[Frontend][Tensorflow] Sparse dense matmul adjoint option added

python/tvm/relay/frontend/tensorflow.py

@@ -941,8 +941,10 @@ def _impl(inputs, attr, params, mod):
             (values_tensor, (rows, cols)), shape=tuple(dense_shape_tensor.tolist())
         )
 
-        if sparse_lhs:
+        if sparse_lhs or attr.get("adjoint_b"):
             data = _op.transpose(data)
+        elif attr.get("adjoint_a"):
+            weight_sp = weight_sp
         else:
             weight_sp = csr_matrix(weight_sp.transpose())
 
@@ -955,21 +957,6 @@ def _impl(inputs, attr, params, mod):
         if not sparse_lhs:
             ret = _op.transpose(ret)
 
-        # Case 1. If both are true means first input was dense and second was sparse
-        # Case 2. If both are false means first input was sparse and second was dense
-        # TODO(ANSHUMAN87): Support other adjoint option too
-        if not (
-            (attr.get("adjoint_a") and attr.get("adjoint_b"))
-            or ((not attr.get("adjoint_a")) and (not attr.get("adjoint_b")))
-        ):
-            raise tvm.error.OpAttributeUnImplemented(
-                "Only tf.sparse.sparse_dense_matmul() with adjoint_a=True and adjoint_b=True"
-                "or with adjoint_a=False and adjoint_b=False"
-                " is supported, but adjoint_a={} and adjoint_b={} was supplied.".format(
-                    attr.get("adjoint_a"), attr.get("adjoint_b")
-                )
-            )
-
         return ret
 
     return _impl

tests/python/frontend/tensorflow/test_forward.py

@@ -1761,9 +1761,11 @@ def test_forward_batch_matmul():
 def _test_sparse_dense_matmul(indices, values, A_shape, B_shape, dtype, flip=False):
     """ One iteration of sparse_dense_matmul """
 
-    # TODO(ANSHUMAN87): Support adjoint options too
-    for adjoint_a in [False]:
-        for adjoint_b in [False]:
+    for adjoint_a in [False, True]:
+        for adjoint_b in [False, True]:
+            A_shape = A_shape[::-1] if adjoint_a else A_shape
+            B_shape = B_shape[::-1] if adjoint_b else B_shape
+
             with tf.Graph().as_default():
                 A_sp = tf.sparse.SparseTensor(indices=indices, values=values, dense_shape=A_shape)
                 B = tf.placeholder(shape=B_shape, dtype=dtype, name="B")