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Merge pull request #24098 from yongtang:24072-sparse_dense_cwise-broadcasting

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PiperOrigin-RevId: 224386209
This commit is contained in:
TensorFlower Gardener 2018-12-06 12:35:01 -08:00
commit 6427950eae
2 changed files with 18 additions and 4 deletions
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8
tensorflow/core/kernels/sparse_dense_binary_op_shared.cc
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@ -88,12 +88,12 @@ class SparseDenseBinaryOpShared : public OpKernel {
const auto rhs_dims = BCast::FromShape(dense_t->shape());
BCast b(lhs_dims, rhs_dims, false); // false for keeping the same num dims.
// True iff (size(lhs) > size(rhs)), or (sizes equal, lhs cwise rhs).
// True iff (size(lhs) >= size(rhs)) and all dims in lhs is greater or equal
// to dims in rhs (from right to left).
auto VecGreaterEq = [](ArraySlice<int64> lhs, ArraySlice<int64> rhs) {
if (lhs.size() > rhs.size()) return true;
if (lhs.size() < rhs.size()) return false;
for (size_t i = 0; i < lhs.size(); ++i) {
if (lhs[i] < rhs[i]) return false;
for (size_t i = 0; i < rhs.size(); ++i) {
if (lhs[lhs.size() - 1 - i] < rhs[rhs.size() - 1 - i]) return false;
}
return true;
};

14
tensorflow/python/kernel_tests/sparse_ops_test.py
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@ -22,6 +22,7 @@ import numpy as np
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import errors
from tensorflow.python.framework import ops
from tensorflow.python.framework import sparse_tensor
from tensorflow.python.framework import test_util
@ -798,6 +799,19 @@ class SparseMathOpsTest(test_util.TensorFlowTestCase):
result_tensor.values).eval()
self.assertAllEqual(result_np, res_densified)
@test_util.run_deprecated_v1
def testCwiseShapeValidation(self):
# Test case for GitHub 24072.
with self.session(use_gpu=False):
a = array_ops.ones([3, 4, 1], dtype=dtypes.int32)
b = sparse_tensor.SparseTensor([[0, 0, 1, 0], [0, 0, 3, 0]], [10, 20],
[1, 1, 4, 2])
c = a * b
with self.assertRaisesRegexp(
errors.InvalidArgumentError,
"broadcasts dense to sparse only; got incompatible shapes"):
c.eval()
@test_util.run_deprecated_v1
def testCwiseDivAndMul(self):
np.random.seed(1618)