File size: 9,161 Bytes
002c3f9 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 | from transformers import PreTrainedModel, PretrainedConfig
from transformers.modeling_outputs import CausalLMOutputWithCrossAttentions
import torch
import torch.nn as nn
from torch.nn import functional as F
from transformers.modeling_outputs import CausalLMOutput
class BVVConfig(PretrainedConfig):
model_type = "model_256_bit"
def __init__(
self,
vocab_size = 65536,
n_embed = 256,
d_model = 1024,
n_head = 32,
n_layer = 16,
block_size = 1024,
pad_id = 57344,
**kwargs
):
super().__init__(**kwargs)
self.vocab_size = vocab_size
self.block_size = block_size
self.n_embed = n_embed
self.d_model = d_model
self.n_layer = n_layer
self.n_head = n_head
self.pad_id = pad_id
self.scale = d_model // n_embed
class RotaryEmbedding(nn.Module):
def __init__(self, dim): # dim = head_dim (?? d_model!)
super().__init__()
inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2).float() / dim))
self.register_buffer("inv_freq", inv_freq, persistent=False)
def forward(self, seq_len, device):
t = torch.arange(seq_len, device=device, dtype=self.inv_freq.dtype)
freqs = torch.einsum('i,j->ij', t, self.inv_freq)
emb = torch.cat([freqs, freqs], dim=-1) # (seq_len, dim)
return emb
def apply_rotary_emb(x, rot_emb):
# x: (B, n_head, seq_len, head_dim)
# rot_emb: (seq_len, head_dim)
seq_len = x.shape[-2]
rot_emb = rot_emb[:seq_len]
cos = torch.cos(rot_emb).unsqueeze(0).unsqueeze(0) # (1, 1, seq_len, head_dim)
sin = torch.sin(rot_emb).unsqueeze(0).unsqueeze(0)
x_shape = x.shape
x = x.reshape(*x_shape[:-1], -1, 2) # (..., head_dim/2, 2)
x1 = x[..., 0]
x2 = x[..., 1]
cos = cos.reshape(*cos.shape[:-1], -1, 2)[..., 0]
sin = sin.reshape(*sin.shape[:-1], -1, 2)[..., 0]
x1_rot = x1 * cos - x2 * sin
x2_rot = x1 * sin + x2 * cos
x_rot = torch.stack([x1_rot, x2_rot], dim=-1)
return x_rot.reshape(x_shape)
class MultiHeadSelfAttention(nn.Module):
def __init__(self, d_model, n_head, block_size):
super().__init__()
assert d_model % n_head == 0
self.d_model = d_model
self.n_head = n_head
self.head_dim = d_model // n_head
self.q_proj = nn.Linear(d_model, d_model, bias=False)
self.k_proj = nn.Linear(d_model, d_model, bias=False)
self.v_proj = nn.Linear(d_model, d_model, bias=False)
self.o_proj = nn.Linear(d_model, d_model, bias=False)
self.rotary_emb = RotaryEmbedding(self.head_dim)
self.dropout = nn.Dropout(0.0)
self.register_buffer(
"tril", torch.tril(torch.ones(block_size, block_size)), persistent=False
)
def forward(self, x):
# x: (B, T, d_model)
B, T, C = x.shape
q = self.q_proj(x) # (B, T, d_model)
k = self.k_proj(x)
v = self.v_proj(x)
q = q.view(B, T, self.n_head, self.head_dim).transpose(1, 2) # (B, n_head, T, head_dim)
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)
# Rotary embeddings
rot_emb = self.rotary_emb(seq_len=T, device=x.device) # (T, head_dim)
q = apply_rotary_emb(q, rot_emb)
k = apply_rotary_emb(k, rot_emb)
# Attention
attn_scores = torch.matmul(q, k.transpose(-2, -1)) * (self.head_dim ** -0.5) # (B, n_head, T, T)
attn_scores = attn_scores.masked_fill(self.tril[:T, :T] == 0, float('-inf'))
attn_probs = F.softmax(attn_scores, dim=-1)
attn_probs = self.dropout(attn_probs)
out = torch.matmul(attn_probs, v) # (B, n_head, T, head_dim)
out = out.transpose(1, 2).contiguous().view(B, T, C) # (B, T, d_model)
return self.o_proj(out)
class TransformerMLP(nn.Module):
def __init__(self, d_model):
super().__init__()
self.net = nn.Sequential(
nn.Linear(d_model, 4 * d_model),
nn.GELU(),
nn.Linear(4 * d_model, d_model),
nn.Dropout(0.0),
)
def forward(self, x):
return self.net(x)
class TransformerBlock(nn.Module):
def __init__(self, d_model, n_head, block_size):
super().__init__()
self.self_attn = MultiHeadSelfAttention(d_model, n_head, block_size)
self.mlp = TransformerMLP(d_model)
self.input_layernorm = nn.LayerNorm(d_model)
self.post_attention_layernorm = nn.LayerNorm(d_model)
def forward(self, x):
x = x + self.self_attn(self.input_layernorm(x))
x = x + self.mlp(self.post_attention_layernorm(x))
return x
class BVVForCausalLM(PreTrainedModel):
config_class = BVVConfig
def __init__(self, config):
super().__init__(config)
self.token_embeddings = nn.Embedding(config.vocab_size, config.n_embed)
self.scale = config.scale
self.transformer_layers = nn.Sequential(*[
TransformerBlock(config.d_model, n_head=config.n_head, block_size=config.block_size) for _ in range(config.n_layer)
])
self.final_layernorm = nn.LayerNorm(config.d_model)
self.lm_head = nn.Linear(config.d_model, config.vocab_size)
self.apply(self._init_weights)
def _init_weights(self, module):
if isinstance(module, nn.Linear):
torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
if module.bias is not None:
torch.nn.init.zeros_(module.bias)
elif isinstance(module, nn.Embedding):
torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
def forward(self, idx, targets=None):
B, T = idx.shape
token_emb = self.token_embeddings(idx) # (B, T, 256)
token_emb = token_emb.repeat_interleave(self.scale, dim=-1) # (B, T, 1024)
x = token_emb
x = self.transformer_layers(x)
x = self.final_layernorm(x)
logits = self.lm_head(x)
loss = None
if targets is not None:
#logits_flat = logits.view(-1, logits.size(-1))
#targets_flat = targets.view(-1)
logits_flat = logits.reshape(-1, logits.size(-1))
targets_flat = targets.reshape(-1)
loss = F.cross_entropy(logits_flat, targets_flat, ignore_index = 57344)
return CausalLMOutput(
logits=logits,
loss=loss,
)
def generate(self,
input_ids=None,
max_new_tokens=None,
max_length=None,
temperature=1.0,
top_k=None,
top_p=None,
do_sample=True,
pad_token_id=None,
eos_token_id=None,
**kwargs):
if input_ids is None:
raise ValueError("Input_ids must be provided")
idx = input_ids
if max_new_tokens is None:
if max_length is not None:
max_new_tokens = max_length - idx.shape[1]
else:
max_new_tokens = 50
with torch.no_grad():
for _ in range(max_new_tokens):
idx_cond = idx[:, -self.config.block_size:]
outputs = self(idx_cond)
logits = outputs.logits[:, -1, :] / temperature
if top_k is not None:
v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
logits[logits < v[:, [-1]]] = float('-inf')
if top_p is not None:
sorted_logits, sorted_indices = torch.sort(logits, descending=True)
cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)
sorted_indices_to_remove = cumulative_probs > top_p
sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
sorted_indices_to_remove[..., 0] = 0
indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove)
logits[indices_to_remove] = float('-inf')
probs = F.softmax(logits, dim=-1)
if do_sample:
idx_next = torch.multinomial(probs, num_samples=1)
else:
idx_next = torch.argmax(logits, dim=-1, keepdim=True)
idx = torch.cat((idx, idx_next), dim=1)
if eos_token_id is not None and (idx_next == eos_token_id).any():
break
return idx |