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
100 lines
3.2 KiB
MLIR
100 lines
3.2 KiB
MLIR
// RUN: mlir-opt %s --sparse-compiler | \
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// RUN: TENSOR0="%mlir_src_dir/test/Integration/data/wide.mtx" \
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// RUN: mlir-cpu-runner \
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// RUN: -e entry -entry-point-result=void \
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// RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
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// RUN: FileCheck %s
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!Filename = !llvm.ptr<i8>
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#SparseMatrix = #sparse_tensor.encoding<{
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dimLevelType = [ "dense", "compressed" ]
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}>
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#spmm = {
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indexing_maps = [
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affine_map<(i,j,k) -> (i,k)>, // A
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affine_map<(i,j,k) -> (k,j)>, // B
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affine_map<(i,j,k) -> (i,j)> // X (out)
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],
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iterator_types = ["parallel", "parallel", "reduction"],
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doc = "X(i,j) += A(i,k) * B(k,j)"
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}
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//
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// Integration test that lowers a kernel annotated as sparse to
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// actual sparse code, initializes a matching sparse storage scheme
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// from file, and runs the resulting code with the JIT compiler.
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//
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module {
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//
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// A kernel that multiplies a sparse matrix A with a dense matrix B
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// into a dense matrix X.
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//
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func.func @kernel_spmm(%arga: tensor<?x?xf64, #SparseMatrix>,
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%argb: tensor<?x?xf64>,
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%argx: tensor<?x?xf64>) -> tensor<?x?xf64> {
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%0 = linalg.generic #spmm
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ins(%arga, %argb: tensor<?x?xf64, #SparseMatrix>, tensor<?x?xf64>)
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outs(%argx: tensor<?x?xf64>) {
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^bb(%a: f64, %b: f64, %x: f64):
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%0 = arith.mulf %a, %b : f64
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%1 = arith.addf %x, %0 : f64
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linalg.yield %1 : f64
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} -> tensor<?x?xf64>
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return %0 : tensor<?x?xf64>
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}
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func.func private @getTensorFilename(index) -> (!Filename)
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//
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// Main driver that reads matrix from file and calls the sparse kernel.
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//
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func.func @entry() {
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%i0 = arith.constant 0.0 : f64
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%c0 = arith.constant 0 : index
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%c1 = arith.constant 1 : index
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%c4 = arith.constant 4 : index
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%c256 = arith.constant 256 : index
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// Read the sparse matrix from file, construct sparse storage.
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%fileName = call @getTensorFilename(%c0) : (index) -> (!Filename)
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%a = sparse_tensor.new %fileName : !Filename to tensor<?x?xf64, #SparseMatrix>
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// Initialize dense tensors.
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%b = tensor.generate %c256, %c4 {
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^bb0(%i : index, %j : index):
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%k0 = arith.muli %i, %c4 : index
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%k1 = arith.addi %j, %k0 : index
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%k2 = arith.index_cast %k1 : index to i32
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%k = arith.sitofp %k2 : i32 to f64
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tensor.yield %k : f64
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} : tensor<?x?xf64>
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%x = tensor.generate %c4, %c4 {
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^bb0(%i : index, %j : index):
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tensor.yield %i0 : f64
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} : tensor<?x?xf64>
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// Call kernel.
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%0 = call @kernel_spmm(%a, %b, %x)
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: (tensor<?x?xf64, #SparseMatrix>, tensor<?x?xf64>, tensor<?x?xf64>) -> tensor<?x?xf64>
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// Print the result for verification.
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//
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// CHECK: ( ( 3548, 3550, 3552, 3554 ), ( 6052, 6053, 6054, 6055 ), ( -56, -63, -70, -77 ), ( -13704, -13709, -13714, -13719 ) )
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//
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%v = vector.transfer_read %0[%c0, %c0], %i0: tensor<?x?xf64>, vector<4x4xf64>
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vector.print %v : vector<4x4xf64>
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// Release the resources.
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bufferization.dealloc_tensor %a : tensor<?x?xf64, #SparseMatrix>
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// TODO(springerm): auto release!
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bufferization.dealloc_tensor %b : tensor<?x?xf64>
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bufferization.dealloc_tensor %x : tensor<?x?xf64>
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return
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}
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}
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