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Running
on
Zero
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# References:
# https://github.com/facebookresearch/dino/blob/master/vision_transformer.py
# https://github.com/rwightman/pytorch-image-models/tree/master/timm/models/vision_transformer.py
import logging
import os
import warnings
from torch import Tensor
from torch import nn
import torch.nn.functional as F
import torch
# from torch.nn.attention import SDPBackend
XFORMERS_AVAILABLE = False
class Attention(nn.Module):
def __init__(
self,
dim: int,
num_heads: int = 8,
qkv_bias: bool = True,
proj_bias: bool = True,
attn_drop: float = 0.0,
proj_drop: float = 0.0,
norm_layer: nn.Module = nn.LayerNorm,
qk_norm: bool = False,
fused_attn: bool = True, # use F.scaled_dot_product_attention or not
rope=None,
) -> None:
super().__init__()
assert dim % num_heads == 0, "dim should be divisible by num_heads"
self.num_heads = num_heads
self.head_dim = dim // num_heads
self.scale = self.head_dim**-0.5
self.fused_attn = fused_attn
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
self.q_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
self.k_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim, bias=proj_bias)
self.proj_drop = nn.Dropout(proj_drop)
self.rope = rope
def forward(self, x: Tensor, pos=None) -> Tensor:
B, N, C = x.shape
qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)
q, k, v = qkv.unbind(0)
q, k = self.q_norm(q), self.k_norm(k)
if self.rope is not None:
q = self.rope(q, pos)
k = self.rope(k, pos)
# orig_dtype = q.dtype
x = F.scaled_dot_product_attention(q, k, v, dropout_p=self.attn_drop.p if self.training else 0.0)
# with torch.cuda.amp.autocast(dtype=torch.bfloat16):
# with nn.attention.sdpa_kernel(SDPBackend.FLASH_ATTENTION):
# if x.dtype != orig_dtype:
# x = x.to(orig_dtype)
x = x.transpose(1, 2).reshape(B, N, C)
x = self.proj(x)
x = self.proj_drop(x)
return x
class MemEffAttention(Attention):
def forward(self, x: Tensor, attn_bias=None, pos=None) -> Tensor:
assert pos is None
if not XFORMERS_AVAILABLE:
if attn_bias is not None:
raise AssertionError("xFormers is required for using nested tensors")
return super().forward(x)
B, N, C = x.shape
qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads)
q, k, v = unbind(qkv, 2)
x = memory_efficient_attention(q, k, v, attn_bias=attn_bias)
x = x.reshape([B, N, C])
x = self.proj(x)
x = self.proj_drop(x)
return x
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