[T5, MT5, UMT5] Add [T5, MT5, UMT5]ForSequenceClassification (#24726)
* Initial addition of t5forsequenceclassification * Adding imports and adding tests * Formatting * Running make fix-copies * Adding mt5forseq * Formatting * run make fix-copies * Adding to docs * Add model_parallel * Fix bug * Fix * Remove TODO * Fixing tests for T5ForSequenceClassification * Undo changes to dependency_versions_table.py * Change classification head to work with T5Config directly * Change seq length to let tests pass * PR comments for formatting * Formatting * Initial addition of UMT5ForSequenceClassification * Adding to inits and formatting * run make fix-copies * Add doc for UMT5ForSeqClass * Update UMT5 config * Fix docs * Skip torch fx test for SequenceClassification * Formatting * Add skip to UMT5 tests as well * Fix umt5 tests * Running make fix-copies * PR comments * Fix for change to sentence_representation * Rename seq_len to hidden_size since that's what it is * Use base_model to follow format of the rest of the library * Update docs * Extract the decoder_input_ids changes and make one liner * Make one-liner
This commit is contained in:
Sebastian Husch Lee
committed by
GitHub
GitHub
parent
21150cb0f3
commit
8f36ab3e22
@@ -15,10 +15,13 @@
|
||||
|
||||
|
||||
import copy
|
||||
import os
|
||||
import pickle
|
||||
import tempfile
|
||||
import unittest
|
||||
|
||||
from transformers import T5Config, is_torch_available
|
||||
from transformers.models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
|
||||
from transformers.testing_utils import (
|
||||
require_accelerate,
|
||||
require_sentencepiece,
|
||||
@@ -27,14 +30,18 @@ from transformers.testing_utils import (
|
||||
slow,
|
||||
torch_device,
|
||||
)
|
||||
from transformers.utils import cached_property
|
||||
from transformers.utils import cached_property, is_torch_fx_available
|
||||
|
||||
from ...generation.test_utils import GenerationTesterMixin
|
||||
from ...test_configuration_common import ConfigTester
|
||||
from ...test_modeling_common import ModelTesterMixin, ids_tensor
|
||||
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, ids_tensor
|
||||
from ...test_pipeline_mixin import PipelineTesterMixin
|
||||
|
||||
|
||||
if is_torch_fx_available():
|
||||
from transformers.utils.fx import symbolic_trace
|
||||
|
||||
|
||||
if is_torch_available():
|
||||
import torch
|
||||
|
||||
@@ -44,6 +51,7 @@ if is_torch_available():
|
||||
T5EncoderModel,
|
||||
T5ForConditionalGeneration,
|
||||
T5ForQuestionAnswering,
|
||||
T5ForSequenceClassification,
|
||||
T5Model,
|
||||
T5Tokenizer,
|
||||
)
|
||||
@@ -57,7 +65,7 @@ class T5ModelTester:
|
||||
vocab_size=99,
|
||||
batch_size=13,
|
||||
encoder_seq_length=7,
|
||||
decoder_seq_length=9,
|
||||
decoder_seq_length=7,
|
||||
# For common tests
|
||||
is_training=True,
|
||||
use_attention_mask=True,
|
||||
@@ -102,7 +110,8 @@ class T5ModelTester:
|
||||
return T5Config.from_pretrained("t5-base")
|
||||
|
||||
def prepare_config_and_inputs(self):
|
||||
input_ids = ids_tensor([self.batch_size, self.encoder_seq_length], self.vocab_size)
|
||||
input_ids = ids_tensor([self.batch_size, self.encoder_seq_length], self.vocab_size).clamp(2)
|
||||
input_ids[:, -1] = self.eos_token_id # Eos Token
|
||||
decoder_input_ids = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size)
|
||||
|
||||
attention_mask = None
|
||||
@@ -251,6 +260,26 @@ class T5ModelTester:
|
||||
self.parent.assertEqual(outputs["logits"].size(), (self.batch_size, self.decoder_seq_length, self.vocab_size))
|
||||
self.parent.assertEqual(outputs["loss"].size(), ())
|
||||
|
||||
def create_and_check_with_sequence_classification_head(
|
||||
self,
|
||||
config,
|
||||
input_ids,
|
||||
decoder_input_ids,
|
||||
attention_mask,
|
||||
decoder_attention_mask,
|
||||
lm_labels,
|
||||
):
|
||||
labels = torch.tensor([1] * self.batch_size, dtype=torch.long, device=torch_device)
|
||||
model = T5ForSequenceClassification(config=config).to(torch_device).eval()
|
||||
outputs = model(
|
||||
input_ids=input_ids,
|
||||
decoder_input_ids=input_ids,
|
||||
labels=labels,
|
||||
)
|
||||
# self.parent.assertEqual(len(outputs), 4)
|
||||
self.parent.assertEqual(outputs["logits"].size(), (self.batch_size, config.num_labels))
|
||||
self.parent.assertEqual(outputs["loss"].size(), ())
|
||||
|
||||
def create_and_check_decoder_model_past(
|
||||
self,
|
||||
config,
|
||||
@@ -521,7 +550,11 @@ class T5ModelTester:
|
||||
|
||||
@require_torch
|
||||
class T5ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
|
||||
all_model_classes = (T5Model, T5ForConditionalGeneration, T5ForQuestionAnswering) if is_torch_available() else ()
|
||||
all_model_classes = (
|
||||
(T5Model, T5ForConditionalGeneration, T5ForSequenceClassification, T5ForQuestionAnswering)
|
||||
if is_torch_available()
|
||||
else ()
|
||||
)
|
||||
all_generative_model_classes = (T5ForConditionalGeneration,) if is_torch_available() else ()
|
||||
pipeline_model_mapping = (
|
||||
{
|
||||
@@ -531,6 +564,8 @@ class T5ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin,
|
||||
"text2text-generation": T5ForConditionalGeneration,
|
||||
"translation": T5ForConditionalGeneration,
|
||||
"question-answering": T5ForQuestionAnswering,
|
||||
"text-classification": T5ForSequenceClassification,
|
||||
"zero-shot": T5ForSequenceClassification,
|
||||
}
|
||||
if is_torch_available()
|
||||
else {}
|
||||
@@ -548,6 +583,126 @@ class T5ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin,
|
||||
self.model_tester = T5ModelTester(self)
|
||||
self.config_tester = ConfigTester(self, config_class=T5Config, d_model=37)
|
||||
|
||||
def _create_and_check_torch_fx_tracing(self, config, inputs_dict, output_loss=False):
|
||||
if not is_torch_fx_available() or not self.fx_compatible:
|
||||
return
|
||||
|
||||
configs_no_init = _config_zero_init(config) # To be sure we have no Nan
|
||||
configs_no_init.return_dict = False
|
||||
|
||||
for model_class in self.all_model_classes:
|
||||
if model_class.__name__ == "T5ForSequenceClassification":
|
||||
continue
|
||||
model = model_class(config=configs_no_init)
|
||||
model.to(torch_device)
|
||||
model.eval()
|
||||
inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=output_loss)
|
||||
|
||||
try:
|
||||
if model.config.is_encoder_decoder:
|
||||
model.config.use_cache = False # FSTM still requires this hack -> FSTM should probably be refactored similar to BART afterward
|
||||
labels = inputs.get("labels", None)
|
||||
input_names = [
|
||||
"attention_mask",
|
||||
"decoder_attention_mask",
|
||||
"decoder_input_ids",
|
||||
"input_features",
|
||||
"input_ids",
|
||||
"input_values",
|
||||
]
|
||||
if labels is not None:
|
||||
input_names.append("labels")
|
||||
|
||||
filtered_inputs = {k: v for (k, v) in inputs.items() if k in input_names}
|
||||
input_names = list(filtered_inputs.keys())
|
||||
|
||||
model_output = model(**filtered_inputs)
|
||||
|
||||
traced_model = symbolic_trace(model, input_names)
|
||||
traced_output = traced_model(**filtered_inputs)
|
||||
else:
|
||||
input_names = [
|
||||
"attention_mask",
|
||||
"bbox",
|
||||
"input_features",
|
||||
"input_ids",
|
||||
"input_values",
|
||||
"pixel_values",
|
||||
"token_type_ids",
|
||||
"visual_feats",
|
||||
"visual_pos",
|
||||
]
|
||||
|
||||
labels = inputs.get("labels", None)
|
||||
start_positions = inputs.get("start_positions", None)
|
||||
end_positions = inputs.get("end_positions", None)
|
||||
if labels is not None:
|
||||
input_names.append("labels")
|
||||
if start_positions is not None:
|
||||
input_names.append("start_positions")
|
||||
if end_positions is not None:
|
||||
input_names.append("end_positions")
|
||||
|
||||
filtered_inputs = {k: v for (k, v) in inputs.items() if k in input_names}
|
||||
input_names = list(filtered_inputs.keys())
|
||||
|
||||
if model.__class__.__name__ in set(MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES.values()) and (
|
||||
not hasattr(model.config, "problem_type") or model.config.problem_type is None
|
||||
):
|
||||
model.config.problem_type = "single_label_classification"
|
||||
|
||||
traced_model = symbolic_trace(model, input_names)
|
||||
traced_output = traced_model(**filtered_inputs)
|
||||
model_output = model(**filtered_inputs)
|
||||
|
||||
except Exception as e:
|
||||
self.fail(f"Couldn't trace module: {e}")
|
||||
|
||||
def flatten_output(output):
|
||||
flatten = []
|
||||
for x in output:
|
||||
if isinstance(x, (tuple, list)):
|
||||
flatten += flatten_output(x)
|
||||
elif not isinstance(x, torch.Tensor):
|
||||
continue
|
||||
else:
|
||||
flatten.append(x)
|
||||
return flatten
|
||||
|
||||
model_output = flatten_output(model_output)
|
||||
traced_output = flatten_output(traced_output)
|
||||
num_outputs = len(model_output)
|
||||
|
||||
for i in range(num_outputs):
|
||||
self.assertTrue(
|
||||
torch.allclose(model_output[i], traced_output[i]),
|
||||
f"traced {i}th output doesn't match model {i}th output for {model_class}",
|
||||
)
|
||||
|
||||
# Test that the model can be serialized and restored properly
|
||||
with tempfile.TemporaryDirectory() as tmp_dir_name:
|
||||
pkl_file_name = os.path.join(tmp_dir_name, "model.pkl")
|
||||
try:
|
||||
with open(pkl_file_name, "wb") as f:
|
||||
pickle.dump(traced_model, f)
|
||||
with open(pkl_file_name, "rb") as f:
|
||||
loaded = pickle.load(f)
|
||||
except Exception as e:
|
||||
self.fail(f"Couldn't serialize / deserialize the traced model: {e}")
|
||||
|
||||
loaded_output = loaded(**filtered_inputs)
|
||||
loaded_output = flatten_output(loaded_output)
|
||||
|
||||
for i in range(num_outputs):
|
||||
self.assertTrue(
|
||||
torch.allclose(model_output[i], loaded_output[i]),
|
||||
f"serialized model {i}th output doesn't match model {i}th output for {model_class}",
|
||||
)
|
||||
|
||||
# Avoid memory leak. Without this, each call increase RAM usage by ~20MB.
|
||||
# (Even with this call, there are still memory leak by ~0.04MB)
|
||||
self.clear_torch_jit_class_registry()
|
||||
|
||||
def test_config(self):
|
||||
self.config_tester.run_common_tests()
|
||||
|
||||
@@ -567,6 +722,36 @@ class T5ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin,
|
||||
config.feed_forward_proj = "gated-gelu"
|
||||
self.model_tester.create_and_check_model(config, *config_and_inputs[1:])
|
||||
|
||||
# T5ForSequenceClassification does not support inputs_embeds
|
||||
def test_inputs_embeds(self):
|
||||
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
||||
|
||||
for model_class in (T5Model, T5ForConditionalGeneration, T5ForQuestionAnswering):
|
||||
model = model_class(config)
|
||||
model.to(torch_device)
|
||||
model.eval()
|
||||
|
||||
inputs = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class))
|
||||
|
||||
if not self.is_encoder_decoder:
|
||||
input_ids = inputs["input_ids"]
|
||||
del inputs["input_ids"]
|
||||
else:
|
||||
encoder_input_ids = inputs["input_ids"]
|
||||
decoder_input_ids = inputs.get("decoder_input_ids", encoder_input_ids)
|
||||
del inputs["input_ids"]
|
||||
inputs.pop("decoder_input_ids", None)
|
||||
|
||||
wte = model.get_input_embeddings()
|
||||
if not self.is_encoder_decoder:
|
||||
inputs["inputs_embeds"] = wte(input_ids)
|
||||
else:
|
||||
inputs["inputs_embeds"] = wte(encoder_input_ids)
|
||||
inputs["decoder_inputs_embeds"] = wte(decoder_input_ids)
|
||||
|
||||
with torch.no_grad():
|
||||
model(**inputs)[0]
|
||||
|
||||
def test_config_and_model_silu_gated(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
config = config_and_inputs[0]
|
||||
@@ -577,6 +762,10 @@ class T5ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin,
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_with_lm_head(*config_and_inputs)
|
||||
|
||||
def test_with_sequence_classification_head(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_with_sequence_classification_head(*config_and_inputs)
|
||||
|
||||
def test_decoder_model_past(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_decoder_model_past(*config_and_inputs)
|
||||
|
||||
Reference in New Issue
Block a user