File size: 9,093 Bytes
d91766b | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 | import os
import torch
import torch.nn as nn
from diffulex.attention import Attention
from diffulex.layer.layernorm import RMSNorm
from diffulex.layer.activation import SiluAndMul
from diffulex.layer.rotary_embedding import get_rope
from diffulex.model.auto_model import AutoModelForDiffusionLM
from diffulex.model.config.llada.configuration_llada import LLaDAConfig
from diffulex.layer.linear import RowParallelLinear, ColumnParallelLinear
from diffulex.layer.embed_head import VocabParallelEmbedding, ParallelLMHead
from diffulex.distributed.parallel_state import fetch_parallel_state
if os.environ.get("TRITON_INTERPRET", None) == "1":
torch._dynamo.reset()
torch._dynamo.config.suppress_errors = True
torch.backends.optimized_mode = False
class LLaDARMSNorm(RMSNorm):
def __init__(self, hidden_size, eps=1e-6):
super().__init__(hidden_size, eps)
class LLaDAAttention(nn.Module):
"""LLaDA attention."""
def __init__(
self,
hidden_size: int,
num_heads: int,
num_kv_heads: int,
max_position: int = 32768,
head_dim: int | None = None,
rms_norm_eps: float = 1e-6,
qkv_bias: bool = True,
rope_theta: float = 10000,
rope_scaling: tuple | None = None,
attn_impl: str = "triton",
) -> None:
super().__init__()
parallel_state = fetch_parallel_state()
tp_size = parallel_state.get_tp_world_size()
self.total_num_heads = num_heads
assert self.total_num_heads % tp_size == 0
self.num_heads = self.total_num_heads // tp_size
self.total_num_kv_heads = num_kv_heads
assert self.total_num_kv_heads % tp_size == 0
self.num_kv_heads = self.total_num_kv_heads // tp_size
self.head_dim = head_dim or hidden_size // self.total_num_heads
self.q_size = self.num_heads * self.head_dim
self.kv_size = self.num_kv_heads * self.head_dim
self.scaling = self.head_dim**-0.5
self.q_proj = ColumnParallelLinear(
hidden_size,
self.total_num_heads * self.head_dim,
bias=qkv_bias,
)
self.k_proj = ColumnParallelLinear(
hidden_size,
self.total_num_kv_heads * self.head_dim,
bias=qkv_bias,
)
self.v_proj = ColumnParallelLinear(
hidden_size,
self.total_num_kv_heads * self.head_dim,
bias=qkv_bias,
)
self.o_proj = RowParallelLinear(
self.total_num_heads * self.head_dim,
hidden_size,
bias=False,
)
self.rotary_emb = get_rope(
self.head_dim,
rotary_dim=self.head_dim,
max_position=max_position,
base=rope_theta,
rope_scaling=rope_scaling,
)
self.attn = Attention(
self.num_heads,
self.head_dim,
self.scaling,
self.num_kv_heads,
attn_impl=attn_impl,
)
def forward(
self,
positions: torch.Tensor,
hidden_states: torch.Tensor,
mask: torch.Tensor | None = None,
) -> torch.Tensor:
q = self.q_proj(hidden_states)
k = self.k_proj(hidden_states)
v = self.v_proj(hidden_states)
q, k = self.rotary_emb(positions, q, k)
o = self.attn(q, k, v, mask)
output = self.o_proj(o)
return output
class LLaDAMLP(nn.Module):
"""LLaDA MLP."""
def __init__(
self,
hidden_size: int,
intermediate_size: int,
hidden_act: str,
) -> None:
super().__init__()
self.gate_proj = ColumnParallelLinear(
hidden_size,
intermediate_size,
bias=False,
)
self.up_proj = ColumnParallelLinear(
hidden_size,
intermediate_size,
bias=False,
)
self.down_proj = RowParallelLinear(
intermediate_size,
hidden_size,
bias=False,
)
assert hidden_act == "silu"
self.act_fn = SiluAndMul()
def forward(self, x):
gate = self.gate_proj(x)
up = self.up_proj(x)
x = self.act_fn(torch.cat([gate, up], dim=-1))
x = self.down_proj(x)
return x
class LLaDABlock(nn.Module):
"""LLaDA transformer block."""
def __init__(
self,
config,
) -> None:
super().__init__()
self.self_attn = LLaDAAttention(
hidden_size=config.hidden_size,
num_heads=config.num_attention_heads,
num_kv_heads=config.n_kv_heads,
max_position=config.max_sequence_length,
rms_norm_eps=config.rms_norm_eps,
qkv_bias=getattr(config, "include_qkv_bias", getattr(config, "use_qkv_bias", False)),
head_dim=getattr(config, "head_dim", None),
rope_theta=getattr(config, "rope_theta", 10000),
rope_scaling=getattr(config, "rope_scaling", None),
attn_impl=getattr(config, "attn_impl", "triton"),
)
self.mlp = LLaDAMLP(
hidden_size=config.hidden_size,
intermediate_size=config.mlp_hidden_size,
hidden_act=config.activation_type,
)
self.input_layernorm = LLaDARMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.post_attention_layernorm = LLaDARMSNorm(config.hidden_size, eps=config.rms_norm_eps)
def forward(
self,
positions: torch.Tensor,
hidden_states: torch.Tensor,
residual: torch.Tensor | None,
mask: torch.Tensor | None = None,
) -> tuple[torch.Tensor, torch.Tensor]:
if residual is None:
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
else:
hidden_states, residual = self.input_layernorm(hidden_states, residual)
hidden_states = self.self_attn(positions, hidden_states, mask)
hidden_states, residual = self.post_attention_layernorm(hidden_states, residual)
hidden_states = self.mlp(hidden_states)
return hidden_states, residual
class LLaDAModel(nn.Module):
"""LLaDA backbone."""
def __init__(
self,
config: LLaDAConfig,
) -> None:
super().__init__()
self.config = config
self.transformer = nn.ModuleDict(
dict(
wte=VocabParallelEmbedding(config.embedding_size or config.vocab_size, config.d_model),
emb_drop=nn.Dropout(config.embedding_dropout),
ln_f=LLaDARMSNorm(config.hidden_size, config.rms_norm_eps),
)
)
blocks = [LLaDABlock(config) for _ in range(config.n_layers)]
self.transformer.update({"blocks": nn.ModuleList(blocks)})
if not (self.config.alibi or self.config.rope):
self.transformer.update(
{
"wpe": nn.Embedding(
config.max_sequence_length,
config.d_model,
device=config.init_device,
)
}
)
def forward(
self,
input_ids: torch.Tensor,
positions: torch.Tensor,
mask: torch.Tensor | None = None,
) -> torch.Tensor:
hidden_states = self.transformer.emb_drop(self.transformer.wte(input_ids))
residual = None
for block_idx, block in enumerate(self.transformer.blocks):
hidden_states, residual = block(positions, hidden_states, residual, mask)
hidden_states, _ = self.transformer.ln_f(hidden_states, residual)
return hidden_states
@AutoModelForDiffusionLM.register("llada")
class LLaDAForDiffusionLM(nn.Module):
"""LLaDA with LM head."""
packed_modules_mapping = {
"q_proj": ("self_attn.q_proj", None),
"k_proj": ("self_attn.k_proj", None),
"v_proj": ("self_attn.v_proj", None),
"attn_out": ("self_attn.o_proj", None),
"attn_norm": ("input_layernorm", None),
"ff_norm": ("post_attention_layernorm", None),
"ff_proj": ("mlp.gate_proj", None),
"up_proj": ("mlp.up_proj", None),
"ff_out": ("mlp.down_proj", None),
"transformer.ff_out": ("lm_head", None),
}
def __init__(
self,
config: LLaDAConfig,
) -> None:
super().__init__()
self.model = LLaDAModel(config)
self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
if getattr(config, "weight_tying", False):
self.lm_head.weight.data = self.model.transformer.wte.weight.data
def forward(
self,
input_ids: torch.Tensor,
positions: torch.Tensor,
mask: torch.Tensor | None = None,
) -> torch.Tensor:
hidden_states = self.model(input_ids, positions, mask)
return hidden_states
def compute_logits(
self,
hidden_states: torch.Tensor,
) -> torch.Tensor:
logits = self.lm_head(hidden_states)
return logits
|