Merge branch 'main' into woosuk/model-runner-v2

.buildkite/scripts/run-prime-rl-test.sh

@@ -0,0 +1,59 @@
+#!/bin/bash
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Setup script for Prime-RL integration tests
+# This script prepares the environment for running Prime-RL tests with nightly vLLM
+
+set -euo pipefail
+
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+REPO_ROOT="$(cd "${SCRIPT_DIR}/../.." && pwd)"
+PRIME_RL_REPO="https://github.com/PrimeIntellect-ai/prime-rl.git"
+PRIME_RL_DIR="${REPO_ROOT}/prime-rl"
+
+echo "Setting up Prime-RL integration test environment..."
+
+# Clean up any existing Prime-RL directory
+if [ -d "${PRIME_RL_DIR}" ]; then
+    echo "Removing existing Prime-RL directory..."
+    rm -rf "${PRIME_RL_DIR}"
+fi
+
+# Install UV if not available
+if ! command -v uv &> /dev/null; then
+    echo "Installing UV package manager..."
+    curl -LsSf https://astral.sh/uv/install.sh | sh
+    source $HOME/.local/bin/env
+fi
+
+# Clone Prime-RL repository at specific branch for reproducible tests
+PRIME_RL_BRANCH="integ-vllm-main"
+echo "Cloning Prime-RL repository at branch: ${PRIME_RL_BRANCH}..."
+git clone --branch "${PRIME_RL_BRANCH}" --single-branch "${PRIME_RL_REPO}" "${PRIME_RL_DIR}"
+cd "${PRIME_RL_DIR}"
+
+echo "Setting up UV project environment..."
+export UV_PROJECT_ENVIRONMENT=/usr/local
+ln -s /usr/bin/python3 /usr/local/bin/python
+
+# Remove vllm pin from pyproject.toml
+echo "Removing vllm pin from pyproject.toml..."
+sed -i '/vllm==/d' pyproject.toml
+
+# Sync Prime-RL dependencies
+echo "Installing Prime-RL dependencies..."
+uv sync --inexact && uv sync --inexact --all-extras
+
+# Verify installation
+echo "Verifying installations..."
+uv run python -c "import vllm; print(f'vLLM version: {vllm.__version__}')"
+uv run python -c "import prime_rl; print('Prime-RL imported successfully')"
+
+echo "Prime-RL integration test environment setup complete!"
+
+echo "Running Prime-RL integration tests..."
+export WANDB_MODE=offline # this makes this test not require a WANDB_API_KEY
+uv run pytest -vs tests/integration/test_rl.py -m gpu
+
+echo "Prime-RL integration tests completed!"

.buildkite/test-pipeline.yaml

@@ -887,6 +887,8 @@ steps:
   - tests/v1/test_external_lb_dp.py
   - tests/v1/entrypoints/openai/test_multi_api_servers.py
   - vllm/v1/engine/
+  - vllm/v1/worker/
+  - tests/v1/worker/test_worker_memory_snapshot.py
   commands:
   - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/test_async_llm_dp.py
   - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/test_external_lb_dp.py
@@ -908,6 +910,7 @@ steps:
   - CUDA_VISIBLE_DEVICES=0,1 pytest -v -s test_sharded_state_loader.py
   - CUDA_VISIBLE_DEVICES=0,1 pytest -v -s v1/shutdown
   - pytest -v -s models/multimodal/generation/test_maverick.py
+  - pytest -v -s v1/worker/test_worker_memory_snapshot.py
 
 - label: Plugin Tests (2 GPUs) # 40min
   timeout_in_minutes: 60
@@ -1042,3 +1045,15 @@ steps:
   commands:
     - pytest -v -s tests/distributed/test_context_parallel.py
     - pytest -v -s tests/distributed/test_nccl_symm_mem_allreduce.py
+
+##### RL Integration Tests #####
+- label: Prime-RL Integration Test # 15min
+  timeout_in_minutes: 30
+  optional: true
+  num_gpus: 2
+  working_dir: "/vllm-workspace"
+  source_file_dependencies:
+  - vllm/
+  - .buildkite/scripts/run-prime-rl-test.sh
+  commands:
+    - bash .buildkite/scripts/run-prime-rl-test.sh

benchmarks/benchmark_serving_structured_output.py

@@ -449,7 +449,8 @@ async def benchmark(
     def prepare_extra_body(request) -> dict:
         extra_body = {}
         # Add the schema to the extra_body
-        extra_body[request.structure_type] = request.schema
+        extra_body["structured_outputs"] = {}
+        extra_body["structured_outputs"][request.structure_type] = request.schema
         return extra_body
 
     print("Starting initial single prompt test run...")

benchmarks/kernels/benchmark_cutlass_moe_fp8.py

@@ -0,0 +1,406 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+Benchmark the performance of the cutlass_moe_fp8 kernel vs the triton_moe
+kernel. Both kernels take in fp8 quantized weights and 16-bit activations,
+but use different quantization strategies and backends.
+"""
+
+import nvtx
+import torch
+
+from vllm import _custom_ops as ops
+from vllm.model_executor.layers.fused_moe.config import fp8_w8a8_moe_quant_config
+from vllm.model_executor.layers.fused_moe.cutlass_moe import cutlass_moe_fp8
+from vllm.model_executor.layers.fused_moe.fused_moe import fused_experts, fused_topk
+from vllm.platforms import current_platform
+from vllm.utils import FlexibleArgumentParser
+
+# Weight shapes for different models: [num_experts, topk, hidden_size,
+# intermediate_size]
+WEIGHT_SHAPES_MOE = {
+    "mixtral-8x7b": [
+        [8, 2, 4096, 14336],
+    ],
+    "deepseek-v2": [
+        [160, 6, 5120, 12288],
+    ],
+    "custom-small": [
+        [8, 2, 2048, 7168],
+    ],
+    "glm45-fp8": [
+        [128, 8, 4096, 1408],
+    ],
+    "Llama-4-Maverick-17B-128E-Instruct-FP8": [
+        [128, 1, 5120, 8192],
+    ],
+}
+
+DEFAULT_MODELS = [
+    "mixtral-8x7b",
+]
+
+DEFAULT_BATCH_SIZES = [4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048]
+DEFAULT_TP_SIZES = [1]
+
+PER_ACT_TOKEN_OPTS = [False, True]
+PER_OUT_CH_OPTS = [False, True]
+
+FP8_DTYPE = current_platform.fp8_dtype()
+
+
+def bench_run(
+    results: list,
+    model: str,
+    num_experts: int,
+    topk: int,
+    per_act_token: bool,
+    per_out_ch: bool,
+    mkn: tuple[int, int, int],
+):
+    (m, k, n) = mkn
+
+    dtype = torch.half
+    device = "cuda"
+
+    # Create input activations
+    a = torch.randn((m, k), device=device, dtype=dtype) / 10
+
+    # Create weights
+    w1 = torch.randn((num_experts, 2 * n, k), device=device, dtype=dtype) / 10
+    w2 = torch.randn((num_experts, k, n), device=device, dtype=dtype) / 10
+
+    # Create FP8 quantized weights and scales for both kernels
+    w1_fp8q = torch.empty((num_experts, 2 * n, k), device=device, dtype=FP8_DTYPE)
+    w2_fp8q = torch.empty((num_experts, k, n), device=device, dtype=FP8_DTYPE)
+
+    # Create scales based on quantization strategy
+    if per_out_ch:
+        # Per-channel quantization
+        w1_scale = torch.empty(
+            (num_experts, 2 * n, 1), device=device, dtype=torch.float32
+        )
+        w2_scale = torch.empty((num_experts, k, 1), device=device, dtype=torch.float32)
+    else:
+        # Per-tensor quantization
+        w1_scale = torch.empty((num_experts, 1, 1), device=device, dtype=torch.float32)
+        w2_scale = torch.empty((num_experts, 1, 1), device=device, dtype=torch.float32)
+
+    # Quantize weights
+    for expert in range(num_experts):
+        if per_out_ch:
+            # Per-channel quantization - not yet implemented properly
+            # For now, fall back to per-tensor quantization
+            w1_fp8q[expert], w1_scale_temp = ops.scaled_fp8_quant(w1[expert])
+            w2_fp8q[expert], w2_scale_temp = ops.scaled_fp8_quant(w2[expert])
+            # Expand scalar scales to the expected per-channel shape
+            w1_scale[expert] = w1_scale_temp.expand(2 * n, 1)
+            w2_scale[expert] = w2_scale_temp.expand(k, 1)
+        else:
+            # Per-tensor quantization
+            w1_fp8q[expert], w1_scale_temp = ops.scaled_fp8_quant(w1[expert])
+            w2_fp8q[expert], w2_scale_temp = ops.scaled_fp8_quant(w2[expert])
+            # Store scalar scales in [1, 1] tensors
+            w1_scale[expert, 0, 0] = w1_scale_temp
+            w2_scale[expert, 0, 0] = w2_scale_temp
+
+    # Prepare weights for CUTLASS (no transpose needed)
+    w1_fp8q_cutlass = w1_fp8q  # Keep original [E, 2N, K]
+    w2_fp8q_cutlass = w2_fp8q  # Keep original [E, K, N]
+
+    # Create router scores and get topk
+    score = torch.randn((m, num_experts), device=device, dtype=dtype)
+    topk_weights, topk_ids, _ = fused_topk(a, score, topk, renormalize=False)
+
+    # WORKAROUND: CUTLASS MoE FP8 has issues with per-token quantization
+    # Force per-tensor quantization for all cases to match working e2e setup
+    a1_scale = torch.full((), 1e-2, device=device, dtype=torch.float32)
+    a2_scale = torch.full((), 1e-2, device=device, dtype=torch.float32)
+
+    # Force per-tensor quantization for all cases
+    per_act_token = False
+
+    # Create stride tensors for CUTLASS
+    ab_strides1 = torch.full((num_experts,), k, dtype=torch.int64, device=device)
+    ab_strides2 = torch.full((num_experts,), n, dtype=torch.int64, device=device)
+    c_strides1 = torch.full((num_experts,), 2 * n, dtype=torch.int64, device=device)
+    c_strides2 = torch.full((num_experts,), k, dtype=torch.int64, device=device)
+
+    def run_triton_moe(
+        a: torch.Tensor,
+        w1: torch.Tensor,
+        w2: torch.Tensor,
+        topk_weights: torch.Tensor,
+        topk_ids: torch.Tensor,
+        w1_scale: torch.Tensor,
+        w2_scale: torch.Tensor,
+        a1_scale: torch.Tensor,
+        a2_scale: torch.Tensor,
+        num_repeats: int,
+    ):
+        quant_config = fp8_w8a8_moe_quant_config(
+            w1_scale=w1_scale,
+            w2_scale=w2_scale,
+            a1_scale=a1_scale,
+            a2_scale=a2_scale,
+            per_act_token_quant=per_act_token,
+            per_out_ch_quant=per_out_ch,
+        )
+
+        for _ in range(num_repeats):
+            fused_experts(
+                a,
+                w1,
+                w2,
+                topk_weights,
+                topk_ids,
+                quant_config=quant_config,
+            )
+
+    def run_cutlass_moe_fp8(
+        a: torch.Tensor,
+        w1: torch.Tensor,
+        w2: torch.Tensor,
+        topk_weights: torch.Tensor,
+        topk_ids: torch.Tensor,
+        ab_strides1: torch.Tensor,
+        ab_strides2: torch.Tensor,
+        c_strides1: torch.Tensor,
+        c_strides2: torch.Tensor,
+        w1_scale: torch.Tensor,
+        w2_scale: torch.Tensor,
+        a1_scale: torch.Tensor,
+        a2_scale: torch.Tensor,
+        num_repeats: int,
+    ):
+        quant_config = fp8_w8a8_moe_quant_config(
+            w1_scale=w1_scale,
+            w2_scale=w2_scale,
+            a1_scale=a1_scale,
+            a2_scale=a2_scale,
+            per_act_token_quant=per_act_token,
+            per_out_ch_quant=per_out_ch,
+        )
+
+        for _ in range(num_repeats):
+            with nvtx.annotate("cutlass_moe_fp8", color="blue"):
+                cutlass_moe_fp8(
+                    a=a,
+                    w1_q=w1,
+                    w2_q=w2,
+                    topk_weights=topk_weights,
+                    topk_ids=topk_ids,
+                    ab_strides1=ab_strides1,
+                    ab_strides2=ab_strides2,
+                    c_strides1=c_strides1,
+                    c_strides2=c_strides2,
+                    quant_config=quant_config,
+                    activation="silu",
+                    global_num_experts=num_experts,
+                )
+
+    # Pre-create quantization config to avoid creating it inside CUDA graph
+    quant_config = fp8_w8a8_moe_quant_config(
+        w1_scale=w1_scale,
+        w2_scale=w2_scale,
+        a1_scale=a1_scale,
+        a2_scale=a2_scale,
+        per_act_token_quant=per_act_token,
+        per_out_ch_quant=per_out_ch,
+    )
+
+    # Create CUDA graphs for CUTLASS (match benchmark_moe.py pattern exactly)
+    cutlass_stream = torch.cuda.Stream()
+    cutlass_graph = torch.cuda.CUDAGraph()
+    with torch.cuda.graph(cutlass_graph, stream=cutlass_stream):
+        # Capture 10 invocations like benchmark_moe.py
+        for _ in range(10):
+            cutlass_moe_fp8(
+                a=a,
+                w1_q=w1_fp8q_cutlass,
+                w2_q=w2_fp8q_cutlass,
+                topk_weights=topk_weights,
+                topk_ids=topk_ids,
+                ab_strides1=ab_strides1,
+                ab_strides2=ab_strides2,
+                c_strides1=c_strides1,
+                c_strides2=c_strides2,
+                quant_config=quant_config,
+                activation="silu",
+                global_num_experts=num_experts,
+            )
+    torch.cuda.synchronize()
+
+    # Create CUDA graphs for Triton (match benchmark_moe.py pattern exactly)
+    triton_stream = torch.cuda.Stream()
+    triton_graph = torch.cuda.CUDAGraph()
+    with torch.cuda.graph(triton_graph, stream=triton_stream):
+        # Capture 10 invocations like benchmark_moe.py
+        for _ in range(10):
+            fused_experts(
+                a,
+                w1_fp8q,
+                w2_fp8q,
+                topk_weights,
+                topk_ids,
+                quant_config=quant_config,
+            )
+    torch.cuda.synchronize()
+
+    def bench_cuda_graph(graph, num_warmup=5, num_iters=100):
+        """Benchmark CUDA graph using events like benchmark_moe.py"""
+        # Warmup
+        for _ in range(num_warmup):
+            graph.replay()
+        torch.cuda.synchronize()
+
+        # Timing
+        start_event = torch.cuda.Event(enable_timing=True)
+        end_event = torch.cuda.Event(enable_timing=True)
+
+        latencies = []
+        for _ in range(num_iters):
+            torch.cuda.synchronize()
+            start_event.record()
+            graph.replay()
+            end_event.record()
+            end_event.synchronize()
+            latencies.append(start_event.elapsed_time(end_event))
+
+        # Divide by 10 since graph contains 10 calls
+        return sum(latencies) / (num_iters * 10)
+
+    # Benchmark parameters
+    num_warmup = 5
+    num_iters = 100
+
+    # Benchmark only CUDA graphs (more reliable and faster)
+    # Benchmark Triton MoE with CUDA graphs
+    triton_graph_time = bench_cuda_graph(
+        triton_graph, num_warmup=num_warmup, num_iters=num_iters
+    )
+
+    # Benchmark CUTLASS MoE with CUDA graphs
+    cutlass_graph_time = bench_cuda_graph(
+        cutlass_graph, num_warmup=num_warmup, num_iters=num_iters
+    )
+
+    # Convert ms to us and return results
+    triton_time_us = triton_graph_time * 1000
+    cutlass_time_us = cutlass_graph_time * 1000
+
+    return {
+        "batch_size": m,
+        "triton_time_us": triton_time_us,
+        "cutlass_time_us": cutlass_time_us,
+    }
+
+
+def main(args):
+    print("Benchmarking models:")
+    for i, model in enumerate(args.models):
+        print(f"[{i}]  {model}")
+
+    all_results = []
+
+    for model in args.models:
+        for tp in args.tp_sizes:
+            for layer in WEIGHT_SHAPES_MOE[model]:
+                num_experts = layer[0]
+                topk = layer[1]
+                size_k = layer[2]
+                size_n = layer[3] // tp
+
+                if len(args.limit_k) > 0 and size_k not in args.limit_k:
+                    continue
+
+                if len(args.limit_n) > 0 and size_n not in args.limit_n:
+                    continue
+
+                for per_act_token in args.per_act_token_opts:
+                    for per_out_ch in args.per_out_ch_opts:
+                        print(
+                            f"\n=== {model}, experts={num_experts}, topk={topk},"
+                            f"per_act={per_act_token}, per_out_ch={per_out_ch} ==="
+                        )
+
+                        config_results = []
+                        for size_m in args.batch_sizes:
+                            mkn = (size_m, size_k, size_n)
+                            result = bench_run(
+                                [],  # Not used anymore
+                                model,
+                                num_experts,
+                                topk,
+                                per_act_token,
+                                per_out_ch,
+                                mkn,
+                            )
+                            if result:
+                                config_results.append(result)
+
+                        # Print results table for this configuration
+                        if config_results:
+                            print(
+                                f"\n{'Batch Size':<12}"
+                                f"{'Triton (us)':<15}"
+                                f"{'CUTLASS (us)':<15}"
+                            )
+                            print("-" * 45)
+                            for result in config_results:
+                                print(
+                                    f"{result['batch_size']:<12}"
+                                    f"{result['triton_time_us']:<15.2f}"
+                                    f"{result['cutlass_time_us']:<15.2f}"
+                                )
+
+                            all_results.extend(config_results)
+
+    print(f"\nTotal benchmarks completed: {len(all_results)}")
+
+
+if __name__ == "__main__":
+    parser = FlexibleArgumentParser(
+        description="""Benchmark CUTLASS FP8 MOE vs Triton FP8 FUSED MOE
+         across specified models/shapes/batches
+
+        Example usage:
+        python benchmark_cutlass_moe_fp8.py  \
+            --model "Llama-4-Maverick-17B-128E-Instruct-FP8"  \
+            --tp-sizes 8 \
+            --batch-size 2 4 8  \
+            --per-act-token-opts false \
+            --per-out-ch-opts false
+
+        """
+    )
+    parser.add_argument(
+        "--models",
+        nargs="+",
+        type=str,
+        default=DEFAULT_MODELS,
+        choices=WEIGHT_SHAPES_MOE.keys(),
+    )
+    parser.add_argument("--tp-sizes", nargs="+", type=int, default=DEFAULT_TP_SIZES)
+    parser.add_argument(
+        "--batch-sizes", nargs="+", type=int, default=DEFAULT_BATCH_SIZES
+    )
+    parser.add_argument("--limit-k", nargs="+", type=int, default=[])
+    parser.add_argument("--limit-n", nargs="+", type=int, default=[])
+    parser.add_argument(
+        "--per-act-token-opts",
+        nargs="+",
+        type=lambda x: x.lower() == "true",
+        default=[False, True],
+        help="Per-activation token quantization options (true/false)",
+    )
+    parser.add_argument(
+        "--per-out-ch-opts",
+        nargs="+",
+        type=lambda x: x.lower() == "true",
+        default=[False, True],
+        help="Per-output channel quantization options (true/false)",
+    )
+
+    args = parser.parse_args()
+    main(args)

cmake/cpu_extension.cmake

@@ -258,7 +258,8 @@ set(VLLM_EXT_SRC
     "csrc/cpu/layernorm.cpp"
     "csrc/cpu/mla_decode.cpp"
     "csrc/cpu/pos_encoding.cpp"
-    "csrc/cpu/torch_bindings.cpp")
+    "csrc/cpu/torch_bindings.cpp"
+    "csrc/moe/dynamic_4bit_int_moe_cpu.cpp")
 
 if (AVX512_FOUND AND NOT AVX512_DISABLED)
     set(VLLM_EXT_SRC

csrc/attention/mla/cutlass_sm100_mla/device/sm100_mla.hpp

@@ -135,10 +135,10 @@ public:
     max_splits = min(16, max_splits);
 
     // TODO: This avoids a hang when the batch size larger than 1 and 
-    // there is more than 4 kv_splits. 
+    // there is more than 1 kv_splits. 
     // Discuss with NVIDIA how this can be fixed.
     if (B > 1) {
-      max_splits = min(2, max_splits);
+      max_splits = min(1, max_splits);
     }
     
     // printf("    max_splits = %d\n", max_splits);

csrc/cpu/torch_bindings.cpp

@@ -88,8 +88,18 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
       "    int tp_rank, int blocksparse_local_blocks,"
       "    int blocksparse_vert_stride, int blocksparse_block_size,"
       "    int blocksparse_head_sliding_step) -> ()");
+
   ops.impl("paged_attention_v1", torch::kCPU, &paged_attention_v1);
 
+  ops.def(
+      "dynamic_4bit_int_moe("
+      "Tensor x, Tensor topk_ids, Tensor topk_weights,"
+      "Tensor w13_packed, Tensor w2_packed, int H, int I, int I2,"
+      "int group_size, bool apply_router_weight_on_input, int activation_kind"
+      ") -> Tensor");
+
+  ops.impl("dynamic_4bit_int_moe", torch::kCPU, &dynamic_4bit_int_moe_cpu);
+
   // PagedAttention V2.
   ops.def(
       "paged_attention_v2("

csrc/moe/dynamic_4bit_int_moe_cpu.cpp

@@ -0,0 +1,156 @@
+#include <ATen/ATen.h>
+#include <ATen/Parallel.h>
+#include <torch/all.h>
+
+// _dyn_quant_matmul_4bit is only available on AArch64.
+#if defined(__aarch64__)
+  #include <ATen/ops/_dyn_quant_matmul_4bit.h>
+#endif
+
+inline torch::Tensor mm(const torch::Tensor& a, const torch::Tensor& packed_w,
+                        int64_t group_size_eff, int64_t in_features,
+                        int64_t out_features) {
+#if defined(__aarch64__)
+  return at::_ops::_dyn_quant_matmul_4bit::call(a, packed_w, group_size_eff,
+                                                in_features, out_features);
+#else
+  TORCH_CHECK(false,
+              "dynamic 4-bit int MoE path requires AArch64 (ARM64); "
+              "_dyn_quant_matmul_4bit is unavailable on this architecture");
+  return {};
+#endif
+}
+
+enum ActivationKind : int64_t {
+  SwiGLU_Gu = 0,  // act = SiLU(g) * u
+  SwiGLUOAI = 1,  // act = SiLU(u) * g
+  SiLU = 2        // SiLU
+};
+
+torch::Tensor dynamic_4bit_int_moe_cpu(
+    torch::Tensor x, torch::Tensor topk_ids, torch::Tensor topk_weights,
+    torch::Tensor w13_packed, torch::Tensor w2_packed, int64_t H, int64_t I,
+    int64_t I2, int64_t group_size, bool apply_router_weight_on_input,
+    int64_t activation_kind) {
+  TORCH_CHECK(x.dim() == 2, "x must be 2D");
+  TORCH_CHECK(topk_ids.dim() == 2 && topk_weights.dim() == 2,
+              "topk tensors must be [T, K]");
+  TORCH_CHECK(
+      w13_packed.size(0) == w2_packed.size(0),
+      "w13_packed and w2_packed must have same number of experts in dim 0");
+  TORCH_CHECK(I2 == 2 * I, "I2 must equal 2*I");
+
+  const int64_t T = x.size(0);
+  const int64_t K = topk_ids.size(1);
+  const int64_t E = w13_packed.size(0);
+  const int64_t N = T * K;
+
+  auto x_c = x.contiguous();
+  auto ids_c = topk_ids.contiguous();
+  auto gates_c = topk_weights.to(at::kFloat).contiguous();
+
+  // bucketing tokens -> experts
+  c10::SmallVector<int64_t, 64> counts(
+      E, 0);  // Small vector uses stack allocation
+  {
+    const auto* ids_ptr = ids_c.data_ptr<int64_t>();
+    for (int64_t i = 0; i < N; ++i) {
+      const int64_t e_id = ids_ptr[i];
+      TORCH_CHECK(0 <= e_id && e_id < E, "expert id out of range");
+      counts[e_id]++;
+    }
+  }
+  c10::SmallVector<int64_t, 65> offsets(E + 1, 0);  // ( E +1 )
+  for (int64_t e = 0; e < E; ++e) offsets[e + 1] = offsets[e] + counts[e];
+
+  auto expert_tokens = at::empty({offsets[E]}, ids_c.options());
+  auto expert_gates = at::empty({offsets[E]}, gates_c.options());
+  {
+    c10::SmallVector<int64_t, 64> cursor(E, 0);
+    const auto* ids_ptr = ids_c.data_ptr<int64_t>();
+    const auto* gts_ptr = gates_c.data_ptr<float>();
+    auto* tok_ptr = expert_tokens.data_ptr<int64_t>();
+    auto* gate_ptr = expert_gates.data_ptr<float>();
+
+    for (int64_t t = 0; t < T; ++t) {
+      const int64_t base = t * K;
+      for (int64_t k = 0; k < K; ++k) {
+        const int64_t idx = base + k;
+        const int64_t e = ids_ptr[idx];
+        const int64_t p = offsets[e] + (cursor[e]++);
+        tok_ptr[p] = t;
+        gate_ptr[p] = gts_ptr[idx];
+      }
+    }
+  }
+
+  const int64_t g_eff_13 = (group_size != -1) ? group_size : H;
+  const int64_t g_eff_2 = (group_size != -1) ? group_size : I;
+
+  // Per-expert outputs filled in parallel
+  std::vector<torch::Tensor> y_list(E);
+  y_list.resize(E);
+
+  at::parallel_for(0, E, 1, [&](int64_t e_begin, int64_t e_end) {
+    for (int64_t e = e_begin; e < e_end; ++e) {
+      const int64_t te = counts[e];
+      if (te == 0) {
+        y_list[e] = at::empty({0, H}, x_c.options());
+        continue;
+      }
+
+      const int64_t start = offsets[e];
+
+      auto sel_tokens =
+          expert_tokens.narrow(/*dim=*/0, /*start=*/start, /*length=*/te);
+      auto gates_e =
+          expert_gates.narrow(/*dim=*/0, /*start=*/start, /*length=*/te);
+
+      auto x_e = x_c.index_select(/*dim=*/0, sel_tokens);
+
+      if (apply_router_weight_on_input) {
+        x_e = x_e.mul(gates_e.unsqueeze(1));
+      }
+
+      auto w13_e = w13_packed.select(/*dim=*/0, e);
+      auto w2_e = w2_packed.select(/*dim=*/0, e);
+
+      // W13
+      auto y13 =
+          mm(x_e, w13_e, g_eff_13, /*in_features=*/H, /*out_features=*/I2);
+
+      auto g_part = y13.narrow(/*dim=*/1, /*start=*/0, /*length=*/I);
+      auto u_part = y13.narrow(/*dim=*/1, /*start=*/I, /*length=*/I);
+
+      torch::Tensor act;
+      if (activation_kind == ActivationKind::SwiGLUOAI) {  // SwiGLUOAI
+        constexpr double kAlpha = 1.702;                   // GPT-OSS default
+        constexpr double kLimit = 7.0;                     // GPT-OSS default
+        auto gate_c = at::clamp_max(g_part, kLimit);
+        auto up_c = at::clamp(u_part, -kLimit, kLimit);
+        auto glu = gate_c.mul(at::sigmoid(gate_c.mul(kAlpha)));
+        act = up_c.add(1.0).mul(glu);
+      } else {  // SiLU , SwiGLU_GU, vLLM maps silu to SiluAndMul()
+        act = at::silu(g_part).mul(u_part);
+      }
+
+      // W2
+      auto y = mm(act, w2_e, g_eff_2, /*in_features=*/I, /*out_features=*/H);
+
+      if (!apply_router_weight_on_input) {
+        y = y.mul(gates_e.unsqueeze(1));
+      }
+
+      // Store per-expert result
+      y_list[e] = y;
+    }
+  });
+
+  // Concatenate all expert outputs to match expert_tokens order
+  auto Y_all = at::cat(y_list, /*dim=*/0);
+  auto out = at::zeros({T, H}, x.options());
+  out =
+      at::index_add(out, /*dim=*/0, /*index=*/expert_tokens, /*source=*/Y_all);
+
+  return out;
+}

csrc/ops.h

@@ -328,6 +328,12 @@ void selective_scan_fwd(const torch::Tensor& u, const torch::Tensor& delta,
                         const std::optional<torch::Tensor>& has_initial_state,
                         const torch::Tensor& ssm_states, int64_t pad_slot_id);
 
+torch::Tensor dynamic_4bit_int_moe_cpu(
+    torch::Tensor x, torch::Tensor topk_ids, torch::Tensor topk_weights,
+    torch::Tensor w13_packed, torch::Tensor w2_packed, int64_t H, int64_t I,
+    int64_t I2, int64_t group_size, bool apply_router_weight_on_input,
+    int64_t activation_kind);
+
 using fptr_t = int64_t;
 fptr_t init_custom_ar(const std::vector<int64_t>& fake_ipc_ptrs,
                       torch::Tensor& rank_data, int64_t rank,

csrc/quantization/activation_kernels.cu

@@ -23,9 +23,14 @@
 typedef __hip_bfloat162 __nv_bfloat162;
 typedef __hip_bfloat16 __nv_bfloat16;
 typedef __hip_bfloat16_raw __nv_bfloat16_raw;
-
+  #if defined(HIP_FP8_TYPE_OCP)
 typedef __hip_fp8_e4m3 __nv_fp8_e4m3;
 typedef __hip_fp8x4_e4m3 __nv_fp8x4_e4m3;
+  #else
+// ROCm 6.2 fallback: only *_fnuz types exist
+typedef __hip_fp8_e4m3_fnuz __nv_fp8_e4m3;
+typedef __hip_fp8x4_e4m3_fnuz __nv_fp8x4_e4m3;
+  #endif
 #endif
 
 #include "core/registration.h"

csrc/rocm/attention.cu

@@ -25,6 +25,12 @@
 #include "../attention/dtype_fp8.cuh"
 #include "../quantization/fp8/amd/quant_utils.cuh"
 
+// ROCm 6.2 compatibility: map OCP fp8 types to FNUZ variants if OCP is absent
+#if !defined(HIP_FP8_TYPE_OCP)
+using __hip_fp8_e4m3 = __hip_fp8_e4m3_fnuz;
+using __hip_fp8_e5m2 = __hip_fp8_e5m2_fnuz;
+#endif
+
 #if defined(__HIPCC__) && \
     (defined(__gfx90a__) || defined(__gfx942__) || defined(__gfx950__))
   #define __HIP__GFX9__

docs/features/disagg_prefill.md

@@ -34,7 +34,7 @@ Now supports 5 types of connectors:
 For NixlConnector, you may also specify one or multiple NIXL_Backend. Such as:
 
   ```bash
-  --kv-transfer-config '{"kv_connector":"NixlConnector","kv_role":"kv_both", "kv_buffer_device":"cuda", "kv_connector_extra_config":{"backend":["UCX", "GDS"]}'
+  --kv-transfer-config '{"kv_connector":"NixlConnector","kv_role":"kv_both", "kv_buffer_device":"cuda", "kv_connector_extra_config":{"backends":["UCX", "GDS"]}}'
   ```
 
 - **OffloadingConnector**: enable offloading of KV data to CPU memory, customizing the CPU block size (in tokens) and number of blocks to allocate (per worker):

docs/serving/expert_parallel_deployment.md

@@ -193,7 +193,7 @@ For production deployments requiring strict SLA guarantees for time-to-first-tok
 
 1. **Install gdrcopy/ucx/nixl**: For maximum performance, run the [install_gdrcopy.sh](gh-file:tools/install_gdrcopy.sh) script to install `gdrcopy` (e.g., `install_gdrcopy.sh "${GDRCOPY_OS_VERSION}" "12.8" "x64"`). You can find available OS versions [here](https://developer.download.nvidia.com/compute/redist/gdrcopy/CUDA%2012.8/). If `gdrcopy` is not installed, things will still work with a plain `pip install nixl`, just with lower performance. `nixl` and `ucx` are installed as dependencies via pip.
 
-2. **Configure Both Instances**: Add this flag to both prefill and decode instances `--kv-transfer-config '{"kv_connector":"NixlConnector","kv_role":"kv_both"}`. Noted, you may also specify one or multiple NIXL_Backend. Such as: `--kv-transfer-config '{"kv_connector":"NixlConnector","kv_role":"kv_both", "kv_connector_extra_config":{"backend":["UCX", "GDS"]}'`
+2. **Configure Both Instances**: Add this flag to both prefill and decode instances `--kv-transfer-config '{"kv_connector":"NixlConnector","kv_role":"kv_both"}`. Noted, you may also specify one or multiple NIXL_Backend. Such as: `--kv-transfer-config '{"kv_connector":"NixlConnector","kv_role":"kv_both", "kv_connector_extra_config":{"backends":["UCX", "GDS"]}}'`
 
 3. **Client Orchestration**: Use the client-side script below to coordinate prefill/decode operations. We are actively working on routing solutions.
 

requirements/common.txt

@@ -24,7 +24,7 @@ outlines_core == 0.2.11
 # required for outlines backend disk cache
 diskcache == 5.6.3
 lark == 1.2.2
-xgrammar == 0.1.24; platform_machine == "x86_64" or platform_machine == "aarch64" or platform_machine == "arm64"
+xgrammar == 0.1.25; platform_machine == "x86_64" or platform_machine == "aarch64" or platform_machine == "arm64"
 typing_extensions >= 4.10
 filelock >= 3.16.1 # need to contain https://github.com/tox-dev/filelock/pull/317
 partial-json-parser # used for parsing partial JSON outputs

tests/conftest.py

@@ -1079,7 +1079,7 @@ def dummy_llava_path():
                           local_dir=_dummy_llava_path,
                           ignore_patterns=[
                               "*.bin", "*.bin.index.json", "*.pt", "*.h5",
-                              "*.msgpack"
+                              "*.msgpack", "*.safetensors"
                           ])
         assert os.path.exists(json_path)
         with open(json_path) as f:
@@ -1098,7 +1098,7 @@ def dummy_gemma2_embedding_path():
                           local_dir=_dummy_gemma2_embedding_path,
                           ignore_patterns=[
                               "*.bin", "*.bin.index.json", "*.pt", "*.h5",
-                              "*.msgpack"
+                              "*.msgpack", "*.safetensors"
                           ])
         assert os.path.exists(json_path)
         with open(json_path) as f:

tests/distributed/test_pipeline_parallel.py

@@ -382,7 +382,6 @@ def test_tp_language_generation(
     test_options: PPTestOptions,
     num_gpus_available,
 ):
-    pytest.skip("Skipping the test until V1 passes it.")
     _compare_tp(model_id,
                 parallel_setup,
                 distributed_backend,
@@ -410,7 +409,6 @@ def test_tp_language_embedding(
     test_options: PPTestOptions,
     num_gpus_available,
 ):
-    pytest.skip("Skipping the test until V1 passes it.")
     _compare_tp(model_id,
                 parallel_setup,
                 distributed_backend,
@@ -438,7 +436,6 @@ def test_tp_multimodal_generation(
     test_options: PPTestOptions,
     num_gpus_available,
 ):
-    pytest.skip("Skipping the test until V1 passes it.")
     _compare_tp(model_id,
                 parallel_setup,
                 distributed_backend,

tests/entrypoints/openai/test_response_api_with_harmony.py

@@ -523,6 +523,7 @@ async def test_function_calling(client: OpenAI, model_name: str):
         input="What's the weather like in Paris today?",
         tools=tools,
         temperature=0.0,
+        extra_body={"request_id": "test_function_calling_non_resp"},
     )
     assert response is not None
     assert response.status == "completed"

tests/entrypoints/openai/test_serving_chat.py

@@ -194,6 +194,7 @@ async def test_gpt_oss_multi_turn_chat(gptoss_client: OpenAI,
     assert tc.function is not None and tc.function.name == "get_current_weather"
     args1 = tc.function.arguments
     assert args1 is not None and len(args1) > 0
+    assert not first_msg.content
 
     messages.append({"role": "assistant", "content": args1})
     messages.append({

tests/kernels/attention/test_attention_selector.py

@@ -85,8 +85,7 @@ def test_env(
         if device == "cpu":
             with patch("vllm.attention.selector.current_platform",
                        CpuPlatform()):
-                backend = get_attn_backend(16, torch.float16, None, block_size,
-                                           False)
+                backend = get_attn_backend(16, torch.float16, None, block_size)
             assert backend.get_name() == "TORCH_SDPA_VLLM_V1"
 
         elif device == "hip":
@@ -106,7 +105,6 @@ def test_env(
                                              torch.float16,
                                              None,
                                              block_size,
-                                             False,
                                              use_mla=use_mla)
                         assert f"The selected backend, {name}" in str(
                             exc_info.value)
@@ -117,7 +115,6 @@ def test_env(
                                              torch.float16,
                                              None,
                                              block_size,
-                                             False,
                                              use_mla=use_mla)
                         assert f"The selected backend, {name}" in str(
                             exc_info.value)
@@ -127,7 +124,6 @@ def test_env(
                                                    torch.float16,
                                                    None,
                                                    block_size,
-                                                   False,
                                                    use_mla=use_mla)
                         expected = f"{name}_VLLM_V1"
                         assert backend.get_name() == expected
@@ -136,7 +132,6 @@ def test_env(
                                                torch.float16,
                                                None,
                                                block_size,
-                                               False,
                                                use_mla=use_mla)
                     expected = "TRITON_ATTN_VLLM_V1"
                     assert backend.get_name() == expected
@@ -164,7 +159,6 @@ def test_env(
                                                        torch.float16,
                                                        None,
                                                        block_size,
-                                                       False,
                                                        use_mla=use_mla)
                             expected = "CUTLASS_MLA_VLLM_V1"
                             assert backend.get_name() == expected
@@ -179,7 +173,6 @@ def test_env(
                                                        torch.float16,
                                                        None,
                                                        block_size,
-                                                       False,
                                                        use_mla=use_mla)
                             expected = "FLASHINFER_MLA"
                             assert backend.get_name() == expected
@@ -199,7 +192,6 @@ def test_env(
                                                            torch.float16,
                                                            None,
                                                            block_size,
-                                                           False,
                                                            use_mla=use_mla)
                                 expected = f"{name}_VLLM_V1"
                                 assert backend.get_name() == expected
@@ -208,7 +200,6 @@ def test_env(
                                                    torch.float16,
                                                    None,
                                                    block_size,
-                                                   False,
                                                    use_mla=use_mla)
                         expected = "FLASH_ATTN_MLA"
                         assert backend.get_name() == expected
@@ -218,7 +209,6 @@ def test_env(
                                                    torch.float16,
                                                    None,
                                                    block_size,
-                                                   False,
                                                    use_mla=use_mla)
                         expected = "TRITON_MLA_VLLM_V1"
                         assert backend.get_name() == expected
@@ -227,7 +217,6 @@ def test_env(
                                                torch.float16,
                                                None,
                                                block_size,
-                                               False,
                                                use_mla=use_mla)
                     expected = "FLASHINFER_VLLM_V1"
                     assert backend.get_name() == expected
@@ -236,7 +225,6 @@ def test_env(
                                                torch.float16,
                                                None,
                                                block_size,
-                                               False,
                                                use_mla=use_mla)
                     expected = "FLASH_ATTN_VLLM_V1"
                     assert backend.get_name() == expected
@@ -245,7 +233,6 @@ def test_env(
                                                torch.float16,
                                                None,
                                                block_size,
-                                               False,
                                                use_mla=use_mla)
                     assert backend.get_name() == "FLEX_ATTENTION", (
                         "Should fallback to FlexAttention if head size is "
@@ -264,13 +251,13 @@ def test_fp32_fallback(
         if device == "cpu":
             with patch("vllm.attention.selector.current_platform",
                        CpuPlatform()):
-                backend = get_attn_backend(16, torch.float32, None, 16, False)
+                backend = get_attn_backend(16, torch.float32, None, 16)
             assert backend.get_name() == "TORCH_SDPA_VLLM_V1"
 
         elif device == "cuda":
             with patch("vllm.attention.selector.current_platform",
                        CudaPlatform()):
-                backend = get_attn_backend(16, torch.float32, None, 16, False)
+                backend = get_attn_backend(16, torch.float32, None, 16)
             assert backend.get_name() == "FLEX_ATTENTION"
 
 
@@ -286,29 +273,29 @@ def test_flash_attn(monkeypatch: pytest.MonkeyPatch):
         monkeypatch.setattr(torch.cuda,
                             "get_device_capability",
                             lambda _=None: (7, 5))
-        backend = get_attn_backend(16, torch.float16, None, 16, False)
+        backend = get_attn_backend(16, torch.float16, None, 16)
         assert backend.get_name() != STR_FLASH_ATTN_VAL
 
         # Reset the monkeypatch for subsequent tests
         monkeypatch.undo()
 
         # Unsupported data type
-        backend = get_attn_backend(16, torch.float8_e4m3fn, None, 16, False)
+        backend = get_attn_backend(16, torch.float8_e4m3fn, None, 16)
         assert backend.get_name() != STR_FLASH_ATTN_VAL
 
         # Unsupported kv cache data type
-        backend = get_attn_backend(16, torch.float16, "fp8", 16, False)
+        backend = get_attn_backend(16, torch.float16, "fp8", 16)
         assert backend.get_name() != STR_FLASH_ATTN_VAL
 
         # Unsupported block size
-        backend = get_attn_backend(16, torch.float16, None, 8, False)
+        backend = get_attn_backend(16, torch.float16, None, 8)
         assert backend.get_name() != STR_FLASH_ATTN_VAL
 
         # flash-attn is not installed
         import sys
         original_module = sys.modules.get('vllm_flash_attn')
         monkeypatch.setitem(sys.modules, 'vllm_flash_attn', None)
-        backend = get_attn_backend(16, torch.float16, None, 16, False)
+        backend = get_attn_backend(16, torch.float16, None, 16)
         assert backend.get_name() != STR_FLASH_ATTN_VAL
 
         # Restore the original module if it existed
@@ -319,11 +306,7 @@ def test_flash_attn(monkeypatch: pytest.MonkeyPatch):
             monkeypatch.delitem(sys.modules, 'vllm_flash_attn', raising=False)
 
         # Unsupported head size
-        backend = get_attn_backend(17, torch.float16, None, 16, False)
-        assert backend.get_name() != STR_FLASH_ATTN_VAL
-
-        # Attention-free models should bypass env and use PlaceholderAttention
-        backend = get_attn_backend(16, torch.float16, None, 16, True)
+        backend = get_attn_backend(17, torch.float16, None, 16)
         assert backend.get_name() != STR_FLASH_ATTN_VAL
 
 
@@ -336,5 +319,5 @@ def test_invalid_env(monkeypatch: pytest.MonkeyPatch):
 
         # Should raise ValueError for invalid backend
         with pytest.raises(ValueError) as exc_info:
-            get_attn_backend(32, torch.float16, None, 16, False)
+            get_attn_backend(32, torch.float16, None, 16)
         assert "Invalid value 'INVALID'" in str(exc_info.value)

tests/models/multimodal/processing/test_common.py

@@ -12,11 +12,11 @@ from mistral_common.protocol.instruct.request import ChatCompletionRequest
 from PIL import Image
 
 from vllm.config import ModelConfig
-from vllm.inputs import InputProcessingContext
 from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalDataDict
 from vllm.multimodal.cache import MultiModalProcessorOnlyCache
 from vllm.multimodal.inputs import MultiModalInputs
-from vllm.multimodal.processing import BaseMultiModalProcessor
+from vllm.multimodal.processing import (BaseMultiModalProcessor,
+                                        InputProcessingContext)
 from vllm.transformers_utils.tokenizer import (AnyTokenizer, MistralTokenizer,
                                                cached_tokenizer_from_config,
                                                encode_tokens)

tests/models/multimodal/processing/test_tensor_schema.py

@@ -18,10 +18,10 @@ from vllm.config import ModelConfig, VllmConfig, set_current_vllm_config
 from vllm.distributed import (cleanup_dist_env_and_memory,
                               init_distributed_environment,
                               initialize_model_parallel)
-from vllm.inputs import InputProcessingContext
 from vllm.model_executor.model_loader.utils import set_default_torch_dtype
 from vllm.multimodal import MULTIMODAL_REGISTRY, BatchedTensorInputs
-from vllm.multimodal.processing import BaseMultiModalProcessor
+from vllm.multimodal.processing import (BaseMultiModalProcessor,
+                                        InputProcessingContext)
 from vllm.multimodal.utils import group_mm_kwargs_by_modality
 from vllm.transformers_utils.tokenizer import cached_tokenizer_from_config
 from vllm.utils import is_list_of

tests/models/test_oot_registration.py

@@ -42,7 +42,6 @@ def test_oot_registration_text_generation(
             assert rest == ""
 
 
-@pytest.mark.skip(reason="This test is skipped because it failed on V1.")
 @create_new_process_for_each_test()
 def test_oot_registration_embedding(
     monkeypatch: pytest.MonkeyPatch,
@@ -63,7 +62,6 @@ def test_oot_registration_embedding(
 image = convert_image_mode(ImageAsset("cherry_blossom").pil_image, "RGB")
 
 
-@pytest.mark.skip(reason="This test is skipped because it failed on V1.")
 @create_new_process_for_each_test()
 def test_oot_registration_multimodal(
     monkeypatch: pytest.MonkeyPatch,

tests/models/utils.py

@@ -11,8 +11,9 @@ import torch.nn.functional as F
 from transformers import PretrainedConfig
 
 from vllm.config import ModelConfig, ModelDType, RunnerOption
-from vllm.inputs import InputContext
 from vllm.logprobs import Logprob, PromptLogprobs, SampleLogprobs
+from vllm.multimodal.processing import InputProcessingContext
+from vllm.transformers_utils.tokenizer import cached_tokenizer_from_config
 
 from .registry import HF_EXAMPLE_MODELS
 
@@ -264,7 +265,7 @@ def build_model_context(
     limit_mm_per_prompt: Optional[dict[str, int]] = None,
     mm_processor_cache_gb: int = 0,
 ):
-    """Creates an InputContext for a given model.
+    """Creates an InputProcessingContext for a given model.
 
     Args:
         model_id: ID of the model being considered.
@@ -273,7 +274,7 @@ def build_model_context(
         limit_mm_per_prompt: Multimodal limits.
 
     Returns:
-        InputContext for the model being considered.
+        InputProcessingContext for the model being considered.
     """
     model_info = HF_EXAMPLE_MODELS.find_hf_info(model_id)
     model_info.check_available_online(on_fail="skip")
@@ -298,7 +299,11 @@ def build_model_context(
         enforce_eager=model_info.enforce_eager,
         **model_config_kwargs,
     )
-    return InputContext(model_config)
+
+    return InputProcessingContext(
+        model_config,
+        tokenizer=cached_tokenizer_from_config(model_config),
+    )
 
 
 def check_embeddings_close(

tests/multimodal/test_processing.py

@@ -8,11 +8,11 @@ import numpy as np
 import pytest
 
 from vllm.config import ModelConfig
-from vllm.inputs import InputProcessingContext
 from vllm.multimodal import MULTIMODAL_REGISTRY
 # yapf conflicts with isort for this block
 # yapf: disable
-from vllm.multimodal.processing import (PlaceholderFeaturesInfo,
+from vllm.multimodal.processing import (InputProcessingContext,
+                                        PlaceholderFeaturesInfo,
                                         PromptIndexTargets, PromptInsertion,
                                         PromptReplacement, apply_text_matches,
                                         apply_token_matches,

tests/reasoning/test_base_thinking_reasoning_parser.py

@@ -0,0 +1,392 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import pytest
+from transformers import AutoTokenizer
+
+from tests.reasoning.utils import run_reasoning_extraction
+from vllm.entrypoints.openai.protocol import ChatCompletionRequest
+from vllm.reasoning.basic_parsers import BaseThinkingReasoningParser
+
+
+# Create a concrete test implementation of BaseThinkingReasoningParser
+class TestThinkingReasoningParser(BaseThinkingReasoningParser):
+    """Test implementation of BaseThinkingReasoningParser."""
+
+    @property
+    def start_token(self) -> str:
+        return "<test:think>"
+
+    @property
+    def end_token(self) -> str:
+        return "</test:think>"
+
+
+class TestThinkingReasoningParserAlt(BaseThinkingReasoningParser):
+    """Alternative test implementation with different tokens."""
+
+    @property
+    def start_token(self) -> str:
+        return "<alt:start>"
+
+    @property
+    def end_token(self) -> str:
+        return "<alt:end>"
+
+
+# Use a test model
+REASONING_MODEL_NAME = "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B"
+
+
+@pytest.fixture(scope="module")
+def test_tokenizer():
+    tokenizer = AutoTokenizer.from_pretrained(REASONING_MODEL_NAME)
+    # Add custom test tokens
+    test_tokens = ["<test:think>", "</test:think>", "<alt:start>", "<alt:end>"]
+    existing_tokens = set(tokenizer.get_vocab().keys())
+    new_tokens = [
+        token for token in test_tokens if token not in existing_tokens
+    ]
+    if new_tokens:
+        tokenizer.add_tokens(new_tokens)
+    return tokenizer
+
+
+class TestBaseThinkingReasoningParserInit:
+    """
+        Test initialization and basic properties of
+        BaseThinkingReasoningParser.
+    """
+
+    def test_successful_initialization(self, test_tokenizer):
+        """Test successful initialization with valid tokens."""
+        parser = TestThinkingReasoningParser(test_tokenizer)
+        assert parser.start_token == "<test:think>"
+        assert parser.end_token == "</test:think>"
+        assert parser.start_token_id is not None
+        assert parser.end_token_id is not None
+
+    def test_initialization_with_missing_tokenizer(self):
+        """Test that initialization fails without tokenizer."""
+        with pytest.raises(ValueError, match="model tokenizer must be passed"):
+            TestThinkingReasoningParser(None)
+
+    def test_initialization_with_missing_tokens(self, test_tokenizer):
+        """Test that initialization fails when tokens are not in vocabulary."""
+
+        # Create a parser with tokens not in vocabulary
+        class MissingTokenParser(BaseThinkingReasoningParser):
+
+            @property
+            def start_token(self) -> str:
+                return "<missing:start>"
+
+            @property
+            def end_token(self) -> str:
+                return "<missing:end>"
+
+        with pytest.raises(RuntimeError,
+                           match="could not locate think start/end tokens"):
+            MissingTokenParser(test_tokenizer)
+
+    def test_initialization_with_empty_tokens(self, test_tokenizer):
+        """Test that initialization fails with empty token strings."""
+
+        class EmptyTokenParser(BaseThinkingReasoningParser):
+
+            @property
+            def start_token(self) -> str:
+                return ""
+
+            @property
+            def end_token(self) -> str:
+                return ""
+
+        with pytest.raises(ValueError,
+                           match="start_token and end_token must be defined"):
+            EmptyTokenParser(test_tokenizer)
+
+
+class TestBaseThinkingReasoningParserMethods:
+    """Test the methods of BaseThinkingReasoningParser."""
+
+    def test_is_reasoning_end(self, test_tokenizer):
+        """Test the is_reasoning_end method."""
+        parser = TestThinkingReasoningParser(test_tokenizer)
+        end_token_id = parser.end_token_id
+
+        # Test with end token present
+        assert parser.is_reasoning_end([1, 2, end_token_id, 4]) is True
+
+        # Test without end token
+        assert parser.is_reasoning_end([1, 2, 3, 4]) is False
+
+        # Test with empty list
+        assert parser.is_reasoning_end([]) is False
+
+    def test_extract_content_ids(self, test_tokenizer):
+        """Test the extract_content_ids method."""
+        parser = TestThinkingReasoningParser(test_tokenizer)
+        end_token_id = parser.end_token_id
+
+        # Test with end token in the middle
+        input_ids = [1, 2, end_token_id, 4, 5]
+        content_ids = parser.extract_content_ids(input_ids)
+        assert content_ids == [4, 5]
+
+        # Test with end token at the end
+        input_ids = [1, 2, 3, end_token_id]
+        content_ids = parser.extract_content_ids(input_ids)
+        assert content_ids == []
+
+        # Test without end token
+        input_ids = [1, 2, 3, 4]
+        content_ids = parser.extract_content_ids(input_ids)
+        assert content_ids == []
+
+        # Test with end token as last element (should not extract)
+        input_ids = [1, 2, 3, end_token_id]
+        content_ids = parser.extract_content_ids(input_ids)
+        assert content_ids == []
+
+
+class TestBaseThinkingReasoningParserExtraction:
+    """Test reasoning content extraction methods."""
+
+    def test_extract_reasoning_content_with_both_tokens(self, test_tokenizer):
+        """Test extraction when both start and end tokens are present."""
+        parser = TestThinkingReasoningParser(test_tokenizer)
+        request = ChatCompletionRequest(messages=[], model="test-model")
+
+        model_output = ("<test:think>This is reasoning"
+                        "</test:think>This is content")
+        reasoning, content = parser.extract_reasoning_content(
+            model_output, request)
+
+        assert reasoning == "This is reasoning"
+        assert content == "This is content"
+
+    def test_extract_reasoning_content_only_end_token(self, test_tokenizer):
+        """Test extraction when only end token is present."""
+        parser = TestThinkingReasoningParser(test_tokenizer)
+        request = ChatCompletionRequest(messages=[], model="test-model")
+
+        model_output = ("This is reasoning</test:think>This is content")
+        reasoning, content = parser.extract_reasoning_content(
+            model_output, request)
+
+        assert reasoning == "This is reasoning"
+        assert content == "This is content"
+
+    def test_extract_reasoning_content_no_end_token(self, test_tokenizer):
+        """Test extraction when no end token is present."""
+        parser = TestThinkingReasoningParser(test_tokenizer)
+        request = ChatCompletionRequest(messages=[], model="test-model")
+
+        model_output = "This is just content"
+        reasoning, content = parser.extract_reasoning_content(
+            model_output, request)
+
+        assert reasoning == "This is just content"
+        assert content is None
+
+    def test_extract_reasoning_content_empty_output(self, test_tokenizer):
+        """Test extraction with empty output."""
+        parser = TestThinkingReasoningParser(test_tokenizer)
+        request = ChatCompletionRequest(messages=[], model="test-model")
+
+        model_output = ""
+        reasoning, content = parser.extract_reasoning_content(
+            model_output, request)
+
+        assert reasoning == ""
+        assert content is None
+
+    def test_extract_reasoning_content_only_tokens(self, test_tokenizer):
+        """Test extraction with only tokens and no content."""
+        parser = TestThinkingReasoningParser(test_tokenizer)
+        request = ChatCompletionRequest(messages=[], model="test-model")
+
+        model_output = ("<test:think></test:think>")
+        reasoning, content = parser.extract_reasoning_content(
+            model_output, request)
+
+        assert reasoning == ""
+        assert content is None
+
+
+class TestBaseThinkingReasoningParserStreaming:
+    """Test streaming functionality of BaseThinkingReasoningParser."""
+
+    @pytest.mark.parametrize("streaming", [True, False])
+    def test_simple_reasoning_extraction(self, test_tokenizer, streaming):
+        """
+            Test basic reasoning extraction in both
+            streaming and non-streaming modes.
+        """
+        parser = TestThinkingReasoningParser(test_tokenizer)
+
+        model_output = [
+            "<test:think>", "Some ", "reasoning ", "content", "</test:think>",
+            "Final ", "answer"
+        ]
+
+        reasoning, content = run_reasoning_extraction(parser,
+                                                      model_output,
+                                                      streaming=streaming)
+
+        assert reasoning == "Some reasoning content"
+        assert content == "Final answer"
+
+    def test_streaming_with_incremental_deltas(self, test_tokenizer):
+        """Test streaming processing with small incremental deltas."""
+        parser = TestThinkingReasoningParser(test_tokenizer)
+
+        deltas = [
+            "<test:think>",
+            "Some ",
+            "reasoning ",
+            "content",
+            "</test:think>",
+            "Final ",
+            "answer",
+        ]
+
+        reasoning, content = run_reasoning_extraction(parser,
+                                                      deltas,
+                                                      streaming=True)
+
+        assert reasoning == "Some reasoning content"
+        assert content == "Final answer"
+
+    def test_streaming_with_start_token(self, test_tokenizer):
+        """Test streaming with start token included."""
+        parser = TestThinkingReasoningParser(test_tokenizer)
+
+        deltas = [
+            "<test:think>",
+            "Some ",
+            "reasoning",
+            "</test:think>",
+            "Answer",
+        ]
+
+        reasoning, content = run_reasoning_extraction(parser,
+                                                      deltas,
+                                                      streaming=True)
+
+        assert reasoning == "Some reasoning"
+        assert content == "Answer"
+
+    def test_streaming_no_end_token(self, test_tokenizer):
+        """Test streaming when no end token is encountered."""
+        parser = TestThinkingReasoningParser(test_tokenizer)
+
+        deltas = [
+            "<test:think>",
+            "Some ",
+            "reasoning ",
+            "without ",
+            "end",
+        ]
+
+        reasoning, content = run_reasoning_extraction(parser,
+                                                      deltas,
+                                                      streaming=True)
+
+        assert reasoning == "Some reasoning without end"
+        assert content is None
+
+    def test_streaming_only_end_token(self, test_tokenizer):
+        """Test streaming when only end token appears."""
+        parser = TestThinkingReasoningParser(test_tokenizer)
+
+        deltas = [
+            "<test:think>",
+            "Reasoning ",
+            "content",
+            "</test:think>",
+            "Final",
+        ]
+
+        reasoning, content = run_reasoning_extraction(parser,
+                                                      deltas,
+                                                      streaming=True)
+
+        assert reasoning == "Reasoning content"
+        assert content == "Final"
+
+
+class TestBaseThinkingReasoningParserMultipleImplementations:
+    """
+        Test that multiple implementations of
+        BaseThinkingReasoningParser work correctly.
+    """
+
+    def test_different_token_implementations(self, test_tokenizer):
+        """
+            Test that different implementations 
+            with different tokens work independently.
+        """
+        parser1 = TestThinkingReasoningParser(test_tokenizer)
+        parser2 = TestThinkingReasoningParserAlt(test_tokenizer)
+
+        # Test parser1
+        model_output1 = ("Reasoning1</test:think>Content1")
+        reasoning1, content1 = run_reasoning_extraction(
+            parser1, [model_output1])
+        assert reasoning1 == "Reasoning1"
+        assert content1 == "Content1"
+
+        # Test parser2
+        model_output2 = "Reasoning2<alt:end>Content2"
+        reasoning2, content2 = run_reasoning_extraction(
+            parser2, [model_output2])
+        assert reasoning2 == "Reasoning2"
+        assert content2 == "Content2"
+
+        # Verify tokens are different
+        assert parser1.start_token != parser2.start_token
+        assert parser1.end_token != parser2.end_token
+        assert parser1.start_token_id != parser2.start_token_id
+        assert parser1.end_token_id != parser2.end_token_id
+
+
+class TestBaseThinkingReasoningParserEdgeCases:
+    """Test edge cases and error conditions."""
+
+    def test_multiple_end_tokens(self, test_tokenizer):
+        """Test behavior with multiple end tokens."""
+        parser = TestThinkingReasoningParser(test_tokenizer)
+
+        model_output = ("First</test:think>Middle</test:think>Last")
+        reasoning, content = run_reasoning_extraction(parser, [model_output])
+
+        # Should stop at first end token
+        assert reasoning == "First"
+        assert content == "Middle</test:think>Last"
+
+    def test_nested_tokens(self, test_tokenizer):
+        """Test behavior with nested-like token patterns."""
+        parser = TestThinkingReasoningParser(test_tokenizer)
+
+        model_output = ("<test:think>Outer"
+                        "<test:think>Inner</test:think>Content")
+        reasoning, content = run_reasoning_extraction(parser, [model_output])
+
+        # Should process normally, start from first start token
+        assert reasoning == "Outer<test:think>Inner"
+        assert content == "Content"
+
+    def test_malformed_tokens(self, test_tokenizer):
+        """Test behavior with malformed token-like strings."""
+        parser = TestThinkingReasoningParser(test_tokenizer)
+
+        model_output = ("<test:thinking>Not a real token"
+                        "</test:thinking>Content")
+        reasoning, content = run_reasoning_extraction(parser, [model_output])
+
+        # Should treat as regular content since tokens don't match exactly
+        assert reasoning == ("<test:thinking>Not a real token"
+                             "</test:thinking>Content")
+        assert content is None

tests/reasoning/test_seedoss_reasoning_parser.py

@@ -0,0 +1,237 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from typing import Any, cast
+
+import pytest
+from transformers import AutoTokenizer
+
+from tests.reasoning.utils import run_reasoning_extraction
+from vllm.reasoning import ReasoningParser, ReasoningParserManager
+
+parser_name = "seed_oss"
+start_token = "<seed:think>"
+end_token = "</seed:think>"
+
+# Use a test model that contains our custom tokens
+REASONING_MODEL_NAME = "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B"
+
+
+@pytest.fixture(scope="module")
+def seedoss_tokenizer():
+    tokenizer = AutoTokenizer.from_pretrained(REASONING_MODEL_NAME)
+    # Add custom SeedOSS tokens if they don't exist
+    if start_token not in tokenizer.get_vocab():
+        tokenizer.add_tokens([start_token, end_token])
+    return tokenizer
+
+
+SIMPLE_REASONING: dict[str, Any] = {
+    "output": "This is a reasoning section</seed:think>This is the rest",
+    "reasoning_content": "This is a reasoning section",
+    "content": "This is the rest",
+    "is_reasoning_end": True,
+}
+COMPLETE_REASONING: dict[str, Any] = {
+    "output": "This is a reasoning section</seed:think>",
+    "reasoning_content": "This is a reasoning section",
+    "content": None,
+    "is_reasoning_end": True,
+}
+NO_CONTENT: dict[str, Any] = {
+    "output": "This is content",
+    "reasoning_content": "This is content",
+    "content": None,
+    "is_reasoning_end": False,
+}
+NO_REASONING_STREAMING: dict[str, Any] = {
+    "output": "This is a reasoning section",
+    "reasoning_content": "This is a reasoning section",
+    "content": None,
+    "is_reasoning_end": False,
+}
+MULTIPLE_LINES: dict[str, Any] = {
+    "output": "This\nThat</seed:think>This is the rest\nThat",
+    "reasoning_content": "This\nThat",
+    "content": "This is the rest\nThat",
+    "is_reasoning_end": True,
+}
+WITH_START_TOKEN: dict[str, Any] = {
+    "output": ("<seed:think>This is a reasoning section"
+               "</seed:think>This is the rest"),
+    "reasoning_content":
+    "This is a reasoning section",
+    "content":
+    "This is the rest",
+    "is_reasoning_end":
+    True,
+}
+ONLY_END_TOKEN: dict[str, Any] = {
+    "output": "Some reasoning</seed:think>This is the rest",
+    "reasoning_content": "Some reasoning",
+    "content": "This is the rest",
+    "is_reasoning_end": True,
+}
+NO_TOKENS: dict[str, Any] = {
+    "output": "This is just content without any reasoning tokens",
+    "reasoning_content": "This is just content without any reasoning tokens",
+    "content": None,
+    "is_reasoning_end": False,
+}
+
+
+def test_seedoss_reasoning_parser_creation(seedoss_tokenizer):
+    """Test that the SeedOSS reasoning parser can be created and registered."""
+    parser_cls = ReasoningParserManager.get_reasoning_parser(parser_name)
+    parser = parser_cls(seedoss_tokenizer)
+    assert isinstance(parser, ReasoningParser)
+    assert parser.start_token == start_token
+    assert parser.end_token == end_token
+
+
+@pytest.mark.parametrize("streaming", [True, False])
+def test_simple_reasoning(seedoss_tokenizer, streaming):
+    """Test basic reasoning extraction with both tokens."""
+    parser_cls = ReasoningParserManager.get_reasoning_parser(parser_name)
+    parser = parser_cls(seedoss_tokenizer)
+
+    reasoning, content = run_reasoning_extraction(
+        parser, [cast(str, SIMPLE_REASONING["output"])], streaming=streaming)
+
+    assert reasoning == SIMPLE_REASONING["reasoning_content"]
+    assert content == SIMPLE_REASONING["content"]
+
+
+@pytest.mark.parametrize("streaming", [True, False])
+def test_complete_reasoning(seedoss_tokenizer, streaming):
+    """Test reasoning extraction when there's no content after reasoning."""
+    parser_cls = ReasoningParserManager.get_reasoning_parser(parser_name)
+    parser = parser_cls(seedoss_tokenizer)
+
+    reasoning, content = run_reasoning_extraction(
+        parser, [cast(str, COMPLETE_REASONING["output"])], streaming=streaming)
+
+    assert reasoning == COMPLETE_REASONING["reasoning_content"]
+    assert content == COMPLETE_REASONING["content"]
+
+
+@pytest.mark.parametrize("streaming", [True, False])
+def test_no_content(seedoss_tokenizer, streaming):
+    """Test when there's no end token - everything is reasoning content."""
+    parser_cls = ReasoningParserManager.get_reasoning_parser(parser_name)
+    parser = parser_cls(seedoss_tokenizer)
+
+    reasoning, content = run_reasoning_extraction(
+        parser, [cast(str, NO_CONTENT["output"])], streaming=streaming)
+
+    assert reasoning == NO_CONTENT["reasoning_content"]
+    assert content == NO_CONTENT["content"]
+
+
+@pytest.mark.parametrize("streaming", [True, False])
+def test_multiple_lines(seedoss_tokenizer, streaming):
+    """Test reasoning extraction with multiline content."""
+    parser_cls = ReasoningParserManager.get_reasoning_parser(parser_name)
+    parser = parser_cls(seedoss_tokenizer)
+
+    reasoning, content = run_reasoning_extraction(
+        parser, [cast(str, MULTIPLE_LINES["output"])], streaming=streaming)
+
+    assert reasoning == MULTIPLE_LINES["reasoning_content"]
+    assert content == MULTIPLE_LINES["content"]
+
+
+@pytest.mark.parametrize("streaming", [True, False])
+def test_with_start_token(seedoss_tokenizer, streaming):
+    """Test reasoning extraction with both start and end tokens."""
+    parser_cls = ReasoningParserManager.get_reasoning_parser(parser_name)
+    parser = parser_cls(seedoss_tokenizer)
+
+    reasoning, content = run_reasoning_extraction(
+        parser, [cast(str, WITH_START_TOKEN["output"])], streaming=streaming)
+
+    assert reasoning == WITH_START_TOKEN["reasoning_content"]
+    assert content == WITH_START_TOKEN["content"]
+
+
+@pytest.mark.parametrize("streaming", [True, False])
+def test_only_end_token(seedoss_tokenizer, streaming):
+    """
+        Test reasoning extraction with only end token
+        (SeedOSS typical behavior).
+    """
+    parser_cls = ReasoningParserManager.get_reasoning_parser(parser_name)
+    parser = parser_cls(seedoss_tokenizer)
+
+    reasoning, content = run_reasoning_extraction(
+        parser, [cast(str, ONLY_END_TOKEN["output"])], streaming=streaming)
+
+    assert reasoning == ONLY_END_TOKEN["reasoning_content"]
+    assert content == ONLY_END_TOKEN["content"]
+
+
+@pytest.mark.parametrize("streaming", [True, False])
+def test_no_tokens(seedoss_tokenizer, streaming):
+    """Test when there are no reasoning tokens at all."""
+    parser_cls = ReasoningParserManager.get_reasoning_parser(parser_name)
+    parser = parser_cls(seedoss_tokenizer)
+
+    reasoning, content = run_reasoning_extraction(
+        parser, [cast(str, NO_TOKENS["output"])], streaming=streaming)
+
+    assert reasoning == NO_TOKENS["reasoning_content"]
+    assert content == NO_TOKENS["content"]
+
+
+def test_is_reasoning_end(seedoss_tokenizer):
+    """Test the is_reasoning_end method."""
+    parser_cls = ReasoningParserManager.get_reasoning_parser(parser_name)
+    parser = parser_cls(seedoss_tokenizer)
+
+    # Test with end token present
+    end_token_id = parser.end_token_id
+    assert parser.is_reasoning_end([1, 2, end_token_id, 4]) is True
+
+    # Test without end token
+    assert parser.is_reasoning_end([1, 2, 3, 4]) is False
+
+
+def test_extract_content_ids(seedoss_tokenizer):
+    """Test the extract_content_ids method."""
+    parser_cls = ReasoningParserManager.get_reasoning_parser(parser_name)
+    parser = parser_cls(seedoss_tokenizer)
+
+    end_token_id = parser.end_token_id
+
+    # Test with end token in the middle
+    input_ids = [1, 2, end_token_id, 4, 5]
+    content_ids = parser.extract_content_ids(input_ids)
+    assert content_ids == [4, 5]
+
+    # Test with end token at the end
+    input_ids = [1, 2, 3, end_token_id]
+    content_ids = parser.extract_content_ids(input_ids)
+    assert content_ids == []
+
+    # Test without end token
+    input_ids = [1, 2, 3, 4]
+    content_ids = parser.extract_content_ids(input_ids)
+    assert content_ids == []
+
+
+def test_streaming_delta_processing(seedoss_tokenizer):
+    """Test streaming processing with small deltas."""
+    parser_cls = ReasoningParserManager.get_reasoning_parser(parser_name)
+    parser = parser_cls(seedoss_tokenizer)
+
+    # Test streaming with incremental tokens
+    deltas = [
+        "Some ", "reasoning ", "content", "</seed:think>", "Final ", "answer"
+    ]
+
+    reasoning, content = run_reasoning_extraction(parser,
+                                                  deltas,
+                                                  streaming=True)
+
+    assert reasoning == "Some reasoning content"
+    assert content == "Final answer"

tests/test_config.py

@@ -1,7 +1,9 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
+import os
 from dataclasses import MISSING, Field, asdict, dataclass, field
+from unittest.mock import patch
 
 import pytest
 
@@ -388,3 +390,108 @@ def test_get_and_verify_max_len(model_id, max_model_len, expected_max_len,
     else:
         actual_max_len = model_config.get_and_verify_max_len(max_model_len)
         assert actual_max_len == expected_max_len
+
+
+class MockConfig:
+    """Simple mock object for testing maybe_pull_model_tokenizer_for_runai"""
+
+    def __init__(self, model: str, tokenizer: str):
+        self.model = model
+        self.tokenizer = tokenizer
+        self.model_weights = None
+
+
+@pytest.mark.parametrize("s3_url", [
+    "s3://example-bucket-1/model/",
+    "s3://example-bucket-2/model/",
+])
+@patch('vllm.transformers_utils.runai_utils.ObjectStorageModel.pull_files')
+def test_s3_url_model_tokenizer_paths(mock_pull_files, s3_url):
+    """Test that S3 URLs create deterministic local directories for model and
+    tokenizer."""
+    # Mock pull_files to avoid actually downloading files during tests
+    mock_pull_files.return_value = None
+
+    # Create first mock and run the method
+    config1 = MockConfig(model=s3_url, tokenizer=s3_url)
+    ModelConfig.maybe_pull_model_tokenizer_for_runai(config1, s3_url, s3_url)
+
+    # Check that model and tokenizer point to existing directories
+    assert os.path.exists(
+        config1.model), f"Model directory does not exist: {config1.model}"
+    assert os.path.isdir(
+        config1.model), f"Model path is not a directory: {config1.model}"
+    assert os.path.exists(
+        config1.tokenizer
+    ), f"Tokenizer directory does not exist: {config1.tokenizer}"
+    assert os.path.isdir(
+        config1.tokenizer
+    ), f"Tokenizer path is not a directory: {config1.tokenizer}"
+
+    # Verify that the paths are different from the original S3 URL
+    assert config1.model != s3_url, (
+        "Model path should be converted to local directory")
+    assert config1.tokenizer != s3_url, (
+        "Tokenizer path should be converted to local directory")
+
+    # Store the original paths
+    created_model_dir = config1.model
+    create_tokenizer_dir = config1.tokenizer
+
+    # Create a new mock and run the method with the same S3 URL
+    config2 = MockConfig(model=s3_url, tokenizer=s3_url)
+    ModelConfig.maybe_pull_model_tokenizer_for_runai(config2, s3_url, s3_url)
+
+    # Check that the new directories exist
+    assert os.path.exists(
+        config2.model), f"Model directory does not exist: {config2.model}"
+    assert os.path.isdir(
+        config2.model), f"Model path is not a directory: {config2.model}"
+    assert os.path.exists(
+        config2.tokenizer
+    ), f"Tokenizer directory does not exist: {config2.tokenizer}"
+    assert os.path.isdir(
+        config2.tokenizer
+    ), f"Tokenizer path is not a directory: {config2.tokenizer}"
+
+    # Verify that the paths are deterministic (same as before)
+    assert config2.model == created_model_dir, (
+        f"Model paths are not deterministic. "
+        f"Original: {created_model_dir}, New: {config2.model}")
+    assert config2.tokenizer == create_tokenizer_dir, (
+        f"Tokenizer paths are not deterministic. "
+        f"Original: {create_tokenizer_dir}, New: {config2.tokenizer}")
+
+
+@patch('vllm.transformers_utils.runai_utils.ObjectStorageModel.pull_files')
+def test_s3_url_different_models_create_different_directories(mock_pull_files):
+    """Test that different S3 URLs create different local directories."""
+    # Mock pull_files to avoid actually downloading files during tests
+    mock_pull_files.return_value = None
+
+    s3_url1 = "s3://example-bucket-1/model/"
+    s3_url2 = "s3://example-bucket-2/model/"
+
+    # Create mocks with different S3 URLs and run the method
+    config1 = MockConfig(model=s3_url1, tokenizer=s3_url1)
+    ModelConfig.maybe_pull_model_tokenizer_for_runai(config1, s3_url1, s3_url1)
+
+    config2 = MockConfig(model=s3_url2, tokenizer=s3_url2)
+    ModelConfig.maybe_pull_model_tokenizer_for_runai(config2, s3_url2, s3_url2)
+
+    # Verify that different URLs produce different directories
+    assert config1.model != config2.model, (
+        f"Different S3 URLs should create different model directories. "
+        f"URL1 model: {config1.model}, URL2 model: {config2.model}")
+    assert config1.tokenizer != config2.tokenizer, (
+        f"Different S3 URLs should create different tokenizer directories. "
+        f"URL1 tokenizer: {config1.tokenizer}, "
+        f"URL2 tokenizer: {config2.tokenizer}")
+
+    # Verify that both sets of directories exist
+    assert os.path.exists(config1.model) and os.path.isdir(config1.model)
+    assert os.path.exists(config1.tokenizer) and os.path.isdir(
+        config1.tokenizer)
+    assert os.path.exists(config2.model) and os.path.isdir(config2.model)
+    assert os.path.exists(config2.tokenizer) and os.path.isdir(
+        config2.tokenizer)

tests/tool_use/test_deepseekv31_tool_parser.py

@@ -0,0 +1,54 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import pytest
+
+from vllm.entrypoints.openai.tool_parsers import DeepSeekV31ToolParser
+from vllm.transformers_utils.tokenizer import get_tokenizer
+
+MODEL = "deepseek-ai/DeepSeek-V3.1"
+
+
+@pytest.fixture(scope="module")
+def deepseekv31_tokenizer():
+    return get_tokenizer(tokenizer_name=MODEL)
+
+
+@pytest.fixture
+def parser(deepseekv31_tokenizer):
+    return DeepSeekV31ToolParser(deepseekv31_tokenizer)
+
+
+def test_extract_tool_calls_with_tool(parser):
+    model_output = (
+        "normal text" + "<｜tool▁calls▁begin｜>" +
+        "<｜tool▁call▁begin｜>foo<｜tool▁sep｜>{\"x\":1}<｜tool▁call▁end｜>" +
+        "<｜tool▁calls▁end｜>")
+    result = parser.extract_tool_calls(model_output, None)
+    assert result.tools_called
+    assert len(result.tool_calls) == 1
+    assert result.tool_calls[0].function.name == "foo"
+    assert result.tool_calls[0].function.arguments == "{\"x\":1}"
+    assert result.content == "normal text"
+
+
+def test_extract_tool_calls_with_multiple_tools(parser):
+    model_output = (
+        "some prefix text" + "<｜tool▁calls▁begin｜>" +
+        "<｜tool▁call▁begin｜>foo<｜tool▁sep｜>{\"x\":1}<｜tool▁call▁end｜>" +
+        "<｜tool▁call▁begin｜>bar<｜tool▁sep｜>{\"y\":2}<｜tool▁call▁end｜>" +
+        "<｜tool▁calls▁end｜>" + " some suffix text")
+
+    result = parser.extract_tool_calls(model_output, None)
+
+    assert result.tools_called
+    assert len(result.tool_calls) == 2
+
+    assert result.tool_calls[0].function.name == "foo"
+    assert result.tool_calls[0].function.arguments == "{\"x\":1}"
+
+    assert result.tool_calls[1].function.name == "bar"
+    assert result.tool_calls[1].function.arguments == "{\"y\":2}"
+
+    # prefix is content
+    assert result.content == "some prefix text"

tests/tool_use/test_openai_tool_parser.py

@@ -70,7 +70,12 @@ def test_extract_tool_calls_no_tools(openai_tool_parser, harmony_encoding):
     assert extracted_info.content == "This is a test"
 
 
-def test_extract_tool_calls_single_tool(openai_tool_parser, harmony_encoding):
+@pytest.mark.parametrize("tool_args", [
+    '{"location": "Tokyo"}',
+    '{\n"location": "Tokyo"\n}',
+])
+def test_extract_tool_calls_single_tool(openai_tool_parser, harmony_encoding,
+                                        tool_args):
     convo = Conversation.from_messages([
         Message.from_role_and_content(Role.USER,
                                       "What is the weather in Tokyo?"),
@@ -80,7 +85,7 @@ def test_extract_tool_calls_single_tool(openai_tool_parser, harmony_encoding):
         ).with_channel("analysis"),
         Message.from_role_and_content(
             Role.ASSISTANT,
-            '{"location": "Tokyo"}').with_channel("commentary").with_recipient(
+            tool_args).with_channel("commentary").with_recipient(
                 "functions.get_current_weather").with_content_type("json"),
     ])
     token_ids = harmony_encoding.render_conversation_for_completion(
@@ -121,6 +126,17 @@ def test_extract_tool_calls_multiple_tools(
             Role.ASSISTANT,
             '{"location": "Tokyo"}').with_channel("commentary").with_recipient(
                 "functions.get_user_location").with_content_type("json"),
+        Message.from_role_and_content(
+            Role.ASSISTANT, '{"location": "Tokyo"}').with_channel(
+                "commentary").with_recipient("functions.no_content_type"),
+        Message.from_role_and_content(Role.ASSISTANT, "foo").with_channel(
+            "commentary").with_recipient("functions.not_json_no_content_type"),
+        Message.from_role_and_content(
+            Role.ASSISTANT, '{}').with_channel("commentary").with_recipient(
+                "functions.empty_args").with_content_type("json"),
+        Message.from_role_and_content(
+            Role.ASSISTANT, '').with_channel("commentary").with_recipient(
+                "functions.no_args").with_content_type("json"),
     ])
     token_ids = harmony_encoding.render_conversation_for_completion(
         convo,
@@ -141,7 +157,63 @@ def test_extract_tool_calls_multiple_tools(
         ToolCall(function=FunctionCall(
             name="get_user_location",
             arguments=json.dumps({"location": "Tokyo"}),
+        )),
+        ToolCall(function=FunctionCall(
+            name="no_content_type",
+            arguments=json.dumps({"location": "Tokyo"}),
+        )),
+        ToolCall(function=FunctionCall(
+            name="not_json_no_content_type",
+            arguments="foo",
+        )),
+        ToolCall(function=FunctionCall(
+            name="empty_args",
+            arguments=json.dumps({}),
+        )),
+        ToolCall(function=FunctionCall(
+            name="no_args",
+            arguments="",
         ))
     ]
     assert_tool_calls(extracted_info.tool_calls, expected_tool_calls)
     assert extracted_info.content is None
+
+
+def test_extract_tool_calls_with_content(
+    openai_tool_parser,
+    harmony_encoding,
+):
+    final_content = "This tool call will get the weather."
+    convo = Conversation.from_messages([
+        Message.from_role_and_content(
+            Role.USER, "What is the weather in Tokyo based on where I'm at?"),
+        Message.from_role_and_content(
+            Role.ASSISTANT,
+            'User asks: "What is the weather in Tokyo?" based on their location. We need to use get_current_weather tool and get_user_location tool.',  #  noqa: E501
+        ).with_channel("analysis"),
+        Message.from_role_and_content(
+            Role.ASSISTANT,
+            '{"location": "Tokyo"}').with_channel("commentary").with_recipient(
+                "functions.get_current_weather").with_content_type("json"),
+        Message.from_role_and_content(Role.ASSISTANT,
+                                      final_content).with_channel("final"),
+    ])
+    token_ids = harmony_encoding.render_conversation_for_completion(
+        convo,
+        Role.ASSISTANT,
+    )
+
+    extracted_info = openai_tool_parser.extract_tool_calls(
+        "",
+        request=None,
+        token_ids=token_ids,
+    )
+    assert extracted_info.tools_called
+    expected_tool_calls = [
+        ToolCall(function=FunctionCall(
+            name="get_current_weather",
+            arguments=json.dumps({"location": "Tokyo"}),
+        )),
+    ]
+    assert_tool_calls(extracted_info.tool_calls, expected_tool_calls)
+    assert extracted_info.content == final_content

tests/tpu/lora/test_lora.py

@@ -31,7 +31,6 @@ def use_v1_only(monkeypatch: pytest.MonkeyPatch):
 def setup_vllm(num_loras: int, tp: int) -> vllm.LLM:
     return vllm.LLM(model="Qwen/Qwen2.5-3B-Instruct",
                     max_model_len=256,
-                    max_seq_len_to_capture=256,
                     max_num_seqs=8,
                     tensor_parallel_size=tp,
                     enable_lora=True,

tests/v1/attention/test_attention_splitting.py

@@ -9,8 +9,9 @@ from tests.v1.attention.utils import BatchSpec, create_common_attn_metadata
 from vllm.v1.attention.backends.utils import (UBatchSlice,
                                               _make_metadata_with_slice,
                                               slice_query_start_locs,
-                                              split_attn_metadata)
-from vllm.v1.worker.ubatch_utils import create_ubatch_slices
+                                              split_attn_metadata,
+                                              split_decodes_and_prefills)
+from vllm.v1.worker.ubatch_splitting import create_ubatch_slices
 
 
 @pytest.fixture
@@ -158,6 +159,112 @@ def test_split_attn_metadata_decode_batch(large_decode_metadata):
     assert torch.equal(results[1].seq_lens, torch.tensor([2048] * mid_point))
 
 
+def apply_split_decodes_and_prefills(query_lens: list[int],
+                                     decode_threshold: int,
+                                     require_uniform: bool):
+    """Helper function to apply split_decodes_and_prefills and return
+    the results."""
+    device = torch.device("cpu")
+    seq_lens = [10 * (i + 1) for i in range(len(query_lens))]
+    common_metadata = create_common_attn_metadata(BatchSpec(
+        seq_lens=seq_lens, query_lens=query_lens),
+                                                  block_size=16,
+                                                  device=device)
+    return split_decodes_and_prefills(common_metadata,
+                                      decode_threshold=decode_threshold,
+                                      require_uniform=require_uniform)
+
+
+def test_split_decodes_and_prefills_nonuniform_all_ones():
+    query_lens = [1, 1, 1]
+    num_decodes, num_prefills, num_decode_tokens, num_prefill_tokens = (
+        apply_split_decodes_and_prefills(query_lens, 1, False))
+    assert num_decodes == 3
+    assert num_prefills == 0
+    assert num_decode_tokens == 3
+    assert num_prefill_tokens == 0
+
+
+def test_split_decodes_and_prefills_nonuniform_all_short_decodes():
+    query_lens = [1, 2, 1, 3, 2, 1, 2]
+    num_decodes, num_prefills, num_decode_tokens, num_prefill_tokens = (
+        apply_split_decodes_and_prefills(query_lens, 3, False))
+    assert num_decodes == 7
+    assert num_prefills == 0
+    assert num_decode_tokens == sum(query_lens)
+    assert num_prefill_tokens == 0
+
+
+def test_split_decodes_and_prefills_nonuniform_all_prefills():
+    query_lens = [4, 5, 6, 7]
+    num_decodes, num_prefills, num_decode_tokens, num_prefill_tokens = (
+        apply_split_decodes_and_prefills(query_lens, 3, False))
+    assert num_decodes == 0
+    assert num_prefills == 4
+    assert num_decode_tokens == 0
+    assert num_prefill_tokens == sum(query_lens)
+
+
+def test_split_decodes_and_prefills_nonuniform_mixed_batch():
+    query_lens = [2, 1, 3, 4, 5, 6, 7, 8]
+    num_decodes, num_prefills, num_decode_tokens, num_prefill_tokens = (
+        apply_split_decodes_and_prefills(query_lens, 4, False))
+    assert num_decodes == 4  # 2, 1, 3, 4 are all <= 4
+    assert num_prefills == 4  # 5, 6, 7, 8 are all > 4
+    assert num_decode_tokens == 10  # 2 + 1 + 3 + 4
+    assert num_prefill_tokens == 26  # 5 + 6 + 7 + 8
+
+
+def test_split_decodes_and_prefills_uniform_all_ones():
+    query_lens = [1, 1, 1]
+    num_decodes, num_prefills, num_decode_tokens, num_prefill_tokens = (
+        apply_split_decodes_and_prefills(query_lens, 1, True))
+    assert num_decodes == 3
+    assert num_prefills == 0
+    assert num_decode_tokens == 3
+    assert num_prefill_tokens == 0
+
+
+def test_split_decodes_and_prefills_uniform_all_short_decodes():
+    query_lens = [2, 2, 1, 3, 2, 1, 2]
+    num_decodes, num_prefills, num_decode_tokens, num_prefill_tokens = (
+        apply_split_decodes_and_prefills(query_lens, 3, True))
+    assert num_decodes == 2
+    assert num_prefills == 5
+    assert num_decode_tokens == 4
+    assert num_prefill_tokens == (1 + 3 + 2 + 1 + 2)
+
+
+def test_split_decodes_and_prefills_uniform_all_prefills():
+    query_lens = [4, 5, 6, 7]
+    num_decodes, num_prefills, num_decode_tokens, num_prefill_tokens = (
+        apply_split_decodes_and_prefills(query_lens, 3, True))
+    assert num_decodes == 0
+    assert num_prefills == 4
+    assert num_decode_tokens == 0
+    assert num_prefill_tokens == sum(query_lens)
+
+
+def test_split_decodes_and_prefills_uniform_mixed_batch_all_uniform_decodes():
+    query_lens = [2, 2, 2, 4, 5, 6, 7, 8]
+    num_decodes, num_prefills, num_decode_tokens, num_prefill_tokens = (
+        apply_split_decodes_and_prefills(query_lens, 4, True))
+    assert num_decodes == 3  # 2, 2, 2 are all <= 4 and uniform
+    assert num_prefills == 5  # 4, 5, 6, 7, 8 are all > 4
+    assert num_decode_tokens == 6  # 2 + 2 + 2
+    assert num_prefill_tokens == 30  # 4 + 5 + 6 + 7 + 8
+
+
+def test_split_decodes_and_prefills_uniform_mixed_batch_non_uniform_decodes():
+    query_lens = [2, 1, 2, 4, 5, 6, 7, 8]
+    num_decodes, num_prefills, num_decode_tokens, num_prefill_tokens = (
+        apply_split_decodes_and_prefills(query_lens, 4, True))
+    assert num_decodes == 1  # only the first 2 is taken as decode
+    assert num_prefills == 7  # 1, 2, 4, 5, 6, 7, 8 are all > 4 or non-uniform
+    assert num_decode_tokens == 2  # only the first 2
+    assert num_prefill_tokens == (sum(query_lens) - 2)  # rest of the tokens
+
+
 @pytest.mark.parametrize(
     "seq_lens,query_lens,split_point,expected_first_reqs,expected_second_reqs",
     [

tests/v1/core/test_prefix_caching.py

@@ -14,10 +14,11 @@ from vllm.multimodal.inputs import (MultiModalFeatureSpec,
                                     MultiModalKwargsItem, PlaceholderRange)
 from vllm.sampling_params import SamplingParams
 from vllm.utils import sha256, sha256_cbor
-from vllm.v1.core.block_pool import BlockPool
+from vllm.v1.core.block_pool import BlockHashToBlockMap, BlockPool
 from vllm.v1.core.kv_cache_manager import KVCacheManager, Request
-from vllm.v1.core.kv_cache_utils import (BlockHash, KVCacheBlock,
-                                         get_block_hash, get_group_id,
+from vllm.v1.core.kv_cache_utils import (BlockHash, BlockHashWithGroupId,
+                                         KVCacheBlock, get_block_hash,
+                                         get_group_id,
                                          get_request_block_hasher,
                                          hash_block_tokens, init_none_hash,
                                          make_block_hash_with_group_id)
@@ -138,7 +139,7 @@ def test_prefill(hash_fn):
     blocks = manager.allocate_slots(req0, 55,
                                     len(computed_blocks.blocks[0]) * 16,
                                     computed_blocks)
-    assert blocks.get_block_ids() == ([1, 2, 3, 4], )
+    assert blocks is not None and blocks.get_block_ids() == ([1, 2, 3, 4], )
 
     # Check full block metadata
     parent_block_hash = None
@@ -171,7 +172,7 @@ def test_prefill(hash_fn):
     blocks = manager.allocate_slots(req1, num_new_tokens,
                                     len(computed_blocks.blocks[0]) * 16,
                                     computed_blocks)
-    assert blocks.get_block_ids() == ([5], )
+    assert blocks is not None and blocks.get_block_ids() == ([5], )
     for block in computed_blocks.blocks[0]:
         assert block.ref_cnt == 2
 
@@ -207,7 +208,7 @@ def test_prefill(hash_fn):
     blocks = manager.allocate_slots(req2, num_new_tokens,
                                     len(computed_blocks.blocks[0]) * 16,
                                     computed_blocks)
-    assert blocks.get_block_ids() == ([6], )
+    assert blocks is not None and blocks.get_block_ids() == ([6], )
 
     # Although we only have 6 free blocks, we have 8 blocks in
     # the free block queue due to lazy removal.
@@ -227,7 +228,9 @@ def test_prefill(hash_fn):
                                     len(computed_blocks.blocks[0]) * 16,
                                     computed_blocks)
     # This block ID order also checks the eviction order.
-    assert blocks.get_block_ids() == ([7, 8, 9, 10, 4, 5, 6, 3, 2, 1], )
+    assert blocks is not None and blocks.get_block_ids() == ([
+        7, 8, 9, 10, 4, 5, 6, 3, 2, 1
+    ], )
 
     assert free_block_queue.num_free_blocks == 0
     assert (free_block_queue.fake_free_list_head.next_free_block
@@ -261,8 +264,9 @@ def test_prefill_hybrid_model():
     blocks = manager.allocate_slots(req0, 55,
                                     len(computed_blocks.blocks[0]) * 16,
                                     computed_blocks)
-    assert blocks.get_block_ids() == ([1, 2, 3, 4], [5, 6, 7,
-                                                     8], [9, 10, 11, 12])
+    assert blocks is not None and blocks.get_block_ids() == ([1, 2, 3, 4], [
+        5, 6, 7, 8
+    ], [9, 10, 11, 12])
 
     # Check full block metadata
     parent_block_hash = None
@@ -298,7 +302,7 @@ def test_prefill_hybrid_model():
     blocks = manager.allocate_slots(req1, num_new_tokens,
                                     len(computed_blocks.blocks[0]) * 16,
                                     computed_blocks)
-    assert blocks.get_block_ids() == ([13], [14], [15])
+    assert blocks is not None and blocks.get_block_ids() == ([13], [14], [15])
     for block_per_group in computed_blocks.blocks:
         for block in block_per_group:
             if block != manager.block_pool.null_block:
@@ -309,14 +313,15 @@ def test_prefill_hybrid_model():
     manager.free(req1)
 
     cached_block_hash_to_block_bak = copy.copy(
-        manager.block_pool.cached_block_hash_to_block)
+        manager.block_pool.cached_block_hash_to_block._cache)
 
-    def test_partial_request_hit(request_id: str, hash_to_evict: list[bytes],
+    def test_partial_request_hit(request_id: str,
+                                 hash_to_evict: list[BlockHashWithGroupId],
                                  expect_hit_length: int):
         req = make_request(request_id, common_token_ids + unique_token_ids,
                            block_size, sha256)
         for hash_with_group_id in hash_to_evict:
-            manager.block_pool.cached_block_hash_to_block.pop(
+            manager.block_pool.cached_block_hash_to_block._cache.pop(
                 hash_with_group_id)
         computed_blocks, num_computed_tokens = manager.get_computed_blocks(req)
         assert len(req.block_hashes) == 3
@@ -324,7 +329,7 @@ def test_prefill_hybrid_model():
         for block_per_group in computed_blocks.blocks:
             assert len(block_per_group) == num_computed_tokens // block_size
         for hash_with_group_id in hash_to_evict:
-            manager.block_pool.cached_block_hash_to_block[
+            manager.block_pool.cached_block_hash_to_block._cache[
                 hash_with_group_id] = cached_block_hash_to_block_bak[
                     hash_with_group_id]
         manager.free(req)
@@ -362,7 +367,8 @@ def test_prefill_hybrid_model():
     # total cache miss.
     # The cache hit length of full attention is 1 * block_size.
     # The cache hit length of sliding window is 2 * block_size.
-    # Then it is cache miss as the two type of layers have different hit length.
+    # Then it is cache miss as the two type of layers
+    # have different hit length.
     test_partial_request_hit("8", [
         make_block_hash_with_group_id(block_hashes[2], 0),
         make_block_hash_with_group_id(block_hashes[0], 1),
@@ -406,7 +412,7 @@ def test_prefill_plp():
     blocks = manager.allocate_slots(req0, 55,
                                     len(computed_blocks.blocks[0]) * 16,
                                     computed_blocks)
-    assert blocks.get_block_ids() == ([1, 2, 3, 4], )
+    assert blocks is not None and blocks.get_block_ids() == ([1, 2, 3, 4], )
     req0_block_hashes = [b.block_hash for b in blocks.blocks[0]]
 
     # Check full block metadata
@@ -441,7 +447,7 @@ def test_prefill_plp():
     blocks = manager.allocate_slots(req1, num_new_tokens,
                                     len(computed_blocks.blocks[0]) * 16,
                                     computed_blocks)
-    assert blocks.get_block_ids() == ([5], )
+    assert blocks is not None and blocks.get_block_ids() == ([5], )
     for block in computed_blocks.blocks[0]:
         assert block.ref_cnt == 2
 
@@ -478,6 +484,7 @@ def test_prefill_plp():
     blocks = manager.allocate_slots(req2, 55,
                                     len(computed_blocks.blocks[0]) * 16,
                                     computed_blocks)
+    assert blocks is not None
     block_ids = blocks.get_block_ids()
     # Duplicate cached blocks have different ids but same hashes vs request #0
     assert [b.block_hash for b in blocks.blocks[0]] == req0_block_hashes
@@ -513,7 +520,7 @@ def test_decode():
     blocks = manager.allocate_slots(req0, 55,
                                     len(computed_blocks.blocks[0]) * 16,
                                     computed_blocks)
-    assert blocks.get_block_ids() == ([1, 2, 3, 4], )
+    assert blocks is not None and blocks.get_block_ids() == ([1, 2, 3, 4], )
 
     # Append slots without allocating a new block.
     req0.num_computed_tokens = 55
@@ -558,7 +565,8 @@ def test_evict():
     blocks = manager.allocate_slots(req0, 5 * 16 + 7,
                                     len(computed_blocks.blocks[0]) * 16,
                                     computed_blocks)
-    assert len(blocks.blocks[0]) == 6  # 5 full + 1 partial
+    # 5 full + 1 partial
+    assert blocks is not None and len(blocks.blocks[0]) == 6
 
     # 3 blocks.
     req1 = make_request("1", list(range(last_token_id,
@@ -570,7 +578,7 @@ def test_evict():
     blocks = manager.allocate_slots(req1, 3 * 16,
                                     len(computed_blocks.blocks[0]) * 16,
                                     computed_blocks)
-    assert len(blocks.blocks[0]) == 3  # 3 full blocks
+    assert blocks is not None and len(blocks.blocks[0]) == 3  # 3 full blocks
     last_token_id += 3 * 16
 
     # 10 - (6 + 3) == 1
@@ -592,7 +600,7 @@ def test_evict():
     blocks = manager.allocate_slots(req2, 3,
                                     len(computed_blocks.blocks[0]) * 16,
                                     computed_blocks)
-    assert blocks.get_block_ids() == ([10], )
+    assert blocks is not None and blocks.get_block_ids() == ([10], )
     assert manager.block_pool.free_block_queue.num_free_blocks == 7
 
 
@@ -617,7 +625,7 @@ def test_hash_block_correct_reuse():
     blocks = manager.allocate_slots(req, num_tokens,
                                     len(computed_blocks.blocks[0]) * 16,
                                     computed_blocks)
-    assert len(blocks.blocks[0]) == 1
+    assert blocks is not None and len(blocks.blocks[0]) == 1
 
     # Deallocate the block.
     manager.free(req)
@@ -631,7 +639,7 @@ def test_hash_block_correct_reuse():
     blocks = manager.allocate_slots(req, num_tokens - 1,
                                     len(computed_blocks.blocks[0]) * 16,
                                     computed_blocks)
-    assert len(blocks.blocks[0]) == 1
+    assert blocks is not None and len(blocks.blocks[0]) == 1
 
     assert manager.block_pool.blocks[blocks.blocks[0]
                                      [0].block_id].block_hash is None
@@ -658,7 +666,7 @@ def test_computed_blocks_not_evicted():
     blocks = manager.allocate_slots(req0, num_tokens,
                                     len(computed_blocks.blocks[0]) * 16,
                                     computed_blocks)
-    assert len(blocks.blocks[0]) == 1
+    assert blocks is not None and len(blocks.blocks[0]) == 1
     assert blocks.blocks[0][0].block_id == 1
 
     # Allocate another block.
@@ -670,7 +678,7 @@ def test_computed_blocks_not_evicted():
     blocks = manager.allocate_slots(req1, num_tokens,
                                     len(computed_blocks.blocks[0]) * 16,
                                     computed_blocks)
-    assert len(blocks.blocks[0]) == 1
+    assert blocks is not None and len(blocks.blocks[0]) == 1
     assert blocks.blocks[0][0].block_id == 2
 
     # Free the blocks.
@@ -688,7 +696,7 @@ def test_computed_blocks_not_evicted():
     blocks = manager.allocate_slots(req2, num_tokens * 2 - num_tokens,
                                     len(computed_blocks.blocks[0]) * 16,
                                     computed_blocks)
-    assert len(blocks.blocks[0]) == 1
+    assert blocks is not None and len(blocks.blocks[0]) == 1
     assert blocks.blocks[0][0].block_id == 2
 
 
@@ -712,7 +720,7 @@ def test_basic_prefix_caching_disabled():
     blocks = manager.allocate_slots(req1, 10,
                                     len(computed_blocks.blocks[0]) * 16,
                                     computed_blocks)
-    assert len(blocks.blocks[0]) == 3
+    assert blocks is not None and len(blocks.blocks[0]) == 3
 
     # Free the blocks.
     manager.free(req1)
@@ -726,7 +734,7 @@ def test_basic_prefix_caching_disabled():
     blocks = manager.allocate_slots(req2, 16,
                                     len(computed_blocks.blocks[0]) * 16,
                                     computed_blocks)
-    assert len(blocks.blocks[0]) == 4
+    assert blocks is not None and len(blocks.blocks[0]) == 4
 
     # New requests should not have any blocks.
     req3 = make_request("3", list(range(4)), block_size, sha256)
@@ -773,7 +781,8 @@ def test_cache_blocks(hash_fn):
     assert len(block_pool.cached_block_hash_to_block) == 2
     assert all([block.block_hash is not None for block in blocks])
 
-    # Test that blocks that don't start from the beginning are cached correctly.
+    # Test that blocks that don't start from the beginning are cached
+    # correctly.
     blocks += [KVCacheBlock(block_id=2)]
     block_pool.cache_full_blocks(
         request=req,
@@ -1101,7 +1110,7 @@ def test_reset_prefix_cache():
     all_token_ids = full_block_token_ids + unique_token_ids
     req0 = make_request("0", all_token_ids, block_size, sha256)
     blocks = manager.allocate_slots(req0, 55)
-    assert blocks.get_block_ids() == ([1, 2, 3, 4], )
+    assert blocks is not None and blocks.get_block_ids() == ([1, 2, 3, 4], )
 
     unique_token_ids = [4] * 7
     all_token_ids = full_block_token_ids + unique_token_ids
@@ -1112,7 +1121,7 @@ def test_reset_prefix_cache():
     blocks = manager.allocate_slots(req1, 7,
                                     len(computed_blocks.blocks[0]) * 16,
                                     computed_blocks)
-    assert blocks.get_block_ids() == ([5], )
+    assert blocks is not None and blocks.get_block_ids() == ([5], )
 
     # Failed to reset prefix cache because some blocks are not freed yet.
     assert not manager.reset_prefix_cache()
@@ -1168,49 +1177,41 @@ def test_maybe_evict_cached_block():
     # Manually add all blocks to cached_blocks
     for block, block_hash in zip(pool.blocks, block_hashes):
         block.block_hash = block_hash
-        pool.cached_block_hash_to_block[block_hash][block.block_id] = block
+        pool.cached_block_hash_to_block.insert(block_hash, block)
 
     block0, block1, block2, block3 = pool.blocks
-    assert pool.cached_block_hash_to_block == {
+    assert pool.cached_block_hash_to_block._cache == {
         block_hash0: {
             block0.block_id: block0,
-            block3.block_id: block3
+            block3.block_id: block3,
         },
-        block_hash1: {
-            block1.block_id: block1
-        },
-        block_hash2: {
-            block2.block_id: block2
-        }
+        block_hash1: block1,
+        block_hash2: block2,
     }
     # Evict block1
     pool._maybe_evict_cached_block(block1)
-    assert pool.cached_block_hash_to_block == {
+    assert pool.cached_block_hash_to_block._cache == {
         block_hash0: {
             block0.block_id: block0,
             block3.block_id: block3
         },
-        block_hash2: {
-            block2.block_id: block2
-        }
+        block_hash2: block2,
     }
     # Evict block0: block_hash0 entry should NOT be removed, as block3
     # also use the same hash
     pool._maybe_evict_cached_block(block0)
-    assert pool.cached_block_hash_to_block == {
+    assert pool.cached_block_hash_to_block._cache == {
         block_hash0: {
             block3.block_id: block3
         },
-        block_hash2: {
-            block2.block_id: block2
-        }
+        block_hash2: block2,
     }
     # Evict block2
     pool._maybe_evict_cached_block(block2)
-    assert pool.cached_block_hash_to_block == {block_hash0: {3: block3}}
+    assert pool.cached_block_hash_to_block._cache == {block_hash0: {3: block3}}
     # Evict block3
     pool._maybe_evict_cached_block(block3)
-    assert pool.cached_block_hash_to_block == {}
+    assert pool.cached_block_hash_to_block._cache == {}
 
 
 @pytest.mark.parametrize("blocks_to_cache", [2, 3, 10])
@@ -1374,7 +1375,7 @@ def test_eagle_with_sliding_window():
     # Evict the first block in the request
     assert manager.block_pool.get_cached_block(
         block_hash_first_block, kv_cache_group_ids=[0]) is not None
-    manager.block_pool.cached_block_hash_to_block.pop(
+    manager.block_pool.cached_block_hash_to_block._cache.pop(
         make_block_hash_with_group_id(block_hash_first_block, 0))
 
     # New request
@@ -1386,3 +1387,78 @@ def test_eagle_with_sliding_window():
     # there will be no matched prefix.
     assert len(computed_blocks.blocks[0]) == 0
     assert num_tokens == 0
+
+
+def test_block_lookup_cache_single_block_per_key():
+    cache = BlockHashToBlockMap()
+    key0 = BlockHashWithGroupId(b"hash0")
+    key1 = BlockHashWithGroupId(b"hash1")
+    key2 = BlockHashWithGroupId(b"hash2")
+    block0 = KVCacheBlock(0)
+    block1 = KVCacheBlock(1)
+
+    assert cache.get_one_block(key0) is None
+    assert cache.get_one_block(key1) is None
+    assert cache.get_one_block(key2) is None
+    # key0 inserted
+    cache.insert(key0, block0)
+    assert cache.get_one_block(key0) is block0
+    assert cache.get_one_block(key1) is None
+    assert cache.get_one_block(key2) is None
+    # key1 inserted
+    cache.insert(key1, block1)
+    assert cache.get_one_block(key0) is block0
+    assert cache.get_one_block(key1) is block1
+    assert cache.get_one_block(key2) is None
+    # No block poped due to block_id mismatch
+    assert cache.pop(key0, 100) is None
+    assert cache.get_one_block(key0) is block0
+    assert cache.get_one_block(key1) is block1
+    assert cache.get_one_block(key2) is None
+    # block poped with (key0, block ID 0)
+    assert cache.pop(key0, 0) is block0
+    assert cache.get_one_block(key0) is None
+    assert cache.get_one_block(key1) is block1
+    assert cache.get_one_block(key2) is None
+    # No block poped due to block_id mismatch
+    assert cache.pop(key0, 1) is None
+    assert cache.get_one_block(key0) is None
+    assert cache.get_one_block(key1) is block1
+    assert cache.get_one_block(key2) is None
+    # block poped with (key1, block ID 1)
+    assert cache.pop(key1, 1) is block1
+    assert cache.get_one_block(key0) is None
+    assert cache.get_one_block(key1) is None
+    assert cache.get_one_block(key2) is None
+
+
+def test_block_lookup_cache_multi_blocks_per_key():
+    cache = BlockHashToBlockMap()
+    key0 = BlockHashWithGroupId(b"hash0")
+    key1 = BlockHashWithGroupId(b"hash1")
+    block00 = KVCacheBlock(0)
+    block01 = KVCacheBlock(1)
+    block10 = KVCacheBlock(10)
+    block11 = KVCacheBlock(11)
+
+    assert cache.get_one_block(key0) is None
+    assert cache.get_one_block(key1) is None
+
+    cache.insert(key0, block00)
+    cache.insert(key0, block01)
+    cache.insert(key1, block10)
+    cache.insert(key1, block11)
+
+    assert cache.get_one_block(key0) is block00
+    assert cache.pop(key0, 0) is block00
+    assert cache.get_one_block(key0) is block01
+    assert cache.pop(key0, 1) is block01
+    assert cache.get_one_block(key0) is None
+    assert cache.pop(key0, 2) is None
+
+    assert cache.get_one_block(key1) is block10
+    assert cache.pop(key1, 10) is block10
+    assert cache.get_one_block(key1) is block11
+    assert cache.pop(key1, 11) is block11
+    assert cache.get_one_block(key1) is None
+    assert cache.pop(key1, 12) is None

tests/v1/core/test_single_type_kv_cache_manager.py

@@ -47,16 +47,15 @@ def test_chunked_local_attention_possible_cached_prefix():
             BlockHash(str(i).encode()) for i in range(len(block_is_cached))
         ]
 
-        block_pool.cached_block_hash_to_block.clear()
+        block_pool.cached_block_hash_to_block._cache.clear()
 
         # Mock the block pool with the cached blocks
         for i, (block_hash,
                 is_cached) in enumerate(zip(block_hash_list, block_is_cached)):
             if is_cached:
-                block_pool.cached_block_hash_to_block[
-                    make_block_hash_with_group_id(block_hash, 0)] = {
-                        i: block_pool.blocks[i + 10],
-                    }
+                block_pool.cached_block_hash_to_block.insert(
+                    make_block_hash_with_group_id(block_hash, 0),
+                    block_pool.blocks[i + 10])
 
         computed_blocks = manager.find_longest_cache_hit(
             block_hashes=block_hash_list,
@@ -112,16 +111,15 @@ def test_sliding_window_possible_cached_prefix():
             BlockHash(str(i).encode()) for i in range(len(block_is_cached))
         ]
 
-        block_pool.cached_block_hash_to_block.clear()
+        block_pool.cached_block_hash_to_block._cache.clear()
 
         # Mock the block pool with the cached blocks
         for i, (block_hash,
                 is_cached) in enumerate(zip(block_hash_list, block_is_cached)):
             if is_cached:
-                block_pool.cached_block_hash_to_block[
-                    make_block_hash_with_group_id(block_hash, 0)] = {
-                        i: block_pool.blocks[i + 10],
-                    }
+                block_pool.cached_block_hash_to_block.insert(
+                    make_block_hash_with_group_id(block_hash, 0),
+                    block_pool.blocks[i + 10])
 
         computed_blocks = manager.find_longest_cache_hit(
             block_hashes=block_hash_list,

tests/v1/entrypoints/llm/test_struct_output_generate.py

@@ -81,16 +81,6 @@ class CarDescription(BaseModel):
     car_type: CarType
 
 
-def _load_json(s: str, backend: str) -> str:
-    if backend != "xgrammar":
-        return json.loads(s)
-
-    # xgrammar specific workarounds
-    # https://github.com/mlc-ai/xgrammar/issues/286
-    s = re.sub(r'[\x00-\x1F\x7F-\xFF]', '', s)
-    return json.loads(s)
-
-
 def test_guided_decoding_deprecated():
     with pytest.warns(DeprecationWarning,
                       match="GuidedDecodingParams is deprecated.*"):
@@ -177,7 +167,12 @@ def test_structured_output(
         if backend != 'lm-format-enforcer':
             assert "\n" not in generated_text
         print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
-        output_json = json.loads(generated_text)
+        try:
+            output_json = json.loads(generated_text)
+        except json.JSONDecodeError as e:
+            pytest.fail(
+                f"Invalid JSON from backend={backend}: {generated_text!r}\n"
+                f"Schema: {sample_json_schema}\nError: {e}")
         jsonschema.validate(instance=output_json, schema=sample_json_schema)
 
     #
@@ -425,7 +420,12 @@ def test_structured_output(
         generated_text = output.outputs[0].text
         assert generated_text is not None
         print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
-        output_json = json.loads(generated_text)
+        try:
+            output_json = json.loads(generated_text)
+        except json.JSONDecodeError as e:
+            pytest.fail(
+                f"Invalid JSON from backend={backend}: {generated_text!r}\n"
+                f"Schema: {json_schema}\nError: {e}")
         jsonschema.validate(instance=output_json, schema=json_schema)
 
     #
@@ -468,7 +468,12 @@ def test_structured_output(
         generated_text = output.outputs[0].text
         assert generated_text is not None
         print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
-        output_json = json.loads(generated_text)
+        try:
+            output_json = json.loads(generated_text)
+        except json.JSONDecodeError as e:
+            pytest.fail(
+                f"Invalid JSON from backend={backend}: {generated_text!r}\n"
+                f"Schema: {json_schema}\nError: {e}")
         jsonschema.validate(instance=output_json, schema=json_schema)
 
     if backend not in ["outlines", "lm-format-enforcer"]:

tests/v1/tpu/test_tpu_int8.py

@@ -48,13 +48,9 @@ def test_model_tpu_int8(vllm_runner, model: str, dtype: str, max_tokens: int,
 
     prompts = [
         "A robot may not injure a human being",
-        "It is only with the heart that one can see rightly;",
-        "The greatest glory in living lies not in never falling,",
     ]
     answers = [
-        "or, being injured, not kill, except in",
-        "without the heart, one can only see wrongly.",
-        "but in rising every time we fall. - Nelson"
+        "or kill a human being",
     ]
 
     with vllm_runner(model, dtype=dtype, hf_overrides=hf_overrides) as vllm:

tests/v1/worker/test_worker_memory_snapshot.py

@@ -0,0 +1,174 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import multiprocessing as mp
+import os
+import tempfile
+from multiprocessing import Queue
+from typing import Optional
+from unittest.mock import patch
+
+import pytest
+import torch
+
+from vllm.engine.arg_utils import EngineArgs
+from vllm.utils import MemorySnapshot
+from vllm.v1.worker.gpu_worker import (Worker,
+                                       init_worker_distributed_environment)
+
+# Global queue to track operation order across processes
+_QUEUE: Optional[Queue] = None
+
+
+def track_operation(operation: str, rank: int):
+    """Track when an operation happens and its rank."""
+    if _QUEUE is not None:
+        _QUEUE.put((operation, rank))
+
+
+def make_operation_tracker(operation_name: str, original_func):
+    """Create a mock function that tracks when an operation is called.
+    
+    Args:
+        operation_name: Name to use when tracking this operation
+        original_func: The original function to wrap
+    
+    Returns:
+        A wrapper function that tracks the operation and calls the original
+    """
+
+    def wrapper(*args, **kwargs):
+        rank = int(os.environ.get("RANK", "-1"))
+        track_operation(operation_name, rank)
+        return original_func(*args, **kwargs)
+
+    return wrapper
+
+
+def worker_process(rank: int, world_size: int, distributed_init_method: str,
+                   queue: Queue, error_queue: Queue):
+    """Worker process that initializes a GPU worker with proper tracking."""
+    global _QUEUE
+    _QUEUE = queue
+
+    try:
+        # Set environment variables
+        os.environ["RANK"] = str(rank)
+        os.environ["LOCAL_RANK"] = str(rank)
+        os.environ["WORLD_SIZE"] = str(world_size)
+
+        # Create vLLM config with small model
+        vllm_config = EngineArgs(model="facebook/opt-125m",
+                                 tensor_parallel_size=2,
+                                 load_format="dummy").create_engine_config()
+
+        # Create worker
+        worker = Worker(
+            vllm_config=vllm_config,
+            local_rank=rank,
+            rank=rank,
+            distributed_init_method=distributed_init_method,
+        )
+
+        # Get original functions before patching
+        original_init_worker = init_worker_distributed_environment
+        original_memory_snapshot_init = MemorySnapshot.__init__
+        original_all_reduce = torch.distributed.all_reduce
+
+        # Apply minimal patches to track operation order
+        init_patch = patch(
+            'vllm.v1.worker.gpu_worker.init_worker_distributed_environment',
+            side_effect=make_operation_tracker("init_distributed",
+                                               original_init_worker))
+        memory_patch = patch.object(
+            MemorySnapshot, '__init__',
+            make_operation_tracker("memory_snapshot",
+                                   original_memory_snapshot_init))
+        all_reduce_patch = patch('torch.distributed.all_reduce',
+                                 side_effect=make_operation_tracker(
+                                     "nccl_all_reduce", original_all_reduce))
+
+        with init_patch, memory_patch, all_reduce_patch:
+
+            # Initialize device (this is where we test the order)
+            worker.init_device()
+
+            # Load model to ensure everything works
+            worker.load_model()
+
+        # Signal success
+        queue.put(("success", rank))
+
+    except Exception as e:
+        error_queue.put((rank, str(e), type(e).__name__))
+        raise
+
+
+@pytest.mark.skipif(torch.cuda.device_count() < 2,
+                    reason="Need at least 2 GPUs for tensor parallelism")
+def test_init_distributed_is_called_before_memory_snapshot():
+    """Test that distributed env is setup before memory snapshot.
+    
+    This test makes sure during worker initialization, the initial memory 
+    snapshot is taken after distributed env is setup to include all the buffers 
+    allocated by distributed env.
+    """
+    world_size = 2
+
+    # Create a temporary file for distributed init
+    with tempfile.NamedTemporaryFile(delete=False) as f:
+        distributed_init_method = f"file://{f.name}"
+
+    # Create queues for inter-process communication
+    ctx = mp.get_context("spawn")
+    operation_queue = ctx.Queue()
+    error_queue = ctx.Queue()
+
+    # Start worker processes
+    processes = []
+    for rank in range(world_size):
+        p = ctx.Process(target=worker_process,
+                        args=(rank, world_size, distributed_init_method,
+                              operation_queue, error_queue))
+        p.start()
+        processes.append(p)
+
+    # Wait for all processes to complete
+    for p in processes:
+        p.join(timeout=60)  # 60 second timeout
+
+    # Check for errors
+    errors = []
+    while not error_queue.empty():
+        rank, error_msg, error_type = error_queue.get()
+        errors.append(f"Rank {rank}: {error_type}: {error_msg}")
+
+    if errors:
+        pytest.fail("Worker processes failed:\n" + "\n".join(errors))
+
+    # Collect all operations from the queue
+    operations = []
+    while not operation_queue.empty():
+        operations.append(operation_queue.get())
+
+    # Verify we got operations from both ranks
+    print(f"Collected operations: {operations}")
+
+    # Check operations for each rank
+    for rank in range(world_size):
+        rank_ops = [op for op, r in operations if r == rank]
+        print(f"\nRank {rank} operations: {rank_ops}")
+
+        # Raises ValueError if the operation is not found
+        init_distributed = rank_ops.index("init_distributed")
+        nccl_all_reduce = rank_ops.index("nccl_all_reduce")
+        memory_snapshot = rank_ops.index("memory_snapshot")
+
+        # Verify order: init_distributed should happen before memory_snapshot
+        assert init_distributed < nccl_all_reduce < memory_snapshot, (
+            f"Rank {rank}: init_distributed (index {init_distributed}) "
+            f"must happen before nccl_all_reduce (index {nccl_all_reduce}) "
+            f"and memory_snapshot (index {memory_snapshot})")
+
+    # Clean up
+    os.unlink(distributed_init_method.replace("file://", ""))

vllm/attention/backends/placeholder_attn.py

@@ -1,314 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-from dataclasses import dataclass
-from itertools import accumulate
-from typing import List, Optional, Tuple, Type
-
-import torch
-
-from vllm.attention.backends.abstract import (AttentionBackend, AttentionImpl,
-                                              AttentionMetadata,
-                                              AttentionMetadataBuilder)
-from vllm.attention.backends.utils import CommonAttentionState
-from vllm.utils import async_tensor_h2d
-
-# Placeholder attention backend for models like Mamba and pooling models that
-# lack attention.
-
-
-class PlaceholderAttentionBackend(AttentionBackend):
-    """Placeholder backend for when no attention is needed."""
-
-    @staticmethod
-    def get_name() -> str:
-        return "NO_ATTENTION"
-
-    @staticmethod
-    def get_impl_cls() -> Type["PlaceholderAttentionImpl"]:
-        return PlaceholderAttentionImpl
-
-    @staticmethod
-    def get_builder_cls() -> Type["PlaceholderAttentionMetadataBuilder"]:
-        return PlaceholderAttentionMetadataBuilder
-
-    @staticmethod
-    def get_metadata_cls() -> Type["PlaceholderAttentionMetadata"]:
-        return PlaceholderAttentionMetadata
-
-    @staticmethod
-    def get_state_cls() -> Type["CommonAttentionState"]:
-        return CommonAttentionState
-
-    @staticmethod
-    def get_kv_cache_shape(
-        num_blocks: int,
-        block_size: int,
-        num_kv_heads: int,
-        head_size: int,
-    ) -> Tuple[int, ...]:
-        return (1, 1, 1, 1, 1)
-
-    @staticmethod
-    def swap_blocks(
-        src_kv_cache: torch.Tensor,
-        dst_kv_cache: torch.Tensor,
-        src_to_dst: torch.Tensor,
-    ) -> None:
-        return
-
-    @staticmethod
-    def copy_blocks(
-        kv_caches: List[torch.Tensor],
-        src_to_dists: torch.Tensor,
-    ) -> None:
-        return
-
-
-@dataclass
-class PlaceholderAttentionMetadata(AttentionMetadata):
-    """Attention metadata for prefill and decode batched together."""
-    # (batch_size,). The sequence length per sequence. Sequence length means
-    # the computed tokens + new tokens None if it is a decoding.
-    seq_lens: Optional[List[int]]
-    # seq_lens stored as a tensor.
-    seq_lens_tensor: Optional[torch.Tensor]
-
-    # Maximum sequence length among prefill batch. 0 if there are decoding
-    # requests only.
-    max_prefill_seq_len: int
-    # Maximum sequence length among decode batch. 0 if there are prefill
-    # requests only.
-    max_decode_seq_len: int
-    # (batch_size,) A tensor of context lengths (tokens that are computed
-    # so far).
-    context_lens_tensor: Optional[torch.Tensor]
-
-    # Whether or not if cuda graph is enabled.
-    # Cuda-graph is currently enabled for decoding only.
-    # TODO(woosuk): Move `use_cuda_graph` out since it's unrelated to attention.
-    use_cuda_graph: bool
-
-    # Maximum query length in the batch.
-    max_query_len: Optional[int]
-
-    # Max number of query tokens among request in the batch.
-    max_decode_query_len: Optional[int]
-
-    # (batch_size + 1,). The cumulative subquery lengths of the sequences in
-    # the batch, used to index into subquery. E.g., if the subquery length
-    # is [4, 6], it is [0, 4, 10].
-    query_start_loc: Optional[torch.Tensor] = None
-    # (batch_size + 1,). The cumulative sequence lengths of the sequences in
-    # the batch, used to index into sequence. E.g., if the sequence length is
-    # [4, 6], it is [0, 4, 10].
-    seq_start_loc: Optional[torch.Tensor] = None
-
-    # Placeholder.
-    block_tables: Optional[torch.Tensor] = None
-
-    _cached_prefill_metadata: Optional["PlaceholderAttentionMetadata"] = None
-    _cached_decode_metadata: Optional["PlaceholderAttentionMetadata"] = None
-
-    @property
-    def prefill_metadata(self) -> Optional["PlaceholderAttentionMetadata"]:
-        if self.num_prefills == 0:
-            return None
-
-        if self._cached_prefill_metadata is not None:
-            return self._cached_prefill_metadata
-
-        # Compute some attn_metadata fields which default to None
-        query_start_loc = (None if self.query_start_loc is None else
-                           self.query_start_loc[:self.num_prefills + 1])
-        seq_lens = (None if self.seq_lens is None else
-                    self.seq_lens[:self.num_prefills])
-        seq_lens_tensor = (None if self.seq_lens_tensor is None else
-                           self.seq_lens_tensor[:self.num_prefills])
-        seq_start_loc = (None if self.seq_start_loc is None else
-                         self.seq_start_loc[:self.num_prefills + 1])
-        context_lens_tensor = (None if self.context_lens_tensor is None else
-                               self.context_lens_tensor[:self.num_prefills])
-
-        # Placeholders
-        slot_mapping = torch.empty(0)
-        block_tables = torch.empty(0)
-
-        self._cached_prefill_metadata = PlaceholderAttentionMetadata(
-            num_prefills=self.num_prefills,
-            num_prefill_tokens=self.num_prefill_tokens,
-            num_decode_tokens=0,
-            slot_mapping=slot_mapping,
-            enable_kv_scales_calculation=self.enable_kv_scales_calculation,
-            seq_lens=seq_lens,
-            seq_lens_tensor=seq_lens_tensor,
-            max_decode_query_len=0,
-            max_query_len=self.max_query_len,
-            max_prefill_seq_len=self.max_prefill_seq_len,
-            max_decode_seq_len=0,
-            query_start_loc=query_start_loc,
-            seq_start_loc=seq_start_loc,
-            context_lens_tensor=context_lens_tensor,
-            block_tables=block_tables,
-            use_cuda_graph=False,
-        )
-        return self._cached_prefill_metadata
-
-    @property
-    def decode_metadata(self) -> Optional["PlaceholderAttentionMetadata"]:
-        if self.num_decode_tokens == 0:
-            return None
-
-        if self._cached_decode_metadata is not None:
-            return self._cached_decode_metadata
-        assert self.seq_lens_tensor is not None
-
-        # Placeholders
-        slot_mapping = torch.empty(0)
-        block_tables = torch.empty(0)
-        seq_lens_tensor = (None if self.seq_lens_tensor is None else
-                           self.seq_lens_tensor[self.num_prefills:])
-
-        self._cached_decode_metadata = PlaceholderAttentionMetadata(
-            num_prefills=0,
-            num_prefill_tokens=0,
-            num_decode_tokens=self.num_decode_tokens,
-            slot_mapping=slot_mapping,
-            enable_kv_scales_calculation=True,
-            seq_lens=None,
-            seq_lens_tensor=seq_lens_tensor,
-            max_decode_query_len=self.max_decode_query_len,
-            max_query_len=None,
-            max_prefill_seq_len=0,
-            max_decode_seq_len=self.max_decode_seq_len,
-            query_start_loc=(self.query_start_loc[self.num_prefills:] -
-                             self.query_start_loc[self.num_prefills])
-            if self.query_start_loc is not None else None,
-            seq_start_loc=self.seq_start_loc[self.num_prefills:]
-            if self.seq_start_loc is not None else None,
-            context_lens_tensor=None,
-            block_tables=block_tables,
-            use_cuda_graph=self.use_cuda_graph,
-        )
-        return self._cached_decode_metadata
-
-
-class PlaceholderAttentionMetadataBuilder(
-        AttentionMetadataBuilder[PlaceholderAttentionMetadata]):
-
-    def __init__(self, input_builder):
-
-        self.input_builder = input_builder
-        self.runner = input_builder.runner
-
-    def prepare(self):
-        self.prefill_seq_lens: List[int] = []
-        self.context_lens: List[int] = []
-        self.curr_seq_lens: List[int] = []
-        self.num_prefills = 0
-        self.num_prefill_tokens = 0
-        self.num_decode_tokens = 0
-
-    def _add_seq_group(self, inter_data, chunked_prefill_enabled: bool):
-        """Add a sequence group to the metadata. Specifically update/append
-        1. context length.
-        """
-        is_prompt = inter_data.is_prompt
-
-        for (seq_id, token_len, seq_len, curr_seq_len, query_len, context_len,
-             curr_sliding_window_block) in zip(
-                 inter_data.seq_ids, [len(t) for t in inter_data.input_tokens],
-                 inter_data.orig_seq_lens, inter_data.seq_lens,
-                 inter_data.query_lens, inter_data.context_lens,
-                 inter_data.curr_sliding_window_blocks):
-            self.context_lens.append(context_len)
-
-            if is_prompt:
-                self.num_prefills += 1
-                self.num_prefill_tokens += token_len
-                self.prefill_seq_lens.append(seq_len)
-            else:
-                self.num_decode_tokens += query_len
-                self.curr_seq_lens.append(curr_seq_len)
-
-    def build(self, seq_lens: List[int], query_lens: List[int],
-              cuda_graph_pad_size: int, batch_size: int):
-        """Build attention metadata with on-device tensors.
-
-        Args:
-            seq_lens: The maybe padded sequence lengths of the input sequences.
-            query_lens: The query lengths of the input sequences.
-            cuda_graph_pad_size: The padding size for cuda graph.
-                                 -1 if cuda graph is not used.
-            batch_size: The maybe padded batch size.
-        """
-
-        # Some input builders such as ModelInputForCPUBuilder do not have the
-        # "inter_data_list" attribute.
-        # Let's check inter_data_list exists before we reference it.
-        if hasattr(self.input_builder, "inter_data_list"):
-            for inter_data in self.input_builder.inter_data_list:
-                self._add_seq_group(inter_data,
-                                    self.input_builder.chunked_prefill_enabled)
-
-        device = self.runner.device
-        use_captured_graph = cuda_graph_pad_size != -1
-
-        max_query_len = max(query_lens)
-        decode_query_lens = query_lens[self.num_prefills:]
-        if len(decode_query_lens) > 0:
-            max_decode_query_len = max(decode_query_lens)
-        else:
-            max_decode_query_len = 1
-        max_prefill_seq_len = max(self.prefill_seq_lens, default=0)
-        max_decode_seq_len = max(self.curr_seq_lens, default=0)
-        num_decode_tokens = self.num_decode_tokens
-        query_start_loc = list(accumulate(query_lens, initial=0))
-        seq_start_loc = list(accumulate(seq_lens, initial=0))
-
-        if use_captured_graph:
-            num_decode_tokens = batch_size - self.num_prefill_tokens
-        assert max_query_len > 0, ("query_lens: {}".format(query_lens))
-
-        assert device is not None
-        context_lens_tensor = async_tensor_h2d(self.context_lens, torch.int,
-                                               device, self.runner.pin_memory)
-        seq_lens_tensor = async_tensor_h2d(seq_lens, torch.int, device,
-                                           self.runner.pin_memory)
-        query_start_loc_tensor = async_tensor_h2d(query_start_loc, torch.int32,
-                                                  device,
-                                                  self.runner.pin_memory)
-        seq_start_loc_tensor = async_tensor_h2d(seq_start_loc, torch.int32,
-                                                device, self.runner.pin_memory)
-
-        # Placeholders
-        slot_mapping_tensor = torch.empty(0)
-        block_tables = torch.empty(0)
-
-        return PlaceholderAttentionMetadata(
-            num_prefills=self.num_prefills,
-            slot_mapping=slot_mapping_tensor,
-            enable_kv_scales_calculation=True,
-            num_prefill_tokens=self.num_prefill_tokens,
-            num_decode_tokens=num_decode_tokens,
-            seq_lens=seq_lens,
-            seq_lens_tensor=seq_lens_tensor,
-            max_query_len=max_query_len,
-            max_decode_query_len=max_decode_query_len,
-            max_prefill_seq_len=max_prefill_seq_len,
-            max_decode_seq_len=max_decode_seq_len,
-            query_start_loc=query_start_loc_tensor,
-            seq_start_loc=seq_start_loc_tensor,
-            context_lens_tensor=context_lens_tensor,
-            block_tables=block_tables,
-            use_cuda_graph=use_captured_graph,
-        )
-
-
-class PlaceholderAttentionImpl(AttentionImpl):
-
-    def __init__(self, *args, **kwargs) -> None:
-        return
-
-    def forward(self, *args, **kwargs) -> torch.Tensor:
-        raise NotImplementedError

vllm/attention/backends/utils.py

@@ -304,7 +304,7 @@ class CommonAttentionState(AttentionState):
             max_query_len=1,
             max_decode_query_len=1,
             max_prefill_seq_len=0,
-            max_decode_seq_len=self.runner.max_seq_len_to_capture,
+            max_decode_seq_len=self.runner.max_model_len,
             query_start_loc=None,
             seq_start_loc=None,
             context_lens_tensor=None,
@@ -390,7 +390,7 @@ class CommonAttentionState(AttentionState):
                                                     dtype=torch.int).cuda()
         attn_metadata.encoder_seq_lens_tensor = torch.full(
             (batch_size, ), 1, dtype=torch.int).cuda()
-        attn_metadata.max_encoder_seq_len = self.runner.max_seq_len_to_capture
+        attn_metadata.max_encoder_seq_len = self.runner.max_model_len
         attn_metadata.num_encoder_tokens = 0
 
     def _add_additional_input_buffers_for_enc_dec_model(

vllm/attention/layer.py

@@ -115,12 +115,10 @@ class Attention(nn.Module, AttentionLayerBase):
         if cache_config is not None:
             kv_cache_dtype = cache_config.cache_dtype
             block_size = cache_config.block_size
-            is_attention_free = cache_config.is_attention_free
             calculate_kv_scales = cache_config.calculate_kv_scales
         else:
             kv_cache_dtype = "auto"
             block_size = 16
-            is_attention_free = False
             calculate_kv_scales = False
         if num_kv_heads is None:
             num_kv_heads = num_heads
@@ -185,7 +183,6 @@ class Attention(nn.Module, AttentionLayerBase):
                                                  dtype,
                                                  kv_cache_dtype,
                                                  block_size,
-                                                 is_attention_free,
                                                  use_mla=use_mla,
                                                  has_sink=self.has_sink)
         else:
@@ -578,9 +575,7 @@ def unified_attention_fake(
 direct_register_custom_op(
     op_name="unified_attention",
     op_func=unified_attention,
-    mutates_args=[],
     fake_impl=unified_attention_fake,
-    dispatch_key=current_platform.dispatch_key,
     tags=tag_cudagraph_unsafe,
 )
 
@@ -631,6 +626,5 @@ direct_register_custom_op(
     op_func=unified_attention_with_output,
     mutates_args=["output", "output_block_scale"],
     fake_impl=unified_attention_with_output_fake,
-    dispatch_key=current_platform.dispatch_key,
     tags=tag_cudagraph_unsafe,
 )

vllm/attention/ops/triton_reshape_and_cache_flash.py

@@ -2,10 +2,9 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import torch
-import triton
-import triton.language as tl
 
 from vllm.platforms import current_platform
+from vllm.triton_utils import tl, triton
 
 
 @triton.jit

vllm/attention/selector.py

@@ -142,7 +142,6 @@ def get_attn_backend(
     dtype: torch.dtype,
     kv_cache_dtype: Optional[str],
     block_size: int,
-    is_attention_free: bool = False,
     use_mla: bool = False,
     has_sink: bool = False,
 ) -> type[AttentionBackend]:
@@ -156,7 +155,6 @@ def get_attn_backend(
         dtype=dtype,
         kv_cache_dtype=kv_cache_dtype,
         block_size=block_size,
-        is_attention_free=is_attention_free,
         use_v1=envs.VLLM_USE_V1,
         use_mla=use_mla,
         has_sink=has_sink,
@@ -169,17 +167,10 @@ def _cached_get_attn_backend(
     dtype: torch.dtype,
     kv_cache_dtype: Optional[str],
     block_size: int,
-    is_attention_free: bool,
     use_v1: bool = False,
     use_mla: bool = False,
     has_sink: bool = False,
 ) -> type[AttentionBackend]:
-    # If there are no attention layers (e.g. we are running Mamba),
-    # use the placeholder NO_ATTENTION
-    if is_attention_free:
-        from vllm.attention.backends.placeholder_attn import (
-            PlaceholderAttentionBackend)
-        return PlaceholderAttentionBackend
 
     # Check whether a particular choice of backend was
     # previously forced.

vllm/compilation/collective_fusion.py

@@ -547,7 +547,6 @@ if flashinfer_comm is not None:
             "scale_out",
         ],
         fake_impl=call_trtllm_fused_allreduce_norm_fake,
-        dispatch_key=current_platform.dispatch_key,
     )
     flashinfer_trtllm_fused_allreduce_norm = (
         torch.ops.vllm.flashinfer_trtllm_fused_allreduce_norm.default)

vllm/compilation/compiler_interface.py

@@ -551,8 +551,9 @@ def set_inductor_config(config, runtime_shape):
     if isinstance(runtime_shape, int):
         # for a specific batchsize, tuning triton kernel parameters
         # can be beneficial
-        config["max_autotune"] = True
-        config["coordinate_descent_tuning"] = True
+        config["max_autotune"] = envs.VLLM_ENABLE_INDUCTOR_MAX_AUTOTUNE
+        config["coordinate_descent_tuning"] = (
+            envs.VLLM_ENABLE_INDUCTOR_COORDINATE_DESCENT_TUNING)
 
 
 class EagerAdaptor(CompilerInterface):

vllm/compilation/decorators.py

@@ -8,6 +8,7 @@ from unittest.mock import patch
 
 import torch
 import torch.nn as nn
+from packaging import version
 from torch._dynamo.symbolic_convert import InliningInstructionTranslator
 
 from vllm.compilation.counter import compilation_counter
@@ -300,13 +301,13 @@ def _support_torch_compile(
                 logger.debug(
                     "enable_cpp_symbolic_shape_guards config not available")
 
-            with patch.object(InliningInstructionTranslator, 'inline_call',
-                              patched_inline_call), torch._dynamo.config.patch(
-                                  **dynamo_config_patches
-                              ), maybe_use_cudagraph_partition_wrapper(
-                                  self.vllm_config):
+            with patch.object(
+                    InliningInstructionTranslator, "inline_call",
+                    patched_inline_call), torch._dynamo.config.patch(
+                        **dynamo_config_patches
+                    ), maybe_use_cudagraph_partition_wrapper(
+                        self.vllm_config), _torch27_patch_tensor_subclasses():
                 output = self.compiled_callable(*args, **kwargs)
-
             return output
 
         # usually, capturing the model once is enough, and then we can
@@ -367,3 +368,33 @@ def maybe_use_cudagraph_partition_wrapper(vllm_config: VllmConfig):
     if (compilation_config.cudagraph_mode != CUDAGraphMode.NONE
             and compilation_config.use_inductor_graph_partition):
         torch._inductor.utils.set_customized_partition_wrappers(None)
+
+
+@contextlib.contextmanager
+def _torch27_patch_tensor_subclasses():
+    """
+    Add support for using tensor subclasses (ie `BasevLLMParameter`, ect) when
+    using torch 2.7.0. This enables using weight_loader_v2 and the use of
+    `BasevLLMParameters` without having to replace them with regular tensors
+    before `torch.compile`-time.
+    """
+    from vllm.model_executor.parameter import (BasevLLMParameter,
+                                               ModelWeightParameter,
+                                               RowvLLMParameter,
+                                               _ColumnvLLMParameter)
+
+    def return_false(*args, **kwargs):
+        return False
+
+    if version.parse("2.7") <= version.parse(
+            torch.__version__) < version.parse("2.8"):
+        yield
+        return
+
+    with (torch._dynamo.config.patch("traceable_tensor_subclasses", [
+            BasevLLMParameter, ModelWeightParameter, _ColumnvLLMParameter,
+            RowvLLMParameter
+    ]),
+          patch("torch._dynamo.variables.torch.can_dispatch_torch_function",
+                return_false)):
+        yield

vllm/config/__init__.py

@@ -27,6 +27,7 @@ from vllm.config.cache import (BlockSize, CacheConfig, CacheDType, MambaDType,
                                PrefixCachingHashAlgo)
 from vllm.config.compilation import (CompilationConfig, CompilationLevel,
                                      CUDAGraphMode, PassConfig)
+from vllm.config.device import Device, DeviceConfig
 from vllm.config.kv_events import KVEventsConfig
 from vllm.config.kv_transfer import KVTransferConfig
 from vllm.config.load import LoadConfig
@@ -38,11 +39,13 @@ from vllm.config.model import (ConvertOption, HfOverrides, LogprobsMode,
                                try_match_architecture_defaults)
 from vllm.config.multimodal import (MMCacheType, MMEncoderTPMode,
                                     MultiModalConfig)
+from vllm.config.observability import DetailedTraceModules, ObservabilityConfig
 from vllm.config.parallel import (DistributedExecutorBackend, EPLBConfig,
                                   ParallelConfig)
 from vllm.config.pooler import PoolerConfig
 from vllm.config.scheduler import RunnerType, SchedulerConfig, SchedulerPolicy
 from vllm.config.speculative import SpeculativeConfig
+from vllm.config.speech_to_text import SpeechToTextConfig
 from vllm.config.structured_outputs import StructuredOutputsConfig
 from vllm.config.utils import ConfigType, config, get_attr_docs, is_init_field
 from vllm.logger import init_logger
@@ -81,158 +84,6 @@ class SupportsMetricsInfo(Protocol):
         ...
 
 
-Device = Literal["auto", "cuda", "cpu", "tpu", "xpu"]
-
-
-@config
-@dataclass(config=ConfigDict(arbitrary_types_allowed=True))
-class DeviceConfig:
-    """Configuration for the device to use for vLLM execution."""
-
-    device: SkipValidation[Optional[Union[Device, torch.device]]] = "auto"
-    """Device type for vLLM execution.
-    This parameter is deprecated and will be
-    removed in a future release.
-    It will now be set automatically based
-    on the current platform."""
-    device_type: str = field(init=False)
-    """Device type from the current platform. This is set in
-    `__post_init__`."""
-
-    def compute_hash(self) -> str:
-        """
-        WARNING: Whenever a new field is added to this config,
-        ensure that it is included in the factors list if
-        it affects the computation graph.
-
-        Provide a hash that uniquely identifies all the configs
-        that affect the structure of the computation
-        graph from input ids/embeddings to the final hidden states,
-        excluding anything before input ids/embeddings and after
-        the final hidden states.
-        """
-        # no factors to consider.
-        # the device/platform information will be summarized
-        # by torch/vllm automatically.
-        factors: list[Any] = []
-        hash_str = hashlib.md5(str(factors).encode(),
-                               usedforsecurity=False).hexdigest()
-        return hash_str
-
-    def __post_init__(self):
-        if self.device == "auto":
-            # Automated device type detection
-            from vllm.platforms import current_platform
-            self.device_type = current_platform.device_type
-            if not self.device_type:
-                raise RuntimeError(
-                    "Failed to infer device type, please set "
-                    "the environment variable `VLLM_LOGGING_LEVEL=DEBUG` "
-                    "to turn on verbose logging to help debug the issue.")
-        else:
-            # Device type is assigned explicitly
-            if isinstance(self.device, str):
-                self.device_type = self.device
-            elif isinstance(self.device, torch.device):
-                self.device_type = self.device.type
-
-        # Some device types require processing inputs on CPU
-        if self.device_type in ["tpu"]:
-            self.device = None
-        else:
-            # Set device with device type
-            self.device = torch.device(self.device_type)
-
-
-DetailedTraceModules = Literal["model", "worker", "all"]
-
-
-@config
-@dataclass
-class ObservabilityConfig:
-    """Configuration for observability - metrics and tracing."""
-
-    show_hidden_metrics_for_version: Optional[str] = None
-    """Enable deprecated Prometheus metrics that have been hidden since the
-    specified version. For example, if a previously deprecated metric has been
-    hidden since the v0.7.0 release, you use
-    `--show-hidden-metrics-for-version=0.7` as a temporary escape hatch while
-    you migrate to new metrics. The metric is likely to be removed completely
-    in an upcoming release."""
-
-    @cached_property
-    def show_hidden_metrics(self) -> bool:
-        """Check if the hidden metrics should be shown."""
-        if self.show_hidden_metrics_for_version is None:
-            return False
-        return version._prev_minor_version_was(
-            self.show_hidden_metrics_for_version)
-
-    otlp_traces_endpoint: Optional[str] = None
-    """Target URL to which OpenTelemetry traces will be sent."""
-
-    collect_detailed_traces: Optional[list[DetailedTraceModules]] = None
-    """It makes sense to set this only if `--otlp-traces-endpoint` is set. If
-    set, it will collect detailed traces for the specified modules. This
-    involves use of possibly costly and or blocking operations and hence might
-    have a performance impact.
-
-    Note that collecting detailed timing information for each request can be
-    expensive."""
-
-    @cached_property
-    def collect_model_forward_time(self) -> bool:
-        """Whether to collect model forward time for the request."""
-        return (self.collect_detailed_traces is not None
-                and ("model" in self.collect_detailed_traces
-                     or "all" in self.collect_detailed_traces))
-
-    @cached_property
-    def collect_model_execute_time(self) -> bool:
-        """Whether to collect model execute time for the request."""
-        return (self.collect_detailed_traces is not None
-                and ("worker" in self.collect_detailed_traces
-                     or "all" in self.collect_detailed_traces))
-
-    def compute_hash(self) -> str:
-        """
-        WARNING: Whenever a new field is added to this config,
-        ensure that it is included in the factors list if
-        it affects the computation graph.
-
-        Provide a hash that uniquely identifies all the configs
-        that affect the structure of the computation
-        graph from input ids/embeddings to the final hidden states,
-        excluding anything before input ids/embeddings and after
-        the final hidden states.
-        """
-        # no factors to consider.
-        # this config will not affect the computation graph.
-        factors: list[Any] = []
-        hash_str = hashlib.md5(str(factors).encode(),
-                               usedforsecurity=False).hexdigest()
-        return hash_str
-
-    def __post_init__(self):
-        if (self.collect_detailed_traces is not None
-                and len(self.collect_detailed_traces) == 1
-                and "," in self.collect_detailed_traces[0]):
-            self._parse_collect_detailed_traces()
-
-        from vllm.tracing import is_otel_available, otel_import_error_traceback
-        if not is_otel_available() and self.otlp_traces_endpoint is not None:
-            raise ValueError(
-                "OpenTelemetry is not available. Unable to configure "
-                "'otlp_traces_endpoint'. Ensure OpenTelemetry packages are "
-                f"installed. Original error:\n{otel_import_error_traceback}")
-
-    def _parse_collect_detailed_traces(self):
-        assert isinstance(self.collect_detailed_traces, list)
-        self.collect_detailed_traces = cast(
-            list[DetailedTraceModules],
-            self.collect_detailed_traces[0].split(","))
-
-
 @config
 @dataclass(config=ConfigDict(arbitrary_types_allowed=True))
 class VllmConfig:
@@ -1009,37 +860,6 @@ def get_layers_from_vllm_config(
     }
 
 
-@config
-@dataclass
-class SpeechToTextConfig:
-    """Configuration for speech-to-text models."""
-
-    sample_rate: float = 16_000
-    """Sample rate (Hz) to resample input audio to. Most speech models expect
-    16kHz audio input. The input audio will be automatically resampled to this
-    rate before processing."""
-
-    max_audio_clip_s: int = 30
-    """Maximum duration in seconds for a single audio clip without chunking.
-    Audio longer than this will be split into smaller chunks if
-    `allow_audio_chunking` evaluates to True, otherwise it will be rejected."""
-
-    overlap_chunk_second: int = 1
-    """Overlap duration in seconds between consecutive audio chunks when
-    splitting long audio. This helps maintain context across chunk boundaries
-    and improves transcription quality at split points."""
-
-    min_energy_split_window_size: Optional[int] = 1600
-    """Window size in samples for finding low-energy (quiet) regions to split
-    audio chunks. The algorithm looks for the quietest moment within this
-    window to minimize cutting through speech. Default 1600 samples ≈ 100ms
-    at 16kHz. If None, no chunking will be done."""
-
-    @property
-    def allow_audio_chunking(self) -> bool:
-        return self.min_energy_split_window_size is not None
-
-
 def update_config(config: DataclassInstanceT,
                   overrides: dict[str, Any]) -> DataclassInstanceT:
     processed_overrides = {}

vllm/config/device.py

@@ -0,0 +1,74 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import hashlib
+from dataclasses import field
+from typing import Any, Literal, Optional, Union
+
+import torch
+from pydantic import ConfigDict, SkipValidation
+from pydantic.dataclasses import dataclass
+
+from vllm.config.utils import config
+
+Device = Literal["auto", "cuda", "cpu", "tpu", "xpu"]
+
+
+@config
+@dataclass(config=ConfigDict(arbitrary_types_allowed=True))
+class DeviceConfig:
+    """Configuration for the device to use for vLLM execution."""
+
+    device: SkipValidation[Optional[Union[Device, torch.device]]] = "auto"
+    """Device type for vLLM execution.
+    This parameter is deprecated and will be
+    removed in a future release.
+    It will now be set automatically based
+    on the current platform."""
+    device_type: str = field(init=False)
+    """Device type from the current platform. This is set in
+    `__post_init__`."""
+
+    def compute_hash(self) -> str:
+        """
+        WARNING: Whenever a new field is added to this config,
+        ensure that it is included in the factors list if
+        it affects the computation graph.
+
+        Provide a hash that uniquely identifies all the configs
+        that affect the structure of the computation
+        graph from input ids/embeddings to the final hidden states,
+        excluding anything before input ids/embeddings and after
+        the final hidden states.
+        """
+        # no factors to consider.
+        # the device/platform information will be summarized
+        # by torch/vllm automatically.
+        factors: list[Any] = []
+        hash_str = hashlib.md5(str(factors).encode(),
+                               usedforsecurity=False).hexdigest()
+        return hash_str
+
+    def __post_init__(self):
+        if self.device == "auto":
+            # Automated device type detection
+            from vllm.platforms import current_platform
+            self.device_type = current_platform.device_type
+            if not self.device_type:
+                raise RuntimeError(
+                    "Failed to infer device type, please set "
+                    "the environment variable `VLLM_LOGGING_LEVEL=DEBUG` "
+                    "to turn on verbose logging to help debug the issue.")
+        else:
+            # Device type is assigned explicitly
+            if isinstance(self.device, str):
+                self.device_type = self.device
+            elif isinstance(self.device, torch.device):
+                self.device_type = self.device.type
+
+        # Some device types require processing inputs on CPU
+        if self.device_type in ["tpu"]:
+            self.device = None
+        else:
+            # Set device with device type
+            self.device = torch.device(self.device_type)

vllm/config/model.py

@@ -177,11 +177,6 @@ class ModelConfig:
     graph and always execute the model in eager mode. If False, we will use
     CUDA graph and eager execution in hybrid for maximal performance and
     flexibility."""
-    max_seq_len_to_capture: int = 8192
-    """Maximum sequence len covered by CUDA graphs. When a sequence has context
-    length larger than this, we fall back to eager mode. Additionally for
-    encoder-decoder models, if the sequence length of the encoder input is
-    larger than this, we fall back to the eager mode."""
     max_logprobs: int = 20
     """Maximum number of log probabilities to return when `logprobs` is
     specified in `SamplingParams`. The default value comes the default for the
@@ -699,11 +694,12 @@ class ModelConfig:
             model: Model name or path
             tokenizer: Tokenizer name or path
         """
+
         if not (is_runai_obj_uri(model) or is_runai_obj_uri(tokenizer)):
             return
 
         if is_runai_obj_uri(model):
-            object_storage_model = ObjectStorageModel()
+            object_storage_model = ObjectStorageModel(url=model)
             object_storage_model.pull_files(
                 model, allow_pattern=["*.model", "*.py", "*.json"])
             self.model_weights = model
@@ -722,7 +718,7 @@ class ModelConfig:
 
         # Only download tokenizer if needed and not already handled
         if is_runai_obj_uri(tokenizer):
-            object_storage_tokenizer = ObjectStorageModel()
+            object_storage_tokenizer = ObjectStorageModel(url=tokenizer)
             object_storage_tokenizer.pull_files(model,
                                                 ignore_pattern=[
                                                     "*.pt", "*.safetensors",
@@ -1023,21 +1019,8 @@ class ModelConfig:
             current_platform.verify_quantization(self.quantization)
 
     def _verify_cuda_graph(self) -> None:
-        # The `max_seq_len_to_capture` was incorrectly
-        # based on the encoder's input length (448)
-        # but not the decoder's larger input length (1500).
-        # This change ensures the CUDA Graph captures the correct,
-        # larger sequence length, allowing it to work as intended.
-        effective_max_seq_len = self.max_model_len
-        if self.is_encoder_decoder:
-            effective_max_seq_len = max(
-                effective_max_seq_len,
-                getattr(self.hf_config, "max_source_positions", 0))
-        self.max_seq_len_to_capture = min(self.max_seq_len_to_capture,
-                                          effective_max_seq_len)
         # CUDAGraph capture not supported for encoder-decoder models on ROCm
         unsupported_rocm = self.is_encoder_decoder
-
         if (unsupported_rocm and not self.enforce_eager
                 and current_platform.is_rocm()):
             logger.warning(

vllm/config/observability.py

@@ -0,0 +1,99 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import hashlib
+from functools import cached_property
+from typing import Any, Literal, Optional, cast
+
+from pydantic.dataclasses import dataclass
+
+from vllm import version
+from vllm.config.utils import config
+
+DetailedTraceModules = Literal["model", "worker", "all"]
+
+
+@config
+@dataclass
+class ObservabilityConfig:
+    """Configuration for observability - metrics and tracing."""
+
+    show_hidden_metrics_for_version: Optional[str] = None
+    """Enable deprecated Prometheus metrics that have been hidden since the
+    specified version. For example, if a previously deprecated metric has been
+    hidden since the v0.7.0 release, you use
+    `--show-hidden-metrics-for-version=0.7` as a temporary escape hatch while
+    you migrate to new metrics. The metric is likely to be removed completely
+    in an upcoming release."""
+
+    @cached_property
+    def show_hidden_metrics(self) -> bool:
+        """Check if the hidden metrics should be shown."""
+        if self.show_hidden_metrics_for_version is None:
+            return False
+        return version._prev_minor_version_was(
+            self.show_hidden_metrics_for_version)
+
+    otlp_traces_endpoint: Optional[str] = None
+    """Target URL to which OpenTelemetry traces will be sent."""
+
+    collect_detailed_traces: Optional[list[DetailedTraceModules]] = None
+    """It makes sense to set this only if `--otlp-traces-endpoint` is set. If
+    set, it will collect detailed traces for the specified modules. This
+    involves use of possibly costly and or blocking operations and hence might
+    have a performance impact.
+
+    Note that collecting detailed timing information for each request can be
+    expensive."""
+
+    @cached_property
+    def collect_model_forward_time(self) -> bool:
+        """Whether to collect model forward time for the request."""
+        return (self.collect_detailed_traces is not None
+                and ("model" in self.collect_detailed_traces
+                     or "all" in self.collect_detailed_traces))
+
+    @cached_property
+    def collect_model_execute_time(self) -> bool:
+        """Whether to collect model execute time for the request."""
+        return (self.collect_detailed_traces is not None
+                and ("worker" in self.collect_detailed_traces
+                     or "all" in self.collect_detailed_traces))
+
+    def compute_hash(self) -> str:
+        """
+        WARNING: Whenever a new field is added to this config,
+        ensure that it is included in the factors list if
+        it affects the computation graph.
+
+        Provide a hash that uniquely identifies all the configs
+        that affect the structure of the computation
+        graph from input ids/embeddings to the final hidden states,
+        excluding anything before input ids/embeddings and after
+        the final hidden states.
+        """
+        # no factors to consider.
+        # this config will not affect the computation graph.
+        factors: list[Any] = []
+        hash_str = hashlib.md5(str(factors).encode(),
+                               usedforsecurity=False).hexdigest()
+        return hash_str
+
+    def __post_init__(self):
+        if (self.collect_detailed_traces is not None
+                and len(self.collect_detailed_traces) == 1
+                and "," in self.collect_detailed_traces[0]):
+            self._parse_collect_detailed_traces()
+
+        from vllm.tracing import is_otel_available, otel_import_error_traceback
+        if not is_otel_available() and self.otlp_traces_endpoint is not None:
+            raise ValueError(
+                "OpenTelemetry is not available. Unable to configure "
+                "'otlp_traces_endpoint'. Ensure OpenTelemetry packages are "
+                f"installed. Original error:\n{otel_import_error_traceback}")
+
+    def _parse_collect_detailed_traces(self):
+        assert isinstance(self.collect_detailed_traces, list)
+        self.collect_detailed_traces = cast(
+            list[DetailedTraceModules],
+            self.collect_detailed_traces[0].split(","))

vllm/config/speculative.py

@@ -285,8 +285,6 @@ class SpeculativeConfig:
                     max_model_len,
                     quantization=self.quantization,
                     enforce_eager=self.target_model_config.enforce_eager,
-                    max_seq_len_to_capture=self.target_model_config.
-                    max_seq_len_to_capture,
                     max_logprobs=self.target_model_config.max_logprobs,
                     hf_overrides=SpeculativeConfig.hf_config_override,
                 )

vllm/config/speech_to_text.py

@@ -0,0 +1,39 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from typing import Optional
+
+from pydantic.dataclasses import dataclass
+
+from vllm.config.utils import config
+
+
+@config
+@dataclass
+class SpeechToTextConfig:
+    """Configuration for speech-to-text models."""
+
+    sample_rate: float = 16_000
+    """Sample rate (Hz) to resample input audio to. Most speech models expect
+    16kHz audio input. The input audio will be automatically resampled to this
+    rate before processing."""
+
+    max_audio_clip_s: int = 30
+    """Maximum duration in seconds for a single audio clip without chunking.
+    Audio longer than this will be split into smaller chunks if
+    `allow_audio_chunking` evaluates to True, otherwise it will be rejected."""
+
+    overlap_chunk_second: int = 1
+    """Overlap duration in seconds between consecutive audio chunks when
+    splitting long audio. This helps maintain context across chunk boundaries
+    and improves transcription quality at split points."""
+
+    min_energy_split_window_size: Optional[int] = 1600
+    """Window size in samples for finding low-energy (quiet) regions to split
+    audio chunks. The algorithm looks for the quietest moment within this
+    window to minimize cutting through speech. Default 1600 samples ≈ 100ms
+    at 16kHz. If None, no chunking will be done."""
+
+    @property
+    def allow_audio_chunking(self) -> bool:
+        return self.min_energy_split_window_size is not None

vllm/distributed/device_communicators/pynccl.py

@@ -46,7 +46,6 @@ def register_nccl_symmetric_ops(pynccl_comm):
     direct_register_custom_op(
         op_name="all_reduce_symmetric_with_copy",
         op_func=all_reduce_symmetric_with_copy_impl,
-        mutates_args=[],
         fake_impl=all_reduce_symmetric_with_copy_fake,
     )
 

vllm/distributed/device_communicators/shm_broadcast.py

@@ -392,7 +392,8 @@ class MessageQueue:
                             > VLLM_RINGBUFFER_WARNING_INTERVAL * n_warning):
                         logger.debug(
                             ("No available shared memory broadcast block found"
-                             " in %s second."),
+                             " in %s seconds. This typically happens when some"
+                             " processes are hanging."),
                             VLLM_RINGBUFFER_WARNING_INTERVAL,
                         )
                         n_warning += 1
@@ -455,7 +456,8 @@ class MessageQueue:
                             > VLLM_RINGBUFFER_WARNING_INTERVAL * n_warning):
                         logger.debug(
                             ("No available shared memory broadcast block found"
-                             " in %s second."),
+                             " in %s seconds. This typically happens when some"
+                             " processes are hanging."),
                             VLLM_RINGBUFFER_WARNING_INTERVAL,
                         )
                         n_warning += 1

vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py

@@ -574,7 +574,6 @@ class NixlConnectorWorker:
                                    self.model_config.dtype,
                                    self.cache_config.cache_dtype,
                                    self.block_size,
-                                   self.model_config.is_attention_free,
                                    use_mla=self.use_mla)
         self.backend_name = backend.get_name()
         attn_backend = backend_name_to_enum(self.backend_name)

vllm/distributed/parallel_state.py

@@ -149,29 +149,22 @@ def all_gather_fake(tensor: torch.Tensor, dim: int, world_size: int,
 
 
 if supports_custom_op():
-    from vllm.platforms import current_platform
     direct_register_custom_op(
         op_name="all_reduce",
         op_func=all_reduce,
-        mutates_args=[],
         fake_impl=all_reduce_fake,
-        dispatch_key=current_platform.dispatch_key,
     )
 
     direct_register_custom_op(
         op_name="reduce_scatter",
         op_func=reduce_scatter,
-        mutates_args=[],
         fake_impl=reduce_scatter_fake,
-        dispatch_key=current_platform.dispatch_key,
     )
 
     direct_register_custom_op(
         op_name="all_gather",
         op_func=all_gather,
-        mutates_args=[],
         fake_impl=all_gather_fake,
-        dispatch_key=current_platform.dispatch_key,
     )
 
 

vllm/engine/arg_utils.py

@@ -373,7 +373,6 @@ class EngineArgs:
     tokenizer_revision: Optional[str] = ModelConfig.tokenizer_revision
     quantization: Optional[QuantizationMethods] = ModelConfig.quantization
     enforce_eager: bool = ModelConfig.enforce_eager
-    max_seq_len_to_capture: int = ModelConfig.max_seq_len_to_capture
     disable_custom_all_reduce: bool = ParallelConfig.disable_custom_all_reduce
     limit_mm_per_prompt: dict[str, int] = \
         get_field(MultiModalConfig, "limit_per_prompt")
@@ -545,8 +544,6 @@ class EngineArgs:
                                  **model_kwargs["quantization"])
         model_group.add_argument("--enforce-eager",
                                  **model_kwargs["enforce_eager"])
-        model_group.add_argument("--max-seq-len-to-capture",
-                                 **model_kwargs["max_seq_len_to_capture"])
         model_group.add_argument("--max-logprobs",
                                  **model_kwargs["max_logprobs"])
         model_group.add_argument("--logprobs-mode",
@@ -1008,7 +1005,6 @@ class EngineArgs:
             max_model_len=self.max_model_len,
             quantization=self.quantization,
             enforce_eager=self.enforce_eager,
-            max_seq_len_to_capture=self.max_seq_len_to_capture,
             max_logprobs=self.max_logprobs,
             logprobs_mode=self.logprobs_mode,
             disable_sliding_window=self.disable_sliding_window,

vllm/entrypoints/llm.py

@@ -130,11 +130,6 @@ class LLM:
         enforce_eager: Whether to enforce eager execution. If True, we will
             disable CUDA graph and always execute the model in eager mode.
             If False, we will use CUDA graph and eager execution in hybrid.
-        max_seq_len_to_capture: Maximum sequence len covered by CUDA graphs.
-            When a sequence has context length larger than this, we fall back
-            to eager mode. Additionally for encoder-decoder models, if the
-            sequence length of the encoder input is larger than this, we fall
-            back to the eager mode.
         disable_custom_all_reduce: See
             [ParallelConfig][vllm.config.ParallelConfig].
         hf_token: The token to use as HTTP bearer authorization for remote files
@@ -184,7 +179,6 @@ class LLM:
         swap_space: float = 4,
         cpu_offload_gb: float = 0,
         enforce_eager: bool = False,
-        max_seq_len_to_capture: int = 8192,
         disable_custom_all_reduce: bool = False,
         hf_token: Optional[Union[bool, str]] = None,
         hf_overrides: Optional[HfOverrides] = None,
@@ -281,7 +275,6 @@ class LLM:
             swap_space=swap_space,
             cpu_offload_gb=cpu_offload_gb,
             enforce_eager=enforce_eager,
-            max_seq_len_to_capture=max_seq_len_to_capture,
             disable_custom_all_reduce=disable_custom_all_reduce,
             hf_token=hf_token,
             hf_overrides=hf_overrides,

vllm/entrypoints/openai/serving_chat.py

@@ -1186,6 +1186,10 @@ class OpenAIServingChat(OpenAIServing):
                 logprobs = None
 
             if self.use_harmony:
+                reasoning_content, content, _ = parse_chat_output(token_ids)
+                if not request.include_reasoning:
+                    reasoning_content = None
+
                 if self.tool_parser is not None:
                     tool_parser = self.tool_parser(tokenizer)
                     # NOTE: We use token_ids for openai tool parser
@@ -1194,10 +1198,7 @@ class OpenAIServingChat(OpenAIServing):
                         request=request,
                         token_ids=token_ids,  # type: ignore
                     )
-                    reasoning_content, content = None, tool_call_info.content
-                    if request.include_reasoning:
-                        reasoning_content, content, _ = parse_chat_output(
-                            token_ids)
+                    content = tool_call_info.content
                     message = ChatMessage(
                         role=role,
                         reasoning_content=reasoning_content,
@@ -1205,10 +1206,6 @@ class OpenAIServingChat(OpenAIServing):
                         tool_calls=tool_call_info.tool_calls,
                     )
                 else:
-                    reasoning_content, content, _ = parse_chat_output(
-                        token_ids)
-                    if not request.include_reasoning:
-                        reasoning_content = None
                     message = ChatMessage(
                         role=role,
                         reasoning_content=reasoning_content,

vllm/entrypoints/openai/serving_responses.py

@@ -235,8 +235,6 @@ class OpenAIServingResponses(OpenAIServing):
         # Handle the previous response ID.
         prev_response_id = request.previous_response_id
         if prev_response_id is not None:
-            if not prev_response_id.startswith("resp_"):
-                return self._make_invalid_id_error(prev_response_id)
             async with self.response_store_lock:
                 prev_response = self.response_store.get(prev_response_id)
             if prev_response is None:
@@ -924,9 +922,6 @@ class OpenAIServingResponses(OpenAIServing):
         stream: Optional[bool],
     ) -> Union[ErrorResponse, ResponsesResponse, AsyncGenerator[
             StreamingResponsesResponse, None]]:
-        if not response_id.startswith("resp_"):
-            return self._make_invalid_id_error(response_id)
-
         async with self.response_store_lock:
             response = self.response_store.get(response_id)
 
@@ -944,9 +939,6 @@ class OpenAIServingResponses(OpenAIServing):
         self,
         response_id: str,
     ) -> Union[ErrorResponse, ResponsesResponse]:
-        if not response_id.startswith("resp_"):
-            return self._make_invalid_id_error(response_id)
-
         async with self.response_store_lock:
             response = self.response_store.get(response_id)
             if response is None:
@@ -972,13 +964,6 @@ class OpenAIServingResponses(OpenAIServing):
                                  response_id)
         return response
 
-    def _make_invalid_id_error(self, response_id: str) -> ErrorResponse:
-        return self.create_error_response(
-            err_type="invalid_request_error",
-            message=(f"Invalid 'response_id': '{response_id}'. "
-                     "Expected an ID that begins with 'resp'."),
-        )
-
     def _make_not_found_error(self, response_id: str) -> ErrorResponse:
         return self.create_error_response(
             err_type="invalid_request_error",

vllm/entrypoints/openai/tool_parsers/deepseekv31_tool_parser.py

@@ -39,7 +39,7 @@ class DeepSeekV31ToolParser(ToolParser):
         self.tool_call_end_token: str = "<｜tool▁call▁end｜>"
 
         self.tool_call_regex = re.compile(
-            r"<｜tool▁call▁begin｜>(?P<function_name>.*)<｜tool▁sep｜>(?P<function_arguments>.*)<｜tool▁call▁end｜>"
+            r"<｜tool▁call▁begin｜>(?P<function_name>.*?)<｜tool▁sep｜>(?P<function_arguments>.*?)<｜tool▁call▁end｜>"
         )
 
         self.stream_tool_call_portion_regex = re.compile(

vllm/entrypoints/openai/tool_parsers/openai_tool_parser.py

@@ -2,6 +2,7 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 from __future__ import annotations
 
+import json
 from collections.abc import Sequence
 from typing import TYPE_CHECKING
 
@@ -12,10 +13,13 @@ from vllm.entrypoints.openai.protocol import (ChatCompletionRequest,
                                               FunctionCall, ToolCall)
 from vllm.entrypoints.openai.tool_parsers.abstract_tool_parser import (
     ToolParser, ToolParserManager)
+from vllm.logger import init_logger
 
 if TYPE_CHECKING:
     from vllm.transformers_utils.tokenizer import AnyTokenizer
 
+logger = init_logger(__name__)
+
 
 @ToolParserManager.register_module("openai")
 class OpenAIToolParser(ToolParser):
@@ -40,17 +44,33 @@ class OpenAIToolParser(ToolParser):
 
         if len(parser.messages) > 0:
             for msg in parser.messages:
+                if len(msg.content) < 1:
+                    continue
+                msg_text = msg.content[0].text
                 if msg.recipient and msg.recipient.startswith("functions."):
+                    # If no content-type is given assume JSON, as that's the
+                    # most common case with gpt-oss models.
+                    if not msg.content_type or "json" in msg.content_type:
+                        # load and dump the JSON text to check validity and
+                        # remove any extra newlines or other odd formatting
+                        try:
+                            tool_args = json.dumps(json.loads(msg_text))
+                        except json.JSONDecodeError:
+                            logger.exception(
+                                "Error decoding JSON tool call from response.")
+                            tool_args = msg_text
+                    else:
+                        tool_args = msg_text
                     tool_calls.append(
                         ToolCall(
                             type="function",
                             function=FunctionCall(
                                 name=msg.recipient.split("functions.")[1],
-                                arguments=msg.content[0].text,
+                                arguments=tool_args,
                             ),
                         ))
                 elif msg.channel == "final":
-                    final_content = msg.content[0].text
+                    final_content = msg_text
 
         return ExtractedToolCallInformation(
             tools_called=len(tool_calls) > 0,

vllm/envs.py

@@ -119,7 +119,7 @@ if TYPE_CHECKING:
     VLLM_SERVER_DEV_MODE: bool = False
     VLLM_V1_OUTPUT_PROC_CHUNK_SIZE: int = 128
     VLLM_MLA_DISABLE: bool = False
-    VLLM_FLASH_ATTN_MAX_NUM_SPLITS_FOR_CUDA_GRAPH: int = 16
+    VLLM_FLASH_ATTN_MAX_NUM_SPLITS_FOR_CUDA_GRAPH: int = 32
     VLLM_RAY_PER_WORKER_GPUS: float = 1.0
     VLLM_RAY_BUNDLE_INDICES: str = ""
     VLLM_CUDART_SO_PATH: Optional[str] = None
@@ -187,12 +187,15 @@ if TYPE_CHECKING:
     VLLM_DISABLE_PAD_FOR_CUDAGRAPH: bool = False
     VLLM_GPT_OSS_HARMONY_SYSTEM_INSTRUCTIONS: bool = False
     VLLM_CUSTOM_SCOPES_FOR_PROFILING: bool = False
+    VLLM_NVTX_SCOPES_FOR_PROFILING: bool = False
     VLLM_KV_EVENTS_USE_INT_BLOCK_HASHES: bool = True
     VLLM_OBJECT_STORAGE_SHM_BUFFER_NAME: str = "VLLM_OBJECT_STORAGE_SHM_BUFFER"
     VLLM_DEEPEP_BUFFER_SIZE_MB: int = 1024
     VLLM_DBO_COMM_SMS: int = 20
     GPT_OSS_SYSTEM_TOOL_MCP_LABELS: list[str] = []
     VLLM_PATTERN_MATCH_DEBUG: Optional[str] = None
+    VLLM_ENABLE_INDUCTOR_MAX_AUTOTUNE: bool = True
+    VLLM_ENABLE_INDUCTOR_COORDINATE_DESCENT_TUNING: bool = True
     VLLM_USE_NCCL_SYMM_MEM: bool = False
     VLLM_NCCL_INCLUDE_PATH: Optional[str] = None
 
@@ -1014,7 +1017,7 @@ environment_variables: dict[str, Callable[[], Any]] = {
     # max number splits for cuda graph decode
     "VLLM_FLASH_ATTN_MAX_NUM_SPLITS_FOR_CUDA_GRAPH":
     lambda: int(os.getenv("VLLM_FLASH_ATTN_MAX_NUM_SPLITS_FOR_CUDA_GRAPH",
-                          "16")),
+                          "32")),
 
     # Number of GPUs per worker in Ray, if it is set to be a fraction,
     # it allows ray to schedule multiple actors on a single GPU,
@@ -1385,6 +1388,10 @@ environment_variables: dict[str, Callable[[], Any]] = {
     "VLLM_CUSTOM_SCOPES_FOR_PROFILING":
     lambda: bool(int(os.getenv("VLLM_CUSTOM_SCOPES_FOR_PROFILING", "0"))),
 
+    # Add optional nvtx scopes for profiling, disable to avoid overheads
+    "VLLM_NVTX_SCOPES_FOR_PROFILING":
+    lambda: bool(int(os.getenv("VLLM_NVTX_SCOPES_FOR_PROFILING", "0"))),
+
     # Represent block hashes in KV cache events as 64-bit integers instead of
     # raw bytes. Defaults to True for backward compatibility.
     "VLLM_KV_EVENTS_USE_INT_BLOCK_HASHES":
@@ -1413,6 +1420,17 @@ environment_variables: dict[str, Callable[[], Any]] = {
                             "code_interpreter",
                             "web_search_preview"]),
 
+    # Enable max_autotune & coordinate_descent_tuning in inductor_config
+    # to compile static shapes passed from compile_sizes in compilation_config
+    # If set to 1, enable max_autotune; By default, this is enabled (1)
+    "VLLM_ENABLE_INDUCTOR_MAX_AUTOTUNE":
+    lambda: bool(int(os.getenv("VLLM_ENABLE_INDUCTOR_MAX_AUTOTUNE", "1"))),
+    # If set to 1, enable coordinate_descent_tuning;
+    # By default, this is enabled (1)
+    "VLLM_ENABLE_INDUCTOR_COORDINATE_DESCENT_TUNING":
+    lambda: bool(int(os.getenv("VLLM_ENABLE_INDUCTOR_COORDINATE_DESCENT_TUNING",
+        "1"))),
+
     # Flag to enable NCCL symmetric memory allocation and registration
     "VLLM_USE_NCCL_SYMM_MEM":
     lambda: bool(int(os.getenv("VLLM_USE_NCCL_SYMM_MEM", "0"))),
@@ -1513,6 +1531,8 @@ def compute_hash() -> str:
         "VLLM_ROCM_QUICK_REDUCE_CAST_BF16_TO_FP16",
         "VLLM_ROCM_QUICK_REDUCE_MAX_SIZE_BYTES_MB",
         "VLLM_ROCM_FP8_MFMA_PAGE_ATTN",
+        "VLLM_ENABLE_INDUCTOR_MAX_AUTOTUNE",
+        "VLLM_ENABLE_INDUCTOR_COORDINATE_DESCENT_TUNING",
     ]
     for key in environment_variables_to_hash:
         # if this goes out of sync with environment_variables,

vllm/inputs/__init__.py

@@ -7,7 +7,6 @@ from .data import (DataPrompt, DecoderOnlyInputs, EmbedsInputs, EmbedsPrompt,
                    SingletonPrompt, TextPrompt, TokenInputs, TokensPrompt,
                    build_explicit_enc_dec_prompt, embeds_inputs,
                    to_enc_dec_tuple_list, token_inputs, zip_enc_dec_prompts)
-from .registry import InputContext, InputProcessingContext
 
 __all__ = [
     "DataPrompt",
@@ -28,6 +27,4 @@ __all__ = [
     "build_explicit_enc_dec_prompt",
     "to_enc_dec_tuple_list",
     "zip_enc_dec_prompts",
-    "InputContext",
-    "InputProcessingContext",
 ]

vllm/inputs/registry.py

@@ -1,186 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-from collections.abc import Mapping
-from dataclasses import dataclass
-from typing import TYPE_CHECKING, Any, Union
-
-import torch
-from transformers import BatchFeature, PretrainedConfig, ProcessorMixin
-from typing_extensions import TypeVar
-
-from vllm.logger import init_logger
-from vllm.transformers_utils.processor import cached_processor_from_config
-from vllm.utils import get_allowed_kwarg_only_overrides
-from vllm.utils.jsontree import JSONTree, json_map_leaves
-
-if TYPE_CHECKING:
-    from vllm.config import ModelConfig
-    from vllm.transformers_utils.tokenizer import AnyTokenizer
-else:
-    ModelConfig = Any
-    AnyTokenizer = Any
-
-_T = TypeVar("_T")
-_C = TypeVar("_C", bound=PretrainedConfig, default=PretrainedConfig)
-_P = TypeVar("_P", bound=ProcessorMixin, default=ProcessorMixin)
-
-logger = init_logger(__name__)
-
-
-@dataclass(frozen=True)
-class InputContext:
-    """
-    Contains information about the model which may be used to
-    modify the inputs.
-    """
-
-    model_config: ModelConfig
-    """The configuration of the model."""
-
-    def get_hf_config(
-        self,
-        typ: Union[type[_C], tuple[type[_C], ...]] = PretrainedConfig,
-        /,
-    ) -> _C:
-        """
-        Get the HuggingFace configuration
-        (`transformers.PretrainedConfig`) of the model,
-        additionally checking its type.
-
-        Raises:
-            TypeError: If the configuration is not of the specified type.
-        """
-        hf_config = self.model_config.hf_config
-        if not isinstance(hf_config, typ):
-            raise TypeError("Invalid type of HuggingFace config. "
-                            f"Expected type: {typ}, but "
-                            f"found type: {type(hf_config)}")
-
-        return hf_config
-
-    def get_hf_image_processor_config(self) -> dict[str, Any]:
-        """
-        Get the HuggingFace image processor configuration of the model.
-        """
-        return self.model_config.hf_image_processor_config
-
-    def get_mm_config(self):
-        """
-        Get the multimodal config of the model.
-
-        Raises:
-            RuntimeError: If the model is not a multimodal model.
-        """
-        mm_config = self.model_config.multimodal_config
-        if mm_config is None:
-            raise RuntimeError("Not a multimodal model")
-
-        return mm_config
-
-    def get_hf_processor(
-        self,
-        typ: Union[type[_P], tuple[type[_P], ...]] = ProcessorMixin,
-        /,
-        **kwargs: object,
-    ) -> _P:
-        """
-        Get the HuggingFace processor
-        (`transformers.ProcessorMixin`) of the model,
-        additionally checking its type.
-
-        Raises:
-            TypeError: If the processor is not of the specified type.
-        """
-        return cached_processor_from_config(
-            self.model_config,
-            processor_cls=typ,
-            **kwargs,
-        )
-
-    def init_processor(
-        self,
-        typ: type[_T],
-        /,
-        **kwargs: object,
-    ) -> _T:
-        """
-        Initialize a HuggingFace-like processor class, merging the
-        keyword arguments with those in the model's configuration.
-        """
-        mm_config = self.model_config.get_multimodal_config()
-        base_kwargs = mm_config.mm_processor_kwargs
-        if base_kwargs is None:
-            base_kwargs = {}
-
-        merged_kwargs = {**base_kwargs, **kwargs}
-
-        return typ(**merged_kwargs)
-
-
-@dataclass(frozen=True)
-class InputProcessingContext(InputContext):
-    tokenizer: AnyTokenizer
-    """The tokenizer used to tokenize the inputs."""
-
-    def get_hf_processor(
-        self,
-        typ: Union[type[_P], tuple[type[_P], ...]] = ProcessorMixin,
-        /,
-        **kwargs: object,
-    ) -> _P:
-        return super().get_hf_processor(
-            typ,
-            tokenizer=self.tokenizer,
-            **kwargs,
-        )
-
-    def call_hf_processor(
-        self,
-        hf_processor: ProcessorMixin,
-        data: Mapping[str, object],
-        kwargs: Mapping[str, object] = {},
-    ) -> Union[BatchFeature, JSONTree]:
-        """
-        Call `hf_processor` on the prompt `data`
-        (text, image, audio...) with configurable options `kwargs`.
-        """
-        assert callable(hf_processor)
-
-        mm_config = self.model_config.get_multimodal_config()
-        merged_kwargs = mm_config.merge_mm_processor_kwargs(kwargs)
-
-        allowed_kwargs = get_allowed_kwarg_only_overrides(
-            hf_processor,
-            merged_kwargs,
-            requires_kw_only=False,
-            allow_var_kwargs=True,
-        )
-
-        def maybe_cast_dtype(x):
-            # This mimics the behavior of transformers.BatchFeature
-            if isinstance(x, torch.Tensor) and x.is_floating_point():
-                return x.to(dtype=self.model_config.dtype)
-            return x
-
-        try:
-            output = hf_processor(**data,
-                                  **allowed_kwargs,
-                                  return_tensors="pt")
-            # this emulates output.to(dtype=self.model_config.dtype)
-            if isinstance(output, BatchFeature):
-                cast_output = json_map_leaves(maybe_cast_dtype, output.data)
-                return BatchFeature(cast_output)
-
-            cast_output = json_map_leaves(maybe_cast_dtype, output)
-
-            logger.warning_once(
-                f"{type(hf_processor).__name__} did not return `BatchFeature`. "
-                "Make sure to match the behaviour of `ProcessorMixin` when "
-                "implementing custom processors.")
-            return cast_output
-
-        except Exception as exc:
-            msg = (f"Failed to apply {type(hf_processor).__name__} "
-                   f"on data={data} with kwargs={allowed_kwargs}")
-
-            raise ValueError(msg) from exc

vllm/lora/ops/triton_ops/lora_expand_op.py

@@ -11,7 +11,6 @@ import torch
 
 from vllm.lora.ops.triton_ops.kernel_utils import do_expand_kernel
 from vllm.lora.ops.triton_ops.utils import _get_lora_b_ptr
-from vllm.platforms import current_platform
 from vllm.triton_utils import tl, triton
 from vllm.utils import direct_register_custom_op
 
@@ -283,7 +282,6 @@ try:
         op_func=_lora_expand,
         mutates_args=["output_tensor"],
         fake_impl=_lora_expand_fake,
-        dispatch_key=current_platform.dispatch_key,
     )
     lora_expand = torch.ops.vllm.lora_expand
 

vllm/lora/ops/triton_ops/lora_shrink_op.py

@@ -11,7 +11,6 @@ import torch
 
 from vllm.lora.ops.triton_ops.kernel_utils import do_shrink_kernel
 from vllm.lora.ops.triton_ops.utils import _get_lora_a_ptr
-from vllm.platforms import current_platform
 from vllm.triton_utils import tl, triton
 from vllm.utils import direct_register_custom_op
 
@@ -237,7 +236,6 @@ try:
         op_func=_lora_shrink,
         mutates_args=["output_tensor"],
         fake_impl=_lora_shrink_fake,
-        dispatch_key=current_platform.dispatch_key,
     )
     lora_shrink = torch.ops.vllm.lora_shrink
 

vllm/model_executor/layers/fla/ops/fused_recurrent.py

@@ -40,8 +40,8 @@ def fused_recurrent_gated_delta_rule_fwd_kernel(
     ssm_state_indices,
     num_accepted_tokens,
     scale,
-    N: tl.constexpr,  # num of sequences
-    T: tl.constexpr,  # num of tokens
+    N: tl.int64,  # num of sequences
+    T: tl.int64,  # num of tokens
     B: tl.constexpr,
     H: tl.constexpr,
     HV: tl.constexpr,

vllm/model_executor/layers/fused_moe/configs/E=128,N=1024,device_name=NVIDIA_H100,dtype=fp8_w8a8.json

@@ -0,0 +1,123 @@
+{
+    "triton_version": "3.4.0",
+    "2": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 32,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "4": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 32,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "8": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 3
+    },
+    "16": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 32,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "32": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 32,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 5
+    },
+    "64": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 3
+    },
+    "128": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 32,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 5
+    },
+    "256": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 32,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 5
+    },
+    "512": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 32,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 5
+    },
+    "1024": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 32,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 5
+    },
+    "2048": {
+        "BLOCK_SIZE_M": 32,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 3
+    },
+    "3072": {
+        "BLOCK_SIZE_M": 32,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 3
+    },
+    "4096": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "8192": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 4
+    },
+    "16384": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 4
+    }
+}

vllm/model_executor/layers/fused_moe/cpu_fused_moe.py

@@ -98,13 +98,16 @@ def select_experts(
                             e_score_correction_bias=e_score_correction_bias)
     elif custom_routing_function is None:
         assert scoring_func == "softmax"
-        topk_weights = torch.nn.functional.softmax(router_logits,
-                                                   dim=1,
-                                                   dtype=torch.float32)
-        topk_weights, topk_ids = torch.topk(topk_weights, top_k, dim=-1)
+        topk_logit_vals, topk_idx = torch.topk(router_logits,
+                                               k=top_k,
+                                               dim=-1,
+                                               sorted=False)
         if renormalize:
-            topk_weights /= topk_weights.sum(dim=-1, keepdim=True)
-        return topk_weights, topk_ids.to(torch.int32)
+            topk_vals = torch.softmax(topk_logit_vals, dim=-1)
+        else:
+            logZ = torch.logsumexp(router_logits, dim=-1, keepdim=True)
+            topk_vals = (topk_logit_vals - logZ).exp()
+        return topk_vals.to(torch.float32), topk_idx.to(torch.int32)
     else:
         return custom_routing_function(hidden_states=hidden_states,
                                        gating_output=router_logits,

vllm/model_executor/layers/fused_moe/flashinfer_trtllm_moe.py

@@ -92,7 +92,6 @@ def flashinfer_fused_moe_blockscale_fp8_fake(
 direct_register_custom_op(
     op_name="flashinfer_fused_moe_blockscale_fp8",
     op_func=flashinfer_fused_moe_blockscale_fp8,
-    mutates_args=[],
     fake_impl=flashinfer_fused_moe_blockscale_fp8_fake,
     tags=(torch.Tag.needs_fixed_stride_order, ),
 )

vllm/model_executor/layers/fused_moe/fused_marlin_moe.py

@@ -235,6 +235,5 @@ def fused_marlin_moe_fake(hidden_states: torch.Tensor,
 direct_register_custom_op(
     op_name="fused_marlin_moe",
     op_func=fused_marlin_moe,
-    mutates_args=[],
     fake_impl=fused_marlin_moe_fake,
 )

vllm/model_executor/layers/fused_moe/fused_moe.py

@@ -1256,7 +1256,6 @@ def outplace_fused_experts_fake(
 direct_register_custom_op(
     op_name="outplace_fused_experts",
     op_func=outplace_fused_experts,
-    mutates_args=[],
     fake_impl=outplace_fused_experts_fake,
     tags=(() if is_torch_equal_or_newer("2.7.0") else
           (torch.Tag.needs_fixed_stride_order, )),

vllm/model_executor/layers/fused_moe/gpt_oss_triton_kernels_moe.py

@@ -23,7 +23,7 @@ if has_triton_kernels():
         from triton_kernels.routing import (RoutingData, routing,
                                             routing_from_bitmatrix)
         from triton_kernels.tensor import Bitmatrix
-    except (ModuleNotFoundError, AttributeError) as e:
+    except (AttributeError, ImportError) as e:
         logger.error(
             "Failed to import Triton kernels. Please make sure your triton "
             "version is compatible. Error: %s", e)

vllm/model_executor/layers/fused_moe/layer.py

@@ -69,8 +69,6 @@ else:
 if is_rocm_aiter_moe_enabled():
     from vllm.model_executor.layers.fused_moe.rocm_aiter_fused_moe import (  # noqa: E501
         rocm_aiter_grouped_topk as grouped_topk)
-elif current_platform.is_cpu():
-    pass
 else:
     from vllm.model_executor.layers.fused_moe.fused_moe import grouped_topk
 if current_platform.is_tpu():
@@ -2040,7 +2038,6 @@ direct_register_custom_op(
     op_func=moe_forward,
     mutates_args=["hidden_states"],
     fake_impl=moe_forward_fake,
-    dispatch_key=current_platform.dispatch_key,
     tags=(torch.Tag.needs_fixed_stride_order, ),
 )
 
@@ -2071,7 +2068,6 @@ direct_register_custom_op(
     op_func=moe_forward_shared,
     mutates_args=["hidden_states"],
     fake_impl=moe_forward_shared_fake,
-    dispatch_key=current_platform.dispatch_key,
     tags=(torch.Tag.needs_fixed_stride_order, ),
 )
 

vllm/model_executor/layers/fused_moe/rocm_aiter_fused_moe.py

@@ -223,17 +223,13 @@ if current_platform.is_rocm():
     direct_register_custom_op(
         op_name="rocm_aiter_asm_moe_tkw1",
         op_func=rocm_aiter_asm_moe_tkw1_impl,
-        mutates_args=[],
         fake_impl=rocm_aiter_asm_moe_tkw1_fake,
-        dispatch_key=current_platform.dispatch_key,
     )
 
     direct_register_custom_op(
         op_name="rocm_aiter_fused_moe",
         op_func=rocm_aiter_fused_moe_impl,
-        mutates_args=[],
         fake_impl=rocm_aiter_fused_moe_fake,
-        dispatch_key=current_platform.dispatch_key,
     )
 
     direct_register_custom_op(
@@ -241,7 +237,6 @@ if current_platform.is_rocm():
         op_func=rocm_aiter_topk_softmax_impl,
         mutates_args=["topk_weights", "topk_indices", "token_expert_indices"],
         fake_impl=rocm_aiter_topk_softmax_fake,
-        dispatch_key=current_platform.dispatch_key,
     )
 
     direct_register_custom_op(
@@ -249,7 +244,6 @@ if current_platform.is_rocm():
         op_func=rocm_aiter_biased_grouped_topk_impl,
         mutates_args=["topk_weights", "topk_ids"],
         fake_impl=rocm_aiter_biased_grouped_topk_fake,
-        dispatch_key=current_platform.dispatch_key,
     )
 
     direct_register_custom_op(
@@ -257,7 +251,6 @@ if current_platform.is_rocm():
         op_func=rocm_aiter_grouped_topk_impl,
         mutates_args=["topk_weights", "topk_ids"],
         fake_impl=rocm_aiter_grouped_topk_fake,
-        dispatch_key=current_platform.dispatch_key,
     )
 
 

vllm/model_executor/layers/layernorm.py

@@ -103,17 +103,13 @@ if current_platform.is_rocm():
     direct_register_custom_op(
         op_name="rocm_aiter_rms_norm",
         op_func=rocm_aiter_rms_norm_impl,
-        mutates_args=[],
         fake_impl=rocm_aiter_rms_norm_fake,
-        dispatch_key=current_platform.dispatch_key,
     )
 
     direct_register_custom_op(
         op_name="rocm_aiter_rmsnorm2d_fwd_with_add",
         op_func=rocm_aiter_rmsnorm2d_fwd_with_add_impl,
-        mutates_args=[],
         fake_impl=rocm_aiter_rmsnorm2d_fwd_with_add_fake,
-        dispatch_key=current_platform.dispatch_key,
     )
 
 

vllm/model_executor/layers/linear.py

@@ -22,6 +22,7 @@ from vllm.model_executor.layers.utils import dispatch_unquantized_gemm
 # yapf: disable
 from vllm.model_executor.parameter import (BasevLLMParameter,
                                            BlockQuantScaleParameter,
+                                           ModelWeightParameter,
                                            PackedColumnParameter,
                                            PackedvLLMParameter,
                                            PerTensorScaleParameter,
@@ -34,6 +35,7 @@ from vllm.utils import GiB_bytes
 logger = init_logger(__name__)
 
 WEIGHT_LOADER_V2_SUPPORTED = [
+    "UnquantizedLinearMethod",
     "CompressedTensorsLinearMethod",
     "CompressedTensorsLinearTransformMethod",
     "BitBLASLinearMethod",
@@ -196,10 +198,14 @@ class UnquantizedLinearMethod(LinearMethodBase):
         # The amount of memory allocated for the weights is
         # sum(output_partition_sizes) * input_size_per_partition.
         try:
-            weight = Parameter(torch.empty(sum(output_partition_sizes),
-                                           input_size_per_partition,
-                                           dtype=params_dtype),
-                               requires_grad=False)
+            weight_loader = extra_weight_attrs.pop("weight_loader")
+            weight = ModelWeightParameter(data=torch.empty(
+                sum(output_partition_sizes),
+                input_size_per_partition,
+                dtype=params_dtype),
+                                          input_dim=1,
+                                          output_dim=0,
+                                          weight_loader=weight_loader)
         except torch.cuda.OutOfMemoryError as e:
             logger.error("Failed to create unquantized linear weights: %s", e)
             if torch.cuda.is_available():
@@ -212,7 +218,7 @@ class UnquantizedLinearMethod(LinearMethodBase):
                 "Failed to create unquantized linear weights. "
                 "This may be caused by insufficient memory to allocate "
                 "the weight.") from e
-        set_weight_attrs(weight, {"input_dim": 1, "output_dim": 0})
+
         layer.register_parameter("weight", weight)
         set_weight_attrs(weight, extra_weight_attrs)
 

vllm/model_executor/layers/mamba/linear_attn.py

@@ -31,7 +31,6 @@ from vllm.model_executor.layers.mamba.mamba_utils import (
     MambaStateDtypeCalculator, MambaStateShapeCalculator)
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig)
-from vllm.platforms import current_platform
 from vllm.utils import direct_register_custom_op
 from vllm.v1.attention.backends.linear_attn import LinearAttentionMetadata
 
@@ -401,5 +400,4 @@ direct_register_custom_op(
     op_func=linear_attention,
     mutates_args=["output"],
     fake_impl=linear_attention_fake,
-    dispatch_key=current_platform.dispatch_key,
 )

vllm/model_executor/layers/mamba/mamba_mixer.py

@@ -27,7 +27,6 @@ from vllm.model_executor.layers.mamba.ops.causal_conv1d import (
 from vllm.model_executor.layers.mamba.ops.mamba_ssm import (
     selective_scan_fn, selective_state_update)
 from vllm.model_executor.utils import set_weight_attrs
-from vllm.platforms import current_platform
 from vllm.utils import direct_register_custom_op
 from vllm.v1.attention.backends.mamba1_attn import Mamba1AttentionMetadata
 
@@ -464,5 +463,4 @@ direct_register_custom_op(
     op_func=mamba_mixer,
     mutates_args=["output"],
     fake_impl=mamba_mixer_fake,
-    dispatch_key=current_platform.dispatch_key,
 )

vllm/model_executor/layers/mamba/mamba_mixer2.py

@@ -34,7 +34,6 @@ from vllm.model_executor.layers.quantization import QuantizationConfig
 from vllm.model_executor.model_loader.weight_utils import (
     LoaderFunction, composed_weight_loader, sharded_weight_loader)
 from vllm.model_executor.utils import set_weight_attrs
-from vllm.platforms import current_platform
 from vllm.utils import direct_register_custom_op
 from vllm.v1.attention.backends.mamba2_attn import Mamba2AttentionMetadata
 
@@ -765,5 +764,4 @@ direct_register_custom_op(
     op_func=mamba_mixer2,
     mutates_args=["output"],
     fake_impl=mamba_mixer2_fake,
-    dispatch_key=current_platform.dispatch_key,
 )

vllm/model_executor/layers/mamba/ops/ssd_chunk_state.py

@@ -17,7 +17,6 @@ from .mamba_ssm import softplus
 
 @triton.autotune(
     configs=[
-        triton.Config({'BLOCK_SIZE_H': 1}),
         triton.Config({'BLOCK_SIZE_H': 2}),
         triton.Config({'BLOCK_SIZE_H': 4}),
         triton.Config({'BLOCK_SIZE_H': 8}),

vllm/model_executor/layers/mamba/short_conv.py

@@ -21,7 +21,6 @@ from vllm.model_executor.layers.mamba.mamba_utils import (
     MambaStateDtypeCalculator, MambaStateShapeCalculator)
 from vllm.model_executor.layers.mamba.ops.causal_conv1d import (
     causal_conv1d_fn, causal_conv1d_update)
-from vllm.platforms import current_platform
 from vllm.utils import direct_register_custom_op
 from vllm.v1.attention.backends.short_conv_attn import (
     ShortConvAttentionMetadata)
@@ -251,5 +250,4 @@ direct_register_custom_op(
     op_func=short_conv,
     mutates_args=["output"],
     fake_impl=short_conv_fake,
-    dispatch_key=current_platform.dispatch_key,
 )

vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe.py

@@ -22,6 +22,7 @@ from vllm.model_executor.layers.fused_moe.config import (
     FusedMoEQuantConfig, fp8_w8a8_moe_quant_config,
     int4_w4a16_moe_quant_config, int8_w8a8_moe_quant_config,
     int8_w8a16_moe_quant_config, nvfp4_moe_quant_config)
+from vllm.model_executor.layers.fused_moe.cpu_fused_moe import select_experts
 from vllm.model_executor.layers.fused_moe.flashinfer_cutlass_moe import (
     is_valid_flashinfer_cutlass_fused_moe)
 from vllm.model_executor.layers.quantization.compressed_tensors.schemes.compressed_tensors_wNa16 import (  # noqa
@@ -47,7 +48,7 @@ from vllm.model_executor.layers.quantization.utils.quant_utils import (
 from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
     all_close_1d, normalize_e4m3fn_to_e4m3fnuz, per_tensor_dequantize)
 from vllm.model_executor.utils import set_weight_attrs
-from vllm.platforms import current_platform
+from vllm.platforms import CpuArchEnum, current_platform
 from vllm.scalar_type import scalar_types
 from vllm.utils.deep_gemm import is_deep_gemm_e8m0_used
 
@@ -63,7 +64,7 @@ __all__ = [
     "CompressedTensorsMoEMethod", "CompressedTensorsW8A8Fp8MoEMethod",
     "CompressedTensorsW8A8Int8MoEMethod",
     "CompressedTensorsWNA16MarlinMoEMethod", "CompressedTensorsWNA16MoEMethod",
-    "CompressedTensorsW4A4MoeMethod"
+    "CompressedTensorsW4A4MoeMethod", "CompressedTensorsW4A8Int8MoEMethod"
 ]
 
 
@@ -139,6 +140,10 @@ class CompressedTensorsMoEMethod(FusedMoEMethodBase):
         elif quant_config._is_dynamic_token_w8a8(weight_quant, input_quant):
             return CompressedTensorsW8A8Int8MoEMethod(quant_config,
                                                       layer.moe_config)
+        elif quant_config._is_dynamic_token_w4a8_int(weight_quant,
+                                                     input_quant):
+            return CompressedTensorsW4A8Int8MoEMethod(quant_config,
+                                                      layer.moe_config)
         else:
             raise RuntimeError(
                 f"Unsupported FusedMoe scheme: {weight_quant}, {input_quant}")
@@ -1769,3 +1774,301 @@ class CompressedTensorsWNA16MoEMethod(CompressedTensorsMoEMethod):
             expert_map=expert_map,
             quant_config=self.moe_quant_config,
         )
+
+
+class CompressedTensorsW4A8Int8MoEMethod(CompressedTensorsMoEMethod):
+    """
+    CPU-only MoE method using dynamic 4-bit matmul kernels on Arm Platform
+    - Weights: int4 (stored as int8 values in [-8,7], packed to uint8 nibbles)
+    - Scales: Fp32 for Channelwise , bf16 for groupwise quantization
+    - Bias: Same data type as original weights
+    - Activations: FP32/Bf16 dynamic per-token (A8 Int),
+      quantized inside the kernel
+    """
+
+    def __init__(
+            self,
+            quant_config: "CompressedTensorsConfig",  # type: ignore # noqa E501
+            moe: FusedMoEConfig):
+        super().__init__(moe)
+        self.has_bias = self.moe.has_bias
+        self.quant_config = quant_config
+
+        # Validate scheme: weights=W4 (channel or group),
+        # activations=dynamic TOKEN (A8)
+        wq = self.quant_config.target_scheme_map["Linear"].get("weights")
+        aq = self.quant_config.target_scheme_map["Linear"].get(
+            "input_activations")
+
+        # Must be dynamic per-token activations
+        if aq.strategy != QuantizationStrategy.TOKEN or not aq.dynamic:
+            raise ValueError(
+                "W4A8-int MoE needs dynamic per-token activation quantization."
+            )
+
+        # Weight can be channel-wise (group_size=None) or group-wise
+        self.group_size = wq.group_size if (wq.group_size is not None) else -1
+        if wq.num_bits != 4:
+            raise ValueError(
+                "This method only supports 4-bit weights (num_bits=4).")
+
+        # CPU only
+        if not current_platform.is_cpu():
+            raise ValueError("CompressedTensorsW4A8Int8MoEMethod is CPU-only.")
+
+        # Arm: check _dyn ops availability
+        if current_platform.get_cpu_architecture() == CpuArchEnum.ARM:
+            try:
+                _ = torch.ops.aten._dyn_quant_matmul_4bit
+                _ = torch.ops.aten._dyn_quant_pack_4bit_weight
+            except AttributeError as err:
+                raise RuntimeError(
+                    f"""PyTorch {torch.__version__} lacks _dyn_quant_* 4bit ops;
+                    install a newer build.""") from err
+        self.static_input_scales = False  # always dynamic per token
+
+    # ---- parameter creation ----
+    def create_weights(self, layer: torch.nn.Module, num_experts: int,
+                       hidden_size: int, intermediate_size_per_partition: int,
+                       params_dtype: torch.dtype, **extra_weight_attrs):
+
+        # Shapes per local rank (TP/EP):
+        #   w13: [E, 2*I_local, H]  int8  (int4 values in [-8,7])
+        #   w2 : [E, H, I_local]    int8
+        # Scales:
+        #   channel-wise: group_size=-1 -> per-output-row, single scale per row
+        #   group-wise  : group_size=g   ->
+        #   per-output-row, (in_features/g) scales
+
+        E = num_experts
+        H = hidden_size
+        IN = intermediate_size_per_partition
+        g = self.group_size
+
+        # Per-row scale columns
+        def _n_scale_cols(in_features: int) -> int:
+            return 1 if g == -1 else (in_features // g)
+
+        # Register unpacked int4-as-int8 weights the loader will fill.
+        w13 = torch.nn.Parameter(torch.empty(E, 2 * IN, H, dtype=torch.int8),
+                                 requires_grad=False)
+        set_weight_attrs(w13, extra_weight_attrs)
+        layer.register_parameter("w13_weight", w13)
+
+        w2 = torch.nn.Parameter(torch.empty(E, H, IN, dtype=torch.int8),
+                                requires_grad=False)
+        set_weight_attrs(w2, extra_weight_attrs)
+        layer.register_parameter("w2_weight", w2)
+
+        # Register scales
+        # KleidiAI groupwise kernels accepts float32 scales
+        # KleidiAI groupwise kernels accepts bfloat16 scales
+        scale_dtype = torch.float32 if g == -1 else torch.bfloat16
+
+        w13_s = torch.nn.Parameter(torch.ones(E,
+                                              2 * IN,
+                                              _n_scale_cols(H),
+                                              dtype=scale_dtype),
+                                   requires_grad=False)
+        set_weight_attrs(
+            w13_s, {
+                "quant_method": "channel" if g == -1 else "group",
+                **extra_weight_attrs
+            })
+        layer.register_parameter("w13_weight_scale", w13_s)
+
+        w2_s = torch.nn.Parameter(torch.ones(E,
+                                             H,
+                                             _n_scale_cols(IN),
+                                             dtype=scale_dtype),
+                                  requires_grad=False)
+        set_weight_attrs(
+            w2_s, {
+                "quant_method": "channel" if g == -1 else "group",
+                **extra_weight_attrs
+            })
+        layer.register_parameter("w2_weight_scale", w2_s)
+
+        if self.has_bias:
+            w13_bias = torch.nn.Parameter(torch.zeros(E,
+                                                      2 * IN,
+                                                      dtype=params_dtype),
+                                          requires_grad=False)
+            layer.register_parameter("w13_bias", w13_bias)
+            set_weight_attrs(w13_bias, extra_weight_attrs)
+
+            w2_bias = torch.nn.Parameter(torch.zeros(num_experts,
+                                                     hidden_size,
+                                                     dtype=params_dtype),
+                                         requires_grad=False)
+            layer.register_parameter("w2_bias", w2_bias)
+            set_weight_attrs(w2_bias, extra_weight_attrs)
+
+        # Placeholders for packed weights (will be replaced after packing)
+        layer.register_parameter(
+            "w13_weight_packed",
+            torch.nn.Parameter(torch.empty(0), requires_grad=False))
+        set_weight_attrs(layer.w13_weight_packed, extra_weight_attrs)
+
+        layer.register_parameter(
+            "w2_weight_packed",
+            torch.nn.Parameter(torch.empty(0), requires_grad=False))
+        set_weight_attrs(layer.w2_weight_packed, extra_weight_attrs)
+
+        # dims for 4 bit fused matmuls
+        layer.w13_in_features = H
+        layer.w13_out_features = 2 * IN
+        layer.w2_in_features = IN
+        layer.w2_out_features = H
+        layer.group_size = g
+
+    # post-load packing to dyn-4bit KleidiAI kernel's format
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        E = layer.w13_weight.shape[0]
+        H = layer.w13_in_features
+        I2 = layer.w13_out_features
+        IN = layer.w2_in_features
+        g = layer.group_size
+
+        def _pack_matrix(int4_as_int8_2d: torch.Tensor,
+                         scales_2d: torch.Tensor,
+                         bias_1d: Optional[torch.Tensor], in_features: int,
+                         out_features: int) -> torch.Tensor:
+            # int4 values are stored as int8 in [-8,7].
+            # Shift to unsigned nibble and pack pairs along input-dim.
+            tmp = int4_as_int8_2d.add(8)  # [out, in]
+            uint8_nibbles = ((tmp[:, 1::2] << 4) | tmp[:, ::2]).to(
+                torch.uint8)  # [out, in//2]
+
+            # KleidiAI groupwise kernels accepts float32 scales
+            # KleidiAI groupwise kernels accepts bfloat16 scales
+            scale_dtype = torch.float32 if g == -1 else torch.bfloat16
+            scales = scales_2d.to(scale_dtype)
+            bias = None if bias_1d is None else bias_1d.to(torch.float32)
+            return torch.ops.aten._dyn_quant_pack_4bit_weight(
+                uint8_nibbles, scales, bias, g if g != -1 else in_features,
+                in_features, out_features)
+
+        # Pack per expert
+        w13_packed_list = []
+        w2_packed_list = []
+
+        has_w13_bias = hasattr(layer,
+                               "w13_bias") and layer.w13_bias is not None
+        has_w2_bias = hasattr(layer, "w2_bias") and layer.w2_bias is not None
+
+        for e in range(E):
+            w13_packed_list.append(
+                _pack_matrix(
+                    layer.w13_weight[e],  # [2I, H]
+                    layer.w13_weight_scale[e],  # [2I, H/g or 1]
+                    layer.w13_bias[e] if has_w13_bias else None,  # [2I]
+                    H,
+                    I2))
+            w2_packed_list.append(
+                _pack_matrix(
+                    # w2 shape is [H, IN]; we need [out, in] == [H, IN].
+                    layer.w2_weight[e],  # [H, IN]
+                    layer.w2_weight_scale[e],  # [H, IN/g or 1]
+                    layer.w2_bias[e] if has_w2_bias else None,  # [H]
+                    IN,
+                    layer.w2_out_features  # in_features=IN, out_features=H
+                ))
+
+        # each packed tensor has identical shape per expert; stack on dim 0
+        w13_packed = torch.stack(w13_packed_list, dim=0)
+        w2_packed = torch.stack(w2_packed_list, dim=0)
+
+        replace_parameter(layer, "w13_weight_packed",
+                          torch.nn.Parameter(w13_packed, requires_grad=False))
+        replace_parameter(layer, "w2_weight_packed",
+                          torch.nn.Parameter(w2_packed, requires_grad=False))
+
+        # free raw tensors/scales/bias now that they're packed into the payload.
+        replace_parameter(
+            layer, "w13_weight",
+            torch.nn.Parameter(torch.empty(0), requires_grad=False))
+        replace_parameter(
+            layer, "w2_weight",
+            torch.nn.Parameter(torch.empty(0), requires_grad=False))
+        replace_parameter(
+            layer, "w13_weight_scale",
+            torch.nn.Parameter(torch.empty(0), requires_grad=False))
+        replace_parameter(
+            layer, "w2_weight_scale",
+            torch.nn.Parameter(torch.empty(0), requires_grad=False))
+        if has_w13_bias:
+            replace_parameter(
+                layer, "w13_bias",
+                torch.nn.Parameter(torch.empty(0), requires_grad=False))
+        if has_w2_bias:
+            replace_parameter(
+                layer, "w2_bias",
+                torch.nn.Parameter(torch.empty(0), requires_grad=False))
+
+    def get_fused_moe_quant_config(
+            self, layer: torch.nn.Module) -> Optional[FusedMoEQuantConfig]:
+        # CPU dynamic 4-bit MoE path does not use modular kernels or
+        # fused_experts; quant config is not needed.
+        return None
+
+    def apply(
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        router_logits: torch.Tensor,
+        top_k: int,
+        renormalize: bool,
+        use_grouped_topk: bool = False,
+        topk_group: Optional[int] = None,
+        num_expert_group: Optional[int] = None,
+        global_num_experts: int = -1,
+        expert_map: Optional[torch.Tensor] = None,
+        custom_routing_function: Optional[Callable] = None,
+        scoring_func: str = "softmax",
+        routed_scaling_factor: float = 1.0,
+        e_score_correction_bias: Optional[torch.Tensor] = None,
+        apply_router_weight_on_input: bool = False,
+        activation: str = "silu",
+        enable_eplb: bool = False,
+        expert_load_view: Optional[torch.Tensor] = None,
+        logical_to_physical_map: Optional[torch.Tensor] = None,
+        logical_replica_count: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        assert not enable_eplb, "EPLB not supported for W4A8-int MoE yet."
+        assert activation in (
+            "silu", "swigluoai",
+            "swiglu"), "Only SiLU/SwiGLUGU/SwiGLUUG are supported."
+        assert expert_map is None, """expert_map/EP not implemented
+        for CPU dyn-4bit MoE."""
+
+        def _act_kind(s: str) -> int:
+            # 0 = SwiGLU_Gu (SiLU(g)*u), 1 = SwiGLU_Ug (SiLU(u)*g), 2 = SiLU
+            if s == "swiglu":
+                return 0
+            if s == "swigluoai":
+                return 1
+            if s == "silu":
+                return 2
+            raise ValueError(f"Unknown activation '{s}'")
+
+        # Apply topk softmax on router output
+        topk_weights, topk_ids = select_experts(
+            hidden_states=x,
+            router_logits=router_logits,
+            use_grouped_topk=use_grouped_topk,
+            top_k=top_k,
+            renormalize=renormalize,
+            topk_group=topk_group,
+            num_expert_group=num_expert_group,
+            custom_routing_function=custom_routing_function,
+            scoring_func=scoring_func,
+            routed_scaling_factor=routed_scaling_factor,
+            e_score_correction_bias=e_score_correction_bias,
+        )
+
+        return torch.ops._C.dynamic_4bit_int_moe(
+            x, topk_ids.to(torch.long), topk_weights, layer.w13_weight_packed,
+            layer.w2_weight_packed, layer.w2_out_features,
+            layer.w2_in_features, layer.w13_out_features, layer.group_size,
+            apply_router_weight_on_input, int(_act_kind(activation)))

vllm/model_executor/layers/quantization/deepgemm.py

@@ -4,7 +4,6 @@ import logging
 
 import torch
 
-from vllm.platforms import current_platform
 from vllm.triton_utils import triton
 from vllm.utils import direct_register_custom_op
 from vllm.utils.deep_gemm import fp8_gemm_nt
@@ -75,7 +74,5 @@ def w8a8_deepgemm_block_scaled_mm_fake(
 direct_register_custom_op(
     op_name="w8a8_deepgemm_block_scaled_mm",
     op_func=w8a8_deepgemm_block_scaled_mm,
-    mutates_args=[],
     fake_impl=w8a8_deepgemm_block_scaled_mm_fake,
-    dispatch_key=current_platform.dispatch_key,
 )

vllm/model_executor/layers/quantization/gguf.py

@@ -161,7 +161,6 @@ try:
     direct_register_custom_op(
         op_name="_fused_mul_mat_gguf",
         op_func=_fused_mul_mat_gguf,
-        mutates_args=[],
         fake_impl=_fused_mul_mat_gguf_fake,
     )
     fused_mul_mat_gguf = torch.ops.vllm._fused_mul_mat_gguf
@@ -273,7 +272,6 @@ try:
     direct_register_custom_op(
         op_name="_fused_moe_gguf",
         op_func=_fused_moe_gguf,
-        mutates_args=[],
         fake_impl=_fused_moe_gguf_fake,
     )
     fused_moe_gguf = torch.ops.vllm._fused_moe_gguf
@@ -319,7 +317,6 @@ try:
     direct_register_custom_op(
         op_name="_apply_gguf_embedding",
         op_func=_apply_gguf_embedding,
-        mutates_args=[],
         fake_impl=_apply_gguf_embedding_fake,
     )
     apply_gguf_embedding = torch.ops.vllm._apply_gguf_embedding

vllm/model_executor/layers/quantization/kernels/scaled_mm/aiter.py

@@ -51,9 +51,7 @@ if current_platform.is_rocm():
     direct_register_custom_op(
         op_name="rocm_aiter_gemm_w8a8",
         op_func=rocm_aiter_gemm_w8a8_impl,
-        mutates_args=[],
         fake_impl=rocm_aiter_gemm_w8a8_fake,
-        dispatch_key=current_platform.dispatch_key,
     )
 
 

vllm/model_executor/layers/quantization/mxfp4.py

@@ -212,12 +212,15 @@ class Mxfp4MoEMethod(FusedMoEMethodBase):
             intermediate_size_per_partition_after_pad = round_up(
                 intermediate_size_per_partition, 256)
             hidden_size = round_up(hidden_size, 256)
-        elif current_platform.is_rocm() or (
-                self.mxfp4_backend == Mxfp4Backend.SM100_FI_MXFP4_MXFP8_CUTLASS
-                or self.mxfp4_backend == Mxfp4Backend.SM90_FI_MXFP4_BF16):
+        elif (self.mxfp4_backend == Mxfp4Backend.SM100_FI_MXFP4_MXFP8_CUTLASS
+              or self.mxfp4_backend == Mxfp4Backend.SM90_FI_MXFP4_BF16):
             intermediate_size_per_partition_after_pad = round_up(
                 intermediate_size_per_partition, 128)
             hidden_size = round_up(hidden_size, 128)
+        elif current_platform.is_rocm():
+            intermediate_size_per_partition_after_pad = round_up(
+                intermediate_size_per_partition, 256)
+            hidden_size = round_up(hidden_size, 256)
         else:
             intermediate_size_per_partition_after_pad = round_up(
                 intermediate_size_per_partition, 64)

vllm/model_executor/layers/quantization/utils/fp8_utils.py

@@ -91,9 +91,7 @@ if current_platform.is_rocm():
     direct_register_custom_op(
         op_name="rocm_aiter_gemm_w8a8_blockscale",
         op_func=rocm_aiter_gemm_w8a8_blockscale_impl,
-        mutates_args=[],
         fake_impl=rocm_aiter_gemm_w8a8_blockscale_fake,
-        dispatch_key=current_platform.dispatch_key,
     )
     if (envs.VLLM_ROCM_USE_AITER and envs.VLLM_ROCM_USE_AITER_LINEAR
             and current_platform.is_fp8_fnuz()):
@@ -132,13 +130,14 @@ def _w8a8_triton_block_scaled_mm_fake(
                        device=qx.device)
 
 
-direct_register_custom_op(
-    "w8a8_triton_block_scaled_mm_func",
-    _w8a8_triton_block_scaled_mm_func,
-    mutates_args=[],
-    fake_impl=_w8a8_triton_block_scaled_mm_fake,
-    dispatch_key="CUDA",
-)
+# Note: the check can be removed when CPU torch > 2.7
+if not current_platform.is_cpu():
+    direct_register_custom_op(
+        "w8a8_triton_block_scaled_mm_func",
+        _w8a8_triton_block_scaled_mm_func,
+        fake_impl=_w8a8_triton_block_scaled_mm_fake,
+        dispatch_key="CUDA",
+    )
 
 
 # TODO fix ROCm->Triton custom path:

vllm/model_executor/layers/quantization/utils/mxfp4_utils.py

@@ -1,6 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-from typing import Callable, Optional
+from typing import Any, Callable, Optional
 
 import torch
 
@@ -21,6 +21,10 @@ def _swizzle_mxfp4(quant_tensor, scale, num_warps):
     from triton_kernels.tensor import FP4, convert_layout, wrap_torch_tensor
     from triton_kernels.tensor_details import layout
     from triton_kernels.tensor_details.layout import StridedLayout
+
+    value_layout_opts: dict[str, Any] = {}
+    scale_layout_opts: dict[str, Any] = {}
+
     if (current_platform.is_cuda()
             and current_platform.is_device_capability(90)
             and not is_torch_equal_or_newer("2.8.1")):
@@ -28,8 +32,15 @@ def _swizzle_mxfp4(quant_tensor, scale, num_warps):
             "Mxfp4 on hopper is running on torch < 2.8.1, "
             "this cause swizling to be disabled, which may "
             "cause performance degradation. Please upgrade to torch nightly")
-        value_layout, value_layout_opts = StridedLayout, dict()
-        scale_layout, scale_layout_opts = StridedLayout, dict()
+        value_layout = StridedLayout
+        scale_layout = StridedLayout
+    elif current_platform.is_rocm():
+        from triton_kernels.tensor_details.layout import (GFX950MXScaleLayout,
+                                                          StridedLayout)
+
+        from vllm.platforms.rocm import on_gfx950
+        value_layout = StridedLayout
+        scale_layout = GFX950MXScaleLayout if on_gfx950() else StridedLayout
     else:
         value_layout, value_layout_opts = \
             layout.make_default_matmul_mxfp4_w_layout(mx_axis=1)
@@ -113,7 +124,6 @@ try:
     direct_register_custom_op(
         op_name="dequant_mxfp4",
         op_func=_dequant_mxfp4,
-        mutates_args=[],
         fake_impl=_dequant_mxfp4_fake,
     )
     dequant_mxfp4 = torch.ops.vllm.dequant_mxfp4
@@ -124,7 +134,6 @@ try:
     direct_register_custom_op(
         op_name="quant_dequant_mxfp4",
         op_func=_quant_dequant_mxfp4,
-        mutates_args=[],
         fake_impl=_quant_dequant_mxfp4_fake,
     )
     quant_dequant_mxfp4 = torch.ops.vllm.quant_dequant_mxfp4

vllm/model_executor/layers/quantization/utils/w8a8_utils.py

@@ -218,9 +218,7 @@ def rocm_per_tensor_w8a8_scaled_mm(*, qinput: torch.Tensor,
 direct_register_custom_op(
     op_name="rocm_per_tensor_w8a8_scaled_mm_impl",
     op_func=rocm_per_tensor_w8a8_scaled_mm_impl,
-    mutates_args=[],
     fake_impl=rocm_per_tensor_w8a8_scaled_mm_fake,
-    dispatch_key=current_platform.dispatch_key,
 )
 
 

vllm/model_executor/layers/rotary_embedding/common.py

@@ -147,5 +147,4 @@ direct_register_custom_op(
     op_func=_flashinfer_rotary_embedding,
     mutates_args=["query", "key"],  # These tensors are modified in-place
     fake_impl=_flashinfer_rotary_embedding_fake,
-    dispatch_key=current_platform.dispatch_key,
 )

vllm/model_executor/layers/utils.py

@@ -136,9 +136,7 @@ def rocm_unquantized_gemm(layer: torch.nn.Module,
 direct_register_custom_op(
     op_name="rocm_unquantized_gemm_impl",
     op_func=rocm_unquantized_gemm_impl,
-    mutates_args=[],
     fake_impl=rocm_unquantized_gemm_impl_fake,
-    dispatch_key=current_platform.dispatch_key,
 )
 
 

vllm/model_executor/models/aya_vision.py

@@ -2,7 +2,7 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 # Adapted from https://github.com/huggingface/transformers/tree/main/src/transformers/models/aya_vision
 from collections.abc import Iterable, Mapping, Sequence
-from typing import Annotated, Literal, Optional, Union, cast
+from typing import Annotated, Literal, Optional, Union
 
 import torch
 from torch import nn
@@ -347,12 +347,16 @@ class AyaVisionForConditionalGeneration(nn.Module, SupportsMultiModal,
         loader = AutoWeightsLoader(self)
         return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
 
-    def _image_pixels_to_features(self, vision_tower: SiglipVisionModel,
-                                  pixel_values: torch.Tensor,
-                                  **kwargs) -> torch.Tensor:
-        target_dtype = vision_tower.get_input_embeddings().weight.dtype
-        image_features = vision_tower(pixel_values.to(dtype=target_dtype),
-                                      **kwargs)
+    def _image_pixels_to_features(
+        self,
+        vision_tower: SiglipVisionModel,
+        pixel_values: torch.Tensor,
+        **kwargs,
+    ) -> Union[torch.Tensor, tuple[torch.Tensor, ...]]:
+        target_dtype: torch.dtype = \
+            vision_tower.get_input_embeddings().weight.dtype
+        image_features: Union[torch.Tensor, tuple[torch.Tensor, ...]] = \
+            vision_tower(pixel_values.to(dtype=target_dtype), **kwargs)
 
         def select_features(leaf: torch.Tensor):
             return self._select_image_features(
@@ -360,10 +364,7 @@ class AyaVisionForConditionalGeneration(nn.Module, SupportsMultiModal,
                 strategy=self.config.vision_feature_select_strategy,
             )
 
-        return cast(
-            Union[torch.Tensor, tuple[torch.Tensor, ...]],
-            json_map_leaves(select_features, image_features),
-        )
+        return json_map_leaves(select_features, image_features)
 
     def _select_image_features(self, image_features: torch.Tensor, *,
                                strategy: str) -> torch.Tensor:

vllm/model_executor/models/config.py

@@ -245,19 +245,6 @@ class SnowflakeGteNewModelConfig(VerifyAndUpdateConfig):
         }
 
 
-class GraniteMoeHybridModelConfig(VerifyAndUpdateConfig):
-
-    @staticmethod
-    def verify_and_update_config(vllm_config: "VllmConfig") -> None:
-        config = vllm_config.model_config
-        config.max_seq_len_to_capture = config.max_model_len
-        logger.info(
-            "Setting max_seq_len_to_capture to %d "
-            "to ensure that CUDA graph capture "
-            "covers sequences of length up to max_model_len.",
-            config.max_model_len)
-
-
 class GptOssForCausalLMConfig(VerifyAndUpdateConfig):
 
     @staticmethod
@@ -426,7 +413,6 @@ MODELS_CONFIG_MAP: dict[str, type[VerifyAndUpdateConfig]] = {
     "XLMRobertaModel": JinaRobertaModelConfig,
     "JinaVLForRanking": JinaVLForSequenceClassificationConfig,
     "JambaForSequenceClassification": JambaForSequenceClassificationConfig,
-    "GraniteMoeHybridForCausalLM": GraniteMoeHybridModelConfig,
     "GptOssForCausalLM": GptOssForCausalLMConfig,
     "MambaForCausalLM": MambaModelConfig,
     "Mamba2ForCausalLM": MambaModelConfig,

vllm/model_executor/models/deepseek_v2.py

@@ -56,7 +56,6 @@ from vllm.model_executor.layers.vocab_parallel_embedding import (
     ParallelLMHead, VocabParallelEmbedding)
 from vllm.model_executor.model_loader.weight_utils import (
     default_weight_loader, maybe_remap_kv_scale_name)
-from vllm.platforms import current_platform
 from vllm.sequence import IntermediateTensors
 from vllm.utils import cdiv, direct_register_custom_op
 
@@ -141,9 +140,7 @@ def sequence_parallel_chunk_fake(x: torch.Tensor) -> torch.Tensor:
 direct_register_custom_op(
     op_name="sequence_parallel_chunk",
     op_func=sequence_parallel_chunk,
-    mutates_args=[],
     fake_impl=sequence_parallel_chunk_fake,
-    dispatch_key=current_platform.dispatch_key,
     tags=(torch.Tag.needs_fixed_stride_order, ),
 )
 

vllm/model_executor/models/gemma3n.py

@@ -26,6 +26,7 @@ from vllm.attention import Attention
 from vllm.compilation.decorators import support_torch_compile
 from vllm.config import CacheConfig, VllmConfig
 from vllm.distributed import get_tensor_model_parallel_world_size
+from vllm.forward_context import get_forward_context
 from vllm.logger import init_logger
 from vllm.model_executor.layers.activation import (_ACTIVATION_REGISTRY,
                                                    GeluAndMul,
@@ -44,6 +45,7 @@ from vllm.model_executor.layers.vocab_parallel_embedding import (
 from vllm.model_executor.model_loader.weight_utils import (
     default_weight_loader, maybe_remap_kv_scale_name)
 from vllm.sequence import IntermediateTensors
+from vllm.v1.attention.backends.utils import KVSharingFastPrefillMetadata
 
 from .interfaces import SupportsQuant
 from .utils import (AutoWeightsLoader, extract_layer_index,
@@ -51,6 +53,8 @@ from .utils import (AutoWeightsLoader, extract_layer_index,
 
 logger = init_logger(__name__)
 
+EPS = torch.tensor(torch.finfo().min)
+
 
 class Gemma3nAltUp(nn.Module):
     """Alternating updates (Altup)
@@ -532,16 +536,29 @@ class Gemma3nDecoderLayer(nn.Module):
         return corrected_predictions
 
 
-@support_torch_compile
-class Gemma3nTextModel(nn.Module, SupportsQuant):
+# This enables torch.compile if --kv-sharing-fast-prefill passed
+@support_torch_compile(enable_if=lambda vllm_config: vllm_config.cache_config.
+                       kv_sharing_fast_prefill)
+class Gemma3nSelfDecoder(nn.Module):
+    """
+    Includes altup embedding and self decoder layers
+    """
 
-    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+    def __init__(
+        self,
+        *,
+        vllm_config: VllmConfig,
+        prefix: str = "",
+        decoder_layers: list[Gemma3nDecoderLayer],
+        layer_idx_start: int,
+    ):
         super().__init__()
+        self.decoder_layers = decoder_layers
+        self.layer_idx_start = layer_idx_start
+
         config = vllm_config.model_config.hf_config
-        cache_config = vllm_config.cache_config
-        quant_config = vllm_config.quant_config
         self.config = config
-        self.quant_config = quant_config
+        quant_config = vllm_config.quant_config
 
         self.embed_tokens = VocabParallelEmbedding(
             config.vocab_size,
@@ -594,32 +611,6 @@ class Gemma3nTextModel(nn.Module, SupportsQuant):
                 prefix=f"{prefix}.altup_projections.{idx-1}",
             ) for idx in range(1, self.config.altup_num_inputs)
         ])
-        self.altup_unembed_projections = nn.ModuleList([
-            ColumnParallelLinear(
-                config.hidden_size,
-                config.hidden_size,
-                bias=False,
-                gather_output=True,
-                return_bias=False,
-                quant_config=quant_config,
-                prefix=f"{prefix}.altup_unembed_projections.{idx-1}",
-            ) for idx in range(1, self.config.altup_num_inputs)
-        ])
-
-        # Transformer blocks.
-        self.start_layer, self.end_layer, self.layers = make_layers(
-            config.num_hidden_layers,
-            lambda prefix: Gemma3nDecoderLayer(
-                config, cache_config, quant_config, prefix=prefix),
-            prefix=f"{prefix}.layers")
-        self.norm = RMSNorm(
-            config.hidden_size,
-            eps=config.rms_norm_eps,
-        )
-        self.eps = torch.tensor(torch.finfo().min)
-
-    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
-        return self.embed_tokens(input_ids) * self.embed_scale
 
     def get_per_layer_input_embeddings(
             self, input_ids: torch.Tensor) -> torch.Tensor:
@@ -633,20 +624,11 @@ class Gemma3nTextModel(nn.Module, SupportsQuant):
         return self.embed_tokens_per_layer(
             per_layer_inputs_tokens) * self.embed_scale_per_layer
 
-    def forward(
+    def get_per_layer_inputs(
         self,
-        input_ids: Optional[torch.Tensor],
-        positions: torch.Tensor,
-        per_layer_inputs: torch.Tensor,
-        intermediate_tensors: Optional[IntermediateTensors] = None,
-        inputs_embeds: Optional[torch.Tensor] = None,
-        **kwargs,
-    ) -> Union[torch.Tensor, IntermediateTensors]:
-        if inputs_embeds is not None:
-            hidden_states_0 = inputs_embeds
-        else:
-            hidden_states_0 = self.get_input_embeddings(input_ids)
-
+        hidden_states_0: torch.Tensor,
+        per_layer_inputs: Optional[torch.Tensor],
+    ) -> torch.Tensor:
         per_layer_projection = self.per_layer_model_projection(hidden_states_0)
         per_layer_projection = per_layer_projection.reshape(
             *hidden_states_0.shape[:-1],
@@ -655,14 +637,18 @@ class Gemma3nTextModel(nn.Module, SupportsQuant):
         )
         per_layer_projection = self.per_layer_projection_norm(
             per_layer_projection)
-
         if per_layer_inputs is not None:
             # Profiling run does not compute per_layer_inputs
             per_layer_inputs = per_layer_projection + per_layer_inputs
             per_layer_inputs *= self.per_layer_input_scale
         else:
             per_layer_inputs = per_layer_projection
+        return per_layer_inputs
 
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.embed_tokens(input_ids) * self.embed_scale
+
+    def altup_embed(self, hidden_states_0: torch.Tensor) -> torch.Tensor:
         # Altup embed.
         hidden_states = [hidden_states_0] * self.config.altup_num_inputs
         target_magnitude = torch.mean(hidden_states_0**2, dim=-1,
@@ -673,11 +659,77 @@ class Gemma3nTextModel(nn.Module, SupportsQuant):
                                        dim=-1,
                                        keepdim=True)**0.5
             hidden_states[i] *= target_magnitude / torch.maximum(
-                new_magnitude, self.eps)
-        hidden_states = torch.stack(hidden_states, dim=0)
+                new_magnitude, EPS)
+        hidden_states = torch.stack(hidden_states, dim=-1)
+        return hidden_states
 
-        # Transformer blocks.
-        for layer_idx, layer in enumerate(self.layers):
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        per_layer_inputs: Optional[torch.Tensor] = None,
+        **kwargs,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        if inputs_embeds is not None:
+            hidden_states_0 = inputs_embeds
+        else:
+            hidden_states_0 = self.get_input_embeddings(input_ids)
+
+        adjusted_per_layer_inputs = self.get_per_layer_inputs(
+            hidden_states_0, per_layer_inputs)
+        hidden_states = self.altup_embed(hidden_states_0)
+
+        # [altnum_inputs, num_tokens, hidden_size]
+        hidden_states = hidden_states.permute(2, 0, 1)
+
+        for idx, layer in enumerate(self.decoder_layers):
+            layer_idx = idx + self.layer_idx_start
+            # [altup_num_inputs, num_tokens, hidden_size]
+            hidden_states = layer(
+                positions=positions,
+                hidden_states=hidden_states,
+                per_layer_input=adjusted_per_layer_inputs[:, layer_idx, :],
+                **kwargs,
+            )
+
+        # [num_tokens, hidden_size, altnum_inputs]
+        hidden_states = hidden_states.permute(1, 2, 0)
+
+        return hidden_states, adjusted_per_layer_inputs
+
+
+# This enables torch.compile if --kv-sharing-fast-prefill passed
+@support_torch_compile(enable_if=lambda vllm_config: vllm_config.cache_config.
+                       kv_sharing_fast_prefill)
+class Gemma3nCrossDecoder(nn.Module):
+    """
+    Cross-decoder layers
+    """
+
+    def __init__(
+        self,
+        *,
+        vllm_config: VllmConfig,
+        prefix: str = "",
+        decoder_layers: list[Gemma3nDecoderLayer],
+        layer_idx_start: int,
+    ):
+        super().__init__()
+        self.decoder_layers = decoder_layers
+        self.layer_idx_start = layer_idx_start
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        per_layer_inputs: torch.Tensor,
+        **kwargs,
+    ) -> torch.Tensor:
+        # [altnum_inputs, num_tokens, hidden_size]
+        hidden_states = hidden_states.permute(2, 0, 1)
+        for idx, layer in enumerate(self.decoder_layers):
+            layer_idx = idx + self.layer_idx_start
             # [altup_num_inputs, num_tokens, hidden_size]
             hidden_states = layer(
                 positions=positions,
@@ -685,22 +737,249 @@ class Gemma3nTextModel(nn.Module, SupportsQuant):
                 per_layer_input=per_layer_inputs[:, layer_idx, :],
                 **kwargs,
             )
+        # [num_tokens, hidden_size, altnum_inputs]
+        hidden_states = hidden_states.permute(1, 2, 0)
+        return hidden_states
 
+
+# This disables torch.compile if --kv-sharing-fast-prefill passed
+@support_torch_compile(enable_if=lambda vllm_config: not vllm_config.
+                       cache_config.kv_sharing_fast_prefill)
+class Gemma3nTextModel(nn.Module, SupportsQuant):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        cache_config = vllm_config.cache_config
+        quant_config = vllm_config.quant_config
+        self.config = config
+        self.quant_config = quant_config
+
+        self.altup_unembed_projections = nn.ModuleList([
+            ColumnParallelLinear(
+                config.hidden_size,
+                config.hidden_size,
+                bias=False,
+                gather_output=True,
+                return_bias=False,
+                quant_config=quant_config,
+                prefix=f"{prefix}.altup_unembed_projections.{idx-1}",
+            ) for idx in range(1, self.config.altup_num_inputs)
+        ])
+
+        # Allocate config.num_kv_shared_layers layers for self-decoder
+        self.start_layer, self.end_layer, self.layers = make_layers(
+            config.num_hidden_layers,
+            lambda prefix: Gemma3nDecoderLayer(
+                config, cache_config, quant_config, prefix=prefix),
+            prefix=f"{prefix}.layers")
+
+        first_kv_shared_layer_idx = (config.num_hidden_layers -
+                                     config.num_kv_shared_layers)
+
+        # NOTE(sarckk): importing this top level seems to cause issues
+        # during running of tests.
+        from vllm.compilation.backends import set_model_tag
+
+        # Layer idx 0-19 are self-decoder layers in You Only Cache Once (YOCO)
+        with set_model_tag("self_decoder"):
+            self.self_decoder = Gemma3nSelfDecoder(
+                vllm_config=vllm_config,
+                prefix=f"{prefix}.self_decoder",
+                decoder_layers=self.layers[:first_kv_shared_layer_idx],
+                layer_idx_start=0,
+            )
+        # Layer idx 20-30 are cross-decoder layers in YOCO
+        with set_model_tag("cross_decoder"):
+            self.cross_decoder = Gemma3nCrossDecoder(
+                vllm_config=vllm_config,
+                prefix=f"{prefix}.cross_decoder",
+                decoder_layers=self.layers[first_kv_shared_layer_idx:],
+                layer_idx_start=first_kv_shared_layer_idx,
+            )
+
+        self.norm = RMSNorm(
+            config.hidden_size,
+            eps=config.rms_norm_eps,
+        )
+
+        self.fast_prefill_enabled = cache_config.kv_sharing_fast_prefill
+
+        if self.fast_prefill_enabled:
+            # Allocate static buffers for CUDAGraph
+            # TODO(sarckk): Extract this functionality to interface
+            max_num_tokens = vllm_config.scheduler_config.max_num_batched_tokens
+            device = next(self.parameters()).device
+            self.positions = torch.zeros(max_num_tokens,
+                                         dtype=torch.int64,
+                                         device=device)
+            self.hidden_states = torch.zeros(
+                (max_num_tokens, config.hidden_size,
+                 self.config.altup_num_inputs),
+                dtype=self.embed_tokens.weight.dtype,
+                device=device,
+            )
+            self.per_layer_inputs = torch.zeros(
+                (max_num_tokens, self.config.num_hidden_layers,
+                 self.config.hidden_size_per_layer_input),
+                dtype=self.embed_tokens.weight.dtype,
+                device=device,
+            )
+
+    @property
+    def embed_tokens(self):
+        return self.self_decoder.embed_tokens
+
+    def get_per_layer_input_embeddings(
+            self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.self_decoder.get_per_layer_input_embeddings(input_ids)
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.self_decoder.get_input_embeddings(input_ids)
+
+    def fast_prefill_forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        per_layer_inputs: Optional[torch.Tensor] = None,
+        **kwargs,
+    ) -> torch.Tensor:
+        logits_indices_padded, num_logits_indices = None, None
+        attn_metadata = get_forward_context().attn_metadata
+
+        # attn_metadata is None during dummy runs
+        if (self.fast_prefill_enabled and attn_metadata is not None):
+            assert isinstance(attn_metadata, dict)
+            # Last layer is a KV sharing layer
+            layer_attn_metadata = attn_metadata[
+                self.layers[-1].self_attn.attn.layer_name]
+            if (isinstance(layer_attn_metadata, KVSharingFastPrefillMetadata)):
+                logits_indices_padded = (
+                    layer_attn_metadata.logits_indices_padded)
+                num_logits_indices = layer_attn_metadata.num_logits_indices
+
+        # Copy inputs for cudagraph
+        batch_size = positions.size(0)
+        self.positions[:batch_size].copy_(positions)
+        self_decoder_hidden_states, per_layer_inputs_adjusted = \
+            self.self_decoder(
+                input_ids=input_ids,
+                positions=self.positions[:batch_size],
+                inputs_embeds=inputs_embeds,
+                per_layer_inputs=per_layer_inputs,
+                **kwargs,
+            )
+
+        if logits_indices_padded is None:
+            logits_indices_padded = torch.arange(
+                positions.size(0),
+                dtype=positions.dtype,
+                device=positions.device,
+            )
+
+        # NOTE(sarckk): There is currently a bug caused by
+        # vLLM converting output of last piecewise CUDA graph
+        # to weakref, causing memory to be prematurely freed
+        # when there are multiple compilation units
+        # Keep .clone() until fix in
+        # https://github.com/vllm-project/vllm/pull/22282
+        hidden_states = self_decoder_hidden_states.clone()
+
+        # Copy inputs for cudagraph
+        num_padded_logits_indices = logits_indices_padded.size(0)
+        self.positions[:num_padded_logits_indices].copy_(
+            positions[logits_indices_padded])
+        self.hidden_states[:num_padded_logits_indices].copy_(
+            self_decoder_hidden_states[logits_indices_padded])
+        self.per_layer_inputs[:num_padded_logits_indices].copy_(
+            per_layer_inputs_adjusted[logits_indices_padded])
+        cross_decoder_hidden_states = self.cross_decoder(
+            positions=self.positions[:num_padded_logits_indices],
+            hidden_states=self.hidden_states[:num_padded_logits_indices],
+            per_layer_inputs=self.per_layer_inputs[:num_padded_logits_indices],
+            **kwargs,
+        )
+
+        if num_logits_indices is not None:
+            assert num_logits_indices > 0
+            # Merge cross-decoder and self-decoder hidden states
+            hidden_states[logits_indices_padded[:num_logits_indices]] = (
+                cross_decoder_hidden_states[:num_logits_indices])
+        else:
+            hidden_states = cross_decoder_hidden_states
+
+        return hidden_states
+
+    def normal_forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        per_layer_inputs: Optional[torch.Tensor] = None,
+        **kwargs,
+    ) -> torch.Tensor:
+        hidden_states, per_layer_inputs = self.self_decoder(
+            input_ids=input_ids,
+            positions=positions,
+            inputs_embeds=inputs_embeds,
+            per_layer_inputs=per_layer_inputs,
+            **kwargs,
+        )
+        hidden_states = self.cross_decoder(
+            positions=positions,
+            hidden_states=hidden_states,
+            per_layer_inputs=per_layer_inputs,
+            **kwargs,
+        )
+        return hidden_states
+
+    def altup_unembed(
+        self,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
         # Altup unembed.
-        target_magnitude = torch.mean(hidden_states[0]**2,
+        target_magnitude = torch.mean(hidden_states[..., 0]**2,
                                       dim=-1,
                                       keepdim=True)**0.5
         for i in range(1, self.config.altup_num_inputs):
-            hidden_states[i] = self.altup_unembed_projections[i - 1](
-                hidden_states[i])
-            new_magnitude = torch.mean(hidden_states[i]**2,
+            hidden_states[..., i] = self.altup_unembed_projections[i - 1](
+                hidden_states[..., i])
+            new_magnitude = torch.mean(hidden_states[..., i]**2,
                                        dim=-1,
                                        keepdim=True)**0.5
-            hidden_states[i] *= target_magnitude / torch.maximum(
-                new_magnitude, self.eps)
-        # [altup_num_inputs,num_tokens,hidden_size] -> [num_tokens,hidden_size]
-        hidden_states = torch.mean(hidden_states, dim=0)
+            hidden_states[..., i] *= target_magnitude / torch.maximum(
+                new_magnitude, EPS)
+        # [num_tokens,hidden_size, altup_num_inputs] -> [num_tokens,hidden_size]
+        hidden_states = torch.mean(hidden_states, dim=-1)
+        return hidden_states
 
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor],
+        positions: torch.Tensor,
+        per_layer_inputs: Optional[torch.Tensor] = None,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        **kwargs,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        if self.fast_prefill_enabled:
+            hidden_states = self.fast_prefill_forward(
+                input_ids,
+                positions,
+                inputs_embeds,
+                per_layer_inputs,
+                **kwargs,
+            )
+        else:
+            hidden_states = self.normal_forward(
+                input_ids,
+                positions,
+                inputs_embeds,
+                per_layer_inputs,
+                **kwargs,
+            )
+        hidden_states = self.altup_unembed(hidden_states)
         return self.norm(hidden_states)
 
     def load_weights(self, weights: Iterable[tuple[str,
@@ -716,6 +995,13 @@ class Gemma3nTextModel(nn.Module, SupportsQuant):
         params_dict = dict(self.named_parameters())
         loaded_params: set[str] = set()
         for name, loaded_weight in weights:
+            # decoder layer weights, altup_unembed_projections and rmsnorm
+            # are initialized in text model, others are in self decoder
+            if (not name.startswith('layers')
+                    and not name.startswith('altup_unembed_projections')
+                    and not name.startswith('norm')):
+                name = f"self_decoder.{name}"
+
             if (self.quant_config is not None and
                 (scale_name := self.quant_config.get_cache_scale(name))):
                 # Loading kv cache scales for compressed-tensors quantization

vllm/model_executor/models/granite.py

@@ -308,13 +308,11 @@ class GraniteModel(nn.Module):
                 hidden_states = inputs_embeds
             else:
                 hidden_states = self.get_input_embeddings(input_ids)
-            residual = None
 
             hidden_states *= self.config.embedding_multiplier
         else:
             assert intermediate_tensors is not None
             hidden_states = intermediate_tensors["hidden_states"]
-            residual = intermediate_tensors["residual"]
 
         for layer in islice(self.layers, self.start_layer, self.end_layer):
             hidden_states = layer(positions, hidden_states)
@@ -322,7 +320,6 @@ class GraniteModel(nn.Module):
         if not get_pp_group().is_last_rank:
             return IntermediateTensors({
                 "hidden_states": hidden_states,
-                "residual": residual
             })
 
         hidden_states = self.norm(hidden_states)
@@ -475,10 +472,6 @@ class GraniteForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
             torch.zeros((batch_size, self.config.hidden_size),
                         dtype=dtype,
                         device=device),
-            "residual":
-            torch.zeros((batch_size, self.config.hidden_size),
-                        dtype=dtype,
-                        device=device),
         })
 
     def load_weights(self, weights: Iterable[tuple[str,

vllm/model_executor/models/granitemoe.py

@@ -298,17 +298,14 @@ class GraniteMoeModel(nn.Module):
             else:
                 hidden_states = self.get_input_embeddings(input_ids)
             hidden_states *= self.embedding_multiplier
-            residual = None
         else:
             assert intermediate_tensors is not None
             hidden_states = intermediate_tensors["hidden_states"]
-            residual = intermediate_tensors["residual"]
         for layer in islice(self.layers, self.start_layer, self.end_layer):
             hidden_states = layer(positions, hidden_states)
         if not get_pp_group().is_last_rank:
             return IntermediateTensors({
                 "hidden_states": hidden_states,
-                "residual": residual
             })
         hidden_states = self.norm(hidden_states)
         return hidden_states
@@ -523,10 +520,6 @@ class GraniteMoeForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
             torch.zeros((batch_size, self.config.hidden_size),
                         dtype=dtype,
                         device=device),
-            "residual":
-            torch.zeros((batch_size, self.config.hidden_size),
-                        dtype=dtype,
-                        device=device),
         })
 
     def load_weights(self, weights: Iterable[tuple[str,

vllm/model_executor/models/granitemoeshared.py

@@ -195,17 +195,14 @@ class GraniteMoeSharedModel(nn.Module):
             else:
                 hidden_states = self.get_input_embeddings(input_ids)
             hidden_states *= self.embedding_multiplier
-            residual = None
         else:
             assert intermediate_tensors is not None
             hidden_states = intermediate_tensors["hidden_states"]
-            residual = intermediate_tensors["residual"]
         for layer in islice(self.layers, self.start_layer, self.end_layer):
             hidden_states = layer(positions, hidden_states)
         if not get_pp_group().is_last_rank:
             return IntermediateTensors({
                 "hidden_states": hidden_states,
-                "residual": residual
             })
         hidden_states = self.norm(hidden_states)
         return hidden_states
@@ -323,10 +320,6 @@ class GraniteMoeSharedForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
             torch.zeros((batch_size, self.config.hidden_size),
                         dtype=dtype,
                         device=device),
-            "residual":
-            torch.zeros((batch_size, self.config.hidden_size),
-                        dtype=dtype,
-                        device=device),
         })
 
     def load_weights(self, weights: Iterable[tuple[str,

vllm/model_executor/models/hyperclovax_vision.py

@@ -29,7 +29,6 @@ from transformers import BatchFeature, CLIPVisionConfig, SiglipVisionConfig
 from transformers.modeling_utils import no_init_weights
 
 from vllm.config import VllmConfig
-from vllm.inputs import InputProcessingContext
 from vllm.model_executor.layers.quantization import QuantizationConfig
 from vllm.multimodal import MULTIMODAL_REGISTRY
 from vllm.multimodal.cache import BaseMultiModalProcessorCache
@@ -37,8 +36,9 @@ from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig,
                                     MultiModalKwargsItems)
 from vllm.multimodal.parse import ImageSize, MultiModalDataItems
 from vllm.multimodal.processing import (BaseMultiModalProcessor,
-                                        BaseProcessingInfo, PromptReplacement,
-                                        PromptUpdate)
+                                        BaseProcessingInfo,
+                                        InputProcessingContext,
+                                        PromptReplacement, PromptUpdate)
 from vllm.multimodal.profiling import BaseDummyInputsBuilder
 from vllm.sequence import IntermediateTensors
 

vllm/model_executor/models/llava.py

@@ -4,7 +4,7 @@
 from abc import abstractmethod
 from collections.abc import Iterable, Mapping, Sequence
 from typing import (Annotated, Final, Literal, Optional, Protocol, TypeVar,
-                    Union, cast)
+                    Union)
 
 import torch
 import torch.nn as nn
@@ -15,7 +15,6 @@ from transformers.models.llava import LlavaProcessor
 from transformers.models.pixtral import PixtralProcessor
 
 from vllm.config import VllmConfig
-from vllm.inputs import InputProcessingContext
 from vllm.model_executor.layers.activation import get_act_fn
 from vllm.model_executor.layers.linear import (ColumnParallelLinear,
                                                RowParallelLinear)
@@ -28,8 +27,10 @@ from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig,
 from vllm.multimodal.parse import (ImageEmbeddingItems, ImageProcessorItems,
                                    ImageSize, MultiModalDataItems)
 from vllm.multimodal.processing import (BaseMultiModalProcessor,
-                                        BaseProcessingInfo, PromptReplacement,
-                                        PromptUpdate, PromptUpdateDetails)
+                                        BaseProcessingInfo,
+                                        InputProcessingContext,
+                                        PromptReplacement, PromptUpdate,
+                                        PromptUpdateDetails)
 from vllm.multimodal.profiling import BaseDummyInputsBuilder
 from vllm.sequence import IntermediateTensors
 from vllm.utils.jsontree import json_map_leaves
@@ -622,7 +623,8 @@ class LlavaForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP):
     ) -> Union[torch.Tensor, tuple[torch.Tensor, ...]]:
         # NOTE: we skip the step to select the vision feature layer since
         # this is already done inside the vision tower
-        image_features = vision_tower(pixel_values)
+        image_features: Union[torch.Tensor, tuple[torch.Tensor, ...]] = \
+            vision_tower(pixel_values)
 
         def select_features(leaf: torch.Tensor):
             return self._select_image_features(
@@ -630,10 +632,7 @@ class LlavaForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP):
                 strategy=self.config.vision_feature_select_strategy,
             )
 
-        return cast(
-            Union[torch.Tensor, tuple[torch.Tensor, ...]],
-            json_map_leaves(select_features, image_features),
-        )
+        return json_map_leaves(select_features, image_features)
 
     def _process_image_pixels(
         self,