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"""
Much of this code is adapted from Andrej Karpathy's NanoGPT
(https://github.com/karpathy/nanoGPT)
"""
import math
from dataclasses import dataclass
import torch
from torch import nn
from torch.nn import functional as F
from .model import GPT, MLP, GPTConfig
class NonCausalSelfAttention(nn.Module):
def __init__(self, config):
super().__init__()
assert config.n_embd % config.n_head == 0
# key, query, value projections for all heads, but in a batch
self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd, bias=config.bias)
# output projection
self.c_proj = nn.Linear(config.n_embd, config.n_embd, bias=config.bias)
# regularization
self.attn_dropout = nn.Dropout(config.dropout)
self.resid_dropout = nn.Dropout(config.dropout)
self.n_head = config.n_head
self.n_embd = config.n_embd
self.dropout = config.dropout
# flash attention make GPU go brrrrr but support is only in PyTorch nightly and still a bit scary
self.flash = hasattr(torch.nn.functional, "scaled_dot_product_attention") and self.dropout == 0.0
def forward(self, x):
B, T, C = x.size() # batch size, sequence length, embedding dimensionality (n_embd)
# calculate query, key, values for all heads in batch and move head forward to be the batch dim
q, k, v = self.c_attn(x).split(self.n_embd, dim=2)
k = k.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)
q = q.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)
v = v.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)
# causal self-attention; Self-attend: (B, nh, T, hs) x (B, nh, hs, T) -> (B, nh, T, T)
if self.flash:
# efficient attention using Flash Attention CUDA kernels
y = torch.nn.functional.scaled_dot_product_attention(
q, k, v, attn_mask=None, dropout_p=self.dropout, is_causal=False
)
else:
# manual implementation of attention
att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))
att = F.softmax(att, dim=-1)
att = self.attn_dropout(att)
y = att @ v # (B, nh, T, T) x (B, nh, T, hs) -> (B, nh, T, hs)
y = y.transpose(1, 2).contiguous().view(B, T, C) # re-assemble all head outputs side by side
# output projection
y = self.resid_dropout(self.c_proj(y))
return y
class FineBlock(nn.Module):
def __init__(self, config):
super().__init__()
self.ln_1 = nn.LayerNorm(config.n_embd)
self.attn = NonCausalSelfAttention(config)
self.ln_2 = nn.LayerNorm(config.n_embd)
self.mlp = MLP(config)
def forward(self, x):
x = x + self.attn(self.ln_1(x))
x = x + self.mlp(self.ln_2(x))
return x
class FineGPT(GPT):
def __init__(self, config):
super().__init__(config)
del self.lm_head
self.config = config
self.n_codes_total = config.n_codes_total
self.transformer = nn.ModuleDict(
dict(
wtes=nn.ModuleList(
[nn.Embedding(config.input_vocab_size, config.n_embd) for _ in range(config.n_codes_total)]
),
wpe=nn.Embedding(config.block_size, config.n_embd),
drop=nn.Dropout(config.dropout),
h=nn.ModuleList([FineBlock(config) for _ in range(config.n_layer)]),
ln_f=nn.LayerNorm(config.n_embd),
)
)
self.lm_heads = nn.ModuleList(
[
nn.Linear(config.n_embd, config.output_vocab_size, bias=False)
for _ in range(config.n_codes_given, self.n_codes_total)
]
)
for i in range(self.n_codes_total - config.n_codes_given):
self.transformer.wtes[i + 1].weight = self.lm_heads[i].weight
def forward(self, pred_idx, idx):
device = idx.device
b, t, codes = idx.size()
assert (
t <= self.config.block_size
), f"Cannot forward sequence of length {t}, block size is only {self.config.block_size}"
assert pred_idx > 0, "cannot predict 0th codebook"
assert codes == self.n_codes_total, (b, t, codes)
pos = torch.arange(0, t, dtype=torch.long, device=device).unsqueeze(0) # shape (1, t)
# forward the GPT model itself
tok_embs = [
wte(idx[:, :, i]).unsqueeze(-1) for i, wte in enumerate(self.transformer.wtes)
] # token embeddings of shape (b, t, n_embd)
tok_emb = torch.cat(tok_embs, dim=-1)
pos_emb = self.transformer.wpe(pos) # position embeddings of shape (1, t, n_embd)
x = tok_emb[:, :, :, : pred_idx + 1].sum(dim=-1)
x = self.transformer.drop(x + pos_emb)
for block in self.transformer.h:
x = block(x)
x = self.transformer.ln_f(x)
logits = self.lm_heads[pred_idx - self.config.n_codes_given](x)
return logits
def get_num_params(self, non_embedding=True):
"""
Return the number of parameters in the model.
For non-embedding count (default), the position embeddings get subtracted.
The token embeddings would too, except due to the parameter sharing these
params are actually used as weights in the final layer, so we include them.
"""
n_params = sum(p.numel() for p in self.parameters())
if non_embedding:
for wte in self.transformer.wtes:
n_params -= wte.weight.numel()
n_params -= self.transformer.wpe.weight.numel()
return n_params
@dataclass
class FineGPTConfig(GPTConfig):
n_codes_total: int = 8
n_codes_given: int = 1
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