78dc1e4978
Add an index_dim annotation to specify the shape to loop mapping of shape-only tensors. A shape-only tensor serves is not accessed withing the body of the operation but is required to span the iteration space of certain operations such as pooling. Differential Revision: https://reviews.llvm.org/D104767
65 lines
2.4 KiB
Python
65 lines
2.4 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, s2)>
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# CHECK: shape_map: affine_map<()[s0, s1, s2] -> (s2, s1)>
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# CHECK: shape_map: affine_map<()[s0, s1, s2] -> (s0, s1)>
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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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C[D.m, D.n] += cast(U, A[D.m, D.k]) * cast(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] += cast(U, A[D.m]) * cast(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] -> (s1)>
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# CHECK: shape_map: affine_map<()[s0, s1, s2] -> (s2)>
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# CHECK: shape_map: affine_map<()[s0, s1, s2] -> (s0)>
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# CHECK: static_indexing_maps:
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# CHECK-NEXT: - affine_map<(d0, d1)[s0, s1, s2] -> (d1 * 2 + d0)>
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# CHECK-NEXT: - affine_map<(d0, d1)[s0, s1, s2] -> (d0)>
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# CHECK-NEXT: - affine_map<(d0, d1)[s0, s1, s2] -> (d1)>
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# CHECK: iterator_types:
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# CHECK-NEXT: - reduction
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# CHECK-NEXT: - parallel
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@linalg_structured_op
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def pool(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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O[D.o] += cast(U, I[D.o * 2 + D.k])
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