Transform SequenceAt to split for special cases

src/Dialect/ONNX/DialectBuilder.cpp

@@ -444,7 +444,7 @@ TensorType OnnxBuilder::toTensor(Type input) const {
 }
 
 TypeRange OnnxBuilder::toTensors(TypeRange inputs) const {
-  assert(inputs.size() >= 2 && "Expect at least two inputs");
+  //assert(inputs.size() >= 2 && "Expect at least two inputs");
   if (llvm::all_of(inputs, [](Type t) { return (mlir::isa<TensorType>(t)); }))
     return inputs;
   assert(llvm::all_of(inputs, [](Type t) {

src/Dialect/ONNX/ONNXOps/OpHelper.cpp

@@ -308,7 +308,8 @@ void ArrayAttrIntVals(ArrayAttr a, mlir::SmallVectorImpl<int64_t> &i) {
 
 ElementsAttr getElementAttributeFromONNXValue(Value value) {
   ONNXConstantOp constantOp = getONNXConstantOp(value);
-  if (constantOp)
+  // In case the ConstantOp has not been normalized yet
+  if (constantOp && constantOp.getValueAttr())
     return mlir::dyn_cast<ElementsAttr>(constantOp.getValueAttr());
   return nullptr;
 }

src/Dialect/ONNX/Transforms/Decompose.cpp

@@ -333,6 +333,122 @@ bool hasStaticSpatialDims(Value v) {
   return llvm::none_of(Ds, ShapedType::isDynamic);
 }
 
+// In the following pattern, a SequenceAt can be replaced with Split
+//   %seq = onnx.SplitToSequence(%input, %split) {%axis : }
+//   %res = onnx.SequenceAt(%seq, %position)
+// We just try to avoid using the sequence related ops, which are less
+// optimized, or even not implemented in onnx-mlir.
+// In the targeted use case, %split and %position are constant scalar and the
+// tensor of %input and %res have static shape.
+// This condition greatly reduces the complexity of code generation to replace
+// SequenceAt with split op
+//   %res = onnx.Split(%input, onnx.expand(%split, %input.shape()[%axis]))
+//   {%axis : } : %position
+// onnx.expand(%split, %input.shape()[%axis]) can be a constant under the
+// assumed condition.
+// Here %position has to be compiler time constant.
+// For multiple SequenceAt from the same SplitToSequence result, the onnx.split
+// for different SequenceAt are expected to be merged by optimization.
+// Alternatively, Slice can be used
+//   %res = onnx.Slice(%input, %start, %end, %step)
+// The start, and end for slice will be onnx.constant:
+//   start: %position*%split for %axis, 0 for other dimensionis
+//   end: (%positiion+1)*%split for %axis, upper bound for other dimension
+//   step: 1 for all dimensions
+// The split approach may have better performance than the alternative slice
+// approach,  because the slicing is done separately.
+
+bool canSequenceAtBeReplaced(Value sequenceAtResult) {
+  if (!hasStaticShape(sequenceAtResult.getType()))
+    return false;
+
+  ONNXSequenceAtOp op =
+      mlir::dyn_cast<ONNXSequenceAtOp>(sequenceAtResult.getDefiningOp());
+
+  Value inputSequence = op.getInputSequence();
+  Value position = op.getPosition();
+
+  if (!isDenseONNXConstant(position))
+    return false;
+
+  // Input sequence should be defined with SplitToSequence
+  ONNXSplitToSequenceOp splitToSequence;
+  if (!(splitToSequence = mlir::dyn_cast<ONNXSplitToSequenceOp>(
+            inputSequence.getDefiningOp())))
+    return false;
+
+  // Check the pattern of the SplitToSequence op
+  Value input = splitToSequence.getInput();
+  if (!hasStaticShape(input.getType()))
+    return false;
+  Value split = splitToSequence.getSplit();
+  if (!isScalarConstantTensor(split))
+    return false;
+
+  return true;
+}
+
+Value replaceSequenceAt(
+    PatternRewriter &rewriter, Location loc, Value sequenceAtResult) {
+  ONNXSequenceAtOp op =
+      mlir::cast<ONNXSequenceAtOp>(sequenceAtResult.getDefiningOp());
+
+  Value inputSequence = op.getInputSequence();
+  Value position = op.getPosition();
+
+  ONNXConstantOp positionConstant =
+      mlir::cast<ONNXConstantOp>(position.getDefiningOp());
+  int64_t positionInt = getScalarValue<int64_t>(positionConstant);
+
+  ONNXSplitToSequenceOp splitToSequence =
+      mlir::cast<ONNXSplitToSequenceOp>(inputSequence.getDefiningOp());
+
+  Value input = splitToSequence.getInput();
+  Value split = splitToSequence.getSplit();
+
+  ONNXConstantOp splitConstant =
+      mlir::cast<ONNXConstantOp>(split.getDefiningOp());
+  int64_t splitInt = getScalarValue<int64_t>(splitConstant);
+  int64_t axisInt = splitToSequence.getAxis();
+
+  auto shape = getShape(input.getType());
+
+  OnnxBuilder create(rewriter, loc);
+
+  Type sequenceElementType =
+      mlir::cast<SeqType>(inputSequence.getType()).getElementType();
+  mlir::SmallVector<mlir::Type, 4> outputTypes(
+      shape[axisInt] / splitInt, sequenceElementType);
+  auto numSplit = create.constantInt64(
+      mlir::SmallVector<int64_t, 4>(shape[axisInt] / splitInt, splitInt));
+  auto resultRange = create.split(outputTypes, input, numSplit, axisInt);
+  auto rawResult = resultRange[positionInt];
+
+  if (rawResult.getType() == sequenceAtResult.getType())
+    return rawResult;
+
+  // Temporary code for the error in the model generated by torch.onnx.export
+  // The the dim of the reuslt of SequenceAt op is different from the element
+  // type of the sequence..
+  // My assumption is that the exporter is confused with  squeeze and unsqueeze
+  // followed by the SequenceAt. There are two cases in the model:
+  // clang-format off
+  // Case #1:
+  //   %16 = "onnx.SequenceAt"(%14, %15) {onnx_node_name = "n0"} :
+  //     (!onnx.Seq<tensor<1x1x100xf32>>, tensor<i64>) -> tensor<1x100xf32>
+  //     %23 = "onnx.Unsqueeze"(%16, %22) {onnx_node_name = "n2"} :
+  //     (tensor<1x100xf32>, tensor<i64>) -> tensor<1x1x100xf32>
+  // Case#2:
+  //   %67 = "onnx.SequenceAt"(%66, %15) {onnx_node_name = "n0"} :
+  //   (!onnx.Seq<tensor<1x1x100xf32>>, tensor<i64>) -> tensor<1x1x100xf32>
+  //   %71 = "onnx.Sigmoid"(%67) {onnx_node_name = "node_Sigmoid_60"} :
+  //   (tensor<1x1x100xf32>) -> tensor<1x1x100xf32>
+  // clang-format on
+  // Thus, the compiler squeeze the tensor if needed.
+  return create.squeeze(
+      sequenceAtResult.getType(), rawResult, create.constantInt64(axisInt));
+}
+
 bool shouldDecomposeConvTransposeOp(Value convTransposeResult) {
   ONNXConvTransposeOp op =
       mlir::cast<ONNXConvTransposeOp>(convTransposeResult.getDefiningOp());
@@ -1246,6 +1362,10 @@ void DecomposeONNXToONNXPass::runOnOperation() {
     return !isConcatFuseMatched(op, shapeOp, transposeOp);
   });
 
+  target.addDynamicallyLegalOp<ONNXSequenceAtOp>([](ONNXSequenceAtOp op) {
+    return !onnx_mlir::canSequenceAtBeReplaced(op.getResult());
+  });
+
   // Rewrite ONNXConstantOp with scalar values into the one using ElementAttrs.
   target.addDynamicallyLegalOp<ONNXConstantOp>([](ONNXConstantOp op) {
     return !(op.getValueFloatAttr() || op.getValueFloatsAttr() ||

src/Dialect/ONNX/Transforms/Decompose.td

@@ -71,6 +71,12 @@ def createScalarDenseAttrRank0
 def ReshapeElementsAttrToRank0 : NativeCodeCall<
     "onnx_mlir::OnnxElementsAttrBuilder($0.getContext()).reshape(cast<ElementsAttr>($0), {})">;
 
+def ReplaceSequenceAt :  NativeCodeCall<
+    "onnx_mlir::replaceSequenceAt($_builder, $_loc, $0)">;
+
+def CanSequenceAtBeReplaced :
+   Constraint<CPred<"::onnx_mlir::canSequenceAtBeReplaced($_self)">, "check whether the SequenceAt can be replaced with split">;
+
 // Create a DenseElementsAttr from a single attribute.
 def createDenseArrayAttrFromSingleAttr
     : NativeCodeCall<"::onnx_mlir::createDenseArrayAttr($_builder, $_builder.getArrayAttr($0))">;
@@ -620,4 +626,16 @@ def ConstantOpNormalizationPattern6: Pat<
    [(AttributeIsNotNull:$stringsAttr)]
 >;
 
+// Optimize for the pattern coming from torch.nn.LSTM exported from pytorch
+//     %32 = "onnx.SplitToSequence"(%30, %27) {axis = 0 : si64, keepdims = 0 : si64, onnx_node_name = "n1"} : (tensor<1x1x100xf32>, tensor<i64>) -> !onnx.Seq<tensor<1x1x100xf32>>
+//    %33 = "onnx.SequenceAt"(%32, %26) {onnx_node_name = "n0"} : (!onnx.Seq<tensor<1x1x100xf32>>, tensor<i64>) -> tensor<1x100xf32>
+// When shape and size/axis related value are constant, this sequence of code
+// can be translated into onnx.slice
+
+def ReplaceSequenceAtPattern: Pat<
+   (ONNXSequenceAtOp:$res $seq, $position),
+   (ReplaceSequenceAt $res),
+   [(CanSequenceAtBeReplaced:$res)]
+>;
+
 #endif // ONNX_DECOMPOSE

test/mlir/onnx/onnx_decompose_canonicalize.mlir

@@ -0,0 +1,43 @@
+
+// RUN: onnx-mlir-opt --decompose-onnx --canonicalize %s -split-input-file | FileCheck %s
+
+// -----
+
+// Test one pattern in lstm_no_data.onnx.
+// The type of output of SequenceAt is not the same as the element type
+// of the input sequence
+func.func @sequence_at_squeezed(%arg0 : tensor<1x1x100xf32>) -> tensor<1x100xf32> {
+  %26 = onnx.Constant dense<0> : tensor<i64>
+  %27 = onnx.Constant dense<1> : tensor<i64>
+  %32 = "onnx.SplitToSequence"(%arg0, %27) {axis = 0 : si64, keepdims = 0 : si64} : (tensor<1x1x100xf32>, tensor<i64>) -> !onnx.Seq<tensor<1x1x100xf32>>
+  %33 = "onnx.SequenceAt"(%32, %26) : (!onnx.Seq<tensor<1x1x100xf32>>, tensor<i64>) -> tensor<1x100xf32>
+  return %33: tensor<1x100xf32>
+// CHECK-LABEL:  func.func @sequence_at_squeezed
+// CHECK-SAME:   ([[PARAM_0_:%.+]]: tensor<1x1x100xf32>) -> tensor<1x100xf32> {
+// CHECK-DAG:       [[VAR_0_:%.+]] = onnx.Constant dense<0> : tensor<1xi64>
+// CHECK-DAG:       [[VAR_1_:%.+]] = onnx.Constant dense<1> : tensor<1xi64>
+// CHECK:           [[VAR_2_:%.+]] = "onnx.Split"([[PARAM_0_]], [[VAR_1_]]) {axis = 0 : si64} : (tensor<1x1x100xf32>, tensor<1xi64>) -> tensor<1x1x100xf32>
+// CHECK:           [[VAR_3_:%.+]] = "onnx.Squeeze"([[VAR_2_]], [[VAR_0_]]) : (tensor<1x1x100xf32>, tensor<1xi64>) -> tensor<1x100xf32>
+// CHECK:           return [[VAR_3_]] : tensor<1x100xf32>
+// CHECK:         }
+}
+
+func.func @sequence_at_multi(%arg0 : tensor<1x1x400xf32>) -> tensor<1x1x100xf32> {
+  %15 = onnx.Constant dense<0> : tensor<i64>
+  %38 = onnx.Constant dense<1> : tensor<i64>
+  %65 = onnx.Constant dense<100> : tensor<i64> 
+  %66 = "onnx.SplitToSequence"(%arg0, %65) {axis = 2 : si64, keepdims = 1 : si64} : (tensor<1x1x400xf32>, tensor<i64>) -> !onnx.Seq<tensor<1x1x100xf32>>
+  %67 = "onnx.SequenceAt"(%66, %15) : (!onnx.Seq<tensor<1x1x100xf32>>, tensor<i64>) -> tensor<1x1x100xf32>
+  %68 = "onnx.SequenceAt"(%66, %38) : (!onnx.Seq<tensor<1x1x100xf32>>, tensor<i64>) -> tensor<1x1x100xf32>
+  %40 = "onnx.Add"(%67, %68) : (tensor<1x1x100xf32>, tensor<1x1x100xf32>) -> tensor<1x1x100xf32>
+  return %40: tensor<1x1x100xf32>
+// CHECK-LABEL:  func.func @sequence_at_multi
+// CHECK-SAME:   ([[PARAM_0_:%.+]]: tensor<1x1x400xf32>) -> tensor<1x1x100xf32> {
+// CHECK:           [[VAR_0_:%.+]] = onnx.Constant dense<100> : tensor<4xi64>
+// CHECK-DAG:       [[VAR_1_:%.+]]:4 = "onnx.Split"([[PARAM_0_]], [[VAR_0_]]) {axis = 2 : si64} : (tensor<1x1x400xf32>, tensor<4xi64>) -> (tensor<1x1x100xf32>, tensor<1x1x100xf32>, tensor<1x1x100xf32>, tensor<1x1x100xf32>)
+// CHECK-DAG:       [[VAR_2_:%.+]]:4 = "onnx.Split"([[PARAM_0_]], [[VAR_0_]]) {axis = 2 : si64} : (tensor<1x1x400xf32>, tensor<4xi64>) -> (tensor<1x1x100xf32>, tensor<1x1x100xf32>, tensor<1x1x100xf32>, tensor<1x1x100xf32>)
+// CHECK:           [[VAR_3_:%.+]] = "onnx.Add"([[VAR_1_]]#0, [[VAR_2_]]#1) : (tensor<1x1x100xf32>, tensor<1x1x100xf32>) -> tensor<1x1x100xf32>
+// CHECK:           return [[VAR_3_]] : tensor<1x1x100xf32>
+// CHECK:         }
+}
+