[Unity] Relax op: search

python/tvm/relax/op/__init__.py

@@ -27,6 +27,7 @@
 from .manipulate import *
 from .op_attrs import *
 from .statistical import *
+from .search import *
 from .set import *
 from .ternary import *
 from .unary import *

python/tvm/relax/op/search.py

@@ -0,0 +1,50 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+# pylint: disable=invalid-name
+"""Search operators."""
+from . import _ffi_api
+from ..expr import Expr
+
+
+def where(condition: Expr, x1: Expr, x2: Expr) -> Expr:
+    """Selecting elements from either the input tensors depending on the value of the
+    condition.
+
+    For a given position, return the corresponding value in `x1` if `condition` is True,
+    and return the corresponding value in `x2` otherwise.
+
+    Parameters
+    ----------
+    condition : relax.Expr
+        When True, yield `x1`; otherwise, yield `x2`.
+        Must be broadcasting compatible with `x1` and `x2`.
+        Must have boolean dtype.
+
+    x1 : relax.Expr
+        The first input tensor.
+        Must be broadcasting compatible with `condition` and `x2`.
+
+    x2 : relax.Expr
+        The second input tensor.
+        Must be broadcasting compatible with `condition` and `x1`.
+
+    Returns
+    -------
+    result : relax.Expr
+        The result tensor.
+    """
+    return _ffi_api.where(condition, x1, x2)  # type: ignore

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

@@ -101,6 +101,7 @@
     tril,
     triu,
     unique,
+    where,
     zeros,
     zeros_like,
     nn,
@@ -547,8 +548,9 @@ def dtype(value: Union[py_str, DataType]) -> Expr:
     "tril",
     "triu",
     "tuple",
-    "variance",
     "unique",
+    "variance",
+    "where",
     "zeros",
     "zeros_like",
     "nn",    

src/relax/op/tensor/search.cc

@@ -0,0 +1,99 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file search.cc
+ * \brief Searching operators.
+ */
+
+#include "search.h"
+
+#include <algorithm>
+#include <utility>
+
+namespace tvm {
+namespace relax {
+
+/* relax.where */
+Expr where(Expr condition, Expr x1, Expr x2) {
+  static const Op& op = Op::Get("relax.where");
+  return Call(op, {std::move(condition), std::move(x1), std::move(x2)}, Attrs(), {});
+}
+
+TVM_REGISTER_GLOBAL("relax.op.where").set_body_typed(where);
+
+StructInfo InferStructInfoWhere(const Call& call, const BlockBuilder& ctx) {
+  Array<TensorStructInfo> input_sinfo = GetInputTensorStructInfo(call, ctx);
+  TensorStructInfo cond_sinfo = input_sinfo[0];
+  TensorStructInfo x1_sinfo = input_sinfo[1];
+  TensorStructInfo x2_sinfo = input_sinfo[2];
+
+  if (!cond_sinfo->dtype.is_bool()) {
+    ctx->ReportFatal(Diagnostic::Error(call)
+                     << "Where requires the input condition tensor to have boolean dtype. However, "
+                        "the given condition dtype is "
+                     << cond_sinfo->dtype);
+  }
+  DataType output_dtype = InferBinaryArithOpOutDtype(call, ctx, x1_sinfo, x2_sinfo);
+
+  int output_ndim;
+  if (cond_sinfo->IsUnknownNdim() || x1_sinfo->IsUnknownNdim() || x2_sinfo->IsUnknownNdim()) {
+    output_ndim = kUnknownNDim;
+  } else {
+    output_ndim = std::max(cond_sinfo->ndim, std::max(x1_sinfo->ndim, x2_sinfo->ndim));
+  }
+
+  const auto* cond_shape = cond_sinfo->shape.as<ShapeExprNode>();
+  const auto* x1_shape = x1_sinfo->shape.as<ShapeExprNode>();
+  const auto* x2_shape = x2_sinfo->shape.as<ShapeExprNode>();
+  if (cond_shape && x1_shape && x2_shape) {
+    // Step 1. Compute the broadcasted shape of x1's and x2's
+    Optional<Array<PrimExpr>> broadcasted_shape =
+        InferBinaryBroadcastShape(call, ctx, x1_shape->values, x2_shape->values);
+    if (!broadcasted_shape.defined()) {
+      return TensorStructInfo(output_dtype, output_ndim);
+    }
+    // Step 2. Compute the broadcasted shape of cond's and the previous broadcasted shape.
+    broadcasted_shape =
+        InferBinaryBroadcastShape(call, ctx, cond_shape->values, broadcasted_shape.value());
+    if (!broadcasted_shape.defined()) {
+      return TensorStructInfo(output_dtype, output_ndim);
+    }
+    ICHECK_EQ(static_cast<int>(broadcasted_shape.value().size()), output_ndim);
+    return TensorStructInfo(ShapeExpr(broadcasted_shape.value()), output_dtype);
+  } else if (cond_sinfo->shape.defined() &&                 //
+             x1_sinfo->shape.defined() &&                   //
+             x2_sinfo->shape.defined() &&                   //
+             cond_sinfo->shape.same_as(x1_sinfo->shape) &&  //
+             cond_sinfo->shape.same_as(x2_sinfo->shape)) {
+    return TensorStructInfo(cond_sinfo->shape.value(), output_dtype);
+  } else {
+    return TensorStructInfo(output_dtype, output_ndim);
+  }
+}
+
+TVM_REGISTER_OP("relax.where")
+    .set_num_inputs(3)
+    .add_argument("condition", "Tensor", "When True, yield `x1`; otherwise, yield `x2`.")
+    .add_argument("x1", "Tensor", "The first input tensor.")
+    .add_argument("x2", "Tensor", "The second input tensor.")
+    .set_attr<FInferStructInfo>("FInferStructInfo", InferStructInfoWhere);
+
+}  // namespace relax
+}  // namespace tvm

src/relax/op/tensor/search.h

@@ -0,0 +1,41 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file search.h
+ * \brief The functions to make Relax searching operator calls.
+ */
+#ifndef TVM_RELAX_OP_TENSOR_SEARCH_H_
+#define TVM_RELAX_OP_TENSOR_SEARCH_H_
+
+#include "../op_common.h"
+
+namespace tvm {
+namespace relax {
+
+/*!
+ * \brief Selecting elements from either the input tensors depending on the value of the
+ * condition.
+ */
+Expr where(Expr condition, Expr x1, Expr x2);
+
+}  // namespace relax
+}  // namespace tvm
+
+#endif  // TVM_RELAX_OP_TENSOR_SEARCH_H_