//--------------------------------------------------------------------------------------------------
// WHEN CREATING A NEW TEST, PLEASE JUST COPY & PASTE WITHOUT EDITS.
//
// Set-up that's shared across all tests in this directory. In principle, this
// config could be moved to lit.local.cfg. However, there are downstream users that
// do not use these LIT config files. Hence why this is kept inline.
//
// DEFINE: %{sparsifier_opts} = enable-runtime-library=true
// DEFINE: %{sparsifier_opts_sve} = enable-arm-sve=true %{sparsifier_opts}
// DEFINE: %{compile} = mlir-opt %s --sparsifier="%{sparsifier_opts}"
// DEFINE: %{compile_sve} = mlir-opt %s --sparsifier="%{sparsifier_opts_sve}"
// DEFINE: %{run_libs} = -shared-libs=%mlir_c_runner_utils,%mlir_runner_utils
// DEFINE: %{run_libs_sve} = -shared-libs=%native_mlir_runner_utils,%native_mlir_c_runner_utils
// DEFINE: %{run_opts} = -e main -entry-point-result=void
// DEFINE: %{run} = mlir-cpu-runner %{run_opts} %{run_libs}
// DEFINE: %{run_sve} = %mcr_aarch64_cmd --march=aarch64 --mattr="+sve" %{run_opts} %{run_libs_sve}
//
// DEFINE: %{env} =
//--------------------------------------------------------------------------------------------------
// REDEFINE: %{sparsifier_opts} = enable-runtime-library=true
// RUN: %{compile} | %{run} | FileCheck %s
//
// Do the same run, but now with direct IR generation.
// REDEFINE: %{sparsifier_opts} = enable-runtime-library=false
// RUN: %{compile} | %{run} | FileCheck %s
//
// Do the same run, but now with direct IR generation and vectorization.
// REDEFINE: %{sparsifier_opts} = enable-runtime-library=false vl=2 reassociate-fp-reductions=true enable-index-optimizations=true
// RUN: %{compile} | %{run} | FileCheck %s
//
// Do the same run, but now with direct IR generation and VLA vectorization.
// RUN: %if mlir_arm_sve_tests %{ %{compile_sve} | %{run_sve} | FileCheck %s %}
#Tensor1 = #sparse_tensor.encoding<{
map = (d0, d1, d2) -> (d0 : dense, d1 : dense, d2 : compressed)
}>
// NOTE: dense after compressed is not currently supported for the target
// of direct-sparse2sparse conversion. (It's fine for the source though.)
#Tensor2 = #sparse_tensor.encoding<{
map = (d0, d1, d2) -> (d0 : dense, d1 : compressed, d2 : dense)
}>
#Tensor3 = #sparse_tensor.encoding<{
map = (d0, d1, d2) -> (d0 : dense, d2 : dense, d1 : compressed)
}>
#SingletonTensor1 = #sparse_tensor.encoding<{
map = (d0, d1, d2) -> (d0 : dense, d1 : compressed(nonunique), d2 : singleton)
}>
// This also checks the singleton->compressed conversion.
#SingletonTensor3 = #sparse_tensor.encoding<{
map = (d0, d1, d2) -> (d0 : dense, d1 : dense, d2 : compressed)
}>
module {
//
// Utility for output.
//
func.func @dump(%arg0: tensor<2x3x4xf64>) {
%c0 = arith.constant 0 : index
%d0 = arith.constant -1.0 : f64
%0 = vector.transfer_read %arg0[%c0, %c0, %c0], %d0: tensor<2x3x4xf64>, vector<2x3x4xf64>
vector.print %0 : vector<2x3x4xf64>
return
}
//
// The first test suite (for non-singleton LevelTypes).
//
func.func @testNonSingleton() {
//
// Initialize a 3-dim dense tensor.
//
%src = arith.constant dense<[
[ [ 1.0, 2.0, 3.0, 4.0 ],
[ 5.0, 6.0, 7.0, 8.0 ],
[ 9.0, 10.0, 11.0, 12.0 ] ],
[ [ 13.0, 14.0, 15.0, 16.0 ],
[ 17.0, 18.0, 19.0, 20.0 ],
[ 21.0, 22.0, 23.0, 24.0 ] ]
]> : tensor<2x3x4xf64>
//
// Convert dense tensor directly to various sparse tensors.
//
%s1 = sparse_tensor.convert %src : tensor<2x3x4xf64> to tensor<2x3x4xf64, #Tensor1>
%s3 = sparse_tensor.convert %src : tensor<2x3x4xf64> to tensor<2x3x4xf64, #Tensor3>
//
// Convert sparse tensor directly to another sparse format.
//
%t13 = sparse_tensor.convert %s1 : tensor<2x3x4xf64, #Tensor1> to tensor<2x3x4xf64, #Tensor3>
%t31 = sparse_tensor.convert %s3 : tensor<2x3x4xf64, #Tensor3> to tensor<2x3x4xf64, #Tensor1>
//
// Convert sparse tensor back to dense.
//
%d13 = sparse_tensor.convert %t13 : tensor<2x3x4xf64, #Tensor3> to tensor<2x3x4xf64>
%d31 = sparse_tensor.convert %t31 : tensor<2x3x4xf64, #Tensor1> to tensor<2x3x4xf64>
//
// Check round-trip equality. And release dense tensors.
//
// CHECK-COUNT-3: ( ( ( 1, 2, 3, 4 ), ( 5, 6, 7, 8 ), ( 9, 10, 11, 12 ) ), ( ( 13, 14, 15, 16 ), ( 17, 18, 19, 20 ), ( 21, 22, 23, 24 ) ) )
call @dump(%src) : (tensor<2x3x4xf64>) -> ()
call @dump(%d13) : (tensor<2x3x4xf64>) -> ()
call @dump(%d31) : (tensor<2x3x4xf64>) -> ()
//
// Release the resources.
//
bufferization.dealloc_tensor %t13 : tensor<2x3x4xf64, #Tensor3>
bufferization.dealloc_tensor %t31 : tensor<2x3x4xf64, #Tensor1>
bufferization.dealloc_tensor %s1 : tensor<2x3x4xf64, #Tensor1>
bufferization.dealloc_tensor %s3 : tensor<2x3x4xf64, #Tensor3>
bufferization.dealloc_tensor %d13 : tensor<2x3x4xf64>
bufferization.dealloc_tensor %d31 : tensor<2x3x4xf64>
return
}
//
// The second test suite (for singleton LevelTypes).
//
func.func @testSingleton() {
//
// Initialize a 3-dim dense tensor with the 3rd dim being singleton.
//
%src = arith.constant dense<[
[ [ 1.0, 0.0, 0.0, 0.0 ],
[ 0.0, 6.0, 0.0, 0.0 ],
[ 0.0, 0.0, 11.0, 0.0 ] ],
[ [ 0.0, 14.0, 0.0, 0.0 ],
[ 0.0, 0.0, 0.0, 20.0 ],
[ 21.0, 0.0, 0.0, 0.0 ] ]
]> : tensor<2x3x4xf64>
//
// Convert dense tensor directly to various sparse tensors.
//
%s1 = sparse_tensor.convert %src : tensor<2x3x4xf64> to tensor<2x3x4xf64, #SingletonTensor1>
%s3 = sparse_tensor.convert %src : tensor<2x3x4xf64> to tensor<2x3x4xf64, #SingletonTensor3>
//
// Convert sparse tensor directly to another sparse format.
//
%t13 = sparse_tensor.convert %s1 : tensor<2x3x4xf64, #SingletonTensor1> to tensor<2x3x4xf64, #SingletonTensor3>
%t31 = sparse_tensor.convert %s3 : tensor<2x3x4xf64, #SingletonTensor3> to tensor<2x3x4xf64, #SingletonTensor1>
//
// Convert sparse tensor back to dense.
//
%d13 = sparse_tensor.convert %t13 : tensor<2x3x4xf64, #SingletonTensor3> to tensor<2x3x4xf64>
%d31 = sparse_tensor.convert %t31 : tensor<2x3x4xf64, #SingletonTensor1> to tensor<2x3x4xf64>
//
// Check round-trip equality. And release dense tensors.
//
// CHECK-COUNT-3: ( ( ( 1, 0, 0, 0 ), ( 0, 6, 0, 0 ), ( 0, 0, 11, 0 ) ), ( ( 0, 14, 0, 0 ), ( 0, 0, 0, 20 ), ( 21, 0, 0, 0 ) ) )
call @dump(%src) : (tensor<2x3x4xf64>) -> ()
call @dump(%d13) : (tensor<2x3x4xf64>) -> ()
call @dump(%d31) : (tensor<2x3x4xf64>) -> ()
//
// Release the resources.
//
bufferization.dealloc_tensor %t13 : tensor<2x3x4xf64, #SingletonTensor3>
bufferization.dealloc_tensor %t31 : tensor<2x3x4xf64, #SingletonTensor1>
bufferization.dealloc_tensor %s1 : tensor<2x3x4xf64, #SingletonTensor1>
bufferization.dealloc_tensor %s3 : tensor<2x3x4xf64, #SingletonTensor3>
bufferization.dealloc_tensor %d13 : tensor<2x3x4xf64>
bufferization.dealloc_tensor %d31 : tensor<2x3x4xf64>
return
}
//
// Main driver.
//
func.func @main() {
call @testNonSingleton() : () -> ()
call @testSingleton() : () -> ()
return
}
}
Codebase Browser
llvm