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import torch
from typing import Optional
from einops import rearrange
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
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
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)
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), :, :]
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)
def create_custom_forward(module):
def custom_forward(*inputs):
return module(*inputs)
return custom_forward
# 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 and use_gradient_checkpointing:
if use_gradient_checkpointing_offload:
with torch.autograd.graph.save_on_cpu():
x = torch.utils.checkpoint.checkpoint(
create_custom_forward(block),
x, context, t_mod, freqs,
use_reentrant=False,
)
else:
x = torch.utils.checkpoint.checkpoint(
create_custom_forward(block),
x, context, t_mod, freqs,
use_reentrant=False,
)
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_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)
x = xFuserLongContextAttention()(
None,
query=q,
key=k,
value=v,
)
x = x.flatten(2)
del q, k, v
torch.cuda.empty_cache()
return self.o(x) |