[Perf] FP8 FlashInfer Attn for ViT (#38065)

Signed-off-by: Zhanda Zhu <[email protected]>
Co-authored-by: Yubo Gao <[email protected]>

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,
+        )

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.

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/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

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,
@@ -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/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/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/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/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/qwen3_vl.py

@@ -136,6 +136,7 @@ from .utils import (
     maybe_prefix,
 )
 from .vision import (
+    get_fp8_padded_hidden_size,
     get_vit_attn_backend,
     is_vit_use_data_parallel,
     run_dp_sharded_mrope_vision_model,
@@ -562,6 +563,13 @@ class Qwen3_VisionTransformer(nn.Module):
 
         norm_layer = partial(nn.LayerNorm, eps=norm_eps)
         head_dim = self.hidden_size // self.num_heads
+
+        # FP8 attention: Q/K/V become independent contiguous tensors
+        # after quantization, so cu_seqlens uses uniform stride (no 3x V).
+        self.fp8_padded_hidden_size = get_fp8_padded_hidden_size(
+            self.num_heads, head_dim
+        )
+
         self.rotary_pos_emb = get_rope(
             head_size=head_dim,
             max_position=8192,
@@ -776,6 +784,7 @@ class Qwen3_VisionTransformer(nn.Module):
             self.hidden_size,
             self.tp_size,
             device,
+            fp8_padded_hidden_size=self.fp8_padded_hidden_size,
         )
 
         return metadata

vllm/model_executor/models/vision.py

@@ -10,7 +10,7 @@ from typing import Final, Generic, Literal, Protocol, TypeAlias, TypeVar
 import torch
 from transformers import PretrainedConfig
 
-from vllm.config import MultiModalConfig, VllmConfig, get_current_vllm_config
+from vllm.config import MultiModalConfig, get_current_vllm_config_or_none
 from vllm.distributed import (
     get_tensor_model_parallel_rank,
     get_tensor_model_parallel_world_size,
@@ -18,6 +18,7 @@ from vllm.distributed import (
 )
 from vllm.logger import init_logger
 from vllm.platforms import current_platform
+from vllm.utils.math_utils import round_up
 from vllm.v1.attention.backends.registry import AttentionBackendEnum
 
 logger = init_logger(__name__)
@@ -102,45 +103,48 @@ def get_vit_attn_backend(
     """
     Get the attention backend for Vision Transformer.
     """
-    try:
-        vllm_config: VllmConfig = get_current_vllm_config()
-        model_config = vllm_config.model_config
-        multimodal_config: MultiModalConfig | None = (
-            model_config.multimodal_config if model_config is not None else None
-        )
-    except (AssertionError, AttributeError):
-        multimodal_config = None
-
+    mm_cfg = get_multimodal_config()
     attn_backend_override = (
-        multimodal_config.mm_encoder_attn_backend
-        if multimodal_config is not None
-        else None
+        mm_cfg.mm_encoder_attn_backend if mm_cfg is not None else None
     )
-    attn_backend = _get_vit_attn_backend(
+    return _get_vit_attn_backend(
         head_size,
         dtype,
         attn_backend_override=attn_backend_override,
     )
-    return attn_backend
+
+
+def get_multimodal_config() -> MultiModalConfig | None:
+    """Return the current ``MultiModalConfig``, or ``None`` when no engine
+    config context is active (e.g., during unit tests) or when the current
+    ``model_config`` does not carry a ``multimodal_config`` (e.g., minimal
+    stubs used in tests)."""
+    vllm_config = get_current_vllm_config_or_none()
+    if vllm_config is None or vllm_config.model_config is None:
+        return None
+    return getattr(vllm_config.model_config, "multimodal_config", None)
+
+
+def get_fp8_padded_hidden_size(num_heads: int, head_dim: int) -> int | None:
+    """Return the padded hidden size for FP8 ViT encoder attention, or
+    ``None`` when FP8 is not enabled.
+
+    cuDNN FP8 prefill attention requires ``head_dim`` to be a multiple of
+    16. For non-aligned ``head_dim`` (e.g. 72), Q/K/V are padded to the
+    nearest multiple of 16.
+    """
+    mm_cfg = get_multimodal_config()
+    if mm_cfg is None or mm_cfg.mm_encoder_attn_dtype != "fp8":
+        return None
+    return num_heads * round_up(head_dim, 16)
 
 
 def is_vit_use_data_parallel():
     """
     Get the tensor parallel type for Vision Transformer.
     """
-    try:
-        vllm_config: VllmConfig = get_current_vllm_config()
-        model_config = vllm_config.model_config
-        multimodal_config: MultiModalConfig | None = (
-            model_config.multimodal_config if model_config is not None else None
-        )
-    except (AssertionError, AttributeError):
-        multimodal_config = None
-
-    mm_encoder_tp_mode = (
-        multimodal_config.mm_encoder_tp_mode if multimodal_config is not None else None
-    )
-    return mm_encoder_tp_mode == "data"
+    mm_cfg = get_multimodal_config()
+    return mm_cfg is not None and mm_cfg.mm_encoder_tp_mode == "data"
 
 
 VisionFeatureSelectStrategyStr = Literal["class", "default", "full"]

vllm/utils/flashinfer.py

@@ -772,6 +772,40 @@ def should_use_flashinfer_for_blockscale_fp8_gemm(
     return should_use_flashinfer
 
 
+_MIN_CUDNN_FP8 = 91701  # cuDNN >= 9.17.1 required for FP8 attention
+
+
+@functools.cache
+def is_flashinfer_cudnn_fp8_prefill_attn_supported() -> bool:
+    """Check if FP8 ViT attention is supported on this platform.
+
+    Requires native FP8 hardware support, the FlashInfer cuDNN backend,
+    and cuDNN >= 9.17.1.
+    """
+    from vllm.v1.attention.backends.registry import AttentionBackendEnum
+
+    # cuDNN SDPA FP8 requires Hopper (SM 90) or newer.
+    if not current_platform.has_device_capability(90):
+        return False
+
+    try:
+        supported = current_platform.get_supported_vit_attn_backends()
+        if AttentionBackendEnum.FLASHINFER not in supported:
+            return False
+    except (ImportError, AttributeError):
+        return False
+
+    try:
+        import torch.backends.cudnn as cudnn
+
+        if cudnn.is_available() and cudnn.version() < _MIN_CUDNN_FP8:
+            return False
+    except (ImportError, AttributeError):
+        pass
+
+    return True
+
+
 __all__ = [
     "has_flashinfer",
     "flashinfer_trtllm_fp8_block_scale_moe",
@@ -803,4 +837,5 @@ __all__ = [
     "flashinfer_fp8_blockscale_gemm",
     "should_use_flashinfer_for_blockscale_fp8_gemm",
     "is_flashinfer_fp8_blockscale_gemm_supported",
+    "is_flashinfer_cudnn_fp8_prefill_attn_supported",
 ]

vllm/v1/attention/ops/vit_attn_wrappers.py

@@ -279,6 +279,10 @@ def flashinfer_wrapper(
     cu_seqlens: torch.Tensor | None = None,
     max_seqlen: torch.Tensor | None = None,
     sequence_lengths: torch.Tensor | None = None,
+    q_scale: torch.Tensor | None = None,
+    k_scale: torch.Tensor | None = None,
+    v_scale: torch.Tensor | None = None,
+    o_data_type: torch.dtype | None = None,
 ) -> torch.Tensor:
     from flashinfer.prefill import cudnn_batch_prefill_with_kv_cache
 
@@ -318,6 +322,10 @@ def flashinfer_wrapper(
         batch_offsets_k=batch_offsets_qko,
         batch_offsets_v=batch_offsets_v,
         batch_offsets_o=batch_offsets_qko,
+        q_scale=q_scale,
+        k_scale=k_scale,
+        v_scale=v_scale,
+        o_data_type=o_data_type,
     )
 
     if is_reshaped:
@@ -335,8 +343,12 @@ def vit_flashinfer_wrapper_fake(
     cu_seqlens: torch.Tensor | None = None,
     max_seqlen: torch.Tensor | None = None,
     sequence_lengths: torch.Tensor | None = None,
+    q_scale: torch.Tensor | None = None,
+    k_scale: torch.Tensor | None = None,
+    v_scale: torch.Tensor | None = None,
+    o_data_type: torch.dtype | None = None,
 ) -> torch.Tensor:
-    return torch.empty_like(q)
+    return torch.empty_like(q, dtype=o_data_type or q.dtype)
 
 
 direct_register_custom_op(
@@ -355,7 +367,22 @@ def vit_flashinfer_wrapper(
     cu_seqlens: torch.Tensor | None = None,
     max_seqlen: torch.Tensor | None = None,
     sequence_lengths: torch.Tensor | None = None,
+    q_scale: torch.Tensor | None = None,
+    k_scale: torch.Tensor | None = None,
+    v_scale: torch.Tensor | None = None,
+    o_data_type: torch.dtype | None = None,
 ) -> torch.Tensor:
     return torch.ops.vllm.flashinfer_wrapper(
-        q, k, v, scale, workspace_buffer, cu_seqlens, max_seqlen, sequence_lengths
+        q,
+        k,
+        v,
+        scale,
+        workspace_buffer,
+        cu_seqlens,
+        max_seqlen,
+        sequence_lengths,
+        q_scale,
+        k_scale,
+        v_scale,
+        o_data_type,
     )