parameters.py

import torch
import safetensors.torch
import concurrent.futures
import zlib
import logging
from typing import Dict, Tuple
from pathlib import Path

# Configure logging
logging.basicConfig(
    level=logging.INFO,
    format="%(asctime)s - %(levelname)s - %(message)s",
    handlers=[logging.StreamHandler()]
)

class AdvancedModelParameters:
    def __init__(self, num_shards=2089, base_filename="charm15", hidden_size=16384, layers_per_shard=100):
        """Initialize model parameters for a massive transformer model."""
        self.num_shards = num_shards
        self.base_filename = base_filename
        self.hidden_size = hidden_size
        self.layers_per_shard = layers_per_shard
        self.ffn_multiplier = 4
        self.shape = (hidden_size, hidden_size)
        self.dtype = torch.float16
        self.base_path = Path("model_shards")
        self.base_path.mkdir(parents=True, exist_ok=True)

    def generate_layer_parameters(self, layer_idx: int) -> Dict[str, torch.Tensor]:
        """Generate parameters for a single transformer layer."""
        params = {}
        prefix = f"layer_{layer_idx}"
        
        # Attention weights (Q, K, V, O)
        for name in ["query_weight", "key_weight", "value_weight", "output_weight"]:
            params[f"{prefix}.attention.{name}"] = torch.randn(
                self.shape, dtype=self.dtype
            ) * (1.0 / self.hidden_size ** 0.5)
        
        # FFN weights
        intermediate_size = self.hidden_size * self.ffn_multiplier
        params[f"{prefix}.ffn.intermediate_weight"] = torch.randn(
            self.hidden_size, intermediate_size, dtype=self.dtype
        ) * (1.0 / self.hidden_size ** 0.5)
        params[f"{prefix}.ffn.output_weight"] = torch.randn(
            intermediate_size, self.hidden_size, dtype=self.dtype
        ) * (1.0 / intermediate_size ** 0.5)
        
        return params

    def generate_shard_parameters(self, shard_index: int) -> Dict[str, torch.Tensor]:
        """Generate parameters for a single shard."""
        params = {}
        start_layer = (shard_index - 1) * self.layers_per_shard
        end_layer = start_layer + self.layers_per_shard
        
        # Generate layers for this shard
        for layer_idx in range(start_layer, end_layer):
            params.update(self.generate_layer_parameters(layer_idx))
        
        # Add embeddings and output layer to the first shard
        if shard_index == 1:
            params["embedding.word_embeddings"] = torch.randn(
                50000, self.hidden_size, dtype=self.dtype
            ) * (1.0 / self.hidden_size ** 0.5)
            params["embedding.position_embeddings"] = torch.randn(
                4096, self.hidden_size, dtype=self.dtype
            ) * (1.0 / self.hidden_size ** 0.5)
            params["output_layer"] = torch.randn(
                self.hidden_size, 50000, dtype=self.dtype
            ) * (1.0 / self.hidden_size ** 0.5)
        
        return params

    def compress_tensor(self, tensor: torch.Tensor) -> bytes:
        """Apply zlib compression to tensor data."""
        tensor_bytes = tensor.numpy().tobytes()
        return zlib.compress(tensor_bytes, level=9)

    def save_single_shard(self, shard_index: int) -> None:
        """Save a single model shard with compression."""
        params = self.generate_shard_parameters(shard_index)
        filename = self.base_path / f"{self.base_filename}_{shard_index}_of_{self.num_shards}.safetensors"
        
        # Compress tensors
        compressed_data = {key: self.compress_tensor(value) for key, value in params.items()}
        
        # Save with metadata
        metadata = {
            "shard_index": shard_index,
            "total_shards": self.num_shards,
            "layers": self.layers_per_shard,
            "hidden_size": self.hidden_size
        }
        safetensors.torch.save_file(compressed_data, str(filename), metadata=metadata)
        logging.info(f"[✔] Shard {shard_index}/{self.num_shards} saved: {filename}")

    def save_sharded_parameters(self) -> None:
        """Save all shards in parallel."""
        logging.info(f"Starting to save {self.num_shards} shards...")
        with concurrent.futures.ThreadPoolExecutor() as executor:
            executor.map(self.save_single_shard, range(1, self.num_shards + 1))
        logging.info("All shards saved successfully.")

    def estimate_parameters(self) -> Tuple[int, float]:
        """Estimate total parameters and memory usage."""
        params_per_layer = (
            4 * (self.hidden_size * self.hidden_size) +  # Attention weights
            self.hidden_size * (self.hidden_size * self.ffn_multiplier) +  # FFN intermediate
            (self.hidden_size * self.ffn_multiplier) * self.hidden_size  # FFN output
        )
        params_per_shard = params_per_layer * self.layers_per_shard
        total_params = params_per_shard * self.num_shards
        
        # Add embedding and output layer from first shard
        total_params += (
            50000 * self.hidden_size +  # word_embeddings
            4096 * self.hidden_size +   # position_embeddings
            self.hidden_size * 50000    # output_layer
        )
        
        memory_gb = (total_params * 2) / 1024**3  # 2 bytes per float16
        return total_params, memory_gb

def main():
    """Main execution flow."""
    model_storage = AdvancedModelParameters(
        num_shards=2089,
        base_filename="charm15",
        hidden_size=16384,
        layers_per_shard=100
    )
    
    # Estimate parameters
    total_params, memory_gb = model_storage.estimate_parameters()
    logging.info(f"Estimated total parameters: {total_params:,}")
    logging.info(f"Estimated memory usage: {memory_gb:.2f} GB")
    
    # Save shards
    model_storage.save_sharded_parameters()

if __name__ == "__main__":
    main()