Split common test from core tests (#24284)
This commit is contained in:
Sylvain Gugger
@@ -17,25 +17,14 @@ import inspect
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import json
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import random
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import tempfile
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import unittest
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from typing import List, Tuple
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import numpy as np
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from huggingface_hub import HfFolder, delete_repo
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from requests.exceptions import HTTPError
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import transformers
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from transformers import BertConfig, is_flax_available, is_torch_available
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from transformers import is_flax_available, is_torch_available
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from transformers.models.auto import get_values
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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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torch_device,
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)
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from transformers.testing_utils import CaptureLogger, is_pt_flax_cross_test, require_flax, torch_device
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from transformers.utils import CONFIG_NAME, GENERATION_CONFIG_NAME, logging
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from transformers.utils.generic import ModelOutput
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@@ -69,14 +58,6 @@ if is_torch_available():
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import torch
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def _config_zero_init(config):
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configs_no_init = copy.deepcopy(config)
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for key in configs_no_init.__dict__.keys():
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if "_range" in key or "_std" in key or "initializer_factor" in key:
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setattr(configs_no_init, key, 1e-10)
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return configs_no_init
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def ids_tensor(shape, vocab_size, rng=None):
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"""Creates a random int32 tensor of the shape within the vocab size."""
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if rng is None:
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@@ -1164,155 +1145,3 @@ class FlaxModelTesterMixin:
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# ensure that the outputs remain precisely equal
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for output, remat_output in zip(outputs, remat_outputs):
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self.assertTrue((output == remat_output).all())
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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", use_auth_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, use_auth_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", use_auth_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, use_auth_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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