coca_ViT-B-32: self._encode_text bug

[haideraltahan]

when doing a forward method on a coca model:
model_out = self.model(images, input_texts)

I get an ValueError: too many values to unpack (expected 2) on line 145:
text_latent, token_emb = self.text(text)

Here is full error:

[rank1]: Traceback (most recent call last):
[rank1]:   File "/opt/hpcaas/.mounts/fs-0565f60d669b6a2d3/home/haideraltahan/RelationCLIP/main.py", line 82, in <module>
[rank1]:     fire.Fire(main)
[rank1]:   File "/data/home/haideraltahan/anaconda3/envs/RClip/lib/python3.12/site-packages/fire/core.py", line 143, in Fire
[rank1]:     component_trace = _Fire(component, args, parsed_flag_args, context, name)
[rank1]:                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank1]:   File "/data/home/haideraltahan/anaconda3/envs/RClip/lib/python3.12/site-packages/fire/core.py", line 477, in _Fire
[rank1]:     component, remaining_args = _CallAndUpdateTrace(
[rank1]:                                 ^^^^^^^^^^^^^^^^^^^^
[rank1]:   File "/data/home/haideraltahan/anaconda3/envs/RClip/lib/python3.12/site-packages/fire/core.py", line 693, in _CallAndUpdateTrace
[rank1]:     component = fn(*varargs, **kwargs)
[rank1]:                 ^^^^^^^^^^^^^^^^^^^^^^
[rank1]:   File "/opt/hpcaas/.mounts/fs-0565f60d669b6a2d3/home/haideraltahan/RelationCLIP/main.py", line 78, in main
[rank1]:     trainer.fit(model=net, datamodule=data, ckpt_path=checkpoint)
[rank1]:   File "/data/home/haideraltahan/anaconda3/envs/RClip/lib/python3.12/site-packages/pytorch_lightning/trainer/trainer.py", line 544, in fit
[rank1]:     call._call_and_handle_interrupt(
[rank1]:   File "/data/home/haideraltahan/anaconda3/envs/RClip/lib/python3.12/site-packages/pytorch_lightning/trainer/call.py", line 43, in _call_and_handle_interrupt
[rank1]:     return trainer.strategy.launcher.launch(trainer_fn, *args, trainer=trainer, **kwargs)
[rank1]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank1]:   File "/data/home/haideraltahan/anaconda3/envs/RClip/lib/python3.12/site-packages/pytorch_lightning/strategies/launchers/subprocess_script.py", line 105, in launch
[rank1]:     return function(*args, **kwargs)
[rank1]:            ^^^^^^^^^^^^^^^^^^^^^^^^^
[rank1]:   File "/data/home/haideraltahan/anaconda3/envs/RClip/lib/python3.12/site-packages/pytorch_lightning/trainer/trainer.py", line 580, in _fit_impl
[rank1]:     self._run(model, ckpt_path=ckpt_path)
[rank1]:   File "/data/home/haideraltahan/anaconda3/envs/RClip/lib/python3.12/site-packages/pytorch_lightning/trainer/trainer.py", line 987, in _run
[rank1]:     results = self._run_stage()
[rank1]:               ^^^^^^^^^^^^^^^^^
[rank1]:   File "/data/home/haideraltahan/anaconda3/envs/RClip/lib/python3.12/site-packages/pytorch_lightning/trainer/trainer.py", line 1033, in _run_stage
[rank1]:     self.fit_loop.run()
[rank1]:   File "/data/home/haideraltahan/anaconda3/envs/RClip/lib/python3.12/site-packages/pytorch_lightning/loops/fit_loop.py", line 205, in run
[rank1]:     self.advance()
[rank1]:   File "/data/home/haideraltahan/anaconda3/envs/RClip/lib/python3.12/site-packages/pytorch_lightning/loops/fit_loop.py", line 363, in advance
[rank1]:     self.epoch_loop.run(self._data_fetcher)
[rank1]:   File "/data/home/haideraltahan/anaconda3/envs/RClip/lib/python3.12/site-packages/pytorch_lightning/loops/training_epoch_loop.py", line 140, in run
[rank1]:     self.advance(data_fetcher)
[rank1]:   File "/data/home/haideraltahan/anaconda3/envs/RClip/lib/python3.12/site-packages/pytorch_lightning/loops/training_epoch_loop.py", line 250, in advance
[rank1]:     batch_output = self.automatic_optimization.run(trainer.optimizers[0], batch_idx, kwargs)
[rank1]:                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank1]:   File "/data/home/haideraltahan/anaconda3/envs/RClip/lib/python3.12/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 190, in run
[rank1]:     self._optimizer_step(batch_idx, closure)
[rank1]:   File "/data/home/haideraltahan/anaconda3/envs/RClip/lib/python3.12/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 268, in _optimizer_step
[rank1]:     call._call_lightning_module_hook(
[rank1]:   File "/data/home/haideraltahan/anaconda3/envs/RClip/lib/python3.12/site-packages/pytorch_lightning/trainer/call.py", line 157, in _call_lightning_module_hook
[rank1]:     output = fn(*args, **kwargs)
[rank1]:              ^^^^^^^^^^^^^^^^^^^
[rank1]:   File "/data/home/haideraltahan/anaconda3/envs/RClip/lib/python3.12/site-packages/pytorch_lightning/core/module.py", line 1303, in optimizer_step
[rank1]:     optimizer.step(closure=optimizer_closure)
[rank1]:   File "/data/home/haideraltahan/anaconda3/envs/RClip/lib/python3.12/site-packages/pytorch_lightning/core/optimizer.py", line 152, in step
[rank1]:     step_output = self._strategy.optimizer_step(self._optimizer, closure, **kwargs)
[rank1]:                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank1]:   File "/data/home/haideraltahan/anaconda3/envs/RClip/lib/python3.12/site-packages/pytorch_lightning/strategies/ddp.py", line 270, in optimizer_step
[rank1]:     optimizer_output = super().optimizer_step(optimizer, closure, model, **kwargs)
[rank1]:                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank1]:   File "/data/home/haideraltahan/anaconda3/envs/RClip/lib/python3.12/site-packages/pytorch_lightning/strategies/strategy.py", line 239, in optimizer_step
[rank1]:     return self.precision_plugin.optimizer_step(optimizer, model=model, closure=closure, **kwargs)
[rank1]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank1]:   File "/data/home/haideraltahan/anaconda3/envs/RClip/lib/python3.12/site-packages/pytorch_lightning/plugins/precision/amp.py", line 77, in optimizer_step
[rank1]:     return super().optimizer_step(optimizer, model=model, closure=closure, **kwargs)
[rank1]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank1]:   File "/data/home/haideraltahan/anaconda3/envs/RClip/lib/python3.12/site-packages/pytorch_lightning/plugins/precision/precision.py", line 122, in optimizer_step
[rank1]:     return optimizer.step(closure=closure, **kwargs)
[rank1]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank1]:   File "/data/home/haideraltahan/anaconda3/envs/RClip/lib/python3.12/site-packages/torch/optim/lr_scheduler.py", line 75, in wrapper
[rank1]:     return wrapped(*args, **kwargs)
[rank1]:            ^^^^^^^^^^^^^^^^^^^^^^^^
[rank1]:   File "/data/home/haideraltahan/anaconda3/envs/RClip/lib/python3.12/site-packages/torch/optim/optimizer.py", line 391, in wrapper
[rank1]:     out = func(*args, **kwargs)
[rank1]:           ^^^^^^^^^^^^^^^^^^^^^
[rank1]:   File "/data/home/haideraltahan/anaconda3/envs/RClip/lib/python3.12/site-packages/torch/optim/optimizer.py", line 76, in _use_grad
[rank1]:     ret = func(self, *args, **kwargs)
[rank1]:           ^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank1]:   File "/data/home/haideraltahan/anaconda3/envs/RClip/lib/python3.12/site-packages/torch/optim/adamw.py", line 165, in step
[rank1]:     loss = closure()
[rank1]:            ^^^^^^^^^
[rank1]:   File "/data/home/haideraltahan/anaconda3/envs/RClip/lib/python3.12/site-packages/pytorch_lightning/plugins/precision/precision.py", line 108, in _wrap_closure
[rank1]:     closure_result = closure()
[rank1]:                      ^^^^^^^^^
[rank1]:   File "/data/home/haideraltahan/anaconda3/envs/RClip/lib/python3.12/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 144, in __call__
[rank1]:     self._result = self.closure(*args, **kwargs)
[rank1]:                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank1]:   File "/data/home/haideraltahan/anaconda3/envs/RClip/lib/python3.12/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
[rank1]:     return func(*args, **kwargs)
[rank1]:            ^^^^^^^^^^^^^^^^^^^^^
[rank1]:   File "/data/home/haideraltahan/anaconda3/envs/RClip/lib/python3.12/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 129, in closure
[rank1]:     step_output = self._step_fn()
[rank1]:                   ^^^^^^^^^^^^^^^
[rank1]:   File "/data/home/haideraltahan/anaconda3/envs/RClip/lib/python3.12/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 318, in _training_step
[rank1]:     training_step_output = call._call_strategy_hook(trainer, "training_step", *kwargs.values())
[rank1]:                            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank1]:   File "/data/home/haideraltahan/anaconda3/envs/RClip/lib/python3.12/site-packages/pytorch_lightning/trainer/call.py", line 309, in _call_strategy_hook
[rank1]:     output = fn(*args, **kwargs)
[rank1]:              ^^^^^^^^^^^^^^^^^^^
[rank1]:   File "/data/home/haideraltahan/anaconda3/envs/RClip/lib/python3.12/site-packages/pytorch_lightning/strategies/strategy.py", line 390, in training_step
[rank1]:     return self._forward_redirection(self.model, self.lightning_module, "training_step", *args, **kwargs)
[rank1]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank1]:   File "/data/home/haideraltahan/anaconda3/envs/RClip/lib/python3.12/site-packages/pytorch_lightning/strategies/strategy.py", line 642, in __call__
[rank1]:     wrapper_output = wrapper_module(*args, **kwargs)
[rank1]:                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank1]:   File "/data/home/haideraltahan/anaconda3/envs/RClip/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[rank1]:     return self._call_impl(*args, **kwargs)
[rank1]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank1]:   File "/data/home/haideraltahan/anaconda3/envs/RClip/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[rank1]:     return forward_call(*args, **kwargs)
[rank1]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank1]:   File "/data/home/haideraltahan/anaconda3/envs/RClip/lib/python3.12/site-packages/torch/nn/parallel/distributed.py", line 1593, in forward
[rank1]:     else self._run_ddp_forward(*inputs, **kwargs)
[rank1]:          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank1]:   File "/data/home/haideraltahan/anaconda3/envs/RClip/lib/python3.12/site-packages/torch/nn/parallel/distributed.py", line 1411, in _run_ddp_forward
[rank1]:     return self.module(*inputs, **kwargs)  # type: ignore[index]
[rank1]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank1]:   File "/data/home/haideraltahan/anaconda3/envs/RClip/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[rank1]:     return self._call_impl(*args, **kwargs)
[rank1]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank1]:   File "/data/home/haideraltahan/anaconda3/envs/RClip/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[rank1]:     return forward_call(*args, **kwargs)
[rank1]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank1]:   File "/data/home/haideraltahan/anaconda3/envs/RClip/lib/python3.12/site-packages/pytorch_lightning/strategies/strategy.py", line 635, in wrapped_forward
[rank1]:     out = method(*_args, **_kwargs)
[rank1]:           ^^^^^^^^^^^^^^^^^^^^^^^^^
[rank1]:   File "/opt/hpcaas/.mounts/fs-0565f60d669b6a2d3/home/haideraltahan/RelationCLIP/modules/clip_module.py", line 49, in training_step
[rank1]:     model_out = self.model(images, input_texts)
[rank1]:                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank1]:   File "/data/home/haideraltahan/anaconda3/envs/RClip/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[rank1]:     return self._call_impl(*args, **kwargs)
[rank1]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank1]:   File "/data/home/haideraltahan/anaconda3/envs/RClip/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[rank1]:     return forward_call(*args, **kwargs)
[rank1]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank1]:   File "/data/home/haideraltahan/anaconda3/envs/RClip/lib/python3.12/site-packages/open_clip/coca_model.py", line 170, in forward
[rank1]:     text_latent, token_embs = self._encode_text(text)
[rank1]:                               ^^^^^^^^^^^^^^^^^^^^^^^
[rank1]:   File "/data/home/haideraltahan/anaconda3/envs/RClip/lib/python3.12/site-packages/open_clip/coca_model.py", line 145, in _encode_text
[rank1]:     text_latent, token_emb = self.text(text)
[rank1]:     ^^^^^^^^^^^^^^^^^^^^^^
[rank1]: ValueError: too many values to unpack (expected 2)

[rwightman]

@haideraltahan I don't think there is a problem here, looks more likely to be an issue with how model is being used/wrapped in whatever this use case is.

Can do model(torch.randn(2,3,224,224), torch.randint(0, 49408, (2, 76))) without issue, call text directly ,etc.

[Srividhya-Sainath]

Adding context_length=76 should help. Something like this:
text_tokens = tokenizer.tokenize(text_descriptions, context_length=76)