File size: 7,720 Bytes
4a2ff77 | 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 | from __future__ import annotations
import math
from typing import Any
import torch
from torch import Tensor, 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_tiny_transformer import TinyTransformerConfig
class CausalSelfAttention(nn.Module):
def __init__(self, config: TinyTransformerConfig):
super().__init__()
self.heads = config.heads
self.head_dim = config.dim // config.heads
self.dropout = config.dropout
self.qkv = nn.Linear(config.dim, 3 * config.dim)
self.output = nn.Linear(config.dim, config.dim)
self.residual_dropout = nn.Dropout(config.dropout)
def forward(self, x: Tensor, attention_mask: Tensor | None = None) -> Tensor:
batch, steps, width = x.shape
qkv = self.qkv(x).view(batch, steps, 3, self.heads, self.head_dim)
q, k, v = qkv.permute(2, 0, 3, 1, 4).unbind(0)
if attention_mask is None:
attended = F.scaled_dot_product_attention(
q,
k,
v,
dropout_p=self.dropout if self.training else 0.0,
is_causal=True,
)
else:
causal = torch.ones(
(steps, steps), dtype=torch.bool, device=x.device
).tril()
valid_keys = attention_mask[:, None, None, :].to(torch.bool)
combined_mask = causal[None, None, :, :] & valid_keys
attended = F.scaled_dot_product_attention(
q,
k,
v,
attn_mask=combined_mask,
dropout_p=self.dropout if self.training else 0.0,
is_causal=False,
)
attended = attended.transpose(1, 2).contiguous().view(batch, steps, width)
return self.residual_dropout(self.output(attended))
class FeedForward(nn.Module):
def __init__(self, config: TinyTransformerConfig):
super().__init__()
self.input = nn.Linear(config.dim, config.ff_dim)
self.output = nn.Linear(config.ff_dim, config.dim)
self.dropout = nn.Dropout(config.dropout)
def forward(self, x: Tensor) -> Tensor:
return self.dropout(self.output(F.gelu(self.input(x))))
class TransformerBlock(nn.Module):
def __init__(self, config: TinyTransformerConfig):
super().__init__()
self.attention_norm = nn.LayerNorm(config.dim)
self.attention = CausalSelfAttention(config)
self.feed_forward_norm = nn.LayerNorm(config.dim)
self.feed_forward = FeedForward(config)
def forward(self, x: Tensor, attention_mask: Tensor | None = None) -> Tensor:
x = x + self.attention(self.attention_norm(x), attention_mask)
return x + self.feed_forward(self.feed_forward_norm(x))
class TinyTransformerForCausalLM(PreTrainedModel, GenerationMixin):
"""Decoder-only TinyStories model compatible with AutoModelForCausalLM."""
config_class = TinyTransformerConfig
model_type = "tiny_transformer"
base_model_prefix = "tiny_transformer"
main_input_name = "input_ids"
_no_split_modules = ["TransformerBlock"]
_supports_sdpa = True
def __init__(self, config: TinyTransformerConfig):
super().__init__(config)
self.token_embedding = nn.Embedding(config.vocab_size, config.dim)
self.position_embedding = nn.Embedding(config.context, config.dim)
self.embedding_dropout = nn.Dropout(config.dropout)
self.blocks = nn.ModuleList(
[TransformerBlock(config) for _ in range(config.layers)]
)
self.final_norm = nn.LayerNorm(config.dim)
self.lm_head = nn.Linear(config.dim, config.vocab_size, bias=False)
self.post_init()
residual_std = 0.02 / math.sqrt(2 * config.layers)
for block in self.blocks:
nn.init.normal_(block.attention.output.weight, mean=0.0, std=residual_std)
nn.init.normal_(block.feed_forward.output.weight, mean=0.0, std=residual_std)
def _init_weights(self, module: nn.Module) -> None:
if isinstance(module, (nn.Linear, nn.Embedding)):
nn.init.normal_(module.weight, mean=0.0, std=0.02)
if isinstance(module, nn.Linear) and module.bias is not None:
nn.init.zeros_(module.bias)
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, value: nn.Linear) -> None:
self.lm_head = value
def forward(
self,
input_ids: Tensor,
attention_mask: Tensor | None = None,
position_ids: Tensor | None = None,
labels: Tensor | None = None,
output_hidden_states: bool | None = None,
return_dict: bool | None = None,
**_: Any,
) -> CausalLMOutput | tuple[Tensor, ...]:
_, steps = input_ids.shape
if steps > self.config.context:
raise ValueError(
f"sequence has {steps} tokens; context limit is {self.config.context}"
)
if position_ids is None:
if attention_mask is None:
position_ids = torch.arange(steps, device=input_ids.device)[None, :]
else:
position_ids = attention_mask.long().cumsum(dim=-1) - 1
position_ids.masked_fill_(attention_mask == 0, 0)
x = self.embedding_dropout(
self.token_embedding(input_ids) + self.position_embedding(position_ids)
)
collect_hidden = bool(
output_hidden_states
if output_hidden_states is not None
else self.config.output_hidden_states
)
hidden_states: list[Tensor] | None = [x] if collect_hidden else None
for block in self.blocks:
x = block(x, attention_mask)
if hidden_states is not None:
hidden_states.append(x)
logits = self.lm_head(self.final_norm(x))
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),
ignore_index=-100,
)
use_return_dict = (
return_dict if return_dict is not None else self.config.return_dict
)
if not use_return_dict:
result: tuple[Tensor, ...] = (logits,)
if hidden_states is not None:
result += (tuple(hidden_states),) # type: ignore[assignment]
return ((loss,) + result) if loss is not None else result
return CausalLMOutput(
loss=loss,
logits=logits,
hidden_states=tuple(hidden_states) if hidden_states is not None else None,
)
def prepare_inputs_for_generation(
self,
input_ids: Tensor,
attention_mask: Tensor | None = None,
**_: Any,
) -> dict[str, Tensor | bool | None]:
if input_ids.size(1) > self.config.context:
input_ids = input_ids[:, -self.config.context :]
if attention_mask is not None:
attention_mask = attention_mask[:, -self.config.context :]
return {
"input_ids": input_ids,
"attention_mask": attention_mask,
"use_cache": False,
}
|