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[mlir][linalg] Add a test for inferConvolutionDimsImpl #90057

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Merged
merged 2 commits into from
Apr 25, 2024
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[mlir][linalg] Add a test for inferConvolutionDimsImpl #90057

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b234d69
[mlir][linalg] Fix inferConvolutionDimsImpl (depthwise convs)
banach-space Apr 25, 2024
825b49a
fixup! [mlir][linalg] Fix inferConvolutionDimsImpl (depthwise convs)
banach-space Apr 25, 2024
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22 changes: 22 additions & 0 deletions mlir/test/Dialect/Linalg/match-ops-interpreter.mlir
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Original file line number Diff line number Diff line change
Expand Up @@ -1062,6 +1062,28 @@ module attributes { transform.target_tag = "start_here" } {
return %result : tensor<10x18x15xf64>
}

func.func @convolution_depthwise(%input: tensor<1x10x196x48xf32>, %filter: tensor<1x4x48xf32>) -> tensor<1x10x191x48xf32> {
%cst = arith.constant 0.0 : f32
%empty = tensor.empty() : tensor<1x10x191x48xf32>
%fill = linalg.fill ins(%cst : f32) outs(%empty : tensor<1x10x191x48xf32>) -> tensor<1x10x191x48xf32>
// expected-remark @below {{convolution}}
// expected-remark @below {{batch dims 0}}
// expected-remark @below {{output image dims 1 : i64, 2 : i64}}
// expected-remark @below {{output channel dims}}
// expected-remark @below {{filter loop dims 4 : i64, 5 : i64}}
// expected-remark @below {{input channel dims}}
// expected-remark @below {{depth dims 3}}
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@qedawkins qedawkins Apr 25, 2024 •
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This doesn't look like what I would expect because it is classifying dim 3 twice. The naming might need to be improved for depthwise convolutions, but the idea with these classifications is that they tell users exactly how each different dimension appears in the indexing maps (and thus input/filter/output tensors). Right now the impl uses this to imply that all dims classified as outputChannel are only featured in the filter and output maps. Dims classified as depth appear in all three of the input, filter, and output.

I'm noticing that there is no description for depth in this comment though:

llvm-project/mlir/lib/Dialect/Linalg/IR/LinalgInterfaces.cpp

Lines 622 to 645 in 76a3be7

/// Find at least 1 parallel (output_image) and reduction (filter_loop)
/// dimension candidates that form a convolution subcomputation within
/// `linalgOp`. The LHS is assumed to be the convolution input while the
/// RHS is assumed as the filter.
/// These dimensions are such that:
/// 1. Optional batch dimensions that appear in the input and filter.
/// 2. The output_image dimension is involved in a cross-correlation along LHS
/// (i.e. it is a permutation on RES and LHS and has an associated
/// filter_loop in RHS).
/// 3. Optional output_channel dimension is involved in an outer-product along
/// RHS (i.e. it is a permutation on RES and RHS and does not appear in
/// LHS).
/// 4. Optional input_channel dimension appears as a permutation on LHS and
/// RHS.
/// 5. The filter_loop dimension appears as a permutation on the RHS and
/// represents the shape of the kernel cross-correlated along a
/// corresponding output_image dim.
/// 6. The input_channel dimension appears as a permutation on LHS and RHS.
/// 7. All dimensions appear only once in any given indexing map.
/// This allows e.g. detecting that some convolution is embedded within
/// `linalgOp` with some orthogonal heuristic.
/// When multiple dimension occurrences exist that match any classification
/// indices are returned in sorted order.
/// Returns a failure if `output_image` (and implicitly `filter_loop`) is empty.

(also the comment is kind of convoluted and could probably be rewritten, maybe with an example).

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Thanks for pointing this out - now I see what's happening here. IIUC, "channel" is only used for non-depthwise convolutions. For depthwise convs (HWC), this logic uses the term "depth" instead. Now, "depth" is:

  • a parallel dim
  • identical for all inputs.

It looks like this PR is breaking rather than fixing things 😂 Let me update and rephrase.

// expected-remark @below {{strides 1 : i64, 1 : i64}}
// expected-remark @below {{dilations 1 : i64, 1 : i64}}
%result = linalg.depthwise_conv_2d_nhwc_hwc {
dilations = dense<1> : tensor<2xi64>,
strides = dense<1> : tensor<2xi64>}
ins(%input, %filter : tensor<1x10x196x48xf32>, tensor<1x4x48xf32>)
outs(%fill : tensor<1x10x191x48xf32>) -> tensor<1x10x191x48xf32>

return %result : tensor<1x10x191x48xf32>
}

func.func @convolution_multi_channel(%input: tensor<2x34x68x16xf32>, %filter: tensor<8x2x3x5x16x16xf32>) -> tensor<8x32x32x16xf32> {
%cst = arith.constant 0.0 : f32
%empty = tensor.empty() : tensor<8x32x32x16xf32>
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