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[HLO] Adopt custom syntax for convolution dimensions and window attributes (HLO) 

PiperOrigin-RevId: 374923250
This commit is contained in:
Rahul Joshi 2021-05-20 12:12:57 -07:00 committed by TensorFlow MLIR Team
parent fc88cf1ff4
commit 41f663ce47
3 changed files with 38 additions and 0 deletions
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9
include/mlir-hlo/Dialect/mhlo/IR/hlo_ops.td
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@ -1409,6 +1409,15 @@ def HLO_ConvOp : HLO_Op<"convolution", [NoSideEffect]> {
[](bool v) { return v; }); [](bool v) { return v; });
} }
}]; }];
let assemblyFormat = [{
`(`operands`)`
`dim_numbers` `=` custom<ConvolutionDimensions>($dimension_numbers) `,`
`window` `=` `{` custom<WindowAttributes>($window_strides, $padding,
$lhs_dilation, $rhs_dilation,
$window_reversal) `}`
attr-dict `:` functional-type(operands, results)
}];
} }
def HLO_CopyOp: HLO_Op<"copy", [NoSideEffect, SameOperandsAndResultType]> { def HLO_CopyOp: HLO_Op<"copy", [NoSideEffect, SameOperandsAndResultType]> {

3
lib/Dialect/mhlo/IR/hlo_ops.cc
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@ -3460,6 +3460,9 @@ OpFoldResult ScatterOp::fold(ArrayRef<Attribute> operands) {
return DenseElementsAttr::get(base_type, results); return DenseElementsAttr::get(base_type, results);
} }
using mlir::hlo::parseWindowAttributes;
using mlir::hlo::printWindowAttributes;
} // namespace mhlo } // namespace mhlo
} // namespace mlir } // namespace mlir

26
tests/ops.mlir
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@ -1440,3 +1440,29 @@ func @rng_uniform_invalid(%arg0: tensor<f32>, %arg1: tensor<f32>, %arg2: tensor<
%0 = "mhlo.rng_uniform"(%arg0, %arg1, %arg2) : (tensor<f32>, tensor<f32>, tensor<7xi64>) -> tensor<?xf32> %0 = "mhlo.rng_uniform"(%arg0, %arg1, %arg2) : (tensor<f32>, tensor<f32>, tensor<7xi64>) -> tensor<?xf32>
return return
} }
// -----
// CHECK: func @conv2d_generic
// CHECK: mhlo.convolution
// CHECK-SAME: dim_numbers = [b, 0, 1, f]x[0, 1, i, o]->[b, 0, 1, f]
// CHECK-SAME{LITERAL}: window = {stride = [1, 1], pad = [[1, 1], [1, 1]], lhs_dilate = [1, 1], rhs_dilate = [1, 1]}
func @conv2d_generic(%arg0: tensor<1x8x8x207xf32>, %arg1: tensor<3x3x207x16xf32>) -> tensor<1x8x8x16xf32> {
%0 = "mhlo.convolution"(%arg0, %arg1) {batch_group_count = 1 : i64, dimension_numbers =
{input_batch_dimension = 0 : i64, input_feature_dimension = 3 : i64, input_spatial_dimensions = dense<[1, 2]> : tensor<2xi64>, kernel_input_feature_dimension = 2 : i64, kernel_output_feature_dimension = 3 : i64, kernel_spatial_dimensions = dense<[0, 1]> : tensor<2xi64>, output_batch_dimension = 0 : i64, output_feature_dimension = 3 : i64, output_spatial_dimensions = dense<[1, 2]> : tensor<2xi64>},
feature_group_count = 1 : i64, lhs_dilation = dense<1> : tensor<2xi64>, padding = dense<1> : tensor<2x2xi64>, precision_config = ["DEFAULT", "DEFAULT"], rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} :
(tensor<1x8x8x207xf32>, tensor<3x3x207x16xf32>) -> tensor<1x8x8x16xf32>
return %0 : tensor<1x8x8x16xf32>
}
// CHECK: func @conv2d
// CHECK: mhlo.convolution
// CHECK-SAME: dim_numbers = [b, 0, 1, f]x[0, 1, i, o]->[b, 0, 1, f]
// CHECK-SAME{LITERAL}: window = {stride = [1, 1], pad = [[1, 1], [1, 1]], lhs_dilate = [1, 1], rhs_dilate = [1, 1]}
func @conv2d(%arg0: tensor<1x8x8x207xf32>, %arg1: tensor<3x3x207x16xf32>) -> tensor<1x8x8x16xf32> {
%0 = mhlo.convolution(%arg0, %arg1)
dim_numbers = [b, 0, 1, f]x[0, 1, i, o]->[b, 0, 1, f],
window = {stride = [1, 1], pad = [[1, 1], [1, 1]], lhs_dilate = [1, 1], rhs_dilate = [1, 1]}
{batch_group_count = 1 : i64, feature_group_count = 1 : i64, precision_config = ["DEFAULT", "DEFAULT"]} :
(tensor<1x8x8x207xf32>, tensor<3x3x207x16xf32>) -> tensor<1x8x8x16xf32>
return %0 : tensor<1x8x8x16xf32>
}