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759d613 | 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 | from configuration_spin import SpinConfig
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import PreTrainedModel
from transformers.modeling_outputs import CausalLMOutputWithPast
class RMSNorm(nn.Module):
def __init__(self, dim: int, eps: float = 1e-5):
super().__init__()
self.eps = eps
self.weight = nn.Parameter(torch.ones(dim))
def forward(self, x: torch.Tensor) -> torch.Tensor:
variance = x.pow(2).mean(-1, keepdim=True)
return x * torch.rsqrt(variance + self.eps) * self.weight
def precompute_freqs_cis(dim: int, max_seq_len: int, theta: float = 10000.0):
freqs = 1.0 / (
theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim)
)
t = torch.arange(max_seq_len, dtype=torch.float32)
freqs = torch.outer(t, freqs)
freqs_cos = torch.cos(freqs)
freqs_sin = torch.sin(freqs)
return freqs_cos, freqs_sin
def apply_rotary_emb(
xq: torch.Tensor,
xk: torch.Tensor,
freqs_cos: torch.Tensor,
freqs_sin: torch.Tensor,
):
xq_r, xq_i = xq.float().reshape(*xq.shape[:-1], -1, 2).unbind(-1)
xk_r, xk_i = xk.float().reshape(*xk.shape[:-1], -1, 2).unbind(-1)
freqs_cos = freqs_cos.unsqueeze(0).unsqueeze(2)
freqs_sin = freqs_sin.unsqueeze(0).unsqueeze(2)
xq_out_r = xq_r * freqs_cos - xq_i * freqs_sin
xq_out_i = xq_r * freqs_sin + xq_i * freqs_cos
xk_out_r = xk_r * freqs_cos - xk_i * freqs_sin
xk_out_i = xk_r * freqs_sin + xk_i * freqs_cos
xq_out = torch.stack([xq_out_r, xq_out_i], dim=-1).flatten(3)
xk_out = torch.stack([xk_out_r, xk_out_i], dim=-1).flatten(3)
return xq_out.type_as(xq), xk_out.type_as(xk)
class SwiGLU(nn.Module):
def __init__(self, d_model: int, d_ff: int):
super().__init__()
self.w_gate = nn.Linear(d_model, d_ff, bias=False)
self.w_up = nn.Linear(d_model, d_ff, bias=False)
self.w_down = nn.Linear(d_ff, d_model, bias=False)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.w_down(F.silu(self.w_gate(x)) * self.w_up(x))
class CausalSelfAttention(nn.Module):
def __init__(self, config: SpinConfig):
super().__init__()
self.n_heads = config.n_heads
self.head_dim = config.d_model // config.n_heads
self.q_proj = nn.Linear(config.d_model, config.d_model, bias=False)
self.k_proj = nn.Linear(config.d_model, config.d_model, bias=False)
self.v_proj = nn.Linear(config.d_model, config.d_model, bias=False)
self.out_proj = nn.Linear(config.d_model, config.d_model, bias=False)
mask = torch.full(
(config.max_seq_len, config.max_seq_len), float("-inf")
)
mask = torch.triu(mask, diagonal=1)
self.register_buffer("causal_mask", mask)
def forward(
self,
x: torch.Tensor,
freqs_cos: torch.Tensor,
freqs_sin: torch.Tensor,
kv_cache: tuple[torch.Tensor, torch.Tensor] | None = None,
) -> tuple[torch.Tensor, tuple[torch.Tensor, torch.Tensor]]:
B, T, C = x.shape
q = self.q_proj(x).view(B, T, self.n_heads, self.head_dim)
k = self.k_proj(x).view(B, T, self.n_heads, self.head_dim)
v = self.v_proj(x).view(B, T, self.n_heads, self.head_dim)
q, k = apply_rotary_emb(q, k, freqs_cos, freqs_sin)
q = q.transpose(1, 2)
k = k.transpose(1, 2)
v = v.transpose(1, 2)
if kv_cache is not None:
prev_k, prev_v = kv_cache
k = torch.cat([prev_k, k], dim=2)
v = torch.cat([prev_v, v], dim=2)
new_kv_cache = (k, v)
total_k_len = k.size(2)
scores = (q @ k.transpose(-2, -1)) / math.sqrt(self.head_dim)
if T > 1:
scores = scores + self.causal_mask[:T, :total_k_len]
attn_weights = F.softmax(scores, dim=-1)
out = attn_weights @ v
out = out.transpose(1, 2).contiguous().view(B, T, C)
return self.out_proj(out), new_kv_cache
class TransformerBlock(nn.Module):
def __init__(self, config: SpinConfig):
super().__init__()
self.attn_norm = RMSNorm(config.d_model, eps=config.norm_eps)
self.attn = CausalSelfAttention(config)
self.ffn_norm = RMSNorm(config.d_model, eps=config.norm_eps)
self.ffn = SwiGLU(config.d_model, config.d_ff)
def forward(
self,
x: torch.Tensor,
freqs_cos: torch.Tensor,
freqs_sin: torch.Tensor,
kv_cache: tuple[torch.Tensor, torch.Tensor] | None = None,
) -> tuple[torch.Tensor, tuple[torch.Tensor, torch.Tensor]]:
norm_x = self.attn_norm(x)
attn_out, next_kv = self.attn(
norm_x, freqs_cos, freqs_sin, kv_cache=kv_cache
)
x = x + attn_out
x = x + self.ffn(self.ffn_norm(x))
return x, next_kv
class SpinForCausalLM(PreTrainedModel):
config_class = SpinConfig
def __init__(self, config: SpinConfig):
super().__init__(config)
self.config = config
self.tok_embeddings = nn.Embedding(config.vocab_size, config.d_model)
self.layers = nn.ModuleList(
[TransformerBlock(config) for _ in range(config.n_layers)]
)
self.norm = RMSNorm(config.d_model, eps=config.norm_eps)
self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False)
self.tok_embeddings.weight = self.lm_head.weight
head_dim = config.d_model // config.n_heads
freqs_cos, freqs_sin = precompute_freqs_cis(
head_dim, config.max_seq_len
)
self.register_buffer("freqs_cos", freqs_cos)
self.register_buffer("freqs_sin", freqs_sin)
self.post_init()
def forward(
self,
input_ids: torch.Tensor,
labels: torch.Tensor = None,
kv_caches=None,
start_pos: int = 0,
return_dict: bool = True,
):
B, T = input_ids.shape
x = self.tok_embeddings(input_ids)
freqs_cos = self.freqs_cos[start_pos : start_pos + T]
freqs_sin = self.freqs_sin[start_pos : start_pos + T]
new_kv_caches = []
for i, layer in enumerate(self.layers):
cache_i = kv_caches[i] if kv_caches is not None else None
x, new_cache = layer(x, freqs_cos, freqs_sin, kv_cache=cache_i)
new_kv_caches.append(new_cache)
x = self.norm(x)
logits = self.lm_head(x)
loss = None
if labels is not None:
loss = F.cross_entropy(
logits.view(-1, self.config.vocab_size),
labels.view(-1),
ignore_index=-100,
)
if not return_dict:
return (logits, loss, new_kv_caches)
return CausalLMOutputWithPast(
loss=loss, logits=logits, past_key_values=new_kv_caches
)
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