26eb2c6b42
Sparse compiler used to generate vectorized code for sparse tensors computation, but it should really be delegated to other vectorization passes for better progressive lowering. https://discourse.llvm.org/t/rfc-structured-codegen-beyond-rectangular-arrays/64707 Reviewed By: aartbik Differential Revision: https://reviews.llvm.org/D136183
260 lines
10 KiB
MLIR
260 lines
10 KiB
MLIR
// RUN: mlir-opt %s --sparse-compiler | \
|
|
// RUN: mlir-cpu-runner \
|
|
// RUN: -e entry -entry-point-result=void \
|
|
// RUN: -shared-libs=%mlir_lib_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.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 @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
|
|
|
|
%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>
|
|
|
|
return
|
|
}
|
|
}
|