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| import math | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from dataclasses import dataclass | |
| 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) | |
| # output projection | |
| self.c_proj = nn.Linear(config.n_embd, config.n_embd) | |
| self.c_proj.NANGPT_SCALE_INIT = 1 | |
| # regularization | |
| self.n_head = config.n_head | |
| self.n_embd = config.n_embd | |
| 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): | |
| 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 | |
| # nh is "number of heads", hs is "head size", and C (number of channels) = nh * hs | |
| # e.g. in GPT-2 (124M), n_head=12, hs=64, so nh*hs=C=768 channels in the Transformer | |
| qkv = self.c_attn(x) | |
| q, k, v = qkv.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) | |
| att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1))) | |
| att = att.masked_fill(self.bias[:, :, :T, :T] == 0, float('-inf')) | |
| att = F.softmax(att, dim=-1) | |
| 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.c_proj(y) | |
| return y | |
| class MLP(nn.Module): | |
| def __init__(self, config): | |
| super().__init__() | |
| self.c_fc = nn.Linear(config.n_embd, 4 * config.n_embd) | |
| self.gelu = nn.GELU(approximate='tanh') | |
| self.c_proj = nn.Linear(4 * config.n_embd, config.n_embd) | |
| self.c_proj.NANOGPT_SCALE_INIT = 1 | |
| def forward(self, x): | |
| x = self.c_fc(x) | |
| x = self.gelu(x) | |
| x = self.c_proj(x) | |
| return x | |
| class Block(nn.Module): | |
| def __init__(self, config): | |
| super().__init__() | |
| self.ln_1 = nn.LayerNorm(config.n_embd) | |
| self.attn = CausalSelfAttention(config) | |
| self.ln_2 = nn.LayerNorm(config.n_embd) | |
| self.mlp = MLP(config) | |
| def forward(self, x): | |
| x = x + self.attn(self.ln_1(x)) | |
| x = x + self.mlp(self.ln_2(x)) | |
| return x | |
| class GPTConfig: | |
| block_size: int = 1024 # max sequence length | |
| vocab_size: int = 50257 # number of tokens: 50,000 BPE merges + 256 bytes tokens + 1 <|endoftext|> token | |
| n_layer: int = 12 # number of layers | |
| n_head: int = 12 # number of heads | |
| n_embd: int = 768 # embedding dimension | |
| class GPT(nn.Module): | |
| def __init__(self, config): | |
| super().__init__() | |
| self.config = config | |
| self.transformer = nn.ModuleDict(dict( | |
| wte = nn.Embedding(config.vocab_size, config.n_embd), | |
| wpe = nn.Embedding(config.block_size, config.n_embd), | |
| h = nn.ModuleList([Block(config) for _ in range(config.n_layer)]), | |
| ln_f = nn.LayerNorm(config.n_embd), | |
| )) | |
| self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False) | |
| # weight sharing | |
| self.transformer.wte.weight = self.lm_head.weight | |
| # weight initialization | |
| self.apply(self._init_weights) | |
| def _init_weights(self, module): | |
| if isinstance(module, nn.Linear): | |
| std = 0.02 | |
| if hasattr(module, 'NANGPT_SCALE_INIT'): | |
| std *= (2 * self.config.n_layer) ** -0.5 | |
| torch.nn.init.normal_(module.weight, mean = 0.0, std = std) | |
| 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, idx, targets=None): | |
| # idx is of shape (B, T) | |
| B, T = idx.size() | |
| assert T <= self.config.block_size, f"Cannot forward sequence of length {T}, block size is only {self.config.block_size}" | |
| # forward the token and posisition embeddings | |
| pos = torch.arange(0, T, dtype=torch.long, device=idx.device) # shape (T) | |
| pos_emb = self.transformer.wpe(pos) # position embeddings of shape (T, n_embd) | |
| tok_emb = self.transformer.wte(idx) # token embeddings of shape (B, T, n_embd) | |
| x = tok_emb + pos_emb | |
| # forward the blocks of the transformer | |
| for block in self.transformer.h: | |
| x = block(x) | |
| # forward the final layernorm and the classifier | |
| x = self.transformer.ln_f(x) | |
| logits = self.lm_head(x) # (B, T, vocab_size) | |
| loss = None | |
| if targets is not None: | |
| loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1)) | |
| return logits, loss | |
| def from_pretrained(cls, model_type): | |
| """Loads pretrained GPT-2 model weights from huggingface""" | |
| assert model_type in {'gpt2', 'gpt2-medium', 'gpt2-large', 'gpt2-xl'} | |
| from transformers import GPT2LMHeadModel | |
| print("loading weights from pretrained gpt: %s" % model_type) | |
| # n_layer, n_head and n_embd are determined from model_type | |
| config_args = { | |
| 'gpt2': dict(n_layer=12, n_head=12, n_embd=768), # 124M params | |
| 'gpt2-medium': dict(n_layer=24, n_head=16, n_embd=1024), # 350M params | |
| 'gpt2-large': dict(n_layer=36, n_head=20, n_embd=1280), # 774M params | |
| 'gpt2-xl': dict(n_layer=48, n_head=25, n_embd=1600), # 1558M params | |
| }[model_type] | |
| config_args['vocab_size'] = 50257 # always 50257 for GPT model checkpoints | |
| config_args['block_size'] = 1024 # always 1024 for GPT model checkpoints | |
| # create a from-scratch initialized minGPT model | |
| config = GPTConfig(**config_args) | |
| model = GPT(config) | |
| sd = model.state_dict() | |
| sd_keys = sd.keys() | |
| sd_keys = [k for k in sd_keys if not k.endswith('.attn.bias')] # discard this mask / buffer, not a param | |
| # init a huggingface/transformers model | |
| model_hf = GPT2LMHeadModel.from_pretrained(model_type) | |
| sd_hf = model_hf.state_dict() | |
| # copy while ensuring all of the parameters are aligned and match in names and shapes | |
| sd_keys_hf = sd_hf.keys() | |
| sd_keys_hf = [k for k in sd_keys_hf if not k.endswith('.attn.masked_bias')] # ignore these, just a buffer | |
| sd_keys_hf = [k for k in sd_keys_hf if not k.endswith('.attn.bias')] # same, just the mask (buffer) | |
| transposed = ['attn.c_attn.weight', 'attn.c_proj.weight', 'mlp.c_fc.weight', 'mlp.c_proj.weight'] | |
| # basically the openai checkpoints use a "Conv1D" module, but we only want to use a vanilla Linear | |
| # this means that we have to transpose these weights when we import them | |
| assert len(sd_keys_hf) == len(sd_keys), f"mismatched keys: {len(sd_keys_hf)} != {len(sd_keys)}" | |
| for k in sd_keys_hf: | |
| if any(k.endswith(w) for w in transposed): | |
| # special treatment for the Conv1D weights we need to transpose | |
| assert sd_hf[k].shape[::-1] == sd[k].shape | |
| with torch.no_grad(): | |
| sd[k].copy_(sd_hf[k].t()) | |
| else: | |
| # vanilla copy over the other parameters | |
| assert sd_hf[k].shape == sd[k].shape | |
| with torch.no_grad(): | |
| sd[k].copy_(sd_hf[k]) | |
| return model | |
| class Config: | |
| vocab_size: int = 50257 | |
| max_seq_len: int = 2048 | |
| dim: int = 768 | |
| num_layers: int = 12 | |
| num_heads: int = 12 | |
| dropout: float = 0.1 | |
| class MultiHeadAttention(nn.Module): | |
| def __init__(self, config): | |
| super().__init__() | |
| self.config = config | |
| self.n_head = config.num_heads | |
| self.n_embd = config.dim | |
| # Linear projections for Q, K, V | |
| self.c_attn = nn.Linear(config.dim, 3 * config.dim) # [n_embd, 3 * n_embd] | |
| self.c_proj = nn.Linear(config.dim, config.dim) # [n_embd, n_embd] | |
| self.attn_dropout = nn.Dropout(config.dropout) | |
| self.resid_dropout = nn.Dropout(config.dropout) | |
| def forward(self, x): | |
| B, T, C = x.size() # [B, T, n_embd] | |
| # Linear projection and split into Q, K, V | |
| q, k, v = self.c_attn(x).split(self.n_embd, dim=2) # [B, T, n_embd] each | |
| # Reshape for multi-head attention | |
| k = k.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # [B, n_head, T, n_embd/n_head] | |
| q = q.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # [B, n_head, T, n_embd/n_head] | |
| v = v.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # [B, n_head, T, n_embd/n_head] | |
| # Attention scores | |
| att = (q @ k.transpose(-2, -1)) * (1.0 / (k.size(-1) ** 0.5)) # [B, n_head, T, T] | |
| att = F.softmax(att, dim=-1) # [B, n_head, T, T] | |
| att = self.attn_dropout(att) # [B, n_head, T, T] | |
| # Weighted sum of values | |
| y = att @ v # [B, n_head, T, n_embd/n_head] | |
| # Reshape and project | |
| y = y.transpose(1, 2).contiguous().view(B, T, C) # [B, T, n_embd] | |
| y = self.c_proj(y) # [B, T, n_embd] | |
| y = self.resid_dropout(y) # [B, T, n_embd] | |
| return y | |
| class FeedForward(nn.Module): | |
| def __init__(self, config): | |
| super().__init__() | |
| self.c_fc = nn.Linear(config.dim, 4 * config.dim) # [n_embd, 4 * n_embd] | |
| self.c_proj = nn.Linear(4 * config.dim, config.dim) # [4 * n_embd, n_embd] | |
| self.dropout = nn.Dropout(config.dropout) | |
| def forward(self, x): | |
| x = self.c_fc(x) # [B, T, 4 * n_embd] | |
| x = F.gelu(x) # [B, T, 4 * n_embd] | |
| x = self.c_proj(x) # [B, T, n_embd] | |
| x = self.dropout(x) # [B, T, n_embd] | |
| return x | |
| class TransformerBlock(nn.Module): | |
| def __init__(self, config): | |
| super().__init__() | |
| self.ln_1 = nn.LayerNorm(config.dim) # [n_embd] | |
| self.attn = MultiHeadAttention(config) | |
| self.ln_2 = nn.LayerNorm(config.dim) # [n_embd] | |
| self.mlp = FeedForward(config) | |
| def forward(self, x): | |
| x = x + self.attn(self.ln_1(x)) # [B, T, n_embd] | |
| x = x + self.mlp(self.ln_2(x)) # [B, T, n_embd] | |
| return x | |
| class DecoderOnlyTransformer(nn.Module): | |
| def __init__(self, config): | |
| super().__init__() | |
| self.config = config | |
| self.wte = nn.Embedding(config.vocab_size, config.dim) # [vocab_size, n_embd] | |
| self.wpe = nn.Embedding(config.max_seq_len, config.dim) # [max_seq_len, n_embd] | |
| self.drop = nn.Dropout(config.dropout) | |
| self.blocks = nn.ModuleList([TransformerBlock(config) for _ in range(config.num_layers)]) | |
| self.ln_f = nn.LayerNorm(config.dim) # [n_embd] | |
| self.lm_head = nn.Linear(config.dim, config.vocab_size, bias=False) # [n_embd, vocab_size] | |
| self.apply(self._init_weights) | |
| def _init_weights(self, module): | |
| if isinstance(module, (nn.Linear, nn.Embedding)): | |
| module.weight.data.normal_(mean=0.0, std=0.02) | |
| if isinstance(module, nn.Linear) and module.bias is not None: | |
| module.bias.data.zero_() | |
| elif isinstance(module, nn.LayerNorm): | |
| module.bias.data.zero_() | |
| module.weight.data.fill_(1.0) | |
| def forward(self, idx): | |
| B, T = idx.size() # [B, T] | |
| # Positional embeddings | |
| pos = torch.arange(0, T, dtype=torch.long, device=idx.device).unsqueeze(0) # [1, T] | |
| # Token and position embeddings | |
| tok_emb = self.wte(idx) # [B, T, n_embd] | |
| pos_emb = self.wpe(pos) # [1, T, n_embd] | |
| # Combine embeddings and apply dropout | |
| x = self.drop(tok_emb + pos_emb) # [B, T, n_embd] | |
| # Transformer blocks | |
| for block in self.blocks: | |
| x = block(x) # [B, T, n_embd] | |
| # Final layer norm and linear projection | |
| x = self.ln_f(x) # [B, T, n_embd] | |
| logits = self.lm_head(x) # [B, T, vocab_size] | |
| return logits | |
| # if __name__ == '__main__': | |
| # config = Config() | |
| # model = DecoderOnlyTransformer(config) | |
| # # Example usage | |
| # batch_size = 4 | |
| # seq_len = 128 | |
| # # Generate random input | |
| # input_ids = torch.randint(0, config.vocab_size, (batch_size, seq_len)) | |
| # # Forward pass | |
| # logits = model(input_ids) | |
| # print("Input shape:", input_ids.shape) | |
| # print("Output shape:", logits.shape) | |