//--------------------------------------------------------------------------------------------------
// WHEN CREATING A NEW TEST, PLEASE JUST COPY & PASTE WITHOUT EDITS.
//
// Set-up that's shared across all tests in this directory. In principle, this
// config could be moved to lit.local.cfg. However, there are downstream users that
// do not use these LIT config files. Hence why this is kept inline.
//
// DEFINE: %{sparsifier_opts} = enable-runtime-library=true
// DEFINE: %{sparsifier_opts_sve} = enable-arm-sve=true %{sparsifier_opts}
// DEFINE: %{compile} = mlir-opt %s --sparsifier="%{sparsifier_opts}"
// DEFINE: %{compile_sve} = mlir-opt %s --sparsifier="%{sparsifier_opts_sve}"
// DEFINE: %{run_libs} = -shared-libs=%mlir_c_runner_utils,%mlir_runner_utils
// DEFINE: %{run_libs_sve} = -shared-libs=%native_mlir_runner_utils,%native_mlir_c_runner_utils
// DEFINE: %{run_opts} = -e main -entry-point-result=void
// DEFINE: %{run} = mlir-cpu-runner %{run_opts} %{run_libs}
// DEFINE: %{run_sve} = %mcr_aarch64_cmd --march=aarch64 --mattr="+sve" %{run_opts} %{run_libs_sve}
//
// DEFINE: %{env} =
//--------------------------------------------------------------------------------------------------
// RUN: %{compile} | %{run} | FileCheck %s
//
// Do the same run, but now with direct IR generation.
// REDEFINE: %{sparsifier_opts} = enable-runtime-library=false enable-buffer-initialization=true
// RUN: %{compile} | %{run} | FileCheck %s
//
// Do the same run, but now with direct IR generation and vectorization.
// REDEFINE: %{sparsifier_opts} = enable-runtime-library=false vl=2 reassociate-fp-reductions=true enable-index-optimizations=true
// RUN: %{compile} | %{run} | FileCheck %s
//
// Do the same run, but now with direct IR generation and VLA vectorization.
// RUN: %if mlir_arm_sve_tests %{ %{compile_sve} | %{run_sve} | FileCheck %s %}
#SV = #sparse_tensor.encoding<{ map = (d0) -> (d0 : 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.func @sparse_cast_s32_to_f32(%arga: tensor<10xi32, #SV>,
%argb: tensor<10xf32>) -> tensor<10xf32> {
%0 = linalg.generic #trait_cast
ins(%arga: tensor<10xi32, #SV>)
outs(%argb: 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.func @sparse_cast_u32_to_f32(%arga: tensor<10xi32, #SV>,
%argb: tensor<10xf32>) -> tensor<10xf32> {
%0 = linalg.generic #trait_cast
ins(%arga: tensor<10xi32, #SV>)
outs(%argb: 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.func @sparse_cast_f32_to_s32(%arga: tensor<10xf32, #SV>,
%argb: tensor<10xi32>) -> tensor<10xi32> {
%0 = linalg.generic #trait_cast
ins(%arga: tensor<10xf32, #SV>)
outs(%argb: 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.func @sparse_cast_f64_to_u32(%arga: tensor<10xf64, #SV>,
%argb: tensor<10xi32>) -> tensor<10xi32> {
%0 = linalg.generic #trait_cast
ins(%arga: tensor<10xf64, #SV>)
outs(%argb: 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.func @sparse_cast_f32_to_f64(%arga: tensor<10xf32, #SV>,
%argb: tensor<10xf64>) -> tensor<10xf64> {
%0 = linalg.generic #trait_cast
ins(%arga: tensor<10xf32, #SV>)
outs(%argb: 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.func @sparse_cast_f64_to_f32(%arga: tensor<10xf64, #SV>,
%argb: tensor<10xf32>) -> tensor<10xf32> {
%0 = linalg.generic #trait_cast
ins(%arga: tensor<10xf64, #SV>)
outs(%argb: 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.func @sparse_cast_s32_to_u64(%arga: tensor<10xi32, #SV>,
%argb: tensor<10xi64>) -> tensor<10xi64> {
%0 = linalg.generic #trait_cast
ins(%arga: tensor<10xi32, #SV>)
outs(%argb: 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.func @sparse_cast_u32_to_s64(%arga: tensor<10xi32, #SV>,
%argb: tensor<10xi64>) -> tensor<10xi64> {
%0 = linalg.generic #trait_cast
ins(%arga: tensor<10xi32, #SV>)
outs(%argb: 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.func @sparse_cast_i32_to_i8(%arga: tensor<10xi32, #SV>,
%argb: tensor<10xi8>) -> tensor<10xi8> {
%0 = linalg.generic #trait_cast
ins(%arga: tensor<10xi32, #SV>)
outs(%argb: 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.func @sparse_cast_f32_as_s32(%arga: tensor<10xf32, #SV>,
%argb: tensor<10xi32>) -> tensor<10xi32> {
%0 = linalg.generic #trait_cast
ins(%arga: tensor<10xf32, #SV>)
outs(%argb: 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.func @main() {
%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
%zero_b = arith.constant dense<0> : tensor<10xi8>
%zero_d = arith.constant dense<0.0> : tensor<10xf64>
%zero_f = arith.constant dense<0.0> : tensor<10xf32>
%zero_i = arith.constant dense<0> : tensor<10xi32>
%zero_l = arith.constant dense<0> : tensor<10xi64>
// 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, %zero_f) : (tensor<10xi32, #SV>, tensor<10xf32>) -> tensor<10xf32>
%v0 = vector.transfer_read %c0[%z], %f: tensor<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, %zero_f) : (tensor<10xi32, #SV>, tensor<10xf32>) -> tensor<10xf32>
%v1 = vector.transfer_read %c1[%z], %f: tensor<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, %zero_i) : (tensor<10xf32, #SV>, tensor<10xi32>) -> tensor<10xi32>
%v2 = vector.transfer_read %c2[%z], %i: tensor<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, %zero_i) : (tensor<10xf64, #SV>, tensor<10xi32>) -> tensor<10xi32>
%v3 = vector.transfer_read %c3[%z], %i: tensor<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, %zero_d) : (tensor<10xf32, #SV>, tensor<10xf64>) -> tensor<10xf64>
%v4 = vector.transfer_read %c4[%z], %d: tensor<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, %zero_f) : (tensor<10xf64, #SV>, tensor<10xf32>) -> tensor<10xf32>
%v5 = vector.transfer_read %c5[%z], %f: tensor<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, %zero_l) : (tensor<10xi32, #SV>, tensor<10xi64>) -> tensor<10xi64>
%v6 = vector.transfer_read %c6[%z], %l: tensor<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, %zero_l) : (tensor<10xi32, #SV>, tensor<10xi64>) -> tensor<10xi64>
%v7 = vector.transfer_read %c7[%z], %l: tensor<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, %zero_b) : (tensor<10xi32, #SV>, tensor<10xi8>) -> tensor<10xi8>
%v8 = vector.transfer_read %c8[%z], %b: tensor<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, %zero_i) : (tensor<10xf32, #SV>, tensor<10xi32>) -> tensor<10xi32>
%v9 = vector.transfer_read %c9[%z], %i: tensor<10xi32>, vector<10xi32>
vector.print %v9 : vector<10xi32>
// Release the resources.
bufferization.dealloc_tensor %1 : tensor<10xi32, #SV>
bufferization.dealloc_tensor %3 : tensor<10xf32, #SV>
bufferization.dealloc_tensor %5 : tensor<10xf64, #SV>
bufferization.dealloc_tensor %7 : tensor<10xf64, #SV>
bufferization.dealloc_tensor %c0 : tensor<10xf32>
bufferization.dealloc_tensor %c1 : tensor<10xf32>
bufferization.dealloc_tensor %c2 : tensor<10xi32>
bufferization.dealloc_tensor %c3 : tensor<10xi32>
bufferization.dealloc_tensor %c4 : tensor<10xf64>
bufferization.dealloc_tensor %c5 : tensor<10xf32>
bufferization.dealloc_tensor %c6 : tensor<10xi64>
bufferization.dealloc_tensor %c7 : tensor<10xi64>
bufferization.dealloc_tensor %c8 : tensor<10xi8>
bufferization.dealloc_tensor %c9 : tensor<10xi32>
return
}
}
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