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Differential D119992

[mlir][sparse] avoid some codeup in sparsification transformation
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Authored by aartbik on Feb 16 2022, 4:33 PM.

Details

Reviewers
bixia
wrengr
penpornk
Commits
rG34381a76c1a3: [mlir][sparse] avoid some codeup in sparsification transformation
Summary

A very small refactoring, but a big impact on tests that expect an exact order.
This revision fixes the tests, but also makes them less brittle for similar
minor changes in the future!

Diff Detail

Event Timeline

aartbik created this revision.Feb 16 2022, 4:33 PM
Herald added subscribers: sdasgup3, wenzhicui, wrengr and 18 others. · View Herald TranscriptFeb 16 2022, 4:33 PM
aartbik requested review of this revision.Feb 16 2022, 4:33 PM
Herald added a project: Restricted Project. · View Herald TranscriptFeb 16 2022, 4:33 PM
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aartbik updated this revision to Diff 409450.Feb 16 2022, 4:37 PM

removed unrelated file

aartbik added reviewers: wrengr, penpornk.Feb 16 2022, 4:40 PM
bixia accepted this revision.Feb 16 2022, 4:45 PM
This revision is now accepted and ready to land.Feb 16 2022, 4:45 PM
Harbormaster completed remote builds in B150109: Diff 409450.Feb 16 2022, 5:17 PM
Closed by commit rG34381a76c1a3: [mlir][sparse] avoid some codeup in sparsification transformation (authored by aartbik). · Explain WhyFeb 16 2022, 5:39 PM
This revision was automatically updated to reflect the committed changes.
aartbik added a commit: rG34381a76c1a3: [mlir][sparse] avoid some codeup in sparsification transformation.

Revision Contents

PathSize
mlir/
lib/
Dialect/
SparseTensor/
Transforms/
10 lines
test/
Dialect/
SparseTensor/
16 lines
448 lines
442 lines
588 lines
36 lines
48 lines
32 lines
32 lines
36 lines
28 lines
24 lines
utils/
bazel/
llvm-project-overlay/
mlir/
2 lines

Diff 409449

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

Show First 20 Lines • Show All 458 Lines • ▼ Show 20 Linesstatic Value genOutputBuffer(CodeGen &codegen, PatternRewriter &rewriter,
if (isInPlace(tensor)) if (isInPlace(tensor))
return rewriter.create<bufferization::ToMemrefOp>(loc, denseTp, tensor); return rewriter.create<bufferization::ToMemrefOp>(loc, denseTp, tensor);
// By default, a new buffer is allocated which is initialized to the // By default, a new buffer is allocated which is initialized to the
// tensor defined in the outs() clause. This is always correct but // tensor defined in the outs() clause. This is always correct but
// introduces a dense initialization component that may negatively // introduces a dense initialization component that may negatively
// impact the running complexity of the sparse kernel. If the tensor // impact the running complexity of the sparse kernel. If the tensor
// materializes into the computation, we need to preserve the zero // materializes into the computation, we need to preserve the zero
// initialization assumption of all sparse output buffers. // initialization assumption of all sparse output buffers.
if (isMaterializing(tensor)) {
Value alloc = rewriter.create<memref::AllocOp>(loc, denseTp, args); Value alloc = rewriter.create<memref::AllocOp>(loc, denseTp, args);
if (isMaterializing(tensor)) {
Value zero = constantZero(rewriter, loc, denseTp.getElementType()); Value zero = constantZero(rewriter, loc, denseTp.getElementType());
rewriter.create<linalg::FillOp>(loc, zero, alloc); rewriter.create<linalg::FillOp>(loc, zero, alloc);
return alloc; } else {
} Value init =
Value init = rewriter.create<bufferization::ToMemrefOp>(loc, denseTp, tensor); rewriter.create<bufferization::ToMemrefOp>(loc, denseTp, tensor);
Value alloc = rewriter.create<memref::AllocOp>(loc, denseTp, args);
rewriter.create<memref::CopyOp>(loc, init, alloc); rewriter.create<memref::CopyOp>(loc, init, alloc);
}
return alloc; return alloc;
} }
/// Local bufferization of all dense and sparse data structures. /// Local bufferization of all dense and sparse data structures.
/// This code enables testing the first prototype sparse compiler. /// This code enables testing the first prototype sparse compiler.
// TODO: replace this with a proliferated bufferization strategy // TODO: replace this with a proliferated bufferization strategy
static void genBuffers(Merger &merger, CodeGen &codegen, static void genBuffers(Merger &merger, CodeGen &codegen,
PatternRewriter &rewriter, linalg::GenericOp op) { PatternRewriter &rewriter, linalg::GenericOp op) {
▲ Show 20 Lines • Show All 1,204 Lines • Show Last 20 Lines

mlir/test/Dialect/SparseTensor/dense.mlir

Show All 29 Lines
// //
// Test with an all-dense-annotated "sparse" matrix as input and // Test with an all-dense-annotated "sparse" matrix as input and
// a non-annotated dense matrix as output that is not inplacable. // a non-annotated dense matrix as output that is not inplacable.
// This results in an explicit allocation to facilitate output. // This results in an explicit allocation to facilitate output.
// //
// CHECK-LABEL: func @dense1( // CHECK-LABEL: func @dense1(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>>, // CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16xf32> {linalg.inplaceable = false}) -> tensor<32x16xf32> { // CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16xf32> {linalg.inplaceable = false}) -> tensor<32x16xf32> {
// CHECK: %[[VAL_2:.*]] = arith.constant 1.000000e+00 : f32 // CHECK-DAG: %[[VAL_2:.*]] = arith.constant 1.000000e+00 : f32
// CHECK: %[[VAL_3:.*]] = arith.constant 32 : index // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index
// CHECK: %[[VAL_4:.*]] = arith.constant 16 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 16 : index
// CHECK: %[[VAL_5:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index
// CHECK: %[[VAL_6:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_6:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> to memref<?xf32> // CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> to memref<?xf32>
// CHECK: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16xf32> // CHECK-DAG: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16xf32>
// CHECK: %[[VAL_9:.*]] = memref.alloc() : memref<32x16xf32> // CHECK-DAG: %[[VAL_9:.*]] = memref.alloc() : memref<32x16xf32>
// CHECK: memref.copy %[[VAL_8]], %[[VAL_9]] : memref<32x16xf32> to memref<32x16xf32> // CHECK: memref.copy %[[VAL_8]], %[[VAL_9]] : memref<32x16xf32> to memref<32x16xf32>
// CHECK: scf.for %[[VAL_10:.*]] = %[[VAL_5]] to %[[VAL_3]] step %[[VAL_6]] { // CHECK: scf.for %[[VAL_10:.*]] = %[[VAL_5]] to %[[VAL_3]] step %[[VAL_6]] {
// CHECK: scf.for %[[VAL_11:.*]] = %[[VAL_5]] to %[[VAL_4]] step %[[VAL_6]] { // CHECK: scf.for %[[VAL_11:.*]] = %[[VAL_5]] to %[[VAL_4]] step %[[VAL_6]] {
// CHECK: %[[VAL_12:.*]] = arith.muli %[[VAL_10]], %[[VAL_4]] : index // CHECK: %[[VAL_12:.*]] = arith.muli %[[VAL_10]], %[[VAL_4]] : index
// CHECK: %[[VAL_13:.*]] = arith.addi %[[VAL_12]], %[[VAL_11]] : index // CHECK: %[[VAL_13:.*]] = arith.addi %[[VAL_12]], %[[VAL_11]] : index
// CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_13]]] : memref<?xf32> // CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_13]]] : memref<?xf32>
// CHECK: %[[VAL_15:.*]] = arith.addf %[[VAL_14]], %[[VAL_2]] : f32 // CHECK: %[[VAL_15:.*]] = arith.addf %[[VAL_14]], %[[VAL_2]] : f32
// CHECK: memref.store %[[VAL_15]], %[[VAL_9]]{{\[}}%[[VAL_10]], %[[VAL_11]]] : memref<32x16xf32> // CHECK: memref.store %[[VAL_15]], %[[VAL_9]]{{\[}}%[[VAL_10]], %[[VAL_11]]] : memref<32x16xf32>
▲ Show 20 Lines • Show All 147 Lines • Show Last 20 Lines

mlir/test/Dialect/SparseTensor/sparse_1d.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
#DV = #sparse_tensor.encoding<{ dimLevelType = [ "dense" ] }> #DV = #sparse_tensor.encoding<{ dimLevelType = [ "dense" ] }>
#SV = #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }> #SV = #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>
#trait1 = { #trait1 = {
indexing_maps = [ indexing_maps = [
affine_map<(i) -> (i)>, // a affine_map<(i) -> (i)>, // a
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" doc = "x(i) = a(i) OP b"
} }
// CHECK-LABEL: func @add_d( // CHECK-LABEL: func @add_d(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, // CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>,
// CHECK-SAME: %[[VAL_1:.*]]: f32, // CHECK-SAME: %[[VAL_1:.*]]: f32,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32xf32>) -> tensor<32xf32> { // CHECK-SAME: %[[VAL_2:.*]]: tensor<32xf32>) -> tensor<32xf32> {
// CHECK: %[[VAL_3:.*]] = arith.constant 32 : index // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index
// CHECK: %[[VAL_4:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
// CHECK: %[[VAL_5:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32> // CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32>
// CHECK: %[[VAL_7:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf32> // CHECK-DAG: %[[VAL_7:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf32>
// CHECK: %[[VAL_8:.*]] = memref.alloc() : memref<32xf32> // CHECK-DAG: %[[VAL_8:.*]] = memref.alloc() : memref<32xf32>
// CHECK: memref.copy %[[VAL_7]], %[[VAL_8]] : memref<32xf32> to memref<32xf32> // CHECK: memref.copy %[[VAL_7]], %[[VAL_8]] : memref<32xf32> to memref<32xf32>
// CHECK: scf.for %[[VAL_9:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] { // CHECK: scf.for %[[VAL_9:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] {
// CHECK: %[[VAL_10:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_9]]] : memref<?xf32> // CHECK: %[[VAL_10:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_9]]] : memref<?xf32>
// CHECK: %[[VAL_11:.*]] = arith.addf %[[VAL_10]], %[[VAL_1]] : f32 // CHECK: %[[VAL_11:.*]] = arith.addf %[[VAL_10]], %[[VAL_1]] : f32
// CHECK: memref.store %[[VAL_11]], %[[VAL_8]]{{\[}}%[[VAL_9]]] : memref<32xf32> // CHECK: memref.store %[[VAL_11]], %[[VAL_8]]{{\[}}%[[VAL_9]]] : memref<32xf32>
// CHECK: } // CHECK: }
// CHECK: %[[VAL_12:.*]] = bufferization.to_tensor %[[VAL_8]] : memref<32xf32> // CHECK: %[[VAL_12:.*]] = bufferization.to_tensor %[[VAL_8]] : memref<32xf32>
// CHECK: return %[[VAL_12]] : tensor<32xf32> // CHECK: return %[[VAL_12]] : tensor<32xf32>
// CHECK: } // CHECK: }
func @add_d(%arga: tensor<32xf32, #DV>, %argb: f32, %argx: tensor<32xf32>) -> tensor<32xf32> { func @add_d(%arga: tensor<32xf32, #DV>, %argb: f32, %argx: tensor<32xf32>) -> tensor<32xf32> {
%0 = linalg.generic #trait1 %0 = linalg.generic #trait1
ins(%arga: tensor<32xf32, #DV>) ins(%arga: tensor<32xf32, #DV>)
outs(%argx: tensor<32xf32>) { outs(%argx: tensor<32xf32>) {
^bb(%a: f32, %x: f32): ^bb(%a: f32, %x: f32):
%0 = arith.addf %a, %argb : f32 %0 = arith.addf %a, %argb : f32
linalg.yield %0 : f32 linalg.yield %0 : f32
} -> tensor<32xf32> } -> tensor<32xf32>
return %0 : tensor<32xf32> return %0 : tensor<32xf32>
} }
// CHECK-LABEL: func @add_d_init( // CHECK-LABEL: func @add_d_init(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, // CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>,
// CHECK-SAME: %[[VAL_1:.*]]: f32) -> tensor<32xf32> { // CHECK-SAME: %[[VAL_1:.*]]: f32) -> tensor<32xf32> {
// CHECK: %[[VAL_2:.*]] = arith.constant 32 : index // CHECK: %[[VAL_2:.*]] = arith.constant 32 : index
// CHECK: %[[VAL_3:.*]] = arith.constant 0.000000e+00 : f32 // CHECK: %[[VAL_3:.*]] = arith.constant 0.000000e+00 : f32
// CHECK: %[[VAL_4:.*]] = arith.constant 0 : index // CHECK: %[[VAL_4:.*]] = arith.constant 0 : index
// CHECK: %[[VAL_5:.*]] = arith.constant 1 : index // CHECK: %[[VAL_5:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32> // CHECK: %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32>
// CHECK: %[[VAL_7:.*]] = memref.alloc() : memref<32xf32> // CHECK: %[[VAL_7:.*]] = memref.alloc() : memref<32xf32>
// CHECK: linalg.fill(%[[VAL_3]], %[[VAL_7]]) : f32, memref<32xf32> // CHECK: linalg.fill(%[[VAL_3]], %[[VAL_7]]) : f32, memref<32xf32>
// CHECK: scf.for %[[VAL_8:.*]] = %[[VAL_4]] to %[[VAL_2]] step %[[VAL_5]] { // CHECK: scf.for %[[VAL_8:.*]] = %[[VAL_4]] to %[[VAL_2]] step %[[VAL_5]] {
Show All 12 Lines outs(%u: tensor<32xf32>) {
^bb(%a: f32, %x: f32): ^bb(%a: f32, %x: f32):
%0 = arith.addf %a, %argb : f32 %0 = arith.addf %a, %argb : f32
linalg.yield %0 : f32 linalg.yield %0 : f32
} -> tensor<32xf32> } -> tensor<32xf32>
return %0 : tensor<32xf32> return %0 : tensor<32xf32>
} }
// CHECK-LABEL: func @mul_d( // CHECK-LABEL: func @mul_d(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, // CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>,
// CHECK-SAME: %[[VAL_1:.*]]: f32, // CHECK-SAME: %[[VAL_1:.*]]: f32,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32xf32>) -> tensor<32xf32> { // CHECK-SAME: %[[VAL_2:.*]]: tensor<32xf32>) -> tensor<32xf32> {
// CHECK: %[[VAL_3:.*]] = arith.constant 32 : index // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index
// CHECK: %[[VAL_4:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
// CHECK: %[[VAL_5:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32> // CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32>
// CHECK: %[[VAL_7:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf32> // CHECK-DAG: %[[VAL_7:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf32>
// CHECK: %[[VAL_8:.*]] = memref.alloc() : memref<32xf32> // CHECK-DAG: %[[VAL_8:.*]] = memref.alloc() : memref<32xf32>
// CHECK: memref.copy %[[VAL_7]], %[[VAL_8]] : memref<32xf32> to memref<32xf32> // CHECK: memref.copy %[[VAL_7]], %[[VAL_8]] : memref<32xf32> to memref<32xf32>
// CHECK: scf.for %[[VAL_9:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] { // CHECK: scf.for %[[VAL_9:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] {
// CHECK: %[[VAL_10:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_9]]] : memref<?xf32> // CHECK: %[[VAL_10:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_9]]] : memref<?xf32>
// CHECK: %[[VAL_11:.*]] = arith.mulf %[[VAL_10]], %[[VAL_1]] : f32 // CHECK: %[[VAL_11:.*]] = arith.mulf %[[VAL_10]], %[[VAL_1]] : f32
// CHECK: memref.store %[[VAL_11]], %[[VAL_8]]{{\[}}%[[VAL_9]]] : memref<32xf32> // CHECK: memref.store %[[VAL_11]], %[[VAL_8]]{{\[}}%[[VAL_9]]] : memref<32xf32>
// CHECK: } // CHECK: }
// CHECK: %[[VAL_12:.*]] = bufferization.to_tensor %[[VAL_8]] : memref<32xf32> // CHECK: %[[VAL_12:.*]] = bufferization.to_tensor %[[VAL_8]] : memref<32xf32>
// CHECK: return %[[VAL_12]] : tensor<32xf32> // CHECK: return %[[VAL_12]] : tensor<32xf32>
// CHECK: } // CHECK: }
func @mul_d(%arga: tensor<32xf32, #DV>, %argb: f32, %argx: tensor<32xf32>) -> tensor<32xf32> { func @mul_d(%arga: tensor<32xf32, #DV>, %argb: f32, %argx: tensor<32xf32>) -> tensor<32xf32> {
%0 = linalg.generic #trait1 %0 = linalg.generic #trait1
ins(%arga: tensor<32xf32, #DV>) ins(%arga: tensor<32xf32, #DV>)
outs(%argx: tensor<32xf32>) { outs(%argx: tensor<32xf32>) {
^bb(%a: f32, %x: f32): ^bb(%a: f32, %x: f32):
%0 = arith.mulf %a, %argb : f32 %0 = arith.mulf %a, %argb : f32
linalg.yield %0 : f32 linalg.yield %0 : f32
} -> tensor<32xf32> } -> tensor<32xf32>
return %0 : tensor<32xf32> return %0 : tensor<32xf32>
} }
// CHECK-LABEL: func @add_s( // CHECK-LABEL: func @add_s(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, // CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>,
// CHECK-SAME: %[[VAL_1:.*]]: f32, // CHECK-SAME: %[[VAL_1:.*]]: f32,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32xf32>) -> tensor<32xf32> { // CHECK-SAME: %[[VAL_2:.*]]: tensor<32xf32>) -> tensor<32xf32> {
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant true // CHECK-DAG: %[[VAL_5:.*]] = arith.constant true
// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_6:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_4]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_4]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32> // CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32>
// CHECK: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf32> // CHECK-DAG: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf32>
// CHECK: %[[VAL_11:.*]] = memref.alloc() : memref<32xf32> // CHECK-DAG: %[[VAL_11:.*]] = memref.alloc() : memref<32xf32>
// CHECK: memref.copy %[[VAL_10]], %[[VAL_11]] : memref<32xf32> to memref<32xf32> // CHECK: memref.copy %[[VAL_10]], %[[VAL_11]] : memref<32xf32> to memref<32xf32>
// CHECK: %[[VAL_12:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_4]]] : memref<?xindex> // CHECK: %[[VAL_12:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_4]]] : memref<?xindex>
// CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_6]]] : memref<?xindex> // CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_6]]] : memref<?xindex>
// CHECK: %[[VAL_14:.*]]:2 = scf.while (%[[VAL_15:.*]] = %[[VAL_12]], %[[VAL_16:.*]] = %[[VAL_4]]) : (index, index) -> (index, index) { // CHECK: %[[VAL_14:.*]]:2 = scf.while (%[[VAL_15:.*]] = %[[VAL_12]], %[[VAL_16:.*]] = %[[VAL_4]]) : (index, index) -> (index, index) {
// CHECK: %[[VAL_17:.*]] = arith.cmpi ult, %[[VAL_15]], %[[VAL_13]] : index // CHECK: %[[VAL_17:.*]] = arith.cmpi ult, %[[VAL_15]], %[[VAL_13]] : index
// CHECK: scf.condition(%[[VAL_17]]) %[[VAL_15]], %[[VAL_16]] : index, index // CHECK: scf.condition(%[[VAL_17]]) %[[VAL_15]], %[[VAL_16]] : index, index
// CHECK: } do { // CHECK: } do {
// CHECK: ^bb0(%[[VAL_18:.*]]: index, %[[VAL_19:.*]]: index): // CHECK: ^bb0(%[[VAL_18:.*]]: index, %[[VAL_19:.*]]: index):
Show All 28 Lines outs(%argx: tensor<32xf32>) {
^bb(%a: f32, %x: f32): ^bb(%a: f32, %x: f32):
%0 = arith.addf %a, %argb : f32 %0 = arith.addf %a, %argb : f32
linalg.yield %0 : f32 linalg.yield %0 : f32
} -> tensor<32xf32> } -> tensor<32xf32>
return %0 : tensor<32xf32> return %0 : tensor<32xf32>
} }
// CHECK-LABEL: func @repeated_add_s( // CHECK-LABEL: func @repeated_add_s(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, // CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32xf32>) -> tensor<32xf32> { // CHECK-SAME: %[[VAL_1:.*]]: tensor<32xf32>) -> tensor<32xf32> {
// CHECK: %[[VAL_2:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_2:.*]] = arith.constant 0 : index
// CHECK: %[[VAL_3:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_4:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_2]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_4:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_2]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_5:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_2]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_2]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32> // CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32>
// CHECK: %[[VAL_7:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32xf32> // CHECK-DAG: %[[VAL_7:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32xf32>
// CHECK: %[[VAL_8:.*]] = memref.alloc() : memref<32xf32> // CHECK-DAG: %[[VAL_8:.*]] = memref.alloc() : memref<32xf32>
// CHECK: memref.copy %[[VAL_7]], %[[VAL_8]] : memref<32xf32> to memref<32xf32> // CHECK: memref.copy %[[VAL_7]], %[[VAL_8]] : memref<32xf32> to memref<32xf32>
// CHECK: %[[VAL_9:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_2]]] : memref<?xindex> // CHECK: %[[VAL_9:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_2]]] : memref<?xindex>
// CHECK: %[[VAL_10:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_3]]] : memref<?xindex> // CHECK: %[[VAL_10:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_3]]] : memref<?xindex>
// CHECK: scf.for %[[VAL_11:.*]] = %[[VAL_9]] to %[[VAL_10]] step %[[VAL_3]] { // CHECK: scf.for %[[VAL_11:.*]] = %[[VAL_9]] to %[[VAL_10]] step %[[VAL_3]] {
// CHECK: %[[VAL_12:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_11]]] : memref<?xindex> // CHECK: %[[VAL_12:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_11]]] : memref<?xindex>
// CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_11]]] : memref<?xf32> // CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_11]]] : memref<?xf32>
// CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_11]]] : memref<?xf32> // CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_11]]] : memref<?xf32>
// CHECK: %[[VAL_15:.*]] = arith.addf %[[VAL_13]], %[[VAL_14]] : f32 // CHECK: %[[VAL_15:.*]] = arith.addf %[[VAL_13]], %[[VAL_14]] : f32
Show All 15 Lines outs(%argx: tensor<32xf32>) {
%1 = arith.addf %a, %a : f32 // should yield %1 = arith.addf %a, %a : f32 // should yield
%2 = arith.addf %0, %1 : f32 // one guard %2 = arith.addf %0, %1 : f32 // one guard
linalg.yield %2 : f32 linalg.yield %2 : f32
} -> tensor<32xf32> } -> tensor<32xf32>
return %0 : tensor<32xf32> return %0 : tensor<32xf32>
} }
// CHECK-LABEL: func @mul_s( // CHECK-LABEL: func @mul_s(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, // CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>,
// CHECK-SAME: %[[VAL_1:.*]]: f32, // CHECK-SAME: %[[VAL_1:.*]]: f32,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32xf32>) -> tensor<32xf32> { // CHECK-SAME: %[[VAL_2:.*]]: tensor<32xf32>) -> tensor<32xf32> {
// CHECK: %[[VAL_3:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index
// CHECK: %[[VAL_4:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_6:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32> // CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32>
// CHECK: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf32> // CHECK-DAG: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf32>
// CHECK: %[[VAL_9:.*]] = memref.alloc() : memref<32xf32> // CHECK-DAG: %[[VAL_9:.*]] = memref.alloc() : memref<32xf32>
// CHECK: memref.copy %[[VAL_8]], %[[VAL_9]] : memref<32xf32> to memref<32xf32> // CHECK: memref.copy %[[VAL_8]], %[[VAL_9]] : memref<32xf32> to memref<32xf32>
// CHECK: %[[VAL_10:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_3]]] : memref<?xindex> // CHECK: %[[VAL_10:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_3]]] : memref<?xindex>
// CHECK: %[[VAL_11:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_4]]] : memref<?xindex> // CHECK: %[[VAL_11:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_4]]] : memref<?xindex>
// CHECK: scf.for %[[VAL_12:.*]] = %[[VAL_10]] to %[[VAL_11]] step %[[VAL_4]] { // CHECK: scf.for %[[VAL_12:.*]] = %[[VAL_10]] to %[[VAL_11]] step %[[VAL_4]] {
// CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_12]]] : memref<?xindex> // CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_12]]] : memref<?xindex>
// CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_12]]] : memref<?xf32> // CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_12]]] : memref<?xf32>
// CHECK: %[[VAL_15:.*]] = arith.mulf %[[VAL_14]], %[[VAL_1]] : f32 // CHECK: %[[VAL_15:.*]] = arith.mulf %[[VAL_14]], %[[VAL_1]] : f32
// CHECK: memref.store %[[VAL_15]], %[[VAL_9]]{{\[}}%[[VAL_13]]] : memref<32xf32> // CHECK: memref.store %[[VAL_15]], %[[VAL_9]]{{\[}}%[[VAL_13]]] : memref<32xf32>
Show All 18 Lines indexing_maps = [
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)"
} }
// CHECK-LABEL: func @add_dd( // CHECK-LABEL: func @add_dd(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, // CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32xf32>, // CHECK-SAME: %[[VAL_1:.*]]: tensor<32xf32>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32xf32>) -> tensor<32xf32> { // CHECK-SAME: %[[VAL_2:.*]]: tensor<32xf32>) -> tensor<32xf32> {
// CHECK: %[[VAL_3:.*]] = arith.constant 32 : index // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index
// CHECK: %[[VAL_4:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
// CHECK: %[[VAL_5:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32> // CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32>
// CHECK: %[[VAL_7:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32xf32> // CHECK-DAG: %[[VAL_7:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32xf32>
// CHECK: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf32> // CHECK-DAG: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf32>
// CHECK: %[[VAL_9:.*]] = memref.alloc() : memref<32xf32> // CHECK-DAG: %[[VAL_9:.*]] = memref.alloc() : memref<32xf32>
// CHECK: memref.copy %[[VAL_8]], %[[VAL_9]] : memref<32xf32> to memref<32xf32> // CHECK: memref.copy %[[VAL_8]], %[[VAL_9]] : memref<32xf32> to memref<32xf32>
// CHECK: scf.for %[[VAL_10:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] { // CHECK: scf.for %[[VAL_10:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] {
// CHECK: %[[VAL_11:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_10]]] : memref<?xf32> // CHECK: %[[VAL_11:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_10]]] : memref<?xf32>
// CHECK: %[[VAL_12:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_10]]] : memref<32xf32> // CHECK: %[[VAL_12:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_10]]] : memref<32xf32>
// CHECK: %[[VAL_13:.*]] = arith.addf %[[VAL_11]], %[[VAL_12]] : f32 // CHECK: %[[VAL_13:.*]] = arith.addf %[[VAL_11]], %[[VAL_12]] : f32
// CHECK: memref.store %[[VAL_13]], %[[VAL_9]]{{\[}}%[[VAL_10]]] : memref<32xf32> // CHECK: memref.store %[[VAL_13]], %[[VAL_9]]{{\[}}%[[VAL_10]]] : memref<32xf32>
// CHECK: } // CHECK: }
// CHECK: %[[VAL_14:.*]] = bufferization.to_tensor %[[VAL_9]] : memref<32xf32> // CHECK: %[[VAL_14:.*]] = bufferization.to_tensor %[[VAL_9]] : memref<32xf32>
// CHECK: return %[[VAL_14]] : tensor<32xf32> // CHECK: return %[[VAL_14]] : tensor<32xf32>
// CHECK: } // CHECK: }
func @add_dd(%arga: tensor<32xf32, #DV>, %argb: tensor<32xf32>, %argx: tensor<32xf32>) -> tensor<32xf32> { func @add_dd(%arga: tensor<32xf32, #DV>, %argb: tensor<32xf32>, %argx: tensor<32xf32>) -> tensor<32xf32> {
%0 = linalg.generic #trait2 %0 = linalg.generic #trait2
ins(%arga, %argb: tensor<32xf32, #DV>, tensor<32xf32>) ins(%arga, %argb: tensor<32xf32, #DV>, tensor<32xf32>)
outs(%argx: tensor<32xf32>) { outs(%argx: tensor<32xf32>) {
^bb(%a: f32, %b: f32, %x: f32): ^bb(%a: f32, %b: f32, %x: f32):
%0 = arith.addf %a, %b : f32 %0 = arith.addf %a, %b : f32
linalg.yield %0 : f32 linalg.yield %0 : f32
} -> tensor<32xf32> } -> tensor<32xf32>
return %0 : tensor<32xf32> return %0 : tensor<32xf32>
} }
// CHECK-LABEL: func @mul_dd( // CHECK-LABEL: func @mul_dd(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, // CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32xf32>, // CHECK-SAME: %[[VAL_1:.*]]: tensor<32xf32>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32xf32>) -> tensor<32xf32> { // CHECK-SAME: %[[VAL_2:.*]]: tensor<32xf32>) -> tensor<32xf32> {
// CHECK: %[[VAL_3:.*]] = arith.constant 32 : index // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index
// CHECK: %[[VAL_4:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
// CHECK: %[[VAL_5:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32> // CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32>
// CHECK: %[[VAL_7:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32xf32> // CHECK-DAG: %[[VAL_7:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32xf32>
// CHECK: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf32> // CHECK-DAG: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf32>
// CHECK: %[[VAL_9:.*]] = memref.alloc() : memref<32xf32> // CHECK-DAG: %[[VAL_9:.*]] = memref.alloc() : memref<32xf32>
// CHECK: memref.copy %[[VAL_8]], %[[VAL_9]] : memref<32xf32> to memref<32xf32> // CHECK: memref.copy %[[VAL_8]], %[[VAL_9]] : memref<32xf32> to memref<32xf32>
// CHECK: scf.for %[[VAL_10:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] { // CHECK: scf.for %[[VAL_10:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] {
// CHECK: %[[VAL_11:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_10]]] : memref<?xf32> // CHECK: %[[VAL_11:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_10]]] : memref<?xf32>
// CHECK: %[[VAL_12:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_10]]] : memref<32xf32> // CHECK: %[[VAL_12:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_10]]] : memref<32xf32>
// CHECK: %[[VAL_13:.*]] = arith.mulf %[[VAL_11]], %[[VAL_12]] : f32 // CHECK: %[[VAL_13:.*]] = arith.mulf %[[VAL_11]], %[[VAL_12]] : f32
// CHECK: memref.store %[[VAL_13]], %[[VAL_9]]{{\[}}%[[VAL_10]]] : memref<32xf32> // CHECK: memref.store %[[VAL_13]], %[[VAL_9]]{{\[}}%[[VAL_10]]] : memref<32xf32>
// CHECK: } // CHECK: }
// CHECK: %[[VAL_14:.*]] = bufferization.to_tensor %[[VAL_9]] : memref<32xf32> // CHECK: %[[VAL_14:.*]] = bufferization.to_tensor %[[VAL_9]] : memref<32xf32>
// CHECK: return %[[VAL_14]] : tensor<32xf32> // CHECK: return %[[VAL_14]] : tensor<32xf32>
// CHECK: } // CHECK: }
func @mul_dd(%arga: tensor<32xf32, #DV>, %argb: tensor<32xf32>, %argx: tensor<32xf32>) -> tensor<32xf32> { func @mul_dd(%arga: tensor<32xf32, #DV>, %argb: tensor<32xf32>, %argx: tensor<32xf32>) -> tensor<32xf32> {
%0 = linalg.generic #trait2 %0 = linalg.generic #trait2
ins(%arga, %argb: tensor<32xf32, #DV>, tensor<32xf32>) ins(%arga, %argb: tensor<32xf32, #DV>, tensor<32xf32>)
outs(%argx: tensor<32xf32>) { 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
linalg.yield %0 : f32 linalg.yield %0 : f32
} -> tensor<32xf32> } -> tensor<32xf32>
return %0 : tensor<32xf32> return %0 : tensor<32xf32>
} }
// CHECK-LABEL: func @add_ds( // CHECK-LABEL: func @add_ds(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32>, // CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, // CHECK-SAME: %[[VAL_1:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32xf32>) -> tensor<32xf32> { // CHECK-SAME: %[[VAL_2:.*]]: tensor<32xf32>) -> tensor<32xf32> {
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant true // CHECK-DAG: %[[VAL_5:.*]] = arith.constant true
// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_6:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_7:.*]] = bufferization.to_memref %[[VAL_0]] : memref<32xf32> // CHECK-DAG: %[[VAL_7:.*]] = bufferization.to_memref %[[VAL_0]] : memref<32xf32>
// CHECK: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_4]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_4]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_4]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_4]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32> // CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32>
// CHECK: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf32> // CHECK-DAG: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf32>
// CHECK: %[[VAL_12:.*]] = memref.alloc() : memref<32xf32> // CHECK-DAG: %[[VAL_12:.*]] = memref.alloc() : memref<32xf32>
// CHECK: memref.copy %[[VAL_11]], %[[VAL_12]] : memref<32xf32> to memref<32xf32> // CHECK: memref.copy %[[VAL_11]], %[[VAL_12]] : memref<32xf32> to memref<32xf32>
// CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_4]]] : memref<?xindex> // CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_4]]] : memref<?xindex>
// CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_6]]] : memref<?xindex> // CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_6]]] : memref<?xindex>
// CHECK: %[[VAL_15:.*]]:2 = scf.while (%[[VAL_16:.*]] = %[[VAL_13]], %[[VAL_17:.*]] = %[[VAL_4]]) : (index, index) -> (index, index) { // CHECK: %[[VAL_15:.*]]:2 = scf.while (%[[VAL_16:.*]] = %[[VAL_13]], %[[VAL_17:.*]] = %[[VAL_4]]) : (index, index) -> (index, index) {
// CHECK: %[[VAL_18:.*]] = arith.cmpi ult, %[[VAL_16]], %[[VAL_14]] : index // CHECK: %[[VAL_18:.*]] = arith.cmpi ult, %[[VAL_16]], %[[VAL_14]] : index
// CHECK: scf.condition(%[[VAL_18]]) %[[VAL_16]], %[[VAL_17]] : index, index // CHECK: scf.condition(%[[VAL_18]]) %[[VAL_16]], %[[VAL_17]] : index, index
// CHECK: } do { // CHECK: } do {
// CHECK: ^bb0(%[[VAL_19:.*]]: index, %[[VAL_20:.*]]: index): // CHECK: ^bb0(%[[VAL_19:.*]]: index, %[[VAL_20:.*]]: index):
Show All 31 Lines outs(%argx: tensor<32xf32>) {
^bb(%a: f32, %b: f32, %x: f32): ^bb(%a: f32, %b: f32, %x: f32):
%0 = arith.addf %a, %b : f32 %0 = arith.addf %a, %b : f32
linalg.yield %0 : f32 linalg.yield %0 : f32
} -> tensor<32xf32> } -> tensor<32xf32>
return %0 : tensor<32xf32> return %0 : tensor<32xf32>
} }
// CHECK-LABEL: func @mul_ds( // CHECK-LABEL: func @mul_ds(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32>, // CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, // CHECK-SAME: %[[VAL_1:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32xf32>) -> tensor<32xf32> { // CHECK-SAME: %[[VAL_2:.*]]: tensor<32xf32>) -> tensor<32xf32> {
// CHECK: %[[VAL_3:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index
// CHECK: %[[VAL_4:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_5:.*]] = bufferization.to_memref %[[VAL_0]] : memref<32xf32> // CHECK-DAG: %[[VAL_5:.*]] = bufferization.to_memref %[[VAL_0]] : memref<32xf32>
// CHECK: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_3]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_3]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_3]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_3]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32> // CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32>
// CHECK: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf32> // CHECK-DAG: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf32>
// CHECK: %[[VAL_10:.*]] = memref.alloc() : memref<32xf32> // CHECK-DAG: %[[VAL_10:.*]] = memref.alloc() : memref<32xf32>
// CHECK: memref.copy %[[VAL_9]], %[[VAL_10]] : memref<32xf32> to memref<32xf32> // CHECK: memref.copy %[[VAL_9]], %[[VAL_10]] : memref<32xf32> to memref<32xf32>
// CHECK: %[[VAL_11:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_3]]] : memref<?xindex> // CHECK: %[[VAL_11:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_3]]] : memref<?xindex>
// CHECK: %[[VAL_12:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex> // CHECK: %[[VAL_12:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex>
// CHECK: scf.for %[[VAL_13:.*]] = %[[VAL_11]] to %[[VAL_12]] step %[[VAL_4]] { // CHECK: scf.for %[[VAL_13:.*]] = %[[VAL_11]] to %[[VAL_12]] step %[[VAL_4]] {
// CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_13]]] : memref<?xindex> // CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_13]]] : memref<?xindex>
// CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_14]]] : memref<32xf32> // CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_14]]] : memref<32xf32>
// CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_13]]] : memref<?xf32> // CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_13]]] : memref<?xf32>
// CHECK: %[[VAL_17:.*]] = arith.mulf %[[VAL_15]], %[[VAL_16]] : f32 // CHECK: %[[VAL_17:.*]] = arith.mulf %[[VAL_15]], %[[VAL_16]] : f32
Show All 9 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
linalg.yield %0 : f32 linalg.yield %0 : f32
} -> tensor<32xf32> } -> tensor<32xf32>
return %0 : tensor<32xf32> return %0 : tensor<32xf32>
} }
// CHECK-LABEL: func @add_sd( // CHECK-LABEL: func @add_sd(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, // CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32xf32>, // CHECK-SAME: %[[VAL_1:.*]]: tensor<32xf32>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32xf32>) -> tensor<32xf32> { // CHECK-SAME: %[[VAL_2:.*]]: tensor<32xf32>) -> tensor<32xf32> {
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant true // CHECK-DAG: %[[VAL_5:.*]] = arith.constant true
// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_6:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_4]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_4]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32> // CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32>
// CHECK: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32xf32> // CHECK-DAG: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32xf32>
// CHECK: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf32> // CHECK-DAG: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf32>
// CHECK: %[[VAL_12:.*]] = memref.alloc() : memref<32xf32> // CHECK-DAG: %[[VAL_12:.*]] = memref.alloc() : memref<32xf32>
// CHECK: memref.copy %[[VAL_11]], %[[VAL_12]] : memref<32xf32> to memref<32xf32> // CHECK: memref.copy %[[VAL_11]], %[[VAL_12]] : memref<32xf32> to memref<32xf32>
// CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_4]]] : memref<?xindex> // CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_4]]] : memref<?xindex>
// CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_6]]] : memref<?xindex> // CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_6]]] : memref<?xindex>
// CHECK: %[[VAL_15:.*]]:2 = scf.while (%[[VAL_16:.*]] = %[[VAL_13]], %[[VAL_17:.*]] = %[[VAL_4]]) : (index, index) -> (index, index) { // CHECK: %[[VAL_15:.*]]:2 = scf.while (%[[VAL_16:.*]] = %[[VAL_13]], %[[VAL_17:.*]] = %[[VAL_4]]) : (index, index) -> (index, index) {
// CHECK: %[[VAL_18:.*]] = arith.cmpi ult, %[[VAL_16]], %[[VAL_14]] : index // CHECK: %[[VAL_18:.*]] = arith.cmpi ult, %[[VAL_16]], %[[VAL_14]] : index
// CHECK: scf.condition(%[[VAL_18]]) %[[VAL_16]], %[[VAL_17]] : index, index // CHECK: scf.condition(%[[VAL_18]]) %[[VAL_16]], %[[VAL_17]] : index, index
// CHECK: } do { // CHECK: } do {
// CHECK: ^bb0(%[[VAL_19:.*]]: index, %[[VAL_20:.*]]: index): // CHECK: ^bb0(%[[VAL_19:.*]]: index, %[[VAL_20:.*]]: index):
Show All 31 Lines outs(%argx: tensor<32xf32>) {
^bb(%a: f32, %b: f32, %x: f32): ^bb(%a: f32, %b: f32, %x: f32):
%0 = arith.addf %a, %b : f32 %0 = arith.addf %a, %b : f32
linalg.yield %0 : f32 linalg.yield %0 : f32
} -> tensor<32xf32> } -> tensor<32xf32>
return %0 : tensor<32xf32> return %0 : tensor<32xf32>
} }
// CHECK-LABEL: func @mul_sd( // CHECK-LABEL: func @mul_sd(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, // CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32xf32>, // CHECK-SAME: %[[VAL_1:.*]]: tensor<32xf32>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32xf32>) -> tensor<32xf32> { // CHECK-SAME: %[[VAL_2:.*]]: tensor<32xf32>) -> tensor<32xf32> {
// CHECK: %[[VAL_3:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index
// CHECK: %[[VAL_4:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_6:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32> // CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32>
// CHECK: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32xf32> // CHECK-DAG: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32xf32>
// CHECK: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf32> // CHECK-DAG: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf32>
// CHECK: %[[VAL_10:.*]] = memref.alloc() : memref<32xf32> // CHECK-DAG: %[[VAL_10:.*]] = memref.alloc() : memref<32xf32>
// CHECK: memref.copy %[[VAL_9]], %[[VAL_10]] : memref<32xf32> to memref<32xf32> // CHECK: memref.copy %[[VAL_9]], %[[VAL_10]] : memref<32xf32> to memref<32xf32>
// CHECK: %[[VAL_11:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_3]]] : memref<?xindex> // CHECK: %[[VAL_11:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_3]]] : memref<?xindex>
// CHECK: %[[VAL_12:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_4]]] : memref<?xindex> // CHECK: %[[VAL_12:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_4]]] : memref<?xindex>
// CHECK: scf.for %[[VAL_13:.*]] = %[[VAL_11]] to %[[VAL_12]] step %[[VAL_4]] { // CHECK: scf.for %[[VAL_13:.*]] = %[[VAL_11]] to %[[VAL_12]] step %[[VAL_4]] {
// CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_13]]] : memref<?xindex> // CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_13]]] : memref<?xindex>
// CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_13]]] : memref<?xf32> // CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_13]]] : memref<?xf32>
// CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_14]]] : memref<32xf32> // CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_14]]] : memref<32xf32>
// CHECK: %[[VAL_17:.*]] = arith.mulf %[[VAL_15]], %[[VAL_16]] : f32 // CHECK: %[[VAL_17:.*]] = arith.mulf %[[VAL_15]], %[[VAL_16]] : f32
Show All 9 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
linalg.yield %0 : f32 linalg.yield %0 : f32
} -> tensor<32xf32> } -> tensor<32xf32>
return %0 : tensor<32xf32> return %0 : tensor<32xf32>
} }
// CHECK-LABEL: func @add_ss( // CHECK-LABEL: func @add_ss(
// CHECK-SAME: %[[VAL_0:.*0]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, // CHECK-SAME: %[[VAL_0:.*0]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>,
// CHECK-SAME: %[[VAL_1:.*1]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, // CHECK-SAME: %[[VAL_1:.*1]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>,
// CHECK-SAME: %[[VAL_2:.*2]]: tensor<32xf32>) -> tensor<32xf32> { // CHECK-SAME: %[[VAL_2:.*2]]: tensor<32xf32>) -> tensor<32xf32> {
// CHECK: %[[VAL_3:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index
// CHECK: %[[VAL_4:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_6:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32> // CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32>
// CHECK: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_3]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_3]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_3]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_3]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32> // CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32>
// CHECK: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf32> // CHECK-DAG: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf32>
// CHECK: %[[VAL_12:.*]] = memref.alloc() : memref<32xf32> // CHECK-DAG: %[[VAL_12:.*]] = memref.alloc() : memref<32xf32>
// CHECK: memref.copy %[[VAL_11]], %[[VAL_12]] : memref<32xf32> to memref<32xf32> // CHECK: memref.copy %[[VAL_11]], %[[VAL_12]] : memref<32xf32> to memref<32xf32>
// CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_3]]] : memref<?xindex> // CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_3]]] : memref<?xindex>
// CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_4]]] : memref<?xindex> // CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_4]]] : memref<?xindex>
// CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_3]]] : memref<?xindex> // CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_3]]] : memref<?xindex>
// CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_4]]] : memref<?xindex> // CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_4]]] : memref<?xindex>
// CHECK: %[[VAL_17:.*]]:2 = scf.while (%[[VAL_18:.*]] = %[[VAL_13]], %[[VAL_19:.*]] = %[[VAL_15]]) : (index, index) -> (index, index) { // CHECK: %[[VAL_17:.*]]:2 = scf.while (%[[VAL_18:.*]] = %[[VAL_13]], %[[VAL_19:.*]] = %[[VAL_15]]) : (index, index) -> (index, index) {
// CHECK: %[[VAL_20:.*]] = arith.cmpi ult, %[[VAL_18]], %[[VAL_14]] : index // CHECK: %[[VAL_20:.*]] = arith.cmpi ult, %[[VAL_18]], %[[VAL_14]] : index
// CHECK: %[[VAL_21:.*]] = arith.cmpi ult, %[[VAL_19]], %[[VAL_16]] : index // CHECK: %[[VAL_21:.*]] = arith.cmpi ult, %[[VAL_19]], %[[VAL_16]] : index
▲ Show 20 Lines • Show All 55 Lines • ▼ Show 20 Lines outs(%argx: tensor<32xf32>) {
^bb(%a: f32, %b: f32, %x: f32): ^bb(%a: f32, %b: f32, %x: f32):
%0 = arith.addf %a, %b : f32 %0 = arith.addf %a, %b : f32
linalg.yield %0 : f32 linalg.yield %0 : f32
} -> tensor<32xf32> } -> tensor<32xf32>
return %0 : tensor<32xf32> return %0 : tensor<32xf32>
} }
// CHECK-LABEL: func @mul_ss( // CHECK-LABEL: func @mul_ss(
// CHECK-SAME: %[[VAL_0:.*0]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, // CHECK-SAME: %[[VAL_0:.*0]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>,
// CHECK-SAME: %[[VAL_1:.*1]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, // CHECK-SAME: %[[VAL_1:.*1]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>,
// CHECK-SAME: %[[VAL_2:.*2]]: tensor<32xf32>) -> tensor<32xf32> { // CHECK-SAME: %[[VAL_2:.*2]]: tensor<32xf32>) -> tensor<32xf32> {
// CHECK: %[[VAL_3:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index
// CHECK: %[[VAL_4:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_6:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32> // CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32>
// CHECK: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_3]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_3]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_3]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_3]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32> // CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32>
// CHECK: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf32> // CHECK-DAG: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf32>
// CHECK: %[[VAL_12:.*]] = memref.alloc() : memref<32xf32> // CHECK-DAG: %[[VAL_12:.*]] = memref.alloc() : memref<32xf32>
// CHECK: memref.copy %[[VAL_11]], %[[VAL_12]] : memref<32xf32> to memref<32xf32> // CHECK: memref.copy %[[VAL_11]], %[[VAL_12]] : memref<32xf32> to memref<32xf32>
// CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_3]]] : memref<?xindex> // CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_3]]] : memref<?xindex>
// CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_4]]] : memref<?xindex> // CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_4]]] : memref<?xindex>
// CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_3]]] : memref<?xindex> // CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_3]]] : memref<?xindex>
// CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_4]]] : memref<?xindex> // CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_4]]] : memref<?xindex>
// CHECK: %[[VAL_17:.*]]:2 = scf.while (%[[VAL_18:.*]] = %[[VAL_13]], %[[VAL_19:.*]] = %[[VAL_15]]) : (index, index) -> (index, index) { // CHECK: %[[VAL_17:.*]]:2 = scf.while (%[[VAL_18:.*]] = %[[VAL_13]], %[[VAL_19:.*]] = %[[VAL_15]]) : (index, index) -> (index, index) {
// CHECK: %[[VAL_20:.*]] = arith.cmpi ult, %[[VAL_18]], %[[VAL_14]] : index // CHECK: %[[VAL_20:.*]] = arith.cmpi ult, %[[VAL_18]], %[[VAL_14]] : index
// CHECK: %[[VAL_21:.*]] = arith.cmpi ult, %[[VAL_19]], %[[VAL_16]] : index // CHECK: %[[VAL_21:.*]] = arith.cmpi ult, %[[VAL_19]], %[[VAL_16]] : index
Show All 33 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
linalg.yield %0 : f32 linalg.yield %0 : f32
} -> tensor<32xf32> } -> tensor<32xf32>
return %0 : tensor<32xf32> return %0 : tensor<32xf32>
} }
// CHECK-LABEL: func @two_way_inv( // CHECK-LABEL: func @two_way_inv(
// CHECK-SAME: %[[VAL_0:.*0]]: tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, // CHECK-SAME: %[[VAL_0:.*0]]: tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>,
// CHECK-SAME: %[[VAL_1:.*1]]: tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, // CHECK-SAME: %[[VAL_1:.*1]]: tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>,
// CHECK-SAME: %[[VAL_2:.*2]]: f32, // CHECK-SAME: %[[VAL_2:.*2]]: f32,
// CHECK-SAME: %[[VAL_3:.*3]]: tensor<16xf32>) -> tensor<16xf32> { // CHECK-SAME: %[[VAL_3:.*3]]: tensor<16xf32>) -> tensor<16xf32> {
// CHECK: %[[VAL_4:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
// CHECK: %[[VAL_5:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_4]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_4]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32> // CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32>
// CHECK: %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_4]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_4]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_4]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_4]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32> // CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32>
// CHECK: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_3]] : memref<16xf32> // CHECK-DAG: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_3]] : memref<16xf32>
// CHECK: %[[VAL_13:.*]] = memref.alloc() : memref<16xf32> // CHECK-DAG: %[[VAL_13:.*]] = memref.alloc() : memref<16xf32>
// CHECK: memref.copy %[[VAL_12]], %[[VAL_13]] : memref<16xf32> to memref<16xf32> // CHECK: memref.copy %[[VAL_12]], %[[VAL_13]] : memref<16xf32> to memref<16xf32>
// CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex> // CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex>
// CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_5]]] : memref<?xindex> // CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_5]]] : memref<?xindex>
// CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_4]]] : memref<?xindex> // CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_4]]] : memref<?xindex>
// CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_5]]] : memref<?xindex> // CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_5]]] : memref<?xindex>
// CHECK: %[[VAL_18:.*]]:2 = scf.while (%[[VAL_19:.*]] = %[[VAL_14]], %[[VAL_20:.*]] = %[[VAL_16]]) : (index, index) -> (index, index) { // CHECK: %[[VAL_18:.*]]:2 = scf.while (%[[VAL_19:.*]] = %[[VAL_14]], %[[VAL_20:.*]] = %[[VAL_16]]) : (index, index) -> (index, index) {
// CHECK: %[[VAL_21:.*]] = arith.cmpi ult, %[[VAL_19]], %[[VAL_15]] : index // CHECK: %[[VAL_21:.*]] = arith.cmpi ult, %[[VAL_19]], %[[VAL_15]] : index
// CHECK: %[[VAL_22:.*]] = arith.cmpi ult, %[[VAL_20]], %[[VAL_17]] : index // CHECK: %[[VAL_22:.*]] = arith.cmpi ult, %[[VAL_20]], %[[VAL_17]] : index
▲ Show 20 Lines • Show All 64 Lines • ▼ Show 20 Lines outs(%argx: tensor<16xf32>) {
%1 = arith.mulf %b, %argc : f32 %1 = arith.mulf %b, %argc : f32
%2 = arith.addf %0, %1 : f32 %2 = arith.addf %0, %1 : f32
linalg.yield %2 : f32 linalg.yield %2 : f32
} -> tensor<16xf32> } -> tensor<16xf32>
return %0 : tensor<16xf32> return %0 : tensor<16xf32>
} }
// CHECK-LABEL: func @two_way_inv_alt( // CHECK-LABEL: func @two_way_inv_alt(
// CHECK-SAME: %[[VAL_0:.*0]]: tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, // CHECK-SAME: %[[VAL_0:.*0]]: tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>,
// CHECK-SAME: %[[VAL_1:.*1]]: tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, // CHECK-SAME: %[[VAL_1:.*1]]: tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>,
// CHECK-SAME: %[[VAL_2:.*2]]: f32, // CHECK-SAME: %[[VAL_2:.*2]]: f32,
// CHECK-SAME: %[[VAL_3:.*3]]: tensor<16xf32>) -> tensor<16xf32> { // CHECK-SAME: %[[VAL_3:.*3]]: tensor<16xf32>) -> tensor<16xf32> {
// CHECK: %[[VAL_4:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
// CHECK: %[[VAL_5:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_4]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_4]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32> // CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32>
// CHECK: %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_4]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_4]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_4]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_4]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32> // CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32>
// CHECK: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_3]] : memref<16xf32> // CHECK-DAG: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_3]] : memref<16xf32>
// CHECK: %[[VAL_13:.*]] = memref.alloc() : memref<16xf32> // CHECK-DAG: %[[VAL_13:.*]] = memref.alloc() : memref<16xf32>
// CHECK: memref.copy %[[VAL_12]], %[[VAL_13]] : memref<16xf32> to memref<16xf32> // CHECK: memref.copy %[[VAL_12]], %[[VAL_13]] : memref<16xf32> to memref<16xf32>
// CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex> // CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex>
// CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_5]]] : memref<?xindex> // CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_5]]] : memref<?xindex>
// CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_4]]] : memref<?xindex> // CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_4]]] : memref<?xindex>
// CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_5]]] : memref<?xindex> // CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_5]]] : memref<?xindex>
// CHECK: %[[VAL_18:.*]]:2 = scf.while (%[[VAL_19:.*]] = %[[VAL_14]], %[[VAL_20:.*]] = %[[VAL_16]]) : (index, index) -> (index, index) { // CHECK: %[[VAL_18:.*]]:2 = scf.while (%[[VAL_19:.*]] = %[[VAL_14]], %[[VAL_20:.*]] = %[[VAL_16]]) : (index, index) -> (index, index) {
// CHECK: %[[VAL_21:.*]] = arith.cmpi ult, %[[VAL_19]], %[[VAL_15]] : index // CHECK: %[[VAL_21:.*]] = arith.cmpi ult, %[[VAL_19]], %[[VAL_15]] : index
// CHECK: %[[VAL_22:.*]] = arith.cmpi ult, %[[VAL_20]], %[[VAL_17]] : index // CHECK: %[[VAL_22:.*]] = arith.cmpi ult, %[[VAL_20]], %[[VAL_17]] : index
▲ Show 20 Lines • Show All 76 Lines • ▼ Show 20 Lines#trait_sum_reduction = {
doc = "x += SUM_i a(i)" doc = "x += SUM_i a(i)"
} }
// CHECK-LABEL: func @sum_reduction( // CHECK-LABEL: func @sum_reduction(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, // CHECK-SAME: %[[VAL_0:.*]]: tensor<?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<f32>) -> tensor<f32> { // CHECK-SAME: %[[VAL_1:.*]]: tensor<f32>) -> tensor<f32> {
// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_2:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_4:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_2]] : tensor<?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_4:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_2]] : tensor<?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_5:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32> // CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32>
// CHECK: %[[VAL_6:.*]] = bufferization.to_memref %[[VAL_1]] : memref<f32> // CHECK-DAG: %[[VAL_6:.*]] = bufferization.to_memref %[[VAL_1]] : memref<f32>
// CHECK: %[[VAL_7:.*]] = memref.alloc() : memref<f32> // CHECK-DAG: %[[VAL_7:.*]] = memref.alloc() : memref<f32>
// CHECK: memref.copy %[[VAL_6]], %[[VAL_7]] : memref<f32> to memref<f32> // CHECK: memref.copy %[[VAL_6]], %[[VAL_7]] : memref<f32> to memref<f32>
// CHECK-DAG: %[[VAL_8:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_2]]] : memref<?xindex> // CHECK-DAG: %[[VAL_8:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_2]]] : memref<?xindex>
// CHECK-DAG: %[[VAL_9:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_3]]] : memref<?xindex> // CHECK-DAG: %[[VAL_9:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_3]]] : memref<?xindex>
// CHECK-DAG: %[[VAL_10:.*]] = memref.load %[[VAL_7]][] : memref<f32> // CHECK-DAG: %[[VAL_10:.*]] = memref.load %[[VAL_7]][] : memref<f32>
// CHECK: %[[VAL_11:.*]] = scf.for %[[VAL_12:.*]] = %[[VAL_8]] to %[[VAL_9]] step %[[VAL_3]] iter_args(%[[VAL_13:.*]] = %[[VAL_10]]) -> (f32) { // CHECK: %[[VAL_11:.*]] = scf.for %[[VAL_12:.*]] = %[[VAL_8]] to %[[VAL_9]] step %[[VAL_3]] iter_args(%[[VAL_13:.*]] = %[[VAL_10]]) -> (f32) {
// CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_12]]] : memref<?xf32> // CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_12]]] : memref<?xf32>
// CHECK: %[[VAL_15:.*]] = arith.addf %[[VAL_13]], %[[VAL_14]] : f32 // CHECK: %[[VAL_15:.*]] = arith.addf %[[VAL_13]], %[[VAL_14]] : f32
// CHECK: scf.yield %[[VAL_15]] : f32 // CHECK: scf.yield %[[VAL_15]] : f32
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} }
// CHECK-LABEL: func @sum_reduction_ss( // CHECK-LABEL: func @sum_reduction_ss(
// CHECK-SAME: %[[VAL_0:.*0]]: tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, // CHECK-SAME: %[[VAL_0:.*0]]: tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>,
// CHECK-SAME: %[[VAL_1:.*1]]: tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, // CHECK-SAME: %[[VAL_1:.*1]]: tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>,
// CHECK-SAME: %[[VAL_2:.*2]]: tensor<f32>) -> tensor<f32> { // CHECK-SAME: %[[VAL_2:.*2]]: tensor<f32>) -> tensor<f32> {
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_6:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32> // CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32>
// CHECK: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_3]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_3]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_3]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_3]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32> // CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32>
// CHECK: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref<f32> // CHECK-DAG: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref<f32>
// CHECK: %[[VAL_12:.*]] = memref.alloc() : memref<f32> // CHECK-DAG: %[[VAL_12:.*]] = memref.alloc() : memref<f32>
// CHECK: memref.copy %[[VAL_11]], %[[VAL_12]] : memref<f32> to memref<f32> // CHECK: memref.copy %[[VAL_11]], %[[VAL_12]] : memref<f32> to memref<f32>
// CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_12]][] : memref<f32> // CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_12]][] : memref<f32>
// CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_3]]] : memref<?xindex> // CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_3]]] : memref<?xindex>
// CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_4]]] : memref<?xindex> // CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_4]]] : memref<?xindex>
// CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_3]]] : memref<?xindex> // CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_3]]] : memref<?xindex>
// CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_4]]] : memref<?xindex> // CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_4]]] : memref<?xindex>
// CHECK: %[[VAL_18:.*]]:3 = scf.while (%[[VAL_19:.*]] = %[[VAL_14]], %[[VAL_20:.*]] = %[[VAL_16]], %[[VAL_21:.*]] = %[[VAL_13]]) : (index, index, f32) -> (index, index, f32) { // CHECK: %[[VAL_18:.*]]:3 = scf.while (%[[VAL_19:.*]] = %[[VAL_14]], %[[VAL_20:.*]] = %[[VAL_16]], %[[VAL_21:.*]] = %[[VAL_13]]) : (index, index, f32) -> (index, index, f32) {
// CHECK: %[[VAL_22:.*]] = arith.cmpi ult, %[[VAL_19]], %[[VAL_15]] : index // CHECK: %[[VAL_22:.*]] = arith.cmpi ult, %[[VAL_19]], %[[VAL_15]] : index
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// CHECK-LABEL: func @sum_reduction_inv( // CHECK-LABEL: func @sum_reduction_inv(
// CHECK-SAME: %[[VAL_0:.*0]]: tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, // CHECK-SAME: %[[VAL_0:.*0]]: tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>,
// CHECK-SAME: %[[VAL_1:.*1]]: tensor<f32>, // CHECK-SAME: %[[VAL_1:.*1]]: tensor<f32>,
// CHECK-SAME: %[[VAL_2:.*2]]: tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, // CHECK-SAME: %[[VAL_2:.*2]]: tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>,
// CHECK-SAME: %[[VAL_3:.*3]]: tensor<f32>) -> tensor<f32> { // CHECK-SAME: %[[VAL_3:.*3]]: tensor<f32>) -> tensor<f32> {
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_4]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_4]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32> // CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32>
// CHECK: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<f32> // CHECK-DAG: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<f32>
// CHECK: %[[VAL_10:.*]] = sparse_tensor.pointers %[[VAL_2]], %[[VAL_4]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.pointers %[[VAL_2]], %[[VAL_4]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_11:.*]] = sparse_tensor.indices %[[VAL_2]], %[[VAL_4]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.indices %[[VAL_2]], %[[VAL_4]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_12:.*]] = sparse_tensor.values %[[VAL_2]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32> // CHECK-DAG: %[[VAL_12:.*]] = sparse_tensor.values %[[VAL_2]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32>
// CHECK: %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_3]] : memref<f32> // CHECK-DAG: %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_3]] : memref<f32>
// CHECK: %[[VAL_14:.*]] = memref.alloc() : memref<f32> // CHECK-DAG: %[[VAL_14:.*]] = memref.alloc() : memref<f32>
// CHECK: memref.copy %[[VAL_13]], %[[VAL_14]] : memref<f32> to memref<f32> // CHECK: memref.copy %[[VAL_13]], %[[VAL_14]] : memref<f32> to memref<f32>
// CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_14]][] : memref<f32> // CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_14]][] : memref<f32>
// CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_9]][] : memref<f32> // CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_9]][] : memref<f32>
// CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex> // CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex>
// CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_5]]] : memref<?xindex> // CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_5]]] : memref<?xindex>
// CHECK: %[[VAL_19:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_4]]] : memref<?xindex> // CHECK: %[[VAL_19:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_4]]] : memref<?xindex>
// CHECK: %[[VAL_20:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_5]]] : memref<?xindex> // CHECK: %[[VAL_20:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_5]]] : memref<?xindex>
// CHECK: %[[VAL_21:.*]]:3 = scf.while (%[[VAL_22:.*]] = %[[VAL_17]], %[[VAL_23:.*]] = %[[VAL_19]], %[[VAL_24:.*]] = %[[VAL_15]]) : (index, index, f32) -> (index, index, f32) { // CHECK: %[[VAL_21:.*]]:3 = scf.while (%[[VAL_22:.*]] = %[[VAL_17]], %[[VAL_23:.*]] = %[[VAL_19]], %[[VAL_24:.*]] = %[[VAL_15]]) : (index, index, f32) -> (index, index, f32) {
▲ Show 20 Lines • Show All 86 Lines • ▼ Show 20 Lines indexing_maps = [
affine_map<(i) -> (i)>, // D affine_map<(i) -> (i)>, // D
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) + C(i) + D(i)" doc = "X(i) = A(i) + B(i) + C(i) + D(i)"
} }
// CHECK-LABEL: func @four_tensors_op( // CHECK-LABEL: func @four_tensors_op(
// CHECK-SAME: %[[VAL_0:.*0]]: tensor<?xf64>, // CHECK-SAME: %[[VAL_0:.*0]]: tensor<?xf64>,
// CHECK-SAME: %[[VAL_1:.*1]]: tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, // CHECK-SAME: %[[VAL_1:.*1]]: tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>,
// CHECK-SAME: %[[VAL_2:.*2]]: tensor<?xf64>, // CHECK-SAME: %[[VAL_2:.*2]]: tensor<?xf64>,
// CHECK-SAME: %[[VAL_3:.*3]]: tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, // CHECK-SAME: %[[VAL_3:.*3]]: tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>,
// CHECK-SAME: %[[VAL_4:.*]]: tensor<?xf64>) -> tensor<?xf64> { // CHECK-SAME: %[[VAL_4:.*]]: tensor<?xf64>) -> tensor<?xf64> {
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_6:.*]] = arith.constant true // CHECK-DAG: %[[VAL_6:.*]] = arith.constant true
// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_7:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_0]] : memref<?xf64> // CHECK-DAG: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_0]] : memref<?xf64>
// CHECK: %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_5]] : tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_5]] : tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_5]] : tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_5]] : tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf64> // CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf64>
// CHECK: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_2]] : memref<?xf64> // CHECK-DAG: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_2]] : memref<?xf64>
// CHECK: %[[VAL_13:.*]] = sparse_tensor.pointers %[[VAL_3]], %[[VAL_5]] : tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_13:.*]] = sparse_tensor.pointers %[[VAL_3]], %[[VAL_5]] : tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_14:.*]] = sparse_tensor.indices %[[VAL_3]], %[[VAL_5]] : tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_14:.*]] = sparse_tensor.indices %[[VAL_3]], %[[VAL_5]] : tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_15:.*]] = sparse_tensor.values %[[VAL_3]] : tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf64> // CHECK-DAG: %[[VAL_15:.*]] = sparse_tensor.values %[[VAL_3]] : tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf64>
// CHECK: %[[VAL_16:.*]] = tensor.dim %[[VAL_4]], %[[VAL_5]] : tensor<?xf64> // CHECK-DAG: %[[VAL_16:.*]] = tensor.dim %[[VAL_4]], %[[VAL_5]] : tensor<?xf64>
// CHECK: %[[VAL_17:.*]] = bufferization.to_memref %[[VAL_4]] : memref<?xf64> // CHECK-DAG: %[[VAL_17:.*]] = bufferization.to_memref %[[VAL_4]] : memref<?xf64>
// CHECK: %[[VAL_18:.*]] = memref.alloc(%[[VAL_16]]) : memref<?xf64> // CHECK-DAG: %[[VAL_18:.*]] = memref.alloc(%[[VAL_16]]) : memref<?xf64>
// CHECK: memref.copy %[[VAL_17]], %[[VAL_18]] : memref<?xf64> to memref<?xf64> // CHECK: memref.copy %[[VAL_17]], %[[VAL_18]] : memref<?xf64> to memref<?xf64>
// CHECK: %[[VAL_19:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_5]]] : memref<?xindex> // CHECK: %[[VAL_19:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_5]]] : memref<?xindex>
// CHECK: %[[VAL_20:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_7]]] : memref<?xindex> // CHECK: %[[VAL_20:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_7]]] : memref<?xindex>
// CHECK: %[[VAL_21:.*]] = memref.load %[[VAL_13]]{{\[}}%[[VAL_5]]] : memref<?xindex> // CHECK: %[[VAL_21:.*]] = memref.load %[[VAL_13]]{{\[}}%[[VAL_5]]] : memref<?xindex>
// CHECK: %[[VAL_22:.*]] = memref.load %[[VAL_13]]{{\[}}%[[VAL_7]]] : memref<?xindex> // CHECK: %[[VAL_22:.*]] = memref.load %[[VAL_13]]{{\[}}%[[VAL_7]]] : memref<?xindex>
// CHECK: %[[VAL_23:.*]]:3 = scf.while (%[[VAL_24:.*]] = %[[VAL_19]], %[[VAL_25:.*]] = %[[VAL_21]], %[[VAL_26:.*]] = %[[VAL_5]]) : (index, index, index) -> (index, index, index) { // CHECK: %[[VAL_23:.*]]:3 = scf.while (%[[VAL_24:.*]] = %[[VAL_19]], %[[VAL_25:.*]] = %[[VAL_21]], %[[VAL_26:.*]] = %[[VAL_5]]) : (index, index, index) -> (index, index, index) {
// CHECK: %[[VAL_27:.*]] = arith.cmpi ult, %[[VAL_24]], %[[VAL_20]] : index // CHECK: %[[VAL_27:.*]] = arith.cmpi ult, %[[VAL_24]], %[[VAL_20]] : index
// CHECK: %[[VAL_28:.*]] = arith.cmpi ult, %[[VAL_25]], %[[VAL_22]] : index // CHECK: %[[VAL_28:.*]] = arith.cmpi ult, %[[VAL_25]], %[[VAL_22]] : index
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// CHECK-LABEL: func @red3s( // CHECK-LABEL: func @red3s(
// CHECK-SAME: %[[VAL_0:.*0]]: tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, // CHECK-SAME: %[[VAL_0:.*0]]: tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>,
// CHECK-SAME: %[[VAL_1:.*1]]: tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, // CHECK-SAME: %[[VAL_1:.*1]]: tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>,
// CHECK-SAME: %[[VAL_2:.*2]]: tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, // CHECK-SAME: %[[VAL_2:.*2]]: tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>,
// CHECK-SAME: %[[VAL_3:.*3]]: tensor<f64>) -> tensor<f64> { // CHECK-SAME: %[[VAL_3:.*3]]: tensor<f64>) -> tensor<f64> {
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_4]] : tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_4]] : tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf64> // CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf64>
// CHECK: %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_4]] : tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_4]] : tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_4]] : tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_4]] : tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf64> // CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf64>
// CHECK: %[[VAL_12:.*]] = sparse_tensor.pointers %[[VAL_2]], %[[VAL_4]] : tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_12:.*]] = sparse_tensor.pointers %[[VAL_2]], %[[VAL_4]] : tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_13:.*]] = sparse_tensor.indices %[[VAL_2]], %[[VAL_4]] : tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_13:.*]] = sparse_tensor.indices %[[VAL_2]], %[[VAL_4]] : tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_14:.*]] = sparse_tensor.values %[[VAL_2]] : tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf64> // CHECK-DAG: %[[VAL_14:.*]] = sparse_tensor.values %[[VAL_2]] : tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf64>
// CHECK: %[[VAL_15:.*]] = bufferization.to_memref %[[VAL_3]] : memref<f64> // CHECK-DAG: %[[VAL_15:.*]] = bufferization.to_memref %[[VAL_3]] : memref<f64>
// CHECK: %[[VAL_16:.*]] = memref.alloc() : memref<f64> // CHECK-DAG: %[[VAL_16:.*]] = memref.alloc() : memref<f64>
// CHECK: memref.copy %[[VAL_15]], %[[VAL_16]] : memref<f64> to memref<f64> // CHECK: memref.copy %[[VAL_15]], %[[VAL_16]] : memref<f64> to memref<f64>
// CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_16]][] : memref<f64> // CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_16]][] : memref<f64>
// CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex> // CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex>
// CHECK: %[[VAL_19:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_5]]] : memref<?xindex> // CHECK: %[[VAL_19:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_5]]] : memref<?xindex>
// CHECK: %[[VAL_20:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_4]]] : memref<?xindex> // CHECK: %[[VAL_20:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_4]]] : memref<?xindex>
// CHECK: %[[VAL_21:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_5]]] : memref<?xindex> // CHECK: %[[VAL_21:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_5]]] : memref<?xindex>
// CHECK: %[[VAL_22:.*]] = memref.load %[[VAL_12]]{{\[}}%[[VAL_4]]] : memref<?xindex> // CHECK: %[[VAL_22:.*]] = memref.load %[[VAL_12]]{{\[}}%[[VAL_4]]] : memref<?xindex>
// CHECK: %[[VAL_23:.*]] = memref.load %[[VAL_12]]{{\[}}%[[VAL_5]]] : memref<?xindex> // CHECK: %[[VAL_23:.*]] = memref.load %[[VAL_12]]{{\[}}%[[VAL_5]]] : memref<?xindex>
▲ Show 20 Lines • Show All 277 Lines • Show Last 20 Lines

mlir/test/Dialect/SparseTensor/sparse_2d.mlir

Show All 11 Lines indexing_maps = [
affine_map<(i,j) -> (i,j)>, // B affine_map<(i,j) -> (i,j)>, // 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) OP B(i,j)" doc = "X(i,j) = A(i,j) OP B(i,j)"
} }
// CHECK-LABEL: func @add_dd( // CHECK-LABEL: func @add_dd(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, // CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16xf32>, // CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16xf32>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16xf32>) -> tensor<32x16xf32> { // CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16xf32>) -> tensor<32x16xf32> {
// CHECK: %[[VAL_3:.*]] = arith.constant 32 : index // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index
// CHECK: %[[VAL_4:.*]] = arith.constant 16 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 16 : index
// CHECK: %[[VAL_5:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index
// CHECK: %[[VAL_6:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_6:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32> // CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32>
// CHECK: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16xf32> // CHECK-DAG: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16xf32>
// CHECK: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf32> // CHECK-DAG: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf32>
// CHECK: %[[VAL_10:.*]] = memref.alloc() : memref<32x16xf32> // CHECK-DAG: %[[VAL_10:.*]] = memref.alloc() : memref<32x16xf32>
// CHECK: memref.copy %[[VAL_9]], %[[VAL_10]] : memref<32x16xf32> to memref<32x16xf32> // CHECK: memref.copy %[[VAL_9]], %[[VAL_10]] : memref<32x16xf32> to memref<32x16xf32>
// CHECK: scf.for %[[VAL_11:.*]] = %[[VAL_5]] to %[[VAL_3]] step %[[VAL_6]] { // CHECK: scf.for %[[VAL_11:.*]] = %[[VAL_5]] to %[[VAL_3]] step %[[VAL_6]] {
// CHECK: scf.for %[[VAL_12:.*]] = %[[VAL_5]] to %[[VAL_4]] step %[[VAL_6]] { // CHECK: scf.for %[[VAL_12:.*]] = %[[VAL_5]] to %[[VAL_4]] step %[[VAL_6]] {
// CHECK: %[[VAL_13:.*]] = arith.muli %[[VAL_11]], %[[VAL_4]] : index // CHECK: %[[VAL_13:.*]] = arith.muli %[[VAL_11]], %[[VAL_4]] : index
// CHECK: %[[VAL_14:.*]] = arith.addi %[[VAL_13]], %[[VAL_12]] : index // CHECK: %[[VAL_14:.*]] = arith.addi %[[VAL_13]], %[[VAL_12]] : index
// CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_14]]] : memref<?xf32> // CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_14]]] : memref<?xf32>
// CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_11]], %[[VAL_12]]] : memref<32x16xf32> // CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_11]], %[[VAL_12]]] : memref<32x16xf32>
// CHECK: %[[VAL_17:.*]] = arith.addf %[[VAL_15]], %[[VAL_16]] : f32 // CHECK: %[[VAL_17:.*]] = arith.addf %[[VAL_15]], %[[VAL_16]] : f32
Show All 10 Lines outs(%argx: tensor<32x16xf32>) {
^bb(%a: f32, %b: f32, %x: f32): ^bb(%a: f32, %b: f32, %x: f32):
%0 = arith.addf %a, %b : f32 %0 = arith.addf %a, %b : f32
linalg.yield %0 : f32 linalg.yield %0 : f32
} -> tensor<32x16xf32> } -> tensor<32x16xf32>
return %0 : tensor<32x16xf32> return %0 : tensor<32x16xf32>
} }
// CHECK-LABEL: func @mul_dd( // CHECK-LABEL: func @mul_dd(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, // CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16xf32>, // CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16xf32>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16xf32>) -> tensor<32x16xf32> { // CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16xf32>) -> tensor<32x16xf32> {
// CHECK: %[[VAL_3:.*]] = arith.constant 32 : index // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index
// CHECK: %[[VAL_4:.*]] = arith.constant 16 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 16 : index
// CHECK: %[[VAL_5:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index
// CHECK: %[[VAL_6:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_6:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32> // CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32>
// CHECK: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16xf32> // CHECK-DAG: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16xf32>
// CHECK: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf32> // CHECK-DAG: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf32>
// CHECK: %[[VAL_10:.*]] = memref.alloc() : memref<32x16xf32> // CHECK-DAG: %[[VAL_10:.*]] = memref.alloc() : memref<32x16xf32>
// CHECK: memref.copy %[[VAL_9]], %[[VAL_10]] : memref<32x16xf32> to memref<32x16xf32> // CHECK: memref.copy %[[VAL_9]], %[[VAL_10]] : memref<32x16xf32> to memref<32x16xf32>
// CHECK: scf.for %[[VAL_11:.*]] = %[[VAL_5]] to %[[VAL_3]] step %[[VAL_6]] { // CHECK: scf.for %[[VAL_11:.*]] = %[[VAL_5]] to %[[VAL_3]] step %[[VAL_6]] {
// CHECK: scf.for %[[VAL_12:.*]] = %[[VAL_5]] to %[[VAL_4]] step %[[VAL_6]] { // CHECK: scf.for %[[VAL_12:.*]] = %[[VAL_5]] to %[[VAL_4]] step %[[VAL_6]] {
// CHECK: %[[VAL_13:.*]] = arith.muli %[[VAL_11]], %[[VAL_4]] : index // CHECK: %[[VAL_13:.*]] = arith.muli %[[VAL_11]], %[[VAL_4]] : index
// CHECK: %[[VAL_14:.*]] = arith.addi %[[VAL_13]], %[[VAL_12]] : index // CHECK: %[[VAL_14:.*]] = arith.addi %[[VAL_13]], %[[VAL_12]] : index
// CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_14]]] : memref<?xf32> // CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_14]]] : memref<?xf32>
// CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_11]], %[[VAL_12]]] : memref<32x16xf32> // CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_11]], %[[VAL_12]]] : memref<32x16xf32>
// CHECK: %[[VAL_17:.*]] = arith.mulf %[[VAL_15]], %[[VAL_16]] : f32 // CHECK: %[[VAL_17:.*]] = arith.mulf %[[VAL_15]], %[[VAL_16]] : f32
Show All 10 Lines outs(%argx: tensor<32x16xf32>) {
^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
linalg.yield %0 : f32 linalg.yield %0 : f32
} -> tensor<32x16xf32> } -> tensor<32x16xf32>
return %0 : tensor<32x16xf32> return %0 : tensor<32x16xf32>
} }
// CHECK-LABEL: func @add_ds( // CHECK-LABEL: func @add_ds(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, // CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16xf32>, // CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16xf32>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16xf32>) -> tensor<32x16xf32> { // CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16xf32>) -> tensor<32x16xf32> {
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 16 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 16 : index
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_6:.*]] = arith.constant true // CHECK-DAG: %[[VAL_6:.*]] = arith.constant true
// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_7:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_7]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_7]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_7]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_7]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32> // CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32>
// CHECK: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16xf32> // CHECK-DAG: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16xf32>
// CHECK: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf32> // CHECK-DAG: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf32>
// CHECK: %[[VAL_13:.*]] = memref.alloc() : memref<32x16xf32> // CHECK-DAG: %[[VAL_13:.*]] = memref.alloc() : memref<32x16xf32>
// CHECK: memref.copy %[[VAL_12]], %[[VAL_13]] : memref<32x16xf32> to memref<32x16xf32> // CHECK: memref.copy %[[VAL_12]], %[[VAL_13]] : memref<32x16xf32> to memref<32x16xf32>
// CHECK: scf.for %[[VAL_14:.*]] = %[[VAL_5]] to %[[VAL_3]] step %[[VAL_7]] { // CHECK: scf.for %[[VAL_14:.*]] = %[[VAL_5]] to %[[VAL_3]] step %[[VAL_7]] {
// CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_14]]] : memref<?xindex> // CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_14]]] : memref<?xindex>
// CHECK: %[[VAL_16:.*]] = arith.addi %[[VAL_14]], %[[VAL_7]] : index // CHECK: %[[VAL_16:.*]] = arith.addi %[[VAL_14]], %[[VAL_7]] : index
// CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_16]]] : memref<?xindex> // CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_16]]] : memref<?xindex>
// CHECK: %[[VAL_18:.*]]:2 = scf.while (%[[VAL_19:.*]] = %[[VAL_15]], %[[VAL_20:.*]] = %[[VAL_5]]) : (index, index) -> (index, index) { // CHECK: %[[VAL_18:.*]]:2 = scf.while (%[[VAL_19:.*]] = %[[VAL_15]], %[[VAL_20:.*]] = %[[VAL_5]]) : (index, index) -> (index, index) {
// CHECK: %[[VAL_21:.*]] = arith.cmpi ult, %[[VAL_19]], %[[VAL_17]] : index // CHECK: %[[VAL_21:.*]] = arith.cmpi ult, %[[VAL_19]], %[[VAL_17]] : index
// CHECK: scf.condition(%[[VAL_21]]) %[[VAL_19]], %[[VAL_20]] : index, index // CHECK: scf.condition(%[[VAL_21]]) %[[VAL_19]], %[[VAL_20]] : index, index
Show All 34 Lines outs(%argx: tensor<32x16xf32>) {
^bb(%a: f32, %b: f32, %x: f32): ^bb(%a: f32, %b: f32, %x: f32):
%0 = arith.addf %a, %b : f32 %0 = arith.addf %a, %b : f32
linalg.yield %0 : f32 linalg.yield %0 : f32
} -> tensor<32x16xf32> } -> tensor<32x16xf32>
return %0 : tensor<32x16xf32> return %0 : tensor<32x16xf32>
} }
// CHECK-LABEL: func @mul_ds( // CHECK-LABEL: func @mul_ds(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, // CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16xf32>, // CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16xf32>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16xf32>) -> tensor<32x16xf32> { // CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16xf32>) -> tensor<32x16xf32> {
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_5]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_5]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_5]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_5]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32> // CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32>
// CHECK: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16xf32> // CHECK-DAG: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16xf32>
// CHECK: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf32> // CHECK-DAG: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf32>
// CHECK: %[[VAL_11:.*]] = memref.alloc() : memref<32x16xf32> // CHECK-DAG: %[[VAL_11:.*]] = memref.alloc() : memref<32x16xf32>
// CHECK: memref.copy %[[VAL_10]], %[[VAL_11]] : memref<32x16xf32> to memref<32x16xf32> // CHECK: memref.copy %[[VAL_10]], %[[VAL_11]] : memref<32x16xf32> to memref<32x16xf32>
// CHECK: scf.for %[[VAL_12:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] { // CHECK: scf.for %[[VAL_12:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] {
// CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_12]]] : memref<?xindex> // CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_12]]] : memref<?xindex>
// CHECK: %[[VAL_14:.*]] = arith.addi %[[VAL_12]], %[[VAL_5]] : index // CHECK: %[[VAL_14:.*]] = arith.addi %[[VAL_12]], %[[VAL_5]] : index
// CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_14]]] : memref<?xindex> // CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_14]]] : memref<?xindex>
// CHECK: scf.for %[[VAL_16:.*]] = %[[VAL_13]] to %[[VAL_15]] step %[[VAL_5]] { // CHECK: scf.for %[[VAL_16:.*]] = %[[VAL_13]] to %[[VAL_15]] step %[[VAL_5]] {
// CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_16]]] : memref<?xindex> // CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_16]]] : memref<?xindex>
// CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_16]]] : memref<?xf32> // CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_16]]] : memref<?xf32>
Show All 12 Lines outs(%argx: tensor<32x16xf32>) {
^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
linalg.yield %0 : f32 linalg.yield %0 : f32
} -> tensor<32x16xf32> } -> tensor<32x16xf32>
return %0 : tensor<32x16xf32> return %0 : tensor<32x16xf32>
} }
// CHECK-LABEL: func @add_sd( // CHECK-LABEL: func @add_sd(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, // CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16xf32>, // CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16xf32>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16xf32>) -> tensor<32x16xf32> { // CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16xf32>) -> tensor<32x16xf32> {
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 16 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 16 : index
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant true // CHECK-DAG: %[[VAL_5:.*]] = arith.constant true
// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_6:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_7:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_6]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_6]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_6]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_6]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32> // CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32>
// CHECK: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16xf32> // CHECK-DAG: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16xf32>
// CHECK: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf32> // CHECK-DAG: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf32>
// CHECK: %[[VAL_13:.*]] = memref.alloc() : memref<32x16xf32> // CHECK-DAG: %[[VAL_13:.*]] = memref.alloc() : memref<32x16xf32>
// CHECK: memref.copy %[[VAL_12]], %[[VAL_13]] : memref<32x16xf32> to memref<32x16xf32> // CHECK: memref.copy %[[VAL_12]], %[[VAL_13]] : memref<32x16xf32> to memref<32x16xf32>
// CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_6]]] : memref<?xindex> // CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_6]]] : memref<?xindex>
// CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_7]]] : memref<?xindex> // CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_7]]] : memref<?xindex>
// CHECK: %[[VAL_16:.*]]:2 = scf.while (%[[VAL_17:.*]] = %[[VAL_14]], %[[VAL_18:.*]] = %[[VAL_6]]) : (index, index) -> (index, index) { // CHECK: %[[VAL_16:.*]]:2 = scf.while (%[[VAL_17:.*]] = %[[VAL_14]], %[[VAL_18:.*]] = %[[VAL_6]]) : (index, index) -> (index, index) {
// CHECK: %[[VAL_19:.*]] = arith.cmpi ult, %[[VAL_17]], %[[VAL_15]] : index // CHECK: %[[VAL_19:.*]] = arith.cmpi ult, %[[VAL_17]], %[[VAL_15]] : index
// CHECK: scf.condition(%[[VAL_19]]) %[[VAL_17]], %[[VAL_18]] : index, index // CHECK: scf.condition(%[[VAL_19]]) %[[VAL_17]], %[[VAL_18]] : index, index
// CHECK: } do { // CHECK: } do {
// CHECK: ^bb0(%[[VAL_20:.*]]: index, %[[VAL_21:.*]]: index): // CHECK: ^bb0(%[[VAL_20:.*]]: index, %[[VAL_21:.*]]: index):
Show All 39 Lines outs(%argx: tensor<32x16xf32>) {
^bb(%a: f32, %b: f32, %x: f32): ^bb(%a: f32, %b: f32, %x: f32):
%0 = arith.addf %a, %b : f32 %0 = arith.addf %a, %b : f32
linalg.yield %0 : f32 linalg.yield %0 : f32
} -> tensor<32x16xf32> } -> tensor<32x16xf32>
return %0 : tensor<32x16xf32> return %0 : tensor<32x16xf32>
} }
// CHECK-LABEL: func @mul_sd( // CHECK-LABEL: func @mul_sd(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, // CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16xf32>, // CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16xf32>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16xf32>) -> tensor<32x16xf32> { // CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16xf32>) -> tensor<32x16xf32> {
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 16 : index // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 16 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_4]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_4]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32> // CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32>
// CHECK: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16xf32> // CHECK-DAG: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16xf32>
// CHECK: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf32> // CHECK-DAG: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf32>
// CHECK: %[[VAL_11:.*]] = memref.alloc() : memref<32x16xf32> // CHECK-DAG: %[[VAL_11:.*]] = memref.alloc() : memref<32x16xf32>
// CHECK: memref.copy %[[VAL_10]], %[[VAL_11]] : memref<32x16xf32> to memref<32x16xf32> // CHECK: memref.copy %[[VAL_10]], %[[VAL_11]] : memref<32x16xf32> to memref<32x16xf32>
// CHECK: %[[VAL_12:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex> // CHECK: %[[VAL_12:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex>
// CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_5]]] : memref<?xindex> // CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_5]]] : memref<?xindex>
// CHECK: scf.for %[[VAL_14:.*]] = %[[VAL_12]] to %[[VAL_13]] step %[[VAL_5]] { // CHECK: scf.for %[[VAL_14:.*]] = %[[VAL_12]] to %[[VAL_13]] step %[[VAL_5]] {
// CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_14]]] : memref<?xindex> // CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_14]]] : memref<?xindex>
// CHECK: scf.for %[[VAL_16:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] { // CHECK: scf.for %[[VAL_16:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] {
// CHECK: %[[VAL_17:.*]] = arith.muli %[[VAL_14]], %[[VAL_3]] : index // CHECK: %[[VAL_17:.*]] = arith.muli %[[VAL_14]], %[[VAL_3]] : index
// CHECK: %[[VAL_18:.*]] = arith.addi %[[VAL_17]], %[[VAL_16]] : index // CHECK: %[[VAL_18:.*]] = arith.addi %[[VAL_17]], %[[VAL_16]] : index
Show All 13 Lines outs(%argx: tensor<32x16xf32>) {
^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
linalg.yield %0 : f32 linalg.yield %0 : f32
} -> tensor<32x16xf32> } -> tensor<32x16xf32>
return %0 : tensor<32x16xf32> return %0 : tensor<32x16xf32>
} }
// CHECK-LABEL: func @add_ss( // CHECK-LABEL: func @add_ss(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, // CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16xf32>, // CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16xf32>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16xf32>) -> tensor<32x16xf32> { // CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16xf32>) -> tensor<32x16xf32> {
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 16 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 16 : index
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant true // CHECK-DAG: %[[VAL_5:.*]] = arith.constant true
// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_6:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_7:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_6]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_6]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_6]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_6]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_10:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_7]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_7]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_11:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_7]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_7]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_12:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32> // CHECK-DAG: %[[VAL_12:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32>
// CHECK: %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16xf32> // CHECK-DAG: %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16xf32>
// CHECK: %[[VAL_14:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf32> // CHECK-DAG: %[[VAL_14:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf32>
// CHECK: %[[VAL_15:.*]] = memref.alloc() : memref<32x16xf32> // CHECK-DAG: %[[VAL_15:.*]] = memref.alloc() : memref<32x16xf32>
// CHECK: memref.copy %[[VAL_14]], %[[VAL_15]] : memref<32x16xf32> to memref<32x16xf32> // CHECK: memref.copy %[[VAL_14]], %[[VAL_15]] : memref<32x16xf32> to memref<32x16xf32>
// CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_6]]] : memref<?xindex> // CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_6]]] : memref<?xindex>
// CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_7]]] : memref<?xindex> // CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_7]]] : memref<?xindex>
// CHECK: %[[VAL_18:.*]]:2 = scf.while (%[[VAL_19:.*]] = %[[VAL_16]], %[[VAL_20:.*]] = %[[VAL_6]]) : (index, index) -> (index, index) { // CHECK: %[[VAL_18:.*]]:2 = scf.while (%[[VAL_19:.*]] = %[[VAL_16]], %[[VAL_20:.*]] = %[[VAL_6]]) : (index, index) -> (index, index) {
// CHECK: %[[VAL_21:.*]] = arith.cmpi ult, %[[VAL_19]], %[[VAL_17]] : index // CHECK: %[[VAL_21:.*]] = arith.cmpi ult, %[[VAL_19]], %[[VAL_17]] : index
// CHECK: scf.condition(%[[VAL_21]]) %[[VAL_19]], %[[VAL_20]] : index, index // CHECK: scf.condition(%[[VAL_21]]) %[[VAL_19]], %[[VAL_20]] : index, index
// CHECK: } do { // CHECK: } do {
// CHECK: ^bb0(%[[VAL_22:.*]]: index, %[[VAL_23:.*]]: index): // CHECK: ^bb0(%[[VAL_22:.*]]: index, %[[VAL_23:.*]]: index):
▲ Show 20 Lines • Show All 63 Lines • ▼ Show 20 Lines outs(%argx: tensor<32x16xf32>) {
^bb(%a: f32, %b: f32, %x: f32): ^bb(%a: f32, %b: f32, %x: f32):
%0 = arith.addf %a, %b : f32 %0 = arith.addf %a, %b : f32
linalg.yield %0 : f32 linalg.yield %0 : f32
} -> tensor<32x16xf32> } -> tensor<32x16xf32>
return %0 : tensor<32x16xf32> return %0 : tensor<32x16xf32>
} }
// CHECK-LABEL: func @mul_ss( // CHECK-LABEL: func @mul_ss(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, // CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16xf32>, // CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16xf32>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16xf32>) -> tensor<32x16xf32> { // CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16xf32>) -> tensor<32x16xf32> {
// CHECK: %[[VAL_3:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index
// CHECK: %[[VAL_4:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_6:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_4]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_4]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32> // CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32>
// CHECK: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16xf32> // CHECK-DAG: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16xf32>
// CHECK: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf32> // CHECK-DAG: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf32>
// CHECK: %[[VAL_12:.*]] = memref.alloc() : memref<32x16xf32> // CHECK-DAG: %[[VAL_12:.*]] = memref.alloc() : memref<32x16xf32>
// CHECK: memref.copy %[[VAL_11]], %[[VAL_12]] : memref<32x16xf32> to memref<32x16xf32> // CHECK: memref.copy %[[VAL_11]], %[[VAL_12]] : memref<32x16xf32> to memref<32x16xf32>
// CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_3]]] : memref<?xindex> // CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_3]]] : memref<?xindex>
// CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_4]]] : memref<?xindex> // CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_4]]] : memref<?xindex>
// CHECK: scf.for %[[VAL_15:.*]] = %[[VAL_13]] to %[[VAL_14]] step %[[VAL_4]] { // CHECK: scf.for %[[VAL_15:.*]] = %[[VAL_13]] to %[[VAL_14]] step %[[VAL_4]] {
// CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_15]]] : memref<?xindex> // CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_15]]] : memref<?xindex>
// CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_15]]] : memref<?xindex> // CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_15]]] : memref<?xindex>
// CHECK: %[[VAL_18:.*]] = arith.addi %[[VAL_15]], %[[VAL_4]] : index // CHECK: %[[VAL_18:.*]] = arith.addi %[[VAL_15]], %[[VAL_4]] : index
// CHECK: %[[VAL_19:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_18]]] : memref<?xindex> // CHECK: %[[VAL_19:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_18]]] : memref<?xindex>
Show All 15 Lines outs(%argx: tensor<32x16xf32>) {
^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
linalg.yield %0 : f32 linalg.yield %0 : f32
} -> tensor<32x16xf32> } -> tensor<32x16xf32>
return %0 : tensor<32x16xf32> return %0 : tensor<32x16xf32>
} }
// CHECK-LABEL: func @add_ss_ss( // CHECK-LABEL: func @add_ss_ss(
// CHECK-SAME: %[[VAL_0:.*0]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, // CHECK-SAME: %[[VAL_0:.*0]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>,
// CHECK-SAME: %[[VAL_1:.*1]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, // CHECK-SAME: %[[VAL_1:.*1]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>,
// CHECK-SAME: %[[VAL_2:.*2]]: tensor<32x16xf32>) -> tensor<32x16xf32> { // CHECK-SAME: %[[VAL_2:.*2]]: tensor<32x16xf32>) -> tensor<32x16xf32> {
// CHECK: %[[VAL_3:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index
// CHECK: %[[VAL_4:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_6:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_4]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_4]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32> // CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32>
// CHECK: %[[VAL_10:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_3]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_3]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_11:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_3]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_3]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_12:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_4]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_12:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_4]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_13:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_4]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_13:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_4]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_14:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32> // CHECK-DAG: %[[VAL_14:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32>
// CHECK: %[[VAL_15:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf32> // CHECK-DAG: %[[VAL_15:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf32>
// CHECK: %[[VAL_16:.*]] = memref.alloc() : memref<32x16xf32> // CHECK-DAG: %[[VAL_16:.*]] = memref.alloc() : memref<32x16xf32>
// CHECK: memref.copy %[[VAL_15]], %[[VAL_16]] : memref<32x16xf32> to memref<32x16xf32> // CHECK: memref.copy %[[VAL_15]], %[[VAL_16]] : memref<32x16xf32> to memref<32x16xf32>
// CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_3]]] : memref<?xindex> // CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_3]]] : memref<?xindex>
// CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_4]]] : memref<?xindex> // CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_4]]] : memref<?xindex>
// CHECK: %[[VAL_19:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_3]]] : memref<?xindex> // CHECK: %[[VAL_19:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_3]]] : memref<?xindex>
// CHECK: %[[VAL_20:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_4]]] : memref<?xindex> // CHECK: %[[VAL_20:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_4]]] : memref<?xindex>
// CHECK: %[[VAL_21:.*]]:2 = scf.while (%[[VAL_22:.*]] = %[[VAL_17]], %[[VAL_23:.*]] = %[[VAL_19]]) : (index, index) -> (index, index) { // CHECK: %[[VAL_21:.*]]:2 = scf.while (%[[VAL_22:.*]] = %[[VAL_17]], %[[VAL_23:.*]] = %[[VAL_19]]) : (index, index) -> (index, index) {
// CHECK: %[[VAL_24:.*]] = arith.cmpi ult, %[[VAL_22]], %[[VAL_18]] : index // CHECK: %[[VAL_24:.*]] = arith.cmpi ult, %[[VAL_22]], %[[VAL_18]] : index
// CHECK: %[[VAL_25:.*]] = arith.cmpi ult, %[[VAL_23]], %[[VAL_20]] : index // CHECK: %[[VAL_25:.*]] = arith.cmpi ult, %[[VAL_23]], %[[VAL_20]] : index
▲ Show 20 Lines • Show All 132 Lines • ▼ Show 20 Lines outs(%argx: tensor<32x16xf32>) {
^bb(%a: f32, %b: f32, %x: f32): ^bb(%a: f32, %b: f32, %x: f32):
%0 = arith.addf %a, %b : f32 %0 = arith.addf %a, %b : f32
linalg.yield %0 : f32 linalg.yield %0 : f32
} -> tensor<32x16xf32> } -> tensor<32x16xf32>
return %0 : tensor<32x16xf32> return %0 : tensor<32x16xf32>
} }
// CHECK-LABEL: func @mul_ss_ss( // CHECK-LABEL: func @mul_ss_ss(
// CHECK-SAME: %[[VAL_0:.*0]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, // CHECK-SAME: %[[VAL_0:.*0]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>,
// CHECK-SAME: %[[VAL_1:.*1]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, // CHECK-SAME: %[[VAL_1:.*1]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>,
// CHECK-SAME: %[[VAL_2:.*2]]: tensor<32x16xf32>) -> tensor<32x16xf32> { // CHECK-SAME: %[[VAL_2:.*2]]: tensor<32x16xf32>) -> tensor<32x16xf32> {
// CHECK: %[[VAL_3:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index
// CHECK: %[[VAL_4:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_6:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_4]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_4]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32> // CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32>
// CHECK: %[[VAL_10:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_3]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_3]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_11:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_3]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_3]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_12:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_4]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_12:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_4]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_13:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_4]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_13:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_4]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_14:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32> // CHECK-DAG: %[[VAL_14:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32>
// CHECK: %[[VAL_15:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf32> // CHECK-DAG: %[[VAL_15:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf32>
// CHECK: %[[VAL_16:.*]] = memref.alloc() : memref<32x16xf32> // CHECK-DAG: %[[VAL_16:.*]] = memref.alloc() : memref<32x16xf32>
// CHECK: memref.copy %[[VAL_15]], %[[VAL_16]] : memref<32x16xf32> to memref<32x16xf32> // CHECK: memref.copy %[[VAL_15]], %[[VAL_16]] : memref<32x16xf32> to memref<32x16xf32>
// CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_3]]] : memref<?xindex> // CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_3]]] : memref<?xindex>
// CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_4]]] : memref<?xindex> // CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_4]]] : memref<?xindex>
// CHECK: %[[VAL_19:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_3]]] : memref<?xindex> // CHECK: %[[VAL_19:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_3]]] : memref<?xindex>
// CHECK: %[[VAL_20:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_4]]] : memref<?xindex> // CHECK: %[[VAL_20:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_4]]] : memref<?xindex>
// CHECK: %[[VAL_21:.*]]:2 = scf.while (%[[VAL_22:.*]] = %[[VAL_17]], %[[VAL_23:.*]] = %[[VAL_19]]) : (index, index) -> (index, index) { // CHECK: %[[VAL_21:.*]]:2 = scf.while (%[[VAL_22:.*]] = %[[VAL_17]], %[[VAL_23:.*]] = %[[VAL_19]]) : (index, index) -> (index, index) {
// CHECK: %[[VAL_24:.*]] = arith.cmpi ult, %[[VAL_22]], %[[VAL_18]] : index // CHECK: %[[VAL_24:.*]] = arith.cmpi ult, %[[VAL_22]], %[[VAL_18]] : index
// CHECK: %[[VAL_25:.*]] = arith.cmpi ult, %[[VAL_23]], %[[VAL_20]] : index // CHECK: %[[VAL_25:.*]] = arith.cmpi ult, %[[VAL_23]], %[[VAL_20]] : index
▲ Show 20 Lines • Show All 64 Lines • ▼ Show 20 Lines outs(%argx: tensor<32x16xf32>) {
^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
linalg.yield %0 : f32 linalg.yield %0 : f32
} -> tensor<32x16xf32> } -> tensor<32x16xf32>
return %0 : tensor<32x16xf32> return %0 : tensor<32x16xf32>
} }
// CHECK-LABEL: func @add_sd_ds( // CHECK-LABEL: func @add_sd_ds(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, // CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, // CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16xf32>) -> tensor<32x16xf32> { // CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16xf32>) -> tensor<32x16xf32> {
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 16 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 16 : index
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_6:.*]] = arith.constant true // CHECK-DAG: %[[VAL_6:.*]] = arith.constant true
// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_7:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_5]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_5]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_5]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_5]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32> // CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32>
// CHECK: %[[VAL_11:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_7]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_7]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_12:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_7]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_12:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_7]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_13:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32> // CHECK-DAG: %[[VAL_13:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32>
// CHECK: %[[VAL_14:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf32> // CHECK-DAG: %[[VAL_14:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf32>
// CHECK: %[[VAL_15:.*]] = memref.alloc() : memref<32x16xf32> // CHECK-DAG: %[[VAL_15:.*]] = memref.alloc() : memref<32x16xf32>
// CHECK: memref.copy %[[VAL_14]], %[[VAL_15]] : memref<32x16xf32> to memref<32x16xf32> // CHECK: memref.copy %[[VAL_14]], %[[VAL_15]] : memref<32x16xf32> to memref<32x16xf32>
// CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_5]]] : memref<?xindex> // CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_5]]] : memref<?xindex>
// CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_7]]] : memref<?xindex> // CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_7]]] : memref<?xindex>
// CHECK: %[[VAL_18:.*]]:2 = scf.while (%[[VAL_19:.*]] = %[[VAL_16]], %[[VAL_20:.*]] = %[[VAL_5]]) : (index, index) -> (index, index) { // CHECK: %[[VAL_18:.*]]:2 = scf.while (%[[VAL_19:.*]] = %[[VAL_16]], %[[VAL_20:.*]] = %[[VAL_5]]) : (index, index) -> (index, index) {
// CHECK: %[[VAL_21:.*]] = arith.cmpi ult, %[[VAL_19]], %[[VAL_17]] : index // CHECK: %[[VAL_21:.*]] = arith.cmpi ult, %[[VAL_19]], %[[VAL_17]] : index
// CHECK: scf.condition(%[[VAL_21]]) %[[VAL_19]], %[[VAL_20]] : index, index // CHECK: scf.condition(%[[VAL_21]]) %[[VAL_19]], %[[VAL_20]] : index, index
// CHECK: } do { // CHECK: } do {
// CHECK: ^bb0(%[[VAL_22:.*]]: index, %[[VAL_23:.*]]: index): // CHECK: ^bb0(%[[VAL_22:.*]]: index, %[[VAL_23:.*]]: index):
▲ Show 20 Lines • Show All 75 Lines • ▼ Show 20 Lines outs(%argx: tensor<32x16xf32>) {
^bb(%a: f32, %b: f32, %x: f32): ^bb(%a: f32, %b: f32, %x: f32):
%0 = arith.addf %a, %b : f32 %0 = arith.addf %a, %b : f32
linalg.yield %0 : f32 linalg.yield %0 : f32
} -> tensor<32x16xf32> } -> tensor<32x16xf32>
return %0 : tensor<32x16xf32> return %0 : tensor<32x16xf32>
} }
// CHECK-LABEL: func @mul_sd_ds( // CHECK-LABEL: func @mul_sd_ds(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, // CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, // CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16xf32>) -> tensor<32x16xf32> { // CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16xf32>) -> tensor<32x16xf32> {
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 16 : index // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 16 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_4]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_4]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32> // CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32>
// CHECK: %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_5]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_5]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_5]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_5]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32> // CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32>
// CHECK: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf32> // CHECK-DAG: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf32>
// CHECK: %[[VAL_13:.*]] = memref.alloc() : memref<32x16xf32> // CHECK-DAG: %[[VAL_13:.*]] = memref.alloc() : memref<32x16xf32>
// CHECK: memref.copy %[[VAL_12]], %[[VAL_13]] : memref<32x16xf32> to memref<32x16xf32> // CHECK: memref.copy %[[VAL_12]], %[[VAL_13]] : memref<32x16xf32> to memref<32x16xf32>
// CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex> // CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex>
// CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_5]]] : memref<?xindex> // CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_5]]] : memref<?xindex>
// CHECK: scf.for %[[VAL_16:.*]] = %[[VAL_14]] to %[[VAL_15]] step %[[VAL_5]] { // CHECK: scf.for %[[VAL_16:.*]] = %[[VAL_14]] to %[[VAL_15]] step %[[VAL_5]] {
// CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_16]]] : memref<?xindex> // CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_16]]] : memref<?xindex>
// CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_17]]] : memref<?xindex> // CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_17]]] : memref<?xindex>
// CHECK: %[[VAL_19:.*]] = arith.addi %[[VAL_17]], %[[VAL_5]] : index // CHECK: %[[VAL_19:.*]] = arith.addi %[[VAL_17]], %[[VAL_5]] : index
// CHECK: %[[VAL_20:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_19]]] : memref<?xindex> // CHECK: %[[VAL_20:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_19]]] : memref<?xindex>
Show All 33 Lines
// CHECK-LABEL: func @matvec( // CHECK-LABEL: func @matvec(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<16x32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, // CHECK-SAME: %[[VAL_0:.*]]: tensor<16x32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32xf32>, // CHECK-SAME: %[[VAL_1:.*]]: tensor<32xf32>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<16xf32>) -> tensor<16xf32> { // CHECK-SAME: %[[VAL_2:.*]]: tensor<16xf32>) -> tensor<16xf32> {
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 16 : index // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 16 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_5]] : tensor<16x32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_5]] : tensor<16x32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_5]] : tensor<16x32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_5]] : tensor<16x32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<16x32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32> // CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<16x32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32>
// CHECK: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32xf32> // CHECK-DAG: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32xf32>
// CHECK: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_2]] : memref<16xf32> // CHECK-DAG: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_2]] : memref<16xf32>
// CHECK: %[[VAL_11:.*]] = memref.alloc() : memref<16xf32> // CHECK-DAG: %[[VAL_11:.*]] = memref.alloc() : memref<16xf32>
// CHECK: memref.copy %[[VAL_10]], %[[VAL_11]] : memref<16xf32> to memref<16xf32> // CHECK: memref.copy %[[VAL_10]], %[[VAL_11]] : memref<16xf32> to memref<16xf32>
// CHECK: scf.for %[[VAL_12:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] { // CHECK: scf.for %[[VAL_12:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] {
// CHECK-DAG: %[[VAL_13:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_12]]] : memref<?xindex> // CHECK-DAG: %[[VAL_13:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_12]]] : memref<?xindex>
// CHECK-DAG: %[[VAL_14:.*]] = arith.addi %[[VAL_12]], %[[VAL_5]] : index // CHECK-DAG: %[[VAL_14:.*]] = arith.addi %[[VAL_12]], %[[VAL_5]] : index
// CHECK-DAG: %[[VAL_15:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_14]]] : memref<?xindex> // CHECK-DAG: %[[VAL_15:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_14]]] : memref<?xindex>
// CHECK-DAG: %[[VAL_16:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_12]]] : memref<16xf32> // CHECK-DAG: %[[VAL_16:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_12]]] : memref<16xf32>
// CHECK: %[[VAL_17:.*]] = scf.for %[[VAL_18:.*]] = %[[VAL_13]] to %[[VAL_15]] step %[[VAL_5]] iter_args(%[[VAL_19:.*]] = %[[VAL_16]]) -> (f32) { // CHECK: %[[VAL_17:.*]] = scf.for %[[VAL_18:.*]] = %[[VAL_13]] to %[[VAL_15]] step %[[VAL_5]] iter_args(%[[VAL_19:.*]] = %[[VAL_16]]) -> (f32) {
// CHECK: %[[VAL_20:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_18]]] : memref<?xindex> // CHECK: %[[VAL_20:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_18]]] : memref<?xindex>
Show All 30 Lines
} }
// CHECK-LABEL: func @sum_reduction( // CHECK-LABEL: func @sum_reduction(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, // CHECK-SAME: %[[VAL_0:.*]]: tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<f32>) -> tensor<f32> { // CHECK-SAME: %[[VAL_1:.*]]: tensor<f32>) -> tensor<f32> {
// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 10 : index // CHECK-DAG: %[[VAL_2:.*]] = arith.constant 10 : index
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
// CHECK: %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32> // CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32>
// CHECK: %[[VAL_7:.*]] = bufferization.to_memref %[[VAL_1]] : memref<f32> // CHECK-DAG: %[[VAL_7:.*]] = bufferization.to_memref %[[VAL_1]] : memref<f32>
// CHECK: %[[VAL_8:.*]] = memref.alloc() : memref<f32> // CHECK-DAG: %[[VAL_8:.*]] = memref.alloc() : memref<f32>
// CHECK: memref.copy %[[VAL_7]], %[[VAL_8]] : memref<f32> to memref<f32> // CHECK: memref.copy %[[VAL_7]], %[[VAL_8]] : memref<f32> to memref<f32>
// CHECK: %[[VAL_9:.*]] = memref.load %[[VAL_8]][] : memref<f32> // CHECK: %[[VAL_9:.*]] = memref.load %[[VAL_8]][] : memref<f32>
// CHECK: %[[VAL_10:.*]] = scf.for %[[VAL_11:.*]] = %[[VAL_4]] to %[[VAL_2]] step %[[VAL_3]] iter_args(%[[VAL_12:.*]] = %[[VAL_9]]) -> (f32) { // CHECK: %[[VAL_10:.*]] = scf.for %[[VAL_11:.*]] = %[[VAL_4]] to %[[VAL_2]] step %[[VAL_3]] iter_args(%[[VAL_12:.*]] = %[[VAL_9]]) -> (f32) {
// CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_11]]] : memref<?xindex> // CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_11]]] : memref<?xindex>
// CHECK: %[[VAL_14:.*]] = arith.addi %[[VAL_11]], %[[VAL_3]] : index // CHECK: %[[VAL_14:.*]] = arith.addi %[[VAL_11]], %[[VAL_3]] : index
// CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_14]]] : memref<?xindex> // CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_14]]] : memref<?xindex>
// CHECK: %[[VAL_16:.*]] = scf.for %[[VAL_17:.*]] = %[[VAL_13]] to %[[VAL_15]] step %[[VAL_3]] iter_args(%[[VAL_18:.*]] = %[[VAL_12]]) -> (f32) { // CHECK: %[[VAL_16:.*]] = scf.for %[[VAL_17:.*]] = %[[VAL_13]] to %[[VAL_15]] step %[[VAL_3]] iter_args(%[[VAL_18:.*]] = %[[VAL_12]]) -> (f32) {
// CHECK: %[[VAL_19:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_17]]] : memref<?xf32> // CHECK: %[[VAL_19:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_17]]] : memref<?xf32>
Show All 22 Lines indexing_maps = [
affine_map<(i,j) -> (i,j)>, // A affine_map<(i,j) -> (i,j)>, // A
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) * SCALE" doc = "X(i,j) = A(i,j) * SCALE"
} }
// CHECK-LABEL: func @scale( // CHECK-LABEL: func @scale(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<?x?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, // CHECK-SAME: %[[VAL_0:.*]]: tensor<?x?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<?x?xf64>) -> tensor<?x?xf64> { // CHECK-SAME: %[[VAL_1:.*]]: tensor<?x?xf64>) -> tensor<?x?xf64> {
// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 2.000000e+00 : f64 // CHECK-DAG: %[[VAL_2:.*]] = arith.constant 2.000000e+00 : f64
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor<?x?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor<?x?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_6:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_4]] : tensor<?x?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_4]] : tensor<?x?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<?x?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf64> // CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<?x?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf64>
// CHECK: %[[VAL_8:.*]] = tensor.dim %[[VAL_1]], %[[VAL_3]] : tensor<?x?xf64> // CHECK-DAG: %[[VAL_8:.*]] = tensor.dim %[[VAL_1]], %[[VAL_3]] : tensor<?x?xf64>
// CHECK: %[[VAL_9:.*]] = tensor.dim %[[VAL_1]], %[[VAL_4]] : tensor<?x?xf64> // CHECK-DAG: %[[VAL_9:.*]] = tensor.dim %[[VAL_1]], %[[VAL_4]] : tensor<?x?xf64>
// CHECK: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_1]] : memref<?x?xf64> // CHECK-DAG: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_1]] : memref<?x?xf64>
// CHECK: %[[VAL_11:.*]] = memref.alloc(%[[VAL_8]], %[[VAL_9]]) : memref<?x?xf64> // CHECK-DAG: %[[VAL_11:.*]] = memref.alloc(%[[VAL_8]], %[[VAL_9]]) : memref<?x?xf64>
// CHECK: memref.copy %[[VAL_10]], %[[VAL_11]] : memref<?x?xf64> to memref<?x?xf64> // CHECK: memref.copy %[[VAL_10]], %[[VAL_11]] : memref<?x?xf64> to memref<?x?xf64>
// CHECK: scf.for %[[VAL_12:.*]] = %[[VAL_3]] to %[[VAL_8]] step %[[VAL_4]] { // CHECK: scf.for %[[VAL_12:.*]] = %[[VAL_3]] to %[[VAL_8]] step %[[VAL_4]] {
// CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_12]]] : memref<?xindex> // CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_12]]] : memref<?xindex>
// CHECK: %[[VAL_14:.*]] = arith.addi %[[VAL_12]], %[[VAL_4]] : index // CHECK: %[[VAL_14:.*]] = arith.addi %[[VAL_12]], %[[VAL_4]] : index
// CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_14]]] : memref<?xindex> // CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_14]]] : memref<?xindex>
// CHECK: scf.for %[[VAL_16:.*]] = %[[VAL_13]] to %[[VAL_15]] step %[[VAL_4]] { // CHECK: scf.for %[[VAL_16:.*]] = %[[VAL_13]] to %[[VAL_15]] step %[[VAL_4]] {
// CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_16]]] : memref<?xindex> // CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_16]]] : memref<?xindex>
// CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_16]]] : memref<?xf64> // CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_16]]] : memref<?xf64>
Show All 27 Lines#trait_sampled_dense_dense = {
doc = "X(i,j) += S(i,j) SUM_k A(i,k) B(k,j)" doc = "X(i,j) += S(i,j) SUM_k A(i,k) B(k,j)"
} }
// CHECK-LABEL: func @sampled_dense_dense( // CHECK-LABEL: func @sampled_dense_dense(
// CHECK-SAME: %[[VAL_0:.*0]]: tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, // CHECK-SAME: %[[VAL_0:.*0]]: tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>,
// CHECK-SAME: %[[VAL_1:.*1]]: tensor<?x?xf32>, // CHECK-SAME: %[[VAL_1:.*1]]: tensor<?x?xf32>,
// CHECK-SAME: %[[VAL_2:.*2]]: tensor<?x?xf32>, // CHECK-SAME: %[[VAL_2:.*2]]: tensor<?x?xf32>,
// CHECK-SAME: %[[VAL_3:.*3]]: tensor<?x?xf32>) -> tensor<?x?xf32> { // CHECK-SAME: %[[VAL_3:.*3]]: tensor<?x?xf32>) -> tensor<?x?xf32> {
// CHECK: %[[VAL_4:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
// CHECK: %[[VAL_5:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_4]] : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_4]] : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_5]] : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_5]] : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_5]] : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_5]] : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32> // CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32>
// CHECK: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_1]] : memref<?x?xf32> // CHECK-DAG: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_1]] : memref<?x?xf32>
// CHECK: %[[VAL_12:.*]] = tensor.dim %[[VAL_2]], %[[VAL_4]] : tensor<?x?xf32> // CHECK-DAG: %[[VAL_12:.*]] = tensor.dim %[[VAL_2]], %[[VAL_4]] : tensor<?x?xf32>
// CHECK: %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_2]] : memref<?x?xf32> // CHECK-DAG: %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_2]] : memref<?x?xf32>
// CHECK: %[[VAL_14:.*]] = tensor.dim %[[VAL_3]], %[[VAL_4]] : tensor<?x?xf32> // CHECK-DAG: %[[VAL_14:.*]] = tensor.dim %[[VAL_3]], %[[VAL_4]] : tensor<?x?xf32>
// CHECK: %[[VAL_15:.*]] = tensor.dim %[[VAL_3]], %[[VAL_5]] : tensor<?x?xf32> // CHECK-DAG: %[[VAL_15:.*]] = tensor.dim %[[VAL_3]], %[[VAL_5]] : tensor<?x?xf32>
// CHECK: %[[VAL_16:.*]] = bufferization.to_memref %[[VAL_3]] : memref<?x?xf32> // CHECK-DAG: %[[VAL_16:.*]] = bufferization.to_memref %[[VAL_3]] : memref<?x?xf32>
// CHECK: %[[VAL_17:.*]] = memref.alloc(%[[VAL_14]], %[[VAL_15]]) : memref<?x?xf32> // CHECK-DAG: %[[VAL_17:.*]] = memref.alloc(%[[VAL_14]], %[[VAL_15]]) : memref<?x?xf32>
// CHECK: memref.copy %[[VAL_16]], %[[VAL_17]] : memref<?x?xf32> to memref<?x?xf32> // CHECK: memref.copy %[[VAL_16]], %[[VAL_17]] : memref<?x?xf32> to memref<?x?xf32>
// CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex> // CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex>
// CHECK: %[[VAL_19:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_5]]] : memref<?xindex> // CHECK: %[[VAL_19:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_5]]] : memref<?xindex>
// CHECK: scf.for %[[VAL_20:.*]] = %[[VAL_18]] to %[[VAL_19]] step %[[VAL_5]] { // CHECK: scf.for %[[VAL_20:.*]] = %[[VAL_18]] to %[[VAL_19]] step %[[VAL_5]] {
// CHECK: %[[VAL_21:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_20]]] : memref<?xindex> // CHECK: %[[VAL_21:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_20]]] : memref<?xindex>
// CHECK: %[[VAL_22:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_20]]] : memref<?xindex> // CHECK: %[[VAL_22:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_20]]] : memref<?xindex>
// CHECK: %[[VAL_23:.*]] = arith.addi %[[VAL_20]], %[[VAL_5]] : index // CHECK: %[[VAL_23:.*]] = arith.addi %[[VAL_20]], %[[VAL_5]] : index
// CHECK: %[[VAL_24:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_23]]] : memref<?xindex> // CHECK: %[[VAL_24:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_23]]] : memref<?xindex>
// CHECK: scf.for %[[VAL_25:.*]] = %[[VAL_22]] to %[[VAL_24]] step %[[VAL_5]] { // CHECK: scf.for %[[VAL_25:.*]] = %[[VAL_22]] to %[[VAL_24]] step %[[VAL_5]] {
// CHECK-DAG: %[[VAL_26:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_25]]] : memref<?xindex> // CHECK-DAG: %[[VAL_26:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_25]]] : memref<?xindex>
// CHECK-DAG: %[[VAL_27:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_25]]] : memref<?xf32> // CHECK-DAG: %[[VAL_27:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_25]]] : memref<?xf32>
// CHECK-DAG: %[[VAL_28:.*]] = memref.load %[[VAL_17]]{{\[}}%[[VAL_21]], %[[VAL_26]]] : memref<?x?xf32> // CHECK-DAG: %[[VAL_28:.*]] = memref.load %[[VAL_17]]{{\[}}%[[VAL_21]], %[[VAL_26]]] : memref<?x?xf32>
// CHECK: %[[VAL_29:.*]] = scf.for %[[VAL_30:.*]] = %[[VAL_4]] to %[[VAL_12]] step %[[VAL_5]] iter_args(%[[VAL_31:.*]] = %[[VAL_28]]) -> (f32) { // CHECK: %[[VAL_29:.*]] = scf.for %[[VAL_30:.*]] = %[[VAL_4]] to %[[VAL_12]] step %[[VAL_5]] iter_args(%[[VAL_31:.*]] = %[[VAL_28]]) -> (f32) {
// CHECK: %[[VAL_32:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_21]], %[[VAL_30]]] : memref<?x?xf32> // CHECK: %[[VAL_32:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_21]], %[[VAL_30]]] : memref<?x?xf32>
// CHECK: %[[VAL_33:.*]] = memref.load %[[VAL_13]]{{\[}}%[[VAL_30]], %[[VAL_26]]] : memref<?x?xf32> // CHECK: %[[VAL_33:.*]] = memref.load %[[VAL_13]]{{\[}}%[[VAL_30]], %[[VAL_26]]] : memref<?x?xf32>
// CHECK: %[[VAL_34:.*]] = arith.mulf %[[VAL_32]], %[[VAL_33]] : f32 // CHECK: %[[VAL_34:.*]] = arith.mulf %[[VAL_32]], %[[VAL_33]] : f32
// CHECK: %[[VAL_35:.*]] = arith.mulf %[[VAL_27]], %[[VAL_34]] : f32 // CHECK: %[[VAL_35:.*]] = arith.mulf %[[VAL_27]], %[[VAL_34]] : f32
// CHECK: %[[VAL_36:.*]] = arith.addf %[[VAL_31]], %[[VAL_35]] : f32 // CHECK: %[[VAL_36:.*]] = arith.addf %[[VAL_31]], %[[VAL_35]] : f32
// CHECK: scf.yield %[[VAL_36]] : f32 // CHECK: scf.yield %[[VAL_36]] : f32
// CHECK: } // CHECK: }
Show All 37 Lines
// CHECK-SAME: %[[VAL_1:.*1]]: tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, // CHECK-SAME: %[[VAL_1:.*1]]: tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>,
// CHECK-SAME: %[[VAL_2:.*2]]: tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, // CHECK-SAME: %[[VAL_2:.*2]]: tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>,
// CHECK-SAME: %[[VAL_3:.*3]]: tensor<?xf32>, // CHECK-SAME: %[[VAL_3:.*3]]: tensor<?xf32>,
// CHECK-SAME: %[[VAL_4:.*4]]: tensor<f32>, // CHECK-SAME: %[[VAL_4:.*4]]: tensor<f32>,
// CHECK-SAME: %[[VAL_5:.*5]]: tensor<?xf32>) -> tensor<?xf32> { // CHECK-SAME: %[[VAL_5:.*5]]: tensor<?xf32>) -> tensor<?xf32> {
// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_6:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_7:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_8:.*]] = arith.constant true // CHECK-DAG: %[[VAL_8:.*]] = arith.constant true
// CHECK: %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_6]] : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_6]] : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_6]] : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_6]] : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_11:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_7]] : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_7]] : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_12:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_7]] : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_12:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_7]] : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_13:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32> // CHECK-DAG: %[[VAL_13:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32>
// CHECK: %[[VAL_14:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_7]] : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_14:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_7]] : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_15:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_7]] : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_15:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_7]] : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_16:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32> // CHECK: %[[VAL_16:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32>
// CHECK: %[[VAL_17:.*]] = sparse_tensor.pointers %[[VAL_2]], %[[VAL_7]] : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_17:.*]] = sparse_tensor.pointers %[[VAL_2]], %[[VAL_7]] : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_18:.*]] = sparse_tensor.indices %[[VAL_2]], %[[VAL_7]] : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_18:.*]] = sparse_tensor.indices %[[VAL_2]], %[[VAL_7]] : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_19:.*]] = sparse_tensor.values %[[VAL_2]] : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32> // CHECK-DAG: %[[VAL_19:.*]] = sparse_tensor.values %[[VAL_2]] : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32>
// CHECK: %[[VAL_20:.*]] = bufferization.to_memref %[[VAL_3]] : memref<?xf32> // CHECK-DAG: %[[VAL_20:.*]] = bufferization.to_memref %[[VAL_3]] : memref<?xf32>
// CHECK: %[[VAL_21:.*]] = bufferization.to_memref %[[VAL_4]] : memref<f32> // CHECK-DAG: %[[VAL_21:.*]] = bufferization.to_memref %[[VAL_4]] : memref<f32>
// CHECK: %[[VAL_22:.*]] = tensor.dim %[[VAL_5]], %[[VAL_6]] : tensor<?xf32> // CHECK-DAG: %[[VAL_22:.*]] = tensor.dim %[[VAL_5]], %[[VAL_6]] : tensor<?xf32>
// CHECK: %[[VAL_23:.*]] = bufferization.to_memref %[[VAL_5]] : memref<?xf32> // CHECK-DAG: %[[VAL_23:.*]] = bufferization.to_memref %[[VAL_5]] : memref<?xf32>
// CHECK: %[[VAL_24:.*]] = memref.alloc(%[[VAL_22]]) : memref<?xf32> // CHECK-DAG: %[[VAL_24:.*]] = memref.alloc(%[[VAL_22]]) : memref<?xf32>
// CHECK: memref.copy %[[VAL_23]], %[[VAL_24]] : memref<?xf32> to memref<?xf32> // CHECK: memref.copy %[[VAL_23]], %[[VAL_24]] : memref<?xf32> to memref<?xf32>
// CHECK: %[[VAL_25:.*]] = memref.load %[[VAL_21]][] : memref<f32> // CHECK: %[[VAL_25:.*]] = memref.load %[[VAL_21]][] : memref<f32>
// CHECK: %[[VAL_26:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_6]]] : memref<?xindex> // CHECK: %[[VAL_26:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_6]]] : memref<?xindex>
// CHECK: %[[VAL_27:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_7]]] : memref<?xindex> // CHECK: %[[VAL_27:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_7]]] : memref<?xindex>
// CHECK: %[[VAL_28:.*]]:2 = scf.while (%[[VAL_29:.*]] = %[[VAL_26]], %[[VAL_30:.*]] = %[[VAL_6]]) : (index, index) -> (index, index) { // CHECK: %[[VAL_28:.*]]:2 = scf.while (%[[VAL_29:.*]] = %[[VAL_26]], %[[VAL_30:.*]] = %[[VAL_6]]) : (index, index) -> (index, index) {
// CHECK: %[[VAL_31:.*]] = arith.cmpi ult, %[[VAL_29]], %[[VAL_27]] : index // CHECK: %[[VAL_31:.*]] = arith.cmpi ult, %[[VAL_29]], %[[VAL_27]] : index
// CHECK: scf.condition(%[[VAL_31]]) %[[VAL_29]], %[[VAL_30]] : index, index // CHECK: scf.condition(%[[VAL_31]]) %[[VAL_29]], %[[VAL_30]] : index, index
// CHECK: } do { // CHECK: } do {
▲ Show 20 Lines • Show All 180 Lines • Show Last 20 Lines

mlir/test/Dialect/SparseTensor/sparse_3d.mlir

Show All 17 Lines indexing_maps = [
affine_map<(i,j,k) -> (i,j,k)>, // B affine_map<(i,j,k) -> (i,j,k)>, // B
affine_map<(i,j,k) -> (i,j,k)> // X (out) affine_map<(i,j,k) -> (i,j,k)> // X (out)
], ],
iterator_types = ["parallel", "parallel", "parallel"], iterator_types = ["parallel", "parallel", "parallel"],
doc = "X(i,j,k) = A(i,j,k) OP B(i,j,k)" doc = "X(i,j,k) = A(i,j,k) OP B(i,j,k)"
} }
// CHECK-LABEL: func @add_ddd( // CHECK-LABEL: func @add_ddd(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, // CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>, // CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> { // CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> {
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 16 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 16 : index
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 8 : index // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 8 : index
// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_6:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_7:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32> // CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32>
// CHECK: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32> // CHECK-DAG: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32>
// CHECK: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32> // CHECK-DAG: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32>
// CHECK: %[[VAL_11:.*]] = memref.alloc() : memref<32x16x8xf32> // CHECK-DAG: %[[VAL_11:.*]] = memref.alloc() : memref<32x16x8xf32>
// CHECK: memref.copy %[[VAL_10]], %[[VAL_11]] : memref<32x16x8xf32> to memref<32x16x8xf32> // CHECK: memref.copy %[[VAL_10]], %[[VAL_11]] : memref<32x16x8xf32> to memref<32x16x8xf32>
// CHECK: scf.for %[[VAL_12:.*]] = %[[VAL_6]] to %[[VAL_3]] step %[[VAL_7]] { // CHECK: scf.for %[[VAL_12:.*]] = %[[VAL_6]] to %[[VAL_3]] step %[[VAL_7]] {
// CHECK: scf.for %[[VAL_13:.*]] = %[[VAL_6]] to %[[VAL_4]] step %[[VAL_7]] { // CHECK: scf.for %[[VAL_13:.*]] = %[[VAL_6]] to %[[VAL_4]] step %[[VAL_7]] {
// CHECK: %[[VAL_14:.*]] = arith.muli %[[VAL_12]], %[[VAL_4]] : index // CHECK: %[[VAL_14:.*]] = arith.muli %[[VAL_12]], %[[VAL_4]] : index
// CHECK: %[[VAL_15:.*]] = arith.addi %[[VAL_14]], %[[VAL_13]] : index // CHECK: %[[VAL_15:.*]] = arith.addi %[[VAL_14]], %[[VAL_13]] : index
// CHECK: scf.for %[[VAL_16:.*]] = %[[VAL_6]] to %[[VAL_5]] step %[[VAL_7]] { // CHECK: scf.for %[[VAL_16:.*]] = %[[VAL_6]] to %[[VAL_5]] step %[[VAL_7]] {
// CHECK: %[[VAL_17:.*]] = arith.muli %[[VAL_15]], %[[VAL_5]] : index // CHECK: %[[VAL_17:.*]] = arith.muli %[[VAL_15]], %[[VAL_5]] : index
// CHECK: %[[VAL_18:.*]] = arith.addi %[[VAL_17]], %[[VAL_16]] : index // CHECK: %[[VAL_18:.*]] = arith.addi %[[VAL_17]], %[[VAL_16]] : index
Show All 14 Lines outs(%argx: tensor<32x16x8xf32>) {
^bb(%a: f32, %b: f32, %x: f32): ^bb(%a: f32, %b: f32, %x: f32):
%0 = arith.addf %a, %b : f32 %0 = arith.addf %a, %b : f32
linalg.yield %0 : f32 linalg.yield %0 : f32
} -> tensor<32x16x8xf32> } -> tensor<32x16x8xf32>
return %0 : tensor<32x16x8xf32> return %0 : tensor<32x16x8xf32>
} }
// CHECK-LABEL: func @mul_ddd( // CHECK-LABEL: func @mul_ddd(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, // CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>, // CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> { // CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> {
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 16 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 16 : index
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 8 : index // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 8 : index
// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_6:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_7:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32> // CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32>
// CHECK: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32> // CHECK-DAG: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32>
// CHECK: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32> // CHECK-DAG: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32>
// CHECK: %[[VAL_11:.*]] = memref.alloc() : memref<32x16x8xf32> // CHECK-DAG: %[[VAL_11:.*]] = memref.alloc() : memref<32x16x8xf32>
// CHECK: memref.copy %[[VAL_10]], %[[VAL_11]] : memref<32x16x8xf32> to memref<32x16x8xf32> // CHECK: memref.copy %[[VAL_10]], %[[VAL_11]] : memref<32x16x8xf32> to memref<32x16x8xf32>
// CHECK: scf.for %[[VAL_12:.*]] = %[[VAL_6]] to %[[VAL_3]] step %[[VAL_7]] { // CHECK: scf.for %[[VAL_12:.*]] = %[[VAL_6]] to %[[VAL_3]] step %[[VAL_7]] {
// CHECK: scf.for %[[VAL_13:.*]] = %[[VAL_6]] to %[[VAL_4]] step %[[VAL_7]] { // CHECK: scf.for %[[VAL_13:.*]] = %[[VAL_6]] to %[[VAL_4]] step %[[VAL_7]] {
// CHECK: %[[VAL_14:.*]] = arith.muli %[[VAL_12]], %[[VAL_4]] : index // CHECK: %[[VAL_14:.*]] = arith.muli %[[VAL_12]], %[[VAL_4]] : index
// CHECK: %[[VAL_15:.*]] = arith.addi %[[VAL_14]], %[[VAL_13]] : index // CHECK: %[[VAL_15:.*]] = arith.addi %[[VAL_14]], %[[VAL_13]] : index
// CHECK: scf.for %[[VAL_16:.*]] = %[[VAL_6]] to %[[VAL_5]] step %[[VAL_7]] { // CHECK: scf.for %[[VAL_16:.*]] = %[[VAL_6]] to %[[VAL_5]] step %[[VAL_7]] {
// CHECK: %[[VAL_17:.*]] = arith.muli %[[VAL_15]], %[[VAL_5]] : index // CHECK: %[[VAL_17:.*]] = arith.muli %[[VAL_15]], %[[VAL_5]] : index
// CHECK: %[[VAL_18:.*]] = arith.addi %[[VAL_17]], %[[VAL_16]] : index // CHECK: %[[VAL_18:.*]] = arith.addi %[[VAL_17]], %[[VAL_16]] : index
Show All 14 Lines outs(%argx: tensor<32x16x8xf32>) {
^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
linalg.yield %0 : f32 linalg.yield %0 : f32
} -> tensor<32x16x8xf32> } -> tensor<32x16x8xf32>
return %0 : tensor<32x16x8xf32> return %0 : tensor<32x16x8xf32>
} }
// CHECK-LABEL: func @add_dds( // CHECK-LABEL: func @add_dds(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, // CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>, // CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> { // CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> {
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 2 : index // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 2 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 32 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 32 : index
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 16 : index // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 16 : index
// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 8 : index // CHECK-DAG: %[[VAL_6:.*]] = arith.constant 8 : index
// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_7:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_8:.*]] = arith.constant true // CHECK-DAG: %[[VAL_8:.*]] = arith.constant true
// CHECK-DAG: %[[VAL_9:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_9:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_10:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_11:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_12:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32> // CHECK-DAG: %[[VAL_12:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32>
// CHECK: %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32> // CHECK-DAG: %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32>
// CHECK: %[[VAL_14:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32> // CHECK-DAG: %[[VAL_14:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32>
// CHECK: %[[VAL_15:.*]] = memref.alloc() : memref<32x16x8xf32> // CHECK-DAG: %[[VAL_15:.*]] = memref.alloc() : memref<32x16x8xf32>
// CHECK: memref.copy %[[VAL_14]], %[[VAL_15]] : memref<32x16x8xf32> to memref<32x16x8xf32> // CHECK: memref.copy %[[VAL_14]], %[[VAL_15]] : memref<32x16x8xf32> to memref<32x16x8xf32>
// CHECK: scf.for %[[VAL_16:.*]] = %[[VAL_7]] to %[[VAL_4]] step %[[VAL_9]] { // CHECK: scf.for %[[VAL_16:.*]] = %[[VAL_7]] to %[[VAL_4]] step %[[VAL_9]] {
// CHECK: scf.for %[[VAL_17:.*]] = %[[VAL_7]] to %[[VAL_5]] step %[[VAL_9]] { // CHECK: scf.for %[[VAL_17:.*]] = %[[VAL_7]] to %[[VAL_5]] step %[[VAL_9]] {
// CHECK: %[[VAL_18:.*]] = arith.muli %[[VAL_16]], %[[VAL_5]] : index // CHECK: %[[VAL_18:.*]] = arith.muli %[[VAL_16]], %[[VAL_5]] : index
// CHECK: %[[VAL_19:.*]] = arith.addi %[[VAL_18]], %[[VAL_17]] : index // CHECK: %[[VAL_19:.*]] = arith.addi %[[VAL_18]], %[[VAL_17]] : index
// CHECK: %[[VAL_20:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_19]]] : memref<?xindex> // CHECK: %[[VAL_20:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_19]]] : memref<?xindex>
// CHECK: %[[VAL_21:.*]] = arith.addi %[[VAL_19]], %[[VAL_9]] : index // CHECK: %[[VAL_21:.*]] = arith.addi %[[VAL_19]], %[[VAL_9]] : index
// CHECK: %[[VAL_22:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_21]]] : memref<?xindex> // CHECK: %[[VAL_22:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_21]]] : memref<?xindex>
Show All 38 Lines outs(%argx: tensor<32x16x8xf32>) {
^bb(%a: f32, %b: f32, %x: f32): ^bb(%a: f32, %b: f32, %x: f32):
%0 = arith.addf %a, %b : f32 %0 = arith.addf %a, %b : f32
linalg.yield %0 : f32 linalg.yield %0 : f32
} -> tensor<32x16x8xf32> } -> tensor<32x16x8xf32>
return %0 : tensor<32x16x8xf32> return %0 : tensor<32x16x8xf32>
} }
// CHECK-LABEL: func @mul_dds( // CHECK-LABEL: func @mul_dds(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, // CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>, // CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> { // CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> {
// CHECK: %[[VAL_3:.*]] = arith.constant 2 : index // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 2 : index
// CHECK: %[[VAL_4:.*]] = arith.constant 32 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 32 : index
// CHECK: %[[VAL_5:.*]] = arith.constant 16 : index // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 16 : index
// CHECK: %[[VAL_6:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_6:.*]] = arith.constant 0 : index
// CHECK: %[[VAL_7:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_7:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32> // CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32>
// CHECK: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32> // CHECK-DAG: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32>
// CHECK: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32> // CHECK-DAG: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32>
// CHECK: %[[VAL_13:.*]] = memref.alloc() : memref<32x16x8xf32> // CHECK-DAG: %[[VAL_13:.*]] = memref.alloc() : memref<32x16x8xf32>
// CHECK: memref.copy %[[VAL_12]], %[[VAL_13]] : memref<32x16x8xf32> to memref<32x16x8xf32> // CHECK: memref.copy %[[VAL_12]], %[[VAL_13]] : memref<32x16x8xf32> to memref<32x16x8xf32>
// CHECK: scf.for %[[VAL_14:.*]] = %[[VAL_6]] to %[[VAL_4]] step %[[VAL_7]] { // CHECK: scf.for %[[VAL_14:.*]] = %[[VAL_6]] to %[[VAL_4]] step %[[VAL_7]] {
// CHECK: scf.for %[[VAL_15:.*]] = %[[VAL_6]] to %[[VAL_5]] step %[[VAL_7]] { // CHECK: scf.for %[[VAL_15:.*]] = %[[VAL_6]] to %[[VAL_5]] step %[[VAL_7]] {
// CHECK: %[[VAL_16:.*]] = arith.muli %[[VAL_14]], %[[VAL_5]] : index // CHECK: %[[VAL_16:.*]] = arith.muli %[[VAL_14]], %[[VAL_5]] : index
// CHECK: %[[VAL_17:.*]] = arith.addi %[[VAL_16]], %[[VAL_15]] : index // CHECK: %[[VAL_17:.*]] = arith.addi %[[VAL_16]], %[[VAL_15]] : index
// CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_17]]] : memref<?xindex> // CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_17]]] : memref<?xindex>
// CHECK: %[[VAL_19:.*]] = arith.addi %[[VAL_17]], %[[VAL_7]] : index // CHECK: %[[VAL_19:.*]] = arith.addi %[[VAL_17]], %[[VAL_7]] : index
// CHECK: %[[VAL_20:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_19]]] : memref<?xindex> // CHECK: %[[VAL_20:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_19]]] : memref<?xindex>
Show All 16 Lines outs(%argx: tensor<32x16x8xf32>) {
^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
linalg.yield %0 : f32 linalg.yield %0 : f32
} -> tensor<32x16x8xf32> } -> tensor<32x16x8xf32>
return %0 : tensor<32x16x8xf32> return %0 : tensor<32x16x8xf32>
} }
// CHECK-LABEL: func @add_dsd( // CHECK-LABEL: func @add_dsd(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, // CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>, // CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> { // CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> {
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 16 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 16 : index
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 8 : index // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 8 : index
// CHECK-DAG: %[[VAL_6:.*]] = arith.constant true // CHECK-DAG: %[[VAL_6:.*]] = arith.constant true
// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_7:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_8:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_8:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_8]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_8]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_8]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_8]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32> // CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32>
// CHECK: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32> // CHECK-DAG: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32>
// CHECK: %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32> // CHECK-DAG: %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32>
// CHECK: %[[VAL_14:.*]] = memref.alloc() : memref<32x16x8xf32> // CHECK-DAG: %[[VAL_14:.*]] = memref.alloc() : memref<32x16x8xf32>
// CHECK: memref.copy %[[VAL_13]], %[[VAL_14]] : memref<32x16x8xf32> to memref<32x16x8xf32> // CHECK: memref.copy %[[VAL_13]], %[[VAL_14]] : memref<32x16x8xf32> to memref<32x16x8xf32>
// CHECK: scf.for %[[VAL_15:.*]] = %[[VAL_7]] to %[[VAL_3]] step %[[VAL_8]] { // CHECK: scf.for %[[VAL_15:.*]] = %[[VAL_7]] to %[[VAL_3]] step %[[VAL_8]] {
// CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_15]]] : memref<?xindex> // CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_15]]] : memref<?xindex>
// CHECK: %[[VAL_17:.*]] = arith.addi %[[VAL_15]], %[[VAL_8]] : index // CHECK: %[[VAL_17:.*]] = arith.addi %[[VAL_15]], %[[VAL_8]] : index
// CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_17]]] : memref<?xindex> // CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_17]]] : memref<?xindex>
// CHECK: %[[VAL_19:.*]]:2 = scf.while (%[[VAL_20:.*]] = %[[VAL_16]], %[[VAL_21:.*]] = %[[VAL_7]]) : (index, index) -> (index, index) { // CHECK: %[[VAL_19:.*]]:2 = scf.while (%[[VAL_20:.*]] = %[[VAL_16]], %[[VAL_21:.*]] = %[[VAL_7]]) : (index, index) -> (index, index) {
// CHECK: %[[VAL_22:.*]] = arith.cmpi ult, %[[VAL_20]], %[[VAL_18]] : index // CHECK: %[[VAL_22:.*]] = arith.cmpi ult, %[[VAL_20]], %[[VAL_18]] : index
// CHECK: scf.condition(%[[VAL_22]]) %[[VAL_20]], %[[VAL_21]] : index, index // CHECK: scf.condition(%[[VAL_22]]) %[[VAL_20]], %[[VAL_21]] : index, index
▲ Show 20 Lines • Show All 42 Lines • ▼ Show 20 Lines outs(%argx: tensor<32x16x8xf32>) {
^bb(%a: f32, %b: f32, %x: f32): ^bb(%a: f32, %b: f32, %x: f32):
%0 = arith.addf %a, %b : f32 %0 = arith.addf %a, %b : f32
linalg.yield %0 : f32 linalg.yield %0 : f32
} -> tensor<32x16x8xf32> } -> tensor<32x16x8xf32>
return %0 : tensor<32x16x8xf32> return %0 : tensor<32x16x8xf32>
} }
// CHECK-LABEL: func @mul_dsd( // CHECK-LABEL: func @mul_dsd(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, // CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>, // CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> { // CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> {
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 8 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 8 : index
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_6:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_6]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_6]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_6]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_6]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32> // CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32>
// CHECK: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32> // CHECK-DAG: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32>
// CHECK: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32> // CHECK-DAG: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32>
// CHECK: %[[VAL_12:.*]] = memref.alloc() : memref<32x16x8xf32> // CHECK-DAG: %[[VAL_12:.*]] = memref.alloc() : memref<32x16x8xf32>
// CHECK: memref.copy %[[VAL_11]], %[[VAL_12]] : memref<32x16x8xf32> to memref<32x16x8xf32> // CHECK: memref.copy %[[VAL_11]], %[[VAL_12]] : memref<32x16x8xf32> to memref<32x16x8xf32>
// CHECK: scf.for %[[VAL_13:.*]] = %[[VAL_5]] to %[[VAL_3]] step %[[VAL_6]] { // CHECK: scf.for %[[VAL_13:.*]] = %[[VAL_5]] to %[[VAL_3]] step %[[VAL_6]] {
// CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_13]]] : memref<?xindex> // CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_13]]] : memref<?xindex>
// CHECK: %[[VAL_15:.*]] = arith.addi %[[VAL_13]], %[[VAL_6]] : index // CHECK: %[[VAL_15:.*]] = arith.addi %[[VAL_13]], %[[VAL_6]] : index
// CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_15]]] : memref<?xindex> // CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_15]]] : memref<?xindex>
// CHECK: scf.for %[[VAL_17:.*]] = %[[VAL_14]] to %[[VAL_16]] step %[[VAL_6]] { // CHECK: scf.for %[[VAL_17:.*]] = %[[VAL_14]] to %[[VAL_16]] step %[[VAL_6]] {
// CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_17]]] : memref<?xindex> // CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_17]]] : memref<?xindex>
// CHECK: scf.for %[[VAL_19:.*]] = %[[VAL_5]] to %[[VAL_4]] step %[[VAL_6]] { // CHECK: scf.for %[[VAL_19:.*]] = %[[VAL_5]] to %[[VAL_4]] step %[[VAL_6]] {
Show All 16 Lines outs(%argx: tensor<32x16x8xf32>) {
^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
linalg.yield %0 : f32 linalg.yield %0 : f32
} -> tensor<32x16x8xf32> } -> tensor<32x16x8xf32>
return %0 : tensor<32x16x8xf32> return %0 : tensor<32x16x8xf32>
} }
// CHECK-LABEL: func @add_dss( // CHECK-LABEL: func @add_dss(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, // CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>, // CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> { // CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> {
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 2 : index // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 2 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 32 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 32 : index
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 16 : index // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 16 : index
// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 8 : index // CHECK-DAG: %[[VAL_6:.*]] = arith.constant 8 : index
// CHECK-DAG: %[[VAL_7:.*]] = arith.constant true // CHECK-DAG: %[[VAL_7:.*]] = arith.constant true
// CHECK-DAG: %[[VAL_8:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_8:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_9:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_9:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_10:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_9]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_9]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_11:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_9]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_9]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_12:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_12:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_13:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_13:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_14:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32> // CHECK-DAG: %[[VAL_14:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32>
// CHECK: %[[VAL_15:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32> // CHECK-DAG: %[[VAL_15:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32>
// CHECK: %[[VAL_16:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32> // CHECK-DAG: %[[VAL_16:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32>
// CHECK: %[[VAL_17:.*]] = memref.alloc() : memref<32x16x8xf32> // CHECK-DAG: %[[VAL_17:.*]] = memref.alloc() : memref<32x16x8xf32>
// CHECK: memref.copy %[[VAL_16]], %[[VAL_17]] : memref<32x16x8xf32> to memref<32x16x8xf32> // CHECK: memref.copy %[[VAL_16]], %[[VAL_17]] : memref<32x16x8xf32> to memref<32x16x8xf32>
// CHECK: scf.for %[[VAL_18:.*]] = %[[VAL_8]] to %[[VAL_4]] step %[[VAL_9]] { // CHECK: scf.for %[[VAL_18:.*]] = %[[VAL_8]] to %[[VAL_4]] step %[[VAL_9]] {
// CHECK: %[[VAL_19:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_18]]] : memref<?xindex> // CHECK: %[[VAL_19:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_18]]] : memref<?xindex>
// CHECK: %[[VAL_20:.*]] = arith.addi %[[VAL_18]], %[[VAL_9]] : index // CHECK: %[[VAL_20:.*]] = arith.addi %[[VAL_18]], %[[VAL_9]] : index
// CHECK: %[[VAL_21:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_20]]] : memref<?xindex> // CHECK: %[[VAL_21:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_20]]] : memref<?xindex>
// CHECK: %[[VAL_22:.*]]:2 = scf.while (%[[VAL_23:.*]] = %[[VAL_19]], %[[VAL_24:.*]] = %[[VAL_8]]) : (index, index) -> (index, index) { // CHECK: %[[VAL_22:.*]]:2 = scf.while (%[[VAL_23:.*]] = %[[VAL_19]], %[[VAL_24:.*]] = %[[VAL_8]]) : (index, index) -> (index, index) {
// CHECK: %[[VAL_25:.*]] = arith.cmpi ult, %[[VAL_23]], %[[VAL_21]] : index // CHECK: %[[VAL_25:.*]] = arith.cmpi ult, %[[VAL_23]], %[[VAL_21]] : index
// CHECK: scf.condition(%[[VAL_25]]) %[[VAL_23]], %[[VAL_24]] : index, index // CHECK: scf.condition(%[[VAL_25]]) %[[VAL_23]], %[[VAL_24]] : index, index
▲ Show 20 Lines • Show All 66 Lines • ▼ Show 20 Lines outs(%argx: tensor<32x16x8xf32>) {
^bb(%a: f32, %b: f32, %x: f32): ^bb(%a: f32, %b: f32, %x: f32):
%0 = arith.addf %a, %b : f32 %0 = arith.addf %a, %b : f32
linalg.yield %0 : f32 linalg.yield %0 : f32
} -> tensor<32x16x8xf32> } -> tensor<32x16x8xf32>
return %0 : tensor<32x16x8xf32> return %0 : tensor<32x16x8xf32>
} }
// CHECK-LABEL: func @mul_dss( // CHECK-LABEL: func @mul_dss(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, // CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>, // CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> { // CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> {
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 2 : index // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 2 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 32 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 32 : index
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_6:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_6]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_6]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_6]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_6]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32> // CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32>
// CHECK: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32> // CHECK-DAG: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32>
// CHECK: %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32> // CHECK-DAG: %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32>
// CHECK: %[[VAL_14:.*]] = memref.alloc() : memref<32x16x8xf32> // CHECK-DAG: %[[VAL_14:.*]] = memref.alloc() : memref<32x16x8xf32>
// CHECK: memref.copy %[[VAL_13]], %[[VAL_14]] : memref<32x16x8xf32> to memref<32x16x8xf32> // CHECK: memref.copy %[[VAL_13]], %[[VAL_14]] : memref<32x16x8xf32> to memref<32x16x8xf32>
// CHECK: scf.for %[[VAL_15:.*]] = %[[VAL_5]] to %[[VAL_4]] step %[[VAL_6]] { // CHECK: scf.for %[[VAL_15:.*]] = %[[VAL_5]] to %[[VAL_4]] step %[[VAL_6]] {
// CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_15]]] : memref<?xindex> // CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_15]]] : memref<?xindex>
// CHECK: %[[VAL_17:.*]] = arith.addi %[[VAL_15]], %[[VAL_6]] : index // CHECK: %[[VAL_17:.*]] = arith.addi %[[VAL_15]], %[[VAL_6]] : index
// CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_17]]] : memref<?xindex> // CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_17]]] : memref<?xindex>
// CHECK: scf.for %[[VAL_19:.*]] = %[[VAL_16]] to %[[VAL_18]] step %[[VAL_6]] { // CHECK: scf.for %[[VAL_19:.*]] = %[[VAL_16]] to %[[VAL_18]] step %[[VAL_6]] {
// CHECK: %[[VAL_20:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_19]]] : memref<?xindex> // CHECK: %[[VAL_20:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_19]]] : memref<?xindex>
// CHECK: %[[VAL_21:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_19]]] : memref<?xindex> // CHECK: %[[VAL_21:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_19]]] : memref<?xindex>
Show All 18 Lines outs(%argx: tensor<32x16x8xf32>) {
^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
linalg.yield %0 : f32 linalg.yield %0 : f32
} -> tensor<32x16x8xf32> } -> tensor<32x16x8xf32>
return %0 : tensor<32x16x8xf32> return %0 : tensor<32x16x8xf32>
} }
// CHECK-LABEL: func @add_sdd( // CHECK-LABEL: func @add_sdd(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, // CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>, // CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> { // CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> {
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 16 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 16 : index
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 8 : index // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 8 : index
// CHECK-DAG: %[[VAL_6:.*]] = arith.constant true // CHECK-DAG: %[[VAL_6:.*]] = arith.constant true
// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_7:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_8:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_8:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_7]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_7]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_7]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_7]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32> // CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32>
// CHECK: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32> // CHECK-DAG: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32>
// CHECK: %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32> // CHECK-DAG: %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32>
// CHECK: %[[VAL_14:.*]] = memref.alloc() : memref<32x16x8xf32> // CHECK-DAG: %[[VAL_14:.*]] = memref.alloc() : memref<32x16x8xf32>
// CHECK: memref.copy %[[VAL_13]], %[[VAL_14]] : memref<32x16x8xf32> to memref<32x16x8xf32> // CHECK: memref.copy %[[VAL_13]], %[[VAL_14]] : memref<32x16x8xf32> to memref<32x16x8xf32>
// CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_7]]] : memref<?xindex> // CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_7]]] : memref<?xindex>
// CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_8]]] : memref<?xindex> // CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_8]]] : memref<?xindex>
// CHECK: %[[VAL_17:.*]]:2 = scf.while (%[[VAL_18:.*]] = %[[VAL_15]], %[[VAL_19:.*]] = %[[VAL_7]]) : (index, index) -> (index, index) { // CHECK: %[[VAL_17:.*]]:2 = scf.while (%[[VAL_18:.*]] = %[[VAL_15]], %[[VAL_19:.*]] = %[[VAL_7]]) : (index, index) -> (index, index) {
// CHECK: %[[VAL_20:.*]] = arith.cmpi ult, %[[VAL_18]], %[[VAL_16]] : index // CHECK: %[[VAL_20:.*]] = arith.cmpi ult, %[[VAL_18]], %[[VAL_16]] : index
// CHECK: scf.condition(%[[VAL_20]]) %[[VAL_18]], %[[VAL_19]] : index, index // CHECK: scf.condition(%[[VAL_20]]) %[[VAL_18]], %[[VAL_19]] : index, index
// CHECK: } do { // CHECK: } do {
// CHECK: ^bb0(%[[VAL_21:.*]]: index, %[[VAL_22:.*]]: index): // CHECK: ^bb0(%[[VAL_21:.*]]: index, %[[VAL_22:.*]]: index):
▲ Show 20 Lines • Show All 47 Lines • ▼ Show 20 Lines outs(%argx: tensor<32x16x8xf32>) {
^bb(%a: f32, %b: f32, %x: f32): ^bb(%a: f32, %b: f32, %x: f32):
%0 = arith.addf %a, %b : f32 %0 = arith.addf %a, %b : f32
linalg.yield %0 : f32 linalg.yield %0 : f32
} -> tensor<32x16x8xf32> } -> tensor<32x16x8xf32>
return %0 : tensor<32x16x8xf32> return %0 : tensor<32x16x8xf32>
} }
// CHECK-LABEL: func @mul_sdd( // CHECK-LABEL: func @mul_sdd(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, // CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>, // CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> { // CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> {
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 16 : index // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 16 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 8 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 8 : index
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_6:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_5]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_5]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_5]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_5]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32> // CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32>
// CHECK: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32> // CHECK-DAG: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32>
// CHECK: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32> // CHECK-DAG: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32>
// CHECK: %[[VAL_12:.*]] = memref.alloc() : memref<32x16x8xf32> // CHECK-DAG: %[[VAL_12:.*]] = memref.alloc() : memref<32x16x8xf32>
// CHECK: memref.copy %[[VAL_11]], %[[VAL_12]] : memref<32x16x8xf32> to memref<32x16x8xf32> // CHECK: memref.copy %[[VAL_11]], %[[VAL_12]] : memref<32x16x8xf32> to memref<32x16x8xf32>
// CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_5]]] : memref<?xindex> // CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_5]]] : memref<?xindex>
// CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_6]]] : memref<?xindex> // CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_6]]] : memref<?xindex>
// CHECK: scf.for %[[VAL_15:.*]] = %[[VAL_13]] to %[[VAL_14]] step %[[VAL_6]] { // CHECK: scf.for %[[VAL_15:.*]] = %[[VAL_13]] to %[[VAL_14]] step %[[VAL_6]] {
// CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_15]]] : memref<?xindex> // CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_15]]] : memref<?xindex>
// CHECK: scf.for %[[VAL_17:.*]] = %[[VAL_5]] to %[[VAL_3]] step %[[VAL_6]] { // CHECK: scf.for %[[VAL_17:.*]] = %[[VAL_5]] to %[[VAL_3]] step %[[VAL_6]] {
// CHECK: %[[VAL_18:.*]] = arith.muli %[[VAL_15]], %[[VAL_3]] : index // CHECK: %[[VAL_18:.*]] = arith.muli %[[VAL_15]], %[[VAL_3]] : index
// CHECK: %[[VAL_19:.*]] = arith.addi %[[VAL_18]], %[[VAL_17]] : index // CHECK: %[[VAL_19:.*]] = arith.addi %[[VAL_18]], %[[VAL_17]] : index
Show All 17 Lines outs(%argx: tensor<32x16x8xf32>) {
^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
linalg.yield %0 : f32 linalg.yield %0 : f32
} -> tensor<32x16x8xf32> } -> tensor<32x16x8xf32>
return %0 : tensor<32x16x8xf32> return %0 : tensor<32x16x8xf32>
} }
// CHECK-LABEL: func @add_sds( // CHECK-LABEL: func @add_sds(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, // CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>, // CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> { // CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> {
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 2 : index // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 2 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 32 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 32 : index
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 16 : index // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 16 : index
// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 8 : index // CHECK-DAG: %[[VAL_6:.*]] = arith.constant 8 : index
// CHECK-DAG: %[[VAL_7:.*]] = arith.constant true // CHECK-DAG: %[[VAL_7:.*]] = arith.constant true
// CHECK-DAG: %[[VAL_8:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_8:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_9:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_9:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_10:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_8]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_8]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_11:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_8]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_8]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_12:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_12:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_13:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_13:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_14:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32> // CHECK-DAG: %[[VAL_14:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32>
// CHECK: %[[VAL_15:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32> // CHECK-DAG: %[[VAL_15:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32>
// CHECK: %[[VAL_16:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32> // CHECK-DAG: %[[VAL_16:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32>
// CHECK: %[[VAL_17:.*]] = memref.alloc() : memref<32x16x8xf32> // CHECK-DAG: %[[VAL_17:.*]] = memref.alloc() : memref<32x16x8xf32>
// CHECK: memref.copy %[[VAL_16]], %[[VAL_17]] : memref<32x16x8xf32> to memref<32x16x8xf32> // CHECK: memref.copy %[[VAL_16]], %[[VAL_17]] : memref<32x16x8xf32> to memref<32x16x8xf32>
// CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_8]]] : memref<?xindex> // CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_8]]] : memref<?xindex>
// CHECK: %[[VAL_19:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_9]]] : memref<?xindex> // CHECK: %[[VAL_19:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_9]]] : memref<?xindex>
// CHECK: %[[VAL_20:.*]]:2 = scf.while (%[[VAL_21:.*]] = %[[VAL_18]], %[[VAL_22:.*]] = %[[VAL_8]]) : (index, index) -> (index, index) { // CHECK: %[[VAL_20:.*]]:2 = scf.while (%[[VAL_21:.*]] = %[[VAL_18]], %[[VAL_22:.*]] = %[[VAL_8]]) : (index, index) -> (index, index) {
// CHECK: %[[VAL_23:.*]] = arith.cmpi ult, %[[VAL_21]], %[[VAL_19]] : index // CHECK: %[[VAL_23:.*]] = arith.cmpi ult, %[[VAL_21]], %[[VAL_19]] : index
// CHECK: scf.condition(%[[VAL_23]]) %[[VAL_21]], %[[VAL_22]] : index, index // CHECK: scf.condition(%[[VAL_23]]) %[[VAL_21]], %[[VAL_22]] : index, index
// CHECK: } do { // CHECK: } do {
// CHECK: ^bb0(%[[VAL_24:.*]]: index, %[[VAL_25:.*]]: index): // CHECK: ^bb0(%[[VAL_24:.*]]: index, %[[VAL_25:.*]]: index):
▲ Show 20 Lines • Show All 71 Lines • ▼ Show 20 Lines outs(%argx: tensor<32x16x8xf32>) {
^bb(%a: f32, %b: f32, %x: f32): ^bb(%a: f32, %b: f32, %x: f32):
%0 = arith.addf %a, %b : f32 %0 = arith.addf %a, %b : f32
linalg.yield %0 : f32 linalg.yield %0 : f32
} -> tensor<32x16x8xf32> } -> tensor<32x16x8xf32>
return %0 : tensor<32x16x8xf32> return %0 : tensor<32x16x8xf32>
} }
// CHECK-LABEL: func @mul_sds( // CHECK-LABEL: func @mul_sds(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, // CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>, // CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> { // CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> {
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 2 : index // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 2 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 16 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 16 : index
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_6:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_5]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_5]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_5]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_5]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32> // CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32>
// CHECK: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32> // CHECK-DAG: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32>
// CHECK: %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32> // CHECK-DAG: %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32>
// CHECK: %[[VAL_14:.*]] = memref.alloc() : memref<32x16x8xf32> // CHECK-DAG: %[[VAL_14:.*]] = memref.alloc() : memref<32x16x8xf32>
// CHECK: memref.copy %[[VAL_13]], %[[VAL_14]] : memref<32x16x8xf32> to memref<32x16x8xf32> // CHECK: memref.copy %[[VAL_13]], %[[VAL_14]] : memref<32x16x8xf32> to memref<32x16x8xf32>
// CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_5]]] : memref<?xindex> // CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_5]]] : memref<?xindex>
// CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_6]]] : memref<?xindex> // CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_6]]] : memref<?xindex>
// CHECK: scf.for %[[VAL_17:.*]] = %[[VAL_15]] to %[[VAL_16]] step %[[VAL_6]] { // CHECK: scf.for %[[VAL_17:.*]] = %[[VAL_15]] to %[[VAL_16]] step %[[VAL_6]] {
// CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_17]]] : memref<?xindex> // CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_17]]] : memref<?xindex>
// CHECK: scf.for %[[VAL_19:.*]] = %[[VAL_5]] to %[[VAL_4]] step %[[VAL_6]] { // CHECK: scf.for %[[VAL_19:.*]] = %[[VAL_5]] to %[[VAL_4]] step %[[VAL_6]] {
// CHECK: %[[VAL_20:.*]] = arith.muli %[[VAL_17]], %[[VAL_4]] : index // CHECK: %[[VAL_20:.*]] = arith.muli %[[VAL_17]], %[[VAL_4]] : index
// CHECK: %[[VAL_21:.*]] = arith.addi %[[VAL_20]], %[[VAL_19]] : index // CHECK: %[[VAL_21:.*]] = arith.addi %[[VAL_20]], %[[VAL_19]] : index
Show All 19 Lines outs(%argx: tensor<32x16x8xf32>) {
^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
linalg.yield %0 : f32 linalg.yield %0 : f32
} -> tensor<32x16x8xf32> } -> tensor<32x16x8xf32>
return %0 : tensor<32x16x8xf32> return %0 : tensor<32x16x8xf32>
} }
// CHECK-LABEL: func @add_ssd( // CHECK-LABEL: func @add_ssd(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, // CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>, // CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> { // CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> {
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 16 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 16 : index
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 8 : index // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 8 : index
// CHECK-DAG: %[[VAL_6:.*]] = arith.constant true // CHECK-DAG: %[[VAL_6:.*]] = arith.constant true
// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_7:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_8:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_8:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_7]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_7]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_7]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_7]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_11:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_8]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_8]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_12:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_8]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_12:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_8]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_13:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32> // CHECK-DAG: %[[VAL_13:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32>
// CHECK: %[[VAL_14:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32> // CHECK-DAG: %[[VAL_14:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32>
// CHECK: %[[VAL_15:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32> // CHECK-DAG: %[[VAL_15:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32>
// CHECK: %[[VAL_16:.*]] = memref.alloc() : memref<32x16x8xf32> // CHECK-DAG: %[[VAL_16:.*]] = memref.alloc() : memref<32x16x8xf32>
// CHECK: memref.copy %[[VAL_15]], %[[VAL_16]] : memref<32x16x8xf32> to memref<32x16x8xf32> // CHECK: memref.copy %[[VAL_15]], %[[VAL_16]] : memref<32x16x8xf32> to memref<32x16x8xf32>
// CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_7]]] : memref<?xindex> // CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_7]]] : memref<?xindex>
// CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_8]]] : memref<?xindex> // CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_8]]] : memref<?xindex>
// CHECK: %[[VAL_19:.*]]:2 = scf.while (%[[VAL_20:.*]] = %[[VAL_17]], %[[VAL_21:.*]] = %[[VAL_7]]) : (index, index) -> (index, index) { // CHECK: %[[VAL_19:.*]]:2 = scf.while (%[[VAL_20:.*]] = %[[VAL_17]], %[[VAL_21:.*]] = %[[VAL_7]]) : (index, index) -> (index, index) {
// CHECK: %[[VAL_22:.*]] = arith.cmpi ult, %[[VAL_20]], %[[VAL_18]] : index // CHECK: %[[VAL_22:.*]] = arith.cmpi ult, %[[VAL_20]], %[[VAL_18]] : index
// CHECK: scf.condition(%[[VAL_22]]) %[[VAL_20]], %[[VAL_21]] : index, index // CHECK: scf.condition(%[[VAL_22]]) %[[VAL_20]], %[[VAL_21]] : index, index
// CHECK: } do { // CHECK: } do {
// CHECK: ^bb0(%[[VAL_23:.*]]: index, %[[VAL_24:.*]]: index): // CHECK: ^bb0(%[[VAL_23:.*]]: index, %[[VAL_24:.*]]: index):
▲ Show 20 Lines • Show All 75 Lines • ▼ Show 20 Lines outs(%argx: tensor<32x16x8xf32>) {
^bb(%a: f32, %b: f32, %x: f32): ^bb(%a: f32, %b: f32, %x: f32):
%0 = arith.addf %a, %b : f32 %0 = arith.addf %a, %b : f32
linalg.yield %0 : f32 linalg.yield %0 : f32
} -> tensor<32x16x8xf32> } -> tensor<32x16x8xf32>
return %0 : tensor<32x16x8xf32> return %0 : tensor<32x16x8xf32>
} }
// CHECK-LABEL: func @mul_ssd( // CHECK-LABEL: func @mul_ssd(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, // CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>, // CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> { // CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> {
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 8 : index // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 8 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_4]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_4]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_5]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_5]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_5]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_5]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32> // CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32>
// CHECK: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32> // CHECK-DAG: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32>
// CHECK: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32> // CHECK-DAG: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32>
// CHECK: %[[VAL_13:.*]] = memref.alloc() : memref<32x16x8xf32> // CHECK-DAG: %[[VAL_13:.*]] = memref.alloc() : memref<32x16x8xf32>
// CHECK: memref.copy %[[VAL_12]], %[[VAL_13]] : memref<32x16x8xf32> to memref<32x16x8xf32> // CHECK: memref.copy %[[VAL_12]], %[[VAL_13]] : memref<32x16x8xf32> to memref<32x16x8xf32>
// CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex> // CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex>
// CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_5]]] : memref<?xindex> // CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_5]]] : memref<?xindex>
// CHECK: scf.for %[[VAL_16:.*]] = %[[VAL_14]] to %[[VAL_15]] step %[[VAL_5]] { // CHECK: scf.for %[[VAL_16:.*]] = %[[VAL_14]] to %[[VAL_15]] step %[[VAL_5]] {
// CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_16]]] : memref<?xindex> // CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_16]]] : memref<?xindex>
// CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_16]]] : memref<?xindex> // CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_16]]] : memref<?xindex>
// CHECK: %[[VAL_19:.*]] = arith.addi %[[VAL_16]], %[[VAL_5]] : index // CHECK: %[[VAL_19:.*]] = arith.addi %[[VAL_16]], %[[VAL_5]] : index
// CHECK: %[[VAL_20:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_19]]] : memref<?xindex> // CHECK: %[[VAL_20:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_19]]] : memref<?xindex>
Show All 19 Lines outs(%argx: tensor<32x16x8xf32>) {
^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
linalg.yield %0 : f32 linalg.yield %0 : f32
} -> tensor<32x16x8xf32> } -> tensor<32x16x8xf32>
return %0 : tensor<32x16x8xf32> return %0 : tensor<32x16x8xf32>
} }
// CHECK-LABEL: func @add_sss( // CHECK-LABEL: func @add_sss(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, // CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>, // CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> { // CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> {
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 2 : index // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 2 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 32 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 32 : index
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 16 : index // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 16 : index
// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 8 : index // CHECK-DAG: %[[VAL_6:.*]] = arith.constant 8 : index
// CHECK-DAG: %[[VAL_7:.*]] = arith.constant true // CHECK-DAG: %[[VAL_7:.*]] = arith.constant true
// CHECK-DAG: %[[VAL_8:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_8:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_9:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_9:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_10:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_8]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_8]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_11:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_8]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_8]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_12:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_9]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_12:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_9]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_13:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_9]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_13:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_9]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_14:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_14:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_15:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_15:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_16:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32> // CHECK-DAG: %[[VAL_16:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32>
// CHECK: %[[VAL_17:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32> // CHECK-DAG: %[[VAL_17:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32>
// CHECK: %[[VAL_18:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32> // CHECK-DAG: %[[VAL_18:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32>
// CHECK: %[[VAL_19:.*]] = memref.alloc() : memref<32x16x8xf32> // CHECK-DAG: %[[VAL_19:.*]] = memref.alloc() : memref<32x16x8xf32>
// CHECK: memref.copy %[[VAL_18]], %[[VAL_19]] : memref<32x16x8xf32> to memref<32x16x8xf32> // CHECK: memref.copy %[[VAL_18]], %[[VAL_19]] : memref<32x16x8xf32> to memref<32x16x8xf32>
// CHECK: %[[VAL_20:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_8]]] : memref<?xindex> // CHECK: %[[VAL_20:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_8]]] : memref<?xindex>
// CHECK: %[[VAL_21:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_9]]] : memref<?xindex> // CHECK: %[[VAL_21:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_9]]] : memref<?xindex>
// CHECK: %[[VAL_22:.*]]:2 = scf.while (%[[VAL_23:.*]] = %[[VAL_20]], %[[VAL_24:.*]] = %[[VAL_8]]) : (index, index) -> (index, index) { // CHECK: %[[VAL_22:.*]]:2 = scf.while (%[[VAL_23:.*]] = %[[VAL_20]], %[[VAL_24:.*]] = %[[VAL_8]]) : (index, index) -> (index, index) {
// CHECK: %[[VAL_25:.*]] = arith.cmpi ult, %[[VAL_23]], %[[VAL_21]] : index // CHECK: %[[VAL_25:.*]] = arith.cmpi ult, %[[VAL_23]], %[[VAL_21]] : index
// CHECK: scf.condition(%[[VAL_25]]) %[[VAL_23]], %[[VAL_24]] : index, index // CHECK: scf.condition(%[[VAL_25]]) %[[VAL_23]], %[[VAL_24]] : index, index
// CHECK: } do { // CHECK: } do {
// CHECK: ^bb0(%[[VAL_26:.*]]: index, %[[VAL_27:.*]]: index): // CHECK: ^bb0(%[[VAL_26:.*]]: index, %[[VAL_27:.*]]: index):
▲ Show 20 Lines • Show All 99 Lines • ▼ Show 20 Lines outs(%argx: tensor<32x16x8xf32>) {
^bb(%a: f32, %b: f32, %x: f32): ^bb(%a: f32, %b: f32, %x: f32):
%0 = arith.addf %a, %b : f32 %0 = arith.addf %a, %b : f32
linalg.yield %0 : f32 linalg.yield %0 : f32
} -> tensor<32x16x8xf32> } -> tensor<32x16x8xf32>
return %0 : tensor<32x16x8xf32> return %0 : tensor<32x16x8xf32>
} }
// CHECK-LABEL: func @mul_sss( // CHECK-LABEL: func @mul_sss(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, // CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>, // CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> { // CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> {
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 2 : index // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 2 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_4]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_4]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_5]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_5]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_5]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_5]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_10:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_11:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_12:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32> // CHECK-DAG: %[[VAL_12:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32>
// CHECK: %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32> // CHECK-DAG: %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32>
// CHECK: %[[VAL_14:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32> // CHECK-DAG: %[[VAL_14:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32>
// CHECK: %[[VAL_15:.*]] = memref.alloc() : memref<32x16x8xf32> // CHECK-DAG: %[[VAL_15:.*]] = memref.alloc() : memref<32x16x8xf32>
// CHECK: memref.copy %[[VAL_14]], %[[VAL_15]] : memref<32x16x8xf32> to memref<32x16x8xf32> // CHECK: memref.copy %[[VAL_14]], %[[VAL_15]] : memref<32x16x8xf32> to memref<32x16x8xf32>
// CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex> // CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex>
// CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_5]]] : memref<?xindex> // CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_5]]] : memref<?xindex>
// CHECK: scf.for %[[VAL_18:.*]] = %[[VAL_16]] to %[[VAL_17]] step %[[VAL_5]] { // CHECK: scf.for %[[VAL_18:.*]] = %[[VAL_16]] to %[[VAL_17]] step %[[VAL_5]] {
// CHECK: %[[VAL_19:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_18]]] : memref<?xindex> // CHECK: %[[VAL_19:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_18]]] : memref<?xindex>
// CHECK: %[[VAL_20:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_18]]] : memref<?xindex> // CHECK: %[[VAL_20:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_18]]] : memref<?xindex>
// CHECK: %[[VAL_21:.*]] = arith.addi %[[VAL_18]], %[[VAL_5]] : index // CHECK: %[[VAL_21:.*]] = arith.addi %[[VAL_18]], %[[VAL_5]] : index
// CHECK: %[[VAL_22:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_21]]] : memref<?xindex> // CHECK: %[[VAL_22:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_21]]] : memref<?xindex>
Show All 32 Lines indexing_maps = [
affine_map<(i,j,k,l) -> (l,j)>, // D affine_map<(i,j,k,l) -> (l,j)>, // D
affine_map<(i,j,k,l) -> (i,j)> // A (out) affine_map<(i,j,k,l) -> (i,j)> // A (out)
], ],
iterator_types = ["parallel", "parallel", "reduction", "reduction"], iterator_types = ["parallel", "parallel", "reduction", "reduction"],
doc = "A(i,j) += SUM_k,l B(i,k,l) * C(k,j) * D(l,j)" doc = "A(i,j) += SUM_k,l B(i,k,l) * C(k,j) * D(l,j)"
} }
// CHECK-LABEL: func @kernel_3d( // CHECK-LABEL: func @kernel_3d(
// CHECK-SAME: %[[VAL_0:.*0]]: tensor<?x?xf32>, // CHECK-SAME: %[[VAL_0:.*0]]: tensor<?x?xf32>,
// CHECK-SAME: %[[VAL_1:.*1]]: tensor<?x?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, // CHECK-SAME: %[[VAL_1:.*1]]: tensor<?x?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>,
// CHECK-SAME: %[[VAL_2:.*2]]: tensor<?x?xf32>, // CHECK-SAME: %[[VAL_2:.*2]]: tensor<?x?xf32>,
// CHECK-SAME: %[[VAL_3:.*3]]: tensor<?x?xf32>) -> tensor<?x?xf32> { // CHECK-SAME: %[[VAL_3:.*3]]: tensor<?x?xf32>) -> tensor<?x?xf32> {
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 2 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 2 : index
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_6:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_4]] : tensor<?x?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_4]] : tensor<?x?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_4]] : tensor<?x?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_4]] : tensor<?x?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?x?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32> // CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?x?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32>
// CHECK: %[[VAL_10:.*]] = tensor.dim %[[VAL_2]], %[[VAL_5]] : tensor<?x?xf32> // CHECK-DAG: %[[VAL_10:.*]] = tensor.dim %[[VAL_2]], %[[VAL_5]] : tensor<?x?xf32>
// CHECK: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref<?x?xf32> // CHECK-DAG: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref<?x?xf32>
// CHECK: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_3]] : memref<?x?xf32> // CHECK-DAG: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_3]] : memref<?x?xf32>
// CHECK: %[[VAL_13:.*]] = tensor.dim %[[VAL_0]], %[[VAL_5]] : tensor<?x?xf32> // CHECK-DAG: %[[VAL_13:.*]] = tensor.dim %[[VAL_0]], %[[VAL_5]] : tensor<?x?xf32>
// CHECK: %[[VAL_14:.*]] = tensor.dim %[[VAL_0]], %[[VAL_6]] : tensor<?x?xf32> // CHECK-DAG: %[[VAL_14:.*]] = tensor.dim %[[VAL_0]], %[[VAL_6]] : tensor<?x?xf32>
// CHECK: %[[VAL_15:.*]] = bufferization.to_memref %[[VAL_0]] : memref<?x?xf32> // CHECK-DAG: %[[VAL_15:.*]] = bufferization.to_memref %[[VAL_0]] : memref<?x?xf32>
// CHECK: %[[VAL_16:.*]] = memref.alloc(%[[VAL_13]], %[[VAL_14]]) : memref<?x?xf32> // CHECK-DAG: %[[VAL_16:.*]] = memref.alloc(%[[VAL_13]], %[[VAL_14]]) : memref<?x?xf32>
// CHECK: memref.copy %[[VAL_15]], %[[VAL_16]] : memref<?x?xf32> to memref<?x?xf32> // CHECK: memref.copy %[[VAL_15]], %[[VAL_16]] : memref<?x?xf32> to memref<?x?xf32>
// CHECK: scf.for %[[VAL_17:.*]] = %[[VAL_5]] to %[[VAL_13]] step %[[VAL_6]] { // CHECK: scf.for %[[VAL_17:.*]] = %[[VAL_5]] to %[[VAL_13]] step %[[VAL_6]] {
// CHECK: scf.for %[[VAL_18:.*]] = %[[VAL_5]] to %[[VAL_10]] step %[[VAL_6]] { // CHECK: scf.for %[[VAL_18:.*]] = %[[VAL_5]] to %[[VAL_10]] step %[[VAL_6]] {
// CHECK: %[[VAL_19:.*]] = arith.muli %[[VAL_10]], %[[VAL_17]] : index // CHECK: %[[VAL_19:.*]] = arith.muli %[[VAL_10]], %[[VAL_17]] : index
// CHECK: %[[VAL_20:.*]] = arith.addi %[[VAL_19]], %[[VAL_18]] : index // CHECK: %[[VAL_20:.*]] = arith.addi %[[VAL_19]], %[[VAL_18]] : index
// CHECK: %[[VAL_21:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_20]]] : memref<?xindex> // CHECK: %[[VAL_21:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_20]]] : memref<?xindex>
// CHECK: %[[VAL_22:.*]] = arith.addi %[[VAL_20]], %[[VAL_6]] : index // CHECK: %[[VAL_22:.*]] = arith.addi %[[VAL_20]], %[[VAL_6]] : index
// CHECK: %[[VAL_23:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_22]]] : memref<?xindex> // CHECK: %[[VAL_23:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_22]]] : memref<?xindex>
Show All 36 Lines indexing_maps = [
affine_map<(i,j,k) -> (i,j,k)>, // A affine_map<(i,j,k) -> (i,j,k)>, // A
affine_map<(i,j,k) -> ()> // x (scalar out) affine_map<(i,j,k) -> ()> // x (scalar out)
], ],
iterator_types = ["reduction", "reduction", "reduction"], iterator_types = ["reduction", "reduction", "reduction"],
doc = "x += SUM_ijk A(i,j,k)" doc = "x += SUM_ijk A(i,j,k)"
} }
// CHECK-LABEL: func @sum_reduction( // CHECK-LABEL: func @sum_reduction(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<10x20x30xf32, #sparse_tensor.encoding<{{{.*}}}>>, // CHECK-SAME: %[[VAL_0:.*]]: tensor<10x20x30xf32, #sparse_tensor.encoding<{{{.*}}}>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<f32>) -> tensor<f32> { // CHECK-SAME: %[[VAL_1:.*]]: tensor<f32>) -> tensor<f32> {
// CHECK: %[[VAL_2:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_2:.*]] = arith.constant 0 : index
// CHECK: %[[VAL_3:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_4:.*]] = arith.constant 2 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 2 : index
// CHECK: %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_2]] : tensor<10x20x30xf32, #sparse_tensor.encoding<{{{.*}}>> // CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_2]] : tensor<10x20x30xf32, #sparse_tensor.encoding<{{{.*}}>>
// CHECK: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<10x20x30xf32, #sparse_tensor.encoding<{{{.*}}>> // CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<10x20x30xf32, #sparse_tensor.encoding<{{{.*}}>>
// CHECK: %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor<10x20x30xf32, #sparse_tensor.encoding<{{{.*}}}>> // CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor<10x20x30xf32, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<10x20x30xf32, #sparse_tensor.encoding<{{{.*}}}>> // CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<10x20x30xf32, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<f32> // CHECK-DAG: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<f32>
// CHECK: %[[VAL_10:.*]] = memref.alloc() : memref<f32> // CHECK-DAG: %[[VAL_10:.*]] = memref.alloc() : memref<f32>
// CHECK: memref.copy %[[VAL_9]], %[[VAL_10]] : memref<f32> to memref<f32> // CHECK: memref.copy %[[VAL_9]], %[[VAL_10]] : memref<f32> to memref<f32>
// CHECK: %[[VAL_11:.*]] = memref.load %[[VAL_10]][] : memref<f32> // CHECK: %[[VAL_11:.*]] = memref.load %[[VAL_10]][] : memref<f32>
// CHECK: %[[VAL_12:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_2]]] : memref<?xindex> // CHECK: %[[VAL_12:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_2]]] : memref<?xindex>
// CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_3]]] : memref<?xindex> // CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_3]]] : memref<?xindex>
// CHECK: %[[VAL_14:.*]] = scf.for %[[VAL_15:.*]] = %[[VAL_12]] to %[[VAL_13]] step %[[VAL_3]] iter_args(%[[VAL_16:.*]] = %[[VAL_11]]) -> (f32) { // CHECK: %[[VAL_14:.*]] = scf.for %[[VAL_15:.*]] = %[[VAL_12]] to %[[VAL_13]] step %[[VAL_3]] iter_args(%[[VAL_16:.*]] = %[[VAL_11]]) -> (f32) {
// CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_15]]] : memref<?xindex> // CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_15]]] : memref<?xindex>
// CHECK: %[[VAL_18:.*]] = arith.addi %[[VAL_15]], %[[VAL_3]] : index // CHECK: %[[VAL_18:.*]] = arith.addi %[[VAL_15]], %[[VAL_3]] : index
// CHECK: %[[VAL_19:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_18]]] : memref<?xindex> // CHECK: %[[VAL_19:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_18]]] : memref<?xindex>
Show All 34 Lines#trait_sum_reduction_inv = {
iterator_types = ["reduction", "reduction", "reduction"], iterator_types = ["reduction", "reduction", "reduction"],
doc = "x += SUM_i A(i,j,k) * b(i)" doc = "x += SUM_i A(i,j,k) * b(i)"
} }
// CHECK-LABEL: func @sum_reduction_inv( // CHECK-LABEL: func @sum_reduction_inv(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<?x?x?xf32>, // CHECK-SAME: %[[VAL_0:.*]]: tensor<?x?x?xf32>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<?xf32, #sparse_tensor.encoding<{{{.*}}}>> // CHECK-SAME: %[[VAL_1:.*]]: tensor<?xf32, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK-SAME: %[[VAL_2:.*]]: tensor<f32>) -> tensor<f32> { // CHECK-SAME: %[[VAL_2:.*]]: tensor<f32>) -> tensor<f32> {
// CHECK: %[[VAL_3:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_4:.*]] = arith.constant 2 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 2 : index
// CHECK: %[[VAL_5:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index
// CHECK: %[[VAL_6:.*]] = tensor.dim %[[VAL_0]], %[[VAL_3]] : tensor<?x?x?xf32> // CHECK-DAG: %[[VAL_6:.*]] = tensor.dim %[[VAL_0]], %[[VAL_3]] : tensor<?x?x?xf32>
// CHECK: %[[VAL_7:.*]] = tensor.dim %[[VAL_0]], %[[VAL_4]] : tensor<?x?x?xf32> // CHECK-DAG: %[[VAL_7:.*]] = tensor.dim %[[VAL_0]], %[[VAL_4]] : tensor<?x?x?xf32>
// CHECK: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_0]] : memref<?x?x?xf32> // CHECK-DAG: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_0]] : memref<?x?x?xf32>
// CHECK: %[[VAL_9:.*]] = tensor.dim %[[VAL_1]], %[[VAL_5]] : tensor<?xf32, #sparse_tensor.encoding<{{{.*}}}>> // CHECK-DAG: %[[VAL_9:.*]] = tensor.dim %[[VAL_1]], %[[VAL_5]] : tensor<?xf32, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xf32, #sparse_tensor.encoding<{{{.*}}}>> // CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xf32, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref<f32> // CHECK-DAG: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref<f32>
// CHECK: %[[VAL_12:.*]] = memref.alloc() : memref<f32> // CHECK-DAG: %[[VAL_12:.*]] = memref.alloc() : memref<f32>
// CHECK: memref.copy %[[VAL_11]], %[[VAL_12]] : memref<f32> to memref<f32> // CHECK: memref.copy %[[VAL_11]], %[[VAL_12]] : memref<f32> to memref<f32>
// CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_12]][] : memref<f32> // CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_12]][] : memref<f32>
// CHECK: %[[VAL_14:.*]] = scf.for %[[VAL_15:.*]] = %[[VAL_5]] to %[[VAL_9]] step %[[VAL_3]] iter_args(%[[VAL_16:.*]] = %[[VAL_13]]) -> (f32) { // CHECK: %[[VAL_14:.*]] = scf.for %[[VAL_15:.*]] = %[[VAL_5]] to %[[VAL_9]] step %[[VAL_3]] iter_args(%[[VAL_16:.*]] = %[[VAL_13]]) -> (f32) {
// CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_15]]] : memref<?xf32> // CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_15]]] : memref<?xf32>
// CHECK: %[[VAL_18:.*]] = scf.for %[[VAL_19:.*]] = %[[VAL_5]] to %[[VAL_6]] step %[[VAL_3]] iter_args(%[[VAL_20:.*]] = %[[VAL_16]]) -> (f32) { // CHECK: %[[VAL_18:.*]] = scf.for %[[VAL_19:.*]] = %[[VAL_5]] to %[[VAL_6]] step %[[VAL_3]] iter_args(%[[VAL_20:.*]] = %[[VAL_16]]) -> (f32) {
// CHECK: %[[VAL_21:.*]] = scf.for %[[VAL_22:.*]] = %[[VAL_5]] to %[[VAL_7]] step %[[VAL_3]] iter_args(%[[VAL_23:.*]] = %[[VAL_20]]) -> (f32) { // CHECK: %[[VAL_21:.*]] = scf.for %[[VAL_22:.*]] = %[[VAL_5]] to %[[VAL_7]] step %[[VAL_3]] iter_args(%[[VAL_23:.*]] = %[[VAL_20]]) -> (f32) {
// CHECK: %[[VAL_24:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_15]], %[[VAL_19]], %[[VAL_22]]] : memref<?x?x?xf32> // CHECK: %[[VAL_24:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_15]], %[[VAL_19]], %[[VAL_22]]] : memref<?x?x?xf32>
// CHECK: %[[VAL_25:.*]] = arith.mulf %[[VAL_24]], %[[VAL_17]] : f32 // CHECK: %[[VAL_25:.*]] = arith.mulf %[[VAL_24]], %[[VAL_17]] : f32
Show All 29 Lines indexing_maps = [
affine_map<(i,j,k) -> (k)>, // c affine_map<(i,j,k) -> (k)>, // c
affine_map<(i,j,k) -> (i,j,k)> // X (out) affine_map<(i,j,k) -> (i,j,k)> // X (out)
], ],
iterator_types = ["parallel", "parallel", "parallel"], iterator_types = ["parallel", "parallel", "parallel"],
doc = "X(i,j,k) = a(i) * b(j) * c(k)" doc = "X(i,j,k) = a(i) * b(j) * c(k)"
} }
// CHECK-LABEL: func @invariants( // CHECK-LABEL: func @invariants(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<10xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, // CHECK-SAME: %[[VAL_0:.*]]: tensor<10xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<20xf32>, // CHECK-SAME: %[[VAL_1:.*]]: tensor<20xf32>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<30xf32>, // CHECK-SAME: %[[VAL_2:.*]]: tensor<30xf32>,
// CHECK-SAME: %[[VAL_3:.*]]: tensor<10x20x30xf32>) -> tensor<10x20x30xf32> { // CHECK-SAME: %[[VAL_3:.*]]: tensor<10x20x30xf32>) -> tensor<10x20x30xf32> {
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 10 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 10 : index
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 20 : index // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 20 : index
// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 30 : index // CHECK-DAG: %[[VAL_6:.*]] = arith.constant 30 : index
// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_7:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_8:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_8:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<10xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32> // CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<10xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32>
// CHECK: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_1]] : memref<20xf32> // CHECK-DAG: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_1]] : memref<20xf32>
// CHECK: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref<30xf32> // CHECK-DAG: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref<30xf32>
// CHECK: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_3]] : memref<10x20x30xf32> // CHECK-DAG: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_3]] : memref<10x20x30xf32>
// CHECK: %[[VAL_13:.*]] = memref.alloc() : memref<10x20x30xf32> // CHECK-DAG: %[[VAL_13:.*]] = memref.alloc() : memref<10x20x30xf32>
// CHECK: memref.copy %[[VAL_12]], %[[VAL_13]] : memref<10x20x30xf32> to memref<10x20x30xf32> // CHECK: memref.copy %[[VAL_12]], %[[VAL_13]] : memref<10x20x30xf32> to memref<10x20x30xf32>
// CHECK: scf.for %[[VAL_14:.*]] = %[[VAL_7]] to %[[VAL_4]] step %[[VAL_8]] { // CHECK: scf.for %[[VAL_14:.*]] = %[[VAL_7]] to %[[VAL_4]] step %[[VAL_8]] {
// CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_14]]] : memref<?xf32> // CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_14]]] : memref<?xf32>
// CHECK: scf.for %[[VAL_16:.*]] = %[[VAL_7]] to %[[VAL_5]] step %[[VAL_8]] { // CHECK: scf.for %[[VAL_16:.*]] = %[[VAL_7]] to %[[VAL_5]] step %[[VAL_8]] {
// CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_16]]] : memref<20xf32> // CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_16]]] : memref<20xf32>
// CHECK: scf.for %[[VAL_18:.*]] = %[[VAL_7]] to %[[VAL_6]] step %[[VAL_8]] { // CHECK: scf.for %[[VAL_18:.*]] = %[[VAL_7]] to %[[VAL_6]] step %[[VAL_8]] {
// CHECK: %[[VAL_19:.*]] = arith.mulf %[[VAL_15]], %[[VAL_17]] : f32 // CHECK: %[[VAL_19:.*]] = arith.mulf %[[VAL_15]], %[[VAL_17]] : f32
// CHECK: %[[VAL_20:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_18]]] : memref<30xf32> // CHECK: %[[VAL_20:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_18]]] : memref<30xf32>
Show All 22 Lines

mlir/test/Dialect/SparseTensor/sparse_affine.mlir

Show All 15 Lines
// CHECK-LABEL: func @mul_inv_dense1d( // CHECK-LABEL: func @mul_inv_dense1d(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>>, // CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<4xf32>, // CHECK-SAME: %[[VAL_1:.*]]: tensor<4xf32>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32xf32>) -> tensor<32xf32> { // CHECK-SAME: %[[VAL_2:.*]]: tensor<32xf32>) -> tensor<32xf32> {
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 3 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 3 : index
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>> // CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>> // CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>> // CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<4xf32> // CHECK-DAG: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<4xf32>
// CHECK: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf32> // CHECK-DAG: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf32>
// CHECK: %[[VAL_11:.*]] = memref.alloc() : memref<32xf32> // CHECK-DAG: %[[VAL_11:.*]] = memref.alloc() : memref<32xf32>
// CHECK: memref.copy %[[VAL_10]], %[[VAL_11]] : memref<32xf32> to memref<32xf32> // CHECK: memref.copy %[[VAL_10]], %[[VAL_11]] : memref<32xf32> to memref<32xf32>
// CHECK: %[[VAL_12:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_4]]] : memref<4xf32> // CHECK: %[[VAL_12:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_4]]] : memref<4xf32>
// CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_3]]] : memref<?xindex> // CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_3]]] : memref<?xindex>
// CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_5]]] : memref<?xindex> // CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_5]]] : memref<?xindex>
// CHECK: scf.for %[[VAL_15:.*]] = %[[VAL_13]] to %[[VAL_14]] step %[[VAL_5]] { // CHECK: scf.for %[[VAL_15:.*]] = %[[VAL_13]] to %[[VAL_14]] step %[[VAL_5]] {
// CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_15]]] : memref<?xindex> // CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_15]]] : memref<?xindex>
// CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_16]]] : memref<32xf32> // CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_16]]] : memref<32xf32>
// CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_15]]] : memref<?xf32> // CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_15]]] : memref<?xf32>
Show All 30 Lines
// CHECK-LABEL: func @and_affine_dense1d( // CHECK-LABEL: func @and_affine_dense1d(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xi32, #sparse_tensor.encoding<{{{.*}}}>>, // CHECK-SAME: %[[VAL_0:.*]]: tensor<32xi32, #sparse_tensor.encoding<{{{.*}}}>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<34xi32>, // CHECK-SAME: %[[VAL_1:.*]]: tensor<34xi32>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32xi32>) -> tensor<32xi32> { // CHECK-SAME: %[[VAL_2:.*]]: tensor<32xi32>) -> tensor<32xi32> {
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 2 : index // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 2 : index
// CHECK: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32xi32, #sparse_tensor.encoding<{{{.*}}}>> // CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32xi32, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32xi32, #sparse_tensor.encoding<{{{.*}}}>> // CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32xi32, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xi32, #sparse_tensor.encoding<{{{.*}}}>> // CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xi32, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<34xi32> // CHECK-DAG: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<34xi32>
// CHECK: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xi32> // CHECK-DAG: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xi32>
// CHECK: %[[VAL_11:.*]] = memref.alloc() : memref<32xi32> // CHECK-DAG: %[[VAL_11:.*]] = memref.alloc() : memref<32xi32>
// CHECK: memref.copy %[[VAL_10]], %[[VAL_11]] : memref<32xi32> to memref<32xi32> // CHECK: memref.copy %[[VAL_10]], %[[VAL_11]] : memref<32xi32> to memref<32xi32>
// CHECK: %[[VAL_12:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_3]]] : memref<?xindex> // CHECK: %[[VAL_12:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_3]]] : memref<?xindex>
// CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex> // CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex>
// CHECK: scf.for %[[VAL_14:.*]] = %[[VAL_12]] to %[[VAL_13]] step %[[VAL_4]] { // CHECK: scf.for %[[VAL_14:.*]] = %[[VAL_12]] to %[[VAL_13]] step %[[VAL_4]] {
// CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_14]]] : memref<?xindex> // CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_14]]] : memref<?xindex>
// CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_14]]] : memref<?xi32> // CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_14]]] : memref<?xi32>
// CHECK: %[[VAL_17:.*]] = arith.addi %[[VAL_15]], %[[VAL_5]] : index // CHECK: %[[VAL_17:.*]] = arith.addi %[[VAL_15]], %[[VAL_5]] : index
// CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_17]]] : memref<34xi32> // CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_17]]] : memref<34xi32>
Show All 30 Lines
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>>, // CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<34x19xf64>, // CHECK-SAME: %[[VAL_1:.*]]: tensor<34x19xf64>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16xf64>) -> tensor<32x16xf64> { // CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16xf64>) -> tensor<32x16xf64> {
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 32 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 32 : index
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 2 : index // CHECK-DAG: %[[VAL_6:.*]] = arith.constant 2 : index
// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 3 : index // CHECK-DAG: %[[VAL_7:.*]] = arith.constant 3 : index
// CHECK: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>> // CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>> // CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>> // CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_1]] : memref<34x19xf64> // CHECK-DAG: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_1]] : memref<34x19xf64>
// CHECK: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf64> // CHECK-DAG: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf64>
// CHECK: %[[VAL_13:.*]] = memref.alloc() : memref<32x16xf64> // CHECK-DAG: %[[VAL_13:.*]] = memref.alloc() : memref<32x16xf64>
// CHECK: memref.copy %[[VAL_12]], %[[VAL_13]] : memref<32x16xf64> to memref<32x16xf64> // CHECK: memref.copy %[[VAL_12]], %[[VAL_13]] : memref<32x16xf64> to memref<32x16xf64>
// CHECK: scf.for %[[VAL_14:.*]] = %[[VAL_5]] to %[[VAL_4]] step %[[VAL_3]] { // CHECK: scf.for %[[VAL_14:.*]] = %[[VAL_5]] to %[[VAL_4]] step %[[VAL_3]] {
// CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_14]]] : memref<?xindex> // CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_14]]] : memref<?xindex>
// CHECK: %[[VAL_16:.*]] = arith.addi %[[VAL_14]], %[[VAL_3]] : index // CHECK: %[[VAL_16:.*]] = arith.addi %[[VAL_14]], %[[VAL_3]] : index
// CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_16]]] : memref<?xindex> // CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_16]]] : memref<?xindex>
// CHECK: scf.for %[[VAL_18:.*]] = %[[VAL_15]] to %[[VAL_17]] step %[[VAL_3]] { // CHECK: scf.for %[[VAL_18:.*]] = %[[VAL_15]] to %[[VAL_17]] step %[[VAL_3]] {
// CHECK: %[[VAL_19:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_18]]] : memref<?xindex> // CHECK: %[[VAL_19:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_18]]] : memref<?xindex>
// CHECK: %[[VAL_20:.*]] = memref.load %[[VAL_13]]{{\[}}%[[VAL_14]], %[[VAL_19]]] : memref<32x16xf64> // CHECK: %[[VAL_20:.*]] = memref.load %[[VAL_13]]{{\[}}%[[VAL_14]], %[[VAL_19]]] : memref<32x16xf64>
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mlir/test/Dialect/SparseTensor/sparse_kernels.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 \ // RUN: mlir-opt %s \
// RUN: --linalg-generalize-named-ops --linalg-fuse-elementwise-ops \ // RUN: --linalg-generalize-named-ops --linalg-fuse-elementwise-ops \
// RUN: --sparsification | FileCheck %s // RUN: --sparsification | FileCheck %s
#DCSR = #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }> #DCSR = #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>
// CHECK-LABEL: func @matmul1( // CHECK-LABEL: func @matmul1(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<10x20xf32, #sparse_tensor.encoding<{{{.*}}}>>, // CHECK-SAME: %[[VAL_0:.*]]: tensor<10x20xf32, #sparse_tensor.encoding<{{{.*}}}>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<20x30xf32>, // CHECK-SAME: %[[VAL_1:.*]]: tensor<20x30xf32>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<10x30xf32>) -> tensor<10x30xf32> { // CHECK-SAME: %[[VAL_2:.*]]: tensor<10x30xf32>) -> tensor<10x30xf32> {
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 30 : index // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 30 : index
// CHECK: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<10x20xf32, #sparse_tensor.encoding<{{{.*}}}>> // CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<10x20xf32, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<10x20xf32, #sparse_tensor.encoding<{{{.*}}}>> // CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<10x20xf32, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor<10x20xf32, #sparse_tensor.encoding<{{{.*}}}>> // CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor<10x20xf32, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_4]] : tensor<10x20xf32, #sparse_tensor.encoding<{{{.*}}}>> // CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_4]] : tensor<10x20xf32, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<10x20xf32, #sparse_tensor.encoding<{{{.*}}}>> // CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<10x20xf32, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_1]] : memref<20x30xf32> // CHECK-DAG: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_1]] : memref<20x30xf32>
// CHECK: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_2]] : memref<10x30xf32> // CHECK-DAG: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_2]] : memref<10x30xf32>
// CHECK: %[[VAL_13:.*]] = memref.alloc() : memref<10x30xf32> // CHECK-DAG: %[[VAL_13:.*]] = memref.alloc() : memref<10x30xf32>
// CHECK: memref.copy %[[VAL_12]], %[[VAL_13]] : memref<10x30xf32> to memref<10x30xf32> // CHECK: memref.copy %[[VAL_12]], %[[VAL_13]] : memref<10x30xf32> to memref<10x30xf32>
// CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_3]]] : memref<?xindex> // CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_3]]] : memref<?xindex>
// CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex> // CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex>
// CHECK: scf.for %[[VAL_16:.*]] = %[[VAL_14]] to %[[VAL_15]] step %[[VAL_4]] { // CHECK: scf.for %[[VAL_16:.*]] = %[[VAL_14]] to %[[VAL_15]] step %[[VAL_4]] {
// CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_16]]] : memref<?xindex> // CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_16]]] : memref<?xindex>
// CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_16]]] : memref<?xindex> // CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_16]]] : memref<?xindex>
// CHECK: %[[VAL_19:.*]] = arith.addi %[[VAL_16]], %[[VAL_4]] : index // CHECK: %[[VAL_19:.*]] = arith.addi %[[VAL_16]], %[[VAL_4]] : index
// CHECK: %[[VAL_20:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_19]]] : memref<?xindex> // CHECK: %[[VAL_20:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_19]]] : memref<?xindex>
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// CHECK-LABEL: func @conv2d( // CHECK-LABEL: func @conv2d(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<8x8xi32>, // CHECK-SAME: %[[VAL_0:.*]]: tensor<8x8xi32>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<3x3xi32, #sparse_tensor.encoding<{{{.*}}}>>, // CHECK-SAME: %[[VAL_1:.*]]: tensor<3x3xi32, #sparse_tensor.encoding<{{{.*}}}>>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<6x6xi32>) -> tensor<6x6xi32> { // CHECK-SAME: %[[VAL_2:.*]]: tensor<6x6xi32>) -> tensor<6x6xi32> {
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 6 : index // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 6 : index
// CHECK: %[[VAL_6:.*]] = bufferization.to_memref %[[VAL_0]] : memref<8x8xi32> // CHECK-DAG: %[[VAL_6:.*]] = bufferization.to_memref %[[VAL_0]] : memref<8x8xi32>
// CHECK: %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_3]] : tensor<3x3xi32, #sparse_tensor.encoding<{{{.*}}}>> // CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_3]] : tensor<3x3xi32, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_3]] : tensor<3x3xi32, #sparse_tensor.encoding<{{{.*}}}>> // CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_3]] : tensor<3x3xi32, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_4]] : tensor<3x3xi32, #sparse_tensor.encoding<{{{.*}}}>> // CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_4]] : tensor<3x3xi32, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_4]] : tensor<3x3xi32, #sparse_tensor.encoding<{{{.*}}}>> // CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_4]] : tensor<3x3xi32, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<3x3xi32, #sparse_tensor.encoding<{{{.*}}}>> // CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<3x3xi32, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_2]] : memref<6x6xi32> // CHECK-DAG: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_2]] : memref<6x6xi32>
// CHECK: %[[VAL_13:.*]] = memref.alloc() : memref<6x6xi32> // CHECK-DAG: %[[VAL_13:.*]] = memref.alloc() : memref<6x6xi32>
// CHECK: memref.copy %[[VAL_12]], %[[VAL_13]] : memref<6x6xi32> to memref<6x6xi32> // CHECK: memref.copy %[[VAL_12]], %[[VAL_13]] : memref<6x6xi32> to memref<6x6xi32>
// CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_3]]] : memref<?xindex> // CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_3]]] : memref<?xindex>
// CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_4]]] : memref<?xindex> // CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_4]]] : memref<?xindex>
// CHECK: scf.for %[[VAL_16:.*]] = %[[VAL_14]] to %[[VAL_15]] step %[[VAL_4]] { // CHECK: scf.for %[[VAL_16:.*]] = %[[VAL_14]] to %[[VAL_15]] step %[[VAL_4]] {
// CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_16]]] : memref<?xindex> // CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_16]]] : memref<?xindex>
// CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_16]]] : memref<?xindex> // CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_16]]] : memref<?xindex>
// CHECK: %[[VAL_19:.*]] = arith.addi %[[VAL_16]], %[[VAL_4]] : index // CHECK: %[[VAL_19:.*]] = arith.addi %[[VAL_16]], %[[VAL_4]] : index
// CHECK: %[[VAL_20:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_19]]] : memref<?xindex> // CHECK: %[[VAL_20:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_19]]] : memref<?xindex>
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// CHECK-LABEL: func @quantized_matmul( // CHECK-LABEL: func @quantized_matmul(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<5x3xi8>, // CHECK-SAME: %[[VAL_0:.*]]: tensor<5x3xi8>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<3x6xi8, #sparse_tensor.encoding<{{{.*}}}>>, // CHECK-SAME: %[[VAL_1:.*]]: tensor<3x6xi8, #sparse_tensor.encoding<{{{.*}}}>>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<5x6xi64>) -> tensor<5x6xi64> { // CHECK-SAME: %[[VAL_2:.*]]: tensor<5x6xi64>) -> tensor<5x6xi64> {
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 2 : i64 // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 2 : i64
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 5 : index // CHECK-DAG: %[[VAL_6:.*]] = arith.constant 5 : index
// CHECK: %[[VAL_7:.*]] = bufferization.to_memref %[[VAL_0]] : memref<5x3xi8> // CHECK-DAG: %[[VAL_7:.*]] = bufferization.to_memref %[[VAL_0]] : memref<5x3xi8>
// CHECK: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_4]] : tensor<3x6xi8, #sparse_tensor.encoding<{{{.*}}}>> // CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_4]] : tensor<3x6xi8, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_4]] : tensor<3x6xi8, #sparse_tensor.encoding<{{{.*}}}>> // CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_4]] : tensor<3x6xi8, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: %[[VAL_10:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_5]] : tensor<3x6xi8, #sparse_tensor.encoding<{{{.*}}}>> // CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_5]] : tensor<3x6xi8, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: %[[VAL_11:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_5]] : tensor<3x6xi8, #sparse_tensor.encoding<{{{.*}}}>> // CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_5]] : tensor<3x6xi8, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: %[[VAL_12:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<3x6xi8, #sparse_tensor.encoding<{{{.*}}}>> // CHECK-DAG: %[[VAL_12:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<3x6xi8, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_2]] : memref<5x6xi64> // CHECK-DAG: %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_2]] : memref<5x6xi64>
// CHECK: %[[VAL_14:.*]] = memref.alloc() : memref<5x6xi64> // CHECK-DAG: %[[VAL_14:.*]] = memref.alloc() : memref<5x6xi64>
// CHECK: memref.copy %[[VAL_13]], %[[VAL_14]] : memref<5x6xi64> to memref<5x6xi64> // CHECK: memref.copy %[[VAL_13]], %[[VAL_14]] : memref<5x6xi64> to memref<5x6xi64>
// CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_4]]] : memref<?xindex> // CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_4]]] : memref<?xindex>
// CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_5]]] : memref<?xindex> // CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_5]]] : memref<?xindex>
// CHECK: scf.for %[[VAL_17:.*]] = %[[VAL_15]] to %[[VAL_16]] step %[[VAL_5]] { // CHECK: scf.for %[[VAL_17:.*]] = %[[VAL_15]] to %[[VAL_16]] step %[[VAL_5]] {
// CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_17]]] : memref<?xindex> // CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_17]]] : memref<?xindex>
// CHECK: %[[VAL_19:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_17]]] : memref<?xindex> // CHECK: %[[VAL_19:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_17]]] : memref<?xindex>
// CHECK: %[[VAL_20:.*]] = arith.addi %[[VAL_17]], %[[VAL_5]] : index // CHECK: %[[VAL_20:.*]] = arith.addi %[[VAL_17]], %[[VAL_5]] : index
// CHECK: %[[VAL_21:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_20]]] : memref<?xindex> // CHECK: %[[VAL_21:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_20]]] : memref<?xindex>
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mlir/test/Dialect/SparseTensor/sparse_lower.mlir

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// CHECK-HIR-LABEL: func @matvec( // CHECK-HIR-LABEL: func @matvec(
// CHECK-HIR-SAME: %[[VAL_0:.*]]: tensor<32x64xf64, #sparse_tensor.encoding<{{{.*}}}>> // CHECK-HIR-SAME: %[[VAL_0:.*]]: tensor<32x64xf64, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK-HIR-SAME: %[[VAL_1:.*]]: tensor<64xf64>, // CHECK-HIR-SAME: %[[VAL_1:.*]]: tensor<64xf64>,
// CHECK-HIR-SAME: %[[VAL_2:.*]]: tensor<32xf64>) -> tensor<32xf64> { // CHECK-HIR-SAME: %[[VAL_2:.*]]: tensor<32xf64>) -> tensor<32xf64> {
// CHECK-HIR-DAG: %[[VAL_3:.*]] = arith.constant 32 : index // CHECK-HIR-DAG: %[[VAL_3:.*]] = arith.constant 32 : index
// CHECK-HIR-DAG: %[[VAL_4:.*]] = arith.constant 0 : index // CHECK-HIR-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
// CHECK-HIR-DAG: %[[VAL_5:.*]] = arith.constant 1 : index // CHECK-HIR-DAG: %[[VAL_5:.*]] = arith.constant 1 : index
// CHECK-HIR: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_5]] : tensor<32x64xf64, #sparse_tensor.encoding<{{{.*}}}>> // CHECK-HIR-DAG: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_5]] : tensor<32x64xf64, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK-HIR: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_5]] : tensor<32x64xf64, #sparse_tensor.encoding<{{{.*}}}>> // CHECK-HIR-DAG: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_5]] : tensor<32x64xf64, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK-HIR: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x64xf64, #sparse_tensor.encoding<{{{.*}}}>> // CHECK-HIR-DAG: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x64xf64, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK-HIR: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<64xf64> // CHECK-HIR-DAG: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<64xf64>
// CHECK-HIR: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf64> // CHECK-HIR-DAG: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf64>
// CHECK-HIR: %[[VAL_11:.*]] = memref.alloc() : memref<32xf64> // CHECK-HIR-DAG: %[[VAL_11:.*]] = memref.alloc() : memref<32xf64>
// CHECK-HIR: memref.copy %[[VAL_10]], %[[VAL_11]] : memref<32xf64> to memref<32xf64> // CHECK-HIR: memref.copy %[[VAL_10]], %[[VAL_11]] : memref<32xf64> to memref<32xf64>
// CHECK-HIR: scf.for %[[VAL_12:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] { // CHECK-HIR: scf.for %[[VAL_12:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] {
// CHECK-HIR-DAG: %[[VAL_13:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_12]]] : memref<?xindex> // CHECK-HIR-DAG: %[[VAL_13:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_12]]] : memref<?xindex>
// CHECK-HIR-DAG: %[[VAL_14:.*]] = arith.addi %[[VAL_12]], %[[VAL_5]] : index // CHECK-HIR-DAG: %[[VAL_14:.*]] = arith.addi %[[VAL_12]], %[[VAL_5]] : index
// CHECK-HIR-DAG: %[[VAL_15:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_14]]] : memref<?xindex> // CHECK-HIR-DAG: %[[VAL_15:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_14]]] : memref<?xindex>
// CHECK-HIR-DAG: %[[VAL_16:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_12]]] : memref<32xf64> // CHECK-HIR-DAG: %[[VAL_16:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_12]]] : memref<32xf64>
// CHECK-HIR: %[[VAL_17:.*]] = scf.for %[[VAL_18:.*]] = %[[VAL_13]] to %[[VAL_15]] step %[[VAL_5]] iter_args(%[[VAL_19:.*]] = %[[VAL_16]]) -> (f64) { // CHECK-HIR: %[[VAL_17:.*]] = scf.for %[[VAL_18:.*]] = %[[VAL_13]] to %[[VAL_15]] step %[[VAL_5]] iter_args(%[[VAL_19:.*]] = %[[VAL_16]]) -> (f64) {
// CHECK-HIR: %[[VAL_20:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_18]]] : memref<?xindex> // CHECK-HIR: %[[VAL_20:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_18]]] : memref<?xindex>
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// CHECK-MIR-LABEL: func @matvec( // CHECK-MIR-LABEL: func @matvec(
// CHECK-MIR-SAME: %[[VAL_0:.*]]: !llvm.ptr<i8>, // CHECK-MIR-SAME: %[[VAL_0:.*]]: !llvm.ptr<i8>,
// CHECK-MIR-SAME: %[[VAL_1:.*]]: tensor<64xf64>, // CHECK-MIR-SAME: %[[VAL_1:.*]]: tensor<64xf64>,
// CHECK-MIR-SAME: %[[VAL_2:.*]]: tensor<32xf64>) -> tensor<32xf64> { // CHECK-MIR-SAME: %[[VAL_2:.*]]: tensor<32xf64>) -> tensor<32xf64> {
// CHECK-MIR-DAG: %[[VAL_3:.*]] = arith.constant 32 : index // CHECK-MIR-DAG: %[[VAL_3:.*]] = arith.constant 32 : index
// CHECK-MIR-DAG: %[[VAL_4:.*]] = arith.constant 0 : index // CHECK-MIR-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
// CHECK-MIR-DAG: %[[VAL_5:.*]] = arith.constant 1 : index // CHECK-MIR-DAG: %[[VAL_5:.*]] = arith.constant 1 : index
// CHECK-MIR: %[[VAL_6:.*]] = call @sparsePointers(%[[VAL_0]], %[[VAL_5]]) : (!llvm.ptr<i8>, index) -> memref<?xindex> // CHECK-MIR-DAG: %[[VAL_6:.*]] = call @sparsePointers(%[[VAL_0]], %[[VAL_5]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
// CHECK-MIR: %[[VAL_7:.*]] = call @sparseIndices(%[[VAL_0]], %[[VAL_5]]) : (!llvm.ptr<i8>, index) -> memref<?xindex> // CHECK-MIR-DAG: %[[VAL_7:.*]] = call @sparseIndices(%[[VAL_0]], %[[VAL_5]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
// CHECK-MIR: %[[VAL_8:.*]] = call @sparseValuesF64(%[[VAL_0]]) : (!llvm.ptr<i8>) -> memref<?xf64> // CHECK-MIR-DAG: %[[VAL_8:.*]] = call @sparseValuesF64(%[[VAL_0]]) : (!llvm.ptr<i8>) -> memref<?xf64>
// CHECK-MIR: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<64xf64> // CHECK-MIR-DAG: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<64xf64>
// CHECK-MIR: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf64> // CHECK-MIR-DAG: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf64>
// CHECK-MIR: %[[VAL_11:.*]] = memref.alloc() : memref<32xf64> // CHECK-MIR-DAG: %[[VAL_11:.*]] = memref.alloc() : memref<32xf64>
// CHECK-MIR: memref.copy %[[VAL_10]], %[[VAL_11]] : memref<32xf64> to memref<32xf64> // CHECK-MIR: memref.copy %[[VAL_10]], %[[VAL_11]] : memref<32xf64> to memref<32xf64>
// CHECK-MIR: scf.for %[[VAL_14:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] { // CHECK-MIR: scf.for %[[VAL_14:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] {
// CHECK-MIR-DAG: %[[VAL_15:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_14]]] : memref<?xindex> // CHECK-MIR-DAG: %[[VAL_15:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_14]]] : memref<?xindex>
// CHECK-MIR-DAG: %[[VAL_16:.*]] = arith.addi %[[VAL_14]], %[[VAL_5]] : index // CHECK-MIR-DAG: %[[VAL_16:.*]] = arith.addi %[[VAL_14]], %[[VAL_5]] : index
// CHECK-MIR-DAG: %[[VAL_17:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_16]]] : memref<?xindex> // CHECK-MIR-DAG: %[[VAL_17:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_16]]] : memref<?xindex>
// CHECK-MIR-DAG: %[[VAL_18:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_14]]] : memref<32xf64> // CHECK-MIR-DAG: %[[VAL_18:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_14]]] : memref<32xf64>
// CHECK-MIR: %[[VAL_19:.*]] = scf.for %[[VAL_20:.*]] = %[[VAL_15]] to %[[VAL_17]] step %[[VAL_5]] iter_args(%[[VAL_21:.*]] = %[[VAL_18]]) -> (f64) { // CHECK-MIR: %[[VAL_19:.*]] = scf.for %[[VAL_20:.*]] = %[[VAL_15]] to %[[VAL_17]] step %[[VAL_5]] iter_args(%[[VAL_21:.*]] = %[[VAL_18]]) -> (f64) {
// CHECK-MIR: %[[VAL_22:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_20]]] : memref<?xindex> // CHECK-MIR: %[[VAL_22:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_20]]] : memref<?xindex>
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// CHECK-LIR-LABEL: func @matvec( // CHECK-LIR-LABEL: func @matvec(
// CHECK-LIR-SAME: %[[VAL_0:.*]]: !llvm.ptr<i8>, // CHECK-LIR-SAME: %[[VAL_0:.*]]: !llvm.ptr<i8>,
// CHECK-LIR-SAME: %[[VAL_1:.*]]: memref<64xf64>, // CHECK-LIR-SAME: %[[VAL_1:.*]]: memref<64xf64>,
// CHECK-LIR-SAME: %[[VAL_2:.*]]: memref<32xf64>) -> memref<32xf64> { // CHECK-LIR-SAME: %[[VAL_2:.*]]: memref<32xf64>) -> memref<32xf64> {
// CHECK-LIR-DAG: %[[VAL_3:.*]] = arith.constant 32 : index // CHECK-LIR-DAG: %[[VAL_3:.*]] = arith.constant 32 : index
// CHECK-LIR-DAG: %[[VAL_4:.*]] = arith.constant 0 : index // CHECK-LIR-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
// CHECK-LIR-DAG: %[[VAL_5:.*]] = arith.constant 1 : index // CHECK-LIR-DAG: %[[VAL_5:.*]] = arith.constant 1 : index
// CHECK-LIR: %[[VAL_6:.*]] = call @sparsePointers(%[[VAL_0]], %[[VAL_5]]) : (!llvm.ptr<i8>, index) -> memref<?xindex> // CHECK-LIR-DAG: %[[VAL_6:.*]] = call @sparsePointers(%[[VAL_0]], %[[VAL_5]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
// CHECK-LIR: %[[VAL_7:.*]] = call @sparseIndices(%[[VAL_0]], %[[VAL_5]]) : (!llvm.ptr<i8>, index) -> memref<?xindex> // CHECK-LIR-DAG: %[[VAL_7:.*]] = call @sparseIndices(%[[VAL_0]], %[[VAL_5]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
// CHECK-LIR: %[[VAL_8:.*]] = call @sparseValuesF64(%[[VAL_0]]) : (!llvm.ptr<i8>) -> memref<?xf64> // CHECK-LIR-DAG: %[[VAL_8:.*]] = call @sparseValuesF64(%[[VAL_0]]) : (!llvm.ptr<i8>) -> memref<?xf64>
// CHECK-LIR: %[[VAL_9:.*]] = memref.alloc() : memref<32xf64> // CHECK-LIR-DAG: %[[VAL_9:.*]] = memref.alloc() : memref<32xf64>
// CHECK-LIR: memref.copy %[[VAL_2]], %[[VAL_9]] : memref<32xf64> to memref<32xf64> // CHECK-LIR: memref.copy %[[VAL_2]], %[[VAL_9]] : memref<32xf64> to memref<32xf64>
// CHECK-LIR: scf.for %[[VAL_12:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] { // CHECK-LIR: scf.for %[[VAL_12:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] {
// CHECK-LIR-DAG: %[[VAL_13:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_12]]] : memref<?xindex> // CHECK-LIR-DAG: %[[VAL_13:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_12]]] : memref<?xindex>
// CHECK-LIR-DAG: %[[VAL_14:.*]] = arith.addi %[[VAL_12]], %[[VAL_5]] : index // CHECK-LIR-DAG: %[[VAL_14:.*]] = arith.addi %[[VAL_12]], %[[VAL_5]] : index
// CHECK-LIR-DAG: %[[VAL_15:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_14]]] : memref<?xindex> // CHECK-LIR-DAG: %[[VAL_15:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_14]]] : memref<?xindex>
// CHECK-LIR-DAG: %[[VAL_16:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_12]]] : memref<32xf64> // CHECK-LIR-DAG: %[[VAL_16:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_12]]] : memref<32xf64>
// CHECK-LIR: %[[VAL_17:.*]] = scf.for %[[VAL_18:.*]] = %[[VAL_13]] to %[[VAL_15]] step %[[VAL_5]] iter_args(%[[VAL_19:.*]] = %[[VAL_16]]) -> (f64) { // CHECK-LIR: %[[VAL_17:.*]] = scf.for %[[VAL_18:.*]] = %[[VAL_13]] to %[[VAL_15]] step %[[VAL_5]] iter_args(%[[VAL_19:.*]] = %[[VAL_16]]) -> (f64) {
// CHECK-LIR: %[[VAL_20:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_18]]] : memref<?xindex> // CHECK-LIR: %[[VAL_20:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_18]]] : memref<?xindex>
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mlir/test/Dialect/SparseTensor/sparse_lower_col.mlir

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// CHECK-HIR-LABEL: func @matvec( // CHECK-HIR-LABEL: func @matvec(
// CHECK-HIR-SAME: %[[VAL_0:.*]]: tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)>, pointerBitWidth = 0, indexBitWidth = 0 }>>, // CHECK-HIR-SAME: %[[VAL_0:.*]]: tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)>, pointerBitWidth = 0, indexBitWidth = 0 }>>,
// CHECK-HIR-SAME: %[[VAL_1:.*]]: tensor<64xf64>, // CHECK-HIR-SAME: %[[VAL_1:.*]]: tensor<64xf64>,
// CHECK-HIR-SAME: %[[VAL_2:.*]]: tensor<32xf64>) -> tensor<32xf64> { // CHECK-HIR-SAME: %[[VAL_2:.*]]: tensor<32xf64>) -> tensor<32xf64> {
// CHECK-HIR-DAG: %[[VAL_3:.*]] = arith.constant 64 : index // CHECK-HIR-DAG: %[[VAL_3:.*]] = arith.constant 64 : index
// CHECK-HIR-DAG: %[[VAL_4:.*]] = arith.constant 0 : index // CHECK-HIR-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
// CHECK-HIR-DAG: %[[VAL_5:.*]] = arith.constant 1 : index // CHECK-HIR-DAG: %[[VAL_5:.*]] = arith.constant 1 : index
// CHECK-HIR: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_5]] : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)>, pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-HIR-DAG: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_5]] : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)>, pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK-HIR: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_5]] : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)>, pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-HIR-DAG: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_5]] : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)>, pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK-HIR: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)>, pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf64> // CHECK-HIR-DAG: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)>, pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf64>
// CHECK-HIR: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<64xf64> // CHECK-HIR-DAG: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<64xf64>
// CHECK-HIR: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf64> // CHECK-HIR-DAG: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf64>
// CHECK-HIR: %[[VAL_11:.*]] = memref.alloc() : memref<32xf64> // CHECK-HIR-DAG: %[[VAL_11:.*]] = memref.alloc() : memref<32xf64>
// CHECK-HIR: memref.copy %[[VAL_10]], %[[VAL_11]] : memref<32xf64> to memref<32xf64> // CHECK-HIR: memref.copy %[[VAL_10]], %[[VAL_11]] : memref<32xf64> to memref<32xf64>
// CHECK-HIR: scf.for %[[VAL_12:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] { // CHECK-HIR: scf.for %[[VAL_12:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] {
// CHECK-HIR: %[[VAL_13:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_12]]] : memref<64xf64> // CHECK-HIR: %[[VAL_13:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_12]]] : memref<64xf64>
// CHECK-HIR: %[[VAL_14:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_12]]] : memref<?xindex> // CHECK-HIR: %[[VAL_14:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_12]]] : memref<?xindex>
// CHECK-HIR: %[[VAL_15:.*]] = arith.addi %[[VAL_12]], %[[VAL_5]] : index // CHECK-HIR: %[[VAL_15:.*]] = arith.addi %[[VAL_12]], %[[VAL_5]] : index
// CHECK-HIR: %[[VAL_16:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_15]]] : memref<?xindex> // CHECK-HIR: %[[VAL_16:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_15]]] : memref<?xindex>
// CHECK-HIR: scf.for %[[VAL_17:.*]] = %[[VAL_14]] to %[[VAL_16]] step %[[VAL_5]] { // CHECK-HIR: scf.for %[[VAL_17:.*]] = %[[VAL_14]] to %[[VAL_16]] step %[[VAL_5]] {
// CHECK-HIR: %[[VAL_18:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_17]]] : memref<?xindex> // CHECK-HIR: %[[VAL_18:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_17]]] : memref<?xindex>
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// CHECK-MIR-LABEL: func @matvec( // CHECK-MIR-LABEL: func @matvec(
// CHECK-MIR-SAME: %[[VAL_0:.*]]: !llvm.ptr<i8>, // CHECK-MIR-SAME: %[[VAL_0:.*]]: !llvm.ptr<i8>,
// CHECK-MIR-SAME: %[[VAL_1:.*]]: tensor<64xf64>, // CHECK-MIR-SAME: %[[VAL_1:.*]]: tensor<64xf64>,
// CHECK-MIR-SAME: %[[VAL_2:.*]]: tensor<32xf64>) -> tensor<32xf64> { // CHECK-MIR-SAME: %[[VAL_2:.*]]: tensor<32xf64>) -> tensor<32xf64> {
// CHECK-MIR-DAG: %[[VAL_3:.*]] = arith.constant 64 : index // CHECK-MIR-DAG: %[[VAL_3:.*]] = arith.constant 64 : index
// CHECK-MIR-DAG: %[[VAL_5:.*]] = arith.constant 0 : index // CHECK-MIR-DAG: %[[VAL_5:.*]] = arith.constant 0 : index
// CHECK-MIR-DAG: %[[VAL_6:.*]] = arith.constant 1 : index // CHECK-MIR-DAG: %[[VAL_6:.*]] = arith.constant 1 : index
// CHECK-MIR: %[[VAL_7:.*]] = call @sparsePointers(%[[VAL_0]], %[[VAL_6]]) : (!llvm.ptr<i8>, index) -> memref<?xindex> // CHECK-MIR-DAG: %[[VAL_7:.*]] = call @sparsePointers(%[[VAL_0]], %[[VAL_6]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
// CHECK-MIR: %[[VAL_8:.*]] = call @sparseIndices(%[[VAL_0]], %[[VAL_6]]) : (!llvm.ptr<i8>, index) -> memref<?xindex> // CHECK-MIR-DAG: %[[VAL_8:.*]] = call @sparseIndices(%[[VAL_0]], %[[VAL_6]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
// CHECK-MIR: %[[VAL_9:.*]] = call @sparseValuesF64(%[[VAL_0]]) : (!llvm.ptr<i8>) -> memref<?xf64> // CHECK-MIR-DAG: %[[VAL_9:.*]] = call @sparseValuesF64(%[[VAL_0]]) : (!llvm.ptr<i8>) -> memref<?xf64>
// CHECK-MIR: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_1]] : memref<64xf64> // CHECK-MIR-DAG: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_1]] : memref<64xf64>
// CHECK-MIR: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf64> // CHECK-MIR-DAG: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf64>
// CHECK-MIR: %[[VAL_12:.*]] = memref.alloc() : memref<32xf64> // CHECK-MIR-DAG: %[[VAL_12:.*]] = memref.alloc() : memref<32xf64>
// CHECK-MIR: memref.copy %[[VAL_11]], %[[VAL_12]] : memref<32xf64> to memref<32xf64> // CHECK-MIR: memref.copy %[[VAL_11]], %[[VAL_12]] : memref<32xf64> to memref<32xf64>
// CHECK-MIR: scf.for %[[VAL_15:.*]] = %[[VAL_5]] to %[[VAL_3]] step %[[VAL_6]] { // CHECK-MIR: scf.for %[[VAL_15:.*]] = %[[VAL_5]] to %[[VAL_3]] step %[[VAL_6]] {
// CHECK-MIR: %[[VAL_16:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_15]]] : memref<64xf64> // CHECK-MIR: %[[VAL_16:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_15]]] : memref<64xf64>
// CHECK-MIR: %[[VAL_17:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_15]]] : memref<?xindex> // CHECK-MIR: %[[VAL_17:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_15]]] : memref<?xindex>
// CHECK-MIR: %[[VAL_18:.*]] = arith.addi %[[VAL_15]], %[[VAL_6]] : index // CHECK-MIR: %[[VAL_18:.*]] = arith.addi %[[VAL_15]], %[[VAL_6]] : index
// CHECK-MIR: %[[VAL_19:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_18]]] : memref<?xindex> // CHECK-MIR: %[[VAL_19:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_18]]] : memref<?xindex>
// CHECK-MIR: scf.for %[[VAL_20:.*]] = %[[VAL_17]] to %[[VAL_19]] step %[[VAL_6]] { // CHECK-MIR: scf.for %[[VAL_20:.*]] = %[[VAL_17]] to %[[VAL_19]] step %[[VAL_6]] {
// CHECK-MIR: %[[VAL_21:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_20]]] : memref<?xindex> // CHECK-MIR: %[[VAL_21:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_20]]] : memref<?xindex>
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// CHECK-LIR-LABEL: func @matvec( // CHECK-LIR-LABEL: func @matvec(
// CHECK-LIR-SAME: %[[VAL_0:.*]]: !llvm.ptr<i8>, // CHECK-LIR-SAME: %[[VAL_0:.*]]: !llvm.ptr<i8>,
// CHECK-LIR-SAME: %[[VAL_1:.*]]: memref<64xf64>, // CHECK-LIR-SAME: %[[VAL_1:.*]]: memref<64xf64>,
// CHECK-LIR-SAME: %[[VAL_2:.*]]: memref<32xf64>) -> memref<32xf64> { // CHECK-LIR-SAME: %[[VAL_2:.*]]: memref<32xf64>) -> memref<32xf64> {
// CHECK-LIR-DAG: %[[VAL_3:.*]] = arith.constant 64 : index // CHECK-LIR-DAG: %[[VAL_3:.*]] = arith.constant 64 : index
// CHECK-LIR-DAG: %[[VAL_5:.*]] = arith.constant 0 : index // CHECK-LIR-DAG: %[[VAL_5:.*]] = arith.constant 0 : index
// CHECK-LIR-DAG: %[[VAL_6:.*]] = arith.constant 1 : index // CHECK-LIR-DAG: %[[VAL_6:.*]] = arith.constant 1 : index
// CHECK-LIR: %[[VAL_7:.*]] = call @sparsePointers(%[[VAL_0]], %[[VAL_6]]) : (!llvm.ptr<i8>, index) -> memref<?xindex> // CHECK-LIR-DAG: %[[VAL_7:.*]] = call @sparsePointers(%[[VAL_0]], %[[VAL_6]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
// CHECK-LIR: %[[VAL_8:.*]] = call @sparseIndices(%[[VAL_0]], %[[VAL_6]]) : (!llvm.ptr<i8>, index) -> memref<?xindex> // CHECK-LIR-DAG: %[[VAL_8:.*]] = call @sparseIndices(%[[VAL_0]], %[[VAL_6]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
// CHECK-LIR: %[[VAL_9:.*]] = call @sparseValuesF64(%[[VAL_0]]) : (!llvm.ptr<i8>) -> memref<?xf64> // CHECK-LIR-DAG: %[[VAL_9:.*]] = call @sparseValuesF64(%[[VAL_0]]) : (!llvm.ptr<i8>) -> memref<?xf64>
// CHECK-LIR: %[[VAL_10:.*]] = memref.alloc() : memref<32xf64> // CHECK-LIR-DAG: %[[VAL_10:.*]] = memref.alloc() : memref<32xf64>
// CHECK-LIR: memref.copy %[[VAL_2]], %[[VAL_10]] : memref<32xf64> to memref<32xf64> // CHECK-LIR: memref.copy %[[VAL_2]], %[[VAL_10]] : memref<32xf64> to memref<32xf64>
// CHECK-LIR: scf.for %[[VAL_13:.*]] = %[[VAL_5]] to %[[VAL_3]] step %[[VAL_6]] { // CHECK-LIR: scf.for %[[VAL_13:.*]] = %[[VAL_5]] to %[[VAL_3]] step %[[VAL_6]] {
// CHECK-LIR: %[[VAL_14:.*]] = memref.load %[[VAL_1]]{{\[}}%[[VAL_13]]] : memref<64xf64> // CHECK-LIR: %[[VAL_14:.*]] = memref.load %[[VAL_1]]{{\[}}%[[VAL_13]]] : memref<64xf64>
// CHECK-LIR: %[[VAL_15:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_13]]] : memref<?xindex> // CHECK-LIR: %[[VAL_15:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_13]]] : memref<?xindex>
// CHECK-LIR: %[[VAL_16:.*]] = arith.addi %[[VAL_13]], %[[VAL_6]] : index // CHECK-LIR: %[[VAL_16:.*]] = arith.addi %[[VAL_13]], %[[VAL_6]] : index
// CHECK-LIR: %[[VAL_17:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_16]]] : memref<?xindex> // CHECK-LIR: %[[VAL_17:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_16]]] : memref<?xindex>
// CHECK-LIR: scf.for %[[VAL_18:.*]] = %[[VAL_15]] to %[[VAL_17]] step %[[VAL_6]] { // CHECK-LIR: scf.for %[[VAL_18:.*]] = %[[VAL_15]] to %[[VAL_17]] step %[[VAL_6]] {
// CHECK-LIR: %[[VAL_19:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_18]]] : memref<?xindex> // CHECK-LIR: %[[VAL_19:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_18]]] : memref<?xindex>
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mlir/test/Dialect/SparseTensor/sparse_nd.mlir

Show All 18 Lines iterator_types = ["parallel", "parallel", "parallel", "parallel",
"parallel", "parallel", "parallel", "parallel"], "parallel", "parallel", "parallel", "parallel"],
doc = "X(i,j,k,l,m,n,o,p) = A(i,j,k,l,m,n,o,p) * B(p,o,n,m,l,k,j,i)" doc = "X(i,j,k,l,m,n,o,p) = A(i,j,k,l,m,n,o,p) * B(p,o,n,m,l,k,j,i)"
} }
// CHECK-LABEL: func @mul( // CHECK-LABEL: func @mul(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<10x20x30x40x50x60x70x80xf32>, // CHECK-SAME: %[[VAL_0:.*]]: tensor<10x20x30x40x50x60x70x80xf32>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<80x70x60x50x40x30x20x10xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense", "compressed", "compressed", "dense", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, // CHECK-SAME: %[[VAL_1:.*]]: tensor<80x70x60x50x40x30x20x10xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense", "compressed", "compressed", "dense", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<10x20x30x40x50x60x70x80xf32>) -> tensor<10x20x30x40x50x60x70x80xf32> { // CHECK-SAME: %[[VAL_2:.*]]: tensor<10x20x30x40x50x60x70x80xf32>) -> tensor<10x20x30x40x50x60x70x80xf32> {
// CHECK: %[[VAL_3:.*]] = arith.constant 3 : index // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 3 : index
// CHECK: %[[VAL_4:.*]] = arith.constant 4 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 4 : index
// CHECK: %[[VAL_5:.*]] = arith.constant 10 : index // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 10 : index
// CHECK: %[[VAL_6:.*]] = arith.constant 20 : index // CHECK-DAG: %[[VAL_6:.*]] = arith.constant 20 : index
// CHECK: %[[VAL_7:.*]] = arith.constant 30 : index // CHECK-DAG: %[[VAL_7:.*]] = arith.constant 30 : index
// CHECK: %[[VAL_8:.*]] = arith.constant 60 : index // CHECK-DAG: %[[VAL_8:.*]] = arith.constant 60 : index
// CHECK: %[[VAL_9:.*]] = arith.constant 70 : index // CHECK-DAG: %[[VAL_9:.*]] = arith.constant 70 : index
// CHECK: %[[VAL_10:.*]] = arith.constant 80 : index // CHECK-DAG: %[[VAL_10:.*]] = arith.constant 80 : index
// CHECK: %[[VAL_11:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_11:.*]] = arith.constant 0 : index
// CHECK: %[[VAL_12:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_12:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_0]] : memref<10x20x30x40x50x60x70x80xf32> // CHECK-DAG: %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_0]] : memref<10x20x30x40x50x60x70x80xf32>
// CHECK: %[[VAL_14:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_3]] : tensor<80x70x60x50x40x30x20x10xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense", "compressed", "compressed", "dense", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_14:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_3]] : tensor<80x70x60x50x40x30x20x10xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense", "compressed", "compressed", "dense", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_15:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_3]] : tensor<80x70x60x50x40x30x20x10xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense", "compressed", "compressed", "dense", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_15:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_3]] : tensor<80x70x60x50x40x30x20x10xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense", "compressed", "compressed", "dense", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_16:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_4]] : tensor<80x70x60x50x40x30x20x10xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense", "compressed", "compressed", "dense", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_16:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_4]] : tensor<80x70x60x50x40x30x20x10xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense", "compressed", "compressed", "dense", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_17:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_4]] : tensor<80x70x60x50x40x30x20x10xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense", "compressed", "compressed", "dense", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex> // CHECK-DAG: %[[VAL_17:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_4]] : tensor<80x70x60x50x40x30x20x10xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense", "compressed", "compressed", "dense", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
// CHECK: %[[VAL_18:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<80x70x60x50x40x30x20x10xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense", "compressed", "compressed", "dense", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32> // CHECK-DAG: %[[VAL_18:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<80x70x60x50x40x30x20x10xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense", "compressed", "compressed", "dense", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32>
// CHECK: %[[VAL_19:.*]] = bufferization.to_memref %[[VAL_2]] : memref<10x20x30x40x50x60x70x80xf32> // CHECK-DAG: %[[VAL_19:.*]] = bufferization.to_memref %[[VAL_2]] : memref<10x20x30x40x50x60x70x80xf32>
// CHECK: %[[VAL_20:.*]] = memref.alloc() : memref<10x20x30x40x50x60x70x80xf32> // CHECK-DAG: %[[VAL_20:.*]] = memref.alloc() : memref<10x20x30x40x50x60x70x80xf32>
// CHECK: memref.copy %[[VAL_19]], %[[VAL_20]] : memref<10x20x30x40x50x60x70x80xf32> to memref<10x20x30x40x50x60x70x80xf32> // CHECK: memref.copy %[[VAL_19]], %[[VAL_20]] : memref<10x20x30x40x50x60x70x80xf32> to memref<10x20x30x40x50x60x70x80xf32>
// CHECK: scf.for %[[VAL_21:.*]] = %[[VAL_11]] to %[[VAL_10]] step %[[VAL_12]] { // CHECK: scf.for %[[VAL_21:.*]] = %[[VAL_11]] to %[[VAL_10]] step %[[VAL_12]] {
// CHECK: scf.for %[[VAL_22:.*]] = %[[VAL_11]] to %[[VAL_9]] step %[[VAL_12]] { // CHECK: scf.for %[[VAL_22:.*]] = %[[VAL_11]] to %[[VAL_9]] step %[[VAL_12]] {
// CHECK: %[[VAL_23:.*]] = arith.muli %[[VAL_21]], %[[VAL_9]] : index // CHECK: %[[VAL_23:.*]] = arith.muli %[[VAL_21]], %[[VAL_9]] : index
// CHECK: %[[VAL_24:.*]] = arith.addi %[[VAL_23]], %[[VAL_22]] : index // CHECK: %[[VAL_24:.*]] = arith.addi %[[VAL_23]], %[[VAL_22]] : index
// CHECK: scf.for %[[VAL_25:.*]] = %[[VAL_11]] to %[[VAL_8]] step %[[VAL_12]] { // CHECK: scf.for %[[VAL_25:.*]] = %[[VAL_11]] to %[[VAL_8]] step %[[VAL_12]] {
// CHECK: %[[VAL_26:.*]] = arith.muli %[[VAL_24]], %[[VAL_8]] : index // CHECK: %[[VAL_26:.*]] = arith.muli %[[VAL_24]], %[[VAL_8]] : index
// CHECK: %[[VAL_27:.*]] = arith.addi %[[VAL_26]], %[[VAL_25]] : index // CHECK: %[[VAL_27:.*]] = arith.addi %[[VAL_26]], %[[VAL_25]] : index
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mlir/test/Dialect/SparseTensor/sparse_perm.mlir

Show All 11 Lines indexing_maps = [
affine_map<(i,j,k) -> (i,j,k)> // X (out) affine_map<(i,j,k) -> (i,j,k)> // X (out)
], ],
iterator_types = ["parallel", "parallel", "parallel"] iterator_types = ["parallel", "parallel", "parallel"]
} }
// CHECK-LABEL: func @sparse_static_dims( // CHECK-LABEL: func @sparse_static_dims(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<10x20x30xf32, #sparse_tensor.encoding<{{{.*}}}>>, // CHECK-SAME: %[[VAL_0:.*]]: tensor<10x20x30xf32, #sparse_tensor.encoding<{{{.*}}}>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<20x30x10xf32>) -> tensor<20x30x10xf32> { // CHECK-SAME: %[[VAL_1:.*]]: tensor<20x30x10xf32>) -> tensor<20x30x10xf32> {
// CHECK: %[[VAL_2:.*]] = arith.constant 20 : index // CHECK-DAG: %[[VAL_2:.*]] = arith.constant 20 : index
// CHECK: %[[VAL_3:.*]] = arith.constant 30 : index // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 30 : index
// CHECK: %[[VAL_4:.*]] = arith.constant 10 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 10 : index
// CHECK: %[[VAL_5:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index
// CHECK: %[[VAL_6:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_6:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<10x20x30xf32, #sparse_tensor.encoding<{{{.*}}}>> // CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<10x20x30xf32, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_1]] : memref<20x30x10xf32> // CHECK-DAG: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_1]] : memref<20x30x10xf32>
// CHECK: %[[VAL_9:.*]] = memref.alloc() : memref<20x30x10xf32> // CHECK-DAG: %[[VAL_9:.*]] = memref.alloc() : memref<20x30x10xf32>
// CHECK: memref.copy %[[VAL_8]], %[[VAL_9]] : memref<20x30x10xf32> to memref<20x30x10xf32> // CHECK: memref.copy %[[VAL_8]], %[[VAL_9]] : memref<20x30x10xf32> to memref<20x30x10xf32>
// CHECK: scf.for %[[VAL_10:.*]] = %[[VAL_5]] to %[[VAL_3]] step %[[VAL_6]] { // CHECK: scf.for %[[VAL_10:.*]] = %[[VAL_5]] to %[[VAL_3]] step %[[VAL_6]] {
// CHECK: scf.for %[[VAL_11:.*]] = %[[VAL_5]] to %[[VAL_4]] step %[[VAL_6]] { // CHECK: scf.for %[[VAL_11:.*]] = %[[VAL_5]] to %[[VAL_4]] step %[[VAL_6]] {
// CHECK: %[[VAL_12:.*]] = arith.muli %[[VAL_10]], %[[VAL_4]] : index // CHECK: %[[VAL_12:.*]] = arith.muli %[[VAL_10]], %[[VAL_4]] : index
// CHECK: %[[VAL_13:.*]] = arith.addi %[[VAL_12]], %[[VAL_11]] : index // CHECK: %[[VAL_13:.*]] = arith.addi %[[VAL_12]], %[[VAL_11]] : index
// CHECK: scf.for %[[VAL_14:.*]] = %[[VAL_5]] to %[[VAL_2]] step %[[VAL_6]] { // CHECK: scf.for %[[VAL_14:.*]] = %[[VAL_5]] to %[[VAL_2]] step %[[VAL_6]] {
// CHECK: %[[VAL_15:.*]] = arith.muli %[[VAL_13]], %[[VAL_2]] : index // CHECK: %[[VAL_15:.*]] = arith.muli %[[VAL_13]], %[[VAL_2]] : index
// CHECK: %[[VAL_16:.*]] = arith.addi %[[VAL_15]], %[[VAL_14]] : index // CHECK: %[[VAL_16:.*]] = arith.addi %[[VAL_15]], %[[VAL_14]] : index
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} }
// CHECK-LABEL: func @sparse_dynamic_dims( // CHECK-LABEL: func @sparse_dynamic_dims(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<?x?x?xf32, #sparse_tensor.encoding<{{{.*}}}>>, // CHECK-SAME: %[[VAL_0:.*]]: tensor<?x?x?xf32, #sparse_tensor.encoding<{{{.*}}}>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<?x?x?xf32>) -> tensor<?x?x?xf32> { // CHECK-SAME: %[[VAL_1:.*]]: tensor<?x?x?xf32>) -> tensor<?x?x?xf32> {
// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 2 : index // CHECK-DAG: %[[VAL_2:.*]] = arith.constant 2 : index
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_5:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<?x?x?xf32, #sparse_tensor.encoding<{{{.*}}}>> // CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<?x?x?xf32, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: %[[VAL_6:.*]] = tensor.dim %[[VAL_1]], %[[VAL_3]] : tensor<?x?x?xf32> // CHECK-DAG: %[[VAL_6:.*]] = tensor.dim %[[VAL_1]], %[[VAL_3]] : tensor<?x?x?xf32>
// CHECK: %[[VAL_7:.*]] = tensor.dim %[[VAL_1]], %[[VAL_4]] : tensor<?x?x?xf32> // CHECK-DAG: %[[VAL_7:.*]] = tensor.dim %[[VAL_1]], %[[VAL_4]] : tensor<?x?x?xf32>
// CHECK: %[[VAL_8:.*]] = tensor.dim %[[VAL_1]], %[[VAL_2]] : tensor<?x?x?xf32> // CHECK-DAG: %[[VAL_8:.*]] = tensor.dim %[[VAL_1]], %[[VAL_2]] : tensor<?x?x?xf32>
// CHECK: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<?x?x?xf32> // CHECK-DAG: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<?x?x?xf32>
// CHECK: %[[VAL_10:.*]] = memref.alloc(%[[VAL_6]], %[[VAL_7]], %[[VAL_8]]) : memref<?x?x?xf32> // CHECK-DAG: %[[VAL_10:.*]] = memref.alloc(%[[VAL_6]], %[[VAL_7]], %[[VAL_8]]) : memref<?x?x?xf32>
// CHECK: memref.copy %[[VAL_9]], %[[VAL_10]] : memref<?x?x?xf32> to memref<?x?x?xf32> // CHECK: memref.copy %[[VAL_9]], %[[VAL_10]] : memref<?x?x?xf32> to memref<?x?x?xf32>
// CHECK: scf.for %[[VAL_11:.*]] = %[[VAL_3]] to %[[VAL_7]] step %[[VAL_4]] { // CHECK: scf.for %[[VAL_11:.*]] = %[[VAL_3]] to %[[VAL_7]] step %[[VAL_4]] {
// CHECK: scf.for %[[VAL_12:.*]] = %[[VAL_3]] to %[[VAL_8]] step %[[VAL_4]] { // CHECK: scf.for %[[VAL_12:.*]] = %[[VAL_3]] to %[[VAL_8]] step %[[VAL_4]] {
// CHECK: %[[VAL_13:.*]] = arith.muli %[[VAL_8]], %[[VAL_11]] : index // CHECK: %[[VAL_13:.*]] = arith.muli %[[VAL_8]], %[[VAL_11]] : index
// CHECK: %[[VAL_14:.*]] = arith.addi %[[VAL_13]], %[[VAL_12]] : index // CHECK: %[[VAL_14:.*]] = arith.addi %[[VAL_13]], %[[VAL_12]] : index
// CHECK: scf.for %[[VAL_15:.*]] = %[[VAL_3]] to %[[VAL_6]] step %[[VAL_4]] { // CHECK: scf.for %[[VAL_15:.*]] = %[[VAL_3]] to %[[VAL_6]] step %[[VAL_4]] {
// CHECK: %[[VAL_16:.*]] = arith.muli %[[VAL_6]], %[[VAL_14]] : index // CHECK: %[[VAL_16:.*]] = arith.muli %[[VAL_6]], %[[VAL_14]] : index
// CHECK: %[[VAL_17:.*]] = arith.addi %[[VAL_16]], %[[VAL_15]] : index // CHECK: %[[VAL_17:.*]] = arith.addi %[[VAL_16]], %[[VAL_15]] : index
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mlir/test/Dialect/SparseTensor/sparse_perm_lower.mlir

Show All 16 Lines
} }
// CHECK-HIR-LABEL: func @sparse_dynamic_dims( // CHECK-HIR-LABEL: func @sparse_dynamic_dims(
// CHECK-HIR-SAME: %[[VAL_0:.*]]: tensor<?x?x?xf32, #sparse_tensor.encoding<{{{.*}}}>>, // CHECK-HIR-SAME: %[[VAL_0:.*]]: tensor<?x?x?xf32, #sparse_tensor.encoding<{{{.*}}}>>,
// CHECK-HIR-SAME: %[[VAL_1:.*]]: tensor<f32>) -> tensor<f32> { // CHECK-HIR-SAME: %[[VAL_1:.*]]: tensor<f32>) -> tensor<f32> {
// CHECK-HIR-DAG: %[[VAL_2:.*]] = arith.constant 1 : index // CHECK-HIR-DAG: %[[VAL_2:.*]] = arith.constant 1 : index
// CHECK-HIR-DAG: %[[VAL_3:.*]] = arith.constant 0 : index // CHECK-HIR-DAG: %[[VAL_3:.*]] = arith.constant 0 : index
// CHECK-HIR-DAG: %[[VAL_4:.*]] = arith.constant 2 : index // CHECK-HIR-DAG: %[[VAL_4:.*]] = arith.constant 2 : index
// CHECK-HIR: %[[VAL_5:.*]] = tensor.dim %[[VAL_0]], %[[VAL_4]] : tensor<?x?x?xf32, #sparse_tensor.encoding<{{{.*}}}>> // CHECK-HIR-DAG: %[[VAL_5:.*]] = tensor.dim %[[VAL_0]], %[[VAL_4]] : tensor<?x?x?xf32, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK-HIR: %[[VAL_6:.*]] = tensor.dim %[[VAL_0]], %[[VAL_3]] : tensor<?x?x?xf32, #sparse_tensor.encoding<{{{.*}}}>> // CHECK-HIR-DAG: %[[VAL_6:.*]] = tensor.dim %[[VAL_0]], %[[VAL_3]] : tensor<?x?x?xf32, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK-HIR: %[[VAL_7:.*]] = tensor.dim %[[VAL_0]], %[[VAL_2]] : tensor<?x?x?xf32, #sparse_tensor.encoding<{{{.*}}}>> // CHECK-HIR-DAG: %[[VAL_7:.*]] = tensor.dim %[[VAL_0]], %[[VAL_2]] : tensor<?x?x?xf32, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK-HIR: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<?x?x?xf32, #sparse_tensor.encoding<{{{.*}}}>> // CHECK-HIR-DAG: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<?x?x?xf32, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK-HIR: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<f32> // CHECK-HIR-DAG: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<f32>
// CHECK-HIR: %[[VAL_10:.*]] = memref.alloc() : memref<f32> // CHECK-HIR-DAG: %[[VAL_10:.*]] = memref.alloc() : memref<f32>
// CHECK-HIR: memref.copy %[[VAL_9]], %[[VAL_10]] : memref<f32> to memref<f32> // CHECK-HIR: memref.copy %[[VAL_9]], %[[VAL_10]] : memref<f32> to memref<f32>
// CHECK-HIR: %[[VAL_11:.*]] = memref.load %[[VAL_10]][] : memref<f32> // CHECK-HIR: %[[VAL_11:.*]] = memref.load %[[VAL_10]][] : memref<f32>
// CHECK-HIR: %[[VAL_12:.*]] = scf.for %[[VAL_13:.*]] = %[[VAL_3]] to %[[VAL_5]] step %[[VAL_2]] iter_args(%[[VAL_14:.*]] = %[[VAL_11]]) -> (f32) { // CHECK-HIR: %[[VAL_12:.*]] = scf.for %[[VAL_13:.*]] = %[[VAL_3]] to %[[VAL_5]] step %[[VAL_2]] iter_args(%[[VAL_14:.*]] = %[[VAL_11]]) -> (f32) {
// CHECK-HIR: %[[VAL_15:.*]] = scf.for %[[VAL_16:.*]] = %[[VAL_3]] to %[[VAL_6]] step %[[VAL_2]] iter_args(%[[VAL_17:.*]] = %[[VAL_14]]) -> (f32) { // CHECK-HIR: %[[VAL_15:.*]] = scf.for %[[VAL_16:.*]] = %[[VAL_3]] to %[[VAL_6]] step %[[VAL_2]] iter_args(%[[VAL_17:.*]] = %[[VAL_14]]) -> (f32) {
// CHECK-HIR: %[[VAL_18:.*]] = arith.muli %[[VAL_6]], %[[VAL_13]] : index // CHECK-HIR: %[[VAL_18:.*]] = arith.muli %[[VAL_6]], %[[VAL_13]] : index
// CHECK-HIR: %[[VAL_19:.*]] = arith.addi %[[VAL_18]], %[[VAL_16]] : index // CHECK-HIR: %[[VAL_19:.*]] = arith.addi %[[VAL_18]], %[[VAL_16]] : index
// CHECK-HIR: %[[VAL_20:.*]] = scf.for %[[VAL_21:.*]] = %[[VAL_3]] to %[[VAL_7]] step %[[VAL_2]] iter_args(%[[VAL_22:.*]] = %[[VAL_17]]) -> (f32) { // CHECK-HIR: %[[VAL_20:.*]] = scf.for %[[VAL_21:.*]] = %[[VAL_3]] to %[[VAL_7]] step %[[VAL_2]] iter_args(%[[VAL_22:.*]] = %[[VAL_17]]) -> (f32) {
// CHECK-HIR: %[[VAL_23:.*]] = arith.muli %[[VAL_7]], %[[VAL_19]] : index // CHECK-HIR: %[[VAL_23:.*]] = arith.muli %[[VAL_7]], %[[VAL_19]] : index
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// CHECK-HIR: } // CHECK-HIR: }
// //
// CHECK-MIR-LABEL: func @sparse_dynamic_dims( // CHECK-MIR-LABEL: func @sparse_dynamic_dims(
// CHECK-MIR-SAME: %[[VAL_0:.*]]: !llvm.ptr<i8>, // CHECK-MIR-SAME: %[[VAL_0:.*]]: !llvm.ptr<i8>,
// CHECK-MIR-SAME: %[[VAL_1:.*]]: tensor<f32>) -> tensor<f32> { // CHECK-MIR-SAME: %[[VAL_1:.*]]: tensor<f32>) -> tensor<f32> {
// CHECK-MIR-DAG: %[[VAL_2:.*]] = arith.constant 2 : index // CHECK-MIR-DAG: %[[VAL_2:.*]] = arith.constant 2 : index
// CHECK-MIR-DAG: %[[VAL_3:.*]] = arith.constant 1 : index // CHECK-MIR-DAG: %[[VAL_3:.*]] = arith.constant 1 : index
// CHECK-MIR-DAG: %[[VAL_4:.*]] = arith.constant 0 : index // CHECK-MIR-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
// CHECK-MIR: %[[VAL_5:.*]] = call @sparseDimSize(%[[VAL_0]], %[[VAL_4]]) : (!llvm.ptr<i8>, index) -> index // CHECK-MIR-DAG: %[[VAL_5:.*]] = call @sparseDimSize(%[[VAL_0]], %[[VAL_4]]) : (!llvm.ptr<i8>, index) -> index
// CHECK-MIR: %[[VAL_6:.*]] = call @sparseDimSize(%[[VAL_0]], %[[VAL_3]]) : (!llvm.ptr<i8>, index) -> index // CHECK-MIR-DAG: %[[VAL_6:.*]] = call @sparseDimSize(%[[VAL_0]], %[[VAL_3]]) : (!llvm.ptr<i8>, index) -> index
// CHECK-MIR: %[[VAL_7:.*]] = call @sparseDimSize(%[[VAL_0]], %[[VAL_2]]) : (!llvm.ptr<i8>, index) -> index // CHECK-MIR-DAG: %[[VAL_7:.*]] = call @sparseDimSize(%[[VAL_0]], %[[VAL_2]]) : (!llvm.ptr<i8>, index) -> index
// CHECK-MIR: %[[VAL_8:.*]] = call @sparseValuesF32(%[[VAL_0]]) : (!llvm.ptr<i8>) -> memref<?xf32> // CHECK-MIR-DAG: %[[VAL_8:.*]] = call @sparseValuesF32(%[[VAL_0]]) : (!llvm.ptr<i8>) -> memref<?xf32>
// CHECK-MIR: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<f32> // CHECK-MIR-DAG: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<f32>
// CHECK-MIR: %[[VAL_10:.*]] = memref.alloc() : memref<f32> // CHECK-MIR-DAG: %[[VAL_10:.*]] = memref.alloc() : memref<f32>
// CHECK-MIR: memref.copy %[[VAL_9]], %[[VAL_10]] : memref<f32> to memref<f32> // CHECK-MIR: memref.copy %[[VAL_9]], %[[VAL_10]] : memref<f32> to memref<f32>
// CHECK-MIR: %[[VAL_11:.*]] = memref.load %[[VAL_10]][] : memref<f32> // CHECK-MIR: %[[VAL_11:.*]] = memref.load %[[VAL_10]][] : memref<f32>
// CHECK-MIR: %[[VAL_12:.*]] = scf.for %[[VAL_13:.*]] = %[[VAL_4]] to %[[VAL_5]] step %[[VAL_3]] iter_args(%[[VAL_14:.*]] = %[[VAL_11]]) -> (f32) { // CHECK-MIR: %[[VAL_12:.*]] = scf.for %[[VAL_13:.*]] = %[[VAL_4]] to %[[VAL_5]] step %[[VAL_3]] iter_args(%[[VAL_14:.*]] = %[[VAL_11]]) -> (f32) {
// CHECK-MIR: %[[VAL_15:.*]] = scf.for %[[VAL_16:.*]] = %[[VAL_4]] to %[[VAL_6]] step %[[VAL_3]] iter_args(%[[VAL_17:.*]] = %[[VAL_14]]) -> (f32) { // CHECK-MIR: %[[VAL_15:.*]] = scf.for %[[VAL_16:.*]] = %[[VAL_4]] to %[[VAL_6]] step %[[VAL_3]] iter_args(%[[VAL_17:.*]] = %[[VAL_14]]) -> (f32) {
// CHECK-MIR: %[[VAL_18:.*]] = arith.muli %[[VAL_6]], %[[VAL_13]] : index // CHECK-MIR: %[[VAL_18:.*]] = arith.muli %[[VAL_6]], %[[VAL_13]] : index
// CHECK-MIR: %[[VAL_19:.*]] = arith.addi %[[VAL_18]], %[[VAL_16]] : index // CHECK-MIR: %[[VAL_19:.*]] = arith.addi %[[VAL_18]], %[[VAL_16]] : index
// CHECK-MIR: %[[VAL_20:.*]] = scf.for %[[VAL_21:.*]] = %[[VAL_4]] to %[[VAL_7]] step %[[VAL_3]] iter_args(%[[VAL_22:.*]] = %[[VAL_17]]) -> (f32) { // CHECK-MIR: %[[VAL_20:.*]] = scf.for %[[VAL_21:.*]] = %[[VAL_4]] to %[[VAL_7]] step %[[VAL_3]] iter_args(%[[VAL_22:.*]] = %[[VAL_17]]) -> (f32) {
// CHECK-MIR: %[[VAL_23:.*]] = arith.muli %[[VAL_7]], %[[VAL_19]] : index // CHECK-MIR: %[[VAL_23:.*]] = arith.muli %[[VAL_7]], %[[VAL_19]] : index
Show All 24 Lines

utils/bazel/llvm-project-overlay/mlir/BUILD.bazel

Show First 20 Lines • Show All 6,991 Lines • ▼ Show 20 Lines deps = [
":LinalgNamedStructuredOpsYamlIncGen", ":LinalgNamedStructuredOpsYamlIncGen",
":LinalgOpsIncGen", ":LinalgOpsIncGen",
":LinalgStructuredOpsIncGen", ":LinalgStructuredOpsIncGen",
":MathDialect", ":MathDialect",
":MemRefDialect", ":MemRefDialect",
":Parser", ":Parser",
":SCFDialect", ":SCFDialect",
":SideEffectInterfaces", ":SideEffectInterfaces",
":SparseTensor",
":StandardOps", ":StandardOps",
":Support", ":Support",
":TensorDialect", ":TensorDialect",
":TilingInterface", ":TilingInterface",
":ViewLikeInterface", ":ViewLikeInterface",
"//llvm:Support", "//llvm:Support",
], ],
) )
▲ Show 20 Lines • Show All 70 Lines • ▼ Show 20 Lines deps = [
":LinalgStructuredOpsIncGen", ":LinalgStructuredOpsIncGen",
":MathDialect", ":MathDialect",
":MemRefDialect", ":MemRefDialect",
":ModuleBufferization", ":ModuleBufferization",
":Pass", ":Pass",
":SCFDialect", ":SCFDialect",
":SCFTransforms", ":SCFTransforms",
":SCFUtils", ":SCFUtils",
":SparseTensor",
":StandardOps", ":StandardOps",
":StandardOpsTransforms", ":StandardOpsTransforms",
":Support", ":Support",
":TensorDialect", ":TensorDialect",
":TensorTilingInterfaceImpl", ":TensorTilingInterfaceImpl",
":TensorTransforms", ":TensorTransforms",
":TensorUtils", ":TensorUtils",
":TransformUtils", ":TransformUtils",
▲ Show 20 Lines • Show All 1,332 Lines • Show Last 20 Lines