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from dataclasses import dataclass
import torch
from torch import nn
from torch.nn import functional as F
from transformers import GenerationMixin, PreTrainedModel, PretrainedConfig
from transformers.modeling_outputs import CausalLMOutputWithPast
class TinyGPTConfig(PretrainedConfig):
model_type = "tiny_gpt"
def __init__(
self,
vocab_size: int = 2048,
context_length: int = 256,
n_layers: int = 4,
n_heads: int = 4,
d_model: int = 128,
d_ff: int = 512,
dropout: float = 0.1,
tie_embeddings: bool = True,
bos_token_id: int = 1,
eos_token_id: int = 2,
pad_token_id: int = 3,
unk_token_id: int = 0,
use_cache: bool = False,
**kwargs,
) -> None:
is_decoder = kwargs.pop("is_decoder", True)
tie_word_embeddings = kwargs.pop("tie_word_embeddings", tie_embeddings)
use_cache = kwargs.pop("use_cache", use_cache)
super().__init__(
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
pad_token_id=pad_token_id,
unk_token_id=unk_token_id,
use_cache=use_cache,
tie_word_embeddings=tie_word_embeddings,
is_decoder=is_decoder,
**kwargs,
)
self.vocab_size = vocab_size
self.context_length = context_length
self.n_layers = n_layers
self.n_heads = n_heads
self.d_model = d_model
self.d_ff = d_ff
self.dropout = dropout
self.tie_embeddings = tie_embeddings
# Standard Transformer aliases used by generation helpers.
self.hidden_size = d_model
self.intermediate_size = d_ff
self.num_attention_heads = n_heads
self.num_hidden_layers = n_layers
self.max_position_embeddings = context_length
self.head_dim = d_model // n_heads
if self.d_model % self.n_heads != 0:
raise ValueError("d_model must be divisible by n_heads")
class TokenEmbedding(nn.Module):
def __init__(self, config: TinyGPTConfig) -> None:
super().__init__()
self.embedding = nn.Embedding(config.vocab_size, config.d_model)
@property
def weight(self) -> torch.Tensor:
return self.embedding.weight
def forward(self, idx: torch.Tensor) -> torch.Tensor:
return self.embedding(idx)
class PositionEmbedding(nn.Module):
def __init__(self, config: TinyGPTConfig) -> None:
super().__init__()
self.embedding = nn.Embedding(config.context_length, config.d_model)
def forward(self, seq_len: int, device: torch.device) -> torch.Tensor:
positions = torch.arange(seq_len, device=device).unsqueeze(0)
return self.embedding(positions)
class CausalSelfAttention(nn.Module):
def __init__(self, config: TinyGPTConfig) -> None:
super().__init__()
self.n_heads = config.n_heads
self.head_dim = config.d_model // config.n_heads
self.q_proj = nn.Linear(config.d_model, config.d_model)
self.k_proj = nn.Linear(config.d_model, config.d_model)
self.v_proj = nn.Linear(config.d_model, config.d_model)
self.out_proj = nn.Linear(config.d_model, config.d_model)
self.attn_dropout = nn.Dropout(config.dropout)
self.resid_dropout = nn.Dropout(config.dropout)
mask = torch.tril(torch.ones(config.context_length, config.context_length))
self.register_buffer(
"causal_mask",
mask.view(1, 1, config.context_length, config.context_length),
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
batch_size, seq_len, d_model = x.shape
query = self._split_heads(self.q_proj(x), batch_size, seq_len)
key = self._split_heads(self.k_proj(x), batch_size, seq_len)
value = self._split_heads(self.v_proj(x), batch_size, seq_len)
scores = query @ key.transpose(-2, -1)
scores = scores / (self.head_dim**0.5)
scores = scores.masked_fill(
self.causal_mask[:, :, :seq_len, :seq_len] == 0,
float("-inf"),
)
attention_weights = F.softmax(scores, dim=-1)
attention_weights = self.attn_dropout(attention_weights)
out = attention_weights @ value
out = out.transpose(1, 2).contiguous().view(batch_size, seq_len, d_model)
out = self.out_proj(out)
return self.resid_dropout(out)
def _split_heads(self, x: torch.Tensor, batch_size: int, seq_len: int) -> torch.Tensor:
x = x.view(batch_size, seq_len, self.n_heads, self.head_dim)
return x.transpose(1, 2)
class FeedForward(nn.Module):
def __init__(self, config: TinyGPTConfig) -> None:
super().__init__()
self.net = nn.Sequential(
nn.Linear(config.d_model, config.d_ff),
nn.GELU(),
nn.Linear(config.d_ff, config.d_model),
nn.Dropout(config.dropout),
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.net(x)
class TransformerBlock(nn.Module):
def __init__(self, config: TinyGPTConfig) -> None:
super().__init__()
self.ln_1 = nn.LayerNorm(config.d_model)
self.attn = CausalSelfAttention(config)
self.ln_2 = nn.LayerNorm(config.d_model)
self.mlp = FeedForward(config)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = x + self.attn(self.ln_1(x))
x = x + self.mlp(self.ln_2(x))
return x
class TinyGPTForCausalLM(PreTrainedModel, GenerationMixin):
config_class = TinyGPTConfig
base_model_prefix = "tiny_gpt"
main_input_name = "input_ids"
_tied_weights_keys = {"lm_head.weight": "token_embedding.embedding.weight"}
def __init__(self, config: TinyGPTConfig) -> None:
super().__init__(config)
self.token_embedding = TokenEmbedding(config)
self.position_embedding = PositionEmbedding(config)
self.dropout = nn.Dropout(config.dropout)
self.blocks = nn.ModuleList(TransformerBlock(config) for _ in range(config.n_layers))
self.final_ln = nn.LayerNorm(config.d_model)
self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False)
self.post_init()
self.tie_weights()
if getattr(self, "generation_config", None) is not None:
self.generation_config.use_cache = False
self.generation_config.cache_implementation = None
def get_input_embeddings(self) -> nn.Module:
return self.token_embedding.embedding
def set_input_embeddings(self, value: nn.Module) -> None:
self.token_embedding.embedding = value
def get_output_embeddings(self) -> nn.Module:
return self.lm_head
def set_output_embeddings(self, new_embeddings: nn.Module) -> None:
self.lm_head = new_embeddings
def tie_weights(self, *args, **kwargs) -> None:
del args, kwargs
if self.config.tie_embeddings:
self.lm_head.weight = self.token_embedding.weight
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)
def forward(
self,
input_ids: torch.Tensor,
attention_mask: torch.Tensor | None = None,
labels: torch.Tensor | None = None,
past_key_values=None,
use_cache: bool | None = None,
return_dict: bool = True,
**kwargs,
) -> CausalLMOutputWithPast | tuple[torch.Tensor, ...]:
del attention_mask, past_key_values, use_cache, kwargs
batch_size, seq_len = input_ids.shape
if seq_len > self.config.context_length:
raise ValueError(
f"Sequence length {seq_len} exceeds context length {self.config.context_length}"
)
if labels is not None and labels.shape != input_ids.shape:
raise ValueError(f"labels shape {labels.shape} must match input_ids shape {input_ids.shape}")
token_embeddings = self.token_embedding(input_ids)
position_embeddings = self.position_embedding(seq_len, input_ids.device)
x = token_embeddings + position_embeddings
x = self.dropout(x)
for block in self.blocks:
x = block(x)
x = self.final_ln(x)
logits = self.lm_head(x)
loss = None
if labels is not None:
if seq_len < 2:
raise ValueError("Need at least 2 tokens to compute causal LM loss")
shift_logits = logits[:, :-1, :].contiguous()
shift_labels = labels[:, 1:].contiguous()
loss = F.cross_entropy(
shift_logits.reshape(-1, self.config.vocab_size),
shift_labels.reshape(-1),
ignore_index=-100,
)
if not return_dict:
output = (logits,)
if loss is not None:
output = (loss,) + output
return output
return CausalLMOutputWithPast(loss=loss, logits=logits, past_key_values=None)
def prepare_inputs_for_generation(
self,
input_ids: torch.Tensor,
past_key_values=None,
attention_mask: torch.Tensor | None = None,
**kwargs,
) -> dict[str, torch.Tensor | None]:
del past_key_values
if input_ids.shape[1] > self.config.context_length:
input_ids = input_ids[:, -self.config.context_length :]
if attention_mask is not None:
attention_mask = attention_mask[:, -self.config.context_length :]
return {
"input_ids": input_ids,
"attention_mask": attention_mask,
"use_cache": False,
**kwargs,
}
TinyGPTConfig.register_for_auto_class()
TinyGPTForCausalLM.register_for_auto_class("AutoModelForCausalLM")
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