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Differential D138172

[mlir][sparse] support affine expression on sparse dimensions (codegen implementation)
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Authored by Peiming on Nov 16 2022, 5:49 PM.

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Reviewers
aartbik
nicolasvasilache
wrengr
bixia
Commits
rGe5e4deca5ee8: [mlir][sparse] support affine expression on sparse dimensions (codegen…

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Peiming created this revision.Nov 16 2022, 5:49 PM
Herald added a reviewer: aartbik. · View Herald TranscriptNov 16 2022, 5:49 PM
Herald added a project: Restricted Project. · View Herald Transcript
Herald added subscribers: Moerafaat, anlunx, bzcheeseman and 21 others. · View Herald Transcript
Peiming requested review of this revision.Nov 16 2022, 5:49 PM
Herald added a reviewer: nicolasvasilache. · View Herald TranscriptNov 16 2022, 5:49 PM
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Herald added subscribers: stephenneuendorffer, nicolasvasilache. · View Herald Transcript
Harbormaster completed remote builds in B198103: Diff 475973.Nov 16 2022, 5:50 PM
Peiming added a parent revision: D138171: [mlir][sparse] support affine expression on sparse dimensions (analysis implementation).Nov 16 2022, 5:51 PM
Peiming added a child revision: D138173: [mlir][sparse] make resolve cycle works with affine expressions..
Peiming added reviewers: wrengr, bixia.Nov 16 2022, 5:54 PM
Peiming mentioned this in D138168: [mlir][sparse] refactoring: move genAffine to loopEmitter.Nov 16 2022, 6:02 PM
Peiming updated this revision to Diff 476524.Nov 18 2022, 10:20 AM

rebase.

Herald added a subscriber: jsetoain. · View Herald TranscriptNov 18 2022, 10:20 AM
Harbormaster completed remote builds in B198500: Diff 476524.Nov 18 2022, 10:37 AM
Peiming updated this revision to Diff 476545.Nov 18 2022, 11:46 AM

fix memory leakage.

Harbormaster completed remote builds in B198514: Diff 476545.Nov 18 2022, 12:04 PM
Peiming mentioned this in rGd083dc63bbaa: [mlir][sparse] refactoring: move genAffine to loopEmitter.Nov 18 2022, 1:39 PM
aartbik accepted this revision.Nov 22 2022, 2:48 PM
aartbik added inline comments.
mlir/lib/Dialect/SparseTensor/Transforms/CodegenUtils.cpp
345

yeah, good todos!

364

an if

This revision is now accepted and ready to land.Nov 22 2022, 2:48 PM
Peiming updated this revision to Diff 477316.Nov 22 2022, 3:02 PM
Peiming marked an inline comment as done.

address comments.

Peiming mentioned this in D138173: [mlir][sparse] make resolve cycle works with affine expressions..Nov 22 2022, 3:06 PM
Peiming updated this revision to Diff 477320.Nov 22 2022, 3:22 PM

add some assertions.

Peiming updated this revision to Diff 477329.Nov 22 2022, 3:42 PM

update comments.

Harbormaster completed remote builds in B199075: Diff 477329.Nov 22 2022, 3:58 PM
Closed by commit rGe5e4deca5ee8: [mlir][sparse] support affine expression on sparse dimensions (codegen… (authored by Peiming). · Explain WhyNov 22 2022, 4:05 PM
This revision was automatically updated to reflect the committed changes.
Peiming added a commit: rGe5e4deca5ee8: [mlir][sparse] support affine expression on sparse dimensions (codegen….

Revision Contents

PathSize
mlir/
lib/
Dialect/
SparseTensor/
Transforms/
64 lines
test/
Dialect/
SparseTensor/
204 lines
Integration/
Dialect/
SparseTensor/
CPU/
128 lines
209 lines
172 lines
219 lines
239 lines

Diff 477316

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

Show First 20 Lines • Show All 326 Lines • ▼ Show 20 LinesOperation *SparseTensorLoopEmitter::enterLoopOverTensorAtDim(
emitExtraLocalsForTensorsAtDenseDims(builder, loc, extraTids, extraDims); emitExtraLocalsForTensorsAtDenseDims(builder, loc, extraTids, extraDims);
return loop; return loop;
} }
Operation *SparseTensorLoopEmitter::enterFilterLoopOverTensorAtDim( Operation *SparseTensorLoopEmitter::enterFilterLoopOverTensorAtDim(
OpBuilder &builder, Location loc, size_t tid, size_t dim, AffineExpr affine, OpBuilder &builder, Location loc, size_t tid, size_t dim, AffineExpr affine,
MutableArrayRef<Value> reduc) { MutableArrayRef<Value> reduc) {
llvm_unreachable("need to be implemented"); assert(!affine.isa<AffineDimExpr>() && !isDenseDLT(dimTypes[tid][dim]));
assert(dimTypes[tid].size() > dim);
// We can not re-enter the same level.
assert(!coord[tid][dim]);
Value step = constantIndex(builder, loc, 1);
Value lo = pidxs[tid][dim];
Value hi = highs[tid][dim];
// TODO: We should instead use a whileOp for filter loop to allow early
aartbikUnsubmitted
Done
ReplyInline Actions

yeah, good todos!

aartbik: yeah, good todos!
// break when exceeding (for ordered dimensions).
// TODO: There are many other potiential opportunities that we might apply in
// the future. E.g., we could use binary search to located the pointer index.
scf::ForOp forOp = builder.create<scf::ForOp>(loc, lo, hi, step, reduc);
// In-place update on the reduction variable vector.
assert(forOp.getNumRegionIterArgs() == reduc.size());
for (int i = 0, e = reduc.size(); i < e; i++)
reduc[i] = forOp.getRegionIterArg(i);
builder.setInsertionPointToStart(forOp.getBody());
Value iv = forOp.getInductionVar();
pidxs[tid][dim] = iv;
// Generating a load on the indices array yields the coordinate.
Value ptr = idxBuffer[tid][dim];
coord[tid][dim] = genIndexLoad(builder, loc, ptr, iv);
// Generate an if condition to filter out indices that is not equal to the
aartbikUnsubmitted
Done
ReplyInline Actions

an if

aartbik: an if
// result of the affine expression.
Value expected = genAffine(builder, affine, loc);
auto pred = builder.create<arith::CmpIOp>(loc, arith::CmpIPredicate::eq,
coord[tid][dim], expected);
SmallVector<Type> types;
for (Value red : reduc) {
types.push_back(red.getType());
}
bool hasReduc = !types.empty();
scf::IfOp ifOp =
builder.create<scf::IfOp>(loc, types, pred, /*else*/ hasReduc);
if (hasReduc) {
// scf.for (a) -> v
// %s = scf.if (a) -> v
// user-generated code.
// else
// yield a
// yield %s
builder.create<scf::YieldOp>(loc, ifOp.getResults());
builder.setInsertionPointToStart(&ifOp.getElseRegion().front());
// On mismatch.
builder.create<scf::YieldOp>(loc, reduc);
}
// Set the insert point to matched branch.
builder.setInsertionPointToStart(&ifOp.getThenRegion().front());
// NOTE: we can also prepares for next dim here in advance
// Push the loop into stack
loopStack.emplace_back(ArrayRef<size_t>(tid), ArrayRef<size_t>(dim), forOp,
coord[tid][dim], nullptr);
return forOp;
} }
void SparseTensorLoopEmitter::genDenseAffineAddressAtCurLevel( void SparseTensorLoopEmitter::genDenseAffineAddressAtCurLevel(
OpBuilder &builder, Location loc, size_t tid, size_t dim, OpBuilder &builder, Location loc, size_t tid, size_t dim,
AffineExpr affine) { AffineExpr affine) {
Value affineV = genAffine(builder, affine, loc); Value affineV = genAffine(builder, affine, loc);
pidxs[tid][dim] = genAddress(builder, loc, tid, dim, affineV); pidxs[tid][dim] = genAddress(builder, loc, tid, dim, affineV);
} }
▲ Show 20 Lines • Show All 171 Lines • ▼ Show 20 Linesvoid SparseTensorLoopEmitter::exitForLoop(RewriterBase &rewriter, Location loc,
MutableArrayRef<Value> reduc) { MutableArrayRef<Value> reduc) {
LoopLevelInfo &loopInfo = loopStack.back(); LoopLevelInfo &loopInfo = loopStack.back();
auto &dims = loopStack.back().dims; auto &dims = loopStack.back().dims;
auto &tids = loopStack.back().tids; auto &tids = loopStack.back().tids;
auto forOp = llvm::dyn_cast<scf::ForOp>(loopInfo.loop); auto forOp = llvm::dyn_cast<scf::ForOp>(loopInfo.loop);
if (forOp) { if (forOp) {
if (!reduc.empty()) { if (!reduc.empty()) {
assert(reduc.size() == forOp.getNumResults()); assert(reduc.size() == forOp.getNumResults());
rewriter.setInsertionPointToEnd(forOp.getBody());
rewriter.create<scf::YieldOp>(loc, reduc); rewriter.create<scf::YieldOp>(loc, reduc);
} }
// Exit the loop. // Exit the loop.
rewriter.setInsertionPointAfter(forOp); rewriter.setInsertionPointAfter(forOp);
// In-place update reduction variables. // In-place update reduction variables.
for (unsigned i = 0, e = forOp.getResults().size(); i < e; i++) for (unsigned i = 0, e = forOp.getResults().size(); i < e; i++)
reduc[i] = forOp.getResult(i); reduc[i] = forOp.getResult(i);
} else { } else {
▲ Show 20 Lines • Show All 578 Lines • Show Last 20 Lines

mlir/test/Dialect/SparseTensor/sparse_affine.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
#SpVec = #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }> #SpVec = #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>
#CSR = #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }> #CSR = #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>
#Row = #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ] }> #Row = #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ] }>
#EncDenseVec = #sparse_tensor.encoding<{ dimLevelType = [ "dense" ] }>
#trait1 = { #trait1 = {
indexing_maps = [ indexing_maps = [
affine_map<(i) -> (i)>, // a affine_map<(i) -> (i)>, // a
affine_map<(i) -> (3)>, // b affine_map<(i) -> (3)>, // b
affine_map<(i) -> (i)> // x (out) affine_map<(i) -> (i)> // x (out)
], ],
iterator_types = ["parallel"], iterator_types = ["parallel"],
Show All 35 Lines outs(%argx: tensor<32xf32>) {
^bb(%a: f32, %b: f32, %x: f32): ^bb(%a: f32, %b: f32, %x: f32):
%0 = arith.mulf %a, %b : f32 %0 = arith.mulf %a, %b : f32
%1 = arith.addf %x, %0 : f32 %1 = arith.addf %x, %0 : f32
linalg.yield %1 : f32 linalg.yield %1 : f32
} -> tensor<32xf32> } -> tensor<32xf32>
return %0 : tensor<32xf32> return %0 : tensor<32xf32>
} }
// CHECK-LABEL: func.func @mul_inv_sparse1d(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<4xf32, #sparse_tensor.encoding<{{{.*}}}>>)
// CHECK: %[[VAL_2:.*]] = arith.constant 0 : index
// CHECK: %[[VAL_3:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_4:.*]] = arith.constant 3 : index
// CHECK: %[[VAL_5:.*]] = arith.constant 0.000000e+00 : f32
// CHECK: %[[VAL_6:.*]] = bufferization.alloc_tensor() : tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
// CHECK: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xf32>
// CHECK: %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 0 : index} : tensor<4xf32, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
// CHECK: %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_1]] {dimension = 0 : index} : tensor<4xf32, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
// CHECK: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<4xf32, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xf32>
// CHECK: %[[VAL_12:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_2]]] : memref<?xindex>
// CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_3]]] : memref<?xindex>
// CHECK: %[[VAL_14:.*]] = scf.for %[[VAL_15:.*]] = %[[VAL_12]] to %[[VAL_13]] step %[[VAL_3]] iter_args(%[[VAL_16:.*]] = %[[VAL_6]]) -> (tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>>) {
// CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_15]]] : memref<?xindex>
// CHECK: %[[VAL_18:.*]] = arith.cmpi eq, %[[VAL_17]], %[[VAL_4]] : index
// CHECK: %[[VAL_19:.*]] = scf.if %[[VAL_18]] -> (tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>>) {
// CHECK: %[[VAL_20:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_15]]] : memref<?xf32>
// CHECK: %[[VAL_21:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_2]]] : memref<?xindex>
// CHECK: %[[VAL_22:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_3]]] : memref<?xindex>
// CHECK: %[[VAL_23:.*]] = scf.for %[[VAL_24:.*]] = %[[VAL_21]] to %[[VAL_22]] step %[[VAL_3]] iter_args(%[[VAL_25:.*]] = %[[VAL_16]]) -> (tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>>) {
// CHECK: %[[VAL_26:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_24]]] : memref<?xf32>
// CHECK: %[[VAL_27:.*]] = arith.mulf %[[VAL_26]], %[[VAL_20]] : f32
// CHECK: %[[VAL_28:.*]] = arith.addf %[[VAL_27]], %[[VAL_5]] : f32
// CHECK: %[[VAL_29:.*]] = sparse_tensor.insert %[[VAL_28]] into %[[VAL_25]]{{\[}}%[[VAL_17]]] : tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: scf.yield %[[VAL_29]] : tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: }
// CHECK: scf.yield %[[VAL_30:.*]] : tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: } else {
// CHECK: scf.yield %[[VAL_16]] : tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: }
// CHECK: scf.yield %[[VAL_31:.*]] : tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: }
// CHECK: %[[VAL_32:.*]] = sparse_tensor.load %[[VAL_33:.*]] hasInserts : tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: return %[[VAL_32]] : tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>>
func.func @mul_inv_sparse1d(%arga: tensor<32xf32, #SpVec>,
%argb: tensor<4xf32, #SpVec>) -> tensor<32xf32, #SpVec> {
%argx = bufferization.alloc_tensor() : tensor<32xf32, #SpVec>
%0 = linalg.generic #trait1
ins(%arga, %argb: tensor<32xf32, #SpVec>, tensor<4xf32, #SpVec>)
outs(%argx: tensor<32xf32, #SpVec>) {
^bb(%a: f32, %b: f32, %x: f32):
%0 = arith.mulf %a, %b : f32
%1 = arith.addf %x, %0 : f32
linalg.yield %1 : f32
} -> tensor<32xf32, #SpVec>
return %0 : tensor<32xf32, #SpVec>
}
// CHECK-LABEL: func.func @mul_inv_enc_dense1d(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<4xf32, #sparse_tensor.encoding<{{{.*}}}>>) -> tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>> {
// CHECK: %[[VAL_2:.*]] = arith.constant 32 : index
// CHECK: %[[VAL_3:.*]] = arith.constant 3 : index
// CHECK: %[[VAL_4:.*]] = arith.constant 0 : index
// CHECK: %[[VAL_5:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_6:.*]] = bufferization.alloc_tensor() : tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xf32>
// CHECK: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<4xf32, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xf32>
// CHECK: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_6]] : tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xf32>
// CHECK: %[[VAL_10:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_3]]] : memref<?xf32>
// CHECK: scf.for %[[VAL_11:.*]] = %[[VAL_4]] to %[[VAL_2]] step %[[VAL_5]] {
// CHECK: %[[VAL_12:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_11]]] : memref<?xf32>
// CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_11]]] : memref<?xf32>
// CHECK: %[[VAL_14:.*]] = arith.mulf %[[VAL_13]], %[[VAL_10]] : f32
// CHECK: %[[VAL_15:.*]] = arith.addf %[[VAL_12]], %[[VAL_14]] : f32
// CHECK: memref.store %[[VAL_15]], %[[VAL_9]]{{\[}}%[[VAL_11]]] : memref<?xf32>
// CHECK: }
// CHECK: %[[VAL_16:.*]] = sparse_tensor.load %[[VAL_6]] : tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: return %[[VAL_16]] : tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: }
func.func @mul_inv_enc_dense1d(%arga: tensor<32xf32, #EncDenseVec>,
%argb: tensor<4xf32, #EncDenseVec>) -> tensor<32xf32, #EncDenseVec> {
%argx = bufferization.alloc_tensor() : tensor<32xf32, #EncDenseVec>
%0 = linalg.generic #trait1
ins(%arga, %argb: tensor<32xf32, #EncDenseVec>, tensor<4xf32, #EncDenseVec>)
outs(%argx: tensor<32xf32, #EncDenseVec>) {
^bb(%a: f32, %b: f32, %x: f32):
%0 = arith.mulf %a, %b : f32
%1 = arith.addf %x, %0 : f32
linalg.yield %1 : f32
} -> tensor<32xf32, #EncDenseVec>
return %0 : tensor<32xf32, #EncDenseVec>
}
#trait2 = { #trait2 = {
indexing_maps = [ indexing_maps = [
affine_map<(i) -> (i)>, // a affine_map<(i) -> (i)>, // a
affine_map<(i) -> (i+2)>, // b affine_map<(i) -> (i+2)>, // b
affine_map<(i) -> (i)> // x (out) affine_map<(i) -> (i)> // x (out)
], ],
iterator_types = ["parallel"], iterator_types = ["parallel"],
doc = "x(i) = a(i) & b(i+2)" doc = "x(i) = a(i) & b(i+2)"
Show All 34 Lines %0 = linalg.generic #trait2
outs(%argx: tensor<32xi32>) { outs(%argx: tensor<32xi32>) {
^bb(%a: i32, %b: i32, %x: i32): ^bb(%a: i32, %b: i32, %x: i32):
%0 = arith.andi %a, %b : i32 %0 = arith.andi %a, %b : i32
linalg.yield %0 : i32 linalg.yield %0 : i32
} -> tensor<32xi32> } -> tensor<32xi32>
return %0 : tensor<32xi32> return %0 : tensor<32xi32>
} }
// CHECK-LABEL: func.func @and_affine_sparse1d(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xi32, #sparse_tensor.encoding<{{{.*}}}>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<34xi32, #sparse_tensor.encoding<{{{.*}}}>>)
// CHECK: %[[VAL_2:.*]] = arith.constant 0 : index
// CHECK: %[[VAL_3:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_4:.*]] = arith.constant 2 : index
// CHECK: %[[VAL_5:.*]] = bufferization.alloc_tensor() : tensor<32xi32, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<32xi32, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
// CHECK: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<32xi32, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
// CHECK: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xi32, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xi32>
// CHECK: %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 0 : index} : tensor<34xi32, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
// CHECK: %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_1]] {dimension = 0 : index} : tensor<34xi32, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
// CHECK: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<34xi32, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xi32>
// CHECK: %[[VAL_12:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_2]]] : memref<?xindex>
// CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_3]]] : memref<?xindex>
// CHECK: %[[VAL_14:.*]] = scf.for %[[VAL_15:.*]] = %[[VAL_12]] to %[[VAL_13]] step %[[VAL_3]] iter_args(%[[VAL_16:.*]] = %[[VAL_5]]) -> (tensor<32xi32, #sparse_tensor.encoding<{{{.*}}}>>) {
// CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_15]]] : memref<?xindex>
// CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_15]]] : memref<?xi32>
// CHECK: %[[VAL_19:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_2]]] : memref<?xindex>
// CHECK: %[[VAL_20:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_3]]] : memref<?xindex>
// CHECK: %[[VAL_21:.*]] = scf.for %[[VAL_22:.*]] = %[[VAL_19]] to %[[VAL_20]] step %[[VAL_3]] iter_args(%[[VAL_23:.*]] = %[[VAL_16]]) -> (tensor<32xi32, #sparse_tensor.encoding<{{{.*}}}>>) {
// CHECK: %[[VAL_24:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_22]]] : memref<?xindex>
// CHECK: %[[VAL_25:.*]] = arith.addi %[[VAL_17]], %[[VAL_4]] : index
// CHECK: %[[VAL_26:.*]] = arith.cmpi eq, %[[VAL_24]], %[[VAL_25]] : index
// CHECK: %[[VAL_27:.*]] = scf.if %[[VAL_26]] -> (tensor<32xi32, #sparse_tensor.encoding<{{{.*}}}>>) {
// CHECK: %[[VAL_28:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_22]]] : memref<?xi32>
// CHECK: %[[VAL_29:.*]] = arith.andi %[[VAL_18]], %[[VAL_28]] : i32
// CHECK: %[[VAL_30:.*]] = sparse_tensor.insert %[[VAL_29]] into %[[VAL_23]]{{\[}}%[[VAL_17]]] : tensor<32xi32, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: scf.yield %[[VAL_30]] : tensor<32xi32, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: } else {
// CHECK: scf.yield %[[VAL_23]] : tensor<32xi32, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: }
// CHECK: scf.yield %[[VAL_31:.*]] : tensor<32xi32, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: }
// CHECK: scf.yield %[[VAL_32:.*]] : tensor<32xi32, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: }
// CHECK: %[[VAL_33:.*]] = sparse_tensor.load %[[VAL_34:.*]] hasInserts : tensor<32xi32, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: return %[[VAL_33]] : tensor<32xi32, #sparse_tensor.encoding<{{{.*}}}>>
func.func @and_affine_sparse1d(%arga: tensor<32xi32, #SpVec>,
%argb: tensor<34xi32, #SpVec>) -> tensor<32xi32, #SpVec> {
%argx = bufferization.alloc_tensor() : tensor<32xi32, #SpVec>
%0 = linalg.generic #trait2
ins(%arga, %argb: tensor<32xi32, #SpVec>, tensor<34xi32, #SpVec>)
outs(%argx: tensor<32xi32, #SpVec>) {
^bb(%a: i32, %b: i32, %x: i32):
%0 = arith.andi %a, %b : i32
linalg.yield %0 : i32
} -> tensor<32xi32, #SpVec>
return %0 : tensor<32xi32, #SpVec>
}
#trait3 = { #trait3 = {
indexing_maps = [ indexing_maps = [
affine_map<(i,j) -> (i,j)>, // a affine_map<(i,j) -> (i,j)>, // a
affine_map<(i,j) -> (i+2,j+3)>, // b affine_map<(i,j) -> (i+2,j+3)>, // b
affine_map<(i,j) -> (i,j)> // x (out) affine_map<(i,j) -> (i,j)> // x (out)
], ],
iterator_types = ["parallel","parallel"], iterator_types = ["parallel","parallel"],
doc = "x(i,j) += a(i,j) * b(i+2,j+3)" doc = "x(i,j) += a(i,j) * b(i+2,j+3)"
▲ Show 20 Lines • Show All 41 Lines • ▼ Show 20 Lines outs(%argx: tensor<32x16xf64>) {
^bb(%a: f64, %b: f64, %x: f64): ^bb(%a: f64, %b: f64, %x: f64):
%0 = arith.mulf %a, %b : f64 %0 = arith.mulf %a, %b : f64
%1 = arith.addf %x, %0 : f64 %1 = arith.addf %x, %0 : f64
linalg.yield %1 : f64 linalg.yield %1 : f64
} -> tensor<32x16xf64> } -> tensor<32x16xf64>
return %0 : tensor<32x16xf64> return %0 : tensor<32x16xf64>
} }
// CHECK-LABEL: func.func @mul_affine_sparse2d(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<34x19xf64, #sparse_tensor.encoding<{{{.*}}}>>) -> tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>> {
// CHECK: %[[VAL_2:.*]] = arith.constant 32 : index
// CHECK: %[[VAL_3:.*]] = arith.constant 0 : index
// CHECK: %[[VAL_4:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_5:.*]] = arith.constant 2 : index
// CHECK: %[[VAL_6:.*]] = arith.constant 0.000000e+00 : f64
// CHECK: %[[VAL_7:.*]] = arith.constant 3 : index
// CHECK: %[[VAL_8:.*]] = bufferization.alloc_tensor() : tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 1 : index} : tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
// CHECK: %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 1 : index} : tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
// CHECK: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xf64>
// CHECK: %[[VAL_12:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 1 : index} : tensor<34x19xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
// CHECK: %[[VAL_13:.*]] = sparse_tensor.indices %[[VAL_1]] {dimension = 1 : index} : tensor<34x19xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
// CHECK: %[[VAL_14:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<34x19xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xf64>
// CHECK: %[[VAL_15:.*]] = scf.for %[[VAL_16:.*]] = %[[VAL_3]] to %[[VAL_2]] step %[[VAL_4]] iter_args(%[[VAL_17:.*]] = %[[VAL_8]]) -> (tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>>) {
// CHECK: %[[VAL_18:.*]] = arith.addi %[[VAL_16]], %[[VAL_5]] : index
// CHECK: %[[VAL_19:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_16]]] : memref<?xindex>
// CHECK: %[[VAL_20:.*]] = arith.addi %[[VAL_16]], %[[VAL_4]] : index
// CHECK: %[[VAL_21:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_20]]] : memref<?xindex>
// CHECK: %[[VAL_22:.*]] = scf.for %[[VAL_23:.*]] = %[[VAL_19]] to %[[VAL_21]] step %[[VAL_4]] iter_args(%[[VAL_24:.*]] = %[[VAL_17]]) -> (tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>>) {
// CHECK: %[[VAL_25:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_23]]] : memref<?xindex>
// CHECK: %[[VAL_26:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_23]]] : memref<?xf64>
// CHECK: %[[VAL_27:.*]] = memref.load %[[VAL_12]]{{\[}}%[[VAL_18]]] : memref<?xindex>
// CHECK: %[[VAL_28:.*]] = arith.addi %[[VAL_18]], %[[VAL_4]] : index
// CHECK: %[[VAL_29:.*]] = memref.load %[[VAL_12]]{{\[}}%[[VAL_28]]] : memref<?xindex>
// CHECK: %[[VAL_30:.*]]:2 = scf.for %[[VAL_31:.*]] = %[[VAL_27]] to %[[VAL_29]] step %[[VAL_4]] iter_args(%[[VAL_32:.*]] = %[[VAL_6]], %[[VAL_33:.*]] = %[[VAL_24]]) -> (f64, tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>>) {
// CHECK: %[[VAL_34:.*]] = memref.load %[[VAL_13]]{{\[}}%[[VAL_31]]] : memref<?xindex>
// CHECK: %[[VAL_35:.*]] = arith.addi %[[VAL_25]], %[[VAL_7]] : index
// CHECK: %[[VAL_36:.*]] = arith.cmpi eq, %[[VAL_34]], %[[VAL_35]] : index
// CHECK: %[[VAL_37:.*]]:2 = scf.if %[[VAL_36]] -> (f64, tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>>) {
// CHECK: %[[VAL_38:.*]] = memref.load %[[VAL_14]]{{\[}}%[[VAL_31]]] : memref<?xf64>
// CHECK: %[[VAL_39:.*]] = arith.mulf %[[VAL_26]], %[[VAL_38]] : f64
// CHECK: %[[VAL_40:.*]] = arith.addf %[[VAL_32]], %[[VAL_39]] : f64
// CHECK: scf.yield %[[VAL_40]], %[[VAL_33]] : f64, tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: } else {
// CHECK: scf.yield %[[VAL_32]], %[[VAL_33]] : f64, tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: }
// CHECK: scf.yield %[[VAL_41:.*]]#0, %[[VAL_41]]#1 : f64, tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: }
// CHECK: %[[VAL_42:.*]] = sparse_tensor.insert %[[VAL_43:.*]]#0 into %[[VAL_43]]#1{{\[}}%[[VAL_16]], %[[VAL_25]]] : tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: scf.yield %[[VAL_42]] : tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: }
// CHECK: scf.yield %[[VAL_44:.*]] : tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: }
// CHECK: %[[VAL_45:.*]] = sparse_tensor.load %[[VAL_46:.*]] hasInserts : tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: return %[[VAL_45]] : tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>>
func.func @mul_affine_sparse2d(%arga: tensor<32x16xf64, #CSR>,
%argb: tensor<34x19xf64, #CSR>) -> tensor<32x16xf64, #CSR> {
%argx = bufferization.alloc_tensor() : tensor<32x16xf64, #CSR>
%0 = linalg.generic #trait3
ins(%arga, %argb: tensor<32x16xf64, #CSR>, tensor<34x19xf64, #CSR>)
outs(%argx: tensor<32x16xf64, #CSR>) {
^bb(%a: f64, %b: f64, %x: f64):
%0 = arith.mulf %a, %b : f64
%1 = arith.addf %x, %0 : f64
linalg.yield %1 : f64
} -> tensor<32x16xf64, #CSR>
return %0 : tensor<32x16xf64, #CSR>
}
#trait4 = { #trait4 = {
indexing_maps = [ indexing_maps = [
affine_map<(i,j) -> (i+2,j)>, // a affine_map<(i,j) -> (i+2,j)>, // a
affine_map<(i,j) -> (i,j+3)>, // b affine_map<(i,j) -> (i,j+3)>, // b
affine_map<(i,j) -> (i,j)> // x (out) affine_map<(i,j) -> (i,j)> // x (out)
], ],
iterator_types = ["parallel","parallel"], iterator_types = ["parallel","parallel"],
doc = "x(i,j) += a(i+2,j) * b(i,j+3)" doc = "x(i,j) += a(i+2,j) * b(i,j+3)"
▲ Show 20 Lines • Show All 108 Lines • ▼ Show 20 Lines %0 = linalg.generic #trait5
outs(%argx: tensor<32x16xf64>) { outs(%argx: tensor<32x16xf64>) {
^bb(%a: f64, %b: f64, %x: f64): ^bb(%a: f64, %b: f64, %x: f64):
%0 = arith.mulf %a, %b : f64 %0 = arith.mulf %a, %b : f64
%1 = arith.addf %x, %0 : f64 %1 = arith.addf %x, %0 : f64
linalg.yield %1 : f64 linalg.yield %1 : f64
} -> tensor<32x16xf64> } -> tensor<32x16xf64>
return %0 : tensor<32x16xf64> return %0 : tensor<32x16xf64>
} }

mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_conv_1d_nwc_wcf.mlir

  • This file was added.
// RUN: mlir-opt %s --sparse-compiler=enable-runtime-library=true | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s
// RUN: mlir-opt %s --sparse-compiler="enable-runtime-library=false enable-buffer-initialization=true" | \
// RUN: mlir-cpu-runner \
// RUN: -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s
#CCC = #sparse_tensor.encoding<{
dimLevelType = [ "compressed", "compressed", "compressed" ] }>
#CDC = #sparse_tensor.encoding<{
dimLevelType = [ "compressed", "dense", "compressed" ]
// FIXME: Still inadmissible might need investigation
// dimOrdering = affine_map<(i,j,k) -> (j,k,i)>
}>
// Creates and returns 3-D buffer of size (%s1, %s2, %s3) filled with the value %f
func.func @alloc_3d_filled_f32(%s1 : index, %s2 : index, %s3 : index, %f : f32) -> tensor<?x?x?xf32> {
%buf = bufferization.alloc_tensor(%s1, %s2, %s3) : tensor<?x?x?xf32>
%ret = linalg.fill ins(%f : f32) outs(%buf : tensor<?x?x?xf32>) -> tensor<?x?x?xf32>
return %ret : tensor<?x?x?xf32>
}
func.func @conv_1d_nwc_wcf(%arg0: tensor<?x?x?xf32>, %arg1: tensor<?x?x?xf32>, %arg2: tensor<?x?x?xf32>) -> tensor<?x?x?xf32> {
%ret = linalg.conv_1d_nwc_wcf {dilations = dense<1> : tensor<1xi64>,
strides = dense<1> : tensor<1xi64>}
ins (%arg0, %arg1: tensor<?x?x?xf32>, tensor<?x?x?xf32>)
outs (%arg2: tensor<?x?x?xf32>) -> tensor<?x?x?xf32>
return %ret : tensor<?x?x?xf32>
}
func.func @conv_1d_nwc_wcf_CCC(%arg0: tensor<?x?x?xf32, #CCC>, %arg1: tensor<?x?x?xf32, #CCC>) -> tensor<?x?x?xf32, #CCC> {
%c1 = arith.constant 1 : index
%c3 = arith.constant 3 : index
%c6 = arith.constant 6 : index
%s = bufferization.alloc_tensor(%c3, %c6, %c1) : tensor<?x?x?xf32, #CCC>
%ret = linalg.conv_1d_nwc_wcf {dilations = dense<1> : tensor<1xi64>,
strides = dense<1> : tensor<1xi64>}
ins (%arg0, %arg1: tensor<?x?x?xf32, #CCC>, tensor<?x?x?xf32, #CCC>)
outs (%s: tensor<?x?x?xf32, #CCC>) -> tensor<?x?x?xf32, #CCC>
return %ret : tensor<?x?x?xf32, #CCC>
}
func.func @conv_1d_nwc_wcf_CDC(%arg0: tensor<?x?x?xf32, #CDC>, %arg1: tensor<?x?x?xf32, #CDC>) -> tensor<?x?x?xf32, #CDC> {
%c1 = arith.constant 1 : index
%c3 = arith.constant 3 : index
%c6 = arith.constant 6 : index
%s = bufferization.alloc_tensor(%c3, %c6, %c1) : tensor<?x?x?xf32, #CDC>
%ret = linalg.conv_1d_nwc_wcf {dilations = dense<1> : tensor<1xi64>,
strides = dense<1> : tensor<1xi64>}
ins (%arg0, %arg1: tensor<?x?x?xf32, #CDC>, tensor<?x?x?xf32, #CDC>)
outs (%s: tensor<?x?x?xf32, #CDC>) -> tensor<?x?x?xf32, #CDC>
return %ret : tensor<?x?x?xf32, #CDC>
}
func.func @entry() {
%c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index
%c3 = arith.constant 3 : index
%c6 = arith.constant 6 : index
%c8 = arith.constant 8 : index
%f10 = arith.constant 10.00000e+00 : f32
%val = arith.constant 2.00000e+00 : f32
%zero = arith.constant 0.00000e+00 : f32
%in1D_tmp = call @alloc_3d_filled_f32(%c3, %c8, %c1, %val) : (index, index, index, f32) -> (tensor<?x?x?xf32>)
%in1D_nwc = tensor.insert %f10 into %in1D_tmp[%c0, %c3, %c0] : tensor<?x?x?xf32>
%filter1D_nwc = call @alloc_3d_filled_f32(%c3, %c1, %c1, %val) : (index, index, index, f32) -> (tensor<?x?x?xf32>)
%out1D_nwc = call @alloc_3d_filled_f32(%c3, %c6, %c1, %zero) : (index, index, index, f32) -> (tensor<?x?x?xf32>)
%in1D_nwc_CCC = sparse_tensor.convert %in1D_nwc
: tensor<?x?x?xf32> to tensor<?x?x?xf32, #CCC>
%filter1D_nwc_CCC = sparse_tensor.convert %filter1D_nwc
: tensor<?x?x?xf32> to tensor<?x?x?xf32, #CCC>
%in1D_nwc_CDC = sparse_tensor.convert %in1D_nwc
: tensor<?x?x?xf32> to tensor<?x?x?xf32, #CDC>
%filter1D_nwc_CDC = sparse_tensor.convert %filter1D_nwc
: tensor<?x?x?xf32> to tensor<?x?x?xf32, #CDC>
%dense_ret = call @conv_1d_nwc_wcf(%in1D_nwc, %filter1D_nwc, %out1D_nwc) : (tensor<?x?x?xf32>, tensor<?x?x?xf32>, tensor<?x?x?xf32>) -> (tensor<?x?x?xf32>)
%CCC_ret = call @conv_1d_nwc_wcf_CCC(%in1D_nwc_CCC, %filter1D_nwc_CCC) : (tensor<?x?x?xf32, #CCC>, tensor<?x?x?xf32, #CCC>) -> (tensor<?x?x?xf32, #CCC>)
%CDC_ret = call @conv_1d_nwc_wcf_CDC(%in1D_nwc_CDC, %filter1D_nwc_CDC) : (tensor<?x?x?xf32, #CDC>, tensor<?x?x?xf32, #CDC>) -> (tensor<?x?x?xf32, #CDC>)
// CHECK: ( ( ( 12 ), ( 28 ), ( 28 ), ( 28 ), ( 12 ), ( 12 ) ),
// CHECK-SAME: ( ( 12 ), ( 12 ), ( 12 ), ( 12 ), ( 12 ), ( 12 ) ),
// CHECK-SAME: ( ( 12 ), ( 12 ), ( 12 ), ( 12 ), ( 12 ), ( 12 ) ) )
%dense_v = vector.transfer_read %dense_ret[%c0, %c0, %c0], %zero
: tensor<?x?x?xf32>, vector<3x6x1xf32>
vector.print %dense_v : vector<3x6x1xf32>
// CHECK: ( ( ( 12 ), ( 28 ), ( 28 ), ( 28 ), ( 12 ), ( 12 ) ),
// CHECK-SAME: ( ( 12 ), ( 12 ), ( 12 ), ( 12 ), ( 12 ), ( 12 ) ),
// CHECK-SAME: ( ( 12 ), ( 12 ), ( 12 ), ( 12 ), ( 12 ), ( 12 ) ) )
%1 = sparse_tensor.convert %CCC_ret
: tensor<?x?x?xf32, #CCC> to tensor<?x?x?xf32>
%v1 = vector.transfer_read %1[%c0, %c0, %c0], %zero
: tensor<?x?x?xf32>, vector<3x6x1xf32>
vector.print %v1 : vector<3x6x1xf32>
// CHECK: ( ( ( 12 ), ( 28 ), ( 28 ), ( 28 ), ( 12 ), ( 12 ) ),
// CHECK-SAME: ( ( 12 ), ( 12 ), ( 12 ), ( 12 ), ( 12 ), ( 12 ) ),
// CHECK-SAME: ( ( 12 ), ( 12 ), ( 12 ), ( 12 ), ( 12 ), ( 12 ) ) )
%2 = sparse_tensor.convert %CDC_ret
: tensor<?x?x?xf32, #CDC> to tensor<?x?x?xf32>
%v2 = vector.transfer_read %2[%c0, %c0, %c0], %zero
: tensor<?x?x?xf32>, vector<3x6x1xf32>
vector.print %v2 : vector<3x6x1xf32>
// Free the resources
bufferization.dealloc_tensor %in1D_nwc : tensor<?x?x?xf32>
bufferization.dealloc_tensor %filter1D_nwc : tensor<?x?x?xf32>
bufferization.dealloc_tensor %out1D_nwc : tensor<?x?x?xf32>
bufferization.dealloc_tensor %in1D_nwc_CDC : tensor<?x?x?xf32, #CDC>
bufferization.dealloc_tensor %filter1D_nwc_CDC : tensor<?x?x?xf32, #CDC>
bufferization.dealloc_tensor %in1D_nwc_CCC : tensor<?x?x?xf32, #CCC>
bufferization.dealloc_tensor %filter1D_nwc_CCC : tensor<?x?x?xf32, #CCC>
bufferization.dealloc_tensor %CCC_ret : tensor<?x?x?xf32, #CCC>
bufferization.dealloc_tensor %CDC_ret : tensor<?x?x?xf32, #CDC>
return
}

mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_conv_2d.mlir

  • This file was added.
// RUN: mlir-opt %s --sparse-compiler=enable-runtime-library=true | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s
// RUN: mlir-opt %s --sparse-compiler="enable-runtime-library=false enable-buffer-initialization=true" | \
// RUN: mlir-cpu-runner \
// RUN: -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s
#DCSR = #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>
#CSR = #sparse_tensor.encoding<{dimLevelType = ["dense", "compressed"]}>
#CSC = #sparse_tensor.encoding<{
dimLevelType = [ "dense", "compressed" ],
dimOrdering = affine_map<(i,j) -> (j,i)>
}>
// An example of a 2D convolution with a sparse filter.
module {
func.func @conv2d(%input: tensor<8x8xi32>,
%filter: tensor<3x3xi32, #DCSR>,
%output: tensor<6x6xi32>) -> tensor<6x6xi32> {
%0 = linalg.conv_2d
ins (%input, %filter: tensor<8x8xi32>, tensor<3x3xi32, #DCSR>)
outs (%output: tensor<6x6xi32>) -> tensor<6x6xi32>
return %0 : tensor<6x6xi32>
}
func.func @conv2d_sparse_out(%input: tensor<8x8xi32>,
%filter: tensor<3x3xi32, #DCSR>) -> tensor<6x6xi32, #DCSR> {
%s = bufferization.alloc_tensor() : tensor<6x6xi32, #DCSR>
%0 = linalg.conv_2d
ins (%input, %filter: tensor<8x8xi32>, tensor<3x3xi32, #DCSR>)
outs (%s: tensor<6x6xi32, #DCSR>) -> tensor<6x6xi32, #DCSR>
return %0 : tensor<6x6xi32, #DCSR>
}
func.func @conv2d_all_sparse_DCSR(%input: tensor<8x8xi32, #DCSR>,
%filter: tensor<3x3xi32, #DCSR>) -> tensor<6x6xi32, #DCSR> {
%s = bufferization.alloc_tensor() : tensor<6x6xi32, #DCSR>
%0 = linalg.conv_2d
ins (%input, %filter: tensor<8x8xi32, #DCSR>, tensor<3x3xi32, #DCSR>)
outs (%s: tensor<6x6xi32, #DCSR>) -> tensor<6x6xi32, #DCSR>
return %0 : tensor<6x6xi32, #DCSR>
}
func.func @conv2d_all_sparse_CSR(%input: tensor<8x8xi32, #CSR>,
%filter: tensor<3x3xi32, #CSR>) -> tensor<6x6xi32, #CSR> {
%s = bufferization.alloc_tensor() : tensor<6x6xi32, #CSR>
%0 = linalg.conv_2d
ins (%input, %filter: tensor<8x8xi32, #CSR>, tensor<3x3xi32, #CSR>)
outs (%s: tensor<6x6xi32, #CSR>) -> tensor<6x6xi32, #CSR>
return %0 : tensor<6x6xi32, #CSR>
}
func.func @conv2d_all_sparse_CSC(%input: tensor<8x8xi32, #CSC>,
%filter: tensor<3x3xi32, #CSC>) -> tensor<6x6xi32, #CSC> {
%s = bufferization.alloc_tensor() : tensor<6x6xi32, #CSC>
%0 = linalg.conv_2d
ins (%input, %filter: tensor<8x8xi32, #CSC>, tensor<3x3xi32, #CSC>)
outs (%s: tensor<6x6xi32, #CSC>) -> tensor<6x6xi32, #CSC>
return %0 : tensor<6x6xi32, #CSC>
}
func.func @entry() {
%c0 = arith.constant 0 : index
%i0 = arith.constant 0 : i32
// A typical edge detection filter.
%filter = arith.constant dense<[
[ 1, 0, -1 ],
[ 0, 0, 0 ],
[ -1, 0, 1 ]
]> : tensor<3x3xi32>
%sparse_filter_DCSR = sparse_tensor.convert %filter
: tensor<3x3xi32> to tensor<3x3xi32, #DCSR>
%sparse_filter_CSR = sparse_tensor.convert %filter
: tensor<3x3xi32> to tensor<3x3xi32, #CSR>
%sparse_filter_CSC = sparse_tensor.convert %filter
: tensor<3x3xi32> to tensor<3x3xi32, #CSC>
%input = arith.constant dense<[
[ 1, 2, 3, 4, 0, 6, 7, 8 ],
[ 2, 2, 4, 4, 0, 0, 6, 8 ],
[ 2, 2, 4, 4, 0, 0, 6, 8 ],
[ 2, 2, 3, 4, 0, 0, 7, 8 ],
[ 1, 3, 3, 4, 0, 0, 6, 8 ],
[ 3, 2, 3, 4, 0, 0, 7, 8 ],
[ 1, 3, 3, 4, 3, 6, 6, 8 ],
[ 1, 3, 3, 4, 3, 0, 7, 8 ]
]> : tensor<8x8xi32>
%sparse_input_DCSR = sparse_tensor.convert %input
: tensor<8x8xi32> to tensor<8x8xi32, #DCSR>
%sparse_input_CSR = sparse_tensor.convert %input
: tensor<8x8xi32> to tensor<8x8xi32, #CSR>
%sparse_input_CSC = sparse_tensor.convert %input
: tensor<8x8xi32> to tensor<8x8xi32, #CSC>
// Call the kernel.
%output = arith.constant dense<0> : tensor<6x6xi32>
%0 = call @conv2d(%input, %sparse_filter_DCSR, %output)
: (tensor<8x8xi32>,
tensor<3x3xi32, #DCSR>, tensor<6x6xi32>) -> tensor<6x6xi32>
%1 = call @conv2d_sparse_out(%input, %sparse_filter_DCSR)
: (tensor<8x8xi32>,
tensor<3x3xi32, #DCSR>) -> tensor<6x6xi32, #DCSR>
%2 = call @conv2d_all_sparse_DCSR(%sparse_input_DCSR, %sparse_filter_DCSR)
: (tensor<8x8xi32, #DCSR>,
tensor<3x3xi32, #DCSR>) -> tensor<6x6xi32, #DCSR>
%3 = call @conv2d_all_sparse_CSR(%sparse_input_CSR, %sparse_filter_CSR)
: (tensor<8x8xi32, #CSR>,
tensor<3x3xi32, #CSR>) -> tensor<6x6xi32, #CSR>
%4 = call @conv2d_all_sparse_CSC(%sparse_input_CSC, %sparse_filter_CSC)
: (tensor<8x8xi32, #CSC>,
tensor<3x3xi32, #CSC>) -> tensor<6x6xi32, #CSC>
// Verify the output.
//
// CHECK: ( ( 0, 0, -1, -6, -1, 6 ),
// CHECK-SAME: ( -1, 0, 1, 0, 1, 0 ),
// CHECK-SAME: ( 0, -1, 1, 0, 0, 0 ),
// CHECK-SAME: ( -1, 0, 0, 0, 0, 0 ),
// CHECK-SAME: ( 0, 0, 3, 6, -3, -6 ),
// CHECK-SAME: ( 2, -1, 3, 0, -3, 0 ) )
//
%v = vector.transfer_read %0[%c0, %c0], %i0
: tensor<6x6xi32>, vector<6x6xi32>
vector.print %v : vector<6x6xi32>
//
// Should be the same as dense output
// CHECK: ( ( 0, 0, -1, -6, -1, 6 ),
// CHECK-SAME: ( -1, 0, 1, 0, 1, 0 ),
// CHECK-SAME: ( 0, -1, 1, 0, 0, 0 ),
// CHECK-SAME: ( -1, 0, 0, 0, 0, 0 ),
// CHECK-SAME: ( 0, 0, 3, 6, -3, -6 ),
// CHECK-SAME: ( 2, -1, 3, 0, -3, 0 ) )
//
%sparse_ret = sparse_tensor.convert %1
: tensor<6x6xi32, #DCSR> to tensor<6x6xi32>
%v1 = vector.transfer_read %sparse_ret[%c0, %c0], %i0
: tensor<6x6xi32>, vector<6x6xi32>
vector.print %v1 : vector<6x6xi32>
//
// Should be the same as dense output
// CHECK: ( ( 0, 0, -1, -6, -1, 6 ),
// CHECK-SAME: ( -1, 0, 1, 0, 1, 0 ),
// CHECK-SAME: ( 0, -1, 1, 0, 0, 0 ),
// CHECK-SAME: ( -1, 0, 0, 0, 0, 0 ),
// CHECK-SAME: ( 0, 0, 3, 6, -3, -6 ),
// CHECK-SAME: ( 2, -1, 3, 0, -3, 0 ) )
//
%all_sparse_DCSR = sparse_tensor.convert %2
: tensor<6x6xi32, #DCSR> to tensor<6x6xi32>
%v2 = vector.transfer_read %all_sparse_DCSR[%c0, %c0], %i0
: tensor<6x6xi32>, vector<6x6xi32>
vector.print %v2 : vector<6x6xi32>
//
// Should be the same as dense output
// CHECK: ( ( 0, 0, -1, -6, -1, 6 ),
// CHECK-SAME: ( -1, 0, 1, 0, 1, 0 ),
// CHECK-SAME: ( 0, -1, 1, 0, 0, 0 ),
// CHECK-SAME: ( -1, 0, 0, 0, 0, 0 ),
// CHECK-SAME: ( 0, 0, 3, 6, -3, -6 ),
// CHECK-SAME: ( 2, -1, 3, 0, -3, 0 ) )
//
%all_sparse_CSR = sparse_tensor.convert %3
: tensor<6x6xi32, #CSR> to tensor<6x6xi32>
%v3 = vector.transfer_read %all_sparse_CSR[%c0, %c0], %i0
: tensor<6x6xi32>, vector<6x6xi32>
vector.print %v3 : vector<6x6xi32>
//
// Should be the same as dense output
// CHECK: ( ( 0, 0, -1, -6, -1, 6 ),
// CHECK-SAME: ( -1, 0, 1, 0, 1, 0 ),
// CHECK-SAME: ( 0, -1, 1, 0, 0, 0 ),
// CHECK-SAME: ( -1, 0, 0, 0, 0, 0 ),
// CHECK-SAME: ( 0, 0, 3, 6, -3, -6 ),
// CHECK-SAME: ( 2, -1, 3, 0, -3, 0 ) )
//
%all_sparse_CSC = sparse_tensor.convert %4
: tensor<6x6xi32, #CSC> to tensor<6x6xi32>
%v4 = vector.transfer_read %all_sparse_CSC[%c0, %c0], %i0
: tensor<6x6xi32>, vector<6x6xi32>
vector.print %v4 : vector<6x6xi32>
// Release the resources.
bufferization.dealloc_tensor %sparse_filter_DCSR : tensor<3x3xi32, #DCSR>
bufferization.dealloc_tensor %sparse_filter_CSR : tensor<3x3xi32, #CSR>
bufferization.dealloc_tensor %sparse_filter_CSC : tensor<3x3xi32, #CSC>
bufferization.dealloc_tensor %sparse_input_DCSR : tensor<8x8xi32, #DCSR>
bufferization.dealloc_tensor %sparse_input_CSR : tensor<8x8xi32, #CSR>
bufferization.dealloc_tensor %sparse_input_CSC : tensor<8x8xi32, #CSC>
bufferization.dealloc_tensor %1 : tensor<6x6xi32, #DCSR>
bufferization.dealloc_tensor %2 : tensor<6x6xi32, #DCSR>
bufferization.dealloc_tensor %3 : tensor<6x6xi32, #CSR>
bufferization.dealloc_tensor %4 : tensor<6x6xi32, #CSC>
return
}
}

mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_conv_2d_nhwc_hwcf.mlir

  • This file was added.
// RUN: mlir-opt %s --sparse-compiler=enable-runtime-library=true | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s
// RUN: mlir-opt %s --sparse-compiler="enable-runtime-library=false enable-buffer-initialization=true" | \
// RUN: mlir-cpu-runner \
// RUN: -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s
#CCCC = #sparse_tensor.encoding<{
dimLevelType = [ "compressed", "compressed", "compressed", "compressed" ]
}>
#CDCD = #sparse_tensor.encoding<{
dimLevelType = [ "compressed", "dense", "compressed", "dense" ]
}>
// Creates and returns 4-D buffer of size (%s1, %s2, %s3, %s4) filled with the value %f
func.func @alloc_4d_filled_f32(%s1 : index, %s2 : index, %s3 : index, %s4 : index, %f : f32) -> tensor<?x?x?x?xf32> {
%buf = bufferization.alloc_tensor(%s1, %s2, %s3, %s4) : tensor<?x?x?x?xf32>
%ret = linalg.fill ins(%f : f32) outs(%buf : tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32>
return %ret : tensor<?x?x?x?xf32>
}
func.func @conv_2d_nhwc_hwcf(%arg0: tensor<?x?x?x?xf32>, %arg1: tensor<?x?x?x?xf32>, %arg2: tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32> {
%ret = linalg.conv_2d_nhwc_hwcf {dilations = dense<1> : tensor<2xi64>,
strides = dense<1> : tensor<2xi64>}
ins (%arg0, %arg1: tensor<?x?x?x?xf32>, tensor<?x?x?x?xf32>)
outs (%arg2: tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32>
return %ret : tensor<?x?x?x?xf32>
}
func.func @conv_2d_nhwc_hwcf_CCCC(%arg0: tensor<?x?x?x?xf32, #CCCC>, %arg1: tensor<?x?x?x?xf32, #CCCC>) -> tensor<?x?x?x?xf32, #CCCC> {
%c1 = arith.constant 1 : index
%c3 = arith.constant 3 : index
%c6 = arith.constant 6 : index
%s = bufferization.alloc_tensor(%c3, %c6, %c6, %c1) : tensor<?x?x?x?xf32, #CCCC>
%ret = linalg.conv_2d_nhwc_hwcf {dilations = dense<1> : tensor<2xi64>,
strides = dense<1> : tensor<2xi64>}
ins (%arg0, %arg1: tensor<?x?x?x?xf32, #CCCC>, tensor<?x?x?x?xf32, #CCCC>)
outs (%s: tensor<?x?x?x?xf32, #CCCC>) -> tensor<?x?x?x?xf32, #CCCC>
return %ret : tensor<?x?x?x?xf32, #CCCC>
}
func.func @conv_2d_nhwc_hwcf_CDCD(%arg0: tensor<?x?x?x?xf32, #CDCD>, %arg1: tensor<?x?x?x?xf32, #CDCD>) -> tensor<?x?x?x?xf32, #CDCD> {
%c1 = arith.constant 1 : index
%c3 = arith.constant 3 : index
%c6 = arith.constant 6 : index
%s = bufferization.alloc_tensor(%c3, %c6, %c6, %c1) : tensor<?x?x?x?xf32, #CDCD>
%ret = linalg.conv_2d_nhwc_hwcf {dilations = dense<1> : tensor<2xi64>,
strides = dense<1> : tensor<2xi64>}
ins (%arg0, %arg1: tensor<?x?x?x?xf32, #CDCD>, tensor<?x?x?x?xf32, #CDCD>)
outs (%s: tensor<?x?x?x?xf32, #CDCD>) -> tensor<?x?x?x?xf32, #CDCD>
return %ret : tensor<?x?x?x?xf32, #CDCD>
}
func.func @entry() {
%c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index
%c3 = arith.constant 3 : index
%c6 = arith.constant 6 : index
%c8 = arith.constant 8 : index
%f10 = arith.constant 10.00000e+00 : f32
%val = arith.constant 2.00000e+00 : f32
%zero = arith.constant 0.00000e+00 : f32
%filter2D_nhwc = call @alloc_4d_filled_f32(%c3, %c3, %c3, %c1, %val) :(index, index, index, index, f32) -> (tensor<?x?x?x?xf32>)
%in2D_tmp = call @alloc_4d_filled_f32(%c3, %c8, %c8, %c3, %val) : (index, index, index, index, f32) -> (tensor<?x?x?x?xf32>)
%in2D_nhwc = tensor.insert %f10 into %in2D_tmp[%c0, %c0, %c3, %c0] : tensor<?x?x?x?xf32>
%out2D_nhwc = call @alloc_4d_filled_f32(%c3, %c6, %c6, %c1, %zero) : (index, index, index, index, f32) -> (tensor<?x?x?x?xf32>)
%in2D_nhwc_CCCC = sparse_tensor.convert %in2D_nhwc
: tensor<?x?x?x?xf32> to tensor<?x?x?x?xf32, #CCCC>
%filter2D_nhwc_CCCC = sparse_tensor.convert %filter2D_nhwc
: tensor<?x?x?x?xf32> to tensor<?x?x?x?xf32, #CCCC>
%in2D_nhwc_CDCD = sparse_tensor.convert %in2D_nhwc
: tensor<?x?x?x?xf32> to tensor<?x?x?x?xf32, #CDCD>
%filter2D_nhwc_CDCD = sparse_tensor.convert %filter2D_nhwc
: tensor<?x?x?x?xf32> to tensor<?x?x?x?xf32, #CDCD>
%dense_ret = call @conv_2d_nhwc_hwcf(%in2D_nhwc, %filter2D_nhwc, %out2D_nhwc) : (tensor<?x?x?x?xf32>, tensor<?x?x?x?xf32>, tensor<?x?x?x?xf32>) -> (tensor<?x?x?x?xf32>)
%CCCC_ret = call @conv_2d_nhwc_hwcf_CCCC(%in2D_nhwc_CCCC, %filter2D_nhwc_CCCC) : (tensor<?x?x?x?xf32, #CCCC>, tensor<?x?x?x?xf32, #CCCC>) -> (tensor<?x?x?x?xf32, #CCCC>)
%CDCD_ret = call @conv_2d_nhwc_hwcf_CDCD(%in2D_nhwc_CDCD, %filter2D_nhwc_CDCD) : (tensor<?x?x?x?xf32, #CDCD>, tensor<?x?x?x?xf32, #CDCD>) -> (tensor<?x?x?x?xf32, #CDCD>)
// CHECK: ( ( ( ( 108 ), ( 124 ), ( 124 ), ( 124 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ) ),
// CHECK-SAME: ( ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ) ),
// CHECK-SAME: ( ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ) ) )
%dense_v = vector.transfer_read %dense_ret[%c0, %c0, %c0, %c0], %zero
: tensor<?x?x?x?xf32>, vector<3x6x6x1xf32>
vector.print %dense_v : vector<3x6x6x1xf32>
// CHECK: ( ( ( ( 108 ), ( 124 ), ( 124 ), ( 124 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ) ),
// CHECK-SAME: ( ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ) ),
// CHECK-SAME: ( ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ) ) )
%1 = sparse_tensor.convert %CCCC_ret
: tensor<?x?x?x?xf32, #CCCC> to tensor<?x?x?x?xf32>
%v1 = vector.transfer_read %1[%c0, %c0, %c0, %c0], %zero
: tensor<?x?x?x?xf32>, vector<3x6x6x1xf32>
vector.print %v1 : vector<3x6x6x1xf32>
// CHECK: ( ( ( ( 108 ), ( 124 ), ( 124 ), ( 124 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ) ),
// CHECK-SAME: ( ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ) ),
// CHECK-SAME: ( ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ) ) )
%2 = sparse_tensor.convert %CDCD_ret
: tensor<?x?x?x?xf32, #CDCD> to tensor<?x?x?x?xf32>
%v2 = vector.transfer_read %2[%c0, %c0, %c0, %c0], %zero
: tensor<?x?x?x?xf32>, vector<3x6x6x1xf32>
vector.print %v2 : vector<3x6x6x1xf32>
// Free the resources
bufferization.dealloc_tensor %in2D_nhwc : tensor<?x?x?x?xf32>
bufferization.dealloc_tensor %filter2D_nhwc : tensor<?x?x?x?xf32>
bufferization.dealloc_tensor %out2D_nhwc : tensor<?x?x?x?xf32>
bufferization.dealloc_tensor %in2D_nhwc_CDCD : tensor<?x?x?x?xf32, #CDCD>
bufferization.dealloc_tensor %filter2D_nhwc_CDCD : tensor<?x?x?x?xf32, #CDCD>
bufferization.dealloc_tensor %in2D_nhwc_CCCC : tensor<?x?x?x?xf32, #CCCC>
bufferization.dealloc_tensor %filter2D_nhwc_CCCC : tensor<?x?x?x?xf32, #CCCC>
bufferization.dealloc_tensor %CCCC_ret : tensor<?x?x?x?xf32, #CCCC>
bufferization.dealloc_tensor %CDCD_ret : tensor<?x?x?x?xf32, #CDCD>
return
}

mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_conv_3d.mlir

  • This file was added.
// RUN: mlir-opt %s --sparse-compiler=enable-runtime-library=true | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s
// RUN: mlir-opt %s --sparse-compiler="enable-runtime-library=false enable-buffer-initialization=true" | \
// RUN: mlir-cpu-runner \
// RUN: -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s
#CCC = #sparse_tensor.encoding<{
dimLevelType = [ "compressed", "compressed", "compressed" ]
}>
#CDC = #sparse_tensor.encoding<{
dimLevelType = [ "compressed", "dense", "compressed" ]
}>
// Creates and returns 3-D buffer of size (%s1, %s2, %s3) filled with the value %f
func.func @alloc_3d_filled_f32(%s1 : index, %s2 : index, %s3 : index, %f : f32) -> tensor<?x?x?xf32> {
%buf = bufferization.alloc_tensor(%s1, %s2, %s3) : tensor<?x?x?xf32>
%ret = linalg.fill ins(%f : f32) outs(%buf : tensor<?x?x?xf32>) -> tensor<?x?x?xf32>
return %ret : tensor<?x?x?xf32>
}
func.func @conv_3d(%arg0: tensor<?x?x?xf32>, %arg1: tensor<?x?x?xf32>, %arg2: tensor<?x?x?xf32>) -> tensor<?x?x?xf32> {
%ret = linalg.conv_3d
ins (%arg0, %arg1: tensor<?x?x?xf32>, tensor<?x?x?xf32>)
outs (%arg2: tensor<?x?x?xf32>) -> tensor<?x?x?xf32>
return %ret : tensor<?x?x?xf32>
}
func.func @conv_3d_CCC(%arg0: tensor<?x?x?xf32, #CCC>, %arg1: tensor<?x?x?xf32, #CCC>) -> tensor<?x?x?xf32, #CCC> {
%c6 = arith.constant 6 : index
%s = bufferization.alloc_tensor(%c6, %c6, %c6) : tensor<?x?x?xf32, #CCC>
%ret = linalg.conv_3d
ins (%arg0, %arg1: tensor<?x?x?xf32, #CCC>, tensor<?x?x?xf32, #CCC>)
outs (%s: tensor<?x?x?xf32, #CCC>) -> tensor<?x?x?xf32, #CCC>
return %ret : tensor<?x?x?xf32, #CCC>
}
func.func @conv_3d_CDC(%arg0: tensor<?x?x?xf32, #CDC>, %arg1: tensor<?x?x?xf32, #CDC>) -> tensor<?x?x?xf32, #CDC> {
%c6 = arith.constant 6 : index
%s = bufferization.alloc_tensor(%c6, %c6, %c6) : tensor<?x?x?xf32, #CDC>
%ret = linalg.conv_3d
ins (%arg0, %arg1: tensor<?x?x?xf32, #CDC>, tensor<?x?x?xf32, #CDC>)
outs (%s: tensor<?x?x?xf32, #CDC>) -> tensor<?x?x?xf32, #CDC>
return %ret : tensor<?x?x?xf32, #CDC>
}
func.func @entry() {
%c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index
%c3 = arith.constant 3 : index
%c6 = arith.constant 6 : index
%c8 = arith.constant 8 : index
%f10 = arith.constant 10.00000e+00 : f32
%val = arith.constant 2.00000e+00 : f32
%zero = arith.constant 0.00000e+00 : f32
%filter3D = call @alloc_3d_filled_f32(%c3, %c3, %c3, %val) : (index, index, index, f32) -> (tensor<?x?x?xf32>)
%in3D_tmp = call @alloc_3d_filled_f32(%c8, %c8, %c8, %val) : (index, index, index, f32) -> (tensor<?x?x?xf32>)
%in3D = tensor.insert %f10 into %in3D_tmp[%c0, %c3, %c0] : tensor<?x?x?xf32>
%out3D = call @alloc_3d_filled_f32(%c6, %c6, %c6, %zero) : (index, index, index, f32) -> (tensor<?x?x?xf32>)
%in3D_CCC = sparse_tensor.convert %in3D
: tensor<?x?x?xf32> to tensor<?x?x?xf32, #CCC>
%filter3D_CCC = sparse_tensor.convert %filter3D
: tensor<?x?x?xf32> to tensor<?x?x?xf32, #CCC>
%in3D_CDC = sparse_tensor.convert %in3D
: tensor<?x?x?xf32> to tensor<?x?x?xf32, #CDC>
%filter3D_CDC = sparse_tensor.convert %filter3D
: tensor<?x?x?xf32> to tensor<?x?x?xf32, #CDC>
%dense_ret = call @conv_3d(%in3D, %filter3D, %out3D) : (tensor<?x?x?xf32>, tensor<?x?x?xf32>, tensor<?x?x?xf32>) -> (tensor<?x?x?xf32>)
%CCC_ret = call @conv_3d_CCC(%in3D_CCC, %filter3D_CCC) : (tensor<?x?x?xf32, #CCC>, tensor<?x?x?xf32, #CCC>) -> (tensor<?x?x?xf32, #CCC>)
%CDC_ret = call @conv_3d_CDC(%in3D_CDC, %filter3D_CDC) : (tensor<?x?x?xf32, #CDC>, tensor<?x?x?xf32, #CDC>) -> (tensor<?x?x?xf32, #CDC>)
// CHECK:( ( ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 124, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 124, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 124, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ) ),
// CHECK-SAME: ( ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ) ),
// CHECK-SAME: ( ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ) ),
// CHECK-SAME: ( ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ) ),
// CHECK-SAME: ( ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ) ),
// CHECK-SAME: ( ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ) ) )
%dense_v = vector.transfer_read %dense_ret[%c0, %c0, %c0], %zero
: tensor<?x?x?xf32>, vector<6x6x6xf32>
vector.print %dense_v : vector<6x6x6xf32>
// CHECK-NEXT:( ( ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 124, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 124, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 124, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ) ),
// CHECK-SAME: ( ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ) ),
// CHECK-SAME: ( ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ) ),
// CHECK-SAME: ( ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ) ),
// CHECK-SAME: ( ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ) ),
// CHECK-SAME: ( ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ) ) )
%1 = sparse_tensor.convert %CCC_ret
: tensor<?x?x?xf32, #CCC> to tensor<?x?x?xf32>
%v1 = vector.transfer_read %1[%c0, %c0, %c0], %zero
: tensor<?x?x?xf32>, vector<6x6x6xf32>
vector.print %v1 : vector<6x6x6xf32>
// CHECK-NEXT:( ( ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 124, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 124, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 124, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ) ),
// CHECK-SAME: ( ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ) ),
// CHECK-SAME: ( ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ) ),
// CHECK-SAME: ( ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ) ),
// CHECK-SAME: ( ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ) ),
// CHECK-SAME: ( ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ),
// CHECK-SAME: ( 108, 108, 108, 108, 108, 108 ) ) )
%2 = sparse_tensor.convert %CCC_ret
: tensor<?x?x?xf32, #CCC> to tensor<?x?x?xf32>
%v2 = vector.transfer_read %2[%c0, %c0, %c0], %zero
: tensor<?x?x?xf32>, vector<6x6x6xf32>
vector.print %v2 : vector<6x6x6xf32>
// Free the resources
bufferization.dealloc_tensor %in3D : tensor<?x?x?xf32>
bufferization.dealloc_tensor %filter3D : tensor<?x?x?xf32>
bufferization.dealloc_tensor %out3D : tensor<?x?x?xf32>
bufferization.dealloc_tensor %in3D_CDC : tensor<?x?x?xf32, #CDC>
bufferization.dealloc_tensor %filter3D_CDC : tensor<?x?x?xf32, #CDC>
bufferization.dealloc_tensor %in3D_CCC : tensor<?x?x?xf32, #CCC>
bufferization.dealloc_tensor %filter3D_CCC : tensor<?x?x?xf32, #CCC>
bufferization.dealloc_tensor %CCC_ret : tensor<?x?x?xf32, #CCC>
bufferization.dealloc_tensor %CDC_ret : tensor<?x?x?xf32, #CDC>
return
}

mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_conv_3d_ndhwc_dhwcf.mlir

  • This file was added.
// RUN: mlir-opt %s --sparse-compiler=enable-runtime-library=true | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s
// RUN: mlir-opt %s --sparse-compiler="enable-runtime-library=false enable-buffer-initialization=true" | \
// RUN: mlir-cpu-runner \
// RUN: -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s
#CCCCC = #sparse_tensor.encoding<{
dimLevelType = [ "compressed", "compressed", "compressed", "compressed", "compressed" ]
}>
#CDCDC = #sparse_tensor.encoding<{
dimLevelType = [ "compressed", "dense", "compressed", "dense", "compressed"]
}>
// Creates and returns 5-D buffer of size (%s1, %s2, %s3, %s4, %s5) filled with the value %f
func.func @alloc_5d_filled_f32(%s1 : index, %s2 : index, %s3 : index, %s4 : index, %s5 : index, %f : f32) -> tensor<?x?x?x?x?xf32> {
%buf = bufferization.alloc_tensor(%s1, %s2, %s3, %s4, %s5) : tensor<?x?x?x?x?xf32>
%ret = linalg.fill ins(%f : f32) outs(%buf : tensor<?x?x?x?x?xf32>) -> tensor<?x?x?x?x?xf32>
return %ret : tensor<?x?x?x?x?xf32>
}
func.func @conv_3d_ndhwc_dhwcf(%arg0: tensor<?x?x?x?x?xf32>,
%arg1: tensor<?x?x?x?x?xf32>,
%arg2: tensor<?x?x?x?x?xf32>) -> tensor<?x?x?x?x?xf32> {
%ret = linalg.conv_3d_ndhwc_dhwcf {dilations = dense<1> : tensor<3xi64>,
strides = dense<1> : tensor<3xi64>}
ins (%arg0, %arg1: tensor<?x?x?x?x?xf32>, tensor<?x?x?x?x?xf32>)
outs (%arg2: tensor<?x?x?x?x?xf32>) -> tensor<?x?x?x?x?xf32>
return %ret : tensor<?x?x?x?x?xf32>
}
func.func @conv_3d_ndhwc_dhwcf_CCCCC(%arg0: tensor<?x?x?x?x?xf32, #CCCCC>,
%arg1: tensor<?x?x?x?x?xf32, #CCCCC>)
-> tensor<?x?x?x?x?xf32, #CCCCC> {
%c1 = arith.constant 1 : index
%c6 = arith.constant 6 : index
%s = bufferization.alloc_tensor(%c1, %c6, %c6, %c6, %c1)
: tensor<?x?x?x?x?xf32, #CCCCC>
%ret = linalg.conv_3d_ndhwc_dhwcf {dilations = dense<1> : tensor<3xi64>,
strides = dense<1> : tensor<3xi64>}
ins (%arg0, %arg1: tensor<?x?x?x?x?xf32, #CCCCC>, tensor<?x?x?x?x?xf32, #CCCCC>)
outs (%s: tensor<?x?x?x?x?xf32, #CCCCC>) -> tensor<?x?x?x?x?xf32, #CCCCC>
return %ret : tensor<?x?x?x?x?xf32, #CCCCC>
}
func.func @conv_3d_ndhwc_dhwcf_CDCDC(%arg0: tensor<?x?x?x?x?xf32, #CDCDC>,
%arg1: tensor<?x?x?x?x?xf32, #CDCDC>)
-> tensor<?x?x?x?x?xf32, #CDCDC> {
%c1 = arith.constant 1 : index
%c6 = arith.constant 6 : index
%s = bufferization.alloc_tensor(%c1, %c6, %c6, %c6, %c1)
: tensor<?x?x?x?x?xf32, #CDCDC>
%ret = linalg.conv_3d_ndhwc_dhwcf {dilations = dense<1> : tensor<3xi64>,
strides = dense<1> : tensor<3xi64>}
ins (%arg0, %arg1: tensor<?x?x?x?x?xf32, #CDCDC>, tensor<?x?x?x?x?xf32, #CDCDC>)
outs (%s: tensor<?x?x?x?x?xf32, #CDCDC>) -> tensor<?x?x?x?x?xf32, #CDCDC>
return %ret : tensor<?x?x?x?x?xf32, #CDCDC>
}
func.func @entry() {
%c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index
%c3 = arith.constant 3 : index
%c6 = arith.constant 6 : index
%c8 = arith.constant 8 : index
%f10 = arith.constant 10.00000e+00 : f32
%val = arith.constant 2.00000e+00 : f32
%zero = arith.constant 0.00000e+00 : f32
%in3D_tmp = call @alloc_5d_filled_f32(%c1, %c8, %c8, %c8, %c1, %val) : (index, index, index, index, index, f32) -> (tensor<?x?x?x?x?xf32>)
%in3D_ndhwc = tensor.insert %f10 into %in3D_tmp[%c0, %c0, %c0, %c3, %c0] : tensor<?x?x?x?x?xf32>
%filter3D_ndhwc = call @alloc_5d_filled_f32(%c3, %c3, %c3, %c1, %c1, %val) : (index, index, index, index, index, f32) -> (tensor<?x?x?x?x?xf32>)
%out3D_ndhwc = call @alloc_5d_filled_f32(%c1, %c6, %c6, %c6, %c1, %zero) : (index, index, index, index, index, f32) -> (tensor<?x?x?x?x?xf32>)
%in3D_ndhwc_CCCCC = sparse_tensor.convert %in3D_ndhwc
: tensor<?x?x?x?x?xf32> to tensor<?x?x?x?x?xf32, #CCCCC>
%filter3D_ndhwc_CCCCC = sparse_tensor.convert %filter3D_ndhwc
: tensor<?x?x?x?x?xf32> to tensor<?x?x?x?x?xf32, #CCCCC>
%in3D_ndhwc_CDCDC = sparse_tensor.convert %in3D_ndhwc
: tensor<?x?x?x?x?xf32> to tensor<?x?x?x?x?xf32, #CDCDC>
%filter3D_ndhwc_CDCDC = sparse_tensor.convert %filter3D_ndhwc
: tensor<?x?x?x?x?xf32> to tensor<?x?x?x?x?xf32, #CDCDC>
// CHECK:( ( ( ( ( 108 ), ( 124 ), ( 124 ), ( 124 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ) ),
// CHECK-SAME: ( ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ) ),
// CHECK-SAME: ( ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ) ),
// CHECK-SAME: ( ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ) ),
// CHECK-SAME: ( ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ) ),
// CHECK-SAME: ( ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ) ) ) )
%dense_ret = call @conv_3d_ndhwc_dhwcf(%in3D_ndhwc, %filter3D_ndhwc, %out3D_ndhwc)
: (tensor<?x?x?x?x?xf32>, tensor<?x?x?x?x?xf32>, tensor<?x?x?x?x?xf32>) -> (tensor<?x?x?x?x?xf32>)
%dense_v = vector.transfer_read %dense_ret[%c0, %c0, %c0, %c0, %c0], %zero
: tensor<?x?x?x?x?xf32>, vector<1x6x6x6x1xf32>
vector.print %dense_v : vector<1x6x6x6x1xf32>
%CCCCC_ret = call @conv_3d_ndhwc_dhwcf_CCCCC(%in3D_ndhwc_CCCCC, %filter3D_ndhwc_CCCCC)
: (tensor<?x?x?x?x?xf32, #CCCCC>,
tensor<?x?x?x?x?xf32, #CCCCC>) -> (tensor<?x?x?x?x?xf32, #CCCCC>)
// CHECK-NEXT:( ( ( ( ( 108 ), ( 124 ), ( 124 ), ( 124 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ) ),
// CHECK-SAME: ( ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ) ),
// CHECK-SAME: ( ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ) ),
// CHECK-SAME: ( ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ) ),
// CHECK-SAME: ( ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ) ),
// CHECK-SAME: ( ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ) ) ) )
%1 = sparse_tensor.convert %CCCCC_ret
: tensor<?x?x?x?x?xf32, #CCCCC> to tensor<?x?x?x?x?xf32>
%v1 = vector.transfer_read %1[%c0, %c0, %c0, %c0, %c0], %zero
: tensor<?x?x?x?x?xf32>, vector<1x6x6x6x1xf32>
vector.print %v1 : vector<1x6x6x6x1xf32>
%CDCDC_ret = call @conv_3d_ndhwc_dhwcf_CDCDC(%in3D_ndhwc_CDCDC, %filter3D_ndhwc_CDCDC)
: (tensor<?x?x?x?x?xf32, #CDCDC>,
tensor<?x?x?x?x?xf32, #CDCDC>) -> (tensor<?x?x?x?x?xf32, #CDCDC>)
// CHECK-NEXT:( ( ( ( ( 108 ), ( 124 ), ( 124 ), ( 124 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ) ),
// CHECK-SAME: ( ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ) ),
// CHECK-SAME: ( ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ) ),
// CHECK-SAME: ( ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ) ),
// CHECK-SAME: ( ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ) ),
// CHECK-SAME: ( ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ) ) ) )
%2 = sparse_tensor.convert %CDCDC_ret
: tensor<?x?x?x?x?xf32, #CDCDC> to tensor<?x?x?x?x?xf32>
%v2 = vector.transfer_read %dense_ret[%c0, %c0, %c0, %c0, %c0], %zero
: tensor<?x?x?x?x?xf32>, vector<1x6x6x6x1xf32>
vector.print %v2 : vector<1x6x6x6x1xf32>
// Free the resources
bufferization.dealloc_tensor %in3D_ndhwc : tensor<?x?x?x?x?xf32>
bufferization.dealloc_tensor %filter3D_ndhwc : tensor<?x?x?x?x?xf32>
bufferization.dealloc_tensor %out3D_ndhwc : tensor<?x?x?x?x?xf32>
bufferization.dealloc_tensor %in3D_ndhwc_CDCDC : tensor<?x?x?x?x?xf32, #CDCDC>
bufferization.dealloc_tensor %filter3D_ndhwc_CDCDC : tensor<?x?x?x?x?xf32, #CDCDC>
bufferization.dealloc_tensor %in3D_ndhwc_CCCCC : tensor<?x?x?x?x?xf32, #CCCCC>
bufferization.dealloc_tensor %filter3D_ndhwc_CCCCC : tensor<?x?x?x?x?xf32, #CCCCC>
bufferization.dealloc_tensor %CCCCC_ret : tensor<?x?x?x?x?xf32, #CCCCC>
bufferization.dealloc_tensor %CDCDC_ret : tensor<?x?x?x?x?xf32, #CDCDC>
return
}