NNEngine
A high-performance, fully native C++ Neural Network engine exposed to Python via pybind11.
Designed for rapid experimentation without the Python Global Interpreter Lock (GIL) overhead, nn-engine-core executes the entire deep learning training loop (forward pass, validation, loss calculation, backpropagation, and weight updates) strictly in native C++ using Eigen. It utilizes a zero-allocation flat-memory Autograd graph, AVX SIMD vectorization, and dynamically compiled OpenBLAS to achieve massive speedups over mainstream Python frameworks.
Highlights
- Native Loop Hoisting: The
JITCompiler::fitloop executes entirely in C++, eliminating the Python GIL overhead across epochs and batches. - CNN & Modern Layer Support: Built-in support for
Conv2dLayer(via parallelizedim2col),BatchNorm1dLayer,DropoutLayer, and Leaky ReLUs. - Zero-Allocation Autograd: Uses arena allocation (
Tape) and flat contiguous memory structs to dynamically build computational graphs without heap allocations. - Native Schedulers: Includes
StepLRlearning rate scheduling calculated natively per-epoch. - Pure Python Extensibility: Easily define custom Autograd operations (
nn.Op) in pure Python. The engine uses PyBind11 trampolines to dispatch the C++ backward pass dynamically to your Python methods. - Native Checkpointing: Dump and restore raw contiguous memory weights directly to disk via C++ streams (
.nnefiles) for blazing fast model saving.
Installation
Install the released wheel from PyPI (macOS, Linux, and Windows supported):
pip install nn-engine-core
Or install in editable/development mode from the repository (requires CMake 3.18+ and a C++17 compiler):
pip install -e .
Quick Start: Building a CNN
import numpy as np
import nnengine as nne
X_train = np.random.rand(100, 1, 64, 64).astype(np.float32)
y_train = np.eye(40, dtype=np.float32)[np.random.choice(40, 100)]
class NNEngineDeepCNN(nne.Module):
def __init__(self, in_h, in_w, num_classes):
super().__init__()
self.conv1 = nne.Conv2dLayer(1, 16, in_h, in_w, kernel_size=5, stride=2, pad=2)
self.act1 = nne.LeakyReLULayer(0.01)
out_h1, out_w1 = in_h // 2, in_w // 2
self.conv2 = nne.Conv2dLayer(16, 32, out_h1, out_w1, kernel_size=3, stride=2, pad=1)
self.act2 = nne.LeakyReLULayer(0.01)
out_h2, out_w2 = out_h1 // 2, out_w1 // 2
self.fc = nne.DenseLayer(32 * out_h2 * out_w2, num_classes)
def forward(self, x):
x = self.act1(self.conv1(x))
x = self.act2(self.conv2(x))
return self.fc(x)
model = NNEngineDeepCNN(64, 64, 40)
optimizer = nne.Adam(learning_rate=0.001)
loss_fn = nne.SoftmaxCrossEntropyLoss()
trainer = nne.JITCompiler(model, optimizer, loss_fn)
dataloader = nne.DataLoader(X_train, y_train, batch_size=32, shuffle=True, drop_last=True)
trainer.fit(dataloader, epochs=40, verbose=False)
trainer.save_checkpoint("faces_model")
Defining Custom Autograd Operations in Python
import numpy as np
import nnengine as nne
class MulOp(nne.Op):
def __init__(self, a, b):
super().__init__()
self.a = a
self.b = b
def forward(self):
self.out_data = self.a.data * self.b.data
def backward(self):
if self.a.requires_grad:
self.a.grad += self.out.grad * self.b.data
if self.b.requires_grad:
self.b.grad += self.out.grad * self.a.data
Testing & Validation Suite
Execute the full validation and benchmark suite via:
python examples/script.py
Latest Benchmark Results (Multi-Seed Selection)
| Benchmark Task | Scikit-Learn | PyTorch (ATen) | NNEngine (C++ JIT) | Speedup |
|---|---|---|---|---|
| Deep Regression | MSE: 0.2811 Time: 9.13s |
MSE: 0.2720 Time: 18.49s |
MSE: 0.2675 Time: 2.76s |
6.70x |
| Deep CNN | N/A | Acc: 97.50% Time: 5.87s |
Acc: 97.50% Time: 4.49s |
1.31x |
1 maintainer
Metadata
1 maintainer
Release files for nn-engine-core 0.1.6
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