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MobulaOP is a simple and flexible cross framework operators toolkit.
You can write custom operators by Python/C++/C/CUDA/HIP/TVM without rebuilding deep learning framework from source.
[中文教程]
[Tutorial]
- Add an addition operator [Code]
import mobula
@mobula.op.register
class MyFirstOP:
def forward(self, x, y):
return x + y
def backward(self, dy):
return [dy, dy]
def infer_shape(self, in_shape):
assert in_shape[0] == in_shape[1]
return in_shape, [in_shape[0]]
# MXNet
import mxnet as mx
a = mx.nd.array([1, 2, 3])
b = mx.nd.array([4, 5, 6])
c = MyFirstOP(a, b)
print (c) # [5, 7, 9]
# PyTorch
import torch
a = torch.tensor([1, 2, 3])
b = torch.tensor([4, 5, 6])
c = MyFirstOP(a, b)
print (c) # [5, 7, 9]
# NumPy
import numpy as np
a = np.array([1, 2, 3])
b = np.array([4, 5, 6])
op = MyFirstOP[np.ndarray]()
c = op(a, b)
print (c) # [5, 7, 9]
# CuPy
import cupy as cp
a = cp.array([1, 2, 3])
b = cp.array([4, 5, 6])
op = MyFirstOP[cp.ndarray]()
c = op(a, b)
print(c) # [5, 7, 9]- Use custom operators without rebuilding the source of deep learning framework [Code]
# Use ROIAlign operator
import mxnet as mx
import numpy as np
import mobula
# Load ROIAlign Module
mobula.op.load('ROIAlign')
ctx = mx.cpu(0)
dtype = np.float32
N, C, H, W = 2, 3, 4, 4
data = mx.nd.array(np.arange(N*C*H*W).astype(dtype).reshape((N,C,H,W)))
rois = mx.nd.array(np.array([[0, 1, 1, 3, 3]], dtype = dtype))
data.attach_grad()
with mx.autograd.record():
# mx.nd.NDArray and mx.sym.Symbol are both available as the inputs.
output = mobula.op.ROIAlign(data = data, rois = rois, pooled_size = (2,2), spatial_scale = 1.0, sampling_ratio = 1)
print (output.asnumpy(), data.grad.asnumpy())- Import Custom C++ Operator Dynamically [Code]
import mobula
# Import Custom Operator Dynamically
mobula.op.load('./AdditionOP')
import mxnet as mx
a = mx.nd.array([1,2,3])
b = mx.nd.array([4,5,6])
c = mobula.op.AdditionOP(a, b)
print ('a + b = c \n {} + {} = {}'.format(a.asnumpy(), b.asnumpy(), c.asnumpy()))# Clone the project
git clone https://github.com/wkcn/MobulaOP
# Enter the directory
cd MobulaOP
# Install MobulaOP
pip install -v -e .
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