331 lines
16 KiB
Python
331 lines
16 KiB
Python
"""
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Full definition of a GPT Language Model, all of it in this single file.
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References:
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1) the official GPT-2 TensorFlow implementation released by OpenAI:
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https://github.com/openai/gpt-2/blob/master/src/model.py
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2) huggingface/transformers PyTorch implementation:
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https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt2/modeling_gpt2.py
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"""
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import math
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import inspect
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from dataclasses import dataclass
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import torch
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import torch.nn as nn
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from torch.nn import functional as F
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class LayerNorm(nn.Module):
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""" LayerNorm but with an optional bias. PyTorch doesn't support simply bias=False """
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def __init__(self, ndim, bias):
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super().__init__()
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self.weight = nn.Parameter(torch.ones(ndim))
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self.bias = nn.Parameter(torch.zeros(ndim)) if bias else None
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def forward(self, input):
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return F.layer_norm(input, self.weight.shape, self.weight, self.bias, 1e-5)
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class CausalSelfAttention(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 >= 2.0
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self.flash = hasattr(torch.nn.functional, 'scaled_dot_product_attention')
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if not self.flash:
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print("WARNING: using slow attention. Flash Attention requires PyTorch >= 2.0")
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# causal mask to ensure that attention is only applied to the left in the input sequence
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self.register_buffer("bias", torch.tril(torch.ones(config.block_size, config.block_size))
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.view(1, 1, config.block_size, config.block_size))
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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(q, k, v, attn_mask=None, dropout_p=self.dropout if self.training else 0, is_causal=True)
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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 = att.masked_fill(self.bias[:,:,:T,:T] == 0, float('-inf'))
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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 MLP(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.c_fc = nn.Linear(config.n_embd, 4 * config.n_embd, bias=config.bias)
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self.gelu = nn.GELU()
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self.c_proj = nn.Linear(4 * config.n_embd, config.n_embd, bias=config.bias)
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self.dropout = nn.Dropout(config.dropout)
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def forward(self, x):
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x = self.c_fc(x)
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x = self.gelu(x)
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x = self.c_proj(x)
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x = self.dropout(x)
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return x
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class Block(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.ln_1 = LayerNorm(config.n_embd, bias=config.bias)
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self.attn = CausalSelfAttention(config)
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self.ln_2 = LayerNorm(config.n_embd, bias=config.bias)
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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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@dataclass
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class GPTConfig:
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block_size: int = 1024
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vocab_size: int = 50304 # GPT-2 vocab_size of 50257, padded up to nearest multiple of 64 for efficiency
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n_layer: int = 12
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n_head: int = 12
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n_embd: int = 768
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dropout: float = 0.0
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bias: bool = True # True: bias in Linears and LayerNorms, like GPT-2. False: a bit better and faster
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class GPT(nn.Module):
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def __init__(self, config):
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super().__init__()
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assert config.vocab_size is not None
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assert config.block_size is not None
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self.config = config
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self.transformer = nn.ModuleDict(dict(
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wte = nn.Embedding(config.vocab_size, config.n_embd),
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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([Block(config) for _ in range(config.n_layer)]),
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ln_f = LayerNorm(config.n_embd, bias=config.bias),
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))
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self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
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# with weight tying when using torch.compile() some warnings get generated:
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# "UserWarning: functional_call was passed multiple values for tied weights.
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# This behavior is deprecated and will be an error in future versions"
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# not 100% sure what this is, so far seems to be harmless. TODO investigate
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self.transformer.wte.weight = self.lm_head.weight # https://paperswithcode.com/method/weight-tying
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# init all weights
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self.apply(self._init_weights)
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# apply special scaled init to the residual projections, per GPT-2 paper
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for pn, p in self.named_parameters():
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if pn.endswith('c_proj.weight'):
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torch.nn.init.normal_(p, mean=0.0, std=0.02/math.sqrt(2 * config.n_layer))
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# report number of parameters
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print("number of parameters: %.2fM" % (self.get_num_params()/1e6,))
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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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n_params -= self.transformer.wpe.weight.numel()
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return n_params
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def _init_weights(self, module):
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if isinstance(module, nn.Linear):
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torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
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if module.bias is not None:
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torch.nn.init.zeros_(module.bias)
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elif isinstance(module, nn.Embedding):
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torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
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def forward(self, idx, targets=None):
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device = idx.device
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b, t = idx.size()
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assert t <= self.config.block_size, f"Cannot forward sequence of length {t}, block size is only {self.config.block_size}"
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pos = torch.arange(0, t, dtype=torch.long, device=device) # shape (t)
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# forward the GPT model itself
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tok_emb = self.transformer.wte(idx) # token embeddings of shape (b, t, n_embd)
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pos_emb = self.transformer.wpe(pos) # position embeddings of shape (t, n_embd)
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x = self.transformer.drop(tok_emb + 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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if targets is not None:
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# if we are given some desired targets also calculate the loss
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logits = self.lm_head(x)
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loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1), ignore_index=-1)
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else:
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# inference-time mini-optimization: only forward the lm_head on the very last position
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logits = self.lm_head(x[:, [-1], :]) # note: using list [-1] to preserve the time dim
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loss = None
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return logits, loss
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def crop_block_size(self, block_size):
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# model surgery to decrease the block size if necessary
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# e.g. we may load the GPT2 pretrained model checkpoint (block size 1024)
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# but want to use a smaller block size for some smaller, simpler model
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assert block_size <= self.config.block_size
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self.config.block_size = block_size
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self.transformer.wpe.weight = nn.Parameter(self.transformer.wpe.weight[:block_size])
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for block in self.transformer.h:
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if hasattr(block.attn, 'bias'):
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block.attn.bias = block.attn.bias[:,:,:block_size,:block_size]
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@classmethod
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def from_pretrained(cls, model_type, override_args=None):
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assert model_type in {'gpt2', 'gpt2-medium', 'gpt2-large', 'gpt2-xl'}
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override_args = override_args or {} # default to empty dict
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# only dropout can be overridden see more notes below
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assert all(k == 'dropout' for k in override_args)
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from transformers import GPT2LMHeadModel
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print("loading weights from pretrained gpt: %s" % model_type)
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# n_layer, n_head and n_embd are determined from model_type
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config_args = {
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'gpt2': dict(n_layer=12, n_head=12, n_embd=768), # 124M params
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'gpt2-medium': dict(n_layer=24, n_head=16, n_embd=1024), # 350M params
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'gpt2-large': dict(n_layer=36, n_head=20, n_embd=1280), # 774M params
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'gpt2-xl': dict(n_layer=48, n_head=25, n_embd=1600), # 1558M params
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}[model_type]
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print("forcing vocab_size=50257, block_size=1024, bias=True")
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config_args['vocab_size'] = 50257 # always 50257 for GPT model checkpoints
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config_args['block_size'] = 1024 # always 1024 for GPT model checkpoints
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config_args['bias'] = True # always True for GPT model checkpoints
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# we can override the dropout rate, if desired
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if 'dropout' in override_args:
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print(f"overriding dropout rate to {override_args['dropout']}")
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config_args['dropout'] = override_args['dropout']
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# create a from-scratch initialized minGPT model
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config = GPTConfig(**config_args)
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model = GPT(config)
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sd = model.state_dict()
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sd_keys = sd.keys()
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sd_keys = [k for k in sd_keys if not k.endswith('.attn.bias')] # discard this mask / buffer, not a param
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# init a huggingface/transformers model
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model_hf = GPT2LMHeadModel.from_pretrained(model_type)
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sd_hf = model_hf.state_dict()
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# copy while ensuring all of the parameters are aligned and match in names and shapes
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sd_keys_hf = sd_hf.keys()
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sd_keys_hf = [k for k in sd_keys_hf if not k.endswith('.attn.masked_bias')] # ignore these, just a buffer
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sd_keys_hf = [k for k in sd_keys_hf if not k.endswith('.attn.bias')] # same, just the mask (buffer)
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transposed = ['attn.c_attn.weight', 'attn.c_proj.weight', 'mlp.c_fc.weight', 'mlp.c_proj.weight']
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# basically the openai checkpoints use a "Conv1D" module, but we only want to use a vanilla Linear
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# this means that we have to transpose these weights when we import them
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assert len(sd_keys_hf) == len(sd_keys), f"mismatched keys: {len(sd_keys_hf)} != {len(sd_keys)}"
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for k in sd_keys_hf:
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if any(k.endswith(w) for w in transposed):
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# special treatment for the Conv1D weights we need to transpose
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assert sd_hf[k].shape[::-1] == sd[k].shape
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with torch.no_grad():
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sd[k].copy_(sd_hf[k].t())
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else:
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# vanilla copy over the other parameters
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assert sd_hf[k].shape == sd[k].shape
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with torch.no_grad():
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sd[k].copy_(sd_hf[k])
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return model
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def configure_optimizers(self, weight_decay, learning_rate, betas, device_type):
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# start with all of the candidate parameters
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param_dict = {pn: p for pn, p in self.named_parameters()}
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# filter out those that do not require grad
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param_dict = {pn: p for pn, p in param_dict.items() if p.requires_grad}
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# create optim groups. Any parameters that is 2D will be weight decayed, otherwise no.
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# i.e. all weight tensors in matmuls + embeddings decay, all biases and layernorms don't.
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decay_params = [p for n, p in param_dict.items() if p.dim() >= 2]
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nodecay_params = [p for n, p in param_dict.items() if p.dim() < 2]
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optim_groups = [
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{'params': decay_params, 'weight_decay': weight_decay},
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{'params': nodecay_params, 'weight_decay': 0.0}
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]
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num_decay_params = sum(p.numel() for p in decay_params)
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num_nodecay_params = sum(p.numel() for p in nodecay_params)
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print(f"num decayed parameter tensors: {len(decay_params)}, with {num_decay_params:,} parameters")
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print(f"num non-decayed parameter tensors: {len(nodecay_params)}, with {num_nodecay_params:,} parameters")
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# Create AdamW optimizer and use the fused version if it is available
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fused_available = 'fused' in inspect.signature(torch.optim.AdamW).parameters
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use_fused = fused_available and device_type == 'cuda'
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extra_args = dict(fused=True) if use_fused else dict()
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optimizer = torch.optim.AdamW(optim_groups, lr=learning_rate, betas=betas, **extra_args)
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print(f"using fused AdamW: {use_fused}")
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return optimizer
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def estimate_mfu(self, fwdbwd_per_iter, dt):
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""" estimate model flops utilization (MFU) in units of A100 bfloat16 peak FLOPS """
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# first estimate the number of flops we do per iteration.
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# see PaLM paper Appendix B as ref: https://arxiv.org/abs/2204.02311
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N = self.get_num_params()
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cfg = self.config
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L, H, Q, T = cfg.n_layer, cfg.n_head, cfg.n_embd//cfg.n_head, cfg.block_size
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flops_per_token = 6*N + 12*L*H*Q*T
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flops_per_fwdbwd = flops_per_token * T
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flops_per_iter = flops_per_fwdbwd * fwdbwd_per_iter
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# express our flops throughput as ratio of A100 bfloat16 peak flops
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flops_achieved = flops_per_iter * (1.0/dt) # per second
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flops_promised = 312e12 # A100 GPU bfloat16 peak flops is 312 TFLOPS
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mfu = flops_achieved / flops_promised
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return mfu
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@torch.no_grad()
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def generate(self, idx, max_new_tokens, temperature=1.0, top_k=None):
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"""
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Take a conditioning sequence of indices idx (LongTensor of shape (b,t)) and complete
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the sequence max_new_tokens times, feeding the predictions back into the model each time.
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Most likely you'll want to make sure to be in model.eval() mode of operation for this.
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"""
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for _ in range(max_new_tokens):
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# if the sequence context is growing too long we must crop it at block_size
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idx_cond = idx if idx.size(1) <= self.config.block_size else idx[:, -self.config.block_size:]
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# forward the model to get the logits for the index in the sequence
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logits, _ = self(idx_cond)
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# pluck the logits at the final step and scale by desired temperature
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logits = logits[:, -1, :] / temperature
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# optionally crop the logits to only the top k options
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if top_k is not None:
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v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
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logits[logits < v[:, [-1]]] = -float('Inf')
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# apply softmax to convert logits to (normalized) probabilities
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probs = F.softmax(logits, dim=-1)
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# sample from the distribution
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idx_next = torch.multinomial(probs, num_samples=1)
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# append sampled index to the running sequence and continue
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idx = torch.cat((idx, idx_next), dim=1)
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return idx
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