[REVIEW] Implement Feature Request from #1077 on Left Padding

nvtabular/loader/tensorflow.py

@@ -140,20 +140,20 @@ class KerasSequenceLoader(tf.keras.utils.Sequence, DataLoader):
 
     Iterator output is of the form `(dict(features), list(labels))`,
     where each element of the features dict is a
-    `feature_name: feature_tensor`  and each elemtn of the labels
+    `feature_name: feature_tensor`  and each element of the labels
     list is a tensor, and all tensors are of shape `(batch_size, 1)`.
     Note that this means vectorized continuous and multi-hot categorical
     features are not currently supported.
     The underlying NVTabular `Dataset` object is stored in the `data`
     attribute, and should be used for updating NVTabular `Workflow`
-    statistics::
+    statistics:
 
         workflow = nvt.Workflow(...)
         dataset = KerasSequenceLoader(...)
         workflow.update_stats(dataset.data.to_iter(), record_stats=True)
 
     Parameters
-    -------------
+    ----------
     - paths_or_dataset: str or list(str)
         Either a string representing a file pattern (see `tf.glob` for
         pattern rules), a list of filenames to be iterated through, or
@@ -205,6 +205,7 @@ class KerasSequenceLoader(tf.keras.utils.Sequence, DataLoader):
         dictionary of key: column_name + value: integer representing max sequence length for column
     sparse_dense : bool
         bool value to activate transforming sparse tensors to dense
+
     """
 
     _use_nnz = True
@@ -238,7 +239,7 @@ def __init__(
             feature_columns, cat_names, cont_names, schema=dataset.schema
         )
 
-        # sort the ccolumns to avoid getting incorrect output
+        # Sort the columns to avoid getting incorrect output.
         # (https://github.com/NVIDIA/NVTabular/issues/412)
         cat_names = _get_embedding_order(cat_names)
         cont_names = _get_embedding_order(cont_names)
@@ -265,19 +266,18 @@ def __init__(
         self._map_fns = []
 
     def __len__(self):
-        """
-        recreating since otherwise Keras yells at you
-        """
+        """Recreating since otherwise Keras yells at you."""
         # TODO: what's a better way to do this inheritance
         # of the appropriate methods? A Metaclass?
         DataLoader.stop(self)
         return DataLoader.__len__(self)
 
     def __getitem__(self, idx):
         """
-        implemented exclusively for consistency
+        Implemented exclusively for consistency
         with Keras model.fit. Does not leverage
-        passed idx in any way
+        passed idx in any way.
+
         """
         return DataLoader.__next__(self)
 
@@ -286,6 +286,7 @@ def map(self, fn):
         Applying a function to each batch.
 
         This can for instance be used to add `sample_weight` to the model.
+
         """
         self._map_fns.append(fn)
 

nvtabular/loader/torch.py

@@ -42,7 +42,7 @@ class TorchAsyncItr(torch.utils.data.IterableDataset, DataLoader):
     batches are the specified size until the final batch.
 
     Parameters
-    -----------
+    ----------
     dataset : NVTabular dataset
     cats : [str]
         the list of categorical columns in the dataset
@@ -174,8 +174,38 @@ def _get_sparse_tensor(self, values, indices, num_rows, seq_limit):
             sparse_tensor = sparse_tensor.to_dense()
         return sparse_tensor
 
-    def _build_sparse_tensor(self, values, offsets, diff_offsets, num_rows, seq_limit):
+    def _build_sparse_tensor(
+        self, values, offsets, diff_offsets, num_rows, seq_limit, padding: str = ""
+    ):
+        """Builds sparse tensors in our torch dataloader.
+
+        Parameters
+        ----------
+        values :
+        offsets :
+        diff_offsets :
+        num_rows :
+        seq_limit :
+        padding : str, optional
+            Padding mode, choose among 'left' for left padding, 'right' for right padding,
+            or '' for no padding, by default ''
+
+        Returns
+        -------
+        torch.sparse
+            Our built torch sparse tensor.
+
+        Raises
+        ------
+        NotImplementedError
+            Raises this error when this method is called with a not implemented
+           padding mode string.
+        """
         indices = self._get_indices(offsets, diff_offsets)
+        if padding == "left":
+            indices[:, 1] = seq_limit - 1 - indices[:, 1]
+        if padding == "right":
+            raise NotImplementedError
         return self._get_sparse_tensor(values, indices, num_rows, seq_limit)
 
 

nvtabular/ops/list_slice.py

@@ -61,7 +61,7 @@ def transform(self, col_selector: ColumnSelector, df: DataFrameType) -> DataFram
         on_cpu = _is_cpu_object(df)
         ret = type(df)()
         for col in col_selector.names:
-            # handle CPU via normal python slicing (not very efficient)
+            # Handle CPU via normal python slicing (not very efficient).
             if on_cpu:
                 ret[col] = [row[self.start : self.end] for row in df[col]]
             else:
@@ -99,8 +99,8 @@ def output_tags(self):
 
 @numba.cuda.jit
 def _calculate_row_sizes(start, end, offsets, row_sizes):
-    """given a slice (start/end) and existing offsets indicating row lengths, this
-    calculates the size for each new row after slicing"""
+    """Given a slice (start/end) and existing offsets indicating row lengths, this
+    calculates the size for each new row after slicing."""
     rowid = numba.cuda.grid(1)
     if rowid < offsets.size - 1:
         original_row_size = offsets[rowid + 1] - offsets[rowid]
@@ -120,9 +120,9 @@ def _calculate_row_sizes(start, end, offsets, row_sizes):
 
 @numba.cuda.jit
 def _slice_rows(start, offsets, elements, new_offsets, new_elements):
-    """slices rows of a list column. requires the 'new_offsets' to
+    """Slices rows of a list column. requires the 'new_offsets' to
     be previously calculated (meaning that we don't need the 'end' slice index
-    since thats baked into the new_offsets"""
+    since thats baked into the new_offsets."""
     rowid = numba.cuda.grid(1)
     if rowid < (new_offsets.size - 1):
         if start >= 0: