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
89 lines
3.2 KiB
MLIR
89 lines
3.2 KiB
MLIR
// DEFINE: %{option} = enable-runtime-library=true
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// DEFINE: %{compile} = mlir-opt %s --sparse-compiler=%{option}
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// DEFINE: %{run} = mlir-cpu-runner \
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// DEFINE: -e entry -entry-point-result=void \
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// DEFINE: -shared-libs=%mlir_c_runner_utils | \
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// DEFINE: FileCheck %s
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//
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// RUN: %{compile} | %{run}
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//
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// Do the same run, but now with direct IR generation.
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// REDEFINE: %{option} = enable-runtime-library=false
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// RUN: %{compile} | %{run}
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//
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// Do the same run, but now with direct IR generation and vectorization.
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// REDEFINE: %{option} = "enable-runtime-library=false vl=2 reassociate-fp-reductions=true enable-index-optimizations=true"
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// RUN: %{compile} | %{run}
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// Do the same run, but now with direct IR generation and, if available, VLA
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// vectorization.
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// REDEFINE: %{option} = "enable-runtime-library=false vl=4 enable-arm-sve=%ENABLE_VLA"
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// REDEFINE: %{run} = %lli_host_or_aarch64_cmd \
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// REDEFINE: --entry-function=entry_lli \
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// REDEFINE: --extra-module=%S/Inputs/main_for_lli.ll \
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// REDEFINE: %VLA_ARCH_ATTR_OPTIONS \
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// REDEFINE: --dlopen=%mlir_native_utils_lib_dir/libmlir_c_runner_utils%shlibext | \
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// REDEFINE: FileCheck %s
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// RUN: %{compile} | mlir-translate -mlir-to-llvmir | %{run}
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#SparseVector = #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>
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module {
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//
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// Sparse kernel.
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//
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func.func @sparse_dot(%a: tensor<1024xf32, #SparseVector>,
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%b: tensor<1024xf32, #SparseVector>,
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%x: tensor<f32>) -> tensor<f32> {
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%dot = linalg.dot ins(%a, %b: tensor<1024xf32, #SparseVector>,
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tensor<1024xf32, #SparseVector>)
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outs(%x: tensor<f32>) -> tensor<f32>
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return %dot : tensor<f32>
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}
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//
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// Main driver.
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//
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func.func @entry() {
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// Setup two sparse vectors.
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%d1 = arith.constant sparse<
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[ [0], [1], [22], [23], [1022] ], [1.0, 2.0, 3.0, 4.0, 5.0]
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> : tensor<1024xf32>
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%d2 = arith.constant sparse<
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[ [22], [1022], [1023] ], [6.0, 7.0, 8.0]
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> : tensor<1024xf32>
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%s1 = sparse_tensor.convert %d1 : tensor<1024xf32> to tensor<1024xf32, #SparseVector>
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%s2 = sparse_tensor.convert %d2 : tensor<1024xf32> to tensor<1024xf32, #SparseVector>
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// Call the kernel and verify the output.
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//
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// CHECK: 53
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//
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%t = bufferization.alloc_tensor() : tensor<f32>
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%z = arith.constant 0.0 : f32
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%x = tensor.insert %z into %t[] : tensor<f32>
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%0 = call @sparse_dot(%s1, %s2, %x) : (tensor<1024xf32, #SparseVector>,
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tensor<1024xf32, #SparseVector>,
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tensor<f32>) -> tensor<f32>
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%1 = tensor.extract %0[] : tensor<f32>
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vector.print %1 : f32
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// Print number of entries in the sparse vectors.
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//
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// CHECK: 5
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// CHECK: 3
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//
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%noe1 = sparse_tensor.number_of_entries %s1 : tensor<1024xf32, #SparseVector>
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%noe2 = sparse_tensor.number_of_entries %s2 : tensor<1024xf32, #SparseVector>
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vector.print %noe1 : index
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vector.print %noe2 : index
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// Release the resources.
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bufferization.dealloc_tensor %s1 : tensor<1024xf32, #SparseVector>
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bufferization.dealloc_tensor %s2 : tensor<1024xf32, #SparseVector>
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return
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}
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}
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