220 lines
7.7 KiB
Python
220 lines
7.7 KiB
Python
# 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 subprocess
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import sys
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from typing import Tuple
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from transformers import BertConfig, BertModel, BertTokenizer, pipeline
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from transformers.testing_utils import TestCasePlus, require_torch
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class OfflineTests(TestCasePlus):
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@require_torch
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def test_offline_mode(self):
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# this test is a bit tricky since TRANSFORMERS_OFFLINE can only be changed before
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# `transformers` is loaded, and it's too late for inside pytest - so we are changing it
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# while running an external program
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# python one-liner segments
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# this must be loaded before socket.socket is monkey-patched
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load = """
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from transformers import BertConfig, BertModel, BertTokenizer, pipeline
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"""
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run = """
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mname = "hf-internal-testing/tiny-random-bert"
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BertConfig.from_pretrained(mname)
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BertModel.from_pretrained(mname)
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BertTokenizer.from_pretrained(mname)
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pipe = pipeline(task="fill-mask", model=mname)
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print("success")
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"""
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mock = """
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import socket
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def offline_socket(*args, **kwargs): raise RuntimeError("Offline mode is enabled, we shouldn't access internet")
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socket.socket = offline_socket
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"""
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# Force fetching the files so that we can use the cache
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mname = "hf-internal-testing/tiny-random-bert"
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BertConfig.from_pretrained(mname)
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BertModel.from_pretrained(mname)
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BertTokenizer.from_pretrained(mname)
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pipeline(task="fill-mask", model=mname)
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# baseline - just load from_pretrained with normal network
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# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
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stdout, _ = self._execute_with_env(load, run, mock, TRANSFORMERS_OFFLINE="1")
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self.assertIn("success", stdout)
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@require_torch
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def test_offline_mode_no_internet(self):
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# python one-liner segments
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# this must be loaded before socket.socket is monkey-patched
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load = """
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from transformers import BertConfig, BertModel, BertTokenizer, pipeline
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"""
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run = """
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mname = "hf-internal-testing/tiny-random-bert"
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BertConfig.from_pretrained(mname)
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BertModel.from_pretrained(mname)
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BertTokenizer.from_pretrained(mname)
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pipe = pipeline(task="fill-mask", model=mname)
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print("success")
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"""
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mock = """
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import socket
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def offline_socket(*args, **kwargs): raise socket.error("Faking flaky internet")
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socket.socket = offline_socket
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"""
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# Force fetching the files so that we can use the cache
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mname = "hf-internal-testing/tiny-random-bert"
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BertConfig.from_pretrained(mname)
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BertModel.from_pretrained(mname)
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BertTokenizer.from_pretrained(mname)
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pipeline(task="fill-mask", model=mname)
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# baseline - just load from_pretrained with normal network
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# should succeed
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stdout, _ = self._execute_with_env(load, run, mock)
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self.assertIn("success", stdout)
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@require_torch
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def test_offline_mode_sharded_checkpoint(self):
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# this test is a bit tricky since TRANSFORMERS_OFFLINE can only be changed before
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# `transformers` is loaded, and it's too late for inside pytest - so we are changing it
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# while running an external program
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# python one-liner segments
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# this must be loaded before socket.socket is monkey-patched
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load = """
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from transformers import BertConfig, BertModel, BertTokenizer
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"""
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run = """
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mname = "hf-internal-testing/tiny-random-bert-sharded"
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BertConfig.from_pretrained(mname)
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BertModel.from_pretrained(mname)
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print("success")
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"""
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mock = """
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import socket
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def offline_socket(*args, **kwargs): raise ValueError("Offline mode is enabled")
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socket.socket = offline_socket
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"""
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# baseline - just load from_pretrained with normal network
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# should succeed
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stdout, _ = self._execute_with_env(load, run)
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self.assertIn("success", stdout)
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# next emulate no network
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# Doesn't fail anymore since the model is in the cache due to other tests, so commenting this.
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# self._execute_with_env(load, mock, run, should_fail=True, TRANSFORMERS_OFFLINE="0")
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# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
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stdout, _ = self._execute_with_env(load, mock, run, TRANSFORMERS_OFFLINE="1")
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self.assertIn("success", stdout)
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@require_torch
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def test_offline_mode_pipeline_exception(self):
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load = """
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from transformers import pipeline
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"""
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run = """
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mname = "hf-internal-testing/tiny-random-bert"
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pipe = pipeline(model=mname)
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"""
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mock = """
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import socket
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def offline_socket(*args, **kwargs): raise socket.error("Offline mode is enabled")
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socket.socket = offline_socket
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"""
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_, stderr = self._execute_with_env(load, mock, run, should_fail=True, TRANSFORMERS_OFFLINE="1")
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self.assertIn(
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"You cannot infer task automatically within `pipeline` when using offline mode",
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stderr.replace("\n", ""),
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)
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@require_torch
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def test_offline_model_dynamic_model(self):
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load = """
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from transformers import AutoModel
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"""
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run = """
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mname = "hf-internal-testing/test_dynamic_model"
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AutoModel.from_pretrained(mname, trust_remote_code=True)
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print("success")
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"""
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# baseline - just load from_pretrained with normal network
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# should succeed
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stdout, _ = self._execute_with_env(load, run)
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self.assertIn("success", stdout)
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# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
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stdout, _ = self._execute_with_env(load, run, TRANSFORMERS_OFFLINE="1")
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self.assertIn("success", stdout)
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def test_is_offline_mode(self):
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"""
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Test `_is_offline_mode` helper (should respect both HF_HUB_OFFLINE and legacy TRANSFORMERS_OFFLINE env vars)
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"""
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load = "from transformers.utils import is_offline_mode"
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run = "print(is_offline_mode())"
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stdout, _ = self._execute_with_env(load, run)
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self.assertIn("False", stdout)
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stdout, _ = self._execute_with_env(load, run, TRANSFORMERS_OFFLINE="1")
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self.assertIn("True", stdout)
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stdout, _ = self._execute_with_env(load, run, HF_HUB_OFFLINE="1")
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self.assertIn("True", stdout)
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def _execute_with_env(self, *commands: Tuple[str, ...], should_fail: bool = False, **env) -> Tuple[str, str]:
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"""Execute Python code with a given environment and return the stdout/stderr as strings.
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If `should_fail=True`, the command is expected to fail. Otherwise, it should succeed.
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Environment variables can be passed as keyword arguments.
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"""
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# Build command
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cmd = [sys.executable, "-c", "\n".join(commands)]
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# Configure env
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new_env = self.get_env()
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new_env.update(env)
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# Run command
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result = subprocess.run(cmd, env=new_env, check=False, capture_output=True)
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# Check execution
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if should_fail:
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self.assertNotEqual(result.returncode, 0, result.stderr)
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else:
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self.assertEqual(result.returncode, 0, result.stderr)
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# Return output
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return result.stdout.decode(), result.stderr.decode()
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