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import torch
import torch.nn as nn
from transformers import PreTrainedModel, PretrainedConfig, GenerationMixin
# Seq2SeqLMOutput for model forward, BaseModelOutputWithPastAndCrossAttentions for encoder forward
from transformers.modeling_outputs import Seq2SeqLMOutput, BaseModelOutputWithPastAndCrossAttentions
from transformers import AutoConfig, AutoModel, AutoModelForSeq2SeqLM
from torch.nn.utils.rnn import pad_sequence
_MAX_CONTEXT_SIZE = 10_000
# ==========================================================
# Config
# ==========================================================
class OriginalTransformerConfig(PretrainedConfig):
model_type = "original_transformer"
def __init__(
self,
num_enc_layers = 6,
num_dec_layers = 6,
embed_dim = 512,
num_heads = 8,
enc_vocab_size = 37000,
dec_vocab_size = 37000,
d_ff = 2048,
dropout=0,
pad_token_id = 0,
bos_token_id = 1,
eos_token_id = 2,
is_encoder_decoder=True,
**kwargs
):
super().__init__(**kwargs)
self.num_enc_layers = num_enc_layers
self.num_dec_layers = num_dec_layers
self.embed_dim = embed_dim
self.num_heads = num_heads
self.enc_vocab_size = enc_vocab_size
self.dec_vocab_size = dec_vocab_size
self.d_ff = d_ff
self.dropout = dropout
self.pad_token_id = pad_token_id
self.bos_token_id = bos_token_id
self.eos_token_id = eos_token_id
self.is_encoder_decoder = is_encoder_decoder
# for using AutoModel.from_pretrained
self.auto_map = {
"AutoModel": "modeling_original_transformer.OrginalTransformer",
"AutoModelForSeq2SeqLM": "modeling_original_transformer.OrginalTransformer"
}
# ==========================================================
# Model
# ==========================================================
# combines both embedding and pos_encoding
class Embed(nn.Module):
def __init__(self, vocab_size, embed_dim, dropout=0):
super().__init__()
self.emb_factor = torch.sqrt(torch.tensor(embed_dim, dtype=torch.float32))
self.embed = nn.Embedding(vocab_size, embed_dim) # vocab x C
self.dropout = nn.Dropout(dropout)
pos_embed = torch.zeros(_MAX_CONTEXT_SIZE, embed_dim) # T x C
position = torch.arange(0, _MAX_CONTEXT_SIZE).unsqueeze(1) # FROM 1 x T to T x 1
# P.E(pos,2i) = sin(pos/10000^(2i/dim))
# div_term = 10000 ^([0,1,2,...,C/2-1] * 2/C) <--
div_term = torch.pow(10_000.0, torch.arange(0, embed_dim//2) * 2/embed_dim) # 1 x C/2 (Embed_dim/2)
pos_embed[:, 0::2] = torch.sin(position / div_term) # T x C/2 ((T x 1) / (1 x C/2) = T x C/2 broadcasted)
pos_embed[:, 1::2] = torch.cos(position / div_term) # T x C/2
self.register_buffer('pos_embed', pos_embed, persistent=False)
def forward(self,x):
# x = B x T (NOT 1-hot)
embed_x = self.embed(x) # B T C
embed_x = embed_x * self.emb_factor # presumably to not be overpowered by the positional encoding
# ================================
# For variable length
# ===============================
seq_len = x.shape[-1] # length of T
truc_pos_embed = self.pos_embed[:seq_len,:]
embed_x = self.dropout(embed_x + truc_pos_embed)
return embed_x
class MultiHeadAttention(nn.Module):
def __init__(self, embed_dim, num_heads, causal_mask = False, bias=True):
super().__init__()
self.dk = embed_dim // num_heads
self.causal_mask = causal_mask
self.combined_projection_q = nn.Linear(embed_dim,embed_dim, bias=bias)
self.combined_projection_k = nn.Linear(embed_dim,embed_dim, bias=bias)
self.combined_projection_v = nn.Linear(embed_dim,embed_dim, bias=bias)
self.num_heads = num_heads
self.multi_linear = nn.Linear(embed_dim,embed_dim, bias=bias)
def attention(self,q,k,v, padding_mask = None):
# input shape is B x h x T x dk
output = (q @ k.transpose(-2,-1)) / torch.sqrt(torch.tensor(self.dk)) # QKt/(sqrt(dk))
#apply mask in decoder layer
if self.causal_mask == True:
seq_len = q.shape[-2]
mask = torch.triu(torch.full((seq_len,seq_len), fill_value=-torch.inf,device=q.device), diagonal=1)
#mask = torch.triu(torch.full((seq_len,seq_len), fill_value=-torch.inf), diagonal=1)
#mask = mask.to(q.device)
output = output + mask
# apply padding mask in encoder self-attention and decoder cross-attention
if padding_mask is not None:
padding_mask = torch.tensor(padding_mask).unsqueeze(1).unsqueeze(1) # B x 1 x 1 x T (broadcasting)
padding_mask = torch.where(padding_mask == 0, -torch.inf, padding_mask) # -inf turns to 0
output = output + padding_mask
output = torch.softmax(output, -1)
output = output @ v
return output
def forward(self,x_q,x_k,x_v, padding_mask = None):
# combined projection, TxC @ CxC
# Equivalent to doing Txhead @ CxC over all heads
p_q = self.combined_projection_q(x_q)
p_k = self.combined_projection_k(x_k)
p_v = self.combined_projection_v(x_v)
# For each of QKV. [B=Batch, T=Time, C=Channels, h=Heads, dk= head dim]
# ========================|======================
# Split | Combine
# ========================|======================
# | B T C /\
# | <view> | <view> |
# | B T h dk |
# | <transpose> | <transpose> |
# \/ B h T dk |
# |
# <attn>
# ===============================================
B = p_q.shape[0]
def split_heads(p):
return p.view(B,-1,self.num_heads,self.dk).transpose(1,2)
p_q = split_heads(p_q)
p_k = split_heads(p_k)
p_v = split_heads(p_v)
output = self.attention(p_q,p_k,p_v, padding_mask=padding_mask)
def combine_heads(p):
return p.transpose(1,2).contiguous().view(B,-1,self.dk*self.num_heads)
output = combine_heads(output)
output = self.multi_linear(output)
return output
# This layer is slightly different from standard linear
class PointwiseFeedForward(nn.Module):
def __init__(self, embed_dim, d_ff):
super(PointwiseFeedForward, self).__init__()
self.linear1 = nn.Linear(embed_dim, d_ff, bias=True)
self.linear2 = nn.Linear(d_ff, embed_dim, bias=True)
def forward(self, x):
return self.linear2(nn.functional.relu(self.linear1(x)))
class EncoderLayer(nn.Module):
def __init__(self, embed_dim, num_heads, d_ff,dropout=0):
super().__init__()
# self attention
self.m_att = MultiHeadAttention(embed_dim, num_heads)
self.att_norm = nn.LayerNorm(embed_dim)
self.dropout1 = nn.Dropout(dropout)
# pointwise feedforward module
self.pwlinear = PointwiseFeedForward(embed_dim, d_ff)
self.lin_norm = nn.LayerNorm(embed_dim)
self.dropout2 = nn.Dropout(dropout)
def forward(self, x, padding_mask = None):
output = self.att_norm(x + self.dropout1(self.m_att(x,x,x, padding_mask=padding_mask)))
output = self.lin_norm(output + self.dropout2(self.pwlinear(output)))
return output
class EncoderStack(nn.Module):
def __init__(self, embed_dim, num_heads, num_layers, d_ff, dropout=0, bos_token_id=1, eos_token_id=2, pad_token_id=0):
super().__init__()
self.layers = nn.ModuleList([EncoderLayer(embed_dim, num_heads, d_ff, dropout) for i in range(num_layers)])
self.bos_token_id = bos_token_id
self.eos_token_id = eos_token_id
self.pad_token_id = pad_token_id
def add_bos_eos(self, input_ids):
modified_input_ids = []
for seq in input_ids: # iterate through each batch element
# Prepend BOS token if needed
if seq[0] != self.bos_token_id:
seq = torch.cat([torch.tensor([self.bos_token_id], device=seq.device), seq])
# Append EOS token if needed
if seq[-1] != self.eos_token_id:
seq = torch.cat([seq, torch.tensor([self.eos_token_id], device=seq.device)])
modified_input_ids.append(seq)
# Pad sequences to the same length
padded_input_ids = pad_sequence(modified_input_ids, batch_first=True, padding_value=self.pad_token_id)
return padded_input_ids
# For huggingface compatibility, input_embeds are calculated inside encoder.
# So encoder must handle both input_ids and input_embeds
# Will use parent's embed layer. Can't transfer emb layer to encoder without breaking saved checkpoints.
def forward(self, input_embeds=None, input_ids=None, padding_mask = None, **kwargs):
input_ids = self.add_bos_eos(input_ids) # add bos and eos tokens if absent
if input_embeds is None:
input_embeds = self.emb(input_ids)
i = 0 # for debugging
for layer in self.layers:
input_embeds = layer(input_embeds, padding_mask = padding_mask)
return BaseModelOutputWithPastAndCrossAttentions(last_hidden_state=input_embeds, hidden_states=None, attentions=None)
class DecoderLayer(nn.Module):
def __init__(self, embed_dim, num_heads, d_ff,dropout=0):
super().__init__()
# self causal mask attention module
self.m_att = MultiHeadAttention(embed_dim, num_heads, causal_mask=True)
self.att_norm = nn.LayerNorm(embed_dim)
self.dropout1 = nn.Dropout(dropout)
# additional cross attention module
self.cross_att = MultiHeadAttention(embed_dim, num_heads, causal_mask=False)
self.cross_att_norm = nn.LayerNorm(embed_dim)
self.dropout2 = nn.Dropout(dropout)
# pointwise feedforward module with its layer norm
self.pwlinear = PointwiseFeedForward(embed_dim, d_ff)
self.lin_norm = nn.LayerNorm(embed_dim)
self.dropout3 = nn.Dropout(dropout)
def forward(self, x, enc_out, enc_padding_mask = None):
output = self.att_norm(x + self.dropout1(self.m_att(x,x,x))) # self attention
output = self.cross_att_norm(output + self.dropout2(self.cross_att(output, enc_out,enc_out, padding_mask=enc_padding_mask))) # cross attention
output = self.lin_norm(output + self.dropout3(self.pwlinear(output))) # pointwise feedforward
return output
class DecoderStack(nn.Module):
def __init__(self, embed_dim, num_heads, num_layers, d_ff,dropout=0):
super().__init__()
self.layers = nn.ModuleList([DecoderLayer(embed_dim, num_heads, d_ff,dropout) for i in range(num_layers)])
def forward(self, x, enc_out, enc_padding_mask = None):
for layer in self.layers:
x = layer(x, enc_out, enc_padding_mask)
return x
class OrginalTransformer(PreTrainedModel, GenerationMixin):
config_class = OriginalTransformerConfig
def __init__(self, config):
super().__init__(config)
self.emb = Embed(config.enc_vocab_size, config.embed_dim) # one embedding for both encoder and decoder
self.enc = EncoderStack(config.embed_dim, config.num_heads, config.num_enc_layers, config.d_ff, config.dropout,
config.bos_token_id, config.eos_token_id, config.pad_token_id)
self.dec = DecoderStack(config.embed_dim, config.num_heads, config.num_dec_layers, config.d_ff, config.dropout)
self.last_lin = nn.Linear(config.embed_dim, config.dec_vocab_size, bias=False) # bias false we're tying its weights with the embedding layer
self.last_lin.weight = self.emb.embed.weight # tying weights
# for accessing emb from inside encoder and decoder (for HF)
self.enc.emb = self.emb
self.dec.emb = self.emb
# huggingface compabile forward
def forward(self, input_ids= None, attention_mask=None, decoder_input_ids=None, decoder_attention_mask=None,
head_mask=None, decoder_head_mask=None, encoder_outputs=None, past_key_values=None, use_cache=None,
output_hidden_states=None, token_type_ids=None, inputs_embeds=None, labels=None, **kwargs):
# Encoder
# Dont actually need this. Encoder automatically called by .generate() method.
if encoder_outputs is None:
encoder_outputs = self.enc(self.emb(input_ids), None) # Encoder
# Decoder
# generate() calls the model with decoder_input_ids
dec_out = self.dec(self.emb(decoder_input_ids), encoder_outputs.last_hidden_state, None)
logits = self.last_lin(dec_out)
output = Seq2SeqLMOutput(logits=logits, encoder_last_hidden_state=encoder_outputs)
return output
def get_encoder(self):
return self.enc
def get_decoder(self):
return self.dec
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