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import math
import torch
import torch.nn as nn
from torch.nn import functional as F
from transformers import PreTrainedModel
from .config import GPTConfig
################################
### Layers ###
################################
class Rotary(torch.nn.Module):
def __init__(self, dim, base=10000):
super().__init__()
inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float() / dim))
self.register_buffer("inv_freq", inv_freq)
self.seq_len_cached = None
self.cos_cached = None
self.sin_cached = None
def forward(self, x):
seq_len = x.shape[1]
if seq_len != self.seq_len_cached:
self.seq_len_cached = seq_len
t = torch.arange(seq_len, device=x.device).type_as(self.inv_freq)
freqs = torch.outer(t, self.inv_freq).to(x.device)
self.cos_cached = freqs.cos()
self.sin_cached = freqs.sin()
return self.cos_cached[None, :, None, :], self.sin_cached[None, :, None, :]
def apply_rotary_emb(x, cos, sin):
assert x.ndim == 4 # multihead attention
d = x.shape[3]//2
x1 = x[..., :d]
x2 = x[..., d:]
y1 = x1 * cos + x2 * sin
y2 = x1 * (-sin) + x2 * cos
return torch.cat([y1, y2], 3)
def rmsnorm(x0, eps=1e-6):
x = x0.float()
x = x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + eps)
return x.type_as(x0)
class RMSNorm(nn.Module):
""" Root Mean Square Normalization """
def __init__(self, dim: int, weight: bool = False, bias: bool = False, eps: float = 1e-6):
super().__init__()
self.eps = eps
if weight:
self.weight = nn.Parameter(torch.ones(dim))
else:
self.register_parameter("weight", None)
if bias:
self.bias = nn.Parameter(torch.zeros(dim))
else:
self.register_parameter("bias", None)
def _norm(self, x):
return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
def forward(self, x):
output = self._norm(x.float()).type_as(x)
if self.weight is not None:
output = output * self.weight
if self.bias is not None:
output = output + self.bias
return output
class CausalSelfAttention(nn.Module):
def __init__(self, config):
super().__init__()
self.n_head = config.n_head
self.n_embd = config.n_embd
self.head_dim = self.n_embd // self.n_head
assert self.n_embd % self.n_head == 0
# key, query, value projections for all heads, but in a batch
self.c_attn = nn.Linear(self.n_embd, 3 * self.n_embd, bias=False)
# output projection
self.c_proj = nn.Linear(self.n_embd, self.n_embd, bias=False)
self.rotary = Rotary(self.head_dim)
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
qkv = self.c_attn(x)
q, k, v = qkv.split(self.n_embd, dim=2)
k = k.view(B, T, self.n_head, self.head_dim)
q = q.view(B, T, self.n_head, self.head_dim)
v = v.view(B, T, self.n_head, self.head_dim)
cos, sin = self.rotary(q)
q = apply_rotary_emb(q, cos, sin)
k = apply_rotary_emb(k, cos, sin)
y = F.scaled_dot_product_attention(q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2), is_causal=True)
y = y.transpose(1, 2).contiguous().view(B, T, C) # re-assemble all head outputs side by side
# output projection
y = self.c_proj(y)
return y
class RMSNorm(nn.Module):
def __init__(self, dim, eps=1e-5):
super().__init__()
self.eps = eps
self.weight = nn.Parameter(torch.ones(dim))
def forward(self, x):
norm = torch.norm(x, dim=-1, keepdim=True)
return self.weight * x / (norm + self.eps)
class Block(nn.Module):
def __init__(self, config):
super().__init__()
self.attn = CausalSelfAttention(config)
self.mlp = MLP(config)
self.attn_scale = (1 / (2 * config.n_layer)**0.5)
def forward(self, x):
x = x + self.attn_scale * self.attn(rmsnorm(x))
x = x + self.mlp(rmsnorm(x))
return x
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.gelu = nn.GELU()
self.c_proj = nn.Linear(4 * config.n_embd, config.n_embd, bias=config.bias)
self.dropout = nn.Dropout(config.dropout)
def forward(self, x):
x = self.c_fc(x)
x = self.gelu(x)
x = self.c_proj(x)
x = self.dropout(x)
return x
################################
### Model ###
################################
class GPT(PreTrainedModel):
config_class = GPTConfig
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.dropout),
h=nn.ModuleList([Block(config) for _ in range(config.n_layer)]),
))
self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
self.apply(self._init_weights)
# GPT-2 style scaled init
for pn, p in self.named_parameters():
if pn.endswith('c_proj.weight'):
torch.nn.init.normal_(p, mean=0.0, std=0.02 / math.sqrt(2 * config.n_layer))
def _init_weights(self, module):
if isinstance(module, nn.Linear):
torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
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=0.02)
def forward(self, input_ids, labels=None):
tok_emb = self.transformer.wte(input_ids)
x = self.transformer.drop(tok_emb)
for block in self.transformer.h:
x = block(x)
x = rmsnorm(x)
logits = self.lm_head(x)
loss = None
if labels is not None:
loss = F.cross_entropy(logits.view(-1, logits.size(-1)), labels.view(-1), ignore_index=-1)
return {'loss': loss, 'logits': logits} if loss is not None else {'logits': logits}
@torch.no_grad()
def generate(self, idx, max_new_tokens, temperature=1.0, top_k=None):
for _ in range(max_new_tokens):
idx_cond = idx if idx.size(1) <= self.config.block_size else idx[:, -self.config.block_size:]
logits = self(idx_cond)['logits']
logits = logits[:, -1, :] / temperature
if top_k is not None:
v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
logits[logits < v[:, [-1]]] = -float('Inf')
probs = F.softmax(logits, dim=-1)
idx_next = torch.multinomial(probs, num_samples=1)
idx = torch.cat((idx, idx_next), dim=1)
return idx
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