SUMIN/HuggingFace_transformer
1
0
Fork 0
Code Issues Pull Requests Actions 7 Packages Projects Releases Wiki Activity

Added support for other features for already supported models (#14358)

Browse Source 
* Added support for other features for already supported models

* Partial support for causal and seq2seq models

* Partial support for causal and seq2seq models

* OnnxSeq2SeqConfigWithPast to support seq2seq models

* Parameterized the onnx tests

* Restored run_mlm.py

* Restored run_mlm.py

* [WIP] BART update

* BART and MBART

* Added comments

* Another sequence length of the past_key_values
This commit is contained in:
Michael Benayoun
2021-12-08 18:39:56 +01:00
committed by GitHub
parent ee4fa2e465
commit 0c70f145d1
17 changed files with 935 additions and 388 deletions
Download Patch File Download Diff File Expand all files Collapse all files

186
tests/test_onnx_v2.py
View File

@@ -3,33 +3,8 @@ from tempfile import NamedTemporaryFile
from unittest import TestCase
from unittest.mock import patch
from transformers import ( # LongformerConfig,; T5Config,
AlbertConfig,
AutoTokenizer,
BartConfig,
DistilBertConfig,
GPT2Config,
GPTNeoConfig,
LayoutLMConfig,
MBartConfig,
RobertaConfig,
XLMRobertaConfig,
is_torch_available,
)
from transformers.models.albert import AlbertOnnxConfig
from transformers.models.bart import BartOnnxConfig
from transformers.models.bert.configuration_bert import BertConfig, BertOnnxConfig
from transformers.models.distilbert import DistilBertOnnxConfig
# from transformers.models.longformer import LongformerOnnxConfig
from transformers.models.gpt2 import GPT2OnnxConfig
from transformers.models.gpt_neo import GPTNeoOnnxConfig
from transformers.models.layoutlm import LayoutLMOnnxConfig
from transformers.models.mbart import MBartOnnxConfig
from transformers.models.roberta import RobertaOnnxConfig
# from transformers.models.t5 import T5OnnxConfig
from transformers.models.xlm_roberta import XLMRobertaOnnxConfig
from parameterized import parameterized
from transformers import AutoConfig, AutoTokenizer
from transformers.onnx import (
EXTERNAL_DATA_FORMAT_SIZE_LIMIT,
OnnxConfig,
@@ -37,7 +12,8 @@ from transformers.onnx import (
export,
validate_model_outputs,
)
from transformers.onnx.config import DEFAULT_ONNX_OPSET, OnnxConfigWithPast
from transformers.onnx.config import OnnxConfigWithPast
from transformers.onnx.features import FeaturesManager
from transformers.onnx.utils import compute_effective_axis_dimension, compute_serialized_parameters_size
from transformers.testing_utils import require_onnx, require_torch, slow
@@ -139,11 +115,12 @@ class OnnxConfigWithPastTestCaseV2(TestCase):
Cover the tests for model which have use_cache feature (i.e. "with_past" for ONNX)
"""
SUPPORTED_WITH_PAST_CONFIGS = {
("BART", BartConfig),
("GPT2", GPT2Config),
# ("T5", T5Config)
}
SUPPORTED_WITH_PAST_CONFIGS = {}
# SUPPORTED_WITH_PAST_CONFIGS = {
# ("BART", BartConfig),
# ("GPT2", GPT2Config),
# # ("T5", T5Config)
# }
@patch.multiple(OnnxConfigWithPast, __abstractmethods__=set())
def test_use_past(self):
@@ -187,40 +164,37 @@ class OnnxConfigWithPastTestCaseV2(TestCase):
)
if is_torch_available():
from transformers import ( # T5Model,
AlbertModel,
BartModel,
BertModel,
DistilBertModel,
GPT2Model,
GPTNeoModel,
LayoutLMModel,
MBartModel,
RobertaModel,
XLMRobertaModel,
)
PYTORCH_EXPORT_MODELS = {
("albert", "hf-internal-testing/tiny-albert"),
("bert", "bert-base-cased"),
("camembert", "camembert-base"),
("distilbert", "distilbert-base-cased"),
# ("longFormer", "longformer-base-4096"),
("roberta", "roberta-base"),
("xlm-roberta", "xlm-roberta-base"),
("layoutlm", "microsoft/layoutlm-base-uncased"),
}
PYTORCH_EXPORT_DEFAULT_MODELS = {
("ALBERT", "hf-internal-testing/tiny-albert", AlbertModel, AlbertConfig, AlbertOnnxConfig),
("BART", "facebook/bart-base", BartModel, BartConfig, BartOnnxConfig),
("BERT", "bert-base-cased", BertModel, BertConfig, BertOnnxConfig),
("DistilBERT", "distilbert-base-cased", DistilBertModel, DistilBertConfig, DistilBertOnnxConfig),
("GPT2", "gpt2", GPT2Model, GPT2Config, GPT2OnnxConfig),
("GPT-Neo", "EleutherAI/gpt-neo-125M", GPTNeoModel, GPTNeoConfig, GPTNeoOnnxConfig),
# ("LongFormer", "longformer-base-4096", LongformerModel, LongformerConfig, LongformerOnnxConfig),
("Roberta", "roberta-base", RobertaModel, RobertaConfig, RobertaOnnxConfig),
("XLM-Roberta", "roberta-base", XLMRobertaModel, XLMRobertaConfig, XLMRobertaOnnxConfig),
("LayoutLM", "microsoft/layoutlm-base-uncased", LayoutLMModel, LayoutLMConfig, LayoutLMOnnxConfig),
("MBart", "sshleifer/tiny-mbart", MBartModel, MBartConfig, MBartOnnxConfig),
# ("T5", "t5-small", T5Model, T5Config, T5OnnxConfig),
}
PYTORCH_EXPORT_WITH_PAST_MODELS = {
("gpt2", "gpt2"),
("gpt-neo", "EleutherAI/gpt-neo-125M"),
}
PYTORCH_EXPORT_WITH_PAST_MODELS = {
# ("BART", "facebook/bart-base", BartModel, BartConfig, BartOnnxConfig),
# ("GPT2", "gpt2", GPT2Model, GPT2Config, GPT2OnnxConfig),
# ("T5", "t5-small", T5Model, T5Config, T5OnnxConfig)
}
PYTORCH_EXPORT_SEQ2SEQ_WITH_PAST_MODELS = {
("bart", "facebook/bart-base"),
("mbart", "sshleifer/tiny-mbart"),
("t5", "t5-small"),
}
def _get_models_to_test(export_models_list):
models_to_test = []
for (name, model) in export_models_list:
for feature, onnx_config_class_constructor in FeaturesManager.get_supported_features_for_model_type(
name
).items():
models_to_test.append((f"{name}_{feature}", name, model, feature, onnx_config_class_constructor))
return models_to_test
class OnnxExportTestCaseV2(TestCase):
@@ -228,52 +202,52 @@ class OnnxExportTestCaseV2(TestCase):
Integration tests ensuring supported models are correctly exported
"""
@slow
@require_torch
def test_pytorch_export_default(self):
def _pytorch_export(self, test_name, name, model_name, feature, onnx_config_class_constructor):
from transformers.onnx import export
for name, model, model_class, config_class, onnx_config_class in PYTORCH_EXPORT_DEFAULT_MODELS:
with self.subTest(name):
self.assertTrue(hasattr(onnx_config_class, "from_model_config"))
tokenizer = AutoTokenizer.from_pretrained(model_name)
config = AutoConfig.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model)
model = model_class(config_class.from_pretrained(model))
onnx_config = onnx_config_class.from_model_config(model.config)
# Useful for causal lm models that do not use pad tokens.
if not getattr(config, "pad_token_id", None):
config.pad_token_id = tokenizer.eos_token_id
with NamedTemporaryFile("w") as output:
onnx_inputs, onnx_outputs = export(
tokenizer, model, onnx_config, DEFAULT_ONNX_OPSET, Path(output.name)
)
model_class = FeaturesManager.get_model_class_for_feature(feature)
model = model_class.from_config(config)
onnx_config = onnx_config_class_constructor(model.config)
try:
validate_model_outputs(onnx_config, tokenizer, model, Path(output.name), onnx_outputs, 1e-5)
except ValueError as ve:
self.fail(f"{name} -> {ve}")
@slow
@require_torch
def test_pytorch_export_with_past(self):
from transformers.onnx import export
for name, model, model_class, config_class, onnx_config_class in PYTORCH_EXPORT_WITH_PAST_MODELS:
with self.subTest(name):
self.assertTrue(hasattr(onnx_config_class, "with_past"), "OnnxConfigWithPast should have with_past()")
tokenizer = AutoTokenizer.from_pretrained(model)
model = model_class(config_class())
onnx_config = onnx_config_class.with_past(model.config)
self.assertTrue(hasattr(onnx_config, "use_past"), "OnnxConfigWithPast should have use_past attribute.")
self.assertTrue(
onnx_config.use_past, "OnnxConfigWithPast.use_past should be if called with with_past()"
with NamedTemporaryFile("w") as output:
onnx_inputs, onnx_outputs = export(
tokenizer, model, onnx_config, onnx_config.default_onnx_opset, Path(output.name)
)
try:
validate_model_outputs(
onnx_config,
tokenizer,
model,
Path(output.name),
onnx_outputs,
onnx_config.atol_for_validation,
)
except ValueError as ve:
self.fail(f"{name}, {feature} -> {ve}")
with NamedTemporaryFile("w") as output:
output = Path(output.name)
onnx_inputs, onnx_outputs = export(tokenizer, model, onnx_config, DEFAULT_ONNX_OPSET, output)
@parameterized.expand(_get_models_to_test(PYTORCH_EXPORT_MODELS))
@slow
@require_torch
def test_pytorch_export(self, test_name, name, model_name, feature, onnx_config_class_constructor):
self._pytorch_export(test_name, name, model_name, feature, onnx_config_class_constructor)
try:
validate_model_outputs(onnx_config, tokenizer, model, output, onnx_outputs, 1e-5)
except ValueError as ve:
self.fail(f"{name} -> {ve}")
@parameterized.expand(_get_models_to_test(PYTORCH_EXPORT_WITH_PAST_MODELS))
@slow
@require_torch
def test_pytorch_export_with_past(self, test_name, name, model_name, feature, onnx_config_class_constructor):
self._pytorch_export(test_name, name, model_name, feature, onnx_config_class_constructor)
@parameterized.expand(_get_models_to_test(PYTORCH_EXPORT_SEQ2SEQ_WITH_PAST_MODELS))
@slow
@require_torch
def test_pytorch_export_seq2seq_with_past(
self, test_name, name, model_name, feature, onnx_config_class_constructor
):
self._pytorch_export(test_name, name, model_name, feature, onnx_config_class_constructor)