Merge branch 'main' into codex/rocm-artifact-tensorizer

Fix ROCm artifact test dependencies
Signed-off-by: Andreas Karatzas <akaratza@amd.com>
2026-06-18 14:28:51 -05:00 · 2026-06-04 21:30:21 -05:00
108 changed files with 787 additions and 2150 deletions
@@ -67,7 +67,7 @@ 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 --ignore=v1/spec_decode/test_speculators_correctness.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/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:
@@ -104,7 +104,6 @@ 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
@@ -101,15 +101,6 @@ 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:
-      - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-      - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=configs/models-mi3xx.txt

 - label: MoE Refactor Integration Test (H100 - TEMPORARY)
  key: moe-refactor-integration-test-h100-temporary
@@ -68,6 +68,7 @@ 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
@@ -1,35 +0,0 @@
---
-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.
@@ -2,14 +2,15 @@
 # for more info about CODEOWNERS file

 # This lists cover the "core" components of vLLM that require careful review
-/vllm/compilation @zou3519 @youkaichao @ProExpertProg @BoyuanFeng
+/vllm/compilation @zou3519 @youkaichao @ProExpertProg @BoyuanFeng @vadiklyutiy
 /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/qwen_gdn_linear_attn.py @tdoublep @ZJY0516 @vadiklyutiy
+/vllm/model_executor/layers/mamba/gdn_linear_attn.py @tdoublep @ZJY0516 @vadiklyutiy
+/vllm/model_executor/layers/rotary_embedding.py @vadiklyutiy
 /vllm/model_executor/model_loader @22quinn
 /vllm/model_executor/layers/batch_invariant.py @yewentao256 
 /vllm/ir @ProExpertProg
@@ -199,9 +199,7 @@ cython_debug/
 .vscode/

 # Claude
-.claude/*
-!.claude/skills/
-!.claude/skills/**
+.claude/

 # Codex
 .codex/
@@ -114,6 +114,17 @@ 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:
@@ -319,35 +319,82 @@ if(VLLM_GPU_LANG STREQUAL "CUDA" OR VLLM_GPU_LANG STREQUAL "HIP")
 endif()

 #
-# Legacy _C extension (ROCm only — CUDA ops migrated to _C_stable_libtorch)
+# _C extension
 #

-if(VLLM_GPU_LANG STREQUAL "HIP")
-  set(VLLM_EXT_SRC
-    "csrc/torch_bindings.cpp"
+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
    "csrc/custom_quickreduce.cu"
    "csrc/cuda_view.cu"
-    "csrc/libtorch_stable/cuda_utils_kernels.cu")
+    "csrc/libtorch_stable/cuda_utils_kernels.cu"
+  )
+# if ROCM endif
+endif()

-  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)
-endif() # _C HIP endif
+# 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(VLLM_GPU_LANG STREQUAL "CUDA" OR VLLM_GPU_LANG STREQUAL "HIP")
  #
@@ -356,7 +403,6 @@ 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"
@@ -383,38 +429,6 @@ 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 ${CUTLASS_REVISION}
-          GIT_PROGRESS TRUE
-
-          # Speed up CUTLASS download by retrieving only the specified GIT_TAG instead of the history.
-          # Important: If GIT_SHALLOW is enabled then GIT_TAG works only with branch names and tags.
-          # So if the GIT_TAG above is updated to a commit hash, GIT_SHALLOW must be set to FALSE
-          GIT_SHALLOW TRUE
-      )
-    endif()
-    FetchContent_MakeAvailable(cutlass)
-
    list(APPEND VLLM_STABLE_EXT_SRC
      "csrc/libtorch_stable/cuda_view.cu"
      "csrc/libtorch_stable/cuda_utils_kernels.cu"
@@ -915,6 +929,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA" OR VLLM_GPU_LANG STREQUAL "HIP")
      SRCS "${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: ${FP4_SM120_ARCHS}")
@@ -947,6 +962,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA" OR VLLM_GPU_LANG STREQUAL "HIP")
      SRCS "${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}")
@@ -60,7 +60,6 @@ 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
    ${qutlass_SOURCE_DIR}/qutlass/csrc/gemm_ada.cu
@@ -79,19 +78,8 @@ if(${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
-      "${qutlass_SOURCE_DIR}/qutlass/third_party/cutlass/include"
-      "${qutlass_SOURCE_DIR}/qutlass/third_party/cutlass/tools/util/include")
+    list(APPEND QUTLASS_INCLUDES "${qutlass_SOURCE_DIR}/qutlass/third_party/cutlass/include")
    message(STATUS "[QUTLASS] Using QuTLASS vendored CUTLASS headers (no vLLM CUTLASS detected).")
  else()
    message(FATAL_ERROR "[QUTLASS] CUTLASS headers not found. "
@@ -103,23 +91,12 @@ if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8 AND QUTLASS_ARCHS)
    CUDA_ARCHS "${QUTLASS_ARCHS}"
  )

-  # 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
+  target_sources(_C PRIVATE ${QUTLASS_SOURCES})
+  target_include_directories(_C PRIVATE ${QUTLASS_INCLUDES})
+  target_compile_definitions(_C PRIVATE
    QUTLASS_DISABLE_PYBIND=1
    TARGET_CUDA_ARCH=${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>
@@ -134,5 +111,4 @@ else()
      "[QUTLASS] Skipping build: no supported arch (12.0f / 10.0f) found in "
      "CUDA_ARCHS='${CUDA_ARCHS}'.")
  endif()
-  add_custom_target(_qutlass_C)
 endif()
@@ -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) {
-        float w = static_cast<float>(src2.val[j]);
-        dst.val[j] = static_cast<scalar_t>(x * s_variance * w);
+        dst.val[j] = normalized * src2.val[j];
      } else {
-        dst.val[j] = static_cast<scalar_t>(x * s_variance);
+        dst.val[j] = normalized;
      }
    }
    v_out[i] = dst;
@@ -151,8 +151,7 @@ fused_add_rms_norm_kernel(
 #pragma unroll
      for (int j = 0; j < width; ++j) {
        float x = Converter::convert(res.data[j]);
-        float wf = Converter::convert(w.data[j]);
-        out.data[j] = Converter::convert(x * s_variance * wf);
+        out.data[j] = Converter::convert(x * s_variance) * w.data[j];
      }
    } else {
 #pragma unroll
@@ -199,8 +198,8 @@ 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) {
-      float w = (float)weight[idx];
-      input[blockIdx.x * input_stride + idx] = (scalar_t)(x * s_variance * w);
+      input[blockIdx.x * input_stride + idx] =
+          (scalar_t)(x * s_variance) * weight[idx];
    } else {
      input[blockIdx.x * input_stride + idx] = (scalar_t)(x * s_variance);
    }
@@ -66,13 +66,8 @@ __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]);
-      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);
+      // 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];
      out[blockIdx.x * hidden_size + idx * VEC_SIZE + j] =
          scaled_fp8_conversion<true, fp8_type>(static_cast<float>(out_norm),
                                                scale_inv);
@@ -142,12 +137,8 @@ fused_add_rms_norm_static_fp8_quant_kernel(
 #pragma unroll
    for (int i = 0; i < width; ++i) {
      float x = Converter::convert(res.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);
+      // 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];
      out[id * width + i] = scaled_fp8_conversion<true, fp8_type>(
          Converter::convert(out_norm_h), scale_inv);
    }
@@ -192,10 +183,8 @@ 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];
-    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);
+    // 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];
    out[blockIdx.x * hidden_size + idx] = scaled_fp8_conversion<true, fp8_type>(
        static_cast<float>(out_norm), scale_inv);
  }
@@ -9,7 +9,7 @@
 #include <torch/headeronly/core/ScalarType.h>

 #include "../../cuda_compat.h"
-#include "libtorch_stable/core/math.hpp"
+#include "core/math.hpp"
 #include "libtorch_stable/dispatch_utils.h"
 #include "libtorch_stable/torch_utils.h"

@@ -2,25 +2,9 @@

 #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,
@@ -387,18 +371,6 @@ 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,
@@ -31,7 +31,7 @@

 #include "cutlass/util/packed_stride.hpp"

-#include "libtorch_stable/core/math.hpp"
+#include "core/math.hpp"
 #include "core/batch_invariant.hpp"

 using namespace cute;
@@ -31,7 +31,7 @@

 #include "cutlass/util/packed_stride.hpp"

-#include "libtorch_stable/core/math.hpp"
+#include "core/math.hpp"
 #include "core/batch_invariant.hpp"

 using namespace cute;
@@ -19,7 +19,7 @@
 #include "cutlass/gemm/collective/collective_builder.hpp"
 #include "cutlass/util/packed_stride.hpp"

-#include "libtorch_stable/core/math.hpp"
+#include "core/math.hpp"
 #include "libtorch_stable/cutlass_extensions/common.hpp"
 // clang-format on

@@ -14,7 +14,7 @@
 #include "cutlass/epilogue/collective/collective_builder.hpp"
 #include "cutlass/gemm/collective/collective_builder.hpp"

-#include "libtorch_stable/core/math.hpp"
+#include "core/math.hpp"
 #include "libtorch_stable/cutlass_extensions/common.hpp"
 // clang-format on

@@ -22,7 +22,7 @@
 #include "cutlass/epilogue/threadblock/fusion/visitors.hpp"
 #include "cutlass/gemm/kernel/default_gemm_universal_with_visitor.h"

-#include "libtorch_stable/core/math.hpp"
+#include "core/math.hpp"
 #include "libtorch_stable/cutlass_extensions/common.hpp"
 // clang-format on

@@ -34,20 +34,6 @@ 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("
@@ -494,11 +480,6 @@ 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) -> ()");

@@ -511,10 +492,6 @@ 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) -> ()");

@@ -713,10 +690,6 @@ 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));
@@ -9,6 +9,28 @@

 #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.
@@ -31,6 +53,16 @@ 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);
@@ -1,12 +1,16 @@
-#include "libtorch_stable/torch_utils.h"
+#include <ATen/cuda/CUDAContext.h>
+#include <torch/all.h>
+#include <c10/cuda/CUDAGuard.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>
@@ -29,6 +33,7 @@ typedef __hip_fp8x4_e4m3_fnuz __nv_fp8x4_e4m3;
  #endif
 #endif

+#include "core/registration.h"
 namespace vllm {

 template <typename T>
@@ -559,47 +564,41 @@ __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 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);                  \
-            });                                                           \
+#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); \
+            });                                                             \
      });

-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);
+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);
  LAUNCH_ACTIVATION_GATE_KERNEL(vllm::silu_kernel);
 }

 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]
+    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]
    bool cast_scale_ue8m0) {
 #ifndef USE_ROCM

@@ -607,18 +606,14 @@ void persistent_masked_m_silu_mul_quant(
  // fixed GROUP_SIZE of 128.
  static constexpr int GROUP_SIZE = 128;

-  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);
+  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);

  bool const 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);
+      (y_s.dtype() == torch::kInt32 && cast_scale_ue8m0);
+  TORCH_CHECK(y_s.dtype() == torch::kFloat32 || is_packed_ue8m0);

  using Idx_t = int64_t;

@@ -636,7 +631,7 @@ void persistent_masked_m_silu_mul_quant(

  int const NUM_GROUPS = H / GROUP_SIZE;

-  const cudaStream_t stream = get_current_cuda_stream(input.get_device_index());
+  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();

  // TODO: Get this from cuda_arch ?
  static constexpr int SILU_V2_BLOCK_COUNT = 132 * 32;
@@ -648,21 +643,18 @@ 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 torch::stable::accelerator::DeviceGuard device_guard(                \
-        input.get_device_index());                                             \
-    VLLM_STABLE_DISPATCH_FP8_TYPES(                                            \
+    const at::cuda::OptionalCUDAGuard device_guard(device_of(input));          \
+    VLLM_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<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,    \
+                  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,       \
                  stride_yq_t, stride_yq_h, STRIDE_YS_E, STRIDE_YS_T,          \
                  STRIDE_YS_G, STRIDE_YS_P, stride_counts_e);                  \
        });
@@ -687,7 +679,7 @@ 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) {
-    STD_TORCH_CHECK(!is_packed_ue8m0);
+    TORCH_CHECK(!is_packed_ue8m0);
    LAUNCH_ON_H(float, stride_ys_e, stride_ys_t, stride_ys_g, stride_ys_p,
                false);
    return;
@@ -700,8 +692,8 @@ void persistent_masked_m_silu_mul_quant(
    return;
  }

-  STD_TORCH_CHECK(cast_scale_ue8m0 && is_packed_ue8m0);
-  STD_TORCH_CHECK(y_s.scalar_type() == torch::headeronly::ScalarType::Int);
+  TORCH_CHECK(cast_scale_ue8m0 && is_packed_ue8m0);
+  TORCH_CHECK(y_s.dtype() == torch::kInt32);

  // Int32 packed ue8m0 scales tensor.
  // Let E, T, G be the number to experts, number of tokens and number of groups
@@ -1,5 +0,0 @@
-#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)
@@ -20,6 +20,17 @@

 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.
@@ -28,6 +39,35 @@ 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
 }

@@ -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/v2.34.4/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 && \
+    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 && \
    dpkg -i *.deb && \
    cd .. && \
    rm -rf neo
@@ -133,7 +133,7 @@ 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 [`NemotronHForCausalLM`](../../../vllm/model_executor/models/nemotron_h.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 [`BambaForCausalLM`](../../../vllm/model_executor/models/bamba.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.
@@ -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) (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.
+* **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.

 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**!

@@ -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. 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.
+- **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.
 - **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
@@ -203,7 +203,6 @@ 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).
@@ -27,7 +27,6 @@ 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
@@ -1,38 +1,10 @@
 # LMCache Examples

-This folder demonstrates how to use LMCache with vLLM v1 for KV cache
-offloading, disaggregated prefilling, and KV cache sharing.
+This folder demonstrates how to use LMCache for disaggregated prefilling, CPU offloading and KV cache sharing.

-## Integration modes
+## 1. Disaggregated Prefill in vLLM v1

-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.
+This example demonstrates how to run LMCache with disaggregated prefill using NIXL on a single node.

 ### Prerequisites

@@ -74,7 +46,15 @@ The main script generates several log files:
 - `decoder.log` - Logs from the decode server
 - `proxy.log` - Logs from the proxy server

-## 4. KV Cache Sharing
+## 2. CPU Offload Examples

-The `kv_cache_sharing_lmcache_v1.py` example demonstrates how to share KV
-caches between vLLM v1 instances through a centralized LMCache server.
+- `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.
@@ -1,8 +1,20 @@
 # 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.
+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)

 Note that `lmcache` is needed to run this example.
 Requirements:
@@ -11,6 +23,7 @@ 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
@@ -26,6 +39,8 @@ 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
@@ -35,9 +50,9 @@ def setup_environment_variables():


@contextlib.contextmanager
-def build_llm_with_lmcache(model: str):
+def build_llm_with_lmcache(lmcache_connector: str, model: str):
    ktc = KVTransferConfig(
-        kv_connector="LMCacheConnectorV1",
+        kv_connector=lmcache_connector,
        kv_role="kv_both",
    )
    # Set GPU memory utilization to 0.8 for an A40 GPU with 40GB
@@ -77,10 +92,23 @@ 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(model) as llm:
+    with build_llm_with_lmcache(lmcache_connector, 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
@@ -1,43 +0,0 @@
-#!/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}}'
@@ -0,0 +1,144 @@
+# 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()
@@ -30,6 +30,7 @@ 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 \
@@ -46,6 +47,7 @@ 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 \
@@ -26,6 +26,8 @@ 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
@@ -11,14 +11,13 @@ 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] >= 0.133.0, < 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
+fastapi[standard] >= 0.115.0 # Required by FastAPI's form models in the OpenAI API server's audio transcriptions endpoint.
 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 >= 8.0.0 # v8 unblocks starlette >= 1.0
+prometheus-fastapi-instrumentator >= 7.0.0
 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"
@@ -11,7 +11,7 @@ numba == 0.65.0 # Required for N-gram speculative decoding
 datasets
 peft
 pytest-asyncio
-tensorizer==2.10.1
+tensorizer==2.12.1
 packaging>=24.2
 setuptools>=77.0.3,<80.0.0
 setuptools-scm>=8
@@ -1,6 +1,6 @@
 # testing
 pytest
-tensorizer==2.10.1
+tensorizer==2.12.1
 pytest-forked
 pytest-asyncio
 pytest-rerunfailures
@@ -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>=4.0.0 # Required for openai schema test.
+schemathesis>=3.39.15 # Required for openai schema test.
 # quantization
 bitsandbytes==0.49.2
 buildkite-test-collector==0.1.9
@@ -35,11 +35,14 @@ 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
@@ -54,7 +57,9 @@ 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
+    # via
+    #   -r requirements/test/cuda.in
+    #   schemathesis
 bitsandbytes==0.49.2
    # via -r requirements/test/cuda.in
 black==24.10.0
@@ -105,6 +110,7 @@ colorama==0.4.6
    # via
    #   perceptron
    #   sacrebleu
+    #   schemathesis
 colorful==0.5.6
    # via ray
 colorlog==6.10.1
@@ -177,7 +183,7 @@ et-xmlfile==2.0.0
    # via openpyxl
 evaluate==0.4.3
    # via lm-eval
-fastapi==0.136.3
+fastapi==0.128.0
    # via
    #   -c requirements/common.txt
    #   gpt-oss
@@ -200,6 +206,8 @@ 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
@@ -261,7 +269,7 @@ h11==0.14.0
    #   uvicorn
 h2==4.3.0
    # via httpx
-harfile==0.5.0
+harfile==0.3.0
    # via schemathesis
 hf-xet==1.4.3
    # via huggingface-hub
@@ -301,7 +309,7 @@ hypothesis==6.131.0
    #   hypothesis-graphql
    #   hypothesis-jsonschema
    #   schemathesis
-hypothesis-graphql==0.13.0
+hypothesis-graphql==0.11.1
    # via schemathesis
 hypothesis-jsonschema==0.23.1
    # via schemathesis
@@ -310,6 +318,7 @@ idna==3.10
    #   anyio
    #   email-validator
    #   httpx
+    #   jsonschema
    #   requests
    #   yarl
 imagehash==4.3.2
@@ -326,6 +335,8 @@ 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
@@ -345,14 +356,15 @@ 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
-jsonschema-rs==0.46.5
-    # via schemathesis
+    #   schemathesis
 jsonschema-specifications==2024.10.1
    # via jsonschema
 junit-xml==1.9
@@ -703,20 +715,18 @@ pydantic-core==2.41.1
 pydantic-extra-types==2.10.5
    # via mistral-common
 pygments==2.18.0
-    # via
-    #   pytest
-    #   rich
+    # via rich
 pyjwt==2.11.0
    # via msal
 pyparsing==3.2.0
    # via matplotlib
-pyrate-limiter==4.4.0
+pyrate-limiter==3.7.0
    # via schemathesis
 pystemmer==3.0.0
    # via mteb
 pytablewriter==1.2.0
    # via lm-eval
-pytest==9.1.0
+pytest==8.3.5
    # via
    #   -r requirements/test/cuda.in
    #   buildkite-test-collector
@@ -727,9 +737,10 @@ pytest==9.1.0
    #   pytest-mock
    #   pytest-rerunfailures
    #   pytest-shard
+    #   pytest-subtests
    #   pytest-timeout
    #   schemathesis
-pytest-asyncio==1.4.0
+pytest-asyncio==0.24.0
    # via -r requirements/test/cuda.in
 pytest-cov==6.3.0
    # via -r requirements/test/cuda.in
@@ -741,10 +752,13 @@ 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
@@ -815,12 +829,15 @@ 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
@@ -851,7 +868,7 @@ safetensors==0.7.0
    #   segmentation-models-pytorch
    #   timm
    #   transformers
-schemathesis==4.21.6
+schemathesis==3.39.15
    # via -r requirements/test/cuda.in
 scikit-image==0.25.2
    # via albumentations
@@ -895,6 +912,7 @@ six==1.16.0
    #   junit-xml
    #   opencensus
    #   python-dateutil
+    #   rfc3339-validator
    #   rouge-score
 smart-open==7.1.0
    # via ray
@@ -920,10 +938,10 @@ sqlalchemy==2.0.41
    #   optuna
 sqlitedict==2.1.0
    # via lm-eval
-starlette==1.3.1
+starlette==0.50.0
    # via
-    #   -c requirements/common.txt
    #   fastapi
+    #   schemathesis
    #   starlette-testclient
 starlette-testclient==0.4.1
    # via schemathesis
@@ -948,8 +966,7 @@ tenacity==9.1.2
    #   gpt-oss
    #   lm-eval
    #   plotly
-    #   schemathesis
-tensorizer==2.10.1
+tensorizer==2.12.1
    # via -r requirements/test/cuda.in
 termcolor==3.1.0
    # via gpt-oss
@@ -973,6 +990,10 @@ 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
@@ -1045,6 +1066,8 @@ 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
@@ -1069,8 +1092,6 @@ typing-extensions==4.15.0
    #   pydantic
    #   pydantic-core
    #   pydantic-extra-types
-    #   pytest-asyncio
-    #   schemathesis
    #   sentence-transformers
    #   sqlalchemy
    #   starlette
@@ -1078,11 +1099,11 @@ typing-extensions==4.15.0
    #   typer
    #   typing-inspection
 typing-inspection==0.4.2
-    # via
-    #   fastapi
-    #   pydantic
+    # via pydantic
 tzdata==2024.2
    # via pandas
+uri-template==1.3.0
+    # via jsonschema
 urllib3==2.2.3
    # via
    #   blobfile
@@ -1101,6 +1122,8 @@ 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
@@ -1112,6 +1135,8 @@ xxhash==3.5.0
    #   datasets
    #   evaluate
 yarl==1.17.1
-    # via aiohttp
+    # via
+    #   aiohttp
+    #   schemathesis
 zipp==3.23.0
    # via importlib-metadata
@@ -1,6 +1,6 @@
 # testing
 pytest
-tensorizer==2.10.1
+tensorizer==2.12.1
 pytest-forked
 pytest-asyncio
 pytest-rerunfailures
@@ -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>=4.0.0 # Required for openai schema test.
+schemathesis>=3.39.15 # Required for openai schema test.
 # quantization
 bitsandbytes>=0.49.2
 buildkite-test-collector==0.1.9
@@ -2,7 +2,7 @@

 # testing
 pytest
-tensorizer==2.10.1
+tensorizer==2.12.1
 pytest-forked
 pytest-asyncio
 pytest-rerunfailures
@@ -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>=4.0.0 # Required for openai schema test
+schemathesis>=3.39.15 # Required for openai schema test
 # quantization
 bitsandbytes==0.49.2
 buildkite-test-collector==0.1.9
@@ -51,12 +51,15 @@ 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
@@ -71,7 +74,9 @@ 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
+    # via
+    #   -r requirements/test/rocm.in
+    #   schemathesis
 bitsandbytes==0.49.2
    # via -r requirements/test/rocm.in
 black==26.3.1
@@ -134,6 +139,7 @@ colorama==0.4.6
    # via
    #   perceptron
    #   sacrebleu
+    #   schemathesis
 colorful==0.5.8
    # via ray
 colorlog==6.10.1
@@ -252,6 +258,8 @@ 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
@@ -320,7 +328,7 @@ h11==0.16.0
    #   uvicorn
 h2==4.3.0
    # via httpx
-harfile==0.5.0
+harfile==0.4.0
    # via schemathesis
 hf-xet==1.4.3
    # via huggingface-hub
@@ -370,7 +378,7 @@ hypothesis==6.151.9
    #   hypothesis-graphql
    #   hypothesis-jsonschema
    #   schemathesis
-hypothesis-graphql==0.13.0
+hypothesis-graphql==0.12.0
    # via schemathesis
 hypothesis-jsonschema==0.23.1
    # via schemathesis
@@ -379,6 +387,7 @@ idna==3.11
    #   anyio
    #   email-validator
    #   httpx
+    #   jsonschema
    #   requests
    #   yarl
 ijson==3.5.0
@@ -399,6 +408,8 @@ 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
@@ -424,6 +435,8 @@ joblib==1.5.3
    #   librosa
    #   nltk
    #   scikit-learn
+jsonpointer==3.1.0
+    # via jsonschema
 jsonschema==4.26.0
    # via
    #   -c requirements/common.txt
@@ -432,8 +445,7 @@ jsonschema==4.26.0
    #   mcp
    #   mistral-common
    #   ray
-jsonschema-rs==0.46.5
-    # via schemathesis
+    #   schemathesis
 jsonschema-specifications==2025.9.1
    # via jsonschema
 junit-xml==1.9
@@ -780,7 +792,7 @@ prometheus-client==0.24.1
    #   opentelemetry-exporter-prometheus
    #   prometheus-fastapi-instrumentator
    #   ray
-prometheus-fastapi-instrumentator==8.0.0
+prometheus-fastapi-instrumentator==7.1.0
    # via
    #   -c requirements/common.txt
    #   -r requirements/test/../common.txt
@@ -864,22 +876,20 @@ pydantic-settings==2.13.1
    #   fastapi
    #   mcp
 pygments==2.19.2
-    # via
-    #   pytest
-    #   rich
+    # via rich
 pyjwt==2.12.1
    # via
    #   mcp
    #   msal
 pyparsing==3.3.2
    # via matplotlib
-pyrate-limiter==4.4.0
+pyrate-limiter==3.9.0
    # via schemathesis
 pystemmer==3.0.0
    # via mteb
 pytablewriter==1.2.1
    # via lm-eval
-pytest==9.1.0
+pytest==8.3.5
    # via
    #   -r requirements/test/rocm.in
    #   buildkite-test-collector
@@ -890,9 +900,10 @@ pytest==9.1.0
    #   pytest-mock
    #   pytest-rerunfailures
    #   pytest-shard
+    #   pytest-subtests
    #   pytest-timeout
    #   schemathesis
-pytest-asyncio==1.4.0
+pytest-asyncio==0.24.0
    # via -r requirements/test/rocm.in
 pytest-cov==6.3.0
    # via -r requirements/test/rocm.in
@@ -904,10 +915,13 @@ 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
@@ -1002,13 +1016,16 @@ 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
@@ -1046,7 +1063,7 @@ safetensors==0.7.0
    #   segmentation-models-pytorch
    #   timm
    #   transformers
-schemathesis==4.21.6
+schemathesis==3.39.15
    # via -r requirements/test/rocm.in
 scikit-image==0.26.0
    # via albumentations
@@ -1103,6 +1120,7 @@ six==1.17.0
    #   junit-xml
    #   opencensus
    #   python-dateutil
+    #   rfc3339-validator
    #   rouge-score
 smart-open==7.5.1
    # via ray
@@ -1131,14 +1149,13 @@ sqlitedict==2.1.0
    # via lm-eval
 sse-starlette==3.3.4
    # via mcp
-starlette==1.3.1
+starlette==0.52.1
    # 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
@@ -1165,8 +1182,7 @@ tenacity==9.1.4
    # via
    #   gpt-oss
    #   lm-eval
-    #   schemathesis
-tensorizer==2.10.1
+tensorizer==2.12.1
    # via
    #   -c requirements/rocm.txt
    #   -r requirements/test/rocm.in
@@ -1199,6 +1215,10 @@ 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
@@ -1281,10 +1301,8 @@ typing-extensions==4.15.0
    #   pydantic
    #   pydantic-core
    #   pydantic-extra-types
-    #   pytest-asyncio
    #   referencing
    #   rich-toolkit
-    #   schemathesis
    #   sentence-transformers
    #   sqlalchemy
    #   starlette
@@ -1299,6 +1317,10 @@ 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
@@ -1329,6 +1351,8 @@ 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
@@ -1346,7 +1370,9 @@ xxhash==3.6.0
    #   datasets
    #   evaluate
 yarl==1.23.0
-    # via aiohttp
+    # via
+    #   aiohttp
+    #   schemathesis
 z3-solver==4.15.4.0
    # via tilelang
 zipp==3.23.0
@@ -593,9 +593,8 @@ soxr==0.5.0.post1
    #   mistral-common
 sqlitedict==2.1.0
    # via lm-eval
-starlette==1.3.1
+starlette==1.0.0
    # via
-    #   -c requirements/common.txt
    #   fastapi
    #   starlette-testclient
 starlette-testclient==0.4.1
@@ -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/v0.1.10/vllm_xpu_kernels-0.1.10-cp38-abi3-manylinux_2_28_x86_64.whl
+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
@@ -769,7 +769,6 @@ class precompiled_wheel_utils:
                            "vllm/_C.abi3.so",
                            "vllm/_C_stable_libtorch.abi3.so",
                            "vllm/_moe_C_stable_libtorch.abi3.so",
-                            "vllm/_qutlass_C.abi3.so",
                            "vllm/_flashmla_C.abi3.so",
                            "vllm/_flashmla_extension_C.abi3.so",
                            "vllm/_sparse_flashmla_C.abi3.so",
@@ -1136,7 +1135,6 @@ if _is_cuda():
        # DeepGEMM requires CUDA 12.3+ (SM90/SM100)
        # Optional since it won't build on unsupported architectures
        ext_modules.append(CMakeExtension(name="vllm._deep_gemm_C", optional=True))
-        ext_modules.append(CMakeExtension(name="vllm._qutlass_C", optional=True))
    # fmha_sm100 is a Python/CuTe-DSL package installed into vllm.third_party.
    ext_modules.append(CMakeExtension(name="vllm.fmha_sm100", optional=True))

@@ -1151,8 +1149,7 @@ if _is_cpu():
        ext_modules.append(CMakeExtension(name="vllm._C"))

 if _build_custom_ops():
-    if _is_hip():
-        ext_modules.append(CMakeExtension(name="vllm._C"))
+    ext_modules.append(CMakeExtension(name="vllm._C"))
    if _is_cuda() or _is_hip():
        ext_modules.append(CMakeExtension(name="vllm._C_stable_libtorch"))
        ext_modules.append(CMakeExtension(name="vllm._moe_C_stable_libtorch"))
@@ -53,6 +53,38 @@ class SPTestSettings:
    runner: RunnerOption
    test_options: SPTestOptions

+    @staticmethod
+    def detailed(
+        *,
+        tp_base: int = 2,
+        pp_base: int = 1,
+        multi_node_only: bool = False,
+        runner: RunnerOption = "auto",
+        load_format: str | None = None,
+    ):
+        parallel_setups = []
+        for eager_mode_val in [False, True]:
+            for pp_multiplier in [1, 2]:
+                for chunked_prefill_val in [False, True]:
+                    parallel_setups.append(
+                        ParallelSetup(
+                            tp_size=tp_base,
+                            pp_size=pp_multiplier * pp_base,
+                            fuse_norm_quant=False,
+                            fuse_act_quant=False,
+                            eager_mode=eager_mode_val,
+                            chunked_prefill=chunked_prefill_val,
+                        )
+                    )
+        return SPTestSettings(
+            parallel_setups=parallel_setups,
+            distributed_backends=["mp", "ray"],
+            runner=runner,
+            test_options=SPTestOptions(
+                multi_node_only=multi_node_only, load_format=load_format
+            ),
+        )
+
    @staticmethod
    def fast(
        *,
@@ -62,26 +94,23 @@ class SPTestSettings:
        multi_node_only: bool = False,
        load_format: str | None = None,
    ):
+        parallel_setups = []
+        for eager_mode_val in [False, True]:
+            for pp_multiplier in [1, 2]:
+                for chunked_prefill_val in [False, True]:
+                    parallel_setups.append(
+                        ParallelSetup(
+                            tp_size=tp_base,
+                            pp_size=pp_multiplier * pp_base,
+                            fuse_norm_quant=False,
+                            fuse_act_quant=False,
+                            eager_mode=eager_mode_val,
+                            chunked_prefill=chunked_prefill_val,
+                        )
+                    )
        return SPTestSettings(
-            parallel_setups=[
-                ParallelSetup(
-                    tp_size=tp_base,
-                    pp_size=pp_base,
-                    fuse_norm_quant=False,
-                    fuse_act_quant=False,
-                    eager_mode=False,
-                    chunked_prefill=True,
-                ),
-                ParallelSetup(
-                    tp_size=tp_base,
-                    pp_size=2 * pp_base,
-                    fuse_norm_quant=False,
-                    fuse_act_quant=False,
-                    eager_mode=False,
-                    chunked_prefill=True,
-                ),
-            ],
-            distributed_backends=["mp"],
+            parallel_setups=parallel_setups,
+            distributed_backends=["mp", "ray"],
            runner=runner,
            test_options=SPTestOptions(
                multi_node_only=multi_node_only, load_format=load_format
@@ -1096,50 +1096,3 @@ class TestMessageStartIncludesTypeAndRole:
        message = events[0][1]["message"]
        assert message["type"] == "message"
        assert message["role"] == "assistant"
-
-
-# ======================================================================
-# Auto-detection of system-first template requirement
-# ======================================================================
-
-
-Q35_TEMPLATE = (
-    "{%- for message in messages %}"
-    "{%- if message.role == 'system' %}"
-    "{%- if not loop.first %}"
-    "{{- raise_exception('System message must be at the beginning.') }}"
-    "{%- endif %}"
-    "{%- endif %}"
-    "{%- endfor %}"
-)
-
-
-class TestDetectMergeInlineSystem:
-    """Verify _detect_merge_inline_system auto-detection.
-
-    Tests three scenarios:
-    1. Template with system-first guard (e.g. Qwen) → merge needed
-    2. Template without restrictions → no merge, cache-friendly
-    3. No template provided → safe default: merge
-    """
-
-    def test_qwen_template_requires_merge(self):
-        """Template with loop.first guard rejects mid-conversation system."""
-        assert (
-            AnthropicServingMessages._detect_merge_inline_system(Q35_TEMPLATE) is True
-        )
-
-    def test_no_restriction_no_merge(self):
-        """Template without restriction accepts mid-conversation system."""
-        assert (
-            AnthropicServingMessages._detect_merge_inline_system(
-                "{%- for message in messages %}"
-                "{{- message.role }}: {{ message.content }}\n"
-                "{%- endfor %}"
-            )
-            is False
-        )
-
-    def test_no_template_defaults_merge(self):
-        """No chat_template → conservative default: merge."""
-        assert AnthropicServingMessages._detect_merge_inline_system(None) is True
@@ -6,22 +6,15 @@ from typing import Final
 import pytest
 import schemathesis
 from hypothesis import HealthCheck, settings
-from schemathesis import GenerationMode
-from schemathesis.config import (
-    ChecksConfig,
-    CoveragePhaseConfig,
-    GenerationConfig,
-    PhasesConfig,
-    PositiveDataAcceptanceConfig,
-    ProjectConfig,
-    ProjectsConfig,
-    SchemathesisConfig,
-)
+from schemathesis import GenerationConfig
+from schemathesis.models import Case

 from vllm.platforms import current_platform

 from ...utils import RemoteOpenAIServer

+schemathesis.experimental.OPEN_API_3_1.enable()
+
 MODEL_NAME = "HuggingFaceTB/SmolVLM-256M-Instruct"
 MAXIMUM_IMAGES = 2
 _ROCM_TIMEOUT_MULTIPLIER = 3 if current_platform.is_rocm() else 1
@@ -51,38 +44,21 @@ def server():
@pytest.fixture(scope="module")
 def get_schema(server):
    # avoid generating null (\x00) bytes in strings during test case generation
-    return schemathesis.openapi.from_url(
+    return schemathesis.openapi.from_uri(
        f"{server.url_root}/openapi.json",
-        config=SchemathesisConfig(
-            projects=ProjectsConfig(
-                default=ProjectConfig(
-                    generation=GenerationConfig(
-                        allow_x00=False,
-                        modes=[GenerationMode.POSITIVE],
-                    ),
-                    checks=ChecksConfig(
-                        positive_data_acceptance=PositiveDataAcceptanceConfig(
-                            enabled=False,
-                        ),
-                    ),
-                    phases=PhasesConfig(
-                        coverage=CoveragePhaseConfig(enabled=False),
-                    ),
-                ),
-            ),
-        ),
+        generation_config=GenerationConfig(allow_x00=False),
    )


-schema = schemathesis.pytest.from_fixture("get_schema")
+schema = schemathesis.from_pytest_fixture("get_schema")


@schemathesis.hook
-def before_generate_case(context: schemathesis.HookContext, strategy):
+def before_generate_case(context: schemathesis.hooks.HookContext, strategy):
    op = context.operation
    assert op is not None

-    def no_invalid_types(case: schemathesis.Case):
+    def no_invalid_types(case: schemathesis.models.Case):
        """
        Skips tool_calls with `"type": "custom"` which schemathesis incorrectly
        generates instead of the valid `"type": "function"`.
@@ -92,25 +68,39 @@ def before_generate_case(context: schemathesis.HookContext, strategy):
            -d '{"messages": [{"role": "assistant", "tool_calls": [{"custom": {"input": "", "name": ""}, "id": "", "type": "custom"}]}]}' \
            http://localhost:8000/v1/chat/completions
        """  # noqa: E501
-        if (
-            hasattr(case, "body")
-            and isinstance(case.body, dict)
-            and "messages" in case.body
-            and isinstance(case.body["messages"], list)
-            and len(case.body["messages"]) > 0
-        ):
-            for message in case.body["messages"]:
-                if not isinstance(message, dict):
-                    continue
+        if hasattr(case, "body") and isinstance(case.body, dict):
+            if (
+                "messages" in case.body
+                and isinstance(case.body["messages"], list)
+                and len(case.body["messages"]) > 0
+            ):
+                for message in case.body["messages"]:
+                    if not isinstance(message, dict):
+                        continue

-                tool_calls = message.get("tool_calls", [])
-                if isinstance(tool_calls, list):
-                    for tool_call in tool_calls:
-                        if isinstance(tool_call, dict):
-                            if tool_call.get("type") != "function":
-                                return False
-                            if "custom" in tool_call:
-                                return False
+                    tool_calls = message.get("tool_calls", [])
+                    if isinstance(tool_calls, list):
+                        for tool_call in tool_calls:
+                            if isinstance(tool_call, dict):
+                                if tool_call.get("type") != "function":
+                                    return False
+                                if "custom" in tool_call:
+                                    return False
+
+            # Sometimes structured_outputs.grammar is generated to be empty
+            # Causing a server error in EBNF grammar parsing
+            # https://github.com/vllm-project/vllm/pull/22587#issuecomment-3195253421
+            structured_outputs = case.body.get("structured_outputs", {})
+            grammar = (
+                structured_outputs.get("grammar")
+                if isinstance(structured_outputs, dict)
+                else None
+            )
+
+            if grammar == "":
+                # Allow None (will be handled as no grammar)
+                # But skip empty strings
+                return False

        return True

@@ -118,6 +108,7 @@ def before_generate_case(context: schemathesis.HookContext, strategy):


@schema.parametrize()
+@schema.override(headers={"Content-Type": "application/json"})
@settings(
    deadline=LONG_TIMEOUT_SECONDS * 1000,
    max_examples=50,
@@ -131,7 +122,7 @@ def before_generate_case(context: schemathesis.HookContext, strategy):
    # generating large-but-valid request bodies before vLLM is called.
    suppress_health_check=[HealthCheck.filter_too_much, HealthCheck.data_too_large],
 )
-def test_openapi_stateless(case: schemathesis.Case):
+def test_openapi_stateless(case: Case):
    key = (
        case.operation.method.upper(),
        case.operation.path,
@@ -160,8 +151,4 @@ def test_openapi_stateless(case: schemathesis.Case):
    }.get(key, DEFAULT_TIMEOUT_SECONDS)

    # No need to verify SSL certificate for localhost
-    case.call_and_validate(
-        verify=False,
-        timeout=timeout,
-        headers={"Content-Type": "application/json"},
-    )
+    case.call_and_validate(verify=False, timeout=timeout)
@@ -1,65 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""Tests that validation_exception_handler populates the `param` field
-in its error response using the Pydantic error's `loc`, even when no
-custom VLLMValidationError context is present.
-
-Previously, `param` was only populated for errors carrying a custom
-VLLMValidationError in their Pydantic `ctx`. Plain validation failures
-(missing fields, wrong types) left `param` as None, even though the
-field name was readily available from `error['loc']`.
-"""
-
-import json
-from types import SimpleNamespace
-
-import pytest
-from fastapi.exceptions import RequestValidationError
-
-from vllm.entrypoints.serve.utils.server_utils import validation_exception_handler
-
-
-def _fake_request(log_error_stack: bool = False) -> SimpleNamespace:
-    """Minimal stand-in for a FastAPI Request - just enough for the
-    handler to read req.app.state.args.log_error_stack."""
-    return SimpleNamespace(
-        app=SimpleNamespace(
-            state=SimpleNamespace(args=SimpleNamespace(log_error_stack=log_error_stack))
-        ),
-        state=SimpleNamespace(),  # no request_metadata -> hasattr(...) is False
-    )
-
-
-class TestValidationErrorParamFallback:
-    """Ensure `param` falls back to the Pydantic error's `loc` when no
-    custom VLLMValidationError context is present."""
-
-    @pytest.mark.parametrize(
-        ("error_type", "msg"),
-        [
-            ("missing", "Field required"),
-            ("list_type", "Input should be a valid list"),
-        ],
-        ids=["missing-field", "wrong-type"],
-    )
-    @pytest.mark.asyncio
-    async def test_param_falls_back_to_loc(self, error_type: str, msg: str):
-        errors = [{"type": error_type, "loc": ("body", "messages"), "msg": msg}]
-        exc = RequestValidationError(errors)
-
-        response = await validation_exception_handler(_fake_request(), exc)
-        body = json.loads(response.body)
-
-        assert body["error"]["param"] == "body.messages"
-
-    @pytest.mark.asyncio
-    async def test_param_fallback_does_not_crash_on_non_dict_error(self):
-        """Schemathesis fuzzing found that errors[0] isn't always a dict.
-        The fallback must not crash in that case - it should just leave
-        param as None instead of raising."""
-        exc = RequestValidationError(["some unexpected non-dict error"])
-
-        response = await validation_exception_handler(_fake_request(), exc)
-        body = json.loads(response.body)
-
-        assert body["error"]["param"] is None
@@ -78,16 +78,7 @@ def test_gsm8k_correctness(config_filename):
            "Skipping DeepSeek-V3.2 and DeepSeek-R1 on ROCm platforms "
            "due to agent pool disk space issues and pod evictions."
        )
-    if current_platform.is_rocm() and (
-        "Qwen3.5-35B-A3B-MXFP4-AITER-TP2" in config_filename.name
-    ):
-        from vllm.platforms.rocm import on_gfx950

-        if not on_gfx950():
-            pytest.skip(
-                "Skipping Qwen3.5-35B-A3B-MXFP4-AITER-TP2 on non-GFX950 platforms. "
-                "The quantization scheme is not supported on non-GFX950 platforms."
-            )
    # Parse server arguments from config (use shlex to handle quoted strings)
    server_args_str = eval_config.get("server_args", "")
    server_args = shlex.split(server_args_str) if server_args_str else []
@@ -60,10 +60,8 @@ def test_rocm_compressed_tensors_w8a8(
    vllm_runner, example_prompts, model_path, max_tokens, num_logprobs
 ):
    dtype = "bfloat16"
-    # Pin to TRITON_ATTN, see https://github.com/vllm-project/vllm/issues/46179
-    with vllm_runner(
-        model_path, dtype=dtype, attention_backend="TRITON_ATTN"
-    ) as vllm_model:
+
+    with vllm_runner(model_path, dtype=dtype) as vllm_model:
        vllm_model.generate_greedy_logprobs(example_prompts, max_tokens, num_logprobs)


@@ -482,127 +482,3 @@ def test_kernels_hidden_size(
            seq_length=128,
            add_inputs=True,
        )
-
-
-@pytest.mark.parametrize("device", DEVICES)
-def test_add_lora_fused_moe_early_exit(device):
-    """
-    Ensures add_lora_fused_moe does not invoke the LoRA kernel or
-    modify the output tensor when no_lora_flag_cpu is True
-    """
-    from types import SimpleNamespace
-
-    from vllm.lora.punica_wrapper.punica_gpu import PunicaWrapperGPU
-
-    torch.set_default_device(device)
-    torch.accelerator.set_device_index(device)
-
-    max_loras, num_tokens = 4, 16
-    num_experts, top_k, max_lora_rank = 8, 2, 16
-    K, N = 256, 128
-
-    # build PunicaWrapperGPU with minimal lora_config mock
-    lora_config = SimpleNamespace(
-        max_loras=max_loras,
-        specialize_active_lora=False,
-    )
-    wrapper = PunicaWrapperGPU(
-        max_num_batched_tokens=num_tokens,
-        max_batches=num_tokens,
-        device=device,
-        lora_config=lora_config,
-    )
-
-    # simulate a prior LoRA batch so the internal mapping is
-    # populated with stale LoRA IDs
-    lora_mapping = torch.zeros(
-        num_tokens,
-        dtype=torch.int32,
-        device=device,
-    )
-    lora_mapping[:8] = 1
-    lora_mapping[8:] = 2
-    wrapper.token_mapping_meta.prepare_tensors(lora_mapping)
-
-    # simulate a base-model batch (all -1)
-    base_mapping = torch.full(
-        (num_tokens,),
-        -1,
-        dtype=torch.int32,
-        device=device,
-    )
-    wrapper.token_mapping_meta.prepare_tensors(base_mapping)
-
-    assert wrapper.token_mapping_meta.no_lora_flag_cpu[0].item() is True
-
-    # dummy tensors for add_lora_fused_moe
-    y = torch.rand(num_tokens, top_k, N, dtype=torch.bfloat16, device=device)
-    y_snapshot = y.clone()
-    x = torch.rand(num_tokens, K, dtype=torch.bfloat16, device=device)
-
-    lora_a_stacked = (
-        torch.rand(
-            max_loras,
-            num_experts,
-            max_lora_rank,
-            K,
-            dtype=torch.bfloat16,
-            device=device,
-        ),
-    )
-    lora_b_stacked = (
-        torch.rand(
-            max_loras,
-            num_experts,
-            N,
-            max_lora_rank,
-            dtype=torch.bfloat16,
-            device=device,
-        ),
-    )
-    topk_weights = torch.ones(
-        num_tokens,
-        top_k,
-        dtype=torch.float32,
-        device=device,
-    )
-    adapter_enabled = torch.ones(
-        max_loras + 1,
-        dtype=torch.int32,
-        device=device,
-    )
-    shrink_config = expand_config = {
-        "BLOCK_SIZE_M": 16,
-        "BLOCK_SIZE_N": 32,
-        "BLOCK_SIZE_K": 64,
-        "GROUP_SIZE_M": 1,
-        "NUM_WARPS": 4,
-        "NUM_STAGES": 3,
-        "SPLIT_K": 1,
-    }
-
-    # call add_lora_fused_moe - the early exit should prevent any
-    # modification to the output
-    wrapper.add_lora_fused_moe(
-        y=y,
-        x=x,
-        lora_a_stacked=lora_a_stacked,
-        lora_b_stacked=lora_b_stacked,
-        topk_weights=topk_weights,
-        sorted_token_ids=None,
-        expert_ids=torch.zeros(
-            num_tokens * top_k,
-            dtype=torch.int32,
-            device=device,
-        ),
-        num_tokens_post_padded=None,
-        max_lora_rank=max_lora_rank,
-        top_k_num=top_k,
-        shrink_config=shrink_config,
-        expand_config=expand_config,
-        adapter_enabled=adapter_enabled,
-    )
-
-    assert torch.equal(y, y_snapshot), (
-        "add_lora_fused_moe modified output tensor despite no_lora_flag_cpu=True"
-    )
@@ -130,12 +130,8 @@ def test_models(
        monkeypatch.setenv("VLLM_ROCM_USE_AITER", "1")
        if model == "TitanML/tiny-mixtral":
            # Untrained model: near-uniform logits make argmax sensitive to
-            # AITER's bfloat16 rounding error. Route the plain rms_norm and the
-            # fused MoE (whose near-uniform router logits flip expert selection
-            # under ~1 ULP drift) through the native kernels for this model.
-            # See ROCm/aiter#3806 for the tracking issue and minimal repro.
+            # AITER's bfloat16 rounding error in plain rms_norm.
            monkeypatch.setenv("VLLM_ROCM_USE_AITER_RMSNORM", "0")
-            monkeypatch.setenv("VLLM_ROCM_USE_AITER_MOE", "0")
    elif use_rocm_aiter and model not in AITER_MODEL_LIST:
        # Skip model that are not using AITER tests.
        # When more AITER kernels are added, this list will not be
@@ -25,7 +25,7 @@ TEST_IMAGE_NAMES = [
 ]
 MAX_MODEL_LEN = 8192
 REQUESTS_PER_ROUND = 4
-WARMUP_ROUNDS = 2
+WARMUP_ROUNDS = 1
 MEASURED_ROUNDS = 16
 GPU_GROWTH_THRESHOLD_MIB = 0
 CPU_PEAK_GROWTH_THRESHOLD_MIB = 0
@@ -2,7 +2,6 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project

 import pytest
-import torch
 import torch.nn.functional as F
 import transformers.utils
 from PIL import Image
@@ -53,7 +52,6 @@ def _get_cherry_blossom_image() -> Image.Image:
    )


-@torch.inference_mode()
 def _run_test(
    hf_runner: type[HfRunner],
    vllm_runner: type[VllmRunner],
@@ -96,9 +96,6 @@ class MockTokenizer:
        return "".join(parts)


-CHUNK_SIZES = [1, 2, 3, 5, 11, 23, None]
-
-
 def make_mock_tokenizer(sample: Sample) -> MockTokenizer:
    """Build a mock tokenizer from a sample's vocab and token data."""
    return MockTokenizer(
@@ -19,7 +19,6 @@ import pytest
 from pydantic import TypeAdapter

 from tests.parser.engine.replay_harness import (
-    CHUNK_SIZES,
    MockTokenizer,
    assert_parse_output,
    collect_output,
@@ -114,6 +113,8 @@ _PAIRINGS = _discover_pairings()

 _ALL_SAMPLES = [(p.parser_cls, s) for p in _PAIRINGS for s in p.samples]

+CHUNK_SIZES = [1, 2, 3, 5, 11, 23, None]
+

@pytest.mark.parametrize("chunk_size", CHUNK_SIZES, ids=lambda c: f"chunk={c}")
@pytest.mark.parametrize(
@@ -1,181 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""Regression test for U+FFFD leak at reasoning→content transition.
-
-When byte-fallback tokens span the reasoning/content boundary,
-decoding isolated content-side token IDs via tokenizer.decode()
-produces U+FFFD (Unicode replacement character).  The fix flushes
-the reasoning parser's engine lexer instead.
-
-Reproduces the bug at various chunk sizes and validates that the
-fix prevents U+FFFD from leaking into streamed content.
-"""
-
-from __future__ import annotations
-
-import pytest
-
-from tests.parser.engine.replay_harness import (
-    CHUNK_SIZES,
-    MockTokenizer,
-    collect_output,
-    replay_streaming,
-)
-from vllm.parser.abstract_parser import DelegatingParser
-from vllm.parser.engine.registered_adapters import (
-    Glm47MoeParserReasoningAdapter,
-    Glm47MoeParserToolAdapter,
-    Qwen3ParserReasoningAdapter,
-    Qwen3ParserToolAdapter,
-)
-
-
-class ByteFallbackMockTokenizer(MockTokenizer):
-    """MockTokenizer that returns U+FFFD for specified token IDs.
-
-    Simulates byte-fallback tokenizer behavior where isolated
-    partial-byte tokens decode to the Unicode replacement character.
-    """
-
-    def __init__(
-        self,
-        vocab: dict[str, int],
-        tokens: list[tuple[int, str]],
-        ufffd_token_ids: set[int],
-    ) -> None:
-        super().__init__(vocab, tokens)
-        self._ufffd_token_ids = frozenset(ufffd_token_ids)
-
-    def decode(self, ids: list[int], skip_special_tokens: bool = False) -> str:
-        parts: list[str] = []
-        for tid in ids:
-            if skip_special_tokens and tid in self._special_ids:
-                continue
-            if tid in self._ufffd_token_ids:
-                parts.append("�")
-            else:
-                text = self._token_decode_map.get(tid, f"?{tid}?")
-                parts.append(text)
-        return "".join(parts)
-
-
-# ── Model-specific DelegatingParser subclasses ───────────────────────
-
-
-class _Glm47Delegating(DelegatingParser):
-    reasoning_parser_cls = Glm47MoeParserReasoningAdapter
-    tool_parser_cls = Glm47MoeParserToolAdapter
-
-
-class _Qwen3Delegating(DelegatingParser):
-    reasoning_parser_cls = Qwen3ParserReasoningAdapter
-    tool_parser_cls = Qwen3ParserToolAdapter
-
-
-# ── Shared test data ─────────────────────────────────────────────────
-
-_SHARED_TOKENS: list[tuple[int, str]] = [
-    (100, "Let me"),
-    (101, " think"),
-    (102, " about"),
-    (103, " Samsung."),
-    (51, "</think>"),
-    (200, "삼성"),
-    (201, "전자의"),
-    (202, " 주가를"),
-    (203, " 분석합니다."),
-]
-
-_SHARED_UFFFD_IDS: set[int] = {200}
-
-EXPECTED_REASONING = "Let me think about Samsung."
-EXPECTED_CONTENT = "삼성전자의 주가를 분석합니다."
-
-_MODEL_CONFIGS = [
-    pytest.param(
-        {
-            "<think>": 50,
-            "</think>": 51,
-            "<tool_call>": 60,
-            "</tool_call>": 61,
-            "<arg_key>": 62,
-            "</arg_key>": 63,
-            "<arg_value>": 64,
-            "</arg_value>": 65,
-        },
-        _Glm47Delegating,
-        id="glm47",
-    ),
-    pytest.param(
-        {
-            "<think>": 50,
-            "</think>": 51,
-            "<tool_call>": 60,
-            "</tool_call>": 61,
-        },
-        _Qwen3Delegating,
-        id="qwen3",
-    ),
-]
-
-
-# ── Tests ────────────────────────────────────────────────────────────
-
-
-class TestUfffdReasoningTransition:
-    """U+FFFD must not appear at the reasoning→content transition."""
-
-    @pytest.mark.parametrize("vocab,delegating_cls", _MODEL_CONFIGS)
-    @pytest.mark.parametrize("chunk_size", CHUNK_SIZES, ids=lambda c: f"chunk={c}")
-    def test_no_ufffd(self, chunk_size, vocab, delegating_cls):
-        tokenizer = ByteFallbackMockTokenizer(vocab, _SHARED_TOKENS, _SHARED_UFFFD_IDS)
-        parser = delegating_cls(tokenizer)
-        deltas = replay_streaming(
-            parser,
-            _SHARED_TOKENS,
-            chunk_size=chunk_size,
-            finished_on_last=True,
-        )
-        output = collect_output(deltas)
-
-        assert "�" not in output.content, (
-            f"U+FFFD leaked into content: {output.content!r}"
-        )
-        assert output.content == EXPECTED_CONTENT
-        assert output.reasoning == EXPECTED_REASONING
-
-    def test_byte_fallback_tokenizer_produces_ufffd(self):
-        """Validate the fixture: decode() returns U+FFFD for isolated
-        byte-fallback token IDs, proving the old code path would leak."""
-        vocab = dict(_MODEL_CONFIGS[0].values[0])
-        tokenizer = ByteFallbackMockTokenizer(vocab, _SHARED_TOKENS, _SHARED_UFFFD_IDS)
-        assert tokenizer.decode([200]) == "�"
-
-    @pytest.mark.parametrize("chunk_size", CHUNK_SIZES, ids=lambda c: f"chunk={c}")
-    def test_multiple_ufffd_tokens_at_boundary(self, chunk_size):
-        """Multiple consecutive byte-fallback tokens at the boundary."""
-        tokens: list[tuple[int, str]] = [
-            (100, "Reasoning."),
-            (51, "</think>"),
-            (200, "삼"),
-            (201, "성"),
-            (202, "전자"),
-        ]
-        ufffd_ids: set[int] = {200, 201}
-        vocab = dict(_MODEL_CONFIGS[0].values[0])
-
-        tokenizer = ByteFallbackMockTokenizer(vocab, tokens, ufffd_ids)
-        parser = _Glm47Delegating(tokenizer)
-        deltas = replay_streaming(
-            parser,
-            tokens,
-            chunk_size=chunk_size,
-            finished_on_last=True,
-        )
-        output = collect_output(deltas)
-
-        assert "�" not in output.content, (
-            f"U+FFFD leaked into content: {output.content!r}"
-        )
-        assert output.content == "삼성전자"
-        assert output.reasoning == "Reasoning."
@@ -143,12 +143,6 @@ SCENARIOS: list[Scenario] = [
        tool_calls=[_READ_TOOL],
        after_tool_response=True,
    ),
-    Scenario(
-        id="empty-tool-block",
-        description="Empty tool block followed by content (edge case recovery)",
-        content="Content after empty tools.",
-        tool_calls=[],
-    ),
 ]


@@ -350,11 +344,8 @@ def _qwen3_segments(scenario: Scenario) -> list[tuple[str, bool]]:
    segs: list[tuple[str, bool]] = []
    if scenario.reasoning is not None:
        segs.append((scenario.reasoning, False))
-    if scenario.content is not None or scenario.tool_calls is not None:
+    if scenario.content is not None or scenario.tool_calls:
        segs.append(("</think>", True))
-    if scenario.tool_calls is not None and not scenario.tool_calls:
-        segs.append(("<tool_call>", True))
-        segs.append(("</tool_call>", True))
    if scenario.content is not None:
        segs.append((scenario.content, False))
    if scenario.tool_calls:
@@ -446,11 +437,8 @@ def _minimax_m2_segments(scenario: Scenario) -> list[tuple[str, bool]]:
    segs: list[tuple[str, bool]] = []
    if scenario.reasoning is not None:
        segs.append((scenario.reasoning, False))
-    if scenario.content is not None or scenario.tool_calls is not None:
+    if scenario.content is not None or scenario.tool_calls:
        segs.append(("</think>", True))
-    if scenario.tool_calls is not None and not scenario.tool_calls:
-        segs.append(("<minimax:tool_call>", True))
-        segs.append(("</minimax:tool_call>", True))
    if scenario.content is not None:
        segs.append((scenario.content, False))
    if scenario.tool_calls:
@@ -546,9 +534,6 @@ def _gemma4_segments(scenario: Scenario) -> list[tuple[str, bool]]:
        segs.append((_GEMMA4_THOUGHT_PREFIX, False))
        segs.append((scenario.reasoning, False))
        segs.append(("<channel|>", True))
-    if scenario.tool_calls is not None and not scenario.tool_calls:
-        segs.append(("<|tool_call>", True))
-        segs.append(("<tool_call|>", True))
    if scenario.content is not None:
        segs.append((scenario.content, False))
    if scenario.tool_calls:
@@ -18,7 +18,6 @@ from vllm.model_executor.kernels.linear.scaled_mm.ScaledMMLinearKernel import (
    FP8ScaledMMLinearLayerConfig,
 )
 from vllm.model_executor.layers.quantization.utils.quant_utils import (
-    get_fp8_min_max,
    kFp8DynamicTokenSym,
    kFp8StaticChannelSym,
    kFp8StaticTensorSym,
@@ -310,7 +309,7 @@ def test_hipb_mm_kernel_forward_accuracy(enable_hipb_mm_kernel):
    _check_bpreshuffle_runtime_support(weight_shape, num_tokens=num_tokens)

    fp8_dtype = current_platform.fp8_dtype()
-    fp8_max = get_fp8_min_max()[1]
+    fp8_max = torch.finfo(fp8_dtype).max
    device = torch.device("cuda")

    # Build a bf16 weight and quantize per output channel (one scale per row).
@@ -1,135 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""Tests for contiguous KV cache packing in _get_kv_cache_config_deepseek_v4."""
-
-from unittest.mock import MagicMock
-
-import pytest
-import torch
-
-from vllm.v1.core.kv_cache_utils import _get_kv_cache_config_deepseek_v4
-from vllm.v1.kv_cache_interface import (
-    KVCacheGroupSpec,
-    MLAAttentionSpec,
-    UniformTypeKVCacheSpecs,
-)
-
-
-def _make_mla_spec(page_size: int, block_size: int = 256) -> MLAAttentionSpec:
-    return MLAAttentionSpec(
-        block_size=block_size,
-        num_kv_heads=1,
-        head_size=512,
-        dtype=torch.uint8,
-        page_size_padded=page_size,
-        cache_dtype_str="fp8_ds_mla",
-        model_version="deepseek_v4",
-        alignment=576,
-    )
-
-
-def _make_groups(n_c4, n_c128, n_swa):
-    PS_C4_MLA = 37440
-    PS_C4_IDX = 8640
-    PS_C128 = 1728
-    PS_SWA = 37440
-
-    mla_specs = {}
-    for i in range(n_c4):
-        mla_specs[f"c4_mla.{i}"] = _make_mla_spec(PS_C4_MLA)
-        mla_specs[f"c4_idx.{i}"] = _make_mla_spec(PS_C4_IDX)
-    for i in range(n_c128):
-        mla_specs[f"c128_mla.{i}"] = _make_mla_spec(PS_C128)
-
-    mla_group = KVCacheGroupSpec(
-        layer_names=list(mla_specs.keys()),
-        kv_cache_spec=UniformTypeKVCacheSpecs(block_size=256, kv_cache_specs=mla_specs),
-    )
-
-    swa_specs = {}
-    for i in range(n_swa):
-        swa_specs[f"swa.{i}"] = _make_mla_spec(PS_SWA)
-
-    swa_group = KVCacheGroupSpec(
-        layer_names=list(swa_specs.keys()),
-        kv_cache_spec=UniformTypeKVCacheSpecs(block_size=256, kv_cache_specs=swa_specs),
-    )
-
-    return [mla_group, swa_group]
-
-
-def _mock_vllm_config():
-    config = MagicMock()
-    config.cache_config.num_gpu_blocks_override = None
-    return config
-
-
-def _run(n_c4=3, n_c128=2, n_swa=5, mem=100 * 1024 * 1024):
-    groups = _make_groups(n_c4, n_c128, n_swa)
-    return _get_kv_cache_config_deepseek_v4(_mock_vllm_config(), groups, mem)
-
-
-def _page_sizes_by_layer(
-    groups: list[KVCacheGroupSpec],
-) -> dict[str, int]:
-    page_sizes = {}
-    for group in groups:
-        specs = group.kv_cache_spec.kv_cache_specs
-        for layer_name in group.layer_names:
-            page_sizes[layer_name] = specs[layer_name].page_size_bytes
-    return page_sizes
-
-
-class TestInterleavedPacking:
-    def test_all_tensors_have_block_stride(self):
-        _, tensors = _run()
-        for t in tensors:
-            assert t.block_stride > 0
-
-    def test_all_tensors_share_same_size(self):
-        _, tensors = _run()
-        sizes = set(t.size for t in tensors)
-        assert len(sizes) == 1
-        assert sizes.pop() > 0
-
-    def test_offsets_within_one_block(self):
-        _, tensors = _run()
-        for t in tensors:
-            assert t.offset < t.block_stride
-
-    def test_all_layers_accounted_for(self):
-        n_c4, n_c128, n_swa = 5, 4, 7
-        _, tensors = _run(n_c4=n_c4, n_c128=n_c128, n_swa=n_swa)
-        all_names = set()
-        for t in tensors:
-            all_names.update(t.shared_by)
-        expected = n_c4 * 2 + n_c128 + n_swa
-        assert len(all_names) == expected
-
-    def test_strided_views_are_independent(self):
-        groups = _make_groups(n_c4=3, n_c128=2, n_swa=5)
-        page_sizes = _page_sizes_by_layer(groups)
-        num_blocks, tensors = _get_kv_cache_config_deepseek_v4(
-            _mock_vllm_config(), groups, 100 * 1024 * 1024
-        )
-        backing = torch.zeros(tensors[0].size, dtype=torch.uint8)
-        views = []
-        for t in tensors:
-            page_size = page_sizes[t.shared_by[0]]
-            v = torch.as_strided(
-                backing,
-                size=(num_blocks, page_size),
-                stride=(t.block_stride, 1),
-                storage_offset=t.offset,
-            )
-            views.append(v)
-
-        for i, v in enumerate(views):
-            v.fill_(i + 1)
-
-        for i, v in enumerate(views):
-            assert (v == i + 1).all(), f"View {i} was corrupted"
-
-
-if __name__ == "__main__":
-    pytest.main([__file__, "-v"])
@@ -1,8 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project

-import threading
-import time
 from unittest.mock import MagicMock, patch

 from vllm.config import set_current_vllm_config
@@ -408,9 +406,7 @@ def test_lookup_key_client_lookup_prepends_typed_tag():
    fake_socket = mock_make_socket.return_value
    fake_socket.recv.return_value = (5).to_bytes(4, "big")

-    # Blocking lookup (non_block defaults to False) runs on the executor and
-    # returns the resolved hit length.
-    assert client.lookup("req0", token_len=128, block_hashes=[]) == 5
+    assert client.lookup(token_len=128, block_hashes=[]) == 5

    sent_frames = fake_socket.send_multipart.call_args[0][0]
    assert sent_frames[0] == protocol.LOOKUP_MSG
@@ -439,127 +435,6 @@ def test_lookup_key_client_reset_uses_typed_protocol():
    assert client.reset() is False


-def _poll_lookup(client, req_id, token_len=128, block_hashes=(), timeout=5.0):
-    """Drive non-blocking lookup until the executor completes it."""
-    deadline = time.monotonic() + timeout
-    while time.monotonic() < deadline:
-        result = client.lookup(req_id, token_len, list(block_hashes), non_block=True)
-        if result is not None:
-            return result
-        time.sleep(0.005)
-    return None
-
-
-def _gated_recv(gate: threading.Event, value: int):
-    """Mock recv side-effect that blocks until ``gate`` is set, so the
-    executor's lookup can be held pending deterministically."""
-
-    def recv():
-        gate.wait()
-        return value.to_bytes(4, "big")
-
-    return recv
-
-
-def test_lookup_key_client_non_block_lookup_async():
-    """Non-blocking lookup defers to the executor: None first, hit once the
-    Future resolves."""
-    vllm_config = _make_vllm_config()
-
-    with patch(
-        "vllm.distributed.kv_transfer.kv_connector.v1.mooncake.store."
-        "worker.make_zmq_socket"
-    ) as mock_make_socket:
-        client = worker.LookupKeyClient(vllm_config)
-
-    fake_socket = mock_make_socket.return_value
-    # Hold the executor's lookup pending until we release the gate.
-    gate = threading.Event()
-    fake_socket.recv.side_effect = _gated_recv(gate, 7)
-
-    # First query submits the lookup and returns None while it is in flight.
-    assert client.lookup("req1", 128, [], non_block=True) is None
-    # Release the executor; a later poll returns the hit length.
-    gate.set()
-    assert _poll_lookup(client, "req1") == 7
-    # Future is consumed (popped) on read.
-    assert "req1" not in client.futures
-
-
-def test_lookup_key_client_discard_clears_state():
-    """discard() drops a completed lookup Future so it is not served stale."""
-    vllm_config = _make_vllm_config()
-
-    with patch(
-        "vllm.distributed.kv_transfer.kv_connector.v1.mooncake.store."
-        "worker.make_zmq_socket"
-    ) as mock_make_socket:
-        client = worker.LookupKeyClient(vllm_config)
-
-    fake_socket = mock_make_socket.return_value
-    gate = threading.Event()
-    fake_socket.recv.side_effect = _gated_recv(gate, 9)
-
-    # Submit while gated so the call returns None and the Future stays in
-    # `futures` (unconsumed) once it resolves.
-    assert client.lookup("req2", 128, [], non_block=True) is None
-    gate.set()
-    deadline = time.monotonic() + 5.0
-    while time.monotonic() < deadline:
-        if client.futures["req2"].done():
-            break
-        time.sleep(0.005)
-    # discard() drops the completed result before any lookup consumes it.
-    client.discard("req2")
-    assert "req2" not in client.futures
-    # A fresh query re-submits rather than returning a stale value: hold the
-    # gate so the resubmitted lookup stays in flight.
-    gate.clear()
-    assert client.lookup("req2", 128, [], non_block=True) is None
-    gate.set()  # release the executor so the worker thread can drain
-
-
-def test_get_num_new_matched_tokens_async_defers_then_reports():
-    """Async lookup returns (None, False) until ready, then the hit count."""
-    vllm_config = create_vllm_config(
-        kv_connector="MooncakeStoreConnector",
-        kv_role="kv_both",
-        kv_connector_extra_config={"lookup_async": True},
-    )
-    kv_cache_config = _make_kv_cache_config()
-
-    with (
-        set_current_vllm_config(vllm_config),
-        patch(
-            "vllm.distributed.kv_transfer.kv_connector.v1.mooncake.store."
-            "scheduler.LookupKeyClient"
-        ) as mock_client_cls,
-    ):
-        sched = scheduler.MooncakeStoreScheduler(vllm_config, kv_cache_config)
-
-    assert sched.lookup_async is True
-    mock_client = mock_client_cls.return_value
-
-    block_size = sched._block_size
-    request = MagicMock()
-    request.request_id = "r1"
-    request.num_tokens = 4 * block_size
-    request.block_hashes = []
-
-    # Lookup not ready -> defer.
-    mock_client.lookup.return_value = None
-    assert sched.get_num_new_matched_tokens(request, 0) == (None, False)
-    assert "r1" not in sched.load_specs
-
-    # Lookup ready with a hit -> report need_to_allocate + async-load flag.
-    hit = 3 * block_size
-    mock_client.lookup.return_value = hit
-    need, load_async = sched.get_num_new_matched_tokens(request, 0)
-    assert need == hit
-    assert load_async == sched.load_async
-    assert sched.load_specs["r1"].kvpool_cached_tokens == hit
-
-
 def test_protocol_tags_are_distinct_and_non_empty():
    """Protocol tags must be unique and non-empty to avoid collision."""
    tags = {protocol.LOOKUP_MSG, protocol.RESET_MSG}
@@ -16,7 +16,6 @@ from vllm.distributed.kv_transfer.kv_connector.v1.mooncake.store.scheduler impor
 def _make_bare_scheduler() -> MooncakeStoreScheduler:
    scheduler = object.__new__(MooncakeStoreScheduler)
    scheduler.kv_role = "kv_both"
-    scheduler.lookup_async = False
    scheduler._block_size = 16
    scheduler.load_specs = {}
    scheduler._preempted_req_ids = set()
@@ -406,13 +405,7 @@ class _StubLookupClient:
    def __init__(self, hit_tokens: int) -> None:
        self._hit_tokens = hit_tokens

-    def lookup(
-        self,
-        req_id: str,
-        token_len: int,
-        block_hashes: list[bytes],
-        non_block: bool = False,
-    ) -> int:
+    def lookup(self, token_len: int, block_hashes: list[bytes]) -> int:
        return self._hit_tokens


@@ -175,17 +175,14 @@ class _FakeModelConfig:


 def _make_vllm_config(
-    *,
-    extra_config: dict[str, object] | None = None,
-    rank: int = 0,
-    decode_context_parallel_size: int = 1,
+    *, extra_config: dict[str, object] | None = None
 ) -> SimpleNamespace:
    return SimpleNamespace(
        model_config=_FakeModelConfig(),
        parallel_config=SimpleNamespace(
            pipeline_parallel_size=1,
-            rank=rank,
-            decode_context_parallel_size=decode_context_parallel_size,
+            rank=0,
+            decode_context_parallel_size=1,
            prefill_context_parallel_size=1,
        ),
        kv_transfer_config=_FakeKVTransferConfig(extra_config=extra_config),
@@ -234,23 +231,13 @@ def _install_fake_mooncake(monkeypatch, store_instance: MagicMock):
    return FakeReplicateConfig


-def _patch_worker_runtime(
-    monkeypatch,
-    *,
-    local_ip: str = "10.0.0.7",
-    tp_rank: int = 0,
-    tp_size: int = 1,
-    dcp_size: int = 1,
-) -> None:
+def _patch_worker_runtime(monkeypatch, *, local_ip: str = "10.0.0.7") -> None:
    single_rank_group = SimpleNamespace(world_size=1, rank_in_group=0)
-    # DCP groups are contiguous splits of the TP group (see
-    # parallel_state.py), so dcp_rank == tp_rank % dcp_size.
-    dcp_group = SimpleNamespace(world_size=dcp_size, rank_in_group=tp_rank % dcp_size)
    monkeypatch.setattr(worker, "get_mooncake_dp_engine_index", lambda _: 0)
-    monkeypatch.setattr(worker, "get_tensor_model_parallel_rank", lambda: tp_rank)
-    monkeypatch.setattr(worker, "get_tensor_model_parallel_world_size", lambda: tp_size)
+    monkeypatch.setattr(worker, "get_tensor_model_parallel_rank", lambda: 0)
+    monkeypatch.setattr(worker, "get_tensor_model_parallel_world_size", lambda: 1)
    monkeypatch.setattr(worker, "get_pcp_group", lambda: single_rank_group)
-    monkeypatch.setattr(worker, "get_dcp_group", lambda: dcp_group)
+    monkeypatch.setattr(worker, "get_dcp_group", lambda: single_rank_group)
    monkeypatch.setattr(worker, "get_ip", lambda: local_ip)


@@ -897,66 +884,6 @@ def test_requester_worker_init_builds_replicate_config_for_preferred_segment(
    assert w.store_replicate_config.preferred_segment == "10.0.0.7:50053"


-@pytest.mark.parametrize("dcp_size", [1, 4])
-def test_worker_put_striding_covers_every_rank_get_namespace(
-    tmp_path, monkeypatch, dcp_size
-):
-    """Every key a rank GETs must have been PUT by some rank.
-
-    When num_kv_head < tp_size, ranks holding the same KV heads stripe
-    their PUTs across one shared key namespace. That dedup is only valid
-    when those ranks really share a namespace: with DCP > 1 each rank GETs
-    every key from its own ``@dcpN`` namespace, so striding must be
-    disabled.
-    """
-    tp_size = 4
-    store = MagicMock()
-    store.setup.return_value = 0
-    _install_fake_mooncake(monkeypatch, store)
-    monkeypatch.setenv(
-        "MOONCAKE_CONFIG_PATH",
-        _write_mooncake_config(
-            tmp_path,
-            {
-                "metadata_server": "http://metadata/endpoint",
-                "protocol": "tcp",
-                "device_name": "",
-                "master_server_address": "10.0.0.7:50051",
-            },
-        ),
-    )
-
-    # _FakeModelConfig has num_kv_head=1 < tp_size, which enables striding.
-    block_hashes = [f"hash-{i}".encode() for i in range(4)]
-    put_keys: set[str] = set()
-    get_keys_per_rank: dict[int, set[str]] = {}
-    for tp_rank in range(tp_size):
-        _patch_worker_runtime(
-            monkeypatch, tp_rank=tp_rank, tp_size=tp_size, dcp_size=dcp_size
-        )
-        w = worker.MooncakeStoreWorker(
-            _make_vllm_config(rank=tp_rank, decode_context_parallel_size=dcp_size),
-            _make_kv_cache_config(),
-        )
-        db = w.token_dbs[0]
-        token_len = len(block_hashes) * db.block_size
-        keys = [
-            key.to_string() for _, _, key in db.process_tokens(token_len, block_hashes)
-        ]
-        assert len(keys) == len(block_hashes)
-        # PUT side: mirrors KVCacheStoreSendingThread's striding slice.
-        put_keys.update(keys[w.tp_rank % w.put_step :: w.put_step])
-        # GET side: KVCacheStoreRecvingThread fetches every key.
-        get_keys_per_rank[tp_rank] = set(keys)
-
-    for tp_rank, rank_keys in get_keys_per_rank.items():
-        missing = rank_keys - put_keys
-        assert not missing, (
-            f"tp_rank={tp_rank} would GET {len(missing)}/{len(rank_keys)} keys "
-            f"that no rank PUT (Mooncake OBJECT_NOT_FOUND): {sorted(missing)}"
-        )
-
-
 # ---------------------------------------------------------------------------
 # Helpers for register_kv_caches tests
 # ---------------------------------------------------------------------------
@@ -1,227 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""Tests for the Model Runner V2 Gumbel-max sampling kernel.
-
-Accuracy: define a target categorical distribution as a non-negative int64
-count tensor summing to N, turn it into logits (= log(count)), sample many
-times with `gumbel_sample`, and check the empirical distribution matches.
-
-The count tensor is deliberately heavy-tailed (one dominant token, the rest
-~18 logits below). That tail is the sensitive part: the fp32 Gumbel noise must
-reach ~18 to ever sample it. A flat distribution would keep every token within
-a few logits of the top and would not exercise the noise tail at all.
-"""
-
-import math
-
-import pytest
-import torch
-
-pytest.importorskip("triton")
-if not torch.cuda.is_available():
-    pytest.skip("CUDA required for Gumbel sampler tests", allow_module_level=True)
-
-from vllm.v1.worker.gpu.sample.gumbel import gumbel_sample
-
-DEVICE = "cuda"
-VOCAB_SIZE = 200_000
-NUM_SAMPLES = 500_000
-# Dominant token is exp(HEAD_LOG_GAP)x larger than the unit-count tail, so the
-# tail sits ~HEAD_LOG_GAP logits below the top.
-HEAD_LOG_GAP = 18.0
-# 10-sigma band: a correct sampler effectively never trips it.
-Z_TOLERANCE = 10.0
-
-
-def _make_heavy_tailed_counts(seed: int = 1234) -> torch.Tensor:
-    """Non-negative int64 counts of shape [VOCAB_SIZE]; target prob = counts/N."""
-    gen = torch.Generator(device=DEVICE).manual_seed(seed)
-    counts = torch.randint(
-        1, 4, (VOCAB_SIZE,), generator=gen, dtype=torch.int64, device=DEVICE
-    )
-    counts[0] = round(math.exp(HEAD_LOG_GAP))  # dominant token
-    return counts
-
-
-def _counts_to_logits(counts: torch.Tensor) -> torch.Tensor:
-    # softmax(log(count)) == count / sum(count); count 0 -> logit -inf -> prob 0.
-    return counts.double().log().to(torch.float32)
-
-
-def _sample(
-    logits_1d: torch.Tensor,
-    num_samples: int,
-    *,
-    use_fp64: bool = False,
-    temperature: float = 1.0,
-) -> torch.Tensor:
-    """Sample `num_samples` tokens from one logit vector.
-
-    Fixed seed with a distinct `pos` per sample gives independent draws; the
-    logits are broadcast with a 0-stride view to avoid materializing
-    [num_samples, vocab_size].
-    """
-    vocab_size = logits_1d.shape[0]
-    logits = logits_1d.unsqueeze(0).expand(num_samples, vocab_size)
-    idx_mapping = torch.zeros(num_samples, dtype=torch.int32, device=DEVICE)
-    temp = torch.tensor([temperature], dtype=torch.float32, device=DEVICE)
-    seed = torch.tensor([0xABCD], dtype=torch.int64, device=DEVICE)
-    pos = torch.arange(num_samples, dtype=torch.int64, device=DEVICE)
-    return gumbel_sample(
-        logits,
-        idx_mapping,
-        temp,
-        seed,
-        pos,
-        apply_temperature=True,
-        use_fp64=use_fp64,
-    )
-
-
-def _z_score(observed: int, expected: float, num_trials: int) -> float:
-    p = expected / num_trials
-    return (observed - expected) / math.sqrt(num_trials * p * (1 - p))
-
-
-def _sample_histogram(
-    logits_1d: torch.Tensor, num_samples: int, *, chunk: int = 1_000_000
-) -> torch.Tensor:
-    """Histogram of `num_samples` draws, accumulated in chunks.
-
-    Chunking keeps the kernel's per-sample scratch ([chunk, num_blocks]) bounded
-    so a large sample count does not blow up memory.
-    """
-    vocab_size = logits_1d.shape[0]
-    hist = torch.zeros(vocab_size, dtype=torch.float64, device=DEVICE)
-    for start in range(0, num_samples, chunk):
-        size = min(chunk, num_samples - start)
-        logits = logits_1d.unsqueeze(0).expand(size, vocab_size)
-        idx_mapping = torch.zeros(size, dtype=torch.int32, device=DEVICE)
-        temp = torch.tensor([1.0], dtype=torch.float32, device=DEVICE)
-        seed = torch.tensor([0xABCD], dtype=torch.int64, device=DEVICE)
-        pos = torch.arange(start, start + size, dtype=torch.int64, device=DEVICE)
-        out = gumbel_sample(
-            logits, idx_mapping, temp, seed, pos, apply_temperature=True
-        )
-        hist += torch.bincount(out, minlength=vocab_size).double()
-    return hist
-
-
-# ----------------------------- Accuracy ------------------------------------
-
-
-@pytest.mark.parametrize("use_fp64", [False, True])
-def test_sampling_matches_target_distribution(use_fp64: bool):
-    counts = _make_heavy_tailed_counts()
-    total = counts.sum().item()
-    logits = _counts_to_logits(counts)
-
-    sampled = _sample(logits, NUM_SAMPLES, use_fp64=use_fp64)
-    assert sampled.min() >= 0 and sampled.max() < VOCAB_SIZE
-
-    # The dominant token (index 0) and the aggregate tail are the two
-    # statistically resolvable bins (individual tail tokens are far below the
-    # ~5/N detectability floor). The tail mass is small but well above noise,
-    # and it lives beyond the fp32 Gumbel cap -- the regime sensitive to noise
-    # precision -- so matching it is the meaningful check.
-    tail_prob = (total - counts[0].item()) / total
-    tail_count = (sampled != 0).sum().item()
-    z = _z_score(tail_count, NUM_SAMPLES * tail_prob, NUM_SAMPLES)
-    assert abs(z) < Z_TOLERANCE, (
-        f"sampled tail mass {tail_count / NUM_SAMPLES:.3e} != target "
-        f"{tail_prob:.3e} (z={z:.2f})"
-    )
-
-
-def test_full_vocab_distribution_fidelity():
-    """The sampled distribution matches the target across the WHOLE vocab.
-
-    A near-flat count tensor makes every one of the 200K bins individually
-    measurable. With ~20 samples/bin, a goodness-of-fit over all bins checks
-    that no part of the vocab is over- or under-represented (the heavy-tailed
-    test above only resolves head vs aggregate tail). Empirically the fp32
-    sampler is as faithful here as torch.multinomial; the residual error is the
-    multinomial sampling-noise floor, not the kernel.
-    """
-    gen = torch.Generator(device=DEVICE).manual_seed(2024)
-    counts = torch.randint(
-        500, 1500, (VOCAB_SIZE,), generator=gen, dtype=torch.int64, device=DEVICE
-    )
-    total = counts.sum().item()
-    logits = _counts_to_logits(counts)
-
-    num_samples = 4_000_000
-    hist = _sample_histogram(logits, num_samples)
-
-    # Diversity: essentially every token must be reachable (no starved region).
-    coverage = (hist > 0).sum().item() / VOCAB_SIZE
-    assert coverage > 0.99, f"only {coverage:.4f} of the vocab was ever sampled"
-
-    # Goodness-of-fit across all bins (each has expected count >= ~10).
-    expected = (counts.double() / total) * num_samples
-    chi2 = (((hist - expected) ** 2) / expected).sum().item()
-    df = VOCAB_SIZE - 1
-    assert chi2 < df + 10 * math.sqrt(2 * df), f"chi2={chi2:.0f}, df={df}"
-
-
-# ----------------------------- Edge cases ----------------------------------
-
-
-def test_greedy_temperature_zero_returns_argmax():
-    """temperature == 0 skips Gumbel noise and returns the exact argmax."""
-    torch.manual_seed(0)
-    num_reqs = 128
-    logits = torch.randn(num_reqs, VOCAB_SIZE, device=DEVICE, dtype=torch.float32)
-    idx_mapping = torch.arange(num_reqs, dtype=torch.int32, device=DEVICE)
-    temp = torch.zeros(num_reqs, dtype=torch.float32, device=DEVICE)
-    seed = torch.arange(num_reqs, dtype=torch.int64, device=DEVICE)
-    pos = torch.arange(num_reqs, dtype=torch.int64, device=DEVICE)
-
-    sampled = gumbel_sample(
-        logits, idx_mapping, temp, seed, pos, apply_temperature=True
-    )
-    assert torch.equal(sampled, logits.argmax(dim=-1))
-
-
-def test_zero_count_tokens_are_never_sampled():
-    """Count 0 -> -inf logit -> probability 0; must never be selected."""
-    counts = _make_heavy_tailed_counts(seed=7)
-    zeroed = torch.arange(1, VOCAB_SIZE, 2, device=DEVICE)  # odd indices (not head)
-    counts[zeroed] = 0
-    logits = _counts_to_logits(counts)
-
-    sampled = _sample(logits, NUM_SAMPLES)
-    assert sampled.min() >= 0 and sampled.max() < VOCAB_SIZE
-    assert not torch.isin(sampled, zeroed).any(), "sampled a zero-probability token"
-
-
-def test_single_nonzero_token_is_always_sampled():
-    """A lone finite logit must win every draw, regardless of its index."""
-    counts = torch.zeros(VOCAB_SIZE, dtype=torch.int64, device=DEVICE)
-    counts[123_456] = 1000
-    logits = _counts_to_logits(counts)
-
-    sampled = _sample(logits, 10_000)
-    assert (sampled == 123_456).all()
-
-
-@pytest.mark.parametrize("vocab_size", [1, 999, 1024, 4097])
-def test_vocab_size_not_multiple_of_block(vocab_size: int):
-    """Per-block tail masking for non-block-aligned vocab; all bins measurable."""
-    gen = torch.Generator(device=DEVICE).manual_seed(vocab_size)
-    counts = torch.randint(
-        20, 200, (vocab_size,), generator=gen, dtype=torch.int64, device=DEVICE
-    )
-    total = counts.sum().item()
-    logits = _counts_to_logits(counts)
-    num_samples = max(40 * vocab_size, 50_000)
-
-    sampled = _sample(logits, num_samples)
-    assert sampled.min() >= 0 and sampled.max() < vocab_size
-
-    observed = torch.bincount(sampled, minlength=vocab_size).double()
-    expected = (counts.double() / total) * num_samples
-    chi2 = (((observed - expected) ** 2) / expected).sum().item()
-    df = vocab_size - 1
-    if df >= 1:
-        assert chi2 < df + 10 * math.sqrt(2 * df), f"chi2={chi2:.1f}, df={df}"
@@ -176,7 +176,7 @@ class MooncakeStoreConnector(KVConnectorBase_V1, SupportsHMA):
        self,
        request: Request,
        num_computed_tokens: int,
-    ) -> tuple[int | None, bool]:
+    ) -> tuple[int, bool]:
        assert self.connector_scheduler is not None
        return self.connector_scheduler.get_num_new_matched_tokens(
            request, num_computed_tokens
@@ -54,9 +54,9 @@ class MooncakeStoreScheduler:
    ):
        assert vllm_config.kv_transfer_config is not None
        self.kv_role = vllm_config.kv_transfer_config.kv_role
-        kvc_extra_config = vllm_config.kv_transfer_config.kv_connector_extra_config
-        self.load_async = kvc_extra_config.get("load_async", True)
-        self.lookup_async = kvc_extra_config.get("lookup_async", False)
+        self.load_async = vllm_config.kv_transfer_config.kv_connector_extra_config.get(
+            "load_async", True
+        )
        self.client = LookupKeyClient(vllm_config)

        # Align with the engine's own scheduler_block_size and hash_block_size.
@@ -75,26 +75,14 @@ class MooncakeStoreScheduler:
        self,
        request: Request,
        num_computed_tokens: int,
-    ) -> tuple[int | None, bool]:
-        """Check for external KV cache hit.
-
-        Returns ``(None, False)`` when an async lookup is still in flight,
-        signaling the scheduler to retry this request on a later step.
-        """
+    ) -> tuple[int, bool]:
+        """Check for external KV cache hit."""
        # Look up against the full prefill range, not just the prompt.
        token_len = request.num_tokens // self._block_size * self._block_size
        if token_len < self._block_size:
            return 0, False

-        num_external_hit_tokens = self.client.lookup(
-            request.request_id,
-            token_len,
-            request.block_hashes,
-            non_block=self.lookup_async,
-        )
-        if num_external_hit_tokens is None:
-            # Lookup not ready yet; scheduler will retry on a later step.
-            return None, False
+        num_external_hit_tokens = self.client.lookup(token_len, request.block_hashes)

        if num_external_hit_tokens == request.num_tokens:
            # Leave a sub-block tail uncomputed for sampling, on a block
@@ -170,7 +158,6 @@ class MooncakeStoreScheduler:
        force_skip_save = self.kv_role == "kv_consumer"

        for finished_req_id in scheduler_output.finished_req_ids:
-            self.client.discard(finished_req_id)
            self.load_specs.pop(finished_req_id, None)
            self._request_trackers.pop(finished_req_id, None)
            self._unfinished_requests.pop(finished_req_id, None)
@@ -19,7 +19,6 @@ import threading
 import time
 from collections import defaultdict
 from collections.abc import Callable
-from concurrent.futures import Future, ThreadPoolExecutor
 from dataclasses import dataclass
 from typing import Any, Literal, TypeVar

@@ -972,13 +971,7 @@ class MooncakeStoreWorker:
        else:
            self.num_kv_head = model_config.get_total_num_kv_heads()

-        if self.num_kv_head < self.tp_size and self.dcp_size <= 1:
-            # Dedup: TP ranks holding the same KV heads stripe PUTs across
-            # one shared key namespace. DCP splits the TP group, so with
-            # DCP>1 those ranks have different `@dcpN` namespaces and
-            # striping would leave keys unwritten (OBJECT_NOT_FOUND on
-            # GET). PCP is outer to TP (pcp_rank is constant within a TP
-            # group), so it needs no guard.
+        if self.num_kv_head < self.tp_size:
            self.put_step = self.tp_size // self.num_kv_head
            self.head_or_tp_rank = self.tp_rank // self.put_step
        else:
@@ -1567,13 +1560,7 @@ class LookupKeyClient:
            bind=False,
        )

-        # Async lookup support
-        self.executor = ThreadPoolExecutor(
-            max_workers=1, thread_name_prefix="MooncakeLookupClient"
-        )
-        self.futures: dict[str, Future[int]] = {}
-
-    def _lookup(self, token_len: int, block_hashes: list[BlockHash]) -> int:
+    def lookup(self, token_len: int, block_hashes: list[BlockHash]) -> int:
        hash_strs = [h.hex() for h in block_hashes]
        hash_frames = self.encoder.encode(hash_strs)
        token_len_bytes = token_len.to_bytes(4, byteorder="big")
@@ -1583,36 +1570,7 @@ class LookupKeyClient:
        result = int.from_bytes(resp, "big")
        return result

-    def lookup(
-        self,
-        req_id: str,
-        token_len: int,
-        block_hashes: list[BlockHash],
-        non_block: bool = False,
-    ) -> int | None:
-        """If non_block is True, will return None until the result is ready,
-        so the caller retries on a later step."""
-        future = self.futures.get(req_id)
-        if future is None:
-            future = self.executor.submit(self._lookup, token_len, list(block_hashes))
-            self.futures[req_id] = future
-        if non_block and not future.done():
-            return None
-        try:
-            return future.result()
-        except Exception as e:
-            logger.error("Async Mooncake lookup failed for %s: %s", req_id, e)
-            return 0
-        finally:
-            del self.futures[req_id]
-
-    def discard(self, req_id: str) -> None:
-        """Drop any cached/in-flight lookup for ``req_id`` (e.g. on abort)."""
-        future = self.futures.pop(req_id, None)
-        if future is not None:
-            future.cancel()
-
-    def _reset(self) -> bool:
+    def reset(self) -> bool:
        """Trigger ``store.remove_all(force=True)`` on worker rank 0.

        Ordering assumption: caller MUST ensure no in-flight Mooncake
@@ -1624,11 +1582,7 @@ class LookupKeyClient:
        resp = self.socket.recv()
        return bytes(resp) == RESP_OK

-    def reset(self) -> bool:
-        return self.executor.submit(self._reset).result()
-
    def close(self):
-        self.executor.shutdown(wait=False, cancel_futures=True)
        self.socket.close(linger=0)


@@ -841,106 +841,8 @@ class NixlBaseConnectorWorker:
        # Forwarding a real layer name rather than a synthetic key
        self.register_kv_caches({first_layer: kv_cache})

-    def _register_packed_kv_cache(
-        self,
-        storage: torch.UntypedStorage,
-    ) -> None:
-        """Register a packed KV cache as a single NIXL region.
-
-        The packed allocation interleaves all layers per block, so each
-        block_stride-byte chunk is one logical block.  We register 1
-        NIXL region and create 1 descriptor per block.
-        """
-        self.transfer_topo = TransferTopology(
-            tp_rank=self.tp_rank,
-            tp_size=self.world_size,
-            block_size=self.block_size,
-            engine_id=self.engine_id,
-            is_mla=self.use_mla,
-            total_num_kv_heads=self.model_config.get_total_num_kv_heads(),
-            attn_backends=self.attn_backends,
-            tensor_shape=None,
-            is_mamba=self._has_mamba,
-        )
-        self.compat_hash = compute_nixl_compatibility_hash(
-            self.vllm_config,
-            self.backend_name,
-            self.transfer_topo.cross_layers_blocks,
-        )
-
-        total_size = storage.nbytes()
-        block_stride = total_size // self.num_blocks
-        base_addr = storage.data_ptr()
-        device_id = storage.device.index
-        assert device_id is not None
-
-        logger.info(
-            "Registering packed KV cache: total_size=%s, block_stride=%s, "
-            "num_blocks=%s, num_regions=1",
-            total_size,
-            block_stride,
-            self.num_blocks,
-        )
-
-        self.device_id = device_id
-        caches_data = [(base_addr, total_size, self.device_id, "")]
-
-        self.block_len_per_layer = [block_stride]
-        self.num_regions = 1
-        self.num_descs = self.num_blocks
-        self.kv_caches_base_addr[self.engine_id][self.tp_rank] = [base_addr]
-
-        descs = self.nixl_wrapper.get_reg_descs(caches_data, self.nixl_memory_type)
-        self.nixl_wrapper.register_memory(descs, backends=self.nixl_backends)
-        self._registered_descs.append(descs)
-
-        self.dst_num_blocks[self.engine_id] = self.num_blocks
-
-        self.src_xfer_handles_by_block_size[self.block_size], (self.src_blocks_data) = (
-            self.register_local_xfer_handler(self.block_size)
-        )
-
-        agent_metadata = NixlAgentMetadata(
-            engine_id=self.engine_id,
-            agent_metadata=self.nixl_wrapper.get_agent_metadata(),
-            device_id=self.device_id,
-            kv_caches_base_addr=(
-                self.kv_caches_base_addr[self.engine_id][self.tp_rank]
-            ),
-            num_blocks=self.num_blocks,
-            block_lens=self.block_len_per_layer,
-            kv_cache_layout=self.kv_cache_layout,
-            block_size=self.block_size,
-            ssm_sizes=self._mamba_ssm_size,
-            attn_backend_name=self.backend_name,
-            physical_blocks_per_logical_kv_block=(
-                self._physical_blocks_per_logical_kv_block
-            ),
-        )
-        assert self.compat_hash is not None
-        encoder = msgspec.msgpack.Encoder()
-        self.xfer_handshake_metadata = NixlHandshakePayload(
-            compatibility_hash=self.compat_hash,
-            agent_metadata_bytes=encoder.encode(agent_metadata),
-        )
-
    def register_kv_caches(self, kv_caches: dict[str, torch.Tensor]):
        """Register the KV Cache data in nixl."""
-
-        # Detect packed allocation: all tensors are strided views into the
-        # same backing storage (different data_ptr but same storage).
-        # This happens with DSv4-style contiguous per-block packing.
-        if len(kv_caches) > 1 and not self._has_mamba:
-            storage = next(iter(kv_caches.values())).untyped_storage()
-            storage_ptrs = {
-                cache.untyped_storage().data_ptr() for cache in kv_caches.values()
-            }
-            data_ptrs = {cache.data_ptr() for cache in kv_caches.values()}
-            if len(storage_ptrs) == 1 and len(data_ptrs) > 1:
-                self._register_packed_kv_cache(storage)
-                self.device_kv_caches = kv_caches
-                return
-
        self.transfer_topo = TransferTopology(
            tp_rank=self.tp_rank,
            tp_size=self.world_size,
@@ -72,22 +72,18 @@ class OffloadingConnectorWorker:
                if isinstance(layer_kv_cache_spec, AttentionSpec):
                    layer_kv_cache = kv_caches[layer_name]
                    assert isinstance(layer_kv_cache, torch.Tensor)
+                    assert layer_kv_cache.storage_offset() == 0

+                    storage = layer_kv_cache.untyped_storage()
                    page = layer_kv_cache_spec.page_size_bytes
-                    elem_size = layer_kv_cache.element_size()
-                    byte_offset = layer_kv_cache.storage_offset() * elem_size
-                    block_stride_bytes = layer_kv_cache.stride(0) * elem_size
                    tensors_per_block[layer_name] = (
                        torch.tensor(
                            [],
                            dtype=torch.int8,
                            device=layer_kv_cache.device,
-                        ).set_(
-                            layer_kv_cache.untyped_storage(),
-                            byte_offset,
-                            (num_blocks, page),
-                            (block_stride_bytes, 1),
-                        ),
+                        )
+                        .set_(storage)
+                        .view(num_blocks, page),
                    )
                    page_size_bytes[layer_name] = layer_kv_cache_spec.page_size_bytes
                    unpadded_page_size_bytes[layer_name] = (
@@ -12,7 +12,6 @@ import uuid
 from collections.abc import AsyncGenerator
 from typing import TYPE_CHECKING, Any

-import jinja2
 from fastapi import Request

 from vllm.engine.protocol import EngineClient
@@ -100,36 +99,6 @@ class AnthropicServingMessages(OpenAIServingChat):
            "length": "max_tokens",
            "tool_calls": "tool_use",
        }
-        self._merge_inline_system = self._detect_merge_inline_system(chat_template)
-
-    @staticmethod
-    def _detect_merge_inline_system(chat_template: str | None) -> bool:
-        """Auto-detect whether the chat template requires system-first ordering.
-
-        Renders a [system, user, system, user] conversation against the
-        template; if it raises (e.g. Qwen's ``loop.first`` guard), the
-        model needs inline system messages merged into the leading block.
-        """
-        if not chat_template:
-            return True
-        try:
-            env = jinja2.sandbox.ImmutableSandboxedEnvironment(
-                trim_blocks=True,
-                lstrip_blocks=True,
-                extensions=[jinja2.ext.loopcontrols],
-            )
-            env.from_string(chat_template).render(
-                messages=[
-                    {"role": "system", "content": "t"},
-                    {"role": "user", "content": "t"},
-                    {"role": "system", "content": "t"},
-                    {"role": "user", "content": "t"},
-                ],
-                add_generation_prompt=False,
-            )
-            return False
-        except jinja2.TemplateError:
-            return True

    @staticmethod
    def _convert_image_source_to_url(source: dict[str, Any]) -> str:
@@ -154,24 +123,13 @@ class AnthropicServingMessages(OpenAIServingChat):

    @classmethod
    def _convert_anthropic_to_openai_request(
-        cls,
-        anthropic_request: AnthropicMessagesRequest | AnthropicCountTokensRequest,
-        *,
-        merge_inline_system: bool = False,
+        cls, anthropic_request: AnthropicMessagesRequest | AnthropicCountTokensRequest
    ) -> ChatCompletionRequest:
        """Convert Anthropic message format to OpenAI format"""
        openai_messages: list[dict[str, Any]] = []

-        cls._convert_system_message(
-            anthropic_request,
-            openai_messages,
-            merge_inline_system=merge_inline_system,
-        )
-        cls._convert_messages(
-            anthropic_request.messages,
-            openai_messages,
-            merge_inline_system=merge_inline_system,
-        )
+        cls._convert_system_message(anthropic_request, openai_messages)
+        cls._convert_messages(anthropic_request.messages, openai_messages)
        req = cls._build_base_request(anthropic_request, openai_messages)
        cls._handle_streaming_options(req, anthropic_request)
        cls._handle_output_config(req, anthropic_request)
@@ -184,8 +142,6 @@ class AnthropicServingMessages(OpenAIServingChat):
        cls,
        anthropic_request: AnthropicMessagesRequest | AnthropicCountTokensRequest,
        openai_messages: list[dict[str, Any]],
-        *,
-        merge_inline_system: bool = False,
    ) -> None:
        """Convert Anthropic system message to OpenAI format"""
        system_parts: list[str] = []
@@ -203,17 +159,6 @@ class AnthropicServingMessages(OpenAIServingChat):
                            continue
                        system_parts.append(block.text)

-        # When the template requires system-first ordering, extract inline
-        # system messages from the messages array and merge them into the
-        # top-level block so the template doesn't reject them.
-        if merge_inline_system:
-            for msg in anthropic_request.messages:
-                if msg.role != "system":
-                    continue
-                text = cls._extract_system_text(msg)
-                if text:
-                    system_parts.append(text)
-
        if system_parts:
            openai_messages.append({"role": "system", "content": "".join(system_parts)})

@@ -235,11 +180,7 @@ class AnthropicServingMessages(OpenAIServingChat):

    @classmethod
    def _convert_messages(
-        cls,
-        messages: list,
-        openai_messages: list[dict[str, Any]],
-        *,
-        merge_inline_system: bool = False,
+        cls, messages: list, openai_messages: list[dict[str, Any]]
    ) -> None:
        """Convert Anthropic messages to OpenAI format"""
        for msg in messages:
@@ -249,8 +190,6 @@ class AnthropicServingMessages(OpenAIServingChat):
            # doesn't strip billing headers and may produce messages with
            # no "content" key.
            if msg.role == "system":
-                if merge_inline_system:
-                    continue  # already merged into top-level by _convert_system_message
                text = cls._extract_system_text(msg)
                if text:
                    openai_messages.append({"role": "system", "content": text})
@@ -558,10 +497,7 @@ class AnthropicServingMessages(OpenAIServingChat):
        """
        if logger.isEnabledFor(logging.DEBUG):
            logger.debug("Received messages request %s", request.model_dump_json())
-        chat_req = self._convert_anthropic_to_openai_request(
-            request,
-            merge_inline_system=self._merge_inline_system,
-        )
+        chat_req = self._convert_anthropic_to_openai_request(request)
        if logger.isEnabledFor(logging.DEBUG):
            logger.debug("Convert to OpenAI request %s", chat_req.model_dump_json())
        generator = await self.create_chat_completion(chat_req, raw_request)
@@ -969,10 +905,7 @@ class AnthropicServingMessages(OpenAIServingChat):
        raw_request: Request | None = None,
    ) -> AnthropicCountTokensResponse | ErrorResponse:
        """Implements Anthropic's messages.count_tokens endpoint."""
-        chat_req = self._convert_anthropic_to_openai_request(
-            request,
-            merge_inline_system=self._merge_inline_system,
-        )
+        chat_req = self._convert_anthropic_to_openai_request(request)
        result = await self.render_chat_request(chat_req)
        if isinstance(result, ErrorResponse):
            return result
@@ -427,12 +427,6 @@ async def validation_exception_handler(req: Request, exc: RequestValidationError
                param = ctx_error.parameter
                break

-    if param is None and errors:
-        first_error = errors[0]
-        loc = first_error.get("loc") if isinstance(first_error, dict) else None
-        if loc:
-            param = ".".join(str(part) for part in loc)
-
    exc_str = str(exc)
    errors_str = str(errors)

@@ -446,17 +446,11 @@ class PunicaWrapperGPU(PunicaWrapperBase):
            _,
            _,
            lora_ids,
-            no_lora_flag,
+            _,
            num_active_loras,
        ) = self.token_mapping_meta.meta_args(
            x.size(0), self.lora_config.specialize_active_lora
        )
-
-        assert no_lora_flag.numel() == 1
-        if no_lora_flag.item():
-            # None of the inputs require LoRA.
-            return
-
        if token_lora_mapping is None:
            token_lora_mapping = token_lora_mapping_meta
        fused_moe_lora(
@@ -349,7 +349,6 @@ class MLAAttention(nn.Module, AttentionLayerBase):
        attn_backend: type[AttentionBackend] | None = None,
        use_sparse: bool = False,
        indexer: object | None = None,
-        topk_indices_buffer: torch.Tensor | None = None,
        **extra_impl_args,
    ):
        super().__init__()
@@ -438,11 +437,6 @@ class MLAAttention(nn.Module, AttentionLayerBase):
            )
            cache_config.enable_prefix_caching = False

-        # Sparse MLA reads top-k indices from a shared buffer. Pass it
-        # explicitly so backbone "skip" layers (indexer=None) still find it.
-        if use_sparse:
-            extra_impl_args["topk_indices_buffer"] = topk_indices_buffer
-
        impl_cls = cast(type[MLAAttentionImpl], self.attn_backend.get_impl_cls())
        self.impl = impl_cls(  # type: ignore[assignment]  # impl_cls always returns an MLAAttentionImpl subclass
            num_heads=self.num_heads,
@@ -59,10 +59,3 @@ class MoELoRAContext:
    # None means no dispatch happened (non-EP path), in which case callers
    # fall back to punica_wrapper.token_mapping_meta.
    local_token_lora_mapping: torch.Tensor | None = None
-
-    # Original unquantized hidden states, stashed by the modular kernel
-    # before the prepare step potentially quantizes them. Used by
-    # apply_w13_lora so the LoRA kernel sees correct-magnitude activations
-    # instead of raw quantized values that are missing the activation scale.
-    # Set per forward pass; None until the modular kernel writes it.
-    original_hidden_states: torch.Tensor | None = None
@@ -77,16 +77,6 @@ class TritonExperts(LoRAExpertsMixin, mk.FusedMoEExpertsModular):
    def activation_format() -> mk.FusedMoEActivationFormat:
        return mk.FusedMoEActivationFormat.Standard

-    @property
-    def expects_unquantized_inputs(self) -> bool:
-        # Defer activation quantization to apply() only when LoRA is active AND
-        # tokens are dispatched across ranks (DP+EP all2all).
-        return (
-            self._lora_context is not None
-            and self.quant_dtype is not None
-            and self.moe_config.moe_parallel_config.use_all2all_kernels
-        )
-
    @staticmethod
    def _supports_current_device() -> bool:
        return current_platform.is_cuda_alike() or current_platform.is_xpu()
@@ -233,25 +223,6 @@ class TritonExperts(LoRAExpertsMixin, mk.FusedMoEExpertsModular):
            torch.float8_e4m3fnuz,
        ]

-        # We declared expects_unquantized_inputs (LoRA + DP/EP all2all), so the
-        # prepare step deferred activation quantization to this kernel:
-        # `hidden_states` arrives unquantized. Keep the unquantized tensor for
-        # the LoRA shrink input and quantize a copy here for the base GEMM
-        # (mirrors what the prepare step would have done, but after the
-        # all-gather so the layout matches the gathered topk_ids / token map).
-        lora_unquantized_hidden_states: torch.Tensor | None = None
-        if self.expects_unquantized_inputs:
-            assert a1q_scale is None
-            lora_unquantized_hidden_states = hidden_states
-            hidden_states, a1q_scale = moe_kernel_quantize_input(
-                hidden_states,
-                self.a1_scale,
-                self.quant_dtype,
-                self.per_act_token_quant,
-                self.block_shape,
-                quantization_emulation=self.quantization_emulation,
-            )
-
        E, num_tokens, N, K, top_k_num = self.moe_problem_size(
            hidden_states, w1, w2, topk_ids
        )
@@ -309,28 +280,12 @@ class TritonExperts(LoRAExpertsMixin, mk.FusedMoEExpertsModular):
        # GEMM on the default stream and the LoRA fast-path on aux_stream;
        # the LoRA writes its delta into a fresh zero buffer (add_inputs=
        # False) and we sum it into intermediate_cache1 after both finish.
-        #
-        # The LoRA shrink kernel needs unquantized, gathered-layout
-        # activations. When activation quant was deferred to this kernel
-        # (expects_unquantized_inputs), the input we quantized above is exactly
-        # that, so use it directly. Otherwise fall back to the context stash
-        # (e.g. weight-only quant), guarding on a row-count match so a
-        # DP-gathered layout never indexes a local stash out of bounds.
+
        sorted_token_ids_lora = None
        expert_ids_lora = None
        num_tokens_post_padded_lora = None
        token_lora_mapping = None
        lora_context = self._lora_context
-        if lora_unquantized_hidden_states is not None:
-            lora_x = lora_unquantized_hidden_states
-        elif (
-            lora_context is not None
-            and lora_context.original_hidden_states is not None
-            and lora_context.original_hidden_states.shape[0] == hidden_states.shape[0]
-        ):
-            lora_x = lora_context.original_hidden_states
-        else:
-            lora_x = hidden_states

        def _base_w13_fn():
            invoke_fused_moe_triton_kernel(
@@ -367,7 +322,7 @@ class TritonExperts(LoRAExpertsMixin, mk.FusedMoEExpertsModular):
                return self.apply_w13_lora(
                    lora_context,
                    y=lora_delta_w13,
-                    x=lora_x,
+                    x=hidden_states,
                    topk_ids=topk_ids,
                    topk_weights=topk_weights,
                    expert_map=expert_map,
@@ -404,7 +359,7 @@ class TritonExperts(LoRAExpertsMixin, mk.FusedMoEExpertsModular):
                ) = self.apply_w13_lora(
                    lora_context,
                    y=intermediate_cache1,
-                    x=lora_x,
+                    x=hidden_states,
                    topk_ids=topk_ids,
                    topk_weights=topk_weights,
                    expert_map=expert_map,
@@ -1407,13 +1407,6 @@ class FusedMoEKernelModularImpl:
            apply_router_weight_on_input,
        )

-        # Stash the original unquantized hidden states on the LoRA context
-        # so apply_w13_lora sees correct-magnitude activations instead of
-        # the potentially quantized values produced by _prepare().
-        lora_ctx = getattr(self.fused_experts, "_lora_context", None)
-        if lora_ctx is not None:
-            lora_ctx.original_hidden_states = hidden_states
-
        fused_out = self._fused_experts(
            in_dtype=hidden_states.dtype,
            a1q=a1q,
@@ -1431,9 +1424,6 @@ class FusedMoEKernelModularImpl:
            output_alias=output,
        )

-        if lora_ctx is not None:
-            lora_ctx.original_hidden_states = None
-
        return self._finalize(
            output,
            fused_out,
@@ -112,7 +112,6 @@ class MultiHeadLatentAttentionWrapper(PluggableLayer):
            kv_b_proj=self.kv_b_proj,
            use_sparse=self.is_sparse,
            indexer=self.indexer,
-            topk_indices_buffer=mla_modules.topk_indices_buffer,
        )

        self.prefix = prefix
@@ -119,12 +119,8 @@ class DeepSeekMultiTokenPredictorLayer(nn.Module):
            hidden_states=hidden_states,
            residual=None,
        )
-        hidden_states = residual + hidden_states  # pre-final-norm (logits hidden)
-        # Recycle the post-final-norm hidden into the next draft step.
-        # compute_logits applies shared_head (== final norm) to the pre-norm
-        # element, so logits and the recycle each get exactly one final-norm.
-        # Matches SGLang's deepseek_nextn.
-        return hidden_states, self.shared_head(hidden_states)
+        hidden_states = residual + hidden_states
+        return hidden_states


 class DeepSeekMultiTokenPredictor(nn.Module):
@@ -998,29 +998,8 @@ class DeepseekV2MLAAttention(nn.Module):

        self.is_v32 = hasattr(config, "index_topk")

-        # IndexCache config
-        # Refer: https://arxiv.org/abs/2603.12201 for more details.
        _skip_topk = False
-        _index_topk_freq = getattr(config, "index_topk_freq", 1)
-        _index_topk_pattern = getattr(config, "index_topk_pattern", None)
-        _index_skip_topk_offset = getattr(config, "index_skip_topk_offset", 2)
-        layer_id = extract_layer_index(prefix)
-
-        if _index_topk_pattern is None:
-            _skip_topk = (
-                max(layer_id - _index_skip_topk_offset + 1, 0) % _index_topk_freq != 0
-            )
-        elif 0 <= layer_id < len(_index_topk_pattern):
-            _skip_topk = _index_topk_pattern[layer_id] == "S"
-
-        # The skip pattern only governs backbone layers. MTP/nextn layers
-        # (layer_id >= num_hidden_layers) always build a full indexer: they
-        # compute indices at draft step 0 and toggle at runtime via
-        # set_skip_topk (index_share_for_mtp_iteration).
-        _num_hidden_layers = getattr(config, "num_hidden_layers", None)
-        is_mtp_layer = _num_hidden_layers is not None and layer_id >= _num_hidden_layers
-
-        if self.is_v32 and (not _skip_topk or is_mtp_layer):
+        if self.is_v32:
            self.indexer_rope_emb = get_rope(
                qk_rope_head_dim,
                max_position=max_position_embeddings,
@@ -1038,6 +1017,22 @@ class DeepseekV2MLAAttention(nn.Module):
                f"{prefix}.indexer",
                is_inplace_rope=self.indexer_rope_emb.enabled(),
            )
+
+            # IndexCache config
+            # Refer: https://arxiv.org/abs/2603.12201 for more details.
+            _index_topk_freq = getattr(config, "index_topk_freq", 1)
+            _index_topk_pattern = getattr(config, "index_topk_pattern", None)
+            _index_skip_topk_offset = getattr(config, "index_skip_topk_offset", 2)
+            layer_id = extract_layer_index(prefix)
+
+            if _index_topk_pattern is None:
+                _skip_topk = (
+                    max(layer_id - _index_skip_topk_offset + 1, 0) % _index_topk_freq
+                    != 0
+                )
+            elif 0 <= layer_id < len(_index_topk_pattern):
+                _skip_topk = _index_topk_pattern[layer_id] == "S"
+
        else:
            self.indexer_rope_emb = None
            self.indexer = None
@@ -64,7 +64,6 @@ from vllm.models.deepseek_v4.nvidia.flashinfer_sparse import (
 from vllm.models.deepseek_v4.nvidia.flashmla import DeepseekV4FlashMLAAttention
 from vllm.models.deepseek_v4.nvidia.ops.prepare_megamoe import prepare_megamoe_inputs
 from vllm.sequence import IntermediateTensors
-from vllm.utils.math_utils import cdiv
 from vllm.v1.attention.backends.registry import AttentionBackendEnum


@@ -86,15 +85,6 @@ class DeepseekV4MLP(nn.Module):
        # across the ranks within the tp_group. In this case the weights are
        # replicated and no collective ops are needed.
        # Otherwise we use standard TP with an allreduce at the end.
-        #
-        # Block-FP8 shards in whole 128-blocks; cdiv rounds the per-rank block
-        # count up so the linear's even TP split stays block-aligned, with the
-        # trailing ranks zero-filled by load_weights.
-        block_size = getattr(quant_config, "weight_block_size", None)
-        if block_size is not None and not is_sequence_parallel:
-            tp_size = get_tensor_model_parallel_world_size()
-            n_local = cdiv(intermediate_size // block_size[0], tp_size)
-            intermediate_size = n_local * block_size[0] * tp_size
        self.gate_up_proj = MergedColumnParallelLinear(
            hidden_size,
            [intermediate_size] * 2,
@@ -902,8 +892,6 @@ class DeepseekV4Model(nn.Module):
        config = vllm_config.model_config.hf_config
        quant_config = vllm_config.quant_config
        self.config = config
-        self.quant_config = quant_config
-        self.parallel_config = vllm_config.parallel_config
        self.use_mega_moe = (
            vllm_config.kernel_config.moe_backend == "deep_gemm_mega_moe"
        )
@@ -1092,17 +1080,7 @@ class DeepseekV4Model(nn.Module):
        # Pre-compute expert mapping ONCE.
        expert_mapping = self.get_expert_mapping()

-        # Block-FP8 shared experts: pad the intermediate up to the TP-uniform
-        # block count so the standard loaders below slice it evenly (trailing
-        # ranks land on the zero pad). SP / unquantized ones need no padding.
-        pad_shared_expert = (
-            getattr(self.quant_config, "weight_block_size", None) is not None
-            and not self.parallel_config.use_sequence_parallel_moe
-        )
-
        for name, loaded_weight in weights:
-            if pad_shared_expert and ".shared_experts." in name:
-                loaded_weight = self._pad_shared_expert_weight(name, loaded_weight)
            for param_name, weight_name, shard_id in stacked_params_mapping:
                # Skip non-stacked layers and experts (experts handled below).
                if ".experts." in name:
@@ -1177,28 +1155,6 @@ class DeepseekV4Model(nn.Module):

        return loaded_params

-    def _pad_shared_expert_weight(
-        self, name: str, loaded_weight: torch.Tensor
-    ) -> torch.Tensor:
-        """Zero-pad a block-FP8 shared-expert weight/scale on its intermediate
-        axis so the standard TP loaders split it into even, block-aligned shards
-        (trailing ranks get the zero pad). gate (w1)/up (w3) [I, H] pad dim 0;
-        down (w2 -> down_proj) [H, I] pads dim 1.
-        """
-        block_size = getattr(self.quant_config, "weight_block_size", None)
-        assert block_size is not None
-        # Round the intermediate axis up to a whole number of TP shards. The axis
-        # is in elements for weights (step = block) and in blocks for scales.
-        step = 1 if name.endswith("weight_scale_inv") else block_size[0]
-        dim = 1 if ".down_proj." in name else 0
-        mult = get_tensor_model_parallel_world_size() * step
-        pad = cdiv(loaded_weight.shape[dim], mult) * mult - loaded_weight.shape[dim]
-        if pad == 0:
-            return loaded_weight
-        pad_shape = list(loaded_weight.shape)
-        pad_shape[dim] = pad
-        return torch.cat([loaded_weight, loaded_weight.new_zeros(pad_shape)], dim=dim)
-
    def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
        first_layer = next(iter(islice(self.layers, self.start_layer, self.end_layer)))
        if first_layer.ffn.use_mega_moe:
@@ -794,10 +794,11 @@ class DelegatingParser(Parser):
                reasoning_transitioned = True
                current_token_ids = self.extract_content_ids(delta_token_ids)
                if self._engine_based:
-                    flush_delta = reasoning_parser.finish_streaming()  # type: ignore[union-attr, attr-defined]
                    current_text = (
-                        (delta_message.content if delta_message else None) or ""
-                    ) + ((flush_delta.content if flush_delta else None) or "")
+                        self.model_tokenizer.decode(current_token_ids)
+                        if current_token_ids
+                        else ""
+                    )
                    if delta_message and self._tool_parser is not None:
                        delta_message.content = None
                else:
@@ -172,9 +172,7 @@ class ParserEngine(Parser):

    def finish_streaming(self) -> DeltaMessage | None:
        events = self._engine.finish()
-        if events or self._deferred_content:
-            return self._events_to_delta(events, finished=True)
-        return None
+        return self._events_to_delta(events) if events else None

    def _reset(self, initial_state: ParserState | None = None) -> None:
        self._engine.reset(initial_state=initial_state)
@@ -674,7 +672,7 @@ class ParserEngine(Parser):
        if len(tool_call_deltas) > 1:
            tool_call_deltas = self._coalesce_tool_call_deltas(tool_call_deltas)

-        if self._deferred_content and (not seen_tool_event or not tool_call_deltas):
+        if self._deferred_content and not seen_tool_event:
            content_parts.insert(0, self._deferred_content)
            self._deferred_content = ""

@@ -375,10 +375,6 @@ def gemma4_config() -> ParserEngineConfig:
                ParserState.TOOL_PREAMBLE,
                (EventType.REASONING_END, EventType.TOOL_CALL_START),
            ),
-            (ParserState.TOOL_PREAMBLE, "TOOL_END"): Transition(
-                ParserState.CONTENT,
-                (EventType.TOOL_CALL_END,),
-            ),
            (ParserState.TOOL_PREAMBLE, "CALL_PREFIX"): Transition(
                ParserState.TOOL_NAME,
                (),
@@ -125,10 +125,6 @@ def qwen3_config(thinking: bool = True) -> ParserEngineConfig:
                ParserState.TOOL_NAME,
                (EventType.TOOL_CALL_START,),
            ),
-            (ParserState.TOOL_PREAMBLE, "TOOL_END"): Transition(
-                ParserState.CONTENT,
-                (EventType.TOOL_CALL_END,),
-            ),
            (ParserState.TOOL_PREAMBLE, "FUNC_PREFIX"): Transition(
                ParserState.TOOL_NAME,
                (),
@@ -19,6 +19,7 @@ from torch.distributed.distributed_c10d import is_nccl_available
 from typing_extensions import ParamSpec

 # import custom ops, trigger op registration
+import vllm._C  # noqa
 import vllm._C_stable_libtorch  # noqa
 import vllm.envs as envs
 from vllm.logger import init_logger
@@ -39,11 +40,6 @@ else:

 logger = init_logger(__name__)

-try:
-    import vllm._qutlass_C  # noqa: F401
-except ImportError as e:
-    logger.warning("Failed to import from vllm._qutlass_C: %r", e)
-
 _P = ParamSpec("_P")
 _R = TypeVar("_R")

@@ -191,22 +187,6 @@ class CudaPlatformBase(Platform):
        "RAY_EXPERIMENTAL_NOSET_CUDA_VISIBLE_DEVICES",
    ]

-    @classmethod
-    def import_kernels(cls) -> None:
-        """Import CUDA kernel extensions (_C_stable_libtorch, optional _qutlass_C)."""
-        try:
-            import vllm._C_stable_libtorch  # noqa: F401
-        except ImportError as e:
-            logger.warning("Failed to import from vllm._C_stable_libtorch: %r", e)
-        try:
-            import vllm._moe_C_stable_libtorch  # noqa: F401
-        except ImportError as e:
-            logger.warning("Failed to import from vllm._moe_C_stable_libtorch: %r", e)
-        try:
-            import vllm._qutlass_C  # noqa: F401
-        except ImportError as e:
-            logger.warning("Failed to import from vllm._qutlass_C: %r", e)
-
    @property
    def supported_dtypes(self) -> list[torch.dtype]:
        if self.has_device_capability(80):
@@ -20,6 +20,7 @@ except ImportError as e:
    ) from e


+from vllm.entrypoints.mcp.tool_server import ToolServer
 from vllm.entrypoints.openai.chat_completion.protocol import (
    ChatCompletionRequest,
 )
@@ -480,6 +481,15 @@ class BaseCohereCommandReasoningParser(ReasoningParser):
    def is_reasoning_end(self, input_ids: Sequence[int]) -> bool:
        return any(tid == self.end_token_id for tid in reversed(input_ids))

+    def prepare_structured_tag(
+        self, original_tag: str | None, tool_server: ToolServer | None
+    ) -> str | None:
+        # Responses API replaces ``structural_tag`` via the reasoning parser.
+        # Default ``ReasoningParser.prepare_structured_tag`` returns None, which
+        # would clear a Cohere tag produced in ``adjust_request`` and break
+        # ``StructuredOutputsParams`` validation. Preserve the existing tag.
+        return original_tag
+
    def adjust_request(
        self, request: ChatCompletionRequest | ResponsesRequest
    ) -> ChatCompletionRequest | ResponsesRequest:
@@ -6,6 +6,7 @@ from typing import TYPE_CHECKING

 from transformers import PreTrainedTokenizerBase

+from vllm.logger import init_logger
 from vllm.reasoning import ReasoningParser
 from vllm.reasoning.deepseek_r1_reasoning_parser import DeepSeekR1ReasoningParser

@@ -16,6 +17,8 @@ if TYPE_CHECKING:
    from vllm.entrypoints.openai.engine.protocol import DeltaMessage
    from vllm.entrypoints.openai.responses.protocol import ResponsesRequest

+logger = init_logger(__name__)
+

 class DeepSeekV3ReasoningParser(ReasoningParser):
    """
@@ -7,12 +7,15 @@ from typing import TYPE_CHECKING
 from transformers import PreTrainedTokenizerBase

 from vllm.entrypoints.openai.engine.protocol import DeltaMessage
+from vllm.logger import init_logger
 from vllm.reasoning.basic_parsers import BaseThinkingReasoningParser

 if TYPE_CHECKING:
    from vllm.entrypoints.openai.chat_completion.protocol import ChatCompletionRequest
    from vllm.entrypoints.openai.responses.protocol import ResponsesRequest

+logger = init_logger(__name__)
+

 class Ernie45ReasoningParser(BaseThinkingReasoningParser):
    """
@@ -8,12 +8,15 @@ import regex as re
 from transformers import PreTrainedTokenizerBase

 from vllm.entrypoints.openai.engine.protocol import DeltaMessage
+from vllm.logger import init_logger
 from vllm.reasoning import ReasoningParser

 if TYPE_CHECKING:
    from vllm.entrypoints.openai.chat_completion.protocol import ChatCompletionRequest
    from vllm.entrypoints.openai.responses.protocol import ResponsesRequest

+logger = init_logger(__name__)
+

 class GraniteReasoningParser(ReasoningParser):
    """
@@ -8,12 +8,15 @@ import regex as re
 from transformers import PreTrainedTokenizerBase

 from vllm.entrypoints.openai.engine.protocol import DeltaMessage
+from vllm.logger import init_logger
 from vllm.reasoning import ReasoningParser

 if TYPE_CHECKING:
    from vllm.entrypoints.openai.chat_completion.protocol import ChatCompletionRequest
    from vllm.entrypoints.openai.responses.protocol import ResponsesRequest

+logger = init_logger(__name__)
+

 class HunyuanA13BReasoningParser(ReasoningParser):
    """
@@ -7,12 +7,15 @@ from typing import TYPE_CHECKING
 from transformers import PreTrainedTokenizerBase

 from vllm.entrypoints.openai.engine.protocol import DeltaMessage
+from vllm.logger import init_logger
 from vllm.reasoning import ReasoningParser

 if TYPE_CHECKING:
    from vllm.entrypoints.openai.chat_completion.protocol import ChatCompletionRequest
    from vllm.entrypoints.openai.responses.protocol import ResponsesRequest

+logger = init_logger(__name__)
+

 class IdentityReasoningParser(ReasoningParser):
    """
@@ -7,6 +7,7 @@ from typing import TYPE_CHECKING
 from vllm.entrypoints.openai.engine.protocol import (
    DeltaMessage,
 )
+from vllm.logger import init_logger
 from vllm.parser.engine.registered_adapters import MinimaxM2ParserReasoningAdapter
 from vllm.reasoning.abs_reasoning_parsers import ReasoningParser
 from vllm.tokenizers import TokenizerLike
@@ -15,6 +16,8 @@ if TYPE_CHECKING:
    from vllm.entrypoints.openai.chat_completion.protocol import ChatCompletionRequest
    from vllm.entrypoints.openai.responses.protocol import ResponsesRequest

+logger = init_logger(__name__)
+

 class MiniMaxM2ReasoningParser(MinimaxM2ParserReasoningAdapter):  # type: ignore[valid-type, misc]
    """
@@ -5,6 +5,7 @@ from collections.abc import Iterable, Sequence
 from functools import cached_property
 from typing import TYPE_CHECKING

+from vllm.logger import init_logger
 from vllm.reasoning import ReasoningParser
 from vllm.reasoning.basic_parsers import BaseThinkingReasoningParser
 from vllm.tokenizers.mistral import MistralTokenizer
@@ -13,6 +14,8 @@ if TYPE_CHECKING:
    from vllm.entrypoints.openai.chat_completion.protocol import ChatCompletionRequest
    from vllm.entrypoints.openai.responses.protocol import ResponsesRequest

+logger = init_logger(__name__)
+

 class MistralReasoningParser(BaseThinkingReasoningParser):
    """
@@ -9,6 +9,7 @@ from typing import TYPE_CHECKING
 import regex as re

 from vllm.entrypoints.openai.engine.protocol import DeltaMessage
+from vllm.logger import init_logger
 from vllm.reasoning import ReasoningParser

 if TYPE_CHECKING:
@@ -16,6 +17,8 @@ if TYPE_CHECKING:
    from vllm.entrypoints.openai.responses.protocol import ResponsesRequest
    from vllm.tokenizers import TokenizerLike

+logger = init_logger(__name__)
+

 class Olmo3ReasoningState(enum.Enum):
    REASONING = 1
@@ -9,12 +9,15 @@ import regex as re
 from transformers import PreTrainedTokenizerBase

 from vllm.entrypoints.openai.engine.protocol import DeltaMessage
+from vllm.logger import init_logger
 from vllm.reasoning import ReasoningParser

 if TYPE_CHECKING:
    from vllm.entrypoints.openai.chat_completion.protocol import ChatCompletionRequest
    from vllm.entrypoints.openai.responses.protocol import ResponsesRequest

+logger = init_logger(__name__)
+

 class Step3ReasoningParser(ReasoningParser):
    """
@@ -271,7 +271,7 @@ class FlashInferMLASparseImpl(SparseMLAAttentionImpl[FlashInferMLASparseMetadata
        attn_type: str,
        kv_sharing_target_layer_name: str | None,
        # MLA Specific Arguments
-        topk_indices_buffer: torch.Tensor | None = None,
+        topk_indice_buffer: torch.Tensor | None = None,
        indexer: "Indexer | None" = None,
        **mla_args,
    ) -> None:
@@ -301,12 +301,8 @@ class FlashInferMLASparseImpl(SparseMLAAttentionImpl[FlashInferMLASparseMetadata
        self.qk_nope_head_dim: int = mla_args["qk_nope_head_dim"]
        self.qk_rope_head_dim: int = mla_args["qk_rope_head_dim"]

-        # The indexer carries the shared buffer for normal layers and tests;
-        # the explicitly-passed buffer covers backbone skip layers, whose
-        # indexer is not constructed (see deepseek_v2.py).
-        self.topk_indices_buffer: torch.Tensor | None = (
-            indexer.topk_indices_buffer if indexer is not None else topk_indices_buffer
-        )
+        assert indexer is not None, "Indexer required for sparse MLA"
+        self.topk_indices_buffer: torch.Tensor | None = indexer.topk_indices_buffer

        self._workspace_buffer: torch.Tensor | None = None
        self.bmm1_scale: float | None = None
@@ -568,12 +568,8 @@ class FlashMLASparseImpl(SparseMLAAttentionImpl[FlashMLASparseMetadata]):
        self.kv_cache_dtype = kv_cache_dtype
        self.kv_lora_rank: int = mla_args["kv_lora_rank"]
        self.softmax_scale = scale
-        # The indexer carries the shared buffer for normal layers and tests;
-        # the explicitly-passed buffer covers backbone skip layers, whose
-        # indexer is not constructed (see deepseek_v2.py).
-        self.topk_indices_buffer: torch.Tensor | None = (
-            indexer.topk_indices_buffer if indexer is not None else topk_indices_buffer
-        )
+        assert indexer is not None
+        self.topk_indices_buffer: torch.Tensor | None = indexer.topk_indices_buffer
        # Prefill BF16 kernel requires 64 on Hopper, 128 on Blackwell
        self.prefill_padding = (
            128 if current_platform.is_device_capability_family(100) else 64
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Andreas KaratzasandGitHub	78d2334aab	Merge branch 'main' into codex/rocm-artifact-tensorizer	2026-06-18 14:28:51 -05:00
Andreas Karatzas	7b375c8502	Fix ROCm artifact test dependencies Signed-off-by: Andreas Karatzas <akaratza@amd.com>	2026-06-04 21:30:21 -05:00