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2dcd089 | 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 | """Transformers wrapper for PakMosaic. Same weights as PakEncoderForMLM."""
from __future__ import annotations
from typing import Any
import torch
import torch.nn.functional as F
from torch import nn
from transformers import PreTrainedModel
from transformers.modeling_outputs import BaseModelOutput, MaskedLMOutput
try:
from .configuration_pakmosaic import PakMosaicConfig
except ImportError:
from configuration_pakmosaic import PakMosaicConfig
def rotate_half(x: torch.Tensor) -> torch.Tensor:
x1, x2 = x.chunk(2, dim=-1)
return torch.cat((-x2, x1), dim=-1)
class RotaryEmbedding(nn.Module):
def __init__(self, dim: int, max_seq_len: int = 512, base: float = 10000.0):
super().__init__()
inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))
self.register_buffer("inv_freq", inv_freq, persistent=False)
self.max_seq_len = max_seq_len
def forward(self, seq_len: int, device: torch.device, dtype: torch.dtype) -> tuple[torch.Tensor, torch.Tensor]:
positions = torch.arange(seq_len, device=device, dtype=self.inv_freq.dtype)
freqs = torch.outer(positions, self.inv_freq)
emb = torch.cat((freqs, freqs), dim=-1)
return emb.cos().to(dtype=dtype), emb.sin().to(dtype=dtype)
def apply_rope(x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor) -> torch.Tensor:
cos = cos.unsqueeze(0).unsqueeze(0)
sin = sin.unsqueeze(0).unsqueeze(0)
return (x * cos) + (rotate_half(x) * sin)
def sliding_window_keep(seq_len: int, window: int, device: torch.device) -> torch.Tensor:
idx = torch.arange(seq_len, device=device)
return (idx[:, None] - idx[None, :]).abs() <= window
class EncoderAttention(nn.Module):
def __init__(self, config: PakMosaicConfig, *, layer_index: int):
super().__init__()
self.config = config
self.num_heads = config.num_attention_heads
self.head_dim = config.head_dim
self.qkv = nn.Linear(config.hidden_size, 3 * config.hidden_size, bias=config.use_bias)
self.out = nn.Linear(config.hidden_size, config.hidden_size, bias=config.use_bias)
self.dropout = config.attention_dropout
self.rope = RotaryEmbedding(self.head_dim, config.max_position_embeddings, config.rope_theta)
self.is_global = config.attention_kind == "full" or (
config.attention_kind == "hybrid" and ((layer_index + 1) % max(1, config.global_every) == 0)
)
def _mask(self, attention_mask: torch.Tensor | None, seq_len: int) -> torch.Tensor | None:
keep = None
if attention_mask is not None:
keep = attention_mask.bool()[:, None, None, :]
if not self.is_global:
window = sliding_window_keep(seq_len, self.config.local_window, self.qkv.weight.device)[None, None, :, :]
keep = window if keep is None else keep & window
return keep
def forward(self, hidden: torch.Tensor, attention_mask: torch.Tensor | None = None) -> torch.Tensor:
batch, seq_len, _ = hidden.shape
qkv = self.qkv(hidden).view(batch, seq_len, 3, self.num_heads, self.head_dim)
qkv = qkv.permute(2, 0, 3, 1, 4)
query, key, value = qkv[0], qkv[1], qkv[2]
cos, sin = self.rope(seq_len, hidden.device, query.dtype)
query = apply_rope(query, cos, sin)
key = apply_rope(key, cos, sin)
context = F.scaled_dot_product_attention(
query,
key,
value,
attn_mask=self._mask(attention_mask, seq_len),
dropout_p=self.dropout if self.training else 0.0,
)
context = context.transpose(1, 2).contiguous().view(batch, seq_len, self.config.hidden_size)
return self.out(context)
class EncoderFFN(nn.Module):
def __init__(self, config: PakMosaicConfig):
super().__init__()
self.kind = config.ffn_activation
bias = config.use_bias
if self.kind in {"geglu", "swiglu"}:
self.gate_up = nn.Linear(config.hidden_size, 2 * config.intermediate_size, bias=bias)
self.down = nn.Linear(config.intermediate_size, config.hidden_size, bias=bias)
else:
self.up = nn.Linear(config.hidden_size, config.intermediate_size, bias=bias)
self.down = nn.Linear(config.intermediate_size, config.hidden_size, bias=bias)
self.act = nn.GELU()
self.dropout = nn.Dropout(config.hidden_dropout)
def forward(self, hidden: torch.Tensor) -> torch.Tensor:
if self.kind in {"geglu", "swiglu"}:
gate, up = self.gate_up(hidden).chunk(2, dim=-1)
hidden = (F.silu(gate) if self.kind == "swiglu" else F.gelu(gate)) * up
else:
hidden = self.act(self.up(hidden))
return self.dropout(self.down(hidden))
class TokenEmbeddings(nn.Module):
def __init__(self, config: PakMosaicConfig):
super().__init__()
self.word = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
self.norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout)
def forward(self, input_ids: torch.Tensor) -> torch.Tensor:
return self.dropout(self.norm(self.word(input_ids)))
class EncoderLayer(nn.Module):
def __init__(self, config: PakMosaicConfig, layer_index: int):
super().__init__()
self.attn_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.ffn_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.attn = EncoderAttention(config, layer_index=layer_index)
self.ffn = EncoderFFN(config)
def forward(self, hidden: torch.Tensor, attention_mask: torch.Tensor | None = None) -> torch.Tensor:
hidden = hidden + self.attn(self.attn_norm(hidden), attention_mask=attention_mask)
return hidden + self.ffn(self.ffn_norm(hidden))
class PakMosaicPreTrainedModel(PreTrainedModel):
config_class = PakMosaicConfig
base_model_prefix = "encoder"
supports_gradient_checkpointing = True
_no_split_modules = ["EncoderLayer"]
def _init_weights(self, module: nn.Module) -> None:
std = self.config.initializer_range
if isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=std)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
elif isinstance(module, nn.LayerNorm):
module.weight.data.fill_(1.0)
module.bias.data.zero_()
class PakMosaicModel(PakMosaicPreTrainedModel):
def __init__(self, config: PakMosaicConfig):
super().__init__(config)
self.embeddings = TokenEmbeddings(config)
self.layers = nn.ModuleList(EncoderLayer(config, i) for i in range(config.num_hidden_layers))
self.final_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.gradient_checkpointing = False
self.post_init()
def get_input_embeddings(self) -> nn.Embedding:
return self.embeddings.word
def set_input_embeddings(self, value: nn.Embedding) -> None:
self.embeddings.word = value
def forward(
self,
input_ids: torch.Tensor | None = None,
attention_mask: torch.Tensor | None = None,
**kwargs: Any,
) -> BaseModelOutput:
hidden = self.embeddings(input_ids)
for layer in self.layers:
if self.gradient_checkpointing and self.training:
hidden = self._gradient_checkpointing_func(layer, hidden, attention_mask)
else:
hidden = layer(hidden, attention_mask=attention_mask)
hidden = self.final_norm(hidden)
return BaseModelOutput(last_hidden_state=hidden)
class MLMHead(nn.Module):
def __init__(self, config: PakMosaicConfig, embedding: nn.Embedding):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size, bias=True)
self.act = nn.GELU()
self.norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.tie = config.tie_word_embeddings
if config.tie_word_embeddings:
self.weight = embedding.weight
self.bias = nn.Parameter(torch.zeros(config.vocab_size))
else:
self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=True)
def forward(self, hidden: torch.Tensor) -> torch.Tensor:
hidden = self.norm(self.act(self.dense(hidden)))
if self.tie:
return F.linear(hidden, self.weight, self.bias)
return self.decoder(hidden)
class PakMosaicForMaskedLM(PakMosaicPreTrainedModel):
_tied_weights_keys = {"mlm.weight": "encoder.embeddings.word.weight"}
def __init__(self, config: PakMosaicConfig):
super().__init__(config)
self.encoder = PakMosaicModel(config)
self.mlm = MLMHead(config, self.encoder.embeddings.word)
self.post_init()
def get_input_embeddings(self) -> nn.Embedding:
return self.encoder.embeddings.word
def forward(
self,
input_ids: torch.Tensor | None = None,
attention_mask: torch.Tensor | None = None,
labels: torch.Tensor | None = None,
**kwargs: Any,
) -> MaskedLMOutput:
hidden = self.encoder(input_ids=input_ids, attention_mask=attention_mask).last_hidden_state
logits = self.mlm(hidden)
loss = None
if labels is not None:
loss = F.cross_entropy(logits.view(-1, self.config.vocab_size), labels.view(-1), ignore_index=-100)
return MaskedLMOutput(loss=loss, logits=logits, hidden_states=(hidden,))
PakMosaicModel.register_for_auto_class("AutoModel")
PakMosaicForMaskedLM.register_for_auto_class("AutoModelForMaskedLM")
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