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test_quantization.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 inspect
# ruff: noqa
import itertools
import logging
import unittest
from collections import defaultdict
from enum import Enum
from functools import partial
from typing import Union
import openvino as ov
import pytest
import evaluate
import numpy as np
import torch
from datasets import load_dataset
from parameterized import parameterized
import nncf
from transformers import (
AutoModelForQuestionAnswering,
AutoModelForSequenceClassification,
AutoTokenizer,
AutoProcessor,
TrainingArguments,
default_data_collator,
)
from transformers.testing_utils import slow
from transformers.utils.quantization_config import QuantizationMethod
from optimum.intel import (
OVConfig,
OVFluxPipeline,
OVLatentConsistencyModelPipeline,
OVModelForAudioClassification,
OVModelForCausalLM,
OVModelForFeatureExtraction,
OVModelForImageClassification,
OVModelForMaskedLM,
OVModelForQuestionAnswering,
OVModelForSeq2SeqLM,
OVModelForSequenceClassification,
OVModelForTokenClassification,
OVModelForSpeechSeq2Seq,
OVStableDiffusionPipeline,
OVStableDiffusionXLPipeline,
OVStableDiffusion3Pipeline,
OVQuantizer,
OVSanaPipeline,
OVQuantizationConfig,
OVMixedQuantizationConfig,
OVWeightQuantizationConfig,
OVDynamicQuantizationConfig,
OVModelOpenCLIPForZeroShotImageClassification,
OVModelForVisualCausalLM,
)
from optimum.intel.openvino.configuration import (
OVQuantizationMethod,
OVQuantizationConfigBase,
_DEFAULT_4BIT_CONFIGS,
_DEFAULT_4BIT_CONFIG,
)
from optimum.intel.openvino.utils import TemporaryDirectory
from copy import deepcopy
from optimum.intel.openvino.quantization import InferRequestWrapper
from optimum.intel.utils.import_utils import is_openvino_version, is_transformers_version
from utils_tests import (
MODEL_NAMES,
get_num_quantized_nodes,
_ARCHITECTURES_TO_EXPECTED_INT8,
check_compression_state_per_model,
)
_TASK_TO_DATASET = {
"text-generation": ("wikitext", "wikitext-2-raw-v1", "text"),
"text-classification": ("glue", "sst2", "sentence"),
}
class OVQuantizerTest(unittest.TestCase):
SUPPORTED_ARCHITECTURES_TORCH_MODEL = (
(OVModelForSequenceClassification, "bert", 32, 35),
(OVModelForCausalLM, "gpt2", 41 if is_transformers_version("<", "4.42.0") else 31, 22),
)
SUPPORTED_ARCHITECTURES_OV_MODEL = (
(OVModelForSequenceClassification, "bert", 32, 35),
(OVModelForCausalLM, "gpt2", 31, 22),
)
SUPPORTED_ARCHITECTURES_OV_MODEL_WITH_AUTO_DATASET = [
(
OVModelForSpeechSeq2Seq,
"whisper",
dict(
dataset="librispeech",
num_samples=1,
processor=MODEL_NAMES["whisper"],
trust_remote_code=True,
smooth_quant_alpha=0.95,
),
[14, 22, 21] if is_transformers_version("<=", "4.36.0") else [14, 22, 25],
[{"int8": 14}, {"int8": 21}, {"int8": 17}]
if is_transformers_version("<=", "4.36.0")
else [{"int8": 14}, {"int8": 22}, {"int8": 18}],
),
(
OVModelForCausalLM,
"llama",
dict(
dataset="wikitext2",
num_samples=1,
dtype="f8e4m3",
weight_only=False,
),
[
13,
],
[
{"f8e4m3": 16},
],
),
(
OVModelForCausalLM,
"llama",
dict(
weight_quantization_config=dict(bits=4, dtype="nf4", group_size=16, weight_only=True, ratio=0.5),
full_quantization_config=dict(dtype="f8e4m3", weight_only=False),
dataset="wikitext2",
num_samples=1,
),
[
14,
],
[
{"f8e4m3": 11, "nf4": 5},
],
),
(
OVModelForCausalLM,
"llama",
OVMixedQuantizationConfig(
weight_quantization_config=OVWeightQuantizationConfig(
bits=4,
dtype="nf4",
group_size=16,
ratio=0.5,
ignored_scope={"patterns": ["^__module.model.layers.0.self_attn"]},
),
full_quantization_config=OVQuantizationConfig(
dtype="f8e4m3", ignored_scope={"patterns": ["^__module.model.layers.0.mlp"]}
),
ignored_scope={"patterns": ["^__module.model.layers.1.self_attn"]},
dataset="wikitext2",
num_samples=1,
),
[
7,
],
[
{"f8e4m3": 8, "nf4": 2},
],
),
(
OVModelForCausalLM,
"llama",
OVMixedQuantizationConfig(
weight_quantization_config=OVWeightQuantizationConfig(
bits=4,
dtype="nf4",
group_size=16,
ratio=0.5,
ignored_scope={"patterns": ["^__module.model.layers.0.self_attn"]},
),
full_quantization_config=OVQuantizationConfig(
dtype="f8e5m2", ignored_scope={"patterns": ["^__module.model.layers.0.mlp"]}
),
ignored_scope={"patterns": ["^__module.model.layers.1.self_attn"]},
dataset="wikitext2",
num_samples=1,
),
[
7,
],
[
{"f8e5m2": 8, "nf4": 2},
],
),
(
OVModelForCausalLM,
"llama",
OVMixedQuantizationConfig(
weight_quantization_config=OVWeightQuantizationConfig(bits=4, group_size=16, ratio=0.5),
full_quantization_config=OVQuantizationConfig(dtype="f8e4m3"),
dataset="wikitext2",
num_samples=1,
),
[
14,
],
[
{"f8e4m3": 11, "int4": 10},
],
),
(
OVModelForCausalLM,
"llama",
OVMixedQuantizationConfig(
weight_quantization_config=OVWeightQuantizationConfig(bits=4, group_size=16),
full_quantization_config=OVQuantizationConfig(dtype="f8e5m2"),
dataset="wikitext2",
num_samples=1,
),
[
13,
],
[
{"f8e5m2": 2, "int4": 28},
],
),
]
@parameterized.expand(SUPPORTED_ARCHITECTURES_TORCH_MODEL)
def test_automodel_static_quantization(self, model_cls, model_name, expected_fake_nodes, expected_int8_nodes):
model_id = MODEL_NAMES[model_name]
task = model_cls.export_feature
dataset_name, dataset_config_name, column_name = _TASK_TO_DATASET[task]
file_name = "openvino_quantized_model.xml"
def preprocess_function(examples, tokenizer):
return tokenizer(examples[column_name], padding="max_length", max_length=128, truncation=True)
with TemporaryDirectory() as tmp_dir:
transformers_model = model_cls.auto_model_class.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
quantizer = OVQuantizer.from_pretrained(transformers_model, task=task)
calibration_dataset = quantizer.get_calibration_dataset(
dataset_name,
dataset_config_name=dataset_config_name,
preprocess_function=partial(preprocess_function, tokenizer=tokenizer),
num_samples=10,
dataset_split="train",
trust_remote_code=True,
)
ov_config = OVConfig(quantization_config=OVQuantizationConfig())
quantizer.quantize(
save_directory=tmp_dir,
calibration_dataset=calibration_dataset,
file_name=file_name,
ov_config=ov_config,
)
model = model_cls.from_pretrained(tmp_dir, file_name=file_name)
num_fake_nodes, num_weight_nodes = get_num_quantized_nodes(model)
self.assertEqual(expected_fake_nodes, num_fake_nodes)
self.assertEqual(expected_int8_nodes, num_weight_nodes["int8"])
tokens = tokenizer("This is a sample input", return_tensors="pt")
outputs = model(**tokens)
self.assertTrue("logits" in outputs)
# Verify that the configuration is correctly saved and loaded
loaded_config = OVConfig.from_pretrained(tmp_dir)
self.assertEqual(ov_config.quantization_config.to_dict(), loaded_config.quantization_config.to_dict())
@parameterized.expand(SUPPORTED_ARCHITECTURES_OV_MODEL)
def test_ovmodel_static_quantization(self, model_cls, model_name, expected_fake_nodes, expected_int8_nodes):
model_id = MODEL_NAMES[model_name]
task = model_cls.export_feature
dataset_name, dataset_config_name, column_name = _TASK_TO_DATASET[task]
def preprocess_function(examples, tokenizer):
return tokenizer(examples[column_name], padding="max_length", max_length=128, truncation=True)
with TemporaryDirectory() as tmp_dir:
ov_model = model_cls.from_pretrained(model_id, export=True)
tokenizer = AutoTokenizer.from_pretrained(model_id)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
quantizer = OVQuantizer.from_pretrained(ov_model, task=task)
calibration_dataset = quantizer.get_calibration_dataset(
dataset_name,
dataset_config_name=dataset_config_name,
preprocess_function=partial(preprocess_function, tokenizer=tokenizer),
num_samples=10,
dataset_split="train",
trust_remote_code=True,
)
ov_config = OVConfig(quantization_config=OVQuantizationConfig())
quantizer.quantize(save_directory=tmp_dir, calibration_dataset=calibration_dataset, ov_config=ov_config)
model = model_cls.from_pretrained(tmp_dir)
num_fake_nodes, num_weight_nodes = get_num_quantized_nodes(model)
self.assertEqual(expected_fake_nodes, num_fake_nodes)
self.assertEqual(expected_int8_nodes, num_weight_nodes["int8"])
tokens = tokenizer("This is a sample input", return_tensors="pt")
outputs = model(**tokens)
self.assertTrue("logits" in outputs)
# Verify that the configuration is correctly saved and loaded
loaded_config = OVConfig.from_pretrained(tmp_dir)
self.assertEqual(ov_config.quantization_config.to_dict(), loaded_config.quantization_config.to_dict())
check_optimization_not_applicable_to_optimized_model(
model, quantization_config=OVWeightQuantizationConfig(bits=8)
)
@parameterized.expand(SUPPORTED_ARCHITECTURES_OV_MODEL_WITH_AUTO_DATASET)
def test_ov_model_static_quantization_with_auto_dataset(
self,
model_cls,
model_name,
quantization_config,
expected_fake_nodes_per_model,
expected_num_weight_nodes_per_model,
):
model_id = MODEL_NAMES[model_name]
with TemporaryDirectory() as tmp_dir:
ov_model = model_cls.from_pretrained(model_id, quantization_config=quantization_config)
ov_model.save_pretrained(tmp_dir)
if model_cls == OVModelForSpeechSeq2Seq:
submodels = [ov_model.encoder.model, ov_model.decoder.model]
if ov_model.decoder_with_past is not None:
submodels.append(ov_model.decoder_with_past.model)
else:
expected_num_weight_nodes_per_model = expected_num_weight_nodes_per_model[:-1]
expected_fake_nodes_per_model = expected_fake_nodes_per_model[:-1]
input_features = torch.randn((1, 128, 3000), dtype=torch.float32)
ov_model.generate(input_features)
elif model_cls == OVModelForCausalLM:
submodels = [ov_model]
tokenizer = AutoTokenizer.from_pretrained(model_id)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
tokens = tokenizer("This is a sample input", return_tensors="pt")
outputs = ov_model(**tokens)
self.assertTrue("logits" in outputs)
else:
raise Exception("Unexpected model class.")
check_compression_state_per_model(
self,
submodels,
expected_num_weight_nodes_per_model,
expected_fake_nodes_per_model,
)
class OVWeightCompressionTest(unittest.TestCase):
SUPPORTED_ARCHITECTURES_WITH_EXPECTED_8BIT_COMPRESSED_MATMULS = (
(OVModelForSequenceClassification, "bert", 70, 70),
(OVModelForCausalLM, "gpt2", 44, 44),
)
SUPPORTED_ARCHITECTURES_WITH_EXPECTED_4BIT_COMPRESSED_MATMULS = ((OVModelForCausalLM, "opt125m", 62, 43),)
SUPPORTED_ARCHITECTURES_WITH_EXPECTED_4BIT_AUTOCOMPRESSED_MATMULS = ((OVModelForCausalLM, "opt125m", 0, 74),)
SUPPORTED_ARCHITECTURES_STATEFUL_WITH_EXPECTED_8BIT_COMPRESSED_MATMULS = ((OVModelForCausalLM, "gpt2", 44, 44),)
LOAD_IN_4_BITS_SCOPE = [
(
OVModelForCausalLM, # model cls
"gpt2", # model name
False, # trust remote code
dict(bits=4, sym=False, group_size=-1, ratio=0.8), # quantization config
[{"int8": 14, "int4": 30}], # reference number of low-precision nodes
),
(
OVModelForCausalLM,
"gpt2",
False,
dict(bits=4, dtype="mxfp4", group_size=32),
[{"int8": 4, "f4e2m1": 20, "f8e8m0": 20}],
),
(
OVModelForCausalLM,
"gpt2",
False,
dict(bits=4, dtype="nf4", group_size=32),
[
{
"int8": 4,
"nf4": 20,
}
],
),
(
OVModelForCausalLM,
"gpt2",
False,
dict(
bits=4,
sym=False,
group_size=32,
ignored_scope={"names": ["__module.model.transformer.h.2.mlp.c_fc/aten::addmm/MatMul"]},
),
[{"int8": 4, "int4": 38}],
),
(
OVModelForCausalLM,
"gpt2",
False,
dict(bits=4, sym=False, group_size=-1, ratio=0.8, all_layers=True),
[{"int8": 18, "int4": 26}],
),
(
OVModelForCausalLM,
"opt",
False,
dict(
bits=4,
sym=True,
group_size=-1,
ratio=0.8,
sensitivity_metric="mean_activation_magnitude",
dataset="c4",
),
[{"int8": 14, "int4": 25}],
),
(
OVModelForCausalLM,
"opt",
False,
dict(
bits=4,
sym=True,
group_size=-1,
ratio=0.8,
sensitivity_metric="mean_activation_magnitude",
dataset=["one two, " * i for i in range(10)],
),
[{"int8": 16, "int4": 24}],
),
(
OVModelForCausalLM,
"llama_awq",
False,
dict(
bits=4,
sym=True,
group_size=16,
ratio=0.8,
sensitivity_metric="mean_activation_magnitude",
dataset="c4",
quant_method=QuantizationMethod.AWQ,
scale_estimation=True,
),
[{"int8": 8, "int4": 12}],
),
(
OVModelForCausalLM,
"llama_awq",
False,
dict(
bits=4,
sym=True,
group_size=16,
ratio=0.8,
sensitivity_metric="mean_activation_magnitude",
dataset="c4",
quant_method="awq",
),
[{"int8": 8, "int4": 12}],
),
(
OVModelForCausalLM,
"llama_awq",
False,
dict(
bits=4,
sym=True,
group_size=16,
ratio=0.8,
sensitivity_metric="mean_activation_magnitude",
dataset="c4",
gptq=True,
),
[{"int8": 8, "int4": 12}],
),
(
OVModelForCausalLM,
"llama_awq",
False,
dict(
bits=4,
group_size=16,
num_samples=16,
dataset="auto",
lora_correction=True,
),
[{"int8": 60, "int4": 28}],
),
(
OVModelForCausalLM,
"llama_awq",
False,
dict(bits=4, backup_precision="none", group_size=16),
[{"int4": 28}],
),
(
OVModelForCausalLM,
"llama_awq",
False,
dict(bits=4, backup_precision="none", group_size=16, ratio=0.5),
[{"int4": 6}],
),
(
OVModelForCausalLM,
"llama_awq",
False,
dict(bits=4, backup_precision="int8_sym", group_size=16, ratio=0.5),
[{"int4": 6, "int8": 13}],
),
(
OVModelForCausalLM,
"llama_awq",
False,
dict(bits=4, backup_precision="int8_asym", group_size=16, ratio=0.5),
[{"int4": 6, "int8": 26}],
),
]
if is_transformers_version(">=", "4.40.0"):
LOAD_IN_4_BITS_SCOPE.extend(
[
(
OVModelForVisualCausalLM,
"llava_next",
False,
dict(
bits=4,
group_size=16,
dataset="contextual",
ratio=0.8,
sensitivity_metric="hessian_input_activation",
num_samples=1,
processor=MODEL_NAMES["llava_next"],
),
[{"int8": 6, "int4": 24}, {"int8": 1}, {"int8": 9}],
),
(
OVModelForVisualCausalLM,
"nanollava",
True,
dict(
bits=4,
group_size=8,
dataset="contextual",
ratio=0.8,
sensitivity_metric="mean_activation_variance",
num_samples=1,
processor=MODEL_NAMES["nanollava_vision_tower"],
tokenizer=MODEL_NAMES["nanollava"],
trust_remote_code=True,
),
[{"int8": 16, "int4": 14}, {"int8": 1}, {"int8": 15}],
),
]
)
if is_transformers_version(">=", "4.45.0"):
LOAD_IN_4_BITS_SCOPE.extend(
[
(
OVModelForVisualCausalLM,
"minicpmv",
True,
dict(
bits=4,
group_size=16,
dataset="contextual",
ratio=0.8,
sensitivity_metric="mean_activation_magnitude",
num_samples=1,
processor=MODEL_NAMES["minicpmv"],
trust_remote_code=True,
),
[{"int8": 8, "int4": 22}, {"int8": 1}, {"int8": 26}, {"int8": 6}],
),
(
OVModelForVisualCausalLM,
"internvl2",
True,
dict(
bits=4,
group_size=4,
dataset="contextual",
ratio=0.8,
sensitivity_metric="mean_activation_magnitude",
num_samples=1,
trust_remote_code=True,
),
[{"int8": 8, "int4": 22}, {"int8": 1}, {"int8": 11}],
),
(
OVModelForVisualCausalLM,
"phi3_v",
True,
dict(
bits=4,
group_size=16,
dataset="contextual",
ratio=0.8,
sensitivity_metric="mean_activation_magnitude",
num_samples=1,
trust_remote_code=True,
),
[{"int8": 4, "int4": 14}, {"int8": 1}, {"int8": 7}, {"int8": 2}],
),
(
OVModelForVisualCausalLM,
"qwen2_vl",
False,
dict(
bits=4,
group_size=16,
dataset="contextual",
ratio=0.8,
sensitivity_metric="mean_activation_magnitude",
num_samples=1,
),
[{"int8": 10, "int4": 20}, {"int8": 1}, {"int8": 1}, {"int8": 10}],
),
]
)
SUPPORTED_ARCHITECTURES_WITH_AUTO_COMPRESSION = [
(OVModelForCausalLM, "gpt2", False),
(OVModelForMaskedLM, "bert", False),
(OVModelForTokenClassification, "roberta", False),
(OVModelForImageClassification, "vit", False),
(OVModelForSeq2SeqLM, "t5", False),
(OVModelForSequenceClassification, "albert", False),
(OVModelForQuestionAnswering, "distilbert", False),
(OVModelForAudioClassification, "wav2vec2", False),
(OVModelForFeatureExtraction, "blenderbot", False),
(OVStableDiffusionPipeline, "stable-diffusion", False),
(OVStableDiffusionXLPipeline, "stable-diffusion-xl", False),
(OVModelOpenCLIPForZeroShotImageClassification, "open-clip", False),
(OVModelForVisualCausalLM, "llava", False),
]
if is_transformers_version(">=", "4.40.0"):
SUPPORTED_ARCHITECTURES_WITH_AUTO_COMPRESSION.append((OVModelForVisualCausalLM, "nanollava", True))
if is_transformers_version(">=", "4.45.0"):
SUPPORTED_ARCHITECTURES_WITH_AUTO_COMPRESSION.append((OVModelForVisualCausalLM, "minicpmv", True))
SUPPORTED_ARCHITECTURES_WITH_AUTO_COMPRESSION.append((OVModelForVisualCausalLM, "qwen2_vl", False))
SUPPORTED_ARCHITECTURES_WITH_HYBRID_QUANTIZATION = [
(OVStableDiffusionPipeline, "stable-diffusion", 72, 195),
(OVStableDiffusionXLPipeline, "stable-diffusion-xl", 84, 331),
(OVLatentConsistencyModelPipeline, "latent-consistency", 50, 135),
]
if is_transformers_version(">=", "4.45.0"):
SUPPORTED_ARCHITECTURES_WITH_HYBRID_QUANTIZATION.extend(
[
(OVStableDiffusion3Pipeline, "stable-diffusion-3", 9, 65),
(OVFluxPipeline, "flux", 7, 56),
(OVSanaPipeline, "sana", 19, 53),
]
)
IS_SUPPORT_STATEFUL = is_openvino_version(">=", "2023.3")
DEFAULT_INT4_CONFIG = {"bits": 4, "sym": True, "group_size": 64, "all_layers": True}
@parameterized.expand(SUPPORTED_ARCHITECTURES_WITH_EXPECTED_8BIT_COMPRESSED_MATMULS)
def test_automodel_weight_compression(self, model_cls, model_name, expected_pt_int8, expected_ov_int8):
task = model_cls.export_feature
model_id = MODEL_NAMES[model_name]
with TemporaryDirectory() as tmp_dir:
transformers_model = model_cls.auto_model_class.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
quantizer = OVQuantizer.from_pretrained(transformers_model, task=task)
quantizer.quantize(save_directory=tmp_dir)
model = model_cls.from_pretrained(tmp_dir)
_, num_weight_nodes = get_num_quantized_nodes(model)
self.assertEqual(expected_pt_int8, num_weight_nodes["int8"])
tokens = tokenizer("This is a sample input", return_tensors="pt")
outputs = model(**tokens)
self.assertTrue("logits" in outputs)
# Verify that the configuration is correctly saved and loaded
loaded_config = OVConfig.from_pretrained(tmp_dir)
original_config_as_dict = OVWeightQuantizationConfig().to_dict()
for k in original_config_as_dict.keys():
v = original_config_as_dict[k]
if isinstance(v, Enum):
original_config_as_dict[k] = v.value
self.assertEqual(original_config_as_dict, loaded_config.quantization_config.to_dict())
self.assertFalse(model.model.has_rt_info(["runtime_options", "KV_CACHE_PRECISION"]))
@parameterized.expand(SUPPORTED_ARCHITECTURES_WITH_EXPECTED_8BIT_COMPRESSED_MATMULS)
def test_ovmodel_8bit_weight_compression(self, model_cls, model_name, expected_pt_int8, expected_ov_int8):
task = model_cls.export_feature
model_id = MODEL_NAMES[model_name]
with TemporaryDirectory() as tmp_dir:
transformers_model = model_cls.from_pretrained(model_id, export=True)
tokenizer = AutoTokenizer.from_pretrained(model_id)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
quantizer = OVQuantizer.from_pretrained(transformers_model, task=task)
quantizer.quantize(save_directory=tmp_dir)
model = model_cls.from_pretrained(tmp_dir)
_, num_weight_nodes = get_num_quantized_nodes(model)
self.assertEqual(expected_ov_int8, num_weight_nodes["int8"])
tokens = tokenizer("This is a sample input", return_tensors="pt")
outputs = model(**tokens)
self.assertTrue("logits" in outputs)
# Verify that the configuration is correctly saved and loaded
loaded_config = OVConfig.from_pretrained(tmp_dir)
self.assertEqual(OVWeightQuantizationConfig().to_dict(), loaded_config.quantization_config.to_dict())
self.assertFalse(model.model.has_rt_info(["runtime_options", "KV_CACHE_PRECISION"]))
@parameterized.expand(SUPPORTED_ARCHITECTURES_WITH_EXPECTED_4BIT_COMPRESSED_MATMULS)
def test_ovmodel_4bit_weight_compression(self, model_cls, model_name, expected_int8_nodes, expected_int4_nodes):
task = model_cls.export_feature
model_id = MODEL_NAMES[model_name]
with TemporaryDirectory() as tmp_dir:
transformers_model = model_cls.from_pretrained(model_id, export=True, stateful=False)
tokenizer = AutoTokenizer.from_pretrained(model_id)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
quantizer = OVQuantizer.from_pretrained(transformers_model, task=task)
ov_config = OVConfig(quantization_config=OVWeightQuantizationConfig(bits=4, sym=True, ratio=0.8))
quantizer.quantize(save_directory=tmp_dir, ov_config=ov_config)
model = model_cls.from_pretrained(tmp_dir)
_, num_weight_nodes = get_num_quantized_nodes(model)
self.assertEqual(expected_int8_nodes, num_weight_nodes["int8"])
self.assertEqual(expected_int4_nodes, num_weight_nodes["int4"])
tokens = tokenizer("This is a sample input", return_tensors="pt")
outputs = model(**tokens)
self.assertTrue("logits" in outputs)
# Verify that the configuration is correctly saved and loaded
loaded_config = OVConfig.from_pretrained(tmp_dir)
self.assertEqual(ov_config.quantization_config.to_dict(), loaded_config.quantization_config.to_dict())
self.assertFalse(model.model.has_rt_info(["runtime_options", "KV_CACHE_PRECISION"]))
@parameterized.expand(SUPPORTED_ARCHITECTURES_STATEFUL_WITH_EXPECTED_8BIT_COMPRESSED_MATMULS)
def test_ovmodel_8bit_weight_compression_stateful(self, model_cls, model_name, expected_pt_int8, expected_ov_int8):
task = model_cls.export_feature
model_id = MODEL_NAMES[model_name]
with TemporaryDirectory() as tmp_dir:
transformers_model = model_cls.from_pretrained(model_id, export=True, stateful=True)
tokenizer = AutoTokenizer.from_pretrained(model_id)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
quantizer = OVQuantizer.from_pretrained(transformers_model, task=task)
quantizer.quantize(save_directory=tmp_dir)
model = model_cls.from_pretrained(tmp_dir)
_, num_weight_nodes = get_num_quantized_nodes(model)
self.assertEqual(expected_ov_int8, num_weight_nodes["int8"])
tokens = tokenizer("This is a sample input", return_tensors="pt")
outputs = model(**tokens)
self.assertTrue("logits" in outputs)
# Verify that the configuration is correctly saved and loaded
loaded_config = OVConfig.from_pretrained(tmp_dir)
self.assertEqual(OVWeightQuantizationConfig().to_dict(), loaded_config.quantization_config.to_dict())
self.assertFalse(model.model.has_rt_info(["runtime_options", "KV_CACHE_PRECISION"]))
@parameterized.expand(SUPPORTED_ARCHITECTURES_WITH_AUTO_COMPRESSION)
def test_ovmodel_load_with_compressed_weights(self, model_cls, model_type, trust_remote_code):
model = model_cls.from_pretrained(
MODEL_NAMES[model_type],
export=True,
load_in_8bit=True,
stateful=False,
trust_remote_code=trust_remote_code,
)
if model_type == "open-clip":
self.assertEqual(model.text_model._openvino_config.quantization_config.bits, 8)
self.assertEqual(model.text_model._openvino_config.dtype, "int8")
self.assertEqual(model.visual_model._openvino_config.quantization_config.bits, 8)
self.assertEqual(model.visual_model._openvino_config.dtype, "int8")
else:
self.assertEqual(model._openvino_config.quantization_config.bits, 8)
self.assertEqual(model._openvino_config.dtype, "int8")
if model.export_feature.startswith("text2text-generation"):
models = [model.encoder, model.decoder]
if model.decoder_with_past is not None:
models.append(model.decoder_with_past)
elif model.export_feature == "text-to-image":
models = [model.unet, model.vae_encoder, model.vae_decoder]
models.append(model.text_encoder if model_type in ["stable-diffusion", "sana"] else model.text_encoder_2)
elif model_type == "open-clip":
models = [model.text_model, model.visual_model]
elif model.export_feature == "image-text-to-text":
models = list(model.submodels.values())
else:
models = [model]
if model_type == "open-clip":
pytest.skip(reason="ticket 161043")
elif model_type == "t5":
pytest.skip(reason="ticket 160958")
else:
check_optimization_not_applicable_to_optimized_model(model, quantization_config={"bits": 8})
expected_ov_int8 = _ARCHITECTURES_TO_EXPECTED_INT8[model_type]
expected_ov_int8 = [{"int8": it} for it in expected_ov_int8]
check_compression_state_per_model(self, models, expected_ov_int8)
@parameterized.expand(SUPPORTED_ARCHITECTURES_WITH_HYBRID_QUANTIZATION)
def test_ovmodel_hybrid_quantization(self, model_cls, model_type, expected_fake_nodes, expected_int8_nodes):
model_id = MODEL_NAMES[model_type]
quantization_config = OVWeightQuantizationConfig(bits=8, dataset="conceptual_captions", num_samples=2)
with TemporaryDirectory() as tmp_dir:
model = model_cls.from_pretrained(model_id, export=True, quantization_config=quantization_config)
num_fake, num_weight_nodes = get_num_quantized_nodes(
model.unet if model.unet is not None else model.transformer
)
self.assertEqual(expected_fake_nodes, num_fake)
self.assertEqual(expected_int8_nodes, num_weight_nodes["int8"])
self.assertEqual(0, num_weight_nodes["int4"])
model.save_pretrained(tmp_dir)
check_optimization_not_applicable_to_optimized_model(model, quantization_config)
def test_stable_diffusion_with_weight_compression(self):
int8_pipe = OVStableDiffusionPipeline.from_pretrained(model_id=MODEL_NAMES["stable-diffusion"], export=True)
quantization_config = OVWeightQuantizationConfig(bits=8, quant_method=OVQuantizationMethod.DEFAULT)
quantizer = OVQuantizer(int8_pipe)
quantizer.quantize(ov_config=OVConfig(quantization_config=quantization_config))
num_fake_nodes, num_weight_nodes = get_num_quantized_nodes(
int8_pipe.unet if int8_pipe.unet is not None else int8_pipe.transformer
)
self.assertEqual(0, num_fake_nodes)
self.assertEqual(242, num_weight_nodes["int8"])
self.assertEqual(0, num_weight_nodes["int4"])
quantization_config = OVWeightQuantizationConfig(
bits=8, dataset="conceptual_captions", num_samples=2, quant_method=OVQuantizationMethod.HYBRID
)
check_optimization_not_applicable_to_optimized_model(int8_pipe, quantization_config)
@parameterized.expand(SUPPORTED_ARCHITECTURES_WITH_HYBRID_QUANTIZATION[-1:])
def test_ovmodel_hybrid_quantization_with_custom_dataset(
self, model_cls, model_type, expected_fake_nodes, expected_int8_nodes
):
model_id = MODEL_NAMES[model_type]
dataset = [
"dream rose covered with clean crystal, sharp edges, transparent, beautiful, highly detailed, high render"
]
model = model_cls.from_pretrained(model_id, export=True)
quantizer = OVQuantizer(model)
quantization_config = OVWeightQuantizationConfig(bits=8, num_samples=3, quant_method="hybrid")
self.assertEqual(quantization_config.quant_method, OVQuantizationMethod.HYBRID)
quantizer.quantize(ov_config=OVConfig(quantization_config=quantization_config), calibration_dataset=dataset)
num_fake_nodes, num_weight_nodes = get_num_quantized_nodes(
model.unet if model.unet is not None else model.transformer
)
self.assertEqual(expected_fake_nodes, num_fake_nodes)
self.assertEqual(expected_int8_nodes, num_weight_nodes["int8"])
self.assertEqual(0, num_weight_nodes["int4"])
@parameterized.expand(SUPPORTED_ARCHITECTURES_WITH_EXPECTED_4BIT_AUTOCOMPRESSED_MATMULS)
@unittest.mock.patch.dict(
"optimum.intel.openvino.configuration._DEFAULT_4BIT_CONFIGS", {"facebook/opt-125m": DEFAULT_INT4_CONFIG}
)
def test_ovmodel_4bit_auto_compression(self, model_cls, model_type, expected_ov_int8, expected_ov_int4):
with TemporaryDirectory() as tmp_dir:
model_id = MODEL_NAMES[model_type]
model = model_cls.from_pretrained(model_id, export=True, quantization_config={"bits": 4})
tokenizer = AutoTokenizer.from_pretrained(model_id)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
_, num_weight_nodes = get_num_quantized_nodes(model)
self.assertEqual(expected_ov_int4, num_weight_nodes["int4"])
self.assertEqual(expected_ov_int8, num_weight_nodes["int8"])
model.save_pretrained(tmp_dir)
openvino_config = OVConfig.from_pretrained(tmp_dir)
self.assertEqual(openvino_config.quantization_config.bits, 4)
self.assertEqual(openvino_config.dtype, "int4")
if model_id == "facebook/opt-125m":
for key, value in self.DEFAULT_INT4_CONFIG.items():
self.assertEqual(value, getattr(openvino_config.quantization_config, key))
check_optimization_not_applicable_to_optimized_model(model, quantization_config={"bits": 8})
@parameterized.expand(LOAD_IN_4_BITS_SCOPE)
def test_ovmodel_4bit_auto_compression_with_config(
self, model_cls, model_name, trust_remote_code, quantization_config, expected_num_weight_nodes_per_model
):
model_id = MODEL_NAMES[model_name]
with TemporaryDirectory() as tmp_dir:
quantization_config = OVWeightQuantizationConfig.from_dict(quantization_config)
model = model_cls.from_pretrained(
model_id, export=True, quantization_config=quantization_config, trust_remote_code=trust_remote_code
)
if quantization_config.quant_method.lower() == "awq":
# TODO: Check that AWQ was actually applied
pass
submodels = []
if isinstance(model, OVModelForCausalLM):
submodels = [model.model]
elif isinstance(model, OVModelForVisualCausalLM):
submodels = list(model.submodels.values())
check_compression_state_per_model(self, submodels, expected_num_weight_nodes_per_model)
model.save_pretrained(tmp_dir)
# At the moment the first model in the list is the only one we apply data-aware compression to
wc_rt_info = submodels[0].get_rt_info()["nncf"]["weight_compression"]
self.assertEqual(quantization_config.quant_method.lower() == "awq", wc_rt_info["awq"].value == "True")
self.assertEqual(
quantization_config.scale_estimation or False, wc_rt_info["scale_estimation"].value == "True"
)
self.assertEqual(quantization_config.gptq or False, wc_rt_info["gptq"].value == "True")
self.assertEqual(
quantization_config.lora_correction or False, wc_rt_info["lora_correction"].value == "True"
)
openvino_config = OVConfig.from_pretrained(tmp_dir)
self.assertEqual(openvino_config.quantization_config.bits, 4)
self.assertEqual(openvino_config.dtype, quantization_config.dtype)
@parameterized.expand(((OVModelForCausalLM, "gpt2"),))
def test_ovmodel_stateful_load_with_compressed_weights(self, model_cls, model_type):
model = model_cls.from_pretrained(MODEL_NAMES[model_type], export=True, load_in_8bit=True, stateful=True)
self.assertTrue(model.stateful)
self.assertTrue(model.use_cache)
expected_ov_int8 = _ARCHITECTURES_TO_EXPECTED_INT8[model_type][0]
_, num_weight_nodes = get_num_quantized_nodes(model)
check_compression_state_per_model(self, [model.model], [{"int8": expected_ov_int8}])
@parameterized.expand(SUPPORTED_ARCHITECTURES_WITH_AUTO_COMPRESSION)
def test_ovmodel_load_with_uncompressed_weights(self, model_cls, model_type, trust_remote_code):
model = model_cls.from_pretrained(
MODEL_NAMES[model_type], export=True, load_in_8bit=False, trust_remote_code=trust_remote_code
)
if model.export_feature.startswith("text2text-generation"):
models = [model.encoder, model.decoder]
if model.decoder_with_past is not None:
models.append(model.decoder_with_past)
elif model.export_feature == "text-to-image":
models = [model.unet, model.vae_encoder, model.vae_decoder]
models.append(model.text_encoder if model_type in ["stable-diffusion", "sana"] else model.text_encoder_2)
elif model_type == "open-clip":
models = [model.text_model, model.visual_model]
elif model.export_feature == "image-text-to-text":
models = list(model.submodels.values())
else:
models = [model]
for i, submodel in enumerate(models):
ov_model = submodel if isinstance(submodel, ov.Model) else submodel.model
_, num_weight_nodes = get_num_quantized_nodes(ov_model)
self.assertEqual(0, num_weight_nodes["int8"])
if "text-generation" in model.export_feature or ("image-text-to-text" in model.export_feature and i == 0):
self.assertTrue(ov_model.has_rt_info(["runtime_options", "KV_CACHE_PRECISION"]))
kv_cache_precision = ov_model.get_rt_info(["runtime_options", "KV_CACHE_PRECISION"]).value
self.assertTrue(kv_cache_precision == "f16")