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Be assertive that we support unordered sparse tensors.

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PiperOrigin-RevId: 360750129
Change-Id: If44b18ce8894a0608069dc09ed705aef0433942f
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A. Unique TensorFlower 2021-03-03 13:55:15 -08:00 committed by TensorFlower Gardener
parent 07ac149508
commit 3a3a7631fa
2 changed files with 31 additions and 16 deletions
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12
tensorflow/python/kernel_tests/sparse_tensor_dense_matmul_op_test.py
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@ -163,6 +163,18 @@ class SparseTensorDenseMatMulTest(test.TestCase):
sparse_ops.sparse_tensor_dense_matmul(
sparse_t, dense_t, adjoint_a=True))
def testUnorderedIndicesForSparseTensorDenseMatmul(self):
indices = np.array([(2, 1), (0, 0)]).astype(np.int64)
values = np.array([10, 11]).astype(np.float32)
shape = [3, 2]
sparse_t = sparse_tensor.SparseTensor(indices, values, shape)
dense_t = np.array([[1] * 500, [2] * 500], dtype=np.float32)
expected_t = np.array([[11] * 500, [0] * 500, [20] * 500], dtype=np.float32)
self.assertAllClose(
expected_t, sparse_ops.sparse_tensor_dense_matmul(sparse_t, dense_t))
@test_util.run_gpu_only
def testInvalidIndicesForSparseTensorDenseMatmulOnGPU(self):
indices = np.array([[1, 10]]).astype(np.int64)

35
tensorflow/python/ops/sparse_ops.py
View File

@ -2431,14 +2431,31 @@ def sparse_tensor_dense_matmul(sp_a,
(or SparseTensor) "B". Please note that one and only one of the inputs MUST
be a SparseTensor and the other MUST be a dense matrix.
No validity checking is performed on the indices of `A`. However, the
following input format is recommended for optimal behavior:
The following input format is recommended (but not required) for optimal
performance:
* If `adjoint_a == false`: `A` should be sorted in lexicographically
increasing order. Use `sparse.reorder` if you're not sure.
* If `adjoint_a == true`: `A` should be sorted in order of increasing
dimension 1 (i.e., "column major" order instead of "row major" order).
Args:
sp_a: SparseTensor (or dense Matrix) A, of rank 2.
b: dense Matrix (or SparseTensor) B, with the same dtype as sp_a.
adjoint_a: Use the adjoint of A in the matrix multiply. If A is complex,
this is transpose(conj(A)). Otherwise it's transpose(A).
adjoint_b: Use the adjoint of B in the matrix multiply. If B is complex,
this is transpose(conj(B)). Otherwise it's transpose(B).
name: A name prefix for the returned tensors (optional)
Returns:
A dense matrix (pseudo-code in dense np.matrix notation):
`A = A.H if adjoint_a else A`
`B = B.H if adjoint_b else B`
`return A*B`
Notes:
Using `tf.nn.embedding_lookup_sparse` for sparse multiplication:
It's not obvious but you can consider `embedding_lookup_sparse` as another
@ -2610,20 +2627,6 @@ def sparse_tensor_dense_matmul(sp_a,
0.8 25 False 1000 1000 0.00211448 0.00752736 3.55992
```
Args:
sp_a: SparseTensor (or dense Matrix) A, of rank 2.
b: dense Matrix (or SparseTensor) B, with the same dtype as sp_a.
adjoint_a: Use the adjoint of A in the matrix multiply. If A is complex,
this is transpose(conj(A)). Otherwise it's transpose(A).
adjoint_b: Use the adjoint of B in the matrix multiply. If B is complex,
this is transpose(conj(B)). Otherwise it's transpose(B).
name: A name prefix for the returned tensors (optional)
Returns:
A dense matrix (pseudo-code in dense np.matrix notation):
`A = A.H if adjoint_a else A`
`B = B.H if adjoint_b else B`
`return A*B`
"""
# pylint: enable=line-too-long