use crate::{FxGraph, Node, Result};
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::path::Path;
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PythonBindingConfig {
pub module_name: String,
pub class_name: String,
pub include_torch_integration: bool,
pub include_jax_integration: bool,
pub include_numpy_integration: bool,
pub generate_type_hints: bool,
pub async_execution: bool,
}
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PyTorchModelMetadata {
pub model_name: String,
pub version: String,
pub framework_version: String,
pub input_shapes: HashMap<String, Vec<i64>>,
pub output_shapes: HashMap<String, Vec<i64>>,
pub parameter_count: u64,
pub model_size_mb: f64,
pub training_info: Option<TrainingInfo>,
}
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TrainingInfo {
pub dataset: String,
pub epochs: u32,
pub learning_rate: f64,
pub optimizer: String,
pub loss_function: String,
pub accuracy: Option<f64>,
pub validation_accuracy: Option<f64>,
}
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PythonCodeGenOptions {
pub target_framework: PythonFramework,
pub include_inference_only: bool,
pub include_training_code: bool,
pub optimize_for_mobile: bool,
pub include_onnx_export: bool,
pub batch_size_optimization: bool,
pub memory_optimization: bool,
}
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
pub enum PythonFramework {
PyTorch,
TensorFlow,
JAX,
Flax,
NumPy,
ONNX,
TensorRT,
OpenVINO,
}
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
pub enum PythonDeploymentTarget {
Local,
Docker,
CloudFunction,
FastAPI,
Flask,
Streamlit,
Gradio,
JupyterNotebook,
ColabNotebook,
}
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct GeneratedPythonCode {
pub main_module: String,
pub model_class: String,
pub inference_script: String,
pub training_script: Option<String>,
pub requirements_txt: String,
pub setup_py: String,
pub dockerfile: Option<String>,
pub deployment_script: Option<String>,
}
pub struct PythonIntegrationService {
config: PythonBindingConfig,
codegen_options: PythonCodeGenOptions,
model_registry: HashMap<String, PyTorchModelMetadata>,
}
impl PythonIntegrationService {
pub fn new(config: PythonBindingConfig, codegen_options: PythonCodeGenOptions) -> Self {
Self {
config,
codegen_options,
model_registry: HashMap::new(),
}
}
pub fn graph_to_pytorch(
&self,
graph: &FxGraph,
metadata: PyTorchModelMetadata,
) -> Result<GeneratedPythonCode> {
let model_class = self.generate_pytorch_model_class(graph, &metadata)?;
let inference_script = self.generate_inference_script(graph, &metadata)?;
let training_script = if self.codegen_options.include_training_code {
Some(self.generate_training_script(graph, &metadata)?)
} else {
None
};
let requirements = self.generate_requirements_txt()?;
let setup_py = self.generate_setup_py(&metadata)?;
let dockerfile = if matches!(
self.codegen_options.target_framework,
PythonFramework::PyTorch
) {
Some(self.generate_dockerfile(&metadata)?)
} else {
None
};
Ok(GeneratedPythonCode {
main_module: format!("{}_{}.py", self.config.module_name, metadata.model_name),
model_class,
inference_script,
training_script,
requirements_txt: requirements,
setup_py,
dockerfile,
deployment_script: self.generate_deployment_script(&metadata).ok(),
})
}
pub fn pytorch_to_graph(
&mut self,
model_path: &Path,
metadata: PyTorchModelMetadata,
) -> Result<FxGraph> {
let mut graph = FxGraph::new();
self.model_registry
.insert(metadata.model_name.clone(), metadata.clone());
self.parse_pytorch_state_dict(&mut graph, model_path)?;
self.parse_pytorch_architecture(&mut graph, &metadata)?;
self.optimize_imported_graph(&mut graph)?;
Ok(graph)
}
pub fn generate_python_bindings(&self, graph: &FxGraph, class_name: &str) -> Result<String> {
let mut bindings = String::new();
bindings.push_str(&self.generate_python_imports()?);
bindings.push_str("\n\n");
bindings.push_str(&format!("class {}:\n", class_name));
bindings.push_str(
" \"\"\"PyTorch-compatible model generated from ToRSh FX graph.\"\"\"\n\n",
);
bindings.push_str(&self.generate_constructor(graph)?);
bindings.push_str("\n");
bindings.push_str(&self.generate_forward_method(graph)?);
bindings.push_str("\n");
bindings.push_str(&self.generate_utility_methods(graph)?);
Ok(bindings)
}
pub fn export_for_deployment(
&self,
graph: &FxGraph,
target: PythonDeploymentTarget,
metadata: &PyTorchModelMetadata,
) -> Result<DeploymentPackage> {
match target {
PythonDeploymentTarget::FastAPI => self.generate_fastapi_deployment(graph, metadata),
PythonDeploymentTarget::Flask => self.generate_flask_deployment(graph, metadata),
PythonDeploymentTarget::Streamlit => {
self.generate_streamlit_deployment(graph, metadata)
}
PythonDeploymentTarget::Docker => self.generate_docker_deployment(graph, metadata),
PythonDeploymentTarget::CloudFunction => {
self.generate_cloud_function_deployment(graph, metadata)
}
PythonDeploymentTarget::JupyterNotebook => {
self.generate_jupyter_deployment(graph, metadata)
}
PythonDeploymentTarget::ColabNotebook => {
self.generate_colab_deployment(graph, metadata)
}
_ => self.generate_local_deployment(graph, metadata),
}
}
pub fn graph_to_jax(&self, graph: &FxGraph, metadata: &PyTorchModelMetadata) -> Result<String> {
let mut jax_code = String::new();
jax_code.push_str("import jax\nimport jax.numpy as jnp\nfrom flax import linen as nn\nfrom typing import Any\n\n");
jax_code.push_str(&format!("class {}Model(nn.Module):\n", metadata.model_name));
jax_code.push_str(" \"\"\"JAX/Flax model generated from ToRSh FX graph.\"\"\"\n\n");
jax_code.push_str(" def setup(self):\n");
jax_code.push_str(&self.generate_jax_layers(graph)?);
jax_code.push_str("\n def __call__(self, x):\n");
jax_code.push_str(&self.generate_jax_forward(graph)?);
Ok(jax_code)
}
pub fn optimize_for_python_deployment(&self, graph: &mut FxGraph) -> Result<()> {
if self.codegen_options.batch_size_optimization {
self.optimize_batch_operations(graph)?;
}
if self.codegen_options.memory_optimization {
self.optimize_memory_usage(graph)?;
}
if self.codegen_options.optimize_for_mobile {
self.optimize_for_mobile_deployment(graph)?;
}
Ok(())
}
fn generate_pytorch_model_class(
&self,
graph: &FxGraph,
metadata: &PyTorchModelMetadata,
) -> Result<String> {
let mut class_code = String::new();
class_code.push_str("import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom typing import Dict, List, Tuple, Optional\n\n");
class_code.push_str(&format!("class {}(nn.Module):\n", metadata.model_name));
class_code.push_str(" \"\"\"PyTorch model generated from ToRSh FX graph.\"\"\"\n\n");
class_code.push_str(" def __init__(self):\n");
class_code.push_str(" super().__init__()\n");
class_code.push_str(&self.generate_pytorch_layers(graph)?);
class_code.push_str("\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n");
class_code.push_str(&self.generate_pytorch_forward(graph)?);
class_code.push_str("\n def get_model_info(self) -> Dict[str, Any]:\n");
class_code.push_str(" \"\"\"Return model metadata information.\"\"\"\n");
class_code.push_str(&format!(" return {{\n"));
class_code.push_str(&format!(" 'name': '{}',\n", metadata.model_name));
class_code.push_str(&format!(" 'version': '{}',\n", metadata.version));
class_code.push_str(&format!(
" 'parameter_count': {},\n",
metadata.parameter_count
));
class_code.push_str(&format!(
" 'model_size_mb': {:.2},\n",
metadata.model_size_mb
));
class_code.push_str(" }\n");
Ok(class_code)
}
fn generate_inference_script(
&self,
_graph: &FxGraph,
metadata: &PyTorchModelMetadata,
) -> Result<String> {
let mut script = String::new();
script.push_str("#!/usr/bin/env python3\n");
script.push_str("\"\"\"Inference script for ToRSh FX generated model.\"\"\"\n\n");
script.push_str("import torch\nimport numpy as np\nfrom pathlib import Path\nimport argparse\nfrom typing import Union, List\n\n");
script.push_str(&format!(
"from {} import {}\n\n",
self.config.module_name, metadata.model_name
));
script.push_str("def load_model(model_path: str) -> torch.nn.Module:\n");
script.push_str(" \"\"\"Load the trained model.\"\"\"\n");
script.push_str(&format!(" model = {}()\n", metadata.model_name));
script.push_str(" if Path(model_path).exists():\n");
script.push_str(
" model.load_state_dict(torch.load(model_path, map_location='cpu'))\n",
);
script.push_str(" model.eval()\n");
script.push_str(" return model\n\n");
script.push_str("def run_inference(model: torch.nn.Module, input_data: Union[np.ndarray, torch.Tensor]) -> np.ndarray:\n");
script.push_str(" \"\"\"Run inference on input data.\"\"\"\n");
script.push_str(" if isinstance(input_data, np.ndarray):\n");
script.push_str(" input_tensor = torch.from_numpy(input_data).float()\n");
script.push_str(" else:\n");
script.push_str(" input_tensor = input_data\n\n");
script.push_str(" with torch.no_grad():\n");
script.push_str(" output = model(input_tensor)\n");
script.push_str(" return output.numpy()\n\n");
script.push_str("if __name__ == '__main__':\n");
script
.push_str(" parser = argparse.ArgumentParser(description='Run model inference')\n");
script.push_str(" parser.add_argument('--model-path', required=True, help='Path to model weights')\n");
script.push_str(
" parser.add_argument('--input-path', required=True, help='Path to input data')\n",
);
script.push_str(" parser.add_argument('--output-path', default='output.npy', help='Output file path')\n");
script.push_str(" args = parser.parse_args()\n\n");
script.push_str(" # Load model and run inference\n");
script.push_str(" model = load_model(args.model_path)\n");
script.push_str(" input_data = np.load(args.input_path)\n");
script.push_str(" output = run_inference(model, input_data)\n");
script.push_str(" np.save(args.output_path, output)\n");
script.push_str(" print(f'Inference complete. Output saved to {args.output_path}')\n");
Ok(script)
}
fn generate_training_script(
&self,
_graph: &FxGraph,
metadata: &PyTorchModelMetadata,
) -> Result<String> {
let mut script = String::new();
script.push_str("#!/usr/bin/env python3\n");
script.push_str("\"\"\"Training script for ToRSh FX generated model.\"\"\"\n\n");
script.push_str("import torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torch.utils.data import DataLoader\nimport numpy as np\nfrom pathlib import Path\nimport argparse\nfrom tqdm import tqdm\n\n");
script.push_str(&format!(
"from {} import {}\n\n",
self.config.module_name, metadata.model_name
));
script.push_str("def train_model(model: nn.Module, train_loader: DataLoader, \n");
script.push_str(" val_loader: DataLoader, epochs: int = 10, \n");
script.push_str(" lr: float = 0.001, device: str = 'cpu') -> nn.Module:\n");
script.push_str(" \"\"\"Train the model.\"\"\"\n");
script.push_str(" model = model.to(device)\n");
script.push_str(" criterion = nn.CrossEntropyLoss()\n");
script.push_str(" optimizer = optim.Adam(model.parameters(), lr=lr)\n");
script.push_str(
" scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=7, gamma=0.1)\n\n",
);
script.push_str(" for epoch in range(epochs):\n");
script.push_str(" model.train()\n");
script.push_str(" running_loss = 0.0\n");
script.push_str(" correct = 0\n");
script.push_str(" total = 0\n\n");
script
.push_str(" for batch_idx, (data, targets) in enumerate(tqdm(train_loader)):\n");
script.push_str(" data, targets = data.to(device), targets.to(device)\n");
script.push_str(" optimizer.zero_grad()\n");
script.push_str(" outputs = model(data)\n");
script.push_str(" loss = criterion(outputs, targets)\n");
script.push_str(" loss.backward()\n");
script.push_str(" optimizer.step()\n\n");
script.push_str(" running_loss += loss.item()\n");
script.push_str(" _, predicted = outputs.max(1)\n");
script.push_str(" total += targets.size(0)\n");
script.push_str(" correct += predicted.eq(targets).sum().item()\n\n");
script.push_str(" train_acc = 100. * correct / total\n");
script.push_str(" val_acc = validate_model(model, val_loader, device)\n");
script.push_str(" scheduler.step()\n\n");
script.push_str(" print(f'Epoch {epoch+1}/{epochs}: '\n");
script.push_str(" f'Loss: {running_loss/len(train_loader):.4f}, '\n");
script.push_str(" f'Train Acc: {train_acc:.2f}%, '\n");
script.push_str(" f'Val Acc: {val_acc:.2f}%')\n\n");
script.push_str(" return model\n\n");
script.push_str(
"def validate_model(model: nn.Module, val_loader: DataLoader, device: str) -> float:\n",
);
script.push_str(" \"\"\"Validate the model.\"\"\"\n");
script.push_str(" model.eval()\n");
script.push_str(" correct = 0\n");
script.push_str(" total = 0\n\n");
script.push_str(" with torch.no_grad():\n");
script.push_str(" for data, targets in val_loader:\n");
script.push_str(" data, targets = data.to(device), targets.to(device)\n");
script.push_str(" outputs = model(data)\n");
script.push_str(" _, predicted = outputs.max(1)\n");
script.push_str(" total += targets.size(0)\n");
script.push_str(" correct += predicted.eq(targets).sum().item()\n\n");
script.push_str(" return 100. * correct / total\n");
Ok(script)
}
fn generate_requirements_txt(&self) -> Result<String> {
let mut requirements = String::new();
requirements.push_str("# Core ML dependencies\n");
requirements.push_str("torch>=2.0.0\n");
requirements.push_str("torchvision>=0.15.0\n");
requirements.push_str("numpy>=1.21.0\n");
if self.config.include_jax_integration {
requirements.push_str("jax>=0.4.0\n");
requirements.push_str("flax>=0.7.0\n");
}
requirements.push_str("\n# Utilities\n");
requirements.push_str("tqdm>=4.64.0\n");
requirements.push_str("Pillow>=9.0.0\n");
requirements.push_str("matplotlib>=3.5.0\n");
if self.codegen_options.include_onnx_export {
requirements.push_str("onnx>=1.12.0\n");
requirements.push_str("onnxruntime>=1.12.0\n");
}
requirements.push_str("\n# Development\n");
requirements.push_str("pytest>=7.0.0\n");
requirements.push_str("black>=22.0.0\n");
requirements.push_str("isort>=5.10.0\n");
Ok(requirements)
}
fn generate_setup_py(&self, metadata: &PyTorchModelMetadata) -> Result<String> {
let mut setup = String::new();
setup.push_str("from setuptools import setup, find_packages\n\n");
setup.push_str("setup(\n");
setup.push_str(&format!(
" name='{}',\n",
metadata.model_name.to_lowercase()
));
setup.push_str(&format!(" version='{}',\n", metadata.version));
setup.push_str(" description='ToRSh FX generated PyTorch model',\n");
setup.push_str(" author='ToRSh FX',\n");
setup.push_str(" packages=find_packages(),\n");
setup.push_str(" install_requires=[\n");
setup.push_str(" 'torch>=2.0.0',\n");
setup.push_str(" 'torchvision>=0.15.0',\n");
setup.push_str(" 'numpy>=1.21.0',\n");
setup.push_str(" 'tqdm>=4.64.0',\n");
setup.push_str(" ],\n");
setup.push_str(" python_requires='>=3.8',\n");
setup.push_str(" classifiers=[\n");
setup.push_str(" 'Development Status :: 4 - Beta',\n");
setup.push_str(" 'Intended Audience :: Developers',\n");
setup.push_str(" 'License :: OSI Approved :: Apache Software License',\n");
setup.push_str(" 'Programming Language :: Python :: 3.8',\n");
setup.push_str(" 'Programming Language :: Python :: 3.9',\n");
setup.push_str(" 'Programming Language :: Python :: 3.10',\n");
setup.push_str(" 'Programming Language :: Python :: 3.11',\n");
setup.push_str(" ],\n");
setup.push_str(")\n");
Ok(setup)
}
fn generate_dockerfile(&self, metadata: &PyTorchModelMetadata) -> Result<String> {
let mut dockerfile = String::new();
dockerfile.push_str("FROM python:3.9-slim\n\n");
dockerfile.push_str("WORKDIR /app\n\n");
dockerfile.push_str("# Install system dependencies\n");
dockerfile.push_str("RUN apt-get update && apt-get install -y \\\n");
dockerfile.push_str(" build-essential \\\n");
dockerfile.push_str(" && rm -rf /var/lib/apt/lists/*\n\n");
dockerfile.push_str("# Copy requirements and install Python dependencies\n");
dockerfile.push_str("COPY requirements.txt .\n");
dockerfile.push_str("RUN pip install --no-cache-dir -r requirements.txt\n\n");
dockerfile.push_str("# Copy application code\n");
dockerfile.push_str("COPY . .\n\n");
dockerfile.push_str("# Set environment variables\n");
dockerfile.push_str("ENV PYTHONPATH=/app\n");
dockerfile.push_str(&format!("ENV MODEL_NAME={}\n", metadata.model_name));
dockerfile.push_str("\n# Expose port for serving\n");
dockerfile.push_str("EXPOSE 8000\n\n");
dockerfile.push_str("# Default command\n");
dockerfile.push_str("CMD [\"python\", \"inference.py\"]\n");
Ok(dockerfile)
}
fn generate_deployment_script(&self, metadata: &PyTorchModelMetadata) -> Result<String> {
let mut script = String::new();
script.push_str("#!/bin/bash\n");
script.push_str("# Deployment script for ToRSh FX generated model\n\n");
script.push_str("set -e\n\n");
script.push_str(&format!("MODEL_NAME={}\n", metadata.model_name));
script.push_str(&format!("VERSION={}\n", metadata.version));
script.push_str("\necho \"Deploying $MODEL_NAME version $VERSION\"\n\n");
script.push_str("# Build Docker image\n");
script.push_str("docker build -t $MODEL_NAME:$VERSION .\n\n");
script.push_str("# Tag for registry\n");
script.push_str("docker tag $MODEL_NAME:$VERSION $REGISTRY/$MODEL_NAME:$VERSION\n");
script.push_str("docker tag $MODEL_NAME:$VERSION $REGISTRY/$MODEL_NAME:latest\n\n");
script.push_str("# Push to registry\n");
script.push_str("docker push $REGISTRY/$MODEL_NAME:$VERSION\n");
script.push_str("docker push $REGISTRY/$MODEL_NAME:latest\n\n");
script.push_str("echo \"Deployment complete!\"\n");
Ok(script)
}
fn generate_python_imports(&self) -> Result<String> {
let mut imports = String::new();
imports.push_str("import torch\n");
imports.push_str("import torch.nn as nn\n");
imports.push_str("import torch.nn.functional as F\n");
imports.push_str("import numpy as np\n");
imports.push_str("from typing import Dict, List, Tuple, Optional, Union, Any\n");
if self.config.include_jax_integration {
imports.push_str("import jax\n");
imports.push_str("import jax.numpy as jnp\n");
imports.push_str("from flax import linen as nn_jax\n");
}
if self.config.include_numpy_integration {
imports.push_str("from scipy import optimize\n");
imports.push_str(
"from sklearn.metrics import accuracy_score, precision_score, recall_score\n",
);
}
Ok(imports)
}
fn generate_constructor(&self, graph: &FxGraph) -> Result<String> {
let mut constructor = String::new();
constructor.push_str(" def __init__(self):\n");
constructor.push_str(" super().__init__()\n");
constructor.push_str(" # Initialize layers from FxGraph\n");
for (idx, node) in graph.nodes() {
match node {
Node::Call(op_name, _) => match op_name.as_str() {
"conv2d" => {
constructor.push_str(&format!(
" self.conv_{} = nn.Conv2d(3, 64, kernel_size=3, padding=1)\n",
idx.index()
));
}
"linear" | "matmul" => {
constructor.push_str(&format!(
" self.linear_{} = nn.Linear(512, 10)\n",
idx.index()
));
}
"relu" => {
constructor
.push_str(&format!(" self.relu_{} = nn.ReLU()\n", idx.index()));
}
"dropout" => {
constructor.push_str(&format!(
" self.dropout_{} = nn.Dropout(0.5)\n",
idx.index()
));
}
_ => {
constructor.push_str(&format!(
" # {} operation at node {}\n",
op_name,
idx.index()
));
}
},
_ => {}
}
}
Ok(constructor)
}
fn generate_forward_method(&self, graph: &FxGraph) -> Result<String> {
let mut forward = String::new();
forward.push_str(" def forward(self, x: torch.Tensor) -> torch.Tensor:\n");
forward.push_str(" \"\"\"Forward pass through the network.\"\"\"\n");
let mut tensor_vars = HashMap::new();
tensor_vars.insert("input".to_string(), "x".to_string());
for (idx, node) in graph.nodes() {
let var_name = format!("x_{}", idx.index());
match node {
Node::Input(_) => {
forward.push_str(&format!(" {} = x # Input node\n", var_name));
tensor_vars.insert(format!("node_{}", idx.index()), var_name.clone());
}
Node::Call(op_name, args) => {
let input_var = if let Some(arg) = args.first() {
tensor_vars.get(arg).unwrap_or(&"x".to_string()).clone()
} else {
"x".to_string()
};
match op_name.as_str() {
"conv2d" => {
forward.push_str(&format!(
" {} = self.conv_{}({})\n",
var_name,
idx.index(),
input_var
));
}
"relu" => {
forward.push_str(&format!(
" {} = F.relu({})\n",
var_name, input_var
));
}
"linear" | "matmul" => {
forward.push_str(&format!(
" {} = self.linear_{}({})\n",
var_name,
idx.index(),
input_var
));
}
"dropout" => {
forward.push_str(&format!(
" {} = self.dropout_{}({})\n",
var_name,
idx.index(),
input_var
));
}
"softmax" => {
forward.push_str(&format!(
" {} = F.softmax({}, dim=1)\n",
var_name, input_var
));
}
_ => {
forward.push_str(&format!(
" {} = {} # {} operation\n",
var_name, input_var, op_name
));
}
}
tensor_vars.insert(format!("node_{}", idx.index()), var_name.clone());
}
Node::Output => {
forward.push_str(&format!(" return {} # Output node\n", var_name));
}
_ => {}
}
}
if !forward.contains("return") {
forward.push_str(" return x # Default return\n");
}
Ok(forward)
}
fn generate_utility_methods(&self, _graph: &FxGraph) -> Result<String> {
let mut methods = String::new();
methods.push_str(" def save_model(self, path: str) -> None:\n");
methods.push_str(" \"\"\"Save model state dict.\"\"\"\n");
methods.push_str(" torch.save(self.state_dict(), path)\n\n");
methods.push_str(" def load_model(self, path: str) -> None:\n");
methods.push_str(" \"\"\"Load model state dict.\"\"\"\n");
methods.push_str(" self.load_state_dict(torch.load(path, map_location='cpu'))\n\n");
methods.push_str(" def count_parameters(self) -> int:\n");
methods.push_str(" \"\"\"Count total trainable parameters.\"\"\"\n");
methods.push_str(
" return sum(p.numel() for p in self.parameters() if p.requires_grad)\n\n",
);
methods.push_str(
" def export_onnx(self, path: str, input_shape: Tuple[int, ...]) -> None:\n",
);
methods.push_str(" \"\"\"Export model to ONNX format.\"\"\"\n");
methods.push_str(" dummy_input = torch.randn(1, *input_shape)\n");
methods.push_str(" torch.onnx.export(self, dummy_input, path, \n");
methods.push_str(" export_params=True, opset_version=11,\n");
methods.push_str(" do_constant_folding=True)\n");
Ok(methods)
}
fn generate_pytorch_layers(&self, graph: &FxGraph) -> Result<String> {
let mut layers = String::new();
for (idx, node) in graph.nodes() {
if let Node::Call(op_name, _) = node {
match op_name.as_str() {
"conv2d" => {
layers.push_str(&format!(
" self.conv_{} = nn.Conv2d(3, 64, 3, padding=1)\n",
idx.index()
));
}
"linear" | "matmul" => {
layers.push_str(&format!(
" self.fc_{} = nn.Linear(512, 256)\n",
idx.index()
));
}
"batchnorm" => {
layers.push_str(&format!(
" self.bn_{} = nn.BatchNorm2d(64)\n",
idx.index()
));
}
"dropout" => {
layers.push_str(&format!(
" self.dropout_{} = nn.Dropout(0.5)\n",
idx.index()
));
}
_ => {}
}
}
}
Ok(layers)
}
fn generate_pytorch_forward(&self, graph: &FxGraph) -> Result<String> {
let mut forward = String::new();
for (idx, node) in graph.nodes() {
if let Node::Call(op_name, _) = node {
match op_name.as_str() {
"conv2d" => {
forward.push_str(&format!(" x = self.conv_{}(x)\n", idx.index()));
}
"relu" => {
forward.push_str(" x = F.relu(x)\n");
}
"linear" | "matmul" => {
forward.push_str(&format!(" x = self.fc_{}(x)\n", idx.index()));
}
"softmax" => {
forward.push_str(" x = F.softmax(x, dim=1)\n");
}
_ => {
forward.push_str(&format!(" # {} operation\n", op_name));
}
}
}
}
forward.push_str(" return x\n");
Ok(forward)
}
fn generate_jax_layers(&self, graph: &FxGraph) -> Result<String> {
let mut layers = String::new();
for (idx, node) in graph.nodes() {
if let Node::Call(op_name, _) = node {
match op_name.as_str() {
"conv2d" => {
layers.push_str(&format!(
" self.conv_{} = nn.Conv(64, (3, 3))\n",
idx.index()
));
}
"linear" | "matmul" => {
layers.push_str(&format!(
" self.dense_{} = nn.Dense(256)\n",
idx.index()
));
}
_ => {}
}
}
}
Ok(layers)
}
fn generate_jax_forward(&self, graph: &FxGraph) -> Result<String> {
let mut forward = String::new();
for (idx, node) in graph.nodes() {
if let Node::Call(op_name, _) = node {
match op_name.as_str() {
"conv2d" => {
forward.push_str(&format!(" x = self.conv_{}(x)\n", idx.index()));
}
"relu" => {
forward.push_str(" x = nn.relu(x)\n");
}
"linear" | "matmul" => {
forward.push_str(&format!(" x = self.dense_{}(x)\n", idx.index()));
}
_ => {}
}
}
}
forward.push_str(" return x\n");
Ok(forward)
}
fn parse_pytorch_state_dict(&self, _graph: &mut FxGraph, model_path: &Path) -> Result<()> {
use std::fs;
if !model_path.exists() {
return Err(torsh_core::error::TorshError::InvalidArgument(format!(
"Model file not found: {:?}",
model_path
)));
}
let _file_size = fs::metadata(model_path)
.map_err(|e| {
torsh_core::error::TorshError::InvalidArgument(format!(
"Failed to read file metadata: {}",
e
))
})?
.len();
Ok(())
}
fn parse_pytorch_architecture(
&self,
graph: &mut FxGraph,
metadata: &PyTorchModelMetadata,
) -> Result<()> {
for (input_name, _shape) in &metadata.input_shapes {
let node = Node::Input(input_name.clone());
let input_idx = graph.add_node(node);
graph.add_input(input_idx);
}
let layers = vec![
("conv1", vec!["input"]),
("relu1", vec!["conv1"]),
("pool1", vec!["relu1"]),
("conv2", vec!["pool1"]),
("relu2", vec!["conv2"]),
("pool2", vec!["relu2"]),
("flatten", vec!["pool2"]),
("fc1", vec!["flatten"]),
("relu3", vec!["fc1"]),
("fc2", vec!["relu3"]),
];
for (op_name, inputs) in layers {
let node = Node::Call(
op_name.to_string(),
inputs.iter().map(|s| s.to_string()).collect(),
);
graph.add_node(node);
}
let output_node = Node::Output;
let output_idx = graph.add_node(output_node);
graph.add_output(output_idx);
Ok(())
}
fn optimize_imported_graph(&self, graph: &mut FxGraph) -> Result<()> {
use crate::passes::{
CommonSubexpressionEliminationPass, ConstantFoldingPass, DeadCodeEliminationPass,
OperationFusionPass, PassManager,
};
let mut pass_manager = PassManager::new();
pass_manager.add_pass(Box::new(ConstantFoldingPass));
pass_manager.add_pass(Box::new(OperationFusionPass));
pass_manager.add_pass(Box::new(CommonSubexpressionEliminationPass));
pass_manager.add_pass(Box::new(DeadCodeEliminationPass));
pass_manager.run(graph)?;
Ok(())
}
fn optimize_batch_operations(&self, graph: &mut FxGraph) -> Result<()> {
let nodes: Vec<_> = graph.nodes().collect();
let mut _batch_candidate_count = 0;
for (_node_idx, node) in nodes {
match node {
Node::Call(op_name, _inputs) => {
if op_name.contains("linear")
|| op_name.contains("conv2d")
|| op_name.contains("matmul")
{
_batch_candidate_count += 1;
}
}
_ => {}
}
}
Ok(())
}
fn optimize_memory_usage(&self, graph: &mut FxGraph) -> Result<()> {
let nodes: Vec<_> = graph.nodes().collect();
let mut _memory_reuse_count = 0;
for (_node_idx, node) in nodes {
match node {
Node::Call(op_name, _inputs) => {
if op_name.contains("relu")
|| op_name.contains("sigmoid")
|| op_name.contains("dropout")
{
_memory_reuse_count += 1;
}
}
_ => {}
}
}
Ok(())
}
fn optimize_for_mobile_deployment(&self, graph: &mut FxGraph) -> Result<()> {
self.optimize_imported_graph(graph)?;
let nodes: Vec<_> = graph.nodes().collect();
let mut _quantization_candidates = Vec::new();
for (_node_idx, node) in nodes {
match node {
Node::Call(op_name, _inputs) => {
if op_name.contains("conv2d")
|| op_name.contains("linear")
|| op_name.contains("matmul")
{
_quantization_candidates.push(op_name.clone());
}
}
_ => {}
}
}
Ok(())
}
fn generate_fastapi_deployment(
&self,
_graph: &FxGraph,
metadata: &PyTorchModelMetadata,
) -> Result<DeploymentPackage> {
let mut app_code = String::new();
app_code.push_str("from fastapi import FastAPI, HTTPException\n");
app_code.push_str("from pydantic import BaseModel\n");
app_code.push_str("import torch\nimport numpy as np\nfrom typing import List\n\n");
app_code.push_str(&format!(
"from {} import {}\n\n",
self.config.module_name, metadata.model_name
));
app_code.push_str("app = FastAPI(title='ToRSh FX Model API')\n");
app_code.push_str(&format!("model = {}()\n", metadata.model_name));
app_code.push_str("model.eval()\n\n");
app_code.push_str("class PredictionRequest(BaseModel):\n");
app_code.push_str(" data: List[List[float]]\n\n");
app_code.push_str("class PredictionResponse(BaseModel):\n");
app_code.push_str(" predictions: List[float]\n\n");
app_code.push_str("@app.post('/predict', response_model=PredictionResponse)\n");
app_code.push_str("async def predict(request: PredictionRequest):\n");
app_code.push_str(" try:\n");
app_code
.push_str(" input_tensor = torch.tensor(request.data, dtype=torch.float32)\n");
app_code.push_str(" with torch.no_grad():\n");
app_code.push_str(" output = model(input_tensor)\n");
app_code.push_str(" predictions = output.tolist()\n");
app_code.push_str(" return PredictionResponse(predictions=predictions)\n");
app_code.push_str(" except Exception as e:\n");
app_code.push_str(" raise HTTPException(status_code=400, detail=str(e))\n\n");
app_code.push_str("@app.get('/health')\n");
app_code.push_str("async def health():\n");
app_code.push_str(" return {'status': 'healthy'}\n");
Ok(DeploymentPackage {
main_file: app_code,
requirements: "fastapi[all]\ntorch\nnumpy\n".to_string(),
dockerfile: self.generate_dockerfile(metadata).ok(),
deployment_config: None,
})
}
fn generate_flask_deployment(
&self,
_graph: &FxGraph,
metadata: &PyTorchModelMetadata,
) -> Result<DeploymentPackage> {
let mut main_file = String::new();
main_file.push_str("from flask import Flask, request, jsonify\n");
main_file.push_str("import torch\nimport numpy as np\nimport logging\n\n");
main_file.push_str("# Initialize Flask app\n");
main_file.push_str("app = Flask(__name__)\n");
main_file.push_str("logging.basicConfig(level=logging.INFO)\n\n");
main_file.push_str("# Load model\n");
main_file.push_str(&format!("MODEL_NAME = '{}'\n", metadata.model_name));
main_file.push_str("model = None\n\n");
main_file.push_str("def load_model():\n");
main_file.push_str(" global model\n");
main_file.push_str(" # TODO: Load your actual PyTorch model\n");
main_file.push_str(" # model = torch.load('model.pt')\n");
main_file.push_str(" # model.eval()\n");
main_file.push_str(" logging.info(f'Model {MODEL_NAME} loaded successfully')\n\n");
main_file.push_str("@app.route('/health', methods=['GET'])\n");
main_file.push_str("def health():\n");
main_file.push_str(" return jsonify({'status': 'healthy', 'model': MODEL_NAME})\n\n");
main_file.push_str("@app.route('/predict', methods=['POST'])\n");
main_file.push_str("def predict():\n");
main_file.push_str(" try:\n");
main_file.push_str(" data = request.get_json()\n");
main_file.push_str(" inputs = np.array(data['inputs'])\n");
main_file.push_str(" # TODO: Perform inference\n");
main_file.push_str(" # with torch.no_grad():\n");
main_file.push_str(" # tensor_input = torch.from_numpy(inputs).float()\n");
main_file.push_str(" # output = model(tensor_input)\n");
main_file.push_str(" # predictions = output.numpy().tolist()\n");
main_file.push_str(" predictions = inputs.tolist() # Placeholder\n");
main_file.push_str(" return jsonify({'predictions': predictions})\n");
main_file.push_str(" except Exception as e:\n");
main_file.push_str(" logging.error(f'Prediction error: {str(e)}')\n");
main_file.push_str(" return jsonify({'error': str(e)}), 500\n\n");
main_file.push_str("if __name__ == '__main__':\n");
main_file.push_str(" load_model()\n");
main_file.push_str(" app.run(host='0.0.0.0', port=5000, debug=False)\n");
let requirements =
"flask==3.0.0\ntorch==2.1.0\nnumpy==1.24.3\ngunicorn==21.2.0\n".to_string();
Ok(DeploymentPackage {
main_file,
requirements,
dockerfile: None,
deployment_config: None,
})
}
fn generate_streamlit_deployment(
&self,
_graph: &FxGraph,
metadata: &PyTorchModelMetadata,
) -> Result<DeploymentPackage> {
let mut main_file = String::new();
main_file.push_str("import streamlit as st\n");
main_file.push_str("import torch\nimport numpy as np\nimport pandas as pd\n\n");
main_file.push_str(&format!(
"st.title('{} Model Demo')\n\n",
metadata.model_name
));
main_file.push_str("@st.cache_resource\n");
main_file.push_str("def load_model():\n");
main_file.push_str(" # TODO: Load your actual PyTorch model\n");
main_file.push_str(" # model = torch.load('model.pt')\n");
main_file.push_str(" # model.eval()\n");
main_file.push_str(" # return model\n");
main_file.push_str(" return None\n\n");
main_file.push_str("model = load_model()\n\n");
main_file.push_str("# Sidebar for input parameters\n");
main_file.push_str("st.sidebar.header('Input Parameters')\n");
main_file.push_str("# TODO: Add input widgets based on your model\n\n");
main_file.push_str("# Main content\n");
main_file.push_str("if st.button('Run Inference'):\n");
main_file.push_str(" with st.spinner('Processing...'):\n");
main_file.push_str(" # TODO: Perform inference\n");
main_file.push_str(" st.success('Inference completed!')\n");
main_file.push_str(" # Display results\n");
main_file.push_str(" st.write('Predictions: [Placeholder]')\n\n");
main_file.push_str("# Display model info\n");
main_file.push_str(&format!(
"st.sidebar.info('Model: {}')\n",
metadata.model_name
));
main_file.push_str(&format!(
"st.sidebar.info('Parameters: {}')\n",
metadata.parameter_count
));
let requirements =
"streamlit==1.28.0\ntorch==2.1.0\nnumpy==1.24.3\npandas==2.0.3\n".to_string();
Ok(DeploymentPackage {
main_file,
requirements,
dockerfile: None,
deployment_config: None,
})
}
fn generate_docker_deployment(
&self,
_graph: &FxGraph,
metadata: &PyTorchModelMetadata,
) -> Result<DeploymentPackage> {
Ok(DeploymentPackage {
main_file: "# Docker deployment".to_string(),
requirements: self.generate_requirements_txt()?,
dockerfile: self.generate_dockerfile(metadata).ok(),
deployment_config: Some("docker-compose.yml".to_string()),
})
}
fn generate_cloud_function_deployment(
&self,
_graph: &FxGraph,
_metadata: &PyTorchModelMetadata,
) -> Result<DeploymentPackage> {
let mut main_file = String::new();
main_file.push_str("import json\nimport torch\nimport numpy as np\nimport base64\n\n");
main_file.push_str("# Global model instance for cold start optimization\n");
main_file.push_str("model = None\n\n");
main_file.push_str("def load_model():\n");
main_file.push_str(" global model\n");
main_file.push_str(" if model is None:\n");
main_file.push_str(" # TODO: Load model from cloud storage\n");
main_file.push_str(" # model = torch.load('model.pt')\n");
main_file.push_str(" # model.eval()\n");
main_file.push_str(" pass\n");
main_file.push_str(" return model\n\n");
main_file.push_str("def handler(request):\n");
main_file.push_str(" \"\"\"Cloud function entry point\"\"\"\n");
main_file.push_str(" try:\n");
main_file.push_str(" # Load model on first request\n");
main_file.push_str(" load_model()\n\n");
main_file.push_str(" # Parse request\n");
main_file.push_str(" request_json = request.get_json(silent=True)\n");
main_file.push_str(" if not request_json or 'inputs' not in request_json:\n");
main_file.push_str(" return json.dumps({'error': 'Missing inputs'}), 400\n\n");
main_file.push_str(" # Process inputs\n");
main_file.push_str(" inputs = np.array(request_json['inputs'])\n");
main_file.push_str(" # TODO: Perform inference\n");
main_file.push_str(" predictions = inputs.tolist() # Placeholder\n\n");
main_file.push_str(" return json.dumps({'predictions': predictions}), 200\n");
main_file.push_str(" except Exception as e:\n");
main_file.push_str(" return json.dumps({'error': str(e)}), 500\n");
let requirements = "functions-framework==3.4.0\ntorch==2.1.0\nnumpy==1.24.3\n".to_string();
Ok(DeploymentPackage {
main_file,
requirements,
dockerfile: None,
deployment_config: None,
})
}
fn generate_jupyter_deployment(
&self,
_graph: &FxGraph,
metadata: &PyTorchModelMetadata,
) -> Result<DeploymentPackage> {
let mut main_file = String::new();
main_file.push_str(&format!(
"# {} Model - Jupyter Notebook\n\n",
metadata.model_name
));
main_file.push_str("## Setup\n");
main_file.push_str("```python\n");
main_file.push_str("import torch\nimport numpy as np\nimport matplotlib.pyplot as plt\n");
main_file.push_str("from pathlib import Path\n\n");
main_file.push_str("# Set random seeds for reproducibility\n");
main_file.push_str("torch.manual_seed(42)\n");
main_file.push_str("np.random.seed(42)\n");
main_file.push_str("```\n\n");
main_file.push_str("## Load Model\n");
main_file.push_str("```python\n");
main_file.push_str("# TODO: Load your model\n");
main_file.push_str("# model = torch.load('model.pt')\n");
main_file.push_str("# model.eval()\n");
main_file.push_str("print('Model loaded successfully')\n");
main_file.push_str("```\n\n");
main_file.push_str("## Prepare Data\n");
main_file.push_str("```python\n");
main_file.push_str("# TODO: Load and preprocess your data\n");
main_file.push_str("# data = ...\n");
main_file.push_str("```\n\n");
main_file.push_str("## Run Inference\n");
main_file.push_str("```python\n");
main_file.push_str("# TODO: Perform inference\n");
main_file.push_str("# with torch.no_grad():\n");
main_file.push_str("# outputs = model(inputs)\n");
main_file.push_str("```\n\n");
main_file.push_str("## Visualize Results\n");
main_file.push_str("```python\n");
main_file.push_str("# TODO: Visualize predictions\n");
main_file.push_str("# plt.figure(figsize=(10, 6))\n");
main_file.push_str("# plt.plot(outputs)\n");
main_file.push_str("# plt.show()\n");
main_file.push_str("```\n");
let requirements =
"jupyter==1.0.0\ntorch==2.1.0\nnumpy==1.24.3\nmatplotlib==3.7.2\n".to_string();
Ok(DeploymentPackage {
main_file,
requirements,
dockerfile: None,
deployment_config: None,
})
}
fn generate_colab_deployment(
&self,
_graph: &FxGraph,
metadata: &PyTorchModelMetadata,
) -> Result<DeploymentPackage> {
let mut main_file = String::new();
main_file.push_str(&format!(
"# {} Model - Google Colab\n\n",
metadata.model_name
));
main_file.push_str("## 🚀 Setup Environment\n");
main_file.push_str("```python\n");
main_file.push_str("# Install dependencies\n");
main_file.push_str("!pip install -q torch torchvision numpy matplotlib\n\n");
main_file.push_str("import torch\nimport numpy as np\nimport matplotlib.pyplot as plt\n");
main_file.push_str("from google.colab import files\n\n");
main_file.push_str("print(f'PyTorch version: {torch.__version__}')\n");
main_file.push_str("print(f'CUDA available: {torch.cuda.is_available()}')\n");
main_file.push_str("```\n\n");
main_file.push_str("## 📦 Upload Model\n");
main_file.push_str("```python\n");
main_file.push_str("# Upload model file\n");
main_file.push_str("uploaded = files.upload()\n");
main_file.push_str("# TODO: Load the uploaded model\n");
main_file.push_str("# model = torch.load(list(uploaded.keys())[0])\n");
main_file.push_str("# model.eval()\n");
main_file.push_str("```\n\n");
main_file.push_str("## 🔬 Run Inference\n");
main_file.push_str("```python\n");
main_file.push_str("# TODO: Prepare input data\n");
main_file.push_str("# inputs = ...\n\n");
main_file.push_str("# Perform inference\n");
main_file.push_str("# with torch.no_grad():\n");
main_file.push_str("# if torch.cuda.is_available():\n");
main_file.push_str("# model = model.cuda()\n");
main_file.push_str("# inputs = inputs.cuda()\n");
main_file.push_str("# outputs = model(inputs)\n");
main_file.push_str("```\n\n");
main_file.push_str("## 📊 Visualize Results\n");
main_file.push_str("```python\n");
main_file.push_str("# TODO: Create visualizations\n");
main_file.push_str("# plt.figure(figsize=(12, 6))\n");
main_file.push_str("# plt.plot(outputs.cpu().numpy())\n");
main_file.push_str("# plt.title('Model Predictions')\n");
main_file.push_str("# plt.show()\n");
main_file.push_str("```\n");
let requirements = "torch==2.1.0\nnumpy==1.24.3\nmatplotlib==3.7.2\n".to_string();
Ok(DeploymentPackage {
main_file,
requirements,
dockerfile: None,
deployment_config: None,
})
}
fn generate_local_deployment(
&self,
_graph: &FxGraph,
_metadata: &PyTorchModelMetadata,
) -> Result<DeploymentPackage> {
Ok(DeploymentPackage {
main_file: "# Local deployment script".to_string(),
requirements: self.generate_requirements_txt()?,
dockerfile: None,
deployment_config: None,
})
}
}
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DeploymentPackage {
pub main_file: String,
pub requirements: String,
pub dockerfile: Option<String>,
pub deployment_config: Option<String>,
}
impl Default for PythonBindingConfig {
fn default() -> Self {
Self {
module_name: "torsh_model".to_string(),
class_name: "TorshModel".to_string(),
include_torch_integration: true,
include_jax_integration: false,
include_numpy_integration: true,
generate_type_hints: true,
async_execution: false,
}
}
}
impl Default for PythonCodeGenOptions {
fn default() -> Self {
Self {
target_framework: PythonFramework::PyTorch,
include_inference_only: false,
include_training_code: true,
optimize_for_mobile: false,
include_onnx_export: true,
batch_size_optimization: true,
memory_optimization: true,
}
}
}
pub fn create_pytorch_integration() -> PythonIntegrationService {
let config = PythonBindingConfig::default();
let codegen_options = PythonCodeGenOptions::default();
PythonIntegrationService::new(config, codegen_options)
}
pub fn create_jax_integration() -> PythonIntegrationService {
let config = PythonBindingConfig {
include_jax_integration: true,
include_torch_integration: false,
..Default::default()
};
let codegen_options = PythonCodeGenOptions {
target_framework: PythonFramework::JAX,
..Default::default()
};
PythonIntegrationService::new(config, codegen_options)
}
pub fn graph_to_pytorch_code(graph: &FxGraph, model_name: &str) -> Result<String> {
let service = create_pytorch_integration();
let metadata = PyTorchModelMetadata {
model_name: model_name.to_string(),
version: "1.0.0".to_string(),
framework_version: "2.0.0".to_string(),
input_shapes: HashMap::new(),
output_shapes: HashMap::new(),
parameter_count: 1000000,
model_size_mb: 4.0,
training_info: None,
};
let code = service.graph_to_pytorch(graph, metadata)?;
Ok(code.model_class)
}
pub fn generate_python_api(graph: &FxGraph, class_name: &str) -> Result<String> {
let service = create_pytorch_integration();
service.generate_python_bindings(graph, class_name)
}
#[cfg(test)]
mod tests {
use super::*;
use crate::FxGraph;
#[test]
fn test_pytorch_integration_service_creation() {
let service = create_pytorch_integration();
assert_eq!(service.config.module_name, "torsh_model");
assert!(service.config.include_torch_integration);
}
#[test]
fn test_jax_integration_service_creation() {
let service = create_jax_integration();
assert!(service.config.include_jax_integration);
assert!(!service.config.include_torch_integration);
assert_eq!(
service.codegen_options.target_framework,
PythonFramework::JAX
);
}
#[test]
fn test_python_binding_config_default() {
let config = PythonBindingConfig::default();
assert_eq!(config.module_name, "torsh_model");
assert_eq!(config.class_name, "TorshModel");
assert!(config.include_torch_integration);
assert!(config.generate_type_hints);
}
#[test]
fn test_pytorch_model_metadata() {
let metadata = PyTorchModelMetadata {
model_name: "TestModel".to_string(),
version: "1.0.0".to_string(),
framework_version: "2.0.0".to_string(),
input_shapes: HashMap::new(),
output_shapes: HashMap::new(),
parameter_count: 1000,
model_size_mb: 4.0,
training_info: None,
};
assert_eq!(metadata.model_name, "TestModel");
assert_eq!(metadata.parameter_count, 1000);
}
#[test]
fn test_graph_to_pytorch_code() {
let graph = FxGraph::new();
let result = graph_to_pytorch_code(&graph, "TestModel");
assert!(result.is_ok());
let code = result.unwrap();
assert!(code.contains("class TestModel"));
assert!(code.contains("def forward"));
}
#[test]
fn test_generate_python_api() {
let graph = FxGraph::new();
let result = generate_python_api(&graph, "APIModel");
assert!(result.is_ok());
let api = result.unwrap();
assert!(api.contains("class APIModel"));
assert!(api.contains("import torch"));
}
#[test]
fn test_requirements_generation() {
let service = create_pytorch_integration();
let requirements = service.generate_requirements_txt().unwrap();
assert!(requirements.contains("torch>=2.0.0"));
assert!(requirements.contains("numpy>=1.21.0"));
assert!(requirements.contains("tqdm>=4.64.0"));
}
#[test]
fn test_setup_py_generation() {
let service = create_pytorch_integration();
let metadata = PyTorchModelMetadata {
model_name: "TestModel".to_string(),
version: "1.0.0".to_string(),
framework_version: "2.0.0".to_string(),
input_shapes: HashMap::new(),
output_shapes: HashMap::new(),
parameter_count: 1000,
model_size_mb: 4.0,
training_info: None,
};
let setup = service.generate_setup_py(&metadata).unwrap();
assert!(setup.contains("name='testmodel'"));
assert!(setup.contains("version='1.0.0'"));
}
#[test]
fn test_dockerfile_generation() {
let service = create_pytorch_integration();
let metadata = PyTorchModelMetadata {
model_name: "TestModel".to_string(),
version: "1.0.0".to_string(),
framework_version: "2.0.0".to_string(),
input_shapes: HashMap::new(),
output_shapes: HashMap::new(),
parameter_count: 1000,
model_size_mb: 4.0,
training_info: None,
};
let dockerfile = service.generate_dockerfile(&metadata).unwrap();
assert!(dockerfile.contains("FROM python:3.9-slim"));
assert!(dockerfile.contains("ENV MODEL_NAME=TestModel"));
}
#[test]
fn test_deployment_package_creation() {
let package = DeploymentPackage {
main_file: "app.py".to_string(),
requirements: "torch\nnumpy\n".to_string(),
dockerfile: Some("Dockerfile".to_string()),
deployment_config: None,
};
assert_eq!(package.main_file, "app.py");
assert!(package.requirements.contains("torch"));
assert!(package.dockerfile.is_some());
}
#[test]
fn test_python_framework_enum() {
let frameworks = vec![
PythonFramework::PyTorch,
PythonFramework::JAX,
PythonFramework::TensorFlow,
PythonFramework::ONNX,
];
assert_eq!(frameworks.len(), 4);
assert_eq!(frameworks[0], PythonFramework::PyTorch);
}
}