Add VLM quantization & loading into transformers-like API (#2116)

Signed-off-by: Kaihui-intel <kaihui.tang@intel.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Sun, Xuehao <xuehao.sun@intel.com>

Author: 3 people

.azure-pipelines/scripts/ut/3x/run_3x_pt.sh

@@ -13,6 +13,7 @@ echo "##[section]import check pass"
 echo "##[group]set up UT env..."
 export LD_LIBRARY_PATH=/usr/local/lib/:$LD_LIBRARY_PATH
 pip install -r /neural-compressor/test/3x/torch/requirements.txt
+pip install torch==2.5.1 torchvision==0.20.1 # For auto-round
 pip install pytest-cov
 pip install pytest-html
 echo "##[endgroup]"

neural_compressor/torch/algorithms/weight_only/autoround.py

@@ -84,7 +84,7 @@ def __init__(
         enable_torch_compile: bool = None,
         # mllm
         is_mllm: bool = False,
-        quant_nontext_module: Union[str, list] = None,
+        quant_nontext_module: bool = False,
         extra_data_dir: str = None,
         image_processor=None,
         processor=None,
@@ -150,7 +150,7 @@ def __init__(
             act_dynamic (bool): Whether to use dynamic activation quantization. Default is True.
             enable_norm_bias_tuning (bool): Whether to enable fast norm/layer_bias tuning.
             enable_torch_compile (bool): Whether to enable torch compile to optimize quant_block/layer, torch>=2.6 True.
-            quant_nontext_module (Union[str, list]): Whether to quantize nontext module.
+            quant_nontext_module (bool): Whether to quantize nontext module.
             is_mllm (bool): Indicates whether the model to be quantized is a multi-modal model (MLLM).
             extra_data_dir (str): The path for extra data such as images, audio or videos.
             processor (transformers.AutoProcessor): Any multi-modal model will require an object to encode or
@@ -383,7 +383,9 @@ def get_mllm_dataloader(
         template, model=model, tokenizer=tokenizer, processor=processor, image_processor=image_processor
     )
     dataset = template.default_dataset if dataset is None else dataset
-    if quant_nontext_module or (dataset in CALIB_DATASETS.keys() and not _only_text_test(model, tokenizer)):
+    if quant_nontext_module or (
+        dataset in CALIB_DATASETS.keys() and not _only_text_test(model, tokenizer, "cpu", template.model_type)
+    ):
         if quant_nontext_module:
             logger.warning(
                 "Quantitative nontext module is not supported for plain text datasets,"
@@ -399,7 +401,7 @@ def get_mllm_dataloader(
         truncation = False
         gradient_accumulate_steps = batch_size * gradient_accumulate_steps
         batch_size = 1
-
+        seed = 42  # The seed is fixed to 42 in transformers
     seqlen = 2048 if seqlen is None else seqlen  # set text only calibration default args
     truncation = True if truncation is None else truncation
     dataset = dataset.replace(" ", "")

neural_compressor/torch/quantization/config.py

@@ -950,7 +950,7 @@ def __init__(
         enable_torch_compile: bool = None,
         # mllm
         is_mllm: bool = False,
-        quant_nontext_module: Union[str, list] = None,
+        quant_nontext_module: bool = False,
         extra_data_dir: str = None,
         processor=None,
         image_processor=None,
@@ -994,7 +994,7 @@ def __init__(
             export_format (str, optional): The format used for exporting the quantized model. Defaults to "itrex".
             enable_norm_bias_tuning (bool): Whether to enable fast norm/layer_bias tuning.
             enable_torch_compile (bool): Whether to enable torch compile to optimize quant_block/layer, torch>=2.6 True.
-            quant_nontext_module (Union[str, list]): Whether to quantize nontext module.
+            quant_nontext_module (bool): Whether to quantize nontext module.
             extra_data_dir (str): The path for extra data such as images, audio or videos.
             is_mllm (bool): Indicates whether the model to be quantized is a multi-modal model (MLLM).
             processor (transformers.AutoProcessor): Any multi-modal model will require an object to encode or

neural_compressor/transformers/__init__.py

@@ -23,4 +23,5 @@
     AutoModelForCausalLM,
     AutoModel,
     AutoModelForSeq2SeqLM,
+    Qwen2VLForConditionalGeneration,
 )

neural_compressor/transformers/models/__init__.py

@@ -13,4 +13,11 @@
 # limitations under the License.
 
 from .modeling_auto import _BaseINCAutoModelClass
-from .modeling_auto import AutoModel, AutoModelForCausalLM, AutoModelForSeq2SeqLM
+from .modeling_auto import (
+    AutoModel,
+    AutoModelForCausalLM,
+    AutoModelForSeq2SeqLM,
+    Qwen2VLForConditionalGeneration,
+    MllamaForConditionalGeneration,
+    LlavaForConditionalGeneration,
+)

neural_compressor/transformers/models/modeling_auto.py

@@ -354,24 +354,27 @@ def load_low_bit(cls, pretrained_model_name_or_path, *model_args, **kwargs):
             else:
                 commit_hash = getattr(config, "_commit_hash", None)
 
-        has_remote_code = hasattr(config, "auto_map") and cls.ORIG_MODEL.__name__ in config.auto_map
-
-        has_local_code = type(config) in cls.ORIG_MODEL._model_mapping.keys()
-        trust_remote_code = resolve_trust_remote_code(
-            trust_remote_code,
-            pretrained_model_name_or_path,
-            has_local_code,
-            has_remote_code,
-        )
-        if has_remote_code and trust_remote_code:
-            class_ref = config.auto_map[cls.ORIG_MODEL.__name__]
-            model_class = get_class_from_dynamic_module(class_ref, pretrained_model_name_or_path, **kwargs_orig)
-            if os.path.isdir(pretrained_model_name_or_path):
-                model_class.register_for_auto_class(cls.ORIG_MODEL.__name__)
-            else:
-                cls.ORIG_MODEL.register(config.__class__, model_class, exist_ok=True)
-        elif type(config) in cls.ORIG_MODEL._model_mapping.keys():
-            model_class = _get_model_class(config, cls.ORIG_MODEL._model_mapping)
+        if "AutoModel" in cls.ORIG_MODEL.__name__:
+            has_remote_code = hasattr(config, "auto_map") and cls.ORIG_MODEL.__name__ in config.auto_map
+            has_local_code = type(config) in cls.ORIG_MODEL._model_mapping.keys()
+
+            trust_remote_code = resolve_trust_remote_code(
+                trust_remote_code,
+                pretrained_model_name_or_path,
+                has_local_code,
+                has_remote_code,
+            )
+            if has_remote_code and trust_remote_code:
+                class_ref = config.auto_map[cls.ORIG_MODEL.__name__]
+                model_class = get_class_from_dynamic_module(class_ref, pretrained_model_name_or_path, **kwargs_orig)
+                if os.path.isdir(pretrained_model_name_or_path):
+                    model_class.register_for_auto_class(cls.ORIG_MODEL.__name__)
+                else:
+                    cls.ORIG_MODEL.register(config.__class__, model_class, exist_ok=True)
+            elif type(config) in cls.ORIG_MODEL._model_mapping.keys():
+                model_class = _get_model_class(config, cls.ORIG_MODEL._model_mapping)
+        else:
+            model_class = cls.ORIG_MODEL
 
         # This variable will flag if we're loading a sharded checkpoint. In this case the archive file is just the
         # index of the files.
@@ -747,3 +750,15 @@ class AutoModel(_BaseINCAutoModelClass):
 
 class AutoModelForSeq2SeqLM(_BaseINCAutoModelClass):
     ORIG_MODEL = transformers.AutoModelForSeq2SeqLM
+
+
+class Qwen2VLForConditionalGeneration(_BaseINCAutoModelClass):
+    ORIG_MODEL = transformers.Qwen2VLForConditionalGeneration
+
+
+class MllamaForConditionalGeneration(_BaseINCAutoModelClass):
+    ORIG_MODEL = transformers.MllamaForConditionalGeneration
+
+
+class LlavaForConditionalGeneration(_BaseINCAutoModelClass):
+    ORIG_MODEL = transformers.LlavaForConditionalGeneration

neural_compressor/transformers/quantization/utils.py

@@ -17,6 +17,7 @@
 import json
 import math
 import os
+import re
 import types
 
 from datasets import load_dataset
@@ -33,11 +34,16 @@
     convert,
     prepare,
 )
-from neural_compressor.torch.utils import is_ipex_available
+from neural_compressor.torch.utils import is_ipex_available, is_package_available
 
 if is_ipex_available():
     import intel_extension_for_pytorch as ipex
 
+if is_package_available("auto_round"):
+    import auto_round
+    import transformers
+    from auto_round.export.export_to_itrex.model_wrapper import WeightOnlyLinear as auto_round_woq_linear
+
 from typing import Union
 
 torch = LazyImport("torch")
@@ -126,10 +132,12 @@ def _replace_linear(
         if (
             isinstance(module, torch.nn.Linear)
             or isinstance(module, INCWeightOnlyLinear)
-            or (is_ipex_available() and isinstance(module, ipex.nn.utils._weight_prepack._IPEXLinear))
+            or (is_package_available("auto_round") and isinstance(module, auto_round_woq_linear))
         ) and (name not in modules_to_not_convert):
             # Check if the current key is not in the `modules_to_not_convert`
-            if not any(key in ".".join(current_key_name) for key in modules_to_not_convert):
+            if not any(key in ".".join(current_key_name) for key in modules_to_not_convert) and not any(
+                re.match(pattern, ".".join(current_key_name)) for pattern in modules_to_not_convert
+            ):
                 in_features = module.in_features
                 out_features = module.out_features
                 if device == "cpu" or device == torch.device("cpu") or device == "auto":
@@ -475,6 +483,54 @@ def convert_to_quantized_model(model, config, device="cpu"):
         run_fn(model, *run_args)
         model = convert(model)
     elif config.quant_method.value == "autoround":
+        if config.is_vlm is True:
+            from transformers import AutoProcessor, AutoTokenizer
+
+            from neural_compressor.torch.algorithms.weight_only.autoround import (
+                get_mllm_dataloader as get_autoround_dataloader,
+            )
+
+            tokenizer = AutoTokenizer.from_pretrained(model.config._name_or_path)
+            processor = AutoProcessor.from_pretrained(model.config._name_or_path, trust_remote_code=True)
+            (
+                dataloader,
+                template,
+                config.truncation,
+                config.batch_size,
+                config.gradient_accumulate_steps,
+                config.seq_len,
+                config.n_samples,
+            ) = get_autoround_dataloader(
+                template=None,
+                model=model,
+                tokenizer=tokenizer,
+                image_processor=None,
+                dataset=config.dataset,
+                extra_data_dir=None,
+                seqlen=config.seq_len,
+                batch_size=config.batch_size,
+                split=None,
+                apply_template=None,
+                truncation=False,
+                nsamples=config.n_samples,
+                seed=42,
+                gradient_accumulate_steps=config.gradient_accumulate_steps,
+                quant_nontext_module=config.quant_nontext_module,
+                processor=processor,
+            )
+        else:
+            from neural_compressor.torch.algorithms.weight_only.autoround import (
+                get_dataloader as get_autoround_dataloader,
+            )
+
+            dataloader = get_autoround_dataloader(
+                tokenizer=config.tokenizer,
+                seqlen=config.seq_len,
+                dataset_name=config.dataset,
+                seed=42,
+                bs=config.batch_size,
+                nsamples=config.n_samples,
+            )
         quant_config = AutoRoundConfig(
             dtype=dtype,
             bits=config.bits,
@@ -486,24 +542,59 @@ def convert_to_quantized_model(model, config, device="cpu"):
             seqlen=config.seq_len,
             nsamples=config.n_samples,
             iters=config.iters,
+            batch_size=config.batch_size,
             scale_dtype=config.scale_dtype,
             use_layer_wise=config.use_layer_wise,
+            # vlm arguments
+            is_mllm=config.is_vlm,
+            quant_nontext_module=config.quant_nontext_module,
+            truncation=config.truncation,
+            gradient_accumulate_steps=config.gradient_accumulate_steps,
+            export_format=config.export_format,
         )
+
+        # vlm set non-text module config
+        if config.is_vlm is True:
+            from neural_compressor.torch.utils.utility import (
+                find_matching_blocks,
+                get_layer_names_in_block,
+                get_multimodal_block_names,
+            )
+
+            def set_nontext_module_config(model, to_quant_block_names, config):
+                all_block_list = get_multimodal_block_names(model, quant_vision=True)
+                all_block_set = set(tuple(block) for block in all_block_list)
+                quant_block_set = set(tuple(block) for block in to_quant_block_names)
+                set_to_full_prec = list(all_block_set - quant_block_set)
+                set_to_full_prec = get_layer_names_in_block(model, to_quant_block_names=set_to_full_prec)
+                for name in set_to_full_prec:
+                    config.modules_to_not_convert.append(name)
+
+                # skip layers not in blocks
+                config.modules_to_not_convert.append("model.vision_embed_tokens.img_projection*")
+                config.modules_to_not_convert.append("transformer.visual.attn_pool.*_proj")
+                config.modules_to_not_convert.append("model.mm_projector*")
+                config.modules_to_not_convert.append("multi_modal_projector")
+                config.modules_to_not_convert.append("visual.merger")
+
+            all_blocks = get_multimodal_block_names(model, quant_config.quant_nontext_module)
+            to_quant_block_names = find_matching_blocks(model, all_blocks, quant_config.to_quant_block_names)
+            set_nontext_module_config(model, to_quant_block_names, config)
+
+            for n, m in model.named_modules():
+                if isinstance(m, torch.nn.Linear) or isinstance(m, transformers.modeling_utils.Conv1D):
+                    if m.weight.shape[0] % 32 != 0 or m.weight.shape[1] % 32 != 0:
+                        config.modules_to_not_convert.append(n)
+                        print(
+                            f"{n} will not be quantized due to its shape not being divisible by 32,"
+                            " resulting in an exporting issue to autogptq"
+                        )
         if config.modules_to_not_convert != []:
             for module in config.modules_to_not_convert:
                 module_name = ".*" + module
                 quant_config.set_local(module_name, AutoRoundConfig(dtype="fp32"))
         logger.info(f"Do AutoRound algorithm with config {quant_config}")
-        from neural_compressor.torch.algorithms.weight_only.autoround import get_dataloader as get_autoround_dataloader
 
-        dataloader = get_autoround_dataloader(
-            tokenizer=config.tokenizer,
-            seqlen=config.seq_len,
-            dataset_name=config.dataset,
-            seed=42,
-            bs=config.batch_size,
-            nsamples=config.n_samples,
-        )
         run_fn = run_fn_for_autoround
         run_args = (dataloader,)
         model = prepare(model=model, quant_config=quant_config)

neural_compressor/transformers/utils/quantization_config.py

@@ -543,6 +543,12 @@ def __init__(
         iters: int = 200,
         use_layer_wise: bool = None,
         quant_lm_head: bool = False,
+        # vlm arguments
+        is_vlm: bool = False,
+        quant_nontext_module: bool = False,
+        truncation: bool = False,
+        gradient_accumulate_steps: int = 1,
+        export_format="itrex",
         **kwargs,
     ):
 
@@ -594,6 +600,13 @@ def __init__(
         self.use_layer_wise = use_layer_wise
         self.model_path = kwargs.get("model_path", "")
 
+        # vlm arguments
+        self.is_vlm = is_vlm
+        self.quant_nontext_module = quant_nontext_module
+        self.truncation = truncation
+        self.gradient_accumulate_steps = gradient_accumulate_steps
+        self.export_format = export_format
+
     def to_diff_dict(self) -> Dict[str, Any]:
         """Removes all attributes from config which correspond to the default config attributes
         for better readability and serializes to a Python dictionary.

test/3x/torch/quantization/weight_only/test_autoround.py

@@ -238,13 +238,13 @@ def test_mllm(self):
             image_processor=None,
             dataset="liuhaotian/llava_conv_58k",
             extra_data_dir=None,
-            seqlen=512,
+            seqlen=32,
             batch_size=1,
             split=None,
             apply_template=None,
             truncation=False,
             seed=42,
-            nsamples=5,
+            nsamples=1,
             gradient_accumulate_steps=1,
             quant_nontext_module=False,
             processor=processor,
@@ -253,9 +253,9 @@ def test_mllm(self):
             bits=4,
             group_size=128,
             is_mllm=True,
-            nsamples=5,
+            nsamples=1,
             batch_size=batch_size,
-            iters=2,
+            iters=1,
             seqlen=seqlen,
             quant_nontext_module=False,
             truncation=truncation,