102 lines
4.7 KiB
MLIR
102 lines
4.7 KiB
MLIR
// RUN: mlir-opt %s -allow-unregistered-dialect -pass-pipeline="builtin.module(func.func(linalg-detensorize{aggressive-mode}))" | FileCheck %s
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#map = affine_map<() -> ()>
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func.func @detensor_simple(%arg1: tensor<f32>, %arg2: tensor<f32>) -> tensor<f32> attributes {iree.module.export} {
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%0 = tensor.empty() : tensor<f32>
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%1 = linalg.generic {indexing_maps = [#map, #map, #map], iterator_types = []}
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ins(%arg1, %arg2 : tensor<f32>, tensor<f32>)
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outs(%0 : tensor<f32>) {
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^bb0(%arg3: f32, %arg4: f32, %arg5: f32):
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%2 = arith.addf %arg3, %arg4 : f32
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linalg.yield %2 : f32
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} -> tensor<f32>
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return %1: tensor<f32>
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}
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// CHECK-LABEL: func @detensor_simple
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// CHECK-SAME: (%[[arg1:.*]]: tensor<f32>, %[[arg2:.*]]: tensor<f32>)
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// CHECK-DAG: %[[arg1_val:.*]] = tensor.extract %[[arg1]]
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// CHECK-DAG: %[[arg2_val:.*]] = tensor.extract %[[arg2]]
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// CHECK: %[[detensored_res:.*]] = arith.addf %[[arg1_val]], %[[arg2_val]]
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// CHECK: %[[new_tensor_res:.*]] = tensor.from_elements %[[detensored_res]]
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// CHECK: return %[[new_tensor_res]]
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func.func @detensor_op_sequence(%arg1: tensor<f32>, %arg2: tensor<f32>) -> tensor<f32> attributes {iree.module.export} {
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%0 = tensor.empty() : tensor<f32>
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%1 = linalg.generic {indexing_maps = [#map, #map, #map], iterator_types = []}
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ins(%arg1, %arg2 : tensor<f32>, tensor<f32>)
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outs(%0 : tensor<f32>) {
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^bb0(%arg3: f32, %arg4: f32, %arg5: f32):
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%2 = arith.addf %arg3, %arg4 : f32
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linalg.yield %2 : f32
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} -> tensor<f32>
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%3 = tensor.empty() : tensor<f32>
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%4 = linalg.generic {indexing_maps = [#map, #map, #map], iterator_types = []}
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ins(%arg1, %1 : tensor<f32>, tensor<f32>)
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outs(%3 : tensor<f32>) {
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^bb0(%arg3: f32, %arg4: f32, %arg5: f32):
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%5 = arith.mulf %arg3, %arg4 : f32
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linalg.yield %5 : f32
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} -> tensor<f32>
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%6 = tensor.empty() : tensor<f32>
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%7 = linalg.generic {indexing_maps = [#map, #map, #map], iterator_types = []}
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ins(%1, %4 : tensor<f32>, tensor<f32>)
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outs(%6 : tensor<f32>) {
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^bb0(%arg3: f32, %arg4: f32, %arg5: f32):
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%5 = arith.divf %arg3, %arg4 : f32
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linalg.yield %5 : f32
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} -> tensor<f32>
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return %7: tensor<f32>
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}
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// CHECK-LABEL: func @detensor_op_sequence
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// CHECK-SAME: (%[[arg1:.*]]: tensor<f32>, %[[arg2:.*]]: tensor<f32>)
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// CHECK-DAG: %[[arg1_val:.*]] = tensor.extract %[[arg1]]
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// CHECK-DAG: %[[arg2_val:.*]] = tensor.extract %[[arg2]]
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// CHECK: %[[detensored_res:.*]] = arith.addf %[[arg1_val]], %[[arg2_val]]
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// CHECK: %[[detensored_res2:.*]] = arith.mulf %[[arg1_val]], %[[detensored_res]]
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// CHECK: %[[detensored_res3:.*]] = arith.divf %[[detensored_res]], %[[detensored_res2]]
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// CHECK: %[[new_tensor_res:.*]] = tensor.from_elements %[[detensored_res3]]
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// CHECK: return %[[new_tensor_res]]
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func.func @detensor_multiple_ops(%arg1: tensor<f32>, %arg2: tensor<f32>) -> tensor<f32> attributes {iree.module.export} {
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%0 = tensor.empty() : tensor<f32>
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%1 = linalg.generic {indexing_maps = [#map, #map, #map], iterator_types = []}
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ins(%arg1, %arg2 : tensor<f32>, tensor<f32>)
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outs(%0 : tensor<f32>) {
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^bb0(%arg3: f32, %arg4: f32, %arg5: f32):
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%2 = arith.addf %arg3, %arg4 : f32
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%3 = arith.mulf %2, %arg4 : f32
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linalg.yield %3 : f32
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} -> tensor<f32>
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return %1: tensor<f32>
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}
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// CHECK-LABEL: func @detensor_multiple_ops
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// CHECK-SAME: (%[[arg1:.*]]: tensor<f32>, %[[arg2:.*]]: tensor<f32>)
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// CHECK-DAG: %[[arg1_val:.*]] = tensor.extract %[[arg1]]
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// CHECK-DAG: %[[arg2_val:.*]] = tensor.extract %[[arg2]]
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// CHECK: %[[detensored_res:.*]] = arith.addf %[[arg1_val]], %[[arg2_val]]
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// CHECK: %[[detensored_res2:.*]] = arith.mulf %[[detensored_res]], %[[arg2_val]]
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// CHECK: %[[new_tensor_res:.*]] = tensor.from_elements %[[detensored_res2]]
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// CHECK: return %[[new_tensor_res]]
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func.func @detensor_foreign_op(%arg1: tensor<f32>, %arg2: tensor<f32>) -> tensor<f32> attributes {iree.module.export} {
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%0 = tensor.empty() : tensor<f32>
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%1 = linalg.generic {indexing_maps = [#map, #map, #map], iterator_types = []}
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ins(%arg1, %arg2 : tensor<f32>, tensor<f32>)
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outs(%0 : tensor<f32>) {
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^bb0(%arg3: f32, %arg4: f32, %arg5: f32):
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%2 = "foreign.do_something"(%arg3, %arg4) {} : (f32, f32) -> f32
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linalg.yield %2 : f32
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} -> tensor<f32>
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return %1: tensor<f32>
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}
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// CHECK-LABEL: func @detensor_foreign_op
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// CHECK-SAME: (%[[arg1:.*]]: tensor<f32>, %[[arg2:.*]]: tensor<f32>)
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// CHECK-DAG: %[[arg1_val:.*]] = tensor.extract %[[arg1]]
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// CHECK-DAG: %[[arg2_val:.*]] = tensor.extract %[[arg2]]
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// CHECK: %[[detensored_res:.*]] = "foreign.do_something"(%[[arg1_val]], %[[arg2_val]])
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// CHECK: %[[new_tensor_res:.*]] = tensor.from_elements %[[detensored_res]]
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// CHECK: return %[[new_tensor_res]]
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