mirror of
https://github.com/FlagOpen/FlagEmbedding.git
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533 lines
22 KiB
Python
533 lines
22 KiB
Python
# --------------------------------------------------------
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# Adapted from https://github.com/microsoft/unilm/tree/master/beit
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# --------------------------------------------------------
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import math
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import os
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from functools import partial
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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try:
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from timm.models.layers import drop_path, to_2tuple, trunc_normal_
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except:
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from timm.layers import drop_path, to_2tuple, trunc_normal_
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from .transformer import PatchDropout
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from .rope import VisionRotaryEmbedding, VisionRotaryEmbeddingFast
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if os.getenv('ENV_TYPE') == 'deepspeed':
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try:
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from deepspeed.runtime.activation_checkpointing.checkpointing import checkpoint
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except:
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from torch.utils.checkpoint import checkpoint
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else:
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from torch.utils.checkpoint import checkpoint
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try:
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import xformers.ops as xops
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except ImportError:
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xops = None
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# print("Please 'pip install xformers'")
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class DropPath(nn.Module):
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"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
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"""
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def __init__(self, drop_prob=None):
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super(DropPath, self).__init__()
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self.drop_prob = drop_prob
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def forward(self, x):
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return drop_path(x, self.drop_prob, self.training)
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def extra_repr(self) -> str:
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return 'p={}'.format(self.drop_prob)
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class Mlp(nn.Module):
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def __init__(
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self,
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in_features,
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hidden_features=None,
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out_features=None,
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act_layer=nn.GELU,
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norm_layer=nn.LayerNorm,
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drop=0.,
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subln=False,
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):
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super().__init__()
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out_features = out_features or in_features
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hidden_features = hidden_features or in_features
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self.fc1 = nn.Linear(in_features, hidden_features)
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self.act = act_layer()
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self.ffn_ln = norm_layer(hidden_features) if subln else nn.Identity()
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self.fc2 = nn.Linear(hidden_features, out_features)
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self.drop = nn.Dropout(drop)
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def forward(self, x):
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x = self.fc1(x)
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x = self.act(x)
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# x = self.drop(x)
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# commit this for the orignal BERT implement
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x = self.ffn_ln(x)
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x = self.fc2(x)
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x = self.drop(x)
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return x
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class SwiGLU(nn.Module):
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def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.SiLU, drop=0.,
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norm_layer=nn.LayerNorm, subln=False):
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super().__init__()
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out_features = out_features or in_features
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hidden_features = hidden_features or in_features
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self.w1 = nn.Linear(in_features, hidden_features)
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self.w2 = nn.Linear(in_features, hidden_features)
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self.act = act_layer()
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self.ffn_ln = norm_layer(hidden_features) if subln else nn.Identity()
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self.w3 = nn.Linear(hidden_features, out_features)
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self.drop = nn.Dropout(drop)
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def forward(self, x):
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x1 = self.w1(x)
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x2 = self.w2(x)
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hidden = self.act(x1) * x2
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x = self.ffn_ln(hidden)
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x = self.w3(x)
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x = self.drop(x)
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return x
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class Attention(nn.Module):
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def __init__(
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self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0.,
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proj_drop=0., window_size=None, attn_head_dim=None, xattn=False, rope=None, subln=False, norm_layer=nn.LayerNorm):
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super().__init__()
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self.num_heads = num_heads
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head_dim = dim // num_heads
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if attn_head_dim is not None:
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head_dim = attn_head_dim
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all_head_dim = head_dim * self.num_heads
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self.scale = qk_scale or head_dim ** -0.5
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self.subln = subln
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if self.subln:
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self.q_proj = nn.Linear(dim, all_head_dim, bias=False)
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self.k_proj = nn.Linear(dim, all_head_dim, bias=False)
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self.v_proj = nn.Linear(dim, all_head_dim, bias=False)
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else:
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self.qkv = nn.Linear(dim, all_head_dim * 3, bias=False)
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if qkv_bias:
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self.q_bias = nn.Parameter(torch.zeros(all_head_dim))
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self.v_bias = nn.Parameter(torch.zeros(all_head_dim))
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else:
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self.q_bias = None
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self.v_bias = None
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if window_size:
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self.window_size = window_size
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self.num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3
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self.relative_position_bias_table = nn.Parameter(
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torch.zeros(self.num_relative_distance, num_heads)) # 2*Wh-1 * 2*Ww-1, nH
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# cls to token & token 2 cls & cls to cls
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# get pair-wise relative position index for each token inside the window
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coords_h = torch.arange(window_size[0])
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coords_w = torch.arange(window_size[1])
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coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww
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coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww
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relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww
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relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
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relative_coords[:, :, 0] += window_size[0] - 1 # shift to start from 0
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relative_coords[:, :, 1] += window_size[1] - 1
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relative_coords[:, :, 0] *= 2 * window_size[1] - 1
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relative_position_index = \
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torch.zeros(size=(window_size[0] * window_size[1] + 1, ) * 2, dtype=relative_coords.dtype)
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relative_position_index[1:, 1:] = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
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relative_position_index[0, 0:] = self.num_relative_distance - 3
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relative_position_index[0:, 0] = self.num_relative_distance - 2
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relative_position_index[0, 0] = self.num_relative_distance - 1
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self.register_buffer("relative_position_index", relative_position_index)
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else:
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self.window_size = None
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self.relative_position_bias_table = None
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self.relative_position_index = None
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self.attn_drop = nn.Dropout(attn_drop)
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self.inner_attn_ln = norm_layer(all_head_dim) if subln else nn.Identity()
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# self.proj = nn.Linear(all_head_dim, all_head_dim)
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self.proj = nn.Linear(all_head_dim, dim)
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self.proj_drop = nn.Dropout(proj_drop)
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self.xattn = xattn
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self.xattn_drop = attn_drop
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self.rope = rope
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def forward(self, x, rel_pos_bias=None, attn_mask=None):
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B, N, C = x.shape
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if self.subln:
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q = F.linear(input=x, weight=self.q_proj.weight, bias=self.q_bias)
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k = F.linear(input=x, weight=self.k_proj.weight, bias=None)
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v = F.linear(input=x, weight=self.v_proj.weight, bias=self.v_bias)
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q = q.reshape(B, N, self.num_heads, -1).permute(0, 2, 1, 3) # B, num_heads, N, C
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k = k.reshape(B, N, self.num_heads, -1).permute(0, 2, 1, 3)
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v = v.reshape(B, N, self.num_heads, -1).permute(0, 2, 1, 3)
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else:
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qkv_bias = None
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if self.q_bias is not None:
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qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias))
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qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias)
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qkv = qkv.reshape(B, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) # 3, B, num_heads, N, C
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q, k, v = qkv[0], qkv[1], qkv[2]
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if self.rope:
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# slightly fast impl
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q_t = q[:, :, 1:, :]
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ro_q_t = self.rope(q_t)
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q = torch.cat((q[:, :, :1, :], ro_q_t), -2).type_as(v)
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k_t = k[:, :, 1:, :]
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ro_k_t = self.rope(k_t)
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k = torch.cat((k[:, :, :1, :], ro_k_t), -2).type_as(v)
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if xops is not None:
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q = q.permute(0, 2, 1, 3) # B, num_heads, N, C -> B, N, num_heads, C
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k = k.permute(0, 2, 1, 3)
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v = v.permute(0, 2, 1, 3)
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x = xops.memory_efficient_attention(
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q, k, v,
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p=self.xattn_drop,
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scale=self.scale,
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)
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x = x.reshape(B, N, -1)
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x = self.inner_attn_ln(x)
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x = self.proj(x)
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x = self.proj_drop(x)
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else:
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q = q * self.scale
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attn = (q @ k.transpose(-2, -1))
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if self.relative_position_bias_table is not None:
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relative_position_bias = \
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self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
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self.window_size[0] * self.window_size[1] + 1,
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self.window_size[0] * self.window_size[1] + 1, -1) # Wh*Ww,Wh*Ww,nH
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relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww
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attn = attn + relative_position_bias.unsqueeze(0).type_as(attn)
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if rel_pos_bias is not None:
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attn = attn + rel_pos_bias.type_as(attn)
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if attn_mask is not None:
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attn_mask = attn_mask.bool()
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attn = attn.masked_fill(~attn_mask[:, None, None, :], float("-inf"))
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attn = attn.softmax(dim=-1)
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attn = self.attn_drop(attn)
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x = (attn @ v).transpose(1, 2).reshape(B, N, -1)
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x = self.inner_attn_ln(x)
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x = self.proj(x)
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x = self.proj_drop(x)
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return x
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class Block(nn.Module):
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def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
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drop_path=0., init_values=None, act_layer=nn.GELU, norm_layer=nn.LayerNorm,
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window_size=None, attn_head_dim=None, xattn=False, rope=None, postnorm=False,
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subln=False, naiveswiglu=False):
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super().__init__()
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self.norm1 = norm_layer(dim)
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self.attn = Attention(
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dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
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attn_drop=attn_drop, proj_drop=drop, window_size=window_size, attn_head_dim=attn_head_dim,
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xattn=xattn, rope=rope, subln=subln, norm_layer=norm_layer)
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# NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
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self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
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self.norm2 = norm_layer(dim)
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mlp_hidden_dim = int(dim * mlp_ratio)
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if naiveswiglu:
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self.mlp = SwiGLU(
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in_features=dim,
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hidden_features=mlp_hidden_dim,
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subln=subln,
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norm_layer=norm_layer,
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)
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else:
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self.mlp = Mlp(
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in_features=dim,
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hidden_features=mlp_hidden_dim,
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act_layer=act_layer,
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subln=subln,
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drop=drop
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)
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if init_values is not None and init_values > 0:
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self.gamma_1 = nn.Parameter(init_values * torch.ones((dim)),requires_grad=True)
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self.gamma_2 = nn.Parameter(init_values * torch.ones((dim)),requires_grad=True)
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else:
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self.gamma_1, self.gamma_2 = None, None
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self.postnorm = postnorm
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def forward(self, x, rel_pos_bias=None, attn_mask=None):
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if self.gamma_1 is None:
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if self.postnorm:
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x = x + self.drop_path(self.norm1(self.attn(x, rel_pos_bias=rel_pos_bias, attn_mask=attn_mask)))
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x = x + self.drop_path(self.norm2(self.mlp(x)))
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else:
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x = x + self.drop_path(self.attn(self.norm1(x), rel_pos_bias=rel_pos_bias, attn_mask=attn_mask))
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x = x + self.drop_path(self.mlp(self.norm2(x)))
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else:
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if self.postnorm:
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x = x + self.drop_path(self.gamma_1 * self.norm1(self.attn(x, rel_pos_bias=rel_pos_bias, attn_mask=attn_mask)))
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x = x + self.drop_path(self.gamma_2 * self.norm2(self.mlp(x)))
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else:
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x = x + self.drop_path(self.gamma_1 * self.attn(self.norm1(x), rel_pos_bias=rel_pos_bias, attn_mask=attn_mask))
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x = x + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x)))
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return x
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class PatchEmbed(nn.Module):
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""" Image to Patch Embedding
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"""
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def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
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super().__init__()
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img_size = to_2tuple(img_size)
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patch_size = to_2tuple(patch_size)
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num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0])
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self.patch_shape = (img_size[0] // patch_size[0], img_size[1] // patch_size[1])
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self.img_size = img_size
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self.patch_size = patch_size
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self.num_patches = num_patches
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self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
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def forward(self, x, **kwargs):
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B, C, H, W = x.shape
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# FIXME look at relaxing size constraints
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assert H == self.img_size[0] and W == self.img_size[1], \
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f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
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x = self.proj(x).flatten(2).transpose(1, 2) # [10, 3, 224, 224] -> [10, 196, 768]
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return x
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class RelativePositionBias(nn.Module):
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def __init__(self, window_size, num_heads):
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super().__init__()
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self.window_size = window_size
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self.num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3
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self.relative_position_bias_table = nn.Parameter(
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torch.zeros(self.num_relative_distance, num_heads)) # 2*Wh-1 * 2*Ww-1, nH
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# cls to token & token 2 cls & cls to cls
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# get pair-wise relative position index for each token inside the window
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coords_h = torch.arange(window_size[0])
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coords_w = torch.arange(window_size[1])
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coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww
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coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww
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relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww
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relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
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relative_coords[:, :, 0] += window_size[0] - 1 # shift to start from 0
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relative_coords[:, :, 1] += window_size[1] - 1
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relative_coords[:, :, 0] *= 2 * window_size[1] - 1
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relative_position_index = \
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torch.zeros(size=(window_size[0] * window_size[1] + 1,) * 2, dtype=relative_coords.dtype)
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relative_position_index[1:, 1:] = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
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relative_position_index[0, 0:] = self.num_relative_distance - 3
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relative_position_index[0:, 0] = self.num_relative_distance - 2
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relative_position_index[0, 0] = self.num_relative_distance - 1
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self.register_buffer("relative_position_index", relative_position_index)
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def forward(self):
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relative_position_bias = \
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self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
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self.window_size[0] * self.window_size[1] + 1,
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self.window_size[0] * self.window_size[1] + 1, -1) # Wh*Ww,Wh*Ww,nH
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return relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww
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class EVAVisionTransformer(nn.Module):
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""" Vision Transformer with support for patch or hybrid CNN input stage
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"""
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def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12,
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num_heads=12, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop_rate=0., attn_drop_rate=0.,
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drop_path_rate=0., norm_layer=nn.LayerNorm, init_values=None, patch_dropout=0.,
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use_abs_pos_emb=True, use_rel_pos_bias=False, use_shared_rel_pos_bias=False, rope=False,
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use_mean_pooling=True, init_scale=0.001, grad_checkpointing=False, xattn=False, postnorm=False,
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pt_hw_seq_len=16, intp_freq=False, naiveswiglu=False, subln=False):
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super().__init__()
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self.image_size = img_size
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self.num_classes = num_classes
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self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models
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self.patch_embed = PatchEmbed(
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img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
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num_patches = self.patch_embed.num_patches
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self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
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# self.mask_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
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if use_abs_pos_emb:
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self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim))
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else:
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self.pos_embed = None
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self.pos_drop = nn.Dropout(p=drop_rate)
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if use_shared_rel_pos_bias:
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|
self.rel_pos_bias = RelativePositionBias(window_size=self.patch_embed.patch_shape, num_heads=num_heads)
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|
else:
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|
self.rel_pos_bias = None
|
|
|
|
if rope:
|
|
half_head_dim = embed_dim // num_heads // 2
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|
hw_seq_len = img_size // patch_size
|
|
self.rope = VisionRotaryEmbeddingFast(
|
|
dim=half_head_dim,
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|
pt_seq_len=pt_hw_seq_len,
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|
ft_seq_len=hw_seq_len if intp_freq else None,
|
|
# patch_dropout=patch_dropout
|
|
)
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|
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 blk in self.blocks:
|
|
if self.grad_checkpointing:
|
|
# x = checkpoint(blk, x, (rel_pos_bias,))
|
|
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=True):
|
|
if return_all_features:
|
|
return self.forward_features(x, return_all_features)
|
|
x = self.forward_features(x)
|
|
x = self.head(x)
|
|
return x
|