_support | Comments |
-|:---|:---|:---|
+| :---------------- | :---------------- | :------- |
 | FlashAttention v2 | `UNIFORM_BATCH` | Actually `ALWAYS` but workaround to fallback to `FULL_AND_PIECEWISE` for performance reason |
 | FlashAttention v3 | `ALWAYS` | has unified routine for both batches, so `FULL` mode is good |
 | Triton Attention | `ALWAYS` | prefer `FULL_AND_PIECEWISE` since it has different kernels for prefill/mixed and pure decode batches |
-| AITER FlashAttention | `UNIFORM_BATCH`| |
+| AITER FlashAttention | `UNIFORM_BATCH` | |
 | FlashInfer | `UNIFORM_SINGLE_TOKEN_DECODE` | Will be set to `UNIFORM_BATCH` when using TRTLLM attention on Blackwell |
 | FlashMLA | `UNIFORM_BATCH` | |
 | FlashInferMLA | `UNIFORM_BATCH` | |
 | FlashInferMLASparse | `UNIFORM_BATCH` | |
 | AITER MLA | `UNIFORM_SINGLE_TOKEN_DECODE` | |
 | CUTLASS MLA | `UNIFORM_SINGLE_TOKEN_DECODE` | |
-| Mamba attention| `UNIFORM_SINGLE_TOKEN_DECODE` | |
+| Mamba attention | `UNIFORM_SINGLE_TOKEN_DECODE` | |
 Unlisted backends are all declared as `NEVER`.

--- a/docs/design/debug_vllm_compile.md
+++ b/docs/design/debug_vllm_compile.md
@@ -5,12 +5,12 @@ TL;DR:
 - use tlparse to acquire torch.compile logs. Include these logs in bug reports and/or support asks.
 - The vLLM-torch.compile integration is multiple pieces. vLLM exposes flags to turn off each piece:
-| Online Flag | Offline Flag   |      Result |
+| Online Flag | Offline Flag | Result |
-|----------|----------|-------------|
+| ----------- | ------------ | ------ |
-| --enforce-eager | enforce_eager=True |  Turn off torch.compile and CUDAGraphs |
+| --enforce-eager | enforce_eager=True | Turn off torch.compile and CUDAGraphs |
-| -cc.mode=0 | mode=CompilationMode.NONE |  Turn off torch.compile only |
+| -cc.mode=0 | mode=CompilationMode.NONE | Turn off torch.compile only |
-| -cc.cudagraph_mode=NONE | compilation_config=CompilationConfig(cudagraph_mode=CUDAGraphMode.NONE) |  Turn off CUDAGraphs only |
+| -cc.cudagraph_mode=NONE | compilation_config=CompilationConfig(cudagraph_mode=CUDAGraphMode.NONE) | Turn off CUDAGraphs only |
-| -cc.backend=eager | compilation_config=CompilationConfig(backend='eager') |  Turn off TorchInductor |
+| -cc.backend=eager | compilation_config=CompilationConfig(backend='eager') | Turn off TorchInductor |
 ## vLLM-torch.compile overview

--- a/docs/design/fusions.md
+++ b/docs/design/fusions.md
@@ -19,7 +19,7 @@ or just on the low or high end.
    If tuning performance by hand, always benchmark your exact use-case with and without the fusion to verify the impact.
 | Fusion                                                                         | `PassConfig` flag            | Fused operations                               | Default at                     | E2E Speedup        | Fullgraph | `num_tokens` |
-|--------------------------------------------------------------------------------|------------------------------|------------------------------------------------|--------------------------------|--------------------|-----------|--------------|
+| ------------------------------------------------------------------------------ | ---------------------------- | ---------------------------------------------- | ------------------------------ | ------------------ | --------- | ------------ |
 | [AllReduce + RMSNorm](#allreduce--rmsnorm-fuse_allreduce_rms)                  | `fuse_allreduce_rms`         | All-reduce → RMSNorm (+residual_add) (→ quant) | O2 (Hopper/Blackwell + TP > 1) | 5-20%              | No        | Low          |
 | [Attention + Quant](#attention--quantization-fuse_attn_quant)                  | `fuse_attn_quant`            | Attention output → FP8/NVFP4 quant             | Off by default                 | 3-7%               | Yes       | Always       |
 | [RoPE + KV-Cache Update](#rope--kv-cache-update-fuse_rope_kvcache)             | `fuse_rope_kvcache`          | Rotary embedding → KV cache write              | O1 (ROCm/AITER only)           | TBD                | No        | Low          |
@@ -37,7 +37,7 @@ The table below lists the quantization schemes supported by each fusion on each
 [#36066](https://github.com/vllm-project/vllm/issues/36066)
 | Fusion                       | SM100 (Blackwell)                        | SM90 (Hopper)                            | SM89 (Ada)                               | SM80 (Ampere) | ROCm                                     |
-|------------------------------|------------------------------------------|------------------------------------------|------------------------------------------|---------------|------------------------------------------|
+| ---------------------------- | ---------------------------------------- | ---------------------------------------- | ---------------------------------------- | ------------- | ---------------------------------------- |
 | `fuse_allreduce_rms`         | FP16/BF16, FP8 static, NVFP4             | FP16/BF16, FP8 static                    | —                                        | —             | —                                        |
 | `fuse_attn_quant`\*          | FP8 static\*, NVFP4\*                    | FP8 static\*                             | FP8 static\*                             | —             | FP8 static\*                             |
 | `fuse_rope_kvcache`          | —                                        | —                                        | —                                        | —             | FP16/BF16                                |

--- a/docs/design/moe_kernel_features.md
+++ b/docs/design/moe_kernel_features.md
@@ -31,7 +31,7 @@ th {
 </style>
 | Backend | Output act. format | Quant. types | Quant. format | Async | Apply Weight On Input | Subclass |
-|---------|--------------------|--------------|---------------|-------|-----------------------|-----------|
+| ------- | ------------------ | ------------ | ------------- | ----- | --------------------- | --------- |
 | naive | standard | all<sup>1</sup> | G,A,T | N | <sup>6</sup> | [layer.py][vllm.model_executor.layers.fused_moe.layer.FusedMoE] |
 | deepep_high_throughput | standard | fp8 | G(128),A,T<sup>2</sup> | Y | Y | [`DeepEPHTPrepareAndFinalize`][vllm.model_executor.layers.fused_moe.deepep_ht_prepare_finalize.DeepEPHTPrepareAndFinalize] |
 | deepep_low_latency | batched | fp8 | G(128),A,T<sup>3</sup> | Y | Y | [`DeepEPLLPrepareAndFinalize`][vllm.model_executor.layers.fused_moe.deepep_ll_prepare_finalize.DeepEPLLPrepareAndFinalize] |
@@ -78,7 +78,7 @@ Most experts flavors include an equivalent modular interface which will be a sub
 To be used with a particular `FusedMoEPrepareAndFinalizeModular` subclass, MoE kernels must have compatible activation formats, quantization types and quantization formats.
 | Kernel | Input act. format | Quant. types | Quant. format | Activation function | Apply Weight On Input | Modular | Source |
-|--------|-------------------|--------------|---------------|---------------------|-----------------------|---------|--------|
+| ------ | ----------------- | ------------ | ------------- | ------------------- | --------------------- | ------- | ------ |
 | triton | standard | all<sup>1</sup> | G,A,T | silu, gelu,</br>swigluoai,</br>silu_no_mul,</br>gelu_no_mul | Y | Y | [`fused_experts`][vllm.model_executor.layers.fused_moe.fused_moe.fused_experts],</br>[`TritonExperts`][vllm.model_executor.layers.fused_moe.fused_moe.TritonExperts] |
 | triton (batched) | batched | all<sup>1</sup> | G,A,T | silu, gelu | <sup>6</sup> | Y | [`BatchedTritonExperts`][vllm.model_executor.layers.fused_moe.fused_batched_moe.BatchedTritonExperts] |
 | deep gemm | standard,</br>batched | fp8 | G(128),A,T | silu, gelu | <sup>6</sup> | Y | </br>[`DeepGemmExperts`][vllm.model_executor.layers.fused_moe.deep_gemm_moe.DeepGemmExperts],</br>[`BatchedDeepGemmExperts`][vllm.model_executor.layers.fused_moe.batched_deep_gemm_moe.BatchedDeepGemmExperts] |
@@ -105,7 +105,7 @@ To be used with a particular `FusedMoEPrepareAndFinalizeModular` subclass, MoE k
 The following table shows "families" of modular kernels that are intended to work together. There are some combinations which may work but have not yet been tested, e.g. flashinfer with other fp8 experts. Note that the "naive" backend will work with any non-modular experts.
 | backend | `FusedMoEPrepareAndFinalizeModular` subclasses | `FusedMoEExpertsModular` subclasses |
-|---------|-----------------------------------------|----------------------------------------------|
+| ------- | ---------------------------------------------- | ----------------------------------- |
-| deepep_high_throughput | `DeepEPHTPrepareAndFinalize` |  `DeepGemmExperts`,</br>`TritonExperts`,</br>`TritonOrDeepGemmExperts`,</br>`CutlassExpertsFp8`, </br>`MarlinExperts` |
+| deepep_high_throughput | `DeepEPHTPrepareAndFinalize` | `DeepGemmExperts`,</br>`TritonExperts`,</br>`TritonOrDeepGemmExperts`,</br>`CutlassExpertsFp8`, </br>`MarlinExperts` |
-| deepep_low_latency | `DeepEPLLPrepareAndFinalize` |  `BatchedDeepGemmExperts`,</br>`BatchedTritonExperts`,</br>`CutlassBatchedExpertsFp8`,</br>`BatchedMarlinExperts` |
+| deepep_low_latency | `DeepEPLLPrepareAndFinalize` | `BatchedDeepGemmExperts`,</br>`BatchedTritonExperts`,</br>`CutlassBatchedExpertsFp8`,</br>`BatchedMarlinExperts` |
 | flashinfer | `FlashInferCutlassMoEPrepareAndFinalize` | `FlashInferExperts` |