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Subject [GitHub] [incubator-tvm] icemelon9 commented on a change in pull request #5078: [DOC] Add doc for Relay op strategy
Date Tue, 17 Mar 2020 05:38:16 GMT
icemelon9 commented on a change in pull request #5078: [DOC] Add doc for Relay op strategy

 File path: docs/dev/relay_op_strategy.rst
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+..  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
+..  Unless required by applicable law or agreed to in writing,
+    software distributed under the License is distributed on an
+    KIND, either express or implied.  See the License for the
+    specific language governing permissions and limitations
+    under the License.
+.. _relay-op-strategy:
+Relay Operator Strategy
+In order to lower Relay operators to implementation defined in TOPI library, the
+compute and schedule functions need to be registered to Relay operators.
+However, compute and schedule functions are usually specialized for each target,
+and further, even for the same target, we may have multiple algorithms and
+implementations available. To deal with the complexity, we introduce operator
+strategy to allow developers to define a flexible lowering strategy for each
+operator and target.
+Operator Strategy Design
+The basic element in operator strategy is an ``OpImplementation``. It includes
+the a pair of compute and schedule function, the name of the implementation,
+and a priority level (the usability of priority level will be explained below).
+The ``OpStrategy`` includes a list of specializations. Each specialization
+contains a list of ``OpImplementation`` associated with a specialized condition
+(see ``SpecializedCondition`` definition in ``include/tvm/te/schedule.h``).  The
+specialized condition can be null, indicating the implementations are generally
+applicable; otherwise, the implementations should only be used when the
+specialized condition is satisfied. ``OpStrategy`` provides only one API,
+adding an implementation to the strategy:
+.. code:: python
+    def add_implementation(self, compute, schedule, name="default", plevel=10)
+Last, a ``FTVMStrategy`` function is registered to each Relay operator.
+``FTVMStrategy`` is a generic function (see ``include/tvm/target/generic_func.h``),
+that can be overwritten for each target. The function signature is
+.. code:: c
+    OpStrategy(const Attrs& attrs, const Array<Tensor>& inputs, const Type&
out_type, const Target& target)
+, that the function returns an ``OpStrategy`` given the op attributes, input
+tensors, output types, and target to compile to,
+Register strategy for a new operator
+There are three methods to register a strategy function for an operator,
+defined in ``python/tvm/relay/op/``.
+First, for operators that have injective, broadcast, or reduction pattern, we
+can call ``register_injective_schedule``, ``register_broadcast_schedule``, and
+``register_reduce_schedule`` repsectively. The schedule function for these
+patterns are already registered by each target and can be applied to these
+operators. We assume the compute function should be same across all targets, and
+``FTVMCompute`` needs to be registered to the op before invoking register
+.. code:: python
+    register_injective_schedule("my_new_op")
+Second, for operators that doesn't have these common patterns mentioned before,
+but also have the same compute function for all targets, we can use
+``register_schedule`` API. But before that, we need to first define the
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