custom_model.py

from typing import Optional
import copy
import math

import torch
from torch import nn, Tensor

from torchtune.modules import (
    CausalSelfAttention,
    FeedForward,
    KVCache,
    RMSNorm,
    RotaryPositionalEmbeddings,
    # TransformerDecoder, replaced with our custom implementation.
    TransformerDecoderLayer,
)

from masked_apply import MaskedApply


def initialize_identity_linear(size):
    layer = nn.Linear(size, size)
    layer.weight.data.copy_(torch.eye(size))
    layer.bias.data.copy_(torch.zeros(size))
    return layer

def initialize_linear(size):
    return nn.Linear(size, size)

def initialize_kaiming_uniform_linear(size):
    layer = nn.Linear(size, size)
    nn.init.kaiming_uniform_(layer.weight, a=math.sqrt(5))
    layer.bias.data.copy_(torch.zeros(size))
    return layer

def initialize_zeros_linear(size):
    layer = nn.Linear(size, size)
    layer.weight.data.copy_(torch.zeros(size))
    layer.bias.data.copy_(torch.zeros(size))
    return layer

INITIALIZATION_OPTIONS = {
    "identity": initialize_identity_linear,
    "default": initialize_linear,
    "kaiming_uniform": initialize_kaiming_uniform_linear,
    "zeros": initialize_zeros_linear,
}

def _get_clones(module: nn.Module, n: int) -> nn.ModuleList:
    """
    Return a list of ``n`` identical layers.

    Args:
        module (nn.Module): module to be cloned
        n (int): number of clones

    Returns:
        nn.ModuleList: list of ``n`` identical layers
    """
    # FIXME: copy.deepcopy() is not defined on nn.module
    return nn.ModuleList([copy.deepcopy(module) for i in range(n)])


class ColoringTransformerDecoder(nn.Module):
    """
    See torchtune.models.llama2.TransformerDecoder for the original implementation.
    """

    def __init__(
        self,
        tok_embeddings: nn.Embedding,
        embedding_transform: nn.Module,
        layer: TransformerDecoderLayer,
        num_layers: int,
        norm: nn.Module,
        output: nn.Linear,
        embedding_norm: nn.Module = None
    ) -> None:
        super().__init__()
        self.tok_embeddings = tok_embeddings
        self.embedding_transform = embedding_transform
        self.embedding_norm = embedding_norm
        self.layers = _get_clones(layer, num_layers)
        self.norm = norm
        self.output = output

    def forward(
        self, 
        tokens: Tensor, 
        mask: Optional[Tensor] = None,
        colors: Optional[Tensor] = None,
        curr_pos: int = 0
    ) -> Tensor:
        """
        Args:
            tokens (Tensor): input tensor with shape [b x s]
            mask (Optional[Tensor]): attention mask tensor, defaults to None.
            curr_pos (int): current position in the seq, defaults to 0.
                Only relevant when incrementally decoding.

        Returns:
            Tensor: output tensor with shape [b x s x v]

        Notation used for tensor shapes:
            - b: batch size
            - s: sequence length
            - v: vocab size
            - d: embed dim
        """
        # input tensor of shape [b, s]
        bsz, seq_len = tokens.shape

        # shape: [b, s, d]
        h = self.tok_embeddings(tokens)

        # Apply normalization before embedding transform to improve
        # training stability.
        ch = h
        if self.embedding_norm is not None:
            # TODO: norm does an in-place operation, so we need to clone the input
            ch = self.embedding_norm(h.clone())
        
        # Apply the embedding transform (e.g. color layer)
        ch = self.embedding_transform(ch, colors)

        # Add the output of the color transform to the embeddings
        h = h + ch

        # TODO: Fix the masking logic to not rely on checking kv_cache
        if seq_len > 1 and self.layers[0].attn.kv_cache is not None:
            mask = torch.full(
                (1, 1, seq_len, seq_len), float("-inf"), device=tokens.device
            )
            mask = torch.triu(mask, diagonal=curr_pos + 1)

        for layer in self.layers:
            # shape: [b, s, d]
            h = layer(h, mask, curr_pos)

        # shape: [b, s, d]
        h = self.norm(h)

        # shape: [b, s, v]
        output = self.output(h).float()
        return output


def coloring_llama2_7b(color_layer_initialization: str = "zeros", norm_before_color_layer: bool = False, max_batch_size: Optional[int] = None) -> ColoringTransformerDecoder:
    """Builder for creating a Llama2 model initialized w/ the default 7b parameter values.
    From https://arxiv.org/abs/2307.09288, these default values are:
    - vocab_size: 32,000
    - embed_dim: 4,096
    - num_layers: 32
    - num_heads: 32
    - num_kv_heads: 32
    - max_seq_len: 4,096
    - norm_eps: 1e-5

    Args:
        max_batch_size (Optional[int]): Maximum batch size to be passed to KVCache.

    Returns:
        A ``TransformerDecoder`` instance of the Llama2 model.
    """
    return coloring_llama2(
        color_layer_initialization=color_layer_initialization,
        vocab_size=32_000,
        num_layers=32,
        num_heads=32,
        num_kv_heads=32,
        embed_dim=4096,
        max_seq_len=4096,
        num_colors=4, # color for default, instruction, input, response
        max_batch_size=max_batch_size,
        attn_dropout=0.0,
        norm_eps=1e-5,
        norm_before_color_layer=norm_before_color_layer
    )

def _scale_hidden_dim_for_mlp(dim: int, multiple_of: int = 256) -> int:
    """Scale hidden dimension for MLP to keep number of parameters and computation constant.

    Args:
        dim (int): Input dimension.
        multiple_of (int): Round scaled dimension to nearest multiple of `multiple_of` for clean computation.

    Returns:
        Scaled hidden dimension.
    """
    # Scale hidden dimension by (2/3)4d for SwiGLU to keep number of
    # parameters and computation constant
    hidden_dim = 4 * int(2 * dim / 3)
    # Round hidden dimension to nearest multiple of `multiple_of`
    hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
    return hidden_dim


def coloring_llama2(
    color_layer_initialization: str,
    vocab_size: int,
    num_layers: int,
    num_heads: int,
    num_kv_heads: int,
    embed_dim: int,
    max_seq_len: int,
    num_colors: int,
    norm_before_color_layer: bool = False,
    attn_dropout: float = 0.0,
    max_batch_size: Optional[int] = None,
    norm_eps: float = 1e-5,
):
    if color_layer_initialization not in INITIALIZATION_OPTIONS:
        raise ValueError(f"Invalid color_layer_initialization: {color_layer_initialization}. Expected one of {list(INITIALIZATION_OPTIONS.keys())}.")
    color_layer_initializer = INITIALIZATION_OPTIONS[color_layer_initialization]

    head_dim = embed_dim // num_heads
    num_kv_heads = num_kv_heads if num_kv_heads else num_heads
    kv_cache = (
        KVCache(
            max_batch_size=max_batch_size,
            max_seq_len=max_seq_len,
            n_kv_heads=num_heads,
            head_dim=head_dim,
        )
        if max_batch_size is not None
        else None
    )
    rope = RotaryPositionalEmbeddings(dim=head_dim, max_seq_len=max_seq_len)
    self_attn = CausalSelfAttention(
        embed_dim=embed_dim,
        num_heads=num_heads,
        num_kv_heads=num_kv_heads,
        head_dim=head_dim,
        q_proj=nn.Linear(embed_dim, num_heads * head_dim, bias=False),
        k_proj=nn.Linear(embed_dim, num_kv_heads * head_dim, bias=False),
        v_proj=nn.Linear(embed_dim, num_kv_heads * head_dim, bias=False),
        output_proj=nn.Linear(embed_dim, embed_dim, bias=False),
        pos_embeddings=rope,
        kv_cache=kv_cache,
        max_seq_len=max_seq_len,
        attn_dropout=attn_dropout,
    )
    hidden_dim = _scale_hidden_dim_for_mlp(embed_dim)
    mlp = FeedForward(dim=embed_dim, hidden_dim=hidden_dim, linear_class=nn.Linear)
    layer = TransformerDecoderLayer(
        attn=self_attn,
        mlp=mlp,
        sa_norm=RMSNorm(dim=embed_dim, eps=norm_eps),
        mlp_norm=RMSNorm(dim=embed_dim, eps=norm_eps),
    )
    tok_embeddings = nn.Embedding(vocab_size, embed_dim)
    output_proj = nn.Linear(embed_dim, vocab_size, bias=False)
    embedding_transform = MaskedApply(
        [color_layer_initializer(embed_dim) for _ in range(num_colors)],
        strict=False
    )
    embedding_norm = RMSNorm(embed_dim, eps=norm_eps) if norm_before_color_layer else None

    return ColoringTransformerDecoder(
        tok_embeddings=tok_embeddings,
        embedding_transform=embedding_transform,
        embedding_norm=embedding_norm,
        layer=layer,
        num_layers=num_layers,
        norm=RMSNorm(embed_dim, eps=norm_eps),
        output=output_proj,
    )