e4384149b5
Update most test passes to use the transform-interpreter pass instead of the test-transform-dialect-interpreter-pass. The new "main" interpreter pass has a named entry point instead of looking up the top-level op with `PossibleTopLevelOpTrait`, which is arguably a more understandable interface. The change is mechanical, rewriting an unnamed sequence into a named one and wrapping the transform IR in to a module when necessary. Add an option to the transform-interpreter pass to target a tagged payload op instead of the root anchor op, which is also useful for repro generation. Only the test in the transform dialect proper and the examples have not been updated yet. These will be updated separately after a more careful consideration of testing coverage of the transform interpreter logic.
49 lines
2.2 KiB
MLIR
49 lines
2.2 KiB
MLIR
// RUN: mlir-opt %s --transform-interpreter --verify-diagnostics --split-input-file
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module attributes { transform.with_named_sequence } {
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transform.named_sequence @match_sparse_structured(%arg0: !transform.any_op {transform.readonly}) -> !transform.any_op {
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%0 = transform.match.structured %arg0 : (!transform.any_op) -> !transform.any_op {
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^bb0(%struct: !transform.any_op):
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%sp_kernel = transform.sparse_tensor.match.sparse_inout %struct
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: (!transform.any_op) -> !transform.any_op
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transform.match.structured.yield %sp_kernel : !transform.any_op
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}
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transform.yield %0 : !transform.any_op
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}
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transform.named_sequence @print_sparse_structured(%arg0: !transform.any_op {transform.readonly}) {
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transform.test_print_remark_at_operand %arg0, "sparse_kernel" : !transform.any_op
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transform.yield
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}
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// Entry point. Match any structured sparse operation and emit at remark.
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transform.named_sequence @__transform_main(%arg0: !transform.any_op {transform.consumed}) {
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transform.foreach_match in %arg0
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@match_sparse_structured -> @print_sparse_structured
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: (!transform.any_op) -> !transform.any_op
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transform.yield
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}
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}
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#CSR = #sparse_tensor.encoding<{map = (d0, d1) -> (d0 : dense, d1 : compressed)}>
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func.func @payload(%lhs: tensor<10x20xf16>,
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%sp_lhs: tensor<10x20xf16, #CSR>,
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%rhs: tensor<20x15xf32>) -> tensor<10x15xf64>{
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%cst = arith.constant 0.0 : f64
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%empty = tensor.empty() : tensor<10x15xf64>
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%fill = linalg.fill ins(%cst : f64) outs(%empty : tensor<10x15xf64>) -> tensor<10x15xf64>
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%result = linalg.matmul ins(%lhs, %rhs: tensor<10x20xf16>, tensor<20x15xf32>)
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outs(%fill: tensor<10x15xf64>) -> tensor<10x15xf64>
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// expected-remark @below {{sparse_kernel}}
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%sp_in = linalg.matmul ins(%sp_lhs, %rhs: tensor<10x20xf16, #CSR>, tensor<20x15xf32>)
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outs(%fill: tensor<10x15xf64>) -> tensor<10x15xf64>
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%sp_empty = tensor.empty() : tensor<10x15xf64, #CSR>
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// expected-remark @below {{sparse_kernel}}
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%sp_out = linalg.matmul ins(%lhs, %rhs: tensor<10x20xf16>, tensor<20x15xf32>)
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outs(%sp_empty: tensor<10x15xf64, #CSR>) -> tensor<10x15xf64, #CSR>
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return %result : tensor<10x15xf64>
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}
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