Pytorch Not Updating Variables in .step()
I'm attempting to convert old code to PyTorch code as an experiment. Ultimately, I will be doing regression on a 10,000+ x 100 Matrix, updating weights and whatnot appropriately.

Trying to learn, I'm slowly scaling up on toy examples. I'm hitting a wall with the following sample code.

import torch 
import torch.nn as nn 
import torch.nn.functional as funct  
from torch.autograd import Variable 



device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") 

x_data = Variable( torch.Tensor( [ [1.0, 2.0], [2.0, 3.0], [3.0, 4.0] ] ), 
requires_grad=True )
y_data = Variable( torch.Tensor( [ [2.0], [4.0], [6.0] ] ) ) 

w = Variable( torch.randn( 2, 1, requires_grad=True ) )

b = Variable( torch.randn( 1, 1, requires_grad=True ) )


class Model(torch.nn.Module) :
    def __init__(self) :
        super( Model, self).__init__()
        self.linear = torch.nn.Linear(2,1) ## 2 features per entry. 1 output
    def forward(self, x2, w2, b2) :
        y_pred = x2 @ w2 + b2
        return y_pred


model = Model()

criterion = torch.nn.MSELoss( size_average=False )
optimizer = torch.optim.SGD( model.parameters(), lr=0.01 )

for epoch in range(10) :
    y_pred = model( x_data,w,b ) # Get prediction
    loss = criterion( y_pred, y_data ) # Calc loss
    print( epoch, loss.data.item() ) # Print loss
    optimizer.zero_grad() # Zero gradient 
    loss.backward() # Calculate gradients
    optimizer.step() # Update w, b


However, doing so, my loss is always the same, and investigating shows my w and b never actually change.  I'm a bit lost at what's going on here.

Ultimately, I'd like to be able to store the results of the "new" w and b to compare across iterations and datasets.

It looks like a case of cargo programming to me.

Notice that your Model class doesn't make use of self in forward, so it is effectively a "regular" (non-method) function, and model is entirely stateless. The simplest fix to your code is to make optimizer aware of w and b, by creating it as optimizer = torch.optim.SGD([w, b], lr=0.01). I also rewrite model to be a function

import torch
import torch.nn as nn
# torch.autograd.Variable is roughly equivalent to requires_grad=True
# and is deprecated in PyTorch 1.0

# your code gives not reason to have `requires_grad=True` on `x_data`
x_data = torch.tensor( [ [1.0, 2.0], [2.0, 3.0], [3.0, 4.0] ])
y_data = torch.tensor( [ [2.0], [4.0], [6.0] ] )

w = torch.randn( 2, 1, requires_grad=True )
b = torch.randn( 1, 1, requires_grad=True )

def model(x2, w2, b2):
    return x2 @ w2 + b2

criterion = torch.nn.MSELoss( size_average=False )
optimizer = torch.optim.SGD([w, b], lr=0.01 )

for epoch in range(10) :
    y_pred = model( x_data,w,b )
    loss = criterion( y_pred, y_data )
    print( epoch, loss.data.item() )
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()


That being said, nn.Linear is built to simplify this procedure. It automatically creates an equivalent of both w and b, called self.weight and self.bias, respectively. Also, self.__call__(x) is equivalent to the definition of forward of your Model, in that it returns self.weight @ x + self.bias. In other words, you can also use alternative code

import torch
import torch.nn as nn

x_data = torch.tensor( [ [1.0, 2.0], [2.0, 3.0], [3.0, 4.0] ] )
y_data = torch.tensor( [ [2.0], [4.0], [6.0] ] )

model = nn.Linear(2, 1)

criterion = torch.nn.MSELoss( size_average=False )
optimizer = torch.optim.SGD(model.parameters(), lr=0.01 )

for epoch in range(10) :
    y_pred = model(x_data)
    loss = criterion( y_pred, y_data )
    print( epoch, loss.data.item() )
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()


where model.parameters() can be used to enumerate model parameters (equivalent to the manually created list [w, b] above). To access your parameters (load, save, print, whatever) use model.weight and model.bias.
