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# 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, snapshot_download
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import (
TOKEN,
CaptureLogger,
TemporaryHubRepo,
is_staging_test,
require_flax,
require_safetensors,
)
from transformers.utils import FLAX_WEIGHTS_NAME, SAFE_WEIGHTS_NAME, logging
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)
def test_push_to_hub(self):
with TemporaryHubRepo(token=self._token) as tmp_repo:
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(tmp_repo.repo_id, token=self._token)
new_model = FlaxBertModel.from_pretrained(tmp_repo.repo_id)
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_via_save_pretrained(self):
with TemporaryHubRepo(token=self._token) as tmp_repo:
config = BertConfig(
vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37
)
model = FlaxBertModel(config)
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir, repo_id=tmp_repo.repo_id, push_to_hub=True, token=self._token)
new_model = FlaxBertModel.from_pretrained(tmp_repo.repo_id)
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):
with TemporaryHubRepo(namespace="valid_org", token=self._token) as tmp_repo:
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(tmp_repo.repo_id, token=self._token)
new_model = FlaxBertModel.from_pretrained(tmp_repo.repo_id)
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_via_save_pretrained(self):
with TemporaryHubRepo(namespace="valid_org", token=self._token) as tmp_repo:
config = BertConfig(
vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37
)
model = FlaxBertModel(config)
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir, repo_id=tmp_repo.repo_id, push_to_hub=True, token=self._token)
new_model = FlaxBertModel.from_pretrained(tmp_repo.repo_id)
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)
@require_safetensors
def test_safetensors_save_and_load(self):
model = FlaxBertModel.from_pretrained("hf-internal-testing/tiny-bert-flax-only")
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir, safe_serialization=True)
# No msgpack 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, FLAX_WEIGHTS_NAME)))
new_model = FlaxBertModel.from_pretrained(tmp_dir)
self.assertTrue(check_models_equal(model, new_model))
@require_safetensors
def test_safetensors_load_from_hub(self):
"""
This test checks that we can load safetensors from a checkpoint that only has those on the Hub
"""
flax_model = FlaxBertModel.from_pretrained("hf-internal-testing/tiny-bert-flax-only")
# Can load from the Flax-formatted checkpoint
safetensors_model = FlaxBertModel.from_pretrained("hf-internal-testing/tiny-bert-flax-safetensors-only")
self.assertTrue(check_models_equal(flax_model, safetensors_model))
@require_safetensors
def test_safetensors_load_from_local(self):
"""
This test checks that we can load safetensors from a checkpoint that only has those on the Hub
"""
with tempfile.TemporaryDirectory() as tmp:
location = snapshot_download("hf-internal-testing/tiny-bert-flax-only", cache_dir=tmp)
flax_model = FlaxBertModel.from_pretrained(location)
with tempfile.TemporaryDirectory() as tmp:
location = snapshot_download("hf-internal-testing/tiny-bert-flax-safetensors-only", cache_dir=tmp)
safetensors_model = FlaxBertModel.from_pretrained(location)
self.assertTrue(check_models_equal(flax_model, safetensors_model))
@require_safetensors
def test_safetensors_load_from_hub_msgpack_before_safetensors(self):
"""
This test checks that we'll first download msgpack weights before safetensors
The safetensors file on that repo is a pt safetensors and therefore cannot be loaded without PyTorch
"""
FlaxBertModel.from_pretrained("hf-internal-testing/tiny-bert-pt-safetensors-msgpack")
@require_safetensors
def test_safetensors_load_from_local_msgpack_before_safetensors(self):
"""
This test checks that we'll first download msgpack weights before safetensors
The safetensors file on that repo is a pt safetensors and therefore cannot be loaded without PyTorch
"""
with tempfile.TemporaryDirectory() as tmp:
location = snapshot_download("hf-internal-testing/tiny-bert-pt-safetensors-msgpack", cache_dir=tmp)
FlaxBertModel.from_pretrained(location)
@require_safetensors
def test_safetensors_flax_from_flax(self):
model = FlaxBertModel.from_pretrained("hf-internal-testing/tiny-bert-flax-only")
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir, safe_serialization=True)
new_model = FlaxBertModel.from_pretrained(tmp_dir)
self.assertTrue(check_models_equal(model, new_model))
@require_safetensors
def test_safetensors_flax_from_sharded_msgpack_with_sharded_safetensors_local(self):
with tempfile.TemporaryDirectory() as tmp_dir:
path = snapshot_download(
"hf-internal-testing/tiny-bert-flax-safetensors-msgpack-sharded", cache_dir=tmp_dir
)
# This should not raise even if there are two types of sharded weights
FlaxBertModel.from_pretrained(path)
@require_safetensors
def test_safetensors_flax_from_sharded_msgpack_with_sharded_safetensors_hub(self):
# This should not raise even if there are two types of sharded weights
# This should discard the safetensors weights in favor of the msgpack sharded weights
FlaxBertModel.from_pretrained("hf-internal-testing/tiny-bert-flax-safetensors-msgpack-sharded")
@require_safetensors
def test_safetensors_from_pt_bf16(self):
# This should not raise; should be able to load bf16-serialized torch safetensors without issue
# and without torch.
logger = logging.get_logger("transformers.modeling_flax_utils")
with CaptureLogger(logger) as cl:
FlaxBertModel.from_pretrained("hf-internal-testing/tiny-bert-pt-safetensors-bf16")
self.assertTrue(
"Some of the weights of FlaxBertModel were initialized in bfloat16 precision from the model checkpoint"
in cl.out
)
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