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Oct 24, 2025
Merged

Add support for 3D Grouped Conv #4354

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72eb4b5
Add support for 3D Grouped Conv
IanWood1 Oct 23, 2025
6338495
Update onnx xfail
IanWood1 Oct 23, 2025
5adfe11
Address comments
IanWood1 Oct 24, 2025
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118 changes: 101 additions & 17 deletions lib/Conversion/TorchToLinalg/Linear.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -1391,9 +1391,13 @@ class ConvertAtenConvolutionOp : public OpConversionPattern<AtenConvolutionOp> {
return success();
}

if (numSpatialDims != 2)
if (numSpatialDims != 2 && numSpatialDims != 3)
return rewriter.notifyMatchFailure(
op, "unimplemented: only 1D and 2D grouped convolution supported");
op, "unimplemented: only 2D and 3D grouped convolution supported");
if (numSpatialDims == 3 && inputZp) {
return rewriter.notifyMatchFailure(
op, "unimplemented: quantized 3D grouped convolution not supported");
}

// Grouped case, use the grouped conv linalg op
auto expandGroups = [&](Value tensor, size_t dim) {
Expand Down Expand Up @@ -1435,21 +1439,101 @@ class ConvertAtenConvolutionOp : public OpConversionPattern<AtenConvolutionOp> {
weight = transposed ? weight : expandWeight(weight);
auto expandOutputTensor = expandGroups(outputTensor, 1);

// TODO: add 1D and 3D case
if (!inputZp) {
conv = rewriter
.create<linalg::Conv2DNgchwGfchwOp>(
loc, expandOutputTensor.getResultType(),
ValueRange{paddedInputExpanded, weight},
expandOutputTensor.getResult(), stridesAttr, dilationAttr)
.getResult(0);
} else {
conv = rewriter
.create<linalg::Conv2DNgchwGfchwQOp>(
loc, expandOutputTensor.getResultType(),
ValueRange{paddedInputExpanded, weight, inputZp, weightZp},
expandOutputTensor.getResult(), stridesAttr, dilationAttr)
.getResult(0);
if (numSpatialDims == 2) {
// 2D grouped convolution
if (!inputZp) {
conv =
rewriter
.create<linalg::Conv2DNgchwGfchwOp>(
loc, expandOutputTensor.getResultType(),
ValueRange{paddedInputExpanded, weight},
expandOutputTensor.getResult(), stridesAttr, dilationAttr)
.getResult(0);
} else {
conv =
rewriter
.create<linalg::Conv2DNgchwGfchwQOp>(
loc, expandOutputTensor.getResultType(),
ValueRange{paddedInputExpanded, weight, inputZp, weightZp},
expandOutputTensor.getResult(), stridesAttr, dilationAttr)
.getResult(0);
}
} else if (numSpatialDims == 3) {
// MLIR does not have a named 3D grouped convolution op, so we use
// linalg.generic instead.
AffineExpr d0, d1, d2, d3, d4, d5, d6, d7, d8, d9;
bindDims(context, d0, d1, d2, d3, d4, d5, d6, d7, d8, d9);

SmallVector<AffineExpr> inputExprs = {
d0, // N
d1, // G
d6, // C/G
d3 * strideInts[0] + d7 * dilationInts[0], // D
d4 * strideInts[1] + d8 * dilationInts[1], // H
d5 * strideInts[2] + d9 * dilationInts[2] // W
};

SmallVector<AffineExpr> weightExprs = {
d1, // G
d2, // F/G
d6, // C/G
d7, // KD
d8, // KH
d9 // KW
};

SmallVector<AffineExpr> outputExprs = {
d0, // N
d1, // G
d2, // F/G
d3, // OD
d4, // OH
d5, // OW
};

SmallVector<AffineMap> indexingMaps = {
AffineMap::get(10, 0, inputExprs, rewriter.getContext()),
AffineMap::get(10, 0, weightExprs, rewriter.getContext()),
AffineMap::get(10, 0, outputExprs, rewriter.getContext())};

SmallVector<utils::IteratorType> iteratorTypes = {
utils::IteratorType::parallel, // N
utils::IteratorType::parallel, // G
utils::IteratorType::parallel, // F/G
utils::IteratorType::parallel, // OD
utils::IteratorType::parallel, // OH
utils::IteratorType::parallel, // OW
utils::IteratorType::reduction, // C/G
utils::IteratorType::reduction, // KD
utils::IteratorType::reduction, // KH
utils::IteratorType::reduction // KW
};

conv =
rewriter
.create<linalg::GenericOp>(
loc, expandOutputTensor.getResultType(),
ValueRange{paddedInputExpanded, weight},
expandOutputTensor.getResult(), indexingMaps, iteratorTypes,
[&](OpBuilder &b, Location loc, ValueRange args) {
Value input = args[0];
Value weight = args[1];
Value output = args[2];

// Convert input and weight to accumulator type if needed
Type accType = output.getType();
if (input.getType() != accType) {
input = b.create<arith::ExtFOp>(loc, accType, input);
}
if (weight.getType() != accType) {
weight = b.create<arith::ExtFOp>(loc, accType, weight);
}

Value mul = b.create<arith::MulFOp>(loc, input, weight);
Value add = b.create<arith::AddFOp>(loc, mul, output);
b.create<linalg::YieldOp>(loc, add);
})
.getResult(0);
}
conv = rewriter.create<tensor::CollapseShapeOp>(
loc, outputTensor.getType(), conv,
Expand Down
9 changes: 9 additions & 0 deletions projects/pt1/e2e_testing/xfail_sets.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2914,6 +2914,9 @@
"Conv3dModule_basic",
"Conv3dWithSamePaddingModule_basic",
"Conv3dWithValidPaddingModule_basic",
"ConvolutionModule3DGroups_basic",
"ConvolutionModule3DGroupsStrided_basic",
"ConvolutionModule3DGroupsDilated_basic",
"ConvTbcModule_basic",
"ConvTranspose2DQInt8_basic",
"Conv_Transpose2dModule_basic",
Expand Down Expand Up @@ -3721,6 +3724,9 @@
"ConvolutionModule2DTransposeStrided_basic",
"ConvolutionModule2DTranspose_basic",
"ConvolutionModule2DGroupedTranspose_basic",
"ConvolutionModule3DGroups_basic",
"ConvolutionModule3DGroupsStrided_basic",
"ConvolutionModule3DGroupsDilated_basic",
"CumsumInputDtypeInt32Module_basic",
"CumsumWithDtypeModule_basic",
"CumsumModule_basic",
Expand Down Expand Up @@ -4369,6 +4375,9 @@
"ConvolutionModule2DTransposeStrided_basic",
"ConvolutionModule2DTranspose_basic",
"ConvolutionModule2DGroupedTranspose_basic",
"ConvolutionModule3DGroups_basic",
"ConvolutionModule3DGroupsStrided_basic",
"ConvolutionModule3DGroupsDilated_basic",
"CopyModule_basic",
"CopyWithDifferentDTypesAndSizesModule_basic",
"CopyWithDifferentDTypesModule_basic",
Expand Down
93 changes: 93 additions & 0 deletions projects/pt1/python/torch_mlir_e2e_test/test_suite/conv.py
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Original file line number Diff line number Diff line change
Expand Up @@ -679,6 +679,99 @@ def ConvolutionModule2DGroups_basic(module, tu: TestUtils):
module.forward(tu.rand(1, 32, 4, 4), tu.rand(32, 8, 3, 3))


class ConvolutionModule3DGroups(torch.nn.Module):
def __init__(self):
super().__init__()

@export
@annotate_args(
[
None,
([-1, -1, -1, -1, -1], torch.float32, True),
([-1, -1, -1, -1, -1], torch.float32, True),
]
)
def forward(self, inputVec, weight):
return torch.ops.aten.convolution(
inputVec,
weight,
bias=None,
stride=[1, 1, 1],
padding=[0, 0, 0],
dilation=[1, 1, 1],
transposed=False,
output_padding=[0, 0, 0],
groups=2,
)


@register_test_case(module_factory=lambda: ConvolutionModule3DGroups())
def ConvolutionModule3DGroups_basic(module, tu: TestUtils):
module.forward(tu.rand(2, 4, 6, 6, 6), tu.rand(8, 2, 3, 3, 3))


class ConvolutionModule3DGroupsStrided(torch.nn.Module):
def __init__(self):
super().__init__()

@export
@annotate_args(
[
None,
([-1, -1, -1, -1, -1], torch.float32, True),
([-1, -1, -1, -1, -1], torch.float32, True),
]
)
def forward(self, inputVec, weight):
return torch.ops.aten.convolution(
inputVec,
weight,
bias=None,
stride=[2, 2, 2],
padding=[1, 1, 1],
dilation=[1, 1, 1],
transposed=False,
output_padding=[0, 0, 0],
groups=4,
)


@register_test_case(module_factory=lambda: ConvolutionModule3DGroupsStrided())
def ConvolutionModule3DGroupsStrided_basic(module, tu: TestUtils):
module.forward(tu.rand(2, 8, 8, 8, 8), tu.rand(16, 2, 3, 3, 3))


class ConvolutionModule3DGroupsDilated(torch.nn.Module):
def __init__(self):
super().__init__()

@export
@annotate_args(
[
None,
([-1, -1, -1, -1, -1], torch.float32, True),
([-1, -1, -1, -1, -1], torch.float32, True),
]
)
def forward(self, inputVec, weight):
return torch.ops.aten.convolution(
inputVec,
weight,
bias=None,
stride=[1, 1, 1],
padding=[2, 2, 2],
dilation=[2, 2, 2],
transposed=False,
output_padding=[0, 0, 0],
groups=2,
)


@register_test_case(module_factory=lambda: ConvolutionModule3DGroupsDilated())
def ConvolutionModule3DGroupsDilated_basic(module, tu: TestUtils):
module.forward(tu.rand(2, 4, 8, 8, 8), tu.rand(8, 2, 3, 3, 3))


# ==============================================================================


Expand Down
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