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c0bba28 | 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 | import torch
import torch.nn as nn
import torch.distributed as dist
import os
from distvae.utils import DistributedEnv
class Patchify(nn.Module):
def __init__(self):
super().__init__()
self.group_world_size = DistributedEnv.get_group_world_size()
self.rank_in_vae_group = DistributedEnv.get_rank_in_vae_group()
def forward(self, hidden_state):
height = hidden_state.shape[2]
start_idx = (height + self.group_world_size - 1) // self.group_world_size * self.rank_in_vae_group
end_idx = min((height + self.group_world_size - 1) // self.group_world_size * (self.rank_in_vae_group + 1), height)
return hidden_state[:, :, start_idx: end_idx, :].clone()
class DePatchify(nn.Module):
def __init__(self):
super().__init__()
self.group_world_size = DistributedEnv.get_group_world_size()
self.rank_in_vae_group = DistributedEnv.get_rank_in_vae_group()
self.local_rank = DistributedEnv.get_local_rank()
def forward(self, patch_hidden_state):
patch_height_list = [torch.empty([1], dtype=torch.int64, device=f"cuda:{self.local_rank}") for _ in range(self.group_world_size)]
dist.all_gather(
patch_height_list,
torch.tensor(
[patch_hidden_state.shape[2]],
dtype=torch.int64,
device=f"cuda:{self.local_rank}"
),
group=DistributedEnv.get_vae_group()
)
patch_hidden_state_list = [
torch.empty(
[patch_hidden_state.shape[0], patch_hidden_state.shape[1], patch_height_list[i].item(), patch_hidden_state.shape[-1]],
dtype=patch_hidden_state.dtype,
device=f"cuda:{self.local_rank}"
) for i in range(self.group_world_size)
]
dist.all_gather(
patch_hidden_state_list,
patch_hidden_state.contiguous(),
group=DistributedEnv.get_vae_group()
)
return torch.cat(patch_hidden_state_list, dim=2)
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