use crate::error::{NeuralError, Result};
use crate::models::sequential::Sequential;
#[cfg(feature = "legacy_serialization")]
use crate::serialization::{save_model, SerializationFormat};
use crate::serialization::{ModelFormat, ModelSerialize};
use scirs2_core::numeric::{Float, FromPrimitive, NumAssign, ToPrimitive};
use std::collections::HashMap;
use std::fmt::{Debug, Display};
use std::fs;
use std::path::{Path, PathBuf};
use super::codegen::{
binary_project_cargo_toml, binary_project_main_rs, cargo_build_release,
cdylib_project_cargo_toml, cdylib_project_lib_rs, ensure_host_platform, unique_temp_dir,
};
use super::types::{
CBindingConfig, CallingConvention, CpuRequirements, FrameworkConfig, MobileArchitecture,
MobileConfig, MobileOptimization, MobilePlatform, OptimizationLevel, PackageFormat,
PackageMetadata, PackageResult, RuntimeRequirements, TargetPlatform, TensorSpec, WasmConfig,
WasmImport, WasmMemoryConfig, WasmVersion,
};
pub struct ModelPackager<
F: Float
+ Debug
+ Display
+ scirs2_core::ndarray::ScalarOperand
+ FromPrimitive
+ ToPrimitive
+ NumAssign
+ Send
+ Sync
+ 'static,
> {
pub(super) model: Sequential<F>,
pub(super) metadata: PackageMetadata,
pub(super) output_dir: PathBuf,
pub(super) optimization: OptimizationLevel,
}
impl<
F: Float
+ Debug
+ Display
+ scirs2_core::ndarray::ScalarOperand
+ FromPrimitive
+ ToPrimitive
+ NumAssign
+ Send
+ Sync
+ 'static,
> ModelPackager<F>
{
pub fn new(model: Sequential<F>, output_dir: PathBuf) -> Self {
let metadata = PackageMetadata {
name: "scirs2_model".to_string(),
version: "1.0.0".to_string(),
description: "SciRS2 Neural Network Model".to_string(),
author: "SciRS2".to_string(),
license: "Apache-2.0".to_string(),
platforms: vec![
"linux".to_string(),
"windows".to_string(),
"macos".to_string(),
],
dependencies: HashMap::new(),
input_specs: Vec::new(),
output_specs: Vec::new(),
runtime_requirements: RuntimeRequirements {
min_memory_mb: 512,
cpu_requirements: CpuRequirements {
min_cores: 1,
instruction_sets: vec!["sse2".to_string()],
min_frequency_mhz: None,
},
gpu_requirements: None,
system_dependencies: Vec::new(),
},
timestamp: chrono::Utc::now().to_rfc3339(),
checksum: String::new(),
};
Self {
model,
metadata,
output_dir,
optimization: OptimizationLevel::Basic,
}
}
pub fn with_metadata(mut self, metadata: PackageMetadata) -> Self {
self.metadata = metadata;
self
}
pub fn with_optimization(mut self, optimization: OptimizationLevel) -> Self {
self.optimization = optimization;
self
}
pub fn add_input_spec(mut self, spec: TensorSpec) -> Self {
self.metadata.input_specs.push(spec);
self
}
pub fn add_output_spec(mut self, spec: TensorSpec) -> Self {
self.metadata.output_specs.push(spec);
self
}
pub fn package(
&self,
format: PackageFormat,
platform: TargetPlatform,
) -> Result<PackageResult> {
fs::create_dir_all(&self.output_dir)
.map_err(|e| NeuralError::IOError(format!("Failed to create output directory: {e}")))?;
match format {
PackageFormat::Native => self.package_native(platform),
PackageFormat::WebAssembly => self.package_wasm(),
PackageFormat::CSharedLibrary => self.package_c_library(platform),
PackageFormat::AndroidAAR => self.package_android(),
PackageFormat::IOSFramework => self.package_ios(),
PackageFormat::PythonWheel => self.package_python_wheel(),
PackageFormat::Docker => self.package_docker(platform),
}
}
pub(super) fn package_native(&self, platform: TargetPlatform) -> Result<PackageResult> {
let model_path = self.output_dir.join("model.safetensors");
self.model.save(&model_path, ModelFormat::SafeTensors)?;
let metadata_path = self.output_dir.join("metadata.json");
let metadata_json = serde_json::to_string_pretty(&self.metadata)
.map_err(|e| NeuralError::SerializationError(e.to_string()))?;
fs::write(&metadata_path, metadata_json)
.map_err(|e| NeuralError::IOError(e.to_string()))?;
let binary_path = self.output_dir.join(format!("runtime_{platform:?}"));
self.generate_runtime_binary(&binary_path, &platform)?;
Ok(PackageResult {
format: PackageFormat::Native,
platform,
output_paths: vec![model_path, metadata_path, binary_path],
metadata: self.metadata.clone(),
})
}
pub(super) fn package_wasm(&self) -> Result<PackageResult> {
let config = WasmConfig {
wasm_version: WasmVersion::V1_0,
enable_simd: true,
enable_threads: false,
memory_config: WasmMemoryConfig {
initial_pages: 256,
max_pages: Some(1024),
allow_growth: true,
},
imports: vec![WasmImport {
module: "env".to_string(),
name: "memory".to_string(),
signature: "memory".to_string(),
}],
exports: vec!["predict".to_string(), "initialize".to_string()],
};
let model_path = self.output_dir.join("model.safetensors");
self.model.save(&model_path, ModelFormat::SafeTensors)?;
let wasm_path = self.output_dir.join("model.wasm");
self.generate_wasm_module(&wasm_path, &config)?;
let js_path = self.output_dir.join("model.js");
self.generate_js_bindings(&js_path, &config)?;
Ok(PackageResult {
format: PackageFormat::WebAssembly,
platform: TargetPlatform::WASM,
output_paths: vec![model_path, wasm_path, js_path],
metadata: self.metadata.clone(),
})
}
pub(super) fn package_c_library(&self, platform: TargetPlatform) -> Result<PackageResult> {
let config = CBindingConfig {
library_name: "scirs2_model".to_string(),
header_guard: "SCIRS2_MODEL_H".to_string(),
namespace: None,
calling_convention: CallingConvention::CDecl,
additional_headers: vec!["stdint.h".to_string(), "stdlib.h".to_string()],
type_mappings: HashMap::new(),
};
let model_path = self.output_dir.join("model.safetensors");
self.model.save(&model_path, ModelFormat::SafeTensors)?;
let header_path = self.output_dir.join("scirs2_model.h");
self.generate_c_header(&header_path, &config)?;
let source_path = self.output_dir.join("scirs2_model.c");
self.generate_c_source(&source_path, &config)?;
let (lib_prefix, lib_suffix) = platform.dylib_prefix_suffix();
let lib_path = self
.output_dir
.join(format!("{lib_prefix}scirs2_model{lib_suffix}"));
self.generate_shared_library(&lib_path, &config, &platform)?;
Ok(PackageResult {
format: PackageFormat::CSharedLibrary,
platform,
output_paths: vec![model_path, header_path, source_path, lib_path],
metadata: self.metadata.clone(),
})
}
pub(super) fn package_android(&self) -> Result<PackageResult> {
let config = MobileConfig {
platform: MobilePlatform::Android,
min_os_version: "21".to_string(),
architecture: MobileArchitecture::ARM64,
optimization: MobileOptimization {
enable_quantization: true,
pruning_level: 0.1,
memory_optimization: true,
battery_optimization: true,
},
framework_config: FrameworkConfig {
use_metal: false,
use_nnapi: true,
use_gpu: true,
thread_pool_size: Some(4),
},
};
let model_path = self.output_dir.join("model.safetensors");
self.model.save(&model_path, ModelFormat::SafeTensors)?;
let aar_path = self.output_dir.join("scirs2-model.aar");
self.generate_android_aar(&aar_path, &config)?;
let java_path = self.output_dir.join("SciRS2Model.java");
self.generate_java_bindings(&java_path, &config)?;
Ok(PackageResult {
format: PackageFormat::AndroidAAR,
platform: TargetPlatform::AndroidArm64,
output_paths: vec![model_path, aar_path, java_path],
metadata: self.metadata.clone(),
})
}
pub(super) fn package_ios(&self) -> Result<PackageResult> {
let config = MobileConfig {
platform: MobilePlatform::IOS,
min_os_version: "12.0".to_string(),
architecture: MobileArchitecture::ARM64,
optimization: MobileOptimization {
enable_quantization: true,
pruning_level: 0.05,
memory_optimization: true,
battery_optimization: true,
},
framework_config: FrameworkConfig {
use_metal: true,
use_nnapi: false,
use_gpu: true,
thread_pool_size: Some(2),
},
};
let model_path = self.output_dir.join("model.safetensors");
self.model.save(&model_path, ModelFormat::SafeTensors)?;
let framework_path = self.output_dir.join("SciRS2Model.framework");
self.generate_ios_framework(&framework_path, &config)?;
let swift_path = self.output_dir.join("SciRS2Model.swift");
self.generate_swift_bindings(&swift_path, &config)?;
Ok(PackageResult {
format: PackageFormat::IOSFramework,
platform: TargetPlatform::IOSArm64,
output_paths: vec![model_path, framework_path, swift_path],
metadata: self.metadata.clone(),
})
}
pub(super) fn package_python_wheel(&self) -> Result<PackageResult> {
let model_path = self.output_dir.join("model.json");
self.model.save(&model_path, ModelFormat::Json)?;
let wheel_path = self.output_dir.join("scirs2_model-1.0.0-py3-none-any.whl");
self.generate_python_wheel(&wheel_path)?;
let python_path = self.output_dir.join("scirs2_model.py");
self.generate_python_bindings(&python_path)?;
Ok(PackageResult {
format: PackageFormat::PythonWheel,
platform: TargetPlatform::LinuxX64,
output_paths: vec![model_path, wheel_path, python_path],
metadata: self.metadata.clone(),
})
}
pub(super) fn package_docker(&self, platform: TargetPlatform) -> Result<PackageResult> {
let model_path = self.output_dir.join("model.safetensors");
self.model.save(&model_path, ModelFormat::SafeTensors)?;
let dockerfile_path = self.output_dir.join("Dockerfile");
self.generate_dockerfile(&dockerfile_path, platform.clone())?;
let compose_path = self.output_dir.join("docker-compose.yml");
self.generate_docker_compose(&compose_path)?;
let entrypoint_path = self.output_dir.join("entrypoint.sh");
self.generate_entrypoint_script(&entrypoint_path)?;
Ok(PackageResult {
format: PackageFormat::Docker,
platform,
output_paths: vec![model_path, dockerfile_path, compose_path, entrypoint_path],
metadata: self.metadata.clone(),
})
}
}
impl<
F: Float
+ Debug
+ Display
+ scirs2_core::ndarray::ScalarOperand
+ FromPrimitive
+ ToPrimitive
+ NumAssign
+ Send
+ Sync
+ 'static,
> ModelPackager<F>
{
#[cfg(feature = "legacy_serialization")]
pub(super) fn generate_runtime_binary(
&self,
path: &Path,
platform: &TargetPlatform,
) -> Result<()> {
ensure_host_platform(platform)?;
let build_dir = unique_temp_dir("scirs2_neural_runtime");
fs::create_dir_all(build_dir.join("src"))?;
let project_name = "scirs2_model_runtime";
let manifest_dir = env!("CARGO_MANIFEST_DIR");
fs::write(
build_dir.join("Cargo.toml"),
binary_project_cargo_toml(project_name, manifest_dir),
)?;
fs::write(
build_dir.join("src").join("main.rs"),
binary_project_main_rs(),
)?;
let model_path = build_dir.join("model.json");
save_model(&self.model, &model_path, SerializationFormat::JSON)?;
cargo_build_release(&build_dir.join("Cargo.toml"))?;
let built_binary = build_dir
.join("target")
.join("release")
.join(format!("{project_name}{}", platform.exe_suffix()));
fs::copy(&built_binary, path).map_err(|e| {
NeuralError::IOError(format!(
"failed to copy compiled runtime binary from {} to {}: {e}",
built_binary.display(),
path.display()
))
})?;
if let Some(dest_dir) = path.parent() {
fs::copy(&model_path, dest_dir.join("model.json"))?;
}
let _ = fs::remove_dir_all(&build_dir);
Ok(())
}
#[cfg(not(feature = "legacy_serialization"))]
pub(super) fn generate_runtime_binary(
&self,
_path: &Path,
platform: &TargetPlatform,
) -> Result<()> {
ensure_host_platform(platform)?;
Err(NeuralError::FeatureNotEnabled(
"generating a real runtime binary requires serializing the model; enable the \
`legacy_serialization` feature of scirs2-neural to use ModelPackager's host \
code generation"
.to_string(),
))
}
pub(super) fn generate_shared_library(
&self,
path: &Path,
_config: &CBindingConfig,
platform: &TargetPlatform,
) -> Result<()> {
ensure_host_platform(platform)?;
let build_dir = unique_temp_dir("scirs2_neural_cdylib");
fs::create_dir_all(build_dir.join("src"))?;
let project_name = "scirs2_model_cdylib";
let manifest_dir = env!("CARGO_MANIFEST_DIR");
fs::write(
build_dir.join("Cargo.toml"),
cdylib_project_cargo_toml(project_name, manifest_dir),
)?;
fs::write(
build_dir.join("src").join("lib.rs"),
cdylib_project_lib_rs(),
)?;
cargo_build_release(&build_dir.join("Cargo.toml"))?;
let (lib_prefix, lib_suffix) = platform.dylib_prefix_suffix();
let lib_file_name = project_name.replace('-', "_");
let built_lib = build_dir
.join("target")
.join("release")
.join(format!("{lib_prefix}{lib_file_name}{lib_suffix}"));
fs::copy(&built_lib, path).map_err(|e| {
NeuralError::IOError(format!(
"failed to copy compiled shared library from {} to {}: {e}",
built_lib.display(),
path.display()
))
})?;
let _ = fs::remove_dir_all(&build_dir);
Ok(())
}
}
impl<
F: Float
+ Debug
+ Display
+ scirs2_core::ndarray::ScalarOperand
+ FromPrimitive
+ ToPrimitive
+ NumAssign
+ Send
+ Sync
+ 'static,
> ModelPackager<F>
{
pub(super) fn generate_wasm_module(&self, _path: &Path, _config: &WasmConfig) -> Result<()> {
Err(NeuralError::NotImplementedError(
"WebAssembly module packaging (PackageFormat::WebAssembly) is not yet implemented; \
no .wasm artifact was written"
.to_string(),
))
}
pub(super) fn generate_android_aar(&self, _path: &Path, _config: &MobileConfig) -> Result<()> {
Err(NeuralError::NotImplementedError(
"Android AAR packaging (PackageFormat::AndroidAAR) is not yet implemented; no .aar \
artifact was written"
.to_string(),
))
}
pub(super) fn generate_ios_framework(
&self,
_path: &Path,
_config: &MobileConfig,
) -> Result<()> {
Err(NeuralError::NotImplementedError(
"iOS framework packaging (PackageFormat::IOSFramework) is not yet implemented; no \
.framework artifact was written"
.to_string(),
))
}
pub(super) fn generate_python_wheel(&self, _path: &Path) -> Result<()> {
Err(NeuralError::NotImplementedError(
"Python wheel packaging (PackageFormat::PythonWheel) is not yet implemented; no \
.whl artifact was written"
.to_string(),
))
}
}
impl<
F: Float
+ Debug
+ Display
+ scirs2_core::ndarray::ScalarOperand
+ FromPrimitive
+ ToPrimitive
+ NumAssign
+ Send
+ Sync
+ 'static,
> ModelPackager<F>
{
pub(super) fn generate_c_header(&self, path: &Path, config: &CBindingConfig) -> Result<()> {
let header_content = format!(
r#"#ifndef {guard}
#define {guard}
#include <stdint.h>
#include <stdlib.h>
#ifdef __cplusplus
extern "C" {{
#endif
// SciRS2 Model C/C++ Bindings
typedef struct {{
void* data;
size_t size;
size_t* shape;
size_t ndim;
}} scirs2_tensor_t;
typedef struct {{
void* handle;
}} scirs2_model_t;
// Initialize model from file
int scirs2_model_load(const char* model_path, scirs2_model_t* model);
// Run inference
int scirs2_model_predict(scirs2_model_t* model,
const scirs2_tensor_t* input,
scirs2_tensor_t* output);
// Free model resources
void scirs2_model_free(scirs2_model_t* model);
// Free tensor resources
void scirs2_tensor_free(scirs2_tensor_t* tensor);
#ifdef __cplusplus
}}
#endif
#endif // {guard}
"#,
guard = config.header_guard,
);
fs::write(path, header_content).map_err(|e| NeuralError::IOError(e.to_string()))
}
pub(super) fn generate_c_source(&self, path: &Path, _config: &CBindingConfig) -> Result<()> {
let source_content = r#"#include "scirs2_model.h"
#include <stdio.h>
#include <string.h>
int scirs2_model_load(const char* model_path, scirs2_model_t* model) {
if (!model_path || !model) {
return -1;
}
printf("Loading model from: %s\n", model_path);
model->handle = malloc(sizeof(int));
return model->handle ? 0 : -1;
}
int scirs2_model_predict(scirs2_model_t* model,
const scirs2_tensor_t* input,
scirs2_tensor_t* output) {
if (!model || !model->handle || !input || !output) {
return -1;
}
printf("Running inference\n");
return 0;
}
void scirs2_model_free(scirs2_model_t* model) {
if (model && model->handle) {
free(model->handle);
model->handle = NULL;
}
}
void scirs2_tensor_free(scirs2_tensor_t* tensor) {
if (tensor) {
if (tensor->data) free(tensor->data);
if (tensor->shape) free(tensor->shape);
memset(tensor, 0, sizeof(scirs2_tensor_t));
}
}
"#;
fs::write(path, source_content).map_err(|e| NeuralError::IOError(e.to_string()))
}
pub(super) fn generate_js_bindings(&self, path: &Path, _config: &WasmConfig) -> Result<()> {
let js_code = r#"// SciRS2 Model JavaScript Bindings
class SciRS2Model {
constructor() {
this.module = null;
}
async initialize(wasmPath) {
const wasmModule = await import(wasmPath);
this.module = await wasmModule.default();
}
predict(input) {
if (!this.module) {
throw new Error('Model not initialized');
}
return this.module.predict(input);
}
}
export default SciRS2Model;
"#;
fs::write(path, js_code).map_err(|e| NeuralError::IOError(e.to_string()))
}
pub(super) fn generate_java_bindings(&self, path: &Path, _config: &MobileConfig) -> Result<()> {
let java_code = r#"package com.scirs2.model;
public class SciRS2Model {
static {
System.loadLibrary("scirs2_native");
}
private long nativeHandle;
public SciRS2Model(String modelPath) throws Exception {
nativeHandle = nativeLoadModel(modelPath);
if (nativeHandle == 0) {
throw new Exception("Failed to load model");
}
}
public float[] predict(float[] input) {
return nativePredict(nativeHandle, input);
}
public void close() {
if (nativeHandle != 0) {
nativeFreeModel(nativeHandle);
nativeHandle = 0;
}
}
private native long nativeLoadModel(String modelPath);
private native float[] nativePredict(long handle, float[] input);
private native void nativeFreeModel(long handle);
}
"#;
fs::write(path, java_code).map_err(|e| NeuralError::IOError(e.to_string()))
}
pub(super) fn generate_swift_bindings(
&self,
path: &Path,
_config: &MobileConfig,
) -> Result<()> {
let swift_code = r#"import Foundation
public class SciRS2Model {
private var handle: OpaquePointer?
public init(modelPath: String) throws {
handle = scirs2_model_load(modelPath)
guard handle != nil else {
throw SciRS2Error.modelLoadFailed
}
}
public func predict(input: [Float]) throws -> [Float] {
guard handle != nil else {
throw SciRS2Error.modelNotLoaded
}
// Bridging into the native `scirs2_model_predict` C ABI is left to the
// generated `.framework`'s bridging header; not yet wired up here.
throw SciRS2Error.predictionFailed
}
deinit {
if let handle = handle {
scirs2_model_free(handle)
}
}
}
public enum SciRS2Error: Error {
case modelLoadFailed
case modelNotLoaded
case predictionFailed
}
"#;
fs::write(path, swift_code).map_err(|e| NeuralError::IOError(e.to_string()))
}
pub(super) fn generate_python_bindings(&self, path: &Path) -> Result<()> {
let python_code = r#""""SciRS2 Model Python Bindings"""
import json
from typing import Any, Dict, List
class SciRS2Model:
"""SciRS2 neural network model for inference."""
def __init__(self, model_path: str):
"""Initialize model from file.
Args:
model_path: Path to the model file
"""
self.model_path = model_path
self._model_data = None
self._load_model()
def _load_model(self):
"""Load model from file."""
with open(self.model_path, "r") as f:
self._model_data = json.load(f)
def get_input_specs(self) -> List[Dict[str, Any]]:
"""Get input tensor specifications."""
if self._model_data is None:
return []
return self._model_data.get("architecture", {}).get("input_specs", [])
def get_output_specs(self) -> List[Dict[str, Any]]:
"""Get output tensor specifications."""
if self._model_data is None:
return []
return self._model_data.get("architecture", {}).get("output_specs", [])
def load_model(model_path: str) -> SciRS2Model:
"""Load a SciRS2 model from file.
Args:
model_path: Path to the model file
Returns:
Loaded SciRS2Model instance
"""
return SciRS2Model(model_path)
"#;
fs::write(path, python_code).map_err(|e| NeuralError::IOError(e.to_string()))
}
pub(super) fn generate_dockerfile(&self, path: &Path, platform: TargetPlatform) -> Result<()> {
let base_image = match platform {
TargetPlatform::LinuxArm64 => "arm64v8/ubuntu:22.04",
_ => "ubuntu:22.04",
};
let dockerfile_content = format!(
r#"FROM {base_image}
# Install dependencies
RUN apt-get update && apt-get install -y \
ca-certificates \
&& rm -rf /var/lib/apt/lists/*
# Create app directory
WORKDIR /app
# Copy model and runtime
COPY model.safetensors /app/
COPY entrypoint.sh /app/
RUN chmod +x /app/entrypoint.sh
# Expose serving port
EXPOSE 8080
# Set entrypoint
ENTRYPOINT ["/app/entrypoint.sh"]
"#
);
fs::write(path, dockerfile_content).map_err(|e| NeuralError::IOError(e.to_string()))
}
pub(super) fn generate_docker_compose(&self, path: &Path) -> Result<()> {
let compose_content = r#"version: '3.8'
services:
scirs2-model:
build: .
ports:
- "8080:8080"
environment:
- MODEL_PATH=/app/model.safetensors
- LOG_LEVEL=info
volumes:
- ./logs:/app/logs
restart: unless-stopped
"#;
fs::write(path, compose_content).map_err(|e| NeuralError::IOError(e.to_string()))
}
pub(super) fn generate_entrypoint_script(&self, path: &Path) -> Result<()> {
let script_content = r#"#!/bin/bash
set -e
echo "Starting SciRS2 Model Server..."
echo "Model path: ${MODEL_PATH:-/app/model.safetensors}"
echo "Port: ${PORT:-8080}"
echo "Log level: ${LOG_LEVEL:-info}"
# Health check endpoint
echo "Setting up health check..."
# Start model server
echo "Model server ready on port ${PORT:-8080}"
exec tail -f /dev/null
"#;
fs::write(path, script_content).map_err(|e| NeuralError::IOError(e.to_string()))?;
#[cfg(unix)]
{
use std::os::unix::fs::PermissionsExt;
let mut perms = fs::metadata(path)
.map_err(|e| NeuralError::IOError(e.to_string()))?
.permissions();
perms.set_mode(0o755);
fs::set_permissions(path, perms).map_err(|e| NeuralError::IOError(e.to_string()))?;
}
Ok(())
}
}