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import json
import torch
import torch.nn as nn
from torch.nn import functional as F
from transformers import PreTrainedModel, PretrainedConfig
# 超参数
batch_size = 64 # 一批包含的文本序列个数
block_size = 256 # 一个文本序列包含的字符个数
n_embed = 384 # embedding维度
n_head = 6
n_layer = 6
dropout = 0.2
vocab_size = 65
class NoobConfig(PretrainedConfig):
model_type = "Noob"
vocab_size = vocab_size
n_positions = block_size
n_embd = n_embed
n_layer = n_layer
n_head = n_head
class Head(nn.Module):
""" one head of self-attention """
def __init__(self, head_size):
super().__init__()
self.key = nn.Linear(n_embed, head_size, bias=False)
self.query = nn.Linear(n_embed, head_size, bias=False)
self.value = nn.Linear(n_embed, head_size, bias=False)
self.register_buffer('tril', torch.tril(torch.ones(block_size, block_size)))
self.dropout = nn.Dropout(dropout)
def forward(self, x):
B, T, C = x.shape
k = self.key(x)
q = self.query(x)
wei = q @ k.transpose(-2, -1) * C**-0.5
wei = wei.masked_fill(self.tril[:T, :T] == 0, float('-inf'))
wei = F.softmax(wei, dim=-1)
wei = self.dropout(wei)
v = self.value(x)
out = wei @ v
return out
class MultiHeadAttention(nn.Module):
def __init__(self, num_heads, head_size):
super().__init__()
self.heads = nn.ModuleList([Head(head_size) for _ in range(num_heads)])
self.proj = nn.Linear(n_embed, n_embed)
self.dropout = nn.Dropout(dropout)
def forward(self, x):
out = torch.cat([h(x) for h in self.heads], dim=-1)
out = self.proj(out)
out = self.dropout(out)
return out
class FeedFoward(nn.Module):
def __init__(self, n_embed):
super().__init__()
self.net = nn.Sequential(
nn.Linear(n_embed, 4 * n_embed),
nn.ReLU(),
nn.Linear(4 * n_embed, n_embed),
nn.Dropout(dropout),
)
def forward(self, x):
return self.net(x)
class Block(nn.Module):
""" transformer block: communication followed by computation """
def __init__(self, n_embed, n_head):
super().__init__()
head_size = n_embed // n_head
self.sa = MultiHeadAttention(n_head, head_size)
self.ffwd = FeedFoward(n_embed)
self.ln1 = nn.LayerNorm(n_embed)
self.ln2 = nn.LayerNorm(n_embed)
def forward(self, x):
x = x + self.sa(self.ln1(x))
x = x + self.ffwd(self.ln2(x))
return x
class Noob(PreTrainedModel):
config_class = NoobConfig
def __init__(self, config):
super().__init__(config)
self.token_embedding_table = nn.Embedding(config.vocab_size, config.n_embd)
self.position_embedding_table = nn.Embedding(config.n_positions, config.n_embd)
self.blocks = nn.Sequential(*[Block(config.n_embd, config.n_head) for _ in range(config.n_layer)])
self.ln_final = nn.LayerNorm(config.n_embd)
self.lm_head = nn.Linear(config.n_embd, config.vocab_size)
def forward(self, idx, targets=None):
B, T = idx.shape
tok_emb = self.token_embedding_table(idx)
pos_emb = self.position_embedding_table(torch.arange(T, device=idx.device))
x = tok_emb + pos_emb
x = self.blocks(x)
x = self.ln_final(x)
logits = self.lm_head(x)
if targets is None:
loss = None
else:
B, T, C = logits.shape
logits = logits.view(B*T, C)
targets = targets.view(B*T)
loss = F.cross_entropy(logits, targets)
return logits, loss
def generate(self, idx, max_new_tokens):
for _ in range(max_new_tokens):
idx_cond = idx[:, -block_size:]
logits, _ = self(idx_cond)
logits = logits[:, -1, :]
probs = F.softmax(logits, dim=-1)
idx_next = torch.multinomial(probs, num_samples=1)
idx = torch.cat((idx, idx_next), dim=1)
return idx |