b919d62eae
In this PR, I added a C API for each (upstream) MLIR type to retrieve
its type name (for example, `IntegerType` -> `mlirIntegerTypeGetName()`
-> `"builtin.integer"`), and exposed a corresponding `type_name` class
attribute in the Python bindings (e.g., `IntegerType.type_name` ->
`"builtin.integer"`). This can be used in various places to avoid
hard-coded strings, such as eliminating the manual string in
`irdl.base("!builtin.integer")`.
Note that parts of this PR (mainly mechanical changes) were produced via
GitHub Copilot and GPT-5.2. I have manually reviewed the changes and
verified them with tests to ensure correctness.
36 lines
1.4 KiB
C++
36 lines
1.4 KiB
C++
//===- NVGPU.cpp - C Interface for NVGPU dialect ------------------===//
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//
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// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
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// See https://llvm.org/LICENSE.txt for license information.
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// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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//
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//===----------------------------------------------------------------------===//
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#include "mlir-c/Dialect/NVGPU.h"
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#include "mlir/CAPI/Registration.h"
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#include "mlir/Dialect/NVGPU/IR/NVGPUDialect.h"
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#include "mlir/IR/BuiltinTypes.h"
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using namespace mlir;
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using namespace mlir::nvgpu;
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MLIR_DEFINE_CAPI_DIALECT_REGISTRATION(NVGPU, nvgpu, mlir::nvgpu::NVGPUDialect)
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bool mlirTypeIsANVGPUTensorMapDescriptorType(MlirType type) {
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return isa<nvgpu::TensorMapDescriptorType>(unwrap(type));
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}
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MlirType mlirNVGPUTensorMapDescriptorTypeGet(MlirContext ctx,
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MlirType tensorMemrefType,
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int swizzle, int l2promo,
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int oobFill, int interleave) {
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return wrap(nvgpu::TensorMapDescriptorType::get(
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unwrap(ctx), cast<MemRefType>(unwrap(tensorMemrefType)),
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TensorMapSwizzleKind(swizzle), TensorMapL2PromoKind(l2promo),
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TensorMapOOBKind(oobFill), TensorMapInterleaveKind(interleave)));
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}
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MlirStringRef mlirNVGPUTensorMapDescriptorTypeGetName(void) {
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return wrap(nvgpu::TensorMapDescriptorType::name);
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}
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