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daveliddellDave Liddell
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Dave Liddell
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[onnx] Added flatten (#2760)
[https://github.com/nod-ai/SHARK-Turbine/issues/328](url) --------- Co-authored-by: Dave Liddell <dliddell@xilinx.com>
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‎lib/Conversion/TorchOnnxToTorch/DefaultDomainAtoF.cpp‎

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Original file line numberDiff line numberDiff line change
@@ -1364,6 +1364,79 @@ void mlir::torch::onnx_c::populateDefaultDomainAtoF(
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binder.op, resultType, data, dimValueList);
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return success();
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});
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patterns.onOp(
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"Flatten", 13, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
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// Flatten means to partition the input tensor's dimensions
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// into a "left range" spanning 0 to axis - 1 and a "right range"
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// spanning axis to rank - 1. Each range is then collapsed
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// into a single dimension, resulting in a 2-D tensor.
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// If either range is empty, it is replaced with a single
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// dimension of size 1.
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//
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// For example, for a 4-D input tensor of shape (a, b, c, d)
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// and axis==2, flatten produces a 2-D tensor of shape
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// (a*b, c*d).
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//
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// If instead axis==0, the left range is empty, and the result
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// is (1, a*b*c*d).
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Torch::ValueTensorType resultType;
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Value operand;
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int64_t axis;
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if (binder.tensorOperand(operand) ||
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binder.s64IntegerAttr(axis, "axis", 1) ||
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binder.tensorResultType(resultType))
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return failure();
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// If axis is negative, count from the right instead of left
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int64_t rank =
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cast<Torch::ValueTensorType>(operand.getType()).getSizes().size();
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if (axis < 0)
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axis = rank + axis;
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Value collapsedRight;
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auto baseType = Torch::ValueTensorType::getWithLeastStaticInformation(
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binder.op->getContext());
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if (axis >= rank) {
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// If the right range is empty, add a dim of size 1 to the
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// right side of the shape:
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// cr = torch.unsqueeze(x, x.ndim)
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Value rankConst = rewriter.create<Torch::ConstantIntOp>(
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binder.getLoc(), rewriter.getI64IntegerAttr(rank));
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collapsedRight = rewriter.create<Torch::AtenUnsqueezeOp>(
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binder.getLoc(), baseType, operand, rankConst);
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} else {
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// Otherwise, collapse the right range into a single dimension:
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// cr = torch._prims.collapse(x, axis, x.ndim - 1)
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Value axisConst = rewriter.create<Torch::ConstantIntOp>(
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binder.getLoc(), rewriter.getI64IntegerAttr(axis));
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Value rankLess1Const = rewriter.create<Torch::ConstantIntOp>(
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binder.getLoc(), rewriter.getI64IntegerAttr(rank - 1));
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collapsedRight = rewriter.create<Torch::PrimsCollapseOp>(
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binder.getLoc(), baseType, operand, axisConst, rankLess1Const);
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}
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Value zero = rewriter.create<Torch::ConstantIntOp>(
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binder.getLoc(), rewriter.getI64IntegerAttr(0));
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if (axis <= 0) {
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// If the left range is empty, add a dim of size 1 to the
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// left side of the shape:
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// torch.unsqueeze(cr, 0)
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rewriter.replaceOpWithNewOp<Torch::AtenUnsqueezeOp>(
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binder.op, resultType, collapsedRight, zero);
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return success();
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}
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// Otherwise, collapse the left range into a single dimension:
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// torch._prims.collapse(cr, 0, axis - 1)
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Value axisLess1Const = rewriter.create<Torch::ConstantIntOp>(
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binder.getLoc(), rewriter.getI64IntegerAttr(axis - 1));
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rewriter.replaceOpWithNewOp<Torch::PrimsCollapseOp>(
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binder.op, resultType, collapsedRight, zero, axisLess1Const);
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return success();
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});
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patterns.onOp("Floor", 13,
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[](OpBinder binder, ConversionPatternRewriter &rewriter) {
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Torch::ValueTensorType resultType;

‎test/Conversion/TorchOnnxToTorch/simple_ops_a_to_f.mlir‎

Lines changed: 113 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -1062,3 +1062,116 @@ func.func @ints_constant() -> !torch.vtensor<[2], si64> attributes {torch.onnx_m
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return %0 : !torch.vtensor<[2],si64>
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}
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// CHECK-LABEL: @test_flatten_4d_axis_2
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func.func @test_flatten_4d_axis_2(%arg0: !torch.vtensor<[2,3,4,5],f32>) -> !torch.vtensor<[6,20],f32> attributes {torch.onnx_meta.ir_version = 7 : si64, torch.onnx_meta.opset_version = 13 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
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// CHECK-DAG: %[[RIGHT_START:.*]] = torch.constant.int 2
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// CHECK-DAG: %[[RIGHT_END:.*]] = torch.constant.int 3
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// CHECK-DAG: %[[CR:.*]] = torch.prims.collapse %arg0, %[[RIGHT_START]], %[[RIGHT_END]] : !torch.vtensor<[2,3,4,5],f32>, !torch.int, !torch.int -> !torch.vtensor
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// CHECK-DAG: %[[LEFT_START:.*]] = torch.constant.int 0
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// CHECK-DAG: %[[LEFT_END:.*]] = torch.constant.int 1
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// CHECK: torch.prims.collapse %[[CR]], %[[LEFT_START]], %[[LEFT_END]] : !torch.vtensor, !torch.int, !torch.int -> !torch.vtensor<[6,20],f32>
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%0 = torch.operator "onnx.Flatten"(%arg0) {torch.onnx.axis = 2 : si64} : (!torch.vtensor<[2,3,4,5],f32>) -> !torch.vtensor<[6,20],f32>
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return %0 : !torch.vtensor<[6,20],f32>
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}
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// CHECK-LABEL: @test_flatten_4d_axis_0
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func.func @test_flatten_4d_axis_0(%arg0: !torch.vtensor<[2,3,4,5],f32>) -> !torch.vtensor<[1,120],f32> attributes {torch.onnx_meta.ir_version = 7 : si64, torch.onnx_meta.opset_version = 13 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
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// CHECK-DAG: %[[RIGHT_START:.*]] = torch.constant.int 0
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// CHECK-DAG: %[[RIGHT_END:.*]] = torch.constant.int 3
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// CHECK-DAG: %[[CR:.*]] = torch.prims.collapse %arg0, %[[RIGHT_START]], %[[RIGHT_END]] : !torch.vtensor<[2,3,4,5],f32>, !torch.int, !torch.int -> !torch.vtensor
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// CHECK-DAG: %[[LEFT_INDEX:.*]] = torch.constant.int 0
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// CHECK: torch.aten.unsqueeze %[[CR]], %[[LEFT_INDEX]] : !torch.vtensor, !torch.int -> !torch.vtensor<[1,120],f32>
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%0 = torch.operator "onnx.Flatten"(%arg0) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[2,3,4,5],f32>) -> !torch.vtensor<[1,120],f32>
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return %0 : !torch.vtensor<[1,120],f32>
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}
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// CHECK-LABEL: @test_flatten_4d_axis_4
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func.func @test_flatten_4d_axis_4(%arg0: !torch.vtensor<[2,3,4,5],f32>) -> !torch.vtensor<[120,1],f32> attributes {torch.onnx_meta.ir_version = 7 : si64, torch.onnx_meta.opset_version = 13 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
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// CHECK-DAG: %[[RIGHT_INDEX:.*]] = torch.constant.int 4
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// CHECK-DAG: %[[CR:.*]] = torch.aten.unsqueeze %arg0, %[[RIGHT_INDEX]] : !torch.vtensor<[2,3,4,5],f32>, !torch.int -> !torch.vtensor
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// CHECK-DAG: %[[LEFT_START:.*]] = torch.constant.int 0
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// CHECK-DAG: %[[LEFT_END:.*]] = torch.constant.int 3
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// CHECK: torch.prims.collapse %[[CR]], %[[LEFT_START]], %[[LEFT_END]] : !torch.vtensor, !torch.int, !torch.int -> !torch.vtensor<[120,1],f32>
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%0 = torch.operator "onnx.Flatten"(%arg0) {torch.onnx.axis = 4 : si64} : (!torch.vtensor<[2,3,4,5],f32>) -> !torch.vtensor<[120,1],f32>
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return %0 : !torch.vtensor<[120,1],f32>
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}
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// CHECK-LABEL: @test_flatten_4d_axis_negative_2
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func.func @test_flatten_4d_axis_negative_2(%arg0: !torch.vtensor<[2,3,4,5],f32>) -> !torch.vtensor<[6,20],f32> attributes {torch.onnx_meta.ir_version = 7 : si64, torch.onnx_meta.opset_version = 13 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
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// CHECK-DAG: %[[RIGHT_START:.*]] = torch.constant.int 2
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// CHECK-DAG: %[[RIGHT_END:.*]] = torch.constant.int 3
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// CHECK-DAG: %[[CR:.*]] = torch.prims.collapse %arg0, %[[RIGHT_START]], %[[RIGHT_END]] : !torch.vtensor<[2,3,4,5],f32>, !torch.int, !torch.int -> !torch.vtensor
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// CHECK-DAG: %[[LEFT_START:.*]] = torch.constant.int 0
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// CHECK-DAG: %[[LEFT_END:.*]] = torch.constant.int 1
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// CHECK: torch.prims.collapse %[[CR]], %[[LEFT_START]], %[[LEFT_END]] : !torch.vtensor, !torch.int, !torch.int -> !torch.vtensor<[6,20],f32>
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%0 = torch.operator "onnx.Flatten"(%arg0) {torch.onnx.axis = -2 : si64} : (!torch.vtensor<[2,3,4,5],f32>) -> !torch.vtensor<[6,20],f32>
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return %0 : !torch.vtensor<[6,20],f32>
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}
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// CHECK-LABEL: @test_flatten_4d_axis_negative_1
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func.func @test_flatten_4d_axis_negative_1(%arg0: !torch.vtensor<[2,3,4,5],f32>) -> !torch.vtensor<[24,5],f32> attributes {torch.onnx_meta.ir_version = 7 : si64, torch.onnx_meta.opset_version = 13 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
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// CHECK-DAG: %[[RIGHT_START:.*]] = torch.constant.int 3
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// CHECK-DAG: %[[RIGHT_END:.*]] = torch.constant.int 3
1115+
// CHECK-DAG: %[[CR:.*]] = torch.prims.collapse %arg0, %[[RIGHT_START]], %[[RIGHT_END]] : !torch.vtensor<[2,3,4,5],f32>, !torch.int, !torch.int -> !torch.vtensor
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// CHECK-DAG: %[[LEFT_START:.*]] = torch.constant.int 0
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// CHECK-DAG: %[[LEFT_END:.*]] = torch.constant.int 2
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// CHECK: torch.prims.collapse %[[CR]], %[[LEFT_START]], %[[LEFT_END]] : !torch.vtensor, !torch.int, !torch.int -> !torch.vtensor<[24,5],f32>
1119+
%0 = torch.operator "onnx.Flatten"(%arg0) {torch.onnx.axis = -1 : si64} : (!torch.vtensor<[2,3,4,5],f32>) -> !torch.vtensor<[24,5],f32>
1120+
return %0 : !torch.vtensor<[24,5],f32>
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}
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// CHECK-LABEL: @test_flatten_4d_axis_negative_4
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func.func @test_flatten_4d_axis_negative_4(%arg0: !torch.vtensor<[2,3,4,5],f32>) -> !torch.vtensor<[1,120],f32> attributes {torch.onnx_meta.ir_version = 7 : si64, torch.onnx_meta.opset_version = 13 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
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// CHECK-DAG: %[[RIGHT_START:.*]] = torch.constant.int 0
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// CHECK-DAG: %[[RIGHT_END:.*]] = torch.constant.int 3
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// CHECK-DAG: %[[CR:.*]] = torch.prims.collapse %arg0, %[[RIGHT_START]], %[[RIGHT_END]] : !torch.vtensor<[2,3,4,5],f32>, !torch.int, !torch.int -> !torch.vtensor
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// CHECK-DAG: %[[LEFT_INDEX:.*]] = torch.constant.int 0
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// CHECK: torch.aten.unsqueeze %[[CR]], %[[LEFT_INDEX]] : !torch.vtensor, !torch.int -> !torch.vtensor<[1,120],f32>
1130+
%0 = torch.operator "onnx.Flatten"(%arg0) {torch.onnx.axis = -4 : si64} : (!torch.vtensor<[2,3,4,5],f32>) -> !torch.vtensor<[1,120],f32>
1131+
return %0 : !torch.vtensor<[1,120],f32>
1132+
}
1133+
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// CHECK-LABEL: @test_flatten_2d_axis_1
1135+
func.func @test_flatten_2d_axis_1(%arg0: !torch.vtensor<[2,3],f32>) -> !torch.vtensor<[2,3],f32> attributes {torch.onnx_meta.ir_version = 7 : si64, torch.onnx_meta.opset_version = 13 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
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// CHECK-DAG: %[[RIGHT_START:.*]] = torch.constant.int 1
1137+
// CHECK-DAG: %[[RIGHT_END:.*]] = torch.constant.int 1
1138+
// CHECK-DAG: %[[CR:.*]] = torch.prims.collapse %arg0, %[[RIGHT_START]], %[[RIGHT_END]] : !torch.vtensor<[2,3],f32>, !torch.int, !torch.int -> !torch.vtensor
1139+
// CHECK-DAG: %[[LEFT_START:.*]] = torch.constant.int 0
1140+
// CHECK-DAG: %[[LEFT_END:.*]] = torch.constant.int 0
1141+
// CHECK: torch.prims.collapse %[[CR]], %[[LEFT_START]], %[[LEFT_END]] : !torch.vtensor, !torch.int, !torch.int -> !torch.vtensor<[2,3],f32>
1142+
%0 = torch.operator "onnx.Flatten"(%arg0) {torch.onnx.axis = 1 : si64} : (!torch.vtensor<[2,3],f32>) -> !torch.vtensor<[2,3],f32>
1143+
return %0 : !torch.vtensor<[2,3],f32>
1144+
}
1145+
1146+
// CHECK-LABEL: @test_flatten_1d_axis_0
1147+
func.func @test_flatten_1d_axis_0(%arg0: !torch.vtensor<[2],f32>) -> !torch.vtensor<[1,2],f32> attributes {torch.onnx_meta.ir_version = 7 : si64, torch.onnx_meta.opset_version = 13 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
1148+
// CHECK-DAG: %[[RIGHT_START:.*]] = torch.constant.int 0
1149+
// CHECK-DAG: %[[RIGHT_END:.*]] = torch.constant.int 0
1150+
// CHECK-DAG: %[[CR:.*]] = torch.prims.collapse %arg0, %[[RIGHT_START]], %[[RIGHT_END]] : !torch.vtensor<[2],f32>, !torch.int, !torch.int -> !torch.vtensor
1151+
// CHECK-DAG: %[[LEFT_INDEX:.*]] = torch.constant.int 0
1152+
// CHECK: torch.aten.unsqueeze %[[CR]], %[[LEFT_INDEX]] : !torch.vtensor, !torch.int -> !torch.vtensor<[1,2],f32>
1153+
%0 = torch.operator "onnx.Flatten"(%arg0) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[2],f32>) -> !torch.vtensor<[1,2],f32>
1154+
return %0 : !torch.vtensor<[1,2],f32>
1155+
}
1156+
1157+
// CHECK-LABEL: @test_flatten_1d_axis_negative_1
1158+
func.func @test_flatten_1d_axis_negative_1(%arg0: !torch.vtensor<[2],f32>) -> !torch.vtensor<[1,2],f32> attributes {torch.onnx_meta.ir_version = 7 : si64, torch.onnx_meta.opset_version = 13 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
1159+
// CHECK-DAG: %[[RIGHT_START:.*]] = torch.constant.int 0
1160+
// CHECK-DAG: %[[RIGHT_END:.*]] = torch.constant.int 0
1161+
// CHECK-DAG: %[[CR:.*]] = torch.prims.collapse %arg0, %[[RIGHT_START]], %[[RIGHT_END]] : !torch.vtensor<[2],f32>, !torch.int, !torch.int -> !torch.vtensor
1162+
// CHECK-DAG: %[[LEFT_INDEX:.*]] = torch.constant.int 0
1163+
// CHECK: torch.aten.unsqueeze %[[CR]], %[[LEFT_INDEX]] : !torch.vtensor, !torch.int -> !torch.vtensor<[1,2],f32>
1164+
%0 = torch.operator "onnx.Flatten"(%arg0) {torch.onnx.axis = -1 : si64} : (!torch.vtensor<[2],f32>) -> !torch.vtensor<[1,2],f32>
1165+
return %0 : !torch.vtensor<[1,2],f32>
1166+
}
1167+
1168+
// COM: CHECK-LABEL: @test_flatten_1d_axis_1
1169+
func.func @test_flatten_1d_axis_1(%arg0: !torch.vtensor<[2],f32>) -> !torch.vtensor<[2,1],f32> attributes {torch.onnx_meta.ir_version = 7 : si64, torch.onnx_meta.opset_version = 13 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
1170+
// CHECK-DAG: %[[RIGHT_INDEX:.*]] = torch.constant.int 1
1171+
// CHECK-DAG: %[[CR:.*]] = torch.aten.unsqueeze %arg0, %[[RIGHT_INDEX]] : !torch.vtensor<[2],f32>, !torch.int -> !torch.vtensor
1172+
// CHECK-DAG: %[[LEFT_START:.*]] = torch.constant.int 0
1173+
// CHECK-DAG: %[[LEFT_END:.*]] = torch.constant.int 0
1174+
// CHECK: torch.prims.collapse %[[CR]], %[[LEFT_START]], %[[LEFT_END]] : !torch.vtensor, !torch.int, !torch.int -> !torch.vtensor<[2,1],f32>
1175+
%0 = torch.operator "onnx.Flatten"(%arg0) {torch.onnx.axis = 1 : si64} : (!torch.vtensor<[2],f32>) -> !torch.vtensor<[2,1],f32>
1176+
return %0 : !torch.vtensor<[2,1],f32>
1177+
}

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