# ----------------------------------------------------------------------------- # From model.py of nanoGPT: https://github.com/karpathy/nanoGPT # Changes are marked by @psando, and described below: # - use in_vocab_size and out_vocab_size, because our models reads both but # only predicts audio # - removed classmethod from_pretrained # ----------------------------------------------------------------------------- import math import inspect from dataclasses import dataclass import torch import torch.nn as nn from torch.nn import functional as F class LayerNorm(nn.Module): """ LayerNorm but with an optional bias. PyTorch doesn't support simply bias=False """ def __init__(self, ndim, bias): super().__init__() self.weight = nn.Parameter(torch.ones(ndim)) self.bias = nn.Parameter(torch.zeros(ndim)) if bias else None def forward(self, input): return F.layer_norm(input, self.weight.shape, self.weight, self.bias, 1e-5) 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, bias=config.bias) # output projection self.c_proj = nn.Linear(config.n_embd, config.n_embd, bias=config.bias) # regularization self.attn_dropout = nn.Dropout(config.dropout) self.resid_dropout = nn.Dropout(config.dropout) self.n_head = config.n_head self.n_embd = config.n_embd self.dropout = config.dropout # flash attention make GPU go brrrrr but support is only in PyTorch >= 2.0 self.flash = hasattr(torch.nn.functional, 'scaled_dot_product_attention') if not self.flash: print("WARNING: using slow attention. Flash Attention requires PyTorch >= 2.0") # causal mask to ensure that attention is only applied to the left in the input sequence 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 q, k, v = self.c_attn(x).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) # causal self-attention; Self-attend: (B, nh, T, hs) x (B, nh, hs, T) -> (B, nh, T, T) if self.flash: # efficient attention using Flash Attention CUDA kernels 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) else: # manual implementation of attention 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) att = self.attn_dropout(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_dropout(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, bias=config.bias) self.gelu = nn.GELU() self.c_proj = nn.Linear(4 * config.n_embd, config.n_embd, bias=config.bias) self.dropout = nn.Dropout(config.dropout) def forward(self, x): x = self.c_fc(x) x = self.gelu(x) x = self.c_proj(x) x = self.dropout(x) return x class Block(nn.Module): def __init__(self, config): super().__init__() self.ln_1 = LayerNorm(config.n_embd, bias=config.bias) self.attn = CausalSelfAttention(config) self.ln_2 = LayerNorm(config.n_embd, bias=config.bias) 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 @dataclass class GPTConfig: block_size: int = 2048 # @psando: double the block size of GPT-2 n_layer: int = 12 n_head: int = 12 n_embd: int = 768 dropout: float = 0.0 bias: bool = False # True: bias in Linears and LayerNorms, like GPT-2. False: a bit better and faster class GPT(nn.Module): def __init__(self, config, joint_tokenizer): # @psando: added joint_tokenizer to constructor super().__init__() assert config.block_size is not None config.in_vocab_size = joint_tokenizer.in_vocab_size config.out_vocab_size = joint_tokenizer.out_vocab_size config.audio_vocab_size = joint_tokenizer.audio_vocab_size config.audio_offset = joint_tokenizer.audio_offset config.in_eos_id = joint_tokenizer.in_eos_id config.out_eos_id = joint_tokenizer.out_eos_id self.config = config self.transformer = nn.ModuleDict(dict( wte = nn.Embedding(config.in_vocab_size, config.n_embd), wpe = nn.Embedding(config.block_size, config.n_embd), drop = nn.Dropout(config.dropout), h = nn.ModuleList([Block(config) for _ in range(config.n_layer)]), ln_f = LayerNorm(config.n_embd, bias=config.bias), )) self.lm_head = nn.Linear(config.n_embd, config.out_vocab_size, bias=False) # TODO: @psando: use num audio tokens here! # with weight tying when using torch.compile() some warnings get generated: # "UserWarning: functional_call was passed multiple values for tied weights. # This behavior is deprecated and will be an error in future versions" # not 100% sure what this is, so far seems to be harmless. TODO investigate # @psando: no weight tying because can't partially tie transformer.wte # self.transformer.wte.weight = self.lm_head.weight # https://paperswithcode.com/method/weight-tying # init all weights self.apply(self._init_weights) # apply special scaled init to the residual projections, per GPT-2 paper for pn, p in self.named_parameters(): if pn.endswith('c_proj.weight'): torch.nn.init.normal_(p, mean=0.0, std=0.02/math.sqrt(2 * config.n_layer)) # report number of parameters print("number of parameters: %.2fM" % (self.get_num_params()/1e6,)) def get_num_params(self, non_embedding=True): """ Return the number of parameters in the model. For non-embedding count (default), the position embeddings get subtracted. The token embeddings would too, except due to the parameter sharing these params are actually used as weights in the final layer, so we include them. """ n_params = sum(p.numel() for p in self.parameters()) if non_embedding: n_params -= self.transformer.wpe.weight.numel() return n_params def _init_weights(self, module): if isinstance(module, nn.Linear): torch.nn.init.normal_(module.weight, mean=0.0, std=0.02) 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): device = idx.device 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}" pos = torch.arange(0, t, dtype=torch.long, device=device) # shape (t) # forward the GPT model itself tok_emb = self.transformer.wte(idx) # token embeddings of shape (b, t, n_embd) pos_emb = self.transformer.wpe(pos) # position embeddings of shape (t, n_embd) x = self.transformer.drop(tok_emb + pos_emb) for block in self.transformer.h: x = block(x) x = self.transformer.ln_f(x) if targets is not None: # if we are given some desired targets also calculate the loss logits = self.lm_head(x) loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1), ignore_index=-100) # @psando: ignore_index=-100 else: # inference-time mini-optimization: only forward the lm_head on the very last position logits = self.lm_head(x[:, [-1], :]) # note: using list [-1] to preserve the time dim loss = None return logits, loss def crop_block_size(self, block_size): # model surgery to decrease the block size if necessary # e.g. we may load the GPT2 pretrained model checkpoint (block size 1024) # but want to use a smaller block size for some smaller, simpler model assert block_size <= self.config.block_size self.config.block_size = block_size self.transformer.wpe.weight = nn.Parameter(self.transformer.wpe.weight[:block_size]) for block in self.transformer.h: if hasattr(block.attn, 'bias'): block.attn.bias = block.attn.bias[:,:,:block_size,:block_size] # @psando: removed classmethod from_pretrained(cls, model_type, override_args=None) def configure_optimizers(self, weight_decay, learning_rate, betas, device_type): # start with all of the candidate parameters param_dict = {pn: p for pn, p in self.named_parameters()} # filter out those that do not require grad param_dict = {pn: p for pn, p in param_dict.items() if p.requires_grad} # create optim groups. Any parameters that is 2D will be weight decayed, otherwise no. # i.e. all weight tensors in matmuls + embeddings decay, all biases and layernorms don't. decay_params = [p for n, p in param_dict.items() if p.dim() >= 2] nodecay_params = [p for n, p in param_dict.items() if p.dim() < 2] optim_groups = [ {'params': decay_params, 'weight_decay': weight_decay}, {'params': nodecay_params, 'weight_decay': 0.0} ] num_decay_params = sum(p.numel() for p in decay_params) num_nodecay_params = sum(p.numel() for p in nodecay_params) print(f"num decayed parameter tensors: {len(decay_params)}, with {num_decay_params:,} parameters") print(f"num non-decayed parameter tensors: {len(nodecay_params)}, with {num_nodecay_params:,} parameters") # Create AdamW optimizer and use the fused version if it is available fused_available = 'fused' in inspect.signature(torch.optim.AdamW).parameters use_fused = fused_available and device_type == 'cuda' extra_args = dict(fused=True) if use_fused else dict() optimizer = torch.optim.AdamW(optim_groups, lr=learning_rate, betas=betas, **extra_args) print(f"using fused AdamW: {use_fused}") return optimizer def estimate_mfu(self, fwdbwd_per_iter, dt): """ estimate model flops utilization (MFU) in units of A100 bfloat16 peak FLOPS """ # first estimate the number of flops we do per iteration. # see PaLM paper Appendix B as ref: https://arxiv.org/abs/2204.02311 N = self.get_num_params() cfg = self.config L, H, Q, T = cfg.n_layer, cfg.n_head, cfg.n_embd//cfg.n_head, cfg.block_size flops_per_token = 6*N + 12*L*H*Q*T flops_per_fwdbwd = flops_per_token * T flops_per_iter = flops_per_fwdbwd * fwdbwd_per_iter # express our flops throughput as ratio of A100 bfloat16 peak flops flops_achieved = flops_per_iter * (1.0/dt) # per second flops_promised = 312e12 # A100 GPU bfloat16 peak flops is 312 TFLOPS mfu = flops_achieved / flops_promised return mfu @torch.no_grad() def generate(self, idx, max_new_tokens, temperature=1.0, top_k=None): """ Take a conditioning sequence of indices idx (LongTensor of shape (b,t)) and complete the sequence max_new_tokens times, feeding the predictions back into the model each time. Most likely you'll want to make sure to be in model.eval() mode of operation for this. """ for _ in range(max_new_tokens): # if the sequence context is growing too long we must crop it at block_size idx_cond = idx if idx.size(1) <= self.config.block_size else idx[:, -self.config.block_size:] # forward the model to get the logits for the index in the sequence logits, _ = self(idx_cond) # 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 if top_k is not None: v, _ = torch.topk(logits, min(top_k, logits.size(-1))) logits[logits < v[:, [-1]]] = -float('Inf') # apply softmax to convert logits to (normalized) probabilities probs = F.softmax(logits, dim=-1) # sample from the distribution idx_next = torch.multinomial(probs, num_samples=1) # append sampled index to the running sequence and continue if idx_next.item() < self.config.audio_vocab_size: # @psando: convert output audio token back to input token space idx_next += self.config.audio_offset elif idx_next.item() == self.config.out_eos_id: # @psando: convert output token to input space idx_next = torch.tensor([[self.config.in_eos_id]], dtype=torch.long, device=idx.device) else: raise ValueError(f"Generated token id {idx_next.item()} is out of range.") idx = torch.cat((idx, idx_next), dim=1) return idx