From abb63121fedeb015ee36b69e255465e8e4415ea6 Mon Sep 17 00:00:00 2001 From: yuwenzho Date: Tue, 28 Nov 2023 15:39:20 +0800 Subject: [PATCH 01/22] update onnxrt woq example Signed-off-by: yuwenzho --- .../llama/quantization/ptq_static/README.md | 3 +++ .../llama/quantization/ptq_static/main.py | 8 +++++++- .../llama/quantization/ptq_static/run_quant.sh | 13 +++++++++++-- 3 files changed, 21 insertions(+), 3 deletions(-) diff --git a/examples/onnxrt/nlp/huggingface_model/text_generation/llama/quantization/ptq_static/README.md b/examples/onnxrt/nlp/huggingface_model/text_generation/llama/quantization/ptq_static/README.md index 938a777ddf6..cacb457c464 100644 --- a/examples/onnxrt/nlp/huggingface_model/text_generation/llama/quantization/ptq_static/README.md +++ b/examples/onnxrt/nlp/huggingface_model/text_generation/llama/quantization/ptq_static/README.md @@ -44,6 +44,9 @@ bash run_quant.sh --input_model=/path/to/model \ # folder path of onnx model --quant_format="QOperator" # or QDQ, optional ``` +Additionally set `--layer-wise=True` to use layer-wise quantization to save your memory. More details please refer to [](). + + ## 2. Benchmark Accuracy: diff --git a/examples/onnxrt/nlp/huggingface_model/text_generation/llama/quantization/ptq_static/main.py b/examples/onnxrt/nlp/huggingface_model/text_generation/llama/quantization/ptq_static/main.py index 46b09a25f86..c1c940ef803 100644 --- a/examples/onnxrt/nlp/huggingface_model/text_generation/llama/quantization/ptq_static/main.py +++ b/examples/onnxrt/nlp/huggingface_model/text_generation/llama/quantization/ptq_static/main.py @@ -116,6 +116,11 @@ type=int, default=4 ) +parser.add_argument( + '--layer_wise', + action='store_true', \ + default=False, +) args = parser.parse_args() # load model @@ -262,7 +267,8 @@ def __iter__(self): calibration_sampling_size=[8], recipes={'optypes_to_exclude_output_quant': ['MatMul'], 'smooth_quant': True, - 'smooth_quant_args': {'alpha': args.smooth_quant_alpha}}, + 'smooth_quant_args': {'alpha': args.smooth_quant_alpha}, + 'layer_wise_quant': True if args.layer_wise else False}, op_type_dict={'^((?!(MatMul|Gather|Conv)).)*$': {'weight': {'dtype': ['fp32']}, 'activation': {'dtype': ['fp32']}}}) for model in ['decoder_model.onnx', 'decoder_with_past_model.onnx']: q_model = quantization.fit( diff --git a/examples/onnxrt/nlp/huggingface_model/text_generation/llama/quantization/ptq_static/run_quant.sh b/examples/onnxrt/nlp/huggingface_model/text_generation/llama/quantization/ptq_static/run_quant.sh index 9c7d2ff8c2a..384f43a84aa 100644 --- a/examples/onnxrt/nlp/huggingface_model/text_generation/llama/quantization/ptq_static/run_quant.sh +++ b/examples/onnxrt/nlp/huggingface_model/text_generation/llama/quantization/ptq_static/run_quant.sh @@ -32,6 +32,9 @@ function init_params { --tokenizer=*) tokenizer=$(echo $var |cut -f2 -d=) ;; + --layer_wise=*) + layer_wise=$(echo $var |cut -f2 -d=) + ;; esac done @@ -59,15 +62,21 @@ function run_tuning { echo "Created directory $output_model" fi + # check if layer_wise option is set to true (case insensitive) + if [ "${layer_wise,,}" = "true" ]; then + extra_cmd="--layer_wise" + fi + python main.py \ --quant_format ${quant_format-QOperator} \ --model_path ${input_model} \ - --tokenizer ${tokenizer-meta-llama/Llama-2-7b-hf} \ + --tokenizer ${tokenizer-meta-llama/Llama-2-7b-hf} \ --output_model ${output_model} \ --batch_size ${batch_size-1} \ --smooth_quant_alpha ${alpha-0.6} \ --dataset ${dataset-NeelNanda/pile-10k} \ - --tune + --tune \ + ${extra_cmd} } main "$@" From 0655119081f6e4298317871c7ab156485ba9f798 Mon Sep 17 00:00:00 2001 From: yuwenzho Date: Thu, 30 Nov 2023 17:08:21 +0800 Subject: [PATCH 02/22] add layer-wise quantization example Signed-off-by: yuwenzho --- examples/.config/model_params_onnxrt.json | 7 +++ .../llama/quantization/ptq_static/README.md | 14 +++++- .../llama/quantization/ptq_static/main.py | 47 ++++++++++++++----- neural_compressor/adaptor/onnxrt.py | 2 +- .../adaptor/ox_utils/calibration.py | 4 +- neural_compressor/adaptor/ox_utils/util.py | 39 +++++++++++++++ neural_compressor/model/onnx_model.py | 13 +++-- 7 files changed, 106 insertions(+), 20 deletions(-) diff --git a/examples/.config/model_params_onnxrt.json b/examples/.config/model_params_onnxrt.json index 8695baa3dc1..238c744ad27 100644 --- a/examples/.config/model_params_onnxrt.json +++ b/examples/.config/model_params_onnxrt.json @@ -763,6 +763,13 @@ "main_script": "main.py", "batch_size": 1 }, + "llama-2-7b-lwq": { + "model_src_dir": "nlp/huggingface_model/text_generation/llama/quantization/ptq_static", + "dataset_location": "", + "input_model": "/tf_dataset2/models/onnx/llama-2-7b", + "main_script": "main.py", + "batch_size": 1 + }, "llama-2-7b-rtn": { "model_src_dir": "nlp/huggingface_model/text_generation/llama/quantization/weight_only", "dataset_location": "", diff --git a/examples/onnxrt/nlp/huggingface_model/text_generation/llama/quantization/ptq_static/README.md b/examples/onnxrt/nlp/huggingface_model/text_generation/llama/quantization/ptq_static/README.md index cacb457c464..5c2bc11ab89 100644 --- a/examples/onnxrt/nlp/huggingface_model/text_generation/llama/quantization/ptq_static/README.md +++ b/examples/onnxrt/nlp/huggingface_model/text_generation/llama/quantization/ptq_static/README.md @@ -34,6 +34,8 @@ optimum-cli export onnx --model meta-llama/Llama-2-7b-hf --task text-generation- ## 1. Quantization +Run SmoothQuant + ```bash bash run_quant.sh --input_model=/path/to/model \ # folder path of onnx model --output_model=/path/to/model_tune \ # folder path to save onnx model @@ -44,7 +46,17 @@ bash run_quant.sh --input_model=/path/to/model \ # folder path of onnx model --quant_format="QOperator" # or QDQ, optional ``` -Additionally set `--layer-wise=True` to use layer-wise quantization to save your memory. More details please refer to [](). +Additionally set `--layer-wise=True` to use layer-wise quantization to save your memory. Please note that layer-wise quantization for ONNX models is still under development and only support W8A8 quantization now. More details please refer to [layer wise quantiation](https://github.com/intel/neural-compressor/blob/master/docs/source/quantization_layer_wise.md). + +```bash +bash run_quant.sh --input_model=/path/to/model \ # folder path of onnx model + --output_model=/path/to/model_tune \ # folder path to save onnx model + --batch_size=batch_size # optional \ + --dataset NeelNanda/pile-10k \ + --tokenizer=meta-llama/Llama-2-7b-hf \ # model name or folder path containing all relevant files for model's tokenizer + --quant_format="QOperator" \ # or QDQ, optional + --layer-wise=True +``` ## 2. Benchmark diff --git a/examples/onnxrt/nlp/huggingface_model/text_generation/llama/quantization/ptq_static/main.py b/examples/onnxrt/nlp/huggingface_model/text_generation/llama/quantization/ptq_static/main.py index c1c940ef803..cb26cc585a4 100644 --- a/examples/onnxrt/nlp/huggingface_model/text_generation/llama/quantization/ptq_static/main.py +++ b/examples/onnxrt/nlp/huggingface_model/text_generation/llama/quantization/ptq_static/main.py @@ -263,16 +263,37 @@ def __iter__(self): if args.tune: from neural_compressor import quantization, PostTrainingQuantConfig - config = PostTrainingQuantConfig( - calibration_sampling_size=[8], - recipes={'optypes_to_exclude_output_quant': ['MatMul'], - 'smooth_quant': True, - 'smooth_quant_args': {'alpha': args.smooth_quant_alpha}, - 'layer_wise_quant': True if args.layer_wise else False}, - op_type_dict={'^((?!(MatMul|Gather|Conv)).)*$': {'weight': {'dtype': ['fp32']}, 'activation': {'dtype': ['fp32']}}}) - for model in ['decoder_model.onnx', 'decoder_with_past_model.onnx']: - q_model = quantization.fit( - os.path.join(args.model_path, model), - config, - calib_dataloader=KVDataloader(os.path.join(args.model_path, model), pad_max=args.pad_max, batch_size=1)) - q_model.save(os.path.join(args.output_model, model)) + if args.layer_wise: + # layer-wise quantization for ONNX models is still under development and only support W8A8 quantization now + config = PostTrainingQuantConfig( + calibration_sampling_size=[8], + recipes={'optypes_to_exclude_output_quant': ['MatMul'], + 'layer_wise_quant': True}, + op_type_dict={'^((?!(MatMul|Gather|Conv)).)*$': {'weight': {'dtype': ['fp32']}, 'activation': {'dtype': ['fp32']}}}) + for model in ['decoder_model.onnx']: + # only test decoder_model + q_model = quantization.fit( + os.path.join(args.model_path, model), + config, + calib_dataloader=KVDataloader(os.path.join(args.model_path, model), pad_max=args.pad_max, batch_size=1)) + q_model.save(os.path.join(args.output_model, model)) + + tokenizer.save_pretrained(args.output_model) + + else: + config = PostTrainingQuantConfig( + calibration_sampling_size=[8], + recipes={'optypes_to_exclude_output_quant': ['MatMul'], + # 'smooth_quant': True, + # 'smooth_quant_args': {'alpha': args.smooth_quant_alpha}, + }, + op_type_dict={'^((?!(MatMul|Gather|Conv)).)*$': {'weight': {'dtype': ['fp32']}, 'activation': {'dtype': ['fp32']}}}) + for model in ['decoder_model.onnx', 'decoder_with_past_model.onnx']: + q_model = quantization.fit( + os.path.join(args.model_path, model), + config, + calib_dataloader=KVDataloader(os.path.join(args.model_path, model), pad_max=args.pad_max, batch_size=1)) + q_model.save(os.path.join(args.output_model, model)) + + tokenizer.save_pretrained(args.output_model) + \ No newline at end of file diff --git a/neural_compressor/adaptor/onnxrt.py b/neural_compressor/adaptor/onnxrt.py index 5e8deff9933..e24dbc23f3b 100644 --- a/neural_compressor/adaptor/onnxrt.py +++ b/neural_compressor/adaptor/onnxrt.py @@ -1021,7 +1021,7 @@ def _pre_optimize(self, model, level=1): from onnx.external_data_helper import load_external_data_for_model load_external_data_for_model(tmp_model, os.path.split(model.model_path)[0]) - model.model_path = sess_options.optimized_model_filepath + model.model_path = sess_options.optimized_model_filepath else: model.model_path = sess_options.optimized_model_filepath diff --git a/neural_compressor/adaptor/ox_utils/calibration.py b/neural_compressor/adaptor/ox_utils/calibration.py index 2e15acd0d1e..26ea0739a9b 100644 --- a/neural_compressor/adaptor/ox_utils/calibration.py +++ b/neural_compressor/adaptor/ox_utils/calibration.py @@ -264,9 +264,9 @@ def get_intermediate_outputs(self, q_config=None): for output in session.get_outputs() ] augment_model_wrapper = ( - ONNXModel(self.augmented_model) + ONNXModel(self.augmented_model, load_external_data=False) if not self.model_wrapper.is_large_model - else ONNXModel(self.model_wrapper.model_path + "_augment.onnx") + else ONNXModel(self.model_wrapper.model_path + "_augment.onnx", load_external_data=False) ) input_name_to_nodes = augment_model_wrapper.input_name_to_nodes output_name_to_node = augment_model_wrapper.output_name_to_node diff --git a/neural_compressor/adaptor/ox_utils/util.py b/neural_compressor/adaptor/ox_utils/util.py index 8e679b432e0..547a7fa919c 100644 --- a/neural_compressor/adaptor/ox_utils/util.py +++ b/neural_compressor/adaptor/ox_utils/util.py @@ -17,6 +17,7 @@ """Helper classes or functions for onnxrt adaptor.""" import os +import logging from enum import Enum import numpy as np @@ -27,6 +28,11 @@ numpy_helper = LazyImport("onnx.numpy_helper") onnx_proto = LazyImport("onnx.onnx_pb") torch = LazyImport("torch") +symbolic_shape_infer = LazyImport("onnxruntime.tools.symbolic_shape_infer") +onnx = LazyImport("onnx") + +logger = logging.getLogger("neural_compressor") + __producer__ = "onnx.quantize" __version__ = "0.1.0" @@ -586,3 +592,36 @@ def to_numpy(data): ) else: return data + +class SymbolicShapeInference(symbolic_shape_infer.SymbolicShapeInference): + def __init__(self, int_max, auto_merge, guess_output_rank, verbose, prefix="", base_dir=""): + super().__init__(int_max, auto_merge, guess_output_rank, verbose, prefix) + self.base_dir = base_dir + + def _get_value(self, node, idx): + name = node.input[idx] + assert name in self.sympy_data_ or name in self.initializers_ + return self.sympy_data_[name] if name in self.sympy_data_ else \ + numpy_helper.to_array(self.initializers_[name], base_dir=self.base_dir) + + @staticmethod + def infer_shapes(in_mp, int_max=2**31 - 1, auto_merge=False, guess_output_rank=False, verbose=0, base_dir=""): + onnx_opset = symbolic_shape_infer.get_opset(in_mp) + if (not onnx_opset) or onnx_opset < 7: + logger.warning("Only support models of onnx opset 7 and above.") + return None + symbolic_shape_inference = SymbolicShapeInference( + int_max, + auto_merge, + guess_output_rank, + verbose, + base_dir=base_dir) + all_shapes_inferred = False + symbolic_shape_inference._preprocess(in_mp) + while symbolic_shape_inference.run_: + all_shapes_inferred = symbolic_shape_inference._infer_impl() + symbolic_shape_inference._update_output_from_vi() + if not all_shapes_inferred: + onnx.save_model(symbolic_shape_inference.out_mp_, "sym_shape_infer_temp.onnx", save_as_external_data=True) + raise Exception("Incomplete symbolic shape inference") + return symbolic_shape_inference.out_mp_ \ No newline at end of file diff --git a/neural_compressor/model/onnx_model.py b/neural_compressor/model/onnx_model.py index c1365668f55..431ddd007e7 100644 --- a/neural_compressor/model/onnx_model.py +++ b/neural_compressor/model/onnx_model.py @@ -40,13 +40,20 @@ def __init__(self, model, **kwargs): Args: model (str or ModelProto): path to onnx model or loaded ModelProto model object. + ignore_warning (bool): ignore large model warning. Default is False. + load_external_data (bool): load external data for large model. """ - self._model = model if not isinstance(model, str) else onnx.load(model) + self._model = model if not isinstance(model, str) else onnx.load(model, load_external_data=False) self._model_path = None if not isinstance(model, str) else model self.check_is_large_model() if self._is_large_model and self._model_path is None and not kwargs.get("ignore_warning", False): logger.warning("Model size > 2GB. Please use model path instead of onnx model object to quantize") + + if self._is_large_model and isinstance(model, str) and kwargs.get("load_external_data", True): + from onnx.external_data_helper import load_external_data_for_model + + load_external_data_for_model(self._model, os.path.dirname(self._model_path)) self._config = None if isinstance(model, str) and os.path.exists(Path(model).parent.joinpath("config.json").as_posix()): @@ -1038,9 +1045,9 @@ def split_model_with_node( if shape_infer: try: # need ort.GraphOptimizationLevel <= ORT_ENABLE_BASIC - import onnxruntime.tools.symbolic_shape_infer as symbolic_shape_infer + from neural_compressor.adaptor.ox_utils.util import SymbolicShapeInference - self._model = symbolic_shape_infer.SymbolicShapeInference.infer_shapes(self._model, auto_merge=True) + self._model = SymbolicShapeInference.infer_shapes(self._model, auto_merge=True, base_dir=os.path.dirname(self._model_path)) except Exception as e: # pragma: no cover logger.error("Shape infer fails for layer-wise quantization") if "Incomplete symbolic shape inference" in str(e): From ca67739816822fa5c6540b87a8c2dcabb0cb821d Mon Sep 17 00:00:00 2001 From: yuwenzho Date: Thu, 30 Nov 2023 17:16:18 +0800 Subject: [PATCH 03/22] fix docstring Signed-off-by: yuwenzho --- .../text_generation/llama/quantization/ptq_static/main.py | 4 ++-- neural_compressor/model/onnx_model.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/examples/onnxrt/nlp/huggingface_model/text_generation/llama/quantization/ptq_static/main.py b/examples/onnxrt/nlp/huggingface_model/text_generation/llama/quantization/ptq_static/main.py index cb26cc585a4..788abf5fc2d 100644 --- a/examples/onnxrt/nlp/huggingface_model/text_generation/llama/quantization/ptq_static/main.py +++ b/examples/onnxrt/nlp/huggingface_model/text_generation/llama/quantization/ptq_static/main.py @@ -284,8 +284,8 @@ def __iter__(self): config = PostTrainingQuantConfig( calibration_sampling_size=[8], recipes={'optypes_to_exclude_output_quant': ['MatMul'], - # 'smooth_quant': True, - # 'smooth_quant_args': {'alpha': args.smooth_quant_alpha}, + 'smooth_quant': True, + 'smooth_quant_args': {'alpha': args.smooth_quant_alpha}, }, op_type_dict={'^((?!(MatMul|Gather|Conv)).)*$': {'weight': {'dtype': ['fp32']}, 'activation': {'dtype': ['fp32']}}}) for model in ['decoder_model.onnx', 'decoder_with_past_model.onnx']: diff --git a/neural_compressor/model/onnx_model.py b/neural_compressor/model/onnx_model.py index 431ddd007e7..3d055d39f04 100644 --- a/neural_compressor/model/onnx_model.py +++ b/neural_compressor/model/onnx_model.py @@ -41,7 +41,7 @@ def __init__(self, model, **kwargs): Args: model (str or ModelProto): path to onnx model or loaded ModelProto model object. ignore_warning (bool): ignore large model warning. Default is False. - load_external_data (bool): load external data for large model. + load_external_data (bool): load external data for large model. Default is True. """ self._model = model if not isinstance(model, str) else onnx.load(model, load_external_data=False) self._model_path = None if not isinstance(model, str) else model From 54179d0603a2d27788e186be0004b7f64e8bafdf Mon Sep 17 00:00:00 2001 From: yuwenzho Date: Thu, 30 Nov 2023 17:18:12 +0800 Subject: [PATCH 04/22] update README.md Signed-off-by: yuwenzho --- .../text_generation/llama/quantization/ptq_static/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/onnxrt/nlp/huggingface_model/text_generation/llama/quantization/ptq_static/README.md b/examples/onnxrt/nlp/huggingface_model/text_generation/llama/quantization/ptq_static/README.md index 5c2bc11ab89..975ef34b617 100644 --- a/examples/onnxrt/nlp/huggingface_model/text_generation/llama/quantization/ptq_static/README.md +++ b/examples/onnxrt/nlp/huggingface_model/text_generation/llama/quantization/ptq_static/README.md @@ -55,7 +55,7 @@ bash run_quant.sh --input_model=/path/to/model \ # folder path of onnx model --dataset NeelNanda/pile-10k \ --tokenizer=meta-llama/Llama-2-7b-hf \ # model name or folder path containing all relevant files for model's tokenizer --quant_format="QOperator" \ # or QDQ, optional - --layer-wise=True + --layer_wise=True ``` From ea2078e710e3bc683ec05ac95c1d1b1e9f7cfb34 Mon Sep 17 00:00:00 2001 From: yuwenzho Date: Thu, 30 Nov 2023 17:24:46 +0800 Subject: [PATCH 05/22] add layer-wise quantization doc Signed-off-by: yuwenzho --- docs/source/imgs/ort_lwq.png | Bin 0 -> 161111 bytes docs/source/quantization_layer_wise.md | 101 +++++++++++++++++++++++++ 2 files changed, 101 insertions(+) create mode 100644 docs/source/imgs/ort_lwq.png create mode 100644 docs/source/quantization_layer_wise.md diff --git a/docs/source/imgs/ort_lwq.png b/docs/source/imgs/ort_lwq.png new file mode 100644 index 0000000000000000000000000000000000000000..e2576acf28722f3c62615e86f49e92a9182b13a8 GIT binary patch literal 161111 zcmeEv30#utzCSa~jAy1vr`6nNw9Pg%bIYx!(l&L<(s04bg(gx|asyF2O_{lkWtn?r zYAG&BE~u!Drlta!0=a=niVBK`h=RcX#q6BQbh`JRd+yx(kI$!%0q^rXzxDfDp67iD z|HaB;$#;t1fk2=o2lnqh3IZ*y0D)vY|Mm^=iT+7P=@(xhj#~T-$|EWd0&l*8?zY?w z0zHgbJbmhG;QhB=`@skhXnVEv-xnKP?K?prNydS_yN?Ax*j>A2U+LXk*JF4>>$?FQ z={EETYol=2*Bp1|K2j7LUk~5)<&oOu`wpyFc`!t6RIBmn?bQ9|YwjEz3IS&R{Q4|` z&l338NCDPjhxcDe|mC{g)fjnJ^#CbiyuYO(CTA9Tl{x-F}M*R(fz zo;^+AnzlPq0tT0SFrfNp-dg?kh##Ztns!Ytb%hCaDw=4VqwTs zW4%cOYP1awt!XcvDn_MP^X{u>_n3NOz)zMFyWe|#%St76PZ3Oet; zysifvv6%QHteo-nPt66q!pwj>PY-)YJZ`N=DOfg-HPa*U^0a1%*}G$;|DV=G9cgF; zn6cwQ9JeFTyvj|G=H=|Q$V z25)i>s8MWpGBZ|p3!L63_)dw?IH$_&k z`=jFzjzbO}UGCAdL91eF%oU+9_J`63SBA#7qsE$3wBl9=4lYsdyQFWHdTy%*Bw*18 z*Nv)=y^dk8U6zJ(s3=e`_l%Oa_llFJO$QX>A}7%wTnq|`rA6#k&wUdtPP22^P}ITB$J(3c*fT|THq+W zqE>}rzXQeDuY12Ef7rCXM8B?OT|Xm<;^m|U?&bxL*e|zc5x{s3{8rAn;IrL2{rv;* z+UqXQ9r8U=HnrI!c^Fk}toBv2Dp+J{rRn>0dtnVWC#$T>1EV*X
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[Introduction](#introduction) + +2. [Supported Framework Model Matrix](#supported-framework-model-matrix) + +3. [Examples](#examples) + +## Introduction + +Large language models (LLMs) have shown exceptional performance across various tasks, meanwhile, the substantial parameter size poses significant challenges for deployment. Layer-wise quantization(LWQ) can greatly reduce the memory footprint of LLMs, usually 80-90% reduction, which means that users can quantize LLMs even on single node using GPU or CPU. We can quantize the model under memory-constrained devices, therefore making the huge-sized LLM quantization possible. + + + +*Figure 1: The process of layer-wise quantization for PyTorch model. The color grey means empty parameters and the color blue represents parameters need to be quantized. Every rectangle inside model represents one layer.* + + + +*Figure 2: The process of layer-wise quantization for ONNX model. The graph of LLM is split into several parts, and each subgraph is quantized in turn.* + +## Supported Framework Model Matrix + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    Types/FrameworkPyTorchONNX Runtime
    W8A8 Post Training Static Quantization✔✔
    Weight-only QuantizationRTN✔✕
    AWQ✕
    GPTQ✔
    TEQ✕
    + +## Examples + +#### PyTorch framework example + +```python +from neural_compressor import PostTrainingQuantConfig, quantization +from neural_compressor.adaptor.torch_utils.layer_wise_quant import load_empty_model + +fp32_model = load_empty_model(model_name_or_path, torchscript=True) +conf = PostTrainingQuantConfig( + approach="weight_only", + recipes={ + "layer_wise_quant": True, + "rtn_args": {"enable_full_range": True}, + }, +) + +q_model = quantization.fit( + fp32_model, + conf, + calib_dataloader=eval_dataloader, + eval_func=lambda x: 0.1, +) +ouput_dir = "./saved_model" +q_model.save(ouput_dir) +q_model = load(ouput_dir, fp32_model, weight_only=True, layer_wise=True) +``` + +#### ONNX Runtime framework example + +```python +from neural_compressor import quantization, PostTrainingQuantConfig + +conf = PostTrainingQuantConfig(recipes={'layer_wise_quant': True}) +q_model = quantization.fit( + fp32_model_path, + conf, + calib_dataloader=dataloader) +q_model.save(int8_model_path) +``` + +Refer to [ONNX Runtime llama-2 LWQ example](../../examples/onnxrt/nlp/huggingface_model/text_generation/llama/quantization/weight_only) \ No newline at end of file From 1acdc9cdec4e0db08a91f818b77793da194b3388 Mon Sep 17 00:00:00 2001 From: yuwenzho Date: Thu, 30 Nov 2023 17:27:33 +0800 Subject: [PATCH 06/22] update onnxrt lwq figure Signed-off-by: yuwenzho --- docs/source/imgs/{ort_lwq.png => lwq_ort.png} | Bin 1 file changed, 0 insertions(+), 0 deletions(-) rename docs/source/imgs/{ort_lwq.png => lwq_ort.png} (100%) diff --git a/docs/source/imgs/ort_lwq.png b/docs/source/imgs/lwq_ort.png similarity index 100% rename from docs/source/imgs/ort_lwq.png rename to docs/source/imgs/lwq_ort.png From a6a656b917227b5b3e411624497424b611530029 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Thu, 30 Nov 2023 09:29:47 +0000 Subject: [PATCH 07/22] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- docs/source/quantization_layer_wise.md | 199 +++++++++--------- .../llama/quantization/ptq_static/main.py | 1 - neural_compressor/adaptor/ox_utils/util.py | 19 +- neural_compressor/model/onnx_model.py | 6 +- 4 files changed, 112 insertions(+), 113 deletions(-) diff --git a/docs/source/quantization_layer_wise.md b/docs/source/quantization_layer_wise.md index f6d3b80a1ba..c9d93761eaa 100644 --- a/docs/source/quantization_layer_wise.md +++ b/docs/source/quantization_layer_wise.md @@ -1,101 +1,98 @@ -Layer Wise Quantization (LWQ) -===== - -1. [Introduction](#introduction) - -2. [Supported Framework Model Matrix](#supported-framework-model-matrix) - -3. [Examples](#examples) - -## Introduction - -Large language models (LLMs) have shown exceptional performance across various tasks, meanwhile, the substantial parameter size poses significant challenges for deployment. Layer-wise quantization(LWQ) can greatly reduce the memory footprint of LLMs, usually 80-90% reduction, which means that users can quantize LLMs even on single node using GPU or CPU. We can quantize the model under memory-constrained devices, therefore making the huge-sized LLM quantization possible. - - - -*Figure 1: The process of layer-wise quantization for PyTorch model. The color grey means empty parameters and the color blue represents parameters need to be quantized. Every rectangle inside model represents one layer.* - - - -*Figure 2: The process of layer-wise quantization for ONNX model. The graph of LLM is split into several parts, and each subgraph is quantized in turn.* - -## Supported Framework Model Matrix - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    Types/FrameworkPyTorchONNX Runtime
    W8A8 Post Training Static Quantization✔✔
    Weight-only QuantizationRTN✔✕
    AWQ✕
    GPTQ✔
    TEQ✕
    - -## Examples - -#### PyTorch framework example - -```python -from neural_compressor import PostTrainingQuantConfig, quantization -from neural_compressor.adaptor.torch_utils.layer_wise_quant import load_empty_model - -fp32_model = load_empty_model(model_name_or_path, torchscript=True) -conf = PostTrainingQuantConfig( - approach="weight_only", - recipes={ - "layer_wise_quant": True, - "rtn_args": {"enable_full_range": True}, - }, -) - -q_model = quantization.fit( - fp32_model, - conf, - calib_dataloader=eval_dataloader, - eval_func=lambda x: 0.1, -) -ouput_dir = "./saved_model" -q_model.save(ouput_dir) -q_model = load(ouput_dir, fp32_model, weight_only=True, layer_wise=True) -``` - -#### ONNX Runtime framework example - -```python -from neural_compressor import quantization, PostTrainingQuantConfig - -conf = PostTrainingQuantConfig(recipes={'layer_wise_quant': True}) -q_model = quantization.fit( - fp32_model_path, - conf, - calib_dataloader=dataloader) -q_model.save(int8_model_path) -``` - -Refer to [ONNX Runtime llama-2 LWQ example](../../examples/onnxrt/nlp/huggingface_model/text_generation/llama/quantization/weight_only) \ No newline at end of file +Layer Wise Quantization (LWQ) +===== + +1. [Introduction](#introduction) + +2. [Supported Framework Model Matrix](#supported-framework-model-matrix) + +3. [Examples](#examples) + +## Introduction + +Large language models (LLMs) have shown exceptional performance across various tasks, meanwhile, the substantial parameter size poses significant challenges for deployment. Layer-wise quantization(LWQ) can greatly reduce the memory footprint of LLMs, usually 80-90% reduction, which means that users can quantize LLMs even on single node using GPU or CPU. We can quantize the model under memory-constrained devices, therefore making the huge-sized LLM quantization possible. + + + +*Figure 1: The process of layer-wise quantization for PyTorch model. The color grey means empty parameters and the color blue represents parameters need to be quantized. Every rectangle inside model represents one layer.* + + + +*Figure 2: The process of layer-wise quantization for ONNX model. The graph of LLM is split into several parts, and each subgraph is quantized in turn.* + +## Supported Framework Model Matrix + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    Types/FrameworkPyTorchONNX Runtime
    W8A8 Post Training Static Quantization✔✔
    Weight-only QuantizationRTN✔✕
    AWQ✕
    GPTQ✔
    TEQ✕
    + +## Examples + +#### PyTorch framework example + +```python +from neural_compressor import PostTrainingQuantConfig, quantization +from neural_compressor.adaptor.torch_utils.layer_wise_quant import load_empty_model + +fp32_model = load_empty_model(model_name_or_path, torchscript=True) +conf = PostTrainingQuantConfig( + approach="weight_only", + recipes={ + "layer_wise_quant": True, + "rtn_args": {"enable_full_range": True}, + }, +) + +q_model = quantization.fit( + fp32_model, + conf, + calib_dataloader=eval_dataloader, + eval_func=lambda x: 0.1, +) +ouput_dir = "./saved_model" +q_model.save(ouput_dir) +q_model = load(ouput_dir, fp32_model, weight_only=True, layer_wise=True) +``` + +#### ONNX Runtime framework example + +```python +from neural_compressor import quantization, PostTrainingQuantConfig + +conf = PostTrainingQuantConfig(recipes={"layer_wise_quant": True}) +q_model = quantization.fit(fp32_model_path, conf, calib_dataloader=dataloader) +q_model.save(int8_model_path) +``` + +Refer to [ONNX Runtime llama-2 LWQ example](../../examples/onnxrt/nlp/huggingface_model/text_generation/llama/quantization/weight_only) diff --git a/examples/onnxrt/nlp/huggingface_model/text_generation/llama/quantization/ptq_static/main.py b/examples/onnxrt/nlp/huggingface_model/text_generation/llama/quantization/ptq_static/main.py index 788abf5fc2d..a41adee578e 100644 --- a/examples/onnxrt/nlp/huggingface_model/text_generation/llama/quantization/ptq_static/main.py +++ b/examples/onnxrt/nlp/huggingface_model/text_generation/llama/quantization/ptq_static/main.py @@ -296,4 +296,3 @@ def __iter__(self): q_model.save(os.path.join(args.output_model, model)) tokenizer.save_pretrained(args.output_model) - \ No newline at end of file diff --git a/neural_compressor/adaptor/ox_utils/util.py b/neural_compressor/adaptor/ox_utils/util.py index d2126b47604..4211e2000fc 100644 --- a/neural_compressor/adaptor/ox_utils/util.py +++ b/neural_compressor/adaptor/ox_utils/util.py @@ -17,8 +17,8 @@ """Helper classes or functions for onnxrt adaptor.""" import importlib -import os import logging +import os from enum import Enum import numpy as np @@ -601,6 +601,7 @@ def to_numpy(data): else: return data + class SymbolicShapeInference(symbolic_shape_infer.SymbolicShapeInference): def __init__(self, int_max, auto_merge, guess_output_rank, verbose, prefix="", base_dir=""): super().__init__(int_max, auto_merge, guess_output_rank, verbose, prefix) @@ -609,9 +610,12 @@ def __init__(self, int_max, auto_merge, guess_output_rank, verbose, prefix="", b def _get_value(self, node, idx): name = node.input[idx] assert name in self.sympy_data_ or name in self.initializers_ - return self.sympy_data_[name] if name in self.sympy_data_ else \ - numpy_helper.to_array(self.initializers_[name], base_dir=self.base_dir) - + return ( + self.sympy_data_[name] + if name in self.sympy_data_ + else numpy_helper.to_array(self.initializers_[name], base_dir=self.base_dir) + ) + @staticmethod def infer_shapes(in_mp, int_max=2**31 - 1, auto_merge=False, guess_output_rank=False, verbose=0, base_dir=""): onnx_opset = symbolic_shape_infer.get_opset(in_mp) @@ -619,11 +623,8 @@ def infer_shapes(in_mp, int_max=2**31 - 1, auto_merge=False, guess_output_rank=F logger.warning("Only support models of onnx opset 7 and above.") return None symbolic_shape_inference = SymbolicShapeInference( - int_max, - auto_merge, - guess_output_rank, - verbose, - base_dir=base_dir) + int_max, auto_merge, guess_output_rank, verbose, base_dir=base_dir + ) all_shapes_inferred = False symbolic_shape_inference._preprocess(in_mp) while symbolic_shape_inference.run_: diff --git a/neural_compressor/model/onnx_model.py b/neural_compressor/model/onnx_model.py index 3d055d39f04..d301964a2eb 100644 --- a/neural_compressor/model/onnx_model.py +++ b/neural_compressor/model/onnx_model.py @@ -49,7 +49,7 @@ def __init__(self, model, **kwargs): self.check_is_large_model() if self._is_large_model and self._model_path is None and not kwargs.get("ignore_warning", False): logger.warning("Model size > 2GB. Please use model path instead of onnx model object to quantize") - + if self._is_large_model and isinstance(model, str) and kwargs.get("load_external_data", True): from onnx.external_data_helper import load_external_data_for_model @@ -1047,7 +1047,9 @@ def split_model_with_node( # need ort.GraphOptimizationLevel <= ORT_ENABLE_BASIC from neural_compressor.adaptor.ox_utils.util import SymbolicShapeInference - self._model = SymbolicShapeInference.infer_shapes(self._model, auto_merge=True, base_dir=os.path.dirname(self._model_path)) + self._model = SymbolicShapeInference.infer_shapes( + self._model, auto_merge=True, base_dir=os.path.dirname(self._model_path) + ) except Exception as e: # pragma: no cover logger.error("Shape infer fails for layer-wise quantization") if "Incomplete symbolic shape inference" in str(e): From 703518771218614b0e14700483f34b2979e29ddd Mon Sep 17 00:00:00 2001 From: yuwenzho Date: Thu, 30 Nov 2023 17:44:53 +0800 Subject: [PATCH 08/22] update quantization_layer_wise.md Signed-off-by: yuwenzho --- docs/source/quantization_layer_wise.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/source/quantization_layer_wise.md b/docs/source/quantization_layer_wise.md index c9d93761eaa..27218452fe1 100644 --- a/docs/source/quantization_layer_wise.md +++ b/docs/source/quantization_layer_wise.md @@ -11,11 +11,11 @@ Layer Wise Quantization (LWQ) Large language models (LLMs) have shown exceptional performance across various tasks, meanwhile, the substantial parameter size poses significant challenges for deployment. Layer-wise quantization(LWQ) can greatly reduce the memory footprint of LLMs, usually 80-90% reduction, which means that users can quantize LLMs even on single node using GPU or CPU. We can quantize the model under memory-constrained devices, therefore making the huge-sized LLM quantization possible. - + *Figure 1: The process of layer-wise quantization for PyTorch model. The color grey means empty parameters and the color blue represents parameters need to be quantized. Every rectangle inside model represents one layer.* - + *Figure 2: The process of layer-wise quantization for ONNX model. The graph of LLM is split into several parts, and each subgraph is quantized in turn.* From 66be652721f2e9de18db98abf31799bf258d4722 Mon Sep 17 00:00:00 2001 From: yuwenzho Date: Thu, 30 Nov 2023 18:01:54 +0800 Subject: [PATCH 09/22] update ox_utils Signed-off-by: yuwenzho --- neural_compressor/adaptor/ox_utils/util.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/neural_compressor/adaptor/ox_utils/util.py b/neural_compressor/adaptor/ox_utils/util.py index 4211e2000fc..5285b22c56a 100644 --- a/neural_compressor/adaptor/ox_utils/util.py +++ b/neural_compressor/adaptor/ox_utils/util.py @@ -17,7 +17,6 @@ """Helper classes or functions for onnxrt adaptor.""" import importlib -import logging import os from enum import Enum @@ -33,8 +32,6 @@ symbolic_shape_infer = LazyImport("onnxruntime.tools.symbolic_shape_infer") onnx = LazyImport("onnx") -logger = logging.getLogger("neural_compressor") - __producer__ = "onnx.quantize" __version__ = "0.1.0" @@ -603,7 +600,10 @@ def to_numpy(data): class SymbolicShapeInference(symbolic_shape_infer.SymbolicShapeInference): + """Shape inference for ONNX model.""" + def __init__(self, int_max, auto_merge, guess_output_rank, verbose, prefix="", base_dir=""): + """Initialize Shape inference class.""" super().__init__(int_max, auto_merge, guess_output_rank, verbose, prefix) self.base_dir = base_dir @@ -618,6 +618,7 @@ def _get_value(self, node, idx): @staticmethod def infer_shapes(in_mp, int_max=2**31 - 1, auto_merge=False, guess_output_rank=False, verbose=0, base_dir=""): + """Symbolic shape inference.""" onnx_opset = symbolic_shape_infer.get_opset(in_mp) if (not onnx_opset) or onnx_opset < 7: logger.warning("Only support models of onnx opset 7 and above.") From 785f6a631d8a79a17f5f03873c78e0f75af58f6a Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Thu, 30 Nov 2023 10:05:23 +0000 Subject: [PATCH 10/22] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- neural_compressor/adaptor/ox_utils/util.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/neural_compressor/adaptor/ox_utils/util.py b/neural_compressor/adaptor/ox_utils/util.py index 5285b22c56a..361edd7f36f 100644 --- a/neural_compressor/adaptor/ox_utils/util.py +++ b/neural_compressor/adaptor/ox_utils/util.py @@ -601,7 +601,7 @@ def to_numpy(data): class SymbolicShapeInference(symbolic_shape_infer.SymbolicShapeInference): """Shape inference for ONNX model.""" - + def __init__(self, int_max, auto_merge, guess_output_rank, verbose, prefix="", base_dir=""): """Initialize Shape inference class.""" super().__init__(int_max, auto_merge, guess_output_rank, verbose, prefix) From de59987b8793a52fb5bc58d7dbe5f1dfa701a776 Mon Sep 17 00:00:00 2001 From: yuwenzho Date: Fri, 1 Dec 2023 10:26:52 +0800 Subject: [PATCH 11/22] fix import bug Signed-off-by: yuwenzho --- neural_compressor/adaptor/ox_utils/util.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/neural_compressor/adaptor/ox_utils/util.py b/neural_compressor/adaptor/ox_utils/util.py index 361edd7f36f..04f5fc45f64 100644 --- a/neural_compressor/adaptor/ox_utils/util.py +++ b/neural_compressor/adaptor/ox_utils/util.py @@ -29,6 +29,7 @@ numpy_helper = LazyImport("onnx.numpy_helper") onnx_proto = LazyImport("onnx.onnx_pb") torch = LazyImport("torch") +onnxruntime = LazyImport("onnxruntime") symbolic_shape_infer = LazyImport("onnxruntime.tools.symbolic_shape_infer") onnx = LazyImport("onnx") @@ -601,7 +602,7 @@ def to_numpy(data): class SymbolicShapeInference(symbolic_shape_infer.SymbolicShapeInference): """Shape inference for ONNX model.""" - + def __init__(self, int_max, auto_merge, guess_output_rank, verbose, prefix="", base_dir=""): """Initialize Shape inference class.""" super().__init__(int_max, auto_merge, guess_output_rank, verbose, prefix) From 595007b85ffc70f25a8dad641bb90a94c6ec60fa Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Fri, 1 Dec 2023 02:28:07 +0000 Subject: [PATCH 12/22] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- neural_compressor/adaptor/ox_utils/util.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/neural_compressor/adaptor/ox_utils/util.py b/neural_compressor/adaptor/ox_utils/util.py index 04f5fc45f64..c30478a1352 100644 --- a/neural_compressor/adaptor/ox_utils/util.py +++ b/neural_compressor/adaptor/ox_utils/util.py @@ -602,7 +602,7 @@ def to_numpy(data): class SymbolicShapeInference(symbolic_shape_infer.SymbolicShapeInference): """Shape inference for ONNX model.""" - + def __init__(self, int_max, auto_merge, guess_output_rank, verbose, prefix="", base_dir=""): """Initialize Shape inference class.""" super().__init__(int_max, auto_merge, guess_output_rank, verbose, prefix) From 966aa9b53e1fc35b5d69b6c56cce307cb7ebbe06 Mon Sep 17 00:00:00 2001 From: yuwenzho Date: Fri, 1 Dec 2023 10:32:41 +0800 Subject: [PATCH 13/22] Update run_quant.sh Signed-off-by: yuwenzho --- .../text_generation/llama/quantization/ptq_static/run_quant.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/onnxrt/nlp/huggingface_model/text_generation/llama/quantization/ptq_static/run_quant.sh b/examples/onnxrt/nlp/huggingface_model/text_generation/llama/quantization/ptq_static/run_quant.sh index 384f43a84aa..f2e62698e16 100644 --- a/examples/onnxrt/nlp/huggingface_model/text_generation/llama/quantization/ptq_static/run_quant.sh +++ b/examples/onnxrt/nlp/huggingface_model/text_generation/llama/quantization/ptq_static/run_quant.sh @@ -70,7 +70,7 @@ function run_tuning { python main.py \ --quant_format ${quant_format-QOperator} \ --model_path ${input_model} \ - --tokenizer ${tokenizer-meta-llama/Llama-2-7b-hf} \ + --tokenizer ${tokenizer-meta-llama/Llama-2-7b-hf} \ --output_model ${output_model} \ --batch_size ${batch_size-1} \ --smooth_quant_alpha ${alpha-0.6} \ From 1f10a92175734a6e8404e76aa765624a3c5b8146 Mon Sep 17 00:00:00 2001 From: yuwenzho Date: Fri, 1 Dec 2023 12:00:42 +0800 Subject: [PATCH 14/22] fix import bug Signed-off-by: yuwenzho --- neural_compressor/adaptor/ox_utils/util.py | 69 ++++++++++------------ neural_compressor/model/onnx_model.py | 4 +- 2 files changed, 34 insertions(+), 39 deletions(-) diff --git a/neural_compressor/adaptor/ox_utils/util.py b/neural_compressor/adaptor/ox_utils/util.py index c30478a1352..2f0bc2caadc 100644 --- a/neural_compressor/adaptor/ox_utils/util.py +++ b/neural_compressor/adaptor/ox_utils/util.py @@ -29,7 +29,6 @@ numpy_helper = LazyImport("onnx.numpy_helper") onnx_proto = LazyImport("onnx.onnx_pb") torch = LazyImport("torch") -onnxruntime = LazyImport("onnxruntime") symbolic_shape_infer = LazyImport("onnxruntime.tools.symbolic_shape_infer") onnx = LazyImport("onnx") @@ -599,40 +598,36 @@ def to_numpy(data): else: return data +def infer_shapes(in_mp, int_max=2**31 - 1, auto_merge=False, guess_output_rank=False, verbose=0, base_dir=""): + """Symbolic shape inference.""" + + class SymbolicShapeInference(symbolic_shape_infer.SymbolicShapeInference): + def __init__(self, int_max, auto_merge, guess_output_rank, verbose, prefix="", base_dir=""): + super().__init__(int_max, auto_merge, guess_output_rank, verbose, prefix) + self.base_dir = base_dir + + def _get_value(self, node, idx): + name = node.input[idx] + assert name in self.sympy_data_ or name in self.initializers_ + return ( + self.sympy_data_[name] + if name in self.sympy_data_ + else numpy_helper.to_array(self.initializers_[name], base_dir=self.base_dir) + ) -class SymbolicShapeInference(symbolic_shape_infer.SymbolicShapeInference): - """Shape inference for ONNX model.""" - - def __init__(self, int_max, auto_merge, guess_output_rank, verbose, prefix="", base_dir=""): - """Initialize Shape inference class.""" - super().__init__(int_max, auto_merge, guess_output_rank, verbose, prefix) - self.base_dir = base_dir - - def _get_value(self, node, idx): - name = node.input[idx] - assert name in self.sympy_data_ or name in self.initializers_ - return ( - self.sympy_data_[name] - if name in self.sympy_data_ - else numpy_helper.to_array(self.initializers_[name], base_dir=self.base_dir) - ) - - @staticmethod - def infer_shapes(in_mp, int_max=2**31 - 1, auto_merge=False, guess_output_rank=False, verbose=0, base_dir=""): - """Symbolic shape inference.""" - onnx_opset = symbolic_shape_infer.get_opset(in_mp) - if (not onnx_opset) or onnx_opset < 7: - logger.warning("Only support models of onnx opset 7 and above.") - return None - symbolic_shape_inference = SymbolicShapeInference( - int_max, auto_merge, guess_output_rank, verbose, base_dir=base_dir - ) - all_shapes_inferred = False - symbolic_shape_inference._preprocess(in_mp) - while symbolic_shape_inference.run_: - all_shapes_inferred = symbolic_shape_inference._infer_impl() - symbolic_shape_inference._update_output_from_vi() - if not all_shapes_inferred: - onnx.save_model(symbolic_shape_inference.out_mp_, "sym_shape_infer_temp.onnx", save_as_external_data=True) - raise Exception("Incomplete symbolic shape inference") - return symbolic_shape_inference.out_mp_ + onnx_opset = symbolic_shape_infer.get_opset(in_mp) + if (not onnx_opset) or onnx_opset < 7: + logger.warning("Only support models of onnx opset 7 and above.") + return None + symbolic_shape_inference = SymbolicShapeInference( + int_max, auto_merge, guess_output_rank, verbose, base_dir=base_dir + ) + all_shapes_inferred = False + symbolic_shape_inference._preprocess(in_mp) + while symbolic_shape_inference.run_: + all_shapes_inferred = symbolic_shape_inference._infer_impl() + symbolic_shape_inference._update_output_from_vi() + if not all_shapes_inferred: + onnx.save_model(symbolic_shape_inference.out_mp_, "sym_shape_infer_temp.onnx", save_as_external_data=True) + raise Exception("Incomplete symbolic shape inference") + return symbolic_shape_inference.out_mp_ diff --git a/neural_compressor/model/onnx_model.py b/neural_compressor/model/onnx_model.py index d301964a2eb..ba9f35abf40 100644 --- a/neural_compressor/model/onnx_model.py +++ b/neural_compressor/model/onnx_model.py @@ -1045,9 +1045,9 @@ def split_model_with_node( if shape_infer: try: # need ort.GraphOptimizationLevel <= ORT_ENABLE_BASIC - from neural_compressor.adaptor.ox_utils.util import SymbolicShapeInference + from neural_compressor.adaptor.ox_utils.util import infer_shapes - self._model = SymbolicShapeInference.infer_shapes( + self._model = infer_shapes( self._model, auto_merge=True, base_dir=os.path.dirname(self._model_path) ) except Exception as e: # pragma: no cover From 472ebbe7b32717e962ac1abee1850e230d6e6f76 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Fri, 1 Dec 2023 04:02:01 +0000 Subject: [PATCH 15/22] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- neural_compressor/adaptor/ox_utils/util.py | 1 + neural_compressor/model/onnx_model.py | 4 +--- 2 files changed, 2 insertions(+), 3 deletions(-) diff --git a/neural_compressor/adaptor/ox_utils/util.py b/neural_compressor/adaptor/ox_utils/util.py index 2f0bc2caadc..7d3cda05915 100644 --- a/neural_compressor/adaptor/ox_utils/util.py +++ b/neural_compressor/adaptor/ox_utils/util.py @@ -598,6 +598,7 @@ def to_numpy(data): else: return data + def infer_shapes(in_mp, int_max=2**31 - 1, auto_merge=False, guess_output_rank=False, verbose=0, base_dir=""): """Symbolic shape inference.""" diff --git a/neural_compressor/model/onnx_model.py b/neural_compressor/model/onnx_model.py index ba9f35abf40..f5542bc120e 100644 --- a/neural_compressor/model/onnx_model.py +++ b/neural_compressor/model/onnx_model.py @@ -1047,9 +1047,7 @@ def split_model_with_node( # need ort.GraphOptimizationLevel <= ORT_ENABLE_BASIC from neural_compressor.adaptor.ox_utils.util import infer_shapes - self._model = infer_shapes( - self._model, auto_merge=True, base_dir=os.path.dirname(self._model_path) - ) + self._model = infer_shapes(self._model, auto_merge=True, base_dir=os.path.dirname(self._model_path)) except Exception as e: # pragma: no cover logger.error("Shape infer fails for layer-wise quantization") if "Incomplete symbolic shape inference" in str(e): From 23ce23b23cc90dcae1d34b072346aa6a184ff8f2 Mon Sep 17 00:00:00 2001 From: yuwenzho Date: Tue, 5 Dec 2023 11:32:11 +0800 Subject: [PATCH 16/22] update requirement Signed-off-by: yuwenzho --- .../text_generation/llama/quantization/ptq_static/README.md | 2 +- .../llama/quantization/ptq_static/requirements.txt | 2 +- .../llama/quantization/weight_only/requirements.txt | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/examples/onnxrt/nlp/huggingface_model/text_generation/llama/quantization/ptq_static/README.md b/examples/onnxrt/nlp/huggingface_model/text_generation/llama/quantization/ptq_static/README.md index 975ef34b617..201f5007864 100644 --- a/examples/onnxrt/nlp/huggingface_model/text_generation/llama/quantization/ptq_static/README.md +++ b/examples/onnxrt/nlp/huggingface_model/text_generation/llama/quantization/ptq_static/README.md @@ -27,7 +27,7 @@ Note that this README.md uses meta-llama/Llama-2-7b-hf as an example. There are Export to ONNX model: ```bash -optimum-cli export onnx --model meta-llama/Llama-2-7b-hf --task text-generation-with-past ./Llama-2-7b-hf +python prepare_model.py --input_model="meta-llama/Llama-2-7b-hf" --output_model="./llama-2-7b-hf" ``` # Run diff --git a/examples/onnxrt/nlp/huggingface_model/text_generation/llama/quantization/ptq_static/requirements.txt b/examples/onnxrt/nlp/huggingface_model/text_generation/llama/quantization/ptq_static/requirements.txt index 77b73003bf4..bb585524476 100644 --- a/examples/onnxrt/nlp/huggingface_model/text_generation/llama/quantization/ptq_static/requirements.txt +++ b/examples/onnxrt/nlp/huggingface_model/text_generation/llama/quantization/ptq_static/requirements.txt @@ -5,7 +5,7 @@ onnx onnxruntime onnxruntime-extensions; python_version < '3.11' datasets -optimum +optimum==1.13.3 evaluate peft git+https://github.com/EleutherAI/lm-evaluation-harness.git@cc9778fbe4fa1a709be2abed9deb6180fd40e7e2 diff --git a/examples/onnxrt/nlp/huggingface_model/text_generation/llama/quantization/weight_only/requirements.txt b/examples/onnxrt/nlp/huggingface_model/text_generation/llama/quantization/weight_only/requirements.txt index 340fe76bbdc..e3a44cc0ab6 100644 --- a/examples/onnxrt/nlp/huggingface_model/text_generation/llama/quantization/weight_only/requirements.txt +++ b/examples/onnxrt/nlp/huggingface_model/text_generation/llama/quantization/weight_only/requirements.txt @@ -5,7 +5,7 @@ onnx onnxruntime onnxruntime-extensions; python_version < '3.11' datasets -optimum +optimum==1.13.3 evaluate peft git+https://github.com/EleutherAI/lm-evaluation-harness.git@cc9778fbe4fa1a709be2abed9deb6180fd40e7e2 From da05ba4c5ccc4c9185c8c9c51724df2132311d55 Mon Sep 17 00:00:00 2001 From: yuwenzho Date: Tue, 5 Dec 2023 11:40:40 +0800 Subject: [PATCH 17/22] update README and doc Signed-off-by: yuwenzho --- README.md | 3 +- docs/source/quantization_weight_only.md | 48 +------------------ docs/source/user_guide.md | 7 +-- .../llama/quantization/ptq_static/README.md | 5 +- 4 files changed, 10 insertions(+), 53 deletions(-) diff --git a/README.md b/README.md index f5346636da8..eca65b77ef5 100644 --- a/README.md +++ b/README.md @@ -122,7 +122,8 @@ q_model = fit( Weight-Only Quantization (INT8/INT4/FP4/NF4) - FP8 Quantization + FP8 Quantization + Layer-Wise Quantization diff --git a/docs/source/quantization_weight_only.md b/docs/source/quantization_weight_only.md index de5d70c6d09..a7d6722306b 100644 --- a/docs/source/quantization_weight_only.md +++ b/docs/source/quantization_weight_only.md @@ -7,9 +7,7 @@ Weight Only Quantization (WOQ) 3. [Examples](#examples) -4. [Layer Wise Quantization](#layer-wise-quantization) - -5. [WOQ Algorithms Tuning](#woq-algorithms-tuning) +4. [WOQ Algorithms Tuning](#woq-algorithms-tuning) ## Introduction @@ -143,50 +141,6 @@ The saved_results folder contains two files: `best_model.pt` and `qconfig.json`, To seek the performance of weight-only quantized models, Please go to [Intel Extension for Transformers](https://github.com/intel/intel-extension-for-transformers/tree/main/examples/huggingface/pytorch/text-generation/quantization#1-performance) to quantize and deploy the model. - -## Layer Wise Quantization - -Large language models (LLMs) have shown exceptional performance across various tasks, meanwhile, the substantial parameter size poses significant challenges for deployment. Layer-wise quantization(LWQ) can greatly reduce the memory footprint of LLMs, usually 80-90% reduction, which means that users can quantize LLMs even on single node using GPU or CPU. We can quantize the model under memory-constrained devices, therefore making the huge-sized LLM quantization possible. - - - -*Figure 1: The process of layer-wise quantization. The color grey means empty parameters and the color blue represents parameters need to be quantized. Every rectangle inside model represents one layer.* - -### Supported Matrix - -| Algorithms/Framework | PyTorch | -|:--------------:|:----------:| -| RTN | ✔ | -| AWQ | ✕ | -| GPTQ | ✔ | -| TEQ | ✕ | - -### Example -```python -from neural_compressor import PostTrainingQuantConfig, quantization -from neural_compressor.adaptor.torch_utils.layer_wise_quant import load_empty_model - -fp32_model = load_empty_model(model_name_or_path, torchscript=True) -conf = PostTrainingQuantConfig( - approach="weight_only", - recipes={ - "layer_wise_quant": True, - "rtn_args": {"enable_full_range": True}, - }, -) - -q_model = quantization.fit( - fp32_model, - conf, - calib_dataloader=eval_dataloader, - eval_func=lambda x: 0.1, -) -ouput_dir = "./saved_model" -q_model.save(ouput_dir) -q_model = load(ouput_dir, fp32_model, weight_only=True, layer_wise=True) -``` - - ## WOQ Algorithms Tuning To find the best algorithm, users can omit specifying a particular algorithm. In comparison to setting a specific algorithm, this tuning process will traverse through a set of pre-defined WOQ configurations and identify the optimal one with the best result. For details usage, please refer to the [tuning strategy](./tuning_strategies.md#Basic). diff --git a/docs/source/user_guide.md b/docs/source/user_guide.md index d9286f099fd..662a2ec177c 100644 --- a/docs/source/user_guide.md +++ b/docs/source/user_guide.md @@ -81,9 +81,10 @@ This part provides the advanced topics that help user dive deep into Intel® Neu Add New Adaptor -Distillation for Quantization -SmoothQuant -Weight-Only Quantization +Distillation for Quantization +SmoothQuant +Weight-Only Quantization +Layer-Wise Quantization diff --git a/examples/onnxrt/nlp/huggingface_model/text_generation/llama/quantization/ptq_static/README.md b/examples/onnxrt/nlp/huggingface_model/text_generation/llama/quantization/ptq_static/README.md index 201f5007864..a82da6d9c7a 100644 --- a/examples/onnxrt/nlp/huggingface_model/text_generation/llama/quantization/ptq_static/README.md +++ b/examples/onnxrt/nlp/huggingface_model/text_generation/llama/quantization/ptq_static/README.md @@ -34,7 +34,7 @@ python prepare_model.py --input_model="meta-llama/Llama-2-7b-hf" --output_model ## 1. Quantization -Run SmoothQuant +### Run SmoothQuant ```bash bash run_quant.sh --input_model=/path/to/model \ # folder path of onnx model @@ -46,7 +46,8 @@ bash run_quant.sh --input_model=/path/to/model \ # folder path of onnx model --quant_format="QOperator" # or QDQ, optional ``` -Additionally set `--layer-wise=True` to use layer-wise quantization to save your memory. Please note that layer-wise quantization for ONNX models is still under development and only support W8A8 quantization now. More details please refer to [layer wise quantiation](https://github.com/intel/neural-compressor/blob/master/docs/source/quantization_layer_wise.md). +### Run layer-wise quantization +Set `--layer-wise=True` to use layer-wise quantization to save your memory. Please note that layer-wise quantization for ONNX models is still under development and only support W8A8 quantization now. More details please refer to [layer wise quantiation](https://github.com/intel/neural-compressor/blob/master/docs/source/quantization_layer_wise.md). ```bash bash run_quant.sh --input_model=/path/to/model \ # folder path of onnx model From e3116d9dc4014bb941862553c5e1fe685fc4b8c3 Mon Sep 17 00:00:00 2001 From: yuwenzho Date: Tue, 5 Dec 2023 13:33:35 +0800 Subject: [PATCH 18/22] update onnx_model.py Signed-off-by: yuwenzho --- neural_compressor/model/onnx_model.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/neural_compressor/model/onnx_model.py b/neural_compressor/model/onnx_model.py index f5542bc120e..6950c742eed 100644 --- a/neural_compressor/model/onnx_model.py +++ b/neural_compressor/model/onnx_model.py @@ -1044,14 +1044,14 @@ def split_model_with_node( # infer shape of the model to be split if shape_infer: try: - # need ort.GraphOptimizationLevel <= ORT_ENABLE_BASIC from neural_compressor.adaptor.ox_utils.util import infer_shapes self._model = infer_shapes(self._model, auto_merge=True, base_dir=os.path.dirname(self._model_path)) except Exception as e: # pragma: no cover - logger.error("Shape infer fails for layer-wise quantization") - if "Incomplete symbolic shape inference" in str(e): - logger.warning("Please set graph optimization level to 'ENABLE_BASIC' for layer-wise quantization.") + logger.error("Shape infer fails for layer-wise quantization. "\ + "We would recommend checking the graph optimization level of your model " \ + "and setting it to levels 'DISABLE_ALL' and 'ENABLE_BASIC', "\ + "as this may help avoid this error.") raise e split_tensor_type, split_tensor_shape = self._get_output_type_shape_by_tensor_name(split_tensor_name) From cf126ce9c5d4652aa0836f2df586f2ebaa0d2f54 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Tue, 5 Dec 2023 05:34:55 +0000 Subject: [PATCH 19/22] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- neural_compressor/model/onnx_model.py | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/neural_compressor/model/onnx_model.py b/neural_compressor/model/onnx_model.py index 6950c742eed..625e149c062 100644 --- a/neural_compressor/model/onnx_model.py +++ b/neural_compressor/model/onnx_model.py @@ -1048,10 +1048,12 @@ def split_model_with_node( self._model = infer_shapes(self._model, auto_merge=True, base_dir=os.path.dirname(self._model_path)) except Exception as e: # pragma: no cover - logger.error("Shape infer fails for layer-wise quantization. "\ - "We would recommend checking the graph optimization level of your model " \ - "and setting it to levels 'DISABLE_ALL' and 'ENABLE_BASIC', "\ - "as this may help avoid this error.") + logger.error( + "Shape infer fails for layer-wise quantization. " + "We would recommend checking the graph optimization level of your model " + "and setting it to levels 'DISABLE_ALL' and 'ENABLE_BASIC', " + "as this may help avoid this error." + ) raise e split_tensor_type, split_tensor_shape = self._get_output_type_shape_by_tensor_name(split_tensor_name) From 449114f57392952775bff4ac5c34baff75051796 Mon Sep 17 00:00:00 2001 From: yuwenzho Date: Tue, 5 Dec 2023 17:29:54 +0800 Subject: [PATCH 20/22] update onnx_model.py Signed-off-by: yuwenzho --- neural_compressor/model/onnx_model.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/neural_compressor/model/onnx_model.py b/neural_compressor/model/onnx_model.py index 625e149c062..56c09af3c7e 100644 --- a/neural_compressor/model/onnx_model.py +++ b/neural_compressor/model/onnx_model.py @@ -1051,7 +1051,7 @@ def split_model_with_node( logger.error( "Shape infer fails for layer-wise quantization. " "We would recommend checking the graph optimization level of your model " - "and setting it to levels 'DISABLE_ALL' and 'ENABLE_BASIC', " + "and setting it to 'DISABLE_ALL' or 'ENABLE_BASIC', " "as this may help avoid this error." ) raise e From ed3f8446a5f357627ffa13898423d49f0e393b12 Mon Sep 17 00:00:00 2001 From: yuwenzho Date: Wed, 6 Dec 2023 09:46:43 +0800 Subject: [PATCH 21/22] update main.py Signed-off-by: yuwenzho --- .../llama/quantization/ptq_static/main.py | 59 +++++++++++++++---- 1 file changed, 48 insertions(+), 11 deletions(-) diff --git a/examples/onnxrt/nlp/huggingface_model/text_generation/llama/quantization/ptq_static/main.py b/examples/onnxrt/nlp/huggingface_model/text_generation/llama/quantization/ptq_static/main.py index a41adee578e..90a68aa453a 100644 --- a/examples/onnxrt/nlp/huggingface_model/text_generation/llama/quantization/ptq_static/main.py +++ b/examples/onnxrt/nlp/huggingface_model/text_generation/llama/quantization/ptq_static/main.py @@ -17,6 +17,7 @@ # pylint:disable=redefined-outer-name,logging-format-interpolation import os import onnx +import json import torch import logging import argparse @@ -136,16 +137,26 @@ def benchmark(model): config = LlamaConfig.from_pretrained(args.model_path) sess_options = ort.SessionOptions() sess_options.intra_op_num_threads = args.intra_op_num_threads - sessions = ORTModelForCausalLM.load_model( - os.path.join(model, 'decoder_model.onnx'), - os.path.join(model, 'decoder_with_past_model.onnx'), + + if os.path.exists(os.path.join(model, "decoder_with_past_model.onnx")): + sessions = ORTModelForCausalLM.load_model( # pylint: disable=E1123 + os.path.join(model, "decoder_model.onnx"), + os.path.join(model, "decoder_with_past_model.onnx"), session_options=sess_options) - model = ORTModelForCausalLM( - sessions[0], - config, - model, - sessions[1], - use_cache=True) + model = ORTModelForCausalLM(sessions[0], # pylint: disable=E1121 + config, + model, + sessions[1], + use_cache=True) + else: + sessions = ORTModelForCausalLM.load_model( # pylint: disable=E1123 + os.path.join(model, "decoder_model.onnx"), + session_options=sess_options) + model = ORTModelForCausalLM(sessions[0], # pylint: disable=E1121 + config, + model, + use_cache=False, + use_io_binding=False) input_tokens = '32' max_new_tokens = 32 @@ -182,19 +193,45 @@ def benchmark(model): print(args) print("Inference latency: %.3f sec." % latency) +def replace_architectures(json_path): + # replace 'LLaMATokenizer' to lowercase 'LlamaTokenizer' + # to avoid bug 'Tokenizer class LLaMATokenizer does not exist or is not currently imported.' + # refer to https://github.com/huggingface/transformers/issues/22222#issuecomment-1477171703 + with open(json_path, "r") as file: + data = json.load(file) + data["architectures"] = ["LlamaForCausalLM"] + + with open(json_path, 'w') as file: + json.dump(data, file, indent=4) + def eval_func(model): + model_dir = model + if isinstance(model, str) and model.endswith(".onnx"): + model_dir = os.path.dirname(model) + + replace_architectures(os.path.join(model_dir, "config.json")) + results = evaluate( model="hf-causal", - model_args='pretrained=' + model + ',tokenizer='+ args.tokenizer, + model_args="pretrained=" + model_dir + ",tokenizer="+ args.tokenizer, batch_size=args.batch_size, tasks=args.tasks, - model_format="onnx" + model_format="onnx", ) + + eval_acc = 0 for task_name in args.tasks: if task_name == "wikitext": print("Accuracy for %s is: %s" % (task_name, results["results"][task_name]["word_perplexity"])) + eval_acc += results["results"][task_name]["word_perplexity"] else: print("Accuracy for %s is: %s" % (task_name, results["results"][task_name]["acc"])) + eval_acc += results["results"][task_name]["acc"] + + if len(args.tasks) != 0: + eval_acc /= len(args.tasks) + + return eval_acc class KVDataloader: def __init__(self, model_path, pad_max=196, batch_size=1, sub_folder='train'): From 95ed5fd41d8306203a5033c6def74d695dfa1511 Mon Sep 17 00:00:00 2001 From: yuwenzho Date: Wed, 6 Dec 2023 14:26:09 +0800 Subject: [PATCH 22/22] update main.py Signed-off-by: yuwenzho --- .../text_generation/llama/quantization/ptq_static/main.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/examples/onnxrt/nlp/huggingface_model/text_generation/llama/quantization/ptq_static/main.py b/examples/onnxrt/nlp/huggingface_model/text_generation/llama/quantization/ptq_static/main.py index 90a68aa453a..71e5aa39a26 100644 --- a/examples/onnxrt/nlp/huggingface_model/text_generation/llama/quantization/ptq_static/main.py +++ b/examples/onnxrt/nlp/huggingface_model/text_generation/llama/quantization/ptq_static/main.py @@ -189,9 +189,10 @@ def benchmark(model): total_time += toc - tic print("\n", "-" * 10, "Summary:", "-" * 10) - latency = total_time / (num_iter - num_warmup) print(args) - print("Inference latency: %.3f sec." % latency) + throughput = (num_iter - num_warmup) / total_time + print("Throughput: {} samples/s".format(throughput)) + def replace_architectures(json_path): # replace 'LLaMATokenizer' to lowercase 'LlamaTokenizer'