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// DEFINE: %{option} = enable-runtime-library=true
// DEFINE: %{compile} = mlir-opt %s --sparse-compiler=%{option}
// DEFINE: %{run} = TENSOR0="%mlir_src_dir/test/Integration/data/mttkrp_b.tns" \
// DEFINE: mlir-cpu-runner \
// DEFINE: -e entry -entry-point-result=void \
// DEFINE: -shared-libs=%mlir_c_runner_utils,%mlir_runner_utils | \
// DEFINE: FileCheck %s
//
// RUN: %{compile} | %{run}
//
// Do the same run, but now with direct IR generation.
// REDEFINE: %{option} = enable-runtime-library=false
// RUN: %{compile} | %{run}
//
// Do the same run, but now with direct IR generation and vectorization.
// REDEFINE: %{option} = "enable-runtime-library=false vl=2 reassociate-fp-reductions=true enable-index-optimizations=true"
// RUN: %{compile} | %{run}
// Do the same run, but now with direct IR generation and, if available, VLA
// vectorization.
// REDEFINE: %{option} = "enable-runtime-library=false vl=4 enable-arm-sve=%ENABLE_VLA"
// REDEFINE: %{run} = TENSOR0="%mlir_src_dir/test/Integration/data/mttkrp_b.tns" \
// REDEFINE: %lli_host_or_aarch64_cmd \
// REDEFINE: --entry-function=entry_lli \
// REDEFINE: --extra-module=%S/Inputs/main_for_lli.ll \
// REDEFINE: %VLA_ARCH_ATTR_OPTIONS \
// REDEFINE: --dlopen=%mlir_native_utils_lib_dir/libmlir_c_runner_utils%shlibext -dlopen=%mlir_runner_utils| \
// REDEFINE: FileCheck %s
// RUN: %{compile} | mlir-translate -mlir-to-llvmir | %{run}
!Filename = !llvm.ptr<i8>
#SparseTensor = #sparse_tensor.encoding<{
lvlTypes = [ "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 {
func.func private @printMemrefF64(%ptr : tensor<*xf64>)
//
// 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-NEXT: [10000, 14225, 19180, 24865, 31280]]
//
%u = tensor.cast %0: tensor<?x?xf64> to tensor<*xf64>
call @printMemrefF64(%u) : (tensor<*xf64>) -> ()
// Release the resources.
bufferization.dealloc_tensor %b : tensor<?x?x?xf64, #SparseTensor>
return
}
}
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