92 lines
3.4 KiB
MLIR
92 lines
3.4 KiB
MLIR
// RUN: mlir-hlo-opt %s -chlo-legalize-to-hlo \
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// RUN: -hlo-legalize-to-lhlo -buffer-hoisting \
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// RUN: -buffer-deallocation -copy-removal -canonicalize -cse \
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// RUN: -lhlo-legalize-to-linalg -lhlo-fuse-linalg -convert-linalg-to-loops \
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// RUN: -lower-affine -convert-scf-to-std -canonicalize -cse \
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// RUN: -convert-std-to-llvm | mlir-cpu-runner -e main \
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// RUN: -entry-point-result=void \
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// RUN: -shared-libs=%mlir_runner_utils_dir/libmlir_runner_utils%shlibext | \
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// RUN: FileCheck %s
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func @main() -> () {
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call @reduce_add() : () -> ()
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call @reduce_max() : () -> ()
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return
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}
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func private @print_memref_f32(memref<*xf32>) attributes { llvm.emit_c_interface }
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func @reduce_add() {
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%c0 = constant 0 : index
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%c1 = constant 1 : index
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// Initialize input.
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%input = memref.alloc() : memref<2x3xf32>
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%dim_x = memref.dim %input, %c0 : memref<2x3xf32>
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%dim_y = memref.dim %input, %c1 : memref<2x3xf32>
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scf.parallel (%i, %j) = (%c0, %c0) to (%dim_x, %dim_y) step (%c1, %c1) {
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%i_i64 = index_cast %i : index to i64
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%i_f32 = sitofp %i_i64 : i64 to f32
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memref.store %i_f32, %input[%i, %j] : memref<2x3xf32>
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}
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%unranked_input = memref.cast %input : memref<2x3xf32> to memref<*xf32>
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call @print_memref_f32(%unranked_input) : (memref<*xf32>) -> ()
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// CHECK: rank = 2 offset = 0 sizes = [2, 3] strides = [3, 1]
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// CHECK: [0, 0, 0]
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// CHECK: [1, 1, 1]
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%in = memref.tensor_load %input : memref<2x3xf32>
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%init = mhlo.constant dense<0.000000e+00> : tensor<f32>
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%reduce = "mhlo.reduce"(%in, %init) ( {
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^bb0(%arg2: tensor<f32>, %arg3: tensor<f32>):
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%1 = mhlo.add %arg2, %arg3 : tensor<f32>
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"mhlo.return"(%1) : (tensor<f32>) -> ()
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}) {dimensions = dense<1> : tensor<1xi64>}
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: (tensor<2x3xf32>, tensor<f32>) -> tensor<2xf32>
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%output = memref.buffer_cast %reduce : memref<2xf32>
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%unranked_output = memref.cast %output : memref<2xf32> to memref<*xf32>
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call @print_memref_f32(%unranked_output) : (memref<*xf32>) -> ()
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// CHECK: rank = 1 offset = 0 sizes = [2] strides = [1]
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// CHECK: [0, 3]
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return
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}
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func @reduce_max() {
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%c0 = constant 0 : index
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%c1 = constant 1 : index
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// Initialize input.
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%input = memref.alloc() : memref<2x3xf32>
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%dim_x = memref.dim %input, %c0 : memref<2x3xf32>
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%dim_y = memref.dim %input, %c1 : memref<2x3xf32>
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scf.parallel (%i, %j) = (%c0, %c0) to (%dim_x, %dim_y) step (%c1, %c1) {
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%i_i64 = index_cast %i : index to i64
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%i_f32 = sitofp %i_i64 : i64 to f32
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memref.store %i_f32, %input[%i, %j] : memref<2x3xf32>
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}
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%unranked_input = memref.cast %input : memref<2x3xf32> to memref<*xf32>
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call @print_memref_f32(%unranked_input) : (memref<*xf32>) -> ()
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// CHECK: rank = 2 offset = 0 sizes = [2, 3] strides = [3, 1]
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// CHECK: [0, 0, 0]
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// CHECK: [1, 1, 1]
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%in = memref.tensor_load %input : memref<2x3xf32>
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%init = mhlo.constant dense<0xff800000> : tensor<f32>
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%reduce = "mhlo.reduce"(%in, %init) ( {
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^bb0(%arg2: tensor<f32>, %arg3: tensor<f32>):
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%1 = mhlo.maximum %arg2, %arg3 : tensor<f32>
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"mhlo.return"(%1) : (tensor<f32>) -> ()
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}) {dimensions = dense<1> : tensor<1xi64>}
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: (tensor<2x3xf32>, tensor<f32>) -> tensor<2xf32>
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%output = memref.buffer_cast %reduce : memref<2xf32>
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%unranked_output = memref.cast %output : memref<2xf32> to memref<*xf32>
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call @print_memref_f32(%unranked_output) : (memref<*xf32>) -> ()
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// CHECK: rank = 1 offset = 0 sizes = [2] strides = [1]
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// CHECK: [0, 1]
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return
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}
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