106 lines
3.8 KiB
C++
106 lines
3.8 KiB
C++
#include "triton/Dialect/TritonGPU/Transforms/TritonGPUConversion.h"
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#include "triton/Dialect/Triton/IR/Dialect.h"
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#include "triton/Dialect/TritonGPU/IR/Dialect.h"
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#include <algorithm>
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#include <llvm-6.0/llvm/Support/ErrorHandling.h>
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using namespace mlir;
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//
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// TypeConverter
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//
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TritonGPUTypeConverter::TritonGPUTypeConverter(MLIRContext *context,
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int numThreads)
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: context(context), numThreads(numThreads) {
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// TODO: how does MLIR pick the right conversion?
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addConversion([](Type type) { return type; });
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addConversion([this](RankedTensorType tensorType) -> RankedTensorType {
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MLIRContext *context = this->context;
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int numThreads = this->numThreads;
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llvm::ArrayRef<int64_t> shape = tensorType.getShape();
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Type elementType = tensorType.getElementType();
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int64_t rank = tensorType.getRank();
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int64_t numElements = tensorType.getNumElements();
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// TODO: we should raise exception here.
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assert(numElements > numThreads);
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assert(numElements % numThreads == 0);
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// or assert no encoding?
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// Now we assume:
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// contiguous = 1, order = 0, 1, 2, ...,
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llvm::SmallVector<unsigned> threadTileSize(rank, 1); // naive layout
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llvm::SmallVector<unsigned> blockTileSize(rank);
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llvm::SmallVector<unsigned> order(rank);
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int remainingThreads = numThreads;
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for (int64_t dim = 0; dim < rank; ++dim) {
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blockTileSize[dim] = std::clamp(remainingThreads, 1, int(shape[dim]));
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order[dim] = dim;
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remainingThreads /= blockTileSize[dim];
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// TODO: will we need repetition?
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}
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Attribute encoding = triton::gpu::TritonGPUDistributedEncodingAttr::get(
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context, threadTileSize, blockTileSize, order);
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return RankedTensorType::get(shape, elementType, encoding);
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});
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// materailizations
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addArgumentMaterialization([&](OpBuilder &builder, RankedTensorType tensorType,
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ValueRange inputs, Location loc) {
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llvm_unreachable("Not implemented");
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return llvm::None;
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});
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addSourceMaterialization([&](OpBuilder &builder, RankedTensorType tensorType,
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ValueRange inputs, Location loc) {
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llvm_unreachable("Not implemented");
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return llvm::None;
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});
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addTargetMaterialization([&](OpBuilder &builder, RankedTensorType tensorType,
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ValueRange inputs, Location loc) {
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assert(inputs.size() == 1);
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llvm_unreachable("Not implemented");
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return llvm::None;
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});
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}
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//
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// TritonGPUConversion
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//
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TritonGPUConversionTarget::TritonGPUConversionTarget(
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MLIRContext &context, TritonGPUTypeConverter &typeConverter)
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: ConversionTarget(context), typeConverter(typeConverter) {
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// TODO: we should also verify ops of TritonGPUDialect
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addLegalDialect<triton::gpu::TritonGPUDialect>();
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// Some ops from SCF are illegal
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addIllegalOp<scf::ExecuteRegionOp, scf::ParallelOp,
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scf::ReduceOp, scf::ReduceReturnOp>();
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addDynamicallyLegalDialect<arith::ArithmeticDialect,
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triton::TritonDialect,
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StandardOpsDialect,
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scf::SCFDialect>([&](Operation *op) {
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if (typeConverter.isLegal(op))
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return true;
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return false;
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});
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// We have requirements for the data layouts
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addDynamicallyLegalOp<triton::DotOp>([this](triton::DotOp dotOp) -> bool {
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Attribute aEncoding = dotOp.a().getType().cast<RankedTensorType>().getEncoding();
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Attribute bEncoding = dotOp.b().getType().cast<RankedTensorType>().getEncoding();
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if (aEncoding && aEncoding.isa<triton::gpu::TritonGPUSharedEncodingAttr>() &&
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bEncoding && bEncoding.isa<triton::gpu::TritonGPUSharedEncodingAttr>())
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return true;
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// TODO: we should delete this
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if (this->typeConverter.isLegal(dotOp))
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return true;
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return false;
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});
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}
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