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Neural Coder adding support of ONNX RT dynamic quant and static quant QDQ (#184)
* Create onnx_inc_static_quant_qdqyaml * Rename onnx_inc_static_quant_qdqyaml to onnx_inc_static_quant_qdq.yaml * Create onnx_inc_dynamic_quant.yaml
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# Copyright (c) 2022 Intel Corporation
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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transformation:
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location:
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- insert_below_model_definition_line
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content:
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- |-
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[+] from neural_compressor.experimental import Quantization, common
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[+] from neural_compressor import options, conf
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[+] conf.model.framework = 'onnxrt_integerops'
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[+] conf.quantization.approach = 'post_training_dynamic_quant'
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[+] quantizer = Quantization(conf)
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[+] quantizer.model = common.Model(MODEL_NAME)
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[+] quantizer.eval_func = EVAL_FUNCTION_NAME
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[+] MODEL_NAME = quantizer()
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order:
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- below:
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above:
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# Copyright (c) 2022 Intel Corporation
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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transformation:
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location:
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- insert_below_model_definition_line
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content:
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- |-
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[+] from neural_compressor.experimental import Quantization, common
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[+] from neural_compressor import options, conf
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[+] conf.model.framework = 'onnxrt_qdqops'
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[+] conf.quantization.approach = 'post_training_static_quant'
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[+] quantizer = Quantization(conf)
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[+] quantizer.model = common.Model(MODEL_NAME)
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[+] quantizer.calib_dataloader = DATALOADER_NAME
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[+] quantizer.eval_func = EVAL_FUNCTION_NAME
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[+] MODEL_NAME = quantizer()
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order:
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- below:
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above:

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