sightly better bufferization
minor fixes
This commit is contained in:
@@ -4,6 +4,7 @@
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#include "src/Accelerators/PIM/Common/IR/AddressAnalysis.hpp"
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#include "src/Accelerators/PIM/Common/IR/AddressAnalysis.hpp"
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#include "src/Accelerators/PIM/Common/IR/ShapeUtils.hpp"
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#include "src/Accelerators/PIM/Common/IR/ShapeUtils.hpp"
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#include "src/Accelerators/PIM/Dialect/Pim/PimOps.hpp"
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namespace onnx_mlir {
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namespace onnx_mlir {
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@@ -227,7 +228,7 @@ llvm::FailureOr<ResolvedContiguousAddress> resolveContiguousAddressImpl(mlir::Va
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continue;
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continue;
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}
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}
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if (mlir::isa<mlir::memref::AllocOp, mlir::memref::GetGlobalOp>(definingOp))
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if (mlir::isa<onnx_mlir::pim::PimEmptyManyOp, mlir::memref::AllocOp, mlir::memref::GetGlobalOp>(definingOp))
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return ResolvedContiguousAddress {value, byteOffset};
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return ResolvedContiguousAddress {value, byteOffset};
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return mlir::failure();
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return mlir::failure();
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@@ -54,7 +54,7 @@ bool isSpatialMvmVmmWeightUse(mlir::OpOperand& use) {
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if (!computeOp || operandIndex >= computeOp.getWeights().size())
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if (!computeOp || operandIndex >= computeOp.getWeights().size())
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return false;
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return false;
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return hasMvmVmmWeightUse<spatial::SpatWeightedMVMOp, spatial::SpatWeightedVMMOp>(computeOp, operandIndex);
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return hasMvmVmmWeightUse<spatial::SpatMVMOp, spatial::SpatVMMOp>(computeOp, operandIndex);
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}
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}
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bool hasOnlySpatialMvmVmmWeightUses(mlir::Value value) {
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bool hasOnlySpatialMvmVmmWeightUses(mlir::Value value) {
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@@ -97,6 +97,11 @@ void PimMemory::allocateHost(ModuleOp moduleOp, func::FuncOp funcOp) {
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if (!allocOp->getParentOfType<pim::PimCoreOp>())
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if (!allocOp->getParentOfType<pim::PimCoreOp>())
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gatherMemEntry(allocOp.getResult());
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gatherMemEntry(allocOp.getResult());
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});
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});
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funcOp.walk([&](pim::PimEmptyManyOp emptyManyOp) {
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if (!emptyManyOp->getParentOfType<pim::PimCoreOp>() && !emptyManyOp->getParentOfType<pim::PimCoreBatchOp>())
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for (mlir::Value output : emptyManyOp.getOutputs())
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gatherMemEntry(output);
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});
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allocateGatheredMemory();
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allocateGatheredMemory();
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@@ -106,6 +111,10 @@ void PimMemory::allocateHost(ModuleOp moduleOp, func::FuncOp funcOp) {
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void PimMemory::allocateCore(Operation* op) {
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void PimMemory::allocateCore(Operation* op) {
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op->walk([&](memref::AllocOp allocOp) { gatherMemEntry(allocOp); });
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op->walk([&](memref::AllocOp allocOp) { gatherMemEntry(allocOp); });
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op->walk([&](pim::PimEmptyManyOp emptyManyOp) {
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for (mlir::Value output : emptyManyOp.getOutputs())
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gatherMemEntry(output);
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});
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allocateGatheredMemory();
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allocateGatheredMemory();
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}
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}
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@@ -957,6 +966,8 @@ static int64_t codeGenCoreOps(Block& block, PimCodeGen& coreCodeGen) {
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coreCodeGen.codeGenVSoftmaxOp(vsoftmaxOp, knowledge);
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coreCodeGen.codeGenVSoftmaxOp(vsoftmaxOp, knowledge);
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else if (auto getGlobalOp = dyn_cast<memref::GetGlobalOp>(op))
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else if (auto getGlobalOp = dyn_cast<memref::GetGlobalOp>(op))
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coreCodeGen.codeGetGlobalOp(getGlobalOp, knowledge);
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coreCodeGen.codeGetGlobalOp(getGlobalOp, knowledge);
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else if (isa<pim::PimEmptyManyOp>(op))
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return success();
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else {
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else {
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op.emitError("Unsupported codegen for this operation");
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op.emitError("Unsupported codegen for this operation");
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op.dump();
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op.dump();
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@@ -381,7 +381,7 @@ LogicalResult GemvToSpatialCompute::matchAndRewrite(ONNXGemmOp gemmOp,
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vmmOutputs.reserve(aHSlicesArgs.size());
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vmmOutputs.reserve(aHSlicesArgs.size());
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for (auto [aHSliceId, computeArg] : llvm::enumerate(aHSlicesArgs))
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for (auto [aHSliceId, computeArg] : llvm::enumerate(aHSlicesArgs))
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vmmOutputs.push_back(
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vmmOutputs.push_back(
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spatial::SpatWeightedVMMOp::create(rewriter, gemmLoc, currOutHSliceType, aHSliceId, computeArg));
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spatial::SpatVMMOp::create(rewriter, gemmLoc, currOutHSliceType, aHSliceId, computeArg));
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if (vmmOutputs.empty()) {
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if (vmmOutputs.empty()) {
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gemmOp.emitOpError("requires at least one non-empty slice when lowering tiled Gemm to Spatial VMMs");
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gemmOp.emitOpError("requires at least one non-empty slice when lowering tiled Gemm to Spatial VMMs");
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return failure();
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return failure();
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@@ -527,7 +527,7 @@ LogicalResult GemmToSpatialComputeBatch::matchAndRewrite(ONNXGemmOp gemmOp,
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&batchOp.getBody(), batchOp.getBody().end(), TypeRange {aSliceType}, SmallVector<Location>(1, loc));
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&batchOp.getBody(), batchOp.getBody().end(), TypeRange {aSliceType}, SmallVector<Location>(1, loc));
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rewriter.setInsertionPointToEnd(body);
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rewriter.setInsertionPointToEnd(body);
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Value vmmResult = spatial::SpatWeightedVMMOp::create(rewriter, loc, outRowType, 0, body->getArgument(0)).getResult();
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Value vmmResult = spatial::SpatVMMOp::create(rewriter, loc, outRowType, 0, body->getArgument(0)).getResult();
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Value laneResult = vmmResult;
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Value laneResult = vmmResult;
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if (sharedBias)
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if (sharedBias)
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laneResult = spatial::SpatVAddOp::create(rewriter, loc, outRowType, vmmResult, sharedBias).getResult();
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laneResult = spatial::SpatVAddOp::create(rewriter, loc, outRowType, vmmResult, sharedBias).getResult();
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@@ -95,7 +95,7 @@ bool hasLaterUserInBlock(mlir::Value value, Operation* operation) {
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return false;
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return false;
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}
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}
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mlir::Value getBestOutputTensorFromOperandsOrAllocate(PatternRewriter& rewriter, Operation* operation) {
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mlir::Value getBestOutputTensorFromOperandsOrAllocate(RewriterBase& rewriter, Operation* operation) {
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assert("Only support operations with a single result" && operation->getNumResults() == 1);
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assert("Only support operations with a single result" && operation->getNumResults() == 1);
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mlir::Value result = operation->getResult(0);
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mlir::Value result = operation->getResult(0);
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auto resultType = result.getType();
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auto resultType = result.getType();
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@@ -41,7 +41,7 @@ mlir::Operation* getEarliestUserWithinBlock(mlir::Value value);
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mlir::SmallVector<mlir::Value> getOpOperandsSortedByUses(mlir::Operation* operation);
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mlir::SmallVector<mlir::Value> getOpOperandsSortedByUses(mlir::Operation* operation);
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mlir::Value getBestOutputTensorFromOperandsOrAllocate(mlir::PatternRewriter& rewriter, mlir::Operation* operation);
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mlir::Value getBestOutputTensorFromOperandsOrAllocate(mlir::RewriterBase& rewriter, mlir::Operation* operation);
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inline mlir::tensor::EmptyOp
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inline mlir::tensor::EmptyOp
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createEmptyTensorFromShaped(mlir::IRRewriter& rewriter, mlir::Location loc, mlir::ShapedType shapedType) {
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createEmptyTensorFromShaped(mlir::IRRewriter& rewriter, mlir::Location loc, mlir::ShapedType shapedType) {
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@@ -16,13 +16,13 @@ def onnxToPimTranspose : Pat<
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>;
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>;
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def spatToPimVMM : Pat<
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def spatToPimVMM : Pat<
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(SpatWeightedVMMOp:$srcOpRes $weightIndex, $vector),
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(SpatVMMOp:$srcOpRes $weightIndex, $vector),
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(PimVMMOp $weightIndex, $vector,
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(PimVMMOp $weightIndex, $vector,
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(NativeCodeCall<"onnx_mlir::getBestOutputTensorFromOperandsOrAllocate($_builder, $0.getDefiningOp())"> $srcOpRes))
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(NativeCodeCall<"onnx_mlir::getBestOutputTensorFromOperandsOrAllocate($_builder, $0.getDefiningOp())"> $srcOpRes))
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>;
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>;
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def spatToPimMVM : Pat<
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def spatToPimMVM : Pat<
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(SpatWeightedMVMOp:$srcOpRes $weightIndex, $vector),
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(SpatMVMOp:$srcOpRes $weightIndex, $vector),
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(PimMVMOp $weightIndex, $vector,
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(PimMVMOp $weightIndex, $vector,
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(NativeCodeCall<"onnx_mlir::getBestOutputTensorFromOperandsOrAllocate($_builder, $0.getDefiningOp())"> $srcOpRes))
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(NativeCodeCall<"onnx_mlir::getBestOutputTensorFromOperandsOrAllocate($_builder, $0.getDefiningOp())"> $srcOpRes))
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>;
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>;
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@@ -252,25 +252,6 @@ static void lowerConcat(spatial::SpatConcatOp concatOp, IRRewriter& rewriter) {
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rewriter.replaceOp(concatOp, concatenated);
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rewriter.replaceOp(concatOp, concatenated);
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}
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}
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static void lowerRemainingSpatialMathOps(func::FuncOp funcOp, IRRewriter& rewriter) {
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SmallVector<spatial::SpatWeightedVMMOp> wvmmOps;
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funcOp.walk([&](spatial::SpatWeightedVMMOp wvmmOp) {
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if (wvmmOp->getParentOfType<pim::PimCoreOp>() || wvmmOp->getParentOfType<pim::PimCoreBatchOp>())
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wvmmOps.push_back(wvmmOp);
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});
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for (auto wvmmOp : wvmmOps) {
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rewriter.setInsertionPoint(wvmmOp);
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auto outputType = cast<ShapedType>(wvmmOp.getOutput().getType());
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Value outputBuffer = createEmptyTensorFromShaped(rewriter, wvmmOp.getLoc(), outputType).getResult();
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rewriter.replaceOpWithNewOp<pim::PimVMMOp>(wvmmOp,
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wvmmOp.getOutput().getType(),
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rewriter.getI32IntegerAttr(wvmmOp.getWeightIndex()),
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wvmmOp.getInput(),
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outputBuffer);
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}
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}
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static void lowerMapOps(func::FuncOp funcOp, IRRewriter& rewriter) {
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static void lowerMapOps(func::FuncOp funcOp, IRRewriter& rewriter) {
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SmallVector<spatial::SpatMapOp> mapOps;
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SmallVector<spatial::SpatMapOp> mapOps;
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funcOp.walk([&](spatial::SpatMapOp mapOp) {
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funcOp.walk([&](spatial::SpatMapOp mapOp) {
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@@ -736,7 +717,7 @@ void SpatialToPimPass::runOnOperation() {
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SmallVector<pim::PimCoreOp> coreOps;
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SmallVector<pim::PimCoreOp> coreOps;
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funcOp.walk([&](pim::PimCoreOp coreOp) { coreOps.push_back(coreOp); });
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funcOp.walk([&](pim::PimCoreOp coreOp) { coreOps.push_back(coreOp); });
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for (auto coreOp : coreOps) {
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for (auto coreOp : coreOps) {
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if (failed(applyPatternsGreedily(coreOp.getOperation(), frozenCoreBodyPatterns))) {
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if (failed(applyPartialConversion(coreOp.getOperation(), target, frozenCoreBodyPatterns))) {
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signalPassFailure();
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signalPassFailure();
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return;
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return;
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}
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}
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@@ -745,15 +726,13 @@ void SpatialToPimPass::runOnOperation() {
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SmallVector<pim::PimCoreBatchOp> coreBatchOps;
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SmallVector<pim::PimCoreBatchOp> coreBatchOps;
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funcOp.walk([&](pim::PimCoreBatchOp coreBatchOp) { coreBatchOps.push_back(coreBatchOp); });
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funcOp.walk([&](pim::PimCoreBatchOp coreBatchOp) { coreBatchOps.push_back(coreBatchOp); });
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for (auto coreBatchOp : coreBatchOps) {
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for (auto coreBatchOp : coreBatchOps) {
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if (failed(applyPatternsGreedily(coreBatchOp.getOperation(), frozenCoreBodyPatterns))) {
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if (failed(applyPartialConversion(coreBatchOp.getOperation(), target, frozenCoreBodyPatterns))) {
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signalPassFailure();
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signalPassFailure();
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return;
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return;
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}
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}
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}
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}
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}
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}
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lowerRemainingSpatialMathOps(funcOp, rewriter);
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RewritePatternSet channelPatterns(ctx);
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RewritePatternSet channelPatterns(ctx);
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populateWithGenerated(channelPatterns);
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populateWithGenerated(channelPatterns);
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if (failed(applyPatternsGreedily(funcOp, std::move(channelPatterns)))) {
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if (failed(applyPatternsGreedily(funcOp, std::move(channelPatterns)))) {
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@@ -96,7 +96,7 @@ def PimEmptyManyOp : PimOp<"empty_many", []> {
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let summary = "Create many identical empty tensors";
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let summary = "Create many identical empty tensors";
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let results = (outs
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let results = (outs
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Variadic<AnyRankedTensor>:$outputs
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Variadic<PimTensor>:$outputs
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);
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);
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let hasVerifier = 1;
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let hasVerifier = 1;
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@@ -79,9 +79,9 @@ LogicalResult PimEmptyManyOp::verify() {
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return emitError("must produce at least one output");
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return emitError("must produce at least one output");
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Type firstType = getOutputs().front().getType();
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Type firstType = getOutputs().front().getType();
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auto firstTensorType = dyn_cast<RankedTensorType>(firstType);
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auto firstShapedType = dyn_cast<ShapedType>(firstType);
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if (!firstTensorType)
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if (!firstShapedType || !firstShapedType.hasRank())
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return emitError("outputs must all be ranked tensor types");
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return emitError("outputs must all be ranked shaped types");
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for (Value output : getOutputs().drop_front())
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for (Value output : getOutputs().drop_front())
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if (output.getType() != firstType)
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if (output.getType() != firstType)
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@@ -34,6 +34,15 @@ static Value materializeContiguousMemRef(Value memrefValue, Location loc, Rewrit
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.getOutput();
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.getOutput();
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}
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}
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static FailureOr<Value> getBufferOrValue(RewriterBase& rewriter,
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Value value,
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const BufferizationOptions& options,
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BufferizationState& state) {
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if (isa<BufferLikeType>(value.getType()))
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return value;
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return getBuffer(rewriter, value, options, state);
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}
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struct MemCopyHostToDevOpInterface
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struct MemCopyHostToDevOpInterface
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: DstBufferizableOpInterfaceExternalModel<MemCopyHostToDevOpInterface, PimMemCopyHostToDevOp> {
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: DstBufferizableOpInterfaceExternalModel<MemCopyHostToDevOpInterface, PimMemCopyHostToDevOp> {
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LogicalResult bufferize(Operation* op,
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LogicalResult bufferize(Operation* op,
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@@ -44,12 +53,12 @@ struct MemCopyHostToDevOpInterface
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auto deviceTarget = memCopyHostToDevOp.getDeviceTarget();
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auto deviceTarget = memCopyHostToDevOp.getDeviceTarget();
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auto hostSource = memCopyHostToDevOp.getHostSource();
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auto hostSource = memCopyHostToDevOp.getHostSource();
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auto deviceTargetOpt = getBuffer(rewriter, deviceTarget, options, state);
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auto deviceTargetOpt = getBufferOrValue(rewriter, deviceTarget, options, state);
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if (failed(deviceTargetOpt))
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if (failed(deviceTargetOpt))
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return failure();
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return failure();
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auto deviceTargetMemRef = *deviceTargetOpt;
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auto deviceTargetMemRef = *deviceTargetOpt;
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auto hostSourceOpt = getBuffer(rewriter, hostSource, options, state);
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auto hostSourceOpt = getBufferOrValue(rewriter, hostSource, options, state);
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if (failed(hostSourceOpt))
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if (failed(hostSourceOpt))
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return failure();
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return failure();
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auto hostSourceMemRef = *hostSourceOpt;
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auto hostSourceMemRef = *hostSourceOpt;
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@@ -73,10 +82,10 @@ struct MemCopyHostToDevBatchOpInterface
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const BufferizationOptions& options,
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const BufferizationOptions& options,
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BufferizationState& state) const {
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BufferizationState& state) const {
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auto memCopyHostToDevOp = cast<PimMemCopyHostToDevBatchOp>(op);
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auto memCopyHostToDevOp = cast<PimMemCopyHostToDevBatchOp>(op);
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auto deviceTargetOpt = getBuffer(rewriter, memCopyHostToDevOp.getDeviceTarget(), options, state);
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auto deviceTargetOpt = getBufferOrValue(rewriter, memCopyHostToDevOp.getDeviceTarget(), options, state);
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if (failed(deviceTargetOpt))
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if (failed(deviceTargetOpt))
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return failure();
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return failure();
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auto hostSourceOpt = getBuffer(rewriter, memCopyHostToDevOp.getHostSource(), options, state);
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auto hostSourceOpt = getBufferOrValue(rewriter, memCopyHostToDevOp.getHostSource(), options, state);
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if (failed(hostSourceOpt))
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if (failed(hostSourceOpt))
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return failure();
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return failure();
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@@ -101,13 +110,13 @@ struct MemCopyDevToHostOpInterface
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auto memCopyDevToHostOp = cast<PimMemCopyDevToHostOp>(op);
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auto memCopyDevToHostOp = cast<PimMemCopyDevToHostOp>(op);
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auto hostTarget = memCopyDevToHostOp.getHostTarget();
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auto hostTarget = memCopyDevToHostOp.getHostTarget();
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auto hostTargetOpt = getBuffer(rewriter, hostTarget, options, state);
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auto hostTargetOpt = getBufferOrValue(rewriter, hostTarget, options, state);
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if (failed(hostTargetOpt))
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if (failed(hostTargetOpt))
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return failure();
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return failure();
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auto hostTargetMemRef = *hostTargetOpt;
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auto hostTargetMemRef = *hostTargetOpt;
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auto deviceSource = memCopyDevToHostOp.getDeviceSource();
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auto deviceSource = memCopyDevToHostOp.getDeviceSource();
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auto deviceSourceOpt = getBuffer(rewriter, deviceSource, options, state);
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auto deviceSourceOpt = getBufferOrValue(rewriter, deviceSource, options, state);
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if (failed(deviceSourceOpt))
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if (failed(deviceSourceOpt))
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return failure();
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return failure();
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auto deviceSourceMemRef = *deviceSourceOpt;
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auto deviceSourceMemRef = *deviceSourceOpt;
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@@ -135,7 +144,7 @@ struct ReceiveOpInterface : DstBufferizableOpInterfaceExternalModel<ReceiveOpInt
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BufferizationState& state) const {
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BufferizationState& state) const {
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auto receiveOp = cast<PimReceiveOp>(op);
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auto receiveOp = cast<PimReceiveOp>(op);
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auto outputBufferOpt = getBuffer(rewriter, receiveOp.getOutputBuffer(), options, state);
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auto outputBufferOpt = getBufferOrValue(rewriter, receiveOp.getOutputBuffer(), options, state);
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if (failed(outputBufferOpt))
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if (failed(outputBufferOpt))
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return failure();
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return failure();
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@@ -159,7 +168,7 @@ struct ReceiveBatchOpInterface : DstBufferizableOpInterfaceExternalModel<Receive
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const BufferizationOptions& options,
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const BufferizationOptions& options,
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BufferizationState& state) const {
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BufferizationState& state) const {
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auto receiveOp = cast<PimReceiveBatchOp>(op);
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auto receiveOp = cast<PimReceiveBatchOp>(op);
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auto outputBufferOpt = getBuffer(rewriter, receiveOp.getOutputBuffer(), options, state);
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auto outputBufferOpt = getBufferOrValue(rewriter, receiveOp.getOutputBuffer(), options, state);
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||||||
if (failed(outputBufferOpt))
|
if (failed(outputBufferOpt))
|
||||||
return failure();
|
return failure();
|
||||||
|
|
||||||
@@ -185,13 +194,11 @@ struct ReceiveManyOpInterface : DstBufferizableOpInterfaceExternalModel<ReceiveM
|
|||||||
auto receiveOp = cast<PimReceiveManyOp>(op);
|
auto receiveOp = cast<PimReceiveManyOp>(op);
|
||||||
SmallVector<Value> outputBuffers;
|
SmallVector<Value> outputBuffers;
|
||||||
SmallVector<Type> resultTypes;
|
SmallVector<Type> resultTypes;
|
||||||
SmallVector<Value> tensorResults;
|
|
||||||
outputBuffers.reserve(receiveOp.getOutputBuffers().size());
|
outputBuffers.reserve(receiveOp.getOutputBuffers().size());
|
||||||
resultTypes.reserve(receiveOp.getOutputBuffers().size());
|
resultTypes.reserve(receiveOp.getOutputBuffers().size());
|
||||||
tensorResults.reserve(receiveOp.getOutputBuffers().size());
|
|
||||||
|
|
||||||
for (Value outputBuffer : receiveOp.getOutputBuffers()) {
|
for (Value outputBuffer : receiveOp.getOutputBuffers()) {
|
||||||
auto outputBufferOpt = getBuffer(rewriter, outputBuffer, options, state);
|
auto outputBufferOpt = getBufferOrValue(rewriter, outputBuffer, options, state);
|
||||||
if (failed(outputBufferOpt))
|
if (failed(outputBufferOpt))
|
||||||
return failure();
|
return failure();
|
||||||
outputBuffers.push_back(*outputBufferOpt);
|
outputBuffers.push_back(*outputBufferOpt);
|
||||||
@@ -200,15 +207,7 @@ struct ReceiveManyOpInterface : DstBufferizableOpInterfaceExternalModel<ReceiveM
|
|||||||
|
|
||||||
auto newOp = PimReceiveManyOp::create(
|
auto newOp = PimReceiveManyOp::create(
|
||||||
rewriter, receiveOp.getLoc(), TypeRange(resultTypes), ValueRange(outputBuffers), receiveOp.getSourceCoreIdsAttr());
|
rewriter, receiveOp.getLoc(), TypeRange(resultTypes), ValueRange(outputBuffers), receiveOp.getSourceCoreIdsAttr());
|
||||||
|
rewriter.replaceOp(receiveOp, newOp.getOutputs());
|
||||||
for (auto [bufferResult, tensorResult] : llvm::zip(newOp.getOutputs(), receiveOp.getOutputs())) {
|
|
||||||
auto tensorType = cast<RankedTensorType>(tensorResult.getType());
|
|
||||||
auto toTensor =
|
|
||||||
bufferization::ToTensorOp::create(rewriter, receiveOp.getLoc(), tensorType, bufferResult, UnitAttr(), UnitAttr());
|
|
||||||
tensorResults.push_back(toTensor.getResult());
|
|
||||||
}
|
|
||||||
|
|
||||||
rewriter.replaceOp(receiveOp, tensorResults);
|
|
||||||
return success();
|
return success();
|
||||||
}
|
}
|
||||||
};
|
};
|
||||||
@@ -226,13 +225,11 @@ struct ReceiveManyBatchOpInterface
|
|||||||
auto receiveOp = cast<PimReceiveManyBatchOp>(op);
|
auto receiveOp = cast<PimReceiveManyBatchOp>(op);
|
||||||
SmallVector<Value> outputBuffers;
|
SmallVector<Value> outputBuffers;
|
||||||
SmallVector<Type> resultTypes;
|
SmallVector<Type> resultTypes;
|
||||||
SmallVector<Value> tensorResults;
|
|
||||||
outputBuffers.reserve(receiveOp.getOutputBuffers().size());
|
outputBuffers.reserve(receiveOp.getOutputBuffers().size());
|
||||||
resultTypes.reserve(receiveOp.getOutputBuffers().size());
|
resultTypes.reserve(receiveOp.getOutputBuffers().size());
|
||||||
tensorResults.reserve(receiveOp.getOutputBuffers().size());
|
|
||||||
|
|
||||||
for (Value outputBuffer : receiveOp.getOutputBuffers()) {
|
for (Value outputBuffer : receiveOp.getOutputBuffers()) {
|
||||||
auto outputBufferOpt = getBuffer(rewriter, outputBuffer, options, state);
|
auto outputBufferOpt = getBufferOrValue(rewriter, outputBuffer, options, state);
|
||||||
if (failed(outputBufferOpt))
|
if (failed(outputBufferOpt))
|
||||||
return failure();
|
return failure();
|
||||||
outputBuffers.push_back(*outputBufferOpt);
|
outputBuffers.push_back(*outputBufferOpt);
|
||||||
@@ -244,15 +241,7 @@ struct ReceiveManyBatchOpInterface
|
|||||||
TypeRange(resultTypes),
|
TypeRange(resultTypes),
|
||||||
ValueRange(outputBuffers),
|
ValueRange(outputBuffers),
|
||||||
receiveOp.getSourceCoreIdsAttr());
|
receiveOp.getSourceCoreIdsAttr());
|
||||||
|
rewriter.replaceOp(receiveOp, newOp.getOutputs());
|
||||||
for (auto [bufferResult, tensorResult] : llvm::zip(newOp.getOutputs(), receiveOp.getOutputs())) {
|
|
||||||
auto tensorType = cast<RankedTensorType>(tensorResult.getType());
|
|
||||||
auto toTensor =
|
|
||||||
bufferization::ToTensorOp::create(rewriter, receiveOp.getLoc(), tensorType, bufferResult, UnitAttr(), UnitAttr());
|
|
||||||
tensorResults.push_back(toTensor.getResult());
|
|
||||||
}
|
|
||||||
|
|
||||||
rewriter.replaceOp(receiveOp, tensorResults);
|
|
||||||
return success();
|
return success();
|
||||||
}
|
}
|
||||||
};
|
};
|
||||||
@@ -267,7 +256,7 @@ struct ExtractRowsOpInterface : DstBufferizableOpInterfaceExternalModel<ExtractR
|
|||||||
const BufferizationOptions& options,
|
const BufferizationOptions& options,
|
||||||
BufferizationState& state) const {
|
BufferizationState& state) const {
|
||||||
auto extractRowsOp = cast<PimExtractRowsOp>(op);
|
auto extractRowsOp = cast<PimExtractRowsOp>(op);
|
||||||
auto inputOpt = getBuffer(rewriter, extractRowsOp.getInput(), options, state);
|
auto inputOpt = getBufferOrValue(rewriter, extractRowsOp.getInput(), options, state);
|
||||||
if (failed(inputOpt))
|
if (failed(inputOpt))
|
||||||
return failure();
|
return failure();
|
||||||
|
|
||||||
@@ -277,7 +266,7 @@ struct ExtractRowsOpInterface : DstBufferizableOpInterfaceExternalModel<ExtractR
|
|||||||
resultTypes.reserve(extractRowsOp.getOutputBuffers().size());
|
resultTypes.reserve(extractRowsOp.getOutputBuffers().size());
|
||||||
|
|
||||||
for (Value outputBuffer : extractRowsOp.getOutputBuffers()) {
|
for (Value outputBuffer : extractRowsOp.getOutputBuffers()) {
|
||||||
auto outputBufferOpt = getBuffer(rewriter, outputBuffer, options, state);
|
auto outputBufferOpt = getBufferOrValue(rewriter, outputBuffer, options, state);
|
||||||
if (failed(outputBufferOpt))
|
if (failed(outputBufferOpt))
|
||||||
return failure();
|
return failure();
|
||||||
outputBuffers.push_back(*outputBufferOpt);
|
outputBuffers.push_back(*outputBufferOpt);
|
||||||
@@ -307,13 +296,13 @@ struct ConcatOpInterface : DstBufferizableOpInterfaceExternalModel<ConcatOpInter
|
|||||||
SmallVector<Value> inputs;
|
SmallVector<Value> inputs;
|
||||||
inputs.reserve(concatOp.getInputs().size());
|
inputs.reserve(concatOp.getInputs().size());
|
||||||
for (Value input : concatOp.getInputs()) {
|
for (Value input : concatOp.getInputs()) {
|
||||||
auto inputOpt = getBuffer(rewriter, input, options, state);
|
auto inputOpt = getBufferOrValue(rewriter, input, options, state);
|
||||||
if (failed(inputOpt))
|
if (failed(inputOpt))
|
||||||
return failure();
|
return failure();
|
||||||
inputs.push_back(materializeContiguousMemRef(*inputOpt, op->getLoc(), rewriter));
|
inputs.push_back(materializeContiguousMemRef(*inputOpt, op->getLoc(), rewriter));
|
||||||
}
|
}
|
||||||
|
|
||||||
auto outputBufferOpt = getBuffer(rewriter, concatOp.getOutputBuffer(), options, state);
|
auto outputBufferOpt = getBufferOrValue(rewriter, concatOp.getOutputBuffer(), options, state);
|
||||||
if (failed(outputBufferOpt))
|
if (failed(outputBufferOpt))
|
||||||
return failure();
|
return failure();
|
||||||
|
|
||||||
@@ -323,6 +312,31 @@ struct ConcatOpInterface : DstBufferizableOpInterfaceExternalModel<ConcatOpInter
|
|||||||
}
|
}
|
||||||
};
|
};
|
||||||
|
|
||||||
|
struct EmptyManyOpInterface : BufferizableOpInterface::ExternalModel<EmptyManyOpInterface, PimEmptyManyOp> {
|
||||||
|
bool bufferizesToAllocation(Operation* op, Value value) const { return true; }
|
||||||
|
|
||||||
|
bool resultBufferizesToMemoryWrite(Operation* op, OpResult opResult, const AnalysisState& state) const {
|
||||||
|
return false;
|
||||||
|
}
|
||||||
|
|
||||||
|
LogicalResult bufferize(Operation* op,
|
||||||
|
RewriterBase& rewriter,
|
||||||
|
const BufferizationOptions& options,
|
||||||
|
BufferizationState& state) const {
|
||||||
|
auto emptyManyOp = cast<PimEmptyManyOp>(op);
|
||||||
|
|
||||||
|
SmallVector<Type> resultTypes;
|
||||||
|
resultTypes.reserve(emptyManyOp.getOutputs().size());
|
||||||
|
for (Value output : emptyManyOp.getOutputs()) {
|
||||||
|
auto shapedType = cast<ShapedType>(output.getType());
|
||||||
|
resultTypes.push_back(MemRefType::get(shapedType.getShape(), shapedType.getElementType()));
|
||||||
|
}
|
||||||
|
|
||||||
|
replaceOpWithNewBufferizedOp<PimEmptyManyOp>(rewriter, emptyManyOp, TypeRange(resultTypes));
|
||||||
|
return success();
|
||||||
|
}
|
||||||
|
};
|
||||||
|
|
||||||
struct MapOpInterface : BufferizableOpInterface::ExternalModel<MapOpInterface, PimMapOp> {
|
struct MapOpInterface : BufferizableOpInterface::ExternalModel<MapOpInterface, PimMapOp> {
|
||||||
bool bufferizesToMemoryRead(Operation* op, OpOperand& opOperand, const AnalysisState& state) const { return true; }
|
bool bufferizesToMemoryRead(Operation* op, OpOperand& opOperand, const AnalysisState& state) const { return true; }
|
||||||
|
|
||||||
@@ -375,7 +389,7 @@ struct MapOpInterface : BufferizableOpInterface::ExternalModel<MapOpInterface, P
|
|||||||
|
|
||||||
for (Value input : mapOp.getInputs()) {
|
for (Value input : mapOp.getInputs()) {
|
||||||
if (isa<TensorType>(input.getType())) {
|
if (isa<TensorType>(input.getType())) {
|
||||||
auto inputOpt = getBuffer(rewriter, input, options, state);
|
auto inputOpt = getBufferOrValue(rewriter, input, options, state);
|
||||||
if (failed(inputOpt))
|
if (failed(inputOpt))
|
||||||
return failure();
|
return failure();
|
||||||
inputs.push_back(*inputOpt);
|
inputs.push_back(*inputOpt);
|
||||||
@@ -453,6 +467,16 @@ struct CoreBatchOpInterface : BufferizableOpInterface::ExternalModel<CoreBatchOp
|
|||||||
BufferizationState& state) const {
|
BufferizationState& state) const {
|
||||||
auto coreBatchOp = cast<PimCoreBatchOp>(op);
|
auto coreBatchOp = cast<PimCoreBatchOp>(op);
|
||||||
|
|
||||||
|
bool alreadyBufferized = llvm::all_of(coreBatchOp.getWeights(), [](Value weight) {
|
||||||
|
return isa<BufferLikeType>(weight.getType());
|
||||||
|
}) && llvm::all_of(coreBatchOp.getInputs(), [](Value input) {
|
||||||
|
return isa<BufferLikeType>(input.getType());
|
||||||
|
}) && llvm::all_of(coreBatchOp.getBody().front().getArguments(), [](BlockArgument arg) {
|
||||||
|
return isa<BufferLikeType>(arg.getType());
|
||||||
|
});
|
||||||
|
if (alreadyBufferized)
|
||||||
|
return success();
|
||||||
|
|
||||||
SmallVector<Value> weights;
|
SmallVector<Value> weights;
|
||||||
SmallVector<Value> inputs;
|
SmallVector<Value> inputs;
|
||||||
weights.reserve(coreBatchOp.getWeights().size());
|
weights.reserve(coreBatchOp.getWeights().size());
|
||||||
@@ -460,7 +484,7 @@ struct CoreBatchOpInterface : BufferizableOpInterface::ExternalModel<CoreBatchOp
|
|||||||
|
|
||||||
for (Value weight : coreBatchOp.getWeights()) {
|
for (Value weight : coreBatchOp.getWeights()) {
|
||||||
if (isa<TensorType>(weight.getType())) {
|
if (isa<TensorType>(weight.getType())) {
|
||||||
auto weightOpt = getBuffer(rewriter, weight, options, state);
|
auto weightOpt = getBufferOrValue(rewriter, weight, options, state);
|
||||||
if (failed(weightOpt))
|
if (failed(weightOpt))
|
||||||
return failure();
|
return failure();
|
||||||
weights.push_back(*weightOpt);
|
weights.push_back(*weightOpt);
|
||||||
@@ -472,7 +496,7 @@ struct CoreBatchOpInterface : BufferizableOpInterface::ExternalModel<CoreBatchOp
|
|||||||
|
|
||||||
for (Value input : coreBatchOp.getInputs()) {
|
for (Value input : coreBatchOp.getInputs()) {
|
||||||
if (isa<TensorType>(input.getType())) {
|
if (isa<TensorType>(input.getType())) {
|
||||||
auto inputOpt = getBuffer(rewriter, input, options, state);
|
auto inputOpt = getBufferOrValue(rewriter, input, options, state);
|
||||||
if (failed(inputOpt))
|
if (failed(inputOpt))
|
||||||
return failure();
|
return failure();
|
||||||
inputs.push_back(*inputOpt);
|
inputs.push_back(*inputOpt);
|
||||||
@@ -510,11 +534,11 @@ struct TransposeOpInterface : DstBufferizableOpInterfaceExternalModel<TransposeO
|
|||||||
BufferizationState& state) const {
|
BufferizationState& state) const {
|
||||||
auto transposeOp = cast<PimTransposeOp>(op);
|
auto transposeOp = cast<PimTransposeOp>(op);
|
||||||
|
|
||||||
auto inputOpt = getBuffer(rewriter, transposeOp.getInput(), options, state);
|
auto inputOpt = getBufferOrValue(rewriter, transposeOp.getInput(), options, state);
|
||||||
if (failed(inputOpt))
|
if (failed(inputOpt))
|
||||||
return failure();
|
return failure();
|
||||||
|
|
||||||
auto outputBufferOpt = getBuffer(rewriter, transposeOp.getOutputBuffer(), options, state);
|
auto outputBufferOpt = getBufferOrValue(rewriter, transposeOp.getOutputBuffer(), options, state);
|
||||||
if (failed(outputBufferOpt))
|
if (failed(outputBufferOpt))
|
||||||
return failure();
|
return failure();
|
||||||
|
|
||||||
@@ -547,11 +571,11 @@ struct VMMOpInterface : DstBufferizableOpInterfaceExternalModel<VMMOpInterface,
|
|||||||
BufferizationState& state) const {
|
BufferizationState& state) const {
|
||||||
auto vmmOp = cast<PimVMMOp>(op);
|
auto vmmOp = cast<PimVMMOp>(op);
|
||||||
|
|
||||||
auto inputOpt = getBuffer(rewriter, vmmOp.getInput(), options, state);
|
auto inputOpt = getBufferOrValue(rewriter, vmmOp.getInput(), options, state);
|
||||||
if (failed(inputOpt))
|
if (failed(inputOpt))
|
||||||
return failure();
|
return failure();
|
||||||
|
|
||||||
auto outputBufferOpt = getBuffer(rewriter, vmmOp.getOutputBuffer(), options, state);
|
auto outputBufferOpt = getBufferOrValue(rewriter, vmmOp.getOutputBuffer(), options, state);
|
||||||
if (failed(outputBufferOpt))
|
if (failed(outputBufferOpt))
|
||||||
return failure();
|
return failure();
|
||||||
|
|
||||||
@@ -574,11 +598,11 @@ struct MVMOpInterface : DstBufferizableOpInterfaceExternalModel<MVMOpInterface,
|
|||||||
BufferizationState& state) const {
|
BufferizationState& state) const {
|
||||||
auto mvmOp = cast<PimMVMOp>(op);
|
auto mvmOp = cast<PimMVMOp>(op);
|
||||||
|
|
||||||
auto inputOpt = getBuffer(rewriter, mvmOp.getInput(), options, state);
|
auto inputOpt = getBufferOrValue(rewriter, mvmOp.getInput(), options, state);
|
||||||
if (failed(inputOpt))
|
if (failed(inputOpt))
|
||||||
return failure();
|
return failure();
|
||||||
|
|
||||||
auto outputBufferOpt = getBuffer(rewriter, mvmOp.getOutputBuffer(), options, state);
|
auto outputBufferOpt = getBufferOrValue(rewriter, mvmOp.getOutputBuffer(), options, state);
|
||||||
if (failed(outputBufferOpt))
|
if (failed(outputBufferOpt))
|
||||||
return failure();
|
return failure();
|
||||||
|
|
||||||
@@ -608,15 +632,15 @@ struct BinaryDstOpInterface : DstBufferizableOpInterfaceExternalModel<BinaryDstO
|
|||||||
BufferizationState& state) const {
|
BufferizationState& state) const {
|
||||||
auto binaryOp = cast<OpTy>(op);
|
auto binaryOp = cast<OpTy>(op);
|
||||||
|
|
||||||
auto lhsOpt = getBuffer(rewriter, binaryOp.getLhs(), options, state);
|
auto lhsOpt = getBufferOrValue(rewriter, binaryOp.getLhs(), options, state);
|
||||||
if (failed(lhsOpt))
|
if (failed(lhsOpt))
|
||||||
return failure();
|
return failure();
|
||||||
|
|
||||||
auto rhsOpt = getBuffer(rewriter, binaryOp.getRhs(), options, state);
|
auto rhsOpt = getBufferOrValue(rewriter, binaryOp.getRhs(), options, state);
|
||||||
if (failed(rhsOpt))
|
if (failed(rhsOpt))
|
||||||
return failure();
|
return failure();
|
||||||
|
|
||||||
auto outputBufferOpt = getBuffer(rewriter, binaryOp.getOutputBuffer(), options, state);
|
auto outputBufferOpt = getBufferOrValue(rewriter, binaryOp.getOutputBuffer(), options, state);
|
||||||
if (failed(outputBufferOpt))
|
if (failed(outputBufferOpt))
|
||||||
return failure();
|
return failure();
|
||||||
|
|
||||||
@@ -647,11 +671,11 @@ struct UnaryDstOpInterface : DstBufferizableOpInterfaceExternalModel<UnaryDstOpI
|
|||||||
BufferizationState& state) const {
|
BufferizationState& state) const {
|
||||||
auto unaryOp = cast<OpTy>(op);
|
auto unaryOp = cast<OpTy>(op);
|
||||||
|
|
||||||
auto inputOpt = getBuffer(rewriter, unaryOp.getInput(), options, state);
|
auto inputOpt = getBufferOrValue(rewriter, unaryOp.getInput(), options, state);
|
||||||
if (failed(inputOpt))
|
if (failed(inputOpt))
|
||||||
return failure();
|
return failure();
|
||||||
|
|
||||||
auto outputBufferOpt = getBuffer(rewriter, unaryOp.getOutputBuffer(), options, state);
|
auto outputBufferOpt = getBufferOrValue(rewriter, unaryOp.getOutputBuffer(), options, state);
|
||||||
if (failed(outputBufferOpt))
|
if (failed(outputBufferOpt))
|
||||||
return failure();
|
return failure();
|
||||||
|
|
||||||
@@ -664,6 +688,7 @@ struct UnaryDstOpInterface : DstBufferizableOpInterfaceExternalModel<UnaryDstOpI
|
|||||||
|
|
||||||
void registerOpBufferizationInterfaces(DialectRegistry& registry) {
|
void registerOpBufferizationInterfaces(DialectRegistry& registry) {
|
||||||
registry.addExtension(+[](MLIRContext* ctx, PimDialect* dialect) {
|
registry.addExtension(+[](MLIRContext* ctx, PimDialect* dialect) {
|
||||||
|
PimEmptyManyOp::attachInterface<EmptyManyOpInterface>(*ctx);
|
||||||
PimMapOp::attachInterface<MapOpInterface>(*ctx);
|
PimMapOp::attachInterface<MapOpInterface>(*ctx);
|
||||||
PimCoreBatchOp::attachInterface<CoreBatchOpInterface>(*ctx);
|
PimCoreBatchOp::attachInterface<CoreBatchOpInterface>(*ctx);
|
||||||
PimReceiveOp::attachInterface<ReceiveOpInterface>(*ctx);
|
PimReceiveOp::attachInterface<ReceiveOpInterface>(*ctx);
|
||||||
|
|||||||
@@ -47,37 +47,26 @@ private:
|
|||||||
|
|
||||||
void PimBufferizationPass::runOnOperation() {
|
void PimBufferizationPass::runOnOperation() {
|
||||||
auto moduleOp = getOperation();
|
auto moduleOp = getOperation();
|
||||||
{
|
|
||||||
SmallVector<pim::PimEmptyManyOp> emptyManyOps;
|
|
||||||
moduleOp.walk([&](pim::PimEmptyManyOp emptyManyOp) { emptyManyOps.push_back(emptyManyOp); });
|
|
||||||
|
|
||||||
IRRewriter rewriter(moduleOp.getContext());
|
|
||||||
for (auto emptyManyOp : emptyManyOps) {
|
|
||||||
SmallVector<Value> replacementValues;
|
|
||||||
replacementValues.reserve(emptyManyOp.getOutputs().size());
|
|
||||||
rewriter.setInsertionPoint(emptyManyOp);
|
|
||||||
for (Value output : emptyManyOp.getOutputs()) {
|
|
||||||
auto outputType = cast<RankedTensorType>(output.getType());
|
|
||||||
replacementValues.push_back(
|
|
||||||
tensor::EmptyOp::create(rewriter, emptyManyOp.getLoc(), outputType.getShape(), outputType.getElementType()));
|
|
||||||
}
|
|
||||||
rewriter.replaceOp(emptyManyOp, replacementValues);
|
|
||||||
}
|
|
||||||
}
|
|
||||||
// Refactor this into a function
|
// Refactor this into a function
|
||||||
{
|
{
|
||||||
auto funcOp = getPimEntryFunc(moduleOp);
|
auto funcOp = *getPimEntryFunc(moduleOp);
|
||||||
|
|
||||||
auto coreOps = llvm::to_vector(funcOp->getOps<pim::PimCoreOp>());
|
SmallVector<Operation*> coreOps;
|
||||||
|
funcOp->walk<WalkOrder::PreOrder>([&](Operation* op) {
|
||||||
|
if (isa<pim::PimCoreOp, pim::PimCoreBatchOp>(op))
|
||||||
|
coreOps.push_back(op);
|
||||||
|
});
|
||||||
MLIRContext* ctx = moduleOp.getContext();
|
MLIRContext* ctx = moduleOp.getContext();
|
||||||
// failableParallelForEach will run the lambda in parallel and stop if any thread fails
|
// failableParallelForEach will run the lambda in parallel and stop if any thread fails
|
||||||
LogicalResult result = mlir::failableParallelForEach(ctx, coreOps, [&](pim::PimCoreOp coreOp) {
|
LogicalResult result = mlir::failableParallelForEach(ctx, coreOps, [&](Operation* coreOp) {
|
||||||
// Again, allocate state LOCALLY per thread/function
|
// Again, allocate state LOCALLY per thread/function
|
||||||
bufferization::OneShotBufferizationOptions options;
|
bufferization::OneShotBufferizationOptions options;
|
||||||
options.allowUnknownOps = true;
|
options.allowUnknownOps = true;
|
||||||
|
if (isa<pim::PimCoreBatchOp>(coreOp))
|
||||||
|
options.opFilter.denyOperation([coreOp](Operation* op) { return op == coreOp; });
|
||||||
bufferization::BufferizationState state;
|
bufferization::BufferizationState state;
|
||||||
if (failed(bufferization::runOneShotBufferize(coreOp, options, state))) {
|
if (failed(bufferization::runOneShotBufferize(coreOp, options, state))) {
|
||||||
coreOp.emitError("Failed to bufferize PIM and Spatial ops");
|
coreOp->emitError("Failed to bufferize PIM and Spatial ops");
|
||||||
return failure();
|
return failure();
|
||||||
}
|
}
|
||||||
return success();
|
return success();
|
||||||
@@ -89,13 +78,16 @@ void PimBufferizationPass::runOnOperation() {
|
|||||||
}
|
}
|
||||||
|
|
||||||
funcOp->walk([&](bufferization::ToTensorOp toTensorOp) {
|
funcOp->walk([&](bufferization::ToTensorOp toTensorOp) {
|
||||||
if (llvm::isa_and_present<pim::PimCoreOp>(toTensorOp->getParentOp()))
|
if (llvm::isa_and_present<pim::PimCoreOp, pim::PimCoreBatchOp>(toTensorOp->getParentOp()))
|
||||||
toTensorOp->setAttr("restrict", UnitAttr::get(ctx));
|
toTensorOp->setAttr("restrict", UnitAttr::get(ctx));
|
||||||
});
|
});
|
||||||
|
|
||||||
// One-Shot-Bufferization
|
// One-Shot-Bufferization
|
||||||
bufferization::OneShotBufferizationOptions options;
|
bufferization::OneShotBufferizationOptions options;
|
||||||
options.allowUnknownOps = true;
|
options.allowUnknownOps = true;
|
||||||
|
options.opFilter.denyOperation([](Operation* op) {
|
||||||
|
return op->getParentOfType<pim::PimCoreOp>() || op->getParentOfType<pim::PimCoreBatchOp>();
|
||||||
|
});
|
||||||
bufferization::BufferizationState state;
|
bufferization::BufferizationState state;
|
||||||
|
|
||||||
if (failed(bufferization::runOneShotBufferize(moduleOp, options, state))) {
|
if (failed(bufferization::runOneShotBufferize(moduleOp, options, state))) {
|
||||||
|
|||||||
@@ -253,7 +253,7 @@ def SpatChannelReceiveManyBatchOp : SpatOp<"channel_receive_many_batch", []> {
|
|||||||
// Math
|
// Math
|
||||||
//===----------------------------------------------------------------------===//
|
//===----------------------------------------------------------------------===//
|
||||||
|
|
||||||
def SpatWeightedVMMOp : SpatOp<"wvmm", []> {
|
def SpatVMMOp : SpatOp<"wvmm", []> {
|
||||||
let summary = "Vector-matrix multiplication within a weighted compute operation";
|
let summary = "Vector-matrix multiplication within a weighted compute operation";
|
||||||
|
|
||||||
let arguments = (ins
|
let arguments = (ins
|
||||||
@@ -272,7 +272,7 @@ def SpatWeightedVMMOp : SpatOp<"wvmm", []> {
|
|||||||
}];
|
}];
|
||||||
}
|
}
|
||||||
|
|
||||||
def SpatWeightedMVMOp : SpatOp<"Wmvm", []> {
|
def SpatMVMOp : SpatOp<"Wmvm", []> {
|
||||||
let summary = "Matrix-vector multiplication within a weighted compute operation";
|
let summary = "Matrix-vector multiplication within a weighted compute operation";
|
||||||
|
|
||||||
let arguments = (ins
|
let arguments = (ins
|
||||||
|
|||||||
@@ -20,7 +20,7 @@ namespace spatial {
|
|||||||
|
|
||||||
namespace {
|
namespace {
|
||||||
|
|
||||||
inline LogicalResult mvmOpVerifySize2(SpatWeightedMVMOp* emitter,
|
inline LogicalResult mvmOpVerifySize2(SpatMVMOp* emitter,
|
||||||
ArrayRef<int64_t>& matrixShape,
|
ArrayRef<int64_t>& matrixShape,
|
||||||
ArrayRef<int64_t>& vectorShape,
|
ArrayRef<int64_t>& vectorShape,
|
||||||
ArrayRef<int64_t>& outputShape) {
|
ArrayRef<int64_t>& outputShape) {
|
||||||
@@ -45,7 +45,7 @@ inline LogicalResult mvmOpVerifySize2(SpatWeightedMVMOp* emitter,
|
|||||||
return success();
|
return success();
|
||||||
}
|
}
|
||||||
|
|
||||||
inline LogicalResult mvmOpVerifySize4(SpatWeightedMVMOp* emitter,
|
inline LogicalResult mvmOpVerifySize4(SpatMVMOp* emitter,
|
||||||
ArrayRef<int64_t>& matrixShape,
|
ArrayRef<int64_t>& matrixShape,
|
||||||
ArrayRef<int64_t>& vectorShape,
|
ArrayRef<int64_t>& vectorShape,
|
||||||
ArrayRef<int64_t>& outputShape) {
|
ArrayRef<int64_t>& outputShape) {
|
||||||
@@ -177,10 +177,10 @@ static LogicalResult verifyBatchBody(Operation* op, Block& block, TypeRange outp
|
|||||||
}
|
}
|
||||||
|
|
||||||
for (auto& bodyOp : block) {
|
for (auto& bodyOp : block) {
|
||||||
if (auto wvmm = dyn_cast<SpatWeightedVMMOp>(&bodyOp))
|
if (auto wvmm = dyn_cast<SpatVMMOp>(&bodyOp))
|
||||||
if (wvmm.getWeightIndex() < 0 || static_cast<size_t>(wvmm.getWeightIndex()) >= weightsPerLane)
|
if (wvmm.getWeightIndex() < 0 || static_cast<size_t>(wvmm.getWeightIndex()) >= weightsPerLane)
|
||||||
return op->emitError("compute_batch body Wvmm weightIndex is out of range for one lane");
|
return op->emitError("compute_batch body Wvmm weightIndex is out of range for one lane");
|
||||||
if (auto wmvm = dyn_cast<SpatWeightedMVMOp>(&bodyOp))
|
if (auto wmvm = dyn_cast<SpatMVMOp>(&bodyOp))
|
||||||
if (wmvm.getWeightIndex() < 0 || static_cast<size_t>(wmvm.getWeightIndex()) >= weightsPerLane)
|
if (wmvm.getWeightIndex() < 0 || static_cast<size_t>(wmvm.getWeightIndex()) >= weightsPerLane)
|
||||||
return op->emitError("compute_batch body Wmvm weightIndex is out of range for one lane");
|
return op->emitError("compute_batch body Wmvm weightIndex is out of range for one lane");
|
||||||
}
|
}
|
||||||
@@ -189,10 +189,10 @@ static LogicalResult verifyBatchBody(Operation* op, Block& block, TypeRange outp
|
|||||||
|
|
||||||
} // namespace
|
} // namespace
|
||||||
|
|
||||||
LogicalResult SpatWeightedMVMOp::verify() {
|
LogicalResult SpatMVMOp::verify() {
|
||||||
auto matrixShapeOpt = getWeightShapeForWeightedOp(this->getOperation(), this->getWeightIndex());
|
auto matrixShapeOpt = getWeightShapeForWeightedOp(this->getOperation(), this->getWeightIndex());
|
||||||
if (failed(matrixShapeOpt))
|
if (failed(matrixShapeOpt))
|
||||||
return emitError("SpatWeightedMVMOp was not within a SpatCompute or Core op");
|
return emitError("SpatMVMOp was not within a SpatCompute or Core op");
|
||||||
auto matrixShape = *matrixShapeOpt;
|
auto matrixShape = *matrixShapeOpt;
|
||||||
auto vectorShape = getInput().getType().getShape();
|
auto vectorShape = getInput().getType().getShape();
|
||||||
auto outputShape = getOutput().getType().getShape();
|
auto outputShape = getOutput().getType().getShape();
|
||||||
@@ -204,10 +204,10 @@ LogicalResult SpatWeightedMVMOp::verify() {
|
|||||||
return emitError("matrix rank must be 2 or 4");
|
return emitError("matrix rank must be 2 or 4");
|
||||||
}
|
}
|
||||||
|
|
||||||
LogicalResult SpatWeightedVMMOp::verify() {
|
LogicalResult SpatVMMOp::verify() {
|
||||||
auto matrixShapeOpt = getWeightShapeForWeightedOp(this->getOperation(), this->getWeightIndex());
|
auto matrixShapeOpt = getWeightShapeForWeightedOp(this->getOperation(), this->getWeightIndex());
|
||||||
if (failed(matrixShapeOpt))
|
if (failed(matrixShapeOpt))
|
||||||
return emitError("SpatWeightedVMMOp was not within a SpatCompute or Core op");
|
return emitError("SpatVMMOp was not within a SpatCompute or Core op");
|
||||||
auto matrixShape = *matrixShapeOpt;
|
auto matrixShape = *matrixShapeOpt;
|
||||||
auto vectorShape = getInput().getType().getShape();
|
auto vectorShape = getInput().getType().getShape();
|
||||||
auto outputShape = getOutput().getType().getShape();
|
auto outputShape = getOutput().getType().getShape();
|
||||||
|
|||||||
@@ -133,7 +133,7 @@ CrossbarUsage getComputeBodyCrossbarUsage(Region& body) {
|
|||||||
CrossbarUsage crossbarUsage = 0;
|
CrossbarUsage crossbarUsage = 0;
|
||||||
for (auto& block : body)
|
for (auto& block : body)
|
||||||
for (auto& op : block)
|
for (auto& op : block)
|
||||||
if (isa<SpatWeightedVMMOp>(op))
|
if (isa<SpatVMMOp>(op))
|
||||||
crossbarUsage = checkedAdd(crossbarUsage, static_cast<CrossbarUsage>(1));
|
crossbarUsage = checkedAdd(crossbarUsage, static_cast<CrossbarUsage>(1));
|
||||||
return crossbarUsage;
|
return crossbarUsage;
|
||||||
}
|
}
|
||||||
|
|||||||
@@ -105,7 +105,7 @@ inline CrossbarUsage getSpatComputeCrossbarUsage(onnx_mlir::spatial::SpatCompute
|
|||||||
CrossbarUsage crossbarUsage = 0;
|
CrossbarUsage crossbarUsage = 0;
|
||||||
for (auto& region : spatCompute.getBody())
|
for (auto& region : spatCompute.getBody())
|
||||||
for (auto& inst : region)
|
for (auto& inst : region)
|
||||||
if (llvm::isa<onnx_mlir::spatial::SpatWeightedVMMOp>(inst))
|
if (llvm::isa<onnx_mlir::spatial::SpatVMMOp>(inst))
|
||||||
crossbarUsage = checkedAdd(crossbarUsage, static_cast<CrossbarUsage>(1));
|
crossbarUsage = checkedAdd(crossbarUsage, static_cast<CrossbarUsage>(1));
|
||||||
return crossbarUsage;
|
return crossbarUsage;
|
||||||
}
|
}
|
||||||
|
|||||||
@@ -838,9 +838,9 @@ void mergeTriviallyConnectedComputes(func::FuncOp funcOp) {
|
|||||||
|
|
||||||
for (auto& op : child.getBody().front()) {
|
for (auto& op : child.getBody().front()) {
|
||||||
auto newInst = rewriter.clone(op, mapper);
|
auto newInst = rewriter.clone(op, mapper);
|
||||||
if (auto weightedMvmOp = dyn_cast<spatial::SpatWeightedMVMOp>(newInst))
|
if (auto weightedMvmOp = dyn_cast<spatial::SpatMVMOp>(newInst))
|
||||||
remapWeightIndex(weightedMvmOp);
|
remapWeightIndex(weightedMvmOp);
|
||||||
if (auto weightedVmmOp = dyn_cast<spatial::SpatWeightedVMMOp>(newInst))
|
if (auto weightedVmmOp = dyn_cast<spatial::SpatVMMOp>(newInst))
|
||||||
remapWeightIndex(weightedVmmOp);
|
remapWeightIndex(weightedVmmOp);
|
||||||
}
|
}
|
||||||
|
|
||||||
@@ -884,9 +884,9 @@ void emitMotifProfile(func::FuncOp funcOp) {
|
|||||||
ComputeMotifInfo& info = computeInfos[index];
|
ComputeMotifInfo& info = computeInfos[index];
|
||||||
for (Operation& op : compute.getBody().front()) {
|
for (Operation& op : compute.getBody().front()) {
|
||||||
info.instructionCount++;
|
info.instructionCount++;
|
||||||
if (isa<spatial::SpatWeightedMVMOp>(&op))
|
if (isa<spatial::SpatMVMOp>(&op))
|
||||||
info.weightedMvmCount++;
|
info.weightedMvmCount++;
|
||||||
if (isa<spatial::SpatWeightedVMMOp>(&op))
|
if (isa<spatial::SpatVMMOp>(&op))
|
||||||
info.weightedVmmCount++;
|
info.weightedVmmCount++;
|
||||||
}
|
}
|
||||||
if (info.weightedVmmCount > 0) {
|
if (info.weightedVmmCount > 0) {
|
||||||
@@ -1617,13 +1617,13 @@ public:
|
|||||||
}
|
}
|
||||||
|
|
||||||
Operation* clonedOp = cpuRewriter.clone(op, mapper);
|
Operation* clonedOp = cpuRewriter.clone(op, mapper);
|
||||||
if (auto oldWeightedMvmOp = dyn_cast<spatial::SpatWeightedMVMOp>(&op)) {
|
if (auto oldWeightedMvmOp = dyn_cast<spatial::SpatMVMOp>(&op)) {
|
||||||
auto newWeightedMvmOp = cast<spatial::SpatWeightedMVMOp>(clonedOp);
|
auto newWeightedMvmOp = cast<spatial::SpatMVMOp>(clonedOp);
|
||||||
Value weight = taskWeights[oldWeightedMvmOp.getWeightIndex()];
|
Value weight = taskWeights[oldWeightedMvmOp.getWeightIndex()];
|
||||||
newWeightedMvmOp.setWeightIndex(program.weightToIndex.at(weight));
|
newWeightedMvmOp.setWeightIndex(program.weightToIndex.at(weight));
|
||||||
}
|
}
|
||||||
if (auto oldWeightedVmmOp = dyn_cast<spatial::SpatWeightedVMMOp>(&op)) {
|
if (auto oldWeightedVmmOp = dyn_cast<spatial::SpatVMMOp>(&op)) {
|
||||||
auto newWeightedVmmOp = cast<spatial::SpatWeightedVMMOp>(clonedOp);
|
auto newWeightedVmmOp = cast<spatial::SpatVMMOp>(clonedOp);
|
||||||
Value weight = taskWeights[oldWeightedVmmOp.getWeightIndex()];
|
Value weight = taskWeights[oldWeightedVmmOp.getWeightIndex()];
|
||||||
newWeightedVmmOp.setWeightIndex(program.weightToIndex.at(weight));
|
newWeightedVmmOp.setWeightIndex(program.weightToIndex.at(weight));
|
||||||
}
|
}
|
||||||
@@ -1643,22 +1643,22 @@ public:
|
|||||||
}
|
}
|
||||||
|
|
||||||
Operation* clonedOp = cpuRewriter.clone(op, mapper);
|
Operation* clonedOp = cpuRewriter.clone(op, mapper);
|
||||||
if (auto oldWeightedMvmOp = dyn_cast<spatial::SpatWeightedMVMOp>(&op)) {
|
if (auto oldWeightedMvmOp = dyn_cast<spatial::SpatMVMOp>(&op)) {
|
||||||
if (oldWeightedMvmOp.getWeightIndex() != 0) {
|
if (oldWeightedMvmOp.getWeightIndex() != 0) {
|
||||||
task.sourceOp->emitOpError("batched per-cpu merge materialization expects lane-local weight index 0");
|
task.sourceOp->emitOpError("batched per-cpu merge materialization expects lane-local weight index 0");
|
||||||
signalPassFailure();
|
signalPassFailure();
|
||||||
return;
|
return;
|
||||||
}
|
}
|
||||||
auto newWeightedMvmOp = cast<spatial::SpatWeightedMVMOp>(clonedOp);
|
auto newWeightedMvmOp = cast<spatial::SpatMVMOp>(clonedOp);
|
||||||
newWeightedMvmOp.setWeightIndex(program.weightToIndex.at(taskWeights[laneOffset]));
|
newWeightedMvmOp.setWeightIndex(program.weightToIndex.at(taskWeights[laneOffset]));
|
||||||
}
|
}
|
||||||
if (auto oldWeightedVmmOp = dyn_cast<spatial::SpatWeightedVMMOp>(&op)) {
|
if (auto oldWeightedVmmOp = dyn_cast<spatial::SpatVMMOp>(&op)) {
|
||||||
if (oldWeightedVmmOp.getWeightIndex() != 0) {
|
if (oldWeightedVmmOp.getWeightIndex() != 0) {
|
||||||
task.sourceOp->emitOpError("batched per-cpu merge materialization expects lane-local weight index 0");
|
task.sourceOp->emitOpError("batched per-cpu merge materialization expects lane-local weight index 0");
|
||||||
signalPassFailure();
|
signalPassFailure();
|
||||||
return;
|
return;
|
||||||
}
|
}
|
||||||
auto newWeightedVmmOp = cast<spatial::SpatWeightedVMMOp>(clonedOp);
|
auto newWeightedVmmOp = cast<spatial::SpatVMMOp>(clonedOp);
|
||||||
newWeightedVmmOp.setWeightIndex(program.weightToIndex.at(taskWeights[laneOffset]));
|
newWeightedVmmOp.setWeightIndex(program.weightToIndex.at(taskWeights[laneOffset]));
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
|
|||||||
@@ -55,7 +55,7 @@ static bool areEquivalentRegularChunks(const RegularChunk& lhs, const RegularChu
|
|||||||
[](auto pair) { return areEquivalentRegularSteps(std::get<0>(pair), std::get<1>(pair)); });
|
[](auto pair) { return areEquivalentRegularSteps(std::get<0>(pair), std::get<1>(pair)); });
|
||||||
}
|
}
|
||||||
|
|
||||||
static FailureOr<RegularChunk> analyzeRegularChunk(spatial::SpatWeightedVMMOp startOp) {
|
static FailureOr<RegularChunk> analyzeRegularChunk(spatial::SpatVMMOp startOp) {
|
||||||
RegularChunk chunk;
|
RegularChunk chunk;
|
||||||
chunk.startOp = startOp.getOperation();
|
chunk.startOp = startOp.getOperation();
|
||||||
chunk.input = startOp.getInput();
|
chunk.input = startOp.getInput();
|
||||||
@@ -376,7 +376,7 @@ void compactRegularOpRuns(func::FuncOp funcOp) {
|
|||||||
|
|
||||||
auto compactInBlock = [&](Block& block) {
|
auto compactInBlock = [&](Block& block) {
|
||||||
for (auto it = block.begin(); it != block.end();) {
|
for (auto it = block.begin(); it != block.end();) {
|
||||||
auto startOp = dyn_cast<spatial::SpatWeightedVMMOp>(&*it);
|
auto startOp = dyn_cast<spatial::SpatVMMOp>(&*it);
|
||||||
if (!startOp) {
|
if (!startOp) {
|
||||||
++it;
|
++it;
|
||||||
continue;
|
continue;
|
||||||
@@ -391,7 +391,7 @@ void compactRegularOpRuns(func::FuncOp funcOp) {
|
|||||||
SmallVector<RegularChunk> run {*anchorChunk};
|
SmallVector<RegularChunk> run {*anchorChunk};
|
||||||
auto runIt = std::next(it, static_cast<std::ptrdiff_t>(anchorChunk->ops.size()));
|
auto runIt = std::next(it, static_cast<std::ptrdiff_t>(anchorChunk->ops.size()));
|
||||||
while (runIt != block.end()) {
|
while (runIt != block.end()) {
|
||||||
auto candidateStart = dyn_cast<spatial::SpatWeightedVMMOp>(&*runIt);
|
auto candidateStart = dyn_cast<spatial::SpatVMMOp>(&*runIt);
|
||||||
if (!candidateStart)
|
if (!candidateStart)
|
||||||
break;
|
break;
|
||||||
|
|
||||||
@@ -425,7 +425,7 @@ void compactRowWiseWvmmRuns(func::FuncOp funcOp) {
|
|||||||
for (auto compute : funcOp.getOps<spatial::SpatCompute>()) {
|
for (auto compute : funcOp.getOps<spatial::SpatCompute>()) {
|
||||||
Block& block = compute.getBody().front();
|
Block& block = compute.getBody().front();
|
||||||
for (auto it = block.begin(); it != block.end();) {
|
for (auto it = block.begin(); it != block.end();) {
|
||||||
auto wvmmOp = dyn_cast<spatial::SpatWeightedVMMOp>(&*it);
|
auto wvmmOp = dyn_cast<spatial::SpatVMMOp>(&*it);
|
||||||
if (!wvmmOp) {
|
if (!wvmmOp) {
|
||||||
++it;
|
++it;
|
||||||
continue;
|
continue;
|
||||||
@@ -440,11 +440,11 @@ void compactRowWiseWvmmRuns(func::FuncOp funcOp) {
|
|||||||
continue;
|
continue;
|
||||||
}
|
}
|
||||||
|
|
||||||
SmallVector<spatial::SpatWeightedVMMOp> run;
|
SmallVector<spatial::SpatVMMOp> run;
|
||||||
auto runIt = it;
|
auto runIt = it;
|
||||||
int64_t expectedRow = static_cast<int64_t>(rowResult.getResultNumber());
|
int64_t expectedRow = static_cast<int64_t>(rowResult.getResultNumber());
|
||||||
while (runIt != block.end()) {
|
while (runIt != block.end()) {
|
||||||
auto current = dyn_cast<spatial::SpatWeightedVMMOp>(&*runIt);
|
auto current = dyn_cast<spatial::SpatVMMOp>(&*runIt);
|
||||||
if (!current || current.getWeightIndex() != wvmmOp.getWeightIndex()
|
if (!current || current.getWeightIndex() != wvmmOp.getWeightIndex()
|
||||||
|| current.getInput().getDefiningOp<spatial::SpatExtractRowsOp>() != extractRowsOp
|
|| current.getInput().getDefiningOp<spatial::SpatExtractRowsOp>() != extractRowsOp
|
||||||
|| current.getInput().getType() != wvmmOp.getInput().getType()
|
|| current.getInput().getType() != wvmmOp.getInput().getType()
|
||||||
@@ -545,7 +545,7 @@ void compactRowWiseWvmmRuns(func::FuncOp funcOp) {
|
|||||||
extractOffsets,
|
extractOffsets,
|
||||||
extractSizes,
|
extractSizes,
|
||||||
extractStrides);
|
extractStrides);
|
||||||
auto loopWvmm = spatial::SpatWeightedVMMOp::create(
|
auto loopWvmm = spatial::SpatVMMOp::create(
|
||||||
rewriter, run.front().getLoc(), outputType, wvmmOp.getWeightIndex(), extractedRow.getResult());
|
rewriter, run.front().getLoc(), outputType, wvmmOp.getWeightIndex(), extractedRow.getResult());
|
||||||
|
|
||||||
SmallVector<OpFoldResult> insertOffsets = {iv, rewriter.getIndexAttr(0)};
|
SmallVector<OpFoldResult> insertOffsets = {iv, rewriter.getIndexAttr(0)};
|
||||||
|
|||||||
@@ -18,6 +18,7 @@ namespace {
|
|||||||
|
|
||||||
static bool isAddressOnlyHostOp(Operation* op) {
|
static bool isAddressOnlyHostOp(Operation* op) {
|
||||||
return isa<arith::ConstantOp,
|
return isa<arith::ConstantOp,
|
||||||
|
pim::PimEmptyManyOp,
|
||||||
memref::AllocOp,
|
memref::AllocOp,
|
||||||
memref::GetGlobalOp,
|
memref::GetGlobalOp,
|
||||||
memref::SubViewOp,
|
memref::SubViewOp,
|
||||||
@@ -36,7 +37,7 @@ static bool isBaseAddressableValue(Value value) {
|
|||||||
Operation* defOp = value.getDefiningOp();
|
Operation* defOp = value.getDefiningOp();
|
||||||
if (!defOp)
|
if (!defOp)
|
||||||
return false;
|
return false;
|
||||||
if (isa<memref::AllocOp, memref::GetGlobalOp>(defOp))
|
if (isa<pim::PimEmptyManyOp, memref::AllocOp, memref::GetGlobalOp>(defOp))
|
||||||
return true;
|
return true;
|
||||||
if (auto subview = dyn_cast<memref::SubViewOp>(defOp)) { value = subview.getSource(); continue; }
|
if (auto subview = dyn_cast<memref::SubViewOp>(defOp)) { value = subview.getSource(); continue; }
|
||||||
if (auto cast = dyn_cast<memref::CastOp>(defOp)) { value = cast.getSource(); continue; }
|
if (auto cast = dyn_cast<memref::CastOp>(defOp)) { value = cast.getSource(); continue; }
|
||||||
@@ -51,7 +52,7 @@ static bool isCodegenAddressableValue(Value value) {
|
|||||||
if (failed(resolvedAddress))
|
if (failed(resolvedAddress))
|
||||||
return false;
|
return false;
|
||||||
return isa<BlockArgument>(resolvedAddress->base)
|
return isa<BlockArgument>(resolvedAddress->base)
|
||||||
|| isa<memref::AllocOp, memref::GetGlobalOp>(resolvedAddress->base.getDefiningOp());
|
|| isa<pim::PimEmptyManyOp, memref::AllocOp, memref::GetGlobalOp>(resolvedAddress->base.getDefiningOp());
|
||||||
}
|
}
|
||||||
|
|
||||||
static bool isExplicitHostOperand(Operation* op, unsigned operandIndex) {
|
static bool isExplicitHostOperand(Operation* op, unsigned operandIndex) {
|
||||||
@@ -184,7 +185,7 @@ private:
|
|||||||
continue;
|
continue;
|
||||||
}
|
}
|
||||||
|
|
||||||
if (!isa<memref::AllocOp>(resolvedAddress->base.getDefiningOp())) {
|
if (!isa<pim::PimEmptyManyOp, memref::AllocOp>(resolvedAddress->base.getDefiningOp())) {
|
||||||
op.emitOpError() << "operand #" << operandIndex
|
op.emitOpError() << "operand #" << operandIndex
|
||||||
<< " must be backed by device-local memory; materialize host values with pim.memcp_hd";
|
<< " must be backed by device-local memory; materialize host values with pim.memcp_hd";
|
||||||
hasFailure = true;
|
hasFailure = true;
|
||||||
|
|||||||
Reference in New Issue
Block a user