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import math
from typing import Final, List, Optional, Tuple, Union
from einops import rearrange
from timm.models import register_model
import torch
from torch import Type, nn
from torch.nn import functional as F
from torch.nn.init import xavier_normal_, xavier_uniform_, zeros_
from .forward_intermediates import forward_intermediates
def _get_init_scale(num_encoder_layers: int, num_decoder_layers: int, is_encoder: bool):
if num_encoder_layers > 0 and num_decoder_layers == 0:
return math.sqrt(math.log(2 * num_encoder_layers))
if num_decoder_layers > 0 and num_encoder_layers == 0:
return math.sqrt(math.log(2 * num_decoder_layers))
if is_encoder:
# Both encoders and decoders
return math.sqrt(
0.33 * math.log(3 * num_decoder_layers) * math.log(2 * num_encoder_layers)
)
return math.sqrt(math.log(3 * num_decoder_layers))
# [1,2] [1,1,2,2]
# [3,4] -> [3,3,4,4]
# [5,6] [5,5,6,6]
def duplicate_interleave(m):
return m.view(-1, 1).repeat(1, 2).view(m.shape[0], -1)
# 0,1,2,3,4,5,6,7 -> -1,0,-3,2,-5,4,-7,6
def rotate_every_two(x):
x1 = x[:, :, ::2]
x2 = x[:, :, 1::2]
x = torch.stack((-x2, x1), dim=-1)
return x.flatten(-2) # in einsum notation: rearrange(x, '... d j -> ... (d j)')\
class XPosEmbedding2D(torch.nn.Module):
"""Implementation of xPos based on RotaryEmbedding from GPT-NeoX.
This implementation is designed to operate on queries and keys that are compatible with
[batch_size, n_heads_per_partition, seq_len, head_dim] (e.g. MinGPTAttention format).
"""
def __init__(
self,
head_dim: int,
base=50000,
scale_base=512
):
super().__init__()
half_dim = head_dim // 2
self.half_dim = half_dim
inv_freq = 1.0 / (base ** (torch.arange(0, half_dim, 2).float() / half_dim))
self.register_buffer("inv_freq", inv_freq, persistent=False)
self.head_dim = head_dim
self.token_shape_cached = None
self.batch_size_cached = None
self.cos_cached: torch.Tensor | None = None
self.sin_cached: torch.Tensor | None = None
self.scale_cached: torch.Tensor | None = None
self.scale_base = scale_base
self.register_buffer("scale",
(torch.arange(0, half_dim, 2) + 0.4 * half_dim) / (1.4 * half_dim))
def cos_sin(
self,
token_shape: Tuple[int, int],
device="cuda",
dtype=torch.bfloat16,
) -> torch.Tensor:
if token_shape != self.token_shape_cached:
self.token_shape_cached = token_shape
y = torch.arange(token_shape[0], device=device, dtype=self.inv_freq.dtype)
x = torch.arange(token_shape[1], device=device, dtype=self.inv_freq.dtype)
x, y = torch.meshgrid(x, y, indexing='xy')
y_freqs = torch.einsum("i,j->ij", y.flatten(), self.inv_freq)
x_freqs = torch.einsum("i,j->ij", x.flatten(), self.inv_freq)
y_scales = self.scale ** y.flatten().div(self.scale_base)[:, None]
x_scales = self.scale ** x.flatten().div(self.scale_base)[:, None]
freqs = torch.cat([y_freqs, x_freqs], dim=-1)
emb = torch.repeat_interleave(freqs, repeats=2, dim=-1)
scales = torch.cat([y_scales, x_scales], dim=-1)
scales = torch.repeat_interleave(scales, repeats=2, dim=-1)
if dtype in [torch.float16, torch.bfloat16]:
emb = emb.float()
self.cos_cached = emb.cos()[None, :, :]
self.sin_cached = emb.sin()[None, :, :]
self.scale_cached = scales[None, :, :]
self.cos_cached = self.cos_cached.type(dtype)
self.sin_cached = self.sin_cached.type(dtype)
self.scale_cached = self.scale_cached.type(dtype)
return self.cos_cached, self.sin_cached, self.scale_cached
def forward(self, q: torch.Tensor, k: torch.Tensor, token_shape: Tuple[int, int]):
batch, seq_len, head_dim = q.shape
cos, sin, scale = self.cos_sin(token_shape, q.device, q.dtype)
# scale = self.scale**torch.arange(seq_len).to(self.scale).div(self.scale_base)[:, None]
# scale = torch.repeat_interleave(scale, 2, dim=-1).to(q.device)
# scale = torch.cat([scale, scale], dim=-1)
# scale = 1
return (
(q * cos * scale) + (rotate_every_two(q) * sin * scale),
(k * cos * (1 / scale)) + (rotate_every_two(k) * sin * (1 / scale)),
)
class MagnetoAttention(nn.Module):
def __init__(self, d_model: int, n_head: int, pos_emb: XPosEmbedding2D):
super().__init__()
self.num_heads = n_head
self.head_dim = d_model // n_head
self.scale = self.head_dim ** -0.5
self.qkv = nn.Linear(d_model, d_model * 3, bias=False)
self.proj = nn.Linear(d_model, d_model)
self.pos_emb = pos_emb
self.norm0 = nn.LayerNorm(d_model)
self.norm1 = nn.LayerNorm(d_model)
def forward(self, x: torch.Tensor, num_prefix_tokens: int, patch_shape: Tuple[int, int]) -> torch.Tensor:
B, N, C = x.shape
x = self.norm0(x)
qkv = self.qkv(x).reshape(B, N, 3, C).permute(2, 0, 1, 3)
q, k, v = qkv.unbind(0)
q_pref = q[:, :num_prefix_tokens]
q_patch = q[:, num_prefix_tokens:]
k_pref = k[:, :num_prefix_tokens]
k_patch = k[:, num_prefix_tokens:]
q_patch, k_patch = self.pos_emb(q_patch, k_patch, patch_shape)
q = torch.cat([q_pref, q_patch], dim=1)
k = torch.cat([k_pref, k_patch], dim=1)
def head_reshape(t: torch.Tensor):
return t.reshape(B, N, self.num_heads, self.head_dim).permute(0, 2, 1, 3)
q = head_reshape(q)
k = head_reshape(k)
v = head_reshape(v)
x = F.scaled_dot_product_attention(q, k, v)
x = x.transpose(1, 2).reshape(B, N, C)
x = self.norm1(x)
x = self.proj(x)
return x
def _reset_parameters(self):
xavier_uniform_(self.qkv.weight)
if self.qkv.bias is not None:
zeros_(self.qkv.bias)
xavier_normal_(self.proj.weight)
zeros_(self.proj.bias)
class MagnetoTransformerEncoderLayer(nn.Module):
def __init__(self, d_model: int, nhead: int, pos_emb: XPosEmbedding2D,
num_encoder_layers: int, num_decoder_layers: int = 0,
dim_mhsa: int = 0,
dim_feedforward: int = 2048,
layer_norm_eps: float = 1e-5,
batch_first: bool = True):
super().__init__()
if dim_mhsa == 0:
dim_mhsa = d_model
self._num_encoder_layers = num_encoder_layers
self._num_decoder_layers = num_decoder_layers
self.attn = MagnetoAttention(d_model, nhead, pos_emb)
self.norm2 = nn.LayerNorm(d_model, eps=layer_norm_eps)
self.linear2 = nn.Linear(d_model, dim_feedforward)
self.norm3 = nn.LayerNorm(dim_feedforward, eps=layer_norm_eps)
self.linear3 = nn.Linear(dim_feedforward, d_model)
def initialize(self):
gamma = _get_init_scale(self._num_encoder_layers, self._num_decoder_layers, is_encoder=True)
# Magneto Initialization
for mod in self.children():
if isinstance(mod, nn.Linear):
xavier_normal_(mod.weight.data, gamma)
elif isinstance(mod, MagnetoAttention):
mod._reset_parameters()
def forward(self, x: torch.Tensor, num_prefix_tokens: int, patch_shape: Tuple[int, int]) -> torch.Tensor:
x = x + self._sa_block(x, num_prefix_tokens, patch_shape)
x = x + self._ff_block(x)
return x
def _sa_block(self, x: torch.Tensor, num_prefix_tokens: int, patch_shape: Tuple[int, int]) -> torch.Tensor:
x = self.attn(x, num_prefix_tokens, patch_shape)
return x
def _ff_block(self, x: torch.Tensor) -> torch.Tensor:
x = self.norm2(x)
x = self.linear2(x)
x = F.gelu(x)
x = self.norm3(x)
x = self.linear3(x)
return x
class VisionTransformer(nn.Module):
""" Vision Transformer
A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale`
- https://arxiv.org/abs/2010.11929
"""
dynamic_img_size: Final[bool]
def __init__(
self,
patch_size: Union[int, Tuple[int, int]] = 16,
in_chans: int = 3,
embed_dim: int = 768,
depth: int = 12,
num_heads: int = 12,
mlp_ratio: float = 4.,
num_cls_tokens: int = 1,
num_reg_tokens: int = 0,
) -> None:
"""
Args:
patch_size: Patch size.
in_chans: Number of image input channels.
embed_dim: Transformer embedding dimension.
depth: Depth of transformer.
num_heads: Number of attention heads.
mlp_ratio: Ratio of mlp hidden dim to embedding dim.
num_cls_tokens: Number of cls tokens
num_reg_tokens: Number of register tokens.
block_fn: Transformer block layer.
"""
super().__init__()
self.patch_size = patch_size
self.embed_dim = embed_dim
self.num_cls_tokens = num_cls_tokens
self.num_reg_tokens = num_reg_tokens
self.patch_embed = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
self.prefix_buffer = nn.Parameter(torch.randn(1, self.num_prefix_tokens, embed_dim) * .02)
pos_emb = XPosEmbedding2D(embed_dim)
self.blocks = nn.ModuleList([
MagnetoTransformerEncoderLayer(
d_model=embed_dim,
nhead=num_heads,
num_encoder_layers=depth,
num_decoder_layers=0,
dim_feedforward=int(embed_dim * mlp_ratio),
pos_emb=pos_emb,
)
for _ in range(depth)
])
for block in self.blocks:
block.initialize()
@property
def num_prefix_tokens(self):
return self.num_cls_tokens + self.num_reg_tokens
@property
def num_summary_tokens(self):
return self.num_prefix_tokens
def forward_features(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
x, patch_shape = self._patchify(x)
for block in self.blocks:
x = block(x, self.num_prefix_tokens, patch_shape)
summary = x[:, :self.num_cls_tokens]
features = x[:, self.num_prefix_tokens:]
return summary, features
def forward_intermediates(self, x: torch.Tensor, norm: bool = False, **kwargs):
patch_shape = tuple(d // self.patch_size for d in x.shape[-2:])
def patch_extractor(x: torch.Tensor):
x, _ = self._patchify(x)
return x
return forward_intermediates(
self,
patch_extractor=patch_extractor,
num_summary_tokens=self.num_prefix_tokens,
num_cls_tokens=self.num_cls_tokens,
norm=lambda y: y,
x=x,
block_kwargs=dict(num_prefix_tokens=self.num_prefix_tokens, patch_shape=patch_shape),
**kwargs,
)
def _patchify(self, x: torch.Tensor):
x = self.patch_embed(x)
patch_shape = x.shape[-2:]
x = rearrange(x, 'b c h w -> b (h w) c')
prefix = self.prefix_buffer.expand(x.shape[0], -1, -1)
x = torch.cat([prefix, x], dim=1)
return x, patch_shape
@register_model
def vit_base_patch16_xpos(num_cls_tokens: int = 1, num_reg_tokens: int = 0, **kwargs) -> VisionTransformer:
return VisionTransformer(patch_size=16, embed_dim=768, depth=12, num_heads=12,
num_cls_tokens=num_cls_tokens, num_reg_tokens=num_reg_tokens)
@register_model
def vit_large_patch16_xpos(num_cls_tokens: int = 1, num_reg_tokens: int = 0, **kwargs) -> VisionTransformer:
return VisionTransformer(patch_size=16, embed_dim=1024, depth=24, num_heads=16,
num_cls_tokens=num_cls_tokens, num_reg_tokens=num_reg_tokens)
@register_model
def vit_huge_patch16_xpos(num_cls_tokens: int = 1, num_reg_tokens: int = 0, **kwargs) -> VisionTransformer:
return VisionTransformer(patch_size=16, embed_dim=1280, depth=32, num_heads=16,
num_cls_tokens=num_cls_tokens, num_reg_tokens=num_reg_tokens)
@register_model
def vit_giant_patch16_xpos(num_cls_tokens: int = 1, num_reg_tokens: int = 0, **kwargs) -> VisionTransformer:
return VisionTransformer(patch_size=16, embed_dim=1408, depth=40, num_heads=16,
num_cls_tokens=num_cls_tokens, num_reg_tokens=num_reg_tokens)
@register_model
def vit_bigG_patch16_xpos(num_cls_tokens: int = 1, num_reg_tokens: int = 0, **kwargs) -> VisionTransformer:
return VisionTransformer(patch_size=16, embed_dim=1664, depth=48, num_heads=16,
num_cls_tokens=num_cls_tokens, num_reg_tokens=num_reg_tokens) |