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Mar 4, 2024
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[mlir][sparse][gpu] migration to sparse_tensor.print #83510

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24 changes: 8 additions & 16 deletions mlir/test/Integration/Dialect/SparseTensor/GPU/CUDA/sparse-gemm-lib.mlir
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Expand Up @@ -62,23 +62,15 @@ module {
tensor<8x8xf32, #CSR>) -> tensor<8x8xf32, #CSR>

//
// Verify computed result (expected output, with only 20 nonzeros).
// Verify computed result.
//
// CHECK: ( ( 1, 39, 52, 0, 0, 0, 45, 51 ),
// CHECK-SAME: ( 0, 0, 0, 0, 0, 0, 0, 0 ),
// CHECK-SAME: ( 0, 0, 16, 0, 0, 0, 0, 0 ),
// CHECK-SAME: ( 0, 0, 0, 25, 0, 0, 0, 0 ),
// CHECK-SAME: ( 0, 0, 0, 0, 36, 0, 0, 0 ),
// CHECK-SAME: ( 0, 117, 158, 0, 0, 0, 135, 144 ),
// CHECK-SAME: ( 0, 156, 318, 0, 0, 0, 301, 324 ),
// CHECK-SAME: ( 0, 208, 430, 0, 0, 0, 405, 436 ) )
// CHECK-NEXT: 20
%d = sparse_tensor.convert %Ccsr : tensor<8x8xf32, #CSR> to tensor<8x8xf32>
%v = vector.transfer_read %d[%c0, %c0], %f0: tensor<8x8xf32>, vector<8x8xf32>
vector.print %v : vector<8x8xf32>
%nnz = sparse_tensor.number_of_entries %Ccsr : tensor<8x8xf32, #CSR>
%x = sparse_tensor.number_of_entries %Ccsr : tensor<8x8xf32, #CSR>
vector.print %nnz : index
// CHECK: ---- Sparse Tensor ----
// CHECK-NEXT: nse = 20
// CHECK-NEXT: pos[1] : ( 0, 5, 5, 6, 7, 8, 12, 16, 20,
// CHECK-NEXT: crd[1] : ( 0, 1, 2, 6, 7, 2, 3, 4, 1, 2, 6, 7, 1, 2, 6, 7, 1, 2, 6, 7,
// CHECK-NEXT: values : ( 1, 39, 52, 45, 51, 16, 25, 36, 117, 158, 135, 144, 156, 318, 301, 324, 208, 430, 405, 436,
// CHECK-NEXT: ----
sparse_tensor.print %Ccsr : tensor<8x8xf32, #CSR>

llvm.call @mgpuDestroySparseEnv(): () -> ()
return
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28 changes: 6 additions & 22 deletions mlir/test/Integration/Dialect/SparseTensor/GPU/CUDA/sparse-matmul-lib.mlir
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Expand Up @@ -66,32 +66,16 @@ module {
return %D: tensor<8x8xf32>
}

// Helper to dump dense tensor as series of vectors.
func.func @dump(%mat: tensor<8x8xf32>) {
%f0 = arith.constant 0.0 : f32
%c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index
%c2 = arith.constant 2 : index
%c3 = arith.constant 3 : index
%c4 = arith.constant 4 : index
%c5 = arith.constant 5 : index
%c6 = arith.constant 6 : index
%c7 = arith.constant 7 : index
%r0 = vector.transfer_read %mat[%c0,%c0], %f0 : tensor<8x8xf32>, vector<8xf32>
vector.print %r0 : vector<8xf32>
%r1 = vector.transfer_read %mat[%c1,%c0], %f0 : tensor<8x8xf32>, vector<8xf32>
vector.print %r1 : vector<8xf32>
%r2 = vector.transfer_read %mat[%c2,%c0], %f0 : tensor<8x8xf32>, vector<8xf32>
vector.print %r2 : vector<8xf32>
%r3 = vector.transfer_read %mat[%c3,%c0], %f0 : tensor<8x8xf32>, vector<8xf32>
vector.print %r3 : vector<8xf32>
%r4 = vector.transfer_read %mat[%c4,%c0], %f0 : tensor<8x8xf32>, vector<8xf32>
vector.print %r4 : vector<8xf32>
%r5 = vector.transfer_read %mat[%c5,%c0], %f0 : tensor<8x8xf32>, vector<8xf32>
vector.print %r5 : vector<8xf32>
%r6 = vector.transfer_read %mat[%c6,%c0], %f0 : tensor<8x8xf32>, vector<8xf32>
vector.print %r6 : vector<8xf32>
%r7 = vector.transfer_read %mat[%c7,%c0], %f0 : tensor<8x8xf32>, vector<8xf32>
vector.print %r7 : vector<8xf32>
%c8 = arith.constant 8 : index
scf.for %i = %c0 to %c8 step %c1 {
%v = vector.transfer_read %mat[%i,%c0], %f0 : tensor<8x8xf32>, vector<8xf32>
vector.print %v : vector<8xf32>
}
return
}

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23 changes: 14 additions & 9 deletions mlir/test/Integration/Dialect/SparseTensor/GPU/CUDA/sparse-sampled-matmul-lib.mlir
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Expand Up @@ -113,11 +113,13 @@ module {
//
// Print the result for verification.
//
// CHECK: ( 11, 41.4, 42, 102.5, 93, 44.1, 164, 105.2, 255 )
//
%vm = sparse_tensor.values %0 : tensor<?x?xf32, #CSR> to memref<?xf32>
%vv = vector.transfer_read %vm[%c0], %d0 : memref<?xf32>, vector<9xf32>
vector.print %vv : vector<9xf32>
// CHECK: ---- Sparse Tensor ----
// CHECK-NEXT: nse = 9
// CHECK-NEXT: pos[1] : ( 0, 2, 4, 5, 7, 9,
// CHECK-NEXT: crd[1] : ( 0, 3, 1, 4, 2, 0, 3, 1, 4,
// CHECK-NEXT: values : ( 11, 41.4, 42, 102.5, 93, 44.1, 164, 105.2, 255,
// CHECK-NEXT: ----
sparse_tensor.print %0 : tensor<?x?xf32, #CSR>

// Create a much sparser sampling matrix.
%t = arith.constant sparse<[[0,0], [0,1], [1,0], [3,4], [7,7]],
Expand All @@ -137,11 +139,14 @@ module {
//
// Print the result for verification.
//
// CHECK: ( ( 17, 18, 0, 0, 0, 0, 0, 0 ), ( 19, 0, 0, 0, 0, 0, 0, 0 ), ( 0, 0, 0, 0, 0, 0, 0, 0 ), ( 0, 0, 0, 0, 20, 0, 0, 0 ), ( 0, 0, 0, 0, 0, 0, 0, 0 ), ( 0, 0, 0, 0, 0, 0, 0, 0 ), ( 0, 0, 0, 0, 0, 0, 0, 0 ), ( 0, 0, 0, 0, 0, 0, 0, 21 ) )
// CHECK: ---- Sparse Tensor ----
// CHECK-NEXT: nse = 5
// CHECK-NEXT: pos[1] : ( 0, 2, 3, 3, 4, 4, 4, 4, 5,
// CHECK-NEXT: crd[1] : ( 0, 1, 0, 4, 7,
// CHECK-NEXT: values : ( 17, 18, 19, 20, 21,
// CHECK-NEXT: ----
//
%d = sparse_tensor.convert %1 : tensor<?x?xf32, #CSR> to tensor<?x?xf32>
%mm = vector.transfer_read %d[%c0, %c0], %d0 : tensor<?x?xf32>, vector<8x8xf32>
vector.print %mm : vector<8x8xf32>
sparse_tensor.print %1 : tensor<?x?xf32, #CSR>

// Release the resources.
bufferization.dealloc_tensor %0 : tensor<?x?xf32, #CSR>
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23 changes: 15 additions & 8 deletions mlir/test/Integration/Dialect/SparseTensor/GPU/CUDA/sparse-sddmm-lib.mlir
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Expand Up @@ -166,15 +166,22 @@ module {
// the result (which is why the block sparse version has actual results
// in the original zero positions).
//
// CHECK: ( 5, 10, 24, 19, 53, 42, 55, 56 )
// CHECK-NEXT: ( 5, 10, 8, 19, 24, 24, 40, 53, 42, 55, 56, 64 )
// CHECK: ---- Sparse Tensor ----
// CHECK-NEXT: nse = 8
// CHECK-NEXT: pos[1] : ( 0, 3, 5, 7, 8,
// CHECK-NEXT: crd[1] : ( 0, 1, 4, 1, 5, 2, 3, 2,
// CHECK-NEXT: values : ( 5, 10, 24, 19, 53, 42, 55, 56,
// CHECK-NEXT: ----
//
%v0 = sparse_tensor.values %0 : tensor<?x?xf32, #CSR> to memref<?xf32>
%vv0 = vector.transfer_read %v0[%c0], %d0 : memref<?xf32>, vector<8xf32>
vector.print %vv0 : vector<8xf32>
%v1 = sparse_tensor.values %1 : tensor<?x?xf32, #BSR> to memref<?xf32>
%vv1 = vector.transfer_read %v1[%c0], %d0 : memref<?xf32>, vector<12xf32>
vector.print %vv1 : vector<12xf32>
// CHECK: ---- Sparse Tensor ----
// CHECK-NEXT: nse = 12
// CHECK-NEXT: pos[1] : ( 0, 2, 3,
// CHECK-NEXT: crd[1] : ( 0, 2, 1,
// CHECK-NEXT: values : ( 5, 10, 8, 19, 24, 24, 40, 53, 42, 55, 56, 64,
// CHECK-NEXT: ----
//
sparse_tensor.print %0 : tensor<?x?xf32, #CSR>
sparse_tensor.print %1 : tensor<?x?xf32, #BSR>

// Release the resources.
bufferization.dealloc_tensor %0 : tensor<?x?xf32, #CSR>
Expand Down