feat(diffusion): add request-gated VAE fast paths

Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>

vllm_omni/diffusion/vae_optimizations/__init__.py

@@ -0,0 +1,19 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Optional VAE decoder optimizations."""
+
+from vllm_omni.diffusion.vae_optimizations.gate import (
+    use_pipeline_vae_fast_path,
+    use_vae_fast_path,
+)
+from vllm_omni.diffusion.vae_optimizations.optimize import (
+    clear_pipeline_vae_fast_path_caches,
+    optimize_pipeline_vaes,
+)
+
+__all__ = [
+    "clear_pipeline_vae_fast_path_caches",
+    "optimize_pipeline_vaes",
+    "use_pipeline_vae_fast_path",
+    "use_vae_fast_path",
+]

vllm_omni/diffusion/vae_optimizations/gate.py

@@ -0,0 +1,67 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Decode-scoped gates for optional, non-bit-exact VAE fast paths."""
+
+from __future__ import annotations
+
+from collections.abc import Iterator
+from contextlib import ExitStack, contextmanager
+from weakref import WeakKeyDictionary
+
+from torch import nn
+
+from vllm_omni.diffusion.offloader.module_collector import ModuleDiscovery
+
+
+class VaeFastPathGate:
+    """Mutable flag shared by wrappers installed on one VAE instance."""
+
+    __slots__ = ("enabled",)
+
+    def __init__(self) -> None:
+        self.enabled = False
+
+
+_VAE_FAST_PATH_GATES: WeakKeyDictionary[nn.Module, VaeFastPathGate] = WeakKeyDictionary()
+
+
+def register_vae_fast_path_gate(vae: nn.Module, gate: VaeFastPathGate) -> None:
+    """Associate an installed fast path with its owning VAE."""
+
+    _VAE_FAST_PATH_GATES[vae] = gate
+
+
+@contextmanager
+def use_vae_fast_path(vae: nn.Module, enabled: bool) -> Iterator[None]:
+    """Enable an installed fast path for one decode and always restore it."""
+
+    gate = _VAE_FAST_PATH_GATES.get(vae)
+    if gate is None:
+        yield
+        return
+
+    previous_enabled = gate.enabled
+    gate.enabled = enabled
+    try:
+        yield
+    finally:
+        gate.enabled = previous_enabled
+
+
+@contextmanager
+def use_pipeline_vae_fast_path(pipeline: nn.Module, enabled: bool) -> Iterator[None]:
+    """Apply one request's quality choice to every declared VAE component."""
+
+    vaes = ModuleDiscovery.discover(pipeline).vaes
+    with ExitStack() as stack:
+        for vae in vaes:
+            stack.enter_context(use_vae_fast_path(vae, enabled))
+        yield
+
+
+__all__ = [
+    "VaeFastPathGate",
+    "register_vae_fast_path_gate",
+    "use_pipeline_vae_fast_path",
+    "use_vae_fast_path",
+]

vllm_omni/diffusion/vae_optimizations/image.py

@@ -0,0 +1,354 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Quality-gated CUDA fast paths for Diffusers-style 2D KL VAE decoders."""
+
+from __future__ import annotations
+
+from types import MethodType
+from typing import Any
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+from vllm.logger import init_logger
+
+from vllm_omni.diffusion.vae_optimizations.gate import (
+    VaeFastPathGate,
+    register_vae_fast_path_gate,
+)
+
+logger = init_logger(__name__)
+
+try:
+    from vllm_omni.diffusion.vae_optimizations.triton_group_norm_silu import (
+        group_norm_silu_4d,
+        group_norm_silu_rows,
+    )
+
+    _HAS_TRITON = True
+except ImportError:  # pragma: no cover - exercised on builds without Triton
+    _HAS_TRITON = False
+
+
+class FusedGroupNormSiLU(nn.Module):
+    """Quality-gated GroupNorm + SiLU with an exact eager fallback."""
+
+    def __init__(self, norm: nn.GroupNorm, gate: VaeFastPathGate) -> None:
+        super().__init__()
+        self.num_groups = norm.num_groups
+        self.num_channels = norm.num_channels
+        self.eps = norm.eps
+        self.affine = norm.affine
+        # Register parameters directly so state-dict FQNs do not change.
+        self.weight = norm.weight
+        self.bias = norm.bias
+        self._vllm_gate = gate
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        if self._vllm_gate.enabled and x.dim() == 4 and not torch.compiler.is_compiling():
+            output = group_norm_silu_4d(
+                x,
+                self.weight,
+                self.bias,
+                self.num_groups,
+                self.eps,
+                apply_silu=True,
+            )
+            if output is not None:
+                return output
+        return F.silu(F.group_norm(x, self.num_groups, self.weight, self.bias, self.eps))
+
+
+def _install_norm_silu(decoder: nn.Module, resnet_cls: type[nn.Module], gate: VaeFastPathGate) -> int:
+    def is_fusable(norm: object) -> bool:
+        return type(norm) is nn.GroupNorm and norm.affine and norm.weight is not None and norm.bias is not None
+
+    count = 0
+    for module in decoder.modules():
+        if (
+            type(module) is resnet_cls
+            and module.time_emb_proj is None
+            and module.time_embedding_norm in ("default", "group")
+            and module.upsample is None
+            and module.downsample is None
+            and isinstance(module.nonlinearity, nn.SiLU)
+            and is_fusable(module.norm1)
+            and is_fusable(module.norm2)
+        ):
+            module.norm1 = FusedGroupNormSiLU(module.norm1, gate)
+            module.norm2 = FusedGroupNormSiLU(module.norm2, gate)
+            module.nonlinearity = nn.Identity()
+            count += 2
+
+    if (
+        type(getattr(decoder, "conv_norm_out", None)) is nn.GroupNorm
+        and isinstance(getattr(decoder, "conv_act", None), nn.SiLU)
+        and is_fusable(decoder.conv_norm_out)
+    ):
+        decoder.conv_norm_out = FusedGroupNormSiLU(decoder.conv_norm_out, gate)
+        decoder.conv_act = nn.Identity()
+        count += 1
+    return count
+
+
+_UPSAMPLE_TAP_MAP = {0: (2,), 1: (1, 2), 2: (0, 1), 3: (0,)}
+
+
+def _fold_upsample2x_conv2d_weight(conv: nn.Conv2d) -> torch.Tensor:
+    weight = conv.weight.detach().float()
+    output_channels, input_channels = weight.shape[:2]
+    folded = weight.new_zeros(input_channels, output_channels, 4, 4)
+    for row in range(4):
+        for column in range(4):
+            value = weight.new_zeros(output_channels, input_channels)
+            for source_row in _UPSAMPLE_TAP_MAP[row]:
+                for source_column in _UPSAMPLE_TAP_MAP[column]:
+                    value += weight[:, :, source_row, source_column]
+            folded[:, :, row, column] = value.t()
+    folded = folded.to(conv.weight.dtype)
+    if conv.weight.is_contiguous(memory_format=torch.channels_last):
+        folded = folded.contiguous(memory_format=torch.channels_last)
+    return folded.to(conv.weight.device)
+
+
+class FusedUpsample2xConv2d(nn.Module):
+    """Quality-gated nearest-2x + Conv2d replacement using ConvTranspose2d."""
+
+    def __init__(self, upsample: nn.Module, gate: VaeFastPathGate) -> None:
+        super().__init__()
+        # Keep only ``conv`` registered so checkpoint parameter names stay
+        # ``...upsamplers.N.conv.*``. The original module is an eager fallback.
+        object.__setattr__(self, "_original", upsample)
+        self.conv = upsample.conv
+        self.channels = upsample.channels
+        self._vllm_gate = gate
+        self.register_buffer("_fused_weight", None, persistent=False)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        output_size: int | None = None,
+        *args: Any,
+        **kwargs: Any,
+    ) -> torch.Tensor:
+        if not self._vllm_gate.enabled or output_size is not None or hidden_states.shape[1] != self.channels:
+            return self._original(hidden_states, output_size=output_size, *args, **kwargs)
+
+        weight = self._fused_weight
+        if weight is None or weight.device != self.conv.weight.device or weight.dtype != self.conv.weight.dtype:
+            weight = _fold_upsample2x_conv2d_weight(self.conv)
+            self._fused_weight = weight
+        return F.conv_transpose2d(hidden_states, weight, self.conv.bias, stride=2, padding=1)
+
+
+def _install_fused_upsample(decoder: nn.Module, upsample_cls: type[nn.Module], gate: VaeFastPathGate) -> int:
+    count = 0
+    for block in decoder.up_blocks:
+        upsamplers = getattr(block, "upsamplers", None)
+        if not upsamplers:
+            continue
+        for index, upsample in enumerate(upsamplers):
+            if type(upsample) is not upsample_cls:
+                continue
+            conv = getattr(upsample, "conv", None)
+            if (
+                upsample.use_conv
+                and not upsample.use_conv_transpose
+                and upsample.interpolate
+                and upsample.norm is None
+                and upsample.name == "conv"
+                and type(conv) is nn.Conv2d
+                and conv.kernel_size == (3, 3)
+                and conv.stride == (1, 1)
+                and conv.padding == (1, 1)
+                and conv.dilation == (1, 1)
+                and conv.groups == 1
+                and conv.padding_mode == "zeros"
+            ):
+                upsamplers[index] = FusedUpsample2xConv2d(upsample, gate)
+                count += 1
+    return count
+
+
+def _fold_attention_value_projection(module: nn.Module) -> tuple[torch.Tensor, torch.Tensor]:
+    value_weight = module.to_v.weight.detach().float()
+    value_bias = module.to_v.bias.detach().float()
+    output_weight = module.to_out[0].weight.detach().float()
+    output_bias = module.to_out[0].bias.detach().float()
+    dtype = module.to_v.weight.dtype
+    return (
+        (output_weight @ value_weight).to(dtype).contiguous(),
+        (output_weight @ value_bias + output_bias).to(dtype),
+    )
+
+
+def _attention_fast_forward(
+    self: nn.Module,
+    hidden_states: torch.Tensor,
+    encoder_hidden_states: torch.Tensor | None = None,
+    attention_mask: torch.Tensor | None = None,
+    temb: torch.Tensor | None = None,
+    **cross_attention_kwargs: Any,
+) -> torch.Tensor:
+    if (
+        not self._vllm_gate.enabled
+        or encoder_hidden_states is not None
+        or attention_mask is not None
+        or temb is not None
+        or hidden_states.ndim != 4
+    ):
+        return type(self).forward(
+            self,
+            hidden_states,
+            encoder_hidden_states=encoder_hidden_states,
+            attention_mask=attention_mask,
+            temb=temb,
+            **cross_attention_kwargs,
+        )
+
+    residual = hidden_states
+    batch_size, channels, height, width = hidden_states.shape
+    hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch_size, height * width, channels)
+
+    if self.group_norm is not None:
+        norm = self.group_norm
+        normalized = None
+        if not torch.compiler.is_compiling():
+            normalized = group_norm_silu_rows(
+                hidden_states,
+                norm.weight,
+                norm.bias,
+                norm.num_groups,
+                norm.eps,
+                apply_silu=False,
+            )
+        hidden_states = normalized if normalized is not None else norm(hidden_states.transpose(1, 2)).transpose(1, 2)
+
+    query = self.to_q(hidden_states)
+    key = self.to_k(hidden_states)
+    folded_weight = self._vllm_folded_value_weight
+    folded_bias = self._vllm_folded_value_bias
+    if (
+        folded_weight is None
+        or folded_bias is None
+        or folded_weight.device != hidden_states.device
+        or folded_weight.dtype != self.to_v.weight.dtype
+    ):
+        folded_weight, folded_bias = _fold_attention_value_projection(self)
+        self._vllm_folded_value_weight = folded_weight
+        self._vllm_folded_value_bias = folded_bias
+    value = F.linear(hidden_states, folded_weight, folded_bias)
+
+    output = F.scaled_dot_product_attention(query.unsqueeze(1), key.unsqueeze(1), value.unsqueeze(1))
+    output = output.squeeze(1).to(query.dtype)
+    output = self.to_out[1](output)
+    output = output.reshape(batch_size, height, width, channels).permute(0, 3, 1, 2)
+    if self.residual_connection:
+        output = output + residual
+    return output / self.rescale_output_factor
+
+
+def _attention_fast_compatible(module: nn.Module, attention_cls: type, processor_cls: type) -> bool:
+    return (
+        type(module) is attention_cls
+        and isinstance(module.processor, processor_cls)
+        and module.heads == 1
+        and module.scale_qk
+        and module.spatial_norm is None
+        and not module.norm_cross
+        and getattr(module, "norm_q", None) is None
+        and getattr(module, "norm_k", None) is None
+        and getattr(module, "add_k_proj", None) is None
+        and type(module.to_q) is nn.Linear
+        and type(module.to_k) is nn.Linear
+        and type(module.to_v) is nn.Linear
+        and type(module.to_out[0]) is nn.Linear
+        and isinstance(module.to_out[1], nn.Dropout)
+        and module.to_v.bias is not None
+        and module.to_out[0].bias is not None
+    )
+
+
+def _decoder_layout_forward(self: nn.Module, *args: Any, **kwargs: Any) -> torch.Tensor:
+    use_channels_last = self._vllm_gate.enabled
+    if use_channels_last != self._vllm_channels_last:
+        memory_format = torch.channels_last if use_channels_last else torch.contiguous_format
+        self.to(memory_format=memory_format)
+        self._vllm_channels_last = use_channels_last
+        logger.debug(
+            "%s decoder switched to %s layout",
+            self._vllm_label,
+            "channels_last" if use_channels_last else "contiguous",
+        )
+    return type(self).forward(self, *args, **kwargs)
+
+
+def maybe_optimize_image_vae(vae: nn.Module) -> nn.Module:
+    """Install image VAE decoder fast paths when the structure is fully supported."""
+
+    if not _HAS_TRITON or getattr(vae, "_vllm_vae_fast_path_installed", False):
+        return vae
+
+    from diffusers.models.attention_processor import Attention, AttnProcessor2_0
+    from diffusers.models.autoencoders import AutoencoderKL
+    from diffusers.models.autoencoders.vae import Decoder
+    from diffusers.models.resnet import ResnetBlock2D
+    from diffusers.models.upsampling import Upsample2D
+
+    supported_types: tuple[type[nn.Module], ...] = (AutoencoderKL,)
+    try:
+        from diffusers.models.autoencoders.autoencoder_kl_flux2 import AutoencoderKLFlux2
+
+        supported_types += (AutoencoderKLFlux2,)
+    except ImportError:  # pragma: no cover - older supported Diffusers builds
+        pass
+
+    if not isinstance(vae, supported_types):
+        return vae
+    decoder = getattr(vae, "decoder", None)
+    if type(decoder) is not Decoder:
+        return vae
+
+    attention_modules = [
+        module for module in decoder.modules() if _attention_fast_compatible(module, Attention, AttnProcessor2_0)
+    ]
+    attention_count = sum(isinstance(module, Attention) for module in decoder.modules())
+    if len(attention_modules) != attention_count:
+        logger.info(
+            "%s has %d/%d attention blocks without a layout-safe rewrite; skipping VAE fast paths",
+            type(vae).__name__,
+            attention_count - len(attention_modules),
+            attention_count,
+        )
+        return vae
+
+    gate = VaeFastPathGate()
+    decoder._vllm_gate = gate
+    decoder._vllm_label = type(vae).__name__
+    decoder._vllm_channels_last = False
+    decoder.forward = MethodType(_decoder_layout_forward, decoder)
+    upsample_count = _install_fused_upsample(decoder, Upsample2D, gate)
+    for module in attention_modules:
+        module._vllm_gate = gate
+        module.register_buffer("_vllm_folded_value_weight", None, persistent=False)
+        module.register_buffer("_vllm_folded_value_bias", None, persistent=False)
+        module.forward = MethodType(_attention_fast_forward, module)
+    norm_count = _install_norm_silu(decoder, ResnetBlock2D, gate)
+    register_vae_fast_path_gate(vae, gate)
+    vae._vllm_vae_fast_path_installed = True
+    logger.info(
+        "%s installed quality-gated VAE decoder fast paths "
+        "(%d upsamplers, %d attention blocks, %d GroupNorm+SiLU fusions)",
+        type(vae).__name__,
+        upsample_count,
+        len(attention_modules),
+        norm_count,
+    )
+    return vae
+
+
+__all__ = [
+    "FusedGroupNormSiLU",
+    "FusedUpsample2xConv2d",
+    "maybe_optimize_image_vae",
+]

vllm_omni/diffusion/vae_optimizations/optimize.py

@@ -0,0 +1,70 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Pipeline-level installation for optional VAE optimizations."""
+
+from __future__ import annotations
+
+from torch import nn
+from vllm.logger import init_logger
+
+from vllm_omni.diffusion.data import OmniDiffusionConfig
+from vllm_omni.diffusion.offloader.module_collector import ModuleDiscovery
+from vllm_omni.diffusion.vae_optimizations.image import maybe_optimize_image_vae
+from vllm_omni.diffusion.vae_optimizations.precision import configure_wan_decode_precision
+from vllm_omni.diffusion.vae_optimizations.wan import maybe_optimize_wan_vae
+from vllm_omni.platforms import current_omni_platform
+
+logger = init_logger(__name__)
+
+_FOLDED_CACHE_NAMES = (
+    "_fused_weight",
+    "_vllm_folded_value_weight",
+    "_vllm_folded_value_bias",
+)
+
+
+def optimize_pipeline_vaes(pipeline: nn.Module, od_config: OmniDiffusionConfig) -> None:
+    """Configure declared VAE components once after checkpoint loading."""
+
+    precision = getattr(od_config, "vae_decode_precision", None)
+    vaes = ModuleDiscovery.discover(pipeline).vaes
+    for vae in vaes:
+        # An explicit precision override is a serving contract, so failures on
+        # a recognized Wan VAE must abort startup instead of being hidden.
+        configure_wan_decode_precision(vae, precision)
+
+    if not current_omni_platform.is_cuda():
+        return
+
+    for vae in vaes:
+        for optimizer in (maybe_optimize_image_vae, maybe_optimize_wan_vae):
+            try:
+                optimizer(vae)
+            except Exception:
+                # A wrapper may already have installed its exact fallback
+                # before a later optional rewrite failed. With no registered
+                # enabled gate, those wrappers remain on the reference path.
+                logger.warning(
+                    "Failed to install optional VAE fast paths on %s; using the reference path",
+                    type(vae).__name__,
+                    exc_info=True,
+                )
+
+
+def clear_pipeline_vae_fast_path_caches(pipeline: nn.Module) -> None:
+    """Release lazily folded buffers before the worker enters sleep mode."""
+
+    cleared = 0
+    for vae in ModuleDiscovery.discover(pipeline).vaes:
+        if not getattr(vae, "_vllm_vae_fast_path_installed", False):
+            continue
+        for module in vae.modules():
+            for name in _FOLDED_CACHE_NAMES:
+                if name in module._buffers and module._buffers[name] is not None:
+                    setattr(module, name, None)
+                    cleared += 1
+    if cleared:
+        logger.debug("Released %d lazy VAE fast-path buffers before sleep", cleared)
+
+
+__all__ = ["clear_pipeline_vae_fast_path_caches", "optimize_pipeline_vaes"]

vllm_omni/diffusion/vae_optimizations/precision.py

@@ -0,0 +1,91 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Decode-only precision configuration for structurally split Wan VAEs."""
+
+from __future__ import annotations
+
+from types import MethodType
+from typing import Any
+
+import torch
+from torch import nn
+from vllm.logger import init_logger
+
+logger = init_logger(__name__)
+
+PRECISION_TO_DTYPE = {
+    "fp32": torch.float32,
+    "fp16": torch.float16,
+    "bf16": torch.bfloat16,
+}
+
+
+def _decode_with_precision(
+    self: nn.Module,
+    latents: torch.Tensor,
+    *args: Any,
+    **kwargs: Any,
+) -> Any:
+    return type(self).decode(
+        self,
+        latents.to(dtype=self._vllm_decode_dtype),
+        *args,
+        **kwargs,
+    )
+
+
+def _encode_in_fp32(
+    self: nn.Module,
+    samples: torch.Tensor,
+    *args: Any,
+    **kwargs: Any,
+) -> Any:
+    return type(self).encode(
+        self,
+        samples.to(dtype=torch.float32),
+        *args,
+        **kwargs,
+    )
+
+
+def configure_wan_decode_precision(vae: nn.Module, precision: str | None) -> nn.Module:
+    """Keep Wan encode in FP32 while materializing only decode modules lower."""
+
+    if precision is None:
+        return vae
+
+    from diffusers.models.autoencoders import AutoencoderKLWan
+
+    if not isinstance(vae, AutoencoderKLWan):
+        return vae
+    try:
+        decode_dtype = PRECISION_TO_DTYPE[precision]
+    except KeyError as exc:
+        raise ValueError(
+            f"Unsupported vae_decode_precision={precision!r}; expected one of {sorted(PRECISION_TO_DTYPE)}"
+        ) from exc
+
+    encoder = getattr(vae, "encoder", None)
+    quant_conv = getattr(vae, "quant_conv", None)
+    decoder = getattr(vae, "decoder", None)
+    post_quant_conv = getattr(vae, "post_quant_conv", None)
+    if not all(isinstance(module, nn.Module) for module in (encoder, decoder, post_quant_conv)):
+        logger.info("Wan VAE lacks separate encoder/decoder modules; skipping decode-only precision")
+        return vae
+
+    encoder.to(dtype=torch.float32)
+    if isinstance(quant_conv, nn.Module):
+        quant_conv.to(dtype=torch.float32)
+    decoder.to(dtype=decode_dtype)
+    post_quant_conv.to(dtype=decode_dtype)
+    vae.encode = MethodType(_encode_in_fp32, vae)
+    vae._vllm_decode_dtype = decode_dtype
+    vae.decode = MethodType(_decode_with_precision, vae)
+    logger.info(
+        "Wan VAE configured with FP32 encode and %s decode",
+        precision,
+    )
+    return vae
+
+
+__all__ = ["PRECISION_TO_DTYPE", "configure_wan_decode_precision"]

vllm_omni/diffusion/vae_optimizations/triton_group_norm_silu.py

@@ -0,0 +1,237 @@
+# ruff: noqa: N803
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Channels-last two-pass GroupNorm(+SiLU) Triton kernels.
+
+The kernels keep the channel dimension innermost throughout a 2D VAE
+decoder. Statistics are accumulated in FP32, then a separate apply pass
+performs the affine transform and optional SiLU epilogue. Unsupported inputs
+return ``None`` so callers can fail closed to the reference implementation.
+"""
+
+from __future__ import annotations
+
+import torch
+import triton
+import triton.language as tl
+
+_SUPPORTED_DTYPES = {torch.float16, torch.bfloat16, torch.float32}
+_MAX_CHANNELS = 2048
+
+
+@triton.jit
+def _gn_partial_rows_kernel(
+    x_ptr,
+    psum_ptr,
+    psq_ptr,
+    rows,
+    rows_per_prog,
+    C: tl.constexpr,
+    BLOCK_R: tl.constexpr,
+):
+    chunk = tl.program_id(0).to(tl.int64)
+    batch = tl.program_id(1).to(tl.int64)
+    num_chunks = tl.num_programs(0)
+    row0 = chunk * rows_per_prog
+    cols = tl.arange(0, C)
+    acc_sum = tl.zeros((C,), tl.float32)
+    acc_sq = tl.zeros((C,), tl.float32)
+    x_base = x_ptr + batch * rows * C
+    for row_offset in range(0, rows_per_prog, BLOCK_R):
+        row_indices = row0 + row_offset + tl.arange(0, BLOCK_R)
+        mask = row_indices < rows
+        x = tl.load(
+            x_base + row_indices[:, None] * C + cols[None, :],
+            mask=mask[:, None],
+            other=0.0,
+        ).to(tl.float32)
+        acc_sum += tl.sum(x, 0)
+        acc_sq += tl.sum(x * x, 0)
+    output_offsets = (batch * num_chunks + chunk) * C + cols
+    tl.store(psum_ptr + output_offsets, acc_sum)
+    tl.store(psq_ptr + output_offsets, acc_sq)
+
+
+@triton.jit
+def _gn_finalize_kernel(
+    psum_ptr,
+    psq_ptr,
+    weight_ptr,
+    bias_ptr,
+    scale_shift_ptr,
+    num_chunks,
+    group_numel,
+    eps,
+    channels,
+    CPG: tl.constexpr,
+    BLOCK_K: tl.constexpr,
+):
+    group = tl.program_id(0).to(tl.int64)
+    batch = tl.program_id(1).to(tl.int64)
+    cols = group * CPG + tl.arange(0, CPG)
+    acc_sum = tl.zeros((), tl.float32)
+    acc_sq = tl.zeros((), tl.float32)
+    for chunk0 in range(0, num_chunks, BLOCK_K):
+        chunks = chunk0 + tl.arange(0, BLOCK_K)
+        mask = chunks < num_chunks
+        offsets = (batch * num_chunks + chunks)[:, None] * channels + cols[None, :]
+        acc_sum += tl.sum(tl.load(psum_ptr + offsets, mask=mask[:, None], other=0.0))
+        acc_sq += tl.sum(tl.load(psq_ptr + offsets, mask=mask[:, None], other=0.0))
+    mean = acc_sum / group_numel
+    variance = acc_sq / group_numel - mean * mean
+    variance = tl.maximum(variance, 0.0)
+    reciprocal_std = tl.rsqrt(variance + eps)
+    weight = tl.load(weight_ptr + cols).to(tl.float32)
+    bias = tl.load(bias_ptr + cols).to(tl.float32)
+    scale = weight * reciprocal_std
+    shift = bias - mean * scale
+    tl.store(scale_shift_ptr + batch * 2 * channels + cols, scale)
+    tl.store(scale_shift_ptr + (batch * 2 + 1) * channels + cols, shift)
+
+
+@triton.jit
+def _gn_apply_rows_kernel(
+    x_ptr,
+    scale_shift_ptr,
+    y_ptr,
+    rows,
+    C: tl.constexpr,
+    BLOCK_R: tl.constexpr,
+    SILU: tl.constexpr,
+):
+    program = tl.program_id(0).to(tl.int64)
+    batch = tl.program_id(1).to(tl.int64)
+    cols = tl.arange(0, C)
+    scale = tl.load(scale_shift_ptr + batch * 2 * C + cols)
+    shift = tl.load(scale_shift_ptr + (batch * 2 + 1) * C + cols)
+    row_indices = program * BLOCK_R + tl.arange(0, BLOCK_R)
+    mask = row_indices < rows
+    offsets = batch * rows * C + row_indices[:, None] * C + cols[None, :]
+    x = tl.load(x_ptr + offsets, mask=mask[:, None], other=0.0).to(tl.float32)
+    y = x * scale[None, :] + shift[None, :]
+    if SILU:
+        y = y * tl.sigmoid(y)
+    tl.store(y_ptr + offsets, y.to(y_ptr.dtype.element_ty), mask=mask[:, None])
+
+
+def _group_norm_silu_rows(
+    x: torch.Tensor,
+    weight: torch.Tensor,
+    bias: torch.Tensor,
+    num_groups: int,
+    eps: float,
+    apply_silu: bool,
+) -> torch.Tensor:
+    batch_size, rows, channels = x.shape
+    channels_per_group = channels // num_groups
+    block_rows = max(1, 8192 // channels)
+    rows_per_program = block_rows * 32
+    num_chunks = triton.cdiv(rows, rows_per_program)
+    partial_sum = torch.empty((batch_size, num_chunks, channels), device=x.device, dtype=torch.float32)
+    partial_sq = torch.empty_like(partial_sum)
+    with torch.cuda.device(x.device):
+        _gn_partial_rows_kernel[(num_chunks, batch_size)](
+            x,
+            partial_sum,
+            partial_sq,
+            rows,
+            rows_per_program,
+            C=channels,
+            BLOCK_R=block_rows,
+            num_warps=4,
+        )
+        scale_shift = torch.empty((batch_size, 2, channels), device=x.device, dtype=torch.float32)
+        block_chunks = max(1, min(4096 // channels_per_group, triton.next_power_of_2(num_chunks)))
+        _gn_finalize_kernel[(num_groups, batch_size)](
+            partial_sum,
+            partial_sq,
+            weight,
+            bias,
+            scale_shift,
+            num_chunks,
+            rows * channels_per_group,
+            eps,
+            channels,
+            CPG=channels_per_group,
+            BLOCK_K=block_chunks,
+            num_warps=4,
+        )
+        output = torch.empty_like(x)
+        _gn_apply_rows_kernel[(triton.cdiv(rows, block_rows), batch_size)](
+            x,
+            scale_shift,
+            output,
+            rows,
+            C=channels,
+            BLOCK_R=block_rows,
+            SILU=apply_silu,
+            num_warps=4,
+        )
+    return output
+
+
+def _is_supported(
+    x: torch.Tensor,
+    weight: torch.Tensor | None,
+    bias: torch.Tensor | None,
+    num_groups: int,
+) -> bool:
+    if not (x.is_cuda and x.numel() > 0 and not torch.is_grad_enabled()):
+        return False
+    if x.requires_grad or x.dtype not in _SUPPORTED_DTYPES:
+        return False
+    if weight is None or bias is None:
+        return False
+    channels = x.shape[1] if x.dim() == 4 else x.shape[-1]
+    if weight.shape != (channels,) or bias.shape != (channels,):
+        return False
+    if not (
+        weight.device == bias.device == x.device
+        and weight.dtype == bias.dtype == x.dtype
+        and weight.is_contiguous()
+        and bias.is_contiguous()
+    ):
+        return False
+    if num_groups < 1 or channels % num_groups != 0:
+        return False
+    return triton.next_power_of_2(channels) == channels and channels <= _MAX_CHANNELS
+
+
+def group_norm_silu_4d(
+    x: torch.Tensor,
+    weight: torch.Tensor,
+    bias: torch.Tensor,
+    num_groups: int,
+    eps: float,
+    apply_silu: bool = True,
+) -> torch.Tensor | None:
+    """Run GroupNorm(+SiLU) on a channels-last ``(N, C, H, W)`` tensor."""
+
+    if x.dim() != 4 or not _is_supported(x, weight, bias, num_groups):
+        return None
+    batch_size, channels, height, width = x.shape
+    if not (channels > 1 and (height > 1 or width > 1) and x.is_contiguous(memory_format=torch.channels_last)):
+        return None
+    rows = x.permute(0, 2, 3, 1).reshape(batch_size, height * width, channels)
+    output = _group_norm_silu_rows(rows, weight, bias, num_groups, eps, apply_silu)
+    return output.reshape(batch_size, height, width, channels).permute(0, 3, 1, 2)
+
+
+def group_norm_silu_rows(
+    x: torch.Tensor,
+    weight: torch.Tensor,
+    bias: torch.Tensor,
+    num_groups: int,
+    eps: float,
+    apply_silu: bool = True,
+) -> torch.Tensor | None:
+    """Run GroupNorm(+SiLU) over contiguous ``(N, rows, C)`` input."""
+
+    if x.dim() != 3 or not x.is_contiguous():
+        return None
+    if not _is_supported(x, weight, bias, num_groups):
+        return None
+    return _group_norm_silu_rows(x, weight, bias, num_groups, eps, apply_silu)
+
+
+__all__ = ["group_norm_silu_4d", "group_norm_silu_rows"]

vllm_omni/diffusion/vae_optimizations/triton_wan_rmsnorm_silu.py

@@ -0,0 +1,160 @@
+# ruff: noqa: N803
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Channels-last-3D Wan VAE RMSNorm(+SiLU) Triton kernel."""
+
+from __future__ import annotations
+
+import torch
+import triton
+import triton.language as tl
+
+_SUPPORTED_DTYPES = {torch.float16, torch.bfloat16, torch.float32}
+_MAX_CHANNELS = 1024
+
+
+@triton.jit
+def _wan_rmsnorm_silu_kernel(
+    x_ptr,
+    gamma_ptr,
+    bias_ptr,
+    output_ptr,
+    channels: tl.constexpr,
+    time_size,
+    height_size,
+    width_size,
+    x_stride_batch,
+    x_stride_channel,
+    x_stride_time,
+    x_stride_height,
+    x_stride_width,
+    output_stride_batch,
+    output_stride_channel,
+    output_stride_time,
+    output_stride_height,
+    output_stride_width,
+    rms_scale,
+    eps,
+    has_bias: tl.constexpr,
+    BLOCK_C: tl.constexpr,
+):
+    row = tl.program_id(0).to(tl.int64)
+    channel_offsets = tl.arange(0, BLOCK_C)
+    channel_mask = channel_offsets < channels
+
+    width = row % width_size
+    remainder = row // width_size
+    height = remainder % height_size
+    remainder = remainder // height_size
+    time = remainder % time_size
+    batch = remainder // time_size
+
+    x_base = batch * x_stride_batch + time * x_stride_time + height * x_stride_height + width * x_stride_width
+    output_base = (
+        batch * output_stride_batch
+        + time * output_stride_time
+        + height * output_stride_height
+        + width * output_stride_width
+    )
+
+    x = tl.load(
+        x_ptr + x_base + channel_offsets * x_stride_channel,
+        mask=channel_mask,
+        other=0.0,
+    ).to(tl.float32)
+    norm = tl.sqrt(tl.sum(x * x, axis=0))
+    inverse_norm = 1.0 / tl.maximum(norm, eps)
+
+    # Match the eager dtype boundaries: normalize and scale in activation
+    # dtype, affine in the promoted dtype, then SiLU in FP32.
+    y = (x * inverse_norm).to(x_ptr.dtype.element_ty)
+    gamma = tl.load(gamma_ptr + channel_offsets, mask=channel_mask, other=1.0)
+    y = (y * rms_scale).to(x_ptr.dtype.element_ty)
+    y = (y.to(tl.float32) * gamma.to(tl.float32)).to(output_ptr.dtype.element_ty)
+    if has_bias:
+        bias = tl.load(bias_ptr + channel_offsets, mask=channel_mask, other=0.0)
+        y = (y.to(tl.float32) + bias.to(tl.float32)).to(output_ptr.dtype.element_ty)
+    y = y.to(tl.float32)
+    y = y * tl.sigmoid(y)
+    tl.store(
+        output_ptr + output_base + channel_offsets * output_stride_channel,
+        y,
+        mask=channel_mask,
+    )
+
+
+def _affine_supported(x: torch.Tensor, affine: torch.Tensor) -> bool:
+    return (
+        affine.is_cuda
+        and affine.device == x.device
+        and (affine.dtype == x.dtype or affine.dtype == torch.float32)
+        and affine.numel() == x.shape[1]
+    )
+
+
+def can_use_wan_rmsnorm_silu(
+    x: torch.Tensor,
+    gamma: torch.Tensor,
+    bias: torch.Tensor | None,
+) -> bool:
+    """Return whether ``wan_rmsnorm_silu`` supports this exact input."""
+
+    return (
+        x.is_cuda
+        and not torch.is_grad_enabled()
+        and not x.requires_grad
+        and x.dtype in _SUPPORTED_DTYPES
+        and x.ndim == 5
+        and x.numel() > 0
+        and 0 < x.shape[1] <= _MAX_CHANNELS
+        and x.is_contiguous(memory_format=torch.channels_last_3d)
+        and _affine_supported(x, gamma)
+        and (bias is None or _affine_supported(x, bias))
+    )
+
+
+def wan_rmsnorm_silu(
+    x: torch.Tensor,
+    gamma: torch.Tensor,
+    bias: torch.Tensor | None = None,
+    rms_scale: float | None = None,
+    eps: float = 1e-12,
+) -> torch.Tensor | None:
+    """Fuse ``SiLU(F.normalize(x, dim=1) * scale * gamma + bias)``."""
+
+    if not can_use_wan_rmsnorm_silu(x, gamma, bias):
+        return None
+
+    batch_size, channels, time_size, height_size, width_size = x.shape
+    output_dtype = torch.promote_types(x.dtype, gamma.dtype)
+    output = torch.empty_strided(x.shape, x.stride(), device=x.device, dtype=output_dtype)
+    block_channels = triton.next_power_of_2(channels)
+    num_warps = 1 if block_channels <= 64 else 4 if block_channels <= 512 else 8
+    gamma = gamma.reshape(channels).contiguous()
+    has_bias = bias is not None
+    bias = gamma if bias is None else bias.reshape(channels).contiguous()
+    if rms_scale is None:
+        rms_scale = channels**0.5
+
+    with torch.cuda.device(x.device):
+        _wan_rmsnorm_silu_kernel[(batch_size * time_size * height_size * width_size,)](
+            x,
+            gamma,
+            bias,
+            output,
+            channels,
+            time_size,
+            height_size,
+            width_size,
+            *x.stride(),
+            *output.stride(),
+            float(rms_scale),
+            eps,
+            has_bias,
+            BLOCK_C=block_channels,
+            num_warps=num_warps,
+        )
+    return output
+
+
+__all__ = ["can_use_wan_rmsnorm_silu", "wan_rmsnorm_silu"]

vllm_omni/diffusion/vae_optimizations/wan.py

@@ -0,0 +1,162 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Quality-gated CUDA fast path for Diffusers Wan VAE decoders."""
+
+from __future__ import annotations
+
+from types import MethodType
+from typing import Any
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+from vllm.logger import init_logger
+
+from vllm_omni.diffusion.vae_optimizations.gate import (
+    VaeFastPathGate,
+    register_vae_fast_path_gate,
+)
+
+logger = init_logger(__name__)
+
+try:
+    from vllm_omni.diffusion.vae_optimizations.triton_wan_rmsnorm_silu import (
+        wan_rmsnorm_silu,
+    )
+
+    _HAS_TRITON = True
+except ImportError:  # pragma: no cover - exercised on builds without Triton
+    _HAS_TRITON = False
+
+
+class FusedWanRMSNormSiLU(nn.Module):
+    """Quality-gated Wan RMSNorm + SiLU with an exact eager fallback."""
+
+    def __init__(self, norm: nn.Module, gate: VaeFastPathGate) -> None:
+        super().__init__()
+        self.gamma = norm.gamma
+        self.bias = norm.bias
+        self.scale = float(norm.scale)
+        self._vllm_gate = gate
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        if self._vllm_gate.enabled and not torch.compiler.is_compiling():
+            bias = self.bias if isinstance(self.bias, torch.Tensor) else None
+            output = wan_rmsnorm_silu(x, self.gamma, bias, rms_scale=self.scale)
+            if output is not None:
+                return output
+
+        needs_fp32_normalize = x.dtype in (torch.float16, torch.bfloat16) or any(
+            token in str(x.dtype) for token in ("float4_", "float8_")
+        )
+        normalized = F.normalize(x.float() if needs_fp32_normalize else x, dim=1).to(x.dtype)
+        return F.silu(normalized * self.scale * self.gamma + self.bias)
+
+
+def _plain_silu(activation: object) -> bool:
+    return isinstance(activation, nn.SiLU) and not activation.inplace
+
+
+def _fusable_norm(norm: object, norm_cls: type) -> bool:
+    return (
+        type(norm) is norm_cls
+        and getattr(norm, "channel_first", False)
+        and isinstance(getattr(norm, "gamma", None), torch.Tensor)
+    )
+
+
+def _install_norm_silu(decoder: nn.Module, gate: VaeFastPathGate) -> int | None:
+    from diffusers.models.autoencoders.autoencoder_kl_wan import WanResidualBlock, WanRMS_norm
+
+    residual_blocks = [module for module in decoder.modules() if isinstance(module, WanResidualBlock)]
+    eligible = [
+        module
+        for module in residual_blocks
+        if type(module) is WanResidualBlock
+        and _plain_silu(module.nonlinearity)
+        and _fusable_norm(module.norm1, WanRMS_norm)
+        and _fusable_norm(module.norm2, WanRMS_norm)
+    ]
+    if len(eligible) != len(residual_blocks):
+        logger.info(
+            "Wan VAE has %d/%d non-standard residual blocks; skipping fast path",
+            len(residual_blocks) - len(eligible),
+            len(residual_blocks),
+        )
+        return None
+    if not (
+        _fusable_norm(getattr(decoder, "norm_out", None), WanRMS_norm)
+        and _plain_silu(getattr(decoder, "nonlinearity", None))
+    ):
+        logger.info("Wan VAE has a non-standard output head; skipping fast path")
+        return None
+
+    count = 0
+    for module in eligible:
+        module.norm1 = FusedWanRMSNormSiLU(module.norm1, gate)
+        module.norm2 = FusedWanRMSNormSiLU(module.norm2, gate)
+        module.nonlinearity = nn.Identity()
+        count += 2
+    decoder.norm_out = FusedWanRMSNormSiLU(decoder.norm_out, gate)
+    decoder.nonlinearity = nn.Identity()
+    return count + 1
+
+
+def _set_wan_decoder_memory_format(decoder: nn.Module, *, channels_last: bool) -> None:
+    for module in decoder.modules():
+        if isinstance(module, nn.Conv3d):
+            module.to(memory_format=torch.channels_last_3d if channels_last else torch.contiguous_format)
+        elif isinstance(module, (nn.Conv2d, nn.ConvTranspose2d)):
+            module.to(memory_format=torch.channels_last if channels_last else torch.contiguous_format)
+
+
+def _decoder_layout_forward(self: nn.Module, *args: Any, **kwargs: Any) -> torch.Tensor:
+    use_channels_last = self._vllm_gate.enabled
+    if use_channels_last != self._vllm_channels_last:
+        _set_wan_decoder_memory_format(self, channels_last=use_channels_last)
+        self._vllm_channels_last = use_channels_last
+        logger.debug(
+            "Wan VAE decoder switched to %s layout",
+            "channels_last_3d" if use_channels_last else "contiguous",
+        )
+    return type(self).forward(self, *args, **kwargs)
+
+
+def maybe_optimize_wan_vae(vae: nn.Module) -> nn.Module:
+    """Install the Wan decoder fast path when its full structure is supported."""
+
+    if not _HAS_TRITON or getattr(vae, "_vllm_vae_fast_path_installed", False):
+        return vae
+
+    from diffusers.models.autoencoders import AutoencoderKLWan
+    from diffusers.models.autoencoders.autoencoder_kl_wan import WanDecoder3d
+
+    if not isinstance(vae, AutoencoderKLWan):
+        return vae
+    decoder = getattr(vae, "decoder", None)
+    if type(decoder) is not WanDecoder3d:
+        return vae
+
+    distributed_executor = getattr(vae, "distributed_executor", None)
+    parallel_mode = getattr(distributed_executor, "parallel_mode", "tile")
+    if parallel_mode in ("auto", "spatial_shard_height", "spatial_shard_width"):
+        logger.info("Wan spatial-shard decode is configured; skipping quality-gated VAE fast path")
+        return vae
+
+    gate = VaeFastPathGate()
+    norm_count = _install_norm_silu(decoder, gate)
+    if norm_count is None:
+        return vae
+    decoder._vllm_gate = gate
+    decoder._vllm_channels_last = False
+    decoder.forward = MethodType(_decoder_layout_forward, decoder)
+    register_vae_fast_path_gate(vae, gate)
+    vae._vllm_vae_fast_path_installed = True
+    logger.info(
+        "Wan VAE installed quality-gated decoder fast path (%d RMSNorm+SiLU fusions)",
+        norm_count,
+    )
+    return vae
+
+
+__all__ = ["FusedWanRMSNormSiLU", "maybe_optimize_wan_vae"]