A torch-like frontend for MindSpore, which use Define-by-run
method to achieve dynamic graph.
- mindspore >= 2.1.0 (which optimized operator excution)
To install MindTorch from source, please run:
pip install git+https://github.com/lvyufeng/mindtorch.git
import numpy as np
from mindtorch import Tensor
from mindtorch.nn import Parameter, Module
from mindtorch.optim import SGD
x_data = Tensor(np.random.randn(100, 1024))
coef = Tensor(np.random.randn(1024, 2048))
y_data = x_data @ coef + 5.
class Model(Module):
def __init__(self) -> None:
super().__init__()
self.w = Parameter(Tensor(np.random.randn(1024, 2048)))
self.b = Parameter(Tensor(np.random.randn(2048)))
def forward(self, inputs: Tensor) -> Tensor:
return inputs @ self.w + self.b
batch_size = 32
model = Model()
optimizer = SGD(model.parameters(), 0.001)
for epoch in range(100):
epoch_loss = 0.0
for start in range(0, 100, batch_size):
end = start + batch_size
optimizer.zero_grad()
inputs = x_data[start:end]
actual = y_data[start:end]
predicted = model(inputs)
errors = predicted - actual
loss = (errors * errors).sum()
loss.backward()
epoch_loss += loss
optimizer.step()
print(epoch, epoch_loss)
This repository refers to the implementation of many deep learning frameworks. Thanks to their inspiration for this project, the following are their links: