From 9edffcefb4632368f20c864630ae2544d928cfb4 Mon Sep 17 00:00:00 2001
From: Kye Gomez <98760976+kyegomez@users.noreply.github.com>
Date: Mon, 11 Nov 2024 06:55:51 -0500
Subject: [PATCH] Update main.py

---
 main.py | 403 ++++++++++++++++++++++++++++++++++++++++++++++++++++----
 1 file changed, 377 insertions(+), 26 deletions(-)

diff --git a/main.py b/main.py
index 53404dd..d6d83cb 100644
--- a/main.py
+++ b/main.py
@@ -1,37 +1,388 @@
-import os
+"""
+BitNet a4.8 Implementation
 
-from dotenv import load_dotenv
+A PyTorch implementation of BitNet a4.8 with 4-bit activations for 1-bit LLMs.
+Includes hybrid quantization and sparsification strategy for efficient inference.
+"""
+
+import math
+from dataclasses import dataclass
+from typing import Optional, Tuple, Union
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
 from loguru import logger
-from swarm_models import OpenAIChat
+from torch import Tensor
+from torch.nn.parameter import Parameter
+
+
+@dataclass
+class BitNetConfig:
+    """Configuration class for BitNet a4.8 model hyperparameters."""
+    
+    hidden_size: int = 4096
+    intermediate_size: int = 11008  # GLU size
+    num_attention_heads: int = 32
+    num_hidden_layers: int = 32
+    max_position_embeddings: int = 2048
+    vocab_size: int = 32000
+    layer_norm_eps: float = 1e-5
+    pad_token_id: int = 0
+    bos_token_id: int = 1
+    eos_token_id: int = 2
+    tie_word_embeddings: bool = False
+
+
+class RMSNorm(nn.Module):
+    """RMSNorm implementation with optional bias."""
+    
+    def __init__(self, hidden_size: int, eps: float = 1e-5):
+        """
+        Args:
+            hidden_size: Dimensionality of input features
+            eps: Small constant for numerical stability
+        """
+        super().__init__()
+        self.weight = Parameter(torch.ones(hidden_size))
+        self.eps = eps
+        
+        logger.debug(f"Initialized RMSNorm with hidden_size={hidden_size}, eps={eps}")
+
+    def forward(self, x: Tensor) -> Tensor:
+        """Apply RMS normalization."""
+        variance = x.pow(2).mean(-1, keepdim=True)
+        x = x * torch.rsqrt(variance + self.eps)
+        return self.weight * x
+
+
+class Int4Quantizer(nn.Module):
+    """4-bit integer quantization using absmean scaling."""
+    
+    def __init__(self):
+        super().__init__()
+        self.min_val = -8
+        self.max_val = 7
+        
+    def forward(self, x: Tensor) -> Tensor:
+        """Quantize input tensor to 4-bit integers."""
+        beta = torch.mean(torch.abs(x), dim=-1, keepdim=True)
+        scale = beta * math.sqrt(7)
+        x_q = torch.round(x * math.sqrt(7) / (beta + 1e-5))
+        x_q = torch.clamp(x_q, self.min_val, self.max_val)
+        return x_q * scale / math.sqrt(7)
+
 
-from swarms.agents.create_agents_from_yaml import (
-    create_agents_from_yaml,
-)
+class Int8Quantizer(nn.Module):
+    """8-bit integer quantization using absmax scaling."""
+    
+    def __init__(self):
+        super().__init__()
+        self.min_val = -128
+        self.max_val = 127
+        
+    def forward(self, x: Tensor) -> Tensor:
+        """Quantize input tensor to 8-bit integers."""
+        gamma = torch.max(torch.abs(x), dim=-1, keepdim=True)[0]
+        x_q = torch.round(x * 127 / (gamma + 1e-5))
+        x_q = torch.clamp(x_q, self.min_val, self.max_val)
+        return x_q * gamma / 127
 
-# Load environment variables
-load_dotenv()
 
-# Path to your YAML file
-yaml_file = "agents.yaml"
+class TopKSparsifier(nn.Module):
+    """Applies top-k sparsification to input tensor."""
+    
+    def __init__(self, k: float = 0.5):
+        """
+        Args:
+            k: Fraction of values to keep (between 0 and 1)
+        """
+        super().__init__()
+        self.k = k
+        
+    def forward(self, x: Tensor) -> Tuple[Tensor, Tensor]:
+        """
+        Apply top-k sparsification.
+        
+        Returns:
+            Tuple of (sparse tensor, binary mask)
+        """
+        abs_x = torch.abs(x)
+        num_keep = int(self.k * x.shape[-1])
+        threshold = torch.kthvalue(abs_x, k=num_keep, dim=-1, keepdim=True)[0]
+        mask = (abs_x >= threshold).float()
+        return x * mask, mask
 
 
-# Get the OpenAI API key from the environment variable
-api_key = os.getenv("GROQ_API_KEY")
+class BitLinear(nn.Module):
+    """1.58-bit quantized linear layer."""
+    
+    def __init__(self, in_features: int, out_features: int):
+        """
+        Args:
+            in_features: Size of input features
+            out_features: Size of output features
+        """
+        super().__init__()
+        self.weight = Parameter(torch.empty((out_features, in_features)))
+        self.scale = Parameter(torch.ones(1))
+        self.reset_parameters()
+        
+    def reset_parameters(self):
+        """Initialize weights."""
+        nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5))
+        
+    def forward(self, x: Tensor) -> Tensor:
+        """Apply quantized linear transformation."""
+        # Quantize weights to {-1, 0, 1}
+        w_abs = torch.abs(self.weight)
+        w_mean = torch.mean(w_abs)
+        w_q = torch.round(self.weight / (w_mean + 1e-5))
+        w_q = torch.clamp(w_q, -1, 1)
+        
+        return F.linear(x, w_q * w_mean * self.scale)
 
-# Model
-model = OpenAIChat(
-    openai_api_base="https://api.groq.com/openai/v1",
-    openai_api_key=api_key,
-    model_name="llama-3.1-70b-versatile",
-    temperature=0.1,
-)
 
-try:
-    # Create agents and run tasks (using 'both' to return agents and task results)
-    task_results = create_agents_from_yaml(
-        model=model, yaml_file=yaml_file, return_type="run_swarm"
+class BitNetAttention(nn.Module):
+    """Multi-head attention with 4-bit activation quantization."""
+    
+    def __init__(self, config: BitNetConfig):
+        """
+        Args:
+            config: Model configuration object
+        """
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = self.hidden_size // self.num_heads
+        
+        self.q_proj = BitLinear(self.hidden_size, self.hidden_size)
+        self.k_proj = BitLinear(self.hidden_size, self.hidden_size)
+        self.v_proj = BitLinear(self.hidden_size, self.hidden_size)
+        self.o_proj = BitLinear(self.hidden_size, self.hidden_size)
+        
+        self.input_quant = Int4Quantizer()
+        self.output_quant = Int8Quantizer()
+        self.output_sparse = TopKSparsifier(k=0.5)
+        
+        logger.debug(f"Initialized BitNetAttention with {self.num_heads} heads")
+        
+    def forward(
+        self,
+        hidden_states: Tensor,
+        attention_mask: Optional[Tensor] = None,
+    ) -> Tensor:
+        """
+        Apply multi-head attention with quantized activations.
+        
+        Args:
+            hidden_states: Input tensor of shape (batch, seq_len, hidden_size)
+            attention_mask: Optional attention mask
+            
+        Returns:
+            Output tensor of shape (batch, seq_len, hidden_size)
+        """
+        batch_size, seq_length, _ = hidden_states.size()
+        
+        # Quantize input
+        hidden_states = self.input_quant(hidden_states)
+        
+        # Project Q/K/V
+        query = self.q_proj(hidden_states)
+        key = self.k_proj(hidden_states) 
+        value = self.v_proj(hidden_states)
+        
+        # Reshape heads
+        query = query.view(batch_size, seq_length, self.num_heads, self.head_dim).transpose(1, 2)
+        key = key.view(batch_size, seq_length, self.num_heads, self.head_dim).transpose(1, 2)
+        value = value.view(batch_size, seq_length, self.num_heads, self.head_dim).transpose(1, 2)
+        
+        # Attention
+        attention_scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(self.head_dim)
+        if attention_mask is not None:
+            attention_scores = attention_scores + attention_mask
+            
+        attention_probs = F.softmax(attention_scores, dim=-1)
+        context = torch.matmul(attention_probs, value)
+        
+        # Reshape back
+        context = context.transpose(1, 2).contiguous()
+        context = context.view(batch_size, seq_length, self.hidden_size)
+        
+        # Output projection with quantization and sparsification
+        context = self.output_quant(context)
+        context, _ = self.output_sparse(context)
+        output = self.o_proj(context)
+        
+        return output
+
+
+class BitNetMLP(nn.Module):
+    """FFN with gated linear unit and hybrid quantization."""
+    
+    def __init__(self, config: BitNetConfig):
+        """
+        Args:
+            config: Model configuration object
+        """
+        super().__init__()
+        self.up_proj = BitLinear(config.hidden_size, config.intermediate_size)
+        self.gate_proj = BitLinear(config.hidden_size, config.intermediate_size)
+        self.down_proj = BitLinear(config.intermediate_size, config.hidden_size)
+        
+        self.input_quant = Int4Quantizer()
+        self.down_quant = Int8Quantizer()
+        
+        logger.debug(f"Initialized BitNetMLP with intermediate_size={config.intermediate_size}")
+        
+    def forward(self, x: Tensor) -> Tensor:
+        """Apply FFN transformation with quantized activations."""
+        # Input quantization
+        x = self.input_quant(x)
+        
+        # Up projection with gating
+        gate = self.gate_proj(x)
+        up = self.up_proj(x)
+        
+        # ReLU^2 activation
+        gate = F.relu(gate).pow(2)
+        
+        # Combine gate and up projections
+        intermediate = up * gate
+        
+        # Down projection with 8-bit quantization
+        intermediate = self.down_quant(intermediate)
+        output = self.down_proj(intermediate)
+        
+        return output
+
+
+class BitNetBlock(nn.Module):
+    """Transformer block with BitNet a4.8 quantization."""
+    
+    def __init__(self, config: BitNetConfig):
+        """
+        Args:
+            config: Model configuration object
+        """
+        super().__init__()
+        self.attention = BitNetAttention(config)
+        self.mlp = BitNetMLP(config)
+        
+        self.input_layernorm = RMSNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.post_attention_layernorm = RMSNorm(config.hidden_size, eps=config.layer_norm_eps)
+        
+    def forward(
+        self,
+        hidden_states: Tensor,
+        attention_mask: Optional[Tensor] = None,
+    ) -> Tensor:
+        """Apply transformer block with quantized operations."""
+        # Self attention
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+        hidden_states = self.attention(hidden_states, attention_mask)
+        hidden_states = residual + hidden_states
+        
+        # MLP
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+        
+        return hidden_states
+
+
+class BitNetModel(nn.Module):
+    """Complete BitNet a4.8 model."""
+    
+    def __init__(self, config: BitNetConfig):
+        """
+        Args:
+            config: Model configuration object
+        """
+        super().__init__()
+        self.config = config
+        
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size)
+        self.layers = nn.ModuleList([BitNetBlock(config) for _ in range(config.num_hidden_layers)])
+        self.norm = RMSNorm(config.hidden_size, eps=config.layer_norm_eps)
+        
+        logger.info(f"Initialized BitNetModel with {config.num_hidden_layers} layers")
+        
+    def forward(
+        self,
+        input_ids: Tensor,
+        attention_mask: Optional[Tensor] = None,
+    ) -> Tensor:
+        """
+        Forward pass through the model.
+        
+        Args:
+            input_ids: Token IDs of shape (batch, seq_len)
+            attention_mask: Optional attention mask
+            
+        Returns:
+            Output tensor of shape (batch, seq_len, hidden_size)
+        """
+        hidden_states = self.embed_tokens(input_ids)
+        
+        for layer in self.layers:
+            hidden_states = layer(hidden_states, attention_mask)
+            
+        hidden_states = self.norm(hidden_states)
+        return hidden_states
+
+
+def create_model(
+    hidden_size: int = 4096,
+    intermediate_size: int = 11008,
+    num_hidden_layers: int = 32,
+    num_attention_heads: int = 32,
+    **kwargs,
+) -> BitNetModel:
+    """
+    Create a BitNet a4.8 model with the specified configuration.
+    
+    Args:
+        hidden_size: Model dimension
+        intermediate_size: FFN intermediate dimension
+        num_hidden_layers: Number of transformer layers
+        num_attention_heads: Number of attention heads
+        **kwargs: Additional config parameters
+        
+    Returns:
+        Initialized BitNetModel
+    """
+    config = BitNetConfig(
+        hidden_size=hidden_size,
+        intermediate_size=intermediate_size,
+        num_hidden_layers=num_hidden_layers,
+        num_attention_heads=num_attention_heads,
+        **kwargs
     )
+    
+    model = BitNetModel(config)
+    return model
 
-    logger.info(f"Results from agents: {task_results}")
-except Exception as e:
-    logger.error(f"An error occurred: {e}")
+
+# Example usage
+if __name__ == "__main__":
+    logger.info("Creating BitNet a4.8 model")
+    
+    model = create_model(
+        hidden_size=816,  # 4096/5
+        intermediate_size=2202,  # 11008/5 
+        num_hidden_layers=6,  # 32/5 rounded down
+        num_attention_heads=6  # 32/5 rounded down
+    )
+    
+    # Generate sample input
+    batch_size = 2
+    seq_length = 512
+    input_ids = torch.randint(0, 32000, (batch_size, seq_length))
+    
+    # Forward pass
+    logger.info("Running forward pass")
+    outputs = model(input_ids)
+    logger.info(f"Output shape: {outputs.shape}")