As described in issue llvm/llvm-project#91518, a previous PR llvm/llvm-project#78484 introduced the `defaultMemorySpaceFn` into bufferization options, allowing one to inform OneShotBufferize that it should use a specified function to derive the memory space attribute from the encoding attribute attached to tensor types. However, introducing this feature exposed unhandled edge cases, examples of which are introduced by this change in the new test under `test/Dialect/Bufferization/Transforms/one-shot-bufferize-encodings.mlir`. Fixing the inconsistencies introduced by `defaultMemorySpaceFn` is pretty simple. This change: - Updates the `bufferization.to_memref` and `bufferization.to_tensor` operations to explicitly include operand and destination types, whereas previously they relied on type inference to deduce the tensor types. Since the type inference cannot recover the correct tensor encoding/memory space, the operand and result types must be explicitly included. This is a small assembly format change, but it touches a large number of test files. - Makes minor updates to other bufferization functions to handle the changes in building the above ops. - Updates bufferization of `tensor.from_elements` to handle memory space. Integration/upgrade guide: In downstream projects, if you have tests or MLIR files that explicitly use `bufferization.to_tensor` or `bufferization.to_memref`, then update them to the new assembly format as follows: ``` %1 = bufferization.to_memref %0 : memref<10xf32> %2 = bufferization.to_tensor %1 : memref<10xf32> ``` becomes ``` %1 = bufferization.to_memref %0 : tensor<10xf32> to memref<10xf32> %2 = bufferization.to_tensor %0 : memref<10xf32> to tensor<10xf32> ```
137 lines
4.8 KiB
MLIR
137 lines
4.8 KiB
MLIR
// RUN: mlir-opt %s -allow-unregistered-dialect -one-shot-bufferize="bufferize-function-boundaries=1" -split-input-file -verify-diagnostics
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func.func @scf_for(%A : tensor<?xf32>,
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%B : tensor<?xf32> {bufferization.writable = true},
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%C : tensor<4xf32>,
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%lb : index, %ub : index, %step : index)
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-> (f32, f32)
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{
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%r0:2 = scf.for %i = %lb to %ub step %step iter_args(%tA = %A, %tB = %B)
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-> (tensor<?xf32>, tensor<?xf32>)
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{
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%ttA = tensor.insert_slice %C into %tA[0][4][1] : tensor<4xf32> into tensor<?xf32>
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%ttB = tensor.insert_slice %C into %tB[0][4][1] : tensor<4xf32> into tensor<?xf32>
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// Throw a wrench in the system by swapping yielded values: this result in a
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// ping-pong of values at each iteration on which we currently want to fail.
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// expected-error @+1 {{Yield operand #0 is not equivalent to the corresponding iter bbArg}}
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scf.yield %ttB, %ttA : tensor<?xf32>, tensor<?xf32>
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}
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%f0 = tensor.extract %r0#0[%step] : tensor<?xf32>
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%f1 = tensor.extract %r0#1[%step] : tensor<?xf32>
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return %f0, %f1: f32, f32
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}
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// -----
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func.func @scf_while_non_equiv_condition(%arg0: tensor<5xi1>,
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%arg1: tensor<5xi1>,
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%idx: index) -> (i1, i1)
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{
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%r0, %r1 = scf.while (%w0 = %arg0, %w1 = %arg1)
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: (tensor<5xi1>, tensor<5xi1>) -> (tensor<5xi1>, tensor<5xi1>) {
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%condition = tensor.extract %w0[%idx] : tensor<5xi1>
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// expected-error @+1 {{Condition arg #0 is not equivalent to the corresponding iter bbArg}}
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scf.condition(%condition) %w1, %w0 : tensor<5xi1>, tensor<5xi1>
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} do {
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^bb0(%b0: tensor<5xi1>, %b1: tensor<5xi1>):
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%pos = "dummy.some_op"() : () -> (index)
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%val = "dummy.another_op"() : () -> (i1)
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%1 = tensor.insert %val into %b0[%pos] : tensor<5xi1>
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scf.yield %1, %b1 : tensor<5xi1>, tensor<5xi1>
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}
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%v0 = tensor.extract %r0[%idx] : tensor<5xi1>
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%v1 = tensor.extract %r1[%idx] : tensor<5xi1>
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return %v0, %v1 : i1, i1
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}
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// -----
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func.func @scf_while_non_equiv_yield(%arg0: tensor<5xi1>,
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%arg1: tensor<5xi1>,
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%idx: index) -> (i1, i1)
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{
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%r0, %r1 = scf.while (%w0 = %arg0, %w1 = %arg1)
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: (tensor<5xi1>, tensor<5xi1>) -> (tensor<5xi1>, tensor<5xi1>) {
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%condition = tensor.extract %w0[%idx] : tensor<5xi1>
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scf.condition(%condition) %w0, %w1 : tensor<5xi1>, tensor<5xi1>
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} do {
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^bb0(%b0: tensor<5xi1>, %b1: tensor<5xi1>):
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%pos = "dummy.some_op"() : () -> (index)
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%val = "dummy.another_op"() : () -> (i1)
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%1 = tensor.insert %val into %b0[%pos] : tensor<5xi1>
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// expected-error @+1 {{Yield operand #0 is not equivalent to the corresponding iter bbArg}}
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scf.yield %b1, %1 : tensor<5xi1>, tensor<5xi1>
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}
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%v0 = tensor.extract %r0[%idx] : tensor<5xi1>
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%v1 = tensor.extract %r1[%idx] : tensor<5xi1>
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return %v0, %v1 : i1, i1
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}
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// -----
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func.func @to_tensor_op_unsupported(%m: memref<?xf32>, %idx: index) -> (f32) {
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// expected-error @+1 {{to_tensor ops without `restrict` are not supported by One-Shot Analysis}}
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%0 = bufferization.to_tensor %m : memref<?xf32> to tensor<?xf32>
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%1 = tensor.extract %0[%idx] : tensor<?xf32>
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return %1 : f32
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}
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// -----
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func.func @yield_alloc_dominance_test_2(%cst : f32, %idx : index,
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%idx2 : index) -> f32 {
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%1 = bufferization.alloc_tensor(%idx) : tensor<?xf32>
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%0 = scf.execute_region -> tensor<?xf32> {
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// This YieldOp returns a value that is defined in a parent block, thus
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// no error.
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scf.yield %1 : tensor<?xf32>
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}
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%2 = tensor.insert %cst into %0[%idx] : tensor<?xf32>
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%r = tensor.extract %2[%idx2] : tensor<?xf32>
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return %r : f32
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}
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// -----
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func.func @copy_of_unranked_tensor(%t: tensor<*xf32>) -> tensor<*xf32> {
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// Unranked tensor OpOperands always bufferize in-place. With this limitation,
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// there is no way to bufferize this IR correctly.
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// expected-error @+1 {{not bufferizable under the given constraints: cannot avoid RaW conflict}}
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func.call @maybe_writing_func(%t) : (tensor<*xf32>) -> ()
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return %t : tensor<*xf32>
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}
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// This function may write to buffer(%ptr).
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func.func private @maybe_writing_func(%ptr : tensor<*xf32>)
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// -----
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func.func @regression_scf_while() {
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%false = arith.constant false
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%8 = bufferization.alloc_tensor() : tensor<10x10xf32>
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scf.while (%arg0 = %8) : (tensor<10x10xf32>) -> () {
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scf.condition(%false)
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} do {
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// expected-error @+1 {{Yield operand #0 is not equivalent to the corresponding iter bbArg}}
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scf.yield %8 : tensor<10x10xf32>
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}
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return
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}
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// -----
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// expected-error @below{{could not infer buffer type of block argument}}
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// expected-error @below{{failed to bufferize op}}
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func.func @func_multiple_yields(%t: tensor<5xf32>) -> tensor<5xf32> {
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func.return %t : tensor<5xf32>
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^bb1(%arg1 : tensor<5xf32>):
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func.return %arg1 : tensor<5xf32>
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}
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