# RusTorch Python Integration Plan
# RusTorch Python連携計画
## Overview / 概要
This document outlines a comprehensive plan for integrating RusTorch with Python, enabling seamless interoperability between Rust-native deep learning capabilities and the rich Python ML ecosystem.
このドキュメントは、RusTorchとPythonを統合し、Rustネイティブの深層学習機能と豊富なPython MLエコシステム間のシームレスな相互運用性を実現する包括的な計画を概説します。
## Architecture Overview / アーキテクチャ概要
```
┌─────────────────────────────────────────────────────────────┐
│ Python Layer │
├─────────────────────────────────────────────────────────────┤
│ rustorch-py │ NumPy/PyTorch │ Jupyter/IPython │
│ Python Package │ Interop │ Integration │
├─────────────────────────────────────────────────────────────┤
│ PyO3 Bindings │
├─────────────────────────────────────────────────────────────┤
│ RusTorch Core │
│ Tensor Ops │ Neural Networks │ Autograd │ Optimizers │
└─────────────────────────────────────────────────────────────┘
```
## Implementation Phases / 実装フェーズ
### Phase 1: Core Python Bindings / フェーズ1: コアPythonバインディング
#### 1.1 PyO3 Infrastructure Setup / PyO3インフラ設定
**目標**: RusTorchのコア機能をPythonから利用可能にする
**実装項目**:
- PyO3による基本的なバインディング
- Tensorクラスの Python ラッパー
- 基本的な演算子オーバーロード
- メモリ管理の Python-Rust 間連携
**ファイル構成**:
```
python/
├── rustorch_py/
│ ├── __init__.py
│ ├── tensor.py
│ ├── nn/
│ │ ├── __init__.py
│ │ ├── linear.py
│ │ └── conv.py
│ ├── optim/
│ │ ├── __init__.py
│ │ ├── sgd.py
│ │ └── adam.py
│ └── autograd/
│ ├── __init__.py
│ └── variable.py
├── src/
│ ├── lib.rs
│ ├── tensor.rs
│ ├── nn.rs
│ └── optim.rs
├── Cargo.toml
├── pyproject.toml
└── setup.py
```
#### 1.2 Basic Tensor Operations / 基本テンソル操作
```python
# Python API Design
import rustorch as rt
# Tensor creation
tensor = rt.tensor([[1.0, 2.0], [3.0, 4.0]])
zeros = rt.zeros((3, 3))
randn = rt.randn((2, 2))
# Operations
result = tensor + 2.0
matmul = tensor @ tensor.T
sum_result = tensor.sum()
# NumPy interop
numpy_array = tensor.numpy()
from_numpy = rt.from_numpy(numpy_array)
```
### Phase 2: Neural Network Integration / フェーズ2: ニューラルネットワーク統合
#### 2.1 Module System / モジュールシステム
```python
# PyTorch-like API
import rustorch.nn as nn
class SimpleNet(nn.Module):
def __init__(self):
super().__init__()
self.fc1 = nn.Linear(784, 128)
self.relu = nn.ReLU()
self.fc2 = nn.Linear(128, 10)
def forward(self, x):
x = self.fc1(x)
x = self.relu(x)
x = self.fc2(x)
return x
model = SimpleNet()
output = model(input_tensor)
```
#### 2.2 Autograd Integration / 自動微分統合
```python
import rustorch.autograd as autograd
# Gradient computation
x = rt.tensor([2.0], requires_grad=True)
y = x ** 2
y.backward()
print(x.grad) # tensor([4.0])
# Custom autograd functions
class SquareFunction(autograd.Function):
@staticmethod
def forward(ctx, input):
ctx.save_for_backward(input)
return input ** 2
@staticmethod
def backward(ctx, grad_output):
input, = ctx.saved_tensors
return 2 * input * grad_output
```
### Phase 3: Advanced Interoperability / フェーズ3: 高度な相互運用性
#### 3.1 PyTorch Model Conversion / PyTorchモデル変換
```python
# PyTorch to RusTorch conversion
import torch
import rustorch.interop as interop
# Convert PyTorch model
pytorch_model = torch.nn.Sequential(
torch.nn.Linear(784, 128),
torch.nn.ReLU(),
torch.nn.Linear(128, 10)
)
# Convert to RusTorch
rustorch_model = interop.from_pytorch(pytorch_model)
# Convert back
converted_back = interop.to_pytorch(rustorch_model)
```
#### 3.2 NumPy Integration / NumPy統合
```python
import numpy as np
import rustorch as rt
# Seamless NumPy integration
np_array = np.array([[1, 2], [3, 4]], dtype=np.float32)
rt_tensor = rt.from_numpy(np_array) # Zero-copy if possible
# Operations maintain compatibility
result = rt_tensor * 2.0
back_to_numpy = result.numpy() # Zero-copy if possible
# Broadcasting compatibility
np_broadcast = np_array + rt_tensor.numpy()
rt_broadcast = rt.from_numpy(np_array) + rt_tensor
```
### Phase 4: Ecosystem Integration / フェーズ4: エコシステム統合
#### 4.1 Jupyter Notebook Support / Jupyter Notebook対応
```python
# Rich display in Jupyter
%load_ext rustorch_magic
# Magic commands
%%rustorch
let tensor = Tensor::randn(&[3, 3]);
println!("{:?}", tensor);
# Interactive plotting
import matplotlib.pyplot as plt
tensor = rt.randn((100, 100))
plt.imshow(tensor.numpy())
plt.colorbar()
plt.show()
# Progress bars for training
from tqdm import tqdm
import rustorch.utils as utils
for epoch in tqdm(range(100)):
loss = utils.train_step(model, batch)
if epoch % 10 == 0:
print(f"Epoch {epoch}, Loss: {loss.item():.4f}")
```
#### 4.2 Scientific Computing Integration / 科学計算統合
```python
# SciPy integration
import scipy.sparse
import rustorch.sparse as rt_sparse
# Sparse tensor operations
sparse_matrix = scipy.sparse.csr_matrix([[1, 0, 2], [0, 0, 3]])
rt_sparse_tensor = rt_sparse.from_scipy(sparse_matrix)
# Scikit-learn integration
from sklearn.preprocessing import StandardScaler
import rustorch.sklearn as rt_sklearn
scaler = StandardScaler()
rt_transformer = rt_sklearn.from_sklearn(scaler)
```
## Technical Implementation Details / 技術実装詳細
### 5.1 Memory Management Strategy / メモリ管理戦略
```rust
// Rust side - PyO3 integration
use pyo3::prelude::*;
use pyo3::types::PyArray1;
use numpy::{IntoPyArray, PyArray, PyReadonlyArray1};
#[pyclass]
struct PyTensor {
inner: crate::tensor::Tensor<f32>,
}
#[pymethods]
impl PyTensor {
#[new]
fn new(data: PyReadonlyArray1<f32>) -> Self {
let array = data.as_array();
let shape = array.shape().to_vec();
let data_vec = array.to_vec();
PyTensor {
inner: Tensor::from_vec(data_vec, shape),
}
}
fn numpy<'py>(&self, py: Python<'py>) -> &'py PyArray1<f32> {
self.inner.data().into_pyarray(py)
}
fn __add__(&self, other: &PyTensor) -> PyResult<PyTensor> {
let result = &self.inner + &other.inner;
Ok(PyTensor { inner: result })
}
}
```
### 5.2 Performance Optimization / パフォーマンス最適化
```python
# Python side - optimized operations
import rustorch.ops as ops
class OptimizedTensor:
def __init__(self, data):
self._data = data
self._cache = {}
def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
# NumPy universal function protocol
if method == '__call__':
return ops.dispatch_ufunc(ufunc, *inputs, **kwargs)
return NotImplemented
def __array_interface__(self):
# NumPy array interface
return {
'shape': self._data.shape(),
'typestr': '<f4', # float32
'data': self._data.data_ptr(),
'version': 3,
}
```
### 5.3 Error Handling / エラーハンドリング
```python
# Unified error handling
import rustorch.errors as errors
try:
result = tensor.matmul(incompatible_tensor)
except errors.ShapeMismatchError as e:
print(f"Shape error: {e}")
print(f"Got shapes: {e.shape1} and {e.shape2}")
except errors.RustorchError as e:
print(f"RusTorch error: {e}")
```
## Development Workflow / 開発ワークフロー
### 6.1 Hybrid Development Pattern / ハイブリッド開発パターン
```python
# Pattern 1: Prototype in Python, Deploy in Rust
# Step 1: Prototype
import torch
model = torch.nn.Sequential(...)
# ... training and validation
# Step 2: Convert to RusTorch
import rustorch.interop as interop
production_model = interop.from_pytorch(model)
production_model.save("model.rustorch")
# Step 3: Deploy
# Rust binary loads the model for production inference
```
```python
# Pattern 2: Compute-heavy operations in Rust
import rustorch.kernels as kernels
def custom_attention(query, key, value):
# Use optimized Rust implementation
return kernels.scaled_dot_product_attention(query, key, value)
class TransformerBlock(nn.Module):
def forward(self, x):
attention_out = custom_attention(x, x, x)
return self.feedforward(attention_out)
```
### 6.2 Performance Profiling / パフォーマンスプロファイリング
```python
# Profiling integration
import rustorch.profiler as profiler
with profiler.profile() as prof:
model = create_model()
for _ in range(100):
output = model(input_data)
loss = criterion(output, target)
loss.backward()
optimizer.step()
# Display results
prof.print_stats()
prof.export_chrome_trace("profile.json")
```
## Package Distribution / パッケージ配布
### 7.1 PyPI Distribution / PyPI配布
```toml
# pyproject.toml
[build-system]
requires = ["setuptools", "wheel", "pyo3-pack"]
build-backend = "pyo3_pack"
[tool.pyo3-pack]
name = "rustorch-py"
bindings = "pyo3"
compatibility = "linux"
[project]
name = "rustorch"
version = "0.3.3"
description = "Python bindings for RusTorch deep learning library"
dependencies = [
"numpy>=1.20.0",
"typing-extensions>=4.0.0",
]
```
### 7.2 Conda Distribution / Conda配布
```yaml
# meta.yaml
package:
name: rustorch
version: {{ environ.get('GIT_DESCRIBE_TAG', 'dev') }}
source:
path: ..
build:
number: 0
script: "{{ PYTHON }} -m pip install . -vv"
requirements:
build:
- {{ compiler('rust') }}
- {{ compiler('c') }}
host:
- python
- pip
- pyo3
- maturin
run:
- python
- numpy
test:
imports:
- rustorch
commands:
- python -c "import rustorch; print(rustorch.__version__)"
```
## Testing Strategy / テスト戦略
### 8.1 Cross-Language Testing / 言語間テスト
```python
# test_interop.py
import pytest
import numpy as np
import torch
import rustorch as rt
class TestInteroperability:
def test_numpy_conversion(self):
np_array = np.random.randn(3, 3).astype(np.float32)
rt_tensor = rt.from_numpy(np_array)
converted_back = rt_tensor.numpy()
assert np.allclose(np_array, converted_back)
def test_pytorch_model_conversion(self):
torch_model = torch.nn.Linear(10, 5)
rt_model = rt.interop.from_pytorch(torch_model)
input_data = torch.randn(1, 10)
torch_output = torch_model(input_data)
rt_output = rt_model(rt.from_numpy(input_data.numpy()))
assert np.allclose(
torch_output.detach().numpy(),
rt_output.numpy(),
atol=1e-6
)
def test_gradient_compatibility(self):
# Test autograd compatibility
x_torch = torch.tensor([2.0], requires_grad=True)
x_rust = rt.tensor([2.0], requires_grad=True)
y_torch = x_torch ** 2
y_rust = x_rust ** 2
y_torch.backward()
y_rust.backward()
assert np.allclose(
x_torch.grad.numpy(),
x_rust.grad.numpy()
)
```
### 8.2 Performance Benchmarking / パフォーマンスベンチマーク
```python
# benchmark_comparison.py
import time
import torch
import rustorch as rt
import numpy as np
def benchmark_operation(name, torch_op, rust_op, *args, iterations=100):
# PyTorch timing
torch_args = [torch.from_numpy(arg) if isinstance(arg, np.ndarray) else arg for arg in args]
start = time.time()
for _ in range(iterations):
torch_result = torch_op(*torch_args)
torch_time = time.time() - start
# RusTorch timing
rust_args = [rt.from_numpy(arg) if isinstance(arg, np.ndarray) else arg for arg in args]
start = time.time()
for _ in range(iterations):
rust_result = rust_op(*rust_args)
rust_time = time.time() - start
speedup = torch_time / rust_time
print(f"{name}: PyTorch={torch_time:.4f}s, RusTorch={rust_time:.4f}s, Speedup={speedup:.2f}x")
# Run benchmarks
if __name__ == "__main__":
a = np.random.randn(1000, 1000).astype(np.float32)
b = np.random.randn(1000, 1000).astype(np.float32)
benchmark_operation("Matrix Multiplication", torch.matmul, rt.matmul, a, b)
benchmark_operation("Element-wise Addition", torch.add, rt.add, a, b)
benchmark_operation("Matrix Sum", torch.sum, rt.sum, a)
```
## Documentation and Examples / ドキュメントと例
### 9.1 API Documentation / APIドキュメント
```python
# Generate documentation
# docs/conf.py
extensions = [
'sphinx.ext.autodoc',
'sphinx.ext.napoleon',
'sphinx.ext.viewcode',
'sphinx_rtd_theme',
]
# Auto-generate from docstrings
def generate_python_docs():
"""
Generate comprehensive API documentation
including both Python and Rust components
"""
pass
```
### 9.2 Tutorial Examples / チュートリアル例
```python
# examples/pytorch_migration.py
"""
Tutorial: Migrating from PyTorch to RusTorch
"""
# Before (PyTorch)
import torch
import torch.nn as nn
import torch.optim as optim
class PyTorchModel(nn.Module):
def __init__(self):
super().__init__()
self.layers = nn.Sequential(
nn.Linear(784, 128),
nn.ReLU(),
nn.Linear(128, 10)
)
def forward(self, x):
return self.layers(x)
# After (RusTorch)
import rustorch as rt
import rustorch.nn as nn
class RusTorchModel(nn.Module):
def __init__(self):
super().__init__()
self.layers = nn.Sequential(
nn.Linear(784, 128),
nn.ReLU(),
nn.Linear(128, 10)
)
def forward(self, x):
return self.layers(x)
# Migration utilities
def migrate_model(pytorch_model):
"""Convert PyTorch model to RusTorch with weights"""
return rt.interop.from_pytorch(pytorch_model)
```
## Deployment and Production / デプロイメントとプロダクション
### 10.1 Production Workflow / プロダクションワークフロー
```python
# production_pipeline.py
import rustorch as rt
import rustorch.serving as serving
# Load model trained in Python
model = rt.load("trained_model.rustorch")
# Create production server
server = serving.ModelServer(model)
server.add_preprocessing(lambda x: x / 255.0)
server.add_postprocessing(lambda x: x.softmax(dim=1))
# Deploy
server.serve(host="0.0.0.0", port=8080)
```
### 10.2 Edge Deployment / エッジデプロイメント
```python
# edge_export.py
import rustorch as rt
# Export for different targets
model = rt.load("model.rustorch")
# WebAssembly export
model.export_wasm("model.wasm")
# Mobile export (iOS/Android)
model.export_mobile("model.mobile")
# Embedded export
model.export_embedded("model.embedded", target="arm-cortex-m4")
```
## Timeline and Milestones / タイムラインとマイルストーン
### Phase 1 (Months 1-2): Foundation
- PyO3 basic bindings
- Core tensor operations
- NumPy interoperability
- Basic testing framework
### Phase 2 (Months 3-4): Neural Networks
- Module system implementation
- Autograd integration
- Optimizer bindings
- Training loop support
### Phase 3 (Months 5-6): Advanced Features
- PyTorch model conversion
- Performance optimization
- Memory management improvements
- Comprehensive error handling
### Phase 4 (Months 7-8): Ecosystem
- Jupyter integration
- Scientific computing libraries
- Documentation and tutorials
- Community feedback integration
## Success Metrics / 成功指標
1. **Performance**: 90%+ of PyTorch performance for equivalent operations
2. **Compatibility**: 95%+ API compatibility for core operations
3. **Adoption**: 1000+ downloads within 3 months of release
4. **Community**: 10+ contributors and 100+ GitHub stars
5. **Use Cases**: 5+ real-world production deployments
## Conclusion / 結論
This comprehensive Python integration plan bridges the gap between RusTorch's performance and safety benefits and Python's rich ML ecosystem. The phased approach ensures incremental value delivery while building towards full ecosystem compatibility.
この包括的なPython統合計画は、RusTorchのパフォーマンスと安全性の利点と、Pythonの豊富なMLエコシステムとの間のギャップを埋めます。段階的アプローチにより、完全なエコシステム互換性を構築しながら段階的な価値提供を保証します。