[CI] Inline env vars lost by CONTINUE_ON_FAILURE subshell wrapping

Postmerge/nightly/daily builds set CONTINUE_ON_FAILURE=1, which wraps
each YAML command in `(cmd) || CI_OVERALL_STATUS=1`. A standalone
`export VAR=val` executes inside the subshell and the variable is
immediately lost — subsequent commands never see it.

This caused real failures (model_executor fastsafetensors crash from
missing VLLM_WORKER_MULTIPROC_METHOD=spawn) and silent coverage gaps
(rust_frontend tests silently running the Python frontend because
VLLM_USE_RUST_FRONTEND=1 was never set).

Fix: inline env vars as command prefixes (`VAR=val command`) instead
of standalone `export` lines across all 11 affected CI YAML files.

Co-authored-by: Claude <noreply@anthropic.com>
Signed-off-by: Tyler Michael Smith <tyler@tylermsmith.com>
Signed-off-by: Tyler Michael Smith <tlrmchlsmth@gmail.com>

.buildkite/hardware_tests/intel_xpu_ci/test-intel.yaml

@@ -21,6 +21,10 @@ steps:
     timeout_in_minutes: 30
     optional: true
     device: intel_gpu
+    agent_tags:
+      label: production
+      gpu: 2+
+      mem: 24+
     no_plugin: true
     env:
       REGISTRY: "public.ecr.aws/q9t5s3a7"
@@ -38,6 +42,10 @@ steps:
     timeout_in_minutes: 30
     optional: true
     device: intel_gpu
+    agent_tags:
+      label: production
+      gpu: 1+
+      mem: 16+
     no_plugin: true
     env:
       REGISTRY: "public.ecr.aws/q9t5s3a7"
@@ -55,6 +63,10 @@ steps:
     timeout_in_minutes: 30
     optional: true
     device: intel_gpu
+    agent_tags:
+      label: production
+      gpu: 1+
+      mem: 16+
     no_plugin: true
     env:
       REGISTRY: "public.ecr.aws/q9t5s3a7"

.buildkite/intel_jobs/basic_correctness.yaml

@@ -5,6 +5,10 @@ steps:
 - label: XPU Sleep Mode
   timeout_in_minutes: 30
   device: intel_gpu
+  agent_tags:
+    label: production
+    gpu: 1+
+    mem: 16+
   no_plugin: true
   working_dir: "."
   env:

.buildkite/intel_jobs/engine_intel.yaml

@@ -5,6 +5,10 @@ steps:
 - label: Engine (1 GPU)
   timeout_in_minutes: 30
   device: intel_gpu
+  agent_tags:
+    label: production
+    gpu: 1+
+    mem: 16+
   no_plugin: true
   working_dir: "."
   env:

.buildkite/intel_jobs/expert_parallelism_intel.yaml

@@ -6,6 +6,10 @@ steps:
   key: eplb-algorithm
   timeout_in_minutes: 45
   device: intel_gpu
+  agent_tags:
+    label: production
+    gpu: 1+
+    mem: 16+
   no_plugin: true
   working_dir: "."
   env:

.buildkite/intel_jobs/kernels_intel.yaml

@@ -5,6 +5,10 @@ steps:
 - label: vLLM IR Tests
   timeout_in_minutes: 30
   device: intel_gpu
+  agent_tags:
+    label: production
+    gpu: 1+
+    mem: 16+
   no_plugin: true
   working_dir: "."
   env:

.buildkite/intel_jobs/lora_intel.yaml

@@ -5,6 +5,10 @@ steps:
 - label: LoRA Runtime + Utils
   timeout_in_minutes: 45
   device: intel_gpu
+  agent_tags:
+    label: production
+    gpu: 1+
+    mem: 24+
   no_plugin: true
   working_dir: "."
   env:
@@ -34,6 +38,10 @@ steps:
 - label: LoRA Fused/MoE Kernels
   timeout_in_minutes: 45
   device: intel_gpu
+  agent_tags:
+    label: production
+    gpu: 1+
+    mem: 16+
   no_plugin: true
   working_dir: "."
   env:
@@ -54,6 +62,10 @@ steps:
 - label: LoRA Punica Kernels
   timeout_in_minutes: 45
   device: intel_gpu
+  agent_tags:
+    label: production
+    gpu: 1+
+    mem: 16+
   no_plugin: true
   working_dir: "."
   env:
@@ -74,6 +86,10 @@ steps:
 - label: LoRA Punica FP8/XPU Ops
   timeout_in_minutes: 45
   device: intel_gpu
+  agent_tags:
+    label: production
+    gpu: 1+
+    mem: 16+
   no_plugin: true
   working_dir: "."
   env:
@@ -94,6 +110,10 @@ steps:
 - label: LoRA Models
   timeout_in_minutes: 45
   device: intel_gpu
+  agent_tags:
+    label: production
+    gpu: 2+
+    mem: 24+
   no_plugin: true
   working_dir: "."
   env:
@@ -117,6 +137,10 @@ steps:
 - label: LoRA Multimodal
   timeout_in_minutes: 45
   device: intel_gpu
+  agent_tags:
+    label: production
+    gpu: 1+
+    mem: 16+
   no_plugin: true
   working_dir: "."
   env:

.buildkite/intel_jobs/misc_intel.yaml

@@ -5,6 +5,10 @@ steps:
 - label: V1 Core + KV + Metrics
   timeout_in_minutes: 30
   device: intel_gpu
+  agent_tags:
+    label: production
+    gpu: 1+
+    mem: 16+
   no_plugin: true
   working_dir: "."
   env:
@@ -31,6 +35,10 @@ steps:
 - label: V1 Sample + Logits
   timeout_in_minutes: 30
   device: intel_gpu
+  agent_tags:
+    label: production
+    gpu: 1+
+    mem: 16+
   no_plugin: true
   working_dir: "."
   env:
@@ -71,6 +79,10 @@ steps:
 - label: XPU CPU Offload
   timeout_in_minutes: 60
   device: intel_gpu
+  agent_tags:
+    label: production
+    gpu: 1+
+    mem: 16+
   no_plugin: true
   working_dir: "."
   env:
@@ -95,6 +107,10 @@ steps:
   key: regression
   timeout_in_minutes: 30
   device: intel_gpu
+  agent_tags:
+    label: production
+    gpu: 1+
+    mem: 16+
   no_plugin: true
   working_dir: "."
   env:
@@ -126,6 +142,10 @@ steps:
   timeout_in_minutes: 30
   num_devices: 2
   device: intel_gpu
+  agent_tags:
+    label: production
+    gpu: 2+
+    mem: 16+
   no_plugin: true
   working_dir: "."
   env:
@@ -157,6 +177,10 @@ steps:
   key: async-engine-inputs-utils-worker
   timeout_in_minutes: 30
   device: intel_gpu
+  agent_tags:
+    label: production
+    gpu: 1+
+    mem: 24+
   no_plugin: true
   working_dir: "."
   env:

.buildkite/intel_jobs/model_runner_v2_intel.yaml

@@ -5,6 +5,10 @@ steps:
 - label: Model Runner V2 Core Tests (Intel)
   timeout_in_minutes: 45
   device: intel_gpu
+  agent_tags:
+    label: production
+    gpu: 2+
+    mem: 16+
   no_plugin: true
   working_dir: "."
   env:
@@ -30,6 +34,10 @@ steps:
 - label: Model Runner V2 Examples (Intel)
   timeout_in_minutes: 45
   device: intel_gpu
+  agent_tags:
+    label: production
+    gpu: 1+
+    mem: 24+
   no_plugin: true
   working_dir: "."
   env:

.buildkite/intel_jobs/models_multimodal_intel.yaml

@@ -6,6 +6,10 @@ steps:
   key: multi-modal-models-standard-1-qwen2
   timeout_in_minutes: 45
   device: intel_gpu
+  agent_tags:
+    label: production
+    gpu: 1+
+    mem: 16+
   no_plugin: true
   working_dir: "."
   env:
@@ -27,6 +31,10 @@ steps:
   key: multi-modal-models-standard-2-qwen3-gemma
   timeout_in_minutes: 45
   device: intel_gpu
+  agent_tags:
+    label: production
+    gpu: 1+
+    mem: 16+
   no_plugin: true
   working_dir: "."
   env:
@@ -47,6 +55,10 @@ steps:
   key: multi-modal-models-standard-3-llava-qwen2-vl
   timeout_in_minutes: 45
   device: intel_gpu
+  agent_tags:
+    label: production
+    gpu: 1+
+    mem: 24+
   no_plugin: true
   working_dir: "."
   env:
@@ -68,6 +80,10 @@ steps:
   key: multi-modal-models-standard-4-other-whisper
   timeout_in_minutes: 45
   device: intel_gpu
+  agent_tags:
+    label: production
+    gpu: 1+
+    mem: 16+
   no_plugin: true
   working_dir: "."
   env:
@@ -88,6 +104,10 @@ steps:
   key: multi-modal-processor
   timeout_in_minutes: 45
   device: intel_gpu
+  agent_tags:
+    label: production
+    gpu: 1+
+    mem: 16+
   no_plugin: true
   working_dir: "."
   env:

.buildkite/intel_jobs/test-intel.yaml

@@ -19,6 +19,10 @@ steps:
       - image-build-xpu
     timeout_in_minutes: 30
     device: intel_gpu
+    agent_tags:
+      label: production
+      gpu: 2+
+      mem: 24+
     no_plugin: true
     env:
       REGISTRY: "public.ecr.aws/q9t5s3a7"
@@ -49,6 +53,10 @@ steps:
       - image-build-xpu
     timeout_in_minutes: 30
     device: intel_gpu
+    agent_tags:
+      label: production
+      gpu: 1+
+      mem: 16+
     no_plugin: true
     env:
       REGISTRY: "public.ecr.aws/q9t5s3a7"
@@ -67,13 +75,17 @@ steps:
         pytest -v -s v1/worker --ignore=v1/worker/test_gpu_model_runner.py --ignore=v1/worker/test_worker_memory_snapshot.py &&
         pytest -v -s v1/structured_output &&
         pytest -v -s v1/test_serial_utils.py &&
-        pytest -v -s v1/spec_decode --ignore=v1/spec_decode/test_max_len.py --ignore=v1/spec_decode/test_speculators_eagle3.py --ignore=v1/spec_decode/test_acceptance_length.py &&
+        pytest -v -s v1/spec_decode --ignore=v1/spec_decode/test_max_len.py --ignore=v1/spec_decode/test_speculators_eagle3.py --ignore=v1/spec_decode/test_acceptance_length.py --ignore=v1/spec_decode/test_speculators_correctness.py &&
         pytest -v -s v1/kv_connector/unit --ignore=v1/kv_connector/unit/test_multi_connector.py --ignore=v1/kv_connector/unit/test_example_connector.py --ignore=v1/kv_connector/unit/test_lmcache_integration.py --ignore=v1/kv_connector/unit/test_hf3fs_client.py --ignore=v1/kv_connector/unit/test_hf3fs_connector.py --ignore=v1/kv_connector/unit/test_hf3fs_metadata_server.py --ignore=v1/kv_connector/unit/test_offloading_connector.py'
   - label: "XPU server test"
     depends_on:
       - image-build-xpu
     timeout_in_minutes: 30
     device: intel_gpu
+    agent_tags:
+      label: production
+      gpu: 1+
+      mem: 16+
     no_plugin: true
     env:
       REGISTRY: "public.ecr.aws/q9t5s3a7"
@@ -93,6 +105,10 @@ steps:
       - image-build-xpu
     timeout_in_minutes: 30
     device: intel_gpu
+    agent_tags:
+      label: production
+      gpu: 1+
+      mem: 16+
     no_plugin: true
     env:
       REGISTRY: "public.ecr.aws/q9t5s3a7"

.buildkite/scripts/hardware_ci/run-intel-ci-test.sh

@@ -34,7 +34,7 @@ case "${test_suite}" in
     pytest -v -s v1/worker --ignore=v1/worker/test_gpu_model_runner.py --ignore=v1/worker/test_worker_memory_snapshot.py
     pytest -v -s v1/structured_output
     pytest -v -s v1/test_serial_utils.py
-    pytest -v -s v1/spec_decode --ignore=v1/spec_decode/test_max_len.py --ignore=v1/spec_decode/test_speculators_eagle3.py --ignore=v1/spec_decode/test_acceptance_length.py
+    pytest -v -s v1/spec_decode --ignore=v1/spec_decode/test_max_len.py --ignore=v1/spec_decode/test_speculators_eagle3.py --ignore=v1/spec_decode/test_acceptance_length.py --ignore=v1/spec_decode/test_speculators_correctness.py
     pytest -v -s v1/kv_connector/unit --ignore=v1/kv_connector/unit/test_multi_connector.py --ignore=v1/kv_connector/unit/test_example_connector.py --ignore=v1/kv_connector/unit/test_lmcache_integration.py --ignore=v1/kv_connector/unit/test_hf3fs_client.py --ignore=v1/kv_connector/unit/test_hf3fs_connector.py --ignore=v1/kv_connector/unit/test_hf3fs_metadata_server.py --ignore=v1/kv_connector/unit/test_offloading_connector.py
     ;;
   server)

.buildkite/scripts/install-kv-connectors.sh

@@ -4,6 +4,11 @@
 
 set -euo pipefail
 
+if python3 -c "import torch; raise SystemExit(0 if torch.version.hip is not None else 1)"; then
+    uv pip install --system -r /vllm-workspace/requirements/kv_connectors_rocm.txt
+    exit 0
+fi
+
 REQUIREMENTS_FILE="${KV_CONNECTORS_REQUIREMENTS:-/vllm-workspace/requirements/kv_connectors.txt}"
 
 uv pip install --system -r "${REQUIREMENTS_FILE}"

.buildkite/test-amd.yaml

@@ -647,7 +647,7 @@ steps:
   - pytest -v -s v1/cudagraph/test_cudagraph_mode.py
 
 - label: e2e Core (1 GPU) # TBD
-  timeout_in_minutes: 180
+  timeout_in_minutes: 35
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
   agent_pool: mi250_1
   optional: true
@@ -1594,9 +1594,10 @@ steps:
 #----------------------------------------------------------  mi300 · kernels  ----------------------------------------------------------#
 
 - label: Kernels Attention Test %N # TBD
-  timeout_in_minutes: 180
+  timeout_in_minutes: 55
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
   agent_pool: mi300_1
+  optional: true
   parallelism: 2
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
@@ -1627,10 +1628,11 @@ steps:
   - pytest -v -s kernels/core --ignore=kernels/core/test_minimax_reduce_rms.py  kernels/test_concat_mla_q.py kernels/test_top_k_per_row.py
 
 - label: Kernels MoE Test %N # TBD
-  timeout_in_minutes: 180
+  timeout_in_minutes: 50
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
   agent_pool: mi300_1
-  parallelism: 4
+  optional: true
+  parallelism: 5
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - csrc/quantization/cutlass_w8a8/moe/
@@ -2075,19 +2077,6 @@ steps:
   - export VLLM_ALLOW_INSECURE_SERIALIZATION=1
   - pytest -v -s v1/spec_decode/test_acceptance_length.py -m slow_test
 
-- label: e2e Core (1 GPU) # TBD
-  timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
-  agent_pool: mi300_1
-  optional: true
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - vllm/v1/
-  - tests/v1/e2e/
-  - vllm/platforms/rocm.py
-  commands:
-  - pytest -v -s v1/e2e/general --ignore v1/e2e/general/test_async_scheduling.py
-
 - label: e2e Scheduling (1 GPU) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
@@ -2133,9 +2122,10 @@ steps:
   - pytest -v -s v1/e2e/spec_decode -k "draft_model or no_sync or batch_inference"
 
 - label: Spec Decode Eagle # TBD
-  timeout_in_minutes: 180
+  timeout_in_minutes: 45
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
   agent_pool: mi300_1
+  optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - vllm/v1/spec_decode/
@@ -3053,7 +3043,7 @@ steps:
 #----------------------------------------------------------  mi355 · kernels  ----------------------------------------------------------#
 
 - label: Kernels (B200-MI355) # TBD
-  timeout_in_minutes: 180
+  timeout_in_minutes: 15
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
   agent_pool: mi355_1
   working_dir: "/vllm-workspace/"
@@ -3077,11 +3067,10 @@ steps:
   - pytest -v -s tests/kernels/attention/test_attention_selector.py
 
 - label: Kernels Attention Test %N # TBD
-  timeout_in_minutes: 180
+  timeout_in_minutes: 60
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
   agent_pool: mi355_1
   parallelism: 2
-  optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - csrc/attention/
@@ -3095,10 +3084,10 @@ steps:
   - pytest -v -s kernels/attention --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
 
 - label: Kernels MoE Test %N # TBD
-  timeout_in_minutes: 180
+  timeout_in_minutes: 50
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
   agent_pool: mi355_1
-  parallelism: 4
+  parallelism: 5
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - csrc/quantization/cutlass_w8a8/moe/

.buildkite/test_areas/basic_correctness.yaml

@@ -12,7 +12,6 @@ steps:
   - tests/basic_correctness/test_cpu_offload
   - tests/basic_correctness/test_mem.py
   commands:
-  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pytest -v -s basic_correctness/test_mem.py
-  - pytest -v -s basic_correctness/test_basic_correctness.py
-  - pytest -v -s basic_correctness/test_cpu_offload.py
+  - VLLM_WORKER_MULTIPROC_METHOD=spawn pytest -v -s basic_correctness/test_mem.py
+  - VLLM_WORKER_MULTIPROC_METHOD=spawn pytest -v -s basic_correctness/test_basic_correctness.py
+  - VLLM_WORKER_MULTIPROC_METHOD=spawn pytest -v -s basic_correctness/test_cpu_offload.py

.buildkite/test_areas/compile.yaml

@@ -14,8 +14,7 @@ steps:
   - vllm/v1/cudagraph_dispatcher.py
   - tests/compile/correctness_e2e/test_sequence_parallel.py
   commands:
-  - export VLLM_TEST_CLEAN_GPU_MEMORY=1
-  - pytest -v -s tests/compile/correctness_e2e/test_sequence_parallel.py
+  - VLLM_TEST_CLEAN_GPU_MEMORY=1 pytest -v -s tests/compile/correctness_e2e/test_sequence_parallel.py
 
 - label: Sequence Parallel Correctness Tests (2xH100)
   key: sequence-parallel-correctness-tests-2xh100
@@ -25,8 +24,7 @@ steps:
   optional: true
   num_devices: 2
   commands:
-  - export VLLM_TEST_CLEAN_GPU_MEMORY=1
-  - pytest -v -s tests/compile/correctness_e2e/test_sequence_parallel.py
+  - VLLM_TEST_CLEAN_GPU_MEMORY=1 pytest -v -s tests/compile/correctness_e2e/test_sequence_parallel.py
 
 - label: AsyncTP Correctness Tests (2xH100)
   key: asynctp-correctness-tests-2xh100
@@ -36,8 +34,7 @@ steps:
   optional: true
   num_devices: 2
   commands:
-  - export VLLM_TEST_CLEAN_GPU_MEMORY=1
-  - pytest -v -s tests/compile/correctness_e2e/test_async_tp.py
+  - VLLM_TEST_CLEAN_GPU_MEMORY=1 pytest -v -s tests/compile/correctness_e2e/test_async_tp.py
 
 - label: AsyncTP Correctness Tests (B200)
   key: asynctp-correctness-tests-b200
@@ -47,8 +44,7 @@ steps:
   optional: true
   num_devices: 2
   commands:
-  - export VLLM_TEST_CLEAN_GPU_MEMORY=1
-  - pytest -v -s tests/compile/correctness_e2e/test_async_tp.py
+  - VLLM_TEST_CLEAN_GPU_MEMORY=1 pytest -v -s tests/compile/correctness_e2e/test_async_tp.py
 
 - label: Distributed Compile Unit Tests (2xH100)
   key: distributed-compile-unit-tests-2xh100
@@ -61,8 +57,7 @@ steps:
   - vllm/model_executor/layers
   - tests/compile/passes/distributed/
   commands:
-  - export VLLM_TEST_CLEAN_GPU_MEMORY=1
-  - pytest -s -v tests/compile/passes/distributed
+  - VLLM_TEST_CLEAN_GPU_MEMORY=1 pytest -s -v tests/compile/passes/distributed
 
 - label: Fusion and Compile Unit Tests (2xB200)
   key: fusion-and-compile-unit-tests-2xb200

.buildkite/test_areas/distributed.yaml

@@ -32,11 +32,10 @@ steps:
   - tests/entrypoints/openai/test_multi_api_servers.py
   commands:
   # https://github.com/NVIDIA/nccl/issues/1838
-  - export NCCL_CUMEM_HOST_ENABLE=0
-  - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_async_llm_dp.py
-  - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_eagle_dp.py
-  - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_external_lb_dp.py
-  - DP_SIZE=2 pytest -v -s entrypoints/openai/test_multi_api_servers.py
+  - NCCL_CUMEM_HOST_ENABLE=0 TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_async_llm_dp.py
+  - NCCL_CUMEM_HOST_ENABLE=0 TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_eagle_dp.py
+  - NCCL_CUMEM_HOST_ENABLE=0 TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_external_lb_dp.py
+  - NCCL_CUMEM_HOST_ENABLE=0 DP_SIZE=2 pytest -v -s entrypoints/openai/test_multi_api_servers.py
 
 - label: Distributed Compile + RPC Tests (2 GPUs)
   key: distributed-compile-rpc-tests-2-gpus
@@ -56,10 +55,9 @@ steps:
   - tests/entrypoints/llm/test_collective_rpc.py
   commands:
   # https://github.com/NVIDIA/nccl/issues/1838
-  - export NCCL_CUMEM_HOST_ENABLE=0
-  - pytest -v -s entrypoints/llm/test_collective_rpc.py
-  - pytest -v -s ./compile/fullgraph/test_basic_correctness.py
-  - pytest -v -s ./compile/test_wrapper.py
+  - NCCL_CUMEM_HOST_ENABLE=0 pytest -v -s entrypoints/llm/test_collective_rpc.py
+  - NCCL_CUMEM_HOST_ENABLE=0 pytest -v -s ./compile/fullgraph/test_basic_correctness.py
+  - NCCL_CUMEM_HOST_ENABLE=0 pytest -v -s ./compile/test_wrapper.py
 
 - label: Distributed Torchrun + Shutdown Tests (2 GPUs)
   key: distributed-torchrun-shutdown-tests-2-gpus
@@ -78,11 +76,10 @@ steps:
   - tests/v1/worker/test_worker_memory_snapshot.py
   commands:
   # https://github.com/NVIDIA/nccl/issues/1838
-  - export NCCL_CUMEM_HOST_ENABLE=0
-  - VLLM_TEST_SAME_HOST=1 torchrun --nproc-per-node=4 distributed/test_same_node.py | grep 'Same node test passed'
-  - VLLM_TEST_SAME_HOST=1 VLLM_TEST_WITH_DEFAULT_DEVICE_SET=1 torchrun --nproc-per-node=4 distributed/test_same_node.py | grep 'Same node test passed'
-  - CUDA_VISIBLE_DEVICES=0,1 pytest -v -s v1/shutdown
-  - pytest -v -s v1/worker/test_worker_memory_snapshot.py
+  - NCCL_CUMEM_HOST_ENABLE=0 VLLM_TEST_SAME_HOST=1 torchrun --nproc-per-node=4 distributed/test_same_node.py | grep 'Same node test passed'
+  - NCCL_CUMEM_HOST_ENABLE=0 VLLM_TEST_SAME_HOST=1 VLLM_TEST_WITH_DEFAULT_DEVICE_SET=1 torchrun --nproc-per-node=4 distributed/test_same_node.py | grep 'Same node test passed'
+  - NCCL_CUMEM_HOST_ENABLE=0 CUDA_VISIBLE_DEVICES=0,1 pytest -v -s v1/shutdown
+  - NCCL_CUMEM_HOST_ENABLE=0 pytest -v -s v1/worker/test_worker_memory_snapshot.py
 
 - label: Distributed Torchrun + Examples (4 GPUs)
   key: distributed-torchrun-examples-4-gpus
@@ -97,24 +94,23 @@ steps:
   - tests/examples/features/data_parallel/data_parallel_offline.py
   commands:
   # https://github.com/NVIDIA/nccl/issues/1838
-  - export NCCL_CUMEM_HOST_ENABLE=0
   # test with torchrun tp=2 and external_dp=2
-  - torchrun --nproc-per-node=4 tests/distributed/test_torchrun_example.py
+  - NCCL_CUMEM_HOST_ENABLE=0 torchrun --nproc-per-node=4 tests/distributed/test_torchrun_example.py
   # test with torchrun tp=2 and pp=2
-  - PP_SIZE=2 torchrun --nproc-per-node=4 tests/distributed/test_torchrun_example.py
+  - NCCL_CUMEM_HOST_ENABLE=0 PP_SIZE=2 torchrun --nproc-per-node=4 tests/distributed/test_torchrun_example.py
   # test with torchrun tp=4 and dp=1
-  - TP_SIZE=4 torchrun --nproc-per-node=4 tests/distributed/test_torchrun_example_moe.py
+  - NCCL_CUMEM_HOST_ENABLE=0 TP_SIZE=4 torchrun --nproc-per-node=4 tests/distributed/test_torchrun_example_moe.py
   # test with torchrun tp=2, pp=2 and dp=1
-  - PP_SIZE=2 TP_SIZE=2 torchrun --nproc-per-node=4 tests/distributed/test_torchrun_example_moe.py
+  - NCCL_CUMEM_HOST_ENABLE=0 PP_SIZE=2 TP_SIZE=2 torchrun --nproc-per-node=4 tests/distributed/test_torchrun_example_moe.py
   # test with torchrun tp=1 and dp=4 with ep
-  - DP_SIZE=4 ENABLE_EP=1 torchrun --nproc-per-node=4 tests/distributed/test_torchrun_example_moe.py
+  - NCCL_CUMEM_HOST_ENABLE=0 DP_SIZE=4 ENABLE_EP=1 torchrun --nproc-per-node=4 tests/distributed/test_torchrun_example_moe.py
   # test with torchrun tp=2 and dp=2 with ep
-  - TP_SIZE=2 DP_SIZE=2 ENABLE_EP=1 torchrun --nproc-per-node=4 tests/distributed/test_torchrun_example_moe.py
+  - NCCL_CUMEM_HOST_ENABLE=0 TP_SIZE=2 DP_SIZE=2 ENABLE_EP=1 torchrun --nproc-per-node=4 tests/distributed/test_torchrun_example_moe.py
   # test with internal dp
-  - python3 examples/features/data_parallel/data_parallel_offline.py --enforce-eager
+  - NCCL_CUMEM_HOST_ENABLE=0 python3 examples/features/data_parallel/data_parallel_offline.py --enforce-eager
   # rlhf examples
-  - VLLM_ALLOW_INSECURE_SERIALIZATION=1 python3 examples/rl/rlhf_nccl.py
-  - VLLM_ALLOW_INSECURE_SERIALIZATION=1 python3 examples/rl/rlhf_ipc.py
+  - NCCL_CUMEM_HOST_ENABLE=0 VLLM_ALLOW_INSECURE_SERIALIZATION=1 python3 examples/rl/rlhf_nccl.py
+  - NCCL_CUMEM_HOST_ENABLE=0 VLLM_ALLOW_INSECURE_SERIALIZATION=1 python3 examples/rl/rlhf_ipc.py
 
 - label: Distributed DP Tests (4 GPUs)
   key: distributed-dp-tests-4-gpus
@@ -128,14 +124,13 @@ steps:
   - tests/distributed/test_utils
   commands:
   # https://github.com/NVIDIA/nccl/issues/1838
-  - export NCCL_CUMEM_HOST_ENABLE=0
-  - TP_SIZE=2 DP_SIZE=2 pytest -v -s v1/distributed/test_async_llm_dp.py
-  - TP_SIZE=2 DP_SIZE=2 pytest -v -s v1/distributed/test_eagle_dp.py
-  - TP_SIZE=2 DP_SIZE=2 pytest -v -s v1/distributed/test_external_lb_dp.py
-  - TP_SIZE=1 DP_SIZE=4 pytest -v -s v1/distributed/test_internal_lb_dp.py
-  - TP_SIZE=1 DP_SIZE=4 pytest -v -s v1/distributed/test_hybrid_lb_dp.py
-  - pytest -v -s v1/engine/test_engine_core_client.py::test_kv_cache_events_dp
-  - pytest -v -s distributed/test_utils.py
+  - NCCL_CUMEM_HOST_ENABLE=0 TP_SIZE=2 DP_SIZE=2 pytest -v -s v1/distributed/test_async_llm_dp.py
+  - NCCL_CUMEM_HOST_ENABLE=0 TP_SIZE=2 DP_SIZE=2 pytest -v -s v1/distributed/test_eagle_dp.py
+  - NCCL_CUMEM_HOST_ENABLE=0 TP_SIZE=2 DP_SIZE=2 pytest -v -s v1/distributed/test_external_lb_dp.py
+  - NCCL_CUMEM_HOST_ENABLE=0 TP_SIZE=1 DP_SIZE=4 pytest -v -s v1/distributed/test_internal_lb_dp.py
+  - NCCL_CUMEM_HOST_ENABLE=0 TP_SIZE=1 DP_SIZE=4 pytest -v -s v1/distributed/test_hybrid_lb_dp.py
+  - NCCL_CUMEM_HOST_ENABLE=0 pytest -v -s v1/engine/test_engine_core_client.py::test_kv_cache_events_dp
+  - NCCL_CUMEM_HOST_ENABLE=0 pytest -v -s distributed/test_utils.py
 
 - label: Distributed Compile + Comm (4 GPUs)
   key: distributed-compile-comm-4-gpus
@@ -151,13 +146,12 @@ steps:
   - tests/distributed/test_multiproc_executor.py
   commands:
   # https://github.com/NVIDIA/nccl/issues/1838
-  - export NCCL_CUMEM_HOST_ENABLE=0
-  - pytest -v -s compile/fullgraph/test_basic_correctness.py
-  - pytest -v -s distributed/test_pynccl.py
-  - pytest -v -s distributed/test_events.py
-  - pytest -v -s distributed/test_symm_mem_allreduce.py
+  - NCCL_CUMEM_HOST_ENABLE=0 pytest -v -s compile/fullgraph/test_basic_correctness.py
+  - NCCL_CUMEM_HOST_ENABLE=0 pytest -v -s distributed/test_pynccl.py
+  - NCCL_CUMEM_HOST_ENABLE=0 pytest -v -s distributed/test_events.py
+  - NCCL_CUMEM_HOST_ENABLE=0 pytest -v -s distributed/test_symm_mem_allreduce.py
   # test multi-node TP with multiproc executor (simulated on single node)
-  - pytest -v -s distributed/test_multiproc_executor.py::test_multiproc_executor_multi_node
+  - NCCL_CUMEM_HOST_ENABLE=0 pytest -v -s distributed/test_multiproc_executor.py::test_multiproc_executor_multi_node
 
 - label: Distributed Tests (8 GPUs)(H100)
   key: distributed-tests-8-gpus-h100
@@ -176,9 +170,8 @@ steps:
 
   commands:
   # https://github.com/NVIDIA/nccl/issues/1838
-  - export NCCL_CUMEM_HOST_ENABLE=0
   # test with torchrun tp=2 and dp=4 with ep
-  - torchrun --nproc-per-node=8 ../examples/features/torchrun/torchrun_dp_example_offline.py --tp-size=2 --pp-size=1 --dp-size=4 --enable-ep
+  - NCCL_CUMEM_HOST_ENABLE=0 torchrun --nproc-per-node=8 ../examples/features/torchrun/torchrun_dp_example_offline.py --tp-size=2 --pp-size=1 --dp-size=4 --enable-ep
 
 - label: Distributed Tests (4 GPUs)(A100)
   key: distributed-tests-4-gpus-a100
@@ -271,9 +264,7 @@ steps:
   - tests/distributed/test_pipeline_parallel.py
   - tests/basic_correctness/test_basic_correctness.py
   commands:
-  - export VLLM_USE_RAY_V2_EXECUTOR_BACKEND=1
-  - export NCCL_CUMEM_HOST_ENABLE=0
-  - pytest -v -s distributed/test_ray_v2_executor.py
-  - pytest -v -s distributed/test_ray_v2_executor_e2e.py
-  - pytest -v -s distributed/test_pipeline_parallel.py -k "ray"
-  - TARGET_TEST_SUITE=L4 pytest -v -s basic_correctness/test_basic_correctness.py -k "ray"
+  - VLLM_USE_RAY_V2_EXECUTOR_BACKEND=1 NCCL_CUMEM_HOST_ENABLE=0 pytest -v -s distributed/test_ray_v2_executor.py
+  - VLLM_USE_RAY_V2_EXECUTOR_BACKEND=1 NCCL_CUMEM_HOST_ENABLE=0 pytest -v -s distributed/test_ray_v2_executor_e2e.py
+  - VLLM_USE_RAY_V2_EXECUTOR_BACKEND=1 NCCL_CUMEM_HOST_ENABLE=0 pytest -v -s distributed/test_pipeline_parallel.py -k "ray"
+  - VLLM_USE_RAY_V2_EXECUTOR_BACKEND=1 NCCL_CUMEM_HOST_ENABLE=0 TARGET_TEST_SUITE=L4 pytest -v -s basic_correctness/test_basic_correctness.py -k "ray"

.buildkite/test_areas/engine.yaml

@@ -74,6 +74,16 @@ steps:
     - tests/v1/e2e/general/
   commands:
     - pytest -v -s v1/e2e/general --ignore v1/e2e/general/test_async_scheduling.py
+  mirror:
+    amd:
+      device: mi250_1
+      timeout_in_minutes: 35
+      depends_on:
+      - image-build-amd
+      source_file_dependencies:
+      - vllm/v1/
+      - tests/v1/e2e/general/
+      - vllm/platforms/rocm.py
 
 - label: V1 e2e (2 GPUs)
   key: v1-e2e-2-gpus

.buildkite/test_areas/entrypoints.yaml

@@ -22,10 +22,9 @@ steps:
   - vllm/
   - tests/entrypoints/llm
   commands:
-  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pytest -v -s entrypoints/llm --ignore=entrypoints/llm/test_generate.py --ignore=entrypoints/llm/test_collective_rpc.py --ignore=entrypoints/llm/offline_mode
-  - pytest -v -s entrypoints/llm/test_generate.py # it needs a clean process
-  - pytest -v -s entrypoints/llm/offline_mode # Needs to avoid interference with other tests
+  - VLLM_WORKER_MULTIPROC_METHOD=spawn pytest -v -s entrypoints/llm --ignore=entrypoints/llm/test_generate.py --ignore=entrypoints/llm/test_collective_rpc.py --ignore=entrypoints/llm/offline_mode
+  - VLLM_WORKER_MULTIPROC_METHOD=spawn pytest -v -s entrypoints/llm/test_generate.py # it needs a clean process
+  - VLLM_WORKER_MULTIPROC_METHOD=spawn pytest -v -s entrypoints/llm/offline_mode # Needs to avoid interference with other tests
   mirror:
     amd:
       device: mi325_1
@@ -41,9 +40,8 @@ steps:
   - vllm/
   - tests/entrypoints/serve
   commands:
-  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pytest -v -s entrypoints/serve --ignore=entrypoints/serve/dev/rpc
-  - PYTHONPATH=/vllm-workspace pytest -v -s entrypoints/serve/dev/rpc
+  - VLLM_WORKER_MULTIPROC_METHOD=spawn pytest -v -s entrypoints/serve --ignore=entrypoints/serve/dev/rpc
+  - VLLM_WORKER_MULTIPROC_METHOD=spawn PYTHONPATH=/vllm-workspace pytest -v -s entrypoints/serve/dev/rpc
   mirror:
     amd:
       device: mi325_1
@@ -59,8 +57,7 @@ steps:
   - tests/entrypoints/openai
   - tests/entrypoints/test_chat_utils
   commands:
-  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pytest -v -s entrypoints/openai --ignore=entrypoints/openai/completion --ignore=entrypoints/openai/chat_completion --ignore=entrypoints/openai/responses --ignore=entrypoints/openai/correctness
+  - VLLM_WORKER_MULTIPROC_METHOD=spawn pytest -v -s entrypoints/openai --ignore=entrypoints/openai/completion --ignore=entrypoints/openai/chat_completion --ignore=entrypoints/openai/responses --ignore=entrypoints/openai/correctness
   mirror:
     amd:
       device: mi325_1
@@ -77,9 +74,8 @@ steps:
   - tests/entrypoints/openai
   - tests/entrypoints/test_chat_utils
   commands:
-  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pytest -v -s entrypoints/openai/chat_completion
-  - pytest -v -s entrypoints/openai/completion --ignore=entrypoints/openai/completion/test_tensorizer_entrypoint.py
+  - VLLM_WORKER_MULTIPROC_METHOD=spawn pytest -v -s entrypoints/openai/chat_completion
+  - VLLM_WORKER_MULTIPROC_METHOD=spawn pytest -v -s entrypoints/openai/completion --ignore=entrypoints/openai/completion/test_tensorizer_entrypoint.py
   mirror:
     amd:
       device: mi325_1
@@ -128,8 +124,7 @@ steps:
   - vllm/
   - tests/entrypoints/speech_to_text
   commands:
-  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pytest -v -s entrypoints/speech_to_text
+  - VLLM_WORKER_MULTIPROC_METHOD=spawn pytest -v -s entrypoints/speech_to_text
 
 - label: Entrypoints Integration (Multimodal)
   device: h200_35gb
@@ -140,8 +135,7 @@ steps:
   - vllm/
   - tests/entrypoints/multimodal
   commands:
-  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pytest -v -s entrypoints/multimodal
+  - VLLM_WORKER_MULTIPROC_METHOD=spawn pytest -v -s entrypoints/multimodal
 
 - label: Entrypoints Integration (Pooling)
   key: entrypoints-integration-pooling
@@ -151,8 +145,7 @@ steps:
   - vllm/
   - tests/entrypoints/pooling
   commands:
-  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pytest -v -s entrypoints/pooling
+  - VLLM_WORKER_MULTIPROC_METHOD=spawn pytest -v -s entrypoints/pooling
 
 - label: OpenAI API Correctness
   key: openai-api-correctness

.buildkite/test_areas/kernels.yaml

@@ -74,6 +74,20 @@ steps:
   commands:
     - pytest -v -s kernels/attention --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
   parallelism: 2
+  mirror:
+    amd:
+      device: mi325_1
+      timeout_in_minutes: 55
+      depends_on:
+      - image-build-amd
+      source_file_dependencies:
+      - csrc/attention/
+      - vllm/v1/attention
+      - vllm/model_executor/layers/attention
+      - tests/kernels/attention
+      - vllm/_aiter_ops.py
+      - vllm/envs.py
+      - vllm/platforms/rocm.py
 
 - label: Kernels Attention DiffKV Test (H100)
   key: kernels-attention-diffkv-test-h100
@@ -104,6 +118,7 @@ steps:
       source_file_dependencies:
       - csrc/quantization/
       - vllm/model_executor/layers/quantization
+      - vllm/config/
       - tests/kernels/quantization
       - tests/kernels/quantization/test_rocm_skinny_gemms.py
       - vllm/_aiter_ops.py
@@ -127,6 +142,22 @@ steps:
     - pytest -v -s kernels/moe --ignore=kernels/moe/test_modular_oai_triton_moe.py --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
     - pytest -v -s kernels/moe/test_modular_oai_triton_moe.py --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
   parallelism: 5
+  mirror:
+    amd:
+      device: mi325_1
+      timeout_in_minutes: 50
+      source_file_dependencies:
+      - csrc/quantization/cutlass_w8a8/moe/
+      - csrc/moe/
+      - tests/kernels/moe
+      - vllm/model_executor/layers/fused_moe/
+      - vllm/distributed/device_communicators/
+      - vllm/envs.py
+      - vllm/config
+      - vllm/_aiter_ops.py
+      - vllm/platforms/rocm.py
+      depends_on:
+      - image-build-amd
 
 - label: Kernels Mamba Test
   key: kernels-mamba-test

.buildkite/test_areas/lm_eval.yaml

@@ -50,8 +50,7 @@ steps:
   - csrc/
   - vllm/model_executor/layers/quantization
   commands:
-    - export VLLM_USE_DEEP_GEMM=0  # We found Triton is faster than DeepGEMM for H100
-    - pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-large-hopper.txt --tp-size=4
+    - VLLM_USE_DEEP_GEMM=0 pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-large-hopper.txt --tp-size=4  # Triton is faster than DeepGEMM for H100
 
 - label: LM Eval Small Models (B200)
   key: lm-eval-small-models-b200
@@ -101,6 +100,14 @@ steps:
   num_devices: 8
   commands:
     - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=configs/models-h200.txt
+  mirror:
+    amd:
+      device: mi300_8
+      timeout_in_minutes: 180
+      depends_on:
+      - image-build-amd
+      commands:
+      - VLLM_WORKER_MULTIPROC_METHOD=spawn PYTORCH_ROCM_ARCH=gfx942 pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=configs/models-mi3xx.txt  # Limit Quark compilation to save time
 
 - label: MoE Refactor Integration Test (H100 - TEMPORARY)
   key: moe-refactor-integration-test-h100-temporary

.buildkite/test_areas/lora.yaml

@@ -36,14 +36,14 @@ steps:
   commands:
     # FIXIT: find out which code initialize cuda before running the test
     # before the fix, we need to use spawn to test it
-    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+    #
     # Alot of these tests are on the edge of OOMing
-    - export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
+    #
     # There is some Tensor Parallelism related processing logic in LoRA that
     # requires multi-GPU testing for validation.
-    - pytest -v -s -x lora/test_chatglm3_tp.py
-    - pytest -v -s -x lora/test_llama_tp.py
-    - pytest -v -s -x lora/test_qwen3_with_multi_loras.py
-    - pytest -v -s -x lora/test_olmoe_tp.py
-    - pytest -v -s -x lora/test_gptoss_tp.py
-    - pytest -v -s -x lora/test_qwen35_densemodel_lora.py
+    - VLLM_WORKER_MULTIPROC_METHOD=spawn PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True pytest -v -s -x lora/test_chatglm3_tp.py
+    - VLLM_WORKER_MULTIPROC_METHOD=spawn PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True pytest -v -s -x lora/test_llama_tp.py
+    - VLLM_WORKER_MULTIPROC_METHOD=spawn PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True pytest -v -s -x lora/test_qwen3_with_multi_loras.py
+    - VLLM_WORKER_MULTIPROC_METHOD=spawn PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True pytest -v -s -x lora/test_olmoe_tp.py
+    - VLLM_WORKER_MULTIPROC_METHOD=spawn PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True pytest -v -s -x lora/test_gptoss_tp.py
+    - VLLM_WORKER_MULTIPROC_METHOD=spawn PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True pytest -v -s -x lora/test_qwen35_densemodel_lora.py

.buildkite/test_areas/misc.yaml

@@ -18,9 +18,8 @@ steps:
     - vllm/v1/
     - tests/v1/spec_decode
   commands:
-    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
     # TODO: create another `optional` test group for slow tests
-    - pytest -v -s -m 'not slow_test' v1/spec_decode
+    - VLLM_WORKER_MULTIPROC_METHOD=spawn pytest -v -s -m 'not slow_test' v1/spec_decode
   mirror:
     amd:
       device: mi300_1
@@ -50,12 +49,11 @@ steps:
     - tests/v1/test_request.py
     - tests/v1/test_outputs.py
   commands:
-    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-    - pytest -v -s v1/sample
-    - pytest -v -s v1/logits_processors
-    - pytest -v -s v1/test_oracle.py
-    - pytest -v -s v1/test_request.py
-    - pytest -v -s v1/test_outputs.py
+    - VLLM_WORKER_MULTIPROC_METHOD=spawn pytest -v -s v1/sample
+    - VLLM_WORKER_MULTIPROC_METHOD=spawn pytest -v -s v1/logits_processors
+    - VLLM_WORKER_MULTIPROC_METHOD=spawn pytest -v -s v1/test_oracle.py
+    - VLLM_WORKER_MULTIPROC_METHOD=spawn pytest -v -s v1/test_request.py
+    - VLLM_WORKER_MULTIPROC_METHOD=spawn pytest -v -s v1/test_outputs.py
   mirror:
     amd:
       device: mi325_1
@@ -93,18 +91,23 @@ steps:
     - tests/entrypoints/openai/correctness/test_lmeval.py
   commands:
     - bash /vllm-workspace/.buildkite/scripts/install-kv-connectors.sh
-    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
     # split the test to avoid interference
-    - pytest -v -s -m 'not cpu_test' v1/core
-    - pytest -v -s v1/executor
-    - pytest -v -s v1/kv_offload
-    - pytest -v -s v1/simple_kv_offload
-    - pytest -v -s v1/worker
-    - pytest -v -s -m 'not cpu_test' v1/kv_connector/unit
-    - pytest -v -s -m 'not cpu_test' v1/metrics
+    - VLLM_WORKER_MULTIPROC_METHOD=spawn pytest -v -s -m 'not cpu_test' v1/core
+    - VLLM_WORKER_MULTIPROC_METHOD=spawn pytest -v -s v1/executor
+    - VLLM_WORKER_MULTIPROC_METHOD=spawn pytest -v -s v1/kv_offload
+    - VLLM_WORKER_MULTIPROC_METHOD=spawn pytest -v -s v1/simple_kv_offload
+    - VLLM_WORKER_MULTIPROC_METHOD=spawn pytest -v -s v1/worker
+    - VLLM_WORKER_MULTIPROC_METHOD=spawn pytest -v -s -m 'not cpu_test' v1/kv_connector/unit
+    - VLLM_WORKER_MULTIPROC_METHOD=spawn pytest -v -s -m 'not cpu_test' v1/metrics
     # Integration test for streaming correctness (requires special branch).
     - pip install -U git+https://github.com/robertgshaw2-redhat/lm-evaluation-harness.git@streaming-api
-    - pytest -v -s entrypoints/openai/correctness/test_lmeval.py::test_lm_eval_accuracy_v1_engine
+    - VLLM_WORKER_MULTIPROC_METHOD=spawn pytest -v -s entrypoints/openai/correctness/test_lmeval.py::test_lm_eval_accuracy_v1_engine
+  mirror:
+    amd:
+      device: mi325_1
+      timeout_in_minutes: 60
+      depends_on:
+      - image-build-amd
 
 - label: V1 Others (CPU)
   key: v1-others-cpu
@@ -147,8 +150,7 @@ steps:
     - vllm/distributed/kv_transfer/kv_connector/v1/example_hidden_states_connector.py
     - tests/v1/kv_connector/extract_hidden_states_integration
   commands:
-    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-    - pytest -v -s v1/kv_connector/extract_hidden_states_integration
+    - VLLM_WORKER_MULTIPROC_METHOD=spawn pytest -v -s v1/kv_connector/extract_hidden_states_integration
 
 - label: Extract Hidden States Integration (2 GPUs)
   key: extract-hidden-states-integration-2-gpus
@@ -161,8 +163,7 @@ steps:
     - vllm/distributed/kv_transfer/kv_connector/v1/example_hidden_states_connector.py
     - tests/v1/kv_connector/extract_hidden_states_integration
   commands:
-    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-    - pytest -v -s -m 'distributed' v1/kv_connector/extract_hidden_states_integration
+    - VLLM_WORKER_MULTIPROC_METHOD=spawn pytest -v -s -m 'distributed' v1/kv_connector/extract_hidden_states_integration
 
 - label: Regression
   key: regression
@@ -354,10 +355,9 @@ steps:
     - vllm/model_executor/layers
     - tests/v1/determinism/
   commands:
-    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
     - pip install pytest-timeout pytest-forked
-    - pytest -v -s v1/determinism/test_batch_invariance.py
-    - VLLM_TEST_MODEL=deepseek-ai/DeepSeek-V2-Lite-Chat pytest -v -s v1/determinism/test_batch_invariance.py::test_v1_generation_is_deterministic_across_batch_sizes_with_needle[TRITON_MLA]
+    - VLLM_WORKER_MULTIPROC_METHOD=spawn pytest -v -s v1/determinism/test_batch_invariance.py
+    - VLLM_WORKER_MULTIPROC_METHOD=spawn VLLM_TEST_MODEL=deepseek-ai/DeepSeek-V2-Lite-Chat pytest -v -s v1/determinism/test_batch_invariance.py::test_v1_generation_is_deterministic_across_batch_sizes_with_needle[TRITON_MLA]
 
 - label: Batch Invariance (H100)
   key: batch-invariance-h100
@@ -368,12 +368,11 @@ steps:
     - vllm/model_executor/layers
     - tests/v1/determinism/
   commands:
-    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
     - pip install pytest-timeout pytest-forked
-    - pytest -v -s v1/determinism/test_batch_invariance.py
-    - pytest -v -s v1/determinism/test_rms_norm_batch_invariant.py
-    - VLLM_TEST_MODEL=deepseek-ai/DeepSeek-V2-Lite-Chat pytest -v -s v1/determinism/test_batch_invariance.py::test_v1_generation_is_deterministic_across_batch_sizes_with_needle[TRITON_MLA]
-    - VLLM_TEST_MODEL=Qwen/Qwen3-30B-A3B-Thinking-2507-FP8 pytest -v -s v1/determinism/test_batch_invariance.py::test_v1_generation_is_deterministic_across_batch_sizes_with_needle[FLASH_ATTN]
+    - VLLM_WORKER_MULTIPROC_METHOD=spawn pytest -v -s v1/determinism/test_batch_invariance.py
+    - VLLM_WORKER_MULTIPROC_METHOD=spawn pytest -v -s v1/determinism/test_rms_norm_batch_invariant.py
+    - VLLM_WORKER_MULTIPROC_METHOD=spawn VLLM_TEST_MODEL=deepseek-ai/DeepSeek-V2-Lite-Chat pytest -v -s v1/determinism/test_batch_invariance.py::test_v1_generation_is_deterministic_across_batch_sizes_with_needle[TRITON_MLA]
+    - VLLM_WORKER_MULTIPROC_METHOD=spawn VLLM_TEST_MODEL=Qwen/Qwen3-30B-A3B-Thinking-2507-FP8 pytest -v -s v1/determinism/test_batch_invariance.py::test_v1_generation_is_deterministic_across_batch_sizes_with_needle[FLASH_ATTN]
 
 - label: Batch Invariance (B200)
   key: batch-invariance-b200
@@ -384,14 +383,13 @@ steps:
     - vllm/model_executor/layers
     - tests/v1/determinism/
   commands:
-    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
     - pip install pytest-timeout pytest-forked
-    - pytest -v -s v1/determinism/test_batch_invariance.py
-    - pytest -v -s v1/determinism/test_rms_norm_batch_invariant.py
-    - VLLM_TEST_MODEL=deepseek-ai/DeepSeek-V2-Lite-Chat pytest -v -s v1/determinism/test_batch_invariance.py::test_v1_generation_is_deterministic_across_batch_sizes_with_needle[TRITON_MLA]
-    - VLLM_TEST_MODEL=Qwen/Qwen3-30B-A3B-Thinking-2507-FP8 pytest -v -s v1/determinism/test_batch_invariance.py::test_v1_generation_is_deterministic_across_batch_sizes_with_needle[FLASH_ATTN]
-    - pytest -v -s v1/determinism/test_nvfp4_batch_invariant.py
-    - pytest -v -s v1/determinism/test_nvfp4_batch_invariant_scaled_mm.py
+    - VLLM_WORKER_MULTIPROC_METHOD=spawn pytest -v -s v1/determinism/test_batch_invariance.py
+    - VLLM_WORKER_MULTIPROC_METHOD=spawn pytest -v -s v1/determinism/test_rms_norm_batch_invariant.py
+    - VLLM_WORKER_MULTIPROC_METHOD=spawn VLLM_TEST_MODEL=deepseek-ai/DeepSeek-V2-Lite-Chat pytest -v -s v1/determinism/test_batch_invariance.py::test_v1_generation_is_deterministic_across_batch_sizes_with_needle[TRITON_MLA]
+    - VLLM_WORKER_MULTIPROC_METHOD=spawn VLLM_TEST_MODEL=Qwen/Qwen3-30B-A3B-Thinking-2507-FP8 pytest -v -s v1/determinism/test_batch_invariance.py::test_v1_generation_is_deterministic_across_batch_sizes_with_needle[FLASH_ATTN]
+    - VLLM_WORKER_MULTIPROC_METHOD=spawn pytest -v -s v1/determinism/test_nvfp4_batch_invariant.py
+    - VLLM_WORKER_MULTIPROC_METHOD=spawn pytest -v -s v1/determinism/test_nvfp4_batch_invariant_scaled_mm.py
   
 - label: Acceptance Length Test (Large Models) # optional
   device: h200_35gb
@@ -406,5 +404,4 @@ steps:
   - vllm/model_executor/models/mlp_speculator.py
   - tests/v1/spec_decode/test_acceptance_length.py
   commands:
-    - export VLLM_ALLOW_INSECURE_SERIALIZATION=1
-    - pytest -v -s v1/spec_decode/test_acceptance_length.py -m slow_test
+    - VLLM_ALLOW_INSECURE_SERIALIZATION=1 pytest -v -s v1/spec_decode/test_acceptance_length.py -m slow_test

.buildkite/test_areas/model_executor.yaml

@@ -13,13 +13,12 @@ steps:
   - tests/entrypoints/openai/completion/test_tensorizer_entrypoint.py
   commands:
     - apt-get update && apt-get install -y curl libsodium23
-    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-    # Dump tracebacks of all threads if a test hangs, so a wedged GPU/CUDA
-    # init surfaces a stack instead of silently stalling.
-    - export PYTHONFAULTHANDLER=1
     # Per-test watchdog: a single hung test (e.g. stuck during engine/CUDA
     # init) fails fast with a traceback instead of running until the global
     # build timeout. The `thread` method also handles hangs inside C/CUDA
     # calls that the signal method cannot interrupt.
-    - pytest -v -s model_executor -m '(not slow_test)' --timeout=900 --timeout-method=thread
-    - pytest -v -s entrypoints/openai/completion/test_tensorizer_entrypoint.py --timeout=900 --timeout-method=thread
+    #
+    # Env vars are inlined because CONTINUE_ON_FAILURE wraps each command
+    # in a subshell, so a standalone `export` would be lost.
+    - VLLM_WORKER_MULTIPROC_METHOD=spawn PYTHONFAULTHANDLER=1 pytest -v -s model_executor -m '(not slow_test)' --timeout=900 --timeout-method=thread
+    - VLLM_WORKER_MULTIPROC_METHOD=spawn PYTHONFAULTHANDLER=1 pytest -v -s entrypoints/openai/completion/test_tensorizer_entrypoint.py --timeout=900 --timeout-method=thread

.buildkite/test_areas/model_runner_v2.yaml

@@ -16,15 +16,14 @@ steps:
   - tests/entrypoints/llm/test_struct_output_generate.py
   commands:
   - set -x
-  - export VLLM_USE_V2_MODEL_RUNNER=1
-  - pytest -v -s v1/engine/test_llm_engine.py -k "not test_engine_metrics"
+  - VLLM_USE_V2_MODEL_RUNNER=1 pytest -v -s v1/engine/test_llm_engine.py -k "not test_engine_metrics"
   # This requires eager until we sort out CG correctness issues.
   # TODO: remove ENFORCE_EAGER here after https://github.com/vllm-project/vllm/pull/32936 is merged.
-  - ENFORCE_EAGER=1 pytest -v -s v1/e2e/general/test_async_scheduling.py -k "not ngram"
-  - pytest -v -s v1/e2e/general/test_context_length.py
-  - pytest -v -s v1/e2e/general/test_min_tokens.py
+  - VLLM_USE_V2_MODEL_RUNNER=1 ENFORCE_EAGER=1 pytest -v -s v1/e2e/general/test_async_scheduling.py -k "not ngram"
+  - VLLM_USE_V2_MODEL_RUNNER=1 pytest -v -s v1/e2e/general/test_context_length.py
+  - VLLM_USE_V2_MODEL_RUNNER=1 pytest -v -s v1/e2e/general/test_min_tokens.py
   # Temporary hack filter to exclude ngram spec decoding based tests.
-  - pytest -v -s entrypoints/llm/test_struct_output_generate.py -k "xgrammar and not speculative_config6 and not speculative_config7 and not speculative_config8 and not speculative_config0"
+  - VLLM_USE_V2_MODEL_RUNNER=1 pytest -v -s entrypoints/llm/test_struct_output_generate.py -k "xgrammar and not speculative_config6 and not speculative_config7 and not speculative_config8 and not speculative_config0"
 
 - label: Model Runner V2 Examples
   device: h200_35gb
@@ -42,26 +41,25 @@ steps:
     - examples/features/tensorize_vllm_model.py
   commands:
     - set -x
-    - export VLLM_USE_V2_MODEL_RUNNER=1
     - pip install tensorizer # for tensorizer test
-    - python3 basic/offline_inference/chat.py # for basic
-    - python3 basic/offline_inference/generate.py --model facebook/opt-125m
-    #- python3 basic/offline_inference/generate.py --model meta-llama/Llama-2-13b-chat-hf --cpu-offload-gb 10  # TODO
-    #- python3 basic/offline_inference/embed.py   # TODO
+    - VLLM_USE_V2_MODEL_RUNNER=1 python3 basic/offline_inference/chat.py # for basic
+    - VLLM_USE_V2_MODEL_RUNNER=1 python3 basic/offline_inference/generate.py --model facebook/opt-125m
+    #- VLLM_USE_V2_MODEL_RUNNER=1 python3 basic/offline_inference/generate.py --model meta-llama/Llama-2-13b-chat-hf --cpu-offload-gb 10  # TODO
+    #- VLLM_USE_V2_MODEL_RUNNER=1 python3 basic/offline_inference/embed.py   # TODO
     # for multi-modal models
-    - python3 generate/multimodal/audio_language_offline.py --seed 0
-    - python3 generate/multimodal/vision_language_offline.py --seed 0
-    - python3 generate/multimodal/vision_language_multi_image_offline.py --seed 0
-    - python3 generate/multimodal/encoder_decoder_multimodal_offline.py --model-type whisper --seed 0
+    - VLLM_USE_V2_MODEL_RUNNER=1 python3 generate/multimodal/audio_language_offline.py --seed 0
+    - VLLM_USE_V2_MODEL_RUNNER=1 python3 generate/multimodal/vision_language_offline.py --seed 0
+    - VLLM_USE_V2_MODEL_RUNNER=1 python3 generate/multimodal/vision_language_multi_image_offline.py --seed 0
+    - VLLM_USE_V2_MODEL_RUNNER=1 python3 generate/multimodal/encoder_decoder_multimodal_offline.py --model-type whisper --seed 0
     # for pooling models
-    - python3 pooling/embed/vision_embedding_offline.py --seed 0
+    - VLLM_USE_V2_MODEL_RUNNER=1 python3 pooling/embed/vision_embedding_offline.py --seed 0
     # for features demo
-    - python3 features/automatic_prefix_caching/prefix_caching_offline.py
-    - python3 deployment/llm_engine_example.py
-    - python3 features/tensorize_vllm_model.py --model facebook/opt-125m serialize --serialized-directory /tmp/ --suffix v1 && python3 features/tensorize_vllm_model.py --model facebook/opt-125m deserialize --path-to-tensors /tmp/vllm/facebook/opt-125m/v1/model.tensors
-    - python3 features/speculative_decoding/spec_decode_offline.py --test --method eagle --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 2048
+    - VLLM_USE_V2_MODEL_RUNNER=1 python3 features/automatic_prefix_caching/prefix_caching_offline.py
+    - VLLM_USE_V2_MODEL_RUNNER=1 python3 deployment/llm_engine_example.py
+    - VLLM_USE_V2_MODEL_RUNNER=1 python3 features/tensorize_vllm_model.py --model facebook/opt-125m serialize --serialized-directory /tmp/ --suffix v1 && VLLM_USE_V2_MODEL_RUNNER=1 python3 features/tensorize_vllm_model.py --model facebook/opt-125m deserialize --path-to-tensors /tmp/vllm/facebook/opt-125m/v1/model.tensors
+    - VLLM_USE_V2_MODEL_RUNNER=1 python3 features/speculative_decoding/spec_decode_offline.py --test --method eagle --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 2048
     # https://github.com/vllm-project/vllm/pull/26682 uses slightly more memory in PyTorch 2.9+ causing this test to OOM in 1xL4 GPU
-    - python3 features/speculative_decoding/spec_decode_offline.py --test --method eagle3 --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 1536
+    - VLLM_USE_V2_MODEL_RUNNER=1 python3 features/speculative_decoding/spec_decode_offline.py --test --method eagle3 --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 1536
 
 - label: Model Runner V2 Distributed (2 GPUs)
   key: model-runner-v2-distributed-2-gpus
@@ -76,13 +74,11 @@ steps:
     - tests/v1/distributed/test_eagle_dp.py
   commands:
     - set -x
-    - export VLLM_USE_V2_MODEL_RUNNER=1
     # The "and not True" here is a hacky way to exclude the prompt_embeds cases which aren't yet supported.
-    - TARGET_TEST_SUITE=L4 pytest -v -s basic_correctness/test_basic_correctness.py -m 'distributed(num_gpus=2)' -k "not ray and not True"
+    - VLLM_USE_V2_MODEL_RUNNER=1 TARGET_TEST_SUITE=L4 pytest -v -s basic_correctness/test_basic_correctness.py -m 'distributed(num_gpus=2)' -k "not ray and not True"
     # https://github.com/NVIDIA/nccl/issues/1838
-    - export NCCL_CUMEM_HOST_ENABLE=0
-    - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_async_llm_dp.py -k "not ray"
-    - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_eagle_dp.py
+    - VLLM_USE_V2_MODEL_RUNNER=1 NCCL_CUMEM_HOST_ENABLE=0 TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_async_llm_dp.py -k "not ray"
+    - VLLM_USE_V2_MODEL_RUNNER=1 NCCL_CUMEM_HOST_ENABLE=0 TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_eagle_dp.py
 
 - label: Model Runner V2 Pipeline Parallelism (4 GPUs)
   key: model-runner-v2-pipeline-parallelism-4-gpus
@@ -97,10 +93,9 @@ steps:
     - tests/v1/distributed/test_pp_dp_v2.py
   commands:
     - set -x
-    - export VLLM_USE_V2_MODEL_RUNNER=1
-    - pytest -v -s distributed/test_pipeline_parallel.py -k "not ray and not Jamba"
-    - pytest -v -s distributed/test_pp_cudagraph.py -k "not ray"
-    - pytest -v -s v1/distributed/test_pp_dp_v2.py
+    - VLLM_USE_V2_MODEL_RUNNER=1 pytest -v -s distributed/test_pipeline_parallel.py -k "not ray and not Jamba"
+    - VLLM_USE_V2_MODEL_RUNNER=1 pytest -v -s distributed/test_pp_cudagraph.py -k "not ray"
+    - VLLM_USE_V2_MODEL_RUNNER=1 pytest -v -s v1/distributed/test_pp_dp_v2.py
 
 - label: Model Runner V2 Spec Decode
   device: h200_35gb
@@ -115,8 +110,7 @@ steps:
   - tests/v1/e2e/spec_decode/test_spec_decode.py
   commands:
   - set -x
-  - export VLLM_USE_V2_MODEL_RUNNER=1
-  - pytest -v -s v1/spec_decode/test_max_len.py -k "eagle or mtp"
-  - pytest -v -s v1/spec_decode/test_rejection_sampler_utils.py
-  - pytest -v -s v1/spec_decode/test_synthetic_rejection_sampler_utils.py
-  - pytest -v -s v1/e2e/spec_decode/test_spec_decode.py -k "eagle or mtp"
+  - VLLM_USE_V2_MODEL_RUNNER=1 pytest -v -s v1/spec_decode/test_max_len.py -k "eagle or mtp"
+  - VLLM_USE_V2_MODEL_RUNNER=1 pytest -v -s v1/spec_decode/test_rejection_sampler_utils.py
+  - VLLM_USE_V2_MODEL_RUNNER=1 pytest -v -s v1/spec_decode/test_synthetic_rejection_sampler_utils.py
+  - VLLM_USE_V2_MODEL_RUNNER=1 pytest -v -s v1/e2e/spec_decode/test_spec_decode.py -k "eagle or mtp"

.buildkite/test_areas/models_multimodal.yaml

@@ -68,7 +68,6 @@ steps:
     - cd .. && VLLM_WORKER_MULTIPROC_METHOD=spawn pytest -v -s tests/models/multimodal/generation/test_whisper.py -m core_model  # Otherwise, mp_method="spawn" doesn't work
   mirror:
     amd:
-      soft_fail: true
       device: mi325_1
       depends_on:
       - image-build-amd

.buildkite/test_areas/pytorch.yaml

@@ -107,6 +107,12 @@ steps:
     - tests/compile/passes
   commands:
   - pytest -s -v compile/passes --ignore compile/passes/distributed
+  mirror:
+    amd:
+      device: mi300_1
+      timeout_in_minutes: 180
+      depends_on:
+      - image-build-amd
 
 - label: PyTorch Fullgraph Smoke Test
   key: pytorch-fullgraph-smoke-test

.buildkite/test_areas/rust_frontend.yaml

@@ -23,17 +23,15 @@ steps:
   # - tests/entrypoints/openai/test_uds.py
   - tests/v1/sample/test_logprobs_e2e.py
   commands:
-  - export VLLM_USE_RUST_FRONTEND=1
-  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pytest -v -s benchmarks/test_serve_cli.py -k "not insecure and not (test_bench_serve and not test_bench_serve_chat)"
-  - pytest -v -s entrypoints/openai/chat_completion/test_chat_completion.py
-  # - pytest -v -s entrypoints/openai/chat_completion/test_chat_logit_bias_validation.py -k "not invalid"
+  - VLLM_USE_RUST_FRONTEND=1 VLLM_WORKER_MULTIPROC_METHOD=spawn pytest -v -s benchmarks/test_serve_cli.py -k "not insecure and not (test_bench_serve and not test_bench_serve_chat)"
+  - VLLM_USE_RUST_FRONTEND=1 VLLM_WORKER_MULTIPROC_METHOD=spawn pytest -v -s entrypoints/openai/chat_completion/test_chat_completion.py
+  # - VLLM_USE_RUST_FRONTEND=1 VLLM_WORKER_MULTIPROC_METHOD=spawn pytest -v -s entrypoints/openai/chat_completion/test_chat_logit_bias_validation.py -k "not invalid"
 
-  # - pytest -v -s entrypoints/openai/completion/test_prompt_validation.py -k "not prompt_embeds"
-  - pytest -v -s entrypoints/openai/completion/test_shutdown.py -k "not engine_failure and not test_abort_timeout_exits_quickly"
-  # - pytest -v -s entrypoints/openai/test_return_token_ids.py
-  # - pytest -v -s entrypoints/openai/test_uds.py
-  - pytest -v -s v1/sample/test_logprobs_e2e.py -k "test_prompt_logprobs_e2e_server"
+  # - VLLM_USE_RUST_FRONTEND=1 VLLM_WORKER_MULTIPROC_METHOD=spawn pytest -v -s entrypoints/openai/completion/test_prompt_validation.py -k "not prompt_embeds"
+  - VLLM_USE_RUST_FRONTEND=1 VLLM_WORKER_MULTIPROC_METHOD=spawn pytest -v -s entrypoints/openai/completion/test_shutdown.py -k "not engine_failure and not test_abort_timeout_exits_quickly"
+  # - VLLM_USE_RUST_FRONTEND=1 VLLM_WORKER_MULTIPROC_METHOD=spawn pytest -v -s entrypoints/openai/test_return_token_ids.py
+  # - VLLM_USE_RUST_FRONTEND=1 VLLM_WORKER_MULTIPROC_METHOD=spawn pytest -v -s entrypoints/openai/test_uds.py
+  - VLLM_USE_RUST_FRONTEND=1 VLLM_WORKER_MULTIPROC_METHOD=spawn pytest -v -s v1/sample/test_logprobs_e2e.py -k "test_prompt_logprobs_e2e_server"
 
 - label: Rust Frontend Serve/Admin Coverage
   timeout_in_minutes: 60
@@ -51,13 +49,11 @@ steps:
   - tests/entrypoints/serve/instrumentator/test_metrics.py
   # - tests/entrypoints/serve/dev/test_sleep.py
   commands:
-  - export VLLM_USE_RUST_FRONTEND=1
-  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  # - pytest -v -s entrypoints/serve/dev/rpc/test_collective_rpc.py
-  - pytest -v -s entrypoints/serve/instrumentator/test_basic.py -k "not show_version and not server_load"
-  - pytest -v -s entrypoints/serve/disagg/test_serving_tokens.py -k "not stream and not lora and not test_generate_logprobs and not stop_string_workflow"
-  - pytest -v -s entrypoints/serve/instrumentator/test_metrics.py -k "text and not show and not run_batch and not test_metrics_counts and not test_metrics_exist"
-  # - pytest -v -s entrypoints/serve/dev/test_sleep.py
+  # - VLLM_USE_RUST_FRONTEND=1 VLLM_WORKER_MULTIPROC_METHOD=spawn pytest -v -s entrypoints/serve/dev/rpc/test_collective_rpc.py
+  - VLLM_USE_RUST_FRONTEND=1 VLLM_WORKER_MULTIPROC_METHOD=spawn pytest -v -s entrypoints/serve/instrumentator/test_basic.py -k "not show_version and not server_load"
+  - VLLM_USE_RUST_FRONTEND=1 VLLM_WORKER_MULTIPROC_METHOD=spawn pytest -v -s entrypoints/serve/disagg/test_serving_tokens.py -k "not stream and not lora and not test_generate_logprobs and not stop_string_workflow"
+  - VLLM_USE_RUST_FRONTEND=1 VLLM_WORKER_MULTIPROC_METHOD=spawn pytest -v -s entrypoints/serve/instrumentator/test_metrics.py -k "text and not show and not run_batch and not test_metrics_counts and not test_metrics_exist"
+  # - VLLM_USE_RUST_FRONTEND=1 VLLM_WORKER_MULTIPROC_METHOD=spawn pytest -v -s entrypoints/serve/dev/test_sleep.py
 
 - label: Rust Frontend Core Correctness
   timeout_in_minutes: 30
@@ -69,9 +65,7 @@ steps:
   - tests/utils.py
   - tests/entrypoints/openai/correctness/test_lmeval.py
   commands:
-  - export VLLM_USE_RUST_FRONTEND=1
-  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pytest -s entrypoints/openai/correctness/test_lmeval.py::test_lm_eval_accuracy_v1_engine
+  - VLLM_USE_RUST_FRONTEND=1 VLLM_WORKER_MULTIPROC_METHOD=spawn pytest -s entrypoints/openai/correctness/test_lmeval.py::test_lm_eval_accuracy_v1_engine
 
 - label: Rust Frontend Tool Use
   timeout_in_minutes: 60
@@ -83,9 +77,7 @@ steps:
   - tests/utils.py
   - tests/tool_use/
   commands:
-  - export VLLM_USE_RUST_FRONTEND=1
-  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pytest -v -s tool_use --ignore=tool_use/mistral --models llama3.2 -k "not test_response_format_with_tool_choice_required and not test_parallel_tool_calls_false and not test_tool_call_and_choice"
+  - VLLM_USE_RUST_FRONTEND=1 VLLM_WORKER_MULTIPROC_METHOD=spawn pytest -v -s tool_use --ignore=tool_use/mistral --models llama3.2 -k "not test_response_format_with_tool_choice_required and not test_parallel_tool_calls_false and not test_tool_call_and_choice"
 
 - label: Rust Frontend Distributed
   timeout_in_minutes: 30
@@ -103,9 +95,6 @@ steps:
   - tests/v1/distributed/test_hybrid_lb_dp.py
   - tests/v1/distributed/test_internal_lb_dp.py
   commands:
-  - export VLLM_USE_RUST_FRONTEND=1
-  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - export NCCL_CUMEM_HOST_ENABLE=0
-  - TP_SIZE=1 DP_SIZE=4 pytest -v -s v1/distributed/test_internal_lb_dp.py -k "not 4 and not server_info"
-  - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_external_lb_dp.py -k "not 4 and not server_info"
-  - TP_SIZE=1 DP_SIZE=4 pytest -v -s v1/distributed/test_hybrid_lb_dp.py -k "not 4 and not server_info"
+  - VLLM_USE_RUST_FRONTEND=1 VLLM_WORKER_MULTIPROC_METHOD=spawn NCCL_CUMEM_HOST_ENABLE=0 TP_SIZE=1 DP_SIZE=4 pytest -v -s v1/distributed/test_internal_lb_dp.py -k "not 4 and not server_info"
+  - VLLM_USE_RUST_FRONTEND=1 VLLM_WORKER_MULTIPROC_METHOD=spawn NCCL_CUMEM_HOST_ENABLE=0 TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_external_lb_dp.py -k "not 4 and not server_info"
+  - VLLM_USE_RUST_FRONTEND=1 VLLM_WORKER_MULTIPROC_METHOD=spawn NCCL_CUMEM_HOST_ENABLE=0 TP_SIZE=1 DP_SIZE=4 pytest -v -s v1/distributed/test_hybrid_lb_dp.py -k "not 4 and not server_info"

.buildkite/test_areas/spec_decode.yaml

@@ -12,6 +12,20 @@ steps:
     - tests/v1/e2e/spec_decode/
   commands:
     - pytest -v -s v1/e2e/spec_decode -k "eagle_correctness"
+  mirror:
+    amd:
+      device: mi325_1
+      timeout_in_minutes: 45
+      depends_on:
+      - image-build-amd
+      source_file_dependencies:
+      - vllm/v1/spec_decode/
+      - vllm/v1/worker/gpu/spec_decode/
+      - vllm/model_executor/model_loader/
+      - vllm/v1/sample/
+      - vllm/model_executor/layers/
+      - tests/v1/e2e/spec_decode/
+      - vllm/platforms/rocm.py
 
 - label: Spec Decode Eagle Nightly B200
   key: spec-decode-eagle-nightly-b200
@@ -139,8 +153,7 @@ steps:
     - vllm/model_executor/models/qwen3_dflash.py
     - tests/v1/spec_decode/test_speculators_correctness.py
   commands:
-    - export VLLM_ALLOW_INSECURE_SERIALIZATION=1
-    - pytest -v -s v1/spec_decode/test_speculators_correctness.py -m slow_test
+    - VLLM_ALLOW_INSECURE_SERIALIZATION=1 pytest -v -s v1/spec_decode/test_speculators_correctness.py -m slow_test
 
 - label: Spec Decode MTP hybrid (B200)
   timeout_in_minutes: 30

.claude/skills/ci-fails-buildkite/SKILL.md

@@ -0,0 +1,35 @@
+---
+name: ci-fails-buildkite
+description: Fetch and diagnose vLLM Buildkite CI failure logs. Use when investigating failing CI jobs on a PR or build, when the user pastes a buildkite.com URL, or asks to fetch/diagnose CI logs.
+---
+
+# Diagnosing vLLM Buildkite CI Failures
+
+Buildkite logs are public; no login needed.
+
+`.buildkite/scripts/ci-fetch-log.sh` saves each log as `ci-<build>-<job-name>.log`, stripped of timestamps and ANSI codes. Existing files are kept; set `CI_FETCH_LOG_FORCE=1` to refetch.
+
+## Fetching logs
+
+```bash
+# All failed jobs in a PR's latest build (current branch's PR if omitted):
+.buildkite/scripts/ci-fetch-log.sh --pr <PR>
+
+# All failed jobs in a build (--soft also includes soft-failed jobs;
+# --all fetches every finished job):
+.buildkite/scripts/ci-fetch-log.sh "https://buildkite.com/vllm/ci/builds/<N>"
+
+# One job — `gh pr checks` URLs (#<job_uuid>) and web UI URLs (?sid=) both
+# work; pass "-" as a second argument to stream to stdout:
+.buildkite/scripts/ci-fetch-log.sh "https://buildkite.com/vllm/ci/builds/<N>#<job_uuid>"
+```
+
+To clean an already-downloaded log with `.buildkite/scripts/ci-clean-log.sh`:
+
+```bash
+./ci-clean-log.sh ci.log
+```
+
+## Reference
+
+See [docs/contributing/ci/failures.md](../../../docs/contributing/ci/failures.md) for the full guide: filing CI failure issues, investigating/bisecting, reproducing flaky tests, and daily triage.

.github/CODEOWNERS

@@ -2,15 +2,14 @@
 # for more info about CODEOWNERS file
 
 # This lists cover the "core" components of vLLM that require careful review
-/vllm/compilation @zou3519 @youkaichao @ProExpertProg @BoyuanFeng @vadiklyutiy
+/vllm/compilation @zou3519 @youkaichao @ProExpertProg @BoyuanFeng
 /vllm/distributed/kv_transfer @NickLucche @ApostaC @orozery @xuechendi
 /vllm/lora @jeejeelee
 /vllm/model_executor/layers/attention @LucasWilkinson @MatthewBonanni
 /vllm/model_executor/layers/fused_moe @mgoin @pavanimajety @zyongye
 /vllm/model_executor/layers/quantization @mgoin @robertgshaw2-redhat @tlrmchlsmth @yewentao256 @pavanimajety @zyongye
 /vllm/model_executor/layers/mamba @tdoublep @tomeras91
-/vllm/model_executor/layers/mamba/gdn_linear_attn.py @tdoublep @ZJY0516 @vadiklyutiy
-/vllm/model_executor/layers/rotary_embedding.py @vadiklyutiy
+/vllm/model_executor/layers/mamba/gdn/qwen_gdn_linear_attn.py @tdoublep @ZJY0516 @vadiklyutiy
 /vllm/model_executor/model_loader @22quinn
 /vllm/model_executor/layers/batch_invariant.py @yewentao256 
 /vllm/ir @ProExpertProg

.gitignore

@@ -199,7 +199,9 @@ cython_debug/
 .vscode/
 
 # Claude
-.claude/
+.claude/*
+!.claude/skills/
+!.claude/skills/**
 
 # Codex
 .codex/

AGENTS.md

@@ -114,17 +114,6 @@ Follow these rules for all code changes in this repository:
 - Keep comments and docstrings minimal and concise.
 - Assume the reader is familiar with vLLM.
 
-### Diagnosing CI failures
-
-Buildkite logs are public; no login needed. Details: [docs/contributing/ci/failures.md](docs/contributing/ci/failures.md).
-
-```bash
-# All failed-job logs for a PR's latest build (current branch's PR if omitted):
-.buildkite/scripts/ci-fetch-log.sh --pr <PR>
-# Any Buildkite build or job URL also works:
-.buildkite/scripts/ci-fetch-log.sh "<buildkite_url>"
-```
-
 ### Commit messages
 
 Add attribution using commit trailers such as `Co-authored-by:` (other projects use `Assisted-by:` or `Generated-by:`). For example:
@@ -146,6 +135,12 @@ Do not modify code in these areas without first reading and following the
 linked guide. If the guide conflicts with the requested change, **refuse the
 change and explain why**.
 
+Security reviewers should start with [`SECURITY.md`](SECURITY.md),
+[`docs/usage/security.md`](docs/usage/security.md), and
+[`docs/contributing/vulnerability_management.md`](docs/contributing/vulnerability_management.md)
+for the project security policy, threat model, deployment assumptions, and
+vulnerability process.
+
 - **Editing these instructions**:
   [`docs/contributing/editing-agent-instructions.md`](docs/contributing/editing-agent-instructions.md)
   — Rules for modifying AGENTS.md or any domain-specific guide it references.

CMakeLists.txt

@@ -319,82 +319,35 @@ if(VLLM_GPU_LANG STREQUAL "CUDA" OR VLLM_GPU_LANG STREQUAL "HIP")
 endif()
 
 #
-# _C extension
+# Legacy _C extension (ROCm only — CUDA ops migrated to _C_stable_libtorch)
 #
 
-set(VLLM_EXT_SRC
-  "csrc/quantization/activation_kernels.cu"
-  "csrc/torch_bindings.cpp")
-
-if(VLLM_GPU_LANG STREQUAL "CUDA")
-  SET(CUTLASS_ENABLE_HEADERS_ONLY ON CACHE BOOL "Enable only the header library")
-
-  # Set CUTLASS_REVISION. Used for FetchContent. Also fixes some bogus messages when building.
-  set(CUTLASS_REVISION "v4.4.2")
-
-  # Use the specified CUTLASS source directory for compilation if VLLM_CUTLASS_SRC_DIR is provided
-  if (DEFINED ENV{VLLM_CUTLASS_SRC_DIR})
-    set(VLLM_CUTLASS_SRC_DIR $ENV{VLLM_CUTLASS_SRC_DIR})
-  endif()
-
-  if(VLLM_CUTLASS_SRC_DIR)
-    if(NOT IS_ABSOLUTE VLLM_CUTLASS_SRC_DIR)
-      get_filename_component(VLLM_CUTLASS_SRC_DIR "${VLLM_CUTLASS_SRC_DIR}" ABSOLUTE)
-    endif()
-    message(STATUS "The VLLM_CUTLASS_SRC_DIR is set, using ${VLLM_CUTLASS_SRC_DIR} for compilation")
-    FetchContent_Declare(cutlass SOURCE_DIR ${VLLM_CUTLASS_SRC_DIR})
-  else()
-    FetchContent_Declare(
-        cutlass
-        GIT_REPOSITORY https://github.com/nvidia/cutlass.git
-        # Please keep this in sync with CUTLASS_REVISION line above.
-        GIT_TAG ${CUTLASS_REVISION}
-        GIT_PROGRESS TRUE
-
-        # Speed up CUTLASS download by retrieving only the specified GIT_TAG instead of the history.
-        # Important: If GIT_SHALLOW is enabled then GIT_TAG works only with branch names and tags.
-        # So if the GIT_TAG above is updated to a commit hash, GIT_SHALLOW must be set to FALSE
-        GIT_SHALLOW TRUE
-    )
-  endif()
-  FetchContent_MakeAvailable(cutlass)
-
-  set_gencode_flags_for_srcs(
-    SRCS "${VLLM_EXT_SRC}"
-    CUDA_ARCHS "${CUDA_ARCHS}")
-
-# if CUDA endif
-endif()
-
-if (VLLM_GPU_LANG STREQUAL "HIP")
-  # Add QuickReduce kernels (ROCm-only; not part of stable ABI migration).
-  # TODO: Remove the cuda_view when ROCm upgrade to torch 2.11.
-  list(APPEND VLLM_EXT_SRC
+if(VLLM_GPU_LANG STREQUAL "HIP")
+  set(VLLM_EXT_SRC
+    "csrc/torch_bindings.cpp"
     "csrc/custom_quickreduce.cu"
     "csrc/cuda_view.cu"
-    "csrc/libtorch_stable/cuda_utils_kernels.cu"
-  )
-# if ROCM endif
-endif()
+    "csrc/libtorch_stable/cuda_utils_kernels.cu")
 
-message(STATUS "Enabling C extension.")
-define_extension_target(
-  _C
-  DESTINATION vllm
-  LANGUAGE ${VLLM_GPU_LANG}
-  SOURCES ${VLLM_EXT_SRC}
-  COMPILE_FLAGS ${VLLM_GPU_FLAGS}
-  ARCHITECTURES ${VLLM_GPU_ARCHES}
-  INCLUDE_DIRECTORIES ${CUTLASS_INCLUDE_DIR}
-  INCLUDE_DIRECTORIES ${CUTLASS_TOOLS_UTIL_INCLUDE_DIR}
-  USE_SABI 3
-  WITH_SOABI)
+  message(STATUS "Enabling C extension.")
+  define_extension_target(
+    _C
+    DESTINATION vllm
+    LANGUAGE ${VLLM_GPU_LANG}
+    SOURCES ${VLLM_EXT_SRC}
+    COMPILE_FLAGS ${VLLM_GPU_FLAGS}
+    ARCHITECTURES ${VLLM_GPU_ARCHES}
+    INCLUDE_DIRECTORIES ${CUTLASS_INCLUDE_DIR}
+    INCLUDE_DIRECTORIES ${CUTLASS_TOOLS_UTIL_INCLUDE_DIR}
+    USE_SABI 3
+    WITH_SOABI)
 
-# If CUTLASS is compiled on NVCC >= 12.5, it by default uses
-# cudaGetDriverEntryPointByVersion as a wrapper to avoid directly calling the
-# driver API. This causes problems when linking with earlier versions of CUDA.
-# Setting this variable sidesteps the issue by calling the driver directly.
-target_compile_definitions(_C PRIVATE CUTLASS_ENABLE_DIRECT_CUDA_DRIVER_CALL=1)
+  # If CUTLASS is compiled on NVCC >= 12.5, it by default uses
+  # cudaGetDriverEntryPointByVersion as a wrapper to avoid directly calling the
+  # driver API. This causes problems when linking with earlier versions of CUDA.
+  # Setting this variable sidesteps the issue by calling the driver directly.
+  target_compile_definitions(_C PRIVATE CUTLASS_ENABLE_DIRECT_CUDA_DRIVER_CALL=1)
+endif() # _C HIP endif
 
 if(VLLM_GPU_LANG STREQUAL "CUDA" OR VLLM_GPU_LANG STREQUAL "HIP")
   #
@@ -403,6 +356,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA" OR VLLM_GPU_LANG STREQUAL "HIP")
   set(VLLM_STABLE_EXT_SRC
     "csrc/libtorch_stable/torch_bindings.cpp"
     "csrc/libtorch_stable/activation_kernels.cu"
+    "csrc/libtorch_stable/quantization/activation_kernels.cu"
     "csrc/libtorch_stable/quantization/w8a8/int8/scaled_quant.cu"
     "csrc/libtorch_stable/quantization/w8a8/fp8/common.cu"
     "csrc/libtorch_stable/quantization/w8a8/fp8/per_token_group_quant.cu"
@@ -429,6 +383,38 @@ if(VLLM_GPU_LANG STREQUAL "CUDA" OR VLLM_GPU_LANG STREQUAL "HIP")
     "csrc/libtorch_stable/fused_deepseek_v4_qnorm_rope_kv_insert_kernel.cu")
 
   if(VLLM_GPU_LANG STREQUAL "CUDA")
+    SET(CUTLASS_ENABLE_HEADERS_ONLY ON CACHE BOOL "Enable only the header library")
+
+    # Set CUTLASS_REVISION. Used for FetchContent. Also fixes some bogus messages when building.
+    set(CUTLASS_REVISION "v4.4.2")
+
+    # Use the specified CUTLASS source directory for compilation if VLLM_CUTLASS_SRC_DIR is provided
+    if (DEFINED ENV{VLLM_CUTLASS_SRC_DIR})
+      set(VLLM_CUTLASS_SRC_DIR $ENV{VLLM_CUTLASS_SRC_DIR})
+    endif()
+
+    if(VLLM_CUTLASS_SRC_DIR)
+      if(NOT IS_ABSOLUTE VLLM_CUTLASS_SRC_DIR)
+        get_filename_component(VLLM_CUTLASS_SRC_DIR "${VLLM_CUTLASS_SRC_DIR}" ABSOLUTE)
+      endif()
+      message(STATUS "The VLLM_CUTLASS_SRC_DIR is set, using ${VLLM_CUTLASS_SRC_DIR} for compilation")
+      FetchContent_Declare(cutlass SOURCE_DIR ${VLLM_CUTLASS_SRC_DIR})
+    else()
+      FetchContent_Declare(
+          cutlass
+          GIT_REPOSITORY https://github.com/nvidia/cutlass.git
+          # Please keep this in sync with CUTLASS_REVISION line above.
+          GIT_TAG ${CUTLASS_REVISION}
+          GIT_PROGRESS TRUE
+
+          # Speed up CUTLASS download by retrieving only the specified GIT_TAG instead of the history.
+          # Important: If GIT_SHALLOW is enabled then GIT_TAG works only with branch names and tags.
+          # So if the GIT_TAG above is updated to a commit hash, GIT_SHALLOW must be set to FALSE
+          GIT_SHALLOW TRUE
+      )
+    endif()
+    FetchContent_MakeAvailable(cutlass)
+
     list(APPEND VLLM_STABLE_EXT_SRC
       "csrc/libtorch_stable/cuda_view.cu"
       "csrc/libtorch_stable/cuda_utils_kernels.cu"
@@ -929,7 +915,6 @@ if(VLLM_GPU_LANG STREQUAL "CUDA" OR VLLM_GPU_LANG STREQUAL "HIP")
       SRCS "${FP4_SM120_SRCS}"
       CUDA_ARCHS "${FP4_SM120_ARCHS}")
     list(APPEND VLLM_STABLE_EXT_SRC "${FP4_SM120_SRCS}")
-    target_compile_definitions(_C PRIVATE ENABLE_NVFP4_SM120=1)
     list(APPEND VLLM_GPU_FLAGS "-DENABLE_NVFP4_SM120=1")
     list(APPEND VLLM_GPU_FLAGS "-DENABLE_CUTLASS_MOE_SM120=1")
     message(STATUS "Building SM12x NVFP4 for archs: ${FP4_SM120_ARCHS}")
@@ -962,7 +947,6 @@ if(VLLM_GPU_LANG STREQUAL "CUDA" OR VLLM_GPU_LANG STREQUAL "HIP")
       SRCS "${FP4_SM100_SRCS}"
       CUDA_ARCHS "${FP4_SM100_ARCHS}")
     list(APPEND VLLM_STABLE_EXT_SRC "${FP4_SM100_SRCS}")
-    target_compile_definitions(_C PRIVATE ENABLE_NVFP4_SM100=1)
     list(APPEND VLLM_GPU_FLAGS "-DENABLE_NVFP4_SM100=1")
     list(APPEND VLLM_GPU_FLAGS "-DENABLE_CUTLASS_MOE_SM100=1")
     message(STATUS "Building SM10x/11x NVFP4/MXFP4 for archs: ${FP4_SM100_ARCHS}")

cmake/external_projects/qutlass.cmake

@@ -60,6 +60,7 @@ endif()
 
 if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8 AND QUTLASS_ARCHS)
   set(QUTLASS_SOURCES
+    csrc/qutlass_registration.cpp
     ${qutlass_SOURCE_DIR}/qutlass/csrc/bindings.cpp
     ${qutlass_SOURCE_DIR}/qutlass/csrc/gemm.cu
     ${qutlass_SOURCE_DIR}/qutlass/csrc/gemm_ada.cu
@@ -78,8 +79,19 @@ if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8 AND QUTLASS_ARCHS)
 
   if(CUTLASS_INCLUDE_DIR AND EXISTS "${CUTLASS_INCLUDE_DIR}/cutlass/cutlass.h")
     list(APPEND QUTLASS_INCLUDES "${CUTLASS_INCLUDE_DIR}")
+    if(CUTLASS_TOOLS_UTIL_INCLUDE_DIR AND
+       EXISTS "${CUTLASS_TOOLS_UTIL_INCLUDE_DIR}/cutlass/util/packed_stride.hpp")
+      list(APPEND QUTLASS_INCLUDES "${CUTLASS_TOOLS_UTIL_INCLUDE_DIR}")
+    else()
+      get_filename_component(_qutlass_cutlass_root "${CUTLASS_INCLUDE_DIR}" DIRECTORY)
+      if(EXISTS "${_qutlass_cutlass_root}/tools/util/include/cutlass/util/packed_stride.hpp")
+        list(APPEND QUTLASS_INCLUDES "${_qutlass_cutlass_root}/tools/util/include")
+      endif()
+    endif()
   elseif(EXISTS "${qutlass_SOURCE_DIR}/qutlass/third_party/cutlass/include/cutlass/cutlass.h")
-    list(APPEND QUTLASS_INCLUDES "${qutlass_SOURCE_DIR}/qutlass/third_party/cutlass/include")
+    list(APPEND QUTLASS_INCLUDES
+      "${qutlass_SOURCE_DIR}/qutlass/third_party/cutlass/include"
+      "${qutlass_SOURCE_DIR}/qutlass/third_party/cutlass/tools/util/include")
     message(STATUS "[QUTLASS] Using QuTLASS vendored CUTLASS headers (no vLLM CUTLASS detected).")
   else()
     message(FATAL_ERROR "[QUTLASS] CUTLASS headers not found. "
@@ -91,12 +103,23 @@ if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8 AND QUTLASS_ARCHS)
     CUDA_ARCHS "${QUTLASS_ARCHS}"
   )
 
-  target_sources(_C PRIVATE ${QUTLASS_SOURCES})
-  target_include_directories(_C PRIVATE ${QUTLASS_INCLUDES})
-  target_compile_definitions(_C PRIVATE
+  # QuTLASS uses legacy ATen headers and cannot be built with TORCH_TARGET_VERSION.
+  # Keep it as its own extension (registers torch.ops._qutlass_C).
+  define_extension_target(
+    _qutlass_C
+    DESTINATION vllm
+    LANGUAGE ${VLLM_GPU_LANG}
+    SOURCES ${QUTLASS_SOURCES}
+    COMPILE_FLAGS ${VLLM_GPU_FLAGS}
+    ARCHITECTURES ${VLLM_GPU_ARCHES}
+    INCLUDE_DIRECTORIES ${QUTLASS_INCLUDES}
+    USE_SABI 3
+    WITH_SOABI)
+
+  target_compile_definitions(_qutlass_C PRIVATE
     QUTLASS_DISABLE_PYBIND=1
     TARGET_CUDA_ARCH=${QUTLASS_TARGET_CC}
-  )
+    CUTLASS_ENABLE_DIRECT_CUDA_DRIVER_CALL=1)
 
   set_property(SOURCE ${QUTLASS_SOURCES} APPEND PROPERTY COMPILE_OPTIONS
     $<$<COMPILE_LANGUAGE:CUDA>:--expt-relaxed-constexpr --use_fast_math -O3>
@@ -111,4 +134,5 @@ else()
       "[QUTLASS] Skipping build: no supported arch (12.0f / 10.0f) found in "
       "CUDA_ARCHS='${CUDA_ARCHS}'.")
   endif()
+  add_custom_target(_qutlass_C)
 endif()

csrc/cpu/cpu_wna16.cpp

@@ -4,6 +4,9 @@
 #ifdef CPU_CAPABILITY_AMXBF16
   #include "cpu/micro_gemm/cpu_micro_gemm_amx.hpp"
 #endif
+#if defined(__riscv_v)
+  #include "cpu/micro_gemm/cpu_micro_gemm_rvv.hpp"
+#endif
 #include "cpu/micro_gemm/cpu_micro_gemm_vec.hpp"
 
 #define VLLM_DISPATCH_CASE_16B_TYPES(...)                 \
@@ -319,6 +322,8 @@ void cpu_gemm_wna16(
       return ISA::AMX;
     } else if (isa_hint == "vec") {
       return ISA::VEC;
+    } else if (isa_hint == "rvv") {
+      return ISA::RVV;
     } else {
       TORCH_CHECK(false, "unsupported isa hint: " + isa_hint);
     }
@@ -397,6 +402,40 @@ void cpu_gemm_wna16(
             pack_factor);
         return;
       }
+    } else if (isa == ISA::RVV) {
+      using gemm_t = cpu_micro_gemm::MicroGemm<ISA::RVV, scalar_t>;
+      if (has_zp) {
+        using dequantizer_t = Dequantizer4b<scalar_t, ISA::RVV, true, false>;
+        cpu_gemm_wna16_impl<scalar_t, dequantizer_t, gemm_t>(
+            input.data_ptr<scalar_t>(), q_weight.data_ptr<int32_t>(),
+            output.data_ptr<scalar_t>(), scales.data_ptr<scalar_t>(), zeros_ptr,
+            g_idx_ptr, bias.has_value() ? bias->data_ptr<scalar_t>() : nullptr,
+            a_m_size, b_n_size, a_k_size, a_m_stride, output_m_stride,
+            scales_group_stride, zeros_group_stride, group_num, group_size,
+            pack_factor);
+        return;
+      }
+      if (use_desc_act) {
+        using dequantizer_t = Dequantizer4b<scalar_t, ISA::RVV, false, true>;
+        cpu_gemm_wna16_impl<scalar_t, dequantizer_t, gemm_t>(
+            input.data_ptr<scalar_t>(), q_weight.data_ptr<int32_t>(),
+            output.data_ptr<scalar_t>(), scales.data_ptr<scalar_t>(), zeros_ptr,
+            g_idx_ptr, bias.has_value() ? bias->data_ptr<scalar_t>() : nullptr,
+            a_m_size, b_n_size, a_k_size, a_m_stride, output_m_stride,
+            scales_group_stride, zeros_group_stride, group_num, group_size,
+            pack_factor);
+        return;
+      } else {
+        using dequantizer_t = Dequantizer4b<scalar_t, ISA::RVV, false, false>;
+        cpu_gemm_wna16_impl<scalar_t, dequantizer_t, gemm_t>(
+            input.data_ptr<scalar_t>(), q_weight.data_ptr<int32_t>(),
+            output.data_ptr<scalar_t>(), scales.data_ptr<scalar_t>(), zeros_ptr,
+            g_idx_ptr, bias.has_value() ? bias->data_ptr<scalar_t>() : nullptr,
+            a_m_size, b_n_size, a_k_size, a_m_stride, output_m_stride,
+            scales_group_stride, zeros_group_stride, group_num, group_size,
+            pack_factor);
+        return;
+      }
     }
   });
 }

csrc/cpu/micro_gemm/cpu_micro_gemm_rvv.hpp

@@ -0,0 +1,228 @@
+#ifndef CPU_MICRO_GEMM_RVV_HPP
+#define CPU_MICRO_GEMM_RVV_HPP
+
+#include "cpu/micro_gemm/cpu_micro_gemm_impl.hpp"
+
+#if defined(__riscv_v)
+
+namespace cpu_micro_gemm {
+namespace {
+
+constexpr int32_t RVV_MGEMM_N8 = 8;
+constexpr int32_t RVV_MGEMM_B_GROUP_STRIDE = 16;
+
+template <typename scalar_t>
+FORCE_INLINE fixed_fp32x8_t load_row8_b_as_f32(const scalar_t* ptr);
+
+template <>
+FORCE_INLINE fixed_fp32x8_t load_row8_b_as_f32<float>(const float* ptr) {
+  return RVVI(__riscv_vle32_v_f32, LMUL_256)(ptr, RVV_MGEMM_N8);
+}
+
+template <>
+FORCE_INLINE fixed_fp32x8_t
+load_row8_b_as_f32<c10::Half>(const c10::Half* ptr) {
+  #if defined(__riscv_zvfh)
+  fixed_fp16x8_t vec = RVVI(__riscv_vle16_v_f16, LMUL_128)(
+      reinterpret_cast<const _Float16*>(ptr), RVV_MGEMM_N8);
+  return RVVI(__riscv_vfwcvt_f_f_v_f32, LMUL_256)(vec, RVV_MGEMM_N8);
+  #else
+  alignas(32) float values[RVV_MGEMM_N8];
+  for (int32_t i = 0; i < RVV_MGEMM_N8; ++i) {
+    values[i] = static_cast<float>(ptr[i]);
+  }
+  return RVVI(__riscv_vle32_v_f32, LMUL_256)(values, RVV_MGEMM_N8);
+  #endif
+}
+
+template <>
+FORCE_INLINE fixed_fp32x8_t
+load_row8_b_as_f32<c10::BFloat16>(const c10::BFloat16* ptr) {
+  #if defined(__riscv_zvfbfmin)
+  fixed_u16x8_t raw = RVVI(__riscv_vle16_v_u16, LMUL_128)(
+      reinterpret_cast<const uint16_t*>(ptr), RVV_MGEMM_N8);
+  fixed_bf16x8_t vec =
+      RVVI4(__riscv_vreinterpret_v_u16, LMUL_128, _bf16, LMUL_128)(raw);
+  return RVVI(__riscv_vfwcvtbf16_f_f_v_f32, LMUL_256)(vec, RVV_MGEMM_N8);
+  #else
+  fixed_u16x8_t raw = RVVI(__riscv_vle16_v_u16, LMUL_128)(
+      reinterpret_cast<const uint16_t*>(ptr), RVV_MGEMM_N8);
+  auto wide = RVVI(__riscv_vzext_vf2_u32, LMUL_256)(raw, RVV_MGEMM_N8);
+  auto shifted = RVVI(__riscv_vsll_vx_u32, LMUL_256)(wide, 16, RVV_MGEMM_N8);
+  return RVVI4(__riscv_vreinterpret_v_u32, LMUL_256, _f32, LMUL_256)(shifted);
+  #endif
+}
+
+// Mx8 RVV kernel. B points at one 8-channel half of a 16-channel packed group,
+// with rows separated by RVV_MGEMM_B_GROUP_STRIDE scalar elements.
+template <int32_t M, typename scalar_t>
+FORCE_INLINE void gemm_micro_rvv_fma_mx8_ku4(const scalar_t* __restrict__ a_ptr,
+                                             const scalar_t* __restrict__ b_ptr,
+                                             float* __restrict__ c_ptr,
+                                             const int64_t lda,
+                                             const int64_t ldc, const int32_t k,
+                                             const bool accum_c) {
+  static_assert(0 < M && M <= 8);
+
+  #define RVV_ROWS_APPLY(OP) OP(0) OP(1) OP(2) OP(3) OP(4) OP(5) OP(6) OP(7)
+  #define RVV_IF_M(i) if constexpr (M > (i))
+
+  #define RVV_DECL_A(i) const scalar_t* __restrict__ a##i = a_ptr + (i) * lda;
+  RVV_ROWS_APPLY(RVV_DECL_A)
+  #undef RVV_DECL_A
+
+  #define RVV_DECL_ACC(i) fixed_fp32x8_t acc##i;
+  RVV_ROWS_APPLY(RVV_DECL_ACC)
+  #undef RVV_DECL_ACC
+
+  #define RVV_INIT_ACC(i)                                                  \
+    RVV_IF_M(i) {                                                          \
+      if (accum_c) {                                                       \
+        acc##i = RVVI(__riscv_vle32_v_f32, LMUL_256)(c_ptr + (i) * ldc,    \
+                                                     RVV_MGEMM_N8);        \
+      } else {                                                             \
+        acc##i = RVVI(__riscv_vfmv_v_f_f32, LMUL_256)(0.0f, RVV_MGEMM_N8); \
+      }                                                                    \
+    }
+  RVV_ROWS_APPLY(RVV_INIT_ACC)
+  #undef RVV_INIT_ACC
+
+  int32_t k_idx = 0;
+  for (; k_idx + 3 < k; k_idx += 4) {
+  #define RVV_FMA_ROW(i, K_OFFSET)                                     \
+    RVV_IF_M(i) {                                                      \
+      acc##i = RVVI(__riscv_vfmacc_vf_f32, LMUL_256)(                  \
+          acc##i, static_cast<float>(*(a##i + k_idx + (K_OFFSET))), b, \
+          RVV_MGEMM_N8);                                               \
+    }
+
+  #define RVV_STEP_K(K_OFFSET)                                      \
+    {                                                               \
+      fixed_fp32x8_t b = load_row8_b_as_f32<scalar_t>(              \
+          b_ptr + (k_idx + (K_OFFSET)) * RVV_MGEMM_B_GROUP_STRIDE); \
+      RVV_FMA_ROW(0, K_OFFSET)                                      \
+      RVV_FMA_ROW(1, K_OFFSET)                                      \
+      RVV_FMA_ROW(2, K_OFFSET)                                      \
+      RVV_FMA_ROW(3, K_OFFSET)                                      \
+      RVV_FMA_ROW(4, K_OFFSET)                                      \
+      RVV_FMA_ROW(5, K_OFFSET)                                      \
+      RVV_FMA_ROW(6, K_OFFSET)                                      \
+      RVV_FMA_ROW(7, K_OFFSET)                                      \
+    }
+
+    RVV_STEP_K(0)
+    RVV_STEP_K(1)
+    RVV_STEP_K(2)
+    RVV_STEP_K(3)
+  #undef RVV_STEP_K
+  #undef RVV_FMA_ROW
+  }
+
+  for (; k_idx < k; ++k_idx) {
+    fixed_fp32x8_t b =
+        load_row8_b_as_f32<scalar_t>(b_ptr + k_idx * RVV_MGEMM_B_GROUP_STRIDE);
+  #define RVV_TAIL_ROW(i)                                                \
+    RVV_IF_M(i) {                                                        \
+      acc##i = RVVI(__riscv_vfmacc_vf_f32, LMUL_256)(                    \
+          acc##i, static_cast<float>(*(a##i + k_idx)), b, RVV_MGEMM_N8); \
+    }
+    RVV_ROWS_APPLY(RVV_TAIL_ROW)
+  #undef RVV_TAIL_ROW
+  }
+
+  #define RVV_STORE_ROW(i)                                           \
+    RVV_IF_M(i) {                                                    \
+      RVVI(__riscv_vse32_v_f32, LMUL_256)(c_ptr + (i) * ldc, acc##i, \
+                                          RVV_MGEMM_N8);             \
+    }
+  RVV_ROWS_APPLY(RVV_STORE_ROW)
+  #undef RVV_STORE_ROW
+
+  #undef RVV_ROWS_APPLY
+  #undef RVV_IF_M
+}
+
+template <int32_t M, typename scalar_t>
+FORCE_INLINE void gemm_micro_rvv_mx32_ku4(DEFINE_CPU_MICRO_GEMM_PARAMS) {
+  static_assert(0 < M && M <= 8);
+  scalar_t* __restrict__ curr_b_0 = b_ptr;
+  scalar_t* __restrict__ curr_b_1 = b_ptr + b_n_group_stride;
+
+  gemm_micro_rvv_fma_mx8_ku4<M>(a_ptr, curr_b_0, c_ptr, lda, ldc, k, accum_c);
+  gemm_micro_rvv_fma_mx8_ku4<M>(a_ptr, curr_b_0 + RVV_MGEMM_N8,
+                                c_ptr + RVV_MGEMM_N8, lda, ldc, k, accum_c);
+  gemm_micro_rvv_fma_mx8_ku4<M>(a_ptr, curr_b_1, c_ptr + 16, lda, ldc, k,
+                                accum_c);
+  gemm_micro_rvv_fma_mx8_ku4<M>(a_ptr, curr_b_1 + RVV_MGEMM_N8, c_ptr + 24, lda,
+                                ldc, k, accum_c);
+}
+
+class TileGemmRVV {
+ public:
+  template <typename scalar_t>
+  FORCE_INLINE static void gemm(DEFINE_CPU_MICRO_GEMM_PARAMS) {
+    switch (m) {
+      case 1:
+        gemm_micro_rvv_mx32_ku4<1>(CPU_MICRO_GEMM_PARAMS);
+        break;
+      case 2:
+        gemm_micro_rvv_mx32_ku4<2>(CPU_MICRO_GEMM_PARAMS);
+        break;
+      case 3:
+        gemm_micro_rvv_mx32_ku4<3>(CPU_MICRO_GEMM_PARAMS);
+        break;
+      case 4:
+        gemm_micro_rvv_mx32_ku4<4>(CPU_MICRO_GEMM_PARAMS);
+        break;
+      case 5:
+        gemm_micro_rvv_mx32_ku4<5>(CPU_MICRO_GEMM_PARAMS);
+        break;
+      case 6:
+        gemm_micro_rvv_mx32_ku4<6>(CPU_MICRO_GEMM_PARAMS);
+        break;
+      case 7:
+        gemm_micro_rvv_mx32_ku4<7>(CPU_MICRO_GEMM_PARAMS);
+        break;
+      case 8:
+        gemm_micro_rvv_mx32_ku4<8>(CPU_MICRO_GEMM_PARAMS);
+        break;
+    }
+  }
+};
+
+}  // namespace
+
+template <typename scalar_t>
+class MicroGemm<cpu_utils::ISA::RVV, scalar_t> {
+ public:
+  static constexpr int32_t MaxMSize = 8;
+  static constexpr int32_t NSize = 32;
+
+ public:
+  void gemm(DEFINE_CPU_MICRO_GEMM_PARAMS) {
+    TileGemmRVV::gemm<scalar_t>(CPU_MICRO_GEMM_PARAMS);
+  }
+
+  static void pack_weight(const scalar_t* __restrict__ weight,
+                          scalar_t* __restrict__ packed_weight,
+                          const int32_t output_size, const int32_t input_size) {
+    TORCH_CHECK_EQ(output_size % 16, 0);
+    for (int32_t o_idx = 0; o_idx < output_size; ++o_idx) {
+      const scalar_t* __restrict__ curr_weight = weight + o_idx * input_size;
+      scalar_t* __restrict__ curr_packed_weight =
+          packed_weight + (o_idx / 16) * (16 * input_size) + o_idx % 16;
+      for (int32_t i_idx = 0; i_idx < input_size; ++i_idx) {
+        *curr_packed_weight = *curr_weight;
+
+        curr_packed_weight += 16;
+        ++curr_weight;
+      }
+    }
+  }
+};
+
+}  // namespace cpu_micro_gemm
+
+#endif  // defined(__riscv_v)
+
+#endif  // CPU_MICRO_GEMM_RVV_HPP

csrc/cpu/utils.hpp

@@ -8,13 +8,15 @@
 #include "cpu/cpu_types.hpp"
 
 namespace cpu_utils {
-enum class ISA { AMX, VEC };
+enum class ISA { AMX, VEC, RVV };
 
 inline ISA get_isa(const std::string& isa) {
   if (isa == "amx") {
     return ISA::AMX;
   } else if (isa == "vec") {
     return ISA::VEC;
+  } else if (isa == "rvv") {
+    return ISA::RVV;
   } else {
     TORCH_CHECK(false, "Invalid isa type: " + isa);
   }

csrc/libtorch_stable/attention/mla/sm100_cutlass_mla_kernel.cu

@@ -136,8 +136,12 @@ typename T::Fmha::Arguments args_from_options(
   StrideQ stride_Q_pe = cute::make_tuple(
       static_cast<int64_t>(q_pe.stride(1)), _1{}, static_cast<int64_t>(q_pe.stride(0)));
 
+  // Read the token and page strides from the cache tensor instead of assuming
+  // packed pages, so strided views (e.g. per-layer views into a cross-layer
+  // block-major cache) are addressed correctly.
   StrideK stride_C = cute::make_tuple(
-      static_cast<int64_t>(0 + D_latent + D_rope), _1{}, static_cast<int64_t>(page_size * (D_latent + D_rope)));
+      static_cast<int64_t>(kv_c_and_k_pe_cache.stride(1)), _1{},
+      static_cast<int64_t>(kv_c_and_k_pe_cache.stride(0)));
   StrideLSE stride_PT = cute::make_stride(_1{}, page_count_per_seq);
   StrideLSE stride_LSE = cute::make_tuple(_1{}, 0 + H);
   StrideO stride_O = cute::make_tuple(static_cast<int64_t>(0 + D_latent), _1{}, static_cast<int64_t>(0 + H * D_latent));
@@ -268,9 +272,14 @@ int64_t sm100_cutlass_mla_get_workspace_size(int64_t max_seq_len, int64_t num_ba
   using TileShapeD = typename MlaSm100Type::TileShapeD;
   arguments.problem_shape =
       cute::make_tuple(TileShapeH{}, static_cast<int>(max_seq_len), TileShapeD{}, static_cast<int>(num_batches));
-  // Assumes device 0 when getting sm_count.
-  arguments.hw_info.sm_count =
-      sm_count <= 0 ? cutlass::KernelHardwareInfo::query_device_multiprocessor_count(/*device_id=*/0) : sm_count;
+  if (sm_count <= 0) {
+    int current_device = 0;
+    cudaGetDevice(&current_device);
+    arguments.hw_info.sm_count =
+        cutlass::KernelHardwareInfo::query_device_multiprocessor_count(current_device);
+  } else {
+    arguments.hw_info.sm_count = sm_count;
+  }
   arguments.split_kv = static_cast<int>(num_kv_splits);
   MlaSm100Type::Fmha::set_split_kv(arguments);
 

csrc/libtorch_stable/cache_kernels.cu

@@ -549,7 +549,7 @@ __global__ void indexer_k_quant_and_cache_kernel(
     const int head_dim,                        // dimension of each head
     const int quant_block_size,                // quantization block size
     const int cache_block_size,                // cache block size
-    const int cache_stride,  // stride for each token in kv_cache
+    const int64_t cache_block_stride,  // stride for each block in kv_cache
 
     const bool use_ue8m0  // use ue8m0 scale format
 ) {
@@ -590,16 +590,15 @@ __global__ void indexer_k_quant_and_cache_kernel(
     scale = exp2f(ceilf(log2f(scale)));
   }
 
-  const int64_t dst_offset = block_idx * cache_block_size * cache_stride +
-                             block_offset * head_dim + head_dim_idx;
+  const int64_t dst_offset =
+      block_idx * cache_block_stride + block_offset * head_dim + head_dim_idx;
   for (int i = 0; i < VEC_SIZE; i++) {
     kv_cache[dst_offset + i] =
         fp8::scaled_convert<cache_t, scalar_t, kv_dt>(k_val_ptr[i], scale);
   }
   if (threadIdx.x == 0) {
     const int64_t dst_scale_idx =
-        block_idx * cache_block_size * cache_stride +
-        cache_block_size * head_dim +
+        block_idx * cache_block_stride + cache_block_size * head_dim +
         (block_offset * head_dim + head_dim_idx) * 4 / quant_block_size;
     reinterpret_cast<float*>(kv_cache)[dst_scale_idx / 4] = scale;
   }
@@ -1452,7 +1451,7 @@ void cp_gather_and_upconvert_fp8_kv_cache(
           reinterpret_cast<KV_T*>(k.data_ptr()),                              \
           reinterpret_cast<CACHE_T*>(kv_cache.data_ptr()),                    \
           slot_mapping.const_data_ptr<int64_t>(), head_dim, quant_block_size, \
-          cache_block_size, cache_stride, use_ue8m0);
+          cache_block_size, cache_block_stride, use_ue8m0);
 
 void indexer_k_quant_and_cache(
     torch::stable::Tensor& k,         // [num_tokens, head_dim]
@@ -1463,7 +1462,7 @@ void indexer_k_quant_and_cache(
   int num_tokens = k.size(0);
   int head_dim = k.size(1);
   int cache_block_size = kv_cache.size(1);
-  int cache_stride = kv_cache.size(2);
+  int64_t cache_block_stride = kv_cache.stride(0);
   bool use_ue8m0 = scale_fmt == "ue8m0";
 
   STD_TORCH_CHECK(k.device() == kv_cache.device(),

csrc/libtorch_stable/layernorm_kernels.cu

@@ -81,11 +81,11 @@ __global__ void rms_norm_kernel(
 #pragma unroll
     for (int j = 0; j < VEC_SIZE; j++) {
       float x = static_cast<float>(src1.val[j]);
-      scalar_t normalized = static_cast<scalar_t>(x * s_variance);
       if constexpr (HasWeight) {
-        dst.val[j] = normalized * src2.val[j];
+        float w = static_cast<float>(src2.val[j]);
+        dst.val[j] = static_cast<scalar_t>(x * s_variance * w);
       } else {
-        dst.val[j] = normalized;
+        dst.val[j] = static_cast<scalar_t>(x * s_variance);
       }
     }
     v_out[i] = dst;
@@ -151,7 +151,8 @@ fused_add_rms_norm_kernel(
 #pragma unroll
       for (int j = 0; j < width; ++j) {
         float x = Converter::convert(res.data[j]);
-        out.data[j] = Converter::convert(x * s_variance) * w.data[j];
+        float wf = Converter::convert(w.data[j]);
+        out.data[j] = Converter::convert(x * s_variance * wf);
       }
     } else {
 #pragma unroll
@@ -198,8 +199,8 @@ fused_add_rms_norm_kernel(
   for (int idx = threadIdx.x; idx < hidden_size; idx += blockDim.x) {
     float x = (float)residual[blockIdx.x * hidden_size + idx];
     if constexpr (HasWeight) {
-      input[blockIdx.x * input_stride + idx] =
-          (scalar_t)(x * s_variance) * weight[idx];
+      float w = (float)weight[idx];
+      input[blockIdx.x * input_stride + idx] = (scalar_t)(x * s_variance * w);
     } else {
       input[blockIdx.x * input_stride + idx] = (scalar_t)(x * s_variance);
     }

csrc/libtorch_stable/layernorm_quant_kernels.cu

@@ -66,8 +66,13 @@ __global__ void rms_norm_static_fp8_quant_kernel(
 #pragma unroll
     for (int j = 0; j < VEC_SIZE; j++) {
       float x = static_cast<float>(src1.val[j]);
-      // Multiply in weight's native dtype to match rms_norm_kernel.
-      scalar_t out_norm = static_cast<scalar_t>(x * s_variance) * src2.val[j];
+      float w = static_cast<float>(src2.val[j]);
+      // Round normalized result through scalar_t to match the precision of the
+      // unfused composite (rms_norm writes scalar_t, then
+      // static_scaled_fp8_quant re-loads it as float before FP8 conversion).
+      // Without this round, the fused path is strictly more accurate and
+      // disagrees with the composite at exact E4M3 quantization tie boundaries.
+      scalar_t out_norm = static_cast<scalar_t>(x * s_variance * w);
       out[blockIdx.x * hidden_size + idx * VEC_SIZE + j] =
           scaled_fp8_conversion<true, fp8_type>(static_cast<float>(out_norm),
                                                 scale_inv);
@@ -137,8 +142,12 @@ fused_add_rms_norm_static_fp8_quant_kernel(
 #pragma unroll
     for (int i = 0; i < width; ++i) {
       float x = Converter::convert(res.data[i]);
-      // Multiply in weight's native dtype to match fused_add_rms_norm_kernel.
-      HipT out_norm_h = Converter::convert(x * s_variance) * w.data[i];
+      float wf = Converter::convert(w.data[i]);
+      // See note in rms_norm_static_fp8_quant_kernel: round through scalar_t
+      // to match the unfused composite path at FP8 boundaries. We use the
+      // backend's hip_type for the intermediate since c10::Half/BFloat16 has
+      // ambiguous conversions on CUDA and no implicit conversion on ROCm.
+      HipT out_norm_h = Converter::convert(x * s_variance * wf);
       out[id * width + i] = scaled_fp8_conversion<true, fp8_type>(
           Converter::convert(out_norm_h), scale_inv);
     }
@@ -183,8 +192,10 @@ fused_add_rms_norm_static_fp8_quant_kernel(
 
   for (int idx = threadIdx.x; idx < hidden_size; idx += blockDim.x) {
     float x = (float)residual[blockIdx.x * hidden_size + idx];
-    // Multiply in weight's native dtype to match fused_add_rms_norm_kernel.
-    scalar_t out_norm = static_cast<scalar_t>(x * s_variance) * weight[idx];
+    float w = (float)weight[idx];
+    // See note in rms_norm_static_fp8_quant_kernel: round through scalar_t
+    // to match the unfused composite path at FP8 boundaries.
+    scalar_t out_norm = static_cast<scalar_t>(x * s_variance * w);
     out[blockIdx.x * hidden_size + idx] = scaled_fp8_conversion<true, fp8_type>(
         static_cast<float>(out_norm), scale_inv);
   }

csrc/libtorch_stable/moe/moe_align_sum_kernels.cu

@@ -9,7 +9,7 @@
 #include <torch/headeronly/core/ScalarType.h>
 
 #include "../../cuda_compat.h"
-#include "core/math.hpp"
+#include "libtorch_stable/core/math.hpp"
 #include "libtorch_stable/dispatch_utils.h"
 #include "libtorch_stable/torch_utils.h"
 

csrc/libtorch_stable/ops.h

@@ -2,9 +2,25 @@
 
 #include <torch/csrc/stable/library.h>
 #include <torch/csrc/stable/tensor.h>
+#include <torch/headeronly/util/Exception.h>
 
 #include <optional>
 #include <string>
+#include <vector>
+
+#include <torch/csrc/stable/ops.h>
+
+inline torch::stable::Tensor weak_ref_tensor(torch::stable::Tensor& tensor) {
+  // Ensure tensor is on CUDA
+  STD_TORCH_CHECK(tensor.device().is_cuda(), "Tensor must be on CUDA device");
+
+  // Get the raw data pointer
+  void* data_ptr = tensor.mutable_data_ptr();
+
+  /// Create a new tensor from the raw data pointer
+  return torch::stable::from_blob(data_ptr, tensor.sizes(), tensor.strides(),
+                                  tensor.device(), tensor.scalar_type());
+}
 
 void per_token_group_quant_fp8(const torch::stable::Tensor& input,
                                torch::stable::Tensor& output_q,
@@ -371,6 +387,18 @@ void silu_and_mul(torch::stable::Tensor& out, torch::stable::Tensor& input);
 void silu_and_mul_clamp(torch::stable::Tensor& out,
                         torch::stable::Tensor& input, double limit,
                         double alpha = 1.0, double beta = 0.0);
+
+void silu_and_mul_quant(torch::stable::Tensor& out,
+                        torch::stable::Tensor& input,
+                        torch::stable::Tensor& scale);
+
+void persistent_masked_m_silu_mul_quant(
+    const torch::stable::Tensor& input,              // (E, T, 2*H)
+    const torch::stable::Tensor& tokens_per_expert,  // (E)
+    torch::stable::Tensor& y_q,                      // (E, T, H) [OUT]
+    torch::stable::Tensor& y_s,  // (E, T, H//group_size) [OUT]
+    bool use_ue8m0);
+
 void mul_and_silu(torch::stable::Tensor& out, torch::stable::Tensor& input);
 void gelu_and_mul(torch::stable::Tensor& out, torch::stable::Tensor& input);
 void gelu_tanh_and_mul(torch::stable::Tensor& out,

csrc/libtorch_stable/quantization/activation_kernels.cu

@@ -1,16 +1,12 @@
-#include <ATen/cuda/CUDAContext.h>
-#include <torch/all.h>
-#include <c10/cuda/CUDAGuard.h>
+#include "libtorch_stable/torch_utils.h"
 
 #include <cmath>
-#include "core/math.hpp"
-#include "../cuda_compat.h"
-#include "dispatch_utils.h"
 
+#include "libtorch_stable/core/math.hpp"
+#include "cuda_compat.h"
+#include "libtorch_stable/dispatch_utils.h"
 #include "quantization/w8a8/fp8/common.cuh"
 
-#include <c10/util/Float8_e4m3fn.h>
-
 #ifndef USE_ROCM
   #include <cuda_bf16.h>
   #include <cuda_fp16.h>
@@ -33,7 +29,6 @@ typedef __hip_fp8x4_e4m3_fnuz __nv_fp8x4_e4m3;
   #endif
 #endif
 
-#include "core/registration.h"
 namespace vllm {
 
 template <typename T>
@@ -564,41 +559,47 @@ __global__ void silu_mul_fp8_quant_deep_gemm_kernel(
 }  // namespace vllm
 
 // Launch activation, gating, and quantize kernel.
-#define LAUNCH_ACTIVATION_GATE_KERNEL(KERNEL)                               \
-  int d = input.size(-1) / 2;                                               \
-  int64_t num_tokens = input.numel() / input.size(-1);                      \
-  dim3 grid(num_tokens, num_tokens > 16 ? num_tokens > 32 ? 1 : 2 : 4);     \
-  dim3 block(std::min(d, 512));                                             \
-  const at::cuda::OptionalCUDAGuard device_guard(device_of(input));         \
-  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();             \
-  VLLM_DISPATCH_FLOATING_TYPES(                                             \
-      input.scalar_type(), "act_and_mul_kernel", [&] {                      \
-        VLLM_DISPATCH_FP8_TYPES(                                            \
-            out.scalar_type(), "fused_add_rms_norm_kernel_fp8_type", [&] {  \
-              vllm::act_and_mul_quant_kernel<scalar_t, KERNEL<scalar_t>,    \
-                                             fp8_t>                         \
-                  <<<grid, block, 0, stream>>>(out.data_ptr<fp8_t>(),       \
-                                               input.data_ptr<scalar_t>(),  \
-                                               scale.data_ptr<float>(), d); \
-            });                                                             \
+#define LAUNCH_ACTIVATION_GATE_KERNEL(KERNEL)                             \
+  int d = input.size(-1) / 2;                                             \
+  int64_t num_tokens = input.numel() / input.size(-1);                    \
+  dim3 grid(num_tokens, num_tokens > 16 ? num_tokens > 32 ? 1 : 2 : 4);   \
+  dim3 block(std::min(d, 512));                                           \
+  const torch::stable::accelerator::DeviceGuard device_guard(             \
+      input.get_device_index());                                          \
+  const cudaStream_t stream =                                             \
+      get_current_cuda_stream(input.get_device_index());                  \
+  VLLM_STABLE_DISPATCH_FLOATING_TYPES(                                    \
+      input.scalar_type(), "act_and_mul_kernel", [&] {                    \
+        VLLM_STABLE_DISPATCH_FP8_TYPES(                                   \
+            out.scalar_type(), "act_and_mul_quant_kernel_fp8_type", [&] { \
+              vllm::act_and_mul_quant_kernel<scalar_t, KERNEL<scalar_t>,  \
+                                             fp8_t>                       \
+                  <<<grid, block, 0, stream>>>(                           \
+                      out.mutable_data_ptr<fp8_t>(),                      \
+                      input.const_data_ptr<scalar_t>(),                   \
+                      scale.const_data_ptr<float>(), d);                  \
+            });                                                           \
       });
 
-void silu_and_mul_quant(torch::Tensor& out,    // [..., d]
-                        torch::Tensor& input,  // [..., 2 * d]
-                        torch::Tensor& scale) {
-  TORCH_CHECK(out.dtype() == torch::kFloat8_e4m3fn ||
-              out.dtype() == torch::kFloat8_e4m3fnuz);
-  TORCH_CHECK(input.dtype() == torch::kFloat16 ||
-              input.dtype() == torch::kBFloat16);
-  TORCH_CHECK(input.size(-1) % 2 == 0);
+void silu_and_mul_quant(torch::stable::Tensor& out,    // [..., d]
+                        torch::stable::Tensor& input,  // [..., 2 * d]
+                        torch::stable::Tensor& scale) {
+  STD_TORCH_CHECK(
+      out.scalar_type() == torch::headeronly::ScalarType::Float8_e4m3fn ||
+      out.scalar_type() == torch::headeronly::ScalarType::Float8_e4m3fnuz);
+  STD_TORCH_CHECK(
+      input.scalar_type() == torch::headeronly::ScalarType::Half ||
+          input.scalar_type() == torch::headeronly::ScalarType::BFloat16,
+      "Input must be FP16 or BF16");
+  STD_TORCH_CHECK(input.size(-1) % 2 == 0);
   LAUNCH_ACTIVATION_GATE_KERNEL(vllm::silu_kernel);
 }
 
 void persistent_masked_m_silu_mul_quant(
-    const at::Tensor& input,              // (E, T, 2*H)
-    const at::Tensor& tokens_per_expert,  // (E)
-    at::Tensor& y_q,                      // (E, T, H) [OUT]
-    at::Tensor& y_s,                      // (E, T, H//group_size) [OUT]
+    const torch::stable::Tensor& input,              // (E, T, 2*H)
+    const torch::stable::Tensor& tokens_per_expert,  // (E)
+    torch::stable::Tensor& y_q,                      // (E, T, H) [OUT]
+    torch::stable::Tensor& y_s,  // (E, T, H//group_size) [OUT]
     bool cast_scale_ue8m0) {
 #ifndef USE_ROCM
 
@@ -606,14 +607,18 @@ void persistent_masked_m_silu_mul_quant(
   // fixed GROUP_SIZE of 128.
   static constexpr int GROUP_SIZE = 128;
 
-  TORCH_CHECK(input.dtype() == torch::kBFloat16);
-  TORCH_CHECK(y_q.dtype() == torch::kFloat8_e4m3fn ||
-              y_q.dtype() == torch::kFloat8_e4m3fnuz);
-  TORCH_CHECK(input.size(-1) % (GROUP_SIZE * 2) == 0);
+  STD_TORCH_CHECK(input.scalar_type() ==
+                  torch::headeronly::ScalarType::BFloat16);
+  STD_TORCH_CHECK(
+      y_q.scalar_type() == torch::headeronly::ScalarType::Float8_e4m3fn ||
+      y_q.scalar_type() == torch::headeronly::ScalarType::Float8_e4m3fnuz);
+  STD_TORCH_CHECK(input.size(-1) % (GROUP_SIZE * 2) == 0);
 
   bool const is_packed_ue8m0 =
-      (y_s.dtype() == torch::kInt32 && cast_scale_ue8m0);
-  TORCH_CHECK(y_s.dtype() == torch::kFloat32 || is_packed_ue8m0);
+      (y_s.scalar_type() == torch::headeronly::ScalarType::Int &&
+       cast_scale_ue8m0);
+  STD_TORCH_CHECK(y_s.scalar_type() == torch::headeronly::ScalarType::Float ||
+                  is_packed_ue8m0);
 
   using Idx_t = int64_t;
 
@@ -631,7 +636,7 @@ void persistent_masked_m_silu_mul_quant(
 
   int const NUM_GROUPS = H / GROUP_SIZE;
 
-  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
+  const cudaStream_t stream = get_current_cuda_stream(input.get_device_index());
 
   // TODO: Get this from cuda_arch ?
   static constexpr int SILU_V2_BLOCK_COUNT = 132 * 32;
@@ -643,18 +648,21 @@ void persistent_masked_m_silu_mul_quant(
     static constexpr int max_shared_mem_bytes =                                \
         GROUP_SIZE * 2 * STAGES * NUM_WARPS * 2;                               \
     dim3 grid(sms), block(THREAD_COUNT);                                       \
-    const at::cuda::OptionalCUDAGuard device_guard(device_of(input));          \
-    VLLM_DISPATCH_FP8_TYPES(                                                   \
+    const torch::stable::accelerator::DeviceGuard device_guard(                \
+        input.get_device_index());                                             \
+    VLLM_STABLE_DISPATCH_FP8_TYPES(                                            \
         y_q.scalar_type(), "silu_mul_fp8_quant_deep_gemm_kernel", [&] {        \
           vllm::silu_mul_fp8_quant_deep_gemm_kernel<                           \
               BLOCK_COUNT, max_shared_mem_bytes, fp8_t, scale_t, THREAD_COUNT, \
               Idx_t, CEIL_UE8M0, GROUP_SIZE, STAGES>                           \
               <<<grid, block, max_shared_mem_bytes + (E + 1) * 16, stream>>>(  \
-                  reinterpret_cast<__nv_bfloat16*>(input.data_ptr()),          \
-                  (fp8_t*)y_q.data_ptr(),                                      \
-                  reinterpret_cast<scale_t*>(y_s.data_ptr()),                  \
-                  reinterpret_cast<int32_t*>(tokens_per_expert.data_ptr()), E, \
-                  T, H, stride_i_e, stride_i_t, stride_i_h, stride_yq_e,       \
+                  reinterpret_cast<const __nv_bfloat16*>(                      \
+                      input.const_data_ptr()),                                 \
+                  y_q.mutable_data_ptr<fp8_t>(),                               \
+                  reinterpret_cast<scale_t*>(y_s.mutable_data_ptr()),          \
+                  reinterpret_cast<const int32_t*>(                            \
+                      tokens_per_expert.const_data_ptr()),                     \
+                  E, T, H, stride_i_e, stride_i_t, stride_i_h, stride_yq_e,    \
                   stride_yq_t, stride_yq_h, STRIDE_YS_E, STRIDE_YS_T,          \
                   STRIDE_YS_G, STRIDE_YS_P, stride_counts_e);                  \
         });
@@ -679,7 +687,7 @@ void persistent_masked_m_silu_mul_quant(
   Idx_t stride_ys_g = y_s.stride(2);
   Idx_t stride_ys_p = 0;
   if (!cast_scale_ue8m0) {
-    TORCH_CHECK(!is_packed_ue8m0);
+    STD_TORCH_CHECK(!is_packed_ue8m0);
     LAUNCH_ON_H(float, stride_ys_e, stride_ys_t, stride_ys_g, stride_ys_p,
                 false);
     return;
@@ -692,8 +700,8 @@ void persistent_masked_m_silu_mul_quant(
     return;
   }
 
-  TORCH_CHECK(cast_scale_ue8m0 && is_packed_ue8m0);
-  TORCH_CHECK(y_s.dtype() == torch::kInt32);
+  STD_TORCH_CHECK(cast_scale_ue8m0 && is_packed_ue8m0);
+  STD_TORCH_CHECK(y_s.scalar_type() == torch::headeronly::ScalarType::Int);
 
   // Int32 packed ue8m0 scales tensor.
   // Let E, T, G be the number to experts, number of tokens and number of groups

csrc/libtorch_stable/quantization/fp4/nvfp4_scaled_mm_kernels.cu

@@ -31,7 +31,7 @@
 
 #include "cutlass/util/packed_stride.hpp"
 
-#include "core/math.hpp"
+#include "libtorch_stable/core/math.hpp"
 #include "core/batch_invariant.hpp"
 
 using namespace cute;

csrc/libtorch_stable/quantization/fp4/nvfp4_scaled_mm_sm120_kernels.cu

@@ -31,7 +31,7 @@
 
 #include "cutlass/util/packed_stride.hpp"
 
-#include "core/math.hpp"
+#include "libtorch_stable/core/math.hpp"
 #include "core/batch_invariant.hpp"
 
 using namespace cute;

csrc/libtorch_stable/quantization/w8a8/cutlass/c3x/cutlass_gemm_caller.cuh

@@ -19,7 +19,7 @@
 #include "cutlass/gemm/collective/collective_builder.hpp"
 #include "cutlass/util/packed_stride.hpp"
 
-#include "core/math.hpp"
+#include "libtorch_stable/core/math.hpp"
 #include "libtorch_stable/cutlass_extensions/common.hpp"
 // clang-format on
 

csrc/libtorch_stable/quantization/w8a8/cutlass/c3x/scaled_mm.cuh

@@ -14,7 +14,7 @@
 #include "cutlass/epilogue/collective/collective_builder.hpp"
 #include "cutlass/gemm/collective/collective_builder.hpp"
 
-#include "core/math.hpp"
+#include "libtorch_stable/core/math.hpp"
 #include "libtorch_stable/cutlass_extensions/common.hpp"
 // clang-format on
 

csrc/libtorch_stable/quantization/w8a8/cutlass/scaled_mm_c2x.cuh

@@ -22,7 +22,7 @@
 #include "cutlass/epilogue/threadblock/fusion/visitors.hpp"
 #include "cutlass/gemm/kernel/default_gemm_universal_with_visitor.h"
 
-#include "core/math.hpp"
+#include "libtorch_stable/core/math.hpp"
 #include "libtorch_stable/cutlass_extensions/common.hpp"
 // clang-format on
 

csrc/libtorch_stable/quantization/w8a8/fp8/per_token_group_quant.cu

@@ -301,8 +301,9 @@ __global__ void per_token_group_quant_8bit_packed_register_kernel(
 
   const int sf_k_local = local_group_id % kGroupsPerBlockX;
   const int row_local = local_group_id / kGroupsPerBlockX;
-  const int sf_k_idx = blockIdx.x * kGroupsPerBlockX + sf_k_local;
-  const int mn_idx = blockIdx.y * kRowsPerBlock + row_local;
+  // Rows on grid.x: mn scales with tokens and can exceed the 65535 grid.y cap.
+  const int sf_k_idx = blockIdx.y * kGroupsPerBlockX + sf_k_local;
+  const int mn_idx = blockIdx.x * kRowsPerBlock + row_local;
 
 #if (defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900))
   asm volatile("griddepcontrol.wait;");
@@ -496,14 +497,15 @@ void per_token_group_quant_8bit_packed(const torch::stable::Tensor& input,
                   " is not a multiple of 4.");
   const int kx = GetGroupsPerBlockX(padded_groups_per_row);
   const int ry = 16 / kx;
-  const int64_t blocks_x = padded_groups_per_row / kx;
-  const int64_t blocks_y = (tma_aligned_mn + ry - 1) / ry;
+  const int64_t row_blocks = (tma_aligned_mn + ry - 1) / ry;
+  const int64_t sf_k_blocks = padded_groups_per_row / kx;
   const int num_threads = (kx * ry) * THREADS_PER_GROUP;
-  // CUDA caps grid.x and grid.y at 2^31 - 1; guard against pathological inputs.
-  STD_TORCH_CHECK(blocks_x <= static_cast<int64_t>(INT32_MAX) &&
-                      blocks_y <= static_cast<int64_t>(INT32_MAX),
+  // CUDA caps grid.x at 2^31 - 1 and grid.y at 2^16 - 1 (65535).
+  constexpr int64_t kMaxGridDimYZ = 65535;
+  STD_TORCH_CHECK(row_blocks <= static_cast<int64_t>(INT32_MAX) &&
+                      sf_k_blocks <= kMaxGridDimYZ,
                   "per_token_group_quant_8bit_packed grid too large: (",
-                  blocks_x, ", ", blocks_y, ").");
+                  row_blocks, ", ", sf_k_blocks, ").");
 
   auto dst_type = output_q.scalar_type();
 
@@ -513,8 +515,8 @@ void per_token_group_quant_8bit_packed(const torch::stable::Tensor& input,
   #define LAUNCH_REG_KERNEL_INST(T, DST_DTYPE, KX, RY)                         \
     do {                                                                       \
       cudaLaunchConfig_t config = {};                                          \
-      config.gridDim = dim3(static_cast<unsigned int>(blocks_x),               \
-                            static_cast<unsigned int>(blocks_y));              \
+      config.gridDim = dim3(static_cast<unsigned int>(row_blocks),             \
+                            static_cast<unsigned int>(sf_k_blocks));           \
       config.blockDim = dim3(num_threads);                                     \
       config.dynamicSmemBytes = 0;                                             \
       config.stream = stream;                                                  \
@@ -539,8 +541,8 @@ void per_token_group_quant_8bit_packed(const torch::stable::Tensor& input,
 #else
   #define LAUNCH_REG_KERNEL_INST(T, DST_DTYPE, KX, RY)                         \
     do {                                                                       \
-      dim3 grid(static_cast<unsigned int>(blocks_x),                           \
-                static_cast<unsigned int>(blocks_y));                          \
+      dim3 grid(static_cast<unsigned int>(row_blocks),                         \
+                static_cast<unsigned int>(sf_k_blocks));                       \
       dim3 block(num_threads);                                                 \
       per_token_group_quant_8bit_packed_register_kernel<T, DST_DTYPE, 128, KX, \
                                                         RY>                    \

csrc/libtorch_stable/torch_bindings.cpp

@@ -34,6 +34,20 @@ STABLE_TORCH_LIBRARY_FRAGMENT(_C, ops) {
   // TODO: Remove this once ROCm upgrade to torch 2.11.
   ops.def("get_cuda_view_from_cpu_tensor(Tensor cpu_tensor) -> Tensor");
 
+  // Note about marlin kernel 'workspace' arguments:
+  // Technically these should be mutable since they are modified by the kernel.
+  // But since they are set back to zero once the kernel is finished we can
+  // hand wave and say that they have no net effect.
+  //
+  // The reason to mark 'workspace' as immutable is so that they don't interfere
+  // with using ScalarType arguments in the ops. If they are marked as mutable,
+  // pytorch throws an assert in
+  // 'torch._higher_order_ops._register_effectful_op' that prevents these
+  // kernels from being torch.compile'd.
+  // See the following document for more info on custom types and ops that use
+  // custom types:
+  // https://docs.google.com/document/d/18fBMPuOJ0fY5ZQ6YyrHUppw9FA332CpNtgB6SOIgyuA
+
   // Machete (Dense) Optimized Mixed Precision GEMM for Hopper.
   ops.def(
       "machete_supported_schedules("
@@ -480,6 +494,11 @@ STABLE_TORCH_LIBRARY_FRAGMENT(_C, ops) {
       "Tensor workspace, int k, int max_seq_len) -> ()");
 
   // Activation ops
+  ops.def(
+      "persistent_masked_m_silu_mul_quant(Tensor input, Tensor counts, Tensor! "
+      "y_q, Tensor! y_s, bool use_ue8m0) -> ()");
+  ops.def("weak_ref_tensor(Tensor input) -> Tensor");
+
   // Activation function used in SwiGLU.
   ops.def("silu_and_mul(Tensor! result, Tensor input) -> ()");
 
@@ -492,6 +511,10 @@ STABLE_TORCH_LIBRARY_FRAGMENT(_C, ops) {
       "silu_and_mul_with_clamp(Tensor! result, Tensor input, float limit, "
       "float alpha=1.0, float beta=0.0) -> ()");
 
+  // SwiGLU activation with FP8 quantization.
+  ops.def(
+      "silu_and_mul_quant(Tensor! result, Tensor input, Tensor scale) -> ()");
+
   // Activation function used in GeGLU with `none` approximation.
   ops.def("gelu_and_mul(Tensor! out, Tensor input) -> ()");
 
@@ -690,6 +713,10 @@ STABLE_TORCH_LIBRARY_IMPL(_C, CUDA, ops) {
   ops.impl("persistent_topk", TORCH_BOX(&persistent_topk));
 
   // Activation kernels (shared CUDA/ROCm)
+  ops.impl("persistent_masked_m_silu_mul_quant",
+           TORCH_BOX(&persistent_masked_m_silu_mul_quant));
+  ops.impl("weak_ref_tensor", TORCH_BOX(&weak_ref_tensor));
+  ops.impl("silu_and_mul_quant", TORCH_BOX(&silu_and_mul_quant));
   ops.impl("silu_and_mul", TORCH_BOX(&silu_and_mul));
   ops.impl("mul_and_silu", TORCH_BOX(&mul_and_silu));
   ops.impl("gelu_and_mul", TORCH_BOX(&gelu_and_mul));

csrc/ops.h

@@ -9,28 +9,6 @@
 
 #include <vector>
 
-torch::Tensor weak_ref_tensor(torch::Tensor& tensor) {
-  // Ensure tensor is on CUDA
-  if (!tensor.is_cuda()) {
-    throw std::runtime_error("Tensor must be on CUDA device");
-  }
-
-  // Get the raw data pointer
-  void* data_ptr = tensor.data_ptr();
-
-  // Get tensor sizes and strides
-  std::vector<int64_t> sizes = tensor.sizes().vec();
-  std::vector<int64_t> strides = tensor.strides().vec();
-
-  // Get tensor options (dtype, device)
-  auto options = tensor.options();
-
-  // Create a new tensor from the raw data pointer
-  auto new_tensor = torch::from_blob(data_ptr, sizes, strides, options);
-
-  return new_tensor;
-}
-
 // rms_norm and fused_add_rms_norm declarations also exist in
 // csrc/libtorch_stable/ops.h (torch::stable ABI for CUDA). They remain here
 // because the CPU build still uses these torch::Tensor declarations.
@@ -53,16 +31,6 @@ void silu_and_mul(torch::Tensor& out, torch::Tensor& input);
 void silu_and_mul_clamp(torch::Tensor& out, torch::Tensor& input, double limit,
                         double alpha = 1.0, double beta = 0.0);
 
-void silu_and_mul_quant(torch::Tensor& out, torch::Tensor& input,
-                        torch::Tensor& scale);
-
-void persistent_masked_m_silu_mul_quant(
-    const at::Tensor& input,   // (E, T, 2*H)
-    const at::Tensor& counts,  // (E)
-    at::Tensor& y_q,           // (E, T, H) [OUT]
-    at::Tensor& y_s,           // (E, T, H//group_size) [OUT]
-    bool use_ue8m0);
-
 void gelu_and_mul(torch::Tensor& out, torch::Tensor& input);
 
 void gelu_tanh_and_mul(torch::Tensor& out, torch::Tensor& input);

csrc/qutlass_registration.cpp

@@ -0,0 +1,5 @@
+#include "core/registration.h"
+
+// QuTLASS registers torch.ops._qutlass_C via TORCH_LIBRARY in bindings.cpp.
+// This stub lets Python import vllm._qutlass_C to trigger op registration.
+REGISTER_EXTENSION(_qutlass_C)

csrc/torch_bindings.cpp

@@ -20,17 +20,6 @@
 
 TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
   // vLLM custom ops
-  //
-
-  ops.def(
-      "persistent_masked_m_silu_mul_quant(Tensor input, Tensor counts, Tensor! "
-      "y_q, Tensor! y_s,"
-      "bool use_ue8m0) -> ()");
-  ops.impl("persistent_masked_m_silu_mul_quant", torch::kCUDA,
-           &persistent_masked_m_silu_mul_quant);
-
-  ops.def("weak_ref_tensor(Tensor input) -> Tensor");
-  ops.impl("weak_ref_tensor", torch::kCUDA, &weak_ref_tensor);
 
 #ifdef USE_ROCM
   // TODO: Remove this once we upgrade to torch 2.11.
@@ -39,35 +28,6 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
   ops.def("get_cuda_view_from_cpu_tensor(Tensor cpu_tensor) -> Tensor");
   ops.impl("get_cuda_view_from_cpu_tensor", torch::kCPU,
            &get_cuda_view_from_cpu_tensor);
-#endif
-
-  // Activation ops (quantized only — basic ops moved to _C_stable_libtorch)
-  ops.def(
-      "silu_and_mul_quant(Tensor! result, Tensor input, Tensor scale) -> ()");
-  ops.impl("silu_and_mul_quant", torch::kCUDA, &silu_and_mul_quant);
-
-  // Horizontally-fused DeepseekV4-MLA: per-head RMSNorm + GPT-J RoPE for Q, and
-  // GPT-J RoPE + UE8M0 FP8 quant + paged cache insert for KV, all in one
-  // kernel launch. Registered in _C_stable_libtorch (incl. the FlashInfer V4
-  // full-cache bf16/fp8 variants).
-
-  // Quantization ops
-#ifndef USE_ROCM
-
-  // Note about marlin kernel 'workspace' arguments:
-  // Technically these should be mutable since they are modified by the kernel.
-  // But since they are set back to zero once the kernel is finished we can
-  // hand wave and say that they have no net effect.
-  //
-  // The reason to mark 'workspace' as immutable is so that they don't interfere
-  // with using ScalarType arguments in the ops. If they are marked as mutable,
-  // pytorch throws an assert in
-  // 'torch._higher_order_ops._register_effectful_op' that prevents these
-  // kernels from being torch.compile'd.
-  // See the following document for more info on custom types and ops that use
-  // custom types:
-  // https://docs.google.com/document/d/18fBMPuOJ0fY5ZQ6YyrHUppw9FA332CpNtgB6SOIgyuA
-
 #endif
 }
 

docker/Dockerfile.xpu

@@ -75,13 +75,13 @@ RUN wget -O- https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRO
 # Install UMD
 RUN mkdir neo && \
     cd neo && \
-    wget https://github.com/intel/intel-graphics-compiler/releases/download/v2.24.8/intel-igc-core-2_2.24.8+20344_amd64.deb && \
-    wget https://github.com/intel/intel-graphics-compiler/releases/download/v2.24.8/intel-igc-opencl-2_2.24.8+20344_amd64.deb && \
-    wget https://github.com/intel/compute-runtime/releases/download/25.48.36300.8/intel-ocloc_25.48.36300.8-0_amd64.deb && \
-    wget https://github.com/intel/compute-runtime/releases/download/25.48.36300.8/intel-opencl-icd_25.48.36300.8-0_amd64.deb && \
-    wget https://github.com/intel/compute-runtime/releases/download/25.48.36300.8/libigdgmm12_22.8.2_amd64.deb && \
-    wget https://github.com/intel/compute-runtime/releases/download/25.48.36300.8/libze-intel-gpu1_25.48.36300.8-0_amd64.deb && \
-    wget https://github.com/oneapi-src/level-zero/releases/download/v1.26.0/level-zero_1.26.0+u24.04_amd64.deb && \
+    wget https://github.com/intel/intel-graphics-compiler/releases/download/v2.34.4/intel-igc-core-2_2.34.4+21428_amd64.deb && \
+    wget https://github.com/intel/intel-graphics-compiler/releases/download/v2.34.4/intel-igc-opencl-2_2.34.4+21428_amd64.deb && \
+    wget https://github.com/intel/compute-runtime/releases/download/26.18.38308.1/intel-ocloc_26.18.38308.1-0_amd64.deb && \
+    wget https://github.com/intel/compute-runtime/releases/download/26.18.38308.1/intel-opencl-icd_26.18.38308.1-0_amd64.deb && \
+    wget https://github.com/intel/compute-runtime/releases/download/26.18.38308.1/libigdgmm12_22.10.0_amd64.deb && \
+    wget https://github.com/intel/compute-runtime/releases/download/26.18.38308.1/libze-intel-gpu1_26.18.38308.1-0_amd64.deb && \
+    wget https://github.com/oneapi-src/level-zero/releases/download/v1.28.2/level-zero_1.28.2+u24.04_amd64.deb && \
     dpkg -i *.deb && \
     cd .. && \
     rm -rf neo
@@ -132,7 +132,7 @@ CMD ["/bin/bash"]
 FROM vllm-base AS ucx-nixl-build
 
 ARG UCX_VERSION=v1.21.0-rc2
-ARG NIXL_VERSION=0.10.1
+ARG NIXL_VERSION=v1.2.0
 
 # Build-time only: compiler, autotools, and verbs dev headers
 RUN apt-get update -y && apt-get install -y --no-install-recommends \
@@ -149,25 +149,25 @@ RUN apt-get update -y && apt-get install -y --no-install-recommends \
 # patchelf (installed via uv) is used by the NIXL wheel build to rewrite
 # RPATH entries, making the wheel portable across stages.
 RUN --mount=type=cache,target=/root/.cache/uv \
-    git clone https://github.com/openucx/ucx /tmp/ucx_source && \
-    cd /tmp/ucx_source && git checkout "${UCX_VERSION}" && \
+    git clone --depth 1 --branch "${UCX_VERSION}" https://github.com/openucx/ucx /tmp/ucx_source && \
+    cd /tmp/ucx_source && \
     bash autogen.sh && \
     ./configure --prefix=/tmp/ucx_install --with-ze=yes --enable-examples --enable-mt && \
-    make CFLAGS="-Wno-error=incompatible-pointer-types" -j8 && make install && \
-    git clone https://github.com/ai-dynamo/nixl /tmp/nixl_source && \
-    cd /tmp/nixl_source && git checkout "${NIXL_VERSION}" && \
+    make CFLAGS="-Wno-error=incompatible-pointer-types" -j"$(nproc)" && make install && \
+    git clone --depth 1 --branch "${NIXL_VERSION}" https://github.com/ai-dynamo/nixl /tmp/nixl_source && \
+    cd /tmp/nixl_source && \
     uv pip install --upgrade meson pybind11 patchelf && \
     uv pip install -r requirements.txt && \
     PKG_CONFIG_PATH=/tmp/ucx_install/lib/pkgconfig \
     LD_LIBRARY_PATH=/tmp/ucx_install/lib \
     python -m pip wheel --no-deps . -w /tmp/nixl_wheels/ && \
     find /tmp/ucx_install -type f \( -name '*.a' -o -name '*.la' \) -delete && \
-    rm -rf /tmp/ucx_install/include /tmp/ucx_install/share /tmp/ucx_install/etc /tmp/ucx_install/lib/cmake /tmp/ucx_install/bin && \
-    rm -rf /tmp/ucx_source /tmp/nixl_source
+    rm -rf /tmp/ucx_install/{include,share,etc,bin} /tmp/ucx_install/lib/cmake \
+           /tmp/ucx_source /tmp/nixl_source
 
 FROM vllm-base AS vllm-openai
 
-ARG NIXL_VERSION=0.10.1
+ARG NIXL_VERSION=v1.2.0
 
 # Copy compiled UCX runtime libraries and the pre-built NIXL wheel.
 # No compiler or autotools are installed in this stage.
@@ -192,7 +192,7 @@ RUN --mount=type=cache,target=/root/.cache/uv \
     librdmacm1t64 \
     && rm -rf /var/lib/apt/lists/* \
     && uv pip install --no-deps /tmp/nixl_wheels/nixl*.whl \
-    && uv pip install nixl==${NIXL_VERSION} \
+    && uv pip install nixl==${NIXL_VERSION} && uv pip uninstall nixl-cu13 \
     && rm -rf /tmp/nixl_wheels
 
 RUN --mount=type=cache,target=/root/.cache/uv \

docs/contributing/README.md

@@ -304,9 +304,15 @@ review process:
   resources. The reviewer will add `ready` label to the PR when the PR is
   ready to merge or a full CI run is needed.
 
-### Escalating Stalled Contributions
+### Pull Request Limits and Escalation
 
-If you have an important contribution that has not yet received maintainer attention, please email us at:
+vLLM uses GitHub's [pull request limit](https://github.blog/open-source/maintainers/how-pull-request-limits-are-cutting-down-the-noise/)
+for contributors without write access. The current cap is 6 open PRs. If this
+blocks well-intentioned critical work, contact a committer to request bypass
+list access.
+
+If you need an expedited review for an important contribution, please email us
+at:
 
 <pr-review-request@vllm.ai>
 

docs/contributing/model/basic.md

@@ -133,10 +133,10 @@ The model should inherit protocol `IsAttentionFree` and also implement class met
 For the mamba layers themselves, please use the [`MambaMixer`](../../../vllm/model_executor/layers/mamba/mamba_mixer.py) (for Mamba-1) or [`MambaMixer2`](../../../vllm/model_executor/layers/mamba/mamba_mixer2.py) (for Mamba-2) classes.
 The model should also be added to the `MODELS_CONFIG_MAP` dictionary in [vllm/model_executor/models/config.py](../../../vllm/model_executor/models/config.py) to ensure that the runtime defaults are optimized.
 
-For case (2), we recommend using as a reference the implementation of [`JambaForCausalLM`](../../../vllm/model_executor/models/jamba.py) (for an example of a model that uses Mamba-1 and attention together) or [`BambaForCausalLM`](../../../vllm/model_executor/models/bamba.py) (for an example of a model that uses Mamba-2 and attention together).
+For case (2), we recommend using as a reference the implementation of [`JambaForCausalLM`](../../../vllm/model_executor/models/jamba.py) (for an example of a model that uses Mamba-1 and attention together) or [`NemotronHForCausalLM`](../../../vllm/model_executor/models/nemotron_h.py) (for an example of a model that uses Mamba-2 and attention together).
 These models should follow the same instructions as case (1), but they should inherit protocol `IsHybrid` (instead of `IsAttentionFree`) and it is *not* necessary to add them to the `MODELS_CONFIG_MAP` (their runtime defaults will be inferred from the protocol).
 
-For case (3), we recommend looking at the implementation of [`MiniMaxText01ForCausalLM`](../../../vllm/model_executor/models/minimax_text_01.py) or [`Lfm2ForCausalLM`](../../../vllm/model_executor/models/lfm2.py) as a reference, which use custom "mamba-like" layers `MiniMaxText01LinearAttention` and `ShortConv` respectively.
+For case (3), we recommend looking at the implementation of [`Lfm2ForCausalLM`](../../../vllm/model_executor/models/lfm2.py) as a reference, which uses a custom "mamba-like" layer `ShortConv`.
 Please follow the same guidelines as case (2) for implementing these models.
 We use "mamba-like" to refer to layers that possess a state that is updated in-place, rather than being appended-to (like KV cache for attention).
 For implementing new custom mamba-like layers, one should inherit from `MambaBase` and implement the methods `get_state_dtype`, `get_state_shape` to calculate the data types and state shapes at runtime, as well as `mamba_type` and `get_attn_backend`.
@@ -144,5 +144,5 @@ It is also necessary to implement the "attention meta-data" class which handles
 Please see [`LinearAttentionMetadata`](../../../vllm/v1/attention/backends/linear_attn.py) or [`ShortConvAttentionMetadata`](../../../vllm/v1/attention/backends/short_conv_attn.py) for examples of this.
 It is also worth noting that we should update `MambaAttentionBackendEnum` in [`registry.py`](../../../vllm/v1/attention/backends/registry.py) when adding a new mamba backend.
 Finally, if one wants to support torch compile and CUDA graphs, it necessary to wrap the call to the mamba-like layer inside a custom op and register it.
-Please see the calls to `direct_register_custom_op` in [vllm/model_executor/models/minimax_text_01.py](../../../vllm/model_executor/models/minimax_text_01.py) or [vllm/model_executor/layers/mamba/short_conv.py](../../../vllm/model_executor/layers/mamba/short_conv.py) for examples of this.
+Please see the calls to `direct_register_custom_op` in [vllm/model_executor/layers/mamba/linear/minimax_linear_attn.py](../../../vllm/model_executor/layers/mamba/linear/minimax_linear_attn.py) or [vllm/model_executor/layers/mamba/short_conv.py](../../../vllm/model_executor/layers/mamba/short_conv.py) for examples of this.
 The new custom op should then be added to the list `_attention_ops` in [vllm/config/compilation.py](../../../vllm/config/compilation.py) to ensure that piecewise CUDA graphs works as intended.

docs/deployment/integrations/production-stack.md

@@ -4,7 +4,7 @@ Deploying vLLM on Kubernetes is a scalable and efficient way to serve machine le
 
 * **Upstream vLLM compatibility** – It wraps around upstream vLLM without modifying its code.
 * **Ease of use** – Simplified deployment via Helm charts and observability through Grafana dashboards.
-* **High performance** – Optimized for LLM workloads with features like multimodel support, model-aware and prefix-aware routing, fast vLLM bootstrapping, and KV cache offloading with [LMCache](https://github.com/LMCache/LMCache), among others.
+* **High performance** – Optimized for LLM workloads with features like multimodel support, model-aware and prefix-aware routing, fast vLLM bootstrapping, and KV cache offloading with [LMCache](https://github.com/LMCache/LMCache) (wired up in vLLM via `--kv-offloading-backend lmcache`; see the [LMCache examples](https://github.com/vllm-project/vllm/tree/main/examples/disaggregated/lmcache) and [docs.lmcache.ai](https://docs.lmcache.ai)), among others.
 
 If you are new to Kubernetes, don't worry: in the vLLM production stack [repo](https://github.com/vllm-project/production-stack), we provide a step-by-step [guide](https://github.com/vllm-project/production-stack/blob/main/tutorials/00-install-kubernetes-env.md) and a [short video](https://www.youtube.com/watch?v=EsTJbQtzj0g) to set up everything and get started in **4 minutes**!
 

docs/design/attention_backends.md

@@ -170,8 +170,8 @@ Priority is **1 = highest** (tried first).
 | 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` | %16 | 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, 128, 256, 512, 1024 | 64, 128, 256, 512 | ❌ | ❌ | ❌ | ✅ | Decoder | 7.x-9.x |
-| `FLASHINFER` | TRTLLM† | fp16, bf16 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3`, `fp8_e5m2`, `nvfp4` | 16, 32, 64, 128, 256, 512, 1024 | 64, 128, 256, 512 | ✅ | ❌ | ❌ | ✅ | Decoder | 10.x |
+| `FLASHINFER` | Native† | fp16, bf16 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3`, `fp8_e5m2` | 16, 32, 64, 128, 256, 512, 1024 | 64, 128, 256, 512 | ❌ | ✅ | ❌ | ✅ | Decoder | 7.x-9.x |
+| `FLASHINFER` | TRTLLM† | fp16, bf16 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3`, `fp8_e5m2`, `nvfp4` | 16, 32, 64, 128, 256, 512, 1024 | 64, 128, 256, 512 | ✅ | ✅ | ❌ | ✅ | 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 |

docs/features/disagg_prefill.md

@@ -20,7 +20,7 @@ Two main reasons:
 Now supports 9 types of connectors:
 
 - **ExampleConnector**: refer to [examples/disaggregated/example_connector/run.sh](../../examples/disaggregated/example_connector/run.sh) for the example usage of ExampleConnector disaggregated prefilling.
-- **LMCacheConnectorV1**: refer to [examples/disaggregated/lmcache/disagg_prefill_lmcache_v1/disagg_example_nixl.sh](../../examples/disaggregated/lmcache/disagg_prefill_lmcache_v1/disagg_example_nixl.sh) for the example usage of LMCacheConnectorV1 disaggregated prefilling which uses NIXL as the underlying KV transmission.
+- **LMCacheConnectorV1**: refer to [examples/disaggregated/lmcache/disagg_prefill_lmcache_v1/disagg_example_nixl.sh](../../examples/disaggregated/lmcache/disagg_prefill_lmcache_v1/disagg_example_nixl.sh) for the example usage of LMCacheConnectorV1 disaggregated prefilling which uses NIXL as the underlying KV transmission. LMCache also offers a multi-process (MP) mode via `LMCacheMPConnector`, where a standalone `lmcache server` holds the KV cache shared by one or more vLLM instances; see the [LMCache examples](../../examples/disaggregated/lmcache/README.md) and the [LMCache docs](https://docs.lmcache.ai) for setup.
 - **NixlConnector**: refer to [tests/v1/kv_connector/nixl_integration/run_accuracy_test.sh](../../tests/v1/kv_connector/nixl_integration/run_accuracy_test.sh) for the example usage of NixlConnector disaggregated prefilling which support fully async send/recv. For detailed usage guide, see [NixlConnector Usage Guide](nixl_connector_usage.md). For feature compatibility details, see [NixlConnector Compatibility Matrix](nixl_connector_compatibility.md). You may specify one or multiple NIXL transfer backends, such as:
 
   ```bash

docs/features/kv_offloading_usage.md

@@ -74,6 +74,7 @@ vllm serve <model> \
 | `max_tracker_size` | no | `64000` | single-tier | Max entries in the lookup tracker. |
 | `secondary_tiers` | no | `[]` | multi-tier | List of secondary tier configs (see below). |
 | `offload_prompt_only` | no | `true` | both | If `true`, only prompt (prefill) blocks are offloaded; decode blocks are skipped. |
+| `self_describing_kv_events` | no | `false` | single-tier | Opt-in. When `true` *and* KV cache events are enabled (`--kv-events-config` with `enable_kv_cache_events`), the connector emits self-describing block-granular `BlockStored`/`BlockRemoved` payloads (constituent block hashes, whole-chunk `token_ids`, per-block `block_size`, parent hash, LoRA + group/cache-spec metadata) instead of the placeholder fallback, so external KV-event consumers can index offloaded blocks. Inert unless events are enabled. Currently rejected by `TieringOffloadingSpec`. Full-attention groups only; sliding-window/SSM groups keep the placeholder fallback. In chunk mode (`block_size` > GPU block size), overlapping chunks re-announce shared per-block hashes, so consumers must reference-count (deduplicate) repeated store/remove announcements. |
 | `spec_module_path` | no | — | both | Python import path for a custom `OffloadingSpec` not in the built-in registry. Required only when `spec_name` is not built-in (advanced). |
 
 ## Secondary Tiers

docs/features/mooncake_store_connector_usage.md

@@ -203,6 +203,7 @@ the vLLM JSON config.
 ### kv_connector_extra_config
 
 - `load_async` (bool): Enable asynchronous loading for better compute-I/O overlap. Default: `true`.
+- `lookup_async` (bool): Run the external prefix-cache lookup on a background thread so it never blocks the scheduler step. The request is held until the in-flight lookup completes, then resumed on a later step. Default: `false`.
 - `enable_cross_layers_blocks` (bool): Enable cross-layer block packing for reduced store operations. Default: `false`.
 - `lookup_rpc_port` (int): Custom port for the ZMQ lookup RPC socket. Default: `0`.
 - `cache_prefix` (str): Namespace prepended to every store key. Lets separate deployments share one Mooncake master without polluting each other — instances configured with different prefixes never see each other's cached blocks, even for identical prompts. All instances that should share a prefix cache must use the same value. Default: `""` (no prefix; keys are byte-identical to the unprefixed format).

docs/features/tool_calling.md

@@ -321,15 +321,6 @@ For Qwen2.5, the chat template in tokenizer_config.json has already included sup
 
 Flags: `--tool-call-parser hermes`
 
-### MiniMax Models (`minimax_m1`)
-
-Supported models:
-
-* `MiniMaxAi/MiniMax-M1-40k` (use with [examples/tool_chat_template_minimax_m1.jinja](../../examples/tool_chat_template_minimax_m1.jinja))
-* `MiniMaxAi/MiniMax-M1-80k` (use with [examples/tool_chat_template_minimax_m1.jinja](../../examples/tool_chat_template_minimax_m1.jinja))
-
-Flags: `--tool-call-parser minimax --chat-template examples/tool_chat_template_minimax_m1.jinja`
-
 ### DeepSeek-V3 Models (`deepseek_v3`)
 
 Supported models:

docs/getting_started/installation/gpu.xpu.inc.md

@@ -27,6 +27,7 @@ Currently, there are no pre-built XPU wheels.
 
 - First, install required [driver](https://dgpu-docs.intel.com/driver/installation.html#installing-gpu-drivers).
 - Second, install Python packages for vLLM XPU backend building (Intel OneAPI dependencies are installed automatically as part of `torch-xpu`, see [PyTorch XPU get started](https://docs.pytorch.org/docs/stable/notes/get_start_xpu.html)):
+- Start from vllm-xpu-kernels v0.1.10, we recommend user upgrade driver to [compute runtime 26.18](https://github.com/intel/compute-runtime/releases/tag/26.14.37833.4) release, to avoid potential compatibility issue.
 
 ```bash
 git clone https://github.com/vllm-project/vllm.git

docs/models/pooling_models/token_embed.md

@@ -61,7 +61,7 @@ Models of any architecture can be converted into embedding models using `--conve
 | `ColModernVBertForRetrieval` | ColModernVBERT | T / I | `ModernVBERT/colmodernvbert-merged` | | |
 | `ColPaliForRetrieval` | ColPali | T / I | `vidore/colpali-v1.3-hf` | | |
 | `ColQwen3` | Qwen3-VL | T / I | `TomoroAI/tomoro-colqwen3-embed-4b`, `TomoroAI/tomoro-colqwen3-embed-8b` | | |
-| `ColQwen3_5` | ColQwen3.5 | T + I + V | `athrael-soju/colqwen3.5-4.5B-v3` | | |
+| `ColQwen3_5` | ColQwen3.5 | T + I + V | `athrael-soju/colqwen3.5-4.5B-v3`, `vultr/VultronRetrieverPrime-Qwen3.5-8B` | | |
 | `OpsColQwen3Model` | Qwen3-VL | T / I | `OpenSearch-AI/Ops-Colqwen3-4B`, `OpenSearch-AI/Ops-Colqwen3-8B` | | |
 | `Qwen3VLNemotronEmbedModel` | Qwen3-VL | T / I | `nvidia/nemotron-colembed-vl-4b-v2`, `nvidia/nemotron-colembed-vl-8b-v2` | ✅︎ | ✅︎ |
 | `*ForConditionalGeneration`<sup>C</sup>, `*ForCausalLM`<sup>C</sup>, etc. | Generative models | \* | N/A | \* | \* |

docs/models/supported_models.md

@@ -441,7 +441,6 @@ th {
 | `MiMoV2ForCausalLM` | MiMoV2Pro | `XiaomiMiMo/MiMo-V2.5-Pro`, etc. | | ✅︎ |
 | `MiniCPMForCausalLM` | MiniCPM | `openbmb/MiniCPM-2B-sft-bf16`, `openbmb/MiniCPM-2B-dpo-bf16`, `openbmb/MiniCPM-S-1B-sft`, etc. | ✅︎ | ✅︎ |
 | `MiniCPM3ForCausalLM` | MiniCPM3 | `openbmb/MiniCPM3-4B`, etc. | ✅︎ | ✅︎ |
-| `MiniMaxForCausalLM` | MiniMax-Text | `MiniMaxAI/MiniMax-Text-01-hf`, etc. | | |
 | `MiniMaxM2ForCausalLM` | MiniMax-M2, MiniMax-M2.1 | `MiniMaxAI/MiniMax-M2`, etc. | ✅︎ | ✅︎ |
 | `MistralForCausalLM` | Ministral-3, Mistral, Mistral-Instruct | `mistralai/Ministral-3-3B-Instruct-2512`, `mistralai/Mistral-7B-v0.1`, `mistralai/Mistral-7B-Instruct-v0.1`, etc. | ✅︎ | ✅︎ |
 | `MistralLarge3ForCausalLM` | Mistral-Large-3-675B-Base-2512, Mistral-Large-3-675B-Instruct-2512 | `mistralai/Mistral-Large-3-675B-Base-2512`, `mistralai/Mistral-Large-3-675B-Instruct-2512`, etc. | ✅︎ | ✅︎ |
@@ -487,8 +486,6 @@ th {
 | `TeleChat2ForCausalLM` | TeleChat2 | `Tele-AI/TeleChat2-3B`, `Tele-AI/TeleChat2-7B`, `Tele-AI/TeleChat2-35B`, etc. | ✅︎ | ✅︎ |
 | `TeleChat3ForCausalLM` | TeleChat3 | `Tele-AI/TeleChat3-36B-Thinking`, `Tele-AI/TeleChat3-Coder-36B-Thinking`, etc. | ✅︎ | ✅︎ |
 | `TeleFLMForCausalLM` | TeleFLM | `CofeAI/FLM-2-52B-Instruct-2407`, `CofeAI/Tele-FLM`, etc. | ✅︎ | ✅︎ |
-| `MiniMaxM1ForCausalLM` | MiniMax-Text | `MiniMaxAI/MiniMax-M1-40k`, `MiniMaxAI/MiniMax-M1-80k`, etc. | | |
-| `MiniMaxText01ForCausalLM` | MiniMax-Text | `MiniMaxAI/MiniMax-Text-01`, etc. | | |
 | `Zamba2ForCausalLM` | Zamba2 | `Zyphra/Zamba2-7B-instruct`, `Zyphra/Zamba2-2.7B-instruct`, `Zyphra/Zamba2-1.2B-instruct`, etc. | | |
 
 !!! note
@@ -595,6 +592,7 @@ These models primarily accept the [`LLM.generate`](./generative_models.md#llmgen
 | `MiMoV2OmniForCausalLM` | MiMo-V2.5-Omni | T + I<sup>E+</sup> + V<sup>E+</sup> + A<sup>+</sup> | `XiaomiMiMo/MiMo-V2.5-Omni` | | ✅︎ |
 | `MiniCPMO` | MiniCPM-O | T + I<sup>E+</sup> + V<sup>E+</sup> + A<sup>E+</sup> | `openbmb/MiniCPM-o-2_6`, etc. | ✅︎ | ✅︎ |
 | `MiniCPMV` | MiniCPM-V | T + I<sup>E+</sup> + V<sup>E+</sup> | `openbmb/MiniCPM-V-2` (see note), `openbmb/MiniCPM-Llama3-V-2_5`, `openbmb/MiniCPM-V-2_6`, `openbmb/MiniCPM-V-4`, `openbmb/MiniCPM-V-4_5`, etc. | ✅︎ | |
+| `MiniMaxM3SparseForConditionalGeneration` | MiniMax-M3 | T + I<sup>+</sup> + V<sup>+</sup> | `MiniMaxAI/MiniMax-M3`, `MiniMaxAI/MiniMax-M3-MXFP8`, etc. | | |
 | `MiniMaxVL01ForConditionalGeneration` | MiniMax-VL | T + I<sup>E+</sup> | `MiniMaxAI/MiniMax-VL-01`, etc. | | ✅︎ |
 | `Mistral3ForConditionalGeneration` | Mistral3 (HF Transformers) | T + I<sup>+</sup> | `mistralai/Mistral-Small-3.1-24B-Instruct-2503`, etc. | ✅︎ | ✅︎ |
 | `MolmoForCausalLM` | Molmo | T + I<sup>+</sup> | `allenai/Molmo-7B-D-0924`, `allenai/Molmo-7B-O-0924`, etc. | ✅︎ | ✅︎ |

docs/usage/v1_guide.md

@@ -128,7 +128,7 @@ Models that use Mamba-2 and Mamba-1 layers (e.g., `Mamba2ForCausalLM`, `MambaFor
 Hybrid models that combine Mamba-2 and Mamba-1 layers with standard attention layers are also supported (e.g., `BambaForCausalLM`,
 `Zamba2ForCausalLM`, `NemotronHForCausalLM`, `FalconH1ForCausalLM` and `GraniteMoeHybridForCausalLM`, `JambaForCausalLM`, `Plamo2ForCausalLM`).
 
-Hybrid models with mechanisms different to Mamba are also supported (e.g, `MiniMaxText01ForCausalLM`, `MiniMaxM1ForCausalLM`, `Lfm2ForCausalLM`).
+Hybrid models with mechanisms different to Mamba are also supported (e.g, `Lfm2ForCausalLM`).
 
 Please note that prefix caching is not yet supported for any of the above models.
 

examples/disaggregated/lmcache/README.md

@@ -1,10 +1,38 @@
 # LMCache Examples
 
-This folder demonstrates how to use LMCache for disaggregated prefilling, CPU offloading and KV cache sharing.
+This folder demonstrates how to use LMCache with vLLM v1 for KV cache
+offloading, disaggregated prefilling, and KV cache sharing.
 
-## 1. Disaggregated Prefill in vLLM v1
+## Integration modes
 
-This example demonstrates how to run LMCache with disaggregated prefill using NIXL on a single node.
+LMCache integrates with vLLM v1 in two ways:
+
+- **In-process mode** (`LMCacheConnectorV1`): LMCache runs inside the vLLM
+  process and is configured through environment variables or a YAML config
+  file (`LMCACHE_CONFIG_FILE`). This is the simplest way to add single-node
+  CPU/disk offloading.
+- **Multi-process (MP) mode** (`LMCacheMPConnector`): LMCache runs as a
+  standalone server (`lmcache server`) that owns the KV cache storage; one or
+  more vLLM instances connect to it. This is the recommended mode for
+  distributed KV storage and for sharing KV cache across instances. See the
+  [LMCache docs](https://docs.lmcache.ai) for the full MP setup.
+
+## 1. CPU offload (in-process)
+
+- `python cpu_offload_lmcache.py` - CPU offloading with `LMCacheConnectorV1`
+  for vLLM v1.
+
+## 2. CPU offload (multi-process)
+
+- `bash cpu_offload_lmcache_mp.sh` - CPU offloading with `LMCacheMPConnector`,
+  using a standalone `lmcache server`. vLLM provides a built-in shortcut for
+  this setup via `--kv-offloading-backend lmcache` and
+  `--kv-offloading-size <GiB>`.
+
+## 3. Disaggregated Prefill in vLLM v1
+
+This example demonstrates how to run LMCache with disaggregated prefill using
+NIXL on a single node.
 
 ### Prerequisites
 
@@ -46,15 +74,7 @@ The main script generates several log files:
 - `decoder.log` - Logs from the decode server
 - `proxy.log` - Logs from the proxy server
 
-## 2. CPU Offload Examples
+## 4. KV Cache Sharing
 
-- `python cpu_offload_lmcache.py -v v0` - CPU offloading implementation for vLLM v0
-- `python cpu_offload_lmcache.py -v v1` - CPU offloading implementation for vLLM v1
-
-## 3. KV Cache Sharing
-
-The `kv_cache_sharing_lmcache_v1.py` example demonstrates how to share KV caches between vLLM v1 instances.
-
-## 4. Disaggregated Prefill in vLLM v0
-
-The `disaggregated_prefill_lmcache_v0.py` provides an example of how to run disaggregated prefill in vLLM v0.
+The `kv_cache_sharing_lmcache_v1.py` example demonstrates how to share KV
+caches between vLLM v1 instances through a centralized LMCache server.

examples/disaggregated/lmcache/cpu_offload_lmcache.py

@@ -1,20 +1,8 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 """
-This file demonstrates the example usage of cpu offloading
-with LMCache in vLLM v1 or v0.
-
-Usage:
-
-    Specify vLLM version
-
-    -v v0 : Use LMCacheConnector
-            model = mistralai/Mistral-7B-Instruct-v0.2
-            (Includes enable_chunked_prefill = True)
-
-    -v v1 : Use LMCacheConnectorV1 (default)
-            model = meta-llama/Meta-Llama-3.1-8B-Instruct
-            (Without enable_chunked_prefill)
+This file demonstrates the example usage of CPU offloading
+with LMCache in vLLM v1.
 
 Note that `lmcache` is needed to run this example.
 Requirements:
@@ -23,7 +11,6 @@ Learn more about LMCache environment setup, please refer to:
 https://docs.lmcache.ai/getting_started/installation.html
 """
 
-import argparse
 import contextlib
 import os
 import time
@@ -39,8 +26,6 @@ from vllm.engine.arg_utils import EngineArgs
 
 def setup_environment_variables():
     # LMCache-related environment variables
-    # Use experimental features in LMCache
-    os.environ["LMCACHE_USE_EXPERIMENTAL"] = "True"
     # LMCache is set to use 256 tokens per chunk
     os.environ["LMCACHE_CHUNK_SIZE"] = "256"
     # Enable local CPU backend in LMCache
@@ -50,9 +35,9 @@ def setup_environment_variables():
 
 
 @contextlib.contextmanager
-def build_llm_with_lmcache(lmcache_connector: str, model: str):
+def build_llm_with_lmcache(model: str):
     ktc = KVTransferConfig(
-        kv_connector=lmcache_connector,
+        kv_connector="LMCacheConnectorV1",
         kv_role="kv_both",
     )
     # Set GPU memory utilization to 0.8 for an A40 GPU with 40GB
@@ -92,23 +77,10 @@ def print_output(
     print("-" * 50)
 
 
-def parse_args():
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "-v",
-        "--version",
-        choices=["v0", "v1"],
-        default="v1",
-        help="Specify vLLM version (default: v1)",
-    )
-    return parser.parse_args()
-
-
 def main():
-    lmcache_connector = "LMCacheConnectorV1"
     model = "meta-llama/Meta-Llama-3.1-8B-Instruct"
     setup_environment_variables()
-    with build_llm_with_lmcache(lmcache_connector, model) as llm:
+    with build_llm_with_lmcache(model) as llm:
         # This example script runs two requests with a shared prefix.
         # Define the shared prompt and specific prompts
         shared_prompt = "Hello, how are you?" * 1000

examples/disaggregated/lmcache/cpu_offload_lmcache_mp.sh

@@ -0,0 +1,43 @@
+#!/bin/bash
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+#
+# CPU offloading with LMCache in multi-process (MP) mode.
+#
+# In MP mode, LMCache runs as a standalone server process (`lmcache server`)
+# that owns the KV cache storage. One or more vLLM instances connect to it via
+# the `LMCacheMPConnector`. This is the recommended way to run LMCache for
+# distributed KV storage and for sharing KV cache across vLLM instances.
+#
+# vLLM ships a built-in shortcut for this setup: pass `--kv-offloading-backend
+# lmcache` together with `--kv-offloading-size <GiB>` and vLLM wires up the
+# `LMCacheMPConnector` for you (it defaults to the LMCache server at
+# tcp://localhost:5555, matching the `lmcache server` default).
+#
+# Requires `lmcache` to be installed (`pip install lmcache`).
+# Learn more: https://docs.lmcache.ai
+set -euo pipefail
+
+MODEL=${MODEL:-meta-llama/Meta-Llama-3.1-8B-Instruct}
+
+# 1. Launch the standalone LMCache server (binds tcp://localhost:5555 by
+#    default). `--l1-size-gb` sets the CPU memory budget for the L1 cache.
+echo "Starting LMCache server..."
+lmcache server --host localhost --port 5555 --l1-size-gb 5 &
+LMCACHE_SERVER_PID=$!
+trap 'kill $LMCACHE_SERVER_PID 2>/dev/null || true' EXIT
+
+# 2. Launch vLLM and offload KV cache to the LMCache server.
+#    The MP connector currently requires the non-hybrid KV cache manager.
+echo "Starting vLLM server with LMCache MP offloading..."
+vllm serve "$MODEL" \
+    --port 8000 \
+    --kv-offloading-size 5 \
+    --kv-offloading-backend lmcache \
+    --disable-hybrid-kv-cache-manager
+
+# Equivalent explicit configuration (instead of the two flags above):
+#   --kv-transfer-config \
+#   '{"kv_connector":"LMCacheMPConnector","kv_role":"kv_both",
+#     "kv_connector_extra_config":{"lmcache.mp.host":"tcp://localhost",
+#                                  "lmcache.mp.port":5555}}'

examples/disaggregated/lmcache/disagg_prefill_lmcache_v0.py

@@ -1,144 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""
-This file demonstrates the example usage of disaggregated prefilling
-with LMCache.
-We will launch 2 vllm instances (GPU 0 for prefill and GPU 1 for decode),
-and launch an additional LMCache server.
-KV cache is transferred in the following manner:
-vLLM prefill node -> LMCache server -> vLLM decode node.
-
-Note that `pip install lmcache` is needed to run this example.
-Learn more about LMCache in https://github.com/LMCache/LMCache.
-"""
-
-import os
-import subprocess
-import time
-from multiprocessing import Event, Process
-
-from lmcache.experimental.cache_engine import LMCacheEngineBuilder
-from lmcache.integration.vllm.utils import ENGINE_NAME
-
-from vllm import LLM, SamplingParams
-from vllm.config import KVTransferConfig
-
-# LMCache-related environment variables
-# The port to start LMCache server
-port = 8100
-# Use experimental features in LMCache
-os.environ["LMCACHE_USE_EXPERIMENTAL"] = "True"
-# LMCache is set to use 256 tokens per chunk
-os.environ["LMCACHE_CHUNK_SIZE"] = "256"
-# Disable local CPU backend in LMCache
-os.environ["LMCACHE_LOCAL_CPU"] = "False"
-# Set local CPU memory buffer limit to 5.0 GB
-os.environ["LMCACHE_MAX_LOCAL_CPU_SIZE"] = "5.0"
-# Set the remote URL for LMCache server
-os.environ["LMCACHE_REMOTE_URL"] = f"lm://localhost:{port}"
-# Set the serializer/deserializer between vllm and LMCache server
-# `naive` indicates using raw bytes of the tensor without any compression
-os.environ["LMCACHE_REMOTE_SERDE"] = "naive"
-
-prompts = [
-    "Hello, how are you?" * 1000,
-]
-
-
-def run_prefill(prefill_done, prompts):
-    # We use GPU 0 for prefill node.
-    os.environ["CUDA_VISIBLE_DEVICES"] = "0"
-
-    sampling_params = SamplingParams(temperature=0, top_p=0.95, max_tokens=1)
-
-    ktc = KVTransferConfig(
-        kv_connector="LMCacheConnector",
-        kv_role="kv_producer",
-        kv_rank=0,
-        kv_parallel_size=2,
-    )
-    # Set GPU memory utilization to 0.8 for an A40 GPU with 40GB
-    # memory. Reduce the value if your GPU has less memory.
-    llm = LLM(
-        model="mistralai/Mistral-7B-Instruct-v0.2",
-        kv_transfer_config=ktc,
-        max_model_len=8000,
-        gpu_memory_utilization=0.8,
-        enforce_eager=True,
-    )
-
-    # llm.generate(prompts, sampling_params)
-    outputs = llm.generate(prompts, sampling_params)
-    for output in outputs:
-        generated_text = output.outputs[0].text
-        print(f"Generated text: {generated_text!r}")
-    print("Prefill node is finished.")
-    prefill_done.set()
-
-    # Clean up lmcache backend
-    LMCacheEngineBuilder.destroy(ENGINE_NAME)
-
-
-def run_decode(prefill_done, prompts, timeout=1):
-    # We use GPU 1 for decode node.
-    os.environ["CUDA_VISIBLE_DEVICES"] = "1"
-
-    sampling_params = SamplingParams(temperature=0, top_p=0.95, max_tokens=10)
-
-    ktc = KVTransferConfig(
-        kv_connector="LMCacheConnector",
-        kv_role="kv_consumer",
-        kv_rank=1,
-        kv_parallel_size=2,
-    )
-    # Set GPU memory utilization to 0.8 for an A40 GPU with 40GB
-    # of memory. Reduce the value if your GPU has less memory.
-    llm = LLM(
-        model="mistralai/Mistral-7B-Instruct-v0.2",
-        kv_transfer_config=ktc,
-        max_model_len=8000,
-        gpu_memory_utilization=0.8,
-        enforce_eager=True,
-    )
-
-    print("Waiting for prefill node to finish...")
-    prefill_done.wait()
-    time.sleep(timeout)
-
-    outputs = llm.generate(prompts, sampling_params)
-    for output in outputs:
-        generated_text = output.outputs[0].text
-        print(f"Generated text: {generated_text!r}")
-
-    # Clean up lmcache backend
-    LMCacheEngineBuilder.destroy(ENGINE_NAME)
-
-
-def run_lmcache_server(port):
-    server_proc = subprocess.Popen(
-        ["python", "-m", "lmcache.experimental.server", "localhost", str(port)]
-    )
-    return server_proc
-
-
-def main():
-    prefill_done = Event()
-    prefill_process = Process(target=run_prefill, args=(prefill_done, prompts))
-    decode_process = Process(target=run_decode, args=(prefill_done, prompts))
-    lmcache_server_process = run_lmcache_server(port)
-
-    # Start prefill node
-    prefill_process.start()
-
-    # Start decode node
-    decode_process.start()
-
-    # Clean up the processes
-    decode_process.join()
-    prefill_process.terminate()
-    lmcache_server_process.terminate()
-    lmcache_server_process.wait()
-
-
-if __name__ == "__main__":
-    main()

examples/disaggregated/lmcache/disagg_prefill_lmcache_v1/disagg_vllm_launcher.sh

@@ -30,7 +30,6 @@ if [[ $1 == "prefiller" ]]; then
 
     UCX_TLS=cuda_ipc,cuda_copy,tcp \
         LMCACHE_CONFIG_FILE=$prefill_config_file \
-        LMCACHE_USE_EXPERIMENTAL=True \
         VLLM_ENABLE_V1_MULTIPROCESSING=1 \
         VLLM_WORKER_MULTIPROC_METHOD=spawn \
         CUDA_VISIBLE_DEVICES=0 \
@@ -47,7 +46,6 @@ elif [[ $1 == "decoder" ]]; then
 
     UCX_TLS=cuda_ipc,cuda_copy,tcp \
         LMCACHE_CONFIG_FILE=$decode_config_file \
-        LMCACHE_USE_EXPERIMENTAL=True \
         VLLM_ENABLE_V1_MULTIPROCESSING=1 \
         VLLM_WORKER_MULTIPROC_METHOD=spawn \
         CUDA_VISIBLE_DEVICES=1 \

examples/disaggregated/lmcache/kv_cache_sharing_lmcache_v1.py

@@ -26,8 +26,6 @@ from vllm.config import KVTransferConfig
 # LMCache-related environment variables
 # The port to start LMCache server
 port = 8100
-# Use experimental features in LMCache
-os.environ["LMCACHE_USE_EXPERIMENTAL"] = "True"
 # LMCache is set to use 256 tokens per chunk
 os.environ["LMCACHE_CHUNK_SIZE"] = "256"
 # Disable local CPU backend in LMCache

examples/generate/multimodal/vision_language_offline.py

@@ -1481,39 +1481,6 @@ def run_minicpmv(questions: list[str], modality: str) -> ModelRequestData:
     return run_minicpmv_base(questions, modality, "openbmb/MiniCPM-V-2_6")
 
 
-def run_minimax_vl_01(questions: list[str], modality: str) -> ModelRequestData:
-    assert modality == "image"
-
-    model_name = "MiniMaxAI/MiniMax-VL-01"
-
-    engine_args = EngineArgs(
-        model=model_name,
-        max_num_seqs=2,
-        limit_mm_per_prompt={modality: 1},
-        trust_remote_code=True,
-        tensor_parallel_size=8,
-    )
-
-    tokenizer = AutoTokenizer.from_pretrained(model_name)
-    messages = [
-        [
-            {
-                "role": "user",
-                "content": [{"type": "image"}, {"type": "text", "text": question}],
-            }
-        ]
-        for question in questions
-    ]
-    prompts = tokenizer.apply_chat_template(
-        messages, add_generation_prompt=True, tokenize=False
-    )
-
-    return ModelRequestData(
-        engine_args=engine_args,
-        prompts=prompts,
-    )
-
-
 # Mistral-3 HF-format
 def run_mistral3(questions: list[str], modality: str) -> ModelRequestData:
     assert modality == "image"
@@ -2485,7 +2452,6 @@ model_example_map = {
     "mantis": run_mantis,
     "minicpmo": run_minicpmo,
     "minicpmv": run_minicpmv,
-    "minimax_vl_01": run_minimax_vl_01,
     "mistral3": run_mistral3,
     "molmo": run_molmo,
     "molmo2": run_molmo2,

examples/pooling/score/colqwen3_5_rerank_online.py

@@ -7,11 +7,27 @@ ColQwen3.5 is a multi-modal ColBERT-style model based on Qwen3.5.
 It produces per-token embeddings and uses MaxSim scoring for retrieval
 and reranking. Supports both text and image inputs.
 
+Works for any ColQwen3.5 checkpoint, e.g. `athrael-soju/colqwen3.5-4.5B-v3`
+or `vultr/VultronRetrieverPrime-Qwen3.5-8B`.
+
 Start the server with:
-    vllm serve athrael-soju/colqwen3.5-4.5B --max-model-len 4096
+    vllm serve athrael-soju/colqwen3.5-4.5B-v3 --max-model-len 4096 \
+        --mm-processor-kwargs '{"min_pixels": 65536, "max_pixels": 1835008}'
 
 Then run this script:
     python colqwen3_5_rerank_online.py
+
+Parity note (matching the native colpali ColQwen3_5Processor pipeline):
+  - Visual-token budget: ColQwen3_5Processor uses max_num_visual_tokens=1792,
+    i.e. max_pixels = 1792 * (patch_size*merge_size)^2 = 1792 * 32^2 = 1835008
+    (with min_pixels = shortest_edge = 65536). Pass these via --mm-processor-kwargs
+    as above; the default budget gives fewer visual tokens and lower retrieval ndcg.
+  - When you build prompts yourself (token_embed), reproduce the processor exactly:
+      image (document): wrap in the instruction template
+        "<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>"
+        "Describe the image.<|im_end|><|endoftext|>"
+      query: append the augmentation suffix  <text> + "<|endoftext|>" * 10
+    Omitting these reproduces a silent ~2.5 ndcg@10 drop vs the native pipeline.
 """
 
 import requests

examples/tool_chat_template_minimax_m1.jinja

@@ -1,91 +0,0 @@
-{{ '<begin_of_document>' -}}
-{%- if custom_tools is defined %}
-    {%- set tools = custom_tools %}
-{%- endif %}
-{%- if not tools is defined %}
-    {%- set tools = none %}
-{%- endif %}
-
-{#- Extract system message #}
-{% set ns = namespace(system_prompt='') -%}
-{%- if messages[0]['role'] == 'system' %}
-    {%- if messages[0]['content'] is string %}
-        {%- set ns.system_prompt = messages[0]['content']|trim %}
-    {%- else %}
-        {%- set ns.system_prompt = messages[0]['content'][0]['text']|trim %}
-    {%- endif %}
-    {%- set messages = messages[1:] %}
-{%- else %}
-    {%- if tools is not none %}
-        {%- set ns.system_prompt = "You are a helpful assistant created by Minimax based on MiniMax-M1 model." %}
-    {%- else %}
-        {%- set ns.system_prompt = "You are a helpful assistant created by Minimax based on MiniMax-M1 model." %}
-    {%- endif %}
-{%- endif %}
-
-{#- System message #}
-{%- if ns.system_prompt != '' %}
-{{ '<beginning_of_sentence>system ai_setting=assistant\n' + ns.system_prompt + '<end_of_sentence>\n' -}}
-{%- endif %}
-
-{#- Tools configuration #}
-{%- if tools is not none %}
-{{ '<beginning_of_sentence>system tool_setting=tools\nYou are provided with these tools:\n<tools>\n' -}}
-{%- for tool in tools %}
-{{ tool | tojson ~ '\n' -}}
-{%- endfor %}
-{{ '</tools>\n\nIf you need to call tools, please respond with <tool_calls></tool_calls> XML tags, and provide tool-name and json-object of arguments, following the format below:\n<tool_calls>\n{"name": <tool-name>, "arguments": <args-json-object>}\n...\n</tool_calls><end_of_sentence>\n' -}}
-{%- endif %}
-
-{#- Process messages #}
-{%- for message in messages %}
-    {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}
-        {%- if message['role'] == 'user' %}
-{{ '<beginning_of_sentence>user name=user\n' -}}
-{%- if message['content'] is string %}
-{{ message['content']|trim -}}
-{%- else %}
-{%- for content in message['content'] %}
-{%- if content['type'] == 'text' %}
-{{ content['text']|trim -}}
-{%- endif %}
-{%- endfor %}
-{%- endif %}
-{{ '<end_of_sentence>\n' -}}
-        {%- elif message['role'] == 'assistant' %}
-{{ '<beginning_of_sentence>ai name=assistant\n' -}}
-{%- if message['content'] is string %}
-{{ message['content']|trim -}}
-{%- else %}
-{%- for content in message['content'] | selectattr('type', 'equalto', 'text') %}
-{{ content['text']|trim -}}
-{%- endfor %}
-{%- endif %}
-{{ '<end_of_sentence>\n' -}}
-        {%- endif %}
-    {%- elif 'tool_calls' in message %}
-{{ '<beginning_of_sentence>ai name=assistant\n<tool_calls>\n' -}}
-{%- for tool_call in message.tool_calls %}
-{{ '{"name": "' + tool_call.function.name + '", "arguments": ' + tool_call.function.arguments | tojson + '}\n' -}}
-{%- endfor %}
-{{ '</tool_calls><end_of_sentence>\n' -}}
-    {%- elif message.role == "tool" or message.role == "ipython" %}
-{{ '<beginning_of_sentence>tool name=tools\n' -}}
-{%- if message.content is string %}
-{{ 'tool result: ' + message.content + '\n\n' -}}
-{%- else %}
-{%- for content in message['content'] %}
-{%- if content['type'] == 'text' %}
-{{ 'tool result: ' + content['text'] + '\n\n' -}}
-{%- elif content.get('name') %}
-{{ 'tool name: ' + content['name'] + '\ntool result: ' + content['text'] + '\n\n' -}}
-{%- endif %}
-{%- endfor %}
-{%- endif %}
-{{ '<end_of_sentence>\n' -}}
-    {%- endif %}
-{%- endfor %}
-
-{%- if add_generation_prompt %}
-{{ '<beginning_of_sentence>ai name=assistant\n' -}}
-{%- endif %}

requirements/common.txt

@@ -11,13 +11,14 @@ transformers >= 5.5.3
 tokenizers >= 0.21.1  # Required for fast incremental detokenization.
 safetensors >= 0.6.2  # MXFP4/MXFP6 dtype support (F8_E8M0, F4) added in 0.6.0: https://github.com/huggingface/safetensors/pull/611
 protobuf >= 5.29.6, !=6.30.*, !=6.31.*, !=6.32.*, !=6.33.0.*, !=6.33.1.*, !=6.33.2.*, !=6.33.3.*, !=6.33.4.* # Required by LlamaTokenizer, gRPC. CVE-2026-0994
-fastapi[standard] >= 0.115.0 # Required by FastAPI's form models in the OpenAI API server's audio transcriptions endpoint.
+fastapi[standard] >= 0.133.0, < 0.137.0 # First version supporting Starlette 1.0; < 0.137.0 avoids route-tree change that breaks model-hosting-container-standards handler overrides.
+starlette >= 1.0.1 # CVE-2026-48710: Host header injection in < 1.0.1
 aiohttp >= 3.13.3
 openai >= 2.0.0  # For Responses API with reasoning content
 pydantic >= 2.12.0
 prometheus_client >= 0.18.0
 pillow  # Required for image processing
-prometheus-fastapi-instrumentator >= 7.0.0
+prometheus-fastapi-instrumentator >= 8.0.0 # v8 unblocks starlette >= 1.0
 tiktoken >= 0.6.0  # Required for DBRX tokenizer
 lm-format-enforcer == 0.11.3
 llguidance >= 1.7.0, < 1.8.0; platform_machine == "x86_64" or platform_machine == "arm64" or platform_machine == "aarch64" or platform_machine == "ppc64le"

requirements/test/cuda.in

@@ -40,7 +40,7 @@ lm-eval[api]>=0.4.12 # required for model evaluation test
 mteb[bm25s]>=2, <3 # required for mteb test
 transformers==5.5.3
 tokenizers==0.22.2
-schemathesis>=3.39.15 # Required for openai schema test.
+schemathesis>=4.0.0 # Required for openai schema test.
 # quantization
 bitsandbytes==0.49.2
 buildkite-test-collector==0.1.9

requirements/test/cuda.txt

@@ -35,14 +35,11 @@ arctic-inference==0.1.1
     # via -r requirements/test/cuda.in
 argcomplete==3.5.1
     # via datamodel-code-generator
-arrow==1.3.0
-    # via isoduration
 attrs==24.2.0
     # via
     #   aiohttp
     #   hypothesis
     #   jsonschema
-    #   pytest-subtests
     #   referencing
 audioread==3.0.1
     # via librosa
@@ -57,9 +54,7 @@ azure-identity==1.25.2
 azure-storage-blob==12.28.0
     # via runai-model-streamer-azure
 backoff==2.2.1
-    # via
-    #   -r requirements/test/cuda.in
-    #   schemathesis
+    # via -r requirements/test/cuda.in
 bitsandbytes==0.49.2
     # via -r requirements/test/cuda.in
 black==24.10.0
@@ -110,7 +105,6 @@ colorama==0.4.6
     # via
     #   perceptron
     #   sacrebleu
-    #   schemathesis
 colorful==0.5.6
     # via ray
 colorlog==6.10.1
@@ -183,7 +177,7 @@ et-xmlfile==2.0.0
     # via openpyxl
 evaluate==0.4.3
     # via lm-eval
-fastapi==0.128.0
+fastapi==0.136.3
     # via
     #   -c requirements/common.txt
     #   gpt-oss
@@ -206,8 +200,6 @@ filelock==3.16.1
     #   virtualenv
 fonttools==4.55.0
     # via matplotlib
-fqdn==1.5.1
-    # via jsonschema
 frozendict==2.4.6
     # via einx
 frozenlist==1.5.0
@@ -269,7 +261,7 @@ h11==0.14.0
     #   uvicorn
 h2==4.3.0
     # via httpx
-harfile==0.3.0
+harfile==0.5.0
     # via schemathesis
 hf-xet==1.4.3
     # via huggingface-hub
@@ -309,7 +301,7 @@ hypothesis==6.131.0
     #   hypothesis-graphql
     #   hypothesis-jsonschema
     #   schemathesis
-hypothesis-graphql==0.11.1
+hypothesis-graphql==0.13.0
     # via schemathesis
 hypothesis-jsonschema==0.23.1
     # via schemathesis
@@ -318,7 +310,6 @@ idna==3.10
     #   anyio
     #   email-validator
     #   httpx
-    #   jsonschema
     #   requests
     #   yarl
 imagehash==4.3.2
@@ -335,8 +326,6 @@ instanttensor==0.1.5
     # via -r requirements/test/cuda.in
 isodate==0.7.2
     # via azure-storage-blob
-isoduration==20.11.0
-    # via jsonschema
 isort==5.13.2
     # via datamodel-code-generator
 jinja2==3.1.6
@@ -356,15 +345,14 @@ joblib==1.4.2
     #   librosa
     #   nltk
     #   scikit-learn
-jsonpointer==3.0.0
-    # via jsonschema
 jsonschema==4.23.0
     # via
     #   -c requirements/common.txt
     #   hypothesis-jsonschema
     #   mistral-common
     #   ray
-    #   schemathesis
+jsonschema-rs==0.46.5
+    # via schemathesis
 jsonschema-specifications==2024.10.1
     # via jsonschema
 junit-xml==1.9
@@ -715,18 +703,20 @@ pydantic-core==2.41.1
 pydantic-extra-types==2.10.5
     # via mistral-common
 pygments==2.18.0
-    # via rich
+    # via
+    #   pytest
+    #   rich
 pyjwt==2.11.0
     # via msal
 pyparsing==3.2.0
     # via matplotlib
-pyrate-limiter==3.7.0
+pyrate-limiter==4.4.0
     # via schemathesis
 pystemmer==3.0.0
     # via mteb
 pytablewriter==1.2.0
     # via lm-eval
-pytest==8.3.5
+pytest==9.1.0
     # via
     #   -r requirements/test/cuda.in
     #   buildkite-test-collector
@@ -737,10 +727,9 @@ pytest==8.3.5
     #   pytest-mock
     #   pytest-rerunfailures
     #   pytest-shard
-    #   pytest-subtests
     #   pytest-timeout
     #   schemathesis
-pytest-asyncio==0.24.0
+pytest-asyncio==1.4.0
     # via -r requirements/test/cuda.in
 pytest-cov==6.3.0
     # via -r requirements/test/cuda.in
@@ -752,13 +741,10 @@ pytest-rerunfailures==14.0
     # via -r requirements/test/cuda.in
 pytest-shard==0.1.2
     # via -r requirements/test/cuda.in
-pytest-subtests==0.14.1
-    # via schemathesis
 pytest-timeout==2.3.1
     # via -r requirements/test/cuda.in
 python-dateutil==2.9.0.post0
     # via
-    #   arrow
     #   botocore
     #   matplotlib
     #   pandas
@@ -829,15 +815,12 @@ requests==2.32.3
     #   tiktoken
 responses==0.25.3
     # via genai-perf
-rfc3339-validator==0.1.4
-    # via jsonschema
-rfc3987==1.3.8
-    # via jsonschema
 rich==13.9.4
     # via
     #   genai-perf
     #   mteb
     #   perceptron
+    #   schemathesis
     #   typer
 rouge-score==0.1.2
     # via lm-eval
@@ -868,7 +851,7 @@ safetensors==0.7.0
     #   segmentation-models-pytorch
     #   timm
     #   transformers
-schemathesis==3.39.15
+schemathesis==4.21.6
     # via -r requirements/test/cuda.in
 scikit-image==0.25.2
     # via albumentations
@@ -912,7 +895,6 @@ six==1.16.0
     #   junit-xml
     #   opencensus
     #   python-dateutil
-    #   rfc3339-validator
     #   rouge-score
 smart-open==7.1.0
     # via ray
@@ -938,10 +920,10 @@ sqlalchemy==2.0.41
     #   optuna
 sqlitedict==2.1.0
     # via lm-eval
-starlette==0.50.0
+starlette==1.3.1
     # via
+    #   -c requirements/common.txt
     #   fastapi
-    #   schemathesis
     #   starlette-testclient
 starlette-testclient==0.4.1
     # via schemathesis
@@ -966,6 +948,7 @@ tenacity==9.1.2
     #   gpt-oss
     #   lm-eval
     #   plotly
+    #   schemathesis
 tensorizer==2.10.1
     # via -r requirements/test/cuda.in
 termcolor==3.1.0
@@ -990,10 +973,6 @@ tokenizers==0.22.2
     #   -c requirements/common.txt
     #   -r requirements/test/cuda.in
     #   transformers
-tomli==2.2.1
-    # via schemathesis
-tomli-w==1.2.0
-    # via schemathesis
 torch==2.11.0+cu130
     # via
     #   -c requirements/cuda.txt
@@ -1066,8 +1045,6 @@ typer==0.15.2
     #   huggingface-hub
     #   perceptron
     #   transformers
-types-python-dateutil==2.9.0.20241206
-    # via arrow
 typing-extensions==4.15.0
     # via
     #   -c requirements/common.txt
@@ -1092,6 +1069,8 @@ typing-extensions==4.15.0
     #   pydantic
     #   pydantic-core
     #   pydantic-extra-types
+    #   pytest-asyncio
+    #   schemathesis
     #   sentence-transformers
     #   sqlalchemy
     #   starlette
@@ -1099,11 +1078,11 @@ typing-extensions==4.15.0
     #   typer
     #   typing-inspection
 typing-inspection==0.4.2
-    # via pydantic
+    # via
+    #   fastapi
+    #   pydantic
 tzdata==2024.2
     # via pandas
-uri-template==1.3.0
-    # via jsonschema
 urllib3==2.2.3
     # via
     #   blobfile
@@ -1122,8 +1101,6 @@ vocos==0.1.0
     # via -r requirements/test/cuda.in
 wcwidth==0.2.13
     # via ftfy
-webcolors==24.11.1
-    # via jsonschema
 werkzeug==3.1.3
     # via schemathesis
 word2number==1.1
@@ -1135,8 +1112,6 @@ xxhash==3.5.0
     #   datasets
     #   evaluate
 yarl==1.17.1
-    # via
-    #   aiohttp
-    #   schemathesis
+    # via aiohttp
 zipp==3.23.0
     # via importlib-metadata

requirements/test/nightly-torch.txt

@@ -31,7 +31,7 @@ lm-eval[api]>=0.4.12 # required for model evaluation test
 mteb[bm25s]>=2, <3 # required for mteb test
 transformers==5.5.3
 tokenizers==0.22.2
-schemathesis>=3.39.15 # Required for openai schema test.
+schemathesis>=4.0.0 # Required for openai schema test.
 # quantization
 bitsandbytes>=0.49.2
 buildkite-test-collector==0.1.9

requirements/test/rocm.in

@@ -39,7 +39,7 @@ lm-eval[api]>=0.4.12 # required for model evaluation test
 mteb[bm25s]>=2, <3 # required for mteb test
 transformers==5.5.3
 tokenizers==0.22.2
-schemathesis>=3.39.15 # Required for openai schema test
+schemathesis>=4.0.0 # Required for openai schema test
 # quantization
 bitsandbytes==0.49.2
 buildkite-test-collector==0.1.9

requirements/test/rocm.txt

@@ -51,15 +51,12 @@ arctic-inference==0.1.1
     # via -r requirements/test/rocm.in
 argcomplete==3.6.3
     # via datamodel-code-generator
-arrow==1.4.0
-    # via isoduration
 astor==0.8.1
     # via depyf
 attrs==26.1.0
     # via
     #   aiohttp
     #   jsonschema
-    #   pytest-subtests
     #   referencing
 audioread==3.0.1
     # via librosa
@@ -74,9 +71,7 @@ azure-identity==1.25.3
 azure-storage-blob==12.28.0
     # via runai-model-streamer-azure
 backoff==2.2.1
-    # via
-    #   -r requirements/test/rocm.in
-    #   schemathesis
+    # via -r requirements/test/rocm.in
 bitsandbytes==0.49.2
     # via -r requirements/test/rocm.in
 black==26.3.1
@@ -139,7 +134,6 @@ colorama==0.4.6
     # via
     #   perceptron
     #   sacrebleu
-    #   schemathesis
 colorful==0.5.8
     # via ray
 colorlog==6.10.1
@@ -258,8 +252,6 @@ filelock==3.25.2
     #   virtualenv
 fonttools==4.62.1
     # via matplotlib
-fqdn==1.5.1
-    # via jsonschema
 frozendict==2.4.7
     # via einx
 frozenlist==1.8.0
@@ -328,7 +320,7 @@ h11==0.16.0
     #   uvicorn
 h2==4.3.0
     # via httpx
-harfile==0.4.0
+harfile==0.5.0
     # via schemathesis
 hf-xet==1.4.3
     # via huggingface-hub
@@ -378,7 +370,7 @@ hypothesis==6.151.9
     #   hypothesis-graphql
     #   hypothesis-jsonschema
     #   schemathesis
-hypothesis-graphql==0.12.0
+hypothesis-graphql==0.13.0
     # via schemathesis
 hypothesis-jsonschema==0.23.1
     # via schemathesis
@@ -387,7 +379,6 @@ idna==3.11
     #   anyio
     #   email-validator
     #   httpx
-    #   jsonschema
     #   requests
     #   yarl
 ijson==3.5.0
@@ -408,8 +399,6 @@ interegular==0.3.3
     # via lm-format-enforcer
 isodate==0.7.2
     # via azure-storage-blob
-isoduration==20.11.0
-    # via jsonschema
 isort==8.0.1
     # via datamodel-code-generator
 jinja2==3.1.6
@@ -435,8 +424,6 @@ joblib==1.5.3
     #   librosa
     #   nltk
     #   scikit-learn
-jsonpointer==3.1.0
-    # via jsonschema
 jsonschema==4.26.0
     # via
     #   -c requirements/common.txt
@@ -445,7 +432,8 @@ jsonschema==4.26.0
     #   mcp
     #   mistral-common
     #   ray
-    #   schemathesis
+jsonschema-rs==0.46.5
+    # via schemathesis
 jsonschema-specifications==2025.9.1
     # via jsonschema
 junit-xml==1.9
@@ -792,7 +780,7 @@ prometheus-client==0.24.1
     #   opentelemetry-exporter-prometheus
     #   prometheus-fastapi-instrumentator
     #   ray
-prometheus-fastapi-instrumentator==7.1.0
+prometheus-fastapi-instrumentator==8.0.0
     # via
     #   -c requirements/common.txt
     #   -r requirements/test/../common.txt
@@ -876,20 +864,22 @@ pydantic-settings==2.13.1
     #   fastapi
     #   mcp
 pygments==2.19.2
-    # via rich
+    # via
+    #   pytest
+    #   rich
 pyjwt==2.12.1
     # via
     #   mcp
     #   msal
 pyparsing==3.3.2
     # via matplotlib
-pyrate-limiter==3.9.0
+pyrate-limiter==4.4.0
     # via schemathesis
 pystemmer==3.0.0
     # via mteb
 pytablewriter==1.2.1
     # via lm-eval
-pytest==8.3.5
+pytest==9.1.0
     # via
     #   -r requirements/test/rocm.in
     #   buildkite-test-collector
@@ -900,10 +890,9 @@ pytest==8.3.5
     #   pytest-mock
     #   pytest-rerunfailures
     #   pytest-shard
-    #   pytest-subtests
     #   pytest-timeout
     #   schemathesis
-pytest-asyncio==0.24.0
+pytest-asyncio==1.4.0
     # via -r requirements/test/rocm.in
 pytest-cov==6.3.0
     # via -r requirements/test/rocm.in
@@ -915,13 +904,10 @@ pytest-rerunfailures==14.0
     # via -r requirements/test/rocm.in
 pytest-shard==0.1.2
     # via -r requirements/test/rocm.in
-pytest-subtests==0.14.2
-    # via schemathesis
 pytest-timeout==2.3.1
     # via -r requirements/test/rocm.in
 python-dateutil==2.9.0.post0
     # via
-    #   arrow
     #   botocore
     #   matplotlib
     #   pandas
@@ -1016,16 +1002,13 @@ requests==2.32.5
     #   tiktoken
 responses==0.26.0
     # via genai-perf
-rfc3339-validator==0.1.4
-    # via jsonschema
-rfc3987==1.3.8
-    # via jsonschema
 rich==14.3.3
     # via
     #   genai-perf
     #   mteb
     #   perceptron
     #   rich-toolkit
+    #   schemathesis
     #   typer
 rich-toolkit==0.19.7
     # via
@@ -1063,7 +1046,7 @@ safetensors==0.7.0
     #   segmentation-models-pytorch
     #   timm
     #   transformers
-schemathesis==3.39.15
+schemathesis==4.21.6
     # via -r requirements/test/rocm.in
 scikit-image==0.26.0
     # via albumentations
@@ -1120,7 +1103,6 @@ six==1.17.0
     #   junit-xml
     #   opencensus
     #   python-dateutil
-    #   rfc3339-validator
     #   rouge-score
 smart-open==7.5.1
     # via ray
@@ -1149,13 +1131,14 @@ sqlitedict==2.1.0
     # via lm-eval
 sse-starlette==3.3.4
     # via mcp
-starlette==0.52.1
+starlette==1.3.1
     # via
+    #   -c requirements/common.txt
+    #   -r requirements/test/../common.txt
     #   fastapi
     #   mcp
     #   model-hosting-container-standards
     #   prometheus-fastapi-instrumentator
-    #   schemathesis
     #   sse-starlette
     #   starlette-testclient
 starlette-testclient==0.4.1
@@ -1182,6 +1165,7 @@ tenacity==9.1.4
     # via
     #   gpt-oss
     #   lm-eval
+    #   schemathesis
 tensorizer==2.10.1
     # via
     #   -c requirements/rocm.txt
@@ -1215,10 +1199,6 @@ tokenizers==0.22.2
     #   -r requirements/test/../common.txt
     #   -r requirements/test/rocm.in
     #   transformers
-tomli==2.4.0
-    # via schemathesis
-tomli-w==1.2.0
-    # via schemathesis
 torch-c-dlpack-ext==0.1.5
     # via tilelang
 tqdm==4.67.3
@@ -1301,8 +1281,10 @@ typing-extensions==4.15.0
     #   pydantic
     #   pydantic-core
     #   pydantic-extra-types
+    #   pytest-asyncio
     #   referencing
     #   rich-toolkit
+    #   schemathesis
     #   sentence-transformers
     #   sqlalchemy
     #   starlette
@@ -1317,10 +1299,6 @@ typing-inspection==0.4.2
     #   mcp
     #   pydantic
     #   pydantic-settings
-tzdata==2025.3
-    # via arrow
-uri-template==1.3.0
-    # via jsonschema
 urllib3==2.6.3
     # via
     #   blobfile
@@ -1351,8 +1329,6 @@ watchfiles==1.1.1
     #   uvicorn
 wcwidth==0.6.0
     # via ftfy
-webcolors==25.10.0
-    # via jsonschema
 websockets==16.0
     # via uvicorn
 werkzeug==3.1.6
@@ -1370,9 +1346,7 @@ xxhash==3.6.0
     #   datasets
     #   evaluate
 yarl==1.23.0
-    # via
-    #   aiohttp
-    #   schemathesis
+    # via aiohttp
 z3-solver==4.15.4.0
     # via tilelang
 zipp==3.23.0

requirements/test/xpu.txt

@@ -593,8 +593,9 @@ soxr==0.5.0.post1
     #   mistral-common
 sqlitedict==2.1.0
     # via lm-eval
-starlette==1.0.0
+starlette==1.3.1
     # via
+    #   -c requirements/common.txt
     #   fastapi
     #   starlette-testclient
 starlette-testclient==0.4.1

requirements/xpu.txt

@@ -17,4 +17,4 @@ torchaudio
 torchvision
 
 auto_round_lib>=0.13.3
-vllm_xpu_kernels @ https://github.com/vllm-project/vllm-xpu-kernels/releases/download/v0.1.9.1/vllm_xpu_kernels-0.1.9.1-cp38-abi3-manylinux_2_28_x86_64.whl
+vllm_xpu_kernels @ https://github.com/vllm-project/vllm-xpu-kernels/releases/download/v0.1.10/vllm_xpu_kernels-0.1.10-cp38-abi3-manylinux_2_28_x86_64.whl

rust/src/chat/src/renderer/hf/format.rs

@@ -386,7 +386,6 @@ mod tests {
             tool_chat_template_llama3.2_pythonic.jinja         => String
             tool_chat_template_llama4_json.jinja               => OpenAi
             tool_chat_template_llama4_pythonic.jinja           => OpenAi
-            tool_chat_template_minimax_m1.jinja                => OpenAi
             tool_chat_template_mistral.jinja                   => String
             tool_chat_template_mistral3.jinja                  => OpenAi
             tool_chat_template_mistral_parallel.jinja          => String

rust/src/chat/tests/templates/vllm_examples/tool_chat_template_minimax_m1.jinja

@@ -1,91 +0,0 @@
-{{ '<begin_of_document>' -}}
-{%- if custom_tools is defined %}
-    {%- set tools = custom_tools %}
-{%- endif %}
-{%- if not tools is defined %}
-    {%- set tools = none %}
-{%- endif %}
-
-{#- Extract system message #}
-{% set ns = namespace(system_prompt='') -%}
-{%- if messages[0]['role'] == 'system' %}
-    {%- if messages[0]['content'] is string %}
-        {%- set ns.system_prompt = messages[0]['content']|trim %}
-    {%- else %}
-        {%- set ns.system_prompt = messages[0]['content'][0]['text']|trim %}
-    {%- endif %}
-    {%- set messages = messages[1:] %}
-{%- else %}
-    {%- if tools is not none %}
-        {%- set ns.system_prompt = "You are a helpful assistant created by Minimax based on MiniMax-M1 model." %}
-    {%- else %}
-        {%- set ns.system_prompt = "You are a helpful assistant created by Minimax based on MiniMax-M1 model." %}
-    {%- endif %}
-{%- endif %}
-
-{#- System message #}
-{%- if ns.system_prompt != '' %}
-{{ '<beginning_of_sentence>system ai_setting=assistant\n' + ns.system_prompt + '<end_of_sentence>\n' -}}
-{%- endif %}
-
-{#- Tools configuration #}
-{%- if tools is not none %}
-{{ '<beginning_of_sentence>system tool_setting=tools\nYou are provided with these tools:\n<tools>\n' -}}
-{%- for tool in tools %}
-{{ tool | tojson ~ '\n' -}}
-{%- endfor %}
-{{ '</tools>\n\nIf you need to call tools, please respond with <tool_calls></tool_calls> XML tags, and provide tool-name and json-object of arguments, following the format below:\n<tool_calls>\n{"name": <tool-name>, "arguments": <args-json-object>}\n...\n</tool_calls><end_of_sentence>\n' -}}
-{%- endif %}
-
-{#- Process messages #}
-{%- for message in messages %}
-    {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}
-        {%- if message['role'] == 'user' %}
-{{ '<beginning_of_sentence>user name=user\n' -}}
-{%- if message['content'] is string %}
-{{ message['content']|trim -}}
-{%- else %}
-{%- for content in message['content'] %}
-{%- if content['type'] == 'text' %}
-{{ content['text']|trim -}}
-{%- endif %}
-{%- endfor %}
-{%- endif %}
-{{ '<end_of_sentence>\n' -}}
-        {%- elif message['role'] == 'assistant' %}
-{{ '<beginning_of_sentence>ai name=assistant\n' -}}
-{%- if message['content'] is string %}
-{{ message['content']|trim -}}
-{%- else %}
-{%- for content in message['content'] | selectattr('type', 'equalto', 'text') %}
-{{ content['text']|trim -}}
-{%- endfor %}
-{%- endif %}
-{{ '<end_of_sentence>\n' -}}
-        {%- endif %}
-    {%- elif 'tool_calls' in message %}
-{{ '<beginning_of_sentence>ai name=assistant\n<tool_calls>\n' -}}
-{%- for tool_call in message.tool_calls %}
-{{ '{"name": "' + tool_call.function.name + '", "arguments": ' + tool_call.function.arguments | tojson + '}\n' -}}
-{%- endfor %}
-{{ '</tool_calls><end_of_sentence>\n' -}}
-    {%- elif message.role == "tool" or message.role == "ipython" %}
-{{ '<beginning_of_sentence>tool name=tools\n' -}}
-{%- if message.content is string %}
-{{ 'tool result: ' + message.content + '\n\n' -}}
-{%- else %}
-{%- for content in message['content'] %}
-{%- if content['type'] == 'text' %}
-{{ 'tool result: ' + content['text'] + '\n\n' -}}
-{%- elif content.get('name') %}
-{{ 'tool name: ' + content['name'] + '\ntool result: ' + content['text'] + '\n\n' -}}
-{%- endif %}
-{%- endfor %}
-{%- endif %}
-{{ '<end_of_sentence>\n' -}}
-    {%- endif %}
-{%- endfor %}
-
-{%- if add_generation_prompt %}
-{{ '<beginning_of_sentence>ai name=assistant\n' -}}
-{%- endif %}

rust/src/engine-core-client/src/protocol/mod.rs

@@ -277,6 +277,12 @@ pub struct EngineCoreSamplingParams {
     pub max_tokens: u32,
     /// Minimum number of tokens to generate before EOS or stop-token handling.
     pub min_tokens: u32,
+    /// Maximum number of reasoning ("thinking") tokens to emit before the
+    /// reasoning section is force-closed. `None` means unlimited; the
+    /// user-facing `-1` sentinel is normalized to `None` by the frontend before
+    /// reaching this DTO, so only non-negative values are sent. Enforced
+    /// engine-side (and only when a reasoning parser is configured).
+    pub thinking_token_budget: Option<u64>,
     /// Number of log probabilities to return per generated token.
     ///
     /// `None` disables sample logprobs. `-1` requests the full vocabulary.
@@ -345,6 +351,7 @@ impl EngineCoreSamplingParams {
             seed: None,
             max_tokens: 65536,
             min_tokens: 0,
+            thinking_token_budget: None,
             logprobs: None,
             prompt_logprobs: None,
             min_p: 0.0,

rust/src/engine-core-client/src/tests/client.rs

@@ -150,6 +150,7 @@ fn sample_request_with_id(request_id: &str) -> EngineCoreRequest {
             top_k: 8,
             max_tokens: 32,
             min_tokens: 1,
+            thinking_token_budget: Some(256),
             stop_token_ids: vec![151643],
             eos_token_id: Some(151645),
             all_stop_token_ids: BTreeSet::from([151643, 151645]),
@@ -2502,6 +2503,7 @@ fn python_msgpack_fixtures_match_rust_encoding() {
             seed: None,
             max_tokens: 16,
             min_tokens: 0,
+            thinking_token_budget: None,
             logprobs: None,
             prompt_logprobs: None,
             min_p: 0.0,

rust/src/engine-core-client/src/tests/python_compat.py

@@ -39,6 +39,7 @@ class EngineCoreSamplingParams(msgspec.Struct, dict=True, omit_defaults=True):
     seed: int | None = None
     max_tokens: int = 16
     min_tokens: int = 0
+    thinking_token_budget: int | None = None
     min_p: float = 0.0
     frequency_penalty: float = 0.0
     presence_penalty: float = 0.0
@@ -122,6 +123,7 @@ request = EngineCoreRequest(
         seed=None,
         max_tokens=32,
         min_tokens=1,
+        thinking_token_budget=256,
         min_p=0.0,
         frequency_penalty=0.0,
         presence_penalty=0.0,

rust/src/server/src/error.rs

@@ -103,6 +103,7 @@ fn is_request_validation_error(error: &vllm_text::Error) -> bool {
             | vllm_text::Error::EmptyPromptTokenIds { .. }
             | vllm_text::Error::Logprobs(_)
             | vllm_text::Error::OutOfVocab(_)
+            | vllm_text::Error::InvalidThinkingTokenBudget
             // An empty tokenized prompt detected later, at request prepare
             // time, surfaces through the transparent Llm wrapper.
             | vllm_text::Error::Llm(vllm_llm::Error::EmptyPromptTokenIds { .. })
@@ -127,6 +128,18 @@ mod tests {
         assert!(response.error.message.contains("9000"));
     }
 
+    #[test]
+    fn invalid_thinking_token_budget_maps_to_invalid_request() {
+        let api_error = text_submit_error(
+            "failed to submit completion request",
+            vllm_text::Error::InvalidThinkingTokenBudget,
+        );
+        assert_eq!(api_error.status_code(), StatusCode::BAD_REQUEST);
+        let response = api_error.to_error_response();
+        assert_eq!(response.error.error_type, "invalid_request_error");
+        assert!(response.error.message.contains("thinking_token_budget"));
+    }
+
     #[test]
     fn chat_wrapped_prompt_too_long_maps_to_invalid_request() {
         let error = vllm_chat::Error::Text(vllm_text::Error::PromptTooLong {

rust/src/server/src/routes/inference/generate/convert.rs

@@ -150,6 +150,33 @@ mod tests {
         );
     }
 
+    #[test]
+    fn prepare_generate_request_forwards_thinking_token_budget() {
+        let request: GenerateRequest = serde_json::from_value(json!({
+            "model": "Qwen/Qwen1.5-0.5B-Chat",
+            "token_ids": [11, 22, 33],
+            "sampling_params": {
+                "thinking_token_budget": 64
+            }
+        }))
+        .expect("parse request");
+
+        let prepared = prepare_generate_request(
+            request,
+            &served(&["Qwen/Qwen1.5-0.5B-Chat"]),
+            ResolvedRequestContext::default(),
+        )
+        .expect("prepare");
+
+        // The raw inference route shares `vllm_text::SamplingParams`, so the
+        // field is carried through to lowering exactly like the OpenAI routes
+        // (normalization/validation then happens in `lower_sampling_params`).
+        assert_eq!(
+            prepared.text_request.sampling_params.thinking_token_budget,
+            Some(64)
+        );
+    }
+
     #[test]
     fn prepare_generate_request_gates_continuous_usage_on_include_usage() {
         let request: GenerateRequest = serde_json::from_value(json!({

rust/src/server/src/routes/openai/chat_completions/convert.rs

@@ -115,6 +115,7 @@ pub(super) fn prepare_chat_request(
             seed: request.seed,
             max_tokens: request.max_completion_tokens,
             min_tokens: request.min_tokens,
+            thinking_token_budget: request.thinking_token_budget,
             logprobs: request.logprobs.then_some(top_logprobs),
             prompt_logprobs,
             min_p: request.min_p,
@@ -613,6 +614,31 @@ mod tests {
         assert_eq!(prepared.chat_request.sampling_params, expected);
     }
 
+    #[test]
+    fn prepare_chat_request_passes_through_thinking_token_budget() {
+        let prepare = |budget: Option<i64>| {
+            prepare_chat_request(
+                ChatCompletionRequest {
+                    thinking_token_budget: budget,
+                    ..base_request()
+                },
+                &served(&["Qwen/Qwen1.5-0.5B-Chat"]),
+                ResolvedRequestContext::default(),
+            )
+            .expect("request is valid")
+            .chat_request
+            .sampling_params
+            .thinking_token_budget
+        };
+
+        // The convert layer forwards the raw value verbatim (including the `-1`
+        // "unlimited" sentinel); normalization/validation happens during
+        // lowering (see `vllm_text::lower`).
+        assert_eq!(prepare(Some(64)), Some(64));
+        assert_eq!(prepare(Some(-1)), Some(-1));
+        assert_eq!(prepare(None), None);
+    }
+
     #[test]
     fn prepare_chat_request_accepts_developer_messages() {
         let request = ChatCompletionRequest {

rust/src/server/src/routes/openai/chat_completions/types.rs

@@ -165,8 +165,10 @@ pub struct ChatCompletionRequest {
     pub bad_words: Option<Vec<String>>,
 
     // -------- Extra vLLM Parameters --------
-    /// Token budget for reasoning/thinking
-    pub thinking_token_budget: Option<u32>,
+    /// Token budget for reasoning/thinking. Accepts a non-negative integer, or
+    /// `-1` for unlimited (mirroring the Python frontend, which normalizes `-1`
+    /// to "no budget").
+    pub thinking_token_budget: Option<i64>,
 
     /// Whether to include reasoning content in the response
     #[serde(default = "default_true")]

rust/src/server/src/routes/openai/chat_completions/validate.rs

@@ -108,11 +108,6 @@ pub(super) fn validate_request_compat(
         "truncate_prompt_tokens",
         "truncate_prompt_tokens is not supported.",
     )?;
-    reject_non_default(
-        request.thinking_token_budget.as_ref(),
-        "thinking_token_budget",
-        "thinking_token_budget is not supported.",
-    )?;
     reject_non_default(
         request.media_io_kwargs.as_ref(),
         "media_io_kwargs",

rust/src/server/src/routes/openai/completions/convert.rs

@@ -108,6 +108,7 @@ pub(super) fn prepare_completion_request(
             seed: request.seed,
             max_tokens,
             min_tokens: request.min_tokens,
+            thinking_token_budget: request.thinking_token_budget,
             logprobs,
             prompt_logprobs,
             min_p: request.min_p,
@@ -266,6 +267,34 @@ mod tests {
         assert!(!prepared.text_request.decode_options.skip_special_tokens);
     }
 
+    #[test]
+    fn prepare_completion_request_passes_through_thinking_token_budget() {
+        let prepare = |budget: serde_json::Value| {
+            let request: CompletionRequest = serde_json::from_value(json!({
+                "model": "Qwen/Qwen1.5-0.5B-Chat",
+                "prompt": "hello",
+                "thinking_token_budget": budget,
+            }))
+            .expect("parse request");
+            prepare_completion_request(
+                request,
+                &served(&["Qwen/Qwen1.5-0.5B-Chat"]),
+                ResolvedRequestContext::default(),
+            )
+            .expect("prepare")
+            .text_request
+            .sampling_params
+            .thinking_token_budget
+        };
+
+        // The convert layer forwards the raw value verbatim (including the `-1`
+        // "unlimited" sentinel); normalization/validation happens during
+        // lowering (see `vllm_text::lower`).
+        assert_eq!(prepare(json!(64)), Some(64));
+        assert_eq!(prepare(json!(-1)), Some(-1));
+        assert_eq!(prepare(json!(null)), None);
+    }
+
     #[test]
     fn prepare_completion_request_maps_stream_usage_and_token_format_options() {
         let request: CompletionRequest = serde_json::from_value(json!({

rust/src/server/src/routes/openai/completions/types.rs

@@ -146,6 +146,11 @@ pub struct CompletionRequest {
     /// Additional kwargs for structured outputs
     pub structured_outputs: Option<Value>,
 
+    /// Token budget for reasoning/thinking. Accepts a non-negative integer, or
+    /// `-1` for unlimited (mirroring the Python frontend, which normalizes `-1`
+    /// to "no budget").
+    pub thinking_token_budget: Option<i64>,
+
     /// Request scheduling priority (lower means earlier; default 0)
     pub priority: Option<i32>,
 

rust/src/text/src/error.rs

@@ -20,6 +20,8 @@ pub enum Error {
     Logprobs(#[from] LogprobsError),
     #[error(transparent)]
     OutOfVocab(#[from] OutOfVocabError),
+    #[error("`thinking_token_budget` must be a non-negative integer or -1 for unlimited.")]
+    InvalidThinkingTokenBudget,
     #[error("text request stream `{request_id}` closed before terminal output")]
     StreamClosedBeforeTerminalOutput { request_id: String },
     #[error(transparent)]

rust/src/text/src/lower.rs

@@ -87,6 +87,7 @@ pub fn lower_sampling_params(
         seed,
         max_tokens,
         min_tokens,
+        thinking_token_budget,
         logprobs,
         prompt_logprobs,
         min_p,
@@ -128,6 +129,7 @@ pub fn lower_sampling_params(
         prompt_len,
     )?;
     let min_tokens = min_tokens.unwrap_or(0);
+    let thinking_token_budget = normalize_thinking_token_budget(thinking_token_budget)?;
     let frequency_penalty = frequency_penalty.unwrap_or(0.0);
     let presence_penalty = presence_penalty.unwrap_or(0.0);
 
@@ -149,6 +151,7 @@ pub fn lower_sampling_params(
         seed,
         max_tokens,
         min_tokens,
+        thinking_token_budget,
         logprobs,
         prompt_logprobs,
         min_p,
@@ -170,6 +173,21 @@ pub fn lower_sampling_params(
     Ok(params)
 }
 
+/// Normalize the user-facing `thinking_token_budget` into the engine value.
+///
+/// Mirrors Python's `validate_thinking_token_budget`
+/// (<https://github.com/vllm-project/vllm/blob/ecf9d83520eb217401b47d8a5451a27c5231b8c2/vllm/sampling_params.py#L35-L55>):
+/// `None` and the `-1` "unlimited" sentinel both map to `None`; any other
+/// negative value is rejected; non-negative values pass through unchanged. Like
+/// Python's `int`, no upper bound is imposed.
+fn normalize_thinking_token_budget(value: Option<i64>) -> Result<Option<u64>> {
+    match value {
+        None | Some(-1) => Ok(None),
+        Some(budget) if budget >= 0 => Ok(Some(budget as u64)),
+        Some(_) => Err(Error::InvalidThinkingTokenBudget),
+    }
+}
+
 /// Convert bad-word strings into token-ID sequences, following the Python vLLM
 /// logic in `SamplingParams.update_from_tokenizer()`.
 ///
@@ -366,6 +384,36 @@ mod tests {
         )
     }
 
+    #[test]
+    fn lower_sampling_params_normalizes_thinking_token_budget() {
+        let lower = |budget: Option<i64>| {
+            lower_sampling_params_with_limits(
+                SamplingParams {
+                    thinking_token_budget: budget,
+                    ..SamplingParams::default()
+                },
+                sample_sampling_limits(),
+            )
+        };
+
+        // Non-negative budgets (including 0) pass through unchanged.
+        assert_eq!(lower(Some(256)).unwrap().thinking_token_budget, Some(256));
+        assert_eq!(lower(Some(0)).unwrap().thinking_token_budget, Some(0));
+        // `None` and the `-1` "unlimited" sentinel both disable the budget.
+        assert_eq!(lower(None).unwrap().thinking_token_budget, None);
+        assert_eq!(lower(Some(-1)).unwrap().thinking_token_budget, None);
+        // No upper bound is imposed, matching Python's `int`.
+        assert_eq!(
+            lower(Some(i64::from(u32::MAX) + 1)).unwrap().thinking_token_budget,
+            Some(u64::from(u32::MAX) + 1)
+        );
+        // Other negatives are rejected.
+        assert!(matches!(
+            lower(Some(-2)),
+            Err(Error::InvalidThinkingTokenBudget)
+        ));
+    }
+
     #[test]
     fn lower_text_request_applies_python_style_eos_hints() {
         let prepared = lower_text_request(
@@ -386,6 +434,7 @@ mod tests {
                 seed: None,
                 max_tokens: 999997,
                 min_tokens: 0,
+                thinking_token_budget: None,
                 logprobs: None,
                 prompt_logprobs: None,
                 min_p: 0.0,
@@ -437,6 +486,7 @@ mod tests {
                 seed: None,
                 max_tokens: 999997,
                 min_tokens: 0,
+                thinking_token_budget: None,
                 logprobs: None,
                 prompt_logprobs: None,
                 min_p: 0.0,
@@ -567,6 +617,7 @@ mod tests {
                 seed: None,
                 max_tokens: 40957,
                 min_tokens: 0,
+                thinking_token_budget: None,
                 logprobs: None,
                 prompt_logprobs: None,
                 min_p: 0.0,
@@ -628,6 +679,7 @@ mod tests {
                 seed: None,
                 max_tokens: 999997,
                 min_tokens: 0,
+                thinking_token_budget: None,
                 logprobs: None,
                 prompt_logprobs: None,
                 min_p: 0.0,
@@ -697,6 +749,7 @@ mod tests {
                 seed: None,
                 max_tokens: 32,
                 min_tokens: 2,
+                thinking_token_budget: None,
                 logprobs: None,
                 prompt_logprobs: None,
                 min_p: 0.1,
@@ -929,6 +982,7 @@ mod tests {
                 seed: None,
                 max_tokens: 128,
                 min_tokens: 0,
+                thinking_token_budget: None,
                 logprobs: None,
                 prompt_logprobs: None,
                 min_p: 0.1,

rust/src/text/src/output/decoded.rs

@@ -309,7 +309,7 @@ fn matches_stop_string(stops: &[String], output: &str, new_bytes: usize) -> Opti
         .find_map(|(ss_idx, (ss, len, start_off))| {
             output[start_off..]
                 .windows(len)
-                .rposition(|w| w == ss)
+                .position(|w| w == ss)
                 .map(|pos| (ss_idx, start_off + pos))
         })
 }
@@ -562,6 +562,13 @@ mod tests {
         assert_eq!(result, Some((0, 4)));
     }
 
+    #[test]
+    fn stop_string_matches_leftmost_with_multiple_new_bytes() {
+        let stops = vec!["\n".to_string()];
+        let result = matches_stop_string(&stops, "Answer\n\n", 2);
+        assert_eq!(result, Some((0, 6)));
+    }
+
     #[test]
     fn stop_string_matches_at_beginning() {
         let stops = vec!["say".to_string()];