id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
10,556 | import inspect
from typing import Callable, List, Optional, Set, Tuple, Union
import torch
from packaging import version
from torch import _softmax_backward_data, nn
from .utils import logging
The provided code snippet includes necessary dependencies for implementing the `find_pruneable_heads_and_indices` function. Write a Python function `def find_pruneable_heads_and_indices( heads: List[int], n_heads: int, head_size: int, already_pruned_heads: Set[int] ) -> Tuple[Set[int], torch.LongTensor]` to solve the following problem:
Finds the heads and their indices taking `already_pruned_heads` into account. Args: heads (`List[int]`): List of the indices of heads to prune. n_heads (`int`): The number of heads in the model. head_size (`int`): The size of each head. already_pruned_heads (`Set[int]`): A set of already pruned heads. Returns: `Tuple[Set[int], torch.LongTensor]`: A tuple with the remaining heads and their corresponding indices.
Here is the function:
def find_pruneable_heads_and_indices(
heads: List[int], n_heads: int, head_size: int, already_pruned_heads: Set[int]
) -> Tuple[Set[int], torch.LongTensor]:
"""
Finds the heads and their indices taking `already_pruned_heads` into account.
Args:
heads (`List[int]`): List of the indices of heads to prune.
n_heads (`int`): The number of heads in the model.
head_size (`int`): The size of each head.
already_pruned_heads (`Set[int]`): A set of already pruned heads.
Returns:
`Tuple[Set[int], torch.LongTensor]`: A tuple with the remaining heads and their corresponding indices.
"""
mask = torch.ones(n_heads, head_size)
heads = set(heads) - already_pruned_heads # Convert to set and remove already pruned heads
for head in heads:
# Compute how many pruned heads are before the head and move the index accordingly
head = head - sum(1 if h < head else 0 for h in already_pruned_heads)
mask[head] = 0
mask = mask.view(-1).contiguous().eq(1)
index: torch.LongTensor = torch.arange(len(mask))[mask].long()
return heads, index | Finds the heads and their indices taking `already_pruned_heads` into account. Args: heads (`List[int]`): List of the indices of heads to prune. n_heads (`int`): The number of heads in the model. head_size (`int`): The size of each head. already_pruned_heads (`Set[int]`): A set of already pruned heads. Returns: `Tuple[Set[int], torch.LongTensor]`: A tuple with the remaining heads and their corresponding indices. |
10,557 | import os
from argparse import ArgumentParser, Namespace
from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand
class TrainCommand(BaseTransformersCLICommand):
def register_subcommand(parser: ArgumentParser):
"""
Register this command to argparse so it's available for the transformer-cli
Args:
parser: Root parser to register command-specific arguments
"""
train_parser = parser.add_parser("train", help="CLI tool to train a model on a task.")
train_parser.add_argument(
"--train_data",
type=str,
required=True,
help="path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences.",
)
train_parser.add_argument(
"--column_label", type=int, default=0, help="Column of the dataset csv file with example labels."
)
train_parser.add_argument(
"--column_text", type=int, default=1, help="Column of the dataset csv file with example texts."
)
train_parser.add_argument(
"--column_id", type=int, default=2, help="Column of the dataset csv file with example ids."
)
train_parser.add_argument(
"--skip_first_row", action="store_true", help="Skip the first row of the csv file (headers)."
)
train_parser.add_argument("--validation_data", type=str, default="", help="path to validation dataset.")
train_parser.add_argument(
"--validation_split",
type=float,
default=0.1,
help="if validation dataset is not provided, fraction of train dataset to use as validation dataset.",
)
train_parser.add_argument("--output", type=str, default="./", help="path to saved the trained model.")
train_parser.add_argument(
"--task", type=str, default="text_classification", help="Task to train the model on."
)
train_parser.add_argument(
"--model", type=str, default="bert-base-uncased", help="Model's name or path to stored model."
)
train_parser.add_argument("--train_batch_size", type=int, default=32, help="Batch size for training.")
train_parser.add_argument("--valid_batch_size", type=int, default=64, help="Batch size for validation.")
train_parser.add_argument("--learning_rate", type=float, default=3e-5, help="Learning rate.")
train_parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon for Adam optimizer.")
train_parser.set_defaults(func=train_command_factory)
def __init__(self, args: Namespace):
self.logger = logging.get_logger("transformers-cli/training")
self.framework = "tf" if is_tf_available() else "torch"
os.makedirs(args.output, exist_ok=True)
self.output = args.output
self.column_label = args.column_label
self.column_text = args.column_text
self.column_id = args.column_id
self.logger.info(f"Loading {args.task} pipeline for {args.model}")
if args.task == "text_classification":
self.pipeline = TextClassificationPipeline.from_pretrained(args.model)
elif args.task == "token_classification":
raise NotImplementedError
elif args.task == "question_answering":
raise NotImplementedError
self.logger.info(f"Loading dataset from {args.train_data}")
self.train_dataset = Processor.create_from_csv(
args.train_data,
column_label=args.column_label,
column_text=args.column_text,
column_id=args.column_id,
skip_first_row=args.skip_first_row,
)
self.valid_dataset = None
if args.validation_data:
self.logger.info(f"Loading validation dataset from {args.validation_data}")
self.valid_dataset = Processor.create_from_csv(
args.validation_data,
column_label=args.column_label,
column_text=args.column_text,
column_id=args.column_id,
skip_first_row=args.skip_first_row,
)
self.validation_split = args.validation_split
self.train_batch_size = args.train_batch_size
self.valid_batch_size = args.valid_batch_size
self.learning_rate = args.learning_rate
self.adam_epsilon = args.adam_epsilon
def run(self):
if self.framework == "tf":
return self.run_tf()
return self.run_torch()
def run_torch(self):
raise NotImplementedError
def run_tf(self):
self.pipeline.fit(
self.train_dataset,
validation_data=self.valid_dataset,
validation_split=self.validation_split,
learning_rate=self.learning_rate,
adam_epsilon=self.adam_epsilon,
train_batch_size=self.train_batch_size,
valid_batch_size=self.valid_batch_size,
)
# Save trained pipeline
self.pipeline.save_pretrained(self.output)
The provided code snippet includes necessary dependencies for implementing the `train_command_factory` function. Write a Python function `def train_command_factory(args: Namespace)` to solve the following problem:
Factory function used to instantiate training command from provided command line arguments. Returns: TrainCommand
Here is the function:
def train_command_factory(args: Namespace):
"""
Factory function used to instantiate training command from provided command line arguments.
Returns: TrainCommand
"""
return TrainCommand(args) | Factory function used to instantiate training command from provided command line arguments. Returns: TrainCommand |
10,558 | import json
import os
import subprocess
import sys
import warnings
from argparse import ArgumentParser
from contextlib import AbstractContextManager
from typing import Dict, List, Optional
import requests
from ..utils import logging
from . import BaseTransformersCLICommand
The provided code snippet includes necessary dependencies for implementing the `write_msg` function. Write a Python function `def write_msg(msg: Dict)` to solve the following problem:
Write out the message in Line delimited JSON.
Here is the function:
def write_msg(msg: Dict):
"""Write out the message in Line delimited JSON."""
msg = json.dumps(msg) + "\n"
sys.stdout.write(msg)
sys.stdout.flush() | Write out the message in Line delimited JSON. |
10,559 | import json
import os
import subprocess
import sys
import warnings
from argparse import ArgumentParser
from contextlib import AbstractContextManager
from typing import Dict, List, Optional
import requests
from ..utils import logging
from . import BaseTransformersCLICommand
logger = logging.get_logger(__name__)
The provided code snippet includes necessary dependencies for implementing the `read_msg` function. Write a Python function `def read_msg() -> Optional[Dict]` to solve the following problem:
Read Line delimited JSON from stdin.
Here is the function:
def read_msg() -> Optional[Dict]:
"""Read Line delimited JSON from stdin."""
msg = json.loads(sys.stdin.readline().strip())
if "terminate" in (msg.get("type"), msg.get("event")):
# terminate message received
return None
if msg.get("event") not in ("download", "upload"):
logger.critical("Received unexpected message")
sys.exit(1)
return msg | Read Line delimited JSON from stdin. |
10,560 | import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List
from ..utils import logging
from . import BaseTransformersCLICommand
class AddNewModelCommand(BaseTransformersCLICommand):
def register_subcommand(parser: ArgumentParser):
add_new_model_parser = parser.add_parser("add-new-model")
add_new_model_parser.add_argument("--testing", action="store_true", help="If in testing mode.")
add_new_model_parser.add_argument("--testing_file", type=str, help="Configuration file on which to run.")
add_new_model_parser.add_argument(
"--path", type=str, help="Path to cookiecutter. Should only be used for testing purposes."
)
add_new_model_parser.set_defaults(func=add_new_model_command_factory)
def __init__(self, testing: bool, testing_file: str, path=None, *args):
self._testing = testing
self._testing_file = testing_file
self._path = path
def run(self):
warnings.warn(
"The command `transformers-cli add-new-model` is deprecated and will be removed in v5 of Transformers. "
"It is not actively maintained anymore, so might give a result that won't pass all tests and quality "
"checks, you should use `transformers-cli add-new-model-like` instead."
)
if not _has_cookiecutter:
raise ImportError(
"Model creation dependencies are required to use the `add_new_model` command. Install them by running "
"the following at the root of your `transformers` clone:\n\n\t$ pip install -e .[modelcreation]\n"
)
# Ensure that there is no other `cookiecutter-template-xxx` directory in the current working directory
directories = [directory for directory in os.listdir() if "cookiecutter-template-" == directory[:22]]
if len(directories) > 0:
raise ValueError(
"Several directories starting with `cookiecutter-template-` in current working directory. "
"Please clean your directory by removing all folders starting with `cookiecutter-template-` or "
"change your working directory."
)
path_to_transformer_root = (
Path(__file__).parent.parent.parent.parent if self._path is None else Path(self._path).parent.parent
)
path_to_cookiecutter = path_to_transformer_root / "templates" / "adding_a_new_model"
# Execute cookiecutter
if not self._testing:
cookiecutter(str(path_to_cookiecutter))
else:
with open(self._testing_file, "r") as configuration_file:
testing_configuration = json.load(configuration_file)
cookiecutter(
str(path_to_cookiecutter if self._path is None else self._path),
no_input=True,
extra_context=testing_configuration,
)
directory = [directory for directory in os.listdir() if "cookiecutter-template-" in directory[:22]][0]
# Retrieve configuration
with open(directory + "/configuration.json", "r") as configuration_file:
configuration = json.load(configuration_file)
lowercase_model_name = configuration["lowercase_modelname"]
generate_tensorflow_pytorch_and_flax = configuration["generate_tensorflow_pytorch_and_flax"]
os.remove(f"{directory}/configuration.json")
output_pytorch = "PyTorch" in generate_tensorflow_pytorch_and_flax
output_tensorflow = "TensorFlow" in generate_tensorflow_pytorch_and_flax
output_flax = "Flax" in generate_tensorflow_pytorch_and_flax
model_dir = f"{path_to_transformer_root}/src/transformers/models/{lowercase_model_name}"
os.makedirs(model_dir, exist_ok=True)
os.makedirs(f"{path_to_transformer_root}/tests/models/{lowercase_model_name}", exist_ok=True)
# Tests require submodules as they have parent imports
with open(f"{path_to_transformer_root}/tests/models/{lowercase_model_name}/__init__.py", "w"):
pass
shutil.move(
f"{directory}/__init__.py",
f"{model_dir}/__init__.py",
)
shutil.move(
f"{directory}/configuration_{lowercase_model_name}.py",
f"{model_dir}/configuration_{lowercase_model_name}.py",
)
def remove_copy_lines(path):
with open(path, "r") as f:
lines = f.readlines()
with open(path, "w") as f:
for line in lines:
if "# Copied from transformers." not in line:
f.write(line)
if output_pytorch:
if not self._testing:
remove_copy_lines(f"{directory}/modeling_{lowercase_model_name}.py")
shutil.move(
f"{directory}/modeling_{lowercase_model_name}.py",
f"{model_dir}/modeling_{lowercase_model_name}.py",
)
shutil.move(
f"{directory}/test_modeling_{lowercase_model_name}.py",
f"{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_{lowercase_model_name}.py",
)
else:
os.remove(f"{directory}/modeling_{lowercase_model_name}.py")
os.remove(f"{directory}/test_modeling_{lowercase_model_name}.py")
if output_tensorflow:
if not self._testing:
remove_copy_lines(f"{directory}/modeling_tf_{lowercase_model_name}.py")
shutil.move(
f"{directory}/modeling_tf_{lowercase_model_name}.py",
f"{model_dir}/modeling_tf_{lowercase_model_name}.py",
)
shutil.move(
f"{directory}/test_modeling_tf_{lowercase_model_name}.py",
f"{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_tf_{lowercase_model_name}.py",
)
else:
os.remove(f"{directory}/modeling_tf_{lowercase_model_name}.py")
os.remove(f"{directory}/test_modeling_tf_{lowercase_model_name}.py")
if output_flax:
if not self._testing:
remove_copy_lines(f"{directory}/modeling_flax_{lowercase_model_name}.py")
shutil.move(
f"{directory}/modeling_flax_{lowercase_model_name}.py",
f"{model_dir}/modeling_flax_{lowercase_model_name}.py",
)
shutil.move(
f"{directory}/test_modeling_flax_{lowercase_model_name}.py",
f"{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_flax_{lowercase_model_name}.py",
)
else:
os.remove(f"{directory}/modeling_flax_{lowercase_model_name}.py")
os.remove(f"{directory}/test_modeling_flax_{lowercase_model_name}.py")
shutil.move(
f"{directory}/{lowercase_model_name}.mdx",
f"{path_to_transformer_root}/docs/source/en/model_doc/{lowercase_model_name}.mdx",
)
shutil.move(
f"{directory}/tokenization_{lowercase_model_name}.py",
f"{model_dir}/tokenization_{lowercase_model_name}.py",
)
shutil.move(
f"{directory}/tokenization_fast_{lowercase_model_name}.py",
f"{model_dir}/tokenization_{lowercase_model_name}_fast.py",
)
from os import fdopen, remove
from shutil import copymode, move
from tempfile import mkstemp
def replace(original_file: str, line_to_copy_below: str, lines_to_copy: List[str]):
# Create temp file
fh, abs_path = mkstemp()
line_found = False
with fdopen(fh, "w") as new_file:
with open(original_file) as old_file:
for line in old_file:
new_file.write(line)
if line_to_copy_below in line:
line_found = True
for line_to_copy in lines_to_copy:
new_file.write(line_to_copy)
if not line_found:
raise ValueError(f"Line {line_to_copy_below} was not found in file.")
# Copy the file permissions from the old file to the new file
copymode(original_file, abs_path)
# Remove original file
remove(original_file)
# Move new file
move(abs_path, original_file)
def skip_units(line):
return (
("generating PyTorch" in line and not output_pytorch)
or ("generating TensorFlow" in line and not output_tensorflow)
or ("generating Flax" in line and not output_flax)
)
def replace_in_files(path_to_datafile):
with open(path_to_datafile) as datafile:
lines_to_copy = []
skip_file = False
skip_snippet = False
for line in datafile:
if "# To replace in: " in line and "##" not in line:
file_to_replace_in = line.split('"')[1]
skip_file = skip_units(line)
elif "# Below: " in line and "##" not in line:
line_to_copy_below = line.split('"')[1]
skip_snippet = skip_units(line)
elif "# End." in line and "##" not in line:
if not skip_file and not skip_snippet:
replace(file_to_replace_in, line_to_copy_below, lines_to_copy)
lines_to_copy = []
elif "# Replace with" in line and "##" not in line:
lines_to_copy = []
elif "##" not in line:
lines_to_copy.append(line)
remove(path_to_datafile)
replace_in_files(f"{directory}/to_replace_{lowercase_model_name}.py")
os.rmdir(directory)
def add_new_model_command_factory(args: Namespace):
return AddNewModelCommand(args.testing, args.testing_file, path=args.path) | null |
10,561 | from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
class ConvertCommand(BaseTransformersCLICommand):
def register_subcommand(parser: ArgumentParser):
"""
Register this command to argparse so it's available for the transformer-cli
Args:
parser: Root parser to register command-specific arguments
"""
train_parser = parser.add_parser(
"convert",
help="CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints.",
)
train_parser.add_argument("--model_type", type=str, required=True, help="Model's type.")
train_parser.add_argument(
"--tf_checkpoint", type=str, required=True, help="TensorFlow checkpoint path or folder."
)
train_parser.add_argument(
"--pytorch_dump_output", type=str, required=True, help="Path to the PyTorch saved model output."
)
train_parser.add_argument("--config", type=str, default="", help="Configuration file path or folder.")
train_parser.add_argument(
"--finetuning_task_name",
type=str,
default=None,
help="Optional fine-tuning task name if the TF model was a finetuned model.",
)
train_parser.set_defaults(func=convert_command_factory)
def __init__(
self,
model_type: str,
tf_checkpoint: str,
pytorch_dump_output: str,
config: str,
finetuning_task_name: str,
*args
):
self._logger = logging.get_logger("transformers-cli/converting")
self._logger.info(f"Loading model {model_type}")
self._model_type = model_type
self._tf_checkpoint = tf_checkpoint
self._pytorch_dump_output = pytorch_dump_output
self._config = config
self._finetuning_task_name = finetuning_task_name
def run(self):
if self._model_type == "albert":
try:
from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
convert_tf_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(IMPORT_ERROR_MESSAGE)
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint, self._config, self._pytorch_dump_output)
elif self._model_type == "bert":
try:
from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
convert_tf_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(IMPORT_ERROR_MESSAGE)
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint, self._config, self._pytorch_dump_output)
elif self._model_type == "funnel":
try:
from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
convert_tf_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(IMPORT_ERROR_MESSAGE)
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint, self._config, self._pytorch_dump_output)
elif self._model_type == "t5":
try:
from ..models.t5.convert_t5_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
except ImportError:
raise ImportError(IMPORT_ERROR_MESSAGE)
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint, self._config, self._pytorch_dump_output)
elif self._model_type == "gpt":
from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
convert_openai_checkpoint_to_pytorch,
)
convert_openai_checkpoint_to_pytorch(self._tf_checkpoint, self._config, self._pytorch_dump_output)
elif self._model_type == "transfo_xl":
try:
from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
convert_transfo_xl_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(IMPORT_ERROR_MESSAGE)
if "ckpt" in self._tf_checkpoint.lower():
TF_CHECKPOINT = self._tf_checkpoint
TF_DATASET_FILE = ""
else:
TF_DATASET_FILE = self._tf_checkpoint
TF_CHECKPOINT = ""
convert_transfo_xl_checkpoint_to_pytorch(
TF_CHECKPOINT, self._config, self._pytorch_dump_output, TF_DATASET_FILE
)
elif self._model_type == "gpt2":
try:
from ..models.gpt2.convert_gpt2_original_tf_checkpoint_to_pytorch import (
convert_gpt2_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(IMPORT_ERROR_MESSAGE)
convert_gpt2_checkpoint_to_pytorch(self._tf_checkpoint, self._config, self._pytorch_dump_output)
elif self._model_type == "xlnet":
try:
from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
convert_xlnet_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(IMPORT_ERROR_MESSAGE)
convert_xlnet_checkpoint_to_pytorch(
self._tf_checkpoint, self._config, self._pytorch_dump_output, self._finetuning_task_name
)
elif self._model_type == "xlm":
from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
convert_xlm_checkpoint_to_pytorch,
)
convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint, self._pytorch_dump_output)
elif self._model_type == "lxmert":
from ..models.lxmert.convert_lxmert_original_pytorch_checkpoint_to_pytorch import (
convert_lxmert_checkpoint_to_pytorch,
)
convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint, self._pytorch_dump_output)
elif self._model_type == "rembert":
from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
convert_rembert_tf_checkpoint_to_pytorch,
)
convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint, self._config, self._pytorch_dump_output)
else:
raise ValueError(
"--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]"
)
The provided code snippet includes necessary dependencies for implementing the `convert_command_factory` function. Write a Python function `def convert_command_factory(args: Namespace)` to solve the following problem:
Factory function used to convert a model TF 1.0 checkpoint in a PyTorch checkpoint. Returns: ServeCommand
Here is the function:
def convert_command_factory(args: Namespace):
"""
Factory function used to convert a model TF 1.0 checkpoint in a PyTorch checkpoint.
Returns: ServeCommand
"""
return ConvertCommand(
args.model_type, args.tf_checkpoint, args.pytorch_dump_output, args.config, args.finetuning_task_name
) | Factory function used to convert a model TF 1.0 checkpoint in a PyTorch checkpoint. Returns: ServeCommand |
10,562 | from argparse import ArgumentParser
from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
def try_infer_format_from_ext(path: str):
if not path:
return "pipe"
for ext in PipelineDataFormat.SUPPORTED_FORMATS:
if path.endswith(ext):
return ext
raise Exception(
f"Unable to determine file format from file extension {path}. "
f"Please provide the format through --format {PipelineDataFormat.SUPPORTED_FORMATS}"
)
class RunCommand(BaseTransformersCLICommand):
def __init__(self, nlp: Pipeline, reader: PipelineDataFormat):
self._nlp = nlp
self._reader = reader
def register_subcommand(parser: ArgumentParser):
run_parser = parser.add_parser("run", help="Run a pipeline through the CLI")
run_parser.add_argument("--task", choices=get_supported_tasks(), help="Task to run")
run_parser.add_argument("--input", type=str, help="Path to the file to use for inference")
run_parser.add_argument("--output", type=str, help="Path to the file that will be used post to write results.")
run_parser.add_argument("--model", type=str, help="Name or path to the model to instantiate.")
run_parser.add_argument("--config", type=str, help="Name or path to the model's config to instantiate.")
run_parser.add_argument(
"--tokenizer", type=str, help="Name of the tokenizer to use. (default: same as the model name)"
)
run_parser.add_argument(
"--column",
type=str,
help="Name of the column to use as input. (For multi columns input as QA use column1,columns2)",
)
run_parser.add_argument(
"--format",
type=str,
default="infer",
choices=PipelineDataFormat.SUPPORTED_FORMATS,
help="Input format to read from",
)
run_parser.add_argument(
"--device",
type=int,
default=-1,
help="Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)",
)
run_parser.add_argument("--overwrite", action="store_true", help="Allow overwriting the output file.")
run_parser.set_defaults(func=run_command_factory)
def run(self):
nlp, outputs = self._nlp, []
for entry in self._reader:
output = nlp(**entry) if self._reader.is_multi_columns else nlp(entry)
if isinstance(output, dict):
outputs.append(output)
else:
outputs += output
# Saving data
if self._nlp.binary_output:
binary_path = self._reader.save_binary(outputs)
logger.warning(f"Current pipeline requires output to be in binary format, saving at {binary_path}")
else:
self._reader.save(outputs)
def pipeline(
task: str = None,
model: Optional = None,
config: Optional[Union[str, PretrainedConfig]] = None,
tokenizer: Optional[Union[str, PreTrainedTokenizer, PreTrainedTokenizerFast]] = None,
feature_extractor: Optional[Union[str, PreTrainedFeatureExtractor]] = None,
framework: Optional[str] = None,
revision: Optional[str] = None,
use_fast: bool = True,
use_auth_token: Optional[Union[str, bool]] = None,
device: Optional[Union[int, str, "torch.device"]] = None,
device_map=None,
torch_dtype=None,
trust_remote_code: Optional[bool] = None,
model_kwargs: Dict[str, Any] = None,
pipeline_class: Optional[Any] = None,
**kwargs,
) -> Pipeline:
"""
Utility factory method to build a [`Pipeline`].
Pipelines are made of:
- A [tokenizer](tokenizer) in charge of mapping raw textual input to token.
- A [model](model) to make predictions from the inputs.
- Some (optional) post processing for enhancing model's output.
Args:
task (`str`):
The task defining which pipeline will be returned. Currently accepted tasks are:
- `"audio-classification"`: will return a [`AudioClassificationPipeline`].
- `"automatic-speech-recognition"`: will return a [`AutomaticSpeechRecognitionPipeline`].
- `"conversational"`: will return a [`ConversationalPipeline`].
- `"feature-extraction"`: will return a [`FeatureExtractionPipeline`].
- `"fill-mask"`: will return a [`FillMaskPipeline`]:.
- `"image-classification"`: will return a [`ImageClassificationPipeline`].
- `"question-answering"`: will return a [`QuestionAnsweringPipeline`].
- `"table-question-answering"`: will return a [`TableQuestionAnsweringPipeline`].
- `"text2text-generation"`: will return a [`Text2TextGenerationPipeline`].
- `"text-classification"` (alias `"sentiment-analysis"` available): will return a
[`TextClassificationPipeline`].
- `"text-generation"`: will return a [`TextGenerationPipeline`]:.
- `"token-classification"` (alias `"ner"` available): will return a [`TokenClassificationPipeline`].
- `"translation"`: will return a [`TranslationPipeline`].
- `"translation_xx_to_yy"`: will return a [`TranslationPipeline`].
- `"summarization"`: will return a [`SummarizationPipeline`].
- `"zero-shot-classification"`: will return a [`ZeroShotClassificationPipeline`].
model (`str` or [`PreTrainedModel`] or [`TFPreTrainedModel`], *optional*):
The model that will be used by the pipeline to make predictions. This can be a model identifier or an
actual instance of a pretrained model inheriting from [`PreTrainedModel`] (for PyTorch) or
[`TFPreTrainedModel`] (for TensorFlow).
If not provided, the default for the `task` will be loaded.
config (`str` or [`PretrainedConfig`], *optional*):
The configuration that will be used by the pipeline to instantiate the model. This can be a model
identifier or an actual pretrained model configuration inheriting from [`PretrainedConfig`].
If not provided, the default configuration file for the requested model will be used. That means that if
`model` is given, its default configuration will be used. However, if `model` is not supplied, this
`task`'s default model's config is used instead.
tokenizer (`str` or [`PreTrainedTokenizer`], *optional*):
The tokenizer that will be used by the pipeline to encode data for the model. This can be a model
identifier or an actual pretrained tokenizer inheriting from [`PreTrainedTokenizer`].
If not provided, the default tokenizer for the given `model` will be loaded (if it is a string). If `model`
is not specified or not a string, then the default tokenizer for `config` is loaded (if it is a string).
However, if `config` is also not given or not a string, then the default tokenizer for the given `task`
will be loaded.
feature_extractor (`str` or [`PreTrainedFeatureExtractor`], *optional*):
The feature extractor that will be used by the pipeline to encode data for the model. This can be a model
identifier or an actual pretrained feature extractor inheriting from [`PreTrainedFeatureExtractor`].
Feature extractors are used for non-NLP models, such as Speech or Vision models as well as multi-modal
models. Multi-modal models will also require a tokenizer to be passed.
If not provided, the default feature extractor for the given `model` will be loaded (if it is a string). If
`model` is not specified or not a string, then the default feature extractor for `config` is loaded (if it
is a string). However, if `config` is also not given or not a string, then the default feature extractor
for the given `task` will be loaded.
framework (`str`, *optional*):
The framework to use, either `"pt"` for PyTorch or `"tf"` for TensorFlow. The specified framework must be
installed.
If no framework is specified, will default to the one currently installed. If no framework is specified and
both frameworks are installed, will default to the framework of the `model`, or to PyTorch if no model is
provided.
revision (`str`, *optional*, defaults to `"main"`):
When passing a task name or a string model identifier: The specific model version to use. It can be a
branch name, a tag name, or a commit id, since we use a git-based system for storing models and other
artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
use_fast (`bool`, *optional*, defaults to `True`):
Whether or not to use a Fast tokenizer if possible (a [`PreTrainedTokenizerFast`]).
use_auth_token (`str` or *bool*, *optional*):
The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated
when running `huggingface-cli login` (stored in `~/.huggingface`).
device (`int` or `str` or `torch.device`):
Defines the device (*e.g.*, `"cpu"`, `"cuda:1"`, `"mps"`, or a GPU ordinal rank like `1`) on which this
pipeline will be allocated.
device_map (`str` or `Dict[str, Union[int, str, torch.device]`, *optional*):
Sent directly as `model_kwargs` (just a simpler shortcut). When `accelerate` library is present, set
`device_map="auto"` to compute the most optimized `device_map` automatically. [More
information](https://huggingface.co/docs/accelerate/main/en/big_modeling#accelerate.cpu_offload)
<Tip warning={true}>
Do not use `device_map` AND `device` at the same time as they will conflict
</Tip>
torch_dtype (`str` or `torch.dtype`, *optional*):
Sent directly as `model_kwargs` (just a simpler shortcut) to use the available precision for this model
(`torch.float16`, `torch.bfloat16`, ... or `"auto"`).
trust_remote_code (`bool`, *optional*, defaults to `False`):
Whether or not to allow for custom code defined on the Hub in their own modeling, configuration,
tokenization or even pipeline files. This option should only be set to `True` for repositories you trust
and in which you have read the code, as it will execute code present on the Hub on your local machine.
model_kwargs:
Additional dictionary of keyword arguments passed along to the model's `from_pretrained(...,
**model_kwargs)` function.
kwargs:
Additional keyword arguments passed along to the specific pipeline init (see the documentation for the
corresponding pipeline class for possible values).
Returns:
[`Pipeline`]: A suitable pipeline for the task.
Examples:
```python
>>> from transformers import pipeline, AutoModelForTokenClassification, AutoTokenizer
>>> # Sentiment analysis pipeline
>>> pipeline("sentiment-analysis")
>>> # Question answering pipeline, specifying the checkpoint identifier
>>> pipeline("question-answering", model="distilbert-base-cased-distilled-squad", tokenizer="bert-base-cased")
>>> # Named entity recognition pipeline, passing in a specific model and tokenizer
>>> model = AutoModelForTokenClassification.from_pretrained("dbmdz/bert-large-cased-finetuned-conll03-english")
>>> tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
>>> pipeline("ner", model=model, tokenizer=tokenizer)
```"""
if model_kwargs is None:
model_kwargs = {}
# Make sure we only pass use_auth_token once as a kwarg (it used to be possible to pass it in model_kwargs,
# this is to keep BC).
use_auth_token = model_kwargs.pop("use_auth_token", use_auth_token)
hub_kwargs = {
"revision": revision,
"use_auth_token": use_auth_token,
"trust_remote_code": trust_remote_code,
"_commit_hash": None,
}
if task is None and model is None:
raise RuntimeError(
"Impossible to instantiate a pipeline without either a task or a model "
"being specified. "
"Please provide a task class or a model"
)
if model is None and tokenizer is not None:
raise RuntimeError(
"Impossible to instantiate a pipeline with tokenizer specified but not the model as the provided tokenizer"
" may not be compatible with the default model. Please provide a PreTrainedModel class or a"
" path/identifier to a pretrained model when providing tokenizer."
)
if model is None and feature_extractor is not None:
raise RuntimeError(
"Impossible to instantiate a pipeline with feature_extractor specified but not the model as the provided"
" feature_extractor may not be compatible with the default model. Please provide a PreTrainedModel class"
" or a path/identifier to a pretrained model when providing feature_extractor."
)
# Config is the primordial information item.
# Instantiate config if needed
if isinstance(config, str):
config = AutoConfig.from_pretrained(config, _from_pipeline=task, **hub_kwargs, **model_kwargs)
hub_kwargs["_commit_hash"] = config._commit_hash
elif config is None and isinstance(model, str):
config = AutoConfig.from_pretrained(model, _from_pipeline=task, **hub_kwargs, **model_kwargs)
hub_kwargs["_commit_hash"] = config._commit_hash
custom_tasks = {}
if config is not None and len(getattr(config, "custom_pipelines", {})) > 0:
custom_tasks = config.custom_pipelines
if task is None and trust_remote_code is not False:
if len(custom_tasks) == 1:
task = list(custom_tasks.keys())[0]
else:
raise RuntimeError(
"We can't infer the task automatically for this model as there are multiple tasks available. Pick "
f"one in {', '.join(custom_tasks.keys())}"
)
if task is None and model is not None:
if not isinstance(model, str):
raise RuntimeError(
"Inferring the task automatically requires to check the hub with a model_id defined as a `str`."
f"{model} is not a valid model_id."
)
task = get_task(model, use_auth_token)
# Retrieve the task
if task in custom_tasks:
normalized_task = task
targeted_task, task_options = clean_custom_task(custom_tasks[task])
if pipeline_class is None:
if not trust_remote_code:
raise ValueError(
"Loading this pipeline requires you to execute the code in the pipeline file in that"
" repo on your local machine. Make sure you have read the code there to avoid malicious use, then"
" set the option `trust_remote_code=True` to remove this error."
)
class_ref = targeted_task["impl"]
module_file, class_name = class_ref.split(".")
pipeline_class = get_class_from_dynamic_module(
model, module_file + ".py", class_name, revision=revision, use_auth_token=use_auth_token
)
else:
normalized_task, targeted_task, task_options = check_task(task)
if pipeline_class is None:
pipeline_class = targeted_task["impl"]
# Use default model/config/tokenizer for the task if no model is provided
if model is None:
# At that point framework might still be undetermined
model, default_revision = get_default_model_and_revision(targeted_task, framework, task_options)
revision = revision if revision is not None else default_revision
logger.warning(
f"No model was supplied, defaulted to {model} and revision"
f" {revision} ({HUGGINGFACE_CO_RESOLVE_ENDPOINT}/{model}).\n"
"Using a pipeline without specifying a model name and revision in production is not recommended."
)
if config is None and isinstance(model, str):
config = AutoConfig.from_pretrained(model, _from_pipeline=task, **hub_kwargs, **model_kwargs)
hub_kwargs["_commit_hash"] = config._commit_hash
if device_map is not None:
if "device_map" in model_kwargs:
raise ValueError(
'You cannot use both `pipeline(... device_map=..., model_kwargs={"device_map":...})` as those'
" arguments might conflict, use only one.)"
)
model_kwargs["device_map"] = device_map
if torch_dtype is not None:
if "torch_dtype" in model_kwargs:
raise ValueError(
'You cannot use both `pipeline(... torch_dtype=..., model_kwargs={"torch_dtype":...})` as those'
" arguments might conflict, use only one.)"
)
model_kwargs["torch_dtype"] = torch_dtype
model_name = model if isinstance(model, str) else None
# Infer the framework from the model
# Forced if framework already defined, inferred if it's None
# Will load the correct model if possible
model_classes = {"tf": targeted_task["tf"], "pt": targeted_task["pt"]}
framework, model = infer_framework_load_model(
model,
model_classes=model_classes,
config=config,
framework=framework,
task=task,
**hub_kwargs,
**model_kwargs,
)
model_config = model.config
hub_kwargs["_commit_hash"] = model.config._commit_hash
load_tokenizer = type(model_config) in TOKENIZER_MAPPING or model_config.tokenizer_class is not None
load_feature_extractor = type(model_config) in FEATURE_EXTRACTOR_MAPPING or feature_extractor is not None
if (
tokenizer is None
and not load_tokenizer
and normalized_task not in NO_TOKENIZER_TASKS
# Using class name to avoid importing the real class.
and model_config.__class__.__name__ in MULTI_MODEL_CONFIGS
):
# This is a special category of models, that are fusions of multiple models
# so the model_config might not define a tokenizer, but it seems to be
# necessary for the task, so we're force-trying to load it.
load_tokenizer = True
if (
feature_extractor is None
and not load_feature_extractor
and normalized_task not in NO_FEATURE_EXTRACTOR_TASKS
# Using class name to avoid importing the real class.
and model_config.__class__.__name__ in MULTI_MODEL_CONFIGS
):
# This is a special category of models, that are fusions of multiple models
# so the model_config might not define a tokenizer, but it seems to be
# necessary for the task, so we're force-trying to load it.
load_feature_extractor = True
if task in NO_TOKENIZER_TASKS:
# These will never require a tokenizer.
# the model on the other hand might have a tokenizer, but
# the files could be missing from the hub, instead of failing
# on such repos, we just force to not load it.
load_tokenizer = False
if task in NO_FEATURE_EXTRACTOR_TASKS:
load_feature_extractor = False
if load_tokenizer:
# Try to infer tokenizer from model or config name (if provided as str)
if tokenizer is None:
if isinstance(model_name, str):
tokenizer = model_name
elif isinstance(config, str):
tokenizer = config
else:
# Impossible to guess what is the right tokenizer here
raise Exception(
"Impossible to guess which tokenizer to use. "
"Please provide a PreTrainedTokenizer class or a path/identifier to a pretrained tokenizer."
)
# Instantiate tokenizer if needed
if isinstance(tokenizer, (str, tuple)):
if isinstance(tokenizer, tuple):
# For tuple we have (tokenizer name, {kwargs})
use_fast = tokenizer[1].pop("use_fast", use_fast)
tokenizer_identifier = tokenizer[0]
tokenizer_kwargs = tokenizer[1]
else:
tokenizer_identifier = tokenizer
tokenizer_kwargs = model_kwargs
tokenizer = AutoTokenizer.from_pretrained(
tokenizer_identifier, use_fast=use_fast, _from_pipeline=task, **hub_kwargs, **tokenizer_kwargs
)
if load_feature_extractor:
# Try to infer feature extractor from model or config name (if provided as str)
if feature_extractor is None:
if isinstance(model_name, str):
feature_extractor = model_name
elif isinstance(config, str):
feature_extractor = config
else:
# Impossible to guess what is the right feature_extractor here
raise Exception(
"Impossible to guess which feature extractor to use. "
"Please provide a PreTrainedFeatureExtractor class or a path/identifier "
"to a pretrained feature extractor."
)
# Instantiate feature_extractor if needed
if isinstance(feature_extractor, (str, tuple)):
feature_extractor = AutoFeatureExtractor.from_pretrained(
feature_extractor, _from_pipeline=task, **hub_kwargs, **model_kwargs
)
if (
feature_extractor._processor_class
and feature_extractor._processor_class.endswith("WithLM")
and isinstance(model_name, str)
):
try:
import kenlm # to trigger `ImportError` if not installed
from pyctcdecode import BeamSearchDecoderCTC
if os.path.isdir(model_name) or os.path.isfile(model_name):
decoder = BeamSearchDecoderCTC.load_from_dir(model_name)
else:
language_model_glob = os.path.join(
BeamSearchDecoderCTC._LANGUAGE_MODEL_SERIALIZED_DIRECTORY, "*"
)
alphabet_filename = BeamSearchDecoderCTC._ALPHABET_SERIALIZED_FILENAME
allow_regex = [language_model_glob, alphabet_filename]
decoder = BeamSearchDecoderCTC.load_from_hf_hub(model_name, allow_regex=allow_regex)
kwargs["decoder"] = decoder
except ImportError as e:
logger.warning(f"Could not load the `decoder` for {model_name}. Defaulting to raw CTC. Error: {e}")
if not is_kenlm_available():
logger.warning("Try to install `kenlm`: `pip install kenlm")
if not is_pyctcdecode_available():
logger.warning("Try to install `pyctcdecode`: `pip install pyctcdecode")
if task == "translation" and model.config.task_specific_params:
for key in model.config.task_specific_params:
if key.startswith("translation"):
task = key
warnings.warn(
f'"translation" task was used, instead of "translation_XX_to_YY", defaulting to "{task}"',
UserWarning,
)
break
if tokenizer is not None:
kwargs["tokenizer"] = tokenizer
if feature_extractor is not None:
kwargs["feature_extractor"] = feature_extractor
if device is not None:
kwargs["device"] = device
return pipeline_class(model=model, framework=framework, task=task, **kwargs)
def run_command_factory(args):
nlp = pipeline(
task=args.task,
model=args.model if args.model else None,
config=args.config,
tokenizer=args.tokenizer,
device=args.device,
)
format = try_infer_format_from_ext(args.input) if args.format == "infer" else args.format
reader = PipelineDataFormat.from_str(
format=format,
output_path=args.output,
input_path=args.input,
column=args.column if args.column else nlp.default_input_names,
overwrite=args.overwrite,
)
return RunCommand(nlp, reader) | null |
10,563 | import inspect
import os
from argparse import ArgumentParser, Namespace
from importlib import import_module
import numpy as np
from packaging import version
import huggingface_hub
from .. import (
FEATURE_EXTRACTOR_MAPPING,
PROCESSOR_MAPPING,
TOKENIZER_MAPPING,
AutoConfig,
AutoFeatureExtractor,
AutoProcessor,
AutoTokenizer,
is_datasets_available,
is_tf_available,
is_torch_available,
)
from ..utils import TF2_WEIGHTS_INDEX_NAME, TF2_WEIGHTS_NAME, logging
from . import BaseTransformersCLICommand
class PTtoTFCommand(BaseTransformersCLICommand):
def register_subcommand(parser: ArgumentParser):
"""
Register this command to argparse so it's available for the transformer-cli
Args:
parser: Root parser to register command-specific arguments
"""
train_parser = parser.add_parser(
"pt-to-tf",
help=(
"CLI tool to run convert a transformers model from a PyTorch checkpoint to a TensorFlow checkpoint."
" Can also be used to validate existing weights without opening PRs, with --no-pr."
),
)
train_parser.add_argument(
"--model-name",
type=str,
required=True,
help="The model name, including owner/organization, as seen on the hub.",
)
train_parser.add_argument(
"--local-dir",
type=str,
default="",
help="Optional local directory of the model repository. Defaults to /tmp/{model_name}",
)
train_parser.add_argument(
"--max-error",
type=float,
default=MAX_ERROR,
help=(
f"Maximum error tolerance. Defaults to {MAX_ERROR}. This flag should be avoided, use at your own risk."
),
)
train_parser.add_argument(
"--new-weights",
action="store_true",
help="Optional flag to create new TensorFlow weights, even if they already exist.",
)
train_parser.add_argument(
"--no-pr", action="store_true", help="Optional flag to NOT open a PR with converted weights."
)
train_parser.add_argument(
"--push",
action="store_true",
help="Optional flag to push the weights directly to `main` (requires permissions)",
)
train_parser.add_argument(
"--extra-commit-description",
type=str,
default="",
help="Optional additional commit description to use when opening a PR (e.g. to tag the owner).",
)
train_parser.set_defaults(func=convert_command_factory)
def find_pt_tf_differences(pt_outputs, tf_outputs):
"""
Compares the TensorFlow and PyTorch outputs, returning a dictionary with all tensor differences.
"""
# 1. All output attributes must be the same
pt_out_attrs = set(pt_outputs.keys())
tf_out_attrs = set(tf_outputs.keys())
if pt_out_attrs != tf_out_attrs:
raise ValueError(
f"The model outputs have different attributes, aborting. (Pytorch: {pt_out_attrs}, TensorFlow:"
f" {tf_out_attrs})"
)
# 2. For each output attribute, computes the difference
def _find_pt_tf_differences(pt_out, tf_out, differences, attr_name=""):
# If the current attribute is a tensor, it is a leaf and we make the comparison. Otherwise, we will dig in
# recursivelly, keeping the name of the attribute.
if isinstance(pt_out, torch.Tensor):
tensor_difference = np.max(np.abs(pt_out.numpy() - tf_out.numpy()))
differences[attr_name] = tensor_difference
else:
root_name = attr_name
for i, pt_item in enumerate(pt_out):
# If it is a named attribute, we keep the name. Otherwise, just its index.
if isinstance(pt_item, str):
branch_name = root_name + pt_item
tf_item = tf_out[pt_item]
pt_item = pt_out[pt_item]
else:
branch_name = root_name + f"[{i}]"
tf_item = tf_out[i]
differences = _find_pt_tf_differences(pt_item, tf_item, differences, branch_name)
return differences
return _find_pt_tf_differences(pt_outputs, tf_outputs, {})
def __init__(
self,
model_name: str,
local_dir: str,
max_error: float,
new_weights: bool,
no_pr: bool,
push: bool,
extra_commit_description: str,
*args
):
self._logger = logging.get_logger("transformers-cli/pt_to_tf")
self._model_name = model_name
self._local_dir = local_dir if local_dir else os.path.join("/tmp", model_name)
self._max_error = max_error
self._new_weights = new_weights
self._no_pr = no_pr
self._push = push
self._extra_commit_description = extra_commit_description
def get_inputs(self, pt_model, config):
"""
Returns the right inputs for the model, based on its signature.
"""
def _get_audio_input():
ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
speech_samples = ds.sort("id").select(range(2))[:2]["audio"]
raw_samples = [x["array"] for x in speech_samples]
return raw_samples
model_config_class = type(pt_model.config)
if model_config_class in PROCESSOR_MAPPING:
processor = AutoProcessor.from_pretrained(self._local_dir)
if model_config_class in TOKENIZER_MAPPING and processor.tokenizer.pad_token is None:
processor.tokenizer.pad_token = processor.tokenizer.eos_token
elif model_config_class in FEATURE_EXTRACTOR_MAPPING:
processor = AutoFeatureExtractor.from_pretrained(self._local_dir)
elif model_config_class in TOKENIZER_MAPPING:
processor = AutoTokenizer.from_pretrained(self._local_dir)
if processor.pad_token is None:
processor.pad_token = processor.eos_token
else:
raise ValueError(f"Unknown data processing type (model config type: {model_config_class})")
model_forward_signature = set(inspect.signature(pt_model.forward).parameters.keys())
processor_inputs = {}
if "input_ids" in model_forward_signature:
processor_inputs.update(
{
"text": ["Hi there!", "I am a batch with more than one row and different input lengths."],
"padding": True,
"truncation": True,
}
)
if "pixel_values" in model_forward_signature:
sample_images = load_dataset("cifar10", "plain_text", split="test")[:2]["img"]
processor_inputs.update({"images": sample_images})
if "input_features" in model_forward_signature:
feature_extractor_signature = inspect.signature(processor.feature_extractor).parameters
# Pad to the largest input length by default but take feature extractor default
# padding value if it exists e.g. "max_length" and is not False or None
if "padding" in feature_extractor_signature:
default_strategy = feature_extractor_signature["padding"].default
if default_strategy is not False and default_strategy is not None:
padding_strategy = default_strategy
else:
padding_strategy = True
else:
padding_strategy = True
processor_inputs.update({"audio": _get_audio_input(), "padding": padding_strategy})
if "input_values" in model_forward_signature: # Wav2Vec2 audio input
processor_inputs.update({"audio": _get_audio_input(), "padding": True})
pt_input = processor(**processor_inputs, return_tensors="pt")
tf_input = processor(**processor_inputs, return_tensors="tf")
# Extra input requirements, in addition to the input modality
if config.is_encoder_decoder or (hasattr(pt_model, "encoder") and hasattr(pt_model, "decoder")):
decoder_input_ids = np.asarray([[1], [1]], dtype=int) * (pt_model.config.decoder_start_token_id or 0)
pt_input.update({"decoder_input_ids": torch.tensor(decoder_input_ids)})
tf_input.update({"decoder_input_ids": tf.convert_to_tensor(decoder_input_ids)})
return pt_input, tf_input
def run(self):
# hub version 0.9.0 introduced the possibility of programmatically opening PRs with normal write tokens.
if version.parse(huggingface_hub.__version__) < version.parse("0.9.0"):
raise ImportError(
"The huggingface_hub version must be >= 0.9.0 to use this command. Please update your huggingface_hub"
" installation."
)
else:
from huggingface_hub import Repository, create_commit
from huggingface_hub._commit_api import CommitOperationAdd
# Fetch remote data
repo = Repository(local_dir=self._local_dir, clone_from=self._model_name)
# Load config and get the appropriate architecture -- the latter is needed to convert the head's weights
config = AutoConfig.from_pretrained(self._local_dir)
architectures = config.architectures
if architectures is None: # No architecture defined -- use auto classes
pt_class = getattr(import_module("transformers"), "AutoModel")
tf_class = getattr(import_module("transformers"), "TFAutoModel")
self._logger.warning("No detected architecture, using AutoModel/TFAutoModel")
else: # Architecture defined -- use it
if len(architectures) > 1:
raise ValueError(f"More than one architecture was found, aborting. (architectures = {architectures})")
self._logger.warning(f"Detected architecture: {architectures[0]}")
pt_class = getattr(import_module("transformers"), architectures[0])
try:
tf_class = getattr(import_module("transformers"), "TF" + architectures[0])
except AttributeError:
raise AttributeError(f"The TensorFlow equivalent of {architectures[0]} doesn't exist in transformers.")
# Load models and acquire a basic input compatible with the model.
pt_model = pt_class.from_pretrained(self._local_dir)
pt_model.eval()
tf_from_pt_model = tf_class.from_pretrained(self._local_dir, from_pt=True)
pt_input, tf_input = self.get_inputs(pt_model, config)
with torch.no_grad():
pt_outputs = pt_model(**pt_input, output_hidden_states=True)
del pt_model # will no longer be used, and may have a large memory footprint
tf_from_pt_model = tf_class.from_pretrained(self._local_dir, from_pt=True)
tf_from_pt_outputs = tf_from_pt_model(**tf_input, output_hidden_states=True)
# Confirms that cross loading PT weights into TF worked.
crossload_differences = self.find_pt_tf_differences(pt_outputs, tf_from_pt_outputs)
output_differences = {k: v for k, v in crossload_differences.items() if "hidden" not in k}
hidden_differences = {k: v for k, v in crossload_differences.items() if "hidden" in k}
if len(output_differences) == 0 and architectures is not None:
raise ValueError(
f"Something went wrong -- the config file has architectures ({architectures}), but no model head"
" output was found. All outputs start with 'hidden'"
)
max_crossload_output_diff = max(output_differences.values()) if output_differences else 0.0
max_crossload_hidden_diff = max(hidden_differences.values())
if max_crossload_output_diff > self._max_error or max_crossload_hidden_diff > self._max_error:
raise ValueError(
"The cross-loaded TensorFlow model has different outputs, something went wrong!\n"
+ f"\nList of maximum output differences above the threshold ({self._max_error}):\n"
+ "\n".join([f"{k}: {v:.3e}" for k, v in output_differences.items() if v > self._max_error])
+ f"\n\nList of maximum hidden layer differences above the threshold ({self._max_error}):\n"
+ "\n".join([f"{k}: {v:.3e}" for k, v in hidden_differences.items() if v > self._max_error])
)
# Save the weights in a TF format (if needed) and confirms that the results are still good
tf_weights_path = os.path.join(self._local_dir, TF2_WEIGHTS_NAME)
tf_weights_index_path = os.path.join(self._local_dir, TF2_WEIGHTS_INDEX_NAME)
if (not os.path.exists(tf_weights_path) and not os.path.exists(tf_weights_index_path)) or self._new_weights:
tf_from_pt_model.save_pretrained(self._local_dir)
del tf_from_pt_model # will no longer be used, and may have a large memory footprint
tf_model = tf_class.from_pretrained(self._local_dir)
tf_outputs = tf_model(**tf_input, output_hidden_states=True)
conversion_differences = self.find_pt_tf_differences(pt_outputs, tf_outputs)
output_differences = {k: v for k, v in conversion_differences.items() if "hidden" not in k}
hidden_differences = {k: v for k, v in conversion_differences.items() if "hidden" in k}
if len(output_differences) == 0 and architectures is not None:
raise ValueError(
f"Something went wrong -- the config file has architectures ({architectures}), but no model head"
" output was found. All outputs start with 'hidden'"
)
max_conversion_output_diff = max(output_differences.values()) if output_differences else 0.0
max_conversion_hidden_diff = max(hidden_differences.values())
if max_conversion_output_diff > self._max_error or max_conversion_hidden_diff > self._max_error:
raise ValueError(
"The converted TensorFlow model has different outputs, something went wrong!\n"
+ f"\nList of maximum output differences above the threshold ({self._max_error}):\n"
+ "\n".join([f"{k}: {v:.3e}" for k, v in output_differences.items() if v > self._max_error])
+ f"\n\nList of maximum hidden layer differences above the threshold ({self._max_error}):\n"
+ "\n".join([f"{k}: {v:.3e}" for k, v in hidden_differences.items() if v > self._max_error])
)
commit_message = "Update TF weights" if self._new_weights else "Add TF weights"
if self._push:
repo.git_add(auto_lfs_track=True)
repo.git_commit(commit_message)
repo.git_push(blocking=True) # this prints a progress bar with the upload
self._logger.warning(f"TF weights pushed into {self._model_name}")
elif not self._no_pr:
self._logger.warning("Uploading the weights into a new PR...")
commit_descrition = (
"Model converted by the [`transformers`' `pt_to_tf`"
" CLI](https://github.com/huggingface/transformers/blob/main/src/transformers/commands/pt_to_tf.py). "
"All converted model outputs and hidden layers were validated against its Pytorch counterpart.\n\n"
f"Maximum crossload output difference={max_crossload_output_diff:.3e}; "
f"Maximum crossload hidden layer difference={max_crossload_hidden_diff:.3e};\n"
f"Maximum conversion output difference={max_conversion_output_diff:.3e}; "
f"Maximum conversion hidden layer difference={max_conversion_hidden_diff:.3e};\n"
)
if self._max_error > MAX_ERROR:
commit_descrition += (
f"\n\nCAUTION: The maximum admissible error was manually increased to {self._max_error}!"
)
if self._extra_commit_description:
commit_descrition += "\n\n" + self._extra_commit_description
# sharded model -> adds all related files (index and .h5 shards)
if os.path.exists(tf_weights_index_path):
operations = [
CommitOperationAdd(path_in_repo=TF2_WEIGHTS_INDEX_NAME, path_or_fileobj=tf_weights_index_path)
]
for shard_path in tf.io.gfile.glob(self._local_dir + "/tf_model-*.h5"):
operations += [
CommitOperationAdd(path_in_repo=os.path.basename(shard_path), path_or_fileobj=shard_path)
]
else:
operations = [CommitOperationAdd(path_in_repo=TF2_WEIGHTS_NAME, path_or_fileobj=tf_weights_path)]
hub_pr_url = create_commit(
repo_id=self._model_name,
operations=operations,
commit_message=commit_message,
commit_description=commit_descrition,
repo_type="model",
create_pr=True,
)
self._logger.warning(f"PR open in {hub_pr_url}")
The provided code snippet includes necessary dependencies for implementing the `convert_command_factory` function. Write a Python function `def convert_command_factory(args: Namespace)` to solve the following problem:
Factory function used to convert a model PyTorch checkpoint in a TensorFlow 2 checkpoint. Returns: ServeCommand
Here is the function:
def convert_command_factory(args: Namespace):
"""
Factory function used to convert a model PyTorch checkpoint in a TensorFlow 2 checkpoint.
Returns: ServeCommand
"""
return PTtoTFCommand(
args.model_name,
args.local_dir,
args.max_error,
args.new_weights,
args.no_pr,
args.push,
args.extra_commit_description,
) | Factory function used to convert a model PyTorch checkpoint in a TensorFlow 2 checkpoint. Returns: ServeCommand |
10,564 | from argparse import ArgumentParser, Namespace
from typing import Any, List, Optional
from ..pipelines import Pipeline, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
def Body(*x, **y):
pass | null |
10,565 | from argparse import ArgumentParser, Namespace
from typing import Any, List, Optional
from ..pipelines import Pipeline, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
class ServeCommand(BaseTransformersCLICommand):
def register_subcommand(parser: ArgumentParser):
"""
Register this command to argparse so it's available for the transformer-cli
Args:
parser: Root parser to register command-specific arguments
"""
serve_parser = parser.add_parser(
"serve", help="CLI tool to run inference requests through REST and GraphQL endpoints."
)
serve_parser.add_argument(
"--task",
type=str,
choices=get_supported_tasks(),
help="The task to run the pipeline on",
)
serve_parser.add_argument("--host", type=str, default="localhost", help="Interface the server will listen on.")
serve_parser.add_argument("--port", type=int, default=8888, help="Port the serving will listen to.")
serve_parser.add_argument("--workers", type=int, default=1, help="Number of http workers")
serve_parser.add_argument("--model", type=str, help="Model's name or path to stored model.")
serve_parser.add_argument("--config", type=str, help="Model's config name or path to stored model.")
serve_parser.add_argument("--tokenizer", type=str, help="Tokenizer name to use.")
serve_parser.add_argument(
"--device",
type=int,
default=-1,
help="Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)",
)
serve_parser.set_defaults(func=serve_command_factory)
def __init__(self, pipeline: Pipeline, host: str, port: int, workers: int):
self._pipeline = pipeline
self.host = host
self.port = port
self.workers = workers
if not _serve_dependencies_installed:
raise RuntimeError(
"Using serve command requires FastAPI and uvicorn. "
'Please install transformers with [serving]: pip install "transformers[serving]".'
"Or install FastAPI and uvicorn separately."
)
else:
logger.info(f"Serving model over {host}:{port}")
self._app = FastAPI(
routes=[
APIRoute(
"/",
self.model_info,
response_model=ServeModelInfoResult,
response_class=JSONResponse,
methods=["GET"],
),
APIRoute(
"/tokenize",
self.tokenize,
response_model=ServeTokenizeResult,
response_class=JSONResponse,
methods=["POST"],
),
APIRoute(
"/detokenize",
self.detokenize,
response_model=ServeDeTokenizeResult,
response_class=JSONResponse,
methods=["POST"],
),
APIRoute(
"/forward",
self.forward,
response_model=ServeForwardResult,
response_class=JSONResponse,
methods=["POST"],
),
],
timeout=600,
)
def run(self):
run(self._app, host=self.host, port=self.port, workers=self.workers)
def model_info(self):
return ServeModelInfoResult(infos=vars(self._pipeline.model.config))
def tokenize(self, text_input: str = Body(None, embed=True), return_ids: bool = Body(False, embed=True)):
"""
Tokenize the provided input and eventually returns corresponding tokens id: - **text_input**: String to
tokenize - **return_ids**: Boolean flags indicating if the tokens have to be converted to their integer
mapping.
"""
try:
tokens_txt = self._pipeline.tokenizer.tokenize(text_input)
if return_ids:
tokens_ids = self._pipeline.tokenizer.convert_tokens_to_ids(tokens_txt)
return ServeTokenizeResult(tokens=tokens_txt, tokens_ids=tokens_ids)
else:
return ServeTokenizeResult(tokens=tokens_txt)
except Exception as e:
raise HTTPException(status_code=500, detail={"model": "", "error": str(e)})
def detokenize(
self,
tokens_ids: List[int] = Body(None, embed=True),
skip_special_tokens: bool = Body(False, embed=True),
cleanup_tokenization_spaces: bool = Body(True, embed=True),
):
"""
Detokenize the provided tokens ids to readable text: - **tokens_ids**: List of tokens ids -
**skip_special_tokens**: Flag indicating to not try to decode special tokens - **cleanup_tokenization_spaces**:
Flag indicating to remove all leading/trailing spaces and intermediate ones.
"""
try:
decoded_str = self._pipeline.tokenizer.decode(tokens_ids, skip_special_tokens, cleanup_tokenization_spaces)
return ServeDeTokenizeResult(model="", text=decoded_str)
except Exception as e:
raise HTTPException(status_code=500, detail={"model": "", "error": str(e)})
async def forward(self, inputs=Body(None, embed=True)):
"""
**inputs**: **attention_mask**: **tokens_type_ids**:
"""
# Check we don't have empty string
if len(inputs) == 0:
return ServeForwardResult(output=[], attention=[])
try:
# Forward through the model
output = self._pipeline(inputs)
return ServeForwardResult(output=output)
except Exception as e:
raise HTTPException(500, {"error": str(e)})
def pipeline(
task: str = None,
model: Optional = None,
config: Optional[Union[str, PretrainedConfig]] = None,
tokenizer: Optional[Union[str, PreTrainedTokenizer, PreTrainedTokenizerFast]] = None,
feature_extractor: Optional[Union[str, PreTrainedFeatureExtractor]] = None,
framework: Optional[str] = None,
revision: Optional[str] = None,
use_fast: bool = True,
use_auth_token: Optional[Union[str, bool]] = None,
device: Optional[Union[int, str, "torch.device"]] = None,
device_map=None,
torch_dtype=None,
trust_remote_code: Optional[bool] = None,
model_kwargs: Dict[str, Any] = None,
pipeline_class: Optional[Any] = None,
**kwargs,
) -> Pipeline:
"""
Utility factory method to build a [`Pipeline`].
Pipelines are made of:
- A [tokenizer](tokenizer) in charge of mapping raw textual input to token.
- A [model](model) to make predictions from the inputs.
- Some (optional) post processing for enhancing model's output.
Args:
task (`str`):
The task defining which pipeline will be returned. Currently accepted tasks are:
- `"audio-classification"`: will return a [`AudioClassificationPipeline`].
- `"automatic-speech-recognition"`: will return a [`AutomaticSpeechRecognitionPipeline`].
- `"conversational"`: will return a [`ConversationalPipeline`].
- `"feature-extraction"`: will return a [`FeatureExtractionPipeline`].
- `"fill-mask"`: will return a [`FillMaskPipeline`]:.
- `"image-classification"`: will return a [`ImageClassificationPipeline`].
- `"question-answering"`: will return a [`QuestionAnsweringPipeline`].
- `"table-question-answering"`: will return a [`TableQuestionAnsweringPipeline`].
- `"text2text-generation"`: will return a [`Text2TextGenerationPipeline`].
- `"text-classification"` (alias `"sentiment-analysis"` available): will return a
[`TextClassificationPipeline`].
- `"text-generation"`: will return a [`TextGenerationPipeline`]:.
- `"token-classification"` (alias `"ner"` available): will return a [`TokenClassificationPipeline`].
- `"translation"`: will return a [`TranslationPipeline`].
- `"translation_xx_to_yy"`: will return a [`TranslationPipeline`].
- `"summarization"`: will return a [`SummarizationPipeline`].
- `"zero-shot-classification"`: will return a [`ZeroShotClassificationPipeline`].
model (`str` or [`PreTrainedModel`] or [`TFPreTrainedModel`], *optional*):
The model that will be used by the pipeline to make predictions. This can be a model identifier or an
actual instance of a pretrained model inheriting from [`PreTrainedModel`] (for PyTorch) or
[`TFPreTrainedModel`] (for TensorFlow).
If not provided, the default for the `task` will be loaded.
config (`str` or [`PretrainedConfig`], *optional*):
The configuration that will be used by the pipeline to instantiate the model. This can be a model
identifier or an actual pretrained model configuration inheriting from [`PretrainedConfig`].
If not provided, the default configuration file for the requested model will be used. That means that if
`model` is given, its default configuration will be used. However, if `model` is not supplied, this
`task`'s default model's config is used instead.
tokenizer (`str` or [`PreTrainedTokenizer`], *optional*):
The tokenizer that will be used by the pipeline to encode data for the model. This can be a model
identifier or an actual pretrained tokenizer inheriting from [`PreTrainedTokenizer`].
If not provided, the default tokenizer for the given `model` will be loaded (if it is a string). If `model`
is not specified or not a string, then the default tokenizer for `config` is loaded (if it is a string).
However, if `config` is also not given or not a string, then the default tokenizer for the given `task`
will be loaded.
feature_extractor (`str` or [`PreTrainedFeatureExtractor`], *optional*):
The feature extractor that will be used by the pipeline to encode data for the model. This can be a model
identifier or an actual pretrained feature extractor inheriting from [`PreTrainedFeatureExtractor`].
Feature extractors are used for non-NLP models, such as Speech or Vision models as well as multi-modal
models. Multi-modal models will also require a tokenizer to be passed.
If not provided, the default feature extractor for the given `model` will be loaded (if it is a string). If
`model` is not specified or not a string, then the default feature extractor for `config` is loaded (if it
is a string). However, if `config` is also not given or not a string, then the default feature extractor
for the given `task` will be loaded.
framework (`str`, *optional*):
The framework to use, either `"pt"` for PyTorch or `"tf"` for TensorFlow. The specified framework must be
installed.
If no framework is specified, will default to the one currently installed. If no framework is specified and
both frameworks are installed, will default to the framework of the `model`, or to PyTorch if no model is
provided.
revision (`str`, *optional*, defaults to `"main"`):
When passing a task name or a string model identifier: The specific model version to use. It can be a
branch name, a tag name, or a commit id, since we use a git-based system for storing models and other
artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
use_fast (`bool`, *optional*, defaults to `True`):
Whether or not to use a Fast tokenizer if possible (a [`PreTrainedTokenizerFast`]).
use_auth_token (`str` or *bool*, *optional*):
The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated
when running `huggingface-cli login` (stored in `~/.huggingface`).
device (`int` or `str` or `torch.device`):
Defines the device (*e.g.*, `"cpu"`, `"cuda:1"`, `"mps"`, or a GPU ordinal rank like `1`) on which this
pipeline will be allocated.
device_map (`str` or `Dict[str, Union[int, str, torch.device]`, *optional*):
Sent directly as `model_kwargs` (just a simpler shortcut). When `accelerate` library is present, set
`device_map="auto"` to compute the most optimized `device_map` automatically. [More
information](https://huggingface.co/docs/accelerate/main/en/big_modeling#accelerate.cpu_offload)
<Tip warning={true}>
Do not use `device_map` AND `device` at the same time as they will conflict
</Tip>
torch_dtype (`str` or `torch.dtype`, *optional*):
Sent directly as `model_kwargs` (just a simpler shortcut) to use the available precision for this model
(`torch.float16`, `torch.bfloat16`, ... or `"auto"`).
trust_remote_code (`bool`, *optional*, defaults to `False`):
Whether or not to allow for custom code defined on the Hub in their own modeling, configuration,
tokenization or even pipeline files. This option should only be set to `True` for repositories you trust
and in which you have read the code, as it will execute code present on the Hub on your local machine.
model_kwargs:
Additional dictionary of keyword arguments passed along to the model's `from_pretrained(...,
**model_kwargs)` function.
kwargs:
Additional keyword arguments passed along to the specific pipeline init (see the documentation for the
corresponding pipeline class for possible values).
Returns:
[`Pipeline`]: A suitable pipeline for the task.
Examples:
```python
>>> from transformers import pipeline, AutoModelForTokenClassification, AutoTokenizer
>>> # Sentiment analysis pipeline
>>> pipeline("sentiment-analysis")
>>> # Question answering pipeline, specifying the checkpoint identifier
>>> pipeline("question-answering", model="distilbert-base-cased-distilled-squad", tokenizer="bert-base-cased")
>>> # Named entity recognition pipeline, passing in a specific model and tokenizer
>>> model = AutoModelForTokenClassification.from_pretrained("dbmdz/bert-large-cased-finetuned-conll03-english")
>>> tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
>>> pipeline("ner", model=model, tokenizer=tokenizer)
```"""
if model_kwargs is None:
model_kwargs = {}
# Make sure we only pass use_auth_token once as a kwarg (it used to be possible to pass it in model_kwargs,
# this is to keep BC).
use_auth_token = model_kwargs.pop("use_auth_token", use_auth_token)
hub_kwargs = {
"revision": revision,
"use_auth_token": use_auth_token,
"trust_remote_code": trust_remote_code,
"_commit_hash": None,
}
if task is None and model is None:
raise RuntimeError(
"Impossible to instantiate a pipeline without either a task or a model "
"being specified. "
"Please provide a task class or a model"
)
if model is None and tokenizer is not None:
raise RuntimeError(
"Impossible to instantiate a pipeline with tokenizer specified but not the model as the provided tokenizer"
" may not be compatible with the default model. Please provide a PreTrainedModel class or a"
" path/identifier to a pretrained model when providing tokenizer."
)
if model is None and feature_extractor is not None:
raise RuntimeError(
"Impossible to instantiate a pipeline with feature_extractor specified but not the model as the provided"
" feature_extractor may not be compatible with the default model. Please provide a PreTrainedModel class"
" or a path/identifier to a pretrained model when providing feature_extractor."
)
# Config is the primordial information item.
# Instantiate config if needed
if isinstance(config, str):
config = AutoConfig.from_pretrained(config, _from_pipeline=task, **hub_kwargs, **model_kwargs)
hub_kwargs["_commit_hash"] = config._commit_hash
elif config is None and isinstance(model, str):
config = AutoConfig.from_pretrained(model, _from_pipeline=task, **hub_kwargs, **model_kwargs)
hub_kwargs["_commit_hash"] = config._commit_hash
custom_tasks = {}
if config is not None and len(getattr(config, "custom_pipelines", {})) > 0:
custom_tasks = config.custom_pipelines
if task is None and trust_remote_code is not False:
if len(custom_tasks) == 1:
task = list(custom_tasks.keys())[0]
else:
raise RuntimeError(
"We can't infer the task automatically for this model as there are multiple tasks available. Pick "
f"one in {', '.join(custom_tasks.keys())}"
)
if task is None and model is not None:
if not isinstance(model, str):
raise RuntimeError(
"Inferring the task automatically requires to check the hub with a model_id defined as a `str`."
f"{model} is not a valid model_id."
)
task = get_task(model, use_auth_token)
# Retrieve the task
if task in custom_tasks:
normalized_task = task
targeted_task, task_options = clean_custom_task(custom_tasks[task])
if pipeline_class is None:
if not trust_remote_code:
raise ValueError(
"Loading this pipeline requires you to execute the code in the pipeline file in that"
" repo on your local machine. Make sure you have read the code there to avoid malicious use, then"
" set the option `trust_remote_code=True` to remove this error."
)
class_ref = targeted_task["impl"]
module_file, class_name = class_ref.split(".")
pipeline_class = get_class_from_dynamic_module(
model, module_file + ".py", class_name, revision=revision, use_auth_token=use_auth_token
)
else:
normalized_task, targeted_task, task_options = check_task(task)
if pipeline_class is None:
pipeline_class = targeted_task["impl"]
# Use default model/config/tokenizer for the task if no model is provided
if model is None:
# At that point framework might still be undetermined
model, default_revision = get_default_model_and_revision(targeted_task, framework, task_options)
revision = revision if revision is not None else default_revision
logger.warning(
f"No model was supplied, defaulted to {model} and revision"
f" {revision} ({HUGGINGFACE_CO_RESOLVE_ENDPOINT}/{model}).\n"
"Using a pipeline without specifying a model name and revision in production is not recommended."
)
if config is None and isinstance(model, str):
config = AutoConfig.from_pretrained(model, _from_pipeline=task, **hub_kwargs, **model_kwargs)
hub_kwargs["_commit_hash"] = config._commit_hash
if device_map is not None:
if "device_map" in model_kwargs:
raise ValueError(
'You cannot use both `pipeline(... device_map=..., model_kwargs={"device_map":...})` as those'
" arguments might conflict, use only one.)"
)
model_kwargs["device_map"] = device_map
if torch_dtype is not None:
if "torch_dtype" in model_kwargs:
raise ValueError(
'You cannot use both `pipeline(... torch_dtype=..., model_kwargs={"torch_dtype":...})` as those'
" arguments might conflict, use only one.)"
)
model_kwargs["torch_dtype"] = torch_dtype
model_name = model if isinstance(model, str) else None
# Infer the framework from the model
# Forced if framework already defined, inferred if it's None
# Will load the correct model if possible
model_classes = {"tf": targeted_task["tf"], "pt": targeted_task["pt"]}
framework, model = infer_framework_load_model(
model,
model_classes=model_classes,
config=config,
framework=framework,
task=task,
**hub_kwargs,
**model_kwargs,
)
model_config = model.config
hub_kwargs["_commit_hash"] = model.config._commit_hash
load_tokenizer = type(model_config) in TOKENIZER_MAPPING or model_config.tokenizer_class is not None
load_feature_extractor = type(model_config) in FEATURE_EXTRACTOR_MAPPING or feature_extractor is not None
if (
tokenizer is None
and not load_tokenizer
and normalized_task not in NO_TOKENIZER_TASKS
# Using class name to avoid importing the real class.
and model_config.__class__.__name__ in MULTI_MODEL_CONFIGS
):
# This is a special category of models, that are fusions of multiple models
# so the model_config might not define a tokenizer, but it seems to be
# necessary for the task, so we're force-trying to load it.
load_tokenizer = True
if (
feature_extractor is None
and not load_feature_extractor
and normalized_task not in NO_FEATURE_EXTRACTOR_TASKS
# Using class name to avoid importing the real class.
and model_config.__class__.__name__ in MULTI_MODEL_CONFIGS
):
# This is a special category of models, that are fusions of multiple models
# so the model_config might not define a tokenizer, but it seems to be
# necessary for the task, so we're force-trying to load it.
load_feature_extractor = True
if task in NO_TOKENIZER_TASKS:
# These will never require a tokenizer.
# the model on the other hand might have a tokenizer, but
# the files could be missing from the hub, instead of failing
# on such repos, we just force to not load it.
load_tokenizer = False
if task in NO_FEATURE_EXTRACTOR_TASKS:
load_feature_extractor = False
if load_tokenizer:
# Try to infer tokenizer from model or config name (if provided as str)
if tokenizer is None:
if isinstance(model_name, str):
tokenizer = model_name
elif isinstance(config, str):
tokenizer = config
else:
# Impossible to guess what is the right tokenizer here
raise Exception(
"Impossible to guess which tokenizer to use. "
"Please provide a PreTrainedTokenizer class or a path/identifier to a pretrained tokenizer."
)
# Instantiate tokenizer if needed
if isinstance(tokenizer, (str, tuple)):
if isinstance(tokenizer, tuple):
# For tuple we have (tokenizer name, {kwargs})
use_fast = tokenizer[1].pop("use_fast", use_fast)
tokenizer_identifier = tokenizer[0]
tokenizer_kwargs = tokenizer[1]
else:
tokenizer_identifier = tokenizer
tokenizer_kwargs = model_kwargs
tokenizer = AutoTokenizer.from_pretrained(
tokenizer_identifier, use_fast=use_fast, _from_pipeline=task, **hub_kwargs, **tokenizer_kwargs
)
if load_feature_extractor:
# Try to infer feature extractor from model or config name (if provided as str)
if feature_extractor is None:
if isinstance(model_name, str):
feature_extractor = model_name
elif isinstance(config, str):
feature_extractor = config
else:
# Impossible to guess what is the right feature_extractor here
raise Exception(
"Impossible to guess which feature extractor to use. "
"Please provide a PreTrainedFeatureExtractor class or a path/identifier "
"to a pretrained feature extractor."
)
# Instantiate feature_extractor if needed
if isinstance(feature_extractor, (str, tuple)):
feature_extractor = AutoFeatureExtractor.from_pretrained(
feature_extractor, _from_pipeline=task, **hub_kwargs, **model_kwargs
)
if (
feature_extractor._processor_class
and feature_extractor._processor_class.endswith("WithLM")
and isinstance(model_name, str)
):
try:
import kenlm # to trigger `ImportError` if not installed
from pyctcdecode import BeamSearchDecoderCTC
if os.path.isdir(model_name) or os.path.isfile(model_name):
decoder = BeamSearchDecoderCTC.load_from_dir(model_name)
else:
language_model_glob = os.path.join(
BeamSearchDecoderCTC._LANGUAGE_MODEL_SERIALIZED_DIRECTORY, "*"
)
alphabet_filename = BeamSearchDecoderCTC._ALPHABET_SERIALIZED_FILENAME
allow_regex = [language_model_glob, alphabet_filename]
decoder = BeamSearchDecoderCTC.load_from_hf_hub(model_name, allow_regex=allow_regex)
kwargs["decoder"] = decoder
except ImportError as e:
logger.warning(f"Could not load the `decoder` for {model_name}. Defaulting to raw CTC. Error: {e}")
if not is_kenlm_available():
logger.warning("Try to install `kenlm`: `pip install kenlm")
if not is_pyctcdecode_available():
logger.warning("Try to install `pyctcdecode`: `pip install pyctcdecode")
if task == "translation" and model.config.task_specific_params:
for key in model.config.task_specific_params:
if key.startswith("translation"):
task = key
warnings.warn(
f'"translation" task was used, instead of "translation_XX_to_YY", defaulting to "{task}"',
UserWarning,
)
break
if tokenizer is not None:
kwargs["tokenizer"] = tokenizer
if feature_extractor is not None:
kwargs["feature_extractor"] = feature_extractor
if device is not None:
kwargs["device"] = device
return pipeline_class(model=model, framework=framework, task=task, **kwargs)
The provided code snippet includes necessary dependencies for implementing the `serve_command_factory` function. Write a Python function `def serve_command_factory(args: Namespace)` to solve the following problem:
Factory function used to instantiate serving server from provided command line arguments. Returns: ServeCommand
Here is the function:
def serve_command_factory(args: Namespace):
"""
Factory function used to instantiate serving server from provided command line arguments.
Returns: ServeCommand
"""
nlp = pipeline(
task=args.task,
model=args.model if args.model else None,
config=args.config,
tokenizer=args.tokenizer,
device=args.device,
)
return ServeCommand(nlp, args.host, args.port, args.workers) | Factory function used to instantiate serving server from provided command line arguments. Returns: ServeCommand |
10,566 | from argparse import ArgumentParser
from . import BaseTransformersCLICommand
class DownloadCommand(BaseTransformersCLICommand):
def register_subcommand(parser: ArgumentParser):
download_parser = parser.add_parser("download")
download_parser.add_argument(
"--cache-dir", type=str, default=None, help="Path to location to store the models"
)
download_parser.add_argument(
"--force", action="store_true", help="Force the model to be download even if already in cache-dir"
)
download_parser.add_argument("model", type=str, help="Name of the model to download")
download_parser.set_defaults(func=download_command_factory)
def __init__(self, model: str, cache: str, force: bool):
self._model = model
self._cache = cache
self._force = force
def run(self):
from ..models.auto import AutoModel, AutoTokenizer
AutoModel.from_pretrained(self._model, cache_dir=self._cache, force_download=self._force)
AutoTokenizer.from_pretrained(self._model, cache_dir=self._cache, force_download=self._force)
def download_command_factory(args):
return DownloadCommand(args.model, args.cache_dir, args.force) | null |
10,567 | import subprocess
from argparse import ArgumentParser
from typing import List, Union
from huggingface_hub.hf_api import HfFolder, create_repo, whoami
from requests.exceptions import HTTPError
from . import BaseTransformersCLICommand
The provided code snippet includes necessary dependencies for implementing the `tabulate` function. Write a Python function `def tabulate(rows: List[List[Union[str, int]]], headers: List[str]) -> str` to solve the following problem:
Inspired by: - stackoverflow.com/a/8356620/593036 - stackoverflow.com/questions/9535954/printing-lists-as-tabular-data
Here is the function:
def tabulate(rows: List[List[Union[str, int]]], headers: List[str]) -> str:
"""
Inspired by:
- stackoverflow.com/a/8356620/593036
- stackoverflow.com/questions/9535954/printing-lists-as-tabular-data
"""
col_widths = [max(len(str(x)) for x in col) for col in zip(*rows, headers)]
row_format = ("{{:{}}} " * len(headers)).format(*col_widths)
lines = []
lines.append(row_format.format(*headers))
lines.append(row_format.format(*["-" * w for w in col_widths]))
for row in rows:
lines.append(row_format.format(*row))
return "\n".join(lines) | Inspired by: - stackoverflow.com/a/8356620/593036 - stackoverflow.com/questions/9535954/printing-lists-as-tabular-data |
10,568 | import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import is_flax_available, is_tf_available, is_torch_available
from . import BaseTransformersCLICommand
class EnvironmentCommand(BaseTransformersCLICommand):
def register_subcommand(parser: ArgumentParser):
download_parser = parser.add_parser("env")
download_parser.set_defaults(func=info_command_factory)
def run(self):
pt_version = "not installed"
pt_cuda_available = "NA"
if is_torch_available():
import torch
pt_version = torch.__version__
pt_cuda_available = torch.cuda.is_available()
tf_version = "not installed"
tf_cuda_available = "NA"
if is_tf_available():
import tensorflow as tf
tf_version = tf.__version__
try:
# deprecated in v2.1
tf_cuda_available = tf.test.is_gpu_available()
except AttributeError:
# returns list of devices, convert to bool
tf_cuda_available = bool(tf.config.list_physical_devices("GPU"))
flax_version = "not installed"
jax_version = "not installed"
jaxlib_version = "not installed"
jax_backend = "NA"
if is_flax_available():
import flax
import jax
import jaxlib
flax_version = flax.__version__
jax_version = jax.__version__
jaxlib_version = jaxlib.__version__
jax_backend = jax.lib.xla_bridge.get_backend().platform
info = {
"`transformers` version": version,
"Platform": platform.platform(),
"Python version": platform.python_version(),
"Huggingface_hub version": huggingface_hub.__version__,
"PyTorch version (GPU?)": f"{pt_version} ({pt_cuda_available})",
"Tensorflow version (GPU?)": f"{tf_version} ({tf_cuda_available})",
"Flax version (CPU?/GPU?/TPU?)": f"{flax_version} ({jax_backend})",
"Jax version": f"{jax_version}",
"JaxLib version": f"{jaxlib_version}",
"Using GPU in script?": "<fill in>",
"Using distributed or parallel set-up in script?": "<fill in>",
}
print("\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n")
print(self.format_dict(info))
return info
def format_dict(d):
return "\n".join([f"- {prop}: {val}" for prop, val in d.items()]) + "\n"
def info_command_factory(_):
return EnvironmentCommand() | null |
10,569 | import difflib
import json
import os
import re
from argparse import ArgumentParser, Namespace
from dataclasses import dataclass
from datetime import date
from itertools import chain
from pathlib import Path
from typing import Any, Callable, Dict, List, Optional, Pattern, Tuple, Union
import transformers.models.auto as auto_module
from transformers.models.auto.configuration_auto import model_type_to_module_name
from ..utils import is_flax_available, is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand
TRANSFORMERS_PATH = Path(__file__).parent.parent
REPO_PATH = TRANSFORMERS_PATH.parent.parent
class ModelPatterns:
"""
Holds the basic information about a new model for the add-new-model-like command.
Args:
model_name (`str`): The model name.
checkpoint (`str`): The checkpoint to use for doc examples.
model_type (`str`, *optional*):
The model type, the identifier used internally in the library like `bert` or `xlm-roberta`. Will default to
`model_name` lowercased with spaces replaced with minuses (-).
model_lower_cased (`str`, *optional*):
The lowercased version of the model name, to use for the module name or function names. Will default to
`model_name` lowercased with spaces and minuses replaced with underscores.
model_camel_cased (`str`, *optional*):
The camel-cased version of the model name, to use for the class names. Will default to `model_name`
camel-cased (with spaces and minuses both considered as word separators.
model_upper_cased (`str`, *optional*):
The uppercased version of the model name, to use for the constant names. Will default to `model_name`
uppercased with spaces and minuses replaced with underscores.
config_class (`str`, *optional*):
The tokenizer class associated with this model. Will default to `"{model_camel_cased}Config"`.
tokenizer_class (`str`, *optional*):
The tokenizer class associated with this model (leave to `None` for models that don't use a tokenizer).
feature_extractor_class (`str`, *optional*):
The feature extractor class associated with this model (leave to `None` for models that don't use a feature
extractor).
processor_class (`str`, *optional*):
The processor class associated with this model (leave to `None` for models that don't use a processor).
"""
model_name: str
checkpoint: str
model_type: Optional[str] = None
model_lower_cased: Optional[str] = None
model_camel_cased: Optional[str] = None
model_upper_cased: Optional[str] = None
config_class: Optional[str] = None
tokenizer_class: Optional[str] = None
feature_extractor_class: Optional[str] = None
processor_class: Optional[str] = None
def __post_init__(self):
if self.model_type is None:
self.model_type = self.model_name.lower().replace(" ", "-")
if self.model_lower_cased is None:
self.model_lower_cased = self.model_name.lower().replace(" ", "_").replace("-", "_")
if self.model_camel_cased is None:
# Split the model name on - and space
words = self.model_name.split(" ")
words = list(chain(*[w.split("-") for w in words]))
# Make sure each word is capitalized
words = [w[0].upper() + w[1:] for w in words]
self.model_camel_cased = "".join(words)
if self.model_upper_cased is None:
self.model_upper_cased = self.model_name.upper().replace(" ", "_").replace("-", "_")
if self.config_class is None:
self.config_class = f"{self.model_camel_cased}Config"
def add_content_to_file(
file_name: Union[str, os.PathLike],
content: str,
add_after: Optional[Union[str, Pattern]] = None,
add_before: Optional[Union[str, Pattern]] = None,
exact_match: bool = False,
):
"""
A utility to add some content inside a given file.
Args:
file_name (`str` or `os.PathLike`): The name of the file in which we want to insert some content.
content (`str`): The content to add.
add_after (`str` or `Pattern`):
The pattern to test on a line of `text`, the new content is added after the first instance matching it.
add_before (`str` or `Pattern`):
The pattern to test on a line of `text`, the new content is added before the first instance matching it.
exact_match (`bool`, *optional*, defaults to `False`):
A line is considered a match with `add_after` or `add_before` if it matches exactly when `exact_match=True`,
otherwise, if `add_after`/`add_before` is present in the line.
<Tip warning={true}>
The arguments `add_after` and `add_before` are mutually exclusive, and one exactly needs to be provided.
</Tip>
"""
with open(file_name, "r", encoding="utf-8") as f:
old_content = f.read()
new_content = add_content_to_text(
old_content, content, add_after=add_after, add_before=add_before, exact_match=exact_match
)
with open(file_name, "w", encoding="utf-8") as f:
f.write(new_content)
def duplicate_module(
module_file: Union[str, os.PathLike],
old_model_patterns: ModelPatterns,
new_model_patterns: ModelPatterns,
dest_file: Optional[str] = None,
add_copied_from: bool = True,
):
"""
Create a new module from an existing one and adapting all function and classes names from old patterns to new ones.
Args:
module_file (`str` or `os.PathLike`): Path to the module to duplicate.
old_model_patterns (`ModelPatterns`): The patterns for the old model.
new_model_patterns (`ModelPatterns`): The patterns for the new model.
dest_file (`str` or `os.PathLike`, *optional*): Path to the new module.
add_copied_from (`bool`, *optional*, defaults to `True`):
Whether or not to add `# Copied from` statements in the duplicated module.
"""
if dest_file is None:
dest_file = str(module_file).replace(
old_model_patterns.model_lower_cased, new_model_patterns.model_lower_cased
)
with open(module_file, "r", encoding="utf-8") as f:
content = f.read()
content = re.sub("# Copyright (\d+)\s", f"# Copyright {CURRENT_YEAR} ", content)
objects = parse_module_content(content)
# Loop and treat all objects
new_objects = []
for obj in objects:
# Special cases
if "PRETRAINED_CONFIG_ARCHIVE_MAP = {" in obj:
# docstyle-ignore
obj = (
f"{new_model_patterns.model_upper_cased}_PRETRAINED_CONFIG_ARCHIVE_MAP = "
+ "{"
+ f"""
"{new_model_patterns.checkpoint}": "https://huggingface.co/{new_model_patterns.checkpoint}/resolve/main/config.json",
"""
+ "}\n"
)
new_objects.append(obj)
continue
elif "PRETRAINED_MODEL_ARCHIVE_LIST = [" in obj:
if obj.startswith("TF_"):
prefix = "TF_"
elif obj.startswith("FLAX_"):
prefix = "FLAX_"
else:
prefix = ""
# docstyle-ignore
obj = f"""{prefix}{new_model_patterns.model_upper_cased}_PRETRAINED_MODEL_ARCHIVE_LIST = [
"{new_model_patterns.checkpoint}",
# See all {new_model_patterns.model_name} models at https://huggingface.co/models?filter={new_model_patterns.model_type}
]
"""
new_objects.append(obj)
continue
special_pattern = False
for pattern, attr in SPECIAL_PATTERNS.items():
if pattern in obj:
obj = obj.replace(getattr(old_model_patterns, attr), getattr(new_model_patterns, attr))
new_objects.append(obj)
special_pattern = True
break
if special_pattern:
continue
# Regular classes functions
old_obj = obj
obj, replacement = replace_model_patterns(obj, old_model_patterns, new_model_patterns)
has_copied_from = re.search("^#\s+Copied from", obj, flags=re.MULTILINE) is not None
if add_copied_from and not has_copied_from and _re_class_func.search(obj) is not None and len(replacement) > 0:
# Copied from statement must be added just before the class/function definition, which may not be the
# first line because of decorators.
module_name = get_module_from_file(module_file)
old_object_name = _re_class_func.search(old_obj).groups()[0]
obj = add_content_to_text(
obj, f"# Copied from {module_name}.{old_object_name} with {replacement}", add_before=_re_class_func
)
# In all cases, we remove Copied from statement with indent on methods.
obj = re.sub("\n[ ]+# Copied from [^\n]*\n", "\n", obj)
new_objects.append(obj)
with open(dest_file, "w", encoding="utf-8") as f:
content = f.write("\n".join(new_objects))
def retrieve_info_for_model(model_type, frameworks: Optional[List[str]] = None):
"""
Retrieves all the information from a given model_type.
Args:
model_type (`str`): A valid model type (like "bert" or "gpt2")
frameworks (`List[str]`, *optional*):
If passed, will only keep the info corresponding to the passed frameworks.
Returns:
`Dict`: A dictionary with the following keys:
- **frameworks** (`List[str]`): The list of frameworks that back this model type.
- **model_classes** (`Dict[str, List[str]]`): The model classes implemented for that model type.
- **model_files** (`Dict[str, Union[Path, List[Path]]]`): The files associated with that model type.
- **model_patterns** (`ModelPatterns`): The various patterns for the model.
"""
if model_type not in auto_module.MODEL_NAMES_MAPPING:
raise ValueError(f"{model_type} is not a valid model type.")
model_name = auto_module.MODEL_NAMES_MAPPING[model_type]
config_class = auto_module.configuration_auto.CONFIG_MAPPING_NAMES[model_type]
archive_map = auto_module.configuration_auto.CONFIG_ARCHIVE_MAP_MAPPING_NAMES.get(model_type, None)
if model_type in auto_module.tokenization_auto.TOKENIZER_MAPPING_NAMES:
tokenizer_classes = auto_module.tokenization_auto.TOKENIZER_MAPPING_NAMES[model_type]
tokenizer_class = tokenizer_classes[0] if tokenizer_classes[0] is not None else tokenizer_classes[1]
else:
tokenizer_class = None
feature_extractor_class = auto_module.feature_extraction_auto.FEATURE_EXTRACTOR_MAPPING_NAMES.get(model_type, None)
processor_class = auto_module.processing_auto.PROCESSOR_MAPPING_NAMES.get(model_type, None)
model_files = get_model_files(model_type, frameworks=frameworks)
model_camel_cased = config_class.replace("Config", "")
available_frameworks = []
for fname in model_files["model_files"]:
if "modeling_tf" in str(fname):
available_frameworks.append("tf")
elif "modeling_flax" in str(fname):
available_frameworks.append("flax")
elif "modeling" in str(fname):
available_frameworks.append("pt")
if frameworks is None:
frameworks = get_default_frameworks()
frameworks = [f for f in frameworks if f in available_frameworks]
model_classes = retrieve_model_classes(model_type, frameworks=frameworks)
# Retrieve model upper-cased name from the constant name of the pretrained archive map.
if archive_map is None:
model_upper_cased = model_camel_cased.upper()
else:
parts = archive_map.split("_")
idx = 0
while idx < len(parts) and parts[idx] != "PRETRAINED":
idx += 1
if idx < len(parts):
model_upper_cased = "_".join(parts[:idx])
else:
model_upper_cased = model_camel_cased.upper()
model_patterns = ModelPatterns(
model_name,
checkpoint=find_base_model_checkpoint(model_type, model_files=model_files),
model_type=model_type,
model_camel_cased=model_camel_cased,
model_lower_cased=model_files["module_name"],
model_upper_cased=model_upper_cased,
config_class=config_class,
tokenizer_class=tokenizer_class,
feature_extractor_class=feature_extractor_class,
processor_class=processor_class,
)
return {
"frameworks": frameworks,
"model_classes": model_classes,
"model_files": model_files,
"model_patterns": model_patterns,
}
def clean_frameworks_in_init(
init_file: Union[str, os.PathLike], frameworks: Optional[List[str]] = None, keep_processing: bool = True
):
"""
Removes all the import lines that don't belong to a given list of frameworks or concern tokenizers/feature
extractors/processors in an init.
Args:
init_file (`str` or `os.PathLike`): The path to the init to treat.
frameworks (`List[str]`, *optional*):
If passed, this will remove all imports that are subject to a framework not in frameworks
keep_processing (`bool`, *optional*, defaults to `True`):
Whether or not to keep the preprocessing (tokenizer, feature extractor, processor) imports in the init.
"""
if frameworks is None:
frameworks = get_default_frameworks()
names = {"pt": "torch"}
to_remove = [names.get(f, f) for f in ["pt", "tf", "flax"] if f not in frameworks]
if not keep_processing:
to_remove.extend(["sentencepiece", "tokenizers", "vision"])
if len(to_remove) == 0:
# Nothing to do
return
remove_pattern = "|".join(to_remove)
re_conditional_imports = re.compile(rf"^\s*if not is_({remove_pattern})_available\(\):\s*$")
re_try = re.compile(r"\s*try:")
re_else = re.compile(r"\s*else:")
re_is_xxx_available = re.compile(rf"is_({remove_pattern})_available")
with open(init_file, "r", encoding="utf-8") as f:
content = f.read()
lines = content.split("\n")
new_lines = []
idx = 0
while idx < len(lines):
# Conditional imports in try-except-else blocks
if (re_conditional_imports.search(lines[idx]) is not None) and (re_try.search(lines[idx - 1]) is not None):
# Remove the preceding `try:`
new_lines.pop()
idx += 1
# Iterate until `else:`
while is_empty_line(lines[idx]) or re_else.search(lines[idx]) is None:
idx += 1
idx += 1
indent = find_indent(lines[idx])
while find_indent(lines[idx]) >= indent or is_empty_line(lines[idx]):
idx += 1
# Remove the import from utils
elif re_is_xxx_available.search(lines[idx]) is not None:
line = lines[idx]
for framework in to_remove:
line = line.replace(f", is_{framework}_available", "")
line = line.replace(f"is_{framework}_available, ", "")
line = line.replace(f"is_{framework}_available,", "")
line = line.replace(f"is_{framework}_available", "")
if len(line.strip()) > 0:
new_lines.append(line)
idx += 1
# Otherwise we keep the line, except if it's a tokenizer import and we don't want to keep it.
elif keep_processing or (
re.search('^\s*"(tokenization|processing|feature_extraction)', lines[idx]) is None
and re.search("^\s*from .(tokenization|processing|feature_extraction)", lines[idx]) is None
):
new_lines.append(lines[idx])
idx += 1
else:
idx += 1
with open(init_file, "w", encoding="utf-8") as f:
f.write("\n".join(new_lines))
def add_model_to_main_init(
old_model_patterns: ModelPatterns,
new_model_patterns: ModelPatterns,
frameworks: Optional[List[str]] = None,
with_processing: bool = True,
):
"""
Add a model to the main init of Transformers.
Args:
old_model_patterns (`ModelPatterns`): The patterns for the old model.
new_model_patterns (`ModelPatterns`): The patterns for the new model.
frameworks (`List[str]`, *optional*):
If specified, only the models implemented in those frameworks will be added.
with_processsing (`bool`, *optional*, defaults to `True`):
Whether the tokenizer/feature extractor/processor of the model should also be added to the init or not.
"""
with open(TRANSFORMERS_PATH / "__init__.py", "r", encoding="utf-8") as f:
content = f.read()
lines = content.split("\n")
idx = 0
new_lines = []
framework = None
while idx < len(lines):
new_framework = False
if not is_empty_line(lines[idx]) and find_indent(lines[idx]) == 0:
framework = None
elif lines[idx].lstrip().startswith("if not is_torch_available"):
framework = "pt"
new_framework = True
elif lines[idx].lstrip().startswith("if not is_tf_available"):
framework = "tf"
new_framework = True
elif lines[idx].lstrip().startswith("if not is_flax_available"):
framework = "flax"
new_framework = True
if new_framework:
# For a new framework, we need to skip until the else: block to get where the imports are.
while lines[idx].strip() != "else:":
new_lines.append(lines[idx])
idx += 1
# Skip if we are in a framework not wanted.
if framework is not None and frameworks is not None and framework not in frameworks:
new_lines.append(lines[idx])
idx += 1
elif re.search(rf'models.{old_model_patterns.model_lower_cased}( |")', lines[idx]) is not None:
block = [lines[idx]]
indent = find_indent(lines[idx])
idx += 1
while find_indent(lines[idx]) > indent:
block.append(lines[idx])
idx += 1
if lines[idx].strip() in [")", "]", "],"]:
block.append(lines[idx])
idx += 1
block = "\n".join(block)
new_lines.append(block)
add_block = True
if not with_processing:
processing_classes = [
old_model_patterns.tokenizer_class,
old_model_patterns.feature_extractor_class,
old_model_patterns.processor_class,
]
# Only keep the ones that are not None
processing_classes = [c for c in processing_classes if c is not None]
for processing_class in processing_classes:
block = block.replace(f' "{processing_class}",', "")
block = block.replace(f', "{processing_class}"', "")
block = block.replace(f" {processing_class},", "")
block = block.replace(f", {processing_class}", "")
if processing_class in block:
add_block = False
if add_block:
new_lines.append(replace_model_patterns(block, old_model_patterns, new_model_patterns)[0])
else:
new_lines.append(lines[idx])
idx += 1
with open(TRANSFORMERS_PATH / "__init__.py", "w", encoding="utf-8") as f:
f.write("\n".join(new_lines))
def add_model_to_auto_classes(
old_model_patterns: ModelPatterns, new_model_patterns: ModelPatterns, model_classes: Dict[str, List[str]]
):
"""
Add a model to the relevant mappings in the auto module.
Args:
old_model_patterns (`ModelPatterns`): The patterns for the old model.
new_model_patterns (`ModelPatterns`): The patterns for the new model.
model_classes (`Dict[str, List[str]]`): A dictionary framework to list of model classes implemented.
"""
for filename in AUTO_CLASSES_PATTERNS:
# Extend patterns with all model classes if necessary
new_patterns = []
for pattern in AUTO_CLASSES_PATTERNS[filename]:
if re.search("any_([a-z]*)_class", pattern) is not None:
framework = re.search("any_([a-z]*)_class", pattern).groups()[0]
if framework in model_classes:
new_patterns.extend(
[
pattern.replace("{" + f"any_{framework}_class" + "}", cls)
for cls in model_classes[framework]
]
)
elif "{config_class}" in pattern:
new_patterns.append(pattern.replace("{config_class}", old_model_patterns.config_class))
elif "{feature_extractor_class}" in pattern:
if (
old_model_patterns.feature_extractor_class is not None
and new_model_patterns.feature_extractor_class is not None
):
new_patterns.append(
pattern.replace("{feature_extractor_class}", old_model_patterns.feature_extractor_class)
)
elif "{processor_class}" in pattern:
if old_model_patterns.processor_class is not None and new_model_patterns.processor_class is not None:
new_patterns.append(pattern.replace("{processor_class}", old_model_patterns.processor_class))
else:
new_patterns.append(pattern)
# Loop through all patterns.
for pattern in new_patterns:
full_name = TRANSFORMERS_PATH / "models" / "auto" / filename
old_model_line = pattern
new_model_line = pattern
for attr in ["model_type", "model_name"]:
old_model_line = old_model_line.replace("{" + attr + "}", getattr(old_model_patterns, attr))
new_model_line = new_model_line.replace("{" + attr + "}", getattr(new_model_patterns, attr))
if "pretrained_archive_map" in pattern:
old_model_line = old_model_line.replace(
"{pretrained_archive_map}", f"{old_model_patterns.model_upper_cased}_PRETRAINED_CONFIG_ARCHIVE_MAP"
)
new_model_line = new_model_line.replace(
"{pretrained_archive_map}", f"{new_model_patterns.model_upper_cased}_PRETRAINED_CONFIG_ARCHIVE_MAP"
)
new_model_line = new_model_line.replace(
old_model_patterns.model_camel_cased, new_model_patterns.model_camel_cased
)
add_content_to_file(full_name, new_model_line, add_after=old_model_line)
# Tokenizers require special handling
insert_tokenizer_in_auto_module(old_model_patterns, new_model_patterns)
def duplicate_doc_file(
doc_file: Union[str, os.PathLike],
old_model_patterns: ModelPatterns,
new_model_patterns: ModelPatterns,
dest_file: Optional[Union[str, os.PathLike]] = None,
frameworks: Optional[List[str]] = None,
):
"""
Duplicate a documentation file and adapts it for a new model.
Args:
module_file (`str` or `os.PathLike`): Path to the doc file to duplicate.
old_model_patterns (`ModelPatterns`): The patterns for the old model.
new_model_patterns (`ModelPatterns`): The patterns for the new model.
dest_file (`str` or `os.PathLike`, *optional*): Path to the new doc file.
Will default to the a file named `{new_model_patterns.model_type}.mdx` in the same folder as `module_file`.
frameworks (`List[str]`, *optional*):
If passed, will only keep the model classes corresponding to this list of frameworks in the new doc file.
"""
with open(doc_file, "r", encoding="utf-8") as f:
content = f.read()
content = re.sub("<!--\s*Copyright (\d+)\s", f"<!--Copyright {CURRENT_YEAR} ", content)
if frameworks is None:
frameworks = get_default_frameworks()
if dest_file is None:
dest_file = Path(doc_file).parent / f"{new_model_patterns.model_type}.mdx"
# Parse the doc file in blocks. One block per section/header
lines = content.split("\n")
blocks = []
current_block = []
for line in lines:
if line.startswith("#"):
blocks.append("\n".join(current_block))
current_block = [line]
else:
current_block.append(line)
blocks.append("\n".join(current_block))
new_blocks = []
in_classes = False
for block in blocks:
# Copyright
if not block.startswith("#"):
new_blocks.append(block)
# Main title
elif re.search("^#\s+\S+", block) is not None:
new_blocks.append(f"# {new_model_patterns.model_name}\n")
# The config starts the part of the doc with the classes.
elif not in_classes and old_model_patterns.config_class in block.split("\n")[0]:
in_classes = True
new_blocks.append(DOC_OVERVIEW_TEMPLATE.format(model_name=new_model_patterns.model_name))
new_block, _ = replace_model_patterns(block, old_model_patterns, new_model_patterns)
new_blocks.append(new_block)
# In classes
elif in_classes:
in_classes = True
block_title = block.split("\n")[0]
block_class = re.search("^#+\s+(\S.*)$", block_title).groups()[0]
new_block, _ = replace_model_patterns(block, old_model_patterns, new_model_patterns)
if "Tokenizer" in block_class:
# We only add the tokenizer if necessary
if old_model_patterns.tokenizer_class != new_model_patterns.tokenizer_class:
new_blocks.append(new_block)
elif "FeatureExtractor" in block_class:
# We only add the feature extractor if necessary
if old_model_patterns.feature_extractor_class != new_model_patterns.feature_extractor_class:
new_blocks.append(new_block)
elif "Processor" in block_class:
# We only add the processor if necessary
if old_model_patterns.processor_class != new_model_patterns.processor_class:
new_blocks.append(new_block)
elif block_class.startswith("Flax"):
# We only add Flax models if in the selected frameworks
if "flax" in frameworks:
new_blocks.append(new_block)
elif block_class.startswith("TF"):
# We only add TF models if in the selected frameworks
if "tf" in frameworks:
new_blocks.append(new_block)
elif len(block_class.split(" ")) == 1:
# We only add PyTorch models if in the selected frameworks
if "pt" in frameworks:
new_blocks.append(new_block)
else:
new_blocks.append(new_block)
with open(dest_file, "w", encoding="utf-8") as f:
f.write("\n".join(new_blocks))
The provided code snippet includes necessary dependencies for implementing the `create_new_model_like` function. Write a Python function `def create_new_model_like( model_type: str, new_model_patterns: ModelPatterns, add_copied_from: bool = True, frameworks: Optional[List[str]] = None, old_checkpoint: Optional[str] = None, )` to solve the following problem:
Creates a new model module like a given model of the Transformers library. Args: model_type (`str`): The model type to duplicate (like "bert" or "gpt2") new_model_patterns (`ModelPatterns`): The patterns for the new model. add_copied_from (`bool`, *optional*, defaults to `True`): Whether or not to add "Copied from" statements to all classes in the new model modeling files. frameworks (`List[str]`, *optional*): If passed, will limit the duplicate to the frameworks specified. old_checkpoint (`str`, *optional*): The name of the base checkpoint for the old model. Should be passed along when it can't be automatically recovered from the `model_type`.
Here is the function:
def create_new_model_like(
model_type: str,
new_model_patterns: ModelPatterns,
add_copied_from: bool = True,
frameworks: Optional[List[str]] = None,
old_checkpoint: Optional[str] = None,
):
"""
Creates a new model module like a given model of the Transformers library.
Args:
model_type (`str`): The model type to duplicate (like "bert" or "gpt2")
new_model_patterns (`ModelPatterns`): The patterns for the new model.
add_copied_from (`bool`, *optional*, defaults to `True`):
Whether or not to add "Copied from" statements to all classes in the new model modeling files.
frameworks (`List[str]`, *optional*):
If passed, will limit the duplicate to the frameworks specified.
old_checkpoint (`str`, *optional*):
The name of the base checkpoint for the old model. Should be passed along when it can't be automatically
recovered from the `model_type`.
"""
# Retrieve all the old model info.
model_info = retrieve_info_for_model(model_type, frameworks=frameworks)
model_files = model_info["model_files"]
old_model_patterns = model_info["model_patterns"]
if old_checkpoint is not None:
old_model_patterns.checkpoint = old_checkpoint
if len(old_model_patterns.checkpoint) == 0:
raise ValueError(
"The old model checkpoint could not be recovered from the model type. Please pass it to the "
"`old_checkpoint` argument."
)
keep_old_processing = True
for processing_attr in ["feature_extractor_class", "processor_class", "tokenizer_class"]:
if getattr(old_model_patterns, processing_attr) != getattr(new_model_patterns, processing_attr):
keep_old_processing = False
model_classes = model_info["model_classes"]
# 1. We create the module for our new model.
old_module_name = model_files["module_name"]
module_folder = TRANSFORMERS_PATH / "models" / new_model_patterns.model_lower_cased
os.makedirs(module_folder, exist_ok=True)
files_to_adapt = model_files["model_files"]
if keep_old_processing:
files_to_adapt = [
f
for f in files_to_adapt
if "tokenization" not in str(f) and "processing" not in str(f) and "feature_extraction" not in str(f)
]
os.makedirs(module_folder, exist_ok=True)
for module_file in files_to_adapt:
new_module_name = module_file.name.replace(
old_model_patterns.model_lower_cased, new_model_patterns.model_lower_cased
)
dest_file = module_folder / new_module_name
duplicate_module(
module_file,
old_model_patterns,
new_model_patterns,
dest_file=dest_file,
add_copied_from=add_copied_from and "modeling" in new_module_name,
)
clean_frameworks_in_init(
module_folder / "__init__.py", frameworks=frameworks, keep_processing=not keep_old_processing
)
# 2. We add our new model to the models init and the main init
add_content_to_file(
TRANSFORMERS_PATH / "models" / "__init__.py",
f" {new_model_patterns.model_lower_cased},",
add_after=f" {old_module_name},",
exact_match=True,
)
add_model_to_main_init(
old_model_patterns, new_model_patterns, frameworks=frameworks, with_processing=not keep_old_processing
)
# 3. Add test files
files_to_adapt = model_files["test_files"]
if keep_old_processing:
files_to_adapt = [
f
for f in files_to_adapt
if "tokenization" not in str(f) and "processor" not in str(f) and "feature_extraction" not in str(f)
]
def disable_fx_test(filename: Path) -> bool:
with open(filename) as fp:
content = fp.read()
new_content = re.sub(r"fx_compatible\s*=\s*True", "fx_compatible = False", content)
with open(filename, "w") as fp:
fp.write(new_content)
return content != new_content
disabled_fx_test = False
tests_folder = REPO_PATH / "tests" / "models" / new_model_patterns.model_lower_cased
os.makedirs(tests_folder, exist_ok=True)
with open(tests_folder / "__init__.py", "w"):
pass
for test_file in files_to_adapt:
new_test_file_name = test_file.name.replace(
old_model_patterns.model_lower_cased, new_model_patterns.model_lower_cased
)
dest_file = test_file.parent.parent / new_model_patterns.model_lower_cased / new_test_file_name
duplicate_module(
test_file,
old_model_patterns,
new_model_patterns,
dest_file=dest_file,
add_copied_from=False,
)
disabled_fx_test = disabled_fx_test | disable_fx_test(dest_file)
if disabled_fx_test:
print(
"The tests for symbolic tracing with torch.fx were disabled, you can add those once symbolic tracing works"
" for your new model."
)
# 4. Add model to auto classes
add_model_to_auto_classes(old_model_patterns, new_model_patterns, model_classes)
# 5. Add doc file
doc_file = REPO_PATH / "docs" / "source" / "en" / "model_doc" / f"{old_model_patterns.model_type}.mdx"
duplicate_doc_file(doc_file, old_model_patterns, new_model_patterns, frameworks=frameworks)
# 6. Warn the user for duplicate patterns
if old_model_patterns.model_type == old_model_patterns.checkpoint:
print(
"The model you picked has the same name for the model type and the checkpoint name "
f"({old_model_patterns.model_type}). As a result, it's possible some places where the new checkpoint "
f"should be, you have {new_model_patterns.model_type} instead. You should search for all instances of "
f"{new_model_patterns.model_type} in the new files and check they're not badly used as checkpoints."
)
elif old_model_patterns.model_lower_cased == old_model_patterns.checkpoint:
print(
"The model you picked has the same name for the model type and the checkpoint name "
f"({old_model_patterns.model_lower_cased}). As a result, it's possible some places where the new "
f"checkpoint should be, you have {new_model_patterns.model_lower_cased} instead. You should search for "
f"all instances of {new_model_patterns.model_lower_cased} in the new files and check they're not badly "
"used as checkpoints."
)
if (
old_model_patterns.model_type == old_model_patterns.model_lower_cased
and new_model_patterns.model_type != new_model_patterns.model_lower_cased
):
print(
"The model you picked has the same name for the model type and the lowercased model name "
f"({old_model_patterns.model_lower_cased}). As a result, it's possible some places where the new "
f"model type should be, you have {new_model_patterns.model_lower_cased} instead. You should search for "
f"all instances of {new_model_patterns.model_lower_cased} in the new files and check they're not badly "
"used as the model type."
)
if not keep_old_processing and old_model_patterns.tokenizer_class is not None:
print(
"The constants at the start of the new tokenizer file created needs to be manually fixed. If your new "
"model has a tokenizer fast, you will also need to manually add the converter in the "
"`SLOW_TO_FAST_CONVERTERS` constant of `convert_slow_tokenizer.py`."
) | Creates a new model module like a given model of the Transformers library. Args: model_type (`str`): The model type to duplicate (like "bert" or "gpt2") new_model_patterns (`ModelPatterns`): The patterns for the new model. add_copied_from (`bool`, *optional*, defaults to `True`): Whether or not to add "Copied from" statements to all classes in the new model modeling files. frameworks (`List[str]`, *optional*): If passed, will limit the duplicate to the frameworks specified. old_checkpoint (`str`, *optional*): The name of the base checkpoint for the old model. Should be passed along when it can't be automatically recovered from the `model_type`. |
10,570 | import difflib
import json
import os
import re
from argparse import ArgumentParser, Namespace
from dataclasses import dataclass
from datetime import date
from itertools import chain
from pathlib import Path
from typing import Any, Callable, Dict, List, Optional, Pattern, Tuple, Union
import transformers.models.auto as auto_module
from transformers.models.auto.configuration_auto import model_type_to_module_name
from ..utils import is_flax_available, is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand
class AddNewModelLikeCommand(BaseTransformersCLICommand):
def register_subcommand(parser: ArgumentParser):
add_new_model_like_parser = parser.add_parser("add-new-model-like")
add_new_model_like_parser.add_argument(
"--config_file", type=str, help="A file with all the information for this model creation."
)
add_new_model_like_parser.add_argument(
"--path_to_repo", type=str, help="When not using an editable install, the path to the Transformers repo."
)
add_new_model_like_parser.set_defaults(func=add_new_model_like_command_factory)
def __init__(self, config_file=None, path_to_repo=None, *args):
if config_file is not None:
with open(config_file, "r", encoding="utf-8") as f:
config = json.load(f)
self.old_model_type = config["old_model_type"]
self.model_patterns = ModelPatterns(**config["new_model_patterns"])
self.add_copied_from = config.get("add_copied_from", True)
self.frameworks = config.get("frameworks", get_default_frameworks())
self.old_checkpoint = config.get("old_checkpoint", None)
else:
(
self.old_model_type,
self.model_patterns,
self.add_copied_from,
self.frameworks,
self.old_checkpoint,
) = get_user_input()
self.path_to_repo = path_to_repo
def run(self):
if self.path_to_repo is not None:
# Adapt constants
global TRANSFORMERS_PATH
global REPO_PATH
REPO_PATH = Path(self.path_to_repo)
TRANSFORMERS_PATH = REPO_PATH / "src" / "transformers"
create_new_model_like(
model_type=self.old_model_type,
new_model_patterns=self.model_patterns,
add_copied_from=self.add_copied_from,
frameworks=self.frameworks,
old_checkpoint=self.old_checkpoint,
)
def add_new_model_like_command_factory(args: Namespace):
return AddNewModelLikeCommand(config_file=args.config_file, path_to_repo=args.path_to_repo) | null |
10,571 | import difflib
import json
import os
import re
from argparse import ArgumentParser, Namespace
from dataclasses import dataclass
from datetime import date
from itertools import chain
from pathlib import Path
from typing import Any, Callable, Dict, List, Optional, Pattern, Tuple, Union
import transformers.models.auto as auto_module
from transformers.models.auto.configuration_auto import model_type_to_module_name
from ..utils import is_flax_available, is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand
class ModelPatterns:
"""
Holds the basic information about a new model for the add-new-model-like command.
Args:
model_name (`str`): The model name.
checkpoint (`str`): The checkpoint to use for doc examples.
model_type (`str`, *optional*):
The model type, the identifier used internally in the library like `bert` or `xlm-roberta`. Will default to
`model_name` lowercased with spaces replaced with minuses (-).
model_lower_cased (`str`, *optional*):
The lowercased version of the model name, to use for the module name or function names. Will default to
`model_name` lowercased with spaces and minuses replaced with underscores.
model_camel_cased (`str`, *optional*):
The camel-cased version of the model name, to use for the class names. Will default to `model_name`
camel-cased (with spaces and minuses both considered as word separators.
model_upper_cased (`str`, *optional*):
The uppercased version of the model name, to use for the constant names. Will default to `model_name`
uppercased with spaces and minuses replaced with underscores.
config_class (`str`, *optional*):
The tokenizer class associated with this model. Will default to `"{model_camel_cased}Config"`.
tokenizer_class (`str`, *optional*):
The tokenizer class associated with this model (leave to `None` for models that don't use a tokenizer).
feature_extractor_class (`str`, *optional*):
The feature extractor class associated with this model (leave to `None` for models that don't use a feature
extractor).
processor_class (`str`, *optional*):
The processor class associated with this model (leave to `None` for models that don't use a processor).
"""
model_name: str
checkpoint: str
model_type: Optional[str] = None
model_lower_cased: Optional[str] = None
model_camel_cased: Optional[str] = None
model_upper_cased: Optional[str] = None
config_class: Optional[str] = None
tokenizer_class: Optional[str] = None
feature_extractor_class: Optional[str] = None
processor_class: Optional[str] = None
def __post_init__(self):
if self.model_type is None:
self.model_type = self.model_name.lower().replace(" ", "-")
if self.model_lower_cased is None:
self.model_lower_cased = self.model_name.lower().replace(" ", "_").replace("-", "_")
if self.model_camel_cased is None:
# Split the model name on - and space
words = self.model_name.split(" ")
words = list(chain(*[w.split("-") for w in words]))
# Make sure each word is capitalized
words = [w[0].upper() + w[1:] for w in words]
self.model_camel_cased = "".join(words)
if self.model_upper_cased is None:
self.model_upper_cased = self.model_name.upper().replace(" ", "_").replace("-", "_")
if self.config_class is None:
self.config_class = f"{self.model_camel_cased}Config"
def retrieve_info_for_model(model_type, frameworks: Optional[List[str]] = None):
"""
Retrieves all the information from a given model_type.
Args:
model_type (`str`): A valid model type (like "bert" or "gpt2")
frameworks (`List[str]`, *optional*):
If passed, will only keep the info corresponding to the passed frameworks.
Returns:
`Dict`: A dictionary with the following keys:
- **frameworks** (`List[str]`): The list of frameworks that back this model type.
- **model_classes** (`Dict[str, List[str]]`): The model classes implemented for that model type.
- **model_files** (`Dict[str, Union[Path, List[Path]]]`): The files associated with that model type.
- **model_patterns** (`ModelPatterns`): The various patterns for the model.
"""
if model_type not in auto_module.MODEL_NAMES_MAPPING:
raise ValueError(f"{model_type} is not a valid model type.")
model_name = auto_module.MODEL_NAMES_MAPPING[model_type]
config_class = auto_module.configuration_auto.CONFIG_MAPPING_NAMES[model_type]
archive_map = auto_module.configuration_auto.CONFIG_ARCHIVE_MAP_MAPPING_NAMES.get(model_type, None)
if model_type in auto_module.tokenization_auto.TOKENIZER_MAPPING_NAMES:
tokenizer_classes = auto_module.tokenization_auto.TOKENIZER_MAPPING_NAMES[model_type]
tokenizer_class = tokenizer_classes[0] if tokenizer_classes[0] is not None else tokenizer_classes[1]
else:
tokenizer_class = None
feature_extractor_class = auto_module.feature_extraction_auto.FEATURE_EXTRACTOR_MAPPING_NAMES.get(model_type, None)
processor_class = auto_module.processing_auto.PROCESSOR_MAPPING_NAMES.get(model_type, None)
model_files = get_model_files(model_type, frameworks=frameworks)
model_camel_cased = config_class.replace("Config", "")
available_frameworks = []
for fname in model_files["model_files"]:
if "modeling_tf" in str(fname):
available_frameworks.append("tf")
elif "modeling_flax" in str(fname):
available_frameworks.append("flax")
elif "modeling" in str(fname):
available_frameworks.append("pt")
if frameworks is None:
frameworks = get_default_frameworks()
frameworks = [f for f in frameworks if f in available_frameworks]
model_classes = retrieve_model_classes(model_type, frameworks=frameworks)
# Retrieve model upper-cased name from the constant name of the pretrained archive map.
if archive_map is None:
model_upper_cased = model_camel_cased.upper()
else:
parts = archive_map.split("_")
idx = 0
while idx < len(parts) and parts[idx] != "PRETRAINED":
idx += 1
if idx < len(parts):
model_upper_cased = "_".join(parts[:idx])
else:
model_upper_cased = model_camel_cased.upper()
model_patterns = ModelPatterns(
model_name,
checkpoint=find_base_model_checkpoint(model_type, model_files=model_files),
model_type=model_type,
model_camel_cased=model_camel_cased,
model_lower_cased=model_files["module_name"],
model_upper_cased=model_upper_cased,
config_class=config_class,
tokenizer_class=tokenizer_class,
feature_extractor_class=feature_extractor_class,
processor_class=processor_class,
)
return {
"frameworks": frameworks,
"model_classes": model_classes,
"model_files": model_files,
"model_patterns": model_patterns,
}
def get_user_field(
question: str,
default_value: Optional[str] = None,
is_valid_answer: Optional[Callable] = None,
convert_to: Optional[Callable] = None,
fallback_message: Optional[str] = None,
) -> Any:
"""
A utility function that asks a question to the user to get an answer, potentially looping until it gets a valid
answer.
Args:
question (`str`): The question to ask the user.
default_value (`str`, *optional*): A potential default value that will be used when the answer is empty.
is_valid_answer (`Callable`, *optional*):
If set, the question will be asked until this function returns `True` on the provided answer.
convert_to (`Callable`, *optional*):
If set, the answer will be passed to this function. If this function raises an error on the procided
answer, the question will be asked again.
fallback_message (`str`, *optional*):
A message that will be displayed each time the question is asked again to the user.
Returns:
`Any`: The answer provided by the user (or the default), passed through the potential conversion function.
"""
if not question.endswith(" "):
question = question + " "
if default_value is not None:
question = f"{question} [{default_value}] "
valid_answer = False
while not valid_answer:
answer = input(question)
if default_value is not None and len(answer) == 0:
answer = default_value
if is_valid_answer is not None:
valid_answer = is_valid_answer(answer)
elif convert_to is not None:
try:
answer = convert_to(answer)
valid_answer = True
except Exception:
valid_answer = False
else:
valid_answer = True
if not valid_answer:
print(fallback_message)
return answer
def convert_to_bool(x: str) -> bool:
"""
Converts a string to a bool.
"""
if x.lower() in ["1", "y", "yes", "true"]:
return True
if x.lower() in ["0", "n", "no", "false"]:
return False
raise ValueError(f"{x} is not a value that can be converted to a bool.")
The provided code snippet includes necessary dependencies for implementing the `get_user_input` function. Write a Python function `def get_user_input()` to solve the following problem:
Ask the user for the necessary inputs to add the new model.
Here is the function:
def get_user_input():
"""
Ask the user for the necessary inputs to add the new model.
"""
model_types = list(auto_module.configuration_auto.MODEL_NAMES_MAPPING.keys())
# Get old model type
valid_model_type = False
while not valid_model_type:
old_model_type = input(
"What is the model you would like to duplicate? Please provide the lowercase `model_type` (e.g. roberta): "
)
if old_model_type in model_types:
valid_model_type = True
else:
print(f"{old_model_type} is not a valid model type.")
near_choices = difflib.get_close_matches(old_model_type, model_types)
if len(near_choices) >= 1:
if len(near_choices) > 1:
near_choices = " or ".join(near_choices)
print(f"Did you mean {near_choices}?")
old_model_info = retrieve_info_for_model(old_model_type)
old_tokenizer_class = old_model_info["model_patterns"].tokenizer_class
old_feature_extractor_class = old_model_info["model_patterns"].feature_extractor_class
old_processor_class = old_model_info["model_patterns"].processor_class
old_frameworks = old_model_info["frameworks"]
old_checkpoint = None
if len(old_model_info["model_patterns"].checkpoint) == 0:
old_checkpoint = get_user_field(
"We couldn't find the name of the base checkpoint for that model, please enter it here."
)
model_name = get_user_field(
"What is the name (with no special casing) for your new model in the paper (e.g. RoBERTa)? "
)
default_patterns = ModelPatterns(model_name, model_name)
model_type = get_user_field(
"What identifier would you like to use for the `model_type` of this model? ",
default_value=default_patterns.model_type,
)
model_lower_cased = get_user_field(
"What lowercase name would you like to use for the module (folder) of this model? ",
default_value=default_patterns.model_lower_cased,
)
model_camel_cased = get_user_field(
"What prefix (camel-cased) would you like to use for the model classes of this model (e.g. Roberta)? ",
default_value=default_patterns.model_camel_cased,
)
model_upper_cased = get_user_field(
"What prefix (upper-cased) would you like to use for the constants relative to this model? ",
default_value=default_patterns.model_upper_cased,
)
config_class = get_user_field(
"What will be the name of the config class for this model? ", default_value=f"{model_camel_cased}Config"
)
checkpoint = get_user_field(
"Please give a checkpoint identifier (on the model Hub) for this new model (e.g. facebook/roberta-base): "
)
old_processing_classes = [
c for c in [old_feature_extractor_class, old_tokenizer_class, old_processor_class] if c is not None
]
old_processing_classes = ", ".join(old_processing_classes)
keep_processing = get_user_field(
f"Will your new model use the same processing class as {old_model_type} ({old_processing_classes}) (yes/no)? ",
convert_to=convert_to_bool,
fallback_message="Please answer yes/no, y/n, true/false or 1/0. ",
)
if keep_processing:
feature_extractor_class = old_feature_extractor_class
processor_class = old_processor_class
tokenizer_class = old_tokenizer_class
else:
if old_tokenizer_class is not None:
tokenizer_class = get_user_field(
"What will be the name of the tokenizer class for this model? ",
default_value=f"{model_camel_cased}Tokenizer",
)
else:
tokenizer_class = None
if old_feature_extractor_class is not None:
feature_extractor_class = get_user_field(
"What will be the name of the feature extractor class for this model? ",
default_value=f"{model_camel_cased}FeatureExtractor",
)
else:
feature_extractor_class = None
if old_processor_class is not None:
processor_class = get_user_field(
"What will be the name of the processor class for this model? ",
default_value=f"{model_camel_cased}Processor",
)
else:
processor_class = None
model_patterns = ModelPatterns(
model_name,
checkpoint,
model_type=model_type,
model_lower_cased=model_lower_cased,
model_camel_cased=model_camel_cased,
model_upper_cased=model_upper_cased,
config_class=config_class,
tokenizer_class=tokenizer_class,
feature_extractor_class=feature_extractor_class,
processor_class=processor_class,
)
add_copied_from = get_user_field(
"Should we add # Copied from statements when creating the new modeling file (yes/no)? ",
convert_to=convert_to_bool,
default_value="yes",
fallback_message="Please answer yes/no, y/n, true/false or 1/0.",
)
all_frameworks = get_user_field(
"Should we add a version of your new model in all the frameworks implemented by"
f" {old_model_type} ({old_frameworks}) (yes/no)? ",
convert_to=convert_to_bool,
default_value="yes",
fallback_message="Please answer yes/no, y/n, true/false or 1/0.",
)
if all_frameworks:
frameworks = None
else:
frameworks = get_user_field(
"Please enter the list of framworks you want (pt, tf, flax) separated by spaces",
is_valid_answer=lambda x: all(p in ["pt", "tf", "flax"] for p in x.split(" ")),
)
frameworks = list(set(frameworks.split(" ")))
return (old_model_type, model_patterns, add_copied_from, frameworks, old_checkpoint) | Ask the user for the necessary inputs to add the new model. |
10,572 | import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional
import torch
from .utils import add_start_docstrings
class MaxLengthCriteria(StoppingCriteria):
"""
This class can be used to stop generation whenever the full generated number of tokens exceeds `max_length`. Keep
in mind for decoder-only type of transformers, this will include the initial prompted tokens.
Args:
max_length (`int`):
The maximum length that the output sequence can have in number of tokens.
"""
def __init__(self, max_length: int):
self.max_length = max_length
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
return input_ids.shape[-1] >= self.max_length
class StoppingCriteriaList(list):
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
return any(criteria(input_ids, scores) for criteria in self)
def max_length(self) -> Optional[int]:
for stopping_criterium in self:
if isinstance(stopping_criterium, MaxLengthCriteria):
return stopping_criterium.max_length
elif isinstance(stopping_criterium, MaxNewTokensCriteria):
return stopping_criterium.max_length
return None
def validate_stopping_criteria(stopping_criteria: StoppingCriteriaList, max_length: int) -> StoppingCriteriaList:
stopping_max_length = stopping_criteria.max_length
new_stopping_criteria = deepcopy(stopping_criteria)
if stopping_max_length is not None and stopping_max_length != max_length:
warnings.warn("You set different `max_length` for stopping criteria and `max_length` parameter", UserWarning)
elif stopping_max_length is None:
new_stopping_criteria.append(MaxLengthCriteria(max_length=max_length))
return new_stopping_criteria | null |
10,573 | import collections
from .utils import ExplicitEnum, is_torch_available, logging
def get_abs_min_max(var, ctx):
abs_var = var.abs()
return f"{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}" | null |
10,574 | import collections
from .utils import ExplicitEnum, is_torch_available, logging
The provided code snippet includes necessary dependencies for implementing the `detect_overflow` function. Write a Python function `def detect_overflow(var, ctx)` to solve the following problem:
Report whether the tensor contains any `nan` or `inf` entries. This is useful for detecting overflows/underflows and best to call right after the function that did some math that modified the tensor in question. This function contains a few other helper features that you can enable and tweak directly if you want to track various other things. Args: var: the tensor variable to check ctx: the message to print as a context Return: `True` if `inf` or `nan` was detected, `False` otherwise
Here is the function:
def detect_overflow(var, ctx):
"""
Report whether the tensor contains any `nan` or `inf` entries.
This is useful for detecting overflows/underflows and best to call right after the function that did some math that
modified the tensor in question.
This function contains a few other helper features that you can enable and tweak directly if you want to track
various other things.
Args:
var: the tensor variable to check
ctx: the message to print as a context
Return:
`True` if `inf` or `nan` was detected, `False` otherwise
"""
detected = False
if torch.isnan(var).any().item():
detected = True
print(f"{ctx} has nans")
if torch.isinf(var).any().item():
detected = True
print(f"{ctx} has infs")
# if needed to monitor large elements can enable the following
if 0: # and detected:
n100 = var[torch.ge(var.abs(), 100)]
if n100.numel() > 0:
print(f"{ctx}: n100={n100.numel()}")
n1000 = var[torch.ge(var.abs(), 1000)]
if n1000.numel() > 0:
print(f"{ctx}: n1000={n1000.numel()}")
n10000 = var[torch.ge(var.abs(), 10000)]
if n10000.numel() > 0:
print(f"{ctx}: n10000={n10000.numel()}")
if 0:
print(f"min={var.min():9.2e} max={var.max():9.2e}")
if 0:
print(f"min={var.min():9.2e} max={var.max():9.2e} var={var.var():9.2e} mean={var.mean():9.2e} ({ctx})")
return detected | Report whether the tensor contains any `nan` or `inf` entries. This is useful for detecting overflows/underflows and best to call right after the function that did some math that modified the tensor in question. This function contains a few other helper features that you can enable and tweak directly if you want to track various other things. Args: var: the tensor variable to check ctx: the message to print as a context Return: `True` if `inf` or `nan` was detected, `False` otherwise |
10,575 | import warnings
from typing import Dict, List, Tuple
from tokenizers import Regex, Tokenizer, decoders, normalizers, pre_tokenizers, processors
from tokenizers.models import BPE, Unigram, WordPiece
from .utils import requires_backends
def check_number_comma(piece: str) -> bool:
return len(piece) < 2 or piece[-1] != "," or not piece[-2].isdigit() | null |
10,576 | import warnings
from typing import Dict, List, Tuple
from tokenizers import Regex, Tokenizer, decoders, normalizers, pre_tokenizers, processors
from tokenizers.models import BPE, Unigram, WordPiece
from .utils import requires_backends
SLOW_TO_FAST_CONVERTERS = {
"AlbertTokenizer": AlbertConverter,
"BartTokenizer": RobertaConverter,
"BarthezTokenizer": BarthezConverter,
"BertTokenizer": BertConverter,
"BigBirdTokenizer": BigBirdConverter,
"BlenderbotTokenizer": BlenderbotConverter,
"CamembertTokenizer": CamembertConverter,
"CLIPTokenizer": CLIPConverter,
"CodeGenTokenizer": GPT2Converter,
"ConvBertTokenizer": BertConverter,
"DebertaTokenizer": DebertaConverter,
"DebertaV2Tokenizer": DebertaV2Converter,
"DistilBertTokenizer": BertConverter,
"DPRReaderTokenizer": BertConverter,
"DPRQuestionEncoderTokenizer": BertConverter,
"DPRContextEncoderTokenizer": BertConverter,
"ElectraTokenizer": BertConverter,
"FNetTokenizer": AlbertConverter,
"FunnelTokenizer": FunnelConverter,
"GPT2Tokenizer": GPT2Converter,
"HerbertTokenizer": HerbertConverter,
"LayoutLMTokenizer": BertConverter,
"LayoutLMv2Tokenizer": BertConverter,
"LayoutLMv3Tokenizer": RobertaConverter,
"LayoutXLMTokenizer": XLMRobertaConverter,
"LongformerTokenizer": RobertaConverter,
"LEDTokenizer": RobertaConverter,
"LxmertTokenizer": BertConverter,
"MarkupLMTokenizer": MarkupLMConverter,
"MBartTokenizer": MBartConverter,
"MBart50Tokenizer": MBart50Converter,
"MPNetTokenizer": MPNetConverter,
"MobileBertTokenizer": BertConverter,
"MvpTokenizer": RobertaConverter,
"NllbTokenizer": NllbConverter,
"OpenAIGPTTokenizer": OpenAIGPTConverter,
"PegasusTokenizer": PegasusConverter,
"RealmTokenizer": BertConverter,
"ReformerTokenizer": ReformerConverter,
"RemBertTokenizer": RemBertConverter,
"RetriBertTokenizer": BertConverter,
"RobertaTokenizer": RobertaConverter,
"RoFormerTokenizer": RoFormerConverter,
"SqueezeBertTokenizer": BertConverter,
"T5Tokenizer": T5Converter,
"XLMRobertaTokenizer": XLMRobertaConverter,
"XLNetTokenizer": XLNetConverter,
"SplinterTokenizer": SplinterConverter,
"XGLMTokenizer": XGLMConverter,
}
The provided code snippet includes necessary dependencies for implementing the `convert_slow_tokenizer` function. Write a Python function `def convert_slow_tokenizer(transformer_tokenizer) -> Tokenizer` to solve the following problem:
Utilities to convert a slow tokenizer instance in a fast tokenizer instance. Args: transformer_tokenizer ([`~tokenization_utils_base.PreTrainedTokenizer`]): Instance of a slow tokenizer to convert in the backend tokenizer for [`~tokenization_utils_base.PreTrainedTokenizerFast`]. Return: A instance of [`~tokenizers.Tokenizer`] to be used as the backend tokenizer of a [`~tokenization_utils_base.PreTrainedTokenizerFast`]
Here is the function:
def convert_slow_tokenizer(transformer_tokenizer) -> Tokenizer:
"""
Utilities to convert a slow tokenizer instance in a fast tokenizer instance.
Args:
transformer_tokenizer ([`~tokenization_utils_base.PreTrainedTokenizer`]):
Instance of a slow tokenizer to convert in the backend tokenizer for
[`~tokenization_utils_base.PreTrainedTokenizerFast`].
Return:
A instance of [`~tokenizers.Tokenizer`] to be used as the backend tokenizer of a
[`~tokenization_utils_base.PreTrainedTokenizerFast`]
"""
tokenizer_class_name = transformer_tokenizer.__class__.__name__
if tokenizer_class_name not in SLOW_TO_FAST_CONVERTERS:
raise ValueError(
f"An instance of tokenizer class {tokenizer_class_name} cannot be converted in a Fast tokenizer instance."
" No converter was found. Currently available slow->fast convertors:"
f" {list(SLOW_TO_FAST_CONVERTERS.keys())}"
)
converter_class = SLOW_TO_FAST_CONVERTERS[tokenizer_class_name]
return converter_class(transformer_tokenizer).converted() | Utilities to convert a slow tokenizer instance in a fast tokenizer instance. Args: transformer_tokenizer ([`~tokenization_utils_base.PreTrainedTokenizer`]): Instance of a slow tokenizer to convert in the backend tokenizer for [`~tokenization_utils_base.PreTrainedTokenizerFast`]. Return: A instance of [`~tokenizers.Tokenizer`] to be used as the backend tokenizer of a [`~tokenization_utils_base.PreTrainedTokenizerFast`] |
10,577 | import re
from typing import Callable, List, Optional, Union
import tensorflow as tf
class WarmUp(tf.keras.optimizers.schedules.LearningRateSchedule):
"""
Applies a warmup schedule on a given learning rate decay schedule.
Args:
initial_learning_rate (`float`):
The initial learning rate for the schedule after the warmup (so this will be the learning rate at the end
of the warmup).
decay_schedule_fn (`Callable`):
The schedule function to apply after the warmup for the rest of training.
warmup_steps (`int`):
The number of steps for the warmup part of training.
power (`float`, *optional*, defaults to 1):
The power to use for the polynomial warmup (defaults is a linear warmup).
name (`str`, *optional*):
Optional name prefix for the returned tensors during the schedule.
"""
def __init__(
self,
initial_learning_rate: float,
decay_schedule_fn: Callable,
warmup_steps: int,
power: float = 1.0,
name: str = None,
):
super().__init__()
self.initial_learning_rate = initial_learning_rate
self.warmup_steps = warmup_steps
self.power = power
self.decay_schedule_fn = decay_schedule_fn
self.name = name
def __call__(self, step):
with tf.name_scope(self.name or "WarmUp") as name:
# Implements polynomial warmup. i.e., if global_step < warmup_steps, the
# learning rate will be `global_step/num_warmup_steps * init_lr`.
global_step_float = tf.cast(step, tf.float32)
warmup_steps_float = tf.cast(self.warmup_steps, tf.float32)
warmup_percent_done = global_step_float / warmup_steps_float
warmup_learning_rate = self.initial_learning_rate * tf.math.pow(warmup_percent_done, self.power)
return tf.cond(
global_step_float < warmup_steps_float,
lambda: warmup_learning_rate,
lambda: self.decay_schedule_fn(step - self.warmup_steps),
name=name,
)
def get_config(self):
return {
"initial_learning_rate": self.initial_learning_rate,
"decay_schedule_fn": self.decay_schedule_fn,
"warmup_steps": self.warmup_steps,
"power": self.power,
"name": self.name,
}
class AdamWeightDecay(tf.keras.optimizers.Adam):
"""
Adam enables L2 weight decay and clip_by_global_norm on gradients. Just adding the square of the weights to the
loss function is *not* the correct way of using L2 regularization/weight decay with Adam, since that will interact
with the m and v parameters in strange ways as shown in [Decoupled Weight Decay
Regularization](https://arxiv.org/abs/1711.05101).
Instead we want ot decay the weights in a manner that doesn't interact with the m/v parameters. This is equivalent
to adding the square of the weights to the loss with plain (non-momentum) SGD.
Args:
learning_rate (`Union[float, tf.keras.optimizers.schedules.LearningRateSchedule]`, *optional*, defaults to 1e-3):
The learning rate to use or a schedule.
beta_1 (`float`, *optional*, defaults to 0.9):
The beta1 parameter in Adam, which is the exponential decay rate for the 1st momentum estimates.
beta_2 (`float`, *optional*, defaults to 0.999):
The beta2 parameter in Adam, which is the exponential decay rate for the 2nd momentum estimates.
epsilon (`float`, *optional*, defaults to 1e-7):
The epsilon parameter in Adam, which is a small constant for numerical stability.
amsgrad (`bool`, *optional*, default to `False`):
Whether to apply AMSGrad variant of this algorithm or not, see [On the Convergence of Adam and
Beyond](https://arxiv.org/abs/1904.09237).
weight_decay_rate (`float`, *optional*, defaults to 0):
The weight decay to apply.
include_in_weight_decay (`List[str]`, *optional*):
List of the parameter names (or re patterns) to apply weight decay to. If none is passed, weight decay is
applied to all parameters by default (unless they are in `exclude_from_weight_decay`).
exclude_from_weight_decay (`List[str]`, *optional*):
List of the parameter names (or re patterns) to exclude from applying weight decay to. If a
`include_in_weight_decay` is passed, the names in it will supersede this list.
name (`str`, *optional*, defaults to 'AdamWeightDecay'):
Optional name for the operations created when applying gradients.
kwargs:
Keyword arguments. Allowed to be {`clipnorm`, `clipvalue`, `lr`, `decay`}. `clipnorm` is clip gradients by
norm; `clipvalue` is clip gradients by value, `decay` is included for backward compatibility to allow time
inverse decay of learning rate. `lr` is included for backward compatibility, recommended to use
`learning_rate` instead.
"""
def __init__(
self,
learning_rate: Union[float, tf.keras.optimizers.schedules.LearningRateSchedule] = 0.001,
beta_1: float = 0.9,
beta_2: float = 0.999,
epsilon: float = 1e-7,
amsgrad: bool = False,
weight_decay_rate: float = 0.0,
include_in_weight_decay: Optional[List[str]] = None,
exclude_from_weight_decay: Optional[List[str]] = None,
name: str = "AdamWeightDecay",
**kwargs
):
super().__init__(learning_rate, beta_1, beta_2, epsilon, amsgrad, name, **kwargs)
self.weight_decay_rate = weight_decay_rate
self._include_in_weight_decay = include_in_weight_decay
self._exclude_from_weight_decay = exclude_from_weight_decay
def from_config(cls, config):
"""Creates an optimizer from its config with WarmUp custom object."""
custom_objects = {"WarmUp": WarmUp}
return super(AdamWeightDecay, cls).from_config(config, custom_objects=custom_objects)
def _prepare_local(self, var_device, var_dtype, apply_state):
super(AdamWeightDecay, self)._prepare_local(var_device, var_dtype, apply_state)
apply_state[(var_device, var_dtype)]["weight_decay_rate"] = tf.constant(
self.weight_decay_rate, name="adam_weight_decay_rate"
)
def _decay_weights_op(self, var, learning_rate, apply_state):
do_decay = self._do_use_weight_decay(var.name)
if do_decay:
return var.assign_sub(
learning_rate * var * apply_state[(var.device, var.dtype.base_dtype)]["weight_decay_rate"],
use_locking=self._use_locking,
)
return tf.no_op()
def apply_gradients(self, grads_and_vars, name=None, **kwargs):
grads, tvars = list(zip(*grads_and_vars))
return super(AdamWeightDecay, self).apply_gradients(zip(grads, tvars), name=name, **kwargs)
def _get_lr(self, var_device, var_dtype, apply_state):
"""Retrieves the learning rate with the given state."""
if apply_state is None:
return self._decayed_lr_t[var_dtype], {}
apply_state = apply_state or {}
coefficients = apply_state.get((var_device, var_dtype))
if coefficients is None:
coefficients = self._fallback_apply_state(var_device, var_dtype)
apply_state[(var_device, var_dtype)] = coefficients
return coefficients["lr_t"], dict(apply_state=apply_state)
def _resource_apply_dense(self, grad, var, apply_state=None):
lr_t, kwargs = self._get_lr(var.device, var.dtype.base_dtype, apply_state)
decay = self._decay_weights_op(var, lr_t, apply_state)
with tf.control_dependencies([decay]):
return super(AdamWeightDecay, self)._resource_apply_dense(grad, var, **kwargs)
def _resource_apply_sparse(self, grad, var, indices, apply_state=None):
lr_t, kwargs = self._get_lr(var.device, var.dtype.base_dtype, apply_state)
decay = self._decay_weights_op(var, lr_t, apply_state)
with tf.control_dependencies([decay]):
return super(AdamWeightDecay, self)._resource_apply_sparse(grad, var, indices, **kwargs)
def get_config(self):
config = super().get_config()
config.update({"weight_decay_rate": self.weight_decay_rate})
return config
def _do_use_weight_decay(self, param_name):
"""Whether to use L2 weight decay for `param_name`."""
if self.weight_decay_rate == 0:
return False
if self._include_in_weight_decay:
for r in self._include_in_weight_decay:
if re.search(r, param_name) is not None:
return True
if self._exclude_from_weight_decay:
for r in self._exclude_from_weight_decay:
if re.search(r, param_name) is not None:
return False
return True
The provided code snippet includes necessary dependencies for implementing the `create_optimizer` function. Write a Python function `def create_optimizer( init_lr: float, num_train_steps: int, num_warmup_steps: int, min_lr_ratio: float = 0.0, adam_beta1: float = 0.9, adam_beta2: float = 0.999, adam_epsilon: float = 1e-8, adam_clipnorm: Optional[float] = None, adam_global_clipnorm: Optional[float] = None, weight_decay_rate: float = 0.0, power: float = 1.0, include_in_weight_decay: Optional[List[str]] = None, )` to solve the following problem:
Creates an optimizer with a learning rate schedule using a warmup phase followed by a linear decay. Args: init_lr (`float`): The desired learning rate at the end of the warmup phase. num_train_steps (`int`): The total number of training steps. num_warmup_steps (`int`): The number of warmup steps. min_lr_ratio (`float`, *optional*, defaults to 0): The final learning rate at the end of the linear decay will be `init_lr * min_lr_ratio`. adam_beta1 (`float`, *optional*, defaults to 0.9): The beta1 to use in Adam. adam_beta2 (`float`, *optional*, defaults to 0.999): The beta2 to use in Adam. adam_epsilon (`float`, *optional*, defaults to 1e-8): The epsilon to use in Adam. adam_clipnorm: (`float`, *optional*, defaults to `None`): If not `None`, clip the gradient norm for each weight tensor to this value. adam_global_clipnorm: (`float`, *optional*, defaults to `None`) If not `None`, clip gradient norm to this value. When using this argument, the norm is computed over all weight tensors, as if they were concatenated into a single vector. weight_decay_rate (`float`, *optional*, defaults to 0): The weight decay to use. power (`float`, *optional*, defaults to 1.0): The power to use for PolynomialDecay. include_in_weight_decay (`List[str]`, *optional*): List of the parameter names (or re patterns) to apply weight decay to. If none is passed, weight decay is applied to all parameters except bias and layer norm parameters.
Here is the function:
def create_optimizer(
init_lr: float,
num_train_steps: int,
num_warmup_steps: int,
min_lr_ratio: float = 0.0,
adam_beta1: float = 0.9,
adam_beta2: float = 0.999,
adam_epsilon: float = 1e-8,
adam_clipnorm: Optional[float] = None,
adam_global_clipnorm: Optional[float] = None,
weight_decay_rate: float = 0.0,
power: float = 1.0,
include_in_weight_decay: Optional[List[str]] = None,
):
"""
Creates an optimizer with a learning rate schedule using a warmup phase followed by a linear decay.
Args:
init_lr (`float`):
The desired learning rate at the end of the warmup phase.
num_train_steps (`int`):
The total number of training steps.
num_warmup_steps (`int`):
The number of warmup steps.
min_lr_ratio (`float`, *optional*, defaults to 0):
The final learning rate at the end of the linear decay will be `init_lr * min_lr_ratio`.
adam_beta1 (`float`, *optional*, defaults to 0.9):
The beta1 to use in Adam.
adam_beta2 (`float`, *optional*, defaults to 0.999):
The beta2 to use in Adam.
adam_epsilon (`float`, *optional*, defaults to 1e-8):
The epsilon to use in Adam.
adam_clipnorm: (`float`, *optional*, defaults to `None`):
If not `None`, clip the gradient norm for each weight tensor to this value.
adam_global_clipnorm: (`float`, *optional*, defaults to `None`)
If not `None`, clip gradient norm to this value. When using this argument, the norm is computed over all
weight tensors, as if they were concatenated into a single vector.
weight_decay_rate (`float`, *optional*, defaults to 0):
The weight decay to use.
power (`float`, *optional*, defaults to 1.0):
The power to use for PolynomialDecay.
include_in_weight_decay (`List[str]`, *optional*):
List of the parameter names (or re patterns) to apply weight decay to. If none is passed, weight decay is
applied to all parameters except bias and layer norm parameters.
"""
# Implements linear decay of the learning rate.
lr_schedule = tf.keras.optimizers.schedules.PolynomialDecay(
initial_learning_rate=init_lr,
decay_steps=num_train_steps - num_warmup_steps,
end_learning_rate=init_lr * min_lr_ratio,
power=power,
)
if num_warmup_steps:
lr_schedule = WarmUp(
initial_learning_rate=init_lr,
decay_schedule_fn=lr_schedule,
warmup_steps=num_warmup_steps,
)
if weight_decay_rate > 0.0:
optimizer = AdamWeightDecay(
learning_rate=lr_schedule,
weight_decay_rate=weight_decay_rate,
beta_1=adam_beta1,
beta_2=adam_beta2,
epsilon=adam_epsilon,
clipnorm=adam_clipnorm,
global_clipnorm=adam_global_clipnorm,
exclude_from_weight_decay=["LayerNorm", "layer_norm", "bias"],
include_in_weight_decay=include_in_weight_decay,
)
else:
optimizer = tf.keras.optimizers.Adam(
learning_rate=lr_schedule,
beta_1=adam_beta1,
beta_2=adam_beta2,
epsilon=adam_epsilon,
clipnorm=adam_clipnorm,
global_clipnorm=adam_global_clipnorm,
)
# We return the optimizer and the LR scheduler in order to better track the
# evolution of the LR independently of the optimizer.
return optimizer, lr_schedule | Creates an optimizer with a learning rate schedule using a warmup phase followed by a linear decay. Args: init_lr (`float`): The desired learning rate at the end of the warmup phase. num_train_steps (`int`): The total number of training steps. num_warmup_steps (`int`): The number of warmup steps. min_lr_ratio (`float`, *optional*, defaults to 0): The final learning rate at the end of the linear decay will be `init_lr * min_lr_ratio`. adam_beta1 (`float`, *optional*, defaults to 0.9): The beta1 to use in Adam. adam_beta2 (`float`, *optional*, defaults to 0.999): The beta2 to use in Adam. adam_epsilon (`float`, *optional*, defaults to 1e-8): The epsilon to use in Adam. adam_clipnorm: (`float`, *optional*, defaults to `None`): If not `None`, clip the gradient norm for each weight tensor to this value. adam_global_clipnorm: (`float`, *optional*, defaults to `None`) If not `None`, clip gradient norm to this value. When using this argument, the norm is computed over all weight tensors, as if they were concatenated into a single vector. weight_decay_rate (`float`, *optional*, defaults to 0): The weight decay to use. power (`float`, *optional*, defaults to 1.0): The power to use for PolynomialDecay. include_in_weight_decay (`List[str]`, *optional*): List of the parameter names (or re patterns) to apply weight decay to. If none is passed, weight decay is applied to all parameters except bias and layer norm parameters. |
10,578 | import argparse
import os
import transformers
from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging
logger = logging.get_logger(__name__)
TOKENIZER_CLASSES = {name: getattr(transformers, name + "Fast") for name in SLOW_TO_FAST_CONVERTERS}
def convert_slow_checkpoint_to_fast(tokenizer_name, checkpoint_name, dump_path, force_download):
if tokenizer_name is not None and tokenizer_name not in TOKENIZER_CLASSES:
raise ValueError(f"Unrecognized tokenizer name, should be one of {list(TOKENIZER_CLASSES.keys())}.")
if tokenizer_name is None:
tokenizer_names = TOKENIZER_CLASSES
else:
tokenizer_names = {tokenizer_name: getattr(transformers, tokenizer_name + "Fast")}
logger.info(f"Loading tokenizer classes: {tokenizer_names}")
for tokenizer_name in tokenizer_names:
tokenizer_class = TOKENIZER_CLASSES[tokenizer_name]
add_prefix = True
if checkpoint_name is None:
checkpoint_names = list(tokenizer_class.max_model_input_sizes.keys())
else:
checkpoint_names = [checkpoint_name]
logger.info(f"For tokenizer {tokenizer_class.__class__.__name__} loading checkpoints: {checkpoint_names}")
for checkpoint in checkpoint_names:
logger.info(f"Loading {tokenizer_class.__class__.__name__} {checkpoint}")
# Load tokenizer
tokenizer = tokenizer_class.from_pretrained(checkpoint, force_download=force_download)
# Save fast tokenizer
logger.info(f"Save fast tokenizer to {dump_path} with prefix {checkpoint} add_prefix {add_prefix}")
# For organization names we create sub-directories
if "/" in checkpoint:
checkpoint_directory, checkpoint_prefix_name = checkpoint.split("/")
dump_path_full = os.path.join(dump_path, checkpoint_directory)
elif add_prefix:
checkpoint_prefix_name = checkpoint
dump_path_full = dump_path
else:
checkpoint_prefix_name = None
dump_path_full = dump_path
logger.info(f"=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}")
if checkpoint in list(tokenizer.pretrained_vocab_files_map.values())[0]:
file_path = list(tokenizer.pretrained_vocab_files_map.values())[0][checkpoint]
next_char = file_path.split(checkpoint)[-1][0]
if next_char == "/":
dump_path_full = os.path.join(dump_path_full, checkpoint_prefix_name)
checkpoint_prefix_name = None
logger.info(f"=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}")
file_names = tokenizer.save_pretrained(
dump_path_full, legacy_format=False, filename_prefix=checkpoint_prefix_name
)
logger.info(f"=> File names {file_names}")
for file_name in file_names:
if not file_name.endswith("tokenizer.json"):
os.remove(file_name)
logger.info(f"=> removing {file_name}") | null |
10,579 | import os
from typing import TYPE_CHECKING, List, Tuple, Union
import numpy as np
from packaging import version
import requests
from .utils import (
ExplicitEnum,
is_jax_tensor,
is_tf_tensor,
is_torch_available,
is_torch_tensor,
is_vision_available,
to_numpy,
)
from .utils.constants import ( # noqa: F401
IMAGENET_DEFAULT_MEAN,
IMAGENET_DEFAULT_STD,
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
)
def is_valid_image(img):
return (
isinstance(img, (PIL.Image.Image, np.ndarray))
or is_torch_tensor(img)
or is_tf_tensor(img)
or is_jax_tensor(img)
)
def valid_images(imgs):
return all(is_valid_image(img) for img in imgs) | null |
10,580 | import os
from typing import TYPE_CHECKING, List, Tuple, Union
import numpy as np
from packaging import version
import requests
from .utils import (
ExplicitEnum,
is_jax_tensor,
is_tf_tensor,
is_torch_available,
is_torch_tensor,
is_vision_available,
to_numpy,
)
from .utils.constants import ( # noqa: F401
IMAGENET_DEFAULT_MEAN,
IMAGENET_DEFAULT_STD,
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
)
def is_valid_image(img):
return (
isinstance(img, (PIL.Image.Image, np.ndarray))
or is_torch_tensor(img)
or is_tf_tensor(img)
or is_jax_tensor(img)
)
def is_batched(img):
if isinstance(img, (list, tuple)):
return is_valid_image(img[0])
return False | null |
10,581 | import os
from typing import TYPE_CHECKING, List, Tuple, Union
import numpy as np
from packaging import version
import requests
from .utils import (
ExplicitEnum,
is_jax_tensor,
is_tf_tensor,
is_torch_available,
is_torch_tensor,
is_vision_available,
to_numpy,
)
from .utils.constants import ( # noqa: F401
IMAGENET_DEFAULT_MEAN,
IMAGENET_DEFAULT_STD,
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
)
The provided code snippet includes necessary dependencies for implementing the `load_image` function. Write a Python function `def load_image(image: Union[str, "PIL.Image.Image"]) -> "PIL.Image.Image"` to solve the following problem:
Loads `image` to a PIL Image. Args: image (`str` or `PIL.Image.Image`): The image to convert to the PIL Image format. Returns: `PIL.Image.Image`: A PIL Image.
Here is the function:
def load_image(image: Union[str, "PIL.Image.Image"]) -> "PIL.Image.Image":
"""
Loads `image` to a PIL Image.
Args:
image (`str` or `PIL.Image.Image`):
The image to convert to the PIL Image format.
Returns:
`PIL.Image.Image`: A PIL Image.
"""
if isinstance(image, str):
if image.startswith("http://") or image.startswith("https://"):
# We need to actually check for a real protocol, otherwise it's impossible to use a local file
# like http_huggingface_co.png
image = PIL.Image.open(requests.get(image, stream=True).raw)
elif os.path.isfile(image):
image = PIL.Image.open(image)
else:
raise ValueError(
f"Incorrect path or url, URLs must start with `http://` or `https://`, and {image} is not a valid path"
)
elif isinstance(image, PIL.Image.Image):
image = image
else:
raise ValueError(
"Incorrect format used for image. Should be an url linking to an image, a local path, or a PIL image."
)
image = PIL.ImageOps.exif_transpose(image)
image = image.convert("RGB")
return image | Loads `image` to a PIL Image. Args: image (`str` or `PIL.Image.Image`): The image to convert to the PIL Image format. Returns: `PIL.Image.Image`: A PIL Image. |
10,582 | import copy
import json
import os
import warnings
from dataclasses import dataclass
from pathlib import Path
from typing import Any, Dict, List, Optional, Union
import requests
import yaml
from huggingface_hub import model_info
from huggingface_hub.utils import HFValidationError
from . import __version__
from .models.auto.modeling_auto import (
MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
MODEL_FOR_CTC_MAPPING_NAMES,
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES,
MODEL_FOR_MASKED_LM_MAPPING_NAMES,
MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,
MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES,
MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES,
MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
)
from .training_args import ParallelMode
from .utils import (
MODEL_CARD_NAME,
cached_file,
is_datasets_available,
is_offline_mode,
is_tf_available,
is_tokenizers_available,
is_torch_available,
logging,
)
def _listify(obj):
if obj is None:
return []
elif isinstance(obj, str):
return [obj]
else:
return obj | null |
10,583 | import copy
import json
import os
import warnings
from dataclasses import dataclass
from pathlib import Path
from typing import Any, Dict, List, Optional, Union
import requests
import yaml
from huggingface_hub import model_info
from huggingface_hub.utils import HFValidationError
from . import __version__
from .models.auto.modeling_auto import (
MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
MODEL_FOR_CTC_MAPPING_NAMES,
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES,
MODEL_FOR_MASKED_LM_MAPPING_NAMES,
MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,
MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES,
MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES,
MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
)
from .training_args import ParallelMode
from .utils import (
MODEL_CARD_NAME,
cached_file,
is_datasets_available,
is_offline_mode,
is_tf_available,
is_tokenizers_available,
is_torch_available,
logging,
)
def _insert_values_as_list(metadata, name, values):
if values is None:
return metadata
if isinstance(values, str):
values = [values]
values = [v for v in values if v is not None]
if len(values) == 0:
return metadata
metadata[name] = values
return metadata | null |
10,584 | import copy
import json
import os
import warnings
from dataclasses import dataclass
from pathlib import Path
from typing import Any, Dict, List, Optional, Union
import requests
import yaml
from huggingface_hub import model_info
from huggingface_hub.utils import HFValidationError
from . import __version__
from .models.auto.modeling_auto import (
MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
MODEL_FOR_CTC_MAPPING_NAMES,
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES,
MODEL_FOR_MASKED_LM_MAPPING_NAMES,
MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,
MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES,
MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES,
MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
)
from .training_args import ParallelMode
from .utils import (
MODEL_CARD_NAME,
cached_file,
is_datasets_available,
is_offline_mode,
is_tf_available,
is_tokenizers_available,
is_torch_available,
logging,
)
METRIC_TAGS = [
"accuracy",
"bleu",
"f1",
"matthews_correlation",
"pearsonr",
"precision",
"recall",
"rouge",
"sacrebleu",
"spearmanr",
"wer",
]
def infer_metric_tags_from_eval_results(eval_results):
if eval_results is None:
return {}
result = {}
for key in eval_results.keys():
if key.lower().replace(" ", "_") in METRIC_TAGS:
result[key.lower().replace(" ", "_")] = key
elif key.lower() == "rouge1":
result["rouge"] = key
return result | null |
10,585 | import copy
import json
import os
import warnings
from dataclasses import dataclass
from pathlib import Path
from typing import Any, Dict, List, Optional, Union
import requests
import yaml
from huggingface_hub import model_info
from huggingface_hub.utils import HFValidationError
from . import __version__
from .models.auto.modeling_auto import (
MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
MODEL_FOR_CTC_MAPPING_NAMES,
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES,
MODEL_FOR_MASKED_LM_MAPPING_NAMES,
MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,
MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES,
MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES,
MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
)
from .training_args import ParallelMode
from .utils import (
MODEL_CARD_NAME,
cached_file,
is_datasets_available,
is_offline_mode,
is_tf_available,
is_tokenizers_available,
is_torch_available,
logging,
)
def _insert_value(metadata, name, value):
if value is None:
return metadata
metadata[name] = value
return metadata | null |
10,586 | import copy
import json
import os
import warnings
from dataclasses import dataclass
from pathlib import Path
from typing import Any, Dict, List, Optional, Union
import requests
import yaml
from huggingface_hub import model_info
from huggingface_hub.utils import HFValidationError
from . import __version__
from .models.auto.modeling_auto import (
MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
MODEL_FOR_CTC_MAPPING_NAMES,
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES,
MODEL_FOR_MASKED_LM_MAPPING_NAMES,
MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,
MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES,
MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES,
MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
)
from .training_args import ParallelMode
from .utils import (
MODEL_CARD_NAME,
cached_file,
is_datasets_available,
is_offline_mode,
is_tf_available,
is_tokenizers_available,
is_torch_available,
logging,
)
def is_hf_dataset(dataset):
if not is_datasets_available():
return False
from datasets import Dataset
return isinstance(dataset, Dataset) | null |
10,587 | import copy
import json
import os
import warnings
from dataclasses import dataclass
from pathlib import Path
from typing import Any, Dict, List, Optional, Union
import requests
import yaml
from huggingface_hub import model_info
from huggingface_hub.utils import HFValidationError
from . import __version__
from .models.auto.modeling_auto import (
MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
MODEL_FOR_CTC_MAPPING_NAMES,
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES,
MODEL_FOR_MASKED_LM_MAPPING_NAMES,
MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,
MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES,
MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES,
MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
)
from .training_args import ParallelMode
from .utils import (
MODEL_CARD_NAME,
cached_file,
is_datasets_available,
is_offline_mode,
is_tf_available,
is_tokenizers_available,
is_torch_available,
logging,
)
def _get_mapping_values(mapping):
result = []
for v in mapping.values():
if isinstance(v, (tuple, list)):
result += list(v)
else:
result.append(v)
return result | null |
10,588 | import copy
import json
import os
import warnings
from dataclasses import dataclass
from pathlib import Path
from typing import Any, Dict, List, Optional, Union
import requests
import yaml
from huggingface_hub import model_info
from huggingface_hub.utils import HFValidationError
from . import __version__
from .models.auto.modeling_auto import (
MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
MODEL_FOR_CTC_MAPPING_NAMES,
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES,
MODEL_FOR_MASKED_LM_MAPPING_NAMES,
MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,
MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES,
MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES,
MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
)
from .training_args import ParallelMode
from .utils import (
MODEL_CARD_NAME,
cached_file,
is_datasets_available,
is_offline_mode,
is_tf_available,
is_tokenizers_available,
is_torch_available,
logging,
)
The provided code snippet includes necessary dependencies for implementing the `parse_keras_history` function. Write a Python function `def parse_keras_history(logs)` to solve the following problem:
Parse the `logs` of either a `tf.keras.History` object returned by `model.fit()` or an accumulated logs `dict` passed to the `PushToHubCallback`. Returns lines and logs compatible with those returned by `parse_log_history`.
Here is the function:
def parse_keras_history(logs):
"""
Parse the `logs` of either a `tf.keras.History` object returned by `model.fit()` or an accumulated logs `dict`
passed to the `PushToHubCallback`. Returns lines and logs compatible with those returned by `parse_log_history`.
"""
if hasattr(logs, "history"):
# This looks like a `History` object
if not hasattr(logs, "epoch"):
# This history looks empty, return empty results
return None, [], dict()
logs.history["epoch"] = logs.epoch
logs = logs.history
else:
# Training logs is a list of dicts, let's invert it to a dict of lists to match a History object
logs = {log_key: [single_dict[log_key] for single_dict in logs] for log_key in logs[0]}
lines = []
for i in range(len(logs["epoch"])):
epoch_dict = {log_key: log_value_list[i] for log_key, log_value_list in logs.items()}
values = dict()
for k, v in epoch_dict.items():
if k.startswith("val_"):
k = "validation_" + k[4:]
elif k != "epoch":
k = "train_" + k
splits = k.split("_")
name = " ".join([part.capitalize() for part in splits])
values[name] = v
lines.append(values)
eval_results = lines[-1]
return logs, lines, eval_results | Parse the `logs` of either a `tf.keras.History` object returned by `model.fit()` or an accumulated logs `dict` passed to the `PushToHubCallback`. Returns lines and logs compatible with those returned by `parse_log_history`. |
10,589 | import copy
import json
import os
import warnings
from dataclasses import dataclass
from pathlib import Path
from typing import Any, Dict, List, Optional, Union
import requests
import yaml
from huggingface_hub import model_info
from huggingface_hub.utils import HFValidationError
from . import __version__
from .models.auto.modeling_auto import (
MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
MODEL_FOR_CTC_MAPPING_NAMES,
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES,
MODEL_FOR_MASKED_LM_MAPPING_NAMES,
MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,
MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES,
MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES,
MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
)
from .training_args import ParallelMode
from .utils import (
MODEL_CARD_NAME,
cached_file,
is_datasets_available,
is_offline_mode,
is_tf_available,
is_tokenizers_available,
is_torch_available,
logging,
)
The provided code snippet includes necessary dependencies for implementing the `parse_log_history` function. Write a Python function `def parse_log_history(log_history)` to solve the following problem:
Parse the `log_history` of a Trainer to get the intermediate and final evaluation results.
Here is the function:
def parse_log_history(log_history):
"""
Parse the `log_history` of a Trainer to get the intermediate and final evaluation results.
"""
idx = 0
while idx < len(log_history) and "train_runtime" not in log_history[idx]:
idx += 1
# If there are no training logs
if idx == len(log_history):
idx -= 1
while idx >= 0 and "eval_loss" not in log_history[idx]:
idx -= 1
if idx >= 0:
return None, None, log_history[idx]
else:
return None, None, None
# From now one we can assume we have training logs:
train_log = log_history[idx]
lines = []
training_loss = "No log"
for i in range(idx):
if "loss" in log_history[i]:
training_loss = log_history[i]["loss"]
if "eval_loss" in log_history[i]:
metrics = log_history[i].copy()
_ = metrics.pop("total_flos", None)
epoch = metrics.pop("epoch", None)
step = metrics.pop("step", None)
_ = metrics.pop("eval_runtime", None)
_ = metrics.pop("eval_samples_per_second", None)
_ = metrics.pop("eval_steps_per_second", None)
values = {"Training Loss": training_loss, "Epoch": epoch, "Step": step}
for k, v in metrics.items():
if k == "eval_loss":
values["Validation Loss"] = v
else:
splits = k.split("_")
name = " ".join([part.capitalize() for part in splits[1:]])
values[name] = v
lines.append(values)
idx = len(log_history) - 1
while idx >= 0 and "eval_loss" not in log_history[idx]:
idx -= 1
if idx > 0:
eval_results = {}
for key, value in log_history[idx].items():
if key.startswith("eval_"):
key = key[5:]
if key not in ["runtime", "samples_per_second", "steps_per_second", "epoch", "step"]:
camel_cased_key = " ".join([part.capitalize() for part in key.split("_")])
eval_results[camel_cased_key] = value
return train_log, lines, eval_results
else:
return train_log, lines, None | Parse the `log_history` of a Trainer to get the intermediate and final evaluation results. |
10,590 | import copy
import json
import os
import warnings
from dataclasses import dataclass
from pathlib import Path
from typing import Any, Dict, List, Optional, Union
import requests
import yaml
from huggingface_hub import model_info
from huggingface_hub.utils import HFValidationError
from . import __version__
from .models.auto.modeling_auto import (
MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
MODEL_FOR_CTC_MAPPING_NAMES,
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES,
MODEL_FOR_MASKED_LM_MAPPING_NAMES,
MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,
MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES,
MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES,
MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
)
from .training_args import ParallelMode
from .utils import (
MODEL_CARD_NAME,
cached_file,
is_datasets_available,
is_offline_mode,
is_tf_available,
is_tokenizers_available,
is_torch_available,
logging,
)
def extract_hyperparameters_from_keras(model):
import tensorflow as tf
hyperparameters = dict()
if hasattr(model, "optimizer") and model.optimizer is not None:
hyperparameters["optimizer"] = model.optimizer.get_config()
else:
hyperparameters["optimizer"] = None
hyperparameters["training_precision"] = tf.keras.mixed_precision.global_policy().name
return hyperparameters | null |
10,591 | import copy
import json
import os
import warnings
from dataclasses import dataclass
from pathlib import Path
from typing import Any, Dict, List, Optional, Union
import requests
import yaml
from huggingface_hub import model_info
from huggingface_hub.utils import HFValidationError
from . import __version__
from .models.auto.modeling_auto import (
MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
MODEL_FOR_CTC_MAPPING_NAMES,
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES,
MODEL_FOR_MASKED_LM_MAPPING_NAMES,
MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,
MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES,
MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES,
MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
)
from .training_args import ParallelMode
from .utils import (
MODEL_CARD_NAME,
cached_file,
is_datasets_available,
is_offline_mode,
is_tf_available,
is_tokenizers_available,
is_torch_available,
logging,
)
def _maybe_round(v, decimals=4):
if isinstance(v, float) and len(str(v).split(".")) > 1 and len(str(v).split(".")[1]) > decimals:
return f"{v:.{decimals}f}"
return str(v)
def _regular_table_line(values, col_widths):
values_with_space = [f"| {v}" + " " * (w - len(v) + 1) for v, w in zip(values, col_widths)]
return "".join(values_with_space) + "|\n"
def _second_table_line(col_widths):
values = ["|:" + "-" * w + ":" for w in col_widths]
return "".join(values) + "|\n"
The provided code snippet includes necessary dependencies for implementing the `make_markdown_table` function. Write a Python function `def make_markdown_table(lines)` to solve the following problem:
Create a nice Markdown table from the results in `lines`.
Here is the function:
def make_markdown_table(lines):
"""
Create a nice Markdown table from the results in `lines`.
"""
if lines is None or len(lines) == 0:
return ""
col_widths = {key: len(str(key)) for key in lines[0].keys()}
for line in lines:
for key, value in line.items():
if col_widths[key] < len(_maybe_round(value)):
col_widths[key] = len(_maybe_round(value))
table = _regular_table_line(list(lines[0].keys()), list(col_widths.values()))
table += _second_table_line(list(col_widths.values()))
for line in lines:
table += _regular_table_line([_maybe_round(v) for v in line.values()], list(col_widths.values()))
return table | Create a nice Markdown table from the results in `lines`. |
10,592 | import copy
import json
import os
import warnings
from dataclasses import dataclass
from pathlib import Path
from typing import Any, Dict, List, Optional, Union
import requests
import yaml
from huggingface_hub import model_info
from huggingface_hub.utils import HFValidationError
from . import __version__
from .models.auto.modeling_auto import (
MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
MODEL_FOR_CTC_MAPPING_NAMES,
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES,
MODEL_FOR_MASKED_LM_MAPPING_NAMES,
MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,
MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES,
MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES,
MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
)
from .training_args import ParallelMode
from .utils import (
MODEL_CARD_NAME,
cached_file,
is_datasets_available,
is_offline_mode,
is_tf_available,
is_tokenizers_available,
is_torch_available,
logging,
)
_TRAINING_ARGS_KEYS = [
"learning_rate",
"train_batch_size",
"eval_batch_size",
"seed",
]
class ParallelMode(Enum):
def extract_hyperparameters_from_trainer(trainer):
hyperparameters = {k: getattr(trainer.args, k) for k in _TRAINING_ARGS_KEYS}
if trainer.args.parallel_mode not in [ParallelMode.NOT_PARALLEL, ParallelMode.NOT_DISTRIBUTED]:
hyperparameters["distributed_type"] = (
"multi-GPU" if trainer.args.parallel_mode == ParallelMode.DISTRIBUTED else trainer.args.parallel_mode.value
)
if trainer.args.world_size > 1:
hyperparameters["num_devices"] = trainer.args.world_size
if trainer.args.gradient_accumulation_steps > 1:
hyperparameters["gradient_accumulation_steps"] = trainer.args.gradient_accumulation_steps
total_train_batch_size = (
trainer.args.train_batch_size * trainer.args.world_size * trainer.args.gradient_accumulation_steps
)
if total_train_batch_size != hyperparameters["train_batch_size"]:
hyperparameters["total_train_batch_size"] = total_train_batch_size
total_eval_batch_size = trainer.args.eval_batch_size * trainer.args.world_size
if total_eval_batch_size != hyperparameters["eval_batch_size"]:
hyperparameters["total_eval_batch_size"] = total_eval_batch_size
if trainer.args.adafactor:
hyperparameters["optimizer"] = "Adafactor"
else:
hyperparameters["optimizer"] = (
f"Adam with betas=({trainer.args.adam_beta1},{trainer.args.adam_beta2}) and"
f" epsilon={trainer.args.adam_epsilon}"
)
hyperparameters["lr_scheduler_type"] = trainer.args.lr_scheduler_type.value
if trainer.args.warmup_ratio != 0.0:
hyperparameters["lr_scheduler_warmup_ratio"] = trainer.args.warmup_ratio
if trainer.args.warmup_steps != 0.0:
hyperparameters["lr_scheduler_warmup_steps"] = trainer.args.warmup_steps
if trainer.args.max_steps != -1:
hyperparameters["training_steps"] = trainer.args.max_steps
else:
hyperparameters["num_epochs"] = trainer.args.num_train_epochs
if trainer.args.fp16:
if trainer.use_cuda_amp:
hyperparameters["mixed_precision_training"] = "Native AMP"
elif trainer.use_apex:
hyperparameters["mixed_precision_training"] = f"Apex, opt level {trainer.args.fp16_opt_level}"
if trainer.args.label_smoothing_factor != 0.0:
hyperparameters["label_smoothing_factor"] = trainer.args.label_smoothing_factor
return hyperparameters | null |
10,593 | import gc
import json
import os
import re
from functools import partial
from pickle import UnpicklingError
from typing import Any, Dict, Set, Tuple, Union
import flax.linen as nn
import jax
import jax.numpy as jnp
import msgpack.exceptions
from flax.core.frozen_dict import FrozenDict, unfreeze
from flax.serialization import from_bytes, to_bytes
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from .configuration_utils import PretrainedConfig
from .dynamic_module_utils import custom_object_save
from .generation_flax_utils import FlaxGenerationMixin
from .modeling_flax_pytorch_utils import load_pytorch_checkpoint_in_flax_state_dict
from .utils import (
FLAX_WEIGHTS_INDEX_NAME,
FLAX_WEIGHTS_NAME,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
PushToHubMixin,
add_code_sample_docstrings,
add_start_docstrings_to_model_forward,
cached_file,
copy_func,
download_url,
has_file,
is_offline_mode,
is_remote_url,
logging,
replace_return_docstrings,
)
from .utils.hub import convert_file_size_to_int, get_checkpoint_shard_files
def quick_gelu(x):
return x * jax.nn.sigmoid(1.702 * x) | null |
10,594 | import gc
import json
import os
import re
from functools import partial
from pickle import UnpicklingError
from typing import Any, Dict, Set, Tuple, Union
import flax.linen as nn
import jax
import jax.numpy as jnp
import msgpack.exceptions
from flax.core.frozen_dict import FrozenDict, unfreeze
from flax.serialization import from_bytes, to_bytes
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from .configuration_utils import PretrainedConfig
from .dynamic_module_utils import custom_object_save
from .generation_flax_utils import FlaxGenerationMixin
from .modeling_flax_pytorch_utils import load_pytorch_checkpoint_in_flax_state_dict
from .utils import (
FLAX_WEIGHTS_INDEX_NAME,
FLAX_WEIGHTS_NAME,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
PushToHubMixin,
add_code_sample_docstrings,
add_start_docstrings_to_model_forward,
cached_file,
copy_func,
download_url,
has_file,
is_offline_mode,
is_remote_url,
logging,
replace_return_docstrings,
)
from .utils.hub import convert_file_size_to_int, get_checkpoint_shard_files
def dtype_byte_size(dtype):
"""
Returns the size (in bytes) occupied by one parameter of type `dtype`. Example:
```py
>>> dtype_byte_size(np.float32)
4
```
"""
if dtype == bool:
return 1 / 8
bit_search = re.search(r"[^\d](\d+)$", dtype.name)
if bit_search is None:
raise ValueError(f"`dtype` is not a valid dtype: {dtype}.")
bit_size = int(bit_search.groups()[0])
return bit_size // 8
FLAX_WEIGHTS_NAME = "flax_model.msgpack"
def convert_file_size_to_int(size: Union[int, str]):
"""
Converts a size expressed as a string with digits an unit (like `"5MB"`) to an integer (in bytes).
Args:
size (`int` or `str`): The size to convert. Will be directly returned if an `int`.
Example:
```py
>>> convert_file_size_to_int("1MiB")
1048576
```
"""
if isinstance(size, int):
return size
if size.upper().endswith("GIB"):
return int(size[:-3]) * (2**30)
if size.upper().endswith("MIB"):
return int(size[:-3]) * (2**20)
if size.upper().endswith("KIB"):
return int(size[:-3]) * (2**10)
if size.upper().endswith("GB"):
int_size = int(size[:-2]) * (10**9)
return int_size // 8 if size.endswith("b") else int_size
if size.upper().endswith("MB"):
int_size = int(size[:-2]) * (10**6)
return int_size // 8 if size.endswith("b") else int_size
if size.upper().endswith("KB"):
int_size = int(size[:-2]) * (10**3)
return int_size // 8 if size.endswith("b") else int_size
raise ValueError("`size` is not in a valid format. Use an integer followed by the unit, e.g., '5GB'.")
The provided code snippet includes necessary dependencies for implementing the `flax_shard_checkpoint` function. Write a Python function `def flax_shard_checkpoint(params, max_shard_size="10GB")` to solve the following problem:
Splits a model state dictionary in sub-checkpoints so that the final size of each sub-checkpoint does not exceed a given size. The sub-checkpoints are determined by iterating through the `state_dict` in the order of its keys, so there is no optimization made to make each sub-checkpoint as close as possible to the maximum size passed. For example, if the limit is 10GB and we have weights of sizes [6GB, 6GB, 2GB, 6GB, 2GB, 2GB] they will get sharded as [6GB], [6+2GB], [6+2+2GB] and not [6+2+2GB], [6+2GB], [6GB]. <Tip warning={true}> If one of the model's weight is bigger that `max_shard_size`, it will end up in its own sub-checkpoint which will have a size greater than `max_shard_size`. </Tip> Args: params (`Union[Dict, FrozenDict]`): A `PyTree` of model parameters. max_shard_size (`int` or `str`, *optional*, defaults to `"10GB"`): The maximum size of each sub-checkpoint. If expressed as a string, needs to be digits followed by a unit (like `"5MB"`).
Here is the function:
def flax_shard_checkpoint(params, max_shard_size="10GB"):
"""
Splits a model state dictionary in sub-checkpoints so that the final size of each sub-checkpoint does not exceed a
given size. The sub-checkpoints are determined by iterating through the `state_dict` in the order of its keys, so
there is no optimization made to make each sub-checkpoint as close as possible to the maximum size passed. For
example, if the limit is 10GB and we have weights of sizes [6GB, 6GB, 2GB, 6GB, 2GB, 2GB] they will get sharded as
[6GB], [6+2GB], [6+2+2GB] and not [6+2+2GB], [6+2GB], [6GB].
<Tip warning={true}>
If one of the model's weight is bigger that `max_shard_size`, it will end up in its own sub-checkpoint which will
have a size greater than `max_shard_size`.
</Tip>
Args:
params (`Union[Dict, FrozenDict]`): A `PyTree` of model parameters.
max_shard_size (`int` or `str`, *optional*, defaults to `"10GB"`):
The maximum size of each sub-checkpoint. If expressed as a string, needs to be digits followed by a unit
(like `"5MB"`).
"""
max_shard_size = convert_file_size_to_int(max_shard_size)
sharded_state_dicts = []
current_block = {}
current_block_size = 0
total_size = 0
# flatten the weights to chunk
weights = flatten_dict(params, sep="/")
for item in weights:
weight_size = weights[item].size * dtype_byte_size(weights[item].dtype)
# If this weight is going to tip up over the maximal size, we split.
if current_block_size + weight_size > max_shard_size:
sharded_state_dicts.append(current_block)
current_block = {}
current_block_size = 0
current_block[item] = weights[item]
current_block_size += weight_size
total_size += weight_size
# Add the last block
sharded_state_dicts.append(current_block)
# If we only have one shard, we return it
if len(sharded_state_dicts) == 1:
return {FLAX_WEIGHTS_NAME: sharded_state_dicts[0]}, None
# Otherwise, let's build the index
weight_map = {}
shards = {}
for idx, shard in enumerate(sharded_state_dicts):
shard_file = FLAX_WEIGHTS_NAME.replace(".msgpack", f"-{idx+1:05d}-of-{len(sharded_state_dicts):05d}.msgpack")
shards[shard_file] = shard
for weight_name in shard.keys():
weight_map[weight_name] = shard_file
# Add the metadata
metadata = {"total_size": total_size}
index = {"metadata": metadata, "weight_map": weight_map}
return shards, index | Splits a model state dictionary in sub-checkpoints so that the final size of each sub-checkpoint does not exceed a given size. The sub-checkpoints are determined by iterating through the `state_dict` in the order of its keys, so there is no optimization made to make each sub-checkpoint as close as possible to the maximum size passed. For example, if the limit is 10GB and we have weights of sizes [6GB, 6GB, 2GB, 6GB, 2GB, 2GB] they will get sharded as [6GB], [6+2GB], [6+2+2GB] and not [6+2+2GB], [6+2GB], [6GB]. <Tip warning={true}> If one of the model's weight is bigger that `max_shard_size`, it will end up in its own sub-checkpoint which will have a size greater than `max_shard_size`. </Tip> Args: params (`Union[Dict, FrozenDict]`): A `PyTree` of model parameters. max_shard_size (`int` or `str`, *optional*, defaults to `"10GB"`): The maximum size of each sub-checkpoint. If expressed as a string, needs to be digits followed by a unit (like `"5MB"`). |
10,595 | import gc
import json
import os
import re
from functools import partial
from pickle import UnpicklingError
from typing import Any, Dict, Set, Tuple, Union
import flax.linen as nn
import jax
import jax.numpy as jnp
import msgpack.exceptions
from flax.core.frozen_dict import FrozenDict, unfreeze
from flax.serialization import from_bytes, to_bytes
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from .configuration_utils import PretrainedConfig
from .dynamic_module_utils import custom_object_save
from .generation_flax_utils import FlaxGenerationMixin
from .modeling_flax_pytorch_utils import load_pytorch_checkpoint_in_flax_state_dict
from .utils import (
FLAX_WEIGHTS_INDEX_NAME,
FLAX_WEIGHTS_NAME,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
PushToHubMixin,
add_code_sample_docstrings,
add_start_docstrings_to_model_forward,
cached_file,
copy_func,
download_url,
has_file,
is_offline_mode,
is_remote_url,
logging,
replace_return_docstrings,
)
from .utils.hub import convert_file_size_to_int, get_checkpoint_shard_files
def overwrite_call_docstring(model_class, docstring):
# copy __call__ function to be sure docstring is changed only for this function
model_class.__call__ = copy_func(model_class.__call__)
# delete existing docstring
model_class.__call__.__doc__ = None
# set correct docstring
model_class.__call__ = add_start_docstrings_to_model_forward(docstring)(model_class.__call__) | null |
10,596 | import gc
import json
import os
import re
from functools import partial
from pickle import UnpicklingError
from typing import Any, Dict, Set, Tuple, Union
import flax.linen as nn
import jax
import jax.numpy as jnp
import msgpack.exceptions
from flax.core.frozen_dict import FrozenDict, unfreeze
from flax.serialization import from_bytes, to_bytes
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from .configuration_utils import PretrainedConfig
from .dynamic_module_utils import custom_object_save
from .generation_flax_utils import FlaxGenerationMixin
from .modeling_flax_pytorch_utils import load_pytorch_checkpoint_in_flax_state_dict
from .utils import (
FLAX_WEIGHTS_INDEX_NAME,
FLAX_WEIGHTS_NAME,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
PushToHubMixin,
add_code_sample_docstrings,
add_start_docstrings_to_model_forward,
cached_file,
copy_func,
download_url,
has_file,
is_offline_mode,
is_remote_url,
logging,
replace_return_docstrings,
)
from .utils.hub import convert_file_size_to_int, get_checkpoint_shard_files
def append_call_sample_docstring(model_class, tokenizer_class, checkpoint, output_type, config_class, mask=None):
model_class.__call__ = copy_func(model_class.__call__)
model_class.__call__ = add_code_sample_docstrings(
processor_class=tokenizer_class,
checkpoint=checkpoint,
output_type=output_type,
config_class=config_class,
model_cls=model_class.__name__,
)(model_class.__call__) | null |
10,597 | import gc
import json
import os
import re
from functools import partial
from pickle import UnpicklingError
from typing import Any, Dict, Set, Tuple, Union
import flax.linen as nn
import jax
import jax.numpy as jnp
import msgpack.exceptions
from flax.core.frozen_dict import FrozenDict, unfreeze
from flax.serialization import from_bytes, to_bytes
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from .configuration_utils import PretrainedConfig
from .dynamic_module_utils import custom_object_save
from .generation_flax_utils import FlaxGenerationMixin
from .modeling_flax_pytorch_utils import load_pytorch_checkpoint_in_flax_state_dict
from .utils import (
FLAX_WEIGHTS_INDEX_NAME,
FLAX_WEIGHTS_NAME,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
PushToHubMixin,
add_code_sample_docstrings,
add_start_docstrings_to_model_forward,
cached_file,
copy_func,
download_url,
has_file,
is_offline_mode,
is_remote_url,
logging,
replace_return_docstrings,
)
from .utils.hub import convert_file_size_to_int, get_checkpoint_shard_files
def append_replace_return_docstrings(model_class, output_type, config_class):
model_class.__call__ = copy_func(model_class.__call__)
model_class.__call__ = replace_return_docstrings(
output_type=output_type,
config_class=config_class,
)(model_class.__call__) | null |
10,598 | import math
import warnings
from typing import Callable, Iterable, Optional, Tuple, Union
import torch
from torch import nn
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .trainer_utils import SchedulerType
from .utils import logging
from .utils.versions import require_version
The provided code snippet includes necessary dependencies for implementing the `get_constant_schedule` function. Write a Python function `def get_constant_schedule(optimizer: Optimizer, last_epoch: int = -1)` to solve the following problem:
Create a schedule with a constant learning rate, using the learning rate set in optimizer. Args: optimizer ([`~torch.optim.Optimizer`]): The optimizer for which to schedule the learning rate. last_epoch (`int`, *optional*, defaults to -1): The index of the last epoch when resuming training. Return: `torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.
Here is the function:
def get_constant_schedule(optimizer: Optimizer, last_epoch: int = -1):
"""
Create a schedule with a constant learning rate, using the learning rate set in optimizer.
Args:
optimizer ([`~torch.optim.Optimizer`]):
The optimizer for which to schedule the learning rate.
last_epoch (`int`, *optional*, defaults to -1):
The index of the last epoch when resuming training.
Return:
`torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.
"""
return LambdaLR(optimizer, lambda _: 1, last_epoch=last_epoch) | Create a schedule with a constant learning rate, using the learning rate set in optimizer. Args: optimizer ([`~torch.optim.Optimizer`]): The optimizer for which to schedule the learning rate. last_epoch (`int`, *optional*, defaults to -1): The index of the last epoch when resuming training. Return: `torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule. |
10,599 | import math
import warnings
from typing import Callable, Iterable, Optional, Tuple, Union
import torch
from torch import nn
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .trainer_utils import SchedulerType
from .utils import logging
from .utils.versions import require_version
The provided code snippet includes necessary dependencies for implementing the `get_constant_schedule_with_warmup` function. Write a Python function `def get_constant_schedule_with_warmup(optimizer: Optimizer, num_warmup_steps: int, last_epoch: int = -1)` to solve the following problem:
Create a schedule with a constant learning rate preceded by a warmup period during which the learning rate increases linearly between 0 and the initial lr set in the optimizer. Args: optimizer ([`~torch.optim.Optimizer`]): The optimizer for which to schedule the learning rate. num_warmup_steps (`int`): The number of steps for the warmup phase. last_epoch (`int`, *optional*, defaults to -1): The index of the last epoch when resuming training. Return: `torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.
Here is the function:
def get_constant_schedule_with_warmup(optimizer: Optimizer, num_warmup_steps: int, last_epoch: int = -1):
"""
Create a schedule with a constant learning rate preceded by a warmup period during which the learning rate
increases linearly between 0 and the initial lr set in the optimizer.
Args:
optimizer ([`~torch.optim.Optimizer`]):
The optimizer for which to schedule the learning rate.
num_warmup_steps (`int`):
The number of steps for the warmup phase.
last_epoch (`int`, *optional*, defaults to -1):
The index of the last epoch when resuming training.
Return:
`torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.
"""
def lr_lambda(current_step: int):
if current_step < num_warmup_steps:
return float(current_step) / float(max(1.0, num_warmup_steps))
return 1.0
return LambdaLR(optimizer, lr_lambda, last_epoch=last_epoch) | Create a schedule with a constant learning rate preceded by a warmup period during which the learning rate increases linearly between 0 and the initial lr set in the optimizer. Args: optimizer ([`~torch.optim.Optimizer`]): The optimizer for which to schedule the learning rate. num_warmup_steps (`int`): The number of steps for the warmup phase. last_epoch (`int`, *optional*, defaults to -1): The index of the last epoch when resuming training. Return: `torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule. |
10,600 | import math
import warnings
from typing import Callable, Iterable, Optional, Tuple, Union
import torch
from torch import nn
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .trainer_utils import SchedulerType
from .utils import logging
from .utils.versions import require_version
The provided code snippet includes necessary dependencies for implementing the `get_linear_schedule_with_warmup` function. Write a Python function `def get_linear_schedule_with_warmup(optimizer, num_warmup_steps, num_training_steps, last_epoch=-1)` to solve the following problem:
Create a schedule with a learning rate that decreases linearly from the initial lr set in the optimizer to 0, after a warmup period during which it increases linearly from 0 to the initial lr set in the optimizer. Args: optimizer ([`~torch.optim.Optimizer`]): The optimizer for which to schedule the learning rate. num_warmup_steps (`int`): The number of steps for the warmup phase. num_training_steps (`int`): The total number of training steps. last_epoch (`int`, *optional*, defaults to -1): The index of the last epoch when resuming training. Return: `torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.
Here is the function:
def get_linear_schedule_with_warmup(optimizer, num_warmup_steps, num_training_steps, last_epoch=-1):
"""
Create a schedule with a learning rate that decreases linearly from the initial lr set in the optimizer to 0, after
a warmup period during which it increases linearly from 0 to the initial lr set in the optimizer.
Args:
optimizer ([`~torch.optim.Optimizer`]):
The optimizer for which to schedule the learning rate.
num_warmup_steps (`int`):
The number of steps for the warmup phase.
num_training_steps (`int`):
The total number of training steps.
last_epoch (`int`, *optional*, defaults to -1):
The index of the last epoch when resuming training.
Return:
`torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.
"""
def lr_lambda(current_step: int):
if current_step < num_warmup_steps:
return float(current_step) / float(max(1, num_warmup_steps))
return max(
0.0, float(num_training_steps - current_step) / float(max(1, num_training_steps - num_warmup_steps))
)
return LambdaLR(optimizer, lr_lambda, last_epoch) | Create a schedule with a learning rate that decreases linearly from the initial lr set in the optimizer to 0, after a warmup period during which it increases linearly from 0 to the initial lr set in the optimizer. Args: optimizer ([`~torch.optim.Optimizer`]): The optimizer for which to schedule the learning rate. num_warmup_steps (`int`): The number of steps for the warmup phase. num_training_steps (`int`): The total number of training steps. last_epoch (`int`, *optional*, defaults to -1): The index of the last epoch when resuming training. Return: `torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule. |
10,601 | import math
import warnings
from typing import Callable, Iterable, Optional, Tuple, Union
import torch
from torch import nn
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .trainer_utils import SchedulerType
from .utils import logging
from .utils.versions import require_version
The provided code snippet includes necessary dependencies for implementing the `get_cosine_schedule_with_warmup` function. Write a Python function `def get_cosine_schedule_with_warmup( optimizer: Optimizer, num_warmup_steps: int, num_training_steps: int, num_cycles: float = 0.5, last_epoch: int = -1 )` to solve the following problem:
Create a schedule with a learning rate that decreases following the values of the cosine function between the initial lr set in the optimizer to 0, after a warmup period during which it increases linearly between 0 and the initial lr set in the optimizer. Args: optimizer ([`~torch.optim.Optimizer`]): The optimizer for which to schedule the learning rate. num_warmup_steps (`int`): The number of steps for the warmup phase. num_training_steps (`int`): The total number of training steps. num_cycles (`float`, *optional*, defaults to 0.5): The number of waves in the cosine schedule (the defaults is to just decrease from the max value to 0 following a half-cosine). last_epoch (`int`, *optional*, defaults to -1): The index of the last epoch when resuming training. Return: `torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.
Here is the function:
def get_cosine_schedule_with_warmup(
optimizer: Optimizer, num_warmup_steps: int, num_training_steps: int, num_cycles: float = 0.5, last_epoch: int = -1
):
"""
Create a schedule with a learning rate that decreases following the values of the cosine function between the
initial lr set in the optimizer to 0, after a warmup period during which it increases linearly between 0 and the
initial lr set in the optimizer.
Args:
optimizer ([`~torch.optim.Optimizer`]):
The optimizer for which to schedule the learning rate.
num_warmup_steps (`int`):
The number of steps for the warmup phase.
num_training_steps (`int`):
The total number of training steps.
num_cycles (`float`, *optional*, defaults to 0.5):
The number of waves in the cosine schedule (the defaults is to just decrease from the max value to 0
following a half-cosine).
last_epoch (`int`, *optional*, defaults to -1):
The index of the last epoch when resuming training.
Return:
`torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.
"""
def lr_lambda(current_step):
if current_step < num_warmup_steps:
return float(current_step) / float(max(1, num_warmup_steps))
progress = float(current_step - num_warmup_steps) / float(max(1, num_training_steps - num_warmup_steps))
return max(0.0, 0.5 * (1.0 + math.cos(math.pi * float(num_cycles) * 2.0 * progress)))
return LambdaLR(optimizer, lr_lambda, last_epoch) | Create a schedule with a learning rate that decreases following the values of the cosine function between the initial lr set in the optimizer to 0, after a warmup period during which it increases linearly between 0 and the initial lr set in the optimizer. Args: optimizer ([`~torch.optim.Optimizer`]): The optimizer for which to schedule the learning rate. num_warmup_steps (`int`): The number of steps for the warmup phase. num_training_steps (`int`): The total number of training steps. num_cycles (`float`, *optional*, defaults to 0.5): The number of waves in the cosine schedule (the defaults is to just decrease from the max value to 0 following a half-cosine). last_epoch (`int`, *optional*, defaults to -1): The index of the last epoch when resuming training. Return: `torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule. |
10,602 | import math
import warnings
from typing import Callable, Iterable, Optional, Tuple, Union
import torch
from torch import nn
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .trainer_utils import SchedulerType
from .utils import logging
from .utils.versions import require_version
The provided code snippet includes necessary dependencies for implementing the `get_cosine_with_hard_restarts_schedule_with_warmup` function. Write a Python function `def get_cosine_with_hard_restarts_schedule_with_warmup( optimizer: Optimizer, num_warmup_steps: int, num_training_steps: int, num_cycles: int = 1, last_epoch: int = -1 )` to solve the following problem:
Create a schedule with a learning rate that decreases following the values of the cosine function between the initial lr set in the optimizer to 0, with several hard restarts, after a warmup period during which it increases linearly between 0 and the initial lr set in the optimizer. Args: optimizer ([`~torch.optim.Optimizer`]): The optimizer for which to schedule the learning rate. num_warmup_steps (`int`): The number of steps for the warmup phase. num_training_steps (`int`): The total number of training steps. num_cycles (`int`, *optional*, defaults to 1): The number of hard restarts to use. last_epoch (`int`, *optional*, defaults to -1): The index of the last epoch when resuming training. Return: `torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.
Here is the function:
def get_cosine_with_hard_restarts_schedule_with_warmup(
optimizer: Optimizer, num_warmup_steps: int, num_training_steps: int, num_cycles: int = 1, last_epoch: int = -1
):
"""
Create a schedule with a learning rate that decreases following the values of the cosine function between the
initial lr set in the optimizer to 0, with several hard restarts, after a warmup period during which it increases
linearly between 0 and the initial lr set in the optimizer.
Args:
optimizer ([`~torch.optim.Optimizer`]):
The optimizer for which to schedule the learning rate.
num_warmup_steps (`int`):
The number of steps for the warmup phase.
num_training_steps (`int`):
The total number of training steps.
num_cycles (`int`, *optional*, defaults to 1):
The number of hard restarts to use.
last_epoch (`int`, *optional*, defaults to -1):
The index of the last epoch when resuming training.
Return:
`torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.
"""
def lr_lambda(current_step):
if current_step < num_warmup_steps:
return float(current_step) / float(max(1, num_warmup_steps))
progress = float(current_step - num_warmup_steps) / float(max(1, num_training_steps - num_warmup_steps))
if progress >= 1.0:
return 0.0
return max(0.0, 0.5 * (1.0 + math.cos(math.pi * ((float(num_cycles) * progress) % 1.0))))
return LambdaLR(optimizer, lr_lambda, last_epoch) | Create a schedule with a learning rate that decreases following the values of the cosine function between the initial lr set in the optimizer to 0, with several hard restarts, after a warmup period during which it increases linearly between 0 and the initial lr set in the optimizer. Args: optimizer ([`~torch.optim.Optimizer`]): The optimizer for which to schedule the learning rate. num_warmup_steps (`int`): The number of steps for the warmup phase. num_training_steps (`int`): The total number of training steps. num_cycles (`int`, *optional*, defaults to 1): The number of hard restarts to use. last_epoch (`int`, *optional*, defaults to -1): The index of the last epoch when resuming training. Return: `torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule. |
10,603 | import math
import warnings
from typing import Callable, Iterable, Optional, Tuple, Union
import torch
from torch import nn
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .trainer_utils import SchedulerType
from .utils import logging
from .utils.versions import require_version
The provided code snippet includes necessary dependencies for implementing the `get_polynomial_decay_schedule_with_warmup` function. Write a Python function `def get_polynomial_decay_schedule_with_warmup( optimizer, num_warmup_steps, num_training_steps, lr_end=1e-7, power=1.0, last_epoch=-1 )` to solve the following problem:
Create a schedule with a learning rate that decreases as a polynomial decay from the initial lr set in the optimizer to end lr defined by *lr_end*, after a warmup period during which it increases linearly from 0 to the initial lr set in the optimizer. Args: optimizer ([`~torch.optim.Optimizer`]): The optimizer for which to schedule the learning rate. num_warmup_steps (`int`): The number of steps for the warmup phase. num_training_steps (`int`): The total number of training steps. lr_end (`float`, *optional*, defaults to 1e-7): The end LR. power (`float`, *optional*, defaults to 1.0): Power factor. last_epoch (`int`, *optional*, defaults to -1): The index of the last epoch when resuming training. Note: *power* defaults to 1.0 as in the fairseq implementation, which in turn is based on the original BERT implementation at https://github.com/google-research/bert/blob/f39e881b169b9d53bea03d2d341b31707a6c052b/optimization.py#L37 Return: `torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.
Here is the function:
def get_polynomial_decay_schedule_with_warmup(
optimizer, num_warmup_steps, num_training_steps, lr_end=1e-7, power=1.0, last_epoch=-1
):
"""
Create a schedule with a learning rate that decreases as a polynomial decay from the initial lr set in the
optimizer to end lr defined by *lr_end*, after a warmup period during which it increases linearly from 0 to the
initial lr set in the optimizer.
Args:
optimizer ([`~torch.optim.Optimizer`]):
The optimizer for which to schedule the learning rate.
num_warmup_steps (`int`):
The number of steps for the warmup phase.
num_training_steps (`int`):
The total number of training steps.
lr_end (`float`, *optional*, defaults to 1e-7):
The end LR.
power (`float`, *optional*, defaults to 1.0):
Power factor.
last_epoch (`int`, *optional*, defaults to -1):
The index of the last epoch when resuming training.
Note: *power* defaults to 1.0 as in the fairseq implementation, which in turn is based on the original BERT
implementation at
https://github.com/google-research/bert/blob/f39e881b169b9d53bea03d2d341b31707a6c052b/optimization.py#L37
Return:
`torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.
"""
lr_init = optimizer.defaults["lr"]
if not (lr_init > lr_end):
raise ValueError(f"lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})")
def lr_lambda(current_step: int):
if current_step < num_warmup_steps:
return float(current_step) / float(max(1, num_warmup_steps))
elif current_step > num_training_steps:
return lr_end / lr_init # as LambdaLR multiplies by lr_init
else:
lr_range = lr_init - lr_end
decay_steps = num_training_steps - num_warmup_steps
pct_remaining = 1 - (current_step - num_warmup_steps) / decay_steps
decay = lr_range * pct_remaining**power + lr_end
return decay / lr_init # as LambdaLR multiplies by lr_init
return LambdaLR(optimizer, lr_lambda, last_epoch) | Create a schedule with a learning rate that decreases as a polynomial decay from the initial lr set in the optimizer to end lr defined by *lr_end*, after a warmup period during which it increases linearly from 0 to the initial lr set in the optimizer. Args: optimizer ([`~torch.optim.Optimizer`]): The optimizer for which to schedule the learning rate. num_warmup_steps (`int`): The number of steps for the warmup phase. num_training_steps (`int`): The total number of training steps. lr_end (`float`, *optional*, defaults to 1e-7): The end LR. power (`float`, *optional*, defaults to 1.0): Power factor. last_epoch (`int`, *optional*, defaults to -1): The index of the last epoch when resuming training. Note: *power* defaults to 1.0 as in the fairseq implementation, which in turn is based on the original BERT implementation at https://github.com/google-research/bert/blob/f39e881b169b9d53bea03d2d341b31707a6c052b/optimization.py#L37 Return: `torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule. |
10,604 | import math
import warnings
from typing import Callable, Iterable, Optional, Tuple, Union
import torch
from torch import nn
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .trainer_utils import SchedulerType
from .utils import logging
from .utils.versions import require_version
TYPE_TO_SCHEDULER_FUNCTION = {
SchedulerType.LINEAR: get_linear_schedule_with_warmup,
SchedulerType.COSINE: get_cosine_schedule_with_warmup,
SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
SchedulerType.CONSTANT: get_constant_schedule,
SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
}
class SchedulerType(ExplicitEnum):
LINEAR = "linear"
COSINE = "cosine"
COSINE_WITH_RESTARTS = "cosine_with_restarts"
POLYNOMIAL = "polynomial"
CONSTANT = "constant"
CONSTANT_WITH_WARMUP = "constant_with_warmup"
The provided code snippet includes necessary dependencies for implementing the `get_scheduler` function. Write a Python function `def get_scheduler( name: Union[str, SchedulerType], optimizer: Optimizer, num_warmup_steps: Optional[int] = None, num_training_steps: Optional[int] = None, )` to solve the following problem:
Unified API to get any scheduler from its name. Args: name (`str` or `SchedulerType`): The name of the scheduler to use. optimizer (`torch.optim.Optimizer`): The optimizer that will be used during training. num_warmup_steps (`int`, *optional*): The number of warmup steps to do. This is not required by all schedulers (hence the argument being optional), the function will raise an error if it's unset and the scheduler type requires it. num_training_steps (`int``, *optional*): The number of training steps to do. This is not required by all schedulers (hence the argument being optional), the function will raise an error if it's unset and the scheduler type requires it.
Here is the function:
def get_scheduler(
name: Union[str, SchedulerType],
optimizer: Optimizer,
num_warmup_steps: Optional[int] = None,
num_training_steps: Optional[int] = None,
):
"""
Unified API to get any scheduler from its name.
Args:
name (`str` or `SchedulerType`):
The name of the scheduler to use.
optimizer (`torch.optim.Optimizer`):
The optimizer that will be used during training.
num_warmup_steps (`int`, *optional*):
The number of warmup steps to do. This is not required by all schedulers (hence the argument being
optional), the function will raise an error if it's unset and the scheduler type requires it.
num_training_steps (`int``, *optional*):
The number of training steps to do. This is not required by all schedulers (hence the argument being
optional), the function will raise an error if it's unset and the scheduler type requires it.
"""
name = SchedulerType(name)
schedule_func = TYPE_TO_SCHEDULER_FUNCTION[name]
if name == SchedulerType.CONSTANT:
return schedule_func(optimizer)
# All other schedulers require `num_warmup_steps`
if num_warmup_steps is None:
raise ValueError(f"{name} requires `num_warmup_steps`, please provide that argument.")
if name == SchedulerType.CONSTANT_WITH_WARMUP:
return schedule_func(optimizer, num_warmup_steps=num_warmup_steps)
# All other schedulers require `num_training_steps`
if num_training_steps is None:
raise ValueError(f"{name} requires `num_training_steps`, please provide that argument.")
return schedule_func(optimizer, num_warmup_steps=num_warmup_steps, num_training_steps=num_training_steps) | Unified API to get any scheduler from its name. Args: name (`str` or `SchedulerType`): The name of the scheduler to use. optimizer (`torch.optim.Optimizer`): The optimizer that will be used during training. num_warmup_steps (`int`, *optional*): The number of warmup steps to do. This is not required by all schedulers (hence the argument being optional), the function will raise an error if it's unset and the scheduler type requires it. num_training_steps (`int``, *optional*): The number of training steps to do. This is not required by all schedulers (hence the argument being optional), the function will raise an error if it's unset and the scheduler type requires it. |
10,605 | import math
import warnings
from typing import Callable, Iterable, Optional, Tuple, Union
import torch
from torch import nn
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .trainer_utils import SchedulerType
from .utils import logging
from .utils.versions import require_version
class AdafactorSchedule(LambdaLR):
"""
Since [`~optimization.Adafactor`] performs its own scheduling, if the training loop relies on a scheduler (e.g.,
for logging), this class creates a proxy object that retrieves the current lr values from the optimizer.
It returns `initial_lr` during startup and the actual `lr` during stepping.
"""
def __init__(self, optimizer, initial_lr=0.0):
def lr_lambda(_):
return initial_lr
for group in optimizer.param_groups:
group["initial_lr"] = initial_lr
super().__init__(optimizer, lr_lambda)
for group in optimizer.param_groups:
del group["initial_lr"]
def get_lr(self):
opt = self.optimizer
lrs = [
opt._get_lr(group, opt.state[group["params"][0]])
for group in opt.param_groups
if group["params"][0].grad is not None
]
if len(lrs) == 0:
lrs = self.base_lrs # if called before stepping
return lrs
The provided code snippet includes necessary dependencies for implementing the `get_adafactor_schedule` function. Write a Python function `def get_adafactor_schedule(optimizer, initial_lr=0.0)` to solve the following problem:
Get a proxy schedule for [`~optimization.Adafactor`] Args: optimizer ([`~torch.optim.Optimizer`]): The optimizer for which to schedule the learning rate. initial_lr (`float`, *optional*, defaults to 0.0): Initial lr Return: [`~optimization.Adafactor`] proxy schedule object.
Here is the function:
def get_adafactor_schedule(optimizer, initial_lr=0.0):
"""
Get a proxy schedule for [`~optimization.Adafactor`]
Args:
optimizer ([`~torch.optim.Optimizer`]):
The optimizer for which to schedule the learning rate.
initial_lr (`float`, *optional*, defaults to 0.0):
Initial lr
Return:
[`~optimization.Adafactor`] proxy schedule object.
"""
return AdafactorSchedule(optimizer, initial_lr) | Get a proxy schedule for [`~optimization.Adafactor`] Args: optimizer ([`~torch.optim.Optimizer`]): The optimizer for which to schedule the learning rate. initial_lr (`float`, *optional*, defaults to 0.0): Initial lr Return: [`~optimization.Adafactor`] proxy schedule object. |
10,606 | import datetime
import json
import math
import os
import sys
import warnings
from collections.abc import Mapping
from contextlib import contextmanager
from dataclasses import dataclass
from logging import StreamHandler
from typing import Any, Dict, Iterator, List, Optional, Union
import numpy as np
import torch
import torch.distributed as dist
from torch import nn
from torch.utils.data import Dataset, IterableDataset, RandomSampler, Sampler
from torch.utils.data.distributed import DistributedSampler
from .tokenization_utils_base import BatchEncoding
from .utils import is_sagemaker_mp_enabled, is_torch_tpu_available, is_training_run_on_sagemaker, logging
try:
from torch.optim.lr_scheduler import SAVE_STATE_WARNING
except ImportError:
SAVE_STATE_WARNING = ""
def torch_pad_and_concatenate(tensor1, tensor2, padding_index=-100):
"""Concatenates `tensor1` and `tensor2` on first axis, applying padding on the second if necessary."""
tensor1 = atleast_1d(tensor1)
tensor2 = atleast_1d(tensor2)
if len(tensor1.shape) == 1 or tensor1.shape[1] == tensor2.shape[1]:
return torch.cat((tensor1, tensor2), dim=0)
# Let's figure out the new shape
new_shape = (tensor1.shape[0] + tensor2.shape[0], max(tensor1.shape[1], tensor2.shape[1])) + tensor1.shape[2:]
# Now let's fill the result tensor
result = tensor1.new_full(new_shape, padding_index)
result[: tensor1.shape[0], : tensor1.shape[1]] = tensor1
result[tensor1.shape[0] :, : tensor2.shape[1]] = tensor2
return result
def numpy_pad_and_concatenate(array1, array2, padding_index=-100):
"""Concatenates `array1` and `array2` on first axis, applying padding on the second if necessary."""
array1 = atleast_1d(array1)
array2 = atleast_1d(array2)
if len(array1.shape) == 1 or array1.shape[1] == array2.shape[1]:
return np.concatenate((array1, array2), axis=0)
# Let's figure out the new shape
new_shape = (array1.shape[0] + array2.shape[0], max(array1.shape[1], array2.shape[1])) + array1.shape[2:]
# Now let's fill the result tensor
result = np.full_like(array1, padding_index, shape=new_shape)
result[: array1.shape[0], : array1.shape[1]] = array1
result[array1.shape[0] :, : array2.shape[1]] = array2
return result
The provided code snippet includes necessary dependencies for implementing the `nested_concat` function. Write a Python function `def nested_concat(tensors, new_tensors, padding_index=-100)` to solve the following problem:
Concat the `new_tensors` to `tensors` on the first dim and pad them on the second if needed. Works for tensors or nested list/tuples/dict of tensors.
Here is the function:
def nested_concat(tensors, new_tensors, padding_index=-100):
"""
Concat the `new_tensors` to `tensors` on the first dim and pad them on the second if needed. Works for tensors or
nested list/tuples/dict of tensors.
"""
assert type(tensors) == type(
new_tensors
), f"Expected `tensors` and `new_tensors` to have the same type but found {type(tensors)} and {type(new_tensors)}."
if isinstance(tensors, (list, tuple)):
return type(tensors)(nested_concat(t, n, padding_index=padding_index) for t, n in zip(tensors, new_tensors))
elif isinstance(tensors, torch.Tensor):
return torch_pad_and_concatenate(tensors, new_tensors, padding_index=padding_index)
elif isinstance(tensors, Mapping):
return type(tensors)(
{k: nested_concat(t, new_tensors[k], padding_index=padding_index) for k, t in tensors.items()}
)
elif isinstance(tensors, np.ndarray):
return numpy_pad_and_concatenate(tensors, new_tensors, padding_index=padding_index)
else:
raise TypeError(f"Unsupported type for concatenation: got {type(tensors)}") | Concat the `new_tensors` to `tensors` on the first dim and pad them on the second if needed. Works for tensors or nested list/tuples/dict of tensors. |
10,607 | import datetime
import json
import math
import os
import sys
import warnings
from collections.abc import Mapping
from contextlib import contextmanager
from dataclasses import dataclass
from logging import StreamHandler
from typing import Any, Dict, Iterator, List, Optional, Union
import numpy as np
import torch
import torch.distributed as dist
from torch import nn
from torch.utils.data import Dataset, IterableDataset, RandomSampler, Sampler
from torch.utils.data.distributed import DistributedSampler
from .tokenization_utils_base import BatchEncoding
from .utils import is_sagemaker_mp_enabled, is_torch_tpu_available, is_training_run_on_sagemaker, logging
try:
from torch.optim.lr_scheduler import SAVE_STATE_WARNING
except ImportError:
SAVE_STATE_WARNING = ""
The provided code snippet includes necessary dependencies for implementing the `find_batch_size` function. Write a Python function `def find_batch_size(tensors)` to solve the following problem:
Find the first dimension of a tensor in a nested list/tuple/dict of tensors.
Here is the function:
def find_batch_size(tensors):
"""
Find the first dimension of a tensor in a nested list/tuple/dict of tensors.
"""
if isinstance(tensors, (list, tuple)):
for t in tensors:
result = find_batch_size(t)
if result is not None:
return result
elif isinstance(tensors, Mapping):
for key, value in tensors.items():
result = find_batch_size(value)
if result is not None:
return result
elif isinstance(tensors, torch.Tensor):
return tensors.shape[0] if len(tensors.shape) >= 1 else None
elif isinstance(tensors, np.ndarray):
return tensors.shape[0] if len(tensors.shape) >= 1 else None | Find the first dimension of a tensor in a nested list/tuple/dict of tensors. |
10,608 | import datetime
import json
import math
import os
import sys
import warnings
from collections.abc import Mapping
from contextlib import contextmanager
from dataclasses import dataclass
from logging import StreamHandler
from typing import Any, Dict, Iterator, List, Optional, Union
import numpy as np
import torch
import torch.distributed as dist
from torch import nn
from torch.utils.data import Dataset, IterableDataset, RandomSampler, Sampler
from torch.utils.data.distributed import DistributedSampler
from .tokenization_utils_base import BatchEncoding
from .utils import is_sagemaker_mp_enabled, is_torch_tpu_available, is_training_run_on_sagemaker, logging
try:
from torch.optim.lr_scheduler import SAVE_STATE_WARNING
except ImportError:
SAVE_STATE_WARNING = ""
The provided code snippet includes necessary dependencies for implementing the `nested_numpify` function. Write a Python function `def nested_numpify(tensors)` to solve the following problem:
Numpify `tensors` (even if it's a nested list/tuple/dict of tensors).
Here is the function:
def nested_numpify(tensors):
"Numpify `tensors` (even if it's a nested list/tuple/dict of tensors)."
if isinstance(tensors, (list, tuple)):
return type(tensors)(nested_numpify(t) for t in tensors)
if isinstance(tensors, Mapping):
return type(tensors)({k: nested_numpify(t) for k, t in tensors.items()})
t = tensors.cpu()
if t.dtype == torch.bfloat16:
# As of Numpy 1.21.4, NumPy does not support bfloat16 (see
# https://github.com/numpy/numpy/blob/a47ecdea856986cd60eabbd53265c2ca5916ad5d/doc/source/user/basics.types.rst ).
# Until Numpy adds bfloat16, we must convert float32.
t = t.to(torch.float32)
return t.numpy() | Numpify `tensors` (even if it's a nested list/tuple/dict of tensors). |
10,609 | import datetime
import json
import math
import os
import sys
import warnings
from collections.abc import Mapping
from contextlib import contextmanager
from dataclasses import dataclass
from logging import StreamHandler
from typing import Any, Dict, Iterator, List, Optional, Union
import numpy as np
import torch
import torch.distributed as dist
from torch import nn
from torch.utils.data import Dataset, IterableDataset, RandomSampler, Sampler
from torch.utils.data.distributed import DistributedSampler
from .tokenization_utils_base import BatchEncoding
from .utils import is_sagemaker_mp_enabled, is_torch_tpu_available, is_training_run_on_sagemaker, logging
The provided code snippet includes necessary dependencies for implementing the `nested_detach` function. Write a Python function `def nested_detach(tensors)` to solve the following problem:
Detach `tensors` (even if it's a nested list/tuple/dict of tensors).
Here is the function:
def nested_detach(tensors):
"Detach `tensors` (even if it's a nested list/tuple/dict of tensors)."
if isinstance(tensors, (list, tuple)):
return type(tensors)(nested_detach(t) for t in tensors)
elif isinstance(tensors, Mapping):
return type(tensors)({k: nested_detach(t) for k, t in tensors.items()})
return tensors.detach() | Detach `tensors` (even if it's a nested list/tuple/dict of tensors). |
10,610 | import datetime
import json
import math
import os
import sys
import warnings
from collections.abc import Mapping
from contextlib import contextmanager
from dataclasses import dataclass
from logging import StreamHandler
from typing import Any, Dict, Iterator, List, Optional, Union
import numpy as np
import torch
import torch.distributed as dist
from torch import nn
from torch.utils.data import Dataset, IterableDataset, RandomSampler, Sampler
from torch.utils.data.distributed import DistributedSampler
from .tokenization_utils_base import BatchEncoding
from .utils import is_sagemaker_mp_enabled, is_torch_tpu_available, is_training_run_on_sagemaker, logging
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
try:
from torch.optim.lr_scheduler import SAVE_STATE_WARNING
except ImportError:
SAVE_STATE_WARNING = ""
def atleast_1d(tensor_or_array: Union[torch.Tensor, np.ndarray]):
if isinstance(tensor_or_array, torch.Tensor):
if hasattr(torch, "atleast_1d"):
tensor_or_array = torch.atleast_1d(tensor_or_array)
elif tensor_or_array.ndim < 1:
tensor_or_array = tensor_or_array[None]
else:
tensor_or_array = np.atleast_1d(tensor_or_array)
return tensor_or_array
def nested_xla_mesh_reduce(tensors, name):
if is_torch_tpu_available():
import torch_xla.core.xla_model as xm
if isinstance(tensors, (list, tuple)):
return type(tensors)(nested_xla_mesh_reduce(t, f"{name}_{i}") for i, t in enumerate(tensors))
if isinstance(tensors, Mapping):
return type(tensors)(
{k: nested_xla_mesh_reduce(t, f"{name}_{i}") for i, (k, t) in enumerate(tensors.items())}
)
tensors = atleast_1d(tensors)
return xm.mesh_reduce(name, tensors, torch.cat)
else:
raise ImportError("Torch xla must be installed to use `nested_xla_mesh_reduce`") | null |
10,611 | import datetime
import json
import math
import os
import sys
import warnings
from collections.abc import Mapping
from contextlib import contextmanager
from dataclasses import dataclass
from logging import StreamHandler
from typing import Any, Dict, Iterator, List, Optional, Union
import numpy as np
import torch
import torch.distributed as dist
from torch import nn
from torch.utils.data import Dataset, IterableDataset, RandomSampler, Sampler
from torch.utils.data.distributed import DistributedSampler
from .tokenization_utils_base import BatchEncoding
from .utils import is_sagemaker_mp_enabled, is_torch_tpu_available, is_training_run_on_sagemaker, logging
try:
from torch.optim.lr_scheduler import SAVE_STATE_WARNING
except ImportError:
SAVE_STATE_WARNING = ""
def atleast_1d(tensor_or_array: Union[torch.Tensor, np.ndarray]):
if isinstance(tensor_or_array, torch.Tensor):
if hasattr(torch, "atleast_1d"):
tensor_or_array = torch.atleast_1d(tensor_or_array)
elif tensor_or_array.ndim < 1:
tensor_or_array = tensor_or_array[None]
else:
tensor_or_array = np.atleast_1d(tensor_or_array)
return tensor_or_array
def distributed_concat(tensor: Any, num_total_examples: Optional[int] = None) -> Any:
try:
if isinstance(tensor, (tuple, list)):
return type(tensor)(distributed_concat(t, num_total_examples) for t in tensor)
tensor = atleast_1d(tensor)
output_tensors = [tensor.clone() for _ in range(dist.get_world_size())]
dist.all_gather(output_tensors, tensor)
concat = torch.cat(output_tensors, dim=0)
# truncate the dummy elements added by SequentialDistributedSampler
if num_total_examples is not None:
concat = concat[:num_total_examples]
return concat
except AssertionError:
raise AssertionError("Not currently using distributed training") | null |
10,612 | import datetime
import json
import math
import os
import sys
import warnings
from collections.abc import Mapping
from contextlib import contextmanager
from dataclasses import dataclass
from logging import StreamHandler
from typing import Any, Dict, Iterator, List, Optional, Union
import numpy as np
import torch
import torch.distributed as dist
from torch import nn
from torch.utils.data import Dataset, IterableDataset, RandomSampler, Sampler
from torch.utils.data.distributed import DistributedSampler
from .tokenization_utils_base import BatchEncoding
from .utils import is_sagemaker_mp_enabled, is_torch_tpu_available, is_training_run_on_sagemaker, logging
try:
from torch.optim.lr_scheduler import SAVE_STATE_WARNING
except ImportError:
SAVE_STATE_WARNING = ""
def distributed_broadcast_scalars(
scalars: List[Union[int, float]],
num_total_examples: Optional[int] = None,
device: Optional[torch.device] = torch.device("cuda"),
) -> torch.Tensor:
try:
tensorized_scalar = torch.tensor(scalars).to(device)
output_tensors = [tensorized_scalar.clone() for _ in range(dist.get_world_size())]
dist.all_gather(output_tensors, tensorized_scalar)
concat = torch.cat(output_tensors, dim=0)
# truncate the dummy elements added by SequentialDistributedSampler
if num_total_examples is not None:
concat = concat[:num_total_examples]
return concat
except AssertionError:
raise AssertionError("Not currently using distributed training") | null |
10,613 | import datetime
import json
import math
import os
import sys
import warnings
from collections.abc import Mapping
from contextlib import contextmanager
from dataclasses import dataclass
from logging import StreamHandler
from typing import Any, Dict, Iterator, List, Optional, Union
import numpy as np
import torch
import torch.distributed as dist
from torch import nn
from torch.utils.data import Dataset, IterableDataset, RandomSampler, Sampler
from torch.utils.data.distributed import DistributedSampler
from .tokenization_utils_base import BatchEncoding
from .utils import is_sagemaker_mp_enabled, is_torch_tpu_available, is_training_run_on_sagemaker, logging
def reissue_pt_warnings(caught_warnings):
# Reissue warnings that are not the SAVE_STATE_WARNING
if len(caught_warnings) > 1:
for w in caught_warnings:
if w.category != UserWarning or w.message != SAVE_STATE_WARNING:
warnings.warn(w.message, w.category) | null |
10,614 | import datetime
import json
import math
import os
import sys
import warnings
from collections.abc import Mapping
from contextlib import contextmanager
from dataclasses import dataclass
from logging import StreamHandler
from typing import Any, Dict, Iterator, List, Optional, Union
import numpy as np
import torch
import torch.distributed as dist
from torch import nn
from torch.utils.data import Dataset, IterableDataset, RandomSampler, Sampler
from torch.utils.data.distributed import DistributedSampler
from .tokenization_utils_base import BatchEncoding
from .utils import is_sagemaker_mp_enabled, is_torch_tpu_available, is_training_run_on_sagemaker, logging
The provided code snippet includes necessary dependencies for implementing the `torch_distributed_zero_first` function. Write a Python function `def torch_distributed_zero_first(local_rank: int)` to solve the following problem:
Decorator to make all processes in distributed training wait for each local_master to do something. Args: local_rank (`int`): The rank of the local process.
Here is the function:
def torch_distributed_zero_first(local_rank: int):
"""
Decorator to make all processes in distributed training wait for each local_master to do something.
Args:
local_rank (`int`): The rank of the local process.
"""
if local_rank not in [-1, 0]:
dist.barrier()
yield
if local_rank == 0:
dist.barrier() | Decorator to make all processes in distributed training wait for each local_master to do something. Args: local_rank (`int`): The rank of the local process. |
10,615 | import datetime
import json
import math
import os
import sys
import warnings
from collections.abc import Mapping
from contextlib import contextmanager
from dataclasses import dataclass
from logging import StreamHandler
from typing import Any, Dict, Iterator, List, Optional, Union
import numpy as np
import torch
import torch.distributed as dist
from torch import nn
from torch.utils.data import Dataset, IterableDataset, RandomSampler, Sampler
from torch.utils.data.distributed import DistributedSampler
from .tokenization_utils_base import BatchEncoding
from .utils import is_sagemaker_mp_enabled, is_torch_tpu_available, is_training_run_on_sagemaker, logging
try:
from torch.optim.lr_scheduler import SAVE_STATE_WARNING
except ImportError:
SAVE_STATE_WARNING = ""
def get_tpu_sampler(dataset: torch.utils.data.Dataset, batch_size: int):
if xm.xrt_world_size() <= 1:
return RandomSampler(dataset)
return DistributedSampler(dataset, num_replicas=xm.xrt_world_size(), rank=xm.get_ordinal()) | null |
10,616 | import datetime
import json
import math
import os
import sys
import warnings
from collections.abc import Mapping
from contextlib import contextmanager
from dataclasses import dataclass
from logging import StreamHandler
from typing import Any, Dict, Iterator, List, Optional, Union
import numpy as np
import torch
import torch.distributed as dist
from torch import nn
from torch.utils.data import Dataset, IterableDataset, RandomSampler, Sampler
from torch.utils.data.distributed import DistributedSampler
from .tokenization_utils_base import BatchEncoding
from .utils import is_sagemaker_mp_enabled, is_torch_tpu_available, is_training_run_on_sagemaker, logging
The provided code snippet includes necessary dependencies for implementing the `nested_new_like` function. Write a Python function `def nested_new_like(arrays, num_samples, padding_index=-100)` to solve the following problem:
Create the same nested structure as `arrays` with a first dimension always at `num_samples`.
Here is the function:
def nested_new_like(arrays, num_samples, padding_index=-100):
"""Create the same nested structure as `arrays` with a first dimension always at `num_samples`."""
if isinstance(arrays, (list, tuple)):
return type(arrays)(nested_new_like(x, num_samples) for x in arrays)
return np.full_like(arrays, padding_index, shape=(num_samples, *arrays.shape[1:])) | Create the same nested structure as `arrays` with a first dimension always at `num_samples`. |
10,617 | import datetime
import json
import math
import os
import sys
import warnings
from collections.abc import Mapping
from contextlib import contextmanager
from dataclasses import dataclass
from logging import StreamHandler
from typing import Any, Dict, Iterator, List, Optional, Union
import numpy as np
import torch
import torch.distributed as dist
from torch import nn
from torch.utils.data import Dataset, IterableDataset, RandomSampler, Sampler
from torch.utils.data.distributed import DistributedSampler
from .tokenization_utils_base import BatchEncoding
from .utils import is_sagemaker_mp_enabled, is_torch_tpu_available, is_training_run_on_sagemaker, logging
The provided code snippet includes necessary dependencies for implementing the `expand_like` function. Write a Python function `def expand_like(arrays, new_seq_length, padding_index=-100)` to solve the following problem:
Expand the `arrays` so that the second dimension grows to `new_seq_length`. Uses `padding_index` for padding.
Here is the function:
def expand_like(arrays, new_seq_length, padding_index=-100):
"""Expand the `arrays` so that the second dimension grows to `new_seq_length`. Uses `padding_index` for padding."""
result = np.full_like(arrays, padding_index, shape=(arrays.shape[0], new_seq_length) + arrays.shape[2:])
result[:, : arrays.shape[1]] = arrays
return result | Expand the `arrays` so that the second dimension grows to `new_seq_length`. Uses `padding_index` for padding. |
10,618 | import datetime
import json
import math
import os
import sys
import warnings
from collections.abc import Mapping
from contextlib import contextmanager
from dataclasses import dataclass
from logging import StreamHandler
from typing import Any, Dict, Iterator, List, Optional, Union
import numpy as np
import torch
import torch.distributed as dist
from torch import nn
from torch.utils.data import Dataset, IterableDataset, RandomSampler, Sampler
from torch.utils.data.distributed import DistributedSampler
from .tokenization_utils_base import BatchEncoding
from .utils import is_sagemaker_mp_enabled, is_torch_tpu_available, is_training_run_on_sagemaker, logging
The provided code snippet includes necessary dependencies for implementing the `nested_truncate` function. Write a Python function `def nested_truncate(tensors, limit)` to solve the following problem:
Truncate `tensors` at `limit` (even if it's a nested list/tuple/dict of tensors).
Here is the function:
def nested_truncate(tensors, limit):
"Truncate `tensors` at `limit` (even if it's a nested list/tuple/dict of tensors)."
if isinstance(tensors, (list, tuple)):
return type(tensors)(nested_truncate(t, limit) for t in tensors)
if isinstance(tensors, Mapping):
return type(tensors)({k: nested_truncate(t, limit) for k, t in tensors.items()})
return tensors[:limit] | Truncate `tensors` at `limit` (even if it's a nested list/tuple/dict of tensors). |
10,619 | import datetime
import json
import math
import os
import sys
import warnings
from collections.abc import Mapping
from contextlib import contextmanager
from dataclasses import dataclass
from logging import StreamHandler
from typing import Any, Dict, Iterator, List, Optional, Union
import numpy as np
import torch
import torch.distributed as dist
from torch import nn
from torch.utils.data import Dataset, IterableDataset, RandomSampler, Sampler
from torch.utils.data.distributed import DistributedSampler
from .tokenization_utils_base import BatchEncoding
from .utils import is_sagemaker_mp_enabled, is_torch_tpu_available, is_training_run_on_sagemaker, logging
try:
from torch.optim.lr_scheduler import SAVE_STATE_WARNING
except ImportError:
SAVE_STATE_WARNING = ""
The provided code snippet includes necessary dependencies for implementing the `get_length_grouped_indices` function. Write a Python function `def get_length_grouped_indices(lengths, batch_size, mega_batch_mult=None, generator=None)` to solve the following problem:
Return a list of indices so that each slice of `batch_size` consecutive indices correspond to elements of similar lengths. To do this, the indices are: - randomly permuted - grouped in mega-batches of size `mega_batch_mult * batch_size` - sorted by length in each mega-batch The result is the concatenation of all mega-batches, with the batch of `batch_size` containing the element of maximum length placed first, so that an OOM happens sooner rather than later.
Here is the function:
def get_length_grouped_indices(lengths, batch_size, mega_batch_mult=None, generator=None):
"""
Return a list of indices so that each slice of `batch_size` consecutive indices correspond to elements of similar
lengths. To do this, the indices are:
- randomly permuted
- grouped in mega-batches of size `mega_batch_mult * batch_size`
- sorted by length in each mega-batch
The result is the concatenation of all mega-batches, with the batch of `batch_size` containing the element of
maximum length placed first, so that an OOM happens sooner rather than later.
"""
# Default for mega_batch_mult: 50 or the number to get 4 megabatches, whichever is smaller.
if mega_batch_mult is None:
mega_batch_mult = min(len(lengths) // (batch_size * 4), 50)
# Just in case, for tiny datasets
if mega_batch_mult == 0:
mega_batch_mult = 1
# We need to use torch for the random part as a distributed sampler will set the random seed for torch.
indices = torch.randperm(len(lengths), generator=generator)
megabatch_size = mega_batch_mult * batch_size
megabatches = [indices[i : i + megabatch_size].tolist() for i in range(0, len(lengths), megabatch_size)]
megabatches = [list(sorted(megabatch, key=lambda i: lengths[i], reverse=True)) for megabatch in megabatches]
# The rest is to get the biggest batch first.
# Since each megabatch is sorted by descending length, the longest element is the first
megabatch_maximums = [lengths[megabatch[0]] for megabatch in megabatches]
max_idx = torch.argmax(torch.tensor(megabatch_maximums)).item()
# Switch to put the longest element in first position
megabatches[0][0], megabatches[max_idx][0] = megabatches[max_idx][0], megabatches[0][0]
return [i for megabatch in megabatches for i in megabatch] | Return a list of indices so that each slice of `batch_size` consecutive indices correspond to elements of similar lengths. To do this, the indices are: - randomly permuted - grouped in mega-batches of size `mega_batch_mult * batch_size` - sorted by length in each mega-batch The result is the concatenation of all mega-batches, with the batch of `batch_size` containing the element of maximum length placed first, so that an OOM happens sooner rather than later. |
10,620 | import datetime
import json
import math
import os
import sys
import warnings
from collections.abc import Mapping
from contextlib import contextmanager
from dataclasses import dataclass
from logging import StreamHandler
from typing import Any, Dict, Iterator, List, Optional, Union
import numpy as np
import torch
import torch.distributed as dist
from torch import nn
from torch.utils.data import Dataset, IterableDataset, RandomSampler, Sampler
from torch.utils.data.distributed import DistributedSampler
from .tokenization_utils_base import BatchEncoding
from .utils import is_sagemaker_mp_enabled, is_torch_tpu_available, is_training_run_on_sagemaker, logging
try:
from torch.optim.lr_scheduler import SAVE_STATE_WARNING
except ImportError:
SAVE_STATE_WARNING = ""
logger = logging.get_logger(__name__)
def _get_learning_rate(self):
if self.deepspeed:
# with deepspeed's fp16 and dynamic loss scale enabled the optimizer/scheduler steps may
# not run for the first few dozen steps while loss scale is too large, and thus during
# that time `get_last_lr` will fail if called during that warm up stage, so work around it:
try:
last_lr = self.lr_scheduler.get_last_lr()[0]
except AssertionError as e:
if "need to call step" in str(e):
logger.warning("tried to get lr value before scheduler/optimizer started stepping, returning lr=0")
last_lr = 0
else:
raise
else:
last_lr = self.lr_scheduler.get_last_lr()[0]
if torch.is_tensor(last_lr):
last_lr = last_lr.item()
return last_lr | null |
10,621 | import datetime
import json
import math
import os
import sys
import warnings
from collections.abc import Mapping
from contextlib import contextmanager
from dataclasses import dataclass
from logging import StreamHandler
from typing import Any, Dict, Iterator, List, Optional, Union
import numpy as np
import torch
import torch.distributed as dist
from torch import nn
from torch.utils.data import Dataset, IterableDataset, RandomSampler, Sampler
from torch.utils.data.distributed import DistributedSampler
from .tokenization_utils_base import BatchEncoding
from .utils import is_sagemaker_mp_enabled, is_torch_tpu_available, is_training_run_on_sagemaker, logging
def metrics_format(self, metrics: Dict[str, float]) -> Dict[str, float]:
"""
Reformat Trainer metrics values to a human-readable format
Args:
metrics (`Dict[str, float]`):
The metrics returned from train/evaluate/predict
Returns:
metrics (`Dict[str, float]`): The reformatted metrics
"""
metrics_copy = metrics.copy()
for k, v in metrics_copy.items():
if "_mem_" in k:
metrics_copy[k] = f"{ v >> 20 }MB"
elif "_runtime" in k:
metrics_copy[k] = _secs2timedelta(v)
elif k == "total_flos":
metrics_copy[k] = f"{ int(v) >> 30 }GF"
elif type(metrics_copy[k]) == float:
metrics_copy[k] = round(v, 4)
return metrics_copy
The provided code snippet includes necessary dependencies for implementing the `log_metrics` function. Write a Python function `def log_metrics(self, split, metrics)` to solve the following problem:
Log metrics in a specially formatted way Under distributed environment this is done only for a process with rank 0. Args: split (`str`): Mode/split name: one of `train`, `eval`, `test` metrics (`Dict[str, float]`): The metrics returned from train/evaluate/predictmetrics: metrics dict Notes on memory reports: In order to get memory usage report you need to install `psutil`. You can do that with `pip install psutil`. Now when this method is run, you will see a report that will include: : ``` init_mem_cpu_alloc_delta = 1301MB init_mem_cpu_peaked_delta = 154MB init_mem_gpu_alloc_delta = 230MB init_mem_gpu_peaked_delta = 0MB train_mem_cpu_alloc_delta = 1345MB train_mem_cpu_peaked_delta = 0MB train_mem_gpu_alloc_delta = 693MB train_mem_gpu_peaked_delta = 7MB ``` **Understanding the reports:** - the first segment, e.g., `train__`, tells you which stage the metrics are for. Reports starting with `init_` will be added to the first stage that gets run. So that if only evaluation is run, the memory usage for the `__init__` will be reported along with the `eval_` metrics. - the third segment, is either `cpu` or `gpu`, tells you whether it's the general RAM or the gpu0 memory metric. - `*_alloc_delta` - is the difference in the used/allocated memory counter between the end and the start of the stage - it can be negative if a function released more memory than it allocated. - `*_peaked_delta` - is any extra memory that was consumed and then freed - relative to the current allocated memory counter - it is never negative. When you look at the metrics of any stage you add up `alloc_delta` + `peaked_delta` and you know how much memory was needed to complete that stage. The reporting happens only for process of rank 0 and gpu 0 (if there is a gpu). Typically this is enough since the main process does the bulk of work, but it could be not quite so if model parallel is used and then other GPUs may use a different amount of gpu memory. This is also not the same under DataParallel where gpu0 may require much more memory than the rest since it stores the gradient and optimizer states for all participating GPUS. Perhaps in the future these reports will evolve to measure those too. The CPU RAM metric measures RSS (Resident Set Size) includes both the memory which is unique to the process and the memory shared with other processes. It is important to note that it does not include swapped out memory, so the reports could be imprecise. The CPU peak memory is measured using a sampling thread. Due to python's GIL it may miss some of the peak memory if that thread didn't get a chance to run when the highest memory was used. Therefore this report can be less than reality. Using `tracemalloc` would have reported the exact peak memory, but it doesn't report memory allocations outside of python. So if some C++ CUDA extension allocated its own memory it won't be reported. And therefore it was dropped in favor of the memory sampling approach, which reads the current process memory usage. The GPU allocated and peak memory reporting is done with `torch.cuda.memory_allocated()` and `torch.cuda.max_memory_allocated()`. This metric reports only "deltas" for pytorch-specific allocations, as `torch.cuda` memory management system doesn't track any memory allocated outside of pytorch. For example, the very first cuda call typically loads CUDA kernels, which may take from 0.5 to 2GB of GPU memory. Note that this tracker doesn't account for memory allocations outside of [`Trainer`]'s `__init__`, `train`, `evaluate` and `predict` calls. Because `evaluation` calls may happen during `train`, we can't handle nested invocations because `torch.cuda.max_memory_allocated` is a single counter, so if it gets reset by a nested eval call, `train`'s tracker will report incorrect info. If this [pytorch issue](https://github.com/pytorch/pytorch/issues/16266) gets resolved it will be possible to change this class to be re-entrant. Until then we will only track the outer level of `train`, `evaluate` and `predict` methods. Which means that if `eval` is called during `train`, it's the latter that will account for its memory usage and that of the former. This also means that if any other tool that is used along the [`Trainer`] calls `torch.cuda.reset_peak_memory_stats`, the gpu peak memory stats could be invalid. And the [`Trainer`] will disrupt the normal behavior of any such tools that rely on calling `torch.cuda.reset_peak_memory_stats` themselves. For best performance you may want to consider turning the memory profiling off for production runs.
Here is the function:
def log_metrics(self, split, metrics):
"""
Log metrics in a specially formatted way
Under distributed environment this is done only for a process with rank 0.
Args:
split (`str`):
Mode/split name: one of `train`, `eval`, `test`
metrics (`Dict[str, float]`):
The metrics returned from train/evaluate/predictmetrics: metrics dict
Notes on memory reports:
In order to get memory usage report you need to install `psutil`. You can do that with `pip install psutil`.
Now when this method is run, you will see a report that will include: :
```
init_mem_cpu_alloc_delta = 1301MB
init_mem_cpu_peaked_delta = 154MB
init_mem_gpu_alloc_delta = 230MB
init_mem_gpu_peaked_delta = 0MB
train_mem_cpu_alloc_delta = 1345MB
train_mem_cpu_peaked_delta = 0MB
train_mem_gpu_alloc_delta = 693MB
train_mem_gpu_peaked_delta = 7MB
```
**Understanding the reports:**
- the first segment, e.g., `train__`, tells you which stage the metrics are for. Reports starting with `init_`
will be added to the first stage that gets run. So that if only evaluation is run, the memory usage for the
`__init__` will be reported along with the `eval_` metrics.
- the third segment, is either `cpu` or `gpu`, tells you whether it's the general RAM or the gpu0 memory
metric.
- `*_alloc_delta` - is the difference in the used/allocated memory counter between the end and the start of the
stage - it can be negative if a function released more memory than it allocated.
- `*_peaked_delta` - is any extra memory that was consumed and then freed - relative to the current allocated
memory counter - it is never negative. When you look at the metrics of any stage you add up `alloc_delta` +
`peaked_delta` and you know how much memory was needed to complete that stage.
The reporting happens only for process of rank 0 and gpu 0 (if there is a gpu). Typically this is enough since the
main process does the bulk of work, but it could be not quite so if model parallel is used and then other GPUs may
use a different amount of gpu memory. This is also not the same under DataParallel where gpu0 may require much more
memory than the rest since it stores the gradient and optimizer states for all participating GPUS. Perhaps in the
future these reports will evolve to measure those too.
The CPU RAM metric measures RSS (Resident Set Size) includes both the memory which is unique to the process and the
memory shared with other processes. It is important to note that it does not include swapped out memory, so the
reports could be imprecise.
The CPU peak memory is measured using a sampling thread. Due to python's GIL it may miss some of the peak memory if
that thread didn't get a chance to run when the highest memory was used. Therefore this report can be less than
reality. Using `tracemalloc` would have reported the exact peak memory, but it doesn't report memory allocations
outside of python. So if some C++ CUDA extension allocated its own memory it won't be reported. And therefore it
was dropped in favor of the memory sampling approach, which reads the current process memory usage.
The GPU allocated and peak memory reporting is done with `torch.cuda.memory_allocated()` and
`torch.cuda.max_memory_allocated()`. This metric reports only "deltas" for pytorch-specific allocations, as
`torch.cuda` memory management system doesn't track any memory allocated outside of pytorch. For example, the very
first cuda call typically loads CUDA kernels, which may take from 0.5 to 2GB of GPU memory.
Note that this tracker doesn't account for memory allocations outside of [`Trainer`]'s `__init__`, `train`,
`evaluate` and `predict` calls.
Because `evaluation` calls may happen during `train`, we can't handle nested invocations because
`torch.cuda.max_memory_allocated` is a single counter, so if it gets reset by a nested eval call, `train`'s tracker
will report incorrect info. If this [pytorch issue](https://github.com/pytorch/pytorch/issues/16266) gets resolved
it will be possible to change this class to be re-entrant. Until then we will only track the outer level of
`train`, `evaluate` and `predict` methods. Which means that if `eval` is called during `train`, it's the latter
that will account for its memory usage and that of the former.
This also means that if any other tool that is used along the [`Trainer`] calls
`torch.cuda.reset_peak_memory_stats`, the gpu peak memory stats could be invalid. And the [`Trainer`] will disrupt
the normal behavior of any such tools that rely on calling `torch.cuda.reset_peak_memory_stats` themselves.
For best performance you may want to consider turning the memory profiling off for production runs.
"""
if not self.is_world_process_zero():
return
print(f"***** {split} metrics *****")
metrics_formatted = self.metrics_format(metrics)
k_width = max(len(str(x)) for x in metrics_formatted.keys())
v_width = max(len(str(x)) for x in metrics_formatted.values())
for key in sorted(metrics_formatted.keys()):
print(f" {key: <{k_width}} = {metrics_formatted[key]:>{v_width}}") | Log metrics in a specially formatted way Under distributed environment this is done only for a process with rank 0. Args: split (`str`): Mode/split name: one of `train`, `eval`, `test` metrics (`Dict[str, float]`): The metrics returned from train/evaluate/predictmetrics: metrics dict Notes on memory reports: In order to get memory usage report you need to install `psutil`. You can do that with `pip install psutil`. Now when this method is run, you will see a report that will include: : ``` init_mem_cpu_alloc_delta = 1301MB init_mem_cpu_peaked_delta = 154MB init_mem_gpu_alloc_delta = 230MB init_mem_gpu_peaked_delta = 0MB train_mem_cpu_alloc_delta = 1345MB train_mem_cpu_peaked_delta = 0MB train_mem_gpu_alloc_delta = 693MB train_mem_gpu_peaked_delta = 7MB ``` **Understanding the reports:** - the first segment, e.g., `train__`, tells you which stage the metrics are for. Reports starting with `init_` will be added to the first stage that gets run. So that if only evaluation is run, the memory usage for the `__init__` will be reported along with the `eval_` metrics. - the third segment, is either `cpu` or `gpu`, tells you whether it's the general RAM or the gpu0 memory metric. - `*_alloc_delta` - is the difference in the used/allocated memory counter between the end and the start of the stage - it can be negative if a function released more memory than it allocated. - `*_peaked_delta` - is any extra memory that was consumed and then freed - relative to the current allocated memory counter - it is never negative. When you look at the metrics of any stage you add up `alloc_delta` + `peaked_delta` and you know how much memory was needed to complete that stage. The reporting happens only for process of rank 0 and gpu 0 (if there is a gpu). Typically this is enough since the main process does the bulk of work, but it could be not quite so if model parallel is used and then other GPUs may use a different amount of gpu memory. This is also not the same under DataParallel where gpu0 may require much more memory than the rest since it stores the gradient and optimizer states for all participating GPUS. Perhaps in the future these reports will evolve to measure those too. The CPU RAM metric measures RSS (Resident Set Size) includes both the memory which is unique to the process and the memory shared with other processes. It is important to note that it does not include swapped out memory, so the reports could be imprecise. The CPU peak memory is measured using a sampling thread. Due to python's GIL it may miss some of the peak memory if that thread didn't get a chance to run when the highest memory was used. Therefore this report can be less than reality. Using `tracemalloc` would have reported the exact peak memory, but it doesn't report memory allocations outside of python. So if some C++ CUDA extension allocated its own memory it won't be reported. And therefore it was dropped in favor of the memory sampling approach, which reads the current process memory usage. The GPU allocated and peak memory reporting is done with `torch.cuda.memory_allocated()` and `torch.cuda.max_memory_allocated()`. This metric reports only "deltas" for pytorch-specific allocations, as `torch.cuda` memory management system doesn't track any memory allocated outside of pytorch. For example, the very first cuda call typically loads CUDA kernels, which may take from 0.5 to 2GB of GPU memory. Note that this tracker doesn't account for memory allocations outside of [`Trainer`]'s `__init__`, `train`, `evaluate` and `predict` calls. Because `evaluation` calls may happen during `train`, we can't handle nested invocations because `torch.cuda.max_memory_allocated` is a single counter, so if it gets reset by a nested eval call, `train`'s tracker will report incorrect info. If this [pytorch issue](https://github.com/pytorch/pytorch/issues/16266) gets resolved it will be possible to change this class to be re-entrant. Until then we will only track the outer level of `train`, `evaluate` and `predict` methods. Which means that if `eval` is called during `train`, it's the latter that will account for its memory usage and that of the former. This also means that if any other tool that is used along the [`Trainer`] calls `torch.cuda.reset_peak_memory_stats`, the gpu peak memory stats could be invalid. And the [`Trainer`] will disrupt the normal behavior of any such tools that rely on calling `torch.cuda.reset_peak_memory_stats` themselves. For best performance you may want to consider turning the memory profiling off for production runs. |
10,622 | import datetime
import json
import math
import os
import sys
import warnings
from collections.abc import Mapping
from contextlib import contextmanager
from dataclasses import dataclass
from logging import StreamHandler
from typing import Any, Dict, Iterator, List, Optional, Union
import numpy as np
import torch
import torch.distributed as dist
from torch import nn
from torch.utils.data import Dataset, IterableDataset, RandomSampler, Sampler
from torch.utils.data.distributed import DistributedSampler
from .tokenization_utils_base import BatchEncoding
from .utils import is_sagemaker_mp_enabled, is_torch_tpu_available, is_training_run_on_sagemaker, logging
The provided code snippet includes necessary dependencies for implementing the `save_metrics` function. Write a Python function `def save_metrics(self, split, metrics, combined=True)` to solve the following problem:
Save metrics into a json file for that split, e.g. `train_results.json`. Under distributed environment this is done only for a process with rank 0. Args: split (`str`): Mode/split name: one of `train`, `eval`, `test`, `all` metrics (`Dict[str, float]`): The metrics returned from train/evaluate/predict combined (`bool`, *optional*, defaults to `True`): Creates combined metrics by updating `all_results.json` with metrics of this call To understand the metrics please read the docstring of [`~Trainer.log_metrics`]. The only difference is that raw unformatted numbers are saved in the current method.
Here is the function:
def save_metrics(self, split, metrics, combined=True):
"""
Save metrics into a json file for that split, e.g. `train_results.json`.
Under distributed environment this is done only for a process with rank 0.
Args:
split (`str`):
Mode/split name: one of `train`, `eval`, `test`, `all`
metrics (`Dict[str, float]`):
The metrics returned from train/evaluate/predict
combined (`bool`, *optional*, defaults to `True`):
Creates combined metrics by updating `all_results.json` with metrics of this call
To understand the metrics please read the docstring of [`~Trainer.log_metrics`]. The only difference is that raw
unformatted numbers are saved in the current method.
"""
if not self.is_world_process_zero():
return
path = os.path.join(self.args.output_dir, f"{split}_results.json")
with open(path, "w") as f:
json.dump(metrics, f, indent=4, sort_keys=True)
if combined:
path = os.path.join(self.args.output_dir, "all_results.json")
if os.path.exists(path):
with open(path, "r") as f:
all_metrics = json.load(f)
else:
all_metrics = {}
all_metrics.update(metrics)
with open(path, "w") as f:
json.dump(all_metrics, f, indent=4, sort_keys=True) | Save metrics into a json file for that split, e.g. `train_results.json`. Under distributed environment this is done only for a process with rank 0. Args: split (`str`): Mode/split name: one of `train`, `eval`, `test`, `all` metrics (`Dict[str, float]`): The metrics returned from train/evaluate/predict combined (`bool`, *optional*, defaults to `True`): Creates combined metrics by updating `all_results.json` with metrics of this call To understand the metrics please read the docstring of [`~Trainer.log_metrics`]. The only difference is that raw unformatted numbers are saved in the current method. |
10,623 | import datetime
import json
import math
import os
import sys
import warnings
from collections.abc import Mapping
from contextlib import contextmanager
from dataclasses import dataclass
from logging import StreamHandler
from typing import Any, Dict, Iterator, List, Optional, Union
import numpy as np
import torch
import torch.distributed as dist
from torch import nn
from torch.utils.data import Dataset, IterableDataset, RandomSampler, Sampler
from torch.utils.data.distributed import DistributedSampler
from .tokenization_utils_base import BatchEncoding
from .utils import is_sagemaker_mp_enabled, is_torch_tpu_available, is_training_run_on_sagemaker, logging
The provided code snippet includes necessary dependencies for implementing the `save_state` function. Write a Python function `def save_state(self)` to solve the following problem:
Saves the Trainer state, since Trainer.save_model saves only the tokenizer with the model Under distributed environment this is done only for a process with rank 0.
Here is the function:
def save_state(self):
"""
Saves the Trainer state, since Trainer.save_model saves only the tokenizer with the model
Under distributed environment this is done only for a process with rank 0.
"""
if not self.is_world_process_zero():
return
path = os.path.join(self.args.output_dir, "trainer_state.json")
self.state.save_to_json(path) | Saves the Trainer state, since Trainer.save_model saves only the tokenizer with the model Under distributed environment this is done only for a process with rank 0. |
10,624 | import datetime
import json
import math
import os
import sys
import warnings
from collections.abc import Mapping
from contextlib import contextmanager
from dataclasses import dataclass
from logging import StreamHandler
from typing import Any, Dict, Iterator, List, Optional, Union
import numpy as np
import torch
import torch.distributed as dist
from torch import nn
from torch.utils.data import Dataset, IterableDataset, RandomSampler, Sampler
from torch.utils.data.distributed import DistributedSampler
from .tokenization_utils_base import BatchEncoding
from .utils import is_sagemaker_mp_enabled, is_torch_tpu_available, is_training_run_on_sagemaker, logging
The provided code snippet includes necessary dependencies for implementing the `get_parameter_names` function. Write a Python function `def get_parameter_names(model, forbidden_layer_types)` to solve the following problem:
Returns the names of the model parameters that are not inside a forbidden layer.
Here is the function:
def get_parameter_names(model, forbidden_layer_types):
"""
Returns the names of the model parameters that are not inside a forbidden layer.
"""
result = []
for name, child in model.named_children():
result += [
f"{name}.{n}"
for n in get_parameter_names(child, forbidden_layer_types)
if not isinstance(child, tuple(forbidden_layer_types))
]
# Add model specific parameters (defined with nn.Parameter) since they are not in any child.
result += list(model._parameters.keys())
return result | Returns the names of the model parameters that are not inside a forbidden layer. |
10,625 | import datetime
import json
import math
import os
import sys
import warnings
from collections.abc import Mapping
from contextlib import contextmanager
from dataclasses import dataclass
from logging import StreamHandler
from typing import Any, Dict, Iterator, List, Optional, Union
import numpy as np
import torch
import torch.distributed as dist
from torch import nn
from torch.utils.data import Dataset, IterableDataset, RandomSampler, Sampler
from torch.utils.data.distributed import DistributedSampler
from .tokenization_utils_base import BatchEncoding
from .utils import is_sagemaker_mp_enabled, is_torch_tpu_available, is_training_run_on_sagemaker, logging
The provided code snippet includes necessary dependencies for implementing the `get_module_class_from_name` function. Write a Python function `def get_module_class_from_name(module, name)` to solve the following problem:
Gets a class from a module by its name. Args: module (`torch.nn.Module`): The module to get the class from. name (`str`): The name of the class.
Here is the function:
def get_module_class_from_name(module, name):
"""
Gets a class from a module by its name.
Args:
module (`torch.nn.Module`): The module to get the class from.
name (`str`): The name of the class.
"""
modules_children = list(module.children())
if module.__class__.__name__ == name:
return module.__class__
elif len(modules_children) == 0:
return
else:
for child_module in modules_children:
module_class = get_module_class_from_name(child_module, name)
if module_class is not None:
return module_class | Gets a class from a module by its name. Args: module (`torch.nn.Module`): The module to get the class from. name (`str`): The name of the class. |
10,626 | import datetime
import json
import math
import os
import sys
import warnings
from collections.abc import Mapping
from contextlib import contextmanager
from dataclasses import dataclass
from logging import StreamHandler
from typing import Any, Dict, Iterator, List, Optional, Union
import numpy as np
import torch
import torch.distributed as dist
from torch import nn
from torch.utils.data import Dataset, IterableDataset, RandomSampler, Sampler
from torch.utils.data.distributed import DistributedSampler
from .tokenization_utils_base import BatchEncoding
from .utils import is_sagemaker_mp_enabled, is_torch_tpu_available, is_training_run_on_sagemaker, logging
def smp_forward_backward(model, inputs, gradient_accumulation_steps=1):
outputs = model(**inputs)
loss = outputs["loss"] if isinstance(outputs, dict) else outputs[0]
loss /= gradient_accumulation_steps
model.backward(loss)
return loss | null |
10,627 | import datetime
import json
import math
import os
import sys
import warnings
from collections.abc import Mapping
from contextlib import contextmanager
from dataclasses import dataclass
from logging import StreamHandler
from typing import Any, Dict, Iterator, List, Optional, Union
import numpy as np
import torch
import torch.distributed as dist
from torch import nn
from torch.utils.data import Dataset, IterableDataset, RandomSampler, Sampler
from torch.utils.data.distributed import DistributedSampler
from .tokenization_utils_base import BatchEncoding
from .utils import is_sagemaker_mp_enabled, is_torch_tpu_available, is_training_run_on_sagemaker, logging
def smp_forward_only(model, inputs):
return model(**inputs) | null |
10,628 | import datetime
import json
import math
import os
import sys
import warnings
from collections.abc import Mapping
from contextlib import contextmanager
from dataclasses import dataclass
from logging import StreamHandler
from typing import Any, Dict, Iterator, List, Optional, Union
import numpy as np
import torch
import torch.distributed as dist
from torch import nn
from torch.utils.data import Dataset, IterableDataset, RandomSampler, Sampler
from torch.utils.data.distributed import DistributedSampler
from .tokenization_utils_base import BatchEncoding
from .utils import is_sagemaker_mp_enabled, is_torch_tpu_available, is_training_run_on_sagemaker, logging
try:
from torch.optim.lr_scheduler import SAVE_STATE_WARNING
except ImportError:
SAVE_STATE_WARNING = ""
def atleast_1d(tensor_or_array: Union[torch.Tensor, np.ndarray]):
if isinstance(tensor_or_array, torch.Tensor):
if hasattr(torch, "atleast_1d"):
tensor_or_array = torch.atleast_1d(tensor_or_array)
elif tensor_or_array.ndim < 1:
tensor_or_array = tensor_or_array[None]
else:
tensor_or_array = np.atleast_1d(tensor_or_array)
return tensor_or_array
def smp_gather(tensor):
if isinstance(tensor, (list, tuple)):
return type(tensor)(smp_gather(t) for t in tensor)
elif isinstance(tensor, dict):
return type(tensor)({k: smp_gather(v) for k, v in tensor.items()})
elif not isinstance(tensor, torch.Tensor):
raise TypeError(
f"Can't gather the values of type {type(tensor)}, only of nested list/tuple/dicts of tensors."
)
all_tensors = smp.allgather(tensor, smp.CommGroup.DP_GROUP)
all_tensors = [atleast_1d(t) for t in all_tensors]
return torch.cat([t.cpu() for t in all_tensors], dim=0) | null |
10,629 | import datetime
import json
import math
import os
import sys
import warnings
from collections.abc import Mapping
from contextlib import contextmanager
from dataclasses import dataclass
from logging import StreamHandler
from typing import Any, Dict, Iterator, List, Optional, Union
import numpy as np
import torch
import torch.distributed as dist
from torch import nn
from torch.utils.data import Dataset, IterableDataset, RandomSampler, Sampler
from torch.utils.data.distributed import DistributedSampler
from .tokenization_utils_base import BatchEncoding
from .utils import is_sagemaker_mp_enabled, is_torch_tpu_available, is_training_run_on_sagemaker, logging
def smp_nested_concat(tensor):
if isinstance(tensor, (list, tuple)):
return type(tensor)(smp_nested_concat(t) for t in tensor)
elif isinstance(tensor, dict):
return type(tensor)({k: smp_nested_concat(v) for k, v in tensor.items()})
# It doesn't seem possible to check here if `tensor` is a StepOutput because StepOutput lives in `smp.step`
# which is also the name of the decorator so Python is confused.
return tensor.concat().detach().cpu() | null |
10,630 | import inspect
import math
from typing import Callable, Iterable, List, Optional, Tuple
import numpy as np
import torch
from .utils import add_start_docstrings
from .utils.logging import get_logger
def _get_ngrams(ngram_size: int, prev_input_ids: torch.Tensor, num_hypos: int):
generated_ngrams = [{} for _ in range(num_hypos)]
for idx in range(num_hypos):
gen_tokens = prev_input_ids[idx].tolist()
generated_ngram = generated_ngrams[idx]
for ngram in zip(*[gen_tokens[i:] for i in range(ngram_size)]):
prev_ngram_tuple = tuple(ngram[:-1])
generated_ngram[prev_ngram_tuple] = generated_ngram.get(prev_ngram_tuple, []) + [ngram[-1]]
return generated_ngrams
def _get_generated_ngrams(banned_ngrams, prev_input_ids, ngram_size, cur_len):
# Before decoding the next token, prevent decoding of ngrams that have already appeared
start_idx = cur_len + 1 - ngram_size
ngram_idx = tuple(prev_input_ids[start_idx:cur_len].tolist())
return banned_ngrams.get(ngram_idx, [])
The provided code snippet includes necessary dependencies for implementing the `_calc_banned_ngram_tokens` function. Write a Python function `def _calc_banned_ngram_tokens( ngram_size: int, prev_input_ids: torch.Tensor, num_hypos: int, cur_len: int ) -> List[Iterable[int]]` to solve the following problem:
Copied from fairseq for no_repeat_ngram in beam_search
Here is the function:
def _calc_banned_ngram_tokens(
ngram_size: int, prev_input_ids: torch.Tensor, num_hypos: int, cur_len: int
) -> List[Iterable[int]]:
"""Copied from fairseq for no_repeat_ngram in beam_search"""
if cur_len + 1 < ngram_size:
# return no banned tokens if we haven't generated no_repeat_ngram_size tokens yet
return [[] for _ in range(num_hypos)]
generated_ngrams = _get_ngrams(ngram_size, prev_input_ids, num_hypos)
banned_tokens = [
_get_generated_ngrams(generated_ngrams[hypo_idx], prev_input_ids[hypo_idx], ngram_size, cur_len)
for hypo_idx in range(num_hypos)
]
return banned_tokens | Copied from fairseq for no_repeat_ngram in beam_search |
10,631 | import collections
import json
import math
import re
import string
from ...models.bert import BasicTokenizer
from ...utils import logging
def find_best_thresh_v2(preds, scores, na_probs, qid_to_has_ans):
num_no_ans = sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k])
cur_score = num_no_ans
best_score = cur_score
best_thresh = 0.0
qid_list = sorted(na_probs, key=lambda k: na_probs[k])
for i, qid in enumerate(qid_list):
if qid not in scores:
continue
if qid_to_has_ans[qid]:
diff = scores[qid]
else:
if preds[qid]:
diff = -1
else:
diff = 0
cur_score += diff
if cur_score > best_score:
best_score = cur_score
best_thresh = na_probs[qid]
has_ans_score, has_ans_cnt = 0, 0
for qid in qid_list:
if not qid_to_has_ans[qid]:
continue
has_ans_cnt += 1
if qid not in scores:
continue
has_ans_score += scores[qid]
return 100.0 * best_score / len(scores), best_thresh, 1.0 * has_ans_score / has_ans_cnt
def find_all_best_thresh_v2(main_eval, preds, exact_raw, f1_raw, na_probs, qid_to_has_ans):
best_exact, exact_thresh, has_ans_exact = find_best_thresh_v2(preds, exact_raw, na_probs, qid_to_has_ans)
best_f1, f1_thresh, has_ans_f1 = find_best_thresh_v2(preds, f1_raw, na_probs, qid_to_has_ans)
main_eval["best_exact"] = best_exact
main_eval["best_exact_thresh"] = exact_thresh
main_eval["best_f1"] = best_f1
main_eval["best_f1_thresh"] = f1_thresh
main_eval["has_ans_exact"] = has_ans_exact
main_eval["has_ans_f1"] = has_ans_f1 | null |
10,632 | import collections
import json
import math
import re
import string
from ...models.bert import BasicTokenizer
from ...utils import logging
def get_raw_scores(examples, preds):
"""
Computes the exact and f1 scores from the examples and the model predictions
"""
exact_scores = {}
f1_scores = {}
for example in examples:
qas_id = example.qas_id
gold_answers = [answer["text"] for answer in example.answers if normalize_answer(answer["text"])]
if not gold_answers:
# For unanswerable questions, only correct answer is empty string
gold_answers = [""]
if qas_id not in preds:
print(f"Missing prediction for {qas_id}")
continue
prediction = preds[qas_id]
exact_scores[qas_id] = max(compute_exact(a, prediction) for a in gold_answers)
f1_scores[qas_id] = max(compute_f1(a, prediction) for a in gold_answers)
return exact_scores, f1_scores
def apply_no_ans_threshold(scores, na_probs, qid_to_has_ans, na_prob_thresh):
new_scores = {}
for qid, s in scores.items():
pred_na = na_probs[qid] > na_prob_thresh
if pred_na:
new_scores[qid] = float(not qid_to_has_ans[qid])
else:
new_scores[qid] = s
return new_scores
def make_eval_dict(exact_scores, f1_scores, qid_list=None):
if not qid_list:
total = len(exact_scores)
return collections.OrderedDict(
[
("exact", 100.0 * sum(exact_scores.values()) / total),
("f1", 100.0 * sum(f1_scores.values()) / total),
("total", total),
]
)
else:
total = len(qid_list)
return collections.OrderedDict(
[
("exact", 100.0 * sum(exact_scores[k] for k in qid_list) / total),
("f1", 100.0 * sum(f1_scores[k] for k in qid_list) / total),
("total", total),
]
)
def merge_eval(main_eval, new_eval, prefix):
for k in new_eval:
main_eval[f"{prefix}_{k}"] = new_eval[k]
def find_all_best_thresh(main_eval, preds, exact_raw, f1_raw, na_probs, qid_to_has_ans):
best_exact, exact_thresh = find_best_thresh(preds, exact_raw, na_probs, qid_to_has_ans)
best_f1, f1_thresh = find_best_thresh(preds, f1_raw, na_probs, qid_to_has_ans)
main_eval["best_exact"] = best_exact
main_eval["best_exact_thresh"] = exact_thresh
main_eval["best_f1"] = best_f1
main_eval["best_f1_thresh"] = f1_thresh
def squad_evaluate(examples, preds, no_answer_probs=None, no_answer_probability_threshold=1.0):
qas_id_to_has_answer = {example.qas_id: bool(example.answers) for example in examples}
has_answer_qids = [qas_id for qas_id, has_answer in qas_id_to_has_answer.items() if has_answer]
no_answer_qids = [qas_id for qas_id, has_answer in qas_id_to_has_answer.items() if not has_answer]
if no_answer_probs is None:
no_answer_probs = {k: 0.0 for k in preds}
exact, f1 = get_raw_scores(examples, preds)
exact_threshold = apply_no_ans_threshold(
exact, no_answer_probs, qas_id_to_has_answer, no_answer_probability_threshold
)
f1_threshold = apply_no_ans_threshold(f1, no_answer_probs, qas_id_to_has_answer, no_answer_probability_threshold)
evaluation = make_eval_dict(exact_threshold, f1_threshold)
if has_answer_qids:
has_ans_eval = make_eval_dict(exact_threshold, f1_threshold, qid_list=has_answer_qids)
merge_eval(evaluation, has_ans_eval, "HasAns")
if no_answer_qids:
no_ans_eval = make_eval_dict(exact_threshold, f1_threshold, qid_list=no_answer_qids)
merge_eval(evaluation, no_ans_eval, "NoAns")
if no_answer_probs:
find_all_best_thresh(evaluation, preds, exact, f1, no_answer_probs, qas_id_to_has_answer)
return evaluation | null |
10,633 | import collections
import json
import math
import re
import string
from ...models.bert import BasicTokenizer
from ...utils import logging
logger = logging.get_logger(__name__)
def get_final_text(pred_text, orig_text, do_lower_case, verbose_logging=False):
"""Project the tokenized prediction back to the original text."""
# When we created the data, we kept track of the alignment between original
# (whitespace tokenized) tokens and our WordPiece tokenized tokens. So
# now `orig_text` contains the span of our original text corresponding to the
# span that we predicted.
#
# However, `orig_text` may contain extra characters that we don't want in
# our prediction.
#
# For example, let's say:
# pred_text = steve smith
# orig_text = Steve Smith's
#
# We don't want to return `orig_text` because it contains the extra "'s".
#
# We don't want to return `pred_text` because it's already been normalized
# (the SQuAD eval script also does punctuation stripping/lower casing but
# our tokenizer does additional normalization like stripping accent
# characters).
#
# What we really want to return is "Steve Smith".
#
# Therefore, we have to apply a semi-complicated alignment heuristic between
# `pred_text` and `orig_text` to get a character-to-character alignment. This
# can fail in certain cases in which case we just return `orig_text`.
def _strip_spaces(text):
ns_chars = []
ns_to_s_map = collections.OrderedDict()
for i, c in enumerate(text):
if c == " ":
continue
ns_to_s_map[len(ns_chars)] = i
ns_chars.append(c)
ns_text = "".join(ns_chars)
return (ns_text, ns_to_s_map)
# We first tokenize `orig_text`, strip whitespace from the result
# and `pred_text`, and check if they are the same length. If they are
# NOT the same length, the heuristic has failed. If they are the same
# length, we assume the characters are one-to-one aligned.
tokenizer = BasicTokenizer(do_lower_case=do_lower_case)
tok_text = " ".join(tokenizer.tokenize(orig_text))
start_position = tok_text.find(pred_text)
if start_position == -1:
if verbose_logging:
logger.info(f"Unable to find text: '{pred_text}' in '{orig_text}'")
return orig_text
end_position = start_position + len(pred_text) - 1
(orig_ns_text, orig_ns_to_s_map) = _strip_spaces(orig_text)
(tok_ns_text, tok_ns_to_s_map) = _strip_spaces(tok_text)
if len(orig_ns_text) != len(tok_ns_text):
if verbose_logging:
logger.info(f"Length not equal after stripping spaces: '{orig_ns_text}' vs '{tok_ns_text}'")
return orig_text
# We then project the characters in `pred_text` back to `orig_text` using
# the character-to-character alignment.
tok_s_to_ns_map = {}
for i, tok_index in tok_ns_to_s_map.items():
tok_s_to_ns_map[tok_index] = i
orig_start_position = None
if start_position in tok_s_to_ns_map:
ns_start_position = tok_s_to_ns_map[start_position]
if ns_start_position in orig_ns_to_s_map:
orig_start_position = orig_ns_to_s_map[ns_start_position]
if orig_start_position is None:
if verbose_logging:
logger.info("Couldn't map start position")
return orig_text
orig_end_position = None
if end_position in tok_s_to_ns_map:
ns_end_position = tok_s_to_ns_map[end_position]
if ns_end_position in orig_ns_to_s_map:
orig_end_position = orig_ns_to_s_map[ns_end_position]
if orig_end_position is None:
if verbose_logging:
logger.info("Couldn't map end position")
return orig_text
output_text = orig_text[orig_start_position : (orig_end_position + 1)]
return output_text
def _get_best_indexes(logits, n_best_size):
"""Get the n-best logits from a list."""
index_and_score = sorted(enumerate(logits), key=lambda x: x[1], reverse=True)
best_indexes = []
for i in range(len(index_and_score)):
if i >= n_best_size:
break
best_indexes.append(index_and_score[i][0])
return best_indexes
def _compute_softmax(scores):
"""Compute softmax probability over raw logits."""
if not scores:
return []
max_score = None
for score in scores:
if max_score is None or score > max_score:
max_score = score
exp_scores = []
total_sum = 0.0
for score in scores:
x = math.exp(score - max_score)
exp_scores.append(x)
total_sum += x
probs = []
for score in exp_scores:
probs.append(score / total_sum)
return probs
The provided code snippet includes necessary dependencies for implementing the `compute_predictions_logits` function. Write a Python function `def compute_predictions_logits( all_examples, all_features, all_results, n_best_size, max_answer_length, do_lower_case, output_prediction_file, output_nbest_file, output_null_log_odds_file, verbose_logging, version_2_with_negative, null_score_diff_threshold, tokenizer, )` to solve the following problem:
Write final predictions to the json file and log-odds of null if needed.
Here is the function:
def compute_predictions_logits(
all_examples,
all_features,
all_results,
n_best_size,
max_answer_length,
do_lower_case,
output_prediction_file,
output_nbest_file,
output_null_log_odds_file,
verbose_logging,
version_2_with_negative,
null_score_diff_threshold,
tokenizer,
):
"""Write final predictions to the json file and log-odds of null if needed."""
if output_prediction_file:
logger.info(f"Writing predictions to: {output_prediction_file}")
if output_nbest_file:
logger.info(f"Writing nbest to: {output_nbest_file}")
if output_null_log_odds_file and version_2_with_negative:
logger.info(f"Writing null_log_odds to: {output_null_log_odds_file}")
example_index_to_features = collections.defaultdict(list)
for feature in all_features:
example_index_to_features[feature.example_index].append(feature)
unique_id_to_result = {}
for result in all_results:
unique_id_to_result[result.unique_id] = result
_PrelimPrediction = collections.namedtuple( # pylint: disable=invalid-name
"PrelimPrediction", ["feature_index", "start_index", "end_index", "start_logit", "end_logit"]
)
all_predictions = collections.OrderedDict()
all_nbest_json = collections.OrderedDict()
scores_diff_json = collections.OrderedDict()
for example_index, example in enumerate(all_examples):
features = example_index_to_features[example_index]
prelim_predictions = []
# keep track of the minimum score of null start+end of position 0
score_null = 1000000 # large and positive
min_null_feature_index = 0 # the paragraph slice with min null score
null_start_logit = 0 # the start logit at the slice with min null score
null_end_logit = 0 # the end logit at the slice with min null score
for feature_index, feature in enumerate(features):
result = unique_id_to_result[feature.unique_id]
start_indexes = _get_best_indexes(result.start_logits, n_best_size)
end_indexes = _get_best_indexes(result.end_logits, n_best_size)
# if we could have irrelevant answers, get the min score of irrelevant
if version_2_with_negative:
feature_null_score = result.start_logits[0] + result.end_logits[0]
if feature_null_score < score_null:
score_null = feature_null_score
min_null_feature_index = feature_index
null_start_logit = result.start_logits[0]
null_end_logit = result.end_logits[0]
for start_index in start_indexes:
for end_index in end_indexes:
# We could hypothetically create invalid predictions, e.g., predict
# that the start of the span is in the question. We throw out all
# invalid predictions.
if start_index >= len(feature.tokens):
continue
if end_index >= len(feature.tokens):
continue
if start_index not in feature.token_to_orig_map:
continue
if end_index not in feature.token_to_orig_map:
continue
if not feature.token_is_max_context.get(start_index, False):
continue
if end_index < start_index:
continue
length = end_index - start_index + 1
if length > max_answer_length:
continue
prelim_predictions.append(
_PrelimPrediction(
feature_index=feature_index,
start_index=start_index,
end_index=end_index,
start_logit=result.start_logits[start_index],
end_logit=result.end_logits[end_index],
)
)
if version_2_with_negative:
prelim_predictions.append(
_PrelimPrediction(
feature_index=min_null_feature_index,
start_index=0,
end_index=0,
start_logit=null_start_logit,
end_logit=null_end_logit,
)
)
prelim_predictions = sorted(prelim_predictions, key=lambda x: (x.start_logit + x.end_logit), reverse=True)
_NbestPrediction = collections.namedtuple( # pylint: disable=invalid-name
"NbestPrediction", ["text", "start_logit", "end_logit"]
)
seen_predictions = {}
nbest = []
for pred in prelim_predictions:
if len(nbest) >= n_best_size:
break
feature = features[pred.feature_index]
if pred.start_index > 0: # this is a non-null prediction
tok_tokens = feature.tokens[pred.start_index : (pred.end_index + 1)]
orig_doc_start = feature.token_to_orig_map[pred.start_index]
orig_doc_end = feature.token_to_orig_map[pred.end_index]
orig_tokens = example.doc_tokens[orig_doc_start : (orig_doc_end + 1)]
tok_text = tokenizer.convert_tokens_to_string(tok_tokens)
# tok_text = " ".join(tok_tokens)
#
# # De-tokenize WordPieces that have been split off.
# tok_text = tok_text.replace(" ##", "")
# tok_text = tok_text.replace("##", "")
# Clean whitespace
tok_text = tok_text.strip()
tok_text = " ".join(tok_text.split())
orig_text = " ".join(orig_tokens)
final_text = get_final_text(tok_text, orig_text, do_lower_case, verbose_logging)
if final_text in seen_predictions:
continue
seen_predictions[final_text] = True
else:
final_text = ""
seen_predictions[final_text] = True
nbest.append(_NbestPrediction(text=final_text, start_logit=pred.start_logit, end_logit=pred.end_logit))
# if we didn't include the empty option in the n-best, include it
if version_2_with_negative:
if "" not in seen_predictions:
nbest.append(_NbestPrediction(text="", start_logit=null_start_logit, end_logit=null_end_logit))
# In very rare edge cases we could only have single null prediction.
# So we just create a nonce prediction in this case to avoid failure.
if len(nbest) == 1:
nbest.insert(0, _NbestPrediction(text="empty", start_logit=0.0, end_logit=0.0))
# In very rare edge cases we could have no valid predictions. So we
# just create a nonce prediction in this case to avoid failure.
if not nbest:
nbest.append(_NbestPrediction(text="empty", start_logit=0.0, end_logit=0.0))
assert len(nbest) >= 1, "No valid predictions"
total_scores = []
best_non_null_entry = None
for entry in nbest:
total_scores.append(entry.start_logit + entry.end_logit)
if not best_non_null_entry:
if entry.text:
best_non_null_entry = entry
probs = _compute_softmax(total_scores)
nbest_json = []
for i, entry in enumerate(nbest):
output = collections.OrderedDict()
output["text"] = entry.text
output["probability"] = probs[i]
output["start_logit"] = entry.start_logit
output["end_logit"] = entry.end_logit
nbest_json.append(output)
assert len(nbest_json) >= 1, "No valid predictions"
if not version_2_with_negative:
all_predictions[example.qas_id] = nbest_json[0]["text"]
else:
# predict "" iff the null score - the score of best non-null > threshold
score_diff = score_null - best_non_null_entry.start_logit - (best_non_null_entry.end_logit)
scores_diff_json[example.qas_id] = score_diff
if score_diff > null_score_diff_threshold:
all_predictions[example.qas_id] = ""
else:
all_predictions[example.qas_id] = best_non_null_entry.text
all_nbest_json[example.qas_id] = nbest_json
if output_prediction_file:
with open(output_prediction_file, "w") as writer:
writer.write(json.dumps(all_predictions, indent=4) + "\n")
if output_nbest_file:
with open(output_nbest_file, "w") as writer:
writer.write(json.dumps(all_nbest_json, indent=4) + "\n")
if output_null_log_odds_file and version_2_with_negative:
with open(output_null_log_odds_file, "w") as writer:
writer.write(json.dumps(scores_diff_json, indent=4) + "\n")
return all_predictions | Write final predictions to the json file and log-odds of null if needed. |
10,634 | import collections
import json
import math
import re
import string
from ...models.bert import BasicTokenizer
from ...utils import logging
logger = logging.get_logger(__name__)
def get_final_text(pred_text, orig_text, do_lower_case, verbose_logging=False):
"""Project the tokenized prediction back to the original text."""
# When we created the data, we kept track of the alignment between original
# (whitespace tokenized) tokens and our WordPiece tokenized tokens. So
# now `orig_text` contains the span of our original text corresponding to the
# span that we predicted.
#
# However, `orig_text` may contain extra characters that we don't want in
# our prediction.
#
# For example, let's say:
# pred_text = steve smith
# orig_text = Steve Smith's
#
# We don't want to return `orig_text` because it contains the extra "'s".
#
# We don't want to return `pred_text` because it's already been normalized
# (the SQuAD eval script also does punctuation stripping/lower casing but
# our tokenizer does additional normalization like stripping accent
# characters).
#
# What we really want to return is "Steve Smith".
#
# Therefore, we have to apply a semi-complicated alignment heuristic between
# `pred_text` and `orig_text` to get a character-to-character alignment. This
# can fail in certain cases in which case we just return `orig_text`.
def _strip_spaces(text):
ns_chars = []
ns_to_s_map = collections.OrderedDict()
for i, c in enumerate(text):
if c == " ":
continue
ns_to_s_map[len(ns_chars)] = i
ns_chars.append(c)
ns_text = "".join(ns_chars)
return (ns_text, ns_to_s_map)
# We first tokenize `orig_text`, strip whitespace from the result
# and `pred_text`, and check if they are the same length. If they are
# NOT the same length, the heuristic has failed. If they are the same
# length, we assume the characters are one-to-one aligned.
tokenizer = BasicTokenizer(do_lower_case=do_lower_case)
tok_text = " ".join(tokenizer.tokenize(orig_text))
start_position = tok_text.find(pred_text)
if start_position == -1:
if verbose_logging:
logger.info(f"Unable to find text: '{pred_text}' in '{orig_text}'")
return orig_text
end_position = start_position + len(pred_text) - 1
(orig_ns_text, orig_ns_to_s_map) = _strip_spaces(orig_text)
(tok_ns_text, tok_ns_to_s_map) = _strip_spaces(tok_text)
if len(orig_ns_text) != len(tok_ns_text):
if verbose_logging:
logger.info(f"Length not equal after stripping spaces: '{orig_ns_text}' vs '{tok_ns_text}'")
return orig_text
# We then project the characters in `pred_text` back to `orig_text` using
# the character-to-character alignment.
tok_s_to_ns_map = {}
for i, tok_index in tok_ns_to_s_map.items():
tok_s_to_ns_map[tok_index] = i
orig_start_position = None
if start_position in tok_s_to_ns_map:
ns_start_position = tok_s_to_ns_map[start_position]
if ns_start_position in orig_ns_to_s_map:
orig_start_position = orig_ns_to_s_map[ns_start_position]
if orig_start_position is None:
if verbose_logging:
logger.info("Couldn't map start position")
return orig_text
orig_end_position = None
if end_position in tok_s_to_ns_map:
ns_end_position = tok_s_to_ns_map[end_position]
if ns_end_position in orig_ns_to_s_map:
orig_end_position = orig_ns_to_s_map[ns_end_position]
if orig_end_position is None:
if verbose_logging:
logger.info("Couldn't map end position")
return orig_text
output_text = orig_text[orig_start_position : (orig_end_position + 1)]
return output_text
def _compute_softmax(scores):
"""Compute softmax probability over raw logits."""
if not scores:
return []
max_score = None
for score in scores:
if max_score is None or score > max_score:
max_score = score
exp_scores = []
total_sum = 0.0
for score in scores:
x = math.exp(score - max_score)
exp_scores.append(x)
total_sum += x
probs = []
for score in exp_scores:
probs.append(score / total_sum)
return probs
The provided code snippet includes necessary dependencies for implementing the `compute_predictions_log_probs` function. Write a Python function `def compute_predictions_log_probs( all_examples, all_features, all_results, n_best_size, max_answer_length, output_prediction_file, output_nbest_file, output_null_log_odds_file, start_n_top, end_n_top, version_2_with_negative, tokenizer, verbose_logging, )` to solve the following problem:
XLNet write prediction logic (more complex than Bert's). Write final predictions to the json file and log-odds of null if needed. Requires utils_squad_evaluate.py
Here is the function:
def compute_predictions_log_probs(
all_examples,
all_features,
all_results,
n_best_size,
max_answer_length,
output_prediction_file,
output_nbest_file,
output_null_log_odds_file,
start_n_top,
end_n_top,
version_2_with_negative,
tokenizer,
verbose_logging,
):
"""
XLNet write prediction logic (more complex than Bert's). Write final predictions to the json file and log-odds of
null if needed.
Requires utils_squad_evaluate.py
"""
_PrelimPrediction = collections.namedtuple( # pylint: disable=invalid-name
"PrelimPrediction", ["feature_index", "start_index", "end_index", "start_log_prob", "end_log_prob"]
)
_NbestPrediction = collections.namedtuple( # pylint: disable=invalid-name
"NbestPrediction", ["text", "start_log_prob", "end_log_prob"]
)
logger.info(f"Writing predictions to: {output_prediction_file}")
example_index_to_features = collections.defaultdict(list)
for feature in all_features:
example_index_to_features[feature.example_index].append(feature)
unique_id_to_result = {}
for result in all_results:
unique_id_to_result[result.unique_id] = result
all_predictions = collections.OrderedDict()
all_nbest_json = collections.OrderedDict()
scores_diff_json = collections.OrderedDict()
for example_index, example in enumerate(all_examples):
features = example_index_to_features[example_index]
prelim_predictions = []
# keep track of the minimum score of null start+end of position 0
score_null = 1000000 # large and positive
for feature_index, feature in enumerate(features):
result = unique_id_to_result[feature.unique_id]
cur_null_score = result.cls_logits
# if we could have irrelevant answers, get the min score of irrelevant
score_null = min(score_null, cur_null_score)
for i in range(start_n_top):
for j in range(end_n_top):
start_log_prob = result.start_logits[i]
start_index = result.start_top_index[i]
j_index = i * end_n_top + j
end_log_prob = result.end_logits[j_index]
end_index = result.end_top_index[j_index]
# We could hypothetically create invalid predictions, e.g., predict
# that the start of the span is in the question. We throw out all
# invalid predictions.
if start_index >= feature.paragraph_len - 1:
continue
if end_index >= feature.paragraph_len - 1:
continue
if not feature.token_is_max_context.get(start_index, False):
continue
if end_index < start_index:
continue
length = end_index - start_index + 1
if length > max_answer_length:
continue
prelim_predictions.append(
_PrelimPrediction(
feature_index=feature_index,
start_index=start_index,
end_index=end_index,
start_log_prob=start_log_prob,
end_log_prob=end_log_prob,
)
)
prelim_predictions = sorted(
prelim_predictions, key=lambda x: (x.start_log_prob + x.end_log_prob), reverse=True
)
seen_predictions = {}
nbest = []
for pred in prelim_predictions:
if len(nbest) >= n_best_size:
break
feature = features[pred.feature_index]
# XLNet un-tokenizer
# Let's keep it simple for now and see if we need all this later.
#
# tok_start_to_orig_index = feature.tok_start_to_orig_index
# tok_end_to_orig_index = feature.tok_end_to_orig_index
# start_orig_pos = tok_start_to_orig_index[pred.start_index]
# end_orig_pos = tok_end_to_orig_index[pred.end_index]
# paragraph_text = example.paragraph_text
# final_text = paragraph_text[start_orig_pos: end_orig_pos + 1].strip()
# Previously used Bert untokenizer
tok_tokens = feature.tokens[pred.start_index : (pred.end_index + 1)]
orig_doc_start = feature.token_to_orig_map[pred.start_index]
orig_doc_end = feature.token_to_orig_map[pred.end_index]
orig_tokens = example.doc_tokens[orig_doc_start : (orig_doc_end + 1)]
tok_text = tokenizer.convert_tokens_to_string(tok_tokens)
# Clean whitespace
tok_text = tok_text.strip()
tok_text = " ".join(tok_text.split())
orig_text = " ".join(orig_tokens)
if hasattr(tokenizer, "do_lower_case"):
do_lower_case = tokenizer.do_lower_case
else:
do_lower_case = tokenizer.do_lowercase_and_remove_accent
final_text = get_final_text(tok_text, orig_text, do_lower_case, verbose_logging)
if final_text in seen_predictions:
continue
seen_predictions[final_text] = True
nbest.append(
_NbestPrediction(text=final_text, start_log_prob=pred.start_log_prob, end_log_prob=pred.end_log_prob)
)
# In very rare edge cases we could have no valid predictions. So we
# just create a nonce prediction in this case to avoid failure.
if not nbest:
nbest.append(_NbestPrediction(text="", start_log_prob=-1e6, end_log_prob=-1e6))
total_scores = []
best_non_null_entry = None
for entry in nbest:
total_scores.append(entry.start_log_prob + entry.end_log_prob)
if not best_non_null_entry:
best_non_null_entry = entry
probs = _compute_softmax(total_scores)
nbest_json = []
for i, entry in enumerate(nbest):
output = collections.OrderedDict()
output["text"] = entry.text
output["probability"] = probs[i]
output["start_log_prob"] = entry.start_log_prob
output["end_log_prob"] = entry.end_log_prob
nbest_json.append(output)
assert len(nbest_json) >= 1, "No valid predictions"
assert best_non_null_entry is not None, "No valid predictions"
score_diff = score_null
scores_diff_json[example.qas_id] = score_diff
# note(zhiliny): always predict best_non_null_entry
# and the evaluation script will search for the best threshold
all_predictions[example.qas_id] = best_non_null_entry.text
all_nbest_json[example.qas_id] = nbest_json
with open(output_prediction_file, "w") as writer:
writer.write(json.dumps(all_predictions, indent=4) + "\n")
with open(output_nbest_file, "w") as writer:
writer.write(json.dumps(all_nbest_json, indent=4) + "\n")
if version_2_with_negative:
with open(output_null_log_odds_file, "w") as writer:
writer.write(json.dumps(scores_diff_json, indent=4) + "\n")
return all_predictions | XLNet write prediction logic (more complex than Bert's). Write final predictions to the json file and log-odds of null if needed. Requires utils_squad_evaluate.py |
10,635 | import random
import warnings
from collections.abc import Mapping
from dataclasses import dataclass
from random import randint
from typing import Any, Callable, Dict, List, NewType, Optional, Tuple, Union
import numpy as np
from ..models.bert import BertTokenizer, BertTokenizerFast
from ..tokenization_utils_base import PreTrainedTokenizerBase
from ..utils import PaddingStrategy
InputDataClass = NewType("InputDataClass", Any)
def torch_default_data_collator(features: List[InputDataClass]) -> Dict[str, Any]:
import torch
if not isinstance(features[0], Mapping):
features = [vars(f) for f in features]
first = features[0]
batch = {}
# Special handling for labels.
# Ensure that tensor is created with the correct type
# (it should be automatically the case, but let's make sure of it.)
if "label" in first and first["label"] is not None:
label = first["label"].item() if isinstance(first["label"], torch.Tensor) else first["label"]
dtype = torch.long if isinstance(label, int) else torch.float
batch["labels"] = torch.tensor([f["label"] for f in features], dtype=dtype)
elif "label_ids" in first and first["label_ids"] is not None:
if isinstance(first["label_ids"], torch.Tensor):
batch["labels"] = torch.stack([f["label_ids"] for f in features])
else:
dtype = torch.long if type(first["label_ids"][0]) is int else torch.float
batch["labels"] = torch.tensor([f["label_ids"] for f in features], dtype=dtype)
# Handling of all other possible keys.
# Again, we will use the first element to figure out which key/values are not None for this model.
for k, v in first.items():
if k not in ("label", "label_ids") and v is not None and not isinstance(v, str):
if isinstance(v, torch.Tensor):
batch[k] = torch.stack([f[k] for f in features])
elif isinstance(v, np.ndarray):
batch[k] = torch.tensor(np.stack([f[k] for f in features]))
else:
batch[k] = torch.tensor([f[k] for f in features])
return batch
def tf_default_data_collator(features: List[InputDataClass]) -> Dict[str, Any]:
import tensorflow as tf
if not isinstance(features[0], Mapping):
features = [vars(f) for f in features]
first = features[0]
batch = {}
# Special handling for labels.
# Ensure that tensor is created with the correct type
# (it should be automatically the case, but let's make sure of it.)
if "label" in first and first["label"] is not None:
label_col_name = "label"
elif "label_ids" in first and first["label_ids"] is not None:
label_col_name = "label_ids"
elif "labels" in first and first["labels"] is not None:
label_col_name = "labels"
else:
label_col_name = None
if label_col_name is not None:
if isinstance(first[label_col_name], tf.Tensor):
dtype = tf.int64 if first[label_col_name].dtype.is_integer() else tf.float32
elif isinstance(first[label_col_name], np.ndarray) or isinstance(first[label_col_name], np.generic):
dtype = tf.int64 if np.issubdtype(first[label_col_name].dtype, np.integer) else tf.float32
elif isinstance(first[label_col_name], (tuple, list)):
dtype = tf.int64 if isinstance(first[label_col_name][0], int) else tf.float32
else:
dtype = tf.int64 if isinstance(first[label_col_name], int) else tf.float32
batch["labels"] = tf.convert_to_tensor([f[label_col_name] for f in features], dtype=dtype)
# Handling of all other possible keys.
# Again, we will use the first element to figure out which key/values are not None for this model.
for k, v in first.items():
if k not in ("label", "label_ids", "labels") and v is not None and not isinstance(v, str):
if isinstance(v, (tf.Tensor, np.ndarray)):
batch[k] = tf.stack([f[k] for f in features])
else:
batch[k] = tf.convert_to_tensor([f[k] for f in features])
return batch
def numpy_default_data_collator(features: List[InputDataClass]) -> Dict[str, Any]:
if not isinstance(features[0], Mapping):
features = [vars(f) for f in features]
first = features[0]
batch = {}
# Special handling for labels.
# Ensure that tensor is created with the correct type
# (it should be automatically the case, but let's make sure of it.)
if "label" in first and first["label"] is not None:
label = first["label"].item() if isinstance(first["label"], np.ndarray) else first["label"]
dtype = np.int64 if isinstance(label, int) else np.float32
batch["labels"] = np.array([f["label"] for f in features], dtype=dtype)
elif "label_ids" in first and first["label_ids"] is not None:
if isinstance(first["label_ids"], np.ndarray):
batch["labels"] = np.stack([f["label_ids"] for f in features])
else:
dtype = np.int64 if type(first["label_ids"][0]) is int else np.float32
batch["labels"] = np.array([f["label_ids"] for f in features], dtype=dtype)
# Handling of all other possible keys.
# Again, we will use the first element to figure out which key/values are not None for this model.
for k, v in first.items():
if k not in ("label", "label_ids") and v is not None and not isinstance(v, str):
if isinstance(v, np.ndarray):
batch[k] = np.stack([f[k] for f in features])
else:
batch[k] = np.array([f[k] for f in features])
return batch
The provided code snippet includes necessary dependencies for implementing the `default_data_collator` function. Write a Python function `def default_data_collator(features: List[InputDataClass], return_tensors="pt") -> Dict[str, Any]` to solve the following problem:
Very simple data collator that simply collates batches of dict-like objects and performs special handling for potential keys named: - `label`: handles a single value (int or float) per object - `label_ids`: handles a list of values per object Does not do any additional preprocessing: property names of the input object will be used as corresponding inputs to the model. See glue and ner for example of how it's useful.
Here is the function:
def default_data_collator(features: List[InputDataClass], return_tensors="pt") -> Dict[str, Any]:
"""
Very simple data collator that simply collates batches of dict-like objects and performs special handling for
potential keys named:
- `label`: handles a single value (int or float) per object
- `label_ids`: handles a list of values per object
Does not do any additional preprocessing: property names of the input object will be used as corresponding inputs
to the model. See glue and ner for example of how it's useful.
"""
# In this function we'll make the assumption that all `features` in the batch
# have the same attributes.
# So we will look at the first element as a proxy for what attributes exist
# on the whole batch.
if return_tensors == "pt":
return torch_default_data_collator(features)
elif return_tensors == "tf":
return tf_default_data_collator(features)
elif return_tensors == "np":
return numpy_default_data_collator(features) | Very simple data collator that simply collates batches of dict-like objects and performs special handling for potential keys named: - `label`: handles a single value (int or float) per object - `label_ids`: handles a list of values per object Does not do any additional preprocessing: property names of the input object will be used as corresponding inputs to the model. See glue and ner for example of how it's useful. |
10,636 | import random
import warnings
from collections.abc import Mapping
from dataclasses import dataclass
from random import randint
from typing import Any, Callable, Dict, List, NewType, Optional, Tuple, Union
import numpy as np
from ..models.bert import BertTokenizer, BertTokenizerFast
from ..tokenization_utils_base import PreTrainedTokenizerBase
from ..utils import PaddingStrategy
The provided code snippet includes necessary dependencies for implementing the `_torch_collate_batch` function. Write a Python function `def _torch_collate_batch(examples, tokenizer, pad_to_multiple_of: Optional[int] = None)` to solve the following problem:
Collate `examples` into a batch, using the information in `tokenizer` for padding if necessary.
Here is the function:
def _torch_collate_batch(examples, tokenizer, pad_to_multiple_of: Optional[int] = None):
"""Collate `examples` into a batch, using the information in `tokenizer` for padding if necessary."""
import torch
# Tensorize if necessary.
if isinstance(examples[0], (list, tuple, np.ndarray)):
examples = [torch.tensor(e, dtype=torch.long) for e in examples]
length_of_first = examples[0].size(0)
# Check if padding is necessary.
are_tensors_same_length = all(x.size(0) == length_of_first for x in examples)
if are_tensors_same_length and (pad_to_multiple_of is None or length_of_first % pad_to_multiple_of == 0):
return torch.stack(examples, dim=0)
# If yes, check if we have a `pad_token`.
if tokenizer._pad_token is None:
raise ValueError(
"You are attempting to pad samples but the tokenizer you are using"
f" ({tokenizer.__class__.__name__}) does not have a pad token."
)
# Creating the full tensor and filling it with our data.
max_length = max(x.size(0) for x in examples)
if pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
result = examples[0].new_full([len(examples), max_length], tokenizer.pad_token_id)
for i, example in enumerate(examples):
if tokenizer.padding_side == "right":
result[i, : example.shape[0]] = example
else:
result[i, -example.shape[0] :] = example
return result | Collate `examples` into a batch, using the information in `tokenizer` for padding if necessary. |
10,637 | import random
import warnings
from collections.abc import Mapping
from dataclasses import dataclass
from random import randint
from typing import Any, Callable, Dict, List, NewType, Optional, Tuple, Union
import numpy as np
from ..models.bert import BertTokenizer, BertTokenizerFast
from ..tokenization_utils_base import PreTrainedTokenizerBase
from ..utils import PaddingStrategy
The provided code snippet includes necessary dependencies for implementing the `_tf_collate_batch` function. Write a Python function `def _tf_collate_batch(examples, tokenizer, pad_to_multiple_of: Optional[int] = None)` to solve the following problem:
Collate `examples` into a batch, using the information in `tokenizer` for padding if necessary.
Here is the function:
def _tf_collate_batch(examples, tokenizer, pad_to_multiple_of: Optional[int] = None):
import tensorflow as tf
"""Collate `examples` into a batch, using the information in `tokenizer` for padding if necessary."""
# Tensorize if necessary.
if isinstance(examples[0], (list, tuple)):
examples = [tf.convert_to_tensor(e, dtype=tf.int64) for e in examples]
# Check if padding is necessary.
length_of_first = len(examples[0])
are_tensors_same_length = all(len(x) == length_of_first for x in examples)
if are_tensors_same_length and (pad_to_multiple_of is None or length_of_first % pad_to_multiple_of == 0):
return tf.stack(examples, axis=0)
# If yes, check if we have a `pad_token`.
if tokenizer._pad_token is None:
raise ValueError(
"You are attempting to pad samples but the tokenizer you are using"
f" ({tokenizer.__class__.__name__}) does not have a pad token."
)
# Creating the full tensor and filling it with our data.
max_length = max(len(x) for x in examples)
if pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
# result = examples[0].new_full([len(examples), max_length], tokenizer.pad_token_id)
result = []
rank = tf.rank(examples[0])
paddings = np.zeros((rank, 2), dtype=np.int32)
for example in examples:
if tokenizer.padding_side == "right":
paddings[0, 1] = max_length - len(example)
else:
paddings[0, 0] = max_length - len(example)
result.append(tf.pad(example, paddings, constant_values=tokenizer.pad_token_id))
return tf.stack(result, axis=0) | Collate `examples` into a batch, using the information in `tokenizer` for padding if necessary. |
10,638 | import random
import warnings
from collections.abc import Mapping
from dataclasses import dataclass
from random import randint
from typing import Any, Callable, Dict, List, NewType, Optional, Tuple, Union
import numpy as np
from ..models.bert import BertTokenizer, BertTokenizerFast
from ..tokenization_utils_base import PreTrainedTokenizerBase
from ..utils import PaddingStrategy
The provided code snippet includes necessary dependencies for implementing the `_numpy_collate_batch` function. Write a Python function `def _numpy_collate_batch(examples, tokenizer, pad_to_multiple_of: Optional[int] = None)` to solve the following problem:
Collate `examples` into a batch, using the information in `tokenizer` for padding if necessary.
Here is the function:
def _numpy_collate_batch(examples, tokenizer, pad_to_multiple_of: Optional[int] = None):
"""Collate `examples` into a batch, using the information in `tokenizer` for padding if necessary."""
# Tensorize if necessary.
if isinstance(examples[0], (list, tuple)):
examples = [np.array(e, dtype=np.int64) for e in examples]
# Check if padding is necessary.
length_of_first = len(examples[0])
are_tensors_same_length = all(len(x) == length_of_first for x in examples)
if are_tensors_same_length and (pad_to_multiple_of is None or length_of_first % pad_to_multiple_of == 0):
return np.stack(examples, axis=0)
# If yes, check if we have a `pad_token`.
if tokenizer._pad_token is None:
raise ValueError(
"You are attempting to pad samples but the tokenizer you are using"
f" ({tokenizer.__class__.__name__}) does not have a pad token."
)
# Creating the full tensor and filling it with our data.
max_length = max(len(x) for x in examples)
if pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
result = np.full(shape=(len(examples), max_length), fill_value=tokenizer.pad_token_id, dtype=examples[0].dtype)
for i, example in enumerate(examples):
if tokenizer.padding_side == "right":
result[i, : example.shape[0]] = example
else:
result[i, -example.shape[0] :] = example
return result | Collate `examples` into a batch, using the information in `tokenizer` for padding if necessary. |
10,639 | import random
import warnings
from collections.abc import Mapping
from dataclasses import dataclass
from random import randint
from typing import Any, Callable, Dict, List, NewType, Optional, Tuple, Union
import numpy as np
from ..models.bert import BertTokenizer, BertTokenizerFast
from ..tokenization_utils_base import PreTrainedTokenizerBase
from ..utils import PaddingStrategy
def tolist(x):
if isinstance(x, list):
return x
elif hasattr(x, "numpy"): # Checks for TF tensors without needing the import
x = x.numpy()
return x.tolist() | null |
10,640 | import json
import os
from functools import partial
from multiprocessing import Pool, cpu_count
import numpy as np
from tqdm import tqdm
from ...models.bert.tokenization_bert import whitespace_tokenize
from ...tokenization_utils_base import BatchEncoding, PreTrainedTokenizerBase, TruncationStrategy
from ...utils import is_tf_available, is_torch_available, logging
from .utils import DataProcessor
The provided code snippet includes necessary dependencies for implementing the `_check_is_max_context` function. Write a Python function `def _check_is_max_context(doc_spans, cur_span_index, position)` to solve the following problem:
Check if this is the 'max context' doc span for the token.
Here is the function:
def _check_is_max_context(doc_spans, cur_span_index, position):
"""Check if this is the 'max context' doc span for the token."""
best_score = None
best_span_index = None
for span_index, doc_span in enumerate(doc_spans):
end = doc_span.start + doc_span.length - 1
if position < doc_span.start:
continue
if position > end:
continue
num_left_context = position - doc_span.start
num_right_context = end - position
score = min(num_left_context, num_right_context) + 0.01 * doc_span.length
if best_score is None or score > best_score:
best_score = score
best_span_index = span_index
return cur_span_index == best_span_index | Check if this is the 'max context' doc span for the token. |
10,641 | import json
import os
from functools import partial
from multiprocessing import Pool, cpu_count
import numpy as np
from tqdm import tqdm
from ...models.bert.tokenization_bert import whitespace_tokenize
from ...tokenization_utils_base import BatchEncoding, PreTrainedTokenizerBase, TruncationStrategy
from ...utils import is_tf_available, is_torch_available, logging
from .utils import DataProcessor
def _is_whitespace(c):
if c == " " or c == "\t" or c == "\r" or c == "\n" or ord(c) == 0x202F:
return True
return False | null |
10,642 | import json
import os
from functools import partial
from multiprocessing import Pool, cpu_count
import numpy as np
from tqdm import tqdm
from ...models.bert.tokenization_bert import whitespace_tokenize
from ...tokenization_utils_base import BatchEncoding, PreTrainedTokenizerBase, TruncationStrategy
from ...utils import is_tf_available, is_torch_available, logging
from .utils import DataProcessor
if is_torch_available():
import torch
from torch.utils.data import TensorDataset
if is_tf_available():
import tensorflow as tf
def squad_convert_example_to_features(
example, max_seq_length, doc_stride, max_query_length, padding_strategy, is_training
):
features = []
if is_training and not example.is_impossible:
# Get start and end position
start_position = example.start_position
end_position = example.end_position
# If the answer cannot be found in the text, then skip this example.
actual_text = " ".join(example.doc_tokens[start_position : (end_position + 1)])
cleaned_answer_text = " ".join(whitespace_tokenize(example.answer_text))
if actual_text.find(cleaned_answer_text) == -1:
logger.warning(f"Could not find answer: '{actual_text}' vs. '{cleaned_answer_text}'")
return []
tok_to_orig_index = []
orig_to_tok_index = []
all_doc_tokens = []
for i, token in enumerate(example.doc_tokens):
orig_to_tok_index.append(len(all_doc_tokens))
if tokenizer.__class__.__name__ in [
"RobertaTokenizer",
"LongformerTokenizer",
"BartTokenizer",
"RobertaTokenizerFast",
"LongformerTokenizerFast",
"BartTokenizerFast",
]:
sub_tokens = tokenizer.tokenize(token, add_prefix_space=True)
else:
sub_tokens = tokenizer.tokenize(token)
for sub_token in sub_tokens:
tok_to_orig_index.append(i)
all_doc_tokens.append(sub_token)
if is_training and not example.is_impossible:
tok_start_position = orig_to_tok_index[example.start_position]
if example.end_position < len(example.doc_tokens) - 1:
tok_end_position = orig_to_tok_index[example.end_position + 1] - 1
else:
tok_end_position = len(all_doc_tokens) - 1
(tok_start_position, tok_end_position) = _improve_answer_span(
all_doc_tokens, tok_start_position, tok_end_position, tokenizer, example.answer_text
)
spans = []
truncated_query = tokenizer.encode(
example.question_text, add_special_tokens=False, truncation=True, max_length=max_query_length
)
# Tokenizers who insert 2 SEP tokens in-between <context> & <question> need to have special handling
# in the way they compute mask of added tokens.
tokenizer_type = type(tokenizer).__name__.replace("Tokenizer", "").lower()
sequence_added_tokens = (
tokenizer.model_max_length - tokenizer.max_len_single_sentence + 1
if tokenizer_type in MULTI_SEP_TOKENS_TOKENIZERS_SET
else tokenizer.model_max_length - tokenizer.max_len_single_sentence
)
sequence_pair_added_tokens = tokenizer.model_max_length - tokenizer.max_len_sentences_pair
span_doc_tokens = all_doc_tokens
while len(spans) * doc_stride < len(all_doc_tokens):
# Define the side we want to truncate / pad and the text/pair sorting
if tokenizer.padding_side == "right":
texts = truncated_query
pairs = span_doc_tokens
truncation = TruncationStrategy.ONLY_SECOND.value
else:
texts = span_doc_tokens
pairs = truncated_query
truncation = TruncationStrategy.ONLY_FIRST.value
encoded_dict = tokenizer.encode_plus( # TODO(thom) update this logic
texts,
pairs,
truncation=truncation,
padding=padding_strategy,
max_length=max_seq_length,
return_overflowing_tokens=True,
stride=max_seq_length - doc_stride - len(truncated_query) - sequence_pair_added_tokens,
return_token_type_ids=True,
)
paragraph_len = min(
len(all_doc_tokens) - len(spans) * doc_stride,
max_seq_length - len(truncated_query) - sequence_pair_added_tokens,
)
if tokenizer.pad_token_id in encoded_dict["input_ids"]:
if tokenizer.padding_side == "right":
non_padded_ids = encoded_dict["input_ids"][: encoded_dict["input_ids"].index(tokenizer.pad_token_id)]
else:
last_padding_id_position = (
len(encoded_dict["input_ids"]) - 1 - encoded_dict["input_ids"][::-1].index(tokenizer.pad_token_id)
)
non_padded_ids = encoded_dict["input_ids"][last_padding_id_position + 1 :]
else:
non_padded_ids = encoded_dict["input_ids"]
tokens = tokenizer.convert_ids_to_tokens(non_padded_ids)
token_to_orig_map = {}
for i in range(paragraph_len):
index = len(truncated_query) + sequence_added_tokens + i if tokenizer.padding_side == "right" else i
token_to_orig_map[index] = tok_to_orig_index[len(spans) * doc_stride + i]
encoded_dict["paragraph_len"] = paragraph_len
encoded_dict["tokens"] = tokens
encoded_dict["token_to_orig_map"] = token_to_orig_map
encoded_dict["truncated_query_with_special_tokens_length"] = len(truncated_query) + sequence_added_tokens
encoded_dict["token_is_max_context"] = {}
encoded_dict["start"] = len(spans) * doc_stride
encoded_dict["length"] = paragraph_len
spans.append(encoded_dict)
if "overflowing_tokens" not in encoded_dict or (
"overflowing_tokens" in encoded_dict and len(encoded_dict["overflowing_tokens"]) == 0
):
break
span_doc_tokens = encoded_dict["overflowing_tokens"]
for doc_span_index in range(len(spans)):
for j in range(spans[doc_span_index]["paragraph_len"]):
is_max_context = _new_check_is_max_context(spans, doc_span_index, doc_span_index * doc_stride + j)
index = (
j
if tokenizer.padding_side == "left"
else spans[doc_span_index]["truncated_query_with_special_tokens_length"] + j
)
spans[doc_span_index]["token_is_max_context"][index] = is_max_context
for span in spans:
# Identify the position of the CLS token
cls_index = span["input_ids"].index(tokenizer.cls_token_id)
# p_mask: mask with 1 for token than cannot be in the answer (0 for token which can be in an answer)
# Original TF implementation also keep the classification token (set to 0)
p_mask = np.ones_like(span["token_type_ids"])
if tokenizer.padding_side == "right":
p_mask[len(truncated_query) + sequence_added_tokens :] = 0
else:
p_mask[-len(span["tokens"]) : -(len(truncated_query) + sequence_added_tokens)] = 0
pad_token_indices = np.where(span["input_ids"] == tokenizer.pad_token_id)
special_token_indices = np.asarray(
tokenizer.get_special_tokens_mask(span["input_ids"], already_has_special_tokens=True)
).nonzero()
p_mask[pad_token_indices] = 1
p_mask[special_token_indices] = 1
# Set the cls index to 0: the CLS index can be used for impossible answers
p_mask[cls_index] = 0
span_is_impossible = example.is_impossible
start_position = 0
end_position = 0
if is_training and not span_is_impossible:
# For training, if our document chunk does not contain an annotation
# we throw it out, since there is nothing to predict.
doc_start = span["start"]
doc_end = span["start"] + span["length"] - 1
out_of_span = False
if not (tok_start_position >= doc_start and tok_end_position <= doc_end):
out_of_span = True
if out_of_span:
start_position = cls_index
end_position = cls_index
span_is_impossible = True
else:
if tokenizer.padding_side == "left":
doc_offset = 0
else:
doc_offset = len(truncated_query) + sequence_added_tokens
start_position = tok_start_position - doc_start + doc_offset
end_position = tok_end_position - doc_start + doc_offset
features.append(
SquadFeatures(
span["input_ids"],
span["attention_mask"],
span["token_type_ids"],
cls_index,
p_mask.tolist(),
example_index=0, # Can not set unique_id and example_index here. They will be set after multiple processing.
unique_id=0,
paragraph_len=span["paragraph_len"],
token_is_max_context=span["token_is_max_context"],
tokens=span["tokens"],
token_to_orig_map=span["token_to_orig_map"],
start_position=start_position,
end_position=end_position,
is_impossible=span_is_impossible,
qas_id=example.qas_id,
)
)
return features
def squad_convert_example_to_features_init(tokenizer_for_convert: PreTrainedTokenizerBase):
global tokenizer
tokenizer = tokenizer_for_convert
The provided code snippet includes necessary dependencies for implementing the `squad_convert_examples_to_features` function. Write a Python function `def squad_convert_examples_to_features( examples, tokenizer, max_seq_length, doc_stride, max_query_length, is_training, padding_strategy="max_length", return_dataset=False, threads=1, tqdm_enabled=True, )` to solve the following problem:
Converts a list of examples into a list of features that can be directly given as input to a model. It is model-dependant and takes advantage of many of the tokenizer's features to create the model's inputs. Args: examples: list of [`~data.processors.squad.SquadExample`] tokenizer: an instance of a child of [`PreTrainedTokenizer`] max_seq_length: The maximum sequence length of the inputs. doc_stride: The stride used when the context is too large and is split across several features. max_query_length: The maximum length of the query. is_training: whether to create features for model evaluation or model training. padding_strategy: Default to "max_length". Which padding strategy to use return_dataset: Default False. Either 'pt' or 'tf'. if 'pt': returns a torch.data.TensorDataset, if 'tf': returns a tf.data.Dataset threads: multiple processing threads. Returns: list of [`~data.processors.squad.SquadFeatures`] Example: ```python processor = SquadV2Processor() examples = processor.get_dev_examples(data_dir) features = squad_convert_examples_to_features( examples=examples, tokenizer=tokenizer, max_seq_length=args.max_seq_length, doc_stride=args.doc_stride, max_query_length=args.max_query_length, is_training=not evaluate, ) ```
Here is the function:
def squad_convert_examples_to_features(
examples,
tokenizer,
max_seq_length,
doc_stride,
max_query_length,
is_training,
padding_strategy="max_length",
return_dataset=False,
threads=1,
tqdm_enabled=True,
):
"""
Converts a list of examples into a list of features that can be directly given as input to a model. It is
model-dependant and takes advantage of many of the tokenizer's features to create the model's inputs.
Args:
examples: list of [`~data.processors.squad.SquadExample`]
tokenizer: an instance of a child of [`PreTrainedTokenizer`]
max_seq_length: The maximum sequence length of the inputs.
doc_stride: The stride used when the context is too large and is split across several features.
max_query_length: The maximum length of the query.
is_training: whether to create features for model evaluation or model training.
padding_strategy: Default to "max_length". Which padding strategy to use
return_dataset: Default False. Either 'pt' or 'tf'.
if 'pt': returns a torch.data.TensorDataset, if 'tf': returns a tf.data.Dataset
threads: multiple processing threads.
Returns:
list of [`~data.processors.squad.SquadFeatures`]
Example:
```python
processor = SquadV2Processor()
examples = processor.get_dev_examples(data_dir)
features = squad_convert_examples_to_features(
examples=examples,
tokenizer=tokenizer,
max_seq_length=args.max_seq_length,
doc_stride=args.doc_stride,
max_query_length=args.max_query_length,
is_training=not evaluate,
)
```"""
# Defining helper methods
features = []
threads = min(threads, cpu_count())
with Pool(threads, initializer=squad_convert_example_to_features_init, initargs=(tokenizer,)) as p:
annotate_ = partial(
squad_convert_example_to_features,
max_seq_length=max_seq_length,
doc_stride=doc_stride,
max_query_length=max_query_length,
padding_strategy=padding_strategy,
is_training=is_training,
)
features = list(
tqdm(
p.imap(annotate_, examples, chunksize=32),
total=len(examples),
desc="convert squad examples to features",
disable=not tqdm_enabled,
)
)
new_features = []
unique_id = 1000000000
example_index = 0
for example_features in tqdm(
features, total=len(features), desc="add example index and unique id", disable=not tqdm_enabled
):
if not example_features:
continue
for example_feature in example_features:
example_feature.example_index = example_index
example_feature.unique_id = unique_id
new_features.append(example_feature)
unique_id += 1
example_index += 1
features = new_features
del new_features
if return_dataset == "pt":
if not is_torch_available():
raise RuntimeError("PyTorch must be installed to return a PyTorch dataset.")
# Convert to Tensors and build dataset
all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)
all_attention_masks = torch.tensor([f.attention_mask for f in features], dtype=torch.long)
all_token_type_ids = torch.tensor([f.token_type_ids for f in features], dtype=torch.long)
all_cls_index = torch.tensor([f.cls_index for f in features], dtype=torch.long)
all_p_mask = torch.tensor([f.p_mask for f in features], dtype=torch.float)
all_is_impossible = torch.tensor([f.is_impossible for f in features], dtype=torch.float)
if not is_training:
all_feature_index = torch.arange(all_input_ids.size(0), dtype=torch.long)
dataset = TensorDataset(
all_input_ids, all_attention_masks, all_token_type_ids, all_feature_index, all_cls_index, all_p_mask
)
else:
all_start_positions = torch.tensor([f.start_position for f in features], dtype=torch.long)
all_end_positions = torch.tensor([f.end_position for f in features], dtype=torch.long)
dataset = TensorDataset(
all_input_ids,
all_attention_masks,
all_token_type_ids,
all_start_positions,
all_end_positions,
all_cls_index,
all_p_mask,
all_is_impossible,
)
return features, dataset
elif return_dataset == "tf":
if not is_tf_available():
raise RuntimeError("TensorFlow must be installed to return a TensorFlow dataset.")
def gen():
for i, ex in enumerate(features):
if ex.token_type_ids is None:
yield (
{
"input_ids": ex.input_ids,
"attention_mask": ex.attention_mask,
"feature_index": i,
"qas_id": ex.qas_id,
},
{
"start_positions": ex.start_position,
"end_positions": ex.end_position,
"cls_index": ex.cls_index,
"p_mask": ex.p_mask,
"is_impossible": ex.is_impossible,
},
)
else:
yield (
{
"input_ids": ex.input_ids,
"attention_mask": ex.attention_mask,
"token_type_ids": ex.token_type_ids,
"feature_index": i,
"qas_id": ex.qas_id,
},
{
"start_positions": ex.start_position,
"end_positions": ex.end_position,
"cls_index": ex.cls_index,
"p_mask": ex.p_mask,
"is_impossible": ex.is_impossible,
},
)
# Why have we split the batch into a tuple? PyTorch just has a list of tensors.
if "token_type_ids" in tokenizer.model_input_names:
train_types = (
{
"input_ids": tf.int32,
"attention_mask": tf.int32,
"token_type_ids": tf.int32,
"feature_index": tf.int64,
"qas_id": tf.string,
},
{
"start_positions": tf.int64,
"end_positions": tf.int64,
"cls_index": tf.int64,
"p_mask": tf.int32,
"is_impossible": tf.int32,
},
)
train_shapes = (
{
"input_ids": tf.TensorShape([None]),
"attention_mask": tf.TensorShape([None]),
"token_type_ids": tf.TensorShape([None]),
"feature_index": tf.TensorShape([]),
"qas_id": tf.TensorShape([]),
},
{
"start_positions": tf.TensorShape([]),
"end_positions": tf.TensorShape([]),
"cls_index": tf.TensorShape([]),
"p_mask": tf.TensorShape([None]),
"is_impossible": tf.TensorShape([]),
},
)
else:
train_types = (
{"input_ids": tf.int32, "attention_mask": tf.int32, "feature_index": tf.int64, "qas_id": tf.string},
{
"start_positions": tf.int64,
"end_positions": tf.int64,
"cls_index": tf.int64,
"p_mask": tf.int32,
"is_impossible": tf.int32,
},
)
train_shapes = (
{
"input_ids": tf.TensorShape([None]),
"attention_mask": tf.TensorShape([None]),
"feature_index": tf.TensorShape([]),
"qas_id": tf.TensorShape([]),
},
{
"start_positions": tf.TensorShape([]),
"end_positions": tf.TensorShape([]),
"cls_index": tf.TensorShape([]),
"p_mask": tf.TensorShape([None]),
"is_impossible": tf.TensorShape([]),
},
)
return tf.data.Dataset.from_generator(gen, train_types, train_shapes)
else:
return features | Converts a list of examples into a list of features that can be directly given as input to a model. It is model-dependant and takes advantage of many of the tokenizer's features to create the model's inputs. Args: examples: list of [`~data.processors.squad.SquadExample`] tokenizer: an instance of a child of [`PreTrainedTokenizer`] max_seq_length: The maximum sequence length of the inputs. doc_stride: The stride used when the context is too large and is split across several features. max_query_length: The maximum length of the query. is_training: whether to create features for model evaluation or model training. padding_strategy: Default to "max_length". Which padding strategy to use return_dataset: Default False. Either 'pt' or 'tf'. if 'pt': returns a torch.data.TensorDataset, if 'tf': returns a tf.data.Dataset threads: multiple processing threads. Returns: list of [`~data.processors.squad.SquadFeatures`] Example: ```python processor = SquadV2Processor() examples = processor.get_dev_examples(data_dir) features = squad_convert_examples_to_features( examples=examples, tokenizer=tokenizer, max_seq_length=args.max_seq_length, doc_stride=args.doc_stride, max_query_length=args.max_query_length, is_training=not evaluate, ) ``` |
10,643 | import importlib.util
import weakref
from copy import deepcopy
from functools import partialmethod
from .dependency_versions_check import dep_version_check
from .utils import is_accelerate_available, is_torch_available, logging
_hf_deepspeed_config_weak_ref = None
def set_hf_deepspeed_config(hf_deepspeed_config_obj):
# this is a special weakref global object to allow us to get to Deepspeed config from APIs
# that don't have an easy way to get to the Deepspeed config outside of the Trainer domain.
global _hf_deepspeed_config_weak_ref
# will go away automatically when HfDeepSpeedConfig is destroyed (when TrainingArguments is destroyed)
_hf_deepspeed_config_weak_ref = weakref.ref(hf_deepspeed_config_obj) | null |
10,644 | import importlib.util
import weakref
from copy import deepcopy
from functools import partialmethod
from .dependency_versions_check import dep_version_check
from .utils import is_accelerate_available, is_torch_available, logging
_hf_deepspeed_config_weak_ref = None
def unset_hf_deepspeed_config():
# useful for unit tests to ensure the global state doesn't leak - call from `tearDown` method
global _hf_deepspeed_config_weak_ref
_hf_deepspeed_config_weak_ref = None | null |
10,645 | import importlib.util
import weakref
from copy import deepcopy
from functools import partialmethod
from .dependency_versions_check import dep_version_check
from .utils import is_accelerate_available, is_torch_available, logging
_hf_deepspeed_config_weak_ref = None
def deepspeed_config():
if _hf_deepspeed_config_weak_ref is not None and _hf_deepspeed_config_weak_ref() is not None:
return _hf_deepspeed_config_weak_ref().config
else:
return None | null |
10,646 | import importlib.util
import weakref
from copy import deepcopy
from functools import partialmethod
from .dependency_versions_check import dep_version_check
from .utils import is_accelerate_available, is_torch_available, logging
logger = logging.get_logger(__name__)
def deepspeed_optim_sched(trainer, hf_deepspeed_config, args, num_training_steps):
"""
A convenience wrapper that deals with optimizer and lr scheduler configuration.
"""
config = hf_deepspeed_config.config
# Optimizer + Scheduler
# Currently supported combos:
# 1. DS scheduler + DS optimizer: Yes
# 2. HF scheduler + HF optimizer: Yes
# 3. DS scheduler + HF optimizer: Yes
# 4. HF scheduler + DS optimizer: Yes
#
# Unless Offload is enabled in which case it's:
# 1. DS scheduler + DS optimizer: Yes
# 2. HF scheduler + HF optimizer: Mostly*
# 3. DS scheduler + HF optimizer: Mostly*
# 4. HF scheduler + DS optimizer: Yes
#
# Mostly*: All non-native DeepSpeed optimizers that have both CPU and GPU implementation should work (except LAMB)
optimizer = None
if "optimizer" in config:
if args.adafactor:
raise ValueError(
"--adafactor was passed, but also found `optimizer` configured in the DeepSpeed config. "
"Only one optimizer can be configured."
)
else:
if hf_deepspeed_config.is_offload():
logger.info(
"Detected ZeRO Offload and non-DeepSpeed optimizers: This combination should work as long as the"
" custom optimizer has both CPU and GPU implementation (except LAMB)"
)
# ds supports Adam, OneBitAdam, and Lamb optimizers and can import other optimizers from torch.
# But trainer uses AdamW by default.
optimizer = trainer.create_optimizer()
# To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
config["zero_allow_untested_optimizer"] = True
def _lr_scheduler_callable(optimizer):
return trainer.create_scheduler(num_training_steps=num_training_steps, optimizer=optimizer)
lr_scheduler = None
if "scheduler" not in config:
if optimizer is None:
# Optimizer is not available, so use callable to defer lr_scheduler creation to DS init
lr_scheduler = _lr_scheduler_callable
else:
lr_scheduler = trainer.create_scheduler(num_training_steps=num_training_steps, optimizer=optimizer)
return optimizer, lr_scheduler
The provided code snippet includes necessary dependencies for implementing the `deepspeed_init` function. Write a Python function `def deepspeed_init(trainer, num_training_steps, resume_from_checkpoint=None, inference=False)` to solve the following problem:
Init DeepSpeed, after updating the DeepSpeed configuration with any relevant Trainer's args. If `resume_from_checkpoint` was passed then an attempt to resume from a previously saved checkpoint will be made. Args: trainer: Trainer object num_training_steps: per single gpu resume_from_checkpoint: path to a checkpoint if to resume from after normal DeepSpeedEngine load inference: launch in inference mode (no optimizer and no lr scheduler) Returns: model, optimizer, lr_scheduler We may use `deepspeed_init` more than once during the life of Trainer, when we do - it's a temp hack based on: https://github.com/microsoft/DeepSpeed/issues/1394#issuecomment-937405374 until Deepspeed fixes a bug where it can't resume from a checkpoint after it did some stepping https://github.com/microsoft/DeepSpeed/issues/1612
Here is the function:
def deepspeed_init(trainer, num_training_steps, resume_from_checkpoint=None, inference=False):
"""
Init DeepSpeed, after updating the DeepSpeed configuration with any relevant Trainer's args.
If `resume_from_checkpoint` was passed then an attempt to resume from a previously saved checkpoint will be made.
Args:
trainer: Trainer object
num_training_steps: per single gpu
resume_from_checkpoint: path to a checkpoint if to resume from after normal DeepSpeedEngine load
inference: launch in inference mode (no optimizer and no lr scheduler)
Returns: model, optimizer, lr_scheduler
We may use `deepspeed_init` more than once during the life of Trainer, when we do - it's a temp hack based on:
https://github.com/microsoft/DeepSpeed/issues/1394#issuecomment-937405374 until Deepspeed fixes a bug where it
can't resume from a checkpoint after it did some stepping https://github.com/microsoft/DeepSpeed/issues/1612
"""
import deepspeed
from deepspeed.utils import logger as ds_logger
model = trainer.model
args = trainer.args
if hasattr(trainer, "hf_deepspeed_config_orig"):
hf_deepspeed_config = deepcopy(trainer.hf_deepspeed_config_orig)
else:
hf_deepspeed_config = args.hf_deepspeed_config
trainer.hf_deepspeed_config_orig = deepcopy(args.hf_deepspeed_config)
# resume config update - some bits like `model` and `num_training_steps` only become available during train
hf_deepspeed_config.trainer_config_finalize(args, model, num_training_steps)
config = hf_deepspeed_config.config
# set the Deepspeed log level consistent with the Trainer
ds_logger.setLevel(args.get_process_log_level())
if inference:
# only Z3 makes sense for the inference
if not hf_deepspeed_config.is_zero3():
raise ValueError("ZeRO inference only makes sense with ZeRO Stage 3 - please adjust your config")
# in case the training config is re-used for inference
hf_deepspeed_config.del_config_sub_tree("optimizer")
hf_deepspeed_config.del_config_sub_tree("lr_scheduler")
optimizer, lr_scheduler = None, None
model_parameters = None
else:
trainer.optimizer = None # important for when deepspeed_init is used as re-init
optimizer, lr_scheduler = deepspeed_optim_sched(trainer, hf_deepspeed_config, args, num_training_steps)
model_parameters = list(filter(lambda p: p.requires_grad, model.parameters()))
# keep for quick debug:
# from pprint import pprint; pprint(config)
kwargs = dict(
model=model,
model_parameters=model_parameters,
config_params=config,
optimizer=optimizer,
lr_scheduler=lr_scheduler,
)
deepspeed_engine, optimizer, _, lr_scheduler = deepspeed.initialize(**kwargs)
if resume_from_checkpoint is not None:
# it's possible that the user is trying to resume from model_path, which doesn't necessarily
# contain a deepspeed checkpoint. e.g. examples just check if the dir exists and assume it's
# a resume from a checkpoint and not just a local pretrained weight. So we check here if the
# path contains what looks like a deepspeed checkpoint
import glob
deepspeed_checkpoint_dirs = sorted(glob.glob(f"{resume_from_checkpoint}/global_step*"))
if len(deepspeed_checkpoint_dirs) > 0:
logger.info(f"Attempting to resume from {resume_from_checkpoint}")
# this magically updates self.optimizer and self.lr_scheduler
load_path, _ = deepspeed_engine.load_checkpoint(
resume_from_checkpoint, load_optimizer_states=True, load_lr_scheduler_states=True
)
if load_path is None:
raise ValueError(f"[deepspeed] failed to resume from checkpoint {resume_from_checkpoint}")
else:
logger.info(f"{resume_from_checkpoint} doesn't have deepspeed checkpoints, doing nothing")
return deepspeed_engine, optimizer, lr_scheduler | Init DeepSpeed, after updating the DeepSpeed configuration with any relevant Trainer's args. If `resume_from_checkpoint` was passed then an attempt to resume from a previously saved checkpoint will be made. Args: trainer: Trainer object num_training_steps: per single gpu resume_from_checkpoint: path to a checkpoint if to resume from after normal DeepSpeedEngine load inference: launch in inference mode (no optimizer and no lr scheduler) Returns: model, optimizer, lr_scheduler We may use `deepspeed_init` more than once during the life of Trainer, when we do - it's a temp hack based on: https://github.com/microsoft/DeepSpeed/issues/1394#issuecomment-937405374 until Deepspeed fixes a bug where it can't resume from a checkpoint after it did some stepping https://github.com/microsoft/DeepSpeed/issues/1612 |
10,647 | import importlib
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from huggingface_hub import HfFolder, model_info
from .utils import HF_MODULES_CACHE, TRANSFORMERS_DYNAMIC_MODULE_NAME, cached_file, is_offline_mode, logging
def get_class_in_module(class_name, module_path):
"""
Import a module on the cache directory for modules and extract a class from it.
"""
module_path = module_path.replace(os.path.sep, ".")
module = importlib.import_module(module_path)
return getattr(module, class_name)
def get_cached_module_file(
pretrained_model_name_or_path: Union[str, os.PathLike],
module_file: str,
cache_dir: Optional[Union[str, os.PathLike]] = None,
force_download: bool = False,
resume_download: bool = False,
proxies: Optional[Dict[str, str]] = None,
use_auth_token: Optional[Union[bool, str]] = None,
revision: Optional[str] = None,
local_files_only: bool = False,
):
"""
Prepares Downloads a module from a local folder or a distant repo and returns its path inside the cached
Transformers module.
Args:
pretrained_model_name_or_path (`str` or `os.PathLike`):
This can be either:
- a string, the *model id* of a pretrained model configuration hosted inside a model repo on
huggingface.co. Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced
under a user or organization name, like `dbmdz/bert-base-german-cased`.
- a path to a *directory* containing a configuration file saved using the
[`~PreTrainedTokenizer.save_pretrained`] method, e.g., `./my_model_directory/`.
module_file (`str`):
The name of the module file containing the class to look for.
cache_dir (`str` or `os.PathLike`, *optional*):
Path to a directory in which a downloaded pretrained model configuration should be cached if the standard
cache should not be used.
force_download (`bool`, *optional*, defaults to `False`):
Whether or not to force to (re-)download the configuration files and override the cached versions if they
exist.
resume_download (`bool`, *optional*, defaults to `False`):
Whether or not to delete incompletely received file. Attempts to resume the download if such a file exists.
proxies (`Dict[str, str]`, *optional*):
A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
'http://hostname': 'foo.bar:4012'}.` The proxies are used on each request.
use_auth_token (`str` or *bool*, *optional*):
The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated
when running `huggingface-cli login` (stored in `~/.huggingface`).
revision (`str`, *optional*, defaults to `"main"`):
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
identifier allowed by git.
local_files_only (`bool`, *optional*, defaults to `False`):
If `True`, will only try to load the tokenizer configuration from local files.
<Tip>
Passing `use_auth_token=True` is required when you want to use a private model.
</Tip>
Returns:
`str`: The path to the module inside the cache.
"""
if is_offline_mode() and not local_files_only:
logger.info("Offline mode: forcing local_files_only=True")
local_files_only = True
# Download and cache module_file from the repo `pretrained_model_name_or_path` of grab it if it's a local file.
pretrained_model_name_or_path = str(pretrained_model_name_or_path)
if os.path.isdir(pretrained_model_name_or_path):
submodule = "local"
else:
submodule = pretrained_model_name_or_path.replace("/", os.path.sep)
try:
# Load from URL or cache if already cached
resolved_module_file = cached_file(
pretrained_model_name_or_path,
module_file,
cache_dir=cache_dir,
force_download=force_download,
proxies=proxies,
resume_download=resume_download,
local_files_only=local_files_only,
use_auth_token=use_auth_token,
)
except EnvironmentError:
logger.error(f"Could not locate the {module_file} inside {pretrained_model_name_or_path}.")
raise
# Check we have all the requirements in our environment
modules_needed = check_imports(resolved_module_file)
# Now we move the module inside our cached dynamic modules.
full_submodule = TRANSFORMERS_DYNAMIC_MODULE_NAME + os.path.sep + submodule
create_dynamic_module(full_submodule)
submodule_path = Path(HF_MODULES_CACHE) / full_submodule
if submodule == "local":
# We always copy local files (we could hash the file to see if there was a change, and give them the name of
# that hash, to only copy when there is a modification but it seems overkill for now).
# The only reason we do the copy is to avoid putting too many folders in sys.path.
shutil.copy(resolved_module_file, submodule_path / module_file)
for module_needed in modules_needed:
module_needed = f"{module_needed}.py"
shutil.copy(os.path.join(pretrained_model_name_or_path, module_needed), submodule_path / module_needed)
else:
# Get the commit hash
# TODO: we will get this info in the etag soon, so retrieve it from there and not here.
if isinstance(use_auth_token, str):
token = use_auth_token
elif use_auth_token is True:
token = HfFolder.get_token()
else:
token = None
commit_hash = model_info(pretrained_model_name_or_path, revision=revision, token=token).sha
# The module file will end up being placed in a subfolder with the git hash of the repo. This way we get the
# benefit of versioning.
submodule_path = submodule_path / commit_hash
full_submodule = full_submodule + os.path.sep + commit_hash
create_dynamic_module(full_submodule)
if not (submodule_path / module_file).exists():
shutil.copy(resolved_module_file, submodule_path / module_file)
# Make sure we also have every file with relative
for module_needed in modules_needed:
if not (submodule_path / module_needed).exists():
get_cached_module_file(
pretrained_model_name_or_path,
f"{module_needed}.py",
cache_dir=cache_dir,
force_download=force_download,
resume_download=resume_download,
proxies=proxies,
use_auth_token=use_auth_token,
revision=revision,
local_files_only=local_files_only,
)
return os.path.join(full_submodule, module_file)
The provided code snippet includes necessary dependencies for implementing the `get_class_from_dynamic_module` function. Write a Python function `def get_class_from_dynamic_module( pretrained_model_name_or_path: Union[str, os.PathLike], module_file: str, class_name: str, cache_dir: Optional[Union[str, os.PathLike]] = None, force_download: bool = False, resume_download: bool = False, proxies: Optional[Dict[str, str]] = None, use_auth_token: Optional[Union[bool, str]] = None, revision: Optional[str] = None, local_files_only: bool = False, **kwargs, )` to solve the following problem:
Extracts a class from a module file, present in the local folder or repository of a model. <Tip warning={true}> Calling this function will execute the code in the module file found locally or downloaded from the Hub. It should therefore only be called on trusted repos. </Tip> Args: pretrained_model_name_or_path (`str` or `os.PathLike`): This can be either: - a string, the *model id* of a pretrained model configuration hosted inside a model repo on huggingface.co. Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced under a user or organization name, like `dbmdz/bert-base-german-cased`. - a path to a *directory* containing a configuration file saved using the [`~PreTrainedTokenizer.save_pretrained`] method, e.g., `./my_model_directory/`. module_file (`str`): The name of the module file containing the class to look for. class_name (`str`): The name of the class to import in the module. cache_dir (`str` or `os.PathLike`, *optional*): Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. force_download (`bool`, *optional*, defaults to `False`): Whether or not to force to (re-)download the configuration files and override the cached versions if they exist. resume_download (`bool`, *optional*, defaults to `False`): Whether or not to delete incompletely received file. Attempts to resume the download if such a file exists. proxies (`Dict[str, str]`, *optional*): A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.` The proxies are used on each request. use_auth_token (`str` or `bool`, *optional*): The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated when running `huggingface-cli login` (stored in `~/.huggingface`). revision (`str`, *optional*, defaults to `"main"`): The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git. local_files_only (`bool`, *optional*, defaults to `False`): If `True`, will only try to load the tokenizer configuration from local files. <Tip> Passing `use_auth_token=True` is required when you want to use a private model. </Tip> Returns: `type`: The class, dynamically imported from the module. Examples: ```python # Download module `modeling.py` from huggingface.co and cache then extract the class `MyBertModel` from this # module. cls = get_class_from_dynamic_module("sgugger/my-bert-model", "modeling.py", "MyBertModel") ```
Here is the function:
def get_class_from_dynamic_module(
pretrained_model_name_or_path: Union[str, os.PathLike],
module_file: str,
class_name: str,
cache_dir: Optional[Union[str, os.PathLike]] = None,
force_download: bool = False,
resume_download: bool = False,
proxies: Optional[Dict[str, str]] = None,
use_auth_token: Optional[Union[bool, str]] = None,
revision: Optional[str] = None,
local_files_only: bool = False,
**kwargs,
):
"""
Extracts a class from a module file, present in the local folder or repository of a model.
<Tip warning={true}>
Calling this function will execute the code in the module file found locally or downloaded from the Hub. It should
therefore only be called on trusted repos.
</Tip>
Args:
pretrained_model_name_or_path (`str` or `os.PathLike`):
This can be either:
- a string, the *model id* of a pretrained model configuration hosted inside a model repo on
huggingface.co. Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced
under a user or organization name, like `dbmdz/bert-base-german-cased`.
- a path to a *directory* containing a configuration file saved using the
[`~PreTrainedTokenizer.save_pretrained`] method, e.g., `./my_model_directory/`.
module_file (`str`):
The name of the module file containing the class to look for.
class_name (`str`):
The name of the class to import in the module.
cache_dir (`str` or `os.PathLike`, *optional*):
Path to a directory in which a downloaded pretrained model configuration should be cached if the standard
cache should not be used.
force_download (`bool`, *optional*, defaults to `False`):
Whether or not to force to (re-)download the configuration files and override the cached versions if they
exist.
resume_download (`bool`, *optional*, defaults to `False`):
Whether or not to delete incompletely received file. Attempts to resume the download if such a file exists.
proxies (`Dict[str, str]`, *optional*):
A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
'http://hostname': 'foo.bar:4012'}.` The proxies are used on each request.
use_auth_token (`str` or `bool`, *optional*):
The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated
when running `huggingface-cli login` (stored in `~/.huggingface`).
revision (`str`, *optional*, defaults to `"main"`):
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
identifier allowed by git.
local_files_only (`bool`, *optional*, defaults to `False`):
If `True`, will only try to load the tokenizer configuration from local files.
<Tip>
Passing `use_auth_token=True` is required when you want to use a private model.
</Tip>
Returns:
`type`: The class, dynamically imported from the module.
Examples:
```python
# Download module `modeling.py` from huggingface.co and cache then extract the class `MyBertModel` from this
# module.
cls = get_class_from_dynamic_module("sgugger/my-bert-model", "modeling.py", "MyBertModel")
```"""
# And lastly we get the class inside our newly created module
final_module = get_cached_module_file(
pretrained_model_name_or_path,
module_file,
cache_dir=cache_dir,
force_download=force_download,
resume_download=resume_download,
proxies=proxies,
use_auth_token=use_auth_token,
revision=revision,
local_files_only=local_files_only,
)
return get_class_in_module(class_name, final_module.replace(".py", "")) | Extracts a class from a module file, present in the local folder or repository of a model. <Tip warning={true}> Calling this function will execute the code in the module file found locally or downloaded from the Hub. It should therefore only be called on trusted repos. </Tip> Args: pretrained_model_name_or_path (`str` or `os.PathLike`): This can be either: - a string, the *model id* of a pretrained model configuration hosted inside a model repo on huggingface.co. Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced under a user or organization name, like `dbmdz/bert-base-german-cased`. - a path to a *directory* containing a configuration file saved using the [`~PreTrainedTokenizer.save_pretrained`] method, e.g., `./my_model_directory/`. module_file (`str`): The name of the module file containing the class to look for. class_name (`str`): The name of the class to import in the module. cache_dir (`str` or `os.PathLike`, *optional*): Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. force_download (`bool`, *optional*, defaults to `False`): Whether or not to force to (re-)download the configuration files and override the cached versions if they exist. resume_download (`bool`, *optional*, defaults to `False`): Whether or not to delete incompletely received file. Attempts to resume the download if such a file exists. proxies (`Dict[str, str]`, *optional*): A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.` The proxies are used on each request. use_auth_token (`str` or `bool`, *optional*): The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated when running `huggingface-cli login` (stored in `~/.huggingface`). revision (`str`, *optional*, defaults to `"main"`): The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git. local_files_only (`bool`, *optional*, defaults to `False`): If `True`, will only try to load the tokenizer configuration from local files. <Tip> Passing `use_auth_token=True` is required when you want to use a private model. </Tip> Returns: `type`: The class, dynamically imported from the module. Examples: ```python # Download module `modeling.py` from huggingface.co and cache then extract the class `MyBertModel` from this # module. cls = get_class_from_dynamic_module("sgugger/my-bert-model", "modeling.py", "MyBertModel") ``` |
10,648 | import importlib
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from huggingface_hub import HfFolder, model_info
from .utils import HF_MODULES_CACHE, TRANSFORMERS_DYNAMIC_MODULE_NAME, cached_file, is_offline_mode, logging
logger = logging.get_logger(__name__)
def get_relative_import_files(module_file):
"""
Get the list of all files that are needed for a given module. Note that this function recurses through the relative
imports (if a imports b and b imports c, it will return module files for b and c).
Args:
module_file (`str` or `os.PathLike`): The module file to inspect.
"""
no_change = False
files_to_check = [module_file]
all_relative_imports = []
# Let's recurse through all relative imports
while not no_change:
new_imports = []
for f in files_to_check:
new_imports.extend(get_relative_imports(f))
module_path = Path(module_file).parent
new_import_files = [str(module_path / m) for m in new_imports]
new_import_files = [f for f in new_import_files if f not in all_relative_imports]
files_to_check = [f"{f}.py" for f in new_import_files]
no_change = len(new_import_files) == 0
all_relative_imports.extend(files_to_check)
return all_relative_imports
The provided code snippet includes necessary dependencies for implementing the `custom_object_save` function. Write a Python function `def custom_object_save(obj, folder, config=None)` to solve the following problem:
Save the modeling files corresponding to a custom model/configuration/tokenizer etc. in a given folder. Optionally adds the proper fields in a config. Args: obj (`Any`): The object for which to save the module files. folder (`str` or `os.PathLike`): The folder where to save. config (`PretrainedConfig` or dictionary, `optional`): A config in which to register the auto_map corresponding to this custom object.
Here is the function:
def custom_object_save(obj, folder, config=None):
"""
Save the modeling files corresponding to a custom model/configuration/tokenizer etc. in a given folder. Optionally
adds the proper fields in a config.
Args:
obj (`Any`): The object for which to save the module files.
folder (`str` or `os.PathLike`): The folder where to save.
config (`PretrainedConfig` or dictionary, `optional`):
A config in which to register the auto_map corresponding to this custom object.
"""
if obj.__module__ == "__main__":
logger.warning(
f"We can't save the code defining {obj} in {folder} as it's been defined in __main__. You should put "
"this code in a separate module so we can include it in the saved folder and make it easier to share via "
"the Hub."
)
def _set_auto_map_in_config(_config):
module_name = obj.__class__.__module__
last_module = module_name.split(".")[-1]
full_name = f"{last_module}.{obj.__class__.__name__}"
# Special handling for tokenizers
if "Tokenizer" in full_name:
slow_tokenizer_class = None
fast_tokenizer_class = None
if obj.__class__.__name__.endswith("Fast"):
# Fast tokenizer: we have the fast tokenizer class and we may have the slow one has an attribute.
fast_tokenizer_class = f"{last_module}.{obj.__class__.__name__}"
if getattr(obj, "slow_tokenizer_class", None) is not None:
slow_tokenizer = getattr(obj, "slow_tokenizer_class")
slow_tok_module_name = slow_tokenizer.__module__
last_slow_tok_module = slow_tok_module_name.split(".")[-1]
slow_tokenizer_class = f"{last_slow_tok_module}.{slow_tokenizer.__name__}"
else:
# Slow tokenizer: no way to have the fast class
slow_tokenizer_class = f"{last_module}.{obj.__class__.__name__}"
full_name = (slow_tokenizer_class, fast_tokenizer_class)
if isinstance(_config, dict):
auto_map = _config.get("auto_map", {})
auto_map[obj._auto_class] = full_name
_config["auto_map"] = auto_map
elif getattr(_config, "auto_map", None) is not None:
_config.auto_map[obj._auto_class] = full_name
else:
_config.auto_map = {obj._auto_class: full_name}
# Add object class to the config auto_map
if isinstance(config, (list, tuple)):
for cfg in config:
_set_auto_map_in_config(cfg)
elif config is not None:
_set_auto_map_in_config(config)
# Copy module file to the output folder.
object_file = sys.modules[obj.__module__].__file__
dest_file = Path(folder) / (Path(object_file).name)
shutil.copy(object_file, dest_file)
# Gather all relative imports recursively and make sure they are copied as well.
for needed_file in get_relative_import_files(object_file):
dest_file = Path(folder) / (Path(needed_file).name)
shutil.copy(needed_file, dest_file) | Save the modeling files corresponding to a custom model/configuration/tokenizer etc. in a given folder. Optionally adds the proper fields in a config. Args: obj (`Any`): The object for which to save the module files. folder (`str` or `os.PathLike`): The folder where to save. config (`PretrainedConfig` or dictionary, `optional`): A config in which to register the auto_map corresponding to this custom object. |
10,649 | import math
from collections import OrderedDict
import torch
from packaging import version
from torch import Tensor, nn
from .utils import logging
ACT2FN = ClassInstantier(ACT2CLS)
def get_activation(activation_string):
if activation_string in ACT2FN:
return ACT2FN[activation_string]
else:
raise KeyError(f"function {activation_string} not found in ACT2FN mapping {list(ACT2FN.keys())}") | null |
10,650 | import subprocess
from typing import Union
import numpy as np
from ..utils import add_end_docstrings, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
The provided code snippet includes necessary dependencies for implementing the `ffmpeg_read` function. Write a Python function `def ffmpeg_read(bpayload: bytes, sampling_rate: int) -> np.array` to solve the following problem:
Helper function to read an audio file through ffmpeg.
Here is the function:
def ffmpeg_read(bpayload: bytes, sampling_rate: int) -> np.array:
"""
Helper function to read an audio file through ffmpeg.
"""
ar = f"{sampling_rate}"
ac = "1"
format_for_conversion = "f32le"
ffmpeg_command = [
"ffmpeg",
"-i",
"pipe:0",
"-ac",
ac,
"-ar",
ar,
"-f",
format_for_conversion,
"-hide_banner",
"-loglevel",
"quiet",
"pipe:1",
]
try:
ffmpeg_process = subprocess.Popen(ffmpeg_command, stdin=subprocess.PIPE, stdout=subprocess.PIPE)
except FileNotFoundError:
raise ValueError("ffmpeg was not found but is required to load audio files from filename")
output_stream = ffmpeg_process.communicate(bpayload)
out_bytes = output_stream[0]
audio = np.frombuffer(out_bytes, np.float32)
if audio.shape[0] == 0:
raise ValueError("Malformed soundfile")
return audio | Helper function to read an audio file through ffmpeg. |
10,651 | import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `ffmpeg_read` function. Write a Python function `def ffmpeg_read(bpayload: bytes, sampling_rate: int) -> np.array` to solve the following problem:
Helper function to read an audio file through ffmpeg.
Here is the function:
def ffmpeg_read(bpayload: bytes, sampling_rate: int) -> np.array:
"""
Helper function to read an audio file through ffmpeg.
"""
ar = f"{sampling_rate}"
ac = "1"
format_for_conversion = "f32le"
ffmpeg_command = [
"ffmpeg",
"-i",
"pipe:0",
"-ac",
ac,
"-ar",
ar,
"-f",
format_for_conversion,
"-hide_banner",
"-loglevel",
"quiet",
"pipe:1",
]
try:
with subprocess.Popen(ffmpeg_command, stdin=subprocess.PIPE, stdout=subprocess.PIPE) as ffmpeg_process:
output_stream = ffmpeg_process.communicate(bpayload)
except FileNotFoundError as error:
raise ValueError("ffmpeg was not found but is required to load audio files from filename") from error
out_bytes = output_stream[0]
audio = np.frombuffer(out_bytes, np.float32)
if audio.shape[0] == 0:
raise ValueError("Malformed soundfile")
return audio | Helper function to read an audio file through ffmpeg. |
10,652 | import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def ffmpeg_microphone(
sampling_rate: int,
chunk_length_s: float,
format_for_conversion: str = "f32le",
):
"""
Helper function ro read raw microphone data.
"""
ar = f"{sampling_rate}"
ac = "1"
if format_for_conversion == "s16le":
size_of_sample = 2
elif format_for_conversion == "f32le":
size_of_sample = 4
else:
raise ValueError(f"Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`")
system = platform.system()
if system == "Linux":
format_ = "alsa"
input_ = "default"
elif system == "Darwin":
format_ = "avfoundation"
input_ = ":0"
elif system == "Windows":
format_ = "dshow"
input_ = "default"
ffmpeg_command = [
"ffmpeg",
"-f",
format_,
"-i",
input_,
"-ac",
ac,
"-ar",
ar,
"-f",
format_for_conversion,
"-fflags",
"nobuffer",
"-hide_banner",
"-loglevel",
"quiet",
"pipe:1",
]
chunk_len = int(round(sampling_rate * chunk_length_s)) * size_of_sample
iterator = _ffmpeg_stream(ffmpeg_command, chunk_len)
for item in iterator:
yield item
def chunk_bytes_iter(iterator, chunk_len: int, stride: Tuple[int, int], stream: bool = False):
"""
Reads raw bytes from an iterator and does chunks of length `chunk_len`. Optionally adds `stride` to each chunks to
get overlaps. `stream` is used to return partial results even if a full `chunk_len` is not yet available.
"""
acc = b""
stride_left, stride_right = stride
if stride_left + stride_right >= chunk_len:
raise ValueError(
f"Stride needs to be strictly smaller than chunk_len: ({stride_left}, {stride_right}) vs {chunk_len}"
)
_stride_left = 0
for raw in iterator:
acc += raw
if stream and len(acc) < chunk_len:
stride = (_stride_left, 0)
yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
else:
while len(acc) >= chunk_len:
# We are flushing the accumulator
stride = (_stride_left, stride_right)
item = {"raw": acc[:chunk_len], "stride": stride}
if stream:
item["partial"] = False
yield item
_stride_left = stride_left
acc = acc[chunk_len - stride_left - stride_right :]
# Last chunk
if len(acc) > stride_left:
item = {"raw": acc, "stride": (_stride_left, 0)}
if stream:
item["partial"] = False
yield item
The provided code snippet includes necessary dependencies for implementing the `ffmpeg_microphone_live` function. Write a Python function `def ffmpeg_microphone_live( sampling_rate: int, chunk_length_s: float, stream_chunk_s: Optional[int] = None, stride_length_s: Optional[Union[Tuple[float, float], float]] = None, format_for_conversion: str = "f32le", )` to solve the following problem:
Helper function to read audio from the microphone file through ffmpeg. This will output `partial` overlapping chunks starting from `stream_chunk_s` (if it is defined) until `chunk_length_s` is reached. It will make use of striding to avoid errors on the "sides" of the various chunks. Arguments: sampling_rate (`int`): The sampling_rate to use when reading the data from the microphone. Try using the model's sampling_rate to avoid resampling later. chunk_length_s (`float` or `int`): The length of the maximum chunk of audio to be sent returned. This includes the eventual striding. stream_chunk_s (`float` or `int`) The length of the minimal temporary audio to be returned. stride_length_s (`float` or `int` or `(float, float)`, *optional*, defaults to `None`) The length of the striding to be used. Stride is used to provide context to a model on the (left, right) of an audio sample but without using that part to actually make the prediction. Setting this does not change the length of the chunk. format_for_conversion: (`str`, defalts to `f32le`) The name of the format of the audio samples to be returned by ffmpeg. The standard is `f32le`, `s16le` could also be used. Return: A generator yielding dictionaries of the following form `{"sampling_rate": int, "raw": np.array(), "partial" bool}` With optionnally a `"stride" (int, int)` key if `stride_length_s` is defined. `stride` and `raw` are all expressed in `samples`, and `partial` is a boolean saying if the current yield item is a whole chunk, or a partial temporary result to be later replaced by another larger chunk.
Here is the function:
def ffmpeg_microphone_live(
sampling_rate: int,
chunk_length_s: float,
stream_chunk_s: Optional[int] = None,
stride_length_s: Optional[Union[Tuple[float, float], float]] = None,
format_for_conversion: str = "f32le",
):
"""
Helper function to read audio from the microphone file through ffmpeg. This will output `partial` overlapping
chunks starting from `stream_chunk_s` (if it is defined) until `chunk_length_s` is reached. It will make use of
striding to avoid errors on the "sides" of the various chunks.
Arguments:
sampling_rate (`int`):
The sampling_rate to use when reading the data from the microphone. Try using the model's sampling_rate to
avoid resampling later.
chunk_length_s (`float` or `int`):
The length of the maximum chunk of audio to be sent returned. This includes the eventual striding.
stream_chunk_s (`float` or `int`)
The length of the minimal temporary audio to be returned.
stride_length_s (`float` or `int` or `(float, float)`, *optional*, defaults to `None`)
The length of the striding to be used. Stride is used to provide context to a model on the (left, right) of
an audio sample but without using that part to actually make the prediction. Setting this does not change
the length of the chunk.
format_for_conversion: (`str`, defalts to `f32le`)
The name of the format of the audio samples to be returned by ffmpeg. The standard is `f32le`, `s16le`
could also be used.
Return:
A generator yielding dictionaries of the following form
`{"sampling_rate": int, "raw": np.array(), "partial" bool}` With optionnally a `"stride" (int, int)` key if
`stride_length_s` is defined.
`stride` and `raw` are all expressed in `samples`, and `partial` is a boolean saying if the current yield item
is a whole chunk, or a partial temporary result to be later replaced by another larger chunk.
"""
if stream_chunk_s is not None:
chunk_s = stream_chunk_s
else:
chunk_s = chunk_length_s
microphone = ffmpeg_microphone(sampling_rate, chunk_s, format_for_conversion=format_for_conversion)
if format_for_conversion == "s16le":
dtype = np.int16
size_of_sample = 2
elif format_for_conversion == "f32le":
dtype = np.float32
size_of_sample = 4
else:
raise ValueError(f"Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`")
if stride_length_s is None:
stride_length_s = chunk_length_s / 6
chunk_len = int(round(sampling_rate * chunk_length_s)) * size_of_sample
if isinstance(stride_length_s, (int, float)):
stride_length_s = [stride_length_s, stride_length_s]
stride_left = int(round(sampling_rate * stride_length_s[0])) * size_of_sample
stride_right = int(round(sampling_rate * stride_length_s[1])) * size_of_sample
for item in chunk_bytes_iter(microphone, chunk_len, stride=(stride_left, stride_right), stream=True):
# Put everything back in numpy scale
item["raw"] = np.frombuffer(item["raw"], dtype=dtype)
item["stride"] = (
item["stride"][0] // size_of_sample,
item["stride"][1] // size_of_sample,
)
item["sampling_rate"] = sampling_rate
yield item | Helper function to read audio from the microphone file through ffmpeg. This will output `partial` overlapping chunks starting from `stream_chunk_s` (if it is defined) until `chunk_length_s` is reached. It will make use of striding to avoid errors on the "sides" of the various chunks. Arguments: sampling_rate (`int`): The sampling_rate to use when reading the data from the microphone. Try using the model's sampling_rate to avoid resampling later. chunk_length_s (`float` or `int`): The length of the maximum chunk of audio to be sent returned. This includes the eventual striding. stream_chunk_s (`float` or `int`) The length of the minimal temporary audio to be returned. stride_length_s (`float` or `int` or `(float, float)`, *optional*, defaults to `None`) The length of the striding to be used. Stride is used to provide context to a model on the (left, right) of an audio sample but without using that part to actually make the prediction. Setting this does not change the length of the chunk. format_for_conversion: (`str`, defalts to `f32le`) The name of the format of the audio samples to be returned by ffmpeg. The standard is `f32le`, `s16le` could also be used. Return: A generator yielding dictionaries of the following form `{"sampling_rate": int, "raw": np.array(), "partial" bool}` With optionnally a `"stride" (int, int)` key if `stride_length_s` is defined. `stride` and `raw` are all expressed in `samples`, and `partial` is a boolean saying if the current yield item is a whole chunk, or a partial temporary result to be later replaced by another larger chunk. |
10,653 | import re
from typing import List, Optional, Tuple, Union
import numpy as np
from ..utils import (
ExplicitEnum,
add_end_docstrings,
is_pytesseract_available,
is_torch_available,
is_vision_available,
logging,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
from .question_answering import select_starts_ends
def normalize_box(box, width, height):
return [
int(1000 * (box[0] / width)),
int(1000 * (box[1] / height)),
int(1000 * (box[2] / width)),
int(1000 * (box[3] / height)),
]
The provided code snippet includes necessary dependencies for implementing the `apply_tesseract` function. Write a Python function `def apply_tesseract(image: "Image.Image", lang: Optional[str], tesseract_config: Optional[str])` to solve the following problem:
Applies Tesseract OCR on a document image, and returns recognized words + normalized bounding boxes.
Here is the function:
def apply_tesseract(image: "Image.Image", lang: Optional[str], tesseract_config: Optional[str]):
"""Applies Tesseract OCR on a document image, and returns recognized words + normalized bounding boxes."""
# apply OCR
data = pytesseract.image_to_data(image, lang=lang, output_type="dict", config=tesseract_config)
words, left, top, width, height = data["text"], data["left"], data["top"], data["width"], data["height"]
# filter empty words and corresponding coordinates
irrelevant_indices = [idx for idx, word in enumerate(words) if not word.strip()]
words = [word for idx, word in enumerate(words) if idx not in irrelevant_indices]
left = [coord for idx, coord in enumerate(left) if idx not in irrelevant_indices]
top = [coord for idx, coord in enumerate(top) if idx not in irrelevant_indices]
width = [coord for idx, coord in enumerate(width) if idx not in irrelevant_indices]
height = [coord for idx, coord in enumerate(height) if idx not in irrelevant_indices]
# turn coordinates into (left, top, left+width, top+height) format
actual_boxes = []
for x, y, w, h in zip(left, top, width, height):
actual_box = [x, y, x + w, y + h]
actual_boxes.append(actual_box)
image_width, image_height = image.size
# finally, normalize the bounding boxes
normalized_boxes = []
for box in actual_boxes:
normalized_boxes.append(normalize_box(box, image_width, image_height))
if len(words) != len(normalized_boxes):
raise ValueError("Not as many words as there are bounding boxes")
return words, normalized_boxes | Applies Tesseract OCR on a document image, and returns recognized words + normalized bounding boxes. |
10,654 | import types
import warnings
from collections.abc import Iterable
from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union
import numpy as np
from ..data import SquadExample, SquadFeatures, squad_convert_examples_to_features
from ..modelcard import ModelCard
from ..tokenization_utils import PreTrainedTokenizer
from ..utils import (
PaddingStrategy,
add_end_docstrings,
is_tf_available,
is_tokenizers_available,
is_torch_available,
logging,
)
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline
def decode_spans(
start: np.ndarray, end: np.ndarray, topk: int, max_answer_len: int, undesired_tokens: np.ndarray
) -> Tuple:
"""
Take the output of any `ModelForQuestionAnswering` and will generate probabilities for each span to be the actual
answer.
In addition, it filters out some unwanted/impossible cases like answer len being greater than max_answer_len or
answer end position being before the starting position. The method supports output the k-best answer through the
topk argument.
Args:
start (`np.ndarray`): Individual start probabilities for each token.
end (`np.ndarray`): Individual end probabilities for each token.
topk (`int`): Indicates how many possible answer span(s) to extract from the model output.
max_answer_len (`int`): Maximum size of the answer to extract from the model's output.
undesired_tokens (`np.ndarray`): Mask determining tokens that can be part of the answer
"""
# Ensure we have batch axis
if start.ndim == 1:
start = start[None]
if end.ndim == 1:
end = end[None]
# Compute the score of each tuple(start, end) to be the real answer
outer = np.matmul(np.expand_dims(start, -1), np.expand_dims(end, 1))
# Remove candidate with end < start and end - start > max_answer_len
candidates = np.tril(np.triu(outer), max_answer_len - 1)
# Inspired by Chen & al. (https://github.com/facebookresearch/DrQA)
scores_flat = candidates.flatten()
if topk == 1:
idx_sort = [np.argmax(scores_flat)]
elif len(scores_flat) < topk:
idx_sort = np.argsort(-scores_flat)
else:
idx = np.argpartition(-scores_flat, topk)[0:topk]
idx_sort = idx[np.argsort(-scores_flat[idx])]
starts, ends = np.unravel_index(idx_sort, candidates.shape)[1:]
desired_spans = np.isin(starts, undesired_tokens.nonzero()) & np.isin(ends, undesired_tokens.nonzero())
starts = starts[desired_spans]
ends = ends[desired_spans]
scores = candidates[0, starts, ends]
return starts, ends, scores
The provided code snippet includes necessary dependencies for implementing the `select_starts_ends` function. Write a Python function `def select_starts_ends( start, end, p_mask, attention_mask, min_null_score=1000000, top_k=1, handle_impossible_answer=False, max_answer_len=15, )` to solve the following problem:
Takes the raw output of any `ModelForQuestionAnswering` and first normalizes its outputs and then uses `decode_spans()` to generate probabilities for each span to be the actual answer. Args: start (`np.ndarray`): Individual start logits for each token. end (`np.ndarray`): Individual end logits for each token. p_mask (`np.ndarray`): A mask with 1 for values that cannot be in the answer attention_mask (`np.ndarray`): The attention mask generated by the tokenizer min_null_score(`float`): The minimum null (empty) answer score seen so far. topk (`int`): Indicates how many possible answer span(s) to extract from the model output. handle_impossible_answer(`bool`): Whether to allow null (empty) answers max_answer_len (`int`): Maximum size of the answer to extract from the model's output.
Here is the function:
def select_starts_ends(
start,
end,
p_mask,
attention_mask,
min_null_score=1000000,
top_k=1,
handle_impossible_answer=False,
max_answer_len=15,
):
"""
Takes the raw output of any `ModelForQuestionAnswering` and first normalizes its outputs and then uses
`decode_spans()` to generate probabilities for each span to be the actual answer.
Args:
start (`np.ndarray`): Individual start logits for each token.
end (`np.ndarray`): Individual end logits for each token.
p_mask (`np.ndarray`): A mask with 1 for values that cannot be in the answer
attention_mask (`np.ndarray`): The attention mask generated by the tokenizer
min_null_score(`float`): The minimum null (empty) answer score seen so far.
topk (`int`): Indicates how many possible answer span(s) to extract from the model output.
handle_impossible_answer(`bool`): Whether to allow null (empty) answers
max_answer_len (`int`): Maximum size of the answer to extract from the model's output.
"""
# Ensure padded tokens & question tokens cannot belong to the set of candidate answers.
undesired_tokens = np.abs(np.array(p_mask) - 1)
if attention_mask is not None:
undesired_tokens = undesired_tokens & attention_mask
# Generate mask
undesired_tokens_mask = undesired_tokens == 0.0
# Make sure non-context indexes in the tensor cannot contribute to the softmax
start = np.where(undesired_tokens_mask, -10000.0, start)
end = np.where(undesired_tokens_mask, -10000.0, end)
# Normalize logits and spans to retrieve the answer
start = np.exp(start - start.max(axis=-1, keepdims=True))
start = start / start.sum()
end = np.exp(end - end.max(axis=-1, keepdims=True))
end = end / end.sum()
if handle_impossible_answer:
min_null_score = min(min_null_score, (start[0, 0] * end[0, 0]).item())
# Mask CLS
start[0, 0] = end[0, 0] = 0.0
starts, ends, scores = decode_spans(start, end, top_k, max_answer_len, undesired_tokens)
return starts, ends, scores, min_null_score | Takes the raw output of any `ModelForQuestionAnswering` and first normalizes its outputs and then uses `decode_spans()` to generate probabilities for each span to be the actual answer. Args: start (`np.ndarray`): Individual start logits for each token. end (`np.ndarray`): Individual end logits for each token. p_mask (`np.ndarray`): A mask with 1 for values that cannot be in the answer attention_mask (`np.ndarray`): The attention mask generated by the tokenizer min_null_score(`float`): The minimum null (empty) answer score seen so far. topk (`int`): Indicates how many possible answer span(s) to extract from the model output. handle_impossible_answer(`bool`): Whether to allow null (empty) answers max_answer_len (`int`): Maximum size of the answer to extract from the model's output. |
10,655 | import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
def sigmoid(_outputs):
return 1.0 / (1.0 + np.exp(-_outputs)) | null |
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