ANSHUMAN87 commented on a change in pull request #6580:
URL: https://github.com/apache/incubator-tvm/pull/6580#discussion_r501970880
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File path: src/relay/op/nn/sparse.cc
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@@ -76,7 +76,36 @@ TVM_REGISTER_GLOBAL("relay.op.nn._make.sparse_dense")
});
RELAY_REGISTER_OP("nn.sparse_dense")
- .describe(R"code(Applies a sparse linear transformation: :math:`Y = XW^T`
with X sparse.
+ .describe(R"code(Applies a sparse linear transformation: :math:`Y = XW^T`
with W sparse.
+
+- **data**: `(x1, x2, ..., xn, input_dim)`
+- **weight**: `(units, input_dim)`
+- **out**: `(x1, x2, ..., xn, units)`.
+
+)code" TVM_ADD_FILELINE)
+ .set_attrs_type<SparseDenseAttrs>()
+ .set_num_inputs(4)
+ .add_argument("data", "nD Tensor", "Input data.")
+ .add_argument("weight_data", "1D Tensor", "Weight data matrix.")
+ .add_argument("weight_indices", "1D Tensor", "Weight indices matrix.")
+ .add_argument("weight_indptr", "1D Tensor", "Weight indptr matrix.")
+ .set_support_level(1)
+ .add_type_rel("SparseDense", SparseDenseRel);
+
+Expr MakeSparseDensePadded(Expr data, Expr weight_data, Expr weight_indices,
Expr weight_indptr) {
+ auto attrs = make_object<SparseDenseAttrs>();
+ static const Op& op = Op::Get("nn.sparse_dense_padded");
+ return Call(op, {data, weight_data, weight_indices, weight_indptr},
Attrs(attrs), {});
+}
+
+TVM_REGISTER_GLOBAL("relay.op.nn._make.sparse_dense_padded")
+ .set_body([](const TVMArgs& args, TVMRetValue* rv) {
+ runtime::detail::unpack_call<Expr, 4>(MakeSparseDensePadded, args, rv);
+ });
+
+RELAY_REGISTER_OP("nn.sparse_dense_padded")
Review comment:
I think "nn.internal.xxx" is a good option.
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