Files
clang-p2996/mlir/lib/Dialect/AMDGPU/IR/AMDGPUDialect.cpp
Tres Popp c1fa60b4cd [mlir] Update method cast calls to function calls
The MLIR classes Type/Attribute/Operation/Op/Value support
cast/dyn_cast/isa/dyn_cast_or_null functionality through llvm's doCast
functionality in addition to defining methods with the same name.
This change begins the migration of uses of the method to the
corresponding function call as has been decided as more consistent.

Note that there still exist classes that only define methods directly,
such as AffineExpr, and this does not include work currently to support
a functional cast/isa call.

Context:

* https://mlir.llvm.org/deprecation/ at "Use the free function variants for dyn_cast/cast/isa/…"
* Original discussion at https://discourse.llvm.org/t/preferred-casting-style-going-forward/68443

Implementation:
This follows a previous patch that updated calls
`op.cast<T>()-> cast<T>(op)`. However some cases could not handle an
unprefixed `cast` call due to occurrences of variables named cast, or
occurring inside of class definitions which would resolve to the method.
All C++ files that did not work automatically with `cast<T>()` are
updated here to `llvm::cast` and similar with the intention that they
can be easily updated after the methods are removed through a
find-replace.

See https://github.com/llvm/llvm-project/compare/main...tpopp:llvm-project:tidy-cast-check
for the clang-tidy check that is used and then update printed
occurrences of the function to include `llvm::` before.

One can then run the following:
```
ninja -C $BUILD_DIR clang-tidy

run-clang-tidy -clang-tidy-binary=$BUILD_DIR/bin/clang-tidy -checks='-*,misc-cast-functions'\
                 -export-fixes /tmp/cast/casts.yaml mlir/*\
                 -header-filter=mlir/ -fix

rm -rf $BUILD_DIR/tools/mlir/**/*.inc
```

Differential Revision: https://reviews.llvm.org/D150348
2023-05-12 11:21:30 +02:00

293 lines
9.8 KiB
C++

//===- AMDGPUDialect.cpp - MLIR AMDGPU dialect implementation --------===//
//
// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
//
//===----------------------------------------------------------------------===//
//
// This file implements the AMDGPU dialect and its operations.
//
//===----------------------------------------------------------------------===//
#include "mlir/Dialect/AMDGPU/IR/AMDGPUDialect.h"
#include "mlir/Dialect/Arith/IR/Arith.h"
#include "mlir/Dialect/GPU/IR/GPUDialect.h"
#include "mlir/IR/Builders.h"
#include "mlir/IR/BuiltinTypes.h"
#include "mlir/IR/Diagnostics.h"
#include "mlir/IR/DialectImplementation.h"
#include "mlir/IR/Matchers.h"
#include "mlir/IR/OpImplementation.h"
#include "mlir/IR/PatternMatch.h"
#include "mlir/IR/TypeUtilities.h"
#include "llvm/ADT/TypeSwitch.h"
#include <limits>
#include <optional>
using namespace mlir;
using namespace mlir::amdgpu;
#include "mlir/Dialect/AMDGPU/IR/AMDGPUDialect.cpp.inc"
void AMDGPUDialect::initialize() {
addOperations<
#define GET_OP_LIST
#include "mlir/Dialect/AMDGPU/IR/AMDGPU.cpp.inc"
>();
addAttributes<
#define GET_ATTRDEF_LIST
#include "mlir/Dialect/AMDGPU/IR/AMDGPUAttributes.cpp.inc"
>();
}
//===----------------------------------------------------------------------===//
// RawBuffer*Op
//===----------------------------------------------------------------------===//
template <typename T>
static LogicalResult verifyRawBufferOp(T &op) {
MemRefType bufferType = llvm::cast<MemRefType>(op.getMemref().getType());
Attribute memorySpace = bufferType.getMemorySpace();
bool isGlobal = false;
if (!memorySpace)
isGlobal = true;
else if (auto intMemorySpace = llvm::dyn_cast<IntegerAttr>(memorySpace))
isGlobal = intMemorySpace.getInt() == 0 || intMemorySpace.getInt() == 1;
else if (auto gpuMemorySpace =
llvm::dyn_cast<gpu::AddressSpaceAttr>(memorySpace))
isGlobal = gpuMemorySpace.getValue() == gpu::AddressSpace::Global;
if (!isGlobal)
return op.emitOpError(
"Buffer ops must operate on a memref in global memory");
if (!bufferType.hasRank())
return op.emitOpError(
"Cannot meaningfully buffer_store to an unranked memref");
if (static_cast<int64_t>(op.getIndices().size()) != bufferType.getRank())
return op.emitOpError("Expected " + Twine(bufferType.getRank()) +
" indices to memref");
return success();
}
LogicalResult RawBufferLoadOp::verify() { return verifyRawBufferOp(*this); }
LogicalResult RawBufferStoreOp::verify() { return verifyRawBufferOp(*this); }
LogicalResult RawBufferAtomicFaddOp::verify() {
return verifyRawBufferOp(*this);
}
LogicalResult RawBufferAtomicFmaxOp::verify() {
return verifyRawBufferOp(*this);
}
LogicalResult RawBufferAtomicSmaxOp::verify() {
return verifyRawBufferOp(*this);
}
LogicalResult RawBufferAtomicUminOp::verify() {
return verifyRawBufferOp(*this);
}
LogicalResult RawBufferAtomicCmpswapOp::verify() {
return verifyRawBufferOp(*this);
}
static std::optional<uint32_t> getConstantUint32(Value v) {
APInt cst;
if (!v.getType().isInteger(32))
return std::nullopt;
if (matchPattern(v, m_ConstantInt(&cst)))
return cst.getZExtValue();
return std::nullopt;
}
template <typename OpType>
static bool staticallyOutOfBounds(OpType op) {
if (!op.getBoundsCheck())
return false;
MemRefType bufferType = op.getMemref().getType();
if (!bufferType.hasStaticShape())
return false;
int64_t offset;
SmallVector<int64_t> strides;
if (failed(getStridesAndOffset(bufferType, strides, offset)))
return false;
int64_t result = offset + op.getIndexOffset().value_or(0);
if (op.getSgprOffset()) {
std::optional<uint32_t> sgprOffset = getConstantUint32(op.getSgprOffset());
if (!sgprOffset)
return false;
result += *sgprOffset;
}
if (strides.size() != op.getIndices().size())
return false;
int64_t indexVal = 0;
for (auto pair : llvm::zip(strides, op.getIndices())) {
int64_t stride = std::get<0>(pair);
Value idx = std::get<1>(pair);
std::optional<uint32_t> idxVal = getConstantUint32(idx);
if (!idxVal)
return false;
indexVal += stride * *idxVal;
}
result += indexVal;
if (result > std::numeric_limits<uint32_t>::max())
// Overflow means don't drop
return false;
return result >= bufferType.getNumElements();
}
namespace {
template <typename OpType>
struct RemoveStaticallyOobBufferLoads final : public OpRewritePattern<OpType> {
using OpRewritePattern<OpType>::OpRewritePattern;
LogicalResult matchAndRewrite(OpType op, PatternRewriter &rw) const override {
if (!staticallyOutOfBounds(op))
return failure();
Type loadType = op.getResult().getType();
rw.replaceOpWithNewOp<arith::ConstantOp>(op, loadType,
rw.getZeroAttr(loadType));
return success();
}
};
template <typename OpType>
struct RemoveStaticallyOobBufferWrites final : public OpRewritePattern<OpType> {
using OpRewritePattern<OpType>::OpRewritePattern;
LogicalResult matchAndRewrite(OpType op, PatternRewriter &rw) const override {
if (!staticallyOutOfBounds(op))
return failure();
rw.eraseOp(op);
return success();
}
};
} // end namespace
void RawBufferLoadOp::getCanonicalizationPatterns(RewritePatternSet &results,
MLIRContext *context) {
results.add<RemoveStaticallyOobBufferLoads<RawBufferLoadOp>>(context);
}
void RawBufferStoreOp::getCanonicalizationPatterns(RewritePatternSet &results,
MLIRContext *context) {
results.add<RemoveStaticallyOobBufferWrites<RawBufferStoreOp>>(context);
}
void RawBufferAtomicFaddOp::getCanonicalizationPatterns(
RewritePatternSet &results, MLIRContext *context) {
results.add<RemoveStaticallyOobBufferWrites<RawBufferAtomicFaddOp>>(context);
}
void RawBufferAtomicFmaxOp::getCanonicalizationPatterns(
RewritePatternSet &results, MLIRContext *context) {
results.add<RemoveStaticallyOobBufferWrites<RawBufferAtomicFmaxOp>>(context);
}
void RawBufferAtomicSmaxOp::getCanonicalizationPatterns(
RewritePatternSet &results, MLIRContext *context) {
results.add<RemoveStaticallyOobBufferWrites<RawBufferAtomicSmaxOp>>(context);
}
void RawBufferAtomicUminOp::getCanonicalizationPatterns(
RewritePatternSet &results, MLIRContext *context) {
results.add<RemoveStaticallyOobBufferWrites<RawBufferAtomicUminOp>>(context);
}
void RawBufferAtomicCmpswapOp::getCanonicalizationPatterns(
RewritePatternSet &results, MLIRContext *context) {
results.add<RemoveStaticallyOobBufferLoads<RawBufferAtomicCmpswapOp>>(
context);
}
//===----------------------------------------------------------------------===//
// MFMAOp
//===----------------------------------------------------------------------===//
LogicalResult MFMAOp::verify() {
constexpr uint32_t waveSize = 64;
Builder b(getContext());
Type sourceType = getSourceA().getType();
Type destType = getDestC().getType();
Type sourceElem = sourceType, destElem = destType;
uint32_t sourceLen = 1, destLen = 1;
if (auto sourceVector = llvm::dyn_cast<VectorType>(sourceType)) {
sourceLen = sourceVector.getNumElements();
sourceElem = sourceVector.getElementType();
}
if (auto destVector = llvm::dyn_cast<VectorType>(destType)) {
destLen = destVector.getNumElements();
destElem = destVector.getElementType();
}
Type sourceBType = getSourceB().getType();
if (sourceElem.isFloat8E5M2FNUZ() || sourceElem.isFloat8E4M3FNUZ()) {
int64_t sourceBLen = 1;
Type sourceBElem = sourceBType;
if (auto sourceBVector = llvm::dyn_cast<VectorType>(sourceBType)) {
sourceBLen = sourceBVector.getNumElements();
sourceBElem = sourceBVector.getElementType();
}
if (!sourceBElem.isFloat8E5M2FNUZ() && !sourceBElem.isFloat8E4M3FNUZ())
return emitOpError("expected both source operands to have f8 elements");
if (sourceLen != sourceBLen)
return emitOpError(
"expected both f8 source vectors to have the same length");
} else {
if (sourceType != sourceBType)
return emitOpError(
"expected both non-f8 source operand types to match exactly");
}
// Normalize the wider integer types the compiler expects to i8
if (sourceElem.isInteger(32)) {
sourceLen *= 4;
sourceElem = b.getI8Type();
}
if (sourceElem.isInteger(64)) {
sourceLen *= 8;
sourceElem = b.getI8Type();
}
int64_t numSourceElems = (getM() * getK() * getBlocks()) / waveSize;
if (sourceLen != numSourceElems)
return emitOpError("expected " + Twine(numSourceElems) +
" source values for this operation but got " +
Twine(sourceLen));
int64_t numDestElems = (getM() * getN() * getBlocks()) / waveSize;
if (destLen != numDestElems)
return emitOpError("expected " + Twine(numDestElems) +
" result values for this operation but got " +
Twine(destLen));
if (destElem.isF64() && getBlgp() != MFMAPermB::none)
return emitOpError(
"double-precision ops do not support permuting lanes of B");
if (destElem.isF64() && getCbsz() != 0)
return emitOpError(
"double-precision ops do not support permuting lanes of A");
if (getAbid() >= (1u << getCbsz()))
return emitOpError(
"block ID for permuting A (abid) must be below 2 ** cbsz");
if ((getNegateA() || getNegateB() || getNegateC()) && !destElem.isF64())
return emitOpError(
"negation flags only available for double-precision operations");
return success();
}
#include "mlir/Dialect/AMDGPU/IR/AMDGPUEnums.cpp.inc"
#define GET_ATTRDEF_CLASSES
#include "mlir/Dialect/AMDGPU/IR/AMDGPUAttributes.cpp.inc"
#define GET_OP_CLASSES
#include "mlir/Dialect/AMDGPU/IR/AMDGPU.cpp.inc"