caio/clang-p2996
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity
Files
0c4d7d14e94de5130d7747bde70c94bce77bb754
clang-p2996/mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_cast.mlir
Cullen Rhodes baafc74ab0 [mlir][test][Integration] Refactor Arm emulator configuration 
The logic enabling the Arm SVE (and now SME) integration tests for
various dialects, that may run under emulation, is now duplicated in
several places.

This patch moves the configuration to the top-level MLIR integration
tests Lit config and renames the '%lli' substitution in contexts where
it will run exclusively (ArmSVE, ArmSME) on AArch64 (and possibly under
emulation) to '%lli_aarch64_cmd', and '%lli_host_or_aarch64_cmd' for
contexts where it may run AArch64 (also possibly under emulation). The
latter is for integration tests that have target-specific and
target-agnostic codepaths such as SparseTensor, which supports scalable
vectors.

The two substitutions have the same effect but the names are different to
convey this information. The '%lli_aarch64_cmd' substitution could be
used in the SparseTensor tests but that would be a misnomer if the host
were x86 and the MLIR_RUN_SVE_TESTS=OFF.

The reason for renaming the '%lli' substitution is to not prevent running other
target-specific integration tests at the same time, since the same substitution
'%lli' is used for lli in other integration tests:

  * mlir/test/Integration/Dialect/Vector/CPU/X86Vector              - (AVX emulation via Intel SDE)
  * mlir/test/Integration/Dialect/Vector/CPU/AMX                    - (AMX emulation via Intel SDE)
  * mlir/test/Integration/Dialect/LLVMIR/CPU/test-vp-intrinsic.mlir - (RISCV emulation via QEMU if supported, native otherwise)

and substituting '%lli' at the top-level with Arm specific logic would override
this.

Reviewed By: awarzynski

Differential Revision: https://reviews.llvm.org/D148929
2023-04-26 09:57:43 +00:00

282 lines
11 KiB
MLIR
Raw Blame History

// DEFINE: %{option} = enable-runtime-library=true
// DEFINE: %{compile} = mlir-opt %s --sparse-compiler=%{option}
// DEFINE: %{run} = mlir-cpu-runner \
// DEFINE: -e entry -entry-point-result=void \
// DEFINE: -shared-libs=%mlir_c_runner_utils | \
// DEFINE: FileCheck %s
//
// RUN: %{compile} | %{run}
//
// Do the same run, but now with direct IR generation.
// REDEFINE: %{option} = enable-runtime-library=false
// RUN: %{compile} | %{run}
//
// Do the same run, but now with direct IR generation and vectorization.
// REDEFINE: %{option} = "enable-runtime-library=false vl=2 reassociate-fp-reductions=true enable-index-optimizations=true"
// RUN: %{compile} | %{run}
// Do the same run, but now with direct IR generation and, if available, VLA
// vectorization.
// REDEFINE: %{option} = "enable-runtime-library=false vl=4 enable-arm-sve=%ENABLE_VLA"
// REDEFINE: %{run} = %lli_host_or_aarch64_cmd \
// REDEFINE: --entry-function=entry_lli \
// REDEFINE: --extra-module=%S/Inputs/main_for_lli.ll \
// REDEFINE: %VLA_ARCH_ATTR_OPTIONS \
// REDEFINE: --dlopen=%mlir_native_utils_lib_dir/libmlir_c_runner_utils%shlibext | \
// REDEFINE: FileCheck %s
// RUN: %{compile} | mlir-translate -mlir-to-llvmir | %{run}
#SV = #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>
#trait_cast = {
indexing_maps = [
affine_map<(i) -> (i)>, // A (in)
affine_map<(i) -> (i)> // X (out)
],
iterator_types = ["parallel"],
doc = "X(i) = cast A(i)"
}
//
// Integration test that lowers a kernel annotated as sparse to actual sparse
// code, initializes a matching sparse storage scheme from a dense vector,
// and runs the resulting code with the JIT compiler.
//
module {
//
// Various kernels that cast a sparse vector from one type to another.
// Arithmetic supports the following casts.
// sitofp
// uitofp
// fptosi
// fptoui
// extf
// truncf
// extsi
// extui
// trunci
// bitcast
// Since all casts are "zero preserving" unary operations, lattice computation
// and conversion to sparse code is straightforward.
//
func.func @sparse_cast_s32_to_f32(%arga: tensor<10xi32, #SV>,
%argb: tensor<10xf32>) -> tensor<10xf32> {
%0 = linalg.generic #trait_cast
ins(%arga: tensor<10xi32, #SV>)
outs(%argb: tensor<10xf32>) {
^bb(%a: i32, %x : f32):
%cst = arith.sitofp %a : i32 to f32
linalg.yield %cst : f32
} -> tensor<10xf32>
return %0 : tensor<10xf32>
}
func.func @sparse_cast_u32_to_f32(%arga: tensor<10xi32, #SV>,
%argb: tensor<10xf32>) -> tensor<10xf32> {
%0 = linalg.generic #trait_cast
ins(%arga: tensor<10xi32, #SV>)
outs(%argb: tensor<10xf32>) {
^bb(%a: i32, %x : f32):
%cst = arith.uitofp %a : i32 to f32
linalg.yield %cst : f32
} -> tensor<10xf32>
return %0 : tensor<10xf32>
}
func.func @sparse_cast_f32_to_s32(%arga: tensor<10xf32, #SV>,
%argb: tensor<10xi32>) -> tensor<10xi32> {
%0 = linalg.generic #trait_cast
ins(%arga: tensor<10xf32, #SV>)
outs(%argb: tensor<10xi32>) {
^bb(%a: f32, %x : i32):
%cst = arith.fptosi %a : f32 to i32
linalg.yield %cst : i32
} -> tensor<10xi32>
return %0 : tensor<10xi32>
}
func.func @sparse_cast_f64_to_u32(%arga: tensor<10xf64, #SV>,
%argb: tensor<10xi32>) -> tensor<10xi32> {
%0 = linalg.generic #trait_cast
ins(%arga: tensor<10xf64, #SV>)
outs(%argb: tensor<10xi32>) {
^bb(%a: f64, %x : i32):
%cst = arith.fptoui %a : f64 to i32
linalg.yield %cst : i32
} -> tensor<10xi32>
return %0 : tensor<10xi32>
}
func.func @sparse_cast_f32_to_f64(%arga: tensor<10xf32, #SV>,
%argb: tensor<10xf64>) -> tensor<10xf64> {
%0 = linalg.generic #trait_cast
ins(%arga: tensor<10xf32, #SV>)
outs(%argb: tensor<10xf64>) {
^bb(%a: f32, %x : f64):
%cst = arith.extf %a : f32 to f64
linalg.yield %cst : f64
} -> tensor<10xf64>
return %0 : tensor<10xf64>
}
func.func @sparse_cast_f64_to_f32(%arga: tensor<10xf64, #SV>,
%argb: tensor<10xf32>) -> tensor<10xf32> {
%0 = linalg.generic #trait_cast
ins(%arga: tensor<10xf64, #SV>)
outs(%argb: tensor<10xf32>) {
^bb(%a: f64, %x : f32):
%cst = arith.truncf %a : f64 to f32
linalg.yield %cst : f32
} -> tensor<10xf32>
return %0 : tensor<10xf32>
}
func.func @sparse_cast_s32_to_u64(%arga: tensor<10xi32, #SV>,
%argb: tensor<10xi64>) -> tensor<10xi64> {
%0 = linalg.generic #trait_cast
ins(%arga: tensor<10xi32, #SV>)
outs(%argb: tensor<10xi64>) {
^bb(%a: i32, %x : i64):
%cst = arith.extsi %a : i32 to i64
linalg.yield %cst : i64
} -> tensor<10xi64>
return %0 : tensor<10xi64>
}
func.func @sparse_cast_u32_to_s64(%arga: tensor<10xi32, #SV>,
%argb: tensor<10xi64>) -> tensor<10xi64> {
%0 = linalg.generic #trait_cast
ins(%arga: tensor<10xi32, #SV>)
outs(%argb: tensor<10xi64>) {
^bb(%a: i32, %x : i64):
%cst = arith.extui %a : i32 to i64
linalg.yield %cst : i64
} -> tensor<10xi64>
return %0 : tensor<10xi64>
}
func.func @sparse_cast_i32_to_i8(%arga: tensor<10xi32, #SV>,
%argb: tensor<10xi8>) -> tensor<10xi8> {
%0 = linalg.generic #trait_cast
ins(%arga: tensor<10xi32, #SV>)
outs(%argb: tensor<10xi8>) {
^bb(%a: i32, %x : i8):
%cst = arith.trunci %a : i32 to i8
linalg.yield %cst : i8
} -> tensor<10xi8>
return %0 : tensor<10xi8>
}
func.func @sparse_cast_f32_as_s32(%arga: tensor<10xf32, #SV>,
%argb: tensor<10xi32>) -> tensor<10xi32> {
%0 = linalg.generic #trait_cast
ins(%arga: tensor<10xf32, #SV>)
outs(%argb: tensor<10xi32>) {
^bb(%a: f32, %x : i32):
%cst = arith.bitcast %a : f32 to i32
linalg.yield %cst : i32
} -> tensor<10xi32>
return %0 : tensor<10xi32>
}
//
// Main driver that converts a dense tensor into a sparse tensor
// and then calls the sparse casting kernel.
//
func.func @entry() {
%z = arith.constant 0 : index
%b = arith.constant 0 : i8
%i = arith.constant 0 : i32
%l = arith.constant 0 : i64
%f = arith.constant 0.0 : f32
%d = arith.constant 0.0 : f64
%zero_b = arith.constant dense<0> : tensor<10xi8>
%zero_d = arith.constant dense<0.0> : tensor<10xf64>
%zero_f = arith.constant dense<0.0> : tensor<10xf32>
%zero_i = arith.constant dense<0> : tensor<10xi32>
%zero_l = arith.constant dense<0> : tensor<10xi64>
// Initialize dense tensors, convert to a sparse vectors.
%0 = arith.constant dense<[ -4, -3, -2, -1, 0, 1, 2, 3, 4, 305 ]> : tensor<10xi32>
%1 = sparse_tensor.convert %0 : tensor<10xi32> to tensor<10xi32, #SV>
%2 = arith.constant dense<[ -4.4, -3.3, -2.2, -1.1, 0.0, 1.1, 2.2, 3.3, 4.4, 305.5 ]> : tensor<10xf32>
%3 = sparse_tensor.convert %2 : tensor<10xf32> to tensor<10xf32, #SV>
%4 = arith.constant dense<[ -4.4, -3.3, -2.2, -1.1, 0.0, 1.1, 2.2, 3.3, 4.4, 305.5 ]> : tensor<10xf64>
%5 = sparse_tensor.convert %4 : tensor<10xf64> to tensor<10xf64, #SV>
%6 = arith.constant dense<[ 4294967295.0, 4294967294.0, 4294967293.0, 4294967292.0,
0.0, 1.1, 2.2, 3.3, 4.4, 305.5 ]> : tensor<10xf64>
%7 = sparse_tensor.convert %6 : tensor<10xf64> to tensor<10xf64, #SV>
//
// CHECK: ( -4, -3, -2, -1, 0, 1, 2, 3, 4, 305 )
//
%c0 = call @sparse_cast_s32_to_f32(%1, %zero_f) : (tensor<10xi32, #SV>, tensor<10xf32>) -> tensor<10xf32>
%v0 = vector.transfer_read %c0[%z], %f: tensor<10xf32>, vector<10xf32>
vector.print %v0 : vector<10xf32>
//
// CHECK: ( 4.29497e+09, 4.29497e+09, 4.29497e+09, 4.29497e+09, 0, 1, 2, 3, 4, 305 )
//
%c1 = call @sparse_cast_u32_to_f32(%1, %zero_f) : (tensor<10xi32, #SV>, tensor<10xf32>) -> tensor<10xf32>
%v1 = vector.transfer_read %c1[%z], %f: tensor<10xf32>, vector<10xf32>
vector.print %v1 : vector<10xf32>
//
// CHECK: ( -4, -3, -2, -1, 0, 1, 2, 3, 4, 305 )
//
%c2 = call @sparse_cast_f32_to_s32(%3, %zero_i) : (tensor<10xf32, #SV>, tensor<10xi32>) -> tensor<10xi32>
%v2 = vector.transfer_read %c2[%z], %i: tensor<10xi32>, vector<10xi32>
vector.print %v2 : vector<10xi32>
//
// CHECK: ( 4294967295, 4294967294, 4294967293, 4294967292, 0, 1, 2, 3, 4, 305 )
//
%c3 = call @sparse_cast_f64_to_u32(%7, %zero_i) : (tensor<10xf64, #SV>, tensor<10xi32>) -> tensor<10xi32>
%v3 = vector.transfer_read %c3[%z], %i: tensor<10xi32>, vector<10xi32>
%vu = vector.bitcast %v3 : vector<10xi32> to vector<10xui32>
vector.print %vu : vector<10xui32>
//
// CHECK: ( -4.4, -3.3, -2.2, -1.1, 0, 1.1, 2.2, 3.3, 4.4, 305.5 )
//
%c4 = call @sparse_cast_f32_to_f64(%3, %zero_d) : (tensor<10xf32, #SV>, tensor<10xf64>) -> tensor<10xf64>
%v4 = vector.transfer_read %c4[%z], %d: tensor<10xf64>, vector<10xf64>
vector.print %v4 : vector<10xf64>
//
// CHECK: ( -4.4, -3.3, -2.2, -1.1, 0, 1.1, 2.2, 3.3, 4.4, 305.5 )
//
%c5 = call @sparse_cast_f64_to_f32(%5, %zero_f) : (tensor<10xf64, #SV>, tensor<10xf32>) -> tensor<10xf32>
%v5 = vector.transfer_read %c5[%z], %f: tensor<10xf32>, vector<10xf32>
vector.print %v5 : vector<10xf32>
//
// CHECK: ( -4, -3, -2, -1, 0, 1, 2, 3, 4, 305 )
//
%c6 = call @sparse_cast_s32_to_u64(%1, %zero_l) : (tensor<10xi32, #SV>, tensor<10xi64>) -> tensor<10xi64>
%v6 = vector.transfer_read %c6[%z], %l: tensor<10xi64>, vector<10xi64>
vector.print %v6 : vector<10xi64>
//
// CHECK: ( 4294967292, 4294967293, 4294967294, 4294967295, 0, 1, 2, 3, 4, 305 )
//
%c7 = call @sparse_cast_u32_to_s64(%1, %zero_l) : (tensor<10xi32, #SV>, tensor<10xi64>) -> tensor<10xi64>
%v7 = vector.transfer_read %c7[%z], %l: tensor<10xi64>, vector<10xi64>
vector.print %v7 : vector<10xi64>
//
// CHECK: ( -4, -3, -2, -1, 0, 1, 2, 3, 4, 49 )
//
%c8 = call @sparse_cast_i32_to_i8(%1, %zero_b) : (tensor<10xi32, #SV>, tensor<10xi8>) -> tensor<10xi8>
%v8 = vector.transfer_read %c8[%z], %b: tensor<10xi8>, vector<10xi8>
vector.print %v8 : vector<10xi8>
//
// CHECK: ( -1064514355, -1068289229, -1072902963, -1081291571, 0, 1066192077, 1074580685, 1079194419, 1082969293, 1134084096 )
//
%c9 = call @sparse_cast_f32_as_s32(%3, %zero_i) : (tensor<10xf32, #SV>, tensor<10xi32>) -> tensor<10xi32>
%v9 = vector.transfer_read %c9[%z], %i: tensor<10xi32>, vector<10xi32>
vector.print %v9 : vector<10xi32>
// Release the resources.
bufferization.dealloc_tensor %1 : tensor<10xi32, #SV>
bufferization.dealloc_tensor %3 : tensor<10xf32, #SV>
bufferization.dealloc_tensor %5 : tensor<10xf64, #SV>
bufferization.dealloc_tensor %7 : tensor<10xf64, #SV>
return
}
}
Reference in New Issue View Git Blame Copy Permalink