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bc8c4af | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 | import torch
from typing import Optional
from einops import rearrange
from yunchang.kernels import AttnType
from xfuser.core.distributed import (get_sequence_parallel_rank,
get_sequence_parallel_world_size,
get_sp_group)
from xfuser.core.long_ctx_attention import xFuserLongContextAttention
from ... import IS_NPU_AVAILABLE
from ...core.device import parse_nccl_backend, parse_device_type
from ...core.gradient import gradient_checkpoint_forward
def initialize_usp(device_type):
import torch.distributed as dist
from xfuser.core.distributed import initialize_model_parallel, init_distributed_environment
dist.init_process_group(backend=parse_nccl_backend(device_type), init_method="env://")
init_distributed_environment(rank=dist.get_rank(), world_size=dist.get_world_size())
initialize_model_parallel(
sequence_parallel_degree=dist.get_world_size(),
ring_degree=1,
ulysses_degree=dist.get_world_size(),
)
getattr(torch, device_type).set_device(dist.get_rank())
def sinusoidal_embedding_1d(dim, position):
sinusoid = torch.outer(position.type(torch.float64), torch.pow(
10000, -torch.arange(dim//2, dtype=torch.float64, device=position.device).div(dim//2)))
x = torch.cat([torch.cos(sinusoid), torch.sin(sinusoid)], dim=1)
return x.to(position.dtype)
def pad_freqs(original_tensor, target_len):
seq_len, s1, s2 = original_tensor.shape
pad_size = target_len - seq_len
original_tensor_device = original_tensor.device
if original_tensor.device == "npu":
original_tensor = original_tensor.cpu()
padding_tensor = torch.ones(
pad_size,
s1,
s2,
dtype=original_tensor.dtype,
device=original_tensor.device)
padded_tensor = torch.cat([original_tensor, padding_tensor], dim=0).to(device=original_tensor_device)
return padded_tensor
def rope_apply(x, freqs, num_heads):
x = rearrange(x, "b s (n d) -> b s n d", n=num_heads)
s_per_rank = x.shape[1]
x_out = torch.view_as_complex(x.to(torch.float64).reshape(
x.shape[0], x.shape[1], x.shape[2], -1, 2))
sp_size = get_sequence_parallel_world_size()
sp_rank = get_sequence_parallel_rank()
freqs = pad_freqs(freqs, s_per_rank * sp_size)
freqs_rank = freqs[(sp_rank * s_per_rank):((sp_rank + 1) * s_per_rank), :, :]
freqs_rank = freqs_rank.to(torch.complex64) if freqs_rank.device.type == "npu" else freqs_rank
x_out = torch.view_as_real(x_out * freqs_rank).flatten(2)
return x_out.to(x.dtype)
def usp_dit_forward(self,
x: torch.Tensor,
timestep: torch.Tensor,
context: torch.Tensor,
clip_feature: Optional[torch.Tensor] = None,
y: Optional[torch.Tensor] = None,
use_gradient_checkpointing: bool = False,
use_gradient_checkpointing_offload: bool = False,
**kwargs,
):
t = self.time_embedding(
sinusoidal_embedding_1d(self.freq_dim, timestep))
t_mod = self.time_projection(t).unflatten(1, (6, self.dim))
context = self.text_embedding(context)
if self.has_image_input:
x = torch.cat([x, y], dim=1) # (b, c_x + c_y, f, h, w)
clip_embdding = self.img_emb(clip_feature)
context = torch.cat([clip_embdding, context], dim=1)
x, (f, h, w) = self.patchify(x)
freqs = torch.cat([
self.freqs[0][:f].view(f, 1, 1, -1).expand(f, h, w, -1),
self.freqs[1][:h].view(1, h, 1, -1).expand(f, h, w, -1),
self.freqs[2][:w].view(1, 1, w, -1).expand(f, h, w, -1)
], dim=-1).reshape(f * h * w, 1, -1).to(x.device)
# Context Parallel
chunks = torch.chunk(x, get_sequence_parallel_world_size(), dim=1)
pad_shape = chunks[0].shape[1] - chunks[-1].shape[1]
chunks = [torch.nn.functional.pad(chunk, (0, 0, 0, chunks[0].shape[1]-chunk.shape[1]), value=0) for chunk in chunks]
x = chunks[get_sequence_parallel_rank()]
for block in self.blocks:
if self.training:
x = gradient_checkpoint_forward(
block,
use_gradient_checkpointing,
use_gradient_checkpointing_offload,
x, context, t_mod, freqs
)
else:
x = block(x, context, t_mod, freqs)
x = self.head(x, t)
# Context Parallel
x = get_sp_group().all_gather(x, dim=1)
x = x[:, :-pad_shape] if pad_shape > 0 else x
# unpatchify
x = self.unpatchify(x, (f, h, w))
return x
def usp_vace_forward(
self, x, vace_context, context, t_mod, freqs,
use_gradient_checkpointing: bool = False,
use_gradient_checkpointing_offload: bool = False,
):
# Compute full sequence length from the sharded x
full_seq_len = x.shape[1] * get_sequence_parallel_world_size()
# Embed vace_context via patch embedding
c = [self.vace_patch_embedding(u.unsqueeze(0)) for u in vace_context]
c = [u.flatten(2).transpose(1, 2) for u in c]
c = torch.cat([
torch.cat([u, u.new_zeros(1, full_seq_len - u.size(1), u.size(2))],
dim=1) for u in c
])
# Chunk VACE context along sequence dim BEFORE processing through blocks
c = torch.chunk(c, get_sequence_parallel_world_size(), dim=1)[get_sequence_parallel_rank()]
# Process through vace_blocks (self_attn already monkey-patched to usp_attn_forward)
for block in self.vace_blocks:
c = gradient_checkpoint_forward(
block,
use_gradient_checkpointing,
use_gradient_checkpointing_offload,
c, x, context, t_mod, freqs
)
# Hints are already sharded per-rank
hints = torch.unbind(c)[:-1]
return hints
def usp_attn_forward(self, x, freqs):
q = self.norm_q(self.q(x))
k = self.norm_k(self.k(x))
v = self.v(x)
q = rope_apply(q, freqs, self.num_heads)
k = rope_apply(k, freqs, self.num_heads)
q = rearrange(q, "b s (n d) -> b s n d", n=self.num_heads)
k = rearrange(k, "b s (n d) -> b s n d", n=self.num_heads)
v = rearrange(v, "b s (n d) -> b s n d", n=self.num_heads)
attn_type = AttnType.FA
ring_impl_type = "basic"
if IS_NPU_AVAILABLE:
attn_type = AttnType.NPU
ring_impl_type = "basic_npu"
x = xFuserLongContextAttention(attn_type=attn_type, ring_impl_type=ring_impl_type)(
None,
query=q,
key=k,
value=v,
)
x = x.flatten(2)
del q, k, v
getattr(torch, parse_device_type(x.device)).empty_cache()
return self.o(x)
def get_current_chunk(x, dim=1):
chunks = torch.chunk(x, get_sequence_parallel_world_size(), dim=dim)
ndims = len(chunks[0].shape)
pad_list = [0] * (2 * ndims)
pad_end_index = 2 * (ndims - 1 - dim) + 1
max_size = chunks[0].size(dim)
chunks = [
torch.nn.functional.pad(
chunk,
tuple(pad_list[:pad_end_index] + [max_size - chunk.size(dim)] + pad_list[pad_end_index+1:]),
value=0
)
for chunk in chunks
]
x = chunks[get_sequence_parallel_rank()]
return x
def gather_all_chunks(x, seq_len=None, dim=1):
x = get_sp_group().all_gather(x, dim=dim)
if seq_len is not None:
slices = [slice(None)] * x.ndim
slices[dim] = slice(0, seq_len)
x = x[tuple(slices)]
return x
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