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# 1st edit by https://github.com/comfyanonymous/ComfyUI
# 2nd edit by Forge Official
import torch
import ldm_patched.modules.model_management
import contextlib
from modules_forge import stream
# https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14855/files
stash = {}
@contextlib.contextmanager
def use_patched_ops(operations):
op_names = ['Linear', 'Conv2d', 'Conv3d', 'GroupNorm', 'LayerNorm']
backups = {op_name: getattr(torch.nn, op_name) for op_name in op_names}
try:
for op_name in op_names:
setattr(torch.nn, op_name, getattr(operations, op_name))
yield
finally:
for op_name in op_names:
setattr(torch.nn, op_name, backups[op_name])
return
def cast_bias_weight(s, input):
weight, bias, signal = None, None, None
non_blocking = ldm_patched.modules.model_management.device_supports_non_blocking(input.device)
if stream.using_stream:
with stream.stream_context()(stream.mover_stream):
if s.bias is not None:
bias = s.bias.to(device=input.device, dtype=input.dtype, non_blocking=non_blocking)
weight = s.weight.to(device=input.device, dtype=input.dtype, non_blocking=non_blocking)
signal = stream.mover_stream.record_event()
else:
if s.bias is not None:
bias = s.bias.to(device=input.device, dtype=input.dtype, non_blocking=non_blocking)
weight = s.weight.to(device=input.device, dtype=input.dtype, non_blocking=non_blocking)
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, (w, b, s) in stash.items():
if s.query():
garbage.append(k)
for k in garbage:
del stash[k]
return
def cleanup_cache():
if not stream.using_stream:
return
stream.current_stream.synchronize()
stream.mover_stream.synchronize()
stash.clear()
return
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):
if dims == 2:
return s.Conv2d(*args, **kwargs)
elif dims == 3:
return s.Conv3d(*args, **kwargs)
else:
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
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