model.py

import torch
import torch.nn as nn
import collections
from torch.nn import functional as F
from torch.nn import RMSNorm

from tokenizer import vocab_size, encode, decode, tiktoken_encoding

# hyperparameters
batch_size = 64 # how many independent sequences will we process in parallel?
block_size = 128 # what is the maximum context length for predictions?
max_iters = 45 * 1000
eval_interval = 500
learning_rate = 1e-3
device = 'cuda' if torch.cuda.is_available() else 'cpu'
eval_iters = 500
n_embd = 128
n_head = 4
n_layer = 10
dropout = 0.02
TRAIN = True
PRETRAIN_PERCENTAGE = 0.6
REP_PENALTY_DECAY = 0.95
# ------------

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),
            SwiGLU(4 * n_embd, 4 * n_embd),
            nn.Linear(4 * n_embd, n_embd),
            nn.Dropout(dropout),
        )

    def forward(self, x):
        return self.net(x)

# NOTE: I AM TESTING CODE FROM https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/activations.py
# be aware I do not know how this works entirely
class SwiGLU(nn.Module):
    r"""
    A [variant](https://arxiv.org/abs/2002.05202) of the gated linear unit activation function. It's similar to `GEGLU`
    but uses SiLU / Swish instead of GeLU.

    Parameters:
        dim_in (`int`): The number of channels in the input.
        dim_out (`int`): The number of channels in the output.
        bias (`bool`, defaults to True): Whether to use a bias in the linear layer.
    """

    def __init__(self, dim_in: int, dim_out: int, bias: bool = True):
        super().__init__()
        self.proj = nn.Linear(dim_in, dim_out * 2, bias=bias)
        self.activation = nn.SiLU()

    def forward(self, hidden_states):
        hidden_states = self.proj(hidden_states)
        hidden_states, gate = hidden_states.chunk(2, dim=-1)
        return hidden_states * self.activation(gate)

class Block(nn.Module):
    """ Transformer block: communication followed by computation """

    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.RMSNorm(n_embd) # orig a LayerNorm
        self.ln2 = nn.RMSNorm(n_embd) # orig a LayerNorm

    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 TokenBasedLanguageModel(nn.Module):

    def __init__(self):
        super().__init__()
        # each token directly reads off the logits for the next token from a lookup table
        self.token_embedding_table = nn.Embedding(vocab_size, n_embd)
        self.position_embedding_table = nn.Embedding(block_size, n_embd)
        self.blocks = nn.Sequential(*[Block(n_embd, n_head=n_head) for _ in range(n_layer)])
        self.ln_f = nn.RMSNorm(n_embd) # final orig layer norm
        self.lm_head = nn.Linear(n_embd, vocab_size)

    def forward(self, idx, targets=None):
        B, T = idx.shape

        # idx and targets are both (B,T) tensor of integers
        tok_emb = self.token_embedding_table(idx) # (B,T,C)
        pos_emb = self.position_embedding_table(torch.arange(T, device=device)) # (T,C)
        x = tok_emb + pos_emb # (B,T,C)
        x = self.blocks(x) # (B,T,C)
        x = self.ln_f(x) # (B,T,C)
        logits = self.lm_head(x) # (B,T,vocab_size)

        if targets is None:
            loss = None
        else:
            B, T, C = logits.shape
            logits = logits.view(B*T, C)
            targets = targets.view(B*T)
            loss = F.cross_entropy(logits, targets)

        return logits, loss
    
    @torch.no_grad
    def generate(self, idx, max_new_tokens, stream = False, stream_probs = False):
        # idx is (B, T) array of indices in the current context
        token_modifiers = collections.defaultdict(lambda x: 1)
        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)
             # apply rep penalty
            #for token in token_modifiers:
            #    token_modifiers[token] *= REP_PENALTY_DECAY
            #    for batch in range(probs.shape[0]):
            #        probs[batch][token] *= (1 - REP_PENALTY_DECAY)
            # print(probs.shape)
            # sample from the distribution
            idx_next = torch.multinomial(probs, num_samples=1) # (B, 1)
            if stream:
                if stream_probs:
                    yield [idx_next, probs[0].tolist()]
                else: 
                    yield idx_next
            token_modifiers[idx_next] = REP_PENALTY_DECAY;
            # append sampled index to the running sequence
            idx = torch.cat((idx, idx_next), dim=1) # (B, T+1)
        if not stream:
            return idx