[TorchToLinalg] Support AtenReplicationPad1d with lowering to linalg backend (#4217)

This PR takes care of #4216.
- Add `AtenReplicationPad1d` support in `torch` dialect.
- Lower to **linalg** backend:
   - `tensor.extract_slice` ops for left & right slices 
- `tensor.concat` ops to **expand** each slice into right shape and join
all slices in order
- Update `AtenPadOp` decomposition to make use of **1d** replication pad
instead of using 2d variant.
- Add tests & update **failing** cases in `xfail` sets.

---------

Signed-off-by: Zahid Wakeel <zahid.wakeel@multicorewareinc.com>

Author: zahidwx

include/torch-mlir/Dialect/Torch/IR/GeneratedTorchOps.td

@@ -10214,6 +10214,30 @@ def Torch_AtenConstantPadNdOp : Torch_Op<"aten.constant_pad_nd", [
   }];
 }
 
+def Torch_AtenReplicationPad1dOp : Torch_Op<"aten.replication_pad1d", [
+    AllowsTypeRefinement,
+    HasValueSemantics,
+    ReadOnly
+  ]> {
+  let summary = "Generated op for `aten::replication_pad1d : (Tensor, int[]) -> (Tensor)`";
+  let arguments = (ins
+    AnyTorchTensorType:$self,
+    AnyTorchListOfTorchIntType:$padding
+  );
+  let results = (outs
+    AnyTorchOptionalTensorType:$result
+  );
+  let hasCustomAssemblyFormat = 1;
+  let extraClassDefinition = [{
+    ParseResult AtenReplicationPad1dOp::parse(OpAsmParser &parser, OperationState &result) {
+      return parseDefaultTorchOp(parser, result, 2, 1);
+    }
+    void AtenReplicationPad1dOp::print(OpAsmPrinter &printer) {
+      printDefaultTorchOp(printer, *this, 2, 1);
+    }
+  }];
+}
+
 def Torch_AtenReplicationPad2dOp : Torch_Op<"aten.replication_pad2d", [
     AllowsTypeRefinement,
     HasValueSemantics,

lib/Conversion/TorchToLinalg/TensorConstructors.cpp

@@ -116,6 +116,83 @@ class ConvertAtenConstantPadNdOp
 
 namespace {
 
+class ConvertAtenReplicationPad1dOp
+    : public OpConversionPattern<AtenReplicationPad1dOp> {
+public:
+  using OpConversionPattern::OpConversionPattern;
+
+  LogicalResult
+  matchAndRewrite(AtenReplicationPad1dOp op, OpAdaptor adaptor,
+                  ConversionPatternRewriter &rewriter) const override {
+    if (failed(verifyLinalgCompatibleTypes(op, rewriter)))
+      return failure();
+
+    Location loc = op.getLoc();
+    Value input = adaptor.getSelf();
+    auto inputType = llvm::cast<RankedTensorType>(input.getType());
+    int64_t inputRank = inputType.getRank();
+
+    if (inputRank < 2)
+      return rewriter.notifyMatchFailure(op, "input rank must be at least 2");
+
+    SmallVector<int64_t> padInts;
+    if (!matchPattern(op.getPadding(), m_TorchListOfConstantInts(padInts)))
+      return rewriter.notifyMatchFailure(
+          op, "only support constant int pad ranges");
+
+    if (padInts.size() != 2)
+      return rewriter.notifyMatchFailure(
+          op, "pad range must have exactly two values");
+
+    int64_t leftPad = padInts[0];
+    int64_t rightPad = padInts[1];
+
+    int64_t dimToPad = inputRank - 1;
+    Value one = rewriter.create<arith::ConstantIndexOp>(loc, 1);
+
+    SmallVector<Value> inputShape = getTensorSizes(rewriter, loc, input);
+    Value widthSize = inputShape[dimToPad];
+    Value widthMinusOne = rewriter.create<arith::SubIOp>(loc, widthSize, one);
+
+    // Build offset and size arrays for slicing
+    SmallVector<OpFoldResult> allOneStrides(inputRank,
+                                            rewriter.getIndexAttr(1));
+    SmallVector<OpFoldResult> leftOffsets(inputRank, rewriter.getIndexAttr(0));
+    SmallVector<OpFoldResult> rightOffsets(inputRank, rewriter.getIndexAttr(0));
+    SmallVector<OpFoldResult> sizes(inputRank, rewriter.getIndexAttr(0));
+    for (int i = 0; i < inputRank; ++i)
+      sizes[i] = (i == dimToPad) ? rewriter.getIndexAttr(1)
+                                 : getAsOpFoldResult(inputShape[i]);
+
+    rightOffsets[dimToPad] = getAsOpFoldResult(widthMinusOne);
+
+    // Extract leftmost and rightmost slices
+    Value leftSlice = rewriter.create<tensor::ExtractSliceOp>(
+        loc, input, leftOffsets, sizes, allOneStrides);
+    Value rightSlice = rewriter.create<tensor::ExtractSliceOp>(
+        loc, input, rightOffsets, sizes, allOneStrides);
+
+    // Aggregate slices to concat together
+    SmallVector<Value> resultParts;
+    resultParts.reserve(leftPad + rightPad + 1);
+
+    resultParts.append(leftPad, leftSlice);
+    resultParts.push_back(input);
+    resultParts.append(rightPad, rightSlice);
+
+    Value result =
+        rewriter.create<tensor::ConcatOp>(loc, dimToPad, resultParts);
+    Type resultType = getTypeConverter()->convertType(op.getType());
+    rewriter.replaceOpWithNewOp<tensor::CastOp>(op, resultType, result);
+
+    return success();
+  }
+};
+
+} // namespace
+
+namespace {
+
 // Lower aten.replication_pad2d operator into a sequence of
 // tensor.extract_slice and tensor.concat operations.
 
@@ -621,6 +698,8 @@ void mlir::torch::torch_to_linalg::
   MLIRContext *context = patterns.getContext();
   target.addIllegalOp<AtenReplicationPad2dOp>();
   patterns.add<ConvertAtenReplicationPad2dOp>(typeConverter, context);
+  target.addIllegalOp<AtenReplicationPad1dOp>();
+  patterns.add<ConvertAtenReplicationPad1dOp>(typeConverter, context);
   target.addIllegalOp<AtenConstantPadNdOp>();
   patterns.add<ConvertAtenConstantPadNdOp>(typeConverter, context);
   target.addIllegalOp<AtenZerosOp, AtenOnesOp>();

lib/Dialect/Torch/Transforms/AbstractInterpLibrary.cpp

@@ -10830,6 +10830,31 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
 "    } : (!torch.int, !torch.bool) -> ()\n"
 "    return %arg0 : !torch.list<int>\n"
 "  }\n"
+"  func.func @\"__torch_mlir_shape_fn.aten.replication_pad1d\"(%arg0: !torch.list<int>, %arg1: !torch.list<int>) -> !torch.list<int> {\n"
+"    %false = torch.constant.bool false\n"
+"    %str = torch.constant.str \"AssertionError: padding size expected to be 2\"\n"
+"    %none = torch.constant.none\n"
+"    %str_0 = torch.constant.str \"AssertionError: \"\n"
+"    %int2 = torch.constant.int 2\n"
+"    %0 = torch.aten.len.t %arg0 : !torch.list<int> -> !torch.int\n"
+"    %1 = torch.aten.ge.int %0, %int2 : !torch.int, !torch.int -> !torch.bool\n"
+"    torch.prim.If %1 -> () {\n"
+"      torch.prim.If.yield\n"
+"    } else {\n"
+"      torch.prim.RaiseException %str_0, %none : !torch.str, !torch.none\n"
+"      torch.prim.If.yield\n"
+"    }\n"
+"    %2 = torch.aten.len.t %arg1 : !torch.list<int> -> !torch.int\n"
+"    %3 = torch.aten.eq.int %2, %int2 : !torch.int, !torch.int -> !torch.bool\n"
+"    torch.prim.If %3 -> () {\n"
+"      torch.prim.If.yield\n"
+"    } else {\n"
+"      torch.prim.RaiseException %str, %none : !torch.str, !torch.none\n"
+"      torch.prim.If.yield\n"
+"    }\n"
+"    %4 = call @__torch__.pad_shape_fn(%arg0, %arg1, %false) : (!torch.list<int>, !torch.list<int>, !torch.bool) -> !torch.list<int>\n"
+"    return %4 : !torch.list<int>\n"
+"  }\n"
 "  func.func @\"__torch_mlir_shape_fn.aten.replication_pad2d\"(%arg0: !torch.list<int>, %arg1: !torch.list<int>) -> !torch.list<int> {\n"
 "    %false = torch.constant.bool false\n"
 "    %str = torch.constant.str \"AssertionError: padding size expected to be 4\"\n"
@@ -10856,6 +10881,10 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
 "    %4 = call @__torch__.pad_shape_fn(%arg0, %arg1, %false) : (!torch.list<int>, !torch.list<int>, !torch.bool) -> !torch.list<int>\n"
 "    return %4 : !torch.list<int>\n"
 "  }\n"
+"  func.func @\"__torch_mlir_dtype_fn.aten.replication_pad1d\"(%arg0: !torch.tuple<int, int>, %arg1: !torch.list<int>) -> !torch.int {\n"
+"    %0:2 = torch.prim.TupleUnpack %arg0 : !torch.tuple<int, int> -> !torch.int, !torch.int\n"
+"    return %0#1 : !torch.int\n"
+"  }\n"
 "  func.func @\"__torch_mlir_dtype_fn.aten.replication_pad2d\"(%arg0: !torch.tuple<int, int>, %arg1: !torch.list<int>) -> !torch.int {\n"
 "    %0:2 = torch.prim.TupleUnpack %arg0 : !torch.tuple<int, int> -> !torch.int, !torch.int\n"
 "    return %0#1 : !torch.int\n"

lib/Dialect/Torch/Transforms/DecomposeComplexOps.cpp

@@ -8490,12 +8490,6 @@ class DecomposeAtenPadOp : public OpRewritePattern<AtenPadOp> {
       }
     }
 
-    // we don't have support for 1-D replicate pad, so pass it as 2d if
-    // possible.
-    // TODO: add support for AtenReplicatePad1dOp and remove this.
-    if (mode == "replicate" && usefulPadIndexEnd == 2 && padValues.size() >= 4)
-      usefulPadIndexEnd = 4;
-
     // make a new list of padding ints if dimensionality reduction can be
     // performed
     if (usefulPadIndexEnd < padValues.size()) {
@@ -8533,11 +8527,20 @@ class DecomposeAtenPadOp : public OpRewritePattern<AtenPadOp> {
     }
 
     if (mode == "replicate") {
-      // only support for replication pad 2d
-      if (numPadDims != 2)
-        return failure();
-      rewriter.replaceOpWithNewOp<AtenReplicationPad2dOp>(
-          op, op.getType(), op.getSelf(), usefulPads);
+      switch (numPadDims) {
+      case 1:
+        rewriter.replaceOpWithNewOp<AtenReplicationPad1dOp>(
+            op, op.getType(), op.getSelf(), usefulPads);
+        break;
+      case 2:
+        rewriter.replaceOpWithNewOp<AtenReplicationPad2dOp>(
+            op, op.getType(), op.getSelf(), usefulPads);
+        break;
+      default:
+        return rewriter.notifyMatchFailure(
+            op, "unsupported number of dims for 'reflect' mode: " +
+                    std::to_string(numPadDims));
+      }
       return success();
     }
 

projects/pt1/e2e_testing/xfail_sets.py

@@ -840,6 +840,8 @@
     "ReflectionPad3dModuleRight_basic",
     "ReflectionPad3dModuleFront_basic",
     "ReflectionPad3dModuleBack_basic",
+    "ReplicationPad1dModule_2DInput_basic",
+    "ReplicationPad1dModule_3DInput_basic",
     "ReplicationPad2dModule_basic",
     "ReplicationPad2dModule_bottom0",
     "ReplicationPad2dModule_left0",
@@ -3927,6 +3929,8 @@
     "IndexPutImpl1DFloatNonAccumulateModule_basic",
     "IndexPutImpl1DIntNonAccumulateModule_basic",
     "UnsafeIndexPutHackedTwin1DFloatNonAccumulateModule_basic",
+    "ReplicationPad1dModule_2DInput_basic",
+    "ReplicationPad1dModule_3DInput_basic",
 }
 
 ONNX_TOSA_CRASHING_SET = {
@@ -4766,6 +4770,8 @@
     "RMSNormWithoutWeightModule_basic",
     "RMSNormAllNormalizeModule_basic",
     "RMSNormDynamicModule_basic",
+    "ReplicationPad1dModule_2DInput_basic",
+    "ReplicationPad1dModule_3DInput_basic",
     "RollModule_basic",
     "RsubIntModule_noalpha_basic",
     "ScalarConstantTupleModule_basic",

projects/pt1/python/torch_mlir/jit_ir_importer/build_tools/abstract_interp_lib_gen.py

@@ -2250,11 +2250,20 @@ def pad_shape_fn(input: List[int], pad: List[int], validate_pad : bool = False):
 def aten〇constant_pad_nd〡shape(self: List[int], pad: List[int], value: float = 0) -> List[int]:
     return pad_shape_fn(self, pad)
 
+def aten〇replication_pad1d〡shape(self: List[int], padding: List[int]) -> List[int]:
+    assert len(self) >= 2
+    assert len(padding) == 2, 'padding size expected to be 2'
+    return pad_shape_fn(self, padding)
+
 def aten〇replication_pad2d〡shape(self: List[int], padding: List[int]) -> List[int]:
     assert len(self) >= 2
     assert len(padding) == 4, 'padding size expected to be 4'
     return pad_shape_fn(self, padding)
 
+def aten〇replication_pad1d〡dtype(self_rank_dtype: Tuple[int, int], padding: List[int]) -> int:
+    self_rank, self_dtype = self_rank_dtype
+    return self_dtype
+
 def aten〇replication_pad2d〡dtype(self_rank_dtype: Tuple[int, int], padding: List[int]) -> int:
     self_rank, self_dtype = self_rank_dtype
     return self_dtype

projects/pt1/python/torch_mlir/jit_ir_importer/build_tools/torch_ods_gen.py

@@ -805,6 +805,7 @@ def emit_with_mutating_variants(key, **kwargs):
 
     # Misc tensor ops.
     emit("aten::constant_pad_nd : (Tensor, int[], Scalar) -> (Tensor)")
+    emit("aten::replication_pad1d : (Tensor, int[]) -> (Tensor)")
     emit("aten::replication_pad2d : (Tensor, int[]) -> (Tensor)")
     emit("aten::reflection_pad1d : (Tensor, int[]) -> (Tensor)")
     emit("aten::reflection_pad2d : (Tensor, int[]) -> (Tensor)")

projects/pt1/python/torch_mlir_e2e_test/test_suite/padding.py

@@ -13,6 +13,52 @@
 # ==============================================================================
 
 
+class ReplicationPad1dModule_3DInput(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args(
+        [
+            None,
+            ([-1, -1, -1], torch.float32, True),
+        ]
+    )
+    def forward(self, x):
+        return torch.ops.aten.replication_pad1d(x, [3, 5])
+
+
+@register_test_case(module_factory=lambda: ReplicationPad1dModule_3DInput())
+def ReplicationPad1dModule_3DInput_basic(module, tu: TestUtils):
+    module.forward(tu.rand(1, 15, 20, low=-1))
+
+
+# ==============================================================================
+
+
+class ReplicationPad1dModule_2DInput(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args(
+        [
+            None,
+            ([-1, -1], torch.float32, True),
+        ]
+    )
+    def forward(self, x):
+        return torch.ops.aten.replication_pad1d(x, [2, 3])
+
+
+@register_test_case(module_factory=lambda: ReplicationPad1dModule_2DInput())
+def ReplicationPad1dModule_2DInput_basic(module, tu: TestUtils):
+    module.forward(tu.rand(7, 12, low=-1))
+
+
+# ==============================================================================
+
+
 class ReflectionPad2dModule(torch.nn.Module):
     def __init__(self):
         super().__init__()