[refactor] Wan single file implementation (huggingface#11918)

* update

* update

* update

* add coauthor

Co-Authored-By: Dhruv Nair <dhruv.nair@gmail.com>

* improve test

* handle ip adapter params correctly

* fix chroma qkv fusion test

* fix fastercache implementation

* remove set_attention_backend related code

* fix more tests

* fight more tests

* add back set_attention_backend

* update

* update

* make style

* make fix-copies

* make ip adapter processor compatible with attention dispatcher

* refactor chroma as well

* attnetion dispatcher support

* remove transpose; fix rope shape

* remove rmsnorm assert

* minify and deprecate npu/xla processors

* remove rmsnorm assert

* minify and deprecate npu/xla processors

* update

* Update src/diffusers/models/transformers/transformer_wan.py

---------

Co-authored-by: Dhruv Nair <dhruv.nair@gmail.com>

Author: 2 people

src/diffusers/models/transformers/transformer_wan.py

@@ -21,9 +21,10 @@
 
 from ...configuration_utils import ConfigMixin, register_to_config
 from ...loaders import FromOriginalModelMixin, PeftAdapterMixin
-from ...utils import USE_PEFT_BACKEND, logging, scale_lora_layers, unscale_lora_layers
-from ..attention import FeedForward
-from ..attention_processor import Attention
+from ...utils import USE_PEFT_BACKEND, deprecate, logging, scale_lora_layers, unscale_lora_layers
+from ...utils.torch_utils import maybe_allow_in_graph
+from ..attention import AttentionMixin, AttentionModuleMixin, FeedForward
+from ..attention_dispatch import dispatch_attention_fn
 from ..cache_utils import CacheMixin
 from ..embeddings import PixArtAlphaTextProjection, TimestepEmbedding, Timesteps, get_1d_rotary_pos_embed
 from ..modeling_outputs import Transformer2DModelOutput
@@ -34,72 +35,117 @@
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
 
-class WanAttnProcessor2_0:
+def _get_qkv_projections(attn: "WanAttention", hidden_states: torch.Tensor, encoder_hidden_states: torch.Tensor):
+    # encoder_hidden_states is only passed for cross-attention
+    if encoder_hidden_states is None:
+        encoder_hidden_states = hidden_states
+
+    if attn.fused_projections:
+        if attn.cross_attention_dim_head is None:
+            # In self-attention layers, we can fuse the entire QKV projection into a single linear
+            query, key, value = attn.to_qkv(hidden_states).chunk(3, dim=-1)
+        else:
+            # In cross-attention layers, we can only fuse the KV projections into a single linear
+            query = attn.to_q(hidden_states)
+            key, value = attn.to_kv(encoder_hidden_states).chunk(2, dim=-1)
+    else:
+        query = attn.to_q(hidden_states)
+        key = attn.to_k(encoder_hidden_states)
+        value = attn.to_v(encoder_hidden_states)
+    return query, key, value
+
+
+def _get_added_kv_projections(attn: "WanAttention", encoder_hidden_states_img: torch.Tensor):
+    if attn.fused_projections:
+        key_img, value_img = attn.to_added_kv(encoder_hidden_states_img).chunk(2, dim=-1)
+    else:
+        key_img = attn.add_k_proj(encoder_hidden_states_img)
+        value_img = attn.add_v_proj(encoder_hidden_states_img)
+    return key_img, value_img
+
+
+class WanAttnProcessor:
+    _attention_backend = None
+
     def __init__(self):
         if not hasattr(F, "scaled_dot_product_attention"):
-            raise ImportError("WanAttnProcessor2_0 requires PyTorch 2.0. To use it, please upgrade PyTorch to 2.0.")
+            raise ImportError(
+                "WanAttnProcessor requires PyTorch 2.0. To use it, please upgrade PyTorch to version 2.0 or higher."
+            )
 
     def __call__(
         self,
-        attn: Attention,
+        attn: "WanAttention",
         hidden_states: torch.Tensor,
         encoder_hidden_states: Optional[torch.Tensor] = None,
         attention_mask: Optional[torch.Tensor] = None,
-        rotary_emb: Optional[torch.Tensor] = None,
+        rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
     ) -> torch.Tensor:
         encoder_hidden_states_img = None
         if attn.add_k_proj is not None:
             # 512 is the context length of the text encoder, hardcoded for now
             image_context_length = encoder_hidden_states.shape[1] - 512
             encoder_hidden_states_img = encoder_hidden_states[:, :image_context_length]
             encoder_hidden_states = encoder_hidden_states[:, image_context_length:]
-        if encoder_hidden_states is None:
-            encoder_hidden_states = hidden_states
 
-        query = attn.to_q(hidden_states)
-        key = attn.to_k(encoder_hidden_states)
-        value = attn.to_v(encoder_hidden_states)
+        query, key, value = _get_qkv_projections(attn, hidden_states, encoder_hidden_states)
 
-        if attn.norm_q is not None:
-            query = attn.norm_q(query)
-        if attn.norm_k is not None:
-            key = attn.norm_k(key)
+        query = attn.norm_q(query)
+        key = attn.norm_k(key)
 
-        query = query.unflatten(2, (attn.heads, -1)).transpose(1, 2)
-        key = key.unflatten(2, (attn.heads, -1)).transpose(1, 2)
-        value = value.unflatten(2, (attn.heads, -1)).transpose(1, 2)
+        query = query.unflatten(2, (attn.heads, -1))
+        key = key.unflatten(2, (attn.heads, -1))
+        value = value.unflatten(2, (attn.heads, -1))
 
         if rotary_emb is not None:
 
-            def apply_rotary_emb(hidden_states: torch.Tensor, freqs: torch.Tensor):
-                dtype = torch.float32 if hidden_states.device.type == "mps" else torch.float64
-                x_rotated = torch.view_as_complex(hidden_states.to(dtype).unflatten(3, (-1, 2)))
-                x_out = torch.view_as_real(x_rotated * freqs).flatten(3, 4)
-                return x_out.type_as(hidden_states)
+            def apply_rotary_emb(
+                hidden_states: torch.Tensor,
+                freqs_cos: torch.Tensor,
+                freqs_sin: torch.Tensor,
+            ):
+                x1, x2 = hidden_states.unflatten(-1, (-1, 2)).unbind(-1)
+                cos = freqs_cos[..., 0::2]
+                sin = freqs_sin[..., 1::2]
+                out = torch.empty_like(hidden_states)
+                out[..., 0::2] = x1 * cos - x2 * sin
+                out[..., 1::2] = x1 * sin + x2 * cos
+                return out.type_as(hidden_states)
 
             query = apply_rotary_emb(query, rotary_emb)
             key = apply_rotary_emb(key, rotary_emb)
 
         # I2V task
         hidden_states_img = None
         if encoder_hidden_states_img is not None:
-            key_img = attn.add_k_proj(encoder_hidden_states_img)
+            key_img, value_img = _get_added_kv_projections(attn, encoder_hidden_states_img)
             key_img = attn.norm_added_k(key_img)
-            value_img = attn.add_v_proj(encoder_hidden_states_img)
-
-            key_img = key_img.unflatten(2, (attn.heads, -1)).transpose(1, 2)
-            value_img = value_img.unflatten(2, (attn.heads, -1)).transpose(1, 2)
 
-            hidden_states_img = F.scaled_dot_product_attention(
-                query, key_img, value_img, attn_mask=None, dropout_p=0.0, is_causal=False
+            key_img = key_img.unflatten(2, (attn.heads, -1))
+            value_img = value_img.unflatten(2, (attn.heads, -1))
+
+            hidden_states_img = dispatch_attention_fn(
+                query,
+                key_img,
+                value_img,
+                attn_mask=None,
+                dropout_p=0.0,
+                is_causal=False,
+                backend=self._attention_backend,
             )
-            hidden_states_img = hidden_states_img.transpose(1, 2).flatten(2, 3)
+            hidden_states_img = hidden_states_img.flatten(2, 3)
             hidden_states_img = hidden_states_img.type_as(query)
 
-        hidden_states = F.scaled_dot_product_attention(
-            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
+        hidden_states = dispatch_attention_fn(
+            query,
+            key,
+            value,
+            attn_mask=attention_mask,
+            dropout_p=0.0,
+            is_causal=False,
+            backend=self._attention_backend,
         )
-        hidden_states = hidden_states.transpose(1, 2).flatten(2, 3)
+        hidden_states = hidden_states.flatten(2, 3)
         hidden_states = hidden_states.type_as(query)
 
         if hidden_states_img is not None:
@@ -110,6 +156,119 @@ def apply_rotary_emb(hidden_states: torch.Tensor, freqs: torch.Tensor):
         return hidden_states
 
 
+class WanAttnProcessor2_0:
+    def __new__(cls, *args, **kwargs):
+        deprecation_message = (
+            "The WanAttnProcessor2_0 class is deprecated and will be removed in a future version. "
+            "Please use WanAttnProcessor instead. "
+        )
+        deprecate("WanAttnProcessor2_0", "1.0.0", deprecation_message, standard_warn=False)
+        return WanAttnProcessor(*args, **kwargs)
+
+
+class WanAttention(torch.nn.Module, AttentionModuleMixin):
+    _default_processor_cls = WanAttnProcessor
+    _available_processors = [WanAttnProcessor]
+
+    def __init__(
+        self,
+        dim: int,
+        heads: int = 8,
+        dim_head: int = 64,
+        eps: float = 1e-5,
+        dropout: float = 0.0,
+        added_kv_proj_dim: Optional[int] = None,
+        cross_attention_dim_head: Optional[int] = None,
+        processor=None,
+    ):
+        super().__init__()
+
+        self.inner_dim = dim_head * heads
+        self.heads = heads
+        self.added_kv_proj_dim = added_kv_proj_dim
+        self.cross_attention_dim_head = cross_attention_dim_head
+        self.kv_inner_dim = self.inner_dim if cross_attention_dim_head is None else cross_attention_dim_head * heads
+
+        self.to_q = torch.nn.Linear(dim, self.inner_dim, bias=True)
+        self.to_k = torch.nn.Linear(dim, self.kv_inner_dim, bias=True)
+        self.to_v = torch.nn.Linear(dim, self.kv_inner_dim, bias=True)
+        self.to_out = torch.nn.ModuleList(
+            [
+                torch.nn.Linear(self.inner_dim, dim, bias=True),
+                torch.nn.Dropout(dropout),
+            ]
+        )
+        self.norm_q = torch.nn.RMSNorm(dim_head * heads, eps=eps, elementwise_affine=True)
+        self.norm_k = torch.nn.RMSNorm(dim_head * heads, eps=eps, elementwise_affine=True)
+
+        self.add_k_proj = self.add_v_proj = None
+        if added_kv_proj_dim is not None:
+            self.add_k_proj = torch.nn.Linear(added_kv_proj_dim, self.inner_dim, bias=True)
+            self.add_v_proj = torch.nn.Linear(added_kv_proj_dim, self.inner_dim, bias=True)
+            self.norm_added_k = torch.nn.RMSNorm(dim_head * heads, eps=eps)
+
+        self.set_processor(processor)
+
+    def fuse_projections(self):
+        if getattr(self, "fused_projections", False):
+            return
+
+        if self.cross_attention_dim_head is None:
+            concatenated_weights = torch.cat([self.to_q.weight.data, self.to_k.weight.data, self.to_v.weight.data])
+            concatenated_bias = torch.cat([self.to_q.bias.data, self.to_k.bias.data, self.to_v.bias.data])
+            out_features, in_features = concatenated_weights.shape
+            with torch.device("meta"):
+                self.to_qkv = nn.Linear(in_features, out_features, bias=True)
+            self.to_qkv.load_state_dict(
+                {"weight": concatenated_weights, "bias": concatenated_bias}, strict=True, assign=True
+            )
+        else:
+            concatenated_weights = torch.cat([self.to_k.weight.data, self.to_v.weight.data])
+            concatenated_bias = torch.cat([self.to_k.bias.data, self.to_v.bias.data])
+            out_features, in_features = concatenated_weights.shape
+            with torch.device("meta"):
+                self.to_kv = nn.Linear(in_features, out_features, bias=True)
+            self.to_kv.load_state_dict(
+                {"weight": concatenated_weights, "bias": concatenated_bias}, strict=True, assign=True
+            )
+
+        if self.added_kv_proj_dim is not None:
+            concatenated_weights = torch.cat([self.add_k_proj.weight.data, self.add_v_proj.weight.data])
+            concatenated_bias = torch.cat([self.add_k_proj.bias.data, self.add_v_proj.bias.data])
+            out_features, in_features = concatenated_weights.shape
+            with torch.device("meta"):
+                self.to_added_kv = nn.Linear(in_features, out_features, bias=True)
+            self.to_added_kv.load_state_dict(
+                {"weight": concatenated_weights, "bias": concatenated_bias}, strict=True, assign=True
+            )
+
+        self.fused_projections = True
+
+    @torch.no_grad()
+    def unfuse_projections(self):
+        if not getattr(self, "fused_projections", False):
+            return
+
+        if hasattr(self, "to_qkv"):
+            delattr(self, "to_qkv")
+        if hasattr(self, "to_kv"):
+            delattr(self, "to_kv")
+        if hasattr(self, "to_added_kv"):
+            delattr(self, "to_added_kv")
+
+        self.fused_projections = False
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+        **kwargs,
+    ) -> torch.Tensor:
+        return self.processor(self, hidden_states, encoder_hidden_states, attention_mask, rotary_emb, **kwargs)
+
+
 class WanImageEmbedding(torch.nn.Module):
     def __init__(self, in_features: int, out_features: int, pos_embed_seq_len=None):
         super().__init__()
@@ -217,11 +376,21 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
             dim=1,
         )
 
-        freqs_f = freqs[0][:ppf].view(ppf, 1, 1, -1).expand(ppf, pph, ppw, -1)
-        freqs_h = freqs[1][:pph].view(1, pph, 1, -1).expand(ppf, pph, ppw, -1)
-        freqs_w = freqs[2][:ppw].view(1, 1, ppw, -1).expand(ppf, pph, ppw, -1)
-        freqs = torch.cat([freqs_f, freqs_h, freqs_w], dim=-1).reshape(1, 1, ppf * pph * ppw, -1)
-        return freqs
+        freqs_cos = self.freqs_cos.split(split_sizes, dim=1)
+        freqs_sin = self.freqs_sin.split(split_sizes, dim=1)
+
+        freqs_cos_f = freqs_cos[0][:ppf].view(ppf, 1, 1, -1).expand(ppf, pph, ppw, -1)
+        freqs_cos_h = freqs_cos[1][:pph].view(1, pph, 1, -1).expand(ppf, pph, ppw, -1)
+        freqs_cos_w = freqs_cos[2][:ppw].view(1, 1, ppw, -1).expand(ppf, pph, ppw, -1)
+
+        freqs_sin_f = freqs_sin[0][:ppf].view(ppf, 1, 1, -1).expand(ppf, pph, ppw, -1)
+        freqs_sin_h = freqs_sin[1][:pph].view(1, pph, 1, -1).expand(ppf, pph, ppw, -1)
+        freqs_sin_w = freqs_sin[2][:ppw].view(1, 1, ppw, -1).expand(ppf, pph, ppw, -1)
+
+        freqs_cos = torch.cat([freqs_cos_f, freqs_cos_h, freqs_cos_w], dim=-1).reshape(1, ppf * pph * ppw, 1, -1)
+        freqs_sin = torch.cat([freqs_sin_f, freqs_sin_h, freqs_sin_w], dim=-1).reshape(1, ppf * pph * ppw, 1, -1)
+
+        return freqs_cos, freqs_sin
 
 
 class WanTransformerBlock(nn.Module):
@@ -239,33 +408,24 @@ def __init__(
 
         # 1. Self-attention
         self.norm1 = FP32LayerNorm(dim, eps, elementwise_affine=False)
-        self.attn1 = Attention(
-            query_dim=dim,
+        self.attn1 = WanAttention(
+            dim=dim,
             heads=num_heads,
-            kv_heads=num_heads,
             dim_head=dim // num_heads,
-            qk_norm=qk_norm,
             eps=eps,
-            bias=True,
-            cross_attention_dim=None,
-            out_bias=True,
-            processor=WanAttnProcessor2_0(),
+            cross_attention_dim_head=None,
+            processor=WanAttnProcessor(),
         )
 
         # 2. Cross-attention
-        self.attn2 = Attention(
-            query_dim=dim,
+        self.attn2 = WanAttention(
+            dim=dim,
             heads=num_heads,
-            kv_heads=num_heads,
             dim_head=dim // num_heads,
-            qk_norm=qk_norm,
             eps=eps,
-            bias=True,
-            cross_attention_dim=None,
-            out_bias=True,
             added_kv_proj_dim=added_kv_proj_dim,
-            added_proj_bias=True,
-            processor=WanAttnProcessor2_0(),
+            cross_attention_dim_head=dim // num_heads,
+            processor=WanAttnProcessor(),
         )
         self.norm2 = FP32LayerNorm(dim, eps, elementwise_affine=True) if cross_attn_norm else nn.Identity()
 
@@ -302,12 +462,12 @@ def forward(
 
         # 1. Self-attention
         norm_hidden_states = (self.norm1(hidden_states.float()) * (1 + scale_msa) + shift_msa).type_as(hidden_states)
-        attn_output = self.attn1(hidden_states=norm_hidden_states, rotary_emb=rotary_emb)
+        attn_output = self.attn1(norm_hidden_states, None, None, rotary_emb)
         hidden_states = (hidden_states.float() + attn_output * gate_msa).type_as(hidden_states)
 
         # 2. Cross-attention
         norm_hidden_states = self.norm2(hidden_states.float()).type_as(hidden_states)
-        attn_output = self.attn2(hidden_states=norm_hidden_states, encoder_hidden_states=encoder_hidden_states)
+        attn_output = self.attn2(norm_hidden_states, encoder_hidden_states, None, None)
         hidden_states = hidden_states + attn_output
 
         # 3. Feed-forward
@@ -320,7 +480,9 @@ def forward(
         return hidden_states
 
 
-class WanTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, FromOriginalModelMixin, CacheMixin):
+class WanTransformer3DModel(
+    ModelMixin, ConfigMixin, PeftAdapterMixin, FromOriginalModelMixin, CacheMixin, AttentionMixin
+):
     r"""
     A Transformer model for video-like data used in the Wan model.