Merge branch 'master' into xinhe/benchmark

.azure-pipelines/docker/Dockerfile.devel

@@ -36,7 +36,7 @@ RUN apt-get update && apt-get install -y --no-install-recommends --fix-missing \
 
 RUN ln -sf $(which python3) /usr/bin/python
 
-RUN python -m pip --no-cache-dir install --upgrade pip
+RUN python -m pip install pip==24.0
 RUN python -m pip install --no-cache-dir setuptools
 
 RUN pip list

.azure-pipelines/model-test.yml

@@ -57,7 +57,7 @@ parameters:
   - name: PyTorchModelList
     type: object
     default:
-      - resnet18
+#      - resnet18
       - resnet18_fx
   - name: ONNXModelList
     type: object

.github/checkgroup.yml

@@ -51,7 +51,6 @@ subprojects:
       - "Model-Test"
       - "Model-Test (Generate Report GenerateReport)"
       - "Model-Test (Run ONNX Model resnet50-v1-12)"
-      - "Model-Test (Run PyTorch Model resnet18)"
       - "Model-Test (Run PyTorch Model resnet18_fx)"
       - "Model-Test (Run TensorFlow Model resnet50v1.5)"
       - "Model-Test (Run TensorFlow Model ssd_resnet50_v1)"

examples/onnxrt/nlp/huggingface_model/token_classification/layoutlmv2/quantization/ptq_dynamic/main.py

@@ -216,8 +216,6 @@ def _process_dataset(self):
         self.label = []
         self.onnx_inputs = []
         for inputs in self.dataset:
-            # import pdb;
-            # pdb.set_trace()
             onnx_inputs = []
             has_labels = all(inputs.get(k) is not None for k in self.label_names)
             if has_labels:
@@ -237,8 +235,6 @@ def _process_dataset(self):
             }
             """
             for key in self.onnx_input_names:
-                # import pdb;
-                # pdb.set_trace()
                 if key in inputs:
                     # onnx_inputs[key] = np.array([inputs[key]])
                     onnx_inputs.append(np.array(inputs[key]))

examples/onnxrt/nlp/huggingface_model/token_classification/layoutlmv2/quantization/ptq_static/main.py

@@ -216,8 +216,6 @@ def _process_dataset(self):
         self.label = []
         self.onnx_inputs = []
         for inputs in self.dataset:
-            # import pdb;
-            # pdb.set_trace()
             onnx_inputs = []
             has_labels = all(inputs.get(k) is not None for k in self.label_names)
             if has_labels:
@@ -237,8 +235,6 @@ def _process_dataset(self):
             }
             """
             for key in self.onnx_input_names:
-                # import pdb;
-                # pdb.set_trace()
                 if key in inputs:
                     # onnx_inputs[key] = np.array([inputs[key]])
                     onnx_inputs.append(np.array(inputs[key]))

examples/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/python/models/utils.py

@@ -59,7 +59,6 @@ def _compute_padding(self, input, dim):
         return additional_padding, total_padding
 
     def forward(self, input):
-        #import pdb; pdb.set_trace()
         if self.padding == "VALID":
             return F.conv2d(
                 input,
@@ -180,7 +179,6 @@ def decode_boxes(rel_codes, boxes, weights):
     dh = dh / wh
 
     pred_ctr_x = dx * widths + ctr_x
-    #import pdb; pdb.set_trace()
     pred_ctr_y = dy * heights + ctr_y
     pred_w = torch.exp(dw) * widths
     pred_h = torch.exp(dh) * heights
@@ -194,5 +192,4 @@ def decode_boxes(rel_codes, boxes, weights):
         ],
         dim=2,
     )
-    #import pdb; pdb.set_trace()
     return pred_boxes

neural_coder/coders/tensorflow/amp.py

@@ -22,8 +22,6 @@ def __init__(self, file) -> None:
         self.keras_edited_flag = False
 
     def transform(self):
-        # import pdb
-        # pdb.set_trace()
         lines = self.file.split("\n")
         for line in lines:
             if self.is_modify(line):

neural_coder/coders/tensorflow/inc.py

@@ -21,8 +21,6 @@ def __init__(self, file) -> None:
         self.result = []
 
     def transform(self):
-        # import pdb
-        # pdb.set_trace()
         lines = self.file.split("\n")
         for line in lines:
             if self.is_modify(line):

neural_compressor/adaptor/keras.py

@@ -24,7 +24,6 @@
 import numpy as np
 import yaml
 
-from ..conf.dotdict import deep_get
 from ..data.dataloaders.base_dataloader import BaseDataLoader
 from ..utils import logger
 from ..utils.utility import (
@@ -34,6 +33,7 @@
     Dequantize,
     LazyImport,
     Statistics,
+    deep_get,
     dump_elapsed_time,
     singleton,
     version1_lt_version2,

neural_compressor/adaptor/mxnet.py

@@ -446,13 +446,6 @@ def _one_shot_query(self):
                 raise ValueError(
                     "Please check if the format of {} follows Neural Compressor yaml schema.".format(self.cfg)
                 )
-        self._update_cfg_with_usr_definition()
-
-    def _update_cfg_with_usr_definition(self):
-        from neural_compressor.conf.pythonic_config import mxnet_config
-
-        if mxnet_config.precisions is not None:
-            self.cur_config["precisions"]["names"] = ",".join(mxnet_config.precisions)
 
     def _get_specified_version_cfg(self, data):
         """Get the configuration for the current runtime.

neural_compressor/adaptor/onnxrt.py

@@ -2242,15 +2242,6 @@ def _one_shot_query(self):
                 raise ValueError(
                     "Please check if the format of {} follows Neural Compressor yaml schema.".format(self.cfg)
                 )
-        self._update_cfg_with_usr_definition()
-
-    def _update_cfg_with_usr_definition(self):
-        from neural_compressor.conf.pythonic_config import onnxruntime_config
-
-        if onnxruntime_config.graph_optimization_level is not None:
-            self.cur_config["graph_optimization"]["level"] = onnxruntime_config.graph_optimization_level
-        if onnxruntime_config.precisions is not None:
-            self.cur_config["precisions"]["names"] = ",".join(onnxruntime_config.precisions)
 
     def _get_specified_version_cfg(self, data):  # pragma: no cover
         """Get the configuration for the current runtime.

neural_compressor/adaptor/pytorch.py

@@ -5102,13 +5102,6 @@ def _one_shot_query(self):
             self.cur_config = self.cur_config[self.device]
         elif "cpu" in self.cur_config:
             self.cur_config = self.cur_config["cpu"]
-        self._update_cfg_with_usr_definition()
-
-    def _update_cfg_with_usr_definition(self):
-        from neural_compressor.conf.pythonic_config import pytorch_config
-
-        if pytorch_config.precisions is not None:
-            self.cur_config["precisions"]["names"] = ",".join(pytorch_config.precisions)
 
     def get_quantization_capability(self, datatype="int8"):
         """Get the supported op types' quantization capability.

neural_compressor/adaptor/tensorflow.py

@@ -24,7 +24,6 @@
 import numpy as np
 import yaml
 
-from ..conf.dotdict import deep_get
 from ..data.dataloaders.base_dataloader import BaseDataLoader
 from ..utils import logger
 from ..utils.utility import (
@@ -34,6 +33,7 @@
     Dequantize,
     LazyImport,
     Statistics,
+    deep_get,
     dump_elapsed_time,
     singleton,
     version1_eq_version2,
@@ -2204,14 +2204,6 @@ def _one_shot_query(self):
                 raise ValueError(
                     "Please check if the format of {} follows Neural Compressor yaml schema.".format(self.cfg)
                 )
-        self._update_cfg_with_usr_definition()
-
-    def _update_cfg_with_usr_definition(self):
-        """Add user defined precision configuration."""
-        from neural_compressor.conf.pythonic_config import tensorflow_config
-
-        if tensorflow_config.precisions is not None:
-            self.cur_config["precisions"]["names"] = ",".join(tensorflow_config.precisions)
 
     def get_version(self):
         """Get the current backend version information.

neural_compressor/adaptor/tf_utils/graph_converter.py

@@ -28,13 +28,13 @@
 from tensorflow.python.platform import gfile
 
 from neural_compressor.adaptor.tf_utils.graph_rewriter.generic.insert_print_node import InsertPrintMinMaxNode
-from neural_compressor.conf.dotdict import deep_get
 from neural_compressor.model import Model
 from neural_compressor.model.tensorflow_model import TensorflowSavedModelModel
 from neural_compressor.utils.utility import (
     CaptureOutputToFile,
     CpuInfo,
     combine_histogram,
+    deep_get,
     get_all_fp32_data,
     get_tensor_histogram,
 )

neural_compressor/adaptor/tf_utils/graph_converter_without_calib.py

@@ -24,8 +24,8 @@
 import tensorflow as tf
 from tensorflow.python.platform import gfile
 
-from neural_compressor.conf.dotdict import deep_get
 from neural_compressor.model import Model
+from neural_compressor.utils.utility import deep_get
 
 from .graph_rewriter.bf16.bf16_convert import BF16Convert
 from .graph_rewriter.generic.fold_batch_norm import FoldBatchNormNodesOptimizer

neural_compressor/compression/pruner/utils.py

@@ -18,90 +18,20 @@
 # limitations under the License.
 
 import re
-from collections import UserDict, defaultdict
+from collections import UserDict
 
 import numpy as np
-import yaml
 
-from ...config import WeightPruningConfig as WeightPruningConf
-
-try:
-    from neural_compressor.conf.config import Pruner
-    from neural_compressor.conf.dotdict import DotDict
-    from neural_compressor.utils import logger
-
-    from ...conf.config import PrunerV2
-    from ...conf.pythonic_config import WeightPruningConfig
-    from ...utils.utility import LazyImport
-
-    torch = LazyImport("torch")
-    nn = LazyImport("torch.nn")
-    F = LazyImport("torch.nn.functional")
-    tf = LazyImport("tensorflow")
-except:
-    import logging
-
-    import tensorflow as tf
-    import torch
-    import torch.nn as nn
-    import torch.nn.functional as F
-
-    from .dot_dict import DotDict  # #TODO
-
-    logger = logging.getLogger(__name__)
-    from .schema_check import PrunerV2
-
-    class WeightPruningConfig:
-        """Similar to torch optimizer's interface."""
-
-        def __init__(
-            self,
-            pruning_configs=[{}],  ##empty dict will use global values
-            target_sparsity=0.9,
-            pruning_type="snip_momentum",
-            pattern="4x1",
-            op_names=[],
-            excluded_op_names=[],
-            start_step=0,
-            end_step=0,
-            pruning_scope="global",
-            pruning_frequency=1,
-            min_sparsity_ratio_per_op=0.0,
-            max_sparsity_ratio_per_op=0.98,
-            sparsity_decay_type="exp",
-            pruning_op_types=["Conv", "Linear"],
-            **kwargs,
-        ):
-            """Init a WeightPruningConfig object."""
-            self.pruning_configs = pruning_configs
-            self._weight_compression = DotDict(
-                {
-                    "target_sparsity": target_sparsity,
-                    "pruning_type": pruning_type,
-                    "pattern": pattern,
-                    "op_names": op_names,
-                    "excluded_op_names": excluded_op_names,  ##global only
-                    "start_step": start_step,
-                    "end_step": end_step,
-                    "pruning_scope": pruning_scope,
-                    "pruning_frequency": pruning_frequency,
-                    "min_sparsity_ratio_per_op": min_sparsity_ratio_per_op,
-                    "max_sparsity_ratio_per_op": max_sparsity_ratio_per_op,
-                    "sparsity_decay_type": sparsity_decay_type,
-                    "pruning_op_types": pruning_op_types,
-                }
-            )
-            self._weight_compression.update(kwargs)
+from neural_compressor.utils import logger
+from neural_compressor.utils.utility import DotDict
 
-        @property
-        def weight_compression(self):
-            """Get weight_compression."""
-            return self._weight_compression
+from ...config import WeightPruningConfig as WeightPruningConf
+from ...utils.utility import LazyImport
 
-        @weight_compression.setter
-        def weight_compression(self, weight_compression):
-            """Set weight_compression."""
-            self._weight_compression = weight_compression
+torch = LazyImport("torch")
+nn = LazyImport("torch.nn")
+F = LazyImport("torch.nn.functional")
+tf = LazyImport("tensorflow")
 
 
 def get_sparsity_ratio(pruners, model):
@@ -423,14 +353,10 @@ def check_key_validity_prunerv2(template_config, usr_cfg_dict):
         for obj in user_config:
             if isinstance(obj, dict):
                 check_key_validity_dict(template_config, obj)
-            elif isinstance(obj, PrunerV2):
-                check_key_validity_prunerv2(template_config, obj)
 
     # single pruner, weightconfig or yaml
     elif isinstance(user_config, dict):
         check_key_validity_dict(template_config, user_config)
-    elif isinstance(user_config, PrunerV2):
-        check_key_validity_prunerv2(template_config, user_config)
     return
 
 
@@ -470,7 +396,7 @@ def process_and_check_config(val):
     default_config.update(default_global_config)
     default_config.update(default_local_config)
     default_config.update(params_default_config)
-    if isinstance(val, WeightPruningConfig) or isinstance(val, WeightPruningConf):
+    if isinstance(val, WeightPruningConf):
         global_configs = val.weight_compression
         pruning_configs = val.pruning_configs
         check_key_validity(default_config, pruning_configs)
@@ -494,21 +420,7 @@ def process_config(config):
     Returns:
         A config dict object.
     """
-    if isinstance(config, str):
-        try:
-            with open(config, "r") as f:
-                content = f.read()
-                val = yaml.safe_load(content)
-                ##schema.validate(val)
-            return process_and_check_config(val)
-        except FileNotFoundError as f:
-            logger.error("{}.".format(f))
-            raise RuntimeError("The yaml file is not exist. Please check the file name or path.")
-        except Exception as e:
-            logger.error("{}.".format(e))
-            raise RuntimeError("The yaml file format is not correct. Please refer to document.")
-
-    if isinstance(config, WeightPruningConfig) or isinstance(config, WeightPruningConf):
+    if isinstance(config, WeightPruningConf):
         return process_and_check_config(config)
     else:
         assert False, f"not supported type {config}"
@@ -618,25 +530,6 @@ def parse_to_prune_tf(config, model):
     return new_modules
 
 
-def generate_pruner_config(info):
-    """Generate pruner config object from prune information.
-
-    Args:
-        info: A dotdict that saves prune information.
-
-    Returns:
-        pruner: A pruner config object.
-    """
-    return Pruner(
-        initial_sparsity=0,
-        method=info.method,
-        target_sparsity=info.target_sparsity,
-        start_epoch=info.start_step,
-        end_epoch=info.end_step,
-        update_frequency=info.pruning_frequency,
-    )
-
-
 def get_layers(model):
     """Get each layer's name and its module.