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"""
This file is part of ComfyUI.
Copyright (C) 2024 Stability AI
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <https://www.gnu.org/licenses/>.
"""
import torch
import comfy.model_management
def cast_to(weight, dtype=None, device=None, non_blocking=False):
return weight.to(device=device, dtype=dtype, non_blocking=non_blocking)
def cast_to_input(weight, input, non_blocking=False):
return cast_to(weight, input.dtype, input.device, non_blocking=non_blocking)
def cast_bias_weight(s, input=None, dtype=None, device=None):
if input is not None:
if dtype is None:
dtype = input.dtype
if device is None:
device = input.device
bias = None
non_blocking = comfy.model_management.device_should_use_non_blocking(device)
if s.bias is not None:
bias = cast_to(s.bias, dtype, device, non_blocking=non_blocking)
if s.bias_function is not None:
bias = s.bias_function(bias)
weight = cast_to(s.weight, dtype, device, non_blocking=non_blocking)
if s.weight_function is not None:
weight = s.weight_function(weight)
return weight, bias
class CastWeightBiasOp:
comfy_cast_weights = False
weight_function = None
bias_function = None
class disable_weight_init:
class Linear(torch.nn.Linear, CastWeightBiasOp):
def reset_parameters(self):
return None
def forward_comfy_cast_weights(self, input):
weight, bias = cast_bias_weight(self, input)
return torch.nn.functional.linear(input, weight, bias)
def forward(self, *args, **kwargs):
if self.comfy_cast_weights:
return self.forward_comfy_cast_weights(*args, **kwargs)
else:
return super().forward(*args, **kwargs)
class Conv1d(torch.nn.Conv1d, CastWeightBiasOp):
def reset_parameters(self):
return None
def forward_comfy_cast_weights(self, input):
weight, bias = cast_bias_weight(self, input)
return self._conv_forward(input, weight, bias)
def forward(self, *args, **kwargs):
if self.comfy_cast_weights:
return self.forward_comfy_cast_weights(*args, **kwargs)
else:
return super().forward(*args, **kwargs)
class Conv2d(torch.nn.Conv2d, CastWeightBiasOp):
def reset_parameters(self):
return None
def forward_comfy_cast_weights(self, input):
weight, bias = cast_bias_weight(self, input)
return self._conv_forward(input, weight, bias)
def forward(self, *args, **kwargs):
if self.comfy_cast_weights:
return self.forward_comfy_cast_weights(*args, **kwargs)
else:
return super().forward(*args, **kwargs)
class Conv3d(torch.nn.Conv3d, CastWeightBiasOp):
def reset_parameters(self):
return None
def forward_comfy_cast_weights(self, input):
weight, bias = cast_bias_weight(self, input)
return self._conv_forward(input, weight, bias)
def forward(self, *args, **kwargs):
if self.comfy_cast_weights:
return self.forward_comfy_cast_weights(*args, **kwargs)
else:
return super().forward(*args, **kwargs)
class GroupNorm(torch.nn.GroupNorm, CastWeightBiasOp):
def reset_parameters(self):
return None
def forward_comfy_cast_weights(self, input):
weight, bias = cast_bias_weight(self, input)
return torch.nn.functional.group_norm(input, self.num_groups, weight, bias, self.eps)
def forward(self, *args, **kwargs):
if self.comfy_cast_weights:
return self.forward_comfy_cast_weights(*args, **kwargs)
else:
return super().forward(*args, **kwargs)
class LayerNorm(torch.nn.LayerNorm, CastWeightBiasOp):
def reset_parameters(self):
return None
def forward_comfy_cast_weights(self, input):
if self.weight is not None:
weight, bias = cast_bias_weight(self, input)
else:
weight = None
bias = None
return torch.nn.functional.layer_norm(input, self.normalized_shape, weight, bias, self.eps)
def forward(self, *args, **kwargs):
if self.comfy_cast_weights:
return self.forward_comfy_cast_weights(*args, **kwargs)
else:
return super().forward(*args, **kwargs)
class ConvTranspose2d(torch.nn.ConvTranspose2d, CastWeightBiasOp):
def reset_parameters(self):
return None
def forward_comfy_cast_weights(self, input, output_size=None):
num_spatial_dims = 2
output_padding = self._output_padding(
input, output_size, self.stride, self.padding, self.kernel_size,
num_spatial_dims, self.dilation)
weight, bias = cast_bias_weight(self, input)
return torch.nn.functional.conv_transpose2d(
input, weight, bias, self.stride, self.padding,
output_padding, self.groups, self.dilation)
def forward(self, *args, **kwargs):
if self.comfy_cast_weights:
return self.forward_comfy_cast_weights(*args, **kwargs)
else:
return super().forward(*args, **kwargs)
class ConvTranspose1d(torch.nn.ConvTranspose1d, CastWeightBiasOp):
def reset_parameters(self):
return None
def forward_comfy_cast_weights(self, input, output_size=None):
num_spatial_dims = 1
output_padding = self._output_padding(
input, output_size, self.stride, self.padding, self.kernel_size,
num_spatial_dims, self.dilation)
weight, bias = cast_bias_weight(self, input)
return torch.nn.functional.conv_transpose1d(
input, weight, bias, self.stride, self.padding,
output_padding, self.groups, self.dilation)
def forward(self, *args, **kwargs):
if self.comfy_cast_weights:
return self.forward_comfy_cast_weights(*args, **kwargs)
else:
return super().forward(*args, **kwargs)
class Embedding(torch.nn.Embedding, CastWeightBiasOp):
def reset_parameters(self):
self.bias = None
return None
def forward_comfy_cast_weights(self, input, out_dtype=None):
output_dtype = out_dtype
if self.weight.dtype == torch.float16 or self.weight.dtype == torch.bfloat16:
out_dtype = None
weight, bias = cast_bias_weight(self, device=input.device, dtype=out_dtype)
return torch.nn.functional.embedding(input, weight, self.padding_idx, self.max_norm, self.norm_type, self.scale_grad_by_freq, self.sparse).to(dtype=output_dtype)
def forward(self, *args, **kwargs):
if self.comfy_cast_weights:
return self.forward_comfy_cast_weights(*args, **kwargs)
else:
if "out_dtype" in kwargs:
kwargs.pop("out_dtype")
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):
comfy_cast_weights = True
class Conv1d(disable_weight_init.Conv1d):
comfy_cast_weights = True
class Conv2d(disable_weight_init.Conv2d):
comfy_cast_weights = True
class Conv3d(disable_weight_init.Conv3d):
comfy_cast_weights = True
class GroupNorm(disable_weight_init.GroupNorm):
comfy_cast_weights = True
class LayerNorm(disable_weight_init.LayerNorm):
comfy_cast_weights = True
class ConvTranspose2d(disable_weight_init.ConvTranspose2d):
comfy_cast_weights = True
class ConvTranspose1d(disable_weight_init.ConvTranspose1d):
comfy_cast_weights = True
class Embedding(disable_weight_init.Embedding):
comfy_cast_weights = True
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