intel
diff --git a/‎.azure-pipelines/scripts/codeScan/pylint/pylint.sh
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+1-1Lines changed: 1 addition & 1 deletion b/‎.azure-pipelines/scripts/codeScan/pylint/pylint.sh
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+1-1Lines changed: 1 addition & 1 deletion
diff --git a/‎neural_compressor/adaptor/pytorch.py
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+15-11Lines changed: 15 additions & 11 deletions b/‎neural_compressor/adaptor/pytorch.py
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+15-11Lines changed: 15 additions & 11 deletions
diff --git a/‎neural_compressor/adaptor/torch_utils/util.py
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+3-3Lines changed: 3 additions & 3 deletions b/‎neural_compressor/adaptor/torch_utils/util.py
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+3-3Lines changed: 3 additions & 3 deletions
diff --git a/‎neural_compressor/torch/algorithms/static_quant/__init__.py
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+17Lines changed: 17 additions & 0 deletions b/‎neural_compressor/torch/algorithms/static_quant/__init__.py
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diff --git a/‎neural_compressor/torch/algorithms/static_quant/static_quant.py
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+130Lines changed: 130 additions & 0 deletions b/‎neural_compressor/torch/algorithms/static_quant/static_quant.py
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+130Lines changed: 130 additions & 0 deletions
@@ -22,7 +22,7 @@ apt-get install -y --no-install-recommends --fix-missing \
 pip install -r /neural-compressor/requirements.txt
 pip install cmake
 
-pip install torch==1.12.0 \
+pip install torch \
             horovod \
             google \
             autograd \
 
@@ -397,12 +397,12 @@ def _cfgs_to_fx_cfgs(op_cfgs, observer_type="post_training_static_quant"):
     for key, value in op_cfgs.items():
         if key == "default_qconfig":
             if version.release >= Version("1.13.0").release:  # pragma: no cover
-                fx_op_cfgs.set_global(value)
+                fx_op_cfgs.set_global(value)  # pylint: disable=E1101
             else:
                 fx_op_cfgs[""] = value
             continue
         if version.release >= Version("1.13.0").release:  # pragma: no cover
-            fx_op_cfgs.set_module_name(key, value)
+            fx_op_cfgs.set_module_name(key, value)  # pylint: disable=E1101
         else:
             op_tuple = (key, value)
             op_tuple_cfg_list.append(op_tuple)
@@ -413,7 +413,7 @@ def _cfgs_to_fx_cfgs(op_cfgs, observer_type="post_training_static_quant"):
         from torch.ao.quantization import get_default_qconfig_mapping
 
         for name, q_config in get_default_qconfig_mapping().to_dict()["object_type"]:
-            fx_op_cfgs.set_object_type(name, q_config)
+            fx_op_cfgs.set_object_type(name, q_config)  # pylint: disable=E1101
 
     return fx_op_cfgs
 
@@ -3619,7 +3619,7 @@ def quantize(self, tune_cfg, model, dataloader, q_func=None):
                         prepare_custom_config=self.prepare_custom_config_dict,
                     )
                 else:
-                    q_model._model = prepare_qat_fx(
+                    q_model._model = prepare_qat_fx(  # pylint: disable=E1120,E1123
                         q_model._model, self.fx_op_cfgs, prepare_custom_config_dict=self.prepare_custom_config_dict
                     )
             else:
@@ -3651,7 +3651,7 @@ def quantize(self, tune_cfg, model, dataloader, q_func=None):
                         prepare_custom_config=self.prepare_custom_config_dict,
                     )
                 else:
-                    q_model._model = prepare_fx(
+                    q_model._model = prepare_fx(  # pylint: disable=E1120,E1123
                         q_model._model, self.fx_op_cfgs, prepare_custom_config_dict=self.prepare_custom_config_dict
                     )
             else:
@@ -3681,7 +3681,9 @@ def quantize(self, tune_cfg, model, dataloader, q_func=None):
                 # pylint: disable=E1123
                 q_model._model = convert_fx(q_model._model, convert_custom_config=self.convert_custom_config_dict)
             else:
-                q_model._model = convert_fx(q_model._model, convert_custom_config_dict=self.convert_custom_config_dict)
+                q_model._model = convert_fx(  # pylint: disable=E1123
+                    q_model._model, convert_custom_config_dict=self.convert_custom_config_dict
+                )
             torch_utils.util.append_attr(q_model._model, tmp_model)
             del tmp_model
             gc.collect()
@@ -3830,7 +3832,7 @@ def _pre_hook_for_qat(self, dataloader=None):
                     ),
                 )
             else:
-                self.model._model = prepare_qat_fx(
+                self.model._model = prepare_qat_fx(  # pylint: disable=E1120,E1123
                     self.model._model,
                     fx_op_cfgs,
                     prepare_custom_config_dict=(
@@ -3877,7 +3879,7 @@ def _post_hook_for_qat(self):
                     ),
                 )
             else:
-                self.model._model = convert_fx(
+                self.model._model = convert_fx(  # pylint: disable=E1123
                     self.model._model,
                     convert_custom_config_dict=(
                         self.model.kwargs.get("convert_custom_config_dict", None)
@@ -4331,15 +4333,15 @@ def prepare_sub_graph(
                 # pragma: no cover
                 if is_qat:
                     module_pre = (
-                        prepare_qat_fx(tmp_module, fx_sub_op_cfgs)
+                        prepare_qat_fx(tmp_module, fx_sub_op_cfgs)  # pylint: disable=E1120
                         if version <= Version("1.12.1")
                         else prepare_qat_fx(tmp_module, fx_sub_op_cfgs, example_inputs=example_inputs)
                     )
                 # pylint: disable=E1123
                 # pragma: no cover
                 else:
                     module_pre = (
-                        prepare_fx(tmp_module, fx_sub_op_cfgs)
+                        prepare_fx(tmp_module, fx_sub_op_cfgs)  # pylint: disable=E1120
                         if version <= Version("1.12.1")
                         else prepare_fx(tmp_module, fx_sub_op_cfgs, example_inputs=example_inputs)
                     )
@@ -4433,7 +4435,9 @@ def fuse_fx_model(self, model, is_qat):
                 fused_model = _fuse_fx(graph_module, is_qat, fuse_custom_config=prepare_custom_config_dict)
             elif self.version.release >= Version("1.11.0").release:  # pragma: no cover
                 # pylint: disable=E1124
-                fused_model = _fuse_fx(graph_module, is_qat, fuse_custom_config_dict=prepare_custom_config_dict)
+                fused_model = _fuse_fx(  # pylint: disable=E1123
+                    graph_module, is_qat, fuse_custom_config_dict=prepare_custom_config_dict
+                )
             else:
                 fused_model = _fuse_fx(graph_module, prepare_custom_config_dict)
         except:
 
@@ -821,7 +821,7 @@ def output_hook(self, input, output):
             if adaptor.version.release >= Version("1.13.0").release:  # pragma: no cover
                 tmp_model = prepare_fx(tmp_model, fx_op_cfgs, example_inp)
             else:
-                tmp_model = prepare_fx(
+                tmp_model = prepare_fx(  # pylint: disable=E1120
                     tmp_model,
                     fx_op_cfgs,
                 )
@@ -877,7 +877,7 @@ def output_hook(self, input, output):
             if adaptor.version.release >= Version("1.13.0").release:  # pragma: no cover
                 tmp_model = prepare_fx(tmp_model, fx_op_cfgs, example_inp)
             else:
-                tmp_model = prepare_fx(
+                tmp_model = prepare_fx(  # pylint: disable=E1120
                     tmp_model,
                     fx_op_cfgs,
                 )
@@ -958,7 +958,7 @@ def output_hook(self, input, output):
                 if adaptor.version.release >= Version("1.13.0").release:  # pragma: no cover
                     tmp_model = prepare_fx(tmp_model, fx_op_cfgs, example_inp)
                 else:
-                    tmp_model = prepare_fx(
+                    tmp_model = prepare_fx(  # pylint: disable=E1120
                         tmp_model,
                         fx_op_cfgs,
                     )
 
@@ -0,0 +1,17 @@
+# 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
+#
+#    https://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.
+
+from .utility import *
+from .static_quant import static_quantize
@@ -0,0 +1,130 @@
+#!/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
+#
+#   https://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.
+
+import json
+
+from neural_compressor.torch.utils import get_ipex_version
+
+try:
+    import intel_extension_for_pytorch as ipex
+except:
+    assert False, "Please install IPEX for static quantization."
+
+import torch
+from packaging.version import Version
+
+from .utility import (
+    cfg_to_qconfig,
+    dump_model_op_stats,
+    get_quantizable_ops_recursively,
+    ipex_config_path,
+    simple_inference,
+)
+
+ipex_ver = get_ipex_version()
+
+
+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()
+
+    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
+
+            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)
+
+        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)
+
+    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
+
+    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
+
+
+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