[CI/Build] Refresh tags before building macOS wheel (#49901 )

Signed-off-by: khluu <khluu000@gmail.com> Co-authored-by: OpenAI Codex <codex@openai.com> (cherry picked from commit 0934b26790)
[ROCm] [Release] [Bugfix] Fix the per commit wheel release pipeline. (#49245 )
2026-07-26 17:57:50 -07:00 · 2026-07-25 00:23:46 -07:00 · 2026-07-24 18:48:21 -07:00 · 2026-07-23 21:20:25 -07:00 · 2026-07-23 21:20:25 -07:00 · 2026-07-23 21:20:25 -07:00
344 changed files with 7536 additions and 10969 deletions
@@ -18,8 +18,6 @@ steps:
  - tests/kernels/quantization/test_cpu_fp8_scaled_mm.py
  - tests/kernels/mamba/cpu/test_cpu_gdn_ops.py
  - tests/kernels/mamba/test_cpu_short_conv.py
-  - tests/kernels/mamba/test_causal_conv1d.py
-  - tests/kernels/mamba/test_mamba_ssm.py
  commands:
    - |
      bash .buildkite/scripts/hardware_ci/run-cpu-test.sh 30m "
@@ -30,9 +28,7 @@ steps:
      pytest -x -v -s tests/kernels/test_onednn.py
      pytest -x -v -s tests/kernels/test_awq_int4_to_int8.py
      pytest -x -v -s tests/kernels/quantization/test_cpu_fp8_scaled_mm.py
-      pytest -x -v -s tests/kernels/mamba/cpu/test_cpu_gdn_ops.py
-      pytest -x -v -s tests/kernels/mamba/test_causal_conv1d.py
-      pytest -x -v -s tests/kernels/mamba/test_mamba_ssm.py"
+      pytest -x -v -s tests/kernels/mamba/cpu/test_cpu_gdn_ops.py"

 # Note: SDE can't be downloaded from CI host because of AWS WAF
 # - label: CPU-Compatibility Tests
@@ -7,6 +7,9 @@

 set -euo pipefail

+# The macmini queue uses persistent checkouts, so refresh tags for setuptools-scm.
+git fetch --tags --force origin
+
 # The Rust frontend build needs protoc.
 if ! command -v protoc >/dev/null 2>&1; then
  brew install protobuf
@@ -40,9 +40,7 @@ function cpu_tests() {
    pytest -x -v -s tests/kernels/moe/test_cpu_fused_moe.py
    pytest -x -v -s tests/kernels/mamba/cpu/test_cpu_gdn_ops.py
    pytest -x -v -s tests/kernels/moe/test_cpu_int4_moe.py
-    pytest -x -v -s tests/kernels/mamba/test_cpu_short_conv.py
-    pytest -x -v -s tests/kernels/mamba/test_causal_conv1d.py
-    pytest -x -v -s tests/kernels/mamba/test_mamba_ssm.py"
+    pytest -x -v -s tests/kernels/mamba/test_cpu_short_conv.py"

  # skip tests requiring model downloads if HF_TOKEN is not set
  # due to rate-limits
@@ -99,4 +97,3 @@ function cpu_tests() {
 # All of CPU tests are expected to be finished less than 40 mins.
 export -f cpu_tests
 timeout 2h bash -c cpu_tests
-
@@ -4,7 +4,7 @@ depends_on:
 steps:
 - label: Basic Correctness
  key: basic-correctness
-  timeout_in_minutes: 45
+  timeout_in_minutes: 68
  device: h200_18gb
  source_file_dependencies:
  - vllm/
@@ -4,7 +4,7 @@ depends_on:
 steps:
 - label: Benchmarks CLI Test
  key: benchmarks-cli-test
-  timeout_in_minutes: 30
+  timeout_in_minutes: 45
  device: h200_18gb
  source_file_dependencies:
  - vllm/
@@ -51,7 +51,7 @@ steps:

 - label: e2e Scheduling (1 GPU)
  key: e2e-scheduling-1-gpu
-  timeout_in_minutes: 35
+  timeout_in_minutes: 53
  device: h200_18gb
  source_file_dependencies:
    - vllm/v1/
@@ -39,7 +39,7 @@ steps:
 - label: Entrypoints Integration (API Server)
  key: entrypoints-integration-api-server
  device: h200_35gb
-  timeout_in_minutes: 50
+  timeout_in_minutes: 75
  working_dir: "/vllm-workspace/tests"
  source_file_dependencies:
  - vllm/
@@ -59,7 +59,7 @@ steps:
 - label: Entrypoints Integration (API Server OpenAI - Part 1)
  device: h200_35gb
  key: entrypoints-integration-api-server-openai-part-1
-  timeout_in_minutes: 45
+  timeout_in_minutes: 68
  working_dir: "/vllm-workspace/tests"
  source_file_dependencies:
  - vllm/
@@ -78,7 +78,7 @@ steps:
 - label: Entrypoints Integration (API Server OpenAI - Part 2)
  device: h200_35gb
  key: entrypoints-integration-api-server-openai-part-2
-  timeout_in_minutes: 45
+  timeout_in_minutes: 83
  working_dir: "/vllm-workspace/tests"
  source_file_dependencies:
  - vllm/
@@ -156,7 +156,7 @@ steps:
 - label: Entrypoints Integration (Pooling)
  device: h200_35gb
  key: entrypoints-integration-pooling
-  timeout_in_minutes: 50
+  timeout_in_minutes: 75
  working_dir: "/vllm-workspace/tests"
  source_file_dependencies:
  - vllm/
@@ -31,7 +31,7 @@ steps:

 - label: V1 Sample + Logits
  key: v1-sample-logits
-  timeout_in_minutes: 45
+  timeout_in_minutes: 83
  device: h200_18gb
  source_file_dependencies:
    - vllm/config/
@@ -5,14 +5,12 @@ steps:
 - label: Model Executor
  device: h200_35gb
  key: model-executor
-  timeout_in_minutes: 45
+  timeout_in_minutes: 60
  source_file_dependencies:
  - vllm/engine/arg_utils.py
  - vllm/config/model.py
  - vllm/model_executor
-  - vllm/model_executor/warmup
  - tests/model_executor
-  - tests/model_executor/test_jit_warmup.py
  - tests/entrypoints/openai/completion/test_tensorizer_entrypoint.py
  commands:
    - apt-get update && apt-get install -y curl libsodium23
@@ -36,9 +34,7 @@ steps:
      - vllm/engine/arg_utils.py
      - vllm/config/model.py
      - vllm/model_executor
-      - vllm/model_executor/warmup
      - tests/model_executor
-      - tests/model_executor/test_jit_warmup.py
      - tests/entrypoints/openai/completion/test_tensorizer_entrypoint.py
      - vllm/_aiter_ops.py
      - vllm/platforms/rocm.py
@@ -137,7 +137,7 @@ steps:

 - label: Language Models Test (MTEB)
  key: language-models-test-mteb
-  timeout_in_minutes: 45
+  timeout_in_minutes: 68
  device: h200_18gb
  optional: true
  source_file_dependencies:
@@ -4,7 +4,7 @@ depends_on:
 steps:
 - label: "Multi-Modal Models (Standard) 1: qwen2"
  key: multi-modal-models-standard-1-qwen2
-  timeout_in_minutes: 45
+  timeout_in_minutes: 68
  device: h200_18gb
  source_file_dependencies:
  - vllm/
@@ -20,7 +20,7 @@ steps:

 - label: "Multi-Modal Models (Standard) 2: qwen3 + gemma"
  key: multi-modal-models-standard-2-qwen3-gemma
-  timeout_in_minutes: 50
+  timeout_in_minutes: 75
  device: h200_18gb
  source_file_dependencies:
  - vllm/
@@ -54,7 +54,7 @@ steps:
 - label: "Multi-Modal Models (Standard) 4: other + whisper"
  device: h200_35gb
  key: multi-modal-models-standard-4-other-whisper
-  timeout_in_minutes: 50
+  timeout_in_minutes: 75
  source_file_dependencies:
  - vllm/
  - tests/models/multimodal
@@ -85,7 +85,7 @@ steps:

 - label: Multi-Modal Processor # 44min
  key: multi-modal-processor
-  timeout_in_minutes: 65
+  timeout_in_minutes: 98
  device: h200_18gb
  source_file_dependencies:
  - vllm/
@@ -5,7 +5,7 @@ steps:
 - label: PyTorch Compilation Unit Tests
  device: h200_35gb
  key: pytorch-compilation-unit-tests
-  timeout_in_minutes: 90
+  timeout_in_minutes: 150
  source_file_dependencies:
    - vllm/__init__.py
    - vllm/_aiter_ops.py
@@ -47,7 +47,6 @@

 # Rust Frontend
 /rust/ @BugenZhao @njhill
-/rust/src/bench @esmeetu
 /build_rust.sh @BugenZhao @njhill
 /rust-toolchain.toml @BugenZhao @njhill
 /.buildkite/test_areas/rust* @BugenZhao @njhill
@@ -48,7 +48,7 @@ vLLM is flexible and easy to use with:
 - Tool calling and reasoning parsers
 - OpenAI-compatible API server, plus Anthropic Messages API and gRPC support
 - Efficient multi-LoRA support for dense and MoE layers
- Support for NVIDIA GPUs, AMD GPUs, Intel GPUs, and x86/ARM/PowerPC CPUs. Additionally, diverse hardware plugins such as Google TPUs, Intel Gaudi, IBM Spyre, Huawei Ascend, Rebellions NPU, Apple Silicon, MetaX GPU, and more.
+- Support for NVIDIA GPUs, AMD GPUs, and x86/ARM/PowerPC CPUs. Additionally, diverse hardware plugins such as Google TPUs, Intel Gaudi, IBM Spyre, Huawei Ascend, Rebellions NPU, Apple Silicon, MetaX GPU, and more.

 vLLM seamlessly supports 200+ model architectures on Hugging Face, including:

@@ -12,7 +12,6 @@ import logging
 import statistics
 import types
 from contextlib import contextmanager
-from math import prod

 import torch
 from batch_spec import parse_batch_spec, reorder_for_flashinfer
@@ -38,13 +37,10 @@ from vllm.config import (
 )
 from vllm.v1.attention.backends.utils import (
    CommonAttentionMetadata,
-    resolve_kv_cache_layout,
-)
-from vllm.v1.kv_cache_interface import (
-    FullAttentionSpec,
-    compute_layer_kv_cache_shape_bytes,
-    reshape_kv_cache,
+    get_kv_cache_layout,
+    set_kv_cache_layout,
 )
+from vllm.v1.kv_cache_interface import FullAttentionSpec

 # ============================================================================
 # Backend Configuration
@@ -341,23 +337,52 @@ def _create_input_tensors(
 def _create_kv_cache(
    config: BenchmarkConfig,
    max_num_blocks: int,
+    backend_class,
    device: torch.device,
    dtype: torch.dtype,
 ) -> list:
-    """Create KV cache tensors for all layers using the standard allocator."""
-    spec = FullAttentionSpec(
+    """Create KV cache tensors for all layers using the backend's methods.
+
+    Uses the backend's get_kv_cache_shape() and get_kv_cache_stride_order()
+    to create the cache with the correct shape and memory layout.
+    """
+    # Get the logical shape from the backend
+    cache_shape = backend_class.get_kv_cache_shape(
+        num_blocks=max_num_blocks,
        block_size=config.block_size,
        num_kv_heads=config.num_kv_heads,
        head_size=config.head_dim,
-        dtype=dtype,
    )
-    layout = resolve_kv_cache_layout()
-    total_bytes = (
-        prod(compute_layer_kv_cache_shape_bytes(spec, max_num_blocks))
-        * config.num_layers
-    )
-    buf = torch.zeros(total_bytes, device=device, dtype=torch.int8)
-    return reshape_kv_cache(buf, spec, max_num_blocks, config.num_layers, layout)
+
+    # Get the stride order for custom memory layout
+    try:
+        stride_order = backend_class.get_kv_cache_stride_order()
+        assert len(stride_order) == len(cache_shape)
+    except (AttributeError, NotImplementedError):
+        stride_order = tuple(range(len(cache_shape)))
+
+    # Permute shape to physical layout order
+    physical_shape = tuple(cache_shape[i] for i in stride_order)
+
+    # Compute inverse permutation to get back to logical view
+    inv_order = [stride_order.index(i) for i in range(len(stride_order))]
+
+    # Use fp8 dtype for cache when requested.
+    cache_dtype = dtype
+    if config.kv_cache_dtype == "fp8":
+        from vllm.platforms import current_platform
+
+        cache_dtype = current_platform.fp8_dtype()
+
+    cache_list = []
+    for _ in range(config.num_layers):
+        # Allocate in physical layout order (contiguous in memory)
+        cache = torch.zeros(*physical_shape, device=device, dtype=cache_dtype)
+        # Permute to logical view
+        cache = cache.permute(*inv_order)
+        cache_list.append(cache)
+
+    return cache_list


 # ============================================================================
@@ -475,6 +500,13 @@ def run_attention_benchmark(config: BenchmarkConfig) -> BenchmarkResult:
                backend_cfg, config, device, dtype
            )

+            # Set KV cache layout if the backend requires a specific one
+            # (e.g., FlashInfer requires HND on SM100/Blackwell for TRTLLM attention)
+            required_layout = backend_class.get_required_kv_cache_layout()
+            if required_layout is not None:
+                set_kv_cache_layout(required_layout)
+                get_kv_cache_layout.cache_clear()
+
            common_metadata = _build_common_attn_metadata(
                q_lens, kv_lens, config.block_size, device
            )
@@ -509,7 +541,9 @@ def run_attention_benchmark(config: BenchmarkConfig) -> BenchmarkResult:
                config, total_q, device, dtype, quantize_query=quantize_query
            )

-            cache_list = _create_kv_cache(config, max_num_blocks, device, dtype)
+            cache_list = _create_kv_cache(
+                config, max_num_blocks, backend_class, device, dtype
+            )

            timing_stats, mem_stats = _run_single_benchmark(
                config,
@@ -430,7 +430,6 @@ set(VLLM_EXT_SRC
    "csrc/cpu/layernorm.cpp"
    "csrc/cpu/mla_decode.cpp"
    "csrc/cpu/pos_encoding.cpp"
-    "csrc/cpu/mamba_cpu.cpp"
    "csrc/moe/dynamic_4bit_int_moe_cpu.cpp"
    "csrc/cpu/cpu_attn.cpp"
    "csrc/cpu/torch_bindings.cpp")
@@ -490,7 +489,6 @@ if (ENABLE_X86_ISA)
        "csrc/cpu/spec_decode_utils.cpp"
        "csrc/cpu/cpu_attn.cpp"
        "csrc/cpu/dnnl_kernels.cpp"
-        "csrc/cpu/mamba_cpu.cpp"
        "csrc/cpu/torch_bindings.cpp"
        # TODO: Remove these files
        "csrc/cpu/activation.cpp"
@@ -504,7 +502,6 @@ if (ENABLE_X86_ISA)
        "csrc/cpu/utils.cpp"
        "csrc/cpu/spec_decode_utils.cpp"
        "csrc/cpu/cpu_attn.cpp"
-        "csrc/cpu/mamba_cpu.cpp"
        "csrc/cpu/dnnl_kernels.cpp"
        "csrc/cpu/torch_bindings.cpp"
        # TODO: Remove these files
@@ -22,7 +22,7 @@ if(QUTLASS_SRC_DIR)
  set(qutlass_BINARY_DIR "${CMAKE_BINARY_DIR}/qutlass-binary-dir-unused")
 else()
  set(_QUTLASS_UPSTREAM_REPO "https://github.com/IST-DASLab/qutlass.git")
-  set(_QUTLASS_UPSTREAM_TAG "e74319e3405ce6d71965732880f5dc1f52371f64")
+  set(_QUTLASS_UPSTREAM_TAG "830d2c4537c7396e14a02a46fbddd18b5d107c65")

  set(_qutlass_fc_root "${FETCHCONTENT_BASE_DIR}")
  if(NOT _qutlass_fc_root)
@@ -125,6 +125,8 @@ if(${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
@@ -137,11 +139,9 @@ if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8 AND QUTLASS_ARCHS)
    WITH_SOABI)

  target_compile_definitions(_qutlass_C PRIVATE
-    QUTLASS_MINIMAL_BUILD=1
+    QUTLASS_DISABLE_PYBIND=1
    TARGET_CUDA_ARCH=${QUTLASS_TARGET_CC}
-    CUTLASS_ENABLE_DIRECT_CUDA_DRIVER_CALL=1
-    TORCH_TARGET_VERSION=0x020B000000000000ULL
-    USE_CUDA)
+    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>
@@ -39,7 +39,7 @@ else()
  FetchContent_Declare(
          vllm-flash-attn
          GIT_REPOSITORY https://github.com/vllm-project/flash-attention.git
-          GIT_TAG 168920233059c48de6199e2cda74003b2ce3d199
+          GIT_TAG caaa4eb59845388a20b1f435ecaafb4bd9517ad8
          GIT_PROGRESS TRUE
          # Don't share the vllm-flash-attn build between build types
          BINARY_DIR ${CMAKE_BINARY_DIR}/vllm-flash-attn
@@ -336,14 +336,13 @@ struct FP32Vec8 : public Vec<FP32Vec8> {
    reg.val[1] = fp16_to_fp32_bits(raw_lo);
  }
  float reduce_sum() const {
-    // VSX horizontal reduction: 3 vector ops instead of 8 scalar adds.
-    // Step 1: pairwise sum of the two 4-wide halves
-    __vector float s = vec_add(reg.val[0], reg.val[1]);
-    // Step 2: rotate by 8 bytes (2 floats) and add
-    s = vec_add(s, vec_sld(s, s, 8));
-    // Step 3: rotate by 4 bytes (1 float) and add  => all lanes hold total
-    s = vec_add(s, vec_sld(s, s, 4));
-    return vec_extract(s, 0);
+    AliasReg ar;
+    ar.reg = reg;
+    float result = 0;
+    unroll_loop<int, VEC_ELEM_NUM>(
+        [&result, &ar](int i) { result += ar.values[i]; });
+
+    return result;
  }
  FP32Vec8 exp() const {
    f32x4x2_t out;
@@ -1,285 +0,0 @@
-// SPDX-License-Identifier: Apache-2.0
-// SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-//
-// CPU at::Tensor wrappers for Mamba decode-step kernels defined in
-// mamba_kernels.hpp.
-
-#include "cpu/mamba_kernels.hpp"
-
-#include <ATen/ATen.h>
-#include <torch/library.h>
-#include <c10/util/Optional.h>
-
-#include "cpu_types.hpp"
-
-// ---------------------------------------------------------------------------
-// causal_conv1d_update
-// ---------------------------------------------------------------------------
-at::Tensor causal_conv1d_update_cpu_impl(
-    at::Tensor& x, at::Tensor& conv_state, const at::Tensor& weight,
-    const c10::optional<at::Tensor>& bias,
-    const c10::optional<std::string>& activation,
-    const c10::optional<at::Tensor>& conv_state_indices,
-    const c10::optional<at::Tensor>& query_start_loc, int64_t pad_slot_id) {
-  bool do_silu = false;
-  if (activation.has_value()) {
-    const std::string& act = activation.value();
-    do_silu = (act == "silu" || act == "swish");
-  }
-
-  at::ScalarType dtype = x.scalar_type();
-
-  // Input x: contiguous in native dtype.
-  at::Tensor x_c = x.is_contiguous() ? x : x.contiguous();
-
-  // conv_state: NEVER copy the full paged tensor just for layout reasons.
-  // If the dtype matches we work directly on conv_state (contiguous or not)
-  // by extracting strides and passing them to the kernel.
-  // Only a dtype-conversion copy is made when types differ (rare for BF16).
-  bool state_type_ok = (conv_state.scalar_type() == dtype);
-  at::Tensor state_c = state_type_ok ? conv_state : conv_state.to(dtype);
-  // state_c and conv_state may be non-contiguous — that is intentional.
-
-  // Weight: coerce to same dtype if needed (should match in practice)
-  at::Tensor w_c =
-      (weight.scalar_type() != dtype)
-          ? weight.to(dtype).contiguous()
-          : (weight.is_contiguous() ? weight : weight.contiguous());
-
-  // Bias stays float32 (small scalar, used only for fp32 accumulation)
-  at::Tensor bias_f32;
-  if (bias.has_value() && bias.value().defined())
-    bias_f32 = bias.value().to(at::kFloat).contiguous();
-
-  int64_t batch = x_c.size(0);
-  int64_t dim = x_c.size(1);
-  int64_t seqlen = (x_c.dim() == 3) ? x_c.size(2) : 1;
-  int64_t width = w_c.size(1);
-  int64_t state_len = state_c.size(2);
-
-  // Extract strides — works for contiguous AND non-contiguous (transposed)
-  // state. stride(0): between cache slots (e.g. num_slots × dim × width-1 in
-  // contiguous) stride(1): between conv channels (dim stride) stride(2):
-  // between state elements (=1 when contiguous, =dim when transposed)
-  int64_t stride_s_slot = state_c.stride(0);
-  int64_t stride_s_dim = state_c.stride(1);
-  int64_t stride_s_state = state_c.stride(2);
-
-  at::Tensor out = x_c.clone();  // native dtype, no float32 alloc
-
-  const int32_t* cache_idx_ptr = nullptr;
-  at::Tensor cache_idx_int;
-  if (conv_state_indices.has_value()) {
-    cache_idx_int = conv_state_indices.value().to(at::kInt).contiguous();
-    cache_idx_ptr = cache_idx_int.data_ptr<int32_t>();
-  }
-
-  VLLM_DISPATCH_FLOATING_TYPES(dtype, "causal_conv1d_update", [&] {
-    mamba_cpu::causal_conv1d_update_kernel<scalar_t>(
-        x_c.data_ptr<scalar_t>(), state_c.data_ptr<scalar_t>(), stride_s_slot,
-        stride_s_dim, stride_s_state, w_c.data_ptr<scalar_t>(),
-        bias_f32.defined() ? bias_f32.data_ptr<float>() : nullptr,
-        out.data_ptr<scalar_t>(), cache_idx_ptr,
-        static_cast<int32_t>(pad_slot_id), batch, dim, seqlen, width, state_len,
-        do_silu);
-  });
-
-  // Write back only when a type-conversion copy was made.
-  // Layout-only non-contiguity is handled via strides above — no copy needed.
-  if (!state_type_ok) conv_state.copy_(state_c);
-
-  return out;
-}
-
-// ---------------------------------------------------------------------------
-// selective_state_update
-// ---------------------------------------------------------------------------
-void selective_state_update_cpu_impl(
-    at::Tensor& state,    // (nstates, nheads, dim, dstate)
-    const at::Tensor& x,  // (N, nheads, dim)
-    const at::Tensor& dt, const at::Tensor& A, const at::Tensor& B,
-    const at::Tensor& C, const c10::optional<at::Tensor>& D,
-    const c10::optional<at::Tensor>& z,
-    const c10::optional<at::Tensor>& dt_bias, bool dt_softplus,
-    const c10::optional<at::Tensor>& state_batch_indices,
-    const c10::optional<at::Tensor>& dst_state_batch_indices,
-    int64_t null_block_id, at::Tensor& out,
-    const c10::optional<at::Tensor>& num_accepted_tokens,
-    const c10::optional<at::Tensor>& cu_seqlens) {
-  at::ScalarType state_type = state.scalar_type();
-  at::ScalarType input_type = x.scalar_type();
-
-  // x, B, C must be contiguous and match input_type
-  auto ensure_input = [input_type](const at::Tensor& t) -> at::Tensor {
-    at::Tensor r = (t.scalar_type() != input_type) ? t.to(input_type) : t;
-    return r.is_contiguous() ? r : r.contiguous();
-  };
-  at::Tensor x_in = ensure_input(x);
-  at::Tensor B_in = ensure_input(B);
-  at::Tensor C_in = ensure_input(C);
-  at::Tensor z_in;
-  if (z.has_value() && z.value().defined()) z_in = ensure_input(z.value());
-
-  // A, D, dt_bias are float32 model parameters that arrive here as expanded
-  // tensors, e.g. A is (nheads, head_dim, dstate) with strides (1, 0, 0).
-  // We need just the scalar value per head as a (nheads,) 1-D array so that
-  // A_ptr[h] in the kernel correctly reads head h's value.
-  //
-  // Strategy: peel trailing expanded (stride=0) dims via .select(), which is
-  // a zero-copy view.  For A: (nheads, head_dim, dstate) strides (1,0,0)
-  //   → .select(2,0) → (nheads, head_dim) strides (1,0)
-  //   → .select(1,0) → (nheads,)          stride  (1,)   ← contiguous, free.
-  // No allocation, no type conversion (A is already float32).
-  auto to_per_head_1d_f32 = [](const at::Tensor& t) -> at::Tensor {
-    at::Tensor r = t;
-    // Peel trailing dimensions that are broadcast (stride=0 or size=1)
-    while (r.dim() > 1) r = r.select(r.dim() - 1, 0);
-    if (r.scalar_type() != at::kFloat) r = r.to(at::kFloat);
-    return r.is_contiguous() ? r : r.contiguous();
-  };
-
-  at::Tensor A_f32 = to_per_head_1d_f32(A);  // (nheads,) float32
-  at::Tensor D_f32, dt_bias_f32;
-  if (D.has_value() && D.value().defined())
-    D_f32 = to_per_head_1d_f32(D.value());
-  if (dt_bias.has_value() && dt_bias.value().defined())
-    dt_bias_f32 = to_per_head_1d_f32(dt_bias.value());
-
-  // dt: reduce (N, nheads, head_dim) expanded tensor → (N, nheads) BEFORE
-  // the type conversion so we convert head_dim x fewer elements.
-  at::Tensor dt_f32;
-  {
-    // If dt was expanded to (N, nheads, head_dim) with stride-0 in dim 2,
-    // take a zero-copy view of index 0 along that dim first.
-    at::Tensor t2 = (dt.dim() == 3) ? dt.select(2, 0) : dt;  // (N, nheads)
-    at::Tensor t3 = (t2.scalar_type() != at::kFloat) ? t2.to(at::kFloat) : t2;
-    dt_f32 = t3.is_contiguous() ? t3 : t3.contiguous();
-  }
-
-  int64_t nheads = state.size(1);
-  int64_t dim = state.size(2);
-  int64_t dstate = state.size(3);
-  int64_t N = (cu_seqlens.has_value() && cu_seqlens.value().defined())
-                  ? cu_seqlens.value().size(0) - 1
-                  : x_in.size(0);
-  int64_t ngroups = B_in.size(1);
-
-  // Strides
-  int64_t stride_state_n = state.stride(0);
-  int64_t stride_state_h = state.stride(1);
-  int64_t stride_state_d = state.stride(2);
-  int64_t stride_x_n = x_in.stride(0);
-  int64_t stride_x_h = x_in.stride(1);
-  int64_t stride_dt_n = dt_f32.stride(0);  // dt is (N, nheads)
-  int64_t stride_BC_n = B_in.stride(0);
-  int64_t stride_BC_g = B_in.stride(1);
-  int64_t stride_out_n = out.stride(0);
-  int64_t stride_out_h = out.stride(1);
-
-  // Optional index pointers
-  auto get_int32_ptr =
-      [](const c10::optional<at::Tensor>& opt) -> const int32_t* {
-    return (opt.has_value() && opt.value().defined())
-               ? opt.value().data_ptr<int32_t>()
-               : nullptr;
-  };
-  const int32_t* sbi_ptr = get_int32_ptr(state_batch_indices);
-  const int32_t* dsbi_ptr = get_int32_ptr(dst_state_batch_indices);
-  const int32_t* nat_ptr = get_int32_ptr(num_accepted_tokens);
-  const int32_t* csl_ptr = get_int32_ptr(cu_seqlens);
-
-  // Dispatch on (state_t, input_t, out_t): write directly into `out`
-  // without any intermediate float32 buffer.
-  VLLM_DISPATCH_FLOATING_TYPES(state_type, "ssu_state", [&] {
-    using state_t = scalar_t;
-    VLLM_DISPATCH_FLOATING_TYPES(input_type, "ssu_input", [&] {
-      using input_t = scalar_t;
-      VLLM_DISPATCH_FLOATING_TYPES(out.scalar_type(), "ssu_out", [&] {
-        using out_t = scalar_t;
-        mamba_cpu::selective_state_update_kernel<state_t, input_t, out_t>(
-            state.data_ptr<state_t>(), stride_state_n, stride_state_h,
-            stride_state_d, x_in.data_ptr<input_t>(), stride_x_n, stride_x_h,
-            dt_f32.data_ptr<float>(), stride_dt_n, A_f32.data_ptr<float>(),
-            B_in.data_ptr<input_t>(), C_in.data_ptr<input_t>(), stride_BC_n,
-            stride_BC_g, D_f32.defined() ? D_f32.data_ptr<float>() : nullptr,
-            z_in.defined() ? z_in.data_ptr<input_t>() : nullptr,
-            dt_bias_f32.defined() ? dt_bias_f32.data_ptr<float>() : nullptr,
-            out.data_ptr<out_t>(), stride_out_n, stride_out_h, sbi_ptr,
-            dsbi_ptr, static_cast<int32_t>(null_block_id), nat_ptr, csl_ptr, N,
-            nheads, ngroups, dim, dstate, dt_softplus);
-      });
-    });
-  });
-}
-
-// ---------------------------------------------------------------------------
-// mamba_chunk_scan_fwd_cpu
-// ---------------------------------------------------------------------------
-void mamba_chunk_scan_fwd_cpu_impl(
-    at::Tensor& out,  // [seqlen, nheads, headdim] — pre-allocated by caller
-    at::Tensor&
-        final_states,     // [batch, nheads, headdim, dstate] float32 contiguous
-    const at::Tensor& x,  // [seqlen, nheads, headdim]
-    const at::Tensor&
-        dt,  // [seqlen, nheads] float32 (preprocessed: bias+softplus+clamp)
-    const at::Tensor& A,                 // [nheads] float32
-    const at::Tensor& B,                 // [seqlen, ngroups, dstate]
-    const at::Tensor& C,                 // [seqlen, ngroups, dstate]
-    const c10::optional<at::Tensor>& D,  // [nheads] float32 (optional)
-    const c10::optional<at::Tensor>& z,  // [seqlen, nheads, headdim] (optional)
-    const at::Tensor& cu_seqlens         // [batch+1] int32
-) {
-  const at::ScalarType input_type = x.scalar_type();
-
-  auto ensure_contig = [input_type](const at::Tensor& t) -> at::Tensor {
-    at::Tensor r = (t.scalar_type() != input_type) ? t.to(input_type) : t;
-    return r.is_contiguous() ? r : r.contiguous();
-  };
-  at::Tensor x_in = ensure_contig(x);
-  at::Tensor B_in = ensure_contig(B);
-  at::Tensor C_in = ensure_contig(C);
-  at::Tensor z_in;
-  if (z.has_value() && z.value().defined()) z_in = ensure_contig(z.value());
-
-  // A and D are float32 model parameters, potentially broadcast-expanded.
-  // Strip trailing broadcast dims to get a contiguous (nheads,) array.
-  auto to_per_head_f32 = [](const at::Tensor& t) -> at::Tensor {
-    at::Tensor r = t;
-    while (r.dim() > 1) r = r.select(r.dim() - 1, 0);
-    if (r.scalar_type() != at::kFloat) r = r.to(at::kFloat);
-    return r.is_contiguous() ? r : r.contiguous();
-  };
-  at::Tensor A_f32 = to_per_head_f32(A);
-  at::Tensor D_f32;
-  if (D.has_value() && D.value().defined()) D_f32 = to_per_head_f32(D.value());
-
-  // dt: [seqlen, nheads] float32 — caller has applied bias+softplus+clamp in
-  // Python.
-  at::Tensor dt_c = dt.is_contiguous() ? dt : dt.contiguous();
-  if (dt_c.scalar_type() != at::kFloat) dt_c = dt_c.to(at::kFloat);
-
-  at::Tensor cu_int = cu_seqlens.to(at::kInt).contiguous();
-
-  const int64_t batch = final_states.size(0);
-  const int64_t nheads = final_states.size(1);
-  const int64_t headdim = final_states.size(2);
-  const int64_t dstate = final_states.size(3);
-  const int64_t ngroups = B_in.size(1);
-
-  TORCH_CHECK(final_states.is_contiguous(),
-              "mamba_chunk_scan_fwd_cpu: final_states must be contiguous");
-  TORCH_CHECK(out.is_contiguous(),
-              "mamba_chunk_scan_fwd_cpu: out must be contiguous (writes via "
-              "raw data_ptr)");
-
-  VLLM_DISPATCH_FLOATING_TYPES(input_type, "mamba_chunk_scan_fwd_cpu", [&] {
-    mamba_cpu::mamba_chunk_scan_fwd_kernel<scalar_t>(
-        final_states.data_ptr<float>(), x_in.data_ptr<scalar_t>(),
-        dt_c.data_ptr<float>(), A_f32.data_ptr<float>(),
-        B_in.data_ptr<scalar_t>(), C_in.data_ptr<scalar_t>(),
-        D_f32.defined() ? D_f32.data_ptr<float>() : nullptr,
-        z_in.defined() ? z_in.data_ptr<scalar_t>() : nullptr,
-        out.data_ptr<scalar_t>(), cu_int.data_ptr<int32_t>(), batch, nheads,
-        ngroups, headdim, dstate);
-  });
-}
@@ -1,382 +0,0 @@
-// SPDX-License-Identifier: Apache-2.0
-// SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-//
-// Fused CPU vector kernels for Mamba decode-step hotspots:
-//   - causal_conv1d_update  (depthwise 1-D conv state roll + compute)
-//   - selective_state_update (SSM recurrence, single-step)
-
-#pragma once
-
-#include "cpu_types.hpp"
-#include <cmath>
-#include <cstring>
-#include <cstdint>
-#include <algorithm>
-
-namespace mamba_cpu {
-
-// ---------------------------------------------------------------------------
-// causal_conv1d_update — templated for native BF16/FP32
-//
-// state_ptr may point to a NON-CONTIGUOUS paged KV cache tensor.
-// Explicit strides are passed so the kernel writes directly into the
-// correct memory locations without making a contiguous copy of the full
-// paged tensor (which was the source of the 34-41% direct_copy_kernel).
-//
-//   stride_s_slot  = state.stride(0)  — between cache slots
-//   stride_s_dim   = state.stride(1)  — between conv_dim channels
-//   stride_s_state = state.stride(2)  — between state elements
-//
-// When stride_s_state == 1 (contiguous), the memmove fast path is used.
-// ---------------------------------------------------------------------------
-template <typename scalar_t>
-inline void causal_conv1d_update_kernel(
-    const scalar_t* __restrict__ x_ptr, scalar_t* __restrict__ state_ptr,
-    int64_t stride_s_slot, int64_t stride_s_dim, int64_t stride_s_state,
-    const scalar_t* __restrict__ weight_ptr, const float* __restrict__ bias_ptr,
-    scalar_t* __restrict__ out_ptr, const int32_t* __restrict__ cache_idxs,
-    int32_t pad_slot_id, int64_t batch, int64_t dim, int64_t seqlen,
-    int64_t width, int64_t state_len, bool do_silu) {
-#pragma omp parallel for
-  for (int64_t b = 0; b < batch; ++b) {
-    int64_t cache_idx = (cache_idxs != nullptr) ? cache_idxs[b] : b;
-    if (cache_idx == pad_slot_id) continue;
-
-    for (int64_t t = 0; t < seqlen; ++t) {
-      const scalar_t* x_b = x_ptr + (b * dim * seqlen + t);
-      scalar_t* out_b = out_ptr + (b * dim * seqlen + t);
-      // Base of this slot in the (possibly non-contiguous) paged state
-      scalar_t* s_base = state_ptr + cache_idx * stride_s_slot;
-
-      for (int64_t d = 0; d < dim; ++d) {
-        float x_val = static_cast<float>(x_b[d * seqlen]);
-        scalar_t* sd = s_base + d * stride_s_dim;  // start of this dim's state
-        const scalar_t* w = weight_ptr + d * width;
-
-        // Accumulate in float32 for precision
-        float acc = (bias_ptr != nullptr) ? bias_ptr[d] : 0.0f;
-        for (int64_t k = 0; k < state_len; ++k) {
-          acc += static_cast<float>(w[k]) *
-                 static_cast<float>(sd[k * stride_s_state]);
-        }
-        acc += static_cast<float>(w[state_len]) * x_val;
-
-        // Shift state left and append new input.
-        // Use memmove when contiguous (stride==1); element loop otherwise.
-        if (stride_s_state == 1) {
-          if (state_len > 1)
-            std::memmove(sd, sd + 1, (state_len - 1) * sizeof(scalar_t));
-          if (state_len > 0) sd[state_len - 1] = static_cast<scalar_t>(x_val);
-        } else {
-          for (int64_t k = 0; k < state_len - 1; ++k)
-            sd[k * stride_s_state] = sd[(k + 1) * stride_s_state];
-          if (state_len > 0)
-            sd[(state_len - 1) * stride_s_state] = static_cast<scalar_t>(x_val);
-        }
-
-        if (do_silu) {
-          float sigmoid = (acc >= 0) ? 1.0f / (1.0f + std::exp(-acc))
-                                     : std::exp(acc) / (1.0f + std::exp(acc));
-          acc *= sigmoid;
-        }
-        out_b[d * seqlen] = static_cast<scalar_t>(acc);
-      }
-    }
-  }
-}
-
-// ---------------------------------------------------------------------------
-// selective_state_update
-//
-// Template parameters:
-//   state_t  - dtype of ssm_state cache (typically BFloat16)
-//   input_t  - dtype of x, B, C (typically BFloat16)
-//   out_t    - dtype of output tensor (typically BFloat16)
-//              Write directly — no float32 intermediate buffer needed.
-//
-// A, D, dt_bias are accepted as const float* (they are always float32
-// model parameters in Mamba2).  This eliminates the per-call float32→BF16
-// conversion and the .contiguous() materialisation of the broadcast-expand.
-//
-// dt is accepted as a (N, nheads) scalar-per-head tensor, not as the
-// (N, nheads, head_dim) expansion, so no .contiguous() copy is needed.
-// ---------------------------------------------------------------------------
-template <typename state_t, typename input_t, typename out_t = float>
-inline void selective_state_update_kernel(
-    state_t* __restrict__ state_ptr, int64_t stride_state_n,
-    int64_t stride_state_h, int64_t stride_state_d,
-    const input_t* __restrict__ x_ptr, int64_t stride_x_n, int64_t stride_x_h,
-    // dt: (N, nheads) — scalar per head, NOT expanded to head_dim
-    const float* __restrict__ dt_ptr, int64_t stride_dt_n,
-    // A: (nheads,) float32 — scalar per head
-    const float* __restrict__ A_ptr, const input_t* __restrict__ B_ptr,
-    const input_t* __restrict__ C_ptr, int64_t stride_BC_n, int64_t stride_BC_g,
-    // D: (nheads,) float32 — scalar per head (nullptr if not used)
-    const float* __restrict__ D_ptr,
-    // z: same shape as x (optional)
-    const input_t* __restrict__ z_ptr,
-    // dt_bias: (nheads,) float32 — scalar per head (nullptr if not used)
-    const float* __restrict__ dt_bias_ptr, out_t* __restrict__ out_ptr,
-    int64_t stride_out_n, int64_t stride_out_h,
-    const int32_t* __restrict__ state_batch_indices,
-    const int32_t* __restrict__ dst_state_batch_indices, int32_t null_block_id,
-    const int32_t* __restrict__ num_accepted_tokens,
-    const int32_t* __restrict__ cu_seqlens, int64_t N, int64_t nheads,
-    int64_t ngroups, int64_t dim, int64_t dstate, bool dt_softplus) {
-  using state_vec_t = vec_op::vec_t<state_t>;
-  using input_vec_t = vec_op::vec_t<input_t>;
-  constexpr int VEC_ELEM_NUM = 8;
-
-  int64_t nheads_per_group = nheads / ngroups;
-
-  for (int64_t seq_idx = 0; seq_idx < N; ++seq_idx) {
-    int64_t bos, seq_len;
-    if (cu_seqlens != nullptr) {
-      bos = cu_seqlens[seq_idx];
-      seq_len = cu_seqlens[seq_idx + 1] - bos;
-    } else {
-      bos = seq_idx;
-      seq_len = 1;
-    }
-
-    int64_t state_read_idx = (state_batch_indices != nullptr)
-                                 ? state_batch_indices[seq_idx]
-                                 : seq_idx;
-    if (state_read_idx == null_block_id) continue;
-
-    int64_t state_write_idx = (num_accepted_tokens == nullptr)
-                                  ? ((dst_state_batch_indices != nullptr)
-                                         ? dst_state_batch_indices[seq_idx]
-                                         : state_read_idx)
-                                  : -1;
-
-    state_t* s = state_ptr + state_read_idx * stride_state_n;
-
-    for (int64_t t = 0; t < seq_len; ++t) {
-      int64_t token_idx = bos + t;
-      const input_t* x_tok = x_ptr + token_idx * stride_x_n;
-      // dt: (N, nheads) — one float per head per token
-      const float* dt_tok = dt_ptr + token_idx * stride_dt_n;
-      const input_t* B_tok = B_ptr + token_idx * stride_BC_n;
-      const input_t* C_tok = C_ptr + token_idx * stride_BC_n;
-      out_t* out_tok = out_ptr + token_idx * stride_out_n;
-
-#pragma omp parallel for
-      for (int64_t h = 0; h < nheads; ++h) {
-        int64_t g = h / nheads_per_group;
-        const input_t* x_h = x_tok + h * stride_x_h;
-        const input_t* B_g = B_tok + g * stride_BC_g;
-        const input_t* C_g = C_tok + g * stride_BC_g;
-        out_t* out_h = out_tok + h * stride_out_h;
-        state_t* s_h = s + h * stride_state_h;
-
-        // Read scalars-per-head (A, dt, dt_bias, D) — no per-dim indexing
-        float dt_val = dt_tok[h];
-        if (dt_bias_ptr != nullptr) dt_val += dt_bias_ptr[h];
-        if (dt_softplus) {
-          dt_val = (dt_val <= 20.0f) ? std::log1p(std::exp(dt_val)) : dt_val;
-        }
-        const float A_val = A_ptr[h];  // scalar: same for all dim, dstate
-        const float D_val = (D_ptr != nullptr) ? D_ptr[h] : 0.0f;
-
-        const input_t* z_h =
-            (z_ptr != nullptr) ? z_ptr + token_idx * stride_x_n + h * stride_x_h
-                               : nullptr;
-
-        vec_op::FP32Vec8 dt_vec(dt_val);
-        // dA = exp(A * dt): A and dt are SCALARS per head, so compute once
-        // and broadcast.  This saves 7 redundant std::exp() calls that
-        // FP32Vec8::exp() would otherwise make on the broadcast vector.
-        const float dA_scalar = std::exp(A_val * dt_val);
-        vec_op::FP32Vec8 dA(dA_scalar);  // broadcast
-
-        for (int64_t d = 0; d < dim; ++d) {
-          float x_val = static_cast<float>(x_h[d]);
-
-          vec_op::FP32Vec8 out_vec(0.0f);
-          state_t* s_hd = s_h + d * stride_state_d;
-          const input_t* B_g_base = B_g;
-          const input_t* C_g_base = C_g;
-
-          vec_op::FP32Vec8 x_vec(x_val);
-          // dBx = B * x * dt — same dA for all dstate (A is scalar)
-          // s_new = s * dA + B * x * dt
-
-          int64_t n = 0;
-          for (; n <= dstate - VEC_ELEM_NUM; n += VEC_ELEM_NUM) {
-            vec_op::FP32Vec8 B_v((input_vec_t(B_g_base + n)));
-            vec_op::FP32Vec8 C_v((input_vec_t(C_g_base + n)));
-            vec_op::FP32Vec8 s_v((state_vec_t(s_hd + n)));
-
-            vec_op::FP32Vec8 dBx = B_v * x_vec * dt_vec;
-            vec_op::FP32Vec8 s_new = s_v * dA + dBx;
-
-            state_vec_t(s_new).save(s_hd + n);
-            out_vec = out_vec + s_new * C_v;
-          }
-
-          float out_val = out_vec.reduce_sum();
-          for (; n < dstate; ++n) {
-            // Reuse dA_scalar computed once per head — no exp() re-call
-            float dBx = static_cast<float>(B_g[n]) * x_val * dt_val;
-            float s_new = static_cast<float>(s_hd[n]) * dA_scalar + dBx;
-            s_hd[n] = static_cast<state_t>(s_new);
-            out_val += s_new * static_cast<float>(C_g[n]);
-          }
-
-          if (D_ptr != nullptr) out_val += x_val * D_val;
-          if (z_h != nullptr) {
-            float z_val = static_cast<float>(z_h[d]);
-            float sigmoid = (z_val >= 0)
-                                ? 1.0f / (1.0f + std::exp(-z_val))
-                                : std::exp(z_val) / (1.0f + std::exp(z_val));
-            out_val *= z_val * sigmoid;
-          }
-          out_h[d] = static_cast<out_t>(out_val);
-        }
-      }
-
-      if (num_accepted_tokens != nullptr &&
-          dst_state_batch_indices != nullptr) {
-        int64_t token_dst_idx = dst_state_batch_indices[seq_idx * seq_len + t];
-        if (token_dst_idx != null_block_id && token_dst_idx != state_read_idx) {
-          state_t* dst_s = state_ptr + token_dst_idx * stride_state_n;
-          std::memmove(dst_s, s, nheads * stride_state_h * sizeof(state_t));
-        }
-      }
-    }
-
-    if (num_accepted_tokens == nullptr && state_write_idx != null_block_id &&
-        state_write_idx != state_read_idx) {
-      state_t* dst_s = state_ptr + state_write_idx * stride_state_n;
-      std::memmove(dst_s, s, nheads * stride_state_h * sizeof(state_t));
-    }
-  }
-}
-
-// ---------------------------------------------------------------------------
-// mamba_chunk_scan_fwd
-//
-// Prefill SSM recurrence for Mamba2 / SSD models.
-//
-// Key difference from selective_state_update_kernel (decode path):
-//   - #pragma omp parallel for collapse(2) is OUTSIDE the time loop.
-//     Each thread owns a (batch, head) slice and runs the entire token
-//     sequence without any per-token OpenMP synchronisation overhead.
-//     For seqlen=256, this eliminates 256 thread-barrier launches per batch.
-//
-// `dt` arrives already processed (float32, after bias + softplus + clamp)
-// to keep this kernel simple. Preprocessing is done in the Python wrapper.
-//
-// `states_ptr` points to the [batch, nheads, headdim, dstate] float32 output
-// tensor, pre-initialised by the caller (zero or from initial_states).
-// Each (b, h) slice is private to exactly one thread via collapse(2), so
-// there are no write conflicts.
-//
-// D is treated as a scalar per head ([nheads] float32).
-// ---------------------------------------------------------------------------
-template <typename input_t>
-inline void mamba_chunk_scan_fwd_kernel(
-    float* __restrict__ states_ptr,     // [batch, nheads, headdim, dstate] f32
-    const input_t* __restrict__ x_ptr,  // [seqlen, nheads, headdim]
-    const float* __restrict__ dt_ptr,   // [seqlen, nheads] f32 (preprocessed)
-    const float* __restrict__ A_ptr,    // [nheads] f32
-    const input_t* __restrict__ B_ptr,  // [seqlen, ngroups, dstate]
-    const input_t* __restrict__ C_ptr,  // [seqlen, ngroups, dstate]
-    const float* __restrict__ D_ptr,    // [nheads] f32 (nullable)
-    const input_t* __restrict__ z_ptr,  // [seqlen, nheads, headdim] (nullable)
-    input_t* __restrict__ out_ptr,      // [seqlen, nheads, headdim]
-    const int32_t* __restrict__ cu_seqlens,  // [batch+1] int32
-    int64_t batch, int64_t nheads, int64_t ngroups, int64_t headdim,
-    int64_t dstate) {
-  using input_vec_t = vec_op::vec_t<input_t>;
-  constexpr int VEC_ELEM_NUM = 8;
-
-  const int64_t nheads_per_group = nheads / ngroups;
-  // states layout: [batch, nheads, headdim, dstate] contiguous (caller
-  // guarantee)
-  const int64_t stride_s_b = nheads * headdim * dstate;
-  const int64_t stride_s_h = headdim * dstate;
-  // stride_s_d = dstate, stride_s_n = 1
-
-#pragma omp parallel for collapse(2) schedule(static)
-  for (int64_t b = 0; b < batch; ++b) {
-    for (int64_t h = 0; h < nheads; ++h) {
-      const int64_t seq_start = cu_seqlens[b];
-      const int64_t seq_end = cu_seqlens[b + 1];
-      const int64_t g = h / nheads_per_group;
-
-      const float A_val = A_ptr[h];
-      const float D_val = (D_ptr != nullptr) ? D_ptr[h] : 0.0f;
-
-      // Working state slice: states[b, h, :, :] — float32, headdim * dstate.
-      // Fits in L1/L2 for typical dims (e.g. 64*128*4 = 32 KB).
-      float* s_bh = states_ptr + b * stride_s_b + h * stride_s_h;
-
-      for (int64_t t = seq_start; t < seq_end; ++t) {
-        const input_t* x_h = x_ptr + t * nheads * headdim + h * headdim;
-        const float* dt_h = dt_ptr + t * nheads + h;
-        const input_t* B_g = B_ptr + t * ngroups * dstate + g * dstate;
-        const input_t* C_g = C_ptr + t * ngroups * dstate + g * dstate;
-        const input_t* z_h = (z_ptr != nullptr)
-                                 ? z_ptr + t * nheads * headdim + h * headdim
-                                 : nullptr;
-        input_t* out_h = out_ptr + t * nheads * headdim + h * headdim;
-
-        const float dt_val = *dt_h;
-        const float dA_val = std::exp(A_val * dt_val);
-        const vec_op::FP32Vec8 dA_vec(dA_val);  // broadcast scalar
-        const vec_op::FP32Vec8 dt_vec(dt_val);
-
-        for (int64_t d = 0; d < headdim; ++d) {
-          const float x_val = static_cast<float>(x_h[d]);
-          float* s_bhd = s_bh + d * dstate;  // [dstate] contiguous float32
-
-          // Vectorised SSM update + readout over dstate:
-          //   s_new = s * dA + x * dt * B
-          //   y    += s_new * C
-          int64_t n = 0;
-          vec_op::FP32Vec8 y_vec(0.0f);
-          const vec_op::FP32Vec8 x_vec(x_val);
-
-          for (; n <= dstate - VEC_ELEM_NUM; n += VEC_ELEM_NUM) {
-            const vec_op::FP32Vec8 B_v((input_vec_t(B_g + n)));
-            const vec_op::FP32Vec8 C_v((input_vec_t(C_g + n)));
-            const vec_op::FP32Vec8 s_v(s_bhd + n);
-
-            const vec_op::FP32Vec8 s_new = s_v * dA_vec + x_vec * dt_vec * B_v;
-            s_new.save(s_bhd + n);
-            y_vec = y_vec + s_new * C_v;
-          }
-
-          float y_val = y_vec.reduce_sum();
-
-          // Scalar tail for remaining dstate elements
-          for (; n < dstate; ++n) {
-            const float B_n = static_cast<float>(B_g[n]);
-            const float C_n = static_cast<float>(C_g[n]);
-            const float s_new = s_bhd[n] * dA_val + x_val * dt_val * B_n;
-            s_bhd[n] = s_new;
-            y_val += s_new * C_n;
-          }
-
-          // D skip connection (scalar per head)
-          if (D_ptr != nullptr) y_val += x_val * D_val;
-
-          // z gating: out = y * z * sigmoid(z)  (SiLU)
-          if (z_h != nullptr) {
-            const float z_val = static_cast<float>(z_h[d]);
-            const float sigmoid =
-                (z_val >= 0.0f) ? 1.0f / (1.0f + std::exp(-z_val))
-                                : std::exp(z_val) / (1.0f + std::exp(z_val));
-            y_val *= z_val * sigmoid;
-          }
-
-          out_h[d] = static_cast<input_t>(y_val);
-        }
-      }
-    }
-  }
-}
-
-}  // namespace mamba_cpu
@@ -213,32 +213,6 @@ void compute_slot_mapping_kernel_impl(const torch::Tensor query_start_loc,
                                      torch::Tensor slot_mapping,
                                      const int64_t block_size);

-at::Tensor causal_conv1d_update_cpu_impl(
-    at::Tensor& x, at::Tensor& conv_state, const at::Tensor& weight,
-    const c10::optional<at::Tensor>& bias,
-    const c10::optional<std::string>& activation,
-    const c10::optional<at::Tensor>& conv_state_indices,
-    const c10::optional<at::Tensor>& query_start_loc, int64_t pad_slot_id);
-
-void selective_state_update_cpu_impl(
-    at::Tensor& state, const at::Tensor& x, const at::Tensor& dt,
-    const at::Tensor& A, const at::Tensor& B, const at::Tensor& C,
-    const c10::optional<at::Tensor>& D, const c10::optional<at::Tensor>& z,
-    const c10::optional<at::Tensor>& dt_bias, bool dt_softplus,
-    const c10::optional<at::Tensor>& state_batch_indices,
-    const c10::optional<at::Tensor>& dst_state_batch_indices,
-    int64_t null_block_id, at::Tensor& out,
-    const c10::optional<at::Tensor>& num_accepted_tokens,
-    const c10::optional<at::Tensor>& cu_seqlens);
-
-void mamba_chunk_scan_fwd_cpu_impl(at::Tensor& out, at::Tensor& final_states,
-                                   const at::Tensor& x, const at::Tensor& dt,
-                                   const at::Tensor& A, const at::Tensor& B,
-                                   const at::Tensor& C,
-                                   const c10::optional<at::Tensor>& D,
-                                   const c10::optional<at::Tensor>& z,
-                                   const at::Tensor& cu_seqlens);
-
 void init_cpu_memory_env(std::vector<int64_t> node_ids);

 namespace cpu_utils {
@@ -621,30 +595,6 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
      "block_size) -> ()",
      &compute_slot_mapping_kernel_impl);

-  // Mamba CPU kernels
-  ops.def(
-      "causal_conv1d_update_cpu_vec("
-      "Tensor(a0!) x, Tensor(a1!) conv_state, Tensor weight, "
-      "Tensor? bias, str? activation, Tensor? conv_state_indices, "
-      "Tensor? query_start_loc, SymInt pad_slot_id) -> Tensor",
-      &causal_conv1d_update_cpu_impl);
-
-  ops.def(
-      "selective_state_update_cpu("
-      "Tensor(a0!) state, Tensor x, Tensor dt, Tensor A, Tensor B, Tensor C, "
-      "Tensor? D, Tensor? z, Tensor? dt_bias, bool dt_softplus, "
-      "Tensor? state_batch_indices, Tensor? dst_state_batch_indices, "
-      "SymInt null_block_id, Tensor(a13!) out, "
-      "Tensor? num_accepted_tokens, Tensor? cu_seqlens) -> ()",
-      &selective_state_update_cpu_impl);
-
-  ops.def(
-      "mamba_chunk_scan_fwd_cpu("
-      "Tensor(a0!) out, Tensor(a1!) final_states, "
-      "Tensor x, Tensor dt, Tensor A, Tensor B, Tensor C, "
-      "Tensor? D, Tensor? z, Tensor cu_seqlens) -> ()",
-      &mamba_chunk_scan_fwd_cpu_impl);
-
  ops.def("init_cpu_memory_env(SymInt[] node_ids) -> ()", &init_cpu_memory_env);

  // Speculative decoding kernels
@@ -39,15 +39,11 @@ __global__ void marlin_int4_fp8_preprocess_kernel_awq(
    // AWQ zeros: (size_k // group_size, size_n // 8)
    const int32_t* __restrict__ qzeros, int32_t size_n, int32_t size_k,
    int32_t group_size) {
-  // Thread mapping: threadIdx.x -> column dim (coalesced read within a row),
-  // blockIdx.x -> row dim. Adjacent threads read consecutive int32 in the
-  // same row (stride 1) instead of striding across rows (stride size_n/8).
-  int col = blockIdx.y * 32 + threadIdx.x;
-  if (col >= size_n / 8) return;
-  (void)size_k;
-
-  int32_t val = qweight[blockIdx.x * (size_n / 8) + col];
-  int32_t zero = qzeros[blockIdx.x / group_size * (size_n / 8) + col];
+  int32_t val =
+      qweight[(blockIdx.x * 32 + threadIdx.x) * size_n / 8 + blockIdx.y];
+  int32_t zero =
+      qzeros[(blockIdx.x * 32 + threadIdx.x) / group_size * size_n / 8 +
+             blockIdx.y];
  int32_t new_val = 0;

 #pragma unroll
@@ -62,7 +58,7 @@ __global__ void marlin_int4_fp8_preprocess_kernel_awq(
    zero >>= 4;
  }

-  output[blockIdx.x * (size_n / 8) + col] = new_val;
+  output[(blockIdx.x * 32 + threadIdx.x) * size_n / 8 + blockIdx.y] = new_val;
 }

 torch::stable::Tensor marlin_int4_fp8_preprocess(
@@ -106,7 +102,7 @@ torch::stable::Tensor marlin_int4_fp8_preprocess(
                    "qweight.size(0) % qzeros.size(0) != 0");
    STD_TORCH_CHECK(group_size % 8 == 0, "group_size % 8 != 0");

-    dim3 blocks(size_k, (size_n / 8 + 31) / 32);
+    dim3 blocks(size_k / 32, size_n / 8);
    marlin_int4_fp8_preprocess_kernel_awq<<<blocks, 32, 0, stream>>>(
        reinterpret_cast<const int32_t*>(qweight.const_data_ptr()),
        reinterpret_cast<int32_t*>(output.mutable_data_ptr()),
@@ -164,8 +164,8 @@ Priority is **1 = highest** (tried first).
 | `FLASHINFER` | XQA† | fp16, bf16 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3`, `fp8_e5m2` | 16, 32, 64, 128, 256, 512, 1024 | 64, 128, 256, 512 | ❌ | ❌ | ❌ | ✅ | Decoder | 9.0 |
 | `FLASHINFER` | trtllm-gen† | fp16, bf16 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3`, `fp8_e5m2`, `nvfp4` | 16, 32, 64, 128, 256, 512, 1024 | 64, 128, 256, 512 | ✅ | ✅ | ❌ | ✅ | Decoder | 10.x |
 | `FLASH_ATTN` | FA2* | fp16, bf16 | `auto`, `float16`, `bfloat16` | %16 | Any | ❌ | ✅ | ❌ | ✅ | All | ≥8.0 |
-| `FLASH_ATTN` | FA3* | fp16, bf16 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3` | %16 | Any | ✅ | ✅ | ❌ | ✅ | All | 9.x |
-| `FLASH_ATTN` | FA4* | fp16, bf16 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3` | %16 | Any | ✅ | ✅ | ❌ | ✅ | All | ≥10.0 |
+| `FLASH_ATTN` | FA3* | fp16, bf16 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3`, `fp8_e5m2` | %16 | Any | ✅ | ✅ | ❌ | ✅ | All | 9.x |
+| `FLASH_ATTN` | FA4* | fp16, bf16 | `auto`, `float16`, `bfloat16` | %16 | Any | ✅ | ✅ | ❌ | ✅ | All | ≥10.0 |
 | `FLASH_ATTN_DIFFKV` | | fp16, bf16 | `auto` | Any | Any | ❌ | ❌ | ❌ | ✅ | Decoder | Any |
 | `FLEX_ATTENTION` | | fp16, bf16, fp32 | `auto`, `float16`, `bfloat16` | %16 | Any | ❌ | ✅ | ✅ | ❌ | Decoder, Encoder Only | Any |
 | `HPC_ATTN` | | fp16, bf16 | `auto`, `bfloat16`, `fp8_e4m3` | 64 | 128 | ❌ | ❌ | ❌ | ❌ | Decoder | ≥9.0 |
@@ -306,7 +306,7 @@ Supported quantization scheme/hardware combinations:

 - Pass: [`vllm/compilation/passes/fusion/rms_quant_fusion.py`](https://github.com/vllm-project/vllm/blob/main/vllm/compilation/passes/fusion/rms_quant_fusion.py)
 - ROCm AITER pass: [`vllm/compilation/passes/fusion/rocm_aiter_fusion.py`](https://github.com/vllm-project/vllm/blob/main/vllm/compilation/passes/fusion/rocm_aiter_fusion.py)
- CUDA/HIP kernels: [`csrc/libtorch_stable/layernorm_quant_kernels.cu`](https://github.com/vllm-project/vllm/blob/main/csrc/libtorch_stable/layernorm_quant_kernels.cu)
+- CUDA/HIP kernels: [`csrc/layernorm_quant_kernels.cu`](https://github.com/vllm-project/vllm/blob/main/csrc/layernorm_quant_kernels.cu)

 ### SiLU+Mul + Quantization (`fuse_act_quant`)

@@ -332,7 +332,7 @@ Supported quantization scheme/hardware combinations:
 - Pass: [`vllm/compilation/passes/fusion/act_quant_fusion.py`](https://github.com/vllm-project/vllm/blob/main/vllm/compilation/passes/fusion/act_quant_fusion.py)
 - ROCm AITER pass: [`vllm/compilation/passes/fusion/rocm_aiter_fusion.py`](https://github.com/vllm-project/vllm/blob/main/vllm/compilation/passes/fusion/rocm_aiter_fusion.py)
 - CUDA/HIP kernels: [`csrc/quantization/`](https://github.com/vllm-project/vllm/blob/main/csrc/quantization/)
- Fused SiLU+Mul+BlockQuant kernel: [`csrc/libtorch_stable/quantization/fused_kernels/fused_silu_mul_block_quant.cu`](https://github.com/vllm-project/vllm/blob/main/csrc/libtorch_stable/quantization/fused_kernels/fused_silu_mul_block_quant.cu)
+- Fused SiLU+Mul+BlockQuant kernel: [`csrc/quantization/fused_kernels/fused_silu_mul_block_quant.cu`](https://github.com/vllm-project/vllm/blob/main/csrc/quantization/fused_kernels/fused_silu_mul_block_quant.cu)

 ### RMSNorm + Padding (`fuse_act_padding`)

@@ -68,14 +68,13 @@ vllm serve <model> \
 | --- | --- | --- | --- | --- |
 | `spec_name` | no | `CPUOffloadingSpec` | both | Set to `TieringOffloadingSpec` for multi-tier. |
 | `cpu_bytes_to_use` | yes | — | both | Total bytes of host memory reserved for the CPU tier across all workers (not per-worker). |
-| `block_size` | no | GPU block size | both | Offloaded block size in tokens; must be a multiple of the GPU block size. Mutually exclusive with `blocks_per_chunk`. |
-| `blocks_per_chunk` | no | `1` | both | Offloaded chunk size in GPU blocks; must be > 0. Alternative to `block_size` for models whose KV cache groups have different block sizes. |
+| `block_size` | no | GPU block size | both | Offloaded block size in tokens; must be a multiple of the GPU block size. |
 | `eviction_policy` | no | `lru` | both | Primary tier policy: `lru` or `arc`. |
 | `store_threshold` | no | `0` | single-tier | Min lookups before a block is offloaded. Values ≥ 2 are rejected by `TieringOffloadingSpec`. |
 | `max_tracker_size` | no | `64000` | single-tier | Max entries in the lookup tracker. |
 | `secondary_tiers` | no | `[]` | multi-tier | List of secondary tier configs (see below). |
 | `offload_prompt_only` | no | `true` | both | If `true`, only prompt (prefill) blocks are offloaded; decode blocks are skipped. |
-| `self_describing_kv_events` | no | `false` | both | Opt-in. When `true` *and* KV cache events are enabled (`--kv-events-config` with `enable_kv_cache_events`), the connector emits self-describing block-granular `BlockStored`/`BlockRemoved` payloads (constituent block hashes, whole-chunk `token_ids`, per-block `block_size`, parent hash, LoRA + group/cache-spec metadata) instead of the placeholder fallback, so external KV-event consumers can index offloaded blocks. Inert unless events are enabled. With `TieringOffloadingSpec`, a CPU promotion is self-describing when a local request observes its primary-tier `HIT` before event translation; otherwise its stored event may retain the placeholder, while a later `HIT` can backfill metadata for removal. Pending-removal/re-promotion races and externally initiated promotions may also produce placeholders, and consumers must ignore removals for unknown hashes. Full-attention groups only; sliding-window/SSM groups keep the placeholder fallback. In chunk mode (`block_size` > GPU block size, or `blocks_per_chunk` > 1), overlapping chunks re-announce shared per-block hashes, so consumers must reference-count (deduplicate) repeated store/remove announcements. |
+| `self_describing_kv_events` | no | `false` | single-tier | Opt-in. When `true` *and* KV cache events are enabled (`--kv-events-config` with `enable_kv_cache_events`), the connector emits self-describing block-granular `BlockStored`/`BlockRemoved` payloads (constituent block hashes, whole-chunk `token_ids`, per-block `block_size`, parent hash, LoRA + group/cache-spec metadata) instead of the placeholder fallback, so external KV-event consumers can index offloaded blocks. Inert unless events are enabled. Currently rejected by `TieringOffloadingSpec`. Full-attention groups only; sliding-window/SSM groups keep the placeholder fallback. In chunk mode (`block_size` > GPU block size), overlapping chunks re-announce shared per-block hashes, so consumers must reference-count (deduplicate) repeated store/remove announcements. |
 | `spec_module_path` | no | — | both | Python import path for a custom `OffloadingSpec` not in the built-in registry. Required only when `spec_name` is not built-in (advanced). |

 ## Secondary Tiers
@@ -180,7 +179,7 @@ Rather than embedding `host`/`port` in each `secondary_tiers` entry, set them on

 - `cpu_bytes_to_use`: a bigger CPU tier means fewer trips to slower secondary tiers and a higher hit rate. The value is total across all workers, not per-worker. Leave headroom for the rest of the host workload.
 - For single-tier (CPU-only) setups, set `cpu_bytes_to_use` larger than the aggregate GPU KV cache. Because offloading is immediate, a smaller CPU tier just mirrors what the GPU already holds and adds no hit rate.
- `block_size` / `blocks_per_chunk`: larger offloaded chunks reduce per-block bookkeeping overhead but increase the granularity of lookups.
+- `block_size`: larger offloaded blocks reduce per-block bookkeeping overhead but increase the granularity of lookups. Must be a multiple of the GPU block size.
 - FS thread counts: tune `n_read_threads` and `n_write_threads` to the parallelism your storage can sustain. Reads are latency-sensitive on the prefill path, so prefer more read threads when prefill hit rates are high.
 - Sharing `root_dir` across runs: runs with the same model, `block_size`, parallelism layout, and dtype share files under the same `<digest>` subdirectory. Changing any of these produces a new subdirectory; old ones are orphaned but harmless. Delete them to reclaim disk.

@@ -59,7 +59,7 @@ th:not(:first-child) {

 <sup>1</sup> P and D instances must use the same speculation configuration.

-<sup>2</sup> Cross-layer contiguity is achieved by using a `BLHNC` layout (set via `VLLM_KV_CACHE_LAYOUT=BLHNC` or `--enable-cross-layers`).
+<sup>2</sup> Requires `FLASH_ATTN` or `FLASHINFER` backend **and** `HND` KV cache layout. Enable via `--kv-transfer-config '{"kv_connector_extra_config": {"enable_cross_layers_blocks": "True"}}'`.

 <sup>3</sup> Supported only when HMA is **not** required (i.e., non-hybrid models). Block IDs are remapped automatically. Only P block size < D block size is supported.

@@ -414,6 +414,15 @@ Support use case: Prefill with 'HND' and decode with 'NHD' with experimental con
 --kv-transfer-config '{..., "enable_permute_local_kv":"True"}'
 ```

+### Cross layers blocks
+
+By default, this feature is disabled. On attention backends that support this feature, each logical block is contiguous in physical memory. This reduces the number of buffers that need to be transferred.
+To enable this feature:
+
+```bash
+--kv-transfer-config '{..., "kv_connector_extra_config": {"enable_cross_layers_blocks": "True"}}'
+```
+
 ## Metrics Reference

 vLLM periodically logs a `KV Transfer metrics` line summarising NIXL transfer
@@ -27,7 +27,7 @@ Currently, there are no pre-built XPU wheels.

 - First, install required [driver](https://dgpu-docs.intel.com/driver/installation.html#installing-gpu-drivers).
 - Second, install Python packages for vLLM XPU backend building (Intel OneAPI dependencies are installed automatically as part of `torch-xpu`, see [PyTorch XPU get started](https://docs.pytorch.org/docs/stable/notes/get_start_xpu.html)):
- Start from vllm-xpu-kernels v0.1.10, we recommend user upgrade driver to [compute runtime 26.18](https://github.com/intel/compute-runtime/releases/tag/26.18.38308.1) release, to avoid potential compatibility issue.
+- 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
@@ -58,40 +58,7 @@ VLLM_TARGET_DEVICE=xpu pip install --no-build-isolation -e . -v
 --8<-- [end:build-wheel-from-source]
 --8<-- [start:pre-built-images]

-vLLM offers official Docker images for deployment.
-The images can be used to run OpenAI compatible server and are available on Docker Hub as [vllm/vllm-openai-xpu](https://hub.docker.com/r/vllm/vllm-openai-xpu/tags).
-
- `vllm/vllm-openai-xpu:latest` — stable release, available starting from v0.26.0
- `vllm/vllm-openai-xpu:nightly` — preview build from the latest development branch, use this if you want the latest features and fixes
-
-```bash
-docker run --rm \
-    --network=host \
-    --device /dev/dri:/dev/dri \
-    -v /dev/dri/by-path:/dev/dri/by-path \
-    -v ~/.cache/huggingface:/root/.cache/huggingface \
-    --env "HF_TOKEN=$HF_TOKEN" \
-    --ipc=host \
-    --privileged \
-    vllm/vllm-openai-xpu:<tag> \
-    --model Qwen/Qwen3-0.6B
-```
-
-To use the docker image as base for development, you can launch it in interactive session through overriding the entrypoint.
-
-???+ console "Commands"
-    ```bash
-    docker run --rm -it \
-        --network=host \
-        --device /dev/dri:/dev/dri \
-        -v /dev/dri/by-path:/dev/dri/by-path \
-        -v ~/.cache/huggingface:/root/.cache/huggingface \
-        --env "HF_TOKEN=$HF_TOKEN" \
-        --ipc=host \
-        --privileged \
-        --entrypoint /bin/bash \
-        vllm/vllm-openai-xpu:<tag>
-    ```
+Currently, we release prebuilt XPU images at docker [hub](https://hub.docker.com/r/intel/vllm/tags) based on vLLM released version. For more information, please refer release [note](https://github.com/intel/ai-containers/blob/main/vllm).

 --8<-- [end:pre-built-images]
 --8<-- [start:build-image-from-source]
@@ -65,15 +65,6 @@ This guide will help you quickly get started with vLLM to perform:
    !!! tip
        A nightly Docker image is also available as [vllm/vllm-openai-rocm:nightly](https://hub.docker.com/r/vllm/vllm-openai-rocm/tags) for testing the latest development builds.

-=== "Intel GPU"
-
-    vLLM supports Intel GPUs through the XPU backend. Pre-built XPU wheels will be available soon.
-
-    Official Docker images for Intel GPUs are added to the vLLM release starting from v0.26.0. Nightly Docker image is also available as [vllm/vllm-openai-xpu:nightly](https://hub.docker.com/r/vllm/vllm-openai-xpu/tags).
-
-    !!! tip
-        For more detailed instructions, including building from source and Docker image setup, please refer to the [GPU installation guide](installation/gpu.md) and select the "Intel XPU" tab.
-
 === "Google TPU"

    To run vLLM on Google TPUs, you need to install the `vllm-tpu` package.
@@ -31,8 +31,10 @@
 | THUDM/CodeGeex4-All-9B                             | CodeGeexForCausalLM                              | ✅                    |                        |       |
 | chuhac/TeleChat2-35B                               | LlamaForCausalLM (TeleChat2 based on Llama arch) | ✅                    |                        |       |
 | 01-ai/Yi1.5-34B-Chat                               | YiForCausalLM                                    | ✅                    |                        |       |
+| THUDM/CodeGeex4-All-9B                             | CodeGeexForCausalLM                              | ✅                    |                        |       |
 | deepseek-ai/DeepSeek-Coder-33B-base                | DeepSeekCoderForCausalLM                         | ✅                    |                        |       |
 | meta-llama/Llama-2-13b-chat-hf                     | LlamaForCausalLM                                 | ✅                    |                        |       |
+| THUDM/CodeGeex4-All-9B                             | CodeGeexForCausalLM                              | ✅                    |                        |       |
 | Qwen/Qwen1.5-14B-Chat                              | QwenForCausalLM                                  | ✅                    |                        |       |
 | Qwen/Qwen1.5-32B-Chat                              | QwenForCausalLM                                  | ✅                    |                        |       |
 | RedHatAI/Meta-Llama-3.1-8B-Instruct-FP8-dynamic    | LlamaForCausalLM                                 |                       | ✅                     |       |
@@ -67,7 +67,7 @@ The Transcriptions API supports uploading audio files in various formats includi
 - `response_format`: Format of the response ("json", "text") (optional)
 - `temperature`: Sampling temperature between 0 and 1 (optional)

-For the complete list of supported parameters including sampling parameters and vLLM extensions, see the [protocol definitions](https://github.com/vllm-project/vllm/blob/main/vllm/entrypoints/speech_to_text/transcription/protocol.py).
+For the complete list of supported parameters including sampling parameters and vLLM extensions, see the [protocol definitions](https://github.com/vllm-project/vllm/blob/main/vllm/entrypoints/openai/protocol.py#L2182).

 **Response Format:**

@@ -3446,6 +3446,7 @@ dependencies = [
 "base64 0.22.1",
 "bytes",
 "encoding_rs",
+ "futures-channel",
 "futures-core",
 "futures-util",
 "h2",
@@ -4875,7 +4876,6 @@ dependencies = [
 "futures-core",
 "pin-project-lite",
 "tokio",
- "tokio-util",
 ]

 [[package]]
@@ -4937,9 +4937,9 @@ dependencies = [

 [[package]]
 name = "tonic"
-version = "0.14.6"
+version = "0.14.5"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "ac2a5518c70fa84342385732db33fb3f44bc4cc748936eb5833d2df34d6445ef"
+checksum = "fec7c61a0695dc1887c1b53952990f3ad2e3a31453e1f49f10e75424943a93ec"
 dependencies = [
 "async-trait",
 "axum",
@@ -4966,9 +4966,9 @@ dependencies = [

 [[package]]
 name = "tonic-build"
-version = "0.14.6"
+version = "0.14.5"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "c68f61875ac5293cf72e6c8cf0158086428c82c37229e98c840878f1706b0322"
+checksum = "1882ac3bf5ef12877d7ed57aad87e75154c11931c2ba7e6cde5e22d63522c734"
 dependencies = [
 "prettyplease",
 "proc-macro2",
@@ -4976,24 +4976,11 @@ dependencies = [
 "syn 2.0.117",
 ]

-[[package]]
-name = "tonic-health"
-version = "0.14.6"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "fcfab99db777fba2802f0dfa861d1628d1ae916fb199d29819941f139ae85082"
-dependencies = [
- "prost",
- "tokio",
- "tokio-stream",
- "tonic",
- "tonic-prost",
-]
-
 [[package]]
 name = "tonic-prost"
-version = "0.14.6"
+version = "0.14.5"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "50849f68853be452acf590cde0b146665b8d507b3b8af17261df47e02c209ea0"
+checksum = "a55376a0bbaa4975a3f10d009ad763d8f4108f067c7c2e74f3001fb49778d309"
 dependencies = [
 "bytes",
 "prost",
@@ -5002,9 +4989,9 @@ dependencies = [

 [[package]]
 name = "tonic-prost-build"
-version = "0.14.6"
+version = "0.14.5"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "654e5643eff75d7f8c99197ce1440ed19a3474eada74c12bbac488b2cafdae27"
+checksum = "f3144df636917574672e93d0f56d7edec49f90305749c668df5101751bb8f95a"
 dependencies = [
 "prettyplease",
 "proc-macro2",
@@ -5497,13 +5484,10 @@ dependencies = [
 "serde",
 "serde_json",
 "thiserror 2.0.18",
- "thiserror-ext",
 "tiktoken-rs 0.9.1",
 "tokenizers",
 "tokio",
 "tokio-stream",
- "tracing",
- "tracing-subscriber",
 "url",
 "uuid",
 ]
@@ -5576,7 +5560,6 @@ dependencies = [
 "tracing",
 "tracing-subscriber",
 "uuid",
- "vllm-bench",
 "vllm-chat",
 "vllm-engine-core-client",
 "vllm-managed-engine",
@@ -5746,7 +5729,6 @@ dependencies = [
 "tokio-stream",
 "tokio-util",
 "tonic",
- "tonic-health",
 "tonic-prost",
 "tonic-prost-build",
 "tower",
@@ -6338,9 +6320,9 @@ checksum = "9edde0db4769d2dc68579893f2306b26c6ecfbe0ef499b013d731b7b9247e0b9"

 [[package]]
 name = "xgrammar-structural-tag"
-version = "0.2.0+xgrammar.0.2.4.dd729e7"
+version = "0.1.0+xgrammar.0.2.2.4d145cc"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "d4d24c842efc3c24e9756aa426d530cbdac0980e49af223cb384e276e981ca0a"
+checksum = "2436dea2393d55a3b188588aa300c5a8afe8f45a77da52c611fb4498a6c876e6"
 dependencies = [
 "auto_impl",
 "serde",
@@ -118,11 +118,10 @@ tokio = { version = "1.47.1", features = [
 tokio-openssl = "0.6"
 tokio-stream = "0.1"
 tokio-util = { version = "0.7.18", features = ["rt"] }
-tonic = "0.14.6"
-tonic-build = "0.14.6"
-tonic-health = "0.14.6"
-tonic-prost = "0.14.6"
-tonic-prost-build = "0.14.6"
+tonic = "0.14.5"
+tonic-build = "0.14.5"
+tonic-prost = "0.14.5"
+tonic-prost-build = "0.14.5"
 tool-parser = "1.2.0"
 tower = { version = "0.5.3", features = ["util"] }
 tower-http = { version = "0.6.8", features = ["cors", "trace"] }
@@ -133,7 +132,6 @@ trait-set = "0.3.0"
 url = "2.5.7"
 uuid = { version = "1.22.0", features = ["v4"] }
 validator = { version = "0.20.0", features = ["derive"] }
-vllm-bench = { path = "src/bench" }
 vllm-chat = { path = "src/chat" }
 vllm-engine-core-client = { path = "src/engine-core-client" }
 vllm-llm = { path = "src/llm" }
@@ -144,7 +142,7 @@ vllm-server = { path = "src/server" }
 vllm-text = { path = "src/text" }
 vllm-tokenizer = { path = "src/tokenizer" }
 winnow = { version = "1.0.2", features = ["simd"] }
-xgrammar-structural-tag = "0.2.0"
+xgrammar-structural-tag = "0.1.0"
 zeromq = { version = "0.6.0", default-features = false, features = [
    "tokio-runtime",
    "all-transport",
@@ -20,19 +20,16 @@ mimalloc.workspace = true
 rand.workspace = true
 rand_distr.workspace = true
 rayon.workspace = true
-reqwest = { workspace = true, features = ["json", "stream", "http2"] }
+reqwest = { workspace = true, features = ["json", "stream", "blocking", "http2"] }
 rlimit.workspace = true
 rustc-hash.workspace = true
 serde = { workspace = true, features = ["rc"] }
 serde_json = { workspace = true, features = ["raw_value"] }
 thiserror.workspace = true
-thiserror-ext.workspace = true
 tiktoken-rs.workspace = true
 tokenizers.workspace = true
 tokio.workspace = true
 tokio-stream.workspace = true
-tracing.workspace = true
-tracing-subscriber.workspace = true
 url.workspace = true
 uuid.workspace = true

@@ -158,10 +158,9 @@ impl PoolingBackend {
                // (mirrors Python async_request_vllm_rerank).
                if let Some(ref list) = input.prompt_list {
                    if list.len() < 2 {
-                        tracing::warn!(
-                            backend = "vllm-rerank",
-                            inputs = list.len(),
-                            "rerank request has no documents"
+                        eprintln!(
+                            "WARNING: vllm-rerank request has no documents \
+                             (prompt_list needs [query, doc, ...])"
                        );
                    }
                    let query = list.first().map(|s| s.as_ref()).unwrap_or("");
@@ -176,10 +175,10 @@ impl PoolingBackend {
                    // Legacy path: text prompt as query, documents via --extra-body.
                    let query = input.prompt.as_ref();
                    if query.is_empty() && input.prompt_token_ids.is_some() {
-                        tracing::warn!(
-                            backend = "vllm-rerank",
-                            dataset = "random",
-                            "rerank request has an empty query; use the random-rerank dataset"
+                        eprintln!(
+                            "WARNING: vllm-rerank received empty query (random dataset uses \
+                             token IDs only). Use --dataset-name random-rerank for meaningful \
+                             rerank benchmarks."
                        );
                    }
                    serde_json::json!({
@@ -6,7 +6,6 @@ use std::sync::Arc;
 use std::time::Instant;

 use indicatif::{ProgressBar, ProgressStyle};
-use thiserror_ext::AsReport as _;
 use tokio::sync::Semaphore;

 use crate::backends::{RequestFuncInput, RequestFuncOutput, get_backend};
@@ -73,12 +72,12 @@ pub fn pre_resolve_dns(
            v4.extend(v6);
            if !v4.is_empty() {
                let ips: Vec<_> = v4.iter().map(|a| a.ip()).collect();
-                tracing::info!(host, addresses = ?ips, "pre-resolved benchmark endpoint DNS");
+                println!("Pre-resolved {host} -> {ips:?}");
                builder = builder.resolve_to_addrs(host, &v4);
            }
        }
        Err(e) => {
-            tracing::warn!(host, error = %e.as_report(), "failed to pre-resolve benchmark endpoint DNS");
+            eprintln!("Warning: DNS pre-resolution for '{host}' failed: {e}");
        }
    }

@@ -347,14 +346,10 @@ pub async fn run_benchmark(config: &BenchConfig) -> Result<serde_json::Value> {
    let (model_id, model_name) = if let Some(ref m) = config.model {
        (m.clone(), config.model_name.clone())
    } else {
-        tracing::info!(base_url = %config.base_url, "fetching first model from server");
+        println!("Model not specified, fetching first model from server...");
        let (name, id) =
            get_first_model_from_server(&config.base_url, &client, &config.extra_headers).await?;
-        tracing::info!(
-            model_name = name,
-            model_id = id,
-            "selected first model from server"
-        );
+        println!("First model name: {name}, first model id: {id}");
        (id, Some(name))
    };

@@ -363,10 +358,10 @@ pub async fn run_benchmark(config: &BenchConfig) -> Result<serde_json::Value> {
        None
    } else {
        let tid = config.tokenizer_id.as_deref().unwrap_or(&model_id);
-        tracing::info!(tokenizer = tid, "loading tokenizer");
+        println!("Loading tokenizer: {tid}");
        let server_info = Some((config.base_url.as_str(), model_id.as_str()));
-        let t =
-            crate::tokenizer::load_tokenizer(tid, config.trust_remote_code, server_info).await?;
+        let t = crate::tokenizer::load_tokenizer(tid, config.trust_remote_code, server_info)?;
+        println!("Tokenizer loaded successfully.");
        Some(t)
    };
    let has_tokenizer = tokenizer.is_some();
@@ -426,12 +421,7 @@ pub async fn run_benchmark(config: &BenchConfig) -> Result<serde_json::Value> {
            config.num_prompts, config.random_batch_size, config.is_reranker,
        ),
    };
-    tracing::info!(
-        dataset = ?config.dataset_name,
-        prompts = config.num_prompts,
-        description = %dataset_label,
-        "generating benchmark dataset"
-    );
+    println!("Generating {dataset_label}...");
    let gen_start = Instant::now();

    let mut input_requests = match config.dataset_name {
@@ -482,7 +472,7 @@ pub async fn run_benchmark(config: &BenchConfig) -> Result<serde_json::Value> {
            let path = match config.dataset_path.as_deref() {
                Some(p) => p,
                None => {
-                    downloaded = crate::datasets::sharegpt::download_sharegpt_dataset().await?;
+                    downloaded = crate::datasets::sharegpt::download_sharegpt_dataset()?;
                    downloaded.as_str()
                }
            };
@@ -522,8 +512,7 @@ pub async fn run_benchmark(config: &BenchConfig) -> Result<serde_json::Value> {
                None => {
                    downloaded = crate::datasets::speed_bench::download_speed_bench(
                        config.speed_bench_config,
-                    )
-                    .await?;
+                    )?;
                    downloaded.as_str()
                }
            };
@@ -554,8 +543,7 @@ pub async fn run_benchmark(config: &BenchConfig) -> Result<serde_json::Value> {
                    config.hf_subset.as_deref(),
                    config.hf_split.as_deref(),
                    config.num_prompts,
-                )
-                .await?;
+                )?;
            crate::datasets::hf_dataset::load_hf_dataset(
                tok,
                &downloaded_path,
@@ -620,19 +608,18 @@ pub async fn run_benchmark(config: &BenchConfig) -> Result<serde_json::Value> {
    };

    let gen_elapsed = gen_start.elapsed();
-    tracing::info!(
-        prompts = input_requests.len(),
-        elapsed_seconds = gen_elapsed.as_secs_f64(),
-        "generated benchmark dataset"
+    println!(
+        "Generated {} prompts in {:.2}s",
+        input_requests.len(),
+        gen_elapsed.as_secs_f64()
    );

    let filtered_count =
        filter_requests_by_max_model_len(&mut input_requests, config.max_model_len);
    if filtered_count > 0 {
-        tracing::info!(
-            filtered_prompts = filtered_count,
-            max_model_len = config.max_model_len.unwrap(),
-            "filtered prompts above maximum model length"
+        println!(
+            "Filtered {filtered_count} prompt(s) above --max-model-len {}.",
+            config.max_model_len.unwrap()
        );
    }
    if input_requests.is_empty() {
@@ -683,7 +670,7 @@ pub async fn run_benchmark(config: &BenchConfig) -> Result<serde_json::Value> {

    // Ready check
    if config.ready_check_timeout_sec > 0 {
-        tracing::info!("starting initial single-prompt test run");
+        println!("Starting initial single prompt test run...");
        let test_output = wait_for_endpoint(
            config.backend,
            &client,
@@ -698,7 +685,7 @@ pub async fn run_benchmark(config: &BenchConfig) -> Result<serde_json::Value> {
                test_output.error
            )));
        }
-        tracing::info!("initial single-prompt test run completed");
+        println!("Initial test run completed.");
    }

    // Verify and fix prompt token lengths against the server's /tokenize endpoint.
@@ -716,15 +703,12 @@ pub async fn run_benchmark(config: &BenchConfig) -> Result<serde_json::Value> {
        DatasetName::Random | DatasetName::PrefixRepetition
    );
    if verifiable_dataset && has_token_ids && !config.backend.is_pooling() {
-        tracing::info!(
-            reason = "prompt_token_ids",
-            "skipping server tokenizer verification"
-        );
+        println!("Using prompt_token_ids, skipping server-side tokenizer verification.");
    }
    if verifiable_dataset && !has_token_ids && !config.backend.is_pooling() {
        let cache_key = tokenizer_verify_cache_key(&config.base_url, &model_id);
        if is_tokenizer_verified(&cache_key) {
-            tracing::info!(reason = "cached", "skipping server tokenizer verification");
+            println!("Tokenizer verified in previous run (cached), skipping verification.");
        } else {
            let num_special =
                tokenizer.as_ref().map(|t| t.num_special_tokens_to_add()).unwrap_or(0);
@@ -739,17 +723,14 @@ pub async fn run_benchmark(config: &BenchConfig) -> Result<serde_json::Value> {
            .await?
            {
                SampleVerifyOutcome::Passed => {
-                    tracing::info!("tokenizer sample verification passed");
+                    println!("Sample verification passed, skipping full verification.");
                    mark_tokenizer_verified(&cache_key);
                }
                SampleVerifyOutcome::Skipped(reason) => {
-                    tracing::warn!(
-                        reason = %reason,
-                        "server tokenizer unavailable; skipping prompt verification"
-                    );
+                    println!("Server /tokenize unavailable ({reason}), skipping verification.");
                }
                SampleVerifyOutcome::Mismatch => {
-                    tracing::warn!("tokenizer sample mismatch; verifying and fixing all prompts");
+                    println!("Sample verification found mismatch, running full verify+fix...");
                    match verify_and_fix_prompt_lengths(
                        &client,
                        &config.base_url,
@@ -761,16 +742,16 @@ pub async fn run_benchmark(config: &BenchConfig) -> Result<serde_json::Value> {
                    .await
                    {
                        Ok(()) => {
-                            tracing::info!(
-                                prompts = input_requests.len(),
-                                "verified exact prompt token lengths"
+                            println!(
+                                "All {} prompts verified: exact token length match.",
+                                input_requests.len()
                            );
                            mark_tokenizer_verified(&cache_key);
                        }
                        Err(BenchError::TokenizeUnavailable(reason)) => {
-                            tracing::warn!(
-                                reason = %reason,
-                                "server tokenizer became unavailable; using client token counts"
+                            println!(
+                                "Server /tokenize became unavailable during verification \
+                                 ({reason}); proceeding with client-side token counts."
                            );
                        }
                        Err(e) => return Err(e),
@@ -782,7 +763,7 @@ pub async fn run_benchmark(config: &BenchConfig) -> Result<serde_json::Value> {

    // Warmup
    if config.num_warmups > 0 {
-        tracing::info!(requests = config.num_warmups, "starting benchmark warmup");
+        println!("Warming up with {} requests...", config.num_warmups);
        run_warmup(
            config.backend,
            &client,
@@ -795,7 +776,7 @@ pub async fn run_benchmark(config: &BenchConfig) -> Result<serde_json::Value> {
            config.disable_tqdm,
        )
        .await;
-        tracing::info!(requests = config.num_warmups, "benchmark warmup completed");
+        println!("Warmup run completed.");
    }

    // Start profiler if requested (immediate mode — no batch threshold)
@@ -833,22 +814,28 @@ pub async fn run_benchmark(config: &BenchConfig) -> Result<serde_json::Value> {
    let spec_decode_before =
        fetch_spec_decode_metrics(&config.base_url, &client, &config.extra_headers).await;
    if spec_decode_before.is_some() {
-        tracing::info!("detected speculative decoding; collecting metrics");
+        println!("Speculative decoding detected, will collect metrics.");
    }

    // Main benchmark
+    println!("Starting main benchmark run...");
    let distribution = if config.burstiness == 1.0 {
        "Poisson process"
    } else {
        "Gamma distribution"
    };
-    tracing::info!(
-        request_rate = config.request_rate,
-        burstiness = config.burstiness,
-        distribution,
-        max_concurrency = config.max_concurrency.unwrap_or(config.num_prompts),
-        prompts = config.num_prompts,
-        "starting main benchmark run"
+    println!(
+        "Traffic request rate: {}",
+        if config.request_rate.is_infinite() {
+            "inf".to_string()
+        } else {
+            format!("{}", config.request_rate)
+        }
+    );
+    println!("Burstiness factor: {} ({distribution})", config.burstiness);
+    println!(
+        "Maximum request concurrency: {}",
+        config.max_concurrency.unwrap_or(config.num_prompts)
    );

    // Pre-assign LoRA adapters to each request (None when --lora-modules not set).
@@ -860,11 +847,11 @@ pub async fn run_benchmark(config: &BenchConfig) -> Result<serde_json::Value> {
    );
    if let (Some(modules), Some(_)) = (config.lora_modules.as_ref(), lora_assignments.as_ref()) {
        let names: Vec<&str> = modules.iter().map(|s| s.as_ref()).collect();
-        tracing::info!(
-            adapters = modules.len(),
-            names = ?names,
-            assignment = ?config.lora_assignment,
-            "assigned LoRA adapters"
+        println!(
+            "LoRA adapters ({}): {:?} [assignment={:?}]",
+            modules.len(),
+            names,
+            config.lora_assignment
        );
    }

@@ -1138,7 +1125,7 @@ pub async fn run_benchmark(config: &BenchConfig) -> Result<serde_json::Value> {
    if let Some((cancel_tx, task)) = profile_task {
        let _ = cancel_tx.send(());
        if let Err(e) = task.await {
-            tracing::error!(error = %e.as_report(), "profiler background task failed");
+            eprintln!("WARNING: Profile background task failed: {e}");
        }
    }

@@ -1302,14 +1289,12 @@ pub(crate) async fn start_profiler_immediate(
    base_url: &str,
    extra_headers: &Option<std::collections::HashMap<String, String>>,
 ) {
+    println!("Starting profiler...");
    let profile_url = format!("{base_url}/start_profile");
-    tracing::info!(url = %profile_url, "starting profiler");
    match send_profile_request(client, &profile_url, extra_headers).await {
-        Ok(true) => tracing::info!(url = %profile_url, "profiler started"),
-        Ok(false) => tracing::warn!(url = %profile_url, "profiler start request was unsuccessful"),
-        Err(e) => {
-            tracing::warn!(url = %profile_url, error = %e.as_report(), "failed to start profiler")
-        }
+        Ok(true) => println!("Profiler started"),
+        Ok(false) => eprintln!("WARNING: Profiler start request returned non-success"),
+        Err(e) => eprintln!("WARNING: Failed to start profiler: {e}"),
    }
 }

@@ -1319,14 +1304,12 @@ pub(crate) async fn stop_profiler_immediate(
    base_url: &str,
    extra_headers: &Option<std::collections::HashMap<String, String>>,
 ) {
+    println!("Stopping profiler...");
    let profile_url = format!("{base_url}/stop_profile");
-    tracing::info!(url = %profile_url, "stopping profiler");
    match send_profile_request(client, &profile_url, extra_headers).await {
-        Ok(true) => tracing::info!(url = %profile_url, "profiler stopped"),
-        Ok(false) => tracing::warn!(url = %profile_url, "profiler stop request was unsuccessful"),
-        Err(e) => {
-            tracing::warn!(url = %profile_url, error = %e.as_report(), "failed to stop profiler")
-        }
+        Ok(true) => println!("Profiler stopped"),
+        Ok(false) => eprintln!("WARNING: Profiler stop request returned non-success"),
+        Err(e) => eprintln!("WARNING: Failed to stop profiler: {e}"),
    }
 }

@@ -1388,30 +1371,25 @@ pub(crate) async fn profile_on_batch_threshold(
    duration_secs: f64,
    mut cancel_rx: tokio::sync::oneshot::Receiver<()>,
 ) {
-    tracing::info!(
-        threshold,
-        duration_seconds = duration_secs,
-        "waiting for profiler batch threshold"
+    println!(
+        "Waiting for batch size >= {threshold} before starting profiler \
+         (will capture {duration_secs}s)..."
    );

    loop {
        if let Some(running) = fetch_num_requests_running(client, base_url).await
            && running >= threshold
        {
-            tracing::info!(
-                running_requests = running,
-                threshold,
-                "profiler batch threshold reached"
-            );
+            println!("Batch size {running} >= {threshold}, starting profiler...");
            break;
        }
        // Wait 500ms or until the benchmark signals cancellation
        tokio::select! {
            _ = tokio::time::sleep(std::time::Duration::from_millis(500)) => {}
            _ = &mut cancel_rx => {
-                tracing::warn!(
-                    threshold,
-                    "benchmark finished before profiler batch threshold; skipping profiling"
+                eprintln!(
+                    "NOTE: Benchmark finished before batch threshold {threshold} was reached; \
+                     profiling skipped."
                );
                return;
            }
@@ -1420,13 +1398,13 @@ pub(crate) async fn profile_on_batch_threshold(

    let start_url = format!("{base_url}/start_profile");
    match send_profile_request(client, &start_url, extra_headers).await {
-        Ok(true) => tracing::info!(url = %start_url, "profiler started"),
+        Ok(true) => println!("Profiler started"),
        Ok(false) => {
-            tracing::warn!(url = %start_url, "profiler start request was unsuccessful");
+            eprintln!("WARNING: Profiler start request returned non-success");
            return;
        }
        Err(e) => {
-            tracing::warn!(url = %start_url, error = %e.as_report(), "failed to start profiler");
+            eprintln!("WARNING: Failed to start profiler: {e}");
            return;
        }
    }
@@ -1435,17 +1413,15 @@ pub(crate) async fn profile_on_batch_threshold(
    tokio::select! {
        _ = tokio::time::sleep(std::time::Duration::from_secs_f64(duration_secs)) => {}
        _ = &mut cancel_rx => {
-            tracing::info!("benchmark finished; stopping profiler early");
+            println!("Benchmark finished, stopping profiler early...");
        }
    }

    let stop_url = format!("{base_url}/stop_profile");
    match send_profile_request(client, &stop_url, extra_headers).await {
-        Ok(true) => tracing::info!(url = %stop_url, "profiler stopped after capture"),
-        Ok(false) => tracing::warn!(url = %stop_url, "profiler stop request was unsuccessful"),
-        Err(e) => {
-            tracing::warn!(url = %stop_url, error = %e.as_report(), "failed to stop profiler")
-        }
+        Ok(true) => println!("Profiler stopped after capturing"),
+        Ok(false) => eprintln!("WARNING: Profiler stop request returned non-success"),
+        Err(e) => eprintln!("WARNING: Failed to stop profiler: {e}"),
    }
 }

@@ -1526,11 +1502,10 @@ async fn verify_and_fix_prompt_lengths(
                let excess = tokens.len().saturating_sub(expected_input_len);
                let compensate = if excess > 0 && last_excess == Some(excess) {
                    if _iter == 1 {
-                        tracing::warn!(
-                            prompt_index = i,
-                            extra_tokens = excess,
-                            adjusted_target = expected_input_len.saturating_sub(excess),
-                            "server consistently adds prompt tokens; compensating verification target"
+                        eprintln!(
+                            "Prompt {i}: server consistently adds {excess} extra token(s) \
+                             (likely BOS), compensating target to {}.",
+                            expected_input_len.saturating_sub(excess),
                        );
                    }
                    excess
@@ -1588,10 +1563,7 @@ async fn verify_and_fix_prompt_lengths(

    let fc = fixed_count.load(std::sync::atomic::Ordering::Relaxed);
    if fc > 0 {
-        tracing::info!(
-            fixed_prompts = fc,
-            "fixed prompt lengths using server tokenizer"
-        );
+        println!("Fixed {fc} prompt(s) via server tokenize/detokenize convergence.");
    }

    Ok(())
@@ -1846,7 +1818,7 @@ async fn sample_verify_prompts(
    let tokenize_url = format!("{base_url}/tokenize");
    let api_key = std::env::var("OPENAI_API_KEY").ok();

-    tracing::info!(sample_size, "sampling prompts for tokenizer verification");
+    println!("Sampling {sample_size} prompts for verification...");

    for (i, request) in requests.iter().enumerate().take(sample_size) {
        let tokens = match server_tokenize(
@@ -1869,11 +1841,9 @@ async fn sample_verify_prompts(

        let expected = request.prompt_len + num_special;
        if tokens.len() != expected {
-            tracing::warn!(
-                prompt_index = i,
-                expected_tokens = expected,
-                actual_tokens = tokens.len(),
-                "tokenizer verification sample mismatch"
+            println!(
+                "Prompt {i}: expected {expected} tokens, server returned {}",
+                tokens.len()
            );
            return Ok(SampleVerifyOutcome::Mismatch);
        }
@@ -3,6 +3,8 @@

 use std::fmt;

+use clap::Parser;
+
 /// Backend type for the benchmark endpoint.
 #[derive(clap::ValueEnum, Debug, Clone, Copy, PartialEq, Eq)]
 pub enum BackendKind {
@@ -75,7 +77,7 @@ pub enum DatasetName {
    ShareGpt,
    #[value(name = "sonnet")]
    Sonnet,
-    #[value(name = "speed-bench", alias = "speed_bench")]
+    #[value(name = "speed-bench")]
    SpeedBench,
    #[value(name = "hf")]
    Hf,
@@ -142,8 +144,13 @@ impl fmt::Display for SpeedBenchConfig {
 }

 /// High-performance benchmark client for vLLM serving endpoints.
-#[derive(clap::Args, Debug, Clone)]
-pub struct BenchServeArgs {
+#[derive(Parser, Debug, Clone)]
+#[command(
+    name = "vllm-bench",
+    about = "Benchmark online serving throughput",
+    version
+)]
+pub struct Cli {
    /// The type of backend or endpoint to use for the benchmark.
    #[arg(long, default_value = "openai")]
    pub backend: BackendKind,
@@ -652,7 +659,7 @@ pub struct BenchServeArgs {
    pub lora_assignment: LoraAssignment,
 }

-impl BenchServeArgs {
+impl Cli {
    /// Resolve the base URL from explicit --base-url or from --host/--port.
    pub fn resolve_base_url(&self) -> String {
        if let Some(ref base) = self.base_url {
@@ -4,9 +4,7 @@
 use std::collections::HashMap;
 use std::sync::Arc;

-use crate::cli::{
-    BackendKind, BenchServeArgs, DatasetName, LoraAssignment, RampUpStrategy, SpeedBenchConfig,
-};
+use crate::cli::{BackendKind, Cli, DatasetName, LoraAssignment, RampUpStrategy, SpeedBenchConfig};
 use crate::datasets::random_mm::{MmBucketKey, MmLimitPerPrompt};
 use crate::error::{BenchError, Result};

@@ -217,63 +215,63 @@ pub struct BenchConfig {
 }

 impl BenchConfig {
-    pub fn from_args(args: &BenchServeArgs) -> Result<Self> {
-        if args.burstiness <= 0.0 {
+    pub fn from_cli(cli: &Cli) -> Result<Self> {
+        if cli.burstiness <= 0.0 {
            return Err(BenchError::Config("Burstiness must be positive".into()));
        }

-        if args.num_prompts == 0 {
+        if cli.num_prompts == 0 {
            return Err(BenchError::Config(
                "--num-prompts must be at least 1".into(),
            ));
        }

-        if args.request_rate <= 0.0 && !args.request_rate.is_infinite() {
+        if cli.request_rate <= 0.0 && !cli.request_rate.is_infinite() {
            return Err(BenchError::Config(
                "--request-rate must be positive (or inf)".into(),
            ));
        }
-        if args.max_model_len == Some(0) {
+        if cli.max_model_len == Some(0) {
            return Err(BenchError::Config(
                "--max-model-len must be at least 1".into(),
            ));
        }

-        let base_url = args.resolve_base_url();
-        let api_url = args.resolve_api_url();
+        let base_url = cli.resolve_base_url();
+        let api_url = cli.resolve_api_url();

-        let extra_headers = args.parse_headers()?;
-        let mut extra_body = args.parse_extra_body()?;
+        let extra_headers = cli.parse_headers()?;
+        let mut extra_body = cli.parse_extra_body()?;

        // Merge sampling parameters into extra_body (matches Python behavior).
        // Python collects non-None sampling params and merges them UNDER extra_body,
        // meaning extra_body keys take precedence over sampling params.
        {
            let mut sampling_params = serde_json::Map::new();
-            if let Some(v) = args.top_p {
+            if let Some(v) = cli.top_p {
                sampling_params.insert("top_p".into(), serde_json::json!(v));
            }
-            if let Some(v) = args.top_k {
+            if let Some(v) = cli.top_k {
                sampling_params.insert("top_k".into(), serde_json::json!(v));
            }
-            if let Some(v) = args.min_p {
+            if let Some(v) = cli.min_p {
                sampling_params.insert("min_p".into(), serde_json::json!(v));
            }
-            if let Some(v) = args.temperature {
+            if let Some(v) = cli.temperature {
                sampling_params.insert("temperature".into(), serde_json::json!(v));
            }
-            if let Some(v) = args.frequency_penalty {
+            if let Some(v) = cli.frequency_penalty {
                sampling_params.insert("frequency_penalty".into(), serde_json::json!(v));
            }
-            if let Some(v) = args.presence_penalty {
+            if let Some(v) = cli.presence_penalty {
                sampling_params.insert("presence_penalty".into(), serde_json::json!(v));
            }
-            if let Some(v) = args.repetition_penalty {
+            if let Some(v) = cli.repetition_penalty {
                sampling_params.insert("repetition_penalty".into(), serde_json::json!(v));
            }

            if !sampling_params.is_empty() {
-                if !args.backend.is_openai_compatible() {
+                if !cli.backend.is_openai_compatible() {
                    return Err(BenchError::Config(
                        "Sampling parameters are only supported by openai-compatible backends."
                            .into(),
@@ -288,18 +286,10 @@ impl BenchConfig {
                    }
                    Some(other) => {
                        // extra_body was not an object — just use sampling params
-                        let value_type = match &other {
-                            serde_json::Value::Null => "null",
-                            serde_json::Value::Bool(_) => "boolean",
-                            serde_json::Value::Number(_) => "number",
-                            serde_json::Value::String(_) => "string",
-                            serde_json::Value::Array(_) => "array",
-                            serde_json::Value::Object(_) => unreachable!(),
-                        };
-                        tracing::warn!(
-                            value_type,
-                            "sampling parameters may be lost because --extra-body is not a JSON object"
+                        eprintln!(
+                            "Warning: --extra-body is not a JSON object, sampling params may be lost"
                        );
+                        let _ = other;
                        sampling_params
                    }
                    None => sampling_params,
@@ -309,7 +299,7 @@ impl BenchConfig {
        }

        // Parse metadata
-        let metadata = match &args.metadata {
+        let metadata = match &cli.metadata {
            None => None,
            Some(items) => {
                let mut pairs = Vec::new();
@@ -324,24 +314,24 @@ impl BenchConfig {
        };

        // Parse goodput SLOs
-        let goodput = parse_goodput(&args.goodput)?;
+        let goodput = parse_goodput(&cli.goodput)?;

        // Parse ramp-up config
-        let ramp_up = parse_ramp_up(args)?;
+        let ramp_up = parse_ramp_up(cli)?;

        // Default percentile metrics based on backend type
-        let default_percentile_metrics = if args.backend.is_pooling() {
+        let default_percentile_metrics = if cli.backend.is_pooling() {
            "e2el"
        } else {
            "ttft,tpot,itl,e2el"
        };
        let percentile_metrics_str =
-            args.percentile_metrics.as_deref().unwrap_or(default_percentile_metrics);
+            cli.percentile_metrics.as_deref().unwrap_or(default_percentile_metrics);
        let selected_percentile_metrics: Vec<String> =
            percentile_metrics_str.split(',').map(|s| s.trim().to_string()).collect();

-        let metric_percentiles = parse_percentiles(&args.metric_percentiles, false)?;
-        let sweep_summary_percentiles = args
+        let metric_percentiles = parse_percentiles(&cli.metric_percentiles, false)?;
+        let sweep_summary_percentiles = cli
            .sweep_summary_percentiles
            .as_deref()
            .map(|raw| parse_percentiles(raw, true))
@@ -354,38 +344,38 @@ impl BenchConfig {
            selected_percentiles.push(90.0);
        }

-        let tokenizer_id = if args.skip_tokenizer_init {
+        let tokenizer_id = if cli.skip_tokenizer_init {
            None
        } else {
-            args.tokenizer.clone().or_else(|| args.model.clone())
+            Some(cli.tokenizer.clone().or_else(|| cli.model.clone()).unwrap_or_default())
        };

        // Resolve input/output lengths
-        let random_input_len = args.resolved_random_input_len();
-        let random_output_len = args.resolved_random_output_len();
-        let per_turn_input_len = args.resolved_per_turn_input_len();
+        let random_input_len = cli.resolved_random_input_len();
+        let random_output_len = cli.resolved_random_output_len();
+        let per_turn_input_len = cli.resolved_per_turn_input_len();

        // Normalized multi-turn turn counts (computed in validation block below, defaults
        // to num_turns if multi-turn mode is not active)
-        let mut multi_turn_min_turns = args.multi_turn_num_turns;
-        let mut multi_turn_max_turns = args.multi_turn_num_turns;
+        let mut multi_turn_min_turns = cli.multi_turn_num_turns;
+        let mut multi_turn_max_turns = cli.multi_turn_num_turns;

        // For random datasets with openai-compatible backends, default to ignore_eos.
        // Exception: multi-turn mode, where ignore_eos causes unbounded context growth
        // across turns. Multi-turn uses min_tokens instead for output length control.
        // Pooling backends don't generate tokens, so ignore_eos is irrelevant.
-        let ignore_eos = if args.backend.is_pooling() {
+        let ignore_eos = if cli.backend.is_pooling() {
            false
        } else {
-            args.ignore_eos
-                || ((args.dataset_name == DatasetName::Random
-                    || args.dataset_name == DatasetName::RandomMm)
-                    && args.backend.is_openai_compatible()
-                    && !args.multi_turn)
+            cli.ignore_eos
+                || ((cli.dataset_name == DatasetName::Random
+                    || cli.dataset_name == DatasetName::RandomMm)
+                    && cli.backend.is_openai_compatible()
+                    && !cli.multi_turn)
        };

        // Pooling backends don't support multi-turn
-        if args.backend.is_pooling() && args.multi_turn {
+        if cli.backend.is_pooling() && cli.multi_turn {
            return Err(BenchError::Config(
                "Pooling/embedding backends do not support --multi-turn".into(),
            ));
@@ -393,7 +383,7 @@ impl BenchConfig {

        // LoRA validation. Adapter names must be non-empty after trim; pooling
        // backends are out of scope (vLLM LoRA routing is for generative paths).
-        let lora_modules = match args.lora_modules.as_ref() {
+        let lora_modules = match cli.lora_modules.as_ref() {
            None => None,
            Some(names) => {
                if names.is_empty() {
@@ -401,7 +391,7 @@ impl BenchConfig {
                        "--lora-modules requires at least one adapter name".into(),
                    ));
                }
-                if args.backend.is_pooling() {
+                if cli.backend.is_pooling() {
                    return Err(BenchError::Config(
                        "--lora-modules is not supported for pooling/embedding backends".into(),
                    ));
@@ -421,18 +411,18 @@ impl BenchConfig {
        };

        // Random-MM validation and config parsing
-        let (random_mm_limit, random_mm_buckets) = if args.dataset_name == DatasetName::RandomMm {
-            if args.backend != BackendKind::OpenaiChat {
+        let (random_mm_limit, random_mm_buckets) = if cli.dataset_name == DatasetName::RandomMm {
+            if cli.backend != BackendKind::OpenaiChat {
                return Err(BenchError::Config(
                    "Multi-modal content (images) is only supported on 'openai-chat' backend."
                        .into(),
                ));
            }
            let limit = crate::datasets::random_mm::parse_limit_mm_per_prompt(
-                &args.random_mm_limit_mm_per_prompt,
+                &cli.random_mm_limit_mm_per_prompt,
            )?;
            let buckets =
-                crate::datasets::random_mm::parse_bucket_config(&args.random_mm_bucket_config)?;
+                crate::datasets::random_mm::parse_bucket_config(&cli.random_mm_bucket_config)?;
            (limit, buckets)
        } else {
            (MmLimitPerPrompt::default(), Vec::new())
@@ -442,18 +432,18 @@ impl BenchConfig {
        // sonnet (uses built-in Shakespeare's sonnets).

        // Range ratio (Python semantics: [len*(1-r), len*(1+r)], each r in [0,1))
-        let random_range_ratio = RangeRatio::parse(&args.random_range_ratio)?;
+        let random_range_ratio = RangeRatio::parse(&cli.random_range_ratio)?;

        // Batched inputs only make sense for pooling backends (the generation
        // backends send one prompt per request).
-        if args.random_batch_size == 0 {
+        if cli.random_batch_size == 0 {
            return Err(BenchError::Config(
                "--random-batch-size must be at least 1".into(),
            ));
        }
-        if args.random_batch_size > 1
-            && !args.backend.is_pooling()
-            && args.dataset_name != DatasetName::RandomRerank
+        if cli.random_batch_size > 1
+            && !cli.backend.is_pooling()
+            && cli.dataset_name != DatasetName::RandomRerank
        {
            return Err(BenchError::Config(
                "--random-batch-size > 1 is only supported with embeddings/pooling backends".into(),
@@ -461,16 +451,16 @@ impl BenchConfig {
        }

        // random-rerank validation (mirrors Python RandomDatasetForReranking)
-        let is_reranker = !args.no_reranker;
-        if args.dataset_name == DatasetName::RandomRerank {
-            if !args.backend.is_pooling() {
+        let is_reranker = !cli.no_reranker;
+        if cli.dataset_name == DatasetName::RandomRerank {
+            if !cli.backend.is_pooling() {
                return Err(BenchError::Config(
                    "--dataset-name random-rerank requires an embeddings/pooling backend \
                     (e.g. --backend vllm-rerank)"
                        .into(),
                ));
            }
-            if !is_reranker && (args.num_prompts < 2 || args.random_batch_size < 2) {
+            if !is_reranker && (cli.num_prompts < 2 || cli.random_batch_size < 2) {
                return Err(BenchError::Config(
                    "--no-reranker requires --num-prompts > 1 and --random-batch-size > 1 \
                     (the query is folded into the first batch slot)"
@@ -480,8 +470,8 @@ impl BenchConfig {
        }

        // Custom dataset validation
-        if args.dataset_name == DatasetName::Custom {
-            match args.dataset_path.as_deref() {
+        if cli.dataset_name == DatasetName::Custom {
+            match cli.dataset_path.as_deref() {
                None => {
                    return Err(BenchError::Config(
                        "--dataset-path is required for --dataset-name custom \
@@ -496,38 +486,38 @@ impl BenchConfig {
                }
                _ => {}
            }
-            if !args.skip_chat_template {
-                tracing::warn!(
-                    dataset = "custom",
-                    "client-side chat template rendering is unsupported; sending prompts raw"
+            if !cli.skip_chat_template {
+                eprintln!(
+                    "NOTE: client-side chat template rendering is not supported; custom \
+                     dataset prompts are sent raw (equivalent to --skip-chat-template)."
                );
            }
        }

        // Prefix repetition validation
-        if args.dataset_name == DatasetName::PrefixRepetition {
-            if args.prefix_repetition_num_prefixes == 0 {
+        if cli.dataset_name == DatasetName::PrefixRepetition {
+            if cli.prefix_repetition_num_prefixes == 0 {
                return Err(BenchError::Config(
                    "--prefix-repetition-num-prefixes must be at least 1".into(),
                ));
            }
-            if args.num_prompts < args.prefix_repetition_num_prefixes {
+            if cli.num_prompts < cli.prefix_repetition_num_prefixes {
                return Err(BenchError::Config(format!(
                    "--num-prompts ({}) must be >= --prefix-repetition-num-prefixes ({})",
-                    args.num_prompts, args.prefix_repetition_num_prefixes
+                    cli.num_prompts, cli.prefix_repetition_num_prefixes
                )));
            }
        }

        // HF dataset validation
-        if args.dataset_name == DatasetName::Hf && args.dataset_path.is_none() {
+        if cli.dataset_name == DatasetName::Hf && cli.dataset_path.is_none() {
            return Err(BenchError::Config(
                "--dataset-path is required for --dataset-name hf \
                 (set to a HuggingFace dataset ID, e.g. 'allenai/WildChat-4.8M')"
                    .into(),
            ));
        }
-        if let Some(len) = args.hf_output_len
+        if let Some(len) = cli.hf_output_len
            && len == 0
        {
            return Err(BenchError::Config(
@@ -536,13 +526,13 @@ impl BenchConfig {
        }

        // Multi-turn validation
-        if args.multi_turn {
-            if args.backend != BackendKind::OpenaiChat {
+        if cli.multi_turn {
+            if cli.backend != BackendKind::OpenaiChat {
                return Err(BenchError::Config(
                    "--multi-turn requires --backend openai-chat".into(),
                ));
            }
-            if args.multi_turn_num_turns == 0 {
+            if cli.multi_turn_num_turns == 0 {
                return Err(BenchError::Config(
                    "--multi-turn-num-turns must be at least 1".into(),
                ));
@@ -551,18 +541,18 @@ impl BenchConfig {
            // Normalize and validate min/max turns. ShareGPT only consumes max_turns
            // (the loader walks all available turns up to the cap), so the
            // min/num/max coupling used for synthetic generation does not apply.
-            if args.dataset_name == DatasetName::ShareGpt {
-                if args.multi_turn_max_turns == 1 {
+            if cli.dataset_name == DatasetName::ShareGpt {
+                if cli.multi_turn_max_turns == 1 {
                    return Err(BenchError::Config(
                        "--multi-turn-max-turns must be at least 2 for ShareGPT multi-turn".into(),
                    ));
                }
            } else {
                (multi_turn_min_turns, multi_turn_max_turns) =
-                    match (args.multi_turn_min_turns, args.multi_turn_max_turns) {
-                        (0, 0) => (args.multi_turn_num_turns, args.multi_turn_num_turns),
-                        (m, 0) => (m, args.multi_turn_num_turns),
-                        (0, x) => (args.multi_turn_num_turns, x),
+                    match (cli.multi_turn_min_turns, cli.multi_turn_max_turns) {
+                        (0, 0) => (cli.multi_turn_num_turns, cli.multi_turn_num_turns),
+                        (m, 0) => (m, cli.multi_turn_num_turns),
+                        (0, x) => (cli.multi_turn_num_turns, x),
                        (m, x) => (m, x),
                    };
                if multi_turn_min_turns < 1 {
@@ -578,16 +568,15 @@ impl BenchConfig {
            }

            if ignore_eos {
-                tracing::warn!(
-                    ignore_eos,
-                    multi_turn = true,
-                    "output length limits may be ignored, causing unbounded context growth"
+                eprintln!(
+                    "WARNING: --ignore-eos is set with --multi-turn. The server may not \
+                     respect output length limits, causing unbounded context growth."
                );
            }

            // Validate prefix sharing ratios
-            let pg = args.multi_turn_prefix_global_ratio;
-            let pc = args.multi_turn_prefix_conversation_ratio;
+            let pg = cli.multi_turn_prefix_global_ratio;
+            let pc = cli.multi_turn_prefix_conversation_ratio;
            if !(0.0..=1.0).contains(&pg) {
                return Err(BenchError::Config(
                    "--multi-turn-prefix-global-ratio must be in [0.0, 1.0]".into(),
@@ -603,20 +592,20 @@ impl BenchConfig {
                    "--multi-turn-prefix-global-ratio + --multi-turn-prefix-conversation-ratio must be < 1.0 (unique suffix required)".into(),
                ));
            }
-            if (pg > 0.0 || pc > 0.0) && args.dataset_name != DatasetName::Random {
+            if (pg > 0.0 || pc > 0.0) && cli.dataset_name != DatasetName::Random {
                return Err(BenchError::Config(
                    "Prefix sharing (--multi-turn-prefix-global-ratio / --multi-turn-prefix-conversation-ratio) only works with --dataset-name random".into(),
                ));
            }
        }

-        if !(args.steady_state_threshold > 0.0 && args.steady_state_threshold <= 1.0) {
+        if !(cli.steady_state_threshold > 0.0 && cli.steady_state_threshold <= 1.0) {
            return Err(BenchError::Config(format!(
                "--steady-state-threshold must be in (0.0, 1.0], got {}",
-                args.steady_state_threshold
+                cli.steady_state_threshold
            )));
        }
-        if let Some(mw) = args.steady_state_min_window
+        if let Some(mw) = cli.steady_state_min_window
            && mw < 0.0
        {
            return Err(BenchError::Config(format!(
@@ -624,122 +613,122 @@ impl BenchConfig {
            )));
        }

-        if args.profile_batch_threshold.is_some() && !args.profile {
+        if cli.profile_batch_threshold.is_some() && !cli.profile {
            return Err(BenchError::Config(
                "--profile-batch-threshold requires --profile".into(),
            ));
        }
-        if args.profile_duration <= 0.0 {
+        if cli.profile_duration <= 0.0 {
            return Err(BenchError::Config(
                "--profile-duration must be positive".into(),
            ));
        }
-        if args.profile_batch_threshold.is_none() && args.profile_duration != 5.0 {
+        if cli.profile_batch_threshold.is_none() && cli.profile_duration != 5.0 {
            return Err(BenchError::Config(
                "--profile-duration requires --profile-batch-threshold".into(),
            ));
        }

        Ok(BenchConfig {
-            backend: args.backend,
+            backend: cli.backend,
            base_url,
            api_url,
-            model: args.model.clone(),
-            model_name: args.served_model_name.clone(),
+            model: cli.model.clone(),
+            model_name: cli.served_model_name.clone(),
            tokenizer_id,
-            tokenizer_mode: args.tokenizer_mode.clone(),
-            trust_remote_code: args.trust_remote_code,
-            skip_tokenizer_init: args.skip_tokenizer_init,
-            dataset_name: args.dataset_name,
-            dataset_path: args.dataset_path.clone(),
-            max_model_len: args.max_model_len,
+            tokenizer_mode: cli.tokenizer_mode.clone(),
+            trust_remote_code: cli.trust_remote_code,
+            skip_tokenizer_init: cli.skip_tokenizer_init,
+            dataset_name: cli.dataset_name,
+            dataset_path: cli.dataset_path.clone(),
+            max_model_len: cli.max_model_len,
            random_input_len,
            random_output_len,
-            random_prefix_len: args.random_prefix_len,
+            random_prefix_len: cli.random_prefix_len,
            random_range_ratio,
-            random_batch_size: args.random_batch_size,
+            random_batch_size: cli.random_batch_size,
            is_reranker,
-            custom_output_len: args.output_len.map(|v| v as i64).unwrap_or(args.custom_output_len),
-            prefix_repetition_prefix_len: args.prefix_repetition_prefix_len,
-            prefix_repetition_suffix_len: args.prefix_repetition_suffix_len,
-            prefix_repetition_num_prefixes: args.prefix_repetition_num_prefixes,
-            prefix_repetition_output_len: args
+            custom_output_len: cli.output_len.map(|v| v as i64).unwrap_or(cli.custom_output_len),
+            prefix_repetition_prefix_len: cli.prefix_repetition_prefix_len,
+            prefix_repetition_suffix_len: cli.prefix_repetition_suffix_len,
+            prefix_repetition_num_prefixes: cli.prefix_repetition_num_prefixes,
+            prefix_repetition_output_len: cli
                .output_len
-                .unwrap_or(args.prefix_repetition_output_len),
-            random_cache_hit_fraction: args.random_cache_hit_fraction,
-            random_cache_ratio: args.random_cache_ratio,
-            sharegpt_output_len: args.sharegpt_output_len,
-            sonnet_input_len: args.sonnet_input_len,
-            sonnet_output_len: args.sonnet_output_len,
-            sonnet_prefix_len: args.sonnet_prefix_len,
-            no_oversample: args.no_oversample,
-            disable_shuffle: args.disable_shuffle,
-            num_prompts: args.num_prompts,
-            request_rate: args.request_rate,
-            burstiness: args.burstiness,
-            max_concurrency: args.max_concurrency,
-            steady_state_threshold: args.steady_state_threshold,
-            steady_state_min_window: args.steady_state_min_window,
-            no_steady_state: args.no_steady_state,
-            disable_tqdm: args.disable_tqdm,
-            num_warmups: args.num_warmups,
-            profile: args.profile,
-            profile_batch_threshold: args.profile_batch_threshold,
-            profile_duration: args.profile_duration,
-            save_result: args.save_result,
-            save_detailed: args.save_detailed,
-            append_result: args.append_result,
-            result_dir: args.result_dir.clone(),
-            result_filename: args.result_filename.clone(),
-            seed: args.seed,
+                .unwrap_or(cli.prefix_repetition_output_len),
+            random_cache_hit_fraction: cli.random_cache_hit_fraction,
+            random_cache_ratio: cli.random_cache_ratio,
+            sharegpt_output_len: cli.sharegpt_output_len,
+            sonnet_input_len: cli.sonnet_input_len,
+            sonnet_output_len: cli.sonnet_output_len,
+            sonnet_prefix_len: cli.sonnet_prefix_len,
+            no_oversample: cli.no_oversample,
+            disable_shuffle: cli.disable_shuffle,
+            num_prompts: cli.num_prompts,
+            request_rate: cli.request_rate,
+            burstiness: cli.burstiness,
+            max_concurrency: cli.max_concurrency,
+            steady_state_threshold: cli.steady_state_threshold,
+            steady_state_min_window: cli.steady_state_min_window,
+            no_steady_state: cli.no_steady_state,
+            disable_tqdm: cli.disable_tqdm,
+            num_warmups: cli.num_warmups,
+            profile: cli.profile,
+            profile_batch_threshold: cli.profile_batch_threshold,
+            profile_duration: cli.profile_duration,
+            save_result: cli.save_result,
+            save_detailed: cli.save_detailed,
+            append_result: cli.append_result,
+            result_dir: cli.result_dir.clone(),
+            result_filename: cli.result_filename.clone(),
+            seed: cli.seed,
            ignore_eos,
-            insecure: args.insecure,
+            insecure: cli.insecure,
            selected_percentile_metrics,
            selected_percentiles,
            sweep_summary_percentiles,
-            label: args.label.clone(),
-            logprobs: args.logprobs,
-            request_id_prefix: args.get_request_id_prefix(),
-            ready_check_timeout_sec: args.ready_check_timeout_sec,
+            label: cli.label.clone(),
+            logprobs: cli.logprobs,
+            request_id_prefix: cli.get_request_id_prefix(),
+            ready_check_timeout_sec: cli.ready_check_timeout_sec,
            extra_headers,
            extra_body,
            metadata,
-            dry_run: args.dry_run,
+            dry_run: cli.dry_run,
            goodput,
            ramp_up,
-            multi_turn: args.multi_turn,
-            multi_turn_num_turns: args.multi_turn_num_turns,
+            multi_turn: cli.multi_turn,
+            multi_turn_num_turns: cli.multi_turn_num_turns,
            multi_turn_min_turns,
            multi_turn_max_turns,
-            sharegpt_multi_turn_max_turns: if args.multi_turn
-                && args.dataset_name == DatasetName::ShareGpt
-                && args.multi_turn_max_turns != 0
+            sharegpt_multi_turn_max_turns: if cli.multi_turn
+                && cli.dataset_name == DatasetName::ShareGpt
+                && cli.multi_turn_max_turns != 0
            {
-                Some(args.multi_turn_max_turns)
+                Some(cli.multi_turn_max_turns)
            } else {
                None
            },
            per_turn_input_len,
-            multi_turn_concurrency: args.multi_turn_concurrency,
-            multi_turn_delay_ms: args.multi_turn_delay_ms,
-            multi_turn_prefix_global_ratio: args.multi_turn_prefix_global_ratio,
-            multi_turn_prefix_conversation_ratio: args.multi_turn_prefix_conversation_ratio,
-            speed_bench_config: args.speed_bench_config,
-            speed_bench_category: args.speed_bench_category.clone(),
-            speed_bench_max_input_len: args.speed_bench_max_input_len,
-            hf_split: args.hf_split.clone(),
-            hf_subset: args.hf_subset.clone(),
-            hf_output_len: args.hf_output_len,
-            hf_text_column: args.hf_text_column.clone(),
-            reset_prefix_cache: args.reset_prefix_cache,
-            prompt_token_ids: args.prompt_token_ids,
-            random_mm_base_items_per_request: args.random_mm_base_items_per_request,
-            random_mm_num_mm_items_range_ratio: args.random_mm_num_mm_items_range_ratio,
+            multi_turn_concurrency: cli.multi_turn_concurrency,
+            multi_turn_delay_ms: cli.multi_turn_delay_ms,
+            multi_turn_prefix_global_ratio: cli.multi_turn_prefix_global_ratio,
+            multi_turn_prefix_conversation_ratio: cli.multi_turn_prefix_conversation_ratio,
+            speed_bench_config: cli.speed_bench_config,
+            speed_bench_category: cli.speed_bench_category.clone(),
+            speed_bench_max_input_len: cli.speed_bench_max_input_len,
+            hf_split: cli.hf_split.clone(),
+            hf_subset: cli.hf_subset.clone(),
+            hf_output_len: cli.hf_output_len,
+            hf_text_column: cli.hf_text_column.clone(),
+            reset_prefix_cache: cli.reset_prefix_cache,
+            prompt_token_ids: cli.prompt_token_ids,
+            random_mm_base_items_per_request: cli.random_mm_base_items_per_request,
+            random_mm_num_mm_items_range_ratio: cli.random_mm_num_mm_items_range_ratio,
            random_mm_limit,
            random_mm_buckets,
-            enable_multimodal_chat: args.enable_multimodal_chat,
+            enable_multimodal_chat: cli.enable_multimodal_chat,
            lora_modules,
-            lora_assignment: args.lora_assignment,
+            lora_assignment: cli.lora_assignment,
        })
    }
 }
@@ -822,17 +811,17 @@ fn parse_goodput(goodput_args: &Option<Vec<String>>) -> Result<GoodputConfig> {
    Ok(config)
 }

-fn parse_ramp_up(args: &BenchServeArgs) -> Result<Option<RampUpConfig>> {
-    let strategy = match args.ramp_up_strategy {
+fn parse_ramp_up(cli: &Cli) -> Result<Option<RampUpConfig>> {
+    let strategy = match cli.ramp_up_strategy {
        None => return Ok(None),
        Some(s) => s,
    };

-    let start_rps = args.ramp_up_start_rps.ok_or_else(|| {
+    let start_rps = cli.ramp_up_start_rps.ok_or_else(|| {
        BenchError::Config("--ramp-up-start-rps is required when --ramp-up-strategy is set".into())
    })?;

-    let end_rps = args.ramp_up_end_rps.ok_or_else(|| {
+    let end_rps = cli.ramp_up_end_rps.ok_or_else(|| {
        BenchError::Config("--ramp-up-end-rps is required when --ramp-up-strategy is set".into())
    })?;

@@ -854,21 +843,7 @@ mod tests {
    use clap::Parser;

    use super::*;
-    use crate::cli::BenchServeArgs;
-
-    #[derive(Parser)]
-    struct TestCli {
-        #[command(flatten)]
-        args: BenchServeArgs,
-    }
-
-    fn parse_args<I, T>(args: I) -> BenchServeArgs
-    where
-        I: IntoIterator<Item = T>,
-        T: Into<std::ffi::OsString> + Clone,
-    {
-        TestCli::parse_from(args).args
-    }
+    use crate::cli::Cli;

    fn base_multi_turn_args() -> Vec<&'static str> {
        vec![
@@ -884,8 +859,8 @@ mod tests {
    #[test]
    fn test_prefix_sharing_defaults_to_zero() {
        let args = base_multi_turn_args();
-        let args = parse_args(args);
-        let config = BenchConfig::from_args(&args).unwrap();
+        let cli = Cli::parse_from(args);
+        let config = BenchConfig::from_cli(&cli).unwrap();
        assert_eq!(config.multi_turn_prefix_global_ratio, 0.0);
        assert_eq!(config.multi_turn_prefix_conversation_ratio, 0.0);
    }
@@ -899,8 +874,8 @@ mod tests {
            "--multi-turn-prefix-conversation-ratio",
            "0.8",
        ]);
-        let args = parse_args(args);
-        let config = BenchConfig::from_args(&args).unwrap();
+        let cli = Cli::parse_from(args);
+        let config = BenchConfig::from_cli(&cli).unwrap();
        assert!((config.multi_turn_prefix_global_ratio - 0.1).abs() < 1e-10);
        assert!((config.multi_turn_prefix_conversation_ratio - 0.8).abs() < 1e-10);
    }
@@ -914,8 +889,8 @@ mod tests {
            "--multi-turn-prefix-conversation-ratio",
            "0.6",
        ]);
-        let args = parse_args(args);
-        assert!(BenchConfig::from_args(&args).is_err());
+        let cli = Cli::parse_from(args);
+        assert!(BenchConfig::from_cli(&cli).is_err());
    }

    #[test]
@@ -927,16 +902,16 @@ mod tests {
            "--multi-turn-prefix-conversation-ratio",
            "0.5",
        ]);
-        let args = parse_args(args);
-        assert!(BenchConfig::from_args(&args).is_err());
+        let cli = Cli::parse_from(args);
+        assert!(BenchConfig::from_cli(&cli).is_err());
    }

    #[test]
    fn test_prefix_sharing_out_of_range_fails() {
        let mut args = base_multi_turn_args();
        args.extend(["--multi-turn-prefix-global-ratio", "1.5"]);
-        let args = parse_args(args);
-        assert!(BenchConfig::from_args(&args).is_err());
+        let cli = Cli::parse_from(args);
+        assert!(BenchConfig::from_cli(&cli).is_err());
    }

    #[test]
@@ -953,8 +928,8 @@ mod tests {
            "--multi-turn-prefix-global-ratio",
            "0.1",
        ];
-        let args = parse_args(args);
-        assert!(BenchConfig::from_args(&args).is_err());
+        let cli = Cli::parse_from(args);
+        assert!(BenchConfig::from_cli(&cli).is_err());
    }

    #[test]
@@ -969,8 +944,8 @@ mod tests {
            "--dataset-name",
            "sharegpt",
        ];
-        let args = parse_args(args);
-        let config = BenchConfig::from_args(&args).unwrap();
+        let cli = Cli::parse_from(args);
+        let config = BenchConfig::from_cli(&cli).unwrap();

        assert_eq!(config.multi_turn_max_turns, 3);
        assert_eq!(config.sharegpt_multi_turn_max_turns, None);
@@ -993,8 +968,8 @@ mod tests {
            "--multi-turn-max-turns",
            "2",
        ];
-        let args = parse_args(args);
-        let config = BenchConfig::from_args(&args).unwrap();
+        let cli = Cli::parse_from(args);
+        let config = BenchConfig::from_cli(&cli).unwrap();
        assert_eq!(config.sharegpt_multi_turn_max_turns, Some(2));
    }

@@ -1012,8 +987,8 @@ mod tests {
            "--multi-turn-max-turns",
            "1",
        ];
-        let args = parse_args(args);
-        let err = BenchConfig::from_args(&args).unwrap_err().to_string();
+        let cli = Cli::parse_from(args);
+        let err = BenchConfig::from_cli(&cli).unwrap_err().to_string();
        assert!(
            err.contains("at least 2 for ShareGPT"),
            "expected ShareGPT-specific error, got: {err}"
@@ -1034,8 +1009,8 @@ mod tests {
            "--multi-turn-max-turns",
            "20",
        ];
-        let args = parse_args(args);
-        let config = BenchConfig::from_args(&args).unwrap();
+        let cli = Cli::parse_from(args);
+        let config = BenchConfig::from_cli(&cli).unwrap();

        assert_eq!(config.sharegpt_multi_turn_max_turns, Some(20));
    }
@@ -1043,8 +1018,8 @@ mod tests {
    #[test]
    fn test_sweep_summary_percentiles_default_empty() {
        let args = base_multi_turn_args();
-        let args = parse_args(args);
-        let config = BenchConfig::from_args(&args).unwrap();
+        let cli = Cli::parse_from(args);
+        let config = BenchConfig::from_cli(&cli).unwrap();

        assert!(config.sweep_summary_percentiles.is_empty());
        assert_eq!(config.selected_percentiles, vec![99.0, 90.0]);
@@ -1059,8 +1034,8 @@ mod tests {
            "--sweep-summary-percentiles",
            "90,95,90",
        ]);
-        let args = parse_args(args);
-        let config = BenchConfig::from_args(&args).unwrap();
+        let cli = Cli::parse_from(args);
+        let config = BenchConfig::from_cli(&cli).unwrap();

        assert_eq!(config.sweep_summary_percentiles, vec![90.0, 95.0]);
        assert_eq!(config.selected_percentiles, vec![99.0, 95.0, 90.0]);
@@ -1070,8 +1045,8 @@ mod tests {
    fn test_invalid_sweep_summary_percentile_fails() {
        let mut args = base_multi_turn_args();
        args.extend(["--sweep-summary-percentiles", "101"]);
-        let args = parse_args(args);
-        assert!(BenchConfig::from_args(&args).is_err());
+        let cli = Cli::parse_from(args);
+        assert!(BenchConfig::from_cli(&cli).is_err());
    }

    #[test]
@@ -1083,20 +1058,12 @@ mod tests {
            "--max-model-len",
            "4096",
        ];
-        let args = parse_args(args);
-        let config = BenchConfig::from_args(&args).unwrap();
+        let cli = Cli::parse_from(args);
+        let config = BenchConfig::from_cli(&cli).unwrap();

        assert_eq!(config.max_model_len, Some(4096));
    }

-    #[test]
-    fn test_tokenizer_id_deferred_when_model_is_unspecified() {
-        let args = parse_args(["vllm-bench"]);
-        let config = BenchConfig::from_args(&args).unwrap();
-
-        assert_eq!(config.tokenizer_id, None);
-    }
-
    #[test]
    fn test_zero_max_model_len_fails() {
        let args = vec![
@@ -1106,9 +1073,9 @@ mod tests {
            "--max-model-len",
            "0",
        ];
-        let args = parse_args(args);
+        let cli = Cli::parse_from(args);

-        assert!(BenchConfig::from_args(&args).is_err());
+        assert!(BenchConfig::from_cli(&cli).is_err());
    }
    #[test]
    fn test_range_ratio_parse_float() {
@@ -128,10 +128,8 @@ mod tests {

    /// gpt2 via built-in tiktoken encoding — loads without network access.
    fn test_tokenizer() -> TokenizerKind {
-        TokenizerKind::Tiktoken(
-            crate::tiktoken::load_builtin_tiktoken("gpt2")
-                .expect("gpt2 built-in tiktoken should always load without network"),
-        )
+        crate::tokenizer::load_tokenizer("gpt2", false, None)
+            .expect("gpt2 built-in tiktoken should always load without network")
    }

    #[test]
@@ -8,7 +8,6 @@ use rand::seq::SliceRandom;
 use rand::{Rng, SeedableRng};

 use super::SampleRequest;
-use super::progress::RowDownloadReporter;
 use crate::error::{BenchError, Result};
 use crate::tokenizer::TokenizerKind;

@@ -51,19 +50,18 @@ enum ColumnFormat {

 /// Make a GET request with retry logic (3 retries with exponential backoff).
 /// Returns the parsed JSON response.
-async fn get_with_retry(
-    client: &reqwest::Client,
+fn get_with_retry(
+    client: &reqwest::blocking::Client,
    url: &str,
    label: &str,
 ) -> Result<serde_json::Value> {
    let max_retries = 3;
    for attempt in 0..=max_retries {
-        let resp = match client.get(url).send().await {
+        let resp = match client.get(url).send() {
            Ok(r) => r,
            Err(e) => {
                if attempt < max_retries {
-                    tokio::time::sleep(std::time::Duration::from_secs(2 * (attempt as u64 + 1)))
-                        .await;
+                    std::thread::sleep(std::time::Duration::from_secs(2 * (attempt as u64 + 1)));
                    continue;
                }
                return Err(BenchError::Config(format!(
@@ -82,7 +80,7 @@ async fn get_with_retry(
        }

        if status.is_server_error() && attempt < max_retries {
-            tokio::time::sleep(std::time::Duration::from_secs(2 * (attempt as u64 + 1))).await;
+            std::thread::sleep(std::time::Duration::from_secs(2 * (attempt as u64 + 1)));
            continue;
        }

@@ -94,7 +92,6 @@ async fn get_with_retry(

        let data: serde_json::Value = resp
            .json()
-            .await
            .map_err(|e| BenchError::Config(format!("Failed to parse {label} response: {e}")))?;
        return Ok(data);
    }
@@ -108,7 +105,7 @@ async fn get_with_retry(
 /// If both `subset` and `split` are provided, the `/info` call is skipped as an optimization.
 /// Paginated download fetches rows in pages of 100 until `num_rows_needed` are collected
 /// or the dataset is exhausted.
-pub async fn download_hf_dataset(
+pub fn download_hf_dataset(
    dataset: &str,
    subset: Option<&str>,
    split: Option<&str>,
@@ -118,7 +115,7 @@ pub async fn download_hf_dataset(
        url::form_urlencoded::byte_serialize(dataset.as_bytes()).collect();

    let mut client_builder =
-        reqwest::Client::builder().timeout(std::time::Duration::from_secs(120));
+        reqwest::blocking::Client::builder().timeout(std::time::Duration::from_secs(120));

    // Add HF_TOKEN auth header if available
    if let Ok(token) = std::env::var("HF_TOKEN") {
@@ -141,7 +138,7 @@ pub async fn download_hf_dataset(
        // Call /info to discover available configs and splits
        let info_url =
            format!("https://datasets-server.huggingface.co/info?dataset={encoded_dataset}");
-        let info = get_with_retry(&client, &info_url, "HF dataset /info").await?;
+        let info = get_with_retry(&client, &info_url, "HF dataset /info")?;

        let dataset_info =
            info.get("dataset_info").and_then(|d| d.as_object()).ok_or_else(|| {
@@ -204,12 +201,7 @@ pub async fn download_hf_dataset(
        (resolved_config, resolved_split)
    };

-    tracing::info!(
-        dataset,
-        config = resolved_config,
-        split = resolved_split,
-        "resolved Hugging Face dataset"
-    );
+    println!("HF dataset: {dataset} (config={resolved_config}, split={resolved_split})");

    // Check cache
    let dir = cache_dir();
@@ -223,16 +215,11 @@ pub async fn download_hf_dataset(

    if cache_path.exists() {
        let path_str = cache_path.to_string_lossy().to_string();
-        tracing::info!(dataset, path = %path_str, "using cached Hugging Face dataset");
+        println!("HF dataset cached: {path_str}");
        return Ok((path_str, resolved_config, resolved_split));
    }

-    tracing::info!(
-        dataset,
-        config = resolved_config,
-        split = resolved_split,
-        "downloading Hugging Face dataset"
-    );
+    println!("Downloading HF dataset '{dataset}' from datasets-server...");

    let encoded_config: String =
        url::form_urlencoded::byte_serialize(resolved_config.as_bytes()).collect();
@@ -242,7 +229,6 @@ pub async fn download_hf_dataset(
    let mut all_rows: Vec<serde_json::Value> = Vec::new();
    let mut offset = 0usize;
    let page_size = 100usize;
-    let mut progress = RowDownloadReporter::new();

    loop {
        let url = format!(
@@ -254,7 +240,7 @@ pub async fn download_hf_dataset(
             &length={page_size}"
        );

-        let data = get_with_retry(&client, &url, "HF dataset /rows").await?;
+        let data = get_with_retry(&client, &url, "HF dataset /rows")?;

        let rows = data["rows"]
            .as_array()
@@ -274,14 +260,14 @@ pub async fn download_hf_dataset(
        offset += fetched;

        let total = data["num_rows_total"].as_u64().unwrap_or(0);
-        progress.update(offset, total);
+        eprint!("\r  Fetched {offset}/{total} rows...");

        // Stop if we have enough rows or reached end of dataset
        if all_rows.len() >= num_rows_needed || fetched < page_size {
            break;
        }
    }
-    progress.finish();
+    eprintln!(); // newline after progress

    if all_rows.is_empty() {
        return Err(BenchError::Config(format!(
@@ -294,12 +280,7 @@ pub async fn download_hf_dataset(
    std::fs::write(&cache_path, &json_str)?;

    let path_str = cache_path.to_string_lossy().to_string();
-    tracing::info!(
-        dataset,
-        rows = all_rows.len(),
-        path = %path_str,
-        "saved Hugging Face dataset"
-    );
+    println!("HF dataset: {} rows saved to {path_str}", all_rows.len());
    Ok((path_str, resolved_config, resolved_split))
 }

@@ -502,31 +483,21 @@ pub fn load_hf_dataset(
    // Detect column format from first row
    let format = detect_column_format(&entries[0], text_column_override)?;

+    // Print detected format
    match &format {
-        ColumnFormat::Chat(col) => {
-            tracing::info!(
-                format = "chat",
-                column = col,
-                "detected Hugging Face dataset format"
-            );
-        }
+        ColumnFormat::Chat(col) => println!("HF dataset: detected chat column '{col}'"),
        ColumnFormat::Text {
            prompt_col,
            output_col,
        } => {
-            tracing::info!(
-                format = "text",
-                prompt_column = prompt_col,
-                output_column = output_col.as_deref().unwrap_or("none"),
-                "detected Hugging Face dataset format"
-            );
+            let out_msg = output_col.as_deref().unwrap_or("none");
+            println!("HF dataset: detected text column '{prompt_col}', output column: {out_msg}");
        }
        ColumnFormat::Combined { cols, output_col } => {
-            tracing::info!(
-                format = "combined",
-                prompt_columns = ?cols,
-                output_column = output_col.as_deref().unwrap_or("none"),
-                "detected Hugging Face dataset format"
+            let out_msg = output_col.as_deref().unwrap_or("none");
+            println!(
+                "HF dataset: detected combined columns {:?}, output column: {out_msg}",
+                cols
            );
        }
    }
@@ -637,10 +608,9 @@ pub fn load_hf_dataset(
                if len == 0 { 128 } else { len }
            } else {
                if !warned_no_output {
-                    tracing::warn!(
-                        path = dataset_path,
-                        default_output_tokens = 128,
-                        "no dataset output column or --hf-output-len; using default output length"
+                    eprintln!(
+                        "WARNING: No output column detected and --hf-output-len not set. \
+                         Using default output length of 128 tokens."
                    );
                    warned_no_output = true;
                }
@@ -648,10 +618,9 @@ pub fn load_hf_dataset(
            }
        } else {
            if !warned_no_output {
-                tracing::warn!(
-                    path = dataset_path,
-                    default_output_tokens = 128,
-                    "no dataset output column or --hf-output-len; using default output length"
+                eprintln!(
+                    "WARNING: No output column detected and --hf-output-len not set. \
+                     Using default output length of 128 tokens."
                );
                warned_no_output = true;
            }
@@ -671,11 +640,9 @@ pub fn load_hf_dataset(
    // Oversample if needed
    if samples.len() < num_requests {
        if no_oversample {
-            tracing::info!(
-                dataset = "hf",
-                samples = samples.len(),
-                requested = num_requests,
-                "skipping dataset oversampling"
+            println!(
+                "Skipping oversampling. Total samples: {} (requested: {num_requests})",
+                samples.len()
            );
        } else if !samples.is_empty() {
            let original_len = samples.len();
@@ -685,11 +652,9 @@ pub fn load_hf_dataset(
                req.request_id = Some(format!("{request_id_prefix}{}", original_len + i));
                samples.push(req);
            }
-            tracing::info!(
-                dataset = "hf",
-                original_samples = original_len,
-                samples = samples.len(),
-                "oversampled dataset"
+            println!(
+                "Oversampled HF dataset from {original_len} to {} total samples.",
+                samples.len()
            );
        }
    }
@@ -1037,10 +1002,8 @@ mod tests {

    /// Build a gpt2 tokenizer using built-in tiktoken encoding (no network required).
    fn builtin_tokenizer() -> crate::tokenizer::TokenizerKind {
-        crate::tokenizer::TokenizerKind::Tiktoken(
-            crate::tiktoken::load_builtin_tiktoken("gpt2")
-                .expect("gpt2 built-in tiktoken should always load without network"),
-        )
+        crate::tokenizer::load_tokenizer("gpt2", false, None)
+            .expect("gpt2 built-in tiktoken should always load without network")
    }

    /// Write JSON data to a unique temp file and return the path string.
@@ -5,7 +5,6 @@ pub mod custom;
 pub mod hf_dataset;
 pub mod multi_turn;
 pub mod prefix_repetition;
-mod progress;
 pub mod random;
 pub mod random_mm;
 pub mod random_rerank;
@@ -91,10 +90,9 @@ pub fn oversample_requests(
        return;
    }
    if no_oversample {
-        tracing::info!(
-            samples = requests.len(),
-            requested = num_requests,
-            "skipping dataset oversampling"
+        println!(
+            "Skipping oversampling. Total samples: {} (requested: {num_requests})",
+            requests.len()
        );
        return;
    }
@@ -105,10 +103,9 @@ pub fn oversample_requests(
        req.request_id = Some(format!("{request_id_prefix}{}", original_len + i));
        requests.push(req);
    }
-    tracing::info!(
-        original_samples = original_len,
-        samples = requests.len(),
-        "oversampled dataset"
+    println!(
+        "Oversampled requests from {original_len} to {} total samples.",
+        requests.len()
    );
 }

@@ -445,10 +445,9 @@ pub fn load_sharegpt_multi_turn(
            conv.conversation_id = format!("{request_id_prefix}conv-{}", original_len + i);
            conversations.push(conv);
        }
-        tracing::info!(
-            original_conversations = original_len,
-            conversations = conversations.len(),
-            "oversampled multi-turn conversations"
+        println!(
+            "Oversampled multi-turn conversations from {original_len} to {} total.",
+            conversations.len()
        );
    }

@@ -526,12 +525,10 @@ mod tests {
        len
    }

-    #[tokio::test]
+    #[test]
    #[ignore]
-    async fn test_prefix_sharing_structure() {
-        let tok = crate::tokenizer::load_tokenizer("nvidia/Kimi-K2.5-NVFP4", false, None)
-            .await
-            .unwrap();
+    fn test_prefix_sharing_structure() {
+        let tok = crate::tokenizer::load_tokenizer("nvidia/Kimi-K2.5-NVFP4", false, None).unwrap();

        let cfg = MultiTurnRandomConfig {
            num_conversations: 5,
@@ -613,12 +610,10 @@ mod tests {
        println!("All prefix sharing checks passed!");
    }

-    #[tokio::test]
+    #[test]
    #[ignore]
-    async fn test_per_turn_input_len_default_mode() {
-        let tok = crate::tokenizer::load_tokenizer("nvidia/Kimi-K2.5-NVFP4", false, None)
-            .await
-            .unwrap();
+    fn test_per_turn_input_len_default_mode() {
+        let tok = crate::tokenizer::load_tokenizer("nvidia/Kimi-K2.5-NVFP4", false, None).unwrap();

        let cfg = MultiTurnRandomConfig {
            num_conversations: 4,
@@ -655,12 +650,10 @@ mod tests {
        println!("per_turn_input_len default-mode checks passed!");
    }

-    #[tokio::test]
+    #[test]
    #[ignore]
-    async fn test_variable_turns_range() {
-        let tok = crate::tokenizer::load_tokenizer("nvidia/Kimi-K2.5-NVFP4", false, None)
-            .await
-            .unwrap();
+    fn test_variable_turns_range() {
+        let tok = crate::tokenizer::load_tokenizer("nvidia/Kimi-K2.5-NVFP4", false, None).unwrap();

        let cfg = MultiTurnRandomConfig {
            num_conversations: 50,
@@ -691,12 +684,10 @@ mod tests {
        println!("variable_turns_range checks passed! counts: {distinct_counts:?}");
    }

-    #[tokio::test]
+    #[test]
    #[ignore]
-    async fn test_variable_turns_fixed() {
-        let tok = crate::tokenizer::load_tokenizer("nvidia/Kimi-K2.5-NVFP4", false, None)
-            .await
-            .unwrap();
+    fn test_variable_turns_fixed() {
+        let tok = crate::tokenizer::load_tokenizer("nvidia/Kimi-K2.5-NVFP4", false, None).unwrap();

        let cfg = MultiTurnRandomConfig {
            num_conversations: 10,
@@ -718,12 +709,10 @@ mod tests {
        println!("variable_turns_fixed checks passed!");
    }

-    #[tokio::test]
+    #[test]
    #[ignore]
-    async fn test_per_turn_input_len_prefix_sharing() {
-        let tok = crate::tokenizer::load_tokenizer("nvidia/Kimi-K2.5-NVFP4", false, None)
-            .await
-            .unwrap();
+    fn test_per_turn_input_len_prefix_sharing() {
+        let tok = crate::tokenizer::load_tokenizer("nvidia/Kimi-K2.5-NVFP4", false, None).unwrap();

        // Turn 0 input_len=1000, turns 1+ per_turn_input_len=600
        // global_len ≈ 100 (10%), conv_len ≈ 800 (80%), unique ≈ 100
@@ -41,13 +41,11 @@ pub fn generate_prefix_repetition_dataset(
    }
    let total = prompts_per_prefix * num_prefixes;
    if total != num_requests {
-        tracing::info!(
-            requested = num_requests,
-            generated = total,
-            prefixes = num_prefixes,
-            prompts_per_prefix,
-            dropped = num_requests - total,
-            "adjusted prefix-repetition request count"
+        println!(
+            "prefix_repetition: generating {total} requests \
+             ({num_prefixes} prefixes x {prompts_per_prefix} prompts each; \
+             {} dropped to divide evenly)",
+            num_requests - total
        );
    }

@@ -111,10 +109,8 @@ mod tests {

    /// gpt2 via built-in tiktoken encoding — loads without network access.
    fn test_tokenizer() -> TokenizerKind {
-        TokenizerKind::Tiktoken(
-            crate::tiktoken::load_builtin_tiktoken("gpt2")
-                .expect("gpt2 built-in tiktoken should always load without network"),
-        )
+        crate::tokenizer::load_tokenizer("gpt2", false, None)
+            .expect("gpt2 built-in tiktoken should always load without network")
    }

    #[test]
@@ -1,77 +0,0 @@
-// SPDX-License-Identifier: Apache-2.0
-// SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-use std::time::{Duration, Instant};
-
-use indicatif::{ProgressBar, ProgressStyle};
-
-const REPORT_INTERVAL: Duration = Duration::from_secs(10);
-
-/// Reports row download progress to an interactive progress bar, or through
-/// periodic tracing events when the progress bar is hidden on a non-TTY.
-pub(super) struct RowDownloadReporter {
-    progress: ProgressBar,
-    next_report: Instant,
-}
-
-impl RowDownloadReporter {
-    /// Creates a reporter that emits non-TTY updates every 10 seconds.
-    pub fn new() -> Self {
-        let progress = ProgressBar::new(0);
-        progress.set_style(
-            ProgressStyle::with_template(
-                "{spinner:.green} Fetching rows [{bar:30.cyan/blue}] {pos}/{len}",
-            )
-            .unwrap()
-            .progress_chars("#>-"),
-        );
-        Self {
-            progress,
-            next_report: Instant::now() + REPORT_INTERVAL,
-        }
-    }
-
-    /// Updates the current row count and reports progress when due.
-    pub fn update(&mut self, rows: usize, total: u64) {
-        let rows = rows as u64;
-        let total = total.max(rows);
-        self.progress.set_length(total);
-        self.progress.set_position(rows);
-
-        if self.should_report(Instant::now()) {
-            tracing::info!(rows, total, "fetching dataset rows");
-        }
-    }
-
-    /// Clears the interactive progress bar after the download completes.
-    pub fn finish(self) {
-        self.progress.finish_and_clear();
-    }
-
-    fn should_report(&mut self, now: Instant) -> bool {
-        if !self.progress.is_hidden() || now < self.next_report {
-            return false;
-        }
-        self.next_report = now + REPORT_INTERVAL;
-        true
-    }
-}
-
-#[cfg(test)]
-mod tests {
-    use super::*;
-
-    #[test]
-    fn hidden_reporter_uses_ten_second_deadline() {
-        let start = Instant::now();
-        let mut reporter = RowDownloadReporter {
-            progress: ProgressBar::hidden(),
-            next_report: start + REPORT_INTERVAL,
-        };
-
-        assert!(!reporter.should_report(start + Duration::from_secs(9)));
-        assert!(reporter.should_report(start + Duration::from_secs(10)));
-        assert!(!reporter.should_report(start + Duration::from_secs(19)));
-        assert!(reporter.should_report(start + Duration::from_secs(20)));
-    }
-}
@@ -49,12 +49,9 @@ pub fn generate_random_dataset(
    let (input_low, input_high) = range_ratio.input_bounds(real_input_len);
    let (output_low, output_high) = range_ratio.output_bounds(output_len);
    if !range_ratio.is_fixed() {
-        tracing::info!(
-            input_low,
-            input_high,
-            output_low,
-            output_high,
-            "sampling random request lengths"
+        println!(
+            "Sampling input_len from [{input_low}, {input_high}] and \
+             output_len from [{output_low}, {output_high}]"
        );
    }

@@ -308,8 +305,7 @@ mod tests {
    #[test]
    #[ignore]
    fn test_generate_random_dataset_token_ids() {
-        let tokenizer =
-            TokenizerKind::Tiktoken(crate::tiktoken::load_builtin_tiktoken("gpt2").unwrap());
+        let tokenizer = tokenizer::load_tokenizer("gpt2", false, None).unwrap();
        let requests = generate_random_dataset(
            &tokenizer,
            10,  // num_requests
@@ -341,8 +337,7 @@ mod tests {
    #[test]
    #[ignore]
    fn test_generate_random_dataset_text() {
-        let tokenizer =
-            TokenizerKind::Tiktoken(crate::tiktoken::load_builtin_tiktoken("gpt2").unwrap());
+        let tokenizer = tokenizer::load_tokenizer("gpt2", false, None).unwrap();
        let requests = generate_random_dataset(
            &tokenizer,
            10,  // num_requests
@@ -376,8 +371,7 @@ mod tests {
    #[test]
    #[ignore]
    fn test_token_length_exact_local() {
-        let tokenizer =
-            TokenizerKind::Tiktoken(crate::tiktoken::load_builtin_tiktoken("gpt2").unwrap());
+        let tokenizer = tokenizer::load_tokenizer("gpt2", false, None).unwrap();
        let target_len = 512;
        let requests = generate_random_dataset(
            &tokenizer,
@@ -411,11 +405,11 @@ mod tests {
    }

    /// Test that tiktoken tokenizer produces exact target token lengths (token ID mode).
-    #[tokio::test]
+    #[test]
    #[ignore]
-    async fn test_token_length_exact_tiktoken() {
+    fn test_token_length_exact_tiktoken() {
        // Use Qwen2.5 which has a tiktoken-format tokenizer
-        let tokenizer = tokenizer::load_tokenizer("Qwen/Qwen2.5-0.5B", false, None).await;
+        let tokenizer = tokenizer::load_tokenizer("Qwen/Qwen2.5-0.5B", false, None);
        let tokenizer = match tokenizer {
            Ok(t) => t,
            Err(e) => {
@@ -459,10 +453,10 @@ mod tests {

    /// Test encode/decode roundtrip stability for tiktoken.
    /// After one decode→encode cycle with UTF-8-safe tokens, length must not drift.
-    #[tokio::test]
+    #[test]
    #[ignore]
-    async fn test_tiktoken_roundtrip_stability() {
-        let tokenizer = tokenizer::load_tokenizer("Qwen/Qwen2.5-0.5B", false, None).await;
+    fn test_tiktoken_roundtrip_stability() {
+        let tokenizer = tokenizer::load_tokenizer("Qwen/Qwen2.5-0.5B", false, None);
        let tokenizer = match tokenizer {
            Ok(t) => t,
            Err(e) => {
@@ -139,10 +139,8 @@ mod tests {

    /// gpt2 via built-in tiktoken encoding — loads without network access.
    fn test_tokenizer() -> TokenizerKind {
-        TokenizerKind::Tiktoken(
-            crate::tiktoken::load_builtin_tiktoken("gpt2")
-                .expect("gpt2 built-in tiktoken should always load without network"),
-        )
+        crate::tokenizer::load_tokenizer("gpt2", false, None)
+            .expect("gpt2 built-in tiktoken should always load without network")
    }

    fn fixed_ratio() -> RangeRatio {
@@ -22,21 +22,18 @@ const DEFAULT_SHAREGPT_FILE: &str = "ShareGPT_V3_unfiltered_cleaned_split.json";

 /// Download the default ShareGPT dataset from HuggingFace Hub.
 /// Uses hf-hub's built-in cache — subsequent calls return the cached path instantly.
-pub async fn download_sharegpt_dataset() -> Result<String> {
-    tracing::info!(
-        repository = DEFAULT_SHAREGPT_REPO,
-        file = DEFAULT_SHAREGPT_FILE,
-        "downloading ShareGPT dataset"
+pub fn download_sharegpt_dataset() -> Result<String> {
+    println!(
+        "Downloading ShareGPT dataset from {DEFAULT_SHAREGPT_REPO}/{DEFAULT_SHAREGPT_FILE} ..."
    );
-    let repo = crate::hub::HubRepo::dataset(DEFAULT_SHAREGPT_REPO.to_string())
-        .map_err(BenchError::Config)?;
-    let path = repo.get(DEFAULT_SHAREGPT_FILE).await.map_err(|e| {
+    let repo = crate::hub::HubRepo::dataset(DEFAULT_SHAREGPT_REPO.to_string());
+    let path = repo.get(DEFAULT_SHAREGPT_FILE).map_err(|e| {
        BenchError::Config(format!(
            "Failed to download ShareGPT dataset from '{DEFAULT_SHAREGPT_REPO}': {e}"
        ))
    })?;
    let path_str = path.to_string_lossy().to_string();
-    tracing::info!(dataset = "sharegpt", path = %path_str, "dataset is ready");
+    println!("ShareGPT dataset ready: {path_str}");
    Ok(path_str)
 }

@@ -138,11 +135,9 @@ pub fn load_sharegpt_dataset(
    // Oversample if dataset is smaller than requested
    if samples.len() < num_requests {
        if no_oversample {
-            tracing::info!(
-                dataset = "sharegpt",
-                samples = samples.len(),
-                requested = num_requests,
-                "skipping dataset oversampling"
+            println!(
+                "Skipping oversampling. Total samples: {} (requested: {num_requests})",
+                samples.len()
            );
        } else if !samples.is_empty() {
            let needed = num_requests - samples.len();
@@ -152,11 +147,9 @@ pub fn load_sharegpt_dataset(
                req.request_id = Some(format!("{request_id_prefix}{}", original_len + i));
                samples.push(req);
            }
-            tracing::info!(
-                dataset = "sharegpt",
-                original_samples = original_len,
-                samples = samples.len(),
-                "oversampled dataset"
+            println!(
+                "Oversampled requests from {original_len} to {} total samples.",
+                samples.len()
            );
        }
    }
@@ -8,7 +8,6 @@ use rand::seq::SliceRandom;
 use rand::{Rng, SeedableRng};

 use super::SampleRequest;
-use super::progress::RowDownloadReporter;
 use crate::cli::SpeedBenchConfig;
 use crate::error::{BenchError, Result};
 use crate::tokenizer::TokenizerKind;
@@ -26,7 +25,7 @@ fn cache_dir() -> std::path::PathBuf {

 /// Download SPEED-Bench dataset from HuggingFace datasets-server API.
 /// Results are cached as JSON locally for subsequent runs.
-pub async fn download_speed_bench(config: SpeedBenchConfig) -> Result<String> {
+pub fn download_speed_bench(config: SpeedBenchConfig) -> Result<String> {
    let config_name = config.as_str();

    let dir = cache_dir();
@@ -36,13 +35,13 @@ pub async fn download_speed_bench(config: SpeedBenchConfig) -> Result<String> {
    // Return cached file if it exists
    if cache_path.exists() {
        let path_str = cache_path.to_string_lossy().to_string();
-        tracing::info!(config = config_name, path = %path_str, "using cached SPEED-Bench dataset");
+        println!("SPEED-Bench ({config_name}) cached: {path_str}");
        return Ok(path_str);
    }

-    tracing::info!(config = config_name, "downloading SPEED-Bench dataset");
+    println!("Downloading SPEED-Bench ({config_name}) from HuggingFace datasets-server...");

-    let client = reqwest::Client::builder()
+    let client = reqwest::blocking::Client::builder()
        .timeout(std::time::Duration::from_secs(120))
        .build()
        .map_err(|e| BenchError::Config(format!("Failed to build HTTP client: {e}")))?;
@@ -50,7 +49,6 @@ pub async fn download_speed_bench(config: SpeedBenchConfig) -> Result<String> {
    let mut all_rows: Vec<serde_json::Value> = Vec::new();
    let mut offset = 0usize;
    let page_size = 100usize;
-    let mut progress = RowDownloadReporter::new();

    loop {
        let url = format!(
@@ -66,14 +64,13 @@ pub async fn download_speed_bench(config: SpeedBenchConfig) -> Result<String> {
        let max_retries = 3;
        let mut data: Option<serde_json::Value> = None;
        for attempt in 0..=max_retries {
-            let resp = match client.get(&url).send().await {
+            let resp = match client.get(&url).send() {
                Ok(r) => r,
                Err(e) => {
                    if attempt < max_retries {
-                        tokio::time::sleep(std::time::Duration::from_secs(
+                        std::thread::sleep(std::time::Duration::from_secs(
                            2 * (attempt as u64 + 1),
-                        ))
-                        .await;
+                        ));
                        continue;
                    }
                    return Err(BenchError::Config(format!(
@@ -83,7 +80,7 @@ pub async fn download_speed_bench(config: SpeedBenchConfig) -> Result<String> {
            };

            if resp.status().is_server_error() && attempt < max_retries {
-                tokio::time::sleep(std::time::Duration::from_secs(2 * (attempt as u64 + 1))).await;
+                std::thread::sleep(std::time::Duration::from_secs(2 * (attempt as u64 + 1)));
                continue;
            }

@@ -94,7 +91,7 @@ pub async fn download_speed_bench(config: SpeedBenchConfig) -> Result<String> {
                )));
            }

-            data = Some(resp.json().await.map_err(|e| {
+            data = Some(resp.json().map_err(|e| {
                BenchError::Config(format!("Failed to parse SPEED-Bench API response: {e}"))
            })?);
            break;
@@ -119,14 +116,15 @@ pub async fn download_speed_bench(config: SpeedBenchConfig) -> Result<String> {
        let fetched = rows.len();
        offset += fetched;

+        // Print progress
        let total = data["num_rows_total"].as_u64().unwrap_or(0);
-        progress.update(offset, total);
+        eprint!("\r  Fetched {offset}/{total} rows...");

        if fetched < page_size {
            break;
        }
    }
-    progress.finish();
+    eprintln!(); // newline after progress

    if all_rows.is_empty() {
        return Err(BenchError::Config(
@@ -139,11 +137,9 @@ pub async fn download_speed_bench(config: SpeedBenchConfig) -> Result<String> {
    std::fs::write(&cache_path, &json_str)?;

    let path_str = cache_path.to_string_lossy().to_string();
-    tracing::info!(
-        config = config_name,
-        rows = all_rows.len(),
-        path = %path_str,
-        "saved SPEED-Bench dataset"
+    println!(
+        "SPEED-Bench ({config_name}): {} rows saved to {path_str}",
+        all_rows.len()
    );
    Ok(path_str)
 }
@@ -267,11 +263,9 @@ pub fn load_speed_bench_dataset(
    // Oversample if needed
    if samples.len() < num_requests {
        if no_oversample {
-            tracing::info!(
-                dataset = "speed-bench",
-                samples = samples.len(),
-                requested = num_requests,
-                "skipping dataset oversampling"
+            println!(
+                "Skipping oversampling. Total samples: {} (requested: {num_requests})",
+                samples.len()
            );
        } else if !samples.is_empty() {
            let original_len = samples.len();
@@ -281,11 +275,9 @@ pub fn load_speed_bench_dataset(
                req.request_id = Some(format!("{request_id_prefix}{}", original_len + i));
                samples.push(req);
            }
-            tracing::info!(
-                dataset = "speed-bench",
-                original_samples = original_len,
-                samples = samples.len(),
-                "oversampled dataset"
+            println!(
+                "Oversampled SPEED-Bench from {original_len} to {} total samples.",
+                samples.len()
            );
        }
    }
@@ -296,6 +288,7 @@ pub fn load_speed_bench_dataset(
        ));
    }

+    // Print category distribution
    let mut cat_counts: std::collections::HashMap<&str, usize> = std::collections::HashMap::new();
    for entry in &filtered[..filtered.len().min(samples.len())] {
        let cat = entry.get("category").and_then(|c| c.as_str()).unwrap_or("unknown");
@@ -304,7 +297,7 @@ pub fn load_speed_bench_dataset(
    let mut cats: Vec<_> = cat_counts.into_iter().collect();
    cats.sort_by_key(|b| std::cmp::Reverse(b.1));
    let cat_str: Vec<String> = cats.iter().map(|(k, v)| format!("{k}:{v}")).collect();
-    tracing::info!(categories = %cat_str.join(", "), "computed SPEED-Bench category distribution");
+    println!("SPEED-Bench categories: {}", cat_str.join(", "));

    Ok(samples)
 }
@@ -1,39 +1,51 @@
 // SPDX-License-Identifier: Apache-2.0
 // SPDX-FileCopyrightText: Copyright contributors to the vLLM project

-use std::path::PathBuf;
+//! Sync facade over the async `hf_hub` API.
+//!
+//! The workspace bans rustls (`rust/deny.toml`), but hf-hub's sync `ureq`
+//! backend unconditionally pulls ureq's default rustls feature. So we use the
+//! reqwest/native-tls tokio API instead, and bridge blocking callers (dataset
+//! loaders, tokenizer fallback in rayon threads) by running each download on a
+//! dedicated thread with its own single-threaded runtime.

-use hf_hub::Repo;
-use hf_hub::api::tokio::{ApiBuilder, ApiRepo};
+use std::path::PathBuf;

 /// A handle to a HuggingFace Hub repo, downloading via hf-hub's on-disk cache.
 pub struct HubRepo {
-    repo: ApiRepo,
+    repo: hf_hub::Repo,
 }

 impl HubRepo {
-    pub fn model(model_id: String) -> Result<Self, String> {
-        Self::new(Repo::model(model_id))
+    pub fn model(model_id: String) -> Self {
+        Self {
+            repo: hf_hub::Repo::model(model_id),
+        }
    }

-    pub fn dataset(repo_id: String) -> Result<Self, String> {
-        Self::new(Repo::dataset(repo_id))
-    }
-
-    fn new(repo: Repo) -> Result<Self, String> {
-        let mut builder = ApiBuilder::from_env();
-        if let Ok(token) = std::env::var("HF_TOKEN") {
-            builder = builder.with_token(Some(token));
+    pub fn dataset(repo_id: String) -> Self {
+        Self {
+            repo: hf_hub::Repo::dataset(repo_id),
        }
-        let api = builder.build().map_err(|e| format!("Failed to init HF API: {e}"))?;
-        Ok(Self {
-            repo: api.repo(repo),
-        })
    }

    /// Download (or fetch from cache) a single file from the repo.
    /// Auth is handled by hf-hub via HF_TOKEN / the cached login token.
-    pub async fn get(&self, filename: &str) -> Result<PathBuf, String> {
-        self.repo.get(filename).await.map_err(|e| format!("{e}"))
+    pub fn get(&self, filename: &str) -> Result<PathBuf, String> {
+        let repo = self.repo.clone();
+        let filename = filename.to_string();
+        std::thread::spawn(move || {
+            let rt = tokio::runtime::Builder::new_current_thread()
+                .enable_all()
+                .build()
+                .map_err(|e| format!("Failed to build download runtime: {e}"))?;
+            rt.block_on(async move {
+                let api = hf_hub::api::tokio::Api::new()
+                    .map_err(|e| format!("Failed to init HF API: {e}"))?;
+                api.repo(repo).get(&filename).await.map_err(|e| format!("{e}"))
+            })
+        })
+        .join()
+        .map_err(|_| "HF Hub download thread panicked".to_string())?
    }
 }
@@ -1,86 +0,0 @@
-// SPDX-License-Identifier: Apache-2.0
-// SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-mod backends;
-mod benchmark;
-mod cli;
-mod compare;
-mod config;
-mod datasets;
-mod error;
-mod hub;
-mod metrics;
-mod multi_run;
-mod multi_turn;
-mod output;
-mod rate_control;
-mod ready_checker;
-mod sweep;
-mod tiktoken;
-mod tokenizer;
-
-use anyhow::Context;
-
-pub use cli::{
-    BackendKind, BenchServeArgs, DatasetName, LoraAssignment, RampUpStrategy, SpeedBenchConfig,
-};
-use config::BenchConfig;
-
-/// Prepare process-wide resources for a benchmark run.
-pub fn prepare_process() {
-    // Raise the open-file soft limit to the hard limit. High-concurrency
-    // benchmarks (1024+ requests) easily exceed the default 1024 fd soft limit.
-    if let Ok(new) = rlimit::increase_nofile_limit(u64::MAX)
-        && new > 1024
-    {
-        tracing::info!(soft_limit = new, "raised open-file limit");
-    }
-}
-
-/// Run the online serving benchmark.
-pub async fn run(args: BenchServeArgs) -> anyhow::Result<()> {
-    // --- Compare mode: no server needed, just diff two JSON files ---
-    if let Some(ref files) = args.compare {
-        return compare::compare_results(&files[0], &files[1]).context("Comparison failed");
-    }
-
-    let config = BenchConfig::from_args(&args).context("Configuration error")?;
-
-    async {
-        if config.multi_turn {
-            if let Some(ref sweep_mc) = args.sweep_max_concurrency {
-                // --- Sweep over concurrency in multi-turn mode ---
-                let values = sweep::parse_concurrency_values(sweep_mc)
-                    .context("Invalid --sweep-max-concurrency")?;
-                sweep::run_multi_turn_concurrency_sweep(
-                    &config,
-                    &values,
-                    args.sweep_num_prompts_factor,
-                )
-                .await?;
-            } else {
-                // --- Single multi-turn conversation benchmark ---
-                multi_turn::run_multi_turn_benchmark(&config).await?;
-            }
-        } else if let Some(ref sweep_mc) = args.sweep_max_concurrency {
-            // --- Sweep over max-concurrency ---
-            let values = sweep::parse_concurrency_values(sweep_mc)
-                .context("Invalid --sweep-max-concurrency")?;
-            sweep::run_concurrency_sweep(&config, &values, args.sweep_num_prompts_factor).await?;
-        } else if let Some(ref sweep_rate) = args.sweep_request_rate {
-            // --- Sweep over request-rate ---
-            let values =
-                sweep::parse_rate_values(sweep_rate).context("Invalid --sweep-request-rate")?;
-            sweep::run_rate_sweep(&config, &values).await?;
-        } else if args.num_runs > 1 {
-            // --- Multi-run with statistical aggregation ---
-            multi_run::run_multi(&config, args.num_runs).await?;
-        } else {
-            // --- Normal single benchmark ---
-            benchmark::run_benchmark(&config).await?;
-        }
-        anyhow::Ok(())
-    }
-    .await
-    .context("Benchmark failed")
-}
@@ -1,44 +1,92 @@
 // SPDX-License-Identifier: Apache-2.0
 // SPDX-FileCopyrightText: Copyright contributors to the vLLM project

+mod backends;
+mod benchmark;
+mod cli;
+mod compare;
+mod config;
+mod datasets;
+mod error;
+mod hub;
+mod metrics;
+mod multi_run;
+mod multi_turn;
+mod output;
+mod rate_control;
+mod ready_checker;
+mod sweep;
+mod tiktoken;
+mod tokenizer;
+
 #[cfg(not(target_env = "msvc"))]
 #[global_allocator]
 static GLOBAL: mimalloc::MiMalloc = mimalloc::MiMalloc;

 use anyhow::Context;
 use clap::Parser;
-
-#[derive(Parser)]
-#[command(
-    name = "vllm-bench",
-    about = "Benchmark online serving throughput",
-    version
-)]
-struct Cli {
-    #[command(flatten)]
-    args: vllm_bench::BenchServeArgs,
-}
-
-// TODO: unify the tracing subscriber used by different binaries.
-fn init_tracing() {
-    let filter = tracing_subscriber::EnvFilter::try_from_default_env()
-        .unwrap_or_else(|_| tracing_subscriber::EnvFilter::new("info"));
-    let _ = tracing_subscriber::fmt()
-        .with_env_filter(filter)
-        .with_writer(std::io::stderr)
-        .try_init();
-}
+use cli::Cli;
+use config::BenchConfig;

 fn main() -> anyhow::Result<()> {
-    init_tracing();
+    // Raise the open-file soft limit to the hard limit. High-concurrency
+    // benchmarks (1024+ requests) easily exceed the default 1024 fd soft limit.
+    if let Ok(new) = rlimit::increase_nofile_limit(u64::MAX)
+        && new > 1024
+    {
+        eprintln!("Open-file limit: {new}");
+    }

    let cli = Cli::parse();
-    vllm_bench::prepare_process();
+
+    // --- Compare mode: no server needed, just diff two JSON files ---
+    if let Some(ref files) = cli.compare {
+        return compare::compare_results(&files[0], &files[1]).context("Comparison failed");
+    }
+
+    let config = BenchConfig::from_cli(&cli).context("Configuration error")?;

    let runtime = tokio::runtime::Builder::new_multi_thread()
        .enable_all()
        .build()
-        .context("Failed to build tokio runtime")?;
+        .expect("Failed to build tokio runtime");

-    runtime.block_on(vllm_bench::run(cli.args))
+    runtime
+        .block_on(async {
+            if config.multi_turn {
+                if let Some(ref sweep_mc) = cli.sweep_max_concurrency {
+                    // --- Sweep over concurrency in multi-turn mode ---
+                    let values = sweep::parse_concurrency_values(sweep_mc)
+                        .context("Invalid --sweep-max-concurrency")?;
+                    sweep::run_multi_turn_concurrency_sweep(
+                        &config,
+                        &values,
+                        cli.sweep_num_prompts_factor,
+                    )
+                    .await?;
+                } else {
+                    // --- Single multi-turn conversation benchmark ---
+                    multi_turn::run_multi_turn_benchmark(&config).await?;
+                }
+            } else if let Some(ref sweep_mc) = cli.sweep_max_concurrency {
+                // --- Sweep over max-concurrency ---
+                let values = sweep::parse_concurrency_values(sweep_mc)
+                    .context("Invalid --sweep-max-concurrency")?;
+                sweep::run_concurrency_sweep(&config, &values, cli.sweep_num_prompts_factor)
+                    .await?;
+            } else if let Some(ref sweep_rate) = cli.sweep_request_rate {
+                // --- Sweep over request-rate ---
+                let values =
+                    sweep::parse_rate_values(sweep_rate).context("Invalid --sweep-request-rate")?;
+                sweep::run_rate_sweep(&config, &values).await?;
+            } else if cli.num_runs > 1 {
+                // --- Multi-run with statistical aggregation ---
+                multi_run::run_multi(&config, cli.num_runs).await?;
+            } else {
+                // --- Normal single benchmark ---
+                benchmark::run_benchmark(&config).await?;
+            }
+            anyhow::Ok(())
+        })
+        .context("Benchmark failed")
 }
@@ -7,22 +7,6 @@ use crate::datasets::SampleRequest;
 use crate::metrics::{BenchmarkMetrics, MultiTurnMetrics};
 use crate::multi_turn::ConversationOutput;

-fn log_failed_requests(outputs: &[RequestFuncOutput]) {
-    let failed_outputs: Vec<_> = outputs.iter().filter(|output| !output.success).collect();
-    if failed_outputs.is_empty() {
-        return;
-    }
-
-    tracing::warn!(
-        failed_requests = failed_outputs.len(),
-        displayed_errors = failed_outputs.len().min(10),
-        "benchmark requests failed"
-    );
-    for (index, output) in failed_outputs.into_iter().take(10).enumerate() {
-        tracing::warn!(index, error = %output.error, "benchmark request failed");
-    }
-}
-
 /// Calculate benchmark metrics from request outputs.
 ///
 /// Mirrors Python's `calculate_metrics()` from serve.py:392-599.
@@ -79,7 +63,14 @@ pub fn calculate_metrics(

    let failed = outputs.len() - completed;

-    log_failed_requests(outputs);
+    // Print failed request errors (capped to 10)
+    let failed_outputs: Vec<&RequestFuncOutput> = outputs.iter().filter(|o| !o.success).collect();
+    if !failed_outputs.is_empty() {
+        eprintln!("Failed requests during benchmark run detected (capping to 10):");
+        for (i, err) in failed_outputs.iter().take(10).enumerate() {
+            eprintln!("Error {i}: {}", err.error);
+        }
+    }

    // Calculate max output tokens per second and max concurrent requests
    let mut max_output_tokens_per_s = 0.0_f64;
@@ -304,7 +295,14 @@ pub fn calculate_embedding_metrics(

    let failed = outputs.len() - completed;

-    log_failed_requests(outputs);
+    // Print failed request errors (capped to 10)
+    let failed_outputs: Vec<&RequestFuncOutput> = outputs.iter().filter(|o| !o.success).collect();
+    if !failed_outputs.is_empty() {
+        eprintln!("Failed requests during benchmark run detected (capping to 10):");
+        for (i, err) in failed_outputs.iter().take(10).enumerate() {
+            eprintln!("Error {i}: {}", err.error);
+        }
+    }

    // Compute peak concurrent requests from start_time + latency windows
    let successful_outputs: Vec<&RequestFuncOutput> =
@@ -6,7 +6,6 @@ use std::sync::Arc;
 use std::time::Instant;

 use indicatif::{ProgressBar, ProgressStyle};
-use thiserror_ext::AsReport as _;
 use tokio::sync::Semaphore;

 use crate::backends::{Backend, RequestFuncInput, RequestFuncOutput, get_backend};
@@ -73,13 +72,9 @@ pub async fn run_multi_turn_benchmark(config: &BenchConfig) -> Result<serde_json
    let (model_id, model_name) = if let Some(ref m) = config.model {
        (m.clone(), config.model_name.clone())
    } else {
-        tracing::info!(base_url = %config.base_url, "fetching first model from server");
+        println!("Model not specified, fetching first model from server...");
        let (name, id) = get_first_model(&config.base_url, &client, &config.extra_headers).await?;
-        tracing::info!(
-            model_name = name,
-            model_id = id,
-            "selected first model from server"
-        );
+        println!("First model name: {name}, first model id: {id}");
        (id, Some(name))
    };

@@ -88,19 +83,15 @@ pub async fn run_multi_turn_benchmark(config: &BenchConfig) -> Result<serde_json
        None
    } else {
        let tid = config.tokenizer_id.as_deref().unwrap_or(&model_id);
-        tracing::info!(tokenizer = tid, "loading tokenizer");
+        println!("Loading tokenizer: {tid}");
        let server_info = Some((config.base_url.as_str(), model_id.as_str()));
-        let t =
-            crate::tokenizer::load_tokenizer(tid, config.trust_remote_code, server_info).await?;
+        let t = crate::tokenizer::load_tokenizer(tid, config.trust_remote_code, server_info)?;
+        println!("Tokenizer loaded successfully.");
        Some(t)
    };

    // Generate/load conversations
-    tracing::info!(
-        dataset = ?config.dataset_name,
-        conversations = config.num_prompts,
-        "generating multi-turn conversations"
-    );
+    println!("Generating multi-turn conversations...");
    let gen_start = Instant::now();

    let mut conversations = match config.dataset_name {
@@ -140,7 +131,7 @@ pub async fn run_multi_turn_benchmark(config: &BenchConfig) -> Result<serde_json
            let path = match config.dataset_path.as_deref() {
                Some(p) => p,
                None => {
-                    downloaded = crate::datasets::sharegpt::download_sharegpt_dataset().await?;
+                    downloaded = crate::datasets::sharegpt::download_sharegpt_dataset()?;
                    downloaded.as_str()
                }
            };
@@ -188,11 +179,8 @@ pub async fn run_multi_turn_benchmark(config: &BenchConfig) -> Result<serde_json
        let (filtered_conversations, filtered_turns) =
            filter_turns_by_max_model_len(&mut conversations, max_model_len, no_history);
        if filtered_turns > 0 || filtered_conversations > 0 {
-            tracing::info!(
-                filtered_turns,
-                filtered_conversations,
-                max_model_len,
-                "filtered conversations above maximum model length"
+            println!(
+                "Filtered {filtered_turns} turn(s) and {filtered_conversations} conversation(s) above --max-model-len {max_model_len}."
            );
        }
        if conversations.is_empty() {
@@ -204,11 +192,11 @@ pub async fn run_multi_turn_benchmark(config: &BenchConfig) -> Result<serde_json

    let gen_elapsed = gen_start.elapsed();
    let total_turns: usize = conversations.iter().map(|c| c.turns.len()).sum();
-    tracing::info!(
-        conversations = conversations.len(),
+    println!(
+        "Generated {} conversations ({} total turns) in {:.2}s",
+        conversations.len(),
        total_turns,
-        elapsed_seconds = gen_elapsed.as_secs_f64(),
-        "generated multi-turn conversations"
+        gen_elapsed.as_secs_f64()
    );

    // Log prefix sharing info
@@ -220,15 +208,18 @@ pub async fn run_multi_turn_benchmark(config: &BenchConfig) -> Result<serde_json
        let conv_tokens =
            (real_input_len as f64 * config.multi_turn_prefix_conversation_ratio).floor() as usize;
        let unique_tokens = real_input_len.saturating_sub(global_tokens + conv_tokens);
-        tracing::info!(
-            global_ratio = config.multi_turn_prefix_global_ratio,
+        println!(
+            "User message prefix sharing: {:.0}% global ({} tokens), {:.0}% per-conversation ({} tokens), {:.0}% unique ({} tokens)",
+            config.multi_turn_prefix_global_ratio * 100.0,
            global_tokens,
-            conversation_ratio = config.multi_turn_prefix_conversation_ratio,
-            conversation_tokens = conv_tokens,
+            config.multi_turn_prefix_conversation_ratio * 100.0,
+            conv_tokens,
+            (1.0 - config.multi_turn_prefix_global_ratio
+                - config.multi_turn_prefix_conversation_ratio)
+                * 100.0,
            unique_tokens,
-            history_accumulation = false,
-            "configured multi-turn prefix sharing"
        );
+        println!("No history accumulation: each turn sends fixed-length prompt only.");
    }

    if config.dry_run {
@@ -262,7 +253,7 @@ pub async fn run_multi_turn_benchmark(config: &BenchConfig) -> Result<serde_json
            ..Default::default()
        };

-        tracing::info!("starting initial single-prompt test run");
+        println!("Starting initial single prompt test run...");
        let test_output = crate::ready_checker::wait_for_endpoint(
            config.backend,
            &client,
@@ -277,7 +268,7 @@ pub async fn run_multi_turn_benchmark(config: &BenchConfig) -> Result<serde_json
                test_output.error
            )));
        }
-        tracing::info!("initial single-prompt test run completed");
+        println!("Initial test run completed.");
    }

    // For random datasets in multi-turn mode, auto-set min_tokens to enforce
@@ -292,10 +283,9 @@ pub async fn run_multi_turn_benchmark(config: &BenchConfig) -> Result<serde_json
                "min_tokens".to_string(),
                serde_json::json!(config.random_output_len),
            );
-            tracing::info!(
-                min_tokens = config.random_output_len,
-                dataset = "random",
-                "set minimum output tokens for multi-turn dataset"
+            println!(
+                "Auto-setting min_tokens={} for multi-turn random dataset (use --extra-body to override)",
+                config.random_output_len
            );
        }
        Some(body)
@@ -307,7 +297,7 @@ pub async fn run_multi_turn_benchmark(config: &BenchConfig) -> Result<serde_json
    let spec_decode_before =
        fetch_spec_decode_metrics(&config.base_url, &client, &config.extra_headers).await;
    if spec_decode_before.is_some() {
-        tracing::info!("detected speculative decoding; collecting metrics");
+        println!("Speculative decoding detected, will collect metrics.");
    }

    // Start profiler if requested (immediate mode — no batch threshold)
@@ -340,13 +330,10 @@ pub async fn run_multi_turn_benchmark(config: &BenchConfig) -> Result<serde_json
    };

    // Main benchmark
-    tracing::info!(
-        conversations = conversations.len(),
-        total_turns,
-        concurrency,
-        inter_turn_delay_ms = config.multi_turn_delay_ms,
-        "starting multi-turn benchmark"
-    );
+    println!("Starting multi-turn benchmark...");
+    println!("Conversations: {}", conversations.len());
+    println!("Concurrency: {concurrency}");
+    println!("Inter-turn delay: {} ms", config.multi_turn_delay_ms);

    let max_turn_count = conversations.iter().map(|c| c.turns.len()).max().unwrap_or(0);

@@ -377,12 +364,11 @@ pub async fn run_multi_turn_benchmark(config: &BenchConfig) -> Result<serde_json
    );
    if let Some(modules) = config.lora_modules.as_ref() {
        let names: Vec<&str> = modules.iter().map(|s| s.as_ref()).collect();
-        tracing::info!(
-            adapters = modules.len(),
-            names = ?names,
-            assignment = ?config.lora_assignment,
-            scope = "conversation",
-            "assigned LoRA adapters"
+        println!(
+            "LoRA adapters ({}): {:?} [assignment={:?}, scope=conversation]",
+            modules.len(),
+            names,
+            config.lora_assignment
        );
    }

@@ -447,7 +433,7 @@ pub async fn run_multi_turn_benchmark(config: &BenchConfig) -> Result<serde_json
        match handle.await {
            Ok(output) => all_outputs.push(output),
            Err(e) => {
-                tracing::error!(error = %e.as_report(), "conversation task panicked");
+                eprintln!("Conversation task panicked: {e}");
            }
        }
    }
@@ -467,7 +453,7 @@ pub async fn run_multi_turn_benchmark(config: &BenchConfig) -> Result<serde_json
    if let Some((cancel_tx, task)) = profile_task {
        let _ = cancel_tx.send(());
        if let Err(e) = task.await {
-            tracing::error!(error = %e.as_report(), "profiler background task failed");
+            eprintln!("WARNING: Profile background task failed: {e}");
        }
    }

@@ -603,7 +603,7 @@ fn add_metric_stats(
 pub fn save_result(json: &Value, file_path: &str) -> Result<()> {
    let content = serde_json::to_string(json)?;
    std::fs::write(file_path, content)?;
-    tracing::info!(path = file_path, "saved benchmark results");
+    println!("Results saved to {file_path}");
    Ok(())
 }

@@ -618,7 +618,7 @@ pub fn append_result(json: &Value, file_path: &str) -> Result<()> {
        file.write_all(b"\n")?;
    }
    file.write_all(content.as_bytes())?;
-    tracing::info!(path = file_path, "appended benchmark results");
+    println!("Results appended to {file_path}");
    Ok(())
 }

@@ -23,11 +23,7 @@ pub async fn wait_for_endpoint(
    let backend = get_backend(backend)?;
    let deadline = Instant::now() + std::time::Duration::from_secs(timeout_seconds);

-    tracing::info!(
-        timeout_seconds,
-        retry_interval,
-        "waiting for endpoint readiness"
-    );
+    println!("Waiting for endpoint to become up in {timeout_seconds}s");

    let pb = ProgressBar::new(timeout_seconds);
    pb.set_style(
@@ -57,9 +53,7 @@ pub async fn wait_for_endpoint(
            Ok(output) => {
                let err = output.error.clone();
                let err_last_line = err.lines().last().unwrap_or(&err);
-                pb.suspend(|| {
-                    tracing::warn!(error = err_last_line, "endpoint is not ready");
-                });
+                eprintln!("Endpoint is not ready. Error='{err_last_line}'");
                last_error = err;
            }
            Err(e) => {
@@ -16,7 +16,7 @@ async fn reset_prefix_cache(base_url: &str) -> Result<()> {
        .await
        .map_err(|e| BenchError::Backend(format!("Failed to reset prefix cache: {e}")))?;
    if resp.status().is_success() {
-        tracing::info!(url = %url, "reset prefix cache");
+        println!("Prefix cache reset successfully.");
    } else {
        let status = resp.status();
        let body = resp.text().await.unwrap_or_default();
@@ -199,17 +199,12 @@ pub fn load_builtin_tiktoken(encoding: &str) -> Result<TiktokenTokenizer> {
        }
    };
    let bpe = bpe.map_err(|e| BenchError::Tokenizer(format!("Failed to load {encoding}: {e}")))?;
-    tracing::info!(
-        encoding,
-        kind = "built-in-tiktoken",
-        vocab_size,
-        "loaded tokenizer"
-    );
+    println!("Tokenizer: Built-in tiktoken {encoding} (vocab_size={vocab_size})");
    Ok(TiktokenTokenizer::from_builtin_bpe(bpe, vocab_size))
 }

 /// Try to load a tiktoken tokenizer from a local directory or HuggingFace model repo.
-pub async fn try_load_tiktoken(model_id: &str) -> Result<TiktokenTokenizer> {
+pub fn try_load_tiktoken(model_id: &str) -> Result<TiktokenTokenizer> {
    // Phase 1: If model_id is a local directory, look for tiktoken files there
    let local_dir = Path::new(model_id);
    if local_dir.is_dir() {
@@ -217,7 +212,7 @@ pub async fn try_load_tiktoken(model_id: &str) -> Result<TiktokenTokenizer> {
    }

    // Phase 2: Fall back to HuggingFace Hub download
-    try_load_tiktoken_from_hf(model_id).await
+    try_load_tiktoken_from_hf(model_id)
 }

 /// Common tiktoken model filenames to search for.
@@ -252,28 +247,25 @@ fn try_load_tiktoken_from_dir(dir: &Path, model_id: &str) -> Result<TiktokenToke
 }

 /// Load a tiktoken tokenizer from a HuggingFace model repo.
-async fn try_load_tiktoken_from_hf(model_id: &str) -> Result<TiktokenTokenizer> {
-    let repo = crate::hub::HubRepo::model(model_id.to_string()).map_err(BenchError::Tokenizer)?;
+fn try_load_tiktoken_from_hf(model_id: &str) -> Result<TiktokenTokenizer> {
+    let repo = crate::hub::HubRepo::model(model_id.to_string());

-    let mut model_path = None;
-    for filename in TIKTOKEN_MODEL_FILENAMES {
-        if let Ok(path) = repo.get(filename).await {
-            model_path = Some(path);
-            break;
-        }
-    }
-    let model_path = model_path.ok_or_else(|| {
-        BenchError::Tokenizer(format!("No tiktoken model file found for '{model_id}'"))
-    })?;
+    let model_path = repo
+        .get("tiktoken.model")
+        .or_else(|_| repo.get("qwen.tiktoken"))
+        .or_else(|_| repo.get("vocab.tiktoken"))
+        .map_err(|_| {
+            BenchError::Tokenizer(format!("No tiktoken model file found for '{model_id}'"))
+        })?;

    let num_base_tokens = count_base_tokens(&model_path)?;

-    let config = match repo.get("tokenizer_config.json").await {
+    let config = match repo.get("tokenizer_config.json") {
        Ok(config_path) => read_tokenizer_config(&config_path),
        Err(_) => None,
    };

-    let pattern = extract_pat_str_from_repo(&repo).await;
+    let pattern = extract_pat_str_from_repo(&repo);

    build_tiktoken(model_id, &model_path, config, pattern, num_base_tokens)
 }
@@ -317,16 +309,15 @@ fn build_tiktoken(
        }
    }

-    tracing::info!(
-        model = model_id,
-        base_tokens = num_base_tokens,
-        special_tokens = all_special_tokens.len(),
-        pattern = if pattern.is_some() {
+    println!(
+        "Loading tiktoken model for '{model_id}' (base={}, special={}, pat={})...",
+        num_base_tokens,
+        all_special_tokens.len(),
+        if pattern.is_some() {
            "custom"
        } else {
            "default"
        },
-        "loading tiktoken model"
    );

    TiktokenTokenizer::from_file(
@@ -406,12 +397,9 @@ fn extract_pat_str_from_local_dir(dir: &Path) -> Option<String> {

 /// Try to download the Python tokenizer source file and extract pat_str via regex.
 /// Returns None if unavailable or unparsable.
-async fn extract_pat_str_from_repo(repo: &crate::hub::HubRepo) -> Option<String> {
+fn extract_pat_str_from_repo(repo: &crate::hub::HubRepo) -> Option<String> {
    // Try common Python tokenizer filenames
-    let py_path = match repo.get("tokenization_kimi.py").await {
-        Ok(path) => path,
-        Err(_) => repo.get("tokenizer.py").await.ok()?,
-    };
+    let py_path = repo.get("tokenization_kimi.py").or_else(|_| repo.get("tokenizer.py")).ok()?;

    let source = std::fs::read_to_string(&py_path).ok()?;

@@ -450,9 +438,9 @@ fn extract_pat_str_from_source(source: &str) -> Option<String> {

                if !fragments.is_empty() {
                    let pattern = fragments.join("|");
-                    tracing::debug!(
-                        fragments = fragments.len(),
-                        "extracted tiktoken pattern from Python source"
+                    println!(
+                        "Extracted pat_str from Python source: {} fragments",
+                        fragments.len()
                    );
                    return Some(pattern);
                }
@@ -2,10 +2,8 @@
 // SPDX-FileCopyrightText: Copyright contributors to the vLLM project

 use std::collections::HashSet;
-use std::future::Future;
 use std::path::Path;

-use thiserror_ext::AsReport as _;
 use tokenizers::Tokenizer;

 use crate::error::{BenchError, Result};
@@ -20,8 +18,7 @@ pub enum TokenizerKind {

 /// Server-side tokenizer using vLLM's /tokenize and /detokenize endpoints.
 pub struct ServerTokenizer {
-    client: reqwest::Client,
-    runtime: tokio::runtime::Handle,
+    client: reqwest::blocking::Client,
    tokenize_url: String,
    detokenize_url: String,
    model: String,
@@ -30,8 +27,8 @@ pub struct ServerTokenizer {

 impl ServerTokenizer {
    /// Create a new server tokenizer and verify connectivity.
-    pub async fn new(base_url: &str, model: &str) -> Result<Self> {
-        let client = reqwest::Client::builder()
+    pub fn new(base_url: &str, model: &str) -> Result<Self> {
+        let client = reqwest::blocking::Client::builder()
            .timeout(std::time::Duration::from_secs(30))
            .build()
            .map_err(|e| BenchError::Tokenizer(format!("Failed to build HTTP client: {e}")))?;
@@ -41,7 +38,6 @@ impl ServerTokenizer {

        let st = Self {
            client,
-            runtime: tokio::runtime::Handle::current(),
            tokenize_url,
            detokenize_url,
            model: model.to_string(),
@@ -49,7 +45,7 @@ impl ServerTokenizer {
        };

        // Probe the endpoint to verify it works and discover vocab size
-        let test_tokens = st.encode_async("test").await?;
+        let test_tokens = st.encode_inner("test")?;
        let max_id = test_tokens.iter().copied().max().unwrap_or(0);
        let estimated_vocab = (max_id * 2).max(131072);

@@ -60,10 +56,6 @@ impl ServerTokenizer {
    }

    fn encode_inner(&self, text: &str) -> Result<Vec<u32>> {
-        self.block_on(self.encode_async(text))
-    }
-
-    async fn encode_async(&self, text: &str) -> Result<Vec<u32>> {
        let payload = serde_json::json!({
            "model": self.model,
            "prompt": text,
@@ -74,7 +66,6 @@ impl ServerTokenizer {
            .post(&self.tokenize_url)
            .json(&payload)
            .send()
-            .await
            .map_err(|e| BenchError::Tokenizer(format!("Server tokenize failed: {e}")))?;

        if !resp.status().is_success() {
@@ -84,7 +75,7 @@ impl ServerTokenizer {
            )));
        }

-        let data: serde_json::Value = resp.json().await.map_err(|e| {
+        let data: serde_json::Value = resp.json().map_err(|e| {
            BenchError::Tokenizer(format!("Failed to parse tokenize response: {e}"))
        })?;

@@ -104,10 +95,6 @@ impl ServerTokenizer {
    }

    fn decode_inner(&self, ids: &[u32]) -> Result<String> {
-        self.block_on(self.decode_async(ids))
-    }
-
-    async fn decode_async(&self, ids: &[u32]) -> Result<String> {
        let payload = serde_json::json!({
            "model": self.model,
            "tokens": ids,
@@ -118,7 +105,6 @@ impl ServerTokenizer {
            .post(&self.detokenize_url)
            .json(&payload)
            .send()
-            .await
            .map_err(|e| BenchError::Tokenizer(format!("Server detokenize failed: {e}")))?;

        if !resp.status().is_success() {
@@ -128,7 +114,7 @@ impl ServerTokenizer {
            )));
        }

-        let data: serde_json::Value = resp.json().await.map_err(|e| {
+        let data: serde_json::Value = resp.json().map_err(|e| {
            BenchError::Tokenizer(format!("Failed to parse detokenize response: {e}"))
        })?;

@@ -137,26 +123,6 @@ impl ServerTokenizer {
            .map(|s| s.to_string())
            .ok_or_else(|| BenchError::Tokenizer("Missing 'prompt' in detokenize response".into()))
    }
-
-    fn block_on<T>(&self, future: impl Future<Output = Result<T>>) -> Result<T> {
-        if matches!(
-            self.runtime.runtime_flavor(),
-            tokio::runtime::RuntimeFlavor::CurrentThread
-        ) {
-            return Err(BenchError::Tokenizer(
-                "Server tokenizer fallback requires a multi-thread Tokio runtime".into(),
-            ));
-        }
-
-        // Sync tokenizer calls can come from a Tokio worker or a Rayon worker.
-        // Tokio workers must enter a blocking region before re-entering the runtime;
-        // Rayon workers can drive the future directly with the saved runtime handle.
-        if tokio::runtime::Handle::try_current().is_ok() {
-            tokio::task::block_in_place(|| self.runtime.block_on(future))
-        } else {
-            self.runtime.block_on(future)
-        }
-    }
 }

 // --- TokenizerKind methods ---
@@ -226,7 +192,7 @@ impl TokenizerKind {
 /// 3. Server-side /tokenize + /detokenize endpoints
 ///
 /// `server_info` is `Some((base_url, model))` to enable server-side fallback.
-pub async fn load_tokenizer(
+pub fn load_tokenizer(
    model_id: &str,
    _trust_remote_code: bool,
    server_info: Option<(&str, &str)>,
@@ -246,48 +212,31 @@ pub async fn load_tokenizer(
    }

    // 1. Try local HuggingFace tokenizer (tokenizer.json)
-    match try_load_local(model_id).await {
+    match try_load_local(model_id) {
        Ok(tok) => {
-            tracing::info!(
-                model = model_id,
-                kind = "local",
-                vocab_size = tok.get_vocab_size(true),
-                "loaded tokenizer"
-            );
+            println!("Tokenizer: Local (vocab_size={})", tok.get_vocab_size(true));
            Ok(TokenizerKind::Local(Box::new(tok)))
        }
        Err(local_err) => {
            // 2. Try tiktoken format
-            tracing::info!(
-                model = model_id,
-                error = %local_err.as_report(),
-                "local tokenizer unavailable; trying tiktoken"
-            );
-            match crate::tiktoken::try_load_tiktoken(model_id).await {
+            println!("No tokenizer.json for '{model_id}', trying tiktoken format...");
+            match crate::tiktoken::try_load_tiktoken(model_id) {
                Ok(tok) => {
-                    tracing::info!(
-                        model = model_id,
-                        kind = "tiktoken",
-                        vocab_size = tok.vocab_size(),
-                        "loaded tokenizer"
-                    );
+                    println!("Tokenizer: Tiktoken (vocab_size={})", tok.vocab_size());
                    Ok(TokenizerKind::Tiktoken(tok))
                }
                Err(tiktoken_err) => {
                    // 3. Try server-side fallback
                    if let Some((base_url, model)) = server_info {
-                        tracing::info!(
-                            model = model_id,
-                            error = %tiktoken_err.as_report(),
-                            "tiktoken unavailable; trying server-side tokenization"
+                        println!(
+                            "Tiktoken also not available ({tiktoken_err}), \
+                             trying server-side tokenization..."
                        );
-                        match ServerTokenizer::new(base_url, model).await {
+                        match ServerTokenizer::new(base_url, model) {
                            Ok(srv) => {
-                                tracing::info!(
-                                    model = model_id,
-                                    kind = "server",
-                                    vocab_size = srv.cached_vocab_size,
-                                    "loaded tokenizer"
+                                println!(
+                                    "Tokenizer: Server (vocab_size≈{})",
+                                    srv.cached_vocab_size
                                );
                                return Ok(TokenizerKind::Server(srv));
                            }
@@ -315,7 +264,7 @@ pub async fn load_tokenizer(
 }

 /// Try loading tokenizer.json from local path or HuggingFace Hub.
-async fn try_load_local(model_id: &str) -> Result<Tokenizer> {
+fn try_load_local(model_id: &str) -> Result<Tokenizer> {
    // 1. Try local directory with tokenizer.json
    let local_path = Path::new(model_id).join("tokenizer.json");
    if local_path.exists() {
@@ -341,37 +290,11 @@ async fn try_load_local(model_id: &str) -> Result<Tokenizer> {
    }

    // 4. Download from HuggingFace Hub (hf-hub handles auth via HF_TOKEN / cached token)
-    let repo = crate::hub::HubRepo::model(model_id.to_string()).map_err(BenchError::Tokenizer)?;
+    let repo = crate::hub::HubRepo::model(model_id.to_string());
    let tokenizer_path = repo
        .get("tokenizer.json")
-        .await
        .map_err(|e| BenchError::Tokenizer(format!("No tokenizer.json for '{model_id}': {e}")))?;

    Tokenizer::from_file(&tokenizer_path)
        .map_err(|e| BenchError::Tokenizer(format!("Failed to load downloaded tokenizer: {e}")))
 }
-
-#[cfg(test)]
-mod tests {
-    use super::*;
-
-    #[tokio::test(flavor = "multi_thread", worker_threads = 2)]
-    async fn test_server_tokenizer_sync_bridge() {
-        let tokenizer = std::sync::Arc::new(ServerTokenizer {
-            client: reqwest::Client::new(),
-            runtime: tokio::runtime::Handle::current(),
-            tokenize_url: String::new(),
-            detokenize_url: String::new(),
-            model: String::new(),
-            cached_vocab_size: 0,
-        });
-
-        assert_eq!(tokenizer.block_on(async { Ok(1) }).unwrap(), 1);
-
-        let (tx, rx) = tokio::sync::oneshot::channel();
-        rayon::spawn(move || {
-            let _ = tx.send(tokenizer.block_on(async { Ok(2) }));
-        });
-        assert_eq!(rx.await.unwrap().unwrap(), 2);
-    }
-}
@@ -38,7 +38,7 @@ pub(super) fn build_batched_items(
            let keep_on_cpu = spec.keep_on_cpu_keys.contains(key);
            let (value, field) = match spec.field_layout_for(key) {
                Some(FieldLayout::Batched) => (
-                    tensor.batched_wire_value_at(index)?,
+                    tensor.batched_value_at(index)?,
                    MmField::Batched(MmBatchedField { keep_on_cpu }),
                ),
                Some(FieldLayout::Flat { sizes_key }) => {
@@ -47,7 +47,7 @@ pub(super) fn build_batched_items(
                    })?;
                    let (start, end) = tensor::flat_range_for_index(sizes, sizes_key, index)?;
                    (
-                        tensor.flat_wire_value_range(start, end)?,
+                        tensor.flat_value_range(start, end)?,
                        MmField::Flat(MmFlatField {
                            slices: vec![MmSlice::Slice(SliceSpec {
                                start: Some(0),
@@ -60,7 +60,7 @@ pub(super) fn build_batched_items(
                    )
                }
                None => (
-                    tensor.try_into()?,
+                    tensor.clone(),
                    MmField::Shared(MmSharedField {
                        batch_size: len,
                        keep_on_cpu,
@@ -71,7 +71,7 @@ pub(super) fn build_batched_items(
            data.insert(
                key.clone(),
                MmFieldElem {
-                    data: Some(value),
+                    data: Some(value.try_into()?),
                    field,
                },
            );
@@ -12,7 +12,7 @@ use vllm_engine_core_client::protocol::tensor::{ShapeExt as _, WireTensor};
 use crate::error::{Error, Result, bail_multimodal, multimodal};

 /// Representation for multimodal kwarg values for transformation.
-#[derive(Debug)]
+#[derive(Debug, Clone)]
 pub(super) enum KwargValue {
    /// Float tensor with row-major flat data and shape.
    F32Tensor { data: Vec<f32>, shape: Vec<usize> },
@@ -107,19 +107,28 @@ impl KwargValue {
    }
 }

-impl TryFrom<&KwargValue> for ProtocolKwargValue {
+impl TryFrom<KwargValue> for ProtocolKwargValue {
    type Error = Error;

-    fn try_from(value: &KwargValue) -> Result<Self> {
-        let tensor = match value {
-            KwargValue::F32Tensor { data, shape } => WireTensor::from_f32(shape.clone(), data),
-            KwargValue::F16Tensor { data, shape } => WireTensor::from_f16(shape.clone(), data),
-            KwargValue::Bf16Tensor { data, shape } => WireTensor::from_bf16(shape.clone(), data),
-            KwargValue::I64Tensor { data, shape } => WireTensor::from_i64(shape.clone(), data),
-            KwargValue::U32Tensor { data, shape } => WireTensor::from_u32(shape.clone(), data),
-            KwargValue::Passthrough(value) => return Ok(value.clone()),
-        };
-        tensor.map(ProtocolKwargValue::Tensor).map_err(Error::Multimodal)
+    fn try_from(value: KwargValue) -> Result<Self> {
+        match value {
+            KwargValue::F32Tensor { data, shape } => Ok(Self::Tensor(
+                WireTensor::from_f32(shape, data).map_err(Error::Multimodal)?,
+            )),
+            KwargValue::F16Tensor { data, shape } => Ok(Self::Tensor(
+                WireTensor::from_f16(shape, data).map_err(Error::Multimodal)?,
+            )),
+            KwargValue::Bf16Tensor { data, shape } => Ok(Self::Tensor(
+                WireTensor::from_bf16(shape, data).map_err(Error::Multimodal)?,
+            )),
+            KwargValue::I64Tensor { data, shape } => Ok(Self::Tensor(
+                WireTensor::from_i64(shape, data).map_err(Error::Multimodal)?,
+            )),
+            KwargValue::U32Tensor { data, shape } => Ok(Self::Tensor(
+                WireTensor::from_u32(shape, data).map_err(Error::Multimodal)?,
+            )),
+            KwargValue::Passthrough(value) => Ok(value),
+        }
    }
 }

@@ -136,55 +145,63 @@ impl KwargValue {
        }
    }

-    /// Convert one media item from a batched tensor field to wire bytes.
+    /// Extract one media item from a batched tensor field.
    ///
    /// Batched fields use their first axis as media-item index and drop that
    /// axis in the per-feature value, matching vLLM's batched-field semantics.
-    pub(super) fn batched_wire_value_at(&self, index: usize) -> Result<ProtocolKwargValue> {
-        self.wire_value_range(index, index + 1, true)
-    }
-
-    /// Convert one media item's flat tensor range directly to wire bytes.
-    ///
-    /// Flat fields keep the first axis as the sliced length for this item.
-    pub(super) fn flat_wire_value_range(
-        &self,
-        start: usize,
-        end: usize,
-    ) -> Result<ProtocolKwargValue> {
-        self.wire_value_range(start, end, false)
-    }
-
-    fn wire_value_range(
-        &self,
-        start: usize,
-        end: usize,
-        drop_axis: bool,
-    ) -> Result<ProtocolKwargValue> {
-        let tensor = match self {
+    pub(super) fn batched_value_at(&self, index: usize) -> Result<Self> {
+        match self {
            Self::F32Tensor { data, shape } => {
-                let (shape, data) = slice_first_axis_range(shape, data, start, end, drop_axis)?;
-                WireTensor::from_f32(shape, data)
+                let (shape, data) = slice_first_axis_range(shape, data, index, index + 1, true)?;
+                Ok(Self::F32Tensor { data, shape })
            }
            Self::F16Tensor { data, shape } => {
-                let (shape, data) = slice_first_axis_range(shape, data, start, end, drop_axis)?;
-                WireTensor::from_f16(shape, data)
+                let (shape, data) = slice_first_axis_range(shape, data, index, index + 1, true)?;
+                Ok(Self::F16Tensor { data, shape })
            }
            Self::Bf16Tensor { data, shape } => {
-                let (shape, data) = slice_first_axis_range(shape, data, start, end, drop_axis)?;
-                WireTensor::from_bf16(shape, data)
+                let (shape, data) = slice_first_axis_range(shape, data, index, index + 1, true)?;
+                Ok(Self::Bf16Tensor { data, shape })
            }
            Self::I64Tensor { data, shape } => {
-                let (shape, data) = slice_first_axis_range(shape, data, start, end, drop_axis)?;
-                WireTensor::from_i64(shape, data)
+                let (shape, data) = slice_first_axis_range(shape, data, index, index + 1, true)?;
+                Ok(Self::I64Tensor { data, shape })
            }
            Self::U32Tensor { data, shape } => {
-                let (shape, data) = slice_first_axis_range(shape, data, start, end, drop_axis)?;
-                WireTensor::from_u32(shape, data)
+                let (shape, data) = slice_first_axis_range(shape, data, index, index + 1, true)?;
+                Ok(Self::U32Tensor { data, shape })
            }
-            Self::Passthrough(value) => return Ok(value.clone()),
-        };
-        tensor.map(ProtocolKwargValue::Tensor).map_err(Error::Multimodal)
+            Self::Passthrough(value) => Ok(Self::Passthrough(value.clone())),
+        }
+    }
+
+    /// Extract one media item's variable-length range from a flat tensor field.
+    ///
+    /// Flat fields keep the first axis as the sliced length for this item.
+    pub(super) fn flat_value_range(&self, start: usize, end: usize) -> Result<Self> {
+        match self {
+            Self::F32Tensor { data, shape } => {
+                let (shape, data) = slice_first_axis_range(shape, data, start, end, false)?;
+                Ok(Self::F32Tensor { data, shape })
+            }
+            Self::F16Tensor { data, shape } => {
+                let (shape, data) = slice_first_axis_range(shape, data, start, end, false)?;
+                Ok(Self::F16Tensor { data, shape })
+            }
+            Self::Bf16Tensor { data, shape } => {
+                let (shape, data) = slice_first_axis_range(shape, data, start, end, false)?;
+                Ok(Self::Bf16Tensor { data, shape })
+            }
+            Self::I64Tensor { data, shape } => {
+                let (shape, data) = slice_first_axis_range(shape, data, start, end, false)?;
+                Ok(Self::I64Tensor { data, shape })
+            }
+            Self::U32Tensor { data, shape } => {
+                let (shape, data) = slice_first_axis_range(shape, data, start, end, false)?;
+                Ok(Self::U32Tensor { data, shape })
+            }
+            Self::Passthrough(value) => Ok(Self::Passthrough(value.clone())),
+        }
    }
 }

@@ -223,13 +240,13 @@ fn tensor_as_usize_vec(tensor: &KwargValue) -> Result<Vec<usize>> {
 }

 /// Slice a flat row-major tensor along its first axis.
-fn slice_first_axis_range<'a, T>(
+fn slice_first_axis_range<T: Clone>(
    shape: &[usize],
-    data: &'a [T],
+    data: &[T],
    start: usize,
    end: usize,
    drop_axis: bool,
-) -> Result<(Vec<usize>, &'a [T])> {
+) -> Result<(Vec<usize>, Vec<T>)> {
    let first_dim = *shape.first().ok_or_else(|| multimodal!("tensor has no first dimension"))?;
    if start > end || end > first_dim {
        bail_multimodal!("invalid tensor slice {start}..{end} for first dimension {first_dim}");
@@ -253,7 +270,7 @@ fn slice_first_axis_range<'a, T>(
        shape[0] = end - start;
        shape
    };
-    Ok((out_shape, &data[data_start..data_end]))
+    Ok((out_shape, data[data_start..data_end].to_vec()))
 }

 #[cfg(test)]
@@ -261,39 +278,35 @@ mod tests {
    use super::*;

    #[test]
-    fn batched_wire_value_at_drops_first_axis() {
+    fn batched_value_at_drops_first_axis() {
        let value = KwargValue::F32Tensor {
            data: vec![1.0, 2.0, 3.0, 4.0],
            shape: vec![2, 2],
        };

-        let ProtocolKwargValue::Tensor(tensor) = value.batched_wire_value_at(1).unwrap() else {
-            panic!("expected tensor");
-        };
+        let value = value.batched_value_at(1).unwrap();

-        assert_eq!(tensor.shape, vec![2]);
-        assert_eq!(
-            tensor.data.into_raw_view().unwrap(),
-            [3.0_f32, 4.0].into_iter().flat_map(f32::to_ne_bytes).collect::<Vec<_>>()
-        );
+        assert!(matches!(
+            value,
+            KwargValue::F32Tensor { data, shape }
+                if shape == vec![2] && data == vec![3.0, 4.0]
+        ));
    }

    #[test]
-    fn flat_wire_value_range_keeps_first_axis() {
+    fn flat_value_range_keeps_first_axis() {
        let value = KwargValue::U32Tensor {
            data: (0..10).collect(),
            shape: vec![5, 2],
        };

-        let ProtocolKwargValue::Tensor(tensor) = value.flat_wire_value_range(1, 3).unwrap() else {
-            panic!("expected tensor");
-        };
+        let value = value.flat_value_range(1, 3).unwrap();

-        assert_eq!(tensor.shape, vec![2, 2]);
-        assert_eq!(
-            tensor.data.into_raw_view().unwrap(),
-            [2_u32, 3, 4, 5].into_iter().flat_map(u32::to_ne_bytes).collect::<Vec<_>>()
-        );
+        assert!(matches!(
+            value,
+            KwargValue::U32Tensor { data, shape }
+                if shape == vec![2, 2] && data == vec![2, 3, 4, 5]
+        ));
    }

    #[test]
@@ -323,7 +336,7 @@ mod tests {
        let value =
            KwargValue::from_f32_tensor(vec![1.0, -1.0], vec![2], ModelDtype::BFloat16).unwrap();

-        let ProtocolKwargValue::Tensor(tensor) = ProtocolKwargValue::try_from(&value).unwrap()
+        let ProtocolKwargValue::Tensor(tensor) = ProtocolKwargValue::try_from(value).unwrap()
        else {
            panic!("expected tensor");
        };
@@ -338,7 +351,7 @@ mod tests {
        let value =
            KwargValue::from_f32_tensor(vec![1.0, -1.0], vec![2], ModelDtype::Float16).unwrap();

-        let ProtocolKwargValue::Tensor(tensor) = ProtocolKwargValue::try_from(&value).unwrap()
+        let ProtocolKwargValue::Tensor(tensor) = ProtocolKwargValue::try_from(value).unwrap()
        else {
            panic!("expected tensor");
        };
@@ -130,7 +130,7 @@ fn build_video_item(
        let keep_on_cpu = support.spec.keep_on_cpu_keys.contains(&key);
        let (value, field) = match support.spec.field_layout_for(&key) {
            Some(FieldLayout::Batched) => (
-                tensor.batched_wire_value_at(0)?,
+                tensor.batched_value_at(0)?,
                MmField::Batched(MmBatchedField { keep_on_cpu }),
            ),
            Some(FieldLayout::Flat { .. }) => {
@@ -138,7 +138,7 @@ fn build_video_item(
                    .first_dim()
                    .ok_or_else(|| multimodal!("flat video input `{key}` is not a tensor"))?;
                (
-                    (&tensor).try_into()?,
+                    tensor,
                    MmField::Flat(MmFlatField {
                        slices: vec![MmSlice::Slice(SliceSpec {
                            start: Some(0),
@@ -151,7 +151,7 @@ fn build_video_item(
                )
            }
            None => (
-                (&tensor).try_into()?,
+                tensor,
                MmField::Shared(MmSharedField {
                    batch_size: 1,
                    keep_on_cpu,
@@ -162,7 +162,7 @@ fn build_video_item(
        data.insert(
            key,
            MmFieldElem {
-                data: Some(value),
+                data: Some(value.try_into()?),
                field,
            },
        );
@@ -74,7 +74,7 @@ impl DefaultChatOutputProcessor {
            Box::new(CombinedParser::new(reasoning_parser, tool_parser)) as Box<dyn UnifiedParser>
        };

-        apply_structural_tag_constraint(request, parser.structural_tag_builder())?;
+        apply_structural_tag_constraint(request, parser.structural_tag_model())?;

        if parser.preserve_special_tokens() {
            request.decode_options.skip_special_tokens = false;
@@ -7,8 +7,7 @@ use thiserror_ext::AsReport;
 use vllm_engine_core_client::protocol::structured_outputs::{
    StructuredOutputBackend, StructuredOutputsParams,
 };
-use vllm_parser::tool::StructuralTagBuilder;
-use xgrammar_structural_tag::builders::StructuralTagOptions;
+use vllm_parser::tool::StructuralTagModel;
 use xgrammar_structural_tag::{
    FunctionDefinition, FunctionToolParam, ToolChoice as StructuralTagToolChoice, ToolParam,
    build_structural_tag,
@@ -21,9 +20,9 @@ use crate::{Error, Result as ChatResult};
 /// support and the request's tool choice.
 pub(super) fn apply_structural_tag_constraint(
    request: &mut ChatRequest,
-    builder: Option<&dyn StructuralTagBuilder>,
+    model: Option<StructuralTagModel>,
 ) -> ChatResult<()> {
-    let Some(builder) = builder else {
+    let Some(model) = model else {
        return Ok(());
    };
    let Some(tool_choice) = structural_tag_tool_choice(request) else {
@@ -43,16 +42,11 @@ pub(super) fn apply_structural_tag_constraint(
        })
        .collect::<Vec<_>>();

-    let structural_tag = build_structural_tag(
-        builder,
-        &tools,
-        tool_choice,
-        StructuralTagOptions::default().with_reasoning(false),
-    )
-    .and_then(|tag| tag.to_json_string())
-    .map_err(|error| Error::StructuralTag {
-        message: error.to_report_string(),
-    })?;
+    let structural_tag = build_structural_tag(model, &tools, tool_choice, false)
+        .and_then(|tag| tag.to_json_string())
+        .map_err(|error| Error::StructuralTag {
+            message: error.to_report_string(),
+        })?;

    // Overwrite any existing structured output settings with the structural tag constraint.
    request.sampling_params.structured_outputs = Some(StructuredOutputsParams {
@@ -147,7 +141,7 @@ mod tests {
        let mut request = request(ChatToolChoice::Auto, vec![chat_tool("search", Some(true))]);
        let parser = qwen3_coder_parser(&request.tools);

-        apply_structural_tag_constraint(&mut request, parser.structural_tag_builder())
+        apply_structural_tag_constraint(&mut request, parser.structural_tag_model())
            .expect("structural tag should build");

        let tag = structural_tag_value(&request);
@@ -160,7 +154,7 @@ mod tests {
        let mut request = request(ChatToolChoice::Auto, vec![chat_tool("search", None)]);
        let parser = qwen3_coder_parser(&request.tools);

-        apply_structural_tag_constraint(&mut request, parser.structural_tag_builder())
+        apply_structural_tag_constraint(&mut request, parser.structural_tag_model())
            .expect("structural tag decision should succeed");

        assert!(request.sampling_params.structured_outputs.is_none());
@@ -175,7 +169,7 @@ mod tests {
        });
        let parser = qwen3_coder_parser(&request.tools);

-        apply_structural_tag_constraint(&mut request, parser.structural_tag_builder())
+        apply_structural_tag_constraint(&mut request, parser.structural_tag_model())
            .expect("structural tag should build");

        let params = structured_outputs(&request);
@@ -190,7 +184,7 @@ mod tests {
        let mut request = request(ChatToolChoice::Required, vec![chat_tool("search", None)]);
        let parser = qwen3_coder_parser(&request.tools);

-        apply_structural_tag_constraint(&mut request, parser.structural_tag_builder())
+        apply_structural_tag_constraint(&mut request, parser.structural_tag_model())
            .expect("structural tag should build");

        let tag = structural_tag_value(&request);
@@ -207,7 +201,7 @@ mod tests {
        });
        let parser = qwen3_coder_parser(&request.tools);

-        apply_structural_tag_constraint(&mut request, parser.structural_tag_builder())
+        apply_structural_tag_constraint(&mut request, parser.structural_tag_model())
            .expect("structural tag should build");

        let params = structured_outputs(&request);
@@ -227,7 +221,7 @@ mod tests {
        );
        let parser = qwen3_coder_parser(&request.tools);

-        apply_structural_tag_constraint(&mut request, parser.structural_tag_builder())
+        apply_structural_tag_constraint(&mut request, parser.structural_tag_model())
            .expect("structural tag should build");

        let tag = structural_tag_value(&request).to_string();
@@ -240,7 +234,7 @@ mod tests {
        let mut request = request(ChatToolChoice::None, vec![chat_tool("search", Some(true))]);
        let parser = qwen3_coder_parser(&request.tools);

-        apply_structural_tag_constraint(&mut request, parser.structural_tag_builder())
+        apply_structural_tag_constraint(&mut request, parser.structural_tag_model())
            .expect("structural tag decision should succeed");

        assert!(request.sampling_params.structured_outputs.is_none());
@@ -255,7 +249,7 @@ mod tests {
        });
        let parser = qwen3_coder_parser(&request.tools);

-        apply_structural_tag_constraint(&mut request, parser.structural_tag_builder())
+        apply_structural_tag_constraint(&mut request, parser.structural_tag_model())
            .expect("structural tag decision should succeed");

        let params = structured_outputs(&request);
@@ -236,10 +236,9 @@ fn has_content_item_loop(root: &Stmt<'_>) -> bool {

    loops.into_iter().any(|loop_ast| {
        matches!(loop_ast.target, Expr::Var(_))
-            && (is_var_access(&loop_ast.iter, "content")
-                || message_varnames.iter().any(|varname| {
-                    is_var_or_elems_access(&loop_ast.iter, varname, Some("content"))
-                }))
+            && message_varnames
+                .iter()
+                .any(|varname| is_var_or_elems_access(&loop_ast.iter, varname, Some("content")))
    })
 }

@@ -316,16 +315,6 @@ mod tests {
        );
    }

-    #[test]
-    fn detects_openai_template_with_content_parameter_loop() {
-        assert_eq!(
-            detect(
-                "{% macro render(content) %}{% for item in content %}{{ item }}{% endfor %}{% endmacro %}{% for message in messages %}{{ render(message.content) }}{% endfor %}"
-            ),
-            ChatTemplateContentFormat::OpenAi
-        );
-    }
-
    #[test]
    fn detects_openai_template_with_messages_alias() {
        assert_eq!(
@@ -1309,26 +1309,6 @@ mod tests {
        .assert_eq(&rendered);
    }

-    #[test]
-    fn qwen35_template_auto_detects_openai_multimodal_content() {
-        let mut request = image_request();
-        request.chat_options.generation_prompt_mode = GenerationPromptMode::NoGenerationPrompt;
-
-        let rendered = render_mm(
-            QWEN3_5_0_8B_TEMPLATE,
-            &request,
-            ChatTemplateContentFormatOption::Auto,
-        )
-        .unwrap();
-
-        expect![[r#"
-            Text(
-                "<|im_start|>user\na<|vision_start|><|image_pad|><|vision_end|>b<|im_end|>\n",
-            )
-        "#]]
-        .assert_debug_eq(&rendered.prompt);
-    }
-
    #[test]
    fn qwen35_template_renders_closed_empty_reasoning_span_when_thinking_disabled() {
        let mut request = sample_request(vec![ChatMessage::text(ChatRole::User, "hello")]);
@@ -29,7 +29,6 @@ tokio-util.workspace = true
 tracing.workspace = true
 tracing-subscriber.workspace = true
 uuid.workspace = true
-vllm-bench.workspace = true
 vllm-chat.workspace = true
 vllm-engine-core-client.workspace = true
 vllm-managed-engine.workspace = true
@@ -79,23 +79,13 @@ impl Cli {
 }

 /// Supported top-level CLI commands.
-#[derive(Debug, Subcommand)]
+#[derive(Debug, Subcommand, PartialEq, Eq)]
 pub enum Command {
    /// Run the Rust OpenAI frontend as a Python-supervised worker.
    Frontend(FrontendArgs),
    /// Launch a managed Python headless engine, then run the Rust OpenAI
    /// frontend.
    Serve(ServeArgs),
-    /// Run vLLM benchmarks.
-    #[command(subcommand)]
-    Bench(BenchCommand),
-}
-
-/// Supported benchmark commands.
-#[derive(Debug, Subcommand)]
-pub enum BenchCommand {
-    /// Benchmark online serving throughput.
-    Serve(vllm_bench::BenchServeArgs),
 }

 /// A JSON-encoded list of strings, matching Python's `json.loads` CLI type for
@@ -5,27 +5,7 @@ use expect_test::expect;
 use vllm_engine_core_client::TransportMode;
 use vllm_server::{Config, HttpListenerMode, ParserSelection, RendererSelection};

-use super::{BenchCommand, Cli, Command};
-
-#[test]
-fn bench_serve_args_parse_without_managed_engine_repartition() {
-    let cli = Cli::try_parse_from([
-        "vllm-rs",
-        "bench",
-        "serve",
-        "--backend",
-        "openai-chat",
-        "--request-rate",
-        "inf",
-    ])
-    .unwrap();
-
-    let Command::Bench(BenchCommand::Serve(args)) = cli.command else {
-        panic!("expected bench serve args");
-    };
-    assert_eq!(args.backend, vllm_bench::BackendKind::OpenaiChat);
-    assert!(args.request_rate.is_infinite());
-}
+use super::{Cli, Command};

 #[test]
 fn serve_args_forward_python_flags_with_separator() {
@@ -26,21 +26,18 @@ const RESET: &str = "\x1b[0m";
 const VLLM_TIME_FORMAT: &[time::format_description::FormatItem<'static>] =
    format_description!("[month]-[day] [hour]:[minute]:[second]");

+const PROCESS_LABEL: &str = "RustFrontend";
+
 /// Install the process-wide vLLM-style tracing subscriber for the CLI binary.
-pub(crate) fn init_tracing(process_label: &str) {
+pub(crate) fn init_tracing() {
    let filter = build_targets_filter(
        env::var("VLLM_LOGGING_LEVEL").ok().as_deref(),
        env::var("RUST_LOG").ok().as_deref(),
    );
-    let formatter = VllmEventFormatter::new(process_label);
+    let formatter = VllmEventFormatter::new();

    let _ = tracing_subscriber::registry()
-        .with(
-            tracing_subscriber::fmt::layer()
-                .event_format(formatter)
-                .with_writer(std::io::stderr)
-                .with_filter(filter),
-        )
+        .with(tracing_subscriber::fmt::layer().event_format(formatter).with_filter(filter))
        .try_init();
 }

@@ -97,9 +94,9 @@ struct VllmEventFormatter {
 }

 impl VllmEventFormatter {
-    fn new(process_label: &str) -> Self {
+    fn new() -> Self {
        Self {
-            prefix: format!("({process_label} pid={})", process::id()),
+            prefix: format!("({} pid={})", PROCESS_LABEL, process::id()),
            timer: VllmLocalTimer::default(),
        }
    }
@@ -294,13 +291,6 @@ fn map_python_log_level(level: &str) -> LevelFilter {
 mod tests {
    use super::*;

-    #[test]
-    fn formatter_prefix_uses_process_label() {
-        let formatter = VllmEventFormatter::new("Bench");
-
-        assert_eq!(formatter.prefix, format!("(Bench pid={})", process::id()));
-    }
-
    #[test]
    fn rust_log_target_overrides_are_merged_with_vllm_default_level() {
        let filter = build_targets_filter(Some("DEBUG"), Some("hyper=warn,tower=error"));
@@ -5,7 +5,6 @@ mod cli;
 mod logging;

 use std::env;
-use std::ffi::OsStr;
 use std::process::ExitStatus;

 use anyhow::{Context, Result, anyhow, bail};
@@ -13,7 +12,7 @@ use tokio_util::sync::CancellationToken;
 use tracing::{info, warn};
 use vllm_managed_engine::ManagedEngineHandle;

-use crate::cli::{BenchCommand, Cli, Command};
+use crate::cli::{Cli, Command};

 #[global_allocator]
 static GLOBAL: mimalloc::MiMalloc = mimalloc::MiMalloc;
@@ -83,14 +82,7 @@ fn shutdown_signal() -> CancellationToken {
 }

 fn main() -> Result<()> {
-    let process_label =
-        match env::args_os().nth(1).as_deref().and_then(OsStr::to_str).unwrap_or_default() {
-            "bench" => "Bench",
-            "serve" | "frontend" => "RustFrontend",
-            _ => "Rust",
-        };
-    logging::init_tracing(process_label);
-
+    logging::init_tracing();
    let cli = Cli::parse();

    let mut runtime = tokio::runtime::Builder::new_multi_thread();
@@ -108,10 +100,6 @@ fn main() -> Result<()> {
 async fn async_main(cli: Cli) -> Result<()> {
    match cli.command {
        Command::Frontend(args) => vllm_server::serve(args.into_config(), shutdown_signal()).await,
-        Command::Bench(BenchCommand::Serve(bench_args)) => {
-            vllm_bench::prepare_process();
-            vllm_bench::run(bench_args).await
-        }
        Command::Serve(args) => {
            let handshake_port = args.managed_engine.resolve_handshake_port()?;

@@ -460,12 +460,6 @@ impl EngineCoreClient {
        self.inner.is_healthy()
    }

-    /// Subscribe to engine health changes. The current value is `true` while
-    /// the client is healthy and changes permanently to `false` on failure.
-    pub fn subscribe_health(&self) -> tokio::sync::watch::Receiver<bool> {
-        self.inner.subscribe_health()
-    }
-
    /// Return the first persistent health error observed by the client, if any.
    pub fn health_error(&self) -> Option<Arc<Error>> {
        self.inner.health_error()
@@ -9,7 +9,7 @@ use arc_swap::ArcSwapOption;
 use parking_lot::Mutex;
 use thiserror_ext::AsReport as _;
 use tokio::runtime::Handle;
-use tokio::sync::{mpsc, watch};
+use tokio::sync::mpsc;
 use tracing::{debug, info, trace, warn};
 use vllm_metrics::METRICS;
 use zeromq::RouterSendHalf;
@@ -36,7 +36,6 @@ pub(crate) struct ClientInner {
    request_reg: Mutex<RequestRegistry>,
    utility_reg: Mutex<UtilityRegistry>,
    health_error: ArcSwapOption<Error>,
-    health_tx: watch::Sender<bool>,
 }

 impl ClientInner {
@@ -58,7 +57,6 @@ impl ClientInner {
            request_reg: Mutex::new(RequestRegistry::new(engines)),
            utility_reg: Mutex::new(UtilityRegistry::default()),
            health_error: ArcSwapOption::empty(),
-            health_tx: watch::Sender::new(true),
        }
    }

@@ -171,7 +169,6 @@ impl ClientInner {
    /// persistent health error.
    pub fn close_registries(&self, error: Arc<Error>) {
        let persistent_error = self.record_health_error(error);
-        self.publish_unhealthy();
        let request_senders = self.request_reg.lock().close();
        let utility_senders = self.utility_reg.lock().close();

@@ -194,12 +191,6 @@ impl ClientInner {
        self.health_error.load().is_none()
    }

-    /// Subscribe to engine health changes. The current value is `true` while
-    /// the client is healthy and changes permanently to `false` on failure.
-    pub fn subscribe_health(&self) -> watch::Receiver<bool> {
-        self.health_tx.subscribe()
-    }
-
    /// Resolve one utility output to the waiting caller. Returns `true` if a
    /// waiting caller existed.
    pub fn resolve_utility_output(&self, output: UtilityOutput) -> bool {
@@ -289,11 +280,6 @@ impl ClientInner {
            .expect("health error must be recorded before registries close")
    }

-    /// Publish the sticky healthy-to-unhealthy transition.
-    fn publish_unhealthy(&self) {
-        self.health_tx.send_if_modified(|healthy| std::mem::replace(healthy, false));
-    }
-
    /// Assert there is a recorded health error and return a `Shared` variant
    /// wrapping it for error returns when the client is already closed.
    fn closed_error(&self) -> Error {
@@ -475,18 +461,13 @@ mod tests {
    #[tokio::test(flavor = "multi_thread", worker_threads = 2)]
    async fn close_registries_records_first_health_error_only() {
        let inner = test_inner().await;
-        let mut health = inner.subscribe_health();
-        assert!(*health.borrow());

        inner.close_registries(Arc::new(Error::EngineCoreDead));
-        health.changed().await.expect("health sender remains open");
        assert!(!inner.is_healthy());
-        assert!(!*health.borrow());
        assert!(matches!(
            inner.health_error().as_deref(),
            Some(Error::EngineCoreDead)
        ));
-        assert!(!*inner.subscribe_health().borrow());

        inner.close_registries(Arc::new(client_closed!("shutdown")));
        assert!(matches!(
@@ -269,19 +269,6 @@ impl WireLogprobs {
            );
        }

-        // Empty position lists may be encoded as either [0, 0] or [0, k + 1].
-        if token_ids.rows == 0 {
-            return Ok(Logprobs {
-                positions: Vec::new(),
-            });
-        }
-        if token_ids.cols == 0 {
-            bail_ext_value_decode!(
-                "{field_prefix}: zero-column logprobs payload with {} rows",
-                token_ids.rows
-            );
-        }
-
        let mut positions = Vec::with_capacity(token_ids.rows);
        for ((token_ids_row, logprobs_row), sampled_rank) in token_ids
            .data
@@ -303,49 +303,3 @@ fn rejects_non_none_cu_num_generated_tokens() {
        "messagepack ext value decode failed: new_logprobs.cu_num_generated_tokens: expected None for per-request engine-core logprobs payload, got [0, 1]"
    );
 }
-
-#[test]
-fn decodes_zero_row_logprobs_as_empty() {
-    for shape in [[0usize, 0], [0, 3]] {
-        let frames = vec![Bytes::from(encode_value(&output_wire_with_custom_fields(
-            None,
-            Some(Value::Array(vec![
-                ndarray_value("<i8", &shape, Value::Ext(3, Vec::new())),
-                ndarray_value("<f4", &shape, Value::Ext(3, Vec::new())),
-                ndarray_value("<i8", &[0], Value::Ext(3, Vec::new())),
-                Value::Nil,
-            ])),
-        )))];
-        let decoded = decode_engine_core_outputs(&frames).unwrap().into_request_batch().unwrap();
-        let logprobs = decoded.outputs[0]
-            .new_prompt_logprobs_tensors
-            .clone()
-            .unwrap()
-            .into_direct()
-            .unwrap();
-        assert!(logprobs.is_empty());
-    }
-}
-
-#[test]
-fn rejects_zero_column_logprobs_with_rows() {
-    let ranks = Value::Ext(3, vec![1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0]);
-    let frames = vec![Bytes::from(encode_value(&output_wire_with_custom_fields(
-        Some(Value::Array(vec![
-            ndarray_value("<i8", &[2, 0], Value::Ext(3, Vec::new())),
-            ndarray_value("<f4", &[2, 0], Value::Ext(3, Vec::new())),
-            ndarray_value("<i8", &[2], ranks),
-            Value::Nil,
-        ])),
-        None,
-    )))];
-
-    let error = decode_engine_core_outputs(&frames).unwrap_err();
-    let crate::error::Error::ExtValueDecode { message } = &error else {
-        panic!("expected ExtValueDecode");
-    };
-    assert_eq!(
-        message,
-        "new_logprobs: zero-column logprobs payload with 2 rows"
-    );
-}
@@ -55,57 +55,52 @@ pub struct WireNdArray {

 impl WireNdArray {
    /// Build a float32 tensor/ndarray backed by native-endian raw-view bytes.
-    pub fn from_f32(shape: Vec<usize>, data: impl AsRef<[f32]>) -> Result<Self, String> {
-        let data = data.as_ref();
+    pub fn from_f32(shape: Vec<usize>, data: Vec<f32>) -> Result<Self, String> {
        validate_element_count(&shape, data.len())?;
        Ok(Self {
            dtype: "float32".to_string(),
            shape,
-            data: WireArrayData::RawView(pod_collect_to_vec::<f32, u8>(data)),
+            data: WireArrayData::RawView(pod_collect_to_vec::<f32, u8>(&data)),
        })
    }

    /// Build a float16 tensor/ndarray backed by native-endian raw-view bytes.
-    pub fn from_f16(shape: Vec<usize>, data: impl AsRef<[f16]>) -> Result<Self, String> {
-        let data = data.as_ref();
+    pub fn from_f16(shape: Vec<usize>, data: Vec<f16>) -> Result<Self, String> {
        validate_element_count(&shape, data.len())?;
        Ok(Self {
            dtype: "float16".to_string(),
            shape,
-            data: WireArrayData::RawView(pod_collect_to_vec::<f16, u8>(data)),
+            data: WireArrayData::RawView(pod_collect_to_vec::<f16, u8>(&data)),
        })
    }

    /// Build a bfloat16 tensor/ndarray backed by native-endian raw-view bytes.
-    pub fn from_bf16(shape: Vec<usize>, data: impl AsRef<[bf16]>) -> Result<Self, String> {
-        let data = data.as_ref();
+    pub fn from_bf16(shape: Vec<usize>, data: Vec<bf16>) -> Result<Self, String> {
        validate_element_count(&shape, data.len())?;
        Ok(Self {
            dtype: "bfloat16".to_string(),
            shape,
-            data: WireArrayData::RawView(pod_collect_to_vec::<bf16, u8>(data)),
+            data: WireArrayData::RawView(pod_collect_to_vec::<bf16, u8>(&data)),
        })
    }

    /// Build an int64 tensor/ndarray backed by native-endian raw-view bytes.
-    pub fn from_i64(shape: Vec<usize>, data: impl AsRef<[i64]>) -> Result<Self, String> {
-        let data = data.as_ref();
+    pub fn from_i64(shape: Vec<usize>, data: Vec<i64>) -> Result<Self, String> {
        validate_element_count(&shape, data.len())?;
        Ok(Self {
            dtype: "int64".to_string(),
            shape,
-            data: WireArrayData::RawView(pod_collect_to_vec::<i64, u8>(data)),
+            data: WireArrayData::RawView(pod_collect_to_vec::<i64, u8>(&data)),
        })
    }

    /// Build a uint32 tensor/ndarray backed by native-endian raw-view bytes.
-    pub fn from_u32(shape: Vec<usize>, data: impl AsRef<[u32]>) -> Result<Self, String> {
-        let data = data.as_ref();
+    pub fn from_u32(shape: Vec<usize>, data: Vec<u32>) -> Result<Self, String> {
        validate_element_count(&shape, data.len())?;
        Ok(Self {
            dtype: "uint32".to_string(),
            shape,
-            data: WireArrayData::RawView(pod_collect_to_vec::<u32, u8>(data)),
+            data: WireArrayData::RawView(pod_collect_to_vec::<u32, u8>(&data)),
        })
    }

@@ -4,7 +4,7 @@
 use std::sync::Arc;

 use vllm_parser::tool::{
-    Result, StructuralTagBuilder, Tool, ToolParser, ToolParserError, ToolParserOutput,
+    Result, StructuralTagModel, Tool, ToolParser, ToolParserError, ToolParserOutput,
 };
 use vllm_parser::unified::{
    UnifiedParser, UnifiedParserError, UnifiedParserEvent, UnifiedParserOutput,
@@ -85,8 +85,8 @@ impl<T: UnifiedParser> ToolParser for UnifiedToolParserAdapter<T> {
        self.inner.preserve_special_tokens()
    }

-    fn structural_tag_builder(&self) -> Option<&dyn StructuralTagBuilder> {
-        self.inner.structural_tag_builder()
+    fn structural_tag_model(&self) -> Option<StructuralTagModel> {
+        self.inner.structural_tag_model()
    }

    fn tool_call_id(&self, tool_index: usize) -> Option<&str> {
@@ -2,7 +2,7 @@
 // SPDX-FileCopyrightText: Copyright contributors to the vLLM project

 use super::{DeepSeekDsmlToolParser, DsmlTokens};
-use crate::tool::{Result, StructuralTagBuilder, Tool, ToolParser, ToolParserOutput};
+use crate::tool::{Result, StructuralTagModel, Tool, ToolParser, ToolParserOutput};

 /// Tool parser for DeepSeek V3.2 models.
 ///
@@ -47,8 +47,8 @@ impl ToolParser for DeepSeekV32ToolParser {
        true
    }

-    fn structural_tag_builder(&self) -> Option<&dyn StructuralTagBuilder> {
-        Some(xgrammar_structural_tag::Model::DeepSeekV32.builder())
+    fn structural_tag_model(&self) -> Option<StructuralTagModel> {
+        Some(StructuralTagModel::DeepSeekV32)
    }

    fn parse_into(&mut self, chunk: &str, output: &mut ToolParserOutput) -> Result<()> {
@@ -2,7 +2,7 @@
 // SPDX-FileCopyrightText: Copyright contributors to the vLLM project

 use super::{DeepSeekDsmlToolParser, DsmlTokens};
-use crate::tool::{Result, StructuralTagBuilder, Tool, ToolParser, ToolParserOutput};
+use crate::tool::{Result, StructuralTagModel, Tool, ToolParser, ToolParserOutput};

 /// Tool parser for DeepSeek V4 models.
 ///
@@ -50,8 +50,8 @@ impl ToolParser for DeepSeekV4ToolParser {
        true
    }

-    fn structural_tag_builder(&self) -> Option<&dyn StructuralTagBuilder> {
-        Some(xgrammar_structural_tag::Model::DeepSeekV4.builder())
+    fn structural_tag_model(&self) -> Option<StructuralTagModel> {
+        Some(StructuralTagModel::DeepSeekV4)
    }

    fn parse_into(&mut self, chunk: &str, output: &mut ToolParserOutput) -> Result<()> {
@@ -73,7 +73,7 @@ mod tests {

    use super::DeepSeekV4ToolParser;
    use crate::tool::test_utils::{collect_stream, test_tools};
-    use crate::tool::{ToolParser, ToolParserTestExt as _};
+    use crate::tool::{StructuralTagModel, ToolParser, ToolParserTestExt as _};

    fn build_tool_call(function_name: &str, params: &[(&str, &str)]) -> String {
        let params = params
@@ -91,10 +91,13 @@ mod tests {
    }

    #[test]
-    fn deepseek_v4_exposes_structural_tag_builder() {
+    fn deepseek_v4_exposes_structural_tag_model() {
        let parser = DeepSeekV4ToolParser::new(&test_tools());

-        assert!(parser.structural_tag_builder().is_some());
+        assert_eq!(
+            parser.structural_tag_model(),
+            Some(StructuralTagModel::DeepSeekV4)
+        );
    }

    #[test]
@@ -2,7 +2,7 @@
 // SPDX-FileCopyrightText: Copyright contributors to the vLLM project

 use super::{DeepSeekJsonFormat, DeepSeekJsonToolParser};
-use crate::tool::{Result, StructuralTagBuilder, Tool, ToolParser, ToolParserOutput};
+use crate::tool::{Result, StructuralTagModel, Tool, ToolParser, ToolParserOutput};

 /// Tool parser for DeepSeek V3 JSON-fenced tool calls.
 ///
@@ -35,8 +35,8 @@ impl ToolParser for DeepSeekV3ToolParser {
        Ok(Box::new(Self::new(tools)))
    }

-    fn structural_tag_builder(&self) -> Option<&dyn StructuralTagBuilder> {
-        Some(xgrammar_structural_tag::Model::DeepSeekR1.builder())
+    fn structural_tag_model(&self) -> Option<StructuralTagModel> {
+        Some(StructuralTagModel::DeepSeekR1)
    }

    fn parse_into(&mut self, chunk: &str, output: &mut ToolParserOutput) -> Result<()> {
@@ -2,7 +2,7 @@
 // SPDX-FileCopyrightText: Copyright contributors to the vLLM project

 use super::{DeepSeekJsonFormat, DeepSeekJsonToolParser};
-use crate::tool::{Result, StructuralTagBuilder, Tool, ToolParser, ToolParserOutput};
+use crate::tool::{Result, StructuralTagModel, Tool, ToolParser, ToolParserOutput};

 /// Tool parser for DeepSeek V3.1 raw JSON tool calls.
 ///
@@ -31,8 +31,8 @@ impl ToolParser for DeepSeekV31ToolParser {
        Ok(Box::new(Self::new(tools)))
    }

-    fn structural_tag_builder(&self) -> Option<&dyn StructuralTagBuilder> {
-        Some(xgrammar_structural_tag::Model::DeepSeekV31.builder())
+    fn structural_tag_model(&self) -> Option<StructuralTagModel> {
+        Some(StructuralTagModel::DeepSeekV31)
    }

    fn parse_into(&mut self, chunk: &str, output: &mut ToolParserOutput) -> Result<()> {
@@ -2,7 +2,7 @@
 // SPDX-FileCopyrightText: Copyright contributors to the vLLM project

 use super::{GlmXmlToolParser, Separator};
-use crate::tool::{Result, StructuralTagBuilder, Tool, ToolParser, ToolParserOutput};
+use crate::tool::{Result, StructuralTagModel, Tool, ToolParser, ToolParserOutput};

 /// Tool parser for GLM-4.7 MoE XML-style tool calls.
 ///
@@ -25,8 +25,8 @@ impl ToolParser for Glm47MoeToolParser {
        Ok(Box::new(Self::new(tools)))
    }

-    fn structural_tag_builder(&self) -> Option<&dyn StructuralTagBuilder> {
-        Some(xgrammar_structural_tag::Model::Glm47.builder())
+    fn structural_tag_model(&self) -> Option<StructuralTagModel> {
+        Some(StructuralTagModel::Glm47)
    }

    fn parse_into(&mut self, chunk: &str, output: &mut ToolParserOutput) -> Result<()> {
@@ -10,7 +10,7 @@ use winnow::token::{literal, rest, take_until};
 use super::parameters::ToolSchemas;
 use super::utils::{MarkerScanState, parse_buffered_event, safe_text_len, take_until_marker};
 use super::{Result, ToolCallDelta, ToolParser, ToolParserOutput};
-use crate::tool::{StructuralTagBuilder, Tool};
+use crate::tool::{StructuralTagModel, Tool};

 const TOOL_CALLS_START: &str = "<tool_calls>";
 const TOOL_CALLS_END: &str = "</tool_calls>";
@@ -116,8 +116,8 @@ impl ToolParser for HyV3ToolParser {
        Ok(Box::new(Self::new(tools)))
    }

-    fn structural_tag_builder(&self) -> Option<&dyn StructuralTagBuilder> {
-        Some(xgrammar_structural_tag::Model::HyV3.builder())
+    fn structural_tag_model(&self) -> Option<StructuralTagModel> {
+        Some(StructuralTagModel::HyV3)
    }

    fn parse_into(&mut self, chunk: &str, output: &mut ToolParserOutput) -> Result<()> {
@@ -2,7 +2,7 @@
 // SPDX-FileCopyrightText: Copyright contributors to the vLLM project

 use super::{JsonToolCallConfig, JsonToolCallParser, JsonToolCallWhitespace};
-use crate::tool::{Result, StructuralTagBuilder, Tool, ToolParser, ToolParserOutput};
+use crate::tool::{Result, StructuralTagModel, Tool, ToolParser, ToolParserOutput};

 const HERMES_CONFIG: JsonToolCallConfig = JsonToolCallConfig {
    parser_name: "Hermes",
@@ -48,8 +48,8 @@ impl ToolParser for HermesToolParser {
        Ok(Box::new(Self::new(tools)))
    }

-    fn structural_tag_builder(&self) -> Option<&dyn StructuralTagBuilder> {
-        Some(xgrammar_structural_tag::Model::Hermes.builder())
+    fn structural_tag_model(&self) -> Option<StructuralTagModel> {
+        Some(StructuralTagModel::Hermes)
    }

    fn parse_into(&mut self, chunk: &str, output: &mut ToolParserOutput) -> Result<()> {
@@ -12,9 +12,7 @@ use super::{
    argument_delta_event, tool_call_header_event,
 };
 use crate::tool::utils::{JsonObjectScanState, parse_buffered_event};
-use crate::tool::{
-    Result, StructuralTagBuilder, Tool, ToolCallDelta, ToolParser, ToolParserOutput,
-};
+use crate::tool::{Result, StructuralTagModel, Tool, ToolCallDelta, ToolParser, ToolParserOutput};

 #[derive(Debug, Clone, PartialEq, Eq)]
 enum LlamaJsonMode {
@@ -138,8 +136,8 @@ impl ToolParser for Llama3JsonToolParser {
        Ok(Box::new(Self::new(tools)))
    }

-    fn structural_tag_builder(&self) -> Option<&dyn StructuralTagBuilder> {
-        Some(xgrammar_structural_tag::Model::Llama.builder())
+    fn structural_tag_model(&self) -> Option<StructuralTagModel> {
+        Some(StructuralTagModel::Llama)
    }

    fn parse_into(&mut self, chunk: &str, output: &mut ToolParserOutput) -> Result<()> {
@@ -2,7 +2,7 @@
 // SPDX-FileCopyrightText: Copyright contributors to the vLLM project

 use super::{JsonToolCallConfig, JsonToolCallParser, JsonToolCallWhitespace};
-use crate::tool::{Result, StructuralTagBuilder, Tool, ToolParser, ToolParserOutput};
+use crate::tool::{Result, StructuralTagModel, Tool, ToolParser, ToolParserOutput};

 const QWEN_XML_CONFIG: JsonToolCallConfig = JsonToolCallConfig {
    parser_name: "Qwen XML",
@@ -50,8 +50,8 @@ impl ToolParser for Qwen3XmlToolParser {
        Ok(Box::new(Self::new(tools)))
    }

-    fn structural_tag_builder(&self) -> Option<&dyn StructuralTagBuilder> {
-        Some(xgrammar_structural_tag::Model::Qwen3.builder())
+    fn structural_tag_model(&self) -> Option<StructuralTagModel> {
+        Some(StructuralTagModel::Qwen3)
    }

    fn parse_into(&mut self, chunk: &str, output: &mut ToolParserOutput) -> Result<()> {
@@ -11,7 +11,7 @@ use winnow::token::{literal, rest, take_until, take_while};

 use super::utils::{JsonObjectScanState, parse_buffered_event, safe_text_len, take_json_object};
 use super::{Result, ToolCallDelta, ToolParser, ToolParserOutput};
-use crate::tool::{StructuralTagBuilder, Tool};
+use crate::tool::{StructuralTagModel, Tool};

 const TOOL_CALLS_START: &str = "<|tool_calls_section_begin|>";
 const TOOL_CALLS_END: &str = "<|tool_calls_section_end|>";
@@ -150,8 +150,8 @@ impl ToolParser for KimiK2ToolParser {
        true
    }

-    fn structural_tag_builder(&self) -> Option<&dyn StructuralTagBuilder> {
-        Some(xgrammar_structural_tag::Model::Kimi.builder())
+    fn structural_tag_model(&self) -> Option<StructuralTagModel> {
+        Some(StructuralTagModel::Kimi)
    }

    fn tool_call_id(&self, tool_index: usize) -> Option<&str> {
@@ -10,7 +10,7 @@ use winnow::token::{literal, rest, take_until};
 use super::parameters::ToolSchemas;
 use super::utils::{MarkerScanState, parse_buffered_event, safe_text_len, take_until_marker};
 use super::{Result, ToolCallDelta, ToolParser, ToolParserOutput};
-use crate::tool::{StructuralTagBuilder, Tool};
+use crate::tool::{StructuralTagModel, Tool};

 const TOOL_CALL_START: &str = "<minimax:tool_call>";
 const TOOL_CALL_END: &str = "</minimax:tool_call>";
@@ -115,8 +115,8 @@ impl ToolParser for MinimaxM2ToolParser {
        Ok(Box::new(Self::new(tools)))
    }

-    fn structural_tag_builder(&self) -> Option<&dyn StructuralTagBuilder> {
-        Some(xgrammar_structural_tag::Model::Minimax.builder())
+    fn structural_tag_model(&self) -> Option<StructuralTagModel> {
+        Some(StructuralTagModel::Minimax)
    }

    fn parse_into(&mut self, chunk: &str, output: &mut ToolParserOutput) -> Result<()> {
@@ -36,7 +36,7 @@ pub use qwen_coder::Qwen3CoderToolParser;
 pub use seed_oss::SeedOssToolParser;
 use serde::{Deserialize, Serialize};
 use serde_json::Value;
-pub use xgrammar_structural_tag::builders::StructuralTagBuilder;
+pub use xgrammar_structural_tag::Model as StructuralTagModel;

 use crate::utils;

@@ -187,8 +187,8 @@ pub trait ToolParser: Send {
        false
    }

-    /// Return the xgrammar structural-tag builder used for strict tool calling.
-    fn structural_tag_builder(&self) -> Option<&dyn StructuralTagBuilder> {
+    /// Return the xgrammar structural-tag model used for strict tool calling.
+    fn structural_tag_model(&self) -> Option<StructuralTagModel> {
        None
    }

@@ -9,8 +9,8 @@ use winnow::token::{literal, take_until};

 use super::parameters::ToolSchemas;
 use super::utils::{MarkerScanState, parse_buffered_event, safe_text_len, take_until_marker};
-use super::{Result, ToolCallDelta, ToolParser, ToolParserOutput};
-use crate::tool::{StructuralTagBuilder, Tool};
+use super::{Result, StructuralTagModel, ToolCallDelta, ToolParser, ToolParserOutput};
+use crate::tool::Tool;

 const TOOL_CALL_START: &str = "<tool_call>";
 const TOOL_CALL_END: &str = "</tool_call>";
@@ -146,8 +146,8 @@ impl ToolParser for Qwen3CoderToolParser {
        Ok(Box::new(Self::new(tools)))
    }

-    fn structural_tag_builder(&self) -> Option<&dyn StructuralTagBuilder> {
-        Some(xgrammar_structural_tag::Model::Qwen3Coder.builder())
+    fn structural_tag_model(&self) -> Option<StructuralTagModel> {
+        Some(StructuralTagModel::Qwen3Coder)
    }

    fn parse_into(&mut self, chunk: &str, output: &mut ToolParserOutput) -> Result<()> {
@@ -294,7 +294,7 @@ mod tests {
    use serde_json::{Value, json};
    use thiserror_ext::AsReport;

-    use super::{Qwen3CoderToolParser, ToolParser};
+    use super::{Qwen3CoderToolParser, StructuralTagModel, ToolParser};
    use crate::tool::test_utils::{collect_stream, split_by_chars, test_tools};
    use crate::tool::{ToolParserOutput, ToolParserTestExt as _};

@@ -308,10 +308,13 @@ mod tests {
    }

    #[test]
-    fn qwen_coder_exposes_structural_tag_builder() {
+    fn qwen_coder_exposes_structural_tag_model() {
        let parser = Qwen3CoderToolParser::new(&test_tools());

-        assert!(parser.structural_tag_builder().is_some());
+        assert_eq!(
+            parser.structural_tag_model(),
+            Some(StructuralTagModel::Qwen3Coder)
+        );
    }

    #[test]
@@ -7,7 +7,7 @@ use vllm_tokenizer::DynTokenizer;

 use super::{Result, UnifiedParser, UnifiedParserError, UnifiedParserOutput};
 use crate::reasoning::ReasoningParser;
-use crate::tool::{StructuralTagBuilder, Tool, ToolParser, ToolParserOutput};
+use crate::tool::{StructuralTagModel, Tool, ToolParser, ToolParserOutput};

 /// Unified parser that composes existing reasoning and tool parsers.
 pub struct CombinedParser {
@@ -79,8 +79,8 @@ impl UnifiedParser for CombinedParser {
            || self.tool.as_ref().is_some_and(|parser| parser.preserve_special_tokens())
    }

-    fn structural_tag_builder(&self) -> Option<&dyn StructuralTagBuilder> {
-        self.tool.as_ref().and_then(|parser| parser.structural_tag_builder())
+    fn structural_tag_model(&self) -> Option<StructuralTagModel> {
+        self.tool.as_ref().and_then(|parser| parser.structural_tag_model())
    }

    fn tool_call_id(&self, tool_index: usize) -> Option<&str> {
@@ -269,7 +269,10 @@ mod tests {
    fn combined_parser_emits_tool_calls_from_visible_content() {
        let tool = Qwen3XmlToolParser::create(&test_tools()).unwrap();
        let mut parser = CombinedParser::new(None, Some(tool));
-        assert!(parser.structural_tag_builder().is_some());
+        assert!(matches!(
+            parser.structural_tag_model(),
+            Some(crate::tool::StructuralTagModel::Qwen3)
+        ));

        let output = collect(
            &mut parser,
@@ -16,7 +16,7 @@ use vllm_tokenizer::DynTokenizer;

 use crate::reasoning::ReasoningError;
 use crate::tool::{
-    StructuralTagBuilder, Tool, ToolCallDelta, ToolParserError, ToolParserEvent, ToolParserOutput,
+    StructuralTagModel, Tool, ToolCallDelta, ToolParserError, ToolParserEvent, ToolParserOutput,
 };

 /// Result alias for unified parser operations.
@@ -171,8 +171,8 @@ pub trait UnifiedParser: Send {
        false
    }

-    /// Return the xgrammar structural-tag builder used for strict tool calling.
-    fn structural_tag_builder(&self) -> Option<&dyn StructuralTagBuilder> {
+    /// Return the xgrammar structural-tag model used for strict tool calling.
+    fn structural_tag_model(&self) -> Option<StructuralTagModel> {
        None
    }

@@ -35,7 +35,6 @@ tokio-openssl.workspace = true
 tokio-stream.workspace = true
 tokio-util.workspace = true
 tonic.workspace = true
-tonic-health.workspace = true
 tonic-prost.workspace = true
 tower.workspace = true
 tower-http.workspace = true
@@ -1,70 +0,0 @@
-// SPDX-License-Identifier: Apache-2.0
-// SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-use tokio::sync::watch;
-use tokio_util::sync::CancellationToken;
-use tonic::server::NamedService;
-use tonic_health::ServingStatus;
-use tonic_health::server::HealthReporter;
-use tracing::{info, warn};
-
-use super::GenerateGrpcService;
-
-pub(crate) async fn monitor_health(
-    mut health_reporter: HealthReporter,
-    mut engine_health: watch::Receiver<bool>,
-    shutdown: CancellationToken,
-) {
-    let generate_service = GenerateGrpcService::NAME;
-    let status = ServingStatus::NotServing;
-    let health_event_first = tokio::select! {
-        result = engine_health.wait_for(|healthy| !*healthy) => {
-            match result {
-                Ok(_) => warn!(
-                    generate_service,
-                    overall_service = true,
-                    status = ?status,
-                    reason = "engine_unhealthy",
-                    "marking gRPC health services as not serving"
-                ),
-                Err(error) => warn!(
-                    %error,
-                    generate_service,
-                    overall_service = true,
-                    status = ?status,
-                    reason = "health_channel_closed",
-                    "engine health channel closed; marking gRPC health services as not serving"
-                ),
-            }
-            true
-        }
-        _ = shutdown.cancelled() => {
-            info!(
-                generate_service,
-                overall_service = true,
-                status = ?status,
-                reason = "server_shutdown",
-                "server shutting down; marking gRPC health services as not serving"
-            );
-            false
-        }
-    };
-
-    health_reporter.set_not_serving::<GenerateGrpcService>().await;
-    // Generate is currently the only engine-backed gRPC service, so overall
-    // server health intentionally mirrors it.
-    health_reporter.set_service_status("", status).await;
-
-    if health_event_first {
-        shutdown.cancelled().await;
-        info!(
-            generate_service,
-            overall_service = true,
-            reason = "server_shutdown",
-            "server shutting down; closing gRPC health watches"
-        );
-    }
-
-    health_reporter.clear_service_status(generate_service).await;
-    health_reporter.clear_service_status("").await;
-}
@@ -4,7 +4,6 @@
 //! gRPC Generate service backed by the shared [`vllm_text::TextLlm`] facade.

 mod convert;
-mod health;

 use std::pin::Pin;
 use std::sync::Arc;
@@ -25,11 +24,8 @@ pub mod pb {
    tonic::include_proto!("vllm");
 }

-pub(crate) use health::monitor_health;
 pub use pb::generate_server::GenerateServer;

-pub(crate) type GenerateGrpcService = GenerateServer<GenerateServiceImpl>;
-
 #[cfg(test)]
 mod tests;

@@ -16,10 +16,6 @@ use tokio::io::{AsyncReadExt as _, AsyncWriteExt as _};
 use tokio::net::TcpStream;
 use tokio_openssl::SslStream;
 use tonic::transport::{Channel, Endpoint, Server as TonicServer, Uri};
-use tonic_health::pb::HealthCheckRequest;
-use tonic_health::pb::health_check_response::ServingStatus as HealthServingStatus;
-use tonic_health::pb::health_client::HealthClient;
-use tonic_health::server::health_reporter;
 use tower::service_fn;
 use vllm_chat::{
    ChatBackend, ChatLlm, ChatRenderer, ChatRequest, ChatTextBackend, DefaultChatOutputProcessor,
@@ -204,11 +200,7 @@ impl ChatRenderer for FakeTextBackend {
 async fn setup_grpc_service(
    engine_id: impl Into<EngineId>,
    output_specs: Vec<(Vec<u32>, Option<EngineCoreFinishReason>)>,
-) -> (
-    GenerateServer<GenerateServiceImpl>,
-    tokio::sync::watch::Receiver<bool>,
-    MockEngineTask,
-) {
+) -> (GenerateServer<GenerateServiceImpl>, MockEngineTask) {
    let ipc = IpcNamespace::new().expect("create ipc namespace");
    let handshake_address = ipc.handshake_endpoint();
    let engine_id = engine_id.into();
@@ -240,7 +232,6 @@ async fn setup_grpc_service(
    )
    .await
    .expect("connect client");
-    let engine_health = client.subscribe_health();

    let chat = ChatLlm::from_shared_backend(
        test_llm(client),
@@ -249,7 +240,6 @@ async fn setup_grpc_service(
    let state = Arc::new(AppState::new(vec!["test-model".to_string()], chat));
    (
        GenerateServer::new(GenerateServiceImpl::new(state)),
-        engine_health,
        engine_task,
    )
 }
@@ -264,51 +254,25 @@ async fn grpc_test_server(
    tokio::task::JoinHandle<()>,
    MockEngineTask,
 ) {
-    let (svc, engine_health, engine_task) = setup_grpc_service(engine_id, output_specs).await;
-    let (channel, server_task) = start_grpc_test_server(
-        svc,
-        engine_health,
-        tokio_util::sync::CancellationToken::new(),
-    )
-    .await;
-    (GenerateClient::new(channel), server_task, engine_task)
-}
-
-async fn start_grpc_test_server(
-    generate_service: GenerateServer<GenerateServiceImpl>,
-    engine_health: tokio::sync::watch::Receiver<bool>,
-    shutdown: tokio_util::sync::CancellationToken,
-) -> (Channel, tokio::task::JoinHandle<()>) {
-    let (health_reporter, health_service) = health_reporter();
-    health_reporter.set_serving::<GenerateServer<GenerateServiceImpl>>().await;
+    let (svc, engine_task) = setup_grpc_service(engine_id, output_specs).await;

    let listener = tokio::net::TcpListener::bind("127.0.0.1:0").await.expect("bind grpc listener");
    let addr = listener.local_addr().expect("local addr");

    let server_task = tokio::spawn(async move {
        let incoming = MaybeTlsListener::plain(Listener::Tcp(listener));
-        let server = TonicServer::builder()
-            .add_service(health_service)
-            .add_service(generate_service)
-            .serve_with_incoming_shutdown(incoming, shutdown.clone().cancelled_owned());
-        let health_monitor =
-            super::monitor_health(health_reporter, engine_health, shutdown.clone());
-        let server = async move {
-            let result = server.await;
-            shutdown.cancel();
-            result
-        };
-        let (server_result, ()) = tokio::join!(server, health_monitor);
-        server_result.expect("grpc server");
+        TonicServer::builder()
+            .add_service(svc)
+            .serve_with_incoming(incoming)
+            .await
+            .expect("grpc server");
    });

-    let channel = Endpoint::from_shared(format!("http://{addr}"))
-        .expect("grpc endpoint")
-        .connect()
+    let grpc_client = GenerateClient::connect(format!("http://{addr}"))
        .await
-        .expect("connect grpc channel");
+        .expect("connect grpc client");

-    (channel, server_task)
+    (grpc_client, server_task, engine_task)
 }

 /// Spin up a TLS gRPC server (server cert from `certs`, `cert_reqs` mTLS mode).
@@ -319,7 +283,7 @@ async fn grpc_tls_test_server(
    certs: &TestCerts,
    cert_reqs: i32,
 ) -> (String, tokio::task::JoinHandle<()>, MockEngineTask) {
-    let (svc, _engine_health, engine_task) = setup_grpc_service(engine_id, output_specs).await;
+    let (svc, engine_task) = setup_grpc_service(engine_id, output_specs).await;
    let context = tls::build_grpc_server_config(&server_tls(certs, cert_reqs))
        .expect("build grpc tls config");

@@ -409,8 +373,7 @@ async fn grpc_server_with_keepalive(
    engine_id: impl Into<EngineId>,
    keepalive: Option<Duration>,
 ) -> (String, tokio::task::JoinHandle<()>, MockEngineTask) {
-    let (svc, _engine_health, engine_task) =
-        setup_grpc_service(engine_id, default_stream_output_specs()).await;
+    let (svc, engine_task) = setup_grpc_service(engine_id, default_stream_output_specs()).await;

    let listener = tokio::net::TcpListener::bind("127.0.0.1:0").await.expect("bind grpc listener");
    let addr = listener.local_addr().expect("local addr").to_string();
@@ -1072,129 +1035,3 @@ async fn grpc_without_keepalive_keeps_unresponsive_connection_open() {

    server_task.abort();
 }
-
-#[tokio::test(flavor = "multi_thread", worker_threads = 2)]
-#[serial]
-async fn grpc_health_transitions_to_not_serving_when_engine_becomes_unhealthy() {
-    let (generate_service, _connected_engine_health, _engine_task) =
-        setup_grpc_service(b"engine-grpc-health-failure", default_stream_output_specs()).await;
-    let (engine_health_tx, engine_health) = tokio::sync::watch::channel(true);
-    let (channel, server_task) = start_grpc_test_server(
-        generate_service,
-        engine_health,
-        tokio_util::sync::CancellationToken::new(),
-    )
-    .await;
-    let mut health_client = HealthClient::new(channel);
-
-    let mut health_streams = Vec::new();
-    for service in ["vllm.Generate", ""] {
-        let service_label = if service.is_empty() {
-            "overall"
-        } else {
-            service
-        };
-        let mut stream = health_client
-            .watch(HealthCheckRequest {
-                service: service.to_string(),
-            })
-            .await
-            .unwrap_or_else(|error| {
-                panic!("failed to start health watch for {service_label}: {error}")
-            })
-            .into_inner();
-        let initial = stream
-            .message()
-            .await
-            .unwrap_or_else(|error| {
-                panic!("failed to read initial health status for {service_label}: {error}")
-            })
-            .unwrap_or_else(|| {
-                panic!("health watch for {service_label} ended before its initial status")
-            });
-        assert_eq!(
-            initial.status,
-            HealthServingStatus::Serving as i32,
-            "unexpected initial health status for {service_label}"
-        );
-        health_streams.push((service_label, stream));
-    }
-
-    engine_health_tx.send(false).expect("publish unhealthy engine state");
-
-    for (service_label, mut stream) in health_streams {
-        let update = tokio::time::timeout(Duration::from_secs(2), stream.message())
-            .await
-            .unwrap_or_else(|_| panic!("timed out waiting for health update for {service_label}"))
-            .unwrap_or_else(|error| {
-                panic!("failed to read health update for {service_label}: {error}")
-            })
-            .unwrap_or_else(|| panic!("health watch for {service_label} ended before its update"));
-        assert_eq!(
-            update.status,
-            HealthServingStatus::NotServing as i32,
-            "unexpected health status for {service_label}"
-        );
-    }
-
-    server_task.abort();
-}
-
-#[tokio::test(flavor = "multi_thread", worker_threads = 2)]
-#[serial]
-async fn grpc_health_watch_closes_on_graceful_shutdown() {
-    let (generate_service, engine_health, _engine_task) = setup_grpc_service(
-        b"engine-grpc-health-shutdown",
-        default_stream_output_specs(),
-    )
-    .await;
-    let shutdown = tokio_util::sync::CancellationToken::new();
-    let (channel, server_task) =
-        start_grpc_test_server(generate_service, engine_health, shutdown.clone()).await;
-    let mut health_client = HealthClient::new(channel);
-    let mut stream = health_client
-        .watch(HealthCheckRequest {
-            service: "vllm.Generate".to_string(),
-        })
-        .await
-        .expect("start health watch for vllm.Generate")
-        .into_inner();
-
-    let initial = stream
-        .message()
-        .await
-        .expect("read initial health status for vllm.Generate")
-        .expect("health watch ended before its initial status");
-    assert_eq!(
-        initial.status,
-        HealthServingStatus::Serving as i32,
-        "unexpected initial health status for vllm.Generate"
-    );
-
-    shutdown.cancel();
-
-    let update = tokio::time::timeout(Duration::from_secs(2), stream.message())
-        .await
-        .expect("timed out waiting for shutdown health update for vllm.Generate")
-        .expect("failed to read shutdown health update for vllm.Generate")
-        .expect("health watch ended before its shutdown update");
-    assert_eq!(
-        update.status,
-        HealthServingStatus::NotServing as i32,
-        "unexpected shutdown health status for vllm.Generate"
-    );
-
-    let stream_end = tokio::time::timeout(Duration::from_secs(2), stream.message())
-        .await
-        .expect("timed out waiting for vllm.Generate health watch to close")
-        .expect("failed while closing vllm.Generate health watch");
-    assert!(
-        stream_end.is_none(),
-        "vllm.Generate health watch remained open"
-    );
-
-    tokio::time::timeout(Duration::from_secs(2), server_task)
-        .await
-        .expect("timed out waiting for gRPC server shutdown")
-        .expect("gRPC server task failed");
-}
@@ -39,7 +39,6 @@ use tokio::net::TcpListener;
 use tokio::time::{Instant, sleep_until};
 use tokio_util::sync::CancellationToken;
 use tonic::transport::Server as TonicServer;
-use tonic_health::server::health_reporter;
 use tower::ServiceExt as _;
 use tracing::{info, trace, warn};
 use vllm_chat::{ChatLlm, LoadModelBackendsOptions, load_model_backends};
@@ -204,19 +203,14 @@ where
            .map(tls::build_grpc_server_config)
            .transpose()
            .context("invalid gRPC TLS configuration")?;
-        let (health_reporter, health_service) = health_reporter();
-        let engine_health = state.engine_core_client().subscribe_health();
-        health_reporter.set_serving::<grpc::GenerateGrpcService>().await;
-        let generate_service =
-            grpc::GenerateGrpcService::new(grpc::GenerateServiceImpl::new(state.clone()));
+        let svc = grpc::GenerateServer::new(grpc::GenerateServiceImpl::new(state.clone()));
        let svc = TonicServer::builder()
            .http2_keepalive_interval(Some(GRPC_KEEPALIVE_INTERVAL))
            .http2_keepalive_timeout(Some(GRPC_KEEPALIVE_TIMEOUT))
            .layer(middleware::request_runtime_layer(state.clone()))
-            .add_service(health_service)
-            .add_service(generate_service);
+            .add_service(svc);
        info!(%addr, tls = grpc_tls.is_some(), "starting gRPC server");
-        Some((grpc_listener, svc, grpc_tls, health_reporter, engine_health))
+        Some((grpc_listener, svc, grpc_tls))
    } else {
        None
    };
@@ -300,8 +294,7 @@ where
        let server_shutdown = server_shutdown.clone();
        let force_shutdown = force_shutdown.clone();
        async move {
-            let Some((grpc_listener, svc, grpc_tls, health_reporter, engine_health)) = grpc_setup
-            else {
+            let Some((grpc_listener, svc, grpc_tls)) = grpc_setup else {
                // No gRPC configured: just wait for shutdown so we do not race the
                // join! by resolving early and tripping the cancellation token.
                shutdown.cancelled().await;
@@ -311,26 +304,19 @@ where
                Some(context) => MaybeTlsListener::tls(grpc_listener, context),
                None => MaybeTlsListener::plain(grpc_listener),
            };
-            let server =
-                svc.serve_with_incoming_shutdown(incoming, shutdown.clone().cancelled_owned());
-            let health_monitor = grpc::monitor_health(health_reporter, engine_health, shutdown);
+            let server = svc.serve_with_incoming_shutdown(incoming, shutdown.cancelled_owned());

-            let server = async move {
-                let result = tokio::select! {
-                    result = server => {
-                        result.context("gRPC server failed")
-                    }
-                    _ = force_shutdown.cancelled() => {
-                        warn!("gRPC graceful shutdown deadline elapsed; aborting server");
-                        Ok(())
-                    }
-                };
-
-                server_shutdown.cancel();
-                result
+            let result = tokio::select! {
+                result = server => {
+                    result.context("gRPC server failed")
+                }
+                _ = force_shutdown.cancelled() => {
+                    warn!("gRPC graceful shutdown deadline elapsed; aborting server");
+                    Ok(())
+                }
            };

-            let (result, ()) = tokio::join!(server, health_monitor);
+            server_shutdown.cancel();
            result
        }
    };
@@ -238,18 +238,13 @@ fn collect_generate(
        None
    };
    let prompt_logprobs = if include_prompt_logprobs {
-        match collected.prompt_logprobs.as_ref() {
-            Some(prompt_logprobs) => Some(raw_prompt_logprobs_to_maps(prompt_logprobs)),
-            // A single-token prompt has no scored positions; same mapping
-            // as /v1/completions.
-            None if collected.prompt_token_ids.len() == 1 => Some(vec![None]),
-            None => {
-                return Err(ApiError::server_error(
-                    "raw generate response requested prompt_logprobs but generation returned none"
-                        .to_string(),
-                ));
-            }
-        }
+        let prompt_logprobs = collected.prompt_logprobs.as_ref().ok_or_else(|| {
+            ApiError::server_error(
+                "raw generate response requested prompt_logprobs but generation returned none"
+                    .to_string(),
+            )
+        })?;
+        Some(raw_prompt_logprobs_to_maps(prompt_logprobs))
    } else {
        None
    };
@@ -477,48 +472,4 @@ mod tests {
            Some(2)
        );
    }
-
-    #[test]
-    fn collect_generate_maps_prompt_logprobs_for_single_token_prompt() {
-        let output_without_payload = |prompt_token_ids: Vec<u32>| CollectedGenerateOutput {
-            request_id: "raw-1".to_string(),
-            prompt_logprobs: None,
-            token_ids: vec![3],
-            logprobs: None,
-            finish_reason: FinishReason::stop_eos(),
-            usage: vllm_llm::TokenUsage {
-                prompt_token_count: prompt_token_ids.len(),
-                output_token_count: 1,
-                cached_token_count: 0,
-            },
-            kv_transfer_params: None,
-            ec_transfer_params: None,
-            prompt_token_ids,
-        };
-
-        let response = collect_generate(
-            output_without_payload(vec![9707]),
-            "raw-1".to_string(),
-            ApiServerOptions::default(),
-            ResponseOptions {
-                include_prompt_logprobs: true,
-                ..Default::default()
-            },
-        )
-        .expect("single-token prompt without payload maps to [None]");
-        let prompt_logprobs = response.prompt_logprobs.expect("prompt logprobs present");
-        assert_eq!(prompt_logprobs.len(), 1);
-        assert!(prompt_logprobs[0].is_none());
-
-        collect_generate(
-            output_without_payload(vec![9707, 11]),
-            "raw-2".to_string(),
-            ApiServerOptions::default(),
-            ResponseOptions {
-                include_prompt_logprobs: true,
-                ..Default::default()
-            },
-        )
-        .expect_err("multi-token prompt without payload is an engine failure");
-    }
 }
@@ -35,7 +35,7 @@ use crate::routes::openai::chat_completions::types::{
    ChatMessageDelta,
 };
 use crate::routes::openai::utils::logprobs::{
-    decoded_logprobs_to_openai_chat, prompt_logprobs_to_maps,
+    decoded_logprobs_to_openai_chat, decoded_prompt_logprobs_to_maps,
 };
 use crate::routes::openai::utils::types::{
    ChatLogProbs, FunctionCallDelta, FunctionCallResponse, ToolCall, ToolCallDelta, Usage,
@@ -181,11 +181,14 @@ async fn collect_chat_completion(
        None
    };
    let prompt_logprobs = if include_prompt_logprobs {
-        Some(prompt_logprobs_to_maps(
-            prompt_logprobs.as_ref(),
-            &prompt_token_ids,
+        Some(decoded_prompt_logprobs_to_maps(
+            prompt_logprobs.as_ref().ok_or_else(|| {
+                server_error!(
+                    "chat response requested prompt_logprobs but generation returned none"
+                )
+            })?,
            return_tokens_as_token_ids,
-        )?)
+        ))
    } else {
        None
    };
@@ -5,6 +5,7 @@ mod convert;
 mod types;
 mod validate;

+use std::collections::HashMap;
 use std::convert::Infallible;
 use std::result::Result;
 use std::sync::Arc;
@@ -28,8 +29,8 @@ use vllm_text::{

 use self::convert::{ResponseOptions, prepare_completion_request};
 use super::utils::logprobs::{
-    collected_logprobs_to_openai, decoded_logprobs_to_openai, decoded_prompt_logprobs_to_openai,
-    prompt_logprobs_to_maps, text_len,
+    collected_logprobs_to_openai, decoded_logprobs_to_openai, decoded_prompt_logprobs_to_maps,
+    decoded_prompt_logprobs_to_openai, text_len,
 };
 use super::utils::types::Usage;
 use crate::config::ApiServerOptions;
@@ -504,6 +505,27 @@ fn prompt_only_logprobs_to_openai(
    ))
 }

+fn prompt_logprobs_to_maps(
+    prompt_logprobs: Option<&DecodedPromptLogprobs>,
+    prompt_token_ids: &[u32],
+    return_tokens_as_token_ids: bool,
+) -> Result<Vec<Option<HashMap<String, f32>>>, ApiError> {
+    if let Some(prompt_logprobs) = prompt_logprobs {
+        return Ok(decoded_prompt_logprobs_to_maps(
+            prompt_logprobs,
+            return_tokens_as_token_ids,
+        ));
+    }
+
+    if let [_token_id] = prompt_token_ids {
+        return Ok(vec![None]);
+    }
+
+    Err(server_error!(
+        "completion response requested prompt_logprobs but generation returned none"
+    ))
+}
+
 fn usage_chunk(
    request_id: &str,
    response_model: &str,
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Kevin H. Luu	568afb3a13	[CI/Build] Refresh tags before building macOS wheel (#49901 ) Signed-off-by: khluu <khluu000@gmail.com> Co-authored-by: OpenAI Codex <codex@openai.com> (cherry picked from commit `0934b26790`)	2026-07-26 17:57:50 -07:00
TJianandkhluu	f2654939e6	[ROCm] [Release] [Bugfix] Fix the per commit wheel release pipeline. (#49245 ) Signed-off-by: tjtanaa <tunjian.tan@embeddedllm.com>	2026-07-25 00:23:46 -07:00
djramicandkhluu	ffd46bfab2	[Bugfix] Register axk1 config to fix A.X-K1 init (#49727 ) Signed-off-by: Djordje Ramic <djoramic@amd.com> (cherry picked from commit `e222c33f2f`)	2026-07-24 18:48:21 -07:00
Andrey Talmanandkhluu	ffd6ee4bcc	[CI] Bump PyTorch Compilation Unit Tests timeout to 150 min (#49606 )	2026-07-23 21:20:25 -07:00
Nick Hillandkhluu	bb26ce8e93	[CI] Increase timeout of pytorch-compilation-unit-tests (#49450 ) Signed-off-by: Nick Hill <nickhill123@gmail.com>	2026-07-23 21:20:25 -07:00
Kevin H. LuuandOpenAI Codex	091db8b58f	[CI] Increase timeouts for jobs exceeding current limits (#49374 ) Signed-off-by: khluu <khluu000@gmail.com> Co-authored-by: OpenAI Codex <codex@openai.com>	2026-07-23 21:20:25 -07:00
Nick Hillandkhluu	ba694b86f2	[CI] Bump timeout of `entrypoints-integration-api-server-openai-part-2` (#49359 ) Signed-off-by: Nick Hill <nickhill123@gmail.com>	2026-07-23 21:20:25 -07:00
zhrrrandkhluu	e5949f1000	[Bugfix] handle grammar compilation failures to avoid engine crash (#47312 ) Signed-off-by: zhuhaoran <zhuhaoran.zhr@alibaba-inc.com> Signed-off-by: Nick Hill <nickhill123@gmail.com> Co-authored-by: Nick Hill <nickhill123@gmail.com> (cherry picked from commit `12213c6795`)	2026-07-23 11:13:25 -07:00
Michael Goinandkhluu	8b30569e83	[Bugfix] Fix DeepGEMM warmup when using `FlashInferFp8DeepGEMMDynamicBlockScaledKernel` (#49467 ) Signed-off-by: mgoin <mgoin64@gmail.com> (cherry picked from commit `917fdb5bf7`)	2026-07-23 11:13:25 -07:00
Lucas Wilkinsonandkhluu	9d37a50c80	[Bugfix][Attention] Ignore empty MLA context chunks during merge (#49294 ) Signed-off-by: Lucas Wilkinson <lwilkins@redhat.com> Co-authored-by: OpenAI Codex <codex@openai.com> (cherry picked from commit `060b5f61dc`)	2026-07-23 11:13:25 -07:00
aoshen02andkhluu	2dd1e7cd3b	Update BGE-M3 token expectations for leading spaces (#49269 ) Signed-off-by: aoshen02 <aoshen02@users.noreply.github.com> Co-authored-by: aoshen02 <aoshen02@users.noreply.github.com> Co-authored-by: Codex <noreply@openai.com> (cherry picked from commit `d9aa35161d`)	2026-07-23 11:13:25 -07:00
Alejandro Paredes La Torreandkhluu	a54c93a146	[Bugfix] Fix WSL circular import from pin_memory warning_once (#48444 ) Signed-off-by: AlejandroParedesLT <alejandroparedeslatorre@gmail.com> Co-authored-by: Shengqi Chen <harry-chen@outlook.com> (cherry picked from commit `0a684ab0c0`)	2026-07-23 11:13:25 -07:00