pytorch - connection between loss.backward() and optimizer.step()

Question

Where is an explicit connection between the optimizer and the loss?

How does the optimizer know where to get the gradients of the loss without a call liks this optimizer.step(loss)?

-More context-

When I minimize the loss, I didn't have to pass the gradients to the optimizer.

loss.backward() # Back Propagation optimizer.step() # Gardient Descent 

Answer 1

Without delving too deep into the internals of pytorch, I can offer a simplistic answer:

Recall that when initializing optimizer you explicitly tell it what parameters (tensors) of the model it should be updating. The gradients are "stored" by the tensors themselves (they have a grad and a requires_grad attributes) once you call backward() on the loss. After computing the gradients for all tensors in the model, calling optimizer.step() makes the optimizer iterate over all parameters (tensors) it is supposed to update and use their internally stored grad to update their values.

More info on computational graphs and the additional "grad" information stored in pytorch tensors can be found in this answer.

Referencing the parameters by the optimizer can sometimes cause troubles, e.g., when the model is moved to GPU after initializing the optimizer. Make sure you are done setting up your model before constructing the optimizer. See this answer for more details.

Answer 2

When you call loss.backward(), all it does is compute gradient of loss w.r.t all the parameters in loss that have requires_grad = True and store them in parameter.grad attribute for every parameter.

optimizer.step() updates all the parameters based on parameter.grad