mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-03 12:50:06 +06:00
286 lines
12 KiB
Python
286 lines
12 KiB
Python
# 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
|
|
)
|