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Tests: detect lines removed from "utils/not_doctested.txt" and doctest ALL generation files (#25763)
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Joao Gante
GitHub
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@ -55,7 +55,7 @@ When you load a model explicitly, you can inspect the generation configuration t
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>>> from transformers import AutoModelForCausalLM
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>>> model = AutoModelForCausalLM.from_pretrained("distilgpt2")
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>>> model.generation_config
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>>> model.generation_config # doctest: +IGNORE_RESULT
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GenerationConfig {
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"_from_model_config": true,
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"bos_token_id": 50256,
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@ -77,7 +77,7 @@ producing highly repetitive results.
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You can override any `generation_config` by passing the parameters and their values directly to the [`generate`] method:
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```python
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>>> my_model.generate(**inputs, num_beams=4, do_sample=True)
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>>> my_model.generate(**inputs, num_beams=4, do_sample=True) # doctest: +SKIP
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```
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Even if the default decoding strategy mostly works for your task, you can still tweak a few things. Some of the
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@ -107,11 +107,11 @@ If you would like to share your fine-tuned model with a specific generation conf
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```python
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>>> from transformers import AutoModelForCausalLM, GenerationConfig
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>>> model = AutoModelForCausalLM.from_pretrained("my_account/my_model")
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>>> model = AutoModelForCausalLM.from_pretrained("my_account/my_model") # doctest: +SKIP
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>>> generation_config = GenerationConfig(
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... max_new_tokens=50, do_sample=True, top_k=50, eos_token_id=model.config.eos_token_id
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... )
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>>> generation_config.save_pretrained("my_account/my_model", push_to_hub=True)
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>>> generation_config.save_pretrained("my_account/my_model", push_to_hub=True) # doctest: +SKIP
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```
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You can also store several generation configurations in a single directory, making use of the `config_file_name`
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@ -133,14 +133,15 @@ one for summarization with beam search). You must have the right Hub permissions
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... pad_token=model.config.pad_token_id,
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... )
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>>> translation_generation_config.save_pretrained("t5-small", "translation_generation_config.json", push_to_hub=True)
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>>> # Tip: add `push_to_hub=True` to push to the Hub
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>>> translation_generation_config.save_pretrained("/tmp", "translation_generation_config.json")
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>>> # You could then use the named generation config file to parameterize generation
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>>> generation_config = GenerationConfig.from_pretrained("t5-small", "translation_generation_config.json")
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>>> generation_config = GenerationConfig.from_pretrained("/tmp", "translation_generation_config.json")
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>>> inputs = tokenizer("translate English to French: Configuration files are easy to use!", return_tensors="pt")
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>>> outputs = model.generate(**inputs, generation_config=generation_config)
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>>> print(tokenizer.batch_decode(outputs, skip_special_tokens=True))
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['Les fichiers de configuration sont faciles à utiliser !']
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['Les fichiers de configuration sont faciles à utiliser!']
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```
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## Streaming
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@ -217,10 +218,9 @@ The two main parameters that enable and control the behavior of contrastive sear
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>>> outputs = model.generate(**inputs, penalty_alpha=0.6, top_k=4, max_new_tokens=100)
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>>> tokenizer.batch_decode(outputs, skip_special_tokens=True)
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['Hugging Face Company is a family owned and operated business. \
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We pride ourselves on being the best in the business and our customer service is second to none.\
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\n\nIf you have any questions about our products or services, feel free to contact us at any time.\
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We look forward to hearing from you!']
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['Hugging Face Company is a family owned and operated business. We pride ourselves on being the best
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in the business and our customer service is second to none.\n\nIf you have any questions about our
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products or services, feel free to contact us at any time. We look forward to hearing from you!']
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```
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### Multinomial sampling
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@ -233,7 +233,8 @@ risk of repetition.
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To enable multinomial sampling set `do_sample=True` and `num_beams=1`.
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```python
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>>> from transformers import AutoTokenizer, AutoModelForCausalLM
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>>> from transformers import AutoTokenizer, AutoModelForCausalLM, set_seed
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>>> set_seed(0) # For reproducibility
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>>> checkpoint = "gpt2-large"
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>>> tokenizer = AutoTokenizer.from_pretrained(checkpoint)
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@ -244,11 +245,8 @@ To enable multinomial sampling set `do_sample=True` and `num_beams=1`.
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>>> outputs = model.generate(**inputs, do_sample=True, num_beams=1, max_new_tokens=100)
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>>> tokenizer.batch_decode(outputs, skip_special_tokens=True)
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['Today was an amazing day because we are now in the final stages of our trip to New York City which was very tough. \
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It is a difficult schedule and a challenging part of the year but still worth it. I have been taking things easier and \
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I feel stronger and more motivated to be out there on their tour. Hopefully, that experience is going to help them with \
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their upcoming events which are currently scheduled in Australia.\n\nWe love that they are here. They want to make a \
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name for themselves and become famous for what they']
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['Today was an amazing day because when you go to the World Cup and you don\'t, or when you don\'t get invited,
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that\'s a terrible feeling."']
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```
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### Beam-search decoding
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@ -272,7 +270,7 @@ To enable this decoding strategy, specify the `num_beams` (aka number of hypothe
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>>> outputs = model.generate(**inputs, num_beams=5, max_new_tokens=50)
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>>> tokenizer.batch_decode(outputs, skip_special_tokens=True)
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['It is astonishing how one can have such a profound impact on the lives of so many people in such a short period of \
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['It is astonishing how one can have such a profound impact on the lives of so many people in such a short period of
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time."\n\nHe added: "I am very proud of the work I have been able to do in the last few years.\n\n"I have']
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```
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@ -282,7 +280,8 @@ As the name implies, this decoding strategy combines beam search with multinomia
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the `num_beams` greater than 1, and set `do_sample=True` to use this decoding strategy.
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```python
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>>> from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
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>>> from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, set_seed
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>>> set_seed(0) # For reproducibility
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>>> prompt = "translate English to German: The house is wonderful."
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>>> checkpoint = "t5-small"
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@ -309,20 +308,22 @@ The diversily penalty ensures the outputs are distinct across groups, and beam s
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>>> from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
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>>> checkpoint = "google/pegasus-xsum"
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>>> prompt = "The Permaculture Design Principles are a set of universal design principles \
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>>> that can be applied to any location, climate and culture, and they allow us to design \
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>>> the most efficient and sustainable human habitation and food production systems. \
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>>> Permaculture is a design system that encompasses a wide variety of disciplines, such \
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>>> as ecology, landscape design, environmental science and energy conservation, and the \
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>>> Permaculture design principles are drawn from these various disciplines. Each individual \
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>>> design principle itself embodies a complete conceptual framework based on sound \
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>>> scientific principles. When we bring all these separate principles together, we can \
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>>> create a design system that both looks at whole systems, the parts that these systems \
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>>> consist of, and how those parts interact with each other to create a complex, dynamic, \
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>>> living system. Each design principle serves as a tool that allows us to integrate all \
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>>> the separate parts of a design, referred to as elements, into a functional, synergistic, \
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>>> whole system, where the elements harmoniously interact and work together in the most \
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>>> efficient way possible."
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>>> prompt = (
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... "The Permaculture Design Principles are a set of universal design principles "
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... "that can be applied to any location, climate and culture, and they allow us to design "
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... "the most efficient and sustainable human habitation and food production systems. "
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... "Permaculture is a design system that encompasses a wide variety of disciplines, such "
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... "as ecology, landscape design, environmental science and energy conservation, and the "
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... "Permaculture design principles are drawn from these various disciplines. Each individual "
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... "design principle itself embodies a complete conceptual framework based on sound "
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... "scientific principles. When we bring all these separate principles together, we can "
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... "create a design system that both looks at whole systems, the parts that these systems "
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... "consist of, and how those parts interact with each other to create a complex, dynamic, "
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... "living system. Each design principle serves as a tool that allows us to integrate all "
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... "the separate parts of a design, referred to as elements, into a functional, synergistic, "
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... "whole system, where the elements harmoniously interact and work together in the most "
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... "efficient way possible."
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... )
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>>> tokenizer = AutoTokenizer.from_pretrained(checkpoint)
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>>> inputs = tokenizer(prompt, return_tensors="pt")
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@ -369,7 +370,8 @@ When using assisted decoding with sampling methods, you can use the `temperarure
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just like in multinomial sampling. However, in assisted decoding, reducing the temperature will help improving latency.
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```python
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>>> from transformers import AutoModelForCausalLM, AutoTokenizer
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>>> from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed
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>>> set_seed(42) # For reproducibility
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>>> prompt = "Alice and Bob"
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>>> checkpoint = "EleutherAI/pythia-1.4b-deduped"
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@ -382,5 +384,5 @@ just like in multinomial sampling. However, in assisted decoding, reducing the t
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>>> assistant_model = AutoModelForCausalLM.from_pretrained(assistant_checkpoint)
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>>> outputs = model.generate(**inputs, assistant_model=assistant_model, do_sample=True, temperature=0.5)
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>>> tokenizer.batch_decode(outputs, skip_special_tokens=True)
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["Alice and Bob are sitting on the sofa. Alice says, 'I'm going to my room"]
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['Alice and Bob are going to the same party. It is a small party, in a small']
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```
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@ -14,14 +14,12 @@ docs/source/en/custom_models.md
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docs/source/en/custom_tools.md
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docs/source/en/debugging.md
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docs/source/en/fast_tokenizers.md
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docs/source/en/generation_strategies.md
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docs/source/en/glossary.md
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docs/source/en/hpo_train.md
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docs/source/en/index.md
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docs/source/en/installation.md
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docs/source/en/internal/audio_utils.md
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docs/source/en/internal/file_utils.md
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docs/source/en/internal/generation_utils.md
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docs/source/en/internal/image_processing_utils.md
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docs/source/en/internal/modeling_utils.md
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docs/source/en/internal/pipelines_utils.md
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@ -45,7 +43,6 @@ docs/source/en/main_classes/output.md
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docs/source/en/main_classes/pipelines.md
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docs/source/en/main_classes/processors.md
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docs/source/en/main_classes/quantization.md
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docs/source/en/main_classes/text_generation.md
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docs/source/en/main_classes/tokenizer.md
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docs/source/en/main_classes/trainer.md
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docs/source/en/model_doc/albert.md
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@ -367,16 +364,6 @@ src/transformers/dynamic_module_utils.py
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src/transformers/feature_extraction_sequence_utils.py
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src/transformers/feature_extraction_utils.py
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src/transformers/file_utils.py
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src/transformers/generation/beam_constraints.py
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src/transformers/generation/beam_search.py
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src/transformers/generation/flax_logits_process.py
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src/transformers/generation/flax_utils.py
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src/transformers/generation/stopping_criteria.py
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src/transformers/generation/streamers.py
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src/transformers/generation/tf_logits_process.py
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src/transformers/generation_flax_utils.py
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src/transformers/generation_tf_utils.py
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src/transformers/generation_utils.py
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src/transformers/hf_argparser.py
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src/transformers/hyperparameter_search.py
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src/transformers/image_processing_utils.py
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@ -413,7 +400,6 @@ src/transformers/models/auto/modeling_tf_auto.py
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src/transformers/models/autoformer/configuration_autoformer.py
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src/transformers/models/autoformer/modeling_autoformer.py
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src/transformers/models/bark/convert_suno_to_hf.py
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src/transformers/models/bark/generation_configuration_bark.py
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src/transformers/models/bart/convert_bart_original_pytorch_checkpoint_to_pytorch.py
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src/transformers/models/bart/modeling_flax_bart.py
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src/transformers/models/bart/modeling_tf_bart.py
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@ -925,9 +911,7 @@ src/transformers/pipelines/object_detection.py
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src/transformers/pipelines/pt_utils.py
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src/transformers/pipelines/question_answering.py
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src/transformers/pipelines/table_question_answering.py
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src/transformers/pipelines/text2text_generation.py
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src/transformers/pipelines/text_classification.py
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src/transformers/pipelines/text_generation.py
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src/transformers/pipelines/token_classification.py
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src/transformers/pipelines/video_classification.py
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src/transformers/pipelines/visual_question_answering.py
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@ -1 +1,2 @@
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docs/source/en/generation_strategies.md
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docs/source/en/task_summary.md
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@ -395,6 +395,31 @@ def get_all_doctest_files() -> List[str]:
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return sorted(test_files_to_run)
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def get_new_doctest_files(repo, base_commit, branching_commit) -> List[str]:
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"""
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Get the list of files that were removed from "utils/not_doctested.txt", between `base_commit` and
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`branching_commit`.
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Returns:
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`List[str]`: List of files that were removed from "utils/not_doctested.txt".
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"""
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for diff_obj in branching_commit.diff(base_commit):
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# Ignores all but the "utils/not_doctested.txt" file.
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if diff_obj.a_path != "utils/not_doctested.txt":
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continue
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# Loads the two versions
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folder = Path(repo.working_dir)
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with checkout_commit(repo, branching_commit):
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with open(folder / "utils/not_doctested.txt", "r", encoding="utf-8") as f:
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old_content = f.read()
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with open(folder / "utils/not_doctested.txt", "r", encoding="utf-8") as f:
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new_content = f.read()
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# Compute the removed lines and return them
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removed_content = set(old_content.split("\n")) - set(new_content.split("\n"))
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return sorted(removed_content)
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return []
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def get_doctest_files(diff_with_last_commit: bool = False) -> List[str]:
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"""
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Return a list of python and Markdown files where doc example have been modified between:
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@ -426,6 +451,10 @@ def get_doctest_files(diff_with_last_commit: bool = False) -> List[str]:
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all_test_files_to_run = get_all_doctest_files()
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# Add to the test files to run any removed entry from "utils/not_doctested.txt".
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new_test_files = get_new_doctest_files(repo, repo.head.commit, repo.refs.main.commit)
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test_files_to_run = list(set(test_files_to_run + new_test_files))
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# Do not run slow doctest tests on CircleCI
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with open("utils/slow_documentation_tests.txt") as fp:
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slow_documentation_tests = set(fp.read().strip().split("\n"))
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