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Check the repo consistency in model templates test (#15141)

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* Check the repo consistency in model templates test

* Fix doc template

* Fix docstrings

* Fix last docstring
This commit is contained in:
Sylvain Gugger 2022-01-14 04:52:38 -05:00 committed by GitHub
parent 96881729ce
commit 5f3c57fc84
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4 changed files with 57 additions and 50 deletions
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2
.github/workflows/model-templates.yml vendored
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@ -61,7 +61,7 @@ jobs:
- name: Run style changes
run: |
git fetch origin master:master
make style && make quality
make style && make quality && make repo-consistency
- name: Failure short reports
if: ${{ always() }}

67
templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/modeling_flax_{{cookiecutter.lowercase_modelname}}.py
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@ -2119,17 +2119,18 @@ class Flax{{cookiecutter.camelcase_modelname}}PreTrainedModel(FlaxPreTrainedMode
r"""
Returns:
Example::
Example:
>>> from transformers import {{cookiecutter.camelcase_modelname}}Tokenizer, Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration
```python
>>> from transformers import {{cookiecutter.camelcase_modelname}}Tokenizer, Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration
>>> model = Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration.from_pretrained('{{cookiecutter.checkpoint_identifier}}')
>>> tokenizer = {{cookiecutter.camelcase_modelname}}Tokenizer.from_pretrained('{{cookiecutter.checkpoint_identifier}}')
>>> model = Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration.from_pretrained('{{cookiecutter.checkpoint_identifier}}')
>>> tokenizer = {{cookiecutter.camelcase_modelname}}Tokenizer.from_pretrained('{{cookiecutter.checkpoint_identifier}}')
>>> text = "My friends are cool but they eat too many carbs."
>>> inputs = tokenizer(text, max_length=1024, return_tensors='np')
>>> encoder_outputs = model.encode(**inputs)
"""
>>> text = "My friends are cool but they eat too many carbs."
>>> inputs = tokenizer(text, max_length=1024, return_tensors='np')
>>> encoder_outputs = model.encode(**inputs)
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
@ -2184,23 +2185,24 @@ class Flax{{cookiecutter.camelcase_modelname}}PreTrainedModel(FlaxPreTrainedMode
r"""
Returns:
Example::
Example:
>>> from transformers import {{cookiecutter.camelcase_modelname}}Tokenizer, Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration
```python
>>> from transformers import {{cookiecutter.camelcase_modelname}}Tokenizer, Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration
>>> model = Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration.from_pretrained('{{cookiecutter.checkpoint_identifier}}')
>>> tokenizer = {{cookiecutter.camelcase_modelname}}Tokenizer.from_pretrained('{{cookiecutter.checkpoint_identifier}}')
>>> model = Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration.from_pretrained('{{cookiecutter.checkpoint_identifier}}')
>>> tokenizer = {{cookiecutter.camelcase_modelname}}Tokenizer.from_pretrained('{{cookiecutter.checkpoint_identifier}}')
>>> text = "My friends are cool but they eat too many carbs."
>>> inputs = tokenizer(text, max_length=1024, return_tensors='np')
>>> encoder_outputs = model.encode(**inputs)
>>> text = "My friends are cool but they eat too many carbs."
>>> inputs = tokenizer(text, max_length=1024, return_tensors='np')
>>> encoder_outputs = model.encode(**inputs)
>>> decoder_start_token_id = model.config.decoder_start_token_id
>>> decoder_input_ids = jnp.ones((inputs.input_ids.shape[0], 1), dtype="i4") * decoder_start_token_id
>>> decoder_start_token_id = model.config.decoder_start_token_id
>>> decoder_input_ids = jnp.ones((inputs.input_ids.shape[0], 1), dtype="i4") * decoder_start_token_id
>>> outputs = model.decode(decoder_input_ids, encoder_outputs)
>>> last_decoder_hidden_states = outputs.last_hidden_state
"""
>>> outputs = model.decode(decoder_input_ids, encoder_outputs)
>>> last_decoder_hidden_states = outputs.last_hidden_state
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
@ -2450,23 +2452,24 @@ class Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration(Flax{{coo
r"""
Returns:
Example::
Example:
>>> from transformers import {{cookiecutter.camelcase_modelname}}Tokenizer, Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration
```python
>>> from transformers import {{cookiecutter.camelcase_modelname}}Tokenizer, Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration
>>> model = Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration.from_pretrained('{{cookiecutter.checkpoint_identifier}}')
>>> tokenizer = {{cookiecutter.camelcase_modelname}}Tokenizer.from_pretrained('{{cookiecutter.checkpoint_identifier}}')
>>> model = Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration.from_pretrained('{{cookiecutter.checkpoint_identifier}}')
>>> tokenizer = {{cookiecutter.camelcase_modelname}}Tokenizer.from_pretrained('{{cookiecutter.checkpoint_identifier}}')
>>> text = "My friends are cool but they eat too many carbs."
>>> inputs = tokenizer(text, max_length=1024, return_tensors='np')
>>> encoder_outputs = model.encode(**inputs)
>>> text = "My friends are cool but they eat too many carbs."
>>> inputs = tokenizer(text, max_length=1024, return_tensors='np')
>>> encoder_outputs = model.encode(**inputs)
>>> decoder_start_token_id = model.config.decoder_start_token_id
>>> decoder_input_ids = jnp.ones((inputs.input_ids.shape[0], 1), dtype="i4") * decoder_start_token_id
>>> decoder_start_token_id = model.config.decoder_start_token_id
>>> decoder_input_ids = jnp.ones((inputs.input_ids.shape[0], 1), dtype="i4") * decoder_start_token_id
>>> outputs = model.decode(decoder_input_ids, encoder_outputs)
>>> logits = outputs.logits
"""
>>> outputs = model.decode(decoder_input_ids, encoder_outputs)
>>> logits = outputs.logits
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states

36
templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/modeling_tf_{{cookiecutter.lowercase_modelname}}.py
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@ -878,7 +878,7 @@ class TF{{cookiecutter.camelcase_modelname}}PreTrainedModel(TFPreTrainedModel):
{{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING = r"""
Args:
input_ids (`np.ndarray`, `tf.Tensor`, `List[tf.Tensor]` ``Dict[str, tf.Tensor]` or `Dict[str, np.ndarray]` and each example must have the shape `({0})`):
input_ids (`np.ndarray`, `tf.Tensor`, `List[tf.Tensor]`, `Dict[str, tf.Tensor]` or `Dict[str, np.ndarray]` and each example must have the shape `({0})`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`BertTokenizer`]. See
@ -925,7 +925,7 @@ class TF{{cookiecutter.camelcase_modelname}}PreTrainedModel(TFPreTrainedModel):
return_dict (`bool`, *optional*):
Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple. This
argument can be used in eager mode, in graph mode the value will always be set to True.
training (`bool`, *optional*, defaults to `False``):
training (`bool`, *optional*, defaults to `False`):
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).
"""
@ -2611,8 +2611,11 @@ class TF{{cookiecutter.camelcase_modelname}}Decoder(tf.keras.layers.Layer):
If `past_key_values` are used, the user can optionally input only the last
`decoder_input_ids` (those that don't have their past key value states given to this model) of
shape `(batch_size, 1)` instead of all ``decoder_input_ids``` of shape `(batch_size,
sequence_length)`. inputs_embeds (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices
shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size,
sequence_length)`.
inputs_embeds (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices
into associated vectors than the model's internal embedding lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
@ -3085,19 +3088,20 @@ class TF{{cookiecutter.camelcase_modelname}}ForConditionalGeneration(TF{{cookiec
"""
Returns:
Examples::
Examples:
>>> from transformers import {{cookiecutter.camelcase_modelname}}Tokenizer, TF{{cookiecutter.camelcase_modelname}}ForConditionalGeneration
>>> import tensorflow as tf
>>> mname = '{{cookiecutter.checkpoint_identifier}}'
>>> tokenizer = {{cookiecutter.camelcase_modelname}}Tokenizer.from_pretrained(mname)
>>> TXT = "My friends are <mask> but they eat too many carbs."
>>> model = TF{{cookiecutter.camelcase_modelname}}ForConditionalGeneration.from_pretrained(mname)
>>> batch = tokenizer([TXT], return_tensors='tf')
>>> logits = model(inputs=batch.input_ids).logits
>>> probs = tf.nn.softmax(logits[0])
>>> # probs[5] is associated with the mask token
"""
```python
>>> from transformers import {{cookiecutter.camelcase_modelname}}Tokenizer, TF{{cookiecutter.camelcase_modelname}}ForConditionalGeneration
>>> import tensorflow as tf
>>> mname = '{{cookiecutter.checkpoint_identifier}}'
>>> tokenizer = {{cookiecutter.camelcase_modelname}}Tokenizer.from_pretrained(mname)
>>> TXT = "My friends are <mask> but they eat too many carbs."
>>> model = TF{{cookiecutter.camelcase_modelname}}ForConditionalGeneration.from_pretrained(mname)
>>> batch = tokenizer([TXT], return_tensors='tf')
>>> logits = model(inputs=batch.input_ids).logits
>>> probs = tf.nn.softmax(logits[0])
>>> # probs[5] is associated with the mask token
```"""
inputs = input_processing(
func=self.call,
config=self.config,

2
templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/{{cookiecutter.lowercase_modelname}}.mdx
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@ -127,7 +127,7 @@ This model was contributed by [INSERT YOUR HF USERNAME HERE](<https://huggingfac
- call
## TF{{cookiecutter.camelcase_modelname}}ForCausalLM[[autodoc]]
## TF{{cookiecutter.camelcase_modelname}}ForCausalLM
[[autodoc]] TF{{cookiecutter.camelcase_modelname}}ForCausalLM
- call