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