Truncated backpropagation in PyTorch (code check) - pytorch

Question

I am trying to implement truncated backpropagation through time in PyTorch, for the simple case where K1=K2. I have an implementation below that produces reasonable output, but I just want to make sure it is correct. When I look online for PyTorch examples of TBTT, they do inconsistent things around detaching the hidden state and zeroing out the gradient, and the ordering of these operations. Please let me know if I have made a mistake.
In the code below, H maintains the current hidden state, and model(weights, H, x) outputs the prediction and the new hidden state.
while i < NUM_STEPS:
# Grab x, y for ith datapoint
x = data[i]
target = true_output[i]
# Run model
output, new_hidden = model(weights, H, x)
H = new_hidden
# Update running error
error += (output - target)**2
if (i+1) % K == 0:
# Backpropagate
error.backward()
opt.step()
opt.zero_grad()
error = 0
H = H.detach()
i += 1

Answer 1

So the idea of your code is to isolate the last variables after each Kth step. Yes, your implementation is absolutely correct and this answer confirms that.
# truncated to the last K timesteps
while i < NUM_STEPS:
out = model(out)
if (i+1) % K == 0:
out.backward()
out.detach()
out.backward()
You can also follow this example for your reference.
import torch
from ignite.engine import Engine, EventEnum, _prepare_batch
from ignite.utils import apply_to_tensor
class Tbptt_Events(EventEnum):
"""Aditional tbptt events.
Additional events for truncated backpropagation throught time dedicated
trainer.
"""
TIME_ITERATION_STARTED = "time_iteration_started"
TIME_ITERATION_COMPLETED = "time_iteration_completed"
def _detach_hidden(hidden):
"""Cut backpropagation graph.
Auxillary function to cut the backpropagation graph by detaching the hidden
vector.
"""
return apply_to_tensor(hidden, torch.Tensor.detach)
def create_supervised_tbptt_trainer(
model, optimizer, loss_fn, tbtt_step, dim=0, device=None, non_blocking=False, prepare_batch=_prepare_batch
):
"""Create a trainer for truncated backprop through time supervised models.
Training recurrent model on long sequences is computationally intensive as
it requires to process the whole sequence before getting a gradient.
However, when the training loss is computed over many outputs
(`X to many <https://karpathy.github.io/2015/05/21/rnn-effectiveness/>`_),
there is an opportunity to compute a gradient over a subsequence. This is
known as
`truncated backpropagation through time <https://machinelearningmastery.com/
gentle-introduction-backpropagation-time/>`_.
This supervised trainer apply gradient optimization step every `tbtt_step`
time steps of the sequence, while backpropagating through the same
`tbtt_step` time steps.
Args:
model (`torch.nn.Module`): the model to train.
optimizer (`torch.optim.Optimizer`): the optimizer to use.
loss_fn (torch.nn loss function): the loss function to use.
tbtt_step (int): the length of time chunks (last one may be smaller).
dim (int): axis representing the time dimension.
device (str, optional): device type specification (default: None).
Applies to batches.
non_blocking (bool, optional): if True and this copy is between CPU and GPU,
the copy may occur asynchronously with respect to the host. For other cases,
this argument has no effect.
prepare_batch (callable, optional): function that receives `batch`, `device`,
`non_blocking` and outputs tuple of tensors `(batch_x, batch_y)`.
.. warning::
The internal use of `device` has changed.
`device` will now *only* be used to move the input data to the correct device.
The `model` should be moved by the user before creating an optimizer.
For more information see:
* `PyTorch Documentation <https://pytorch.org/docs/stable/optim.html#constructing-it>`_
* `PyTorch's Explanation <https://github.com/pytorch/pytorch/issues/7844#issuecomment-503713840>`_
Returns:
Engine: a trainer engine with supervised update function.
"""
def _update(engine, batch):
loss_list = []
hidden = None
x, y = batch
for batch_t in zip(x.split(tbtt_step, dim=dim), y.split(tbtt_step, dim=dim)):
x_t, y_t = prepare_batch(batch_t, device=device, non_blocking=non_blocking)
# Fire event for start of iteration
engine.fire_event(Tbptt_Events.TIME_ITERATION_STARTED)
# Forward, backward and
model.train()
optimizer.zero_grad()
if hidden is None:
y_pred_t, hidden = model(x_t)
else:
hidden = _detach_hidden(hidden)
y_pred_t, hidden = model(x_t, hidden)
loss_t = loss_fn(y_pred_t, y_t)
loss_t.backward()
optimizer.step()
# Setting state of engine for consistent behaviour
engine.state.output = loss_t.item()
loss_list.append(loss_t.item())
# Fire event for end of iteration
engine.fire_event(Tbptt_Events.TIME_ITERATION_COMPLETED)
# return average loss over the time splits
return sum(loss_list) / len(loss_list)
engine = Engine(_update)
engine.register_events(*Tbptt_Events)
return engine