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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
import torch.nn as nn
from torch.nn import functional as F
class LayerNorm(nn.Module):
""" LayerNorm but with an optional bias. PyTorch doesn't support simply bias=False """
def __init__(self, ndim, bias):
super().__init__()
self.weight = nn.Parameter(torch.ones(ndim))
self.bias = nn.Parameter(torch.zeros(ndim)) if bias else None
def forward(self, input):
return F.layer_norm(input, self.weight.shape, self.weight, self.bias, 1e-5)
class CausalSelfAttention(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')
if not self.flash:
# print("WARNING: using slow attention. Flash Attention atm needs PyTorch nightly and dropout=0.0")
# causal mask to ensure that attention is only applied to the left in the input sequence
self.register_buffer("bias", torch.tril(torch.ones(config.block_size, config.block_size))
.view(1, 1, config.block_size, config.block_size))
def forward(self, x, past_kv=None, use_cache=False):
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)
if past_kv is not None:
past_key = past_kv[0]
past_value = past_kv[1]
k = torch.cat((past_key, k), dim=-2)
v = torch.cat((past_value, v), dim=-2)
FULL_T = k.shape[-2]
if use_cache is True:
present = (k, v)
else:
present = None
# 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
if past_kv is not None:
# When `past_kv` is provided, we're doing incremental decoding and `q.shape[2] == 1`: q only contains
# the query for the last token. scaled_dot_product_attention interprets this as the first token in the
# sequence, so if is_causal=True it will mask out all attention from it. This is not what we want, so
# to work around this we set is_causal=False.
is_causal = False
else:
is_causal = True
y = torch.nn.functional.scaled_dot_product_attention(q, k, v, dropout_p=self.dropout, is_causal=is_causal)
else:
# manual implementation of attention
att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))
att = att.masked_fill(self.bias[:,:,FULL_T-T:FULL_T,:FULL_T] == 0, float('-inf'))
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, present)
class MLP(nn.Module):
def __init__(self, config):
super().__init__()
self.c_fc = nn.Linear(config.n_embd, 4 * config.n_embd, bias=config.bias)
self.c_proj = nn.Linear(4 * config.n_embd, config.n_embd, bias=config.bias)
self.dropout = nn.Dropout(config.dropout)
self.gelu = nn.GELU()
def forward(self, x):
x = self.c_fc(x)
x = self.gelu(x)
x = self.c_proj(x)
x = self.dropout(x)
return x
class Block(nn.Module):
def __init__(self, config, layer_idx):
super().__init__()
self.ln_1 = LayerNorm(config.n_embd, bias=config.bias)
self.attn = CausalSelfAttention(config)
self.ln_2 = LayerNorm(config.n_embd, bias=config.bias)
self.mlp = MLP(config)
self.layer_idx = layer_idx
def forward(self, x, past_kv=None, use_cache=False):
attn_output, prev_kvs = self.attn(self.ln_1(x), past_kv=past_kv, use_cache=use_cache)
x = x + attn_output
x = x + self.mlp(self.ln_2(x))
return (x, prev_kvs)
@dataclass
class GPTConfig:
block_size: int = 1024
input_vocab_size: int = 10_048
output_vocab_size: int = 10_048
n_layer: int = 12
n_head: int = 12
n_embd: int = 768
dropout: float = 0.0
bias: bool = True # True: bias in Linears and LayerNorms, like GPT-2. False: a bit better and faster
class GPT(nn.Module):
def __init__(self, config):
super().__init__()
assert config.input_vocab_size is not None
assert config.output_vocab_size is not None
assert config.block_size is not None
self.config = config
self.transformer = nn.ModuleDict(dict(
wte = nn.Embedding(config.input_vocab_size, config.n_embd),
wpe = nn.Embedding(config.block_size, config.n_embd),
drop = nn.Dropout(config.dropout),
h = nn.ModuleList([Block(config, idx) for idx in range(config.n_layer)]),
ln_f = LayerNorm(config.n_embd, bias=config.bias),
))
self.lm_head = nn.Linear(config.n_embd, config.output_vocab_size, bias=False)
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:
n_params -= self.transformer.wte.weight.numel()
n_params -= self.transformer.wpe.weight.numel()
return n_params
def forward(self, idx, merge_context=False, past_kv=None, position_ids=None, use_cache=False):
device = idx.device
b, t = idx.size()
if past_kv is not None:
assert t == 1
tok_emb = self.transformer.wte(idx) # token embeddings of shape (b, t, n_embd)
else:
if merge_context:
assert(idx.shape[1] >= 256+256+1)
t = idx.shape[1] - 256
else:
assert t <= self.config.block_size, f"Cannot forward sequence of length {t}, block size is only {self.config.block_size}"
# forward the GPT model itself
if merge_context:
tok_emb = torch.cat([
self.transformer.wte(idx[:,:256]) + self.transformer.wte(idx[:,256:256+256]),
self.transformer.wte(idx[:,256+256:])
], dim=1)
else:
tok_emb = self.transformer.wte(idx) # token embeddings of shape (b, t, n_embd)
if past_kv is None:
past_length = 0
past_kv = tuple([None] * len(self.transformer.h))
else:
past_length = past_kv[0][0].size(-2)
if position_ids is None:
position_ids = torch.arange(past_length, t + past_length, dtype=torch.long, device=device)
position_ids = position_ids.unsqueeze(0) # shape (1, t)
assert position_ids.shape == (1, t)
pos_emb = self.transformer.wpe(position_ids) # position embeddings of shape (1, t, n_embd)
x = self.transformer.drop(tok_emb + pos_emb)
new_kv = () if use_cache else None
for i, (block, past_layer_kv) in enumerate(zip(self.transformer.h, past_kv)):
x, kv = block(x, past_kv=past_layer_kv, use_cache=use_cache)
if use_cache:
new_kv = new_kv + (kv,)
x = self.transformer.ln_f(x)
# inference-time mini-optimization: only forward the lm_head on the very last position
logits = self.lm_head(x[:, [-1], :]) # note: using list [-1] to preserve the time dim
return (logits, new_kv)
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