[ONNX][TOPI] Support select_last_index for argmin/max (#8816)

* support select_last_index for argmin/max

* reverse conditions which made on accident

* forward args in reduce.py

* make proper nodes for reduction ops

* remove complicated nested lambdas

* fix lambda capture for conversion

* forward more arguments

* forward more args

* enable onnx tests

* wrapping casts to remove ambiguity

* revert changes extraneous

* correct incorrect attrs being used for ops

* change attributes

* remove old impl

* register new attribute node

* clean up test

* reformat

* reformat

* coolio

* stable comparison

* casts to avoid ambiguity

* casting more

* correct arg passing

* support select_last_index for argmin/max

* reverse conditions which made on accident

* forward args in reduce.py

* make proper nodes for reduction ops

* remove complicated nested lambdas

* fix lambda capture for conversion

* forward more arguments

* forward more args

* enable onnx tests

* wrapping casts to remove ambiguity

* revert changes extraneous

* correct incorrect attrs being used for ops

* change attributes

* remove old impl

* register new attribute node

* clean up test

* reformat

* reformat

* coolio

* stable comparison

* casts to avoid ambiguity

* casting more

* correct arg passing

* fix broken input

* OneElementReduceAttrs-->ArgReduceAttrs"

* reduce boilerplate

* change names

* remove log statement

* jostle ci

Co-authored-by: Andrew Zhao Luo <andrewzhaoluo@system76-pc.localdomain>

include/tvm/relay/attrs/reduce.h

@@ -61,6 +61,42 @@ struct ReduceAttrs : public tvm::AttrsNode<ReduceAttrs> {
   }
 };
 
+/*! \brief Attributes for Reduce operators which reduce by finding a single element. E.g. argmin */
+struct ArgReduceAttrs : public tvm::AttrsNode<ArgReduceAttrs> {
+  Array<Integer> axis;
+  bool keepdims;
+  bool select_last_index;
+  bool exclude;
+
+  TVM_DECLARE_ATTRS(ArgReduceAttrs, "relay.attrs.ArgReduceAttrs") {
+    TVM_ATTR_FIELD(axis)
+        .set_default(NullValue<Array<Integer>>())
+        .describe(R"code(The axis or axes along which to perform the reduction.
+
+      The default, `axis=()`, will compute over all elements into a
+      scalar array with shape `(1,)`.
+
+      If `axis` is int, a reduction is performed on a particular axis.
+
+      If `axis` is a tuple of ints, a reduction is performed on all the axes
+      specified in the tuple.
+
+      If `exclude` is true, reduction will be performed on the axes that are
+      NOT in axis instead.)code");
+
+    TVM_ATTR_FIELD(keepdims).set_default(false).describe(
+        "If this is set to `True`, the reduced axes are left "
+        "in the result as dimension with size one.");
+    TVM_ATTR_FIELD(select_last_index)
+        .set_default(false)
+        .describe(
+            "Whether to select the last index if the target element appears multiple times, else "
+            "select the first index which the target element appears");
+    TVM_ATTR_FIELD(exclude).set_default(false).describe(
+        "Whether to perform reduction on axis that are NOT in axis instead.");
+  }
+};
+
 struct VarianceAttrs : public tvm::AttrsNode<VarianceAttrs> {
   Array<Integer> axis;
   bool keepdims;

include/tvm/topi/reduction.h

@@ -431,6 +431,45 @@ inline Tensor max(const Tensor& data, const Array<Integer>& axis, bool keepdims
   return CommReduce(data, axis, MaxOp, keepdims, atleast1d);
 }
 
+inline FCommReduce MakeArgminReducer(bool select_last_index = false) {
+  // Create a Commutative Reducer with a comparison operation, and method to get the initial value.
+  auto fcombine = [=](Array<Var> lhs, Array<Var> rhs) {
+    Array<PrimExpr> result;
+
+    // Casting to avoid operator ambiguity
+    PrimExpr lhs_idx = static_cast<PrimExpr>(lhs[0]);
+    PrimExpr rhs_idx = static_cast<PrimExpr>(rhs[0]);
+    PrimExpr lhs_val = static_cast<PrimExpr>(lhs[1]);
+    PrimExpr rhs_val = static_cast<PrimExpr>(rhs[1]);
+
+    // These variables compare the actual values of the array
+    auto is_smaller = lhs_val < rhs_val;
+    auto is_same = lhs_val == rhs_val;
+
+    // This checks if the indices are correct for the reduction. E.g. for select_last_index
+    // it gives precedence for later indices of the same element and precedence for sooner
+    // indices if not select_last_index;
+    PrimExpr proper_index;
+    if (select_last_index) {
+      proper_index = lhs_idx > rhs_idx;
+    } else {
+      proper_index = lhs_idx < rhs_idx;
+    }
+
+    PrimExpr update_index = is_smaller || (is_same && proper_index);
+    result.push_back(tvm::tir::Select(update_index, lhs[0], rhs[0]));  // idx
+    result.push_back(tvm::tir::Select(is_smaller, lhs[1], rhs[1]));    // val
+    return result;
+  };
+  auto fidentity = [&](std::vector<DataType> types) {
+    Array<PrimExpr> result;
+    result.push_back(tvm::tir::make_const(types[0], -1));  // idx
+    result.push_back(tvm::max_value(types[1]));            // val
+    return result;
+  };
+  return MakeCommReducer(fcombine, fidentity, "argmin");
+}
+
 /*!
  * \brief Creates an operation that finds the indices of the minimum
  * values over a given axis.
@@ -442,35 +481,48 @@ inline Tensor max(const Tensor& data, const Array<Integer>& axis, bool keepdims
  * left in the result as dimensions with size one. This enables the result
  * to broadcast correctly against the input array.
  * \param atleast1d Whether the output need to be atleast1d.
+ * \param select_last_index Whether to select the last index if the minimum element
+ * appears multiple times, else select the first index.
  *
  * \return A Tensor whose op member is the argmin operation
  */
 inline Tensor argmin(const Tensor& data, const Array<Integer>& axis, bool keepdims = false,
-                     bool atleast1d = false) {
-  auto fcombine = [](Array<Var> lhs, Array<Var> rhs) {
-    Array<PrimExpr> result;
-    result.push_back(tvm::tir::Select(lhs[1] <= rhs[1], lhs[0], rhs[0]));  // idx
-    result.push_back(tvm::tir::Select(lhs[1] <= rhs[1], lhs[1], rhs[1]));  // val
-    return result;
-  };
-  auto fidentity = [](std::vector<DataType> types) {
-    Array<PrimExpr> result;
-    result.push_back(tvm::tir::make_const(types[0], -1));  // idx
-    result.push_back(tvm::max_value(types[1]));            // val
-    return result;
-  };
-  auto func = MakeCommReducer(fcombine, fidentity, "argmin");
-  return CommReduceIdx(data, axis, func, keepdims, atleast1d);
+                     bool atleast1d = false, bool select_last_index = false) {
+  auto reducer = MakeArgminReducer(select_last_index);
+  return CommReduceIdx(data, axis, reducer, keepdims, atleast1d);
 }
 
-inline FCommReduce MakeArgmaxReducer() {
-  auto fcombine = [](Array<Var> lhs, Array<Var> rhs) {
+inline FCommReduce MakeArgmaxReducer(bool select_last_index = false) {
+  // Create a Commutative Reducer with a comparison operation, and method to get the initial value.
+  auto fcombine = [=](Array<Var> lhs, Array<Var> rhs) {
     Array<PrimExpr> result;
-    result.push_back(tvm::tir::Select(lhs[1] >= rhs[1], lhs[0], rhs[0]));  // idx
-    result.push_back(tvm::tir::Select(lhs[1] >= rhs[1], lhs[1], rhs[1]));  // val
+
+    // Casting to avoid operator ambiguity
+    PrimExpr lhs_idx = static_cast<PrimExpr>(lhs[0]);
+    PrimExpr rhs_idx = static_cast<PrimExpr>(rhs[0]);
+    PrimExpr lhs_val = static_cast<PrimExpr>(lhs[1]);
+    PrimExpr rhs_val = static_cast<PrimExpr>(rhs[1]);
+
+    // These variables compare the actual values of the array
+    auto is_bigger = lhs_val > rhs_val;
+    auto is_same = lhs_val == rhs_val;
+
+    // This checks if the indices are correct for the reduction. E.g. for select_last_index
+    // it gives precedence for later indices of the same element and precedence for sooner
+    // indices if not select_last_index;
+    PrimExpr proper_index;
+    if (select_last_index) {
+      proper_index = lhs_idx > rhs_idx;
+    } else {
+      proper_index = lhs_idx < rhs_idx;
+    }
+
+    PrimExpr update_index = is_bigger || (is_same && proper_index);
+    result.push_back(tvm::tir::Select(update_index, lhs[0], rhs[0]));  // idx
+    result.push_back(tvm::tir::Select(is_bigger, lhs[1], rhs[1]));     // val
     return result;
   };
-  auto fidentity = [](std::vector<DataType> types) {
+  auto fidentity = [&](std::vector<DataType> types) {
     Array<PrimExpr> result;
     result.push_back(tvm::tir::make_const(types[0], -1));  // idx
     result.push_back(tvm::min_value(types[1]));            // val
@@ -490,12 +542,13 @@ inline FCommReduce MakeArgmaxReducer() {
  * left in the result as dimensions with size one. This enables the result
  * to broadcast correctly against the input array.
  * \param atleast1d Whether the output need to be atleast1d.
- *
+ * \param select_last_index Whether to select the last index if the maximum element
+ * appears multiple times, else select the first index.
  * \return A Tensor whose op member is the argmax operation
  */
 inline Tensor argmax(const Tensor& data, const Array<Integer>& axis, bool keepdims = false,
-                     bool atleast1d = false) {
-  auto reducer = MakeArgmaxReducer();
+                     bool atleast1d = false, bool select_last_index = false) {
+  auto reducer = MakeArgmaxReducer(select_last_index);
   return CommReduceIdx(data, axis, reducer, keepdims, atleast1d);
 }
 

python/tvm/relay/frontend/onnx.py

@@ -32,23 +32,23 @@
 from .. import loops as _loops
 from .. import op as _op
 from .. import qnn as _qnn
+from .. import random as _random
 from .. import ty as _ty
 from .. import vision as _vision
-from .. import random as _random
 from .common import (
     AttrCvt,
     Renamer,
     fold_constant,
     get_name,
     get_relay_op,
+    gru_cell,
     infer_channels,
     infer_shape,
     infer_type,
     infer_value,
+    lstm_cell,
     new_var,
     unbind,
-    gru_cell,
-    lstm_cell,
 )
 
 __all__ = ["from_onnx"]
@@ -1786,25 +1786,23 @@ class ArgMax(OnnxOpConverter):
     """Operator converter for ArgMax."""
 
     @classmethod
-    def _impl_v1(cls, inputs, attr, params):
-        if "select_last_index" in attr:
-            raise NotImplementedError("select_last_index not supported in ArgMax")
+    def _impl_v13(cls, inputs, attr, params):
         axis = attr.get("axis", 0)
         keepdims = attr.get("keepdims", True)
-        attr = {"axis": axis, "keepdims": keepdims}
+        select_last_index = attr.get("select_last_index", False)
+        attr = {"axis": axis, "keepdims": keepdims, "select_last_index": select_last_index}
         return _op.cast(AttrCvt("argmax")(inputs, attr), "int64")
 
 
 class ArgMin(OnnxOpConverter):
     """Operator converter for ArgMin."""
 
     @classmethod
-    def _impl_v1(cls, inputs, attr, params):
-        if "select_last_index" in attr:
-            raise NotImplementedError("select_last_index not supported in ArgMin")
+    def _impl_v13(cls, inputs, attr, params):
         axis = attr.get("axis", 0)
         keepdims = attr.get("keepdims", True)
-        attr = {"axis": axis, "keepdims": keepdims}
+        select_last_index = attr.get("select_last_index", False)
+        attr = {"axis": axis, "keepdims": keepdims, "select_last_index": select_last_index}
         return _op.cast(AttrCvt("argmin")(inputs, attr), "int64")
 
 

python/tvm/relay/op/reduce.py

@@ -17,13 +17,13 @@
 """Reduce operators."""
 # pylint: disable=redefined-builtin
 
+from ..expr import Tuple, TupleWrapper
 from . import _make
-from .tensor import sqrt, log, exp
+from .tensor import exp, log, sqrt
 from .transform import squeeze
-from ..expr import Tuple, TupleWrapper
 
 
-def argmax(data, axis=None, keepdims=False, exclude=False):
+def argmax(data, axis=None, keepdims=False, exclude=False, select_last_index=False):
     """Returns the indices of the maximum values along an axis.
 
     Parameters
@@ -45,16 +45,20 @@ def argmax(data, axis=None, keepdims=False, exclude=False):
         If `exclude` is true, reduction will be performed on the axes that are
         NOT in axis instead.
 
+    select_last_index : bool
+        Whether to select the last index or the first index if the max element appears in
+        multiple indices, default is False (first index).
+
     Returns
     -------
     result : relay.Expr
         The computed result.
     """
     axis = [axis] if isinstance(axis, int) else axis
-    return _make.argmax(data, axis, keepdims, exclude)
+    return _make.argmax(data, axis, keepdims, exclude, select_last_index)
 
 
-def argmin(data, axis=None, keepdims=False, exclude=False):
+def argmin(data, axis=None, keepdims=False, exclude=False, select_last_index=False):
     """Returns the indices of the minimum values along an axis.
 
     Parameters
@@ -76,13 +80,17 @@ def argmin(data, axis=None, keepdims=False, exclude=False):
         If `exclude` is true, reduction will be performed on the axes that are
         NOT in axis instead.
 
+    select_last_index : bool
+        Whether to select the last index or the first index if the min element appears in
+        multiple indices, default is False (first index).
+
     Returns
     -------
     result : relay.Expr
         The computed result.
     """
     axis = [axis] if isinstance(axis, int) else axis
-    return _make.argmin(data, axis, keepdims, exclude)
+    return _make.argmin(data, axis, keepdims, exclude, select_last_index)
 
 
 def sum(data, axis=None, keepdims=False, exclude=False):

python/tvm/topi/reduction.py

@@ -167,7 +167,7 @@ def min(data, axis=None, keepdims=False):
     return cpp.min(data, axis, keepdims)
 
 
-def argmax(data, axis=None, keepdims=False):
+def argmax(data, axis=None, keepdims=False, select_last_index=False):
     """Returns the indices of the maximum values along an axis.
 
     Parameters
@@ -185,14 +185,18 @@ def argmax(data, axis=None, keepdims=False):
         with size one.
         With this option, the result will broadcast correctly against the input array.
 
+    select_last_index: bool
+        Whether to select the last index if the maximum element appears multiple times, else
+        select the first index.
+
     Returns
     -------
     ret : tvm.te.Tensor
     """
-    return cpp.argmax(data, axis, keepdims)
+    return cpp.argmax(data, axis, keepdims, select_last_index)
 
 
-def argmin(data, axis=None, keepdims=False):
+def argmin(data, axis=None, keepdims=False, select_last_index=False):
     """Returns the indices of the minimum values along an axis.
 
     Parameters
@@ -210,11 +214,15 @@ def argmin(data, axis=None, keepdims=False):
         with size one.
         With this option, the result will broadcast correctly against the input array.
 
+    select_last_index: bool
+        Whether to select the last index if the minimum element appears multiple times, else
+        select the first index.
+
     Returns
     -------
     ret : tvm.te.Tensor
     """
-    return cpp.argmin(data, axis, keepdims)
+    return cpp.argmin(data, axis, keepdims, select_last_index)
 
 
 def prod(data, axis=None, keepdims=False):