gpt_infer.py

import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from dataclasses import dataclass


def norm(x: torch.Tensor) -> torch.Tensor:
    return F.rms_norm(x, (x.size(-1),))


def apply_rotary_emb(x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor) -> torch.Tensor:
    assert x.ndim == 4
    d = x.shape[3] // 2
    x1, x2 = x[..., :d], x[..., d:]
    y1 = x1 * cos + x2 * sin
    y2 = x1 * (-sin) + x2 * cos
    out = torch.cat([y1, y2], 3)
    out = out.to(x.dtype)
    return out


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


@dataclass
class GPTConfig:
    sequence_len: int = 1024
    vocab_size: int = 50304
    n_layer: int = 12
    n_head: int = 6
    n_kv_head: int = 6
    n_embd: int = 768


class CausalSelfAttention(nn.Module):
    def __init__(self, config: GPTConfig, layer_idx: int):
        super().__init__()
        self.layer_idx = layer_idx
        self.n_head = config.n_head
        self.n_kv_head = config.n_kv_head
        self.n_embd = config.n_embd
        self.head_dim = self.n_embd // self.n_head
        assert self.n_embd % self.n_head == 0
        assert self.n_kv_head <= self.n_head and self.n_head % self.n_kv_head == 0
        self.c_q = nn.Linear(self.n_embd, self.n_head * self.head_dim, bias=False)
        self.c_k = nn.Linear(self.n_embd, self.n_kv_head * self.head_dim, bias=False)
        self.c_v = nn.Linear(self.n_embd, self.n_kv_head * self.head_dim, bias=False)
        self.c_proj = nn.Linear(self.n_embd, self.n_embd, bias=False)

    def forward(self, x: torch.Tensor, cos_sin, kv_cache=None):
        B, T, C = x.size()
        q = self.c_q(x).view(B, T, self.n_head, self.head_dim)
        k = self.c_k(x).view(B, T, self.n_kv_head, self.head_dim)
        v = self.c_v(x).view(B, T, self.n_kv_head, self.head_dim)
        cos, sin = cos_sin
        q, k = apply_rotary_emb(q, cos, sin), apply_rotary_emb(k, cos, sin)
        q, k = norm(q), norm(k)
        q, k, v = q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2)
        if kv_cache is not None:
            k, v = kv_cache.insert_kv(self.layer_idx, k, v)
        Tq = q.size(2)
        Tk = k.size(2)
        nrep = self.n_head // self.n_kv_head
        k, v = repeat_kv(k, nrep), repeat_kv(v, nrep)
        if kv_cache is None or Tq == Tk:
            y = F.scaled_dot_product_attention(q, k, v, is_causal=True)
        elif Tq == 1:
            y = F.scaled_dot_product_attention(q, k, v, is_causal=False)
        else:
            attn_mask = torch.zeros((Tq, Tk), dtype=torch.bool, device=q.device)
            prefix_len = Tk - Tq
            if prefix_len > 0:
                attn_mask[:, :prefix_len] = True
            attn_mask[:, prefix_len:] = torch.tril(torch.ones((Tq, Tq), dtype=torch.bool, device=q.device))
            y = F.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask)
        y = y.transpose(1, 2).contiguous().view(B, T, -1)
        y = self.c_proj(y)
        return y


class MLP(nn.Module):
    def __init__(self, config: GPTConfig):
        super().__init__()
        self.c_fc = nn.Linear(config.n_embd, 4 * config.n_embd, bias=False)
        self.c_proj = nn.Linear(4 * config.n_embd, config.n_embd, bias=False)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = self.c_fc(x)
        x = F.relu(x).square()
        x = self.c_proj(x)
        return x


class Block(nn.Module):
    def __init__(self, config: GPTConfig, layer_idx: int):
        super().__init__()
        self.attn = CausalSelfAttention(config, layer_idx)
        self.mlp = MLP(config)

    def forward(self, x: torch.Tensor, cos_sin, kv_cache=None) -> torch.Tensor:
        x = x + self.attn(norm(x), cos_sin, kv_cache)
        x = x + self.mlp(norm(x))
        return x


class GPT(nn.Module):
    def __init__(self, config: GPTConfig):
        super().__init__()
        self.config = config
        self.transformer = nn.ModuleDict({
            'wte': nn.Embedding(config.vocab_size, config.n_embd),
            'h': nn.ModuleList([Block(config, layer_idx) for layer_idx in range(config.n_layer)]),
        })
        self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
        self.rotary_seq_len = config.sequence_len * 10
        head_dim = config.n_embd // config.n_head
        cos, sin = self._precompute_rotary_embeddings(self.rotary_seq_len, head_dim)
        self.register_buffer('cos', cos, persistent=False)
        self.register_buffer('sin', sin, persistent=False)
        self.transformer.wte.to(dtype=torch.bfloat16)

    def _precompute_rotary_embeddings(self, seq_len: int, head_dim: int, base: int = 10000, device=None):
        if device is None:
            device = self.transformer.wte.weight.device
        channel_range = torch.arange(0, head_dim, 2, dtype=torch.float32, device=device)
        inv_freq = 1.0 / (base ** (channel_range / head_dim))
        t = torch.arange(seq_len, dtype=torch.float32, device=device)
        freqs = torch.outer(t, inv_freq)
        cos, sin = freqs.cos(), freqs.sin()
        cos, sin = cos.bfloat16(), sin.bfloat16()
        cos, sin = cos[None, :, None, :], sin[None, :, None, :]
        return cos, sin

    def forward(self, idx: torch.Tensor, targets: torch.Tensor | None = None, kv_cache=None, loss_reduction: str = 'mean'):
        B, T = idx.size()
        assert T <= self.cos.size(1)
        assert idx.device == self.cos.device
        assert self.cos.dtype == torch.bfloat16
        T0 = 0 if kv_cache is None else kv_cache.get_pos()
        cos_sin = self.cos[:, T0:T0+T], self.sin[:, T0:T0+T]
        x = self.transformer.wte(idx)
        x = norm(x)
        for block in self.transformer.h:
            x = block(x, cos_sin, kv_cache)
        x = norm(x)
        softcap = 15
        if targets is not None:
            logits = self.lm_head(x)
            logits = softcap * torch.tanh(logits / softcap)
            logits = logits.float()
            loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1), ignore_index=-1, reduction=loss_reduction)
            return loss
        else:
            logits = self.lm_head(x)
            logits = softcap * torch.tanh(logits / softcap)
            return logits

    @torch.inference_mode()
    def generate(self, tokens: list[int], max_tokens: int, temperature: float = 1.0, top_k: int | None = None, seed: int = 42):
        assert isinstance(tokens, list)
        device = self.transformer.wte.weight.device
        rng = None
        if temperature > 0:
            rng = torch.Generator(device=device)
            rng.manual_seed(seed)
        ids = torch.tensor([tokens], dtype=torch.long, device=device)
        for _ in range(max_tokens):
            logits = self.forward(ids)
            logits = logits[:, -1, :]
            if top_k is not None:
                v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
                logits[logits < v[:, [-1]]] = -float('Inf')
            if temperature > 0:
                logits = logits / max(temperature, 1e-6)
                probs = F.softmax(logits, dim=-1)
                next_ids = torch.multinomial(probs, num_samples=1, generator=rng)
            else:
                next_ids = torch.argmax(logits, dim=-1, keepdim=True)
            ids = torch.cat((ids, next_ids), dim=1)
            yield next_ids.item()