infimm-hd / eva_vit_model.py

Update eva_vit_model.py

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	# --------------------------------------------------------
	# Adapted from https://github.com/baaivision/EVA/blob/master/EVA-CLIP/rei/eva_clip/eva_vit_model.py
	# --------------------------------------------------------
	import math
	import os
	import tempfile
	from dataclasses import dataclass
	from functools import partial
	from typing import Optional, Tuple, Union
	import torch
	import torch.nn as nn
	import torch.nn.functional as F
	import yaml
	from timm.models.layers import drop_path, to_2tuple, trunc_normal_



	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

	from .utils import resize_eva_pos_embed

	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=0.0,
	subln=False,
	):
	super().__init__()
	out_features = out_features or in_features
	hidden_features = hidden_features or in_features


	use_ft_linear = False

	if use_ft_linear:
	self.fc1 = FTLinear(in_features, hidden_features)
	else:
	self.fc1 = nn.Linear(in_features, hidden_features)
	self.act = act_layer()

	self.ffn_ln = norm_layer(hidden_features) if subln else nn.Identity()

	if use_ft_linear:
	self.fc2 = FTLinear(hidden_features, out_features)
	else:
	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=0.0,
	norm_layer=nn.LayerNorm,
	subln=False,
	):
	super().__init__()
	out_features = out_features or in_features
	hidden_features = hidden_features or in_features

	use_ft_linear = False

	if use_ft_linear:
	self.w1 = FTLinear(in_features, hidden_features)
	self.w2 = FTLinear(in_features, hidden_features)
	else:
	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()

	if use_ft_linear:
	self.w3 = FTLinear(hidden_features, out_features)
	else:
	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=0.0,
	proj_drop=0.0,
	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.use_ft_flash_attention = False

	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

	if self.use_ft_flash_attention:
	assert FTFlashAttention is not None
	self.ft_flash_attn = FTFlashAttention()

	self.rope = rope

	def forward(self, x, rel_pos_bias=None, attn_mask=None):
	B, N, C = x.shape
	if self.subln:
	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.use_ft_flash_attention:
	q = q.permute(0, 2, 1, 3).contiguous()
	q = q.view(
	q.shape[0], q.shape[1], -1
	) # B, num_heads, N, C -> B, N, num_heads, C
	k = k.permute(0, 2, 1, 3).contiguous()
	k = k.view(k.shape[0], k.shape[1], -1)
	v = v.permute(0, 2, 1, 3).contiguous()
	v = v.view(v.shape[0], v.shape[1], -1)
	x = self.ft_flash_attn(
	[q, k, v],
	self.num_heads,
	attn_mask=None,
	causal=False,
	attention_dropout=self.xattn_drop if self.training else 0.0,
	softmax_scale=self.scale,
	use_rmpad_attn=False,
	)

	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=4.0,
	qkv_bias=False,
	qk_scale=None,
	drop=0.0,
	attn_drop=0.0,
	drop_path=0.0,
	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 > 0.0 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"
	f" ({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=4.0,
	qkv_bias=False,
	qk_scale=None,
	drop_rate=0.0,
	attn_drop_rate=0.0,
	drop_path_rate=0.0,
	norm_layer=nn.LayerNorm,
	init_values=None,
	patch_dropout=0.0,
	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=0.02)

	trunc_normal_(self.cls_token, std=0.02)
	# 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=0.02)
	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 > 0.0 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=0.02)
	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 blk in self.blocks:
	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


	@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 = 0.0 # 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 = ( # feature pooling for timm model ('abs_attn', 'rot_attn', 'avg', '')
	"avg"
	)
	timm_proj: str = ( # linear projection for timm model output ('linear', 'mlp', '')
	"linear"
	)
	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
	embed_dim: int = 1024
	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 load_state_dict(
	checkpoint_path: str,
	map_location: str = "cpu",
	model_key: str = "model\|module\|state_dict",
	is_openai: bool = False,
	skip_list: list = [],
	):
	if is_openai:
	model = torch.jit.load(checkpoint_path, map_location="cpu").eval()
	state_dict = model.state_dict()
	for key in ["input_resolution", "context_length", "vocab_size"]:
	state_dict.pop(key, None)
	else:
	checkpoint = torch.load(checkpoint_path, map_location=map_location)
	for mk in model_key.split("\|"):
	if isinstance(checkpoint, dict) and mk in checkpoint:
	state_dict = checkpoint[mk]
	break
	else:
	state_dict = checkpoint
	if next(iter(state_dict.items()))[0].startswith("module"):
	state_dict = {k[7:]: v for k, v in state_dict.items()}

	for k in skip_list:
	if k in list(state_dict.keys()):
	print(f"Removing key {k} from pretrained checkpoint")
	del state_dict[k]

	if os.getenv("RoPE") == "1":
	for k in list(state_dict.keys()):
	if "freqs_cos" in k or "freqs_sin" in k:
	del state_dict[k]
	return state_dict


	def load_clip_visual_state_dict(
	checkpoint_path: str,
	map_location: str = "cpu",
	is_openai: bool = False,
	skip_list: list = [],
	):
	state_dict = load_state_dict(
	checkpoint_path,
	map_location=map_location,
	is_openai=is_openai,
	skip_list=skip_list,
	)

	for k in list(state_dict.keys()):
	if not k.startswith("visual."):
	del state_dict[k]
	for k in list(state_dict.keys()):
	if k.startswith("visual."):
	new_k = k[7:]
	state_dict[new_k] = state_dict[k]
	del state_dict[k]
	return state_dict