Examples reorg (#11350)
* Base move * Examples reorganization * Update references * Put back test data * Move conftest * More fixes * Move test data to test fixtures * Update path * Apply suggestions from code review Co-authored-by: Lysandre Debut <lysandre@huggingface.co> * Address review comments and clean Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
This commit is contained in:
Sylvain Gugger
31
examples/pytorch/text-generation/README.md
Normal file
31
examples/pytorch/text-generation/README.md
Normal file
@@ -0,0 +1,31 @@
|
||||
<!---
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License");
|
||||
you may not use this file except in compliance with the License.
|
||||
You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software
|
||||
distributed under the License is distributed on an "AS IS" BASIS,
|
||||
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
See the License for the specific language governing permissions and
|
||||
limitations under the License.
|
||||
-->
|
||||
|
||||
## Language generation
|
||||
|
||||
Based on the script [`run_generation.py`](https://github.com/huggingface/transformers/blob/master/examples/text-generation/run_generation.py).
|
||||
|
||||
Conditional text generation using the auto-regressive models of the library: GPT, GPT-2, Transformer-XL, XLNet, CTRL.
|
||||
A similar script is used for our official demo [Write With Transfomer](https://transformer.huggingface.co), where you
|
||||
can try out the different models available in the library.
|
||||
|
||||
Example usage:
|
||||
|
||||
```bash
|
||||
python run_generation.py \
|
||||
--model_type=gpt2 \
|
||||
--model_name_or_path=gpt2
|
||||
```
|
||||
3
examples/pytorch/text-generation/requirements.txt
Normal file
3
examples/pytorch/text-generation/requirements.txt
Normal file
@@ -0,0 +1,3 @@
|
||||
sentencepiece != 0.1.92
|
||||
protobuf
|
||||
torch >= 1.3
|
||||
290
examples/pytorch/text-generation/run_generation.py
Executable file
290
examples/pytorch/text-generation/run_generation.py
Executable file
@@ -0,0 +1,290 @@
|
||||
#!/usr/bin/env python
|
||||
# coding=utf-8
|
||||
# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team.
|
||||
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
""" Conditional text generation with the auto-regressive models of the library (GPT/GPT-2/CTRL/Transformer-XL/XLNet)
|
||||
"""
|
||||
|
||||
|
||||
import argparse
|
||||
import logging
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
|
||||
from transformers import (
|
||||
CTRLLMHeadModel,
|
||||
CTRLTokenizer,
|
||||
GPT2LMHeadModel,
|
||||
GPT2Tokenizer,
|
||||
OpenAIGPTLMHeadModel,
|
||||
OpenAIGPTTokenizer,
|
||||
TransfoXLLMHeadModel,
|
||||
TransfoXLTokenizer,
|
||||
XLMTokenizer,
|
||||
XLMWithLMHeadModel,
|
||||
XLNetLMHeadModel,
|
||||
XLNetTokenizer,
|
||||
)
|
||||
|
||||
|
||||
logging.basicConfig(
|
||||
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
|
||||
datefmt="%m/%d/%Y %H:%M:%S",
|
||||
level=logging.INFO,
|
||||
)
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
MAX_LENGTH = int(10000) # Hardcoded max length to avoid infinite loop
|
||||
|
||||
MODEL_CLASSES = {
|
||||
"gpt2": (GPT2LMHeadModel, GPT2Tokenizer),
|
||||
"ctrl": (CTRLLMHeadModel, CTRLTokenizer),
|
||||
"openai-gpt": (OpenAIGPTLMHeadModel, OpenAIGPTTokenizer),
|
||||
"xlnet": (XLNetLMHeadModel, XLNetTokenizer),
|
||||
"transfo-xl": (TransfoXLLMHeadModel, TransfoXLTokenizer),
|
||||
"xlm": (XLMWithLMHeadModel, XLMTokenizer),
|
||||
}
|
||||
|
||||
# Padding text to help Transformer-XL and XLNet with short prompts as proposed by Aman Rusia
|
||||
# in https://github.com/rusiaaman/XLNet-gen#methodology
|
||||
# and https://medium.com/@amanrusia/xlnet-speaks-comparison-to-gpt-2-ea1a4e9ba39e
|
||||
PREFIX = """In 1991, the remains of Russian Tsar Nicholas II and his family
|
||||
(except for Alexei and Maria) are discovered.
|
||||
The voice of Nicholas's young son, Tsarevich Alexei Nikolaevich, narrates the
|
||||
remainder of the story. 1883 Western Siberia,
|
||||
a young Grigori Rasputin is asked by his father and a group of men to perform magic.
|
||||
Rasputin has a vision and denounces one of the men as a horse thief. Although his
|
||||
father initially slaps him for making such an accusation, Rasputin watches as the
|
||||
man is chased outside and beaten. Twenty years later, Rasputin sees a vision of
|
||||
the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous,
|
||||
with people, even a bishop, begging for his blessing. <eod> </s> <eos>"""
|
||||
|
||||
|
||||
def set_seed(args):
|
||||
np.random.seed(args.seed)
|
||||
torch.manual_seed(args.seed)
|
||||
if args.n_gpu > 0:
|
||||
torch.cuda.manual_seed_all(args.seed)
|
||||
|
||||
|
||||
#
|
||||
# Functions to prepare models' input
|
||||
#
|
||||
|
||||
|
||||
def prepare_ctrl_input(args, _, tokenizer, prompt_text):
|
||||
if args.temperature > 0.7:
|
||||
logger.info("CTRL typically works better with lower temperatures (and lower top_k).")
|
||||
|
||||
encoded_prompt = tokenizer.encode(prompt_text, add_special_tokens=False)
|
||||
if not any(encoded_prompt[0] == x for x in tokenizer.control_codes.values()):
|
||||
logger.info("WARNING! You are not starting your generation from a control code so you won't get good results")
|
||||
return prompt_text
|
||||
|
||||
|
||||
def prepare_xlm_input(args, model, tokenizer, prompt_text):
|
||||
# kwargs = {"language": None, "mask_token_id": None}
|
||||
|
||||
# Set the language
|
||||
use_lang_emb = hasattr(model.config, "use_lang_emb") and model.config.use_lang_emb
|
||||
if hasattr(model.config, "lang2id") and use_lang_emb:
|
||||
available_languages = model.config.lang2id.keys()
|
||||
if args.xlm_language in available_languages:
|
||||
language = args.xlm_language
|
||||
else:
|
||||
language = None
|
||||
while language not in available_languages:
|
||||
language = input("Using XLM. Select language in " + str(list(available_languages)) + " >>> ")
|
||||
|
||||
model.config.lang_id = model.config.lang2id[language]
|
||||
# kwargs["language"] = tokenizer.lang2id[language]
|
||||
|
||||
# TODO fix mask_token_id setup when configurations will be synchronized between models and tokenizers
|
||||
# XLM masked-language modeling (MLM) models need masked token
|
||||
# is_xlm_mlm = "mlm" in args.model_name_or_path
|
||||
# if is_xlm_mlm:
|
||||
# kwargs["mask_token_id"] = tokenizer.mask_token_id
|
||||
|
||||
return prompt_text
|
||||
|
||||
|
||||
def prepare_xlnet_input(args, _, tokenizer, prompt_text):
|
||||
prefix = args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
|
||||
prompt_text = prefix + prompt_text
|
||||
return prompt_text
|
||||
|
||||
|
||||
def prepare_transfoxl_input(args, _, tokenizer, prompt_text):
|
||||
prefix = args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
|
||||
prompt_text = prefix + prompt_text
|
||||
return prompt_text
|
||||
|
||||
|
||||
PREPROCESSING_FUNCTIONS = {
|
||||
"ctrl": prepare_ctrl_input,
|
||||
"xlm": prepare_xlm_input,
|
||||
"xlnet": prepare_xlnet_input,
|
||||
"transfo-xl": prepare_transfoxl_input,
|
||||
}
|
||||
|
||||
|
||||
def adjust_length_to_model(length, max_sequence_length):
|
||||
if length < 0 and max_sequence_length > 0:
|
||||
length = max_sequence_length
|
||||
elif 0 < max_sequence_length < length:
|
||||
length = max_sequence_length # No generation bigger than model size
|
||||
elif length < 0:
|
||||
length = MAX_LENGTH # avoid infinite loop
|
||||
return length
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument(
|
||||
"--model_type",
|
||||
default=None,
|
||||
type=str,
|
||||
required=True,
|
||||
help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()),
|
||||
)
|
||||
parser.add_argument(
|
||||
"--model_name_or_path",
|
||||
default=None,
|
||||
type=str,
|
||||
required=True,
|
||||
help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(MODEL_CLASSES.keys()),
|
||||
)
|
||||
|
||||
parser.add_argument("--prompt", type=str, default="")
|
||||
parser.add_argument("--length", type=int, default=20)
|
||||
parser.add_argument("--stop_token", type=str, default=None, help="Token at which text generation is stopped")
|
||||
|
||||
parser.add_argument(
|
||||
"--temperature",
|
||||
type=float,
|
||||
default=1.0,
|
||||
help="temperature of 1.0 has no effect, lower tend toward greedy sampling",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--repetition_penalty", type=float, default=1.0, help="primarily useful for CTRL model; in that case, use 1.2"
|
||||
)
|
||||
parser.add_argument("--k", type=int, default=0)
|
||||
parser.add_argument("--p", type=float, default=0.9)
|
||||
|
||||
parser.add_argument("--prefix", type=str, default="", help="Text added prior to input.")
|
||||
parser.add_argument("--padding_text", type=str, default="", help="Deprecated, the use of `--prefix` is preferred.")
|
||||
parser.add_argument("--xlm_language", type=str, default="", help="Optional language when used with the XLM model.")
|
||||
|
||||
parser.add_argument("--seed", type=int, default=42, help="random seed for initialization")
|
||||
parser.add_argument("--no_cuda", action="store_true", help="Avoid using CUDA when available")
|
||||
parser.add_argument("--num_return_sequences", type=int, default=1, help="The number of samples to generate.")
|
||||
parser.add_argument(
|
||||
"--fp16",
|
||||
action="store_true",
|
||||
help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit",
|
||||
)
|
||||
args = parser.parse_args()
|
||||
|
||||
args.device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
|
||||
args.n_gpu = 0 if args.no_cuda else torch.cuda.device_count()
|
||||
|
||||
logger.warning(f"device: {args.device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fp16}")
|
||||
|
||||
set_seed(args)
|
||||
|
||||
# Initialize the model and tokenizer
|
||||
try:
|
||||
args.model_type = args.model_type.lower()
|
||||
model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
|
||||
except KeyError:
|
||||
raise KeyError("the model {} you specified is not supported. You are welcome to add it and open a PR :)")
|
||||
|
||||
tokenizer = tokenizer_class.from_pretrained(args.model_name_or_path)
|
||||
model = model_class.from_pretrained(args.model_name_or_path)
|
||||
model.to(args.device)
|
||||
|
||||
if args.fp16:
|
||||
model.half()
|
||||
|
||||
args.length = adjust_length_to_model(args.length, max_sequence_length=model.config.max_position_embeddings)
|
||||
logger.info(args)
|
||||
|
||||
prompt_text = args.prompt if args.prompt else input("Model prompt >>> ")
|
||||
|
||||
# Different models need different input formatting and/or extra arguments
|
||||
requires_preprocessing = args.model_type in PREPROCESSING_FUNCTIONS.keys()
|
||||
if requires_preprocessing:
|
||||
prepare_input = PREPROCESSING_FUNCTIONS.get(args.model_type)
|
||||
preprocessed_prompt_text = prepare_input(args, model, tokenizer, prompt_text)
|
||||
|
||||
if model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
|
||||
tokenizer_kwargs = {"add_space_before_punct_symbol": True}
|
||||
else:
|
||||
tokenizer_kwargs = {}
|
||||
|
||||
encoded_prompt = tokenizer.encode(
|
||||
preprocessed_prompt_text, add_special_tokens=False, return_tensors="pt", **tokenizer_kwargs
|
||||
)
|
||||
else:
|
||||
prefix = args.prefix if args.prefix else args.padding_text
|
||||
encoded_prompt = tokenizer.encode(prefix + prompt_text, add_special_tokens=False, return_tensors="pt")
|
||||
encoded_prompt = encoded_prompt.to(args.device)
|
||||
|
||||
if encoded_prompt.size()[-1] == 0:
|
||||
input_ids = None
|
||||
else:
|
||||
input_ids = encoded_prompt
|
||||
|
||||
output_sequences = model.generate(
|
||||
input_ids=input_ids,
|
||||
max_length=args.length + len(encoded_prompt[0]),
|
||||
temperature=args.temperature,
|
||||
top_k=args.k,
|
||||
top_p=args.p,
|
||||
repetition_penalty=args.repetition_penalty,
|
||||
do_sample=True,
|
||||
num_return_sequences=args.num_return_sequences,
|
||||
)
|
||||
|
||||
# Remove the batch dimension when returning multiple sequences
|
||||
if len(output_sequences.shape) > 2:
|
||||
output_sequences.squeeze_()
|
||||
|
||||
generated_sequences = []
|
||||
|
||||
for generated_sequence_idx, generated_sequence in enumerate(output_sequences):
|
||||
print(f"=== GENERATED SEQUENCE {generated_sequence_idx + 1} ===")
|
||||
generated_sequence = generated_sequence.tolist()
|
||||
|
||||
# Decode text
|
||||
text = tokenizer.decode(generated_sequence, clean_up_tokenization_spaces=True)
|
||||
|
||||
# Remove all text after the stop token
|
||||
text = text[: text.find(args.stop_token) if args.stop_token else None]
|
||||
|
||||
# Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
|
||||
total_sequence = (
|
||||
prompt_text + text[len(tokenizer.decode(encoded_prompt[0], clean_up_tokenization_spaces=True)) :]
|
||||
)
|
||||
|
||||
generated_sequences.append(total_sequence)
|
||||
print(total_sequence)
|
||||
|
||||
return generated_sequences
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user