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Unverified Commit 372f5003 authored by Sylvain Gugger's avatar Sylvain Gugger Committed by GitHub
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Split common test from core tests (#24284)

parent a611ac9b
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Showing with 2663 additions and 2478 deletions
tests/test_configuration_common.py
View file @ 372f5003
...@@ -16,80 +16,11 @@ ...@@ -16,80 +16,11 @@
import copy import copy
import json import json
import os import os
import shutil
import sys
import tempfile import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo from transformers import is_torch_available
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConfig, GPT2Config, is_torch_available from .test_configuration_utils import config_common_kwargs
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / "utils"))
from test_module.custom_configuration import CustomConfig # noqa E402
config_common_kwargs = {
"return_dict": False,
"output_hidden_states": True,
"output_attentions": True,
"torchscript": True,
"torch_dtype": "float16",
"use_bfloat16": True,
"tf_legacy_loss": True,
"pruned_heads": {"a": 1},
"tie_word_embeddings": False,
"is_decoder": True,
"cross_attention_hidden_size": 128,
"add_cross_attention": True,
"tie_encoder_decoder": True,
"max_length": 50,
"min_length": 3,
"do_sample": True,
"early_stopping": True,
"num_beams": 3,
"num_beam_groups": 3,
"diversity_penalty": 0.5,
"temperature": 2.0,
"top_k": 10,
"top_p": 0.7,
"typical_p": 0.2,
"repetition_penalty": 0.8,
"length_penalty": 0.8,
"no_repeat_ngram_size": 5,
"encoder_no_repeat_ngram_size": 5,
"bad_words_ids": [1, 2, 3],
"num_return_sequences": 3,
"chunk_size_feed_forward": 5,
"output_scores": True,
"return_dict_in_generate": True,
"forced_bos_token_id": 2,
"forced_eos_token_id": 3,
"remove_invalid_values": True,
"architectures": ["BertModel"],
"finetuning_task": "translation",
"id2label": {0: "label"},
"label2id": {"label": "0"},
"tokenizer_class": "BertTokenizerFast",
"prefix": "prefix",
"bos_token_id": 6,
"pad_token_id": 7,
"eos_token_id": 8,
"sep_token_id": 9,
"decoder_start_token_id": 10,
"exponential_decay_length_penalty": (5, 1.01),
"suppress_tokens": [0, 1],
"begin_suppress_tokens": 2,
"task_specific_params": {"translation": "some_params"},
"problem_type": "regression",
}
class ConfigTester(object): class ConfigTester(object):
...@@ -220,200 +151,3 @@ class ConfigTester(object): ...@@ -220,200 +151,3 @@ class ConfigTester(object):
self.create_and_test_config_with_num_labels() self.create_and_test_config_with_num_labels()
self.check_config_can_be_init_without_params() self.check_config_can_be_init_without_params()
self.check_config_arguments_init() self.check_config_arguments_init()
@is_staging_test
class ConfigPushToHubTester(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls._token = TOKEN
HfFolder.save_token(TOKEN)
@classmethod
def tearDownClass(cls):
try:
delete_repo(token=cls._token, repo_id="test-config")
except HTTPError:
pass
try:
delete_repo(token=cls._token, repo_id="valid_org/test-config-org")
except HTTPError:
pass
try:
delete_repo(token=cls._token, repo_id="test-dynamic-config")
except HTTPError:
pass
def test_push_to_hub(self):
config = BertConfig(
vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37
)
config.push_to_hub("test-config", use_auth_token=self._token)
new_config = BertConfig.from_pretrained(f"{USER}/test-config")
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(v, getattr(new_config, k))
# Reset repo
delete_repo(token=self._token, repo_id="test-config")
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(tmp_dir, repo_id="test-config", push_to_hub=True, use_auth_token=self._token)
new_config = BertConfig.from_pretrained(f"{USER}/test-config")
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(v, getattr(new_config, k))
def test_push_to_hub_in_organization(self):
config = BertConfig(
vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37
)
config.push_to_hub("valid_org/test-config-org", use_auth_token=self._token)
new_config = BertConfig.from_pretrained("valid_org/test-config-org")
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(v, getattr(new_config, k))
# Reset repo
delete_repo(token=self._token, repo_id="valid_org/test-config-org")
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
tmp_dir, repo_id="valid_org/test-config-org", push_to_hub=True, use_auth_token=self._token
)
new_config = BertConfig.from_pretrained("valid_org/test-config-org")
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(v, getattr(new_config, k))
def test_push_to_hub_dynamic_config(self):
CustomConfig.register_for_auto_class()
config = CustomConfig(attribute=42)
config.push_to_hub("test-dynamic-config", use_auth_token=self._token)
# This has added the proper auto_map field to the config
self.assertDictEqual(config.auto_map, {"AutoConfig": "custom_configuration.CustomConfig"})
new_config = AutoConfig.from_pretrained(f"{USER}/test-dynamic-config", trust_remote_code=True)
# Can't make an isinstance check because the new_config is from the FakeConfig class of a dynamic module
self.assertEqual(new_config.__class__.__name__, "CustomConfig")
self.assertEqual(new_config.attribute, 42)
class ConfigTestUtils(unittest.TestCase):
def test_config_from_string(self):
c = GPT2Config()
# attempt to modify each of int/float/bool/str config records and verify they were updated
n_embd = c.n_embd + 1 # int
resid_pdrop = c.resid_pdrop + 1.0 # float
scale_attn_weights = not c.scale_attn_weights # bool
summary_type = c.summary_type + "foo" # str
c.update_from_string(
f"n_embd={n_embd},resid_pdrop={resid_pdrop},scale_attn_weights={scale_attn_weights},summary_type={summary_type}"
)
self.assertEqual(n_embd, c.n_embd, "mismatch for key: n_embd")
self.assertEqual(resid_pdrop, c.resid_pdrop, "mismatch for key: resid_pdrop")
self.assertEqual(scale_attn_weights, c.scale_attn_weights, "mismatch for key: scale_attn_weights")
self.assertEqual(summary_type, c.summary_type, "mismatch for key: summary_type")
def test_config_common_kwargs_is_complete(self):
base_config = PretrainedConfig()
missing_keys = [key for key in base_config.__dict__ if key not in config_common_kwargs]
# If this part of the test fails, you have arguments to addin config_common_kwargs above.
self.assertListEqual(
missing_keys, ["is_encoder_decoder", "_name_or_path", "_commit_hash", "transformers_version"]
)
keys_with_defaults = [key for key, value in config_common_kwargs.items() if value == getattr(base_config, key)]
if len(keys_with_defaults) > 0:
raise ValueError(
"The following keys are set with the default values in"
" `test_configuration_common.config_common_kwargs` pick another value for them:"
f" {', '.join(keys_with_defaults)}."
)
def test_from_pretrained_subfolder(self):
with self.assertRaises(OSError):
# config is in subfolder, the following should not work without specifying the subfolder
_ = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert-subfolder")
config = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert-subfolder", subfolder="bert")
self.assertIsNotNone(config)
def test_cached_files_are_used_when_internet_is_down(self):
# A mock response for an HTTP head request to emulate server down
response_mock = mock.Mock()
response_mock.status_code = 500
response_mock.headers = {}
response_mock.raise_for_status.side_effect = HTTPError
response_mock.json.return_value = {}
# Download this model to make sure it's in the cache.
_ = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert")
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch("requests.Session.request", return_value=response_mock) as mock_head:
_ = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert")
# This check we did call the fake head request
mock_head.assert_called()
def test_legacy_load_from_url(self):
# This test is for deprecated behavior and can be removed in v5
_ = BertConfig.from_pretrained(
"https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/config.json"
)
class ConfigurationVersioningTest(unittest.TestCase):
def test_local_versioning(self):
configuration = AutoConfig.from_pretrained("bert-base-cased")
configuration.configuration_files = ["config.4.0.0.json"]
with tempfile.TemporaryDirectory() as tmp_dir:
configuration.save_pretrained(tmp_dir)
configuration.hidden_size = 2
json.dump(configuration.to_dict(), open(os.path.join(tmp_dir, "config.4.0.0.json"), "w"))
# This should pick the new configuration file as the version of Transformers is > 4.0.0
new_configuration = AutoConfig.from_pretrained(tmp_dir)
self.assertEqual(new_configuration.hidden_size, 2)
# Will need to be adjusted if we reach v42 and this test is still here.
# Should pick the old configuration file as the version of Transformers is < 4.42.0
configuration.configuration_files = ["config.42.0.0.json"]
configuration.hidden_size = 768
configuration.save_pretrained(tmp_dir)
shutil.move(os.path.join(tmp_dir, "config.4.0.0.json"), os.path.join(tmp_dir, "config.42.0.0.json"))
new_configuration = AutoConfig.from_pretrained(tmp_dir)
self.assertEqual(new_configuration.hidden_size, 768)
def test_repo_versioning_before(self):
# This repo has two configuration files, one for v4.0.0 and above with a different hidden size.
repo = "hf-internal-testing/test-two-configs"
import transformers as new_transformers
new_transformers.configuration_utils.__version__ = "v4.0.0"
new_configuration, kwargs = new_transformers.models.auto.AutoConfig.from_pretrained(
repo, return_unused_kwargs=True
)
self.assertEqual(new_configuration.hidden_size, 2)
# This checks `_configuration_file` ia not kept in the kwargs by mistake.
self.assertDictEqual(kwargs, {})
# Testing an older version by monkey-patching the version in the module it's used.
import transformers as old_transformers
old_transformers.configuration_utils.__version__ = "v3.0.0"
old_configuration = old_transformers.models.auto.AutoConfig.from_pretrained(repo)
self.assertEqual(old_configuration.hidden_size, 768)
tests/test_configuration_utils.py 0 → 100644
View file @ 372f5003
# coding=utf-8
# Copyright 2019 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConfig, GPT2Config
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / "utils"))
from test_module.custom_configuration import CustomConfig # noqa E402
config_common_kwargs = {
"return_dict": False,
"output_hidden_states": True,
"output_attentions": True,
"torchscript": True,
"torch_dtype": "float16",
"use_bfloat16": True,
"tf_legacy_loss": True,
"pruned_heads": {"a": 1},
"tie_word_embeddings": False,
"is_decoder": True,
"cross_attention_hidden_size": 128,
"add_cross_attention": True,
"tie_encoder_decoder": True,
"max_length": 50,
"min_length": 3,
"do_sample": True,
"early_stopping": True,
"num_beams": 3,
"num_beam_groups": 3,
"diversity_penalty": 0.5,
"temperature": 2.0,
"top_k": 10,
"top_p": 0.7,
"typical_p": 0.2,
"repetition_penalty": 0.8,
"length_penalty": 0.8,
"no_repeat_ngram_size": 5,
"encoder_no_repeat_ngram_size": 5,
"bad_words_ids": [1, 2, 3],
"num_return_sequences": 3,
"chunk_size_feed_forward": 5,
"output_scores": True,
"return_dict_in_generate": True,
"forced_bos_token_id": 2,
"forced_eos_token_id": 3,
"remove_invalid_values": True,
"architectures": ["BertModel"],
"finetuning_task": "translation",
"id2label": {0: "label"},
"label2id": {"label": "0"},
"tokenizer_class": "BertTokenizerFast",
"prefix": "prefix",
"bos_token_id": 6,
"pad_token_id": 7,
"eos_token_id": 8,
"sep_token_id": 9,
"decoder_start_token_id": 10,
"exponential_decay_length_penalty": (5, 1.01),
"suppress_tokens": [0, 1],
"begin_suppress_tokens": 2,
"task_specific_params": {"translation": "some_params"},
"problem_type": "regression",
}
@is_staging_test
class ConfigPushToHubTester(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls._token = TOKEN
HfFolder.save_token(TOKEN)
@classmethod
def tearDownClass(cls):
try:
delete_repo(token=cls._token, repo_id="test-config")
except HTTPError:
pass
try:
delete_repo(token=cls._token, repo_id="valid_org/test-config-org")
except HTTPError:
pass
try:
delete_repo(token=cls._token, repo_id="test-dynamic-config")
except HTTPError:
pass
def test_push_to_hub(self):
config = BertConfig(
vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37
)
config.push_to_hub("test-config", use_auth_token=self._token)
new_config = BertConfig.from_pretrained(f"{USER}/test-config")
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(v, getattr(new_config, k))
# Reset repo
delete_repo(token=self._token, repo_id="test-config")
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(tmp_dir, repo_id="test-config", push_to_hub=True, use_auth_token=self._token)
new_config = BertConfig.from_pretrained(f"{USER}/test-config")
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(v, getattr(new_config, k))
def test_push_to_hub_in_organization(self):
config = BertConfig(
vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37
)
config.push_to_hub("valid_org/test-config-org", use_auth_token=self._token)
new_config = BertConfig.from_pretrained("valid_org/test-config-org")
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(v, getattr(new_config, k))
# Reset repo
delete_repo(token=self._token, repo_id="valid_org/test-config-org")
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
tmp_dir, repo_id="valid_org/test-config-org", push_to_hub=True, use_auth_token=self._token
)
new_config = BertConfig.from_pretrained("valid_org/test-config-org")
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(v, getattr(new_config, k))
def test_push_to_hub_dynamic_config(self):
CustomConfig.register_for_auto_class()
config = CustomConfig(attribute=42)
config.push_to_hub("test-dynamic-config", use_auth_token=self._token)
# This has added the proper auto_map field to the config
self.assertDictEqual(config.auto_map, {"AutoConfig": "custom_configuration.CustomConfig"})
new_config = AutoConfig.from_pretrained(f"{USER}/test-dynamic-config", trust_remote_code=True)
# Can't make an isinstance check because the new_config is from the FakeConfig class of a dynamic module
self.assertEqual(new_config.__class__.__name__, "CustomConfig")
self.assertEqual(new_config.attribute, 42)
class ConfigTestUtils(unittest.TestCase):
def test_config_from_string(self):
c = GPT2Config()
# attempt to modify each of int/float/bool/str config records and verify they were updated
n_embd = c.n_embd + 1 # int
resid_pdrop = c.resid_pdrop + 1.0 # float
scale_attn_weights = not c.scale_attn_weights # bool
summary_type = c.summary_type + "foo" # str
c.update_from_string(
f"n_embd={n_embd},resid_pdrop={resid_pdrop},scale_attn_weights={scale_attn_weights},summary_type={summary_type}"
)
self.assertEqual(n_embd, c.n_embd, "mismatch for key: n_embd")
self.assertEqual(resid_pdrop, c.resid_pdrop, "mismatch for key: resid_pdrop")
self.assertEqual(scale_attn_weights, c.scale_attn_weights, "mismatch for key: scale_attn_weights")
self.assertEqual(summary_type, c.summary_type, "mismatch for key: summary_type")
def test_config_common_kwargs_is_complete(self):
base_config = PretrainedConfig()
missing_keys = [key for key in base_config.__dict__ if key not in config_common_kwargs]
# If this part of the test fails, you have arguments to addin config_common_kwargs above.
self.assertListEqual(
missing_keys, ["is_encoder_decoder", "_name_or_path", "_commit_hash", "transformers_version"]
)
keys_with_defaults = [key for key, value in config_common_kwargs.items() if value == getattr(base_config, key)]
if len(keys_with_defaults) > 0:
raise ValueError(
"The following keys are set with the default values in"
" `test_configuration_common.config_common_kwargs` pick another value for them:"
f" {', '.join(keys_with_defaults)}."
)
def test_from_pretrained_subfolder(self):
with self.assertRaises(OSError):
# config is in subfolder, the following should not work without specifying the subfolder
_ = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert-subfolder")
config = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert-subfolder", subfolder="bert")
self.assertIsNotNone(config)
def test_cached_files_are_used_when_internet_is_down(self):
# A mock response for an HTTP head request to emulate server down
response_mock = mock.Mock()
response_mock.status_code = 500
response_mock.headers = {}
response_mock.raise_for_status.side_effect = HTTPError
response_mock.json.return_value = {}
# Download this model to make sure it's in the cache.
_ = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert")
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch("requests.Session.request", return_value=response_mock) as mock_head:
_ = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert")
# This check we did call the fake head request
mock_head.assert_called()
def test_legacy_load_from_url(self):
# This test is for deprecated behavior and can be removed in v5
_ = BertConfig.from_pretrained(
"https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/config.json"
)
def test_local_versioning(self):
configuration = AutoConfig.from_pretrained("bert-base-cased")
configuration.configuration_files = ["config.4.0.0.json"]
with tempfile.TemporaryDirectory() as tmp_dir:
configuration.save_pretrained(tmp_dir)
configuration.hidden_size = 2
json.dump(configuration.to_dict(), open(os.path.join(tmp_dir, "config.4.0.0.json"), "w"))
# This should pick the new configuration file as the version of Transformers is > 4.0.0
new_configuration = AutoConfig.from_pretrained(tmp_dir)
self.assertEqual(new_configuration.hidden_size, 2)
# Will need to be adjusted if we reach v42 and this test is still here.
# Should pick the old configuration file as the version of Transformers is < 4.42.0
configuration.configuration_files = ["config.42.0.0.json"]
configuration.hidden_size = 768
configuration.save_pretrained(tmp_dir)
shutil.move(os.path.join(tmp_dir, "config.4.0.0.json"), os.path.join(tmp_dir, "config.42.0.0.json"))
new_configuration = AutoConfig.from_pretrained(tmp_dir)
self.assertEqual(new_configuration.hidden_size, 768)
def test_repo_versioning_before(self):
# This repo has two configuration files, one for v4.0.0 and above with a different hidden size.
repo = "hf-internal-testing/test-two-configs"
import transformers as new_transformers
new_transformers.configuration_utils.__version__ = "v4.0.0"
new_configuration, kwargs = new_transformers.models.auto.AutoConfig.from_pretrained(
repo, return_unused_kwargs=True
)
self.assertEqual(new_configuration.hidden_size, 2)
# This checks `_configuration_file` ia not kept in the kwargs by mistake.
self.assertDictEqual(kwargs, {})
# Testing an older version by monkey-patching the version in the module it's used.
import transformers as old_transformers
old_transformers.configuration_utils.__version__ = "v3.0.0"
old_configuration = old_transformers.models.auto.AutoConfig.from_pretrained(repo)
self.assertEqual(old_configuration.hidden_size, 768)
tests/test_feature_extraction_common.py
View file @ 372f5003
...@@ -16,25 +16,9 @@ ...@@ -16,25 +16,9 @@
import json import json
import os import os
import sys
import tempfile import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo from transformers.testing_utils import check_json_file_has_correct_format
from requests.exceptions import HTTPError
from transformers import AutoFeatureExtractor, Wav2Vec2FeatureExtractor
from transformers.testing_utils import TOKEN, USER, check_json_file_has_correct_format, get_tests_dir, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / "utils"))
from test_module.custom_feature_extraction import CustomFeatureExtractor # noqa E402
SAMPLE_FEATURE_EXTRACTION_CONFIG_DIR = get_tests_dir("fixtures")
class FeatureExtractionSavingTestMixin: class FeatureExtractionSavingTestMixin:
...@@ -69,112 +53,3 @@ class FeatureExtractionSavingTestMixin: ...@@ -69,112 +53,3 @@ class FeatureExtractionSavingTestMixin:
def test_init_without_params(self): def test_init_without_params(self):
feat_extract = self.feature_extraction_class() feat_extract = self.feature_extraction_class()
self.assertIsNotNone(feat_extract) self.assertIsNotNone(feat_extract)
class FeatureExtractorUtilTester(unittest.TestCase):
def test_cached_files_are_used_when_internet_is_down(self):
# A mock response for an HTTP head request to emulate server down
response_mock = mock.Mock()
response_mock.status_code = 500
response_mock.headers = {}
response_mock.raise_for_status.side_effect = HTTPError
response_mock.json.return_value = {}
# Download this model to make sure it's in the cache.
_ = Wav2Vec2FeatureExtractor.from_pretrained("hf-internal-testing/tiny-random-wav2vec2")
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch("requests.Session.request", return_value=response_mock) as mock_head:
_ = Wav2Vec2FeatureExtractor.from_pretrained("hf-internal-testing/tiny-random-wav2vec2")
# This check we did call the fake head request
mock_head.assert_called()
def test_legacy_load_from_url(self):
# This test is for deprecated behavior and can be removed in v5
_ = Wav2Vec2FeatureExtractor.from_pretrained(
"https://huggingface.co/hf-internal-testing/tiny-random-wav2vec2/resolve/main/preprocessor_config.json"
)
@is_staging_test
class FeatureExtractorPushToHubTester(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls._token = TOKEN
HfFolder.save_token(TOKEN)
@classmethod
def tearDownClass(cls):
try:
delete_repo(token=cls._token, repo_id="test-feature-extractor")
except HTTPError:
pass
try:
delete_repo(token=cls._token, repo_id="valid_org/test-feature-extractor-org")
except HTTPError:
pass
try:
delete_repo(token=cls._token, repo_id="test-dynamic-feature-extractor")
except HTTPError:
pass
def test_push_to_hub(self):
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(SAMPLE_FEATURE_EXTRACTION_CONFIG_DIR)
feature_extractor.push_to_hub("test-feature-extractor", use_auth_token=self._token)
new_feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(f"{USER}/test-feature-extractor")
for k, v in feature_extractor.__dict__.items():
self.assertEqual(v, getattr(new_feature_extractor, k))
# Reset repo
delete_repo(token=self._token, repo_id="test-feature-extractor")
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
feature_extractor.save_pretrained(
tmp_dir, repo_id="test-feature-extractor", push_to_hub=True, use_auth_token=self._token
)
new_feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(f"{USER}/test-feature-extractor")
for k, v in feature_extractor.__dict__.items():
self.assertEqual(v, getattr(new_feature_extractor, k))
def test_push_to_hub_in_organization(self):
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(SAMPLE_FEATURE_EXTRACTION_CONFIG_DIR)
feature_extractor.push_to_hub("valid_org/test-feature-extractor", use_auth_token=self._token)
new_feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("valid_org/test-feature-extractor")
for k, v in feature_extractor.__dict__.items():
self.assertEqual(v, getattr(new_feature_extractor, k))
# Reset repo
delete_repo(token=self._token, repo_id="valid_org/test-feature-extractor")
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
feature_extractor.save_pretrained(
tmp_dir, repo_id="valid_org/test-feature-extractor-org", push_to_hub=True, use_auth_token=self._token
)
new_feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("valid_org/test-feature-extractor-org")
for k, v in feature_extractor.__dict__.items():
self.assertEqual(v, getattr(new_feature_extractor, k))
def test_push_to_hub_dynamic_feature_extractor(self):
CustomFeatureExtractor.register_for_auto_class()
feature_extractor = CustomFeatureExtractor.from_pretrained(SAMPLE_FEATURE_EXTRACTION_CONFIG_DIR)
feature_extractor.push_to_hub("test-dynamic-feature-extractor", use_auth_token=self._token)
# This has added the proper auto_map field to the config
self.assertDictEqual(
feature_extractor.auto_map,
{"AutoFeatureExtractor": "custom_feature_extraction.CustomFeatureExtractor"},
)
new_feature_extractor = AutoFeatureExtractor.from_pretrained(
f"{USER}/test-dynamic-feature-extractor", trust_remote_code=True
)
# Can't make an isinstance check because the new_feature_extractor is from the CustomFeatureExtractor class of a dynamic module
self.assertEqual(new_feature_extractor.__class__.__name__, "CustomFeatureExtractor")
tests/test_feature_extraction_utils.py 0 → 100644
View file @ 372f5003
# coding=utf-8
# Copyright 2021 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoFeatureExtractor, Wav2Vec2FeatureExtractor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / "utils"))
from test_module.custom_feature_extraction import CustomFeatureExtractor # noqa E402
SAMPLE_FEATURE_EXTRACTION_CONFIG_DIR = get_tests_dir("fixtures")
class FeatureExtractorUtilTester(unittest.TestCase):
def test_cached_files_are_used_when_internet_is_down(self):
# A mock response for an HTTP head request to emulate server down
response_mock = mock.Mock()
response_mock.status_code = 500
response_mock.headers = {}
response_mock.raise_for_status.side_effect = HTTPError
response_mock.json.return_value = {}
# Download this model to make sure it's in the cache.
_ = Wav2Vec2FeatureExtractor.from_pretrained("hf-internal-testing/tiny-random-wav2vec2")
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch("requests.Session.request", return_value=response_mock) as mock_head:
_ = Wav2Vec2FeatureExtractor.from_pretrained("hf-internal-testing/tiny-random-wav2vec2")
# This check we did call the fake head request
mock_head.assert_called()
def test_legacy_load_from_url(self):
# This test is for deprecated behavior and can be removed in v5
_ = Wav2Vec2FeatureExtractor.from_pretrained(
"https://huggingface.co/hf-internal-testing/tiny-random-wav2vec2/resolve/main/preprocessor_config.json"
)
@is_staging_test
class FeatureExtractorPushToHubTester(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls._token = TOKEN
HfFolder.save_token(TOKEN)
@classmethod
def tearDownClass(cls):
try:
delete_repo(token=cls._token, repo_id="test-feature-extractor")
except HTTPError:
pass
try:
delete_repo(token=cls._token, repo_id="valid_org/test-feature-extractor-org")
except HTTPError:
pass
try:
delete_repo(token=cls._token, repo_id="test-dynamic-feature-extractor")
except HTTPError:
pass
def test_push_to_hub(self):
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(SAMPLE_FEATURE_EXTRACTION_CONFIG_DIR)
feature_extractor.push_to_hub("test-feature-extractor", use_auth_token=self._token)
new_feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(f"{USER}/test-feature-extractor")
for k, v in feature_extractor.__dict__.items():
self.assertEqual(v, getattr(new_feature_extractor, k))
# Reset repo
delete_repo(token=self._token, repo_id="test-feature-extractor")
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
feature_extractor.save_pretrained(
tmp_dir, repo_id="test-feature-extractor", push_to_hub=True, use_auth_token=self._token
)
new_feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(f"{USER}/test-feature-extractor")
for k, v in feature_extractor.__dict__.items():
self.assertEqual(v, getattr(new_feature_extractor, k))
def test_push_to_hub_in_organization(self):
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(SAMPLE_FEATURE_EXTRACTION_CONFIG_DIR)
feature_extractor.push_to_hub("valid_org/test-feature-extractor", use_auth_token=self._token)
new_feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("valid_org/test-feature-extractor")
for k, v in feature_extractor.__dict__.items():
self.assertEqual(v, getattr(new_feature_extractor, k))
# Reset repo
delete_repo(token=self._token, repo_id="valid_org/test-feature-extractor")
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
feature_extractor.save_pretrained(
tmp_dir, repo_id="valid_org/test-feature-extractor-org", push_to_hub=True, use_auth_token=self._token
)
new_feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("valid_org/test-feature-extractor-org")
for k, v in feature_extractor.__dict__.items():
self.assertEqual(v, getattr(new_feature_extractor, k))
def test_push_to_hub_dynamic_feature_extractor(self):
CustomFeatureExtractor.register_for_auto_class()
feature_extractor = CustomFeatureExtractor.from_pretrained(SAMPLE_FEATURE_EXTRACTION_CONFIG_DIR)
feature_extractor.push_to_hub("test-dynamic-feature-extractor", use_auth_token=self._token)
# This has added the proper auto_map field to the config
self.assertDictEqual(
feature_extractor.auto_map,
{"AutoFeatureExtractor": "custom_feature_extraction.CustomFeatureExtractor"},
)
new_feature_extractor = AutoFeatureExtractor.from_pretrained(
f"{USER}/test-dynamic-feature-extractor", trust_remote_code=True
)
# Can't make an isinstance check because the new_feature_extractor is from the CustomFeatureExtractor class of a dynamic module
self.assertEqual(new_feature_extractor.__class__.__name__, "CustomFeatureExtractor")
tests/test_image_processing_common.py
View file @ 372f5003
...@@ -13,34 +13,12 @@ ...@@ -13,34 +13,12 @@
# See the License for the specific language governing permissions and # See the License for the specific language governing permissions and
# limitations under the License. # limitations under the License.
import json import json
import os import os
import sys
import tempfile import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import (
TOKEN,
USER,
check_json_file_has_correct_format,
get_tests_dir,
is_staging_test,
require_torch,
require_vision,
)
from transformers.utils import is_torch_available, is_vision_available
sys.path.append(str(Path(__file__).parent.parent / "utils"))
from test_module.custom_image_processing import CustomImageProcessor # noqa E402 from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
if is_torch_available(): if is_torch_available():
...@@ -51,9 +29,6 @@ if is_vision_available(): ...@@ -51,9 +29,6 @@ if is_vision_available():
from PIL import Image from PIL import Image
SAMPLE_IMAGE_PROCESSING_CONFIG_DIR = get_tests_dir("fixtures")
def prepare_image_inputs(image_processor_tester, equal_resolution=False, numpify=False, torchify=False): def prepare_image_inputs(image_processor_tester, equal_resolution=False, numpify=False, torchify=False):
"""This function prepares a list of PIL images, or a list of numpy arrays if one specifies numpify=True, """This function prepares a list of PIL images, or a list of numpy arrays if one specifies numpify=True,
or a list of PyTorch tensors if one specifies torchify=True. or a list of PyTorch tensors if one specifies torchify=True.
...@@ -201,123 +176,3 @@ class ImageProcessingSavingTestMixin: ...@@ -201,123 +176,3 @@ class ImageProcessingSavingTestMixin:
self.assertEqual(encoding.pixel_values.device, torch.device("cpu")) self.assertEqual(encoding.pixel_values.device, torch.device("cpu"))
self.assertEqual(encoding.pixel_values.dtype, torch.float16) self.assertEqual(encoding.pixel_values.dtype, torch.float16)
self.assertEqual(encoding.input_ids.dtype, torch.long) self.assertEqual(encoding.input_ids.dtype, torch.long)
class ImageProcessorUtilTester(unittest.TestCase):
def test_cached_files_are_used_when_internet_is_down(self):
# A mock response for an HTTP head request to emulate server down
response_mock = mock.Mock()
response_mock.status_code = 500
response_mock.headers = {}
response_mock.raise_for_status.side_effect = HTTPError
response_mock.json.return_value = {}
# Download this model to make sure it's in the cache.
_ = ViTImageProcessor.from_pretrained("hf-internal-testing/tiny-random-vit")
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch("requests.Session.request", return_value=response_mock) as mock_head:
_ = ViTImageProcessor.from_pretrained("hf-internal-testing/tiny-random-vit")
# This check we did call the fake head request
mock_head.assert_called()
def test_legacy_load_from_url(self):
# This test is for deprecated behavior and can be removed in v5
_ = ViTImageProcessor.from_pretrained(
"https://huggingface.co/hf-internal-testing/tiny-random-vit/resolve/main/preprocessor_config.json"
)
@is_staging_test
class ImageProcessorPushToHubTester(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls._token = TOKEN
HfFolder.save_token(TOKEN)
@classmethod
def tearDownClass(cls):
try:
delete_repo(token=cls._token, repo_id="test-image-processor")
except HTTPError:
pass
try:
delete_repo(token=cls._token, repo_id="valid_org/test-image-processor-org")
except HTTPError:
pass
try:
delete_repo(token=cls._token, repo_id="test-dynamic-image-processor")
except HTTPError:
pass
def test_push_to_hub(self):
image_processor = ViTImageProcessor.from_pretrained(SAMPLE_IMAGE_PROCESSING_CONFIG_DIR)
image_processor.push_to_hub("test-image-processor", use_auth_token=self._token)
new_image_processor = ViTImageProcessor.from_pretrained(f"{USER}/test-image-processor")
for k, v in image_processor.__dict__.items():
self.assertEqual(v, getattr(new_image_processor, k))
# Reset repo
delete_repo(token=self._token, repo_id="test-image-processor")
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(
tmp_dir, repo_id="test-image-processor", push_to_hub=True, use_auth_token=self._token
)
new_image_processor = ViTImageProcessor.from_pretrained(f"{USER}/test-image-processor")
for k, v in image_processor.__dict__.items():
self.assertEqual(v, getattr(new_image_processor, k))
def test_push_to_hub_in_organization(self):
image_processor = ViTImageProcessor.from_pretrained(SAMPLE_IMAGE_PROCESSING_CONFIG_DIR)
image_processor.push_to_hub("valid_org/test-image-processor", use_auth_token=self._token)
new_image_processor = ViTImageProcessor.from_pretrained("valid_org/test-image-processor")
for k, v in image_processor.__dict__.items():
self.assertEqual(v, getattr(new_image_processor, k))
# Reset repo
delete_repo(token=self._token, repo_id="valid_org/test-image-processor")
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(
tmp_dir, repo_id="valid_org/test-image-processor-org", push_to_hub=True, use_auth_token=self._token
)
new_image_processor = ViTImageProcessor.from_pretrained("valid_org/test-image-processor-org")
for k, v in image_processor.__dict__.items():
self.assertEqual(v, getattr(new_image_processor, k))
def test_push_to_hub_dynamic_image_processor(self):
CustomImageProcessor.register_for_auto_class()
image_processor = CustomImageProcessor.from_pretrained(SAMPLE_IMAGE_PROCESSING_CONFIG_DIR)
image_processor.push_to_hub("test-dynamic-image-processor", use_auth_token=self._token)
# This has added the proper auto_map field to the config
self.assertDictEqual(
image_processor.auto_map,
{"ImageProcessor": "custom_image_processing.CustomImageProcessor"},
)
new_image_processor = AutoImageProcessor.from_pretrained(
f"{USER}/test-dynamic-image-processor", trust_remote_code=True
)
# Can't make an isinstance check because the new_image_processor is from the CustomImageProcessor class of a dynamic module
self.assertEqual(new_image_processor.__class__.__name__, "CustomImageProcessor")
def test_image_processor_from_pretrained_subfolder(self):
with self.assertRaises(OSError):
# config is in subfolder, the following should not work without specifying the subfolder
_ = AutoImageProcessor.from_pretrained("hf-internal-testing/stable-diffusion-all-variants")
config = AutoImageProcessor.from_pretrained(
"hf-internal-testing/stable-diffusion-all-variants", subfolder="feature_extractor"
)
self.assertIsNotNone(config)
tests/test_image_processing_utils.py 0 → 100644
View file @ 372f5003
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / "utils"))
from test_module.custom_image_processing import CustomImageProcessor # noqa E402
SAMPLE_IMAGE_PROCESSING_CONFIG_DIR = get_tests_dir("fixtures")
class ImageProcessorUtilTester(unittest.TestCase):
def test_cached_files_are_used_when_internet_is_down(self):
# A mock response for an HTTP head request to emulate server down
response_mock = mock.Mock()
response_mock.status_code = 500
response_mock.headers = {}
response_mock.raise_for_status.side_effect = HTTPError
response_mock.json.return_value = {}
# Download this model to make sure it's in the cache.
_ = ViTImageProcessor.from_pretrained("hf-internal-testing/tiny-random-vit")
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch("requests.Session.request", return_value=response_mock) as mock_head:
_ = ViTImageProcessor.from_pretrained("hf-internal-testing/tiny-random-vit")
# This check we did call the fake head request
mock_head.assert_called()
def test_legacy_load_from_url(self):
# This test is for deprecated behavior and can be removed in v5
_ = ViTImageProcessor.from_pretrained(
"https://huggingface.co/hf-internal-testing/tiny-random-vit/resolve/main/preprocessor_config.json"
)
@is_staging_test
class ImageProcessorPushToHubTester(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls._token = TOKEN
HfFolder.save_token(TOKEN)
@classmethod
def tearDownClass(cls):
try:
delete_repo(token=cls._token, repo_id="test-image-processor")
except HTTPError:
pass
try:
delete_repo(token=cls._token, repo_id="valid_org/test-image-processor-org")
except HTTPError:
pass
try:
delete_repo(token=cls._token, repo_id="test-dynamic-image-processor")
except HTTPError:
pass
def test_push_to_hub(self):
image_processor = ViTImageProcessor.from_pretrained(SAMPLE_IMAGE_PROCESSING_CONFIG_DIR)
image_processor.push_to_hub("test-image-processor", use_auth_token=self._token)
new_image_processor = ViTImageProcessor.from_pretrained(f"{USER}/test-image-processor")
for k, v in image_processor.__dict__.items():
self.assertEqual(v, getattr(new_image_processor, k))
# Reset repo
delete_repo(token=self._token, repo_id="test-image-processor")
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(
tmp_dir, repo_id="test-image-processor", push_to_hub=True, use_auth_token=self._token
)
new_image_processor = ViTImageProcessor.from_pretrained(f"{USER}/test-image-processor")
for k, v in image_processor.__dict__.items():
self.assertEqual(v, getattr(new_image_processor, k))
def test_push_to_hub_in_organization(self):
image_processor = ViTImageProcessor.from_pretrained(SAMPLE_IMAGE_PROCESSING_CONFIG_DIR)
image_processor.push_to_hub("valid_org/test-image-processor", use_auth_token=self._token)
new_image_processor = ViTImageProcessor.from_pretrained("valid_org/test-image-processor")
for k, v in image_processor.__dict__.items():
self.assertEqual(v, getattr(new_image_processor, k))
# Reset repo
delete_repo(token=self._token, repo_id="valid_org/test-image-processor")
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(
tmp_dir, repo_id="valid_org/test-image-processor-org", push_to_hub=True, use_auth_token=self._token
)
new_image_processor = ViTImageProcessor.from_pretrained("valid_org/test-image-processor-org")
for k, v in image_processor.__dict__.items():
self.assertEqual(v, getattr(new_image_processor, k))
def test_push_to_hub_dynamic_image_processor(self):
CustomImageProcessor.register_for_auto_class()
image_processor = CustomImageProcessor.from_pretrained(SAMPLE_IMAGE_PROCESSING_CONFIG_DIR)
image_processor.push_to_hub("test-dynamic-image-processor", use_auth_token=self._token)
# This has added the proper auto_map field to the config
self.assertDictEqual(
image_processor.auto_map,
{"ImageProcessor": "custom_image_processing.CustomImageProcessor"},
)
new_image_processor = AutoImageProcessor.from_pretrained(
f"{USER}/test-dynamic-image-processor", trust_remote_code=True
)
# Can't make an isinstance check because the new_image_processor is from the CustomImageProcessor class of a dynamic module
self.assertEqual(new_image_processor.__class__.__name__, "CustomImageProcessor")
def test_image_processor_from_pretrained_subfolder(self):
with self.assertRaises(OSError):
# config is in subfolder, the following should not work without specifying the subfolder
_ = AutoImageProcessor.from_pretrained("hf-internal-testing/stable-diffusion-all-variants")
config = AutoImageProcessor.from_pretrained(
"hf-internal-testing/stable-diffusion-all-variants", subfolder="feature_extractor"
)
self.assertIsNotNone(config)
tests/test_modeling_common.py
View file @ 372f5003
...@@ -16,32 +16,22 @@ ...@@ -16,32 +16,22 @@
import collections import collections
import copy import copy
import gc import gc
import glob
import inspect import inspect
import json
import os import os
import os.path import os.path
import pickle import pickle
import random import random
import re import re
import sys
import tempfile import tempfile
import unittest
import unittest.mock as mock
import warnings import warnings
from collections import defaultdict from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple from typing import Dict, List, Tuple
import numpy as np import numpy as np
from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from pytest import mark from pytest import mark
from requests.exceptions import HTTPError
import transformers import transformers
from transformers import ( from transformers import (
AutoConfig,
AutoModel, AutoModel,
AutoModelForSequenceClassification, AutoModelForSequenceClassification,
PretrainedConfig, PretrainedConfig,
...@@ -70,28 +60,20 @@ from transformers.models.auto.modeling_auto import ( ...@@ -70,28 +60,20 @@ from transformers.models.auto.modeling_auto import (
MODEL_MAPPING_NAMES, MODEL_MAPPING_NAMES,
) )
from transformers.testing_utils import ( from transformers.testing_utils import (
TOKEN,
USER,
CaptureLogger, CaptureLogger,
TestCasePlus,
is_pt_flax_cross_test, is_pt_flax_cross_test,
is_pt_tf_cross_test, is_pt_tf_cross_test,
is_staging_test,
require_accelerate, require_accelerate,
require_safetensors, require_safetensors,
require_torch, require_torch,
require_torch_gpu, require_torch_gpu,
require_torch_multi_gpu, require_torch_multi_gpu,
require_usr_bin_time,
slow, slow,
torch_device, torch_device,
) )
from transformers.utils import ( from transformers.utils import (
CONFIG_NAME, CONFIG_NAME,
GENERATION_CONFIG_NAME, GENERATION_CONFIG_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME, WEIGHTS_NAME,
is_accelerate_available, is_accelerate_available,
is_flax_available, is_flax_available,
...@@ -101,65 +83,17 @@ from transformers.utils import ( ...@@ -101,65 +83,17 @@ from transformers.utils import (
from transformers.utils.generic import ModelOutput from transformers.utils.generic import ModelOutput
sys.path.append(str(Path(__file__).parent.parent / "utils"))
from test_module.custom_configuration import CustomConfig, NoSuperInitConfig # noqa E402
if is_accelerate_available(): if is_accelerate_available():
from accelerate.utils import compute_module_sizes from accelerate.utils import compute_module_sizes
if is_torch_available(): if is_torch_available():
import torch import torch
from test_module.custom_modeling import CustomModel, NoSuperInitModel
from torch import nn from torch import nn
from transformers import ( from transformers import MODEL_MAPPING, AdaptiveEmbedding
BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
MODEL_MAPPING,
AdaptiveEmbedding,
AutoModelForCausalLM,
AutoTokenizer,
BertConfig,
BertModel,
CLIPTextModel,
PreTrainedModel,
T5Config,
T5ForConditionalGeneration,
)
from transformers.modeling_utils import shard_checkpoint
from transformers.pytorch_utils import id_tensor_storage from transformers.pytorch_utils import id_tensor_storage
# Fake pretrained models for tests
class BaseModel(PreTrainedModel):
config_class = PretrainedConfig
def __init__(self, config):
super().__init__(config)
self.linear = nn.Linear(4, 5)
self.linear_2 = nn.Linear(5, 6)
def forward(self, x):
return self.linear_2(self.linear(x))
class ModelWithHead(PreTrainedModel):
base_model_prefix = "base"
config_class = PretrainedConfig
def _init_weights(self, module):
pass
def __init__(self, config):
super().__init__(config)
self.base = BaseModel(config)
# linear is a common name between Base and Head on purpose.
self.linear = nn.Linear(6, 3)
self.linear2 = nn.Linear(3, 5)
def forward(self, x):
return self.linear2(self.linear(self.base(x)))
if is_tf_available(): if is_tf_available():
import tensorflow as tf import tensorflow as tf
...@@ -187,10 +121,6 @@ def _config_zero_init(config): ...@@ -187,10 +121,6 @@ def _config_zero_init(config):
return configs_no_init return configs_no_init
TINY_T5 = "patrickvonplaten/t5-tiny-random"
TINY_BERT_FOR_TOKEN_CLASSIFICATION = "hf-internal-testing/tiny-bert-for-token-classification"
def _mock_init_weights(self, module): def _mock_init_weights(self, module):
for name, param in module.named_parameters(recurse=False): for name, param in module.named_parameters(recurse=False):
# Use the first letter of the name to get a value and go from a <> -13 to z <> 12 # Use the first letter of the name to get a value and go from a <> -13 to z <> 12
...@@ -2778,865 +2708,3 @@ def floats_tensor(shape, scale=1.0, rng=None, name=None): ...@@ -2778,865 +2708,3 @@ def floats_tensor(shape, scale=1.0, rng=None, name=None):
values.append(rng.random() * scale) values.append(rng.random() * scale)
return torch.tensor(data=values, dtype=torch.float, device=torch_device).view(shape).contiguous() return torch.tensor(data=values, dtype=torch.float, device=torch_device).view(shape).contiguous()
def check_models_equal(model1, model2):
models_are_equal = True
for model1_p, model2_p in zip(model1.parameters(), model2.parameters()):
if model1_p.data.ne(model2_p.data).sum() > 0:
models_are_equal = False
return models_are_equal
@require_torch
class ModelUtilsTest(TestCasePlus):
@slow
def test_model_from_pretrained(self):
for model_name in BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
config = BertConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, PretrainedConfig)
model = BertModel.from_pretrained(model_name)
model, loading_info = BertModel.from_pretrained(model_name, output_loading_info=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, PreTrainedModel)
self.assertEqual(len(loading_info["missing_keys"]), 0)
self.assertEqual(len(loading_info["unexpected_keys"]), 8)
self.assertEqual(len(loading_info["mismatched_keys"]), 0)
self.assertEqual(len(loading_info["error_msgs"]), 0)
config = BertConfig.from_pretrained(model_name, output_attentions=True, output_hidden_states=True)
# Not sure this is the intended behavior. TODO fix Lysandre & Thom
config.name_or_path = model_name
model = BertModel.from_pretrained(model_name, output_attentions=True, output_hidden_states=True)
self.assertEqual(model.config.output_hidden_states, True)
self.assertEqual(model.config, config)
def test_model_from_pretrained_subfolder(self):
config = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert")
model = BertModel(config)
subfolder = "bert"
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(os.path.join(tmp_dir, subfolder))
with self.assertRaises(OSError):
_ = BertModel.from_pretrained(tmp_dir)
model_loaded = BertModel.from_pretrained(tmp_dir, subfolder=subfolder)
self.assertTrue(check_models_equal(model, model_loaded))
def test_model_from_pretrained_subfolder_sharded(self):
config = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert")
model = BertModel(config)
subfolder = "bert"
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(os.path.join(tmp_dir, subfolder), max_shard_size="10KB")
with self.assertRaises(OSError):
_ = BertModel.from_pretrained(tmp_dir)
model_loaded = BertModel.from_pretrained(tmp_dir, subfolder=subfolder)
self.assertTrue(check_models_equal(model, model_loaded))
def test_model_from_pretrained_hub_subfolder(self):
subfolder = "bert"
model_id = "hf-internal-testing/tiny-random-bert-subfolder"
with self.assertRaises(OSError):
_ = BertModel.from_pretrained(model_id)
model = BertModel.from_pretrained(model_id, subfolder=subfolder)
self.assertIsNotNone(model)
def test_model_from_pretrained_hub_subfolder_sharded(self):
subfolder = "bert"
model_id = "hf-internal-testing/tiny-random-bert-sharded-subfolder"
with self.assertRaises(OSError):
_ = BertModel.from_pretrained(model_id)
model = BertModel.from_pretrained(model_id, subfolder=subfolder)
self.assertIsNotNone(model)
def test_model_from_pretrained_with_different_pretrained_model_name(self):
model = T5ForConditionalGeneration.from_pretrained(TINY_T5)
self.assertIsNotNone(model)
logger = logging.get_logger("transformers.configuration_utils")
with CaptureLogger(logger) as cl:
BertModel.from_pretrained(TINY_T5)
self.assertTrue("You are using a model of type t5 to instantiate a model of type bert" in cl.out)
def test_model_from_config_torch_dtype(self):
# test that the model can be instantiated with dtype of user's choice - as long as it's a
# float dtype. To make it happen config.torch_dtype needs to be set before instantiating the
# model from the config object.
config = T5Config.from_pretrained(TINY_T5)
model = AutoModel.from_config(config)
# XXX: isn't supported
# model = T5ForConditionalGeneration.from_config(config)
self.assertEqual(model.dtype, torch.float32)
model = AutoModel.from_config(config, torch_dtype=torch.float16)
self.assertEqual(model.dtype, torch.float16)
# torch.set_default_dtype() supports only float dtypes, so will fail with non-float type
with self.assertRaises(ValueError):
model = AutoModel.from_config(config, torch_dtype=torch.int64)
def test_model_from_pretrained_torch_dtype(self):
# test that the model can be instantiated with dtype of either
# 1. explicit from_pretrained's torch_dtype argument
# 2. via autodiscovery by looking at model weights (torch_dtype="auto")
# so if a model.half() was saved, we want it to be instantiated as such.
#
# test an explicit model class, but also AutoModel separately as the latter goes through a different code path
model_path = self.get_auto_remove_tmp_dir()
# baseline - we know TINY_T5 is fp32 model
model = T5ForConditionalGeneration.from_pretrained(TINY_T5)
self.assertEqual(model.dtype, torch.float32)
def remove_torch_dtype(model_path):
file = f"{model_path}/config.json"
with open(file, "r", encoding="utf-8") as f:
s = json.load(f)
s.pop("torch_dtype")
with open(file, "w", encoding="utf-8") as f:
json.dump(s, f)
# test the default fp32 save_pretrained => from_pretrained cycle
model.save_pretrained(model_path)
model = T5ForConditionalGeneration.from_pretrained(model_path)
self.assertEqual(model.dtype, torch.float32)
# 1. test torch_dtype="auto" via `config.torch_dtype`
model = T5ForConditionalGeneration.from_pretrained(model_path, torch_dtype="auto")
self.assertEqual(model.dtype, torch.float32)
# 2. test torch_dtype="auto" via auto-derivation
# now remove the torch_dtype entry from config.json and try "auto" again which should
# perform auto-derivation from weights
remove_torch_dtype(model_path)
model = T5ForConditionalGeneration.from_pretrained(model_path, torch_dtype="auto")
self.assertEqual(model.dtype, torch.float32)
# test forced loading in fp16 (even though the weights are in fp32)
model = T5ForConditionalGeneration.from_pretrained(model_path, torch_dtype=torch.float16)
self.assertEqual(model.dtype, torch.float16)
# test fp16 save_pretrained, loaded with auto-detection
model = model.half()
model.save_pretrained(model_path)
# 1. test torch_dtype="auto" via `config.torch_dtype`
model = T5ForConditionalGeneration.from_pretrained(model_path, torch_dtype="auto")
self.assertEqual(model.config.torch_dtype, torch.float16)
self.assertEqual(model.dtype, torch.float16)
# tests `config.torch_dtype` saving
with open(f"{model_path}/config.json") as f:
config_dict = json.load(f)
self.assertEqual(config_dict["torch_dtype"], "float16")
# 2. test torch_dtype="auto" via auto-derivation
# now same with using config info
remove_torch_dtype(model_path)
model = T5ForConditionalGeneration.from_pretrained(model_path, torch_dtype="auto")
self.assertEqual(model.dtype, torch.float16)
# 3. now retest that AutoModel behaves the same wrt torch_dtype="auto" as T5ForConditionalGeneration
model = AutoModel.from_pretrained(model_path, torch_dtype="auto")
self.assertEqual(model.dtype, torch.float16)
# test fp16 save_pretrained, loaded with the explicit fp16
model = T5ForConditionalGeneration.from_pretrained(model_path, torch_dtype=torch.float16)
self.assertEqual(model.dtype, torch.float16)
# test AutoModel separately as it goes through a different path
# test auto-detection - as currently TINY_T5 doesn't have torch_dtype entry
model = AutoModel.from_pretrained(TINY_T5, torch_dtype="auto")
# test that the config object didn't get polluted with torch_dtype="auto"
# there was a bug that after this call we ended up with config.torch_dtype=="auto"
self.assertNotEqual(model.config.torch_dtype, "auto")
# now test the outcome
self.assertEqual(model.dtype, torch.float32)
model = AutoModel.from_pretrained(TINY_T5, torch_dtype=torch.float16)
self.assertEqual(model.dtype, torch.float16)
# test model whose first param is not of a floating type, but int
model = AutoModel.from_pretrained(TINY_BERT_FOR_TOKEN_CLASSIFICATION, torch_dtype="auto")
self.assertEqual(model.dtype, torch.float32)
def test_no_super_init_config_and_model(self):
config = NoSuperInitConfig(attribute=32)
model = NoSuperInitModel(config)
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir)
new_model = NoSuperInitModel.from_pretrained(tmp_dir)
for p1, p2 in zip(model.parameters(), new_model.parameters()):
self.assertTrue(torch.equal(p1, p2))
def test_shard_checkpoint(self):
# This is the model we will use, total size 340,000 bytes.
model = torch.nn.Sequential(
torch.nn.Linear(100, 200, bias=False), # size 80,000
torch.nn.Linear(200, 200, bias=False), # size 160,000
torch.nn.Linear(200, 100, bias=False), # size 80,000
torch.nn.Linear(100, 50, bias=False), # size 20,000
)
state_dict = model.state_dict()
with self.subTest("No shard when max size is bigger than model size"):
shards, index = shard_checkpoint(state_dict)
self.assertIsNone(index)
self.assertDictEqual(shards, {WEIGHTS_NAME: state_dict})
with self.subTest("Test sharding, no weights bigger than max size"):
shards, index = shard_checkpoint(state_dict, max_shard_size="300kB")
# Split is first two layers then last two.
self.assertDictEqual(
index,
{
"metadata": {"total_size": 340000},
"weight_map": {
"0.weight": "pytorch_model-00001-of-00002.bin",
"1.weight": "pytorch_model-00001-of-00002.bin",
"2.weight": "pytorch_model-00002-of-00002.bin",
"3.weight": "pytorch_model-00002-of-00002.bin",
},
},
)
shard1 = {"0.weight": state_dict["0.weight"], "1.weight": state_dict["1.weight"]}
shard2 = {"2.weight": state_dict["2.weight"], "3.weight": state_dict["3.weight"]}
self.assertDictEqual(
shards, {"pytorch_model-00001-of-00002.bin": shard1, "pytorch_model-00002-of-00002.bin": shard2}
)
with self.subTest("Test sharding with weights bigger than max size"):
shards, index = shard_checkpoint(state_dict, max_shard_size="100kB")
# Split is first layer, second layer then last 2.
self.assertDictEqual(
index,
{
"metadata": {"total_size": 340000},
"weight_map": {
"0.weight": "pytorch_model-00001-of-00003.bin",
"1.weight": "pytorch_model-00002-of-00003.bin",
"2.weight": "pytorch_model-00003-of-00003.bin",
"3.weight": "pytorch_model-00003-of-00003.bin",
},
},
)
shard1 = {"0.weight": state_dict["0.weight"]}
shard2 = {"1.weight": state_dict["1.weight"]}
shard3 = {"2.weight": state_dict["2.weight"], "3.weight": state_dict["3.weight"]}
self.assertDictEqual(
shards,
{
"pytorch_model-00001-of-00003.bin": shard1,
"pytorch_model-00002-of-00003.bin": shard2,
"pytorch_model-00003-of-00003.bin": shard3,
},
)
def test_checkpoint_sharding_local(self):
model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
with tempfile.TemporaryDirectory() as tmp_dir:
# We use the same folder for various sizes to make sure a new save erases the old checkpoint.
for max_size in ["50kB", "50kiB", "100kB", "100kiB", "200kB", "200kiB"]:
model.save_pretrained(tmp_dir, max_shard_size=max_size)
# Get each shard file and its size
shard_to_size = {}
for shard in os.listdir(tmp_dir):
if shard.endswith(".bin"):
shard_file = os.path.join(tmp_dir, shard)
shard_to_size[shard_file] = os.path.getsize(shard_file)
index_file = os.path.join(tmp_dir, WEIGHTS_INDEX_NAME)
# Check there is an index but no regular weight file
self.assertTrue(os.path.isfile(index_file))
self.assertFalse(os.path.isfile(os.path.join(tmp_dir, WEIGHTS_NAME)))
# Check a file is bigger than max_size only when it has a single weight
for shard_file, size in shard_to_size.items():
if max_size.endswith("kiB"):
max_size_int = int(max_size[:-3]) * 2**10
else:
max_size_int = int(max_size[:-2]) * 10**3
# Note: pickle adds some junk so the weight of the file can end up being slightly bigger than
# the size asked for (since we count parameters)
if size >= max_size_int + 50000:
state_dict = torch.load(shard_file)
self.assertEqual(len(state_dict), 1)
# Check the index and the shard files found match
with open(index_file, "r", encoding="utf-8") as f:
index = json.loads(f.read())
all_shards = set(index["weight_map"].values())
shards_found = {f for f in os.listdir(tmp_dir) if f.endswith(".bin")}
self.assertSetEqual(all_shards, shards_found)
# Finally, check the model can be reloaded
new_model = BertModel.from_pretrained(tmp_dir)
for p1, p2 in zip(model.parameters(), new_model.parameters()):
self.assertTrue(torch.allclose(p1, p2))
def test_checkpoint_sharding_from_hub(self):
model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert-sharded")
# the model above is the same as the model below, just a sharded version.
ref_model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
for p1, p2 in zip(model.parameters(), ref_model.parameters()):
self.assertTrue(torch.allclose(p1, p2))
def test_checkpoint_variant_local(self):
model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir, variant="v2")
weights_name = ".".join(WEIGHTS_NAME.split(".")[:-1] + ["v2"] + ["bin"])
weights_file = os.path.join(tmp_dir, weights_name)
self.assertTrue(os.path.isfile(weights_file))
self.assertFalse(os.path.isfile(os.path.join(tmp_dir, WEIGHTS_NAME)))
with self.assertRaises(EnvironmentError):
_ = BertModel.from_pretrained(tmp_dir)
new_model = BertModel.from_pretrained(tmp_dir, variant="v2")
for p1, p2 in zip(model.parameters(), new_model.parameters()):
self.assertTrue(torch.allclose(p1, p2))
def test_checkpoint_variant_local_sharded(self):
model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir, variant="v2", max_shard_size="50kB")
weights_index_name = ".".join(WEIGHTS_INDEX_NAME.split(".")[:-1] + ["v2"] + ["json"])
weights_index_file = os.path.join(tmp_dir, weights_index_name)
self.assertTrue(os.path.isfile(weights_index_file))
self.assertFalse(os.path.isfile(os.path.join(tmp_dir, WEIGHTS_INDEX_NAME)))
for i in range(1, 6):
weights_name = ".".join(WEIGHTS_NAME.split(".")[:-1] + [f"v2-0000{i}-of-00006"] + ["bin"])
weights_name_file = os.path.join(tmp_dir, weights_name)
self.assertTrue(os.path.isfile(weights_name_file))
with self.assertRaises(EnvironmentError):
_ = BertModel.from_pretrained(tmp_dir)
new_model = BertModel.from_pretrained(tmp_dir, variant="v2")
for p1, p2 in zip(model.parameters(), new_model.parameters()):
self.assertTrue(torch.allclose(p1, p2))
@require_safetensors
def test_checkpoint_variant_local_safe(self):
model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir, variant="v2", safe_serialization=True)
weights_name = ".".join(SAFE_WEIGHTS_NAME.split(".")[:-1] + ["v2"] + ["safetensors"])
weights_file = os.path.join(tmp_dir, weights_name)
self.assertTrue(os.path.isfile(weights_file))
self.assertFalse(os.path.isfile(os.path.join(tmp_dir, SAFE_WEIGHTS_NAME)))
with self.assertRaises(EnvironmentError):
_ = BertModel.from_pretrained(tmp_dir)
new_model = BertModel.from_pretrained(tmp_dir, variant="v2")
for p1, p2 in zip(model.parameters(), new_model.parameters()):
self.assertTrue(torch.allclose(p1, p2))
@require_safetensors
def test_checkpoint_variant_local_sharded_safe(self):
model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir, variant="v2", max_shard_size="50kB", safe_serialization=True)
weights_index_name = ".".join(SAFE_WEIGHTS_INDEX_NAME.split(".")[:-1] + ["v2"] + ["json"])
weights_index_file = os.path.join(tmp_dir, weights_index_name)
self.assertTrue(os.path.isfile(weights_index_file))
self.assertFalse(os.path.isfile(os.path.join(tmp_dir, SAFE_WEIGHTS_INDEX_NAME)))
for i in range(1, 6):
weights_name = ".".join(SAFE_WEIGHTS_NAME.split(".")[:-1] + [f"v2-0000{i}-of-00006"] + ["safetensors"])
weights_name_file = os.path.join(tmp_dir, weights_name)
self.assertTrue(os.path.isfile(weights_name_file))
with self.assertRaises(EnvironmentError):
_ = BertModel.from_pretrained(tmp_dir)
new_model = BertModel.from_pretrained(tmp_dir, variant="v2")
for p1, p2 in zip(model.parameters(), new_model.parameters()):
self.assertTrue(torch.allclose(p1, p2))
def test_checkpoint_variant_hub(self):
with tempfile.TemporaryDirectory() as tmp_dir:
with self.assertRaises(EnvironmentError):
_ = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert-variant", cache_dir=tmp_dir)
model = BertModel.from_pretrained(
"hf-internal-testing/tiny-random-bert-variant", cache_dir=tmp_dir, variant="v2"
)
self.assertIsNotNone(model)
def test_checkpoint_variant_hub_sharded(self):
with tempfile.TemporaryDirectory() as tmp_dir:
with self.assertRaises(EnvironmentError):
_ = BertModel.from_pretrained(
"hf-internal-testing/tiny-random-bert-variant-sharded", cache_dir=tmp_dir
)
model = BertModel.from_pretrained(
"hf-internal-testing/tiny-random-bert-variant-sharded", cache_dir=tmp_dir, variant="v2"
)
self.assertIsNotNone(model)
@require_safetensors
def test_checkpoint_variant_hub_safe(self):
with tempfile.TemporaryDirectory() as tmp_dir:
with self.assertRaises(EnvironmentError):
_ = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert-variant-safe", cache_dir=tmp_dir)
model = BertModel.from_pretrained(
"hf-internal-testing/tiny-random-bert-variant-safe", cache_dir=tmp_dir, variant="v2"
)
self.assertIsNotNone(model)
@require_safetensors
def test_checkpoint_variant_hub_sharded_safe(self):
with tempfile.TemporaryDirectory() as tmp_dir:
with self.assertRaises(EnvironmentError):
_ = BertModel.from_pretrained(
"hf-internal-testing/tiny-random-bert-variant-sharded-safe", cache_dir=tmp_dir
)
model = BertModel.from_pretrained(
"hf-internal-testing/tiny-random-bert-variant-sharded-safe", cache_dir=tmp_dir, variant="v2"
)
self.assertIsNotNone(model)
def test_checkpoint_variant_save_load(self):
with tempfile.TemporaryDirectory() as tmp_dir:
model = BertModel.from_pretrained(
"hf-internal-testing/tiny-random-bert-variant", cache_dir=tmp_dir, variant="v2"
)
weights_name = ".".join(WEIGHTS_NAME.split(".")[:-1] + ["v2"] + ["bin"])
model.save_pretrained(tmp_dir, variant="v2")
# saving will create a variant checkpoint
self.assertTrue(os.path.isfile(os.path.join(tmp_dir, weights_name)))
model.save_pretrained(tmp_dir)
# saving shouldn't delete variant checkpoints
weights_name = ".".join(WEIGHTS_NAME.split(".")[:-1] + ["v2"] + ["bin"])
self.assertTrue(os.path.isfile(os.path.join(tmp_dir, weights_name)))
# there should be a normal checkpoint
self.assertTrue(os.path.isfile(os.path.join(tmp_dir, WEIGHTS_NAME)))
self.assertIsNotNone(model)
@require_accelerate
@mark.accelerate_tests
def test_from_pretrained_low_cpu_mem_usage_functional(self):
# test that we can use `from_pretrained(..., low_cpu_mem_usage=True)` with normal and
# sharded models
mnames = [
"hf-internal-testing/tiny-random-bert-sharded",
"hf-internal-testing/tiny-random-bert",
]
for mname in mnames:
_ = BertModel.from_pretrained(mname, low_cpu_mem_usage=True)
@require_usr_bin_time
@require_accelerate
@mark.accelerate_tests
def test_from_pretrained_low_cpu_mem_usage_measured(self):
# test that `from_pretrained(..., low_cpu_mem_usage=True)` uses less cpu memory than default
mname = "bert-base-cased"
preamble = "from transformers import AutoModel"
one_liner_str = f'{preamble}; AutoModel.from_pretrained("{mname}", low_cpu_mem_usage=False)'
max_rss_normal = self.python_one_liner_max_rss(one_liner_str)
# print(f"{max_rss_normal=}")
one_liner_str = f'{preamble}; AutoModel.from_pretrained("{mname}", low_cpu_mem_usage=True)'
max_rss_low_mem = self.python_one_liner_max_rss(one_liner_str)
# print(f"{max_rss_low_mem=}")
diff_bytes = max_rss_normal - max_rss_low_mem
diff_percent = diff_bytes / max_rss_low_mem
# print(f"{diff_bytes=}, {diff_percent=}")
# ideally we would compare that the diff is close to ~1x checkpoint size in bytes, but
# measuring cpu memory on linux is very tricky and inconsistent, so instead let's check that
# it's at least 15% less cpu memory consumed
self.assertGreater(
diff_percent,
0.15,
"should use less CPU memory for low_cpu_mem_usage=True, "
f"but got max_rss_normal={max_rss_normal} and max_rss_low_mem={max_rss_low_mem}",
)
# if you want to compare things manually, let's first look at the size of the model in bytes
# model = BertModel.from_pretrained(mname, low_cpu_mem_usage=False)
# total_numel = sum(dict((p.data_ptr(), p.numel()) for p in model.parameters()).values())
# total_bytes = total_numel * 4 # 420MB
# Now the diff_bytes should be very close to total_bytes, but the reports are inconsistent.
# The easiest way to test this is to switch the model and torch.load to do all the work on
# gpu - that way one can measure exactly the total and peak memory used. Perhaps once we add
# functionality to load models directly on gpu, this test can be rewritten to use torch's
# cuda memory tracking and then we should be able to do a much more precise test.
@require_accelerate
@mark.accelerate_tests
@require_torch_multi_gpu
@slow
def test_model_parallelism_gpt2(self):
device_map = {"transformer.wte": 0, "transformer.wpe": 0, "lm_head": 0, "transformer.ln_f": 1}
for i in range(12):
device_map[f"transformer.h.{i}"] = 0 if i <= 5 else 1
model = AutoModelForCausalLM.from_pretrained("gpt2", device_map=device_map)
tokenizer = AutoTokenizer.from_pretrained("gpt2")
inputs = tokenizer("Hello, my name is", return_tensors="pt")
output = model.generate(inputs["input_ids"].to(0))
text_output = tokenizer.decode(output[0].tolist())
self.assertEqual(text_output, "Hello, my name is John. I'm a writer, and I'm a writer. I'm")
@require_accelerate
@mark.accelerate_tests
@require_torch_gpu
def test_from_pretrained_disk_offload_task_model(self):
model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-gpt2")
device_map = {
"transformer.wte": 0,
"transformer.wpe": 0,
"transformer.h.0": "cpu",
"transformer.h.1": "cpu",
"transformer.h.2": "cpu",
"transformer.h.3": "disk",
"transformer.h.4": "disk",
"transformer.ln_f": 0,
"lm_head": 0,
}
with tempfile.TemporaryDirectory() as tmp_dir:
inputs = torch.tensor([[1, 2, 3]]).to(0)
model.save_pretrained(tmp_dir)
new_model = AutoModelForCausalLM.from_pretrained(tmp_dir).to(0)
outputs1 = new_model.to(0)(inputs)
offload_folder = os.path.join(tmp_dir, "offload")
new_model_with_offload = AutoModelForCausalLM.from_pretrained(
tmp_dir, device_map=device_map, offload_folder=offload_folder
)
outputs2 = new_model_with_offload(inputs)
self.assertTrue(torch.allclose(outputs1.logits.cpu(), outputs2.logits.cpu()))
# With state dict temp offload
offload_folder = os.path.join(tmp_dir, "offload")
new_model_with_offload = AutoModelForCausalLM.from_pretrained(
tmp_dir,
device_map=device_map,
offload_folder=offload_folder,
offload_state_dict=True,
)
outputs2 = new_model_with_offload(inputs)
self.assertTrue(torch.allclose(outputs1.logits.cpu(), outputs2.logits.cpu()))
def test_cached_files_are_used_when_internet_is_down(self):
# A mock response for an HTTP head request to emulate server down
response_mock = mock.Mock()
response_mock.status_code = 500
response_mock.headers = {}
response_mock.raise_for_status.side_effect = HTTPError
response_mock.json.return_value = {}
# Download this model to make sure it's in the cache.
_ = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch("requests.Session.request", return_value=response_mock) as mock_head:
_ = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
# This check we did call the fake head request
mock_head.assert_called()
def test_load_from_one_file(self):
try:
tmp_file = tempfile.mktemp()
with open(tmp_file, "wb") as f:
http_get(
"https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/pytorch_model.bin", f
)
config = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert")
_ = BertModel.from_pretrained(tmp_file, config=config)
finally:
os.remove(tmp_file)
def test_legacy_load_from_url(self):
# This test is for deprecated behavior and can be removed in v5
config = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert")
_ = BertModel.from_pretrained(
"https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/pytorch_model.bin", config=config
)
@require_safetensors
def test_use_safetensors(self):
# test nice error message if no safetensor files available
with self.assertRaises(OSError) as env_error:
AutoModel.from_pretrained("hf-internal-testing/tiny-random-RobertaModel", use_safetensors=True)
self.assertTrue(
"model.safetensors or model.safetensors.index.json and thus cannot be loaded with `safetensors`"
in str(env_error.exception)
)
# test that error if only safetensors is available
with self.assertRaises(OSError) as env_error:
BertModel.from_pretrained("hf-internal-testing/tiny-random-bert-safetensors", use_safetensors=False)
self.assertTrue("does not appear to have a file named pytorch_model.bin" in str(env_error.exception))
# test that only safetensors if both available and use_safetensors=False
with tempfile.TemporaryDirectory() as tmp_dir:
CLIPTextModel.from_pretrained(
"hf-internal-testing/diffusers-stable-diffusion-tiny-all",
subfolder="text_encoder",
use_safetensors=False,
cache_dir=tmp_dir,
)
all_downloaded_files = glob.glob(os.path.join(tmp_dir, "*", "snapshots", "*", "*", "*"))
self.assertTrue(any(f.endswith("bin") for f in all_downloaded_files))
self.assertFalse(any(f.endswith("safetensors") for f in all_downloaded_files))
# test that no safetensors if both available and use_safetensors=True
with tempfile.TemporaryDirectory() as tmp_dir:
CLIPTextModel.from_pretrained(
"hf-internal-testing/diffusers-stable-diffusion-tiny-all",
subfolder="text_encoder",
use_safetensors=True,
cache_dir=tmp_dir,
)
all_downloaded_files = glob.glob(os.path.join(tmp_dir, "*", "snapshots", "*", "*", "*"))
self.assertTrue(any(f.endswith("safetensors") for f in all_downloaded_files))
self.assertFalse(any(f.endswith("bin") for f in all_downloaded_files))
@require_safetensors
def test_safetensors_save_and_load(self):
model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir, safe_serialization=True)
# No pytorch_model.bin file, only a model.safetensors
self.assertTrue(os.path.isfile(os.path.join(tmp_dir, SAFE_WEIGHTS_NAME)))
self.assertFalse(os.path.isfile(os.path.join(tmp_dir, WEIGHTS_NAME)))
new_model = BertModel.from_pretrained(tmp_dir)
# Check models are equal
for p1, p2 in zip(model.parameters(), new_model.parameters()):
self.assertTrue(torch.allclose(p1, p2))
@require_safetensors
def test_safetensors_load_from_hub(self):
safetensors_model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert-safetensors")
pytorch_model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
# Check models are equal
for p1, p2 in zip(safetensors_model.parameters(), pytorch_model.parameters()):
self.assertTrue(torch.allclose(p1, p2))
@require_safetensors
def test_safetensors_save_and_load_sharded(self):
model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir, safe_serialization=True, max_shard_size="100kB")
# No pytorch_model.bin index file, only a model.safetensors index
self.assertFalse(os.path.isfile(os.path.join(tmp_dir, WEIGHTS_INDEX_NAME)))
self.assertTrue(os.path.isfile(os.path.join(tmp_dir, SAFE_WEIGHTS_INDEX_NAME)))
# No regular weights file
self.assertFalse(os.path.isfile(os.path.join(tmp_dir, WEIGHTS_NAME)))
self.assertFalse(os.path.isfile(os.path.join(tmp_dir, SAFE_WEIGHTS_NAME)))
new_model = BertModel.from_pretrained(tmp_dir)
# Check models are equal
for p1, p2 in zip(model.parameters(), new_model.parameters()):
self.assertTrue(torch.allclose(p1, p2))
@require_safetensors
def test_safetensors_load_from_hub_sharded(self):
safetensors_model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert-sharded-safetensors")
pytorch_model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert-sharded")
# Check models are equal
for p1, p2 in zip(safetensors_model.parameters(), pytorch_model.parameters()):
self.assertTrue(torch.allclose(p1, p2))
def test_base_model_to_head_model_load(self):
base_model = BaseModel(PretrainedConfig())
with tempfile.TemporaryDirectory() as tmp_dir:
base_model.save_pretrained(tmp_dir)
# Can load a base model in a model with head
model = ModelWithHead.from_pretrained(tmp_dir)
for p1, p2 in zip(model.base.parameters(), base_model.parameters()):
self.assertTrue(torch.allclose(p1, p2))
# It doesn't work if the state dict has a mix of keys of the head and base without prefix though.
base_state_dict = base_model.state_dict()
head_state_dict = model.state_dict()
base_state_dict["linear2.weight"] = head_state_dict["linear2.weight"]
base_state_dict["linear2.bias"] = head_state_dict["linear2.bias"]
torch.save(base_state_dict, os.path.join(tmp_dir, WEIGHTS_NAME))
with self.assertRaisesRegex(
ValueError, "The state dictionary of the model you are trying to load is corrupted."
):
_ = ModelWithHead.from_pretrained(tmp_dir)
@require_torch_gpu
@slow
def test_pretrained_low_mem_new_config(self):
# Checking for 1 model(the same one which was described in the issue) .
model_ids = ["gpt2"]
for model_id in model_ids:
model_config = AutoConfig.from_pretrained(pretrained_model_name_or_path=model_id)
model_config.n_layer = 48
model_config.n_head = 25
model_config.n_embd = 1600
model = AutoModelForCausalLM.from_pretrained(
pretrained_model_name_or_path=model_id,
config=model_config,
ignore_mismatched_sizes=True,
torch_dtype=torch.float16,
low_cpu_mem_usage=True,
)
model_ref = AutoModelForCausalLM.from_pretrained(pretrained_model_name_or_path=model_id)
self.assertEqual(model.__class__.__name__, model_ref.__class__.__name__)
@require_torch
@is_staging_test
class ModelPushToHubTester(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls._token = TOKEN
HfFolder.save_token(TOKEN)
@classmethod
def tearDownClass(cls):
try:
delete_repo(token=cls._token, repo_id="test-model")
except HTTPError:
pass
try:
delete_repo(token=cls._token, repo_id="valid_org/test-model-org")
except HTTPError:
pass
try:
delete_repo(token=cls._token, repo_id="test-dynamic-model")
except HTTPError:
pass
def test_push_to_hub(self):
config = BertConfig(
vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37
)
model = BertModel(config)
model.push_to_hub("test-model", use_auth_token=self._token)
new_model = BertModel.from_pretrained(f"{USER}/test-model")
for p1, p2 in zip(model.parameters(), new_model.parameters()):
self.assertTrue(torch.equal(p1, p2))
# Reset repo
delete_repo(token=self._token, repo_id="test-model")
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir, repo_id="test-model", push_to_hub=True, use_auth_token=self._token)
new_model = BertModel.from_pretrained(f"{USER}/test-model")
for p1, p2 in zip(model.parameters(), new_model.parameters()):
self.assertTrue(torch.equal(p1, p2))
def test_push_to_hub_in_organization(self):
config = BertConfig(
vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37
)
model = BertModel(config)
model.push_to_hub("valid_org/test-model-org", use_auth_token=self._token)
new_model = BertModel.from_pretrained("valid_org/test-model-org")
for p1, p2 in zip(model.parameters(), new_model.parameters()):
self.assertTrue(torch.equal(p1, p2))
# Reset repo
delete_repo(token=self._token, repo_id="valid_org/test-model-org")
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(
tmp_dir, push_to_hub=True, use_auth_token=self._token, repo_id="valid_org/test-model-org"
)
new_model = BertModel.from_pretrained("valid_org/test-model-org")
for p1, p2 in zip(model.parameters(), new_model.parameters()):
self.assertTrue(torch.equal(p1, p2))
def test_push_to_hub_dynamic_model(self):
CustomConfig.register_for_auto_class()
CustomModel.register_for_auto_class()
config = CustomConfig(hidden_size=32)
model = CustomModel(config)
model.push_to_hub("test-dynamic-model", use_auth_token=self._token)
# checks
self.assertDictEqual(
config.auto_map,
{"AutoConfig": "custom_configuration.CustomConfig", "AutoModel": "custom_modeling.CustomModel"},
)
new_model = AutoModel.from_pretrained(f"{USER}/test-dynamic-model", trust_remote_code=True)
# Can't make an isinstance check because the new_model is from the CustomModel class of a dynamic module
self.assertEqual(new_model.__class__.__name__, "CustomModel")
for p1, p2 in zip(model.parameters(), new_model.parameters()):
self.assertTrue(torch.equal(p1, p2))
config = AutoConfig.from_pretrained(f"{USER}/test-dynamic-model", trust_remote_code=True)
new_model = AutoModel.from_config(config, trust_remote_code=True)
self.assertEqual(new_model.__class__.__name__, "CustomModel")
tests/test_modeling_flax_common.py
View file @ 372f5003
...@@ -17,25 +17,14 @@ import inspect ...@@ -17,25 +17,14 @@ import inspect
import json import json
import random import random
import tempfile import tempfile
import unittest
from typing import List, Tuple from typing import List, Tuple
import numpy as np import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
import transformers import transformers
from transformers import BertConfig, is_flax_available, is_torch_available from transformers import is_flax_available, is_torch_available
from transformers.models.auto import get_values from transformers.models.auto import get_values
from transformers.testing_utils import ( from transformers.testing_utils import CaptureLogger, is_pt_flax_cross_test, require_flax, torch_device
TOKEN,
USER,
CaptureLogger,
is_pt_flax_cross_test,
is_staging_test,
require_flax,
torch_device,
)
from transformers.utils import CONFIG_NAME, GENERATION_CONFIG_NAME, logging from transformers.utils import CONFIG_NAME, GENERATION_CONFIG_NAME, logging
from transformers.utils.generic import ModelOutput from transformers.utils.generic import ModelOutput
...@@ -69,14 +58,6 @@ if is_torch_available(): ...@@ -69,14 +58,6 @@ if is_torch_available():
import torch import torch
def _config_zero_init(config):
configs_no_init = copy.deepcopy(config)
for key in configs_no_init.__dict__.keys():
if "_range" in key or "_std" in key or "initializer_factor" in key:
setattr(configs_no_init, key, 1e-10)
return configs_no_init
def ids_tensor(shape, vocab_size, rng=None): def ids_tensor(shape, vocab_size, rng=None):
"""Creates a random int32 tensor of the shape within the vocab size.""" """Creates a random int32 tensor of the shape within the vocab size."""
if rng is None: if rng is None:
...@@ -1164,155 +1145,3 @@ class FlaxModelTesterMixin: ...@@ -1164,155 +1145,3 @@ class FlaxModelTesterMixin:
# ensure that the outputs remain precisely equal # ensure that the outputs remain precisely equal
for output, remat_output in zip(outputs, remat_outputs): for output, remat_output in zip(outputs, remat_outputs):
self.assertTrue((output == remat_output).all()) self.assertTrue((output == remat_output).all())
@require_flax
@is_staging_test
class FlaxModelPushToHubTester(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls._token = TOKEN
HfFolder.save_token(TOKEN)
@classmethod
def tearDownClass(cls):
try:
delete_repo(token=cls._token, repo_id="test-model-flax")
except HTTPError:
pass
try:
delete_repo(token=cls._token, repo_id="valid_org/test-model-flax-org")
except HTTPError:
pass
def test_push_to_hub(self):
config = BertConfig(
vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37
)
model = FlaxBertModel(config)
model.push_to_hub("test-model-flax", use_auth_token=self._token)
new_model = FlaxBertModel.from_pretrained(f"{USER}/test-model-flax")
base_params = flatten_dict(unfreeze(model.params))
new_params = flatten_dict(unfreeze(new_model.params))
for key in base_params.keys():
max_diff = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical")
# Reset repo
delete_repo(token=self._token, repo_id="test-model-flax")
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir, repo_id="test-model-flax", push_to_hub=True, use_auth_token=self._token)
new_model = FlaxBertModel.from_pretrained(f"{USER}/test-model-flax")
base_params = flatten_dict(unfreeze(model.params))
new_params = flatten_dict(unfreeze(new_model.params))
for key in base_params.keys():
max_diff = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical")
def test_push_to_hub_in_organization(self):
config = BertConfig(
vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37
)
model = FlaxBertModel(config)
model.push_to_hub("valid_org/test-model-flax-org", use_auth_token=self._token)
new_model = FlaxBertModel.from_pretrained("valid_org/test-model-flax-org")
base_params = flatten_dict(unfreeze(model.params))
new_params = flatten_dict(unfreeze(new_model.params))
for key in base_params.keys():
max_diff = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical")
# Reset repo
delete_repo(token=self._token, repo_id="valid_org/test-model-flax-org")
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(
tmp_dir, repo_id="valid_org/test-model-flax-org", push_to_hub=True, use_auth_token=self._token
)
new_model = FlaxBertModel.from_pretrained("valid_org/test-model-flax-org")
base_params = flatten_dict(unfreeze(model.params))
new_params = flatten_dict(unfreeze(new_model.params))
for key in base_params.keys():
max_diff = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical")
def check_models_equal(model1, model2):
models_are_equal = True
flat_params_1 = flatten_dict(model1.params)
flat_params_2 = flatten_dict(model2.params)
for key in flat_params_1.keys():
if np.sum(np.abs(flat_params_1[key] - flat_params_2[key])) > 1e-4:
models_are_equal = False
return models_are_equal
@require_flax
class FlaxModelUtilsTest(unittest.TestCase):
def test_model_from_pretrained_subfolder(self):
config = BertConfig.from_pretrained("hf-internal-testing/tiny-bert-flax-only")
model = FlaxBertModel(config)
subfolder = "bert"
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(os.path.join(tmp_dir, subfolder))
with self.assertRaises(OSError):
_ = FlaxBertModel.from_pretrained(tmp_dir)
model_loaded = FlaxBertModel.from_pretrained(tmp_dir, subfolder=subfolder)
self.assertTrue(check_models_equal(model, model_loaded))
def test_model_from_pretrained_subfolder_sharded(self):
config = BertConfig.from_pretrained("hf-internal-testing/tiny-bert-flax-only")
model = FlaxBertModel(config)
subfolder = "bert"
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(os.path.join(tmp_dir, subfolder), max_shard_size="10KB")
with self.assertRaises(OSError):
_ = FlaxBertModel.from_pretrained(tmp_dir)
model_loaded = FlaxBertModel.from_pretrained(tmp_dir, subfolder=subfolder)
self.assertTrue(check_models_equal(model, model_loaded))
def test_model_from_pretrained_hub_subfolder(self):
subfolder = "bert"
model_id = "hf-internal-testing/tiny-random-bert-subfolder"
with self.assertRaises(OSError):
_ = FlaxBertModel.from_pretrained(model_id)
model = FlaxBertModel.from_pretrained(model_id, subfolder=subfolder)
self.assertIsNotNone(model)
def test_model_from_pretrained_hub_subfolder_sharded(self):
subfolder = "bert"
model_id = "hf-internal-testing/tiny-random-bert-sharded-subfolder"
with self.assertRaises(OSError):
_ = FlaxBertModel.from_pretrained(model_id)
model = FlaxBertModel.from_pretrained(model_id, subfolder=subfolder)
self.assertIsNotNone(model)
tests/test_modeling_flax_utils.py 0 → 100644
View file @ 372f5003
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import tempfile
import unittest
import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax
if is_flax_available():
import os
from flax.core.frozen_dict import unfreeze
from flax.traverse_util import flatten_dict
from transformers import FlaxBertModel
os.environ["XLA_PYTHON_CLIENT_MEM_FRACTION"] = "0.12" # assumed parallelism: 8
@require_flax
@is_staging_test
class FlaxModelPushToHubTester(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls._token = TOKEN
HfFolder.save_token(TOKEN)
@classmethod
def tearDownClass(cls):
try:
delete_repo(token=cls._token, repo_id="test-model-flax")
except HTTPError:
pass
try:
delete_repo(token=cls._token, repo_id="valid_org/test-model-flax-org")
except HTTPError:
pass
def test_push_to_hub(self):
config = BertConfig(
vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37
)
model = FlaxBertModel(config)
model.push_to_hub("test-model-flax", use_auth_token=self._token)
new_model = FlaxBertModel.from_pretrained(f"{USER}/test-model-flax")
base_params = flatten_dict(unfreeze(model.params))
new_params = flatten_dict(unfreeze(new_model.params))
for key in base_params.keys():
max_diff = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical")
# Reset repo
delete_repo(token=self._token, repo_id="test-model-flax")
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir, repo_id="test-model-flax", push_to_hub=True, use_auth_token=self._token)
new_model = FlaxBertModel.from_pretrained(f"{USER}/test-model-flax")
base_params = flatten_dict(unfreeze(model.params))
new_params = flatten_dict(unfreeze(new_model.params))
for key in base_params.keys():
max_diff = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical")
def test_push_to_hub_in_organization(self):
config = BertConfig(
vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37
)
model = FlaxBertModel(config)
model.push_to_hub("valid_org/test-model-flax-org", use_auth_token=self._token)
new_model = FlaxBertModel.from_pretrained("valid_org/test-model-flax-org")
base_params = flatten_dict(unfreeze(model.params))
new_params = flatten_dict(unfreeze(new_model.params))
for key in base_params.keys():
max_diff = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical")
# Reset repo
delete_repo(token=self._token, repo_id="valid_org/test-model-flax-org")
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(
tmp_dir, repo_id="valid_org/test-model-flax-org", push_to_hub=True, use_auth_token=self._token
)
new_model = FlaxBertModel.from_pretrained("valid_org/test-model-flax-org")
base_params = flatten_dict(unfreeze(model.params))
new_params = flatten_dict(unfreeze(new_model.params))
for key in base_params.keys():
max_diff = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical")
def check_models_equal(model1, model2):
models_are_equal = True
flat_params_1 = flatten_dict(model1.params)
flat_params_2 = flatten_dict(model2.params)
for key in flat_params_1.keys():
if np.sum(np.abs(flat_params_1[key] - flat_params_2[key])) > 1e-4:
models_are_equal = False
return models_are_equal
@require_flax
class FlaxModelUtilsTest(unittest.TestCase):
def test_model_from_pretrained_subfolder(self):
config = BertConfig.from_pretrained("hf-internal-testing/tiny-bert-flax-only")
model = FlaxBertModel(config)
subfolder = "bert"
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(os.path.join(tmp_dir, subfolder))
with self.assertRaises(OSError):
_ = FlaxBertModel.from_pretrained(tmp_dir)
model_loaded = FlaxBertModel.from_pretrained(tmp_dir, subfolder=subfolder)
self.assertTrue(check_models_equal(model, model_loaded))
def test_model_from_pretrained_subfolder_sharded(self):
config = BertConfig.from_pretrained("hf-internal-testing/tiny-bert-flax-only")
model = FlaxBertModel(config)
subfolder = "bert"
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(os.path.join(tmp_dir, subfolder), max_shard_size="10KB")
with self.assertRaises(OSError):
_ = FlaxBertModel.from_pretrained(tmp_dir)
model_loaded = FlaxBertModel.from_pretrained(tmp_dir, subfolder=subfolder)
self.assertTrue(check_models_equal(model, model_loaded))
def test_model_from_pretrained_hub_subfolder(self):
subfolder = "bert"
model_id = "hf-internal-testing/tiny-random-bert-subfolder"
with self.assertRaises(OSError):
_ = FlaxBertModel.from_pretrained(model_id)
model = FlaxBertModel.from_pretrained(model_id, subfolder=subfolder)
self.assertIsNotNone(model)
def test_model_from_pretrained_hub_subfolder_sharded(self):
subfolder = "bert"
model_id = "hf-internal-testing/tiny-random-bert-sharded-subfolder"
with self.assertRaises(OSError):
_ = FlaxBertModel.from_pretrained(model_id)
model = FlaxBertModel.from_pretrained(model_id, subfolder=subfolder)
self.assertIsNotNone(model)
tests/test_modeling_tf_common.py
View file @ 372f5003
...@@ -23,42 +23,24 @@ import os ...@@ -23,42 +23,24 @@ import os
import random import random
import tempfile import tempfile
import unittest import unittest
import unittest.mock as mock
from importlib import import_module from importlib import import_module
from math import isnan from math import isnan
from typing import List, Tuple from typing import List, Tuple
from datasets import Dataset from datasets import Dataset
from huggingface_hub import HfFolder, Repository, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError
from transformers import is_tf_available, is_torch_available from transformers import is_tf_available, is_torch_available
from transformers.configuration_utils import PretrainedConfig
from transformers.models.auto import get_values from transformers.models.auto import get_values
from transformers.testing_utils import ( # noqa: F401 from transformers.testing_utils import ( # noqa: F401
TOKEN,
USER,
CaptureLogger, CaptureLogger,
CaptureStdout,
_tf_gpu_memory_limit, _tf_gpu_memory_limit,
is_pt_tf_cross_test, is_pt_tf_cross_test,
is_staging_test,
require_safetensors,
require_tf, require_tf,
require_tf2onnx, require_tf2onnx,
slow, slow,
tooslow,
torch_device, torch_device,
) )
from transformers.utils import ( from transformers.utils import CONFIG_NAME, GENERATION_CONFIG_NAME, logging
CONFIG_NAME,
GENERATION_CONFIG_NAME,
SAFE_WEIGHTS_NAME,
TF2_WEIGHTS_INDEX_NAME,
TF2_WEIGHTS_NAME,
logging,
)
from transformers.utils.generic import ModelOutput from transformers.utils.generic import ModelOutput
...@@ -66,7 +48,6 @@ logger = logging.get_logger(__name__) ...@@ -66,7 +48,6 @@ logger = logging.get_logger(__name__)
if is_tf_available(): if is_tf_available():
import h5py
import numpy as np import numpy as np
import tensorflow as tf import tensorflow as tf
...@@ -85,17 +66,8 @@ if is_tf_available(): ...@@ -85,17 +66,8 @@ if is_tf_available():
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING, TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING, TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING, TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
BertConfig,
PreTrainedModel,
PushToHubCallback,
RagRetriever,
TFAutoModel, TFAutoModel,
TFAutoModelForSequenceClassification, TFAutoModelForSequenceClassification,
TFBertForMaskedLM,
TFBertForSequenceClassification,
TFBertModel,
TFPreTrainedModel,
TFRagModel,
TFSharedEmbeddings, TFSharedEmbeddings,
) )
from transformers.generation import ( from transformers.generation import (
...@@ -108,8 +80,6 @@ if is_tf_available(): ...@@ -108,8 +80,6 @@ if is_tf_available():
TFSampleDecoderOnlyOutput, TFSampleDecoderOnlyOutput,
TFSampleEncoderDecoderOutput, TFSampleEncoderDecoderOutput,
) )
from transformers.modeling_tf_utils import tf_shard_checkpoint, unpack_inputs
from transformers.tf_utils import stable_softmax
tf.config.experimental.enable_tensor_float_32_execution(False) tf.config.experimental.enable_tensor_float_32_execution(False)
...@@ -130,8 +100,6 @@ if is_tf_available(): ...@@ -130,8 +100,6 @@ if is_tf_available():
if is_torch_available(): if is_torch_available():
import torch import torch
from transformers import BertModel
def _config_zero_init(config): def _config_zero_init(config):
configs_no_init = copy.deepcopy(config) configs_no_init = copy.deepcopy(config)
...@@ -1995,544 +1963,3 @@ def floats_tensor(shape, scale=1.0, rng=None, name=None, dtype=None): ...@@ -1995,544 +1963,3 @@ def floats_tensor(shape, scale=1.0, rng=None, name=None, dtype=None):
values.append(rng.random() * scale) values.append(rng.random() * scale)
return tf.reshape(tf.constant(values, dtype=dtype if dtype is not None else tf.float32), shape=shape) return tf.reshape(tf.constant(values, dtype=dtype if dtype is not None else tf.float32), shape=shape)
@require_tf
class UtilsFunctionsTest(unittest.TestCase):
def test_cached_files_are_used_when_internet_is_down(self):
# A mock response for an HTTP head request to emulate server down
response_mock = mock.Mock()
response_mock.status_code = 500
response_mock.headers = {}
response_mock.raise_for_status.side_effect = HTTPError
response_mock.json.return_value = {}
# Download this model to make sure it's in the cache.
_ = TFBertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch("requests.Session.request", return_value=response_mock) as mock_head:
_ = TFBertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
# This check we did call the fake head request
mock_head.assert_called()
def test_load_from_one_file(self):
try:
tmp_file = tempfile.mktemp()
with open(tmp_file, "wb") as f:
http_get("https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/tf_model.h5", f)
config = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert")
_ = TFBertModel.from_pretrained(tmp_file, config=config)
finally:
os.remove(tmp_file)
def test_legacy_load_from_url(self):
# This test is for deprecated behavior and can be removed in v5
config = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert")
_ = TFBertModel.from_pretrained(
"https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/tf_model.h5", config=config
)
# tests whether the unpack_inputs function behaves as expected
def test_unpack_inputs(self):
class DummyModel:
def __init__(self):
config_kwargs = {"output_attentions": False, "output_hidden_states": False, "return_dict": False}
self.config = PretrainedConfig(**config_kwargs)
self.main_input_name = "input_ids"
@unpack_inputs
def call(
self,
input_ids=None,
past_key_values=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
return input_ids, past_key_values, output_attentions, output_hidden_states, return_dict
@unpack_inputs
def foo(self, pixel_values, output_attentions=None, output_hidden_states=None, return_dict=None):
return pixel_values, output_attentions, output_hidden_states, return_dict
dummy_model = DummyModel()
input_ids = tf.constant([0, 1, 2, 3], dtype=tf.int32)
past_key_values = tf.constant([4, 5, 6, 7], dtype=tf.int32)
pixel_values = tf.constant([8, 9, 10, 11], dtype=tf.int32)
# test case 1: Pass inputs as keyword arguments; Booleans are inherited from the config.
output = dummy_model.call(input_ids=input_ids, past_key_values=past_key_values)
tf.debugging.assert_equal(output[0], input_ids)
tf.debugging.assert_equal(output[1], past_key_values)
self.assertFalse(output[2])
self.assertFalse(output[3])
self.assertFalse(output[4])
# test case 2: Same as above, but with positional arguments.
output = dummy_model.call(input_ids, past_key_values)
tf.debugging.assert_equal(output[0], input_ids)
tf.debugging.assert_equal(output[1], past_key_values)
self.assertFalse(output[2])
self.assertFalse(output[3])
self.assertFalse(output[4])
# test case 3: We can also pack everything in the first input.
output = dummy_model.call(input_ids={"input_ids": input_ids, "past_key_values": past_key_values})
tf.debugging.assert_equal(output[0], input_ids)
tf.debugging.assert_equal(output[1], past_key_values)
self.assertFalse(output[2])
self.assertFalse(output[3])
self.assertFalse(output[4])
# test case 4: Explicit boolean arguments should override the config.
output = dummy_model.call(
input_ids=input_ids, past_key_values=past_key_values, output_attentions=False, return_dict=True
)
tf.debugging.assert_equal(output[0], input_ids)
tf.debugging.assert_equal(output[1], past_key_values)
self.assertFalse(output[2])
self.assertFalse(output[3])
self.assertTrue(output[4])
# test case 5: Unexpected arguments should raise an exception.
with self.assertRaises(ValueError):
output = dummy_model.call(input_ids=input_ids, past_key_values=past_key_values, foo="bar")
# test case 6: the decorator is independent from `main_input_name` -- it treats the first argument of the
# decorated function as its main input.
output = dummy_model.foo(pixel_values=pixel_values)
tf.debugging.assert_equal(output[0], pixel_values)
self.assertFalse(output[1])
self.assertFalse(output[2])
self.assertFalse(output[3])
# Tests whether the stable softmax is stable on CPU, with and without XLA
def test_xla_stable_softmax(self):
large_penalty = -1e9
n_tokens = 10
batch_size = 8
def masked_softmax(x, boolean_mask):
numerical_mask = (1.0 - tf.cast(boolean_mask, dtype=tf.float32)) * large_penalty
masked_x = x + numerical_mask
return stable_softmax(masked_x)
xla_masked_softmax = tf.function(masked_softmax, jit_compile=True)
xla_stable_softmax = tf.function(stable_softmax, jit_compile=True)
x = tf.random.normal((batch_size, n_tokens))
# Same outcome regardless of the boolean mask here
masked_tokens = random.randint(0, n_tokens)
boolean_mask = tf.convert_to_tensor([[1] * (n_tokens - masked_tokens) + [0] * masked_tokens], dtype=tf.int32)
# We can randomly mask a random numerical input OUTSIDE XLA
numerical_mask = (1.0 - tf.cast(boolean_mask, dtype=tf.float32)) * large_penalty
masked_x = x + numerical_mask
xla_out = xla_stable_softmax(masked_x)
out = stable_softmax(masked_x)
assert tf.experimental.numpy.allclose(xla_out, out)
# The stable softmax has the same output as the original softmax
unstable_out = tf.nn.softmax(masked_x)
assert tf.experimental.numpy.allclose(unstable_out, out)
# We can randomly mask a random numerical input INSIDE XLA
xla_out = xla_masked_softmax(x, boolean_mask)
out = masked_softmax(x, boolean_mask)
assert tf.experimental.numpy.allclose(xla_out, out)
def test_checkpoint_sharding_from_hub(self):
model = TFBertModel.from_pretrained("ArthurZ/tiny-random-bert-sharded")
# the model above is the same as the model below, just a sharded version.
ref_model = TFBertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
for p1, p2 in zip(model.weights, ref_model.weights):
assert np.allclose(p1.numpy(), p2.numpy())
def test_sharded_checkpoint_with_prefix(self):
model = TFBertModel.from_pretrained("hf-internal-testing/tiny-random-bert", load_weight_prefix="a/b")
sharded_model = TFBertModel.from_pretrained("ArthurZ/tiny-random-bert-sharded", load_weight_prefix="a/b")
for p1, p2 in zip(model.weights, sharded_model.weights):
self.assertTrue(np.allclose(p1.numpy(), p2.numpy()))
self.assertTrue(p1.name.startswith("a/b/"))
self.assertTrue(p2.name.startswith("a/b/"))
def test_sharded_checkpoint_transfer(self):
# If this doesn't throw an error then the test passes
TFBertForSequenceClassification.from_pretrained("ArthurZ/tiny-random-bert-sharded")
@is_pt_tf_cross_test
def test_checkpoint_sharding_local_from_pt(self):
with tempfile.TemporaryDirectory() as tmp_dir:
_ = Repository(local_dir=tmp_dir, clone_from="hf-internal-testing/tiny-random-bert-sharded")
model = TFBertModel.from_pretrained(tmp_dir, from_pt=True)
# the model above is the same as the model below, just a sharded pytorch version.
ref_model = TFBertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
for p1, p2 in zip(model.weights, ref_model.weights):
assert np.allclose(p1.numpy(), p2.numpy())
@is_pt_tf_cross_test
def test_checkpoint_loading_with_prefix_from_pt(self):
model = TFBertModel.from_pretrained(
"hf-internal-testing/tiny-random-bert", from_pt=True, load_weight_prefix="a/b"
)
ref_model = TFBertModel.from_pretrained("hf-internal-testing/tiny-random-bert", from_pt=True)
for p1, p2 in zip(model.weights, ref_model.weights):
self.assertTrue(np.allclose(p1.numpy(), p2.numpy()))
self.assertTrue(p1.name.startswith("a/b/"))
@is_pt_tf_cross_test
def test_checkpoint_sharding_hub_from_pt(self):
model = TFBertModel.from_pretrained("hf-internal-testing/tiny-random-bert-sharded", from_pt=True)
# the model above is the same as the model below, just a sharded pytorch version.
ref_model = TFBertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
for p1, p2 in zip(model.weights, ref_model.weights):
assert np.allclose(p1.numpy(), p2.numpy())
def test_shard_checkpoint(self):
# This is the model we will use, total size 340,000 bytes.
model = tf.keras.Sequential(
[
tf.keras.layers.Dense(200, use_bias=False), # size 80,000
tf.keras.layers.Dense(200, use_bias=False), # size 160,000
tf.keras.layers.Dense(100, use_bias=False), # size 80,000
tf.keras.layers.Dense(50, use_bias=False), # size 20,000
]
)
inputs = tf.zeros((1, 100), dtype=tf.float32)
model(inputs)
weights = model.weights
weights_dict = {w.name: w for w in weights}
with self.subTest("No shard when max size is bigger than model size"):
shards, index = tf_shard_checkpoint(weights)
self.assertIsNone(index)
self.assertDictEqual(shards, {TF2_WEIGHTS_NAME: weights})
with self.subTest("Test sharding, no weights bigger than max size"):
shards, index = tf_shard_checkpoint(weights, max_shard_size="300kB")
# Split is first two layers then last two.
self.assertDictEqual(
index,
{
"metadata": {"total_size": 340000},
"weight_map": {
"dense/kernel:0": "tf_model-00001-of-00002.h5",
"dense_1/kernel:0": "tf_model-00001-of-00002.h5",
"dense_2/kernel:0": "tf_model-00002-of-00002.h5",
"dense_3/kernel:0": "tf_model-00002-of-00002.h5",
},
},
)
shard1 = [weights_dict["dense/kernel:0"], weights_dict["dense_1/kernel:0"]]
shard2 = [weights_dict["dense_2/kernel:0"], weights_dict["dense_3/kernel:0"]]
self.assertDictEqual(shards, {"tf_model-00001-of-00002.h5": shard1, "tf_model-00002-of-00002.h5": shard2})
with self.subTest("Test sharding with weights bigger than max size"):
shards, index = tf_shard_checkpoint(weights, max_shard_size="100kB")
# Split is first layer, second layer then last 2.
self.assertDictEqual(
index,
{
"metadata": {"total_size": 340000},
"weight_map": {
"dense/kernel:0": "tf_model-00001-of-00003.h5",
"dense_1/kernel:0": "tf_model-00002-of-00003.h5",
"dense_2/kernel:0": "tf_model-00003-of-00003.h5",
"dense_3/kernel:0": "tf_model-00003-of-00003.h5",
},
},
)
shard1 = [weights_dict["dense/kernel:0"]]
shard2 = [weights_dict["dense_1/kernel:0"]]
shard3 = [weights_dict["dense_2/kernel:0"], weights_dict["dense_3/kernel:0"]]
self.assertDictEqual(
shards,
{
"tf_model-00001-of-00003.h5": shard1,
"tf_model-00002-of-00003.h5": shard2,
"tf_model-00003-of-00003.h5": shard3,
},
)
@slow
def test_special_layer_name_sharding(self):
retriever = RagRetriever.from_pretrained("facebook/rag-token-nq", index_name="exact", use_dummy_dataset=True)
model = TFRagModel.from_pretrained("facebook/rag-token-nq", retriever=retriever)
with tempfile.TemporaryDirectory() as tmp_dir:
for max_size in ["150kB", "150kiB", "200kB", "200kiB"]:
model.save_pretrained(tmp_dir, max_shard_size=max_size)
ref_model = TFRagModel.from_pretrained(tmp_dir, retriever=retriever)
for p1, p2 in zip(model.weights, ref_model.weights):
assert np.allclose(p1.numpy(), p2.numpy())
def test_checkpoint_sharding_local(self):
model = TFBertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
with tempfile.TemporaryDirectory() as tmp_dir:
# We use the same folder for various sizes to make sure a new save erases the old checkpoint.
for max_size in ["150kB", "150kiB", "200kB", "200kiB"]:
model.save_pretrained(tmp_dir, max_shard_size=max_size)
# Get each shard file and its size
shard_to_size = {}
for shard in os.listdir(tmp_dir):
if shard.endswith(".h5"):
shard_file = os.path.join(tmp_dir, shard)
shard_to_size[shard_file] = os.path.getsize(shard_file)
index_file = os.path.join(tmp_dir, TF2_WEIGHTS_INDEX_NAME)
# Check there is an index but no regular weight file
self.assertTrue(os.path.isfile(index_file))
self.assertFalse(os.path.isfile(os.path.join(tmp_dir, TF2_WEIGHTS_NAME)))
# Check a file is bigger than max_size only when it has a single weight
for shard_file, size in shard_to_size.items():
if max_size.endswith("kiB"):
max_size_int = int(max_size[:-3]) * 2**10
else:
max_size_int = int(max_size[:-2]) * 10**3
# Note: pickle adds some junk so the weight of the file can end up being slightly bigger than
# the size asked for (since we count parameters)
if size >= max_size_int + 50000:
with h5py.File(shard_file, "r") as state_file:
self.assertEqual(len(state_file), 1)
# Check the index and the shard files found match
with open(index_file, "r", encoding="utf-8") as f:
index = json.loads(f.read())
all_shards = set(index["weight_map"].values())
shards_found = {f for f in os.listdir(tmp_dir) if f.endswith(".h5")}
self.assertSetEqual(all_shards, shards_found)
# Finally, check the model can be reloaded
new_model = TFBertModel.from_pretrained(tmp_dir)
model.build()
new_model.build()
for p1, p2 in zip(model.weights, new_model.weights):
self.assertTrue(np.allclose(p1.numpy(), p2.numpy()))
@slow
def test_save_pretrained_signatures(self):
model = TFBertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
# Short custom TF signature function.
# `input_signature` is specific to BERT.
@tf.function(
input_signature=[
[
tf.TensorSpec([None, None], tf.int32, name="input_ids"),
tf.TensorSpec([None, None], tf.int32, name="token_type_ids"),
tf.TensorSpec([None, None], tf.int32, name="attention_mask"),
]
]
)
def serving_fn(input):
return model(input)
# Using default signature (default behavior) overrides 'serving_default'
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir, saved_model=True, signatures=None)
model_loaded = tf.keras.models.load_model(f"{tmp_dir}/saved_model/1")
self.assertTrue("serving_default" in list(model_loaded.signatures.keys()))
# Providing custom signature function
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir, saved_model=True, signatures={"custom_signature": serving_fn})
model_loaded = tf.keras.models.load_model(f"{tmp_dir}/saved_model/1")
self.assertTrue("custom_signature" in list(model_loaded.signatures.keys()))
# Providing multiple custom signature function
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(
tmp_dir,
saved_model=True,
signatures={"custom_signature_1": serving_fn, "custom_signature_2": serving_fn},
)
model_loaded = tf.keras.models.load_model(f"{tmp_dir}/saved_model/1")
self.assertTrue("custom_signature_1" in list(model_loaded.signatures.keys()))
self.assertTrue("custom_signature_2" in list(model_loaded.signatures.keys()))
@require_safetensors
def test_safetensors_save_and_load(self):
model = TFBertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir, safe_serialization=True)
# No tf_model.h5 file, only a model.safetensors
self.assertTrue(os.path.isfile(os.path.join(tmp_dir, SAFE_WEIGHTS_NAME)))
self.assertFalse(os.path.isfile(os.path.join(tmp_dir, TF2_WEIGHTS_NAME)))
new_model = TFBertModel.from_pretrained(tmp_dir)
# Check models are equal
for p1, p2 in zip(model.weights, new_model.weights):
self.assertTrue(np.allclose(p1.numpy(), p2.numpy()))
@is_pt_tf_cross_test
def test_safetensors_save_and_load_pt_to_tf(self):
model = TFBertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
pt_model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
with tempfile.TemporaryDirectory() as tmp_dir:
pt_model.save_pretrained(tmp_dir, safe_serialization=True)
# Check we have a model.safetensors file
self.assertTrue(os.path.isfile(os.path.join(tmp_dir, SAFE_WEIGHTS_NAME)))
new_model = TFBertModel.from_pretrained(tmp_dir)
# Check models are equal
for p1, p2 in zip(model.weights, new_model.weights):
self.assertTrue(np.allclose(p1.numpy(), p2.numpy()))
@require_safetensors
def test_safetensors_load_from_hub(self):
tf_model = TFBertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
# Can load from the TF-formatted checkpoint
safetensors_model = TFBertModel.from_pretrained("hf-internal-testing/tiny-random-bert-safetensors-tf")
# Check models are equal
for p1, p2 in zip(safetensors_model.weights, tf_model.weights):
self.assertTrue(np.allclose(p1.numpy(), p2.numpy()))
# Can load from the PyTorch-formatted checkpoint
safetensors_model = TFBertModel.from_pretrained("hf-internal-testing/tiny-random-bert-safetensors")
# Check models are equal
for p1, p2 in zip(safetensors_model.weights, tf_model.weights):
self.assertTrue(np.allclose(p1.numpy(), p2.numpy()))
@require_tf
@is_staging_test
class TFModelPushToHubTester(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls._token = TOKEN
HfFolder.save_token(TOKEN)
@classmethod
def tearDownClass(cls):
try:
delete_repo(token=cls._token, repo_id="test-model-tf")
except HTTPError:
pass
try:
delete_repo(token=cls._token, repo_id="test-model-tf-callback")
except HTTPError:
pass
try:
delete_repo(token=cls._token, repo_id="valid_org/test-model-tf-org")
except HTTPError:
pass
def test_push_to_hub(self):
config = BertConfig(
vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37
)
model = TFBertModel(config)
# Make sure model is properly initialized
model.build()
logging.set_verbosity_info()
logger = logging.get_logger("transformers.utils.hub")
with CaptureLogger(logger) as cl:
model.push_to_hub("test-model-tf", use_auth_token=self._token)
logging.set_verbosity_warning()
# Check the model card was created and uploaded.
self.assertIn("Uploading the following files to __DUMMY_TRANSFORMERS_USER__/test-model-tf", cl.out)
new_model = TFBertModel.from_pretrained(f"{USER}/test-model-tf")
models_equal = True
for p1, p2 in zip(model.weights, new_model.weights):
if not tf.math.reduce_all(p1 == p2):
models_equal = False
break
self.assertTrue(models_equal)
# Reset repo
delete_repo(token=self._token, repo_id="test-model-tf")
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir, repo_id="test-model-tf", push_to_hub=True, use_auth_token=self._token)
new_model = TFBertModel.from_pretrained(f"{USER}/test-model-tf")
models_equal = True
for p1, p2 in zip(model.weights, new_model.weights):
if not tf.math.reduce_all(p1 == p2):
models_equal = False
break
self.assertTrue(models_equal)
@is_pt_tf_cross_test
def test_push_to_hub_callback(self):
config = BertConfig(
vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37
)
model = TFBertForMaskedLM(config)
model.compile()
with tempfile.TemporaryDirectory() as tmp_dir:
push_to_hub_callback = PushToHubCallback(
output_dir=tmp_dir,
hub_model_id="test-model-tf-callback",
hub_token=self._token,
)
model.fit(model.dummy_inputs, model.dummy_inputs, epochs=1, callbacks=[push_to_hub_callback])
new_model = TFBertForMaskedLM.from_pretrained(f"{USER}/test-model-tf-callback")
models_equal = True
for p1, p2 in zip(model.weights, new_model.weights):
if not tf.math.reduce_all(p1 == p2):
models_equal = False
break
self.assertTrue(models_equal)
tf_push_to_hub_params = dict(inspect.signature(TFPreTrainedModel.push_to_hub).parameters)
tf_push_to_hub_params.pop("base_model_card_args")
pt_push_to_hub_params = dict(inspect.signature(PreTrainedModel.push_to_hub).parameters)
pt_push_to_hub_params.pop("deprecated_kwargs")
self.assertDictEaual(tf_push_to_hub_params, pt_push_to_hub_params)
def test_push_to_hub_in_organization(self):
config = BertConfig(
vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37
)
model = TFBertModel(config)
# Make sure model is properly initialized
model.build()
model.push_to_hub("valid_org/test-model-tf-org", use_auth_token=self._token)
new_model = TFBertModel.from_pretrained("valid_org/test-model-tf-org")
models_equal = True
for p1, p2 in zip(model.weights, new_model.weights):
if not tf.math.reduce_all(p1 == p2):
models_equal = False
break
self.assertTrue(models_equal)
# Reset repo
delete_repo(token=self._token, repo_id="valid_org/test-model-tf-org")
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(
tmp_dir, push_to_hub=True, use_auth_token=self._token, repo_id="valid_org/test-model-tf-org"
)
new_model = TFBertModel.from_pretrained("valid_org/test-model-tf-org")
models_equal = True
for p1, p2 in zip(model.weights, new_model.weights):
if not tf.math.reduce_all(p1 == p2):
models_equal = False
break
self.assertTrue(models_equal)
tests/test_modeling_tf_utils.py 0 → 100644
View file @ 372f5003
# coding=utf-8
# Copyright 2019 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
import inspect
import json
import os
import random
import tempfile
import unittest
import unittest.mock as mock
from huggingface_hub import HfFolder, Repository, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError
from transformers import is_tf_available, is_torch_available
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import ( # noqa: F401
TOKEN,
USER,
CaptureLogger,
_tf_gpu_memory_limit,
is_pt_tf_cross_test,
is_staging_test,
require_safetensors,
require_tf,
slow,
)
from transformers.utils import SAFE_WEIGHTS_NAME, TF2_WEIGHTS_INDEX_NAME, TF2_WEIGHTS_NAME, logging
logger = logging.get_logger(__name__)
if is_tf_available():
import h5py
import numpy as np
import tensorflow as tf
from transformers import (
BertConfig,
PreTrainedModel,
PushToHubCallback,
RagRetriever,
TFBertForMaskedLM,
TFBertForSequenceClassification,
TFBertModel,
TFPreTrainedModel,
TFRagModel,
)
from transformers.modeling_tf_utils import tf_shard_checkpoint, unpack_inputs
from transformers.tf_utils import stable_softmax
tf.config.experimental.enable_tensor_float_32_execution(False)
if _tf_gpu_memory_limit is not None:
gpus = tf.config.list_physical_devices("GPU")
for gpu in gpus:
# Restrict TensorFlow to only allocate x GB of memory on the GPUs
try:
tf.config.set_logical_device_configuration(
gpu, [tf.config.LogicalDeviceConfiguration(memory_limit=_tf_gpu_memory_limit)]
)
logical_gpus = tf.config.list_logical_devices("GPU")
print("Logical GPUs", logical_gpus)
except RuntimeError as e:
# Virtual devices must be set before GPUs have been initialized
print(e)
if is_torch_available():
from transformers import BertModel
@require_tf
class TFModelUtilsTest(unittest.TestCase):
def test_cached_files_are_used_when_internet_is_down(self):
# A mock response for an HTTP head request to emulate server down
response_mock = mock.Mock()
response_mock.status_code = 500
response_mock.headers = {}
response_mock.raise_for_status.side_effect = HTTPError
response_mock.json.return_value = {}
# Download this model to make sure it's in the cache.
_ = TFBertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch("requests.Session.request", return_value=response_mock) as mock_head:
_ = TFBertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
# This check we did call the fake head request
mock_head.assert_called()
def test_load_from_one_file(self):
try:
tmp_file = tempfile.mktemp()
with open(tmp_file, "wb") as f:
http_get("https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/tf_model.h5", f)
config = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert")
_ = TFBertModel.from_pretrained(tmp_file, config=config)
finally:
os.remove(tmp_file)
def test_legacy_load_from_url(self):
# This test is for deprecated behavior and can be removed in v5
config = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert")
_ = TFBertModel.from_pretrained(
"https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/tf_model.h5", config=config
)
# tests whether the unpack_inputs function behaves as expected
def test_unpack_inputs(self):
class DummyModel:
def __init__(self):
config_kwargs = {"output_attentions": False, "output_hidden_states": False, "return_dict": False}
self.config = PretrainedConfig(**config_kwargs)
self.main_input_name = "input_ids"
@unpack_inputs
def call(
self,
input_ids=None,
past_key_values=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
return input_ids, past_key_values, output_attentions, output_hidden_states, return_dict
@unpack_inputs
def foo(self, pixel_values, output_attentions=None, output_hidden_states=None, return_dict=None):
return pixel_values, output_attentions, output_hidden_states, return_dict
dummy_model = DummyModel()
input_ids = tf.constant([0, 1, 2, 3], dtype=tf.int32)
past_key_values = tf.constant([4, 5, 6, 7], dtype=tf.int32)
pixel_values = tf.constant([8, 9, 10, 11], dtype=tf.int32)
# test case 1: Pass inputs as keyword arguments; Booleans are inherited from the config.
output = dummy_model.call(input_ids=input_ids, past_key_values=past_key_values)
tf.debugging.assert_equal(output[0], input_ids)
tf.debugging.assert_equal(output[1], past_key_values)
self.assertFalse(output[2])
self.assertFalse(output[3])
self.assertFalse(output[4])
# test case 2: Same as above, but with positional arguments.
output = dummy_model.call(input_ids, past_key_values)
tf.debugging.assert_equal(output[0], input_ids)
tf.debugging.assert_equal(output[1], past_key_values)
self.assertFalse(output[2])
self.assertFalse(output[3])
self.assertFalse(output[4])
# test case 3: We can also pack everything in the first input.
output = dummy_model.call(input_ids={"input_ids": input_ids, "past_key_values": past_key_values})
tf.debugging.assert_equal(output[0], input_ids)
tf.debugging.assert_equal(output[1], past_key_values)
self.assertFalse(output[2])
self.assertFalse(output[3])
self.assertFalse(output[4])
# test case 4: Explicit boolean arguments should override the config.
output = dummy_model.call(
input_ids=input_ids, past_key_values=past_key_values, output_attentions=False, return_dict=True
)
tf.debugging.assert_equal(output[0], input_ids)
tf.debugging.assert_equal(output[1], past_key_values)
self.assertFalse(output[2])
self.assertFalse(output[3])
self.assertTrue(output[4])
# test case 5: Unexpected arguments should raise an exception.
with self.assertRaises(ValueError):
output = dummy_model.call(input_ids=input_ids, past_key_values=past_key_values, foo="bar")
# test case 6: the decorator is independent from `main_input_name` -- it treats the first argument of the
# decorated function as its main input.
output = dummy_model.foo(pixel_values=pixel_values)
tf.debugging.assert_equal(output[0], pixel_values)
self.assertFalse(output[1])
self.assertFalse(output[2])
self.assertFalse(output[3])
# Tests whether the stable softmax is stable on CPU, with and without XLA
def test_xla_stable_softmax(self):
large_penalty = -1e9
n_tokens = 10
batch_size = 8
def masked_softmax(x, boolean_mask):
numerical_mask = (1.0 - tf.cast(boolean_mask, dtype=tf.float32)) * large_penalty
masked_x = x + numerical_mask
return stable_softmax(masked_x)
xla_masked_softmax = tf.function(masked_softmax, jit_compile=True)
xla_stable_softmax = tf.function(stable_softmax, jit_compile=True)
x = tf.random.normal((batch_size, n_tokens))
# Same outcome regardless of the boolean mask here
masked_tokens = random.randint(0, n_tokens)
boolean_mask = tf.convert_to_tensor([[1] * (n_tokens - masked_tokens) + [0] * masked_tokens], dtype=tf.int32)
# We can randomly mask a random numerical input OUTSIDE XLA
numerical_mask = (1.0 - tf.cast(boolean_mask, dtype=tf.float32)) * large_penalty
masked_x = x + numerical_mask
xla_out = xla_stable_softmax(masked_x)
out = stable_softmax(masked_x)
assert tf.experimental.numpy.allclose(xla_out, out)
# The stable softmax has the same output as the original softmax
unstable_out = tf.nn.softmax(masked_x)
assert tf.experimental.numpy.allclose(unstable_out, out)
# We can randomly mask a random numerical input INSIDE XLA
xla_out = xla_masked_softmax(x, boolean_mask)
out = masked_softmax(x, boolean_mask)
assert tf.experimental.numpy.allclose(xla_out, out)
def test_checkpoint_sharding_from_hub(self):
model = TFBertModel.from_pretrained("ArthurZ/tiny-random-bert-sharded")
# the model above is the same as the model below, just a sharded version.
ref_model = TFBertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
for p1, p2 in zip(model.weights, ref_model.weights):
assert np.allclose(p1.numpy(), p2.numpy())
def test_sharded_checkpoint_with_prefix(self):
model = TFBertModel.from_pretrained("hf-internal-testing/tiny-random-bert", load_weight_prefix="a/b")
sharded_model = TFBertModel.from_pretrained("ArthurZ/tiny-random-bert-sharded", load_weight_prefix="a/b")
for p1, p2 in zip(model.weights, sharded_model.weights):
self.assertTrue(np.allclose(p1.numpy(), p2.numpy()))
self.assertTrue(p1.name.startswith("a/b/"))
self.assertTrue(p2.name.startswith("a/b/"))
def test_sharded_checkpoint_transfer(self):
# If this doesn't throw an error then the test passes
TFBertForSequenceClassification.from_pretrained("ArthurZ/tiny-random-bert-sharded")
@is_pt_tf_cross_test
def test_checkpoint_sharding_local_from_pt(self):
with tempfile.TemporaryDirectory() as tmp_dir:
_ = Repository(local_dir=tmp_dir, clone_from="hf-internal-testing/tiny-random-bert-sharded")
model = TFBertModel.from_pretrained(tmp_dir, from_pt=True)
# the model above is the same as the model below, just a sharded pytorch version.
ref_model = TFBertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
for p1, p2 in zip(model.weights, ref_model.weights):
assert np.allclose(p1.numpy(), p2.numpy())
@is_pt_tf_cross_test
def test_checkpoint_loading_with_prefix_from_pt(self):
model = TFBertModel.from_pretrained(
"hf-internal-testing/tiny-random-bert", from_pt=True, load_weight_prefix="a/b"
)
ref_model = TFBertModel.from_pretrained("hf-internal-testing/tiny-random-bert", from_pt=True)
for p1, p2 in zip(model.weights, ref_model.weights):
self.assertTrue(np.allclose(p1.numpy(), p2.numpy()))
self.assertTrue(p1.name.startswith("a/b/"))
@is_pt_tf_cross_test
def test_checkpoint_sharding_hub_from_pt(self):
model = TFBertModel.from_pretrained("hf-internal-testing/tiny-random-bert-sharded", from_pt=True)
# the model above is the same as the model below, just a sharded pytorch version.
ref_model = TFBertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
for p1, p2 in zip(model.weights, ref_model.weights):
assert np.allclose(p1.numpy(), p2.numpy())
def test_shard_checkpoint(self):
# This is the model we will use, total size 340,000 bytes.
model = tf.keras.Sequential(
[
tf.keras.layers.Dense(200, use_bias=False), # size 80,000
tf.keras.layers.Dense(200, use_bias=False), # size 160,000
tf.keras.layers.Dense(100, use_bias=False), # size 80,000
tf.keras.layers.Dense(50, use_bias=False), # size 20,000
]
)
inputs = tf.zeros((1, 100), dtype=tf.float32)
model(inputs)
weights = model.weights
weights_dict = {w.name: w for w in weights}
with self.subTest("No shard when max size is bigger than model size"):
shards, index = tf_shard_checkpoint(weights)
self.assertIsNone(index)
self.assertDictEqual(shards, {TF2_WEIGHTS_NAME: weights})
with self.subTest("Test sharding, no weights bigger than max size"):
shards, index = tf_shard_checkpoint(weights, max_shard_size="300kB")
# Split is first two layers then last two.
self.assertDictEqual(
index,
{
"metadata": {"total_size": 340000},
"weight_map": {
"dense/kernel:0": "tf_model-00001-of-00002.h5",
"dense_1/kernel:0": "tf_model-00001-of-00002.h5",
"dense_2/kernel:0": "tf_model-00002-of-00002.h5",
"dense_3/kernel:0": "tf_model-00002-of-00002.h5",
},
},
)
shard1 = [weights_dict["dense/kernel:0"], weights_dict["dense_1/kernel:0"]]
shard2 = [weights_dict["dense_2/kernel:0"], weights_dict["dense_3/kernel:0"]]
self.assertDictEqual(shards, {"tf_model-00001-of-00002.h5": shard1, "tf_model-00002-of-00002.h5": shard2})
with self.subTest("Test sharding with weights bigger than max size"):
shards, index = tf_shard_checkpoint(weights, max_shard_size="100kB")
# Split is first layer, second layer then last 2.
self.assertDictEqual(
index,
{
"metadata": {"total_size": 340000},
"weight_map": {
"dense/kernel:0": "tf_model-00001-of-00003.h5",
"dense_1/kernel:0": "tf_model-00002-of-00003.h5",
"dense_2/kernel:0": "tf_model-00003-of-00003.h5",
"dense_3/kernel:0": "tf_model-00003-of-00003.h5",
},
},
)
shard1 = [weights_dict["dense/kernel:0"]]
shard2 = [weights_dict["dense_1/kernel:0"]]
shard3 = [weights_dict["dense_2/kernel:0"], weights_dict["dense_3/kernel:0"]]
self.assertDictEqual(
shards,
{
"tf_model-00001-of-00003.h5": shard1,
"tf_model-00002-of-00003.h5": shard2,
"tf_model-00003-of-00003.h5": shard3,
},
)
@slow
def test_special_layer_name_sharding(self):
retriever = RagRetriever.from_pretrained("facebook/rag-token-nq", index_name="exact", use_dummy_dataset=True)
model = TFRagModel.from_pretrained("facebook/rag-token-nq", retriever=retriever)
with tempfile.TemporaryDirectory() as tmp_dir:
for max_size in ["150kB", "150kiB", "200kB", "200kiB"]:
model.save_pretrained(tmp_dir, max_shard_size=max_size)
ref_model = TFRagModel.from_pretrained(tmp_dir, retriever=retriever)
for p1, p2 in zip(model.weights, ref_model.weights):
assert np.allclose(p1.numpy(), p2.numpy())
def test_checkpoint_sharding_local(self):
model = TFBertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
with tempfile.TemporaryDirectory() as tmp_dir:
# We use the same folder for various sizes to make sure a new save erases the old checkpoint.
for max_size in ["150kB", "150kiB", "200kB", "200kiB"]:
model.save_pretrained(tmp_dir, max_shard_size=max_size)
# Get each shard file and its size
shard_to_size = {}
for shard in os.listdir(tmp_dir):
if shard.endswith(".h5"):
shard_file = os.path.join(tmp_dir, shard)
shard_to_size[shard_file] = os.path.getsize(shard_file)
index_file = os.path.join(tmp_dir, TF2_WEIGHTS_INDEX_NAME)
# Check there is an index but no regular weight file
self.assertTrue(os.path.isfile(index_file))
self.assertFalse(os.path.isfile(os.path.join(tmp_dir, TF2_WEIGHTS_NAME)))
# Check a file is bigger than max_size only when it has a single weight
for shard_file, size in shard_to_size.items():
if max_size.endswith("kiB"):
max_size_int = int(max_size[:-3]) * 2**10
else:
max_size_int = int(max_size[:-2]) * 10**3
# Note: pickle adds some junk so the weight of the file can end up being slightly bigger than
# the size asked for (since we count parameters)
if size >= max_size_int + 50000:
with h5py.File(shard_file, "r") as state_file:
self.assertEqual(len(state_file), 1)
# Check the index and the shard files found match
with open(index_file, "r", encoding="utf-8") as f:
index = json.loads(f.read())
all_shards = set(index["weight_map"].values())
shards_found = {f for f in os.listdir(tmp_dir) if f.endswith(".h5")}
self.assertSetEqual(all_shards, shards_found)
# Finally, check the model can be reloaded
new_model = TFBertModel.from_pretrained(tmp_dir)
model.build()
new_model.build()
for p1, p2 in zip(model.weights, new_model.weights):
self.assertTrue(np.allclose(p1.numpy(), p2.numpy()))
@slow
def test_save_pretrained_signatures(self):
model = TFBertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
# Short custom TF signature function.
# `input_signature` is specific to BERT.
@tf.function(
input_signature=[
[
tf.TensorSpec([None, None], tf.int32, name="input_ids"),
tf.TensorSpec([None, None], tf.int32, name="token_type_ids"),
tf.TensorSpec([None, None], tf.int32, name="attention_mask"),
]
]
)
def serving_fn(input):
return model(input)
# Using default signature (default behavior) overrides 'serving_default'
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir, saved_model=True, signatures=None)
model_loaded = tf.keras.models.load_model(f"{tmp_dir}/saved_model/1")
self.assertTrue("serving_default" in list(model_loaded.signatures.keys()))
# Providing custom signature function
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir, saved_model=True, signatures={"custom_signature": serving_fn})
model_loaded = tf.keras.models.load_model(f"{tmp_dir}/saved_model/1")
self.assertTrue("custom_signature" in list(model_loaded.signatures.keys()))
# Providing multiple custom signature function
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(
tmp_dir,
saved_model=True,
signatures={"custom_signature_1": serving_fn, "custom_signature_2": serving_fn},
)
model_loaded = tf.keras.models.load_model(f"{tmp_dir}/saved_model/1")
self.assertTrue("custom_signature_1" in list(model_loaded.signatures.keys()))
self.assertTrue("custom_signature_2" in list(model_loaded.signatures.keys()))
@require_safetensors
def test_safetensors_save_and_load(self):
model = TFBertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir, safe_serialization=True)
# No tf_model.h5 file, only a model.safetensors
self.assertTrue(os.path.isfile(os.path.join(tmp_dir, SAFE_WEIGHTS_NAME)))
self.assertFalse(os.path.isfile(os.path.join(tmp_dir, TF2_WEIGHTS_NAME)))
new_model = TFBertModel.from_pretrained(tmp_dir)
# Check models are equal
for p1, p2 in zip(model.weights, new_model.weights):
self.assertTrue(np.allclose(p1.numpy(), p2.numpy()))
@is_pt_tf_cross_test
def test_safetensors_save_and_load_pt_to_tf(self):
model = TFBertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
pt_model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
with tempfile.TemporaryDirectory() as tmp_dir:
pt_model.save_pretrained(tmp_dir, safe_serialization=True)
# Check we have a model.safetensors file
self.assertTrue(os.path.isfile(os.path.join(tmp_dir, SAFE_WEIGHTS_NAME)))
new_model = TFBertModel.from_pretrained(tmp_dir)
# Check models are equal
for p1, p2 in zip(model.weights, new_model.weights):
self.assertTrue(np.allclose(p1.numpy(), p2.numpy()))
@require_safetensors
def test_safetensors_load_from_hub(self):
tf_model = TFBertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
# Can load from the TF-formatted checkpoint
safetensors_model = TFBertModel.from_pretrained("hf-internal-testing/tiny-random-bert-safetensors-tf")
# Check models are equal
for p1, p2 in zip(safetensors_model.weights, tf_model.weights):
self.assertTrue(np.allclose(p1.numpy(), p2.numpy()))
# Can load from the PyTorch-formatted checkpoint
safetensors_model = TFBertModel.from_pretrained("hf-internal-testing/tiny-random-bert-safetensors")
# Check models are equal
for p1, p2 in zip(safetensors_model.weights, tf_model.weights):
self.assertTrue(np.allclose(p1.numpy(), p2.numpy()))
@require_tf
@is_staging_test
class TFModelPushToHubTester(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls._token = TOKEN
HfFolder.save_token(TOKEN)
@classmethod
def tearDownClass(cls):
try:
delete_repo(token=cls._token, repo_id="test-model-tf")
except HTTPError:
pass
try:
delete_repo(token=cls._token, repo_id="test-model-tf-callback")
except HTTPError:
pass
try:
delete_repo(token=cls._token, repo_id="valid_org/test-model-tf-org")
except HTTPError:
pass
def test_push_to_hub(self):
config = BertConfig(
vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37
)
model = TFBertModel(config)
# Make sure model is properly initialized
model.build()
logging.set_verbosity_info()
logger = logging.get_logger("transformers.utils.hub")
with CaptureLogger(logger) as cl:
model.push_to_hub("test-model-tf", use_auth_token=self._token)
logging.set_verbosity_warning()
# Check the model card was created and uploaded.
self.assertIn("Uploading the following files to __DUMMY_TRANSFORMERS_USER__/test-model-tf", cl.out)
new_model = TFBertModel.from_pretrained(f"{USER}/test-model-tf")
models_equal = True
for p1, p2 in zip(model.weights, new_model.weights):
if not tf.math.reduce_all(p1 == p2):
models_equal = False
break
self.assertTrue(models_equal)
# Reset repo
delete_repo(token=self._token, repo_id="test-model-tf")
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir, repo_id="test-model-tf", push_to_hub=True, use_auth_token=self._token)
new_model = TFBertModel.from_pretrained(f"{USER}/test-model-tf")
models_equal = True
for p1, p2 in zip(model.weights, new_model.weights):
if not tf.math.reduce_all(p1 == p2):
models_equal = False
break
self.assertTrue(models_equal)
@is_pt_tf_cross_test
def test_push_to_hub_callback(self):
config = BertConfig(
vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37
)
model = TFBertForMaskedLM(config)
model.compile()
with tempfile.TemporaryDirectory() as tmp_dir:
push_to_hub_callback = PushToHubCallback(
output_dir=tmp_dir,
hub_model_id="test-model-tf-callback",
hub_token=self._token,
)
model.fit(model.dummy_inputs, model.dummy_inputs, epochs=1, callbacks=[push_to_hub_callback])
new_model = TFBertForMaskedLM.from_pretrained(f"{USER}/test-model-tf-callback")
models_equal = True
for p1, p2 in zip(model.weights, new_model.weights):
if not tf.math.reduce_all(p1 == p2):
models_equal = False
break
self.assertTrue(models_equal)
tf_push_to_hub_params = dict(inspect.signature(TFPreTrainedModel.push_to_hub).parameters)
tf_push_to_hub_params.pop("base_model_card_args")
pt_push_to_hub_params = dict(inspect.signature(PreTrainedModel.push_to_hub).parameters)
pt_push_to_hub_params.pop("deprecated_kwargs")
self.assertDictEaual(tf_push_to_hub_params, pt_push_to_hub_params)
def test_push_to_hub_in_organization(self):
config = BertConfig(
vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37
)
model = TFBertModel(config)
# Make sure model is properly initialized
model.build()
model.push_to_hub("valid_org/test-model-tf-org", use_auth_token=self._token)
new_model = TFBertModel.from_pretrained("valid_org/test-model-tf-org")
models_equal = True
for p1, p2 in zip(model.weights, new_model.weights):
if not tf.math.reduce_all(p1 == p2):
models_equal = False
break
self.assertTrue(models_equal)
# Reset repo
delete_repo(token=self._token, repo_id="valid_org/test-model-tf-org")
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(
tmp_dir, push_to_hub=True, use_auth_token=self._token, repo_id="valid_org/test-model-tf-org"
)
new_model = TFBertModel.from_pretrained("valid_org/test-model-tf-org")
models_equal = True
for p1, p2 in zip(model.weights, new_model.weights):
if not tf.math.reduce_all(p1 == p2):
models_equal = False
break
self.assertTrue(models_equal)
tests/test_modeling_utils.py 0 → 100755
View file @ 372f5003
# coding=utf-8
# Copyright 2019 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import glob
import json
import os
import os.path
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from pytest import mark
from requests.exceptions import HTTPError
from transformers import (
AutoConfig,
AutoModel,
PretrainedConfig,
is_torch_available,
logging,
)
from transformers.testing_utils import (
TOKEN,
USER,
CaptureLogger,
TestCasePlus,
is_staging_test,
require_accelerate,
require_safetensors,
require_torch,
require_torch_gpu,
require_torch_multi_gpu,
require_usr_bin_time,
slow,
)
from transformers.utils import (
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
sys.path.append(str(Path(__file__).parent.parent / "utils"))
from test_module.custom_configuration import CustomConfig, NoSuperInitConfig # noqa E402
if is_torch_available():
import torch
from test_module.custom_modeling import CustomModel, NoSuperInitModel
from torch import nn
from transformers import (
BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
AutoModelForCausalLM,
AutoTokenizer,
BertConfig,
BertModel,
CLIPTextModel,
PreTrainedModel,
T5Config,
T5ForConditionalGeneration,
)
from transformers.modeling_utils import shard_checkpoint
# Fake pretrained models for tests
class BaseModel(PreTrainedModel):
config_class = PretrainedConfig
def __init__(self, config):
super().__init__(config)
self.linear = nn.Linear(4, 5)
self.linear_2 = nn.Linear(5, 6)
def forward(self, x):
return self.linear_2(self.linear(x))
class ModelWithHead(PreTrainedModel):
base_model_prefix = "base"
config_class = PretrainedConfig
def _init_weights(self, module):
pass
def __init__(self, config):
super().__init__(config)
self.base = BaseModel(config)
# linear is a common name between Base and Head on purpose.
self.linear = nn.Linear(6, 3)
self.linear2 = nn.Linear(3, 5)
def forward(self, x):
return self.linear2(self.linear(self.base(x)))
TINY_T5 = "patrickvonplaten/t5-tiny-random"
TINY_BERT_FOR_TOKEN_CLASSIFICATION = "hf-internal-testing/tiny-bert-for-token-classification"
def check_models_equal(model1, model2):
models_are_equal = True
for model1_p, model2_p in zip(model1.parameters(), model2.parameters()):
if model1_p.data.ne(model2_p.data).sum() > 0:
models_are_equal = False
return models_are_equal
@require_torch
class ModelUtilsTest(TestCasePlus):
@slow
def test_model_from_pretrained(self):
for model_name in BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
config = BertConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, PretrainedConfig)
model = BertModel.from_pretrained(model_name)
model, loading_info = BertModel.from_pretrained(model_name, output_loading_info=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, PreTrainedModel)
self.assertEqual(len(loading_info["missing_keys"]), 0)
self.assertEqual(len(loading_info["unexpected_keys"]), 8)
self.assertEqual(len(loading_info["mismatched_keys"]), 0)
self.assertEqual(len(loading_info["error_msgs"]), 0)
config = BertConfig.from_pretrained(model_name, output_attentions=True, output_hidden_states=True)
# Not sure this is the intended behavior. TODO fix Lysandre & Thom
config.name_or_path = model_name
model = BertModel.from_pretrained(model_name, output_attentions=True, output_hidden_states=True)
self.assertEqual(model.config.output_hidden_states, True)
self.assertEqual(model.config, config)
def test_model_from_pretrained_subfolder(self):
config = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert")
model = BertModel(config)
subfolder = "bert"
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(os.path.join(tmp_dir, subfolder))
with self.assertRaises(OSError):
_ = BertModel.from_pretrained(tmp_dir)
model_loaded = BertModel.from_pretrained(tmp_dir, subfolder=subfolder)
self.assertTrue(check_models_equal(model, model_loaded))
def test_model_from_pretrained_subfolder_sharded(self):
config = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert")
model = BertModel(config)
subfolder = "bert"
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(os.path.join(tmp_dir, subfolder), max_shard_size="10KB")
with self.assertRaises(OSError):
_ = BertModel.from_pretrained(tmp_dir)
model_loaded = BertModel.from_pretrained(tmp_dir, subfolder=subfolder)
self.assertTrue(check_models_equal(model, model_loaded))
def test_model_from_pretrained_hub_subfolder(self):
subfolder = "bert"
model_id = "hf-internal-testing/tiny-random-bert-subfolder"
with self.assertRaises(OSError):
_ = BertModel.from_pretrained(model_id)
model = BertModel.from_pretrained(model_id, subfolder=subfolder)
self.assertIsNotNone(model)
def test_model_from_pretrained_hub_subfolder_sharded(self):
subfolder = "bert"
model_id = "hf-internal-testing/tiny-random-bert-sharded-subfolder"
with self.assertRaises(OSError):
_ = BertModel.from_pretrained(model_id)
model = BertModel.from_pretrained(model_id, subfolder=subfolder)
self.assertIsNotNone(model)
def test_model_from_pretrained_with_different_pretrained_model_name(self):
model = T5ForConditionalGeneration.from_pretrained(TINY_T5)
self.assertIsNotNone(model)
logger = logging.get_logger("transformers.configuration_utils")
with CaptureLogger(logger) as cl:
BertModel.from_pretrained(TINY_T5)
self.assertTrue("You are using a model of type t5 to instantiate a model of type bert" in cl.out)
def test_model_from_config_torch_dtype(self):
# test that the model can be instantiated with dtype of user's choice - as long as it's a
# float dtype. To make it happen config.torch_dtype needs to be set before instantiating the
# model from the config object.
config = T5Config.from_pretrained(TINY_T5)
model = AutoModel.from_config(config)
# XXX: isn't supported
# model = T5ForConditionalGeneration.from_config(config)
self.assertEqual(model.dtype, torch.float32)
model = AutoModel.from_config(config, torch_dtype=torch.float16)
self.assertEqual(model.dtype, torch.float16)
# torch.set_default_dtype() supports only float dtypes, so will fail with non-float type
with self.assertRaises(ValueError):
model = AutoModel.from_config(config, torch_dtype=torch.int64)
def test_model_from_pretrained_torch_dtype(self):
# test that the model can be instantiated with dtype of either
# 1. explicit from_pretrained's torch_dtype argument
# 2. via autodiscovery by looking at model weights (torch_dtype="auto")
# so if a model.half() was saved, we want it to be instantiated as such.
#
# test an explicit model class, but also AutoModel separately as the latter goes through a different code path
model_path = self.get_auto_remove_tmp_dir()
# baseline - we know TINY_T5 is fp32 model
model = T5ForConditionalGeneration.from_pretrained(TINY_T5)
self.assertEqual(model.dtype, torch.float32)
def remove_torch_dtype(model_path):
file = f"{model_path}/config.json"
with open(file, "r", encoding="utf-8") as f:
s = json.load(f)
s.pop("torch_dtype")
with open(file, "w", encoding="utf-8") as f:
json.dump(s, f)
# test the default fp32 save_pretrained => from_pretrained cycle
model.save_pretrained(model_path)
model = T5ForConditionalGeneration.from_pretrained(model_path)
self.assertEqual(model.dtype, torch.float32)
# 1. test torch_dtype="auto" via `config.torch_dtype`
model = T5ForConditionalGeneration.from_pretrained(model_path, torch_dtype="auto")
self.assertEqual(model.dtype, torch.float32)
# 2. test torch_dtype="auto" via auto-derivation
# now remove the torch_dtype entry from config.json and try "auto" again which should
# perform auto-derivation from weights
remove_torch_dtype(model_path)
model = T5ForConditionalGeneration.from_pretrained(model_path, torch_dtype="auto")
self.assertEqual(model.dtype, torch.float32)
# test forced loading in fp16 (even though the weights are in fp32)
model = T5ForConditionalGeneration.from_pretrained(model_path, torch_dtype=torch.float16)
self.assertEqual(model.dtype, torch.float16)
# test fp16 save_pretrained, loaded with auto-detection
model = model.half()
model.save_pretrained(model_path)
# 1. test torch_dtype="auto" via `config.torch_dtype`
model = T5ForConditionalGeneration.from_pretrained(model_path, torch_dtype="auto")
self.assertEqual(model.config.torch_dtype, torch.float16)
self.assertEqual(model.dtype, torch.float16)
# tests `config.torch_dtype` saving
with open(f"{model_path}/config.json") as f:
config_dict = json.load(f)
self.assertEqual(config_dict["torch_dtype"], "float16")
# 2. test torch_dtype="auto" via auto-derivation
# now same with using config info
remove_torch_dtype(model_path)
model = T5ForConditionalGeneration.from_pretrained(model_path, torch_dtype="auto")
self.assertEqual(model.dtype, torch.float16)
# 3. now retest that AutoModel behaves the same wrt torch_dtype="auto" as T5ForConditionalGeneration
model = AutoModel.from_pretrained(model_path, torch_dtype="auto")
self.assertEqual(model.dtype, torch.float16)
# test fp16 save_pretrained, loaded with the explicit fp16
model = T5ForConditionalGeneration.from_pretrained(model_path, torch_dtype=torch.float16)
self.assertEqual(model.dtype, torch.float16)
# test AutoModel separately as it goes through a different path
# test auto-detection - as currently TINY_T5 doesn't have torch_dtype entry
model = AutoModel.from_pretrained(TINY_T5, torch_dtype="auto")
# test that the config object didn't get polluted with torch_dtype="auto"
# there was a bug that after this call we ended up with config.torch_dtype=="auto"
self.assertNotEqual(model.config.torch_dtype, "auto")
# now test the outcome
self.assertEqual(model.dtype, torch.float32)
model = AutoModel.from_pretrained(TINY_T5, torch_dtype=torch.float16)
self.assertEqual(model.dtype, torch.float16)
# test model whose first param is not of a floating type, but int
model = AutoModel.from_pretrained(TINY_BERT_FOR_TOKEN_CLASSIFICATION, torch_dtype="auto")
self.assertEqual(model.dtype, torch.float32)
def test_no_super_init_config_and_model(self):
config = NoSuperInitConfig(attribute=32)
model = NoSuperInitModel(config)
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir)
new_model = NoSuperInitModel.from_pretrained(tmp_dir)
for p1, p2 in zip(model.parameters(), new_model.parameters()):
self.assertTrue(torch.equal(p1, p2))
def test_shard_checkpoint(self):
# This is the model we will use, total size 340,000 bytes.
model = torch.nn.Sequential(
torch.nn.Linear(100, 200, bias=False), # size 80,000
torch.nn.Linear(200, 200, bias=False), # size 160,000
torch.nn.Linear(200, 100, bias=False), # size 80,000
torch.nn.Linear(100, 50, bias=False), # size 20,000
)
state_dict = model.state_dict()
with self.subTest("No shard when max size is bigger than model size"):
shards, index = shard_checkpoint(state_dict)
self.assertIsNone(index)
self.assertDictEqual(shards, {WEIGHTS_NAME: state_dict})
with self.subTest("Test sharding, no weights bigger than max size"):
shards, index = shard_checkpoint(state_dict, max_shard_size="300kB")
# Split is first two layers then last two.
self.assertDictEqual(
index,
{
"metadata": {"total_size": 340000},
"weight_map": {
"0.weight": "pytorch_model-00001-of-00002.bin",
"1.weight": "pytorch_model-00001-of-00002.bin",
"2.weight": "pytorch_model-00002-of-00002.bin",
"3.weight": "pytorch_model-00002-of-00002.bin",
},
},
)
shard1 = {"0.weight": state_dict["0.weight"], "1.weight": state_dict["1.weight"]}
shard2 = {"2.weight": state_dict["2.weight"], "3.weight": state_dict["3.weight"]}
self.assertDictEqual(
shards, {"pytorch_model-00001-of-00002.bin": shard1, "pytorch_model-00002-of-00002.bin": shard2}
)
with self.subTest("Test sharding with weights bigger than max size"):
shards, index = shard_checkpoint(state_dict, max_shard_size="100kB")
# Split is first layer, second layer then last 2.
self.assertDictEqual(
index,
{
"metadata": {"total_size": 340000},
"weight_map": {
"0.weight": "pytorch_model-00001-of-00003.bin",
"1.weight": "pytorch_model-00002-of-00003.bin",
"2.weight": "pytorch_model-00003-of-00003.bin",
"3.weight": "pytorch_model-00003-of-00003.bin",
},
},
)
shard1 = {"0.weight": state_dict["0.weight"]}
shard2 = {"1.weight": state_dict["1.weight"]}
shard3 = {"2.weight": state_dict["2.weight"], "3.weight": state_dict["3.weight"]}
self.assertDictEqual(
shards,
{
"pytorch_model-00001-of-00003.bin": shard1,
"pytorch_model-00002-of-00003.bin": shard2,
"pytorch_model-00003-of-00003.bin": shard3,
},
)
def test_checkpoint_sharding_local(self):
model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
with tempfile.TemporaryDirectory() as tmp_dir:
# We use the same folder for various sizes to make sure a new save erases the old checkpoint.
for max_size in ["50kB", "50kiB", "100kB", "100kiB", "200kB", "200kiB"]:
model.save_pretrained(tmp_dir, max_shard_size=max_size)
# Get each shard file and its size
shard_to_size = {}
for shard in os.listdir(tmp_dir):
if shard.endswith(".bin"):
shard_file = os.path.join(tmp_dir, shard)
shard_to_size[shard_file] = os.path.getsize(shard_file)
index_file = os.path.join(tmp_dir, WEIGHTS_INDEX_NAME)
# Check there is an index but no regular weight file
self.assertTrue(os.path.isfile(index_file))
self.assertFalse(os.path.isfile(os.path.join(tmp_dir, WEIGHTS_NAME)))
# Check a file is bigger than max_size only when it has a single weight
for shard_file, size in shard_to_size.items():
if max_size.endswith("kiB"):
max_size_int = int(max_size[:-3]) * 2**10
else:
max_size_int = int(max_size[:-2]) * 10**3
# Note: pickle adds some junk so the weight of the file can end up being slightly bigger than
# the size asked for (since we count parameters)
if size >= max_size_int + 50000:
state_dict = torch.load(shard_file)
self.assertEqual(len(state_dict), 1)
# Check the index and the shard files found match
with open(index_file, "r", encoding="utf-8") as f:
index = json.loads(f.read())
all_shards = set(index["weight_map"].values())
shards_found = {f for f in os.listdir(tmp_dir) if f.endswith(".bin")}
self.assertSetEqual(all_shards, shards_found)
# Finally, check the model can be reloaded
new_model = BertModel.from_pretrained(tmp_dir)
for p1, p2 in zip(model.parameters(), new_model.parameters()):
self.assertTrue(torch.allclose(p1, p2))
def test_checkpoint_sharding_from_hub(self):
model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert-sharded")
# the model above is the same as the model below, just a sharded version.
ref_model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
for p1, p2 in zip(model.parameters(), ref_model.parameters()):
self.assertTrue(torch.allclose(p1, p2))
def test_checkpoint_variant_local(self):
model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir, variant="v2")
weights_name = ".".join(WEIGHTS_NAME.split(".")[:-1] + ["v2"] + ["bin"])
weights_file = os.path.join(tmp_dir, weights_name)
self.assertTrue(os.path.isfile(weights_file))
self.assertFalse(os.path.isfile(os.path.join(tmp_dir, WEIGHTS_NAME)))
with self.assertRaises(EnvironmentError):
_ = BertModel.from_pretrained(tmp_dir)
new_model = BertModel.from_pretrained(tmp_dir, variant="v2")
for p1, p2 in zip(model.parameters(), new_model.parameters()):
self.assertTrue(torch.allclose(p1, p2))
def test_checkpoint_variant_local_sharded(self):
model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir, variant="v2", max_shard_size="50kB")
weights_index_name = ".".join(WEIGHTS_INDEX_NAME.split(".")[:-1] + ["v2"] + ["json"])
weights_index_file = os.path.join(tmp_dir, weights_index_name)
self.assertTrue(os.path.isfile(weights_index_file))
self.assertFalse(os.path.isfile(os.path.join(tmp_dir, WEIGHTS_INDEX_NAME)))
for i in range(1, 6):
weights_name = ".".join(WEIGHTS_NAME.split(".")[:-1] + [f"v2-0000{i}-of-00006"] + ["bin"])
weights_name_file = os.path.join(tmp_dir, weights_name)
self.assertTrue(os.path.isfile(weights_name_file))
with self.assertRaises(EnvironmentError):
_ = BertModel.from_pretrained(tmp_dir)
new_model = BertModel.from_pretrained(tmp_dir, variant="v2")
for p1, p2 in zip(model.parameters(), new_model.parameters()):
self.assertTrue(torch.allclose(p1, p2))
@require_safetensors
def test_checkpoint_variant_local_safe(self):
model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir, variant="v2", safe_serialization=True)
weights_name = ".".join(SAFE_WEIGHTS_NAME.split(".")[:-1] + ["v2"] + ["safetensors"])
weights_file = os.path.join(tmp_dir, weights_name)
self.assertTrue(os.path.isfile(weights_file))
self.assertFalse(os.path.isfile(os.path.join(tmp_dir, SAFE_WEIGHTS_NAME)))
with self.assertRaises(EnvironmentError):
_ = BertModel.from_pretrained(tmp_dir)
new_model = BertModel.from_pretrained(tmp_dir, variant="v2")
for p1, p2 in zip(model.parameters(), new_model.parameters()):
self.assertTrue(torch.allclose(p1, p2))
@require_safetensors
def test_checkpoint_variant_local_sharded_safe(self):
model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir, variant="v2", max_shard_size="50kB", safe_serialization=True)
weights_index_name = ".".join(SAFE_WEIGHTS_INDEX_NAME.split(".")[:-1] + ["v2"] + ["json"])
weights_index_file = os.path.join(tmp_dir, weights_index_name)
self.assertTrue(os.path.isfile(weights_index_file))
self.assertFalse(os.path.isfile(os.path.join(tmp_dir, SAFE_WEIGHTS_INDEX_NAME)))
for i in range(1, 6):
weights_name = ".".join(SAFE_WEIGHTS_NAME.split(".")[:-1] + [f"v2-0000{i}-of-00006"] + ["safetensors"])
weights_name_file = os.path.join(tmp_dir, weights_name)
self.assertTrue(os.path.isfile(weights_name_file))
with self.assertRaises(EnvironmentError):
_ = BertModel.from_pretrained(tmp_dir)
new_model = BertModel.from_pretrained(tmp_dir, variant="v2")
for p1, p2 in zip(model.parameters(), new_model.parameters()):
self.assertTrue(torch.allclose(p1, p2))
def test_checkpoint_variant_hub(self):
with tempfile.TemporaryDirectory() as tmp_dir:
with self.assertRaises(EnvironmentError):
_ = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert-variant", cache_dir=tmp_dir)
model = BertModel.from_pretrained(
"hf-internal-testing/tiny-random-bert-variant", cache_dir=tmp_dir, variant="v2"
)
self.assertIsNotNone(model)
def test_checkpoint_variant_hub_sharded(self):
with tempfile.TemporaryDirectory() as tmp_dir:
with self.assertRaises(EnvironmentError):
_ = BertModel.from_pretrained(
"hf-internal-testing/tiny-random-bert-variant-sharded", cache_dir=tmp_dir
)
model = BertModel.from_pretrained(
"hf-internal-testing/tiny-random-bert-variant-sharded", cache_dir=tmp_dir, variant="v2"
)
self.assertIsNotNone(model)
@require_safetensors
def test_checkpoint_variant_hub_safe(self):
with tempfile.TemporaryDirectory() as tmp_dir:
with self.assertRaises(EnvironmentError):
_ = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert-variant-safe", cache_dir=tmp_dir)
model = BertModel.from_pretrained(
"hf-internal-testing/tiny-random-bert-variant-safe", cache_dir=tmp_dir, variant="v2"
)
self.assertIsNotNone(model)
@require_safetensors
def test_checkpoint_variant_hub_sharded_safe(self):
with tempfile.TemporaryDirectory() as tmp_dir:
with self.assertRaises(EnvironmentError):
_ = BertModel.from_pretrained(
"hf-internal-testing/tiny-random-bert-variant-sharded-safe", cache_dir=tmp_dir
)
model = BertModel.from_pretrained(
"hf-internal-testing/tiny-random-bert-variant-sharded-safe", cache_dir=tmp_dir, variant="v2"
)
self.assertIsNotNone(model)
def test_checkpoint_variant_save_load(self):
with tempfile.TemporaryDirectory() as tmp_dir:
model = BertModel.from_pretrained(
"hf-internal-testing/tiny-random-bert-variant", cache_dir=tmp_dir, variant="v2"
)
weights_name = ".".join(WEIGHTS_NAME.split(".")[:-1] + ["v2"] + ["bin"])
model.save_pretrained(tmp_dir, variant="v2")
# saving will create a variant checkpoint
self.assertTrue(os.path.isfile(os.path.join(tmp_dir, weights_name)))
model.save_pretrained(tmp_dir)
# saving shouldn't delete variant checkpoints
weights_name = ".".join(WEIGHTS_NAME.split(".")[:-1] + ["v2"] + ["bin"])
self.assertTrue(os.path.isfile(os.path.join(tmp_dir, weights_name)))
# there should be a normal checkpoint
self.assertTrue(os.path.isfile(os.path.join(tmp_dir, WEIGHTS_NAME)))
self.assertIsNotNone(model)
@require_accelerate
@mark.accelerate_tests
def test_from_pretrained_low_cpu_mem_usage_functional(self):
# test that we can use `from_pretrained(..., low_cpu_mem_usage=True)` with normal and
# sharded models
mnames = [
"hf-internal-testing/tiny-random-bert-sharded",
"hf-internal-testing/tiny-random-bert",
]
for mname in mnames:
_ = BertModel.from_pretrained(mname, low_cpu_mem_usage=True)
@require_usr_bin_time
@require_accelerate
@mark.accelerate_tests
def test_from_pretrained_low_cpu_mem_usage_measured(self):
# test that `from_pretrained(..., low_cpu_mem_usage=True)` uses less cpu memory than default
mname = "bert-base-cased"
preamble = "from transformers import AutoModel"
one_liner_str = f'{preamble}; AutoModel.from_pretrained("{mname}", low_cpu_mem_usage=False)'
max_rss_normal = self.python_one_liner_max_rss(one_liner_str)
# print(f"{max_rss_normal=}")
one_liner_str = f'{preamble}; AutoModel.from_pretrained("{mname}", low_cpu_mem_usage=True)'
max_rss_low_mem = self.python_one_liner_max_rss(one_liner_str)
# print(f"{max_rss_low_mem=}")
diff_bytes = max_rss_normal - max_rss_low_mem
diff_percent = diff_bytes / max_rss_low_mem
# print(f"{diff_bytes=}, {diff_percent=}")
# ideally we would compare that the diff is close to ~1x checkpoint size in bytes, but
# measuring cpu memory on linux is very tricky and inconsistent, so instead let's check that
# it's at least 15% less cpu memory consumed
self.assertGreater(
diff_percent,
0.15,
"should use less CPU memory for low_cpu_mem_usage=True, "
f"but got max_rss_normal={max_rss_normal} and max_rss_low_mem={max_rss_low_mem}",
)
# if you want to compare things manually, let's first look at the size of the model in bytes
# model = BertModel.from_pretrained(mname, low_cpu_mem_usage=False)
# total_numel = sum(dict((p.data_ptr(), p.numel()) for p in model.parameters()).values())
# total_bytes = total_numel * 4 # 420MB
# Now the diff_bytes should be very close to total_bytes, but the reports are inconsistent.
# The easiest way to test this is to switch the model and torch.load to do all the work on
# gpu - that way one can measure exactly the total and peak memory used. Perhaps once we add
# functionality to load models directly on gpu, this test can be rewritten to use torch's
# cuda memory tracking and then we should be able to do a much more precise test.
@require_accelerate
@mark.accelerate_tests
@require_torch_multi_gpu
@slow
def test_model_parallelism_gpt2(self):
device_map = {"transformer.wte": 0, "transformer.wpe": 0, "lm_head": 0, "transformer.ln_f": 1}
for i in range(12):
device_map[f"transformer.h.{i}"] = 0 if i <= 5 else 1
model = AutoModelForCausalLM.from_pretrained("gpt2", device_map=device_map)
tokenizer = AutoTokenizer.from_pretrained("gpt2")
inputs = tokenizer("Hello, my name is", return_tensors="pt")
output = model.generate(inputs["input_ids"].to(0))
text_output = tokenizer.decode(output[0].tolist())
self.assertEqual(text_output, "Hello, my name is John. I'm a writer, and I'm a writer. I'm")
@require_accelerate
@mark.accelerate_tests
@require_torch_gpu
def test_from_pretrained_disk_offload_task_model(self):
model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-gpt2")
device_map = {
"transformer.wte": 0,
"transformer.wpe": 0,
"transformer.h.0": "cpu",
"transformer.h.1": "cpu",
"transformer.h.2": "cpu",
"transformer.h.3": "disk",
"transformer.h.4": "disk",
"transformer.ln_f": 0,
"lm_head": 0,
}
with tempfile.TemporaryDirectory() as tmp_dir:
inputs = torch.tensor([[1, 2, 3]]).to(0)
model.save_pretrained(tmp_dir)
new_model = AutoModelForCausalLM.from_pretrained(tmp_dir).to(0)
outputs1 = new_model.to(0)(inputs)
offload_folder = os.path.join(tmp_dir, "offload")
new_model_with_offload = AutoModelForCausalLM.from_pretrained(
tmp_dir, device_map=device_map, offload_folder=offload_folder
)
outputs2 = new_model_with_offload(inputs)
self.assertTrue(torch.allclose(outputs1.logits.cpu(), outputs2.logits.cpu()))
# With state dict temp offload
offload_folder = os.path.join(tmp_dir, "offload")
new_model_with_offload = AutoModelForCausalLM.from_pretrained(
tmp_dir,
device_map=device_map,
offload_folder=offload_folder,
offload_state_dict=True,
)
outputs2 = new_model_with_offload(inputs)
self.assertTrue(torch.allclose(outputs1.logits.cpu(), outputs2.logits.cpu()))
def test_cached_files_are_used_when_internet_is_down(self):
# A mock response for an HTTP head request to emulate server down
response_mock = mock.Mock()
response_mock.status_code = 500
response_mock.headers = {}
response_mock.raise_for_status.side_effect = HTTPError
response_mock.json.return_value = {}
# Download this model to make sure it's in the cache.
_ = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch("requests.Session.request", return_value=response_mock) as mock_head:
_ = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
# This check we did call the fake head request
mock_head.assert_called()
def test_load_from_one_file(self):
try:
tmp_file = tempfile.mktemp()
with open(tmp_file, "wb") as f:
http_get(
"https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/pytorch_model.bin", f
)
config = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert")
_ = BertModel.from_pretrained(tmp_file, config=config)
finally:
os.remove(tmp_file)
def test_legacy_load_from_url(self):
# This test is for deprecated behavior and can be removed in v5
config = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert")
_ = BertModel.from_pretrained(
"https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/pytorch_model.bin", config=config
)
@require_safetensors
def test_use_safetensors(self):
# test nice error message if no safetensor files available
with self.assertRaises(OSError) as env_error:
AutoModel.from_pretrained("hf-internal-testing/tiny-random-RobertaModel", use_safetensors=True)
self.assertTrue(
"model.safetensors or model.safetensors.index.json and thus cannot be loaded with `safetensors`"
in str(env_error.exception)
)
# test that error if only safetensors is available
with self.assertRaises(OSError) as env_error:
BertModel.from_pretrained("hf-internal-testing/tiny-random-bert-safetensors", use_safetensors=False)
self.assertTrue("does not appear to have a file named pytorch_model.bin" in str(env_error.exception))
# test that only safetensors if both available and use_safetensors=False
with tempfile.TemporaryDirectory() as tmp_dir:
CLIPTextModel.from_pretrained(
"hf-internal-testing/diffusers-stable-diffusion-tiny-all",
subfolder="text_encoder",
use_safetensors=False,
cache_dir=tmp_dir,
)
all_downloaded_files = glob.glob(os.path.join(tmp_dir, "*", "snapshots", "*", "*", "*"))
self.assertTrue(any(f.endswith("bin") for f in all_downloaded_files))
self.assertFalse(any(f.endswith("safetensors") for f in all_downloaded_files))
# test that no safetensors if both available and use_safetensors=True
with tempfile.TemporaryDirectory() as tmp_dir:
CLIPTextModel.from_pretrained(
"hf-internal-testing/diffusers-stable-diffusion-tiny-all",
subfolder="text_encoder",
use_safetensors=True,
cache_dir=tmp_dir,
)
all_downloaded_files = glob.glob(os.path.join(tmp_dir, "*", "snapshots", "*", "*", "*"))
self.assertTrue(any(f.endswith("safetensors") for f in all_downloaded_files))
self.assertFalse(any(f.endswith("bin") for f in all_downloaded_files))
@require_safetensors
def test_safetensors_save_and_load(self):
model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir, safe_serialization=True)
# No pytorch_model.bin file, only a model.safetensors
self.assertTrue(os.path.isfile(os.path.join(tmp_dir, SAFE_WEIGHTS_NAME)))
self.assertFalse(os.path.isfile(os.path.join(tmp_dir, WEIGHTS_NAME)))
new_model = BertModel.from_pretrained(tmp_dir)
# Check models are equal
for p1, p2 in zip(model.parameters(), new_model.parameters()):
self.assertTrue(torch.allclose(p1, p2))
@require_safetensors
def test_safetensors_load_from_hub(self):
safetensors_model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert-safetensors")
pytorch_model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
# Check models are equal
for p1, p2 in zip(safetensors_model.parameters(), pytorch_model.parameters()):
self.assertTrue(torch.allclose(p1, p2))
@require_safetensors
def test_safetensors_save_and_load_sharded(self):
model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir, safe_serialization=True, max_shard_size="100kB")
# No pytorch_model.bin index file, only a model.safetensors index
self.assertFalse(os.path.isfile(os.path.join(tmp_dir, WEIGHTS_INDEX_NAME)))
self.assertTrue(os.path.isfile(os.path.join(tmp_dir, SAFE_WEIGHTS_INDEX_NAME)))
# No regular weights file
self.assertFalse(os.path.isfile(os.path.join(tmp_dir, WEIGHTS_NAME)))
self.assertFalse(os.path.isfile(os.path.join(tmp_dir, SAFE_WEIGHTS_NAME)))
new_model = BertModel.from_pretrained(tmp_dir)
# Check models are equal
for p1, p2 in zip(model.parameters(), new_model.parameters()):
self.assertTrue(torch.allclose(p1, p2))
@require_safetensors
def test_safetensors_load_from_hub_sharded(self):
safetensors_model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert-sharded-safetensors")
pytorch_model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert-sharded")
# Check models are equal
for p1, p2 in zip(safetensors_model.parameters(), pytorch_model.parameters()):
self.assertTrue(torch.allclose(p1, p2))
def test_base_model_to_head_model_load(self):
base_model = BaseModel(PretrainedConfig())
with tempfile.TemporaryDirectory() as tmp_dir:
base_model.save_pretrained(tmp_dir)
# Can load a base model in a model with head
model = ModelWithHead.from_pretrained(tmp_dir)
for p1, p2 in zip(model.base.parameters(), base_model.parameters()):
self.assertTrue(torch.allclose(p1, p2))
# It doesn't work if the state dict has a mix of keys of the head and base without prefix though.
base_state_dict = base_model.state_dict()
head_state_dict = model.state_dict()
base_state_dict["linear2.weight"] = head_state_dict["linear2.weight"]
base_state_dict["linear2.bias"] = head_state_dict["linear2.bias"]
torch.save(base_state_dict, os.path.join(tmp_dir, WEIGHTS_NAME))
with self.assertRaisesRegex(
ValueError, "The state dictionary of the model you are trying to load is corrupted."
):
_ = ModelWithHead.from_pretrained(tmp_dir)
@require_torch_gpu
@slow
def test_pretrained_low_mem_new_config(self):
# Checking for 1 model(the same one which was described in the issue) .
model_ids = ["gpt2"]
for model_id in model_ids:
model_config = AutoConfig.from_pretrained(pretrained_model_name_or_path=model_id)
model_config.n_layer = 48
model_config.n_head = 25
model_config.n_embd = 1600
model = AutoModelForCausalLM.from_pretrained(
pretrained_model_name_or_path=model_id,
config=model_config,
ignore_mismatched_sizes=True,
torch_dtype=torch.float16,
low_cpu_mem_usage=True,
)
model_ref = AutoModelForCausalLM.from_pretrained(pretrained_model_name_or_path=model_id)
self.assertEqual(model.__class__.__name__, model_ref.__class__.__name__)
@require_torch
@is_staging_test
class ModelPushToHubTester(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls._token = TOKEN
HfFolder.save_token(TOKEN)
@classmethod
def tearDownClass(cls):
try:
delete_repo(token=cls._token, repo_id="test-model")
except HTTPError:
pass
try:
delete_repo(token=cls._token, repo_id="valid_org/test-model-org")
except HTTPError:
pass
try:
delete_repo(token=cls._token, repo_id="test-dynamic-model")
except HTTPError:
pass
def test_push_to_hub(self):
config = BertConfig(
vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37
)
model = BertModel(config)
model.push_to_hub("test-model", use_auth_token=self._token)
new_model = BertModel.from_pretrained(f"{USER}/test-model")
for p1, p2 in zip(model.parameters(), new_model.parameters()):
self.assertTrue(torch.equal(p1, p2))
# Reset repo
delete_repo(token=self._token, repo_id="test-model")
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir, repo_id="test-model", push_to_hub=True, use_auth_token=self._token)
new_model = BertModel.from_pretrained(f"{USER}/test-model")
for p1, p2 in zip(model.parameters(), new_model.parameters()):
self.assertTrue(torch.equal(p1, p2))
def test_push_to_hub_in_organization(self):
config = BertConfig(
vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37
)
model = BertModel(config)
model.push_to_hub("valid_org/test-model-org", use_auth_token=self._token)
new_model = BertModel.from_pretrained("valid_org/test-model-org")
for p1, p2 in zip(model.parameters(), new_model.parameters()):
self.assertTrue(torch.equal(p1, p2))
# Reset repo
delete_repo(token=self._token, repo_id="valid_org/test-model-org")
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(
tmp_dir, push_to_hub=True, use_auth_token=self._token, repo_id="valid_org/test-model-org"
)
new_model = BertModel.from_pretrained("valid_org/test-model-org")
for p1, p2 in zip(model.parameters(), new_model.parameters()):
self.assertTrue(torch.equal(p1, p2))
def test_push_to_hub_dynamic_model(self):
CustomConfig.register_for_auto_class()
CustomModel.register_for_auto_class()
config = CustomConfig(hidden_size=32)
model = CustomModel(config)
model.push_to_hub("test-dynamic-model", use_auth_token=self._token)
# checks
self.assertDictEqual(
config.auto_map,
{"AutoConfig": "custom_configuration.CustomConfig", "AutoModel": "custom_modeling.CustomModel"},
)
new_model = AutoModel.from_pretrained(f"{USER}/test-dynamic-model", trust_remote_code=True)
# Can't make an isinstance check because the new_model is from the CustomModel class of a dynamic module
self.assertEqual(new_model.__class__.__name__, "CustomModel")
for p1, p2 in zip(model.parameters(), new_model.parameters()):
self.assertTrue(torch.equal(p1, p2))
config = AutoConfig.from_pretrained(f"{USER}/test-dynamic-model", trust_remote_code=True)
new_model = AutoModel.from_config(config, trust_remote_code=True)
self.assertEqual(new_model.__class__.__name__, "CustomModel")
tests/test_tokenization_common.py
View file @ 372f5003
...@@ -21,28 +21,20 @@ import os ...@@ -21,28 +21,20 @@ import os
import pickle import pickle
import re import re
import shutil import shutil
import sys
import tempfile import tempfile
import traceback import traceback
import unittest import unittest
import unittest.mock as mock
from collections import OrderedDict from collections import OrderedDict
from itertools import takewhile from itertools import takewhile
from pathlib import Path
from typing import TYPE_CHECKING, Any, Dict, List, Tuple, Union from typing import TYPE_CHECKING, Any, Dict, List, Tuple, Union
from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from parameterized import parameterized from parameterized import parameterized
from requests.exceptions import HTTPError
from transformers import ( from transformers import (
AlbertTokenizer, AlbertTokenizer,
AlbertTokenizerFast, AlbertTokenizerFast,
AutoTokenizer,
BertTokenizer, BertTokenizer,
BertTokenizerFast, BertTokenizerFast,
GPT2TokenizerFast,
PreTrainedTokenizer, PreTrainedTokenizer,
PreTrainedTokenizerBase, PreTrainedTokenizerBase,
PreTrainedTokenizerFast, PreTrainedTokenizerFast,
...@@ -51,24 +43,20 @@ from transformers import ( ...@@ -51,24 +43,20 @@ from transformers import (
TrainingArguments, TrainingArguments,
is_flax_available, is_flax_available,
is_tf_available, is_tf_available,
is_tokenizers_available,
is_torch_available, is_torch_available,
logging, logging,
) )
from transformers.testing_utils import ( from transformers.testing_utils import (
TOKEN,
USER,
check_json_file_has_correct_format, check_json_file_has_correct_format,
get_tests_dir, get_tests_dir,
is_pt_tf_cross_test, is_pt_tf_cross_test,
is_staging_test,
require_tf, require_tf,
require_tokenizers, require_tokenizers,
require_torch, require_torch,
run_test_in_subprocess, run_test_in_subprocess,
slow, slow,
) )
from transformers.tokenization_utils import AddedToken, Trie from transformers.tokenization_utils import AddedToken
if is_torch_available(): if is_torch_available():
...@@ -79,15 +67,6 @@ if TYPE_CHECKING: ...@@ -79,15 +67,6 @@ if TYPE_CHECKING:
from transformers import PretrainedConfig, PreTrainedModel, TFPreTrainedModel from transformers import PretrainedConfig, PreTrainedModel, TFPreTrainedModel
sys.path.append(str(Path(__file__).parent.parent / "utils"))
from test_module.custom_tokenization import CustomTokenizer # noqa E402
if is_tokenizers_available():
from test_module.custom_tokenization_fast import CustomTokenizerFast
logger = logging.get_logger(__name__) logger = logging.get_logger(__name__)
NON_ENGLISH_TAGS = ["chinese", "dutch", "french", "finnish", "german", "multilingual"] NON_ENGLISH_TAGS = ["chinese", "dutch", "french", "finnish", "german", "multilingual"]
...@@ -3974,238 +3953,3 @@ class TokenizerTesterMixin: ...@@ -3974,238 +3953,3 @@ class TokenizerTesterMixin:
tokenizer.clean_up_tokenization_spaces = True tokenizer.clean_up_tokenization_spaces = True
decoded = tokenizer.decode(tokens) decoded = tokenizer.decode(tokens)
assert decoded == "[CLS] this shouldn't be! he'll go. [SEP]" assert decoded == "[CLS] this shouldn't be! he'll go. [SEP]"
class TokenizerUtilTester(unittest.TestCase):
def test_cached_files_are_used_when_internet_is_down(self):
# A mock response for an HTTP head request to emulate server down
response_mock = mock.Mock()
response_mock.status_code = 500
response_mock.headers = {}
response_mock.raise_for_status.side_effect = HTTPError
response_mock.json.return_value = {}
# Download this model to make sure it's in the cache.
_ = BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-bert")
# Under the mock environment we get a 500 error when trying to reach the tokenizer.
with mock.patch("requests.Session.request", return_value=response_mock) as mock_head:
_ = BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-bert")
# This check we did call the fake head request
mock_head.assert_called()
@require_tokenizers
def test_cached_files_are_used_when_internet_is_down_missing_files(self):
# A mock response for an HTTP head request to emulate server down
response_mock = mock.Mock()
response_mock.status_code = 500
response_mock.headers = {}
response_mock.raise_for_status.side_effect = HTTPError
response_mock.json.return_value = {}
# Download this model to make sure it's in the cache.
_ = GPT2TokenizerFast.from_pretrained("gpt2")
# Under the mock environment we get a 500 error when trying to reach the tokenizer.
with mock.patch("requests.Session.request", return_value=response_mock) as mock_head:
_ = GPT2TokenizerFast.from_pretrained("gpt2")
# This check we did call the fake head request
mock_head.assert_called()
def test_legacy_load_from_one_file(self):
# This test is for deprecated behavior and can be removed in v5
try:
tmp_file = tempfile.mktemp()
with open(tmp_file, "wb") as f:
http_get("https://huggingface.co/albert-base-v1/resolve/main/spiece.model", f)
_ = AlbertTokenizer.from_pretrained(tmp_file)
finally:
os.remove(tmp_file)
# Supporting this legacy load introduced a weird bug where the tokenizer would load local files if they are in
# the current folder and have the right name.
if os.path.isfile("tokenizer.json"):
# We skip the test if the user has a `tokenizer.json` in this folder to avoid deleting it.
return
try:
with open("tokenizer.json", "wb") as f:
http_get("https://huggingface.co/hf-internal-testing/tiny-random-bert/blob/main/tokenizer.json", f)
tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
# The tiny random BERT has a vocab size of 1024, tiny gpt2 as a vocab size of 1000
self.assertEqual(tokenizer.vocab_size, 1000)
# Tokenizer should depend on the remote checkpoint, not the local tokenizer.json file.
finally:
os.remove("tokenizer.json")
def test_legacy_load_from_url(self):
# This test is for deprecated behavior and can be removed in v5
_ = AlbertTokenizer.from_pretrained("https://huggingface.co/albert-base-v1/resolve/main/spiece.model")
@is_staging_test
class TokenizerPushToHubTester(unittest.TestCase):
vocab_tokens = ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "bla", "blou"]
@classmethod
def setUpClass(cls):
cls._token = TOKEN
HfFolder.save_token(TOKEN)
@classmethod
def tearDownClass(cls):
try:
delete_repo(token=cls._token, repo_id="test-tokenizer")
except HTTPError:
pass
try:
delete_repo(token=cls._token, repo_id="valid_org/test-tokenizer-org")
except HTTPError:
pass
try:
delete_repo(token=cls._token, repo_id="test-dynamic-tokenizer")
except HTTPError:
pass
def test_push_to_hub(self):
with tempfile.TemporaryDirectory() as tmp_dir:
vocab_file = os.path.join(tmp_dir, "vocab.txt")
with open(vocab_file, "w", encoding="utf-8") as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]))
tokenizer = BertTokenizer(vocab_file)
tokenizer.push_to_hub("test-tokenizer", use_auth_token=self._token)
new_tokenizer = BertTokenizer.from_pretrained(f"{USER}/test-tokenizer")
self.assertDictEqual(new_tokenizer.vocab, tokenizer.vocab)
# Reset repo
delete_repo(token=self._token, repo_id="test-tokenizer")
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(tmp_dir, repo_id="test-tokenizer", push_to_hub=True, use_auth_token=self._token)
new_tokenizer = BertTokenizer.from_pretrained(f"{USER}/test-tokenizer")
self.assertDictEqual(new_tokenizer.vocab, tokenizer.vocab)
def test_push_to_hub_in_organization(self):
with tempfile.TemporaryDirectory() as tmp_dir:
vocab_file = os.path.join(tmp_dir, "vocab.txt")
with open(vocab_file, "w", encoding="utf-8") as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]))
tokenizer = BertTokenizer(vocab_file)
tokenizer.push_to_hub("valid_org/test-tokenizer-org", use_auth_token=self._token)
new_tokenizer = BertTokenizer.from_pretrained("valid_org/test-tokenizer-org")
self.assertDictEqual(new_tokenizer.vocab, tokenizer.vocab)
# Reset repo
delete_repo(token=self._token, repo_id="valid_org/test-tokenizer-org")
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(
tmp_dir, repo_id="valid_org/test-tokenizer-org", push_to_hub=True, use_auth_token=self._token
)
new_tokenizer = BertTokenizer.from_pretrained("valid_org/test-tokenizer-org")
self.assertDictEqual(new_tokenizer.vocab, tokenizer.vocab)
@require_tokenizers
def test_push_to_hub_dynamic_tokenizer(self):
CustomTokenizer.register_for_auto_class()
with tempfile.TemporaryDirectory() as tmp_dir:
vocab_file = os.path.join(tmp_dir, "vocab.txt")
with open(vocab_file, "w", encoding="utf-8") as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]))
tokenizer = CustomTokenizer(vocab_file)
# No fast custom tokenizer
tokenizer.push_to_hub("test-dynamic-tokenizer", use_auth_token=self._token)
tokenizer = AutoTokenizer.from_pretrained(f"{USER}/test-dynamic-tokenizer", trust_remote_code=True)
# Can't make an isinstance check because the new_model.config is from the CustomTokenizer class of a dynamic module
self.assertEqual(tokenizer.__class__.__name__, "CustomTokenizer")
# Fast and slow custom tokenizer
CustomTokenizerFast.register_for_auto_class()
with tempfile.TemporaryDirectory() as tmp_dir:
vocab_file = os.path.join(tmp_dir, "vocab.txt")
with open(vocab_file, "w", encoding="utf-8") as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]))
bert_tokenizer = BertTokenizerFast.from_pretrained(tmp_dir)
bert_tokenizer.save_pretrained(tmp_dir)
tokenizer = CustomTokenizerFast.from_pretrained(tmp_dir)
tokenizer.push_to_hub("test-dynamic-tokenizer", use_auth_token=self._token)
tokenizer = AutoTokenizer.from_pretrained(f"{USER}/test-dynamic-tokenizer", trust_remote_code=True)
# Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
self.assertEqual(tokenizer.__class__.__name__, "CustomTokenizerFast")
tokenizer = AutoTokenizer.from_pretrained(
f"{USER}/test-dynamic-tokenizer", use_fast=False, trust_remote_code=True
)
# Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
self.assertEqual(tokenizer.__class__.__name__, "CustomTokenizer")
class TrieTest(unittest.TestCase):
def test_trie(self):
trie = Trie()
trie.add("Hello 友達")
self.assertEqual(trie.data, {"H": {"e": {"l": {"l": {"o": {" ": {"友": {"達": {"": 1}}}}}}}}})
trie.add("Hello")
trie.data
self.assertEqual(trie.data, {"H": {"e": {"l": {"l": {"o": {"": 1, " ": {"友": {"達": {"": 1}}}}}}}}})
def test_trie_split(self):
trie = Trie()
self.assertEqual(trie.split("[CLS] This is a extra_id_100"), ["[CLS] This is a extra_id_100"])
trie.add("[CLS]")
trie.add("extra_id_1")
trie.add("extra_id_100")
self.assertEqual(trie.split("[CLS] This is a extra_id_100"), ["[CLS]", " This is a ", "extra_id_100"])
def test_trie_single(self):
trie = Trie()
trie.add("A")
self.assertEqual(trie.split("ABC"), ["A", "BC"])
self.assertEqual(trie.split("BCA"), ["BC", "A"])
def test_trie_final(self):
trie = Trie()
trie.add("TOKEN]")
trie.add("[SPECIAL_TOKEN]")
self.assertEqual(trie.split("This is something [SPECIAL_TOKEN]"), ["This is something ", "[SPECIAL_TOKEN]"])
def test_trie_subtokens(self):
trie = Trie()
trie.add("A")
trie.add("P")
trie.add("[SPECIAL_TOKEN]")
self.assertEqual(trie.split("This is something [SPECIAL_TOKEN]"), ["This is something ", "[SPECIAL_TOKEN]"])
def test_trie_suffix_tokens(self):
trie = Trie()
trie.add("AB")
trie.add("B")
trie.add("C")
self.assertEqual(trie.split("ABC"), ["AB", "C"])
def test_trie_skip(self):
trie = Trie()
trie.add("ABC")
trie.add("B")
trie.add("CD")
self.assertEqual(trie.split("ABCD"), ["ABC", "D"])
def test_cut_text_hardening(self):
# Even if the offsets are wrong, we necessarily output correct string
# parts.
trie = Trie()
parts = trie.cut_text("ABC", [0, 0, 2, 1, 2, 3])
self.assertEqual(parts, ["AB", "C"])
tests/test_tokenization_utils.py 0 → 100644
View file @ 372f5003
# coding=utf-8
# Copyright 2019 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError
from transformers import (
AlbertTokenizer,
AutoTokenizer,
BertTokenizer,
BertTokenizerFast,
GPT2TokenizerFast,
is_tokenizers_available,
)
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_tokenizers
from transformers.tokenization_utils import Trie
sys.path.append(str(Path(__file__).parent.parent / "utils"))
from test_module.custom_tokenization import CustomTokenizer # noqa E402
if is_tokenizers_available():
from test_module.custom_tokenization_fast import CustomTokenizerFast
class TokenizerUtilTester(unittest.TestCase):
def test_cached_files_are_used_when_internet_is_down(self):
# A mock response for an HTTP head request to emulate server down
response_mock = mock.Mock()
response_mock.status_code = 500
response_mock.headers = {}
response_mock.raise_for_status.side_effect = HTTPError
response_mock.json.return_value = {}
# Download this model to make sure it's in the cache.
_ = BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-bert")
# Under the mock environment we get a 500 error when trying to reach the tokenizer.
with mock.patch("requests.Session.request", return_value=response_mock) as mock_head:
_ = BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-bert")
# This check we did call the fake head request
mock_head.assert_called()
@require_tokenizers
def test_cached_files_are_used_when_internet_is_down_missing_files(self):
# A mock response for an HTTP head request to emulate server down
response_mock = mock.Mock()
response_mock.status_code = 500
response_mock.headers = {}
response_mock.raise_for_status.side_effect = HTTPError
response_mock.json.return_value = {}
# Download this model to make sure it's in the cache.
_ = GPT2TokenizerFast.from_pretrained("gpt2")
# Under the mock environment we get a 500 error when trying to reach the tokenizer.
with mock.patch("requests.Session.request", return_value=response_mock) as mock_head:
_ = GPT2TokenizerFast.from_pretrained("gpt2")
# This check we did call the fake head request
mock_head.assert_called()
def test_legacy_load_from_one_file(self):
# This test is for deprecated behavior and can be removed in v5
try:
tmp_file = tempfile.mktemp()
with open(tmp_file, "wb") as f:
http_get("https://huggingface.co/albert-base-v1/resolve/main/spiece.model", f)
_ = AlbertTokenizer.from_pretrained(tmp_file)
finally:
os.remove(tmp_file)
# Supporting this legacy load introduced a weird bug where the tokenizer would load local files if they are in
# the current folder and have the right name.
if os.path.isfile("tokenizer.json"):
# We skip the test if the user has a `tokenizer.json` in this folder to avoid deleting it.
return
try:
with open("tokenizer.json", "wb") as f:
http_get("https://huggingface.co/hf-internal-testing/tiny-random-bert/blob/main/tokenizer.json", f)
tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
# The tiny random BERT has a vocab size of 1024, tiny gpt2 as a vocab size of 1000
self.assertEqual(tokenizer.vocab_size, 1000)
# Tokenizer should depend on the remote checkpoint, not the local tokenizer.json file.
finally:
os.remove("tokenizer.json")
def test_legacy_load_from_url(self):
# This test is for deprecated behavior and can be removed in v5
_ = AlbertTokenizer.from_pretrained("https://huggingface.co/albert-base-v1/resolve/main/spiece.model")
@is_staging_test
class TokenizerPushToHubTester(unittest.TestCase):
vocab_tokens = ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "bla", "blou"]
@classmethod
def setUpClass(cls):
cls._token = TOKEN
HfFolder.save_token(TOKEN)
@classmethod
def tearDownClass(cls):
try:
delete_repo(token=cls._token, repo_id="test-tokenizer")
except HTTPError:
pass
try:
delete_repo(token=cls._token, repo_id="valid_org/test-tokenizer-org")
except HTTPError:
pass
try:
delete_repo(token=cls._token, repo_id="test-dynamic-tokenizer")
except HTTPError:
pass
def test_push_to_hub(self):
with tempfile.TemporaryDirectory() as tmp_dir:
vocab_file = os.path.join(tmp_dir, "vocab.txt")
with open(vocab_file, "w", encoding="utf-8") as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]))
tokenizer = BertTokenizer(vocab_file)
tokenizer.push_to_hub("test-tokenizer", use_auth_token=self._token)
new_tokenizer = BertTokenizer.from_pretrained(f"{USER}/test-tokenizer")
self.assertDictEqual(new_tokenizer.vocab, tokenizer.vocab)
# Reset repo
delete_repo(token=self._token, repo_id="test-tokenizer")
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(tmp_dir, repo_id="test-tokenizer", push_to_hub=True, use_auth_token=self._token)
new_tokenizer = BertTokenizer.from_pretrained(f"{USER}/test-tokenizer")
self.assertDictEqual(new_tokenizer.vocab, tokenizer.vocab)
def test_push_to_hub_in_organization(self):
with tempfile.TemporaryDirectory() as tmp_dir:
vocab_file = os.path.join(tmp_dir, "vocab.txt")
with open(vocab_file, "w", encoding="utf-8") as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]))
tokenizer = BertTokenizer(vocab_file)
tokenizer.push_to_hub("valid_org/test-tokenizer-org", use_auth_token=self._token)
new_tokenizer = BertTokenizer.from_pretrained("valid_org/test-tokenizer-org")
self.assertDictEqual(new_tokenizer.vocab, tokenizer.vocab)
# Reset repo
delete_repo(token=self._token, repo_id="valid_org/test-tokenizer-org")
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(
tmp_dir, repo_id="valid_org/test-tokenizer-org", push_to_hub=True, use_auth_token=self._token
)
new_tokenizer = BertTokenizer.from_pretrained("valid_org/test-tokenizer-org")
self.assertDictEqual(new_tokenizer.vocab, tokenizer.vocab)
@require_tokenizers
def test_push_to_hub_dynamic_tokenizer(self):
CustomTokenizer.register_for_auto_class()
with tempfile.TemporaryDirectory() as tmp_dir:
vocab_file = os.path.join(tmp_dir, "vocab.txt")
with open(vocab_file, "w", encoding="utf-8") as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]))
tokenizer = CustomTokenizer(vocab_file)
# No fast custom tokenizer
tokenizer.push_to_hub("test-dynamic-tokenizer", use_auth_token=self._token)
tokenizer = AutoTokenizer.from_pretrained(f"{USER}/test-dynamic-tokenizer", trust_remote_code=True)
# Can't make an isinstance check because the new_model.config is from the CustomTokenizer class of a dynamic module
self.assertEqual(tokenizer.__class__.__name__, "CustomTokenizer")
# Fast and slow custom tokenizer
CustomTokenizerFast.register_for_auto_class()
with tempfile.TemporaryDirectory() as tmp_dir:
vocab_file = os.path.join(tmp_dir, "vocab.txt")
with open(vocab_file, "w", encoding="utf-8") as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]))
bert_tokenizer = BertTokenizerFast.from_pretrained(tmp_dir)
bert_tokenizer.save_pretrained(tmp_dir)
tokenizer = CustomTokenizerFast.from_pretrained(tmp_dir)
tokenizer.push_to_hub("test-dynamic-tokenizer", use_auth_token=self._token)
tokenizer = AutoTokenizer.from_pretrained(f"{USER}/test-dynamic-tokenizer", trust_remote_code=True)
# Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
self.assertEqual(tokenizer.__class__.__name__, "CustomTokenizerFast")
tokenizer = AutoTokenizer.from_pretrained(
f"{USER}/test-dynamic-tokenizer", use_fast=False, trust_remote_code=True
)
# Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
self.assertEqual(tokenizer.__class__.__name__, "CustomTokenizer")
class TrieTest(unittest.TestCase):
def test_trie(self):
trie = Trie()
trie.add("Hello 友達")
self.assertEqual(trie.data, {"H": {"e": {"l": {"l": {"o": {" ": {"友": {"達": {"": 1}}}}}}}}})
trie.add("Hello")
trie.data
self.assertEqual(trie.data, {"H": {"e": {"l": {"l": {"o": {"": 1, " ": {"友": {"達": {"": 1}}}}}}}}})
def test_trie_split(self):
trie = Trie()
self.assertEqual(trie.split("[CLS] This is a extra_id_100"), ["[CLS] This is a extra_id_100"])
trie.add("[CLS]")
trie.add("extra_id_1")
trie.add("extra_id_100")
self.assertEqual(trie.split("[CLS] This is a extra_id_100"), ["[CLS]", " This is a ", "extra_id_100"])
def test_trie_single(self):
trie = Trie()
trie.add("A")
self.assertEqual(trie.split("ABC"), ["A", "BC"])
self.assertEqual(trie.split("BCA"), ["BC", "A"])
def test_trie_final(self):
trie = Trie()
trie.add("TOKEN]")
trie.add("[SPECIAL_TOKEN]")
self.assertEqual(trie.split("This is something [SPECIAL_TOKEN]"), ["This is something ", "[SPECIAL_TOKEN]"])
def test_trie_subtokens(self):
trie = Trie()
trie.add("A")
trie.add("P")
trie.add("[SPECIAL_TOKEN]")
self.assertEqual(trie.split("This is something [SPECIAL_TOKEN]"), ["This is something ", "[SPECIAL_TOKEN]"])
def test_trie_suffix_tokens(self):
trie = Trie()
trie.add("AB")
trie.add("B")
trie.add("C")
self.assertEqual(trie.split("ABC"), ["AB", "C"])
def test_trie_skip(self):
trie = Trie()
trie.add("ABC")
trie.add("B")
trie.add("CD")
self.assertEqual(trie.split("ABCD"), ["ABC", "D"])
def test_cut_text_hardening(self):
# Even if the offsets are wrong, we necessarily output correct string
# parts.
trie = Trie()
parts = trie.cut_text("ABC", [0, 0, 2, 1, 2, 3])
self.assertEqual(parts, ["AB", "C"])
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