[Bugfix][Kernel] Pad Marlin small-N output shards by parameter layout

.buildkite/release-pipeline.yaml

@@ -723,7 +723,7 @@ steps:
       - "bash tools/vllm-rocm/generate-rocm-wheels-root-index.sh"
     env:
       S3_BUCKET: "vllm-wheels"
-      VARIANT: "rocm721"
+      VARIANT: "rocm722"
 
   # ROCm Job 6: Build ROCm Release Docker Image
   - label: ":docker: Build release image - x86_64 - ROCm"

docker/Dockerfile.rocm_base

@@ -1,4 +1,4 @@
-ARG BASE_IMAGE=rocm/dev-ubuntu-22.04:7.2.1-complete
+ARG BASE_IMAGE=rocm/dev-ubuntu-22.04:7.2.2-complete
 ARG TRITON_BRANCH="ba5c1517"
 ARG TRITON_REPO="https://github.com/ROCm/triton.git"
 ARG PYTORCH_BRANCH="8514f051" # release/2.10 as of 3/17
@@ -9,7 +9,7 @@ ARG PYTORCH_AUDIO_BRANCH="v2.9.0"
 ARG PYTORCH_AUDIO_REPO="https://github.com/pytorch/audio.git"
 ARG FA_BRANCH="0e60e394"
 ARG FA_REPO="https://github.com/Dao-AILab/flash-attention.git"
-ARG AITER_BRANCH="v0.1.10.post3"
+ARG AITER_BRANCH="v0.1.12.post2"
 ARG AITER_REPO="https://github.com/ROCm/aiter.git"
 ARG MORI_BRANCH="v1.1.0"
 ARG MORI_REPO="https://github.com/ROCm/mori.git"
@@ -104,6 +104,28 @@ ENV SCCACHE_REGION=${USE_SCCACHE:+${SCCACHE_REGION_NAME}}
 ENV SCCACHE_S3_NO_CREDENTIALS=${USE_SCCACHE:+${SCCACHE_S3_NO_CREDENTIALS}}
 ENV SCCACHE_IDLE_TIMEOUT=${USE_SCCACHE:+0}
 
+# torch profiler hotfix for 7.2.2: rebuild CLR with https://github.com/ROCm/rocm-systems/pull/5062
+# will be removed once we move to ROCm 7.2.3
+RUN apt-get update && apt-get install -y rocm-llvm-dev
+RUN pip install CppHeaderParser
+RUN git clone --no-checkout --filter=blob:none https://github.com/ROCm/rocm-systems /tmp/rocm-systems \
+    && cd /tmp/rocm-systems \
+    && git sparse-checkout init --cone \
+    && git sparse-checkout set projects/hip projects/clr \
+    && git checkout 35e8c7bf8911862e5389509800e65fdf125412b3 \
+    && export CLR_DIR=/tmp/rocm-systems/projects/clr \
+    && export HIP_DIR=/tmp/rocm-systems/projects/hip \
+    && mkdir -p $CLR_DIR/build && cd $CLR_DIR/build \
+    && cmake \
+        -DHIP_COMMON_DIR=$HIP_DIR \
+        -DCMAKE_PREFIX_PATH="/opt/rocm/" \
+        -DCLR_BUILD_HIP=ON \
+        -DCLR_BUILD_OCL=OFF \
+        -DHIP_PLATFORM=amd \
+        .. \
+    && make -j$(nproc) \
+    && make install \
+    && rm -rf /tmp/rocm-systems
 
 ###
 ### Triton Build
@@ -153,8 +175,6 @@ RUN git clone ${PYTORCH_REPO} pytorch
 RUN cd pytorch && git checkout ${PYTORCH_BRANCH}
 RUN cd pytorch \
     && pip install -r requirements.txt && git submodule update --init --recursive
-RUN cd pytorch/third_party/kineto \
-    && git remote add rocm https://github.com/ROCm/kineto && git fetch rocm && git checkout 2d73be3 
 RUN cd pytorch && python3 tools/amd_build/build_amd.py \
     && if [ "$USE_SCCACHE" = "1" ]; then \
            export HIP_CLANG_PATH=/opt/sccache-wrappers \

docs/design/attention_backends.md

@@ -155,6 +155,7 @@ Priority is **1 = highest** (tried first).
 | **Block Sizes** | Supported KV cache block sizes (%N means multiples of N) |
 | **Head Sizes** | Supported attention head sizes |
 | **Sink** | Attention sink support (for StreamingLLM) |
+| **Non-Causal** | Non-causal (bidirectional) attention support for decoder models |
 | **Sparse** | Sparse attention support (MLA only) |
 | **MM Prefix** | Multimodal prefix full attention support |
 | **DCP** | Decode Context Parallelism support (`--decode-context-parallel-size`) |
@@ -165,22 +166,22 @@ 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`, `fp8`, `fp8_e4m3`, `fp8_e5m2` | Any | 32, 64, 80, 96, 112, 128, 160, 192, 224, 256, 512 | ❌ | ❌ | ❌ | All | N/A |
-| `FLASHINFER` | Native† | fp16, bf16 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3`, `fp8_e5m2` | 16, 32, 64 | 64, 128, 256 | ❌ | ❌ | ✅ | Decoder | 7.x-9.x |
-| `FLASHINFER` | TRTLLM† | fp16, bf16 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3`, `fp8_e5m2`, `nvfp4` | 16, 32, 64 | 64, 128, 256 | ✅ | ❌ | ✅ | Decoder | 10.x |
-| `FLASH_ATTN` | FA2* | fp16, bf16 | `auto`, `float16`, `bfloat16` | %16 | Any | ❌ | ❌ | ✅ | All | ≥8.0 |
-| `FLASH_ATTN` | FA3* | fp16, bf16 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3`, `fp8_e5m2` | %16 | Any | ✅ | ❌ | ✅ | All | 9.x |
-| `FLASH_ATTN` | FA4* | fp16, bf16 | `auto`, `float16`, `bfloat16` | %16 | Any | ✅ | ❌ | ✅ | All | ≥10.0 |
-| `FLASH_ATTN_DIFFKV` | | fp16, bf16 | `auto` | Any | Any | ❌ | ❌ | ✅ | Decoder | Any |
-| `FLEX_ATTENTION` | | fp16, bf16, fp32 | `auto`, `float16`, `bfloat16` | %16 | Any | ❌ | ✅ | ❌ | Decoder, Encoder Only | Any |
-| `ROCM_AITER_FA` | | fp16, bf16 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3`, `fp8_e5m2` | 16, 32 | 64, 128, 256 | ❌ | ❌ | ❌ | Decoder | N/A |
-| `ROCM_AITER_UNIFIED_ATTN` | | fp16, bf16 | `auto` | %16 | Any | ✅ | ✅ | ❌ | All | N/A |
-| `ROCM_ATTN` | | fp16, bf16, fp32 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3`, `fp8_e5m2` | %16 | 32, 64, 80, 96, 128, 160, 192, 224, 256 | ❌ | ✅ | ❌ | Decoder, Encoder, Encoder Only | N/A |
-| `TREE_ATTN` | | fp16, bf16 | `auto`, `float16`, `bfloat16` | %16 | 32, 64, 96, 128, 160, 192, 224, 256 | ❌ | ❌ | ❌ | Decoder | Any |
-| `TRITON_ATTN` | | fp16, bf16, fp32 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3`, `fp8_e5m2`, `int8_per_token_head`, `fp8_per_token_head` | %16 | Any | ✅ | ✅ | ❌ | All | Any |
-| `TURBOQUANT` | | fp16, bf16 | `turboquant_k8v4`, `turboquant_4bit_nc`, `turboquant_k3v4_nc`, `turboquant_3bit_nc` | 16, 32, 64, 128 | Any | ❌ | ❌ | ❌ | Decoder | Any |
+| Backend | Version | Dtypes | KV Dtypes | Block Sizes | Head Sizes | Sink | Non-Causal | MM Prefix | DCP | Attention Types | Compute Cap. |
+| ------- | ------- | ------ | --------- | ----------- | ---------- | ---- | ---------- | --------- | --- | --------------- | ------------ |
+| `CPU_ATTN` | | fp16, bf16, fp32 | `auto`, `fp8`, `fp8_e4m3`, `fp8_e5m2` | Any | 32, 64, 80, 96, 112, 128, 160, 192, 224, 256, 512 | ❌ | ❌ | ❌ | ❌ | All | N/A |
+| `FLASHINFER` | Native† | fp16, bf16 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3`, `fp8_e5m2` | 16, 32, 64 | 64, 128, 256 | ❌ | ❌ | ❌ | ✅ | Decoder | 7.x-9.x |
+| `FLASHINFER` | TRTLLM† | fp16, bf16 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3`, `fp8_e5m2`, `nvfp4` | 16, 32, 64 | 64, 128, 256 | ✅ | ❌ | ❌ | ✅ | Decoder | 10.x |
+| `FLASH_ATTN` | FA2* | fp16, bf16 | `auto`, `float16`, `bfloat16` | %16 | Any | ❌ | ✅ | ❌ | ✅ | All | ≥8.0 |
+| `FLASH_ATTN` | FA3* | fp16, bf16 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3`, `fp8_e5m2` | %16 | Any | ✅ | ✅ | ❌ | ✅ | All | 9.x |
+| `FLASH_ATTN` | FA4* | fp16, bf16 | `auto`, `float16`, `bfloat16` | %16 | Any | ✅ | ✅ | ❌ | ✅ | All | ≥10.0 |
+| `FLASH_ATTN_DIFFKV` | | fp16, bf16 | `auto` | Any | Any | ❌ | ❌ | ❌ | ✅ | Decoder | Any |
+| `FLEX_ATTENTION` | | fp16, bf16, fp32 | `auto`, `float16`, `bfloat16` | %16 | Any | ❌ | ✅ | ✅ | ❌ | Decoder, Encoder Only | Any |
+| `ROCM_AITER_FA` | | fp16, bf16 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3`, `fp8_e5m2` | 16, 32 | 64, 128, 256 | ❌ | ✅ | ❌ | ❌ | Decoder | N/A |
+| `ROCM_AITER_UNIFIED_ATTN` | | fp16, bf16 | `auto` | %16 | Any | ✅ | ❌ | ✅ | ❌ | All | N/A |
+| `ROCM_ATTN` | | fp16, bf16, fp32 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3`, `fp8_e5m2` | %16 | 32, 64, 80, 96, 128, 160, 192, 224, 256 | ❌ | ✅ | ✅ | ❌ | Decoder, Encoder, Encoder Only | N/A |
+| `TREE_ATTN` | | fp16, bf16 | `auto`, `float16`, `bfloat16` | %16 | 32, 64, 96, 128, 160, 192, 224, 256 | ❌ | ❌ | ❌ | ❌ | Decoder | Any |
+| `TRITON_ATTN` | | fp16, bf16, fp32 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3`, `fp8_e5m2`, `int8_per_token_head`, `fp8_per_token_head` | %16 | Any | ✅ | ❌ | ✅ | ❌ | All | Any |
+| `TURBOQUANT` | | fp16, bf16 | `turboquant_k8v4`, `turboquant_4bit_nc`, `turboquant_k3v4_nc`, `turboquant_3bit_nc` | 16, 32, 64, 128 | Any | ❌ | ❌ | ❌ | ❌ | Decoder | Any |
 
 > **†** FlashInfer uses TRTLLM attention on Blackwell (SM100), which supports sinks. Disable via `--attention-config.use_trtllm_attention=0`.
 >
@@ -211,16 +212,16 @@ hardware and configuration.
 MLA decode backends are selected using the standard
 `-ac.backend=<BACKEND>` argument (e.g., `FLASHMLA`, `TRITON_MLA`).
 
-| Backend | Dtypes | KV Dtypes | Block Sizes | Head Sizes | Sink | Sparse | MM Prefix | DCP | Attention Types | Compute Cap. |
-| ------- | ------ | --------- | ----------- | ---------- | ---- | ------ | --------- | --- | --------------- | ------------ |
-| `CUTLASS_MLA` | fp16, bf16 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3` | 128 | Any | ❌ | ❌ | ❌ | ✅ | Decoder | 10.x |
-| `FLASHINFER_MLA` | fp16, bf16 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3` | 32, 64 | Any | ❌ | ❌ | ❌ | ❌ | Decoder | 10.x |
-| `FLASHINFER_MLA_SPARSE` | fp16, bf16 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3` | 32, 64 | 576 | ❌ | ✅ | ❌ | ❌ | Decoder | 10.x |
-| `FLASHMLA` | fp16, bf16 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3` | 64 | Any | ❌ | ❌ | ❌ | ✅ | Decoder | 9.x-10.x |
-| `FLASHMLA_SPARSE` | bf16 | `auto`, `bfloat16`, `fp8_ds_mla` | 64 | 512, 576 | ❌ | ✅ | ❌ | ❌ | Decoder | 9.x-10.x |
-| `FLASH_ATTN_MLA` | fp16, bf16 | `auto`, `float16`, `bfloat16` | %16 | Any | ❌ | ❌ | ❌ | ✅ | Decoder | 9.x |
-| `ROCM_AITER_MLA` | fp16, bf16 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3`, `fp8_e5m2` | %1 | Any | ❌ | ❌ | ❌ | ❌ | Decoder | N/A |
-| `ROCM_AITER_MLA_SPARSE` | fp16, bf16 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3` | 1, 64 | Any | ❌ | ✅ | ❌ | ❌ | Decoder | N/A |
-| `ROCM_AITER_TRITON_MLA` | fp16, bf16 | `auto` | Any | Any | ❌ | ❌ | ❌ | ❌ | Decoder | N/A |
-| `TRITON_MLA` | fp16, bf16 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3` | %16 | Any | ❌ | ❌ | ❌ | ✅ | Decoder | Any |
-| `XPU_MLA_SPARSE` | fp16, bf16 | `auto`, `float16`, `bfloat16` | Any | 576 | ❌ | ✅ | ❌ | ❌ | Decoder | Any |
+| Backend | Dtypes | KV Dtypes | Block Sizes | Head Sizes | Sink | Non-Causal | Sparse | MM Prefix | DCP | Attention Types | Compute Cap. |
+| ------- | ------ | --------- | ----------- | ---------- | ---- | ---------- | ------ | --------- | --- | --------------- | ------------ |
+| `CUTLASS_MLA` | fp16, bf16 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3` | 128 | Any | ❌ | ❌ | ❌ | ❌ | ✅ | Decoder | 10.x |
+| `FLASHINFER_MLA` | fp16, bf16 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3` | 32, 64 | Any | ❌ | ❌ | ❌ | ❌ | ❌ | Decoder | 10.x |
+| `FLASHINFER_MLA_SPARSE` | fp16, bf16 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3` | 32, 64 | 576 | ❌ | ❌ | ✅ | ❌ | ❌ | Decoder | 10.x |
+| `FLASHMLA` | fp16, bf16 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3` | 64 | Any | ❌ | ❌ | ❌ | ❌ | ✅ | Decoder | 9.x-10.x |
+| `FLASHMLA_SPARSE` | bf16 | `auto`, `bfloat16`, `fp8_ds_mla` | 64 | 512, 576 | ❌ | ❌ | ✅ | ❌ | ❌ | Decoder | 9.x-10.x |
+| `FLASH_ATTN_MLA` | fp16, bf16 | `auto`, `float16`, `bfloat16` | %16 | Any | ❌ | ❌ | ❌ | ❌ | ✅ | Decoder | 9.x |
+| `ROCM_AITER_MLA` | fp16, bf16 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3`, `fp8_e5m2` | %1 | Any | ❌ | ❌ | ❌ | ❌ | ❌ | Decoder | N/A |
+| `ROCM_AITER_MLA_SPARSE` | fp16, bf16 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3` | 1, 64 | Any | ❌ | ❌ | ✅ | ❌ | ❌ | Decoder | N/A |
+| `ROCM_AITER_TRITON_MLA` | fp16, bf16 | `auto` | Any | Any | ❌ | ❌ | ❌ | ❌ | ❌ | Decoder | N/A |
+| `TRITON_MLA` | fp16, bf16 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3` | %16 | Any | ❌ | ❌ | ❌ | ❌ | ✅ | Decoder | Any |
+| `XPU_MLA_SPARSE` | fp16, bf16 | `auto`, `float16`, `bfloat16` | Any | 576 | ❌ | ❌ | ✅ | ❌ | ❌ | Decoder | Any |

docs/features/batch_invariance.md

@@ -105,7 +105,7 @@ Batch invariance has been tested and verified on the following models:
 
 - **DeepSeek series**: `deepseek-ai/DeepSeek-V3`, `deepseek-ai/DeepSeek-V3-0324`, `deepseek-ai/DeepSeek-R1`, `deepseek-ai/DeepSeek-V3.1`
 - **Qwen3 (Dense)**: `Qwen/Qwen3-1.7B`, `Qwen/Qwen3-8B`, `Qwen/Qwen3-4B-AWQ`, `Qwen/Qwen3-8B-AWQ`
-- **Qwen3 (MoE)**: `Qwen/Qwen3-30B-A3B`, `Qwen/Qwen3-Next-80B-A3B-Instruct`
+- **Qwen3 (MoE)**: `Qwen/Qwen3-30B-A3B`, `Qwen/Qwen3-Next-80B-A3B-Instruct`, `Qwen/Qwen3-30B-A3B-Thinking-2507-FP8`
 - **Qwen2.5**: `Qwen/Qwen2.5-0.5B-Instruct`, `Qwen/Qwen2.5-1.5B-Instruct`, `Qwen/Qwen2.5-3B-Instruct`, `Qwen/Qwen2.5-7B-Instruct`, `Qwen/Qwen2.5-14B-Instruct`, `Qwen/Qwen2.5-32B-Instruct`
 - **Llama 3**: `meta-llama/Llama-3.1-8B-Instruct`, `meta-llama/Llama-3.2-1B-Instruct`
 - **GPT-OSS**: `openai/gpt-oss-20b`, `openai/gpt-oss-120b`

docs/serving/data_parallel_deployment.md

@@ -98,7 +98,7 @@ For larger scale deployments especially, it can make sense to handle the orchest
 
 In this case, it's more convenient to treat each DP rank like a separate vLLM deployment, with its own endpoint, and have an external router balance HTTP requests between them, making use of appropriate real-time telemetry from each server for routing decisions.
 
-This can already be done trivially for non-MoE models, since each deployed server is fully independent. No data parallel CLI options need to be used for this.
+This can already be done trivially for non-MoE models, since each deployed server is fully independent. In that case, launch independent vLLM instances without any `--data-parallel-*` arguments; external DP CLI options are only supported for MoE deployments.
 
 We support an equivalent topology for MoE DP+EP which can be configured via the following CLI arguments.
 

mkdocs.yaml

@@ -105,8 +105,7 @@ plugins:
           - https://docs.aiohttp.org/en/stable/objects.inv
           - https://pillow.readthedocs.io/en/stable/objects.inv
           - https://numpy.org/doc/stable/objects.inv
-          # TODO revert to stable once https://github.com/pytorch/pytorch/issues/182007 is fixed
-          - https://pytorch.org/docs/2.11/objects.inv
+          - https://pytorch.org/docs/stable/objects.inv
   - redirects:
       redirect_maps:
         features/spec_decode/README.md: features/speculative_decoding/README.md

requirements/common.txt

@@ -24,7 +24,7 @@ outlines_core == 0.2.14
 # required for outlines backend disk cache
 diskcache == 5.6.3
 lark == 1.2.2
-xgrammar >= 0.1.32, < 1.0.0; platform_machine == "x86_64" or platform_machine == "aarch64" or platform_machine == "arm64" or platform_machine == "s390x" or platform_machine == "ppc64le"
+xgrammar >= 0.2.0, < 1.0.0; platform_machine == "x86_64" or platform_machine == "aarch64" or platform_machine == "arm64" or platform_machine == "s390x" or platform_machine == "ppc64le"
 typing_extensions >= 4.10
 filelock >= 3.16.1 # need to contain https://github.com/tox-dev/filelock/pull/317
 partial-json-parser # used for parsing partial JSON outputs

requirements/test/rocm.txt

@@ -42,6 +42,8 @@ anyio==4.13.0
     #   sse-starlette
     #   starlette
     #   watchfiles
+apache-tvm-ffi==0.1.10
+    # via xgrammar
 arctic-inference==0.1.1
     # via -r requirements/test/rocm.in
 argcomplete==3.6.3
@@ -1264,6 +1266,7 @@ typing-extensions==4.15.0
     #   alembic
     #   anthropic
     #   anyio
+    #   apache-tvm-ffi
     #   azure-core
     #   azure-identity
     #   azure-storage-blob
@@ -1345,7 +1348,7 @@ word2number==1.1
     # via lm-eval
 wrapt==2.1.2
     # via smart-open
-xgrammar==0.1.33
+xgrammar==0.2.0
     # via
     #   -c requirements/common.txt
     #   -r requirements/test/../common.txt

tests/benchmarks/test_custom_dataset_seed.py

@@ -0,0 +1,77 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import argparse
+import json
+from pathlib import Path
+
+import pytest
+from transformers import AutoTokenizer, PreTrainedTokenizerBase
+
+from vllm.benchmarks.datasets import get_samples
+
+
+@pytest.fixture(scope="session")
+def hf_tokenizer() -> PreTrainedTokenizerBase:
+    return AutoTokenizer.from_pretrained("gpt2")
+
+
+def _write_jsonl(path: Path, n_rows: int) -> None:
+    with path.open("w") as f:
+        for i in range(n_rows):
+            f.write(json.dumps({"prompt": f"row {i}: unique prompt content."}) + "\n")
+
+
+def _args_for_custom(dataset_path: str, seed: int) -> argparse.Namespace:
+    return argparse.Namespace(
+        dataset_name="custom",
+        dataset_path=dataset_path,
+        disable_shuffle=False,
+        num_prompts=30,
+        custom_output_len=32,
+        skip_chat_template=True,
+        no_oversample=False,
+        seed=seed,
+        request_id_prefix="",
+    )
+
+
+@pytest.mark.benchmark
+def test_custom_dataset_seed_propagates(
+    hf_tokenizer: PreTrainedTokenizerBase, tmp_path: Path
+) -> None:
+    """--seed must control the CustomDataset shuffle used by get_samples.
+
+    Without the fix, CustomDataset was instantiated without random_seed,
+    so its load-time shuffle always used DEFAULT_SEED=0 regardless of
+    args.seed, causing every run with --dataset-name custom to pick the
+    same subset of rows from a larger file.
+    """
+    jsonl = tmp_path / "data.jsonl"
+    _write_jsonl(jsonl, n_rows=60)
+
+    samples_a = get_samples(_args_for_custom(str(jsonl), seed=0), hf_tokenizer)
+    samples_b = get_samples(_args_for_custom(str(jsonl), seed=42), hf_tokenizer)
+
+    prompts_a = {s.prompt for s in samples_a}
+    prompts_b = {s.prompt for s in samples_b}
+
+    assert len(prompts_a) == 30
+    assert len(prompts_b) == 30
+    assert prompts_a != prompts_b
+
+
+@pytest.mark.benchmark
+def test_custom_dataset_same_seed_is_deterministic(
+    hf_tokenizer: PreTrainedTokenizerBase, tmp_path: Path
+) -> None:
+    """Same --seed must yield the same CustomDataset subset."""
+    jsonl = tmp_path / "data.jsonl"
+    _write_jsonl(jsonl, n_rows=60)
+
+    samples_a = get_samples(_args_for_custom(str(jsonl), seed=7), hf_tokenizer)
+    samples_b = get_samples(_args_for_custom(str(jsonl), seed=7), hf_tokenizer)
+
+    prompts_a = [s.prompt for s in samples_a]
+    prompts_b = [s.prompt for s in samples_b]
+
+    assert prompts_a == prompts_b

tests/conftest.py

@@ -996,6 +996,8 @@ def generate(
             req_sample_output_ids: list[list[int]] = []
             req_sample_output_strs: list[str] = []
             req_logprobs = []
+            if req_output.prompt_logprobs:
+                req_logprobs.extend(req_output.prompt_logprobs)
             for sample in req_output.outputs:
                 output_str = sample.text
                 output_ids = list(sample.token_ids)

tests/entrypoints/openai/correctness/test_transcription_api_correctness.py

@@ -27,7 +27,8 @@
 from ....utils import RemoteOpenAIServer
 
 # Tuned to prevent OOM on 18GB GPUs in transcription correctness tests.
-MAX_SEQS_FOR_TRANSCRIPTION_TEST = 32
+MAX_SEQS_FOR_TRANSCRIPTION_TEST = 8
+GPU_UTIL_FOR_TRANSCRIPTION_TEST = 0.5
 
 
 def to_bytes(y, sr):
@@ -188,6 +189,7 @@ def test_wer_correctness(
         "--enforce-eager",
         f"--tokenizer_mode={model_info.tokenizer_mode}",
         f"--max_num_seqs={MAX_SEQS_FOR_TRANSCRIPTION_TEST}",
+        f"--gpu_memory_utilization={GPU_UTIL_FOR_TRANSCRIPTION_TEST}",
     ]
     if model_info.trust_remote_code:
         server_args.append("--trust-remote-code")