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Credit: ComfyUI
https://github.com/comfyanonymous/ComfyUI
- Edited by. Forge Official
- Edited by. Haoming02
"""
import contextlib
import torch
from ldm_patched.modules.model_management import cast_to_device, device_supports_non_blocking
from modules_forge import stream
stash = {}
@contextlib.contextmanager
def use_patched_ops(operations):
names = ("Linear", "Conv2d", "Conv3d", "GroupNorm", "LayerNorm")
backups = {name: getattr(torch.nn, name) for name in names}
try:
for name in names:
setattr(torch.nn, name, getattr(operations, name))
yield
finally:
for name in names:
setattr(torch.nn, name, backups[name])
return
def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None):
if input is not None:
if dtype is None:
dtype = input.dtype
if bias_dtype is None:
bias_dtype = dtype
if device is None:
device = input.device
bias, signal = None, None
with stream.stream_context()(stream.mover_stream) if stream.using_stream else contextlib.nullcontext():
if s.bias is not None:
bias = cast_to_device(
s.bias,
device=device,
dtype=bias_dtype,
)
weight = cast_to_device(
s.weight,
device=device,
dtype=dtype,
)
if stream.using_stream:
signal = stream.mover_stream.record_event()
return weight, bias, signal
@contextlib.contextmanager
def main_stream_worker(weight, bias, signal):
if not stream.using_stream or signal is None:
yield
return
with stream.stream_context()(stream.current_stream):
stream.current_stream.wait_event(signal)
yield
finished_signal = stream.current_stream.record_event()
stash[id(finished_signal)] = (weight, bias, finished_signal)
garbage = []
for k, (_, _, s) in stash.items():
if s.query():
garbage.append(k)
for k in garbage:
del stash[k]
def cleanup_cache():
if not stream.using_stream:
return
stream.current_stream.synchronize()
stream.mover_stream.synchronize()
stash.clear()
class disable_weight_init:
class Linear(torch.nn.Linear):
ldm_patched_cast_weights = False
def reset_parameters(self):
return None
def forward_ldm_patched_cast_weights(self, input):
weight, bias, signal = cast_bias_weight(self, input)
with main_stream_worker(weight, bias, signal):
return torch.nn.functional.linear(input, weight, bias)
def forward(self, *args, **kwargs):
if self.ldm_patched_cast_weights:
return self.forward_ldm_patched_cast_weights(*args, **kwargs)
else:
return super().forward(*args, **kwargs)
class Conv2d(torch.nn.Conv2d):
ldm_patched_cast_weights = False
def reset_parameters(self):
return None
def forward_ldm_patched_cast_weights(self, input):
weight, bias, signal = cast_bias_weight(self, input)
with main_stream_worker(weight, bias, signal):
return self._conv_forward(input, weight, bias)
def forward(self, *args, **kwargs):
if self.ldm_patched_cast_weights:
return self.forward_ldm_patched_cast_weights(*args, **kwargs)
else:
return super().forward(*args, **kwargs)
class Conv3d(torch.nn.Conv3d):
ldm_patched_cast_weights = False
def reset_parameters(self):
return None
def forward_ldm_patched_cast_weights(self, input):
weight, bias, signal = cast_bias_weight(self, input)
with main_stream_worker(weight, bias, signal):
return self._conv_forward(input, weight, bias)
def forward(self, *args, **kwargs):
if self.ldm_patched_cast_weights:
return self.forward_ldm_patched_cast_weights(*args, **kwargs)
else:
return super().forward(*args, **kwargs)
class GroupNorm(torch.nn.GroupNorm):
ldm_patched_cast_weights = False
def reset_parameters(self):
return None
def forward_ldm_patched_cast_weights(self, input):
weight, bias, signal = cast_bias_weight(self, input)
with main_stream_worker(weight, bias, signal):
return torch.nn.functional.group_norm(
input,
self.num_groups,
weight,
bias,
self.eps,
)
def forward(self, *args, **kwargs):
if self.ldm_patched_cast_weights:
return self.forward_ldm_patched_cast_weights(*args, **kwargs)
else:
return super().forward(*args, **kwargs)
class LayerNorm(torch.nn.LayerNorm):
ldm_patched_cast_weights = False
def reset_parameters(self):
return None
def forward_ldm_patched_cast_weights(self, input):
weight, bias, signal = cast_bias_weight(self, input)
with main_stream_worker(weight, bias, signal):
return torch.nn.functional.layer_norm(input, self.normalized_shape, weight, bias, self.eps)
def forward(self, *args, **kwargs):
if self.ldm_patched_cast_weights:
return self.forward_ldm_patched_cast_weights(*args, **kwargs)
else:
return super().forward(*args, **kwargs)
@classmethod
def conv_nd(s, dims, *args, **kwargs):
match dims:
case 2:
return s.Conv2d(*args, **kwargs)
case 3:
return s.Conv3d(*args, **kwargs)
case _:
raise ValueError(f"unsupported dimensions: {dims}")
class manual_cast(disable_weight_init):
class Linear(disable_weight_init.Linear):
ldm_patched_cast_weights = True
class Conv2d(disable_weight_init.Conv2d):
ldm_patched_cast_weights = True
class Conv3d(disable_weight_init.Conv3d):
ldm_patched_cast_weights = True
class GroupNorm(disable_weight_init.GroupNorm):
ldm_patched_cast_weights = True
class LayerNorm(disable_weight_init.LayerNorm):
ldm_patched_cast_weights = True
def fp8_linear(self, input):
dtype = self.weight.dtype
if dtype is not torch.float8_e4m3fn:
return None
tensor_2d = False
if len(input.shape) == 2:
tensor_2d = True
input = input.unsqueeze(1)
if len(input.shape) != 3:
return None
input_shape = input.shape
input_dtype = input.dtype
input_device = input.device
w, bias, signal = cast_bias_weight(self, input, dtype=dtype, bias_dtype=input_dtype)
w = w.t()
scale_weight = self.scale_weight
scale_input = self.scale_input
if scale_weight is None:
scale_weight = torch.ones((), device=input_device, dtype=torch.float32)
else:
scale_weight = scale_weight.to(input_device)
if scale_input is None:
scale_input = torch.ones((), device=input_device, dtype=torch.float32)
inn = torch.clamp(input, min=-448, max=448).view(-1, input_shape[2]).to(dtype)
else:
scale_input = scale_input.to(input_device)
inn = (input * (1.0 / scale_input).to(input_dtype)).view(-1, input_shape[2]).to(dtype)
with main_stream_worker(w, bias, signal):
o = torch._scaled_mm(
input=inn,
mat2=w,
bias=bias,
out_dtype=input_dtype,
scale_a=scale_input,
scale_b=scale_weight,
)
if isinstance(o, tuple):
o = o[0]
if tensor_2d:
return o.view(input_shape[0], -1)
else:
return o.view((-1, input_shape[1], self.weight.shape[0]))
class fp8_ops(manual_cast):
class Linear(manual_cast.Linear):
def reset_parameters(self):
self.scale_weight = None
self.scale_input = None
return None
def forward_ldm_patched_cast_weights(self, input):
if (out := fp8_linear(self, input)) is not None:
return out
weight, bias, signal = cast_bias_weight(self, input)
with main_stream_worker(weight, bias, signal):
return torch.nn.functional.linear(input, weight, bias)
try:
from cublas_ops import CublasLinear
except ImportError:
pass
else:
class cublas_ops(disable_weight_init):
class Linear(CublasLinear, disable_weight_init.Linear):
def reset_parameters(self):
return None
def forward_ldm_patched_cast_weights(self, input):
return super().forward(input)
def forward(self, *args, **kwargs):
return super().forward(*args, **kwargs)
class tiled_ops(disable_weight_init):
class Conv2d(disable_weight_init.Conv2d):
tile_size: int
def __init__(self, *arg, **kwargs):
from modules.shared import opts
super().__init__(*arg, **kwargs)
self._3x1x1: bool = self.kernel_size == (3, 3) and self.stride == (1, 1) and self.padding == (1, 1)
self.tile_size = opts.sd_vae_tiled_size
@torch.inference_mode()
def forward(self, x: torch.Tensor):
if not self._3x1x1:
return super().forward(x)
B, C, H, W = x.shape
if H <= self.tile_size and W <= self.tile_size:
return super().forward(x)
out = torch.empty((B, C if self.out_channels is None else self.out_channels, H, W), device=x.device, dtype=x.dtype, memory_format=torch.contiguous_format)
non_blocking = device_supports_non_blocking(x.device)
for i in range(0, H, self.tile_size):
for j in range(0, W, self.tile_size):
i0 = max(i - 1, 0)
j0 = max(j - 1, 0)
i1 = min(i + self.tile_size + 1, H)
j1 = min(j + self.tile_size + 1, W)
tile = x[:, :, i0:i1, j0:j1]
tile_conv = super().forward(tile)
pi = i - i0
pj = j - j0
ph = min(self.tile_size, H - i)
pw = min(self.tile_size, W - j)
out[:, :, i : i + ph, j : j + pw].copy_(tile_conv[:, :, pi : pi + ph, pj : pj + pw], non_blocking=non_blocking)
del tile_conv
return out
class Upsample(torch.nn.Module):
tile_size: int
def __init__(self, in_channels, with_conv):
from modules.shared import opts
super().__init__()
self.with_conv = with_conv
if self.with_conv:
self.conv = tiled_ops.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1)
self.tile_size = opts.sd_vae_tiled_size
@torch.inference_mode()
def forward(self, x: torch.Tensor):
B, C, H, W = x.shape
out_H, out_W = (H * 2, W * 2)
if out_H <= self.tile_size and out_W <= self.tile_size:
x_up = torch.nn.functional.interpolate(x, size=(out_H, out_W), mode="nearest")
return x_up if not self.with_conv else self.conv(x_up)
scale_h = out_H / H
scale_w = out_W / W
out = torch.empty((B, C, out_H, out_W), device=x.device, dtype=x.dtype, memory_format=torch.contiguous_format)
non_blocking = device_supports_non_blocking(x.device)
for i in range(0, H, self.tile_size):
for j in range(0, W, self.tile_size):
i0 = max(i - 1, 0)
j0 = max(j - 1, 0)
i1 = min(i + self.tile_size + 1, H)
j1 = min(j + self.tile_size + 1, W)
tile = x[:, :, i0:i1, j0:j1]
tile_up = torch.nn.functional.interpolate(tile, scale_factor=(scale_h, scale_w), mode="nearest")
if self.with_conv:
tile_up = self.conv(tile_up)
pi = int((i - i0) * scale_h)
pj = int((j - j0) * scale_w)
ph = int(min(self.tile_size, H - i) * scale_h)
pw = int(min(self.tile_size, W - j) * scale_w)
oi = int(i * scale_h)
oj = int(j * scale_w)
# Clip output patch to not exceed requested output size
ph = min(ph, out_H - oi)
pw = min(pw, out_W - oj)
out[:, :, oi : oi + ph, oj : oj + pw].copy_(tile_up[:, :, pi : pi + ph, pj : pj + pw], non_blocking=non_blocking)
del tile_up
return out
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