| """
|
| # Adapted from https://github.com/baaivision/EVA/tree/master/EVA-CLIP
|
| """
|
|
|
| from math import pi
|
| import torch
|
| from torch import nn
|
| from einops import rearrange, repeat
|
| import logging
|
| from llava.utils import rank0_print
|
|
|
|
|
| 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=0.0):
|
| super().__init__()
|
| if custom_freqs:
|
| freqs = custom_freqs
|
| elif freqs_for == "lang":
|
| freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim))
|
| elif freqs_for == "pixel":
|
| freqs = torch.linspace(1.0, 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
|
|
|
|
|
| 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)
|
|
|
|
|
| class PatchDropout(nn.Module):
|
| """
|
| https://arxiv.org/abs/2212.00794
|
| """
|
|
|
| def __init__(self, prob, exclude_first_token=True):
|
| super().__init__()
|
| assert 0 <= prob < 1.0
|
| self.prob = prob
|
| self.exclude_first_token = exclude_first_token
|
| logging.info(f"os.getenv('RoPE')={os.getenv('RoPE')}")
|
|
|
| def forward(self, x):
|
| if not self.training or self.prob == 0.0:
|
| 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
|
|
|
|
|
|
|
|
|
|
|
| import math
|
| import os
|
| import torch.nn as nn
|
| import torch.nn.functional as F
|
|
|
| try:
|
| from timm.models.layers import drop_path, to_2tuple, trunc_normal_
|
| except:
|
| from timm.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
|
|
|
| try:
|
| import xformers.ops as xops
|
| except ImportError:
|
| xops = None
|
|
|
|
|
|
|
| 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
|
| 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.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
|
|
|
| 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=0.0, 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.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))
|
|
|
|
|
|
|
| coords_h = torch.arange(window_size[0])
|
| coords_w = torch.arange(window_size[1])
|
| coords = torch.stack(torch.meshgrid([coords_h, coords_w]))
|
| coords_flatten = torch.flatten(coords, 1)
|
| relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]
|
| relative_coords = relative_coords.permute(1, 2, 0).contiguous()
|
| relative_coords[:, :, 0] += window_size[0] - 1
|
| 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)
|
| 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, 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:
|
| 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)
|
| 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)
|
| q, k, v = qkv[0], qkv[1], qkv[2]
|
|
|
| if self.rope:
|
|
|
| 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 and xops is not None:
|
| q = q.permute(0, 2, 1, 3)
|
| 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)
|
| relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()
|
| 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
|
| )
|
|
|
| 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
|
|
|
| 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))
|
|
|
|
|
|
|
| coords_h = torch.arange(window_size[0])
|
| coords_w = torch.arange(window_size[1])
|
| coords = torch.stack(torch.meshgrid([coords_h, coords_w]))
|
| coords_flatten = torch.flatten(coords, 1)
|
| relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]
|
| relative_coords = relative_coords.permute(1, 2, 0).contiguous()
|
| relative_coords[:, :, 0] += window_size[0] - 1
|
| 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)
|
| 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)
|
| return relative_position_bias.permute(2, 0, 1).contiguous()
|
|
|
|
|
| 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
|
|
|
| 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))
|
|
|
| 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,
|
|
|
| )
|
| else:
|
| self.rope = None
|
|
|
| self.naiveswiglu = naiveswiglu
|
|
|
| dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]
|
| 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)
|
|
|
|
|
| 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)
|
|
|
|
|
| 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)
|
| 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)
|
|
|
|
|
| if os.getenv("RoPE") == "1":
|
| if self.training and not isinstance(self.patch_dropout, nn.Identity):
|
| x, patch_indices_keep = self.patch_dropout(x)
|
|
|
| x = self.rope.forward(x, patch_indices_keep=patch_indices_keep)
|
| else:
|
|
|
| x = self.rope.forward(x, 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
|
|
|
|
|
| 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()):
|
| logging.info(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)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| return state_dict
|
|
|
|
|
| from dataclasses import dataclass
|
| from typing import Optional, Tuple, Union
|
|
|
| try:
|
| from apex.normalization import FusedLayerNorm
|
| except:
|
| FusedLayerNorm = LayerNorm
|
|
|
|
|
|
|
| @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
|
| patch_dropout: float = 0.0
|
| global_average_pool: bool = False
|
| drop_path_rate: Optional[float] = None
|
| timm_model_name: str = None
|
| timm_model_pretrained: bool = False
|
| timm_pool: str = "avg"
|
| timm_proj: str = "linear"
|
| timm_proj_bias: bool = False
|
| eva_model_name: str = None
|
| qkv_bias: bool = True
|
| fusedLN: bool = False
|
| xattn: bool = False
|
| postnorm: bool = False
|
| rope: bool = False
|
| pt_hw_seq_len: int = 16
|
| intp_freq: bool = False
|
| naiveswiglu: bool = False
|
| subln: bool = False
|
|
|
|
|
| def create_norm_layer_factory(use_fused_ln, eps=1e-6):
|
|
|
| return lambda num_features: nn.LayerNorm(num_features, eps=eps)
|
|
|
|
|
| def _build_vision_tower(vision_tower_path: str, embed_dim: int, vision_cfg: CLIPVisionCfg, **kwargs):
|
| 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_factory = create_norm_layer_factory(vision_cfg.fusedLN, eps=1e-6)
|
|
|
| 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,
|
| 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=norm_layer_factory,
|
| xattn=vision_cfg.xattn,
|
| rope=vision_cfg.rope,
|
| postnorm=vision_cfg.postnorm,
|
| pt_hw_seq_len=vision_cfg.pt_hw_seq_len,
|
| intp_freq=vision_cfg.intp_freq,
|
| naiveswiglu=vision_cfg.naiveswiglu,
|
| subln=vision_cfg.subln,
|
| )
|
|
|
| state_dict = load_clip_visual_state_dict(vision_tower_path)
|
| incompatible_keys = visual.load_state_dict(state_dict, strict=False)
|
| rank0_print("EVA-CLIP incompatible_keys:", incompatible_keys)
|
|
|
| return visual
|
|
|
|
|
| class EVAEncoderWrapper(nn.Module):
|
| def __init__(self, vision_tower_pretrained, config):
|
| super(EVAEncoderWrapper, self).__init__()
|
| self.config = config
|
| self.config["vision_tower_path"] = vision_tower_pretrained
|
| self.model = _build_vision_tower(**self.config)
|
|
|
| def forward(self, image, **kwargs):
|
| encode = self.model(image, return_all_features=True)[:, 1:, :]
|
| return encode
|
|
|
| @property
|
| def dtype(self):
|
| return list(self.parameters())[-1].dtype
|
|
|
| @property
|
| def device(self):
|
| return list(self.parameters())[-1].device
|
|
|
