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Switch return_dict to True by default. (#8530)

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* Use the CI to identify failing tests

* Remove from all examples and tests

* More default switch

* Fixes

* More test fixes

* More fixes

* Last fixes hopefully

* Use the CI to identify failing tests

* Remove from all examples and tests

* More default switch

* Fixes

* More test fixes

* More fixes

* Last fixes hopefully

* Run on the real suite

* Fix slow tests
This commit is contained in:
Sylvain Gugger
2020-11-16 11:43:00 -05:00
committed by GitHub
parent 0d0a0785fd
commit 1073a2bde5
106 changed files with 138 additions and 234 deletions
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28
src/transformers/file_utils.py
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@@ -559,7 +559,7 @@ PT_TOKEN_CLASSIFICATION_SAMPLE = r"""
>>> import torch
>>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
>>> model = {model_class}.from_pretrained('{checkpoint}', return_dict=True)
>>> model = {model_class}.from_pretrained('{checkpoint}')
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> labels = torch.tensor([1] * inputs["input_ids"].size(1)).unsqueeze(0) # Batch size 1
@@ -576,7 +576,7 @@ PT_QUESTION_ANSWERING_SAMPLE = r"""
>>> import torch
>>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
>>> model = {model_class}.from_pretrained('{checkpoint}', return_dict=True)
>>> model = {model_class}.from_pretrained('{checkpoint}')
>>> question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
>>> inputs = tokenizer(question, text, return_tensors='pt')
@@ -596,7 +596,7 @@ PT_SEQUENCE_CLASSIFICATION_SAMPLE = r"""
>>> import torch
>>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
>>> model = {model_class}.from_pretrained('{checkpoint}', return_dict=True)
>>> model = {model_class}.from_pretrained('{checkpoint}')
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> labels = torch.tensor([1]).unsqueeze(0) # Batch size 1
@@ -612,7 +612,7 @@ PT_MASKED_LM_SAMPLE = r"""
>>> import torch
>>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
>>> model = {model_class}.from_pretrained('{checkpoint}', return_dict=True)
>>> model = {model_class}.from_pretrained('{checkpoint}')
>>> inputs = tokenizer("The capital of France is {mask}.", return_tensors="pt")
>>> labels = tokenizer("The capital of France is Paris.", return_tensors="pt")["input_ids"]
@@ -629,7 +629,7 @@ PT_BASE_MODEL_SAMPLE = r"""
>>> import torch
>>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
>>> model = {model_class}.from_pretrained('{checkpoint}', return_dict=True)
>>> model = {model_class}.from_pretrained('{checkpoint}')
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> outputs = model(**inputs)
@@ -644,7 +644,7 @@ PT_MULTIPLE_CHOICE_SAMPLE = r"""
>>> import torch
>>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
>>> model = {model_class}.from_pretrained('{checkpoint}', return_dict=True)
>>> model = {model_class}.from_pretrained('{checkpoint}')
>>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
>>> choice0 = "It is eaten with a fork and a knife."
@@ -666,7 +666,7 @@ PT_CAUSAL_LM_SAMPLE = r"""
>>> from transformers import {tokenizer_class}, {model_class}
>>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
>>> model = {model_class}.from_pretrained('{checkpoint}', return_dict=True)
>>> model = {model_class}.from_pretrained('{checkpoint})
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> outputs = model(**inputs, labels=inputs["input_ids"])
@@ -681,7 +681,7 @@ TF_TOKEN_CLASSIFICATION_SAMPLE = r"""
>>> import tensorflow as tf
>>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
>>> model = {model_class}.from_pretrained('{checkpoint}', return_dict=True)
>>> model = {model_class}.from_pretrained('{checkpoint}')
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
>>> input_ids = inputs["input_ids"]
@@ -699,7 +699,7 @@ TF_QUESTION_ANSWERING_SAMPLE = r"""
>>> import tensorflow as tf
>>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
>>> model = {model_class}.from_pretrained('{checkpoint}', return_dict=True)
>>> model = {model_class}.from_pretrained('{checkpoint}')
>>> question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
>>> input_dict = tokenizer(question, text, return_tensors='tf')
@@ -718,7 +718,7 @@ TF_SEQUENCE_CLASSIFICATION_SAMPLE = r"""
>>> import tensorflow as tf
>>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
>>> model = {model_class}.from_pretrained('{checkpoint}', return_dict=True)
>>> model = {model_class}.from_pretrained('{checkpoint}')
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
>>> inputs["labels"] = tf.reshape(tf.constant(1), (-1, 1)) # Batch size 1
@@ -735,7 +735,7 @@ TF_MASKED_LM_SAMPLE = r"""
>>> import tensorflow as tf
>>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
>>> model = {model_class}.from_pretrained('{checkpoint}', return_dict=True)
>>> model = {model_class}.from_pretrained('{checkpoint}')
>>> inputs = tokenizer("The capital of France is {mask}.", return_tensors="tf")
>>> inputs["labels"] = tokenizer("The capital of France is Paris.", return_tensors="tf")["input_ids"]
@@ -752,7 +752,7 @@ TF_BASE_MODEL_SAMPLE = r"""
>>> import tensorflow as tf
>>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
>>> model = {model_class}.from_pretrained('{checkpoint}', return_dict=True)
>>> model = {model_class}.from_pretrained('{checkpoint}')
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
>>> outputs = model(inputs)
@@ -767,7 +767,7 @@ TF_MULTIPLE_CHOICE_SAMPLE = r"""
>>> import tensorflow as tf
>>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
>>> model = {model_class}.from_pretrained('{checkpoint}', return_dict=True)
>>> model = {model_class}.from_pretrained('{checkpoint}')
>>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
>>> choice0 = "It is eaten with a fork and a knife."
@@ -788,7 +788,7 @@ TF_CAUSAL_LM_SAMPLE = r"""
>>> import tensorflow as tf
>>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
>>> model = {model_class}.from_pretrained('{checkpoint}', return_dict=True)
>>> model = {model_class}.from_pretrained('{checkpoint}')
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
>>> outputs = model(inputs)