This release has been yanked by its maintainers, and will be ignored by installers, except when explicitly specified.
Consider using release 0.1.1 instead.
Reason given by maintainers: Wheels only support AVX512
AffineFlow-NN
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, AffineFlow-NN executes the entire deep learning training loop 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
- Dual Execution Modes: Train dynamic graphs using the
@nn.eagerdecorator for zero-allocation step-by-step execution, or compile static graphs withJITCompilerfor maximum throughput. - Dynamic Hardware Dispatch: Automatically detects your CPU at runtime to dispatch highly optimized AVX2 or AVX-512 instructions.
- CNN & Modern Layer Support: Built-in support for
Conv2dLayer,MaxPool2dLayer,AvgPool2dLayer,FlattenLayer,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.
Installation
Install the released wheel from PyPI:
pip install AffineFlow-NN
Quick Start: Building a CNN
import numpy as np
import affineflow_nn as nn
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 AffineFlowNNDeepCNN(nn.Module):
def __init__(self, in_h, in_w, num_classes):
super().__init__()
self.conv1 = nn.Conv2dLayer(1, 16, in_h, in_w, kernel_size=5, stride=1, pad=2)
self.act1 = nn.LeakyReLULayer(0.01)
self.pool1 = nn.MaxPool2dLayer(16, in_h, in_w, kernel_size=2, stride=2, pad=0)
out_h1, out_w1 = in_h // 2, in_w // 2
self.conv2 = nn.Conv2dLayer(16, 32, out_h1, out_w1, kernel_size=3, stride=1, pad=1)
self.act2 = nn.LeakyReLULayer(0.01)
self.pool2 = nn.MaxPool2dLayer(32, out_h1, out_w1, kernel_size=2, stride=2, pad=0)
out_h2, out_w2 = out_h1 // 2, out_w1 // 2
self.flatten = nn.FlattenLayer()
self.fc = nn.DenseLayer(32 * out_h2 * out_w2, num_classes)
def forward(self, x):
x = self.pool1(self.act1(self.conv1(x)))
x = self.pool2(self.act2(self.conv2(x)))
x = self.flatten(x)
return self.fc(x)
model = AffineFlowNNDeepCNN(64, 64, 40)
optimizer = nn.Adam(learning_rate=0.001)
optimizer.set_parameters(model.parameters())
loss_fn = nn.SoftmaxCrossEntropyLoss()
trainer = nn.JITCompiler(model, optimizer, loss_fn)
dataloader = nn.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")
Latest Benchmark Results
| Benchmark Task | Scikit-Learn | PyTorch (ATen) | AffineFlow-NN (Eager) | AffineFlow-NN (C++ JIT) | Max Speedup (vs PyTorch) |
|---|---|---|---|---|---|
| Deep Regression (Housing) | MSE: 0.2811 Time: 4.94s |
MSE: 0.2720 Time: 13.32s |
MSE: 0.2662 Time: 2.47s |
MSE: 0.2669 Time: 4.02s |
5.39x |
| Deep CNN (Faces) | N/A | Acc: 98.75% Time: 15.19s |
Acc: 98.75% Time: 12.17s |
Acc: 98.75% Time: 11.22s |
1.35x |
1 maintainer
Metadata
1 maintainer
Release files for AffineFlow-NN 0.1.0
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