model.py

import logging

from torch.nn import Module
from zeta.structs import AutoregressiveWrapper
from zeta.structs.transformer import (
    Decoder,
    Encoder,
    Transformer,
    ViTransformerWrapper,
)


# logging
logging.basicConfig(
    level=logging.DEBUG, format="%(asctime)s - %(levelname)s - %(message)s"
)


# main model
class CM3(Module):
    """
    Andromeda is a transformer-based model architecture. It initializes with
    a Transformer and AutoregressiveWrapper with default or user-specified parameters.

    Initialize the model with specified or default parameters.
        Args:
        - num_tokens: Number of tokens in the vocabulary
        - max_seq_len: Maximum sequence length
        - dim: Dimension of the model
        - depth: Depth of the model
        - dim_head: Dimension of the model head
        - heads: Number of heads
        - use_abs_pos_emb: Whether to use absolute position embedding
        - alibi_pos_bias: Alibi position bias
        - alibi_num_heads: Number of alibi heads
        - rotary_xpos: Rotary position
        - attn_flash: Attention flash
        - deepnorm: Deep normalization
        - shift_tokens: Number of tokens to shift
        - attn_one_kv_head: Attention one key/value head
        - qk_norm: Query-key normalization
        - attn_qk_norm: Attention query-key normalization
        - attn_qk_norm_dim_scale: Attention query-key normalization dimension scale
    """

    def __init__(
        self,
        num_tokens=50432,
        max_seq_len=8192,
        dim=2560,
        depth=32,
        dim_head=128,
        heads=24,
        use_abs_pos_emb=False,
        alibi_pos_bias=True,
        alibi_num_heads=12,
        rotary_xpos=True,
        attn_flash=True,
        image_size=256,
        patch_size=32,
        attn_one_kv_head=True,  # multiquery attention
        qk_norm=True,
        attn_qk_norm=True,
        attn_qk_norm_dim_scale=True,
    ):
        super().__init__()

        self.encoder = ViTransformerWrapper(
            image_size=image_size,
            patch_size=patch_size,
            attn_layers=Encoder(dim=dim, depth=depth, dim_head=dim_head, heads=heads),
        )

        self.transformer = Transformer(
            num_tokens=num_tokens,
            max_seq_len=max_seq_len,
            use_abs_pos_emb=use_abs_pos_emb,
            attn_layers=Decoder(
                dim=dim,
                depth=depth,
                dim_head=dim_head,
                heads=heads,
                alibi_pos_bias=alibi_pos_bias,
                alibi_num_heads=alibi_num_heads,
                rotary_xpos=rotary_xpos,
                attn_flash=attn_flash,
                # attn_one_kv_head=attn_one_kv_head,
                # qk_norm=qk_norm,
                # attn_qk_norm=attn_qk_norm,
                # attn_qk_norm_dim_scale=attn_qk_norm_dim_scale,
                cross_attend=True,
            ),
        )

        self.decoder = AutoregressiveWrapper(self.transformer)

    def forward(self, img, text):
        try:
            encoded = self.encoder(img, return_embeddings=True)
            return self.decoder(text, context=encoded)
        except Exception as error:
            print(f"Failed in forward method: {error}")
            raise

    def generate(self, text, seq):
        text = text[:, None, ...]
        return self.decoder.generate(text, seq)