Create parameters.py

parameters.py

@@ -0,0 +1,143 @@
+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()