[Bugbash] update doc (#5075)

docs/source/tutorials/index.rst

@@ -170,13 +170,13 @@ Tutorials
 .. only:: html
 
   .. image:: /tutorials/images/thumb/sphx_glr_pruning_bert_glue_thumb.png
-    :alt: Pruning Transformer with NNI
+    :alt: Pruning Bert on Task MNLI
 
   :ref:`sphx_glr_tutorials_pruning_bert_glue.py`
 
 .. raw:: html
 
-      <div class="sphx-glr-thumbnail-title">Pruning Transformer with NNI</div>
+      <div class="sphx-glr-thumbnail-title">Pruning Bert on Task MNLI</div>
     </div>
 
 

docs/source/tutorials/pruning_bert_glue.py.md5

-7d8ff24fe5a88d208ad2ad051f060df4
\ No newline at end of file
+4935f5727dd073c91bcfab8b9f0676d7
\ No newline at end of file

docs/source/tutorials/sg_execution_times.rst

@@ -5,12 +5,12 @@
 
 Computation times
 =================
-**01:38.004** total execution time for **tutorials** files:
+**00:41.637** total execution time for **tutorials** files:
 
 +-----------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorials_darts.py` (``darts.py``)                                                   | 01:38.004 | 0.0 MB |
+| :ref:`sphx_glr_tutorials_pruning_bert_glue.py` (``pruning_bert_glue.py``)                           | 00:41.637 | 0.0 MB |
 +-----------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorials_pruning_bert_glue.py` (``pruning_bert_glue.py``)                           | 00:27.206 | 0.0 MB |
+| :ref:`sphx_glr_tutorials_darts.py` (``darts.py``)                                                   | 00:00.000 | 0.0 MB |
 +-----------------------------------------------------------------------------------------------------+-----------+--------+
 | :ref:`sphx_glr_tutorials_hello_nas.py` (``hello_nas.py``)                                           | 00:00.000 | 0.0 MB |
 +-----------------------------------------------------------------------------------------------------+-----------+--------+

nni/algorithms/compression/pytorch/quantization/native_quantizer.py

@@ -22,7 +22,7 @@ class NaiveQuantizer(Quantizer):
     config_list : List[Dict]
         List of configurations for quantization. Supported keys:
             - quant_types : List[str]
-                Type of quantization you want to apply, currently support 'weight', 'input', 'output'.
+                Type of quantization you want to apply, currently support 'weight'.
             - quant_bits : Union[int, Dict[str, int]]
                 Bits length of quantization, key is the quantization type, value is the length, eg. {'weight': 8},
                 when the type is int, all quantization types share same bits length.

nni/algorithms/compression/pytorch/quantization/qat_quantizer.py

@@ -228,27 +228,6 @@ class QAT_Quantizer(Quantizer):
             'quant_dtype': 'uint',
             'quant_scheme': 'per_tensor_affine'
         }]
-
-    **Multi-GPU training**
-
-    QAT quantizer natively supports multi-gpu training (DataParallel and DistributedDataParallel). Note that the quantizer
-    instantiation should happen before you wrap your model with DataParallel or DistributedDataParallel. For example:
-
-    .. code-block:: python
-
-        from torch.nn.parallel import DistributedDataParallel as DDP
-        from nni.algorithms.compression.pytorch.quantization import QAT_Quantizer
-
-        model = define_your_model()
-
-        model = QAT_Quantizer(model, **other_params)  # <--- QAT_Quantizer instantiation
-
-        model = DDP(model)
-
-        for i in range(epochs):
-            train(model)
-            eval(model)
-
     """
 
     def __init__(self, model, config_list, optimizer, dummy_input=None):

nni/algorithms/compression/v2/pytorch/pruning/basic_pruner.py

@@ -175,8 +175,8 @@ class EvaluatorBasedPruner(BasicPruner):
             else:
                 self.optimizer_helper = OptimizerConstructHelper.from_trace(model, traced_optimizer)
             self.using_evaluator = False
-            warn_msg = f"The old API ...{','.join(old_api)} will be deprecated after NNI v3.0, " + \
-                       "please using the new one ...{','.join(new_api)}"
+            warn_msg = f"The old API {','.join(old_api)} will be deprecated after NNI v3.0, " + \
+                       f"please using the new one {','.join(new_api)}"
             _logger.warning(warn_msg)
         return init_kwargs
 

nni/algorithms/compression/v2/pytorch/utils/evaluator.py

@@ -760,7 +760,10 @@ class TorchEvaluator(Evaluator):
 
     def evaluate(self) -> float | None | Tuple[float, Dict[str, Any]] | Tuple[None, Dict[str, Any]]:
         assert self.model is not None
-        assert self.evaluating_func is not None
+        if self.evaluating_func is None:
+            warn_msg = f'Did not pass evaluation_func to {self.__class__.__name__}, will return None for calling evaluate()'
+            _logger.warning(warn_msg)
+            return None
         metric = self.evaluating_func(self.model)
         if isinstance(metric, dict):
             nni_used_metric = metric.get('default', None)