[CI] run clang-format (#24)
This commit is contained in:
Philippe Tillet
GitHub
@@ -1,7 +1,7 @@
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#include "triton/Dialect/TritonGPU/Transforms/TritonGPUConversion.h"
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#include "mlir/IR/BlockAndValueMapping.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 "mlir/IR/BlockAndValueMapping.h"
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#include <algorithm>
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using namespace mlir;
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@@ -10,7 +10,7 @@ using namespace mlir::triton::gpu;
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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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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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@@ -38,14 +38,14 @@ TritonGPUTypeConverter::TritonGPUTypeConverter(MLIRContext *context,
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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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// 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> warpTileSize(rank, 1);
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llvm::SmallVector<unsigned> blockTileSize(rank);
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llvm::SmallVector<unsigned> order(rank);
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llvm::SmallVector<unsigned> broadcastAxis;
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int remainingThreads = numThreads;
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int remainingLanes = /*warp size*/32;
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int remainingLanes = /*warp size*/ 32;
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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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warpTileSize[dim] = std::clamp(remainingLanes, 1, int(shape[dim]));
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@@ -56,7 +56,8 @@ TritonGPUTypeConverter::TritonGPUTypeConverter(MLIRContext *context,
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// TODO: will we need repetition?
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}
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Attribute encoding = triton::gpu::TritonGPUBlockedEncodingAttr::get(
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context, threadTileSize, warpTileSize, blockTileSize, order, broadcastAxis);
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context, threadTileSize, warpTileSize, blockTileSize, order,
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broadcastAxis);
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return RankedTensorType::get(shape, elementType, encoding);
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});
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@@ -65,8 +66,9 @@ TritonGPUTypeConverter::TritonGPUTypeConverter(MLIRContext *context,
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//
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// This will be called when (newArgType != origArgType)
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// This will create newArg, and map(origArg, newArg)
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addArgumentMaterialization([&](OpBuilder &builder, RankedTensorType tensorType,
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ValueRange inputs, Location loc) {
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addArgumentMaterialization([&](OpBuilder &builder,
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RankedTensorType tensorType, ValueRange inputs,
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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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@@ -74,7 +76,7 @@ TritonGPUTypeConverter::TritonGPUTypeConverter(MLIRContext *context,
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// If the origValue still has live user(s), use this to
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// convert origValue to newValue
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addSourceMaterialization([&](OpBuilder &builder, RankedTensorType tensorType,
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ValueRange inputs, Location loc) {
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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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@@ -83,7 +85,7 @@ TritonGPUTypeConverter::TritonGPUTypeConverter(MLIRContext *context,
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// where, desiredType = typeConverter->convertType(origType)
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// NOTE: only for remapped values.
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addTargetMaterialization([&](OpBuilder &builder, RankedTensorType tensorType,
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ValueRange inputs, Location loc) {
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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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@@ -93,30 +95,31 @@ TritonGPUTypeConverter::TritonGPUTypeConverter(MLIRContext *context,
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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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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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addIllegalOp<scf::ExecuteRegionOp, scf::ParallelOp, scf::ReduceOp,
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scf::ReduceReturnOp>();
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addDynamicallyLegalDialect<arith::ArithmeticDialect, triton::TritonDialect,
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StandardOpsDialect, scf::SCFDialect>(
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[&](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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Attribute aEncoding =
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dotOp.a().getType().cast<RankedTensorType>().getEncoding();
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Attribute bEncoding =
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dotOp.b().getType().cast<RankedTensorType>().getEncoding();
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if (aEncoding &&
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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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@@ -124,7 +127,6 @@ TritonGPUConversionTarget::TritonGPUConversionTarget(
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// return true;
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return false;
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});
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}
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// %dst = tt.broadcast %src
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@@ -133,12 +135,10 @@ TritonGPUConversionTarget::TritonGPUConversionTarget(
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// %bcst = tt.broadcast %newSrc
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// %dst = convert_layout %bcst
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LogicalResult TritonGPUConversionTarget::refineLayouts(ModuleOp mod,
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int numThreads) {
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int numThreads) {
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// collect broadcasts
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SmallVector<triton::BroadcastOp> broadcasts;
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mod.walk([&](triton::BroadcastOp op) {
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broadcasts.push_back(op);
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});
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mod.walk([&](triton::BroadcastOp op) { broadcasts.push_back(op); });
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BlockAndValueMapping mapping;
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for (auto broadcast : broadcasts) {
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@@ -161,20 +161,14 @@ LogicalResult TritonGPUConversionTarget::refineLayouts(ModuleOp mod,
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broadcastAxis.push_back(ax);
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Attribute originSrcEnc = tensorType.getEncoding();
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if (auto blockedEnc = originSrcEnc.dyn_cast<TritonGPUBlockedEncodingAttr>()) {
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if (auto blockedEnc =
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originSrcEnc.dyn_cast<TritonGPUBlockedEncodingAttr>()) {
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auto newSrcEnc = TritonGPUBlockedMulticastEncodingAttr::get(
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blockedEnc.getContext(),
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blockedEnc.getThreadTileSize(),
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blockedEnc.getWarpTileSize(),
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blockedEnc.getBlockTileSize(),
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blockedEnc.getOrder(),
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broadcastAxis
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);
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blockedEnc.getContext(), blockedEnc.getThreadTileSize(),
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blockedEnc.getWarpTileSize(), blockedEnc.getBlockTileSize(),
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blockedEnc.getOrder(), broadcastAxis);
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newSrcType = RankedTensorType::get(
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tensorType.getShape(),
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tensorType.getElementType(),
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newSrcEnc
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);
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tensorType.getShape(), tensorType.getElementType(), newSrcEnc);
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} else
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llvm_unreachable("src of broadcast should have blocked encoding");
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} else {
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@@ -186,34 +180,25 @@ LogicalResult TritonGPUConversionTarget::refineLayouts(ModuleOp mod,
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// create new src
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if (!isSrcScalar) // we don't need to convert layout for scalar values
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src = builder.create<triton::gpu::ConvertLayoutOp>(
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src.getLoc(), newSrcType, src
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);
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src = builder.create<triton::gpu::ConvertLayoutOp>(src.getLoc(),
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newSrcType, src);
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// create new broadcast
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// compute new type (encoding)
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auto originDstEnc = originDstTensorType.getEncoding()
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.dyn_cast<TritonGPUBlockedEncodingAttr>();
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.dyn_cast<TritonGPUBlockedEncodingAttr>();
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auto newEnc = TritonGPUBlockedEncodingAttr::get(
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originDstEnc.getContext(),
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originDstEnc.getThreadTileSize(),
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originDstEnc.getWarpTileSize(),
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originDstEnc.getBlockTileSize(),
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originDstEnc.getOrder(),
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broadcastAxis
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);
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auto newType = RankedTensorType::get(
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originDstTensorType.getShape(),
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originDstTensorType.getElementType(),
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newEnc
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);
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Value newBroadcast = builder.create<triton::BroadcastOp>(
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broadcast.getLoc(), newType, src
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);
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originDstEnc.getContext(), originDstEnc.getThreadTileSize(),
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originDstEnc.getWarpTileSize(), originDstEnc.getBlockTileSize(),
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originDstEnc.getOrder(), broadcastAxis);
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auto newType =
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RankedTensorType::get(originDstTensorType.getShape(),
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originDstTensorType.getElementType(), newEnc);
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Value newBroadcast =
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builder.create<triton::BroadcastOp>(broadcast.getLoc(), newType, src);
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// we don't want to change the encoding of the result
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Value newDst = builder.create<triton::gpu::ConvertLayoutOp>(
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broadcast.getLoc(), originDstType, newBroadcast
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);
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broadcast.getLoc(), originDstType, newBroadcast);
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broadcast.replaceAllUsesWith(newDst);
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mapping.map(broadcast, newDst);
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