[Relax] Add scatter_nd op support

include/tvm/relax/attrs/manipulate.h

@@ -164,6 +164,18 @@ struct ScatterElementsAttrs : public tvm::AttrsNode<ScatterElementsAttrs> {
         "either \"update\", \"add\", \"mul\", \"mean\", \"min\" or \"max\".");
   }
 };  // struct ScatterElementsAttrs
+
+/*! \brief Attributes used in scatter_nd operators */
+struct ScatterNDAttrs : public tvm::AttrsNode<ScatterNDAttrs> {
+  String reduction;
+
+  TVM_DECLARE_ATTRS(ScatterNDAttrs, "relax.attrs.ScatterNDAttrs") {
+    TVM_ATTR_FIELD(reduction).set_default("update").describe(
+        "Accumulation mode of the ScatterND, "
+        "either \"update\", \"add\", \"mul\", \"min\" or \"max\".");
+  }
+};  // struct ScatterNDAttrs
+
 }  // namespace relax
 }  // namespace tvm
 

python/tvm/relax/frontend/onnx/onnx_frontend.py

@@ -604,6 +604,36 @@ def _impl_v11(cls, bb, inputs, attr, params):
         return relax.op.scatter_elements(inputs[0], inputs[1], inputs[2], axis=axis)
 
 
+class ScatterND(OnnxOpConverter):
+    """Convert an onnx ScatterND node into an equivalent Relax expression."""
+
+    @staticmethod
+    def _reduction_check(attr, valid_reductions: List[str]):
+        reduction = attr.get("reduction", None)
+        reduction = reduction or b"update"
+        reduction = reduction.decode("utf-8")
+        reduction = "update" if reduction == "none" else reduction
+        assert (
+            reduction in valid_reductions
+        ), f"Only {valid_reductions} reductions are supported, but {reduction} is gotten"
+
+        return reduction
+
+    @classmethod
+    def _impl_v11(cls, bb, inputs, attr, params):
+        return relax.op.scatter_nd(inputs[0], inputs[1], inputs[2])
+
+    @classmethod
+    def _impl_v16(cls, bb, inputs, attr, params):
+        reduction = cls._reduction_check(attr, ["update", "add", "mul"])
+        return relax.op.scatter_nd(inputs[0], inputs[1], inputs[2], reduction)
+
+    @classmethod
+    def _impl_v18(cls, bb, inputs, attr, params):
+        reduction = cls._reduction_check(attr, ["update", "add", "mul", "min", "max"])
+        return relax.op.scatter_nd(inputs[0], inputs[1], inputs[2], reduction)
+
+
 class Size(OnnxOpConverter):
     """Convert an onnx Size node into an equivalent Relax expression."""
 
@@ -2729,7 +2759,7 @@ def _get_convert_map():
         # "GatherND": GatherND,
         "Scatter": Scatter,
         "ScatterElements": ScatterElements,
-        # "ScatterND": ScatterND,
+        "ScatterND": ScatterND,
         # "Compress": Compress,
         "Size": Size,
         # "EyeLike": EyeLike,

python/tvm/relax/op/__init__.py

@@ -91,6 +91,7 @@
     repeat,
     reshape,
     scatter_elements,
+    scatter_nd,
     split,
     squeeze,
     tile,

python/tvm/relax/op/manipulate.py

@@ -511,3 +511,42 @@ def scatter_elements(
 
     """
     return _ffi_api.scatter_elements(data, indices, updates, axis, reduction)  # type: ignore
+
+
+def scatter_nd(data: Expr, indices: Expr, updates: Expr, reduction: str = "update") -> Expr:
+    """Scatter updates into an array according to indices.
+
+    Parameters
+    ----------
+    data: relax.Expr
+        The input data to be updated.
+
+    indices: relax.Expr
+        The index positions to update in `data`.
+
+    updates: relax.Expr
+        Values to replace to.
+
+    reduction: str
+        Type of reduction to apply: update, add, mul, max, min.
+        It is "update" by default.
+
+    Returns
+    -------
+    result : relax.Expr
+        The result has the same shape as data.
+
+    Examples
+    --------
+    .. code-block:: python
+
+       # inputs
+       data = [1, 2, 3, 4, 5, 6, 7, 8]
+       indices = [[4], [3], [1], [7]]
+       updates = [9, 10, 11, 12]
+
+       # output
+       output = [1, 11, 3, 10, 9, 6, 7, 12]
+
+    """
+    return _ffi_api.scatter_nd(data, indices, updates, reduction)  # type: ignore

python/tvm/relax/transform/legalize_ops/manipulate.py

@@ -168,6 +168,23 @@ def _scatter_elements(bb: BlockBuilder, call: Call) -> Expr:
     )
 
 
+@register_legalize("relax.scatter_nd")
+def _scatter_nd(bb: BlockBuilder, call: Call) -> Expr:
+    # TODO(relax-team): Support native scatter_nd without te extern
+    def scatter_nd(data, indices, updates, reduction):
+        axes = list(range(len(indices.shape)))
+        indices = topi.transpose(indices, axes[-1:] + axes[:-1])
+        return topi.scatter_nd(data, indices, updates, reduction)
+
+    return bb.call_te(
+        scatter_nd,
+        call.args[0],
+        call.args[1],
+        call.args[2],
+        call.attrs.reduction,
+    )
+
+
 @register_legalize("relax.layout_transform")
 def _layout_transform(bb: BlockBuilder, call: Call) -> Expr:
     def te_layout_transform(data, name):

python/tvm/script/ir_builder/relax/ir.py

@@ -136,6 +136,7 @@
     round,
     rsqrt,
     scatter_elements,
+    scatter_nd,
     shape_of,
     shape_to_tensor,
     sigmoid,
@@ -736,6 +737,7 @@ def dtype(value: Union[py_str, DataType]) -> Expr:
     "cumsum",
     "einsum",
     "scatter_elements",
+    "scatter_nd",
     "dataflow",
     "device",
     "divide",

src/relax/op/tensor/manipulate.cc

@@ -1531,5 +1531,139 @@ TVM_REGISTER_OP("relax.scatter_elements")
     .set_attr<FInferStructInfo>("FInferStructInfo", InferStructInfoScatterElements)
     .set_attr<Bool>("FPurity", Bool(true));
 
+/* relax.scatter_nd */
+TVM_REGISTER_NODE_TYPE(ScatterNDAttrs);
+
+Expr scatter_nd(Expr data, Expr indices, Expr updates, String reduction) {
+  auto attrs = make_object<ScatterNDAttrs>();
+  attrs->reduction = std::move(reduction);
+  static const Op& op = Op::Get("relax.scatter_nd");
+  return Call(op, {data, indices, updates}, Attrs(attrs), {});
+}
+
+TVM_REGISTER_GLOBAL("relax.op.scatter_nd").set_body_typed(scatter_nd);
+
+StructInfo InferStructInfoScatterND(const Call& call, const BlockBuilder& ctx) {
+  // `call->args` contains: [data, indices, updates]
+  arith::Analyzer* analyzer = ctx->GetAnalyzer();
+  ICHECK_EQ(call->args.size(), 3);
+  const auto* data_sinfo = GetStructInfoAs<TensorStructInfoNode>(call->args[0]);
+  const auto* indices_sinfo = GetStructInfoAs<TensorStructInfoNode>(call->args[1]);
+  const auto* updates_sinfo = GetStructInfoAs<TensorStructInfoNode>(call->args[2]);
+
+  if (data_sinfo == nullptr) {
+    ctx->ReportFatal(
+        Diagnostic::Error(call)
+        << "ScatterND op requires the input data to be a tensor. However, the given type is "
+        << call->args[0]->GetTypeKey());
+  }
+  if (indices_sinfo == nullptr) {
+    ctx->ReportFatal(
+        Diagnostic::Error(call)
+        << "ScatterND op requires the input indices to be a tensor. However, the given type is "
+        << call->args[1]->GetTypeKey());
+  }
+  if (updates_sinfo == nullptr) {
+    ctx->ReportFatal(
+        Diagnostic::Error(call)
+        << "ScatterND op requires the input updates to be a tensor. However, the given type is "
+        << call->args[2]->GetTypeKey());
+  }
+
+  if (data_sinfo->IsUnknownDtype() || updates_sinfo->IsUnknownDtype()) {
+    ctx->ReportFatal(Diagnostic::Error(call)
+                     << "ScatterND op requires the input data and updates to have known dtype. "
+                        "However, the given types are "
+                     << "data: " << data_sinfo->dtype << ", updates: " << updates_sinfo->dtype);
+  }
+
+  if (data_sinfo->dtype != updates_sinfo->dtype) {
+    ctx->ReportFatal(Diagnostic::Error(call)
+                     << "ScatterND op requires the input data to have same type with updates. "
+                        "However, the given types are "
+                     << "data: " << data_sinfo->dtype << ", updates: " << updates_sinfo->dtype);
+  }
+
+  if (indices_sinfo->IsUnknownDtype()) {
+    LOG(WARNING) << "Data type of indices has not been specified. Assume it has an integer type.";
+  } else if (!(indices_sinfo->dtype.is_int() || indices_sinfo->dtype.is_uint())) {
+    ctx->ReportFatal(Diagnostic::Error(call)
+                     << "ScatterND op requires the input indices to have integer dtype. However, "
+                        "the given indices dtype is "
+                     << indices_sinfo->dtype);
+  }
+
+  const auto* data_shape = data_sinfo->shape.as<ShapeExprNode>();
+  const auto* indices_shape = indices_sinfo->shape.as<ShapeExprNode>();
+  const auto* updates_shape = updates_sinfo->shape.as<ShapeExprNode>();
+
+  if (data_shape && indices_shape && updates_shape) {
+    const IntImmNode* k_dim = indices_shape->values[indices_sinfo->ndim - 1].as<IntImmNode>();
+    if (!k_dim) {
+      ctx->ReportFatal(Diagnostic::Error(call)
+                       << "ScatterND needs a static shape for the last axis of indices, got "
+                       << indices_shape->values);
+    }
+    const size_t data_ndim = data_sinfo->ndim;
+    const size_t indices_ndim = indices_sinfo->ndim;
+    const size_t updates_ndim = updates_sinfo->ndim;
+    if (data_ndim + indices_ndim - k_dim->value - 1 != updates_ndim) {
+      ctx->ReportFatal(Diagnostic::Error(call)
+                       << "ScatterND op requires the updates tensor to have the rank of "
+                          "`data tensor + indices tensor - last axis of indices tensor - 1`. "
+                          "However, the given shapes are "
+                       << "data: " << ShapeExpr(data_shape->values)
+                       << ", indices: " << ShapeExpr(indices_shape->values)
+                       << ", updates: " << ShapeExpr(updates_shape->values));
+    }
+    if (k_dim->value > static_cast<int>(data_ndim)) {
+      ctx->ReportFatal(Diagnostic::Error(call)
+                       << "ScatterND op requires the last axis of indices tensor to be less than "
+                          "or equal to the rank of data tensor. However, the given shapes are "
+                       << "data: " << ShapeExpr(data_shape->values)
+                       << ", indices: " << ShapeExpr(indices_shape->values));
+    }
+    Array<PrimExpr> expected_updates_shape;
+    for (size_t i = 0; i < indices_ndim - 1; i++) {
+      expected_updates_shape.push_back(indices_shape->values[i]);
+    }
+    for (size_t i = k_dim->value; i < data_ndim; i++) {
+      expected_updates_shape.push_back(data_shape->values[i]);
+    }
+    auto check_shape = [&](const Array<PrimExpr>& expected, const Array<PrimExpr>& actual) {
+      if (expected.size() != actual.size()) {
+        return false;
+      }
+      for (size_t i = 0; i < expected.size(); i++) {
+        if (!analyzer->CanProve(expected[i] == actual[i])) {
+          return false;
+        }
+      }
+      return true;
+    };
+    if (!check_shape(expected_updates_shape, updates_shape->values)) {
+      ctx->ReportFatal(
+          Diagnostic::Error(call)
+          << "ScatterND op requires the updates tensor to have the shape with constraint: "
+          << "`updates.shape = indices.shape[:-1] + data.shape[K:]`, but got "
+          << "updates.shape: " << ShapeExpr(updates_shape->values) << ", indices.shape: "
+          << ShapeExpr(indices_shape->values) << ", data.shape: " << ShapeExpr(data_shape->values));
+    }
+  }
+  if (data_shape) {
+    return TensorStructInfo(ShapeExpr(data_shape->values), data_sinfo->dtype, data_sinfo->vdevice);
+  }
+  return TensorStructInfo(data_sinfo->dtype, data_sinfo->ndim, data_sinfo->vdevice);
+}
+
+TVM_REGISTER_OP("relax.scatter_nd")
+    .set_attrs_type<ScatterNDAttrs>()
+    .set_num_inputs(3)
+    .add_argument("data", "Tensor", "The input tensor.")
+    .add_argument("indices", "Tensor", "The indices tensor.")
+    .add_argument("updates", "Tensor", "The input tensor of updates.")
+    .set_attr<FInferStructInfo>("FInferStructInfo", InferStructInfoScatterND)
+    .set_attr<Bool>("FPurity", Bool(true));
+
 }  // namespace relax
 }  // namespace tvm

src/relax/op/tensor/manipulate.h

@@ -173,6 +173,39 @@ Expr tile(Expr data, Array<Integer> repeats);
  */
 Expr flip(Expr data, Integer axis);
 
+/*!
+ * \brief Scatter updates into an array according to indices.
+ * \param data The input tensor.
+ * \param indices The index positions to update in `data`.
+ * \param updates The values to replace to.
+ * \param axis The axis along which to scatter the elements.
+ * \param reduction The reduction mode of the scatter elements,
+ * either "update", "add", "mul", "mean", "max" or "min".
+ * \return The computed result.
+ */
+Expr scatter_elements(Expr data, Expr indices, Expr updates, int axis, String reduction);
+
+/*!
+ * \brief Scatter updates into an array according to indices.
+ * \param data The input tensor to be updated.
+ * \param indices The index positions to update in `data`.
+ * \param updates The values to replace to.
+ * \param reduction The reduction mode of the scatter operation.
+ *        Supported modes are:
+ *        - "update": Replace the values at the indices with the update values.
+ *        - "add": Add the update values to the existing values at the indices.
+ *        - "mul": Multiply the existing values at the indices by the update values.
+ *        - "max": Take the maximum of the existing value and the update value at each index.
+ *        - "min": Take the minimum of the existing value and the update value at each index.
+ * \return The computed result tensor with the same shape as `data`.
+ *
+ * \note The shape of `indices` defines the shape of the scattered tensor.
+ *       The last dimension of `indices` corresponds to the depth of each index vector.
+ *       The shape of `updates` must match the shape of `indices` except for the last dimension,
+ *       which must match the slice shape at each index.
+ */
+Expr scatter_nd(Expr data, Expr indices, Expr updates, String reduction);
+
 }  // namespace relax
 }  // namespace tvm
 

tests/python/relax/test_frontend_onnx.py

@@ -118,7 +118,6 @@ def check_correctness(
     tvm_model = relax.transform.DecomposeOpsForInference()(tvm_model)
     # Legalize any relax ops into tensorir.
     tvm_model = relax.transform.LegalizeOps()(tvm_model)
-    print(tvm_model)
 
     # Separate model from parameters.
     tvm_model, params = relax.frontend.detach_params(tvm_model)
@@ -487,6 +486,38 @@ def test_scatter(axis: int, name: str, opset: int):
     check_correctness(model, inputs={"indices": indices}, opset=opset)
 
 
+@pytest.mark.parametrize("reduction", ["none", "add", "mul"])
+def test_scatter_nd(reduction):
+    def verify_scatter_nd(data_shape, indices_shape, updates_shape):
+        scatter_nd_node = helper.make_node(
+            "ScatterND",
+            ["data", "indices", "updates"],
+            ["output"],
+            reduction=reduction,
+        )
+
+        graph = helper.make_graph(
+            [scatter_nd_node],
+            "scatter_nd_test",
+            inputs=[
+                helper.make_tensor_value_info("data", TensorProto.FLOAT, data_shape),
+                helper.make_tensor_value_info("indices", TensorProto.INT64, indices_shape),
+                helper.make_tensor_value_info("updates", TensorProto.FLOAT, updates_shape),
+            ],
+            outputs=[helper.make_tensor_value_info("output", TensorProto.FLOAT, data_shape)],
+        )
+
+        model = helper.make_model(graph, producer_name="scatter_nd_test")
+
+        indices = np.random.choice(data_shape[0], indices_shape)
+        check_correctness(model, inputs={"indices": indices}, opset=16)
+
+    verify_scatter_nd([8], [4, 1], [4])
+    verify_scatter_nd([4, 4, 4], [2, 1], [2, 4, 4])
+    verify_scatter_nd([4, 5, 6], [2, 3, 2], [2, 3, 6])
+    verify_scatter_nd([10], [5, 1], [5])
+
+
 def test_size():
     test_node = helper.make_node("Size", ["x"], ["y"])
     graph = helper.make_graph(