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
126 lines
4.2 KiB
MLIR
126 lines
4.2 KiB
MLIR
// RUN: mlir-opt %s --sparse-compiler | \
|
|
// RUN: TENSOR0="%mlir_src_dir/test/Integration/data/mttkrp_b.tns" \
|
|
// 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
|
|
|
|
!Filename = !llvm.ptr<i8>
|
|
|
|
#SparseTensor = #sparse_tensor.encoding<{
|
|
dimLevelType = [ "compressed", "compressed", "compressed" ]
|
|
}>
|
|
|
|
#mttkrp = {
|
|
indexing_maps = [
|
|
affine_map<(i,j,k,l) -> (i,k,l)>, // B
|
|
affine_map<(i,j,k,l) -> (k,j)>, // C
|
|
affine_map<(i,j,k,l) -> (l,j)>, // D
|
|
affine_map<(i,j,k,l) -> (i,j)> // A (out)
|
|
],
|
|
iterator_types = ["parallel", "parallel", "reduction", "reduction"],
|
|
doc = "A(i,j) += B(i,k,l) * D(l,j) * C(k,j)"
|
|
}
|
|
|
|
//
|
|
// Integration test that lowers a kernel annotated as sparse to
|
|
// actual sparse code, initializes a matching sparse storage scheme
|
|
// from file, and runs the resulting code with the JIT compiler.
|
|
//
|
|
module {
|
|
//
|
|
// Computes Matricized Tensor Times Khatri-Rao Product (MTTKRP) kernel. See
|
|
// http://tensor-compiler.org/docs/data_analytics/index.html.
|
|
//
|
|
func.func @kernel_mttkrp(%argb: tensor<?x?x?xf64, #SparseTensor>,
|
|
%argc: tensor<?x?xf64>,
|
|
%argd: tensor<?x?xf64>,
|
|
%arga: tensor<?x?xf64>)
|
|
-> tensor<?x?xf64> {
|
|
%0 = linalg.generic #mttkrp
|
|
ins(%argb, %argc, %argd:
|
|
tensor<?x?x?xf64, #SparseTensor>, tensor<?x?xf64>, tensor<?x?xf64>)
|
|
outs(%arga: tensor<?x?xf64>) {
|
|
^bb(%b: f64, %c: f64, %d: f64, %a: f64):
|
|
%0 = arith.mulf %b, %c : f64
|
|
%1 = arith.mulf %d, %0 : f64
|
|
%2 = arith.addf %a, %1 : f64
|
|
linalg.yield %2 : f64
|
|
} -> tensor<?x?xf64>
|
|
return %0 : tensor<?x?xf64>
|
|
}
|
|
|
|
func.func private @getTensorFilename(index) -> (!Filename)
|
|
|
|
//
|
|
// Main driver that reads matrix from file and calls the sparse kernel.
|
|
//
|
|
func.func @entry() {
|
|
%f0 = arith.constant 0.0 : f64
|
|
%cst0 = arith.constant 0 : index
|
|
%cst1 = arith.constant 1 : index
|
|
%cst2 = arith.constant 2 : index
|
|
|
|
// Read the sparse input tensor B from a file.
|
|
%fileName = call @getTensorFilename(%cst0) : (index) -> (!Filename)
|
|
%b = sparse_tensor.new %fileName
|
|
: !Filename to tensor<?x?x?xf64, #SparseTensor>
|
|
|
|
// Get sizes from B, pick a fixed size for dim-2 of A.
|
|
%isz = tensor.dim %b, %cst0 : tensor<?x?x?xf64, #SparseTensor>
|
|
%jsz = arith.constant 5 : index
|
|
%ksz = tensor.dim %b, %cst1 : tensor<?x?x?xf64, #SparseTensor>
|
|
%lsz = tensor.dim %b, %cst2 : tensor<?x?x?xf64, #SparseTensor>
|
|
|
|
// Initialize dense input matrix C.
|
|
%c = tensor.generate %ksz, %jsz {
|
|
^bb0(%k : index, %j : index):
|
|
%k0 = arith.muli %k, %jsz : index
|
|
%k1 = arith.addi %k0, %j : index
|
|
%k2 = arith.index_cast %k1 : index to i32
|
|
%kf = arith.sitofp %k2 : i32 to f64
|
|
tensor.yield %kf : f64
|
|
} : tensor<?x?xf64>
|
|
|
|
// Initialize dense input matrix D.
|
|
%d = tensor.generate %lsz, %jsz {
|
|
^bb0(%l : index, %j : index):
|
|
%k0 = arith.muli %l, %jsz : index
|
|
%k1 = arith.addi %k0, %j : index
|
|
%k2 = arith.index_cast %k1 : index to i32
|
|
%kf = arith.sitofp %k2 : i32 to f64
|
|
tensor.yield %kf : f64
|
|
} : tensor<?x?xf64>
|
|
|
|
// Initialize dense output matrix A.
|
|
%a = tensor.generate %isz, %jsz {
|
|
^bb0(%i : index, %j: index):
|
|
tensor.yield %f0 : f64
|
|
} : tensor<?x?xf64>
|
|
|
|
// Call kernel.
|
|
%0 = call @kernel_mttkrp(%b, %c, %d, %a)
|
|
: (tensor<?x?x?xf64, #SparseTensor>,
|
|
tensor<?x?xf64>, tensor<?x?xf64>, tensor<?x?xf64>) -> tensor<?x?xf64>
|
|
|
|
// Print the result for verification.
|
|
//
|
|
// CHECK: ( ( 16075, 21930, 28505, 35800, 43815 ),
|
|
// CHECK: ( 10000, 14225, 19180, 24865, 31280 ) )
|
|
//
|
|
%v = vector.transfer_read %0[%cst0, %cst0], %f0
|
|
: tensor<?x?xf64>, vector<2x5xf64>
|
|
vector.print %v : vector<2x5xf64>
|
|
|
|
// Release the resources.
|
|
bufferization.dealloc_tensor %b : tensor<?x?x?xf64, #SparseTensor>
|
|
|
|
// TODO(springerm): auto release!
|
|
bufferization.dealloc_tensor %a : tensor<?x?xf64>
|
|
bufferization.dealloc_tensor %c : tensor<?x?xf64>
|
|
bufferization.dealloc_tensor %d : tensor<?x?xf64>
|
|
|
|
return
|
|
}
|
|
}
|