[torch-dialect] emit aten.index_add and decompose it to scatter.add op

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

@@ -5703,6 +5703,59 @@ def Torch_AtenTril_Op : Torch_Op<"aten.tril_", [
   }];
 }
 
+def Torch_AtenIndexAddOp : Torch_Op<"aten.index_add", [
+    AllowsTypeRefinement,
+    HasValueSemantics,
+    ReadOnly
+  ]> {
+  let summary = "Generated op for `aten::index_add : (Tensor, int, Tensor, Tensor, Scalar) -> (Tensor)`";
+  let arguments = (ins
+    AnyTorchTensorType:$self,
+    Torch_IntType:$dim,
+    AnyTorchTensorType:$index,
+    AnyTorchTensorType:$source,
+    AnyTorchScalarType:$alpha
+  );
+  let results = (outs
+    AnyTorchTensorType:$result
+  );
+  let hasCustomAssemblyFormat = 1;
+  let extraClassDefinition = [{
+    ParseResult AtenIndexAddOp::parse(OpAsmParser &parser, OperationState &result) {
+      return parseDefaultTorchOp(parser, result, 5, 1);
+    }
+    void AtenIndexAddOp::print(OpAsmPrinter &printer) {
+      printDefaultTorchOp(printer, *this, 5, 1);
+    }
+  }];
+}
+
+def Torch_AtenIndexAdd_Op : Torch_Op<"aten.index_add_", [
+    IsTrailingUnderscoreInplaceVariant,
+    AllowsTypeRefinement
+  ]> {
+  let summary = "Generated op for `aten::index_add_ : (Tensor, int, Tensor, Tensor, Scalar) -> (Tensor)`";
+  let arguments = (ins
+    Torch_NonValueTensorType:$self,
+    Torch_IntType:$dim,
+    Torch_NonValueTensorType:$index,
+    Torch_NonValueTensorType:$source,
+    AnyTorchScalarType:$alpha
+  );
+  let results = (outs
+    Torch_NonValueTensorType:$result
+  );
+  let hasCustomAssemblyFormat = 1;
+  let extraClassDefinition = [{
+    ParseResult AtenIndexAdd_Op::parse(OpAsmParser &parser, OperationState &result) {
+      return parseDefaultTorchOp(parser, result, 5, 1);
+    }
+    void AtenIndexAdd_Op::print(OpAsmPrinter &printer) {
+      printDefaultTorchOp(printer, *this, 5, 1);
+    }
+  }];
+}
+
 def Torch_AtenIndexPutOp : Torch_Op<"aten.index_put", [
     AllowsTypeRefinement,
     HasValueSemantics,

lib/Dialect/Torch/Transforms/AbstractInterpLibrary.cpp

@@ -9185,6 +9185,14 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
 "    %0 = call @__torch__.torch.jit._shape_functions.index_select(%arg0, %arg1, %arg2) : (!torch.list<int>, !torch.int, !torch.list<int>) -> !torch.list<int>\n"
 "    return %0 : !torch.list<int>\n"
 "  }\n"
+"  func.func @\"__torch_mlir_shape_fn.aten.scatter_add\"(%arg0: !torch.list<int>, %arg1: !torch.int, %arg2: !torch.list<int>, %arg3: !torch.list<int>) -> !torch.list<int> {\n"
+"    %0 = call @__torch__.torch.jit._shape_functions.unary(%arg0) : (!torch.list<int>) -> !torch.list<int>\n"
+"    return %0 : !torch.list<int>\n"
+"  }\n"
+"  func.func @\"__torch_mlir_shape_fn.aten.index_add\"(%arg0: !torch.list<int>, %arg1: !torch.int, %arg2: !torch.list<int>, %arg3: !torch.list<int>, %arg4: !torch.float) -> !torch.list<int> {\n"
+"    %0 = call @__torch__.torch.jit._shape_functions.unary(%arg0) : (!torch.list<int>) -> !torch.list<int>\n"
+"    return %0 : !torch.list<int>\n"
+"  }\n"
 "  func.func @\"__torch_mlir_shape_fn.aten.index_put\"(%arg0: !torch.list<int>, %arg1: !torch.list<optional<list<int>>>, %arg2: !torch.list<int>, %arg3: !torch.bool) -> !torch.list<int> {\n"
 "    %0 = call @__torch__.torch.jit._shape_functions.unary(%arg0) : (!torch.list<int>) -> !torch.list<int>\n"
 "    return %0 : !torch.list<int>\n"
@@ -10399,6 +10407,14 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
 "    %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.index_add\"(%arg0: !torch.tuple<int, int>, %arg1: !torch.int, %arg2: !torch.tuple<int, int>, %arg3: !torch.tuple<int, int>, %arg4: !torch.number) -> !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.scatter_add\"(%arg0: !torch.tuple<int, int>, %arg1: !torch.int, %arg2: !torch.tuple<int, int>, %arg3: !torch.tuple<int, 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.index_select\"(%arg0: !torch.tuple<int, int>, %arg1: !torch.int, %arg2: !torch.tuple<int, 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

@@ -5621,6 +5621,75 @@ public:
 };
 } // namespace
 
+namespace {
+// Decompose `aten.index_add` op into `aten.index_put`
+class DecomposeAtenIndexAddOp : public OpRewritePattern<AtenIndexAddOp> {
+public:
+  using OpRewritePattern::OpRewritePattern;
+  LogicalResult matchAndRewrite(AtenIndexAddOp op,
+                                PatternRewriter &rewriter) const override {
+    Location loc = op.getLoc();
+    Value src = op.getSource();
+    Value input = op.getSelf();
+    Value index = op.getIndex();
+    Value alpha = op.getAlpha();
+
+    int64_t dim;
+    if (!matchPattern(op.getDim(), m_TorchConstantInt(&dim))) {
+      return rewriter.notifyMatchFailure(op,
+                                         "dim of index_add must be a constant");
+    }
+    std::optional<unsigned> maybeInputRank = getTensorRank(input);
+    if (!maybeInputRank) {
+      return rewriter.notifyMatchFailure(op, "expected input to have a rank");
+    }
+    int64_t inputRank = static_cast<int64_t>(*maybeInputRank);
+    dim = toPositiveDim(dim, inputRank);
+    if (!isValidDim(dim, inputRank)) {
+      return rewriter.notifyMatchFailure(op, "index dim is not a valid dim");
+    }
+
+    auto resType = op.getType().cast<BaseTensorType>();
+    auto srcType = src.getType().cast<BaseTensorType>();
+    auto indexType = index.getType().cast<BaseTensorType>();
+    if (!indexType.hasDtype()) {
+      return rewriter.notifyMatchFailure(op, "index should have dtype");
+    }
+    auto indexDtype = indexType.getDtype();
+
+    // calculate src * alpha first.
+    Value newSrc =
+        rewriter.create<Torch::AtenMulScalarOp>(loc, srcType, src, alpha);
+
+    // broadcast index to have the same shape as src.
+    Value constMinusOne = rewriter.create<Torch::ConstantIntOp>(
+        loc, rewriter.getI64IntegerAttr(-1));
+    for (int64_t i = dim + 1; i < inputRank; ++i) {
+      index = *unsqueezeTensor(rewriter, op, index, /*dim=*/constMinusOne);
+    }
+
+    SmallVector<int64_t> bcastShape;
+    SmallVector<Value> bcastShapeValue;
+    computeBroadcastShape(rewriter, loc, index, src, bcastShape,
+                          bcastShapeValue);
+
+    Type bcastType = ValueTensorType::get(
+        op.getContext(), llvm::ArrayRef(bcastShape), indexDtype);
+
+    Value indexBcastShapeTorchList = rewriter.create<PrimListConstructOp>(
+        loc, Torch::ListType::get(Torch::IntType::get(op.getContext())),
+        bcastShapeValue);
+
+    index = rewriter.create<Torch::AtenBroadcastToOp>(loc, bcastType, index,
+                                                      indexBcastShapeTorchList);
+
+    rewriter.replaceOpWithNewOp<Torch::AtenScatterAddOp>(op, resType, input,
+                                                         op.getDim(), index, newSrc);
+    return success();
+  }
+};
+} // namespace
+
 namespace {
 class DecomposeAtenExpandAsOp : public OpRewritePattern<AtenExpandAsOp> {
   using OpRewritePattern::OpRewritePattern;
@@ -8021,6 +8090,7 @@ public:
     addPatternIfTargetOpIsIllegal<DecomposeAtenMishOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenFullLikeOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenNewFullOp>(patterns);
+    addPatternIfTargetOpIsIllegal<DecomposeAtenIndexAddOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenExpandAsOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAten_ToCopyOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenCopyOp>(patterns);

lib/Dialect/Torch/Transforms/LowerToBackendContract.cpp

@@ -471,6 +471,7 @@ static void markDecomposedOpsAsIllegal(MLIRContext *context,
   target.addIllegalOp<AtenMishOp>();
   target.addIllegalOp<AtenFullLikeOp>();
   target.addIllegalOp<AtenNewFullOp>();
+  target.addIllegalOp<AtenIndexAddOp>();
   target.addIllegalOp<AtenExpandAsOp>();
   target.addIllegalOp<Aten_ToCopyOp>();
   target.addIllegalOp<AtenDropoutOp>();

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

@@ -1607,15 +1607,18 @@ def aten〇scatter〇value〡shape(self: List[int], dim: int, index: List[int],
 def aten〇index_select〡shape(self: List[int], dim: int, index: List[int]) -> List[int]:
     return upstream_shape_functions.index_select(self, dim, index)
 
+def aten〇scatter_add〡shape(self: List[int], dim: int, index: List[int], src: List[int]) -> List[int]:
+    return upstream_shape_functions.unary(self)
+
+def aten〇index_add〡shape(self: List[int], dim: int, index: List[int], source: List[int], alpha: float = 1) -> List[int]:
+    return upstream_shape_functions.unary(self)
+
 def aten〇index_put〡shape(self: List[int], indices: List[Optional[List[int]]], values: List[int], accumulate: bool = False) -> List[int]:
     return upstream_shape_functions.unary(self)
 
 def aten〇index_put〇hacked_twin〡shape(self: List[int], indices: List[List[int]], values: List[int], accumulate: bool = False) -> List[int]:
     return upstream_shape_functions.unary(self)
 
-def aten〇embedding〡shape(weight: List[int], indices: List[int], padding_idx: int = -1, scale_grad_by_freq: bool = False, sparse: bool = False) -> List[int]:
-    return upstream_shape_functions.embedding(weight, indices, padding_idx, scale_grad_by_freq, sparse)
-
 def aten〇embedding_bag〇padding_idx〡shape(weight: List[int], indices: List[int], offsets: List[int], scale_grad_by_freq: bool, mode: int, sparse: bool, per_sample_weights: Optional[List[int]], include_last_offset: bool, padding_idx: Optional[int]) -> Tuple[List[int], List[int], List[int], List[int]]:
     return _embedding_bag_helper(weight, indices, offsets, include_last_offset,
                                  mode, per_sample_weights, padding_idx)
@@ -2534,6 +2537,16 @@ def aten〇index_put〡dtype(self_rank_dtype: Tuple[int, int], indices_rank_dtyp
     self_rank, self_dtype = self_rank_dtype
     return self_dtype
 
+@check_dtype_function([Invocation(TensorOfShape(3, dtype=dtype), 0, TensorOfShape(3, dtype=torch.int64), TensorOfShape(3, dtype=dtype)) for dtype in _SORTED_TORCH_TYPES])
+def aten〇index_add〡dtype(self_rank_dtype: Tuple[int, int], dim: int, index_rank_dtype: Tuple[int, int], source_rank_dtype: Tuple[int, int], alpha: Union[int, float, complex] = 1) -> int:
+    self_rank, self_dtype = self_rank_dtype
+    return self_dtype
+
+@check_dtype_function([Invocation(TensorOfShape(3, dtype=dtype), 0, TensorOfShape(3, dtype=torch.int64), TensorOfShape(3, dtype=dtype)) for dtype in _SORTED_TORCH_TYPES])
+def aten〇scatter_add〡dtype(self_rank_dtype: Tuple[int, int], dim: int, index_rank_dtype: Tuple[int, int], src_rank_dtype: Tuple[int, int]) -> int:
+    self_rank, self_dtype = self_rank_dtype
+    return self_dtype
+
 @check_dtype_function(_check_tensors_with_the_same_dtype(None, [(5,)], None, None, 0, TensorOfShape(1, dtype=torch.int64)))
 def aten〇index_select〡dtype(self_rank_dtype: Tuple[int, int], dim: int, index_rank_dtype: Tuple[int, int]) -> int:
     self_rank, self_dtype = self_rank_dtype

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

@@ -516,6 +516,8 @@ def emit_ops(emitter_td: TextEmitter, registry: Registry):
 
     emit_with_mutating_variants("aten::triu : (Tensor, int) -> (Tensor)")
     emit_with_mutating_variants("aten::tril : (Tensor, int) -> (Tensor)")
+    emit_with_mutating_variants("aten::index_add : (Tensor, int, Tensor, Tensor, Scalar) -> (Tensor)")
+    
     emit_with_mutating_variants(
         "aten::index_put : (Tensor, Tensor?[], Tensor, bool) -> (Tensor)"
     )