def backward(self, loss):
"""Wrapper for backward pass. Some optimizer requires ownership of the
backward pass."""
if self._with_fp16_wrapper:
kwargs = {}
if "update_master_grads" in fn_args(self._optimizer.backward):
kwargs["update_master_grads"] = True
self._optimizer.backward(loss, **kwargs)
else:
loss.backward()
def backward(self, loss):
"""Wrapper for backward pass. Some optimizer requires ownership of the
backward pass."""
if self._with_fp16_wrapper:
kwargs = {}
if "update_master_grads" in fn_args(self._optimizer.backward):
kwargs["update_master_grads"] = True
self._optimizer.backward(loss, **kwargs)
else:
loss.backward()
def backward(self, loss, retain_graph=False):
"""Wrapper for backward pass. Some optimizer requires ownership of the
backward pass."""
#with torch.autograd.detect_anomaly():
if self._with_fp16_wrapper:
kwargs = {"retain_graph": retain_graph}
if "update_master_grads" in fn_args(self._optimizer.backward):
kwargs["update_master_grads"] = True
self._optimizer.backward(loss, **kwargs)
else:
loss.backward(retain_graph=retain_graph)
def backward(self, loss):
"""Wrapper for backward pass. Some optimizer requires ownership of the
backward pass."""
if self.amp:
self._scaler.scale(loss).backward()
elif self._fp16 == "legacy":
kwargs = {}
if "update_master_grads" in fn_args(self._optimizer.backward):
kwargs["update_master_grads"] = True
self._optimizer.backward(loss, **kwargs)
else:
loss.backward()
def backward(self, loss):
"""Wrapper for backward pass. Some optimizer requires ownership of the
backward pass."""
if self._fp16 == "amp":
import apex
with apex.amp.scale_loss(loss, self._optimizer) as scaled_loss:
scaled_loss.backward()
elif self._fp16 == "legacy":
kwargs = {}
if "update_master_grads" in fn_args(self._optimizer.backward):
kwargs["update_master_grads"] = True
self._optimizer.backward(loss, **kwargs)
else:
loss.backward()
