modeling_latent_recurrent_depth.py

import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional, Tuple
import math
from transformers import PretrainedConfig, PreTrainedModel

class MultiHeadAttention(nn.Module):
    def __init__(self, d_model: int, num_heads: int, dropout: float = 0.1):
        super().__init__()
        assert d_model % num_heads == 0

        self.d_model = d_model
        self.num_heads = num_heads
        self.head_dim = d_model // num_heads

        self.q_proj = nn.Linear(d_model, d_model)
        self.k_proj = nn.Linear(d_model, d_model)
        self.v_proj = nn.Linear(d_model, d_model)
        self.o_proj = nn.Linear(d_model, d_model)

        self.dropout = nn.Dropout(dropout)

    def forward(self, x: torch.Tensor, mask: Optional[torch.Tensor] = None) -> torch.Tensor:
        batch_size, seq_len, d_model = x.shape

        # Project and reshape for multi-head attention
        q = self.q_proj(x).reshape(batch_size, seq_len, self.num_heads, self.head_dim)
        k = self.k_proj(x).reshape(batch_size, seq_len, self.num_heads, self.head_dim)
        v = self.v_proj(x).reshape(batch_size, seq_len, self.num_heads, self.head_dim)

        # Transpose for attention computation
        q = q.transpose(1, 2)  # (batch_size, num_heads, seq_len, head_dim)
        k = k.transpose(1, 2)
        v = v.transpose(1, 2)

        # Compute attention scores
        scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.head_dim)

        if mask is not None:
            scores = scores.masked_fill(mask == 0, float('-inf'))

        attn_weights = F.softmax(scores, dim=-1)
        attn_weights = self.dropout(attn_weights)

        # Apply attention to values
        out = torch.matmul(attn_weights, v)  # (batch_size, num_heads, seq_len, head_dim)
        out = out.transpose(1, 2)  # (batch_size, seq_len, num_heads, head_dim)
        out = out.reshape(batch_size, seq_len, d_model)

        return self.o_proj(out)

class PreludeBlock(nn.Module):
    def __init__(self, vocab_size: int, d_model: int, num_heads: int, dropout: float = 0.1):
        super().__init__()
        self.token_embedding = nn.Embedding(vocab_size, d_model)
        self.pos_encoding = nn.Parameter(torch.zeros(1, 1024, d_model))  # Max sequence length of 1024
        self.attention = MultiHeadAttention(d_model, num_heads, dropout)
        self.norm1 = nn.LayerNorm(d_model)
        self.norm2 = nn.LayerNorm(d_model)
        self.feed_forward = nn.Sequential(
            nn.Linear(d_model, 4 * d_model),
            nn.GELU(),
            nn.Linear(4 * d_model, d_model),
            nn.Dropout(dropout)
        )

    def forward(self, x: torch.Tensor, mask: Optional[torch.Tensor] = None) -> torch.Tensor:
        seq_len = x.size(1)
        # Embed tokens and add positional encoding
        x = self.token_embedding(x) + self.pos_encoding[:, :seq_len, :]

        # Self-attention block
        attended = self.attention(self.norm1(x), mask)
        x = x + attended

        # Feed-forward block
        x = x + self.feed_forward(self.norm2(x))
        return x

class RecurrentBlock(nn.Module):
    def __init__(self, d_model: int, num_heads: int, dropout: float = 0.1):
        super().__init__()
        self.attention = MultiHeadAttention(d_model, num_heads, dropout)
        self.norm1 = nn.LayerNorm(d_model)
        self.norm2 = nn.LayerNorm(d_model)
        self.feed_forward = nn.Sequential(
            nn.Linear(d_model, 4 * d_model),
            nn.GELU(),
            nn.Linear(4 * d_model, d_model),
            nn.Dropout(dropout)
        )

        # Recurrent state projection
        self.state_proj = nn.Linear(d_model, d_model)

    def forward(self, x: torch.Tensor, recurrent_state: torch.Tensor,
                mask: Optional[torch.Tensor] = None) -> Tuple[torch.Tensor, torch.Tensor]:
        # Update recurrent state
        recurrent_state = self.state_proj(recurrent_state)

        # Combine input with recurrent state
        x = x + recurrent_state

        # Self-attention block
        attended = self.attention(self.norm1(x), mask)
        x = x + attended

        # Feed-forward block
        x = x + self.feed_forward(self.norm2(x))

        return x, x  # Return both output and new recurrent state

class CodaBlock(nn.Module):
    def __init__(self, d_model: int, vocab_size: int):
        super().__init__()
        self.norm = nn.LayerNorm(d_model)
        self.output_proj = nn.Linear(d_model, vocab_size)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = self.norm(x)
        return self.output_proj(x)

class LatentRecurrentDepthLM(nn.Module):
    def __init__(self, vocab_size: int, d_model: int, num_heads: int, dropout: float = 0.1):
        super().__init__()
        self.prelude = PreludeBlock(vocab_size, d_model, num_heads, dropout)
        self.recurrent = RecurrentBlock(d_model, num_heads, dropout)
        self.coda = CodaBlock(d_model, vocab_size)

    def forward(self, x: torch.Tensor, num_iterations: int,
                mask: Optional[torch.Tensor] = None) -> torch.Tensor:
        # Initial embedding and processing
        hidden = self.prelude(x, mask)

        # Initialize recurrent state
        recurrent_state = torch.zeros_like(hidden)

        # Apply recurrent block multiple times
        for _ in range(num_iterations):
            hidden, recurrent_state = self.recurrent(hidden, recurrent_state, mask)

        # Final output projection
        return self.coda(hidden)



# Configuration for the Latent Recurrent Depth Model
class LatentRecurrentDepthConfig(PretrainedConfig):
    model_type = "latent_recurrent_depth"

    def __init__(self, vocab_size=50257, d_model=768, num_heads=12, dropout=0.1, **kwargs):
        super().__init__(**kwargs)
        self.vocab_size = vocab_size
        self.d_model = d_model
        self.num_heads = num_heads
        self.dropout = dropout


# Hugging Face-Compatible Model Wrapper
class LatentRecurrentDepthModel(PreTrainedModel):
    config_class = LatentRecurrentDepthConfig
    base_model_prefix = "latent_recurrent_depth"

    def __init__(self, config: LatentRecurrentDepthConfig):
        super().__init__(config)
        self.latent_model = LatentRecurrentDepthLM(config.vocab_size, config.d_model, config.num_heads, config.dropout)
        self.init_weights()

    def forward(self, input_ids: torch.Tensor, num_iterations: int, mask: Optional[torch.Tensor] = None) -> torch.Tensor:
        return self.latent_model(input_ids, num_iterations, mask)

    def generate(
        self,
        input_ids: torch.Tensor,
        max_length: int = 20,
        num_iterations: int = 3,
        temperature: float = 1.0,
        top_k: Optional[int] = 50,
    ) -> torch.Tensor:
        """
        Generate a sequence of tokens given input_ids.

        Args:
          input_ids: torch.Tensor of shape (batch, seq_length) containing the prompt.
          max_length: The number of tokens to generate.
          num_iterations: The number of recurrent iterations to use in each forward pass.
          temperature: Temperature for scaling logits.
          top_k: If set, only sample from the top k tokens.

        Returns:
          generated: torch.Tensor containing the generated sequence.
        """
        generated = input_ids.clone()
        self.eval()
        with torch.no_grad():
            for _ in range(max_length):
                # Get logits from the model for the current sequence.
                logits = self.forward(generated, num_iterations=num_iterations)
                # Use only the logits for the last token in the sequence.
                next_token_logits = logits[:, -1, :] / temperature
                if top_k is not None:
                    # Top-k filtering
                    top_k_logits, top_k_indices = torch.topk(next_token_logits, top_k)
                    probabilities = F.softmax(top_k_logits, dim=-1)
                    next_token = top_k_indices.gather(-1, torch.multinomial(probabilities, num_samples=1))
                else:
                    probabilities = F.softmax(next_token_logits, dim=-1)
                    next_token = torch.multinomial(probabilities, num_samples=1)
                generated = torch.cat([generated, next_token], dim=1)
                # Optionally, break if the EOS token is generated.
                if next_token.item() == self.config.eos_token_id:
                    break
        return generated