wefml 0.1.5

A C++ deep learning library with Vulkan GPU acceleration, exposed to Python

wefml

C++ deep learning library with optional CUDA/Vulkan GPU acceleration, packaged for Python.

Install

pip install wefml

Requires Python >= 3.8, CMake >= 3.15, C++17 compiler. CUDA or Vulkan are auto-detected at build time; CPU-only mode works out of the box.

Quick Start

import wefml
import numpy as np

a = wefml.Tensor.from_numpy(np.random.randn(2, 3).astype(np.float32))
b = wefml.Tensor.from_numpy(np.random.randn(3, 4).astype(np.float32))
c = wefml.ops.matmul(a, b)
print(c.numpy())

Sequential Model

model = wefml.Model([
    wefml.Linear(128, use_bias=True),
    wefml.ReLU(),
    wefml.Linear(10, use_bias=True),
])
model.fit(labels, inputs, epochs=10, lr=0.01)
pred = model.predict(test_inputs)

Transformer (English-to-Spanish)

A built-in encoder-decoder transformer for seq2seq tasks. The bundled english_spanish_tab.txt is resolved automatically.

import wefml

tok = wefml.Tokenizer()
tok.process("english_spanish_tab.txt", early_stop=4000)
data = wefml.prepare_translation_data(tok, val_size=50)

model = wefml.Transformer(
    vocab_size=data["vocab_size"],
    d_model=64,
    num_heads=4,
    batch_size=50,
)

model.train(
    data["enc"], data["dec"], data["target"],
    data["val_enc"], data["val_dec"], data["val_target"],
    epochs=10, lr=0.05,
)
print(model.loss)

test_input = data["val_enc"]  # single [1, maxlen] sample
output = model.generate(test_input, data["start_token"], data["end_token"], max_tokens=10)
print(output.numpy())

GPU Mode

print(wefml.gpu_backend())   # 'cuda', 'vulkan', or 'none'
wefml.use_gpu(True)          # Linear/Conv2D/MaxPool2D auto-pick GPU variants

model = wefml.Model([...], use_gpu=True)
# or
model = wefml.Transformer(vocab_size=vocab, use_gpu=True)

API Reference

GPU -- use_gpu(bool), is_gpu_available(), is_cuda_available(), is_vulkan_available(), gpu_backend()

Tensor -- Tensor.from_numpy(arr), Tensor.zeros(shape), .numpy(), .shape, .rank, .size, arithmetic (+, -, *, /)

Ops (wefml.ops) -- matmul, matmul_mt, transpose, argmax, softmax, relu, sigmoid, reducesum, positional_encoding, l2, binarycrossentropy

Layers -- Linear, Conv2D, MaxPool2D, ReLU, Sigmoid, Flatten, LayerNorm, ReduceSum, Embedding, MHA

Model -- Model(layers, use_gpu) with .add(), .fit(), .predict(), .summary()

Transformer -- Transformer(vocab_size, d_model=128, num_heads=4, batch_size=50, use_gpu=False) with .train(), .generate(), .loss

Data -- Tokenizer().process(path, early_stop), prepare_translation_data(tok, val_size), load_mnist_images(path), load_mnist_labels(path)

License

MIT