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d52dd01 | 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 | """CORe model architecture for HuggingFace transformers."""
import math
from typing import Optional, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import PreTrainedModel, PretrainedConfig, GenerationMixin
from transformers.modeling_outputs import CausalLMOutput
class COReConfig(PretrainedConfig):
model_type = "core"
def __init__(
self,
n_layer=12,
n_head=16,
n_embd=1024,
block_size=512,
vocab_size=16384,
rope=False,
dropout=0.0,
**kwargs,
):
super().__init__(**kwargs)
self.n_layer = n_layer
self.n_head = n_head
self.n_embd = n_embd
self.block_size = block_size
self.vocab_size = vocab_size
self.rope = rope
self.dropout = dropout
class CausalSelfAttention(nn.Module):
def __init__(self, config):
super().__init__()
assert config.n_embd % config.n_head == 0
self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd)
self.c_proj = nn.Linear(config.n_embd, config.n_embd)
self.attn_dropout = nn.Dropout(config.dropout)
self.resid_dropout = nn.Dropout(config.dropout)
self.n_head = config.n_head
self.head_dim = config.n_embd // config.n_head
self.register_buffer(
"causal_mask",
torch.tril(torch.ones(config.block_size, config.block_size)).view(
1, 1, config.block_size, config.block_size
),
persistent=False,
)
def forward(self, x):
B, T, C = x.size()
q, k, v = self.c_attn(x).split(C, dim=2)
q = q.view(B, T, self.n_head, self.head_dim).transpose(1, 2)
k = k.view(B, T, self.n_head, self.head_dim).transpose(1, 2)
v = v.view(B, T, self.n_head, self.head_dim).transpose(1, 2)
y = F.scaled_dot_product_attention(
q, k, v,
dropout_p=self.attn_dropout.p if self.training else 0.0,
is_causal=True,
)
y = y.transpose(1, 2).contiguous().view(B, T, C)
return self.resid_dropout(self.c_proj(y))
class MLP(nn.Module):
def __init__(self, config):
super().__init__()
self.c_fc = nn.Linear(config.n_embd, 4 * config.n_embd)
self.c_proj = nn.Linear(4 * config.n_embd, config.n_embd)
self.dropout = nn.Dropout(config.dropout)
def forward(self, x):
return self.dropout(self.c_proj(F.gelu(self.c_fc(x))))
class Block(nn.Module):
def __init__(self, config):
super().__init__()
self.ln_1 = nn.LayerNorm(config.n_embd)
self.attn = CausalSelfAttention(config)
self.ln_2 = nn.LayerNorm(config.n_embd)
self.mlp = MLP(config)
def forward(self, x):
x = x + self.attn(self.ln_1(x))
x = x + self.mlp(self.ln_2(x))
return x
class COReModel(PreTrainedModel):
config_class = COReConfig
base_model_prefix = "core"
def __init__(self, config):
super().__init__(config)
self.config = config
self.tok_emb = nn.Embedding(config.vocab_size, config.n_embd)
self.pos_emb = None if config.rope else nn.Embedding(config.block_size, config.n_embd)
self.drop = nn.Dropout(config.dropout)
self.blocks = nn.ModuleList(Block(config) for _ in range(config.n_layer))
self.ln_f = nn.LayerNorm(config.n_embd)
self.post_init()
def forward(self, input_ids, attention_mask=None, **kwargs):
B, T = input_ids.size()
if self.pos_emb is not None:
pos = torch.arange(0, T, device=input_ids.device)
x = self.drop(self.tok_emb(input_ids) + self.pos_emb(pos))
else:
x = self.drop(self.tok_emb(input_ids))
for block in self.blocks:
x = block(x)
return self.ln_f(x)
class COReForCausalLM(PreTrainedModel, GenerationMixin):
config_class = COReConfig
base_model_prefix = "core"
main_input_name = "input_ids"
_supports_cache_class = False
_supports_static_cache = False
def __init__(self, config):
super().__init__(config)
self.config = config
self.tok_emb = nn.Embedding(config.vocab_size, config.n_embd)
self.pos_emb = None if config.rope else nn.Embedding(config.block_size, config.n_embd)
self.drop = nn.Dropout(config.dropout)
self.blocks = nn.ModuleList(Block(config) for _ in range(config.n_layer))
self.ln_f = nn.LayerNorm(config.n_embd)
self.head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
self.tok_emb.weight = self.head.weight
self.post_init()
def forward(self, input_ids, attention_mask=None, labels=None, **kwargs):
B, T = input_ids.size()
if self.pos_emb is not None:
pos = torch.arange(0, T, device=input_ids.device)
x = self.drop(self.tok_emb(input_ids) + self.pos_emb(pos))
else:
x = self.drop(self.tok_emb(input_ids))
for block in self.blocks:
x = block(x)
x = self.ln_f(x)
logits = self.head(x)
loss = None
if labels is not None:
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
loss = F.cross_entropy(
shift_logits.view(-1, shift_logits.size(-1)),
shift_labels.view(-1),
ignore_index=-100,
)
return CausalLMOutput(loss=loss, logits=logits)
def prepare_inputs_for_generation(self, input_ids, **kwargs):
if input_ids.size(1) > self.config.block_size:
input_ids = input_ids[:, -self.config.block_size:]
return {"input_ids": input_ids}
def _reorder_cache(self, past, beam_idx):
return past
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