Add updated transformer and attention
This commit is contained in:
ExponentialML
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import torch
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from torch import nn, einsum
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import torch.nn.functional as F
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from einops import rearrange, repeat
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from functools import partial
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from ..common import (
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checkpoint,
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exists,
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default,
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)
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from ..basics import zero_module
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import comfy.ops
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ops = comfy.ops.disable_weight_init
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from comfy import model_management
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from comfy.ldm.modules.attention import optimized_attention, optimized_attention_masked
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if model_management.xformers_enabled():
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import xformers
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import xformers.ops
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XFORMERS_IS_AVAILBLE = True
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else:
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XFORMERS_IS_AVAILBLE = False
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class RelativePosition(nn.Module):
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""" https://github.com/evelinehong/Transformer_Relative_Position_PyTorch/blob/master/relative_position.py """
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def __init__(self, num_units, max_relative_position):
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super().__init__()
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self.num_units = num_units
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self.max_relative_position = max_relative_position
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self.embeddings_table = nn.Parameter(torch.Tensor(max_relative_position * 2 + 1, num_units))
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nn.init.xavier_uniform_(self.embeddings_table)
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def forward(self, length_q, length_k):
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device = self.embeddings_table.device
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range_vec_q = torch.arange(length_q, device=device)
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range_vec_k = torch.arange(length_k, device=device)
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distance_mat = range_vec_k[None, :] - range_vec_q[:, None]
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distance_mat_clipped = torch.clamp(distance_mat, -self.max_relative_position, self.max_relative_position)
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final_mat = distance_mat_clipped + self.max_relative_position
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final_mat = final_mat.long()
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embeddings = self.embeddings_table[final_mat]
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return embeddings
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# TODO Add native Comfy optimized attention.
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class CrossAttention(nn.Module):
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def __init__(
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self,
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query_dim,
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context_dim=None,
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heads=8,
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dim_head=64,
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dropout=0.,
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relative_position=False,
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temporal_length=None,
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video_length=None,
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image_cross_attention=False,
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image_cross_attention_scale=1.0,
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image_cross_attention_scale_learnable=False,
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text_context_len=77,
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device=None,
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dtype=None,
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operations=ops
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):
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super().__init__()
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inner_dim = dim_head * heads
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context_dim = default(context_dim, query_dim)
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self.scale = dim_head**-0.5
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self.heads = heads
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self.dim_head = dim_head
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self.to_q = operations.Linear(query_dim, inner_dim, bias=False, device=device, dtype=dtype)
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self.to_k = operations.Linear(context_dim, inner_dim, bias=False, device=device, dtype=dtype)
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self.to_v = operations.Linear(context_dim, inner_dim, bias=False, device=device, dtype=dtype)
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self.to_out = nn.Sequential(
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operations.Linear(inner_dim, query_dim, device=device, dtype=dtype),
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nn.Dropout(dropout)
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)
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self.relative_position = relative_position
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if self.relative_position:
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assert(temporal_length is not None)
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self.relative_position_k = RelativePosition(num_units=dim_head, max_relative_position=temporal_length)
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self.relative_position_v = RelativePosition(num_units=dim_head, max_relative_position=temporal_length)
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else:
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## only used for spatial attention, while NOT for temporal attention
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if XFORMERS_IS_AVAILBLE and temporal_length is None:
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self.forward = self.efficient_forward
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else:
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self.forward = self.comfy_efficient_forward
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self.video_length = video_length
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self.image_cross_attention = image_cross_attention
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self.image_cross_attention_scale = image_cross_attention_scale
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self.text_context_len = text_context_len
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self.image_cross_attention_scale_learnable = image_cross_attention_scale_learnable
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if self.image_cross_attention:
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self.to_k_ip = operations.Linear(context_dim, inner_dim, bias=False, device=device, dtype=dtype)
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self.to_v_ip = operations.Linear(context_dim, inner_dim, bias=False, device=device, dtype=dtype)
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if image_cross_attention_scale_learnable:
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self.register_parameter('alpha', nn.Parameter(torch.tensor(0.)) )
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def comfy_efficient_forward(self, x, context=None, mask=None):
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spatial_self_attn = (context is None)
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k_ip, v_ip, out_ip = None, None, None
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h = self.heads
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q = self.to_q(x)
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context = default(context, x)
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if self.image_cross_attention and not spatial_self_attn:
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context, context_image = context[:,:self.text_context_len,:], context[:,self.text_context_len:,:]
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k = self.to_k(context)
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v = self.to_v(context)
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k_ip = self.to_k_ip(context_image)
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v_ip = self.to_v_ip(context_image)
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else:
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if not spatial_self_attn:
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context = context[:,:self.text_context_len,:]
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k = self.to_k(context)
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v = self.to_v(context)
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out = optimized_attention(q, k, v, h)
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if exists(mask):
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## feasible for causal attention mask only
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out = optimized_attention_masked(q, k, v, h)
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## for image cross-attention
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if k_ip is not None:
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q = rearrange(q, 'b n (h d) -> (b h) n d', h=h)
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k_ip, v_ip = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (k_ip, v_ip))
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sim_ip = torch.einsum('b i d, b j d -> b i j', q, k_ip) * self.scale
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del k_ip
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sim_ip = sim_ip.softmax(dim=-1)
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out_ip = torch.einsum('b i j, b j d -> b i d', sim_ip, v_ip)
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out_ip = rearrange(out_ip, '(b h) n d -> b n (h d)', h=h)
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if out_ip is not None:
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if self.image_cross_attention_scale_learnable:
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out = out + self.image_cross_attention_scale * out_ip * (torch.tanh(self.alpha)+1)
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else:
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out = out + self.image_cross_attention_scale * out_ip
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return self.to_out(out)
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def forward(self, x, context=None, mask=None):
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spatial_self_attn = (context is None)
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k_ip, v_ip, out_ip = None, None, None
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h = self.heads
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q = self.to_q(x)
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context = default(context, x)
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if self.image_cross_attention and not spatial_self_attn:
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context, context_image = context[:,:self.text_context_len,:], context[:,self.text_context_len:,:]
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k = self.to_k(context)
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v = self.to_v(context)
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k_ip = self.to_k_ip(context_image)
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v_ip = self.to_v_ip(context_image)
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else:
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# Assumed Spatial Attention (b c h w)
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if not spatial_self_attn:
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context = context[:,:self.text_context_len,:]
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k = self.to_k(context)
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v = self.to_v(context)
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q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
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sim = torch.einsum('b i d, b j d -> b i j', q, k) * self.scale
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if self.relative_position:
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len_q, len_k, len_v = q.shape[1], k.shape[1], v.shape[1]
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k2 = self.relative_position_k(len_q, len_k)
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sim2 = einsum('b t d, t s d -> b t s', q, k2) * self.scale # TODO check
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sim += sim2
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del k
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if exists(mask):
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## feasible for causal attention mask only
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max_neg_value = -torch.finfo(sim.dtype).max
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mask = repeat(mask, 'b i j -> (b h) i j', h=h)
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sim.masked_fill_(~(mask>0.5), max_neg_value)
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# attention, what we cannot get enough of
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sim = sim.softmax(dim=-1)
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out = torch.einsum('b i j, b j d -> b i d', sim, v)
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if self.relative_position:
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v2 = self.relative_position_v(len_q, len_v)
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out2 = einsum('b t s, t s d -> b t d', sim, v2) # TODO check
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out += out2
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out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
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## for image cross-attention
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if k_ip is not None:
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k_ip, v_ip = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (k_ip, v_ip))
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sim_ip = torch.einsum('b i d, b j d -> b i j', q, k_ip) * self.scale
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del k_ip
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sim_ip = sim_ip.softmax(dim=-1)
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out_ip = torch.einsum('b i j, b j d -> b i d', sim_ip, v_ip)
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out_ip = rearrange(out_ip, '(b h) n d -> b n (h d)', h=h)
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if out_ip is not None:
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if self.image_cross_attention_scale_learnable:
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out = out + self.image_cross_attention_scale * out_ip * (torch.tanh(self.alpha)+1)
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else:
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out = out + self.image_cross_attention_scale * out_ip
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return self.to_out(out)
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def efficient_forward(self, x, context=None, mask=None):
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spatial_self_attn = (context is None)
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k_ip, v_ip, out_ip = None, None, None
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q = self.to_q(x)
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context = default(context, x)
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if self.image_cross_attention and not spatial_self_attn:
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context, context_image = context[:,:self.text_context_len,:], context[:,self.text_context_len:,:]
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k = self.to_k(context)
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v = self.to_v(context)
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k_ip = self.to_k_ip(context_image)
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v_ip = self.to_v_ip(context_image)
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else:
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if not spatial_self_attn:
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context = context[:,:self.text_context_len,:]
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k = self.to_k(context)
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v = self.to_v(context)
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b, _, _ = q.shape
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q, k, v = map(
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lambda t: t.unsqueeze(3)
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.reshape(b, t.shape[1], self.heads, self.dim_head)
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.permute(0, 2, 1, 3)
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.reshape(b * self.heads, t.shape[1], self.dim_head)
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.contiguous(),
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(q, k, v),
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)
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# actually compute the attention, what we cannot get enough of
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out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=None)
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## for image cross-attention
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if k_ip is not None:
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k_ip, v_ip = map(
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lambda t: t.unsqueeze(3)
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.reshape(b, t.shape[1], self.heads, self.dim_head)
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.permute(0, 2, 1, 3)
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.reshape(b * self.heads, t.shape[1], self.dim_head)
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.contiguous(),
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(k_ip, v_ip),
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)
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out_ip = xformers.ops.memory_efficient_attention(q, k_ip, v_ip, attn_bias=None, op=None)
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out_ip = (
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out_ip.unsqueeze(0)
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.reshape(b, self.heads, out.shape[1], self.dim_head)
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.permute(0, 2, 1, 3)
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.reshape(b, out.shape[1], self.heads * self.dim_head)
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)
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if exists(mask):
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raise NotImplementedError
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out = (
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out.unsqueeze(0)
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.reshape(b, self.heads, out.shape[1], self.dim_head)
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.permute(0, 2, 1, 3)
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.reshape(b, out.shape[1], self.heads * self.dim_head)
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)
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if out_ip is not None:
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if self.image_cross_attention_scale_learnable:
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out = out + self.image_cross_attention_scale * out_ip * (torch.tanh(self.alpha)+1)
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else:
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out = out + self.image_cross_attention_scale * out_ip
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return self.to_out(out)
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class BasicTransformerBlock(nn.Module):
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def __init__(
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self,
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dim,
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n_heads,
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d_head,
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dropout=0.,
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context_dim=None,
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gated_ff=True,
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checkpoint=True,
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disable_self_attn=False,
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attention_cls=None,
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video_length=None,
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image_cross_attention=False,
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image_cross_attention_scale=1.0,
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image_cross_attention_scale_learnable=False,
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text_context_len=77,
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device=None,
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dtype=None,
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operations=ops
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):
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super().__init__()
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attn_cls = CrossAttention if attention_cls is None else attention_cls
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self.disable_self_attn = disable_self_attn
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self.attn1 = attn_cls(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout,
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context_dim=None, device=device, dtype=dtype if self.disable_self_attn else None)
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self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff, device=device, dtype=dtype)
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self.attn2 = attn_cls(
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query_dim=dim,
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context_dim=context_dim,
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heads=n_heads,
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dim_head=d_head,
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dropout=dropout,
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video_length=video_length,
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image_cross_attention=image_cross_attention,
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image_cross_attention_scale=image_cross_attention_scale,
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image_cross_attention_scale_learnable=image_cross_attention_scale_learnable,
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text_context_len=text_context_len,
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device=device,
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dtype=dtype
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)
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self.image_cross_attention = image_cross_attention
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self.norm1 = operations.LayerNorm(dim, device=device, dtype=dtype)
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self.norm2 = operations.LayerNorm(dim, device=device, dtype=dtype)
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self.norm3 = operations.LayerNorm(dim, device=device, dtype=dtype)
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self.checkpoint = checkpoint
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def forward(self, x, context=None, mask=None, **kwargs):
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## implementation tricks: because checkpointing doesn't support non-tensor (e.g. None or scalar) arguments
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input_tuple = (x,) ## should not be (x), otherwise *input_tuple will decouple x into multiple arguments
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if context is not None:
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input_tuple = (x, context)
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if mask is not None:
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forward_mask = partial(self._forward, mask=mask)
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return checkpoint(forward_mask, (x,), self.parameters(), self.checkpoint)
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return checkpoint(self._forward, input_tuple, self.parameters(), self.checkpoint)
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def _forward(self, x, context=None, mask=None):
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x = self.attn1(self.norm1(x), context=context if self.disable_self_attn else None, mask=mask) + x
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x = self.attn2(self.norm2(x), context=context, mask=mask) + x
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x = self.ff(self.norm3(x)) + x
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return x
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class SpatialTransformer(nn.Module):
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"""
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Transformer block for image-like data in spatial axis.
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First, project the input (aka embedding)
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and reshape to b, t, d.
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Then apply standard transformer action.
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Finally, reshape to image
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NEW: use_linear for more efficiency instead of the 1x1 convs
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"""
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def __init__(
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self,
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in_channels,
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n_heads,
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d_head,
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depth=1,
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dropout=0.,
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context_dim=None,
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use_checkpoint=True,
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disable_self_attn=False,
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use_linear=False,
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video_length=None,
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image_cross_attention=False,
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image_cross_attention_scale_learnable=False,
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device=None,
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dtype=None,
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operations=ops
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):
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super().__init__()
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self.in_channels = in_channels
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inner_dim = n_heads * d_head
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self.norm = operations.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True, device=device, dtype=dtype)
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if not use_linear:
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self.proj_in = opeations.Conv2d(in_channels, inner_dim, kernel_size=1, stride=1, padding=0, device=device, dtype=dtype)
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else:
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self.proj_in = operations.Linear(in_channels, inner_dim, device=device, dtype=dtype)
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attention_cls = None
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self.transformer_blocks = nn.ModuleList([
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BasicTransformerBlock(
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inner_dim,
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n_heads,
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d_head,
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dropout=dropout,
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context_dim=context_dim,
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disable_self_attn=disable_self_attn,
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checkpoint=use_checkpoint,
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attention_cls=attention_cls,
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video_length=video_length,
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image_cross_attention=image_cross_attention,
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image_cross_attention_scale_learnable=image_cross_attention_scale_learnable,
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device=device,
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dtype=dtype
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) for d in range(depth)
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])
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if not use_linear:
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self.proj_out = zero_module(operations.Conv2d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0, device=device, dtype=dtype))
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else:
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self.proj_out = zero_module(operations.Linear(inner_dim, in_channels, device=device, dtype=dtype))
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self.use_linear = use_linear
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def forward(self, x, context=None, **kwargs):
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b, c, h, w = x.shape
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x_in = x
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x = self.norm(x)
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if not self.use_linear:
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x = self.proj_in(x)
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x = rearrange(x, 'b c h w -> b (h w) c').contiguous()
|
||||
if self.use_linear:
|
||||
x = self.proj_in(x)
|
||||
for i, block in enumerate(self.transformer_blocks):
|
||||
x = block(x, context=context, **kwargs)
|
||||
if self.use_linear:
|
||||
x = self.proj_out(x)
|
||||
x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w).contiguous()
|
||||
if not self.use_linear:
|
||||
x = self.proj_out(x)
|
||||
return x + x_in
|
||||
|
||||
|
||||
class TemporalTransformer(nn.Module):
|
||||
"""
|
||||
Transformer block for image-like data in temporal axis.
|
||||
First, reshape to b, t, d.
|
||||
Then apply standard transformer action.
|
||||
Finally, reshape to image
|
||||
"""
|
||||
def __init__(
|
||||
self,
|
||||
in_channels,
|
||||
n_heads,
|
||||
d_head,
|
||||
depth=1,
|
||||
dropout=0.,
|
||||
context_dim=None,
|
||||
use_checkpoint=True,
|
||||
use_linear=False,
|
||||
only_self_att=True,
|
||||
causal_attention=False,
|
||||
causal_block_size=1,
|
||||
relative_position=False,
|
||||
temporal_length=None,
|
||||
device=None,
|
||||
dtype=None,
|
||||
operations=ops
|
||||
):
|
||||
super().__init__()
|
||||
self.only_self_att = only_self_att
|
||||
self.relative_position = relative_position
|
||||
self.causal_attention = causal_attention
|
||||
self.causal_block_size = causal_block_size
|
||||
|
||||
if only_self_att:
|
||||
context_dim = None
|
||||
|
||||
self.in_channels = in_channels
|
||||
inner_dim = n_heads * d_head
|
||||
self.norm = operations.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True, device=device, dtype=dtype)
|
||||
self.proj_in = nn.Conv1d(in_channels, inner_dim, kernel_size=1, stride=1, padding=0).to(device, dtype)
|
||||
if not use_linear:
|
||||
self.proj_in = nn.Conv1d(in_channels, inner_dim, kernel_size=1, stride=1, padding=0).to(device, dtype)
|
||||
else:
|
||||
self.proj_in = operations.Linear(in_channels, inner_dim, device=device, dtype=dtype)
|
||||
|
||||
if relative_position:
|
||||
assert(temporal_length is not None)
|
||||
attention_cls = partial(CrossAttention, relative_position=True, temporal_length=temporal_length, device=device, dtype=dtype)
|
||||
else:
|
||||
attention_cls = partial(CrossAttention, temporal_length=temporal_length, device=device, dtype=dtype)
|
||||
if self.causal_attention:
|
||||
assert(temporal_length is not None)
|
||||
self.mask = torch.tril(torch.ones([1, temporal_length, temporal_length]))
|
||||
|
||||
if self.only_self_att:
|
||||
context_dim = None
|
||||
self.transformer_blocks = nn.ModuleList([
|
||||
BasicTransformerBlock(
|
||||
inner_dim,
|
||||
n_heads,
|
||||
d_head,
|
||||
dropout=dropout,
|
||||
context_dim=context_dim,
|
||||
attention_cls=attention_cls,
|
||||
checkpoint=use_checkpoint,
|
||||
device=device,
|
||||
dtype=dtype
|
||||
) for d in range(depth)
|
||||
])
|
||||
if not use_linear:
|
||||
self.proj_out = zero_module(nn.Conv1d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0).to(device, dtype))
|
||||
else:
|
||||
self.proj_out = zero_module(operations.Linear(inner_dim, in_channels, device=device, dtype=dtype))
|
||||
self.use_linear = use_linear
|
||||
|
||||
def forward(self, x, context=None):
|
||||
b, c, t, h, w = x.shape
|
||||
x_in = x
|
||||
x = self.norm(x)
|
||||
x = rearrange(x, 'b c t h w -> (b h w) c t').contiguous()
|
||||
if not self.use_linear:
|
||||
x = self.proj_in(x)
|
||||
x = rearrange(x, 'bhw c t -> bhw t c').contiguous()
|
||||
if self.use_linear:
|
||||
x = self.proj_in(x)
|
||||
|
||||
temp_mask = None
|
||||
if self.causal_attention:
|
||||
# slice the from mask map
|
||||
temp_mask = self.mask[:,:t,:t].to(x.device)
|
||||
|
||||
if temp_mask is not None:
|
||||
mask = temp_mask.to(x.device)
|
||||
mask = repeat(mask, 'l i j -> (l bhw) i j', bhw=b*h*w)
|
||||
else:
|
||||
mask = None
|
||||
|
||||
if self.only_self_att:
|
||||
## note: if no context is given, cross-attention defaults to self-attention
|
||||
for i, block in enumerate(self.transformer_blocks):
|
||||
x = block(x, mask=mask)
|
||||
x = rearrange(x, '(b hw) t c -> b hw t c', b=b).contiguous()
|
||||
else:
|
||||
x = rearrange(x, '(b hw) t c -> b hw t c', b=b).contiguous()
|
||||
context = rearrange(context, '(b t) l con -> b t l con', t=t).contiguous()
|
||||
for i, block in enumerate(self.transformer_blocks):
|
||||
# calculate each batch one by one (since number in shape could not greater then 65,535 for some package)
|
||||
for j in range(b):
|
||||
context_j = repeat(
|
||||
context[j],
|
||||
't l con -> (t r) l con', r=(h * w) // t, t=t).contiguous()
|
||||
## note: causal mask will not applied in cross-attention case
|
||||
x[j] = block(x[j], context=context_j)
|
||||
|
||||
if self.use_linear:
|
||||
x = self.proj_out(x)
|
||||
x = rearrange(x, 'b (h w) t c -> b c t h w', h=h, w=w).contiguous()
|
||||
if not self.use_linear:
|
||||
x = rearrange(x, 'b hw t c -> (b hw) c t').contiguous()
|
||||
x = self.proj_out(x)
|
||||
x = rearrange(x, '(b h w) c t -> b c t h w', b=b, h=h, w=w).contiguous()
|
||||
|
||||
return x + x_in
|
||||
|
||||
|
||||
class GEGLU(nn.Module):
|
||||
def __init__(self, dim_in, dim_out, device=None, dtype=None, operations=ops):
|
||||
super().__init__()
|
||||
self.proj = operations.Linear(dim_in, dim_out * 2, device=device, dtype=dtype)
|
||||
|
||||
def forward(self, x):
|
||||
x, gate = self.proj(x).chunk(2, dim=-1)
|
||||
return x * F.gelu(gate)
|
||||
|
||||
|
||||
class FeedForward(nn.Module):
|
||||
def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0., device=None, dtype=None, operations=ops):
|
||||
super().__init__()
|
||||
inner_dim = int(dim * mult)
|
||||
dim_out = default(dim_out, dim)
|
||||
project_in = nn.Sequential(
|
||||
operations.Linear(dim, inner_dim, device=device, dtype=dtype),
|
||||
nn.GELU()
|
||||
) if not glu else GEGLU(dim, inner_dim)
|
||||
|
||||
self.net = nn.Sequential(
|
||||
project_in,
|
||||
nn.Dropout(dropout),
|
||||
operations.Linear(inner_dim, dim_out, device=device, dtype=dtype)
|
||||
)
|
||||
|
||||
def forward(self, x):
|
||||
return self.net(x)
|
||||
|
||||
|
||||
class LinearAttention(nn.Module):
|
||||
def __init__(self, dim, heads=4, dim_head=32, device=None, dtype=None, operations=ops):
|
||||
super().__init__()
|
||||
self.heads = heads
|
||||
hidden_dim = dim_head * heads
|
||||
self.to_qkv = operations.Conv2d(dim, hidden_dim * 3, 1, bias = False, device=device, dtype=dtype)
|
||||
self.to_out = operations.Conv2d(hidden_dim, dim, 1, device=device, dtype=dtype)
|
||||
|
||||
def forward(self, x):
|
||||
b, c, h, w = x.shape
|
||||
qkv = self.to_qkv(x)
|
||||
q, k, v = rearrange(qkv, 'b (qkv heads c) h w -> qkv b heads c (h w)', heads = self.heads, qkv=3)
|
||||
k = k.softmax(dim=-1)
|
||||
context = torch.einsum('bhdn,bhen->bhde', k, v)
|
||||
out = torch.einsum('bhde,bhdn->bhen', context, q)
|
||||
out = rearrange(out, 'b heads c (h w) -> b (heads c) h w', heads=self.heads, h=h, w=w)
|
||||
return self.to_out(out)
|
||||
|
||||
|
||||
class SpatialSelfAttention(nn.Module):
|
||||
def __init__(self, in_channels, device=None, dtype=None, operations=ops):
|
||||
super().__init__()
|
||||
self.in_channels = in_channels
|
||||
|
||||
self.norm = operations.GroupNorm(
|
||||
num_groups=32,
|
||||
num_channels=in_channels,
|
||||
eps=1e-6,
|
||||
affine=True,
|
||||
device=device,
|
||||
dtype=dtype
|
||||
)
|
||||
self.q = operations.Conv2d(
|
||||
in_channels,
|
||||
in_channels,
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
padding=0,
|
||||
device=device,
|
||||
dtype=dtype
|
||||
)
|
||||
self.k = operations.Conv2d(
|
||||
in_channels,
|
||||
in_channels,
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
padding=0,
|
||||
device=device,
|
||||
dtype=dtype
|
||||
)
|
||||
self.v = operations.Conv2d(
|
||||
in_channels,
|
||||
in_channels,
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
padding=0,
|
||||
device=device,
|
||||
dtype=dtype
|
||||
)
|
||||
self.proj_out = operations.Conv2d(
|
||||
in_channels,
|
||||
in_channels,
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
padding=0,
|
||||
device=device,
|
||||
dtype=dtype
|
||||
)
|
||||
|
||||
def forward(self, x):
|
||||
h_ = x
|
||||
h_ = self.norm(h_)
|
||||
q = self.q(h_)
|
||||
k = self.k(h_)
|
||||
v = self.v(h_)
|
||||
|
||||
# compute attention
|
||||
b,c,h,w = q.shape
|
||||
q = rearrange(q, 'b c h w -> b (h w) c')
|
||||
k = rearrange(k, 'b c h w -> b c (h w)')
|
||||
w_ = torch.einsum('bij,bjk->bik', q, k)
|
||||
|
||||
w_ = w_ * (int(c)**(-0.5))
|
||||
w_ = torch.nn.functional.softmax(w_, dim=2)
|
||||
|
||||
# attend to values
|
||||
v = rearrange(v, 'b c h w -> b c (h w)')
|
||||
w_ = rearrange(w_, 'b i j -> b j i')
|
||||
h_ = torch.einsum('bij,bjk->bik', v, w_)
|
||||
h_ = rearrange(h_, 'b c (h w) -> b c h w', h=h)
|
||||
h_ = self.proj_out(h_)
|
||||
|
||||
return x+h_
|
||||
@@ -0,0 +1,389 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import kornia
|
||||
import open_clip
|
||||
from torch.utils.checkpoint import checkpoint
|
||||
from transformers import T5Tokenizer, T5EncoderModel, CLIPTokenizer, CLIPTextModel
|
||||
from ..common import autocast
|
||||
from utils.utils import count_params
|
||||
|
||||
|
||||
class AbstractEncoder(nn.Module):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
|
||||
def encode(self, *args, **kwargs):
|
||||
raise NotImplementedError
|
||||
|
||||
|
||||
class IdentityEncoder(AbstractEncoder):
|
||||
def encode(self, x):
|
||||
return x
|
||||
|
||||
|
||||
class ClassEmbedder(nn.Module):
|
||||
def __init__(self, embed_dim, n_classes=1000, key='class', ucg_rate=0.1):
|
||||
super().__init__()
|
||||
self.key = key
|
||||
self.embedding = nn.Embedding(n_classes, embed_dim)
|
||||
self.n_classes = n_classes
|
||||
self.ucg_rate = ucg_rate
|
||||
|
||||
def forward(self, batch, key=None, disable_dropout=False):
|
||||
if key is None:
|
||||
key = self.key
|
||||
# this is for use in crossattn
|
||||
c = batch[key][:, None]
|
||||
if self.ucg_rate > 0. and not disable_dropout:
|
||||
mask = 1. - torch.bernoulli(torch.ones_like(c) * self.ucg_rate)
|
||||
c = mask * c + (1 - mask) * torch.ones_like(c) * (self.n_classes - 1)
|
||||
c = c.long()
|
||||
c = self.embedding(c)
|
||||
return c
|
||||
|
||||
def get_unconditional_conditioning(self, bs, device="cuda"):
|
||||
uc_class = self.n_classes - 1 # 1000 classes --> 0 ... 999, one extra class for ucg (class 1000)
|
||||
uc = torch.ones((bs,), device=device) * uc_class
|
||||
uc = {self.key: uc}
|
||||
return uc
|
||||
|
||||
|
||||
def disabled_train(self, mode=True):
|
||||
"""Overwrite model.train with this function to make sure train/eval mode
|
||||
does not change anymore."""
|
||||
return self
|
||||
|
||||
|
||||
class FrozenT5Embedder(AbstractEncoder):
|
||||
"""Uses the T5 transformer encoder for text"""
|
||||
|
||||
def __init__(self, version="google/t5-v1_1-large", device="cuda", max_length=77,
|
||||
freeze=True): # others are google/t5-v1_1-xl and google/t5-v1_1-xxl
|
||||
super().__init__()
|
||||
self.tokenizer = T5Tokenizer.from_pretrained(version)
|
||||
self.transformer = T5EncoderModel.from_pretrained(version)
|
||||
self.device = device
|
||||
self.max_length = max_length # TODO: typical value?
|
||||
if freeze:
|
||||
self.freeze()
|
||||
|
||||
def freeze(self):
|
||||
self.transformer = self.transformer.eval()
|
||||
# self.train = disabled_train
|
||||
for param in self.parameters():
|
||||
param.requires_grad = False
|
||||
|
||||
def forward(self, text):
|
||||
batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
|
||||
return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
|
||||
tokens = batch_encoding["input_ids"].to(self.device)
|
||||
outputs = self.transformer(input_ids=tokens)
|
||||
|
||||
z = outputs.last_hidden_state
|
||||
return z
|
||||
|
||||
def encode(self, text):
|
||||
return self(text)
|
||||
|
||||
|
||||
class FrozenCLIPEmbedder(AbstractEncoder):
|
||||
"""Uses the CLIP transformer encoder for text (from huggingface)"""
|
||||
LAYERS = [
|
||||
"last",
|
||||
"pooled",
|
||||
"hidden"
|
||||
]
|
||||
|
||||
def __init__(self, version="openai/clip-vit-large-patch14", device="cuda", max_length=77,
|
||||
freeze=True, layer="last", layer_idx=None): # clip-vit-base-patch32
|
||||
super().__init__()
|
||||
assert layer in self.LAYERS
|
||||
self.tokenizer = CLIPTokenizer.from_pretrained(version)
|
||||
self.transformer = CLIPTextModel.from_pretrained(version)
|
||||
self.device = device
|
||||
self.max_length = max_length
|
||||
if freeze:
|
||||
self.freeze()
|
||||
self.layer = layer
|
||||
self.layer_idx = layer_idx
|
||||
if layer == "hidden":
|
||||
assert layer_idx is not None
|
||||
assert 0 <= abs(layer_idx) <= 12
|
||||
|
||||
def freeze(self):
|
||||
self.transformer = self.transformer.eval()
|
||||
# self.train = disabled_train
|
||||
for param in self.parameters():
|
||||
param.requires_grad = False
|
||||
|
||||
def forward(self, text):
|
||||
batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
|
||||
return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
|
||||
tokens = batch_encoding["input_ids"].to(self.device)
|
||||
outputs = self.transformer(input_ids=tokens, output_hidden_states=self.layer == "hidden")
|
||||
if self.layer == "last":
|
||||
z = outputs.last_hidden_state
|
||||
elif self.layer == "pooled":
|
||||
z = outputs.pooler_output[:, None, :]
|
||||
else:
|
||||
z = outputs.hidden_states[self.layer_idx]
|
||||
return z
|
||||
|
||||
def encode(self, text):
|
||||
return self(text)
|
||||
|
||||
|
||||
class ClipImageEmbedder(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
model,
|
||||
jit=False,
|
||||
device='cuda' if torch.cuda.is_available() else 'cpu',
|
||||
antialias=True,
|
||||
ucg_rate=0.
|
||||
):
|
||||
super().__init__()
|
||||
from clip import load as load_clip
|
||||
self.model, _ = load_clip(name=model, device=device, jit=jit)
|
||||
|
||||
self.antialias = antialias
|
||||
|
||||
self.register_buffer('mean', torch.Tensor([0.48145466, 0.4578275, 0.40821073]), persistent=False)
|
||||
self.register_buffer('std', torch.Tensor([0.26862954, 0.26130258, 0.27577711]), persistent=False)
|
||||
self.ucg_rate = ucg_rate
|
||||
|
||||
def preprocess(self, x):
|
||||
# normalize to [0,1]
|
||||
x = kornia.geometry.resize(x, (224, 224),
|
||||
interpolation='bicubic', align_corners=True,
|
||||
antialias=self.antialias)
|
||||
x = (x + 1.) / 2.
|
||||
# re-normalize according to clip
|
||||
x = kornia.enhance.normalize(x, self.mean, self.std)
|
||||
return x
|
||||
|
||||
def forward(self, x, no_dropout=False):
|
||||
# x is assumed to be in range [-1,1]
|
||||
out = self.model.encode_image(self.preprocess(x))
|
||||
out = out.to(x.dtype)
|
||||
if self.ucg_rate > 0. and not no_dropout:
|
||||
out = torch.bernoulli((1. - self.ucg_rate) * torch.ones(out.shape[0], device=out.device))[:, None] * out
|
||||
return out
|
||||
|
||||
|
||||
class FrozenOpenCLIPEmbedder(AbstractEncoder):
|
||||
"""
|
||||
Uses the OpenCLIP transformer encoder for text
|
||||
"""
|
||||
LAYERS = [
|
||||
# "pooled",
|
||||
"last",
|
||||
"penultimate"
|
||||
]
|
||||
|
||||
def __init__(self, arch="ViT-H-14", version="laion2b_s32b_b79k", device="cuda", max_length=77,
|
||||
freeze=True, layer="last"):
|
||||
super().__init__()
|
||||
assert layer in self.LAYERS
|
||||
model, _, _ = open_clip.create_model_and_transforms(arch, device=torch.device('cpu'), pretrained=version)
|
||||
del model.visual
|
||||
self.model = model
|
||||
|
||||
self.device = device
|
||||
self.max_length = max_length
|
||||
if freeze:
|
||||
self.freeze()
|
||||
self.layer = layer
|
||||
if self.layer == "last":
|
||||
self.layer_idx = 0
|
||||
elif self.layer == "penultimate":
|
||||
self.layer_idx = 1
|
||||
else:
|
||||
raise NotImplementedError()
|
||||
|
||||
def freeze(self):
|
||||
self.model = self.model.eval()
|
||||
for param in self.parameters():
|
||||
param.requires_grad = False
|
||||
|
||||
def forward(self, text):
|
||||
tokens = open_clip.tokenize(text) ## all clip models use 77 as context length
|
||||
z = self.encode_with_transformer(tokens.to(self.device))
|
||||
return z
|
||||
|
||||
def encode_with_transformer(self, text):
|
||||
x = self.model.token_embedding(text) # [batch_size, n_ctx, d_model]
|
||||
x = x + self.model.positional_embedding
|
||||
x = x.permute(1, 0, 2) # NLD -> LND
|
||||
x = self.text_transformer_forward(x, attn_mask=self.model.attn_mask)
|
||||
x = x.permute(1, 0, 2) # LND -> NLD
|
||||
x = self.model.ln_final(x)
|
||||
return x
|
||||
|
||||
def text_transformer_forward(self, x: torch.Tensor, attn_mask=None):
|
||||
for i, r in enumerate(self.model.transformer.resblocks):
|
||||
if i == len(self.model.transformer.resblocks) - self.layer_idx:
|
||||
break
|
||||
if self.model.transformer.grad_checkpointing and not torch.jit.is_scripting():
|
||||
x = checkpoint(r, x, attn_mask)
|
||||
else:
|
||||
x = r(x, attn_mask=attn_mask)
|
||||
return x
|
||||
|
||||
def encode(self, text):
|
||||
return self(text)
|
||||
|
||||
|
||||
class FrozenOpenCLIPImageEmbedder(AbstractEncoder):
|
||||
"""
|
||||
Uses the OpenCLIP vision transformer encoder for images
|
||||
"""
|
||||
|
||||
def __init__(self, arch="ViT-H-14", version="laion2b_s32b_b79k", device="cuda", max_length=77,
|
||||
freeze=True, layer="pooled", antialias=True, ucg_rate=0.):
|
||||
super().__init__()
|
||||
model, _, _ = open_clip.create_model_and_transforms(arch, device=torch.device('cpu'),
|
||||
pretrained=version, )
|
||||
del model.transformer
|
||||
self.model = model
|
||||
# self.mapper = torch.nn.Linear(1280, 1024)
|
||||
self.device = device
|
||||
self.max_length = max_length
|
||||
if freeze:
|
||||
self.freeze()
|
||||
self.layer = layer
|
||||
if self.layer == "penultimate":
|
||||
raise NotImplementedError()
|
||||
self.layer_idx = 1
|
||||
|
||||
self.antialias = antialias
|
||||
|
||||
self.register_buffer('mean', torch.Tensor([0.48145466, 0.4578275, 0.40821073]), persistent=False)
|
||||
self.register_buffer('std', torch.Tensor([0.26862954, 0.26130258, 0.27577711]), persistent=False)
|
||||
self.ucg_rate = ucg_rate
|
||||
|
||||
def preprocess(self, x):
|
||||
# normalize to [0,1]
|
||||
x = kornia.geometry.resize(x, (224, 224),
|
||||
interpolation='bicubic', align_corners=True,
|
||||
antialias=self.antialias)
|
||||
x = (x + 1.) / 2.
|
||||
# renormalize according to clip
|
||||
x = kornia.enhance.normalize(x, self.mean, self.std)
|
||||
return x
|
||||
|
||||
def freeze(self):
|
||||
self.model = self.model.eval()
|
||||
for param in self.model.parameters():
|
||||
param.requires_grad = False
|
||||
|
||||
@autocast
|
||||
def forward(self, image, no_dropout=False):
|
||||
z = self.encode_with_vision_transformer(image)
|
||||
if self.ucg_rate > 0. and not no_dropout:
|
||||
z = torch.bernoulli((1. - self.ucg_rate) * torch.ones(z.shape[0], device=z.device))[:, None] * z
|
||||
return z
|
||||
|
||||
def encode_with_vision_transformer(self, img):
|
||||
img = self.preprocess(img)
|
||||
x = self.model.visual(img)
|
||||
return x
|
||||
|
||||
def encode(self, text):
|
||||
return self(text)
|
||||
|
||||
class FrozenOpenCLIPImageEmbedderV2(AbstractEncoder):
|
||||
"""
|
||||
Uses the OpenCLIP vision transformer encoder for images
|
||||
"""
|
||||
|
||||
def __init__(self, arch="ViT-H-14", version="laion2b_s32b_b79k", device="cuda",
|
||||
freeze=True, layer="pooled", antialias=True):
|
||||
super().__init__()
|
||||
model, _, _ = open_clip.create_model_and_transforms(arch, device=torch.device('cpu'),
|
||||
pretrained=version, )
|
||||
del model.transformer
|
||||
self.model = model
|
||||
self.device = device
|
||||
|
||||
if freeze:
|
||||
self.freeze()
|
||||
self.layer = layer
|
||||
if self.layer == "penultimate":
|
||||
raise NotImplementedError()
|
||||
self.layer_idx = 1
|
||||
|
||||
self.antialias = antialias
|
||||
|
||||
self.register_buffer('mean', torch.Tensor([0.48145466, 0.4578275, 0.40821073]), persistent=False)
|
||||
self.register_buffer('std', torch.Tensor([0.26862954, 0.26130258, 0.27577711]), persistent=False)
|
||||
|
||||
|
||||
def preprocess(self, x):
|
||||
# normalize to [0,1]
|
||||
x = kornia.geometry.resize(x, (224, 224),
|
||||
interpolation='bicubic', align_corners=True,
|
||||
antialias=self.antialias)
|
||||
x = (x + 1.) / 2.
|
||||
# renormalize according to clip
|
||||
x = kornia.enhance.normalize(x, self.mean, self.std)
|
||||
return x
|
||||
|
||||
def freeze(self):
|
||||
self.model = self.model.eval()
|
||||
for param in self.model.parameters():
|
||||
param.requires_grad = False
|
||||
|
||||
def forward(self, image, no_dropout=False):
|
||||
## image: b c h w
|
||||
z = self.encode_with_vision_transformer(image)
|
||||
return z
|
||||
|
||||
def encode_with_vision_transformer(self, x):
|
||||
x = self.preprocess(x)
|
||||
|
||||
# to patches - whether to use dual patchnorm - https://arxiv.org/abs/2302.01327v1
|
||||
if self.model.visual.input_patchnorm:
|
||||
# einops - rearrange(x, 'b c (h p1) (w p2) -> b (h w) (c p1 p2)')
|
||||
x = x.reshape(x.shape[0], x.shape[1], self.model.visual.grid_size[0], self.model.visual.patch_size[0], self.model.visual.grid_size[1], self.model.visual.patch_size[1])
|
||||
x = x.permute(0, 2, 4, 1, 3, 5)
|
||||
x = x.reshape(x.shape[0], self.model.visual.grid_size[0] * self.model.visual.grid_size[1], -1)
|
||||
x = self.model.visual.patchnorm_pre_ln(x)
|
||||
x = self.model.visual.conv1(x)
|
||||
else:
|
||||
x = self.model.visual.conv1(x) # shape = [*, width, grid, grid]
|
||||
x = x.reshape(x.shape[0], x.shape[1], -1) # shape = [*, width, grid ** 2]
|
||||
x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width]
|
||||
|
||||
# class embeddings and positional embeddings
|
||||
x = torch.cat(
|
||||
[self.model.visual.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device),
|
||||
x], dim=1) # shape = [*, grid ** 2 + 1, width]
|
||||
x = x + self.model.visual.positional_embedding.to(x.dtype)
|
||||
|
||||
# a patch_dropout of 0. would mean it is disabled and this function would do nothing but return what was passed in
|
||||
x = self.model.visual.patch_dropout(x)
|
||||
x = self.model.visual.ln_pre(x)
|
||||
|
||||
x = x.permute(1, 0, 2) # NLD -> LND
|
||||
x = self.model.visual.transformer(x)
|
||||
x = x.permute(1, 0, 2) # LND -> NLD
|
||||
|
||||
return x
|
||||
|
||||
class FrozenCLIPT5Encoder(AbstractEncoder):
|
||||
def __init__(self, clip_version="openai/clip-vit-large-patch14", t5_version="google/t5-v1_1-xl", device="cuda",
|
||||
clip_max_length=77, t5_max_length=77):
|
||||
super().__init__()
|
||||
self.clip_encoder = FrozenCLIPEmbedder(clip_version, device, max_length=clip_max_length)
|
||||
self.t5_encoder = FrozenT5Embedder(t5_version, device, max_length=t5_max_length)
|
||||
print(f"{self.clip_encoder.__class__.__name__} has {count_params(self.clip_encoder) * 1.e-6:.2f} M parameters, "
|
||||
f"{self.t5_encoder.__class__.__name__} comes with {count_params(self.t5_encoder) * 1.e-6:.2f} M params.")
|
||||
|
||||
def encode(self, text):
|
||||
return self(text)
|
||||
|
||||
def forward(self, text):
|
||||
clip_z = self.clip_encoder.encode(text)
|
||||
t5_z = self.t5_encoder.encode(text)
|
||||
return [clip_z, t5_z]
|
||||
@@ -0,0 +1,145 @@
|
||||
# modified from https://github.com/mlfoundations/open_flamingo/blob/main/open_flamingo/src/helpers.py
|
||||
# and https://github.com/lucidrains/imagen-pytorch/blob/main/imagen_pytorch/imagen_pytorch.py
|
||||
# and https://github.com/tencent-ailab/IP-Adapter/blob/main/ip_adapter/resampler.py
|
||||
import math
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
|
||||
|
||||
class ImageProjModel(nn.Module):
|
||||
"""Projection Model"""
|
||||
def __init__(self, cross_attention_dim=1024, clip_embeddings_dim=1024, clip_extra_context_tokens=4):
|
||||
super().__init__()
|
||||
self.cross_attention_dim = cross_attention_dim
|
||||
self.clip_extra_context_tokens = clip_extra_context_tokens
|
||||
self.proj = nn.Linear(clip_embeddings_dim, self.clip_extra_context_tokens * cross_attention_dim)
|
||||
self.norm = nn.LayerNorm(cross_attention_dim)
|
||||
|
||||
def forward(self, image_embeds):
|
||||
#embeds = image_embeds
|
||||
embeds = image_embeds.type(list(self.proj.parameters())[0].dtype)
|
||||
clip_extra_context_tokens = self.proj(embeds).reshape(-1, self.clip_extra_context_tokens, self.cross_attention_dim)
|
||||
clip_extra_context_tokens = self.norm(clip_extra_context_tokens)
|
||||
return clip_extra_context_tokens
|
||||
|
||||
|
||||
# FFN
|
||||
def FeedForward(dim, mult=4):
|
||||
inner_dim = int(dim * mult)
|
||||
return nn.Sequential(
|
||||
nn.LayerNorm(dim),
|
||||
nn.Linear(dim, inner_dim, bias=False),
|
||||
nn.GELU(),
|
||||
nn.Linear(inner_dim, dim, bias=False),
|
||||
)
|
||||
|
||||
|
||||
def reshape_tensor(x, heads):
|
||||
bs, length, width = x.shape
|
||||
#(bs, length, width) --> (bs, length, n_heads, dim_per_head)
|
||||
x = x.view(bs, length, heads, -1)
|
||||
# (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
|
||||
x = x.transpose(1, 2)
|
||||
# (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
|
||||
x = x.reshape(bs, heads, length, -1)
|
||||
return x
|
||||
|
||||
|
||||
class PerceiverAttention(nn.Module):
|
||||
def __init__(self, *, dim, dim_head=64, heads=8):
|
||||
super().__init__()
|
||||
self.scale = dim_head**-0.5
|
||||
self.dim_head = dim_head
|
||||
self.heads = heads
|
||||
inner_dim = dim_head * heads
|
||||
|
||||
self.norm1 = nn.LayerNorm(dim)
|
||||
self.norm2 = nn.LayerNorm(dim)
|
||||
|
||||
self.to_q = nn.Linear(dim, inner_dim, bias=False)
|
||||
self.to_kv = nn.Linear(dim, inner_dim * 2, bias=False)
|
||||
self.to_out = nn.Linear(inner_dim, dim, bias=False)
|
||||
|
||||
|
||||
def forward(self, x, latents):
|
||||
"""
|
||||
Args:
|
||||
x (torch.Tensor): image features
|
||||
shape (b, n1, D)
|
||||
latent (torch.Tensor): latent features
|
||||
shape (b, n2, D)
|
||||
"""
|
||||
x = self.norm1(x)
|
||||
latents = self.norm2(latents)
|
||||
|
||||
b, l, _ = latents.shape
|
||||
|
||||
q = self.to_q(latents)
|
||||
kv_input = torch.cat((x, latents), dim=-2)
|
||||
k, v = self.to_kv(kv_input).chunk(2, dim=-1)
|
||||
|
||||
q = reshape_tensor(q, self.heads)
|
||||
k = reshape_tensor(k, self.heads)
|
||||
v = reshape_tensor(v, self.heads)
|
||||
|
||||
# attention
|
||||
scale = 1 / math.sqrt(math.sqrt(self.dim_head))
|
||||
weight = (q * scale) @ (k * scale).transpose(-2, -1) # More stable with f16 than dividing afterwards
|
||||
weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)
|
||||
out = weight @ v
|
||||
|
||||
out = out.permute(0, 2, 1, 3).reshape(b, l, -1)
|
||||
|
||||
return self.to_out(out)
|
||||
|
||||
|
||||
class Resampler(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
dim=1024,
|
||||
depth=8,
|
||||
dim_head=64,
|
||||
heads=16,
|
||||
num_queries=8,
|
||||
embedding_dim=768,
|
||||
output_dim=1024,
|
||||
ff_mult=4,
|
||||
video_length=None, # using frame-wise version or not
|
||||
):
|
||||
super().__init__()
|
||||
## queries for a single frame / image
|
||||
self.num_queries = num_queries
|
||||
self.video_length = video_length
|
||||
|
||||
## <num_queries> queries for each frame
|
||||
if video_length is not None:
|
||||
num_queries = num_queries * video_length
|
||||
|
||||
self.latents = nn.Parameter(torch.randn(1, num_queries, dim) / dim**0.5)
|
||||
self.proj_in = nn.Linear(embedding_dim, dim)
|
||||
self.proj_out = nn.Linear(dim, output_dim)
|
||||
self.norm_out = nn.LayerNorm(output_dim)
|
||||
|
||||
self.layers = nn.ModuleList([])
|
||||
for _ in range(depth):
|
||||
self.layers.append(
|
||||
nn.ModuleList(
|
||||
[
|
||||
PerceiverAttention(dim=dim, dim_head=dim_head, heads=heads),
|
||||
FeedForward(dim=dim, mult=ff_mult),
|
||||
]
|
||||
)
|
||||
)
|
||||
|
||||
def forward(self, x):
|
||||
latents = self.latents.repeat(x.size(0), 1, 1) ## B (T L) C
|
||||
x = self.proj_in(x)
|
||||
|
||||
for attn, ff in self.layers:
|
||||
latents = attn(x, latents) + latents
|
||||
latents = ff(latents) + latents
|
||||
|
||||
latents = self.proj_out(latents)
|
||||
latents = self.norm_out(latents) # B L C or B (T L) C
|
||||
|
||||
return latents
|
||||
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,822 @@
|
||||
from functools import partial
|
||||
from abc import abstractmethod
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from einops import rearrange
|
||||
import torch.nn.functional as F
|
||||
from ...models.utils_diffusion import timestep_embedding
|
||||
from ...common import checkpoint
|
||||
from ...basics import (
|
||||
zero_module,
|
||||
conv_nd,
|
||||
linear,
|
||||
avg_pool_nd,
|
||||
normalization
|
||||
)
|
||||
from ...modules.attention import SpatialTransformer, TemporalTransformer
|
||||
import comfy.ops
|
||||
import logging
|
||||
|
||||
ops = comfy.ops.disable_weight_init
|
||||
|
||||
class TimestepBlock(nn.Module):
|
||||
"""
|
||||
Any module where forward() takes timestep embeddings as a second argument.
|
||||
"""
|
||||
@abstractmethod
|
||||
def forward(self, x, emb):
|
||||
"""
|
||||
Apply the module to `x` given `emb` timestep embeddings.
|
||||
"""
|
||||
|
||||
#This is needed because accelerate makes a copy of transformer_options which breaks "transformer_index"
|
||||
def forward_timestep_embed(ts, x, emb, context=None, batch_size=None, transformer_options={}):
|
||||
for layer in ts:
|
||||
if isinstance(layer, TimestepBlock):
|
||||
x = layer(x, emb, batch_size=batch_size)
|
||||
elif isinstance(layer, SpatialTransformer):
|
||||
x = layer(x, context)
|
||||
if "transformer_index" in transformer_options:
|
||||
transformer_options["transformer_index"] += 1
|
||||
elif isinstance(layer, TemporalTransformer):
|
||||
x = rearrange(x, '(b f) c h w -> b c f h w', b=batch_size)
|
||||
x = layer(x, context)
|
||||
if "transformer_index" in transformer_options:
|
||||
transformer_options["transformer_index"] += 1
|
||||
x = rearrange(x, 'b c f h w -> (b f) c h w')
|
||||
else:
|
||||
x = layer(x)
|
||||
return x
|
||||
|
||||
class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
|
||||
"""
|
||||
A sequential module that passes timestep embeddings to the children that
|
||||
support it as an extra input.
|
||||
"""
|
||||
|
||||
def forward(self, *args, **kwargs):
|
||||
return forward_timestep_embed(self, *args, **kwargs)
|
||||
|
||||
class Downsample(nn.Module):
|
||||
"""
|
||||
A downsampling layer with an optional convolution.
|
||||
:param channels: channels in the inputs and outputs.
|
||||
:param use_conv: a bool determining if a convolution is applied.
|
||||
:param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
|
||||
downsampling occurs in the inner-two dimensions.
|
||||
"""
|
||||
|
||||
def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1, dtype=None, device=None, operations=ops):
|
||||
super().__init__()
|
||||
self.channels = channels
|
||||
self.out_channels = out_channels or channels
|
||||
self.use_conv = use_conv
|
||||
self.dims = dims
|
||||
stride = 2 if dims != 3 else (1, 2, 2)
|
||||
if use_conv:
|
||||
self.op = operations.conv_nd(
|
||||
dims, self.channels, self.out_channels, 3, stride=stride, padding=padding
|
||||
)
|
||||
else:
|
||||
assert self.channels == self.out_channels
|
||||
self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride)
|
||||
|
||||
def forward(self, x):
|
||||
assert x.shape[1] == self.channels
|
||||
return self.op(x)
|
||||
|
||||
class Upsample(nn.Module):
|
||||
"""
|
||||
An upsampling layer with an optional convolution.
|
||||
:param channels: channels in the inputs and outputs.
|
||||
:param use_conv: a bool determining if a convolution is applied.
|
||||
:param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
|
||||
upsampling occurs in the inner-two dimensions.
|
||||
"""
|
||||
|
||||
def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1, dtype=None, device=None, operations=ops):
|
||||
super().__init__()
|
||||
self.channels = channels
|
||||
self.out_channels = out_channels or channels
|
||||
self.use_conv = use_conv
|
||||
self.dims = dims
|
||||
if use_conv:
|
||||
self.conv = operations.conv_nd(dims, self.channels, self.out_channels, 3, padding=padding, dtype=dtype, device=device)
|
||||
|
||||
def forward(self, x):
|
||||
assert x.shape[1] == self.channels
|
||||
if self.dims == 3:
|
||||
x = F.interpolate(x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode='nearest')
|
||||
else:
|
||||
x = F.interpolate(x, scale_factor=2, mode='nearest')
|
||||
if self.use_conv:
|
||||
x = self.conv(x)
|
||||
return x
|
||||
|
||||
class ResBlock(TimestepBlock):
|
||||
"""
|
||||
A residual block that can optionally change the number of channels.
|
||||
:param channels: the number of input channels.
|
||||
:param emb_channels: the number of timestep embedding channels.
|
||||
:param dropout: the rate of dropout.
|
||||
:param out_channels: if specified, the number of out channels.
|
||||
:param use_conv: if True and out_channels is specified, use a spatial
|
||||
convolution instead of a smaller 1x1 convolution to change the
|
||||
channels in the skip connection.
|
||||
:param dims: determines if the signal is 1D, 2D, or 3D.
|
||||
:param up: if True, use this block for upsampling.
|
||||
:param down: if True, use this block for downsampling.
|
||||
:param use_temporal_conv: if True, use the temporal convolution.
|
||||
:param use_image_dataset: if True, the temporal parameters will not be optimized.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
channels,
|
||||
emb_channels,
|
||||
dropout,
|
||||
out_channels=None,
|
||||
use_scale_shift_norm=False,
|
||||
dims=2,
|
||||
use_checkpoint=False,
|
||||
use_conv=False,
|
||||
up=False,
|
||||
down=False,
|
||||
kernel_size=3,
|
||||
use_temporal_conv=False,
|
||||
tempspatial_aware=False,
|
||||
dtype=None,
|
||||
device=None,
|
||||
operations=ops
|
||||
):
|
||||
super().__init__()
|
||||
self.channels = channels
|
||||
self.emb_channels = emb_channels
|
||||
self.dropout = dropout
|
||||
self.out_channels = out_channels or channels
|
||||
self.use_conv = use_conv
|
||||
self.use_checkpoint = use_checkpoint
|
||||
self.use_scale_shift_norm = use_scale_shift_norm
|
||||
self.use_temporal_conv = use_temporal_conv
|
||||
|
||||
if isinstance(kernel_size, list):
|
||||
padding =[k // 2 for k in kernel_size]
|
||||
else:
|
||||
padding = kernel_size // 2
|
||||
|
||||
# operations used in normalization function
|
||||
self.in_layers = nn.Sequential(
|
||||
normalization(channels, dtype=dtype, device=device),
|
||||
nn.SiLU(),
|
||||
operations.conv_nd(dims, channels, self.out_channels, 3, padding=1, dtype=dtype, device=device),
|
||||
)
|
||||
|
||||
self.updown = up or down
|
||||
|
||||
if up:
|
||||
self.h_upd = Upsample(channels, False, dims, dtype=dtype, device=device)
|
||||
self.x_upd = Upsample(channels, False, dims, dtype=dtype, device=device)
|
||||
elif down:
|
||||
self.h_upd = Downsample(channels, False, dims, dtype=dtype, device=device)
|
||||
self.x_upd = Downsample(channels, False, dims, dtype=dtype, device=device)
|
||||
else:
|
||||
self.h_upd = self.x_upd = nn.Identity()
|
||||
|
||||
self.emb_layers = nn.Sequential(
|
||||
nn.SiLU(),
|
||||
operations.Linear(
|
||||
emb_channels,
|
||||
2 * self.out_channels if use_scale_shift_norm else self.out_channels,
|
||||
dtype=dtype,
|
||||
device=device
|
||||
),
|
||||
)
|
||||
self.out_layers = nn.Sequential(
|
||||
normalization(self.out_channels, dtype=dtype, device=device),
|
||||
nn.SiLU(),
|
||||
nn.Dropout(p=dropout),
|
||||
zero_module(operations.Conv2d(self.out_channels, self.out_channels, 3, padding=1, dtype=dtype, device=device)),
|
||||
)
|
||||
|
||||
if self.out_channels == channels:
|
||||
self.skip_connection = nn.Identity()
|
||||
elif use_conv:
|
||||
self.skip_connection = operations.conv_nd(dims, channels, self.out_channels, 3, padding=1, dtype=dtype, device=device)
|
||||
else:
|
||||
self.skip_connection = operations.conv_nd(dims, channels, self.out_channels, 1, dtype=dtype, device=device)
|
||||
|
||||
if self.use_temporal_conv:
|
||||
self.temopral_conv = TemporalConvBlock(
|
||||
self.out_channels,
|
||||
self.out_channels,
|
||||
dropout=0.1,
|
||||
spatial_aware=tempspatial_aware,
|
||||
dtype=dtype,
|
||||
device=device
|
||||
)
|
||||
|
||||
def forward(self, x, emb, batch_size=None):
|
||||
"""
|
||||
Apply the block to a Tensor, conditioned on a timestep embedding.
|
||||
:param x: an [N x C x ...] Tensor of features.
|
||||
:param emb: an [N x emb_channels] Tensor of timestep embeddings.
|
||||
:return: an [N x C x ...] Tensor of outputs.
|
||||
"""
|
||||
input_tuple = (x, emb)
|
||||
if batch_size:
|
||||
forward_batchsize = partial(self._forward, batch_size=batch_size)
|
||||
return checkpoint(forward_batchsize, input_tuple, self.parameters(), self.use_checkpoint)
|
||||
return checkpoint(self._forward, input_tuple, self.parameters(), self.use_checkpoint)
|
||||
|
||||
def _forward(self, x, emb, batch_size=None):
|
||||
if self.updown:
|
||||
in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
|
||||
h = in_rest(x)
|
||||
h = self.h_upd(h)
|
||||
x = self.x_upd(x)
|
||||
h = in_conv(h)
|
||||
else:
|
||||
h = self.in_layers(x)
|
||||
emb_out = self.emb_layers(emb).type(h.dtype)
|
||||
while len(emb_out.shape) < len(h.shape):
|
||||
emb_out = emb_out[..., None]
|
||||
if self.use_scale_shift_norm:
|
||||
out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
|
||||
scale, shift = torch.chunk(emb_out, 2, dim=1)
|
||||
h = out_norm(h) * (1 + scale) + shift
|
||||
h = out_rest(h)
|
||||
else:
|
||||
h = h + emb_out
|
||||
h = self.out_layers(h)
|
||||
h = self.skip_connection(x) + h
|
||||
|
||||
if self.use_temporal_conv and batch_size:
|
||||
h = rearrange(h, '(b t) c h w -> b c t h w', b=batch_size)
|
||||
h = self.temopral_conv(h)
|
||||
h = rearrange(h, 'b c t h w -> (b t) c h w')
|
||||
return h
|
||||
|
||||
class TemporalConvBlock(nn.Module):
|
||||
"""
|
||||
Adapted from modelscope: https://github.com/modelscope/modelscope/blob/master/modelscope/models/multi_modal/video_synthesis/unet_sd.py
|
||||
"""
|
||||
def __init__(
|
||||
self,
|
||||
in_channels,
|
||||
out_channels=None,
|
||||
dropout=0.0,
|
||||
spatial_aware=False,
|
||||
dtype=None,
|
||||
device=None,
|
||||
operations=ops
|
||||
):
|
||||
super(TemporalConvBlock, self).__init__()
|
||||
if out_channels is None:
|
||||
out_channels = in_channels
|
||||
self.in_channels = in_channels
|
||||
self.out_channels = out_channels
|
||||
th_kernel_shape = (3, 1, 1) if not spatial_aware else (3, 3, 1)
|
||||
th_padding_shape = (1, 0, 0) if not spatial_aware else (1, 1, 0)
|
||||
tw_kernel_shape = (3, 1, 1) if not spatial_aware else (3, 1, 3)
|
||||
tw_padding_shape = (1, 0, 0) if not spatial_aware else (1, 0, 1)
|
||||
|
||||
# conv layers
|
||||
self.conv1 = nn.Sequential(
|
||||
operations.GroupNorm(32, in_channels, device=device, dtype=dtype), nn.SiLU(),
|
||||
operations.Conv3d(in_channels, out_channels, th_kernel_shape, padding=th_padding_shape, device=device, dtype=dtype))
|
||||
self.conv2 = nn.Sequential(
|
||||
operations.GroupNorm(32, out_channels, device=device, dtype=dtype), nn.SiLU(), nn.Dropout(dropout),
|
||||
operations.Conv3d(out_channels, in_channels, tw_kernel_shape, padding=tw_padding_shape, device=device, dtype=dtype))
|
||||
self.conv3 = nn.Sequential(
|
||||
operations.GroupNorm(32, out_channels, device=device, dtype=dtype), nn.SiLU(), nn.Dropout(dropout),
|
||||
operations.Conv3d(out_channels, in_channels, th_kernel_shape, padding=th_padding_shape, device=device, dtype=dtype))
|
||||
self.conv4 = nn.Sequential(
|
||||
operations.GroupNorm(32, out_channels, device=device, dtype=dtype), nn.SiLU(), nn.Dropout(dropout),
|
||||
operations.Conv3d(out_channels, in_channels, tw_kernel_shape, padding=tw_padding_shape, device=device, dtype=dtype))
|
||||
|
||||
# zero out the last layer params,so the conv block is identity
|
||||
nn.init.zeros_(self.conv4[-1].weight)
|
||||
nn.init.zeros_(self.conv4[-1].bias)
|
||||
|
||||
def forward(self, x):
|
||||
identity = x
|
||||
x = self.conv1(x)
|
||||
x = self.conv2(x)
|
||||
x = self.conv3(x)
|
||||
x = self.conv4(x)
|
||||
|
||||
return identity + x
|
||||
|
||||
def context_processor(context, t, img_emb=None, temporal_size=16, concat_only=False, disable_concat=False):
|
||||
if disable_concat:
|
||||
return context
|
||||
|
||||
## repeat t times for context [(b t) 77 768] & time embedding
|
||||
## check if we use per-frame image conditioning
|
||||
|
||||
if img_emb is not None:
|
||||
context = torch.cat([context, img_emb.to(context.device, context.dtype)], dim=1)
|
||||
|
||||
if concat_only:
|
||||
return context
|
||||
|
||||
b, l_context, _ = context.shape
|
||||
if l_context == 77 + t * temporal_size:
|
||||
context_text, context_img = context[:,:77,:], context[:,77:,:]
|
||||
context_text = context_text.repeat_interleave(repeats=t, dim=0)
|
||||
context_img = rearrange(context_img, 'b (t l) c -> (b t) l c', t=t)
|
||||
context = torch.cat([context_text, context_img], dim=1)
|
||||
else:
|
||||
context = context.repeat_interleave(repeats=t, dim=0)
|
||||
|
||||
return context
|
||||
|
||||
def apply_control(h, control, name, cond_idx=None):
|
||||
if control is not None and name in control and len(control[name]) > 0:
|
||||
frames = h.shape[0]
|
||||
ctrl = control[name].pop()
|
||||
if ctrl is not None:
|
||||
try:
|
||||
if cond_idx is not None and ctrl.shape[0] > frames:
|
||||
ctrl_frames_list = list(range(ctrl.shape[0]))
|
||||
ctrl_frames = len(ctrl_frames_list)
|
||||
|
||||
idxs = (
|
||||
ctrl_frames_list[:ctrl_frames // 2] if cond_idx == 0 else \
|
||||
ctrl_frames_list[ctrl_frames // 2:]
|
||||
)
|
||||
|
||||
ctrl = ctrl[idxs]
|
||||
|
||||
h += ctrl
|
||||
except Exception as e:
|
||||
if h.shape != ctrl.shape:
|
||||
logging.warning(
|
||||
"warning control could not be applied {} {}".format(h.shape, ctrl.shape)
|
||||
)
|
||||
logging.warning(e)
|
||||
return h
|
||||
|
||||
class UNetModel(nn.Module):
|
||||
"""
|
||||
The full UNet model with attention and timestep embedding.
|
||||
:param in_channels: in_channels in the input Tensor.
|
||||
:param model_channels: base channel count for the model.
|
||||
:param out_channels: channels in the output Tensor.
|
||||
:param num_res_blocks: number of residual blocks per downsample.
|
||||
:param attention_resolutions: a collection of downsample rates at which
|
||||
attention will take place. May be a set, list, or tuple.
|
||||
For example, if this contains 4, then at 4x downsampling, attention
|
||||
will be used.
|
||||
:param dropout: the dropout probability.
|
||||
:param channel_mult: channel multiplier for each level of the UNet.
|
||||
:param conv_resample: if True, use learned convolutions for upsampling and
|
||||
downsampling.
|
||||
:param dims: determines if the signal is 1D, 2D, or 3D.
|
||||
:param num_classes: if specified (as an int), then this model will be
|
||||
class-conditional with `num_classes` classes.
|
||||
:param use_checkpoint: use gradient checkpointing to reduce memory usage.
|
||||
:param num_heads: the number of attention heads in each attention layer.
|
||||
:param num_heads_channels: if specified, ignore num_heads and instead use
|
||||
a fixed channel width per attention head.
|
||||
:param num_heads_upsample: works with num_heads to set a different number
|
||||
of heads for upsampling. Deprecated.
|
||||
:param use_scale_shift_norm: use a FiLM-like conditioning mechanism.
|
||||
:param resblock_updown: use residual blocks for up/downsampling.
|
||||
:param use_new_attention_order: use a different attention pattern for potentially
|
||||
increased efficiency.
|
||||
"""
|
||||
|
||||
def __init__(self,
|
||||
in_channels,
|
||||
model_channels,
|
||||
out_channels,
|
||||
num_res_blocks,
|
||||
attention_resolutions,
|
||||
dropout=0.0,
|
||||
channel_mult=(1, 2, 4, 8),
|
||||
conv_resample=True,
|
||||
dims=2,
|
||||
context_dim=None,
|
||||
use_scale_shift_norm=False,
|
||||
resblock_updown=False,
|
||||
num_heads=-1,
|
||||
num_head_channels=-1,
|
||||
transformer_depth=1,
|
||||
use_linear=False,
|
||||
use_checkpoint=False,
|
||||
temporal_conv=False,
|
||||
tempspatial_aware=False,
|
||||
temporal_attention=True,
|
||||
use_relative_position=True,
|
||||
use_causal_attention=False,
|
||||
temporal_length=None,
|
||||
use_fp16=False,
|
||||
addition_attention=False,
|
||||
temporal_selfatt_only=True,
|
||||
image_cross_attention=False,
|
||||
image_cross_attention_scale_learnable=False,
|
||||
default_fs=4,
|
||||
fs_condition=False,
|
||||
device=None,
|
||||
dtype=torch.float16,
|
||||
operations=ops
|
||||
):
|
||||
super(UNetModel, self).__init__()
|
||||
if num_heads == -1:
|
||||
assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set'
|
||||
if num_head_channels == -1:
|
||||
assert num_heads != -1, 'Either num_heads or num_head_channels has to be set'
|
||||
|
||||
self.in_channels = in_channels
|
||||
self.model_channels = model_channels
|
||||
self.out_channels = out_channels
|
||||
self.num_res_blocks = num_res_blocks
|
||||
self.attention_resolutions = attention_resolutions
|
||||
self.dropout = dropout
|
||||
self.channel_mult = channel_mult
|
||||
self.conv_resample = conv_resample
|
||||
self.temporal_attention = temporal_attention
|
||||
time_embed_dim = model_channels * 4
|
||||
self.use_checkpoint = use_checkpoint
|
||||
temporal_self_att_only = True
|
||||
self.addition_attention = addition_attention
|
||||
self.temporal_length = temporal_length
|
||||
self.image_cross_attention = image_cross_attention
|
||||
self.image_cross_attention_scale_learnable = image_cross_attention_scale_learnable
|
||||
self.default_fs = default_fs
|
||||
self.fs_condition = fs_condition
|
||||
self.device = device
|
||||
#self.dtype = dtype
|
||||
self.dtype = torch.float32
|
||||
|
||||
## Time embedding blocks
|
||||
self.time_embed = nn.Sequential(
|
||||
linear(model_channels, time_embed_dim, device=device, dtype=self.dtype),
|
||||
nn.SiLU(),
|
||||
linear(time_embed_dim, time_embed_dim, device=device, dtype=self.dtype),
|
||||
)
|
||||
if fs_condition:
|
||||
self.fps_embedding = nn.Sequential(
|
||||
linear(model_channels, time_embed_dim, device=device, dtype=self.dtype),
|
||||
nn.SiLU(),
|
||||
linear(time_embed_dim, time_embed_dim, device=device, dtype=self.dtype),
|
||||
)
|
||||
nn.init.zeros_(self.fps_embedding[-1].weight)
|
||||
nn.init.zeros_(self.fps_embedding[-1].bias)
|
||||
## Input Block
|
||||
self.input_blocks = nn.ModuleList(
|
||||
[
|
||||
TimestepEmbedSequential(
|
||||
operations.conv_nd(
|
||||
dims,
|
||||
in_channels,
|
||||
model_channels,
|
||||
3,
|
||||
padding=1,
|
||||
device=device,
|
||||
dtype=self.dtype
|
||||
))
|
||||
]
|
||||
)
|
||||
if self.addition_attention:
|
||||
self.init_attn=TimestepEmbedSequential(
|
||||
TemporalTransformer(
|
||||
model_channels,
|
||||
n_heads=8,
|
||||
d_head=num_head_channels,
|
||||
depth=transformer_depth,
|
||||
context_dim=context_dim,
|
||||
use_checkpoint=use_checkpoint, only_self_att=temporal_selfatt_only,
|
||||
causal_attention=False, relative_position=use_relative_position,
|
||||
temporal_length=temporal_length,
|
||||
device=device,
|
||||
dtype=self.dtype
|
||||
))
|
||||
|
||||
input_block_chans = [model_channels]
|
||||
ch = model_channels
|
||||
ds = 1
|
||||
for level, mult in enumerate(channel_mult):
|
||||
for _ in range(num_res_blocks):
|
||||
layers = [
|
||||
ResBlock(ch, time_embed_dim, dropout,
|
||||
out_channels=mult * model_channels, dims=dims, use_checkpoint=use_checkpoint,
|
||||
use_scale_shift_norm=use_scale_shift_norm, tempspatial_aware=tempspatial_aware,
|
||||
use_temporal_conv=temporal_conv,
|
||||
device=device,
|
||||
dtype=self.dtype
|
||||
)
|
||||
]
|
||||
ch = mult * model_channels
|
||||
if ds in attention_resolutions:
|
||||
if num_head_channels == -1:
|
||||
dim_head = ch // num_heads
|
||||
else:
|
||||
num_heads = ch // num_head_channels
|
||||
dim_head = num_head_channels
|
||||
layers.append(
|
||||
SpatialTransformer(ch, num_heads, dim_head,
|
||||
depth=transformer_depth, context_dim=context_dim, use_linear=use_linear,
|
||||
use_checkpoint=use_checkpoint, disable_self_attn=False,
|
||||
video_length=temporal_length, image_cross_attention=self.image_cross_attention,
|
||||
image_cross_attention_scale_learnable=self.image_cross_attention_scale_learnable,
|
||||
device=device,
|
||||
dtype=self.dtype
|
||||
)
|
||||
)
|
||||
if self.temporal_attention:
|
||||
layers.append(
|
||||
TemporalTransformer(ch, num_heads, dim_head,
|
||||
depth=transformer_depth, context_dim=context_dim, use_linear=use_linear,
|
||||
use_checkpoint=use_checkpoint, only_self_att=temporal_self_att_only,
|
||||
causal_attention=use_causal_attention, relative_position=use_relative_position,
|
||||
temporal_length=temporal_length,
|
||||
device=device,
|
||||
dtype=self.dtype
|
||||
)
|
||||
)
|
||||
self.input_blocks.append(TimestepEmbedSequential(*layers))
|
||||
input_block_chans.append(ch)
|
||||
if level != len(channel_mult) - 1:
|
||||
out_ch = ch
|
||||
self.input_blocks.append(
|
||||
TimestepEmbedSequential(
|
||||
ResBlock(ch, time_embed_dim, dropout,
|
||||
out_channels=out_ch, dims=dims, use_checkpoint=use_checkpoint,
|
||||
use_scale_shift_norm=use_scale_shift_norm,
|
||||
down=True,
|
||||
device=device,
|
||||
dtype=self.dtype
|
||||
)
|
||||
if resblock_updown
|
||||
else Downsample(
|
||||
ch,
|
||||
conv_resample,
|
||||
dims=dims,
|
||||
out_channels=out_ch,
|
||||
device=device,
|
||||
dtype=self.dtype
|
||||
)
|
||||
)
|
||||
)
|
||||
ch = out_ch
|
||||
input_block_chans.append(ch)
|
||||
ds *= 2
|
||||
|
||||
if num_head_channels == -1:
|
||||
dim_head = ch // num_heads
|
||||
else:
|
||||
num_heads = ch // num_head_channels
|
||||
dim_head = num_head_channels
|
||||
layers = [
|
||||
ResBlock(ch, time_embed_dim, dropout,
|
||||
dims=dims, use_checkpoint=use_checkpoint,
|
||||
use_scale_shift_norm=use_scale_shift_norm, tempspatial_aware=tempspatial_aware,
|
||||
use_temporal_conv=temporal_conv,
|
||||
device=device,
|
||||
dtype=self.dtype
|
||||
),
|
||||
SpatialTransformer(ch, num_heads, dim_head,
|
||||
depth=transformer_depth, context_dim=context_dim, use_linear=use_linear,
|
||||
use_checkpoint=use_checkpoint, disable_self_attn=False, video_length=temporal_length,
|
||||
image_cross_attention=self.image_cross_attention,image_cross_attention_scale_learnable=self.image_cross_attention_scale_learnable,
|
||||
device=device,
|
||||
dtype=self.dtype
|
||||
)
|
||||
]
|
||||
if self.temporal_attention:
|
||||
layers.append(
|
||||
TemporalTransformer(ch, num_heads, dim_head,
|
||||
depth=transformer_depth, context_dim=context_dim, use_linear=use_linear,
|
||||
use_checkpoint=use_checkpoint, only_self_att=temporal_self_att_only,
|
||||
causal_attention=use_causal_attention, relative_position=use_relative_position,
|
||||
temporal_length=temporal_length,
|
||||
device=device,
|
||||
dtype=self.dtype
|
||||
)
|
||||
)
|
||||
layers.append(
|
||||
ResBlock(ch, time_embed_dim, dropout,
|
||||
dims=dims, use_checkpoint=use_checkpoint,
|
||||
use_scale_shift_norm=use_scale_shift_norm, tempspatial_aware=tempspatial_aware,
|
||||
use_temporal_conv=temporal_conv,
|
||||
device=device,
|
||||
dtype=self.dtype
|
||||
)
|
||||
)
|
||||
|
||||
## Middle Block
|
||||
self.middle_block = TimestepEmbedSequential(*layers)
|
||||
|
||||
## Output Block
|
||||
self.output_blocks = nn.ModuleList([])
|
||||
for level, mult in list(enumerate(channel_mult))[::-1]:
|
||||
for i in range(num_res_blocks + 1):
|
||||
ich = input_block_chans.pop()
|
||||
layers = [
|
||||
ResBlock(ch + ich, time_embed_dim, dropout,
|
||||
out_channels=mult * model_channels, dims=dims, use_checkpoint=use_checkpoint,
|
||||
use_scale_shift_norm=use_scale_shift_norm, tempspatial_aware=tempspatial_aware,
|
||||
use_temporal_conv=temporal_conv,
|
||||
device=device,
|
||||
dtype=self.dtype
|
||||
)
|
||||
]
|
||||
ch = model_channels * mult
|
||||
if ds in attention_resolutions:
|
||||
if num_head_channels == -1:
|
||||
dim_head = ch // num_heads
|
||||
else:
|
||||
num_heads = ch // num_head_channels
|
||||
dim_head = num_head_channels
|
||||
layers.append(
|
||||
SpatialTransformer(ch, num_heads, dim_head,
|
||||
depth=transformer_depth, context_dim=context_dim, use_linear=use_linear,
|
||||
use_checkpoint=use_checkpoint, disable_self_attn=False, video_length=temporal_length,
|
||||
image_cross_attention=self.image_cross_attention,image_cross_attention_scale_learnable=self.image_cross_attention_scale_learnable,
|
||||
device=device,
|
||||
dtype=self.dtype
|
||||
)
|
||||
)
|
||||
if self.temporal_attention:
|
||||
layers.append(
|
||||
TemporalTransformer(ch, num_heads, dim_head,
|
||||
depth=transformer_depth, context_dim=context_dim, use_linear=use_linear,
|
||||
use_checkpoint=use_checkpoint, only_self_att=temporal_self_att_only,
|
||||
causal_attention=use_causal_attention, relative_position=use_relative_position,
|
||||
temporal_length=temporal_length,
|
||||
device=device,
|
||||
dtype=self.dtype
|
||||
)
|
||||
)
|
||||
if level and i == num_res_blocks:
|
||||
out_ch = ch
|
||||
layers.append(
|
||||
ResBlock(ch, time_embed_dim, dropout,
|
||||
out_channels=out_ch, dims=dims, use_checkpoint=use_checkpoint,
|
||||
use_scale_shift_norm=use_scale_shift_norm,
|
||||
up=True,
|
||||
device=device,
|
||||
dtype=self.dtype
|
||||
)
|
||||
if resblock_updown
|
||||
else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch)
|
||||
)
|
||||
ds //= 2
|
||||
self.output_blocks.append(TimestepEmbedSequential(*layers))
|
||||
|
||||
self.out = nn.Sequential(
|
||||
normalization(ch, device=device, dtype=self.dtype),
|
||||
nn.SiLU(),
|
||||
zero_module(
|
||||
operations.conv_nd(
|
||||
dims,
|
||||
model_channels,
|
||||
out_channels,
|
||||
3,
|
||||
padding=1,
|
||||
device=device,
|
||||
dtype=self.dtype
|
||||
)
|
||||
),
|
||||
)
|
||||
|
||||
# TODO Add Transformer options to leverage the usage of patches.
|
||||
def forward(
|
||||
self,
|
||||
x,
|
||||
timesteps,
|
||||
context=None,
|
||||
context_in=None,
|
||||
cc_concat=None,
|
||||
num_video_frames=16,
|
||||
features_adapter=None,
|
||||
fs=None,
|
||||
img_emb=None,
|
||||
control=None,
|
||||
transformer_options={},
|
||||
cond_idx=None,
|
||||
**kwargs
|
||||
):
|
||||
|
||||
if any([fs is None, img_emb is None, cc_concat is None]):
|
||||
raise ValueError("One or more of the required inputs for UNet Forward is None.")
|
||||
|
||||
cond_idx = transformer_options.get("cond_idx", None)
|
||||
transformer_options['original_shape'] = list(x.shape)
|
||||
transformer_options['transformer_index'] = 0
|
||||
transformer_patches = transformer_options.get("patches", {})
|
||||
|
||||
# In ComfyUI, the frames are always with the batch, so we deconstruct it here.
|
||||
# This is mandatory as this is a video based model.
|
||||
# We usually denote "f" as frames, but will use "t" (time) to be consistent with DynamiCrafter.
|
||||
b,_,t,_,_ = x.shape
|
||||
|
||||
context = context_in
|
||||
cc_concat = cc_concat.to(x.device, x.dtype)
|
||||
x = torch.cat([x, cc_concat], dim=1)
|
||||
|
||||
fs = fs.to(x.device, x.dtype)
|
||||
|
||||
timestep = timesteps
|
||||
context = context_processor(context, num_video_frames, img_emb=img_emb)
|
||||
|
||||
t_emb = timestep_embedding(timestep, self.model_channels, repeat_only=False, dtype=self.dtype)
|
||||
emb = self.time_embed(t_emb)
|
||||
emb = emb.repeat_interleave(repeats=t, dim=0)
|
||||
|
||||
## always in shape (b t) c h w, except for temporal layer
|
||||
x = rearrange(x, 'b c t h w -> (b t) c h w')
|
||||
|
||||
## combine emb
|
||||
if self.fs_condition:
|
||||
if fs is None:
|
||||
fs = torch.tensor(
|
||||
[self.default_fs] * b, dtype=torch.long, device=x.device)
|
||||
fs_emb = timestep_embedding(fs, self.model_channels, repeat_only=False, dtype=self.dtype).type(x.dtype)
|
||||
|
||||
fs_embed = self.fps_embedding(fs_emb)
|
||||
fs_embed = fs_embed.repeat_interleave(repeats=t, dim=0)
|
||||
|
||||
emb = emb + fs_embed
|
||||
|
||||
h = x.type(self.dtype)
|
||||
adapter_idx = 0
|
||||
hs = []
|
||||
|
||||
for id, module in enumerate(self.input_blocks):
|
||||
transformer_options["block"] = ("input", id)
|
||||
#h = module(h, emb, context=context, batch_size=b)
|
||||
h = forward_timestep_embed(
|
||||
module,
|
||||
h,
|
||||
emb,
|
||||
context=context,
|
||||
batch_size=b,
|
||||
transformer_options=transformer_options
|
||||
)
|
||||
h = apply_control(h, control, 'input', cond_idx)
|
||||
|
||||
if "input_block_patch" in transformer_patches:
|
||||
patch = transformer_patches["input_block_patch"]
|
||||
for p in patch:
|
||||
h = p(h, transformer_options)
|
||||
|
||||
if id ==0 and self.addition_attention:
|
||||
h = forward_timestep_embed(
|
||||
self.init_attn,
|
||||
h,
|
||||
emb,
|
||||
context=context,
|
||||
batch_size=b,
|
||||
transformer_options=transformer_options
|
||||
)
|
||||
## plug-in adapter features
|
||||
if ((id+1)%3 == 0) and features_adapter is not None:
|
||||
h = h + features_adapter[adapter_idx]
|
||||
adapter_idx += 1
|
||||
hs.append(h)
|
||||
if "input_block_patch_after_skip" in transformer_patches:
|
||||
patch = transformer_patches["input_block_patch_after_skip"]
|
||||
for p in patch:
|
||||
h = p(h, transformer_options)
|
||||
if features_adapter is not None:
|
||||
assert len(features_adapter)==adapter_idx, 'Wrong features_adapter'
|
||||
transformer_options["block"] = ("middle", 0)
|
||||
h = forward_timestep_embed(
|
||||
self.middle_block,
|
||||
h,
|
||||
emb,
|
||||
context=context,
|
||||
batch_size=b,
|
||||
transformer_options=transformer_options
|
||||
)
|
||||
h = apply_control(h, control, 'middle', cond_idx)
|
||||
for id, module in enumerate(self.output_blocks):
|
||||
transformer_options["block"] = ("output", id)
|
||||
hsp = hs.pop()
|
||||
hsp = apply_control(hsp, control, 'output', cond_idx)
|
||||
|
||||
if "output_block_patch" in transformer_patches:
|
||||
patch = transformer_patches["output_block_patch"]
|
||||
for p in patch:
|
||||
h, hsp = p(h, hsp, transformer_options)
|
||||
|
||||
h = torch.cat([h, hsp], dim=1)
|
||||
del hsp
|
||||
h = forward_timestep_embed(
|
||||
module,
|
||||
h,
|
||||
emb,
|
||||
context=context,
|
||||
batch_size=b,
|
||||
transformer_options=transformer_options
|
||||
)
|
||||
h = h.type(x.dtype)
|
||||
h = self.out(h)
|
||||
|
||||
# We output with the tensor unfolded framewise, then reshape them to batched using ComfyUI nodes.
|
||||
h = rearrange(h, '(b t) c h w -> b c t h w', t=num_video_frames)
|
||||
|
||||
return h
|
||||
@@ -0,0 +1,639 @@
|
||||
"""shout-out to https://github.com/lucidrains/x-transformers/tree/main/x_transformers"""
|
||||
from functools import partial
|
||||
from inspect import isfunction
|
||||
from collections import namedtuple
|
||||
from einops import rearrange, repeat
|
||||
import torch
|
||||
from torch import nn, einsum
|
||||
import torch.nn.functional as F
|
||||
|
||||
# constants
|
||||
DEFAULT_DIM_HEAD = 64
|
||||
|
||||
Intermediates = namedtuple('Intermediates', [
|
||||
'pre_softmax_attn',
|
||||
'post_softmax_attn'
|
||||
])
|
||||
|
||||
LayerIntermediates = namedtuple('Intermediates', [
|
||||
'hiddens',
|
||||
'attn_intermediates'
|
||||
])
|
||||
|
||||
|
||||
class AbsolutePositionalEmbedding(nn.Module):
|
||||
def __init__(self, dim, max_seq_len):
|
||||
super().__init__()
|
||||
self.emb = nn.Embedding(max_seq_len, dim)
|
||||
self.init_()
|
||||
|
||||
def init_(self):
|
||||
nn.init.normal_(self.emb.weight, std=0.02)
|
||||
|
||||
def forward(self, x):
|
||||
n = torch.arange(x.shape[1], device=x.device)
|
||||
return self.emb(n)[None, :, :]
|
||||
|
||||
|
||||
class FixedPositionalEmbedding(nn.Module):
|
||||
def __init__(self, dim):
|
||||
super().__init__()
|
||||
inv_freq = 1. / (10000 ** (torch.arange(0, dim, 2).float() / dim))
|
||||
self.register_buffer('inv_freq', inv_freq)
|
||||
|
||||
def forward(self, x, seq_dim=1, offset=0):
|
||||
t = torch.arange(x.shape[seq_dim], device=x.device).type_as(self.inv_freq) + offset
|
||||
sinusoid_inp = torch.einsum('i , j -> i j', t, self.inv_freq)
|
||||
emb = torch.cat((sinusoid_inp.sin(), sinusoid_inp.cos()), dim=-1)
|
||||
return emb[None, :, :]
|
||||
|
||||
|
||||
# helpers
|
||||
|
||||
def exists(val):
|
||||
return val is not None
|
||||
|
||||
|
||||
def default(val, d):
|
||||
if exists(val):
|
||||
return val
|
||||
return d() if isfunction(d) else d
|
||||
|
||||
|
||||
def always(val):
|
||||
def inner(*args, **kwargs):
|
||||
return val
|
||||
return inner
|
||||
|
||||
|
||||
def not_equals(val):
|
||||
def inner(x):
|
||||
return x != val
|
||||
return inner
|
||||
|
||||
|
||||
def equals(val):
|
||||
def inner(x):
|
||||
return x == val
|
||||
return inner
|
||||
|
||||
|
||||
def max_neg_value(tensor):
|
||||
return -torch.finfo(tensor.dtype).max
|
||||
|
||||
|
||||
# keyword argument helpers
|
||||
|
||||
def pick_and_pop(keys, d):
|
||||
values = list(map(lambda key: d.pop(key), keys))
|
||||
return dict(zip(keys, values))
|
||||
|
||||
|
||||
def group_dict_by_key(cond, d):
|
||||
return_val = [dict(), dict()]
|
||||
for key in d.keys():
|
||||
match = bool(cond(key))
|
||||
ind = int(not match)
|
||||
return_val[ind][key] = d[key]
|
||||
return (*return_val,)
|
||||
|
||||
|
||||
def string_begins_with(prefix, str):
|
||||
return str.startswith(prefix)
|
||||
|
||||
|
||||
def group_by_key_prefix(prefix, d):
|
||||
return group_dict_by_key(partial(string_begins_with, prefix), d)
|
||||
|
||||
|
||||
def groupby_prefix_and_trim(prefix, d):
|
||||
kwargs_with_prefix, kwargs = group_dict_by_key(partial(string_begins_with, prefix), d)
|
||||
kwargs_without_prefix = dict(map(lambda x: (x[0][len(prefix):], x[1]), tuple(kwargs_with_prefix.items())))
|
||||
return kwargs_without_prefix, kwargs
|
||||
|
||||
|
||||
# classes
|
||||
class Scale(nn.Module):
|
||||
def __init__(self, value, fn):
|
||||
super().__init__()
|
||||
self.value = value
|
||||
self.fn = fn
|
||||
|
||||
def forward(self, x, **kwargs):
|
||||
x, *rest = self.fn(x, **kwargs)
|
||||
return (x * self.value, *rest)
|
||||
|
||||
|
||||
class Rezero(nn.Module):
|
||||
def __init__(self, fn):
|
||||
super().__init__()
|
||||
self.fn = fn
|
||||
self.g = nn.Parameter(torch.zeros(1))
|
||||
|
||||
def forward(self, x, **kwargs):
|
||||
x, *rest = self.fn(x, **kwargs)
|
||||
return (x * self.g, *rest)
|
||||
|
||||
|
||||
class ScaleNorm(nn.Module):
|
||||
def __init__(self, dim, eps=1e-5):
|
||||
super().__init__()
|
||||
self.scale = dim ** -0.5
|
||||
self.eps = eps
|
||||
self.g = nn.Parameter(torch.ones(1))
|
||||
|
||||
def forward(self, x):
|
||||
norm = torch.norm(x, dim=-1, keepdim=True) * self.scale
|
||||
return x / norm.clamp(min=self.eps) * self.g
|
||||
|
||||
|
||||
class RMSNorm(nn.Module):
|
||||
def __init__(self, dim, eps=1e-8):
|
||||
super().__init__()
|
||||
self.scale = dim ** -0.5
|
||||
self.eps = eps
|
||||
self.g = nn.Parameter(torch.ones(dim))
|
||||
|
||||
def forward(self, x):
|
||||
norm = torch.norm(x, dim=-1, keepdim=True) * self.scale
|
||||
return x / norm.clamp(min=self.eps) * self.g
|
||||
|
||||
|
||||
class Residual(nn.Module):
|
||||
def forward(self, x, residual):
|
||||
return x + residual
|
||||
|
||||
|
||||
class GRUGating(nn.Module):
|
||||
def __init__(self, dim):
|
||||
super().__init__()
|
||||
self.gru = nn.GRUCell(dim, dim)
|
||||
|
||||
def forward(self, x, residual):
|
||||
gated_output = self.gru(
|
||||
rearrange(x, 'b n d -> (b n) d'),
|
||||
rearrange(residual, 'b n d -> (b n) d')
|
||||
)
|
||||
|
||||
return gated_output.reshape_as(x)
|
||||
|
||||
|
||||
# feedforward
|
||||
|
||||
class GEGLU(nn.Module):
|
||||
def __init__(self, dim_in, dim_out):
|
||||
super().__init__()
|
||||
self.proj = nn.Linear(dim_in, dim_out * 2)
|
||||
|
||||
def forward(self, x):
|
||||
x, gate = self.proj(x).chunk(2, dim=-1)
|
||||
return x * F.gelu(gate)
|
||||
|
||||
|
||||
class FeedForward(nn.Module):
|
||||
def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):
|
||||
super().__init__()
|
||||
inner_dim = int(dim * mult)
|
||||
dim_out = default(dim_out, dim)
|
||||
project_in = nn.Sequential(
|
||||
nn.Linear(dim, inner_dim),
|
||||
nn.GELU()
|
||||
) if not glu else GEGLU(dim, inner_dim)
|
||||
|
||||
self.net = nn.Sequential(
|
||||
project_in,
|
||||
nn.Dropout(dropout),
|
||||
nn.Linear(inner_dim, dim_out)
|
||||
)
|
||||
|
||||
def forward(self, x):
|
||||
return self.net(x)
|
||||
|
||||
|
||||
# attention.
|
||||
class Attention(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
dim,
|
||||
dim_head=DEFAULT_DIM_HEAD,
|
||||
heads=8,
|
||||
causal=False,
|
||||
mask=None,
|
||||
talking_heads=False,
|
||||
sparse_topk=None,
|
||||
use_entmax15=False,
|
||||
num_mem_kv=0,
|
||||
dropout=0.,
|
||||
on_attn=False
|
||||
):
|
||||
super().__init__()
|
||||
if use_entmax15:
|
||||
raise NotImplementedError("Check out entmax activation instead of softmax activation!")
|
||||
self.scale = dim_head ** -0.5
|
||||
self.heads = heads
|
||||
self.causal = causal
|
||||
self.mask = mask
|
||||
|
||||
inner_dim = dim_head * heads
|
||||
|
||||
self.to_q = nn.Linear(dim, inner_dim, bias=False)
|
||||
self.to_k = nn.Linear(dim, inner_dim, bias=False)
|
||||
self.to_v = nn.Linear(dim, inner_dim, bias=False)
|
||||
self.dropout = nn.Dropout(dropout)
|
||||
|
||||
# talking heads
|
||||
self.talking_heads = talking_heads
|
||||
if talking_heads:
|
||||
self.pre_softmax_proj = nn.Parameter(torch.randn(heads, heads))
|
||||
self.post_softmax_proj = nn.Parameter(torch.randn(heads, heads))
|
||||
|
||||
# explicit topk sparse attention
|
||||
self.sparse_topk = sparse_topk
|
||||
|
||||
# entmax
|
||||
#self.attn_fn = entmax15 if use_entmax15 else F.softmax
|
||||
self.attn_fn = F.softmax
|
||||
|
||||
# add memory key / values
|
||||
self.num_mem_kv = num_mem_kv
|
||||
if num_mem_kv > 0:
|
||||
self.mem_k = nn.Parameter(torch.randn(heads, num_mem_kv, dim_head))
|
||||
self.mem_v = nn.Parameter(torch.randn(heads, num_mem_kv, dim_head))
|
||||
|
||||
# attention on attention
|
||||
self.attn_on_attn = on_attn
|
||||
self.to_out = nn.Sequential(nn.Linear(inner_dim, dim * 2), nn.GLU()) if on_attn else nn.Linear(inner_dim, dim)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
x,
|
||||
context=None,
|
||||
mask=None,
|
||||
context_mask=None,
|
||||
rel_pos=None,
|
||||
sinusoidal_emb=None,
|
||||
prev_attn=None,
|
||||
mem=None
|
||||
):
|
||||
b, n, _, h, talking_heads, device = *x.shape, self.heads, self.talking_heads, x.device
|
||||
kv_input = default(context, x)
|
||||
|
||||
q_input = x
|
||||
k_input = kv_input
|
||||
v_input = kv_input
|
||||
|
||||
if exists(mem):
|
||||
k_input = torch.cat((mem, k_input), dim=-2)
|
||||
v_input = torch.cat((mem, v_input), dim=-2)
|
||||
|
||||
if exists(sinusoidal_emb):
|
||||
# in shortformer, the query would start at a position offset depending on the past cached memory
|
||||
offset = k_input.shape[-2] - q_input.shape[-2]
|
||||
q_input = q_input + sinusoidal_emb(q_input, offset=offset)
|
||||
k_input = k_input + sinusoidal_emb(k_input)
|
||||
|
||||
q = self.to_q(q_input)
|
||||
k = self.to_k(k_input)
|
||||
v = self.to_v(v_input)
|
||||
|
||||
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h=h), (q, k, v))
|
||||
|
||||
input_mask = None
|
||||
if any(map(exists, (mask, context_mask))):
|
||||
q_mask = default(mask, lambda: torch.ones((b, n), device=device).bool())
|
||||
k_mask = q_mask if not exists(context) else context_mask
|
||||
k_mask = default(k_mask, lambda: torch.ones((b, k.shape[-2]), device=device).bool())
|
||||
q_mask = rearrange(q_mask, 'b i -> b () i ()')
|
||||
k_mask = rearrange(k_mask, 'b j -> b () () j')
|
||||
input_mask = q_mask * k_mask
|
||||
|
||||
if self.num_mem_kv > 0:
|
||||
mem_k, mem_v = map(lambda t: repeat(t, 'h n d -> b h n d', b=b), (self.mem_k, self.mem_v))
|
||||
k = torch.cat((mem_k, k), dim=-2)
|
||||
v = torch.cat((mem_v, v), dim=-2)
|
||||
if exists(input_mask):
|
||||
input_mask = F.pad(input_mask, (self.num_mem_kv, 0), value=True)
|
||||
|
||||
dots = einsum('b h i d, b h j d -> b h i j', q, k) * self.scale
|
||||
mask_value = max_neg_value(dots)
|
||||
|
||||
if exists(prev_attn):
|
||||
dots = dots + prev_attn
|
||||
|
||||
pre_softmax_attn = dots
|
||||
|
||||
if talking_heads:
|
||||
dots = einsum('b h i j, h k -> b k i j', dots, self.pre_softmax_proj).contiguous()
|
||||
|
||||
if exists(rel_pos):
|
||||
dots = rel_pos(dots)
|
||||
|
||||
if exists(input_mask):
|
||||
dots.masked_fill_(~input_mask, mask_value)
|
||||
del input_mask
|
||||
|
||||
if self.causal:
|
||||
i, j = dots.shape[-2:]
|
||||
r = torch.arange(i, device=device)
|
||||
mask = rearrange(r, 'i -> () () i ()') < rearrange(r, 'j -> () () () j')
|
||||
mask = F.pad(mask, (j - i, 0), value=False)
|
||||
dots.masked_fill_(mask, mask_value)
|
||||
del mask
|
||||
|
||||
if exists(self.sparse_topk) and self.sparse_topk < dots.shape[-1]:
|
||||
top, _ = dots.topk(self.sparse_topk, dim=-1)
|
||||
vk = top[..., -1].unsqueeze(-1).expand_as(dots)
|
||||
mask = dots < vk
|
||||
dots.masked_fill_(mask, mask_value)
|
||||
del mask
|
||||
|
||||
attn = self.attn_fn(dots, dim=-1)
|
||||
post_softmax_attn = attn
|
||||
|
||||
attn = self.dropout(attn)
|
||||
|
||||
if talking_heads:
|
||||
attn = einsum('b h i j, h k -> b k i j', attn, self.post_softmax_proj).contiguous()
|
||||
|
||||
out = einsum('b h i j, b h j d -> b h i d', attn, v)
|
||||
out = rearrange(out, 'b h n d -> b n (h d)')
|
||||
|
||||
intermediates = Intermediates(
|
||||
pre_softmax_attn=pre_softmax_attn,
|
||||
post_softmax_attn=post_softmax_attn
|
||||
)
|
||||
|
||||
return self.to_out(out), intermediates
|
||||
|
||||
|
||||
class AttentionLayers(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
dim,
|
||||
depth,
|
||||
heads=8,
|
||||
causal=False,
|
||||
cross_attend=False,
|
||||
only_cross=False,
|
||||
use_scalenorm=False,
|
||||
use_rmsnorm=False,
|
||||
use_rezero=False,
|
||||
rel_pos_num_buckets=32,
|
||||
rel_pos_max_distance=128,
|
||||
position_infused_attn=False,
|
||||
custom_layers=None,
|
||||
sandwich_coef=None,
|
||||
par_ratio=None,
|
||||
residual_attn=False,
|
||||
cross_residual_attn=False,
|
||||
macaron=False,
|
||||
pre_norm=True,
|
||||
gate_residual=False,
|
||||
**kwargs
|
||||
):
|
||||
super().__init__()
|
||||
ff_kwargs, kwargs = groupby_prefix_and_trim('ff_', kwargs)
|
||||
attn_kwargs, _ = groupby_prefix_and_trim('attn_', kwargs)
|
||||
|
||||
dim_head = attn_kwargs.get('dim_head', DEFAULT_DIM_HEAD)
|
||||
|
||||
self.dim = dim
|
||||
self.depth = depth
|
||||
self.layers = nn.ModuleList([])
|
||||
|
||||
self.has_pos_emb = position_infused_attn
|
||||
self.pia_pos_emb = FixedPositionalEmbedding(dim) if position_infused_attn else None
|
||||
self.rotary_pos_emb = always(None)
|
||||
|
||||
assert rel_pos_num_buckets <= rel_pos_max_distance, 'number of relative position buckets must be less than the relative position max distance'
|
||||
self.rel_pos = None
|
||||
|
||||
self.pre_norm = pre_norm
|
||||
|
||||
self.residual_attn = residual_attn
|
||||
self.cross_residual_attn = cross_residual_attn
|
||||
|
||||
norm_class = ScaleNorm if use_scalenorm else nn.LayerNorm
|
||||
norm_class = RMSNorm if use_rmsnorm else norm_class
|
||||
norm_fn = partial(norm_class, dim)
|
||||
|
||||
norm_fn = nn.Identity if use_rezero else norm_fn
|
||||
branch_fn = Rezero if use_rezero else None
|
||||
|
||||
if cross_attend and not only_cross:
|
||||
default_block = ('a', 'c', 'f')
|
||||
elif cross_attend and only_cross:
|
||||
default_block = ('c', 'f')
|
||||
else:
|
||||
default_block = ('a', 'f')
|
||||
|
||||
if macaron:
|
||||
default_block = ('f',) + default_block
|
||||
|
||||
if exists(custom_layers):
|
||||
layer_types = custom_layers
|
||||
elif exists(par_ratio):
|
||||
par_depth = depth * len(default_block)
|
||||
assert 1 < par_ratio <= par_depth, 'par ratio out of range'
|
||||
default_block = tuple(filter(not_equals('f'), default_block))
|
||||
par_attn = par_depth // par_ratio
|
||||
depth_cut = par_depth * 2 // 3 # 2 / 3 attention layer cutoff suggested by PAR paper
|
||||
par_width = (depth_cut + depth_cut // par_attn) // par_attn
|
||||
assert len(default_block) <= par_width, 'default block is too large for par_ratio'
|
||||
par_block = default_block + ('f',) * (par_width - len(default_block))
|
||||
par_head = par_block * par_attn
|
||||
layer_types = par_head + ('f',) * (par_depth - len(par_head))
|
||||
elif exists(sandwich_coef):
|
||||
assert sandwich_coef > 0 and sandwich_coef <= depth, 'sandwich coefficient should be less than the depth'
|
||||
layer_types = ('a',) * sandwich_coef + default_block * (depth - sandwich_coef) + ('f',) * sandwich_coef
|
||||
else:
|
||||
layer_types = default_block * depth
|
||||
|
||||
self.layer_types = layer_types
|
||||
self.num_attn_layers = len(list(filter(equals('a'), layer_types)))
|
||||
|
||||
for layer_type in self.layer_types:
|
||||
if layer_type == 'a':
|
||||
layer = Attention(dim, heads=heads, causal=causal, **attn_kwargs)
|
||||
elif layer_type == 'c':
|
||||
layer = Attention(dim, heads=heads, **attn_kwargs)
|
||||
elif layer_type == 'f':
|
||||
layer = FeedForward(dim, **ff_kwargs)
|
||||
layer = layer if not macaron else Scale(0.5, layer)
|
||||
else:
|
||||
raise Exception(f'invalid layer type {layer_type}')
|
||||
|
||||
if isinstance(layer, Attention) and exists(branch_fn):
|
||||
layer = branch_fn(layer)
|
||||
|
||||
if gate_residual:
|
||||
residual_fn = GRUGating(dim)
|
||||
else:
|
||||
residual_fn = Residual()
|
||||
|
||||
self.layers.append(nn.ModuleList([
|
||||
norm_fn(),
|
||||
layer,
|
||||
residual_fn
|
||||
]))
|
||||
|
||||
def forward(
|
||||
self,
|
||||
x,
|
||||
context=None,
|
||||
mask=None,
|
||||
context_mask=None,
|
||||
mems=None,
|
||||
return_hiddens=False
|
||||
):
|
||||
hiddens = []
|
||||
intermediates = []
|
||||
prev_attn = None
|
||||
prev_cross_attn = None
|
||||
|
||||
mems = mems.copy() if exists(mems) else [None] * self.num_attn_layers
|
||||
|
||||
for ind, (layer_type, (norm, block, residual_fn)) in enumerate(zip(self.layer_types, self.layers)):
|
||||
is_last = ind == (len(self.layers) - 1)
|
||||
|
||||
if layer_type == 'a':
|
||||
hiddens.append(x)
|
||||
layer_mem = mems.pop(0)
|
||||
|
||||
residual = x
|
||||
|
||||
if self.pre_norm:
|
||||
x = norm(x)
|
||||
|
||||
if layer_type == 'a':
|
||||
out, inter = block(x, mask=mask, sinusoidal_emb=self.pia_pos_emb, rel_pos=self.rel_pos,
|
||||
prev_attn=prev_attn, mem=layer_mem)
|
||||
elif layer_type == 'c':
|
||||
out, inter = block(x, context=context, mask=mask, context_mask=context_mask, prev_attn=prev_cross_attn)
|
||||
elif layer_type == 'f':
|
||||
out = block(x)
|
||||
|
||||
x = residual_fn(out, residual)
|
||||
|
||||
if layer_type in ('a', 'c'):
|
||||
intermediates.append(inter)
|
||||
|
||||
if layer_type == 'a' and self.residual_attn:
|
||||
prev_attn = inter.pre_softmax_attn
|
||||
elif layer_type == 'c' and self.cross_residual_attn:
|
||||
prev_cross_attn = inter.pre_softmax_attn
|
||||
|
||||
if not self.pre_norm and not is_last:
|
||||
x = norm(x)
|
||||
|
||||
if return_hiddens:
|
||||
intermediates = LayerIntermediates(
|
||||
hiddens=hiddens,
|
||||
attn_intermediates=intermediates
|
||||
)
|
||||
|
||||
return x, intermediates
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class Encoder(AttentionLayers):
|
||||
def __init__(self, **kwargs):
|
||||
assert 'causal' not in kwargs, 'cannot set causality on encoder'
|
||||
super().__init__(causal=False, **kwargs)
|
||||
|
||||
|
||||
|
||||
class TransformerWrapper(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
*,
|
||||
num_tokens,
|
||||
max_seq_len,
|
||||
attn_layers,
|
||||
emb_dim=None,
|
||||
max_mem_len=0.,
|
||||
emb_dropout=0.,
|
||||
num_memory_tokens=None,
|
||||
tie_embedding=False,
|
||||
use_pos_emb=True
|
||||
):
|
||||
super().__init__()
|
||||
assert isinstance(attn_layers, AttentionLayers), 'attention layers must be one of Encoder or Decoder'
|
||||
|
||||
dim = attn_layers.dim
|
||||
emb_dim = default(emb_dim, dim)
|
||||
|
||||
self.max_seq_len = max_seq_len
|
||||
self.max_mem_len = max_mem_len
|
||||
self.num_tokens = num_tokens
|
||||
|
||||
self.token_emb = nn.Embedding(num_tokens, emb_dim)
|
||||
self.pos_emb = AbsolutePositionalEmbedding(emb_dim, max_seq_len) if (
|
||||
use_pos_emb and not attn_layers.has_pos_emb) else always(0)
|
||||
self.emb_dropout = nn.Dropout(emb_dropout)
|
||||
|
||||
self.project_emb = nn.Linear(emb_dim, dim) if emb_dim != dim else nn.Identity()
|
||||
self.attn_layers = attn_layers
|
||||
self.norm = nn.LayerNorm(dim)
|
||||
|
||||
self.init_()
|
||||
|
||||
self.to_logits = nn.Linear(dim, num_tokens) if not tie_embedding else lambda t: t @ self.token_emb.weight.t()
|
||||
|
||||
# memory tokens (like [cls]) from Memory Transformers paper
|
||||
num_memory_tokens = default(num_memory_tokens, 0)
|
||||
self.num_memory_tokens = num_memory_tokens
|
||||
if num_memory_tokens > 0:
|
||||
self.memory_tokens = nn.Parameter(torch.randn(num_memory_tokens, dim))
|
||||
|
||||
# let funnel encoder know number of memory tokens, if specified
|
||||
if hasattr(attn_layers, 'num_memory_tokens'):
|
||||
attn_layers.num_memory_tokens = num_memory_tokens
|
||||
|
||||
def init_(self):
|
||||
nn.init.normal_(self.token_emb.weight, std=0.02)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
x,
|
||||
return_embeddings=False,
|
||||
mask=None,
|
||||
return_mems=False,
|
||||
return_attn=False,
|
||||
mems=None,
|
||||
**kwargs
|
||||
):
|
||||
b, n, device, num_mem = *x.shape, x.device, self.num_memory_tokens
|
||||
x = self.token_emb(x)
|
||||
x += self.pos_emb(x)
|
||||
x = self.emb_dropout(x)
|
||||
|
||||
x = self.project_emb(x)
|
||||
|
||||
if num_mem > 0:
|
||||
mem = repeat(self.memory_tokens, 'n d -> b n d', b=b)
|
||||
x = torch.cat((mem, x), dim=1)
|
||||
|
||||
# auto-handle masking after appending memory tokens
|
||||
if exists(mask):
|
||||
mask = F.pad(mask, (num_mem, 0), value=True)
|
||||
|
||||
x, intermediates = self.attn_layers(x, mask=mask, mems=mems, return_hiddens=True, **kwargs)
|
||||
x = self.norm(x)
|
||||
|
||||
mem, x = x[:, :num_mem], x[:, num_mem:]
|
||||
|
||||
out = self.to_logits(x) if not return_embeddings else x
|
||||
|
||||
if return_mems:
|
||||
hiddens = intermediates.hiddens
|
||||
new_mems = list(map(lambda pair: torch.cat(pair, dim=-2), zip(mems, hiddens))) if exists(mems) else hiddens
|
||||
new_mems = list(map(lambda t: t[..., -self.max_mem_len:, :].detach(), new_mems))
|
||||
return out, new_mems
|
||||
|
||||
if return_attn:
|
||||
attn_maps = list(map(lambda t: t.post_softmax_attn, intermediates.attn_intermediates))
|
||||
return out, attn_maps
|
||||
|
||||
return out
|
||||
Reference in New Issue
Block a user