497 lines
15 KiB
Python
497 lines
15 KiB
Python
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import ixformer.functions as ixf_F
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def time_embed(t_emb, weight1, bias1, weight2, bias2):
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# unet time_emd
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# linear + silu + linear
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emb = ixf_F.act_bias_mm(
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t_emb, weight1, act_type="silu", bias=bias1, scale=1, trans_format="TN"
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)
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emb = ixf_F.act_bias_mm(
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emb, weight2, act_type="none", bias=bias2, scale=1, trans_format="TN"
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)
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return emb
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def ixf_layer_norm(input, normalized_shape, weight=None, bias=None, eps=1e-05):
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return ixf_F.layernorm(input, weight, bias, normalized_shape)
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def ixf_pt_scaled_dot_product_attention(
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query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False
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):
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if (
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not query.is_contiguous()
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and query.transpose(1, 2).is_contiguous()
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and key.transpose(1, 2).is_contiguous()
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and value.transpose(1, 2).is_contiguous()
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and attn_mask is None
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):
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batch_size, head_num, seq_len_q, head_dim = query.shape
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_, _, seq_len_k, _ = key.shape
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query = query.transpose(1, 2).view(batch_size * seq_len_q, head_num, head_dim)
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key = key.transpose(1, 2).view(batch_size * seq_len_k, head_num, head_dim)
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value = value.transpose(1, 2).view(batch_size * seq_len_k, head_num, head_dim)
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cu_seqlens_q = torch.arange(
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0,
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seq_len_q * (batch_size + 1),
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seq_len_q,
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dtype=torch.int32,
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device=query.device,
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)
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if seq_len_q == seq_len_k:
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cu_seqlens_k = cu_seqlens_q
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else:
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cu_seqlens_k = torch.arange(
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0,
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seq_len_k * (batch_size + 1),
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seq_len_k,
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dtype=torch.int32,
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device=query.device,
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)
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res = ixf_F.flash_attn_varlen_func(
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query,
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key,
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value,
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cu_seqlens_q.int(),
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cu_seqlens_k.int(),
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seq_len_q,
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seq_len_k,
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)
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res = res.view(batch_size, seq_len_q, head_num, head_dim).transpose(1, 2)
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return res
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if not query.is_contiguous():
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query = query.contiguous()
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if not key.is_contiguous():
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key = key.contiguous()
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if not value.is_contiguous():
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value = value.contiguous()
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return ixf_F.scaled_dot_product_attention(
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query, key, value, attn_mask=attn_mask, is_causal=is_causal
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)
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class UnetIxformerFunction:
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def __init__(self) -> None:
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self.ixf_linear = ixf_F.linear
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self.pt_linear = F.linear
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self.pt_layer_norm = F.layer_norm
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self.pt_scaled_dot_product_attention = F.scaled_dot_product_attention
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def __enter__(self):
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F.linear = self.ixf_linear
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F.layer_norm = ixf_layer_norm
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F.scaled_dot_product_attention = ixf_pt_scaled_dot_product_attention
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return self
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def __exit__(self, exc_type, exc_val, exc_tb):
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F.linear = self.pt_linear
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F.layer_norm = self.pt_layer_norm
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F.scaled_dot_product_attention = self.pt_scaled_dot_product_attention
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if exc_tb is not None:
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print(f"{exc_type} {exc_val}")
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return False
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return True
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def ForwardWrapper(fun):
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def wrap(*args, **kwargs):
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with UnetIxformerFunction() as w:
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return fun(*args, **kwargs)
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return wrap
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class IxformerComfyWrapper(nn.Module):
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def __init__(self):
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super().__init__()
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self.is_ixf_wrapper = True
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class Conv2dNhwcWrapper(IxformerComfyWrapper):
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def __init__(self, module):
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super().__init__()
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module.weight.data = module.weight.permute(0, 2, 3, 1).contiguous()
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module.bias.data = module.bias.float()
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self.weight = module.weight.data
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self.bias = module.bias.data
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self.stride = module.stride
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self.padding = module.padding
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self.dilation = module.dilation
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self.groups = module.groups
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def forward(self, x):
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h2 = ixf_F.conv2d(
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x,
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self.weight,
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self.bias,
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self.stride,
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self.padding,
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self.dilation,
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self.groups,
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)
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return h2
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class ResBlockNhwcWrapper(IxformerComfyWrapper):
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def __init__(self, module) -> None:
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super().__init__()
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assert not module.updown
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assert not module.use_scale_shift_norm
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assert not module.skip_t_emb
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assert not module.exchange_temb_dims
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if isinstance(module.skip_connection, nn.Identity):
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self.skip_connection = module.skip_connection
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elif get_class_name(module.skip_connection) == "Conv2d":
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self.skip_connection = Conv2dNhwcWrapper(module.skip_connection)
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else:
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raise NotImplementedError(
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f"ResBlockNhwcWrapper support Conv2d or nn.Identity, but got {module.skip_connection}"
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)
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self.in_layers = module.in_layers
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self.out_layers = module.out_layers
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self.emb_layers = module.emb_layers
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self.in_layers_conv = Conv2dNhwcWrapper(module.in_layers[2])
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self.out_layers_conv = Conv2dNhwcWrapper(module.out_layers[3])
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def forward(self, x, emb):
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# x: nhwc
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x1 = x
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# print(x1.shape)
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# fused group_norm silu
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h = ixf_F.group_norm(
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x1, # nchw->nhwc
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self.in_layers[0].num_groups,
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self.in_layers[0].weight,
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self.in_layers[0].bias,
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format=False,
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act_type=1,
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)
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h = self.in_layers_conv(h)
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emb_out = self.emb_layers(emb)
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while len(emb_out.shape) < len(h.shape):
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emb_out = emb_out[..., None]
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h = h + emb_out.permute(0, 2, 3, 1)
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# print(h.shape)
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h = ixf_F.group_norm(
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h,
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self.out_layers[0].num_groups,
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self.out_layers[0].weight,
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self.out_layers[0].bias,
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format=False,
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act_type=1,
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)
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h = self.out_layers[2](h)
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h = self.out_layers_conv(h)
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# TODO: support other skip_connection
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return self.skip_connection(x) + h
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class DownsampleNhwcWrapper(IxformerComfyWrapper):
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def __init__(self, module) -> None:
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# TODO: support avg_pool_nd
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super().__init__()
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assert module.use_conv
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self.channels = module.channels
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self.op = Conv2dNhwcWrapper(module.op)
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def forward(self, x):
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assert x.shape[-1] == self.channels
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return self.op(x)
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def ffn_forward(self, x):
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if get_class_name(self.net[0]) == "GEGLU":
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net = self.net[1:]
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geglu_net = self.net[0]
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x = geglu_net.proj(x)
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x = ixf_F.gelu_and_mul(x)
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return net(x)
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else:
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return self.net(x)
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# ComfyUI/comfy/ldm/modules/attention.py `class BasicTransformerBlock(nn.Module)`
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def transformer_block_forward(self, x, context=None, transformer_options={}):
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extra_options = {}
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block = transformer_options.get("block", None)
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block_index = transformer_options.get("block_index", 0)
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transformer_patches = {}
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transformer_patches_replace = {}
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for k in transformer_options:
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if k == "patches":
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transformer_patches = transformer_options[k]
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elif k == "patches_replace":
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transformer_patches_replace = transformer_options[k]
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else:
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extra_options[k] = transformer_options[k]
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extra_options["n_heads"] = self.n_heads
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extra_options["dim_head"] = self.d_head
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if self.ff_in:
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x_skip = x
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x = self.ff_in(self.norm_in(x))
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if self.is_res:
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x += x_skip
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n = self.norm1(x)
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if self.disable_self_attn:
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context_attn1 = context
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else:
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context_attn1 = None
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value_attn1 = None
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if "attn1_patch" in transformer_patches:
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patch = transformer_patches["attn1_patch"]
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if context_attn1 is None:
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context_attn1 = n
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value_attn1 = context_attn1
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for p in patch:
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n, context_attn1, value_attn1 = p(
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n, context_attn1, value_attn1, extra_options
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)
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if block is not None:
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transformer_block = (block[0], block[1], block_index)
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else:
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transformer_block = None
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attn1_replace_patch = transformer_patches_replace.get("attn1", {})
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block_attn1 = transformer_block
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if block_attn1 not in attn1_replace_patch:
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block_attn1 = block
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if block_attn1 in attn1_replace_patch:
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if context_attn1 is None:
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context_attn1 = n
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value_attn1 = n
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n = self.attn1.to_q(n)
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context_attn1 = self.attn1.to_k(context_attn1)
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value_attn1 = self.attn1.to_v(value_attn1)
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n = attn1_replace_patch[block_attn1](
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n, context_attn1, value_attn1, extra_options
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)
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n = self.attn1.to_out(n)
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else:
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n = self.attn1(n, context=context_attn1, value=value_attn1)
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if "attn1_output_patch" in transformer_patches:
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patch = transformer_patches["attn1_output_patch"]
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for p in patch:
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n = p(n, extra_options)
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x += n
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if "middle_patch" in transformer_patches:
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patch = transformer_patches["middle_patch"]
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for p in patch:
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x = p(x, extra_options)
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if self.attn2 is not None:
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n = self.norm2(x)
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if self.switch_temporal_ca_to_sa:
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context_attn2 = n
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else:
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context_attn2 = context
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value_attn2 = None
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if "attn2_patch" in transformer_patches:
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patch = transformer_patches["attn2_patch"]
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value_attn2 = context_attn2
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for p in patch:
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n, context_attn2, value_attn2 = p(
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n, context_attn2, value_attn2, extra_options
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)
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attn2_replace_patch = transformer_patches_replace.get("attn2", {})
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block_attn2 = transformer_block
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if block_attn2 not in attn2_replace_patch:
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block_attn2 = block
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if block_attn2 in attn2_replace_patch:
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if value_attn2 is None:
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value_attn2 = context_attn2
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n = self.attn2.to_q(n)
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context_attn2 = self.attn2.to_k(context_attn2)
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value_attn2 = self.attn2.to_v(value_attn2)
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n = attn2_replace_patch[block_attn2](
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n, context_attn2, value_attn2, extra_options
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)
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n = self.attn2.to_out(n)
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else:
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n = self.attn2(n, context=context_attn2, value=value_attn2)
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if "attn2_output_patch" in transformer_patches:
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patch = transformer_patches["attn2_output_patch"]
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for p in patch:
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n = p(n, extra_options)
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# x += n
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# if self.is_res:
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# x_skip = x
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# x = self.ff(self.norm3(x))
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x, x_skip = ixf_F.residual_layer_norm(
|
|||
|
|
n,
|
|||
|
|
self.norm3.normalized_shape,
|
|||
|
|
self.norm3.weight,
|
|||
|
|
self.norm3.bias,
|
|||
|
|
x,
|
|||
|
|
eps=self.norm3.eps,
|
|||
|
|
is_post_ln=False,
|
|||
|
|
)
|
|||
|
|
x = ffn_forward(self.ff, x)
|
|||
|
|
|
|||
|
|
# x = ffn_forward(self.ff, self.norm3(x))
|
|||
|
|
if self.is_res:
|
|||
|
|
x += x_skip
|
|||
|
|
|
|||
|
|
return x
|
|||
|
|
|
|||
|
|
|
|||
|
|
class SpatialTransformerNhwcWrapper(IxformerComfyWrapper):
|
|||
|
|
def __init__(self, module):
|
|||
|
|
super().__init__()
|
|||
|
|
self.use_linear = module.use_linear
|
|||
|
|
self.transformer_blocks = module.transformer_blocks
|
|||
|
|
self.norm = module.norm
|
|||
|
|
if not self.use_linear:
|
|||
|
|
self.proj_in = Conv2dNhwcWrapper(module.proj_in)
|
|||
|
|
self.proj_out = Conv2dNhwcWrapper(module.proj_out)
|
|||
|
|
else:
|
|||
|
|
self.proj_in = module.proj_in
|
|||
|
|
self.proj_out = module.proj_out
|
|||
|
|
|
|||
|
|
@ForwardWrapper
|
|||
|
|
def forward(self, x, context=None, transformer_options={}):
|
|||
|
|
# note: if no context is given, cross-attention defaults to self-attention
|
|||
|
|
if not isinstance(context, list):
|
|||
|
|
context = [context] * len(self.transformer_blocks)
|
|||
|
|
|
|||
|
|
b, h, w, c = x.shape
|
|||
|
|
x_in = x
|
|||
|
|
|
|||
|
|
# group_norm
|
|||
|
|
x = ixf_F.group_norm(
|
|||
|
|
x,
|
|||
|
|
self.norm.num_groups,
|
|||
|
|
self.norm.weight,
|
|||
|
|
self.norm.bias,
|
|||
|
|
format=False,
|
|||
|
|
)
|
|||
|
|
# conv2d
|
|||
|
|
if not self.use_linear:
|
|||
|
|
x = self.proj_in(x)
|
|||
|
|
# n,(hw),c
|
|||
|
|
x = x.view(x.shape[0], -1, x.shape[-1])
|
|||
|
|
if self.use_linear:
|
|||
|
|
x = self.proj_in(x)
|
|||
|
|
|
|||
|
|
for i, block in enumerate(self.transformer_blocks):
|
|||
|
|
transformer_options["block_index"] = i
|
|||
|
|
# x = block(x, context=context[i], transformer_options=transformer_options)
|
|||
|
|
x = transformer_block_forward(
|
|||
|
|
block, x, context=context[i], transformer_options=transformer_options
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
if self.use_linear:
|
|||
|
|
x = self.proj_out(x)
|
|||
|
|
x = x.view(b, h, w, c)
|
|||
|
|
if not self.use_linear:
|
|||
|
|
x = self.proj_out(x)
|
|||
|
|
return x + x_in
|
|||
|
|
|
|||
|
|
|
|||
|
|
class UpsampleNhwcWrapper(IxformerComfyWrapper):
|
|||
|
|
def __init__(self, module) -> None:
|
|||
|
|
# TODO: support mhwc interpolate
|
|||
|
|
super().__init__()
|
|||
|
|
self.dims = module.dims
|
|||
|
|
self.use_conv = module.use_conv
|
|||
|
|
self.channels = module.channels
|
|||
|
|
if self.use_conv:
|
|||
|
|
self.conv = Conv2dNhwcWrapper(module.conv)
|
|||
|
|
|
|||
|
|
def forward(self, x, output_shape=None):
|
|||
|
|
# print("================== Upsample is running ==================")
|
|||
|
|
assert x.shape[-1] == self.channels
|
|||
|
|
assert len(x.shape) == 4
|
|||
|
|
|
|||
|
|
# nhwc -> nchw
|
|||
|
|
if output_shape is not None:
|
|||
|
|
assert len(output_shape) == 4
|
|||
|
|
output_shape = [
|
|||
|
|
output_shape[0],
|
|||
|
|
output_shape[3],
|
|||
|
|
output_shape[1],
|
|||
|
|
output_shape[2],
|
|||
|
|
]
|
|||
|
|
x = x.permute(0, 3, 1, 2).contiguous()
|
|||
|
|
if self.dims == 3:
|
|||
|
|
shape = [x.shape[2], x.shape[3] * 2, x.shape[4] * 2]
|
|||
|
|
if output_shape is not None:
|
|||
|
|
shape[1] = output_shape[3]
|
|||
|
|
shape[2] = output_shape[4]
|
|||
|
|
else:
|
|||
|
|
shape = [x.shape[2] * 2, x.shape[3] * 2]
|
|||
|
|
if output_shape is not None:
|
|||
|
|
shape[0] = output_shape[2]
|
|||
|
|
shape[1] = output_shape[3]
|
|||
|
|
# TODO: interpolate 支持 nhwc， 去掉前后转置
|
|||
|
|
x = F.interpolate(x, size=shape, mode="nearest")
|
|||
|
|
# nchw -> nhwc
|
|||
|
|
x = x.permute(0, 2, 3, 1).contiguous()
|
|||
|
|
if self.use_conv:
|
|||
|
|
x = self.conv(x)
|
|||
|
|
return x
|
|||
|
|
|
|||
|
|
|
|||
|
|
unet_wrappers = {
|
|||
|
|
"Conv2d": Conv2dNhwcWrapper,
|
|||
|
|
"ResBlock": ResBlockNhwcWrapper,
|
|||
|
|
"Downsample": DownsampleNhwcWrapper,
|
|||
|
|
"SpatialTransformer": SpatialTransformerNhwcWrapper,
|
|||
|
|
"Upsample": UpsampleNhwcWrapper,
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
|
|||
|
|
def get_class_name(module):
|
|||
|
|
return module.__class__.__name__
|
|||
|
|
|
|||
|
|
|
|||
|
|
def module_wrapper(module):
|
|||
|
|
# 将原始的 module 封装为 nhwc 模式
|
|||
|
|
module_name = get_class_name(module)
|
|||
|
|
assert (
|
|||
|
|
module_name == "TimestepEmbedSequential"
|
|||
|
|
), f"ixformer unet_model_wrapper only support 'TimestepEmbedSequential' now, but got {module_name}"
|
|||
|
|
|
|||
|
|
num_sequential = len(module)
|
|||
|
|
for idx_seq in range(num_sequential):
|
|||
|
|
sub_module = module[idx_seq]
|
|||
|
|
sub_module_name = get_class_name(sub_module)
|
|||
|
|
# 判断模块是否已经封装
|
|||
|
|
if not getattr(sub_module, "is_ixf_wrapper", False):
|
|||
|
|
if sub_module_name in unet_wrappers:
|
|||
|
|
module[idx_seq].forward = unet_wrappers[sub_module_name](
|
|||
|
|
sub_module
|
|||
|
|
).forward
|
|||
|
|
module[idx_seq].is_ixf_wrapper = True
|
|||
|
|
else:
|
|||
|
|
raise NotImplementedError(f"{sub_module_name} not support")
|
|||
|
|
return module
|