torch-einops-utils
Some utility functions to help myself (and perhaps others) go faster with ML/AI work
maybe_return
return just the primary output by default, extras only on request
from torch_einops_utils import maybe_return
@maybe_return('memories')
def forward(self, ids, return_memories = False):
memories = build_memories(ids) if return_memories else None
return backbone(ids), memories
model(ids) # logits
model(ids, return_memories = True) # Output(logits, memories)
logits, memories = model(ids, return_memories = True) # unpacks
any number of extras, individually or all at once
@maybe_return('memories', 'hiddens')
def forward(self, ids, return_memories = False, return_hiddens = False):
...
return logits, memories, hiddens
model(ids, return_hiddens = True) # Output(logits, hiddens)
model(ids, return_all = True) # Output(logits, memories, hiddens)
primary may be a tuple of names, or a callable receiving the bound call arguments, for when the shape of the primary depends on the mode
def primary_fields(kwargs):
if not kwargs.get('return_loss', False):
return ('logits', 'memory') if kwargs.get('return_memory', False) else 'logits'
return ('loss', 'loss_mask') if not kwargs.get('reduce_loss', False) else 'loss'
@maybe_return('pooled_repr', primary = primary_fields)
def forward(self, ids, return_loss = False, return_memory = False, reduce_loss = False, return_pooled_repr = False):
...
if return_loss:
return loss, loss_mask, pooled_repr
return logits, memory, pooled_repr
model(ids) # logits
logits, memory = model(ids, return_memory = True)
loss, loss_mask = model(ids, return_loss = True)
loss = model(ids, return_loss = True, reduce_loss = True)
loss, pooled = model(ids, return_loss = True, reduce_loss = True, return_pooled_repr = True)
1 maintainer
Metadata
1 maintainer
Release files for torch-einops-utils 0.1.39
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