Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

f32 precision for compare-with-transformers tests #508

Merged
merged 3 commits into from
Jan 10, 2024
Merged

f32 precision for compare-with-transformers tests #508

Changes from 1 commit
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
e7c15c8
f32 precision for compare-with-transformers tests
helena-intel Jan 9, 2024
4f9ea99
Add F32_CONFIG constant for modeling tests
helena-intel Jan 9, 2024
fcc4497
Replace get_version with is_openvino_version
helena-intel Jan 9, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
66 changes: 50 additions & 16 deletions tests/openvino/test_modeling.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -25,6 +25,7 @@
import torch
from datasets import load_dataset
from evaluate import evaluator
from openvino.runtime import get_version
from parameterized import parameterized
from PIL import Image
from transformers import (
Expand Down Expand Up @@ -125,7 +126,10 @@ def test_load_from_hub_and_save_model(self):
loaded_model = OVModelForSequenceClassification.from_pretrained(self.OV_MODEL_ID, ov_config=ov_config)
self.assertTrue(manual_openvino_cache_dir.is_dir())
self.assertGreaterEqual(len(list(manual_openvino_cache_dir.glob("*.blob"))), 1)
self.assertEqual(loaded_model.request.get_property("PERFORMANCE_HINT").name, "THROUGHPUT")
if get_version() < "2023.3":
Copy link
Collaborator

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

minor comment : it could be replaced with is_openvino_version for clarity

Suggested change
if get_version() < "2023.3":
if is_openvino_version("<", "2023.3"):

Copy link
Collaborator Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Thanks! Changed.

self.assertEqual(loaded_model.request.get_property("PERFORMANCE_HINT").name, "THROUGHPUT")
else:
self.assertEqual(loaded_model.request.get_property("PERFORMANCE_HINT"), "THROUGHPUT")

with tempfile.TemporaryDirectory() as tmpdirname:
loaded_model.save_pretrained(tmpdirname)
Expand Down Expand Up @@ -247,7 +251,9 @@ class OVModelForSequenceClassificationIntegrationTest(unittest.TestCase):
def test_compare_to_transformers(self, model_arch):
model_id = MODEL_NAMES[model_arch]
set_seed(SEED)
ov_model = OVModelForSequenceClassification.from_pretrained(model_id, export=True)
ov_model = OVModelForSequenceClassification.from_pretrained(
model_id, export=True, ov_config={"CACHE_DIR": "", "INFERENCE_PRECISION_HINT": "f32"}
)
self.assertIsInstance(ov_model.config, PretrainedConfig)
transformers_model = AutoModelForSequenceClassification.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)
Expand Down Expand Up @@ -313,7 +319,9 @@ class OVModelForQuestionAnsweringIntegrationTest(unittest.TestCase):
def test_compare_to_transformers(self, model_arch):
model_id = MODEL_NAMES[model_arch]
set_seed(SEED)
ov_model = OVModelForQuestionAnswering.from_pretrained(model_id, export=True)
ov_model = OVModelForQuestionAnswering.from_pretrained(
model_id, export=True, ov_config={"CACHE_DIR": "", "INFERENCE_PRECISION_HINT": "f32"}
)
self.assertIsInstance(ov_model.config, PretrainedConfig)
transformers_model = AutoModelForQuestionAnswering.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)
Expand Down Expand Up @@ -386,7 +394,9 @@ class OVModelForTokenClassificationIntegrationTest(unittest.TestCase):
def test_compare_to_transformers(self, model_arch):
model_id = MODEL_NAMES[model_arch]
set_seed(SEED)
ov_model = OVModelForTokenClassification.from_pretrained(model_id, export=True)
ov_model = OVModelForTokenClassification.from_pretrained(
model_id, export=True, ov_config={"CACHE_DIR": "", "INFERENCE_PRECISION_HINT": "f32"}
)
self.assertIsInstance(ov_model.config, PretrainedConfig)
transformers_model = AutoModelForTokenClassification.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)
Expand Down Expand Up @@ -430,7 +440,9 @@ class OVModelForFeatureExtractionIntegrationTest(unittest.TestCase):
def test_compare_to_transformers(self, model_arch):
model_id = MODEL_NAMES[model_arch]
set_seed(SEED)
ov_model = OVModelForFeatureExtraction.from_pretrained(model_id, export=True)
ov_model = OVModelForFeatureExtraction.from_pretrained(
model_id, export=True, ov_config={"CACHE_DIR": "", "INFERENCE_PRECISION_HINT": "f32"}
Copy link

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

I suggest using OV_CONFIG const in utils and loading it via:
ov_model = OVModelForFeatureExtraction.from_pretrained(
model_id, export=True, ov_config=OV_CONFIG)

Copy link

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

also setting NUN_STREAMS = 1 could be useful

Copy link
Collaborator Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

I suggest using OV_CONFIG const in utils and loading it via:
ov_model = OVModelForFeatureExtraction.from_pretrained(
model_id, export=True, ov_config=OV_CONFIG)

I like being explicit in tests, especially when we deviate from defaults. But I can add an F32_CONFIG at the top of this file.

also setting NUN_STREAMS = 1 could be useful

Agreed that this can be useful, but we should run some tests and assuming no issues, set this by default in optimum-intel, not just for the tests.

Copy link
Collaborator Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

I like being explicit in tests, especially when we deviate from defaults. But I can add an F32_CONFIG at the top of this file.

Done

Copy link

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

the same will be needed also in stable diffusion tests

Copy link
Collaborator Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

the same will be needed also in stable diffusion tests

Thanks @dtrawins I did that in this PR #518

)
self.assertIsInstance(ov_model.config, PretrainedConfig)
transformers_model = AutoModel.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)
Expand Down Expand Up @@ -492,7 +504,9 @@ class OVModelForCausalLMIntegrationTest(unittest.TestCase):
def test_compare_to_transformers(self, model_arch):
model_id = MODEL_NAMES[model_arch]
set_seed(SEED)
ov_model = OVModelForCausalLM.from_pretrained(model_id, export=True)
ov_model = OVModelForCausalLM.from_pretrained(
model_id, export=True, ov_config={"CACHE_DIR": "", "INFERENCE_PRECISION_HINT": "f32"}
)
self.assertIsInstance(ov_model.config, PretrainedConfig)
transformers_model = AutoModelForCausalLM.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)
Expand Down Expand Up @@ -637,7 +651,9 @@ class OVModelForMaskedLMIntegrationTest(unittest.TestCase):
def test_compare_to_transformers(self, model_arch):
model_id = MODEL_NAMES[model_arch]
set_seed(SEED)
ov_model = OVModelForMaskedLM.from_pretrained(model_id, export=True)
ov_model = OVModelForMaskedLM.from_pretrained(
model_id, export=True, ov_config={"CACHE_DIR": "", "INFERENCE_PRECISION_HINT": "f32"}
)
self.assertIsInstance(ov_model.config, PretrainedConfig)
transformers_model = AutoModelForMaskedLM.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)
Expand Down Expand Up @@ -693,7 +709,9 @@ class OVModelForImageClassificationIntegrationTest(unittest.TestCase):
def test_compare_to_transformers(self, model_arch):
model_id = MODEL_NAMES[model_arch]
set_seed(SEED)
ov_model = OVModelForImageClassification.from_pretrained(model_id, export=True)
ov_model = OVModelForImageClassification.from_pretrained(
model_id, export=True, ov_config={"CACHE_DIR": "", "INFERENCE_PRECISION_HINT": "f32"}
)
self.assertIsInstance(ov_model.config, PretrainedConfig)
transformers_model = AutoModelForImageClassification.from_pretrained(model_id)
preprocessor = AutoFeatureExtractor.from_pretrained(model_id)
Expand Down Expand Up @@ -729,7 +747,9 @@ def test_pipeline(self, model_arch):

@parameterized.expand(TIMM_MODELS)
def test_compare_to_timm(self, model_id):
ov_model = OVModelForImageClassification.from_pretrained(model_id, export=True)
ov_model = OVModelForImageClassification.from_pretrained(
model_id, export=True, ov_config={"CACHE_DIR": "", "INFERENCE_PRECISION_HINT": "f32"}
)
self.assertIsInstance(ov_model.config, PretrainedConfig)
timm_model = timm.create_model(model_id, pretrained=True)
preprocessor = TimmImageProcessor.from_pretrained(model_id)
Expand Down Expand Up @@ -781,7 +801,9 @@ class OVModelForSeq2SeqLMIntegrationTest(unittest.TestCase):
def test_compare_to_transformers(self, model_arch):
model_id = MODEL_NAMES[model_arch]
set_seed(SEED)
ov_model = OVModelForSeq2SeqLM.from_pretrained(model_id, export=True)
ov_model = OVModelForSeq2SeqLM.from_pretrained(
model_id, export=True, ov_config={"CACHE_DIR": "", "INFERENCE_PRECISION_HINT": "f32"}
)

self.assertIsInstance(ov_model.encoder, OVEncoder)
self.assertIsInstance(ov_model.decoder, OVDecoder)
Expand Down Expand Up @@ -920,7 +942,9 @@ def _generate_random_audio_data(self):
def test_compare_to_transformers(self, model_arch):
model_id = MODEL_NAMES[model_arch]
set_seed(SEED)
ov_model = OVModelForAudioClassification.from_pretrained(model_id, export=True)
ov_model = OVModelForAudioClassification.from_pretrained(
model_id, export=True, ov_config={"CACHE_DIR": "", "INFERENCE_PRECISION_HINT": "f32"}
)
self.assertIsInstance(ov_model.config, PretrainedConfig)
transformers_model = AutoModelForAudioClassification.from_pretrained(model_id)
preprocessor = AutoFeatureExtractor.from_pretrained(model_id)
Expand Down Expand Up @@ -985,7 +1009,9 @@ def test_load_vanilla_transformers_which_is_not_supported(self):
def test_compare_to_transformers(self, model_arch):
model_id = MODEL_NAMES[model_arch]
set_seed(SEED)
ov_model = OVModelForCTC.from_pretrained(model_id, export=True)
ov_model = OVModelForCTC.from_pretrained(
model_id, export=True, ov_config={"CACHE_DIR": "", "INFERENCE_PRECISION_HINT": "f32"}
)
self.assertIsInstance(ov_model.config, PretrainedConfig)

set_seed(SEED)
Expand Down Expand Up @@ -1037,7 +1063,9 @@ def test_load_vanilla_transformers_which_is_not_supported(self):
def test_compare_to_transformers(self, model_arch):
model_id = MODEL_NAMES[model_arch]
set_seed(SEED)
ov_model = OVModelForAudioXVector.from_pretrained(model_id, export=True)
ov_model = OVModelForAudioXVector.from_pretrained(
model_id, export=True, ov_config={"CACHE_DIR": "", "INFERENCE_PRECISION_HINT": "f32"}
)
self.assertIsInstance(ov_model.config, PretrainedConfig)

set_seed(SEED)
Expand Down Expand Up @@ -1091,7 +1119,9 @@ def test_load_vanilla_transformers_which_is_not_supported(self):
def test_compare_to_transformers(self, model_arch):
model_id = MODEL_NAMES[model_arch]
set_seed(SEED)
ov_model = OVModelForAudioFrameClassification.from_pretrained(model_id, export=True)
ov_model = OVModelForAudioFrameClassification.from_pretrained(
model_id, export=True, ov_config={"CACHE_DIR": "", "INFERENCE_PRECISION_HINT": "f32"}
)
self.assertIsInstance(ov_model.config, PretrainedConfig)

set_seed(SEED)
Expand Down Expand Up @@ -1134,7 +1164,9 @@ class OVModelForPix2StructIntegrationTest(unittest.TestCase):
def test_compare_to_transformers(self, model_arch):
model_id = MODEL_NAMES[model_arch]
set_seed(SEED)
ov_model = OVModelForPix2Struct.from_pretrained(model_id, export=True)
ov_model = OVModelForPix2Struct.from_pretrained(
model_id, export=True, ov_config={"CACHE_DIR": "", "INFERENCE_PRECISION_HINT": "f32"}
)

self.assertIsInstance(ov_model.encoder, OVEncoder)
self.assertIsInstance(ov_model.decoder, OVDecoder)
Expand Down Expand Up @@ -1223,7 +1255,9 @@ def _generate_random_audio_data(self):
def test_compare_to_transformers(self, model_arch):
model_id = MODEL_NAMES[model_arch]
set_seed(SEED)
ov_model = OVModelForSpeechSeq2Seq.from_pretrained(model_id, export=True)
ov_model = OVModelForSpeechSeq2Seq.from_pretrained(
model_id, export=True, ov_config={"CACHE_DIR": "", "INFERENCE_PRECISION_HINT": "f32"}
)
self.assertIsInstance(ov_model.config, PretrainedConfig)
transformers_model = AutoModelForSpeechSeq2Seq.from_pretrained(model_id)
processor = get_preprocessor(model_id)
Expand Down