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Differential D105731

[mlir][sparse] add restrictive versions of division support
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Authored by aartbik on Jul 9 2021, 1:53 PM.

Details

Reviewers
bixia
penpornk
gussmith23
tatianashp
Commits
rG622eb169f650: [mlir][sparse] add restrictive versions of division support
Summary

Right now, we only accept x/c with nonzero c, since this
conceptually can be treated as a x*(1/c) conjunction for both
FP and INT as far as lattice computations go. The codegen
keeps the division though to preserve precise semantics.

See discussion:
https://llvm.discourse.group/t/sparse-tensors-in-mlir/3389/28

Diff Detail

Event Timeline

aartbik created this revision.Jul 9 2021, 1:53 PM
Herald added subscribers: dcaballe, cota, teijeong and 15 others. · View Herald TranscriptJul 9 2021, 1:53 PM
aartbik requested review of this revision.Jul 9 2021, 1:53 PM
Herald added a project: Restricted Project. · View Herald TranscriptJul 9 2021, 1:53 PM
Herald added subscribers: stephenneuendorffer, nicolasvasilache. · View Herald Transcript
aartbik added reviewers: bixia, penpornk, gussmith23, tatianashp.Jul 9 2021, 1:56 PM
aartbik updated this revision to Diff 357638.Jul 9 2021, 2:20 PM

extra table

Harbormaster completed remote builds in B113292: Diff 357638.Jul 9 2021, 4:16 PM
aartbik updated this revision to Diff 357694.Jul 9 2021, 9:02 PM

more compact if

Harbormaster completed remote builds in B113340: Diff 357694.Jul 9 2021, 9:32 PM
• post.kadirselcuk added a child revision: D34362: [LNT] Support for different DataSet usage in Polybench for "lnt runtest nt".Jul 10 2021, 5:55 PM
• post.kadirselcuk added a parent revision: D105762: [X86] Teach X86FloatingPoint's handleCall to only erase the FP stack if there is a regmask operand that clobbers the FP stack..Jul 10 2021, 8:06 PM
craig.topper removed a parent revision: D105762: [X86] Teach X86FloatingPoint's handleCall to only erase the FP stack if there is a regmask operand that clobbers the FP stack..Jul 10 2021, 9:47 PM
gussmith23 added inline comments.Jul 12 2021, 2:28 PM
mlir/lib/Dialect/SparseTensor/Utils/Merger.cpp
338

Out of curiosity, any reason for the brackets here when you don't have them on the kMulI/kMulF cases?

mlir/test/Dialect/SparseTensor/sparse_int_ops.mlir
249

Is it possible to test the situations where we can't prove that the denominator is zero, and thus code can't be generated? At first I thought that could go in invalid.mlir, but it doesn't seem like that would be the right place.

aartbik marked 2 inline comments as done.Jul 12 2021, 2:33 PM
aartbik added inline comments.
mlir/lib/Dialect/SparseTensor/Utils/Merger.cpp
338

ah, left over from when I had a local. Thanks. Removed!

mlir/test/Dialect/SparseTensor/sparse_int_ops.mlir
249

Not easy with the CHECK, but it will become possible with your unit test!

aartbik updated this revision to Diff 358071.Jul 12 2021, 2:38 PM
aartbik marked 2 inline comments as done.

removed braces

gussmith23 accepted this revision.Jul 12 2021, 2:42 PM

This looks good to me!

This revision is now accepted and ready to land.Jul 12 2021, 2:42 PM
This revision was landed with ongoing or failed builds.Jul 12 2021, 3:00 PM
Closed by commit rG622eb169f650: [mlir][sparse] add restrictive versions of division support (authored by aartbik). · Explain Why
This revision was automatically updated to reflect the committed changes.
aartbik added a commit: rG622eb169f650: [mlir][sparse] add restrictive versions of division support.
Harbormaster completed remote builds in B113595: Diff 358071.Jul 12 2021, 3:52 PM
efriedma removed a child revision: D34362: [LNT] Support for different DataSet usage in Polybench for "lnt runtest nt".Jul 17 2021, 3:02 PM

Revision Contents

PathSize
mlir/
include/
mlir/
Dialect/
SparseTensor/
Utils/
5 lines
lib/
Dialect/
SparseTensor/
Transforms/
6 lines
Utils/
56 lines
test/
Dialect/
SparseTensor/
44 lines
77 lines

Diff 357694

mlir/include/mlir/Dialect/SparseTensor/Utils/Merger.h

Show All 26 Lines
enum class Kind { enum class Kind {
// Leaf. // Leaf.
kTensor, kTensor,
kInvariant, kInvariant,
kZero, kZero,
// Operation. // Operation.
kMulF, kMulF,
kMulI, kMulI,
kDivF,
kDivS, // signed
kDivU, // unsigned
kAddF, kAddF,
kAddI, kAddI,
kSubF, kSubF,
kSubI kSubI
}; };
/// Children subexpressions of tensor operations. /// Children subexpressions of tensor operations.
struct Children { struct Children {
▲ Show 20 Lines • Show All 149 Lines • ▼ Show 20 Lines#endif
/// Returns index of the root expression. /// Returns index of the root expression.
unsigned buildLattices(unsigned exp, unsigned idx); unsigned buildLattices(unsigned exp, unsigned idx);
/// Builds a tensor expression from the given Linalg operation. /// Builds a tensor expression from the given Linalg operation.
/// Returns index of the root expression on success. /// Returns index of the root expression on success.
Optional<unsigned> buildTensorExpFromLinalg(linalg::GenericOp op); Optional<unsigned> buildTensorExpFromLinalg(linalg::GenericOp op);
private: private:
bool maybeZero(unsigned e);
/// Traverses the SSA tree (possibly a DAG) to build a tensor expression. /// Traverses the SSA tree (possibly a DAG) to build a tensor expression.
Optional<unsigned> buildTensorExp(linalg::GenericOp op, Value val); Optional<unsigned> buildTensorExp(linalg::GenericOp op, Value val);
const unsigned outTensor; const unsigned outTensor;
const unsigned syntheticTensor; const unsigned syntheticTensor;
const unsigned numTensors; const unsigned numTensors;
const unsigned numLoops; const unsigned numLoops;
Show All 10 Lines

mlir/lib/Dialect/SparseTensor/Transforms/Sparsification.cpp

Show First 20 Lines • Show All 640 Lines • ▼ Show 20 Linesstatic Value genExp(Merger &merger, CodeGen &codegen, PatternRewriter &rewriter,
case Kind::kTensor: case Kind::kTensor:
case Kind::kInvariant: case Kind::kInvariant:
case Kind::kZero: case Kind::kZero:
llvm_unreachable("handled above"); llvm_unreachable("handled above");
case Kind::kMulF: case Kind::kMulF:
return rewriter.create<MulFOp>(loc, v0, v1); return rewriter.create<MulFOp>(loc, v0, v1);
case Kind::kMulI: case Kind::kMulI:
return rewriter.create<MulIOp>(loc, v0, v1); return rewriter.create<MulIOp>(loc, v0, v1);
case Kind::kDivF:
return rewriter.create<DivFOp>(loc, v0, v1);
case Kind::kDivS:
return rewriter.create<SignedDivIOp>(loc, v0, v1);
case Kind::kDivU:
return rewriter.create<UnsignedDivIOp>(loc, v0, v1);
case Kind::kAddF: case Kind::kAddF:
return rewriter.create<AddFOp>(loc, v0, v1); return rewriter.create<AddFOp>(loc, v0, v1);
case Kind::kAddI: case Kind::kAddI:
return rewriter.create<AddIOp>(loc, v0, v1); return rewriter.create<AddIOp>(loc, v0, v1);
case Kind::kSubF: case Kind::kSubF:
return rewriter.create<SubFOp>(loc, v0, v1); return rewriter.create<SubFOp>(loc, v0, v1);
case Kind::kSubI: case Kind::kSubI:
return rewriter.create<SubIOp>(loc, v0, v1); return rewriter.create<SubIOp>(loc, v0, v1);
▲ Show 20 Lines • Show All 569 Lines • Show Last 20 Lines

mlir/lib/Dialect/SparseTensor/Utils/Merger.cpp

Show First 20 Lines • Show All 195 Lines • ▼ Show 20 Lines
// Print methods (for debugging). // Print methods (for debugging).
// //
static char kindToOpSymbol(Kind kind) { static char kindToOpSymbol(Kind kind) {
switch (kind) { switch (kind) {
case Kind::kMulF: case Kind::kMulF:
case Kind::kMulI: case Kind::kMulI:
return '*'; return '*';
case Kind::kDivF:
case Kind::kDivS:
case Kind::kDivU:
return '/';
case Kind::kAddF: case Kind::kAddF:
case Kind::kAddI: case Kind::kAddI:
return '+'; return '+';
case Kind::kSubF: case Kind::kSubF:
case Kind::kSubI: case Kind::kSubI:
return '-'; return '-';
default: default:
break; break;
▲ Show 20 Lines • Show All 85 Lines • ▼ Show 20 Lines case Kind::kZero: {
// is set to a synthetic tensor with undefined indices only. // is set to a synthetic tensor with undefined indices only.
unsigned s = addSet(); unsigned s = addSet();
unsigned t = kind == Kind::kTensor ? tensorExps[e].tensor : syntheticTensor; unsigned t = kind == Kind::kTensor ? tensorExps[e].tensor : syntheticTensor;
latSets[s].push_back(addLat(t, idx, e)); latSets[s].push_back(addLat(t, idx, e));
return s; return s;
} }
case Kind::kMulF: case Kind::kMulF:
case Kind::kMulI: case Kind::kMulI:
// A multiplicative operation only needs to be performed
// for the conjunction of sparse iteration spaces.
//
// x*y|!y | y |
// ---+---+---+
// !x | 0 | 0 |
// x | 0 |x*y|
return takeConj(kind, // take binary conjunction
buildLattices(tensorExps[e].children.e0, idx),
buildLattices(tensorExps[e].children.e1, idx));
case Kind::kDivF:
case Kind::kDivS:
case Kind::kDivU: {
// A division is tricky, since 0/0, 0/c, c/0 all have
// specific outcomes for floating-point and integers.
// Thus, we need to traverse the full iteration space.
//
// x/y|!y | y |
// ---+---+---+
// !x |0/0|0/y| FP: 0/0=NaN,c/0=Inf,0/c=0 with c true nonzero
// x |x/0|x/y| INT: x/0=exception for any x
//
// TODO: for now we "fixed" this by only accepting x/c cases
// during expression building, so that the conjunction
// rules applies (viz. x/c = x*(1/c) as far as lattice
// construction is concerned).
return takeConj(kind, // take binary conjunction return takeConj(kind, // take binary conjunction
buildLattices(tensorExps[e].children.e0, idx), buildLattices(tensorExps[e].children.e0, idx),
buildLattices(tensorExps[e].children.e1, idx)); buildLattices(tensorExps[e].children.e1, idx));
}
gussmith23Unsubmitted
Done
ReplyInline Actions

Out of curiosity, any reason for the brackets here when you don't have them on the kMulI/kMulF cases?

gussmith23: Out of curiosity, any reason for the brackets here when you don't have them on the…
aartbikAuthorUnsubmitted
Done
ReplyInline Actions

ah, left over from when I had a local. Thanks. Removed!

aartbik: ah, left over from when I had a local. Thanks. Removed!
case Kind::kSubF: case Kind::kSubF:
case Kind::kSubI: case Kind::kSubI:
// Special case: 0-y is -y.
if (tensorExps[tensorExps[e].children.e0].kind == Kind::kZero) if (tensorExps[tensorExps[e].children.e0].kind == Kind::kZero)
return mapZero(kind, // maps to 0-y with just y's lattices return mapZero(kind, // maps to 0-y with just y's lattices
buildLattices(tensorExps[e].children.e1, idx)); buildLattices(tensorExps[e].children.e1, idx));
LLVM_FALLTHROUGH; LLVM_FALLTHROUGH;
case Kind::kAddF: case Kind::kAddF:
case Kind::kAddI: case Kind::kAddI:
// An additive operation needs to be performed
// for the disjunction of sparse iteration spaces.
//
// x+y|!y | y | x-y|!y | y |
// ---+---+---+ ---+---+---+
// !x | 0 | y | !x | 0 |-y |
// x | x |x+y| x | x |x-y|
return takeDisj(kind, // take binary disjunction return takeDisj(kind, // take binary disjunction
buildLattices(tensorExps[e].children.e0, idx), buildLattices(tensorExps[e].children.e0, idx),
buildLattices(tensorExps[e].children.e1, idx)); buildLattices(tensorExps[e].children.e1, idx));
} }
llvm_unreachable("unexpected expression kind"); llvm_unreachable("unexpected expression kind");
} }
Optional<unsigned> Merger::buildTensorExpFromLinalg(linalg::GenericOp op) { Optional<unsigned> Merger::buildTensorExpFromLinalg(linalg::GenericOp op) {
Operation *yield = op.region().front().getTerminator(); Operation *yield = op.region().front().getTerminator();
return buildTensorExp(op, yield->getOperand(0)); return buildTensorExp(op, yield->getOperand(0));
} }
bool Merger::maybeZero(unsigned e) {
if (tensorExps[e].kind == Kind::kInvariant) {
if (auto c = tensorExps[e].val.getDefiningOp<ConstantIntOp>())
return c.getValue() == 0;
if (auto c = tensorExps[e].val.getDefiningOp<ConstantFloatOp>())
return c.getValue().isZero();
}
return true;
}
Optional<unsigned> Merger::buildTensorExp(linalg::GenericOp op, Value val) { Optional<unsigned> Merger::buildTensorExp(linalg::GenericOp op, Value val) {
if (auto arg = val.dyn_cast<BlockArgument>()) { if (auto arg = val.dyn_cast<BlockArgument>()) {
unsigned argN = arg.getArgNumber(); unsigned argN = arg.getArgNumber();
// Any argument of the generic op that is not marked as a scalar // Any argument of the generic op that is not marked as a scalar
// argument is considered a tensor, indexed by the implicit loop // argument is considered a tensor, indexed by the implicit loop
// bounds. This includes rank-0 tensor arguments. // bounds. This includes rank-0 tensor arguments.
if (arg.getOwner()->getParentOp() == op) { if (arg.getOwner()->getParentOp() == op) {
OpOperand *t = op.getInputAndOutputOperands()[argN]; OpOperand *t = op.getInputAndOutputOperands()[argN];
Show All 16 Lines if (x.hasValue()) {
unsigned e0 = addExp(Kind::kZero); unsigned e0 = addExp(Kind::kZero);
unsigned e1 = x.getValue(); unsigned e1 = x.getValue();
if (isa<NegFOp>(def)) if (isa<NegFOp>(def))
return addExp(Kind::kSubF, e0, e1); return addExp(Kind::kSubF, e0, e1);
// TODO: no negi in std? // TODO: no negi in std?
} }
} }
// Construct binary operations if subexpressions can be built. // Construct binary operations if subexpressions can be built.
// TODO: see buildLattices() for an explanation of rejecting certain divisions
if (def->getNumOperands() == 2) { if (def->getNumOperands() == 2) {
auto x = buildTensorExp(op, def->getOperand(0)); auto x = buildTensorExp(op, def->getOperand(0));
auto y = buildTensorExp(op, def->getOperand(1)); auto y = buildTensorExp(op, def->getOperand(1));
if (x.hasValue() && y.hasValue()) { if (x.hasValue() && y.hasValue()) {
unsigned e0 = x.getValue(); unsigned e0 = x.getValue();
unsigned e1 = y.getValue(); unsigned e1 = y.getValue();
if (isa<MulFOp>(def)) if (isa<MulFOp>(def))
return addExp(Kind::kMulF, e0, e1); return addExp(Kind::kMulF, e0, e1);
if (isa<MulIOp>(def)) if (isa<MulIOp>(def))
return addExp(Kind::kMulI, e0, e1); return addExp(Kind::kMulI, e0, e1);
if (isa<DivFOp>(def) && !maybeZero(e1))
return addExp(Kind::kDivF, e0, e1);
if (isa<SignedDivIOp>(def) && !maybeZero(e1))
return addExp(Kind::kDivS, e0, e1);
if (isa<UnsignedDivIOp>(def) && !maybeZero(e1))
return addExp(Kind::kDivU, e0, e1);
if (isa<AddFOp>(def)) if (isa<AddFOp>(def))
return addExp(Kind::kAddF, e0, e1); return addExp(Kind::kAddF, e0, e1);
if (isa<AddIOp>(def)) if (isa<AddIOp>(def))
return addExp(Kind::kAddI, e0, e1); return addExp(Kind::kAddI, e0, e1);
if (isa<SubFOp>(def)) if (isa<SubFOp>(def))
return addExp(Kind::kSubF, e0, e1); return addExp(Kind::kSubF, e0, e1);
if (isa<SubIOp>(def)) if (isa<SubIOp>(def))
return addExp(Kind::kSubI, e0, e1); return addExp(Kind::kSubI, e0, e1);
} }
} }
// Cannot build. // Cannot build.
return None; return None;
} }
} // namespace sparse_tensor } // namespace sparse_tensor
} // namespace mlir } // namespace mlir

mlir/test/Dialect/SparseTensor/sparse_fp_ops.mlir

Show All 16 Lines indexing_maps = [
affine_map<(i) -> (i)>, // a affine_map<(i) -> (i)>, // a
affine_map<(i) -> (i)>, // b affine_map<(i) -> (i)>, // b
affine_map<(i) -> (i)> // x (out) affine_map<(i) -> (i)> // x (out)
], ],
iterator_types = ["parallel"], iterator_types = ["parallel"],
doc = "x(i) = a(i) OP b(i)" doc = "x(i) = a(i) OP b(i)"
} }
#traitc = {
indexing_maps = [
affine_map<(i) -> (i)>, // a
affine_map<(i) -> (i)> // x (out)
],
iterator_types = ["parallel"],
doc = "x(i) = a(i) OP c"
}
// CHECK-LABEL: func @neg( // CHECK-LABEL: func @neg(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>>, // CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32xf64> {linalg.inplaceable = true}) -> tensor<32xf64> { // CHECK-SAME: %[[VAL_1:.*]]: tensor<32xf64> {linalg.inplaceable = true}) -> tensor<32xf64> {
// CHECK: %[[VAL_2:.*]] = constant 0 : index // CHECK: %[[VAL_2:.*]] = constant 0 : index
// CHECK: %[[VAL_3:.*]] = constant 1 : index // CHECK: %[[VAL_3:.*]] = constant 1 : index
// CHECK: %[[VAL_4:.*]] = constant 0.000000e+00 : f64 // CHECK: %[[VAL_4:.*]] = constant 0.000000e+00 : f64
// CHECK: %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_2]] : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>> // CHECK: %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_2]] : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: %[[VAL_6:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_2]] : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>> // CHECK: %[[VAL_6:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_2]] : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>>
▲ Show 20 Lines • Show All 175 Lines • ▼ Show 20 Lines %0 = linalg.generic #trait2
ins(%arga, %argb: tensor<32xf64, #SV>, tensor<32xf64>) ins(%arga, %argb: tensor<32xf64, #SV>, tensor<32xf64>)
outs(%argx: tensor<32xf64>) { outs(%argx: tensor<32xf64>) {
^bb(%a: f64, %b: f64, %x: f64): ^bb(%a: f64, %b: f64, %x: f64):
%0 = mulf %a, %b : f64 %0 = mulf %a, %b : f64
linalg.yield %0 : f64 linalg.yield %0 : f64
} -> tensor<32xf64> } -> tensor<32xf64>
return %0 : tensor<32xf64> return %0 : tensor<32xf64>
} }
// CHECK-LABEL: func @divbyc(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32xf64> {linalg.inplaceable = true}) -> tensor<32xf64> {
// CHECK: %[[VAL_2:.*]] = constant 2.000000e+00 : f64
// CHECK: %[[VAL_3:.*]] = constant 0 : index
// CHECK: %[[VAL_4:.*]] = constant 1 : index
// CHECK: %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: %[[VAL_6:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: %[[VAL_8:.*]] = memref.buffer_cast %[[VAL_1]] : memref<32xf64>
// CHECK: %[[VAL_9:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_3]]] : memref<?xindex>
// CHECK: %[[VAL_10:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_4]]] : memref<?xindex>
// CHECK: scf.for %[[VAL_11:.*]] = %[[VAL_9]] to %[[VAL_10]] step %[[VAL_4]] {
// CHECK: %[[VAL_12:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_11]]] : memref<?xindex>
// CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_11]]] : memref<?xf64>
// CHECK: %[[VAL_14:.*]] = divf %[[VAL_13]], %[[VAL_2]] : f64
// CHECK: memref.store %[[VAL_14]], %[[VAL_8]]{{\[}}%[[VAL_12]]] : memref<32xf64>
// CHECK: }
// CHECK: %[[VAL_15:.*]] = memref.tensor_load %[[VAL_8]] : memref<32xf64>
// CHECK: return %[[VAL_15]] : tensor<32xf64>
// CHECK: }
func @divbyc(%arga: tensor<32xf64, #SV>,
%argx: tensor<32xf64> {linalg.inplaceable = true}) -> tensor<32xf64> {
%c = constant 2.0 : f64
%0 = linalg.generic #traitc
ins(%arga: tensor<32xf64, #SV>)
outs(%argx: tensor<32xf64>) {
^bb(%a: f64, %x: f64):
%0 = divf %a, %c : f64
linalg.yield %0 : f64
} -> tensor<32xf64>
return %0 : tensor<32xf64>
}

mlir/test/Dialect/SparseTensor/sparse_int_ops.mlir

// NOTE: Assertions have been autogenerated by utils/generate-test-checks.py // NOTE: Assertions have been autogenerated by utils/generate-test-checks.py
// RUN: mlir-opt %s -sparsification | FileCheck %s // RUN: mlir-opt %s -sparsification | FileCheck %s
#SV = #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }> #SV = #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>
#trait2 = { #trait2 = {
indexing_maps = [ indexing_maps = [
affine_map<(i) -> (i)>, // a affine_map<(i) -> (i)>, // a
affine_map<(i) -> (i)>, // b affine_map<(i) -> (i)>, // b
affine_map<(i) -> (i)> // x (out) affine_map<(i) -> (i)> // x (out)
], ],
iterator_types = ["parallel"], iterator_types = ["parallel"],
doc = "x(i) = a(i) OP b(i)" doc = "x(i) = a(i) OP b(i)"
} }
#traitc = {
indexing_maps = [
affine_map<(i) -> (i)>, // a
affine_map<(i) -> (i)> // x (out)
],
iterator_types = ["parallel"],
doc = "x(i) = a(i) OP c"
}
// CHECK-LABEL: func @add( // CHECK-LABEL: func @add(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xi64, #sparse_tensor.encoding<{{{.*}}}>>, // CHECK-SAME: %[[VAL_0:.*]]: tensor<32xi64, #sparse_tensor.encoding<{{{.*}}}>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32xi64>, // CHECK-SAME: %[[VAL_1:.*]]: tensor<32xi64>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32xi64> {linalg.inplaceable = true}) -> tensor<32xi64> { // CHECK-SAME: %[[VAL_2:.*]]: tensor<32xi64> {linalg.inplaceable = true}) -> tensor<32xi64> {
// CHECK: %[[VAL_3:.*]] = constant 32 : index // CHECK: %[[VAL_3:.*]] = constant 32 : index
// CHECK: %[[VAL_4:.*]] = constant 0 : index // CHECK: %[[VAL_4:.*]] = constant 0 : index
// CHECK: %[[VAL_5:.*]] = constant true // CHECK: %[[VAL_5:.*]] = constant true
// CHECK: %[[VAL_6:.*]] = constant 1 : index // CHECK: %[[VAL_6:.*]] = constant 1 : index
▲ Show 20 Lines • Show All 142 Lines • ▼ Show 20 Lines %0 = linalg.generic #trait2
ins(%arga, %argb: tensor<32xi64, #SV>, tensor<32xi64>) ins(%arga, %argb: tensor<32xi64, #SV>, tensor<32xi64>)
outs(%argx: tensor<32xi64>) { outs(%argx: tensor<32xi64>) {
^bb(%a: i64, %b: i64, %x: i64): ^bb(%a: i64, %b: i64, %x: i64):
%0 = muli %a, %b : i64 %0 = muli %a, %b : i64
linalg.yield %0 : i64 linalg.yield %0 : i64
} -> tensor<32xi64> } -> tensor<32xi64>
return %0 : tensor<32xi64> return %0 : tensor<32xi64>
} }
// CHECK-LABEL: func @divsbyc(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xi64, #sparse_tensor.encoding<{{{.*}}}>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32xi64> {linalg.inplaceable = true}) -> tensor<32xi64> {
// CHECK: %[[VAL_2:.*]] = constant 2 : i64
// CHECK: %[[VAL_3:.*]] = constant 0 : index
// CHECK: %[[VAL_4:.*]] = constant 1 : index
// CHECK: %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32xi64, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: %[[VAL_6:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32xi64, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xi64, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: %[[VAL_8:.*]] = memref.buffer_cast %[[VAL_1]] : memref<32xi64>
// CHECK: %[[VAL_9:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_3]]] : memref<?xindex>
// CHECK: %[[VAL_10:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_4]]] : memref<?xindex>
// CHECK: scf.for %[[VAL_11:.*]] = %[[VAL_9]] to %[[VAL_10]] step %[[VAL_4]] {
// CHECK: %[[VAL_12:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_11]]] : memref<?xindex>
// CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_11]]] : memref<?xi64>
// CHECK: %[[VAL_14:.*]] = divi_signed %[[VAL_13]], %[[VAL_2]] : i64
// CHECK: memref.store %[[VAL_14]], %[[VAL_8]]{{\[}}%[[VAL_12]]] : memref<32xi64>
// CHECK: }
// CHECK: %[[VAL_15:.*]] = memref.tensor_load %[[VAL_8]] : memref<32xi64>
// CHECK: return %[[VAL_15]] : tensor<32xi64>
// CHECK: }
func @divsbyc(%arga: tensor<32xi64, #SV>,
%argx: tensor<32xi64> {linalg.inplaceable = true}) -> tensor<32xi64> {
%c = constant 2 : i64
%0 = linalg.generic #traitc
ins(%arga: tensor<32xi64, #SV>)
outs(%argx: tensor<32xi64>) {
^bb(%a: i64, %x: i64):
%0 = divi_signed %a, %c : i64
linalg.yield %0 : i64
} -> tensor<32xi64>
return %0 : tensor<32xi64>
}
// CHECK-LABEL: func @divubyc(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xi64, #sparse_tensor.encoding<{{{.*}}}>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32xi64> {linalg.inplaceable = true}) -> tensor<32xi64> {
// CHECK: %[[VAL_2:.*]] = constant 2 : i64
// CHECK: %[[VAL_3:.*]] = constant 0 : index
// CHECK: %[[VAL_4:.*]] = constant 1 : index
// CHECK: %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32xi64, #sparse_tensor.encoding<{{.*}}}>>
// CHECK: %[[VAL_6:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32xi64, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xi64, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: %[[VAL_8:.*]] = memref.buffer_cast %[[VAL_1]] : memref<32xi64>
// CHECK: %[[VAL_9:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_3]]] : memref<?xindex>
// CHECK: %[[VAL_10:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_4]]] : memref<?xindex>
// CHECK: scf.for %[[VAL_11:.*]] = %[[VAL_9]] to %[[VAL_10]] step %[[VAL_4]] {
// CHECK: %[[VAL_12:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_11]]] : memref<?xindex>
// CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_11]]] : memref<?xi64>
// CHECK: %[[VAL_14:.*]] = divi_unsigned %[[VAL_13]], %[[VAL_2]] : i64
// CHECK: memref.store %[[VAL_14]], %[[VAL_8]]{{\[}}%[[VAL_12]]] : memref<32xi64>
// CHECK: }
// CHECK: %[[VAL_15:.*]] = memref.tensor_load %[[VAL_8]] : memref<32xi64>
// CHECK: return %[[VAL_15]] : tensor<32xi64>
// CHECK: }
func @divubyc(%arga: tensor<32xi64, #SV>,
%argx: tensor<32xi64> {linalg.inplaceable = true}) -> tensor<32xi64> {
%c = constant 2 : i64
%0 = linalg.generic #traitc
ins(%arga: tensor<32xi64, #SV>)
outs(%argx: tensor<32xi64>) {
^bb(%a: i64, %x: i64):
%0 = divi_unsigned %a, %c : i64
linalg.yield %0 : i64
} -> tensor<32xi64>
return %0 : tensor<32xi64>
gussmith23Unsubmitted
Done
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Is it possible to test the situations where we can't prove that the denominator is zero, and thus code can't be generated? At first I thought that could go in invalid.mlir, but it doesn't seem like that would be the right place.

gussmith23: Is it possible to test the situations where we can't prove that the denominator is zero, and…
aartbikAuthorUnsubmitted
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Not easy with the CHECK, but it will become possible with your unit test!

aartbik: Not easy with the CHECK, but it will become possible with your unit test!
}