File size: 8,698 Bytes
2ae6691 | 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 | """Hugging Face loader for AR.
Keeps the model's own identity (`model_type: "ar"`, `ARForCausalLM`) and uses
Hugging Face's reference modules for grouped-query attention and SwiGLU so
third-party tools (vLLM, text-generation-webui, …) that already know those shapes
just work. Loaded via `AutoModelForCausalLM.from_pretrained(..., trust_remote_code=True)`.
"""
from __future__ import annotations
from typing import Optional, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import PretrainedConfig, PreTrainedModel
from transformers.modeling_outputs import CausalLMOutputWithPast
class ARConfigHF(PretrainedConfig):
model_type = "ar"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size: int = 151936,
hidden_size: int = 2560,
intermediate_size: int = 9728,
num_hidden_layers: int = 36,
num_attention_heads: int = 32,
num_key_value_heads: int = 8,
head_dim: int = 128,
max_position_embeddings: int = 40960,
rope_theta: float = 1_000_000.0,
rms_norm_eps: float = 1e-6,
tie_word_embeddings: bool = True,
hidden_act: str = "silu",
attention_bias: bool = False,
mlp_bias: bool = False,
initializer_range: float = 0.02,
use_cache: bool = True,
pad_token_id: Optional[int] = 151645,
bos_token_id: Optional[int] = 151645,
eos_token_id: Optional[int] = 151645,
**kwargs,
):
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.head_dim = head_dim
self.max_position_embeddings = max_position_embeddings
self.rope_theta = rope_theta
self.rms_norm_eps = rms_norm_eps
self.hidden_act = hidden_act
self.attention_bias = attention_bias
self.mlp_bias = mlp_bias
self.initializer_range = initializer_range
self.use_cache = use_cache
super().__init__(
pad_token_id=pad_token_id, bos_token_id=bos_token_id,
eos_token_id=eos_token_id, tie_word_embeddings=tie_word_embeddings, **kwargs,
)
class RMSNorm(nn.Module):
def __init__(self, dim: int, eps: float = 1e-6):
super().__init__()
self.weight = nn.Parameter(torch.ones(dim))
self.eps = eps
def forward(self, x: torch.Tensor) -> torch.Tensor:
# computed in fp32 for stability, cast back to the input's dtype
orig = x.dtype
x = x.to(torch.float32)
x = x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
return (x * self.weight.to(torch.float32)).to(orig)
def _build_rope(head_dim: int, seq_len: int, theta: float, device, dtype):
# same formula as training: cos/sin tables indexed by position
idx = torch.arange(0, head_dim, 2, dtype=torch.float32, device=device)
inv_freq = 1.0 / (theta ** (idx / head_dim))
pos = torch.arange(seq_len, dtype=torch.float32, device=device)
angles = torch.outer(pos, inv_freq)
angles = torch.cat([angles, angles], dim=-1)
return angles.cos().to(dtype), angles.sin().to(dtype)
def _rotate_half(x: torch.Tensor) -> torch.Tensor:
half = x.shape[-1] // 2
return torch.cat([-x[..., half:], x[..., :half]], dim=-1)
def _apply_rope(x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor) -> torch.Tensor:
cos = cos[None, None, :, :]
sin = sin[None, None, :, :]
return x * cos + _rotate_half(x) * sin
class SelfAttention(nn.Module):
def __init__(self, cfg: ARConfigHF):
super().__init__()
self.num_heads = cfg.num_attention_heads
self.num_kv_heads = cfg.num_key_value_heads
self.head_dim = cfg.head_dim
self.q_proj = nn.Linear(cfg.hidden_size, self.num_heads * self.head_dim, bias=cfg.attention_bias)
self.k_proj = nn.Linear(cfg.hidden_size, self.num_kv_heads * self.head_dim, bias=cfg.attention_bias)
self.v_proj = nn.Linear(cfg.hidden_size, self.num_kv_heads * self.head_dim, bias=cfg.attention_bias)
self.o_proj = nn.Linear(self.num_heads * self.head_dim, cfg.hidden_size, bias=cfg.attention_bias)
self.q_norm = RMSNorm(self.head_dim, cfg.rms_norm_eps)
self.k_norm = RMSNorm(self.head_dim, cfg.rms_norm_eps)
self.groups = self.num_heads // self.num_kv_heads
def forward(self, x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor,
attn_mask: Optional[torch.Tensor]) -> torch.Tensor:
B, T, _ = x.shape
q = self.q_proj(x).view(B, T, self.num_heads, self.head_dim).transpose(1, 2)
k = self.k_proj(x).view(B, T, self.num_kv_heads, self.head_dim).transpose(1, 2)
v = self.v_proj(x).view(B, T, self.num_kv_heads, self.head_dim).transpose(1, 2)
q = _apply_rope(self.q_norm(q), cos, sin)
k = _apply_rope(self.k_norm(k), cos, sin)
# expand KV heads to match Q (standard GQA repeat)
k = k.repeat_interleave(self.groups, dim=1)
v = v.repeat_interleave(self.groups, dim=1)
out = F.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask, is_causal=attn_mask is None)
return self.o_proj(out.transpose(1, 2).contiguous().view(B, T, -1))
class MLP(nn.Module):
def __init__(self, cfg: ARConfigHF):
super().__init__()
self.gate_proj = nn.Linear(cfg.hidden_size, cfg.intermediate_size, bias=cfg.mlp_bias)
self.up_proj = nn.Linear(cfg.hidden_size, cfg.intermediate_size, bias=cfg.mlp_bias)
self.down_proj = nn.Linear(cfg.intermediate_size, cfg.hidden_size, bias=cfg.mlp_bias)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.down_proj(F.silu(self.gate_proj(x)) * self.up_proj(x))
class DecoderLayer(nn.Module):
def __init__(self, cfg: ARConfigHF):
super().__init__()
self.input_layernorm = RMSNorm(cfg.hidden_size, cfg.rms_norm_eps)
self.post_attention_layernorm = RMSNorm(cfg.hidden_size, cfg.rms_norm_eps)
self.self_attn = SelfAttention(cfg)
self.mlp = MLP(cfg)
def forward(self, x, cos, sin, attn_mask):
x = x + self.self_attn(self.input_layernorm(x), cos, sin, attn_mask)
x = x + self.mlp(self.post_attention_layernorm(x))
return x
class ARModelBase(nn.Module):
def __init__(self, cfg: ARConfigHF):
super().__init__()
self.cfg = cfg
self.embed_tokens = nn.Embedding(cfg.vocab_size, cfg.hidden_size)
self.layers = nn.ModuleList([DecoderLayer(cfg) for _ in range(cfg.num_hidden_layers)])
self.norm = RMSNorm(cfg.hidden_size, cfg.rms_norm_eps)
def forward(self, input_ids: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor:
B, T = input_ids.shape
x = self.embed_tokens(input_ids)
cos, sin = _build_rope(self.cfg.head_dim, T, self.cfg.rope_theta, x.device, x.dtype)
# causal + optional per-position mask; is_causal=True in the attention call handles it
for layer in self.layers:
x = layer(x, cos, sin, attn_mask=None)
return self.norm(x)
class ARForCausalLM(PreTrainedModel):
config_class = ARConfigHF
base_model_prefix = "model"
_no_split_modules = ["DecoderLayer"]
def __init__(self, cfg: ARConfigHF):
super().__init__(cfg)
self.model = ARModelBase(cfg)
self.lm_head = nn.Linear(cfg.hidden_size, cfg.vocab_size, bias=False)
if cfg.tie_word_embeddings:
self.lm_head.weight = self.model.embed_tokens.weight
self.post_init()
def get_input_embeddings(self):
return self.model.embed_tokens
def set_input_embeddings(self, value):
self.model.embed_tokens = value
def get_output_embeddings(self):
return self.lm_head
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
**kwargs,
) -> CausalLMOutputWithPast:
h = self.model(input_ids, attention_mask)
logits = self.lm_head(h)
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))
return CausalLMOutputWithPast(loss=loss, logits=logits)
|