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# coding=utf-8
# Copyright 2022 The HuggingFace Team Inc.
#
# 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 clone 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 copy
import os
import tempfile
import unittest
import warnings
from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError
from transformers import AutoConfig, GenerationConfig
from transformers.generation import GenerationMode
from transformers.testing_utils import TOKEN, USER, is_staging_test
class GenerationConfigTest(unittest.TestCase):
@parameterized.expand([(None,), ("foo.json",)])
def test_save_load_config(self, config_name):
config = GenerationConfig(
do_sample=True,
temperature=0.7,
length_penalty=1.0,
bad_words_ids=[[1, 2, 3], [4, 5]],
)
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(tmp_dir, config_name=config_name)
loaded_config = GenerationConfig.from_pretrained(tmp_dir, config_name=config_name)
# Checks parameters that were specified
self.assertEqual(loaded_config.do_sample, True)
self.assertEqual(loaded_config.temperature, 0.7)
self.assertEqual(loaded_config.length_penalty, 1.0)
self.assertEqual(loaded_config.bad_words_ids, [[1, 2, 3], [4, 5]])
# Checks parameters that were not specified (defaults)
self.assertEqual(loaded_config.top_k, 50)
self.assertEqual(loaded_config.max_length, 20)
self.assertEqual(loaded_config.max_time, None)
def test_from_model_config(self):
model_config = AutoConfig.from_pretrained("openai-community/gpt2")
generation_config_from_model = GenerationConfig.from_model_config(model_config)
default_generation_config = GenerationConfig()
# The generation config has loaded a few non-default parameters from the model config
self.assertNotEqual(generation_config_from_model, default_generation_config)
# One of those parameters is eos_token_id -- check if it matches
self.assertNotEqual(generation_config_from_model.eos_token_id, default_generation_config.eos_token_id)
self.assertEqual(generation_config_from_model.eos_token_id, model_config.eos_token_id)
def test_update(self):
generation_config = GenerationConfig()
update_kwargs = {
"max_new_tokens": 1024,
"foo": "bar",
}
update_kwargs_copy = copy.deepcopy(update_kwargs)
unused_kwargs = generation_config.update(**update_kwargs)
# update_kwargs was not modified (no side effects)
self.assertEqual(update_kwargs, update_kwargs_copy)
# update_kwargs was used to update the config on valid attributes
self.assertEqual(generation_config.max_new_tokens, 1024)
# `.update()` returns a dictionary of unused kwargs
self.assertEqual(unused_kwargs, {"foo": "bar"})
def test_initialize_new_kwargs(self):
generation_config = GenerationConfig()
generation_config.foo = "bar"
with tempfile.TemporaryDirectory("test-generation-config") as tmp_dir:
generation_config.save_pretrained(tmp_dir)
new_config = GenerationConfig.from_pretrained(tmp_dir)
# update_kwargs was used to update the config on valid attributes
self.assertEqual(new_config.foo, "bar")
generation_config = GenerationConfig.from_model_config(new_config)
assert not hasattr(generation_config, "foo") # no new kwargs should be initialized if from config
def test_kwarg_init(self):
"""Tests that we can overwrite attributes at `from_pretrained` time."""
default_config = GenerationConfig()
self.assertEqual(default_config.temperature, 1.0)
self.assertEqual(default_config.do_sample, False)
self.assertEqual(default_config.num_beams, 1)
config = GenerationConfig(
do_sample=True,
temperature=0.7,
length_penalty=1.0,
bad_words_ids=[[1, 2, 3], [4, 5]],
)
self.assertEqual(config.temperature, 0.7)
self.assertEqual(config.do_sample, True)
self.assertEqual(config.num_beams, 1)
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(tmp_dir)
loaded_config = GenerationConfig.from_pretrained(tmp_dir, temperature=1.0)
self.assertEqual(loaded_config.temperature, 1.0)
self.assertEqual(loaded_config.do_sample, True)
self.assertEqual(loaded_config.num_beams, 1) # default value
def test_validate(self):
"""
Tests that the `validate` method is working as expected. Note that `validate` is called at initialization time
"""
# A correct configuration will not throw any warning
with warnings.catch_warnings(record=True) as captured_warnings:
GenerationConfig()
self.assertEqual(len(captured_warnings), 0)
# Inconsequent but technically wrong configuration will throw a warning (e.g. setting sampling
# parameters with `do_sample=False`). May be escalated to an error in the future.
with warnings.catch_warnings(record=True) as captured_warnings:
GenerationConfig(do_sample=False, temperature=0.5)
self.assertEqual(len(captured_warnings), 1)
# Expanding on the case above, we can update a bad configuration to get rid of the warning. Ideally,
# that is done by unsetting the parameter (i.e. setting it to None)
generation_config_bad_temperature = GenerationConfig(do_sample=False, temperature=0.5)
with warnings.catch_warnings(record=True) as captured_warnings:
# BAD - 0.9 means it is still set, we should warn
generation_config_bad_temperature.update(temperature=0.9)
self.assertEqual(len(captured_warnings), 1)
generation_config_bad_temperature = GenerationConfig(do_sample=False, temperature=0.5)
with warnings.catch_warnings(record=True) as captured_warnings:
# CORNER CASE - 1.0 is the default, we can't detect whether it is set by the user or not, we shouldn't warn
generation_config_bad_temperature.update(temperature=1.0)
self.assertEqual(len(captured_warnings), 0)
generation_config_bad_temperature = GenerationConfig(do_sample=False, temperature=0.5)
with warnings.catch_warnings(record=True) as captured_warnings:
# OK - None means it is unset, nothing to warn about
generation_config_bad_temperature.update(temperature=None)
self.assertEqual(len(captured_warnings), 0)
# Impossible sets of contraints/parameters will raise an exception
with self.assertRaises(ValueError):
GenerationConfig(do_sample=False, num_beams=1, num_return_sequences=2)
with self.assertRaises(ValueError):
# dummy constraint
GenerationConfig(do_sample=True, num_beams=2, constraints=["dummy"])
with self.assertRaises(ValueError):
GenerationConfig(do_sample=True, num_beams=2, force_words_ids=[[[1, 2, 3]]])
# Passing `generate()`-only flags to `validate` will raise an exception
with self.assertRaises(ValueError):
GenerationConfig(logits_processor="foo")
# Model-specific parameters will NOT raise an exception or a warning
with warnings.catch_warnings(record=True) as captured_warnings:
GenerationConfig(foo="bar")
self.assertEqual(len(captured_warnings), 0)
def test_refuse_to_save(self):
"""Tests that we refuse to save a generation config that fails validation."""
# setting the temperature alone is invalid, as we also need to set do_sample to True -> throws a warning that
# is caught, doesn't save, and raises an exception
config = GenerationConfig()
config.temperature = 0.5
with tempfile.TemporaryDirectory() as tmp_dir:
with self.assertRaises(ValueError) as exc:
config.save_pretrained(tmp_dir)
self.assertTrue("Fix these issues to save the configuration." in str(exc.exception))
self.assertTrue(len(os.listdir(tmp_dir)) == 0)
# greedy decoding throws an exception if we try to return multiple sequences -> throws an exception that is
# caught, doesn't save, and raises a warning
config = GenerationConfig()
config.num_return_sequences = 2
with tempfile.TemporaryDirectory() as tmp_dir:
with self.assertRaises(ValueError) as exc:
config.save_pretrained(tmp_dir)
self.assertTrue("Fix these issues to save the configuration." in str(exc.exception))
self.assertTrue(len(os.listdir(tmp_dir)) == 0)
# final check: no warnings/exceptions thrown if it is correct, and file is saved
config = GenerationConfig()
with tempfile.TemporaryDirectory() as tmp_dir:
with warnings.catch_warnings(record=True) as captured_warnings:
config.save_pretrained(tmp_dir)
self.assertEqual(len(captured_warnings), 0)
self.assertTrue(len(os.listdir(tmp_dir)) == 1)
def test_generation_mode(self):
"""Tests that the `get_generation_mode` method is working as expected."""
config = GenerationConfig()
self.assertEqual(config.get_generation_mode(), GenerationMode.GREEDY_SEARCH)
config = GenerationConfig(do_sample=True)
self.assertEqual(config.get_generation_mode(), GenerationMode.SAMPLE)
config = GenerationConfig(num_beams=2)
self.assertEqual(config.get_generation_mode(), GenerationMode.BEAM_SEARCH)
config = GenerationConfig(top_k=10, do_sample=False, penalty_alpha=0.6)
self.assertEqual(config.get_generation_mode(), GenerationMode.CONTRASTIVE_SEARCH)
config = GenerationConfig()
self.assertEqual(config.get_generation_mode(assistant_model="foo"), GenerationMode.ASSISTED_GENERATION)
@is_staging_test
class ConfigPushToHubTester(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls._token = TOKEN
HfFolder.save_token(TOKEN)
@classmethod
def tearDownClass(cls):
try:
delete_repo(token=cls._token, repo_id="test-generation-config")
except HTTPError:
pass
try:
delete_repo(token=cls._token, repo_id="valid_org/test-generation-config-org")
except HTTPError:
pass
def test_push_to_hub(self):
config = GenerationConfig(
do_sample=True,
temperature=0.7,
length_penalty=1.0,
)
config.push_to_hub("test-generation-config", token=self._token)
new_config = GenerationConfig.from_pretrained(f"{USER}/test-generation-config")
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(v, getattr(new_config, k))
# Reset repo
delete_repo(token=self._token, repo_id="test-generation-config")
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(tmp_dir, repo_id="test-generation-config", push_to_hub=True, token=self._token)
new_config = GenerationConfig.from_pretrained(f"{USER}/test-generation-config")
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(v, getattr(new_config, k))
def test_push_to_hub_in_organization(self):
config = GenerationConfig(
do_sample=True,
temperature=0.7,
length_penalty=1.0,
)
config.push_to_hub("valid_org/test-generation-config-org", token=self._token)
new_config = GenerationConfig.from_pretrained("valid_org/test-generation-config-org")
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(v, getattr(new_config, k))
# Reset repo
delete_repo(token=self._token, repo_id="valid_org/test-generation-config-org")
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
tmp_dir, repo_id="valid_org/test-generation-config-org", push_to_hub=True, token=self._token
)
new_config = GenerationConfig.from_pretrained("valid_org/test-generation-config-org")
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(v, getattr(new_config, k))
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