feat: NPU operator adaptations for MindIE-SD

diffsynth_engine/args.py

@@ -17,8 +17,10 @@ def _parse_tuple(value: str) -> Tuple[int, int] | int:
         raise ValueError(f"Cannot parse tuple: {value}, format should be '256,256' or '256'")
 
 
-def _parse_attention_type(attn_type_str: str) -> AttentionType:
+def _parse_attention_type(attn_type_str: str | None) -> AttentionType | None:
     """Convert string to AttentionType enum"""
+    if attn_type_str is None:
+        return None
     return AttentionType[attn_type_str.upper()]
 
 
@@ -106,9 +108,9 @@ def parse_cli_args() -> Dict[str, Any]:
     attn_group.add_argument(
         "--attn-type",
         type=str,
-        default="sdpa",
+        default=None,
         choices=attn_type_choices,
-        help="Attention type (default: sdpa)",
+        help="Attention type (default: auto, SDPA on GPU, MINDIE on NPU)",
     )
     attn_group.add_argument(
         "--sparge-topk",

diffsynth_engine/configs/base.py

@@ -36,7 +36,7 @@ class PipelineConfig:
     vae_tile_stride: int | Tuple[int, int] = (192, 192)
 
     # attention
-    attn_type: AttentionType = AttentionType.SDPA
+    attn_type: AttentionType | None = None  # None = auto-detect
     attn_params: Optional[AttentionParams] = None
 
     # parallelism

diffsynth_engine/layers/attention/backends/abstract.py

@@ -22,6 +22,7 @@ class AttentionType(enum.Enum):
     SAGE2 = enum.auto()
     SAGE3 = enum.auto()
     SPARGE = enum.auto()
+    MINDIE = enum.auto()
 
     def __str__(self) -> str:
         return self.name.lower()

diffsynth_engine/layers/attention/backends/mindie_attn.py

@@ -0,0 +1,79 @@
+import torch
+from diffsynth_engine.layers.attention.backends.abstract import (
+    AttentionBackend,
+    AttentionImpl,
+    AttentionMetadata,
+    AttentionType,
+)
+from diffsynth_engine.utils.import_utils import is_npu_available
+
+
+class MindieAttentionBackend(AttentionBackend):
+    @staticmethod
+    def check_availability() -> None:
+        if not is_npu_available():
+            raise RuntimeError("NPU is not available, cannot use MINDIE attention backend")
+
+    @staticmethod
+    def get_type() -> AttentionType:
+        return AttentionType.MINDIE
+
+    @staticmethod
+    def get_impl_cls() -> type["AttentionImpl"]:
+        return MindieAttentionImpl
+
+    @staticmethod
+    def get_metadata_cls() -> type["AttentionMetadata"]:
+        return AttentionMetadata
+
+    @staticmethod
+    def get_builder_cls() -> type:
+        return None
+
+    @staticmethod
+    def get_supported_head_sizes() -> list[int]:
+        return []
+
+
+class MindieAttentionImpl(AttentionImpl):
+    def __init__(
+        self,
+        num_heads: int,
+        head_size: int,
+        softmax_scale: float | None = None,
+        causal: bool = False,
+        num_kv_heads: int | None = None,
+        **extra_impl_args,
+    ) -> None:
+        if num_kv_heads is None:
+            num_kv_heads = num_heads
+        self.num_kv_groups = num_heads // num_kv_heads
+        self.causal = causal
+        self.softmax_scale = softmax_scale
+        self.num_heads = num_heads
+        self.head_size = head_size
+
+    def forward(
+        self,
+        query: torch.Tensor,
+        key: torch.Tensor,
+        value: torch.Tensor,
+        attn_mask: torch.Tensor | None = None,
+        attn_metadata=None,
+    ) -> torch.Tensor:
+        from mindiesd.layers.flash_attn.attention_forward import attention_forward
+
+        scale = self.softmax_scale
+        if scale is None:
+            scale = self.head_size ** -0.5
+
+        out = attention_forward(
+            query=query,
+            key=key,
+            value=value,
+            attn_mask=attn_mask,
+            scale=scale,
+            fused=True,
+            head_first=False,
+        )
+        return out

diffsynth_engine/layers/attention/selector.py

@@ -1,7 +1,7 @@
 from functools import cache
 
 from diffsynth_engine.layers.attention.backends.abstract import AttentionBackend, AttentionType
-from diffsynth_engine.utils.import_utils import LazyImport
+from diffsynth_engine.utils.import_utils import LazyImport, is_npu_available
 
 AiterBackend = LazyImport("diffsynth_engine.layers.attention.backends.aiter", "AiterBackend")
 AiterFP8Backend = LazyImport("diffsynth_engine.layers.attention.backends.aiter", "AiterFP8Backend")
@@ -15,6 +15,7 @@
 SageAttention3Backend = LazyImport("diffsynth_engine.layers.attention.backends.sage_attn_3", "SageAttention3Backend")
 SDPABackend = LazyImport("diffsynth_engine.layers.attention.backends.sdpa", "SDPABackend")
 SpargeAttentionBackend = LazyImport("diffsynth_engine.layers.attention.backends.sparge_attn", "SpargeAttentionBackend")
+MindieAttentionBackend = LazyImport("diffsynth_engine.layers.attention.backends.mindie_attn", "MindieAttentionBackend")
 
 _attention_backends = {
     AttentionType.AITER: AiterBackend,
@@ -27,14 +28,16 @@
     AttentionType.SAGE3: SageAttention3Backend,
     AttentionType.SDPA: SDPABackend,
     AttentionType.SPARGE: SpargeAttentionBackend,
+    AttentionType.MINDIE: MindieAttentionBackend,
 }
 
 
 @cache
 def get_attn_backend(head_size: int, attn_type: AttentionType | None = None) -> type["AttentionBackend"]:
-    # use SDPA as default
     if attn_type is None:
-        attn_type = AttentionType.SDPA
+        # Auto-detect: NPU → MINDIE, otherwise → SDPA
+        attn_type = AttentionType.MINDIE if is_npu_available() else AttentionType.SDPA
+
     selected_backend = _attention_backends[attn_type]
     selected_backend.check_availability()
     if not selected_backend.supports_head_size(head_size):

diffsynth_engine/layers/mlp.py

@@ -0,0 +1,72 @@
+import torch.nn as nn
+import torch.nn.functional as F
+from diffsynth_engine.utils.import_utils import is_npu_available
+
+try:
+    import torch_npu
+except ImportError:
+    torch_npu = None
+
+
+class _GELUProj(nn.Module):
+    """Wrapper to match diffusers FeedForward GELU structure with internal proj.
+
+    This wrapper holds the first Linear layer as .proj to match checkpoint keys.
+    """
+
+    def __init__(self, dim, inner_dim):
+        super().__init__()
+        self.proj = nn.Linear(dim, inner_dim)
+
+    def forward(self, x):
+        return F.gelu(x, approximate="tanh")
+
+
+class FastGELUMLP(nn.Module):
+    """MLP with npu_fast_gelu on NPU, fallback to F.gelu on other devices.
+
+    Functionally equivalent to diffusers.models.attention.FeedForward(
+        dim=dim, dim_out=dim, activation_fn="gelu-approximate"
+    )
+    """
+
+    def __init__(self, dim, dim_out=None, mult=4):
+        """Initialize MLP.
+
+        Args:
+            dim: Input and output dimension
+            dim_out: Output dimension, defaults to dim
+            mult: inner_dim = dim * mult, defaults to 4
+        """
+        super().__init__()
+        inner_dim = int(dim * mult)
+        dim_out = dim_out or dim
+
+        # Match diffusers FeedForward structure: net[0]=GELU(proj), net[2]=output
+        # net[1] is Dropout which is skipped in inference
+        self.net = nn.ModuleList([
+            _GELUProj(dim, inner_dim),
+            nn.Dropout(0.0),
+            nn.Linear(inner_dim, dim_out),
+        ])
+
+    def forward(self, hidden_states):
+        """Forward pass.
+
+        Args:
+            hidden_states: Input tensor, shape [B, S, dim]
+
+        Returns:
+            Output tensor, shape [B, S, dim_out]
+        """
+        # net[0] = _GELUProj with internal proj (dim → inner_dim)
+        hidden_states = self.net[0].proj(hidden_states)
+
+        if is_npu_available() and torch_npu is not None:
+            hidden_states = torch_npu.npu_fast_gelu(hidden_states)
+        else:
+            hidden_states = F.gelu(hidden_states, approximate="tanh")
+
+        # net[2] = output Linear (inner_dim → dim_out)
+        hidden_states = self.net[2](hidden_states)
+        return hidden_states

diffsynth_engine/layers/norm.py

@@ -0,0 +1,78 @@
+import torch
+import torch.nn as nn
+from diffusers.models.normalization import RMSNorm as DiffusersRMSNorm
+from diffsynth_engine.utils.import_utils import is_npu_available
+
+try:
+    import torch_npu
+except ImportError:
+    torch_npu = None
+
+try:
+    from mindiesd.layers import layernorm_scale_shift
+except ImportError:
+    layernorm_scale_shift = None
+
+
+class RMSNorm(nn.Module):
+    """NPU-optimized RMSNorm wrapper with fallback to diffusers implementation."""
+
+    def __init__(self, hidden_size, eps=1e-6):
+        super().__init__()
+        self.hidden_size = hidden_size
+        self.eps = eps
+        # Cache the fallback instance so forward() reuses the same weight
+        # tensor. register_parameter is reference assignment (no copy), so
+        # self.weight and self._fallback.weight share the same storage.
+        # When a checkpoint writes to "weight", both paths see the update.
+        self._fallback = DiffusersRMSNorm(hidden_size, eps)
+        self.register_parameter("weight", self._fallback.weight)
+
+    def forward(self, hidden_states):
+        if is_npu_available() and torch_npu is not None:
+            return torch_npu.npu_rms_norm(hidden_states, self.weight, epsilon=self.eps)[0]
+        else:
+            return self._fallback(hidden_states)
+
+
+class AdaLayerNorm(nn.Module):
+    """NPU-optimized AdaLayerNorm with fallback to original implementation.
+
+    Performs: output = layernorm(x) * (1 + scale) + shift
+
+    Args:
+        layernorm: The underlying nn.LayerNorm module (elementwise_affine=False)
+    """
+
+    def __init__(self, layernorm: nn.LayerNorm):
+        super().__init__()
+        self.layernorm = layernorm
+
+    def forward(self, hidden_states: torch.Tensor, scale: torch.Tensor, shift: torch.Tensor) -> torch.Tensor:
+        """
+        Args:
+            hidden_states: Input tensor, shape [B, S, H]
+            scale: Scale parameter, shape [B, H] or [B, 1, H]
+            shift: Shift parameter, shape [B, H] or [B, 1, H]
+
+        Returns:
+            layernorm(x) * (1 + scale) + shift
+        """
+        if is_npu_available() and layernorm_scale_shift is not None:
+            # NPU path: use MindIE-SD fused operator
+            return layernorm_scale_shift(
+                layernorm=self.layernorm,
+                x=hidden_states,
+                scale=scale,
+                shift=shift,
+                fused=True
+            )
+        else:
+            # Fallback: original Python implementation
+            normed = self.layernorm(hidden_states)
+            # Handle [B, 1, H] -> [B, H] dimension
+            if scale.dim() == 2:
+                scale = scale.unsqueeze(1)
+            if shift.dim() == 2:
+                shift = shift.unsqueeze(1)
+            return normed * (1 + scale) + shift