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
226 lines
8.4 KiB
MLIR
226 lines
8.4 KiB
MLIR
// RUN: mlir-opt %s -sparsification -cse -split-input-file | \
|
|
// RUN: FileCheck %s
|
|
|
|
#DenseVector = #sparse_tensor.encoding<{ dimLevelType = [ "dense" ] }>
|
|
|
|
#trait_scale_d = {
|
|
indexing_maps = [
|
|
affine_map<(i) -> (i)>, // a
|
|
affine_map<(i) -> (i)> // x (out)
|
|
],
|
|
iterator_types = ["parallel"],
|
|
doc = "x(i) = a(i) * b"
|
|
}
|
|
|
|
//
|
|
// CHECK-LABEL: func @scale_d
|
|
// CHECK-DAG: %[[c0:.*]] = arith.constant 0 : index
|
|
// CHECK-DAG: %[[c1:.*]] = arith.constant 1 : index
|
|
// CHECK-DAG: %[[c1024:.*]] = arith.constant 1024 : index
|
|
// CHECK: scf.for %[[i:.*]] = %[[c0]] to %[[c1024]] step %[[c1]] {
|
|
// CHECK: %[[l:.*]] = memref.load %{{.*}}[%[[i]]] : memref<?xf32>
|
|
// CHECK: %[[m:.*]] = arith.mulf %[[l]], %{{.*}} : f32
|
|
// CHECK: store %[[m]], %{{.*}}[%[[i]]] : memref<1024xf32>
|
|
// CHECK: }
|
|
// CHECK: return
|
|
//
|
|
|
|
func.func @scale_d(%arga: tensor<1024xf32, #DenseVector>, %b: f32, %argx: tensor<1024xf32>) -> tensor<1024xf32> {
|
|
%0 = linalg.generic #trait_scale_d
|
|
ins(%arga: tensor<1024xf32, #DenseVector>)
|
|
outs(%argx: tensor<1024xf32>) {
|
|
^bb(%a: f32, %x: f32):
|
|
%0 = arith.mulf %a, %b : f32
|
|
linalg.yield %0 : f32
|
|
} -> tensor<1024xf32>
|
|
return %0 : tensor<1024xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
#SparseVector = #sparse_tensor.encoding<{
|
|
dimLevelType = [ "compressed" ],
|
|
pointerBitWidth = 32,
|
|
indexBitWidth = 32
|
|
}>
|
|
|
|
#trait_mul_s = {
|
|
indexing_maps = [
|
|
affine_map<(i) -> (i)>, // a
|
|
affine_map<(i) -> (i)>, // b
|
|
affine_map<(i) -> (i)> // x (out)
|
|
],
|
|
iterator_types = ["parallel"],
|
|
doc = "x(i) = a(i) * b(i)"
|
|
}
|
|
|
|
//
|
|
// CHECK-LABEL: func @mul_s
|
|
// CHECK-DAG: %[[c0:.*]] = arith.constant 0 : index
|
|
// CHECK-DAG: %[[c1:.*]] = arith.constant 1 : index
|
|
// CHECK: %[[p:.*]] = memref.load %{{.*}}[%[[c0]]] : memref<?xi32>
|
|
// CHECK: %[[a:.*]] = arith.extui %[[p]] : i32 to i64
|
|
// CHECK: %[[q:.*]] = arith.index_cast %[[a]] : i64 to index
|
|
// CHECK: %[[r:.*]] = memref.load %{{.*}}[%[[c1]]] : memref<?xi32>
|
|
// CHECK: %[[b:.*]] = arith.extui %[[r]] : i32 to i64
|
|
// CHECK: %[[s:.*]] = arith.index_cast %[[b]] : i64 to index
|
|
// CHECK: scf.for %[[i:.*]] = %[[q]] to %[[s]] step %[[c1]] {
|
|
// CHECK: %[[li:.*]] = memref.load %{{.*}}[%[[i]]] : memref<?xi32>
|
|
// CHECK: %[[zi:.*]] = arith.extui %[[li]] : i32 to i64
|
|
// CHECK: %[[ci:.*]] = arith.index_cast %[[zi]] : i64 to index
|
|
// CHECK: %[[la:.*]] = memref.load %{{.*}}[%[[i]]] : memref<?xf32>
|
|
// CHECK: %[[lb:.*]] = memref.load %{{.*}}[%[[ci]]] : memref<1024xf32>
|
|
// CHECK: %[[m:.*]] = arith.mulf %[[la]], %[[lb]] : f32
|
|
// CHECK: store %[[m]], %{{.*}}[%[[ci]]] : memref<1024xf32>
|
|
// CHECK: }
|
|
// CHECK: return
|
|
//
|
|
func.func @mul_s(%arga: tensor<1024xf32, #SparseVector>, %argb: tensor<1024xf32>, %argx: tensor<1024xf32>) -> tensor<1024xf32> {
|
|
%0 = linalg.generic #trait_mul_s
|
|
ins(%arga, %argb: tensor<1024xf32, #SparseVector>, tensor<1024xf32>)
|
|
outs(%argx: tensor<1024xf32>) {
|
|
^bb(%a: f32, %b: f32, %x: f32):
|
|
%0 = arith.mulf %a, %b : f32
|
|
linalg.yield %0 : f32
|
|
} -> tensor<1024xf32>
|
|
return %0 : tensor<1024xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
#DenseVector = #sparse_tensor.encoding<{ dimLevelType = [ "dense" ] }>
|
|
|
|
#trait_reduction_d = {
|
|
indexing_maps = [
|
|
affine_map<(i) -> (i)>, // a
|
|
affine_map<(i) -> (i)>, // b
|
|
affine_map<(i) -> ()> // x (out)
|
|
],
|
|
iterator_types = ["reduction"],
|
|
doc = "x += a(i) * b(i)"
|
|
}
|
|
|
|
//
|
|
// CHECK-LABEL: func @reduction_d
|
|
// CHECK-DAG: %[[c0:.*]] = arith.constant 0 : index
|
|
// CHECK-DAG: %[[c1:.*]] = arith.constant 1 : index
|
|
// CHECK-DAG: %[[c1024:.*]] = arith.constant 1024 : index
|
|
// CHECK: %[[red:.*]] = scf.for %[[i:.*]] = %[[c0]] to %[[c1024]] step %[[c1]] iter_args(%[[red_in:.*]] = %{{.*}}) -> (f32) {
|
|
// CHECK: %[[la:.*]] = memref.load %{{.*}}[%[[i]]] : memref<?xf32>
|
|
// CHECK: %[[lb:.*]] = memref.load %{{.*}}[%[[i]]] : memref<1024xf32>
|
|
// CHECK: %[[m:.*]] = arith.mulf %[[la]], %[[lb]] : f32
|
|
// CHECK: %[[a:.*]] = arith.addf %[[red_in]], %[[m]] : f32
|
|
// CHECK: scf.yield %[[a]] : f32
|
|
// CHECK: }
|
|
// CHECK: return
|
|
//
|
|
func.func @reduction_d(%arga: tensor<1024xf32, #DenseVector>, %argb: tensor<1024xf32>, %argx: tensor<f32>) -> tensor<f32> {
|
|
%0 = linalg.generic #trait_reduction_d
|
|
ins(%arga, %argb: tensor<1024xf32, #DenseVector>, tensor<1024xf32>)
|
|
outs(%argx: tensor<f32>) {
|
|
^bb(%a: f32, %b: f32, %x: f32):
|
|
%0 = arith.mulf %a, %b : f32
|
|
%1 = arith.addf %x, %0 : f32
|
|
linalg.yield %1 : f32
|
|
} -> tensor<f32>
|
|
return %0 : tensor<f32>
|
|
}
|
|
|
|
// -----
|
|
|
|
#SparseMatrix = #sparse_tensor.encoding<{
|
|
dimLevelType = [ "dense", "compressed" ],
|
|
pointerBitWidth = 32,
|
|
indexBitWidth = 32
|
|
}>
|
|
|
|
#trait_mul_ds = {
|
|
indexing_maps = [
|
|
affine_map<(i,j) -> (i,j)>, // A
|
|
affine_map<(i,j) -> (i,j)>, // B
|
|
affine_map<(i,j) -> (i,j)> // X (out)
|
|
],
|
|
iterator_types = ["parallel", "parallel"],
|
|
doc = "X(i,j) = A(i,j) * B(i,j)"
|
|
}
|
|
|
|
//
|
|
// CHECK-LABEL: func @mul_ds
|
|
// CHECK-DAG: %[[c0:.*]] = arith.constant 0 : index
|
|
// CHECK-DAG: %[[c1:.*]] = arith.constant 1 : index
|
|
// CHECK-DAG: %[[c512:.*]] = arith.constant 512 : index
|
|
// CHECK: scf.for %[[i:.*]] = %[[c0]] to %[[c512]] step %[[c1]] {
|
|
// CHECK: %[[p:.*]] = memref.load %{{.*}}[%[[i]]] : memref<?xi32>
|
|
// CHECK: %[[a:.*]] = arith.extui %[[p]] : i32 to i64
|
|
// CHECK: %[[q:.*]] = arith.index_cast %[[a]] : i64 to index
|
|
// CHECK: %[[a:.*]] = arith.addi %[[i]], %[[c1]] : index
|
|
// CHECK: %[[r:.*]] = memref.load %{{.*}}[%[[a]]] : memref<?xi32>
|
|
// CHECK: %[[b:.*]] = arith.extui %[[r]] : i32 to i64
|
|
// CHECK: %[[s:.*]] = arith.index_cast %[[b]] : i64 to index
|
|
// CHECK: scf.for %[[j:.*]] = %[[q]] to %[[s]] step %[[c1]] {
|
|
// CHECK: %[[lj:.*]] = memref.load %{{.*}}[%[[j]]] : memref<?xi32>
|
|
// CHECK: %[[zj:.*]] = arith.extui %[[lj]] : i32 to i64
|
|
// CHECK: %[[cj:.*]] = arith.index_cast %[[zj]] : i64 to index
|
|
// CHECK: %[[la:.*]] = memref.load %{{.*}}[%[[j]]] : memref<?xf32>
|
|
// CHECK: %[[lb:.*]] = memref.load %{{.*}}[%[[i]], %[[cj]]] : memref<512x1024xf32>
|
|
// CHECK: %[[m:.*]] = arith.mulf %[[la]], %[[lb]] : f32
|
|
// CHECK: store %[[m]], %{{.*}}[%[[i]], %[[cj]]] : memref<512x1024xf32>
|
|
// CHECK: }
|
|
// CHECK: }
|
|
// CHECK: return
|
|
//
|
|
func.func @mul_ds(%arga: tensor<512x1024xf32, #SparseMatrix>, %argb: tensor<512x1024xf32>, %argx: tensor<512x1024xf32>) -> tensor<512x1024xf32> {
|
|
%0 = linalg.generic #trait_mul_ds
|
|
ins(%arga, %argb: tensor<512x1024xf32, #SparseMatrix>, tensor<512x1024xf32>)
|
|
outs(%argx: tensor<512x1024xf32>) {
|
|
^bb(%a: f32, %b: f32, %x: f32):
|
|
%0 = arith.mulf %a, %b : f32
|
|
linalg.yield %0 : f32
|
|
} -> tensor<512x1024xf32>
|
|
return %0 : tensor<512x1024xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
#SparseMatrix = #sparse_tensor.encoding<{dimLevelType = ["dense","compressed"]}>
|
|
|
|
#trait_affine = {
|
|
indexing_maps = [
|
|
affine_map<(i,j) -> (i,j)>,
|
|
affine_map<(i,j) -> (i+1,j)>
|
|
],
|
|
iterator_types = ["parallel","parallel"],
|
|
doc = "X(i+1,j) += A(i,j)"
|
|
}
|
|
|
|
//
|
|
// CHECK-LABEL: func @add_dense
|
|
// CHECK-DAG: %[[c0:.*]] = arith.constant 0 : index
|
|
// CHECK-DAG: %[[c1:.*]] = arith.constant 1 : index
|
|
// CHECK-DAG: %[[c32:.*]] = arith.constant 32 : index
|
|
// CHECK: scf.for %[[i:.*]] = %[[c0]] to %[[c32]] step %[[c1]] {
|
|
// CHECK: %[[lo:.*]] = memref.load %{{.*}}[%[[i]]] : memref<?xindex>
|
|
// CHECK: %[[i1:.*]] = arith.addi %[[i]], %[[c1]] : index
|
|
// CHECK: %[[hi:.*]] = memref.load %{{.*}}[%[[i1]]] : memref<?xindex>
|
|
// CHECK: scf.for %[[jj:.*]] = %[[lo]] to %[[hi]] step %[[c1]] {
|
|
// CHECK: %[[j:.*]] = memref.load %{{.*}}[%[[jj]]] : memref<?xindex>
|
|
// CHECK: %[[x:.*]] = memref.load %{{.*}}[%[[i1]], %[[j]]] : memref<33x64xf64>
|
|
// CHECK: %[[a:.*]] = memref.load %{{.*}}[%[[jj]]] : memref<?xf64>
|
|
// CHECK: %[[s:.*]] = arith.addf %[[x]], %[[a]] : f64
|
|
// CHECK: memref.store %[[s]], %{{.*}}[%[[i1]], %[[j]]] : memref<33x64xf64>
|
|
// CHECK: }
|
|
// CHECK: }
|
|
// CHECK: return
|
|
//
|
|
func.func @add_dense(%arga: tensor<32x64xf64, #SparseMatrix>,
|
|
%argx: tensor<33x64xf64>) -> tensor<33x64xf64> {
|
|
%0 = linalg.generic #trait_affine
|
|
ins(%arga: tensor<32x64xf64, #SparseMatrix>)
|
|
outs(%argx: tensor<33x64xf64>) {
|
|
^bb(%a: f64, %x: f64):
|
|
%0 = arith.addf %x, %a : f64
|
|
linalg.yield %0 : f64
|
|
} -> tensor<33x64xf64>
|
|
return %0 : tensor<33x64xf64>
|
|
}
|