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[mlir][tosa] Fixed depthwise conv parallel/reduction indices order 

Reduction axis should come after all parallel axis to work with vectorization.

Reviewed By: NatashaKnk

Differential Revision: https://reviews.llvm.org/D108005
This commit is contained in:
Rob Suderman 2021-08-16 13:47:00 -07:00
parent f22ba51873
commit f328f72e60
2 changed files with 12 additions and 12 deletions
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20
mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml
View File

@ -949,19 +949,19 @@ structured_op: !LinalgStructuredOpConfig
indexing_maps: !LinalgIndexingMapsConfig
static_indexing_maps:
- affine_map<(d0, d1, d2, d3, d4, d5, d6)[s0, s1, s2, s3, s4, s5, s6, s7, s8,
s9, s10, s11, s12] -> (d0, d1 * s9 + d3 * s11, d2 * s10 + d4 * s12, d5)>
s9, s10, s11, s12] -> (d0, d1 * s9 + d5 * s11, d2 * s10 + d6 * s12, d3)>
- affine_map<(d0, d1, d2, d3, d4, d5, d6)[s0, s1, s2, s3, s4, s5, s6, s7, s8,
s9, s10, s11, s12] -> (d3, d4, d5, d6)>
s9, s10, s11, s12] -> (d5, d6, d3, d4)>
- affine_map<(d0, d1, d2, d3, d4, d5, d6)[s0, s1, s2, s3, s4, s5, s6, s7, s8,
s9, s10, s11, s12] -> (d0, d1, d2, d5, d6)>
s9, s10, s11, s12] -> (d0, d1, d2, d3, d4)>
iterator_types:
- parallel
- parallel
- parallel
- reduction
- reduction
- parallel
- parallel
- reduction
- reduction
assignments:
- !ScalarAssign
arg: O
@ -1039,23 +1039,23 @@ structured_op: !LinalgStructuredOpConfig
indexing_maps: !LinalgIndexingMapsConfig
static_indexing_maps:
- affine_map<(d0, d1, d2, d3, d4, d5, d6)[s0, s1, s2, s3, s4, s5, s6, s7, s8,
s9, s10, s11, s12] -> (d0, d1 * s9 + d3 * s11, d2 * s10 + d4 * s12, d5)>
s9, s10, s11, s12] -> (d0, d1 * s9 + d5 * s11, d2 * s10 + d6 * s12, d3)>
- affine_map<(d0, d1, d2, d3, d4, d5, d6)[s0, s1, s2, s3, s4, s5, s6, s7, s8,
s9, s10, s11, s12] -> (d3, d4, d5, d6)>
s9, s10, s11, s12] -> (d5, d6, d3, d4)>
- affine_map<(d0, d1, d2, d3, d4, d5, d6)[s0, s1, s2, s3, s4, s5, s6, s7, s8,
s9, s10, s11, s12] -> ()>
- affine_map<(d0, d1, d2, d3, d4, d5, d6)[s0, s1, s2, s3, s4, s5, s6, s7, s8,
s9, s10, s11, s12] -> ()>
- affine_map<(d0, d1, d2, d3, d4, d5, d6)[s0, s1, s2, s3, s4, s5, s6, s7, s8,
s9, s10, s11, s12] -> (d0, d1, d2, d5, d6)>
s9, s10, s11, s12] -> (d0, d1, d2, d3, d4)>
iterator_types:
- parallel
- parallel
- parallel
- reduction
- reduction
- parallel
- parallel
- reduction
- reduction
assignments:
- !ScalarAssign
arg: O

4
mlir/python/mlir/dialects/linalg/opdsl/ops/core_named_ops.py
View File

@ -209,7 +209,7 @@ def depthwise_conv2D_nhwc(
Numeric casting is performed on the operands to the inner multiply, promoting
them to the same data type as the accumulator/output.
"""
domain(D.n, D.oh, D.ow, D.kh, D.kw, D.ic, D.cm)
domain(D.n, D.oh, D.ow, D.ic, D.cm, D.kh, D.kw)
O[D.n, D.oh, D.ow, D.ic, D.cm] += cast(
U, I[D.n, D.oh * S.SH + D.kh * S.DH, D.ow * S.SW + D.kw * S.DW,
D.ic]) * cast(U, K[D.kh, D.kw, D.ic, D.cm])
@ -228,7 +228,7 @@ def depthwise_conv2D_nhwc_q(
Numeric casting is performed on the operands to the inner multiply, promoting
them to the same data type as the accumulator/output.
"""
domain(D.n, D.oh, D.ow, D.kh, D.kw, D.ic, D.cm)
domain(D.n, D.oh, D.ow, D.ic, D.cm, D.kh, D.kw)
O[D.n, D.oh, D.ow, D.ic, D.cm] += (
(cast(U, I[D.n, D.oh * S.SH + D.kh * S.DH, D.ow * S.SW + D.kw * S.DW,
D.ic]) - cast(U, IZp)) *