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Update tiny model creation script (#22202)

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* Update UNCONVERTIBLE_MODEL_ARCHITECTURES

* Deal with 2 model tester classes in single test file

* Deal with 2 model tester classes in single test file

* Deal with 2 model tester classes in single test file

* make style and quality

---------

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
This commit is contained in:
Yih-Dar 2023-03-16 14:21:58 +01:00 committed by GitHub
parent 464d420775
commit 4c5c0af7e5
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1 changed files with 20 additions and 7 deletions
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27
utils/create_dummy_models.py
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@ -16,18 +16,17 @@
import argparse
import collections.abc
import copy
import importlib
import inspect
import json
import os
import shutil
import sys
import tempfile
import traceback
from pathlib import Path
from check_config_docstrings import get_checkpoint_from_config_class
from datasets import load_dataset
from get_test_info import get_model_to_tester_mapping, get_tester_classes_for_model
from huggingface_hub import Repository, create_repo, upload_folder
from transformers import (
@ -58,7 +57,6 @@ logging.set_verbosity_error()
logging.disable_progress_bar()
logger = logging.get_logger(__name__)
sys.path.append(".")
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
if not is_torch_available():
@ -67,6 +65,7 @@ if not is_torch_available():
if not is_tf_available():
raise ValueError("Please install TensorFlow.")
FRAMEWORKS = ["pytorch", "tensorflow"]
INVALID_ARCH = []
TARGET_VOCAB_SIZE = 1024
@ -94,8 +93,12 @@ UNCONVERTIBLE_MODEL_ARCHITECTURES = {
"TFCamembertModel",
"TFCamembertForCausalLM",
"DecisionTransformerModel",
"GraphormerModel",
"InformerModel",
"JukeboxModel",
"MarianForCausalLM",
"MaskFormerSwinModel",
"MaskFormerSwinBackbone",
"MT5Model",
"MT5ForConditionalGeneration",
"TFMT5ForConditionalGeneration",
@ -126,6 +129,7 @@ UNCONVERTIBLE_MODEL_ARCHITECTURES = {
"XLMRobertaForQuestionAnswering",
"TFXLMRobertaForSequenceClassification",
"TFXLMRobertaForMaskedLM",
"TFXLMRobertaForCausalLM",
"TFXLMRobertaForQuestionAnswering",
"TFXLMRobertaModel",
"TFXLMRobertaForMultipleChoice",
@ -355,7 +359,7 @@ def build_processor(config_class, processor_class, allow_no_checkpoint=False):
return processor
def get_tiny_config(config_class, **model_tester_kwargs):
def get_tiny_config(config_class, model_class=None, **model_tester_kwargs):
"""Retrieve a tiny configuration from `config_class` using each model's `ModelTester`.
Args:
@ -378,9 +382,18 @@ def get_tiny_config(config_class, **model_tester_kwargs):
module_name = model_type_to_module_name(model_type)
if not modeling_name.startswith(module_name):
raise ValueError(f"{modeling_name} doesn't start with {module_name}!")
module = importlib.import_module(f".models.{module_name}.test_modeling_{modeling_name}", package="tests")
camel_case_model_name = config_class.__name__.split("Config")[0]
model_tester_class = getattr(module, f"{camel_case_model_name}ModelTester", None)
test_file = os.path.join("tests", "models", module_name, f"test_modeling_{modeling_name}.py")
models_to_model_testers = get_model_to_tester_mapping(test_file)
# Find the model tester class
model_tester_class = None
tester_classes = []
if model_class is not None:
tester_classes = get_tester_classes_for_model(test_file, model_class)
else:
for _tester_classes in models_to_model_testers.values():
tester_classes.extend(_tester_classes)
if len(tester_classes) > 0:
model_tester_class = sorted(tester_classes, key=lambda x: x.__name__)[0]
except ModuleNotFoundError:
error = f"Tiny config not created for {model_type} - cannot find the testing module from the model name."
raise ValueError(error)