from math import pi
import torch
from torch import nn
from einops import rearrange, repeat
import logging
def broadcat(tensors, dim = -1):
num_tensors = len(tensors)
shape_lens = set(list(map(lambda t: len(t.shape), tensors)))
assert len(shape_lens) == 1, 'tensors must all have the same number of dimensions'
shape_len = list(shape_lens)[0]
dim = (dim + shape_len) if dim < 0 else dim
dims = list(zip(*map(lambda t: list(t.shape), tensors)))
expandable_dims = [(i, val) for i, val in enumerate(dims) if i != dim]
assert all([*map(lambda t: len(set(t[1])) <= 2, expandable_dims)]), 'invalid dimensions for broadcastable concatentation'
max_dims = list(map(lambda t: (t[0], max(t[1])), expandable_dims))
expanded_dims = list(map(lambda t: (t[0], (t[1],) * num_tensors), max_dims))
expanded_dims.insert(dim, (dim, dims[dim]))
expandable_shapes = list(zip(*map(lambda t: t[1], expanded_dims)))
tensors = list(map(lambda t: t[0].expand(*t[1]), zip(tensors, expandable_shapes)))
return torch.cat(tensors, dim = dim)
def rotate_half(x):
x = rearrange(x, '... (d r) -> ... d r', r = 2)
x1, x2 = x.unbind(dim = -1)
x = torch.stack((-x2, x1), dim = -1)
return rearrange(x, '... d r -> ... (d r)')
class VisionRotaryEmbeddingFast(nn.Module):
def __init__(
self,
dim,
pt_seq_len,
ft_seq_len=None,
custom_freqs = None,
freqs_for = 'lang',
theta = 10000,
max_freq = 10,
num_freqs = 1,
patch_dropout = 0.
):
super().__init__()
if custom_freqs:
freqs = custom_freqs
elif freqs_for == 'lang':
freqs = 1. / (theta ** (torch.arange(0, dim, 2)[:(dim // 2)].float() / dim))
elif freqs_for == 'pixel':
freqs = torch.linspace(1., max_freq / 2, dim // 2) * pi
elif freqs_for == 'constant':
freqs = torch.ones(num_freqs).float()
else:
raise ValueError(f'unknown modality {freqs_for}')
if ft_seq_len is None: ft_seq_len = pt_seq_len
t = torch.arange(ft_seq_len) / ft_seq_len * pt_seq_len
freqs = torch.einsum('..., f -> ... f', t, freqs)
freqs = repeat(freqs, '... n -> ... (n r)', r = 2)
freqs = broadcat((freqs[:, None, :], freqs[None, :, :]), dim = -1)
freqs_cos = freqs.cos().view(-1, freqs.shape[-1])
freqs_sin = freqs.sin().view(-1, freqs.shape[-1])
self.patch_dropout = patch_dropout
self.register_buffer("freqs_cos", freqs_cos)
self.register_buffer("freqs_sin", freqs_sin)
logging.info(f'Shape of rope freq: {self.freqs_cos.shape}')
def forward(self, t, patch_indices_keep=None):
if patch_indices_keep is not None:
batch = t.size()[0]
batch_indices = torch.arange(batch)
batch_indices = batch_indices[..., None]
freqs_cos = repeat(self.freqs_cos, 'i j -> n i m j', n=t.shape[0], m=t.shape[1])
freqs_sin = repeat(self.freqs_sin, 'i j -> n i m j', n=t.shape[0], m=t.shape[1])
freqs_cos = freqs_cos[batch_indices, patch_indices_keep]
freqs_cos = rearrange(freqs_cos, 'n i m j -> n m i j')
freqs_sin = freqs_sin[batch_indices, patch_indices_keep]
freqs_sin = rearrange(freqs_sin, 'n i m j -> n m i j')
return t * freqs_cos + rotate_half(t) * freqs_sin
return t * self.freqs_cos + rotate_half(t) * self.freqs_sin
import torch.nn as nn
import os
from dataclasses import dataclass
from typing import Optional, Tuple, Union
from functools import partial
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
# --------------------------------------------------------
# Adapted from https://github.com/microsoft/unilm/tree/master/beit
# --------------------------------------------------------
import math
import os
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
import logging
try:
from timm.models.layers import drop_path, to_2tuple, trunc_normal_
except:
from timm.layers import drop_path, to_2tuple, trunc_normal_
class PatchDropout(nn.Module):
"""
https://arxiv.org/abs/2212.00794
"""
def __init__(self, prob, exclude_first_token=True):
super().__init__()
assert 0 <= prob < 1.
self.prob = prob
self.exclude_first_token = exclude_first_token # exclude CLS token
logging.info(f"os.getenv('RoPE')={os.getenv('RoPE')}")
def forward(self, x):
if not self.training or self.prob == 0.:
return x
if self.exclude_first_token:
cls_tokens, x = x[:, :1], x[:, 1:]
else:
cls_tokens = torch.jit.annotate(torch.Tensor, x[:, :1])
batch = x.size()[0]
num_tokens = x.size()[1]
batch_indices = torch.arange(batch)
batch_indices = batch_indices[..., None]
keep_prob = 1 - self.prob
num_patches_keep = max(1, int(num_tokens * keep_prob))
rand = torch.randn(batch, num_tokens)
patch_indices_keep = rand.topk(num_patches_keep, dim=-1).indices
x = x[batch_indices, patch_indices_keep]
if self.exclude_first_token:
x = torch.cat((cls_tokens, x), dim=1)
if self.training and os.getenv('RoPE') == '1':
return x, patch_indices_keep
return x
if os.getenv('ENV_TYPE') == 'deepspeed':
try:
from deepspeed.runtime.activation_checkpointing.checkpointing import checkpoint
except:
from torch.utils.checkpoint import checkpoint
else:
from torch.utils.checkpoint import checkpoint
import xformers.ops as xops
class DropPath(nn.Module):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
"""
def __init__(self, drop_prob=None):
super(DropPath, self).__init__()
self.drop_prob = drop_prob
def forward(self, x):
return drop_path(x, self.drop_prob, self.training)
def extra_repr(self) -> str:
return 'p={}'.format(self.drop_prob)
class Mlp(nn.Module):
def __init__(
self,
in_features,
hidden_features=None,
out_features=None,
act_layer=nn.GELU,
norm_layer=nn.LayerNorm,
drop=0.,
subln=False,
):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
self.fc1 = nn.Linear(in_features, hidden_features)
self.act = act_layer()
self.ffn_ln = norm_layer(hidden_features) if subln else nn.Identity()
self.fc2 = nn.Linear(hidden_features, out_features)
self.drop = nn.Dropout(drop)
def forward(self, x):
x = self.fc1(x)
x = self.act(x)
# x = self.drop(x)
# commit this for the orignal BERT implement
x = self.ffn_ln(x)
x = self.fc2(x)
x = self.drop(x)
return x
class SwiGLU(nn.Module):
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.SiLU, drop=0.,
norm_layer=nn.LayerNorm, subln=False):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
self.w1 = nn.Linear(in_features, hidden_features)
self.w2 = nn.Linear(in_features, hidden_features)
self.act = act_layer()
self.ffn_ln = norm_layer(hidden_features) if subln else nn.Identity()
self.w3 = nn.Linear(hidden_features, out_features)
self.drop = nn.Dropout(drop)
def forward(self, x):
x1 = self.w1(x)
x2 = self.w2(x)
hidden = self.act(x1) * x2
x = self.ffn_ln(hidden)
x = self.w3(x)
x = self.drop(x)
return x
class Attention(nn.Module):
def __init__(
self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0.,
proj_drop=0., window_size=None, attn_head_dim=None, xattn=False, rope=None, subln=False, norm_layer=nn.LayerNorm):
super().__init__()
self.num_heads = num_heads
head_dim = dim // num_heads
if attn_head_dim is not None:
head_dim = attn_head_dim
all_head_dim = head_dim * self.num_heads
self.scale = qk_scale or head_dim ** -0.5
self.subln = subln
if self.subln:
self.q_proj = nn.Linear(dim, all_head_dim, bias=False)
self.k_proj = nn.Linear(dim, all_head_dim, bias=False)
self.v_proj = nn.Linear(dim, all_head_dim, bias=False)
else:
self.qkv = nn.Linear(dim, all_head_dim * 3, bias=False)
if qkv_bias:
self.q_bias = nn.Parameter(torch.zeros(all_head_dim))
self.v_bias = nn.Parameter(torch.zeros(all_head_dim))
else:
self.q_bias = None
self.v_bias = None
if window_size:
self.window_size = window_size
self.num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3
self.relative_position_bias_table = nn.Parameter(
torch.zeros(self.num_relative_distance, num_heads)) # 2*Wh-1 * 2*Ww-1, nH
# cls to token & token 2 cls & cls to cls
# get pair-wise relative position index for each token inside the window
coords_h = torch.arange(window_size[0])
coords_w = torch.arange(window_size[1])
coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww
coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww
relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww
relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
relative_coords[:, :, 0] += window_size[0] - 1 # shift to start from 0
relative_coords[:, :, 1] += window_size[1] - 1
relative_coords[:, :, 0] *= 2 * window_size[1] - 1
relative_position_index = \
torch.zeros(size=(window_size[0] * window_size[1] + 1, ) * 2, dtype=relative_coords.dtype)
relative_position_index[1:, 1:] = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
relative_position_index[0, 0:] = self.num_relative_distance - 3
relative_position_index[0:, 0] = self.num_relative_distance - 2
relative_position_index[0, 0] = self.num_relative_distance - 1
self.register_buffer("relative_position_index", relative_position_index)
else:
self.window_size = None
self.relative_position_bias_table = None
self.relative_position_index = None
self.attn_drop = nn.Dropout(attn_drop)
self.inner_attn_ln = norm_layer(all_head_dim) if subln else nn.Identity()
# self.proj = nn.Linear(all_head_dim, all_head_dim)
self.proj = nn.Linear(all_head_dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
self.xattn = xattn
self.xattn_drop = attn_drop
self.rope = rope
def forward(self, x, rel_pos_bias=None, attn_mask=None):
B, N, C = x.shape
if self.subln:
if self.q_proj.weight.dtype == torch.uint8:
import bitsandbytes as bnb
q = bnb.matmul_4bit(x, self.q_proj.weight.t(), bias=self.q_bias, quant_state=self.q_proj.weight.quant_state)
k = bnb.matmul_4bit(x, self.k_proj.weight.t(), bias=None, quant_state=self.k_proj.weight.quant_state)
v = bnb.matmul_4bit(x, self.v_proj.weight.t(), bias=self.v_bias, quant_state=self.v_proj.weight.quant_state)
else:
q = F.linear(input=x, weight=self.q_proj.weight, bias=self.q_bias)
k = F.linear(input=x, weight=self.k_proj.weight, bias=None)
v = F.linear(input=x, weight=self.v_proj.weight, bias=self.v_bias)
q = q.reshape(B, N, self.num_heads, -1).permute(0, 2, 1, 3) # B, num_heads, N, C
k = k.reshape(B, N, self.num_heads, -1).permute(0, 2, 1, 3)
v = v.reshape(B, N, self.num_heads, -1).permute(0, 2, 1, 3)
else:
qkv_bias = None
if self.q_bias is not None:
qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias))
qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias)
qkv = qkv.reshape(B, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) # 3, B, num_heads, N, C
q, k, v = qkv[0], qkv[1], qkv[2]
if self.rope:
# slightly fast impl
q_t = q[:, :, 1:, :]
ro_q_t = self.rope(q_t)
q = torch.cat((q[:, :, :1, :], ro_q_t), -2).type_as(v)
k_t = k[:, :, 1:, :]
ro_k_t = self.rope(k_t)
k = torch.cat((k[:, :, :1, :], ro_k_t), -2).type_as(v)
if self.xattn:
q = q.permute(0, 2, 1, 3) # B, num_heads, N, C -> B, N, num_heads, C
k = k.permute(0, 2, 1, 3)
v = v.permute(0, 2, 1, 3)
x = xops.memory_efficient_attention(
q, k, v,
p=self.xattn_drop,
scale=self.scale,
)
x = x.reshape(B, N, -1)
x = self.inner_attn_ln(x)
x = self.proj(x)
x = self.proj_drop(x)
else:
q = q * self.scale
attn = (q @ k.transpose(-2, -1))
if self.relative_position_bias_table is not None:
relative_position_bias = \
self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
self.window_size[0] * self.window_size[1] + 1,
self.window_size[0] * self.window_size[1] + 1, -1) # Wh*Ww,Wh*Ww,nH
relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww
attn = attn + relative_position_bias.unsqueeze(0).type_as(attn)
if rel_pos_bias is not None:
attn = attn + rel_pos_bias.type_as(attn)
if attn_mask is not None:
attn_mask = attn_mask.bool()
attn = attn.masked_fill(~attn_mask[:, None, None, :], float("-inf"))
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
x = (attn @ v).transpose(1, 2).reshape(B, N, -1)
x = self.inner_attn_ln(x)
x = self.proj(x)
x = self.proj_drop(x)
return x
class Block(nn.Module):
def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
drop_path=0., init_values=None, act_layer=nn.GELU, norm_layer=nn.LayerNorm,
window_size=None, attn_head_dim=None, xattn=False, rope=None, postnorm=False,
subln=False, naiveswiglu=False):
super().__init__()
self.norm1 = norm_layer(dim)
self.attn = Attention(
dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
attn_drop=attn_drop, proj_drop=drop, window_size=window_size, attn_head_dim=attn_head_dim,
xattn=xattn, rope=rope, subln=subln, norm_layer=norm_layer)
# NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.norm2 = norm_layer(dim)
mlp_hidden_dim = int(dim * mlp_ratio)
if naiveswiglu:
self.mlp = SwiGLU(
in_features=dim,
hidden_features=mlp_hidden_dim,
subln=subln,
norm_layer=norm_layer,
)
else:
self.mlp = Mlp(
in_features=dim,
hidden_features=mlp_hidden_dim,
act_layer=act_layer,
subln=subln,
drop=drop
)
if init_values is not None and init_values > 0:
self.gamma_1 = nn.Parameter(init_values * torch.ones((dim)),requires_grad=True)
self.gamma_2 = nn.Parameter(init_values * torch.ones((dim)),requires_grad=True)
else:
self.gamma_1, self.gamma_2 = None, None
self.postnorm = postnorm
def forward(self, x, rel_pos_bias=None, attn_mask=None):
if self.gamma_1 is None:
if self.postnorm:
x = x + self.drop_path(self.norm1(self.attn(x, rel_pos_bias=rel_pos_bias, attn_mask=attn_mask)))
x = x + self.drop_path(self.norm2(self.mlp(x)))
else:
x = x + self.drop_path(self.attn(self.norm1(x), rel_pos_bias=rel_pos_bias, attn_mask=attn_mask))
x = x + self.drop_path(self.mlp(self.norm2(x)))
else:
if self.postnorm:
x = x + self.drop_path(self.gamma_1 * self.norm1(self.attn(x, rel_pos_bias=rel_pos_bias, attn_mask=attn_mask)))
x = x + self.drop_path(self.gamma_2 * self.norm2(self.mlp(x)))
else:
x = x + self.drop_path(self.gamma_1 * self.attn(self.norm1(x), rel_pos_bias=rel_pos_bias, attn_mask=attn_mask))
x = x + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x)))
return x
class PatchEmbed(nn.Module):
""" Image to Patch Embedding
"""
def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
super().__init__()
img_size = to_2tuple(img_size)
patch_size = to_2tuple(patch_size)
num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0])
self.patch_shape = (img_size[0] // patch_size[0], img_size[1] // patch_size[1])
self.img_size = img_size
self.patch_size = patch_size
self.num_patches = num_patches
self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
def forward(self, x, **kwargs):
B, C, H, W = x.shape
# FIXME look at relaxing size constraints
assert H == self.img_size[0] and W == self.img_size[1], \
f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
x = self.proj(x).flatten(2).transpose(1, 2)
return x
class RelativePositionBias(nn.Module):
def __init__(self, window_size, num_heads):
super().__init__()
self.window_size = window_size
self.num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3
self.relative_position_bias_table = nn.Parameter(
torch.zeros(self.num_relative_distance, num_heads)) # 2*Wh-1 * 2*Ww-1, nH
# cls to token & token 2 cls & cls to cls
# get pair-wise relative position index for each token inside the window
coords_h = torch.arange(window_size[0])
coords_w = torch.arange(window_size[1])
coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww
coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww
relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww
relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
relative_coords[:, :, 0] += window_size[0] - 1 # shift to start from 0
relative_coords[:, :, 1] += window_size[1] - 1
relative_coords[:, :, 0] *= 2 * window_size[1] - 1
relative_position_index = \
torch.zeros(size=(window_size[0] * window_size[1] + 1,) * 2, dtype=relative_coords.dtype)
relative_position_index[1:, 1:] = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
relative_position_index[0, 0:] = self.num_relative_distance - 3
relative_position_index[0:, 0] = self.num_relative_distance - 2
relative_position_index[0, 0] = self.num_relative_distance - 1
self.register_buffer("relative_position_index", relative_position_index)
def forward(self):
relative_position_bias = \
self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
self.window_size[0] * self.window_size[1] + 1,
self.window_size[0] * self.window_size[1] + 1, -1) # Wh*Ww,Wh*Ww,nH
return relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww
class EVAVisionTransformer(nn.Module):
""" Vision Transformer with support for patch or hybrid CNN input stage
"""
def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12,
num_heads=12, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop_rate=0., attn_drop_rate=0.,
drop_path_rate=0., norm_layer=nn.LayerNorm, init_values=None, patch_dropout=0.,
use_abs_pos_emb=True, use_rel_pos_bias=False, use_shared_rel_pos_bias=False, rope=False,
use_mean_pooling=True, init_scale=0.001, grad_checkpointing=False, xattn=False, postnorm=False,
pt_hw_seq_len=16, intp_freq=False, naiveswiglu=False, subln=False):
super().__init__()
self.image_size = img_size
self.num_classes = num_classes
self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models
self.patch_embed = PatchEmbed(
img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
num_patches = self.patch_embed.num_patches
self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
# self.mask_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
if use_abs_pos_emb:
self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim))
else:
self.pos_embed = None
self.pos_drop = nn.Dropout(p=drop_rate)
if use_shared_rel_pos_bias:
self.rel_pos_bias = RelativePositionBias(window_size=self.patch_embed.patch_shape, num_heads=num_heads)
else:
self.rel_pos_bias = None
if rope:
half_head_dim = embed_dim // num_heads // 2
hw_seq_len = img_size // patch_size
self.rope = VisionRotaryEmbeddingFast(
dim=half_head_dim,
pt_seq_len=pt_hw_seq_len,
ft_seq_len=hw_seq_len if intp_freq else None,
# patch_dropout=patch_dropout
)
else:
self.rope = None
self.naiveswiglu = naiveswiglu
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule
self.use_rel_pos_bias = use_rel_pos_bias
self.blocks = nn.ModuleList([
Block(
dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer,
init_values=init_values, window_size=self.patch_embed.patch_shape if use_rel_pos_bias else None,
xattn=xattn, rope=self.rope, postnorm=postnorm, subln=subln, naiveswiglu=naiveswiglu)
for i in range(depth)])
self.norm = nn.Identity() if use_mean_pooling else norm_layer(embed_dim)
self.fc_norm = norm_layer(embed_dim) if use_mean_pooling else None
self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity()
if self.pos_embed is not None:
trunc_normal_(self.pos_embed, std=.02)
trunc_normal_(self.cls_token, std=.02)
# trunc_normal_(self.mask_token, std=.02)
self.apply(self._init_weights)
self.fix_init_weight()
if isinstance(self.head, nn.Linear):
trunc_normal_(self.head.weight, std=.02)
self.head.weight.data.mul_(init_scale)
self.head.bias.data.mul_(init_scale)
# setting a patch_dropout of 0. would mean it is disabled and this function would be the identity fn
self.patch_dropout = PatchDropout(patch_dropout) if patch_dropout > 0. else nn.Identity()
self.grad_checkpointing = grad_checkpointing
def fix_init_weight(self):
def rescale(param, layer_id):
param.div_(math.sqrt(2.0 * layer_id))
for layer_id, layer in enumerate(self.blocks):
rescale(layer.attn.proj.weight.data, layer_id + 1)
if self.naiveswiglu:
rescale(layer.mlp.w3.weight.data, layer_id + 1)
else:
rescale(layer.mlp.fc2.weight.data, layer_id + 1)
def get_cast_dtype(self) -> torch.dtype:
return self.blocks[0].mlp.fc2.weight.dtype
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
def get_num_layers(self):
return len(self.blocks)
def lock(self, unlocked_groups=0, freeze_bn_stats=False):
assert unlocked_groups == 0, 'partial locking not currently supported for this model'
for param in self.parameters():
param.requires_grad = False
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
self.grad_checkpointing = enable
@torch.jit.ignore
def no_weight_decay(self):
return {'pos_embed', 'cls_token'}
def get_classifier(self):
return self.head
def reset_classifier(self, num_classes, global_pool=''):
self.num_classes = num_classes
self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
def forward_features(self, x, return_all_features=False):
x = self.patch_embed(x)
batch_size, seq_len, _ = x.size()
cls_tokens = self.cls_token.expand(batch_size, -1, -1) # stole cls_tokens impl from Phil Wang, thanks
x = torch.cat((cls_tokens, x), dim=1)
if self.pos_embed is not None:
x = x + self.pos_embed
x = self.pos_drop(x)
# a patch_dropout of 0. would mean it is disabled and this function would do nothing but return what was passed in
if os.getenv('RoPE') == '1':
if self.training and not isinstance(self.patch_dropout, nn.Identity):
x, patch_indices_keep = self.patch_dropout(x)
self.rope.forward = partial(self.rope.forward, patch_indices_keep=patch_indices_keep)
else:
self.rope.forward = partial(self.rope.forward, patch_indices_keep=None)
x = self.patch_dropout(x)
else:
x = self.patch_dropout(x)
rel_pos_bias = self.rel_pos_bias() if self.rel_pos_bias is not None else None
for i, blk in enumerate(self.blocks):
if i == len(self.blocks)-1:
continue
if self.grad_checkpointing:
x = checkpoint(blk, x, (rel_pos_bias,))
else:
x = blk(x, rel_pos_bias=rel_pos_bias)
if not return_all_features:
x = self.norm(x)
if self.fc_norm is not None:
return self.fc_norm(x.mean(1))
else:
return x[:, 0]
return x
def forward(self, x, return_all_features=False):
if return_all_features:
return self.forward_features(x, return_all_features)
x = self.forward_features(x)
x = self.head(x)
return x
class LayerNorm(nn.LayerNorm):
"""Subclass torch's LayerNorm (with cast back to input dtype)."""
def forward(self, x: torch.Tensor):
orig_type = x.dtype
x = F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
return x.to(orig_type)
try:
from apex.normalization import FusedLayerNorm
except:
FusedLayerNorm = LayerNorm
print("Please 'pip install apex'")
@dataclass
class CLIPVisionCfg:
layers: Union[Tuple[int, int, int, int], int] = 12
width: int = 768
head_width: int = 64
mlp_ratio: float = 4.0
patch_size: int = 16
image_size: Union[Tuple[int, int], int] = 224
ls_init_value: Optional[float] = None # layer scale initial value
patch_dropout: float = 0. # what fraction of patches to dropout during training (0 would mean disabled and no patches dropped) - 0.5 to 0.75 recommended in the paper for optimal results
global_average_pool: bool = False # whether to global average pool the last embedding layer, instead of using CLS token (https://arxiv.org/abs/2205.01580)
drop_path_rate: Optional[float] = None # drop path rate
timm_model_name: str = None # a valid model name overrides layers, width, patch_size
timm_model_pretrained: bool = False # use (imagenet) pretrained weights for named model
timm_pool: str = 'avg' # feature pooling for timm model ('abs_attn', 'rot_attn', 'avg', '')
timm_proj: str = 'linear' # linear projection for timm model output ('linear', 'mlp', '')
timm_proj_bias: bool = False # enable bias final projection
eva_model_name: str = None # a valid eva model name overrides layers, width, patch_size
qkv_bias: bool = True
fusedLN: bool = False
xattn: bool = False
postnorm: bool = False
rope: bool = False
pt_hw_seq_len: int = 16 # 224/14
intp_freq: bool = False
naiveswiglu: bool = False
subln: bool = False
def _build_vision_tower(
embed_dim: int,
vision_cfg: CLIPVisionCfg
):
if isinstance(vision_cfg, dict):
vision_cfg = CLIPVisionCfg(**vision_cfg)
if vision_cfg.eva_model_name:
vision_heads = vision_cfg.width // vision_cfg.head_width
norm_layer = LayerNorm
visual = EVAVisionTransformer(
img_size=vision_cfg.image_size,
patch_size=vision_cfg.patch_size,
num_classes=embed_dim,
use_mean_pooling=vision_cfg.global_average_pool, #False
init_values=vision_cfg.ls_init_value,
patch_dropout=vision_cfg.patch_dropout,
embed_dim=vision_cfg.width,
depth=vision_cfg.layers,
num_heads=vision_heads,
mlp_ratio=vision_cfg.mlp_ratio,
qkv_bias=vision_cfg.qkv_bias,
drop_path_rate=vision_cfg.drop_path_rate,
norm_layer= partial(FusedLayerNorm, eps=1e-6) if vision_cfg.fusedLN else partial(norm_layer, eps=1e-6),
xattn=vision_cfg.xattn,
rope=vision_cfg.rope,
postnorm=vision_cfg.postnorm,
pt_hw_seq_len= vision_cfg.pt_hw_seq_len, # 224/14
intp_freq= vision_cfg.intp_freq,
naiveswiglu= vision_cfg.naiveswiglu,
subln= vision_cfg.subln
)
return visual
class Eva2LargeEncoder(nn.Module):
def __init__(self, image_size=224):
super(Eva2LargeEncoder, self).__init__()
self.config = {
"embed_dim": 768,
"vision_cfg": {
"image_size": 336,
"layers": 24,
"width": 1024,
"drop_path_rate": 0,
"head_width": 64,
"mlp_ratio": 2.6667,
"patch_size": 14,
"eva_model_name": "eva-clip-l-14-336",
"xattn": True,
"fusedLN": True,
"rope": True,
"pt_hw_seq_len": 16,
"intp_freq": True,
"naiveswiglu": True,
"subln": True
}
}
self.config['vision_cfg']['image_size'] = image_size
import os
os.environ['delRoPE'] = '1' # to avoid error in rope params when changing image size
self.model = _build_vision_tower(**self.config)
def forward(self, images):
encode = self.model(images, return_all_features=True)[:, 1:, :]
return encode
class CrossVisionModel(nn.Module):
def __init__(self, config):
super().__init__()
self.vit = Eva2LargeEncoder(image_size=config.cross_image_size)
self.pos_embed = nn.Parameter(torch.zeros((self.vit.config['vision_cfg']['image_size'] // self.vit.config['vision_cfg']['patch_size']) ** 2, self.vit.config['vision_cfg']['width']))
def forward(self, images):
enc = self.vit(images)
return enc + self.pos_embed.to(enc.device).unsqueeze(0)