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d073da3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 | from transformers import GenerationMixin, PreTrainedModel
from transformers.modeling_outputs import CausalLMOutput
import torch.nn as nn
import torch
import math
try:
from .configuration_qraxai import GPTConfig
except ImportError:
from configuration_qraxai import GPTConfig
class CausalSelfAttention(nn.Module):
def __init__(self, embed_dim, num_heads):
super().__init__()
assert embed_dim % num_heads == 0, \
"embed_dim must be divisible by num_heads"
self.embed_dim = embed_dim
self.num_heads = num_heads
self.head_dim = embed_dim // num_heads
# Q, K, V projections
self.q_proj = nn.Linear(embed_dim, embed_dim)
self.k_proj = nn.Linear(embed_dim, embed_dim)
self.v_proj = nn.Linear(embed_dim, embed_dim)
self.o_proj = nn.Linear(embed_dim, embed_dim)
def forward(self, x):
batch_size, seq_len, embed_dim = x.shape
Q = self.q_proj(x)
V = self.v_proj(x)
K = self.k_proj(x)
Q = Q.view(
batch_size,
seq_len,
self.num_heads,
self.head_dim
)
K = K.view(
batch_size,
seq_len,
self.num_heads,
self.head_dim
)
V = V.view(
batch_size,
seq_len,
self.num_heads,
self.head_dim
)
Q = Q.transpose(1, 2)
K = K.transpose(1, 2)
V = V.transpose(1, 2)
scores = Q @ K.transpose(-2, -1)
scores = scores / math.sqrt(self.head_dim)
mask = torch.triu(
torch.ones(
seq_len,
seq_len,
device=x.device
),
diagonal=1
).bool()
scores = scores.masked_fill(
mask,
torch.finfo(scores.dtype).min
)
attention_w = torch.softmax(
scores,
dim=-1
)
output = attention_w @ V
output = output.transpose(1, 2)
output = output.contiguous().view(
batch_size,
seq_len,
embed_dim
)
# Final projection
output = self.o_proj(output)
return output
class TransformerBlock(nn.Module):
def __init__(
self,
embed_dim
):
super().__init__()
self.ln1 = nn.LayerNorm(embed_dim)
self.attention = CausalSelfAttention(embed_dim, 8)
self.ln2 = nn.LayerNorm(embed_dim)
# feed forward network
self.ffn = nn.Sequential(
nn.Linear(
in_features=embed_dim,
out_features=4*embed_dim
),
nn.GELU(),
nn.Linear(
in_features=4*embed_dim,
out_features=embed_dim
)
)
def forward(self, x):
x = x + self.attention(
self.ln1(x)
)
x = x + self.ffn(
self.ln2(x)
)
return x
class QraXAiForCausalLM(PreTrainedModel, GenerationMixin):
config_class = GPTConfig
def __init__(
self,
config
):
super().__init__(config)
self.token_embedding = nn.Embedding(
config.vocab_size,
config.embed_dim
)
self.position_embedding = nn.Embedding(
config.max_seq_len,
config.embed_dim
)
self.transformer_blocks = nn.ModuleList([
TransformerBlock(config.embed_dim)
for _ in range(config.n_layers)
])
self.ln_f = nn.LayerNorm(
config.embed_dim
)
self.lm_head = nn.Linear(
config.embed_dim,
config.vocab_size,
bias=False
)
self.post_init()
def forward(
self,
input_ids,
labels=None,
**kwargs
):
batch_size, seq_len = input_ids.shape
if seq_len > self.config.max_seq_len:
raise ValueError(
f"Sequence length ({seq_len}) "
f"cannot be greater than "
f"max_seq_len ({self.config.max_seq_len})"
)
positions = torch.arange(
seq_len,
device=input_ids.device
)
token_emb = self.token_embedding(
input_ids
)
pos_emb = self.position_embedding(
positions
)
x = token_emb + pos_emb
for block in self.transformer_blocks:
x = block(x)
x = self.ln_f(x)
logits = self.lm_head(x)
loss = None
if labels is not None:
shift_logits = logits[
:, :-1, :
].contiguous()
shift_labels = labels[
:, 1:
].contiguous()
loss = nn.functional.cross_entropy(
shift_logits.view(
-1,
shift_logits.size(-1)
),
shift_labels.view(-1)
)
return CausalLMOutput(
loss=loss,
logits=logits
) |