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[Test] add more plaidML test (#511)
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‎test/PlaidML/CppEdsl.ConvI8.mlir

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// RUN: %python_executable %imex_runner -i %s --pass-pipeline-file=%p/linalg-to-cpu.pp \
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// RUN: --runner mlir-cpu-runner -e main \
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// RUN: --shared-libs=%mlir_runner_utils \
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// RUN: --entry-point-result=void | FileCheck %s
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// RUN: %gpu_skip || %python_executable %imex_runner -i %s --pass-pipeline-file=%p/linalg-to-llvm.pp \
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// RUN: --runner mlir-cpu-runner -e main \
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// RUN: --entry-point-result=void \
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// RUN: --shared-libs=%mlir_runner_utils,%mlir_c_runner_utils,%sycl_runtime | FileCheck %s
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#map0 = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>
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#map1 = affine_map<(d0, d1, d2, d3, d4, d5, d6) -> (d0, d1 + d4, d2 + d5, d6)>
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#map2 = affine_map<(d0, d1, d2, d3, d4, d5, d6) -> (d4, d5, d6, d3)>
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#map3 = affine_map<(d0, d1, d2, d3, d4, d5, d6) -> (d0, d1, d2, d3)>
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module @convolution {
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func.func @test(%arg0: tensor<1x224x224x3xi8>, %arg1: tensor<3x3x3x32xi8>) -> tensor<1x224x224x32xi8> {
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%c0_i8 = arith.constant 0 : i8
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%0 = tensor.empty() : tensor<1x224x224x3xi8>
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%1 = linalg.generic {indexing_maps = [#map0, #map0], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%arg0 : tensor<1x224x224x3xi8>) outs(%0 : tensor<1x224x224x3xi8>) {
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^bb0(%arg2: i8, %arg3: i8):
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linalg.yield %arg2 : i8
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} -> tensor<1x224x224x3xi8>
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%c0_i8_0 = arith.constant 0 : i8
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%2 = tensor.pad %1 low[0, 1, 1, 0] high[0, 1, 1, 0] {
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^bb0(%arg2: index, %arg3: index, %arg4: index, %arg5: index):
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tensor.yield %c0_i8_0 : i8
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} : tensor<1x224x224x3xi8> to tensor<1x226x226x3xi8>
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%3 = tensor.empty() : tensor<1x224x224x32xi8>
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%4 = linalg.fill ins(%c0_i8 : i8) outs(%3 : tensor<1x224x224x32xi8>) -> tensor<1x224x224x32xi8>
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%5 = linalg.generic {indexing_maps = [#map1, #map2, #map3], iterator_types = ["parallel", "parallel", "parallel", "parallel", "reduction", "reduction", "reduction"]} ins(%2, %arg1 : tensor<1x226x226x3xi8>, tensor<3x3x3x32xi8>) outs(%4 : tensor<1x224x224x32xi8>) attrs = {iterator_ranges = [1, 224, 224, 32, 3, 3, 3]} {
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^bb0(%arg2: i8, %arg3: i8, %arg4: i8):
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%6 = arith.muli %arg2, %arg3 : i8
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%7 = arith.addi %arg4, %6 : i8
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linalg.yield %7 : i8
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} -> tensor<1x224x224x32xi8>
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return %5 : tensor<1x224x224x32xi8>
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}
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func.func @main() {
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%0 = arith.constant dense<1> : tensor<1x224x224x3xi8>
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%1 = arith.constant dense<1> : tensor<3x3x3x32xi8>
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%2 = call @test(%0, %1) : (tensor<1x224x224x3xi8>, tensor<3x3x3x32xi8>) -> tensor<1x224x224x32xi8>
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%unranked = tensor.cast %2 : tensor<1x224x224x32xi8> to tensor<*xi8>
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call @printMemrefI8(%unranked) : (tensor<*xi8>) -> ()
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return
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}
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// CHECK: Unranked Memref base@ = {{(0x)?[-9a-f]*}}
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// CHECK-NEXT:
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func.func private @printMemrefI8(tensor<*xi8>)
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}

‎test/PlaidML/CppEdsl.Convolution.mlir

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// RUN: %python_executable %imex_runner -i %s --pass-pipeline-file=%p/linalg-to-cpu.pp \
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// RUN: --runner mlir-cpu-runner -e main \
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// RUN: --shared-libs=%mlir_runner_utils,%mlir_c_runner_utils \
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// RUN: --entry-point-result=void | FileCheck %s
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// RUN: %gpu_skip || %python_executable %imex_runner -i %s --pass-pipeline-file=%p/linalg-to-llvm.pp \
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// RUN: --runner mlir-cpu-runner -e main \
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// RUN: --entry-point-result=void \
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// RUN: --shared-libs=%mlir_runner_utils,%mlir_c_runner_utils,%sycl_runtime | FileCheck %s
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#map0 = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>
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#map1 = affine_map<(d0, d1, d2, d3, d4, d5, d6) -> (d0, d1 + d4, d2 + d5, d6)>
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#map2 = affine_map<(d0, d1, d2, d3, d4, d5, d6) -> (d4, d5, d6, d3)>
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#map3 = affine_map<(d0, d1, d2, d3, d4, d5, d6) -> (d0, d1, d2, d3)>
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module @convolution {
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func.func @test(%arg0: tensor<1x56x56x64xf32>, %arg1: tensor<3x3x64x64xf32>) -> tensor<1x56x56x64xf32> {
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%cst = arith.constant 0.000000e+00 : f32
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%0 = tensor.empty() : tensor<1x56x56x64xf32>
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%1 = linalg.generic {indexing_maps = [#map0, #map0], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%arg0 : tensor<1x56x56x64xf32>) outs(%0 : tensor<1x56x56x64xf32>) {
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^bb0(%arg2: f32, %arg3: f32):
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linalg.yield %arg2 : f32
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} -> tensor<1x56x56x64xf32>
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%cst_0 = arith.constant 0.000000e+00 : f32
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%2 = tensor.pad %1 low[0, 1, 1, 0] high[0, 1, 1, 0] {
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^bb0(%arg2: index, %arg3: index, %arg4: index, %arg5: index):
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tensor.yield %cst_0 : f32
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} : tensor<1x56x56x64xf32> to tensor<1x58x58x64xf32>
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%3 = tensor.empty() : tensor<1x56x56x64xf32>
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%4 = linalg.fill ins(%cst : f32) outs(%3 : tensor<1x56x56x64xf32>) -> tensor<1x56x56x64xf32>
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%5 = linalg.generic {indexing_maps = [#map1, #map2, #map3], iterator_types = ["parallel", "parallel", "parallel", "parallel", "reduction", "reduction", "reduction"]} ins(%2, %arg1 : tensor<1x58x58x64xf32>, tensor<3x3x64x64xf32>) outs(%4 : tensor<1x56x56x64xf32>) attrs = {iterator_ranges = [1, 56, 56, 64, 3, 3, 64]} {
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^bb0(%arg2: f32, %arg3: f32, %arg4: f32):
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%6 = arith.mulf %arg2, %arg3 : f32
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%7 = arith.addf %arg4, %6 : f32
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linalg.yield %7 : f32
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} -> tensor<1x56x56x64xf32>
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return %5 : tensor<1x56x56x64xf32>
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}
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func.func @main() {
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%0 = arith.constant dense<1.0> : tensor<1x56x56x64xf32>
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%1 = arith.constant dense<0.5> : tensor<3x3x64x64xf32>
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%2 = call @test(%0, %1) : (tensor<1x56x56x64xf32>, tensor<3x3x64x64xf32>) -> tensor<1x56x56x64xf32>
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%unranked = tensor.cast %2 : tensor<1x56x56x64xf32> to tensor<*xf32>
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call @printMemrefF32(%unranked) : (tensor<*xf32>) -> ()
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// CHECK: Unranked Memref base@ = {{(0x)?[-9a-f]*}}
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// CHECK-NEXT: [128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128
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return
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}
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func.func private @printMemrefF32(%ptr : tensor<*xf32>)
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}

‎test/PlaidML/CppEdsl.DefractLong.mlir

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// RUN: %python_executable %imex_runner -i %s --pass-pipeline-file=%p/linalg-to-cpu.pp \
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// RUN: --runner mlir-cpu-runner -e main \
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// RUN: --shared-libs=%mlir_runner_utils \
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// RUN: --entry-point-result=void | FileCheck %s
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// RUN: %gpu_skip || %python_executable %imex_runner -i %s --pass-pipeline-file=%p/linalg-to-llvm.pp \
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// RUN: --runner mlir-cpu-runner -e main \
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// RUN: --entry-point-result=void \
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// RUN: --shared-libs=%mlir_runner_utils,%mlir_c_runner_utils,%sycl_runtime | FileCheck %s
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#map0 = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>
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#map1 = affine_map<(d0, d1, d2, d3, d4, d5, d6, d7, d8, d9, d10) -> (d1, -d0 + d3 + d8 + 1, -d0 + d4 + d5 + d9, d10)>
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#map2 = affine_map<(d0, d1, d2, d3, d4, d5, d6, d7, d8, d9, d10) -> (d0 * 4 + d2 * 2 + d7 - d8 * 2, d0 * 4 + d2 - d4 + d7 - d9 * 2 + 3, d6, d10)>
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#map3 = affine_map<(d0, d1, d2, d3, d4, d5, d6, d7, d8, d9, d10) -> (d1, d2 + d3 * 2 + 1, d4 + d5 * 2, d6)>
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#set = affine_set<(d0, d1, d2, d3, d4, d5, d6, d7, d8, d9, d10) : (d2 + d4 + d7 + (d9 - 1) * 2 + 1 >= 0, -d4 - d5 * 2 + 4 >= 0)>
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module @defract_long {
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func.func @main(%arg0: tensor<1x3x3x1xf32>, %arg1: tensor<1x3x3x1xf32>) -> tensor<1x5x5x1xf32> {
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%cst = arith.constant 0.000000e+00 : f32
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%0 = tensor.empty() : tensor<1x3x3x1xf32>
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%1 = linalg.generic {indexing_maps = [#map0, #map0], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%arg0 : tensor<1x3x3x1xf32>) outs(%0 : tensor<1x3x3x1xf32>) {
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^bb0(%arg2: f32, %arg3: f32):
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linalg.yield %arg2 : f32
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} -> tensor<1x3x3x1xf32>
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%cst_0 = arith.constant 0.000000e+00 : f32
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%2 = tensor.pad %1 low[0, 0, 1, 0] high[0, 0, 1, 0] {
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^bb0(%arg2: index, %arg3: index, %arg4: index, %arg5: index):
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tensor.yield %cst_0 : f32
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} : tensor<1x3x3x1xf32> to tensor<1x3x5x1xf32>
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%3 = tensor.empty() : tensor<1x3x3x1xf32>
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%4 = linalg.generic {indexing_maps = [#map0, #map0], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%arg1 : tensor<1x3x3x1xf32>) outs(%3 : tensor<1x3x3x1xf32>) {
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^bb0(%arg2: f32, %arg3: f32):
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linalg.yield %arg2 : f32
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} -> tensor<1x3x3x1xf32>
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%cst_1 = arith.constant 0.000000e+00 : f32
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%5 = tensor.pad %4 low[0, 0, 0, 0] high[0, 1, 0, 0] {
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^bb0(%arg2: index, %arg3: index, %arg4: index, %arg5: index):
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tensor.yield %cst_1 : f32
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} : tensor<1x3x3x1xf32> to tensor<1x4x3x1xf32>
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%6 = tensor.empty() : tensor<1x5x5x1xf32>
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%7 = linalg.fill ins(%cst : f32) outs(%6 : tensor<1x5x5x1xf32>) -> tensor<1x5x5x1xf32>
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//%8 = linalg.generic {indexing_maps = [#map1, #map2, #map3], iterator_types = ["reduction", "parallel", "window", "window", "window", "window",
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%8 = linalg.generic {indexing_maps = [#map1, #map2, #map3], iterator_types = ["reduction", "parallel", "parallel", "parallel", "parallel", "parallel", "parallel", "reduction", "reduction", "reduction", "reduction"]} ins(%2, %5 : tensor<1x3x5x1xf32>, tensor<1x4x3x1xf32>) outs(%7 : tensor<1x5x5x1xf32>) attrs = {constraints = #set, iterator_ranges = [1, 1, 1, 2, 2, 3, 1, 1, 1, 2, 1]} {
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^bb0(%arg2: f32, %arg3: f32, %arg4: f32):
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%9 = arith.mulf %arg2, %arg3 : f32
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%10 = arith.addf %arg4, %9 : f32
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linalg.yield %10 : f32
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} -> tensor<1x5x5x1xf32>
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return %8 : tensor<1x5x5x1xf32>
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}
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}

‎test/PlaidML/CppEdsl.DoubleDot.mlir

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// RUN: %python_executable %imex_runner -i %s --pass-pipeline-file=%p/linalg-to-cpu.pp \
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// RUN: --runner mlir-cpu-runner -e main \
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// RUN: --shared-libs=%mlir_runner_utils \
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// RUN: --entry-point-result=void | FileCheck %s
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// RUN: %gpu_skip || %python_executable %imex_runner -i %s --pass-pipeline-file=%p/linalg-to-llvm.pp \
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// RUN: --runner mlir-cpu-runner -e main \
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// RUN: --entry-point-result=void \
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// RUN: --shared-libs=%mlir_runner_utils,%mlir_c_runner_utils,%sycl_runtime | FileCheck %s
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#map0 = affine_map<(d0, d1, d2) -> (d0, d2)>
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#map1 = affine_map<(d0, d1, d2) -> (d2, d1)>
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#map2 = affine_map<(d0, d1, d2) -> (d0, d1)>
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module @double_dot {
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func.func @test(%arg0: tensor<10x20xf32>, %arg1: tensor<20x30xf32>, %arg2: tensor<30x40xf32>) -> tensor<10x40xf32> {
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%cst = arith.constant 0.000000e+00 : f32
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%0 = tensor.empty() : tensor<10x30xf32>
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%1 = linalg.fill ins(%cst : f32) outs(%0 : tensor<10x30xf32>) -> tensor<10x30xf32>
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%2 = linalg.generic {indexing_maps = [#map0, #map1, #map2], iterator_types = ["parallel", "parallel", "reduction"]} ins(%arg0, %arg1 : tensor<10x20xf32>, tensor<20x30xf32>) outs(%1 : tensor<10x30xf32>) attrs = {iterator_ranges = [10, 30, 20]} {
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^bb0(%arg3: f32, %arg4: f32, %arg5: f32):
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%6 = arith.mulf %arg3, %arg4 : f32
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%7 = arith.addf %arg5, %6 : f32
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linalg.yield %7 : f32
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} -> tensor<10x30xf32>
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%3 = tensor.empty() : tensor<10x40xf32>
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%4 = linalg.fill ins(%cst : f32) outs(%3 : tensor<10x40xf32>) -> tensor<10x40xf32>
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%5 = linalg.generic {indexing_maps = [#map0, #map1, #map2], iterator_types = ["parallel", "parallel", "reduction"]} ins(%2, %arg2 : tensor<10x30xf32>, tensor<30x40xf32>) outs(%4 : tensor<10x40xf32>) attrs = {iterator_ranges = [10, 40, 30]} {
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^bb0(%arg3: f32, %arg4: f32, %arg5: f32):
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%6 = arith.mulf %arg3, %arg4 : f32
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%7 = arith.addf %arg5, %6 : f32
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linalg.yield %7 : f32
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} -> tensor<10x40xf32>
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return %5 : tensor<10x40xf32>
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}
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func.func @main() {
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%0 = arith.constant dense<1.0> : tensor<10x20xf32>
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%1 = arith.constant dense<2.0> : tensor<20x30xf32>
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%2 = arith.constant dense<3.0> : tensor<30x40xf32>
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%3 = call @test(%0, %1, %2) : (tensor<10x20xf32>, tensor<20x30xf32>, tensor<30x40xf32>) -> tensor<10x40xf32>
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%unranked = tensor.cast %3 : tensor<10x40xf32> to tensor<*xf32>
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call @printMemrefF32(%unranked) : (tensor<*xf32>) -> ()
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// CHECK: Unranked Memref base@ = {{(0x)?[-9a-f]*}}
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// CHECK-NEXT: [3600, 3600, 3600, 3600, 3600, 3600, 3600, 3600, 3600, 3600, 3600, 3600, 3600, 3600, 3600, 3600, 3600
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return
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}
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func.func private @printMemrefF32(%ptr : tensor<*xf32>)
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}

‎test/PlaidML/CppEdsl.DupOut.mlir

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// RUN: %python_executable %imex_runner -i %s --pass-pipeline-file=%p/linalg-to-cpu.pp \
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// RUN: --runner mlir-cpu-runner -e main \
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// RUN: --shared-libs=%mlir_runner_utils \
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// RUN: --entry-point-result=void | FileCheck %s
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// RUN: %gpu_skip || %python_executable %imex_runner -i %s --pass-pipeline-file=%p/linalg-to-llvm.pp \
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// RUN: --runner mlir-cpu-runner -e main \
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// RUN: --entry-point-result=void \
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// RUN: --shared-libs=%mlir_runner_utils,%mlir_c_runner_utils,%sycl_runtime | FileCheck %s
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#map0 = affine_map<(d0, d1, d2) -> (d0, d2)>
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#map1 = affine_map<(d0, d1, d2) -> (d2, d1)>
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#map2 = affine_map<(d0, d1, d2) -> (d0, d1)>
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#map3 = affine_map<(d0, d1) -> (d0, d1)>
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module @dup_out {
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func.func @test(%arg0: tensor<10x20xf32>, %arg1: tensor<20x30xf32>, %arg2: tensor<30x40xf32>) -> (tensor<10x40xf32>, tensor<10x40xf32>, tensor<10x40xf32>) {
15+
%cst = arith.constant 0.000000e+00 : f32
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%0 = tensor.empty() : tensor<10x30xf32>
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%1 = linalg.fill ins(%cst : f32) outs(%0 : tensor<10x30xf32>) -> tensor<10x30xf32>
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%2 = linalg.generic {indexing_maps = [#map0, #map1, #map2], iterator_types = ["parallel", "parallel", "reduction"]} ins(%arg0, %arg1 : tensor<10x20xf32>, tensor<20x30xf32>) outs(%1 : tensor<10x30xf32>) attrs = {iterator_ranges = [10, 30, 20]} {
19+
^bb0(%arg3: f32, %arg4: f32, %arg5: f32):
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%10 = arith.mulf %arg3, %arg4 : f32
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%11 = arith.addf %arg5, %10 : f32
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linalg.yield %11 : f32
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} -> tensor<10x30xf32>
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%3 = tensor.empty() : tensor<10x40xf32>
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%4 = linalg.fill ins(%cst : f32) outs(%3 : tensor<10x40xf32>) -> tensor<10x40xf32>
26+
%5 = linalg.generic {indexing_maps = [#map0, #map1, #map2], iterator_types = ["parallel", "parallel", "reduction"]} ins(%2, %arg2 : tensor<10x30xf32>, tensor<30x40xf32>) outs(%4 : tensor<10x40xf32>) attrs = {iterator_ranges = [10, 40, 30]} {
27+
^bb0(%arg3: f32, %arg4: f32, %arg5: f32):
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%10 = arith.mulf %arg3, %arg4 : f32
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%11 = arith.addf %arg5, %10 : f32
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linalg.yield %11 : f32
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} -> tensor<10x40xf32>
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%6 = tensor.empty() : tensor<10x40xf32>
33+
%7 = linalg.generic {indexing_maps = [#map3, #map3], iterator_types = ["parallel", "parallel"]} ins(%5 : tensor<10x40xf32>) outs(%6 : tensor<10x40xf32>) {
34+
^bb0(%arg3: f32, %arg4: f32):
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linalg.yield %arg3 : f32
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} -> tensor<10x40xf32>
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%8 = tensor.empty() : tensor<10x40xf32>
38+
%9 = linalg.generic {indexing_maps = [#map3, #map3], iterator_types = ["parallel", "parallel"]} ins(%5 : tensor<10x40xf32>) outs(%8 : tensor<10x40xf32>) {
39+
^bb0(%arg3: f32, %arg4: f32):
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linalg.yield %arg3 : f32
41+
} -> tensor<10x40xf32>
42+
return %5, %7, %9 : tensor<10x40xf32>, tensor<10x40xf32>, tensor<10x40xf32>
43+
}
44+
func.func @main() {
45+
%0 = arith.constant dense<1.0> : tensor<10x20xf32>
46+
%1 = arith.constant dense<2.0> : tensor<20x30xf32>
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%2 = arith.constant dense<3.0> : tensor<30x40xf32>
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%3:3 = call @test(%0, %1, %2) : (tensor<10x20xf32>, tensor<20x30xf32>, tensor<30x40xf32>) -> (tensor<10x40xf32>, tensor<10x40xf32>, tensor<10x40xf32>)
49+
%unranked0 = tensor.cast %3#0 : tensor<10x40xf32> to tensor<*xf32>
50+
call @printMemrefF32(%unranked0) : (tensor<*xf32>) -> ()
51+
// CHECK: Unranked Memref base@ = {{(0x)?[-9a-f]*}}
52+
// CHECK-NEXT: [3600, 3600, 3600, 3600, 3600, 3600, 3600, 3600, 3600, 3600, 3600, 3600, 3600, 3600,
53+
%unranked1 = tensor.cast %3#1 : tensor<10x40xf32> to tensor<*xf32>
54+
call @printMemrefF32(%unranked1) : (tensor<*xf32>) -> ()
55+
// CHECK: Unranked Memref base@ = {{(0x)?[-9a-f]*}}
56+
// CHECK-NEXT: [3600, 3600, 3600, 3600, 3600, 3600, 3600, 3600, 3600, 3600, 3600, 3600, 3600, 3600,
57+
%unranked2 = tensor.cast %3#2 : tensor<10x40xf32> to tensor<*xf32>
58+
call @printMemrefF32(%unranked2) : (tensor<*xf32>) -> ()
59+
// CHECK: Unranked Memref base@ = {{(0x)?[-9a-f]*}}
60+
// CHECK-NEXT: [3600, 3600, 3600, 3600, 3600, 3600, 3600, 3600, 3600, 3600, 3600, 3600, 3600, 3600,
61+
return
62+
}
63+
64+
func.func private @printMemrefF32(%ptr : tensor<*xf32>)
65+
}

‎test/PlaidML/lit.local.cfg

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -14,7 +14,6 @@ local_excludes = [
1414
'OpTest.LogicalOr_int32.mlir',
1515
'OpTest.MnistCnn.mlir',
1616
'OpTest.SinH.mlir',
17-
'OpTest.Sum.mlir',
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'OpTest.Tan.mlir',
1918
'OpTest.GEMV_INT8.mlir',
2019
'OpTest.GEMM_INT8.mlir',
@@ -27,7 +26,8 @@ local_excludes = [
2726
'OpTest.LogicalOr_int32.mlir',
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'OpTest.MnistCnn.mlir',
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'OpTest.SinH.mlir',
30-
'OpTest.Tan.mlir',
29+
'CppEdsl.ConvI8.mlir',
30+
'CppEdsl.DefractLong.mlir'
3131
]
3232
config.excludes.update(local_excludes)
3333

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