# -*- coding: utf-8 -*- # Yan Chen 2023.10 # yanchen@xjtu.edu.com """ GPT model: - the initial stem consists of a combination of token encoding and a positional encoding - the meat of it is a uniform sequence of Transformer blocks - each Transformer is a sequential combination of a 1-hidden-layer MLP block and a self-attention block - all blocks feed into a central residual pathway similar to resnets - the final decoder is a linear projection into a vanilla Softmax classifier """ import math,json import torch import torch.nn as nn from torch.nn import functional as F class GPTConfig: """ base GPT config, params common to all GPT versions """ embd_pdrop = 0.1 resid_pdrop = 0.1 attn_pdrop = 0.1 def __init__(self, vocab_size, block_size, **kwargs): self.vocab_size = vocab_size self.block_size = block_size for k,v in kwargs.items(): setattr(self, k, v) class GPT1Config(GPTConfig): """ GPT-1 like network roughly 125M params """ n_layer = 12 n_head = 12 n_embd = 768 class CausalSelfAttention(nn.Module): """ A vanilla multi-head masked self-attention layer with a projection at the end. It is possible to use torch.nn.MultiheadAttention here but I am including an explicit implementation here to show that there is nothing too scary here. """ def __init__(self, config): super().__init__() assert config.n_embd % config.n_head == 0 # key, query, value projections for all heads self.key = nn.Linear(config.n_embd, config.n_embd) self.query = nn.Linear(config.n_embd, config.n_embd) self.value = nn.Linear(config.n_embd, config.n_embd) # regularization self.attn_drop = nn.Dropout(config.attn_pdrop) self.resid_drop = nn.Dropout(config.resid_pdrop) # output projection self.proj = nn.Linear(config.n_embd, config.n_embd) # causal mask to ensure that attention is only applied to the left in the input sequence num = int(bool(config.num_props)) # num = 1 self.register_buffer("mask", torch.tril(torch.ones(config.block_size + num, config.block_size + num)) .view(1, 1, config.block_size + num, config.block_size + num)) self.n_head = config.n_head def forward(self, x, layer_past=None): B, T, C = x.size() # calculate query, key, values for all heads in batch and move head forward to be the batch dim k = self.key(x).view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs) q = self.query(x).view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs) v = self.value(x).view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs) # causal self-attention; Self-attend: (B, nh, T, hs) x (B, nh, hs, T) -> (B, nh, T, T) att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1))) att = att.masked_fill(self.mask[:,:,:T,:T] == 0, float('-inf')) att = F.softmax(att, dim=-1) attn_save = att att = self.attn_drop(att) 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.resid_drop(self.proj(y)) return y, attn_save class Block(nn.Module): """ an unassuming Transformer block """ def __init__(self, config): super().__init__() self.ln1 = nn.LayerNorm(config.n_embd) self.ln2 = nn.LayerNorm(config.n_embd) self.attn = CausalSelfAttention(config) self.mlp = nn.Sequential( nn.Linear(config.n_embd, 4 * config.n_embd), nn.GELU(), nn.Linear(4 * config.n_embd, config.n_embd), nn.Dropout(config.resid_pdrop), ) def forward(self, x): y, attn = self.attn(self.ln1(x)) x = x + y x = x + self.mlp(self.ln2(x)) return x, attn class GPT(nn.Module): """ the full GPT language model, with a context size of block_size """ def __init__(self, config): super().__init__() #print(json.dumps(config.__dict__, indent=2)) # input embedding stem self.config = config self.tok_emb = nn.Embedding(config.vocab_size, config.n_embd) self.type_emb = nn.Embedding(2, config.n_embd) if config.num_props: self.prop_nn = nn.Linear(config.num_props, config.n_embd) self.pos_emb = nn.Parameter(torch.zeros(1, config.block_size, config.n_embd)) self.drop = nn.Dropout(config.embd_pdrop) # transformer self.blocks = nn.Sequential(*[Block(config) for _ in range(config.n_layer)]) # decoder head self.ln_f = nn.LayerNorm(config.n_embd) self.head = nn.Linear(config.n_embd, config.vocab_size, bias=False) self.block_size = config.block_size if config.lstm: self.lstm = nn.LSTM(input_size = config.n_embd, hidden_size = config.n_embd, num_layers = config.lstm_layers, dropout = 0.3, bidirectional = False) self.apply(self._init_weights) #logger.info("number of parameters: %e", sum(p.numel() for p in self.parameters())) def get_block_size(self): return self.block_size 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 configure_optimizers(self, train_config): """ This long function is unfortunately doing something very simple and is being very defensive: We are separating out all parameters of the model into two buckets: those that will experience weight decay for regularization and those that won't (biases, and layernorm/embedding weights). We are then returning the PyTorch optimizer object. """ # separate out all parameters to those that will and won't experience regularizing weight decay decay = set() no_decay = set() whitelist_weight_modules = (torch.nn.Linear, torch.nn.LSTM) blacklist_weight_modules = (torch.nn.LayerNorm, torch.nn.Embedding) for mn, m in self.named_modules(): for pn, p in m.named_parameters(): fpn = '%s.%s' % (mn, pn) if mn else pn # full param name if pn.endswith('bias') or ('bias' in pn): # all biases will not be decayed no_decay.add(fpn) elif (pn.endswith('weight') or ('weight' in pn)) and isinstance(m, whitelist_weight_modules): # weights of whitelist modules will be weight decayed decay.add(fpn) elif pn.endswith('weight') and isinstance(m, blacklist_weight_modules): # weights of blacklist modules will NOT be weight decayed no_decay.add(fpn) # special case the position embedding parameter in the root GPT module as not decayed no_decay.add('pos_emb') # validate that we considered every parameter param_dict = {pn: p for pn, p in self.named_parameters()} inter_params = decay & no_decay union_params = decay | no_decay assert len(inter_params) == 0, "parameters %s made it into both decay/no_decay sets!" % (str(inter_params), ) assert len(param_dict.keys() - union_params) == 0, "parameters %s were not separated into either decay/no_decay set!" \ % (str(param_dict.keys() - union_params), ) # create the pytorch optimizer object optim_groups = [ {"params": [param_dict[pn] for pn in sorted(list(decay))], "weight_decay": train_config.weight_decay}, {"params": [param_dict[pn] for pn in sorted(list(no_decay))], "weight_decay": 0.0}, ] optimizer = torch.optim.AdamW(optim_groups, lr=train_config.learning_rate, betas=train_config.betas) return optimizer def forward(self, idx, targets=None, prop = None): b, t = idx.size() assert t <= self.block_size, "Cannot forward, model block size is exhausted." if self.config.num_props: assert prop.size(-1) == self.config.num_props, "Num_props should be equal to last dim of property vector" # forward the GPT model token_embeddings = self.tok_emb(idx) # each index maps to a (learnable) vector position_embeddings = self.pos_emb[:, :t, :] # each position maps to a (learnable) vector type_embeddings = self.type_emb(torch.ones((b,t), dtype = torch.long, device = idx.device)) x = self.drop(token_embeddings + position_embeddings + type_embeddings) embed = x if self.config.num_props: type_embd = self.type_emb(torch.zeros((b, 1), dtype = torch.long, device = idx.device)) if prop.ndim == 2: p = self.prop_nn(prop.unsqueeze(1)) # for single property else: p = self.prop_nn(prop) # for multiproperty p += type_embd x = torch.cat([p, x], 1) # x = self.blocks(x) attn_maps = [] for layer in self.blocks: x, attn = layer(x) attn_maps.append(attn) x = self.ln_f(x) logits = self.head(x) if self.config.num_props: num = int(bool(self.config.num_props)) else: num = 0 logits = logits[:, num:, :] # if we are given some desired targets also calculate the loss loss = None if targets is not None: loss = F.cross_entropy(logits.reshape(-1, logits.size(-1)), targets.view(-1)) return logits, loss, attn_maps, embed # (num_layers, batch_size, num_heads, max_seq_len, max_seq_len) @torch.no_grad() def sample(self, x, steps, temperature=1.0, do_sample=False, top_k=None, top_p=None, prop=None): """ Take a conditioning sequence of indices in x (of shape (b,t)) and predict the next token in the sequence, feeding the predictions back into the model each time. Clearly the sampling has quadratic complexity unlike an RNN that is only linear, and has a finite context window of block_size, unlike an RNN that has an infinite context window. Most likely you'll want to make sure to be in model.eval() mode of operation for this. """ #model.eval() def top_k_top_p_filtering(logits, top_k=0, top_p=0.0, filter_value=-float('Inf')): """ Filter a distribution of logits using top-k and/or nucleus (top-p) filtering Args: logits: logits distribution shape (batch size x vocabulary size) top_k > 0: keep only top k tokens with highest probability (top-k filtering). top_p > 0.0: keep the top tokens with cumulative probability >= top_p (nucleus filtering). Nucleus filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751) From: https://gist.github.com/thomwolf/1a5a29f6962089e871b94cbd09daf317 """ top_k = min(top_k, logits.size(-1)) # Safety check if top_k > 0: # Remove all tokens with a probability less than the last token of the top-k indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None] logits[indices_to_remove] = filter_value if top_p > 0.0: sorted_logits, sorted_indices = torch.sort(logits, descending=True) cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1) # Remove tokens with cumulative probability above the threshold sorted_indices_to_remove = cumulative_probs > top_p # Shift the indices to the right to keep also the first token above the threshold sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone() sorted_indices_to_remove[..., 0] = 0 # scatter sorted tensors to original indexing indices_to_remove = sorted_indices_to_remove.scatter(dim=1, index=sorted_indices, src=sorted_indices_to_remove) logits[indices_to_remove] = filter_value return logits for k in range(steps): x_cond = x if x.size(1) <= self.block_size else x[:, -self.block_size:] # crop context if needed # forward the model to get the logits for the index in the sequence logits, _, _, _ = self(x_cond, prop = prop) # for sampling, no target # pluck the logits at the final step and scale by desired temperature logits = logits[:, -1, :] / temperature # optionally crop the logits to only the top k options OR using nucleus (top-p) filtering #if top_k is not None: # v, _ = torch.topk(logits, top_k) # logits[logits < v[:, [-1]]] = -float('Inf') logits = top_k_top_p_filtering(logits, top_p=top_p, top_k=top_k) # apply softmax to convert logits to (normalized) probabilities probs = F.softmax(logits, dim=-1) # sample from the distribution or take the most likely if do_sample: x_next = torch.multinomial(probs, num_samples=1) else: _, x_next = torch.topk(probs, k=1, dim=-1) # append sampled index to the running sequence and continue x = torch.cat((x, x_next), dim=1) return x[:, 1:]