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081b4cb | 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 | """Transformers-compatible implementation of the pinyin-code causal LM."""
from __future__ import annotations
import math
import torch
from torch import nn
from torch.nn import functional as F
from transformers import PreTrainedModel
from transformers.generation import GenerationMixin
from transformers.modeling_outputs import CausalLMOutput
from .configuration_pinyin_code import PinyinCodeConfig
class CausalSelfAttention(nn.Module):
"""Multi-head masked self-attention matching the original training module."""
def __init__(self, config: PinyinCodeConfig) -> None:
super().__init__()
if config.n_embd % config.n_head != 0:
raise ValueError("n_embd must be divisible by n_head")
self.n_head = config.n_head
self.head_dim = config.n_embd // config.n_head
self.qkv = nn.Linear(config.n_embd, 3 * config.n_embd)
self.proj = nn.Linear(config.n_embd, config.n_embd)
self.attn_dropout = nn.Dropout(config.dropout)
self.resid_dropout = nn.Dropout(config.dropout)
self.register_buffer(
"mask",
torch.tril(torch.ones(config.block_size, config.block_size)).view(
1, 1, config.block_size, config.block_size
),
persistent=False,
)
def forward(self, x: torch.Tensor, attention_mask: torch.Tensor | None = None) -> torch.Tensor:
batch_size, seq_len, embd = x.shape
q, k, v = self.qkv(x).split(embd, dim=2)
q = q.view(batch_size, seq_len, self.n_head, self.head_dim).transpose(1, 2)
k = k.view(batch_size, seq_len, self.n_head, self.head_dim).transpose(1, 2)
v = v.view(batch_size, seq_len, self.n_head, self.head_dim).transpose(1, 2)
att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(self.head_dim))
causal_mask = self.mask[:, :, :seq_len, :seq_len] == 0
att = att.masked_fill(causal_mask, torch.finfo(att.dtype).min)
if attention_mask is not None:
key_mask = attention_mask[:, None, None, :seq_len].to(dtype=torch.bool)
att = att.masked_fill(~key_mask, torch.finfo(att.dtype).min)
att = F.softmax(att, dim=-1)
att = self.attn_dropout(att)
y = att @ v
y = y.transpose(1, 2).contiguous().view(batch_size, seq_len, embd)
return self.resid_dropout(self.proj(y))
class FeedForward(nn.Module):
"""Transformer MLP block."""
def __init__(self, config: PinyinCodeConfig) -> None:
super().__init__()
self.net = nn.Sequential(
nn.Linear(config.n_embd, 4 * config.n_embd),
nn.GELU(),
nn.Linear(4 * config.n_embd, config.n_embd),
nn.Dropout(config.dropout),
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.net(x)
class TransformerBlock(nn.Module):
"""Pre-norm Transformer block."""
def __init__(self, config: PinyinCodeConfig) -> None:
super().__init__()
self.ln_1 = nn.LayerNorm(config.n_embd)
self.attn = CausalSelfAttention(config)
self.ln_2 = nn.LayerNorm(config.n_embd)
self.mlp = FeedForward(config)
def forward(self, x: torch.Tensor, attention_mask: torch.Tensor | None = None) -> torch.Tensor:
x = x + self.attn(self.ln_1(x), attention_mask=attention_mask)
x = x + self.mlp(self.ln_2(x))
return x
class PinyinCodePreTrainedModel(PreTrainedModel):
"""Base class for pinyin-code Transformers models."""
config_class = PinyinCodeConfig
base_model_prefix = "pinyin_code"
supports_gradient_checkpointing = False
def _init_weights(self, module: nn.Module) -> None:
if isinstance(module, nn.Linear):
nn.init.normal_(module.weight, mean=0.0, std=0.02)
if module.bias is not None:
nn.init.zeros_(module.bias)
elif isinstance(module, nn.Embedding):
nn.init.normal_(module.weight, mean=0.0, std=0.02)
class PinyinCodeForCausalLM(PinyinCodePreTrainedModel, GenerationMixin):
"""Compact GPT-style causal language model using the original architecture."""
_tied_weights_keys = {"lm_head.weight": "token_embedding.weight"}
_keys_to_ignore_on_load_missing = [r"lm_head\.weight"]
def __init__(self, config: PinyinCodeConfig) -> None:
super().__init__(config)
self.token_embedding = nn.Embedding(config.vocab_size, config.n_embd)
self.position_embedding = nn.Embedding(config.block_size, config.n_embd)
self.dropout = nn.Dropout(config.dropout)
self.blocks = nn.ModuleList(TransformerBlock(config) for _ in range(config.n_layer))
self.ln_f = nn.LayerNorm(config.n_embd)
self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
self.post_init()
self.tie_weights()
def get_input_embeddings(self) -> nn.Embedding:
return self.token_embedding
def set_input_embeddings(self, value: nn.Embedding) -> None:
self.token_embedding = value
def get_output_embeddings(self) -> nn.Linear:
return self.lm_head
def set_output_embeddings(self, new_embeddings: nn.Linear) -> None:
self.lm_head = new_embeddings
def tie_weights(self, *args, **kwargs) -> None:
self.lm_head.weight = self.token_embedding.weight
def prepare_inputs_for_generation(
self,
input_ids: torch.Tensor,
past_key_values=None,
attention_mask: torch.Tensor | None = None,
**kwargs,
) -> dict:
if input_ids.shape[1] > self.config.block_size:
input_ids = input_ids[:, -self.config.block_size :]
if attention_mask is not None:
attention_mask = attention_mask[:, -self.config.block_size :]
return {"input_ids": input_ids, "attention_mask": attention_mask}
def forward(
self,
input_ids: torch.Tensor | None = None,
attention_mask: torch.Tensor | None = None,
labels: torch.Tensor | None = None,
inputs_embeds: torch.Tensor | None = None,
return_dict: bool | None = None,
**kwargs,
) -> CausalLMOutput | tuple:
return_dict = True if return_dict is None else return_dict
if input_ids is None and inputs_embeds is None:
raise ValueError("You must provide either input_ids or inputs_embeds")
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot provide both input_ids and inputs_embeds")
if inputs_embeds is None:
_, seq_len = input_ids.shape
if seq_len > self.config.block_size:
raise ValueError(
f"Sequence length {seq_len} exceeds block size {self.config.block_size}"
)
inputs_embeds = self.token_embedding(input_ids)
else:
seq_len = inputs_embeds.shape[1]
if seq_len > self.config.block_size:
raise ValueError(
f"Sequence length {seq_len} exceeds block size {self.config.block_size}"
)
positions = torch.arange(seq_len, device=inputs_embeds.device)
x = inputs_embeds + self.position_embedding(positions)
x = self.dropout(x)
for block in self.blocks:
x = block(x, attention_mask=attention_mask)
logits = self.lm_head(self.ln_f(x))
loss = None
if labels is not None:
loss = F.cross_entropy(
logits[:, :-1, :].contiguous().view(-1, logits.size(-1)),
labels[:, 1:].contiguous().view(-1),
ignore_index=-100,
)
if not return_dict:
output = (logits,)
return ((loss,) + output) if loss is not None else output
return CausalLMOutput(loss=loss, logits=logits)
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