llama2.py

# based on model.py from https://github.com/karpathy/llama2.c by Andrej Karpathy, MIT licenced

# modifications by okuvshynov include:
# - no weight tying 
# - using blackbox offloadable modules
# - simplify init/generation as we only use it for fine-tuning experiments
# - manual backprop 
# - support for ffn_dim_multiplier which llama2-70b uses
# - LoRA

import logging
import math

from typing import Optional, Tuple

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

from blackbox import BlackboxDisk
from utils import save_rng_state, restore_rng_state, device_map, cleanup_cache
from model_config import ModelArgs

import logging


class RMSNorm(torch.nn.Module):
    def __init__(self, dim: int, eps: float):
        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_cos = torch.cos(freqs)  # real part
    freqs_sin = torch.sin(freqs)  # imaginary part
    return freqs_cos, freqs_sin

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_cos: torch.Tensor,
    freqs_sin: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor]:

    # reshape xq and xk to match the complex representation
    xq_r, xq_i = xq.float().reshape(xq.shape[:-1] + (-1, 2)).unbind(-1)
    xk_r, xk_i = xk.float().reshape(xk.shape[:-1] + (-1, 2)).unbind(-1)

    # reshape freqs_cos and freqs_sin for broadcasting
    freqs_cos = reshape_for_broadcast(freqs_cos, xq_r)
    freqs_sin = reshape_for_broadcast(freqs_sin, xq_r)

    # apply rotation using real numbers
    xq_out_r = xq_r * freqs_cos - xq_i * freqs_sin
    xq_out_i = xq_r * freqs_sin + xq_i * freqs_cos
    xk_out_r = xk_r * freqs_cos - xk_i * freqs_sin
    xk_out_i = xk_r * freqs_sin + xk_i * freqs_cos

    # flatten last two dimensions
    xq_out = torch.stack([xq_out_r, xq_out_i], dim=-1).flatten(3)
    xk_out = torch.stack([xk_out_r, xk_out_i], dim=-1).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, slen, n_kv_heads, head_dim = x.shape
    if n_rep == 1:
        return x
    return (
        x[:, :, :, None, :]
        .expand(bs, slen, n_kv_heads, n_rep, head_dim)
        .reshape(bs, slen, n_kv_heads * n_rep, head_dim)
    )

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

        # here's where we inject LoRA
        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)

        # here's where we inject LoRA
        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)

        # TODO: probably don't need dropout here as we don't plan to do full finetune
        # or maybe we do.
        self.attn_dropout = nn.Dropout(args.dropout)
        self.resid_dropout = nn.Dropout(args.dropout)
        self.dropout = args.dropout

        # use flash attention or a manual implementation?
        self.flash = hasattr(torch.nn.functional, 'scaled_dot_product_attention')
        if not self.flash:
            logging.warn("using slow attention. Flash Attention requires PyTorch >= 2.0")
            mask = torch.full((1, 1, args.max_seq_len, args.max_seq_len), float("-inf"))
            mask = torch.triu(mask, diagonal=1)
            self.register_buffer("mask", mask)

        self.attention_norm = RMSNorm(args.dim, eps=args.norm_eps)

    def forward(
        self,
        x: torch.Tensor,
        freqs_cos: torch.Tensor,
        freqs_sin: torch.Tensor,
        q_lora: nn.Module,
        v_lora: nn.Module
    ):
        bsz, seqlen, _ = x.shape

        x_base = x
        x = self.attention_norm(x)

        # QKV
        xq, xk, xv = self.wq(x) + q_lora(x), self.wk(x), self.wv(x) + v_lora(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)

        # RoPE relative positional embeddings
        xq, xk = apply_rotary_emb(xq, xk, freqs_cos, freqs_sin)

        # grouped multiquery attention: expand out keys and values
        xk = repeat_kv(xk, self.n_rep)  # (bs, seqlen, n_heads, head_dim)
        xv = repeat_kv(xv, self.n_rep)  # (bs, seqlen, n_heads, head_dim)

        # make heads into a batch dimension
        xq = xq.transpose(1, 2)  # (bs, n_heads, seqlen, head_dim)
        xk = xk.transpose(1, 2)
        xv = xv.transpose(1, 2)

        # flash implementation
        if self.flash:
            output = torch.nn.functional.scaled_dot_product_attention(xq, xk, xv, attn_mask=None, dropout_p=self.dropout if self.training else 0.0, is_causal=True)
        else:
            # manual implementation
            scores = torch.matmul(xq, xk.transpose(2, 3)) / math.sqrt(self.head_dim)
            assert hasattr(self, 'mask')
            scores = scores + self.mask[:, :, :seqlen, :seqlen]   # (bs, n_heads, seqlen, cache_len + seqlen)
            scores = F.softmax(scores.float(), dim=-1).type_as(xq)
            scores = self.attn_dropout(scores)
            output = torch.matmul(scores, xv)  # (bs, n_heads, seqlen, head_dim)

        # restore time as batch dimension and concat heads
        output = output.transpose(1, 2).contiguous().view(bsz, seqlen, -1)

        # final projection into the residual stream
        output = self.wo(output)
        output = self.resid_dropout(output)
        return x_base + output


class FeedForward(nn.Module):
    def __init__(self, dim: int, hidden_dim: int, multiple_of: int, dropout: float, ffn_dim_multiplier: Optional[float], args: ModelArgs):
        super().__init__()
        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)
        self.dropout = nn.Dropout(dropout)
        self.ffn_norm = RMSNorm(args.dim, eps=args.norm_eps)

    def forward(self, x):
        x_base = x
        x = self.ffn_norm(x)
        return x_base + self.dropout(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 = BlackboxDisk(Attention(args), args)
        self.feed_forward = BlackboxDisk(FeedForward(
            dim=args.dim,
            hidden_dim=4 * args.dim,
            multiple_of=args.multiple_of,
            dropout=args.dropout,
            ffn_dim_multiplier=args.ffn_dim_multiplier,
            args=args
        ), args)
        self.layer_id = layer_id


    def forward(self, x, freqs_cos, freqs_sin, lora_q, lora_v):
        h = self.attention(x, freqs_cos, freqs_sin, lora_q, lora_v)
        out = self.feed_forward(h) 
        return out
    
class LoRA(nn.Module):
    def __init__(self, original_layer, rank, alpha, dropout):
        super().__init__()
        n, m = original_layer.weight.shape
        self.A = nn.Linear(m, rank, bias=False)
        self.B = nn.Linear(rank, n, bias=False)
        nn.init.zeros_(self.B.weight)
        self.dropout = nn.Dropout(dropout)
        self.scale = alpha / rank

    # return matrix to add to original weight
    def expanded(self):
        res = self.B.weight.mm(self.A.weight) * self.scale
        return res

    def forward(self, x):
        return self.dropout(self.B(self.A(x))) * self.scale

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 = BlackboxDisk(nn.Embedding(params.vocab_size, params.dim), params)
        self.dropout = nn.Dropout(params.dropout)
        self.layers = torch.nn.ModuleList()

        # we create LoRA adapters separately. As we don't want to load/save them continously
        self.lora_layers = []
        for layer_id in range(params.n_layers):
            block = TransformerBlock(layer_id, params)

            # TODO: remove this one 
            attn = block.attention.loaded_inner()
            q_lora = LoRA(attn.wq, rank=params.lora_rank, alpha=params.lora_alpha, dropout=params.lora_dropout).to(params.compute_dtype)
            v_lora = LoRA(attn.wv, rank=params.lora_rank, alpha=params.lora_alpha, dropout=params.lora_dropout).to(params.compute_dtype)
            self.lora_layers.append({ 'q_lora': q_lora, 'v_lora': v_lora})
            self.add_module(f'q_lora_{layer_id}', q_lora)
            self.add_module(f'v_lora_{layer_id}', v_lora)
            self.layers.append(block)
            logging.debug(f'created transformer block {layer_id}')

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

        # some useful precompute for the RoPE relative positional embeddings
        freqs_cos, freqs_sin = precompute_freqs_cis(self.params.dim // self.params.n_heads, self.params.max_seq_len, theta=params.rope_theta)
        self.register_buffer("freqs_cos", freqs_cos, persistent=False)
        self.register_buffer("freqs_sin", freqs_sin, persistent=False)

    def forward(self, tokens: torch.Tensor) -> torch.Tensor:
        _bsz, seqlen = tokens.shape

        # dummy input to force gradient propagation to blackbox modules
        h = self.tok_embeddings(tokens)
        h = self.dropout(h)
        freqs_cos = self.freqs_cos[:seqlen]
        freqs_sin = self.freqs_sin[:seqlen]

        for layer, lora in zip(self.layers, self.lora_layers):
            h = layer(h, freqs_cos, freqs_sin, lora['q_lora'], lora['v_lora'])
        h = self.norm(h)

        return self.output(h[:, [-1], :])
    
    def backprop_w_lora(self, blackbox_module, output_grad, *args):
        device = output_grad.device
        module = blackbox_module.load(device)

        # we use LoRA and only updated attached low-rank modules
        # no part of original model is getting any updates, so no need for gradient
        for param in module.parameters():
            param.requires_grad = False

        input = blackbox_module.load_input(device)
        input.requires_grad = True
        
        output = module(input, *args)
        output.backward(output_grad)

        return input.grad if input.requires_grad else None

    # this is a manual implementation on forward/backward passes
    def manual_loop(self, tokens, targets):
        logging.log(level=logging.DEBUG, msg=f'starting manual loop')
        device = device_map(tokens.device)

        embd_out = self.tok_embeddings(tokens)
        embd_out = embd_out.detach()
        embd_out.requires_grad = True
        logging.log(level=logging.DEBUG, msg=f'done embedding')

        _, seqlen = tokens.shape

        freqs_cos = self.freqs_cos[:seqlen]
        freqs_sin = self.freqs_sin[:seqlen]

        current = self.dropout(embd_out)
        del embd_out

        rng_before = []

        for i, (layer, lora) in enumerate(zip(self.layers, self.lora_layers)):
            rng_before.append(save_rng_state(device))
            current = layer(current, freqs_cos, freqs_sin, lora['q_lora'], lora['v_lora'])
            logging.log(level=logging.DEBUG, msg=f'forward: transformer block {i} done')

        current = current.detach()
        current.requires_grad = True

        norm_out = self.norm(current)
        norm_out = norm_out.detach()
        norm_out.requires_grad = True

        # TODO: micro-optimization: as output is last layer, we can skip loading and running it second time 
        logging.log(level=logging.DEBUG, msg=f'output layer')
        logits = self.output(norm_out)
        del norm_out

        logging.log(level=logging.DEBUG, msg=f'output layer done')

        if (self.params.compute_dtype != torch.float32):
            logits = logits.to(torch.float32)

        logits = logits.detach()
        logits.requires_grad = True

        loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1), ignore_index=-1)
        logging.log(level=logging.DEBUG, msg=f'forward: computed loss')

        loss.backward()

        norm_out_grad = self.backprop_w_lora(self.output, logits.grad.to(self.params.compute_dtype))
        del logits
        logging.log(level=logging.DEBUG, msg=f'combined: output layer done')

        norm_out2 = self.norm(current)
        norm_out2.backward(norm_out_grad)
        del norm_out_grad
        del norm_out2

        last_grad = current.grad
        del current

        for i, (layer, rng_state, lora) in enumerate(zip(reversed(self.layers), reversed(rng_before), reversed(self.lora_layers))):
            cleanup_cache(device)
            restore_rng_state(rng_state, device=device)
            # first, do feed_forward
            last_grad = self.backprop_w_lora(layer.feed_forward, last_grad)

            # now, do attention
            cleanup_cache(device)
            last_grad = self.backprop_w_lora(layer.attention, last_grad, freqs_cos, freqs_sin, lora['q_lora'], lora['v_lora'])
            logging.log(level=logging.DEBUG, msg=f'combined: transformer block {i} done')

        # no need to backpropagate through embeddings no LoRA layers there.
        return loss.item()