[Mlir-commits] [mlir] 256ac46 - [mlir][sparse] Change tests to use new syntax for ELL and slice (#67569)
llvmlistbot at llvm.org
llvmlistbot at llvm.org
Wed Sep 27 16:40:57 PDT 2023
Author: Yinying Li
Date: 2023-09-27T19:40:52-04:00
New Revision: 256ac4619b5c515434281c8ff5cd5a6960b07413
URL: https://github.com/llvm/llvm-project/commit/256ac4619b5c515434281c8ff5cd5a6960b07413
DIFF: https://github.com/llvm/llvm-project/commit/256ac4619b5c515434281c8ff5cd5a6960b07413.diff
LOG: [mlir][sparse] Change tests to use new syntax for ELL and slice (#67569)
Examples:
1. `#ELL = #sparse_tensor.encoding<{ lvlTypes = [ "dense", "dense",
"compressed" ], dimToLvl = affine_map<(i,j)[c] -> (c*4*i, i, j)>
}>`
to
`#ELL = #sparse_tensor.encoding<{ map = [s0](d0, d1) -> (d0 * (s0 * 4) :
dense, d0 : dense, d1 : compressed)
}>`
2. `#CSR_SLICE = #sparse_tensor.encoding<{ lvlTypes = [ "dense",
"compressed" ], dimSlices = [ (1, 4, 1), (1, 4, 2) ]
}>`
to
`#CSR_SLICE = #sparse_tensor.encoding<{ map = (d0 :
#sparse_tensor<slice(1, 4, 1)>, d1 : #sparse_tensor<slice(1, 4, 2)>) ->
(d0 : dense, d1 : compressed)
}>`
Added:
Modified:
mlir/include/mlir/Dialect/SparseTensor/IR/SparseTensorAttrDefs.td
mlir/test/CAPI/sparse_tensor.c
mlir/test/Dialect/SparseTensor/convert_sparse2sparse.mlir
mlir/test/Dialect/SparseTensor/invalid.mlir
mlir/test/Dialect/SparseTensor/invalid_encoding.mlir
mlir/test/Dialect/SparseTensor/pre_rewriting.mlir
mlir/test/Dialect/SparseTensor/roundtrip.mlir
mlir/test/Dialect/SparseTensor/roundtrip_encoding.mlir
mlir/test/Dialect/SparseTensor/sparse_extract_slice.mlir
mlir/test/Dialect/SparseTensor/sparse_foreach.mlir
mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_foreach_slices.mlir
mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_matmul_slice.mlir
Removed:
################################################################################
diff --git a/mlir/include/mlir/Dialect/SparseTensor/IR/SparseTensorAttrDefs.td b/mlir/include/mlir/Dialect/SparseTensor/IR/SparseTensorAttrDefs.td
index 9c20c94802a4267..d311fe7801cc18f 100644
--- a/mlir/include/mlir/Dialect/SparseTensor/IR/SparseTensorAttrDefs.td
+++ b/mlir/include/mlir/Dialect/SparseTensor/IR/SparseTensorAttrDefs.td
@@ -237,11 +237,33 @@ def SparseTensorEncodingAttr : SparseTensor_Attr<"SparseTensorEncoding",
}>
... tensor<20x30xf32, #BSR_explicit> ...
+ // ELL format.
+ // In the simple format for matrix, one array stores values and another
+ // array stores column indices. The arrays have the same number of rows
+ // as the original matrix, but only have as many columns as
+ // the maximum number of nonzeros on a row of the original matrix.
+ // There are many variants for ELL such as jagged diagonal scheme.
+ // To implement ELL, map provides a notion of "counting a
+ // dimension", where every stored element with the same coordinate
+ // is mapped to a new slice. For instance, ELL storage of a 2-d
+ // tensor can be defined with the mapping (i, j) -> (#i, i, j)
+ // using the notation of [Chou20]. Lacking the # symbol in MLIR's
+ // affine mapping, we use a free symbol c to define such counting,
+ // together with a constant that denotes the number of resulting
+ // slices. For example, the mapping [c](i, j) -> (c * 3 * i, i, j)
+ // with the level-types ["dense", "dense", "compressed"] denotes ELL
+ // storage with three jagged diagonals that count the dimension i.
+ #ELL = #sparse_tensor.encoding<{
+ map = [c](i, j) -> (c * 3 * i : dense, i : dense, j : compressed)
+ }>
+ ... tensor<?x?xf64, #ELL> ...
+
// CSR slice (offset = 0, size = 4, stride = 1 on the first dimension;
// offset = 0, size = 8, and a dynamic stride on the second dimension).
#CSR_SLICE = #sparse_tensor.encoding<{
- lvlTypes = [ "dense", "compressed" ],
- dimSlices = [ (0, 4, 1), (0, 8, ?) ]
+ map = (i : #sparse_tensor<slice(0, 4, 1)>,
+ j : #sparse_tensor<slice(0, 8, ?)>) ->
+ (i : dense, j : compressed)
}>
... tensor<?x?xf64, #CSC_SLICE> ...
diff --git a/mlir/test/CAPI/sparse_tensor.c b/mlir/test/CAPI/sparse_tensor.c
index 30ef1557e73302f..33ee8e784096a18 100644
--- a/mlir/test/CAPI/sparse_tensor.c
+++ b/mlir/test/CAPI/sparse_tensor.c
@@ -25,8 +25,7 @@ static int testRoundtripEncoding(MlirContext ctx) {
// clang-format off
const char *originalAsm =
"#sparse_tensor.encoding<{ "
- "lvlTypes = [ \"dense\", \"compressed\", \"compressed\"], "
- "dimToLvl = affine_map<(d0, d1)[s0] -> (s0, d0, d1)>, "
+ "map = [s0](d0, d1) -> (s0 : dense, d0 : compressed, d1 : compressed), "
"posWidth = 32, crdWidth = 64 }>";
// clang-format on
MlirAttribute originalAttr =
diff --git a/mlir/test/Dialect/SparseTensor/convert_sparse2sparse.mlir b/mlir/test/Dialect/SparseTensor/convert_sparse2sparse.mlir
index 54cdfc690952d9a..2a2619daf493654 100644
--- a/mlir/test/Dialect/SparseTensor/convert_sparse2sparse.mlir
+++ b/mlir/test/Dialect/SparseTensor/convert_sparse2sparse.mlir
@@ -39,8 +39,7 @@
}>
#COOSlice = #sparse_tensor.encoding<{
- lvlTypes = [ "compressed_nu", "singleton" ],
- dimSlices = [ (2, 2, 1), (12, 13, 1) ]
+ map = (d0 : #sparse_tensor<slice(2, 2, 1)>, d1 : #sparse_tensor<slice(12, 13, 1)>) -> (d0 : compressed(nonunique), d1 : singleton)
}>
// CHECK-LABEL: func @sparse_nop_convert(
diff --git a/mlir/test/Dialect/SparseTensor/invalid.mlir b/mlir/test/Dialect/SparseTensor/invalid.mlir
index c0e813dcde7c57e..2a13f208fa225d3 100644
--- a/mlir/test/Dialect/SparseTensor/invalid.mlir
+++ b/mlir/test/Dialect/SparseTensor/invalid.mlir
@@ -201,8 +201,7 @@ func.func @mismatch_values_types(%arg0: tensor<?xf64, #SparseVector>) -> memref<
// -----
#CSR_SLICE = #sparse_tensor.encoding<{
- lvlTypes = [ "dense", "compressed" ],
- dimSlices = [ (1, 4, 1), (1, 4, 2) ]
+ map = (d0 : #sparse_tensor<slice(1, 4, 1)>, d1 : #sparse_tensor<slice(1, 4, 2)>) -> (d0 : dense, d1 : compressed)
}>
func.func @sparse_slice_offset(%arg0: tensor<2x8xf64, #CSR_SLICE>) -> index {
@@ -214,8 +213,7 @@ func.func @sparse_slice_offset(%arg0: tensor<2x8xf64, #CSR_SLICE>) -> index {
// -----
#CSR_SLICE = #sparse_tensor.encoding<{
- lvlTypes = [ "dense", "compressed" ],
- dimSlices = [ (1, 4, 1), (1, 4, 2) ]
+ map = (d0 : #sparse_tensor<slice(1, 4, 1)>, d1 : #sparse_tensor<slice(1, 4, 2)>) -> (d0 : dense, d1 : compressed)
}>
func.func @sparse_slice_stride(%arg0: tensor<2x8xf64, #CSR_SLICE>) -> index {
@@ -400,8 +398,7 @@ func.func @invalid_out_dense(%arg0: tensor<10xf64>, %arg1: !llvm.ptr<i8>) {
// -----
#CSR = #sparse_tensor.encoding<{
- lvlTypes = ["dense", "compressed"],
- dimSlices = [ (1, 4, 1), (1, 4, 2) ]
+ map = (d0 : #sparse_tensor<slice(1, 4, 1)>, d1 : #sparse_tensor<slice(1, 4, 2)>) -> (d0 : dense, d1 : compressed)
}>
func.func @sparse_convert_to_slice(%arg0: tensor<10x?xf32>) -> tensor<10x10xf32, #CSR> {
diff --git a/mlir/test/Dialect/SparseTensor/invalid_encoding.mlir b/mlir/test/Dialect/SparseTensor/invalid_encoding.mlir
index 8adf981d00051c5..ef1dd3ee41f8576 100644
--- a/mlir/test/Dialect/SparseTensor/invalid_encoding.mlir
+++ b/mlir/test/Dialect/SparseTensor/invalid_encoding.mlir
@@ -218,8 +218,7 @@ func.func private @tensor_invalid_key(%arg0: tensor<16x32xf32, #a>) -> ()
// -----
#CSR_SLICE = #sparse_tensor.encoding<{
- lvlTypes = [ "dense", "compressed" ],
- dimSlices = [ (-1, ?, 1), (?, 4, 2) ] // expected-error{{expect positive value or ? for slice offset/size/stride}}
+ map = (d0 : #sparse_tensor<slice(-1, ?, 1)>, d1 : #sparse_tensor<slice(?, 4, 2)>) -> (d0 : dense, d1 : compressed)// expected-error{{expect positive value or ? for slice offset/size/stride}}
}>
func.func private @sparse_slice(tensor<?x?xf64, #CSR_SLICE>)
diff --git a/mlir/test/Dialect/SparseTensor/pre_rewriting.mlir b/mlir/test/Dialect/SparseTensor/pre_rewriting.mlir
index ac20ec1b950c258..c2cab30edec9180 100644
--- a/mlir/test/Dialect/SparseTensor/pre_rewriting.mlir
+++ b/mlir/test/Dialect/SparseTensor/pre_rewriting.mlir
@@ -13,8 +13,7 @@
}>
#Slice = #sparse_tensor.encoding<{
- lvlTypes = [ "compressed_nu", "singleton" ],
- dimSlices = [ (?, 1, 1), (?, 3, 1) ]
+ map = (d0 : #sparse_tensor<slice(?, 1, 1)>, d1 : #sparse_tensor<slice(?, 3, 1)>) -> (d0 : compressed(nonunique), d1 : singleton)
}>
#sel_trait = {
diff --git a/mlir/test/Dialect/SparseTensor/roundtrip.mlir b/mlir/test/Dialect/SparseTensor/roundtrip.mlir
index d252fa559a1543f..33471497cc69767 100644
--- a/mlir/test/Dialect/SparseTensor/roundtrip.mlir
+++ b/mlir/test/Dialect/SparseTensor/roundtrip.mlir
@@ -143,8 +143,7 @@ func.func @sparse_values(%arg0: tensor<128xf64, #SparseVector>) -> memref<?xf64>
// -----
#CSR_SLICE = #sparse_tensor.encoding<{
- lvlTypes = [ "dense", "compressed" ],
- dimSlices = [ (1, 4, 1), (1, 4, 2) ]
+ map = (d0 : #sparse_tensor<slice(1, 4, 1)>, d1 : #sparse_tensor<slice(1, 4, 2)>) -> (d0 : dense, d1 : compressed)
}>
// CHECK-LABEL: func @sparse_slice_offset(
@@ -159,8 +158,7 @@ func.func @sparse_slice_offset(%arg0: tensor<2x8xf64, #CSR_SLICE>) -> index {
// -----
#CSR_SLICE = #sparse_tensor.encoding<{
- lvlTypes = [ "dense", "compressed" ],
- dimSlices = [ (1, 4, 1), (1, 4, 2) ]
+ map = (d0 : #sparse_tensor<slice(1, 4, 1)>, d1 : #sparse_tensor<slice(1, 4, 2)>) -> (d0 : dense, d1 : compressed)
}>
// CHECK-LABEL: func @sparse_slice_stride(
@@ -188,8 +186,7 @@ func.func @sparse_metadata_init() -> !sparse_tensor.storage_specifier<#SparseVec
#SparseVector = #sparse_tensor.encoding<{map = (d0) -> (d0 : compressed)}>
#SparseVector_Slice = #sparse_tensor.encoding<{
- lvlTypes = ["compressed"],
- dimSlices = [ (?, ?, ?) ]
+ map = (d0 : #sparse_tensor<slice(?, ?, ?)>) -> (d0 : compressed)
}>
// CHECK-LABEL: func @sparse_metadata_init(
@@ -220,8 +217,7 @@ func.func @sparse_get_md(%arg0: !sparse_tensor.storage_specifier<#SparseVector>)
// -----
#SparseVector_Slice = #sparse_tensor.encoding<{
- lvlTypes = ["compressed"],
- dimSlices = [ (?, ?, ?) ]
+ map = (d0 : #sparse_tensor<slice(?, ?, ?)>) -> (d0 : compressed)
}>
// CHECK-LABEL: func @sparse_get_md(
@@ -237,8 +233,7 @@ func.func @sparse_get_md(%arg0: !sparse_tensor.storage_specifier<#SparseVector_S
// -----
#SparseVector = #sparse_tensor.encoding<{
- lvlTypes = ["compressed"],
- dimSlices = [ (?, ?, ?) ]
+ map = (d0 : #sparse_tensor<slice(?, ?, ?)>) -> (d0 : compressed)
}>
// CHECK-LABEL: func @sparse_get_md(
diff --git a/mlir/test/Dialect/SparseTensor/roundtrip_encoding.mlir b/mlir/test/Dialect/SparseTensor/roundtrip_encoding.mlir
index 0e77889242925c9..d82c59a714d14ac 100644
--- a/mlir/test/Dialect/SparseTensor/roundtrip_encoding.mlir
+++ b/mlir/test/Dialect/SparseTensor/roundtrip_encoding.mlir
@@ -101,8 +101,7 @@ func.func private @sparse_bcsr(tensor<10x60xf64, #BCSR>)
// -----
#ELL = #sparse_tensor.encoding<{
- lvlTypes = [ "dense", "dense", "compressed" ],
- dimToLvl = affine_map<(i,j)[c] -> (c*4*i, i, j)>
+ map = [s0](d0, d1) -> (d0 * (s0 * 4) : dense, d0 : dense, d1 : compressed)
}>
// CHECK-LABEL: func private @sparse_ell(
@@ -112,8 +111,7 @@ func.func private @sparse_ell(tensor<?x?xf64, #ELL>)
// -----
#CSR_SLICE = #sparse_tensor.encoding<{
- lvlTypes = [ "dense", "compressed" ],
- dimSlices = [ (1, 4, 1), (1, 4, 2) ]
+ map = (d0 : #sparse_tensor<slice(1, 4, 1)>, d1 : #sparse_tensor<slice(1, 4, 2)>) -> (d0 : dense, d1 : compressed)
}>
// CHECK-LABEL: func private @sparse_slice(
@@ -123,19 +121,7 @@ func.func private @sparse_slice(tensor<?x?xf64, #CSR_SLICE>)
// -----
#CSR_SLICE = #sparse_tensor.encoding<{
- lvlTypes = [ "dense", "compressed" ],
- dimSlices = [ (1, 4, 1), (1, 4, 2) ]
-}>
-
-// CHECK-LABEL: func private @sparse_slice(
-// CHECK-SAME: tensor<?x?xf64, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ], dimSlices = [ (1, 4, 1), (1, 4, 2) ] }>>
-func.func private @sparse_slice(tensor<?x?xf64, #CSR_SLICE>)
-
-// -----
-
-#CSR_SLICE = #sparse_tensor.encoding<{
- lvlTypes = [ "dense", "compressed" ],
- dimSlices = [ (1, ?, 1), (?, 4, 2) ]
+ map = (d0 : #sparse_tensor<slice(1, ?, 1)>, d1 : #sparse_tensor<slice(?, 4, 2)>) -> (d0 : dense, d1 : compressed)
}>
// CHECK-LABEL: func private @sparse_slice(
diff --git a/mlir/test/Dialect/SparseTensor/sparse_extract_slice.mlir b/mlir/test/Dialect/SparseTensor/sparse_extract_slice.mlir
index 0a33ecfa4abe7ef..ae53f4d854e2b13 100644
--- a/mlir/test/Dialect/SparseTensor/sparse_extract_slice.mlir
+++ b/mlir/test/Dialect/SparseTensor/sparse_extract_slice.mlir
@@ -5,8 +5,7 @@
}>
#CSR_SLICE = #sparse_tensor.encoding<{
- lvlTypes = [ "dense", "compressed" ],
- dimSlices = [ (0, 4, 1), (0, 8, 1) ]
+ map = (d0 : #sparse_tensor<slice(0, 4, 1)>, d1 : #sparse_tensor<slice(0, 8, 1)>) -> (d0 : dense, d1 : compressed)
}>
// CHECK-LABEL: func.func @sparse_slice(
diff --git a/mlir/test/Dialect/SparseTensor/sparse_foreach.mlir b/mlir/test/Dialect/SparseTensor/sparse_foreach.mlir
index d05d3d5a49cfa27..8c1836b1c2ef8f1 100644
--- a/mlir/test/Dialect/SparseTensor/sparse_foreach.mlir
+++ b/mlir/test/Dialect/SparseTensor/sparse_foreach.mlir
@@ -29,13 +29,11 @@ func.func @sparse_foreach_constant() -> () {
}
#CSR_SLICE = #sparse_tensor.encoding<{
- lvlTypes = [ "compressed", "compressed" ],
- dimSlices = [ (0, 4, 1), (2, 4, 1) ]
+ map = (d0 : #sparse_tensor<slice(0, 4, 1)>, d1 : #sparse_tensor<slice(2, 4, 1)>) -> (d0 : compressed, d1 : compressed)
}>
#CSR_SLICE_DYN = #sparse_tensor.encoding<{
- lvlTypes = [ "compressed", "compressed" ],
- dimSlices = [ (?, ?, ?), (?, ?, ?) ]
+ map = (d0 : #sparse_tensor<slice(?, ?, ?)>, d1 : #sparse_tensor<slice(?, ?, ?)>) -> (d0 : compressed, d1 : compressed)
}>
diff --git a/mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_foreach_slices.mlir b/mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_foreach_slices.mlir
index d10ae8aee8141f4..e0dd31b2ca8671c 100644
--- a/mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_foreach_slices.mlir
+++ b/mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_foreach_slices.mlir
@@ -28,13 +28,11 @@
}>
#CSR_SLICE = #sparse_tensor.encoding<{
- lvlTypes = [ "dense", "compressed" ],
- dimSlices = [ (1, 4, 1), (1, 4, 2) ]
+ map = (d0 : #sparse_tensor<slice(1, 4, 1)>, d1 : #sparse_tensor<slice(1, 4, 2)>) -> (d0 : dense, d1 : compressed)
}>
#CSR_SLICE_DYN = #sparse_tensor.encoding<{
- lvlTypes = [ "dense", "compressed" ],
- dimSlices = [ (?, ?, ?), (?, ?, ?) ]
+ map = (d0 : #sparse_tensor<slice(?, ?, ?)>, d1 : #sparse_tensor<slice(?, ?, ?)>) -> (d0 : dense, d1 : compressed)
}>
#COO = #sparse_tensor.encoding<{
@@ -42,13 +40,11 @@
}>
#COO_SLICE = #sparse_tensor.encoding<{
- lvlTypes = [ "compressed_nu", "singleton" ],
- dimSlices = [ (1, 4, 1), (1, 4, 2) ]
+ map = (d0 : #sparse_tensor<slice(1, 4, 1)>, d1 : #sparse_tensor<slice(1, 4, 2)>) -> (d0 : compressed(nonunique), d1 : singleton)
}>
#COO_SLICE_DYN = #sparse_tensor.encoding<{
- lvlTypes = [ "compressed_nu", "singleton" ],
- dimSlices = [ (?, ?, ?), (?, ?, ?) ]
+ map = (d0 : #sparse_tensor<slice(?, ?, ?)>, d1 : #sparse_tensor<slice(?, ?, ?)>) -> (d0 : compressed(nonunique), d1 : singleton)
}>
diff --git a/mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_matmul_slice.mlir b/mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_matmul_slice.mlir
index c04d9b82e811270..21934fd72f018e9 100644
--- a/mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_matmul_slice.mlir
+++ b/mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_matmul_slice.mlir
@@ -27,8 +27,7 @@
}>
#DCSR_SLICE = #sparse_tensor.encoding<{
- lvlTypes = [ "compressed", "compressed" ],
- dimSlices = [ (0, 4, 1), (0, 8, 1) ]
+ map = (d0 : #sparse_tensor<slice(0, 4, 1)>, d1 : #sparse_tensor<slice(0, 8, 1)>) -> (d0 : compressed, d1 : compressed)
}>
#CSR = #sparse_tensor.encoding<{
@@ -36,8 +35,7 @@
}>
#CSR_SLICE = #sparse_tensor.encoding<{
- lvlTypes = [ "dense", "compressed" ],
- dimSlices = [ (0, 4, 1), (0, 8, 1) ]
+ map = (d0 : #sparse_tensor<slice(0, 4, 1)>, d1 : #sparse_tensor<slice(0, 8, 1)>) -> (d0 : dense, d1 : compressed)
}>
#COO = #sparse_tensor.encoding<{
@@ -45,33 +43,27 @@
}>
#CSR_SLICE_1 = #sparse_tensor.encoding<{
- lvlTypes = [ "dense", "compressed" ],
- dimSlices = [ (0, 4, 2), (0, 4, 1) ]
+ map = (d0 : #sparse_tensor<slice(0, 4, 2)>, d1 : #sparse_tensor<slice(0, 4, 1)>) -> (d0 : dense, d1 : compressed)
}>
#DCSR_SLICE_1 = #sparse_tensor.encoding<{
- lvlTypes = [ "compressed", "compressed" ],
- dimSlices = [ (0, 4, 2), (1, 4, 1) ]
+ map = (d0 : #sparse_tensor<slice(0, 4, 2)>, d1 : #sparse_tensor<slice(1, 4, 1)>) -> (d0 : compressed, d1 : compressed)
}>
#COO_SLICE_1 = #sparse_tensor.encoding<{
- lvlTypes = [ "compressed_nu", "singleton" ],
- dimSlices = [ (0, 4, 2), (0, 4, 1) ]
+ map = (d0 : #sparse_tensor<slice(0, 4, 2)>, d1 : #sparse_tensor<slice(0, 4, 1)>) -> (d0 : compressed(nonunique), d1 : singleton)
}>
#COO_SLICE_2 = #sparse_tensor.encoding<{
- lvlTypes = [ "compressed_nu", "singleton" ],
- dimSlices = [ (0, 4, 2), (1, 4, 1) ]
+ map = (d0 : #sparse_tensor<slice(0, 4, 2)>, d1 : #sparse_tensor<slice(1, 4, 1)>) -> (d0 : compressed(nonunique), d1 : singleton)
}>
#CSR_SLICE_dyn = #sparse_tensor.encoding<{
- lvlTypes = [ "dense", "compressed" ],
- dimSlices = [ (?, 4, ?), (?, 4, ?) ]
+ map = (d0 : #sparse_tensor<slice(?, 4, ?)>, d1 : #sparse_tensor<slice(?, 4, ?)>) -> (d0 : dense, d1 : compressed)
}>
#DCSR_SLICE_dyn = #sparse_tensor.encoding<{
- lvlTypes = [ "compressed", "compressed" ],
- dimSlices = [ (?, 4, ?), (?, 4, ?) ]
+ map = (d0 : #sparse_tensor<slice(?, 4, ?)>, d1 : #sparse_tensor<slice(?, 4, ?)>) -> (d0 : compressed, d1 : compressed)
}>
module {
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