[v1.9.x] Port apache#20759 from v1.x (apache#20815)

* [v1.x] Restore quantized RNN operator from MXNet 1.6 (apache#20759)

* restore but seg fault

* Refactor & seg fault fixed

* apply formatter

* fix sanity

* Fix docs build

* anko review

* Remove copyright by contributors from touched files

* remove comments / apply formatter

* Update call to work with older mkldnn version.

Co-authored-by: bgawrych <[email protected]>

docs/python_docs/environment.yml

@@ -22,6 +22,7 @@ dependencies:
 - conda>=4.6.13
 - pip
 - python
+- setuptools==49.6.0
 - jupyter
 - sphinx==2.4.0
 - matplotlib

include/mxnet/op_attr_types.h

@@ -347,6 +347,21 @@ using FAvoidQuantizeInput = std::function<bool (const NodeAttrs& attrs,
                                                 const size_t index,
                                                 const std::string quantize_granularity)>;
 
+/*!
+ * \brief Register a function to determine if the input of a quantized operator
+ * needs to be quantized asymmetrically.
+ */
+using FNeedAsymQuantizeInput = std::function<bool (const NodeAttrs& attrs,
+                                                   const size_t index)>;
+
+/*!
+ * \brief Register a function to determine if the output of a quantized operator
+ * needs to be dequantized. This is usually used for the quantized operators
+ * which can produce fp32 outputs directly.
+ */
+using FAvoidDequantizeOutput = std::function<bool (const NodeAttrs& attrs,
+                                                   const size_t index)>;
+
 /*!
  * \brief Register a function to determine if the input of a quantized operator
  * needs to be calibrated. This is usually used for the quantized operators

python/mxnet/io/io.py

@@ -37,7 +37,7 @@
 from ..ndarray import array
 from ..ndarray import concat, tile
 
-from .utils import _init_data, _has_instance, _getdata_by_idx
+from .utils import _init_data, _has_instance, _getdata_by_idx, _slice_along_batch_axis
 
 class DataDesc(namedtuple('DataDesc', ['name', 'shape'])):
     """DataDesc is used to store name, shape, type and layout
@@ -602,10 +602,12 @@ class NDArrayIter(DataIter):
         The data name.
     label_name : str, optional
         The label name.
+    layout : str, optional
+        The data layout
     """
     def __init__(self, data, label=None, batch_size=1, shuffle=False,
                  last_batch_handle='pad', data_name='data',
-                 label_name='softmax_label'):
+                 label_name='softmax_label', layout='NCHW'):
         super(NDArrayIter, self).__init__(batch_size)
 
         self.data = _init_data(data, allow_empty=False, default_name=data_name)
@@ -631,20 +633,27 @@ def __init__(self, data, label=None, batch_size=1, shuffle=False,
         # used for 'roll_over'
         self._cache_data = None
         self._cache_label = None
+        self.layout = layout
 
     @property
     def provide_data(self):
         """The name and shape of data provided by this iterator."""
+        batch_axis = self.layout.find('N')
         return [
-            DataDesc(k, tuple([self.batch_size] + list(v.shape[1:])), v.dtype)
+            DataDesc(k, tuple(list(v.shape[:batch_axis]) + \
+                              [self.batch_size] + list(v.shape[batch_axis + 1:])),
+                     v.dtype, layout=self.layout)
             for k, v in self.data
         ]
 
     @property
     def provide_label(self):
         """The name and shape of label provided by this iterator."""
+        batch_axis = self.layout.find('N')
         return [
-            DataDesc(k, tuple([self.batch_size] + list(v.shape[1:])), v.dtype)
+            DataDesc(k, tuple(list(v.shape[:batch_axis]) + \
+                              [self.batch_size] + list(v.shape[batch_axis + 1:])),
+                     v.dtype, layout=self.layout)
             for k, v in self.label
         ]
 
@@ -681,7 +690,7 @@ def next(self):
         data = self.getdata()
         label = self.getlabel()
         # iter should stop when last batch is not complete
-        if data[0].shape[0] != self.batch_size:
+        if data[0].shape[self.layout.find('N')] != self.batch_size:
         # in this case, cache it for next epoch
             self._cache_data = data
             self._cache_label = label
@@ -697,7 +706,7 @@ def _getdata(self, data_source, start=None, end=None):
             end = data_source[0][1].shape[0] if data_source else 0
         s = slice(start, end)
         return [
-            x[1][s]
+            _slice_along_batch_axis(x[1], s, self.layout.find('N'))
             if isinstance(x[1], (np.ndarray, NDArray)) else
             # h5py (only supports indices in increasing order)
             array(x[1][sorted(self.idx[s])][[
@@ -716,7 +725,7 @@ def _concat(self, first_data, second_data):
             concat(
                 first_data[i],
                 second_data[i],
-                dim=0
+                dim=self.layout.find('N')
             ) for i in range(len(first_data))
         ]
 

python/mxnet/io/utils.py

@@ -84,3 +84,8 @@ def _getdata_by_idx(data, idx):
             shuffle_data.append((k, array(v.asnumpy()[idx], v.context)))
 
     return shuffle_data
+
+def _slice_along_batch_axis(data, s, batch_axis):
+    """Apply slice along the batch axis"""
+    ret = data.slice_axis(axis=batch_axis, begin=s.start, end=s.stop)
+    return ret