[OV] Introduce --quant-mode cli argument enabling full quantization…

… via optimum-cli (#1061)

* Introduce --quant-mode cli argument

* Make int8 by default

* Add a test

* Add documentation

* Fix command

* Replace 'int8/int8' by 'int8'

* Add missing docstring

* Add trust_remote_code

* Fix condition

* Trigger Tests

* Trigger Tests

docs/source/openvino/export.mdx

@@ -31,13 +31,14 @@ Check out the help for more options:
 
 ```text
 usage: optimum-cli export openvino [-h] -m MODEL [--task TASK] [--framework {pt,tf}] [--trust-remote-code]
-                                   [--weight-format {fp32,fp16,int8,int4,mxfp4,nf4}]
+                                   [--weight-format {fp32,fp16,int8,int4,mxfp4,nf4}] [--quant-mode {int8}]
                                    [--library {transformers,diffusers,timm,sentence_transformers,open_clip}]
                                    [--cache_dir CACHE_DIR] [--pad-token-id PAD_TOKEN_ID] [--ratio RATIO] [--sym]
                                    [--group-size GROUP_SIZE] [--backup-precision {none,int8_sym,int8_asym}]
                                    [--dataset DATASET] [--all-layers] [--awq] [--scale-estimation] [--gptq]
                                    [--lora-correction] [--sensitivity-metric SENSITIVITY_METRIC]
                                    [--num-samples NUM_SAMPLES] [--disable-stateful] [--disable-convert-tokenizer]
+                                   [--smooth-quant-alpha SMOOTH_QUANT_ALPHA]
                                    output
 
 optional arguments:
@@ -66,6 +67,10 @@ Optional arguments:
                         on your local machine arbitrary code present in the model repository.
   --weight-format {fp32,fp16,int8,int4,mxfp4,nf4}
                         The weight format of the exported model.
+  --quant-mode {int8}
+                        Quantization precision mode. This is used for applying full model quantization including
+                        activations. The only currently supported choice is 'int8' for int8 quantization of both
+                        weights and activations.
   --library {transformers,diffusers,timm,sentence_transformers,open_clip}
                         The library used to load the model before export. If not provided, will attempt to infer the
                         local checkpoint's library
@@ -102,8 +107,8 @@ Optional arguments:
                         weight compression is applied, they are compressed to INT8.
   --awq                 Whether to apply AWQ algorithm. AWQ improves generation quality of INT4-compressed LLMs, but
                         requires additional time for tuning weights on a calibration dataset. To run AWQ, please also
-                        provide a dataset argument. Note: it is possible that there will be no matching patterns in the
-                        model to apply AWQ, in such case it will be skipped.
+                        provide a dataset argument. Note: it is possible that there will be no matching patterns in
+                        the model to apply AWQ, in such case it will be skipped.
   --scale-estimation    Indicates whether to apply a scale estimation algorithm that minimizes the L2 error between
                         the original and compressed layers. Providing a dataset is required to run scale estimation.
                         Please note, that applying scale estimation takes additional memory and time.
@@ -128,6 +133,9 @@ Optional arguments:
                         OpenVINO native inference code that expects KV-cache inputs and outputs in the model.
   --disable-convert-tokenizer
                         Do not add converted tokenizer and detokenizer OpenVINO models.
+  --smooth-quant-alpha SMOOTH_QUANT_ALPHA
+                        SmoothQuant alpha parameter that improves the distribution of activations before MatMul layers
+                        and reduces quantization error. Valid only when activations quantization is enabled.
 ```
 
 You can also apply fp16, 8-bit or 4-bit weight-only quantization on the Linear, Convolutional and Embedding layers when exporting your model by setting `--weight-format` to respectively `fp16`, `int8` or `int4`.
@@ -158,6 +166,12 @@ Models larger than 1 billion parameters are exported to the OpenVINO format with
 </Tip>
 
 
+Besides weight-only quantization, you can also apply full model quantization including activations by setting `--quant-mode` to `int8`. This will quantize both weights and activations of Linear, Convolutional and some other layers to int8. Currently this is only supported for speech-to-text models. Please see example below.
+
+```bash
+optimum-cli export openvino -m openai/whisper-large-v3-turbo --quant-mode int8 --dataset librispeech --num-samples 32 --smooth-quant-alpha 0.9 ./whisper-large-v3-turbo
+```
+
 ### Decoder models
 
 For models with a decoder, we enable the re-use of past keys and values by default. This allows to avoid recomputing the same intermediate activations at each generation step. To export the model without, you will need to remove the `-with-past` suffix when specifying the task.

optimum/commands/export/openvino.py

@@ -75,6 +75,16 @@ def parse_args_openvino(parser: "ArgumentParser"):
         default=None,
         help="The weight format of the exported model.",
     )
+    optional_group.add_argument(
+        "--quant-mode",
+        type=str,
+        choices=["int8"],
+        default=None,
+        help=(
+            "Quantization precision mode. This is used for applying full model quantization including activations. "
+            "The only currently supported choice is 'int8' for int8 quantization of both weights and activations."
+        ),
+    )
     optional_group.add_argument(
         "--library",
         type=str,
@@ -228,6 +238,15 @@ def parse_args_openvino(parser: "ArgumentParser"):
         action="store_true",
         help="Do not add converted tokenizer and detokenizer OpenVINO models.",
     )
+    optional_group.add_argument(
+        "--smooth-quant-alpha",
+        type=float,
+        default=None,
+        help=(
+            "SmoothQuant alpha parameter that improves the distribution of activations before MatMul layers and "
+            "reduces quantization error. Valid only when activations quantization is enabled."
+        ),
+    )
 
 
 def no_compression_parameter_provided(args):
@@ -252,6 +271,20 @@ def no_compression_parameter_provided(args):
     )
 
 
+def no_quantization_parameter_provided(args):
+    return all(
+        (
+            it is None
+            for it in (
+                args.sym,
+                args.dataset,
+                args.num_samples,
+                args.smooth_quant_alpha,
+            )
+        )
+    )
+
+
 class OVExportCommand(BaseOptimumCLICommand):
     COMMAND = CommandInfo(name="openvino", help="Export PyTorch models to OpenVINO IR.")
 
@@ -291,16 +324,21 @@ def run(self):
         else:
             library_name = self.args.library
 
-        if self.args.weight_format is None:
+        if self.args.weight_format is None and self.args.quant_mode is None:
             ov_config = None
             if not no_compression_parameter_provided(self.args):
                 raise ValueError(
                     "Some compression parameters are provided, but the weight format is not specified. "
                     "Please provide it with --weight-format argument."
                 )
+            if not no_quantization_parameter_provided(self.args):
+                raise ValueError(
+                    "Some quantization parameters are provided, but the quantization mode is not specified. "
+                    "Please provide it with --quant-mode argument."
+                )
         elif self.args.weight_format in {"fp16", "fp32"}:
             ov_config = OVConfig(dtype=self.args.weight_format)
-        else:
+        elif self.args.weight_format is not None:
             # For int4 quantization if no parameter is provided, then use the default config if exists
             if no_compression_parameter_provided(self.args) and self.args.weight_format == "int4":
                 quantization_config = get_default_int4_config(self.args.model)
@@ -326,6 +364,21 @@ def run(self):
             if quantization_config.get("dataset", None) is not None:
                 quantization_config["trust_remote_code"] = self.args.trust_remote_code
             ov_config = OVConfig(quantization_config=quantization_config)
+        else:
+            if self.args.quant_mode != "int8":
+                raise ValueError("Only 'int8' quantization mode is currently supported.")
+
+            quantization_config = {
+                "weight_format": self.args.quant_mode,
+                "activation_format": self.args.quant_mode,
+                "bits": 8,
+                "sym": self.args.sym or False,
+                "dataset": self.args.dataset,
+                "num_samples": self.args.num_samples,
+                "smooth_quant_alpha": self.args.smooth_quant_alpha,
+                "trust_remote_code": self.args.trust_remote_code,
+            }
+            ov_config = OVConfig(quantization_config=quantization_config)
 
         quantization_config = ov_config.quantization_config if ov_config else None
         quantize_with_dataset = quantization_config and getattr(quantization_config, "dataset", None) is not None
@@ -368,17 +421,23 @@ def run(self):
             model.save_pretrained(self.args.output)
             if not self.args.disable_convert_tokenizer:
                 maybe_convert_tokenizers(library_name, self.args.output, model, task=task)
-        elif (task.startswith("text-generation") and quantize_with_dataset) or (
-            task == "image-text-to-text" and quantization_config is not None
+        elif (
+            quantize_with_dataset
+            and (task.startswith("text-generation") or task == "automatic-speech-recognition")
+            or (task == "image-text-to-text" and quantization_config is not None)
         ):
             if task.startswith("text-generation"):
                 from optimum.intel import OVModelForCausalLM
 
                 model_cls = OVModelForCausalLM
-            else:
+            elif task == "image-text-to-text":
                 from optimum.intel import OVModelForVisualCausalLM
 
                 model_cls = OVModelForVisualCausalLM
+            else:
+                from optimum.intel import OVModelForSpeechSeq2Seq
+
+                model_cls = OVModelForSpeechSeq2Seq
 
             # In this case, to apply quantization an instance of a model class is required
             model = model_cls.from_pretrained(

optimum/intel/openvino/configuration.py

@@ -266,6 +266,7 @@ def __init__(
         tokenizer: Optional[str] = None,
         processor: Optional[str] = None,
         trust_remote_code: bool = False,
+        weight_format: Optional[str] = None,
         **kwargs,
     ):
         """
@@ -279,6 +280,18 @@ def __init__(
                 entries provided via this argument are used to create an instance of `nncf.IgnoredScope` class.
             num_samples (`int`, *optional*):
                 The maximum number of samples composing the calibration dataset.
+            dataset (`str or List[str]`, *optional*):
+                The dataset used for data-aware optimization with NNCF.
+            tokenizer (`str`, *optional*):
+                The tokenizer used to process the dataset.
+            processor (`str`, *optional*):
+                A transformers processor used to process the dataset inputs.
+            trust_remote_code (`bool`, defaults to `False`):
+                Allows to use custom code for the modeling hosted in the model repository. This option should only be
+                set for repositories you trust and in which you have read the code, as it will execute on your local
+                machine arbitrary code present in the model repository.
+            weight_format (`str`, *optional*):
+                Data format weights are compressed to.
         """
         self.bits = bits
         self.sym = sym
@@ -287,6 +300,7 @@ def __init__(
         self.tokenizer = tokenizer
         self.processor = processor
         self.trust_remote_code = trust_remote_code
+        self.weight_format = weight_format
 
         if isinstance(ignored_scope, nncf.IgnoredScope):
             ignored_scope = ignored_scope.__dict__
@@ -370,7 +384,7 @@ class OVWeightQuantizationConfig(OVQuantizationConfigBase):
         scale_estimation (`bool`, *optional*):
             Indicates whether to apply a scale estimation algorithm that minimizes the L2 error between the original and
             compressed layers. Providing a dataset is required to run scale estimation.
-        weight_format (`str`, defaults to 'int'):
+        weight_format (`str`, *optional*):
             Data format weights are compressed to. Possible values: ['int4', 'int8', 'mxfp4', 'nf4'].
         qptq (`bool`, *optional*):
             Whether to apply GPTQ algorithm. GPTQ optimizes compressed weights in a layer-wise fashion to minimize the
@@ -425,14 +439,14 @@ def __init__(
             tokenizer=tokenizer,
             processor=processor,
             trust_remote_code=trust_remote_code,
+            weight_format=weight_format,
         )
         self.group_size = group_size or (-1 if bits == 8 else 128)
         self.ratio = ratio
         self.all_layers = all_layers
         self.sensitivity_metric = sensitivity_metric
         self.quant_method = OVQuantizationMethod(quant_method) if isinstance(quant_method, str) else quant_method
         self.scale_estimation = scale_estimation
-        self.weight_format = weight_format
         self.gptq = gptq
         self.lora_correction = lora_correction
         self.backup_precision = backup_precision
@@ -578,6 +592,8 @@ def __init__(
         processor: Optional[str] = None,
         trust_remote_code: bool = False,
         smooth_quant_alpha: Optional[float] = None,
+        weight_format: Optional[str] = "int8",
+        activation_format: Optional[str] = "int8",
         **kwargs,
     ):
         """
@@ -621,6 +637,10 @@ def __init__(
             smooth_quant_alpha (`float`, *optional*):
                 SmoothQuant alpha parameter that improves the distribution of activations before MatMul layers and
                 reduces quantization error.
+            weight_format (`str`, defaults to "int8"):
+                Data format weights are quantized to. Possible values: ['int8'].
+            activation_format (`str`, defaults to "int8"):
+                Data format activations are compressed to. Possible values: ['int8'].
         """
         super().__init__(
             bits=bits,
@@ -631,11 +651,13 @@ def __init__(
             tokenizer=tokenizer,
             processor=processor,
             trust_remote_code=trust_remote_code,
+            weight_format=weight_format,
         )
         self.model_type = model_type
         self.fast_bias_correction = fast_bias_correction
         self.overflow_fix = overflow_fix
         self.smooth_quant_alpha = smooth_quant_alpha
+        self.activation_format = activation_format
         self.post_init()
 
     def post_init(self):
@@ -659,6 +681,12 @@ def post_init(self):
                 f"SmoothQuant alpha parameter must be in range [0, 1], but found {self.smooth_quant_alpha}"
             )
 
+        if self.weight_format != "int8":
+            raise ValueError("Only 'int8' weight format is currently supported.")
+
+        if self.activation_format != "int8":
+            raise ValueError("Only 'int8' activation format is currently supported.")
+
 
 class OVConfig(BaseConfig):
     CONFIG_NAME = "openvino_config.json"

optimum/intel/openvino/quantization.py

@@ -458,6 +458,11 @@ def _quantize_ovbasemodel(
             if calibration_dataset is None:
                 raise ValueError("Calibration dataset is required to run quantization.")
 
+            if quantization_config.weight_format != "int8":
+                raise ValueError("Only 'int8' weight format is currently supported.")
+            if quantization_config.activation_format != "int8":
+                raise ValueError("Only 'int8' activation format is currently supported.")
+
             # Quantize model(s)
             if isinstance(self.model, _OVModelForWhisper):
                 self._quantize_whisper_model(quantization_config, calibration_dataset, **kwargs)

optimum/intel/openvino/utils.py

@@ -131,6 +131,7 @@
     "open_clip_text": "OVModelOpenCLIPText",
     "open_clip_vision": "OVModelOpenCLIPVisual",
     "open_clip": "OVModelOpenCLIPForZeroShotImageClassification",
+    "automatic-speech-recognition": "OVModelForSpeechSeq2Seq",
 }
 
 

tests/openvino/test_exporters_cli.py

@@ -14,7 +14,7 @@
 import subprocess
 import unittest
 from pathlib import Path
-from typing import Dict, List
+from typing import Dict, List, Tuple
 
 from parameterized import parameterized
 from transformers import AutoModelForCausalLM
@@ -37,6 +37,7 @@
     OVModelForQuestionAnswering,
     OVModelForSeq2SeqLM,
     OVModelForSequenceClassification,
+    OVModelForSpeechSeq2Seq,
     OVModelForTokenClassification,
     OVModelForVisualCausalLM,
     OVModelOpenCLIPForZeroShotImageClassification,
@@ -109,6 +110,16 @@ class OVCLIExportTestCase(unittest.TestCase):
         SUPPORTED_SD_HYBRID_ARCHITECTURES.append(("stable-diffusion-3", 9, 65))
         SUPPORTED_SD_HYBRID_ARCHITECTURES.append(("flux", 7, 56))
 
+    SUPPORTED_QUANTIZATION_ARCHITECTURES = [
+        (
+            "automatic-speech-recognition",
+            "whisper",
+            "--quant-mode int8 --dataset librispeech --num-samples 1 --smooth-quant-alpha 0.9 --trust-remote-code",
+            (14, 22, 21) if is_transformers_version("<=", "4.36.0") else (14, 22, 25),
+            (14, 21, 17) if is_transformers_version("<=", "4.36.0") else (14, 22, 18),
+        ),
+    ]
+
     TEST_4BIT_CONFIGURATIONS = [
         ("text-generation-with-past", "opt125m", "int4 --sym --group-size 128", [{"int8": 4, "int4": 72}]),
         ("text-generation-with-past", "opt125m", "int4 --group-size 64", [{"int8": 4, "int4": 144}]),
@@ -391,6 +402,32 @@ def test_exporters_cli_4bit(
                 "--lora-correction" not in option or b"with correction of low-rank adapters" in result.stdout
             )
 
+    @parameterized.expand(SUPPORTED_QUANTIZATION_ARCHITECTURES)
+    def test_exporters_cli_full_quantization(
+        self,
+        task: str,
+        model_type: str,
+        option: str,
+        expected_num_fq_nodes_per_model: Tuple[int],
+        expected_num_weight_nodes_per_model: Tuple[int],
+    ):
+        with TemporaryDirectory() as tmpdir:
+            subprocess.run(
+                f"optimum-cli export openvino --model {MODEL_NAMES[model_type]} {option} {tmpdir}",
+                shell=True,
+                check=True,
+            )
+            model = eval(_HEAD_TO_AUTOMODELS[task]).from_pretrained(tmpdir)
+
+            submodels = []
+            if task == "automatic-speech-recognition":
+                submodels = [model.encoder, model.decoder, model.decoder_with_past]
+            self.assertEqual(len(expected_num_fq_nodes_per_model), len(submodels))
+            for i, model in enumerate(submodels):
+                actual_num_fq_nodes, actual_num_weight_nodes = get_num_quantized_nodes(model)
+                self.assertEqual(expected_num_fq_nodes_per_model[i], actual_num_fq_nodes)
+                self.assertEqual(expected_num_weight_nodes_per_model[i], actual_num_weight_nodes["int8"])
+
     def test_exporters_cli_int4_with_local_model_and_default_config(self):
         with TemporaryDirectory() as tmpdir:
             pt_model = AutoModelForCausalLM.from_pretrained(MODEL_NAMES["falcon-40b"])