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Migrate static quant ipex backend to 3.x API (#1596)
Signed-off-by: Cheng, Zixuan <zixuan.cheng@intel.com> Signed-off-by: chensuyue <suyue.chen@intel.com> Signed-off-by: yiliu30 <yi4.liu@intel.com>
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17 changes: 17 additions & 0 deletions
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neural_compressor/torch/algorithms/static_quant/__init__.py
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| # Copyright (c) 2024 Intel Corporation | ||
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| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
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| from .utility import * | ||
| from .static_quant import static_quantize |
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neural_compressor/torch/algorithms/static_quant/static_quant.py
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| #!/usr/bin/env python | ||
| # -*- coding: utf-8 -*- | ||
| # | ||
| # Copyright (c) 2024 Intel Corporation | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
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| import json | ||
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| from neural_compressor.torch.utils import get_ipex_version | ||
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| try: | ||
| import intel_extension_for_pytorch as ipex | ||
| except: | ||
| assert False, "Please install IPEX for static quantization." | ||
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| import torch | ||
| from packaging.version import Version | ||
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| from .utility import ( | ||
| cfg_to_qconfig, | ||
| dump_model_op_stats, | ||
| get_quantizable_ops_recursively, | ||
| ipex_config_path, | ||
| simple_inference, | ||
| ) | ||
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| ipex_ver = get_ipex_version() | ||
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| def static_quantize(model, tune_cfg, run_fn, example_inputs, inplace=True): | ||
| """Execute the quantize process on the specified model. | ||
| Args: | ||
| model: a float model to be quantized. | ||
| tune_cfg: quantization config for ops. | ||
| run_fn: a calibration function for calibrating the model. | ||
| example_inputs: used to trace torch model. | ||
| inplace: whether to carry out model transformations in-place. | ||
| Returns: | ||
| A quantized model. | ||
| """ | ||
| model.eval() | ||
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| if ipex_ver.release >= Version("1.12.0").release: | ||
| # Check save_qconf_summary part is a workaround for IPEX bug. | ||
| # Sometimes the prepared model from get_op_capablitiy loss this attribute | ||
| if not hasattr(model, "save_qconf_summary") or not hasattr(model, "load_qconf_summary"): | ||
| from torch.ao.quantization import MinMaxObserver, PerChannelMinMaxObserver, QConfig | ||
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| if ipex_ver.release >= Version("2.1").release: | ||
| static_qconfig = ipex.quantization.default_static_qconfig_mapping | ||
| else: | ||
| static_qconfig = QConfig( | ||
| activation=MinMaxObserver.with_args(qscheme=torch.per_tensor_affine, dtype=torch.quint8), | ||
| weight=PerChannelMinMaxObserver.with_args(dtype=torch.qint8, qscheme=torch.per_channel_symmetric), | ||
| ) | ||
| if isinstance(example_inputs, dict): | ||
| model = ipex.quantization.prepare( | ||
| model, static_qconfig, example_kwarg_inputs=example_inputs, inplace=inplace | ||
| ) | ||
| else: | ||
| model = ipex.quantization.prepare(model, static_qconfig, example_inputs=example_inputs, inplace=inplace) | ||
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| model.load_qconf_summary(qconf_summary=ipex_config_path) | ||
| run_fn(model) | ||
| model.save_qconf_summary(qconf_summary=ipex_config_path) | ||
| model = _ipex_post_quant_process(model, example_inputs, inplace=inplace) | ||
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| else: # pragma: no cover | ||
| # for IPEX version < 1.12 | ||
| _, cfgs, default_cfgs, fuse_ops = get_quantizable_ops_recursively(model, example_inputs) | ||
| qscheme = cfg_to_qconfig(tune_cfg, cfgs, default_cfgs, fuse_ops) | ||
| ipex_conf = ipex.quantization.QuantConf( | ||
| configure_file=ipex_config_path, qscheme=qscheme | ||
| ) # pylint: disable=E1101 | ||
| run_fn(model) | ||
| ipex_conf.save(ipex_config_path) | ||
| ipex_conf = ipex.quantization.QuantConf(ipex_config_path) # pylint: disable=E1101 | ||
| model = ipex.quantization.convert(model, ipex_conf, example_inputs, inplace=True) # pylint: disable=E1121 | ||
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| with open(ipex_config_path, "r") as f: | ||
| model.tune_cfg = json.load(f) | ||
| model.ipex_config_path = ipex_config_path | ||
| if ipex_ver.release >= Version("1.12.0").release: | ||
| dump_model_op_stats(tune_cfg) | ||
| return model | ||
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| def _ipex_post_quant_process(model, example_inputs, inplace=False): | ||
| """Convert to a jit model. | ||
| Args: | ||
| model: a prepared model. | ||
| example_inputs: used to trace torch model. | ||
| inplace: whether to carry out model transformations in-place. | ||
| Returns: | ||
| A converted jit model. | ||
| """ | ||
| model = ipex.quantization.convert(model, inplace=inplace) | ||
| with torch.no_grad(): | ||
| try: | ||
| if isinstance(example_inputs, dict): | ||
| model = torch.jit.trace(model, example_kwarg_inputs=example_inputs) | ||
| else: | ||
| model = torch.jit.trace(model, example_inputs) | ||
| model = torch.jit.freeze(model.eval()) | ||
| except: | ||
| if isinstance(example_inputs, dict): | ||
| model = torch.jit.trace(model, example_kwarg_inputs=example_inputs, strict=False, check_trace=False) | ||
| else: | ||
| model = torch.jit.trace(model, example_inputs, strict=False) | ||
| model = torch.jit.freeze(model.eval()) | ||
| # After freezing, run 1 time to warm up the profiling graph executor to insert prim::profile | ||
| # At the 2nd run, the llga pass will be triggered and the model is turned into | ||
| # an int8 model: prim::profile will be removed and will have LlgaFusionGroup in the graph | ||
| simple_inference(model, example_inputs, iterations=2) | ||
| return model |
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