[Weight-adapter/Trainer] Bypass forward mode in Weight adapter system (#11958)

* Add API of bypass forward module

* bypass implementation

* add bypass fwd into nodes list/trainer

comfy/weight_adapter/boft.py

@@ -62,9 +62,13 @@ class BOFTAdapter(WeightAdapterBase):
         alpha = v[2]
         dora_scale = v[3]
 
-        blocks = comfy.model_management.cast_to_device(blocks, weight.device, intermediate_dtype)
+        blocks = comfy.model_management.cast_to_device(
+            blocks, weight.device, intermediate_dtype
+        )
         if rescale is not None:
-            rescale = comfy.model_management.cast_to_device(rescale, weight.device, intermediate_dtype)
+            rescale = comfy.model_management.cast_to_device(
+                rescale, weight.device, intermediate_dtype
+            )
 
         boft_m, block_num, boft_b, *_ = blocks.shape
 
@@ -74,7 +78,7 @@ class BOFTAdapter(WeightAdapterBase):
             # for Q = -Q^T
             q = blocks - blocks.transpose(-1, -2)
             normed_q = q
-            if alpha > 0: # alpha in boft/bboft is for constraint
+            if alpha > 0:  # alpha in boft/bboft is for constraint
                 q_norm = torch.norm(q) + 1e-8
                 if q_norm > alpha:
                     normed_q = q * alpha / q_norm
@@ -83,13 +87,13 @@ class BOFTAdapter(WeightAdapterBase):
             r = r.to(weight)
             inp = org = weight
 
-            r_b = boft_b//2
+            r_b = boft_b // 2
             for i in range(boft_m):
                 bi = r[i]
                 g = 2
                 k = 2**i * r_b
                 if strength != 1:
-                    bi = bi * strength + (1-strength) * I
+                    bi = bi * strength + (1 - strength) * I
                 inp = (
                     inp.unflatten(0, (-1, g, k))
                     .transpose(1, 2)
@@ -98,18 +102,117 @@ class BOFTAdapter(WeightAdapterBase):
                 )
                 inp = torch.einsum("b i j, b j ...-> b i ...", bi, inp)
                 inp = (
-                    inp.flatten(0, 1).unflatten(0, (-1, k, g)).transpose(1, 2).flatten(0, 2)
+                    inp.flatten(0, 1)
+                    .unflatten(0, (-1, k, g))
+                    .transpose(1, 2)
+                    .flatten(0, 2)
                 )
 
             if rescale is not None:
                 inp = inp * rescale
 
             lora_diff = inp - org
-            lora_diff = comfy.model_management.cast_to_device(lora_diff, weight.device, intermediate_dtype)
+            lora_diff = comfy.model_management.cast_to_device(
+                lora_diff, weight.device, intermediate_dtype
+            )
             if dora_scale is not None:
-                weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype, function)
+                weight = weight_decompose(
+                    dora_scale,
+                    weight,
+                    lora_diff,
+                    alpha,
+                    strength,
+                    intermediate_dtype,
+                    function,
+                )
             else:
                 weight += function((strength * lora_diff).type(weight.dtype))
         except Exception as e:
             logging.error("ERROR {} {} {}".format(self.name, key, e))
         return weight
+
+    def _get_orthogonal_matrices(self, device, dtype):
+        """Compute the orthogonal rotation matrices R from BOFT blocks."""
+        v = self.weights
+        blocks = v[0].to(device=device, dtype=dtype)
+        alpha = v[2]
+        if alpha is None:
+            alpha = 0
+
+        boft_m, block_num, boft_b, _ = blocks.shape
+        I = torch.eye(boft_b, device=device, dtype=dtype)
+
+        # Q = blocks - blocks^T (skew-symmetric)
+        q = blocks - blocks.transpose(-1, -2)
+        normed_q = q
+
+        # Apply constraint if alpha > 0
+        if alpha > 0:
+            q_norm = torch.norm(q) + 1e-8
+            if q_norm > alpha:
+                normed_q = q * alpha / q_norm
+
+        # Cayley transform: R = (I + Q)(I - Q)^-1
+        r = (I + normed_q) @ (I - normed_q).float().inverse()
+        return r, boft_m, boft_b
+
+    def g(self, y: torch.Tensor) -> torch.Tensor:
+        """
+        Output transformation for BOFT: applies butterfly orthogonal transform.
+
+        BOFT uses multiple stages of butterfly-structured orthogonal transforms.
+
+        Reference: LyCORIS ButterflyOFTModule._bypass_forward
+        """
+        v = self.weights
+        rescale = v[1]
+
+        r, boft_m, boft_b = self._get_orthogonal_matrices(y.device, y.dtype)
+        r_b = boft_b // 2
+
+        # Apply multiplier
+        multiplier = getattr(self, "multiplier", 1.0)
+        I = torch.eye(boft_b, device=y.device, dtype=y.dtype)
+
+        # Use module info from bypass injection to determine conv vs linear
+        is_conv = getattr(self, "is_conv", y.dim() > 2)
+
+        if is_conv:
+            # Conv output: (N, C, H, W, ...) -> transpose to (N, H, W, ..., C)
+            y = y.transpose(1, -1)
+
+        # Apply butterfly transform stages
+        inp = y
+        for i in range(boft_m):
+            bi = r[i]  # (block_num, boft_b, boft_b)
+            g = 2
+            k = 2**i * r_b
+
+            # Interpolate with identity based on multiplier
+            if multiplier != 1:
+                bi = bi * multiplier + (1 - multiplier) * I
+
+            # Reshape for butterfly: unflatten last dim, transpose, flatten, unflatten
+            inp = (
+                inp.unflatten(-1, (-1, g, k))
+                .transpose(-2, -1)
+                .flatten(-3)
+                .unflatten(-1, (-1, boft_b))
+            )
+            # Apply block-diagonal orthogonal transform
+            inp = torch.einsum("b i j, ... b j -> ... b i", bi, inp)
+            # Reshape back
+            inp = (
+                inp.flatten(-2).unflatten(-1, (-1, k, g)).transpose(-2, -1).flatten(-3)
+            )
+
+        # Apply rescale if present
+        if rescale is not None:
+            rescale = rescale.to(device=y.device, dtype=y.dtype)
+            inp = inp * rescale.transpose(0, -1)
+
+        if is_conv:
+            # Transpose back: (N, H, W, ..., C) -> (N, C, H, W, ...)
+            inp = inp.transpose(1, -1)
+
+        return inp