016e1eb9c8
This patch adds the `#gpu.kernel_metadata` and `#gpu.kernel_table`
attributes. The `#gpu.kernel_metadata` attribute allows storing metadata
related to a compiled kernel, for example, the number of scalar
registers used by the kernel. The attribute only has 2 required
parameters, the name and function type. It also has 2 optional
parameters, the arguments attributes and generic dictionary for storing
all other metadata.
The `#gpu.kernel_table` stores a table of `#gpu.kernel_metadata`,
mapping the name of the kernel to the metadata.
Finally, the function `ROCDL::getAMDHSAKernelsELFMetadata` was added to
collect ELF metadata from a binary, and to test the class methods in
both attributes.
Example:
```mlir
gpu.binary @binary [#gpu.object<#rocdl.target<chip = "gfx900">, kernels = #gpu.kernel_table<[
#gpu.kernel_metadata<"kernel0", (i32) -> (), metadata = {sgpr_count = 255}>,
#gpu.kernel_metadata<"kernel1", (i32, f32) -> (), arg_attrs = [{llvm.read_only}, {}]>
]> , bin = "BLOB">]
```
The motivation behind these attributes is to provide useful information
for things like tunning.
---------
Co-authored-by: Mehdi Amini <joker.eph@gmail.com>
91 lines
3.8 KiB
C++
91 lines
3.8 KiB
C++
//===- DialectGPU.cpp - Pybind module for the GPU passes ------------------===//
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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/GPU.h"
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#include "mlir-c/IR.h"
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#include "mlir-c/Support.h"
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#include "mlir/Bindings/Python/PybindAdaptors.h"
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#include <pybind11/detail/common.h>
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#include <pybind11/pybind11.h>
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namespace py = pybind11;
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using namespace mlir;
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using namespace mlir::python;
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using namespace mlir::python::adaptors;
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// -----------------------------------------------------------------------------
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// Module initialization.
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// -----------------------------------------------------------------------------
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PYBIND11_MODULE(_mlirDialectsGPU, m) {
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m.doc() = "MLIR GPU Dialect";
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//===-------------------------------------------------------------------===//
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// AsyncTokenType
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//===-------------------------------------------------------------------===//
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auto mlirGPUAsyncTokenType =
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mlir_type_subclass(m, "AsyncTokenType", mlirTypeIsAGPUAsyncTokenType);
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mlirGPUAsyncTokenType.def_classmethod(
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"get",
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[](py::object cls, MlirContext ctx) {
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return cls(mlirGPUAsyncTokenTypeGet(ctx));
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},
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"Gets an instance of AsyncTokenType in the same context", py::arg("cls"),
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py::arg("ctx") = py::none());
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//===-------------------------------------------------------------------===//
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// ObjectAttr
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//===-------------------------------------------------------------------===//
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mlir_attribute_subclass(m, "ObjectAttr", mlirAttributeIsAGPUObjectAttr)
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.def_classmethod(
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"get",
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[](py::object cls, MlirAttribute target, uint32_t format,
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py::bytes object, std::optional<MlirAttribute> mlirObjectProps,
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std::optional<MlirAttribute> mlirKernelsAttr) {
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py::buffer_info info(py::buffer(object).request());
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MlirStringRef objectStrRef =
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mlirStringRefCreate(static_cast<char *>(info.ptr), info.size);
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return cls(mlirGPUObjectAttrGetWithKernels(
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mlirAttributeGetContext(target), target, format, objectStrRef,
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mlirObjectProps.has_value() ? *mlirObjectProps
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: MlirAttribute{nullptr},
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mlirKernelsAttr.has_value() ? *mlirKernelsAttr
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: MlirAttribute{nullptr}));
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},
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"cls"_a, "target"_a, "format"_a, "object"_a,
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"properties"_a = py::none(), "kernels"_a = py::none(),
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"Gets a gpu.object from parameters.")
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.def_property_readonly(
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"target",
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[](MlirAttribute self) { return mlirGPUObjectAttrGetTarget(self); })
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.def_property_readonly(
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"format",
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[](MlirAttribute self) { return mlirGPUObjectAttrGetFormat(self); })
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.def_property_readonly(
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"object",
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[](MlirAttribute self) {
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MlirStringRef stringRef = mlirGPUObjectAttrGetObject(self);
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return py::bytes(stringRef.data, stringRef.length);
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})
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.def_property_readonly("properties",
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[](MlirAttribute self) {
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if (mlirGPUObjectAttrHasProperties(self))
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return py::cast(
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mlirGPUObjectAttrGetProperties(self));
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return py::none().cast<py::object>();
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})
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.def_property_readonly("kernels", [](MlirAttribute self) {
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if (mlirGPUObjectAttrHasKernels(self))
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return py::cast(mlirGPUObjectAttrGetKernels(self));
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return py::none().cast<py::object>();
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});
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}
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