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44498e1 253ca21 44498e1 0a72cae | 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 | import torch
import torch.nn as nn
import math
from transformers import PreTrainedModel
from transformers.modeling_outputs import CausalLMOutput
try:
from .configuration_rapnss import RapnssConfig
except ImportError:
from configuration_rapnss import RapnssConfig
class RMSNorm(nn.Module):
def __init__(self, dim, eps=1e-6):
super().__init__()
self.eps = eps
self.weight = nn.Parameter(torch.ones(dim))
def _norm(self, x):
return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
def forward(self, x):
return self.weight * self._norm(x.float()).type_as(x)
def precompute_freqs_cis(dim: int, end: int, theta: float = 10000.0):
freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim))
t = torch.arange(end, device=freqs.device, dtype=torch.float32)
freqs = torch.outer(t, freqs)
freqs_cis = torch.polar(torch.ones_like(freqs), freqs)
return freqs_cis
def apply_rotary_emb(xq: torch.Tensor, xk: torch.Tensor, freqs_cis: torch.Tensor):
xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2))
xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2))
freqs_cis = freqs_cis[:xq_.shape[1]].unsqueeze(0).unsqueeze(2)
xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(3)
xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(3)
return xq_out.type_as(xq), xk_out.type_as(xk)
class CausalSelfAttention(nn.Module):
def __init__(self, config):
super().__init__()
assert config.d_model % config.n_heads == 0
self.c_attn = nn.Linear(config.d_model, 3 * config.d_model, bias=False)
self.c_proj = nn.Linear(config.d_model, config.d_model, bias=False)
self.n_heads = config.n_heads
self.d_model = config.d_model
self.register_buffer("bias", torch.tril(torch.ones(config.max_seq_len, config.max_seq_len))
.view(1, 1, config.max_seq_len, config.max_seq_len))
freqs_cis = precompute_freqs_cis(self.d_model // self.n_heads, config.max_seq_len)
self.register_buffer("freqs_cis", freqs_cis, persistent=False)
self.dropout = nn.Dropout(config.dropout)
def forward(self, x):
B, T, C = x.size()
qkv = self.c_attn(x)
q, k, v = qkv.split(self.d_model, dim=2)
q = q.view(B, T, self.n_heads, C // self.n_heads)
k = k.view(B, T, self.n_heads, C // self.n_heads)
v = v.view(B, T, self.n_heads, C // self.n_heads)
q, k = apply_rotary_emb(q, k, self.freqs_cis)
q = q.transpose(1, 2)
k = k.transpose(1, 2)
v = v.transpose(1, 2)
att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))
att = att.masked_fill(self.bias[:,:,:T,:T] == 0, float('-inf'))
att = torch.softmax(att, dim=-1)
att = self.dropout(att)
y = att @ v
y = y.transpose(1, 2).contiguous().view(B, T, C)
y = self.c_proj(y)
return y
class SwiGLU(nn.Module):
def forward(self, x):
x, gate = x.chunk(2, dim=-1)
return torch.nn.functional.silu(gate) * x
class FeedForward(nn.Module):
def __init__(self, config):
super().__init__()
hidden_dim = int(2 * config.d_ff / 3)
self.w1 = nn.Linear(config.d_model, hidden_dim * 2, bias=False)
self.swiglu = SwiGLU()
self.w2 = nn.Linear(hidden_dim, config.d_model, bias=False)
self.dropout = nn.Dropout(config.dropout)
def forward(self, x):
x = self.w1(x)
x = self.swiglu(x)
x = self.w2(x)
x = self.dropout(x)
return x
class RapnssTransformerBlock(nn.Module):
def __init__(self, config):
super().__init__()
self.ln_1 = RMSNorm(config.d_model)
self.attn = CausalSelfAttention(config)
self.ln_2 = RMSNorm(config.d_model)
self.mlp = FeedForward(config)
def forward(self, x):
x = x + self.attn(self.ln_1(x))
x = x + self.mlp(self.ln_2(x))
return x
class RapnssPreTrainedModel(PreTrainedModel):
config_class = RapnssConfig
base_model_prefix = "transformer"
supports_gradient_checkpointing = True
def _init_weights(self, module):
if isinstance(module, nn.Linear):
torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
elif isinstance(module, nn.Embedding):
torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
class RapnssForCausalLM(RapnssPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.config = config
self.wte = nn.Embedding(config.vocab_size, config.d_model)
self.drop = nn.Dropout(config.dropout)
self.h = nn.ModuleList([RapnssTransformerBlock(config) for _ in range(config.n_layers)])
self.ln_f = RMSNorm(config.d_model)
self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False)
self.wte.weight = nn.Parameter(self.lm_head.weight.clone())
self.post_init()
def get_input_embeddings(self):
return self.wte
def set_input_embeddings(self, value):
self.wte = value
def get_output_embeddings(self):
return self.lm_head
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings
def forward(self, input_ids=None, labels=None, **kwargs):
tok_emb = self.wte(input_ids)
x = self.drop(tok_emb)
for block in self.h:
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_fct = nn.CrossEntropyLoss()
loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
return CausalLMOutput(loss=loss, logits=logits)
def prepare_inputs_for_generation(self, input_ids, **kwargs):
return {"input_ids": input_ids}
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