Add OFT/BOFT algorithm in weight adapter (#7725)

comfy/weight_adapter/oft.py

@@ -0,0 +1,94 @@
+import logging
+from typing import Optional
+
+import torch
+import comfy.model_management
+from .base import WeightAdapterBase, weight_decompose
+
+
+class OFTAdapter(WeightAdapterBase):
+    name = "oft"
+
+    def __init__(self, loaded_keys, weights):
+        self.loaded_keys = loaded_keys
+        self.weights = weights
+
+    @classmethod
+    def load(
+        cls,
+        x: str,
+        lora: dict[str, torch.Tensor],
+        alpha: float,
+        dora_scale: torch.Tensor,
+        loaded_keys: set[str] = None,
+    ) -> Optional["OFTAdapter"]:
+        if loaded_keys is None:
+            loaded_keys = set()
+        blocks_name = "{}.oft_blocks".format(x)
+        rescale_name = "{}.rescale".format(x)
+
+        blocks = None
+        if blocks_name in lora.keys():
+            blocks = lora[blocks_name]
+            if blocks.ndim == 3:
+                loaded_keys.add(blocks_name)
+
+        rescale = None
+        if rescale_name in lora.keys():
+            rescale = lora[rescale_name]
+            loaded_keys.add(rescale_name)
+
+        if blocks is not None:
+            weights = (blocks, rescale, alpha, dora_scale)
+            return cls(loaded_keys, weights)
+        else:
+            return None
+
+    def calculate_weight(
+        self,
+        weight,
+        key,
+        strength,
+        strength_model,
+        offset,
+        function,
+        intermediate_dtype=torch.float32,
+        original_weight=None,
+    ):
+        v = self.weights
+        blocks = v[0]
+        rescale = v[1]
+        alpha = v[2]
+        dora_scale = v[3]
+
+        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)
+
+        block_num, block_size, *_ = blocks.shape
+
+        try:
+            # Get r
+            I = torch.eye(block_size, device=blocks.device, dtype=blocks.dtype)
+            # for Q = -Q^T
+            q = blocks - blocks.transpose(1, 2)
+            normed_q = q
+            if alpha > 0: # alpha in oft/boft is for constraint
+                q_norm = torch.norm(q) + 1e-8
+                if q_norm > alpha:
+                    normed_q = q * alpha / q_norm
+            # use float() to prevent unsupported type in .inverse()
+            r = (I + normed_q) @ (I - normed_q).float().inverse()
+            r = r.to(original_weight)
+            lora_diff = torch.einsum(
+                "k n m, k n ... -> k m ...",
+                (r * strength) - strength * I,
+                original_weight,
+            )
+            if dora_scale is not None:
+                weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype, function)
+            else:
+                weight += function(((strength * alpha) * lora_diff).type(weight.dtype))
+        except Exception as e:
+            logging.error("ERROR {} {} {}".format(self.name, key, e))
+        return weight