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from collections import OrderedDict
from typing import Tuple, Union
import logging
import os
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
from timm.models.layers import DropPath, trunc_normal_
from .registry import register_lang_encoder
logger = logging.getLogger(__name__)
class LayerNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-12):
"""Construct a layernorm module in the TF style (epsilon inside the square root).
"""
super(LayerNorm, self).__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.bias = nn.Parameter(torch.zeros(hidden_size))
self.variance_epsilon = eps
def forward(self, x):
pdtype = x.dtype
x = x.float()
u = x.mean(-1, keepdim=True)
s = (x - u).pow(2).mean(-1, keepdim=True)
x = (x - u) / torch.sqrt(s + self.variance_epsilon)
return self.weight * x.to(pdtype) + self.bias
class QuickGELU(nn.Module):
def forward(self, x: torch.Tensor):
return x * torch.sigmoid(1.702 * x)
class ResidualAttentionBlock(nn.Module):
def __init__(self,
d_model: int,
n_head: int,
attn_mask: torch.Tensor = None,
drop_path: float = 0.0):
super().__init__()
self.attn = nn.MultiheadAttention(d_model, n_head)
self.ln_1 = LayerNorm(d_model)
self.mlp = nn.Sequential(OrderedDict([
("c_fc", nn.Linear(d_model, d_model * 4)),
("gelu", QuickGELU()),
("c_proj", nn.Linear(d_model * 4, d_model))
]))
self.ln_2 = LayerNorm(d_model)
self.attn_mask = attn_mask
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
def attention(self, x: torch.Tensor, key_padding_mask: torch.Tensor = None):
self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) \
if self.attn_mask is not None else None
return self.attn(
x, x, x,
key_padding_mask=key_padding_mask,
need_weights=False,
attn_mask=self.attn_mask
)[0]
def forward(self, x: torch.Tensor, key_padding_mask: torch.Tensor = None):
x = x + self.drop_path(self.attention(self.ln_1(x), key_padding_mask=key_padding_mask))
x = x + self.drop_path(self.mlp(self.ln_2(x)))
return x
class Transformer(nn.Module):
def __init__(self,
context_length: int,
vocab_size: int,
width: int,
layers: int,
heads: int,
drop_path: float = 0.0,
autogressive: bool =True):
super().__init__()
self.token_embedding = nn.Embedding(vocab_size, width)
self.context_length = context_length
self.positional_embedding = nn.Parameter(
torch.empty(self.context_length, width)
)
self.width = width
self.layers = layers
self.autogressive = autogressive
attn_mask = self.build_attention_mask() if autogressive else None
dpr = [x.item() for x in torch.linspace(0, drop_path, layers)] # stochastic depth decay rule
self.resblocks = nn.ModuleList(
[
ResidualAttentionBlock(width, heads, attn_mask, dpr[i])
for i in range(layers)
]
)
self.ln_final = LayerNorm(width)
trunc_normal_(self.positional_embedding, std=.02)
# nn.init.normal_(self.token_embedding, std=.02)
trunc_normal_(self.token_embedding.weight, std=.02)
self.apply(self._init_weights)
@property
def dim_out(self):
return self.width
def build_attention_mask(self):
# lazily create causal attention mask, with full attention between the vision tokens
# pytorch uses additive attention mask; fill with -inf
mask = torch.empty(self.context_length, self.context_length)
mask.fill_(float("-inf"))
mask.triu_(1) # zero out the lower diagonal
return mask
def _init_weights(self, m):
if isinstance(m, (nn.Linear, nn.Conv2d)):
logger.info('=> init weight of Linear/Conv2d from trunc norm')
trunc_normal_(m.weight, std=0.02)
if m.bias is not None:
logger.info('=> init bias of Linear/Conv2d to zeros')
nn.init.constant_(m.bias, 0)
elif isinstance(m, (nn.LayerNorm, nn.BatchNorm2d)):
nn.init.constant_(m.bias, 0)
def load_pretrained(self, pretrained='', pretrained_layers=[], verbose=True):
if os.path.isfile(pretrained):
pretrained_dict = torch.load(pretrained, map_location='cpu')
logging.info(f'=> loading pretrained model {pretrained}')
model_dict = self.state_dict()
pretrained_dict = {
k: v for k, v in pretrained_dict.items()
if k in model_dict.keys()
}
need_init_state_dict = {}
for k, v in pretrained_dict.items():
need_init = (
k.split('.')[0] in pretrained_layers
or pretrained_layers[0] == '*'
)
if need_init:
if verbose:
logging.info(f'=> init {k} from {pretrained}')
need_init_state_dict[k] = v
self.load_state_dict(need_init_state_dict, strict=False)
@torch.jit.ignore
def no_weight_decay(self):
return {
'positional_embedding',
'token_embedding',
}
def forward(self, input_ids, attention_mask=None):
key_padding_mask = (input_ids == 0) if not self.autogressive else None
x = self.token_embedding(input_ids) # [batch_size, n_ctx, d_model]
x = x + self.positional_embedding
x = x.permute(1, 0, 2) # NLD -> LND
for block in self.resblocks:
x = block(x, key_padding_mask)
x = x.permute(1, 0, 2) # LND -> NLD
x = self.ln_final(x)
return {'last_hidden_state': x}
@register_lang_encoder
def lang_encoder(config_encoder, tokenizer, verbose, **kwargs):
transformer = Transformer(
context_length=config_encoder['CONTEXT_LENGTH'],
vocab_size=tokenizer.vocab_size,
width=config_encoder['WIDTH'],
layers=config_encoder['LAYERS'],
heads=config_encoder['HEADS'],
autogressive=config_encoder.get('AUTOGRESSIVE', True)
)
if config_encoder['LOAD_PRETRAINED']:
transformer.load_pretrained()
return transformer
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