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Adding stream.tensor.dispatch op. #19817

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Adding stream.tensor.dispatch op. #19817

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46 changes: 24 additions & 22 deletions compiler/src/iree/compiler/Dialect/Stream/Conversion/FlowToStream/Patterns.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -782,17 +782,11 @@ struct ConvertDispatchOp
IREE::Flow::DispatchOp op, OneToNOpAdaptor adaptor,
IREE::Stream::AffinityAttr executionAffinityAttr,
ConversionPatternRewriter &rewriter) const override {
// Zero is going to be used for each operand to start.
auto zeroOffset = rewriter.create<arith::ConstantIndexOp>(op.getLoc(), 0);

// Query and resolve all operands and their sizes.
SmallVector<Value> dispatchOperands;
SmallVector<Value> dispatchOperandSizes;
SmallVector<Value> dispatchOperandOffsets;
SmallVector<Value> dispatchOperandEnds;
SmallVector<Value> dispatchOperandLengths;
SmallVector<Value> operands;
SmallVector<Value> operandSizes;

SmallVector<Value> allOperandSizes;
SmallVector<Type> operandEncodings;
for (auto [oldOperand, convertedOperands] :
llvm::zip_equal(op.getArguments(), adaptor.getArguments())) {
Value newOperand;
Expand All @@ -801,50 +795,58 @@ struct ConvertDispatchOp
transferTensorOperands(op.getLoc(), oldOperand, convertedOperands,
executionAffinityAttr, rewriter);
newOperand = newOperandCast.resource;
dispatchOperandSizes.push_back(newOperandCast.resourceSize);
operandSizes.push_back(newOperandCast.resourceSize);
dispatchOperandOffsets.push_back(zeroOffset);
dispatchOperandEnds.push_back(newOperandCast.resourceSize);
dispatchOperandLengths.push_back(newOperandCast.resourceSize);
allOperandSizes.push_back(newOperandCast.resourceSize);
operandEncodings.push_back(oldOperand.getType());
} else {
operandSizes.push_back({});
allOperandSizes.push_back({});
operandEncodings.push_back(rewriter.getType<IREE::Util::UnusedType>());
newOperand = convertedOperands.front();
}
dispatchOperands.push_back(newOperand);
operands.push_back(newOperand);
}

// Construct result sizes or reuse tied operand sizes from above.
SmallVector<Value> resultSizes;
SmallVector<Type> resultTypes;
SmallVector<Type> resultEncodings;
auto unknownType = rewriter.getType<IREE::Stream::ResourceType>();
auto tiedOperandBase = op.getTiedOperandsIndexAndLength().first;
for (auto result : llvm::enumerate(op.getResults())) {
auto oldResultType = result.value().getType();
if (!llvm::isa<ShapedType>(oldResultType)) {
resultTypes.push_back(getTypeConverter()->convertType(oldResultType));
resultEncodings.push_back(rewriter.getType<IREE::Util::UnusedType>());
continue;
}
auto tiedOperand = op.getTiedResultOperandIndex(result.index());
if (tiedOperand.has_value()) {
auto operandIndex = tiedOperand.value() - tiedOperandBase;
resultSizes.push_back(operandSizes[operandIndex]);
resultTypes.push_back(dispatchOperands[operandIndex].getType());
resultSizes.push_back(allOperandSizes[operandIndex]);
resultTypes.push_back(operands[operandIndex].getType());
resultEncodings.push_back(operandEncodings[operandIndex]);
} else {
auto resultDynamicDims = IREE::Util::buildDynamicDimsForValue(
op.getLoc(), result.value(), rewriter);
resultSizes.push_back(
buildResultSizeOf(op.getLoc(), result.value(), resultDynamicDims,
executionAffinityAttr, rewriter));
resultTypes.push_back(unknownType);
resultEncodings.push_back(oldResultType);
}
}

auto newOp = rewriter.create<IREE::Stream::AsyncDispatchOp>(
auto newOp = rewriter.create<IREE::Stream::TensorDispatchOp>(
op.getLoc(), resultTypes, flattenValues(adaptor.getWorkload()),
adaptor.getEntryPointsAttr(), dispatchOperands, dispatchOperandSizes,
dispatchOperandOffsets, dispatchOperandEnds, dispatchOperandLengths,
resultSizes, adaptor.getTiedOperandsAttr(), executionAffinityAttr);
newOp->setDialectAttrs(op->getDialectAttrs());
adaptor.getEntryPointsAttr(), operands, operandSizes,
rewriter.getTypeArrayAttr(operandEncodings), op.getArgumentDims(),
resultSizes, rewriter.getTypeArrayAttr(resultEncodings),
op.getResultDims(), adaptor.getTiedOperandsAttr(),
executionAffinityAttr);
newOp->setDialectAttrs(
llvm::make_filter_range(op->getDialectAttrs(), [](NamedAttribute attr) {
return attr.getName() != "stream.affinity";
}));
SmallVector<SmallVector<Value>> replacementsVec = llvm::map_to_vector(
llvm::zip_equal(newOp->getResults(), resultSizes), [](auto it) {
return SmallVector<Value>{std::get<0>(it), std::get<1>(it)};
Expand Down
45 changes: 25 additions & 20 deletions compiler/src/iree/compiler/Dialect/Stream/Conversion/FlowToStream/test/dispatch_ops.mlir
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Expand Up @@ -3,28 +3,29 @@
// CHECK-LABEL: @dispatchNoWorkload
// CHECK-SAME: (%[[INPUT:.+]]: !stream.resource<*>, %[[INPUT_SIZE:.+]]: index, %[[DIM1:.+]]: index, %[[DIM3:.+]]: index)
util.func public @dispatchNoWorkload(%input: tensor<7x?x24x?xf32>, %dim1: index, %dim3: index) -> tensor<?x?x1024xf32> {
// CHECK: %[[RESULT_SIZE:.+]] = stream.tensor.sizeof tensor<?x?x1024xf32>{%[[DIM1]], %[[DIM3]]}
// CHECK: %[[RESULT:.+]] = stream.async.dispatch @ex::@entry(%[[INPUT]][%c0 to %[[INPUT_SIZE]] for %[[INPUT_SIZE]]]) :
// CHECK-SAME: (!stream.resource<*>{%[[INPUT_SIZE]]}) -> !stream.resource<*>{%[[RESULT_SIZE]]}
// CHECK: %[[RESULT_SIZE:.+]] = stream.tensor.sizeof tensor<?x?x1024xf32>{%[[DIM1]], %[[DIM3]]}
// CHECK: %[[RESULT:.+]] = stream.tensor.dispatch @ex::@entry(%[[INPUT]]) :
// CHECK-SAME: (tensor<7x?x24x?xf32>{%[[DIM1]], %[[DIM3]]} in !stream.resource<*>{%[[INPUT_SIZE]]}) -> tensor<?x?x1024xf32>{%[[DIM1]], %[[DIM3]]} in !stream.resource<*>{%[[RESULT_SIZE]]}
%0 = flow.dispatch @ex::@entry(%input) : (tensor<7x?x24x?xf32>{%dim1, %dim3}) -> tensor<?x?x1024xf32>{%dim1, %dim3}
// return %[[RESULT]], %[[RESULT_SIZE]] : !stream.resource<*>, index
// CHECK: util.return %[[RESULT]], %[[RESULT_SIZE]] : !stream.resource<*>, index
util.return %0 : tensor<?x?x1024xf32>
}

// -----

// CHECK-LABEL: @dispatch
// CHECK-SAME: (%[[INPUT:.+]]: !stream.resource<*>, %[[INPUT_SIZE:.+]]: index, %[[DIM1:.+]]: index, %[[DIM3:.+]]: index)
util.func public @dispatch(%input: tensor<7x?x24x?xf32>, %dim1: index, %dim3: index) -> tensor<?x?x1024xf32> {
util.func public @dispatch(%input: tensor<7x?x24x?xf32>, %dim1: index, %dim3: index) -> (tensor<?x?x1024xf32>, tensor<1024x?x?xf32>) {
%c1 = arith.constant 1 : index
%c2 = arith.constant 2 : index
%c3 = arith.constant 3 : index
// CHECK: %[[RESULT_SIZE:.+]] = stream.tensor.sizeof tensor<?x?x1024xf32>{%[[DIM1]], %[[DIM3]]}
// CHECK: %[[RESULT:.+]] = stream.async.dispatch @ex::@entry[%c1, %c2, %c3](%[[INPUT]][%c0 to %[[INPUT_SIZE]] for %[[INPUT_SIZE]]]) :
// CHECK-SAME: (!stream.resource<*>{%[[INPUT_SIZE]]}) -> !stream.resource<*>{%[[RESULT_SIZE]]}
%0 = flow.dispatch @ex::@entry[%c1, %c2, %c3](%input) : (tensor<7x?x24x?xf32>{%dim1, %dim3}) -> tensor<?x?x1024xf32>{%dim1, %dim3}
// return %[[RESULT]], %[[RESULT_SIZE]] : !stream.resource<*>, index
util.return %0 : tensor<?x?x1024xf32>
// CHECK: %[[RESULT0_SIZE:.+]] = stream.tensor.sizeof tensor<?x?x1024xf32>{%[[DIM1]], %[[DIM3]]}
// CHECK: %[[RESULT1_SIZE:.+]] = stream.tensor.sizeof tensor<1024x?x?xf32>{%[[DIM3]], %[[DIM1]]}
// CHECK: %[[RESULTS:.+]]:2 = stream.tensor.dispatch @ex::@entry[%c1, %c2, %c3](%[[INPUT]]) :
// CHECK-SAME: (tensor<7x?x24x?xf32>{%[[DIM1]], %[[DIM3]]} in !stream.resource<*>{%[[INPUT_SIZE]]}) -> (tensor<?x?x1024xf32>{%[[DIM1]], %[[DIM3]]} in !stream.resource<*>{%[[RESULT0_SIZE]]}, tensor<1024x?x?xf32>{%[[DIM3]], %[[DIM1]]} in !stream.resource<*>{%[[RESULT1_SIZE]]})
%results:2 = flow.dispatch @ex::@entry[%c1, %c2, %c3](%input) : (tensor<7x?x24x?xf32>{%dim1, %dim3}) -> (tensor<?x?x1024xf32>{%dim1, %dim3}, tensor<1024x?x?xf32>{%dim3, %dim1})
// CHECK: util.return %[[RESULTS]]#0, %[[RESULT0_SIZE]], %[[RESULTS]]#1, %[[RESULT1_SIZE]] : !stream.resource<*>, index, !stream.resource<*>, index
util.return %results#0, %results#1 : tensor<?x?x1024xf32>, tensor<1024x?x?xf32>
}

// -----
Expand All @@ -36,9 +37,11 @@ util.func public @tiedDispatch(%input0: tensor<i32>, %input1: tensor<2x3xi32>) -
%c2 = arith.constant 2 : index
%c3 = arith.constant 3 : index
// CHECK: %[[T_SIZE:.+]] = stream.tensor.sizeof tensor<3x9xi32> : index
// CHECK: %[[T:.+]] = stream.async.dispatch @ex::@entry0[%c1, %c2, %c3](%[[INPUT0]][%c0 to %[[INPUT0_SIZE]] for %[[INPUT0_SIZE]]]) : (!stream.resource<*>{%[[INPUT0_SIZE]]}) -> !stream.resource<*>{%[[T_SIZE]]}
// CHECK: %[[T:.+]] = stream.tensor.dispatch @ex::@entry0[%c1, %c2, %c3](%[[INPUT0]]) :
// CHECK-SAME: (tensor<i32> in !stream.resource<*>{%[[INPUT0_SIZE]]}) -> tensor<3x9xi32> in !stream.resource<*>{%[[T_SIZE]]}
%0 = flow.dispatch @ex::@entry0[%c1, %c2, %c3](%input0) : (tensor<i32>) -> tensor<3x9xi32>
// CHECK: %[[RESULT:.+]] = stream.async.dispatch @ex::@entry1[%c1, %c2, %c3](%[[INPUT1]][%c0 to %[[INPUT1_SIZE]] for %[[INPUT1_SIZE]]], %[[T]][%c0 to %[[T_SIZE]] for %[[T_SIZE]]]) : (!stream.resource<*>{%[[INPUT1_SIZE]]}, !stream.resource<*>{%[[T_SIZE]]}) -> %[[T]]{%[[T_SIZE]]}
// CHECK: %[[RESULT:.+]] = stream.tensor.dispatch @ex::@entry1[%c1, %c2, %c3](%[[INPUT1]], %[[T]]) :
// CHECK-SAME: (tensor<2x3xi32> in !stream.resource<*>{%[[INPUT1_SIZE]]}, tensor<3x9xi32> in !stream.resource<*>{%[[T_SIZE]]}) -> tensor<3x9xi32> in %[[T]]{%[[T_SIZE]]}
%1 = flow.dispatch @ex::@entry1[%c1, %c2, %c3](%input1, %0) : (tensor<2x3xi32>, tensor<3x9xi32>) -> %0
// CHECK: util.return %[[RESULT]], %[[T_SIZE]] : !stream.resource<*>, index
util.return %1 : tensor<3x9xi32>
Expand All @@ -52,18 +55,20 @@ util.global private @device_b : !hal.device
// CHECK-LABEL: @dispatchAffinity
// CHECK-SAME: (%[[INPUT:.+]]: !stream.resource<*>, %[[INPUT_SIZE:.+]]: index, %[[DIM1:.+]]: index, %[[DIM3:.+]]: index)
util.func public @dispatchAffinity(%input: tensor<7x?x24x?xf32>, %dim1: index, %dim3: index) -> (tensor<?x?x1024xf32>, tensor<?x?x1024xf32>) {
// CHECK: %[[INPUT_A:.+]] = stream.async.transfer %[[INPUT]] : !stream.resource<*>{%[[INPUT_SIZE]]} -> to(#hal.device.affinity<@device_a>) !stream.resource<*>{%[[INPUT_SIZE]]}
// CHECK: %[[RESULT0_SIZE:.+]] = stream.tensor.sizeof on(#hal.device.affinity<@device_a>) tensor<?x?x1024xf32>{%[[DIM1]], %[[DIM3]]}
// CHECK: %[[RESULT0:.+]] = stream.async.dispatch on(#hal.device.affinity<@device_a>) @ex::@entry0(%[[INPUT_A]][%c0 to %[[INPUT_SIZE]] for %[[INPUT_SIZE]]])
// CHECK: %[[INPUT_A:.+]] = stream.async.transfer %[[INPUT]] : !stream.resource<*>{%[[INPUT_SIZE]]} -> to(#hal.device.affinity<@device_a>) !stream.resource<*>{%[[INPUT_SIZE]]}
// CHECK: %[[RESULT0_SIZE:.+]] = stream.tensor.sizeof on(#hal.device.affinity<@device_a>) tensor<?x?x1024xf32>{%[[DIM1]], %[[DIM3]]}
// CHECK: %[[RESULT0:.+]] = stream.tensor.dispatch on(#hal.device.affinity<@device_a>) @ex::@entry0(%[[INPUT_A]])
// CHECK-SAME: (tensor<7x?x24x?xf32>{%[[DIM1]], %[[DIM3]]} in !stream.resource<*>{%[[INPUT_SIZE]]}) -> tensor<?x?x1024xf32>{%[[DIM1]], %[[DIM3]]} in !stream.resource<*>{%[[RESULT0_SIZE]]}
%0 = flow.dispatch @ex::@entry0(%input) {
stream.affinity = #hal.device.affinity<@device_a>
} : (tensor<7x?x24x?xf32>{%dim1, %dim3}) -> tensor<?x?x1024xf32>{%dim1, %dim3}
// CHECK: %[[INPUT_B:.+]] = stream.async.transfer %[[INPUT]] : !stream.resource<*>{%[[INPUT_SIZE]]} -> to(#hal.device.affinity<@device_b>) !stream.resource<*>{%[[INPUT_SIZE]]}
// CHECK: %[[RESULT1_SIZE:.+]] = stream.tensor.sizeof on(#hal.device.affinity<@device_b>) tensor<?x?x1024xf32>{%[[DIM3]], %[[DIM1]]}
// CHECK: %[[RESULT1:.+]] = stream.async.dispatch on(#hal.device.affinity<@device_b>) @ex::@entry1(%[[INPUT_B]][%c0 to %[[INPUT_SIZE]] for %[[INPUT_SIZE]]])
// CHECK: %[[INPUT_B:.+]] = stream.async.transfer %[[INPUT]] : !stream.resource<*>{%[[INPUT_SIZE]]} -> to(#hal.device.affinity<@device_b>) !stream.resource<*>{%[[INPUT_SIZE]]}
// CHECK: %[[RESULT1_SIZE:.+]] = stream.tensor.sizeof on(#hal.device.affinity<@device_b>) tensor<?x?x1024xf32>{%[[DIM3]], %[[DIM1]]}
// CHECK: %[[RESULT1:.+]] = stream.tensor.dispatch on(#hal.device.affinity<@device_b>) @ex::@entry1(%[[INPUT_B]])
// CHECK-SAME: (tensor<7x?x24x?xf32>{%[[DIM1]], %[[DIM3]]} in !stream.resource<*>{%[[INPUT_SIZE]]}) -> tensor<?x?x1024xf32>{%[[DIM3]], %[[DIM1]]} in !stream.resource<*>{%[[RESULT1_SIZE]]}
%1 = flow.dispatch @ex::@entry1(%input) {
stream.affinity = #hal.device.affinity<@device_b>
} : (tensor<7x?x24x?xf32>{%dim1, %dim3}) -> tensor<?x?x1024xf32>{%dim3, %dim1}
// return %[[RESULT0]], %[[RESULT0_SIZE]], %[[RESULT1]], %[[RESULT1_SIZE]]
// CHECK: return %[[RESULT0]], %[[RESULT0_SIZE]], %[[RESULT1]], %[[RESULT1_SIZE]]
util.return %0, %1 : tensor<?x?x1024xf32>, tensor<?x?x1024xf32>
}
12 changes: 4 additions & 8 deletions compiler/src/iree/compiler/Dialect/Stream/Conversion/FlowToStream/test/tensor_ops.mlir
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Expand Up @@ -141,16 +141,12 @@ util.global private @device : !hal.device
// CHECK-LABEL: @tensorBarrierDispatch
// CHECK-SAME: (%[[INPUT:.+]]: !stream.resource<*>, %[[DIM0:.+]]: index, %[[DIM1:.+]]: index)
util.func public @tensorBarrierDispatch(%input: tensor<?x128xi8>, %dim0: index) -> tensor<?x128xi8> {
%c0 = arith.constant 0 : index
%barrier = flow.tensor.barrier %input : tensor<?x128xi8>{%dim0} on #hal.device.affinity<@device>
%0 = flow.dispatch @ex::@entry[%c0](%barrier) : (tensor<?x128xi8>{%dim0}) -> tensor<?x128xi8>{%dim0}

// CHECK: %[[C0:.+]] = arith.constant 0 : index
// CHECK: %[[BARRIER:.+]] = stream.async.barrier %[[INPUT]] : !stream.resource<*>{%[[DIM0]]} -> !stream.resource<*>
// CHECK: %[[C0_2:.+]] = arith.constant 0 : index
%barrier = flow.tensor.barrier %input : tensor<?x128xi8>{%dim0} on #hal.device.affinity<@device>
// CHECK: %[[SIZE:.+]] = stream.tensor.sizeof on(#hal.device.affinity<@device>) tensor<?x128xi8>{%arg2} : index
// CHECK: %[[DISP:.+]] = stream.async.dispatch on(#hal.device.affinity<@device>) @ex::@entry[%[[C0]]](%[[BARRIER]][%[[C0_2]] to %[[DIM0]] for %[[DIM0]]])
// CHECK: util.return %[[DISP]], %[[SIZE]]
// CHECK: %[[RESULT:.+]] = stream.tensor.dispatch on(#hal.device.affinity<@device>) @ex::@entry(%[[BARRIER]])
%0 = flow.dispatch @ex::@entry(%barrier) : (tensor<?x128xi8>{%dim0}) -> tensor<?x128xi8>{%dim0}
// CHECK: util.return %[[RESULT]], %[[SIZE]]
util.return %0 : tensor<?x128xi8>
}

Expand Down
30 changes: 30 additions & 0 deletions compiler/src/iree/compiler/Dialect/Stream/IR/StreamOpFolders.cpp
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Expand Up @@ -1363,6 +1363,36 @@ void TensorStoreOp::getCanonicalizationPatterns(RewritePatternSet &results,
// TODO(benvanik): combine multiple stores to the same target if contiguous.
}

//===----------------------------------------------------------------------===//
// stream.tensor.dispatch
//===----------------------------------------------------------------------===//

namespace {

struct DeduplicateTensorDispatchEntryRefs final
: public OpRewritePattern<TensorDispatchOp> {
using OpRewritePattern::OpRewritePattern;
LogicalResult matchAndRewrite(TensorDispatchOp dispatchOp,
PatternRewriter &rewriter) const override {
auto originalAttr = dispatchOp.getEntryPointsAttr();
auto newAttr = deduplicateArrayElements(originalAttr);
if (newAttr == originalAttr)
return failure();
rewriter.modifyOpInPlace(dispatchOp,
[&]() { dispatchOp.setEntryPointsAttr(newAttr); });
return success();
}
};

} // namespace

void TensorDispatchOp::getCanonicalizationPatterns(RewritePatternSet &results,
MLIRContext *context) {
// TODO(benvanik): maybe tied type/lifetime updates?
results.insert<ElideUnusedOp<TensorDispatchOp>>(context);
results.insert<DeduplicateTensorDispatchEntryRefs>(context);
}

//===----------------------------------------------------------------------===//
// stream.async.alloca
//===----------------------------------------------------------------------===//
Expand Down
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