c780184a84
Expose `function-boundary-type-conversion` in `OneShotBufferizeOp`. To reuse options between passes and transform operations, create a `BufferizationEnums.td`. Reviewed By: springerm Differential Revision: https://reviews.llvm.org/D137833
121 lines
4.2 KiB
MLIR
121 lines
4.2 KiB
MLIR
// RUN: mlir-opt --test-transform-dialect-interpreter %s -split-input-file -verify-diagnostics | FileCheck %s
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// Test One-Shot Bufferize.
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transform.sequence failures(propagate) {
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^bb0(%arg1: !pdl.operation):
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%0 = transform.structured.match ops{["func.func"]} in %arg1
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transform.bufferization.one_shot_bufferize %0
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{target_is_module = false}
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}
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// CHECK-LABEL: func @test_function(
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// CHECK-SAME: %[[A:.*]]: tensor<?xf32>
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func.func @test_function(%A : tensor<?xf32>, %v : vector<4xf32>) -> (tensor<?xf32>) {
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%c0 = arith.constant 0 : index
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// CHECK: %[[A_memref:.*]] = bufferization.to_memref %[[A]]
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// CHECK: %[[dim:.*]] = memref.dim %[[A_memref]]
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// CHECK: %[[alloc:.*]] = memref.alloc(%[[dim]])
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// CHECK: memref.copy %[[A_memref]], %[[alloc]]
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// CHECK: vector.transfer_write %{{.*}}, %[[alloc]]
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// CHECK: %[[res_tensor:.*]] = bufferization.to_tensor %[[alloc]]
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%0 = vector.transfer_write %v, %A[%c0] : vector<4xf32>, tensor<?xf32>
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// CHECK: memref.dealloc %[[alloc]]
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// CHECK: return %[[res_tensor]]
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return %0 : tensor<?xf32>
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}
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// -----
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// Test analysis of One-Shot Bufferize only.
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transform.sequence failures(propagate) {
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^bb0(%arg1: !pdl.operation):
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%0 = transform.structured.match ops{["func.func"]} in %arg1
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transform.bufferization.one_shot_bufferize %0
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{target_is_module = false, test_analysis_only = true}
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}
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// CHECK-LABEL: func @test_function_analysis(
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// CHECK-SAME: %[[A:.*]]: tensor<?xf32>
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func.func @test_function_analysis(%A : tensor<?xf32>, %v : vector<4xf32>) -> (tensor<?xf32>) {
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%c0 = arith.constant 0 : index
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// CHECK: vector.transfer_write
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// CHECK-SAME: {__inplace_operands_attr__ = ["none", "false", "none"]}
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// CHECK-SAME: tensor<?xf32>
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%0 = vector.transfer_write %v, %A[%c0] : vector<4xf32>, tensor<?xf32>
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return %0 : tensor<?xf32>
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}
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// -----
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// Test One-Shot Bufferize transform failure with an unknown op. This would be
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// allowed with `allow_unknown_ops`.
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transform.sequence failures(propagate) {
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^bb0(%arg1: !pdl.operation):
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%0 = transform.structured.match ops{["func.func"]} in %arg1
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// expected-error @+1 {{bufferization failed}}
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transform.bufferization.one_shot_bufferize %0 {target_is_module = false}
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}
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func.func @test_unknown_op_failure() -> (tensor<?xf32>) {
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// expected-error @+1 {{op was not bufferized}}
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%0 = "test.dummy_op"() : () -> (tensor<?xf32>)
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return %0 : tensor<?xf32>
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}
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// -----
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// Test One-Shot Bufferize transform failure with a module op.
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transform.sequence failures(propagate) {
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^bb0(%arg1: !pdl.operation):
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// %arg1 is the module
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transform.bufferization.one_shot_bufferize %arg1
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}
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module {
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// CHECK-LABEL: func @test_function(
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// CHECK-SAME: %[[A:.*]]: tensor<?xf32>
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func.func @test_function(%A : tensor<?xf32>, %v : vector<4xf32>) -> (tensor<?xf32>) {
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%c0 = arith.constant 0 : index
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// CHECK: %[[A_memref:.*]] = bufferization.to_memref %[[A]]
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// CHECK: %[[dim:.*]] = memref.dim %[[A_memref]]
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// CHECK: %[[alloc:.*]] = memref.alloc(%[[dim]])
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// CHECK: memref.copy %[[A_memref]], %[[alloc]]
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// CHECK: vector.transfer_write %{{.*}}, %[[alloc]]
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// CHECK: %[[res_tensor:.*]] = bufferization.to_tensor %[[alloc]]
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%0 = vector.transfer_write %v, %A[%c0] : vector<4xf32>, tensor<?xf32>
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// CHECK: memref.dealloc %[[alloc]]
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// CHECK: return %[[res_tensor]]
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return %0 : tensor<?xf32>
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}
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}
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// -----
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// Test we use identity layout at function boundaries.
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transform.sequence failures(propagate) {
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^bb0(%arg1: !pdl.operation):
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transform.bufferization.one_shot_bufferize layout{IdentityLayoutMap} %arg1 {
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target_is_module = true,
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bufferize_function_boundaries = true }
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}
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// CHECK: func.func @matmul(
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// CHECK-SAME: %[[A:.*]]: memref<12x9xf32>,
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// CHECK-SAME: %[[B:.*]]: memref<9x6xf32>,
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// CHECK-SAME: %[[C:.*]]: memref<12x6xf32>) -> memref<12x6xf32> {
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func.func @matmul(%A: tensor<12x9xf32>, %B: tensor<9x6xf32>, %C: tensor<12x6xf32>) -> tensor<12x6xf32> {
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// CHECK: linalg.matmul ins(%[[A]], %[[B]] : memref<12x9xf32>, memref<9x6xf32>) outs(%[[C]] : memref<12x6xf32>)
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%D = linalg.matmul ins(%A, %B: tensor<12x9xf32>, tensor<9x6xf32>) outs(%C: tensor<12x6xf32>) -> tensor<12x6xf32>
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// CHECK: return %[[C]] : memref<12x6xf32>
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return %D : tensor<12x6xf32>
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}
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