apply rope and optimized attention

comfy/ldm/trellis2/attention.py

@@ -26,21 +26,21 @@ def scaled_dot_product_attention(*args, **kwargs):
         k = args[1] if len(args) > 1 else kwargs['k']
         v = args[2] if len(args) > 2 else kwargs['v']
 
+    # TODO verify
+    heads = q or qkv
+    heads = heads.shape[2]
+
     if optimized_attention.__name__ == 'attention_xformers':
-        if 'xops' not in globals():
-            import xformers.ops as xops
         if num_all_args == 1:
             q, k, v = qkv.unbind(dim=2)
         elif num_all_args == 2:
             k, v = kv.unbind(dim=2)
-        out = xops.memory_efficient_attention(q, k, v)
+        #out = xops.memory_efficient_attention(q, k, v)
+        out = optimized_attention(q, k, v, heads, skip_output_reshape=True, skip_reshape=True)
     elif optimized_attention.__name__ == 'attention_flash' and not FLASH_ATTN_3_AVA:
-        if 'flash_attn' not in globals():
-            import flash_attn
         if num_all_args == 2:
-            out = flash_attn.flash_attn_kvpacked_func(q, kv)
+            k, v = kv.unbind(dim=2)
-        elif num_all_args == 3:
+        out = optimized_attention(q, k, v, heads, skip_output_reshape=True, skip_reshape=True)
-            out = flash_attn.flash_attn_func(q, k, v)
     elif optimized_attention.__name__ == 'attention_flash': # TODO
         if 'flash_attn_3' not in globals():
             import flash_attn_interface as flash_attn_3
@@ -59,15 +59,14 @@ def scaled_dot_product_attention(*args, **kwargs):
         q = q.permute(0, 2, 1, 3)   # [N, H, L, C]
         k = k.permute(0, 2, 1, 3)   # [N, H, L, C]
         v = v.permute(0, 2, 1, 3)   # [N, H, L, C]
-        out = sdpa(q, k, v)         # [N, H, L, C]
+        out = optimized_attention(q, k, v, heads, skip_output_reshape=True, skip_reshape=True)
         out = out.permute(0, 2, 1, 3)   # [N, L, H, C]
     elif optimized_attention.__name__ == 'attention_basic':
         if num_all_args == 1:
             q, k, v = qkv.unbind(dim=2)
         elif num_all_args == 2:
             k, v = kv.unbind(dim=2)
-        q = q.shape[2] # TODO
+        out = optimized_attention(q, k, v, heads, skip_output_reshape=True, skip_reshape=True)
-        out = optimized_attention(q, k, v)
 
     return out
 
@@ -86,19 +85,21 @@ def sparse_windowed_scaled_dot_product_self_attention(
         fwd_indices, bwd_indices, seq_lens, attn_func_args = serialization_spatial_cache
 
     qkv_feats = qkv.feats[fwd_indices]      # [M, 3, H, C]
+    heads = qkv_feats.shape[2]
 
     if optimized_attention.__name__ == 'attention_xformers':
-        if 'xops' not in globals():
-            import xformers.ops as xops
         q, k, v = qkv_feats.unbind(dim=1)
         q = q.unsqueeze(0)                                                              # [1, M, H, C]
         k = k.unsqueeze(0)                                                              # [1, M, H, C]
         v = v.unsqueeze(0)                                                              # [1, M, H, C]
-        out = xops.memory_efficient_attention(q, k, v, **attn_func_args)[0]             # [M, H, C]
+        #out = xops.memory_efficient_attention(q, k, v, **attn_func_args)[0]             # [M, H, C]
+        out = optimized_attention(q, k, v, heads, skip_output_reshape=True, skip_reshape=True)
     elif optimized_attention.__name__ == 'attention_flash':
         if 'flash_attn' not in globals():
             import flash_attn
         out = flash_attn.flash_attn_varlen_qkvpacked_func(qkv_feats, **attn_func_args)  # [M, H, C]
+    else:
+        out = optimized_attention(q, k, v, heads, skip_output_reshape=True, skip_reshape=True)
 
     out = out[bwd_indices]      # [T, H, C]
 

comfy/ldm/trellis2/model.py

@@ -8,6 +8,7 @@ from comfy.ldm.trellis2.attention import (
 )
 from comfy.ldm.genmo.joint_model.layers import TimestepEmbedder
 from comfy.nested_tensor import NestedTensor
+from comfy.ldm.flux.math import apply_rope, apply_rope1
 
 class SparseGELU(nn.GELU):
     def forward(self, input: VarLenTensor) -> VarLenTensor:
@@ -52,7 +53,6 @@ class SparseMultiHeadRMSNorm(nn.Module):
             x = F.normalize(x, dim=-1) * self.gamma * self.scale
         return x.to(x_type)
 
-# TODO: replace with apply_rope1
 class SparseRotaryPositionEmbedder(nn.Module):
     def __init__(
         self,
@@ -61,7 +61,6 @@ class SparseRotaryPositionEmbedder(nn.Module):
         rope_freq: Tuple[float, float] = (1.0, 10000.0)
     ):
         super().__init__()
-        assert head_dim % 2 == 0, "Head dim must be divisible by 2"
         self.head_dim = head_dim
         self.dim = dim
         self.rope_freq = rope_freq
@@ -69,46 +68,48 @@ class SparseRotaryPositionEmbedder(nn.Module):
         self.freqs = torch.arange(self.freq_dim, dtype=torch.float32) / self.freq_dim
         self.freqs = rope_freq[0] / (rope_freq[1] ** (self.freqs))
 
-    def _get_phases(self, indices: torch.Tensor) -> torch.Tensor:
+    def _get_freqs_cis(self, coords: torch.Tensor) -> torch.Tensor:
-        self.freqs = self.freqs.to(indices.device)
+        phases_list = []
-        phases = torch.outer(indices, self.freqs)
+        for i in range(self.dim):
-        phases = torch.polar(torch.ones_like(phases), phases)
+            phases_list.append(torch.outer(coords[..., i], self.freqs.to(coords.device)))
-        return phases
 
-    def _rotary_embedding(self, x: torch.Tensor, phases: torch.Tensor) -> torch.Tensor:
+        phases = torch.cat(phases_list, dim=-1)
-        x_complex = torch.view_as_complex(x.float().reshape(*x.shape[:-1], -1, 2))
+
-        x_rotated = x_complex * phases.unsqueeze(-2)
+        if phases.shape[-1] < self.head_dim // 2:
-        x_embed = torch.view_as_real(x_rotated).reshape(*x_rotated.shape[:-1], -1).to(x.dtype)
+            padn = self.head_dim // 2 - phases.shape[-1]
-        return x_embed
+            phases = torch.cat([phases, torch.zeros(*phases.shape[:-1], padn, device=phases.device)], dim=-1)
+
+        cos = torch.cos(phases)
+        sin = torch.sin(phases)
+
+        f_cis_0 = torch.stack([cos, sin], dim=-1)
+        f_cis_1 = torch.stack([-sin, cos], dim=-1)
+        freqs_cis = torch.stack([f_cis_0, f_cis_1], dim=-1)
+
+        return freqs_cis
+
+    def forward(self, q, k=None):
+        cache_name = f'rope_cis_{self.dim}d_f{self.rope_freq[1]}_hd{self.head_dim}'
+        freqs_cis = q.get_spatial_cache(cache_name)
+
+        if freqs_cis is None:
+            coords = q.coords[..., 1:].to(torch.float32)
+            freqs_cis = self._get_freqs_cis(coords)
+            q.register_spatial_cache(cache_name, freqs_cis)
+
+        if q.feats.ndim == 3:
+            f_cis = freqs_cis.unsqueeze(1)
+        else:
+            f_cis = freqs_cis
 
-    def forward(self, q: SparseTensor, k: Optional[SparseTensor] = None) -> Tuple[torch.Tensor, torch.Tensor]:
-        """
-        Args:
-            q (SparseTensor): [..., N, H, D] tensor of queries
-            k (SparseTensor): [..., N, H, D] tensor of keys
-        """
-        assert q.coords.shape[-1] == self.dim + 1, "Last dimension of coords must be equal to dim+1"
-        phases_cache_name = f'rope_phase_{self.dim}d_freq{self.rope_freq[0]}-{self.rope_freq[1]}_hd{self.head_dim}'
-        phases = q.get_spatial_cache(phases_cache_name)
-        if phases is None:
-            coords = q.coords[..., 1:]
-            phases = self._get_phases(coords.reshape(-1)).reshape(*coords.shape[:-1], -1)
-            if phases.shape[-1] < self.head_dim // 2:
-                padn = self.head_dim // 2 - phases.shape[-1]
-                phases = torch.cat([phases, torch.polar(
-                    torch.ones(*phases.shape[:-1], padn, device=phases.device),
-                    torch.zeros(*phases.shape[:-1], padn, device=phases.device)
-                )], dim=-1)
-            q.register_spatial_cache(phases_cache_name, phases)
-        q_embed = q.replace(self._rotary_embedding(q.feats, phases))
         if k is None:
-            return q_embed
+            return q.replace(apply_rope1(q.feats, f_cis))
-        k_embed = k.replace(self._rotary_embedding(k.feats, phases))
+
-        return q_embed, k_embed
+        q_feats, k_feats = apply_rope(q.feats, k.feats, f_cis)
+        return q.replace(q_feats), k.replace(k_feats)
 
 class RotaryPositionEmbedder(SparseRotaryPositionEmbedder):
     def forward(self, indices: torch.Tensor) -> torch.Tensor:
-        assert indices.shape[-1] == self.dim, f"Last dim of indices must be {self.dim}"
         phases = self._get_phases(indices.reshape(-1)).reshape(*indices.shape[:-1], -1)
         if phases.shape[-1] < self.head_dim // 2:
                 padn = self.head_dim // 2 - phases.shape[-1]
@@ -228,9 +229,6 @@ class SparseMultiHeadAttention(nn.Module):
         return h
 
 class ModulatedSparseTransformerBlock(nn.Module):
-    """
-    Sparse Transformer block (MSA + FFN) with adaptive layer norm conditioning.
-    """
     def __init__(
         self,
         channels: int,