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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2018/08/23 18:15:42 UTC
[GitHub] szha closed pull request #11984: Generalized broadcast_like operator
szha closed pull request #11984: Generalized broadcast_like operator
URL: https://github.com/apache/incubator-mxnet/pull/11984
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diff --git a/src/operator/tensor/broadcast_reduce_op.h b/src/operator/tensor/broadcast_reduce_op.h
index 351315ab0c8..0944d255a45 100644
--- a/src/operator/tensor/broadcast_reduce_op.h
+++ b/src/operator/tensor/broadcast_reduce_op.h
@@ -147,6 +147,17 @@ struct BroadcastToParam : public dmlc::Parameter<BroadcastToParam> {
}
};
+struct BroadcastLikeParam : public dmlc::Parameter<BroadcastLikeParam> {
+ dmlc::optional<TShape> lhs_axes;
+ dmlc::optional<TShape> rhs_axes;
+ DMLC_DECLARE_PARAMETER(BroadcastLikeParam) {
+ DMLC_DECLARE_FIELD(lhs_axes).set_default(dmlc::optional<TShape>())
+ .describe("Axes to perform broadcast on in the first input array");
+ DMLC_DECLARE_FIELD(rhs_axes).set_default(dmlc::optional<TShape>())
+ .describe("Axes to copy from the second input array");
+ }
+};
+
inline int CheckAxis(int axis, int ndim) {
CHECK(axis < ndim && axis >= -ndim)
<< "axis " << axis << " exceeds the input dimension of " << ndim;
@@ -350,20 +361,60 @@ inline bool BroadcastLikeShape(const nnvm::NodeAttrs& attrs,
CHECK_EQ(out_attrs->size(), 1U);
TShape& lhs_shape = (*in_attrs)[0];
TShape& rhs_shape = (*in_attrs)[1];
- TShape oshape = TShape(rhs_shape);
- if (lhs_shape.ndim() == 0 || lhs_shape.ndim() == 0) return false;
- CHECK_EQ(lhs_shape.ndim(), rhs_shape.ndim())
- << "Operand of shape " << lhs_shape << " cannot be broadcasted to " << rhs_shape;
+ if ((lhs_shape.ndim() == 0) || (lhs_shape.ndim() == 0)) {
+ return false;
+ }
- for (index_t i = 0; i < lhs_shape.ndim(); ++i) {
- if (rhs_shape[i] != 0) {
- CHECK(lhs_shape[i] == rhs_shape[i] || lhs_shape[i] == 1)
- << "Array cannot be broadcasted from " << lhs_shape << " to " << rhs_shape;
- } else {
- oshape[i] = lhs_shape[i];
+ const BroadcastLikeParam& param = nnvm::get<BroadcastLikeParam>(attrs.parsed);
+ TShape oshape;
+
+ // lhs or rhs or both params were not specified
+ if (!param.lhs_axes.has_value() || !param.rhs_axes.has_value()) {
+ CHECK_EQ(lhs_shape.ndim(), rhs_shape.ndim())
+ << "Operand of shape " << lhs_shape << " cannot be broadcasted to " << rhs_shape;
+
+ oshape = TShape(rhs_shape);
+ for (index_t i = 0; i < lhs_shape.ndim(); ++i) {
+ if (rhs_shape[i] != 0) {
+ CHECK(lhs_shape[i] == rhs_shape[i] || lhs_shape[i] == 1)
+ << "Array cannot be broadcasted from " << lhs_shape << " to " << rhs_shape;
+ } else {
+ oshape[i] = lhs_shape[i];
+ }
+ }
+ } else {
+ auto lhs_axes = param.lhs_axes.value();
+ auto rhs_axes = param.rhs_axes.value();
+
+ CHECK(rhs_axes.ndim() == lhs_axes.ndim())
+ << "Input_axis and other_axis size does not match";
+
+ CHECK(lhs_axes.ndim() > 0)
+ << "Empty axes tuple is not allowed";
+
+ oshape = TShape(lhs_shape);
+ for (index_t i = 0; i < lhs_axes.ndim(); ++i) {
+ auto copyfrom = lhs_axes[i];
+ if (copyfrom < 0) {
+ copyfrom = lhs_shape.ndim() + copyfrom;
+ }
+ CHECK(copyfrom >= 0 && copyfrom < oshape.ndim())
+ << "Invalid dimension specified in lhs_axes: " << lhs_axes[i];
+
+ auto copyto = rhs_axes[i];
+ if (copyto < 0) {
+ copyto = rhs_shape.ndim() + copyto;
+ }
+ CHECK(copyto >= 0 && copyto < rhs_shape.ndim())
+ << "Invalid dimension specified in rhs_axes: " << rhs_axes[i];
+
+ CHECK(lhs_shape[copyfrom] == 1) << "Input axis " << lhs_axes[i]
+ << " at dimension " << i << " cannot be broadcasted to " << rhs_shape[copyto];
+ oshape[copyfrom] = rhs_shape[copyto];
}
}
+
SHAPE_ASSIGN_CHECK(*out_attrs, 0, oshape);
return true;
}
diff --git a/src/operator/tensor/broadcast_reduce_op_value.cc b/src/operator/tensor/broadcast_reduce_op_value.cc
index 929c3dfcf0a..c3bc9cfd3f0 100644
--- a/src/operator/tensor/broadcast_reduce_op_value.cc
+++ b/src/operator/tensor/broadcast_reduce_op_value.cc
@@ -31,6 +31,7 @@ DMLC_REGISTER_PARAMETER(NormParam);
DMLC_REGISTER_PARAMETER(ReduceAxisParam);
DMLC_REGISTER_PARAMETER(BroadcastAxesParam);
DMLC_REGISTER_PARAMETER(BroadcastToParam);
+DMLC_REGISTER_PARAMETER(BroadcastLikeParam);
inline std::string get_reduce_axes_description(const std::string& op_name, int line) {
std::string doc = R"code(Computes the __op__ of array elements over given axes.
@@ -309,7 +310,11 @@ For example::
broadcast_like([[1,2,3]], [[5,6,7],[7,8,9]]) = [[ 1., 2., 3.],
[ 1., 2., 3.]])
+ broadcast_like([9], [1,2,3,4,5], lhs_axes=(0,), rhs_axes=(-1,)) = [9,9,9,9,9]
+
)code" ADD_FILELINE)
+.set_attr_parser(ParamParser<BroadcastLikeParam>)
+.add_arguments(BroadcastLikeParam::__FIELDS__())
.set_attr<nnvm::FInferShape>("FInferShape", BroadcastLikeShape)
.set_attr<FCompute>("FCompute<cpu>", BroadcastCompute<cpu>);
diff --git a/tests/python/unittest/test_ndarray.py b/tests/python/unittest/test_ndarray.py
index 2db39d5dd53..36522601814 100644
--- a/tests/python/unittest/test_ndarray.py
+++ b/tests/python/unittest/test_ndarray.py
@@ -551,8 +551,27 @@ def test_broadcast_like():
err = np.square(ndarray_ret - numpy_ret).mean()
assert err < 1E-8
+ def test_broadcast_like_axis():
+ testcases = [
+ # Lhs shape, rhs shape, lhs axis, rhs axis, result
+ [(1, 2, 1, 3), (5, 6, 7, 8), (0,2), (1,3), (6, 2, 8, 3)],
+ [(1,), (5,), (0,), (-1,), (5,)],
+ [(1, 7, 9, 1, 1), (9,), (-2,), (0,), (1, 7, 9, 9, 1)],
+ [(1, 7, 9, 1, 1), (9, 1), (-2, -1), (-2, -1), (1, 7, 9, 9, 1)],
+ [(2, 1), (1, 7, 9, 1, 1), (1,), (-3,), (2, 9)]
+ ]
+
+ for test_data in testcases:
+ lhs = mx.nd.random.uniform(shape=test_data[0])
+ rhs = mx.nd.random.uniform(shape=test_data[1])
+ output = mx.nd.broadcast_like(lhs, rhs, lhs_axes=test_data[2], rhs_axes=test_data[3])
+
+ assert_exception(mx.nd.broadcast_like, mx.base.MXNetError, lhs, rhs, lhs_axes=(), rhs_axes=())
+ assert output.shape == test_data[4]
+
test_broadcast_to()
test_broadcast_like()
+ test_broadcast_like_axis()
@with_seed()
diff --git a/tests/python/unittest/test_symbol.py b/tests/python/unittest/test_symbol.py
index aece9a37812..d022c68237a 100644
--- a/tests/python/unittest/test_symbol.py
+++ b/tests/python/unittest/test_symbol.py
@@ -171,7 +171,7 @@ def test_symbol_infer_shape_var():
def test_symbol_fluent():
has_grad = set(['flatten', 'expand_dims', 'flip', 'tile', 'transpose', 'sum', 'nansum', 'prod',
'nanprod', 'mean', 'max', 'min', 'reshape', 'broadcast_to', 'split',
- 'broadcast_axes', 'pad', 'swapaxes', 'slice', 'slice_axis', 'slice_like',
+ 'broadcast_axes', 'broadcast_like', 'pad', 'swapaxes', 'slice', 'slice_axis', 'slice_like',
'take', 'one_hot', 'pick', 'sort', 'topk', 'argsort', 'argmax', 'argmin',
'clip', 'abs', 'sign', 'sin', 'cos', 'tan', 'arcsin', 'arccos', 'arctan',
'degrees', 'radians', 'sinh', 'cosh', 'tanh', 'arcsinh', 'arccosh', 'arctanh',
@@ -212,6 +212,7 @@ def check_fluent_regular(func, kwargs, shape=(5, 17, 1), equal_nan=False):
check_fluent_regular('slice_like', {'axes': (0, -2), 'shape_like': mx.sym.zeros((3, 3))})
check_fluent_regular('clip', {'a_min': 0.25, 'a_max': 0.75})
check_fluent_regular('broadcast_axes', {'axis': (2,), 'size': (5,)})
+ check_fluent_regular('broadcast_like', {'rhs': mx.sym.ones((1, 5)), 'lhs_axes': (0,), 'rhs_axes': (1,)}, shape=(1,9))
check_fluent_regular('pad', {'mode': 'constant', 'pad_width': (0,0,0,0,3,0,0,4)}, shape=(5, 17, 2, 3))
check_fluent_regular('reshape_like', {'rhs': mx.sym.ones((30, 17))}, shape=(5, 17, 2, 3))
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