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from typing import Optional, Tuple, Dict, Any
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import PreTrainedModel, GenerationMixin, AutoConfig, AutoModelForCausalLM
from transformers import PretrainedConfig
class GPT2CustomConfig(PretrainedConfig):
model_type = "gpt2_custom"
auto_map = {
"AutoConfig": "configuration_gpt2.GPT2CustomConfig",
"AutoModelForCausalLM": "modeling_gpt2.GPT2CustomLMHeadModel"
}
def __init__(
self,
vocab_size=16384,
n_positions=512,
n_embd=512,
n_layer=12,
n_head=8,
n_inner=1360,
activation_function="gelu_new",
resid_pdrop=0.1,
embd_pdrop=0.1,
attn_pdrop=0.1,
layer_norm_epsilon=1e-5,
initializer_range=0.02,
bos_token_id=2,
eos_token_id=3,
**kwargs
):
self.vocab_size = vocab_size
self.n_positions = n_positions
self.n_embd = n_embd
self.n_layer = n_layer
self.n_head = n_head
self.n_inner = n_inner
self.activation_function = activation_function
self.resid_pdrop = resid_pdrop
self.embd_pdrop = embd_pdrop
self.attn_pdrop = attn_pdrop
self.layer_norm_epsilon = layer_norm_epsilon
self.initializer_range = initializer_range
self.hidden_size = n_embd
self.num_attention_heads = n_head
self.num_hidden_layers = n_layer
super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
# ─────────────────────────────────────────────────────────────
# ROPE HELPERS
# ─────────────────────────────────────────────────────────────
def _precompute_rope_freqs(head_dim: int, seq_len: int, device: torch.device, theta: float = 10000.0):
assert head_dim % 2 == 0, "head_dim must be divisible by 2 for RoPE"
inv_freq = 1.0 / (theta ** (torch.arange(0, head_dim, 2, device=device).float() / head_dim))
t = torch.arange(seq_len, device=device).float()
freqs = torch.outer(t, inv_freq)
emb = torch.cat((freqs, freqs), dim=-1)
return emb.cos(), emb.sin()
def _apply_rope(x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor):
L = x.size(2)
cos = cos[:L, :].unsqueeze(0).unsqueeze(1)
sin = sin[:L, :].unsqueeze(0).unsqueeze(1)
half_dim = x.size(-1) // 2
x1 = x[..., :half_dim]
x2 = x[..., half_dim:]
rotated_x = torch.cat((-x2, x1), dim=-1)
return (x * cos) + (rotated_x * sin)
class CausalSelfAttention(nn.Module):
def __init__(self, config):
super().__init__()
assert config.n_embd % config.n_head == 0
self.num_heads = config.n_head
self.head_dim = config.n_embd // config.n_head
self.q_proj = nn.Linear(config.n_embd, config.n_embd, bias=False)
self.k_proj = nn.Linear(config.n_embd, config.n_embd, bias=False)
self.v_proj = nn.Linear(config.n_embd, config.n_embd, bias=False)
self.o_proj = nn.Linear(config.n_embd, config.n_embd, bias=False)
self.attn_dropout = nn.Dropout(config.attn_pdrop)
self.resid_dropout = nn.Dropout(config.resid_pdrop)
def forward(self, x, past_kv=None, use_cache: bool = False):
B, L, D = x.size()
q = self.q_proj(x).view(B, L, self.num_heads, self.head_dim).transpose(1, 2)
k = self.k_proj(x).view(B, L, self.num_heads, self.head_dim).transpose(1, 2)
v = self.v_proj(x).view(B, L, self.num_heads, self.head_dim).transpose(1, 2)
if past_kv is not None:
past_k, past_v = past_kv
past_len = past_k.size(2)
q_cos, q_sin = _precompute_rope_freqs(self.head_dim, past_len + L, x.device)
q = _apply_rope(q, q_cos[past_len:, :], q_sin[past_len:, :])
k = _apply_rope(k, q_cos[past_len:, :], q_sin[past_len:, :])
k = torch.cat([past_k, k], dim=2)
v = torch.cat([past_v, v], dim=2)
else:
cos, sin = _precompute_rope_freqs(self.head_dim, L, x.device)
q = _apply_rope(q, cos, sin)
k = _apply_rope(k, cos, sin)
L_kv = k.size(2)
scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.head_dim)
# Standard full context causal attention mask (no sliding window)
past_len = L_kv - L
pos_i = (past_len + torch.arange(L, device=x.device)).unsqueeze(1)
pos_j = torch.arange(L_kv, device=x.device).unsqueeze(0)
causal_mask = (pos_i - pos_j) >= 0
scores = scores.masked_fill(~causal_mask.unsqueeze(0).unsqueeze(0), float('-inf'))
attn = torch.softmax(scores, dim=-1)
attn = self.attn_dropout(attn)
out = torch.matmul(attn, v)
out = out.transpose(1, 2).contiguous().view(B, L, D)
out = self.resid_dropout(self.o_proj(out))
present_kv = (k, v) if use_cache else None
return out, present_kv
class SwiGLU(nn.Module):
def __init__(self, dim: int, hidden_dim: int):
super().__init__()
self.fc1 = nn.Linear(dim, hidden_dim, bias=False)
self.fc2 = nn.Linear(dim, hidden_dim, bias=False)
self.fc3 = nn.Linear(hidden_dim, dim, bias=False)
def forward(self, x):
return self.fc3(F.silu(self.fc1(x)) * self.fc2(x))
class GPT2Block(nn.Module):
def __init__(self, config):
super().__init__()
self.ln_1 = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
self.attn = CausalSelfAttention(config)
self.ln_2 = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
self.mlp = SwiGLU(config.n_embd, config.n_inner)
def forward(self, x, past_kv=None, use_cache: bool = False):
attn_out, present_kv = self.attn(self.ln_1(x), past_kv=past_kv, use_cache=use_cache)
x = x + attn_out
x = x + self.mlp(self.ln_2(x))
return x, present_kv
class GPT2CustomLMHeadModel(PreTrainedModel, GenerationMixin):
config_class = GPT2CustomConfig
base_model_prefix = "transformer"
_tied_weights_keys = {"lm_head.weight": "transformer.wte.weight"}
def __init__(self, config):
super().__init__(config)
self.transformer = nn.ModuleDict(dict(
wte = nn.Embedding(config.vocab_size, config.n_embd),
drop = nn.Dropout(config.embd_pdrop),
h = nn.ModuleList([GPT2Block(config) for _ in range(config.n_layer)]),
ln_f = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon),
))
self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
self.post_init()
def get_input_embeddings(self):
return self.transformer.wte
def set_input_embeddings(self, new_embeddings):
self.transformer.wte = new_embeddings
def get_output_embeddings(self):
return self.lm_head
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings
def _init_weights(self, module):
if isinstance(module, nn.Linear):
torch.nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
torch.nn.init.zeros_(module.bias)
elif isinstance(module, nn.Embedding):
torch.nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range)
def forward(self, input_ids, labels=None, past_key_values=None, use_cache: bool = False, **kwargs):
device = input_ids.device
x = self.transformer.wte(input_ids)
x = self.transformer.drop(x)
new_kvs = []
for i, block in enumerate(self.transformer.h):
pkv = past_key_values[i] if past_key_values is not None else None
x, nkv = block(x, past_kv=pkv, use_cache=use_cache)
if use_cache:
new_kvs.append(nkv)
x = self.transformer.ln_f(x)
logits = self.lm_head(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)
present_kvs = tuple(new_kvs) if use_cache else None
from transformers.modeling_outputs import CausalLMOutputWithPast
return CausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=present_kvs
)
def prepare_inputs_for_generation(self, input_ids, past_key_values=None, **kwargs):
return {"input_ids": input_ids, "past_key_values": past_key_values}
# Register configuration and model for auto mapping
AutoConfig.register("gpt2_custom", GPT2CustomConfig)
AutoModelForCausalLM.register(GPT2CustomConfig, GPT2CustomLMHeadModel)
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