model.py

# Copyright (c) Meta Platforms, Inc. and affiliates.
# This software may be used and distributed according to the terms of the GNU General Public License version 3.

import math
from dataclasses import dataclass
from typing import Optional, Tuple

import torch
import torch.nn.functional as F
from torch import nn
from torch.nn import Embedding, Linear


@dataclass
class ModelArgs:
    dim: int = 512
    n_layers: int = 8
    n_heads: int = 8
    vocab_size: int = -1  # defined later by tokenizer
    multiple_of: int = 256  # make SwiGLU hidden layer size multiple of large power of 2
    norm_eps: float = 1e-5

    max_batch_size: int = 32
    max_seq_len: int = 2048
    adapter_len: int = 10
    adapter_layer: int = 30


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)  # type: ignore
    freqs = torch.outer(t, freqs).float()  # type: ignore
    freqs_cis = torch.polar(torch.ones_like(freqs), freqs)  # complex64
    return freqs_cis


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])
    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)


class Attention(nn.Module):
    def __init__(self, args: ModelArgs):
        super().__init__()

        self.n_local_heads = args.n_heads
        self.head_dim = args.dim // args.n_heads

        self.wq = Linear(args.dim, args.n_heads * self.head_dim, bias=False)
        self.wk = Linear(args.dim, args.n_heads * self.head_dim, bias=False)
        self.wv = Linear(args.dim, args.n_heads * self.head_dim, bias=False)
        self.wo = 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_local_heads, self.head_dim)).cuda()
        self.cache_v = torch.zeros((args.max_batch_size, args.max_seq_len, self.n_local_heads, self.head_dim)).cuda()
        self.gate = torch.nn.Parameter(torch.zeros(1, self.n_local_heads, 1, 1))

    def forward(
        self, x: torch.Tensor, start_pos: int, freqs_cis: torch.Tensor, mask: Optional[torch.Tensor], adapter=None
    ):

        bsz, seqlen, _ = x.shape
        xq, xk, xv = self.wq(x), self.wk(x), self.wv(x)

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

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

        if adapter is not None:
            adapter_len = adapter.shape[1]
            adapter_k = self.wk(adapter).view(1, adapter_len, self.n_local_heads, self.head_dim).repeat(bsz, 1, 1, 1)
            adapter_v = self.wv(adapter).view(1, adapter_len, self.n_local_heads, self.head_dim).repeat(bsz, 1, 1, 1)
            xk = torch.cat([adapter_k, xk], dim=1)
            xv = torch.cat([adapter_v, xv], dim=1)
            extra_mask = torch.zeros(1, 1, seqlen, adapter_len).to(mask)
            mask = torch.cat([extra_mask, mask], dim=-1)
        keys = xk
        values = xv

        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  # (bs, n_local_heads, slen, cache_len + slen)
        if adapter is not None:
            scores = torch.cat(
                [
                    self.gate.tanh().half() * F.softmax(scores[:, :, :, :adapter_len].float(), dim=-1).type_as(xq),
                    F.softmax(scores[:, :, :, adapter_len:].float(), dim=-1).type_as(xq),
                ],
                dim=-1,
            )
        else:
            scores = F.softmax(scores.float(), dim=-1).type_as(xq)
        output = torch.matmul(scores, values)  # (bs, n_local_heads, slen, head_dim)
        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,
    ):
        super().__init__()
        hidden_dim = int(2 * hidden_dim / 3)
        hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)

        self.w1 = Linear(dim, hidden_dim, bias=False)
        self.w2 = Linear(hidden_dim, dim, bias=False)
        self.w3 = 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, layer_id: int, 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)
        self.layer_id = layer_id
        self.attention_norm = RMSNorm(args.dim, eps=args.norm_eps)
        self.ffn_norm = RMSNorm(args.dim, eps=args.norm_eps)

    def forward(
        self, x: torch.Tensor, start_pos: int, freqs_cis: torch.Tensor, mask: Optional[torch.Tensor], adapter=None
    ):

        h = x + self.attention.forward(self.attention_norm(x), start_pos, freqs_cis, mask, adapter)
        out = h + self.feed_forward.forward(self.ffn_norm(h))
        return out


class Transformer(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.tok_embeddings = Embedding(params.vocab_size, params.dim)

        self.adapter_query = nn.Embedding(params.adapter_len * params.adapter_layer, params.dim)
        self.adapter_len = params.adapter_len
        self.adapter_layer = params.adapter_layer

        self.criterion = torch.nn.CrossEntropyLoss(ignore_index=0)

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

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

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

    def forward(self, examples, labels):

        _bsz, seqlen = examples.shape

        with torch.no_grad():
            h = self.tok_embeddings(examples)
            freqs_cis = self.freqs_cis.to(h.device)
            freqs_cis = freqs_cis[:seqlen]
            mask = None
            mask = torch.full((1, 1, seqlen, seqlen), float("-inf"), device=h.device)
            mask = torch.triu(mask, diagonal=0 + 1).type_as(h)
            start_pos = 0
            for layer in self.layers[: -1 * self.adapter_layer]:
                h = layer(h, start_pos, freqs_cis, mask)

        adapter_index = 0
        adapter = self.adapter_query.weight.reshape(-1, self.adapter_len, self.params.dim).unsqueeze(1)
        for layer in self.layers[-1 * self.adapter_layer :]:
            h = layer(h, start_pos, freqs_cis, mask, adapter[adapter_index].half())
            adapter_index = adapter_index + 1

        h = self.norm(h)
        output = self.output(h)
        output = output[:, :-1, :].reshape(-1, self.vocab_size)
        labels = labels[:, 1:].flatten()

        c_loss = self.criterion(output, labels)
        return c_loss