mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_cast.mlir - llvm-project - Git at Google

 // RUN: mlir-opt %s \
 // RUN:   --sparsification --sparse-tensor-conversion \
 // RUN:   --convert-vector-to-scf --convert-scf-to-std \
 // RUN:   --func-bufferize --tensor-constant-bufferize --tensor-bufferize \
 // RUN:   --std-bufferize --finalizing-bufferize --lower-affine \
 // RUN:   --convert-vector-to-llvm --convert-memref-to-llvm \
 // RUN:   --convert-std-to-llvm --reconcile-unrealized-casts | \
 // RUN: mlir-cpu-runner \
 // RUN:  -e entry -entry-point-result=void  \
 // RUN:  -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \
 // RUN: FileCheck %s
 //
 // Do the same run, but now with SIMDization as well. This should not change the outcome.
 //
 // RUN: mlir-opt %s \
 // RUN:   --sparsification="vectorization-strategy=2 vl=2" --sparse-tensor-conversion \
 // RUN:   --convert-vector-to-scf --convert-scf-to-std \
 // RUN:   --func-bufferize --tensor-constant-bufferize --tensor-bufferize \
 // RUN:   --std-bufferize --finalizing-bufferize --lower-affine \
 // RUN:   --convert-vector-to-llvm --convert-memref-to-llvm \
 // RUN:   --convert-std-to-llvm --reconcile-unrealized-casts | \
 // RUN: mlir-cpu-runner \
 // RUN:  -e entry -entry-point-result=void  \
 // RUN:  -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \
 // RUN: FileCheck %s

 #SV = #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>

 #trait_cast = {
   indexing_maps = [
     affine_map<(i) -> (i)>,  // A (in)
     affine_map<(i) -> (i)>   // X (out)
   ],
   iterator_types = ["parallel"],
   doc = "X(i) = cast A(i)"
 }

 //
 // Integration test that lowers a kernel annotated as sparse to actual sparse
 // code, initializes a matching sparse storage scheme from a dense vector,
 // and runs the resulting code with the JIT compiler.
 //
 module {
   //
   // Various kernels that cast a sparse vector from one type to another.
   // Arithmetic supports the following casts.
   //   sitofp
   //   uitofp
   //   fptosi
   //   fptoui
   //   extf
   //   truncf
   //   extsi
   //   extui
   //   trunci
   //   bitcast
   // Since all casts are "zero preserving" unary operations, lattice computation
   // and conversion to sparse code is straightforward.
   //
   func @sparse_cast_s32_to_f32(%arga: tensor<10xi32, #SV>) -> tensor<10xf32> {
     %argx = arith.constant dense<0.0> : tensor<10xf32>
     %0 = linalg.generic #trait_cast
       ins(%arga: tensor<10xi32, #SV>)
       outs(%argx: tensor<10xf32>) {
         ^bb(%a: i32, %x : f32):
           %cst = arith.sitofp %a : i32 to f32
           linalg.yield %cst : f32
     } -> tensor<10xf32>
     return %0 : tensor<10xf32>
   }
   func @sparse_cast_u32_to_f32(%arga: tensor<10xi32, #SV>) -> tensor<10xf32> {
     %argx = arith.constant dense<0.0> : tensor<10xf32>
     %0 = linalg.generic #trait_cast
       ins(%arga: tensor<10xi32, #SV>)
       outs(%argx: tensor<10xf32>) {
         ^bb(%a: i32, %x : f32):
           %cst = arith.uitofp %a : i32 to f32
           linalg.yield %cst : f32
     } -> tensor<10xf32>
     return %0 : tensor<10xf32>
   }
   func @sparse_cast_f32_to_s32(%arga: tensor<10xf32, #SV>) -> tensor<10xi32> {
     %argx = arith.constant dense<0> : tensor<10xi32>
     %0 = linalg.generic #trait_cast
       ins(%arga: tensor<10xf32, #SV>)
       outs(%argx: tensor<10xi32>) {
         ^bb(%a: f32, %x : i32):
           %cst = arith.fptosi %a : f32 to i32
           linalg.yield %cst : i32
     } -> tensor<10xi32>
     return %0 : tensor<10xi32>
   }
   func @sparse_cast_f64_to_u32(%arga: tensor<10xf64, #SV>) -> tensor<10xi32> {
     %argx = arith.constant dense<0> : tensor<10xi32>
     %0 = linalg.generic #trait_cast
       ins(%arga: tensor<10xf64, #SV>)
       outs(%argx: tensor<10xi32>) {
         ^bb(%a: f64, %x : i32):
           %cst = arith.fptoui %a : f64 to i32
           linalg.yield %cst : i32
     } -> tensor<10xi32>
     return %0 : tensor<10xi32>
   }
   func @sparse_cast_f32_to_f64(%arga: tensor<10xf32, #SV>) -> tensor<10xf64> {
     %argx = arith.constant dense<0.0> : tensor<10xf64>
     %0 = linalg.generic #trait_cast
       ins(%arga: tensor<10xf32, #SV>)
       outs(%argx: tensor<10xf64>) {
         ^bb(%a: f32, %x : f64):
           %cst = arith.extf %a : f32 to f64
           linalg.yield %cst : f64
     } -> tensor<10xf64>
     return %0 : tensor<10xf64>
   }
   func @sparse_cast_f64_to_f32(%arga: tensor<10xf64, #SV>) -> tensor<10xf32> {
     %argx = arith.constant dense<0.0> : tensor<10xf32>
     %0 = linalg.generic #trait_cast
       ins(%arga: tensor<10xf64, #SV>)
       outs(%argx: tensor<10xf32>) {
         ^bb(%a: f64, %x : f32):
           %cst = arith.truncf %a : f64 to f32
           linalg.yield %cst : f32
     } -> tensor<10xf32>
     return %0 : tensor<10xf32>
   }
   func @sparse_cast_s32_to_u64(%arga: tensor<10xi32, #SV>) -> tensor<10xi64> {
     %argx = arith.constant dense<0> : tensor<10xi64>
     %0 = linalg.generic #trait_cast
       ins(%arga: tensor<10xi32, #SV>)
       outs(%argx: tensor<10xi64>) {
         ^bb(%a: i32, %x : i64):
           %cst = arith.extsi %a : i32 to i64
           linalg.yield %cst : i64
     } -> tensor<10xi64>
     return %0 : tensor<10xi64>
   }
   func @sparse_cast_u32_to_s64(%arga: tensor<10xi32, #SV>) -> tensor<10xi64> {
     %argx = arith.constant dense<0> : tensor<10xi64>
     %0 = linalg.generic #trait_cast
       ins(%arga: tensor<10xi32, #SV>)
       outs(%argx: tensor<10xi64>) {
         ^bb(%a: i32, %x : i64):
           %cst = arith.extui %a : i32 to i64
           linalg.yield %cst : i64
     } -> tensor<10xi64>
     return %0 : tensor<10xi64>
   }
   func @sparse_cast_i32_to_i8(%arga: tensor<10xi32, #SV>) -> tensor<10xi8> {
     %argx = arith.constant dense<0> : tensor<10xi8>
     %0 = linalg.generic #trait_cast
       ins(%arga: tensor<10xi32, #SV>)
       outs(%argx: tensor<10xi8>) {
         ^bb(%a: i32, %x : i8):
           %cst = arith.trunci %a : i32 to i8
           linalg.yield %cst : i8
     } -> tensor<10xi8>
     return %0 : tensor<10xi8>
   }
   func @sparse_cast_f32_as_s32(%arga: tensor<10xf32, #SV>) -> tensor<10xi32> {
     %argx = arith.constant dense<0> : tensor<10xi32>
     %0 = linalg.generic #trait_cast
       ins(%arga: tensor<10xf32, #SV>)
       outs(%argx: tensor<10xi32>) {
         ^bb(%a: f32, %x : i32):
           %cst = arith.bitcast %a : f32 to i32
           linalg.yield %cst : i32
     } -> tensor<10xi32>
     return %0 : tensor<10xi32>
   }

   //
   // Main driver that converts a dense tensor into a sparse tensor
   // and then calls the sparse casting kernel.
   //
   func @entry() {
     %z = arith.constant 0 : index
     %b = arith.constant 0 : i8
     %i = arith.constant 0 : i32
     %l = arith.constant 0 : i64
     %f = arith.constant 0.0 : f32
     %d = arith.constant 0.0 : f64

     // Initialize dense tensors, convert to a sparse vectors.
     %0 = arith.constant dense<[ -4, -3, -2, -1, 0, 1, 2, 3, 4, 305 ]> : tensor<10xi32>
     %1 = sparse_tensor.convert %0 : tensor<10xi32> to tensor<10xi32, #SV>
     %2 = arith.constant dense<[ -4.4, -3.3, -2.2, -1.1, 0.0, 1.1, 2.2, 3.3, 4.4, 305.5 ]> : tensor<10xf32>
     %3 = sparse_tensor.convert %2 : tensor<10xf32> to tensor<10xf32, #SV>
     %4 = arith.constant dense<[ -4.4, -3.3, -2.2, -1.1, 0.0, 1.1, 2.2, 3.3, 4.4, 305.5 ]> : tensor<10xf64>
     %5 = sparse_tensor.convert %4 : tensor<10xf64> to tensor<10xf64, #SV>
     %6 = arith.constant dense<[ 4294967295.0, 4294967294.0, 4294967293.0, 4294967292.0,
                           0.0, 1.1, 2.2, 3.3, 4.4, 305.5 ]> : tensor<10xf64>
     %7 = sparse_tensor.convert %6 : tensor<10xf64> to tensor<10xf64, #SV>

     //
     // CHECK: ( -4, -3, -2, -1, 0, 1, 2, 3, 4, 305 )
     //
     %c0 = call @sparse_cast_s32_to_f32(%1) : (tensor<10xi32, #SV>) -> tensor<10xf32>
     %m0 = bufferization.to_memref %c0 : memref<10xf32>
     %v0 = vector.transfer_read %m0[%z], %f: memref<10xf32>, vector<10xf32>
     vector.print %v0 : vector<10xf32>

     //
     // CHECK: ( 4.29497e+09, 4.29497e+09, 4.29497e+09, 4.29497e+09, 0, 1, 2, 3, 4, 305 )
     //
     %c1 = call @sparse_cast_u32_to_f32(%1) : (tensor<10xi32, #SV>) -> tensor<10xf32>
     %m1 = bufferization.to_memref %c1 : memref<10xf32>
     %v1 = vector.transfer_read %m1[%z], %f: memref<10xf32>, vector<10xf32>
     vector.print %v1 : vector<10xf32>

     //
     // CHECK: ( -4, -3, -2, -1, 0, 1, 2, 3, 4, 305 )
     //
     %c2 = call @sparse_cast_f32_to_s32(%3) : (tensor<10xf32, #SV>) -> tensor<10xi32>
     %m2 = bufferization.to_memref %c2 : memref<10xi32>
     %v2 = vector.transfer_read %m2[%z], %i: memref<10xi32>, vector<10xi32>
     vector.print %v2 : vector<10xi32>

     //
     // CHECK: ( 4294967295, 4294967294, 4294967293, 4294967292, 0, 1, 2, 3, 4, 305 )
     //
     %c3 = call @sparse_cast_f64_to_u32(%7) : (tensor<10xf64, #SV>) -> tensor<10xi32>
     %m3 = bufferization.to_memref %c3 : memref<10xi32>
     %v3 = vector.transfer_read %m3[%z], %i: memref<10xi32>, vector<10xi32>
     %vu = vector.bitcast %v3 : vector<10xi32> to vector<10xui32>
     vector.print %vu : vector<10xui32>

     //
     // CHECK: ( -4.4, -3.3, -2.2, -1.1, 0, 1.1, 2.2, 3.3, 4.4, 305.5 )
     //
     %c4 = call @sparse_cast_f32_to_f64(%3) : (tensor<10xf32, #SV>) -> tensor<10xf64>
     %m4 = bufferization.to_memref %c4 : memref<10xf64>
     %v4 = vector.transfer_read %m4[%z], %d: memref<10xf64>, vector<10xf64>
     vector.print %v4 : vector<10xf64>

     //
     // CHECK: ( -4.4, -3.3, -2.2, -1.1, 0, 1.1, 2.2, 3.3, 4.4, 305.5 )
     //
     %c5 = call @sparse_cast_f64_to_f32(%5) : (tensor<10xf64, #SV>) -> tensor<10xf32>
     %m5 = bufferization.to_memref %c5 : memref<10xf32>
     %v5 = vector.transfer_read %m5[%z], %f: memref<10xf32>, vector<10xf32>
     vector.print %v5 : vector<10xf32>

     //
     // CHECK: ( -4, -3, -2, -1, 0, 1, 2, 3, 4, 305 )
     //
     %c6 = call @sparse_cast_s32_to_u64(%1) : (tensor<10xi32, #SV>) -> tensor<10xi64>
     %m6 = bufferization.to_memref %c6 : memref<10xi64>
     %v6 = vector.transfer_read %m6[%z], %l: memref<10xi64>, vector<10xi64>
     vector.print %v6 : vector<10xi64>

     //
     // CHECK: ( 4294967292, 4294967293, 4294967294, 4294967295, 0, 1, 2, 3, 4, 305 )
     //
     %c7 = call @sparse_cast_u32_to_s64(%1) : (tensor<10xi32, #SV>) -> tensor<10xi64>
     %m7 = bufferization.to_memref %c7 : memref<10xi64>
     %v7 = vector.transfer_read %m7[%z], %l: memref<10xi64>, vector<10xi64>
     vector.print %v7 : vector<10xi64>

     //
     // CHECK: ( -4, -3, -2, -1, 0, 1, 2, 3, 4, 49 )
     //
     %c8 = call @sparse_cast_i32_to_i8(%1) : (tensor<10xi32, #SV>) -> tensor<10xi8>
     %m8 = bufferization.to_memref %c8 : memref<10xi8>
     %v8 = vector.transfer_read %m8[%z], %b: memref<10xi8>, vector<10xi8>
     vector.print %v8 : vector<10xi8>

     //
     // CHECK: ( -1064514355, -1068289229, -1072902963, -1081291571, 0, 1066192077, 1074580685, 1079194419, 1082969293, 1134084096 )
     //
     %c9 = call @sparse_cast_f32_as_s32(%3) : (tensor<10xf32, #SV>) -> tensor<10xi32>
     %m9 = bufferization.to_memref %c9 : memref<10xi32>
     %v9 = vector.transfer_read %m9[%z], %i: memref<10xi32>, vector<10xi32>
     vector.print %v9 : vector<10xi32>

     // Release the resources.
     sparse_tensor.release %1 : tensor<10xi32, #SV>
     sparse_tensor.release %3 : tensor<10xf32, #SV>
     sparse_tensor.release %5 : tensor<10xf64, #SV>
     sparse_tensor.release %7 : tensor<10xf64, #SV>
     memref.dealloc %m0 : memref<10xf32>
     memref.dealloc %m1 : memref<10xf32>
     memref.dealloc %m2 : memref<10xi32>
     memref.dealloc %m3 : memref<10xi32>
     memref.dealloc %m4 : memref<10xf64>
     memref.dealloc %m5 : memref<10xf32>
     memref.dealloc %m6 : memref<10xi64>
     memref.dealloc %m7 : memref<10xi64>
     memref.dealloc %m8 : memref<10xi8>
     memref.dealloc %m9 : memref<10xi32>

     return
   }
 }
	// RUN: mlir-opt %s \
	// RUN: --sparsification --sparse-tensor-conversion \
	// RUN: --convert-vector-to-scf --convert-scf-to-std \
	// RUN: --func-bufferize --tensor-constant-bufferize --tensor-bufferize \
	// RUN: --std-bufferize --finalizing-bufferize --lower-affine \
	// RUN: --convert-vector-to-llvm --convert-memref-to-llvm \
	// RUN: --convert-std-to-llvm --reconcile-unrealized-casts \| \
	// RUN: mlir-cpu-runner \
	// RUN: -e entry -entry-point-result=void \
	// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext \| \
	// RUN: FileCheck %s
	//
	// Do the same run, but now with SIMDization as well. This should not change the outcome.
	//
	// RUN: mlir-opt %s \
	// RUN: --sparsification="vectorization-strategy=2 vl=2" --sparse-tensor-conversion \
	// RUN: --convert-vector-to-scf --convert-scf-to-std \
	// RUN: --func-bufferize --tensor-constant-bufferize --tensor-bufferize \
	// RUN: --std-bufferize --finalizing-bufferize --lower-affine \
	// RUN: --convert-vector-to-llvm --convert-memref-to-llvm \
	// RUN: --convert-std-to-llvm --reconcile-unrealized-casts \| \
	// RUN: mlir-cpu-runner \
	// RUN: -e entry -entry-point-result=void \
	// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext \| \
	// RUN: FileCheck %s

	#SV = #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>

	#trait_cast = {
	indexing_maps = [
	affine_map<(i) -> (i)>, // A (in)
	affine_map<(i) -> (i)> // X (out)
	],
	iterator_types = ["parallel"],
	doc = "X(i) = cast A(i)"
	}

	//
	// Integration test that lowers a kernel annotated as sparse to actual sparse
	// code, initializes a matching sparse storage scheme from a dense vector,
	// and runs the resulting code with the JIT compiler.
	//
	module {
	//
	// Various kernels that cast a sparse vector from one type to another.
	// Arithmetic supports the following casts.
	// sitofp
	// uitofp
	// fptosi
	// fptoui
	// extf
	// truncf
	// extsi
	// extui
	// trunci
	// bitcast
	// Since all casts are "zero preserving" unary operations, lattice computation
	// and conversion to sparse code is straightforward.
	//
	func @sparse_cast_s32_to_f32(%arga: tensor<10xi32, #SV>) -> tensor<10xf32> {
	%argx = arith.constant dense<0.0> : tensor<10xf32>
	%0 = linalg.generic #trait_cast
	ins(%arga: tensor<10xi32, #SV>)
	outs(%argx: tensor<10xf32>) {
	^bb(%a: i32, %x : f32):
	%cst = arith.sitofp %a : i32 to f32
	linalg.yield %cst : f32
	} -> tensor<10xf32>
	return %0 : tensor<10xf32>
	}
	func @sparse_cast_u32_to_f32(%arga: tensor<10xi32, #SV>) -> tensor<10xf32> {
	%argx = arith.constant dense<0.0> : tensor<10xf32>
	%0 = linalg.generic #trait_cast
	ins(%arga: tensor<10xi32, #SV>)
	outs(%argx: tensor<10xf32>) {
	^bb(%a: i32, %x : f32):
	%cst = arith.uitofp %a : i32 to f32
	linalg.yield %cst : f32
	} -> tensor<10xf32>
	return %0 : tensor<10xf32>
	}
	func @sparse_cast_f32_to_s32(%arga: tensor<10xf32, #SV>) -> tensor<10xi32> {
	%argx = arith.constant dense<0> : tensor<10xi32>
	%0 = linalg.generic #trait_cast
	ins(%arga: tensor<10xf32, #SV>)
	outs(%argx: tensor<10xi32>) {
	^bb(%a: f32, %x : i32):
	%cst = arith.fptosi %a : f32 to i32
	linalg.yield %cst : i32
	} -> tensor<10xi32>
	return %0 : tensor<10xi32>
	}
	func @sparse_cast_f64_to_u32(%arga: tensor<10xf64, #SV>) -> tensor<10xi32> {
	%argx = arith.constant dense<0> : tensor<10xi32>
	%0 = linalg.generic #trait_cast
	ins(%arga: tensor<10xf64, #SV>)
	outs(%argx: tensor<10xi32>) {
	^bb(%a: f64, %x : i32):
	%cst = arith.fptoui %a : f64 to i32
	linalg.yield %cst : i32
	} -> tensor<10xi32>
	return %0 : tensor<10xi32>
	}
	func @sparse_cast_f32_to_f64(%arga: tensor<10xf32, #SV>) -> tensor<10xf64> {
	%argx = arith.constant dense<0.0> : tensor<10xf64>
	%0 = linalg.generic #trait_cast
	ins(%arga: tensor<10xf32, #SV>)
	outs(%argx: tensor<10xf64>) {
	^bb(%a: f32, %x : f64):
	%cst = arith.extf %a : f32 to f64
	linalg.yield %cst : f64
	} -> tensor<10xf64>
	return %0 : tensor<10xf64>
	}
	func @sparse_cast_f64_to_f32(%arga: tensor<10xf64, #SV>) -> tensor<10xf32> {
	%argx = arith.constant dense<0.0> : tensor<10xf32>
	%0 = linalg.generic #trait_cast
	ins(%arga: tensor<10xf64, #SV>)
	outs(%argx: tensor<10xf32>) {
	^bb(%a: f64, %x : f32):
	%cst = arith.truncf %a : f64 to f32
	linalg.yield %cst : f32
	} -> tensor<10xf32>
	return %0 : tensor<10xf32>
	}
	func @sparse_cast_s32_to_u64(%arga: tensor<10xi32, #SV>) -> tensor<10xi64> {
	%argx = arith.constant dense<0> : tensor<10xi64>
	%0 = linalg.generic #trait_cast
	ins(%arga: tensor<10xi32, #SV>)
	outs(%argx: tensor<10xi64>) {
	^bb(%a: i32, %x : i64):
	%cst = arith.extsi %a : i32 to i64
	linalg.yield %cst : i64
	} -> tensor<10xi64>
	return %0 : tensor<10xi64>
	}
	func @sparse_cast_u32_to_s64(%arga: tensor<10xi32, #SV>) -> tensor<10xi64> {
	%argx = arith.constant dense<0> : tensor<10xi64>
	%0 = linalg.generic #trait_cast
	ins(%arga: tensor<10xi32, #SV>)
	outs(%argx: tensor<10xi64>) {
	^bb(%a: i32, %x : i64):
	%cst = arith.extui %a : i32 to i64
	linalg.yield %cst : i64
	} -> tensor<10xi64>
	return %0 : tensor<10xi64>
	}
	func @sparse_cast_i32_to_i8(%arga: tensor<10xi32, #SV>) -> tensor<10xi8> {
	%argx = arith.constant dense<0> : tensor<10xi8>
	%0 = linalg.generic #trait_cast
	ins(%arga: tensor<10xi32, #SV>)
	outs(%argx: tensor<10xi8>) {
	^bb(%a: i32, %x : i8):
	%cst = arith.trunci %a : i32 to i8
	linalg.yield %cst : i8
	} -> tensor<10xi8>
	return %0 : tensor<10xi8>
	}
	func @sparse_cast_f32_as_s32(%arga: tensor<10xf32, #SV>) -> tensor<10xi32> {
	%argx = arith.constant dense<0> : tensor<10xi32>
	%0 = linalg.generic #trait_cast
	ins(%arga: tensor<10xf32, #SV>)
	outs(%argx: tensor<10xi32>) {
	^bb(%a: f32, %x : i32):
	%cst = arith.bitcast %a : f32 to i32
	linalg.yield %cst : i32
	} -> tensor<10xi32>
	return %0 : tensor<10xi32>
	}

	//
	// Main driver that converts a dense tensor into a sparse tensor
	// and then calls the sparse casting kernel.
	//
	func @entry() {
	%z = arith.constant 0 : index
	%b = arith.constant 0 : i8
	%i = arith.constant 0 : i32
	%l = arith.constant 0 : i64
	%f = arith.constant 0.0 : f32
	%d = arith.constant 0.0 : f64

	// Initialize dense tensors, convert to a sparse vectors.
	%0 = arith.constant dense<[ -4, -3, -2, -1, 0, 1, 2, 3, 4, 305 ]> : tensor<10xi32>
	%1 = sparse_tensor.convert %0 : tensor<10xi32> to tensor<10xi32, #SV>
	%2 = arith.constant dense<[ -4.4, -3.3, -2.2, -1.1, 0.0, 1.1, 2.2, 3.3, 4.4, 305.5 ]> : tensor<10xf32>
	%3 = sparse_tensor.convert %2 : tensor<10xf32> to tensor<10xf32, #SV>
	%4 = arith.constant dense<[ -4.4, -3.3, -2.2, -1.1, 0.0, 1.1, 2.2, 3.3, 4.4, 305.5 ]> : tensor<10xf64>
	%5 = sparse_tensor.convert %4 : tensor<10xf64> to tensor<10xf64, #SV>
	%6 = arith.constant dense<[ 4294967295.0, 4294967294.0, 4294967293.0, 4294967292.0,
	0.0, 1.1, 2.2, 3.3, 4.4, 305.5 ]> : tensor<10xf64>
	%7 = sparse_tensor.convert %6 : tensor<10xf64> to tensor<10xf64, #SV>

	//
	// CHECK: ( -4, -3, -2, -1, 0, 1, 2, 3, 4, 305 )
	//
	%c0 = call @sparse_cast_s32_to_f32(%1) : (tensor<10xi32, #SV>) -> tensor<10xf32>
	%m0 = bufferization.to_memref %c0 : memref<10xf32>
	%v0 = vector.transfer_read %m0[%z], %f: memref<10xf32>, vector<10xf32>
	vector.print %v0 : vector<10xf32>

	//
	// CHECK: ( 4.29497e+09, 4.29497e+09, 4.29497e+09, 4.29497e+09, 0, 1, 2, 3, 4, 305 )
	//
	%c1 = call @sparse_cast_u32_to_f32(%1) : (tensor<10xi32, #SV>) -> tensor<10xf32>
	%m1 = bufferization.to_memref %c1 : memref<10xf32>
	%v1 = vector.transfer_read %m1[%z], %f: memref<10xf32>, vector<10xf32>
	vector.print %v1 : vector<10xf32>

	//
	// CHECK: ( -4, -3, -2, -1, 0, 1, 2, 3, 4, 305 )
	//
	%c2 = call @sparse_cast_f32_to_s32(%3) : (tensor<10xf32, #SV>) -> tensor<10xi32>
	%m2 = bufferization.to_memref %c2 : memref<10xi32>
	%v2 = vector.transfer_read %m2[%z], %i: memref<10xi32>, vector<10xi32>
	vector.print %v2 : vector<10xi32>

	//
	// CHECK: ( 4294967295, 4294967294, 4294967293, 4294967292, 0, 1, 2, 3, 4, 305 )
	//
	%c3 = call @sparse_cast_f64_to_u32(%7) : (tensor<10xf64, #SV>) -> tensor<10xi32>
	%m3 = bufferization.to_memref %c3 : memref<10xi32>
	%v3 = vector.transfer_read %m3[%z], %i: memref<10xi32>, vector<10xi32>
	%vu = vector.bitcast %v3 : vector<10xi32> to vector<10xui32>
	vector.print %vu : vector<10xui32>

	//
	// CHECK: ( -4.4, -3.3, -2.2, -1.1, 0, 1.1, 2.2, 3.3, 4.4, 305.5 )
	//
	%c4 = call @sparse_cast_f32_to_f64(%3) : (tensor<10xf32, #SV>) -> tensor<10xf64>
	%m4 = bufferization.to_memref %c4 : memref<10xf64>
	%v4 = vector.transfer_read %m4[%z], %d: memref<10xf64>, vector<10xf64>
	vector.print %v4 : vector<10xf64>

	//
	// CHECK: ( -4.4, -3.3, -2.2, -1.1, 0, 1.1, 2.2, 3.3, 4.4, 305.5 )
	//
	%c5 = call @sparse_cast_f64_to_f32(%5) : (tensor<10xf64, #SV>) -> tensor<10xf32>
	%m5 = bufferization.to_memref %c5 : memref<10xf32>
	%v5 = vector.transfer_read %m5[%z], %f: memref<10xf32>, vector<10xf32>
	vector.print %v5 : vector<10xf32>

	//
	// CHECK: ( -4, -3, -2, -1, 0, 1, 2, 3, 4, 305 )
	//
	%c6 = call @sparse_cast_s32_to_u64(%1) : (tensor<10xi32, #SV>) -> tensor<10xi64>
	%m6 = bufferization.to_memref %c6 : memref<10xi64>
	%v6 = vector.transfer_read %m6[%z], %l: memref<10xi64>, vector<10xi64>
	vector.print %v6 : vector<10xi64>

	//
	// CHECK: ( 4294967292, 4294967293, 4294967294, 4294967295, 0, 1, 2, 3, 4, 305 )
	//
	%c7 = call @sparse_cast_u32_to_s64(%1) : (tensor<10xi32, #SV>) -> tensor<10xi64>
	%m7 = bufferization.to_memref %c7 : memref<10xi64>
	%v7 = vector.transfer_read %m7[%z], %l: memref<10xi64>, vector<10xi64>
	vector.print %v7 : vector<10xi64>

	//
	// CHECK: ( -4, -3, -2, -1, 0, 1, 2, 3, 4, 49 )
	//
	%c8 = call @sparse_cast_i32_to_i8(%1) : (tensor<10xi32, #SV>) -> tensor<10xi8>
	%m8 = bufferization.to_memref %c8 : memref<10xi8>
	%v8 = vector.transfer_read %m8[%z], %b: memref<10xi8>, vector<10xi8>
	vector.print %v8 : vector<10xi8>

	//
	// CHECK: ( -1064514355, -1068289229, -1072902963, -1081291571, 0, 1066192077, 1074580685, 1079194419, 1082969293, 1134084096 )
	//
	%c9 = call @sparse_cast_f32_as_s32(%3) : (tensor<10xf32, #SV>) -> tensor<10xi32>
	%m9 = bufferization.to_memref %c9 : memref<10xi32>
	%v9 = vector.transfer_read %m9[%z], %i: memref<10xi32>, vector<10xi32>
	vector.print %v9 : vector<10xi32>

	// Release the resources.
	sparse_tensor.release %1 : tensor<10xi32, #SV>
	sparse_tensor.release %3 : tensor<10xf32, #SV>
	sparse_tensor.release %5 : tensor<10xf64, #SV>
	sparse_tensor.release %7 : tensor<10xf64, #SV>
	memref.dealloc %m0 : memref<10xf32>
	memref.dealloc %m1 : memref<10xf32>
	memref.dealloc %m2 : memref<10xi32>
	memref.dealloc %m3 : memref<10xi32>
	memref.dealloc %m4 : memref<10xf64>
	memref.dealloc %m5 : memref<10xf32>
	memref.dealloc %m6 : memref<10xi64>
	memref.dealloc %m7 : memref<10xi64>
	memref.dealloc %m8 : memref<10xi8>
	memref.dealloc %m9 : memref<10xi32>

	return
	}
	}