109 lines
3.6 KiB
MLIR
109 lines
3.6 KiB
MLIR
// RUN: mlir-opt %s -one-shot-bufferize="allow-unknown-ops" -split-input-file | FileCheck %s
|
|
|
|
// Run fuzzer with different seeds.
|
|
// RUN: mlir-opt %s -one-shot-bufferize="test-analysis-only analysis-fuzzer-seed=23" -split-input-file -o /dev/null
|
|
// RUN: mlir-opt %s -one-shot-bufferize="test-analysis-only analysis-fuzzer-seed=59" -split-input-file -o /dev/null
|
|
// RUN: mlir-opt %s -one-shot-bufferize="test-analysis-only analysis-fuzzer-seed=91" -split-input-file -o /dev/null
|
|
|
|
// CHECK-LABEL: func @use_tensor_func_arg(
|
|
// CHECK-SAME: %[[A:.*]]: tensor<?xf32>
|
|
func @use_tensor_func_arg(%A : tensor<?xf32>) -> (vector<4xf32>) {
|
|
%c0 = arith.constant 0 : index
|
|
%f0 = arith.constant 0.0 : f32
|
|
|
|
// CHECK: %[[A_memref:.*]] = bufferization.to_memref %[[A]]
|
|
// CHECK: %[[res:.*]] = vector.transfer_read %[[A_memref]]
|
|
%0 = vector.transfer_read %A[%c0], %f0 : tensor<?xf32>, vector<4xf32>
|
|
|
|
// CHECK: return %[[res]]
|
|
return %0 : vector<4xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
// CHECK-LABEL: func @return_tensor(
|
|
// CHECK-SAME: %[[A:.*]]: tensor<?xf32>
|
|
func @return_tensor(%A : tensor<?xf32>, %v : vector<4xf32>) -> (tensor<?xf32>) {
|
|
%c0 = arith.constant 0 : index
|
|
|
|
// CHECK: %[[A_memref:.*]] = bufferization.to_memref %[[A]]
|
|
// CHECK: %[[dim:.*]] = tensor.dim %[[A]]
|
|
// CHECK: %[[alloc:.*]] = memref.alloc(%[[dim]])
|
|
// CHECK: %[[casted:.*]] = memref.cast %[[alloc]]
|
|
// CHECK: memref.copy %[[A_memref]], %[[alloc]]
|
|
// CHECK: vector.transfer_write %{{.*}}, %[[alloc]]
|
|
// CHECK: %[[res_tensor:.*]] = bufferization.to_tensor %[[casted]]
|
|
%0 = vector.transfer_write %v, %A[%c0] : vector<4xf32>, tensor<?xf32>
|
|
|
|
// CHECK: memref.dealloc %[[alloc]]
|
|
// CHECK: return %[[res_tensor]]
|
|
return %0 : tensor<?xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
// CHECK-LABEL: func @func_without_tensor_args
|
|
func @func_without_tensor_args(%v : vector<10xf32>) -> () {
|
|
// CHECK: %[[alloc:.*]] = memref.alloc()
|
|
%0 = linalg.init_tensor[10] : tensor<10xf32>
|
|
|
|
%c0 = arith.constant 0 : index
|
|
// CHECK: vector.transfer_write %{{.*}}, %[[alloc]]
|
|
%1 = vector.transfer_write %v, %0[%c0] : vector<10xf32>, tensor<10xf32>
|
|
|
|
%cst = arith.constant 0.0 : f32
|
|
// CHECK: vector.transfer_read %[[alloc]]
|
|
%r = vector.transfer_read %1[%c0], %cst : tensor<10xf32>, vector<11xf32>
|
|
|
|
vector.print %r : vector<11xf32>
|
|
return
|
|
}
|
|
|
|
// -----
|
|
|
|
// CHECK-LABEL: func private @private_func
|
|
func private @private_func(tensor<?xf32>) -> ()
|
|
|
|
// CHECK-LABEL: func @empty_func()
|
|
func @empty_func() -> () {
|
|
return
|
|
}
|
|
|
|
// -----
|
|
|
|
// CHECK-LABEL: func @read_after_write_conflict(
|
|
func @read_after_write_conflict(%cst : f32, %idx : index, %idx2 : index)
|
|
-> (f32, f32) {
|
|
// CHECK-DAG: %[[alloc:.*]] = memref.alloc
|
|
// CHECK-DAG: %[[dummy:.*]] = "test.dummy_op"
|
|
// CHECK-DAG: %[[dummy_m:.*]] = bufferization.to_memref %[[dummy]]
|
|
%t = "test.dummy_op"() : () -> (tensor<10xf32>)
|
|
|
|
// CHECK: memref.copy %[[dummy_m]], %[[alloc]]
|
|
// CHECK: memref.store %{{.*}}, %[[alloc]]
|
|
%write = tensor.insert %cst into %t[%idx2] : tensor<10xf32>
|
|
|
|
// CHECK: %[[read:.*]] = "test.some_use"(%[[dummy]])
|
|
%read = "test.some_use"(%t) : (tensor<10xf32>) -> (f32)
|
|
// CHECK: %[[read2:.*]] = memref.load %[[alloc]]
|
|
%read2 = tensor.extract %write[%idx] : tensor<10xf32>
|
|
|
|
// CHECK: memref.dealloc %[[alloc]]
|
|
// CHECK: return %[[read]], %[[read2]]
|
|
return %read, %read2 : f32, f32
|
|
}
|
|
|
|
// -----
|
|
|
|
// CHECK-LABEL: func @copy_deallocated(
|
|
func @copy_deallocated() -> tensor<10xf32> {
|
|
// CHECK: %[[alloc:.*]] = memref.alloc()
|
|
%0 = linalg.init_tensor[10] : tensor<10xf32>
|
|
// CHECK: %[[alloc_tensor:.*]] = bufferization.to_tensor %[[alloc]]
|
|
// CHECK: memref.dealloc %[[alloc]]
|
|
// CHECK: return %[[alloc_tensor]]
|
|
return %0 : tensor<10xf32>
|
|
}
|
|
|
|
|