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.
* current working example!
* commit regex and result file
* update
* nit
* push the conversion file
* oups
* roadmap and nits
* attempt diffs for 3 files
* persimmon
* nit
* add diff file that is the same as the modeling_llama.py
* fix rope nits
* updates
* updates with converted versions
* give some breathing space to the code
* delete
* update
* update
* push the actual result
* update regex patterns
* update regex patterns
* fix some issues
* fix some issues
* fix some issues
* updates
* updates
* updates
* updates
* updates
* revert changes done to llama
* updates
* update gemma
* updates
* oups
* current state
* current state
* update
* ouiiii
* nit
* clear diffs
* nit
* fixup
* update
* doc 🚀
* 🔥
* for now use gemma
* deal with comments
* style
* handle funtions
* deal with assigns
* todos
* process inheritage
* keep decorators?
* 🤗
* deal with duplicates
* fixup
* correctly remove duplicate code
* run ruff post script
* ruff deals pretty well with imports, let's leave it to him
* ah maybe not lol
* for now remove all imports from child.
* nit
* conversion of llama
* okay
* convert starcoder2
* synch with main
* update llama diff
* updates
* https://docs.astral.sh/ruff/rules/redefined-while-unused/ fixes the imports, bit needs later version of ruff
* updates
* okay actual state
* non zero exit
* update!
* revert unrelated
* remove other diff files
* updates
* cleanup
* update
* less diff!
* stash
* current updates
* updates
* No need for call
* finished fining deps
* update
* current changes
* current state
* current state
* new status
* nit
* finally
* fixes
* nits
* order is now expected
* use logger info instead of prints
* fixup
* up
* nit
* update
* nits
* update
* correct merge
* update
* update
* update
* add warning
* update caution message
* update
* better merging strategy
* copy class statements :wink
* fixups
* nits
* update
* Apply suggestions from code review
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* nits
* smaller header
* do cleanup some stuff
* even simpler header?
* fixup
* updates
* ruff
* update examples
* nit
* TODO
* state
* OUUUUUUF
* current state
* nits
* final state
* add a readme
* fixup
* remove diff llama
* fix
* nit
* dummy noy funny
* ruff format tests src utils --check
* everless diffs
* less diffs and fix test
* fixes
* naming nit?
* update converter and add supper example
* nits
* updated for function signatures
* update
* update
* add converted dummies
* autoformat
* single target assign fix
* fixup
* fix some imports
* fixes
* don't push them
* `# noqa: F841`
---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Description of quantization_config
Added missing description about quantization_config in replace_with_bnb_linear for better readability.
* Removed trailing spaces
`mask` variable is not defined. probably a writing mistake. it should be `segmentation_map`. `segmentation_map` should be a `1` channel image rather than `RGB`.
[on a different note, the `mask_url` is the same as `raw_image`. could provide a better example.
