cogagent-chat-hf / cross_visual.py

Update cross_visual.py

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	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)) # 2Wh-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, WhWw, WhWw
	relative_coords = relative_coords.permute(1, 2, 0).contiguous() # WhWw, WhWw, 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) # WhWw, WhWw
	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) # WhWw,WhWw,nH
	relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, WhWw, WhWw
	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)) # 2Wh-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, WhWw, WhWw
	relative_coords = relative_coords.permute(1, 2, 0).contiguous() # WhWw, WhWw, 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) # WhWw, WhWw
	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) # WhWw,WhWw,nH
	return relative_position_bias.permute(2, 0, 1).contiguous() # nH, WhWw, WhWw


	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)