[Relay][Frontend] SparseTensorDenseMatMul support for Tensorflow (apa…

…che#6685)

* [Relay][Frontend] SparseTensorDenseMatMul support for Tensorflow

* Lint error resolved

* [1] Review comments handled

* [2] Review comments handled

python/tvm/relay/frontend/tensorflow.py

@@ -903,6 +903,51 @@ def _impl(inputs, attr, params, mod):
     return _impl
 
 
+def _sparse_tensor_dense_matmul():
+    # Sparse utility from scipy
+    from scipy.sparse import csr_matrix
+
+    def _impl(inputs, attr, params, mod):
+        assert len(inputs) == 4, "There should be 4 input tensors"
+
+        indices_tensor = _infer_value(inputs[0], params, mod).asnumpy()
+        values_tensor = _infer_value(inputs[1], params, mod).asnumpy()
+        dense_shape_tensor = _infer_value(inputs[2], params, mod).asnumpy()
+
+        data = inputs[3]
+
+        rows = [x[0] for x in indices_tensor]
+        cols = [x[1] for x in indices_tensor]
+
+        # Create scipy sparse Tensor(CSR)
+        weight_sp = csr_matrix(
+            (values_tensor, (rows, cols)), shape=tuple(dense_shape_tensor.tolist())
+        )
+        weight_sp = csr_matrix(weight_sp.transpose())
+
+        weight_data = _expr.const(weight_sp.data, weight_sp.data.dtype)
+        weight_indptrs = _expr.const(weight_sp.indptr, weight_sp.indptr.dtype)
+        weight_indices = _expr.const(weight_sp.indices, weight_sp.indices.dtype)
+
+        ret = _op.nn.sparse_dense(data, [weight_data, weight_indices, weight_indptrs])
+
+        # If both are true means First input was dense and second was sparse
+        # TODO(ANSHUMAN87): Support other adjoint option too
+        if attr.get("adjoint_a") and attr.get("adjoint_b"):
+            ret = _op.transpose(ret)
+        else:
+            raise tvm.error.OpAttributeUnImplemented(
+                "Only tf.sparse.sparse_dense_matmul() with adjoint_a=True and adjoint_b=True"
+                " is supported, but adjoint_a={} and adjoint_b={} was supplied.".format(
+                    attr.get("adjoint_a"), attr.get("adjoint_b")
+                )
+            )
+
+        return ret
+
+    return _impl
+
+
 def _identity():
     def _impl(inputs, attr, params, mod):
         return inputs[0]
@@ -2411,6 +2456,7 @@ def _impl(inputs, attr, params, mod):
     "SpaceToBatchND": _space_to_batch_nd(),
     "SpaceToDepth": _space_to_depth(),
     "SparseToDense": _sparse_to_dense(),
+    "SparseTensorDenseMatMul": _sparse_tensor_dense_matmul(),
     "Split": _split(False),
     "SplitV": _split(True),
     "Sqrt": AttrCvt("sqrt"),

python/tvm/relay/op/nn/nn.py

@@ -2046,7 +2046,7 @@ def sparse_transpose(x):
 
     Parameters
     ----------
-    x : namedtuple.
+    x : Union[namedtuple, Tuple[ndarray, ndarray, ndarray]].
         The sparse weight matrix for the fast matrix transpose.
 
     Returns
@@ -2055,7 +2055,9 @@ def sparse_transpose(x):
         Tuple of output sparse tensor (same shape and format as input),
         i.e. if CSR then output is in ([data, indices, indptr]) form
     """
-    return expr.TupleWrapper(_make.sparse_transpose(x.data, x.indices, x.indptr), 3)
+    if hasattr(x, "indices"):
+        return expr.TupleWrapper(_make.sparse_transpose(x.data, x.indices, x.indptr), 3)
+    return expr.TupleWrapper(_make.sparse_transpose(x[0], x[1], x[2]), 3)
 
 
 def contrib_conv2d_winograd_without_weight_transform(

python/tvm/topi/cuda/sparse.py

@@ -180,7 +180,7 @@ def gen_ir(data, w_data, w_indices, w_indptr, out):
         assert (
             mb >= mi
         ), "Number of block rows in dense matrix must be larger than warp size: {} vs {}.".format(
-            warp_size, m
+            warp_size, mb
         )
         mo = ceil_div(mb, mi)
         ni = 1  # TODO(tkonolige): how do I compute the number of warps per block?
@@ -367,9 +367,14 @@ def _alter_sparse_dense_layout(_attrs, inputs, _tinfos, _out_type):
         and isinstance(inputs[2], relay.Constant)
         and isinstance(inputs[3], relay.Constant)
     ):
-        sparse_matrix = sp.bsr_matrix(
-            (inputs[1].data.asnumpy(), inputs[2].data.asnumpy(), inputs[3].data.asnumpy())
-        )
+        if len(inputs[1].data.asnumpy().shape) == 1:
+            sparse_matrix = sp.csr_matrix(
+                (inputs[1].data.asnumpy(), inputs[2].data.asnumpy(), inputs[3].data.asnumpy())
+            ).tobsr()
+        else:
+            sparse_matrix = sp.bsr_matrix(
+                (inputs[1].data.asnumpy(), inputs[2].data.asnumpy(), inputs[3].data.asnumpy())
+            )
         warp_size = int(tvm.target.Target.current(allow_none=False).thread_warp_size)
         sparse_matrix = pad_sparse_matrix(sparse_matrix, warp_size)
         return relay.nn._make.sparse_dense_padded(

tests/python/frontend/tensorflow/test_forward.py

@@ -1765,6 +1765,62 @@ def test_forward_batch_matmul():
     _test_batch_matmul((2, 3, 4, 2, 3, 4, 5, 6), (2, 3, 4, 2, 3, 4, 5, 6), "float32", False, True)
 
 
+#######################################################################
+# SparseTensorDenseMatMul
+# ----------------------------------
+
+
+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]:
+            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")
+
+                if flip:
+                    result = tf.sparse.sparse_dense_matmul(
+                        B, A_sp, adjoint_a=adjoint_a, adjoint_b=adjoint_b
+                    )
+                else:
+                    result = tf.sparse.sparse_dense_matmul(
+                        A_sp, B, adjoint_a=adjoint_a, adjoint_b=adjoint_b
+                    )
+
+                B_np = np.random.uniform(high=5.0, size=B_shape).astype(dtype)
+
+                # TODO(ANSHUMAN87): There is an issue in cuda scheduling for csr, work in progress
+                compare_tf_with_tvm([B_np], [B.name], result.name, no_gpu=True)
+
+
+def test_forward_sparse_dense_matmul():
+    """ sparse_dense_matmul op test"""
+    ###################################################################
+    #
+    # In order to create a SparseTensor, it requires 3 input as below:
+    #    SparseTensor(indices=[[0, 0], [1, 2]], values=[1, 2], dense_shape=[3, 4])
+    #
+    # Above Sparse can be represented in Dense as below :
+    #    [[1, 0, 0, 0]
+    #     [0, 0, 2, 0]
+    #     [0, 0, 0, 0]]
+    #
+    # ------------------------------------------------------------------
+
+    # TODO(ANSHUMAN87): False case for flip need to be supported
+    # _test_sparse_dense_matmul([[0, 0], [1, 2]], [4.0, 8.0], [3, 4], [4, 3], "float32")
+    _test_sparse_dense_matmul([[0, 0], [1, 2]], [4.0, 8.0], [3, 5], [4, 3], "float32", True)
+    _test_sparse_dense_matmul([[0, 0], [1, 2]], [4.0, 8.0], [3, 3], [3, 3], "float32", True)
+    _test_sparse_dense_matmul(
+        [[0, 0], [1, 3], [4, 3]], [3.0, 6.0, 9.0], [5, 5], [5, 5], "float32", True
+    )
+    _test_sparse_dense_matmul(
+        [[0, 0], [1, 3], [4, 3]], [3.0, 6.0, 9.0], [9, 5], [7, 9], "float32", True
+    )
+
+
 #######################################################################
 # StridedSlice
 # ------------