a0615d020a
This commit is part of the migration of towards the new STEA syntax/design. In particular, this commit includes the following changes:
* Renaming compiler-internal functions/methods:
* `SparseTensorEncodingAttr::{getDimLevelType => getLvlTypes}`
* `Merger::{getDimLevelType => getLvlType}` (for consistency)
* `sparse_tensor::{getDimLevelType => buildLevelType}` (to help reduce confusion vs actual getter methods)
* Renaming external facets to match:
* the STEA parser and printer
* the C and Python bindings
* PyTACO
However, the actual renaming of the `DimLevelType` itself (along with all the "dlt" names) will be handled in a separate commit.
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D150330
95 lines
3.2 KiB
MLIR
95 lines
3.2 KiB
MLIR
// DEFINE: %{option} = enable-runtime-library=true
|
|
// DEFINE: %{compile} = mlir-opt %s --sparse-compiler=%{option}
|
|
// DEFINE: %{run} = mlir-cpu-runner \
|
|
// DEFINE: -e entry -entry-point-result=void \
|
|
// DEFINE: -shared-libs=%mlir_c_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, if available, VLA
|
|
// vectorization.
|
|
// REDEFINE: %{option} = "enable-runtime-library=false vl=4 enable-arm-sve=%ENABLE_VLA"
|
|
// REDEFINE: %{run} = %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 | \
|
|
// REDEFINE: FileCheck %s
|
|
// RUN: %{compile} | mlir-translate -mlir-to-llvmir | %{run}
|
|
|
|
#CSR = #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>
|
|
|
|
#trait_scale = {
|
|
indexing_maps = [
|
|
affine_map<(i,j) -> (i,j)> // X (out)
|
|
],
|
|
iterator_types = ["parallel", "parallel"],
|
|
doc = "X(i,j) = X(i,j) * 2"
|
|
}
|
|
|
|
//
|
|
// Integration test that lowers a kernel annotated as sparse to actual sparse
|
|
// code, initializes a matching sparse storage scheme from a dense tensor,
|
|
// and runs the resulting code with the JIT compiler.
|
|
//
|
|
module {
|
|
//
|
|
// A kernel that scales a sparse matrix A by a factor of 2.0.
|
|
//
|
|
func.func @sparse_scale(%argx: tensor<8x8xf32, #CSR>) -> tensor<8x8xf32, #CSR> {
|
|
%c = arith.constant 2.0 : f32
|
|
%0 = linalg.generic #trait_scale
|
|
outs(%argx: tensor<8x8xf32, #CSR>) {
|
|
^bb(%x: f32):
|
|
%1 = arith.mulf %x, %c : f32
|
|
linalg.yield %1 : f32
|
|
} -> tensor<8x8xf32, #CSR>
|
|
return %0 : tensor<8x8xf32, #CSR>
|
|
}
|
|
|
|
//
|
|
// Main driver that converts a dense tensor into a sparse tensor
|
|
// and then calls the sparse scaling kernel with the sparse tensor
|
|
// as input argument.
|
|
//
|
|
func.func @entry() {
|
|
%c0 = arith.constant 0 : index
|
|
%f0 = arith.constant 0.0 : f32
|
|
|
|
// Initialize a dense tensor.
|
|
%0 = arith.constant dense<[
|
|
[1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0],
|
|
[0.0, 2.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
|
|
[0.0, 0.0, 3.0, 0.0, 0.0, 0.0, 0.0, 0.0],
|
|
[0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0],
|
|
[0.0, 1.0, 0.0, 0.0, 5.0, 0.0, 0.0, 0.0],
|
|
[0.0, 1.0, 1.0, 0.0, 0.0, 6.0, 0.0, 0.0],
|
|
[0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 7.0, 1.0],
|
|
[0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 8.0]
|
|
]> : tensor<8x8xf32>
|
|
|
|
// Convert dense tensor to sparse tensor and call sparse kernel.
|
|
%1 = sparse_tensor.convert %0 : tensor<8x8xf32> to tensor<8x8xf32, #CSR>
|
|
%2 = call @sparse_scale(%1)
|
|
: (tensor<8x8xf32, #CSR>) -> tensor<8x8xf32, #CSR>
|
|
|
|
// Print the resulting compacted values for verification.
|
|
//
|
|
// CHECK: ( 2, 2, 2, 4, 6, 8, 2, 10, 2, 2, 12, 2, 14, 2, 2, 16 )
|
|
//
|
|
%m = sparse_tensor.values %2 : tensor<8x8xf32, #CSR> to memref<?xf32>
|
|
%v = vector.transfer_read %m[%c0], %f0: memref<?xf32>, vector<16xf32>
|
|
vector.print %v : vector<16xf32>
|
|
|
|
// Release the resources.
|
|
bufferization.dealloc_tensor %1 : tensor<8x8xf32, #CSR>
|
|
|
|
return
|
|
}
|
|
}
|