deprecate timeout

Signed-off-by: NickLucche <nlucches@redhat.com>

.buildkite/test-amd.yaml

@@ -388,10 +388,10 @@ steps:
     - python3 basic/offline_inference/embed.py
     - python3 basic/offline_inference/score.py
     # Multi-modal models
-    - python3 offline_inference/audio_language.py --seed 0
-    - python3 offline_inference/vision_language.py --seed 0
-    - python3 offline_inference/vision_language_multi_image.py --seed 0
-    - python3 offline_inference/encoder_decoder_multimodal.py --model-type whisper --seed 0
+    - python3 generate/multimodal/audio_language_offline.py --seed 0
+    - python3 generate/multimodal/vision_language_offline.py --seed 0
+    - python3 generate/multimodal/vision_language_multi_image_offline.py --seed 0
+    - python3 generate/multimodal/encoder_decoder_multimodal_offline.py --model-type whisper --seed 0
     # Pooling models
     - python3 pooling/embed/vision_embedding_offline.py --seed 0
     # Features demo
@@ -1647,10 +1647,10 @@ steps:
     - python3 basic/offline_inference/embed.py
     - python3 basic/offline_inference/score.py
     # Multi-modal models
-    - python3 offline_inference/audio_language.py --seed 0
-    - python3 offline_inference/vision_language.py --seed 0
-    - python3 offline_inference/vision_language_multi_image.py --seed 0
-    - python3 offline_inference/encoder_decoder_multimodal.py --model-type whisper --seed 0
+    - python3 generate/multimodal/audio_language_offline.py --seed 0
+    - python3 generate/multimodal/vision_language_offline.py --seed 0
+    - python3 generate/multimodal/vision_language_multi_image_offline.py --seed 0
+    - python3 generate/multimodal/encoder_decoder_multimodal_offline.py --model-type whisper --seed 0
     # Pooling models
     - python3 pooling/embed/vision_embedding_offline.py --seed 0
     # Features demo
@@ -1951,8 +1951,8 @@ steps:
   - pytest -v -s tests/models/multimodal/processing/
   - pytest -v -s tests/models/multimodal/test_mapping.py
   - python3 examples/basic/offline_inference/chat.py
-  - python3 examples/offline_inference/vision_language.py --model-type qwen2_5_vl
-  - VLLM_WORKER_MULTIPROC_METHOD=spawn python3 examples/offline_inference/audio_language.py --model-type whisper
+  - python3 examples/generate/multimodal/vision_language_offline.py --model-type qwen2_5_vl
+  - VLLM_WORKER_MULTIPROC_METHOD=spawn python3 examples/generate/multimodal/audio_language_offline.py --model-type whisper
 
 #-------------------------------------------------------  mi300 · quantization  --------------------------------------------------------#
 
@@ -2930,10 +2930,10 @@ steps:
   - python3 basic/offline_inference/embed.py
   - python3 basic/offline_inference/score.py
   # Multi-modal models
-  - python3 offline_inference/audio_language.py --seed 0
-  - python3 offline_inference/vision_language.py --seed 0
-  - python3 offline_inference/vision_language_multi_image.py --seed 0
-  - python3 offline_inference/encoder_decoder_multimodal.py --model-type whisper --seed 0
+  - python3 generate/multimodal/audio_language_offline.py --seed 0
+  - python3 generate/multimodal/vision_language_offline.py --seed 0
+  - python3 generate/multimodal/vision_language_multi_image_offline.py --seed 0
+  - python3 generate/multimodal/encoder_decoder_multimodal_offline.py --model-type whisper --seed 0
   # Pooling models
   - python3 pooling/embed/vision_embedding_offline.py --seed 0
   # Features demo

.buildkite/test_areas/kernels.yaml

@@ -95,11 +95,13 @@ steps:
   - tests/kernels/moe/test_deepgemm.py
   - tests/kernels/moe/test_batched_deepgemm.py
   - tests/kernels/attention/test_deepgemm_attention.py
+  - tests/quantization/test_cutlass_w4a16.py
   commands:
     - pytest -v -s kernels/quantization/test_block_fp8.py
     - pytest -v -s kernels/moe/test_deepgemm.py
     - pytest -v -s kernels/moe/test_batched_deepgemm.py
     - pytest -v -s kernels/attention/test_deepgemm_attention.py
+    - pytest -v -s quantization/test_cutlass_w4a16.py
 
 - label: Kernels (B200)
   timeout_in_minutes: 30

.buildkite/test_areas/misc.yaml

@@ -113,10 +113,10 @@ steps:
     - python3 basic/offline_inference/embed.py
     - python3 basic/offline_inference/score.py
     # for multi-modal models
-    - python3 offline_inference/audio_language.py --seed 0
-    - python3 offline_inference/vision_language.py --seed 0
-    - python3 offline_inference/vision_language_multi_image.py --seed 0
-    - python3 offline_inference/encoder_decoder_multimodal.py --model-type whisper --seed 0
+    - python3 generate/multimodal/audio_language_offline.py --seed 0
+    - python3 generate/multimodal/vision_language_offline.py --seed 0
+    - python3 generate/multimodal/vision_language_multi_image_offline.py --seed 0
+    - python3 generate/multimodal/encoder_decoder_multimodal_offline.py --model-type whisper --seed 0
      # for pooling models
     - python3 pooling/embed/vision_embedding_offline.py --seed 0
     # for features demo

.buildkite/test_areas/model_runner_v2.yaml

@@ -44,10 +44,10 @@ steps:
     #- python3 basic/offline_inference/generate.py --model meta-llama/Llama-2-13b-chat-hf --cpu-offload-gb 10  # TODO
     #- python3 basic/offline_inference/embed.py   # TODO
     # for multi-modal models
-    - python3 offline_inference/audio_language.py --seed 0
-    - python3 offline_inference/vision_language.py --seed 0
-    - python3 offline_inference/vision_language_multi_image.py --seed 0
-    - python3 offline_inference/encoder_decoder_multimodal.py --model-type whisper --seed 0
+    - python3 generate/multimodal/audio_language_offline.py --seed 0
+    - python3 generate/multimodal/vision_language_offline.py --seed 0
+    - python3 generate/multimodal/vision_language_multi_image_offline.py --seed 0
+    - python3 generate/multimodal/encoder_decoder_multimodal_offline.py --model-type whisper --seed 0
     # for pooling models
     - python3 pooling/embed/vision_embedding_offline.py --seed 0
     # for features demo

.buildkite/test_areas/models_basic.yaml

@@ -69,9 +69,9 @@ steps:
     - pytest -v -s tests/models/multimodal/processing/
     - pytest -v -s tests/models/multimodal/test_mapping.py
     - python3 examples/basic/offline_inference/chat.py
-    - python3 examples/offline_inference/vision_language.py --model-type qwen2_5_vl
+    - python3 examples/generate/multimodal/vision_language_offline.py --model-type qwen2_5_vl
     # Whisper needs spawn method to avoid deadlock
-    - VLLM_WORKER_MULTIPROC_METHOD=spawn python3 examples/offline_inference/audio_language.py --model-type whisper
+    - VLLM_WORKER_MULTIPROC_METHOD=spawn python3 examples/generate/multimodal/audio_language_offline.py --model-type whisper
 
 - label: Transformers Backward Compatibility Models Test
   working_dir: "/vllm-workspace/"
@@ -83,7 +83,7 @@ steps:
     - pytest -v -s tests/models/test_transformers.py
     - pytest -v -s tests/models/multimodal/processing/
     - pytest -v -s tests/models/multimodal/test_mapping.py
-    - python3 examples/offline_inference/basic/chat.py
-    - python3 examples/offline_inference/vision_language.py --model-type qwen2_5_vl
+    - python3 examples/basic/offline_inference/chat.py
+    - python3 examples/generate/multimodal/vision_language_offline.py --model-type qwen2_5_vl
     # Whisper needs spawn method to avoid deadlock
-    - VLLM_WORKER_MULTIPROC_METHOD=spawn python3 examples/offline_inference/audio_language.py --model-type whisper
+    - VLLM_WORKER_MULTIPROC_METHOD=spawn python3 examples/generate/multimodal/audio_language_offline.py --model-type whisper

.github/mergify.yml

@@ -389,11 +389,7 @@ pull_request_rules:
       - files~=^tests/entrypoints/anthropic/.*tool.*
       - files~=^vllm/tool_parsers/
       - files=docs/features/tool_calling.md
-      - files~=^examples/tool_chat_*
-      - files=examples/offline_inference/chat_with_tools.py
-      - files=examples/online_serving/openai_chat_completion_client_with_tools_required.py
-      - files=examples/online_serving/openai_chat_completion_tool_calls_with_reasoning.py
-      - files=examples/online_serving/openai_chat_completion_client_with_tools.py
+      - files~=^examples/tool_calling/
   actions:
     label:
       add:

benchmarks/kernels/benchmark_vit_fp8_attn.py

@@ -0,0 +1,324 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Benchmarks FP8 vs BF16 ViT attention via FlashInfer cuDNN backend.
+#
+# == Usage Examples ==
+#
+# Benchmark mode (default, FlashInfer CUDAGraph Bench)
+#   python3 benchmark_vit_fp8_attn.py
+#
+# Profile mode (PyTorch profiler, saves TensorBoard traces):
+#   python3 benchmark_vit_fp8_attn.py --profile
+#   python3 benchmark_vit_fp8_attn.py --profile --profile-output-dir ./profile_traces
+#
+# Custom seq_lens:
+#   python3 benchmark_vit_fp8_attn.py --seq-lens 4096 8192 16384
+
+from functools import partial
+
+import numpy as np
+import torch
+from torch.profiler import ProfilerActivity, profile, record_function
+
+from vllm.utils.argparse_utils import FlexibleArgumentParser
+
+# Qwen3-VL defaults
+NUM_HEADS = 16
+HEAD_DIM = 72
+DEFAULT_SEQ_LENS = [2304, 4096, 8192, 16384]
+
+
+def _setup_fp8_attention(num_heads: int, head_dim: int) -> tuple:
+    """Create FP8 and BF16 attention modules + workspace."""
+    from types import SimpleNamespace
+    from unittest.mock import patch
+
+    from vllm.config import VllmConfig, set_current_vllm_config
+    from vllm.config.multimodal import MultiModalConfig
+    from vllm.model_executor.layers.attention.mm_encoder_attention import (
+        MMEncoderAttention,
+        _get_flashinfer_workspace_buffer,
+    )
+    from vllm.v1.attention.backends.registry import AttentionBackendEnum
+
+    old_dtype = torch.get_default_dtype()
+    torch.set_default_dtype(torch.bfloat16)
+
+    backend_patch = patch(
+        "vllm.model_executor.layers.attention.mm_encoder_attention"
+        ".get_vit_attn_backend",
+        return_value=AttentionBackendEnum.FLASHINFER,
+    )
+
+    # FP8 attention
+    mm_config_fp8 = MultiModalConfig(mm_encoder_attn_dtype="fp8")
+    vllm_config_fp8 = VllmConfig()
+    vllm_config_fp8.model_config = SimpleNamespace(multimodal_config=mm_config_fp8)
+    with set_current_vllm_config(vllm_config_fp8), backend_patch:
+        attn_fp8 = MMEncoderAttention(
+            num_heads=num_heads,
+            head_size=head_dim,
+            prefix="visual.blocks.0.attn",
+        ).to("cuda")
+
+    # BF16 attention (no FP8)
+    with set_current_vllm_config(VllmConfig()), backend_patch:
+        attn_bf16 = MMEncoderAttention(
+            num_heads=num_heads,
+            head_size=head_dim,
+            prefix="visual.blocks.0.attn",
+        ).to("cuda")
+
+    torch.set_default_dtype(old_dtype)
+
+    workspace = _get_flashinfer_workspace_buffer()
+    return attn_fp8, attn_bf16, workspace
+
+
+def _build_meta(
+    seq_len: int,
+    num_heads: int,
+    head_dim: int,
+    fp8: bool,
+):
+    """Build cu_seqlens, max_seqlen, sequence_lengths."""
+    from vllm.model_executor.layers.attention.mm_encoder_attention import (
+        MMEncoderAttention,
+    )
+    from vllm.utils.math_utils import round_up
+    from vllm.v1.attention.backends.registry import AttentionBackendEnum
+
+    cu_np = np.array([0, seq_len], dtype=np.int32)
+    fp8_padded = num_heads * round_up(head_dim, 16) if fp8 else None
+
+    seq_lengths = MMEncoderAttention.maybe_compute_seq_lens(
+        AttentionBackendEnum.FLASHINFER, cu_np, torch.device("cuda")
+    )
+    max_seqlen = torch.tensor(
+        MMEncoderAttention.compute_max_seqlen(AttentionBackendEnum.FLASHINFER, cu_np),
+        dtype=torch.int32,
+    )
+    cu_seqlens = MMEncoderAttention.maybe_recompute_cu_seqlens(
+        AttentionBackendEnum.FLASHINFER,
+        cu_np,
+        num_heads * head_dim,
+        1,
+        torch.device("cuda"),
+        fp8_padded_hidden_size=fp8_padded,
+    )
+    return cu_seqlens, max_seqlen, seq_lengths
+
+
+def run_benchmark(
+    seq_lens: list[int],
+    num_heads: int,
+    head_dim: int,
+    method: str,
+):
+    """Benchmark FP8 vs BF16 attention across seq_lens.
+
+    Uses FlashInfer GPU-level timing to measure pure kernel time,
+    excluding CPU launch overhead.
+    """
+    if method == "cupti":
+        from flashinfer.testing import bench_gpu_time_with_cupti as bench_fn
+
+        bench_fn = partial(bench_fn, use_cuda_graph=True, cold_l2_cache=False)
+    elif method == "cudagraph":
+        from flashinfer.testing import (
+            bench_gpu_time_with_cudagraph as bench_fn,
+        )
+
+        bench_fn = partial(bench_fn, cold_l2_cache=False)
+    else:
+        raise ValueError(f"Invalid method: {method}")
+
+    attn_fp8, attn_bf16, workspace = _setup_fp8_attention(num_heads, head_dim)
+
+    print(f"Timing method: {method}")
+    print(f"{'seq_len':>8} {'BF16 (us)':>12} {'FP8 (us)':>12} {'Speedup':>10}")
+    print("-" * 46)
+
+    for seq_len in seq_lens:
+        torch.manual_seed(42)
+
+        q = torch.randn(
+            seq_len,
+            num_heads,
+            head_dim,
+            device="cuda",
+            dtype=torch.bfloat16,
+        )
+        k = torch.randn_like(q)
+        v = torch.randn_like(q)
+
+        cu_fp8, max_s, seq_l = _build_meta(seq_len, num_heads, head_dim, fp8=True)
+        # we can reuse cu_fp8 for cu_bf16 since q, k, and v are contiguous
+        cu_bf16 = cu_fp8.clone()
+
+        def bf16_fn(q=q, k=k, v=v, cu=cu_bf16, ms=max_s, sl=seq_l):
+            attn_bf16._forward_flashinfer(q, k, v, cu, ms, sl)
+
+        def fp8_fn(q=q, k=k, v=v, cu=cu_fp8, ms=max_s, sl=seq_l):
+            attn_fp8._forward_flashinfer(q, k, v, cu, ms, sl)
+
+        # bench_fn returns List[float] of per-iteration times in ms
+        bf16_times = bench_fn(bf16_fn)
+        fp8_times = bench_fn(fp8_fn)
+
+        bf16_us = np.median(bf16_times) * 1e3  # ms -> us
+        fp8_us = np.median(fp8_times) * 1e3
+        speedup = bf16_us / fp8_us if fp8_us > 0 else float("inf")
+
+        print(f"{seq_len:>8} {bf16_us:>12.1f} {fp8_us:>12.1f} {speedup:>9.2f}x")
+
+
+def _make_trace_handler(output_dir: str, worker_name: str, label: str):
+    """Create a trace handler that saves to TensorBoard and prints summary."""
+
+    def handler(prof):
+        torch.profiler.tensorboard_trace_handler(output_dir, worker_name)(prof)
+        print(f"\n{'=' * 80}")
+        print(label)
+        print(f"{'=' * 80}")
+        print(prof.key_averages().table(sort_by="cuda_time_total", row_limit=20))
+
+    return handler
+
+
+def run_profile(
+    seq_len: int,
+    num_heads: int,
+    head_dim: int,
+    warmup: int,
+    output_dir: str,
+):
+    """Profile FP8 vs BF16 attention with PyTorch profiler."""
+    attn_fp8, attn_bf16, workspace = _setup_fp8_attention(num_heads, head_dim)
+
+    torch.manual_seed(42)
+    q = torch.randn(
+        seq_len,
+        num_heads,
+        head_dim,
+        device="cuda",
+        dtype=torch.bfloat16,
+    )
+    k = torch.randn_like(q)
+    v = torch.randn_like(q)
+
+    cu_fp8, max_s, seq_l = _build_meta(seq_len, num_heads, head_dim, fp8=True)
+    # we can reuse cu_fp8 for cu_bf16 since q, k, and v are contiguous
+    cu_bf16 = cu_fp8.clone()
+
+    sched = torch.profiler.schedule(wait=0, warmup=warmup, active=1)
+
+    # Profile BF16 (warmup handled by profiler schedule)
+    with profile(
+        activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA],
+        schedule=sched,
+        on_trace_ready=_make_trace_handler(
+            output_dir,
+            f"bf16_h{head_dim}_s{seq_len}",
+            f"BF16 Attention (seq_len={seq_len}, heads={num_heads}, "
+            f"head_dim={head_dim})",
+        ),
+    ) as prof_bf16:
+        for _ in range(warmup + 1):
+            with record_function("bf16_attention"):
+                attn_bf16._forward_flashinfer(
+                    q.clone(), k.clone(), v.clone(), cu_bf16, max_s, seq_l
+                )
+                torch.accelerator.synchronize()
+            prof_bf16.step()
+
+    # Profile FP8 (warmup handled by profiler schedule)
+    with profile(
+        activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA],
+        schedule=sched,
+        on_trace_ready=_make_trace_handler(
+            output_dir,
+            f"fp8_h{head_dim}_s{seq_len}",
+            f"FP8 Attention (seq_len={seq_len}, heads={num_heads}, "
+            f"head_dim={head_dim})",
+        ),
+    ) as prof_fp8:
+        for _ in range(warmup + 1):
+            with record_function("fp8_attention"):
+                attn_fp8._forward_flashinfer(
+                    q.clone(), k.clone(), v.clone(), cu_fp8, max_s, seq_l
+                )
+                torch.accelerator.synchronize()
+            prof_fp8.step()
+
+    print(f"\nTensorBoard traces saved to: {output_dir}")
+    print(f"View with: tensorboard --logdir={output_dir}")
+
+
+if __name__ == "__main__":
+    parser = FlexibleArgumentParser(description="Benchmark FP8 vs BF16 ViT attention.")
+    parser.add_argument(
+        "--seq-lens",
+        type=int,
+        nargs="+",
+        default=DEFAULT_SEQ_LENS,
+        help="Sequence lengths to benchmark",
+    )
+    parser.add_argument(
+        "--num-heads",
+        type=int,
+        default=NUM_HEADS,
+    )
+    parser.add_argument(
+        "--head-dim",
+        type=int,
+        default=HEAD_DIM,
+    )
+    parser.add_argument(
+        "--method",
+        choices=["cupti", "cudagraph"],
+        default="cudagraph",
+        help="GPU timing method: cupti (CUPTI kernel timing) or "
+        "cudagraph (CUDA graph capture/replay). Default: cudagraph",
+    )
+    parser.add_argument(
+        "--warmup",
+        type=int,
+        default=10,
+        help="Warmup iterations (profile mode only)",
+    )
+    parser.add_argument(
+        "--profile",
+        action="store_true",
+        help="Run PyTorch profiler instead of benchmark",
+    )
+    parser.add_argument(
+        "--profile-seq-len",
+        type=int,
+        default=8192,
+        help="Sequence length for profiling (default: 8192)",
+    )
+    parser.add_argument(
+        "--profile-output-dir",
+        type=str,
+        default="./profile_traces",
+        help="Output directory for TensorBoard traces (default: ./profile_traces)",
+    )
+    args = parser.parse_args()
+
+    if args.profile:
+        run_profile(
+            args.profile_seq_len,
+            args.num_heads,
+            args.head_dim,
+            args.warmup,
+            args.profile_output_dir,
+        )
+    else:
+        run_benchmark(
+            args.seq_lens,
+            args.num_heads,
+            args.head_dim,
+            args.method,
+        )

docker/Dockerfile

@@ -538,11 +538,9 @@ RUN CUDA_VERSION_DASH=$(echo $CUDA_VERSION | cut -d. -f1,2 | tr '.' '-') && \
         cuda-nvrtc-${CUDA_VERSION_DASH} \
         cuda-cuobjdump-${CUDA_VERSION_DASH} \
         libcurand-dev-${CUDA_VERSION_DASH} \
-        libcublas-${CUDA_VERSION_DASH} \
+        libcublas-dev-${CUDA_VERSION_DASH} \
         # Required by fastsafetensors (fixes #20384)
-        libnuma-dev \
-        # numactl CLI for NUMA binding at runtime
-        numactl && \
+        libnuma-dev && \
     # Fixes nccl_allocator requiring nccl.h at runtime
     # https://github.com/vllm-project/vllm/blob/1336a1ea244fa8bfd7e72751cabbdb5b68a0c11a/vllm/distributed/device_communicators/pynccl_allocator.py#L22
     # NCCL packages don't use the cuda-MAJOR-MINOR naming convention,

docs/features/multimodal_inputs.md

@@ -68,7 +68,7 @@ You can pass a single image to the `'image'` field of the multi-modal dictionary
         print(generated_text)
     ```
 
-Full example: [examples/offline_inference/vision_language.py](../../examples/offline_inference/vision_language.py)
+Full example: [examples/generate/multimodal/vision_language_offline.py](../../examples/generate/multimodal/vision_language_offline.py)
 
 To substitute multiple images inside the same text prompt, you can pass in a list of images instead:
 
@@ -101,7 +101,7 @@ To substitute multiple images inside the same text prompt, you can pass in a lis
         print(generated_text)
     ```
 
-Full example: [examples/offline_inference/vision_language_multi_image.py](../../examples/offline_inference/vision_language_multi_image.py)
+Full example: [examples/generate/multimodal/vision_language_multi_image_offline.py](../../examples/generate/multimodal/vision_language_multi_image_offline.py)
 
 If using the [LLM.chat](../models/generative_models.md#llmchat) method, you can pass images directly in the message content using various formats: image URLs, PIL Image objects, or pre-computed embeddings:
 
@@ -287,13 +287,13 @@ Instead of NumPy arrays, you can also pass `'torch.Tensor'` instances, as shown
     !!! note
         'process_vision_info' is only applicable to Qwen2.5-VL and similar models.
 
-Full example: [examples/offline_inference/vision_language.py](../../examples/offline_inference/vision_language.py)
+Full example: [examples/generate/multimodal/vision_language_offline.py](../../examples/generate/multimodal/vision_language_offline.py)
 
 ### Audio Inputs
 
 You can pass a tuple `(array, sampling_rate)` to the `'audio'` field of the multi-modal dictionary.
 
-Full example: [examples/offline_inference/audio_language.py](../../examples/offline_inference/audio_language.py)
+Full example: [examples/generate/multimodal/audio_language_offline.py](../../examples/generate/multimodal/audio_language_offline.py)
 
 #### Chunking Long Audio for Transcription
 
@@ -674,7 +674,7 @@ Then, you can use the OpenAI client as follows:
     print("Chat completion output:", chat_response.choices[0].message.content)
     ```
 
-Full example: [examples/online_serving/openai_chat_completion_client_for_multimodal.py](../../examples/online_serving/openai_chat_completion_client_for_multimodal.py)
+Full example: [examples/generate/multimodal/openai_chat_completion_client_for_multimodal.py](../../examples/generate/multimodal/openai_chat_completion_client_for_multimodal.py)
 
 !!! tip
     Loading from local file paths is also supported on vLLM: You can specify the allowed local media path via `--allowed-local-media-path` when launching the API server/engine,
@@ -745,7 +745,7 @@ Then, you can use the OpenAI client as follows:
     print("Chat completion output from image url:", result)
     ```
 
-Full example: [examples/online_serving/openai_chat_completion_client_for_multimodal.py](../../examples/online_serving/openai_chat_completion_client_for_multimodal.py)
+Full example: [examples/generate/multimodal/openai_chat_completion_client_for_multimodal.py](../../examples/generate/multimodal/openai_chat_completion_client_for_multimodal.py)
 
 !!! note
     By default, the timeout for fetching videos through HTTP URL is `30` seconds.
@@ -958,7 +958,7 @@ Alternatively, you can pass `audio_url`, which is the audio counterpart of `imag
     print("Chat completion output from audio url:", result)
     ```
 
-Full example: [examples/online_serving/openai_chat_completion_client_for_multimodal.py](../../examples/online_serving/openai_chat_completion_client_for_multimodal.py)
+Full example: [examples/generate/multimodal/openai_chat_completion_client_for_multimodal.py](../../examples/generate/multimodal/openai_chat_completion_client_for_multimodal.py)
 
 !!! note
     By default, the timeout for fetching audios through HTTP URL is `10` seconds.

docs/features/quantization/README.md

@@ -20,6 +20,7 @@ The following are the supported quantization formats for vLLM:
 - [AMD Quark](quark.md)
 - [Quantized KV Cache](quantized_kvcache.md)
 - [TorchAO](torchao.md)
+- [FP8 ViT Encoder Attention](fp8_vit_attn.md)
 
 ## Supported Hardware
 

docs/features/quantization/fp8_vit_attn.md

@@ -0,0 +1,109 @@
+# FP8 ViT Encoder Attention
+
+For visual understanding workloads with large images (e.g. QHD, 4K) and relatively
+short text prompts/generation, the ViT encoder attention can become a significant
+bottleneck, especially when the text model is quantized (e.g. NVFP4). vLLM
+supports optional FP8 quantization for the ViT encoder attention via the
+FlashInfer cuDNN backend. Q/K/V are quantized on-the-fly to FP8 before the
+cuDNN attention call.
+
+!!! note
+    - Currently supports Qwen3-VL family models only (`qwen3_vl`, `qwen3_vl_moe`,
+      `qwen3_5`, `qwen3_5_moe`, and other models using Qwen3 ViT).
+    - Dynamic scaling is not compatible with ViT full CUDA graphs.
+    - Performance gains are mostly visible at QHD/4K resolutions or multi-image
+      requests. Smaller images may see no speedup due to quantization overhead
+      (3 quantization kernel launches + un-padding).
+    - FP8 tensor-core speedup is more pronounced on GB300 than GB200.
+
+## Requirements
+
+- FlashInfer cuDNN backend with cuDNN >= 9.17.1.
+
+## Usage
+
+Enable FP8 ViT attention by passing `--mm-encoder-attn-dtype fp8` together
+with `--mm-encoder-attn-backend FLASHINFER`:
+
+```bash
+vllm serve $MODEL \
+    --mm-encoder-attn-backend FLASHINFER \
+    --mm-encoder-attn-dtype fp8
+```
+
+By default (no scale file), **dynamic scaling** is used: a 16-entry circular
+buffer of observed Q/K/V amax values drives per-forward scale updates. This
+matches BF16 accuracy without any calibration but adds a small per-forward
+overhead.
+
+## Calibrate-Once, Reuse Workflow (Recommended)
+
+For production, calibrate static scales on a representative dataset once and
+reuse them to avoid the dynamic overhead:
+
+```bash
+# Step 1: calibrate and save scales (runs dynamic scaling for 16 passes,
+# then dumps the learned scales to JSON).
+vllm bench mm-processor \
+    --model $MODEL --mm-encoder-attn-backend FLASHINFER \
+    --mm-encoder-attn-dtype fp8 \
+    --mm-encoder-fp8-scale-save-path /path/to/scales.json \
+    --dataset-name hf --dataset-path lmarena-ai/VisionArena-Chat \
+    --num-prompts 100
+
+# Step 2: serve with static scales (no dynamic overhead).
+vllm serve $MODEL \
+    --mm-encoder-attn-backend FLASHINFER \
+    --mm-encoder-attn-dtype fp8 \
+    --mm-encoder-fp8-scale-path /path/to/scales.json
+```
+
+Saved scales are multiplied by `--mm-encoder-fp8-scale-save-margin` (default
+`1.5`) to leave headroom against activation outliers not present in the
+calibration set. The default has been validated to generalize across datasets
+(e.g. VisionArena-Chat calibration maintains BF16 accuracy on ChartQA).
+
+## Scale File Format
+
+```json
+{
+    "visual.blocks.0.attn.attn": {"q": 224.0, "k": 198.0, "v": 210.0},
+    "visual.blocks.1.attn.attn": {"q": 218.0, "k": 195.0, "v": 207.0}
+}
+```
+
+Keys `q_scale` / `k_scale` / `v_scale` are accepted as aliases.
+
+## Performance
+
+**Core cuDNN attention kernel** (PyTorch profiler, `cudnn_generated_fort_native_sdpa_sm100_flash_fprop`, head_dim=128, seq_len=8192):
+
+| Hardware | BF16 | FP8 | Speedup |
+| -------- | ---- | ---- | ------- |
+| GB200 | 350 us | 312 us | **1.12x** |
+| GB300 | 300 us | 211 us | **1.42x** |
+
+**End-to-end encoder forward time** (Qwen3-VL-30B-A3B-Instruct on GB200, 3 images/request):
+
+| Resolution | BF16 median | FP8 median | Speedup |
+| ---------- | ----------- | ---------- | ------- |
+| HD (720x1280) | 31.77 ms | 36.39 ms | 0.87x |
+| FullHD (1080x1920) | 57.99 ms | 58.73 ms | ~same |
+| QHD (1440x2560) | 131.83 ms | 122.30 ms | **1.08x** |
+| 4K (2160x3840) | 543.44 ms | 460.31 ms | **1.18x** |
+
+Crossover is around FullHD with 3 images/request. At QHD and above, FP8 wins.
+
+## Accuracy
+
+ChartQA, Qwen3-VL-8B-Instruct, 500 samples. FP8 static uses scales calibrated
+on VisionArena-Chat (with default 1.5x margin):
+
+| Metric | BF16 | FP8 dynamic | FP8 static |
+| ------ | ---- | ----------- | ---------- |
+| relaxed_accuracy | 0.780 | 0.776 | 0.780 |
+| anywhere_accuracy | 0.806 | 0.816 | 0.814 |
+| exact_match | 0.584 | 0.582 | 0.578 |
+
+All three configurations match within statistical noise, confirming that
+static scales calibrated on one dataset generalize to another.

docs/features/reasoning_outputs.md

@@ -202,7 +202,7 @@ The reasoning content is also available when both tool calling and the reasoning
     print(f"Arguments: {tool_call.arguments}")
     ```
 
-For more examples, please refer to [examples/online_serving/openai_chat_completion_tool_calls_with_reasoning.py](../../examples/online_serving/openai_chat_completion_tool_calls_with_reasoning.py).
+For more examples, please refer to [examples/reasoning/openai_chat_completion_tool_calls_with_reasoning.py](../../examples/reasoning/openai_chat_completion_tool_calls_with_reasoning.py).
 
 ## Server-Level Default Chat Template Kwargs
 

docs/models/pooling_models/README.md

@@ -292,10 +292,10 @@ Pooling models now support token-wise task.
 
 ### Score task
 
-`score` task is deprecated and will be removed in v0.20. Please use `classify` instead. Only when a
-classification model outputs num_labels equal to 1 can it be used as a scoring model and have its scoring API enabled.
+`score` task have has been removed in v0.21, use `classify` instead. Only when a classification model outputs num_labels
+equal to 1 can it be used as a scoring model and have its scoring API enabled.
 
 ### Pooling multitask support
 
-Pooling multitask support is deprecated and will be removed in v0.20. When the default pooling task is not what you want,
+Pooling multitask support has been removed in v0.21. When the default pooling task is not what you want,
 you need to manually specify it via `PoolerConfig(task=<task>)` offline or `--pooler-config.task <task>` online.

docs/models/supported_models.md

@@ -439,6 +439,7 @@ th {
 | `Mamba2ForCausalLM` | Mamba2 | `mistralai/Mamba-Codestral-7B-v0.1`, etc. | | ✅︎ |
 | `MiMoForCausalLM` | MiMo | `XiaomiMiMo/MiMo-7B-RL`, etc. | ✅︎ | ✅︎ |
 | `MiMoV2FlashForCausalLM` | MiMoV2Flash | `XiaomiMiMo/MiMo-V2-Flash`, etc. | | ✅︎ |
+| `MiMoV2ProForCausalLM` | MiMoV2Pro | `XiaomiMiMo/MiMo-V2.5-Pro`, etc. | | ✅︎ |
 | `MiniCPMForCausalLM` | MiniCPM | `openbmb/MiniCPM-2B-sft-bf16`, `openbmb/MiniCPM-2B-dpo-bf16`, `openbmb/MiniCPM-S-1B-sft`, etc. | ✅︎ | ✅︎ |
 | `MiniCPM3ForCausalLM` | MiniCPM3 | `openbmb/MiniCPM3-4B`, etc. | ✅︎ | ✅︎ |
 | `MiniMaxForCausalLM` | MiniMax-Text | `MiniMaxAI/MiniMax-Text-01-hf`, etc. | | |
@@ -590,6 +591,7 @@ These models primarily accept the [`LLM.generate`](./generative_models.md#llmgen
 | `LlavaNextVideoForConditionalGeneration` | LLaVA-NeXT-Video | T + V | `llava-hf/LLaVA-NeXT-Video-7B-hf`, etc. | | ✅︎ |
 | `LlavaOnevisionForConditionalGeneration` | LLaVA-Onevision | T + I<sup>+</sup> + V<sup>+</sup> | `llava-hf/llava-onevision-qwen2-7b-ov-hf`, `llava-hf/llava-onevision-qwen2-0.5b-ov-hf`, etc. | | ✅︎ |
 | `MiDashengLMModel` | MiDashengLM | T + A<sup>+</sup> | `mispeech/midashenglm-7b` | | ✅︎ |
+| `MiMoV2OmniForCausalLM` | MiMo-V2.5-Omni | T + I<sup>E+</sup> + V<sup>E+</sup> + A<sup>+</sup> | `XiaomiMiMo/MiMo-V2.5-Omni` | | ✅︎ |
 | `MiniCPMO` | MiniCPM-O | T + I<sup>E+</sup> + V<sup>E+</sup> + A<sup>E+</sup> | `openbmb/MiniCPM-o-2_6`, etc. | ✅︎ | ✅︎ |
 | `MiniCPMV` | MiniCPM-V | T + I<sup>E+</sup> + V<sup>E+</sup> | `openbmb/MiniCPM-V-2` (see note), `openbmb/MiniCPM-Llama3-V-2_5`, `openbmb/MiniCPM-V-2_6`, `openbmb/MiniCPM-V-4`, `openbmb/MiniCPM-V-4_5`, etc. | ✅︎ | |
 | `MiniMaxVL01ForConditionalGeneration` | MiniMax-VL | T + I<sup>E+</sup> | `MiniMaxAI/MiniMax-VL-01`, etc. | | ✅︎ |

docs/serving/openai_compatible_server.md

@@ -251,7 +251,7 @@ The following extra parameters are supported:
 Our Responses API is compatible with [OpenAI's Responses API](https://platform.openai.com/docs/api-reference/responses);
 you can use the [official OpenAI Python client](https://github.com/openai/openai-python) to interact with it.
 
-Code example: [examples/online_serving/openai_responses_client_with_tools.py](../../examples/online_serving/openai_responses_client_with_tools.py)
+Code example: [examples/online_serving/openai_responses_client_with_tools.py](../../examples/tool_calling/openai_responses_client_with_tools.py)
 
 #### Extra parameters
 
@@ -279,7 +279,7 @@ you can use the [official OpenAI Python client](https://github.com/openai/openai
 !!! note
     To use the Transcriptions API, please install with extra audio dependencies using `pip install vllm[audio]`.
 
-Code example: [examples/online_serving/openai_transcription_client.py](../../examples/online_serving/openai_transcription_client.py)
+Code example: [examples/speech_to_text/openai/openai_transcription_client.py](../../examples/speech_to_text/openai/openai_transcription_client.py)
 
 NOTE: beam search is currently supported in the transcriptions endpoint for encoder-decoder multimodal models, e.g., whisper, but highly inefficient as work for handling the encoder/decoder cache is actively ongoing. This is an active point of ongoing optimization and will be handled properly in the very near future.
 
@@ -397,7 +397,7 @@ Please mind that the popular `openai/whisper-large-v3-turbo` model does not supp
 !!! note
     To use the Translation API, please install with extra audio dependencies using `pip install vllm[audio]`.
 
-Code example: [examples/online_serving/openai_translation_client.py](../../examples/online_serving/openai_translation_client.py)
+Code example: [examples/speech_to_text/openai/openai_translation_client.py](../../examples/speech_to_text/openai/openai_translation_client.py)
 
 #### Extra Parameters
 

examples/generate/multimodal/qwen2_5_omni/README.md

@@ -6,15 +6,15 @@ This folder provides several example scripts on how to inference Qwen2.5-Omni of
 
 ```bash
 # Audio + image + video
-python examples/offline_inference/qwen2_5_omni/only_thinker.py \
+python examples/generate/multimodal/qwen2_5_omni/only_thinker.py \
     -q mixed_modalities
 
 # Read vision and audio inputs from a single video file
-python examples/offline_inference/qwen2_5_omni/only_thinker.py \
+python examples/generate/multimodal/qwen2_5_omni/only_thinker.py \
     -q use_audio_in_video
 
 # Multiple audios
-python examples/offline_inference/qwen2_5_omni/only_thinker.py \
+python examples/generate/multimodal/qwen2_5_omni/only_thinker.py \
     -q multi_audios
 ```
 
@@ -24,16 +24,16 @@ You can also test Qwen2.5-Omni on a single modality:
 
 ```bash
 # Process audio inputs
-python examples/offline_inference/audio_language.py \
+python examples/generate/multimodal/audio_language_offline.py \
     --model-type qwen2_5_omni
 
 # Process image inputs
-python examples/offline_inference/vision_language.py \
+python examples/generate/multimodal/vision_language_offline.py \
     --modality image \
     --model-type qwen2_5_omni
 
 # Process video inputs
-python examples/offline_inference/vision_language.py \
+python examples/generate/multimodal/vision_language_offline.py \
     --modality video \
     --model-type qwen2_5_omni
 ```

examples/generate/multimodal/vision_language_offline.py

@@ -1402,7 +1402,7 @@ def run_mantis(questions: list[str], modality: str) -> ModelRequestData:
 # MiniCPM-V
 def run_minicpmv_base(questions: list[str], modality: str, model_name):
     assert modality in ["image", "video", "image+video"]
-    # If you want to use `MiniCPM-o-2_6` with audio inputs, check `audio_language.py` # noqa
+    # If you want to use `MiniCPM-o-2_6` with audio inputs, check `audio_language_offline.py` # noqa
 
     # 2.0
     # The official repo doesn't work yet, so we need to use a fork for now

examples/pooling/token_embed/jina_embeddings_v4_offline.py

@@ -4,68 +4,74 @@
 import torch
 
 from vllm import LLM
+from vllm.config import PoolerConfig
 from vllm.inputs import TextPrompt
 from vllm.multimodal.utils import fetch_image
 
-# Initialize model
-model = LLM(
-    model="jinaai/jina-embeddings-v4-vllm-text-matching",
-    runner="pooling",
-    max_model_len=1024,
-    gpu_memory_utilization=0.8,
-)
 
-# Create text prompts
-text1 = "Ein wunderschöner Sonnenuntergang am Strand"
-text1_prompt = TextPrompt(prompt=f"Query: {text1}")
+def main():
+    # Initialize model
+    model = LLM(
+        model="jinaai/jina-embeddings-v4-vllm-text-matching",
+        pooler_config=PoolerConfig(task="token_embed"),
+        runner="pooling",
+        max_model_len=1024,
+        gpu_memory_utilization=0.8,
+    )
 
-text2 = "浜辺に沈む美しい夕日"
-text2_prompt = TextPrompt(prompt=f"Query: {text2}")
+    # Create text prompts
+    text1 = "Ein wunderschöner Sonnenuntergang am Strand"
+    text1_prompt = TextPrompt(prompt=f"Query: {text1}")
 
-# Create image prompt
-image = fetch_image(
-    "https://vllm-public-assets.s3.us-west-2.amazonaws.com/multimodal_asset/eskimo.jpg"  # noqa: E501
-)
-image_prompt = TextPrompt(
-    prompt="<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>Describe the image.<|im_end|>\n",  # noqa: E501
-    multi_modal_data={"image": image},
-)
+    text2 = "浜辺に沈む美しい夕日"
+    text2_prompt = TextPrompt(prompt=f"Query: {text2}")
 
-# Encode all prompts
-prompts = [text1_prompt, text2_prompt, image_prompt]
-outputs = model.encode(prompts, pooling_task="token_embed")
+    # Create image prompt
+    image = fetch_image(
+        "https://vllm-public-assets.s3.us-west-2.amazonaws.com/multimodal_asset/eskimo.jpg"  # noqa: E501
+    )
+    image_prompt = TextPrompt(
+        prompt="<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>Describe the image.<|im_end|>\n",  # noqa: E501
+        multi_modal_data={"image": image},
+    )
+
+    # Encode all prompts
+    prompts = [text1_prompt, text2_prompt, image_prompt]
+    outputs = model.encode(prompts, pooling_task="token_embed")
+
+    def get_embeddings(outputs):
+        VISION_START_TOKEN_ID, VISION_END_TOKEN_ID = 151652, 151653
+
+        embeddings = []
+        for output in outputs:
+            if VISION_START_TOKEN_ID in output.prompt_token_ids:
+                # Gather only vision tokens
+                img_start_pos = torch.where(
+                    torch.tensor(output.prompt_token_ids) == VISION_START_TOKEN_ID
+                )[0][0]
+                img_end_pos = torch.where(
+                    torch.tensor(output.prompt_token_ids) == VISION_END_TOKEN_ID
+                )[0][0]
+                embeddings_tensor = output.outputs.data.detach().clone()[
+                    img_start_pos : img_end_pos + 1
+                ]
+            else:
+                # Use all tokens for text-only prompts
+                embeddings_tensor = output.outputs.data.detach().clone()
+
+            # Pool and normalize embeddings
+            pooled_output = (
+                embeddings_tensor.sum(dim=0, dtype=torch.float32)
+                / embeddings_tensor.shape[0]
+            )
+            embeddings.append(torch.nn.functional.normalize(pooled_output, dim=-1))
+        return embeddings
+
+    embeddings = get_embeddings(outputs)
+
+    for embedding in embeddings:
+        print(embedding.shape)
 
 
-def get_embeddings(outputs):
-    VISION_START_TOKEN_ID, VISION_END_TOKEN_ID = 151652, 151653
-
-    embeddings = []
-    for output in outputs:
-        if VISION_START_TOKEN_ID in output.prompt_token_ids:
-            # Gather only vision tokens
-            img_start_pos = torch.where(
-                torch.tensor(output.prompt_token_ids) == VISION_START_TOKEN_ID
-            )[0][0]
-            img_end_pos = torch.where(
-                torch.tensor(output.prompt_token_ids) == VISION_END_TOKEN_ID
-            )[0][0]
-            embeddings_tensor = output.outputs.data.detach().clone()[
-                img_start_pos : img_end_pos + 1
-            ]
-        else:
-            # Use all tokens for text-only prompts
-            embeddings_tensor = output.outputs.data.detach().clone()
-
-        # Pool and normalize embeddings
-        pooled_output = (
-            embeddings_tensor.sum(dim=0, dtype=torch.float32)
-            / embeddings_tensor.shape[0]
-        )
-        embeddings.append(torch.nn.functional.normalize(pooled_output, dim=-1))
-    return embeddings
-
-
-embeddings = get_embeddings(outputs)
-
-for embedding in embeddings:
-    print(embedding.shape)
+if __name__ == "__main__":
+    main()

examples/pooling/token_embed/multi_vector_retrieval_offline.py

@@ -4,6 +4,7 @@
 from argparse import Namespace
 
 from vllm import LLM, EngineArgs
+from vllm.config import PoolerConfig
 from vllm.utils.argparse_utils import FlexibleArgumentParser
 
 
@@ -13,6 +14,7 @@ def parse_args():
     # Set example specific arguments
     parser.set_defaults(
         model="BAAI/bge-m3",
+        pooler_config=PoolerConfig(task="token_embed"),
         runner="pooling",
         enforce_eager=True,
     )
@@ -32,15 +34,6 @@ def main(args: Namespace):
     # You should pass runner="pooling" for embedding models
     llm = LLM(**vars(args))
 
-    # Generate embedding. The output is a list of EmbeddingRequestOutputs.
-    outputs = llm.embed(prompts)
-
-    # Print the outputs.
-    print("\nGenerated Outputs:\n" + "-" * 60)
-    for prompt, output in zip(prompts, outputs):
-        embeds = output.outputs.embedding
-        print(len(embeds))
-
     # Generate embedding for each token. The output is a list of PoolingRequestOutput.
     outputs = llm.encode(prompts, pooling_task="token_embed")
 
@@ -50,6 +43,20 @@ def main(args: Namespace):
         multi_vector = output.outputs.data
         print(multi_vector.shape)
 
+    query = "What is the capital of France?"
+    documents = [
+        "The capital of Brazil is Brasilia.",
+        "The capital of France is Paris.",
+    ]
+    # Generate scores.
+    outputs = llm.score(query, documents)
+    # Print the outputs.
+    print("\nGenerated Outputs:\n" + "-" * 60)
+    for document, output in zip(documents, outputs):
+        score = output.outputs.score
+        print(f"Pair: {[query, document]!r} \nScore: {score}")
+        print("-" * 60)
+
 
 if __name__ == "__main__":
     args = parse_args()

examples/pooling/token_embed/multi_vector_retrieval_online.py

@@ -7,10 +7,11 @@ Example online usage of Pooling API for multi vector retrieval.
 Run `vllm serve <model> --runner pooling`
 to start up the server in vLLM. e.g.
 
-vllm serve BAAI/bge-m3
+vllm serve BAAI/bge-m3 --pooler-config.task token_embed
 """
 
 import argparse
+import pprint
 
 import requests
 import torch
@@ -32,7 +33,8 @@ def parse_args():
 
 
 def main(args):
-    api_url = f"http://{args.host}:{args.port}/pooling"
+    pooling_url = f"http://{args.host}:{args.port}/pooling"
+    score_url = f"http://{args.host}:{args.port}/score"
     model_name = args.model
 
     prompts = [
@@ -43,11 +45,23 @@ def main(args):
     ]
     prompt = {"model": model_name, "input": prompts}
 
-    pooling_response = post_http_request(prompt=prompt, api_url=api_url)
+    pooling_response = post_http_request(prompt=prompt, api_url=pooling_url)
     for output in pooling_response.json()["data"]:
         multi_vector = torch.tensor(output["data"])
         print(multi_vector.shape)
 
+    queries = "What is the capital of France?"
+    documents = [
+        "The capital of Brazil is Brasilia.",
+        "The capital of France is Paris.",
+    ]
+    prompt = {"model": model_name, "queries": queries, "documents": documents}
+    score_response = post_http_request(prompt=prompt, api_url=score_url)
+    print("\nPrompt when queries is string and documents is a list:")
+    pprint.pprint(prompt)
+    print("\nScore Response:")
+    pprint.pprint(score_response.json())
+
 
 if __name__ == "__main__":
     args = parse_args()

tests/config/test_multimodal_config.py

@@ -41,3 +41,21 @@ def test_language_model_only_affects_model_hash():
     base_hash = ModelConfig(model).compute_hash()
     lm_only_hash = ModelConfig(model, language_model_only=True).compute_hash()
     assert base_hash != lm_only_hash
+
+
+def test_mm_encoder_fp8_scale_path_requires_fp8():
+    with pytest.raises(ValueError, match="mm_encoder_attn_dtype"):
+        MultiModalConfig(mm_encoder_fp8_scale_path="/tmp/scales.json")
+
+
+def test_mm_encoder_attn_dtype_hash_updates(tmp_path):
+    scale_file = tmp_path / "scales.json"
+    scale_file.write_text("{}")
+    base_hash = MultiModalConfig().compute_hash()
+    fp8_hash = MultiModalConfig(mm_encoder_attn_dtype="fp8").compute_hash()
+    fp8_static_hash = MultiModalConfig(
+        mm_encoder_attn_dtype="fp8",
+        mm_encoder_fp8_scale_path=str(scale_file),
+    ).compute_hash()
+    assert base_hash != fp8_hash
+    assert fp8_hash != fp8_static_hash

tests/entrypoints/openai/test_fingerprint.py

@@ -0,0 +1,76 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Unit tests for ``system_fingerprint`` construction."""
+
+from types import SimpleNamespace
+
+import pytest
+
+from vllm.entrypoints.openai import fingerprint as fp
+
+
+def _cfg(tp=1, pp=1, dp=1, ep=False, digest="a3b21f94deadbeef"):
+    c = SimpleNamespace(
+        parallel_config=SimpleNamespace(
+            tensor_parallel_size=tp,
+            pipeline_parallel_size=pp,
+            data_parallel_size=dp,
+            enable_expert_parallel=ep,
+        )
+    )
+    c.compute_hash = lambda: digest  # type: ignore[attr-defined]
+    return c
+
+
+@pytest.fixture(autouse=True)
+def _reset():
+    fp.set_default_fingerprint_mode("full")
+    yield
+    fp.set_default_fingerprint_mode("full")
+
+
+def test_four_modes_produce_expected_shapes():
+    from vllm import __version__ as v
+
+    cfg = _cfg(tp=8, ep=True)
+
+    assert fp.build_system_fingerprint(cfg, "full") == (f"vllm-{v}-tp8-ep-a3b21f94")
+    assert fp.build_system_fingerprint(cfg, "hash") == f"vllm-{v}-a3b21f94"
+    assert fp.build_system_fingerprint(cfg, "custom", "my-fp") == "my-fp"
+    assert fp.build_system_fingerprint(cfg, "none") is None
+
+
+def test_full_mode_emits_only_non_trivial_parallelism():
+    from vllm import __version__ as v
+
+    # Single-GPU: nothing between version and hash.
+    assert fp.build_system_fingerprint(_cfg(), "full") == f"vllm-{v}-a3b21f94"
+    # All parallelism axes.
+    assert (
+        fp.build_system_fingerprint(_cfg(tp=8, pp=2, dp=4, ep=True), "full")
+        == f"vllm-{v}-tp8-pp2-dp4-ep-a3b21f94"
+    )
+
+
+def test_get_respects_set_default():
+    cfg = _cfg(tp=8)
+    full = fp.get_system_fingerprint(cfg)
+    assert full == fp.get_system_fingerprint(cfg)
+
+    fp.set_default_fingerprint_mode("hash")
+    hashed = fp.get_system_fingerprint(cfg)
+    assert hashed != full
+    assert "tp8" not in hashed
+
+    fp.set_default_fingerprint_mode("custom", "deploy-42")
+    assert fp.get_system_fingerprint(cfg) == "deploy-42"
+
+    fp.set_default_fingerprint_mode("none")
+    assert fp.get_system_fingerprint(cfg) is None
+
+
+def test_compute_hash_failure_does_not_raise():
+    cfg = _cfg()
+    cfg.compute_hash = lambda: (_ for _ in ()).throw(RuntimeError("boom"))
+    assert fp.build_system_fingerprint(cfg, "full").endswith("-nohash")
+    assert fp.build_system_fingerprint(cfg, "hash").endswith("-nohash")

tests/entrypoints/pooling/classify/test_offline.py

@@ -1,13 +1,12 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-import logging
 import weakref
 
 import pytest
 import torch
 
 from tests.models.utils import softmax
-from vllm import LLM, ClassificationRequestOutput, PoolingParams, PoolingRequestOutput
+from vllm import LLM, ClassificationRequestOutput, PoolingParams
 from vllm.distributed import cleanup_dist_env_and_memory
 from vllm.tasks import PoolingTask
 
@@ -66,18 +65,6 @@ def test_list_prompts(llm: LLM):
         assert len(outputs[i].outputs.probs) == num_labels
 
 
-@pytest.mark.skip_global_cleanup
-def test_token_classify(llm: LLM, caplog_vllm):
-    with caplog_vllm.at_level(level=logging.WARNING, logger="vllm"):
-        outputs = llm.encode(prompt, pooling_task="token_classify", use_tqdm=False)
-        assert "deprecated" in caplog_vllm.text
-
-    assert len(outputs) == 1
-    assert isinstance(outputs[0], PoolingRequestOutput)
-    assert outputs[0].prompt_token_ids == prompt_token_ids
-    assert outputs[0].outputs.data.shape == (len(prompt_token_ids), num_labels)
-
-
 @pytest.mark.skip_global_cleanup
 def test_pooling_params(llm: LLM):
     def get_outputs(use_activation):
@@ -110,10 +97,12 @@ def test_score_api(llm: LLM):
         llm.score("ping", "pong", use_tqdm=False)
 
 
-@pytest.mark.parametrize("task", ["embed", "token_embed", "plugin"])
+@pytest.mark.parametrize("task", ["embed", "token_embed", "token_classify", "plugin"])
 def test_unsupported_tasks(llm: LLM, task: PoolingTask):
     if task == "plugin":
         err_msg = "No IOProcessor plugin installed."
+    elif task == "token_classify":
+        err_msg = "Try switching the model's pooling_task via.+"
     else:
         err_msg = "Embedding API is not supported by this model.+"
     with pytest.raises(ValueError, match=err_msg):

tests/entrypoints/pooling/classify/test_online.py

@@ -436,26 +436,7 @@ async def test_pooling_classify(server: RemoteOpenAIServer, model_name: str):
 
 @pytest.mark.asyncio
 @pytest.mark.parametrize("model_name", [MODEL_NAME])
-async def test_pooling_token_classify(server: RemoteOpenAIServer, model_name: str):
-    task = "token_classify"
-    response = requests.post(
-        server.url_for("pooling"),
-        json={
-            "model": model_name,
-            "input": input_text,
-            "encoding_format": "float",
-            "task": task,
-        },
-    )
-    poolings = PoolingResponse.model_validate(response.json())
-    assert len(poolings.data) == 1
-    assert len(poolings.data[0].data) == 8
-    assert len(poolings.data[0].data[0]) == 2
-
-
-@pytest.mark.asyncio
-@pytest.mark.parametrize("model_name", [MODEL_NAME])
-@pytest.mark.parametrize("task", ["embed", "token_embed", "plugin"])
+@pytest.mark.parametrize("task", ["embed", "token_embed", "token_classify", "plugin"])
 async def test_pooling_not_supported(
     server: RemoteOpenAIServer, model_name: str, task: str
 ):
@@ -469,8 +450,11 @@ async def test_pooling_not_supported(
         },
     )
     assert response.json()["error"]["type"] == "BadRequestError"
+
     if task == "plugin":
         err_msg = "No IOProcessor plugin installed."
+    elif task == "token_classify":
+        err_msg = "Try switching the model's pooling_task via"
     else:
         err_msg = f"Unsupported task: {task!r}"
     assert response.json()["error"]["message"].startswith(err_msg)

tests/entrypoints/pooling/embed/test_offline.py

@@ -1,6 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-import logging
 import weakref
 
 import pytest
@@ -38,11 +37,11 @@ def llm():
         seed=0,
         attention_config=attention_config,
     )
+    assert embedding_size == llm.model_config.embedding_size
 
     yield weakref.proxy(llm)
 
     del llm
-
     cleanup_dist_env_and_memory()
 
 
@@ -74,16 +73,6 @@ def test_list_prompts(llm: LLM):
         assert len(outputs[i].outputs.embedding) == embedding_size
 
 
-@pytest.mark.skip_global_cleanup
-def test_token_embed(llm: LLM, caplog_vllm):
-    with caplog_vllm.at_level(level=logging.WARNING, logger="vllm"):
-        outputs = llm.encode(prompt, pooling_task="token_embed", use_tqdm=False)
-        assert "deprecated" in caplog_vllm.text
-
-    multi_vector = outputs[0].outputs.data
-    assert multi_vector.shape == (11, 384)
-
-
 @pytest.mark.skip_global_cleanup
 def test_pooling_params(llm: LLM):
     def get_outputs(normalize):
@@ -107,10 +96,14 @@ def test_pooling_params(llm: LLM):
     )
 
 
-@pytest.mark.parametrize("task", ["token_classify", "classify", "plugin"])
+@pytest.mark.parametrize(
+    "task", ["token_classify", "classify", "token_embed", "plugin"]
+)
 def test_unsupported_tasks(llm: LLM, task: PoolingTask):
     if task == "plugin":
         err_msg = "No IOProcessor plugin installed."
+    elif task == "token_embed":
+        err_msg = "Try switching the model's pooling_task via.+"
     else:
         err_msg = "Classification API is not supported by this model.+"
     with pytest.raises(ValueError, match=err_msg):

tests/entrypoints/pooling/embed/test_online.py

@@ -732,28 +732,9 @@ async def test_pooling_embed(server: RemoteOpenAIServer, model_name: str):
 
 @pytest.mark.asyncio
 @pytest.mark.parametrize("model_name", [MODEL_NAME])
-async def test_pooling_token_embed(server: RemoteOpenAIServer, model_name: str):
-    task = "token_embed"
-    response = requests.post(
-        server.url_for("pooling"),
-        json={
-            "model": model_name,
-            "input": input_text,
-            "encoding_format": "float",
-            "task": task,
-        },
-    )
-
-    poolings = PoolingResponse.model_validate(response.json())
-
-    assert len(poolings.data) == 1
-    assert len(poolings.data[0].data) == len(input_tokens)
-    assert len(poolings.data[0].data[0]) == 384
-
-
-@pytest.mark.asyncio
-@pytest.mark.parametrize("model_name", [MODEL_NAME])
-@pytest.mark.parametrize("task", ["classify", "token_classify", "plugin"])
+@pytest.mark.parametrize(
+    "task", ["classify", "token_classify", "token_embed", "plugin"]
+)
 async def test_pooling_not_supported(
     server: RemoteOpenAIServer, model_name: str, task: str
 ):
@@ -769,6 +750,8 @@ async def test_pooling_not_supported(
     assert response.json()["error"]["type"] == "BadRequestError"
     if task == "plugin":
         err_msg = "No IOProcessor plugin installed."
+    elif task == "token_embed":
+        err_msg = "Try switching the model's pooling_task via"
     else:
         err_msg = f"Unsupported task: {task!r}"
     assert response.json()["error"]["message"].startswith(err_msg)

tests/entrypoints/pooling/scoring/test_cross_encoder_online.py

@@ -452,25 +452,6 @@ async def test_pooling_classify(server: RemoteOpenAIServer):
     assert len(poolings.data[0].data) == 1
 
 
-@pytest.mark.asyncio
-async def test_pooling_token_classify(server: RemoteOpenAIServer):
-    response = requests.post(
-        server.url_for("pooling"),
-        json={
-            "model": MODEL_NAME,
-            "task": "token_classify",
-            "input": input_text,
-            "encoding_format": "float",
-        },
-    )
-
-    poolings = PoolingResponse.model_validate(response.json())
-
-    assert len(poolings.data) == 1
-    assert len(poolings.data[0].data) == len(input_tokens)
-    assert len(poolings.data[0].data[0]) == 1
-
-
 @pytest.mark.asyncio
 async def test_rerank_max_tokens_per_doc(
     server: RemoteOpenAIServer,
@@ -544,7 +525,7 @@ async def test_rerank_max_tokens_per_doc_validation(
 
 
 @pytest.mark.asyncio
-@pytest.mark.parametrize("task", ["embed", "token_embed", "plugin"])
+@pytest.mark.parametrize("task", ["embed", "token_embed", "token_classify", "plugin"])
 async def test_pooling_not_supported(server: RemoteOpenAIServer, task: str):
     response = requests.post(
         server.url_for("pooling"),
@@ -558,6 +539,8 @@ async def test_pooling_not_supported(server: RemoteOpenAIServer, task: str):
     assert response.json()["error"]["type"] == "BadRequestError"
     if task == "plugin":
         err_msg = "No IOProcessor plugin installed."
+    elif task == "token_classify":
+        err_msg = "Try switching the model's pooling_task via"
     else:
         err_msg = f"Unsupported task: {task!r}"
     assert response.json()["error"]["message"].startswith(err_msg)

tests/entrypoints/pooling/token_classify/test_offline.py

@@ -1,6 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-import logging
 import weakref
 
 import pytest
@@ -60,22 +59,19 @@ def test_token_ids_prompts(llm: LLM):
 
 @pytest.mark.skip_global_cleanup
 def test_score_api(llm: LLM):
-    err_msg = "Scoring API is only enabled for num_labels == 1."
+    err_msg = "This model does not support the Scoring API."
     with pytest.raises(ValueError, match=err_msg):
         llm.score("ping", "pong", use_tqdm=False)
 
 
 @pytest.mark.parametrize("task", ["classify", "embed", "token_embed", "plugin"])
 def test_unsupported_tasks(llm: LLM, task: PoolingTask, caplog_vllm):
-    if task == "classify":
-        with caplog_vllm.at_level(level=logging.WARNING, logger="vllm"):
-            llm.encode(prompt, pooling_task=task, use_tqdm=False)
-        assert "deprecated" in caplog_vllm.text
+    if task == "plugin":
+        err_msg = "No IOProcessor plugin installed."
+    elif task == "classify":
+        err_msg = "Try switching the model's pooling_task via.+"
     else:
-        if task == "plugin":
-            err_msg = "No IOProcessor plugin installed."
-        else:
-            err_msg = "Embedding API is not supported by this model.+"
+        err_msg = "Embedding API is not supported by this model.+"
 
-        with pytest.raises(ValueError, match=err_msg):
-            llm.encode(prompt, pooling_task=task, use_tqdm=False)
+    with pytest.raises(ValueError, match=err_msg):
+        llm.encode(prompt, pooling_task=task, use_tqdm=False)

tests/entrypoints/pooling/token_classify/test_online.py

@@ -50,7 +50,7 @@ async def test_pooling_token_classify(server: RemoteOpenAIServer, model_name: st
 
 @pytest.mark.asyncio
 @pytest.mark.parametrize("model_name", [MODEL_NAME])
-@pytest.mark.parametrize("task", ["embed", "token_embed", "plugin"])
+@pytest.mark.parametrize("task", ["classify", "embed", "token_embed", "plugin"])
 async def test_pooling_not_supported(
     server: RemoteOpenAIServer, model_name: str, task: str
 ):
@@ -63,9 +63,12 @@ async def test_pooling_not_supported(
             "task": task,
         },
     )
+    assert response.json()["error"]["type"] == "BadRequestError"
 
     if task == "plugin":
         err_msg = "No IOProcessor plugin installed."
+    elif task == "classify":
+        err_msg = "Try switching the model's pooling_task via"
     else:
         err_msg = f"Unsupported task: {task!r}"
     assert response.json()["error"]["message"].startswith(err_msg)

tests/entrypoints/pooling/token_embed/test_offline.py

@@ -1,6 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-import logging
 import weakref
 
 import pytest
@@ -64,15 +63,12 @@ def test_token_ids_prompts(llm: LLM):
 
 @pytest.mark.parametrize("task", ["embed", "classify", "token_classify", "plugin"])
 def test_unsupported_tasks(llm: LLM, task: PoolingTask, caplog_vllm):
-    if task == "embed":
-        with caplog_vllm.at_level(level=logging.WARNING, logger="vllm"):
-            llm.encode(prompt, pooling_task=task, use_tqdm=False)
-        assert "deprecated" in caplog_vllm.text
+    if task == "plugin":
+        err_msg = "No IOProcessor plugin installed."
+    elif task == "embed":
+        err_msg = "Try switching the model's pooling_task via.+"
     else:
-        if task == "plugin":
-            err_msg = "No IOProcessor plugin installed."
-        else:
-            err_msg = "Classification API is not supported by this model.+"
+        err_msg = "Classification API is not supported by this model.+"
 
-        with pytest.raises(ValueError, match=err_msg):
-            llm.encode(prompt, pooling_task=task, use_tqdm=False)
+    with pytest.raises(ValueError, match=err_msg):
+        llm.encode(prompt, pooling_task=task, use_tqdm=False)

tests/entrypoints/pooling/token_embed/test_online.py

@@ -73,7 +73,7 @@ async def test_pooling_token_embed(server: RemoteOpenAIServer, model_name: str):
 
 @pytest.mark.asyncio
 @pytest.mark.parametrize("model_name", [MODEL_NAME])
-@pytest.mark.parametrize("task", ["classify", "token_classify", "plugin"])
+@pytest.mark.parametrize("task", ["embed", "classify", "token_classify", "plugin"])
 async def test_pooling_not_supported(
     server: RemoteOpenAIServer, model_name: str, task: str
 ):
@@ -86,9 +86,12 @@ async def test_pooling_not_supported(
             "task": task,
         },
     )
+    assert response.json()["error"]["type"] == "BadRequestError"
 
     if task == "plugin":
         err_msg = "No IOProcessor plugin installed."
+    elif task == "embed":
+        err_msg = "Try switching the model's pooling_task via"
     else:
         err_msg = f"Unsupported task: {task!r}"
     assert response.json()["error"]["message"].startswith(err_msg)

tests/kernels/core/test_vit_fp8_attn.py

@@ -0,0 +1,279 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Tests for the full FP8 ViT attention path (quantize -> cuDNN -> un-pad)."""
+
+import contextlib
+
+import pytest
+import torch
+
+from vllm.triton_utils import HAS_TRITON
+from vllm.utils.flashinfer import (
+    is_flashinfer_cudnn_fp8_prefill_attn_supported,
+)
+from vllm.v1.attention.backends.registry import AttentionBackendEnum
+
+
+def _has_flashinfer_cudnn() -> bool:
+    """Check if FlashInfer cuDNN backend is available."""
+    try:
+        from flashinfer.prefill import (
+            cudnn_batch_prefill_with_kv_cache,  # noqa: F401
+        )
+
+        return True
+    except ImportError:
+        return False
+
+
+HEAD_DIMS = [72, 80]
+SEQ_LENS = [256]
+NUM_HEADS = [16]
+
+
+@pytest.fixture
+def _fp8_attention():
+    """Create FP8-enabled MMEncoderAttention via config."""
+    from types import SimpleNamespace
+    from unittest.mock import patch
+
+    from vllm.config import VllmConfig, set_current_vllm_config
+    from vllm.config.multimodal import MultiModalConfig
+
+    if not is_flashinfer_cudnn_fp8_prefill_attn_supported():
+        pytest.skip("FlashInfer cuDNN FP8 prefill attention not supported")
+
+    mm_config = MultiModalConfig(mm_encoder_attn_dtype="fp8")
+    vllm_config = VllmConfig()
+    vllm_config.model_config = SimpleNamespace(multimodal_config=mm_config)
+
+    # MMEncoderAttention reads torch.get_default_dtype() during init
+    # to determine the output dtype. In real model loading this is bf16.
+    old_dtype = torch.get_default_dtype()
+    torch.set_default_dtype(torch.bfloat16)
+
+    with (
+        set_current_vllm_config(vllm_config),
+        patch(
+            "vllm.model_executor.layers.attention.mm_encoder_attention"
+            ".get_vit_attn_backend",
+            return_value=AttentionBackendEnum.FLASHINFER,
+        ),
+    ):
+        yield
+
+    torch.set_default_dtype(old_dtype)
+
+
+def _build_cu_seqlens_and_meta(
+    seq_len: int,
+    num_heads: int,
+    head_dim: int,
+    fp8_padded_hidden_size: int | None = None,
+) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+    """Build cu_seqlens, max_seqlen, sequence_lengths for a single sequence."""
+    import numpy as np
+
+    from vllm.model_executor.layers.attention.mm_encoder_attention import (
+        MMEncoderAttention,
+    )
+
+    cu_seqlens_np = np.array([0, seq_len], dtype=np.int32)
+
+    sequence_lengths = MMEncoderAttention.maybe_compute_seq_lens(
+        AttentionBackendEnum.FLASHINFER,
+        cu_seqlens_np,
+        torch.device("cuda"),
+    )
+
+    max_seqlen = torch.tensor(
+        MMEncoderAttention.compute_max_seqlen(
+            AttentionBackendEnum.FLASHINFER, cu_seqlens_np
+        ),
+        dtype=torch.int32,
+    )
+
+    cu_seqlens = MMEncoderAttention.maybe_recompute_cu_seqlens(
+        AttentionBackendEnum.FLASHINFER,
+        cu_seqlens_np,
+        num_heads * head_dim,
+        1,  # tp_size
+        torch.device("cuda"),
+        fp8_padded_hidden_size=fp8_padded_hidden_size,
+    )
+
+    return cu_seqlens, max_seqlen, sequence_lengths
+
+
+@pytest.mark.skipif(
+    not (HAS_TRITON and _has_flashinfer_cudnn()),
+    reason="Triton and FlashInfer cuDNN required",
+)
+@pytest.mark.parametrize("head_dim", HEAD_DIMS)
+@pytest.mark.parametrize("seq_len", SEQ_LENS)
+@pytest.mark.parametrize("num_heads", NUM_HEADS)
+def test_fp8_attn_output_shape(
+    head_dim: int,
+    seq_len: int,
+    num_heads: int,
+    _fp8_attention,
+) -> None:
+    """Verify FP8 attention produces correct output shape after un-padding."""
+    from vllm.model_executor.layers.attention.mm_encoder_attention import (
+        MMEncoderAttention,
+    )
+    from vllm.utils.math_utils import round_up
+
+    attn = None
+    with contextlib.suppress(ValueError, ImportError):
+        attn = MMEncoderAttention(
+            num_heads=num_heads,
+            head_size=head_dim,
+            prefix="visual.blocks.0.attn",
+        ).to("cuda")
+
+    if attn is None or not attn.fp8_enabled:
+        pytest.skip("FP8 MMEncoderAttention not available")
+    assert attn is not None  # mypy narrowing
+
+    # FP8 always needs fp8_padded_hidden_size for correct cu_seqlens
+    fp8_padded_hidden_size = num_heads * round_up(head_dim, 16)
+
+    cu_seqlens, max_seqlen, sequence_lengths = _build_cu_seqlens_and_meta(
+        seq_len, num_heads, head_dim, fp8_padded_hidden_size=fp8_padded_hidden_size
+    )
+
+    q = torch.randn(
+        seq_len,
+        num_heads,
+        head_dim,
+        device="cuda",
+        dtype=torch.bfloat16,
+    )
+    k = torch.randn_like(q)
+    v = torch.randn_like(q)
+
+    output = attn._forward_flashinfer(q, k, v, cu_seqlens, max_seqlen, sequence_lengths)
+
+    # Output should have original head_dim (un-padded)
+    assert output.shape[-1] == head_dim
+    assert output.dtype == torch.bfloat16
+
+
+@pytest.mark.skipif(
+    not (HAS_TRITON and _has_flashinfer_cudnn()),
+    reason="Triton and FlashInfer cuDNN required",
+)
+@pytest.mark.parametrize("head_dim", HEAD_DIMS)
+@pytest.mark.parametrize("seq_len", SEQ_LENS)
+@pytest.mark.parametrize("num_heads", NUM_HEADS)
+def test_fp8_vs_bf16_close(
+    head_dim: int, seq_len: int, num_heads: int, _fp8_attention
+) -> None:
+    """FP8 attention output should be reasonably close to BF16 baseline."""
+    from vllm.model_executor.layers.attention.mm_encoder_attention import (
+        MMEncoderAttention,
+    )
+    from vllm.utils.math_utils import round_up
+
+    torch.manual_seed(42)
+    q = torch.randn(
+        1,
+        seq_len,
+        num_heads,
+        head_dim,
+        device="cuda",
+        dtype=torch.bfloat16,
+    )
+    k = torch.randn_like(q)
+    v = torch.randn_like(q)
+
+    # FP8 path
+    attn_fp8 = None
+    with contextlib.suppress(ValueError, ImportError):
+        attn_fp8 = MMEncoderAttention(
+            num_heads=num_heads,
+            head_size=head_dim,
+            prefix="visual.blocks.0.attn",
+        ).to("cuda")
+
+    if attn_fp8 is None or not attn_fp8.fp8_enabled:
+        pytest.skip("FP8 MMEncoderAttention not available")
+    assert attn_fp8 is not None  # mypy narrowing
+
+    fp8_padded_hidden_size = num_heads * round_up(head_dim, 16)
+    cu_seqlens, max_seqlen, seq_lengths = _build_cu_seqlens_and_meta(
+        seq_len,
+        num_heads,
+        head_dim,
+        fp8_padded_hidden_size=fp8_padded_hidden_size,
+    )
+
+    out_fp8 = attn_fp8._forward_flashinfer(
+        q.clone(),
+        k.clone(),
+        v.clone(),
+        cu_seqlens,
+        max_seqlen,
+        seq_lengths,
+    )
+
+    # BF16 baseline (create non-FP8 attention by using scale=attn_fp8.scale
+    # and calling the wrapper directly without FP8 quantization)
+    from vllm.model_executor.layers.attention.mm_encoder_attention import (
+        _get_flashinfer_workspace_buffer,
+    )
+    from vllm.v1.attention.ops.vit_attn_wrappers import (
+        vit_flashinfer_wrapper,
+    )
+
+    out_bf16 = vit_flashinfer_wrapper(
+        q=q.clone(),
+        k=k.clone(),
+        v=v.clone(),
+        scale=attn_fp8.scale,
+        workspace_buffer=_get_flashinfer_workspace_buffer(),
+        cu_seqlens=cu_seqlens,
+        max_seqlen=max_seqlen,
+        sequence_lengths=seq_lengths,
+    )
+
+    out_fp8_f = out_fp8.float()
+    out_bf16_f = out_bf16.float()
+
+    abs_diff = (out_fp8_f - out_bf16_f).abs()
+    abs_diff_flat = abs_diff.flatten()
+
+    # Relative diff (avoid division by zero)
+    denom = out_bf16_f.abs().clamp(min=1e-6)
+    rel_diff_flat = (abs_diff / denom).flatten()
+
+    cosine_sim = torch.nn.functional.cosine_similarity(
+        out_fp8_f.flatten().unsqueeze(0),
+        out_bf16_f.flatten().unsqueeze(0),
+    ).item()
+
+    pcts = [50, 90, 95, 99, 99.9]
+    abs_pct = {p: torch.quantile(abs_diff_flat, p / 100).item() for p in pcts}
+    rel_pct = {p: torch.quantile(rel_diff_flat, p / 100).item() for p in pcts}
+
+    print(f"\nFP8 vs BF16 (head_dim={head_dim}, seq_len={seq_len}):")
+    print(f"  cosine_sim={cosine_sim:.6f}")
+    print(
+        f"  abs_diff: max={abs_diff_flat.max().item():.6f}, "
+        f"mean={abs_diff_flat.mean().item():.6f}, "
+        + ", ".join(f"p{p}={abs_pct[p]:.6f}" for p in pcts)
+    )
+    print(
+        f"  rel_diff: max={rel_diff_flat.max().item():.6f}, "
+        f"mean={rel_diff_flat.mean().item():.6f}, "
+        + ", ".join(f"p{p}={rel_pct[p]:.6f}" for p in pcts)
+    )
+
+    assert abs_diff_flat.max().item() < 0.3, (
+        f"FP8 vs BF16 max abs diff too large: {abs_diff_flat.max().item()}"
+    )
+    assert abs_diff_flat.mean().item() < 0.03, (
+        f"FP8 vs BF16 mean abs diff too large: {abs_diff_flat.mean().item()}"
+    )
+    assert cosine_sim > 0.99, f"Cosine similarity too low: {cosine_sim:.6f}"

tests/kernels/core/test_vit_fp8_quant.py

@@ -0,0 +1,124 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Tests for the stride-aware FP8 quantization kernel with head_dim padding."""
+
+import pytest
+import torch
+
+from vllm.platforms import current_platform
+from vllm.triton_utils import HAS_TRITON
+
+if HAS_TRITON:
+    from vllm.kernels.triton.qkv_padded_fp8_quant import (
+        quantize_fp8_pad_head_dim_triton,
+    )
+
+HEAD_DIMS = [72, 80, 128]
+SEQ_LENS = [64, 256]
+NUM_HEADS = [16]
+SCALES = [0.01, 0.1, 1.0]
+
+
+def _naive_fp8_quantize(
+    tensor: torch.Tensor, scale: torch.Tensor, skip_scale: bool
+) -> torch.Tensor:
+    """Reference FP8 quantization in PyTorch."""
+    fp8_dtype = current_platform.fp8_dtype()
+    fp8_max = torch.finfo(fp8_dtype).max
+    fp8_min = -fp8_max
+
+    x = tensor.float()
+    if not skip_scale:
+        x = x / scale.item()
+    x = x.clamp(fp8_min, fp8_max)
+    return x.to(fp8_dtype)
+
+
+@pytest.mark.skipif(not HAS_TRITON, reason="Triton not available")
+@pytest.mark.parametrize("head_dim", HEAD_DIMS)
+@pytest.mark.parametrize("seq_len", SEQ_LENS)
+@pytest.mark.parametrize("num_heads", NUM_HEADS)
+@pytest.mark.parametrize("scale_val", SCALES)
+def test_quantize_contiguous(
+    head_dim: int, seq_len: int, num_heads: int, scale_val: float
+) -> None:
+    """Test quantization of contiguous 3D tensors."""
+    torch.manual_seed(42)
+    tensor = torch.randn(
+        seq_len, num_heads, head_dim, device="cuda", dtype=torch.bfloat16
+    )
+    scale = torch.tensor([scale_val], dtype=torch.float32, device="cuda").view(
+        1, 1, 1, 1
+    )
+
+    result = quantize_fp8_pad_head_dim_triton(tensor, scale)
+
+    padded_dim = (head_dim + 15) // 16 * 16
+    assert result.shape == (seq_len, num_heads, padded_dim)
+    assert result.is_contiguous()
+    assert result.dtype == current_platform.fp8_dtype()
+
+    # Compare unpadded portion against reference
+    ref = _naive_fp8_quantize(tensor, scale, skip_scale=False)
+    torch.testing.assert_close(result[:, :, :head_dim].float(), ref.float())
+
+    # Padded region should be zero
+    if padded_dim > head_dim:
+        assert (result[:, :, head_dim:].float() == 0).all()
+
+
+@pytest.mark.skipif(not HAS_TRITON, reason="Triton not available")
+@pytest.mark.parametrize("head_dim", [72, 80])
+def test_quantize_non_contiguous(head_dim: int) -> None:
+    """Test quantization from non-contiguous QKV views (interleaved buffer)."""
+    seq_len, num_heads = 64, 16
+    # Simulate interleaved QKV buffer: shape (seq_len, 3 * num_heads, head_dim)
+    qkv = torch.randn(
+        seq_len, 3 * num_heads, head_dim, device="cuda", dtype=torch.bfloat16
+    )
+    # Q is every 3rd head slice - non-contiguous view
+    q = qkv[:, 0::3, :]
+    assert not q.is_contiguous()
+
+    scale = torch.tensor([0.1], dtype=torch.float32, device="cuda").view(1, 1, 1, 1)
+    result = quantize_fp8_pad_head_dim_triton(q, scale)
+
+    padded_dim = (head_dim + 15) // 16 * 16
+    assert result.shape == (seq_len, num_heads, padded_dim)
+    assert result.is_contiguous()
+
+    # Compare against contiguous reference
+    ref = _naive_fp8_quantize(q.contiguous(), scale, skip_scale=False)
+    torch.testing.assert_close(result[:, :, :head_dim].float(), ref.float())
+
+
+@pytest.mark.skipif(not HAS_TRITON, reason="Triton not available")
+def test_skip_scale() -> None:
+    """Test skip_scale=True produces cast-only output (no division)."""
+    seq_len, num_heads, head_dim = 32, 8, 80
+    tensor = torch.randn(
+        seq_len, num_heads, head_dim, device="cuda", dtype=torch.bfloat16
+    )
+    scale = torch.tensor([0.5], dtype=torch.float32, device="cuda").view(1, 1, 1, 1)
+
+    result_skip = quantize_fp8_pad_head_dim_triton(tensor, scale, skip_scale=True)
+    result_noskip = quantize_fp8_pad_head_dim_triton(tensor, scale, skip_scale=False)
+
+    # skip_scale should just cast, not divide
+    ref_cast = _naive_fp8_quantize(tensor, scale, skip_scale=True)
+    torch.testing.assert_close(result_skip[:, :, :head_dim].float(), ref_cast.float())
+
+    # With scale != 1.0, skip and no-skip should differ
+    assert not torch.equal(result_skip.float(), result_noskip.float())
+
+
+@pytest.mark.skipif(not HAS_TRITON, reason="Triton not available")
+def test_4d_input() -> None:
+    """Test that 4D input (B, S, H, D) is handled correctly."""
+    B, S, H, D = 2, 32, 8, 72
+    tensor = torch.randn(B, S, H, D, device="cuda", dtype=torch.bfloat16)
+    scale = torch.tensor([0.1], dtype=torch.float32, device="cuda").view(1, 1, 1, 1)
+
+    result = quantize_fp8_pad_head_dim_triton(tensor, scale)
+    padded_dim = (D + 15) // 16 * 16
+    assert result.shape == (B, S, H, padded_dim)

tests/kernels/core/test_vit_fp8_scaling.py

@@ -0,0 +1,251 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Tests for FP8 scaling (dynamic and static) in MMEncoderAttention."""
+
+import contextlib
+import json
+from types import SimpleNamespace
+from unittest.mock import patch
+
+import pytest
+import torch
+
+from vllm.model_executor.layers.attention.mm_encoder_attention import (
+    _FP8_AMAX_HISTORY_LEN,
+    _FP8_MAX,
+)
+from vllm.utils.flashinfer import (
+    is_flashinfer_cudnn_fp8_prefill_attn_supported,
+)
+
+LAYER_0 = "visual.blocks.0.attn.attn"
+LAYER_1 = "visual.blocks.1.attn.attn"
+NUM_HEADS = 16
+HEAD_DIM = 72
+
+
+@contextlib.contextmanager
+def _build_attention(mm_config):
+    """Yield an MMEncoderAttention with the given multimodal config.
+
+    The VllmConfig context stays active while the test runs so that
+    ``get_multimodal_config()`` calls during the forward path resolve. Also
+    invokes ``process_weights_after_loading`` to simulate the model loader's
+    auto-scan. Yields ``None`` if FlashInfer cuDNN is not available.
+    """
+    from vllm.config import VllmConfig, set_current_vllm_config
+    from vllm.model_executor.layers.attention.mm_encoder_attention import (
+        MMEncoderAttention,
+    )
+    from vllm.v1.attention.backends.registry import AttentionBackendEnum
+
+    if not is_flashinfer_cudnn_fp8_prefill_attn_supported():
+        yield None
+        return
+
+    vllm_config = VllmConfig()
+    vllm_config.model_config = SimpleNamespace(multimodal_config=mm_config)
+
+    with (
+        set_current_vllm_config(vllm_config),
+        patch(
+            "vllm.model_executor.layers.attention.mm_encoder_attention"
+            ".get_vit_attn_backend",
+            return_value=AttentionBackendEnum.FLASHINFER,
+        ),
+    ):
+        attn = MMEncoderAttention(
+            num_heads=NUM_HEADS,
+            head_size=HEAD_DIM,
+            prefix=LAYER_0,
+        )
+        attn.process_weights_after_loading(torch.bfloat16)
+        yield attn
+
+
+@pytest.fixture
+def _make_attention():
+    """Create an MMEncoderAttention with dynamic FP8 scaling."""
+    from vllm.config.multimodal import MultiModalConfig
+
+    with _build_attention(MultiModalConfig(mm_encoder_attn_dtype="fp8")) as attn:
+        yield attn
+
+
+@pytest.fixture
+def _make_static_attention(tmp_path):
+    """Create an MMEncoderAttention with static FP8 scales from a file."""
+    from vllm.config.multimodal import MultiModalConfig
+
+    scale_file = tmp_path / "scales.json"
+    scale_file.write_text(
+        json.dumps(
+            {
+                LAYER_0: {"q": 224.0, "k": 198.0, "v": 210.0},
+                LAYER_1: {"q": 100.0, "k": 110.0, "v": 120.0},
+            }
+        )
+    )
+    with _build_attention(
+        MultiModalConfig(
+            mm_encoder_attn_dtype="fp8",
+            mm_encoder_fp8_scale_path=str(scale_file),
+        )
+    ) as attn:
+        yield attn
+
+
+def test_dynamic_scaling_updates_scales(_make_attention) -> None:
+    """Verify that _record_amax_and_update_scales updates scale buffers."""
+    attn = _make_attention
+    if attn is None or not attn.fp8_enabled:
+        pytest.skip("FP8 attention not available (FlashInfer backend required)")
+
+    attn = attn.to("cuda")
+
+    S, H, D = 32, NUM_HEADS, HEAD_DIM
+    q = torch.full((S, H, D), 2.0, device="cuda", dtype=torch.bfloat16)
+    k = torch.full((S, H, D), 3.0, device="cuda", dtype=torch.bfloat16)
+    v = torch.full((S, H, D), 4.0, device="cuda", dtype=torch.bfloat16)
+
+    attn._record_amax_and_update_scales(q, k, v)
+
+    expected_q_scale = 2.0 / _FP8_MAX
+    expected_k_scale = 3.0 / _FP8_MAX
+    expected_v_scale = 4.0 / _FP8_MAX
+
+    torch.testing.assert_close(attn._fp8_q_scale.item(), expected_q_scale)
+    torch.testing.assert_close(attn._fp8_k_scale.item(), expected_k_scale)
+    torch.testing.assert_close(attn._fp8_v_scale.item(), expected_v_scale)
+
+
+def test_circular_buffer_wraps(_make_attention) -> None:
+    """Verify the amax circular buffer wraps at HISTORY_LEN."""
+    attn = _make_attention
+    if attn is None or not attn.fp8_enabled:
+        pytest.skip("FP8 attention not available (FlashInfer backend required)")
+
+    attn = attn.to("cuda")
+    S, H, D = 16, NUM_HEADS, HEAD_DIM
+
+    for i in range(_FP8_AMAX_HISTORY_LEN + 2):
+        mag = float(i + 1)
+        q = torch.full((S, H, D), mag, device="cuda", dtype=torch.bfloat16)
+        k = torch.full((S, H, D), mag, device="cuda", dtype=torch.bfloat16)
+        v = torch.full((S, H, D), mag, device="cuda", dtype=torch.bfloat16)
+        attn._record_amax_and_update_scales(q, k, v)
+
+    assert attn._fp8_amax_pos == 2
+
+    expected_max = float(_FP8_AMAX_HISTORY_LEN + 2)
+    expected_scale = expected_max / _FP8_MAX
+    torch.testing.assert_close(attn._fp8_q_scale.item(), expected_scale)
+
+
+def test_static_scales_loaded(_make_static_attention) -> None:
+    """Verify static scales are loaded from the JSON file."""
+    attn = _make_static_attention
+    if attn is None or not attn.fp8_enabled:
+        pytest.skip("FP8 attention not available (FlashInfer backend required)")
+
+    assert attn.fp8_enabled
+    assert not attn._fp8_dynamic_scale
+
+    # Layer 0 scales (the layer this attention was created with).
+    assert attn._fp8_q_scale.item() == 224.0
+    assert attn._fp8_k_scale.item() == 198.0
+    assert attn._fp8_v_scale.item() == 210.0
+
+    assert not attn.skip_scale_q
+    assert not attn.skip_scale_k
+    assert not attn.skip_scale_v
+
+    # No amax history buffers for static scaling.
+    assert not hasattr(attn, "_fp8_q_amax")
+
+
+def test_static_scales_missing_layer(tmp_path) -> None:
+    """Verify error when requested layer is not in the scale file."""
+    from vllm.config import VllmConfig, set_current_vllm_config
+    from vllm.config.multimodal import MultiModalConfig
+    from vllm.v1.attention.backends.registry import AttentionBackendEnum
+
+    if not is_flashinfer_cudnn_fp8_prefill_attn_supported():
+        pytest.skip("FlashInfer cuDNN not available")
+
+    scale_file = tmp_path / "wrong_layer.json"
+    scale_file.write_text(
+        json.dumps({"visual.blocks.99.attn": {"q": 1.0, "k": 1.0, "v": 1.0}})
+    )
+    mm_config = MultiModalConfig(
+        mm_encoder_attn_dtype="fp8",
+        mm_encoder_fp8_scale_path=str(scale_file),
+    )
+    vllm_config = VllmConfig()
+    vllm_config.model_config = SimpleNamespace(multimodal_config=mm_config)
+
+    from vllm.model_executor.layers.attention.mm_encoder_attention import (
+        MMEncoderAttention,
+    )
+
+    with (
+        set_current_vllm_config(vllm_config),
+        patch(
+            "vllm.model_executor.layers.attention.mm_encoder_attention"
+            ".get_vit_attn_backend",
+            return_value=AttentionBackendEnum.FLASHINFER,
+        ),
+    ):
+        attn = MMEncoderAttention(
+            num_heads=NUM_HEADS,
+            head_size=HEAD_DIM,
+            prefix=LAYER_0,
+        )
+        with pytest.raises(ValueError, match="scales not found for layer"):
+            attn.process_weights_after_loading(torch.bfloat16)
+
+
+def test_dynamic_scales_auto_save(tmp_path) -> None:
+    """Verify scales are saved to disk after the amax buffer fills."""
+    import vllm.model_executor.layers.attention.mm_encoder_attention as _mod
+    from vllm.config.multimodal import MultiModalConfig
+
+    if not is_flashinfer_cudnn_fp8_prefill_attn_supported():
+        pytest.skip("FlashInfer cuDNN not available")
+
+    # Reset module-level state between runs (other tests may have left
+    # state behind after triggering a save).
+    _mod._fp8_scale_save_path = None
+    _mod._fp8_saved_scale_refs.clear()
+
+    save_file = tmp_path / "auto_scales.json"
+    with _build_attention(
+        MultiModalConfig(
+            mm_encoder_attn_dtype="fp8",
+            mm_encoder_fp8_scale_save_path=str(save_file),
+        )
+    ) as attn:
+        if attn is None or not attn.fp8_enabled:
+            pytest.skip("FP8 attention not available")
+
+        attn = attn.to("cuda")
+        S, H, D = 16, NUM_HEADS, HEAD_DIM
+
+        # Run exactly _FP8_AMAX_HISTORY_LEN forward passes.
+        for i in range(_FP8_AMAX_HISTORY_LEN):
+            mag = float(i + 1)
+            q = torch.full((S, H, D), mag, device="cuda", dtype=torch.bfloat16)
+            k = torch.full((S, H, D), mag * 0.5, device="cuda", dtype=torch.bfloat16)
+            v = torch.full((S, H, D), mag * 0.3, device="cuda", dtype=torch.bfloat16)
+            attn._record_amax_and_update_scales(q, k, v)
+
+    # File should have been written on the 16th call (buffer wrap).
+    assert save_file.is_file(), "Scale file was not saved"
+    scales = json.loads(save_file.read_text())
+    assert LAYER_0 in scales
+    assert set(scales[LAYER_0].keys()) == {"q", "k", "v"}
+    for val in scales[LAYER_0].values():
+        assert isinstance(val, float) and val > 0
+
+    # Path is cleared after the one-shot save fires.
+    assert _mod._fp8_scale_save_path is None

tests/models/language/pooling/test_all_pooling_plus_chunked_prefill.py

@@ -6,6 +6,7 @@ from transformers import AutoModel
 
 from tests.models.utils import check_embeddings_close
 from vllm import TokensPrompt
+from vllm.config import PoolerConfig
 
 
 @pytest.mark.parametrize(
@@ -21,6 +22,7 @@ def test_embed_models(hf_runner, vllm_runner, model: str):
     with vllm_runner(
         model,
         runner="pooling",
+        pooler_config=PoolerConfig(task="token_embed"),
         max_model_len=128,
         max_num_batched_tokens=chunk_size,
         enforce_eager=True,

tests/models/language/pooling/test_bge_m3.py

@@ -3,7 +3,6 @@
 import httpx
 import openai
 import pytest
-import pytest_asyncio
 import torch
 
 from ....utils import RemoteOpenAIServer
@@ -25,29 +24,42 @@ sentences_2 = [
 similarity_reference = [[0.6259, 0.3474], [0.3309, 0.6734]]
 lexical_score_reference = [0.19554901123046875, 0.0]
 colbert_score_reference = [0.7797, 0.4620]
+SUPPORTED_TASKS = ["embed", "token_embed", "token_classify"]
+
+
+@pytest.fixture(scope="module", params=SUPPORTED_TASKS)
+def pooling_task(request):
+    yield request.param
 
 
 @pytest.fixture(scope="module")
-def server():
+def server(pooling_task):
     args = [
         "--max-model-len",
         str(MAX_MODEL_LEN),
         "--hf-overrides",
         '{"architectures": ["BgeM3EmbeddingModel"]}',
+        "--pooler-config.task",
+        pooling_task,
     ]
 
     with RemoteOpenAIServer(MODEL_NAME, args) as remote_server:
         yield remote_server
 
 
-@pytest_asyncio.fixture
-async def client(server):
-    async with server.get_async_client() as async_client:
-        yield async_client
-
-
 @pytest.mark.asyncio
-async def test_bge_m3_api_server_embedding(client: openai.AsyncOpenAI):
+async def test_bge_m3_api_server_embedding(server, pooling_task):
+    client = server.get_async_client()
+
+    if pooling_task != "embed":
+        with pytest.raises(openai.InternalServerError):
+            await run_client_embeddings(
+                client,
+                MODEL_NAME,
+                sentences_1,
+            )
+        return
+
     embeddings_list_1 = await run_client_embeddings(
         client,
         MODEL_NAME,
@@ -117,7 +129,14 @@ def compute_lexical_matching_score(
 
 
 @pytest.mark.asyncio
-async def test_bge_m3_api_server_sparse_embedding(client: openai.AsyncOpenAI):
+async def test_bge_m3_api_server_sparse_embedding(server, pooling_task):
+    client = server.get_async_client()
+
+    if pooling_task != "token_classify":
+        with pytest.raises(openai.BadRequestError):
+            await sparse_embeddings(client, sentences_1)
+        return
+
     embeddings_1 = await sparse_embeddings(client, sentences_1)
     embeddings_2 = await sparse_embeddings(client, sentences_2)
 
@@ -137,9 +156,11 @@ async def test_bge_m3_api_server_sparse_embedding(client: openai.AsyncOpenAI):
 
 
 @pytest.mark.asyncio
-async def test_bge_m3_api_server_sparse_embedding_corner_case(
-    client: openai.AsyncOpenAI,
-):
+async def test_bge_m3_api_server_sparse_embedding_corner_case(server, pooling_task):
+    if pooling_task != "token_classify":
+        return
+
+    client = server.get_async_client()
     embeddings = await sparse_embeddings(client, ["Hi"])
     assert len(embeddings) == 1
     assert 2673 in embeddings[0]
@@ -155,7 +176,18 @@ def colbert_score(q_reps: torch.Tensor, p_reps: torch.Tensor) -> torch.Tensor:
 
 
 @pytest.mark.asyncio
-async def test_bge_m3_api_server_multi_vector(client: openai.AsyncOpenAI):
+async def test_bge_m3_api_server_multi_vector(server, pooling_task):
+    client = server.get_async_client()
+
+    if pooling_task != "token_embed":
+        with pytest.raises(openai.BadRequestError):
+            await client.post(
+                "../pooling",
+                body={"model": MODEL_NAME, "input": sentences_1, "task": "token_embed"},
+                cast_to=httpx.Response,
+            )
+        return
+
     result_1 = await client.post(
         "../pooling",
         body={"model": MODEL_NAME, "input": sentences_1, "task": "token_embed"},

tests/models/language/pooling/test_extract_hidden_states.py

@@ -4,6 +4,7 @@ import pytest
 import torch
 
 from vllm import TokensPrompt
+from vllm.config import PoolerConfig
 
 
 @pytest.mark.parametrize(
@@ -20,6 +21,7 @@ def test_extract_hidden_states(hf_runner, vllm_runner, model: str):
         max_model_len=128,
         enforce_eager=True,
         runner="pooling",
+        pooler_config=PoolerConfig(task="token_embed"),
         enable_prefix_caching=True,
     ) as vllm_model:
         pooling_outputs = vllm_model.llm.encode(
@@ -44,14 +46,3 @@ def test_extract_hidden_states(hf_runner, vllm_runner, model: str):
             assert len(output.prompt_token_ids) == n
             assert len(output.outputs.data) == n
             assert output.num_cached_tokens == 0
-
-        # skip_reading_prefix_cache can still write to cache
-        # to accelerate following requests
-        pooling_outputs = vllm_model.llm.encode(
-            [TokensPrompt(prompt_token_ids=t) for t in token_prompts],
-            pooling_task="embed",
-        )
-
-        for n, output in zip(n_prompt_tokens, pooling_outputs):
-            assert len(output.prompt_token_ids) == n
-            assert output.num_cached_tokens > 0

tests/models/language/pooling/test_multi_vector_retrieval.py

@@ -5,6 +5,7 @@ import torch
 from transformers import AutoModel
 
 from tests.models.utils import check_embeddings_close
+from vllm.config import PoolerConfig
 
 
 @pytest.mark.parametrize(
@@ -17,6 +18,7 @@ def test_embed_models(hf_runner, vllm_runner, example_prompts, model: str, dtype
     with vllm_runner(
         model,
         runner="pooling",
+        pooler_config=PoolerConfig(task="token_embed"),
         max_model_len=None,
     ) as vllm_model:
         vllm_outputs = vllm_model.token_embed(example_prompts)

tests/models/language/pooling/test_pooler_config_init_behaviour.py

@@ -146,7 +146,7 @@ def test_multi_vector_retrieval_models_using_normalize(
         model,
         max_model_len=512,
         dtype=dtype,
-        pooler_config=PoolerConfig(use_activation=False),
+        pooler_config=PoolerConfig(use_activation=False, task="token_embed"),
     ) as vllm_model:
         wo_normalize = vllm_model.token_embed(example_prompts)
 
@@ -154,7 +154,7 @@ def test_multi_vector_retrieval_models_using_normalize(
         model,
         max_model_len=512,
         dtype=dtype,
-        pooler_config=PoolerConfig(use_activation=True),
+        pooler_config=PoolerConfig(use_activation=True, task="token_embed"),
     ) as vllm_model:
         w_normalize = vllm_model.token_embed(example_prompts)
 

tests/models/registry.py

@@ -594,6 +594,9 @@ _TEXT_GENERATION_EXAMPLE_MODELS = {
     "MiMoV2FlashForCausalLM": _HfExamplesInfo(
         "XiaomiMiMo/MiMo-V2-Flash", trust_remote_code=True
     ),
+    "MiMoV2ProForCausalLM": _HfExamplesInfo(
+        "XiaomiMiMo/MiMo-V2.5-Pro", trust_remote_code=True, is_available_online=False
+    ),
     "Dots1ForCausalLM": _HfExamplesInfo("rednote-hilab/dots.llm1.inst"),
 }
 
@@ -961,6 +964,18 @@ _MULTIMODAL_EXAMPLE_MODELS = {
         "PerceptronAI/Isaac-0.1",
         trust_remote_code=True,
         extras={"0.2-2B-Preview": "PerceptronAI/Isaac-0.2-2B-Preview"},
+        max_transformers_version="4.57",
+        transformers_version_reason={
+            "vllm": (
+                "Custom Isaac code is not compatible with Transformers v5. "
+                "The model should be upstreamed to Transformers for "
+                "long-term support."
+            ),
+            "hf": (
+                "Isaac's remote model and processor code import or configure "
+                "APIs that changed in Transformers v5."
+            ),
+        },
     ),
     "InternS1ForConditionalGeneration": _HfExamplesInfo(
         "internlm/Intern-S1",
@@ -1057,6 +1072,9 @@ _MULTIMODAL_EXAMPLE_MODELS = {
     "MiDashengLMModel": _HfExamplesInfo(
         "mispeech/midashenglm-7b", trust_remote_code=True
     ),
+    "MiMoV2OmniForCausalLM": _HfExamplesInfo(
+        "XiaomiMiMo/MiMo-V2.5-Omni", trust_remote_code=True, is_available_online=False
+    ),
     "MiniCPMO": _HfExamplesInfo(
         "openbmb/MiniCPM-o-2_6",
         trust_remote_code=True,
@@ -1540,6 +1558,18 @@ _SPECULATIVE_DECODING_EXAMPLE_MODELS = {
         trust_remote_code=True,
         speculative_model="XiaomiMiMo/MiMo-7B-RL",
     ),
+    "MiMoV2MTPModel": _HfExamplesInfo(
+        "XiaomiMiMo/MiMo-V2.5-Pro",
+        trust_remote_code=True,
+        speculative_model="XiaomiMiMo/MiMo-V2.5-Pro",
+        is_available_online=False,
+    ),
+    "MiMoV2OmniMTPModel": _HfExamplesInfo(
+        "XiaomiMiMo/MiMo-V2.5-Omni",
+        trust_remote_code=True,
+        speculative_model="XiaomiMiMo/MiMo-V2.5-Omni",
+        is_available_online=False,
+    ),
     "NemotronHMTPModel": _HfExamplesInfo(
         "nvidia/Nemotron-Super-Placeholder",
         speculative_model="nvidia/Nemotron-Super-Placeholder",

tests/multimodal/test_registry.py

@@ -5,6 +5,8 @@ Unit tests for MultiModalRegistry.supports_multimodal_inputs and
 Qwen2.5-VL visual component loading behavior.
 """
 
+from types import SimpleNamespace
+
 import pytest
 
 from vllm.multimodal import MULTIMODAL_REGISTRY
@@ -32,3 +34,17 @@ def test_supports_multimodal_inputs(model_id, limit_mm_per_prompt, expected):
         limit_mm_per_prompt=limit_mm_per_prompt,
     )
     assert MULTIMODAL_REGISTRY.supports_multimodal_inputs(ctx.model_config) is expected
+
+
+def test_create_processor_error_uses_served_model_name():
+    model_config = SimpleNamespace(
+        is_multimodal_model=False,
+        model="/path/to/model/weights",
+        served_model_name="friendly-model-name",
+    )
+
+    with pytest.raises(
+        ValueError,
+        match="friendly-model-name is not a multimodal model",
+    ):
+        MULTIMODAL_REGISTRY.create_processor(model_config)

tests/quantization/test_cutlass_w4a16.py

@@ -0,0 +1,185 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Tests for Cutlass W4A16 (Machete) kernel on Hopper.
+
+Verifies that W4A16 quantized models loaded through vllm select the
+MacheteLinearKernel on sm_90 GPUs, that weights are correctly repacked,
+and that inference produces valid output.
+
+Run `pytest tests/quantization/test_cutlass_w4a16.py`.
+"""
+
+import pytest
+import torch
+
+from vllm.platforms import current_platform
+
+if not current_platform.has_device_capability(90):
+    pytest.skip(
+        "Machete W4A16 requires Hopper (sm_90).",
+        allow_module_level=True,
+    )
+
+from vllm.model_executor.kernels.linear import (
+    MPLinearLayerConfig,
+    choose_mp_linear_kernel,
+)
+from vllm.model_executor.kernels.linear.mixed_precision import (
+    MacheteLinearKernel,
+)
+from vllm.model_executor.layers.quantization.compressed_tensors.compressed_tensors import (  # noqa: E501
+    CompressedTensorsLinearMethod,
+    CompressedTensorsWNA16,
+)
+from vllm.scalar_type import scalar_types
+
+
+@pytest.fixture(scope="function", autouse=True)
+def enable_pickle(monkeypatch):
+    """`LLM.apply_model` requires pickling a function."""
+    monkeypatch.setenv("VLLM_ALLOW_INSECURE_SERIALIZATION", "1")
+
+
+@pytest.mark.parametrize(
+    "act_type,weight_type,group_size,zero_points",
+    [
+        (torch.float16, scalar_types.uint4b8, 128, False),
+        (torch.bfloat16, scalar_types.uint4b8, 128, False),
+        (torch.float16, scalar_types.uint4, 128, True),
+        (torch.float16, scalar_types.uint4b8, -1, False),
+    ],
+    ids=[
+        "fp16-gptq-g128",
+        "bf16-gptq-g128",
+        "fp16-awq-g128",
+        "fp16-channelwise",
+    ],
+)
+def test_machete_kernel_selected(act_type, weight_type, group_size, zero_points):
+    """Verify choose_mp_linear_kernel picks MacheteLinearKernel."""
+    config = MPLinearLayerConfig(
+        full_weight_shape=(4096, 4096),
+        partition_weight_shape=(4096, 4096),
+        act_type=act_type,
+        weight_type=weight_type,
+        group_size=group_size,
+        zero_points=zero_points,
+        has_g_idx=False,
+    )
+    kernel = choose_mp_linear_kernel(config)
+    assert kernel is MacheteLinearKernel, (
+        f"Expected MacheteLinearKernel, got {kernel.__name__}"
+    )
+
+
+@pytest.mark.parametrize(
+    "full_shape,part_shape,weight_type,group_size,has_g_idx,expected_reason",
+    [
+        ((4096, 4096), (2048, 4096), scalar_types.uint4b8, 128, True, "Act reordering"),
+        (
+            (4096, 4096),
+            (4096, 4096),
+            scalar_types.float6_e3m2f,
+            128,
+            False,
+            "Quant type",
+        ),
+        ((4096, 4096), (4096, 4096), scalar_types.uint4b8, 32, False, "Group size"),
+    ],
+    ids=["partitioned-g_idx", "unsupported-quant-type", "unsupported-group-size"],
+)
+def test_machete_rejects_invalid_config(
+    full_shape, part_shape, weight_type, group_size, has_g_idx, expected_reason
+):
+    """Verify Machete rejects unsupported configurations."""
+    config = MPLinearLayerConfig(
+        full_weight_shape=full_shape,
+        partition_weight_shape=part_shape,
+        act_type=torch.float16,
+        weight_type=weight_type,
+        group_size=group_size,
+        zero_points=False,
+        has_g_idx=has_g_idx,
+    )
+    can_impl, reason = MacheteLinearKernel.can_implement(config)
+    assert not can_impl
+    assert expected_reason in reason
+
+
+def test_kernel_selection_with_disabled_machete(monkeypatch):
+    """Verify kernel selection falls back when Machete is disabled."""
+    monkeypatch.setattr("vllm.envs.VLLM_DISABLED_KERNELS", ["MacheteLinearKernel"])
+
+    config = MPLinearLayerConfig(
+        full_weight_shape=(4096, 4096),
+        partition_weight_shape=(4096, 4096),
+        act_type=torch.float16,
+        weight_type=scalar_types.uint4b8,
+        group_size=128,
+        zero_points=False,
+        has_g_idx=False,
+    )
+    kernel = choose_mp_linear_kernel(config)
+    assert kernel is not MacheteLinearKernel, "MacheteLinearKernel should be disabled"
+
+
+@pytest.mark.parametrize(
+    "model_name",
+    [
+        "nm-testing/tinyllama-oneshot-w4a16-channel-v2",
+        "nm-testing/TinyLlama-1.1B-Chat-v1.0-W4A16-G128-Asym-Updated-ActOrder",
+    ],
+)
+def test_w4a16_machete_e2e(vllm_runner, model_name):
+    """Load a W4A16 model, verify Machete kernel is used, and generate."""
+    with vllm_runner(model_name, enforce_eager=True, gpu_memory_utilization=0.5) as llm:
+
+        def check_model(model):
+            layer = model.model.layers[0]
+            qkv_proj = layer.self_attn.qkv_proj
+
+            assert isinstance(qkv_proj.quant_method, CompressedTensorsLinearMethod)
+            assert isinstance(qkv_proj.scheme, CompressedTensorsWNA16)
+            assert isinstance(qkv_proj.scheme.kernel, MacheteLinearKernel), (
+                f"Expected MacheteLinearKernel on Hopper, "
+                f"got {type(qkv_proj.scheme.kernel).__name__}"
+            )
+
+            assert hasattr(qkv_proj, "weight_packed")
+            assert hasattr(qkv_proj, "weight_scale")
+            assert qkv_proj.weight_packed.dtype == torch.int32
+
+        llm.apply_model(check_model)
+
+        output = llm.generate_greedy("Hello my name is", max_tokens=10)
+        assert output
+        assert len(output[0][1]) > 0
+
+
+def test_w4a16_machete_bfloat16_deterministic(vllm_runner):
+    """Verify Machete works with bf16 activations and is deterministic."""
+    model_name = "nm-testing/tinyllama-oneshot-w4a16-channel-v2"
+    prompt = "The capital of France is"
+
+    with vllm_runner(
+        model_name,
+        enforce_eager=True,
+        dtype="bfloat16",
+        gpu_memory_utilization=0.5,
+    ) as llm:
+
+        def check_kernel_type(model):
+            layer = model.model.layers[0]
+            scheme = layer.self_attn.qkv_proj.scheme
+            assert isinstance(scheme.kernel, MacheteLinearKernel), (
+                f"Expected MacheteLinearKernel with bf16, "
+                f"got {type(scheme.kernel).__name__}"
+            )
+
+        llm.apply_model(check_kernel_type)
+
+        out1 = llm.generate_greedy(prompt, max_tokens=10)
+        out2 = llm.generate_greedy(prompt, max_tokens=10)
+        assert out1[0][1] == out2[0][1], (
+            f"Non-deterministic: '{out1[0][1]}' vs '{out2[0][1]}'"
+        )

vllm/config/model.py

@@ -326,6 +326,10 @@ class ModelConfig:
     mm_encoder_only: InitVar[bool | None] = None
     mm_encoder_tp_mode: InitVar[MMEncoderTPMode | None] = None
     mm_encoder_attn_backend: InitVar[AttentionBackendEnum | str | None] = None
+    mm_encoder_attn_dtype: InitVar[str | None] = None
+    mm_encoder_fp8_scale_path: InitVar[str | None] = None
+    mm_encoder_fp8_scale_save_path: InitVar[str | None] = None
+    mm_encoder_fp8_scale_save_margin: InitVar[float | None] = None
     interleave_mm_strings: InitVar[bool | None] = None
     skip_mm_profiling: InitVar[bool | None] = None
     video_pruning_rate: InitVar[float | None] = None
@@ -447,6 +451,10 @@ class ModelConfig:
         mm_encoder_only: bool | None,
         mm_encoder_tp_mode: MMEncoderTPMode | None,
         mm_encoder_attn_backend: AttentionBackendEnum | str | None,
+        mm_encoder_attn_dtype: str | None,
+        mm_encoder_fp8_scale_path: str | None,
+        mm_encoder_fp8_scale_save_path: str | None,
+        mm_encoder_fp8_scale_save_margin: float | None,
         interleave_mm_strings: bool | None,
         skip_mm_profiling: bool | None,
         video_pruning_rate: float | None,
@@ -513,6 +521,7 @@ class ModelConfig:
         if dict_overrides:
             self._apply_dict_overrides(hf_config, dict_overrides)
         self.hf_text_config = get_hf_text_config(self.hf_config)
+        self.model_arch_config = self.get_model_arch_config()
         self.attention_chunk_size = getattr(
             self.hf_text_config, "attention_chunk_size", None
         )
@@ -520,7 +529,6 @@ class ModelConfig:
         self.hf_image_processor_config = get_hf_image_processor_config(
             self.model, hf_token=self.hf_token, revision=self.revision
         )
-        self.model_arch_config = self.get_model_arch_config()
 
         architectures = self.architectures
         registry = self.registry
@@ -643,6 +651,10 @@ class ModelConfig:
                 mm_encoder_only=mm_encoder_only,
                 mm_encoder_tp_mode=mm_encoder_tp_mode,
                 mm_encoder_attn_backend=mm_encoder_attn_backend,
+                mm_encoder_attn_dtype=mm_encoder_attn_dtype,
+                mm_encoder_fp8_scale_path=mm_encoder_fp8_scale_path,
+                mm_encoder_fp8_scale_save_path=mm_encoder_fp8_scale_save_path,
+                mm_encoder_fp8_scale_save_margin=mm_encoder_fp8_scale_save_margin,
                 interleave_mm_strings=interleave_mm_strings,
                 skip_mm_profiling=skip_mm_profiling,
                 video_pruning_rate=video_pruning_rate,

vllm/config/multimodal.py

@@ -2,6 +2,7 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 from collections.abc import Mapping
+from pathlib import Path
 from typing import Any, Literal, TypeAlias, TypedDict, final
 
 from pydantic import ConfigDict, Field, field_validator, model_validator
@@ -158,6 +159,24 @@ class MultiModalConfig:
     """Optional override for the multi-modal encoder attention backend when
     using vision transformers. Accepts any value from
     `vllm.v1.attention.backends.registry.AttentionBackendEnum` (e.g. `FLASH_ATTN`)."""
+    mm_encoder_attn_dtype: Literal["fp8"] | None = None
+    """Optional dtype override for ViT encoder attention. Set to `"fp8"` to
+    enable FP8 quantization via the FlashInfer cuDNN backend. When set to
+    `"fp8"` without a scale file, dynamic scaling is used automatically.
+    See docs/features/quantization/fp8_vit_attn.md for details."""
+    mm_encoder_fp8_scale_path: str | None = None
+    """Path to a JSON file containing per-layer FP8 Q/K/V scales for ViT
+    encoder attention. When provided (with `mm_encoder_attn_dtype="fp8"`),
+    static scaling is used. When omitted, dynamic scaling is used."""
+    mm_encoder_fp8_scale_save_path: str | None = None
+    """When set with dynamic FP8 scaling (`mm_encoder_attn_dtype="fp8"`
+    and no `mm_encoder_fp8_scale_path`), saves the calibrated scales to
+    this file after the amax history buffer is full. The saved file can
+    then be used as `mm_encoder_fp8_scale_path` in subsequent runs."""
+    mm_encoder_fp8_scale_save_margin: float = Field(default=1.5, gt=0.0)
+    """Safety margin multiplied onto scales when auto-saving. A value > 1
+    leaves headroom so that inputs with larger activations than the
+    calibration set do not overflow FP8 range. Default 1.5."""
     interleave_mm_strings: bool = False
     """Enable fully interleaved support for multimodal prompts, while using
     --chat-template-content-format=string."""
@@ -233,6 +252,36 @@ class MultiModalConfig:
                 "'mm_shm_cache_max_object_size_mb' should only be set when "
                 "'mm_processor_cache_type' is 'shm'."
             )
+        # Validate FP8 scale path combinations.
+        if self.mm_encoder_attn_dtype != "fp8" and (
+            self.mm_encoder_fp8_scale_path is not None
+            or self.mm_encoder_fp8_scale_save_path is not None
+        ):
+            raise ValueError(
+                "'mm_encoder_fp8_scale_path' and "
+                "'mm_encoder_fp8_scale_save_path' require "
+                "'mm_encoder_attn_dtype' to be 'fp8'."
+            )
+        if (
+            self.mm_encoder_fp8_scale_path is not None
+            and self.mm_encoder_fp8_scale_save_path is not None
+        ):
+            raise ValueError(
+                "'mm_encoder_fp8_scale_save_path' cannot be used with "
+                "'mm_encoder_fp8_scale_path' (saving requires dynamic scaling)."
+            )
+
+        # Validate file paths exist.
+        if self.mm_encoder_fp8_scale_path is not None:
+            scale_path = Path(self.mm_encoder_fp8_scale_path)
+            if not scale_path.is_file():
+                raise FileNotFoundError(f"FP8 scale file not found: {scale_path}")
+        if self.mm_encoder_fp8_scale_save_path is not None:
+            save_parent = Path(self.mm_encoder_fp8_scale_save_path).parent
+            if not save_parent.is_dir():
+                raise FileNotFoundError(
+                    f"Parent directory for FP8 scale save path not found: {save_parent}"
+                )
         return self
 
     def compute_hash(self) -> str:
@@ -252,6 +301,8 @@ class MultiModalConfig:
             if self.mm_encoder_attn_backend is not None
             else None,
             self.mm_encoder_tp_mode,
+            self.mm_encoder_attn_dtype,
+            self.mm_encoder_fp8_scale_path,
         ]
         hash_str = safe_hash(str(factors).encode(), usedforsecurity=False).hexdigest()
         return hash_str

vllm/config/speculative.py

@@ -34,6 +34,7 @@ logger = init_logger(__name__)
 MTPModelTypes = Literal[
     "deepseek_mtp",
     "mimo_mtp",
+    "mimo_v2_mtp",
     "glm4_moe_mtp",
     "glm4_moe_lite_mtp",
     "glm_ocr_mtp",
@@ -63,7 +64,8 @@ SpeculativeMethod = Literal[
     EagleModelTypes,
     NgramGPUTypes,
 ]
-RejectionSampleMethod = Literal["strict", "probabilistic", "synthetic"]
+RejectionSampleMethod = Literal["standard", "synthetic"]
+DraftSampleMethod = Literal["greedy", "gumbel"]
 
 
 @config
@@ -183,11 +185,11 @@ class SpeculativeConfig:
     """Load config for the draft model. If not specified, will use the load
     config from the target model."""
 
-    rejection_sample_method: RejectionSampleMethod = "strict"
-    """Whether to use strict (target and draft sampled tokens match exactly)
-    or probabilistic rejection sampling. Both respect the target model
-    distribution, but the latter yields a higher acceptance rate at the cost
-    of more memory to cache draft logits."""
+    rejection_sample_method: RejectionSampleMethod = "standard"
+    """The rejection sampling method to use. 'standard' uses probabilistic
+    rejection sampling (with or without cached draft logits, controlled by
+    draft_sample_method). 'synthetic' accepts draft tokens with a decaying
+    probability calibrated to synthetic_acceptance_rate."""
 
     synthetic_acceptance_rates: list[float] | None = None
     """Per-position *unconditional* acceptance rates for synthetic rejection
@@ -248,6 +250,14 @@ class SpeculativeConfig:
             )
         return SpeculativeConfig._acceptance_length_to_rates(length, n)
 
+    draft_sample_method: DraftSampleMethod = "greedy"
+    """How the draft model samples tokens. 'greedy' always picks the argmax
+    token, and the draft probabilities are treated as one-hot during rejection
+    sampling. 'gumbel' adds Gumbel noise for stochastic sampling, and the full
+    draft logits are used for the probability ratio test during rejection
+    sampling. This comes at the cost of additional GPU memory usage. This
+    parameter currently only applies to Model Runner V2."""
+
     def compute_hash(self) -> str:
         """
         WARNING: Whenever a new field is added to this config,
@@ -323,6 +333,48 @@ class SpeculativeConfig:
                 }
             )
 
+        if (arch := hf_config.architectures[0]) in (
+            "MiMoV2ProForCausalLM",
+            "MiMoV2OmniForCausalLM",
+        ):
+            from vllm.model_executor.models.mimo_v2_mtp import (
+                _MIMO_V2_PRO_NUM_MTP_LAYERS,
+            )
+
+            mtp_arch_maps = {
+                "MiMoV2ProForCausalLM": "MiMoV2MTPModel",
+                "MiMoV2OmniForCausalLM": "MiMoV2OmniMTPModel",
+            }
+
+            hf_config.model_type = "mimo_v2_mtp"
+            # vLLM currently supports only the first MiMo-V2 MTP layer.
+            n_predict = _MIMO_V2_PRO_NUM_MTP_LAYERS
+            hf_config.update(
+                {
+                    "num_hidden_layers": 0,
+                    "n_predict": n_predict,
+                    "num_nextn_predict_layers": n_predict,
+                    "architectures": [mtp_arch_maps[arch]],
+                }
+            )
+
+        if hf_config.architectures[0] == "MiMoV2FlashForCausalLM":
+            from vllm.model_executor.models.mimo_v2_mtp import (
+                _MIMO_V2_FLASH_NUM_MTP_LAYERS,
+            )
+
+            hf_config.model_type = "mimo_v2_mtp"
+            # vLLM currently supports only the first MiMo-V2 MTP layer.
+            n_predict = _MIMO_V2_FLASH_NUM_MTP_LAYERS
+            hf_config.update(
+                {
+                    "num_hidden_layers": 0,
+                    "n_predict": n_predict,
+                    "num_nextn_predict_layers": n_predict,
+                    "architectures": ["MiMoV2MTPModel"],
+                }
+            )
+
         if hf_config.architectures[0] == "Glm4MoeForCausalLM":
             hf_config.model_type = "glm4_moe_mtp"
             n_predict = getattr(hf_config, "num_nextn_predict_layers", None)

vllm/device_allocator/cumem.py

@@ -128,13 +128,6 @@ class CuMemAllocator:
         return CuMemAllocator.instance
 
     def __init__(self):
-        conf = os.environ.get("PYTORCH_CUDA_ALLOC_CONF", "")
-        assert "expandable_segments:True" not in conf, (
-            "Expandable segments are not compatible with memory pool. "
-            "Please track https://github.com/pytorch/pytorch/issues/147851 "
-            "for the latest updates."
-        )
-
         self.pointer_to_data: dict[int, AllocationData] = {}
         self.current_tag: str = CuMemAllocator.default_tag
         self.allocator_and_pools: dict[str, Any] = {}
@@ -264,34 +257,49 @@ class CuMemAllocator:
 
         assert isinstance(tag, str)
 
+        # Expandable segments are incompatible with the memory pool used for
+        # sleep mode (see https://github.com/pytorch/pytorch/issues/147851).
+        # If the user has enabled expandable segments via
+        # PYTORCH_CUDA_ALLOC_CONF, temporarily disable them for the duration
+        # of the memory pool context and restore on exit.
+        conf = os.environ.get("PYTORCH_CUDA_ALLOC_CONF", "")
+        expandable_was_enabled = "expandable_segments:True" in conf
+        if expandable_was_enabled:
+            torch.cuda.memory._set_allocator_settings("expandable_segments:False")
+
         old_tag = self.current_tag
         self.current_tag = tag
-        with use_memory_pool_with_allocator(
-            self.python_malloc_callback, self.python_free_callback
-        ) as data:
-            # start to hit another PyTorch bug in PyTorch 2.6,
-            # possibly because of gc-related issue w.r.t. the allocator and
-            # the memory pool.
-            # to avoid the issue, we keep a reference of the data.
-            # see https://github.com/pytorch/pytorch/issues/146431 .
-            self.allocator_and_pools[tag] = data
-            yield
-            # PyTorch's bug, calling torch.cuda.empty_cache() will error
-            # when using pluggable allocator, see
-            # https://github.com/pytorch/pytorch/issues/145168 .
-            # if we have some memory allocated and then freed,
-            # the memory will not be released, e.g. in online quantization,
-            # where the model is created in higher precision, and then
-            # quantized in lower precision.
-            # Find all unused allocations and manually release them.
-            # TODO: we should expose `empty_cache` method in the memory pool.
-            # TODO: ask for help from PyTorch team to expose this method.
-            allocations = data[0].snapshot()
-            for allocation in allocations:
-                if allocation["allocated_size"] == 0:
-                    handle = self._python_free_callback(allocation["address"])
-                    unmap_and_release(handle)
+        try:
+            with use_memory_pool_with_allocator(
+                self.python_malloc_callback, self.python_free_callback
+            ) as data:
+                # start to hit another PyTorch bug in PyTorch 2.6,
+                # possibly because of gc-related issue w.r.t. the allocator
+                # and the memory pool.
+                # to avoid the issue, we keep a reference of the data.
+                # see https://github.com/pytorch/pytorch/issues/146431 .
+                self.allocator_and_pools[tag] = data
+                yield
+                # PyTorch's bug, calling torch.cuda.empty_cache() will error
+                # when using pluggable allocator, see
+                # https://github.com/pytorch/pytorch/issues/145168 .
+                # if we have some memory allocated and then freed,
+                # the memory will not be released, e.g. in online
+                # quantization, where the model is created in higher
+                # precision, and then quantized in lower precision.
+                # Find all unused allocations and manually release them.
+                # TODO: we should expose `empty_cache` method in the memory
+                # pool.
+                # TODO: ask for help from PyTorch team to expose this method.
+                allocations = data[0].snapshot()
+                for allocation in allocations:
+                    if allocation["allocated_size"] == 0:
+                        handle = self._python_free_callback(allocation["address"])
+                        unmap_and_release(handle)
+        finally:
             self.current_tag = old_tag
+            if expandable_was_enabled:
+                torch.cuda.memory._set_allocator_settings("expandable_segments:True")
 
     def get_current_usage(self) -> int:
         """

vllm/distributed/kv_transfer/kv_connector/v1/nixl/scheduler.py

@@ -2,6 +2,7 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 """Scheduler-side logic for the NIXL connector."""
 
+import os
 import threading
 import time
 from typing import TYPE_CHECKING, Any
@@ -88,6 +89,12 @@ class NixlConnectorScheduler:
         if vllm_config.scheduler_config.disable_hybrid_kv_cache_manager:
             logger.info("Hybrid Memory Allocator is enabled with NIXL")
 
+        if os.environ.get("VLLM_NIXL_ABORT_REQUEST_TIMEOUT") is not None:
+            logger.warning(
+                "VLLM_NIXL_ABORT_REQUEST_TIMEOUT is deprecated and will be "
+                "removed in release 0.22.0."
+            )
+
         # Background thread for handling new handshake requests.
         self._nixl_handshake_listener_t: threading.Thread | None = None
         self._stop_event = threading.Event()

vllm/engine/arg_utils.py

@@ -542,6 +542,14 @@ class EngineArgs:
     mm_encoder_attn_backend: AttentionBackendEnum | str | None = (
         MultiModalConfig.mm_encoder_attn_backend
     )
+    mm_encoder_attn_dtype: str | None = MultiModalConfig.mm_encoder_attn_dtype
+    mm_encoder_fp8_scale_path: str | None = MultiModalConfig.mm_encoder_fp8_scale_path
+    mm_encoder_fp8_scale_save_path: str | None = (
+        MultiModalConfig.mm_encoder_fp8_scale_save_path
+    )
+    mm_encoder_fp8_scale_save_margin: float = (
+        MultiModalConfig.mm_encoder_fp8_scale_save_margin
+    )
     io_processor_plugin: str | None = None
     renderer_num_workers: int = 1
     skip_mm_profiling: bool = MultiModalConfig.skip_mm_profiling
@@ -1179,6 +1187,22 @@ class EngineArgs:
             "--mm-encoder-attn-backend",
             **multimodal_kwargs["mm_encoder_attn_backend"],
         )
+        multimodal_group.add_argument(
+            "--mm-encoder-attn-dtype",
+            **multimodal_kwargs["mm_encoder_attn_dtype"],
+        )
+        multimodal_group.add_argument(
+            "--mm-encoder-fp8-scale-path",
+            **multimodal_kwargs["mm_encoder_fp8_scale_path"],
+        )
+        multimodal_group.add_argument(
+            "--mm-encoder-fp8-scale-save-path",
+            **multimodal_kwargs["mm_encoder_fp8_scale_save_path"],
+        )
+        multimodal_group.add_argument(
+            "--mm-encoder-fp8-scale-save-margin",
+            **multimodal_kwargs["mm_encoder_fp8_scale_save_margin"],
+        )
         multimodal_group.add_argument(
             "--interleave-mm-strings", **multimodal_kwargs["interleave_mm_strings"]
         )
@@ -1517,6 +1541,10 @@ class EngineArgs:
             mm_encoder_only=self.mm_encoder_only,
             mm_encoder_tp_mode=self.mm_encoder_tp_mode,
             mm_encoder_attn_backend=self.mm_encoder_attn_backend,
+            mm_encoder_attn_dtype=self.mm_encoder_attn_dtype,
+            mm_encoder_fp8_scale_path=self.mm_encoder_fp8_scale_path,
+            mm_encoder_fp8_scale_save_path=self.mm_encoder_fp8_scale_save_path,
+            mm_encoder_fp8_scale_save_margin=self.mm_encoder_fp8_scale_save_margin,
             pooler_config=self.pooler_config,
             generation_config=self.generation_config,
             override_generation_config=self.override_generation_config,

vllm/entrypoints/llm.py

@@ -79,7 +79,7 @@ from vllm.renderers.inputs.preprocess import (
     prompt_to_seq,
 )
 from vllm.sampling_params import BeamSearchParams, RequestOutputKind, SamplingParams
-from vllm.tasks import PoolingTask
+from vllm.tasks import SCORE_TYPE_MAP, PoolingTask
 from vllm.tokenizers import TokenizerLike
 from vllm.usage.usage_lib import UsageContext
 from vllm.utils.counter import Counter
@@ -1204,12 +1204,9 @@ class LLM:
                     f"Supported tasks: {self.supported_tasks}"
                 )
             else:
-                logger.warning_once(
-                    "Pooling multitask support is deprecated and will "
-                    "be removed in v0.20. When the default pooling task is "
-                    "not what you want, you need to manually specify it "
-                    'via PoolerConfig(task="%s"). ',
-                    pooling_task,
+                raise ValueError(
+                    f"Try switching the model's pooling_task "
+                    f'via `PoolerConfig(task="{pooling_task}")`'
                 )
 
         if pooling_task == "plugin" and "plugin" not in self.pooling_io_processors:
@@ -1412,7 +1409,7 @@ class LLM:
                 "pooling model."
             )
 
-        score_type = self.model_config.score_type
+        score_type: str | None = SCORE_TYPE_MAP.get(self.pooling_task, None)  # type: ignore[arg-type]
         if (
             score_type == "cross-encoder"
             and getattr(self.model_config.hf_config, "num_labels", 0) != 1

vllm/entrypoints/openai/chat_completion/batch_serving.py

@@ -317,4 +317,5 @@ class OpenAIServingChatBatch(OpenAIServingChat):
             model=model_name,
             choices=choices,
             usage=usage,
+            system_fingerprint=self.system_fingerprint,
         )

vllm/entrypoints/openai/chat_completion/protocol.py

@@ -129,6 +129,9 @@ class ChatCompletionStreamResponse(OpenAIBaseModel):
     model: str
     choices: list[ChatCompletionResponseStreamChoice]
     usage: UsageInfo | None = Field(default=None)
+    # Set only on the final chunk of a stream to mirror non-streaming responses
+    # without the per-chunk serialization overhead.
+    system_fingerprint: str | None = None
     # not part of the OpenAI spec but for tracing the tokens
     prompt_token_ids: list[int] | None = None
 

vllm/entrypoints/openai/chat_completion/serving.py

@@ -1195,6 +1195,16 @@ class OpenAIServingChat(OpenAIServing):
                         choices=[choice_data],
                         model=model_name,
                     )
+                    # Stamp the fingerprint on terminal chunks only (those with
+                    # finish_reason set). When ``include_usage`` is on, the
+                    # trailing usage chunk below overrides this as the true
+                    # final message.
+                    if (
+                        not include_usage
+                        and self.system_fingerprint is not None
+                        and choice_data.finish_reason is not None
+                    ):
+                        chunk.system_fingerprint = self.system_fingerprint
 
                     # handle usage stats if requested & if continuous
                     if include_continuous_usage:
@@ -1229,6 +1239,7 @@ class OpenAIServingChat(OpenAIServing):
                     choices=[],
                     model=model_name,
                     usage=final_usage,
+                    system_fingerprint=self.system_fingerprint,
                 )
                 final_usage_data = final_usage_chunk.model_dump_json(
                     exclude_unset=True, exclude_none=True
@@ -1637,6 +1648,7 @@ class OpenAIServingChat(OpenAIServing):
             model=model_name,
             choices=choices,
             usage=usage,
+            system_fingerprint=self.system_fingerprint,
             prompt_logprobs=clamp_prompt_logprobs(final_res.prompt_logprobs),
             prompt_token_ids=(
                 final_res.prompt_token_ids if request.return_token_ids else None

vllm/entrypoints/openai/cli_args.py

@@ -153,9 +153,21 @@ class BaseFrontendArgs:
     """If set to True, log the stack trace of error responses"""
     tokens_only: bool = False
     """
-    If set to True, only enable the Tokens In<>Out endpoint. 
+    If set to True, only enable the Tokens In<>Out endpoint.
     This is intended for use in a Disaggregated Everything setup.
     """
+    fingerprint_mode: Literal["full", "hash", "custom", "none"] = "full"
+    """Controls the ``system_fingerprint`` field on responses.
+
+    - ``full`` (default): ``vllm-<version>[-<parallelism>]-<hash8>``. Encodes
+      server version, non-trivial parallelism degrees (tp/pp/dp/ep), and an
+      8-char config hash.
+    - ``hash``: ``vllm-<version>-<hash8>``. Parallelism stripped.
+    - ``custom``: emits the literal string from ``--fingerprint-value``.
+    - ``none``: the field is omitted (serialized as ``null``).
+    """
+    fingerprint_value: str | None = None
+    """Literal fingerprint string used when ``--fingerprint-mode=custom``."""
 
     @classmethod
     def _customize_cli_kwargs(

vllm/entrypoints/openai/completion/protocol.py

@@ -512,3 +512,6 @@ class CompletionStreamResponse(OpenAIBaseModel):
     model: str
     choices: list[CompletionResponseStreamChoice]
     usage: UsageInfo | None = Field(default=None)
+    # Set only on the final chunk of a stream to mirror non-streaming responses
+    # without the per-chunk serialization overhead.
+    system_fingerprint: str | None = None

vllm/entrypoints/openai/completion/serving.py

@@ -383,6 +383,7 @@ class OpenAIServingCompletion(OpenAIServing):
 
                     chunk = CompletionStreamResponse(
                         id=request_id,
+                        object="text_completion",
                         created=created_time,
                         model=model_name,
                         choices=[
@@ -401,6 +402,14 @@ class OpenAIServingCompletion(OpenAIServing):
                             )
                         ],
                     )
+                    # Stamp on terminal chunk only when no trailing usage chunk
+                    # will follow (that one is the true final message).
+                    if (
+                        not include_usage
+                        and self.system_fingerprint is not None
+                        and finish_reason is not None
+                    ):
+                        chunk.system_fingerprint = self.system_fingerprint
                     if include_continuous_usage:
                         prompt_tokens = num_prompt_tokens[prompt_idx]
                         completion_tokens = previous_num_tokens[i]
@@ -410,7 +419,7 @@ class OpenAIServingCompletion(OpenAIServing):
                             total_tokens=prompt_tokens + completion_tokens,
                         )
 
-                    response_json = chunk.model_dump_json(exclude_unset=False)
+                    response_json = chunk.model_dump_json(exclude_unset=True)
                     yield f"data: {response_json}\n\n"
 
             total_prompt_tokens = sum(num_prompt_tokens)
@@ -433,6 +442,7 @@ class OpenAIServingCompletion(OpenAIServing):
                     model=model_name,
                     choices=[],
                     usage=final_usage_info,
+                    system_fingerprint=self.system_fingerprint,
                 )
                 final_usage_data = final_usage_chunk.model_dump_json(
                     exclude_unset=False, exclude_none=True
@@ -562,6 +572,7 @@ class OpenAIServingCompletion(OpenAIServing):
             model=model_name,
             choices=choices,
             usage=usage,
+            system_fingerprint=self.system_fingerprint,
             kv_transfer_params=kv_transfer_params,
         )
 

vllm/entrypoints/openai/engine/serving.py

@@ -157,6 +157,19 @@ class OpenAIServing:
         self.renderer = engine_client.renderer
         self.input_processor = engine_client.input_processor
 
+        # Computed once at startup (cached by ``vllm_config`` identity) and
+        # stamped on non-streaming responses. Streaming chunks deliberately
+        # omit it to avoid per-chunk overhead.
+        from vllm.entrypoints.openai.fingerprint import get_system_fingerprint
+
+        try:
+            self.system_fingerprint: str | None = get_system_fingerprint(
+                engine_client.vllm_config
+            )
+        except Exception:
+            # Never fail server startup over the fingerprint.
+            self.system_fingerprint = None
+
     async def beam_search(
         self,
         prompt: EngineInput,

vllm/entrypoints/openai/fingerprint.py

@@ -0,0 +1,84 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Build the ``system_fingerprint`` string returned by the OpenAI-compatible
+server.
+
+Four modes, configured via ``--fingerprint-mode``:
+
+* ``full`` (default): ``vllm-<version>[-<parallelism>]-<hash8>`` — encodes
+  server version, any non-trivial parallelism degree (tp/pp/dp/ep), and an
+  8-char prefix of ``vllm_config.compute_hash()`` (covers model identity,
+  quant config, speculative, attention backend, etc.).
+* ``hash``: ``vllm-<version>-<hash8>`` — parallelism stripped.
+* ``custom``: user-provided literal via ``--fingerprint-value``.
+* ``none``: the field is omitted (serialized as ``null``).
+
+``get_system_fingerprint`` is only called at serving-class init (a handful
+of times per server); each subclass caches the returned string on
+``self.system_fingerprint``, so per-request cost is one attribute read.
+"""
+
+from __future__ import annotations
+
+from typing import Any, Literal
+
+FingerprintMode = Literal["full", "hash", "custom", "none"]
+
+_DEFAULT_MODE: FingerprintMode = "full"
+_CUSTOM_VALUE: str | None = None
+
+
+def set_default_fingerprint_mode(
+    mode: FingerprintMode,
+    custom_value: str | None = None,
+) -> None:
+    """Configure the fingerprint mode for subsequent ``get_system_fingerprint``
+    calls. Called once at server startup."""
+    global _DEFAULT_MODE, _CUSTOM_VALUE
+    _DEFAULT_MODE = mode
+    _CUSTOM_VALUE = custom_value
+
+
+def get_system_fingerprint(vllm_config: Any) -> str | None:
+    """Return the fingerprint for ``vllm_config`` using the mode configured by
+    ``set_default_fingerprint_mode``."""
+    return build_system_fingerprint(vllm_config, _DEFAULT_MODE, _CUSTOM_VALUE)
+
+
+def build_system_fingerprint(
+    vllm_config: Any,
+    mode: FingerprintMode = "full",
+    custom_value: str | None = None,
+) -> str | None:
+    if mode == "none":
+        return None
+    if mode == "custom":
+        return custom_value
+
+    from vllm import __version__ as vllm_version
+
+    try:
+        hash8 = vllm_config.compute_hash()[:8]
+    except Exception:
+        hash8 = "nohash"
+
+    if mode == "hash":
+        return f"vllm-{vllm_version}-{hash8}"
+
+    # mode == "full"
+    parts: list[str] = [f"vllm-{vllm_version}"]
+    pc = getattr(vllm_config, "parallel_config", None)
+    if pc is not None:
+        tp = getattr(pc, "tensor_parallel_size", 1)
+        if tp > 1:
+            parts.append(f"tp{tp}")
+        pp = getattr(pc, "pipeline_parallel_size", 1)
+        if pp > 1:
+            parts.append(f"pp{pp}")
+        dp = getattr(pc, "data_parallel_size", 1)
+        if dp > 1:
+            parts.append(f"dp{dp}")
+        if getattr(pc, "enable_expert_parallel", False):
+            parts.append("ep")
+    parts.append(hash8)
+    return "-".join(parts)

vllm/entrypoints/openai/generate/api_router.py

@@ -61,9 +61,17 @@ async def init_generate_state(
     )
     from vllm.entrypoints.openai.chat_completion.serving import OpenAIServingChat
     from vllm.entrypoints.openai.completion.serving import OpenAIServingCompletion
+    from vllm.entrypoints.openai.fingerprint import set_default_fingerprint_mode
     from vllm.entrypoints.openai.responses.serving import OpenAIServingResponses
     from vllm.entrypoints.serve.disagg.serving import ServingTokens
 
+    # Applied before any serving class is constructed so that each one picks
+    # up the chosen mode on its first cache miss.
+    set_default_fingerprint_mode(
+        getattr(args, "fingerprint_mode", "full"),
+        getattr(args, "fingerprint_value", None),
+    )
+
     if args.tool_server == "demo":
         tool_server: ToolServer | None = DemoToolServer()
         assert isinstance(tool_server, DemoToolServer)

vllm/entrypoints/pooling/base/serving.py

@@ -15,10 +15,7 @@ from starlette.datastructures import Headers
 from vllm import PoolingParams, PoolingRequestOutput, envs
 from vllm.config import VllmConfig
 from vllm.engine.protocol import EngineClient
-from vllm.entrypoints.chat_utils import (
-    ChatTemplateConfig,
-    ChatTemplateContentFormatOption,
-)
+from vllm.entrypoints.chat_utils import ChatTemplateConfig
 from vllm.entrypoints.logger import RequestLogger
 from vllm.entrypoints.openai.engine.protocol import ErrorResponse
 from vllm.entrypoints.openai.models.serving import OpenAIServingModels
@@ -48,9 +45,7 @@ class PoolingServingBase(ABC):
         models: OpenAIServingModels,
         *,
         request_logger: RequestLogger | None,
-        chat_template: str | None = None,
-        chat_template_content_format: ChatTemplateContentFormatOption = "auto",
-        trust_request_chat_template: bool = False,
+        chat_template_config: ChatTemplateConfig,
         return_tokens_as_token_ids: bool = False,
         log_error_stack: bool = False,
     ):
@@ -63,11 +58,7 @@ class PoolingServingBase(ABC):
         self.request_logger = request_logger
         self.return_tokens_as_token_ids = return_tokens_as_token_ids
         self.log_error_stack = log_error_stack
-        self.chat_template_config = ChatTemplateConfig(
-            chat_template=chat_template,
-            chat_template_content_format=chat_template_content_format,
-            trust_request_chat_template=trust_request_chat_template,
-        )
+        self.chat_template_config = chat_template_config
 
         # Shared thread pool executor for preprocessing and postprocessing.
         self._executor: Executor = models.renderer._executor

vllm/entrypoints/pooling/factories.py

@@ -10,7 +10,7 @@ from vllm.entrypoints.chat_utils import ChatTemplateConfig
 from vllm.logger import init_logger
 from vllm.plugins.io_processors import has_io_processor
 from vllm.renderers import BaseRenderer
-from vllm.tasks import POOLING_TASKS, SupportedTask
+from vllm.tasks import POOLING_TASKS, SCORE_TYPE_MAP, SupportedTask
 
 from .base.io_processor import PoolingIOProcessor
 from .utils import enable_scoring_api
@@ -43,23 +43,24 @@ def init_pooling_io_processors(
 ) -> dict[str, PoolingIOProcessor]:
     model_config = vllm_config.model_config
     processors: dict[str, type[PoolingIOProcessor]] = {}
+    pooling_task = model_config.get_pooling_task(supported_tasks)
 
-    if "classify" in supported_tasks:
+    if pooling_task == "classify":
         from .classify.io_processor import ClassifyIOProcessor
 
         processors["classify"] = ClassifyIOProcessor
 
-    if "token_classify" in supported_tasks:
+    if pooling_task == "token_classify":
         from .classify.io_processor import TokenClassifyIOProcessor
 
         processors["token_classify"] = TokenClassifyIOProcessor
 
-    if "embed" in supported_tasks:
+    if pooling_task == "embed":
         from .embed.io_processor import EmbedIOProcessor
 
         processors["embed"] = EmbedIOProcessor
 
-    if "token_embed" in supported_tasks:
+    if pooling_task == "token_embed":
         from .embed.io_processor import TokenEmbedIOProcessor
 
         processors["token_embed"] = TokenEmbedIOProcessor
@@ -71,15 +72,15 @@ def init_pooling_io_processors(
         from .pooling.io_processor import PluginWithIOProcessorPlugins
 
         processors["plugin"] = PluginWithIOProcessorPlugins
-    elif "plugin" in supported_tasks:
+    elif pooling_task == "plugin":
         from .pooling.io_processor import PluginWithoutIOProcessorPlugins
 
         processors["plugin"] = PluginWithoutIOProcessorPlugins
 
     if enable_scoring_api(supported_tasks, model_config):
-        score_type = model_config.score_type
         from .scoring.io_processor import ScoringIOProcessors
 
+        score_type: str | None = SCORE_TYPE_MAP.get(pooling_task, None)  # type: ignore[arg-type]
         if score_type is not None and score_type in ScoringIOProcessors:
             processors[score_type] = ScoringIOProcessors[score_type]
 
@@ -140,6 +141,10 @@ def init_pooling_state(
     request_logger: RequestLogger | None,
     supported_tasks: tuple["SupportedTask", ...],
 ):
+    model_config = engine_client.model_config
+    if model_config is None:
+        return
+
     from vllm.entrypoints.chat_utils import load_chat_template
     from vllm.tasks import POOLING_TASKS
 
@@ -148,8 +153,14 @@ def init_pooling_state(
     from .pooling.serving import ServingPooling
     from .scoring.serving import ServingScores
 
-    model_config = engine_client.model_config
     resolved_chat_template = load_chat_template(args.chat_template)
+    pooling_task = model_config.get_pooling_task(supported_tasks)
+
+    chat_template_config = ChatTemplateConfig(
+        chat_template=resolved_chat_template,
+        chat_template_content_format=args.chat_template_content_format,
+        trust_request_chat_template=args.trust_request_chat_template,
+    )
 
     state.serving_pooling = (
         (
@@ -158,9 +169,7 @@ def init_pooling_state(
                 state.openai_serving_models,
                 supported_tasks=supported_tasks,
                 request_logger=request_logger,
-                chat_template=resolved_chat_template,
-                chat_template_content_format=args.chat_template_content_format,
-                trust_request_chat_template=args.trust_request_chat_template,
+                chat_template_config=chat_template_config,
             )
         )
         if any(t in supported_tasks for t in POOLING_TASKS)
@@ -171,11 +180,9 @@ def init_pooling_state(
             engine_client,
             state.openai_serving_models,
             request_logger=request_logger,
-            chat_template=resolved_chat_template,
-            chat_template_content_format=args.chat_template_content_format,
-            trust_request_chat_template=args.trust_request_chat_template,
+            chat_template_config=chat_template_config,
         )
-        if "embed" in supported_tasks
+        if pooling_task == "embed"
         else None
     )
     state.serving_classification = (
@@ -183,21 +190,18 @@ def init_pooling_state(
             engine_client,
             state.openai_serving_models,
             request_logger=request_logger,
-            chat_template=resolved_chat_template,
-            chat_template_content_format=args.chat_template_content_format,
-            trust_request_chat_template=args.trust_request_chat_template,
+            chat_template_config=chat_template_config,
         )
-        if "classify" in supported_tasks
+        if pooling_task == "classify"
         else None
     )
     state.serving_scores = (
         ServingScores(
             engine_client,
             state.openai_serving_models,
+            supported_tasks=supported_tasks,
             request_logger=request_logger,
-            chat_template=resolved_chat_template,
-            chat_template_content_format=args.chat_template_content_format,
-            trust_request_chat_template=args.trust_request_chat_template,
+            chat_template_config=chat_template_config,
             enable_flash_late_interaction=getattr(
                 args, "enable_flash_late_interaction", True
             ),
@@ -214,7 +218,12 @@ def get_pooling_invocation_types(
     # NOTE: Items defined earlier take higher priority
     invocation_types: list[tuple[RequestType, tuple[GetHandlerFn, EndpointFn]]] = []
 
-    if "embed" in supported_tasks:
+    if model_config is None:
+        return invocation_types
+
+    pooling_task = model_config.get_pooling_task(supported_tasks)
+
+    if pooling_task == "embed":
         from .embed.api_router import create_embedding, embedding
         from .embed.protocol import EmbeddingRequest
 
@@ -222,7 +231,7 @@ def get_pooling_invocation_types(
             (EmbeddingRequest, (embedding, create_embedding)),
         ]
 
-    if "classify" in supported_tasks:
+    if pooling_task == "classify":
         from .classify.api_router import classify, create_classify
         from .classify.protocol import ClassificationRequest
 

vllm/entrypoints/pooling/pooling/serving.py

@@ -78,17 +78,15 @@ class ServingPooling(PoolingServingBase):
 
         # plugin task uses io_processor.parse_request to verify inputs
         if pooling_task != "plugin" and pooling_task != self.pooling_task:
-            if pooling_task not in self.io_processors:
+            if pooling_task not in self.supported_tasks:
                 raise ValueError(
                     f"Unsupported task: {pooling_task!r} "
                     f"Supported tasks: {self.supported_tasks}"
                 )
             else:
-                logger.warning_once(
-                    "Pooling multitask support is deprecated and will be removed "
-                    "in v0.20. When the default pooling task is not what you want, you "
-                    "need to manually specify it via --pooler-config.task %s. ",
-                    pooling_task,
+                raise ValueError(
+                    "Try switching the model's pooling_task "
+                    f"via --pooler-config.task {request.task}."
                 )
 
         if pooling_task == "plugin" and "plugin" not in self.io_processors:

vllm/entrypoints/pooling/scoring/serving.py

@@ -8,6 +8,7 @@ from vllm.engine.protocol import EngineClient
 from vllm.entrypoints.openai.engine.protocol import UsageInfo
 from vllm.logger import init_logger
 from vllm.outputs import PoolingRequestOutput, ScoringRequestOutput
+from vllm.tasks import SCORE_TYPE_MAP, SupportedTask
 from vllm.v1.pool.late_interaction import (
     build_late_interaction_doc_params,
     build_late_interaction_query_params,
@@ -38,10 +39,15 @@ class ServingScores(PoolingServing):
         self,
         engine_client: EngineClient,
         *args,
+        supported_tasks: tuple[SupportedTask, ...],
         enable_flash_late_interaction: bool = True,
         **kwargs,
     ):
-        self.io_processor_name: str = engine_client.model_config.score_type
+        pooling_task = engine_client.model_config.get_pooling_task(supported_tasks)
+        score_type = SCORE_TYPE_MAP.get(pooling_task, None)  # type: ignore[arg-type]
+        assert score_type is not None
+
+        self.io_processor_name: str = score_type
         self.enable_flash_late_interaction = (
             self.io_processor_name == "late-interaction"
             and enable_flash_late_interaction

vllm/entrypoints/pooling/utils.py

@@ -141,10 +141,14 @@ def enable_scoring_api(
     supported_tasks: tuple["SupportedTask", ...],
     model_config: ModelConfig | None = None,
 ) -> bool:
-    if any(t in supported_tasks for t in ("embed", "token_embed")):
+    if model_config is None:
+        return False
+
+    pooling_task = model_config.get_pooling_task(supported_tasks)
+    if pooling_task in ("embed", "token_embed"):
         return True
 
-    if model_config is not None and "classify" in supported_tasks:
+    if pooling_task == "classify":
         num_labels = getattr(model_config.hf_config, "num_labels", 0)
         if num_labels != 1:
             logger.debug_once("Scoring API is only enabled for num_labels == 1.")

vllm/kernels/triton/__init__.py

@@ -0,0 +1,3 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Triton kernel implementations."""

vllm/kernels/triton/qkv_padded_fp8_quant.py

@@ -0,0 +1,180 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Stride-aware FP8 quantization with head_dim padding for ViT attention.
+
+Reads directly from non-contiguous QKV views using 3D strides and pads
+head_dim to a multiple of 16 for cuDNN compatibility.
+"""
+
+import torch
+
+from vllm.model_executor.layers.quantization.input_quant_fp8 import QuantFP8
+from vllm.model_executor.layers.quantization.utils.quant_utils import (
+    get_fp8_min_max,
+)
+from vllm.platforms import current_platform
+from vllm.triton_utils import HAS_TRITON, tl, triton
+from vllm.utils.math_utils import round_up
+
+_FP8_MIN, _FP8_MAX = get_fp8_min_max()
+
+
+@triton.jit
+def _quantize_pad_fp8_kernel(
+    x_ptr,
+    y_ptr,
+    scale_ptr,
+    stride_xs,
+    stride_xh,
+    stride_xd,
+    stride_ys,
+    stride_yh,
+    stride_yd,
+    num_heads,
+    n_rows,
+    n_cols,
+    n_cols_padded,
+    fp8_min,
+    fp8_max,
+    SKIP_SCALE: tl.constexpr,
+    BLOCK_M: tl.constexpr,
+    BLOCK_N: tl.constexpr,
+):
+    pid_m = tl.program_id(0)
+    pid_n = tl.program_id(1)
+
+    offs_m = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
+    offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
+    mask_m = offs_m < n_rows
+    mask_out = mask_m[:, None] & (offs_n[None, :] < n_cols_padded)
+    mask_in = mask_m[:, None] & (offs_n[None, :] < n_cols)
+
+    # Decompose flattened row into (token, head) for 3D stride indexing.
+    s = offs_m // num_heads
+    h = offs_m % num_heads
+
+    x_ptrs = (
+        x_ptr
+        + s[:, None] * stride_xs
+        + h[:, None] * stride_xh
+        + offs_n[None, :] * stride_xd
+    )
+    x = tl.load(x_ptrs, mask=mask_in, other=0.0).to(tl.float32)
+    if SKIP_SCALE:
+        x_q = x
+    else:
+        scale = tl.load(scale_ptr)
+        x_q = x / scale
+    x_q = tl.clamp(x_q, fp8_min, fp8_max).to(y_ptr.dtype.element_ty)
+
+    y_ptrs = (
+        y_ptr
+        + s[:, None] * stride_ys
+        + h[:, None] * stride_yh
+        + offs_n[None, :] * stride_yd
+    )
+    tl.store(y_ptrs, x_q, mask=mask_out)
+
+
+def _get_fp8_pad_quant_config(padded_head_dim: int) -> tuple[int, int, int]:
+    block_n = triton.next_power_of_2(padded_head_dim)
+    block_n = max(16, min(block_n, 128))
+    block_m = 16
+    num_warps = 4
+    return block_m, block_n, num_warps
+
+
+def quantize_fp8_pad_head_dim_triton(
+    tensor: torch.Tensor,
+    scale: torch.Tensor,
+    skip_scale: bool = False,
+    block_m: int | None = None,
+    block_n: int | None = None,
+    num_warps: int | None = None,
+) -> torch.Tensor:
+    """Quantize a 3D/4D tensor to FP8, padding head_dim to a multiple of 16.
+
+    Reads directly from the input using its 3D strides, so non-contiguous
+    views (e.g. Q/K/V slices from an interleaved QKV buffer) are handled
+    without an extra copy.  Output is always a fresh contiguous tensor
+    with shape (S, H, padded_D).
+    """
+    if not HAS_TRITON:
+        raise RuntimeError("Triton is required to quantize with head_dim padding.")
+
+    original_shape = tensor.shape
+    if tensor.dim() == 4:
+        tensor = tensor.view(-1, tensor.shape[-2], tensor.shape[-1])
+    assert tensor.dim() == 3, f"Expected 3D input (S, H, D), got {tensor.dim()}D"
+    S, H, D = tensor.shape
+    padded_head_dim = round_up(D, 16)
+    out_dtype = current_platform.fp8_dtype()
+    output = torch.empty(
+        (S, H, padded_head_dim),
+        device=tensor.device,
+        dtype=out_dtype,
+    )
+
+    scale_1d = scale.reshape(-1)
+    n_rows = S * H
+
+    if block_m is None or block_n is None or num_warps is None:
+        block_m, block_n, num_warps = _get_fp8_pad_quant_config(padded_head_dim)
+
+    grid = (
+        triton.cdiv(n_rows, block_m),
+        triton.cdiv(padded_head_dim, block_n),
+    )
+
+    _quantize_pad_fp8_kernel[grid](
+        tensor,
+        output,
+        scale_1d,
+        tensor.stride(0),
+        tensor.stride(1),
+        tensor.stride(2),
+        output.stride(0),
+        output.stride(1),
+        output.stride(2),
+        H,
+        n_rows,
+        D,
+        padded_head_dim,
+        _FP8_MIN,
+        _FP8_MAX,
+        SKIP_SCALE=skip_scale,
+        BLOCK_M=block_m,
+        BLOCK_N=block_n,
+        num_warps=num_warps,
+    )
+
+    return output.view((*original_shape[:-1], padded_head_dim))
+
+
+def quantize_fp8_maybe_pad_head_dim(
+    tensor: torch.Tensor,
+    scale: torch.Tensor,
+    fp8_quant: QuantFP8,
+    skip_scale: bool = False,
+) -> torch.Tensor:
+    """Quantize a 3D/4D tensor to FP8, padding head_dim to a multiple of 16
+    only when needed.
+
+    Accepts (S, H, D) or (B, S, H, D) input. Uses ``fp8_quant`` (a
+    :class:`QuantFP8` CustomOp) when head_dim is already aligned to 16
+    (no padding); otherwise falls back to a stride-aware Triton kernel
+    that pads head_dim to a multiple of 16.
+    """
+    head_dim = tensor.shape[-1]
+    if head_dim % 16 != 0:
+        return quantize_fp8_pad_head_dim_triton(tensor, scale, skip_scale=skip_scale)
+
+    if skip_scale:
+        return tensor.to(current_platform.fp8_dtype())
+
+    # QuantFP8 expects 2D: flatten all dims except (H, D).
+    orig_shape = tensor.shape
+    total_tokens = tensor.numel() // (orig_shape[-1] * orig_shape[-2])
+    tensor_2d = tensor.reshape(total_tokens, -1)
+    fp8_tensor, _ = fp8_quant(tensor_2d, scale=scale)
+    return fp8_tensor.reshape(orig_shape)

vllm/model_executor/layers/attention/attention.py

@@ -386,10 +386,6 @@ class Attention(nn.Module, AttentionLayerBase):
         # Initialize KV cache quantization attributes
         _init_kv_cache_quant(self, quant_config, prefix)
 
-        # Initialize TurboQuant buffers (Pi, S, centroids) if tq cache dtype
-        if kv_cache_dtype.startswith("turboquant_"):
-            self._init_turboquant_buffers(kv_cache_dtype, head_size, prefix)
-
         # for attn backends supporting query quantization
         self.query_quant = None
         if (
@@ -410,50 +406,6 @@ class Attention(nn.Module, AttentionLayerBase):
                 else GroupShape.PER_TENSOR,
             )
 
-    def _init_turboquant_buffers(
-        self, cache_dtype: str, head_size: int, prefix: str
-    ) -> None:
-        """Initialize TurboQuant centroids for Lloyd-Max quantization."""
-        from vllm.model_executor.layers.quantization.turboquant.centroids import (
-            get_centroids,
-        )
-        from vllm.model_executor.layers.quantization.turboquant.config import (
-            TurboQuantConfig,
-        )
-
-        tq_config = TurboQuantConfig.from_cache_dtype(cache_dtype, head_size)
-
-        self.register_buffer(
-            "_tq_centroids",
-            get_centroids(head_size, tq_config.centroid_bits),
-        )
-        self._tq_config = tq_config
-
-        # Pre-allocate decode intermediate buffers so model.to(device) moves
-        # them to GPU *before* the memory profiler runs.  Without this the
-        # profiler gives all free memory to KV cache blocks and the first
-        # decode OOMs when these buffers are lazily allocated.
-        _vllm_cfg = get_current_vllm_config()
-        B = _vllm_cfg.scheduler_config.max_num_seqs
-        Hq = self.num_heads
-        S = _vllm_cfg.attention_config.tq_max_kv_splits_for_cuda_graph
-        D = head_size
-        self.register_buffer(
-            "_tq_mid_o_buf",
-            torch.empty(B, Hq, S, D + 1, dtype=torch.float32),
-            persistent=False,
-        )
-        self.register_buffer(
-            "_tq_output_buf",
-            torch.empty(B, Hq, D, dtype=torch.float32),
-            persistent=False,
-        )
-        self.register_buffer(
-            "_tq_lse_buf",
-            torch.empty(B, Hq, dtype=torch.float32),
-            persistent=False,
-        )
-
     def forward(
         self,
         query: torch.Tensor,

vllm/model_executor/layers/attention/mm_encoder_attention.py

@@ -1,13 +1,32 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
+import functools
+import json
 
 import numpy as np
 import torch
 
+from vllm.config import MultiModalConfig
+from vllm.kernels.triton.qkv_padded_fp8_quant import (
+    quantize_fp8_maybe_pad_head_dim,
+)
 from vllm.logger import init_logger
 from vllm.model_executor.custom_op import CustomOp, maybe_get_oot_by_class
-from vllm.model_executor.models.vision import get_vit_attn_backend
+from vllm.model_executor.layers.quantization.input_quant_fp8 import (
+    QuantFP8,
+)
+from vllm.model_executor.layers.quantization.utils.quant_utils import (
+    GroupShape,
+    get_fp8_min_max,
+)
+from vllm.model_executor.models.vision import (
+    get_multimodal_config,
+    get_vit_attn_backend,
+)
+from vllm.utils.flashinfer import (
+    is_flashinfer_cudnn_fp8_prefill_attn_supported,
+)
 from vllm.utils.math_utils import round_up
 from vllm.v1.attention.backends.fa_utils import get_flash_attn_version
 from vllm.v1.attention.backends.registry import AttentionBackendEnum
@@ -20,6 +39,108 @@ from vllm.v1.attention.ops.vit_attn_wrappers import (
 
 logger = init_logger(__name__)
 
+_, _FP8_MAX = get_fp8_min_max()
+_FP8_AMAX_HISTORY_LEN = 16
+
+# Module-level state for auto-saving dynamic scales. The save is a one-shot
+# triggered by the first layer whose amax buffer wraps. Path and margin are
+# captured during layer init (set_current_vllm_config context only lives
+# across model init, not forward passes).
+_fp8_scale_save_path: str | None = None
+_fp8_scale_save_margin: float = MultiModalConfig.mm_encoder_fp8_scale_save_margin
+_fp8_saved_scale_refs: dict[str, tuple[torch.Tensor, torch.Tensor, torch.Tensor]] = {}
+
+
+@functools.cache
+def _load_fp8_scales_file(path: str | None) -> dict[str, dict[str, float]]:
+    """Load per-layer FP8 Q/K/V scales from a JSON file. Results are cached.
+
+    Expected format (keys ``q_scale`` / ``k_scale`` / ``v_scale`` also accepted)::
+
+        {
+            "visual.blocks.0.attn.attn": {"q": 224.0, "k": 198.0, "v": 210.0},
+            "visual.blocks.1.attn.attn": {"q": 218.0, "k": 195.0, "v": 207.0},
+        }
+
+    To produce such a file, run with ``mm_encoder_fp8_scale_save_path`` set.
+    """
+    if path is None:
+        return {}
+
+    with open(path, encoding="utf-8") as f:
+        data = json.load(f)
+
+    # Handle nested "layers" format
+    if "layers" in data and isinstance(data["layers"], dict):
+        data = data["layers"]
+
+    scales: dict[str, dict[str, float]] = {}
+    for layer_name, layer_scales in data.items():
+        if not isinstance(layer_scales, dict):
+            continue
+        q = layer_scales.get("q", layer_scales.get("q_scale"))
+        k = layer_scales.get("k", layer_scales.get("k_scale"))
+        v = layer_scales.get("v", layer_scales.get("v_scale"))
+        if q is not None and k is not None and v is not None:
+            q_f, k_f, v_f = float(q), float(k), float(v)
+            if q_f <= 0 or k_f <= 0 or v_f <= 0:
+                raise ValueError(
+                    f"FP8 scales must be positive, got q={q_f}, "
+                    f"k={k_f}, v={v_f} for layer '{layer_name}'"
+                )
+            scales[layer_name] = {"q": q_f, "k": k_f, "v": v_f}
+
+    logger.info_once(
+        "Loaded FP8 attention scales from %s (%d layers)", path, len(scales)
+    )
+    return scales
+
+
+def _maybe_save_fp8_scales(
+    layer_name: str,
+    q_scale: torch.Tensor,
+    k_scale: torch.Tensor,
+    v_scale: torch.Tensor,
+    buffer_wrapped: bool,
+) -> None:
+    """Accumulate a layer's scale tensors; on the first amax buffer wrap,
+    dump all accumulated scales to ``mm_encoder_fp8_scale_save_path``.
+
+    No-op unless auto-save is configured. Tensor references are stored on
+    every call (no GPU->CPU sync); ``.item()`` is only called at the single
+    save point to avoid stalling the forward path.
+    """
+    global _fp8_scale_save_path
+    # Fast path: auto-save either disabled or already finished. Path is
+    # captured at layer init and cleared once the save fires.
+    if _fp8_scale_save_path is None:
+        return
+
+    # Stash scale tensor refs (no GPU->CPU sync yet); wait until the amax
+    # history has seen a full cycle before committing scales to disk.
+    _fp8_saved_scale_refs[layer_name] = (q_scale, k_scale, v_scale)
+    if not buffer_wrapped:
+        return
+
+    # Buffer just wrapped for the first time: materialize scales (with
+    # safety margin) and dump to disk. Clearing _fp8_scale_save_path
+    # makes this a one-shot across all layers.
+    path, margin = _fp8_scale_save_path, _fp8_scale_save_margin
+    scales = {
+        name: {
+            "q": q.item() * margin,
+            "k": k.item() * margin,
+            "v": v.item() * margin,
+        }
+        for name, (q, k, v) in _fp8_saved_scale_refs.items()
+    }
+    _fp8_scale_save_path = None
+    _fp8_saved_scale_refs.clear()
+    with open(path, "w", encoding="utf-8") as f:
+        json.dump(scales, f, indent=2)
+    logger.info("Saved FP8 scales (%d layers) to %s", len(scales), path)
+
+
 # Batch buckets for cuDNN graph caching.
 # Graphs use batch size and max sequence length as cache key.
 # This avoids creating a new graph for each unique set of
@@ -148,27 +269,47 @@ class MMEncoderAttention(CustomOp):
         hidden_size: int,
         tp_size: int,
         device: torch.device,
+        fp8_padded_hidden_size: int | None = None,
     ) -> torch.Tensor:
         if (oot_class := maybe_get_oot_by_class(cls)) is not cls:
             return oot_class.maybe_recompute_cu_seqlens(  # type: ignore[attr-defined]
-                attn_backend, cu_seqlens, hidden_size, tp_size, device
+                attn_backend,
+                cu_seqlens,
+                hidden_size,
+                tp_size,
+                device,
+                fp8_padded_hidden_size=fp8_padded_hidden_size,
             )
 
         if attn_backend == AttentionBackendEnum.FLASHINFER:
             batch_size = len(cu_seqlens) - 1
-            scale = hidden_size // tp_size
-            cu_seqlens = cu_seqlens * scale
 
-            cu_seqlens_qko = cu_seqlens
-            cu_seqlens_v = cu_seqlens * 3
+            if fp8_padded_hidden_size is not None:
+                # FP8 path: after quantization Q/K/V are each independent
+                # contiguous tensors with stride H * padded_D per token.
+                # All sections use the same element stride.
+                scale = fp8_padded_hidden_size // tp_size
+                cu_seqlens = cu_seqlens * scale
+                cu_seqlens_padded = add_padding_to_seqlens(
+                    cu_seqlens, batch_size, cu_seqlens[-1]
+                )
+                cu_seqlens = np.concatenate([cu_seqlens_padded, cu_seqlens_padded])
+            else:
+                # BF16 path: Q/K/V are non-contiguous views into shared
+                # buffers. V section has 3x stride from interleaved QKV.
+                scale = hidden_size // tp_size
+                cu_seqlens = cu_seqlens * scale
 
-            cu_seqlens_qko = add_padding_to_seqlens(
-                cu_seqlens_qko, batch_size, cu_seqlens_qko[-1]
-            )
-            cu_seqlens_v = add_padding_to_seqlens(
-                cu_seqlens_v, batch_size, cu_seqlens_v[-1]
-            )
-            cu_seqlens = np.concatenate([cu_seqlens_qko, cu_seqlens_v])
+                cu_seqlens_qko = cu_seqlens
+                cu_seqlens_v = cu_seqlens * 3
+
+                cu_seqlens_qko = add_padding_to_seqlens(
+                    cu_seqlens_qko, batch_size, cu_seqlens_qko[-1]
+                )
+                cu_seqlens_v = add_padding_to_seqlens(
+                    cu_seqlens_v, batch_size, cu_seqlens_v[-1]
+                )
+                cu_seqlens = np.concatenate([cu_seqlens_qko, cu_seqlens_v])
 
         cu_seqlens = torch.from_numpy(cu_seqlens).to(device, non_blocking=True)
         return cu_seqlens
@@ -206,6 +347,7 @@ class MMEncoderAttention(CustomOp):
         # During model initialization, the default dtype is set as the model
         # weight and activation dtype.
         dtype = torch.get_default_dtype()
+        self.dtype = dtype
 
         # Get device-specific vision attention backend.
         self.attn_backend = get_vit_attn_backend(
@@ -229,6 +371,113 @@ class MMEncoderAttention(CustomOp):
 
         logger.info_once(f"Using {self.attn_backend} for MMEncoderAttention.")
 
+        self._init_fp8_state()
+
+    def _init_fp8_state(self) -> None:
+        """Initialize FP8 attention state from multimodal config.
+
+        No-op if FP8 is not requested. Raises ``ValueError`` if FP8 is
+        requested but the platform does not support it.
+        """
+        # Populate defaults so ``_forward_flashinfer`` can
+        # check ``self.fp8_enabled`` and others without AttributeError.
+        self.fp8_enabled = False
+        self._fp8_dynamic_scale = False
+        self.fp8_quant: QuantFP8 | None = None
+        self.skip_scale_q = False
+        self.skip_scale_k = False
+        self.skip_scale_v = False
+
+        mm_cfg = get_multimodal_config()
+        if mm_cfg is None or mm_cfg.mm_encoder_attn_dtype != "fp8":
+            return
+
+        # FP8 path
+        if not is_flashinfer_cudnn_fp8_prefill_attn_supported():
+            raise ValueError(
+                "mm_encoder_attn_dtype='fp8' requires the FlashInfer "
+                "cuDNN backend with cuDNN >= 9.17.1 on a GPU with native "
+                "FP8 support."
+            )
+
+        self.fp8_enabled = True
+        self._fp8_dynamic_scale = mm_cfg.mm_encoder_fp8_scale_path is None
+        self.fp8_quant = QuantFP8(static=True, group_shape=GroupShape.PER_TENSOR)
+
+        # Register buffers pre-device-move; values populated in
+        # process_weights_after_loading. Shape (1, 1, 1, 1) is required by cuDNN.
+        for attr in ("_fp8_q_scale", "_fp8_k_scale", "_fp8_v_scale"):
+            self.register_buffer(
+                attr, torch.ones(1, dtype=torch.float32).view(1, 1, 1, 1)
+            )
+        if self._fp8_dynamic_scale:
+            for attr in ("_fp8_q_amax", "_fp8_k_amax", "_fp8_v_amax"):
+                self.register_buffer(
+                    attr,
+                    torch.zeros(_FP8_AMAX_HISTORY_LEN, dtype=torch.float32),
+                    persistent=False,
+                )
+            self._fp8_amax_pos = 0
+
+        # Capture auto-save config now: the VllmConfig context only lives
+        # across model init, not forward passes, so ``_maybe_save_fp8_scales``
+        # reads these globals instead of re-querying ``get_multimodal_config``.
+        if (
+            mm_cfg.mm_encoder_fp8_scale_save_path is not None
+            and self._fp8_dynamic_scale
+        ):
+            global _fp8_scale_save_path, _fp8_scale_save_margin
+            _fp8_scale_save_path = mm_cfg.mm_encoder_fp8_scale_save_path
+            _fp8_scale_save_margin = mm_cfg.mm_encoder_fp8_scale_save_margin
+
+    def process_weights_after_loading(self, act_dtype: torch.dtype) -> None:
+        """Populate FP8 scale buffers after weights are loaded.
+
+        ``act_dtype`` matches the signature used by :class:`Attention` and
+        :class:`MLAAttention` for the loader auto-scan but is unused:
+        FP8 scales are always float32.
+        """
+        if not self.fp8_enabled:
+            return
+
+        mm_cfg = get_multimodal_config()
+        scale_path = mm_cfg.mm_encoder_fp8_scale_path if mm_cfg is not None else None
+        if scale_path is None:
+            logger.info_once(
+                "FP8 attention enabled with dynamic scaling "
+                "(no scale file provided). Scales will adapt from "
+                "observed Q/K/V amax values (history_len=%d).",
+                _FP8_AMAX_HISTORY_LEN,
+            )
+            return
+
+        all_scales = _load_fp8_scales_file(scale_path)
+        layer_scales = all_scales.get(self.layer_name)
+        if layer_scales is None:
+            raise ValueError(
+                "FP8 attention enabled but scales not found for layer "
+                f"'{self.layer_name}' in {scale_path}. "
+                f"Available layers: {list(all_scales.keys())}"
+            )
+
+        for attr, key in (
+            ("_fp8_q_scale", "q"),
+            ("_fp8_k_scale", "k"),
+            ("_fp8_v_scale", "v"),
+        ):
+            getattr(self, attr).fill_(layer_scales[key])
+        self.skip_scale_q = layer_scales["q"] == 1.0
+        self.skip_scale_k = layer_scales["k"] == 1.0
+        self.skip_scale_v = layer_scales["v"] == 1.0
+
+        logger.debug(
+            "FP8 attention enabled for %s: q=%.4f, k=%.4f, v=%.4f",
+            self.layer_name if self.layer_name else "MMEncoderAttention",
+            layer_scales["q"],
+            layer_scales["k"],
+            layer_scales["v"],
+        )
+
     @classmethod
     def enabled(cls) -> bool:
         return True
@@ -353,6 +602,44 @@ class MMEncoderAttention(CustomOp):
             output = output.reshape(bsz, q_len, -1)
         return output
 
+    @torch.no_grad()
+    def _record_amax_and_update_scales(
+        self,
+        query: torch.Tensor,
+        key: torch.Tensor,
+        value: torch.Tensor,
+    ) -> None:
+        """Record Q/K/V amax into circular history and recompute scales.
+
+        All work stays on GPU with no device-to-host sync. The Python-side
+        history position counter is mutated, so this method must NOT be
+        called inside CUDA graph capture/replay. When CUDA graphs are
+        used for the encoder, dynamic scaling should be disabled by
+        providing a static scale file via --mm-encoder-fp8-scale-path.
+        """
+        pos = self._fp8_amax_pos
+        self._fp8_amax_pos = (pos + 1) % _FP8_AMAX_HISTORY_LEN
+
+        for tensor, amax_buf, scale_buf in (
+            (query, self._fp8_q_amax, self._fp8_q_scale),
+            (key, self._fp8_k_amax, self._fp8_k_scale),
+            (value, self._fp8_v_amax, self._fp8_v_scale),
+        ):
+            amax_buf[pos] = tensor.amax()
+            max_amax = amax_buf.max()
+            scale_buf.fill_(
+                torch.clamp(max_amax, min=torch.finfo(torch.float32).tiny) / _FP8_MAX
+            )
+
+        buffer_wrapped = self._fp8_amax_pos == 0 and pos == _FP8_AMAX_HISTORY_LEN - 1
+        _maybe_save_fp8_scales(
+            self.layer_name,
+            self._fp8_q_scale,
+            self._fp8_k_scale,
+            self._fp8_v_scale,
+            buffer_wrapped,
+        )
+
     def _forward_flashinfer(
         self,
         query: torch.Tensor,
@@ -363,7 +650,32 @@ class MMEncoderAttention(CustomOp):
         sequence_lengths: torch.Tensor
         | None = None,  # Only used for FlashInfer CuDNN backend
     ) -> torch.Tensor:
-        return vit_flashinfer_wrapper(
+        if self.fp8_enabled:
+            assert self.fp8_quant is not None
+
+            if self._fp8_dynamic_scale:
+                self._record_amax_and_update_scales(query, key, value)
+
+            query = quantize_fp8_maybe_pad_head_dim(
+                query,
+                self._fp8_q_scale,
+                skip_scale=self.skip_scale_q,
+                fp8_quant=self.fp8_quant,
+            )
+            key = quantize_fp8_maybe_pad_head_dim(
+                key,
+                self._fp8_k_scale,
+                skip_scale=self.skip_scale_k,
+                fp8_quant=self.fp8_quant,
+            )
+            value = quantize_fp8_maybe_pad_head_dim(
+                value,
+                self._fp8_v_scale,
+                skip_scale=self.skip_scale_v,
+                fp8_quant=self.fp8_quant,
+            )
+
+        output = vit_flashinfer_wrapper(
             q=query,
             k=key,
             v=value,
@@ -372,8 +684,17 @@ class MMEncoderAttention(CustomOp):
             cu_seqlens=cu_seqlens,
             max_seqlen=max_seqlen,
             sequence_lengths=sequence_lengths,
+            q_scale=self._fp8_q_scale if self.fp8_enabled else None,
+            k_scale=self._fp8_k_scale if self.fp8_enabled else None,
+            v_scale=self._fp8_v_scale if self.fp8_enabled else None,
+            o_data_type=self.dtype if self.fp8_enabled else None,
         )
 
+        if self.fp8_enabled and output.shape[-1] != self.head_size:
+            output = output[..., : self.head_size].contiguous()
+
+        return output
+
     def forward_native(
         self,
         query: torch.Tensor,

vllm/model_executor/layers/linear.py

@@ -1120,7 +1120,12 @@ class QKVParallelLinear(ColumnParallelLinear):
             # Special case for Quantization.
             # If quantized, we need to adjust the offset and size to account
             # for the packing.
-            if (
+            if isinstance(param, BlockQuantScaleParameter):
+                weight_block_size = getattr(self, "weight_block_size", None)
+                shard_size, shard_offset = adjust_block_scale_shard(
+                    weight_block_size, shard_size, shard_offset
+                )
+            elif (
                 isinstance(param, (PackedColumnParameter, PackedvLLMParameter))
                 and param.packed_dim == param.output_dim
             ):

vllm/model_executor/layers/mamba/linear_attn.py

@@ -3,6 +3,7 @@
 
 import math
 from collections.abc import Callable
+from functools import partial
 
 import torch
 import torch.nn.functional as F
@@ -33,28 +34,46 @@ from vllm.v1.attention.backend import AttentionMetadata
 from vllm.v1.attention.backends.linear_attn import LinearAttentionMetadata
 
 
+@CustomOp.register("minimax_text01_rmsnorm_tp")
 class MiniMaxText01RMSNormTP(CustomOp):
-    name = "MiniMaxText01RMSNormTP"
-
-    def __init__(self, hidden_size: int, eps: float = 1e-6) -> None:
+    def __init__(
+        self,
+        hidden_size: int,
+        eps: float = 1e-6,
+        *,
+        weight_shard_world_size: int | None = None,
+        weight_shard_rank: int | None = None,
+    ) -> None:
         super().__init__()
         self.tp_world = get_tensor_model_parallel_world_size()
         self.tp_rank = get_tensor_model_parallel_rank()
-        self.weight = nn.Parameter(torch.ones(int(hidden_size / self.tp_world)))
+        self.weight_shard_world = weight_shard_world_size or self.tp_world
+        self.weight_shard_rank = (
+            self.tp_rank if weight_shard_rank is None else weight_shard_rank
+        )
 
-        self.weight.weight_loader = self.weight_loader
+        self.weight = nn.Parameter(torch.ones(hidden_size // self.weight_shard_world))
+        self.weight.weight_loader = partial(
+            self.weight_loader,
+            shard_world_size=self.weight_shard_world,
+            shard_rank=self.weight_shard_rank,
+        )
         self.variance_epsilon = eps
 
     @staticmethod
     def weight_loader(
         param: nn.Parameter,
         loaded_weight: torch.Tensor,
+        shard_world_size: int | None = None,
+        shard_rank: int | None = None,
     ) -> None:
-        tp_world = get_tensor_model_parallel_world_size()
-        tp_rank = get_tensor_model_parallel_rank()
+        if shard_world_size is None:
+            shard_world_size = get_tensor_model_parallel_world_size()
+        if shard_rank is None:
+            shard_rank = get_tensor_model_parallel_rank()
 
-        shard_size = loaded_weight.shape[0] // tp_world
-        shard = slice(tp_rank * shard_size, (tp_rank + 1) * shard_size)
+        shard_size = loaded_weight.shape[0] // shard_world_size
+        shard = slice(shard_rank * shard_size, (shard_rank + 1) * shard_size)
         param.data.copy_(loaded_weight[shard])
 
     def _forward(

vllm/model_executor/model_loader/utils.py

@@ -15,7 +15,11 @@ from typing_extensions import assert_never
 import vllm.envs as envs
 from vllm.config import ModelConfig, VllmConfig, set_current_vllm_config
 from vllm.logger import init_logger
-from vllm.model_executor.layers.attention import Attention, MLAAttention
+from vllm.model_executor.layers.attention import (
+    Attention,
+    MLAAttention,
+    MMEncoderAttention,
+)
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig,
     QuantizeMethodBase,
@@ -106,12 +110,12 @@ def process_weights_after_loading(
             with device_loading_context(module, target_device):
                 quant_method.process_weights_after_loading(module)
 
-    # Initialize post-load attention weights for both Attention and MLA.
+    # Initialize post-load attention weights for Attention, MLA, and MM encoder.
     # NOTE: Happens after other modules so we can easily decompress weights.
     for _, module in model.named_modules():
-        if isinstance(module, (Attention, MLAAttention)) and hasattr(
-            module, "process_weights_after_loading"
-        ):
+        if isinstance(
+            module, (Attention, MLAAttention, MMEncoderAttention)
+        ) and hasattr(module, "process_weights_after_loading"):
             # TODO(lucas): see if there is a way to unify the signatures
             # of process_weights_after_loading
             with device_loading_context(module, target_device):

vllm/model_executor/models/mimo_v2.py

@@ -6,6 +6,7 @@ from itertools import islice
 import torch
 from torch import nn
 
+from vllm.compilation.decorators import support_torch_compile
 from vllm.config import (
     CacheConfig,
     VllmConfig,
@@ -268,7 +269,7 @@ class MiMoV2Attention(nn.Module):
             self.total_num_heads * self.v_head_dim,
             hidden_size,
             bias=False,
-            quant_config=quant_config,
+            quant_config=quant_config if "mtp.layers" not in prefix else None,
             reduce_results=True,
             prefix=f"{prefix}.o_proj",
         )
@@ -440,6 +441,7 @@ class MiMoV2FlashDecoderLayer(nn.Module):
         return self.config.hybrid_layer_pattern[self.layer_id] == 1
 
 
+@support_torch_compile
 class MiMoV2Model(nn.Module):
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         super().__init__()
@@ -603,7 +605,13 @@ class MiMoV2Model(nn.Module):
 
             if expert_matched:
                 continue
-
+            # Support fused qkv_proj checkpoint (Pro format)
+            if "qkv_proj" in name:
+                if name in params_dict:
+                    param = params_dict[name]
+                    loaded_weight = loaded_weight.chunk(tp_size, dim=0)[tp_rank]
+                    default_weight_loader(param, loaded_weight)
+                continue
             stacked_matched = False
             for param_name, weight_name, shard_id in stacked_params_mapping:
                 if weight_name not in name:
@@ -662,6 +670,11 @@ class MiMoV2Model(nn.Module):
 
 
 class MiMoV2FlashForCausalLM(nn.Module, SupportsPP, MixtureOfExperts):
+    packed_modules_mapping = {
+        "qkv_proj": ["q_proj", "k_proj", "v_proj"],
+        "gate_up_proj": ["gate_proj", "up_proj"],
+    }
+
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         super().__init__()
         config = vllm_config.model_config.hf_config
@@ -718,3 +731,10 @@ class MiMoV2FlashForCausalLM(nn.Module, SupportsPP, MixtureOfExperts):
     def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
         loader = AutoWeightsLoader(self)
         return loader.load_weights(weights)
+
+
+class MiMoV2ProForCausalLM(MiMoV2FlashForCausalLM):
+    packed_modules_mapping = {
+        "qkv_proj": ["qkv_proj"],
+        "gate_up_proj": ["gate_proj", "up_proj"],
+    }

vllm/model_executor/models/mimo_v2_mtp.py

@@ -0,0 +1,373 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+"""Inference-only MiMo-V2 MTP (Multi-Token Prediction) draft model.
+
+Supports both MiMo-V2-Pro and MiMo-V2-Flash checkpoints.
+
+Checkpoint weight layout (model.mtp.layers.{idx}.*):
+  enorm            - RMSNorm for token embeddings
+  hnorm            - RMSNorm for previous hidden states
+  eh_proj          - ReplicatedLinear(hidden*2 -> hidden)
+  input_layernorm  - pre-attention RMSNorm
+  self_attn.*      - attention weights; format differs by variant:
+                       Pro:   fused qkv_proj  [Q;K;V] concatenated
+                       Flash: separate q_proj, k_proj, v_proj
+  pre_mlp_layernorm - post-attention / pre-MLP RMSNorm
+  mlp.*            - dense MLP (gate_proj / up_proj / down_proj)
+  final_layernorm  - norm applied before logit computation
+"""
+
+from collections.abc import Iterable
+
+import torch
+import torch.nn as nn
+from transformers import PretrainedConfig
+
+from vllm.config import VllmConfig
+from vllm.distributed import (
+    get_tensor_model_parallel_rank,
+    get_tensor_model_parallel_world_size,
+)
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.linear import ReplicatedLinear
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    ParallelLMHead,
+    VocabParallelEmbedding,
+)
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.sequence import IntermediateTensors
+
+from .interfaces import (
+    MultiModalEmbeddings,
+    SupportsMultiModal,
+    _require_is_multimodal,
+)
+from .mimo_v2 import MiMoV2Attention, MiMoV2MLP
+from .utils import _merge_multimodal_embeddings, maybe_prefix
+
+# MiMo-V2 checkpoints contain multiple MTP layers, but vLLM currently supports
+# only the first layer and only one speculative token.
+_MIMO_V2_PRO_NUM_MTP_LAYERS = 1
+_MIMO_V2_FLASH_NUM_MTP_LAYERS = 1
+
+
+class MiMoV2MTPLayer(nn.Module):
+    """Single MTP predictor layer for MiMo-V2 (Pro and Flash).
+
+    Mirrors the single-layer MiMo-V2 nextn reference implementation.
+    """
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        prefix: str,
+        quant_config: QuantizationConfig | None = None,
+    ) -> None:
+        super().__init__()
+
+        # Predictor head components
+        self.enorm = RMSNorm(config.hidden_size, eps=config.layernorm_epsilon)
+        self.hnorm = RMSNorm(config.hidden_size, eps=config.layernorm_epsilon)
+        self.eh_proj = ReplicatedLinear(
+            config.hidden_size * 2, config.hidden_size, bias=False
+        )
+
+        # MTP uses the SWA attention configuration
+        # implementation.
+        swa_rope_theta = getattr(
+            config,
+            "swa_rope_theta",
+            getattr(config, "rope_theta", 1000000),
+        )
+        sliding_window_size = getattr(config, "sliding_window_size", -1)
+
+        self.input_layernorm = RMSNorm(config.hidden_size, eps=config.layernorm_epsilon)
+        self.self_attn = MiMoV2Attention(
+            hidden_size=config.hidden_size,
+            num_heads=config.swa_num_attention_heads,
+            num_kv_heads=config.swa_num_key_value_heads,
+            head_dim=config.swa_head_dim,
+            v_head_dim=getattr(config, "swa_v_head_dim", None),
+            v_scale=getattr(config, "attention_value_scale", None),
+            sliding_window_size=sliding_window_size,
+            attention_bias=config.attention_bias,
+            add_swa_attention_sink_bias=getattr(
+                config, "add_swa_attention_sink_bias", False
+            ),
+            layer_id=0,
+            rope_theta=swa_rope_theta,
+            max_position_embeddings=getattr(config, "max_position_embeddings", 32768),
+            quant_config=quant_config,
+            partial_rotary_factor=getattr(config, "partial_rotary_factor", 1.0),
+            prefix=f"{prefix}.self_attn",
+        )
+        self.pre_mlp_layernorm = RMSNorm(
+            config.hidden_size, eps=config.layernorm_epsilon
+        )
+        self.mlp = MiMoV2MLP(
+            hidden_size=config.hidden_size,
+            intermediate_size=config.intermediate_size,
+            hidden_act=config.hidden_act,
+            quant_config=quant_config,
+            prefix=f"{prefix}.mlp",
+        )
+        self.final_layernorm = RMSNorm(config.hidden_size, eps=config.layernorm_epsilon)
+
+    def forward(
+        self,
+        inputs_embeds: torch.Tensor,
+        positions: torch.Tensor,
+        previous_hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        # Combine token embedding and previous hidden state
+        h, _ = self.eh_proj(
+            torch.cat(
+                [self.enorm(inputs_embeds), self.hnorm(previous_hidden_states)], dim=-1
+            )
+        )
+
+        # Transformer block with fused residual norms
+        residual = h
+        h = self.input_layernorm(h)
+        h = self.self_attn(positions=positions, hidden_states=h)
+        h, residual = self.pre_mlp_layernorm(h, residual)
+        h = self.mlp(h)
+        h = h + residual
+
+        return self.final_layernorm(h)
+
+
+class _MiMoV2MTPLayers(nn.Module):
+    """Thin wrapper so parameter paths match checkpoint: model.mtp.layers.*"""
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        num_mtp_layers: int,
+        quant_config: QuantizationConfig | None,
+        prefix: str,
+    ) -> None:
+        super().__init__()
+        self.layers = nn.ModuleDict(
+            {
+                str(i): MiMoV2MTPLayer(
+                    config=config,
+                    prefix=f"{prefix}.{i}",
+                    quant_config=quant_config,
+                )
+                for i in range(num_mtp_layers)
+            }
+        )
+
+
+class MiMoV2MultiTokenPredictor(nn.Module):
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = "") -> None:
+        super().__init__()
+
+        config = vllm_config.model_config.hf_config
+        spec_cfg = vllm_config.speculative_config
+        assert spec_cfg is not None
+        if spec_cfg.num_speculative_tokens != 1:
+            raise ValueError(
+                "MiMo-V2 MTP in vLLM only supports num_speculative_tokens=1."
+            )
+        num_mtp_layers = 1
+
+        self.num_mtp_layers = num_mtp_layers
+
+        self.embed_tokens = VocabParallelEmbedding(
+            config.vocab_size,
+            config.hidden_size,
+        )
+
+        self.mtp = _MiMoV2MTPLayers(
+            config=config,
+            num_mtp_layers=num_mtp_layers,
+            quant_config=vllm_config.quant_config,
+            prefix=maybe_prefix(prefix, "mtp.layers"),
+        )
+
+        self.logits_processor = LogitsProcessor(config.vocab_size)
+
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.embed_tokens(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        previous_hidden_states: torch.Tensor,
+        inputs_embeds: torch.Tensor | None = None,
+        spec_step_idx: int = 0,
+    ) -> torch.Tensor:
+        assert spec_step_idx == 0, "MiMo-V2 MTP only supports one speculative token."
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_input_ids(input_ids)
+        return self.mtp.layers[str(spec_step_idx)](
+            inputs_embeds, positions, previous_hidden_states
+        )
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        lm_head: ParallelLMHead,
+        spec_step_idx: int = 0,
+    ) -> torch.Tensor:
+        assert spec_step_idx == 0, "MiMo-V2 MTP only supports one speculative token."
+        return self.logits_processor(lm_head, hidden_states)
+
+
+class MiMoV2MTP(nn.Module):
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = "") -> None:
+        super().__init__()
+        self.config = vllm_config.model_config.hf_config
+        self.model = MiMoV2MultiTokenPredictor(
+            vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
+        )
+        self.lm_head = ParallelLMHead(
+            self.config.vocab_size,
+            self.config.hidden_size,
+            prefix=maybe_prefix(prefix, "lm_head"),
+        )
+
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.embed_input_ids(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor | None,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        intermediate_tensors: IntermediateTensors | None = None,
+        inputs_embeds: torch.Tensor | None = None,
+        spec_step_idx: int = 0,
+    ) -> torch.Tensor:
+        assert spec_step_idx == 0, "MiMo-V2 MTP only supports one speculative token."
+        return self.model(
+            input_ids, positions, hidden_states, inputs_embeds, spec_step_idx
+        )
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        spec_step_idx: int = 0,
+    ) -> torch.Tensor | None:
+        assert spec_step_idx == 0, "MiMo-V2 MTP only supports one speculative token."
+        return self.model.compute_logits(hidden_states, self.lm_head, spec_step_idx)
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        tp_rank = get_tensor_model_parallel_rank()
+        tp_size = get_tensor_model_parallel_world_size()
+
+        stacked_params_mapping = [
+            ("gate_up_proj", "gate_proj", 0),
+            ("gate_up_proj", "up_proj", 1),
+            # Flash format: separate projections → fused qkv_proj
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+        ]
+
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+
+        for name, loaded_weight in weights:
+            if "rotary_emb.inv_freq" in name:
+                continue
+
+            # Only load MTP-related weights, shared embeddings, and lm_head
+            if (
+                "model.mtp" not in name
+                and "model.embed_tokens" not in name
+                and not name.startswith("lm_head")
+            ):
+                continue
+
+            # Support fused qkv_proj checkpoint (Pro format).
+            # The checkpoint is stored pre-sharded for TP=8 as
+            # [Q_rank0, K_rank0, V_rank0, Q_rank1, ...], so splitting along
+            # dim 0 with chunk(tp_size) gives each rank its Q+K+V slice for
+            # both the FP8 weight and the block weight_scale_inv. This matches
+            # how the main model loads the same layout.
+            if "qkv_proj" in name:
+                if name in params_dict:
+                    param = params_dict[name]
+                    loaded_weight = loaded_weight.chunk(tp_size, dim=0)[tp_rank]
+                    default_weight_loader(param, loaded_weight)
+                    loaded_params.add(name)
+                continue
+
+            # gate_proj/up_proj → gate_up_proj stacking (both formats);
+            # Flash: q_proj/k_proj/v_proj → qkv_proj merging.
+            stacked_matched = False
+            for param_name, weight_name, shard_id in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                name_rewritten = name.replace(weight_name, param_name)
+                if (
+                    name_rewritten.endswith(".bias")
+                    and name_rewritten not in params_dict
+                ):
+                    continue
+                if name_rewritten not in params_dict:
+                    continue
+                param = params_dict[name_rewritten]
+                weight_loader = getattr(param, "weight_loader", default_weight_loader)
+                weight_loader(param, loaded_weight, shard_id)
+                loaded_params.add(name_rewritten)
+                stacked_matched = True
+                break
+
+            if stacked_matched:
+                continue
+
+            if name.endswith(".bias") and name not in params_dict:
+                continue
+            if name not in params_dict:
+                continue
+
+            param = params_dict[name]
+            # attention_sink_bias is head-parallel; slice by tp
+            if "attention_sink_bias" in name:
+                total_heads = loaded_weight.shape[0]
+                heads_per_rank = total_heads // tp_size
+                loaded_weight = loaded_weight.narrow(
+                    0, tp_rank * heads_per_rank, heads_per_rank
+                )
+
+            weight_loader = getattr(param, "weight_loader", default_weight_loader)
+            weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+
+        return loaded_params
+
+
+class MiMoV2OmniMTP(MiMoV2MTP, SupportsMultiModal):
+    def embed_input_ids(
+        self,
+        input_ids: torch.Tensor,
+        multimodal_embeddings: MultiModalEmbeddings | None = None,
+        *,
+        is_multimodal: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        inputs_embeds = self._embed_text_input_ids(
+            input_ids,
+            self.model.embed_input_ids,
+            is_multimodal=is_multimodal,
+        )
+
+        if multimodal_embeddings is None or len(multimodal_embeddings) == 0:
+            return inputs_embeds
+
+        is_multimodal = _require_is_multimodal(is_multimodal)
+
+        inputs_embeds = _merge_multimodal_embeddings(
+            inputs_embeds=inputs_embeds,
+            multimodal_embeddings=multimodal_embeddings,
+            is_multimodal=is_multimodal,
+        )
+
+        return inputs_embeds