284 lines
10 KiB
Python
284 lines
10 KiB
Python
"""
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This file is part of ComfyUI.
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Copyright (C) 2024 Stability AI
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This program is free software: you can redistribute it and/or modify
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it under the terms of the GNU General Public License as published by
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the Free Software Foundation, either version 3 of the License, or
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(at your option) any later version.
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This program is distributed in the hope that it will be useful,
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but WITHOUT ANY WARRANTY; without even the implied warranty of
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MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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GNU General Public License for more details.
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You should have received a copy of the GNU General Public License
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along with this program. If not, see <https://www.gnu.org/licenses/>.
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"""
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import torch
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import comfy.model_management
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from comfy.cli_args import args
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def cast_to(weight, dtype=None, device=None, non_blocking=False):
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if (dtype is None or weight.dtype == dtype) and (device is None or weight.device == device):
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return weight
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r = torch.empty_like(weight, dtype=dtype, device=device)
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r.copy_(weight, non_blocking=non_blocking)
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return r
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def cast_to_input(weight, input, non_blocking=False):
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return cast_to(weight, input.dtype, input.device, non_blocking=non_blocking)
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def cast_bias_weight(s, input=None, dtype=None, device=None):
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if input is not None:
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if dtype is None:
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dtype = input.dtype
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if device is None:
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device = input.device
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bias = None
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non_blocking = comfy.model_management.device_supports_non_blocking(device)
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if s.bias is not None:
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bias = cast_to(s.bias, dtype, device, non_blocking=non_blocking)
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if s.bias_function is not None:
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bias = s.bias_function(bias)
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weight = cast_to(s.weight, dtype, device, non_blocking=non_blocking)
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if s.weight_function is not None:
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weight = s.weight_function(weight)
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return weight, bias
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class CastWeightBiasOp:
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comfy_cast_weights = False
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weight_function = None
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bias_function = None
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class disable_weight_init:
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class Linear(torch.nn.Linear, CastWeightBiasOp):
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def reset_parameters(self):
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return None
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def forward_comfy_cast_weights(self, input):
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weight, bias = cast_bias_weight(self, input)
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return torch.nn.functional.linear(input, weight, bias)
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def forward(self, *args, **kwargs):
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if self.comfy_cast_weights:
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return self.forward_comfy_cast_weights(*args, **kwargs)
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else:
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return super().forward(*args, **kwargs)
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class Conv1d(torch.nn.Conv1d, CastWeightBiasOp):
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def reset_parameters(self):
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return None
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def forward_comfy_cast_weights(self, input):
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weight, bias = cast_bias_weight(self, input)
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return self._conv_forward(input, weight, bias)
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def forward(self, *args, **kwargs):
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if self.comfy_cast_weights:
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return self.forward_comfy_cast_weights(*args, **kwargs)
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else:
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return super().forward(*args, **kwargs)
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class Conv2d(torch.nn.Conv2d, CastWeightBiasOp):
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def reset_parameters(self):
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return None
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def forward_comfy_cast_weights(self, input):
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weight, bias = cast_bias_weight(self, input)
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return self._conv_forward(input, weight, bias)
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def forward(self, *args, **kwargs):
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if self.comfy_cast_weights:
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return self.forward_comfy_cast_weights(*args, **kwargs)
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else:
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return super().forward(*args, **kwargs)
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class Conv3d(torch.nn.Conv3d, CastWeightBiasOp):
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def reset_parameters(self):
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return None
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def forward_comfy_cast_weights(self, input):
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weight, bias = cast_bias_weight(self, input)
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return self._conv_forward(input, weight, bias)
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def forward(self, *args, **kwargs):
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if self.comfy_cast_weights:
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return self.forward_comfy_cast_weights(*args, **kwargs)
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else:
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return super().forward(*args, **kwargs)
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class GroupNorm(torch.nn.GroupNorm, CastWeightBiasOp):
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def reset_parameters(self):
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return None
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def forward_comfy_cast_weights(self, input):
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weight, bias = cast_bias_weight(self, input)
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return torch.nn.functional.group_norm(input, self.num_groups, weight, bias, self.eps)
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def forward(self, *args, **kwargs):
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if self.comfy_cast_weights:
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return self.forward_comfy_cast_weights(*args, **kwargs)
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else:
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return super().forward(*args, **kwargs)
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class LayerNorm(torch.nn.LayerNorm, CastWeightBiasOp):
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def reset_parameters(self):
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return None
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def forward_comfy_cast_weights(self, input):
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if self.weight is not None:
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weight, bias = cast_bias_weight(self, input)
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else:
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weight = None
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bias = None
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return torch.nn.functional.layer_norm(input, self.normalized_shape, weight, bias, self.eps)
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def forward(self, *args, **kwargs):
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if self.comfy_cast_weights:
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return self.forward_comfy_cast_weights(*args, **kwargs)
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else:
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return super().forward(*args, **kwargs)
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class ConvTranspose2d(torch.nn.ConvTranspose2d, CastWeightBiasOp):
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def reset_parameters(self):
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return None
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def forward_comfy_cast_weights(self, input, output_size=None):
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num_spatial_dims = 2
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output_padding = self._output_padding(
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input, output_size, self.stride, self.padding, self.kernel_size,
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num_spatial_dims, self.dilation)
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weight, bias = cast_bias_weight(self, input)
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return torch.nn.functional.conv_transpose2d(
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input, weight, bias, self.stride, self.padding,
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output_padding, self.groups, self.dilation)
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def forward(self, *args, **kwargs):
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if self.comfy_cast_weights:
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return self.forward_comfy_cast_weights(*args, **kwargs)
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else:
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return super().forward(*args, **kwargs)
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class ConvTranspose1d(torch.nn.ConvTranspose1d, CastWeightBiasOp):
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def reset_parameters(self):
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return None
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def forward_comfy_cast_weights(self, input, output_size=None):
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num_spatial_dims = 1
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output_padding = self._output_padding(
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input, output_size, self.stride, self.padding, self.kernel_size,
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num_spatial_dims, self.dilation)
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weight, bias = cast_bias_weight(self, input)
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return torch.nn.functional.conv_transpose1d(
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input, weight, bias, self.stride, self.padding,
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output_padding, self.groups, self.dilation)
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def forward(self, *args, **kwargs):
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if self.comfy_cast_weights:
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return self.forward_comfy_cast_weights(*args, **kwargs)
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else:
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return super().forward(*args, **kwargs)
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class Embedding(torch.nn.Embedding, CastWeightBiasOp):
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def reset_parameters(self):
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self.bias = None
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return None
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def forward_comfy_cast_weights(self, input, out_dtype=None):
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output_dtype = out_dtype
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if self.weight.dtype == torch.float16 or self.weight.dtype == torch.bfloat16:
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out_dtype = None
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weight, bias = cast_bias_weight(self, device=input.device, dtype=out_dtype)
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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)
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def forward(self, *args, **kwargs):
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if self.comfy_cast_weights:
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return self.forward_comfy_cast_weights(*args, **kwargs)
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else:
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if "out_dtype" in kwargs:
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kwargs.pop("out_dtype")
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return super().forward(*args, **kwargs)
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@classmethod
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def conv_nd(s, dims, *args, **kwargs):
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if dims == 2:
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return s.Conv2d(*args, **kwargs)
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elif dims == 3:
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return s.Conv3d(*args, **kwargs)
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else:
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raise ValueError(f"unsupported dimensions: {dims}")
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class manual_cast(disable_weight_init):
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class Linear(disable_weight_init.Linear):
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comfy_cast_weights = True
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class Conv1d(disable_weight_init.Conv1d):
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comfy_cast_weights = True
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class Conv2d(disable_weight_init.Conv2d):
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comfy_cast_weights = True
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class Conv3d(disable_weight_init.Conv3d):
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comfy_cast_weights = True
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class GroupNorm(disable_weight_init.GroupNorm):
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comfy_cast_weights = True
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class LayerNorm(disable_weight_init.LayerNorm):
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comfy_cast_weights = True
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class ConvTranspose2d(disable_weight_init.ConvTranspose2d):
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comfy_cast_weights = True
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class ConvTranspose1d(disable_weight_init.ConvTranspose1d):
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comfy_cast_weights = True
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class Embedding(disable_weight_init.Embedding):
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comfy_cast_weights = True
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def fp8_linear(self, input):
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dtype = self.weight.dtype
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if dtype not in [torch.float8_e4m3fn]:
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return None
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if len(input.shape) == 3:
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out = torch.empty((input.shape[0], input.shape[1], self.weight.shape[0]), device=input.device, dtype=input.dtype)
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inn = input.to(dtype)
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non_blocking = comfy.model_management.device_supports_non_blocking(input.device)
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w = cast_to(self.weight, device=input.device, non_blocking=non_blocking).t()
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for i in range(input.shape[0]):
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if self.bias is not None:
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o, _ = torch._scaled_mm(inn[i], w, out_dtype=input.dtype, bias=cast_to_input(self.bias, input, non_blocking=non_blocking))
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else:
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o, _ = torch._scaled_mm(inn[i], w, out_dtype=input.dtype)
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out[i] = o
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return out
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return None
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class fp8_ops(manual_cast):
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class Linear(manual_cast.Linear):
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def forward_comfy_cast_weights(self, input):
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out = fp8_linear(self, input)
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if out is not None:
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return out
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weight, bias = cast_bias_weight(self, input)
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return torch.nn.functional.linear(input, weight, bias)
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def pick_operations(weight_dtype, compute_dtype, load_device=None):
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if compute_dtype is None or weight_dtype == compute_dtype:
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return disable_weight_init
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if args.fast:
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if comfy.model_management.supports_fp8_compute(load_device):
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return fp8_ops
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return manual_cast
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