* Fix contrastive_search for new cache structure, and improve performance by removing inneficient torch.stack(torch.split(x, top_k, dim=0))
* Fix _contrastive_search for non-standard cache using ellipsis slicing
* Fix all outputs.logits memory leaks for all decoding strategies!
* Fix small error in _contrastive_search()
* Make all necessary change and revert for the new class
* Apply coding style
* Remove pipes in type hints for compatibility
* correct type hint
* apply style
* Use DynamicCache by default and solve conflicts
* Fix rebase issues
* Add `_supports_dynamic_cache_class` in models for models that support DynamicCache but not other caches to make DynamicCache the default for more models
* Create generation config to return legacy format by default, or to choose not to
* style
* Fix case when use_cache is False
* Remove default DynamicCache in assiste_decoding if assistant_model does not support it + fix _seen_tokens when cropping cache
* Update prepare_inputs_for_generation() for case with empty DynamicCache
* Correct return of args in _assisted_decoding
* Remove EfficientDynamicCache as it is no longer needed
* Correct mistake in generation config
* Move cache logic of assisted decoding to AssistedCandidateGenerator.__init__
* change DynamicCache function names from "split" to "batch_split" for readability + apply coding style
* Remove `_supports_dynamic_cache_class` attribute after rebase
* Correct missing line lost in conflict resolution during rebasing
* Add special case for Jamba
* Fix jamba test
* Coding style
* coding style
* Correct missing import in rebasing
* Simplify _validate_model_kwargs based on removal of _supports_dynamic_cache attribute
* Simplify code paths in _contrastive_search
* coding style
* Update docstrings of cache methods
* Update prepare_inputs_for_generation() -> past_key_values are always Cache objects
The StoppingCriteriaList allocates is_done without specifying dtype=torch.bool. On XLA this allocates a float tensor and causes a failure on the following line:
is_done = is_done | criteria(input_ids, scores, **kwargs)
by attempting to OR float with bool.
* Added interpolate pos encoding feature and test to deit
* Added interpolate pos encoding feature and test for deit TF model
* readded accidentally delted test for multi_gpu
* storing only patch_size instead of entire config and removed commented code
* Update modeling_tf_deit.py to remove extra line
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* add tokenizer_summary to es/_toctree.yml
* add tokenizer_summary to es/
* fix link to Transformes XL in en/
* translate until Subword tokenization section
* fix GPT link in en/
* fix other GPT link in en/
* fix typo in en/
* translate the doc
* run make fixup
* Remove .md in Transformer XL link
* fix some link issues in es/
* fix typo
* fix the get_size_with_aspect_ratio in max_size situation
* make fix-up
* add more general solution
* consider when max_size is not defined
* fix typo
* fix typo
* simple fix
* fix error
* fix if else error
* fix error of size overwrite
* fix yolos image processing
* fix detr image processing
* make
* add longest related test script
* Update src/transformers/models/yolos/image_processing_yolos.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* add more test
* add test script about longest size
* remove deprecated
---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
While running the model.prepare_tf_dataset() method,
it raises the error below:
```
TypeError: Cannot convert [array([322., 1.])] to EagerTensor of dtype int64
```
This happens, in "DataCollatorForSeq2Seq" function when we are try
to convert the labels to tensors. While converting the labels to tensors,
the labels can be in the format of list of list or list of ndarrays.
There is no problem converting the list of list lables. There is a problem
when the list of ndarrays are float values(like below).
```
[array([322., 1.])]
```
so the exception raises while trying to convert this label to tensors using
below code.
```
batch["labels"] = tf.constant(batch["labels"], dtype=tf.int64)
```
The labels are always integer values, so this got converted to float
values in the label padding operation below.
```
batch["labels"] = [
call(label)
if padding_side == "right"
else np.concatenate([[self.label_pad_token_id] * (max_label_length - len(label)), label])
for label in labels
]
```
Here we have 2 cases:
1 - Concatenating an array having integer padding token value with labels.
2 - Concatenating an empty array with labels.
----------------------------------------------------------------------------------------
case 1: Concatenating an array having integer padding token value with labels.
WORKS EXPECTED:
----------------------------------------------------------------------------------------
```
label = np.array([233, 1])
max_label_length = 4
label_pad_token_id = -100
np.concatenate([[label_pad_token_id] * (max_label_length - len(label)), label])
o/p:
array([-100, -100, 233, 1])
```
----------------------------------------------------------------------------------------
Case 2: Concatenating an empty array with labels.
GIVES THE ISSUE:
This scenorio can happen when the label has the maximum label length -- No padding needed.
----------------------------------------------------------------------------------------
```
label = np.array([233, 1])
max_label_length = 2
label_pad_token_id = -100
np.concatenate([[label_pad_token_id] * (max_label_length - len(label)), label])
o/p:
array([233., 1.])
```
----------------------------------------------------------------------------------------
Solution:
----------------------------------------------------------------------------------------
We need to concatenate a ndarray of dtype int with labels.
AFTER FIX:
----------
case 1:
```
label = np.array([233, 1])
max_label_length = 4
label_pad_token_id = -100
np.concatenate([np.array([label_pad_token_id] * (max_label_length - len(label)), dtype=np.int64),label])
o/p:
array([-100, -100, 233, 1])
```
case 2:
```
label = np.array([233, 1])
max_label_length = 2
label_pad_token_id = -100
np.concatenate([np.array([label_pad_token_id] * (max_label_length - len(label)), dtype=np.int64),label])
o/p:
array([233, 1])
```
* token healing impl + trie with extensions
* make fixup
* prefix-robust space tokenization
* examples readme and requirements
* make fixup
* allow input prompt and model
* redundant defaults
* Specialized Trie
* make fixup
* updated tests with new inherited Tree
* input ids to auto device_map
* rm unused import
* Update src/transformers/generation/utils.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* naming convention
* Revert "naming convention"
This reverts commit dd39d9c5b7a969e2d8a8d2a8e54f121b82dc44f0.
* naming convention
* last -hopefully- changes
---------
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Corrected a typo in security.md. Changed `use_safetenstors` to `use_safetensors` in the section discussing the usage of safe formats for loading models to prevent arbitrary code execution.
