llama/model.py

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

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

import clip
from timm.models.vision_transformer import Block

import fairscale.nn.model_parallel.initialize as fs_init
from fairscale.nn.model_parallel.layers import (
    ParallelEmbedding,
    RowParallelLinear,
    ColumnParallelLinear,
)


@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

    cap_adapter_len: int = 10
    cap_adapter_layer: int = 30
    cap_vision_model: str = "ViT-L/14"
    cap_vision_dim: int = 512
    cap_vision_block: int = 2


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 // fs_init.get_model_parallel_world_size()
        self.head_dim = args.dim // args.n_heads

        self.wq = ColumnParallelLinear(
            args.dim,
            args.n_heads * self.head_dim,
            bias=False,
            gather_output=False,
            init_method=lambda x: x,
        )
        self.wk = ColumnParallelLinear(
            args.dim,
            args.n_heads * self.head_dim,
            bias=False,
            gather_output=False,
            init_method=lambda x: x,
        )
        self.wv = ColumnParallelLinear(
            args.dim,
            args.n_heads * self.head_dim,
            bias=False,
            gather_output=False,
            init_method=lambda x: x,
        )
        self.wo = RowParallelLinear(
            args.n_heads * self.head_dim,
            args.dim,
            bias=False,
            input_is_parallel=True,
            init_method=lambda x: x,
        )

        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.cap_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, mode='instruct'):
        if mode == 'instruct':
            return self.forward_instruct(x, start_pos, freqs_cis, mask, adapter)
        elif mode == 'caption':
            return self.forward_caption(x, start_pos, freqs_cis, mask, adapter)

    def forward_instruct(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)

        self.cache_k = self.cache_k.to(xq)
        self.cache_v = self.cache_v.to(xq)

        self.cache_k[:bsz, start_pos: start_pos + seqlen] = xk
        self.cache_v[:bsz, start_pos: start_pos + seqlen] = xv

        keys = self.cache_k[:bsz, : start_pos + seqlen]
        values = self.cache_v[:bsz, : start_pos + seqlen]

        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)
            adapter_k = adapter_k.transpose(1, 2)
            adapter_v = adapter_v.transpose(1, 2)
        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)
        scores = F.softmax(scores.float(), dim=-1).type_as(xq)
        output = torch.matmul(scores, values)  # (bs, n_local_heads, slen, head_dim)
        if adapter is not None:
            adapter_scores = torch.matmul(xq, adapter_k.transpose(2, 3)) / math.sqrt(self.head_dim)
            adapter_scores = self.gate * F.softmax(adapter_scores.float(), dim=-1).type_as(xq)
            output = output + torch.matmul(adapter_scores, adapter_v)
        output = output.transpose(
            1, 2
        ).contiguous().view(bsz, seqlen, -1)

        return self.wo(output)

    def forward_caption(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)

        self.cache_k = self.cache_k.to(xq)
        self.cache_v = self.cache_v.to(xq)

        self.cache_k[:bsz, start_pos: start_pos + seqlen] = xk
        self.cache_v[:bsz, start_pos: start_pos + seqlen] = xv

        keys = self.cache_k[:bsz, : start_pos + seqlen]
        values = self.cache_v[:bsz, : start_pos + seqlen]

        if adapter is not None:
            adapter_len = adapter.shape[1]
            adapter_k = self.wk(adapter).view(bsz, adapter_len, self.n_local_heads, self.head_dim)
            adapter_v = self.wv(adapter).view(bsz, adapter_len, self.n_local_heads, self.head_dim)
            adapter_k = adapter_k.transpose(1, 2)
            adapter_v = adapter_v.transpose(1, 2)
        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)
        scores = F.softmax(scores.float(), dim=-1).type_as(xq)
        output = torch.matmul(scores, values)  # (bs, n_local_heads, slen, head_dim)
        if adapter is not None:
            adapter_scores = torch.matmul(xq, adapter_k.transpose(2, 3)) / math.sqrt(self.head_dim)
            adapter_scores = self.cap_gate.tanh() * F.softmax(adapter_scores.float(), dim=-1).type_as(xq)

            output = output + torch.matmul(adapter_scores, adapter_v)
        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 = ColumnParallelLinear(
            dim, hidden_dim, bias=False, gather_output=False, init_method=lambda x: x
        )
        self.w2 = RowParallelLinear(
            hidden_dim, dim, bias=False, input_is_parallel=True, init_method=lambda x: x
        )
        self.w3 = ColumnParallelLinear(
            dim, hidden_dim, bias=False, gather_output=False, init_method=lambda x: x
        )

    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, mode='instruct'):
        h = x + self.attention.forward(self.attention_norm(x), start_pos, freqs_cis, mask, adapter, mode=mode)
        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 = ParallelEmbedding(
            params.vocab_size, params.dim, init_method=lambda x: x
        )

        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 = ColumnParallelLinear(
            params.dim, params.vocab_size, bias=False, init_method=lambda x: x
        )

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

        # Note: this is only a preview of multimodal LLaMA-Adapter
        # and requires more efforts to decouple LLaMA-Adapter from LLaMA.
        # instruct model
        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

        # caption model
        self.cap_adapter_query = nn.Embedding(params.cap_adapter_len * params.cap_adapter_layer, params.dim)
        self.cap_adapter_len = params.cap_adapter_len
        self.cap_adapter_layer = params.cap_adapter_layer

    @torch.inference_mode()
    def forward(self, tokens: torch.Tensor, start_pos: int, visual_tokens: torch.Tensor = None, mode: str = 'instruct'):
        if mode == 'instruct':
            return self.forward_instruct(tokens, start_pos, mode)
        elif mode == 'caption':
            return self.forward_caption(tokens, start_pos, visual_tokens, mode)

    def forward_instruct(self, tokens: torch.Tensor, start_pos: int, mode=None):
        _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]
        adapter = self.adapter_query.weight.reshape(self.params.adapter_layer, self.params.adapter_len,
                                                    self.params.dim).unsqueeze(1)
        mask = None
        if seqlen > 1:
            mask = torch.full((1, 1, seqlen, seqlen), float("-inf"), device=tokens.device)
            mask = torch.triu(mask, diagonal=start_pos + 1).type_as(h)

        for layer in self.layers[: -1 * self.params.adapter_layer]:
            h = layer(h, start_pos, freqs_cis, mask)
        layer_index = 0
        for layer in self.layers[-1 * self.params.adapter_layer:]:
            h = layer(h, start_pos, freqs_cis, mask, adapter[layer_index], mode=mode)
            layer_index = layer_index + 1
        h = self.norm(h)
        output = self.output(h[:, -1, :])  # only compute last logits
        return output.float()

    def forward_caption(self, tokens: torch.Tensor, start_pos: int, visual_tokens: torch.Tensor = None, mode=None):
        _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]
        adapter = self.cap_adapter_query.weight.reshape(self.params.cap_adapter_layer, self.params.cap_adapter_len,
                                                        self.params.dim).unsqueeze(1)
        mask = None
        if seqlen > 1:
            mask = torch.full((1, 1, seqlen, seqlen), float("-inf"), device=tokens.device)
            mask = torch.triu(mask, diagonal=start_pos + 1).type_as(h)

        for layer in self.layers[: -1 * self.params.cap_adapter_layer]:
            h = layer(h, start_pos, freqs_cis, mask)
        layer_index = 0
        for layer in self.layers[-1 * self.params.cap_adapter_layer:]:
            adapter_per_layer = adapter[layer_index]
            if visual_tokens is not None:
                adapter_per_layer = adapter_per_layer + visual_tokens
            h = layer(h, start_pos, freqs_cis, mask, adapter_per_layer, mode=mode)
            layer_index = layer_index + 1
        h = self.norm(h)
        output = self.output(h[:, -1, :])  # only compute last logits
        return output.float()


class VisionModel(nn.Module):
    def __init__(self, params: ModelArgs):
        super().__init__()

        self.params = params

        self.clip, self.clip_transform = clip.load(params.cap_vision_model)
        self.clip.float()
        for param in self.clip.parameters():
            param.requires_grad = False

        self.clip_proj = nn.Linear(self.clip.visual.output_dim, params.cap_vision_dim)
        self.clip_proj_norm = nn.LayerNorm(params.cap_vision_dim)

        self.visual_query = nn.Embedding(params.cap_adapter_len, params.cap_vision_dim)

        self.visual_blocks = nn.ModuleList([
            Block(params.cap_vision_dim, 16, 4, qkv_bias=True, qk_scale=None, norm_layer=nn.LayerNorm)
            for i in range(params.cap_vision_block)])

        self.visual_proj = nn.Linear(params.cap_vision_dim, params.dim)
        self.visual_proj_norm = nn.LayerNorm(params.dim)

    def clip_encode_image(self, x):
        x = self.clip.visual.conv1(x)  # shape = [*, width, grid, grid]
        x = x.reshape(x.shape[0], x.shape[1], -1)  # shape = [*, width, grid ** 2]
        x = x.permute(0, 2, 1)  # shape = [*, grid ** 2, width]
        x = torch.cat([self.clip.visual.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1],
                                                                                  dtype=x.dtype, device=x.device), x],
                      dim=1)  # shape = [*, grid ** 2 + 1, width]
        x = x + self.clip.visual.positional_embedding.to(x.dtype)
        x = self.clip.visual.ln_pre(x)

        x = x.permute(1, 0, 2)  # NLD -> LND
        x = self.clip.visual.transformer(x)
        x = x.permute(1, 0, 2)  # LND -> NLD

        x = self.clip.visual.ln_post(x[:, :, :])

        if self.clip.visual.proj is not None:
            x = x @ self.clip.visual.proj

        return x

    def forward(self, imgs):
        x = [self.clip_transform(img) for img in imgs]
        x = torch.stack(x, dim=0).to(self.visual_query.weight.device)
        _bsz = x.shape[0]

        visual_feats = self.clip_encode_image(x).half()
        visual_feats = self.clip_proj_norm(self.clip_proj(visual_feats))
        visual_query = self.visual_query.weight.unsqueeze(0).repeat(_bsz, 1, 1)
        visual_query = torch.cat([visual_query, visual_feats], dim=1)
        for block in self.visual_blocks:
            visual_query = block(visual_query)
        visual_query = visual_query[:, :self.params.cap_adapter_len, :]
        visual_query = self.visual_proj(visual_query)
        visual_query = self.visual_proj_norm(visual_query)

        return visual_query