comaniac commented on a change in pull request #4947: [TUTORIAL] Fix tedd
tutorial after strategy change
URL: https://github.com/apache/incubator-tvm/pull/4947#discussion_r384668104
##########
File path: tutorials/language/tedd.py
##########
@@ -63,102 +63,102 @@
A = tvm.placeholder((in_size, in_size, in_channel, batch), name='A')
W = tvm.placeholder((kernel, kernel, in_channel, num_filter), name='W')
B = tvm.placeholder((1, num_filter, 1), name='bias')
-with tvm.target.create("cuda"):
+with tvm.target.create("llvm"):
t_conv = topi.nn.conv2d(A, W, stride, padding, dilation, layout='HWCN')
t_bias = topi.add(t_conv, B)
t_relu = topi.nn.relu(t_bias)
- s = topi.generic.schedule_conv2d_hwcn([t_relu])
-
-######################################################################
+ s = topi.generic.schedule_conv2d_hwcn([t_relu])
+
+######################################################################
# 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
+# 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)
+tedd.viz_dataflow_graph(s, dot_file_path = '/tmp/dfg.dot')
+#tedd.viz_dataflow_graph(s, show_svg = True)
######################################################################
# .. 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.
+# 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)
+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
+# 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 after normalize() for its impact.
#
s = s.normalize()
-tedd.viz_schedule_tree(s, dot_file_path = '/tmp/scheduletree2.dot')
-#tedd.viz_schedule_tree(s, show_svg = True)
+tedd.viz_schedule_tree(s, dot_file_path = '/tmp/scheduletree2.dot')
+#tedd.viz_schedule_tree(s, show_svg = True)
######################################################################
# .. 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.
-# The middle rows are for IterVars, the higher the outer, the lower the inner.
-# An IterVar row contains its index, name, type, and other optional
information.
-# Let's use the W.shared stage as an example. The top row tells
-# its name, "W.shared", and memory scope, "Shared". Its compute is
-# :code:`W(ax0, ax1, ax2, ax3)`.
-# Its outer most loop IterVar is ax0.ax1.fused.ax2.fused.ax3.fused.outer,
-# indexed with 0, of kDataPar, bound to threadIdx.y, and with range(min=0,
ext=8).
-# You can also tell
-# IterVar type with the index box color, shown in the legend.
-#
-# 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.
+# 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.
+# The middle rows are for IterVars, the higher the outer, the lower the inner.
+# An IterVar row contains its index, name, type, and other optional
information.
+# Let's use the W.shared stage as an example. The top row tells
+# its name, "W.shared", and memory scope, "Shared". Its compute is
+# :code:`W(ax0, ax1, ax2, ax3)`.
+# Its outer most loop IterVar is ax0.ax1.fused.ax2.fused.ax3.fused.outer,
+# indexed with 0, of kDataPar, bound to threadIdx.y, and with range(min=0,
ext=8).
+# You can also tell
+# IterVar type with the index box color, shown in the legend.
+#
+# 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
+######################################################################
+# .. 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)
+tedd.viz_itervar_relationship_graph(s, dot_file_path = '/tmp/itervar.dot')
+#tedd.viz_itervar_relationship_graph(s, show_svg = True)
######################################################################
# .. image::
https://github.com/dmlc/web-data/raw/master/tvm/tutorial/tedd_itervar_rel.png
# :align: center
# :scale: 100%
Review comment:
Remove this line. `:scale` will be ignored because Sphinx cannot get the
image size.
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