mirror of
https://github.com/comfyanonymous/ComfyUI.git
synced 2026-02-03 18:20:26 +08:00
500 lines
19 KiB
Python
500 lines
19 KiB
Python
import torch
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import torch.nn.functional as F
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import torch.nn as nn
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from vae import SparseTensor, SparseLinear, sparse_cat, VarLenTensor
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from typing import Optional, Tuple, Literal, Union, List
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from attention import sparse_windowed_scaled_dot_product_self_attention, sparse_scaled_dot_product_attention
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from comfy.ldm.genmo.joint_model.layers import TimestepEmbedder
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class SparseGELU(nn.GELU):
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def forward(self, input: VarLenTensor) -> VarLenTensor:
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return input.replace(super().forward(input.feats))
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class SparseFeedForwardNet(nn.Module):
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def __init__(self, channels: int, mlp_ratio: float = 4.0):
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super().__init__()
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self.mlp = nn.Sequential(
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SparseLinear(channels, int(channels * mlp_ratio)),
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SparseGELU(approximate="tanh"),
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SparseLinear(int(channels * mlp_ratio), channels),
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)
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def forward(self, x: VarLenTensor) -> VarLenTensor:
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return self.mlp(x)
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def manual_cast(tensor, dtype):
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if not torch.is_autocast_enabled():
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return tensor.type(dtype)
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return tensor
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class LayerNorm32(nn.LayerNorm):
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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x_dtype = x.dtype
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x = manual_cast(x, torch.float32)
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o = super().forward(x)
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return manual_cast(o, x_dtype)
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class SparseMultiHeadRMSNorm(nn.Module):
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def __init__(self, dim: int, heads: int):
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super().__init__()
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self.scale = dim ** 0.5
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self.gamma = nn.Parameter(torch.ones(heads, dim))
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def forward(self, x: Union[VarLenTensor, torch.Tensor]) -> Union[VarLenTensor, torch.Tensor]:
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x_type = x.dtype
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x = x.float()
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if isinstance(x, VarLenTensor):
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x = x.replace(F.normalize(x.feats, dim=-1) * self.gamma * self.scale)
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else:
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x = F.normalize(x, dim=-1) * self.gamma * self.scale
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return x.to(x_type)
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# TODO: replace with apply_rope1
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class SparseRotaryPositionEmbedder(nn.Module):
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def __init__(
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self,
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head_dim: int,
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dim: int = 3,
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rope_freq: Tuple[float, float] = (1.0, 10000.0)
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):
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super().__init__()
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assert head_dim % 2 == 0, "Head dim must be divisible by 2"
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self.head_dim = head_dim
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self.dim = dim
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self.rope_freq = rope_freq
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self.freq_dim = head_dim // 2 // dim
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self.freqs = torch.arange(self.freq_dim, dtype=torch.float32) / self.freq_dim
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self.freqs = rope_freq[0] / (rope_freq[1] ** (self.freqs))
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def _get_phases(self, indices: torch.Tensor) -> torch.Tensor:
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self.freqs = self.freqs.to(indices.device)
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phases = torch.outer(indices, self.freqs)
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phases = torch.polar(torch.ones_like(phases), phases)
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return phases
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def _rotary_embedding(self, x: torch.Tensor, phases: torch.Tensor) -> torch.Tensor:
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x_complex = torch.view_as_complex(x.float().reshape(*x.shape[:-1], -1, 2))
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x_rotated = x_complex * phases.unsqueeze(-2)
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x_embed = torch.view_as_real(x_rotated).reshape(*x_rotated.shape[:-1], -1).to(x.dtype)
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return x_embed
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def forward(self, q: SparseTensor, k: Optional[SparseTensor] = None) -> Tuple[torch.Tensor, torch.Tensor]:
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"""
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Args:
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q (SparseTensor): [..., N, H, D] tensor of queries
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k (SparseTensor): [..., N, H, D] tensor of keys
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"""
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assert q.coords.shape[-1] == self.dim + 1, "Last dimension of coords must be equal to dim+1"
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phases_cache_name = f'rope_phase_{self.dim}d_freq{self.rope_freq[0]}-{self.rope_freq[1]}_hd{self.head_dim}'
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phases = q.get_spatial_cache(phases_cache_name)
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if phases is None:
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coords = q.coords[..., 1:]
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phases = self._get_phases(coords.reshape(-1)).reshape(*coords.shape[:-1], -1)
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if phases.shape[-1] < self.head_dim // 2:
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padn = self.head_dim // 2 - phases.shape[-1]
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phases = torch.cat([phases, torch.polar(
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torch.ones(*phases.shape[:-1], padn, device=phases.device),
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torch.zeros(*phases.shape[:-1], padn, device=phases.device)
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)], dim=-1)
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q.register_spatial_cache(phases_cache_name, phases)
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q_embed = q.replace(self._rotary_embedding(q.feats, phases))
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if k is None:
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return q_embed
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k_embed = k.replace(self._rotary_embedding(k.feats, phases))
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return q_embed, k_embed
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class SparseMultiHeadAttention(nn.Module):
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def __init__(
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self,
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channels: int,
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num_heads: int,
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ctx_channels: Optional[int] = None,
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type: Literal["self", "cross"] = "self",
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attn_mode: Literal["full", "windowed", "double_windowed"] = "full",
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window_size: Optional[int] = None,
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shift_window: Optional[Tuple[int, int, int]] = None,
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qkv_bias: bool = True,
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use_rope: bool = False,
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rope_freq: Tuple[int, int] = (1.0, 10000.0),
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qk_rms_norm: bool = False,
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):
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super().__init__()
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self.channels = channels
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self.head_dim = channels // num_heads
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self.ctx_channels = ctx_channels if ctx_channels is not None else channels
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self.num_heads = num_heads
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self._type = type
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self.attn_mode = attn_mode
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self.window_size = window_size
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self.shift_window = shift_window
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self.use_rope = use_rope
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self.qk_rms_norm = qk_rms_norm
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if self._type == "self":
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self.to_qkv = nn.Linear(channels, channels * 3, bias=qkv_bias)
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else:
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self.to_q = nn.Linear(channels, channels, bias=qkv_bias)
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self.to_kv = nn.Linear(self.ctx_channels, channels * 2, bias=qkv_bias)
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if self.qk_rms_norm:
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self.q_rms_norm = SparseMultiHeadRMSNorm(self.head_dim, num_heads)
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self.k_rms_norm = SparseMultiHeadRMSNorm(self.head_dim, num_heads)
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self.to_out = nn.Linear(channels, channels)
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if use_rope:
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self.rope = SparseRotaryPositionEmbedder(self.head_dim, rope_freq=rope_freq)
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@staticmethod
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def _linear(module: nn.Linear, x: Union[VarLenTensor, torch.Tensor]) -> Union[VarLenTensor, torch.Tensor]:
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if isinstance(x, VarLenTensor):
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return x.replace(module(x.feats))
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else:
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return module(x)
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@staticmethod
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def _reshape_chs(x: Union[VarLenTensor, torch.Tensor], shape: Tuple[int, ...]) -> Union[VarLenTensor, torch.Tensor]:
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if isinstance(x, VarLenTensor):
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return x.reshape(*shape)
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else:
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return x.reshape(*x.shape[:2], *shape)
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def _fused_pre(self, x: Union[VarLenTensor, torch.Tensor], num_fused: int) -> Union[VarLenTensor, torch.Tensor]:
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if isinstance(x, VarLenTensor):
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x_feats = x.feats.unsqueeze(0)
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else:
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x_feats = x
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x_feats = x_feats.reshape(*x_feats.shape[:2], num_fused, self.num_heads, -1)
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return x.replace(x_feats.squeeze(0)) if isinstance(x, VarLenTensor) else x_feats
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def forward(self, x: SparseTensor, context: Optional[Union[VarLenTensor, torch.Tensor]] = None) -> SparseTensor:
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if self._type == "self":
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qkv = self._linear(self.to_qkv, x)
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qkv = self._fused_pre(qkv, num_fused=3)
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if self.qk_rms_norm or self.use_rope:
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q, k, v = qkv.unbind(dim=-3)
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if self.qk_rms_norm:
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q = self.q_rms_norm(q)
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k = self.k_rms_norm(k)
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if self.use_rope:
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q, k = self.rope(q, k)
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qkv = qkv.replace(torch.stack([q.feats, k.feats, v.feats], dim=1))
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if self.attn_mode == "full":
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h = sparse_scaled_dot_product_attention(qkv)
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elif self.attn_mode == "windowed":
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h = sparse_windowed_scaled_dot_product_self_attention(
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qkv, self.window_size, shift_window=self.shift_window
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)
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elif self.attn_mode == "double_windowed":
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qkv0 = qkv.replace(qkv.feats[:, :, self.num_heads//2:])
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qkv1 = qkv.replace(qkv.feats[:, :, :self.num_heads//2])
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h0 = sparse_windowed_scaled_dot_product_self_attention(
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qkv0, self.window_size, shift_window=(0, 0, 0)
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)
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h1 = sparse_windowed_scaled_dot_product_self_attention(
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qkv1, self.window_size, shift_window=tuple([self.window_size//2] * 3)
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)
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h = qkv.replace(torch.cat([h0.feats, h1.feats], dim=1))
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else:
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q = self._linear(self.to_q, x)
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q = self._reshape_chs(q, (self.num_heads, -1))
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kv = self._linear(self.to_kv, context)
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kv = self._fused_pre(kv, num_fused=2)
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if self.qk_rms_norm:
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q = self.q_rms_norm(q)
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k, v = kv.unbind(dim=-3)
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k = self.k_rms_norm(k)
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h = sparse_scaled_dot_product_attention(q, k, v)
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else:
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h = sparse_scaled_dot_product_attention(q, kv)
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h = self._reshape_chs(h, (-1,))
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h = self._linear(self.to_out, h)
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return h
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class ModulatedSparseTransformerBlock(nn.Module):
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"""
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Sparse Transformer block (MSA + FFN) with adaptive layer norm conditioning.
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"""
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def __init__(
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self,
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channels: int,
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num_heads: int,
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mlp_ratio: float = 4.0,
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attn_mode: Literal["full", "swin"] = "full",
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window_size: Optional[int] = None,
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shift_window: Optional[Tuple[int, int, int]] = None,
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use_checkpoint: bool = False,
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use_rope: bool = False,
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rope_freq: Tuple[float, float] = (1.0, 10000.0),
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qk_rms_norm: bool = False,
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qkv_bias: bool = True,
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share_mod: bool = False,
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):
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super().__init__()
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self.use_checkpoint = use_checkpoint
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self.share_mod = share_mod
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self.norm1 = LayerNorm32(channels, elementwise_affine=False, eps=1e-6)
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self.norm2 = LayerNorm32(channels, elementwise_affine=False, eps=1e-6)
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self.attn = SparseMultiHeadAttention(
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channels,
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num_heads=num_heads,
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attn_mode=attn_mode,
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window_size=window_size,
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shift_window=shift_window,
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qkv_bias=qkv_bias,
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use_rope=use_rope,
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rope_freq=rope_freq,
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qk_rms_norm=qk_rms_norm,
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)
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self.mlp = SparseFeedForwardNet(
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channels,
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mlp_ratio=mlp_ratio,
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)
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if not share_mod:
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self.adaLN_modulation = nn.Sequential(
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nn.SiLU(),
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nn.Linear(channels, 6 * channels, bias=True)
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)
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else:
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self.modulation = nn.Parameter(torch.randn(6 * channels) / channels ** 0.5)
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def _forward(self, x: SparseTensor, mod: torch.Tensor) -> SparseTensor:
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if self.share_mod:
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shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (self.modulation + mod).type(mod.dtype).chunk(6, dim=1)
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else:
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shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(mod).chunk(6, dim=1)
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h = x.replace(self.norm1(x.feats))
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h = h * (1 + scale_msa) + shift_msa
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h = self.attn(h)
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h = h * gate_msa
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x = x + h
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h = x.replace(self.norm2(x.feats))
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h = h * (1 + scale_mlp) + shift_mlp
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h = self.mlp(h)
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h = h * gate_mlp
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x = x + h
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return x
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def forward(self, x: SparseTensor, mod: torch.Tensor) -> SparseTensor:
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if self.use_checkpoint:
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return torch.utils.checkpoint.checkpoint(self._forward, x, mod, use_reentrant=False)
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else:
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return self._forward(x, mod)
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class ModulatedSparseTransformerCrossBlock(nn.Module):
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"""
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Sparse Transformer cross-attention block (MSA + MCA + FFN) with adaptive layer norm conditioning.
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"""
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def __init__(
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self,
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channels: int,
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ctx_channels: int,
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num_heads: int,
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mlp_ratio: float = 4.0,
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attn_mode: Literal["full", "swin"] = "full",
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window_size: Optional[int] = None,
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shift_window: Optional[Tuple[int, int, int]] = None,
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use_checkpoint: bool = False,
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use_rope: bool = False,
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rope_freq: Tuple[float, float] = (1.0, 10000.0),
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qk_rms_norm: bool = False,
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qk_rms_norm_cross: bool = False,
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qkv_bias: bool = True,
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share_mod: bool = False,
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):
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super().__init__()
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self.use_checkpoint = use_checkpoint
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self.share_mod = share_mod
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self.norm1 = LayerNorm32(channels, elementwise_affine=False, eps=1e-6)
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self.norm2 = LayerNorm32(channels, elementwise_affine=True, eps=1e-6)
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self.norm3 = LayerNorm32(channels, elementwise_affine=False, eps=1e-6)
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self.self_attn = SparseMultiHeadAttention(
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channels,
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num_heads=num_heads,
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type="self",
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attn_mode=attn_mode,
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window_size=window_size,
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shift_window=shift_window,
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qkv_bias=qkv_bias,
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use_rope=use_rope,
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rope_freq=rope_freq,
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qk_rms_norm=qk_rms_norm,
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)
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self.cross_attn = SparseMultiHeadAttention(
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channels,
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ctx_channels=ctx_channels,
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num_heads=num_heads,
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type="cross",
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attn_mode="full",
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qkv_bias=qkv_bias,
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qk_rms_norm=qk_rms_norm_cross,
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)
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self.mlp = SparseFeedForwardNet(
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channels,
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mlp_ratio=mlp_ratio,
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)
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if not share_mod:
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self.adaLN_modulation = nn.Sequential(
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nn.SiLU(),
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nn.Linear(channels, 6 * channels, bias=True)
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)
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else:
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self.modulation = nn.Parameter(torch.randn(6 * channels) / channels ** 0.5)
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def _forward(self, x: SparseTensor, mod: torch.Tensor, context: Union[torch.Tensor, VarLenTensor]) -> SparseTensor:
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if self.share_mod:
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shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (self.modulation + mod).type(mod.dtype).chunk(6, dim=1)
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else:
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shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(mod).chunk(6, dim=1)
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h = x.replace(self.norm1(x.feats))
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h = h * (1 + scale_msa) + shift_msa
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h = self.self_attn(h)
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h = h * gate_msa
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x = x + h
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h = x.replace(self.norm2(x.feats))
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h = self.cross_attn(h, context)
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x = x + h
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h = x.replace(self.norm3(x.feats))
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h = h * (1 + scale_mlp) + shift_mlp
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h = self.mlp(h)
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h = h * gate_mlp
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x = x + h
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return x
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def forward(self, x: SparseTensor, mod: torch.Tensor, context: Union[torch.Tensor, VarLenTensor]) -> SparseTensor:
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return self._forward(x, mod, context)
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class SLatFlowModel(nn.Module):
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def __init__(
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self,
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resolution: int,
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in_channels: int,
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model_channels: int,
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cond_channels: int,
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out_channels: int,
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num_blocks: int,
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num_heads: Optional[int] = None,
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num_head_channels: Optional[int] = 64,
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mlp_ratio: float = 4,
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pe_mode: Literal["ape", "rope"] = "rope",
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rope_freq: Tuple[float, float] = (1.0, 10000.0),
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use_checkpoint: bool = False,
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share_mod: bool = False,
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initialization: str = 'vanilla',
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qk_rms_norm: bool = False,
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qk_rms_norm_cross: bool = False,
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dtype = None,
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device = None,
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operations = None,
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):
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super().__init__()
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self.resolution = resolution
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self.in_channels = in_channels
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self.model_channels = model_channels
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self.cond_channels = cond_channels
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self.out_channels = out_channels
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self.num_blocks = num_blocks
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self.num_heads = num_heads or model_channels // num_head_channels
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self.mlp_ratio = mlp_ratio
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self.pe_mode = pe_mode
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self.use_checkpoint = use_checkpoint
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self.share_mod = share_mod
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self.initialization = initialization
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self.qk_rms_norm = qk_rms_norm
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self.qk_rms_norm_cross = qk_rms_norm_cross
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self.dtype = dtype
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self.t_embedder = TimestepEmbedder(model_channels)
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if share_mod:
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self.adaLN_modulation = nn.Sequential(
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nn.SiLU(),
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nn.Linear(model_channels, 6 * model_channels, bias=True)
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)
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self.input_layer = SparseLinear(in_channels, model_channels)
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self.blocks = nn.ModuleList([
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ModulatedSparseTransformerCrossBlock(
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model_channels,
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cond_channels,
|
|
num_heads=self.num_heads,
|
|
mlp_ratio=self.mlp_ratio,
|
|
attn_mode='full',
|
|
use_checkpoint=self.use_checkpoint,
|
|
use_rope=(pe_mode == "rope"),
|
|
rope_freq=rope_freq,
|
|
share_mod=self.share_mod,
|
|
qk_rms_norm=self.qk_rms_norm,
|
|
qk_rms_norm_cross=self.qk_rms_norm_cross,
|
|
)
|
|
for _ in range(num_blocks)
|
|
])
|
|
|
|
self.out_layer = SparseLinear(model_channels, out_channels)
|
|
|
|
@property
|
|
def device(self) -> torch.device:
|
|
return next(self.parameters()).device
|
|
|
|
def forward(
|
|
self,
|
|
x: SparseTensor,
|
|
t: torch.Tensor,
|
|
cond: Union[torch.Tensor, List[torch.Tensor]],
|
|
concat_cond: Optional[SparseTensor] = None,
|
|
**kwargs
|
|
) -> SparseTensor:
|
|
if concat_cond is not None:
|
|
x = sparse_cat([x, concat_cond], dim=-1)
|
|
if isinstance(cond, list):
|
|
cond = VarLenTensor.from_tensor_list(cond)
|
|
|
|
h = self.input_layer(x)
|
|
h = manual_cast(h, self.dtype)
|
|
t_emb = self.t_embedder(t)
|
|
if self.share_mod:
|
|
t_emb = self.adaLN_modulation(t_emb)
|
|
t_emb = manual_cast(t_emb, self.dtype)
|
|
cond = manual_cast(cond, self.dtype)
|
|
|
|
if self.pe_mode == "ape":
|
|
pe = self.pos_embedder(h.coords[:, 1:])
|
|
h = h + manual_cast(pe, self.dtype)
|
|
for block in self.blocks:
|
|
h = block(h, t_emb, cond)
|
|
|
|
h = manual_cast(h, x.dtype)
|
|
h = h.replace(F.layer_norm(h.feats, h.feats.shape[-1:]))
|
|
h = self.out_layer(h)
|
|
return h
|
|
|
|
class Trellis2(nn.Module):
|
|
def __init__(self, resolution,
|
|
in_channels = 32,
|
|
out_channels = 32,
|
|
model_channels = 1536,
|
|
cond_channels = 1024,
|
|
num_blocks = 30,
|
|
num_heads = 12,
|
|
mlp_ratio = 5.3334,
|
|
share_mod = True,
|
|
qk_rms_norm = True,
|
|
qk_rms_norm_cross = True,
|
|
dtype=None, device=None, operations=None):
|
|
args = {
|
|
"out_channels":out_channels, "num_blocks":num_blocks, "cond_channels" :cond_channels,
|
|
"model_channels":model_channels, "num_heads":num_heads, "mlp_ratio": mlp_ratio, "share_mod": share_mod,
|
|
"qk_rms_norm": qk_rms_norm, "qk_rms_norm_cross": qk_rms_norm_cross, "device": device, "dtype": dtype, "operations": operations
|
|
}
|
|
# TODO: update the names/checkpoints
|
|
self.img2shape = SLatFlowModel(resolution, in_channels=in_channels, *args)
|
|
self.shape2txt = SLatFlowModel(resolution, in_channels=in_channels*2, *args)
|
|
self.shape_generation = True
|
|
|
|
def forward(self, x, timestep, context):
|
|
pass
|