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transformers/tests/models/modernbert/__init__.py Normal file
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# 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 json
import os
import tempfile
import unittest
import pytest
from packaging import version
from transformers import AutoTokenizer, ModernBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import (
CaptureLogger,
require_flash_attn,
require_torch,
require_torch_gpu,
slow,
torch_device,
)
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
ModernBertForMaskedLM,
ModernBertForMultipleChoice,
ModernBertForQuestionAnswering,
ModernBertForSequenceClassification,
ModernBertForTokenClassification,
ModernBertModel,
logging,
)
class ModernBertModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_input_mask=True,
use_labels=True,
vocab_size=99,
pad_token_id=0,
hidden_size=32,
num_hidden_layers=2,
num_attention_heads=4,
intermediate_size=37,
hidden_activation="gelu",
mlp_dropout=0.0,
attention_dropout=0.0,
embedding_dropout=0.0,
classifier_dropout=0.0,
max_position_embeddings=512,
type_vocab_size=16,
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=3,
num_choices=4,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_input_mask = use_input_mask
self.use_labels = use_labels
self.vocab_size = vocab_size
self.pad_token_id = pad_token_id
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_activation = hidden_activation
self.mlp_dropout = mlp_dropout
self.attention_dropout = attention_dropout
self.embedding_dropout = embedding_dropout
self.classifier_dropout = classifier_dropout
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_labels = num_labels
self.num_choices = num_choices
self.scope = scope
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.seq_length])
sequence_labels = None
token_labels = None
choice_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
choice_labels = ids_tensor([self.batch_size], self.num_choices)
config = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def get_config(self):
"""
Returns a tiny configuration by default.
"""
config = ModernBertConfig(
vocab_size=self.vocab_size,
pad_token_id=self.pad_token_id,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_activation=self.hidden_activation,
mlp_dropout=self.mlp_dropout,
attention_dropout=self.attention_dropout,
embedding_dropout=self.embedding_dropout,
classifier_dropout=self.classifier_dropout,
max_position_embeddings=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
is_decoder=False,
initializer_range=self.initializer_range,
)
if test := os.environ.get("PYTEST_CURRENT_TEST", None):
test_name = test.split(":")[-1].split(" ")[0]
# If we're testing `test_retain_grad_hidden_states_attentions`, we normally get an error
# that compilation doesn't work. Users can then set compile=False when loading the model,
# much like here. We're testing whether it works once they've done that.
# If we're testing `test_inputs_embeds_matches_input_ids`, then we'd like to test with `reference_compile`
# set to False, otherwise the input_ids with compiled input embeddings will not match the inputs_embeds
# with atol=1e-8 and rtol=1e-5
if test_name in ("test_retain_grad_hidden_states_attentions", "test_inputs_embeds_matches_input_ids"):
config.reference_compile = False
# Some tests require attentions to be outputted, in that case we'll set the attention implementation to eager
# as the others don't support outputted attentions
if test_name in (
"test_attention_outputs",
"test_hidden_states_output",
"test_retain_grad_hidden_states_attentions",
):
config._attn_implementation = "eager"
return config
def create_and_check_model(self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels):
model = ModernBertModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask)
result = model(input_ids)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def create_and_check_for_masked_lm(
self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = ModernBertForMaskedLM(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_for_sequence_classification(
self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = ModernBertForSequenceClassification(config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, labels=sequence_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def create_and_check_for_token_classification(
self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = ModernBertForTokenClassification(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
def create_and_check_for_multiple_choice(
self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = ModernBertForMultipleChoice(config=config)
model.to(torch_device)
model.eval()
multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
result = model(
multiple_choice_inputs_ids,
attention_mask=multiple_choice_input_mask,
labels=choice_labels,
)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = config_and_inputs
inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class ModernBertModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
test_torchscript = False
all_model_classes = (
(
ModernBertModel,
ModernBertForMaskedLM,
ModernBertForSequenceClassification,
ModernBertForTokenClassification,
ModernBertForQuestionAnswering,
ModernBertForMultipleChoice,
)
if is_torch_available()
else ()
)
pipeline_model_mapping = (
{
"feature-extraction": ModernBertModel,
"fill-mask": ModernBertForMaskedLM,
"text-classification": ModernBertForSequenceClassification,
"token-classification": ModernBertForTokenClassification,
"zero-shot": ModernBertForSequenceClassification,
"question-answering": ModernBertForQuestionAnswering,
}
if is_torch_available()
else {}
)
fx_compatible = False
test_head_masking = False
test_pruning = False
model_split_percents = [0.5, 0.8, 0.9]
# special case for ForPreTraining model
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
if inputs_dict.get("output_attentions", False):
inputs_dict["output_attentions"] = True
if return_labels:
if model_class in get_values(MODEL_FOR_PRETRAINING_MAPPING):
inputs_dict["labels"] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device
)
inputs_dict["next_sentence_label"] = torch.zeros(
self.model_tester.batch_size, dtype=torch.long, device=torch_device
)
return inputs_dict
def setUp(self):
self.model_tester = ModernBertModelTester(self)
self.config_tester = ConfigTester(self, config_class=ModernBertConfig, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_model_various_embeddings(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
config_and_inputs[0].position_embedding_type = type
self.model_tester.create_and_check_model(*config_and_inputs)
def test_initialization(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
configs_no_init = _config_zero_init(config)
for model_class in self.all_model_classes:
model = model_class(config=configs_no_init)
for name, param in model.named_parameters():
# The classifier.weight from ModernBertForSequenceClassification and ModernBertForTokenClassification
# are initialized without `initializer_range`, so they're not set to ~0 via the _config_zero_init
if param.requires_grad and not (
name == "classifier.weight"
and model_class
in [
ModernBertForSequenceClassification,
ModernBertForTokenClassification,
ModernBertForQuestionAnswering,
ModernBertForMultipleChoice,
]
):
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item(),
[0.0, 1.0],
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
)
def test_for_masked_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
def test_for_sequence_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)
def test_for_token_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
def test_for_multiple_choice(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs)
def test_for_warning_if_padding_and_no_attention_mask(self):
(
config,
input_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = self.model_tester.prepare_config_and_inputs()
# Set pad tokens in the input_ids
input_ids[0, 0] = config.pad_token_id
# Check for warnings if the attention_mask is missing.
logger = logging.get_logger("transformers.modeling_utils")
# clear cache so we can test the warning is emitted (from `warning_once`).
logger.warning_once.cache_clear()
with CaptureLogger(logger) as cl:
model = ModernBertModel(config=config)
model.to(torch_device)
model.eval()
model(input_ids, attention_mask=None)
self.assertIn("We strongly recommend passing in an `attention_mask`", cl.out)
@unittest.skip("ModernBert doesn't use separate classes for SDPA, but a function instead.")
def test_sdpa_can_dispatch_non_composite_models(self):
pass
@slow
def test_model_from_pretrained(self):
model_name = "google-bert/bert-base-uncased"
model = ModernBertModel.from_pretrained(model_name)
self.assertIsNotNone(model)
@require_flash_attn
@require_torch_gpu
@pytest.mark.flash_attn_test
@slow
def test_flash_attn_2_inference_equivalence_right_padding(self):
self.skipTest(reason="ModernBert flash attention does not support right padding")
@require_flash_attn
@require_torch_gpu
@pytest.mark.flash_attn_test
@slow
def test_flash_attn_2_conversion(self):
self.skipTest(reason="ModernBert doesn't use the ModernBertFlashAttention2 class method.")
@pytest.mark.torch_compile_test
def test_saved_config_excludes_reference_compile(self):
config = ModernBertConfig(reference_compile=True)
with tempfile.TemporaryDirectory() as tmpdirname:
config.save_pretrained(tmpdirname)
with open(os.path.join(tmpdirname, "config.json")) as f:
config_dict = json.load(f)
self.assertNotIn("reference_compile", config_dict)
@require_flash_attn
@require_torch_gpu
@pytest.mark.flash_attn_test
def test_flash_attention_dispatches_by_default(self):
"ModernBert should dispatch to FA2 by default, not SDPA"
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config=config)
self.assertTrue(model.config._attn_implementation == "flash_attention_2")
@require_torch
class ModernBertModelIntegrationTest(unittest.TestCase):
@slow
def test_inference_masked_lm(self):
if version.parse(torch.__version__) < version.parse("2.4.0"):
self.skipTest(reason="This test requires torch >= 2.4 to run.")
model = ModernBertForMaskedLM.from_pretrained(
"answerdotai/ModernBERT-base", reference_compile=False, attn_implementation="sdpa"
)
tokenizer = AutoTokenizer.from_pretrained("answerdotai/ModernBERT-base")
inputs = tokenizer("Hello World!", return_tensors="pt")
with torch.no_grad():
output = model(**inputs)[0]
expected_shape = torch.Size((1, 5, 50368))
self.assertEqual(output.shape, expected_shape)
# compare the actual values for a slice.
expected_slice = torch.tensor(
[[[3.8387, -0.2017, 12.2839], [3.6300, 0.6869, 14.7123], [-5.1137, -3.8122, 11.9874]]]
)
torch.testing.assert_close(output[:, :3, :3], expected_slice, rtol=1e-4, atol=1e-4)
@slow
def test_inference_no_head(self):
if version.parse(torch.__version__) < version.parse("2.4.0"):
self.skipTest(reason="This test requires torch >= 2.4 to run.")
model = ModernBertModel.from_pretrained(
"answerdotai/ModernBERT-base", reference_compile=False, attn_implementation="sdpa"
)
tokenizer = AutoTokenizer.from_pretrained("answerdotai/ModernBERT-base")
inputs = tokenizer("Hello World!", return_tensors="pt")
with torch.no_grad():
output = model(**inputs)[0]
expected_shape = torch.Size((1, 5, 768))
self.assertEqual(output.shape, expected_shape)
# compare the actual values for a slice.
expected_slice = torch.tensor(
[[[0.3151, -0.6417, -0.7027], [-0.7834, -1.5810, 0.4576], [1.0614, -0.7268, -0.0871]]]
)
torch.testing.assert_close(output[:, :3, :3], expected_slice, rtol=1e-4, atol=1e-4)
@slow
def test_inference_token_classification(self):
if version.parse(torch.__version__) < version.parse("2.4.0"):
self.skipTest(reason="This test requires torch >= 2.4 to run.")
model = ModernBertForTokenClassification.from_pretrained(
"hf-internal-testing/tiny-random-ModernBertForTokenClassification",
reference_compile=False,
attn_implementation="sdpa",
)
tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-ModernBertForTokenClassification")
inputs = tokenizer("Hello World!", return_tensors="pt")
with torch.no_grad():
output = model(**inputs)[0]
expected_shape = torch.Size((1, 5, 2))
self.assertEqual(output.shape, expected_shape)
expected = torch.tensor(
[[[2.0159, 4.6569], [-0.9430, 3.1595], [-3.8770, 3.2653], [1.5752, 4.5167], [-1.6939, 1.2524]]]
)
torch.testing.assert_close(output, expected, rtol=1e-4, atol=1e-4)
@slow
def test_inference_sequence_classification(self):
if version.parse(torch.__version__) < version.parse("2.4.0"):
self.skipTest(reason="This test requires torch >= 2.4 to run.")
model = ModernBertForSequenceClassification.from_pretrained(
"hf-internal-testing/tiny-random-ModernBertForSequenceClassification",
reference_compile=False,
attn_implementation="sdpa",
)
tokenizer = AutoTokenizer.from_pretrained(
"hf-internal-testing/tiny-random-ModernBertForSequenceClassification"
)
inputs = tokenizer("Hello World!", return_tensors="pt")
with torch.no_grad():
output = model(**inputs)[0]
expected_shape = torch.Size((1, 2))
self.assertEqual(output.shape, expected_shape)
expected = torch.tensor([[1.6466, 4.5662]])
torch.testing.assert_close(output, expected, rtol=1e-4, atol=1e-4)
@pytest.mark.torch_export_test
@slow
def test_export(self):
if version.parse(torch.__version__) < version.parse("2.4.0"):
self.skipTest(reason="This test requires torch >= 2.4 to run.")
bert_model = "answerdotai/ModernBERT-base"
device = "cpu"
attn_implementation = "sdpa"
max_length = 512
tokenizer = AutoTokenizer.from_pretrained(bert_model)
inputs = tokenizer(
"the man worked as a [MASK].",
return_tensors="pt",
padding="max_length",
max_length=max_length,
)
model = ModernBertForMaskedLM.from_pretrained(
bert_model,
device_map=device,
attn_implementation=attn_implementation,
)
logits = model(**inputs).logits
eg_predicted_mask = tokenizer.decode(logits[0, 6].topk(5).indices)
self.assertEqual(eg_predicted_mask.split(), ["lawyer", "mechanic", "teacher", "doctor", "waiter"])
exported_program = torch.export.export(
model,
args=(inputs["input_ids"],),
kwargs={"attention_mask": inputs["attention_mask"]},
strict=True,
)
result = exported_program.module().forward(inputs["input_ids"], inputs["attention_mask"])
ep_predicted_mask = tokenizer.decode(result.logits[0, 6].topk(5).indices)
self.assertEqual(eg_predicted_mask, ep_predicted_mask)