b9589545c4
`compressed(hi)` is similar to `compressed`, but instead of reusing the previous position high as the current position low, it uses a pair of positions for each sparse index. The patch only introduces the definition (syntax) but does not provide codegen implementation. Reviewed By: aartbik Differential Revision: https://reviews.llvm.org/D148664
133 lines
4.6 KiB
MLIR
133 lines
4.6 KiB
MLIR
// RUN: mlir-opt %s -split-input-file | mlir-opt | FileCheck %s
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// CHECK-LABEL: func private @sparse_1d_tensor(
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// CHECK-SAME: tensor<32xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>)
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func.func private @sparse_1d_tensor(tensor<32xf64, #sparse_tensor.encoding<{ dimLevelType = ["compressed"] }>>)
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// -----
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#CSR = #sparse_tensor.encoding<{
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dimLevelType = [ "dense", "compressed" ],
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dimOrdering = affine_map<(i,j) -> (i,j)>,
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posWidth = 64,
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crdWidth = 64
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}>
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// CHECK-LABEL: func private @sparse_csr(
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// CHECK-SAME: tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], posWidth = 64, crdWidth = 64 }>>)
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func.func private @sparse_csr(tensor<?x?xf32, #CSR>)
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// -----
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#CSC = #sparse_tensor.encoding<{
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dimLevelType = [ "dense", "compressed" ],
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dimOrdering = affine_map<(i,j) -> (j,i)>,
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posWidth = 0,
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crdWidth = 0
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}>
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// CHECK-LABEL: func private @sparse_csc(
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// CHECK-SAME: tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>>)
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func.func private @sparse_csc(tensor<?x?xf32, #CSC>)
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// -----
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#DCSC = #sparse_tensor.encoding<{
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dimLevelType = [ "compressed", "compressed" ],
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dimOrdering = affine_map<(i,j) -> (j,i)>,
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posWidth = 0,
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crdWidth = 64
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}>
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// CHECK-LABEL: func private @sparse_dcsc(
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// CHECK-SAME: tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)>, crdWidth = 64 }>>)
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func.func private @sparse_dcsc(tensor<?x?xf32, #DCSC>)
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// -----
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#COO = #sparse_tensor.encoding<{
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dimLevelType = [ "compressed-nu-no", "singleton-no" ]
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}>
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// CHECK-LABEL: func private @sparse_coo(
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// CHECK-SAME: tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu-no", "singleton-no" ] }>>)
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func.func private @sparse_coo(tensor<?x?xf32, #COO>)
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// -----
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#BCOO = #sparse_tensor.encoding<{
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dimLevelType = [ "dense", "compressed-hi-nu", "singleton" ]
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}>
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// CHECK-LABEL: func private @sparse_bcoo(
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// CHECK-SAME: tensor<?x?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed-hi-nu", "singleton" ] }>>)
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func.func private @sparse_bcoo(tensor<?x?x?xf32, #BCOO>)
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// -----
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#SortedCOO = #sparse_tensor.encoding<{
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dimLevelType = [ "compressed-nu", "singleton" ]
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}>
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// CHECK-LABEL: func private @sparse_sorted_coo(
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// CHECK-SAME: tensor<10x10xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>>)
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func.func private @sparse_sorted_coo(tensor<10x10xf64, #SortedCOO>)
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// -----
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#BCSR = #sparse_tensor.encoding<{
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dimLevelType = [ "compressed", "compressed", "dense", "dense" ],
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dimOrdering = affine_map<(ii, jj, i, j) -> (ii, jj, i, j)>,
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higherOrdering = affine_map<(i, j) -> (i floordiv 2, j floordiv 3, i mod 2, j mod 3)>
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}>
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// CHECK-LABEL: func private @sparse_bcsr(
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// CHECK-SAME: tensor<10x60xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense", "dense" ], higherOrdering = affine_map<(d0, d1) -> (d0 floordiv 2, d1 floordiv 3, d0 mod 2, d1 mod 3)> }>>
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func.func private @sparse_bcsr(tensor<10x60xf64, #BCSR>)
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// -----
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#ELL = #sparse_tensor.encoding<{
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dimLevelType = [ "dense", "dense", "compressed" ],
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dimOrdering = affine_map<(ii, i, j) -> (ii, i, j)>,
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higherOrdering = affine_map<(i,j)[c] -> (c*4*i, i, j)>
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}>
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// CHECK-LABEL: func private @sparse_ell(
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// CHECK-SAME: tensor<?x?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "compressed" ], higherOrdering = affine_map<(d0, d1)[s0] -> (d0 * (s0 * 4), d0, d1)> }>>
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func.func private @sparse_ell(tensor<?x?xf64, #ELL>)
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// -----
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#CSR_SLICE = #sparse_tensor.encoding<{
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dimLevelType = [ "dense", "compressed" ],
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slice = [ (1, 4, 1), (1, 4, 2) ]
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}>
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// CHECK-LABEL: func private @sparse_slice(
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// CHECK-SAME: tensor<?x?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], slice = [ (1, 4, 1), (1, 4, 2) ] }>>
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func.func private @sparse_slice(tensor<?x?xf64, #CSR_SLICE>)
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// -----
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#CSR_SLICE = #sparse_tensor.encoding<{
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dimLevelType = [ "dense", "compressed" ],
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slice = [ (1, 4, 1), (1, 4, 2) ]
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}>
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// CHECK-LABEL: func private @sparse_slice(
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// CHECK-SAME: tensor<?x?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], slice = [ (1, 4, 1), (1, 4, 2) ] }>>
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func.func private @sparse_slice(tensor<?x?xf64, #CSR_SLICE>)
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// -----
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#CSR_SLICE = #sparse_tensor.encoding<{
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dimLevelType = [ "dense", "compressed" ],
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slice = [ (1, ?, 1), (?, 4, 2) ]
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}>
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// CHECK-LABEL: func private @sparse_slice(
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// CHECK-SAME: tensor<?x?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], slice = [ (1, ?, 1), (?, 4, 2) ] }>>
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func.func private @sparse_slice(tensor<?x?xf64, #CSR_SLICE>)
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