Error: unbiased linear model

[mcksshg]

Thank you for this great project.
When I ported a PyTorch sample code to torch-rb, I met an error.

Example Code

require "torch"
require "npy"

# y = 2 x1 + 3 x2
w_true = Torch::Tensor.new([2,3])

x = Torch.randn(100,2)
y = Torch.mv(x, w_true) + Torch.randn(100)*0.5

net = Torch::NN::Linear.new(2, 1, bias: false)
optimizer = Torch::Optim::SGD.new(net.parameters, lr: 0.1)
loss_fn = Torch::NN::MSELoss.new
100.times do |epoc|
  optimizer.zero_grad
  y_pred = net.call(x)
  loss = loss_fn.call(y_pred.view_as(y),y)
  loss.backward
  optimizer.step
  puts loss.item
  puts net.parameters
end

Result

Error message is

53:in `block (2 levels) in zero_grad': undefined method `grad' for nil:NilClass (NoMethodError)

It seems that unbiased linear model does not work in using `grad'.

Biased Linear Model

If I change the bias into true, it works correctly:

net = Torch::NN::Linear.new(2, 1, bias: true)

Python Code

Corresponding python code is here

from torch import nn, optim
import torch
# y = 2 x1 + 3 x2
w_true = torch.Tensor([2,3])

x = torch.randn(100,2)
y = torch.mv(x, w_true) + torch.randn(100)*0.5

net = nn.Linear(2, 1, bias = False)
optimizer = optim.SGD(net.parameters(), lr= 0.1)
loss_fn = nn.MSELoss()
for epoc in range(100):
  optimizer.zero_grad()
  y_pred = net(x)
  loss = loss_fn(y_pred.view_as(y),y)
  loss.backward()
  optimizer.step()
  print(loss.item())
  print(list(net.parameters()))

[Andrew Kane]

Hey @mcksshg, thanks for reporting and the great repro steps! Fixed on master.