[Mlir-commits] [mlir] [mlir][sparse] Print new syntax (PR #68130)
Yinying Li
llvmlistbot at llvm.org
Tue Oct 3 11:39:39 PDT 2023
https://github.com/yinying-lisa-li updated https://github.com/llvm/llvm-project/pull/68130
>From 47b34bb327e1078678d3ba0c96ebce3fc89cf2ae Mon Sep 17 00:00:00 2001
From: Yinying Li <yinyingli at google.com>
Date: Tue, 3 Oct 2023 16:43:50 +0000
Subject: [PATCH 1/2] [mlir][sparse] Print new syntax
Printing changes from #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }> to map = (d0) -> (d0 : compressed). Level properties, ELL and slice are also supported.
---
.../mlir/Dialect/SparseTensor/IR/Enums.h | 20 +--
.../SparseTensor/IR/SparseTensorDialect.cpp | 64 ++++---
mlir/test/Dialect/SparseTensor/codegen.mlir | 8 +-
.../SparseTensor/roundtrip_encoding.mlir | 32 ++--
.../Dialect/SparseTensor/sparse_reshape.mlir | 8 +-
.../SparseTensor/sparse_tensor_reshape.mlir | 2 +-
.../python/dialects/sparse_tensor/dialect.py | 160 +++++++++---------
7 files changed, 159 insertions(+), 135 deletions(-)
diff --git a/mlir/include/mlir/Dialect/SparseTensor/IR/Enums.h b/mlir/include/mlir/Dialect/SparseTensor/IR/Enums.h
index bc351ec52c0946b..2920ef79f461c6a 100644
--- a/mlir/include/mlir/Dialect/SparseTensor/IR/Enums.h
+++ b/mlir/include/mlir/Dialect/SparseTensor/IR/Enums.h
@@ -215,29 +215,29 @@ constexpr const char *toMLIRString(DimLevelType dlt) {
case DimLevelType::Compressed:
return "compressed";
case DimLevelType::CompressedNu:
- return "compressed_nu";
+ return "compressed(nonunique)";
case DimLevelType::CompressedNo:
- return "compressed_no";
+ return "compressed(nonordered)";
case DimLevelType::CompressedNuNo:
- return "compressed_nu_no";
+ return "compressed(nonunique, nonordered)";
case DimLevelType::Singleton:
return "singleton";
case DimLevelType::SingletonNu:
- return "singleton_nu";
+ return "singleton(nonunique)";
case DimLevelType::SingletonNo:
- return "singleton_no";
+ return "singleton(nonordered)";
case DimLevelType::SingletonNuNo:
- return "singleton_nu_no";
+ return "singleton(nonunique, nonordered)";
case DimLevelType::LooseCompressed:
return "loose_compressed";
case DimLevelType::LooseCompressedNu:
- return "loose_compressed_nu";
+ return "loose_compressed(nonunique)";
case DimLevelType::LooseCompressedNo:
- return "loose_compressed_no";
+ return "loose_compressed(nonordered)";
case DimLevelType::LooseCompressedNuNo:
- return "loose_compressed_nu_no";
+ return "loose_compressed(nonunique, nonordered)";
case DimLevelType::TwoOutOfFour:
- return "compressed24";
+ return "block2_4";
}
return "";
}
diff --git a/mlir/lib/Dialect/SparseTensor/IR/SparseTensorDialect.cpp b/mlir/lib/Dialect/SparseTensor/IR/SparseTensorDialect.cpp
index 3897e1b9ea3597c..4c8dccdda6c0c7c 100644
--- a/mlir/lib/Dialect/SparseTensor/IR/SparseTensorDialect.cpp
+++ b/mlir/lib/Dialect/SparseTensor/IR/SparseTensorDialect.cpp
@@ -586,30 +586,56 @@ Attribute SparseTensorEncodingAttr::parse(AsmParser &parser, Type type) {
}
void SparseTensorEncodingAttr::print(AsmPrinter &printer) const {
- // Print the struct-like storage in dictionary fashion.
- printer << "<{ lvlTypes = [ ";
- llvm::interleaveComma(getLvlTypes(), printer, [&](DimLevelType dlt) {
- printer << "\"" << toMLIRString(dlt) << "\"";
- });
- printer << " ]";
+ auto map = static_cast<AffineMap>(getDimToLvl());
+ auto lvlTypes = getLvlTypes();
+ // Empty affine map indicates identity map
+ if (!map) {
+ map = AffineMap::getMultiDimIdentityMap(getLvlTypes().size(), getContext());
+ }
+ // Modified version of AsmPrinter::Impl::printAffineMap.
+ printer << "<{ map = ";
+ // Symbolic identifiers.
+ if (map.getNumSymbols() != 0) {
+ printer << '[';
+ for (unsigned i = 0; i < map.getNumSymbols() - 1; ++i)
+ printer << 's' << i << ", ";
+ if (map.getNumSymbols() >= 1)
+ printer << 's' << map.getNumSymbols() - 1;
+ printer << ']';
+ }
+ // Dimension identifiers.
+ printer << '(';
+ auto dimSlices = getDimSlices();
+ if (!dimSlices.empty()) {
+ for (unsigned i = 0; i < map.getNumDims() - 1; ++i)
+ printer << 'd' << i << " : " << dimSlices[i] << ", ";
+ if (map.getNumDims() >= 1)
+ printer << 'd' << map.getNumDims() - 1 << " : "
+ << dimSlices[map.getNumDims() - 1];
+ } else {
+ for (unsigned i = 0; i < map.getNumDims() - 1; ++i)
+ printer << 'd' << i << ", ";
+ if (map.getNumDims() >= 1)
+ printer << 'd' << map.getNumDims() - 1;
+ }
+ printer << ')';
+ // Level format and properties.
+ printer << " -> (";
+ for (unsigned i = 0; i < map.getNumResults() - 1; ++i) {
+ map.getResult(i).print(printer.getStream());
+ printer << " : " << toMLIRString(lvlTypes[i]) << ", ";
+ }
+ if (map.getNumResults() >= 1) {
+ auto lastIndex = map.getNumResults() - 1;
+ map.getResult(lastIndex).print(printer.getStream());
+ printer << " : " << toMLIRString(lvlTypes[lastIndex]);
+ }
+ printer << ')';
// Print remaining members only for non-default values.
- if (!isIdentity())
- printer << ", dimToLvl = affine_map<" << getDimToLvl() << ">";
if (getPosWidth())
printer << ", posWidth = " << getPosWidth();
if (getCrdWidth())
printer << ", crdWidth = " << getCrdWidth();
- if (!getDimSlices().empty()) {
- printer << ", dimSlices = [ ";
- llvm::interleaveComma(getDimSlices(), printer,
- [&](SparseTensorDimSliceAttr attr) {
- // Calls SparseTensorDimSliceAttr::print directly to
- // skip mnemonic.
- attr.print(printer);
- });
- printer << " ]";
- }
-
printer << " }>";
}
diff --git a/mlir/test/Dialect/SparseTensor/codegen.mlir b/mlir/test/Dialect/SparseTensor/codegen.mlir
index 69a9c274a861ce1..c3b16807a7c18a6 100644
--- a/mlir/test/Dialect/SparseTensor/codegen.mlir
+++ b/mlir/test/Dialect/SparseTensor/codegen.mlir
@@ -507,7 +507,7 @@ func.func @sparse_compression(%tensor: tensor<8x8xf64, #CSR>,
return %1 : tensor<8x8xf64, #CSR>
}
-// CHECK-LABEL: func.func private @_insert_dense_compressed_no_8_8_f64_0_0(
+// CHECK-LABEL: func.func private @"_insert_dense_compressed(nonordered)_8_8_f64_0_0"(
// CHECK-SAME: %[[A1:.*0]]: memref<?xindex>,
// CHECK-SAME: %[[A2:.*1]]: memref<?xindex>,
// CHECK-SAME: %[[A3:.*2]]: memref<?xf64>,
@@ -533,7 +533,7 @@ func.func @sparse_compression(%tensor: tensor<8x8xf64, #CSR>,
// CHECK: %[[A13:.*]]:4 = scf.for %[[A14:.*]] = %[[A11]] to %[[A7]] step %[[A12]] iter_args(%[[A15:.*]] = %[[A0]], %[[A16:.*]] = %[[A1]], %[[A17:.*]] = %[[A2]], %[[A18:.*]] = %[[A3]]) -> (memref<?xindex>, memref<?xindex>, memref<?xf64>, !sparse_tensor.storage_specifier
// CHECK: %[[A19:.*]] = memref.load %[[A6]]{{\[}}%[[A14]]] : memref<?xindex>
// CHECK: %[[A20:.*]] = memref.load %[[A4]]{{\[}}%[[A19]]] : memref<?xf64>
-// CHECK: %[[A21:.*]]:4 = func.call @_insert_dense_compressed_no_8_8_f64_0_0(%[[A15]], %[[A16]], %[[A17]], %[[A18]], %[[A8]], %[[A19]], %[[A20]]) : (memref<?xindex>, memref<?xindex>, memref<?xf64>, !sparse_tensor.storage_specifier
+// CHECK: %[[A21:.*]]:4 = func.call @"_insert_dense_compressed(nonordered)_8_8_f64_0_0"(%[[A15]], %[[A16]], %[[A17]], %[[A18]], %[[A8]], %[[A19]], %[[A20]]) : (memref<?xindex>, memref<?xindex>, memref<?xf64>, !sparse_tensor.storage_specifier
// CHECK: memref.store %[[A10]], %[[A4]]{{\[}}%[[A19]]] : memref<?xf64>
// CHECK: memref.store %[[A9]], %[[A5]]{{\[}}%[[A19]]] : memref<?xi1>
// CHECK: scf.yield %[[A21]]#0, %[[A21]]#1, %[[A21]]#2, %[[A21]]#3 : memref<?xindex>, memref<?xindex>, memref<?xf64>, !sparse_tensor.storage_specifier
@@ -611,7 +611,7 @@ func.func @sparse_insert_typed(%arg0: tensor<128xf64, #SparseVector>, %arg1: ind
return %1 : tensor<128xf64, #SparseVector>
}
-// CHECK-LABEL: func.func private @_insert_compressed_nu_singleton_5_6_f64_0_0(
+// CHECK-LABEL: func.func private @"_insert_compressed(nonunique)_singleton_5_6_f64_0_0"(
// CHECK-SAME: %[[A1:.*0]]: memref<?xindex>,
// CHECK-SAME: %[[A2:.*1]]: memref<?xindex>,
// CHECK-SAME: %[[A3:.*2]]: memref<?xf64>,
@@ -627,7 +627,7 @@ func.func @sparse_insert_typed(%arg0: tensor<128xf64, #SparseVector>, %arg1: ind
// CHECK-SAME: %[[A3:.*3]]: !sparse_tensor.storage_specifier
// CHECK-SAME: %[[A4:.*4]]: index,
// CHECK-SAME: %[[A5:.*5]]: f64)
-// CHECK: %[[R:.*]]:4 = call @_insert_compressed_nu_singleton_5_6_f64_0_0(%[[A0]], %[[A1]], %[[A2]], %[[A3]], %[[A4]], %[[A4]], %[[A5]])
+// CHECK: %[[R:.*]]:4 = call @"_insert_compressed(nonunique)_singleton_5_6_f64_0_0"(%[[A0]], %[[A1]], %[[A2]], %[[A3]], %[[A4]], %[[A4]], %[[A5]])
// CHECK: return %[[R]]#0, %[[R]]#1, %[[R]]#2, %[[R]]#3
func.func @sparse_insert_coo(%arg0: tensor<5x6xf64, #Coo>, %arg1: index, %arg2: f64) -> tensor<5x6xf64, #Coo> {
%0 = sparse_tensor.insert %arg2 into %arg0[%arg1, %arg1] : tensor<5x6xf64, #Coo>
diff --git a/mlir/test/Dialect/SparseTensor/roundtrip_encoding.mlir b/mlir/test/Dialect/SparseTensor/roundtrip_encoding.mlir
index 39e3ef102423524..c4ef50bee01ea2c 100644
--- a/mlir/test/Dialect/SparseTensor/roundtrip_encoding.mlir
+++ b/mlir/test/Dialect/SparseTensor/roundtrip_encoding.mlir
@@ -1,7 +1,7 @@
// RUN: mlir-opt %s -split-input-file | mlir-opt | FileCheck %s
// CHECK-LABEL: func private @sparse_1d_tensor(
-// CHECK-SAME: tensor<32xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>>)
+// CHECK-SAME: tensor<32xf64, #sparse_tensor.encoding<{ map = (d0) -> (d0 : compressed) }>>)
func.func private @sparse_1d_tensor(tensor<32xf64, #sparse_tensor.encoding<{ map = (d0) -> (d0 : compressed) }>>)
// -----
@@ -13,7 +13,7 @@ func.func private @sparse_1d_tensor(tensor<32xf64, #sparse_tensor.encoding<{ map
}>
// CHECK-LABEL: func private @sparse_csr(
-// CHECK-SAME: tensor<?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ], posWidth = 64, crdWidth = 64 }>>)
+// CHECK-SAME: tensor<?x?xf32, #sparse_tensor.encoding<{ map = (d0, d1) -> (d0 : dense, d1 : compressed), posWidth = 64, crdWidth = 64 }>>)
func.func private @sparse_csr(tensor<?x?xf32, #CSR>)
// -----
@@ -23,7 +23,7 @@ func.func private @sparse_csr(tensor<?x?xf32, #CSR>)
}>
// CHECK-LABEL: func private @CSR_explicit(
-// CHECK-SAME: tensor<?x?xf64, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>>
+// CHECK-SAME: tensor<?x?xf64, #sparse_tensor.encoding<{ map = (d0, d1) -> (d0 : dense, d1 : compressed) }>>
func.func private @CSR_explicit(%arg0: tensor<?x?xf64, #CSR_explicit>) {
return
}
@@ -37,7 +37,7 @@ func.func private @CSR_explicit(%arg0: tensor<?x?xf64, #CSR_explicit>) {
}>
// CHECK-LABEL: func private @sparse_csc(
-// CHECK-SAME: tensor<?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ], dimToLvl = affine_map<(d0, d1) -> (d1, d0)> }>>)
+// CHECK-SAME: tensor<?x?xf32, #sparse_tensor.encoding<{ map = (d0, d1) -> (d1 : dense, d0 : compressed) }>>)
func.func private @sparse_csc(tensor<?x?xf32, #CSC>)
// -----
@@ -49,7 +49,7 @@ func.func private @sparse_csc(tensor<?x?xf32, #CSC>)
}>
// CHECK-LABEL: func private @sparse_dcsc(
-// CHECK-SAME: tensor<?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ], dimToLvl = affine_map<(d0, d1) -> (d1, d0)>, crdWidth = 64 }>>)
+// CHECK-SAME: tensor<?x?xf32, #sparse_tensor.encoding<{ map = (d0, d1) -> (d1 : compressed, d0 : compressed), crdWidth = 64 }>>)
func.func private @sparse_dcsc(tensor<?x?xf32, #DCSC>)
// -----
@@ -59,7 +59,7 @@ func.func private @sparse_dcsc(tensor<?x?xf32, #DCSC>)
}>
// CHECK-LABEL: func private @sparse_coo(
-// CHECK-SAME: tensor<?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed_nu_no", "singleton_no" ] }>>)
+// CHECK-SAME: tensor<?x?xf32, #sparse_tensor.encoding<{ map = (d0, d1) -> (d0 : compressed(nonunique, nonordered), d1 : singleton(nonordered)) }>>)
func.func private @sparse_coo(tensor<?x?xf32, #COO>)
// -----
@@ -69,7 +69,7 @@ func.func private @sparse_coo(tensor<?x?xf32, #COO>)
}>
// CHECK-LABEL: func private @sparse_bcoo(
-// CHECK-SAME: tensor<?x?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "loose_compressed_nu", "singleton" ] }>>)
+// CHECK-SAME: tensor<?x?x?xf32, #sparse_tensor.encoding<{ map = (d0, d1, d2) -> (d0 : dense, d1 : loose_compressed(nonunique), d2 : singleton) }>>)
func.func private @sparse_bcoo(tensor<?x?x?xf32, #BCOO>)
// -----
@@ -79,7 +79,7 @@ func.func private @sparse_bcoo(tensor<?x?x?xf32, #BCOO>)
}>
// CHECK-LABEL: func private @sparse_sorted_coo(
-// CHECK-SAME: tensor<10x10xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed_nu", "singleton" ] }>>)
+// CHECK-SAME: tensor<10x10xf64, #sparse_tensor.encoding<{ map = (d0, d1) -> (d0 : compressed(nonunique), d1 : singleton) }>>)
func.func private @sparse_sorted_coo(tensor<10x10xf64, #SortedCOO>)
// -----
@@ -94,7 +94,7 @@ func.func private @sparse_sorted_coo(tensor<10x10xf64, #SortedCOO>)
}>
// CHECK-LABEL: func private @sparse_bcsr(
-// CHECK-SAME: tensor<10x60xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed", "dense", "dense" ], dimToLvl = affine_map<(d0, d1) -> (d0 floordiv 2, d1 floordiv 3, d0 mod 2, d1 mod 3)> }>>
+// CHECK-SAME: tensor<10x60xf64, #sparse_tensor.encoding<{ map = (d0, d1) -> (d0 floordiv 2 : compressed, d1 floordiv 3 : compressed, d0 mod 2 : dense, d1 mod 3 : dense) }>>
func.func private @sparse_bcsr(tensor<10x60xf64, #BCSR>)
@@ -105,7 +105,7 @@ func.func private @sparse_bcsr(tensor<10x60xf64, #BCSR>)
}>
// CHECK-LABEL: func private @sparse_ell(
-// CHECK-SAME: tensor<?x?xf64, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "dense", "compressed" ], dimToLvl = affine_map<(d0, d1)[s0] -> (d0 * (s0 * 4), d0, d1)> }>>
+// CHECK-SAME: tensor<?x?xf64, #sparse_tensor.encoding<{ map = [s0](d0, d1) -> (d0 * (s0 * 4) : dense, d0 : dense, d1 : compressed) }>>
func.func private @sparse_ell(tensor<?x?xf64, #ELL>)
// -----
@@ -115,7 +115,7 @@ func.func private @sparse_ell(tensor<?x?xf64, #ELL>)
}>
// CHECK-LABEL: func private @sparse_slice(
-// CHECK-SAME: tensor<?x?xf64, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ], dimSlices = [ (1, 4, 1), (1, 4, 2) ] }>>
+// CHECK-SAME: tensor<?x?xf64, #sparse_tensor.encoding<{ map = (d0 : #sparse_tensor<slice(1, 4, 1)>, d1 : #sparse_tensor<slice(1, 4, 2)>) -> (d0 : dense, d1 : compressed) }>>
func.func private @sparse_slice(tensor<?x?xf64, #CSR_SLICE>)
// -----
@@ -125,7 +125,7 @@ func.func private @sparse_slice(tensor<?x?xf64, #CSR_SLICE>)
}>
// CHECK-LABEL: func private @sparse_slice(
-// CHECK-SAME: tensor<?x?xf64, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ], dimSlices = [ (1, ?, 1), (?, 4, 2) ] }>>
+// CHECK-SAME: tensor<?x?xf64, #sparse_tensor.encoding<{ map = (d0 : #sparse_tensor<slice(1, ?, 1)>, d1 : #sparse_tensor<slice(?, 4, 2)>) -> (d0 : dense, d1 : compressed) }>>
func.func private @sparse_slice(tensor<?x?xf64, #CSR_SLICE>)
// -----
@@ -138,7 +138,7 @@ func.func private @sparse_slice(tensor<?x?xf64, #CSR_SLICE>)
}>
// CHECK-LABEL: func private @sparse_2_out_of_4(
-// CHECK-SAME: tensor<?x?xf64, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed24" ] }>>
+// CHECK-SAME: tensor<?x?xf64, #sparse_tensor.encoding<{ map = (d0, d1) -> (d0 : dense, d1 : block2_4) }>>
func.func private @sparse_2_out_of_4(tensor<?x?xf64, #NV_24>)
// -----
@@ -153,7 +153,7 @@ func.func private @sparse_2_out_of_4(tensor<?x?xf64, #NV_24>)
}>
// CHECK-LABEL: func private @BCSR(
-// CHECK-SAME: tensor<?x?xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed", "dense", "dense" ], dimToLvl = affine_map<(d0, d1) -> (d0 floordiv 2, d1 floordiv 3, d0 mod 2, d1 mod 3)> }>>
+// CHECK-SAME: tensor<?x?xf64, #sparse_tensor.encoding<{ map = (d0, d1) -> (d0 floordiv 2 : compressed, d1 floordiv 3 : compressed, d0 mod 2 : dense, d1 mod 3 : dense) }>>
func.func private @BCSR(%arg0: tensor<?x?xf64, #BCSR>) {
return
}
@@ -174,7 +174,7 @@ func.func private @BCSR(%arg0: tensor<?x?xf64, #BCSR>) {
}>
// CHECK-LABEL: func private @BCSR_explicit(
-// CHECK-SAME: tensor<?x?xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed", "dense", "dense" ], dimToLvl = affine_map<(d0, d1) -> (d0 floordiv 2, d1 floordiv 3, d0 mod 2, d1 mod 3)> }>>
+// CHECK-SAME: tensor<?x?xf64, #sparse_tensor.encoding<{ map = (d0, d1) -> (d0 floordiv 2 : compressed, d1 floordiv 3 : compressed, d0 mod 2 : dense, d1 mod 3 : dense) }>>
func.func private @BCSR_explicit(%arg0: tensor<?x?xf64, #BCSR_explicit>) {
return
}
@@ -190,7 +190,7 @@ func.func private @BCSR_explicit(%arg0: tensor<?x?xf64, #BCSR_explicit>) {
}>
// CHECK-LABEL: func private @NV_24(
-// CHECK-SAME: tensor<?x?xf64, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "dense", "compressed24" ], dimToLvl = affine_map<(d0, d1) -> (d0, d1 floordiv 4, d1 mod 4)> }>>
+// CHECK-SAME: tensor<?x?xf64, #sparse_tensor.encoding<{ map = (d0, d1) -> (d0 : dense, d1 floordiv 4 : dense, d1 mod 4 : block2_4) }>>
func.func private @NV_24(%arg0: tensor<?x?xf64, #NV_24>) {
return
}
diff --git a/mlir/test/Dialect/SparseTensor/sparse_reshape.mlir b/mlir/test/Dialect/SparseTensor/sparse_reshape.mlir
index 7f8edac15302616..3a2376f75654af9 100644
--- a/mlir/test/Dialect/SparseTensor/sparse_reshape.mlir
+++ b/mlir/test/Dialect/SparseTensor/sparse_reshape.mlir
@@ -16,7 +16,7 @@
// CHECK-ROUND: return %[[E]] : tensor<10x10xf64, #sparse_tensor.encoding<{{{.*}}}>>
//
// CHECK-LABEL: func.func @sparse_expand(
-// CHECK-SAME: %[[S:.*]]:
+// CHECK-SAME: %[[S:[a-zA-Z0-9_]*]]:
// CHECK-DAG: %[[C10:.*]] = arith.constant 10 : index
// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[C1:.*]] = arith.constant 1 : index
@@ -53,7 +53,7 @@ func.func @sparse_expand(%arg0: tensor<100xf64, #SparseVector>) -> tensor<10x10x
// CHECK-ROUND: return %[[C]] : tensor<100xf64, #sparse_tensor.encoding<{{{.*}}}>>
//
// CHECK-LABEL: func.func @sparse_collapse(
-// CHECK-SAME: %[[S:.*]]:
+// CHECK-SAME: %[[S:[a-zA-Z0-9_]*]]:
// CHECK-DAG: %[[C10:.*]] = arith.constant 10 : index
// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[C1:.*]] = arith.constant 1 : index
@@ -99,7 +99,7 @@ func.func @sparse_collapse(%arg0: tensor<10x10xf64, #SparseMatrix>) -> tensor<10
// CHECK-ROUND: return %[[E]] : tensor<?x10xf64, #sparse_tensor.encoding<{{{.*}}}>>
//
// CHECK-LABEL: func.func @dynamic_sparse_expand(
-// CHECK-SAME: %[[S:.*]]:
+// CHECK-SAME: %[[S:[a-zA-Z0-9_]*]]:
// CHECK-DAG: %[[C10:.*]] = arith.constant 10 : index
// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[C1:.*]] = arith.constant 1 : index
@@ -142,7 +142,7 @@ func.func @dynamic_sparse_expand(%arg0: tensor<?xf64, #SparseVector>) -> tensor<
// CHECK-ROUND: return %[[C]] : tensor<?xf64, #sparse_tensor.encoding<{{{.*}}}>>
//
// CHECK-LABEL: func.func @dynamic_sparse_collapse(
-// CHECK-SAME: %[[S:.*]]:
+// CHECK-SAME: %[[S:[a-zA-Z0-9_]*]]:
// CHECK-DAG: %[[C10:.*]] = arith.constant 10 : index
// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[C1:.*]] = arith.constant 1 : index
diff --git a/mlir/test/Dialect/SparseTensor/sparse_tensor_reshape.mlir b/mlir/test/Dialect/SparseTensor/sparse_tensor_reshape.mlir
index 9368cc71c5faa42..e0111c89df65a2d 100644
--- a/mlir/test/Dialect/SparseTensor/sparse_tensor_reshape.mlir
+++ b/mlir/test/Dialect/SparseTensor/sparse_tensor_reshape.mlir
@@ -4,7 +4,7 @@
#SparseMatrix = #sparse_tensor.encoding<{ map = (d0, d1) -> (d0 : compressed, d1 : compressed) }>
// CHECK: func.func @sparse_reshape(
-// CHECK-SAME: %[[S:.*]]:
+// CHECK-SAME: %[[S:[a-zA-Z0-9_]*]]:
// CHECK-DAG: %[[C25:.*]] = arith.constant 25 : index
// CHECK-DAG: %[[C10:.*]] = arith.constant 10 : index
// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
diff --git a/mlir/test/python/dialects/sparse_tensor/dialect.py b/mlir/test/python/dialects/sparse_tensor/dialect.py
index e1048edce184a51..6d15363fb17118d 100644
--- a/mlir/test/python/dialects/sparse_tensor/dialect.py
+++ b/mlir/test/python/dialects/sparse_tensor/dialect.py
@@ -13,95 +13,93 @@ def run(f):
# CHECK-LABEL: TEST: testEncodingAttr1D
@run
def testEncodingAttr1D():
- with Context() as ctx:
- parsed = Attribute.parse(
- "#sparse_tensor.encoding<{"
- " map = (d0) -> (d0 : compressed),"
- " posWidth = 16,"
- " crdWidth = 32"
- "}>"
- )
- # CHECK: #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ], posWidth = 16, crdWidth = 32 }>
- print(parsed)
-
- casted = st.EncodingAttr(parsed)
- # CHECK: equal: True
- print(f"equal: {casted == parsed}")
-
- # CHECK: lvl_types: [<DimLevelType.compressed: 8>]
- print(f"lvl_types: {casted.lvl_types}")
- # CHECK: dim_to_lvl: None
- print(f"dim_to_lvl: {casted.dim_to_lvl}")
- # CHECK: pos_width: 16
- print(f"pos_width: {casted.pos_width}")
- # CHECK: crd_width: 32
- print(f"crd_width: {casted.crd_width}")
-
- created = st.EncodingAttr.get(casted.lvl_types, None, 0, 0)
- # CHECK: #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>
- print(created)
- # CHECK: created_equal: False
- print(f"created_equal: {created == casted}")
-
- # Verify that the factory creates an instance of the proper type.
- # CHECK: is_proper_instance: True
- print(f"is_proper_instance: {isinstance(created, st.EncodingAttr)}")
- # CHECK: created_pos_width: 0
- print(f"created_pos_width: {created.pos_width}")
+ with Context() as ctx:
+ parsed = Attribute.parse(
+ "#sparse_tensor.encoding<{"
+ " map = (d0) -> (d0 : compressed),"
+ " posWidth = 16,"
+ " crdWidth = 32"
+ "}>"
+ )
+ # CHECK: #sparse_tensor.encoding<{ map = (d0) -> (d0 : compressed), posWidth = 16, crdWidth = 32 }>
+ print(parsed)
+
+ casted = st.EncodingAttr(parsed)
+ # CHECK: equal: True
+ print(f"equal: {casted == parsed}")
+
+ # CHECK: lvl_types: [<DimLevelType.compressed: 8>]
+ print(f"lvl_types: {casted.lvl_types}")
+ # CHECK: dim_to_lvl: None
+ print(f"dim_to_lvl: {casted.dim_to_lvl}")
+ # CHECK: pos_width: 16
+ print(f"pos_width: {casted.pos_width}")
+ # CHECK: crd_width: 32
+ print(f"crd_width: {casted.crd_width}")
+
+ created = st.EncodingAttr.get(casted.lvl_types, None, 0, 0)
+ # CHECK: #sparse_tensor.encoding<{ map = (d0) -> (d0 : compressed) }>
+ print(created)
+ # CHECK: created_equal: False
+ print(f"created_equal: {created == casted}")
+
+ # Verify that the factory creates an instance of the proper type.
+ # CHECK: is_proper_instance: True
+ print(f"is_proper_instance: {isinstance(created, st.EncodingAttr)}")
+ # CHECK: created_pos_width: 0
+ print(f"created_pos_width: {created.pos_width}")
# CHECK-LABEL: TEST: testEncodingAttr2D
@run
def testEncodingAttr2D():
- with Context() as ctx:
- parsed = Attribute.parse(
- "#sparse_tensor.encoding<{"
- " map = (d0, d1) -> (d1 : dense, d0 : compressed),"
- " posWidth = 8,"
- " crdWidth = 32"
- "}>"
- )
- # CHECK: #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ], dimToLvl = affine_map<(d0, d1) -> (d1, d0)>, posWidth = 8, crdWidth = 32 }>
- print(parsed)
-
- casted = st.EncodingAttr(parsed)
- # CHECK: equal: True
- print(f"equal: {casted == parsed}")
-
- # CHECK: lvl_types: [<DimLevelType.dense: 4>, <DimLevelType.compressed: 8>]
- print(f"lvl_types: {casted.lvl_types}")
- # CHECK: dim_to_lvl: (d0, d1) -> (d1, d0)
- print(f"dim_to_lvl: {casted.dim_to_lvl}")
- # CHECK: pos_width: 8
- print(f"pos_width: {casted.pos_width}")
- # CHECK: crd_width: 32
- print(f"crd_width: {casted.crd_width}")
-
- created = st.EncodingAttr.get(
- casted.lvl_types, casted.dim_to_lvl, 8, 32
- )
- # CHECK: #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ], dimToLvl = affine_map<(d0, d1) -> (d1, d0)>, posWidth = 8, crdWidth = 32 }>
- print(created)
- # CHECK: created_equal: True
- print(f"created_equal: {created == casted}")
+ with Context() as ctx:
+ parsed = Attribute.parse(
+ "#sparse_tensor.encoding<{"
+ " map = (d0, d1) -> (d1 : dense, d0 : compressed),"
+ " posWidth = 8,"
+ " crdWidth = 32"
+ "}>"
+ )
+ # CHECK: #sparse_tensor.encoding<{ map = (d0, d1) -> (d1 : dense, d0 : compressed), posWidth = 8, crdWidth = 32 }>
+ print(parsed)
+
+ casted = st.EncodingAttr(parsed)
+ # CHECK: equal: True
+ print(f"equal: {casted == parsed}")
+
+ # CHECK: lvl_types: [<DimLevelType.dense: 4>, <DimLevelType.compressed: 8>]
+ print(f"lvl_types: {casted.lvl_types}")
+ # CHECK: dim_to_lvl: (d0, d1) -> (d1, d0)
+ print(f"dim_to_lvl: {casted.dim_to_lvl}")
+ # CHECK: pos_width: 8
+ print(f"pos_width: {casted.pos_width}")
+ # CHECK: crd_width: 32
+ print(f"crd_width: {casted.crd_width}")
+
+ created = st.EncodingAttr.get(casted.lvl_types, casted.dim_to_lvl, 8, 32)
+ # CHECK: #sparse_tensor.encoding<{ map = (d0, d1) -> (d1 : dense, d0 : compressed), posWidth = 8, crdWidth = 32 }>
+ print(created)
+ # CHECK: created_equal: True
+ print(f"created_equal: {created == casted}")
# CHECK-LABEL: TEST: testEncodingAttrOnTensorType
@run
def testEncodingAttrOnTensorType():
- with Context() as ctx, Location.unknown():
- encoding = st.EncodingAttr(
- Attribute.parse(
- "#sparse_tensor.encoding<{"
- " map = (d0) -> (d0 : compressed), "
- " posWidth = 64,"
- " crdWidth = 32"
- "}>"
- )
+ with Context() as ctx, Location.unknown():
+ encoding = st.EncodingAttr(
+ Attribute.parse(
+ "#sparse_tensor.encoding<{"
+ " map = (d0) -> (d0 : compressed), "
+ " posWidth = 64,"
+ " crdWidth = 32"
+ "}>"
)
- tt = RankedTensorType.get((1024,), F32Type.get(), encoding=encoding)
- # CHECK: tensor<1024xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ], posWidth = 64, crdWidth = 32 }>>
- print(tt)
- # CHECK: #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ], posWidth = 64, crdWidth = 32 }>
- print(tt.encoding)
- assert tt.encoding == encoding
+ )
+ tt = RankedTensorType.get((1024,), F32Type.get(), encoding=encoding)
+ # CHECK: tensor<1024xf32, #sparse_tensor.encoding<{ map = (d0) -> (d0 : compressed), posWidth = 64, crdWidth = 32 }>>
+ print(tt)
+ # CHECK: #sparse_tensor.encoding<{ map = (d0) -> (d0 : compressed), posWidth = 64, crdWidth = 32 }>
+ print(tt.encoding)
+ assert tt.encoding == encoding
>From 2be69066192995ff171e08a54f7c7fdd3e35ab44 Mon Sep 17 00:00:00 2001
From: Yinying Li <yinyingli at google.com>
Date: Tue, 3 Oct 2023 18:39:17 +0000
Subject: [PATCH 2/2] format
---
.../python/dialects/sparse_tensor/dialect.py | 158 +++++++++---------
1 file changed, 79 insertions(+), 79 deletions(-)
diff --git a/mlir/test/python/dialects/sparse_tensor/dialect.py b/mlir/test/python/dialects/sparse_tensor/dialect.py
index 6d15363fb17118d..d80b878323377a4 100644
--- a/mlir/test/python/dialects/sparse_tensor/dialect.py
+++ b/mlir/test/python/dialects/sparse_tensor/dialect.py
@@ -13,93 +13,93 @@ def run(f):
# CHECK-LABEL: TEST: testEncodingAttr1D
@run
def testEncodingAttr1D():
- with Context() as ctx:
- parsed = Attribute.parse(
- "#sparse_tensor.encoding<{"
- " map = (d0) -> (d0 : compressed),"
- " posWidth = 16,"
- " crdWidth = 32"
- "}>"
- )
- # CHECK: #sparse_tensor.encoding<{ map = (d0) -> (d0 : compressed), posWidth = 16, crdWidth = 32 }>
- print(parsed)
-
- casted = st.EncodingAttr(parsed)
- # CHECK: equal: True
- print(f"equal: {casted == parsed}")
-
- # CHECK: lvl_types: [<DimLevelType.compressed: 8>]
- print(f"lvl_types: {casted.lvl_types}")
- # CHECK: dim_to_lvl: None
- print(f"dim_to_lvl: {casted.dim_to_lvl}")
- # CHECK: pos_width: 16
- print(f"pos_width: {casted.pos_width}")
- # CHECK: crd_width: 32
- print(f"crd_width: {casted.crd_width}")
-
- created = st.EncodingAttr.get(casted.lvl_types, None, 0, 0)
- # CHECK: #sparse_tensor.encoding<{ map = (d0) -> (d0 : compressed) }>
- print(created)
- # CHECK: created_equal: False
- print(f"created_equal: {created == casted}")
-
- # Verify that the factory creates an instance of the proper type.
- # CHECK: is_proper_instance: True
- print(f"is_proper_instance: {isinstance(created, st.EncodingAttr)}")
- # CHECK: created_pos_width: 0
- print(f"created_pos_width: {created.pos_width}")
+ with Context() as ctx:
+ parsed = Attribute.parse(
+ "#sparse_tensor.encoding<{"
+ " map = (d0) -> (d0 : compressed),"
+ " posWidth = 16,"
+ " crdWidth = 32"
+ "}>"
+ )
+ # CHECK: #sparse_tensor.encoding<{ map = (d0) -> (d0 : compressed), posWidth = 16, crdWidth = 32 }>
+ print(parsed)
+
+ casted = st.EncodingAttr(parsed)
+ # CHECK: equal: True
+ print(f"equal: {casted == parsed}")
+
+ # CHECK: lvl_types: [<DimLevelType.compressed: 8>]
+ print(f"lvl_types: {casted.lvl_types}")
+ # CHECK: dim_to_lvl: None
+ print(f"dim_to_lvl: {casted.dim_to_lvl}")
+ # CHECK: pos_width: 16
+ print(f"pos_width: {casted.pos_width}")
+ # CHECK: crd_width: 32
+ print(f"crd_width: {casted.crd_width}")
+
+ created = st.EncodingAttr.get(casted.lvl_types, None, 0, 0)
+ # CHECK: #sparse_tensor.encoding<{ map = (d0) -> (d0 : compressed) }>
+ print(created)
+ # CHECK: created_equal: False
+ print(f"created_equal: {created == casted}")
+
+ # Verify that the factory creates an instance of the proper type.
+ # CHECK: is_proper_instance: True
+ print(f"is_proper_instance: {isinstance(created, st.EncodingAttr)}")
+ # CHECK: created_pos_width: 0
+ print(f"created_pos_width: {created.pos_width}")
# CHECK-LABEL: TEST: testEncodingAttr2D
@run
def testEncodingAttr2D():
- with Context() as ctx:
- parsed = Attribute.parse(
- "#sparse_tensor.encoding<{"
- " map = (d0, d1) -> (d1 : dense, d0 : compressed),"
- " posWidth = 8,"
- " crdWidth = 32"
- "}>"
- )
- # CHECK: #sparse_tensor.encoding<{ map = (d0, d1) -> (d1 : dense, d0 : compressed), posWidth = 8, crdWidth = 32 }>
- print(parsed)
-
- casted = st.EncodingAttr(parsed)
- # CHECK: equal: True
- print(f"equal: {casted == parsed}")
-
- # CHECK: lvl_types: [<DimLevelType.dense: 4>, <DimLevelType.compressed: 8>]
- print(f"lvl_types: {casted.lvl_types}")
- # CHECK: dim_to_lvl: (d0, d1) -> (d1, d0)
- print(f"dim_to_lvl: {casted.dim_to_lvl}")
- # CHECK: pos_width: 8
- print(f"pos_width: {casted.pos_width}")
- # CHECK: crd_width: 32
- print(f"crd_width: {casted.crd_width}")
-
- created = st.EncodingAttr.get(casted.lvl_types, casted.dim_to_lvl, 8, 32)
- # CHECK: #sparse_tensor.encoding<{ map = (d0, d1) -> (d1 : dense, d0 : compressed), posWidth = 8, crdWidth = 32 }>
- print(created)
- # CHECK: created_equal: True
- print(f"created_equal: {created == casted}")
+ with Context() as ctx:
+ parsed = Attribute.parse(
+ "#sparse_tensor.encoding<{"
+ " map = (d0, d1) -> (d1 : dense, d0 : compressed),"
+ " posWidth = 8,"
+ " crdWidth = 32"
+ "}>"
+ )
+ # CHECK: #sparse_tensor.encoding<{ map = (d0, d1) -> (d1 : dense, d0 : compressed), posWidth = 8, crdWidth = 32 }>
+ print(parsed)
+
+ casted = st.EncodingAttr(parsed)
+ # CHECK: equal: True
+ print(f"equal: {casted == parsed}")
+
+ # CHECK: lvl_types: [<DimLevelType.dense: 4>, <DimLevelType.compressed: 8>]
+ print(f"lvl_types: {casted.lvl_types}")
+ # CHECK: dim_to_lvl: (d0, d1) -> (d1, d0)
+ print(f"dim_to_lvl: {casted.dim_to_lvl}")
+ # CHECK: pos_width: 8
+ print(f"pos_width: {casted.pos_width}")
+ # CHECK: crd_width: 32
+ print(f"crd_width: {casted.crd_width}")
+
+ created = st.EncodingAttr.get(casted.lvl_types, casted.dim_to_lvl, 8, 32)
+ # CHECK: #sparse_tensor.encoding<{ map = (d0, d1) -> (d1 : dense, d0 : compressed), posWidth = 8, crdWidth = 32 }>
+ print(created)
+ # CHECK: created_equal: True
+ print(f"created_equal: {created == casted}")
# CHECK-LABEL: TEST: testEncodingAttrOnTensorType
@run
def testEncodingAttrOnTensorType():
- with Context() as ctx, Location.unknown():
- encoding = st.EncodingAttr(
- Attribute.parse(
- "#sparse_tensor.encoding<{"
- " map = (d0) -> (d0 : compressed), "
- " posWidth = 64,"
- " crdWidth = 32"
- "}>"
+ with Context() as ctx, Location.unknown():
+ encoding = st.EncodingAttr(
+ Attribute.parse(
+ "#sparse_tensor.encoding<{"
+ " map = (d0) -> (d0 : compressed), "
+ " posWidth = 64,"
+ " crdWidth = 32"
+ "}>"
+ )
)
- )
- tt = RankedTensorType.get((1024,), F32Type.get(), encoding=encoding)
- # CHECK: tensor<1024xf32, #sparse_tensor.encoding<{ map = (d0) -> (d0 : compressed), posWidth = 64, crdWidth = 32 }>>
- print(tt)
- # CHECK: #sparse_tensor.encoding<{ map = (d0) -> (d0 : compressed), posWidth = 64, crdWidth = 32 }>
- print(tt.encoding)
- assert tt.encoding == encoding
+ tt = RankedTensorType.get((1024,), F32Type.get(), encoding=encoding)
+ # CHECK: tensor<1024xf32, #sparse_tensor.encoding<{ map = (d0) -> (d0 : compressed), posWidth = 64, crdWidth = 32 }>>
+ print(tt)
+ # CHECK: #sparse_tensor.encoding<{ map = (d0) -> (d0 : compressed), posWidth = 64, crdWidth = 32 }>
+ print(tt.encoding)
+ assert tt.encoding == encoding
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