diff --git a/docs/langref/relay_op.rst b/docs/langref/relay_op.rst
index f4a65023ee53aba0873f60aca6d89297633ce16d..5c3ab8b1ffdabdfa4a4a250bdf6e3c4bd4ef847d 100644
--- a/docs/langref/relay_op.rst
+++ b/docs/langref/relay_op.rst
@@ -124,6 +124,7 @@ This level enables additional math and transform operators.
tvm.relay.mean
tvm.relay.prod
tvm.relay.strided_slice
+ tvm.relay.broadcast_to
**Level 5: Vision/Image Operators**
diff --git a/python/tvm/relay/op/_transform.py b/python/tvm/relay/op/_transform.py
index c1e71e9133eaf1636777ec49adefb18b419fb2a7..085a8ceed5d1d26e77e25537fa65e939137d7edf 100644
--- a/python/tvm/relay/op/_transform.py
+++ b/python/tvm/relay/op/_transform.py
@@ -11,6 +11,7 @@ schedule_broadcast = _reg.schedule_injective
_reg.register_schedule("collapse_sum_like", _schedule_reduce)
+_reg.register_schedule("broadcast_to", schedule_broadcast)
_reg.register_schedule("broadcast_to_like", schedule_broadcast)
_reg.register_schedule("expand_dims", schedule_broadcast)
_reg.register_schedule("squeeze", schedule_injective)
diff --git a/python/tvm/relay/op/transform.py b/python/tvm/relay/op/transform.py
index f536e75fd9b401d901ef3084013ecf9d8d88a3ff..2791eaf7d9dbe9aea4b283d004102024deafcc5e 100644
--- a/python/tvm/relay/op/transform.py
+++ b/python/tvm/relay/op/transform.py
@@ -267,6 +267,24 @@ def where(condition, x, y):
"""
return _make.where(condition, x, y)
+def broadcast_to(data, shape):
+ """Return an scalar value array with the same type, broadcast to
+ the provided shape.
+
+ Parameters
+ ----------
+ data : relay.Expr
+ The input tensor.
+
+ shape : shape
+ Provide the shape to broadcast to.
+
+ Returns
+ -------
+ result : relay.Expr
+ The resulting tensor.
+ """
+ return _make.broadcast_to(data, shape)
def broadcast_to_like(data, broadcast_type):
"""Return an scalar value array with the same shape and type as the input array.
diff --git a/src/relay/op/nn/pooling.cc b/src/relay/op/nn/pooling.cc
index 6233e6d51776bd8bf06fd4075fc8ac5647415e8e..6cf37668cab59ed2f3d3f7f399e2e069b1e29e62 100644
--- a/src/relay/op/nn/pooling.cc
+++ b/src/relay/op/nn/pooling.cc
@@ -258,8 +258,7 @@ bool GlobalPool2DRel(const Array<Type>& types,
const TypeReporter& reporter) {
CHECK_EQ(types.size(), 2);
const auto* data = types[0].as<TensorTypeNode>();
-
- CHECK(data != nullptr);
+ if (data == nullptr) { return false; }
const auto dshape = data->shape;
CHECK_NE(dshape.size(), 0);
CHECK_GE(dshape.size(), 2U)
diff --git a/src/relay/op/tensor/transform.cc b/src/relay/op/tensor/transform.cc
index fcf7f6fe329911a40c5694f9b7f11b30fec084e8..eb8b4f13fb3fd9f601d2ecd41694d97504688374 100644
--- a/src/relay/op/tensor/transform.cc
+++ b/src/relay/op/tensor/transform.cc
@@ -1084,6 +1084,52 @@ RELAY_REGISTER_OP("collapse_sum_like")
.set_attr<FTVMCompute>("FTVMCompute", CollapseSumLikeCompute)
.set_attr<TOpPattern>("TOpPattern", kCommReduce);
+// BroadCastTo: <A, B> -> B where BroadCast(A, B) = B
+bool BroadCastToRel(const Array<Type>& types,
+ int num_inputs,
+ const Attrs& attrs,
+ const TypeReporter& reporter) {
+ CHECK_EQ(types.size(), 2);
+ auto ioattrs = attrs.as<InitOpAttrs>();
+ CHECK(ioattrs);
+ auto intt = types[0].as<TensorTypeNode>();
+ if (intt == nullptr) { return false; }
+ auto type = TensorTypeNode::make(ioattrs->shape, intt->dtype);
+ reporter->Assign(types[1], type);
+ return true;
+}
+
+Expr MakeBroadCastTo(Expr data, Array<IndexExpr> shape) {
+ static const Op& op = Op::Get("broadcast_to");
+ auto attrs = make_node<InitOpAttrs>();
+ attrs->shape = std::move(shape);
+ return CallNode::make(op, {data}, Attrs(attrs), {});
+}
+
+Array<Tensor> BroadCastToCompute(const Attrs& attrs,
+ const Array<Tensor>& inputs,
+ const Type& out_type,
+ const Target& target) {
+ auto ioattrs = attrs.as<InitOpAttrs>();
+ CHECK(ioattrs != nullptr);
+ return { topi::broadcast_to(inputs[0], ioattrs->shape) };
+}
+
+TVM_REGISTER_API("relay.op._make.broadcast_to")
+.set_body([](const TVMArgs& args, TVMRetValue* rv) {
+ runtime::detail::unpack_call<Expr, 2>(MakeBroadCastTo, args, rv);
+ });
+
+RELAY_REGISTER_OP("broadcast_to")
+.describe(R"code(Broadcast the first input to match the shape argument.
+)code" TVM_ADD_FILELINE)
+.set_num_inputs(1)
+.add_argument("data", "Tensor", "The input tensor.")
+.set_support_level(4)
+.add_type_rel("BroadCastTo", BroadCastToRel)
+.set_attr<FTVMCompute>("FTVMCompute", BroadCastToCompute)
+.set_attr<TOpPattern>("TOpPattern", kBroadcast);
+
// BroadCastToLike: <A, B> -> B where BroadCast(A, B) = B
bool BroadCastToLikeRel(const Array<Type>& types,
int num_inputs,
diff --git a/tests/python/relay/test_op_level10.py b/tests/python/relay/test_op_level10.py
index 5d65691a2ad59ef5ef11b7decb36b30d7ebd9d4a..2c0ed73a753595fd0bd1b7393d40dc9dc6d6e6fc 100644
--- a/tests/python/relay/test_op_level10.py
+++ b/tests/python/relay/test_op_level10.py
@@ -25,6 +25,24 @@ def test_collapse_sum_like():
op_res = intrp.evaluate(func)(x, y)
tvm.testing.assert_allclose(op_res.asnumpy(), ref_res, rtol=1e-5)
+def test_broadcast_to():
+ shape = (4, 1, 6)
+ shape_like = (3, 4, 5, 6)
+ dtype = "float32"
+ x = relay.Var("x", relay.ty.TensorType(shape , dtype))
+ z = relay.broadcast_to(x, shape=shape_like)
+ zz = relay.ir_pass.infer_type(z)
+ assert zz.checked_type == relay.ty.TensorType(shape_like, dtype)
+
+ func = relay.Function([x], z)
+ x = np.random.uniform(size=shape).astype(dtype)
+ ref_res = np.broadcast_to(x, shape_like)
+ for target, ctx in ctx_list():
+ for kind in ["graph", "debug"]:
+ intrp = relay.create_executor(kind, ctx=ctx, target=target)
+ op_res = intrp.evaluate(func)(x)
+ tvm.testing.assert_allclose(op_res.asnumpy(), ref_res, rtol=1e-5)
+
def test_broadcast_to_like():
shape = (4, 1, 6)
shape_like = (3, 4, 5, 6)