test_offline.py

# 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 subprocess
import sys
from typing import Tuple

from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch


class OfflineTests(TestCasePlus):
    @require_torch
    def test_offline_mode(self):
        # this test is a bit tricky since TRANSFORMERS_OFFLINE can only be changed before
        # `transformers` is loaded, and it's too late for inside pytest - so we are changing it
        # while running an external program

        # python one-liner segments

        # this must be loaded before socket.socket is monkey-patched
        load = """
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
        """

        run = """
mname = "hf-internal-testing/tiny-random-bert"
BertConfig.from_pretrained(mname)
BertModel.from_pretrained(mname)
BertTokenizer.from_pretrained(mname)
pipe = pipeline(task="fill-mask", model=mname)
print("success")
        """

        mock = """
import socket
def offline_socket(*args, **kwargs): raise RuntimeError("Offline mode is enabled, we shouldn't access internet")
socket.socket = offline_socket
        """

        # Force fetching the files so that we can use the cache
        mname = "hf-internal-testing/tiny-random-bert"
        BertConfig.from_pretrained(mname)
        BertModel.from_pretrained(mname)
        BertTokenizer.from_pretrained(mname)
        pipeline(task="fill-mask", model=mname)

        # baseline - just load from_pretrained with normal network
        # should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
        stdout, _ = self._execute_with_env(load, run, mock, TRANSFORMERS_OFFLINE="1")
        self.assertIn("success", stdout)

    @require_torch
    def test_offline_mode_no_internet(self):
        # python one-liner segments
        # this must be loaded before socket.socket is monkey-patched
        load = """
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
        """

        run = """
mname = "hf-internal-testing/tiny-random-bert"
BertConfig.from_pretrained(mname)
BertModel.from_pretrained(mname)
BertTokenizer.from_pretrained(mname)
pipe = pipeline(task="fill-mask", model=mname)
print("success")
        """

        mock = """
import socket
def offline_socket(*args, **kwargs): raise socket.error("Faking flaky internet")
socket.socket = offline_socket
        """

        # Force fetching the files so that we can use the cache
        mname = "hf-internal-testing/tiny-random-bert"
        BertConfig.from_pretrained(mname)
        BertModel.from_pretrained(mname)
        BertTokenizer.from_pretrained(mname)
        pipeline(task="fill-mask", model=mname)

        # baseline - just load from_pretrained with normal network
        # should succeed
        stdout, _ = self._execute_with_env(load, run, mock)
        self.assertIn("success", stdout)

    @require_torch
    def test_offline_mode_sharded_checkpoint(self):
        # this test is a bit tricky since TRANSFORMERS_OFFLINE can only be changed before
        # `transformers` is loaded, and it's too late for inside pytest - so we are changing it
        # while running an external program

        # python one-liner segments

        # this must be loaded before socket.socket is monkey-patched
        load = """
from transformers import BertConfig, BertModel, BertTokenizer
        """

        run = """
mname = "hf-internal-testing/tiny-random-bert-sharded"
BertConfig.from_pretrained(mname)
BertModel.from_pretrained(mname)
print("success")
        """

        mock = """
import socket
def offline_socket(*args, **kwargs): raise ValueError("Offline mode is enabled")
socket.socket = offline_socket
        """

        # baseline - just load from_pretrained with normal network
        # should succeed
        stdout, _ = self._execute_with_env(load, run)
        self.assertIn("success", stdout)

        # next emulate no network
        # Doesn't fail anymore since the model is in the cache due to other tests, so commenting this.
        # self._execute_with_env(load, mock, run, should_fail=True, TRANSFORMERS_OFFLINE="0")

        # should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
        stdout, _ = self._execute_with_env(load, mock, run, TRANSFORMERS_OFFLINE="1")
        self.assertIn("success", stdout)

    @require_torch
    def test_offline_mode_pipeline_exception(self):
        load = """
from transformers import pipeline
        """
        run = """
mname = "hf-internal-testing/tiny-random-bert"
pipe = pipeline(model=mname)
        """

        mock = """
import socket
def offline_socket(*args, **kwargs): raise socket.error("Offline mode is enabled")
socket.socket = offline_socket
        """

        _, stderr = self._execute_with_env(load, mock, run, should_fail=True, TRANSFORMERS_OFFLINE="1")
        self.assertIn(
            "You cannot infer task automatically within `pipeline` when using offline mode",
            stderr.replace("\n", ""),
        )

    @require_torch
    def test_offline_model_dynamic_model(self):
        load = """
from transformers import AutoModel
        """
        run = """
mname = "hf-internal-testing/test_dynamic_model"
AutoModel.from_pretrained(mname, trust_remote_code=True)
print("success")
        """

        # baseline - just load from_pretrained with normal network
        # should succeed
        stdout, _ = self._execute_with_env(load, run)
        self.assertIn("success", stdout)

        # should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
        stdout, _ = self._execute_with_env(load, run, TRANSFORMERS_OFFLINE="1")
        self.assertIn("success", stdout)

    def test_is_offline_mode(self):
        """
        Test `_is_offline_mode` helper (should respect both HF_HUB_OFFLINE and legacy TRANSFORMERS_OFFLINE env vars)
        """
        load = "from transformers.utils import is_offline_mode"
        run = "print(is_offline_mode())"

        stdout, _ = self._execute_with_env(load, run)
        self.assertIn("False", stdout)

        stdout, _ = self._execute_with_env(load, run, TRANSFORMERS_OFFLINE="1")
        self.assertIn("True", stdout)

        stdout, _ = self._execute_with_env(load, run, HF_HUB_OFFLINE="1")
        self.assertIn("True", stdout)

    def _execute_with_env(self, *commands: Tuple[str, ...], should_fail: bool = False, **env) -> Tuple[str, str]:
        """Execute Python code with a given environment and return the stdout/stderr as strings.

        If `should_fail=True`, the command is expected to fail. Otherwise, it should succeed.
        Environment variables can be passed as keyword arguments.
        """
        # Build command
        cmd = [sys.executable, "-c", "\n".join(commands)]

        # Configure env
        new_env = self.get_env()
        new_env.update(env)

        # Run command
        result = subprocess.run(cmd, env=new_env, check=False, capture_output=True)

        # Check execution
        if should_fail:
            self.assertNotEqual(result.returncode, 0, result.stderr)
        else:
            self.assertEqual(result.returncode, 0, result.stderr)

        # Return output
        return result.stdout.decode(), result.stderr.decode()