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test_modeling.py
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# Copyright 2021 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 gc
import os
import tempfile
import time
import unittest
from typing import Dict
import numpy as np
import pytest
import requests
import timm
import torch
from datasets import load_dataset
from evaluate import evaluator
from parameterized import parameterized
from PIL import Image
from transformers import (
AutoConfig,
AutoFeatureExtractor,
AutoImageProcessor,
AutoModel,
AutoModelForAudioClassification,
AutoModelForAudioFrameClassification,
AutoModelForAudioXVector,
AutoModelForCausalLM,
AutoModelForCTC,
AutoModelForImageClassification,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
AutoModelForSeq2SeqLM,
AutoModelForSequenceClassification,
AutoModelForSpeechSeq2Seq,
AutoModelForTokenClassification,
AutoModelForVision2Seq,
AutoTokenizer,
GenerationConfig,
Pix2StructForConditionalGeneration,
PretrainedConfig,
pipeline,
set_seed,
)
from transformers.onnx.utils import get_preprocessor
from transformers.testing_utils import slow
from utils_tests import MODEL_NAMES
from optimum.intel import (
OVModelForAudioClassification,
OVModelForAudioFrameClassification,
OVModelForAudioXVector,
OVModelForCausalLM,
OVModelForCTC,
OVModelForCustomTasks,
OVModelForFeatureExtraction,
OVModelForImageClassification,
OVModelForMaskedLM,
OVModelForPix2Struct,
OVModelForQuestionAnswering,
OVModelForSeq2SeqLM,
OVModelForSequenceClassification,
OVModelForSpeechSeq2Seq,
OVModelForTokenClassification,
OVModelForVision2Seq,
OVStableDiffusionPipeline,
)
from optimum.intel.openvino import OV_DECODER_NAME, OV_DECODER_WITH_PAST_NAME, OV_ENCODER_NAME, OV_XML_FILE_NAME
from optimum.intel.openvino.modeling_seq2seq import OVDecoder, OVEncoder
from optimum.intel.openvino.modeling_timm import TimmImageProcessor
from optimum.intel.openvino.utils import _print_compiled_model_properties
from optimum.intel.utils.import_utils import is_openvino_version
from optimum.utils import (
DIFFUSION_MODEL_TEXT_ENCODER_SUBFOLDER,
DIFFUSION_MODEL_UNET_SUBFOLDER,
DIFFUSION_MODEL_VAE_DECODER_SUBFOLDER,
DIFFUSION_MODEL_VAE_ENCODER_SUBFOLDER,
)
from optimum.utils.testing_utils import require_diffusers
TENSOR_ALIAS_TO_TYPE = {
"pt": torch.Tensor,
"np": np.ndarray,
}
SEED = 42
F32_CONFIG = {"INFERENCE_PRECISION_HINT": "f32"}
class Timer(object):
def __enter__(self):
self.elapsed = time.perf_counter()
return self
def __exit__(self, type, value, traceback):
self.elapsed = (time.perf_counter() - self.elapsed) * 1e3
class OVModelIntegrationTest(unittest.TestCase):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.OV_MODEL_ID = "echarlaix/distilbert-base-uncased-finetuned-sst-2-english-openvino"
self.OV_DECODER_MODEL_ID = "helenai/gpt2-ov"
self.OV_SEQ2SEQ_MODEL_ID = "echarlaix/t5-small-openvino"
self.OV_DIFFUSION_MODEL_ID = "hf-internal-testing/tiny-stable-diffusion-openvino"
def test_load_from_hub_and_save_model(self):
tokenizer = AutoTokenizer.from_pretrained(self.OV_MODEL_ID)
tokens = tokenizer("This is a sample input", return_tensors="pt")
loaded_model = OVModelForSequenceClassification.from_pretrained(self.OV_MODEL_ID)
self.assertIsInstance(loaded_model.config, PretrainedConfig)
# Test that PERFORMANCE_HINT is set to LATENCY by default
self.assertEqual(loaded_model.ov_config.get("PERFORMANCE_HINT"), "LATENCY")
self.assertEqual(loaded_model.request.get_property("PERFORMANCE_HINT"), "LATENCY")
loaded_model_outputs = loaded_model(**tokens)
# Test specifying ov_config with throughput hint and manual cache dir
manual_openvino_cache_dir = loaded_model.model_save_dir / "manual_model_cache"
ov_config = {"CACHE_DIR": str(manual_openvino_cache_dir), "PERFORMANCE_HINT": "THROUGHPUT"}
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)
if is_openvino_version("<", "2023.3"):
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)
folder_contents = os.listdir(tmpdirname)
self.assertTrue(OV_XML_FILE_NAME in folder_contents)
self.assertTrue(OV_XML_FILE_NAME.replace(".xml", ".bin") in folder_contents)
model = OVModelForSequenceClassification.from_pretrained(tmpdirname, ov_config={"NUM_STREAMS": 2})
# Test that PERFORMANCE_HINT is set to LATENCY by default even with ov_config provided
self.assertEqual(model.ov_config.get("PERFORMANCE_HINT"), "LATENCY")
self.assertEqual(model.request.get_property("PERFORMANCE_HINT"), "LATENCY")
outputs = model(**tokens)
self.assertTrue(torch.equal(loaded_model_outputs.logits, outputs.logits))
del loaded_model
del model
gc.collect()
@parameterized.expand((True, False))
def test_load_from_hub_and_save_decoder_model(self, use_cache):
model_id = "vuiseng9/ov-gpt2-fp32-kv-cache" if use_cache else "vuiseng9/ov-gpt2-fp32-no-cache"
tokenizer = AutoTokenizer.from_pretrained(model_id)
tokens = tokenizer("This is a sample input", return_tensors="pt")
loaded_model = OVModelForCausalLM.from_pretrained(model_id, use_cache=use_cache)
self.assertIsInstance(loaded_model.config, PretrainedConfig)
# Test that PERFORMANCE_HINT is set to LATENCY by default
self.assertEqual(loaded_model.ov_config.get("PERFORMANCE_HINT"), "LATENCY")
self.assertEqual(loaded_model.request.get_compiled_model().get_property("PERFORMANCE_HINT"), "LATENCY")
loaded_model_outputs = loaded_model(**tokens)
with tempfile.TemporaryDirectory() as tmpdirname:
loaded_model.save_pretrained(tmpdirname)
folder_contents = os.listdir(tmpdirname)
self.assertTrue(OV_XML_FILE_NAME in folder_contents)
self.assertTrue(OV_XML_FILE_NAME.replace(".xml", ".bin") in folder_contents)
model = OVModelForCausalLM.from_pretrained(tmpdirname, use_cache=use_cache)
self.assertEqual(model.use_cache, use_cache)
outputs = model(**tokens)
self.assertTrue(torch.equal(loaded_model_outputs.logits, outputs.logits))
del loaded_model
del model
gc.collect()
def test_load_from_hub_and_save_seq2seq_model(self):
tokenizer = AutoTokenizer.from_pretrained(self.OV_SEQ2SEQ_MODEL_ID)
tokens = tokenizer("This is a sample input", return_tensors="pt")
loaded_model = OVModelForSeq2SeqLM.from_pretrained(self.OV_SEQ2SEQ_MODEL_ID, compile=False)
self.assertIsInstance(loaded_model.config, PretrainedConfig)
loaded_model.to("cpu")
loaded_model.compile()
# Test that PERFORMANCE_HINT is set to LATENCY by default
self.assertEqual(loaded_model.ov_config.get("PERFORMANCE_HINT"), "LATENCY")
self.assertEqual(loaded_model.decoder.request.get_compiled_model().get_property("PERFORMANCE_HINT"), "LATENCY")
loaded_model_outputs = loaded_model.generate(**tokens)
with tempfile.TemporaryDirectory() as tmpdirname:
loaded_model.save_pretrained(tmpdirname)
folder_contents = os.listdir(tmpdirname)
self.assertTrue(OV_ENCODER_NAME in folder_contents)
self.assertTrue(OV_DECODER_NAME in folder_contents)
self.assertTrue(OV_DECODER_WITH_PAST_NAME in folder_contents)
model = OVModelForSeq2SeqLM.from_pretrained(tmpdirname, device="cpu")
outputs = model.generate(**tokens)
self.assertTrue(torch.equal(loaded_model_outputs, outputs))
del loaded_model
del model
gc.collect()
@require_diffusers
def test_load_from_hub_and_save_stable_diffusion_model(self):
loaded_pipeline = OVStableDiffusionPipeline.from_pretrained(self.OV_DIFFUSION_MODEL_ID, compile=False)
self.assertIsInstance(loaded_pipeline.config, Dict)
# Test that PERFORMANCE_HINT is set to LATENCY by default
self.assertEqual(loaded_pipeline.ov_config.get("PERFORMANCE_HINT"), "LATENCY")
loaded_pipeline.compile()
self.assertEqual(loaded_pipeline.unet.request.get_property("PERFORMANCE_HINT"), "LATENCY")
batch_size, height, width = 2, 16, 16
np.random.seed(0)
inputs = {
"prompt": ["sailing ship in storm by Leonardo da Vinci"] * batch_size,
"height": height,
"width": width,
"num_inference_steps": 2,
"output_type": "np",
}
pipeline_outputs = loaded_pipeline(**inputs).images
self.assertEqual(pipeline_outputs.shape, (batch_size, height, width, 3))
with tempfile.TemporaryDirectory() as tmpdirname:
loaded_pipeline.save_pretrained(tmpdirname)
pipeline = OVStableDiffusionPipeline.from_pretrained(tmpdirname)
folder_contents = os.listdir(tmpdirname)
self.assertIn(loaded_pipeline.config_name, folder_contents)
for subfoler in {
DIFFUSION_MODEL_UNET_SUBFOLDER,
DIFFUSION_MODEL_TEXT_ENCODER_SUBFOLDER,
DIFFUSION_MODEL_VAE_ENCODER_SUBFOLDER,
DIFFUSION_MODEL_VAE_DECODER_SUBFOLDER,
}:
folder_contents = os.listdir(os.path.join(tmpdirname, subfoler))
self.assertIn(OV_XML_FILE_NAME, folder_contents)
self.assertIn(OV_XML_FILE_NAME.replace(".xml", ".bin"), folder_contents)
np.random.seed(0)
outputs = pipeline(**inputs).images
self.assertTrue(np.array_equal(pipeline_outputs, outputs))
del pipeline
gc.collect()
class OVModelForSequenceClassificationIntegrationTest(unittest.TestCase):
SUPPORTED_ARCHITECTURES = (
"albert",
"bert",
# "camembert",
"convbert",
# "data2vec_text",
# "deberta_v2",
"distilbert",
"electra",
"flaubert",
"ibert",
# "mobilebert",
# "nystromformer",
"roberta",
"roformer",
"squeezebert",
"xlm",
# "xlm_roberta",
)
@parameterized.expand(SUPPORTED_ARCHITECTURES)
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_config=F32_CONFIG)
self.assertIsInstance(ov_model.config, PretrainedConfig)
transformers_model = AutoModelForSequenceClassification.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)
inputs = "This is a sample input"
tokens = tokenizer(inputs, return_tensors="pt")
with torch.no_grad():
transformers_outputs = transformers_model(**tokens)
for input_type in ["pt", "np"]:
tokens = tokenizer(inputs, return_tensors=input_type)
ov_outputs = ov_model(**tokens)
self.assertIn("logits", ov_outputs)
self.assertIsInstance(ov_outputs.logits, TENSOR_ALIAS_TO_TYPE[input_type])
# Compare tensor outputs
self.assertTrue(torch.allclose(torch.Tensor(ov_outputs.logits), transformers_outputs.logits, atol=1e-4))
del transformers_model
del ov_model
gc.collect()
@parameterized.expand(SUPPORTED_ARCHITECTURES)
def test_pipeline(self, model_arch):
model_id = MODEL_NAMES[model_arch]
model = OVModelForSequenceClassification.from_pretrained(model_id, export=True, compile=False)
model.eval()
tokenizer = AutoTokenizer.from_pretrained(model_id)
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "This restaurant is awesome"
outputs = pipe(text)
self.assertTrue(model.is_dynamic)
self.assertEqual(pipe.device, model.device)
self.assertGreaterEqual(outputs[0]["score"], 0.0)
self.assertIsInstance(outputs[0]["label"], str)
if model_arch == "bert":
# Test FP16 conversion
model.half()
model.to("cpu")
model.compile()
outputs = pipe(text)
self.assertGreaterEqual(outputs[0]["score"], 0.0)
self.assertIsInstance(outputs[0]["label"], str)
# Test static shapes
model.reshape(1, 25)
model.compile()
outputs = pipe(text)
self.assertTrue(not model.is_dynamic)
self.assertGreaterEqual(outputs[0]["score"], 0.0)
self.assertIsInstance(outputs[0]["label"], str)
# Test that model caching was not automatically enabled for exported model
openvino_cache_dir = model.model_save_dir / "model_cache"
self.assertFalse(openvino_cache_dir.is_dir())
del model
del pipe
gc.collect()
class OVModelForQuestionAnsweringIntegrationTest(unittest.TestCase):
SUPPORTED_ARCHITECTURES = (
"bert",
"distilbert",
"roberta",
)
@parameterized.expand(SUPPORTED_ARCHITECTURES)
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_config=F32_CONFIG)
self.assertIsInstance(ov_model.config, PretrainedConfig)
transformers_model = AutoModelForQuestionAnswering.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)
inputs = "This is a sample input"
tokens = tokenizer(inputs, return_tensors="pt")
with torch.no_grad():
transformers_outputs = transformers_model(**tokens)
for input_type in ["pt", "np"]:
tokens = tokenizer(inputs, return_tensors=input_type)
ov_outputs = ov_model(**tokens)
self.assertIn("start_logits", ov_outputs)
self.assertIn("end_logits", ov_outputs)
self.assertIsInstance(ov_outputs.start_logits, TENSOR_ALIAS_TO_TYPE[input_type])
self.assertIsInstance(ov_outputs.end_logits, TENSOR_ALIAS_TO_TYPE[input_type])
# Compare tensor outputs
self.assertTrue(
torch.allclose(torch.Tensor(ov_outputs.start_logits), transformers_outputs.start_logits, atol=1e-4)
)
self.assertTrue(
torch.allclose(torch.Tensor(ov_outputs.end_logits), transformers_outputs.end_logits, atol=1e-4)
)
del ov_model
del transformers_model
gc.collect()
@parameterized.expand(SUPPORTED_ARCHITECTURES)
@pytest.mark.run_slow
@slow
def test_pipeline(self, model_arch):
model_id = MODEL_NAMES[model_arch]
model = OVModelForQuestionAnswering.from_pretrained(model_id, export=True)
model.eval()
tokenizer = AutoTokenizer.from_pretrained(model_id)
pipe = pipeline("question-answering", model=model, tokenizer=tokenizer)
question = "What's my name?"
context = "My Name is Arthur and I live in Lyon."
outputs = pipe(question, context)
self.assertEqual(pipe.device, model.device)
self.assertGreaterEqual(outputs["score"], 0.0)
self.assertIsInstance(outputs["answer"], str)
del model
gc.collect()
@pytest.mark.run_slow
@slow
def test_metric(self):
model_id = "distilbert-base-cased-distilled-squad"
set_seed(SEED)
ov_model = OVModelForQuestionAnswering.from_pretrained(model_id, export=True)
transformers_model = AutoModelForQuestionAnswering.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)
data = load_dataset("squad", split="validation").select(range(50))
task_evaluator = evaluator("question-answering")
transformers_pipe = pipeline("question-answering", model=transformers_model, tokenizer=tokenizer)
ov_pipe = pipeline("question-answering", model=ov_model, tokenizer=tokenizer)
transformers_metric = task_evaluator.compute(model_or_pipeline=transformers_pipe, data=data, metric="squad")
ov_metric = task_evaluator.compute(model_or_pipeline=ov_pipe, data=data, metric="squad")
self.assertEqual(ov_metric["exact_match"], transformers_metric["exact_match"])
self.assertEqual(ov_metric["f1"], transformers_metric["f1"])
del transformers_pipe
del transformers_model
del ov_pipe
del ov_model
gc.collect()
class OVModelForTokenClassificationIntegrationTest(unittest.TestCase):
SUPPORTED_ARCHITECTURES = (
"bert",
"distilbert",
"roberta",
)
@parameterized.expand(SUPPORTED_ARCHITECTURES)
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_config=F32_CONFIG)
self.assertIsInstance(ov_model.config, PretrainedConfig)
transformers_model = AutoModelForTokenClassification.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)
inputs = "This is a sample input"
tokens = tokenizer(inputs, return_tensors="pt")
with torch.no_grad():
transformers_outputs = transformers_model(**tokens)
for input_type in ["pt", "np"]:
tokens = tokenizer(inputs, return_tensors=input_type)
ov_outputs = ov_model(**tokens)
self.assertIn("logits", ov_outputs)
self.assertIsInstance(ov_outputs.logits, TENSOR_ALIAS_TO_TYPE[input_type])
# Compare tensor outputs
self.assertTrue(torch.allclose(torch.Tensor(ov_outputs.logits), transformers_outputs.logits, atol=1e-4))
del transformers_model
del ov_model
gc.collect()
@parameterized.expand(SUPPORTED_ARCHITECTURES)
@pytest.mark.run_slow
@slow
def test_pipeline(self, model_arch):
model_id = MODEL_NAMES[model_arch]
model = OVModelForTokenClassification.from_pretrained(model_id, export=True)
model.eval()
tokenizer = AutoTokenizer.from_pretrained(model_id)
pipe = pipeline("token-classification", model=model, tokenizer=tokenizer)
outputs = pipe("My Name is Arthur and I live in Lyon.")
self.assertEqual(pipe.device, model.device)
self.assertTrue(all(item["score"] > 0.0 for item in outputs))
del model
del pipe
gc.collect()
class OVModelForFeatureExtractionIntegrationTest(unittest.TestCase):
SUPPORTED_ARCHITECTURES = (
"bert",
"distilbert",
"roberta",
"sentence-transformers-bert",
)
@parameterized.expand(SUPPORTED_ARCHITECTURES)
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_config=F32_CONFIG)
self.assertIsInstance(ov_model.config, PretrainedConfig)
transformers_model = AutoModel.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)
inputs = "This is a sample input"
tokens = tokenizer(inputs, return_tensors="pt")
with torch.no_grad():
transformers_outputs = transformers_model(**tokens)
for input_type in ["pt", "np"]:
tokens = tokenizer(inputs, return_tensors=input_type)
ov_outputs = ov_model(**tokens)
self.assertIn("last_hidden_state", ov_outputs)
self.assertIsInstance(ov_outputs.last_hidden_state, TENSOR_ALIAS_TO_TYPE[input_type])
# Compare tensor outputs
self.assertTrue(
torch.allclose(
torch.Tensor(ov_outputs.last_hidden_state), transformers_outputs.last_hidden_state, atol=1e-4
)
)
del transformers_model
del ov_model
gc.collect()
@parameterized.expand(SUPPORTED_ARCHITECTURES)
@pytest.mark.run_slow
@slow
def test_pipeline(self, model_arch):
model_id = MODEL_NAMES[model_arch]
model = OVModelForFeatureExtraction.from_pretrained(model_id, export=True)
model.eval()
tokenizer = AutoTokenizer.from_pretrained(model_id)
pipe = pipeline("feature-extraction", model=model, tokenizer=tokenizer)
outputs = pipe("My Name is Arthur and I live in Lyon.")
self.assertEqual(pipe.device, model.device)
self.assertTrue(all(all(isinstance(item, float) for item in row) for row in outputs[0]))
del pipe
del model
gc.collect()
class OVModelForCausalLMIntegrationTest(unittest.TestCase):
SUPPORTED_ARCHITECTURES = (
"bart",
"baichuan2",
"gpt_bigcode",
"blenderbot",
"blenderbot-small",
"bloom",
"chatglm",
"codegen",
# "data2vec-text", # TODO : enable when enabled in exporters
"gemma",
"gpt2",
"gpt_neo",
"gpt_neox",
"llama",
# "llama_gptq",
"marian",
"minicpm",
"mistral",
"mixtral",
"mpt",
"opt",
"pegasus",
"qwen",
"qwen2",
"stablelm",
"starcoder2",
"phi",
"internlm2",
"orion",
"falcon",
)
GENERATION_LENGTH = 100
REMOTE_CODE_MODELS = ("chatglm", "minicpm", "baichuan2", "jais", "qwen", "internlm2", "olmo", "orion")
@parameterized.expand(SUPPORTED_ARCHITECTURES)
def test_compare_to_transformers(self, model_arch):
model_id = MODEL_NAMES[model_arch]
not_stateful = ["gpt_bigcode"]
if is_openvino_version("<", "2024.0"):
not_stateful.append("mixtral")
if is_openvino_version("<", "2024.1"):
not_stateful.extend(["llama", "gemma"])
if "gptq" in model_arch:
self.skipTest("GPTQ model loading unsupported with AutoModelForCausalLM")
set_seed(SEED)
model_kwargs = {}
if model_arch in self.REMOTE_CODE_MODELS:
model_kwargs = {"trust_remote_code": True}
ov_model = OVModelForCausalLM.from_pretrained(model_id, export=True, ov_config=F32_CONFIG, **model_kwargs)
self.assertIsInstance(ov_model.config, PretrainedConfig)
self.assertTrue(ov_model.use_cache)
self.assertEqual(ov_model.stateful, ov_model.config.model_type not in not_stateful)
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=model_arch in self.REMOTE_CODE_MODELS)
tokens = tokenizer("This is a sample output", return_tensors="pt")
ov_outputs = ov_model(**tokens)
self.assertTrue("logits" in ov_outputs)
self.assertIsInstance(ov_outputs.logits, torch.Tensor)
self.assertTrue("past_key_values" in ov_outputs)
self.assertIsInstance(ov_outputs.past_key_values, tuple)
is_stateful = ov_model.config.model_type not in not_stateful
self.assertEqual(ov_model.stateful, is_stateful)
if is_stateful:
self.assertTrue(len(ov_outputs.past_key_values) == 1 and len(ov_outputs.past_key_values[0]) == 0)
set_seed(SEED)
transformers_model = AutoModelForCausalLM.from_pretrained(model_id, **model_kwargs)
if model_arch == "qwen":
transformers_model.to(torch.float32)
with torch.no_grad():
transformers_outputs = transformers_model(**tokens)
# Compare tensor outputs
self.assertTrue(torch.allclose(ov_outputs.logits, transformers_outputs.logits, equal_nan=True, atol=1e-4))
# Qwen tokenizer does not support padding
if model_arch == "qwen":
return
if model_arch != "chatglm":
tokenizer.pad_token_id = tokenizer.eos_token_id
# Compare batched generation
tokenizer.padding_side = "left"
tokens = tokenizer(["Today is a nice day and I am longer", "This is me"], return_tensors="pt", padding=True)
ov_model.generation_config.eos_token_id = None
transformers_model.generation_config.eos_token_id = None
ov_model.config.eos_token_id = None
transformers_model.config.eos_token_id = None
gen_config = GenerationConfig(
max_new_tokens=30,
min_new_tokens=30,
num_beams=3,
do_sample=False,
eos_token_id=None,
)
ov_outputs = ov_model.generate(**tokens, generation_config=gen_config)
transformers_outputs = transformers_model.generate(**tokens, generation_config=gen_config)
self.assertTrue(torch.allclose(ov_outputs, transformers_outputs))
del transformers_model
del ov_model
gc.collect()
@parameterized.expand(SUPPORTED_ARCHITECTURES)
@pytest.mark.run_slow
@slow
def test_pipeline(self, model_arch):
model_kwargs = {}
model_id = MODEL_NAMES[model_arch]
if model_arch in self.REMOTE_CODE_MODELS:
model_kwargs = {
"config": AutoConfig.from_pretrained(model_id, trust_remote_code=True),
"trust_remote_code": True,
}
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=model_arch in self.REMOTE_CODE_MODELS)
if model_arch == "qwen":
tokenizer._convert_tokens_to_ids = lambda x: 0
model = OVModelForCausalLM.from_pretrained(
model_id, export=True, use_cache=False, compile=False, **model_kwargs
)
model.eval()
model.config.encoder_no_repeat_ngram_size = 0
model.to("cpu")
model.half()
model.compile()
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
outputs = pipe("This is a sample", max_length=20)
self.assertEqual(pipe.device, model.device)
self.assertTrue(all("This is a sample" in item["generated_text"] for item in outputs))
del pipe
del model
gc.collect()
def test_model_and_decoder_same_device(self):
model_id = MODEL_NAMES["gpt2"]
model = OVModelForCausalLM.from_pretrained(model_id, export=True)
model.to("TEST")
self.assertEqual(model._device, "TEST")
# Verify that request is being reset
self.assertEqual(model.request, None)
del model
gc.collect()
def test_compare_with_and_without_past_key_values(self):
model_id = MODEL_NAMES["gpt2"]
tokenizer = AutoTokenizer.from_pretrained(model_id)
tokens = tokenizer("This is a sample input", return_tensors="pt")
model_with_pkv = OVModelForCausalLM.from_pretrained(model_id, export=True, use_cache=True, stateful=False)
outputs_model_with_pkv = model_with_pkv.generate(
**tokens, min_length=self.GENERATION_LENGTH, max_length=self.GENERATION_LENGTH, num_beams=1
)
model_without_pkv = OVModelForCausalLM.from_pretrained(model_id, export=True, use_cache=False)
outputs_model_without_pkv = model_without_pkv.generate(
**tokens, min_length=self.GENERATION_LENGTH, max_length=self.GENERATION_LENGTH, num_beams=1
)
self.assertTrue(torch.equal(outputs_model_with_pkv, outputs_model_without_pkv))
self.assertEqual(outputs_model_with_pkv.shape[1], self.GENERATION_LENGTH)
self.assertEqual(outputs_model_without_pkv.shape[1], self.GENERATION_LENGTH)
model_stateful = OVModelForCausalLM.from_pretrained(model_id, export=True, use_cache=True, stateful=True)
outputs_model_stateful = model_stateful.generate(
**tokens, min_length=self.GENERATION_LENGTH, max_length=self.GENERATION_LENGTH, num_beams=1
)
self.assertTrue(torch.equal(outputs_model_without_pkv, outputs_model_stateful))
del model_with_pkv
del model_without_pkv
gc.collect()
def test_print_model_properties(self):
# test setting OPENVINO_LOG_LEVEL to 3, which calls _print_compiled_model_properties
openvino_log_level = os.environ.get("OPENVINO_LOG_LEVEL", None)
os.environ["OPENVINO_LOG_LEVEL"] = "3"
model = OVModelForSequenceClassification.from_pretrained(MODEL_NAMES["bert"], export=True)
if openvino_log_level is not None:
os.environ["OPENVINO_LOG_LEVEL"] = openvino_log_level
# test calling function directly
_print_compiled_model_properties(model.request)
def test_auto_device_loading(self):
OV_MODEL_ID = "echarlaix/distilbert-base-uncased-finetuned-sst-2-english-openvino"
for device in ("AUTO", "AUTO:CPU"):
model = OVModelForSequenceClassification.from_pretrained(OV_MODEL_ID, device=device)
model.half()
self.assertEqual(model._device, device)
if device == "AUTO:CPU":
model = OVModelForSequenceClassification.from_pretrained(OV_MODEL_ID, device=device)
message = "Model should not be loaded from cache without explicitly setting CACHE_DIR"
self.assertFalse(model.request.get_property("LOADED_FROM_CACHE"), message)
del model
gc.collect()
def test_default_filling_attention_mask(self):
model_id = MODEL_NAMES["gpt2"]
model_with_cache = OVModelForCausalLM.from_pretrained(model_id, export=True, use_cache=True)
tokenizer = AutoTokenizer.from_pretrained(model_id)
tokenizer.pad_token = tokenizer.eos_token
texts = ["this is a simple input"]
tokens = tokenizer(texts, return_tensors="pt")
self.assertTrue("attention_mask" in model_with_cache.input_names)
outs = model_with_cache(**tokens)
attention_mask = tokens.pop("attention_mask")
outs_without_attn_mask = model_with_cache(**tokens)
self.assertTrue(torch.allclose(outs.logits, outs_without_attn_mask.logits))
input_ids = torch.argmax(outs.logits[:, -1:, :], dim=2)
past_key_values = outs.past_key_values
attention_mask = torch.ones((input_ids.shape[0], tokens.input_ids.shape[1] + 1), dtype=torch.long)
outs_step2 = model_with_cache(
input_ids=input_ids, attention_mask=attention_mask, past_key_values=past_key_values
)
outs_without_attn_mask_step2 = model_with_cache(input_ids=input_ids, past_key_values=past_key_values)
self.assertTrue(torch.allclose(outs_step2.logits, outs_without_attn_mask_step2.logits))
del model_with_cache
gc.collect()
def test_default_filling_attention_mask_and_position_ids(self):
model_id = MODEL_NAMES["llama"]
model_with_cache = OVModelForCausalLM.from_pretrained(model_id, export=True, use_cache=True)
tokenizer = AutoTokenizer.from_pretrained(model_id)
tokenizer.pad_token = tokenizer.eos_token
texts = ["this is a simple input"]
tokens = tokenizer(texts, return_tensors="pt")
self.assertTrue("position_ids" in model_with_cache.input_names)
outs = model_with_cache(**tokens)
attention_mask = tokens.pop("attention_mask")
outs_without_attn_mask = model_with_cache(**tokens)
self.assertTrue(torch.allclose(outs.logits, outs_without_attn_mask.logits))
input_ids = torch.argmax(outs.logits[:, -1:, :], dim=2)
past_key_values = outs.past_key_values
attention_mask = torch.ones((input_ids.shape[0], tokens.input_ids.shape[1] + 1), dtype=torch.long)
outs_step2 = model_with_cache(
input_ids=input_ids, attention_mask=attention_mask, past_key_values=past_key_values
)
outs_without_attn_mask_step2 = model_with_cache(input_ids=input_ids, past_key_values=past_key_values)
self.assertTrue(torch.allclose(outs_step2.logits, outs_without_attn_mask_step2.logits))
del model_with_cache
gc.collect()
class OVModelForMaskedLMIntegrationTest(unittest.TestCase):
SUPPORTED_ARCHITECTURES = (
"albert",
"bert",
# "camembert",
# "convbert",
# "data2vec_text",
"deberta",
# "deberta_v2",
"distilbert",
"electra",
"flaubert",
"ibert",
# "mobilebert",
"roberta",
"roformer",
"squeezebert",
"xlm",
"xlm_roberta",
)
@parameterized.expand(SUPPORTED_ARCHITECTURES)
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_config=F32_CONFIG)
self.assertIsInstance(ov_model.config, PretrainedConfig)
transformers_model = AutoModelForMaskedLM.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)
inputs = f"This is a sample {tokenizer.mask_token}"
tokens = tokenizer(inputs, return_tensors="pt")
with torch.no_grad():
transformers_outputs = transformers_model(**tokens)
for input_type in ["pt", "np"]:
tokens = tokenizer(inputs, return_tensors=input_type)
ov_outputs = ov_model(**tokens)
self.assertIn("logits", ov_outputs)
self.assertIsInstance(ov_outputs.logits, TENSOR_ALIAS_TO_TYPE[input_type])
# Compare tensor outputs
self.assertTrue(torch.allclose(torch.Tensor(ov_outputs.logits), transformers_outputs.logits, atol=1e-4))
del transformers_model
del ov_model
gc.collect()
@parameterized.expand(SUPPORTED_ARCHITECTURES)
def test_pipeline(self, model_arch):
model_id = MODEL_NAMES[model_arch]
model = OVModelForMaskedLM.from_pretrained(model_id, export=True)
model.eval()
tokenizer = AutoTokenizer.from_pretrained(model_id)
pipe = pipeline("fill-mask", model=model, tokenizer=tokenizer)
outputs = pipe(f"This is a {tokenizer.mask_token}.")
self.assertEqual(pipe.device, model.device)
self.assertTrue(all(item["score"] > 0.0 for item in outputs))
del pipe
del model
gc.collect()
class OVModelForImageClassificationIntegrationTest(unittest.TestCase):
SUPPORTED_ARCHITECTURES = (
"beit",
"convnext",
# "data2vec_vision",
# "deit",
"levit",
"mobilenet_v1",
"mobilenet_v2",
"mobilevit",
# "poolformer",
"resnet",
# "segformer",
# "swin",
"vit",
)
TIMM_MODELS = ("timm/pit_s_distilled_224.in1k", "timm/vit_tiny_patch16_224.augreg_in21k")
@parameterized.expand(SUPPORTED_ARCHITECTURES)
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_config=F32_CONFIG)
self.assertIsInstance(ov_model.config, PretrainedConfig)
transformers_model = AutoModelForImageClassification.from_pretrained(model_id)
preprocessor = AutoFeatureExtractor.from_pretrained(model_id)
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
inputs = preprocessor(images=image, return_tensors="pt")
with torch.no_grad():
transformers_outputs = transformers_model(**inputs)
for input_type in ["pt", "np"]:
inputs = preprocessor(images=image, return_tensors=input_type)
ov_outputs = ov_model(**inputs)
self.assertIn("logits", ov_outputs)
self.assertIsInstance(ov_outputs.logits, TENSOR_ALIAS_TO_TYPE[input_type])
# Compare tensor outputs
self.assertTrue(torch.allclose(torch.Tensor(ov_outputs.logits), transformers_outputs.logits, atol=1e-4))
del transformers_model
del ov_model
gc.collect()
@parameterized.expand(SUPPORTED_ARCHITECTURES)
@pytest.mark.run_slow
@slow
def test_pipeline(self, model_arch):
model_id = MODEL_NAMES[model_arch]
model = OVModelForImageClassification.from_pretrained(model_id, export=True)
model.eval()
preprocessor = AutoFeatureExtractor.from_pretrained(model_id)
pipe = pipeline("image-classification", model=model, feature_extractor=preprocessor)
outputs = pipe("http://images.cocodataset.org/val2017/000000039769.jpg")
self.assertEqual(pipe.device, model.device)
self.assertGreaterEqual(outputs[0]["score"], 0.0)
self.assertTrue(isinstance(outputs[0]["label"], str))
del model
del pipe
gc.collect()
@parameterized.expand(TIMM_MODELS)
def test_compare_to_timm(self, model_id):
ov_model = OVModelForImageClassification.from_pretrained(model_id, export=True, ov_config=F32_CONFIG)
self.assertEqual(ov_model.request.get_property("INFERENCE_PRECISION_HINT").to_string(), "f32")
self.assertIsInstance(ov_model.config, PretrainedConfig)
timm_model = timm.create_model(model_id, pretrained=True)
preprocessor = TimmImageProcessor.from_pretrained(model_id)
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
inputs = preprocessor(images=image, return_tensors="pt")
with torch.no_grad():
timm_model.eval()
timm_outputs = timm_model(inputs["pixel_values"].float())
for input_type in ["pt", "np"]:
inputs = preprocessor(images=image, return_tensors=input_type)
ov_outputs = ov_model(**inputs)
self.assertIn("logits", ov_outputs)
self.assertIsInstance(ov_outputs.logits, TENSOR_ALIAS_TO_TYPE[input_type])
# Compare tensor outputs
self.assertTrue(torch.allclose(torch.Tensor(ov_outputs.logits), timm_outputs, atol=1e-3))
gc.collect()
@parameterized.expand(TIMM_MODELS)
def test_timm_save_and_infer(self, model_id):
ov_model = OVModelForImageClassification.from_pretrained(model_id, export=True)
with tempfile.TemporaryDirectory() as tmpdirname:
model_save_path = os.path.join(tmpdirname, "timm_ov_model")
ov_model.save_pretrained(model_save_path)
model = OVModelForImageClassification.from_pretrained(model_save_path)
model(pixel_values=torch.zeros((5, 3, model.config.image_size, model.config.image_size)))
gc.collect()
class OVModelForSeq2SeqLMIntegrationTest(unittest.TestCase):
SUPPORTED_ARCHITECTURES = (
"bart",
# "bigbird_pegasus",
"blenderbot",
"blenderbot-small",
# "longt5",
"m2m_100",
"marian",
"mbart",
"mt5",
"pegasus",
"t5",
)
GENERATION_LENGTH = 100
SPEEDUP_CACHE = 1.1
@parameterized.expand(SUPPORTED_ARCHITECTURES)
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_config=F32_CONFIG)
self.assertIsInstance(ov_model.encoder, OVEncoder)
self.assertIsInstance(ov_model.decoder, OVDecoder)
self.assertIsInstance(ov_model.decoder_with_past, OVDecoder)
self.assertIsInstance(ov_model.config, PretrainedConfig)
transformers_model = AutoModelForSeq2SeqLM.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)
tokens = tokenizer("This is a sample input", return_tensors="pt")
decoder_start_token_id = transformers_model.config.decoder_start_token_id if model_arch != "mbart" else 2
decoder_inputs = {"decoder_input_ids": torch.ones((1, 1), dtype=torch.long) * decoder_start_token_id}
ov_outputs = ov_model(**tokens, **decoder_inputs)
self.assertTrue("logits" in ov_outputs)
self.assertIsInstance(ov_outputs.logits, torch.Tensor)
with torch.no_grad():
transformers_outputs = transformers_model(**tokens, **decoder_inputs)
# Compare tensor outputs
self.assertTrue(torch.allclose(ov_outputs.logits, transformers_outputs.logits, atol=1e-4))
del transformers_model
del ov_model
gc.collect()
@parameterized.expand(SUPPORTED_ARCHITECTURES)
def test_pipeline(self, model_arch):
model_id = MODEL_NAMES[model_arch]
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = OVModelForSeq2SeqLM.from_pretrained(model_id, export=True, compile=False)
model.eval()
model.half()
model.to("cpu")
model.compile()
# Text2Text generation
pipe = pipeline("text2text-generation", model=model, tokenizer=tokenizer)
text = "This is a test"
outputs = pipe(text)
self.assertEqual(pipe.device, model.device)
self.assertIsInstance(outputs[0]["generated_text"], str)
# Summarization
pipe = pipeline("summarization", model=model, tokenizer=tokenizer)
text = "This is a test"
outputs = pipe(text)
self.assertEqual(pipe.device, model.device)
self.assertIsInstance(outputs[0]["summary_text"], str)
# Translation
pipe = pipeline("translation_en_to_fr", model=model, tokenizer=tokenizer)
text = "This is a test"
outputs = pipe(text)
self.assertEqual(pipe.device, model.device)
self.assertIsInstance(outputs[0]["translation_text"], str)
del pipe
del model
gc.collect()
@parameterized.expand(SUPPORTED_ARCHITECTURES)
@pytest.mark.run_slow
@slow
def test_generate_utils(self, model_arch):
model_id = MODEL_NAMES[model_arch]
model = OVModelForSeq2SeqLM.from_pretrained(model_id, export=True)
tokenizer = AutoTokenizer.from_pretrained(model_id)
text = "This is a sample input"
tokens = tokenizer(text, return_tensors="pt")