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on
Zero
Running
on
Zero
import logging | |
from typing import Optional | |
import torch | |
import comfy.model_management | |
from .base import WeightAdapterBase, weight_decompose | |
class BOFTAdapter(WeightAdapterBase): | |
name = "boft" | |
def __init__(self, loaded_keys, weights): | |
self.loaded_keys = loaded_keys | |
self.weights = weights | |
def load( | |
cls, | |
x: str, | |
lora: dict[str, torch.Tensor], | |
alpha: float, | |
dora_scale: torch.Tensor, | |
loaded_keys: set[str] = None, | |
) -> Optional["BOFTAdapter"]: | |
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 == 4: | |
loaded_keys.add(blocks_name) | |
else: | |
blocks = None | |
if blocks is None: | |
return None | |
rescale = None | |
if rescale_name in lora.keys(): | |
rescale = lora[rescale_name] | |
loaded_keys.add(rescale_name) | |
weights = (blocks, rescale, alpha, dora_scale) | |
return cls(loaded_keys, weights) | |
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) | |
boft_m, block_num, boft_b, *_ = blocks.shape | |
try: | |
# Get r | |
I = torch.eye(boft_b, device=blocks.device, dtype=blocks.dtype) | |
# for Q = -Q^T | |
q = blocks - blocks.transpose(-1, -2) | |
normed_q = q | |
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 | |
# use float() to prevent unsupported type in .inverse() | |
r = (I + normed_q) @ (I - normed_q).float().inverse() | |
r = r.to(weight) | |
inp = org = weight | |
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 | |
inp = ( | |
inp.unflatten(0, (-1, g, k)) | |
.transpose(1, 2) | |
.flatten(0, 2) | |
.unflatten(0, (-1, boft_b)) | |
) | |
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) | |
) | |
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) | |
if dora_scale is not None: | |
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 | |