e9085d0d25
The revision renames the following OpDSL functions: ``` TypeFn.cast -> TypeFn.cast_signed BinaryFn.min -> BinaryFn.min_signed BinaryFn.max -> BinaryFn.max_signed ``` The corresponding enum values on the C++ side are renamed accordingly: ``` #linalg.type_fn<cast> -> #linalg.type_fn<cast_signed> #linalg.binary_fn<min> -> #linalg.binary_fn<min_signed> #linalg.binary_fn<max> -> #linalg.binary_fn<max_signed> ``` Depends On D120110 Reviewed By: aartbik Differential Revision: https://reviews.llvm.org/D120562
70 lines
2.5 KiB
Python
70 lines
2.5 KiB
Python
# RUN: %PYTHON -m mlir.dialects.linalg.opdsl.dump_oplib --file %s | FileCheck %s
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from mlir.dialects.linalg.opdsl.lang import *
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# Verify that simple case with iteration order defined lexically and reduction
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# dims auto discovered emits the right shape, indexing maps and iterator types.
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# CHECK: ---
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# CHECK-LABEL: matmul
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# CHECK: shape_map: affine_map<()[s0, s1, s2] -> (s0, s1)>
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# CHECK: shape_map: affine_map<()[s0, s1, s2] -> (s1, s2)>
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# CHECK: shape_map: affine_map<()[s0, s1, s2] -> (s0, s2)>
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# CHECK: static_indexing_maps:
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# CHECK-NEXT: - affine_map<(d0, d1, d2)[s0, s1, s2] -> (d0, d2)>
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# CHECK-NEXT: - affine_map<(d0, d1, d2)[s0, s1, s2] -> (d2, d1)>
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# CHECK-NEXT: - affine_map<(d0, d1, d2)[s0, s1, s2] -> (d0, d1)>
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# CHECK: iterator_types:
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# CHECK-NEXT: - parallel
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# CHECK-NEXT: - parallel
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# CHECK-NEXT: - reduction
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@linalg_structured_op
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def matmul(
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A=TensorDef(T, S.M, S.K),
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B=TensorDef(T, S.K, S.N),
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C=TensorDef(U, S.M, S.N, output=True)):
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domain(D.m, D.n, D.k)
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C[D.m, D.n] += TypeFn.cast_signed(U, A[D.m, D.k]) * TypeFn.cast_signed(
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U, B[D.k, D.n])
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# Verifies that assignment to a scalar (represented as [None]) is represented
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# correctly.
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# CHECK: ---
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# CHECK-LABEL: dot
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# CHECK: shape_map: affine_map<()[s0] -> (s0)>
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# CHECK: shape_map: affine_map<()[s0] -> (s0)>
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# CHECK: shape_map: affine_map<()[s0] -> ()>
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# CHECK: static_indexing_maps:
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# CHECK-NEXT: - affine_map<(d0)[s0] -> (d0)>
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# CHECK-NEXT: - affine_map<(d0)[s0] -> (d0)>
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# CHECK-NEXT: - affine_map<(d0)[s0] -> ()>
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# CHECK: iterator_types:
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# CHECK-NEXT: - reduction
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@linalg_structured_op
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def dot(A=TensorDef(T, S.M), B=TensorDef(T, S.M), C=TensorDef(U, output=True)):
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C[None] += TypeFn.cast_signed(U, A[D.m]) * TypeFn.cast_signed(U, B[D.m])
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# Verifies that the index_dims of shape-only operands translate to correct
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# indexing maps.
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# CHECK: ---
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# CHECK-LABEL: pool
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# CHECK: shape_map: affine_map<()[s0, s1, s2] -> (s0)>
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# CHECK: shape_map: affine_map<()[s0, s1, s2] -> (s1)>
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# CHECK: shape_map: affine_map<()[s0, s1, s2] -> (s2)>
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# CHECK: static_indexing_maps:
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# CHECK-NEXT: - affine_map<(d0, d1)[s0, s1, s2] -> (d0 * 2 + d1)>
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# CHECK-NEXT: - affine_map<(d0, d1)[s0, s1, s2] -> (d1)>
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# CHECK-NEXT: - affine_map<(d0, d1)[s0, s1, s2] -> (d0)>
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# CHECK: iterator_types:
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# CHECK-NEXT: - parallel
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# CHECK-NEXT: - reduction
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@linalg_structured_op
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def pool(
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I=TensorDef(T, S.I),
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K=TensorDef(T, S.K, index_dims=[D.k]),
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O=TensorDef(U, S.O, output=True)):
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domain(D.o, D.k)
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O[D.o] += TypeFn.cast_signed(U, I[D.o * 2 + D.k])
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