1d4ce574a4
In the origin implementation, the empty tensor elimination will add a `tensor.cast` and eliminate the tensor even if they have different element type(f32, bf16). Here add a check for element type and skip the elimination if they are different.
79 lines
3.4 KiB
MLIR
79 lines
3.4 KiB
MLIR
// RUN: mlir-opt %s -eliminate-empty-tensors -empty-tensor-to-alloc-tensor -one-shot-bufferize="bufferize-function-boundaries test-analysis-only" -split-input-file | FileCheck %s
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// CHECK-LABEL: func @buffer_forwarding_conflict
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func.func @buffer_forwarding_conflict(%arg0: tensor<?xf32> {bufferization.writable = true}, %arg1: index) -> (tensor<?xf32>, tensor<?xf32>) {
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%cst = arith.constant 0.000000e+00 : f32
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// CHECK: tensor.extract_slice
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// CHECK-SAME: {__inplace_operands_attr__ = ["false", "none"]
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// Instead of allocating, share buffer with some inplace bufferization?
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%0 = tensor.empty(%arg1) : tensor<?xf32>
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// CHECK: linalg.fill
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// CHECK-SAME: {__inplace_operands_attr__ = ["none", "true"]
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%1 = linalg.fill ins(%cst : f32) outs(%0 : tensor<?xf32>) -> tensor<?xf32>
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// CHECK: tensor.insert_slice
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// CHECK-SAME: {__inplace_operands_attr__ = ["true", "false", "none"]
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%2 = tensor.insert_slice %1 into %arg0[0] [%arg1] [1] : tensor<?xf32> into tensor<?xf32>
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// CHECK: tensor.insert_slice
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// CHECK-SAME: {__inplace_operands_attr__ = ["true", "true", "none"]
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%3 = tensor.insert_slice %1 into %arg0[42] [%arg1] [1] : tensor<?xf32> into tensor<?xf32>
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// CHECK: return
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// CHECK-SAME: __equivalent_func_args__ = [-1, 0]
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return %2, %3 : tensor<?xf32>, tensor<?xf32>
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}
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// -----
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// CHECK-LABEL: func @buffer_forwarding_no_conflict
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func.func @buffer_forwarding_no_conflict(%arg0: tensor<?xf32> {bufferization.writable = true}, %arg1: index) -> (tensor<?xf32>, tensor<?xf32>) {
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%cst = arith.constant 0.000000e+00 : f32
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// CHECK: tensor.extract_slice
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// CHECK-SAME: {__inplace_operands_attr__ = ["true", "none"]
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// Instead of allocating, share buffer with some inplace bufferization?
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%0 = tensor.empty(%arg1) : tensor<?xf32>
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// CHECK: linalg.fill
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// CHECK-SAME: {__inplace_operands_attr__ = ["none", "true"]
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%1 = linalg.fill ins(%cst : f32) outs(%0 : tensor<?xf32>) -> tensor<?xf32>
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// CHECK: tensor.insert_slice
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// CHECK-SAME: {__inplace_operands_attr__ = ["true", "true", "none"]
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%2 = tensor.insert_slice %1 into %arg0[42] [%arg1] [1] : tensor<?xf32> into tensor<?xf32>
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// CHECK: return
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// CHECK-SAME: __equivalent_func_args__ = [0, 0]
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return %2, %2 : tensor<?xf32>, tensor<?xf32>
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}
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// -----
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// CHECK-LABEL: func @buffer_forwarding_conflict_with_different_element_type
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func.func @buffer_forwarding_conflict_with_different_element_type(%arg0: tensor<?xf32> {bufferization.writable = true}, %arg1: index) -> (tensor<?xf32>, tensor<?xf32>) {
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// CHECK: tensor.extract_slice
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// CHECK-SAME: {__inplace_operands_attr__ = ["true", "none"]
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%cst = arith.constant 0.000000e+00 : f32
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%0 = tensor.empty(%arg1) : tensor<?xf32>
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// CHECK: bufferization.alloc_tensor(%arg1)
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%1 = tensor.empty(%arg1) : tensor<?xbf16>
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// CHECK: linalg.copy
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// CHECK-SAME: {__inplace_operands_attr__ = ["true", "true"]
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%2 = linalg.copy ins(%0 : tensor<?xf32>) outs(%1 : tensor<?xbf16>) -> tensor<?xbf16>
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// CHECK: linalg.copy
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// CHECK-SAME: {__inplace_operands_attr__ = ["true", "true"]
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%3 = linalg.copy ins(%2 : tensor<?xbf16>) outs(%0 : tensor<?xf32>) -> tensor<?xf32>
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// CHECK: tensor.insert_slice
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// CHECK-SAME: {__inplace_operands_attr__ = ["true", "true", "none"]
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%4 = tensor.insert_slice %3 into %arg0[42] [%arg1] [1] : tensor<?xf32> into tensor<?xf32>
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// CHECK: return
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// CHECK-SAME: __equivalent_func_args__ = [0, 0]
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return %4, %4 : tensor<?xf32>, tensor<?xf32>
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}
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