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// RUN: mlir-opt %s \
// RUN: --pass-pipeline="builtin.module(test-transform-dialect-interpreter{ \
// RUN: bind-first-extra-to-ops=linalg.matmul \
// RUN: bind-second-extra-to-ops=linalg.elemwise_binary \
// RUN: enable-expensive-checks},canonicalize,cse,symbol-dce)" \
// RUN: --split-input-file --verify-diagnostics
// ****************************** IMPORTANT NOTE ******************************
//
// If you are changing this file, you may also need to change
// mlir/docs/Tutorials/Transform accordingly.
//
// ****************************************************************************
transform.sequence failures(propagate) {
^bb0(%arg0: !transform.any_op,
// expected-note @below {{handle to invalidated ops}}
%arg1: !transform.op<"linalg.matmul">,
%arg2: !transform.op<"linalg.elemwise_binary">):
// The actual tiling transformation takes tile sizes as attributes.
// expected-note @below {{invalidated by this transform op that consumes its operand #0 and invalidates all handles to payload IR entities associated with this operand and entities nested in them}}
%loop, %tiled = transform.structured.tile_to_forall_op %arg1 tile_sizes [4, 32]
: (!transform.op<"linalg.matmul">) -> (!transform.any_op, !transform.any_op)
// This is trying to use an invalidated handle leading to undefined behavior.
// expected-error @below {{uses a handle invalidated by a previously executed transform op}}
transform.test_print_remark_at_operand %arg1, "remark" : !transform.op<"linalg.matmul">
transform.yield
}
// Original function to optimize.
func.func @fc_relu(%lhs: tensor<512x512xf32>, %rhs: tensor<512x512xf32>,
%bias: tensor<512x512xf32>, %output: tensor<512x512xf32>)
-> tensor<512x512xf32> {
// Matrix-matrix multiplication.
// expected-note @below {{payload op}}
%matmul = linalg.matmul ins(%lhs, %rhs: tensor<512x512xf32>, tensor<512x512xf32>)
outs(%output: tensor<512x512xf32>) -> tensor<512x512xf32>
// Elementwise addition.
%biased = linalg.elemwise_binary { fun = #linalg.binary_fn<add> }
ins(%matmul, %bias : tensor<512x512xf32>, tensor<512x512xf32>)
outs(%output : tensor<512x512xf32>) -> tensor<512x512xf32>
// Elementwise max with 0 (ReLU).
%c0f = arith.constant 0.0 : f32
%relued = linalg.elemwise_binary { fun = #linalg.binary_fn<max_signed> }
ins(%biased, %c0f : tensor<512x512xf32>, f32)
outs(%output : tensor<512x512xf32>) -> tensor<512x512xf32>
func.return %relued : tensor<512x512xf32>
}
// -----
transform.sequence failures(propagate) {
^bb0(%arg0: !transform.any_op,
%arg1: !transform.op<"linalg.matmul">,
%arg2: !transform.op<"linalg.elemwise_binary">):
// We can cast one type to another as long as operations are compatible
// with both types. This creates "aliasing" handles.
// expected-note @below {{handle to invalidated ops}}
%casted = transform.cast %arg1 : !transform.op<"linalg.matmul"> to
!transform.any_op
// The actual tiling transformation takes tile sizes as attributes.
// expected-note @below {{invalidated by this transform op that consumes its operand #0 and invalidates all handles to payload IR entities associated with this operand and entities nested in them}}
%loop, %tiled = transform.structured.tile_to_forall_op %arg1 tile_sizes [4, 32]
: (!transform.op<"linalg.matmul">) -> (!transform.any_op, !transform.any_op)
// Consuming an operand invalidates the consumed handle and any other handle that is
// associated with the same payload operations, or payload operations nested in them.
// expected-error @below {{uses a handle invalidated by a previously executed transform op}}
transform.test_print_remark_at_operand %casted, "remark"
: !transform.any_op
transform.yield
}
// Original function to optimize.
func.func @fc_relu(%lhs: tensor<512x512xf32>, %rhs: tensor<512x512xf32>,
%bias: tensor<512x512xf32>, %output: tensor<512x512xf32>)
-> tensor<512x512xf32> {
// Matrix-matrix multiplication.
// expected-note @below {{payload op}}
%matmul = linalg.matmul ins(%lhs, %rhs: tensor<512x512xf32>, tensor<512x512xf32>)
outs(%output: tensor<512x512xf32>) -> tensor<512x512xf32>
// Elementwise addition.
%biased = linalg.elemwise_binary { fun = #linalg.binary_fn<add> }
ins(%matmul, %bias : tensor<512x512xf32>, tensor<512x512xf32>)
outs(%output : tensor<512x512xf32>) -> tensor<512x512xf32>
// Elementwise max with 0 (ReLU).
%c0f = arith.constant 0.0 : f32
%relued = linalg.elemwise_binary { fun = #linalg.binary_fn<max_signed> }
ins(%biased, %c0f : tensor<512x512xf32>, f32)
outs(%output : tensor<512x512xf32>) -> tensor<512x512xf32>
func.return %relued : tensor<512x512xf32>
}
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