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Lower convolution to KRNL dialect. (#65) 

* Ensure data shape is at least 4.

* First version of convolution.

* Simplify code for KRNL lowering.

* Add test without padding or strides.

* Refactor code for lowering frontend operations to KRNL dialect.

* Add test for conv with no bias and no padding.

* Add test with group greater than one.

* Address comment.
This commit is contained in:
Gheorghe-Teodor Bercea 2020-02-07 16:51:32 -05:00 committed by GitHub
parent 0564c0eaef
commit 0272451521
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4 changed files with 457 additions and 174 deletions
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4
src/dialect/onnx/onnx_ops.cpp
View File

@ -628,6 +628,10 @@ void ONNXConvNoBiasOp::inferShapes() {
auto dataShape = dataTy.getShape();
auto weightShape = weightTy.getShape();
// Lowest ranked input supported is of shape (N x C x H x W).
if (dataShape.size() < 4)
emitError("Data input shape must be at least (NxCxHxW).");
// Check that shape of weight and data have same length.
if (dataShape.size() != weightShape.size())
emitError("Weight size not compatible with data size.");

524
src/pass/lower_frontend_to_krnl.cpp
View File

@ -130,6 +130,78 @@ static bool checkInsertDealloc(Operation *currentOp) {
return insertDealloc;
}
// Add bounds associated with the op operand to the KRNL iteration pack.
// Dynamic dimenions are supported.
static void addDimensionToPack(ConversionPatternRewriter &rewriter,
Location loc, KrnlIterateOperandPack &pack, Value operand, int index) {
auto shape = operand.getType().cast<MemRefType>().getShape();
if (shape[index] < 0) {
pack.pushConstantBound(0);
pack.pushOperandBound(
rewriter.create<DimOp>(loc, operand, index).getResult());
} else {
pack.pushConstantBound(0);
pack.pushConstantBound(shape[index]);
}
}
// Function that defines the KRNL dialect loops and their respective
// optimized version.
static KrnlOptimizeLoopsOp emitOptimizedLoops(
ConversionPatternRewriter &rewriter, Location loc,
std::vector<Value> &loops, std::vector<Value> &optimizedLoops,
int64_t numLoops) {
// Define loops.
auto loopsOp = rewriter.create<KrnlDefineLoopsOp>(loc, numLoops);
loops.reserve(numLoops);
for (auto result : loopsOp.getResults())
loops.push_back(result);
// Define optimized version of the loops.
auto optimizedLoopsOp = rewriter.create<KrnlOptimizeLoopsOp>(loc, numLoops);
optimizedLoops.reserve(numLoops);
for (auto result : optimizedLoopsOp.getResults())
optimizedLoops.push_back(result);
return optimizedLoopsOp;
}
// Function that emits the loops and their optimized version.
// The function returns a reference to the inner optimization block.
static Block* defineLoops(ConversionPatternRewriter &rewriter,
Location loc, std::vector<Value> &loops,
std::vector<Value> &optimizedLoops, int64_t numLoops) {
KrnlOptimizeLoopsOp optimizedLoopsOp = emitOptimizedLoops(
rewriter, loc, loops, optimizedLoops, numLoops);
return &optimizedLoopsOp.region().front();
}
// Function which emits a basic set of loops and optimized loops
// for a given operation argument. A reference to the loop optimization
// block is returned in the last argument of the function.
static void emitKrnlLoopsAndIterationForOperand(
ConversionPatternRewriter &rewriter, Location loc,
Value operand, std::vector<Value> &originalLoops,
KrnlOptimizeLoopsOp &optimizedLoopsOp, KrnlIterateOp &iterateOp) {
// Operand shape.
auto shape = operand.getType().cast<MemRefType>().getShape();
// Number of loops.
int64_t rank = shape.size();
// Define loops and optimized loops.
std::vector<Value> optimizedLoops;
optimizedLoopsOp = emitOptimizedLoops(rewriter, loc, originalLoops,
optimizedLoops, rank);
KrnlIterateOperandPack pack(rewriter, originalLoops, optimizedLoops);
// Iterate over the loop nest.
for (int i = 0; i < rank; ++i)
addDimensionToPack(rewriter, loc, pack, operand, i);
iterateOp = rewriter.create<KrnlIterateOp>(loc, pack);
}
unsigned getMemRefEltSizeInBytes(MemRefType memRefType) {
auto elementType = memRefType.getElementType();
@ -749,55 +821,21 @@ struct ONNXElementwiseUnaryOpLowering : public ConversionPattern {
alloc = insertAllocAndDealloc(memRefType, loc, rewriter, insertDealloc,
{operands[0]});
// Number of loops
auto memRefShape = memRefType.getShape();
int64_t rank = memRefShape.size();
// Define loops.
auto loopsOp = rewriter.create<KrnlDefineLoopsOp>(loc, rank);
std::vector<Value> originalLoops;
originalLoops.reserve(rank);
for (auto result : loopsOp.getResults()) {
originalLoops.push_back(result);
}
// Define loop optimization.
auto optimizedLoopsOp = rewriter.create<KrnlOptimizeLoopsOp>(loc, rank);
std::vector<Value> optimizedLoops;
optimizedLoops.reserve(rank);
for (auto result : optimizedLoopsOp.getResults()) {
optimizedLoops.push_back(result);
}
KrnlOptimizeLoopsOp optimizedLoopsOp;
KrnlIterateOp iterateOp;
emitKrnlLoopsAndIterationForOperand(
rewriter, loc, operands[0], originalLoops,
optimizedLoopsOp, iterateOp);
Block &optimizationBlock = optimizedLoopsOp.region().front();
KrnlIterateOperandPack pack(rewriter, originalLoops, optimizedLoops);
// Iterate over the loop nest.
// TODO (Tian): move this logic inside KrnlIterateOp. Pass MemRefShape
// to KrnlIterateOp instead.
for (int i = 0; i < rank; ++i) {
if (memRefShape[i] < 0) {
pack.pushConstantBound(0);
pack.pushOperandBound(
rewriter.create<DimOp>(loc, operands[0], i).getResult());
} else {
pack.pushConstantBound(0);
pack.pushConstantBound(memRefShape[i]);
}
}
auto iterateOp = rewriter.create<KrnlIterateOp>(loc, pack);
Block &iterationBlock = iterateOp.bodyRegion().front();
// Now perform the insertions into the body of the
// just generated instructions:
// 1. Insert any optimizations in the KrnlOptimizeLoopsOp body.
rewriter.setInsertionPointToEnd(&optimizationBlock);
// Return from KrnlOptimizeLoopsOp body.
// When no optimizations are present we just return the loops
// unchaged.
rewriter.create<KrnlReturnLoopsOp>(loc, originalLoops);
rewriter.setInsertionPoint(optimizedLoopsOp);
// 2. Insert instructions inside the KernelIterateOp body.
rewriter.setInsertionPointToStart(&iterationBlock);
@ -851,59 +889,25 @@ struct ONNXElementwiseVariadicOpLowering : public ConversionPattern {
alloc = insertAllocAndDealloc(memRefType, loc, rewriter, insertDealloc,
operands);
// Number of loops
auto memRefShape = memRefType.getShape();
int64_t rank = memRefShape.size();
// Define loops.
auto loopsOp = rewriter.create<KrnlDefineLoopsOp>(loc, rank);
std::vector<Value> originalLoops;
originalLoops.reserve(rank);
for (auto result : loopsOp.getResults()) {
originalLoops.push_back(result);
}
// Define loop optimization.
auto optimizedLoopsOp = rewriter.create<KrnlOptimizeLoopsOp>(loc, rank);
std::vector<Value> optimizedLoops;
optimizedLoops.reserve(rank);
for (auto result : optimizedLoopsOp.getResults()) {
optimizedLoops.push_back(result);
}
Block &optimizationBlock = optimizedLoopsOp.region().front();
KrnlIterateOperandPack pack(rewriter, originalLoops, optimizedLoops);
// Iterate over the loop nest.
// TODO (Tian): move this logic inside KrnlIterateOp. Pass MemRefShape
// to KrnlIterateOp instead.
for (int i = 0; i < rank; ++i) {
if (memRefShape[i] < 0) {
pack.pushConstantBound(0);
pack.pushOperandBound(
rewriter.create<DimOp>(loc, alloc, i).getResult());
} else {
pack.pushConstantBound(0);
pack.pushConstantBound(memRefShape[i]);
}
}
// Get run-time dimension information for unknown dimensions used for
// broadcasting.
std::map<int, std::map<int, Value>> broadcastedDimInfo =
getBroadcastedDimInfo(loc, rewriter, memRefType, operands);
auto iterateOp = rewriter.create<KrnlIterateOp>(loc, pack);
std::vector<Value> originalLoops;
KrnlOptimizeLoopsOp optimizedLoopsOp;
KrnlIterateOp iterateOp;
emitKrnlLoopsAndIterationForOperand(
rewriter, loc, alloc, originalLoops,
optimizedLoopsOp, iterateOp);
Block &optimizationBlock = optimizedLoopsOp.region().front();
Block &iterationBlock = iterateOp.bodyRegion().front();
// Now perform the insertions into the body of the
// just generated instructions:
// 1. Insert any optimizations in the KrnlOptimizeLoopsOp body.
rewriter.setInsertionPointToEnd(&optimizationBlock);
// Return from KrnlOptimizeLoopsOp body.
// When no optimizations are present we just return the loops unchaged.
rewriter.create<KrnlReturnLoopsOp>(loc, originalLoops);
rewriter.setInsertionPoint(optimizedLoopsOp);
// 2. Insert instructions inside the KernelIterateOp body.
rewriter.setInsertionPointToStart(&iterationBlock);
@ -978,21 +982,10 @@ struct ONNXSoftmaxOpLowering : public ConversionPattern {
FloatAttr::get(elementType, -std::numeric_limits<float>::infinity()));
// Define loops.
auto loopsOp = rewriter.create<KrnlDefineLoopsOp>(loc, rank);
std::vector<Value> originalLoops;
originalLoops.reserve(rank);
for (auto result : loopsOp.getResults()) {
originalLoops.push_back(result);
}
// Define loop optimization.
auto optimizedLoopsOp = rewriter.create<KrnlOptimizeLoopsOp>(loc, rank);
std::vector<Value> optimizedLoops;
optimizedLoops.reserve(rank);
for (auto result : optimizedLoopsOp.getResults()) {
optimizedLoops.push_back(result);
}
Block &optimizationBlock = optimizedLoopsOp.region().front();
Block *optimizationBlock = defineLoops(rewriter, loc, originalLoops,
optimizedLoops, rank);
// Coerce the input into a 2-D tensor. `axis` will be the coercing point.
// This coercing follows the softmax definition in ONNX:
@ -1009,16 +1002,9 @@ struct ONNXSoftmaxOpLowering : public ConversionPattern {
optimizedOuterLoops.push_back(optimizedLoops[i]);
}
KrnlIterateOperandPack outerPack(rewriter, outerLoops, optimizedOuterLoops);
for (int i = 0; i < axis; ++i) {
if (memRefShape[i] < 0) {
outerPack.pushConstantBound(0);
outerPack.pushOperandBound(
rewriter.create<DimOp>(loc, operands[0], i).getResult());
} else {
outerPack.pushConstantBound(0);
outerPack.pushConstantBound(memRefShape[i]);
}
}
for (int i = 0; i < axis; ++i)
addDimensionToPack(rewriter, loc, outerPack, operands[0], i);
// Define an inner loop with respect to axis.
std::vector<Value> innerLoops, optimizedInnerLoops;
innerLoops.reserve(rank - axis);
@ -1028,16 +1014,8 @@ struct ONNXSoftmaxOpLowering : public ConversionPattern {
optimizedInnerLoops.push_back(optimizedLoops[i]);
}
KrnlIterateOperandPack innerPack(rewriter, innerLoops, optimizedInnerLoops);
for (int i = axis; i < rank; ++i) {
if (memRefShape[i] < 0) {
innerPack.pushConstantBound(0);
innerPack.pushOperandBound(
rewriter.create<DimOp>(loc, operands[0], i).getResult());
} else {
innerPack.pushConstantBound(0);
innerPack.pushConstantBound(memRefShape[i]);
}
}
for (int i = axis; i < rank; ++i)
addDimensionToPack(rewriter, loc, innerPack, operands[0], i);
KrnlIterateOp outerIterateOp, maxIterateOp, sumIterateOp, softmaxIterateOp;
SmallVector<Value, 4> outerLoopIVs;
@ -1045,9 +1023,8 @@ struct ONNXSoftmaxOpLowering : public ConversionPattern {
outerIterateOp = rewriter.create<KrnlIterateOp>(loc, outerPack);
// No optimization
rewriter.setInsertionPointToEnd(&optimizationBlock);
rewriter.setInsertionPointToEnd(optimizationBlock);
rewriter.create<KrnlReturnLoopsOp>(loc, originalLoops);
rewriter.setInsertionPoint(optimizedLoopsOp);
// Insert instructions inside the outer loop.
Block &outerIterationBlock = outerIterateOp.bodyRegion().front();
@ -1078,9 +1055,8 @@ struct ONNXSoftmaxOpLowering : public ConversionPattern {
softmaxIterateOp = rewriter.create<KrnlIterateOp>(loc, innerPack);
// No optimization
rewriter.setInsertionPointToEnd(&optimizationBlock);
rewriter.setInsertionPointToEnd(optimizationBlock);
rewriter.create<KrnlReturnLoopsOp>(loc, originalLoops);
rewriter.setInsertionPoint(optimizedLoopsOp);
}
// Insert instructions inside the max loop.
@ -1291,20 +1267,10 @@ struct ONNXGemmOpLowering : public ConversionPattern {
int64_t numLoops = 3;
// Define loops.
auto loopsOp = rewriter.create<KrnlDefineLoopsOp>(loc, numLoops);
std::vector<Value> originalLoops;
originalLoops.reserve(numLoops);
for (auto result : loopsOp.getResults()) {
originalLoops.push_back(result);
}
auto optimizedLoopsOp = rewriter.create<KrnlOptimizeLoopsOp>(loc, numLoops);
std::vector<Value> optimizedLoops;
optimizedLoops.reserve(numLoops);
for (auto result : optimizedLoopsOp.getResults()) {
optimizedLoops.push_back(result);
}
Block &optimizationBlock = optimizedLoopsOp.region().front();
Block *optimizationBlock = defineLoops(rewriter, loc, originalLoops,
optimizedLoops, numLoops);
// We have two Krnl loops:
// - Outer loop iterates over the output matrix dimensions, and
@ -1321,16 +1287,9 @@ struct ONNXGemmOpLowering : public ConversionPattern {
KrnlIterateOperandPack outerPack(rewriter, outerLoops,
optimizedOuterLoops);
// Induction variables for the outer loops
for (int i = 0; i < 2; ++i) {
if (memRefShape[i] < 0) {
outerPack.pushConstantBound(0);
outerPack.pushOperandBound(
rewriter.create<DimOp>(loc, alloc, i).getResult());
} else {
outerPack.pushConstantBound(0);
outerPack.pushConstantBound(memRefShape[i]);
}
}
for (int i = 0; i < 2; ++i)
addDimensionToPack(rewriter, loc, outerPack, alloc, i);
// Reduction loop
std::vector<Value> reductionLoops, optimizedReductionLoops;
reductionLoops.reserve(1);
@ -1378,9 +1337,8 @@ struct ONNXGemmOpLowering : public ConversionPattern {
// just generated instructions:
// No optimization
rewriter.setInsertionPointToEnd(&optimizationBlock);
rewriter.setInsertionPointToEnd(optimizationBlock);
rewriter.create<KrnlReturnLoopsOp>(loc, originalLoops);
rewriter.setInsertionPoint(optimizedLoopsOp);
// Insert instructions inside the outer loop.
Block &outerIterationBlock = outerIterateOp.bodyRegion().front();
@ -1544,36 +1502,15 @@ struct ONNXTransposeOpLowering : public ConversionPattern {
int64_t rank = memRefShape.size();
// Define loops.
auto loopsOp = rewriter.create<KrnlDefineLoopsOp>(loc, rank);
std::vector<Value> originalLoops;
originalLoops.reserve(rank);
for (auto result : loopsOp.getResults()) {
originalLoops.push_back(result);
}
// Define loop optimization.
auto optimizedLoopsOp = rewriter.create<KrnlOptimizeLoopsOp>(loc, rank);
std::vector<Value> optimizedLoops;
optimizedLoops.reserve(rank);
Block *optimizationBlock = defineLoops(rewriter, loc, originalLoops,
optimizedLoops, rank);
for (auto result : optimizedLoopsOp.getResults()) {
optimizedLoops.push_back(result);
}
Block &optimizationBlock = optimizedLoopsOp.region().front();
KrnlIterateOperandPack pack(rewriter, originalLoops, optimizedLoops);
// Iterate over the loop nest using the input shape.
auto inputShape = operands[0].getType().cast<MemRefType>().getShape();
for (int i = 0; i < rank; ++i) {
if (inputShape[i] < 0) {
pack.pushConstantBound(0);
pack.pushOperandBound(
rewriter.create<DimOp>(loc, operands[0], i).getResult());
} else {
pack.pushConstantBound(0);
pack.pushConstantBound(inputShape[i]);
}
}
for (int i = 0; i < rank; ++i)
addDimensionToPack(rewriter, loc, pack, operands[0], i);
auto iterateOp = rewriter.create<KrnlIterateOp>(loc, pack);
Block &iterationBlock = iterateOp.bodyRegion().front();
@ -1582,12 +1519,11 @@ struct ONNXTransposeOpLowering : public ConversionPattern {
// just generated instructions:
// 1. Insert any optimizations in the KrnlOptimizeLoopsOp body.
rewriter.setInsertionPointToEnd(&optimizationBlock);
rewriter.setInsertionPointToEnd(optimizationBlock);
// Return from KrnlOptimizeLoopsOp body.
// When no optimizations are present we just return the loops
// unchaged.
rewriter.create<KrnlReturnLoopsOp>(loc, originalLoops);
rewriter.setInsertionPoint(optimizedLoopsOp);
// 2. Insert instructions inside the KernelIterateOp body.
rewriter.setInsertionPointToStart(&iterationBlock);
@ -1638,6 +1574,255 @@ struct ONNXIdentityOpLowering : public ConversionPattern {
}
};
struct ONNXConvNoBiasOpLowering : public ConversionPattern {
ONNXConvNoBiasOpLowering(MLIRContext *ctx)
: ConversionPattern(mlir::ONNXConvNoBiasOp::getOperationName(), 1, ctx) {}
PatternMatchResult
matchAndRewrite(Operation *op, ArrayRef<Value> operands,
ConversionPatternRewriter &rewriter) const final {
auto tensorType = (*op->result_type_begin()).cast<TensorType>();
auto loc = op->getLoc();
// Insert an allocation and deallocation for the result of this operation.
auto memRefType = convertTensorToMemRef(tensorType);
Value alloc;
bool insertDealloc = checkInsertDealloc(op);
ONNXConvNoBiasOp convOp = llvm::dyn_cast<ONNXConvNoBiasOp>(op);
if (hasAllConstantDimensions(memRefType))
alloc = insertAllocAndDealloc(memRefType, loc, rewriter, insertDealloc);
else
alloc = insertAllocAndDealloc(memRefType, loc, rewriter, insertDealloc,
{operands[0]});
auto resultShape = memRefType.getShape();
auto inputShape = operands[0].getType().cast<MemRefType>().getShape();
auto kernelShape = operands[1].getType().cast<MemRefType>().getShape();
// R = ConvNoBias(D, K)
//
// The input/output shapes will look like this:
//
// D (NxCxHxW) x K (MxC/groupxKHxKW) -> R (NxMxRHxRW)
//
// M is a multiple of the number of groups:
// M = group * kernelsPerGroup
//
// The loop nest will look as follows:
//
// kernelsPerGroup = M / group;
// for n = 0 .. N:
// for g = 0 .. group:
// for m = 0 .. kernelsPerGroup:
// kernel = g * kernelsPerGroup + m;
// for r1 = 0 .. RH:
// for r2 = 0 .. RW:
// R[n][kernel][r1][r2] = 0;
// for c = 0 .. C/group:
// for k1 = 0 .. KH:
// for k2 = 0 .. KW:
// R[n][kernel][r1][r2] =
// D[n][g * (C / group) + c][r1 + k1][r2 + k2] *
// K[kernel][c][k1][k2];
//
// TODO: handle padding.
//
// In the general case:
//
// D (NxCxD1xD2x...xDdim) x K (MxC/groupxK1xK2x...xKdim)
// -> R (NxMxR1xR2x...xRdim)
//
// The above loop nest can be adapted by increasing the number
// of r- and k-index loop i.e. r1 r2 and k1 k2 loops.
// Set up outermost loops: n g m r1 r2 ... rdim
// Skip g if group is 1.
// Before we start the iteration we need to compute the number of
// unsplit kernels and fetch the number of groups from the attribute
// list. Group is always a compilation constant.
int64_t group = convOp.group().getSExtValue();
// Compute the number of unsplit kernels. The number of kernels
// must be a multiple of the number of groups.
int64_t kernelsPerGroup = floor(kernelShape[0] / group);
auto kernelsPerGroupValue =
rewriter.create<ConstantIndexOp>(loc, kernelsPerGroup);
auto zero = rewriter.create<ConstantOp>(
loc, FloatAttr::get(memRefType.getElementType(), 0));
Value subchannels;
if (kernelShape[1] < 0) {
subchannels =
rewriter.create<DimOp>(loc, operands[1], 1).getResult();
} else {
subchannels = rewriter.create<ConstantIndexOp>(
loc, kernelShape[1]);
}
// 1. Define outer loops and emit empty optimization block:
int64_t nOuterLoops = (group > 1) ? 3 : 2;
std::vector<Value> outerLoops;
std::vector<Value> optimizedOuterLoops;
Block *optimizationBlock = defineLoops(rewriter, loc, outerLoops,
optimizedOuterLoops, nOuterLoops);
// Prepare iteration arguments over outer loop nest.
KrnlIterateOperandPack pack(
rewriter, outerLoops, optimizedOuterLoops);
// for n = 0 .. N:
pack.pushConstantBound(0);
if (inputShape[0] < 0)
pack.pushOperandBound(
rewriter.create<DimOp>(loc, operands[0], 0).getResult());
else
pack.pushConstantBound(inputShape[0]);
// for g = 0 .. N:
if (group > 1) {
pack.pushConstantBound(0);
pack.pushConstantBound(group);
}
// for m = 0 .. kernelsPerGroup:
pack.pushConstantBound(0);
pack.pushConstantBound(kernelsPerGroup);
// Outer loop iteration.
auto iterateOp = rewriter.create<KrnlIterateOp>(loc, pack);
Block &outerIterationBlock = iterateOp.bodyRegion().front();
// Emit optimizations for outer loops:
rewriter.setInsertionPointToEnd(optimizationBlock);
rewriter.create<KrnlReturnLoopsOp>(loc, outerLoops);
rewriter.setInsertionPointToStart(&outerIterationBlock);
{
// 2. Emit the body of the outer loop nest.
// 2.1 Compute kernel order number: kernel = g * kernelsPerGroup + m;
// If group is not set then the value of the kernel ID is
// identical to that of the loop over kernels.
Value kernel = outerIterationBlock.getArguments()[1];
if (group > 1) {
// Middle loop is over groups and third loop is over the
// kernel identifiers in the current group.
auto kernelsOffset = rewriter.create<MulIOp>(loc,
outerIterationBlock.getArguments()[1],
kernelsPerGroupValue);
kernel = rewriter.create<AddIOp>(loc, kernelsOffset,
outerIterationBlock.getArguments()[2]);
}
// 2.2 Define spatial loops
int64_t nSpatialLoops = resultShape.size() - 2;
std::vector<Value> spatialLoops;
std::vector<Value> optimizedSpatialLoops;
Block *optSpatialLoopBlock = defineLoops(rewriter, loc, spatialLoops,
optimizedSpatialLoops, nSpatialLoops);
// 2.3 Prepare iteration arguments for spatial loop nest.
KrnlIterateOperandPack spatialPack(
rewriter, spatialLoops, optimizedSpatialLoops);
for (int i = 2; i < resultShape.size(); ++i)
addDimensionToPack(rewriter, loc, spatialPack, alloc, i);
// 2.4 Emit loop nest over output spatial dimensions.
// for rX = 0 .. RX
auto spatialIterateOp =
rewriter.create<KrnlIterateOp>(loc, spatialPack);
Block &spatialIterationBlock = spatialIterateOp.bodyRegion().front();
// 2.5 Emit optimizations for outer loops:
rewriter.setInsertionPointToEnd(optSpatialLoopBlock);
rewriter.create<KrnlReturnLoopsOp>(loc, spatialLoops);
rewriter.setInsertionPointToStart(&spatialIterationBlock);
{
// 3. Emit the body of the spatial loop nest.
// 3.1 Emit: R[n][kernel][r1][r2] = 0;
SmallVector<Value, 4> resultIndices;
// n
resultIndices.emplace_back(outerIterationBlock.getArguments()[0]);
// kernel
resultIndices.emplace_back(kernel);
// rX
for (auto arg : spatialIterationBlock.getArguments())
resultIndices.emplace_back(arg);
// Store initializer value into output location.
rewriter.create<StoreOp>(loc, zero, alloc, resultIndices);
// 3.2 Define inner loops.
int64_t nInnerLoops = 1 + (kernelShape.size() - 2);
std::vector<Value> innerLoops;
std::vector<Value> optimizedInnerLoops;
Block *optInnerLoopBlock = defineLoops(rewriter, loc, innerLoops,
optimizedInnerLoops, nInnerLoops);
// 3.3 Prepare iteration arguments for inner loop nest.
KrnlIterateOperandPack innerPack(
rewriter, innerLoops, optimizedInnerLoops);
// for c = 0 .. C/group
innerPack.pushConstantBound(0);
innerPack.pushConstantBound(kernelShape[1]);
// for Kx = 0 .. KX
for (int i = 2; i < kernelShape.size(); ++i)
addDimensionToPack(rewriter, loc, innerPack, operands[1], i);
// 3.4 Emit inner loop nest.
auto innerIterateOp =
rewriter.create<KrnlIterateOp>(loc, innerPack);
Block &innerIterationBlock = innerIterateOp.bodyRegion().front();
// 3.5 Emit optimizations for outer loops:
rewriter.setInsertionPointToEnd(optInnerLoopBlock);
rewriter.create<KrnlReturnLoopsOp>(loc, innerLoops);
rewriter.setInsertionPointToStart(&innerIterationBlock);
{
// 4. Emit inner loop body
// R[n][kernel][r1][r2] =
// D[n][g * (C / group) + c][r1 + k1][r2 + k2] *
// K[kernel][c][k1][k2];
// 4.1 Prepare indices for accesing the data tensor.
SmallVector<Value, 4> dataIndices;
// n
dataIndices.emplace_back(outerIterationBlock.getArguments()[0]);
// g * (C / group) + c
Value channelDepth = innerIterationBlock.getArguments()[0];
if (group > 1)
channelDepth = rewriter.create<AddIOp>(loc, channelDepth,
rewriter.create<MulIOp>(loc, subchannels,
outerIterationBlock.getArguments()[1]));
dataIndices.emplace_back(channelDepth);
// rX + kX
for (int i = 0; i < kernelShape.size() - 2; ++i)
dataIndices.emplace_back(
rewriter.create<AddIOp>(loc,
spatialIterationBlock.getArguments()[i],
innerIterationBlock.getArguments()[i+1]));
// 4.2 Prepare indices for accessing the kernel tensor.
SmallVector<Value, 4> kernelIndices;
// kernel
kernelIndices.emplace_back(kernel);
// c
kernelIndices.emplace_back(innerIterationBlock.getArguments()[0]);
// kX
for (int i = 0; i < kernelShape.size() - 2; ++i)
kernelIndices.emplace_back(
innerIterationBlock.getArguments()[i+1]);
// 4.3 Compute convolution.
auto loadData =
rewriter.create<LoadOp>(loc, operands[0], dataIndices);
auto loadKernel =
rewriter.create<LoadOp>(loc, operands[1], kernelIndices);
auto loadPartialSum =
rewriter.create<LoadOp>(loc, alloc, resultIndices);
Value result = rewriter.create<AddFOp>(loc, loadPartialSum,
rewriter.create<MulFOp>(loc, loadData, loadKernel));
// 4.4 Store computed value into output location.
rewriter.create<StoreOp>(loc, result, alloc, resultIndices);
}
}
}
rewriter.replaceOp(op, alloc);
return matchSuccess();
}
};
//===----------------------------------------------------------------------===//
// EntryPoint Op lowering to Krnl Entry Point.
//===----------------------------------------------------------------------===//
@ -1769,7 +1954,8 @@ void FrontendToKrnlLoweringPass::runOnModule() {
ONNXReshapeOpLowering, ONNXEntryPointLowering,
ONNXSoftmaxOpLowering, ONNXGemmOpLowering,
ONNXUnsqueezeOpLowering, ONNXTransposeOpLowering,
ONNXIdentityOpLowering>(&getContext());
ONNXIdentityOpLowering, ONNXConvNoBiasOpLowering
>(&getContext());
// With the target and rewrite patterns defined, we can now attempt the
// conversion. The conversion will signal failure if any of our `illegal`

3
test/backend/test.py
View File

@ -202,6 +202,9 @@ test_to_enable = [
"test_transpose_all_permutations_4_cpu",
"test_transpose_all_permutations_5_cpu",
# Conv
"test_basic_conv_without_padding_cpu",
# Sign Op:
"test_sign_cpu",
]

100
test/mlir/onnx/onnx_lowering.mlir
View File

@ -568,15 +568,15 @@ func @test_add_with_broadcasting(%arg0 : tensor<?xf32>, %arg1 : tensor<?x10xf32>
// CHECK-LABEL: test_add_with_broadcasting
// CHECK: [[DIM1:%.+]] = dim %arg1, 0 : memref<?x10xf32>
// CHECK: [[RES:%.+]] = alloc([[DIM1]]) : memref<?x10xf32>
// CHECK: [[DIM2:%.+]] = dim %arg0, 0 : memref<?xf32>
// CHECK: [[ONE:%.+]] = constant 1 : index
// CHECK: [[IS_ONE:%.+]] = cmpi "eq", [[DIM2]], [[ONE]] : index
// CHECK: [[DEF_LOOPS:%.+]]:2 = krnl.define_loops 2
// CHECK: [[OPT_LOOPS:%.+]]:2 = krnl.optimize_loops {
// CHECK: krnl.return_loops [[DEF_LOOPS]]#0, [[DEF_LOOPS]]#1
// CHECK: } : () -> (!krnl.loop, !krnl.loop)
// CHECK: [[DIM2:%.+]] = dim [[RES]], 0 : memref<?x10xf32>
// CHECK: [[DIM3:%.+]] = dim %arg0, 0 : memref<?xf32>
// CHECK: [[ONE:%.+]] = constant 1 : index
// CHECK: [[IS_ONE:%.+]] = cmpi "eq", [[DIM3]], [[ONE]] : index
// CHECK: krnl.iterate([[OPT_LOOPS]]#0, [[OPT_LOOPS]]#1) with ([[DEF_LOOPS]]#0 -> %arg2 = 0 to [[DIM2]], [[DEF_LOOPS]]#1 -> %arg3 = 0 to 10) {
// CHECK: [[DIM3:%.+]] = dim [[RES]], 0 : memref<?x10xf32>
// CHECK: krnl.iterate([[OPT_LOOPS]]#0, [[OPT_LOOPS]]#1) with ([[DEF_LOOPS]]#0 -> %arg2 = 0 to [[DIM3]], [[DEF_LOOPS]]#1 -> %arg3 = 0 to 10) {
// CHECK: [[ZERO:%.+]] = constant 0 : index
// CHECK: %[[SELECT1:.+]] = select [[IS_ONE]], [[ZERO]], %arg3 : index
// CHECK: [[LOAD1:%.+]] = load %arg0[%[[SELECT1]]] : memref<?xf32>
@ -788,3 +788,93 @@ func @test_sign_i(%arg0 : tensor<?x10xi32>) -> tensor<*xi32> {
// CHECK: store [[SIGN_RES]], [[RES]][%arg1, %arg2] : memref<?x10xi32>
// CHECK: return [[RES]] : memref<?x10xi32>
}
func @test_conv_no_bias_no_pad(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x7xf32>) -> tensor<*xf32> {
%0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i64} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<*xf32>
"std.return"(%0) : (tensor<*xf32>) -> ()
// CHECK-LABEL: test_conv_no_bias_no_pad
// CHECK: [[RES:%.+]] = alloc() : memref<1x5x27x58xf32>
// CHECK: [[CONST0:%.+]] = constant 5 : index
// CHECK: [[CONST1:%.+]] = constant 0.000000e+00 : f32
// CHECK: [[CONST2:%.+]] = constant 2 : index
// CHECK: [[OUTER_LOOPS:%.+]]:2 = krnl.define_loops 2
// CHECK: [[OPT_OUTER_LOOPS:%.+]]:2 = krnl.optimize_loops {
// CHECK: krnl.return_loops [[OUTER_LOOPS]]#0, [[OUTER_LOOPS]]#1
// CHECK: } : () -> (!krnl.loop, !krnl.loop)
// CHECK: krnl.iterate([[OPT_OUTER_LOOPS]]#0, [[OPT_OUTER_LOOPS]]#1) with ([[OUTER_LOOPS]]#0 -> %arg2 = 0 to 1, [[OUTER_LOOPS]]#1 -> %arg3 = 0 to 5) {
// CHECK: [[SPATIAL_LOOPS:%.+]]:2 = krnl.define_loops 2
// CHECK: [[OPT_SPATIAL_LOOPS:%.+]]:2 = krnl.optimize_loops {
// CHECK: krnl.return_loops [[SPATIAL_LOOPS]]#0, [[SPATIAL_LOOPS]]#1
// CHECK: } : () -> (!krnl.loop, !krnl.loop)
// CHECK: krnl.iterate([[OPT_SPATIAL_LOOPS]]#0, [[OPT_SPATIAL_LOOPS]]#1) with ([[SPATIAL_LOOPS]]#0 -> %arg4 = 0 to 27, [[SPATIAL_LOOPS]]#1 -> %arg5 = 0 to 58) {
// CHECK: store [[CONST1]], [[RES]][%arg2, %arg3, %arg4, %arg5] : memref<1x5x27x58xf32>
// CHECK: [[INNER_LOOPS:%.+]]:3 = krnl.define_loops 3
// CHECK: [[OPT_INNER_LOOPS:%.+]]:3 = krnl.optimize_loops {
// CHECK: krnl.return_loops [[INNER_LOOPS]]#0, [[INNER_LOOPS]]#1, [[INNER_LOOPS]]#2
// CHECK: } : () -> (!krnl.loop, !krnl.loop, !krnl.loop)
// CHECK: krnl.iterate([[OPT_INNER_LOOPS]]#0, [[OPT_INNER_LOOPS]]#1, [[OPT_INNER_LOOPS]]#2) with ([[INNER_LOOPS]]#0 -> %arg6 = 0 to 2, [[INNER_LOOPS]]#1 -> %arg7 = 0 to 6, [[INNER_LOOPS]]#2 -> %arg8 = 0 to 7) {
// CHECK: [[R1PLUSK1:%.+]] = addi %arg4, %arg7 : index
// CHECK: [[R2PLUSK2:%.+]] = addi %arg5, %arg8 : index
// CHECK: [[DATA:%.+]] = load %arg0[%arg2, %arg6, [[R1PLUSK1]], [[R2PLUSK2]]] : memref<1x2x32x64xf32>
// CHECK: [[KERNEL:%.+]] = load %arg1[%arg3, %arg6, %arg7, %arg8] : memref<5x2x6x7xf32>
// CHECK: [[ACC_RES:%.+]] = load %0[%arg2, %arg3, %arg4, %arg5] : memref<1x5x27x58xf32>
// CHECK: [[MUL:%.+]] = mulf [[DATA]], [[KERNEL]] : f32
// CHECK: [[ADD:%.+]] = addf [[ACC_RES]], [[MUL]] : f32
// CHECK: store [[ADD]], [[RES]][%arg2, %arg3, %arg4, %arg5] : memref<1x5x27x58xf32>
// CHECK: }
// CHECK: }
// CHECK: }
// CHECK: return [[RES]] : memref<1x5x27x58xf32>
}
func @test_conv_no_bias_no_pad_w_group(%arg0 : tensor<1x9x32x64xf32>, %arg1 : tensor<5x3x6x7xf32>) -> tensor<*xf32> {
%0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 3 : i64} : (tensor<1x9x32x64xf32>, tensor<5x3x6x7xf32>) -> tensor<*xf32>
"std.return"(%0) : (tensor<*xf32>) -> ()
// CHECK-LABEL: test_conv_no_bias_no_pad_w_group
// CHECK: [[RES:%.+]] = alloc() : memref<1x5x27x58xf32>
// CHECK: [[CONST0:%.+]] = constant 1 : index
// CHECK: [[CONST1:%.+]] = constant 0.000000e+00 : f32
// CHECK: [[CONST2:%.+]] = constant 3 : index
// CHECK: [[OUTER_LOOPS:%.+]]:3 = krnl.define_loops 3
// CHECK: [[OPT_OUTER_LOOPS:%.+]]:3 = krnl.optimize_loops {
// CHECK: krnl.return_loops [[OUTER_LOOPS]]#0, [[OUTER_LOOPS]]#1, [[OUTER_LOOPS]]#2
// CHECK: } : () -> (!krnl.loop, !krnl.loop, !krnl.loop)
// CHECK: krnl.iterate([[OPT_OUTER_LOOPS]]#0, [[OPT_OUTER_LOOPS]]#1, [[OPT_OUTER_LOOPS]]#2) with ([[OUTER_LOOPS]]#0 -> %arg2 = 0 to 1, [[OUTER_LOOPS]]#1 -> %arg3 = 0 to 3, [[OUTER_LOOPS]]#2 -> %arg4 = 0 to 1) {
// CHECK: [[MUL1:%.+]] = muli %arg3, [[CONST0]] : index
// CHECK: %[[ADD1:.+]] = addi [[MUL1]], %arg4 : index
// CHECK: [[SPATIAL_LOOPS:%.+]]:2 = krnl.define_loops 2
// CHECK: [[OPT_SPATIAL_LOOPS:%.+]]:2 = krnl.optimize_loops {
// CHECK: krnl.return_loops [[SPATIAL_LOOPS]]#0, [[SPATIAL_LOOPS]]#1
// CHECK: } : () -> (!krnl.loop, !krnl.loop)
// CHECK: krnl.iterate([[OPT_SPATIAL_LOOPS]]#0, [[OPT_SPATIAL_LOOPS]]#1) with ([[SPATIAL_LOOPS]]#0 -> %arg5 = 0 to 27, [[SPATIAL_LOOPS]]#1 -> %arg6 = 0 to 58) {
// CHECK: store [[CONST1]], [[RES]][%arg2, %[[ADD1]], %arg5, %arg6] : memref<1x5x27x58xf32>
// CHECK: [[INNER_LOOPS:%.+]]:3 = krnl.define_loops 3
// CHECK: [[OPT_INNER_LOOPS:%.+]]:3 = krnl.optimize_loops {
// CHECK: krnl.return_loops [[INNER_LOOPS]]#0, [[INNER_LOOPS]]#1, [[INNER_LOOPS]]#2
// CHECK: } : () -> (!krnl.loop, !krnl.loop, !krnl.loop)
// CHECK: krnl.iterate([[OPT_INNER_LOOPS]]#0, [[OPT_INNER_LOOPS]]#1, [[OPT_INNER_LOOPS]]#2) with ([[INNER_LOOPS]]#0 -> %arg7 = 0 to 3, [[INNER_LOOPS]]#1 -> %arg8 = 0 to 6, [[INNER_LOOPS]]#2 -> %arg9 = 0 to 7) {
// CHECK: [[MUL2:%.+]] = muli [[CONST2]], %arg3 : index
// CHECK: [[ADD2:%.+]] = addi %arg7, [[MUL2]] : index
// CHECK: [[R1PLUSK1:%.+]] = addi %arg5, %arg8 : index
// CHECK: [[R2PLUSK2:%.+]] = addi %arg6, %arg9 : index
// CHECK: [[DATA:%.+]] = load %arg0[%arg2, [[ADD2]], [[R1PLUSK1]], [[R2PLUSK2]]] : memref<1x9x32x64xf32>
// CHECK: [[KERNEL:%.+]] = load %arg1[%[[ADD1]], %arg7, %arg8, %arg9] : memref<5x3x6x7xf32>
// CHECK: [[ACC_RES:%.+]] = load %0[%arg2, %[[ADD1]], %arg5, %arg6] : memref<1x5x27x58xf32>
// CHECK: [[MUL:%.+]] = mulf [[DATA]], [[KERNEL]] : f32
// CHECK: [[ADD:%.+]] = addf [[ACC_RES]], [[MUL]] : f32
// CHECK: store [[ADD]], [[RES]][%arg2, %[[ADD1]], %arg5, %arg6] : memref<1x5x27x58xf32>
// CHECK: }
// CHECK: }
// CHECK: }
// CHECK: return [[RES]] : memref<1x5x27x58xf32>
}