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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,96 @@ | ||
| // RUN: buddy-opt %s \ | ||
| // RUN: -pass-pipeline "builtin.module(func.func(tosa-to-linalg-named),func.func(tosa-to-linalg),func.func(tosa-to-tensor),func.func(tosa-to-arith))" \ | ||
| // RUN: | buddy-opt \ | ||
| // RUN: -arith-expand \ | ||
| // RUN: -eliminate-empty-tensors \ | ||
| // RUN: -empty-tensor-to-alloc-tensor \ | ||
| // RUN: -one-shot-bufferize \ | ||
| // RUN: -convert-linalg-to-affine-loops \ | ||
| // RUN: -affine-loop-fusion \ | ||
| // RUN: -lower-affine \ | ||
| // RUN: -func-bufferize \ | ||
| // RUN: -arith-bufferize \ | ||
| // RUN: -tensor-bufferize \ | ||
| // RUN: -buffer-deallocation \ | ||
| // RUN: -finalizing-bufferize \ | ||
| // RUN: -convert-vector-to-scf \ | ||
| // RUN: -expand-strided-metadata \ | ||
| // RUN: -convert-vector-to-llvm \ | ||
| // RUN: -memref-expand \ | ||
| // RUN: -arith-expand \ | ||
| // RUN: -convert-arith-to-llvm \ | ||
| // RUN: -finalize-memref-to-llvm \ | ||
| // RUN: -convert-scf-to-cf \ | ||
| // RUN: -convert-openmp-to-llvm \ | ||
| // RUN: -convert-arith-to-llvm \ | ||
| // RUN: -convert-math-to-llvm \ | ||
| // RUN: -convert-math-to-libm \ | ||
| // RUN: -convert-func-to-llvm \ | ||
| // RUN: -reconcile-unrealized-casts \ | ||
| // RUN: | mlir-cpu-runner -e main -entry-point-result=void \ | ||
| // RUN: -shared-libs=%mlir_runner_utils_dir/libmlir_runner_utils%shlibext \ | ||
| // RUN: -shared-libs=%mlir_runner_utils_dir/libmlir_c_runner_utils%shlibext \ | ||
| // RUN: | FileCheck %s | ||
| module { | ||
| func.func private @printMemrefF32(tensor<*xf32>) | ||
| func.func private @rtclock() -> f64 | ||
|
|
||
| func.func @uvue_original() { | ||
| %t0_original = call @rtclock() : () -> f64 | ||
|
|
||
| %84 = arith.constant dense<2.0> : tensor<1x32x40x128xf32> | ||
| %92 = "tosa.const"() <{value = dense<0.000000e+00> : tensor<1x32x40x128xf32>}> : () -> tensor<1x32x40x128xf32> | ||
| %93 = tosa.add %84, %92 : (tensor<1x32x40x128xf32>, tensor<1x32x40x128xf32>) -> tensor<1x32x40x128xf32> | ||
| %94 = tosa.reshape %93 {new_shape = array<i64: 32, 40, 128>} : (tensor<1x32x40x128xf32>) -> tensor<32x40x128xf32> | ||
|
|
||
| %t1_original = call @rtclock() : () -> f64 | ||
| %tensor_unranked = tensor.cast %94 : tensor<32x40x128xf32> to tensor<*xf32> | ||
|
|
||
| // All the elements of the MemRef are the same, | ||
| // only check the first line to verify the correctness. | ||
| // CHECK: Unranked Memref base@ = {{.*}} rank = 3 offset = 0 sizes = [32, 40, 128] strides = [5120, 128, 1] data = | ||
| // CHECK-NEXT: [ | ||
| // CHECK-SAME: [ | ||
| // CHECK-SAME: [2{{(, 2)*}}], | ||
|
|
||
| // Print results. | ||
| call @printMemrefF32(%tensor_unranked) : (tensor<*xf32>) -> () | ||
| // Print timings. | ||
|
|
||
| %t_original = arith.subf %t1_original, %t0_original : f64 | ||
| vector.print str "original operation time: " | ||
| vector.print %t_original : f64 | ||
| return | ||
| } | ||
|
|
||
| func.func @uve_optimized() { | ||
| %t0_optimized = call @rtclock() : () -> f64 | ||
|
|
||
| %84 = arith.constant dense<2.0> : tensor<1x32x40x128xf32> | ||
| %94 = tosa.reshape %84 {new_shape = array<i64: 32, 40, 128>} : (tensor<1x32x40x128xf32>) -> tensor<32x40x128xf32> | ||
| %t1_optimized = call @rtclock() : () -> f64 | ||
|
|
||
| %tensor_unranked = tensor.cast %94 : tensor<32x40x128xf32> to tensor<*xf32> | ||
|
|
||
|
|
||
|
|
||
| // Print results. | ||
| call @printMemrefF32(%tensor_unranked) : (tensor<*xf32>) -> () | ||
| // Print timings. | ||
|
|
||
| %t_optimized = arith.subf %t1_optimized, %t0_optimized : f64 | ||
| vector.print str "optimized operation time: " | ||
| vector.print %t_optimized : f64 | ||
| return | ||
| } | ||
|
|
||
|
|
||
| func.func @main() { | ||
| %84 = arith.constant dense<2.0> : tensor<1x32x40x128xf32> | ||
|
|
||
| call @uvue_original() : () -> () | ||
| call @uve_optimized() : () -> () | ||
|
|
||
| return | ||
| } | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,96 @@ | ||
| // RUN: buddy-opt %s \ | ||
| // RUN: -pass-pipeline "builtin.module(func.func(tosa-to-linalg-named),func.func(tosa-to-linalg),func.func(tosa-to-tensor),func.func(tosa-to-arith))" \ | ||
| // RUN: | buddy-opt \ | ||
| // RUN: -arith-expand \ | ||
| // RUN: -eliminate-empty-tensors \ | ||
| // RUN: -empty-tensor-to-alloc-tensor \ | ||
| // RUN: -one-shot-bufferize \ | ||
| // RUN: -convert-linalg-to-affine-loops \ | ||
| // RUN: -affine-loop-fusion \ | ||
| // RUN: -lower-affine \ | ||
| // RUN: -func-bufferize \ | ||
| // RUN: -arith-bufferize \ | ||
| // RUN: -tensor-bufferize \ | ||
| // RUN: -buffer-deallocation \ | ||
| // RUN: -finalizing-bufferize \ | ||
| // RUN: -convert-vector-to-scf \ | ||
| // RUN: -expand-strided-metadata \ | ||
| // RUN: -convert-vector-to-llvm \ | ||
| // RUN: -memref-expand \ | ||
| // RUN: -arith-expand \ | ||
| // RUN: -convert-arith-to-llvm \ | ||
| // RUN: -finalize-memref-to-llvm \ | ||
| // RUN: -convert-scf-to-cf \ | ||
| // RUN: -convert-openmp-to-llvm \ | ||
| // RUN: -convert-arith-to-llvm \ | ||
| // RUN: -convert-math-to-llvm \ | ||
| // RUN: -convert-math-to-libm \ | ||
| // RUN: -convert-func-to-llvm \ | ||
| // RUN: -reconcile-unrealized-casts \ | ||
| // RUN: | mlir-cpu-runner -e main -entry-point-result=void \ | ||
| // RUN: -shared-libs=%mlir_runner_utils_dir/libmlir_runner_utils%shlibext \ | ||
| // RUN: -shared-libs=%mlir_runner_utils_dir/libmlir_c_runner_utils%shlibext \ | ||
| // RUN: | FileCheck %s | ||
| module { | ||
| func.func private @printMemrefF32(tensor<*xf32>) | ||
| func.func private @rtclock() -> f64 | ||
|
|
||
| func.func @ie_original() { | ||
| %t0_original = call @rtclock() : () -> f64 | ||
|
|
||
| %119 = arith.constant dense<1.0> : tensor<1x40x32x128xf32> | ||
| %120 = tosa.identity %119 : (tensor<1x40x32x128xf32>) -> tensor<1x40x32x128xf32> | ||
| %121 = tosa.reshape %120 {new_shape = array<i64: 1, 40, 4096>} : (tensor<1x40x32x128xf32>) -> tensor<1x40x4096xf32> | ||
| %t1_original = call @rtclock() : () -> f64 | ||
|
|
||
| %tensor_unranked = tensor.cast %121 : tensor<1x40x4096xf32> to tensor<*xf32> | ||
| // All the elements of the MemRef are the same, | ||
| // only check the first line to verify the correctness. | ||
| // CHECK: Unranked Memref base@ = {{.*}} rank = 3 offset = 0 sizes = [1, 40, 4096] strides = [163840, 4096, 1] data = | ||
| // CHECK-NEXT: [ | ||
| // CHECK-SAME: [ | ||
| // CHECK-SAME: [1{{(, 1)*}}], | ||
|
|
||
| // Print results. | ||
| call @printMemrefF32(%tensor_unranked) : (tensor<*xf32>) -> () | ||
| // Print timings. | ||
|
|
||
| %t_original = arith.subf %t1_original, %t0_original : f64 | ||
| vector.print str "original operation time: " | ||
| vector.print %t_original : f64 | ||
| return | ||
| } | ||
|
|
||
| func.func @ie_optimized() { | ||
| %t0_optimized = call @rtclock() : () -> f64 | ||
|
|
||
| %119 = arith.constant dense<1.0> : tensor<1x40x32x128xf32> | ||
| %121 = tosa.reshape %119 {new_shape = array<i64: 1, 40, 4096>} : (tensor<1x40x32x128xf32>) -> tensor<1x40x4096xf32> | ||
| %t1_optimized = call @rtclock() : () -> f64 | ||
|
|
||
| %tensor_unranked = tensor.cast %121 : tensor<1x40x4096xf32> to tensor<*xf32> | ||
| // All the elements of the MemRef are the same, | ||
| // only check the first line to verify the correctness. | ||
| // CHECK: Unranked Memref base@ = {{.*}} rank = 3 offset = 0 sizes = [1, 40, 4096] strides = [163840, 4096, 1] data = | ||
| // CHECK-NEXT: [ | ||
| // CHECK-SAME: [ | ||
| // CHECK-SAME: [1{{(, 1)*}}], | ||
|
|
||
| // Print results. | ||
| call @printMemrefF32(%tensor_unranked) : (tensor<*xf32>) -> () | ||
| // Print timings. | ||
|
|
||
| %t_optimized = arith.subf %t1_optimized, %t0_optimized : f64 | ||
| vector.print str "optimized operation time: " | ||
| vector.print %t_optimized : f64 | ||
| return | ||
| } | ||
|
|
||
| func.func @main() { | ||
|
|
||
| call @ie_original() : () -> () | ||
| call @ie_optimized() : () -> () | ||
|
|
||
| return | ||
| } | ||
| } |
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