Update model.py

model.py

@@ -47,78 +47,78 @@ n = int(0.9*len(data)) # first 90% will be train, rest val
 train_data = data[:n]
 val_data = data[n:]
 
-# data loading
-def get_batch(split):
-    # generate a small batch of data of inputs x and targets y
-    data = train_data if split == 'train' else val_data
-    ix = torch.randint(len(data) - block_size, (batch_size,))
-    x = torch.stack([data[i:i+block_size] for i in ix])
-    y = torch.stack([data[i+1:i+block_size+1] for i in ix])
-    x, y = x.to(device), y.to(device)
-    return x, y
+class Head(nn.Module):
+      """ one head of self-attention """
 
+      def __init__(self, head_size):
+          super().__init__()
+          self.key = nn.Linear(n_embd, head_size, bias=False)
+          self.query = nn.Linear(n_embd, head_size, bias=False)
+          self.value = nn.Linear(n_embd, head_size, bias=False)
+          self.register_buffer('tril', torch.tril(torch.ones(block_size, block_size)))
+
+          self.dropout = nn.Dropout(dropout)
+
+      def forward(self, x):
+          B,T,C = x.shape
+          k = self.key(x)   # (B,T,C)
+          q = self.query(x) # (B,T,C)
+          # compute attention scores ("affinities")
+          wei = q @ k.transpose(-2,-1) * C**-0.5 # (B, T, C) @ (B, C, T) -> (B, T, T)
+          wei = wei.masked_fill(self.tril[:T, :T] == 0, float('-inf')) # (B, T, T)
+          wei = F.softmax(wei, dim=-1) # (B, T, T)
+          wei = self.dropout(wei)
+          # perform the weighted aggregation of the values
+          v = self.value(x) # (B,T,C)
+          out = wei @ v # (B, T, T) @ (B, T, C) -> (B, T, C)
+          return out
+
+class MultiHeadAttention(nn.Module):
+      """ multiple heads of self-attention in parallel """
+
+      def __init__(self, num_heads, head_size):
+          super().__init__()
+          self.heads = nn.ModuleList([Head(head_size) for _ in range(num_heads)])
+          self.proj = nn.Linear(n_embd, n_embd)
+          self.dropout = nn.Dropout(dropout)
+
+      def forward(self, x):
+          out = torch.cat([h(x) for h in self.heads], dim=-1)
+          out = self.dropout(self.proj(out))
+          return out
+class FeedFoward(nn.Module):
+      """ a simple linear layer followed by a non-linearity """
+
+      def __init__(self, n_embd):
+          super().__init__()
+          self.net = nn.Sequential(
+              nn.Linear(n_embd, 4 * n_embd),
+              nn.ReLU(),
+              nn.Linear(4 * n_embd, n_embd),
+              nn.Dropout(dropout),
+          )
 
-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
+      def forward(self, x):
+          return self.net(x)
 
-        # output projection
-        y = self.resid_dropout(self.c_proj(y))
-        return y
+class Block(nn.Module):
+      """ Transformer block: communication followed by computation """
 
-# super simple bigram model
+      def __init__(self, n_embd, n_head):
+          # n_embd: embedding dimension, n_head: the number of heads we'd like
+          super().__init__()
+          head_size = n_embd // n_head
+          self.sa = MultiHeadAttention(n_head, head_size)
+          self.ffwd = FeedFoward(n_embd)
+          self.ln1 = nn.LayerNorm(n_embd)
+          self.ln2 = nn.LayerNorm(n_embd)
+
+      def forward(self, x):
+          x = x + self.sa(self.ln1(x))
+          x = x + self.ffwd(self.ln2(x))
+          return x
+
+  # super simple bigram model
 class BigramLanguageModel(nn.Module):
 
       def __init__(self):
@@ -151,257 +151,19 @@ class BigramLanguageModel(nn.Module):
 
           return logits, loss
 
-
-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 = 1024
-    vocab_size: int = 50304 # GPT-2 vocab_size of 50257, padded up to nearest multiple of 64 for efficiency
-    n_layer: int = 12
-    n_head: int = 12
-    n_embd: int = 768
-    dropout: float = 0.0
-    bias: bool = True # True: bias in Linears and LayerNorms, like GPT-2. False: a bit better and faster
-
-class GPT(nn.Module):
-
-    def __init__(self, config):
-        super().__init__()
-        assert config.vocab_size is not None
-        assert config.block_size is not None
-        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),
-            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.vocab_size, bias=False)
-        # 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
-        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=-1)
-        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]
-
-    @classmethod
-    def from_pretrained(cls, model_type, override_args=None):
-        assert model_type in {'gpt2', 'gpt2-medium', 'gpt2-large', 'gpt2-xl'}
-        override_args = override_args or {} # default to empty dict
-        # only dropout can be overridden see more notes below
-        assert all(k == 'dropout' for k in override_args)
-        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]
-        print("forcing vocab_size=50257, block_size=1024, bias=True")
-        config_args['vocab_size'] = 50257 # always 50257 for GPT model checkpoints
-        config_args['block_size'] = 1024 # always 1024 for GPT model checkpoints
-        config_args['bias'] = True # always True for GPT model checkpoints
-        # we can override the dropout rate, if desired
-        if 'dropout' in override_args:
-            print(f"overriding dropout rate to {override_args['dropout']}")
-            config_args['dropout'] = override_args['dropout']
-        # 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
-
-    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
-            idx = torch.cat((idx, idx_next), dim=1)
-
-        return idx
+      def generate(self, idx, max_new_tokens):
+          # idx is (B, T) array of indices in the current context
+          for _ in range(max_new_tokens):
+              # crop idx to the last block_size tokens
+              idx_cond = idx[:, -block_size:]
+              # get the predictions
+              logits, loss = self(idx_cond)
+              # focus only on the last time step
+              logits = logits[:, -1, :] # becomes (B, C)
+              # apply softmax to get probabilities
+              probs = F.softmax(logits, dim=-1) # (B, C)
+              # sample from the distribution
+              idx_next = torch.multinomial(probs, num_samples=1) # (B, 1)
+              # append sampled index to the running sequence
+              idx = torch.cat((idx, idx_next), dim=1) # (B, T+1)
+          return idx