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qwenillustrious / transformers /tests /utils /test_generic.py
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# Copyright 2019-present, the HuggingFace Inc. team.
#
# 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 unittest
import warnings
import numpy as np
from transformers.configuration_utils import PretrainedConfig
from transformers.modeling_outputs import BaseModelOutput
from transformers.testing_utils import require_torch
from transformers.utils import (
can_return_tuple,
expand_dims,
filter_out_non_signature_kwargs,
flatten_dict,
is_torch_available,
reshape,
squeeze,
to_py_obj,
transpose,
)
if is_torch_available():
import torch
class GenericTester(unittest.TestCase):
def test_flatten_dict(self):
input_dict = {
"task_specific_params": {
"summarization": {"length_penalty": 1.0, "max_length": 128, "min_length": 12, "num_beams": 4},
"summarization_cnn": {"length_penalty": 2.0, "max_length": 142, "min_length": 56, "num_beams": 4},
"summarization_xsum": {"length_penalty": 1.0, "max_length": 62, "min_length": 11, "num_beams": 6},
}
}
expected_dict = {
"task_specific_params.summarization.length_penalty": 1.0,
"task_specific_params.summarization.max_length": 128,
"task_specific_params.summarization.min_length": 12,
"task_specific_params.summarization.num_beams": 4,
"task_specific_params.summarization_cnn.length_penalty": 2.0,
"task_specific_params.summarization_cnn.max_length": 142,
"task_specific_params.summarization_cnn.min_length": 56,
"task_specific_params.summarization_cnn.num_beams": 4,
"task_specific_params.summarization_xsum.length_penalty": 1.0,
"task_specific_params.summarization_xsum.max_length": 62,
"task_specific_params.summarization_xsum.min_length": 11,
"task_specific_params.summarization_xsum.num_beams": 6,
}
self.assertEqual(flatten_dict(input_dict), expected_dict)
def test_transpose_numpy(self):
x = np.random.randn(3, 4)
self.assertTrue(np.allclose(transpose(x), x.transpose()))
x = np.random.randn(3, 4, 5)
self.assertTrue(np.allclose(transpose(x, axes=(1, 2, 0)), x.transpose((1, 2, 0))))
@require_torch
def test_transpose_torch(self):
x = np.random.randn(3, 4)
t = torch.tensor(x)
self.assertTrue(np.allclose(transpose(x), transpose(t).numpy()))
x = np.random.randn(3, 4, 5)
t = torch.tensor(x)
self.assertTrue(np.allclose(transpose(x, axes=(1, 2, 0)), transpose(t, axes=(1, 2, 0)).numpy()))
@require_torch
def test_reshape_torch(self):
x = np.random.randn(3, 4)
t = torch.tensor(x)
self.assertTrue(np.allclose(reshape(x, (4, 3)), reshape(t, (4, 3)).numpy()))
x = np.random.randn(3, 4, 5)
t = torch.tensor(x)
self.assertTrue(np.allclose(reshape(x, (12, 5)), reshape(t, (12, 5)).numpy()))
@require_torch
def test_squeeze_torch(self):
x = np.random.randn(1, 3, 4)
t = torch.tensor(x)
self.assertTrue(np.allclose(squeeze(x), squeeze(t).numpy()))
x = np.random.randn(1, 4, 1, 5)
t = torch.tensor(x)
self.assertTrue(np.allclose(squeeze(x, axis=2), squeeze(t, axis=2).numpy()))
def test_expand_dims_numpy(self):
x = np.random.randn(3, 4)
self.assertTrue(np.allclose(expand_dims(x, axis=1), np.expand_dims(x, axis=1)))
@require_torch
def test_expand_dims_torch(self):
x = np.random.randn(3, 4)
t = torch.tensor(x)
self.assertTrue(np.allclose(expand_dims(x, axis=1), expand_dims(t, axis=1).numpy()))
def test_to_py_obj_native(self):
self.assertTrue(to_py_obj(1) == 1)
self.assertTrue(to_py_obj([1, 2, 3]) == [1, 2, 3])
self.assertTrue(to_py_obj([((1.0, 1.1), 1.2), (2, 3)]) == [[[1.0, 1.1], 1.2], [2, 3]])
def test_to_py_obj_numpy(self):
x1 = [[1, 2, 3], [4, 5, 6]]
t1 = np.array(x1)
self.assertTrue(to_py_obj(t1) == x1)
x2 = [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]
t2 = np.array(x2)
self.assertTrue(to_py_obj(t2) == x2)
self.assertTrue(to_py_obj([t1, t2]) == [x1, x2])
@require_torch
def test_to_py_obj_torch(self):
x1 = [[1, 2, 3], [4, 5, 6]]
t1 = torch.tensor(x1)
self.assertTrue(to_py_obj(t1) == x1)
x2 = [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]
t2 = torch.tensor(x2)
self.assertTrue(to_py_obj(t2) == x2)
self.assertTrue(to_py_obj([t1, t2]) == [x1, x2])
class ValidationDecoratorTester(unittest.TestCase):
def test_cases_no_warning(self):
with warnings.catch_warnings(record=True) as raised_warnings:
warnings.simplefilter("always")
# basic test
@filter_out_non_signature_kwargs()
def func1(a):
return a
result = func1(1)
self.assertEqual(result, 1)
# include extra kwarg
@filter_out_non_signature_kwargs(extra=["extra_arg"])
def func2(a, **kwargs):
return a, kwargs
a, kwargs = func2(1)
self.assertEqual(a, 1)
self.assertEqual(kwargs, {})
a, kwargs = func2(1, extra_arg=2)
self.assertEqual(a, 1)
self.assertEqual(kwargs, {"extra_arg": 2})
# multiple extra kwargs
@filter_out_non_signature_kwargs(extra=["extra_arg", "extra_arg2"])
def func3(a, **kwargs):
return a, kwargs
a, kwargs = func3(2)
self.assertEqual(a, 2)
self.assertEqual(kwargs, {})
a, kwargs = func3(3, extra_arg2=3)
self.assertEqual(a, 3)
self.assertEqual(kwargs, {"extra_arg2": 3})
a, kwargs = func3(1, extra_arg=2, extra_arg2=3)
self.assertEqual(a, 1)
self.assertEqual(kwargs, {"extra_arg": 2, "extra_arg2": 3})
# Check that no warnings were raised
self.assertEqual(len(raised_warnings), 0, f"Warning raised: {[w.message for w in raised_warnings]}")
def test_cases_with_warnings(self):
@filter_out_non_signature_kwargs()
def func1(a):
return a
with self.assertWarns(UserWarning):
func1(1, extra_arg=2)
@filter_out_non_signature_kwargs(extra=["extra_arg"])
def func2(a, **kwargs):
return kwargs
with self.assertWarns(UserWarning):
kwargs = func2(1, extra_arg=2, extra_arg2=3)
self.assertEqual(kwargs, {"extra_arg": 2})
@filter_out_non_signature_kwargs(extra=["extra_arg", "extra_arg2"])
def func3(a, **kwargs):
return kwargs
with self.assertWarns(UserWarning):
kwargs = func3(1, extra_arg=2, extra_arg2=3, extra_arg3=4)
self.assertEqual(kwargs, {"extra_arg": 2, "extra_arg2": 3})
@require_torch
class CanReturnTupleDecoratorTester(unittest.TestCase):
def _get_model(self, config, store_config=True, raise_in_forward=False):
# Simple model class for testing can_return_tuple decorator.
class SimpleTestModel(torch.nn.Module):
def __init__(self, config):
super().__init__()
if store_config:
self.config = config
@can_return_tuple
def forward(self, x):
if raise_in_forward:
raise ValueError("Test error")
return BaseModelOutput(
last_hidden_state=x,
hidden_states=None,
attentions=None,
)
return SimpleTestModel(config)
def test_decorator_eager(self):
"""Test that the can_return_tuple decorator works with eager mode."""
# test nothing is set
config = PretrainedConfig()
model = self._get_model(config)
inputs = torch.tensor(10)
output = model(inputs)
self.assertIsInstance(
output, BaseModelOutput, "output should be a BaseModelOutput when return_dict is not set"
)
# test all explicit cases
for config_return_dict in [True, False, None]:
for return_dict in [True, False, None]:
config = PretrainedConfig(return_dict=config_return_dict)
model = self._get_model(config)
output = model(torch.tensor(10), return_dict=return_dict)
expected_type = (
tuple
if return_dict is False
else (tuple if config_return_dict is False and return_dict is None else BaseModelOutput)
)
if config_return_dict is None and return_dict is None:
expected_type = tuple
message = f"output should be a {expected_type.__name__} when config.use_return_dict={config_return_dict} and return_dict={return_dict}"
self.assertIsInstance(output, expected_type, message)
def test_decorator_compiled(self):
"""Test that the can_return_tuple decorator works with compiled mode."""
config = PretrainedConfig()
# Output object
model = self._get_model(config)
compiled_model = torch.compile(model)
output = compiled_model(torch.tensor(10))
self.assertIsInstance(output, BaseModelOutput)
# Tuple output
model = self._get_model(config)
compiled_model = torch.compile(model)
output = compiled_model(torch.tensor(10), return_dict=False)
self.assertIsInstance(output, tuple)
def test_decorator_torch_export(self):
"""Test that the can_return_tuple decorator works with torch.export."""
config = PretrainedConfig()
model = self._get_model(config)
torch.export.export(model, args=(torch.tensor(10),))
def test_decorator_torchscript(self):
"""Test that the can_return_tuple decorator works with torch.jit.trace."""
config = PretrainedConfig(return_dict=False)
model = self._get_model(config)
inputs = torch.tensor(10)
traced_module = torch.jit.trace(model, inputs)
output = traced_module(inputs)
self.assertIsInstance(output, tuple)
def test_attribute_cleanup(self):
"""Test that the `_is_top_level_module` attribute is removed after the forward call."""
config = PretrainedConfig(return_dict=False)
inputs = torch.tensor(10)
# working case
model = self._get_model(config)
output = model(inputs)
self.assertIsInstance(output, tuple)
for name, module in model.named_modules():
self.assertFalse(
hasattr(module, "_is_top_level_module"),
f"Module `{name}` should not have `_is_top_level_module` attribute",
)
# model without config
no_config_model = self._get_model(config, store_config=False)
output = no_config_model(inputs)
self.assertIsInstance(output, BaseModelOutput)
for name, module in no_config_model.named_modules():
self.assertFalse(
hasattr(module, "_is_top_level_module"),
f"Module `{name}` should not have `_is_top_level_module` attribute",
)
# model with raise in forward
model_with_raise = self._get_model(config, raise_in_forward=True)
with self.assertRaises(ValueError):
model_with_raise(inputs)
for name, module in model_with_raise.named_modules():
self.assertFalse(
hasattr(module, "_is_top_level_module"),
f"Module `{name}` should not have `_is_top_level_module` attribute",
)