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Differential D133270

[MLIR] Switch lit tests to %mlir_lib_dir and %mlir_src_dir replacements.
ClosedPublic

Authored by csigg on Sep 4 2022, 1:48 AM.

Details

Reviewers
mehdi_amini
antiagainst
aartbik
ftynse
nicolasvasilache
herhut
dcaballe
Commits
rG0f2ec35691cd: [MLIR] Switch lit tests to %mlir_lib_dir and %mlir_src_dir replacements.
Summary

The old replacements will be removed soon:

  • %linalg_test_lib_dir
  • %cuda_wrapper_library_dir
  • %spirv_wrapper_library_dir
  • %vulkan_wrapper_library_dir
  • %mlir_runner_utils_dir
  • %mlir_integration_test_dir

Diff Detail

Event Timeline

csigg created this revision.Sep 4 2022, 1:48 AM
Herald added a reviewer: antiagainst. · View Herald TranscriptSep 4 2022, 1:48 AM
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csigg requested review of this revision.Sep 4 2022, 1:48 AM
Herald added a reviewer: nicolasvasilache. · View Herald TranscriptSep 4 2022, 1:48 AM
Herald added a reviewer: herhut. · View Herald Transcript
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csigg mentioned this in D133190: [MLIR] Fix runner library paths for integration tests.Sep 4 2022, 2:11 AM
Harbormaster completed remote builds in B185000: Diff 457845.Sep 4 2022, 2:39 AM
csigg updated this revision to Diff 457847.Sep 4 2022, 2:56 AM

Remove libmlir_test_spirv_cpu_runner_c_wrappers.so bazel target for now.

The corresponding test package will be added later.

Harbormaster completed remote builds in B185002: Diff 457847.Sep 4 2022, 3:11 AM
csigg updated this revision to Diff 457848.Sep 4 2022, 4:03 AM

Tag cuda-dependent targets with 'manual'.

Harbormaster completed remote builds in B185003: Diff 457848.Sep 4 2022, 4:21 AM
herhut added a comment.Sep 5 2022, 1:38 AM

+1 for the direction. Just a bazil question.

utils/bazel/llvm-project-overlay/mlir/BUILD.bazel
6485

Why are these aliases needed?

csigg added inline comments.Sep 5 2022, 3:36 AM
utils/bazel/llvm-project-overlay/mlir/BUILD.bazel
6485

They are needed because the shared library name (lib<name>.so) of the cc_library target would otherwise clash with the cc_binary target below.

I will add a comment explaining this.

csigg updated this revision to Diff 457998.Sep 5 2022, 7:43 AM

Add comments why alias are needed.

Harbormaster completed remote builds in B185095: Diff 457998.Sep 5 2022, 8:01 AM
herhut accepted this revision.Sep 6 2022, 1:02 AM

Thanks for the explanation. My bazil-foo is not as strong as yours.

This revision is now accepted and ready to land.Sep 6 2022, 1:02 AM
Closed by commit rG0f2ec35691cd: [MLIR] Switch lit tests to %mlir_lib_dir and %mlir_src_dir replacements. (authored by csigg). · Explain WhySep 6 2022, 3:34 AM
This revision was automatically updated to reflect the committed changes.
csigg added a commit: rG0f2ec35691cd: [MLIR] Switch lit tests to %mlir_lib_dir and %mlir_src_dir replacements..

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Diff 457845

mlir/test/CMakeLists.txt

Show All 29 Linesif (MLIR_INCLUDE_INTEGRATION_TESTS)
option(MLIR_RUN_ARM_SVE_TESTS "Run Arm SVE tests.") option(MLIR_RUN_ARM_SVE_TESTS "Run Arm SVE tests.")
# The native target may not be enabled when cross compiling, raise an error. # The native target may not be enabled when cross compiling, raise an error.
if(NOT MLIR_ENABLE_EXECUTION_ENGINE) if(NOT MLIR_ENABLE_EXECUTION_ENGINE)
message(FATAL_ERROR "MLIR_INCLUDE_INTEGRATION_TESTS requires a native target") message(FATAL_ERROR "MLIR_INCLUDE_INTEGRATION_TESTS requires a native target")
endif() endif()
# Copy test data over.
# TODO: drop copy and load files relative to MLIR_SOURCE_DIR / %mlir_src_root.
file(COPY ${CMAKE_CURRENT_SOURCE_DIR}/Integration/data/mttkrp_b.tns
${CMAKE_CURRENT_SOURCE_DIR}/Integration/data/test.mtx
${CMAKE_CURRENT_SOURCE_DIR}/Integration/data/test_symmetric.mtx
${CMAKE_CURRENT_SOURCE_DIR}/Integration/data/test_symmetric_complex.mtx
${CMAKE_CURRENT_SOURCE_DIR}/Integration/data/test.tns
${CMAKE_CURRENT_SOURCE_DIR}/Integration/data/wide.mtx
DESTINATION ${MLIR_LIB_DIR}/data/)
endif() endif()
llvm_canonicalize_cmake_booleans( llvm_canonicalize_cmake_booleans(
LLVM_BUILD_EXAMPLES LLVM_BUILD_EXAMPLES
MLIR_ENABLE_BINDINGS_PYTHON MLIR_ENABLE_BINDINGS_PYTHON
MLIR_ENABLE_CUDA_CONVERSIONS MLIR_ENABLE_CUDA_CONVERSIONS
MLIR_ENABLE_CUDA_RUNNER MLIR_ENABLE_CUDA_RUNNER
MLIR_ENABLE_ROCM_CONVERSIONS MLIR_ENABLE_ROCM_CONVERSIONS
▲ Show 20 Lines • Show All 118 Lines • Show Last 20 Lines

mlir/test/Integration/Dialect/Async/CPU/microbench-linalg-async-parallel-for.mlir

// RUN: mlir-opt %s \ // RUN: mlir-opt %s \
// RUN: -convert-linalg-to-parallel-loops \ // RUN: -convert-linalg-to-parallel-loops \
// RUN: -async-parallel-for \ // RUN: -async-parallel-for \
// RUN: -async-to-async-runtime \ // RUN: -async-to-async-runtime \
// RUN: -async-runtime-ref-counting \ // RUN: -async-runtime-ref-counting \
// RUN: -async-runtime-ref-counting-opt \ // RUN: -async-runtime-ref-counting-opt \
// RUN: -convert-async-to-llvm \ // RUN: -convert-async-to-llvm \
// RUN: -convert-scf-to-cf \ // RUN: -convert-scf-to-cf \
// RUN: -arith-expand \ // RUN: -arith-expand \
// RUN: -memref-expand \ // RUN: -memref-expand \
// RUN: -convert-vector-to-llvm \ // RUN: -convert-vector-to-llvm \
// RUN: -convert-memref-to-llvm \ // RUN: -convert-memref-to-llvm \
// RUN: -convert-func-to-llvm \ // RUN: -convert-func-to-llvm \
// RUN: -reconcile-unrealized-casts \ // RUN: -reconcile-unrealized-casts \
// RUN: | mlir-cpu-runner \ // RUN: | mlir-cpu-runner \
// RUN: -e entry -entry-point-result=void -O3 \ // RUN: -e entry -entry-point-result=void -O3 \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext\ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext\
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_async_runtime%shlibext \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_async_runtime%shlibext \
// RUN: | FileCheck %s --dump-input=always // RUN: | FileCheck %s --dump-input=always
// RUN: mlir-opt %s \ // RUN: mlir-opt %s \
// RUN: -convert-linalg-to-loops \ // RUN: -convert-linalg-to-loops \
// RUN: -convert-scf-to-cf \ // RUN: -convert-scf-to-cf \
// RUN: -convert-vector-to-llvm \ // RUN: -convert-vector-to-llvm \
// RUN: -convert-memref-to-llvm \ // RUN: -convert-memref-to-llvm \
// RUN: -convert-func-to-llvm \ // RUN: -convert-func-to-llvm \
// RUN: -reconcile-unrealized-casts \ // RUN: -reconcile-unrealized-casts \
// RUN: | mlir-cpu-runner \ // RUN: | mlir-cpu-runner \
// RUN: -e entry -entry-point-result=void -O3 \ // RUN: -e entry -entry-point-result=void -O3 \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext\ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext\
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_async_runtime%shlibext \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_async_runtime%shlibext \
// RUN: | FileCheck %s --dump-input=always // RUN: | FileCheck %s --dump-input=always
#map0 = affine_map<(d0, d1) -> (d0, d1)> #map0 = affine_map<(d0, d1) -> (d0, d1)>
func.func @linalg_generic(%lhs: memref<?x?xf32>, func.func @linalg_generic(%lhs: memref<?x?xf32>,
%rhs: memref<?x?xf32>, %rhs: memref<?x?xf32>,
%sum: memref<?x?xf32>) { %sum: memref<?x?xf32>) {
linalg.generic { linalg.generic {
▲ Show 20 Lines • Show All 93 Lines • Show Last 20 Lines

mlir/test/Integration/Dialect/Async/CPU/microbench-scf-async-parallel-for.mlir

// RUN: mlir-opt %s \ // RUN: mlir-opt %s \
// RUN: -async-parallel-for \ // RUN: -async-parallel-for \
// RUN: -async-to-async-runtime \ // RUN: -async-to-async-runtime \
// RUN: -async-runtime-ref-counting \ // RUN: -async-runtime-ref-counting \
// RUN: -async-runtime-ref-counting-opt \ // RUN: -async-runtime-ref-counting-opt \
// RUN: -convert-async-to-llvm \ // RUN: -convert-async-to-llvm \
// RUN: -convert-linalg-to-loops \ // RUN: -convert-linalg-to-loops \
// RUN: -convert-scf-to-cf \ // RUN: -convert-scf-to-cf \
// RUN: -arith-expand \ // RUN: -arith-expand \
// RUN: -memref-expand \ // RUN: -memref-expand \
// RUN: -convert-vector-to-llvm \ // RUN: -convert-vector-to-llvm \
// RUN: -convert-memref-to-llvm \ // RUN: -convert-memref-to-llvm \
// RUN: -convert-func-to-llvm \ // RUN: -convert-func-to-llvm \
// RUN: -reconcile-unrealized-casts \ // RUN: -reconcile-unrealized-casts \
// RUN: | mlir-cpu-runner \ // RUN: | mlir-cpu-runner \
// RUN: -e entry -entry-point-result=void -O3 \ // RUN: -e entry -entry-point-result=void -O3 \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext\ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext\
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_async_runtime%shlibext \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_async_runtime%shlibext \
// RUN: | FileCheck %s --dump-input=always // RUN: | FileCheck %s --dump-input=always
// RUN: mlir-opt %s \ // RUN: mlir-opt %s \
// RUN: -async-parallel-for=async-dispatch=false \ // RUN: -async-parallel-for=async-dispatch=false \
// RUN: -async-to-async-runtime \ // RUN: -async-to-async-runtime \
// RUN: -async-runtime-ref-counting \ // RUN: -async-runtime-ref-counting \
// RUN: -async-runtime-ref-counting-opt \ // RUN: -async-runtime-ref-counting-opt \
// RUN: -convert-async-to-llvm \ // RUN: -convert-async-to-llvm \
// RUN: -convert-linalg-to-loops \ // RUN: -convert-linalg-to-loops \
// RUN: -convert-scf-to-cf \ // RUN: -convert-scf-to-cf \
// RUN: -arith-expand \ // RUN: -arith-expand \
// RUN: -memref-expand \ // RUN: -memref-expand \
// RUN: -convert-vector-to-llvm \ // RUN: -convert-vector-to-llvm \
// RUN: -convert-memref-to-llvm \ // RUN: -convert-memref-to-llvm \
// RUN: -convert-func-to-llvm \ // RUN: -convert-func-to-llvm \
// RUN: -reconcile-unrealized-casts \ // RUN: -reconcile-unrealized-casts \
// RUN: | mlir-cpu-runner \ // RUN: | mlir-cpu-runner \
// RUN: -e entry -entry-point-result=void -O3 \ // RUN: -e entry -entry-point-result=void -O3 \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext\ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext\
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_async_runtime%shlibext \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_async_runtime%shlibext \
// RUN: | FileCheck %s --dump-input=always // RUN: | FileCheck %s --dump-input=always
// RUN: mlir-opt %s \ // RUN: mlir-opt %s \
// RUN: -convert-linalg-to-loops \ // RUN: -convert-linalg-to-loops \
// RUN: -convert-scf-to-cf \ // RUN: -convert-scf-to-cf \
// RUN: -convert-vector-to-llvm \ // RUN: -convert-vector-to-llvm \
// RUN: -convert-memref-to-llvm \ // RUN: -convert-memref-to-llvm \
// RUN: -convert-func-to-llvm \ // RUN: -convert-func-to-llvm \
// RUN: -reconcile-unrealized-casts \ // RUN: -reconcile-unrealized-casts \
// RUN: | mlir-cpu-runner \ // RUN: | mlir-cpu-runner \
// RUN: -e entry -entry-point-result=void -O3 \ // RUN: -e entry -entry-point-result=void -O3 \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext\ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext\
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_async_runtime%shlibext \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_async_runtime%shlibext \
// RUN: | FileCheck %s --dump-input=always // RUN: | FileCheck %s --dump-input=always
#map0 = affine_map<(d0, d1) -> (d0, d1)> #map0 = affine_map<(d0, d1) -> (d0, d1)>
func.func @scf_parallel(%lhs: memref<?x?xf32>, func.func @scf_parallel(%lhs: memref<?x?xf32>,
%rhs: memref<?x?xf32>, %rhs: memref<?x?xf32>,
%sum: memref<?x?xf32>) { %sum: memref<?x?xf32>) {
%c0 = arith.constant 0 : index %c0 = arith.constant 0 : index
▲ Show 20 Lines • Show All 94 Lines • Show Last 20 Lines

mlir/test/Integration/Dialect/Async/CPU/test-async-parallel-for-1d.mlir

// RUN: mlir-opt %s -async-parallel-for \ // RUN: mlir-opt %s -async-parallel-for \
// RUN: -async-to-async-runtime \ // RUN: -async-to-async-runtime \
// RUN: -async-runtime-ref-counting \ // RUN: -async-runtime-ref-counting \
// RUN: -async-runtime-ref-counting-opt \ // RUN: -async-runtime-ref-counting-opt \
// RUN: -convert-async-to-llvm \ // RUN: -convert-async-to-llvm \
// RUN: -convert-scf-to-cf \ // RUN: -convert-scf-to-cf \
// RUN: -convert-memref-to-llvm \ // RUN: -convert-memref-to-llvm \
// RUN: -arith-expand \ // RUN: -arith-expand \
// RUN: -memref-expand \ // RUN: -memref-expand \
// RUN: -convert-func-to-llvm \ // RUN: -convert-func-to-llvm \
// RUN: -reconcile-unrealized-casts \ // RUN: -reconcile-unrealized-casts \
// RUN: | mlir-cpu-runner \ // RUN: | mlir-cpu-runner \
// RUN: -e entry -entry-point-result=void -O0 \ // RUN: -e entry -entry-point-result=void -O0 \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_async_runtime%shlibext\ // RUN: -shared-libs=%mlir_lib_dir/libmlir_async_runtime%shlibext\
// RUN: | FileCheck %s --dump-input=always // RUN: | FileCheck %s --dump-input=always
// RUN: mlir-opt %s -async-parallel-for \ // RUN: mlir-opt %s -async-parallel-for \
// RUN: -async-to-async-runtime \ // RUN: -async-to-async-runtime \
// RUN: -async-runtime-policy-based-ref-counting \ // RUN: -async-runtime-policy-based-ref-counting \
// RUN: -convert-async-to-llvm \ // RUN: -convert-async-to-llvm \
// RUN: -convert-scf-to-cf \ // RUN: -convert-scf-to-cf \
// RUN: -convert-memref-to-llvm \ // RUN: -convert-memref-to-llvm \
// RUN: -arith-expand \ // RUN: -arith-expand \
// RUN: -memref-expand \ // RUN: -memref-expand \
// RUN: -convert-func-to-llvm \ // RUN: -convert-func-to-llvm \
// RUN: -reconcile-unrealized-casts \ // RUN: -reconcile-unrealized-casts \
// RUN: | mlir-cpu-runner \ // RUN: | mlir-cpu-runner \
// RUN: -e entry -entry-point-result=void -O0 \ // RUN: -e entry -entry-point-result=void -O0 \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_async_runtime%shlibext\ // RUN: -shared-libs=%mlir_lib_dir/libmlir_async_runtime%shlibext\
// RUN: | FileCheck %s --dump-input=always // RUN: | FileCheck %s --dump-input=always
// RUN: mlir-opt %s -async-parallel-for="async-dispatch=false \ // RUN: mlir-opt %s -async-parallel-for="async-dispatch=false \
// RUN: num-workers=20 \ // RUN: num-workers=20 \
// RUN: min-task-size=1" \ // RUN: min-task-size=1" \
// RUN: -async-to-async-runtime \ // RUN: -async-to-async-runtime \
// RUN: -async-runtime-ref-counting \ // RUN: -async-runtime-ref-counting \
// RUN: -async-runtime-ref-counting-opt \ // RUN: -async-runtime-ref-counting-opt \
// RUN: -convert-async-to-llvm \ // RUN: -convert-async-to-llvm \
// RUN: -convert-scf-to-cf \ // RUN: -convert-scf-to-cf \
// RUN: -convert-memref-to-llvm \ // RUN: -convert-memref-to-llvm \
// RUN: -arith-expand \ // RUN: -arith-expand \
// RUN: -memref-expand \ // RUN: -memref-expand \
// RUN: -convert-func-to-llvm \ // RUN: -convert-func-to-llvm \
// RUN: -reconcile-unrealized-casts \ // RUN: -reconcile-unrealized-casts \
// RUN: | mlir-cpu-runner \ // RUN: | mlir-cpu-runner \
// RUN: -e entry -entry-point-result=void -O0 \ // RUN: -e entry -entry-point-result=void -O0 \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_async_runtime%shlibext\ // RUN: -shared-libs=%mlir_lib_dir/libmlir_async_runtime%shlibext\
// RUN: | FileCheck %s --dump-input=always // RUN: | FileCheck %s --dump-input=always
// Suppress constant folding by introducing "dynamic" zero value at runtime. // Suppress constant folding by introducing "dynamic" zero value at runtime.
func.func private @zero() -> index { func.func private @zero() -> index {
%0 = arith.constant 0 : index %0 = arith.constant 0 : index
return %0 : index return %0 : index
} }
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mlir/test/Integration/Dialect/Async/CPU/test-async-parallel-for-2d.mlir

// RUN: mlir-opt %s -async-parallel-for \ // RUN: mlir-opt %s -async-parallel-for \
// RUN: -async-to-async-runtime \ // RUN: -async-to-async-runtime \
// RUN: -async-runtime-ref-counting \ // RUN: -async-runtime-ref-counting \
// RUN: -async-runtime-ref-counting-opt \ // RUN: -async-runtime-ref-counting-opt \
// RUN: -arith-expand \ // RUN: -arith-expand \
// RUN: -convert-async-to-llvm \ // RUN: -convert-async-to-llvm \
// RUN: -convert-scf-to-cf \ // RUN: -convert-scf-to-cf \
// RUN: -convert-memref-to-llvm \ // RUN: -convert-memref-to-llvm \
// RUN: -convert-func-to-llvm \ // RUN: -convert-func-to-llvm \
// RUN: -reconcile-unrealized-casts \ // RUN: -reconcile-unrealized-casts \
// RUN: | mlir-cpu-runner \ // RUN: | mlir-cpu-runner \
// RUN: -e entry -entry-point-result=void -O0 \ // RUN: -e entry -entry-point-result=void -O0 \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_async_runtime%shlibext\ // RUN: -shared-libs=%mlir_lib_dir/libmlir_async_runtime%shlibext\
// RUN: | FileCheck %s --dump-input=always // RUN: | FileCheck %s --dump-input=always
// RUN: mlir-opt %s -async-parallel-for \ // RUN: mlir-opt %s -async-parallel-for \
// RUN: -async-to-async-runtime \ // RUN: -async-to-async-runtime \
// RUN: -async-runtime-policy-based-ref-counting \ // RUN: -async-runtime-policy-based-ref-counting \
// RUN: -arith-expand \ // RUN: -arith-expand \
// RUN: -convert-async-to-llvm \ // RUN: -convert-async-to-llvm \
// RUN: -convert-scf-to-cf \ // RUN: -convert-scf-to-cf \
// RUN: -convert-memref-to-llvm \ // RUN: -convert-memref-to-llvm \
// RUN: -convert-func-to-llvm \ // RUN: -convert-func-to-llvm \
// RUN: -reconcile-unrealized-casts \ // RUN: -reconcile-unrealized-casts \
// RUN: | mlir-cpu-runner \ // RUN: | mlir-cpu-runner \
// RUN: -e entry -entry-point-result=void -O0 \ // RUN: -e entry -entry-point-result=void -O0 \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_async_runtime%shlibext\ // RUN: -shared-libs=%mlir_lib_dir/libmlir_async_runtime%shlibext\
// RUN: | FileCheck %s --dump-input=always // RUN: | FileCheck %s --dump-input=always
// RUN: mlir-opt %s -async-parallel-for="async-dispatch=false \ // RUN: mlir-opt %s -async-parallel-for="async-dispatch=false \
// RUN: num-workers=20 \ // RUN: num-workers=20 \
// RUN: min-task-size=1" \ // RUN: min-task-size=1" \
// RUN: -async-to-async-runtime \ // RUN: -async-to-async-runtime \
// RUN: -async-runtime-ref-counting \ // RUN: -async-runtime-ref-counting \
// RUN: -async-runtime-ref-counting-opt \ // RUN: -async-runtime-ref-counting-opt \
// RUN: -arith-expand \ // RUN: -arith-expand \
// RUN: -convert-async-to-llvm \ // RUN: -convert-async-to-llvm \
// RUN: -convert-scf-to-cf \ // RUN: -convert-scf-to-cf \
// RUN: -convert-memref-to-llvm \ // RUN: -convert-memref-to-llvm \
// RUN: -convert-func-to-llvm \ // RUN: -convert-func-to-llvm \
// RUN: -reconcile-unrealized-casts \ // RUN: -reconcile-unrealized-casts \
// RUN: | mlir-cpu-runner \ // RUN: | mlir-cpu-runner \
// RUN: -e entry -entry-point-result=void -O0 \ // RUN: -e entry -entry-point-result=void -O0 \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_async_runtime%shlibext\ // RUN: -shared-libs=%mlir_lib_dir/libmlir_async_runtime%shlibext\
// RUN: | FileCheck %s --dump-input=always // RUN: | FileCheck %s --dump-input=always
func.func @entry() { func.func @entry() {
%c0 = arith.constant 0.0 : f32 %c0 = arith.constant 0.0 : f32
%c1 = arith.constant 1 : index %c1 = arith.constant 1 : index
%c2 = arith.constant 2 : index %c2 = arith.constant 2 : index
%c8 = arith.constant 8 : index %c8 = arith.constant 8 : index
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mlir/test/Integration/Dialect/Complex/CPU/correctness.mlir

// RUN: mlir-opt %s \ // RUN: mlir-opt %s \
// RUN: -func-bufferize -tensor-bufferize -arith-bufferize --canonicalize \ // RUN: -func-bufferize -tensor-bufferize -arith-bufferize --canonicalize \
// RUN: -convert-scf-to-cf --convert-complex-to-standard \ // RUN: -convert-scf-to-cf --convert-complex-to-standard \
// RUN: -convert-memref-to-llvm -convert-math-to-llvm -convert-math-to-libm \ // RUN: -convert-memref-to-llvm -convert-math-to-llvm -convert-math-to-libm \
// RUN: -convert-vector-to-llvm -convert-complex-to-llvm \ // RUN: -convert-vector-to-llvm -convert-complex-to-llvm \
// RUN: -convert-func-to-llvm -reconcile-unrealized-casts |\ // RUN: -convert-func-to-llvm -reconcile-unrealized-casts |\
// RUN: mlir-cpu-runner \ // RUN: mlir-cpu-runner \
// RUN: -e entry -entry-point-result=void \ // RUN: -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext |\ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext |\
// RUN: FileCheck %s // RUN: FileCheck %s
func.func @test_unary(%input: tensor<?xcomplex<f32>>, func.func @test_unary(%input: tensor<?xcomplex<f32>>,
%func: (complex<f32>) -> complex<f32>) { %func: (complex<f32>) -> complex<f32>) {
%c0 = arith.constant 0 : index %c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index %c1 = arith.constant 1 : index
%size = tensor.dim %input, %c0: tensor<?xcomplex<f32>> %size = tensor.dim %input, %c0: tensor<?xcomplex<f32>>
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mlir/test/Integration/Dialect/LLVMIR/CPU/X86/test-inline-asm-vector.mlir

// RUN: mlir-opt %s -convert-vector-to-llvm | \ // RUN: mlir-opt %s -convert-vector-to-llvm | \
// RUN: mlir-cpu-runner -e entry_point_with_all_constants -entry-point-result=void \ // RUN: mlir-cpu-runner -e entry_point_with_all_constants -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext
module { module {
llvm.func @function_to_run(%a: vector<8xf32>, %b: vector<8xf32>) { llvm.func @function_to_run(%a: vector<8xf32>, %b: vector<8xf32>) {
// CHECK: ( 8, 10, 12, 14, 16, 18, 20, 22 ) // CHECK: ( 8, 10, 12, 14, 16, 18, 20, 22 )
%r0 = llvm.inline_asm asm_dialect = intel %r0 = llvm.inline_asm asm_dialect = intel
"vaddps $0, $1, $2", "=x,x,x" %a, %b: "vaddps $0, $1, $2", "=x,x,x" %a, %b:
(vector<8xf32>, vector<8xf32>) -> vector<8xf32> (vector<8xf32>, vector<8xf32>) -> vector<8xf32>
vector.print %r0: vector<8xf32> vector.print %r0: vector<8xf32>
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mlir/test/Integration/Dialect/LLVMIR/CPU/X86/test-inline-asm.mlir

// RUN: mlir-cpu-runner %s -e entry -entry-point-result=void \ // RUN: mlir-cpu-runner %s -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
module { module {
llvm.func @printI64(i64) llvm.func @printI64(i64)
llvm.func @entry() { llvm.func @entry() {
%c2 = llvm.mlir.constant(-42: i64) :i64 %c2 = llvm.mlir.constant(-42: i64) :i64
%val = llvm.inline_asm "xor $0, $0", "=r,r" %c2 : %val = llvm.inline_asm "xor $0, $0", "=r,r" %c2 :
(i64) -> i64 (i64) -> i64
// CHECK: 0 // CHECK: 0
llvm.call @printI64(%val) : (i64) -> () llvm.call @printI64(%val) : (i64) -> ()
llvm.return llvm.return
} }
} }

mlir/test/Integration/Dialect/LLVMIR/CPU/test-vector-reductions-fp.mlir

// RUN: mlir-cpu-runner %s -e entry -entry-point-result=void \ // RUN: mlir-cpu-runner %s -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
// End-to-end test of all fp reduction intrinsics (not exhaustive unit tests). // End-to-end test of all fp reduction intrinsics (not exhaustive unit tests).
module { module {
llvm.func @printNewline() llvm.func @printNewline()
llvm.func @printF32(f32) llvm.func @printF32(f32)
llvm.func @entry() { llvm.func @entry() {
// Setup (1,2,3,4). // Setup (1,2,3,4).
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mlir/test/Integration/Dialect/LLVMIR/CPU/test-vector-reductions-int.mlir

// RUN: mlir-cpu-runner %s -e entry -entry-point-result=void \ // RUN: mlir-cpu-runner %s -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
// End-to-end test of all int reduction intrinsics (not exhaustive unit tests). // End-to-end test of all int reduction intrinsics (not exhaustive unit tests).
module { module {
llvm.func @printNewline() llvm.func @printNewline()
llvm.func @printI64(i64) llvm.func @printI64(i64)
llvm.func @entry() { llvm.func @entry() {
// Setup (1,2,3,4). // Setup (1,2,3,4).
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mlir/test/Integration/Dialect/Linalg/CPU/matmul-vs-matvec.mlir

// RUN: mlir-opt %s -convert-linalg-to-loops -convert-scf-to-cf -convert-linalg-to-llvm -convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \ // RUN: mlir-opt %s -convert-linalg-to-loops -convert-scf-to-cf -convert-linalg-to-llvm -convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \
// RUN: mlir-cpu-runner -O3 -e main -entry-point-result=void \ // RUN: mlir-cpu-runner -O3 -e main -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext \
// RUN: | FileCheck %s // RUN: | FileCheck %s
func.func private @printMemrefF32(memref<*xf32>) func.func private @printMemrefF32(memref<*xf32>)
func.func @matmul(%A: memref<?x?xf32>, %B: memref<?x?xf32>) -> (memref<?x?xf32>) { func.func @matmul(%A: memref<?x?xf32>, %B: memref<?x?xf32>) -> (memref<?x?xf32>) {
%c0 = arith.constant 0 : index %c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index %c1 = arith.constant 1 : index
%f0 = arith.constant 0.0 : f32 %f0 = arith.constant 0.0 : f32
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mlir/test/Integration/Dialect/Linalg/CPU/rank-reducing-subview.mlir

// RUN: mlir-opt %s -convert-linalg-to-loops -convert-scf-to-cf -convert-linalg-to-llvm -convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \ // RUN: mlir-opt %s -convert-linalg-to-loops -convert-scf-to-cf -convert-linalg-to-llvm -convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \
// RUN: mlir-cpu-runner -O3 -e main -entry-point-result=void \ // RUN: mlir-cpu-runner -O3 -e main -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext \
// RUN: | FileCheck %s // RUN: | FileCheck %s
func.func private @printMemrefF32(memref<*xf32>) func.func private @printMemrefF32(memref<*xf32>)
func.func @main() { func.func @main() {
%c0 = arith.constant 0 : index %c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index %c1 = arith.constant 1 : index
%c2 = arith.constant 2 : index %c2 = arith.constant 2 : index
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mlir/test/Integration/Dialect/Linalg/CPU/test-collapse-tensor.mlir

// RUN: mlir-opt %s -linalg-bufferize \ // RUN: mlir-opt %s -linalg-bufferize \
// RUN: -arith-bufferize -tensor-bufferize -func-bufferize \ // RUN: -arith-bufferize -tensor-bufferize -func-bufferize \
// RUN: -finalizing-bufferize -buffer-deallocation -convert-linalg-to-llvm \ // RUN: -finalizing-bufferize -buffer-deallocation -convert-linalg-to-llvm \
// RUN: -convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \ // RUN: -convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \
// RUN: mlir-cpu-runner -e main -entry-point-result=void \ // RUN: mlir-cpu-runner -e main -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext \
// RUN: | FileCheck %s // RUN: | FileCheck %s
func.func @main() { func.func @main() {
%const = arith.constant dense<[[[[-3.9058,0.9072],[-2.9470,-2.2055],[18.3946,8.2997]],[[3.4700,5.9006],[-17.2267,4.9777],[1.0450,-0.8201]]],[[[17.6996,-11.1763],[26.7775,-3.8823],[-4.2492,-5.8966]],[[2.1259,13.1794],[-10.7136,0.8428],[16.4233,9.4589]]]]> : tensor<2x2x3x2xf32> %const = arith.constant dense<[[[[-3.9058,0.9072],[-2.9470,-2.2055],[18.3946,8.2997]],[[3.4700,5.9006],[-17.2267,4.9777],[1.0450,-0.8201]]],[[[17.6996,-11.1763],[26.7775,-3.8823],[-4.2492,-5.8966]],[[2.1259,13.1794],[-10.7136,0.8428],[16.4233,9.4589]]]]> : tensor<2x2x3x2xf32>
%dynamic = tensor.cast %const: tensor<2x2x3x2xf32> to tensor<2x?x?x?xf32> %dynamic = tensor.cast %const: tensor<2x2x3x2xf32> to tensor<2x?x?x?xf32>
%collapsed = call @collapse_dynamic_shape(%dynamic) : (tensor<2x?x?x?xf32>) -> (tensor<2x?x?xf32>) %collapsed = call @collapse_dynamic_shape(%dynamic) : (tensor<2x?x?x?xf32>) -> (tensor<2x?x?xf32>)
%unranked = tensor.cast %collapsed: tensor<2x?x?xf32> to tensor<*xf32> %unranked = tensor.cast %collapsed: tensor<2x?x?xf32> to tensor<*xf32>
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mlir/test/Integration/Dialect/Linalg/CPU/test-conv-1d-call.mlir

// RUN: mlir-opt %s -convert-linalg-to-loops -convert-scf-to-cf -convert-linalg-to-llvm -lower-affine -convert-scf-to-cf --convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \ // RUN: mlir-opt %s -convert-linalg-to-loops -convert-scf-to-cf -convert-linalg-to-llvm -lower-affine -convert-scf-to-cf --convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \
// RUN: mlir-cpu-runner -e main -entry-point-result=void \ // RUN: mlir-cpu-runner -e main -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext \
// RUN: | FileCheck %s // RUN: | FileCheck %s
// RUN: mlir-opt %s -linalg-tile="tile-sizes=4" -convert-linalg-to-loops -convert-scf-to-cf \ // RUN: mlir-opt %s -linalg-tile="tile-sizes=4" -convert-linalg-to-loops -convert-scf-to-cf \
// RUN: -convert-linalg-to-llvm -lower-affine -convert-scf-to-cf --convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \ // RUN: -convert-linalg-to-llvm -lower-affine -convert-scf-to-cf --convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \
// RUN: mlir-cpu-runner -e main -entry-point-result=void \ // RUN: mlir-cpu-runner -e main -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext \
// RUN: | FileCheck %s // RUN: | FileCheck %s
func.func private @printMemrefF32(memref<*xf32>) func.func private @printMemrefF32(memref<*xf32>)
// Creates and returns a 1-D buffer of size %s1 filled with the value %f // Creates and returns a 1-D buffer of size %s1 filled with the value %f
func.func @alloc_1d_filled_f32(%s1 : index, %f : f32) -> memref<?xf32> { func.func @alloc_1d_filled_f32(%s1 : index, %f : f32) -> memref<?xf32> {
%buf = memref.alloc(%s1) : memref<?xf32> %buf = memref.alloc(%s1) : memref<?xf32>
linalg.fill ins(%f : f32) outs(%buf : memref<?xf32>) linalg.fill ins(%f : f32) outs(%buf : memref<?xf32>)
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mlir/test/Integration/Dialect/Linalg/CPU/test-conv-1d-nwc-wcf-call.mlir

// RUN: mlir-opt %s -convert-linalg-to-loops -convert-scf-to-cf -convert-linalg-to-llvm -lower-affine -convert-scf-to-cf --convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \ // RUN: mlir-opt %s -convert-linalg-to-loops -convert-scf-to-cf -convert-linalg-to-llvm -lower-affine -convert-scf-to-cf --convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \
// RUN: mlir-cpu-runner -e main -entry-point-result=void \ // RUN: mlir-cpu-runner -e main -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext \
// RUN: | FileCheck %s // RUN: | FileCheck %s
// RUN: mlir-opt %s -linalg-tile="tile-sizes=2,4" -convert-linalg-to-loops -convert-scf-to-cf \ // RUN: mlir-opt %s -linalg-tile="tile-sizes=2,4" -convert-linalg-to-loops -convert-scf-to-cf \
// RUN: -convert-linalg-to-llvm -lower-affine -convert-scf-to-cf --convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \ // RUN: -convert-linalg-to-llvm -lower-affine -convert-scf-to-cf --convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \
// RUN: mlir-cpu-runner -e main -entry-point-result=void \ // RUN: mlir-cpu-runner -e main -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext \
// RUN: | FileCheck %s // RUN: | FileCheck %s
func.func private @printMemrefF32(memref<*xf32>) func.func private @printMemrefF32(memref<*xf32>)
// Creates and returns 3-D buffer of size (%s1, %s2, %s3) filled with the value %f // Creates and returns 3-D buffer of size (%s1, %s2, %s3) filled with the value %f
func.func @alloc_3d_filled_f32(%s1 : index, %s2 : index, %s3 : index, %f : f32) -> memref<?x?x?xf32> { func.func @alloc_3d_filled_f32(%s1 : index, %s2 : index, %s3 : index, %f : f32) -> memref<?x?x?xf32> {
%buf = memref.alloc(%s1, %s2, %s3) : memref<?x?x?xf32> %buf = memref.alloc(%s1, %s2, %s3) : memref<?x?x?xf32>
linalg.fill ins(%f : f32) outs(%buf : memref<?x?x?xf32>) linalg.fill ins(%f : f32) outs(%buf : memref<?x?x?xf32>)
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mlir/test/Integration/Dialect/Linalg/CPU/test-conv-2d-call.mlir

// RUN: mlir-opt %s -convert-linalg-to-loops -convert-scf-to-cf -convert-linalg-to-llvm -lower-affine -convert-scf-to-cf --convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \ // RUN: mlir-opt %s -convert-linalg-to-loops -convert-scf-to-cf -convert-linalg-to-llvm -lower-affine -convert-scf-to-cf --convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \
// RUN: mlir-cpu-runner -e main -entry-point-result=void \ // RUN: mlir-cpu-runner -e main -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext \
// RUN: | FileCheck %s // RUN: | FileCheck %s
// RUN: mlir-opt %s -linalg-tile="tile-sizes=2,2" -convert-linalg-to-loops -convert-scf-to-cf \ // RUN: mlir-opt %s -linalg-tile="tile-sizes=2,2" -convert-linalg-to-loops -convert-scf-to-cf \
// RUN: -convert-linalg-to-llvm -lower-affine -convert-scf-to-cf --convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \ // RUN: -convert-linalg-to-llvm -lower-affine -convert-scf-to-cf --convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \
// RUN: mlir-cpu-runner -e main -entry-point-result=void \ // RUN: mlir-cpu-runner -e main -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext \
// RUN: | FileCheck %s // RUN: | FileCheck %s
func.func private @printMemrefF32(memref<*xf32>) func.func private @printMemrefF32(memref<*xf32>)
// Creates and returns a 2-D buffer of size (%s1, %s2) filled with the value %f // Creates and returns a 2-D buffer of size (%s1, %s2) filled with the value %f
func.func @alloc_2d_filled_f32(%s1 : index, %s2 : index, %f : f32) -> memref<?x?xf32> { func.func @alloc_2d_filled_f32(%s1 : index, %s2 : index, %f : f32) -> memref<?x?xf32> {
%buf = memref.alloc(%s1, %s2) : memref<?x?xf32> %buf = memref.alloc(%s1, %s2) : memref<?x?xf32>
linalg.fill ins(%f : f32) outs(%buf : memref<?x?xf32>) linalg.fill ins(%f : f32) outs(%buf : memref<?x?xf32>)
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mlir/test/Integration/Dialect/Linalg/CPU/test-conv-2d-nhwc-hwcf-call.mlir

// RUN: mlir-opt %s -convert-linalg-to-loops -convert-scf-to-cf -convert-linalg-to-llvm -lower-affine -convert-scf-to-cf --convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \ // RUN: mlir-opt %s -convert-linalg-to-loops -convert-scf-to-cf -convert-linalg-to-llvm -lower-affine -convert-scf-to-cf --convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \
// RUN: mlir-cpu-runner -e main -entry-point-result=void \ // RUN: mlir-cpu-runner -e main -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext \
// RUN: | FileCheck %s // RUN: | FileCheck %s
// RUN: mlir-opt %s -linalg-tile="tile-sizes=2,3,3,2" -convert-linalg-to-loops -convert-scf-to-cf \ // RUN: mlir-opt %s -linalg-tile="tile-sizes=2,3,3,2" -convert-linalg-to-loops -convert-scf-to-cf \
// RUN: -convert-linalg-to-llvm -lower-affine -convert-scf-to-cf --convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \ // RUN: -convert-linalg-to-llvm -lower-affine -convert-scf-to-cf --convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \
// RUN: mlir-cpu-runner -e main -entry-point-result=void \ // RUN: mlir-cpu-runner -e main -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext \
// RUN: | FileCheck %s // RUN: | FileCheck %s
func.func private @printMemrefF32(memref<*xf32>) func.func private @printMemrefF32(memref<*xf32>)
// Creates and returns 4-D buffer of size (%s1, %s2, %s3, %s4) filled with the value %f // Creates and returns 4-D buffer of size (%s1, %s2, %s3, %s4) filled with the value %f
func.func @alloc_4d_filled_f32(%s1 : index, %s2 : index, %s3 : index, %s4 : index, %f : f32) -> memref<?x?x?x?xf32> { func.func @alloc_4d_filled_f32(%s1 : index, %s2 : index, %s3 : index, %s4 : index, %f : f32) -> memref<?x?x?x?xf32> {
%buf = memref.alloc(%s1, %s2, %s3, %s4) : memref<?x?x?x?xf32> %buf = memref.alloc(%s1, %s2, %s3, %s4) : memref<?x?x?x?xf32>
linalg.fill ins(%f : f32) outs(%buf : memref<?x?x?x?xf32>) linalg.fill ins(%f : f32) outs(%buf : memref<?x?x?x?xf32>)
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mlir/test/Integration/Dialect/Linalg/CPU/test-conv-3d-call.mlir

// RUN: mlir-opt %s -convert-linalg-to-loops -convert-scf-to-cf -convert-linalg-to-llvm -lower-affine -convert-scf-to-cf --convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \ // RUN: mlir-opt %s -convert-linalg-to-loops -convert-scf-to-cf -convert-linalg-to-llvm -lower-affine -convert-scf-to-cf --convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \
// RUN: mlir-cpu-runner -e main -entry-point-result=void \ // RUN: mlir-cpu-runner -e main -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext \
// RUN: | FileCheck %s // RUN: | FileCheck %s
// RUN: mlir-opt %s -linalg-tile="tile-sizes=2,2,2" -convert-linalg-to-loops -convert-scf-to-cf \ // RUN: mlir-opt %s -linalg-tile="tile-sizes=2,2,2" -convert-linalg-to-loops -convert-scf-to-cf \
// RUN: -convert-linalg-to-llvm -lower-affine -convert-scf-to-cf --convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \ // RUN: -convert-linalg-to-llvm -lower-affine -convert-scf-to-cf --convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \
// RUN: mlir-cpu-runner -e main -entry-point-result=void \ // RUN: mlir-cpu-runner -e main -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext \
// RUN: | FileCheck %s // RUN: | FileCheck %s
func.func private @printMemrefF32(memref<*xf32>) func.func private @printMemrefF32(memref<*xf32>)
// Creates and returns 3-D buffer of size (%s1, %s2, %s3) filled with the value %f // Creates and returns 3-D buffer of size (%s1, %s2, %s3) filled with the value %f
func.func @alloc_3d_filled_f32(%s1 : index, %s2 : index, %s3 : index, %f : f32) -> memref<?x?x?xf32> { func.func @alloc_3d_filled_f32(%s1 : index, %s2 : index, %s3 : index, %f : f32) -> memref<?x?x?xf32> {
%buf = memref.alloc(%s1, %s2, %s3) : memref<?x?x?xf32> %buf = memref.alloc(%s1, %s2, %s3) : memref<?x?x?xf32>
linalg.fill ins(%f : f32) outs(%buf : memref<?x?x?xf32>) linalg.fill ins(%f : f32) outs(%buf : memref<?x?x?xf32>)
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mlir/test/Integration/Dialect/Linalg/CPU/test-conv-3d-ndhwc-dhwcf-call.mlir

// RUN: mlir-opt %s -convert-linalg-to-loops -convert-scf-to-cf -convert-linalg-to-llvm -lower-affine -convert-scf-to-cf --convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \ // RUN: mlir-opt %s -convert-linalg-to-loops -convert-scf-to-cf -convert-linalg-to-llvm -lower-affine -convert-scf-to-cf --convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \
// RUN: mlir-cpu-runner -e main -entry-point-result=void \ // RUN: mlir-cpu-runner -e main -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext \
// RUN: | FileCheck %s // RUN: | FileCheck %s
// RUN: mlir-opt %s -linalg-tile="tile-sizes=0,5,5,5" -convert-linalg-to-loops -convert-scf-to-cf \ // RUN: mlir-opt %s -linalg-tile="tile-sizes=0,5,5,5" -convert-linalg-to-loops -convert-scf-to-cf \
// RUN: -convert-linalg-to-llvm -lower-affine -convert-scf-to-cf --convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \ // RUN: -convert-linalg-to-llvm -lower-affine -convert-scf-to-cf --convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \
// RUN: mlir-cpu-runner -e main -entry-point-result=void \ // RUN: mlir-cpu-runner -e main -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext \
// RUN: | FileCheck %s // RUN: | FileCheck %s
func.func private @printMemrefF32(memref<*xf32>) func.func private @printMemrefF32(memref<*xf32>)
// Creates and returns 5-D buffer of size (%s1, %s2, %s3, %s4, %s5) filled with the value %f // Creates and returns 5-D buffer of size (%s1, %s2, %s3, %s4, %s5) filled with the value %f
func.func @alloc_5d_filled_f32(%s1 : index, %s2 : index, %s3 : index, %s4 : index, %s5 : index, %f : f32) -> memref<?x?x?x?x?xf32> { func.func @alloc_5d_filled_f32(%s1 : index, %s2 : index, %s3 : index, %s4 : index, %s5 : index, %f : f32) -> memref<?x?x?x?x?xf32> {
%buf = memref.alloc(%s1, %s2, %s3, %s4, %s5) : memref<?x?x?x?x?xf32> %buf = memref.alloc(%s1, %s2, %s3, %s4, %s5) : memref<?x?x?x?x?xf32>
linalg.fill ins(%f : f32) outs(%buf : memref<?x?x?x?x?xf32>) linalg.fill ins(%f : f32) outs(%buf : memref<?x?x?x?x?xf32>)
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mlir/test/Integration/Dialect/Linalg/CPU/test-elementwise.mlir

// RUN: mlir-opt %s -convert-elementwise-to-linalg \ // RUN: mlir-opt %s -convert-elementwise-to-linalg \
// RUN: -arith-bufferize -linalg-bufferize -tensor-bufferize \ // RUN: -arith-bufferize -linalg-bufferize -tensor-bufferize \
// RUN: -func-bufferize -buffer-deallocation -convert-linalg-to-loops \ // RUN: -func-bufferize -buffer-deallocation -convert-linalg-to-loops \
// RUN: -convert-linalg-to-llvm --convert-memref-to-llvm -convert-func-to-llvm \ // RUN: -convert-linalg-to-llvm --convert-memref-to-llvm -convert-func-to-llvm \
// RUN: -reconcile-unrealized-casts | \ // RUN: -reconcile-unrealized-casts | \
// RUN: mlir-cpu-runner -e main -entry-point-result=void \ // RUN: mlir-cpu-runner -e main -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext \
// RUN: | FileCheck %s // RUN: | FileCheck %s
func.func @main() { func.func @main() {
%a = arith.constant dense<[1.0, 2.0, 3.0]> : tensor<3xf32> %a = arith.constant dense<[1.0, 2.0, 3.0]> : tensor<3xf32>
%b = arith.constant dense<[10.0, 20.0, 30.0]> : tensor<3xf32> %b = arith.constant dense<[10.0, 20.0, 30.0]> : tensor<3xf32>
%addf = arith.addf %a, %b : tensor<3xf32> %addf = arith.addf %a, %b : tensor<3xf32>
%addf_unranked = tensor.cast %addf : tensor<3xf32> to tensor<*xf32> %addf_unranked = tensor.cast %addf : tensor<3xf32> to tensor<*xf32>
call @printMemrefF32(%addf_unranked) : (tensor<*xf32>) -> () call @printMemrefF32(%addf_unranked) : (tensor<*xf32>) -> ()
// CHECK: Unranked Memref base@ = {{.*}} rank = 1 offset = 0 sizes = [3] strides = [1] data = // CHECK: Unranked Memref base@ = {{.*}} rank = 1 offset = 0 sizes = [3] strides = [1] data =
// CHECK-NEXT: [11, 22, 33] // CHECK-NEXT: [11, 22, 33]
return return
} }
func.func private @printMemrefF32(%ptr : tensor<*xf32>) func.func private @printMemrefF32(%ptr : tensor<*xf32>)

mlir/test/Integration/Dialect/Linalg/CPU/test-expand-tensor.mlir

// RUN: mlir-opt %s -linalg-bufferize \ // RUN: mlir-opt %s -linalg-bufferize \
// RUN: -arith-bufferize -tensor-bufferize -func-bufferize \ // RUN: -arith-bufferize -tensor-bufferize -func-bufferize \
// RUN: -finalizing-bufferize -buffer-deallocation -convert-linalg-to-llvm \ // RUN: -finalizing-bufferize -buffer-deallocation -convert-linalg-to-llvm \
// RUN: -convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \ // RUN: -convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \
// RUN: mlir-cpu-runner -e main -entry-point-result=void \ // RUN: mlir-cpu-runner -e main -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext \
// RUN: | FileCheck %s // RUN: | FileCheck %s
func.func @main() { func.func @main() {
%const = arith.constant dense<[[[-3.9058,0.9072],[-2.9470,-2.2055],[18.3946,8.2997],[3.4700,5.9006],[-17.2267,4.9777],[1.0450,-0.8201]],[[17.6996,-11.1763],[26.7775,-3.8823],[-4.2492,-5.8966],[2.1259,13.1794],[-10.7136,0.8428],[16.4233,9.4589]]]> : tensor<2x6x2xf32> %const = arith.constant dense<[[[-3.9058,0.9072],[-2.9470,-2.2055],[18.3946,8.2997],[3.4700,5.9006],[-17.2267,4.9777],[1.0450,-0.8201]],[[17.6996,-11.1763],[26.7775,-3.8823],[-4.2492,-5.8966],[2.1259,13.1794],[-10.7136,0.8428],[16.4233,9.4589]]]> : tensor<2x6x2xf32>
%dynamic = tensor.cast %const: tensor<2x6x2xf32> to tensor<2x?x?xf32> %dynamic = tensor.cast %const: tensor<2x6x2xf32> to tensor<2x?x?xf32>
%expanded = call @expand_dynamic_shape(%dynamic) : (tensor<2x?x?xf32>) -> (tensor<2x2x?x1x?xf32>) %expanded = call @expand_dynamic_shape(%dynamic) : (tensor<2x?x?xf32>) -> (tensor<2x2x?x1x?xf32>)
%unranked = tensor.cast %expanded: tensor<2x2x?x1x?xf32> to tensor<*xf32> %unranked = tensor.cast %expanded: tensor<2x2x?x1x?xf32> to tensor<*xf32>
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mlir/test/Integration/Dialect/Linalg/CPU/test-one-shot-bufferize.mlir

// RUN: mlir-opt %s -pass-pipeline="func.func(canonicalize,cse),one-shot-bufferize{bufferize-function-boundaries}" |\ // RUN: mlir-opt %s -pass-pipeline="func.func(canonicalize,cse),one-shot-bufferize{bufferize-function-boundaries}" |\
// RUN: mlir-opt -pass-pipeline="func.func(buffer-deallocation,convert-vector-to-scf,lower-affine,convert-linalg-to-loops)" |\ // RUN: mlir-opt -pass-pipeline="func.func(buffer-deallocation,convert-vector-to-scf,lower-affine,convert-linalg-to-loops)" |\
// RUN: mlir-opt -pass-pipeline="func.func(canonicalize,convert-scf-to-cf),convert-vector-to-llvm,convert-memref-to-llvm,convert-func-to-llvm,reconcile-unrealized-casts" | \ // RUN: mlir-opt -pass-pipeline="func.func(canonicalize,convert-scf-to-cf),convert-vector-to-llvm,convert-memref-to-llvm,convert-func-to-llvm,reconcile-unrealized-casts" | \
// RUN: mlir-cpu-runner -O3 -e main -entry-point-result=void \ // RUN: mlir-cpu-runner -O3 -e main -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext,%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext |\ // RUN: -shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext,%mlir_lib_dir/libmlir_c_runner_utils%shlibext |\
// RUN: FileCheck %s // RUN: FileCheck %s
#map0 = affine_map<(d0, d1)[s0] -> ((d1 - d0) ceildiv s0)> #map0 = affine_map<(d0, d1)[s0] -> ((d1 - d0) ceildiv s0)>
#map1 = affine_map<(d0, d1)[s0] -> ((d0 - d1) ceildiv s0)> #map1 = affine_map<(d0, d1)[s0] -> ((d0 - d1) ceildiv s0)>
func.func @init_and_dot(%arg0: tensor<64xf32>, %arg1: tensor<64xf32>, %arg2: tensor<f32>) -> tensor<f32> { func.func @init_and_dot(%arg0: tensor<64xf32>, %arg1: tensor<64xf32>, %arg2: tensor<f32>) -> tensor<f32> {
%c64 = arith.constant 64 : index %c64 = arith.constant 64 : index
%cst = arith.constant 0.000000e+00 : f32 %cst = arith.constant 0.000000e+00 : f32
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mlir/test/Integration/Dialect/Linalg/CPU/test-padtensor.mlir

// RUN: mlir-opt %s -test-linalg-transform-patterns=test-linalg-to-vector-patterns \ // RUN: mlir-opt %s -test-linalg-transform-patterns=test-linalg-to-vector-patterns \
// RUN: -linalg-init-tensor-to-alloc-tensor -linalg-bufferize -arith-bufferize \ // RUN: -linalg-init-tensor-to-alloc-tensor -linalg-bufferize -arith-bufferize \
// RUN: -bufferization-bufferize -tensor-bufferize -func-bufferize \ // RUN: -bufferization-bufferize -tensor-bufferize -func-bufferize \
// RUN: -finalizing-bufferize -buffer-deallocation \ // RUN: -finalizing-bufferize -buffer-deallocation \
// RUN: -convert-linalg-to-loops -convert-scf-to-cf -convert-linalg-to-llvm -convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \ // RUN: -convert-linalg-to-loops -convert-scf-to-cf -convert-linalg-to-llvm -convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \
// RUN: mlir-cpu-runner -e main -entry-point-result=void \ // RUN: mlir-cpu-runner -e main -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext,%mlir_integration_test_dir/libmlir_runner_utils%shlibext \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext,%mlir_lib_dir/libmlir_runner_utils%shlibext \
// RUN: | FileCheck %s // RUN: | FileCheck %s
func.func @main() { func.func @main() {
%const = arith.constant dense<[[[1.0, 2.0, 3.0], [2.0, 3.0, 4.0]]]> : tensor<1x2x3xf32> %const = arith.constant dense<[[[1.0, 2.0, 3.0], [2.0, 3.0, 4.0]]]> : tensor<1x2x3xf32>
%dynamic = tensor.cast %const: tensor<1x2x3xf32> to tensor<1x?x3xf32> %dynamic = tensor.cast %const: tensor<1x2x3xf32> to tensor<1x?x3xf32>
%offset = arith.constant 2 : index %offset = arith.constant 2 : index
%cst = arith.constant 2.3 : f32 %cst = arith.constant 2.3 : f32
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mlir/test/Integration/Dialect/Linalg/CPU/test-subtensor-insert-multiple-uses.mlir

// RUN: mlir-opt %s -linalg-bufferize \ // RUN: mlir-opt %s -linalg-bufferize \
// RUN: -arith-bufferize -tensor-bufferize -func-bufferize \ // RUN: -arith-bufferize -tensor-bufferize -func-bufferize \
// RUN: -finalizing-bufferize -buffer-deallocation \ // RUN: -finalizing-bufferize -buffer-deallocation \
// RUN: -convert-linalg-to-loops -convert-scf-to-cf -convert-linalg-to-llvm --convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \ // RUN: -convert-linalg-to-loops -convert-scf-to-cf -convert-linalg-to-llvm --convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \
// RUN: mlir-cpu-runner -e main -entry-point-result=void \ // RUN: mlir-cpu-runner -e main -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext,%mlir_integration_test_dir/libmlir_runner_utils%shlibext \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext,%mlir_lib_dir/libmlir_runner_utils%shlibext \
// RUN: | FileCheck %s // RUN: | FileCheck %s
func.func @main() { func.func @main() {
%const = arith.constant dense<10.0> : tensor<2xf32> %const = arith.constant dense<10.0> : tensor<2xf32>
%insert_val = arith.constant dense<20.0> : tensor<1xf32> %insert_val = arith.constant dense<20.0> : tensor<1xf32>
// Both of these insert_slice ops insert into the same original tensor // Both of these insert_slice ops insert into the same original tensor
// value `%const`. This can easily cause bugs if at the memref level // value `%const`. This can easily cause bugs if at the memref level
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mlir/test/Integration/Dialect/Linalg/CPU/test-subtensor-insert.mlir

// RUN: mlir-opt %s -linalg-bufferize \ // RUN: mlir-opt %s -linalg-bufferize \
// RUN: -arith-bufferize -tensor-bufferize -func-bufferize \ // RUN: -arith-bufferize -tensor-bufferize -func-bufferize \
// RUN: -finalizing-bufferize -buffer-deallocation \ // RUN: -finalizing-bufferize -buffer-deallocation \
// RUN: -convert-linalg-to-loops -convert-scf-to-cf -convert-linalg-to-llvm --convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \ // RUN: -convert-linalg-to-loops -convert-scf-to-cf -convert-linalg-to-llvm --convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \
// RUN: mlir-cpu-runner -e main -entry-point-result=void \ // RUN: mlir-cpu-runner -e main -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext \
// RUN: | FileCheck %s // RUN: | FileCheck %s
func.func @main() { func.func @main() {
%const = arith.constant dense<10.0> : tensor<2xf32> %const = arith.constant dense<10.0> : tensor<2xf32>
%insert_val = arith.constant dense<20.0> : tensor<1xf32> %insert_val = arith.constant dense<20.0> : tensor<1xf32>
%inserted = tensor.insert_slice %insert_val into %const[0][1][1] : tensor<1xf32> into tensor<2xf32> %inserted = tensor.insert_slice %insert_val into %const[0][1][1] : tensor<1xf32> into tensor<2xf32>
%unranked = tensor.cast %inserted : tensor<2xf32> to tensor<*xf32> %unranked = tensor.cast %inserted : tensor<2xf32> to tensor<*xf32>
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mlir/test/Integration/Dialect/Linalg/CPU/test-tensor-e2e.mlir

// RUN: mlir-opt %s -arith-bufferize -linalg-bufferize \ // RUN: mlir-opt %s -arith-bufferize -linalg-bufferize \
// RUN: -tensor-bufferize -func-bufferize -finalizing-bufferize -buffer-deallocation -convert-linalg-to-loops \ // RUN: -tensor-bufferize -func-bufferize -finalizing-bufferize -buffer-deallocation -convert-linalg-to-loops \
// RUN: -convert-linalg-to-llvm --convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \ // RUN: -convert-linalg-to-llvm --convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \
// RUN: mlir-cpu-runner -e main -entry-point-result=void \ // RUN: mlir-cpu-runner -e main -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext \
// RUN: | FileCheck %s // RUN: | FileCheck %s
func.func @foo() -> tensor<4xf32> { func.func @foo() -> tensor<4xf32> {
%0 = arith.constant dense<[1.0, 2.0, 3.0, 4.0]> : tensor<4xf32> %0 = arith.constant dense<[1.0, 2.0, 3.0, 4.0]> : tensor<4xf32>
return %0 : tensor<4xf32> return %0 : tensor<4xf32>
} }
func.func @main() { func.func @main() {
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mlir/test/Integration/Dialect/Linalg/CPU/test-tensor-matmul.mlir

// UNSUPPORTED: asan // UNSUPPORTED: asan
// RUN: mlir-opt %s -linalg-bufferize -arith-bufferize \ // RUN: mlir-opt %s -linalg-bufferize -arith-bufferize \
// RUN: -tensor-bufferize -func-bufferize -finalizing-bufferize -buffer-deallocation -convert-linalg-to-loops -convert-scf-to-cf \ // RUN: -tensor-bufferize -func-bufferize -finalizing-bufferize -buffer-deallocation -convert-linalg-to-loops -convert-scf-to-cf \
// RUN: -convert-linalg-to-llvm -lower-affine -convert-scf-to-cf --convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \ // RUN: -convert-linalg-to-llvm -lower-affine -convert-scf-to-cf --convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \
// RUN: mlir-cpu-runner -e main -entry-point-result=void \ // RUN: mlir-cpu-runner -e main -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext,%mlir_integration_test_dir/libmlir_runner_utils%shlibext \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext,%mlir_lib_dir/libmlir_runner_utils%shlibext \
// RUN: | FileCheck %s // RUN: | FileCheck %s
// RUN: mlir-opt %s -linalg-tile="tile-sizes=1,2,3" -linalg-bufferize \ // RUN: mlir-opt %s -linalg-tile="tile-sizes=1,2,3" -linalg-bufferize \
// RUN: -scf-bufferize -arith-bufferize -tensor-bufferize \ // RUN: -scf-bufferize -arith-bufferize -tensor-bufferize \
// RUN: -func-bufferize \ // RUN: -func-bufferize \
// RUN: -finalizing-bufferize -convert-linalg-to-loops -convert-scf-to-cf -convert-scf-to-cf \ // RUN: -finalizing-bufferize -convert-linalg-to-loops -convert-scf-to-cf -convert-scf-to-cf \
// RUN: -convert-linalg-to-llvm -lower-affine -convert-scf-to-cf --convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \ // RUN: -convert-linalg-to-llvm -lower-affine -convert-scf-to-cf --convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \
// RUN: mlir-cpu-runner -e main -entry-point-result=void \ // RUN: mlir-cpu-runner -e main -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext,%mlir_integration_test_dir/libmlir_runner_utils%shlibext \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext,%mlir_lib_dir/libmlir_runner_utils%shlibext \
// RUN: | FileCheck %s // RUN: | FileCheck %s
func.func @main() { func.func @main() {
%A = arith.constant dense<[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]> : tensor<2x3xf32> %A = arith.constant dense<[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]> : tensor<2x3xf32>
%B = arith.constant dense<[[1.0, 2.0, 3.0, 4.0], %B = arith.constant dense<[[1.0, 2.0, 3.0, 4.0],
[5.0, 6.0, 7.0, 8.0], [5.0, 6.0, 7.0, 8.0],
[9.0, 10.0, 11.0, 12.0]]> : tensor<3x4xf32> [9.0, 10.0, 11.0, 12.0]]> : tensor<3x4xf32>
%C = arith.constant dense<1000.0> : tensor<2x4xf32> %C = arith.constant dense<1000.0> : tensor<2x4xf32>
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mlir/test/Integration/Dialect/SparseTensor/CPU/concatenate.mlir

// RUN: mlir-opt %s --sparse-compiler | \ // RUN: mlir-opt %s --sparse-compiler | \
// RUN: mlir-cpu-runner \ // RUN: mlir-cpu-runner \
// RUN: -e entry -entry-point-result=void \ // RUN: -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
#MAT_C_C = #sparse_tensor.encoding<{dimLevelType = ["compressed", "compressed"]}> #MAT_C_C = #sparse_tensor.encoding<{dimLevelType = ["compressed", "compressed"]}>
#MAT_D_C = #sparse_tensor.encoding<{dimLevelType = ["dense", "compressed"]}> #MAT_D_C = #sparse_tensor.encoding<{dimLevelType = ["dense", "compressed"]}>
#MAT_C_D = #sparse_tensor.encoding<{dimLevelType = ["compressed", "dense"]}> #MAT_C_D = #sparse_tensor.encoding<{dimLevelType = ["compressed", "dense"]}>
#MAT_C_C_P = #sparse_tensor.encoding<{ #MAT_C_C_P = #sparse_tensor.encoding<{
dimLevelType = [ "compressed", "compressed" ], dimLevelType = [ "compressed", "compressed" ],
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mlir/test/Integration/Dialect/SparseTensor/CPU/dense_output.mlir

// RUN: mlir-opt %s --sparse-compiler | \ // RUN: mlir-opt %s --sparse-compiler | \
// RUN: TENSOR0="%mlir_integration_test_dir/data/test.mtx" \ // RUN: TENSOR0="%mlir_src_dir/test/Integration/data/test.mtx" \
// RUN: mlir-cpu-runner \ // RUN: mlir-cpu-runner \
// RUN: -e entry -entry-point-result=void \ // RUN: -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
!Filename = !llvm.ptr<i8> !Filename = !llvm.ptr<i8>
#DenseMatrix = #sparse_tensor.encoding<{ #DenseMatrix = #sparse_tensor.encoding<{
dimLevelType = [ "dense", "dense" ], dimLevelType = [ "dense", "dense" ],
dimOrdering = affine_map<(i,j) -> (i,j)> dimOrdering = affine_map<(i,j) -> (i,j)>
}> }>
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mlir/test/Integration/Dialect/SparseTensor/CPU/dense_output_bf16.mlir

// RUN: mlir-opt %s --sparse-compiler | \ // RUN: mlir-opt %s --sparse-compiler | \
// RUN: mlir-cpu-runner \ // RUN: mlir-cpu-runner \
// RUN: -e entry -entry-point-result=void \ // RUN: -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}> #SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}>
#DenseVector = #sparse_tensor.encoding<{dimLevelType = ["dense"]}> #DenseVector = #sparse_tensor.encoding<{dimLevelType = ["dense"]}>
#trait_vec_op = { #trait_vec_op = {
indexing_maps = [ indexing_maps = [
affine_map<(i) -> (i)>, // a (in) affine_map<(i) -> (i)>, // a (in)
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mlir/test/Integration/Dialect/SparseTensor/CPU/dense_output_f16.mlir

// RUN: mlir-opt %s --sparse-compiler | \ // RUN: mlir-opt %s --sparse-compiler | \
// RUN: mlir-cpu-runner \ // RUN: mlir-cpu-runner \
// RUN: -e entry -entry-point-result=void \ // RUN: -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}> #SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}>
#DenseVector = #sparse_tensor.encoding<{dimLevelType = ["dense"]}> #DenseVector = #sparse_tensor.encoding<{dimLevelType = ["dense"]}>
#trait_vec_op = { #trait_vec_op = {
indexing_maps = [ indexing_maps = [
affine_map<(i) -> (i)>, // a (in) affine_map<(i) -> (i)>, // a (in)
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mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_abs.mlir

// RUN: mlir-opt %s --sparse-compiler | \ // RUN: mlir-opt %s --sparse-compiler | \
// RUN: mlir-cpu-runner \ // RUN: mlir-cpu-runner \
// RUN: -e entry -entry-point-result=void \ // RUN: -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
#SparseVector = #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }> #SparseVector = #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>
#trait_op = { #trait_op = {
indexing_maps = [ indexing_maps = [
affine_map<(i) -> (i)>, // a affine_map<(i) -> (i)>, // a
affine_map<(i) -> (i)> // x (out) affine_map<(i) -> (i)> // x (out)
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mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_binary.mlir

// RUN: mlir-opt %s --sparse-compiler | \ // RUN: mlir-opt %s --sparse-compiler | \
// RUN: mlir-cpu-runner \ // RUN: mlir-cpu-runner \
// RUN: -e entry -entry-point-result=void \ // RUN: -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}> #SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}>
#DCSR = #sparse_tensor.encoding<{dimLevelType = ["compressed", "compressed"]}> #DCSR = #sparse_tensor.encoding<{dimLevelType = ["compressed", "compressed"]}>
// //
// Traits for tensor operations. // Traits for tensor operations.
// //
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mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_cast.mlir

// RUN: mlir-opt %s --sparse-compiler | \ // RUN: mlir-opt %s --sparse-compiler | \
// RUN: mlir-cpu-runner \ // RUN: mlir-cpu-runner \
// RUN: -e entry -entry-point-result=void \ // RUN: -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
// //
// Do the same run, but now with SIMDization as well. This should not change the outcome. // Do the same run, but now with SIMDization as well. This should not change the outcome.
// //
// RUN: mlir-opt %s --sparse-compiler="vectorization-strategy=any-storage-inner-loop vl=2" | \ // RUN: mlir-opt %s --sparse-compiler="vectorization-strategy=any-storage-inner-loop vl=2" | \
// RUN: mlir-cpu-runner \ // RUN: mlir-cpu-runner \
// RUN: -e entry -entry-point-result=void \ // RUN: -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
#SV = #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }> #SV = #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>
#trait_cast = { #trait_cast = {
indexing_maps = [ indexing_maps = [
affine_map<(i) -> (i)>, // A (in) affine_map<(i) -> (i)>, // A (in)
affine_map<(i) -> (i)> // X (out) affine_map<(i) -> (i)> // X (out)
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mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_complex32.mlir

// RUN: mlir-opt %s --sparse-compiler | \ // RUN: mlir-opt %s --sparse-compiler | \
// RUN: mlir-cpu-runner \ // RUN: mlir-cpu-runner \
// RUN: -e entry -entry-point-result=void \ // RUN: -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}> #SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}>
#trait_op = { #trait_op = {
indexing_maps = [ indexing_maps = [
affine_map<(i) -> (i)>, // a (in) affine_map<(i) -> (i)>, // a (in)
affine_map<(i) -> (i)>, // b (in) affine_map<(i) -> (i)>, // b (in)
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mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_complex64.mlir

// RUN: mlir-opt %s --sparse-compiler | \ // RUN: mlir-opt %s --sparse-compiler | \
// RUN: mlir-cpu-runner \ // RUN: mlir-cpu-runner \
// RUN: -e entry -entry-point-result=void \ // RUN: -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}> #SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}>
#trait_op = { #trait_op = {
indexing_maps = [ indexing_maps = [
affine_map<(i) -> (i)>, // a (in) affine_map<(i) -> (i)>, // a (in)
affine_map<(i) -> (i)>, // b (in) affine_map<(i) -> (i)>, // b (in)
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mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_complex_ops.mlir

// RUN: mlir-opt %s --sparse-compiler | \ // RUN: mlir-opt %s --sparse-compiler | \
// RUN: mlir-cpu-runner \ // RUN: mlir-cpu-runner \
// RUN: -e entry -entry-point-result=void \ // RUN: -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}> #SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}>
#trait_op1 = { #trait_op1 = {
indexing_maps = [ indexing_maps = [
affine_map<(i) -> (i)>, // a (in) affine_map<(i) -> (i)>, // a (in)
affine_map<(i) -> (i)> // x (out) affine_map<(i) -> (i)> // x (out)
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mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_constant_to_sparse_tensor.mlir

// RUN: mlir-opt %s --sparse-compiler | \ // RUN: mlir-opt %s --sparse-compiler | \
// RUN: mlir-cpu-runner \ // RUN: mlir-cpu-runner \
// RUN: -e entry -entry-point-result=void \ // RUN: -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
#Tensor1 = #sparse_tensor.encoding<{ #Tensor1 = #sparse_tensor.encoding<{
dimLevelType = [ "compressed", "compressed"] dimLevelType = [ "compressed", "compressed"]
}> }>
// //
// Integration tests for conversions from sparse constants to sparse tensors. // Integration tests for conversions from sparse constants to sparse tensors.
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mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_conversion.mlir

// RUN: mlir-opt %s --sparse-compiler | \ // RUN: mlir-opt %s --sparse-compiler | \
// RUN: mlir-cpu-runner \ // RUN: mlir-cpu-runner \
// RUN: -e entry -entry-point-result=void \ // RUN: -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
#Tensor1 = #sparse_tensor.encoding<{ #Tensor1 = #sparse_tensor.encoding<{
dimLevelType = [ "compressed", "compressed", "compressed" ], dimLevelType = [ "compressed", "compressed", "compressed" ],
dimOrdering = affine_map<(i,j,k) -> (i,j,k)> dimOrdering = affine_map<(i,j,k) -> (i,j,k)>
}> }>
#Tensor2 = #sparse_tensor.encoding<{ #Tensor2 = #sparse_tensor.encoding<{
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mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_conversion_dyn.mlir

// RUN: mlir-opt %s --sparse-compiler | \ // RUN: mlir-opt %s --sparse-compiler | \
// RUN: mlir-cpu-runner \ // RUN: mlir-cpu-runner \
// RUN: -e entry -entry-point-result=void \ // RUN: -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
#DCSR = #sparse_tensor.encoding<{ #DCSR = #sparse_tensor.encoding<{
dimLevelType = [ "compressed", "compressed" ] dimLevelType = [ "compressed", "compressed" ]
}> }>
#DCSC = #sparse_tensor.encoding<{ #DCSC = #sparse_tensor.encoding<{
dimLevelType = [ "compressed", "compressed" ], dimLevelType = [ "compressed", "compressed" ],
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mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_conversion_ptr.mlir

// RUN: mlir-opt %s --sparse-compiler | \ // RUN: mlir-opt %s --sparse-compiler | \
// RUN: mlir-cpu-runner \ // RUN: mlir-cpu-runner \
// RUN: -e entry -entry-point-result=void \ // RUN: -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
#DCSR = #sparse_tensor.encoding<{ #DCSR = #sparse_tensor.encoding<{
dimLevelType = [ "compressed", "compressed" ], dimLevelType = [ "compressed", "compressed" ],
pointerBitWidth = 8, pointerBitWidth = 8,
indexBitWidth = 8 indexBitWidth = 8
}> }>
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mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_conversion_sparse2dense.mlir

// RUN: mlir-opt %s --sparse-compiler | \ // RUN: mlir-opt %s --sparse-compiler | \
// RUN: mlir-cpu-runner \ // RUN: mlir-cpu-runner \
// RUN: -e entry -entry-point-result=void \ // RUN: -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext \
// RUN: | \ // RUN: | \
// RUN: FileCheck %s // RUN: FileCheck %s
#Tensor1 = #sparse_tensor.encoding<{ #Tensor1 = #sparse_tensor.encoding<{
dimLevelType = [ "compressed", "compressed", "compressed" ], dimLevelType = [ "compressed", "compressed", "compressed" ],
dimOrdering = affine_map<(i,j,k) -> (i,j,k)> dimOrdering = affine_map<(i,j,k) -> (i,j,k)>
}> }>
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mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_conversion_sparse2sparse.mlir

// Force this file to use the kDirect method for sparse2sparse. // Force this file to use the kDirect method for sparse2sparse.
// RUN: mlir-opt %s --sparse-compiler="s2s-strategy=2" | \ // RUN: mlir-opt %s --sparse-compiler="s2s-strategy=2" | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \ // RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
#Tensor1 = #sparse_tensor.encoding<{ #Tensor1 = #sparse_tensor.encoding<{
dimLevelType = [ "dense", "dense", "compressed" ] dimLevelType = [ "dense", "dense", "compressed" ]
}> }>
// NOTE: dense after compressed is not currently supported for the target // NOTE: dense after compressed is not currently supported for the target
// of direct-sparse2sparse conversion. (It's fine for the source though.) // of direct-sparse2sparse conversion. (It's fine for the source though.)
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mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_dot.mlir

// RUN: mlir-opt %s --sparse-compiler | \ // RUN: mlir-opt %s --sparse-compiler | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \ // RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
#SparseVector = #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }> #SparseVector = #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>
module { module {
// //
// Sparse kernel. // Sparse kernel.
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mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_filter_conv2d.mlir

// RUN: mlir-opt %s --sparse-compiler | \ // RUN: mlir-opt %s --sparse-compiler | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \ // RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
// //
// Do the same run, but now with SIMDization as well. This should not change the outcome. // Do the same run, but now with SIMDization as well. This should not change the outcome.
// //
// RUN: mlir-opt %s --sparse-compiler="vectorization-strategy=any-storage-inner-loop vl=2" | \ // RUN: mlir-opt %s --sparse-compiler="vectorization-strategy=any-storage-inner-loop vl=2" | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \ // RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
#DCSR = #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }> #DCSR = #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>
// An example of a 2D convolution with a sparse filter. // An example of a 2D convolution with a sparse filter.
module { module {
func.func @conv2d(%input: tensor<8x8xi32>, func.func @conv2d(%input: tensor<8x8xi32>,
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mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_flatten.mlir

// RUN: mlir-opt %s --sparse-compiler | \ // RUN: mlir-opt %s --sparse-compiler | \
// RUN: TENSOR0="%mlir_integration_test_dir/data/test.tns" \ // RUN: TENSOR0="%mlir_src_dir/test/Integration/data/test.tns" \
// RUN: mlir-cpu-runner \ // RUN: mlir-cpu-runner \
// RUN: -e entry -entry-point-result=void \ // RUN: -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
// //
// Do the same run, but now with SIMDization as well. This should not change the outcome. // Do the same run, but now with SIMDization as well. This should not change the outcome.
// //
// RUN: mlir-opt %s --sparse-compiler="vectorization-strategy=any-storage-inner-loop vl=4" | \ // RUN: mlir-opt %s --sparse-compiler="vectorization-strategy=any-storage-inner-loop vl=4" | \
// RUN: TENSOR0="%mlir_integration_test_dir/data/test.tns" \ // RUN: TENSOR0="%mlir_src_dir/test/Integration/data/test.tns" \
// RUN: mlir-cpu-runner \ // RUN: mlir-cpu-runner \
// RUN: -e entry -entry-point-result=void \ // RUN: -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
!Filename = !llvm.ptr<i8> !Filename = !llvm.ptr<i8>
#SparseTensor = #sparse_tensor.encoding<{ #SparseTensor = #sparse_tensor.encoding<{
dimLevelType = [ "compressed", "compressed", "compressed", "compressed", dimLevelType = [ "compressed", "compressed", "compressed", "compressed",
"compressed", "compressed", "compressed", "compressed" ], "compressed", "compressed", "compressed", "compressed" ],
// Note that any dimOrdering permutation should give the same results // Note that any dimOrdering permutation should give the same results
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mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_index.mlir

// RUN: mlir-opt %s --sparse-compiler | \ // RUN: mlir-opt %s --sparse-compiler | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \ // RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
#SparseVector = #sparse_tensor.encoding<{ #SparseVector = #sparse_tensor.encoding<{
dimLevelType = ["compressed"] dimLevelType = ["compressed"]
}> }>
#SparseMatrix = #sparse_tensor.encoding<{ #SparseMatrix = #sparse_tensor.encoding<{
dimLevelType = ["compressed", "compressed"] dimLevelType = ["compressed", "compressed"]
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mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_index_dense.mlir

// RUN: mlir-opt %s --sparse-compiler | \ // RUN: mlir-opt %s --sparse-compiler | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \ // RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
// //
// Do the same run, but now with SIMDization as well. This should not change the outcome. // Do the same run, but now with SIMDization as well. This should not change the outcome.
// //
// RUN: mlir-opt %s --sparse-compiler="vectorization-strategy=any-storage-inner-loop vl=4" | \ // RUN: mlir-opt %s --sparse-compiler="vectorization-strategy=any-storage-inner-loop vl=4" | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \ // RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
#SparseVector = #sparse_tensor.encoding<{ #SparseVector = #sparse_tensor.encoding<{
dimLevelType = ["compressed"] dimLevelType = ["compressed"]
}> }>
#SparseMatrix = #sparse_tensor.encoding<{ #SparseMatrix = #sparse_tensor.encoding<{
dimLevelType = ["compressed", "compressed"] dimLevelType = ["compressed", "compressed"]
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mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_matmul.mlir

// RUN: mlir-opt %s --sparse-compiler | \ // RUN: mlir-opt %s --sparse-compiler | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \ // RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
#CSR = #sparse_tensor.encoding<{ #CSR = #sparse_tensor.encoding<{
dimLevelType = [ "dense", "compressed" ], dimLevelType = [ "dense", "compressed" ],
dimOrdering = affine_map<(i,j) -> (i,j)> dimOrdering = affine_map<(i,j) -> (i,j)>
}> }>
#DCSR = #sparse_tensor.encoding<{ #DCSR = #sparse_tensor.encoding<{
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mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_matrix_ops.mlir

// RUN: mlir-opt %s --sparse-compiler | \ // RUN: mlir-opt %s --sparse-compiler | \
// RUN: mlir-cpu-runner \ // RUN: mlir-cpu-runner \
// RUN: -e entry -entry-point-result=void \ // RUN: -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
#DCSR = #sparse_tensor.encoding<{dimLevelType = ["compressed", "compressed"]}> #DCSR = #sparse_tensor.encoding<{dimLevelType = ["compressed", "compressed"]}>
// //
// Traits for 2-d tensor (aka matrix) operations. // Traits for 2-d tensor (aka matrix) operations.
// //
#trait_scale = { #trait_scale = {
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mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_matvec.mlir

// RUN: mlir-opt %s --sparse-compiler | \ // RUN: mlir-opt %s --sparse-compiler | \
// RUN: TENSOR0="%mlir_integration_test_dir/data/wide.mtx" \ // RUN: TENSOR0="%mlir_src_dir/test/Integration/data/wide.mtx" \
// RUN: mlir-cpu-runner \ // RUN: mlir-cpu-runner \
// RUN: -e entry -entry-point-result=void \ // RUN: -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
// //
// Do the same run, but now with SIMDization as well. This should not change the outcome. // Do the same run, but now with SIMDization as well. This should not change the outcome.
// //
// RUN: mlir-opt %s \ // RUN: mlir-opt %s \
// RUN: --sparse-compiler="vectorization-strategy=any-storage-inner-loop vl=16 enable-simd-index32" | \ // RUN: --sparse-compiler="vectorization-strategy=any-storage-inner-loop vl=16 enable-simd-index32" | \
// RUN: TENSOR0="%mlir_integration_test_dir/data/wide.mtx" \ // RUN: TENSOR0="%mlir_src_dir/test/Integration/data/wide.mtx" \
// RUN: mlir-cpu-runner \ // RUN: mlir-cpu-runner \
// RUN: -e entry -entry-point-result=void \ // RUN: -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
!Filename = !llvm.ptr<i8> !Filename = !llvm.ptr<i8>
#SparseMatrix = #sparse_tensor.encoding<{ #SparseMatrix = #sparse_tensor.encoding<{
dimLevelType = [ "dense", "compressed" ], dimLevelType = [ "dense", "compressed" ],
pointerBitWidth = 8, pointerBitWidth = 8,
indexBitWidth = 8 indexBitWidth = 8
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mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_mttkrp.mlir

// RUN: mlir-opt %s --sparse-compiler | \ // RUN: mlir-opt %s --sparse-compiler | \
// RUN: TENSOR0="%mlir_integration_test_dir/data/mttkrp_b.tns" \ // RUN: TENSOR0="%mlir_src_dir/test/Integration/data/mttkrp_b.tns" \
// RUN: mlir-cpu-runner \ // RUN: mlir-cpu-runner \
// RUN: -e entry -entry-point-result=void \ // RUN: -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
// //
// Do the same run, but now with SIMDization as well. This should not change the outcome. // Do the same run, but now with SIMDization as well. This should not change the outcome.
// //
// RUN: mlir-opt %s --sparse-compiler="vectorization-strategy=any-storage-inner-loop vl=4" | \ // RUN: mlir-opt %s --sparse-compiler="vectorization-strategy=any-storage-inner-loop vl=4" | \
// RUN: TENSOR0="%mlir_integration_test_dir/data/mttkrp_b.tns" \ // RUN: TENSOR0="%mlir_src_dir/test/Integration/data/mttkrp_b.tns" \
// RUN: mlir-cpu-runner \ // RUN: mlir-cpu-runner \
// RUN: -e entry -entry-point-result=void \ // RUN: -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
!Filename = !llvm.ptr<i8> !Filename = !llvm.ptr<i8>
#SparseTensor = #sparse_tensor.encoding<{ #SparseTensor = #sparse_tensor.encoding<{
dimLevelType = [ "compressed", "compressed", "compressed" ] dimLevelType = [ "compressed", "compressed", "compressed" ]
}> }>
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mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_out_mult_elt.mlir

// RUN: mlir-opt %s --sparse-compiler | \ // RUN: mlir-opt %s --sparse-compiler | \
// RUN: mlir-cpu-runner \ // RUN: mlir-cpu-runner \
// RUN: -e entry -entry-point-result=void \ // RUN: -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
#DCSR = #sparse_tensor.encoding<{ #DCSR = #sparse_tensor.encoding<{
dimLevelType = [ "compressed", "compressed" ] dimLevelType = [ "compressed", "compressed" ]
}> }>
#trait_mult_elt = { #trait_mult_elt = {
indexing_maps = [ indexing_maps = [
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mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_out_reduction.mlir

// RUN: mlir-opt %s --sparse-compiler | \ // RUN: mlir-opt %s --sparse-compiler | \
// RUN: mlir-cpu-runner \ // RUN: mlir-cpu-runner \
// RUN: -e entry -entry-point-result=void \ // RUN: -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
#SparseMatrix = #sparse_tensor.encoding<{ #SparseMatrix = #sparse_tensor.encoding<{
dimLevelType = [ "compressed", "compressed" ] dimLevelType = [ "compressed", "compressed" ]
}> }>
#SparseTensor = #sparse_tensor.encoding<{ #SparseTensor = #sparse_tensor.encoding<{
dimLevelType = [ "compressed", "compressed", "compressed" ] dimLevelType = [ "compressed", "compressed", "compressed" ]
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mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_out_simple.mlir

// RUN: mlir-opt %s --sparse-compiler | \ // RUN: mlir-opt %s --sparse-compiler | \
// RUN: TENSOR0="%mlir_integration_test_dir/data/test.mtx" \ // RUN: TENSOR0="%mlir_src_dir/test/Integration/data/test.mtx" \
// RUN: mlir-cpu-runner \ // RUN: mlir-cpu-runner \
// RUN: -e entry -entry-point-result=void \ // RUN: -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
// //
// Do the same run, but now with SIMDization as well. This should not change the outcome. // Do the same run, but now with SIMDization as well. This should not change the outcome.
// //
// RUN: mlir-opt %s --sparse-compiler="vectorization-strategy=any-storage-inner-loop vl=4" | \ // RUN: mlir-opt %s --sparse-compiler="vectorization-strategy=any-storage-inner-loop vl=4" | \
// RUN: TENSOR0="%mlir_integration_test_dir/data/test.mtx" \ // RUN: TENSOR0="%mlir_src_dir/test/Integration/data/test.mtx" \
// RUN: mlir-cpu-runner \ // RUN: mlir-cpu-runner \
// RUN: -e entry -entry-point-result=void \ // RUN: -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
!Filename = !llvm.ptr<i8> !Filename = !llvm.ptr<i8>
#DCSR = #sparse_tensor.encoding<{ #DCSR = #sparse_tensor.encoding<{
dimLevelType = [ "compressed", "compressed" ], dimLevelType = [ "compressed", "compressed" ],
dimOrdering = affine_map<(i,j) -> (i,j)> dimOrdering = affine_map<(i,j) -> (i,j)>
}> }>
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mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_quantized_matmul.mlir

// RUN: mlir-opt %s --sparse-compiler | \ // RUN: mlir-opt %s --sparse-compiler | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \ // RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
// //
// Do the same run, but now with SIMDization as well. This should not change the outcome. // Do the same run, but now with SIMDization as well. This should not change the outcome.
// //
// RUN: mlir-opt %s --sparse-compiler="vectorization-strategy=any-storage-inner-loop vl=2" | \ // RUN: mlir-opt %s --sparse-compiler="vectorization-strategy=any-storage-inner-loop vl=2" | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \ // RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
#DCSR = #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }> #DCSR = #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>
// An example of a quantized sparse matmul. With the zero offset for the // An example of a quantized sparse matmul. With the zero offset for the
// sparse input, the sparse compiler generates very efficient code for the // sparse input, the sparse compiler generates very efficient code for the
// x(i,j) += (ext(a(i,k)) - 2) * ext(b(k,j)) // x(i,j) += (ext(a(i,k)) - 2) * ext(b(k,j))
// operation. // operation.
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mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_re_im.mlir

// RUN: mlir-opt %s --sparse-compiler | \ // RUN: mlir-opt %s --sparse-compiler | \
// RUN: mlir-cpu-runner \ // RUN: mlir-cpu-runner \
// RUN: -e entry -entry-point-result=void \ // RUN: -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}> #SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}>
#trait_op = { #trait_op = {
indexing_maps = [ indexing_maps = [
affine_map<(i) -> (i)>, // a (in) affine_map<(i) -> (i)>, // a (in)
affine_map<(i) -> (i)> // x (out) affine_map<(i) -> (i)> // x (out)
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mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_reduce_custom.mlir

// RUN: mlir-opt %s --sparse-compiler | \ // RUN: mlir-opt %s --sparse-compiler | \
// RUN: mlir-cpu-runner \ // RUN: mlir-cpu-runner \
// RUN: -e entry -entry-point-result=void \ // RUN: -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}> #SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}>
#CSR = #sparse_tensor.encoding<{dimLevelType = ["dense", "compressed"]}> #CSR = #sparse_tensor.encoding<{dimLevelType = ["dense", "compressed"]}>
#CSC = #sparse_tensor.encoding<{ #CSC = #sparse_tensor.encoding<{
dimLevelType = [ "dense", "compressed" ], dimLevelType = [ "dense", "compressed" ],
dimOrdering = affine_map<(i,j) -> (j,i)> dimOrdering = affine_map<(i,j) -> (j,i)>
}> }>
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mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_reductions.mlir

// RUN: mlir-opt %s --sparse-compiler | \ // RUN: mlir-opt %s --sparse-compiler | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \ // RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
// //
// Do the same run, but now with SIMDization as well. This should not change the outcome. // Do the same run, but now with SIMDization as well. This should not change the outcome.
// //
// RUN: mlir-opt %s -sparse-compiler="vectorization-strategy=any-storage-inner-loop vl=8" | \ // RUN: mlir-opt %s -sparse-compiler="vectorization-strategy=any-storage-inner-loop vl=8" | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \ // RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
#SV = #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }> #SV = #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>
#DV = #sparse_tensor.encoding<{ dimLevelType = [ "dense" ] }> #DV = #sparse_tensor.encoding<{ dimLevelType = [ "dense" ] }>
#trait_reduction = { #trait_reduction = {
indexing_maps = [ indexing_maps = [
affine_map<(i) -> (i)>, // a affine_map<(i) -> (i)>, // a
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mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_reshape.mlir

// RUN: mlir-opt %s --sparse-compiler | \ // RUN: mlir-opt %s --sparse-compiler | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \ // RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
#SparseVector = #sparse_tensor.encoding<{ #SparseVector = #sparse_tensor.encoding<{
dimLevelType = ["compressed"] dimLevelType = ["compressed"]
}> }>
#SparseMatrix = #sparse_tensor.encoding<{ #SparseMatrix = #sparse_tensor.encoding<{
dimLevelType = ["compressed", "compressed"] dimLevelType = ["compressed", "compressed"]
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mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_sampled_matmul.mlir

// RUN: mlir-opt %s --sparse-compiler | \ // RUN: mlir-opt %s --sparse-compiler | \
// RUN: TENSOR0="%mlir_integration_test_dir/data/test.mtx" \ // RUN: TENSOR0="%mlir_src_dir/test/Integration/data/test.mtx" \
// RUN: mlir-cpu-runner \ // RUN: mlir-cpu-runner \
// RUN: -e entry -entry-point-result=void \ // RUN: -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
// //
// Do the same run, but now with SIMDization as well. This should not change the outcome. // Do the same run, but now with SIMDization as well. This should not change the outcome.
// //
// RUN: mlir-opt %s \ // RUN: mlir-opt %s \
// RUN: --sparse-compiler="vectorization-strategy=any-storage-inner-loop vl=4 enable-simd-index32" | \ // RUN: --sparse-compiler="vectorization-strategy=any-storage-inner-loop vl=4 enable-simd-index32" | \
// RUN: TENSOR0="%mlir_integration_test_dir/data/test.mtx" \ // RUN: TENSOR0="%mlir_src_dir/test/Integration/data/test.mtx" \
// RUN: mlir-cpu-runner \ // RUN: mlir-cpu-runner \
// RUN: -e entry -entry-point-result=void \ // RUN: -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
// //
!Filename = !llvm.ptr<i8> !Filename = !llvm.ptr<i8>
#SparseMatrix = #sparse_tensor.encoding<{ #SparseMatrix = #sparse_tensor.encoding<{
dimLevelType = [ "compressed", "compressed" ], dimLevelType = [ "compressed", "compressed" ],
pointerBitWidth = 32, pointerBitWidth = 32,
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mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_sampled_mm_fusion.mlir

// RUN: mlir-opt %s --sparse-compiler | \ // RUN: mlir-opt %s --sparse-compiler | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \ // RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
// //
// Do the same run, but now with SIMDization as well. This should not change the outcome. // Do the same run, but now with SIMDization as well. This should not change the outcome.
// //
// RUN: mlir-opt %s -sparse-compiler="vl=8" | \ // RUN: mlir-opt %s -sparse-compiler="vl=8" | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \ // RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
#SM = #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }> #SM = #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>
#trait_sampled_dense_dense = { #trait_sampled_dense_dense = {
indexing_maps = [ indexing_maps = [
affine_map<(i,j,k) -> (i,j)>, // S affine_map<(i,j,k) -> (i,j)>, // S
affine_map<(i,j,k) -> (i,k)>, // A affine_map<(i,j,k) -> (i,k)>, // A
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mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_scale.mlir

// RUN: mlir-opt %s --sparse-compiler | \ // RUN: mlir-opt %s --sparse-compiler | \
// RUN: mlir-cpu-runner \ // RUN: mlir-cpu-runner \
// RUN: -e entry -entry-point-result=void \ // RUN: -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
// //
// Do the same run, but now with SIMDization as well. This should not change the outcome. // Do the same run, but now with SIMDization as well. This should not change the outcome.
// //
// RUN: mlir-opt %s --sparse-compiler="vectorization-strategy=any-storage-inner-loop vl=4" | \ // RUN: mlir-opt %s --sparse-compiler="vectorization-strategy=any-storage-inner-loop vl=4" | \
// RUN: mlir-cpu-runner \ // RUN: mlir-cpu-runner \
// RUN: -e entry -entry-point-result=void \ // RUN: -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
#CSR = #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }> #CSR = #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>
#trait_scale = { #trait_scale = {
indexing_maps = [ indexing_maps = [
affine_map<(i,j) -> (i,j)> // X (out) affine_map<(i,j) -> (i,j)> // X (out)
], ],
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mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_scf_nested.mlir

// RUN: mlir-opt %s --sparse-compiler | \ // RUN: mlir-opt %s --sparse-compiler | \
// RUN: mlir-cpu-runner \ // RUN: mlir-cpu-runner \
// RUN: -e entry -entry-point-result=void \ // RUN: -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
#map = affine_map<(d0, d1, d2) -> (d0, d1, d2)> #map = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
#SparseMatrix = #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }> #SparseMatrix = #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>
module @func_sparse.2 { module @func_sparse.2 {
// Do elementwise x+1 when true, x-1 when false // Do elementwise x+1 when true, x-1 when false
func.func public @condition(%cond: i1, %arg0: tensor<2x3x4xf64, #SparseMatrix>) -> tensor<2x3x4xf64, #SparseMatrix> { func.func public @condition(%cond: i1, %arg0: tensor<2x3x4xf64, #SparseMatrix>) -> tensor<2x3x4xf64, #SparseMatrix> {
▲ Show 20 Lines • Show All 62 Lines • ▼ Show 20 Lines func.func public @entry() {
call @dump(%sm_f) : (tensor<2x3x4xf64, #SparseMatrix>) -> () call @dump(%sm_f) : (tensor<2x3x4xf64, #SparseMatrix>) -> ()
bufferization.dealloc_tensor %sm : tensor<2x3x4xf64, #SparseMatrix> bufferization.dealloc_tensor %sm : tensor<2x3x4xf64, #SparseMatrix>
bufferization.dealloc_tensor %sm_t : tensor<2x3x4xf64, #SparseMatrix> bufferization.dealloc_tensor %sm_t : tensor<2x3x4xf64, #SparseMatrix>
bufferization.dealloc_tensor %sm_f : tensor<2x3x4xf64, #SparseMatrix> bufferization.dealloc_tensor %sm_f : tensor<2x3x4xf64, #SparseMatrix>
return return
} }
} }
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mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_sign.mlir

// RUN: mlir-opt %s --sparse-compiler | \ // RUN: mlir-opt %s --sparse-compiler | \
// RUN: mlir-cpu-runner \ // RUN: mlir-cpu-runner \
// RUN: -e entry -entry-point-result=void \ // RUN: -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
#SparseVector = #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }> #SparseVector = #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>
#trait_op = { #trait_op = {
indexing_maps = [ indexing_maps = [
affine_map<(i) -> (i)>, // a affine_map<(i) -> (i)>, // a
affine_map<(i) -> (i)> // x (out) affine_map<(i) -> (i)> // x (out)
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mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_spmm.mlir

// RUN: mlir-opt %s --sparse-compiler | \ // RUN: mlir-opt %s --sparse-compiler | \
// RUN: TENSOR0="%mlir_integration_test_dir/data/wide.mtx" \ // RUN: TENSOR0="%mlir_src_dir/test/Integration/data/wide.mtx" \
// RUN: mlir-cpu-runner \ // RUN: mlir-cpu-runner \
// RUN: -e entry -entry-point-result=void \ // RUN: -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
// //
// Do the same run, but now with SIMDization as well. This should not change the outcome. // Do the same run, but now with SIMDization as well. This should not change the outcome.
// //
// RUN: mlir-opt %s --sparse-compiler="vectorization-strategy=any-storage-inner-loop vl=2" | \ // RUN: mlir-opt %s --sparse-compiler="vectorization-strategy=any-storage-inner-loop vl=2" | \
// RUN: TENSOR0="%mlir_integration_test_dir/data/wide.mtx" \ // RUN: TENSOR0="%mlir_src_dir/test/Integration/data/wide.mtx" \
// RUN: mlir-cpu-runner \ // RUN: mlir-cpu-runner \
// RUN: -e entry -entry-point-result=void \ // RUN: -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
!Filename = !llvm.ptr<i8> !Filename = !llvm.ptr<i8>
#SparseMatrix = #sparse_tensor.encoding<{ #SparseMatrix = #sparse_tensor.encoding<{
dimLevelType = [ "dense", "compressed" ] dimLevelType = [ "dense", "compressed" ]
}> }>
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mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_storage.mlir

// RUN: mlir-opt %s --sparse-compiler | \ // RUN: mlir-opt %s --sparse-compiler | \
// RUN: mlir-cpu-runner \ // RUN: mlir-cpu-runner \
// RUN: -e entry -entry-point-result=void \ // RUN: -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
// //
// Several common sparse storage schemes. // Several common sparse storage schemes.
// //
#Dense = #sparse_tensor.encoding<{ #Dense = #sparse_tensor.encoding<{
dimLevelType = [ "dense", "dense" ] dimLevelType = [ "dense", "dense" ]
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mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_sum.mlir

// RUN: mlir-opt %s --sparse-compiler | \ // RUN: mlir-opt %s --sparse-compiler | \
// RUN: TENSOR0="%mlir_integration_test_dir/data/test_symmetric.mtx" \ // RUN: TENSOR0="%mlir_src_dir/test/Integration/data/test_symmetric.mtx" \
// RUN: mlir-cpu-runner \ // RUN: mlir-cpu-runner \
// RUN: -e entry -entry-point-result=void \ // RUN: -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
// //
// Do the same run, but now with SIMDization as well. This should not change the outcome. // Do the same run, but now with SIMDization as well. This should not change the outcome.
// //
// RUN: mlir-opt %s --sparse-compiler="vectorization-strategy=any-storage-inner-loop vl=2" | \ // RUN: mlir-opt %s --sparse-compiler="vectorization-strategy=any-storage-inner-loop vl=2" | \
// RUN: TENSOR0="%mlir_integration_test_dir/data/test_symmetric.mtx" \ // RUN: TENSOR0="%mlir_src_dir/test/Integration/data/test_symmetric.mtx" \
// RUN: mlir-cpu-runner \ // RUN: mlir-cpu-runner \
// RUN: -e entry -entry-point-result=void \ // RUN: -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
!Filename = !llvm.ptr<i8> !Filename = !llvm.ptr<i8>
#SparseMatrix = #sparse_tensor.encoding<{ #SparseMatrix = #sparse_tensor.encoding<{
dimLevelType = [ "compressed", "compressed" ] dimLevelType = [ "compressed", "compressed" ]
}> }>
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mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_sum_bf16.mlir

// RUN: mlir-opt %s --sparse-compiler | \ // RUN: mlir-opt %s --sparse-compiler | \
// RUN: mlir-cpu-runner \ // RUN: mlir-cpu-runner \
// RUN: -e entry -entry-point-result=void \ // RUN: -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
!Filename = !llvm.ptr<i8> !Filename = !llvm.ptr<i8>
#SparseMatrix = #sparse_tensor.encoding<{ #SparseMatrix = #sparse_tensor.encoding<{
dimLevelType = [ "compressed", "compressed" ] dimLevelType = [ "compressed", "compressed" ]
}> }>
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mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_sum_c32.mlir

// RUN: mlir-opt %s --sparse-compiler | \ // RUN: mlir-opt %s --sparse-compiler | \
// RUN: TENSOR0="%mlir_integration_test_dir/data/test_symmetric_complex.mtx" \ // RUN: TENSOR0="%mlir_src_dir/test/Integration/data/test_symmetric_complex.mtx" \
// RUN: mlir-cpu-runner \ // RUN: mlir-cpu-runner \
// RUN: -e entry -entry-point-result=void \ // RUN: -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
!Filename = !llvm.ptr<i8> !Filename = !llvm.ptr<i8>
#SparseMatrix = #sparse_tensor.encoding<{ #SparseMatrix = #sparse_tensor.encoding<{
dimLevelType = [ "compressed", "compressed" ] dimLevelType = [ "compressed", "compressed" ]
}> }>
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mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_sum_f16.mlir

// RUN: mlir-opt %s --sparse-compiler | \ // RUN: mlir-opt %s --sparse-compiler | \
// RUN: mlir-cpu-runner \ // RUN: mlir-cpu-runner \
// RUN: -e entry -entry-point-result=void \ // RUN: -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
!Filename = !llvm.ptr<i8> !Filename = !llvm.ptr<i8>
#SparseMatrix = #sparse_tensor.encoding<{ #SparseMatrix = #sparse_tensor.encoding<{
dimLevelType = [ "compressed", "compressed" ] dimLevelType = [ "compressed", "compressed" ]
}> }>
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mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_tanh.mlir

// RUN: mlir-opt %s --sparse-compiler | \ // RUN: mlir-opt %s --sparse-compiler | \
// RUN: mlir-cpu-runner \ // RUN: mlir-cpu-runner \
// RUN: -e entry -entry-point-result=void \ // RUN: -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
#SparseVector = #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }> #SparseVector = #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>
#trait_op = { #trait_op = {
indexing_maps = [ indexing_maps = [
affine_map<(i) -> (i)> // X (out) affine_map<(i) -> (i)> // X (out)
], ],
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mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_tensor_mul.mlir

// RUN: mlir-opt %s --sparse-compiler | \ // RUN: mlir-opt %s --sparse-compiler | \
// RUN: mlir-cpu-runner \ // RUN: mlir-cpu-runner \
// RUN: -e entry -entry-point-result=void \ // RUN: -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
#ST = #sparse_tensor.encoding<{dimLevelType = ["compressed", "compressed", "compressed"]}> #ST = #sparse_tensor.encoding<{dimLevelType = ["compressed", "compressed", "compressed"]}>
// //
// Trait for 3-d tensor element wise multiplication. // Trait for 3-d tensor element wise multiplication.
// //
#trait_mul = { #trait_mul = {
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mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_tensor_ops.mlir

// RUN: mlir-opt %s --sparse-compiler | \ // RUN: mlir-opt %s --sparse-compiler | \
// RUN: mlir-cpu-runner \ // RUN: mlir-cpu-runner \
// RUN: -e entry -entry-point-result=void \ // RUN: -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
#ST1 = #sparse_tensor.encoding<{dimLevelType = ["compressed", "compressed", "compressed"]}> #ST1 = #sparse_tensor.encoding<{dimLevelType = ["compressed", "compressed", "compressed"]}>
#ST2 = #sparse_tensor.encoding<{dimLevelType = ["compressed", "compressed", "dense"]}> #ST2 = #sparse_tensor.encoding<{dimLevelType = ["compressed", "compressed", "dense"]}>
// //
// Trait for 3-d tensor operation. // Trait for 3-d tensor operation.
// //
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mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_transpose.mlir

// RUN: mlir-opt %s --sparse-compiler | \ // RUN: mlir-opt %s --sparse-compiler | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \ // RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
#DCSR = #sparse_tensor.encoding<{ #DCSR = #sparse_tensor.encoding<{
dimLevelType = [ "compressed", "compressed" ] dimLevelType = [ "compressed", "compressed" ]
}> }>
#DCSC = #sparse_tensor.encoding<{ #DCSC = #sparse_tensor.encoding<{
dimLevelType = [ "compressed", "compressed" ], dimLevelType = [ "compressed", "compressed" ],
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mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_unary.mlir

// RUN: mlir-opt %s --sparse-compiler | \ // RUN: mlir-opt %s --sparse-compiler | \
// RUN: mlir-cpu-runner \ // RUN: mlir-cpu-runner \
// RUN: -e entry -entry-point-result=void \ // RUN: -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}> #SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}>
#DCSR = #sparse_tensor.encoding<{dimLevelType = ["compressed", "compressed"]}> #DCSR = #sparse_tensor.encoding<{dimLevelType = ["compressed", "compressed"]}>
// //
// Traits for tensor operations. // Traits for tensor operations.
// //
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mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_vector_ops.mlir

// RUN: mlir-opt %s --sparse-compiler | \ // RUN: mlir-opt %s --sparse-compiler | \
// RUN: mlir-cpu-runner \ // RUN: mlir-cpu-runner \
// RUN: -e entry -entry-point-result=void \ // RUN: -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}> #SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}>
#DenseVector = #sparse_tensor.encoding<{dimLevelType = ["dense"]}> #DenseVector = #sparse_tensor.encoding<{dimLevelType = ["dense"]}>
// //
// Traits for 1-d tensor (aka vector) operations. // Traits for 1-d tensor (aka vector) operations.
// //
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mlir/test/Integration/Dialect/SparseTensor/python/test_SDDMM.py

# RUN: SUPPORT_LIB=%mlir_runner_utils_dir/libmlir_c_runner_utils%shlibext \ # RUN: SUPPORT_LIB=%mlir_lib_dir/libmlir_c_runner_utils%shlibext \
# RUN: %PYTHON %s | FileCheck %s # RUN: %PYTHON %s | FileCheck %s
import ctypes import ctypes
import numpy as np import numpy as np
import os import os
import sys import sys
from mlir import ir from mlir import ir
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from tools import sparse_compiler from tools import sparse_compiler
@dsl.linalg_structured_op @dsl.linalg_structured_op
def sddmm_dsl( def sddmm_dsl(
A=dsl.TensorDef(dsl.T, dsl.S.M, dsl.S.K), A=dsl.TensorDef(dsl.T, dsl.S.M, dsl.S.K),
B=dsl.TensorDef(dsl.T, dsl.S.K, dsl.S.N), B=dsl.TensorDef(dsl.T, dsl.S.K, dsl.S.N),
S=dsl.TensorDef(dsl.T, dsl.S.M, dsl.S.N), S=dsl.TensorDef(dsl.T, dsl.S.M, dsl.S.N),
C=dsl.TensorDef(dsl.T, dsl.S.M, dsl.S.N, output=True)): C=dsl.TensorDef(dsl.T, dsl.S.M, dsl.S.N, output=True)):
C[dsl.D.m, C[dsl.D.m,
dsl.D.n] += S[dsl.D.m, dsl.D.n] * A[dsl.D.m, dsl.D.k] * B[dsl.D.k, dsl.D.n] dsl.D.n] += S[dsl.D.m, dsl.D.n] * A[dsl.D.m, dsl.D.k] * B[dsl.D.k, dsl.D.n]
def build_SDDMM(attr: st.EncodingAttr): def build_SDDMM(attr: st.EncodingAttr):
"""Build SDDMM kernel. """Build SDDMM kernel.
This method generates a linalg op with for matrix multiplication using This method generates a linalg op with for matrix multiplication using
just the Python API. Effectively, a generic linalg op is constructed just the Python API. Effectively, a generic linalg op is constructed
that computes C(i,j) += S(i,j) SUM_k A(i,k) B(k,j) for sparse S. that computes C(i,j) += S(i,j) SUM_k A(i,k) B(k,j) for sparse S.
""" """
module = ir.Module.create() module = ir.Module.create()
f64 = ir.F64Type.get() f64 = ir.F64Type.get()
a = ir.RankedTensorType.get([8, 8], f64) a = ir.RankedTensorType.get([8, 8], f64)
b = ir.RankedTensorType.get([8, 8], f64) b = ir.RankedTensorType.get([8, 8], f64)
c = ir.RankedTensorType.get([8, 8], f64) c = ir.RankedTensorType.get([8, 8], f64)
s = ir.RankedTensorType.get([8, 8], f64, attr) s = ir.RankedTensorType.get([8, 8], f64, attr)
arguments = [a, b, s, c] arguments = [a, b, s, c]
with ir.InsertionPoint(module.body): with ir.InsertionPoint(module.body):
@func.FuncOp.from_py_func(*arguments) @func.FuncOp.from_py_func(*arguments)
def sddmm(*args): def sddmm(*args):
return sddmm_dsl(args[0], args[1], args[2], outs=[args[3]]) return sddmm_dsl(args[0], args[1], args[2], outs=[args[3]])
return module return module
def boilerplate(attr: st.EncodingAttr): def boilerplate(attr: st.EncodingAttr):
"""Returns boilerplate code for main driver.""" """Returns boilerplate code for main driver."""
return f""" return f"""
func.func @main(%a: tensor<8x8xf64>, func.func @main(%a: tensor<8x8xf64>,
%b: tensor<8x8xf64>, %b: tensor<8x8xf64>,
%c: tensor<8x8xf64>) -> tensor<8x8xf64> attributes {{ llvm.emit_c_interface }} {{ %c: tensor<8x8xf64>) -> tensor<8x8xf64> attributes {{ llvm.emit_c_interface }} {{
%t = arith.constant sparse<[[0,0], [0,2], [4,1]], [1.0, 2.0, 3.0]> : tensor<8x8xf64> %t = arith.constant sparse<[[0,0], [0,2], [4,1]], [1.0, 2.0, 3.0]> : tensor<8x8xf64>
%s = sparse_tensor.convert %t : tensor<8x8xf64> to tensor<8x8xf64, {attr}> %s = sparse_tensor.convert %t : tensor<8x8xf64> to tensor<8x8xf64, {attr}>
%0 = call @sddmm(%a, %b, %s, %c) : (tensor<8x8xf64>, %0 = call @sddmm(%a, %b, %s, %c) : (tensor<8x8xf64>,
tensor<8x8xf64>, tensor<8x8xf64>,
tensor<8x8xf64, {attr}>, tensor<8x8xf64, {attr}>,
tensor<8x8xf64>) -> tensor<8x8xf64> tensor<8x8xf64>) -> tensor<8x8xf64>
return %0 : tensor<8x8xf64> return %0 : tensor<8x8xf64>
}} }}
""" """
def build_compile_and_run_SDDMMM(attr: st.EncodingAttr, compiler): def build_compile_and_run_SDDMMM(attr: st.EncodingAttr, compiler):
# Build. # Build.
module = build_SDDMM(attr) module = build_SDDMM(attr)
func = str(module.operation.regions[0].blocks[0].operations[0].operation) func = str(module.operation.regions[0].blocks[0].operations[0].operation)
module = ir.Module.parse(func + boilerplate(attr)) module = ir.Module.parse(func + boilerplate(attr))
# Compile. # Compile.
engine = compiler.compile_and_jit(module) engine = compiler.compile_and_jit(module)
# Set up numpy input and buffer for output. # Set up numpy input and buffer for output.
a = np.array([[1.1, 2.1, 3.1, 4.1, 5.1, 6.1, 7.1, 8.1], a = np.array([[1.1, 2.1, 3.1, 4.1, 5.1, 6.1, 7.1, 8.1],
[1.2, 2.2, 3.2, 4.2, 5.2, 6.2, 7.2, 8.2], [1.2, 2.2, 3.2, 4.2, 5.2, 6.2, 7.2, 8.2],
[1.3, 2.3, 3.3, 4.3, 5.3, 6.3, 7.3, 8.3], [1.3, 2.3, 3.3, 4.3, 5.3, 6.3, 7.3, 8.3],
[1.4, 2.4, 3.4, 4.4, 5.4, 6.4, 7.4, 8.4], [1.4, 2.4, 3.4, 4.4, 5.4, 6.4, 7.4, 8.4],
[1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5], [1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5],
[1.6, 2.6, 3.6, 4.6, 5.6, 6.6, 7.6, 8.6], [1.6, 2.6, 3.6, 4.6, 5.6, 6.6, 7.6, 8.6],
[1.7, 2.7, 3.7, 4.7, 5.7, 6.7, 7.7, 8.7], [1.7, 2.7, 3.7, 4.7, 5.7, 6.7, 7.7, 8.7],
[1.8, 2.8, 3.8, 4.8, 5.8, 6.8, 7.8, 8.8]], np.float64) [1.8, 2.8, 3.8, 4.8, 5.8, 6.8, 7.8, 8.8]], np.float64)
b = np.ones((8, 8), np.float64) b = np.ones((8, 8), np.float64)
c = np.zeros((8, 8), np.float64) c = np.zeros((8, 8), np.float64)
mem_a = ctypes.pointer(ctypes.pointer(rt.get_ranked_memref_descriptor(a))) mem_a = ctypes.pointer(ctypes.pointer(rt.get_ranked_memref_descriptor(a)))
mem_b = ctypes.pointer(ctypes.pointer(rt.get_ranked_memref_descriptor(b))) mem_b = ctypes.pointer(ctypes.pointer(rt.get_ranked_memref_descriptor(b)))
mem_c = ctypes.pointer(ctypes.pointer(rt.get_ranked_memref_descriptor(c))) mem_c = ctypes.pointer(ctypes.pointer(rt.get_ranked_memref_descriptor(c)))
# Allocate a MemRefDescriptor to receive the output tensor. # Allocate a MemRefDescriptor to receive the output tensor.
# The buffer itself is allocated inside the MLIR code generation. # The buffer itself is allocated inside the MLIR code generation.
ref_out = rt.make_nd_memref_descriptor(2, ctypes.c_double)() ref_out = rt.make_nd_memref_descriptor(2, ctypes.c_double)()
mem_out = ctypes.pointer(ctypes.pointer(ref_out)) mem_out = ctypes.pointer(ctypes.pointer(ref_out))
# Invoke the kernel and get numpy output. # Invoke the kernel and get numpy output.
# Built-in bufferization uses in-out buffers. # Built-in bufferization uses in-out buffers.
# TODO: replace with inplace comprehensive bufferization. # TODO: replace with inplace comprehensive bufferization.
engine.invoke('main', mem_out, mem_a, mem_b, mem_c) engine.invoke('main', mem_out, mem_a, mem_b, mem_c)
# Sanity check on computed result. Only a few elements # Sanity check on computed result. Only a few elements
# are sampled from the full dense matrix multiplication. # are sampled from the full dense matrix multiplication.
full_matmul = np.matmul(a, b) full_matmul = np.matmul(a, b)
expected = np.zeros((8, 8), np.float64) expected = np.zeros((8, 8), np.float64)
expected[0, 0] = 1.0 * full_matmul[0, 0] expected[0, 0] = 1.0 * full_matmul[0, 0]
expected[0, 2] = 2.0 * full_matmul[0, 2] expected[0, 2] = 2.0 * full_matmul[0, 2]
expected[4, 1] = 3.0 * full_matmul[4, 1] expected[4, 1] = 3.0 * full_matmul[4, 1]
c = rt.ranked_memref_to_numpy(mem_out[0]) c = rt.ranked_memref_to_numpy(mem_out[0])
if np.allclose(c, expected): if np.allclose(c, expected):
pass pass
else: else:
quit(f'FAILURE') quit(f'FAILURE')
def main(): def main():
support_lib = os.getenv('SUPPORT_LIB') support_lib = os.getenv('SUPPORT_LIB')
assert support_lib is not None, 'SUPPORT_LIB is undefined' assert support_lib is not None, 'SUPPORT_LIB is undefined'
if not os.path.exists(support_lib): if not os.path.exists(support_lib):
raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT),
support_lib) support_lib)
# CHECK-LABEL: TEST: testSDDMMM # CHECK-LABEL: TEST: testSDDMMM
print('\nTEST: testSDDMMM') print('\nTEST: testSDDMMM')
with ir.Context() as ctx, ir.Location.unknown(): with ir.Context() as ctx, ir.Location.unknown():
count = 0 count = 0
# Loop over various ways to compile and annotate the SDDMM kernel with # Loop over various ways to compile and annotate the SDDMM kernel with
# a *single* sparse tensor. Note that we deliberate do not exhaustively # a *single* sparse tensor. Note that we deliberate do not exhaustively
# search the full state space to reduce runtime of the test. It is # search the full state space to reduce runtime of the test. It is
# straightforward to adapt the code below to explore more combinations. # straightforward to adapt the code below to explore more combinations.
levels = [[st.DimLevelType.dense, st.DimLevelType.dense], levels = [[st.DimLevelType.dense, st.DimLevelType.dense],
[st.DimLevelType.dense, st.DimLevelType.compressed], [st.DimLevelType.dense, st.DimLevelType.compressed],
[st.DimLevelType.compressed, st.DimLevelType.dense], [st.DimLevelType.compressed, st.DimLevelType.dense],
[st.DimLevelType.compressed, st.DimLevelType.compressed]] [st.DimLevelType.compressed, st.DimLevelType.compressed]]
orderings = [ orderings = [
ir.AffineMap.get_permutation([0, 1]), ir.AffineMap.get_permutation([0, 1]),
ir.AffineMap.get_permutation([1, 0]) ir.AffineMap.get_permutation([1, 0])
] ]
vec_strategy = [ vec_strategy = [
'none', 'dense-inner-loop' 'none', 'dense-inner-loop'
] ]for level in levels:
for level in levels:
for ordering in orderings: for ordering in orderings:
for pwidth in [32]: for pwidth in [32]:
for iwidth in [32]: for iwidth in [32]:
for vec in vec_strategy: for vec in vec_strategy:
for e in [True]: for e in [True]:
vl = 1 if vec == 0 else 16 vl = 1 if vec == 0 else 16
attr = st.EncodingAttr.get(level, ordering, pwidth, iwidth) attr = st.EncodingAttr.get(level, ordering, pwidth, iwidth)
opt = (f'parallelization-strategy=none ' opt = (f'parallelization-strategy=none '
f'vectorization-strategy={vec} ' f'vectorization-strategy={vec} '
f'vl={vl} enable-simd-index32={e}') f'vl={vl} enable-simd-index32={e}')
compiler = sparse_compiler.SparseCompiler( compiler = sparse_compiler.SparseCompiler(
options=opt, opt_level=0, shared_libs=[support_lib]) options=opt, opt_level=0, shared_libs=[support_lib])
build_compile_and_run_SDDMMM(attr, compiler) build_compile_and_run_SDDMMM(attr, compiler)
count = count + 1 count = count + 1
# CHECK: Passed 16 tests # CHECK: Passed 16 tests
print('Passed ', count, 'tests') print('Passed ', count, 'tests')
if __name__ == '__main__': if __name__ == '__main__':
main() main()

mlir/test/Integration/Dialect/SparseTensor/python/test_SpMM.py

# RUN: SUPPORT_LIB=%mlir_runner_utils_dir/libmlir_c_runner_utils%shlibext \ # RUN: SUPPORT_LIB=%mlir_lib_dir/libmlir_c_runner_utils%shlibext \
# RUN: %PYTHON %s | FileCheck %s # RUN: %PYTHON %s | FileCheck %s
import ctypes import ctypes
import numpy as np import numpy as np
import os import os
import sys import sys
from mlir import ir from mlir import ir
from mlir import runtime as rt from mlir import runtime as rt
from mlir.dialects import sparse_tensor as st from mlir.dialects import sparse_tensor as st
from mlir.dialects import builtin from mlir.dialects import builtin
from mlir.dialects import func from mlir.dialects import func
from mlir.dialects.linalg.opdsl import lang as dsl from mlir.dialects.linalg.opdsl import lang as dsl
_SCRIPT_PATH = os.path.dirname(os.path.abspath(__file__)) _SCRIPT_PATH = os.path.dirname(os.path.abspath(__file__))
sys.path.append(_SCRIPT_PATH) sys.path.append(_SCRIPT_PATH)
from tools import sparse_compiler from tools import sparse_compiler
@dsl.linalg_structured_op @dsl.linalg_structured_op
def matmul_dsl( def matmul_dsl(
A=dsl.TensorDef(dsl.T, dsl.S.M, dsl.S.K), A=dsl.TensorDef(dsl.T, dsl.S.M, dsl.S.K),
B=dsl.TensorDef(dsl.T, dsl.S.K, dsl.S.N), B=dsl.TensorDef(dsl.T, dsl.S.K, dsl.S.N),
C=dsl.TensorDef(dsl.T, dsl.S.M, dsl.S.N, output=True)): C=dsl.TensorDef(dsl.T, dsl.S.M, dsl.S.N, output=True)):
C[dsl.D.m, dsl.D.n] += A[dsl.D.m, dsl.D.k] * B[dsl.D.k, dsl.D.n] C[dsl.D.m, dsl.D.n] += A[dsl.D.m, dsl.D.k] * B[dsl.D.k, dsl.D.n]
def build_SpMM(attr: st.EncodingAttr): def build_SpMM(attr: st.EncodingAttr):
"""Build SpMM kernel. """Build SpMM kernel.
This method generates a linalg op with for matrix multiplication using This method generates a linalg op with for matrix multiplication using
just the Python API. Effectively, a generic linalg op is constructed just the Python API. Effectively, a generic linalg op is constructed
that computes C(i,j) += A(i,k) * B(k,j) for annotated matrix A. that computes C(i,j) += A(i,k) * B(k,j) for annotated matrix A.
""" """
module = ir.Module.create() module = ir.Module.create()
f64 = ir.F64Type.get() f64 = ir.F64Type.get()
a = ir.RankedTensorType.get([3, 4], f64, attr) a = ir.RankedTensorType.get([3, 4], f64, attr)
b = ir.RankedTensorType.get([4, 2], f64) b = ir.RankedTensorType.get([4, 2], f64)
c = ir.RankedTensorType.get([3, 2], f64) c = ir.RankedTensorType.get([3, 2], f64)
arguments = [a, b, c] arguments = [a, b, c]
with ir.InsertionPoint(module.body): with ir.InsertionPoint(module.body):
@func.FuncOp.from_py_func(*arguments) @func.FuncOp.from_py_func(*arguments)
def spMxM(*args): def spMxM(*args):
return matmul_dsl(args[0], args[1], outs=[args[2]]) return matmul_dsl(args[0], args[1], outs=[args[2]])
return module return module
def boilerplate(attr: st.EncodingAttr): def boilerplate(attr: st.EncodingAttr):
"""Returns boilerplate main method. """Returns boilerplate main method.
This method sets up a boilerplate main method that takes three tensors This method sets up a boilerplate main method that takes three tensors
(a, b, c), converts the first tensor a into s sparse tensor, and then (a, b, c), converts the first tensor a into s sparse tensor, and then
calls the sparse kernel for matrix multiplication. For convenience, calls the sparse kernel for matrix multiplication. For convenience,
this part is purely done as string input. this part is purely done as string input.
""" """
return f""" return f"""
func.func @main(%ad: tensor<3x4xf64>, %b: tensor<4x2xf64>, %c: tensor<3x2xf64>) -> tensor<3x2xf64> func.func @main(%ad: tensor<3x4xf64>, %b: tensor<4x2xf64>, %c: tensor<3x2xf64>) -> tensor<3x2xf64>
attributes {{ llvm.emit_c_interface }} {{ attributes {{ llvm.emit_c_interface }} {{
%a = sparse_tensor.convert %ad : tensor<3x4xf64> to tensor<3x4xf64, {attr}> %a = sparse_tensor.convert %ad : tensor<3x4xf64> to tensor<3x4xf64, {attr}>
%0 = call @spMxM(%a, %b, %c) : (tensor<3x4xf64, {attr}>, %0 = call @spMxM(%a, %b, %c) : (tensor<3x4xf64, {attr}>,
tensor<4x2xf64>, tensor<4x2xf64>,
tensor<3x2xf64>) -> tensor<3x2xf64> tensor<3x2xf64>) -> tensor<3x2xf64>
return %0 : tensor<3x2xf64> return %0 : tensor<3x2xf64>
}} }}
""" """
def build_compile_and_run_SpMM(attr: st.EncodingAttr, compiler): def build_compile_and_run_SpMM(attr: st.EncodingAttr, compiler):
# Build. # Build.
module = build_SpMM(attr) module = build_SpMM(attr)
func = str(module.operation.regions[0].blocks[0].operations[0].operation) func = str(module.operation.regions[0].blocks[0].operations[0].operation)
module = ir.Module.parse(func + boilerplate(attr)) module = ir.Module.parse(func + boilerplate(attr))
# Compile. # Compile.
engine = compiler.compile_and_jit(module) engine = compiler.compile_and_jit(module)
# Set up numpy input and buffer for output. # Set up numpy input and buffer for output.
a = np.array( a = np.array(
[[1.1, 0.0, 0.0, 1.4], [0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 3.3, 0.0]], [[1.1, 0.0, 0.0, 1.4], [0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 3.3, 0.0]],
np.float64) np.float64)
b = np.array([[1.0, 2.0], [4.0, 3.0], [5.0, 6.0], [8.0, 7.0]], np.float64) b = np.array([[1.0, 2.0], [4.0, 3.0], [5.0, 6.0], [8.0, 7.0]], np.float64)
c = np.zeros((3, 2), np.float64) c = np.zeros((3, 2), np.float64)
mem_a = ctypes.pointer(ctypes.pointer(rt.get_ranked_memref_descriptor(a))) mem_a = ctypes.pointer(ctypes.pointer(rt.get_ranked_memref_descriptor(a)))
mem_b = ctypes.pointer(ctypes.pointer(rt.get_ranked_memref_descriptor(b))) mem_b = ctypes.pointer(ctypes.pointer(rt.get_ranked_memref_descriptor(b)))
mem_c = ctypes.pointer(ctypes.pointer(rt.get_ranked_memref_descriptor(c))) mem_c = ctypes.pointer(ctypes.pointer(rt.get_ranked_memref_descriptor(c)))
# Allocate a MemRefDescriptor to receive the output tensor. # Allocate a MemRefDescriptor to receive the output tensor.
# The buffer itself is allocated inside the MLIR code generation. # The buffer itself is allocated inside the MLIR code generation.
ref_out = rt.make_nd_memref_descriptor(2, ctypes.c_double)() ref_out = rt.make_nd_memref_descriptor(2, ctypes.c_double)()
mem_out = ctypes.pointer(ctypes.pointer(ref_out)) mem_out = ctypes.pointer(ctypes.pointer(ref_out))
# Invoke the kernel and get numpy output. # Invoke the kernel and get numpy output.
# Built-in bufferization uses in-out buffers. # Built-in bufferization uses in-out buffers.
# TODO: replace with inplace comprehensive bufferization. # TODO: replace with inplace comprehensive bufferization.
engine.invoke('main', mem_out, mem_a, mem_b, mem_c) engine.invoke('main', mem_out, mem_a, mem_b, mem_c)
# Sanity check on computed result. # Sanity check on computed result.
expected = np.matmul(a, b); expected = np.matmul(a, b);
c = rt.ranked_memref_to_numpy(mem_out[0]) c = rt.ranked_memref_to_numpy(mem_out[0])
if np.allclose(c, expected): if np.allclose(c, expected):
pass pass
else: else:
quit(f'FAILURE') quit(f'FAILURE')
def main(): def main():
support_lib = os.getenv('SUPPORT_LIB') support_lib = os.getenv('SUPPORT_LIB')
assert support_lib is not None, 'SUPPORT_LIB is undefined' assert support_lib is not None, 'SUPPORT_LIB is undefined'
if not os.path.exists(support_lib): if not os.path.exists(support_lib):
raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), support_lib) raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), support_lib)
# CHECK-LABEL: TEST: testSpMM # CHECK-LABEL: TEST: testSpMM
print('\nTEST: testSpMM') print('\nTEST: testSpMM')
with ir.Context() as ctx, ir.Location.unknown(): with ir.Context() as ctx, ir.Location.unknown():
count = 0 count = 0
# Loop over various ways to compile and annotate the SpMM kernel with # Loop over various ways to compile and annotate the SpMM kernel with
# a *single* sparse tensor. Note that we deliberate do not exhaustively # a *single* sparse tensor. Note that we deliberate do not exhaustively
# search the full state space to reduce runtime of the test. It is # search the full state space to reduce runtime of the test. It is
# straightforward to adapt the code below to explore more combinations. # straightforward to adapt the code below to explore more combinations.
vl = 1 vl = 1
e = False e = False
opt = (f'parallelization-strategy=none ' opt = (f'parallelization-strategy=none '
f'vectorization-strategy=none ' f'vectorization-strategy=none '
f'vl={vl} enable-simd-index32={e}') f'vl={vl} enable-simd-index32={e}')
levels = [[st.DimLevelType.dense, st.DimLevelType.dense], levels = [[st.DimLevelType.dense, st.DimLevelType.dense],
[st.DimLevelType.dense, st.DimLevelType.compressed], [st.DimLevelType.dense, st.DimLevelType.compressed],
[st.DimLevelType.compressed, st.DimLevelType.dense], [st.DimLevelType.compressed, st.DimLevelType.dense],
[st.DimLevelType.compressed, st.DimLevelType.compressed]] [st.DimLevelType.compressed, st.DimLevelType.compressed]]
orderings = [ orderings = [
ir.AffineMap.get_permutation([0, 1]), ir.AffineMap.get_permutation([0, 1]),
ir.AffineMap.get_permutation([1, 0]) ir.AffineMap.get_permutation([1, 0])
] ]
bitwidths = [0] bitwidths = [0]
compiler = sparse_compiler.SparseCompiler( compiler = sparse_compiler.SparseCompiler(
options=opt, opt_level=0, shared_libs=[support_lib]) options=opt, opt_level=0, shared_libs=[support_lib])
for level in levels: for level in levels:
for ordering in orderings: for ordering in orderings:
for pwidth in bitwidths: for pwidth in bitwidths:
for iwidth in bitwidths: for iwidth in bitwidths:
attr = st.EncodingAttr.get(level, ordering, pwidth, iwidth) attr = st.EncodingAttr.get(level, ordering, pwidth, iwidth)
build_compile_and_run_SpMM(attr, compiler) build_compile_and_run_SpMM(attr, compiler)
count = count + 1 count = count + 1
# CHECK: Passed 8 tests # CHECK: Passed 8 tests
print('Passed ', count, 'tests') print('Passed ', count, 'tests')
if __name__ == '__main__': if __name__ == '__main__':
main() main()

mlir/test/Integration/Dialect/SparseTensor/python/test_elementwise_add_sparse_output.py

# RUN: SUPPORT_LIB=%mlir_runner_utils_dir/libmlir_c_runner_utils%shlibext %PYTHON %s | FileCheck %s # RUN: SUPPORT_LIB=%mlir_lib_dir/libmlir_c_runner_utils%shlibext %PYTHON %s | FileCheck %s
import ctypes import ctypes
import numpy as np import numpy as np
import os import os
import sys import sys
from mlir import ir from mlir import ir
from mlir import runtime as rt from mlir import runtime as rt
▲ Show 20 Lines • Show All 96 Lines • Show Last 20 Lines

mlir/test/Integration/Dialect/SparseTensor/python/test_output.py

# RUN: SUPPORT_LIB=%mlir_runner_utils_dir/libmlir_c_runner_utils%shlibext \ # RUN: SUPPORT_LIB=%mlir_lib_dir/libmlir_c_runner_utils%shlibext \
# RUN: %PYTHON %s | FileCheck %s # RUN: %PYTHON %s | FileCheck %s
import ctypes import ctypes
import os import os
import sys import sys
import tempfile import tempfile
from mlir import ir from mlir import ir
from mlir import runtime as rt from mlir import runtime as rt
from mlir.dialects import builtin from mlir.dialects import builtin
from mlir.dialects import sparse_tensor as st from mlir.dialects import sparse_tensor as st
_SCRIPT_PATH = os.path.dirname(os.path.abspath(__file__)) _SCRIPT_PATH = os.path.dirname(os.path.abspath(__file__))
sys.path.append(_SCRIPT_PATH) sys.path.append(_SCRIPT_PATH)
from tools import sparse_compiler from tools import sparse_compiler
# TODO: move more into actual IR building. # TODO: move more into actual IR building.
def boilerplate(attr: st.EncodingAttr): def boilerplate(attr: st.EncodingAttr):
"""Returns boilerplate main method.""" """Returns boilerplate main method."""
return f""" return f"""
func.func @main(%p : !llvm.ptr<i8>) -> () attributes {{ llvm.emit_c_interface }} {{ func.func @main(%p : !llvm.ptr<i8>) -> () attributes {{ llvm.emit_c_interface }} {{
%d = arith.constant sparse<[[0, 0], [1, 1], [0, 9], [9, 0], [4, 4]], %d = arith.constant sparse<[[0, 0], [1, 1], [0, 9], [9, 0], [4, 4]],
[1.0, 2.0, 3.0, 4.0, 5.0]> : tensor<10x10xf64> [1.0, 2.0, 3.0, 4.0, 5.0]> : tensor<10x10xf64>
%a = sparse_tensor.convert %d : tensor<10x10xf64> to tensor<10x10xf64, {attr}> %a = sparse_tensor.convert %d : tensor<10x10xf64> to tensor<10x10xf64, {attr}>
sparse_tensor.out %a, %p : tensor<10x10xf64, {attr}>, !llvm.ptr<i8> sparse_tensor.out %a, %p : tensor<10x10xf64, {attr}>, !llvm.ptr<i8>
return return
}} }}
""" """
def expected(): def expected():
"""Returns expected contents of output. """Returns expected contents of output.
Regardless of the dimension ordering, compression, and bitwidths that are Regardless of the dimension ordering, compression, and bitwidths that are
used in the sparse tensor, the output is always lexicographically sorted used in the sparse tensor, the output is always lexicographically sorted
by natural index order. by natural index order.
""" """
return f"""; extended FROSTT format return f"""; extended FROSTT format
2 5 2 5
10 10 10 10
1 1 1 1 1 1
1 10 3 1 10 3
2 2 2 2 2 2
5 5 5 5 5 5
10 1 4 10 1 4
""" """
def build_compile_and_run_output(attr: st.EncodingAttr, compiler): def build_compile_and_run_output(attr: st.EncodingAttr, compiler):
# Build and Compile. # Build and Compile.
module = ir.Module.parse(boilerplate(attr)) module = ir.Module.parse(boilerplate(attr))
engine = compiler.compile_and_jit(module) engine = compiler.compile_and_jit(module)
# Invoke the kernel and compare output. # Invoke the kernel and compare output.
with tempfile.TemporaryDirectory() as test_dir: with tempfile.TemporaryDirectory() as test_dir:
out = os.path.join(test_dir, 'out.tns') out = os.path.join(test_dir, 'out.tns')
buf = out.encode('utf-8') buf = out.encode('utf-8')
mem_a = ctypes.pointer(ctypes.pointer(ctypes.create_string_buffer(buf))) mem_a = ctypes.pointer(ctypes.pointer(ctypes.create_string_buffer(buf)))
engine.invoke('main', mem_a) engine.invoke('main', mem_a)
actual = open(out).read() actual = open(out).read()
if actual != expected(): if actual != expected():
quit('FAILURE') quit('FAILURE')
def main(): def main():
support_lib = os.getenv('SUPPORT_LIB') support_lib = os.getenv('SUPPORT_LIB')
assert support_lib is not None, 'SUPPORT_LIB is undefined' assert support_lib is not None, 'SUPPORT_LIB is undefined'
if not os.path.exists(support_lib): if not os.path.exists(support_lib):
raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT),
support_lib) support_lib)
# CHECK-LABEL: TEST: test_output # CHECK-LABEL: TEST: test_output
print('\nTEST: test_output') print('\nTEST: test_output')
count = 0 count = 0
with ir.Context() as ctx, ir.Location.unknown(): with ir.Context() as ctx, ir.Location.unknown():
# Loop over various sparse types: CSR, DCSR, CSC, DCSC. # Loop over various sparse types: CSR, DCSR, CSC, DCSC.
levels = [[st.DimLevelType.dense, st.DimLevelType.compressed], levels = [[st.DimLevelType.dense, st.DimLevelType.compressed],
[st.DimLevelType.compressed, st.DimLevelType.compressed]] [st.DimLevelType.compressed, st.DimLevelType.compressed]]
orderings = [ orderings = [
ir.AffineMap.get_permutation([0, 1]), ir.AffineMap.get_permutation([0, 1]),
ir.AffineMap.get_permutation([1, 0]) ir.AffineMap.get_permutation([1, 0])
] ]
bitwidths = [8, 16, 32, 64] bitwidths = [8, 16, 32, 64]
compiler = sparse_compiler.SparseCompiler( compiler = sparse_compiler.SparseCompiler(
options='', opt_level=2, shared_libs=[support_lib]) options='', opt_level=2, shared_libs=[support_lib])
for level in levels: for level in levels:
for ordering in orderings: for ordering in orderings:
for bwidth in bitwidths: for bwidth in bitwidths:
attr = st.EncodingAttr.get(level, ordering, bwidth, bwidth) attr = st.EncodingAttr.get(level, ordering, bwidth, bwidth)
build_compile_and_run_output(attr, compiler) build_compile_and_run_output(attr, compiler)
count = count + 1 count = count + 1
# CHECK: Passed 16 tests # CHECK: Passed 16 tests
print('Passed', count, 'tests') print('Passed', count, 'tests')
if __name__ == '__main__': if __name__ == '__main__':
main() main()

mlir/test/Integration/Dialect/SparseTensor/python/test_stress.py

# RUN: SUPPORT_LIB=%mlir_runner_utils_dir/libmlir_c_runner_utils%shlibext \ # RUN: SUPPORT_LIB=%mlir_lib_dir/libmlir_c_runner_utils%shlibext \
# RUN: %PYTHON %s | FileCheck %s # RUN: %PYTHON %s | FileCheck %s
import ctypes import ctypes
import errno import errno
import itertools import itertools
import os import os
import sys import sys
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mlir/test/Integration/Dialect/SparseTensor/taco/test_MTTKRP.py

# RUN: SUPPORTLIB=%mlir_runner_utils_dir/libmlir_c_runner_utils%shlibext %PYTHON %s | FileCheck %s # RUN: SUPPORTLIB=%mlir_lib_dir/libmlir_c_runner_utils%shlibext %PYTHON %s | FileCheck %s
import numpy as np import numpy as np
import os import os
import sys import sys
import tempfile import tempfile
_SCRIPT_PATH = os.path.dirname(os.path.abspath(__file__)) _SCRIPT_PATH = os.path.dirname(os.path.abspath(__file__))
sys.path.append(_SCRIPT_PATH) sys.path.append(_SCRIPT_PATH)
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mlir/test/Integration/Dialect/SparseTensor/taco/test_SDDMM.py

# RUN: SUPPORTLIB=%mlir_runner_utils_dir/libmlir_c_runner_utils%shlibext %PYTHON %s | FileCheck %s # RUN: SUPPORTLIB=%mlir_lib_dir/libmlir_c_runner_utils%shlibext %PYTHON %s | FileCheck %s
import filecmp import filecmp
import numpy as np import numpy as np
import os import os
import sys import sys
import tempfile import tempfile
_SCRIPT_PATH = os.path.dirname(os.path.abspath(__file__)) _SCRIPT_PATH = os.path.dirname(os.path.abspath(__file__))
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mlir/test/Integration/Dialect/SparseTensor/taco/test_SpMM.py

# RUN: SUPPORTLIB=%mlir_runner_utils_dir/libmlir_c_runner_utils%shlibext %PYTHON %s | FileCheck %s # RUN: SUPPORTLIB=%mlir_lib_dir/libmlir_c_runner_utils%shlibext %PYTHON %s | FileCheck %s
import filecmp import filecmp
import numpy as np import numpy as np
import os import os
import sys import sys
import tempfile import tempfile
_SCRIPT_PATH = os.path.dirname(os.path.abspath(__file__)) _SCRIPT_PATH = os.path.dirname(os.path.abspath(__file__))
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mlir/test/Integration/Dialect/SparseTensor/taco/test_SpMV.py

# RUN: SUPPORTLIB=%mlir_runner_utils_dir/libmlir_c_runner_utils%shlibext %PYTHON %s | FileCheck %s # RUN: SUPPORTLIB=%mlir_lib_dir/libmlir_c_runner_utils%shlibext %PYTHON %s | FileCheck %s
import numpy as np import numpy as np
import os import os
import sys import sys
import tempfile import tempfile
_SCRIPT_PATH = os.path.dirname(os.path.abspath(__file__)) _SCRIPT_PATH = os.path.dirname(os.path.abspath(__file__))
sys.path.append(_SCRIPT_PATH) sys.path.append(_SCRIPT_PATH)
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mlir/test/Integration/Dialect/SparseTensor/taco/test_Tensor.py

# RUN: SUPPORTLIB=%mlir_runner_utils_dir/libmlir_c_runner_utils%shlibext %PYTHON %s | FileCheck %s # RUN: SUPPORTLIB=%mlir_lib_dir/libmlir_c_runner_utils%shlibext %PYTHON %s | FileCheck %s
import filecmp import filecmp
import numpy as np import numpy as np
import os import os
import sys import sys
import tempfile import tempfile
_SCRIPT_PATH = os.path.dirname(os.path.abspath(__file__)) _SCRIPT_PATH = os.path.dirname(os.path.abspath(__file__))
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mlir/test/Integration/Dialect/SparseTensor/taco/test_scalar_tensor_algebra.py

# RUN: SUPPORTLIB=%mlir_runner_utils_dir/libmlir_c_runner_utils%shlibext %PYTHON %s | FileCheck %s # RUN: SUPPORTLIB=%mlir_lib_dir/libmlir_c_runner_utils%shlibext %PYTHON %s | FileCheck %s
import numpy as np import numpy as np
import os import os
import sys import sys
_SCRIPT_PATH = os.path.dirname(os.path.abspath(__file__)) _SCRIPT_PATH = os.path.dirname(os.path.abspath(__file__))
sys.path.append(_SCRIPT_PATH) sys.path.append(_SCRIPT_PATH)
from tools import mlir_pytaco_api as pt from tools import mlir_pytaco_api as pt
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mlir/test/Integration/Dialect/SparseTensor/taco/test_simple_tensor_algebra.py

# RUN: SUPPORTLIB=%mlir_runner_utils_dir/libmlir_c_runner_utils%shlibext %PYTHON %s | FileCheck %s # RUN: SUPPORTLIB=%mlir_lib_dir/libmlir_c_runner_utils%shlibext %PYTHON %s | FileCheck %s
import numpy as np import numpy as np
import os import os
import sys import sys
_SCRIPT_PATH = os.path.dirname(os.path.abspath(__file__)) _SCRIPT_PATH = os.path.dirname(os.path.abspath(__file__))
sys.path.append(_SCRIPT_PATH) sys.path.append(_SCRIPT_PATH)
from tools import mlir_pytaco_api as pt from tools import mlir_pytaco_api as pt
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mlir/test/Integration/Dialect/SparseTensor/taco/test_tensor_complex.py

# RUN: SUPPORTLIB=%mlir_runner_utils_dir/libmlir_c_runner_utils%shlibext %PYTHON %s | FileCheck %s # RUN: SUPPORTLIB=%mlir_lib_dir/libmlir_c_runner_utils%shlibext %PYTHON %s | FileCheck %s
import numpy as np import numpy as np
import os import os
import sys import sys
_SCRIPT_PATH = os.path.dirname(os.path.abspath(__file__)) _SCRIPT_PATH = os.path.dirname(os.path.abspath(__file__))
sys.path.append(_SCRIPT_PATH) sys.path.append(_SCRIPT_PATH)
from tools import mlir_pytaco_api as pt from tools import mlir_pytaco_api as pt
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mlir/test/Integration/Dialect/SparseTensor/taco/test_tensor_types.py

# RUN: SUPPORTLIB=%mlir_runner_utils_dir/libmlir_c_runner_utils%shlibext %PYTHON %s | FileCheck %s # RUN: SUPPORTLIB=%mlir_lib_dir/libmlir_c_runner_utils%shlibext %PYTHON %s | FileCheck %s
import numpy as np import numpy as np
import os import os
import sys import sys
_SCRIPT_PATH = os.path.dirname(os.path.abspath(__file__)) _SCRIPT_PATH = os.path.dirname(os.path.abspath(__file__))
sys.path.append(_SCRIPT_PATH) sys.path.append(_SCRIPT_PATH)
from tools import mlir_pytaco_api as pt from tools import mlir_pytaco_api as pt
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mlir/test/Integration/Dialect/SparseTensor/taco/test_tensor_unary_ops.py

# RUN: SUPPORTLIB=%mlir_runner_utils_dir/libmlir_c_runner_utils%shlibext %PYTHON %s | FileCheck %s # RUN: SUPPORTLIB=%mlir_lib_dir/libmlir_c_runner_utils%shlibext %PYTHON %s | FileCheck %s
import numpy as np import numpy as np
import os import os
import sys import sys
_SCRIPT_PATH = os.path.dirname(os.path.abspath(__file__)) _SCRIPT_PATH = os.path.dirname(os.path.abspath(__file__))
sys.path.append(_SCRIPT_PATH) sys.path.append(_SCRIPT_PATH)
from tools import mlir_pytaco_api as pt from tools import mlir_pytaco_api as pt
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mlir/test/Integration/Dialect/SparseTensor/taco/test_true_dense_tensor_algebra.py

# RUN: SUPPORTLIB=%mlir_runner_utils_dir/libmlir_c_runner_utils%shlibext %PYTHON %s | FileCheck %s # RUN: SUPPORTLIB=%mlir_lib_dir/libmlir_c_runner_utils%shlibext %PYTHON %s | FileCheck %s
import numpy as np import numpy as np
import os import os
import sys import sys
_SCRIPT_PATH = os.path.dirname(os.path.abspath(__file__)) _SCRIPT_PATH = os.path.dirname(os.path.abspath(__file__))
sys.path.append(_SCRIPT_PATH) sys.path.append(_SCRIPT_PATH)
from tools import mlir_pytaco_api as pt from tools import mlir_pytaco_api as pt
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mlir/test/Integration/Dialect/SparseTensor/taco/unit_test_tensor_core.py

# RUN: SUPPORTLIB=%mlir_runner_utils_dir/libmlir_c_runner_utils%shlibext %PYTHON %s | FileCheck %s # RUN: SUPPORTLIB=%mlir_lib_dir/libmlir_c_runner_utils%shlibext %PYTHON %s | FileCheck %s
from string import Template from string import Template
import numpy as np import numpy as np
import os import os
import sys import sys
import tempfile import tempfile
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mlir/test/Integration/Dialect/SparseTensor/taco/unit_test_tensor_io.py

# RUN: SUPPORTLIB=%mlir_runner_utils_dir/libmlir_c_runner_utils%shlibext %PYTHON %s | FileCheck %s # RUN: SUPPORTLIB=%mlir_lib_dir/libmlir_c_runner_utils%shlibext %PYTHON %s | FileCheck %s
from string import Template from string import Template
import numpy as np import numpy as np
import os import os
import sys import sys
import tempfile import tempfile
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mlir/test/Integration/Dialect/SparseTensor/taco/unit_test_tensor_utils.py

# RUN: SUPPORTLIB=%mlir_runner_utils_dir/libmlir_c_runner_utils%shlibext %PYTHON %s | FileCheck %s # RUN: SUPPORTLIB=%mlir_lib_dir/libmlir_c_runner_utils%shlibext %PYTHON %s | FileCheck %s
from typing import Sequence from typing import Sequence
import dataclasses import dataclasses
import numpy as np import numpy as np
import os import os
import sys import sys
import tempfile import tempfile
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mlir/test/Integration/Dialect/Standard/CPU/test-ceil-floor-pos-neg.mlir

// RUN: mlir-opt %s -pass-pipeline="func.func(convert-vector-to-scf,lower-affine,convert-scf-to-cf,memref-expand,arith-expand),convert-vector-to-llvm,convert-memref-to-llvm,convert-func-to-llvm,reconcile-unrealized-casts" | \ // RUN: mlir-opt %s -pass-pipeline="func.func(convert-vector-to-scf,lower-affine,convert-scf-to-cf,memref-expand,arith-expand),convert-vector-to-llvm,convert-memref-to-llvm,convert-func-to-llvm,reconcile-unrealized-casts" | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \ // RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
func.func @transfer_read_2d(%A : memref<40xi32>, %base1: index) { func.func @transfer_read_2d(%A : memref<40xi32>, %base1: index) {
%i42 = arith.constant -42: i32 %i42 = arith.constant -42: i32
%f = vector.transfer_read %A[%base1], %i42 %f = vector.transfer_read %A[%base1], %i42
{permutation_map = affine_map<(d0) -> (d0)>} : {permutation_map = affine_map<(d0) -> (d0)>} :
memref<40xi32>, vector<40xi32> memref<40xi32>, vector<40xi32>
vector.print %f: vector<40xi32> vector.print %f: vector<40xi32>
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mlir/test/Integration/Dialect/Standard/CPU/test_subview.mlir

// RUN: mlir-opt %s -convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \ // RUN: mlir-opt %s -convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \
// RUN: mlir-cpu-runner -e main -entry-point-result=void \ // RUN: mlir-cpu-runner -e main -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext | FileCheck %s // RUN: -shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext | FileCheck %s
memref.global "private" constant @__constant_5x3xf32 : memref<5x3xf32> = memref.global "private" constant @__constant_5x3xf32 : memref<5x3xf32> =
dense<[[0.0, 1.0, 2.0], dense<[[0.0, 1.0, 2.0],
[3.0, 4.0, 5.0], [3.0, 4.0, 5.0],
[6.0, 7.0, 8.0], [6.0, 7.0, 8.0],
[9.0, 10.0, 11.0], [9.0, 10.0, 11.0],
[12.0, 13.0, 14.0]]> [12.0, 13.0, 14.0]]>
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mlir/test/Integration/Dialect/Vector/CPU/AMX/test-mulf-full.mlir

// RUN: mlir-opt %s -convert-vector-to-scf -lower-affine -convert-scf-to-cf \ // RUN: mlir-opt %s -convert-vector-to-scf -lower-affine -convert-scf-to-cf \
// RUN: -arith-bufferize -convert-vector-to-llvm="enable-amx" \ // RUN: -arith-bufferize -convert-vector-to-llvm="enable-amx" \
// RUN: -convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \ // RUN: -convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \
// RUN: mlir-translate -mlir-to-llvmir | \ // RUN: mlir-translate -mlir-to-llvmir | \
// RUN: %lli --entry-function=entry --mattr="+amx-tile,+amx-int8,+amx-bf16" \ // RUN: %lli --entry-function=entry --mattr="+amx-tile,+amx-int8,+amx-bf16" \
// RUN: --dlopen=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: --dlopen=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
// Note: To run this test, your CPU must support AMX. // Note: To run this test, your CPU must support AMX.
// Multiply full size tiles into zero destination. // Multiply full size tiles into zero destination.
func.func @kernel(%arg0: memref<16x32xbf16>, func.func @kernel(%arg0: memref<16x32xbf16>,
%arg1: memref<16x32xbf16>, %arg1: memref<16x32xbf16>,
%arg2: memref<16x16xf32>) { %arg2: memref<16x16xf32>) {
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mlir/test/Integration/Dialect/Vector/CPU/AMX/test-mulf.mlir

// RUN: mlir-opt %s -convert-vector-to-scf -lower-affine -convert-scf-to-cf -convert-vector-to-llvm="enable-amx" -convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \ // RUN: mlir-opt %s -convert-vector-to-scf -lower-affine -convert-scf-to-cf -convert-vector-to-llvm="enable-amx" -convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \
// RUN: mlir-translate -mlir-to-llvmir | \ // RUN: mlir-translate -mlir-to-llvmir | \
// RUN: %lli --entry-function=entry --mattr="+amx-tile,+amx-int8,+amx-bf16" --dlopen=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: %lli --entry-function=entry --mattr="+amx-tile,+amx-int8,+amx-bf16" --dlopen=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
// Note: To run this test, your CPU must support AMX. // Note: To run this test, your CPU must support AMX.
// Multiply into zeroed destination. // Multiply into zeroed destination.
func.func @kernel1(%arg0: memref<2x4xbf16>, func.func @kernel1(%arg0: memref<2x4xbf16>,
%arg1: memref<2x4xbf16>, %arg1: memref<2x4xbf16>,
%arg2: memref<2x2xf32>) { %arg2: memref<2x2xf32>) {
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mlir/test/Integration/Dialect/Vector/CPU/AMX/test-muli-ext.mlir

// RUN: mlir-opt %s -convert-vector-to-scf -lower-affine -convert-scf-to-cf -convert-vector-to-llvm="enable-amx" -convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \ // RUN: mlir-opt %s -convert-vector-to-scf -lower-affine -convert-scf-to-cf -convert-vector-to-llvm="enable-amx" -convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \
// RUN: mlir-translate -mlir-to-llvmir | \ // RUN: mlir-translate -mlir-to-llvmir | \
// RUN: %lli --entry-function=entry --mattr="+amx-tile,+amx-int8,+amx-bf16" --dlopen=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: %lli --entry-function=entry --mattr="+amx-tile,+amx-int8,+amx-bf16" --dlopen=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
// Note: To run this test, your CPU must support AMX. // Note: To run this test, your CPU must support AMX.
func.func @print(%arg0: memref<16x4xi32>) { func.func @print(%arg0: memref<16x4xi32>) {
%iu = arith.constant -1: i32 %iu = arith.constant -1: i32
%c0 = arith.constant 0: index %c0 = arith.constant 0: index
%c1 = arith.constant 1: index %c1 = arith.constant 1: index
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mlir/test/Integration/Dialect/Vector/CPU/AMX/test-muli-full.mlir

// RUN: mlir-opt %s -convert-vector-to-scf -lower-affine -convert-scf-to-cf \ // RUN: mlir-opt %s -convert-vector-to-scf -lower-affine -convert-scf-to-cf \
// RUN: -arith-bufferize -convert-vector-to-llvm="enable-amx" \ // RUN: -arith-bufferize -convert-vector-to-llvm="enable-amx" \
// RUN: -convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \ // RUN: -convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \
// RUN: mlir-translate -mlir-to-llvmir | \ // RUN: mlir-translate -mlir-to-llvmir | \
// RUN: %lli --entry-function=entry --mattr="+amx-tile,+amx-int8,+amx-bf16" \ // RUN: %lli --entry-function=entry --mattr="+amx-tile,+amx-int8,+amx-bf16" \
// RUN: --dlopen=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: --dlopen=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
// Note: To run this test, your CPU must support AMX. // Note: To run this test, your CPU must support AMX.
// Multiply full size tiles into zero destination. // Multiply full size tiles into zero destination.
func.func @kernel(%arg0: memref<16x64xi8>, func.func @kernel(%arg0: memref<16x64xi8>,
%arg1: memref<16x64xi8>, %arg1: memref<16x64xi8>,
%arg2: memref<16x16xi32>) { %arg2: memref<16x16xi32>) {
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mlir/test/Integration/Dialect/Vector/CPU/AMX/test-muli.mlir

// RUN: mlir-opt %s -convert-vector-to-scf -lower-affine -convert-scf-to-cf -convert-vector-to-llvm="enable-amx" -convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \ // RUN: mlir-opt %s -convert-vector-to-scf -lower-affine -convert-scf-to-cf -convert-vector-to-llvm="enable-amx" -convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \
// RUN: mlir-translate -mlir-to-llvmir | \ // RUN: mlir-translate -mlir-to-llvmir | \
// RUN: %lli --entry-function=entry --mattr="+amx-tile,+amx-int8,+amx-bf16" --dlopen=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: %lli --entry-function=entry --mattr="+amx-tile,+amx-int8,+amx-bf16" --dlopen=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
// Note: To run this test, your CPU must support AMX. // Note: To run this test, your CPU must support AMX.
// Multiply into zeroed destination. // Multiply into zeroed destination.
func.func @kernel1(%arg0: memref<2x8xi8>, func.func @kernel1(%arg0: memref<2x8xi8>,
%arg1: memref<2x8xi8>, %arg1: memref<2x8xi8>,
%arg2: memref<2x2xi32>) { %arg2: memref<2x2xi32>) {
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mlir/test/Integration/Dialect/Vector/CPU/AMX/test-tilezero-block.mlir

// RUN: mlir-opt %s -convert-vector-to-scf -lower-affine -convert-scf-to-cf -convert-vector-to-llvm="enable-amx" -convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \ // RUN: mlir-opt %s -convert-vector-to-scf -lower-affine -convert-scf-to-cf -convert-vector-to-llvm="enable-amx" -convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \
// RUN: mlir-translate -mlir-to-llvmir | \ // RUN: mlir-translate -mlir-to-llvmir | \
// RUN: %lli --entry-function=entry --mattr="+amx-tile,+amx-int8,+amx-bf16" --dlopen=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: %lli --entry-function=entry --mattr="+amx-tile,+amx-int8,+amx-bf16" --dlopen=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
// Note: To run this test, your CPU must support AMX. // Note: To run this test, your CPU must support AMX.
func.func @print(%arg0: memref<4x32xf32>) { func.func @print(%arg0: memref<4x32xf32>) {
%fu = arith.constant -1.0: f32 %fu = arith.constant -1.0: f32
%c0 = arith.constant 0: index %c0 = arith.constant 0: index
%c1 = arith.constant 1: index %c1 = arith.constant 1: index
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mlir/test/Integration/Dialect/Vector/CPU/AMX/test-tilezero.mlir

// RUN: mlir-opt %s -convert-vector-to-scf -lower-affine -convert-scf-to-cf -convert-vector-to-llvm="enable-amx" -convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \ // RUN: mlir-opt %s -convert-vector-to-scf -lower-affine -convert-scf-to-cf -convert-vector-to-llvm="enable-amx" -convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \
// RUN: mlir-translate -mlir-to-llvmir | \ // RUN: mlir-translate -mlir-to-llvmir | \
// RUN: %lli --entry-function=entry --mattr="+amx-tile,+amx-int8,+amx-bf16" --dlopen=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: %lli --entry-function=entry --mattr="+amx-tile,+amx-int8,+amx-bf16" --dlopen=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
// Note: To run this test, your CPU must support AMX. // Note: To run this test, your CPU must support AMX.
func.func @tilezero(%arg0: memref<?x?xi32>, %i: index, %j: index) { func.func @tilezero(%arg0: memref<?x?xi32>, %i: index, %j: index) {
%1 = amx.tile_zero : vector<16x16xi32> %1 = amx.tile_zero : vector<16x16xi32>
amx.tile_store %arg0[%i, %j], %1 : memref<?x?xi32>, vector<16x16xi32> amx.tile_store %arg0[%i, %j], %1 : memref<?x?xi32>, vector<16x16xi32>
return return
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mlir/test/Integration/Dialect/Vector/CPU/X86Vector/test-dot.mlir

// RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm="enable-x86vector" -convert-func-to-llvm -reconcile-unrealized-casts | \ // RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm="enable-x86vector" -convert-func-to-llvm -reconcile-unrealized-casts | \
// RUN: mlir-translate --mlir-to-llvmir | \ // RUN: mlir-translate --mlir-to-llvmir | \
// RUN: %lli --entry-function=entry --mattr="avx" --dlopen=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: %lli --entry-function=entry --mattr="avx" --dlopen=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
func.func @entry() -> i32 { func.func @entry() -> i32 {
%i0 = arith.constant 0 : i32 %i0 = arith.constant 0 : i32
%i4 = arith.constant 4 : i32 %i4 = arith.constant 4 : i32
%a = arith.constant dense<[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0]> : vector<8xf32> %a = arith.constant dense<[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0]> : vector<8xf32>
%b = arith.constant dense<[9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0]> : vector<8xf32> %b = arith.constant dense<[9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0]> : vector<8xf32>
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mlir/test/Integration/Dialect/Vector/CPU/X86Vector/test-inline-asm-vector-avx512.mlir

// RUN: mlir-opt %s -convert-linalg-to-loops -convert-vector-to-scf='full-unroll=true' -lower-affine -convert-scf-to-cf -convert-vector-to-llvm -convert-memref-to-llvm -convert-func-to-llvm='use-bare-ptr-memref-call-conv=1' -convert-arith-to-llvm -reconcile-unrealized-casts |\ // RUN: mlir-opt %s -convert-linalg-to-loops -convert-vector-to-scf='full-unroll=true' -lower-affine -convert-scf-to-cf -convert-vector-to-llvm -convert-memref-to-llvm -convert-func-to-llvm='use-bare-ptr-memref-call-conv=1' -convert-arith-to-llvm -reconcile-unrealized-casts |\
// RUN: mlir-translate --mlir-to-llvmir |\ // RUN: mlir-translate --mlir-to-llvmir |\
// RUN: %lli --entry-function=entry --mattr="avx512f" --dlopen=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext |\ // RUN: %lli --entry-function=entry --mattr="avx512f" --dlopen=%mlir_lib_dir/libmlir_c_runner_utils%shlibext |\
// RUN: FileCheck %s // RUN: FileCheck %s
module { module {
// an array of 16 i32 of values [0..15] // an array of 16 i32 of values [0..15]
llvm.mlir.global private @const16( llvm.mlir.global private @const16(
dense<[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]> : tensor<16 x i32>) dense<[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]> : tensor<16 x i32>)
: !llvm.array<16 x i32> : !llvm.array<16 x i32>
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mlir/test/Integration/Dialect/Vector/CPU/X86Vector/test-mask-compress.mlir

// RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm="enable-x86vector" -convert-func-to-llvm -reconcile-unrealized-casts | \ // RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm="enable-x86vector" -convert-func-to-llvm -reconcile-unrealized-casts | \
// RUN: mlir-translate --mlir-to-llvmir | \ // RUN: mlir-translate --mlir-to-llvmir | \
// RUN: %lli --entry-function=entry --mattr="avx512bw" --dlopen=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: %lli --entry-function=entry --mattr="avx512bw" --dlopen=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
func.func @entry() -> i32 { func.func @entry() -> i32 {
%i0 = arith.constant 0 : i32 %i0 = arith.constant 0 : i32
%a = arith.constant dense<[1., 0., 0., 2., 4., 3., 5., 7., 8., 1., 5., 5., 3., 1., 0., 7.]> : vector<16xf32> %a = arith.constant dense<[1., 0., 0., 2., 4., 3., 5., 7., 8., 1., 5., 5., 3., 1., 0., 7.]> : vector<16xf32>
%k = arith.constant dense<[1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0]> : vector<16xi1> %k = arith.constant dense<[1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0]> : vector<16xi1>
%r1 = x86vector.avx512.mask.compress %k, %a : vector<16xf32> %r1 = x86vector.avx512.mask.compress %k, %a : vector<16xf32>
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mlir/test/Integration/Dialect/Vector/CPU/X86Vector/test-rsqrt.mlir

// RUN: mlir-opt %s -convert-vector-to-llvm="enable-x86vector" -convert-func-to-llvm | \ // RUN: mlir-opt %s -convert-vector-to-llvm="enable-x86vector" -convert-func-to-llvm | \
// RUN: mlir-translate --mlir-to-llvmir | \ // RUN: mlir-translate --mlir-to-llvmir | \
// RUN: %lli --entry-function=entry --mattr="avx" --dlopen=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: %lli --entry-function=entry --mattr="avx" --dlopen=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
func.func @entry() -> i32 { func.func @entry() -> i32 {
%i0 = arith.constant 0 : i32 %i0 = arith.constant 0 : i32
%v = arith.constant dense<[0.125, 0.25, 0.5, 1.0, 2.0, 4.0, 8.0, 16.0]> : vector<8xf32> %v = arith.constant dense<[0.125, 0.25, 0.5, 1.0, 2.0, 4.0, 8.0, 16.0]> : vector<8xf32>
%r = x86vector.avx.rsqrt %v : vector<8xf32> %r = x86vector.avx.rsqrt %v : vector<8xf32>
// `rsqrt` may produce slightly different results on Intel and AMD machines: accept both results here. // `rsqrt` may produce slightly different results on Intel and AMD machines: accept both results here.
// CHECK: {{( 2.82[0-9]*, 1.99[0-9]*, 1.41[0-9]*, 0.99[0-9]*, 0.70[0-9]*, 0.49[0-9]*, 0.35[0-9]*, 0.24[0-9]* )}} // CHECK: {{( 2.82[0-9]*, 1.99[0-9]*, 1.41[0-9]*, 0.99[0-9]*, 0.70[0-9]*, 0.49[0-9]*, 0.35[0-9]*, 0.24[0-9]* )}}
vector.print %r : vector<8xf32> vector.print %r : vector<8xf32>
return %i0 : i32 return %i0 : i32
} }

mlir/test/Integration/Dialect/Vector/CPU/X86Vector/test-sparse-dot-product.mlir

// RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm="enable-x86vector" -convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \ // RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm="enable-x86vector" -convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \
// RUN: mlir-translate --mlir-to-llvmir | \ // RUN: mlir-translate --mlir-to-llvmir | \
// RUN: %lli --entry-function=entry --mattr="avx512bw,avx512vp2intersect" --dlopen=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: %lli --entry-function=entry --mattr="avx512bw,avx512vp2intersect" --dlopen=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
// This test shows how to implement a sparse vector-vector dot product with // This test shows how to implement a sparse vector-vector dot product with
// AVX512. It uses vp2intersect, mask.compress and vector.contract to compute // AVX512. It uses vp2intersect, mask.compress and vector.contract to compute
// the dot product of two sparse HW vectors of 8 float64 elements ("segment"). // the dot product of two sparse HW vectors of 8 float64 elements ("segment").
// Each sparse vector is represented by an index memref (A or C) and by a data // Each sparse vector is represented by an index memref (A or C) and by a data
// memref (B or D), containing M or N elements. // memref (B or D), containing M or N elements.
// //
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mlir/test/Integration/Dialect/Vector/CPU/X86Vector/test-vp2intersect-i32.mlir

// RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm="enable-x86vector" -convert-func-to-llvm -reconcile-unrealized-casts | \ // RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm="enable-x86vector" -convert-func-to-llvm -reconcile-unrealized-casts | \
// RUN: mlir-translate --mlir-to-llvmir | \ // RUN: mlir-translate --mlir-to-llvmir | \
// RUN: %lli --entry-function=entry --mattr="avx512bw,avx512vp2intersect" --dlopen=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: %lli --entry-function=entry --mattr="avx512bw,avx512vp2intersect" --dlopen=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
// Note: To run this test, your CPU must support AVX512 vp2intersect. // Note: To run this test, your CPU must support AVX512 vp2intersect.
func.func @entry() -> i32 { func.func @entry() -> i32 {
%i0 = arith.constant 0 : i32 %i0 = arith.constant 0 : i32
%i1 = arith.constant 1: i32 %i1 = arith.constant 1: i32
%i2 = arith.constant 2: i32 %i2 = arith.constant 2: i32
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mlir/test/Integration/Dialect/Vector/CPU/test-0-d-vectors.mlir

// RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm -convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \ // RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm -convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \ // RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
func.func @extract_element_0d(%a: vector<f32>) { func.func @extract_element_0d(%a: vector<f32>) {
%1 = vector.extractelement %a[] : vector<f32> %1 = vector.extractelement %a[] : vector<f32>
// CHECK: 42 // CHECK: 42
vector.print %1: f32 vector.print %1: f32
return return
} }
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mlir/test/Integration/Dialect/Vector/CPU/test-broadcast.mlir

// RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \ // RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \ // RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
func.func @entry() { func.func @entry() {
%i = arith.constant 2147483647: i32 %i = arith.constant 2147483647: i32
%l = arith.constant 9223372036854775807 : i64 %l = arith.constant 9223372036854775807 : i64
%f0 = arith.constant 0.0: f32 %f0 = arith.constant 0.0: f32
%f1 = arith.constant 1.0: f32 %f1 = arith.constant 1.0: f32
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mlir/test/Integration/Dialect/Vector/CPU/test-compress.mlir

// RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm -convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \ // RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm -convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \ // RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
func.func @compress16(%base: memref<?xf32>, func.func @compress16(%base: memref<?xf32>,
%mask: vector<16xi1>, %value: vector<16xf32>) { %mask: vector<16xi1>, %value: vector<16xf32>) {
%c0 = arith.constant 0: index %c0 = arith.constant 0: index
vector.compressstore %base[%c0], %mask, %value vector.compressstore %base[%c0], %mask, %value
: memref<?xf32>, vector<16xi1>, vector<16xf32> : memref<?xf32>, vector<16xi1>, vector<16xf32>
return return
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mlir/test/Integration/Dialect/Vector/CPU/test-constant-mask.mlir

// RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \ // RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \ // RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
func.func @entry() { func.func @entry() {
%0 = vector.constant_mask [4] : vector<8xi1> %0 = vector.constant_mask [4] : vector<8xi1>
vector.print %0 : vector<8xi1> vector.print %0 : vector<8xi1>
// CHECK: ( 1, 1, 1, 1, 0, 0, 0, 0 ) // CHECK: ( 1, 1, 1, 1, 0, 0, 0, 0 )
%1 = vector.constant_mask [1, 3] : vector<4x4xi1> %1 = vector.constant_mask [1, 3] : vector<4x4xi1>
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mlir/test/Integration/Dialect/Vector/CPU/test-contraction.mlir

// RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \ // RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \ // RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
#dotp_accesses = [ #dotp_accesses = [
affine_map<(i) -> (i)>, affine_map<(i) -> (i)>,
affine_map<(i) -> (i)>, affine_map<(i) -> (i)>,
affine_map<(i) -> ()> affine_map<(i) -> ()>
] ]
#dotp_trait = { #dotp_trait = {
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mlir/test/Integration/Dialect/Vector/CPU/test-create-mask-v4i1.mlir

// RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \ // RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \ // RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
// NOTE: This is similar to test-create-mask.mlir, but with a different length, // NOTE: This is similar to test-create-mask.mlir, but with a different length,
// because the v4i1 vector specifically exposed bugs in the LLVM backend. // because the v4i1 vector specifically exposed bugs in the LLVM backend.
func.func @entry() { func.func @entry() {
%c0 = arith.constant 0 : index %c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index %c1 = arith.constant 1 : index
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mlir/test/Integration/Dialect/Vector/CPU/test-create-mask.mlir

// RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \ // RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \ // RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
func.func @entry() { func.func @entry() {
%cneg1 = arith.constant -1 : index %cneg1 = arith.constant -1 : index
%c0 = arith.constant 0 : index %c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index %c1 = arith.constant 1 : index
%c2 = arith.constant 2 : index %c2 = arith.constant 2 : index
%c3 = arith.constant 3 : index %c3 = arith.constant 3 : index
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mlir/test/Integration/Dialect/Vector/CPU/test-expand.mlir

// RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm -convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \ // RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm -convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \ // RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
func.func @expand16(%base: memref<?xf32>, func.func @expand16(%base: memref<?xf32>,
%mask: vector<16xi1>, %mask: vector<16xi1>,
%pass_thru: vector<16xf32>) -> vector<16xf32> { %pass_thru: vector<16xf32>) -> vector<16xf32> {
%c0 = arith.constant 0: index %c0 = arith.constant 0: index
%e = vector.expandload %base[%c0], %mask, %pass_thru %e = vector.expandload %base[%c0], %mask, %pass_thru
: memref<?xf32>, vector<16xi1>, vector<16xf32> into vector<16xf32> : memref<?xf32>, vector<16xi1>, vector<16xf32> into vector<16xf32>
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mlir/test/Integration/Dialect/Vector/CPU/test-extract-strided-slice.mlir

// RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \ // RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \ // RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
func.func @entry() { func.func @entry() {
%f0 = arith.constant 0.0: f32 %f0 = arith.constant 0.0: f32
%f1 = arith.constant 1.0: f32 %f1 = arith.constant 1.0: f32
%f2 = arith.constant 2.0: f32 %f2 = arith.constant 2.0: f32
%f3 = arith.constant 3.0: f32 %f3 = arith.constant 3.0: f32
%f4 = arith.constant 4.0: f32 %f4 = arith.constant 4.0: f32
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mlir/test/Integration/Dialect/Vector/CPU/test-flat-transpose-col.mlir

// RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \ // RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \ // RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -O0 -enable-matrix -matrix-allow-contract -matrix-default-layout=column-major \ // RUN: -O0 -enable-matrix -matrix-allow-contract -matrix-default-layout=column-major \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
func.func @entry() { func.func @entry() {
%f0 = arith.constant 0.0: f64 %f0 = arith.constant 0.0: f64
%f1 = arith.constant 1.0: f64 %f1 = arith.constant 1.0: f64
%f2 = arith.constant 2.0: f64 %f2 = arith.constant 2.0: f64
%f3 = arith.constant 3.0: f64 %f3 = arith.constant 3.0: f64
%f4 = arith.constant 4.0: f64 %f4 = arith.constant 4.0: f64
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mlir/test/Integration/Dialect/Vector/CPU/test-flat-transpose-row.mlir

// RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \ // RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \ // RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -O0 -enable-matrix -matrix-allow-contract -matrix-default-layout=row-major \ // RUN: -O0 -enable-matrix -matrix-allow-contract -matrix-default-layout=row-major \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
func.func @entry() { func.func @entry() {
%f0 = arith.constant 0.0: f64 %f0 = arith.constant 0.0: f64
%f1 = arith.constant 1.0: f64 %f1 = arith.constant 1.0: f64
%f2 = arith.constant 2.0: f64 %f2 = arith.constant 2.0: f64
%f3 = arith.constant 3.0: f64 %f3 = arith.constant 3.0: f64
%f4 = arith.constant 4.0: f64 %f4 = arith.constant 4.0: f64
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mlir/test/Integration/Dialect/Vector/CPU/test-fma.mlir

// RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \ // RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \ // RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
func.func @entry() { func.func @entry() {
%f1 = arith.constant 1.0: f32 %f1 = arith.constant 1.0: f32
%f3 = arith.constant 3.0: f32 %f3 = arith.constant 3.0: f32
%f7 = arith.constant 7.0: f32 %f7 = arith.constant 7.0: f32
%v1 = vector.broadcast %f1 : f32 to vector<8xf32> %v1 = vector.broadcast %f1 : f32 to vector<8xf32>
%v3 = vector.broadcast %f3 : f32 to vector<8xf32> %v3 = vector.broadcast %f3 : f32 to vector<8xf32>
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mlir/test/Integration/Dialect/Vector/CPU/test-gather.mlir

// RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm -convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \ // RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm -convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \ // RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
func.func @gather8(%base: memref<?xf32>, %indices: vector<8xi32>, func.func @gather8(%base: memref<?xf32>, %indices: vector<8xi32>,
%mask: vector<8xi1>, %pass_thru: vector<8xf32>) -> vector<8xf32> { %mask: vector<8xi1>, %pass_thru: vector<8xf32>) -> vector<8xf32> {
%c0 = arith.constant 0: index %c0 = arith.constant 0: index
%g = vector.gather %base[%c0][%indices], %mask, %pass_thru %g = vector.gather %base[%c0][%indices], %mask, %pass_thru
: memref<?xf32>, vector<8xi32>, vector<8xi1>, vector<8xf32> into vector<8xf32> : memref<?xf32>, vector<8xi32>, vector<8xi1>, vector<8xf32> into vector<8xf32>
return %g : vector<8xf32> return %g : vector<8xf32>
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mlir/test/Integration/Dialect/Vector/CPU/test-index-vectors.mlir

// RUN: mlir-opt %s -convert-vector-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \ // RUN: mlir-opt %s -convert-vector-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \ // RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
func.func @entry() { func.func @entry() {
%c0 = arith.constant dense<[0, 1, 2, 3]>: vector<4xindex> %c0 = arith.constant dense<[0, 1, 2, 3]>: vector<4xindex>
%c1 = arith.constant dense<[0, 1]>: vector<2xindex> %c1 = arith.constant dense<[0, 1]>: vector<2xindex>
%c2 = arith.constant 2 : index %c2 = arith.constant 2 : index
%v1 = vector.broadcast %c0 : vector<4xindex> to vector<2x4xindex> %v1 = vector.broadcast %c0 : vector<4xindex> to vector<2x4xindex>
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mlir/test/Integration/Dialect/Vector/CPU/test-insert-strided-slice.mlir

// RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \ // RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \ // RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
func.func @entry() { func.func @entry() {
%f1 = arith.constant 1.0: f32 %f1 = arith.constant 1.0: f32
%f2 = arith.constant 2.0: f32 %f2 = arith.constant 2.0: f32
%f3 = arith.constant 3.0: f32 %f3 = arith.constant 3.0: f32
%f4 = arith.constant 4.0: f32 %f4 = arith.constant 4.0: f32
%v1 = vector.broadcast %f1 : f32 to vector<4xf32> %v1 = vector.broadcast %f1 : f32 to vector<4xf32>
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mlir/test/Integration/Dialect/Vector/CPU/test-maskedload.mlir

// RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm -convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \ // RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm -convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \ // RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
func.func @maskedload16(%base: memref<?xf32>, %mask: vector<16xi1>, func.func @maskedload16(%base: memref<?xf32>, %mask: vector<16xi1>,
%pass_thru: vector<16xf32>) -> vector<16xf32> { %pass_thru: vector<16xf32>) -> vector<16xf32> {
%c0 = arith.constant 0: index %c0 = arith.constant 0: index
%ld = vector.maskedload %base[%c0], %mask, %pass_thru %ld = vector.maskedload %base[%c0], %mask, %pass_thru
: memref<?xf32>, vector<16xi1>, vector<16xf32> into vector<16xf32> : memref<?xf32>, vector<16xi1>, vector<16xf32> into vector<16xf32>
return %ld : vector<16xf32> return %ld : vector<16xf32>
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mlir/test/Integration/Dialect/Vector/CPU/test-maskedstore.mlir

// RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm -convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \ // RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm -convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \ // RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
func.func @maskedstore16(%base: memref<?xf32>, func.func @maskedstore16(%base: memref<?xf32>,
%mask: vector<16xi1>, %value: vector<16xf32>) { %mask: vector<16xi1>, %value: vector<16xf32>) {
%c0 = arith.constant 0: index %c0 = arith.constant 0: index
vector.maskedstore %base[%c0], %mask, %value vector.maskedstore %base[%c0], %mask, %value
: memref<?xf32>, vector<16xi1>, vector<16xf32> : memref<?xf32>, vector<16xi1>, vector<16xf32>
return return
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mlir/test/Integration/Dialect/Vector/CPU/test-matrix-multiply-col.mlir

// RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \ // RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \ // RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -O0 -enable-matrix -matrix-allow-contract -matrix-default-layout=column-major \ // RUN: -O0 -enable-matrix -matrix-allow-contract -matrix-default-layout=column-major \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
func.func @entry() { func.func @entry() {
%f0 = arith.constant 0.0: f64 %f0 = arith.constant 0.0: f64
%f1 = arith.constant 1.0: f64 %f1 = arith.constant 1.0: f64
%f2 = arith.constant 2.0: f64 %f2 = arith.constant 2.0: f64
%f3 = arith.constant 3.0: f64 %f3 = arith.constant 3.0: f64
%f4 = arith.constant 4.0: f64 %f4 = arith.constant 4.0: f64
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mlir/test/Integration/Dialect/Vector/CPU/test-matrix-multiply-row.mlir

// RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \ // RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \ // RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -O0 -enable-matrix -matrix-allow-contract -matrix-default-layout=row-major \ // RUN: -O0 -enable-matrix -matrix-allow-contract -matrix-default-layout=row-major \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
func.func @entry() { func.func @entry() {
%f0 = arith.constant 0.0: f64 %f0 = arith.constant 0.0: f64
%f1 = arith.constant 1.0: f64 %f1 = arith.constant 1.0: f64
%f2 = arith.constant 2.0: f64 %f2 = arith.constant 2.0: f64
%f3 = arith.constant 3.0: f64 %f3 = arith.constant 3.0: f64
%f4 = arith.constant 4.0: f64 %f4 = arith.constant 4.0: f64
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mlir/test/Integration/Dialect/Vector/CPU/test-outerproduct-f32.mlir

// RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \ // RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \ // RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
!vector_type_A = vector<8xf32> !vector_type_A = vector<8xf32>
!vector_type_B = vector<8xf32> !vector_type_B = vector<8xf32>
!vector_type_C = vector<8x8xf32> !vector_type_C = vector<8x8xf32>
!vector_type_X = vector<2xf32> !vector_type_X = vector<2xf32>
!vector_type_Y = vector<3xf32> !vector_type_Y = vector<3xf32>
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mlir/test/Integration/Dialect/Vector/CPU/test-outerproduct-i64.mlir

// RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \ // RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \ // RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
!vector_type_A = vector<8xi64> !vector_type_A = vector<8xi64>
!vector_type_B = vector<8xi64> !vector_type_B = vector<8xi64>
!vector_type_C = vector<8x8xi64> !vector_type_C = vector<8x8xi64>
!vector_type_X = vector<2xi64> !vector_type_X = vector<2xi64>
!vector_type_Y = vector<3xi64> !vector_type_Y = vector<3xi64>
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mlir/test/Integration/Dialect/Vector/CPU/test-print-int.mlir

// RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \ // RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \ // RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
// //
// Test various signless, signed, unsigned integer types. // Test various signless, signed, unsigned integer types.
// //
func.func @entry() { func.func @entry() {
%0 = arith.constant dense<[true, false, -1, 0, 1]> : vector<5xi1> %0 = arith.constant dense<[true, false, -1, 0, 1]> : vector<5xi1>
vector.print %0 : vector<5xi1> vector.print %0 : vector<5xi1>
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mlir/test/Integration/Dialect/Vector/CPU/test-reductions-f32-reassoc.mlir

// RUN: mlir-opt %s -convert-scf-to-cf \ // RUN: mlir-opt %s -convert-scf-to-cf \
// RUN: -convert-vector-to-llvm='reassociate-fp-reductions' \ // RUN: -convert-vector-to-llvm='reassociate-fp-reductions' \
// RUN: -convert-func-to-llvm -reconcile-unrealized-casts | \ // RUN: -convert-func-to-llvm -reconcile-unrealized-casts | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \ // RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
func.func @entry() { func.func @entry() {
// Construct test vector, numerically very stable. // Construct test vector, numerically very stable.
%f1 = arith.constant 1.0: f32 %f1 = arith.constant 1.0: f32
%f2 = arith.constant 2.0: f32 %f2 = arith.constant 2.0: f32
%f3 = arith.constant 3.0: f32 %f3 = arith.constant 3.0: f32
%v0 = vector.broadcast %f1 : f32 to vector<64xf32> %v0 = vector.broadcast %f1 : f32 to vector<64xf32>
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mlir/test/Integration/Dialect/Vector/CPU/test-reductions-f32.mlir

// RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \ // RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \ // RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
func.func @entry() { func.func @entry() {
// Construct test vector. // Construct test vector.
%f1 = arith.constant 1.5: f32 %f1 = arith.constant 1.5: f32
%f2 = arith.constant 2.0: f32 %f2 = arith.constant 2.0: f32
%f3 = arith.constant 3.0: f32 %f3 = arith.constant 3.0: f32
%f4 = arith.constant 4.0: f32 %f4 = arith.constant 4.0: f32
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mlir/test/Integration/Dialect/Vector/CPU/test-reductions-f64-reassoc.mlir

// RUN: mlir-opt %s -convert-scf-to-cf \ // RUN: mlir-opt %s -convert-scf-to-cf \
// RUN: -convert-vector-to-llvm='reassociate-fp-reductions' \ // RUN: -convert-vector-to-llvm='reassociate-fp-reductions' \
// RUN: -convert-func-to-llvm -reconcile-unrealized-casts | \ // RUN: -convert-func-to-llvm -reconcile-unrealized-casts | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \ // RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
func.func @entry() { func.func @entry() {
// Construct test vector, numerically very stable. // Construct test vector, numerically very stable.
%f1 = arith.constant 1.0: f64 %f1 = arith.constant 1.0: f64
%f2 = arith.constant 2.0: f64 %f2 = arith.constant 2.0: f64
%f3 = arith.constant 3.0: f64 %f3 = arith.constant 3.0: f64
%v0 = vector.broadcast %f1 : f64 to vector<64xf64> %v0 = vector.broadcast %f1 : f64 to vector<64xf64>
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mlir/test/Integration/Dialect/Vector/CPU/test-reductions-f64.mlir

// RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \ // RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \ // RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
func.func @entry() { func.func @entry() {
// Construct test vector. // Construct test vector.
%f1 = arith.constant 1.5: f64 %f1 = arith.constant 1.5: f64
%f2 = arith.constant 2.0: f64 %f2 = arith.constant 2.0: f64
%f3 = arith.constant 3.0: f64 %f3 = arith.constant 3.0: f64
%f4 = arith.constant 4.0: f64 %f4 = arith.constant 4.0: f64
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mlir/test/Integration/Dialect/Vector/CPU/test-reductions-i32.mlir

// RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \ // RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \ // RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
func.func @entry() { func.func @entry() {
// Construct test vector. // Construct test vector.
%i1 = arith.constant 1: i32 %i1 = arith.constant 1: i32
%i2 = arith.constant 2: i32 %i2 = arith.constant 2: i32
%i3 = arith.constant 3: i32 %i3 = arith.constant 3: i32
%i4 = arith.constant 4: i32 %i4 = arith.constant 4: i32
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mlir/test/Integration/Dialect/Vector/CPU/test-reductions-i4.mlir

// RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \ // RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \ // RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
func.func @entry() { func.func @entry() {
%v = arith.constant dense<[-8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]> : vector<24xi4> %v = arith.constant dense<[-8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]> : vector<24xi4>
vector.print %v : vector<24xi4> vector.print %v : vector<24xi4>
// //
// Test vector: // Test vector:
// //
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mlir/test/Integration/Dialect/Vector/CPU/test-reductions-i64.mlir

// RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \ // RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \ // RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
func.func @entry() { func.func @entry() {
// Construct test vector. // Construct test vector.
%i1 = arith.constant 1: i64 %i1 = arith.constant 1: i64
%i2 = arith.constant 2: i64 %i2 = arith.constant 2: i64
%i3 = arith.constant 3: i64 %i3 = arith.constant 3: i64
%i4 = arith.constant 4: i64 %i4 = arith.constant 4: i64
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mlir/test/Integration/Dialect/Vector/CPU/test-reductions-si4.mlir

// RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \ // RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \ // RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
func.func @entry() { func.func @entry() {
%v0 = arith.constant dense<[-8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7]> : vector<16xi4> %v0 = arith.constant dense<[-8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7]> : vector<16xi4>
%v = vector.bitcast %v0 : vector<16xi4> to vector<16xsi4> %v = vector.bitcast %v0 : vector<16xi4> to vector<16xsi4>
vector.print %v : vector<16xsi4> vector.print %v : vector<16xsi4>
// //
// Test vector: // Test vector:
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mlir/test/Integration/Dialect/Vector/CPU/test-reductions-ui4.mlir

// RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \ // RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \ // RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
func.func @entry() { func.func @entry() {
%v0 = arith.constant dense<[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]> : vector<16xi4> %v0 = arith.constant dense<[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]> : vector<16xi4>
%v = vector.bitcast %v0 : vector<16xi4> to vector<16xui4> %v = vector.bitcast %v0 : vector<16xi4> to vector<16xui4>
vector.print %v : vector<16xui4> vector.print %v : vector<16xui4>
// //
// Test vector: // Test vector:
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mlir/test/Integration/Dialect/Vector/CPU/test-scan.mlir

// RUN: mlir-opt %s -test-vector-scan-lowering -convert-scf-to-cf -convert-vector-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \ // RUN: mlir-opt %s -test-vector-scan-lowering -convert-scf-to-cf -convert-vector-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \ // RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
func.func @entry() { func.func @entry() {
%f1 = arith.constant 1.0: f32 %f1 = arith.constant 1.0: f32
%f2 = arith.constant 2.0: f32 %f2 = arith.constant 2.0: f32
%f3 = arith.constant 3.0: f32 %f3 = arith.constant 3.0: f32
%f4 = arith.constant 4.0: f32 %f4 = arith.constant 4.0: f32
%f5 = arith.constant 5.0: f32 %f5 = arith.constant 5.0: f32
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mlir/test/Integration/Dialect/Vector/CPU/test-scatter.mlir

// RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm -convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \ // RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm -convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \ // RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
func.func @scatter8(%base: memref<?xf32>, func.func @scatter8(%base: memref<?xf32>,
%indices: vector<8xi32>, %indices: vector<8xi32>,
%mask: vector<8xi1>, %value: vector<8xf32>) { %mask: vector<8xi1>, %value: vector<8xf32>) {
%c0 = arith.constant 0: index %c0 = arith.constant 0: index
vector.scatter %base[%c0][%indices], %mask, %value vector.scatter %base[%c0][%indices], %mask, %value
: memref<?xf32>, vector<8xi32>, vector<8xi1>, vector<8xf32> : memref<?xf32>, vector<8xi32>, vector<8xi1>, vector<8xf32>
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mlir/test/Integration/Dialect/Vector/CPU/test-shape-cast.mlir

// RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \ // RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \ // RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
func.func @entry() { func.func @entry() {
%f1 = arith.constant 1.0: f32 %f1 = arith.constant 1.0: f32
%f2 = arith.constant 2.0: f32 %f2 = arith.constant 2.0: f32
%f3 = arith.constant 3.0: f32 %f3 = arith.constant 3.0: f32
%f4 = arith.constant 4.0: f32 %f4 = arith.constant 4.0: f32
%f5 = arith.constant 5.0: f32 %f5 = arith.constant 5.0: f32
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mlir/test/Integration/Dialect/Vector/CPU/test-shuffle.mlir

// RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \ // RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \ // RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
func.func @entry() { func.func @entry() {
%f1 = arith.constant 1.0: f32 %f1 = arith.constant 1.0: f32
%f2 = arith.constant 2.0: f32 %f2 = arith.constant 2.0: f32
%v1 = vector.broadcast %f1 : f32 to vector<2x4xf32> %v1 = vector.broadcast %f1 : f32 to vector<2x4xf32>
%v2 = vector.broadcast %f2 : f32 to vector<2x4xf32> %v2 = vector.broadcast %f2 : f32 to vector<2x4xf32>
vector.print %v1 : vector<2x4xf32> vector.print %v1 : vector<2x4xf32>
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mlir/test/Integration/Dialect/Vector/CPU/test-sparse-dot-matvec.mlir

// RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm -convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \ // RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm -convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \ // RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
// Illustrates an 8x8 Sparse Matrix x Vector implemented with only operations // Illustrates an 8x8 Sparse Matrix x Vector implemented with only operations
// of the vector dialect (and some std/scf). Essentially, this example performs // of the vector dialect (and some std/scf). Essentially, this example performs
// the following multiplication: // the following multiplication:
// //
// 0 1 2 3 4 5 6 7 // 0 1 2 3 4 5 6 7
// +------------------------+ // +------------------------+
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mlir/test/Integration/Dialect/Vector/CPU/test-sparse-saxpy-jagged-matvec.mlir

// RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm -convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \ // RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm -convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \ // RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
// Illustrates an 8x8 Sparse Matrix x Vector implemented with only operations // Illustrates an 8x8 Sparse Matrix x Vector implemented with only operations
// of the vector dialect (and some std/scf). Essentially, this example performs // of the vector dialect (and some std/scf). Essentially, this example performs
// the following multiplication: // the following multiplication:
// //
// 0 1 2 3 4 5 6 7 // 0 1 2 3 4 5 6 7
// +------------------------+ // +------------------------+
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mlir/test/Integration/Dialect/Vector/CPU/test-transfer-read-1d.mlir

// RUN: mlir-opt %s -pass-pipeline="func.func(convert-vector-to-scf,lower-affine,convert-scf-to-cf),convert-vector-to-llvm,convert-memref-to-llvm,convert-func-to-llvm,reconcile-unrealized-casts" | \ // RUN: mlir-opt %s -pass-pipeline="func.func(convert-vector-to-scf,lower-affine,convert-scf-to-cf),convert-vector-to-llvm,convert-memref-to-llvm,convert-func-to-llvm,reconcile-unrealized-casts" | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \ // RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
// RUN: mlir-opt %s -pass-pipeline="func.func(convert-vector-to-scf{lower-permutation-maps=true},lower-affine,convert-scf-to-cf),convert-vector-to-llvm,convert-memref-to-llvm,convert-func-to-llvm,reconcile-unrealized-casts" | \ // RUN: mlir-opt %s -pass-pipeline="func.func(convert-vector-to-scf{lower-permutation-maps=true},lower-affine,convert-scf-to-cf),convert-vector-to-llvm,convert-memref-to-llvm,convert-func-to-llvm,reconcile-unrealized-casts" | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \ // RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
// RUN: mlir-opt %s -pass-pipeline="func.func(convert-vector-to-scf{full-unroll=true},lower-affine,convert-scf-to-cf),convert-vector-to-llvm,convert-memref-to-llvm,convert-func-to-llvm,reconcile-unrealized-casts" | \ // RUN: mlir-opt %s -pass-pipeline="func.func(convert-vector-to-scf{full-unroll=true},lower-affine,convert-scf-to-cf),convert-vector-to-llvm,convert-memref-to-llvm,convert-func-to-llvm,reconcile-unrealized-casts" | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \ // RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
// RUN: mlir-opt %s -pass-pipeline="func.func(convert-vector-to-scf{full-unroll=true lower-permutation-maps=true},lower-affine,convert-scf-to-cf),convert-vector-to-llvm,convert-memref-to-llvm,convert-func-to-llvm,reconcile-unrealized-casts" | \ // RUN: mlir-opt %s -pass-pipeline="func.func(convert-vector-to-scf{full-unroll=true lower-permutation-maps=true},lower-affine,convert-scf-to-cf),convert-vector-to-llvm,convert-memref-to-llvm,convert-func-to-llvm,reconcile-unrealized-casts" | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \ // RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
// Test for special cases of 1D vector transfer ops. // Test for special cases of 1D vector transfer ops.
memref.global "private" @gv : memref<5x6xf32> = memref.global "private" @gv : memref<5x6xf32> =
dense<[[0. , 1. , 2. , 3. , 4. , 5. ], dense<[[0. , 1. , 2. , 3. , 4. , 5. ],
[10., 11., 12., 13., 14., 15.], [10., 11., 12., 13., 14., 15.],
[20., 21., 22., 23., 24., 25.], [20., 21., 22., 23., 24., 25.],
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mlir/test/Integration/Dialect/Vector/CPU/test-transfer-read-2d.mlir

// RUN: mlir-opt %s -pass-pipeline="func.func(convert-vector-to-scf,lower-affine,convert-scf-to-cf),convert-vector-to-llvm,convert-memref-to-llvm,convert-func-to-llvm,reconcile-unrealized-casts" | \ // RUN: mlir-opt %s -pass-pipeline="func.func(convert-vector-to-scf,lower-affine,convert-scf-to-cf),convert-vector-to-llvm,convert-memref-to-llvm,convert-func-to-llvm,reconcile-unrealized-casts" | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \ // RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
// RUN: mlir-opt %s -pass-pipeline="func.func(convert-vector-to-scf{lower-permutation-maps=true},lower-affine,convert-scf-to-cf),convert-vector-to-llvm,convert-memref-to-llvm,convert-func-to-llvm,reconcile-unrealized-casts" | \ // RUN: mlir-opt %s -pass-pipeline="func.func(convert-vector-to-scf{lower-permutation-maps=true},lower-affine,convert-scf-to-cf),convert-vector-to-llvm,convert-memref-to-llvm,convert-func-to-llvm,reconcile-unrealized-casts" | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \ // RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
// RUN: mlir-opt %s -pass-pipeline="func.func(convert-vector-to-scf{full-unroll=true},lower-affine,convert-scf-to-cf),convert-vector-to-llvm,convert-memref-to-llvm,convert-func-to-llvm,reconcile-unrealized-casts" | \ // RUN: mlir-opt %s -pass-pipeline="func.func(convert-vector-to-scf{full-unroll=true},lower-affine,convert-scf-to-cf),convert-vector-to-llvm,convert-memref-to-llvm,convert-func-to-llvm,reconcile-unrealized-casts" | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \ // RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
// RUN: mlir-opt %s -pass-pipeline="func.func(convert-vector-to-scf{full-unroll=true lower-permutation-maps=true},lower-affine,convert-scf-to-cf),convert-vector-to-llvm,convert-memref-to-llvm,convert-func-to-llvm,reconcile-unrealized-casts" | \ // RUN: mlir-opt %s -pass-pipeline="func.func(convert-vector-to-scf{full-unroll=true lower-permutation-maps=true},lower-affine,convert-scf-to-cf),convert-vector-to-llvm,convert-memref-to-llvm,convert-func-to-llvm,reconcile-unrealized-casts" | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \ // RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
memref.global "private" @gv : memref<3x4xf32> = dense<[[0. , 1. , 2. , 3. ], memref.global "private" @gv : memref<3x4xf32> = dense<[[0. , 1. , 2. , 3. ],
[10., 11., 12., 13.], [10., 11., 12., 13.],
[20., 21., 22., 23.]]> [20., 21., 22., 23.]]>
// Vector load. // Vector load.
func.func @transfer_read_2d(%A : memref<?x?xf32>, %base1: index, %base2: index) { func.func @transfer_read_2d(%A : memref<?x?xf32>, %base1: index, %base2: index) {
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mlir/test/Integration/Dialect/Vector/CPU/test-transfer-read-3d.mlir

// RUN: mlir-opt %s -pass-pipeline="func.func(convert-vector-to-scf,lower-affine,convert-scf-to-cf),convert-vector-to-llvm,convert-memref-to-llvm,convert-func-to-llvm,reconcile-unrealized-casts" | \ // RUN: mlir-opt %s -pass-pipeline="func.func(convert-vector-to-scf,lower-affine,convert-scf-to-cf),convert-vector-to-llvm,convert-memref-to-llvm,convert-func-to-llvm,reconcile-unrealized-casts" | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \ // RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
// RUN: mlir-opt %s -pass-pipeline="func.func(convert-vector-to-scf{lower-permutation-maps=true},lower-affine,convert-scf-to-cf),convert-vector-to-llvm,convert-memref-to-llvm,convert-func-to-llvm,reconcile-unrealized-casts" | \ // RUN: mlir-opt %s -pass-pipeline="func.func(convert-vector-to-scf{lower-permutation-maps=true},lower-affine,convert-scf-to-cf),convert-vector-to-llvm,convert-memref-to-llvm,convert-func-to-llvm,reconcile-unrealized-casts" | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \ // RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
// RUN: mlir-opt %s -pass-pipeline="func.func(convert-vector-to-scf{full-unroll=true},lower-affine,convert-scf-to-cf),convert-vector-to-llvm,convert-memref-to-llvm,convert-func-to-llvm,reconcile-unrealized-casts" | \ // RUN: mlir-opt %s -pass-pipeline="func.func(convert-vector-to-scf{full-unroll=true},lower-affine,convert-scf-to-cf),convert-vector-to-llvm,convert-memref-to-llvm,convert-func-to-llvm,reconcile-unrealized-casts" | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \ // RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
// RUN: mlir-opt %s -pass-pipeline="func.func(convert-vector-to-scf{full-unroll=true lower-permutation-maps=true},lower-affine,convert-scf-to-cf),convert-vector-to-llvm,convert-memref-to-llvm,convert-func-to-llvm,reconcile-unrealized-casts" | \ // RUN: mlir-opt %s -pass-pipeline="func.func(convert-vector-to-scf{full-unroll=true lower-permutation-maps=true},lower-affine,convert-scf-to-cf),convert-vector-to-llvm,convert-memref-to-llvm,convert-func-to-llvm,reconcile-unrealized-casts" | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \ // RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
func.func @transfer_read_3d(%A : memref<?x?x?x?xf32>, func.func @transfer_read_3d(%A : memref<?x?x?x?xf32>,
%o: index, %a: index, %b: index, %c: index) { %o: index, %a: index, %b: index, %c: index) {
%fm42 = arith.constant -42.0: f32 %fm42 = arith.constant -42.0: f32
%f = vector.transfer_read %A[%o, %a, %b, %c], %fm42 %f = vector.transfer_read %A[%o, %a, %b, %c], %fm42
: memref<?x?x?x?xf32>, vector<2x5x3xf32> : memref<?x?x?x?xf32>, vector<2x5x3xf32>
vector.print %f: vector<2x5x3xf32> vector.print %f: vector<2x5x3xf32>
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mlir/test/Integration/Dialect/Vector/CPU/test-transfer-read.mlir

// RUN: mlir-opt %s -pass-pipeline="func.func(convert-vector-to-scf,lower-affine,convert-scf-to-cf),convert-vector-to-llvm,convert-memref-to-llvm,convert-func-to-llvm,reconcile-unrealized-casts" | \ // RUN: mlir-opt %s -pass-pipeline="func.func(convert-vector-to-scf,lower-affine,convert-scf-to-cf),convert-vector-to-llvm,convert-memref-to-llvm,convert-func-to-llvm,reconcile-unrealized-casts" | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \ // RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
// RUN: mlir-opt %s -pass-pipeline="func.func(convert-vector-to-scf{full-unroll=true},lower-affine,convert-scf-to-cf),convert-vector-to-llvm,convert-memref-to-llvm,convert-func-to-llvm,reconcile-unrealized-casts" | \ // RUN: mlir-opt %s -pass-pipeline="func.func(convert-vector-to-scf{full-unroll=true},lower-affine,convert-scf-to-cf),convert-vector-to-llvm,convert-memref-to-llvm,convert-func-to-llvm,reconcile-unrealized-casts" | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \ // RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
func.func @transfer_read_1d(%A : memref<?xf32>, %base: index) { func.func @transfer_read_1d(%A : memref<?xf32>, %base: index) {
%fm42 = arith.constant -42.0: f32 %fm42 = arith.constant -42.0: f32
%f = vector.transfer_read %A[%base], %fm42 %f = vector.transfer_read %A[%base], %fm42
{permutation_map = affine_map<(d0) -> (d0)>} : {permutation_map = affine_map<(d0) -> (d0)>} :
memref<?xf32>, vector<13xf32> memref<?xf32>, vector<13xf32>
vector.print %f: vector<13xf32> vector.print %f: vector<13xf32>
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mlir/test/Integration/Dialect/Vector/CPU/test-transfer-to-loops.mlir

// RUN: mlir-opt %s -pass-pipeline="func.func(convert-vector-to-scf,lower-affine,convert-scf-to-cf),convert-vector-to-llvm,convert-memref-to-llvm,convert-func-to-llvm,reconcile-unrealized-casts" | \ // RUN: mlir-opt %s -pass-pipeline="func.func(convert-vector-to-scf,lower-affine,convert-scf-to-cf),convert-vector-to-llvm,convert-memref-to-llvm,convert-func-to-llvm,reconcile-unrealized-casts" | \
// RUN: mlir-cpu-runner -e main -entry-point-result=void \ // RUN: mlir-cpu-runner -e main -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext,%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext,%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
// RUN: mlir-opt %s -pass-pipeline="func.func(convert-vector-to-scf{full-unroll=true},lower-affine,convert-scf-to-cf),convert-vector-to-llvm,convert-memref-to-llvm,convert-func-to-llvm,reconcile-unrealized-casts" | \ // RUN: mlir-opt %s -pass-pipeline="func.func(convert-vector-to-scf{full-unroll=true},lower-affine,convert-scf-to-cf),convert-vector-to-llvm,convert-memref-to-llvm,convert-func-to-llvm,reconcile-unrealized-casts" | \
// RUN: mlir-cpu-runner -e main -entry-point-result=void \ // RUN: mlir-cpu-runner -e main -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext,%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext,%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
#map0 = affine_map<(d0, d1) -> (d1, d0)> #map0 = affine_map<(d0, d1) -> (d1, d0)>
#map1 = affine_map<(d0, d1) -> (d1)> #map1 = affine_map<(d0, d1) -> (d1)>
func.func private @printMemrefF32(memref<*xf32>) func.func private @printMemrefF32(memref<*xf32>)
func.func @alloc_2d_filled_f32(%arg0: index, %arg1: index) -> memref<?x?xf32> { func.func @alloc_2d_filled_f32(%arg0: index, %arg1: index) -> memref<?x?xf32> {
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mlir/test/Integration/Dialect/Vector/CPU/test-transfer-write.mlir

// RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm -convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \ // RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm -convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \ // RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
func.func @transfer_write16_inbounds_1d(%A : memref<?xf32>, %base: index) { func.func @transfer_write16_inbounds_1d(%A : memref<?xf32>, %base: index) {
%f = arith.constant 16.0 : f32 %f = arith.constant 16.0 : f32
%v = vector.splat %f : vector<16xf32> %v = vector.splat %f : vector<16xf32>
vector.transfer_write %v, %A[%base] vector.transfer_write %v, %A[%base]
{permutation_map = affine_map<(d0) -> (d0)>, in_bounds = [true]} {permutation_map = affine_map<(d0) -> (d0)>, in_bounds = [true]}
: vector<16xf32>, memref<?xf32> : vector<16xf32>, memref<?xf32>
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mlir/test/Integration/Dialect/Vector/CPU/test-transpose.mlir

// RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \ // RUN: mlir-opt %s -convert-scf-to-cf -convert-vector-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \ // RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
func.func @entry() { func.func @entry() {
%f0 = arith.constant 0.0: f32 %f0 = arith.constant 0.0: f32
%f1 = arith.constant 1.0: f32 %f1 = arith.constant 1.0: f32
%f2 = arith.constant 2.0: f32 %f2 = arith.constant 2.0: f32
%f3 = arith.constant 3.0: f32 %f3 = arith.constant 3.0: f32
%f4 = arith.constant 4.0: f32 %f4 = arith.constant 4.0: f32
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mlir/test/Integration/Dialect/Vector/CPU/test-vector-distribute.mlir

// RUN: mlir-opt %s -pass-pipeline="func.func(test-vector-to-forloop,convert-vector-to-scf,lower-affine,convert-scf-to-cf),convert-vector-to-llvm,convert-memref-to-llvm,convert-func-to-llvm,reconcile-unrealized-casts" | \ // RUN: mlir-opt %s -pass-pipeline="func.func(test-vector-to-forloop,convert-vector-to-scf,lower-affine,convert-scf-to-cf),convert-vector-to-llvm,convert-memref-to-llvm,convert-func-to-llvm,reconcile-unrealized-casts" | \
// RUN: mlir-cpu-runner -e main -entry-point-result=void \ // RUN: mlir-cpu-runner -e main -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
// RUN: mlir-opt %s -pass-pipeline="func.func(convert-vector-to-scf,lower-affine,convert-scf-to-cf),convert-vector-to-llvm,convert-memref-to-llvm,convert-func-to-llvm,reconcile-unrealized-casts" | mlir-cpu-runner -e main \ // RUN: mlir-opt %s -pass-pipeline="func.func(convert-vector-to-scf,lower-affine,convert-scf-to-cf),convert-vector-to-llvm,convert-memref-to-llvm,convert-func-to-llvm,reconcile-unrealized-casts" | mlir-cpu-runner -e main \
// RUN: -entry-point-result=void \ // RUN: -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
// RUN: mlir-opt %s -pass-pipeline="func.func(test-vector-to-forloop)" | FileCheck %s -check-prefix=TRANSFORM // RUN: mlir-opt %s -pass-pipeline="func.func(test-vector-to-forloop)" | FileCheck %s -check-prefix=TRANSFORM
func.func private @printMemrefF32(memref<*xf32>) func.func private @printMemrefF32(memref<*xf32>)
func.func @alloc_1d_filled_inc_f32(%arg0: index, %arg1: f32) -> memref<?xf32> { func.func @alloc_1d_filled_inc_f32(%arg0: index, %arg1: f32) -> memref<?xf32> {
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mlir/test/Integration/Dialect/Vector/GPU/CUDA/test-reduction-distribute.mlir

// RUN: mlir-opt %s -test-vector-warp-distribute="hoist-uniform distribute-transfer-write propagate-distribution" -canonicalize |\ // RUN: mlir-opt %s -test-vector-warp-distribute="hoist-uniform distribute-transfer-write propagate-distribution" -canonicalize |\
// RUN: mlir-opt -test-vector-warp-distribute=rewrite-warp-ops-to-scf-if |\ // RUN: mlir-opt -test-vector-warp-distribute=rewrite-warp-ops-to-scf-if |\
// RUN: mlir-opt -lower-affine -convert-scf-to-cf -convert-vector-to-llvm \ // RUN: mlir-opt -lower-affine -convert-scf-to-cf -convert-vector-to-llvm \
// RUN: -convert-arith-to-llvm -gpu-kernel-outlining \ // RUN: -convert-arith-to-llvm -gpu-kernel-outlining \
// RUN: -pass-pipeline='gpu.module(strip-debuginfo,convert-gpu-to-nvvm,reconcile-unrealized-casts,gpu-to-cubin)' \ // RUN: -pass-pipeline='gpu.module(strip-debuginfo,convert-gpu-to-nvvm,reconcile-unrealized-casts,gpu-to-cubin)' \
// RUN: -gpu-to-llvm -reconcile-unrealized-casts |\ // RUN: -gpu-to-llvm -reconcile-unrealized-casts |\
// RUN: mlir-cpu-runner -e main -entry-point-result=void \ // RUN: mlir-cpu-runner -e main -entry-point-result=void \
// RUN: -shared-libs=%mlir_runner_utils_dir/libmlir_cuda_runtime%shlibext \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_cuda_runtime%shlibext \
// RUN: -shared-libs=%mlir_runner_utils_dir/libmlir_c_runner_utils%shlibext \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext \
// RUN: -shared-libs=%mlir_runner_utils_dir/libmlir_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
// Run a tiled reduction fused with an elementwise op. // Run a tiled reduction fused with an elementwise op.
func.func @gpu_func(%in: memref<1024xf32>, %out: memref<1xf32>) { func.func @gpu_func(%in: memref<1024xf32>, %out: memref<1xf32>) {
%c1 = arith.constant 1 : index %c1 = arith.constant 1 : index
%cst = arith.constant dense<100.0000> : vector<1xf32> %cst = arith.constant dense<100.0000> : vector<1xf32>
%cst_0 = arith.constant 0.000000e+00 : f32 %cst_0 = arith.constant 0.000000e+00 : f32
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mlir/test/Integration/Dialect/Vector/GPU/CUDA/test-warp-distribute.mlir

// Run the test cases without distributing ops to test default lowering. Run // Run the test cases without distributing ops to test default lowering. Run
// everything on the same thread. // everything on the same thread.
// RUN: mlir-opt %s -test-vector-warp-distribute=rewrite-warp-ops-to-scf-if -canonicalize | \ // RUN: mlir-opt %s -test-vector-warp-distribute=rewrite-warp-ops-to-scf-if -canonicalize | \
// RUN: mlir-opt -convert-scf-to-cf -convert-cf-to-llvm -convert-vector-to-llvm -convert-arith-to-llvm \ // RUN: mlir-opt -convert-scf-to-cf -convert-cf-to-llvm -convert-vector-to-llvm -convert-arith-to-llvm \
// RUN: -gpu-kernel-outlining \ // RUN: -gpu-kernel-outlining \
// RUN: -pass-pipeline='gpu.module(strip-debuginfo,convert-gpu-to-nvvm,reconcile-unrealized-casts,gpu-to-cubin)' \ // RUN: -pass-pipeline='gpu.module(strip-debuginfo,convert-gpu-to-nvvm,reconcile-unrealized-casts,gpu-to-cubin)' \
// RUN: -gpu-to-llvm -reconcile-unrealized-casts |\ // RUN: -gpu-to-llvm -reconcile-unrealized-casts |\
// RUN: mlir-cpu-runner -e main -entry-point-result=void \ // RUN: mlir-cpu-runner -e main -entry-point-result=void \
// RUN: -shared-libs=%mlir_runner_utils_dir/libmlir_cuda_runtime%shlibext \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_cuda_runtime%shlibext \
// RUN: -shared-libs=%mlir_runner_utils_dir/libmlir_c_runner_utils%shlibext \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext \
// RUN: -shared-libs=%mlir_runner_utils_dir/libmlir_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
// Run the same test cases with distribution and propagation. // Run the same test cases with distribution and propagation.
// RUN: mlir-opt %s -test-vector-warp-distribute="hoist-uniform distribute-transfer-write" \ // RUN: mlir-opt %s -test-vector-warp-distribute="hoist-uniform distribute-transfer-write" \
// RUN: -test-vector-warp-distribute=rewrite-warp-ops-to-scf-if -canonicalize | \ // RUN: -test-vector-warp-distribute=rewrite-warp-ops-to-scf-if -canonicalize | \
// RUN: mlir-opt -convert-scf-to-cf -convert-cf-to-llvm -convert-vector-to-llvm -convert-arith-to-llvm \ // RUN: mlir-opt -convert-scf-to-cf -convert-cf-to-llvm -convert-vector-to-llvm -convert-arith-to-llvm \
// RUN: -gpu-kernel-outlining \ // RUN: -gpu-kernel-outlining \
// RUN: -pass-pipeline='gpu.module(strip-debuginfo,convert-gpu-to-nvvm,reconcile-unrealized-casts,gpu-to-cubin)' \ // RUN: -pass-pipeline='gpu.module(strip-debuginfo,convert-gpu-to-nvvm,reconcile-unrealized-casts,gpu-to-cubin)' \
// RUN: -gpu-to-llvm -reconcile-unrealized-casts |\ // RUN: -gpu-to-llvm -reconcile-unrealized-casts |\
// RUN: mlir-cpu-runner -e main -entry-point-result=void \ // RUN: mlir-cpu-runner -e main -entry-point-result=void \
// RUN: -shared-libs=%mlir_runner_utils_dir/libmlir_cuda_runtime%shlibext \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_cuda_runtime%shlibext \
// RUN: -shared-libs=%mlir_runner_utils_dir/libmlir_c_runner_utils%shlibext \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext \
// RUN: -shared-libs=%mlir_runner_utils_dir/libmlir_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
// RUN: mlir-opt %s -test-vector-warp-distribute="hoist-uniform distribute-transfer-write propagate-distribution" \ // RUN: mlir-opt %s -test-vector-warp-distribute="hoist-uniform distribute-transfer-write propagate-distribution" \
// RUN: -test-vector-warp-distribute=rewrite-warp-ops-to-scf-if -canonicalize | \ // RUN: -test-vector-warp-distribute=rewrite-warp-ops-to-scf-if -canonicalize | \
// RUN: mlir-opt -convert-scf-to-cf -convert-cf-to-llvm -convert-vector-to-llvm -convert-arith-to-llvm \ // RUN: mlir-opt -convert-scf-to-cf -convert-cf-to-llvm -convert-vector-to-llvm -convert-arith-to-llvm \
// RUN: -gpu-kernel-outlining \ // RUN: -gpu-kernel-outlining \
// RUN: -pass-pipeline='gpu.module(strip-debuginfo,convert-gpu-to-nvvm,reconcile-unrealized-casts,gpu-to-cubin)' \ // RUN: -pass-pipeline='gpu.module(strip-debuginfo,convert-gpu-to-nvvm,reconcile-unrealized-casts,gpu-to-cubin)' \
// RUN: -gpu-to-llvm -reconcile-unrealized-casts |\ // RUN: -gpu-to-llvm -reconcile-unrealized-casts |\
// RUN: mlir-cpu-runner -e main -entry-point-result=void \ // RUN: mlir-cpu-runner -e main -entry-point-result=void \
// RUN: -shared-libs=%mlir_runner_utils_dir/libmlir_cuda_runtime%shlibext \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_cuda_runtime%shlibext \
// RUN: -shared-libs=%mlir_runner_utils_dir/libmlir_c_runner_utils%shlibext \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext \
// RUN: -shared-libs=%mlir_runner_utils_dir/libmlir_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
func.func @gpu_func(%arg1: memref<32xf32>, %arg2: memref<32xf32>) { func.func @gpu_func(%arg1: memref<32xf32>, %arg2: memref<32xf32>) {
%c1 = arith.constant 1 : index %c1 = arith.constant 1 : index
%c0 = arith.constant 0 : index %c0 = arith.constant 0 : index
%c32 = arith.constant 32 : index %c32 = arith.constant 32 : index
%cst = arith.constant 0.000000e+00 : f32 %cst = arith.constant 0.000000e+00 : f32
gpu.launch blocks(%arg3, %arg4, %arg5) gpu.launch blocks(%arg3, %arg4, %arg5)
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mlir/test/Integration/GPU/CUDA/TensorCore/wmma-matmul-f16.mlir

// RUN: mlir-opt %s \ // RUN: mlir-opt %s \
// RUN: -gpu-kernel-outlining \ // RUN: -gpu-kernel-outlining \
// RUN: -pass-pipeline='gpu.module(strip-debuginfo,convert-gpu-to-nvvm,gpu-to-cubin{chip=sm_70})' \ // RUN: -pass-pipeline='gpu.module(strip-debuginfo,convert-gpu-to-nvvm,gpu-to-cubin{chip=sm_70})' \
// RUN: --convert-scf-to-cf -gpu-to-llvm \ // RUN: --convert-scf-to-cf -gpu-to-llvm \
// RUN: | mlir-cpu-runner \ // RUN: | mlir-cpu-runner \
// RUN: --shared-libs=%linalg_test_lib_dir/libmlir_cuda_runtime%shlibext \ // RUN: --shared-libs=%mlir_lib_dir/libmlir_cuda_runtime%shlibext \
// RUN: --shared-libs=%linalg_test_lib_dir/libmlir_runner_utils%shlibext \ // RUN: --shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext \
// RUN: --entry-point-result=void \ // RUN: --entry-point-result=void \
// RUN: | FileCheck %s // RUN: | FileCheck %s
// Test case to check the working of Tensor cores on Nvidia GPUs. The kernel has already // Test case to check the working of Tensor cores on Nvidia GPUs. The kernel has already
// been outlined to prevent crashing due to introduction of an empty basic block by --gpu- // been outlined to prevent crashing due to introduction of an empty basic block by --gpu-
// kernel-outling. // kernel-outling.
func.func @main() { func.func @main() {
%0 = memref.alloc() : memref<16x16xf16> %0 = memref.alloc() : memref<16x16xf16>
%22 = memref.alloc() : memref<16x16xf16> %22 = memref.alloc() : memref<16x16xf16>
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mlir/test/Integration/GPU/CUDA/TensorCore/wmma-matmul-f32.mlir

// RUN: mlir-opt %s \ // RUN: mlir-opt %s \
// RUN: -gpu-kernel-outlining \ // RUN: -gpu-kernel-outlining \
// RUN: -pass-pipeline='gpu.module(strip-debuginfo,convert-gpu-to-nvvm,gpu-to-cubin{chip=sm_70})' \ // RUN: -pass-pipeline='gpu.module(strip-debuginfo,convert-gpu-to-nvvm,gpu-to-cubin{chip=sm_70})' \
// RUN: --convert-scf-to-cf -gpu-to-llvm \ // RUN: --convert-scf-to-cf -gpu-to-llvm \
// RUN: | mlir-cpu-runner \ // RUN: | mlir-cpu-runner \
// RUN: --shared-libs=%linalg_test_lib_dir/libmlir_cuda_runtime%shlibext \ // RUN: --shared-libs=%mlir_lib_dir/libmlir_cuda_runtime%shlibext \
// RUN: --shared-libs=%linalg_test_lib_dir/libmlir_runner_utils%shlibext \ // RUN: --shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext \
// RUN: --entry-point-result=void \ // RUN: --entry-point-result=void \
// RUN: | FileCheck %s // RUN: | FileCheck %s
func.func @main() { func.func @main() {
%0 = memref.alloc() : memref<16x16xf16> %0 = memref.alloc() : memref<16x16xf16>
%22 = memref.alloc() : memref<16x16xf32> %22 = memref.alloc() : memref<16x16xf32>
%1 = memref.alloc() : memref<16x16xf32> %1 = memref.alloc() : memref<16x16xf32>
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mlir/test/Integration/GPU/CUDA/all-reduce-and.mlir

// RUN: mlir-opt %s \ // RUN: mlir-opt %s \
// RUN: -gpu-kernel-outlining \ // RUN: -gpu-kernel-outlining \
// RUN: -pass-pipeline='gpu.module(strip-debuginfo,convert-gpu-to-nvvm,gpu-to-cubin)' \ // RUN: -pass-pipeline='gpu.module(strip-debuginfo,convert-gpu-to-nvvm,gpu-to-cubin)' \
// RUN: -gpu-to-llvm \ // RUN: -gpu-to-llvm \
// RUN: | mlir-cpu-runner \ // RUN: | mlir-cpu-runner \
// RUN: --shared-libs=%linalg_test_lib_dir/libmlir_cuda_runtime%shlibext \ // RUN: --shared-libs=%mlir_lib_dir/libmlir_cuda_runtime%shlibext \
// RUN: --shared-libs=%linalg_test_lib_dir/libmlir_runner_utils%shlibext \ // RUN: --shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext \
// RUN: --entry-point-result=void \ // RUN: --entry-point-result=void \
// RUN: | FileCheck %s // RUN: | FileCheck %s
func.func @main() { func.func @main() {
%data = memref.alloc() : memref<2x6xi32> %data = memref.alloc() : memref<2x6xi32>
%sum = memref.alloc() : memref<2xi32> %sum = memref.alloc() : memref<2xi32>
%cst0 = arith.constant 0 : i32 %cst0 = arith.constant 0 : i32
%cst1 = arith.constant 1 : i32 %cst1 = arith.constant 1 : i32
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mlir/test/Integration/GPU/CUDA/all-reduce-max.mlir

// RUN: mlir-opt %s \ // RUN: mlir-opt %s \
// RUN: -gpu-kernel-outlining \ // RUN: -gpu-kernel-outlining \
// RUN: -pass-pipeline='gpu.module(strip-debuginfo,convert-gpu-to-nvvm,gpu-to-cubin)' \ // RUN: -pass-pipeline='gpu.module(strip-debuginfo,convert-gpu-to-nvvm,gpu-to-cubin)' \
// RUN: -gpu-to-llvm \ // RUN: -gpu-to-llvm \
// RUN: | mlir-cpu-runner \ // RUN: | mlir-cpu-runner \
// RUN: --shared-libs=%linalg_test_lib_dir/libmlir_cuda_runtime%shlibext \ // RUN: --shared-libs=%mlir_lib_dir/libmlir_cuda_runtime%shlibext \
// RUN: --shared-libs=%linalg_test_lib_dir/libmlir_runner_utils%shlibext \ // RUN: --shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext \
// RUN: --entry-point-result=void \ // RUN: --entry-point-result=void \
// RUN: | FileCheck %s // RUN: | FileCheck %s
func.func @main() { func.func @main() {
%data = memref.alloc() : memref<2x6xi32> %data = memref.alloc() : memref<2x6xi32>
%sum = memref.alloc() : memref<2xi32> %sum = memref.alloc() : memref<2xi32>
%cst0 = arith.constant 0 : i32 %cst0 = arith.constant 0 : i32
%cst1 = arith.constant 1 : i32 %cst1 = arith.constant 1 : i32
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mlir/test/Integration/GPU/CUDA/all-reduce-min.mlir

// RUN: mlir-opt %s \ // RUN: mlir-opt %s \
// RUN: -gpu-kernel-outlining \ // RUN: -gpu-kernel-outlining \
// RUN: -pass-pipeline='gpu.module(strip-debuginfo,convert-gpu-to-nvvm,gpu-to-cubin)' \ // RUN: -pass-pipeline='gpu.module(strip-debuginfo,convert-gpu-to-nvvm,gpu-to-cubin)' \
// RUN: -gpu-to-llvm \ // RUN: -gpu-to-llvm \
// RUN: | mlir-cpu-runner \ // RUN: | mlir-cpu-runner \
// RUN: --shared-libs=%linalg_test_lib_dir/libmlir_cuda_runtime%shlibext \ // RUN: --shared-libs=%mlir_lib_dir/libmlir_cuda_runtime%shlibext \
// RUN: --shared-libs=%linalg_test_lib_dir/libmlir_runner_utils%shlibext \ // RUN: --shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext \
// RUN: --entry-point-result=void \ // RUN: --entry-point-result=void \
// RUN: | FileCheck %s // RUN: | FileCheck %s
func.func @main() { func.func @main() {
%data = memref.alloc() : memref<2x6xi32> %data = memref.alloc() : memref<2x6xi32>
%sum = memref.alloc() : memref<2xi32> %sum = memref.alloc() : memref<2xi32>
%cst0 = arith.constant 0 : i32 %cst0 = arith.constant 0 : i32
%cst1 = arith.constant 1 : i32 %cst1 = arith.constant 1 : i32
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mlir/test/Integration/GPU/CUDA/all-reduce-op.mlir

// RUN: mlir-opt %s \ // RUN: mlir-opt %s \
// RUN: -gpu-kernel-outlining \ // RUN: -gpu-kernel-outlining \
// RUN: -pass-pipeline='gpu.module(strip-debuginfo,convert-gpu-to-nvvm,gpu-to-cubin)' \ // RUN: -pass-pipeline='gpu.module(strip-debuginfo,convert-gpu-to-nvvm,gpu-to-cubin)' \
// RUN: -gpu-to-llvm \ // RUN: -gpu-to-llvm \
// RUN: | mlir-cpu-runner \ // RUN: | mlir-cpu-runner \
// RUN: --shared-libs=%linalg_test_lib_dir/libmlir_cuda_runtime%shlibext \ // RUN: --shared-libs=%mlir_lib_dir/libmlir_cuda_runtime%shlibext \
// RUN: --shared-libs=%linalg_test_lib_dir/libmlir_runner_utils%shlibext \ // RUN: --shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext \
// RUN: --entry-point-result=void \ // RUN: --entry-point-result=void \
// RUN: | FileCheck %s // RUN: | FileCheck %s
// CHECK-COUNT-8: [{{(5356, ){12}5356}}] // CHECK-COUNT-8: [{{(5356, ){12}5356}}]
func.func @main() { func.func @main() {
%arg = memref.alloc() : memref<2x4x13xf32> %arg = memref.alloc() : memref<2x4x13xf32>
%dst = memref.cast %arg : memref<2x4x13xf32> to memref<?x?x?xf32> %dst = memref.cast %arg : memref<2x4x13xf32> to memref<?x?x?xf32>
%c0 = arith.constant 0 : index %c0 = arith.constant 0 : index
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mlir/test/Integration/GPU/CUDA/all-reduce-or.mlir

// RUN: mlir-opt %s \ // RUN: mlir-opt %s \
// RUN: -gpu-kernel-outlining \ // RUN: -gpu-kernel-outlining \
// RUN: -pass-pipeline='gpu.module(strip-debuginfo,convert-gpu-to-nvvm,gpu-to-cubin)' \ // RUN: -pass-pipeline='gpu.module(strip-debuginfo,convert-gpu-to-nvvm,gpu-to-cubin)' \
// RUN: -gpu-to-llvm \ // RUN: -gpu-to-llvm \
// RUN: | mlir-cpu-runner \ // RUN: | mlir-cpu-runner \
// RUN: --shared-libs=%linalg_test_lib_dir/libmlir_cuda_runtime%shlibext \ // RUN: --shared-libs=%mlir_lib_dir/libmlir_cuda_runtime%shlibext \
// RUN: --shared-libs=%linalg_test_lib_dir/libmlir_runner_utils%shlibext \ // RUN: --shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext \
// RUN: --entry-point-result=void \ // RUN: --entry-point-result=void \
// RUN: | FileCheck %s // RUN: | FileCheck %s
func.func @main() { func.func @main() {
%data = memref.alloc() : memref<2x6xi32> %data = memref.alloc() : memref<2x6xi32>
%sum = memref.alloc() : memref<2xi32> %sum = memref.alloc() : memref<2xi32>
%cst0 = arith.constant 0 : i32 %cst0 = arith.constant 0 : i32
%cst1 = arith.constant 1 : i32 %cst1 = arith.constant 1 : i32
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mlir/test/Integration/GPU/CUDA/all-reduce-region.mlir

// RUN: mlir-opt %s \ // RUN: mlir-opt %s \
// RUN: -gpu-kernel-outlining \ // RUN: -gpu-kernel-outlining \
// RUN: -pass-pipeline='gpu.module(strip-debuginfo,convert-gpu-to-nvvm,gpu-to-cubin)' \ // RUN: -pass-pipeline='gpu.module(strip-debuginfo,convert-gpu-to-nvvm,gpu-to-cubin)' \
// RUN: -gpu-to-llvm \ // RUN: -gpu-to-llvm \
// RUN: | mlir-cpu-runner \ // RUN: | mlir-cpu-runner \
// RUN: --shared-libs=%linalg_test_lib_dir/libmlir_cuda_runtime%shlibext \ // RUN: --shared-libs=%mlir_lib_dir/libmlir_cuda_runtime%shlibext \
// RUN: --shared-libs=%linalg_test_lib_dir/libmlir_runner_utils%shlibext \ // RUN: --shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext \
// RUN: --entry-point-result=void \ // RUN: --entry-point-result=void \
// RUN: | FileCheck %s // RUN: | FileCheck %s
// CHECK: [{{(35, ){34}35}}] // CHECK: [{{(35, ){34}35}}]
func.func @main() { func.func @main() {
%arg = memref.alloc() : memref<35xf32> %arg = memref.alloc() : memref<35xf32>
%dst = memref.cast %arg : memref<35xf32> to memref<?xf32> %dst = memref.cast %arg : memref<35xf32> to memref<?xf32>
%one = arith.constant 1 : index %one = arith.constant 1 : index
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mlir/test/Integration/GPU/CUDA/all-reduce-xor.mlir

// RUN: mlir-opt %s \ // RUN: mlir-opt %s \
// RUN: -gpu-kernel-outlining \ // RUN: -gpu-kernel-outlining \
// RUN: -pass-pipeline='gpu.module(strip-debuginfo,convert-gpu-to-nvvm,gpu-to-cubin)' \ // RUN: -pass-pipeline='gpu.module(strip-debuginfo,convert-gpu-to-nvvm,gpu-to-cubin)' \
// RUN: -gpu-to-llvm \ // RUN: -gpu-to-llvm \
// RUN: | mlir-cpu-runner \ // RUN: | mlir-cpu-runner \
// RUN: --shared-libs=%linalg_test_lib_dir/libmlir_cuda_runtime%shlibext \ // RUN: --shared-libs=%mlir_lib_dir/libmlir_cuda_runtime%shlibext \
// RUN: --shared-libs=%linalg_test_lib_dir/libmlir_runner_utils%shlibext \ // RUN: --shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext \
// RUN: --entry-point-result=void \ // RUN: --entry-point-result=void \
// RUN: | FileCheck %s // RUN: | FileCheck %s
func.func @main() { func.func @main() {
%data = memref.alloc() : memref<2x6xi32> %data = memref.alloc() : memref<2x6xi32>
%sum = memref.alloc() : memref<2xi32> %sum = memref.alloc() : memref<2xi32>
%cst0 = arith.constant 0 : i32 %cst0 = arith.constant 0 : i32
%cst1 = arith.constant 1 : i32 %cst1 = arith.constant 1 : i32
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mlir/test/Integration/GPU/CUDA/async.mlir

// RUN: mlir-opt %s \ // RUN: mlir-opt %s \
// RUN: -gpu-kernel-outlining \ // RUN: -gpu-kernel-outlining \
// RUN: -pass-pipeline='gpu.module(strip-debuginfo,convert-gpu-to-nvvm,gpu-to-cubin)' \ // RUN: -pass-pipeline='gpu.module(strip-debuginfo,convert-gpu-to-nvvm,gpu-to-cubin)' \
// RUN: -gpu-async-region -gpu-to-llvm \ // RUN: -gpu-async-region -gpu-to-llvm \
// RUN: -async-to-async-runtime -async-runtime-ref-counting \ // RUN: -async-to-async-runtime -async-runtime-ref-counting \
// RUN: -convert-async-to-llvm -convert-func-to-llvm \ // RUN: -convert-async-to-llvm -convert-func-to-llvm \
// RUN: | mlir-cpu-runner \ // RUN: | mlir-cpu-runner \
// RUN: --shared-libs=%linalg_test_lib_dir/libmlir_cuda_runtime%shlibext \ // RUN: --shared-libs=%mlir_lib_dir/libmlir_cuda_runtime%shlibext \
// RUN: --shared-libs=%linalg_test_lib_dir/libmlir_async_runtime%shlibext \ // RUN: --shared-libs=%mlir_lib_dir/libmlir_async_runtime%shlibext \
// RUN: --shared-libs=%linalg_test_lib_dir/libmlir_runner_utils%shlibext \ // RUN: --shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext \
// RUN: --entry-point-result=void -O0 \ // RUN: --entry-point-result=void -O0 \
// RUN: | FileCheck %s // RUN: | FileCheck %s
func.func @main() { func.func @main() {
%c0 = arith.constant 0 : index %c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index %c1 = arith.constant 1 : index
%count = arith.constant 2 : index %count = arith.constant 2 : index
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mlir/test/Integration/GPU/CUDA/gpu-to-cubin.mlir

// RUN: mlir-opt %s \ // RUN: mlir-opt %s \
// RUN: -gpu-kernel-outlining \ // RUN: -gpu-kernel-outlining \
// RUN: -pass-pipeline='gpu.module(strip-debuginfo,convert-gpu-to-nvvm,gpu-to-cubin)' \ // RUN: -pass-pipeline='gpu.module(strip-debuginfo,convert-gpu-to-nvvm,gpu-to-cubin)' \
// RUN: -gpu-to-llvm \ // RUN: -gpu-to-llvm \
// RUN: | mlir-cpu-runner \ // RUN: | mlir-cpu-runner \
// RUN: --shared-libs=%linalg_test_lib_dir/libmlir_cuda_runtime%shlibext \ // RUN: --shared-libs=%mlir_lib_dir/libmlir_cuda_runtime%shlibext \
// RUN: --shared-libs=%linalg_test_lib_dir/libmlir_runner_utils%shlibext \ // RUN: --shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext \
// RUN: --shared-libs=%linalg_test_lib_dir/libmlir_c_runner_utils%shlibext \ // RUN: --shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext \
// RUN: --entry-point-result=void \ // RUN: --entry-point-result=void \
// RUN: | FileCheck %s // RUN: | FileCheck %s
func.func @other_func(%arg0 : f32, %arg1 : memref<?xf32>) { func.func @other_func(%arg0 : f32, %arg1 : memref<?xf32>) {
%cst = arith.constant 1 : index %cst = arith.constant 1 : index
%c0 = arith.constant 0 : index %c0 = arith.constant 0 : index
%cst2 = memref.dim %arg1, %c0 : memref<?xf32> %cst2 = memref.dim %arg1, %c0 : memref<?xf32>
gpu.launch blocks(%bx, %by, %bz) in (%grid_x = %cst, %grid_y = %cst, %grid_z = %cst) gpu.launch blocks(%bx, %by, %bz) in (%grid_x = %cst, %grid_y = %cst, %grid_z = %cst)
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mlir/test/Integration/GPU/CUDA/multiple-all-reduce.mlir

// RUN: mlir-opt %s \ // RUN: mlir-opt %s \
// RUN: -gpu-kernel-outlining \ // RUN: -gpu-kernel-outlining \
// RUN: -pass-pipeline='gpu.module(strip-debuginfo,convert-gpu-to-nvvm,gpu-to-cubin)' \ // RUN: -pass-pipeline='gpu.module(strip-debuginfo,convert-gpu-to-nvvm,gpu-to-cubin)' \
// RUN: -gpu-to-llvm \ // RUN: -gpu-to-llvm \
// RUN: | mlir-cpu-runner \ // RUN: | mlir-cpu-runner \
// RUN: --shared-libs=%linalg_test_lib_dir/libmlir_cuda_runtime%shlibext \ // RUN: --shared-libs=%mlir_lib_dir/libmlir_cuda_runtime%shlibext \
// RUN: --shared-libs=%linalg_test_lib_dir/libmlir_runner_utils%shlibext \ // RUN: --shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext \
// RUN: --entry-point-result=void \ // RUN: --entry-point-result=void \
// RUN: | FileCheck %s // RUN: | FileCheck %s
func.func @main() { func.func @main() {
%data = memref.alloc() : memref<2x6xf32> %data = memref.alloc() : memref<2x6xf32>
%sum = memref.alloc() : memref<2xf32> %sum = memref.alloc() : memref<2xf32>
%mul = memref.alloc() : memref<2xf32> %mul = memref.alloc() : memref<2xf32>
%cst0 = arith.constant 0.0 : f32 %cst0 = arith.constant 0.0 : f32
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mlir/test/Integration/GPU/CUDA/shuffle.mlir

// RUN: mlir-opt %s \ // RUN: mlir-opt %s \
// RUN: -gpu-kernel-outlining \ // RUN: -gpu-kernel-outlining \
// RUN: -pass-pipeline='gpu.module(strip-debuginfo,convert-gpu-to-nvvm,gpu-to-cubin)' \ // RUN: -pass-pipeline='gpu.module(strip-debuginfo,convert-gpu-to-nvvm,gpu-to-cubin)' \
// RUN: -gpu-to-llvm \ // RUN: -gpu-to-llvm \
// RUN: | mlir-cpu-runner \ // RUN: | mlir-cpu-runner \
// RUN: --shared-libs=%linalg_test_lib_dir/libmlir_cuda_runtime%shlibext \ // RUN: --shared-libs=%mlir_lib_dir/libmlir_cuda_runtime%shlibext \
// RUN: --shared-libs=%linalg_test_lib_dir/libmlir_runner_utils%shlibext \ // RUN: --shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext \
// RUN: --entry-point-result=void \ // RUN: --entry-point-result=void \
// RUN: | FileCheck %s // RUN: | FileCheck %s
// CHECK: [4, 5, 6, 7, 0, 1, 2, 3, 12, -1, -1, -1, 8] // CHECK: [4, 5, 6, 7, 0, 1, 2, 3, 12, -1, -1, -1, 8]
func.func @main() { func.func @main() {
%arg = memref.alloc() : memref<13xf32> %arg = memref.alloc() : memref<13xf32>
%dst = memref.cast %arg : memref<13xf32> to memref<?xf32> %dst = memref.cast %arg : memref<13xf32> to memref<?xf32>
%one = arith.constant 1 : index %one = arith.constant 1 : index
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mlir/test/Integration/GPU/CUDA/two-modules.mlir

// RUN: mlir-opt %s \ // RUN: mlir-opt %s \
// RUN: -gpu-kernel-outlining \ // RUN: -gpu-kernel-outlining \
// RUN: -pass-pipeline='gpu.module(strip-debuginfo,convert-gpu-to-nvvm,gpu-to-cubin)' \ // RUN: -pass-pipeline='gpu.module(strip-debuginfo,convert-gpu-to-nvvm,gpu-to-cubin)' \
// RUN: -gpu-to-llvm \ // RUN: -gpu-to-llvm \
// RUN: | mlir-cpu-runner \ // RUN: | mlir-cpu-runner \
// RUN: --shared-libs=%linalg_test_lib_dir/libmlir_cuda_runtime%shlibext \ // RUN: --shared-libs=%mlir_lib_dir/libmlir_cuda_runtime%shlibext \
// RUN: --shared-libs=%linalg_test_lib_dir/libmlir_runner_utils%shlibext \ // RUN: --shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext \
// RUN: --entry-point-result=void \ // RUN: --entry-point-result=void \
// RUN: | FileCheck %s // RUN: | FileCheck %s
// CHECK: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12] // CHECK: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
func.func @main() { func.func @main() {
%arg = memref.alloc() : memref<13xi32> %arg = memref.alloc() : memref<13xi32>
%dst = memref.cast %arg : memref<13xi32> to memref<?xi32> %dst = memref.cast %arg : memref<13xi32> to memref<?xi32>
%one = arith.constant 1 : index %one = arith.constant 1 : index
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mlir/test/Integration/GPU/ROCM/gpu-to-hsaco.mlir

// RUN: mlir-opt %s \ // RUN: mlir-opt %s \
// RUN: -gpu-kernel-outlining \ // RUN: -gpu-kernel-outlining \
// RUN: -pass-pipeline='gpu.module(strip-debuginfo,convert-gpu-to-rocdl,gpu-to-hsaco{chip=%chip})' \ // RUN: -pass-pipeline='gpu.module(strip-debuginfo,convert-gpu-to-rocdl,gpu-to-hsaco{chip=%chip})' \
// RUN: -gpu-to-llvm \ // RUN: -gpu-to-llvm \
// RUN: | mlir-cpu-runner \ // RUN: | mlir-cpu-runner \
// RUN: --shared-libs=%linalg_test_lib_dir/libmlir_rocm_runtime%shlibext \ // RUN: --shared-libs=%mlir_lib_dir/libmlir_rocm_runtime%shlibext \
// RUN: --shared-libs=%linalg_test_lib_dir/libmlir_runner_utils%shlibext \ // RUN: --shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext \
// RUN: --entry-point-result=void \ // RUN: --entry-point-result=void \
// RUN: | FileCheck %s // RUN: | FileCheck %s
func.func @other_func(%arg0 : f32, %arg1 : memref<?xf32>) { func.func @other_func(%arg0 : f32, %arg1 : memref<?xf32>) {
%c0 = arith.constant 0 : index %c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index %c1 = arith.constant 1 : index
%block_dim = memref.dim %arg1, %c0 : memref<?xf32> %block_dim = memref.dim %arg1, %c0 : memref<?xf32>
gpu.launch blocks(%bx, %by, %bz) in (%grid_x = %c1, %grid_y = %c1, %grid_z = %c1) gpu.launch blocks(%bx, %by, %bz) in (%grid_x = %c1, %grid_y = %c1, %grid_z = %c1)
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mlir/test/Integration/GPU/ROCM/printf.mlir

// RUN: mlir-opt %s \ // RUN: mlir-opt %s \
// RUN: -pass-pipeline='gpu.module(strip-debuginfo,convert-gpu-to-rocdl{index-bitwidth=32 runtime=HIP},gpu-to-hsaco{chip=%chip})' \ // RUN: -pass-pipeline='gpu.module(strip-debuginfo,convert-gpu-to-rocdl{index-bitwidth=32 runtime=HIP},gpu-to-hsaco{chip=%chip})' \
// RUN: -gpu-to-llvm \ // RUN: -gpu-to-llvm \
// RUN: | mlir-cpu-runner \ // RUN: | mlir-cpu-runner \
// RUN: --shared-libs=%linalg_test_lib_dir/libmlir_rocm_runtime%shlibext \ // RUN: --shared-libs=%mlir_lib_dir/libmlir_rocm_runtime%shlibext \
// RUN: --shared-libs=%linalg_test_lib_dir/libmlir_runner_utils%shlibext \ // RUN: --shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext \
// RUN: --entry-point-result=void \ // RUN: --entry-point-result=void \
// RUN: | FileCheck %s // RUN: | FileCheck %s
// CHECK: Hello from 0 // CHECK: Hello from 0
// CHECK: Hello from 1 // CHECK: Hello from 1
module attributes {gpu.container_module} { module attributes {gpu.container_module} {
gpu.module @kernels { gpu.module @kernels {
gpu.func @hello() kernel { gpu.func @hello() kernel {
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mlir/test/Integration/GPU/ROCM/two-modules.mlir

// RUN: mlir-opt %s \ // RUN: mlir-opt %s \
// RUN: -gpu-kernel-outlining \ // RUN: -gpu-kernel-outlining \
// RUN: -pass-pipeline='gpu.module(strip-debuginfo,convert-gpu-to-rocdl,gpu-to-hsaco{chip=%chip})' \ // RUN: -pass-pipeline='gpu.module(strip-debuginfo,convert-gpu-to-rocdl,gpu-to-hsaco{chip=%chip})' \
// RUN: -gpu-to-llvm \ // RUN: -gpu-to-llvm \
// RUN: | mlir-cpu-runner \ // RUN: | mlir-cpu-runner \
// RUN: --shared-libs=%linalg_test_lib_dir/libmlir_rocm_runtime%shlibext \ // RUN: --shared-libs=%mlir_lib_dir/libmlir_rocm_runtime%shlibext \
// RUN: --shared-libs=%linalg_test_lib_dir/libmlir_runner_utils%shlibext \ // RUN: --shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext \
// RUN: --entry-point-result=void \ // RUN: --entry-point-result=void \
// RUN: | FileCheck %s // RUN: | FileCheck %s
// CHECK: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12] // CHECK: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
func.func @main() { func.func @main() {
%arg = memref.alloc() : memref<13xi32> %arg = memref.alloc() : memref<13xi32>
%dst = memref.cast %arg : memref<13xi32> to memref<?xi32> %dst = memref.cast %arg : memref<13xi32> to memref<?xi32>
%c0 = arith.constant 0 : index %c0 = arith.constant 0 : index
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mlir/test/Integration/GPU/ROCM/vecadd.mlir

// RUN: mlir-opt %s \ // RUN: mlir-opt %s \
// RUN: -convert-scf-to-cf \ // RUN: -convert-scf-to-cf \
// RUN: -gpu-kernel-outlining \ // RUN: -gpu-kernel-outlining \
// RUN: -pass-pipeline='gpu.module(strip-debuginfo,convert-gpu-to-rocdl{use-bare-ptr-memref-call-conv=true},gpu-to-hsaco{chip=%chip})' \ // RUN: -pass-pipeline='gpu.module(strip-debuginfo,convert-gpu-to-rocdl{use-bare-ptr-memref-call-conv=true},gpu-to-hsaco{chip=%chip})' \
// RUN: -gpu-to-llvm=use-bare-pointers-for-kernels=true \ // RUN: -gpu-to-llvm=use-bare-pointers-for-kernels=true \
// RUN: | mlir-cpu-runner \ // RUN: | mlir-cpu-runner \
// RUN: --shared-libs=%linalg_test_lib_dir/libmlir_rocm_runtime%shlibext \ // RUN: --shared-libs=%mlir_lib_dir/libmlir_rocm_runtime%shlibext \
// RUN: --shared-libs=%linalg_test_lib_dir/libmlir_runner_utils%shlibext \ // RUN: --shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext \
// RUN: --entry-point-result=void \ // RUN: --entry-point-result=void \
// RUN: | FileCheck %s // RUN: | FileCheck %s
func.func @vecadd(%arg0 : memref<5xf32>, %arg1 : memref<5xf32>, %arg2 : memref<5xf32>) { func.func @vecadd(%arg0 : memref<5xf32>, %arg1 : memref<5xf32>, %arg2 : memref<5xf32>) {
%c0 = arith.constant 0 : index %c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index %c1 = arith.constant 1 : index
%block_dim = arith.constant 5 : index %block_dim = arith.constant 5 : index
gpu.launch blocks(%bx, %by, %bz) in (%grid_x = %c1, %grid_y = %c1, %grid_z = %c1) gpu.launch blocks(%bx, %by, %bz) in (%grid_x = %c1, %grid_y = %c1, %grid_z = %c1)
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mlir/test/Integration/GPU/ROCM/vector-transferops.mlir

// RUN: mlir-opt %s \ // RUN: mlir-opt %s \
// RUN: -convert-scf-to-cf \ // RUN: -convert-scf-to-cf \
// RUN: -gpu-kernel-outlining \ // RUN: -gpu-kernel-outlining \
// RUN: -pass-pipeline='gpu.module(strip-debuginfo,convert-gpu-to-rocdl{chipset=%chip index-bitwidth=32},gpu-to-hsaco{chip=%chip})' \ // RUN: -pass-pipeline='gpu.module(strip-debuginfo,convert-gpu-to-rocdl{chipset=%chip index-bitwidth=32},gpu-to-hsaco{chip=%chip})' \
// RUN: -gpu-to-llvm \ // RUN: -gpu-to-llvm \
// RUN: | mlir-cpu-runner \ // RUN: | mlir-cpu-runner \
// RUN: --shared-libs=%linalg_test_lib_dir/libmlir_rocm_runtime%shlibext \ // RUN: --shared-libs=%mlir_lib_dir/libmlir_rocm_runtime%shlibext \
// RUN: --shared-libs=%linalg_test_lib_dir/libmlir_runner_utils%shlibext \ // RUN: --shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext \
// RUN: --entry-point-result=void \ // RUN: --entry-point-result=void \
// RUN: | FileCheck %s // RUN: | FileCheck %s
// TODO: swap for vector transfer reads if we ever create a --vector-to-amdgpu // TODO: swap for vector transfer reads if we ever create a --vector-to-amdgpu
func.func @vectransferx2(%arg0 : memref<?xf32>, %arg1 : memref<?xf32>) { func.func @vectransferx2(%arg0 : memref<?xf32>, %arg1 : memref<?xf32>) {
%cst = arith.constant 1 : index %cst = arith.constant 1 : index
gpu.launch blocks(%bx, %by, %bz) in (%grid_x = %cst, %grid_y = %cst, %grid_z = %cst) gpu.launch blocks(%bx, %by, %bz) in (%grid_x = %cst, %grid_y = %cst, %grid_z = %cst)
threads(%tx, %ty, %tz) in (%block_x = %cst, %block_y = %cst, %block_z = %cst) { threads(%tx, %ty, %tz) in (%block_x = %cst, %block_y = %cst, %block_z = %cst) {
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mlir/test/lit.cfg.py

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# The following tools are optional # The following tools are optional
tools.extend([ tools.extend([
ToolSubst('toy-ch1', unresolved='ignore'), ToolSubst('toy-ch1', unresolved='ignore'),
ToolSubst('toy-ch2', unresolved='ignore'), ToolSubst('toy-ch2', unresolved='ignore'),
ToolSubst('toy-ch3', unresolved='ignore'), ToolSubst('toy-ch3', unresolved='ignore'),
ToolSubst('toy-ch4', unresolved='ignore'), ToolSubst('toy-ch4', unresolved='ignore'),
ToolSubst('toy-ch5', unresolved='ignore'), ToolSubst('toy-ch5', unresolved='ignore'),
ToolSubst('%mlir_lib_dir', config.mlir_lib_dir, unresolved='ignore'), ToolSubst('%mlir_lib_dir', config.mlir_lib_dir, unresolved='ignore'),
ToolSubst('%mlir_src_dir', config.mlir_src_root, unresolved='ignore'),
# The substitutions below will be replaced by '%mlir_lib_dir' shortly. # The substitutions below will be replaced by '%mlir_lib_dir' shortly.
ToolSubst('%linalg_test_lib_dir', config.mlir_lib_dir, unresolved='ignore'), ToolSubst('%linalg_test_lib_dir', config.mlir_lib_dir, unresolved='ignore'),
ToolSubst('%mlir_runner_utils_dir', config.mlir_lib_dir, unresolved='ignore'), ToolSubst('%mlir_runner_utils_dir', config.mlir_lib_dir, unresolved='ignore'),
ToolSubst('%spirv_wrapper_library_dir', config.mlir_lib_dir, unresolved='ignore'), ToolSubst('%spirv_wrapper_library_dir', config.mlir_lib_dir, unresolved='ignore'),
ToolSubst('%vulkan_wrapper_library_dir', config.mlir_lib_dir, unresolved='ignore'), ToolSubst('%vulkan_wrapper_library_dir', config.mlir_lib_dir, unresolved='ignore'),
ToolSubst('%mlir_integration_test_dir', config.mlir_lib_dir, unresolved='ignore'), ToolSubst('%mlir_integration_test_dir', config.mlir_lib_dir, unresolved='ignore'),
]) ])
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# it can be explicitly opted-in by prefixing the variable name with $ # it can be explicitly opted-in by prefixing the variable name with $
config.environment['FILECHECK_OPTS'] = "-enable-var-scope --allow-unused-prefixes=false" config.environment['FILECHECK_OPTS'] = "-enable-var-scope --allow-unused-prefixes=false"
# Add the python path for both the source and binary tree. # Add the python path for both the source and binary tree.
# Note that presently, the python sources come from the source tree and the # Note that presently, the python sources come from the source tree and the
# binaries come from the build tree. This should be unified to the build tree # binaries come from the build tree. This should be unified to the build tree
# by copying/linking sources to build. # by copying/linking sources to build.
if config.enable_bindings_python: if config.enable_bindings_python:
llvm_config.with_environment('PYTHONPATH', [ llvm_config.with_environment('PYTHONPATH', [
os.path.join(config.mlir_obj_root, 'python_packages', 'mlir_core'), os.path.join(config.mlir_obj_root, 'python_packages', 'mlir_core'),
os.path.join(config.mlir_obj_root, 'python_packages', 'mlir_test'), os.path.join(config.mlir_obj_root, 'python_packages', 'mlir_test'),
], append_path=True) ], append_path=True)
if config.enable_assertions: if config.enable_assertions:
config.available_features.add('asserts') config.available_features.add('asserts')
else: else:
config.available_features.add('noasserts') config.available_features.add('noasserts')
def have_host_jit_feature_support(feature_name): def have_host_jit_feature_support(feature_name):
mlir_cpu_runner_exe = lit.util.which('mlir-cpu-runner', config.mlir_tools_dir) mlir_cpu_runner_exe = lit.util.which('mlir-cpu-runner', config.mlir_tools_dir)
if not mlir_cpu_runner_exe: if not mlir_cpu_runner_exe:
return False return False
try: try:
mlir_cpu_runner_cmd = subprocess.Popen( mlir_cpu_runner_cmd = subprocess.Popen(
[mlir_cpu_runner_exe, '--host-supports-' + feature_name], stdout=subprocess.PIPE) [mlir_cpu_runner_exe, '--host-supports-' + feature_name], stdout=subprocess.PIPE)
except OSError: except OSError:
print('could not exec mlir-cpu-runner') print('could not exec mlir-cpu-runner')
return False return False
mlir_cpu_runner_out = mlir_cpu_runner_cmd.stdout.read().decode('ascii') mlir_cpu_runner_out = mlir_cpu_runner_cmd.stdout.read().decode('ascii')
mlir_cpu_runner_cmd.wait() mlir_cpu_runner_cmd.wait()
return 'true' in mlir_cpu_runner_out return 'true' in mlir_cpu_runner_out
if have_host_jit_feature_support('jit'): if have_host_jit_feature_support('jit'):
config.available_features.add('host-supports-jit') config.available_features.add('host-supports-jit')

mlir/test/mlir-cpu-runner/X86Vector/math-polynomial-approx-avx2.mlir

// RUN: mlir-opt %s -test-math-polynomial-approximation="enable-avx2" \ // RUN: mlir-opt %s -test-math-polynomial-approximation="enable-avx2" \
// RUN: -convert-arith-to-llvm \ // RUN: -convert-arith-to-llvm \
// RUN: -convert-vector-to-llvm="enable-x86vector" \ // RUN: -convert-vector-to-llvm="enable-x86vector" \
// RUN: -convert-math-to-llvm \ // RUN: -convert-math-to-llvm \
// RUN: -convert-func-to-llvm \ // RUN: -convert-func-to-llvm \
// RUN: -reconcile-unrealized-casts \ // RUN: -reconcile-unrealized-casts \
// RUN: | mlir-cpu-runner \ // RUN: | mlir-cpu-runner \
// RUN: -e main -entry-point-result=void -O0 \ // RUN: -e main -entry-point-result=void -O0 \
// RUN: -shared-libs=%linalg_test_lib_dir/libmlir_c_runner_utils%shlibext \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext \
// RUN: -shared-libs=%linalg_test_lib_dir/libmlir_runner_utils%shlibext \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext \
// RUN: | FileCheck %s // RUN: | FileCheck %s
// -------------------------------------------------------------------------- // // -------------------------------------------------------------------------- //
// rsqrt. // rsqrt.
// -------------------------------------------------------------------------- // // -------------------------------------------------------------------------- //
func.func @rsqrt() { func.func @rsqrt() {
// Sanity-check that the scalar rsqrt still works OK. // Sanity-check that the scalar rsqrt still works OK.
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mlir/test/mlir-cpu-runner/async-error.mlir

// RUN: mlir-opt %s -pass-pipeline="async-to-async-runtime,func.func(async-runtime-ref-counting,async-runtime-ref-counting-opt),convert-async-to-llvm,func.func(convert-linalg-to-loops,convert-scf-to-cf),convert-linalg-to-llvm,convert-vector-to-llvm,func.func(convert-arith-to-llvm),convert-func-to-llvm,reconcile-unrealized-casts" \ // RUN: mlir-opt %s -pass-pipeline="async-to-async-runtime,func.func(async-runtime-ref-counting,async-runtime-ref-counting-opt),convert-async-to-llvm,func.func(convert-linalg-to-loops,convert-scf-to-cf),convert-linalg-to-llvm,convert-vector-to-llvm,func.func(convert-arith-to-llvm),convert-func-to-llvm,reconcile-unrealized-casts" \
// RUN: | mlir-cpu-runner \ // RUN: | mlir-cpu-runner \
// RUN: -e main -entry-point-result=void -O0 \ // RUN: -e main -entry-point-result=void -O0 \
// RUN: -shared-libs=%linalg_test_lib_dir/libmlir_c_runner_utils%shlibext \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext \
// RUN: -shared-libs=%linalg_test_lib_dir/libmlir_runner_utils%shlibext \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext \
// RUN: -shared-libs=%linalg_test_lib_dir/libmlir_async_runtime%shlibext \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_async_runtime%shlibext \
// RUN: | FileCheck %s --dump-input=always // RUN: | FileCheck %s --dump-input=always
// FIXME: https://github.com/llvm/llvm-project/issues/57231 // FIXME: https://github.com/llvm/llvm-project/issues/57231
// UNSUPPORTED: hwasan // UNSUPPORTED: hwasan
func.func @main() { func.func @main() {
%false = arith.constant 0 : i1 %false = arith.constant 0 : i1
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mlir/test/mlir-cpu-runner/async-group.mlir

// RUN: mlir-opt %s -pass-pipeline="async-to-async-runtime,func.func(async-runtime-ref-counting,async-runtime-ref-counting-opt),convert-async-to-llvm,func.func(convert-arith-to-llvm),convert-func-to-llvm,reconcile-unrealized-casts" \ // RUN: mlir-opt %s -pass-pipeline="async-to-async-runtime,func.func(async-runtime-ref-counting,async-runtime-ref-counting-opt),convert-async-to-llvm,func.func(convert-arith-to-llvm),convert-func-to-llvm,reconcile-unrealized-casts" \
// RUN: | mlir-cpu-runner \ // RUN: | mlir-cpu-runner \
// RUN: -e main -entry-point-result=void -O0 \ // RUN: -e main -entry-point-result=void -O0 \
// RUN: -shared-libs=%linalg_test_lib_dir/libmlir_c_runner_utils%shlibext \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext \
// RUN: -shared-libs=%linalg_test_lib_dir/libmlir_runner_utils%shlibext \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext \
// RUN: -shared-libs=%linalg_test_lib_dir/libmlir_async_runtime%shlibext \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_async_runtime%shlibext \
// RUN: | FileCheck %s // RUN: | FileCheck %s
// This is crashing in CI "most of the time" on a AMD Rome CPU VM on GCP with: // This is crashing in CI "most of the time" on a AMD Rome CPU VM on GCP with:
// Tracer caught signal 11: addr=0x7a800028 pc=0x2e81ba sp=0x7efd2a7ffd50 // Tracer caught signal 11: addr=0x7a800028 pc=0x2e81ba sp=0x7efd2a7ffd50
// LeakSanitizer has encountered a fatal error. // LeakSanitizer has encountered a fatal error.
// This is hard to reproduce locally unfortunately. Disable it with ASAN/LSAN // This is hard to reproduce locally unfortunately. Disable it with ASAN/LSAN
// to keep the bot green for now. // to keep the bot green for now.
// UNSUPPORTED: asan // UNSUPPORTED: asan
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mlir/test/mlir-cpu-runner/async-value.mlir

// RUN: mlir-opt %s -pass-pipeline="async-to-async-runtime,func.func(async-runtime-ref-counting,async-runtime-ref-counting-opt),convert-async-to-llvm,func.func(convert-arith-to-llvm),convert-vector-to-llvm,convert-memref-to-llvm,convert-func-to-llvm,reconcile-unrealized-casts" \ // RUN: mlir-opt %s -pass-pipeline="async-to-async-runtime,func.func(async-runtime-ref-counting,async-runtime-ref-counting-opt),convert-async-to-llvm,func.func(convert-arith-to-llvm),convert-vector-to-llvm,convert-memref-to-llvm,convert-func-to-llvm,reconcile-unrealized-casts" \
// RUN: | mlir-cpu-runner \ // RUN: | mlir-cpu-runner \
// RUN: -e main -entry-point-result=void -O0 \ // RUN: -e main -entry-point-result=void -O0 \
// RUN: -shared-libs=%linalg_test_lib_dir/libmlir_c_runner_utils%shlibext \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext \
// RUN: -shared-libs=%linalg_test_lib_dir/libmlir_runner_utils%shlibext \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext \
// RUN: -shared-libs=%linalg_test_lib_dir/libmlir_async_runtime%shlibext \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_async_runtime%shlibext \
// RUN: | FileCheck %s --dump-input=always // RUN: | FileCheck %s --dump-input=always
// FIXME: https://github.com/llvm/llvm-project/issues/57231 // FIXME: https://github.com/llvm/llvm-project/issues/57231
// UNSUPPORTED: hwasan // UNSUPPORTED: hwasan
func.func @main() { func.func @main() {
// ------------------------------------------------------------------------ // // ------------------------------------------------------------------------ //
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mlir/test/mlir-cpu-runner/async.mlir

// RUN: mlir-opt %s -pass-pipeline="async-to-async-runtime,func.func(async-runtime-ref-counting,async-runtime-ref-counting-opt),convert-async-to-llvm,func.func(convert-linalg-to-loops,convert-scf-to-cf),convert-linalg-to-llvm,convert-memref-to-llvm,func.func(convert-arith-to-llvm),convert-func-to-llvm,reconcile-unrealized-casts" \ // RUN: mlir-opt %s -pass-pipeline="async-to-async-runtime,func.func(async-runtime-ref-counting,async-runtime-ref-counting-opt),convert-async-to-llvm,func.func(convert-linalg-to-loops,convert-scf-to-cf),convert-linalg-to-llvm,convert-memref-to-llvm,func.func(convert-arith-to-llvm),convert-func-to-llvm,reconcile-unrealized-casts" \
// RUN: | mlir-cpu-runner \ // RUN: | mlir-cpu-runner \
// RUN: -e main -entry-point-result=void -O0 \ // RUN: -e main -entry-point-result=void -O0 \
// RUN: -shared-libs=%linalg_test_lib_dir/libmlir_c_runner_utils%shlibext \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext \
// RUN: -shared-libs=%linalg_test_lib_dir/libmlir_runner_utils%shlibext \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext \
// RUN: -shared-libs=%linalg_test_lib_dir/libmlir_async_runtime%shlibext \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_async_runtime%shlibext \
// RUN: | FileCheck %s // RUN: | FileCheck %s
// FIXME: https://github.com/llvm/llvm-project/issues/57231 // FIXME: https://github.com/llvm/llvm-project/issues/57231
// UNSUPPORTED: hwasan // UNSUPPORTED: hwasan
func.func @main() { func.func @main() {
%i0 = arith.constant 0 : index %i0 = arith.constant 0 : index
%i1 = arith.constant 1 : index %i1 = arith.constant 1 : index
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mlir/test/mlir-cpu-runner/bare-ptr-call-conv.mlir

// RUN: mlir-opt %s -pass-pipeline="func.func(convert-scf-to-cf,convert-arith-to-llvm),convert-memref-to-llvm,convert-func-to-llvm{use-bare-ptr-memref-call-conv=1}" -reconcile-unrealized-casts | mlir-cpu-runner -shared-libs=%linalg_test_lib_dir/libmlir_c_runner_utils%shlibext -entry-point-result=void | FileCheck %s // RUN: mlir-opt %s -pass-pipeline="func.func(convert-scf-to-cf,convert-arith-to-llvm),convert-memref-to-llvm,convert-func-to-llvm{use-bare-ptr-memref-call-conv=1}" -reconcile-unrealized-casts | mlir-cpu-runner -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext -entry-point-result=void | FileCheck %s
// Verify bare pointer memref calling convention. `simple_add1_add2_test` // Verify bare pointer memref calling convention. `simple_add1_add2_test`
// gets two 2xf32 memrefs, adds 1.0f to the first one and 2.0f to the second // gets two 2xf32 memrefs, adds 1.0f to the first one and 2.0f to the second
// one. 'main' calls 'simple_add1_add2_test' with {1, 1} and {2, 2} so {2, 2} // one. 'main' calls 'simple_add1_add2_test' with {1, 1} and {2, 2} so {2, 2}
// and {4, 4} are the expected outputs. // and {4, 4} are the expected outputs.
func.func @simple_add1_add2_test(%arg0: memref<2xf32>, %arg1: memref<2xf32>) { func.func @simple_add1_add2_test(%arg0: memref<2xf32>, %arg1: memref<2xf32>) {
%c2 = arith.constant 2 : index %c2 = arith.constant 2 : index
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mlir/test/mlir-cpu-runner/copy.mlir

// RUN: mlir-opt %s -pass-pipeline="func.func(convert-scf-to-cf,convert-arith-to-llvm),convert-memref-to-llvm,convert-func-to-llvm,reconcile-unrealized-casts" \ // RUN: mlir-opt %s -pass-pipeline="func.func(convert-scf-to-cf,convert-arith-to-llvm),convert-memref-to-llvm,convert-func-to-llvm,reconcile-unrealized-casts" \
// RUN: | mlir-cpu-runner -e main -entry-point-result=void \ // RUN: | mlir-cpu-runner -e main -entry-point-result=void \
// RUN: -shared-libs=%mlir_runner_utils_dir/libmlir_runner_utils%shlibext,%mlir_runner_utils_dir/libmlir_c_runner_utils%shlibext \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext,%mlir_lib_dir/libmlir_c_runner_utils%shlibext \
// RUN: | FileCheck %s // RUN: | FileCheck %s
func.func private @printMemrefF32(memref<*xf32>) attributes { llvm.emit_c_interface } func.func private @printMemrefF32(memref<*xf32>) attributes { llvm.emit_c_interface }
func.func @main() -> () { func.func @main() -> () {
%c0 = arith.constant 0 : index %c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index %c1 = arith.constant 1 : index
%c42 = arith.constant 42.0 : f32 %c42 = arith.constant 42.0 : f32
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mlir/test/mlir-cpu-runner/global-memref.mlir

// RUN: mlir-opt %s -pass-pipeline="func.func(convert-arith-to-llvm),convert-memref-to-llvm,convert-func-to-llvm,reconcile-unrealized-casts" | mlir-cpu-runner -e main -entry-point-result=void -shared-libs=%mlir_runner_utils_dir/libmlir_runner_utils%shlibext,%mlir_runner_utils_dir/libmlir_c_runner_utils%shlibext | FileCheck %s // RUN: mlir-opt %s -pass-pipeline="func.func(convert-arith-to-llvm),convert-memref-to-llvm,convert-func-to-llvm,reconcile-unrealized-casts" | mlir-cpu-runner -e main -entry-point-result=void -shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext,%mlir_lib_dir/libmlir_c_runner_utils%shlibext | FileCheck %s
func.func private @printMemrefF32(memref<*xf32>) attributes { llvm.emit_c_interface } func.func private @printMemrefF32(memref<*xf32>) attributes { llvm.emit_c_interface }
func.func private @printMemrefI32(memref<*xi32>) attributes { llvm.emit_c_interface } func.func private @printMemrefI32(memref<*xi32>) attributes { llvm.emit_c_interface }
func.func private @printNewline() -> () func.func private @printNewline() -> ()
memref.global "private" @gv0 : memref<4xf32> = dense<[0.0, 1.0, 2.0, 3.0]> memref.global "private" @gv0 : memref<4xf32> = dense<[0.0, 1.0, 2.0, 3.0]>
func.func @test1DMemref() { func.func @test1DMemref() {
%0 = memref.get_global @gv0 : memref<4xf32> %0 = memref.get_global @gv0 : memref<4xf32>
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mlir/test/mlir-cpu-runner/math-polynomial-approx.mlir

// RUN: mlir-opt %s -pass-pipeline="func.func(test-math-polynomial-approximation,convert-arith-to-llvm),convert-vector-to-llvm,func.func(convert-math-to-llvm),convert-func-to-llvm,reconcile-unrealized-casts" \ // RUN: mlir-opt %s -pass-pipeline="func.func(test-math-polynomial-approximation,convert-arith-to-llvm),convert-vector-to-llvm,func.func(convert-math-to-llvm),convert-func-to-llvm,reconcile-unrealized-casts" \
// RUN: | mlir-cpu-runner \ // RUN: | mlir-cpu-runner \
// RUN: -e main -entry-point-result=void -O0 \ // RUN: -e main -entry-point-result=void -O0 \
// RUN: -shared-libs=%linalg_test_lib_dir/libmlir_c_runner_utils%shlibext \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext \
// RUN: -shared-libs=%linalg_test_lib_dir/libmlir_runner_utils%shlibext \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext \
// RUN: | FileCheck %s // RUN: | FileCheck %s
// -------------------------------------------------------------------------- // // -------------------------------------------------------------------------- //
// Tanh. // Tanh.
// -------------------------------------------------------------------------- // // -------------------------------------------------------------------------- //
func.func @tanh() { func.func @tanh() {
// CHECK: 0.848284 // CHECK: 0.848284
%0 = arith.constant 1.25 : f32 %0 = arith.constant 1.25 : f32
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mlir/test/mlir-cpu-runner/memref-reinterpret-cast.mlir

// RUN: mlir-opt %s -pass-pipeline="func.func(convert-scf-to-cf),convert-memref-to-llvm,func.func(convert-arith-to-llvm),convert-func-to-llvm,reconcile-unrealized-casts" \ // RUN: mlir-opt %s -pass-pipeline="func.func(convert-scf-to-cf),convert-memref-to-llvm,func.func(convert-arith-to-llvm),convert-func-to-llvm,reconcile-unrealized-casts" \
// RUN: | mlir-cpu-runner -e main -entry-point-result=void \ // RUN: | mlir-cpu-runner -e main -entry-point-result=void \
// RUN: -shared-libs=%mlir_runner_utils_dir/libmlir_runner_utils%shlibext,%mlir_runner_utils_dir/libmlir_c_runner_utils%shlibext \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext,%mlir_lib_dir/libmlir_c_runner_utils%shlibext \
// RUN: | FileCheck %s // RUN: | FileCheck %s
func.func private @printMemrefF32(memref<*xf32>) attributes { llvm.emit_c_interface } func.func private @printMemrefF32(memref<*xf32>) attributes { llvm.emit_c_interface }
func.func @main() -> () { func.func @main() -> () {
%c0 = arith.constant 0 : index %c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index %c1 = arith.constant 1 : index
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mlir/test/mlir-cpu-runner/memref-reshape.mlir

// RUN: mlir-opt %s -pass-pipeline="func.func(convert-scf-to-cf,memref-expand,convert-arith-to-llvm),convert-memref-to-llvm,convert-func-to-llvm,reconcile-unrealized-casts" \ // RUN: mlir-opt %s -pass-pipeline="func.func(convert-scf-to-cf,memref-expand,convert-arith-to-llvm),convert-memref-to-llvm,convert-func-to-llvm,reconcile-unrealized-casts" \
// RUN: | mlir-cpu-runner -e main -entry-point-result=void \ // RUN: | mlir-cpu-runner -e main -entry-point-result=void \
// RUN: -shared-libs=%mlir_runner_utils_dir/libmlir_runner_utils%shlibext,%mlir_runner_utils_dir/libmlir_c_runner_utils%shlibext \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext,%mlir_lib_dir/libmlir_c_runner_utils%shlibext \
// RUN: | FileCheck %s // RUN: | FileCheck %s
func.func private @printMemrefF32(memref<*xf32>) attributes { llvm.emit_c_interface } func.func private @printMemrefF32(memref<*xf32>) attributes { llvm.emit_c_interface }
func.func @main() -> () { func.func @main() -> () {
%c0 = arith.constant 0 : index %c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index %c1 = arith.constant 1 : index
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mlir/test/mlir-cpu-runner/print.mlir

// RUN: mlir-opt %s -pass-pipeline="convert-func-to-llvm,reconcile-unrealized-casts" \ // RUN: mlir-opt %s -pass-pipeline="convert-func-to-llvm,reconcile-unrealized-casts" \
// RUN: | mlir-cpu-runner -e main -entry-point-result=void \ // RUN: | mlir-cpu-runner -e main -entry-point-result=void \
// RUN: -shared-libs=%mlir_runner_utils_dir/libmlir_runner_utils%shlibext,%mlir_runner_utils_dir/libmlir_c_runner_utils%shlibext \ // RUN: -shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext,%mlir_lib_dir/libmlir_c_runner_utils%shlibext \
// RUN: | FileCheck %s // RUN: | FileCheck %s
llvm.mlir.global internal constant @str_global("String to print\0A") llvm.mlir.global internal constant @str_global("String to print\0A")
llvm.func @printCString(!llvm.ptr<i8>) llvm.func @printCString(!llvm.ptr<i8>)
func.func @main() { func.func @main() {
%0 = llvm.mlir.addressof @str_global : !llvm.ptr<array<16 x i8>> %0 = llvm.mlir.addressof @str_global : !llvm.ptr<array<16 x i8>>
%1 = llvm.mlir.constant(0 : index) : i64 %1 = llvm.mlir.constant(0 : index) : i64
%2 = llvm.getelementptr %0[%1, %1] %2 = llvm.getelementptr %0[%1, %1]
: (!llvm.ptr<array<16 x i8>>, i64, i64) -> !llvm.ptr<i8> : (!llvm.ptr<array<16 x i8>>, i64, i64) -> !llvm.ptr<i8>
llvm.call @printCString(%2) : (!llvm.ptr<i8>) -> () llvm.call @printCString(%2) : (!llvm.ptr<i8>) -> ()
return return
} }
// CHECK: String to print // CHECK: String to print

mlir/test/mlir-cpu-runner/sgemm-naive-codegen.mlir

// RUN: mlir-opt -pass-pipeline="func.func(convert-linalg-to-loops,lower-affine,convert-scf-to-cf,convert-arith-to-llvm),convert-vector-to-llvm,convert-memref-to-llvm,convert-func-to-llvm,reconcile-unrealized-casts" %s | mlir-cpu-runner -O3 -e main -entry-point-result=void -shared-libs=%mlir_runner_utils_dir/libmlir_c_runner_utils%shlibext | FileCheck %s // RUN: mlir-opt -pass-pipeline="func.func(convert-linalg-to-loops,lower-affine,convert-scf-to-cf,convert-arith-to-llvm),convert-vector-to-llvm,convert-memref-to-llvm,convert-func-to-llvm,reconcile-unrealized-casts" %s | mlir-cpu-runner -O3 -e main -entry-point-result=void -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | FileCheck %s
func.func @main() { func.func @main() {
%A = memref.alloc() : memref<16x16xf32> %A = memref.alloc() : memref<16x16xf32>
%B = memref.alloc() : memref<16x16xf32> %B = memref.alloc() : memref<16x16xf32>
%C = memref.alloc() : memref<16x16xf32> %C = memref.alloc() : memref<16x16xf32>
%cf1 = arith.constant 1.00000e+00 : f32 %cf1 = arith.constant 1.00000e+00 : f32
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mlir/test/mlir-cpu-runner/unranked-memref.mlir

// RUN: mlir-opt %s -pass-pipeline="func.func(convert-linalg-to-loops,convert-scf-to-cf,convert-arith-to-llvm),convert-linalg-to-llvm,convert-memref-to-llvm,convert-func-to-llvm,reconcile-unrealized-casts" | \ // RUN: mlir-opt %s -pass-pipeline="func.func(convert-linalg-to-loops,convert-scf-to-cf,convert-arith-to-llvm),convert-linalg-to-llvm,convert-memref-to-llvm,convert-func-to-llvm,reconcile-unrealized-casts" | \
// RUN: mlir-cpu-runner -e main -entry-point-result=void \ // RUN: mlir-cpu-runner -e main -entry-point-result=void \
// RUN: -shared-libs=%mlir_runner_utils_dir/libmlir_runner_utils%shlibext,%mlir_runner_utils_dir/libmlir_c_runner_utils%shlibext | FileCheck %s // RUN: -shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext,%mlir_lib_dir/libmlir_c_runner_utils%shlibext | FileCheck %s
// CHECK: rank = 2 // CHECK: rank = 2
// CHECK-SAME: sizes = [10, 3] // CHECK-SAME: sizes = [10, 3]
// CHECK-SAME: strides = [3, 1] // CHECK-SAME: strides = [3, 1]
// CHECK-COUNT-10: [10, 10, 10] // CHECK-COUNT-10: [10, 10, 10]
// //
// CHECK: rank = 2 // CHECK: rank = 2
// CHECK-SAME: sizes = [10, 3] // CHECK-SAME: sizes = [10, 3]
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mlir/test/mlir-cpu-runner/utils.mlir

// RUN: mlir-opt %s -pass-pipeline="func.func(convert-linalg-to-loops,convert-scf-to-cf,convert-arith-to-llvm),convert-linalg-to-llvm,convert-memref-to-llvm,convert-func-to-llvm,reconcile-unrealized-casts" | mlir-cpu-runner -e print_0d -entry-point-result=void -shared-libs=%linalg_test_lib_dir/libmlir_runner_utils%shlibext -shared-libs=%linalg_test_lib_dir/libmlir_c_runner_utils%shlibext | FileCheck %s --check-prefix=PRINT-0D // RUN: mlir-opt %s -pass-pipeline="func.func(convert-linalg-to-loops,convert-scf-to-cf,convert-arith-to-llvm),convert-linalg-to-llvm,convert-memref-to-llvm,convert-func-to-llvm,reconcile-unrealized-casts" | mlir-cpu-runner -e print_0d -entry-point-result=void -shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | FileCheck %s --check-prefix=PRINT-0D
// RUN: mlir-opt %s -pass-pipeline="func.func(convert-linalg-to-loops,convert-scf-to-cf,convert-arith-to-llvm),convert-linalg-to-llvm,convert-memref-to-llvm,convert-func-to-llvm,reconcile-unrealized-casts" | mlir-cpu-runner -e print_1d -entry-point-result=void -shared-libs=%linalg_test_lib_dir/libmlir_runner_utils%shlibext -shared-libs=%linalg_test_lib_dir/libmlir_c_runner_utils%shlibext | FileCheck %s --check-prefix=PRINT-1D // RUN: mlir-opt %s -pass-pipeline="func.func(convert-linalg-to-loops,convert-scf-to-cf,convert-arith-to-llvm),convert-linalg-to-llvm,convert-memref-to-llvm,convert-func-to-llvm,reconcile-unrealized-casts" | mlir-cpu-runner -e print_1d -entry-point-result=void -shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | FileCheck %s --check-prefix=PRINT-1D
// RUN: mlir-opt %s -pass-pipeline="func.func(convert-linalg-to-loops,convert-scf-to-cf,convert-arith-to-llvm),convert-linalg-to-llvm,convert-memref-to-llvm,convert-func-to-llvm,reconcile-unrealized-casts" | mlir-cpu-runner -e print_3d -entry-point-result=void -shared-libs=%linalg_test_lib_dir/libmlir_runner_utils%shlibext -shared-libs=%linalg_test_lib_dir/libmlir_c_runner_utils%shlibext | FileCheck %s --check-prefix=PRINT-3D // RUN: mlir-opt %s -pass-pipeline="func.func(convert-linalg-to-loops,convert-scf-to-cf,convert-arith-to-llvm),convert-linalg-to-llvm,convert-memref-to-llvm,convert-func-to-llvm,reconcile-unrealized-casts" | mlir-cpu-runner -e print_3d -entry-point-result=void -shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | FileCheck %s --check-prefix=PRINT-3D
// RUN: mlir-opt %s -pass-pipeline="func.func(convert-linalg-to-loops,convert-scf-to-cf,convert-arith-to-llvm),convert-linalg-to-llvm,convert-memref-to-llvm,convert-func-to-llvm,reconcile-unrealized-casts" | mlir-cpu-runner -e vector_splat_2d -entry-point-result=void -shared-libs=%linalg_test_lib_dir/libmlir_runner_utils%shlibext -shared-libs=%linalg_test_lib_dir/libmlir_c_runner_utils%shlibext | FileCheck %s --check-prefix=PRINT-VECTOR-SPLAT-2D // RUN: mlir-opt %s -pass-pipeline="func.func(convert-linalg-to-loops,convert-scf-to-cf,convert-arith-to-llvm),convert-linalg-to-llvm,convert-memref-to-llvm,convert-func-to-llvm,reconcile-unrealized-casts" | mlir-cpu-runner -e vector_splat_2d -entry-point-result=void -shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | FileCheck %s --check-prefix=PRINT-VECTOR-SPLAT-2D
func.func @print_0d() { func.func @print_0d() {
%f = arith.constant 2.00000e+00 : f32 %f = arith.constant 2.00000e+00 : f32
%A = memref.alloc() : memref<f32> %A = memref.alloc() : memref<f32>
memref.store %f, %A[]: memref<f32> memref.store %f, %A[]: memref<f32>
%U = memref.cast %A : memref<f32> to memref<*xf32> %U = memref.cast %A : memref<f32> to memref<*xf32>
call @printMemrefF32(%U): (memref<*xf32>) -> () call @printMemrefF32(%U): (memref<*xf32>) -> ()
memref.dealloc %A : memref<f32> memref.dealloc %A : memref<f32>
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mlir/test/mlir-spirv-cpu-runner/double.mlir

// RUN: mlir-spirv-cpu-runner %s -e main --entry-point-result=void --shared-libs=%mlir_runner_utils_dir/libmlir_runner_utils%shlibext,%spirv_wrapper_library_dir/libmlir_test_spirv_cpu_runner_c_wrappers%shlibext // RUN: mlir-spirv-cpu-runner %s -e main --entry-point-result=void --shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext,%mlir_lib_dir/libmlir_test_spirv_cpu_runner_c_wrappers%shlibext
// CHECK: [8, 8, 8, 8, 8, 8] // CHECK: [8, 8, 8, 8, 8, 8]
module attributes { module attributes {
gpu.container_module, gpu.container_module,
spv.target_env = #spv.target_env< spv.target_env = #spv.target_env<
#spv.vce<v1.0, [Shader], [SPV_KHR_variable_pointers]>, #spv.vce<v1.0, [Shader], [SPV_KHR_variable_pointers]>,
#spv.resource_limits< #spv.resource_limits<
max_compute_workgroup_invocations = 128, max_compute_workgroup_invocations = 128,
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mlir/test/mlir-spirv-cpu-runner/simple_add.mlir

// RUN: mlir-spirv-cpu-runner %s -e main --entry-point-result=void --shared-libs=%mlir_runner_utils_dir/libmlir_runner_utils%shlibext,%spirv_wrapper_library_dir/libmlir_test_spirv_cpu_runner_c_wrappers%shlibext // RUN: mlir-spirv-cpu-runner %s -e main --entry-point-result=void --shared-libs=%mlir_lib_dir/libmlir_runner_utils%shlibext,%mlir_lib_dir/libmlir_test_spirv_cpu_runner_c_wrappers%shlibext
// CHECK: [[[7.7, 0, 0], [7.7, 0, 0], [7.7, 0, 0]], [[0, 7.7, 0], [0, 7.7, 0], [0, 7.7, 0]], [[0, 0, 7.7], [0, 0, 7.7], [0, 0, 7.7]]] // CHECK: [[[7.7, 0, 0], [7.7, 0, 0], [7.7, 0, 0]], [[0, 7.7, 0], [0, 7.7, 0], [0, 7.7, 0]], [[0, 0, 7.7], [0, 0, 7.7], [0, 0, 7.7]]]
module attributes { module attributes {
gpu.container_module, gpu.container_module,
spv.target_env = #spv.target_env< spv.target_env = #spv.target_env<
#spv.vce<v1.0, [Shader], [SPV_KHR_storage_buffer_storage_class, SPV_KHR_8bit_storage]>, #spv.vce<v1.0, [Shader], [SPV_KHR_storage_buffer_storage_class, SPV_KHR_8bit_storage]>,
#spv.resource_limits< #spv.resource_limits<
max_compute_workgroup_invocations = 128, max_compute_workgroup_invocations = 128,
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mlir/test/mlir-vulkan-runner/addf.mlir

// RUN: mlir-vulkan-runner %s --shared-libs=%vulkan_wrapper_library_dir/libvulkan-runtime-wrappers%shlibext,%linalg_test_lib_dir/libmlir_runner_utils%shlibext --entry-point-result=void | FileCheck %s // RUN: mlir-vulkan-runner %s --shared-libs=%mlir_lib_dir/libvulkan-runtime-wrappers%shlibext,%mlir_lib_dir/libmlir_runner_utils%shlibext --entry-point-result=void | FileCheck %s
// CHECK: [3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3] // CHECK: [3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3]
module attributes { module attributes {
gpu.container_module, gpu.container_module,
spv.target_env = #spv.target_env< spv.target_env = #spv.target_env<
#spv.vce<v1.0, [Shader], [SPV_KHR_storage_buffer_storage_class]>, #spv.resource_limits<>> #spv.vce<v1.0, [Shader], [SPV_KHR_storage_buffer_storage_class]>, #spv.resource_limits<>>
} { } {
gpu.module @kernels { gpu.module @kernels {
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mlir/test/mlir-vulkan-runner/addi.mlir

// RUN: mlir-vulkan-runner %s --shared-libs=%vulkan_wrapper_library_dir/libvulkan-runtime-wrappers%shlibext,%linalg_test_lib_dir/libmlir_runner_utils%shlibext --entry-point-result=void | FileCheck %s // RUN: mlir-vulkan-runner %s --shared-libs=%mlir_lib_dir/libvulkan-runtime-wrappers%shlibext,%mlir_lib_dir/libmlir_runner_utils%shlibext --entry-point-result=void | FileCheck %s
// CHECK-COUNT-64: [3, 3, 3, 3, 3, 3, 3, 3] // CHECK-COUNT-64: [3, 3, 3, 3, 3, 3, 3, 3]
module attributes { module attributes {
gpu.container_module, gpu.container_module,
spv.target_env = #spv.target_env< spv.target_env = #spv.target_env<
#spv.vce<v1.0, [Shader], [SPV_KHR_storage_buffer_storage_class]>, #spv.resource_limits<>> #spv.vce<v1.0, [Shader], [SPV_KHR_storage_buffer_storage_class]>, #spv.resource_limits<>>
} { } {
gpu.module @kernels { gpu.module @kernels {
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mlir/test/mlir-vulkan-runner/addi8.mlir

// RUN: mlir-vulkan-runner %s --shared-libs=%vulkan_wrapper_library_dir/libvulkan-runtime-wrappers%shlibext,%linalg_test_lib_dir/libmlir_runner_utils%shlibext --entry-point-result=void | FileCheck %s // RUN: mlir-vulkan-runner %s --shared-libs=%mlir_lib_dir/libvulkan-runtime-wrappers%shlibext,%mlir_lib_dir/libmlir_runner_utils%shlibext --entry-point-result=void | FileCheck %s
// CHECK-COUNT-64: [3, 3, 3, 3, 3, 3, 3, 3] // CHECK-COUNT-64: [3, 3, 3, 3, 3, 3, 3, 3]
module attributes { module attributes {
gpu.container_module, gpu.container_module,
spv.target_env = #spv.target_env< spv.target_env = #spv.target_env<
#spv.vce<v1.0, [Shader], [SPV_KHR_storage_buffer_storage_class, SPV_KHR_8bit_storage]>, #spv.resource_limits<>> #spv.vce<v1.0, [Shader], [SPV_KHR_storage_buffer_storage_class, SPV_KHR_8bit_storage]>, #spv.resource_limits<>>
} { } {
gpu.module @kernels { gpu.module @kernels {
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mlir/test/mlir-vulkan-runner/mulf.mlir

// RUN: mlir-vulkan-runner %s --shared-libs=%vulkan_wrapper_library_dir/libvulkan-runtime-wrappers%shlibext,%linalg_test_lib_dir/libmlir_runner_utils%shlibext --entry-point-result=void | FileCheck %s // RUN: mlir-vulkan-runner %s --shared-libs=%mlir_lib_dir/libvulkan-runtime-wrappers%shlibext,%mlir_lib_dir/libmlir_runner_utils%shlibext --entry-point-result=void | FileCheck %s
// CHECK-COUNT-4: [6, 6, 6, 6] // CHECK-COUNT-4: [6, 6, 6, 6]
module attributes { module attributes {
gpu.container_module, gpu.container_module,
spv.target_env = #spv.target_env< spv.target_env = #spv.target_env<
#spv.vce<v1.0, [Shader], [SPV_KHR_storage_buffer_storage_class]>, #spv.resource_limits<>> #spv.vce<v1.0, [Shader], [SPV_KHR_storage_buffer_storage_class]>, #spv.resource_limits<>>
} { } {
gpu.module @kernels { gpu.module @kernels {
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mlir/test/mlir-vulkan-runner/subf.mlir

// RUN: mlir-vulkan-runner %s --shared-libs=%vulkan_wrapper_library_dir/libvulkan-runtime-wrappers%shlibext,%linalg_test_lib_dir/libmlir_runner_utils%shlibext --entry-point-result=void | FileCheck %s // RUN: mlir-vulkan-runner %s --shared-libs=%mlir_lib_dir/libvulkan-runtime-wrappers%shlibext,%mlir_lib_dir/libmlir_runner_utils%shlibext --entry-point-result=void | FileCheck %s
// CHECK-COUNT-32: [2.2, 2.2, 2.2, 2.2] // CHECK-COUNT-32: [2.2, 2.2, 2.2, 2.2]
module attributes { module attributes {
gpu.container_module, gpu.container_module,
spv.target_env = #spv.target_env< spv.target_env = #spv.target_env<
#spv.vce<v1.0, [Shader], [SPV_KHR_storage_buffer_storage_class]>, #spv.vce<v1.0, [Shader], [SPV_KHR_storage_buffer_storage_class]>,
#spv.resource_limits<>> #spv.resource_limits<>>
} { } {
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mlir/test/mlir-vulkan-runner/time.mlir

// RUN: mlir-vulkan-runner %s --shared-libs=%vulkan_wrapper_library_dir/libvulkan-runtime-wrappers%shlibext,%linalg_test_lib_dir/libmlir_runner_utils%shlibext --entry-point-result=void | FileCheck %s // RUN: mlir-vulkan-runner %s --shared-libs=%mlir_lib_dir/libvulkan-runtime-wrappers%shlibext,%mlir_lib_dir/libmlir_runner_utils%shlibext --entry-point-result=void | FileCheck %s
// CHECK: Compute shader execution time // CHECK: Compute shader execution time
// CHECK: Command buffer submit time // CHECK: Command buffer submit time
// CHECK: Wait idle time // CHECK: Wait idle time
module attributes { module attributes {
gpu.container_module, gpu.container_module,
spv.target_env = #spv.target_env< spv.target_env = #spv.target_env<
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utils/bazel/llvm-project-overlay/mlir/BUILD.bazel

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":Support", ":Support",
"//llvm:Core", "//llvm:Core",
"//llvm:OrcJIT", "//llvm:OrcJIT",
"//llvm:Support", "//llvm:Support",
], ],
) )
cc_library( cc_library(
name = "mlir_c_runner_utils", name = "mlir_async_runtime_api",
hdrs = ["include/mlir/ExecutionEngine/AsyncRuntime.h"],
includes = ["include"],
)
cc_library(
name = "_mlir_async_runtime",
srcs = ["lib/ExecutionEngine/AsyncRuntime.cpp"],
copts = ["-Dmlir_async_runtime_EXPORTS"],
deps = [
":mlir_async_runtime_api",
"//llvm:Support",
],
)
alias(
herhutUnsubmitted
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Why are these aliases needed?

herhut: Why are these aliases needed?
csiggAuthorUnsubmitted
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They are needed because the shared library name (lib<name>.so) of the cc_library target would otherwise clash with the cc_binary target below.

I will add a comment explaining this.

csigg: They are needed because the shared library name (`lib<name>.so`) of the `cc_library` target…
name = "mlir_async_runtime",
actual = "_mlir_async_runtime",
)
cc_binary(
name = "libmlir_async_runtime.so",
linkshared = True,
linkstatic = False,
deps = [":mlir_async_runtime"],
)
cc_library(
name = "_mlir_c_runner_utils",
srcs = [ srcs = [
"lib/ExecutionEngine/CRunnerUtils.cpp", "lib/ExecutionEngine/CRunnerUtils.cpp",
"lib/ExecutionEngine/Float16bits.cpp", "lib/ExecutionEngine/Float16bits.cpp",
"lib/ExecutionEngine/SparseTensorUtils.cpp", "lib/ExecutionEngine/SparseTensorUtils.cpp",
], ],
hdrs = [ hdrs = [
"include/mlir/ExecutionEngine/CRunnerUtils.h", "include/mlir/ExecutionEngine/CRunnerUtils.h",
"include/mlir/ExecutionEngine/Float16bits.h", "include/mlir/ExecutionEngine/Float16bits.h",
"include/mlir/ExecutionEngine/Msan.h", "include/mlir/ExecutionEngine/Msan.h",
"include/mlir/ExecutionEngine/SparseTensorUtils.h", "include/mlir/ExecutionEngine/SparseTensorUtils.h",
], ],
includes = ["include"], includes = ["include"],
) )
cc_library( alias(
name = "mlir_async_runtime_api", name = "mlir_c_runner_utils",
hdrs = ["include/mlir/ExecutionEngine/AsyncRuntime.h"], actual = "_mlir_c_runner_utils",
includes = ["include"],
) )
cc_library( cc_binary(
name = "mlir_async_runtime", name = "libmlir_c_runner_utils.so",
srcs = ["lib/ExecutionEngine/AsyncRuntime.cpp"], linkshared = True,
copts = ["-Dmlir_async_runtime_EXPORTS"], linkstatic = False,
deps = [ deps = [":mlir_c_runner_utils"],
":mlir_async_runtime_api",
"//llvm:Support",
],
) )
cc_library( cc_library(
name = "mlir_runner_utils", name = "_mlir_runner_utils",
srcs = ["lib/ExecutionEngine/RunnerUtils.cpp"], srcs = ["lib/ExecutionEngine/RunnerUtils.cpp"],
hdrs = ["include/mlir/ExecutionEngine/RunnerUtils.h"], hdrs = ["include/mlir/ExecutionEngine/RunnerUtils.h"],
includes = ["include"], includes = ["include"],
deps = [":mlir_c_runner_utils"], deps = [":mlir_c_runner_utils"],
) )
alias(
name = "mlir_runner_utils",
actual = "_mlir_runner_utils",
)
cc_binary(
name = "libmlir_runner_utils.so",
linkshared = True,
linkstatic = False,
deps = [":mlir_runner_utils"],
)
cc_binary( cc_binary(
name = "mlir-cpu-runner", name = "mlir-cpu-runner",
srcs = ["tools/mlir-cpu-runner/mlir-cpu-runner.cpp"], srcs = ["tools/mlir-cpu-runner/mlir-cpu-runner.cpp"],
deps = [ deps = [
":AllToLLVMIRTranslations", ":AllToLLVMIRTranslations",
":ExecutionEngineUtils", ":ExecutionEngineUtils",
":IR", ":IR",
":LLVMDialect", ":LLVMDialect",
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# BUILD_SHARED_LIBS), skipping them if none of the symbols are used. # BUILD_SHARED_LIBS), skipping them if none of the symbols are used.
# See also https://reviews.llvm.org/D95613. # See also https://reviews.llvm.org/D95613.
cc_headers_only( cc_headers_only(
name = "LLVMSupportHeaders", name = "LLVMSupportHeaders",
src = "//llvm:Support", src = "//llvm:Support",
) )
cc_library( cc_library(
name = "mlir_cuda_runtime", name = "_mlir_cuda_runtime",
srcs = ["lib/ExecutionEngine/CudaRuntimeWrappers.cpp"], srcs = ["lib/ExecutionEngine/CudaRuntimeWrappers.cpp"],
# Prevent needing EnableABIBreakingChecks symbol from LLVMSupport. # Prevent needing EnableABIBreakingChecks symbol from LLVMSupport.
copts = ["-DLLVM_DISABLE_ABI_BREAKING_CHECKS_ENFORCING=1"], copts = ["-DLLVM_DISABLE_ABI_BREAKING_CHECKS_ENFORCING=1"],
tags = [ tags = [
"manual", # External dependency "manual", # External dependency
"nobuildkite", # TODO(gcmn): Add support for this target "nobuildkite", # TODO(gcmn): Add support for this target
], ],
deps = [ deps = [
":LLVMSupportHeaders", ":LLVMSupportHeaders",
":mlir_c_runner_utils", ":mlir_c_runner_utils",
"@cuda//:cuda_headers", "@cuda//:cuda_headers",
"@cuda//:libcuda", "@cuda//:libcuda",
], ],
) )
alias(
name = "mlir_cuda_runtime",
actual = "_mlir_cuda_runtime",
)
cc_binary(
name = "libmlir_cuda_runtime.so",
linkshared = True,
linkstatic = False,
deps = [":mlir_cuda_runtime"],
)
cc_library( cc_library(
name = "VulkanRuntime", name = "VulkanRuntime",
srcs = ["tools/mlir-vulkan-runner/VulkanRuntime.cpp"], srcs = ["tools/mlir-vulkan-runner/VulkanRuntime.cpp"],
hdrs = ["tools/mlir-vulkan-runner/VulkanRuntime.h"], hdrs = ["tools/mlir-vulkan-runner/VulkanRuntime.h"],
tags = [ tags = [
"manual", # External dependency "manual", # External dependency
], ],
deps = [ deps = [
":FuncDialect", ":FuncDialect",
":IR", ":IR",
":Pass", ":Pass",
":SPIRVDialect", ":SPIRVDialect",
":SideEffectInterfaces", ":SideEffectInterfaces",
":Support", ":Support",
"//llvm:Support", "//llvm:Support",
"@vulkan_headers", "@vulkan_headers",
"@vulkan_sdk//:sdk", "@vulkan_sdk//:sdk",
], ],
) )
cc_binary( cc_binary(
name = "tools/libvulkan-runtime-wrappers.so", name = "libvulkan-runtime-wrappers.so",
srcs = ["tools/mlir-vulkan-runner/vulkan-runtime-wrappers.cpp"], srcs = ["tools/mlir-vulkan-runner/vulkan-runtime-wrappers.cpp"],
linkshared = True, linkshared = True,
tags = [ tags = [
"manual", # External dependency "manual", # External dependency
], ],
deps = [ deps = [
":VulkanRuntime", ":VulkanRuntime",
"//llvm:Support", "//llvm:Support",
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":SPIRVTransforms", ":SPIRVTransforms",
":ToLLVMIRTranslation", ":ToLLVMIRTranslation",
"//llvm:Core", "//llvm:Core",
"//llvm:Linker", "//llvm:Linker",
"//llvm:Support", "//llvm:Support",
], ],
) )
cc_binary(
name = "libmlir_test_spirv_cpu_runner_c_wrappers.so",
srcs = ["mlir_test_spirv_cpu_runner_c_wrappers.cpp"],
linkshared = True,
linkstatic = False,
deps = [":mlir_runner_utils"],
)
cc_library( cc_library(
name = "TableGen", name = "TableGen",
srcs = glob(["lib/TableGen/*.cpp"]), srcs = glob(["lib/TableGen/*.cpp"]),
hdrs = glob(["include/mlir/TableGen/*.h"]), hdrs = glob(["include/mlir/TableGen/*.h"]),
includes = ["include"], includes = ["include"],
deps = [ deps = [
":Support", ":Support",
"//llvm:Support", "//llvm:Support",
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