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import math
from dataclasses import dataclass
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import PreTrainedModel
from .configuration_nanogpt import NanoGPTConfig
def _rms_norm(x: torch.Tensor) -> torch.Tensor:
return F.rms_norm(x, (x.size(-1),))
def _apply_rotary_emb(x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor) -> torch.Tensor:
assert x.ndim == 4
d = x.shape[3] // 2
x1, x2 = x[..., :d], x[..., d:]
y1 = x1 * cos + x2 * sin
y2 = x1 * (-sin) + x2 * cos
out = torch.cat([y1, y2], 3)
return out.to(x.dtype)
def _repeat_kv(x: torch.Tensor, n_rep: int) -> torch.Tensor:
if n_rep == 1:
return x
bs, n_kv_heads, slen, head_dim = x.shape
return (
x[:, :, None, :, :]
.expand(bs, n_kv_heads, n_rep, slen, head_dim)
.reshape(bs, n_kv_heads * n_rep, slen, head_dim)
)
class CausalSelfAttention(nn.Module):
def __init__(self, config: NanoGPTConfig, layer_idx: int):
super().__init__()
self.layer_idx = layer_idx
self.n_head = config.n_head
self.n_kv_head = config.n_kv_head
self.n_embd = config.n_embd
self.head_dim = self.n_embd // self.n_head
assert self.n_embd % self.n_head == 0
assert self.n_kv_head <= self.n_head and self.n_head % self.n_kv_head == 0
self.c_q = nn.Linear(self.n_embd, self.n_head * self.head_dim, bias=False)
self.c_k = nn.Linear(self.n_embd, self.n_kv_head * self.head_dim, bias=False)
self.c_v = nn.Linear(self.n_embd, self.n_kv_head * self.head_dim, bias=False)
self.c_proj = nn.Linear(self.n_embd, self.n_embd, bias=False)
def forward(self, x: torch.Tensor, cos_sin, kv_cache=None) -> torch.Tensor:
B, T, C = x.size()
q = self.c_q(x).view(B, T, self.n_head, self.head_dim)
k = self.c_k(x).view(B, T, self.n_kv_head, self.head_dim)
v = self.c_v(x).view(B, T, self.n_kv_head, self.head_dim)
cos, sin = cos_sin
q, k = _apply_rotary_emb(q, cos, sin), _apply_rotary_emb(k, cos, sin)
q, k = _rms_norm(q), _rms_norm(k)
q, k, v = q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2)
Tq = q.size(2)
Tk = k.size(2)
nrep = self.n_head // self.n_kv_head
k, v = _repeat_kv(k, nrep), _repeat_kv(v, nrep)
if Tq == Tk:
y = F.scaled_dot_product_attention(q, k, v, is_causal=True)
elif Tq == 1:
y = F.scaled_dot_product_attention(q, k, v, is_causal=False)
else:
attn_mask = torch.zeros((Tq, Tk), dtype=torch.bool, device=q.device)
prefix_len = Tk - Tq
if prefix_len > 0:
attn_mask[:, :prefix_len] = True
attn_mask[:, prefix_len:] = torch.tril(torch.ones((Tq, Tq), dtype=torch.bool, device=q.device))
y = F.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask)
y = y.transpose(1, 2).contiguous().view(B, T, -1)
y = self.c_proj(y)
return y
class MLP(nn.Module):
def __init__(self, config: NanoGPTConfig):
super().__init__()
self.c_fc = nn.Linear(config.n_embd, 4 * config.n_embd, bias=False)
self.c_proj = nn.Linear(4 * config.n_embd, config.n_embd, bias=False)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.c_fc(x)
x = F.relu(x).square()
x = self.c_proj(x)
return x
class Block(nn.Module):
def __init__(self, config: NanoGPTConfig, layer_idx: int):
super().__init__()
self.attn = CausalSelfAttention(config, layer_idx)
self.mlp = MLP(config)
def forward(self, x: torch.Tensor, cos_sin, kv_cache=None) -> torch.Tensor:
x = x + self.attn(_rms_norm(x), cos_sin, kv_cache)
x = x + self.mlp(_rms_norm(x))
return x
class NanoGPTModel(PreTrainedModel):
config_class = NanoGPTConfig
def __init__(self, config: NanoGPTConfig):
super().__init__(config)
self.transformer = nn.ModuleDict({
"wte": nn.Embedding(config.vocab_size, config.n_embd),
"h": nn.ModuleList([Block(config, layer_idx) for layer_idx in range(config.n_layer)]),
})
self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
self.rotary_seq_len = config.sequence_len * 10
head_dim = config.n_embd // config.n_head
cos, sin = self._precompute_rotary_embeddings(self.rotary_seq_len, head_dim)
self.register_buffer("cos", cos, persistent=False)
self.register_buffer("sin", sin, persistent=False)
# ensure fp32 activations
self.transformer.wte.to(dtype=torch.bfloat16)
# following HF API expectations
self.post_init()
def _init_weights(self, module: nn.Module):
if isinstance(module, nn.Linear):
fan_out = module.weight.size(0)
fan_in = module.weight.size(1)
std = 1.0 / math.sqrt(fan_in) * min(1.0, math.sqrt(fan_out / fan_in))
torch.nn.init.normal_(module.weight, mean=0.0, std=std)
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=1.0)
def _precompute_rotary_embeddings(self, seq_len: int, head_dim: int, base: int = 10000, device=None):
if device is None:
device = self.transformer.wte.weight.device
# Handle meta device case - use CPU as fallback
if device.type == 'meta':
device = torch.device('cpu')
channel_range = torch.arange(0, head_dim, 2, dtype=torch.float32, device=device)
inv_freq = 1.0 / (base ** (channel_range / head_dim))
t = torch.arange(seq_len, dtype=torch.float32, device=device)
freqs = torch.outer(t, inv_freq)
cos, sin = freqs.cos(), freqs.sin()
cos, sin = cos.bfloat16(), sin.bfloat16()
cos, sin = cos[None, :, None, :], sin[None, :, None, :]
return cos, sin
def forward(self, input_ids: torch.Tensor, labels=None, **kwargs):
idx = input_ids
B, T = idx.size()
T0 = 0
cos_sin = self.cos[:, T0:T0+T], self.sin[:, T0:T0+T]
x = self.transformer.wte(idx)
x = x.float()
x = _rms_norm(x)
for block in self.transformer.h:
x = block(x, cos_sin, None)
x = _rms_norm(x)
softcap = 15
logits = self.lm_head(x)
logits = softcap * torch.tanh(logits / softcap)
loss = None
if labels is not None:
loss = F.cross_entropy(logits.view(-1, logits.size(-1)), labels.view(-1), ignore_index=-1, reduction='mean')
return {"loss": loss, "logits": logits}
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