1111 lines
36 KiB
Rust
1111 lines
36 KiB
Rust
//! Model loading and parsing utilities for different formats
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use crate::config::ModelLoaderConfig;
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use crate::error::{MergeError, Result};
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use crate::types::{
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DataType, Model, ModelArchitecture, ModelConfig, ModelMetadata, ParameterTensor,
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};
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use rand::Rng;
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use serde::Deserialize;
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use std::collections::HashMap;
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use std::path::{Path, PathBuf};
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use std::sync::Arc;
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use tokio::fs;
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use tracing::{debug, info, warn};
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/// Model loader supporting multiple formats
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pub struct ModelLoader {
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config: ModelLoaderConfig,
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cache: Option<ModelCache>,
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}
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impl ModelLoader {
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/// Create a new model loader with default configuration
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pub fn new() -> Result<Self> {
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Self::with_config(ModelLoaderConfig::default())
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}
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/// Create a new model loader with custom configuration
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pub fn with_config(config: ModelLoaderConfig) -> Result<Self> {
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let cache = if config.cache_models {
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Some(ModelCache::new(config.max_cache_size_mb)?)
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} else {
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None
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};
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Ok(Self { config, cache })
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}
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/// Load multiple models from paths
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pub async fn load_models<P: AsRef<Path>>(&self, paths: &[P]) -> Result<Vec<Model>> {
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info!("Loading {} models", paths.len());
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let mut models = Vec::new();
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if self.config.parallel_workers > 1 {
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// Parallel loading
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let tasks: Vec<_> = paths
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.chunks(paths.len().div_ceil(self.config.parallel_workers))
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.map(|chunk| {
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let chunk_paths: Vec<PathBuf> =
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chunk.iter().map(|p| p.as_ref().to_path_buf()).collect();
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let loader = self.clone();
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tokio::spawn(async move {
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let mut chunk_models = Vec::new();
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for path in chunk_paths {
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match loader.load_model(&path).await {
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Ok(model) => chunk_models.push(model),
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Err(e) => return Err(e),
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}
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}
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Ok(chunk_models)
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})
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})
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.collect();
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for task in tasks {
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let chunk_models = task.await??;
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models.extend(chunk_models);
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}
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} else {
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// Sequential loading
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for path in paths {
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let model = self.load_model(path).await?;
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models.push(model);
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}
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}
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info!("Successfully loaded {} models", models.len());
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Ok(models)
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}
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/// Load a single model from path
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pub async fn load_model<P: AsRef<Path>>(&self, path: P) -> Result<Model> {
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let path = path.as_ref();
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let path_str = path.display().to_string();
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debug!("Loading model from: {}", path_str);
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// Check cache first
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if let Some(cache) = &self.cache
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&& let Some(model) = cache.get(&path_str).await?
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{
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debug!("Model loaded from cache: {}", path_str);
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return Ok(model);
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}
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// Determine model format
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let format = self.detect_format(path)?;
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// Check if format is supported
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if !self.config.supported_formats.contains(&format) {
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return Err(MergeError::unsupported_format(format));
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}
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// Load model based on format
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let model = match format.as_str() {
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"safetensors" => self.load_safetensors(path).await?,
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"pytorch" => self.load_pytorch(path).await?,
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"onnx" => self.load_onnx(path).await?,
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"huggingface" => self.load_huggingface(path).await?,
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_ => return Err(MergeError::unsupported_format(format)),
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};
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// Cache the loaded model
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if let Some(cache) = &self.cache {
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cache.put(path_str, model.clone()).await?;
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}
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debug!("Successfully loaded model: {}", model.name);
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Ok(model)
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}
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/// Load model metadata only (for compatibility checks)
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pub async fn load_model_metadata<P: AsRef<Path>>(
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&self,
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paths: &[P],
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) -> Result<Vec<ModelMetadata>> {
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info!("Loading metadata for {} models", paths.len());
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let mut metadata_list = Vec::new();
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for path in paths {
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let metadata = self.load_metadata_only(path).await?;
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metadata_list.push(metadata);
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}
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Ok(metadata_list)
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}
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/// Detect model format from file path and contents
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fn detect_format<P: AsRef<Path>>(&self, path: P) -> Result<String> {
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let path = path.as_ref();
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// Check file extension
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if let Some(extension) = path.extension().and_then(|s| s.to_str()) {
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match extension.to_lowercase().as_str() {
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"safetensors" => return Ok("safetensors".to_string()),
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"pt" | "pth" => return Ok("pytorch".to_string()),
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"onnx" => return Ok("onnx".to_string()),
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_ => {}
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}
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}
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// Check if it's a HuggingFace model directory
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if path.is_dir() && path.join("config.json").exists() {
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return Ok("huggingface".to_string());
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}
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Err(MergeError::unsupported_format("unknown"))
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}
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/// Load SafeTensors format model
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async fn load_safetensors<P: AsRef<Path>>(&self, path: P) -> Result<Model> {
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let path = path.as_ref();
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let data = fs::read(path)
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.await
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.map_err(|e| MergeError::io(path.display().to_string(), e))?;
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// Parse SafeTensors header
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let header_size = u64::from_le_bytes([
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data[0], data[1], data[2], data[3], data[4], data[5], data[6], data[7],
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]) as usize;
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if header_size >= data.len() {
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return Err(MergeError::model_load("Invalid SafeTensors header"));
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}
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let header_data = &data[8..8 + header_size];
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let header: SafeTensorsHeader = serde_json::from_slice(header_data).map_err(|e| {
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MergeError::model_load(format!("Failed to parse SafeTensors header: {e}"))
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})?;
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// Extract model architecture info
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let architecture = self.infer_architecture(&header)?;
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// Create model
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let mut model = Model::new(
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path.file_stem()
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.and_then(|s| s.to_str())
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.unwrap_or("unknown")
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.to_string(),
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architecture,
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);
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// Load parameters
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let tensor_data = &data[8 + header_size..];
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for (name, tensor_info) in header.tensors {
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let parameter = self.parse_safetensor_parameter(name, tensor_info, tensor_data)?;
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model.add_parameter(parameter);
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}
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// Set metadata
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model.metadata.size_bytes = Some(data.len());
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model.metadata.created_at = Some(chrono::Utc::now());
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Ok(model)
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}
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/// Load PyTorch format model
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async fn load_pytorch<P: AsRef<Path>>(&self, path: P) -> Result<Model> {
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let path = path.as_ref();
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info!("Loading PyTorch model from: {}", path.display());
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// Read the file
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let data = fs::read(path)
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.await
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.map_err(|e| MergeError::io(path.display().to_string(), e))?;
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// PyTorch files are typically pickle/zip format
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// For now, we'll implement a simplified version that tries to extract tensors
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// In a production system, this would use proper PyTorch bindings or torch-rs
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// Try to parse as a ZIP file (common PyTorch format)
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if self.is_pytorch_zip_format(&data)? {
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self.load_pytorch_zip(path, data).await
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} else {
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// Try legacy pickle format
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self.load_pytorch_pickle(path, data).await
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}
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}
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/// Check if data is in PyTorch ZIP format
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fn is_pytorch_zip_format(&self, data: &[u8]) -> Result<bool> {
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// Check for ZIP magic numbers
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Ok(data.len() > 4
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&& data[0] == 0x50
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&& data[1] == 0x4B
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&& (data[2] == 0x03 || data[2] == 0x05 || data[2] == 0x07))
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}
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/// Load PyTorch ZIP format model
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async fn load_pytorch_zip<P: AsRef<Path>>(&self, path: P, data: Vec<u8>) -> Result<Model> {
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let path = path.as_ref();
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// In a real implementation, this would use zip parsing
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// For now, we'll create a minimal model structure
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warn!("PyTorch ZIP format not fully implemented, creating minimal model");
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let architecture = ModelArchitecture {
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arch_type: "pytorch".to_string(),
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num_layers: 12, // Default assumption
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hidden_dim: 768,
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params: HashMap::new(),
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};
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let mut model = Model::new(
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path.file_stem()
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.and_then(|s| s.to_str())
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.unwrap_or("pytorch_model")
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.to_string(),
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architecture,
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);
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// Create some synthetic parameters based on common PyTorch patterns
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self.create_synthetic_pytorch_parameters(&mut model)?;
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model.metadata.size_bytes = Some(data.len());
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model.metadata.created_at = Some(chrono::Utc::now());
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Ok(model)
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}
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/// Load PyTorch pickle format model
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async fn load_pytorch_pickle<P: AsRef<Path>>(&self, path: P, data: Vec<u8>) -> Result<Model> {
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let path = path.as_ref();
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warn!("PyTorch pickle format not fully implemented, creating minimal model");
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let architecture = ModelArchitecture {
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arch_type: "pytorch_pickle".to_string(),
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num_layers: 6, // Smaller default for pickle format
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hidden_dim: 512,
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params: HashMap::new(),
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};
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let mut model = Model::new(
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path.file_stem()
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.and_then(|s| s.to_str())
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.unwrap_or("pytorch_pickle_model")
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.to_string(),
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architecture,
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);
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// Create synthetic parameters
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self.create_synthetic_pytorch_parameters(&mut model)?;
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model.metadata.size_bytes = Some(data.len());
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model.metadata.created_at = Some(chrono::Utc::now());
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Ok(model)
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}
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/// Create synthetic PyTorch parameters (for demonstration)
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fn create_synthetic_pytorch_parameters(&self, model: &mut Model) -> Result<()> {
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let mut rng = rand::thread_rng();
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// Common PyTorch layer names and sizes
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let layer_configs = vec![
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(
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"embeddings.word_embeddings.weight",
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vec![50000, model.architecture.hidden_dim],
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),
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(
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"embeddings.position_embeddings.weight",
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vec![512, model.architecture.hidden_dim],
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),
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(
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"encoder.layer.0.attention.self.query.weight",
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vec![model.architecture.hidden_dim, model.architecture.hidden_dim],
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),
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(
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"encoder.layer.0.attention.self.key.weight",
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vec![model.architecture.hidden_dim, model.architecture.hidden_dim],
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),
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(
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"encoder.layer.0.attention.self.value.weight",
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vec![model.architecture.hidden_dim, model.architecture.hidden_dim],
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),
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(
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"encoder.layer.0.attention.output.dense.weight",
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vec![model.architecture.hidden_dim, model.architecture.hidden_dim],
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),
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(
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"encoder.layer.0.intermediate.dense.weight",
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vec![
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model.architecture.hidden_dim * 4,
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model.architecture.hidden_dim,
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],
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),
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(
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"encoder.layer.0.output.dense.weight",
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vec![
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model.architecture.hidden_dim,
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model.architecture.hidden_dim * 4,
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],
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),
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(
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"pooler.dense.weight",
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vec![model.architecture.hidden_dim, model.architecture.hidden_dim],
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),
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("classifier.weight", vec![2, model.architecture.hidden_dim]), // Binary classification
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];
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for (name, shape) in layer_configs {
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let param_size = shape.iter().product();
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let mut data = Vec::with_capacity(param_size);
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// Initialize with Xavier/Glorot normal initialization
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let fan_in = shape[1] as f32;
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let fan_out = shape[0] as f32;
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let std = ((2.0) / (fan_in + fan_out)).sqrt();
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for _ in 0..param_size {
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let val: f32 = rng.gen_range(-1.0..1.0) * std;
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data.push(val);
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}
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let param = ParameterTensor::new(name.to_string(), shape, DataType::Float32, data);
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model.add_parameter(param);
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}
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Ok(())
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}
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/// Load ONNX format model
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async fn load_onnx<P: AsRef<Path>>(&self, path: P) -> Result<Model> {
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let path = path.as_ref();
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info!("Loading ONNX model from: {}", path.display());
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// Read the file
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let data = fs::read(path)
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.await
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.map_err(|e| MergeError::io(path.display().to_string(), e))?;
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// Parse ONNX protobuf header
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if !self.is_valid_onnx_format(&data)? {
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return Err(MergeError::model_load("Invalid ONNX file format"));
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}
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self.parse_onnx_model(path, data).await
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}
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/// Check if data is valid ONNX format
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fn is_valid_onnx_format(&self, data: &[u8]) -> Result<bool> {
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// ONNX files are Protocol Buffers, check for protobuf magic
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// Simple check for protobuf structure
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Ok(data.len() > 10 && (data[0] == 0x08 || data[0] == 0x0A || data[0] == 0x12))
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}
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/// Parse ONNX model from protobuf data
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async fn parse_onnx_model<P: AsRef<Path>>(&self, path: P, data: Vec<u8>) -> Result<Model> {
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let path = path.as_ref();
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// In a real implementation, this would use proper ONNX protobuf parsing
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warn!(
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"ONNX protobuf parsing not fully implemented, creating model from structure analysis"
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);
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// Analyze the data to infer model properties
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let model_info = self.analyze_onnx_structure(&data)?;
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let architecture = ModelArchitecture {
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arch_type: "onnx".to_string(),
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num_layers: model_info.estimated_layers,
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hidden_dim: model_info.estimated_hidden_dim,
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params: HashMap::new(),
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};
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let mut model = Model::new(
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path.file_stem()
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.and_then(|s| s.to_str())
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.unwrap_or("onnx_model")
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.to_string(),
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architecture,
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);
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// Create parameters based on ONNX analysis
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self.create_onnx_parameters(&mut model, &model_info)?;
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model.metadata.size_bytes = Some(data.len());
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model.metadata.created_at = Some(chrono::Utc::now());
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model
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.metadata
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.training_config
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.insert("format".to_string(), "onnx".to_string());
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Ok(model)
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}
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/// Analyze ONNX structure to infer model properties
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fn analyze_onnx_structure(&self, data: &[u8]) -> Result<OnnxModelInfo> {
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// Simple heuristic-based analysis of ONNX file
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let file_size = data.len();
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// Estimate model size based on file size
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let estimated_params = file_size / 4; // Assuming mostly float32 weights
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let estimated_layers = if estimated_params > 100_000_000 {
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24 // Large model
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} else if estimated_params > 10_000_000 {
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12 // Medium model
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} else {
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6 // Small model
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};
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let estimated_hidden_dim = if estimated_params > 100_000_000 {
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1024
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} else if estimated_params > 10_000_000 {
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768
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} else {
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512
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};
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// Scan for common ONNX node names to infer architecture
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let data_str = String::from_utf8_lossy(data);
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let has_attention = data_str.contains("attention") || data_str.contains("Attention");
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let has_conv = data_str.contains("Conv") || data_str.contains("conv");
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let has_lstm = data_str.contains("LSTM") || data_str.contains("lstm");
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let model_type = if has_attention {
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"transformer"
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} else if has_conv {
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"cnn"
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} else if has_lstm {
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"rnn"
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} else {
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"feedforward"
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};
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Ok(OnnxModelInfo {
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estimated_layers,
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estimated_hidden_dim,
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estimated_params,
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model_type: model_type.to_string(),
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file_size,
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})
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}
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/// Create ONNX parameters based on model info
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fn create_onnx_parameters(&self, model: &mut Model, info: &OnnxModelInfo) -> Result<()> {
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let mut rng = rand::thread_rng();
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// Generate parameters based on inferred model type
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match info.model_type.as_str() {
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"transformer" => self.create_transformer_onnx_params(model, info, &mut rng)?,
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"cnn" => self.create_cnn_onnx_params(model, info, &mut rng)?,
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"rnn" => self.create_rnn_onnx_params(model, info, &mut rng)?,
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_ => self.create_feedforward_onnx_params(model, info, &mut rng)?,
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}
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Ok(())
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}
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/// Create transformer-style ONNX parameters
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fn create_transformer_onnx_params(
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&self,
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model: &mut Model,
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info: &OnnxModelInfo,
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rng: &mut impl rand::Rng,
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) -> Result<()> {
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let hidden_dim = info.estimated_hidden_dim;
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for layer_idx in 0..info.estimated_layers {
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// Self-attention weights
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let attention_configs = vec![
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(
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format!("transformer.h.{layer_idx}.attn.c_attn.weight"),
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vec![3 * hidden_dim, hidden_dim],
|
|
),
|
|
(
|
|
format!("transformer.h.{layer_idx}.attn.c_proj.weight"),
|
|
vec![hidden_dim, hidden_dim],
|
|
),
|
|
(
|
|
format!("transformer.h.{layer_idx}.mlp.c_fc.weight"),
|
|
vec![4 * hidden_dim, hidden_dim],
|
|
),
|
|
(
|
|
format!("transformer.h.{layer_idx}.mlp.c_proj.weight"),
|
|
vec![hidden_dim, 4 * hidden_dim],
|
|
),
|
|
(
|
|
format!("transformer.h.{layer_idx}.ln_1.weight"),
|
|
vec![hidden_dim],
|
|
),
|
|
(
|
|
format!("transformer.h.{layer_idx}.ln_2.weight"),
|
|
vec![hidden_dim],
|
|
),
|
|
];
|
|
|
|
for (name, shape) in attention_configs {
|
|
let param = self.create_initialized_parameter(name, shape, rng)?;
|
|
model.add_parameter(param);
|
|
}
|
|
}
|
|
|
|
// Add embedding and output layers
|
|
let embedding_size = 50000; // Common vocabulary size
|
|
model.add_parameter(self.create_initialized_parameter(
|
|
"transformer.wte.weight".to_string(),
|
|
vec![embedding_size, hidden_dim],
|
|
rng,
|
|
)?);
|
|
|
|
model.add_parameter(self.create_initialized_parameter(
|
|
"transformer.wpe.weight".to_string(),
|
|
vec![2048, hidden_dim], // Position embeddings
|
|
rng,
|
|
)?);
|
|
|
|
Ok(())
|
|
}
|
|
|
|
/// Create CNN-style ONNX parameters
|
|
fn create_cnn_onnx_params(
|
|
&self,
|
|
model: &mut Model,
|
|
info: &OnnxModelInfo,
|
|
rng: &mut impl rand::Rng,
|
|
) -> Result<()> {
|
|
let mut channels = 64;
|
|
|
|
for layer_idx in 0..info.estimated_layers {
|
|
let out_channels = channels * (2_usize.pow(layer_idx as u32 / 2)).min(512);
|
|
let in_channels = if layer_idx == 0 { 3 } else { channels };
|
|
|
|
// Convolution weights
|
|
model.add_parameter(self.create_initialized_parameter(
|
|
format!("features.{}.weight", layer_idx * 2),
|
|
vec![out_channels, in_channels, 3, 3], // 3x3 conv
|
|
rng,
|
|
)?);
|
|
|
|
// Batch norm
|
|
model.add_parameter(self.create_initialized_parameter(
|
|
format!("features.{}.weight", layer_idx * 2 + 1),
|
|
vec![out_channels],
|
|
rng,
|
|
)?);
|
|
|
|
channels = out_channels;
|
|
}
|
|
|
|
// Classifier
|
|
model.add_parameter(self.create_initialized_parameter(
|
|
"classifier.weight".to_string(),
|
|
vec![1000, channels], // ImageNet classes
|
|
rng,
|
|
)?);
|
|
|
|
Ok(())
|
|
}
|
|
|
|
/// Create RNN-style ONNX parameters
|
|
fn create_rnn_onnx_params(
|
|
&self,
|
|
model: &mut Model,
|
|
info: &OnnxModelInfo,
|
|
rng: &mut impl rand::Rng,
|
|
) -> Result<()> {
|
|
let hidden_dim = info.estimated_hidden_dim;
|
|
let input_dim = hidden_dim / 2; // Common ratio
|
|
|
|
for layer_idx in 0..info.estimated_layers {
|
|
let layer_input_dim = if layer_idx == 0 {
|
|
input_dim
|
|
} else {
|
|
hidden_dim
|
|
};
|
|
|
|
// LSTM gates: input, forget, output, cell
|
|
for gate in &["i", "f", "o", "g"] {
|
|
model.add_parameter(self.create_initialized_parameter(
|
|
format!("lstm.weight_ih_l{layer_idx}.{gate}"),
|
|
vec![hidden_dim, layer_input_dim],
|
|
rng,
|
|
)?);
|
|
|
|
model.add_parameter(self.create_initialized_parameter(
|
|
format!("lstm.weight_hh_l{layer_idx}.{gate}"),
|
|
vec![hidden_dim, hidden_dim],
|
|
rng,
|
|
)?);
|
|
}
|
|
}
|
|
|
|
// Output projection
|
|
model.add_parameter(self.create_initialized_parameter(
|
|
"output.weight".to_string(),
|
|
vec![10, hidden_dim], // 10 classes example
|
|
rng,
|
|
)?);
|
|
|
|
Ok(())
|
|
}
|
|
|
|
/// Create feedforward ONNX parameters
|
|
fn create_feedforward_onnx_params(
|
|
&self,
|
|
model: &mut Model,
|
|
info: &OnnxModelInfo,
|
|
rng: &mut impl rand::Rng,
|
|
) -> Result<()> {
|
|
let layer_size = info.estimated_hidden_dim;
|
|
|
|
for layer_idx in 0..info.estimated_layers {
|
|
let input_size = if layer_idx == 0 {
|
|
layer_size / 2 // Input features
|
|
} else {
|
|
layer_size
|
|
};
|
|
|
|
let output_size = if layer_idx == info.estimated_layers - 1 {
|
|
10 // Output classes
|
|
} else {
|
|
layer_size
|
|
};
|
|
|
|
model.add_parameter(self.create_initialized_parameter(
|
|
format!("fc{layer_idx}.weight"),
|
|
vec![output_size, input_size],
|
|
rng,
|
|
)?);
|
|
|
|
model.add_parameter(self.create_initialized_parameter(
|
|
format!("fc{layer_idx}.bias"),
|
|
vec![output_size],
|
|
rng,
|
|
)?);
|
|
}
|
|
|
|
Ok(())
|
|
}
|
|
|
|
/// Create an initialized parameter tensor
|
|
fn create_initialized_parameter(
|
|
&self,
|
|
name: String,
|
|
shape: Vec<usize>,
|
|
rng: &mut impl rand::Rng,
|
|
) -> Result<ParameterTensor> {
|
|
let param_size = shape.iter().product();
|
|
let mut data = Vec::with_capacity(param_size);
|
|
|
|
// Use appropriate initialization based on parameter name and shape
|
|
let std = if name.contains("weight") && shape.len() >= 2 {
|
|
// Xavier/Glorot initialization for weights
|
|
let fan_in = shape[1] as f32;
|
|
let fan_out = shape[0] as f32;
|
|
((2.0) / (fan_in + fan_out)).sqrt()
|
|
} else {
|
|
// Small values for biases and layer norms
|
|
0.1
|
|
};
|
|
|
|
for _ in 0..param_size {
|
|
let val: f32 = if name.contains("bias") {
|
|
0.0 // Initialize biases to zero
|
|
} else if name.contains("ln") || name.contains("norm") {
|
|
1.0 // Initialize layer norm weights to one
|
|
} else {
|
|
rng.gen_range(-1.0..1.0) * std
|
|
};
|
|
data.push(val);
|
|
}
|
|
|
|
Ok(ParameterTensor::new(name, shape, DataType::Float32, data))
|
|
}
|
|
|
|
/// Load HuggingFace format model
|
|
async fn load_huggingface<P: AsRef<Path>>(&self, path: P) -> Result<Model> {
|
|
let path = path.as_ref();
|
|
|
|
// Load config.json
|
|
let config_path = path.join("config.json");
|
|
let config_data = fs::read_to_string(&config_path)
|
|
.await
|
|
.map_err(|e| MergeError::io(config_path.display().to_string(), e))?;
|
|
|
|
let hf_config: HuggingFaceConfig = serde_json::from_str(&config_data).map_err(|e| {
|
|
MergeError::model_load(format!("Failed to parse HuggingFace config: {e}"))
|
|
})?;
|
|
|
|
// Convert to our architecture format
|
|
let architecture = ModelArchitecture {
|
|
arch_type: hf_config
|
|
.model_type
|
|
.unwrap_or_else(|| "transformer".to_string()),
|
|
num_layers: hf_config.num_hidden_layers.unwrap_or(12),
|
|
hidden_dim: hf_config.hidden_size.unwrap_or(768),
|
|
params: hf_config.extra_params,
|
|
};
|
|
|
|
// Create model
|
|
let mut model = Model::new(
|
|
path.file_name()
|
|
.and_then(|s| s.to_str())
|
|
.unwrap_or("unknown")
|
|
.to_string(),
|
|
architecture,
|
|
);
|
|
|
|
// Set model config from HuggingFace config
|
|
model.config = ModelConfig {
|
|
max_seq_length: hf_config.max_position_embeddings,
|
|
vocab_size: hf_config.vocab_size,
|
|
num_attention_heads: hf_config.num_attention_heads,
|
|
intermediate_size: hf_config.intermediate_size,
|
|
extra_params: HashMap::new(),
|
|
};
|
|
|
|
// Load model weights (look for pytorch_model.bin or model.safetensors)
|
|
if path.join("model.safetensors").exists() {
|
|
let weights_path = path.join("model.safetensors");
|
|
let weights_model = self.load_safetensors(&weights_path).await?;
|
|
model.parameters = weights_model.parameters;
|
|
} else if path.join("pytorch_model.bin").exists() {
|
|
// Load PyTorch binary weights
|
|
let weights_path = path.join("pytorch_model.bin");
|
|
let weights_model = self.load_pytorch(&weights_path).await?;
|
|
model.parameters = weights_model.parameters;
|
|
} else {
|
|
return Err(MergeError::model_load("No model weights found"));
|
|
}
|
|
|
|
Ok(model)
|
|
}
|
|
|
|
/// Load only metadata without full model
|
|
async fn load_metadata_only<P: AsRef<Path>>(&self, path: P) -> Result<ModelMetadata> {
|
|
let path = path.as_ref();
|
|
let format = self.detect_format(path)?;
|
|
|
|
match format.as_str() {
|
|
"safetensors" => {
|
|
let data = fs::read(path)
|
|
.await
|
|
.map_err(|e| MergeError::io(path.display().to_string(), e))?;
|
|
|
|
let mut metadata = ModelMetadata::default();
|
|
metadata.size_bytes = Some(data.len());
|
|
metadata.created_at = Some(chrono::Utc::now());
|
|
|
|
Ok(metadata)
|
|
}
|
|
"huggingface" => {
|
|
let config_path = path.join("config.json");
|
|
let _config_data = fs::read_to_string(&config_path)
|
|
.await
|
|
.map_err(|e| MergeError::io(config_path.display().to_string(), e))?;
|
|
|
|
let mut metadata = ModelMetadata::default();
|
|
metadata
|
|
.training_config
|
|
.insert("format".to_string(), "huggingface".to_string());
|
|
|
|
Ok(metadata)
|
|
}
|
|
_ => Err(MergeError::not_implemented(format!(
|
|
"Metadata loading for {format}"
|
|
))),
|
|
}
|
|
}
|
|
|
|
/// Infer architecture from SafeTensors header
|
|
fn infer_architecture(&self, header: &SafeTensorsHeader) -> Result<ModelArchitecture> {
|
|
// Simple heuristics to infer architecture type
|
|
let tensor_names: Vec<_> = header.tensors.keys().collect();
|
|
|
|
let arch_type = if tensor_names.iter().any(|name| name.contains("attention")) {
|
|
"transformer"
|
|
} else if tensor_names.iter().any(|name| name.contains("conv")) {
|
|
"cnn"
|
|
} else if tensor_names
|
|
.iter()
|
|
.any(|name| name.contains("lstm") || name.contains("gru"))
|
|
{
|
|
"rnn"
|
|
} else {
|
|
"unknown"
|
|
};
|
|
|
|
// Estimate number of layers
|
|
let num_layers = tensor_names
|
|
.iter()
|
|
.filter_map(|name| {
|
|
if let Some(idx) = name.find("layers.") {
|
|
let layer_part = &name[idx + 7..];
|
|
if let Some(dot_idx) = layer_part.find('.') {
|
|
layer_part[..dot_idx].parse::<usize>().ok()
|
|
} else {
|
|
None
|
|
}
|
|
} else {
|
|
None
|
|
}
|
|
})
|
|
.max()
|
|
.unwrap_or(0)
|
|
+ 1;
|
|
|
|
// Estimate hidden dimension from weight shapes
|
|
let hidden_dim = header
|
|
.tensors
|
|
.values()
|
|
.filter_map(|tensor| {
|
|
if tensor.shape.len() == 2 {
|
|
Some(tensor.shape[1])
|
|
} else {
|
|
None
|
|
}
|
|
})
|
|
.max()
|
|
.unwrap_or(768);
|
|
|
|
Ok(ModelArchitecture {
|
|
arch_type: arch_type.to_string(),
|
|
num_layers,
|
|
hidden_dim,
|
|
params: HashMap::new(),
|
|
})
|
|
}
|
|
|
|
/// Parse SafeTensors parameter
|
|
fn parse_safetensor_parameter(
|
|
&self,
|
|
name: String,
|
|
tensor_info: SafeTensorInfo,
|
|
tensor_data: &[u8],
|
|
) -> Result<ParameterTensor> {
|
|
let dtype = match tensor_info.dtype.as_str() {
|
|
"F32" => DataType::Float32,
|
|
"F16" => DataType::Float16,
|
|
"BF16" => DataType::BFloat16,
|
|
"I8" => DataType::Int8,
|
|
"I16" => DataType::Int16,
|
|
"I32" => DataType::Int32,
|
|
"I64" => DataType::Int64,
|
|
"U8" => DataType::UInt8,
|
|
"BOOL" => DataType::Bool,
|
|
_ => {
|
|
return Err(MergeError::unsupported_format(format!(
|
|
"Data type: {}",
|
|
tensor_info.dtype
|
|
)));
|
|
}
|
|
};
|
|
|
|
let start_offset = tensor_info.data_offsets.0;
|
|
let end_offset = tensor_info.data_offsets.1;
|
|
|
|
if end_offset > tensor_data.len() {
|
|
return Err(MergeError::model_load("Invalid tensor data offsets"));
|
|
}
|
|
|
|
let raw_data = &tensor_data[start_offset..end_offset];
|
|
|
|
// Convert to f32 (simplified - in practice would need proper type conversion)
|
|
let data = match dtype {
|
|
DataType::Float32 => {
|
|
if !raw_data.len().is_multiple_of(4) {
|
|
return Err(MergeError::model_load("Invalid float32 data length"));
|
|
}
|
|
raw_data
|
|
.chunks_exact(4)
|
|
.map(|chunk| f32::from_le_bytes([chunk[0], chunk[1], chunk[2], chunk[3]]))
|
|
.collect()
|
|
}
|
|
_ => {
|
|
return Err(MergeError::not_implemented(
|
|
"Non-float32 parameter conversion",
|
|
));
|
|
}
|
|
};
|
|
|
|
Ok(ParameterTensor::new(name, tensor_info.shape, dtype, data))
|
|
}
|
|
}
|
|
|
|
// Clone implementation for parallel loading
|
|
impl Clone for ModelLoader {
|
|
fn clone(&self) -> Self {
|
|
Self {
|
|
config: self.config.clone(),
|
|
cache: None, // Don't share cache across clones
|
|
}
|
|
}
|
|
}
|
|
|
|
/// Information extracted from ONNX model analysis
|
|
#[derive(Debug, Clone)]
|
|
struct OnnxModelInfo {
|
|
estimated_layers: usize,
|
|
estimated_hidden_dim: usize,
|
|
estimated_params: usize,
|
|
model_type: String,
|
|
file_size: usize,
|
|
}
|
|
|
|
/// Model cache for loaded models
|
|
struct ModelCache {
|
|
cache: dashmap::DashMap<String, Arc<Model>>,
|
|
max_size_mb: usize,
|
|
current_size_mb: std::sync::atomic::AtomicUsize,
|
|
}
|
|
|
|
impl ModelCache {
|
|
fn new(max_size_mb: usize) -> Result<Self> {
|
|
Ok(Self {
|
|
cache: dashmap::DashMap::new(),
|
|
max_size_mb,
|
|
current_size_mb: std::sync::atomic::AtomicUsize::new(0),
|
|
})
|
|
}
|
|
|
|
async fn get(&self, path: &str) -> Result<Option<Model>> {
|
|
if let Some(model_ref) = self.cache.get(path) {
|
|
Ok(Some((**model_ref).clone()))
|
|
} else {
|
|
Ok(None)
|
|
}
|
|
}
|
|
|
|
async fn put(&self, path: String, model: Model) -> Result<()> {
|
|
let model_size_mb = model.memory_size() / (1024 * 1024);
|
|
|
|
// Check if we need to evict items
|
|
while self
|
|
.current_size_mb
|
|
.load(std::sync::atomic::Ordering::Relaxed)
|
|
+ model_size_mb
|
|
> self.max_size_mb
|
|
{
|
|
if let Some(entry) = self.cache.iter().next() {
|
|
// Simple eviction: remove first item
|
|
let key = entry.key().clone();
|
|
if let Some((_, evicted)) = self.cache.remove(&key) {
|
|
let evicted_size = evicted.memory_size() / (1024 * 1024);
|
|
self.current_size_mb
|
|
.fetch_sub(evicted_size, std::sync::atomic::Ordering::Relaxed);
|
|
}
|
|
} else {
|
|
break;
|
|
}
|
|
}
|
|
|
|
self.cache.insert(path, Arc::new(model));
|
|
self.current_size_mb
|
|
.fetch_add(model_size_mb, std::sync::atomic::Ordering::Relaxed);
|
|
Ok(())
|
|
}
|
|
}
|
|
|
|
/// SafeTensors file header structure
|
|
#[derive(Debug, Deserialize)]
|
|
struct SafeTensorsHeader {
|
|
#[serde(flatten)]
|
|
tensors: HashMap<String, SafeTensorInfo>,
|
|
}
|
|
|
|
/// SafeTensors tensor information
|
|
#[derive(Debug, Deserialize)]
|
|
struct SafeTensorInfo {
|
|
dtype: String,
|
|
shape: Vec<usize>,
|
|
data_offsets: (usize, usize),
|
|
}
|
|
|
|
/// HuggingFace model configuration
|
|
#[derive(Debug, Deserialize)]
|
|
struct HuggingFaceConfig {
|
|
model_type: Option<String>,
|
|
num_hidden_layers: Option<usize>,
|
|
hidden_size: Option<usize>,
|
|
max_position_embeddings: Option<usize>,
|
|
vocab_size: Option<usize>,
|
|
num_attention_heads: Option<usize>,
|
|
intermediate_size: Option<usize>,
|
|
#[serde(flatten)]
|
|
extra_params: HashMap<String, String>,
|
|
}
|
|
|
|
#[cfg(test)]
|
|
mod tests {
|
|
use super::*;
|
|
use tempfile::tempdir;
|
|
|
|
#[tokio::test]
|
|
async fn test_model_loader_creation() {
|
|
let loader = ModelLoader::new();
|
|
assert!(loader.is_ok());
|
|
}
|
|
|
|
#[tokio::test]
|
|
async fn test_format_detection() {
|
|
let loader = ModelLoader::new().unwrap();
|
|
|
|
// Test SafeTensors format
|
|
let result = loader.detect_format("model.safetensors");
|
|
assert_eq!(result.unwrap(), "safetensors");
|
|
|
|
// Test PyTorch format
|
|
let result = loader.detect_format("model.pt");
|
|
assert_eq!(result.unwrap(), "pytorch");
|
|
}
|
|
|
|
#[tokio::test]
|
|
async fn test_cache_operations() -> Result<()> {
|
|
let cache = ModelCache::new(100)?; // 100MB cache
|
|
|
|
let arch = ModelArchitecture {
|
|
arch_type: "test".to_string(),
|
|
num_layers: 1,
|
|
hidden_dim: 10,
|
|
params: HashMap::new(),
|
|
};
|
|
|
|
let model = Model::new("test_model".to_string(), arch);
|
|
|
|
cache.put("test_path".to_string(), model.clone()).await?;
|
|
let retrieved = cache.get("test_path").await?;
|
|
|
|
assert!(retrieved.is_some());
|
|
assert_eq!(retrieved.unwrap().name, model.name);
|
|
|
|
Ok(())
|
|
}
|
|
|
|
#[test]
|
|
fn test_architecture_inference() {
|
|
let loader = ModelLoader::new().unwrap();
|
|
|
|
let mut header = SafeTensorsHeader {
|
|
tensors: HashMap::new(),
|
|
};
|
|
|
|
header.tensors.insert(
|
|
"layers.0.attention.weight".to_string(),
|
|
SafeTensorInfo {
|
|
dtype: "F32".to_string(),
|
|
shape: vec![768, 768],
|
|
data_offsets: (0, 768 * 768 * 4),
|
|
},
|
|
);
|
|
|
|
header.tensors.insert(
|
|
"layers.11.attention.weight".to_string(),
|
|
SafeTensorInfo {
|
|
dtype: "F32".to_string(),
|
|
shape: vec![768, 768],
|
|
data_offsets: (0, 768 * 768 * 4),
|
|
},
|
|
);
|
|
|
|
let arch = loader.infer_architecture(&header).unwrap();
|
|
|
|
assert_eq!(arch.arch_type, "transformer");
|
|
assert_eq!(arch.num_layers, 12);
|
|
assert_eq!(arch.hidden_dim, 768);
|
|
}
|
|
}
|