model.py

import torch
import torch.nn as nn
import torch.nn.functional as F
import math

from dataclasses import dataclass
from typing import Optional, Tuple

import tiktoken
import os
import numpy as np

torch.set_float32_matmul_precision('high')

def load_tokens(filename):
    npt = np.load(filename)
    npt = npt.astype(np.int32) # added after video
    ptt = torch.tensor(npt, dtype=torch.long)
    return ptt

class DataLoaderLite:
    def __init__(self, B, T, split):
        self.B = B
        self.T = T
        assert split in {'train', 'val'}

        # get the shard filenames
        data_root = "tinystories"
        shards = os.listdir(data_root)
        shards = [s for s in shards if split in s]
        shards = sorted(shards)
        shards = [os.path.join(data_root, s) for s in shards]
        self.shards = shards
        assert len(shards) > 0, f"no shards found for split {split}"
        print(f"found {len(shards)} shards for split {split}")
        self.reset()

    def reset(self):
        # state, init at shard zero
        self.current_shard = 0
        self.tokens = load_tokens(self.shards[self.current_shard])
        self.current_position = self.B * self.T

    def next_batch(self):
        B, T = self.B, self.T
        buf = self.tokens[self.current_position : self.current_position+B*T+1]
        x = (buf[:-1]).view(B, T) # inputs
        y = (buf[1:]).view(B, T) # targets
        # advance the position in the tensor
        self.current_position += B * T
        # if loading the next batch would be out of bounds, advance to next shard
        if self.current_position + (B * T ) > len(self.tokens):
            self.current_shard = (self.current_shard + 1) % len(self.shards)
            self.tokens = load_tokens(self.shards[self.current_shard])
            self.current_position = B * T
        return x, y

@dataclass # the hyperparameters 
class ModelArgs:
    dim: int = 768 
    n_layers: int = 16 
    n_heads: int = 16 
    n_kv_heads: Optional[int] = 4
    vocab_size: int = 50304
    multiple_of: int = 256 
    ffn_dim_multiplier: Optional[float] = None
    norm_eps: float = 1e-5
    rope_theta: float = 50000 
    max_batch_size: int = 4
    max_seq_len: int = 1024 
    device: str = 'cuda' if torch.cuda.is_available() else 'cpu'
    dropout_rate: float = 0.1 

params = ModelArgs()

class RMSNorm(torch.nn.Module):
    def __init__(self, dim: int, eps: float = 1e-6):
        super().__init__()
        self.eps = eps
        self.weight = nn.Parameter(torch.ones(dim))

    def _norm(self, x):
        return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)

    def forward(self, x):
        output = self._norm(x.float()).type_as(x)
        return output * self.weight


def precompute_freqs_cis(dim: int, end: int, theta: float = 10000.0):
    freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim))
    t = torch.arange(end, device=freqs.device, dtype=torch.float32)
    freqs = torch.outer(t, freqs)
    freqs_cis = torch.polar(torch.ones_like(freqs), freqs)  # complex64
    return freqs_cis.to(params.device)

def reshape_for_broadcast(freqs_cis: torch.Tensor, x: torch.Tensor):
    ndim = x.ndim
    assert 0 <= 1 < ndim
    assert freqs_cis.shape == (x.shape[1], x.shape[-1]), f'freqs_cis.shape {freqs_cis.shape} != (x.shape[1], x.shape[-1]) {(x.shape[1], x.shape[-1])}'
    shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)]
    return freqs_cis.view(*shape)

def apply_rotary_emb(
    xq: torch.Tensor,
    xk: torch.Tensor,
    freqs_cis: torch.Tensor,
) -> Tuple[torch.Tensor, torch.Tensor]:
    xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2))
    xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2))
    freqs_cis = reshape_for_broadcast(freqs_cis, xq_)
    xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(3)
    xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(3)
    return xq_out.type_as(xq), xk_out.type_as(xk)

def repeat_kv(x: torch.Tensor, n_rep: int) -> torch.Tensor:
    """torch.repeat_interleave(x, dim=2, repeats=n_rep)"""
    bs, seqlen, n_kv_heads, head_dim = x.shape
    if n_rep == 1:
        return x
    return (
        x[:, :, :, None, :]
        .expand(bs, seqlen, n_kv_heads, n_rep, head_dim)
        .reshape(bs, seqlen, n_kv_heads * n_rep, head_dim)
    ) 

class Attention(nn.Module):
    def __init__(self, args: ModelArgs):
        super().__init__()
        self.n_heads = args.n_heads
        self.n_kv_heads = args.n_heads if args.n_kv_heads is None else args.n_kv_heads
        self.n_rep = args.n_heads // self.n_kv_heads
        self.head_dim = args.dim // args.n_heads

        self.wq = nn.Linear(args.dim, args.n_heads * self.head_dim, bias=False)
        self.wk = nn.Linear(args.dim, self.n_kv_heads * self.head_dim, bias=False)
        self.wv = nn.Linear(args.dim, self.n_kv_heads * self.head_dim, bias=False)
        self.wo = nn.Linear(args.n_heads * self.head_dim, args.dim, bias=False)

        self.cache_k = torch.zeros(
            (args.max_batch_size, args.max_seq_len, self.n_kv_heads, self.head_dim),
            requires_grad = False
        ).to(args.device)
        self.cache_v = torch.zeros(
            (args.max_batch_size, args.max_seq_len, self.n_kv_heads, self.head_dim),
            requires_grad = False
        ).to(args.device)

    def forward(
        self,
        x: torch.Tensor,
        freqs_cis: torch.Tensor,
        mask: Optional[torch.Tensor],
        start_pos: int = None,
    ):
        bsz, seqlen, _ = x.shape
        xq, xk, xv = self.wq(x), self.wk(x), self.wv(x)

        xq = xq.view(bsz, seqlen, self.n_heads, self.head_dim)
        xk = xk.view(bsz, seqlen, self.n_kv_heads, self.head_dim)
        xv = xv.view(bsz, seqlen, self.n_kv_heads, self.head_dim)

        xq, xk = apply_rotary_emb(xq, xk, freqs_cis=freqs_cis)

        if start_pos is not None: # if we're performing inference, use kv caching 
            self.cache_k = self.cache_k.to(xq)
            self.cache_v = self.cache_v.to(xq)

            # set the values in our cache according to the current input
            self.cache_k[:bsz, start_pos : start_pos + seqlen] = xk
            self.cache_v[:bsz, start_pos : start_pos + seqlen] = xv

            # grab our key and value matrixes which have a longer sequence length than our queries
            keys = self.cache_k[:bsz, : start_pos + seqlen]
            values = self.cache_v[:bsz, : start_pos + seqlen]
        else:
            # if we're training, do full sequence length 
            keys, values = xk, xv

        keys = repeat_kv(keys, self.n_rep) 
        values = repeat_kv(values, self.n_rep) 

        xq = xq.transpose(1, 2)  
        keys = keys.transpose(1, 2)  
        values = values.transpose(1, 2) 

        scores = torch.matmul(xq, keys.transpose(2, 3)) / math.sqrt(self.head_dim)
        if mask is not None:
            scores = scores + mask 
        scores = F.softmax(scores.float(), dim=-1).type_as(xq)

        output = torch.matmul(scores, values) 
        output = output.transpose(1, 2).contiguous().view(bsz, seqlen, -1)
        return self.wo(output)

class FeedForward(nn.Module):
    def __init__(
        self,
        dim: int,
        hidden_dim: int,
        multiple_of: int,
        ffn_dim_multiplier: Optional[float],
    ):
        super().__init__()
        # custom dim factor multiplier that ensures we're using a multiple of "multiple_of" for hardware efficiency reasons
        hidden_dim = int(2 * hidden_dim / 3)
        if ffn_dim_multiplier is not None:
            hidden_dim = int(ffn_dim_multiplier * hidden_dim)
        hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)

        self.w1 = nn.Linear(dim, hidden_dim, bias=False)
        self.w2 = nn.Linear(hidden_dim, dim, bias=False)
        self.w3 = nn.Linear(dim, hidden_dim, bias=False)

    def forward(self, x):
        return self.w2(F.silu(self.w1(x)) * self.w3(x))

class TransformerBlock(nn.Module):
    def __init__(self, args: ModelArgs):
        super().__init__()
        self.n_heads = args.n_heads
        self.dim = args.dim
        self.head_dim = args.dim // args.n_heads
        self.attention = Attention(args)
        self.feed_forward = FeedForward(
            dim=args.dim,
            hidden_dim=4 * args.dim,
            multiple_of=args.multiple_of,
            ffn_dim_multiplier=args.ffn_dim_multiplier,
        )
        self.attention_norm = RMSNorm(args.dim, eps=args.norm_eps)
        self.ffn_norm = RMSNorm(args.dim, eps=args.norm_eps)
        self.dropout_rate = args.dropout_rate

    def forward(
        self,
        x: torch.Tensor,
        freqs_cis: torch.Tensor,
        mask: Optional[torch.Tensor],
        start_pos: int = None,
        training = False,
    ):
        h = x + F.dropout(self.attention(self.attention_norm(x), freqs_cis, mask, start_pos), p=self.dropout_rate, training=training)
        out = h + F.dropout(self.feed_forward(self.ffn_norm(h)), p=self.dropout_rate, training=training)
        return out

class Moose(nn.Module):
    def __init__(self, params: ModelArgs):
        super().__init__()
        self.params = params
        self.vocab_size = params.vocab_size
        self.n_layers = params.n_layers
        self.max_seq_len = params.max_seq_len

        self.tok_embeddings = nn.Embedding(params.vocab_size, params.dim)

        self.layers = torch.nn.ModuleList()
        for _ in range(params.n_layers):
            self.layers.append(TransformerBlock(params))

        self.norm = RMSNorm(params.dim, eps=params.norm_eps)
        self.output = nn.Linear(
            params.dim,
            params.vocab_size,
            bias=False)

        self.freqs_cis = precompute_freqs_cis(
            params.dim // params.n_heads,
            params.max_seq_len * 2,
            params.rope_theta,)

        # precompute the causal attention mask
        mask = torch.full((params.max_seq_len, params.max_seq_len),
                          float("-inf"),
                          device=params.device)
        mask = torch.triu(mask, diagonal=1)
        self.register_buffer('mask', mask)

        self.criterion = nn.CrossEntropyLoss()

    def forward(self,
                tokens: torch.Tensor,
                targets: torch.Tensor):
        bsz, seqlen = tokens.shape
        assert tokens.shape == targets.shape
        assert seqlen == self.max_seq_len

        h = self.tok_embeddings(tokens)

        freqs_cis = self.freqs_cis.to(h.device)
        freqs_cis = self.freqs_cis[:seqlen]

        for layer in self.layers:
            h = layer(
                h,
                freqs_cis,
                self.mask,
                start_pos = None,
                training = True
            )

        h = self.norm(h)
        logits = self.output(h).float()

        loss = self.criterion(
            logits.view(bsz * seqlen, self.vocab_size),
            targets.reshape(bsz * seqlen))

        return logits, loss

    @torch.inference_mode()
    def forward_inference(self,
                          tokens: torch.Tensor,
                          start_pos: int,
                          max_context_window: int,
                         ):
        _bsz, seqlen = tokens.shape
        h = self.tok_embeddings(tokens)
        self.freqs_cis = self.freqs_cis.to(h.device)
        freqs_cis = self.freqs_cis[start_pos : start_pos + seqlen]

        mask = self.mask[:seqlen, :seqlen]
        mask = torch.hstack(
            [torch.zeros((seqlen, start_pos), device=tokens.device), mask]
        ).type_as(h)

        for layer in self.layers:
            h = layer(
                h,
                freqs_cis,
                mask,
                start_pos = start_pos
            )
        h = self.norm(h)
        logits = self.output(h).float()
        return logits

    @torch.inference_mode() 
    def Sampler(
        self,
        logits: torch.Tensor, 
        temperature: float,
        top_p: float
    ) -> torch.Tensor:
        logits = logits[:,-1,:]

        logits.div_(temperature) 

        probs = torch.softmax(logits, dim=-1, dtype=torch.float) 

        probs_sort, probs_idx = torch.sort(probs, dim=-1, descending=True)

        probs_sum = torch.cumsum(probs_sort, dim=-1)
        top_ps_mask = (probs_sum - probs_sort) > top_p
        probs_sort = torch.where(top_ps_mask, 0, probs_sort)
        probs_sort.div_(probs_sort.sum(dim=-1, keepdim=True))
        probs = torch.gather(probs_sort, dim=-1, index=torch.argsort(probs_idx, dim=-1))
        next_token_id = torch.multinomial(probs, num_samples=1)

        return next_token_id

    @torch.inference_mode()
    def generate(
        self,
        prompt: str,
        max_gen_len: int = 100,
        memory_saver_div: int = 1, 
        temperature: float = 0.6,
        top_p: float = 0.9, 
    ) -> str:
        assert ((memory_saver_div & (memory_saver_div-1)) == 0) & (memory_saver_div > 0), f'memory_saver_div {memory_saver_div} must be power of 2'
        max_context_window = self.max_seq_len // memory_saver_div

        enc = tiktoken.get_encoding('gpt2')
        tokens = enc.encode(prompt)

        tokens = torch.tensor(tokens, device=self.params.device)
        tokens = tokens.unsqueeze(0) if len(tokens.shape)==1 else tokens

        start_pos = max(tokens.shape[1] - max_context_window, 0)
        eot = enc._special_tokens['<|endoftext|>'] # end of text token

        while True:
            logits = self.forward_inference(
                tokens[:,-max_context_window:],
                start_pos = start_pos,
                max_context_window = max_context_window
            )

            next_token = self.Sampler(
                logits = logits,
                temperature = temperature,
                top_p = top_p,
            )

            tokens = torch.cat((tokens, next_token), dim=1)

            if next_token.item() == eot:
                break

            if tokens.shape[1] >= max_context_window:
                start_pos += 1

        output = enc.decode(tokens.squeeze(0).tolist())

        return output[:-13]