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[TorchToLinalg] Direct lowering from Torch to Linalg for torch.aten.convolution_backward #4384

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[TorchToLinalg] Direct lowering from Torch to Linalg for torch.aten.convolution_backward #4384

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627 changes: 627 additions & 0 deletions lib/Conversion/TorchToLinalg/Linear.cpp
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401 changes: 0 additions & 401 deletions lib/Dialect/Torch/Transforms/DecomposeComplexOps.cpp
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1 change: 0 additions & 1 deletion lib/Dialect/Torch/Transforms/LowerToBackendContract.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -444,7 +444,6 @@ static void markDecomposedOpsAsIllegal(MLIRContext *context,
target.addIllegalOp<AtenNativeGroupNormOp>();
target.addIllegalOp<AtenNativeBatchNormOp>();
target.addIllegalOp<Aten_ConvolutionOp, Aten_ConvolutionDeprecatedOp>();
target.addIllegalOp<AtenConvolutionBackwardOp>();
target.addIllegalOp<AtenConvTbcOp>();
target.addIllegalOp<AtenConv1dOp>();
target.addIllegalOp<AtenConv2dOp>();
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1 change: 1 addition & 0 deletions projects/pt1/python/torch_mlir_e2e_test/configs/jit_importer_backend.py
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Expand Up @@ -34,6 +34,7 @@
"aten.flatten.using_ints",
"aten.adaptive_avg_pool1d",
"aten.adaptive_avg_pool2d",
"aten.convolution_backward",
"aten.unflatten.int",
],
OutputType.STABLEHLO: [
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69 changes: 69 additions & 0 deletions projects/pt1/python/torch_mlir_e2e_test/test_suite/backprop.py
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Expand Up @@ -228,6 +228,75 @@ def ConvolutionBackwardModule2DStrided_basic(module, tu: TestUtils):
module.forward(tu.rand(1, 2, 4, 4), tu.rand(1, 2, 8, 8), tu.rand(2, 2, 3, 3))


class ConvolutionBackwardModule2DDilated(torch.nn.Module):
def __init__(self):
super().__init__()

@export
@annotate_args(
[
None,
([1, 2, 6, 6], torch.float32, True),
([1, 4, 8, 8], torch.float32, True),
([2, 4, 3, 3], torch.float32, True),
]
)
def forward(self, grad_out, input_vec, weight):
return torch.ops.aten.convolution_backward(
grad_out,
input_vec,
weight,
bias_sizes=[4],
stride=[1, 1],
padding=[1, 1],
dilation=[2, 2],
transposed=False,
output_padding=[0, 0],
groups=1,
output_mask=[True, True, True],
)


@register_test_case(module_factory=lambda: ConvolutionBackwardModule2DDilated())
def ConvolutionBackwardModule2DDilated_basic(module, tu: TestUtils):
with torch.backends.mkldnn.flags(enabled=False):
module.forward(tu.rand(1, 2, 6, 6), tu.rand(1, 4, 8, 8), tu.rand(2, 4, 3, 3))


class ConvolutionBackwardModule2DStridedPaddedDilatedGrouped(torch.nn.Module):
def __init__(self):
super().__init__()

@export
@annotate_args(
[
None,
([2, 16, 32, 32], torch.float32, True),
([2, 128, 64, 64], torch.float32, True),
([16, 32, 2, 2], torch.float32, True),
]
)
def forward(self, grad_out, input_vec, weight):
return torch.ops.aten.convolution_backward(
grad_out,
input_vec,
weight,
bias_sizes=[4],
stride=[2, 2],
padding=[2, 2],
dilation=[4, 4],
transposed=False,
output_padding=[0, 0],
groups=4,
output_mask=[True, True, True],
)


@register_test_case(module_factory=lambda: ConvolutionBackwardModule2DStridedPaddedDilatedGrouped())
def ConvolutionBackwardModule2DStridedPaddedDilatedGrouped_basic(module, tu: TestUtils):
with torch.backends.mkldnn.flags(enabled=False):
module.forward(tu.rand(2, 16, 32, 32), tu.rand(2, 128, 64, 64), tu.rand(16, 32, 2, 2))

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


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318 changes: 318 additions & 0 deletions test/Conversion/TorchToLinalg/convolution_bwd.mlir
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34 changes: 0 additions & 34 deletions test/Dialect/Torch/decompose-complex-ops.mlir
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Expand Up @@ -273,40 +273,6 @@ func.func @torch.aten._assert_scalar(%arg0: !torch.int) -> !torch.int {
return %arg0 : !torch.int
}

// -----

// CHECK-LABEL: func.func @convolution_backward_none_result(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[1,1,3,3],f32>, %[[VAL_1:.*]]: !torch.vtensor<[1,1,5,5],f32>,
// CHECK-SAME: %[[VAL_2:.*]]: !torch.vtensor<[1,1,3,3],f32>,
// CHECK-SAME: %[[VAL_3:.*]]: !torch.vtensor<[],f32>) -> (!torch.vtensor<[1,1,3,3],f32>, !torch.vtensor<[1],f32>) {
func.func @convolution_backward_none_result(%arg0: !torch.vtensor<[1,1,3,3],f32>, %arg1: !torch.vtensor<[1,1,5,5],f32>, %arg2: !torch.vtensor<[1,1,3,3],f32>, %arg3: !torch.vtensor<[],f32>) -> (!torch.vtensor<[1,1,3,3],f32>, !torch.vtensor<[1],f32>) {
// CHECK: %[[VAL_4:.*]] = torch.constant.int 3
// CHECK: %[[VAL_5:.*]] = torch.constant.int 2
// CHECK: %[[VAL_6:.*]] = torch.constant.none
// CHECK: %[[VAL_7:.*]] = torch.constant.int 0
// CHECK: %[[VAL_8:.*]] = torch.constant.bool false
// CHECK: %[[VAL_9:.*]] = torch.constant.int 1
// CHECK: %[[VAL_10:.*]] = torch.prim.ListConstruct %[[VAL_9]], %[[VAL_9]] : (!torch.int, !torch.int) -> !torch.list<int>
// CHECK: %[[VAL_11:.*]] = torch.prim.ListConstruct %[[VAL_7]], %[[VAL_7]] : (!torch.int, !torch.int) -> !torch.list<int>
// CHECK: %[[VAL_12:.*]] = torch.aten.transpose.int %[[VAL_1]], %[[VAL_7]], %[[VAL_9]] : !torch.vtensor<[1,1,5,5],f32>, !torch.int, !torch.int -> !torch.vtensor<[1,1,5,5],f32>
// CHECK: %[[VAL_13:.*]] = torch.aten.transpose.int %[[VAL_0]], %[[VAL_7]], %[[VAL_9]] : !torch.vtensor<[1,1,3,3],f32>, !torch.int, !torch.int -> !torch.vtensor<[1,1,3,3],f32>
// CHECK: %[[VAL_14:.*]] = torch.aten.convolution %[[VAL_12]], %[[VAL_13]], %[[VAL_6]], %[[VAL_10]], %[[VAL_11]], %[[VAL_10]], %[[VAL_8]], %[[VAL_11]], %[[VAL_9]] : !torch.vtensor<[1,1,5,5],f32>, !torch.vtensor<[1,1,3,3],f32>, !torch.none, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.bool, !torch.list<int>, !torch.int -> !torch.vtensor<[1,1,3,3],f32>
// CHECK: %[[VAL_15:.*]] = torch.aten.transpose.int %[[VAL_14]], %[[VAL_7]], %[[VAL_9]] : !torch.vtensor<[1,1,3,3],f32>, !torch.int, !torch.int -> !torch.vtensor<[1,1,3,3],f32>
// CHECK: %[[VAL_16:.*]] = torch.prim.ListConstruct %[[VAL_7]], %[[VAL_5]], %[[VAL_4]] : (!torch.int, !torch.int, !torch.int) -> !torch.list<int>
// CHECK: %[[VAL_17:.*]] = torch.aten.sum.dim_IntList %[[VAL_0]], %[[VAL_16]], %[[VAL_8]], %[[VAL_6]] : !torch.vtensor<[1,1,3,3],f32>, !torch.list<int>, !torch.bool, !torch.none -> !torch.vtensor<[1],f32>
// CHECK: return %[[VAL_15]], %[[VAL_17]] : !torch.vtensor<[1,1,3,3],f32>, !torch.vtensor<[1],f32>
%true = torch.constant.bool true
%int0 = torch.constant.int 0
%false = torch.constant.bool false
%int1 = torch.constant.int 1
%0 = torch.prim.ListConstruct %int1 : (!torch.int) -> !torch.list<int>
%1 = torch.prim.ListConstruct %int1, %int1 : (!torch.int, !torch.int) -> !torch.list<int>
%2 = torch.prim.ListConstruct %int0, %int0 : (!torch.int, !torch.int) -> !torch.list<int>
%3 = torch.prim.ListConstruct %false, %true, %true : (!torch.bool, !torch.bool, !torch.bool) -> !torch.list<bool>
%result0, %result1, %result2 = torch.aten.convolution_backward %arg0, %arg1, %arg2, %0, %1, %2, %1, %false, %2, %int1, %3 : !torch.vtensor<[1,1,3,3],f32>, !torch.vtensor<[1,1,5,5],f32>, !torch.vtensor<[1,1,3,3],f32>, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.bool, !torch.list<int>, !torch.int, !torch.list<bool> -> !torch.none, !torch.vtensor<[1,1,3,3],f32>, !torch.vtensor<[1],f32>
return %result1, %result2 : !torch.vtensor<[1,1,3,3],f32>, !torch.vtensor<[1],f32>
}

// -----
// CHECK-LABEL: func.func @emptyLikeNoneDtype(
// CHECK-SAME: %[[ARG0:.*]]: !torch.vtensor<[200,200,26],f64>) -> !torch.vtensor<[200,200,26],f64> {
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