Tianqi Chen
275114b327
## Summary - Make `PrimExpr` a typed C++ view over `Expr` values whose `ExprNode::ty` is `PrimType`, instead of using a separate runtime node class as the proof of primitive-ness. - Use the shared `ir::Call` node for Relax, TIRX, and primitive-valued calls, while keeping primitive-only APIs explicit at their semantic boundaries. - Keep Python on the general `Expr` surface for primitive-typed values so `isinstance` behavior does not imply a nominal primitive-expression subclass. ## Design Rationale The main advantage of this change is that common expression nodes such as `Call` can be unified without specializing each one to `PrimType`. A single `ir::Call` can represent a Relax tensor call, a Relax scalar call, or a primitive-valued intrinsic call; the result type stored in `ExprNode::ty` determines whether that particular value can be viewed as `PrimExpr`. This keeps the IR node hierarchy focused on expression structure rather than result-type categories. Nodes that are intrinsically primitive, such as integer and floating-point literals or TIRX primitive operators, still have strongly typed C++ APIs and data structures. General nodes whose result type may vary, such as `Call`, remain general `Expr` nodes and are narrowed to `PrimExpr` only where primitive-only semantics are required. The PR also keeps the compatibility surface practical: C++ primitive-only APIs continue to accept `PrimExpr`, Python exposes a compatibility predicate for checking the primitive typed category, and visitors/printers use one natural `Call` path rather than duplicating Relax and primitive call handling. Missing expression types are represented explicitly with `Type::Missing()` so constructors can leave type inference to later analysis without relying on nullable `Type` values.
41 lines
1.3 KiB
Python
41 lines
1.3 KiB
Python
# Licensed to the Apache Software Foundation (ASF) under one
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# or more contributor license agreements. See the NOTICE file
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# distributed with this work for additional information
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# regarding copyright ownership. The ASF licenses this file
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# to you under the Apache License, Version 2.0 (the
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# "License"); you may not use this file except in compliance
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# with the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing,
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# software distributed under the License is distributed on an
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# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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# KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations
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# under the License.
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"""Bound deduction."""
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from . import _ffi_api
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def deduce_bound(var, cond, hint_map, relax_map):
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"""Deduce the bound of the target variable in the cond.
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Parameters
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----------
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var : tvm.tirx.Var
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The target variable to be deduced.
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cond : Expr
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The condition
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hint_map : Map[tvm.tirx.Var, IntSet]
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Domain of variables used to help deduction.
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relax_map : Map[tvm.tirx.Var, IntSet]
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The fomain of the variables to be relaxed
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using the provided domain.
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"""
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return _ffi_api.DeduceBound(var, cond, hint_map, relax_map)
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