Move some test files (tets/test_xxx_utils.py) to tests/utils (#31730)
* move * move * move * move * Update tests/utils/test_image_processing_utils.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> --------- Co-authored-by: ydshieh <ydshieh@users.noreply.github.com> Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
This commit is contained in:
Yih-Dar
403
tests/utils/test_modeling_flax_utils.py
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403
tests/utils/test_modeling_flax_utils.py
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# Copyright 2020 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import tempfile
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import unittest
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import numpy as np
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from huggingface_hub import HfFolder, delete_repo, snapshot_download
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from requests.exceptions import HTTPError
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from transformers import BertConfig, BertModel, is_flax_available, is_torch_available
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from transformers.testing_utils import (
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TOKEN,
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USER,
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CaptureLogger,
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is_pt_flax_cross_test,
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is_staging_test,
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require_flax,
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require_safetensors,
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require_torch,
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)
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from transformers.utils import FLAX_WEIGHTS_NAME, SAFE_WEIGHTS_NAME, logging
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if is_flax_available():
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import os
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from flax.core.frozen_dict import unfreeze
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from flax.traverse_util import flatten_dict
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from transformers import FlaxBertModel
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os.environ["XLA_PYTHON_CLIENT_MEM_FRACTION"] = "0.12" # assumed parallelism: 8
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if is_torch_available():
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import torch
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@require_flax
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@is_staging_test
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class FlaxModelPushToHubTester(unittest.TestCase):
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@classmethod
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def setUpClass(cls):
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cls._token = TOKEN
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HfFolder.save_token(TOKEN)
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@classmethod
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def tearDownClass(cls):
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try:
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delete_repo(token=cls._token, repo_id="test-model-flax")
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except HTTPError:
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pass
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try:
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delete_repo(token=cls._token, repo_id="valid_org/test-model-flax-org")
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except HTTPError:
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pass
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def test_push_to_hub(self):
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config = BertConfig(
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vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37
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)
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model = FlaxBertModel(config)
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model.push_to_hub("test-model-flax", token=self._token)
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new_model = FlaxBertModel.from_pretrained(f"{USER}/test-model-flax")
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base_params = flatten_dict(unfreeze(model.params))
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new_params = flatten_dict(unfreeze(new_model.params))
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for key in base_params.keys():
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max_diff = (base_params[key] - new_params[key]).sum().item()
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self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical")
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# Reset repo
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delete_repo(token=self._token, repo_id="test-model-flax")
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# Push to hub via save_pretrained
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with tempfile.TemporaryDirectory() as tmp_dir:
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model.save_pretrained(tmp_dir, repo_id="test-model-flax", push_to_hub=True, token=self._token)
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new_model = FlaxBertModel.from_pretrained(f"{USER}/test-model-flax")
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base_params = flatten_dict(unfreeze(model.params))
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new_params = flatten_dict(unfreeze(new_model.params))
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for key in base_params.keys():
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max_diff = (base_params[key] - new_params[key]).sum().item()
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self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical")
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def test_push_to_hub_in_organization(self):
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config = BertConfig(
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vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37
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)
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model = FlaxBertModel(config)
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model.push_to_hub("valid_org/test-model-flax-org", token=self._token)
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new_model = FlaxBertModel.from_pretrained("valid_org/test-model-flax-org")
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base_params = flatten_dict(unfreeze(model.params))
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new_params = flatten_dict(unfreeze(new_model.params))
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for key in base_params.keys():
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max_diff = (base_params[key] - new_params[key]).sum().item()
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self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical")
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# Reset repo
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delete_repo(token=self._token, repo_id="valid_org/test-model-flax-org")
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# Push to hub via save_pretrained
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with tempfile.TemporaryDirectory() as tmp_dir:
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model.save_pretrained(
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tmp_dir, repo_id="valid_org/test-model-flax-org", push_to_hub=True, token=self._token
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)
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new_model = FlaxBertModel.from_pretrained("valid_org/test-model-flax-org")
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base_params = flatten_dict(unfreeze(model.params))
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new_params = flatten_dict(unfreeze(new_model.params))
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for key in base_params.keys():
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max_diff = (base_params[key] - new_params[key]).sum().item()
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self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical")
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def check_models_equal(model1, model2):
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models_are_equal = True
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flat_params_1 = flatten_dict(model1.params)
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flat_params_2 = flatten_dict(model2.params)
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for key in flat_params_1.keys():
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if np.sum(np.abs(flat_params_1[key] - flat_params_2[key])) > 1e-4:
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models_are_equal = False
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return models_are_equal
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@require_flax
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class FlaxModelUtilsTest(unittest.TestCase):
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def test_model_from_pretrained_subfolder(self):
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config = BertConfig.from_pretrained("hf-internal-testing/tiny-bert-flax-only")
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model = FlaxBertModel(config)
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subfolder = "bert"
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with tempfile.TemporaryDirectory() as tmp_dir:
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model.save_pretrained(os.path.join(tmp_dir, subfolder))
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with self.assertRaises(OSError):
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_ = FlaxBertModel.from_pretrained(tmp_dir)
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model_loaded = FlaxBertModel.from_pretrained(tmp_dir, subfolder=subfolder)
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self.assertTrue(check_models_equal(model, model_loaded))
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def test_model_from_pretrained_subfolder_sharded(self):
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config = BertConfig.from_pretrained("hf-internal-testing/tiny-bert-flax-only")
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model = FlaxBertModel(config)
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subfolder = "bert"
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with tempfile.TemporaryDirectory() as tmp_dir:
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model.save_pretrained(os.path.join(tmp_dir, subfolder), max_shard_size="10KB")
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with self.assertRaises(OSError):
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_ = FlaxBertModel.from_pretrained(tmp_dir)
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model_loaded = FlaxBertModel.from_pretrained(tmp_dir, subfolder=subfolder)
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self.assertTrue(check_models_equal(model, model_loaded))
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def test_model_from_pretrained_hub_subfolder(self):
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subfolder = "bert"
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model_id = "hf-internal-testing/tiny-random-bert-subfolder"
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with self.assertRaises(OSError):
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_ = FlaxBertModel.from_pretrained(model_id)
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model = FlaxBertModel.from_pretrained(model_id, subfolder=subfolder)
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self.assertIsNotNone(model)
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def test_model_from_pretrained_hub_subfolder_sharded(self):
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subfolder = "bert"
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model_id = "hf-internal-testing/tiny-random-bert-sharded-subfolder"
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with self.assertRaises(OSError):
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_ = FlaxBertModel.from_pretrained(model_id)
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model = FlaxBertModel.from_pretrained(model_id, subfolder=subfolder)
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self.assertIsNotNone(model)
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@require_safetensors
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def test_safetensors_save_and_load(self):
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model = FlaxBertModel.from_pretrained("hf-internal-testing/tiny-bert-flax-only")
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with tempfile.TemporaryDirectory() as tmp_dir:
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model.save_pretrained(tmp_dir, safe_serialization=True)
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# No msgpack file, only a model.safetensors
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self.assertTrue(os.path.isfile(os.path.join(tmp_dir, SAFE_WEIGHTS_NAME)))
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self.assertFalse(os.path.isfile(os.path.join(tmp_dir, FLAX_WEIGHTS_NAME)))
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new_model = FlaxBertModel.from_pretrained(tmp_dir)
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self.assertTrue(check_models_equal(model, new_model))
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@require_flax
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@require_torch
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@is_pt_flax_cross_test
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def test_safetensors_save_and_load_pt_to_flax(self):
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model = FlaxBertModel.from_pretrained("hf-internal-testing/tiny-random-bert", from_pt=True)
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pt_model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
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with tempfile.TemporaryDirectory() as tmp_dir:
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pt_model.save_pretrained(tmp_dir)
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# Check we have a model.safetensors file
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self.assertTrue(os.path.isfile(os.path.join(tmp_dir, SAFE_WEIGHTS_NAME)))
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new_model = FlaxBertModel.from_pretrained(tmp_dir)
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# Check models are equal
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self.assertTrue(check_models_equal(model, new_model))
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@require_safetensors
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def test_safetensors_load_from_hub(self):
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"""
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This test checks that we can load safetensors from a checkpoint that only has those on the Hub
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"""
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flax_model = FlaxBertModel.from_pretrained("hf-internal-testing/tiny-bert-flax-only")
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# Can load from the Flax-formatted checkpoint
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safetensors_model = FlaxBertModel.from_pretrained("hf-internal-testing/tiny-bert-flax-safetensors-only")
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self.assertTrue(check_models_equal(flax_model, safetensors_model))
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@require_safetensors
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def test_safetensors_load_from_local(self):
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"""
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This test checks that we can load safetensors from a checkpoint that only has those on the Hub
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"""
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with tempfile.TemporaryDirectory() as tmp:
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location = snapshot_download("hf-internal-testing/tiny-bert-flax-only", cache_dir=tmp)
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flax_model = FlaxBertModel.from_pretrained(location)
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with tempfile.TemporaryDirectory() as tmp:
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location = snapshot_download("hf-internal-testing/tiny-bert-flax-safetensors-only", cache_dir=tmp)
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safetensors_model = FlaxBertModel.from_pretrained(location)
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self.assertTrue(check_models_equal(flax_model, safetensors_model))
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@require_safetensors
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@is_pt_flax_cross_test
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def test_safetensors_load_from_hub_from_safetensors_pt(self):
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"""
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This test checks that we can load safetensors from a checkpoint that only has those on the Hub.
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saved in the "pt" format.
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"""
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flax_model = FlaxBertModel.from_pretrained("hf-internal-testing/tiny-bert-msgpack")
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# Can load from the PyTorch-formatted checkpoint
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safetensors_model = FlaxBertModel.from_pretrained("hf-internal-testing/tiny-bert-pt-safetensors")
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self.assertTrue(check_models_equal(flax_model, safetensors_model))
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@require_safetensors
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@require_torch
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@is_pt_flax_cross_test
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def test_safetensors_load_from_hub_from_safetensors_pt_bf16(self):
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"""
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This test checks that we can load safetensors from a checkpoint that only has those on the Hub.
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saved in the "pt" format.
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"""
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import torch
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model = BertModel.from_pretrained("hf-internal-testing/tiny-bert-pt-safetensors")
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model.to(torch.bfloat16)
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with tempfile.TemporaryDirectory() as tmp:
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model.save_pretrained(tmp)
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flax_model = FlaxBertModel.from_pretrained(tmp)
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# Can load from the PyTorch-formatted checkpoint
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safetensors_model = FlaxBertModel.from_pretrained("hf-internal-testing/tiny-bert-pt-safetensors-bf16")
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self.assertTrue(check_models_equal(flax_model, safetensors_model))
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@require_safetensors
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@is_pt_flax_cross_test
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def test_safetensors_load_from_local_from_safetensors_pt(self):
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"""
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This test checks that we can load safetensors from a checkpoint that only has those on the Hub.
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saved in the "pt" format.
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"""
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with tempfile.TemporaryDirectory() as tmp:
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location = snapshot_download("hf-internal-testing/tiny-bert-msgpack", cache_dir=tmp)
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flax_model = FlaxBertModel.from_pretrained(location)
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# Can load from the PyTorch-formatted checkpoint
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with tempfile.TemporaryDirectory() as tmp:
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location = snapshot_download("hf-internal-testing/tiny-bert-pt-safetensors", cache_dir=tmp)
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safetensors_model = FlaxBertModel.from_pretrained(location)
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self.assertTrue(check_models_equal(flax_model, safetensors_model))
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@require_safetensors
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def test_safetensors_load_from_hub_msgpack_before_safetensors(self):
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"""
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This test checks that we'll first download msgpack weights before safetensors
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The safetensors file on that repo is a pt safetensors and therefore cannot be loaded without PyTorch
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"""
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FlaxBertModel.from_pretrained("hf-internal-testing/tiny-bert-pt-safetensors-msgpack")
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@require_safetensors
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def test_safetensors_load_from_local_msgpack_before_safetensors(self):
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"""
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This test checks that we'll first download msgpack weights before safetensors
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The safetensors file on that repo is a pt safetensors and therefore cannot be loaded without PyTorch
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"""
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with tempfile.TemporaryDirectory() as tmp:
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location = snapshot_download("hf-internal-testing/tiny-bert-pt-safetensors-msgpack", cache_dir=tmp)
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FlaxBertModel.from_pretrained(location)
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@require_safetensors
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def test_safetensors_flax_from_flax(self):
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model = FlaxBertModel.from_pretrained("hf-internal-testing/tiny-bert-flax-only")
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with tempfile.TemporaryDirectory() as tmp_dir:
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model.save_pretrained(tmp_dir, safe_serialization=True)
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new_model = FlaxBertModel.from_pretrained(tmp_dir)
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self.assertTrue(check_models_equal(model, new_model))
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@require_safetensors
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@require_torch
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@is_pt_flax_cross_test
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def test_safetensors_flax_from_torch(self):
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hub_model = FlaxBertModel.from_pretrained("hf-internal-testing/tiny-bert-flax-only")
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model = BertModel.from_pretrained("hf-internal-testing/tiny-bert-pt-only")
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with tempfile.TemporaryDirectory() as tmp_dir:
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model.save_pretrained(tmp_dir, safe_serialization=True)
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new_model = FlaxBertModel.from_pretrained(tmp_dir)
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self.assertTrue(check_models_equal(hub_model, new_model))
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@require_safetensors
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def test_safetensors_flax_from_sharded_msgpack_with_sharded_safetensors_local(self):
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with tempfile.TemporaryDirectory() as tmp_dir:
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path = snapshot_download(
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"hf-internal-testing/tiny-bert-flax-safetensors-msgpack-sharded", cache_dir=tmp_dir
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)
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# This should not raise even if there are two types of sharded weights
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FlaxBertModel.from_pretrained(path)
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@require_safetensors
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def test_safetensors_flax_from_sharded_msgpack_with_sharded_safetensors_hub(self):
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# This should not raise even if there are two types of sharded weights
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# This should discard the safetensors weights in favor of the msgpack sharded weights
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FlaxBertModel.from_pretrained("hf-internal-testing/tiny-bert-flax-safetensors-msgpack-sharded")
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@require_safetensors
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def test_safetensors_from_pt_bf16(self):
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# This should not raise; should be able to load bf16-serialized torch safetensors without issue
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# and without torch.
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logger = logging.get_logger("transformers.modeling_flax_utils")
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with CaptureLogger(logger) as cl:
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FlaxBertModel.from_pretrained("hf-internal-testing/tiny-bert-pt-safetensors-bf16")
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self.assertTrue(
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"Some of the weights of FlaxBertModel were initialized in bfloat16 precision from the model checkpoint"
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in cl.out
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)
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@require_torch
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@require_safetensors
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@is_pt_flax_cross_test
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def test_from_pt_bf16(self):
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model = BertModel.from_pretrained("hf-internal-testing/tiny-bert-pt-only")
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model.to(torch.bfloat16)
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with tempfile.TemporaryDirectory() as tmp_dir:
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model.save_pretrained(tmp_dir, safe_serialization=False)
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logger = logging.get_logger("transformers.modeling_flax_utils")
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with CaptureLogger(logger) as cl:
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new_model = FlaxBertModel.from_pretrained("hf-internal-testing/tiny-bert-pt-safetensors-bf16")
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self.assertTrue(
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"Some of the weights of FlaxBertModel were initialized in bfloat16 precision from the model checkpoint"
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in cl.out
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)
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flat_params_1 = flatten_dict(new_model.params)
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for value in flat_params_1.values():
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self.assertEqual(value.dtype, "bfloat16")
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