-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements. See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership. The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License. You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied. See the License for the
-# specific language governing permissions and limitations
-# under the License.
-"""
-Use Tensor Expression Debug Display (TEDD) for Visualization
-============================================================
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# 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.
+This is an introduction about using TEDD to visualize tensor expressions.
-Tensor Expressions are scheduled with primitives. Although individual
-primitives are usually easy to understand, they become complicated quickly
-when you put them together. We have introduced an operational model of
-schedule primitives in Tensor Expression in this document
-(https://docs.google.com/document/d/1nmz00_n4Ju-SpYN0QFl3abTHTlR_P0dRyo5zsWC0Q1k/edit?usp=sharing)
-to make it easier to understand
+Tensor Expressions are scheduled with primitives. Although individual
+primitives are usually easy to understand, they become complicated quickly
+when you put them together. We have introduced an operational model of
+schedule primitives in Tensor Expression.
-* the interactions between different schedule primitives,
-* the impact of the schedule primitives on the final code generation.
+* the interactions between different schedule primitives,
+* the impact of the schedule primitives on the final code generation.
-The operational model is based on a Dataflow Graph, a Schedule Tree and an
-IterVar Relationship Graph. Schedule primitives perform operations on these
+The operational model is based on a Dataflow Graph, a Schedule Tree and an
+IterVar Relationship Graph. Schedule primitives perform operations on these
graphs.
-TEDD renders these three graphs from a given schedule. This tutorial demonstrates
-how to use TEDD and how to interpret the rendered graphs.
+TEDD renders these three graphs from a given schedule. This tutorial demonstrates
+how to use TEDD and how to interpret the rendered graphs.
"""
from __future__ import absolute_import, print_function
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
#
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%
#
-# 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,
+# 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
+######################################################################
+# 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 its effect.
-#
\ No newline at end of file
+#
projects / platform / upstream / tvm.git / commitdiff