.. 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. Parser utilities ================ A few helpers act at **parse time** (when TVMScript is turned into TIRx), letting you inline Python-computed values, factor out reusable fragments, and bundle parser-side state. ``T.meta_var`` — inline a Python value -------------------------------------- ``T.meta_var(x)`` tells the parser to treat ``x`` — a value computed in **Python** — as a compile-time *meta* value and inline it directly into the IR, rather than parse it as a script variable. It avoids a throwaway local, and it drives metaprogramming: a plain Python ``for`` over a meta value unrolls in the parser. .. code-block:: python n = T.meta_var(4) # n is a Python int, inlined for j in range(n): # unrolled at parse time acc[0] = acc[0] + A[tx, j] ``@T.inline`` — inline functions -------------------------------- ``@T.inline`` defines a function whose body is **inlined at each call site** during parsing — no call appears in the generated code. It follows Python's lexical (LEGB) scoping with late binding, so a parameter shadows an enclosing variable: .. code-block:: python @T.inline def add_into(acc, x): acc[0] = acc[0] + x add_into(s.acc, A[tx, j]) # inlined -> s.acc[0] = s.acc[0] + A[tx, j] ``@T.meta_class`` — parser-side state objects --------------------------------------------- ``@T.meta_class`` marks a plain Python class whose **instances are parser meta values**: their fields can hold buffers and scalars, so you can bundle related allocations and state into one object and use it in the kernel body. .. code-block:: python @T.meta_class class State: def __init__(self, smem): self.acc = T.alloc_local([1], "float32") self.buf = T.decl_buffer([64], "float16", smem, scope="shared.dyn") s = State(smem.data) s.acc[0] = T.float32(0.0) # use its fields like ordinary buffers # ... s.buf[i] ... This is handy for grouping a kernel's pipeline state (barriers, accumulators, scratch views) instead of threading many separate locals through the body. ``T.constexpr`` --------------- ``T.constexpr`` marks a compile-time kernel parameter, baked in by ``@T.jit``'s ``.specialize(...)``. See :doc:`functions` for the details.