1. Import IPython only if SVG is requested. This is required to fix a…

… tutorial publishing faliure. 2. Fix test about IPython availability check.

python/tvm/contrib/tedd.py

@@ -14,14 +14,13 @@
 # KIND, either express or implied.  See the License for the
 # specific language governing permissions and limitations
 # under the License.
+# pylint: disable=import-outside-toplevel
 """Tensor Expression Debug Display (TEDD), visualizing Tensor Expression"""
 import html
 import json
 import warnings
 from graphviz import Digraph
 from graphviz import Source
-from IPython.display import display
-from IPython.display import SVG
 import tvm
 
 TVMDD_TABLE_BODY_WIDTH = 30
@@ -208,6 +207,8 @@ def dump_graph(dot_string,
         except IOError:
             print('Cannot open file: ' + dot_file_path)
     if show_svg:
+        from IPython.display import display
+        from IPython.display import SVG
         src = Source(dot_string)
         display(SVG(src.pipe(format='svg')))
     if output_dot_string:

tests/python/contrib/test_tedd.py

@@ -28,7 +28,7 @@ def findany(pattern, str):
 
 def checkdepdency():
     import pkg_resources
-    return not {'graphviz', 'IPython'} - {pkg.key for pkg in pkg_resources.working_set}
+    return not {'graphviz', 'ipython'} - {pkg.key for pkg in pkg_resources.working_set}
 
 def test_dfg():
     A = tvm.placeholder((1024, 4096), dtype='float32', name='A')

tutorials/language/tedd.py

@@ -16,7 +16,7 @@
 # under the License. 
 """ 
 Use Tensor Expression Debug Display (TEDD) for Visualization 
-============================================= 
+============================================================ 
 **Author**: `Yongfeng Gu <https://github.com/yongfeng-nv>`_
 
 This is an introduction about using TEDD to visualize tensor expressions. 
@@ -47,10 +47,11 @@
 
 ######################################################################
 # Define and Schedule Convolution with Bias and ReLU
-# ----------------------------
+# --------------------------------------------------
 # Let's build an example Tensor Expression for a convolution followed by Bias and ReLU.
 # We first connect conv2d, add, and relu TOPIs.  Then, we create a TOPI generic schedule.
 #
+
 batch = 1
 in_channel = 256
 in_size = 32
@@ -70,11 +71,12 @@
 
 ###################################################################### 
 # Render Graphs with TEDD
-# ------------------- 
+# -----------------------
 # We render graphs to see the computation  
 # and how it is scheduled.   
 # If you run the tutorial in a Jupyter notebook, you can use the following commented lines  
 # to render SVG figures showing in notebook directly.
+#
 
 tedd.viz_dataflow_graph(s, dot_file_path = '/tmp/dfg.dot') 
 #tedd.viz_dataflow_graph(s, show_svg = True) 
@@ -83,18 +85,22 @@
 # .. image:: https://github.com/dmlc/web-data/raw/master/tvm/tutorial/tedd_dfg.png
 #      :align: center
 #      :scale: 100%
+#
 # The first one is a dataflow graph.  Every node represents a stage with name and memory  
 # scope shown in the middle and inputs/outputs information on the sides.   
 # Edges show nodes' dependency.   
+#
 
 tedd.viz_schedule_tree(s, dot_file_path = '/tmp/scheduletree.dot') 
 #tedd.viz_schedule_tree(s, show_svg = True) 
+
 ######################################################################
 # We just rendered the schedule tree graph.  You may notice an warning about ranges not 
 # available.
 # The message also suggests to call normalize() to infer range information.  We will
 # skip inspecting the first schedule tree and encourage you to compare the graphs before
-# and and after normalize() for its impact.
+# and after normalize() for its impact.
+#
 
 s = s.normalize()
 tedd.viz_schedule_tree(s, dot_file_path = '/tmp/scheduletree2.dot') 
@@ -104,6 +110,7 @@
 # .. image:: https://github.com/dmlc/web-data/raw/master/tvm/tutorial/tedd_st.png
 #      :align: center
 #      :scale: 100%
+#
 # Now, let us take a close look at the second schedule tree.  Every block under ROOT 
 # represents a  
 # stage.  Stage name shows in the top row and compute shows in the bottom row.   
@@ -120,13 +127,15 @@
 # If a stage doesn't compute_at any other stage, it has an edge directly to the  
 # ROOT node.  Otherwise, it has an edge pointing to the IterVar it attaches to,  
 # such as W.shared attaches to rx.outer in the middle compute stage. 
+#
 
 ###################################################################### 
 # .. note:: 
 # 
 #   By definition, IterVars are internal nodes and computes are leaf nodes in 
 #   a schedule tree.   The edges among IterVars and compute within one stage are 
 #   omitted, making every stage a block, for better readability.
+#
 
 tedd.viz_itervar_relationship_graph(s, dot_file_path = '/tmp/itervar.dot') 
 #tedd.viz_itervar_relationship_graph(s, show_svg = True) 
@@ -135,18 +144,21 @@
 # .. image:: https://github.com/dmlc/web-data/raw/master/tvm/tutorial/tedd_itervar_rel.png
 #      :align: center
 #      :scale: 100%
+#
 # The last one is an IterVar Relationship Graph.  Every subgraph represents a  
 # stage and contains IterVar nodes and transformation nodes.  For example,   
 # W.shared has three split nodes and three fuse nodes.  The rest are IterVar  
 # nodes of the same format as the IterVar rows in Schedule Trees.  Root  
 # IterVars are those not driven by any transformation node, such as ax0; leaf  
 # IterVars don't drive any transformation node and have non-negative indices,  
 # such as ax0.ax1.fused.ax2.fused.ax3.fused.outer with index of 0.
+#
 
 
 ###################################################################### 
 # Summary 
 # ------- 
 # This tutorial demonstrates the usage of TEDD.  We use an example built 
 # with TOPI to show the schedules under the hood.  You can also use 
-# it before and after any schedule primitive to inspect the impact.
+# it before and after any schedule primitive to inspect its effect.
+#