.. 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. Hybrid Frontend Developer Guide =============================== If you are a developer: 1. who is trying writing some preliminary patterns that have not been supported by TVM yet, maybe :ref:`hybrid-langref-label` is a better place for you. 2. who wants to know the implementation details of this module, you are right here! Features -------- Software Emulation ~~~~~~~~~~~~~~~~~~ In software emulation, the most interesting thing is the decorator ``tvm.te.hybrid.script``. This decorator helps 2 things: 1. Importing runtime variables 2. Overloading the function according to the arguments passed Correct me if I am wrong: I believe that how 1. is implemented is dangerous, but I have no choice. What I did is to add those names into python dict ``func.__global__`` and after the call to ``func`` is done, those names will be cleaned up. Overload is simple: the decorator checks the arguments' types and determines which function should be actually called. Backend Compilation ~~~~~~~~~~~~~~~~~~~ Compilation is a large module, you can see ``python/tvm/te/hybrid/`` for more details. The first stage determines the usage, or more accurately the declaration of each variable and the second stage does the actual IR generation. Attributes ~~~~~~~~~~ So far, ONLY tensors' `shape` attribute is supported. You can see ``visit_Subscript`` in ``python/tvm/te/hybrid/parser.py`` for more details. This is a hacky solution, I just check the attributes when subscript. Loops ~~~~~ In HalideIR, loops have in total 4 types: ``serial``, ``unrolled``, ``parallel``, and ``vectorized``. .. note:: Unlike what that is in HalideIR, in ``loop_type(a, b)``, ``a`` is the starting point and ``b`` is the trip count of iterations. Here ``loop_type(a, b)`` indicates ``[a, b)``. Thus, when lowering it to HalideIR, we need to do ``start, extent = a, b - a`` .. note:: In HalideIR those are enums, they are in passive form. Here we use active form to annotate loops, because they are ready to run. Variables ~~~~~~~~~ Because there is no variables in ``HalideIR``, all the mutable variables will be lowered to an array with size 1. It takes the first store of a variable as its declaration. Math Intrinsics ~~~~~~~~~~~~~~~ So far, these math intrinsics, ``log``, ``exp``, ``sigmoid``, ``tanh``, ``power``, and ``popcount``, are supported. Math intrinsics will be imported by the decorator. Most of the intrinsics are borrowed by library implementation except ``popcount`` and ``sigmoid``. I implemented them manually. Casting ~~~~~~~ You can cast values by using the keywords ``uint8``, ``uint16`` ``uint32``, ``uint64``, ``int8``, ``int16``, ``int32``, ``int64``, ``float16``, ``float32``, ``float64``.