tkonolige commented on a change in pull request #7126:
URL: https://github.com/apache/tvm/pull/7126#discussion_r546836442
##########
File path: python/tvm/relay/op/transform.py
##########
@@ -1320,3 +1320,84 @@ def adv_index(inputs):
Output tensor.
"""
return _make.adv_index(Tuple(inputs))
+
+
+def sparsefillemptyrows(sparse_indices, sparse_values, dense_shape,
default_value):
+ """
+ Fill first column of the empty rows with default values for a sparse array.
+
+ Parameters
+ ----------
+ sparse_indices : relay.Expr
+ A 2-D tensor[N, n_dim] of integers containing location of sparse
values, where N is the
+ number of sparse values and n_dim is the number of dimensions of the
dense_shape
+
+ sparse_values : relay.Expr
+ A 1-D tensor[N] containing the sparse values for the sparse indices.
+
+ dense_shape : relay.Expr
+ A list of integers. Shape of the dense output tensor.
+
+ default_value : relay.Expr
+ A 0-D tensor containing the default value for the remaining locations.
+ Defaults to 0.
+
+ Returns
+ -------
+ TupleWrapper with the following four outputs
+
+ new_sparse_indices : relay.Expr
+ A 2-D tensor[N + dense_shape[0], n_dim] of integers containing
location of new sparse
+ indices where N is the number of sparse values. It is filled with -1
at to_be_discarded
Review comment:
What is `to_be_discarded` it is not mentioned anywhere?
##########
File path: python/tvm/relay/op/_transform.py
##########
@@ -63,6 +63,7 @@
_reg.register_injective_schedule("sparse_to_dense")
_reg.register_injective_schedule("matrix_set_diag")
_reg.register_injective_schedule("adv_index")
+_reg.register_injective_schedule("sparsefillemptyrows")
Review comment:
I think `sparse_fill_empty_rows` fits better with our current naming
convention.
##########
File path: src/relay/op/tensor/transform.cc
##########
@@ -1553,6 +1553,63 @@ RELAY_REGISTER_OP("meshgrid")
.set_attr<FTVMCompute>("FTVMCompute", MeshgridCompute)
.set_attr<TOpPattern>("TOpPattern", kInjective);
+TVM_REGISTER_NODE_TYPE(SparseFillEmptyRowsAttrs);
+
+bool SparseFillEmptyRowsRel(const Array<Type>& types, int num_inputs, const
Attrs& attrs,
+ const TypeReporter& reporter) {
+ // types: [ sparse_indices, sparse_values, default_values, result]
+ ICHECK_EQ(types.size(), 4);
+ ICHECK_EQ(num_inputs, 3);
+ std::vector<Type> fields;
+ auto sparse_indices = types[0].as<TensorTypeNode>();
+ auto default_value = types[2].as<TensorTypeNode>();
+ const auto* param = attrs.as<SparseFillEmptyRowsAttrs>();
+ CHECK(param != nullptr);
+
+ Array<IndexExpr> sp_ordered_output_shape;
+ sp_ordered_output_shape.push_back(param->dense_shape[0] +
sparse_indices->shape[0]);
+ if (sparse_indices->shape.size() > 1) {
+ sp_ordered_output_shape.push_back(sparse_indices->shape[1]);
+ }
+ fields.push_back(TensorType(sp_ordered_output_shape, sparse_indices->dtype));
+ fields.push_back(TensorType(Array<PrimExpr>{param->dense_shape[0]},
tvm::DataType::Bool()));
+ fields.push_back(TensorType(Array<PrimExpr>{sp_ordered_output_shape[0]},
default_value->dtype));
+ fields.push_back(TensorType(Array<PrimExpr>{1}, tvm::DataType::Int(32)));
+ reporter->Assign(types[3], TupleType(Array<Type>(fields)));
+ return true;
+}
+
+Array<te::Tensor> SparseFillEmptyRowsCompute(const Attrs& attrs, const
Array<te::Tensor>& inputs,
+ const Type& out_type) {
+ CHECK_EQ(inputs.size(), 3);
+ const auto* param = attrs.as<SparseFillEmptyRowsAttrs>();
+ CHECK(param != nullptr);
+ return {topi::SparseFillEmptyRows(inputs[0], inputs[1], inputs[2],
param->dense_shape)};
+}
+
+Expr MakeSparseFillEmptyRows(Expr sparse_indices, Expr sparse_values, Expr
default_value,
+ Array<Integer> dense_shape) {
+ auto attrs = make_object<SparseFillEmptyRowsAttrs>();
+ attrs->dense_shape = std::move(dense_shape);
+ static const Op& op = Op::Get("sparsefillemptyrows");
+ return Call(op, {sparse_indices, sparse_values, default_value},
Attrs(attrs), {});
+}
+
+TVM_REGISTER_GLOBAL("relay.op._make.sparsefillemptyrows").set_body_typed(MakeSparseFillEmptyRows);
+
+RELAY_REGISTER_OP("sparsefillemptyrows")
+ .describe(R"code(Return twice of normal addition of two tensors.
Review comment:
Could you update the description.
##########
File path: src/relay/op/tensor/transform.cc
##########
@@ -1553,6 +1553,63 @@ RELAY_REGISTER_OP("meshgrid")
.set_attr<FTVMCompute>("FTVMCompute", MeshgridCompute)
.set_attr<TOpPattern>("TOpPattern", kInjective);
+TVM_REGISTER_NODE_TYPE(SparseFillEmptyRowsAttrs);
+
+bool SparseFillEmptyRowsRel(const Array<Type>& types, int num_inputs, const
Attrs& attrs,
+ const TypeReporter& reporter) {
+ // types: [ sparse_indices, sparse_values, default_values, result]
+ ICHECK_EQ(types.size(), 4);
+ ICHECK_EQ(num_inputs, 3);
+ std::vector<Type> fields;
+ auto sparse_indices = types[0].as<TensorTypeNode>();
+ auto default_value = types[2].as<TensorTypeNode>();
+ const auto* param = attrs.as<SparseFillEmptyRowsAttrs>();
+ CHECK(param != nullptr);
+
+ Array<IndexExpr> sp_ordered_output_shape;
+ sp_ordered_output_shape.push_back(param->dense_shape[0] +
sparse_indices->shape[0]);
+ if (sparse_indices->shape.size() > 1) {
+ sp_ordered_output_shape.push_back(sparse_indices->shape[1]);
+ }
+ fields.push_back(TensorType(sp_ordered_output_shape, sparse_indices->dtype));
+ fields.push_back(TensorType(Array<PrimExpr>{param->dense_shape[0]},
tvm::DataType::Bool()));
+ fields.push_back(TensorType(Array<PrimExpr>{sp_ordered_output_shape[0]},
default_value->dtype));
+ fields.push_back(TensorType(Array<PrimExpr>{1}, tvm::DataType::Int(32)));
+ reporter->Assign(types[3], TupleType(Array<Type>(fields)));
+ return true;
+}
+
+Array<te::Tensor> SparseFillEmptyRowsCompute(const Attrs& attrs, const
Array<te::Tensor>& inputs,
+ const Type& out_type) {
+ CHECK_EQ(inputs.size(), 3);
Review comment:
Can you add an error message here.
##########
File path: python/tvm/relay/op/transform.py
##########
@@ -1320,3 +1320,84 @@ def adv_index(inputs):
Output tensor.
"""
return _make.adv_index(Tuple(inputs))
+
+
+def sparsefillemptyrows(sparse_indices, sparse_values, dense_shape,
default_value):
+ """
+ Fill first column of the empty rows with default values for a sparse array.
+
+ Parameters
+ ----------
+ sparse_indices : relay.Expr
+ A 2-D tensor[N, n_dim] of integers containing location of sparse
values, where N is the
+ number of sparse values and n_dim is the number of dimensions of the
dense_shape
+
+ sparse_values : relay.Expr
+ A 1-D tensor[N] containing the sparse values for the sparse indices.
+
+ dense_shape : relay.Expr
+ A list of integers. Shape of the dense output tensor.
+
+ default_value : relay.Expr
+ A 0-D tensor containing the default value for the remaining locations.
+ Defaults to 0.
+
+ Returns
+ -------
+ TupleWrapper with the following four outputs
+
+ new_sparse_indices : relay.Expr
+ A 2-D tensor[N + dense_shape[0], n_dim] of integers containing
location of new sparse
+ indices where N is the number of sparse values. It is filled with -1
at to_be_discarded
+ indices.
+
+ empty_row_indicator : relay.Expr
+ A 1-D Boolean tensor[dense_shape[0]] indicating whether the particular
row is empty
+
+ new_sparse_values : relay.Expr
+ A 1-D tensor[dense_shape[0]] containing the sparse values for the
sparse indices. It is
+ filled with -1 at to_be_discarded indices.
+
+ slice_element_index : relay.Expr
+ A 1-D tensor containing the amount of elements in the sparse_indices
and new_sparse_values
+ expression to be sliced in a future op discarding non-useful elements
in new_sparse_indices
+ and new_sparse_values
+
+ Examples
+ -------
+
+ .. code-block:: python
+
+ sparse_indices = [[0, 1],
+ [0, 3],
+ [2, 0],
+ [3, 1]]
+ sparse_values = [1, 2, 3, 4]
+ default_value = [10]
+ dense_shape = [5, 6]
+ new_sparse_indices, empty_row_indicator, new_sparse_values,
slice_element_index =
+ relay.sparsereshape(
+ sparse_indices,
+ sparse_values,
+ prev_shape,
+ new_shape)
+ new_sparse_indices = [[0, 1],
+ [0, 3],
+ [2, 0],
+ [3, 1],
+ [1, 0],
+ [4, 0],
+ [-1, -1],
Review comment:
I'm not sure this example is correct. `[-1, -1]` is not a valid sparse
index. Shouldn't the output be
```
new_sparse_indices=[[0, 1],
[0, 3]
[1, 0]
[2, 0]
[3, 1]
[4, 0]]
new_sparse_values = [1, 2, 10, 3, 4, 10]
```
##########
File path: src/relay/op/tensor/transform.cc
##########
@@ -1553,6 +1553,63 @@ RELAY_REGISTER_OP("meshgrid")
.set_attr<FTVMCompute>("FTVMCompute", MeshgridCompute)
.set_attr<TOpPattern>("TOpPattern", kInjective);
+TVM_REGISTER_NODE_TYPE(SparseFillEmptyRowsAttrs);
+
+bool SparseFillEmptyRowsRel(const Array<Type>& types, int num_inputs, const
Attrs& attrs,
+ const TypeReporter& reporter) {
+ // types: [ sparse_indices, sparse_values, default_values, result]
+ ICHECK_EQ(types.size(), 4);
Review comment:
Could you add an error message here. Something like
"SparseFillEmptyRowsRel expects 4 arguments, but X were provided".
##########
File path: src/relay/op/tensor/transform.cc
##########
@@ -1553,6 +1553,63 @@ RELAY_REGISTER_OP("meshgrid")
.set_attr<FTVMCompute>("FTVMCompute", MeshgridCompute)
.set_attr<TOpPattern>("TOpPattern", kInjective);
+TVM_REGISTER_NODE_TYPE(SparseFillEmptyRowsAttrs);
+
+bool SparseFillEmptyRowsRel(const Array<Type>& types, int num_inputs, const
Attrs& attrs,
+ const TypeReporter& reporter) {
+ // types: [ sparse_indices, sparse_values, default_values, result]
+ ICHECK_EQ(types.size(), 4);
+ ICHECK_EQ(num_inputs, 3);
+ std::vector<Type> fields;
+ auto sparse_indices = types[0].as<TensorTypeNode>();
+ auto default_value = types[2].as<TensorTypeNode>();
+ const auto* param = attrs.as<SparseFillEmptyRowsAttrs>();
+ CHECK(param != nullptr);
Review comment:
```suggestion
ICHECK(param != nullptr);
```
##########
File path: src/relay/op/tensor/transform.cc
##########
@@ -1553,6 +1553,63 @@ RELAY_REGISTER_OP("meshgrid")
.set_attr<FTVMCompute>("FTVMCompute", MeshgridCompute)
.set_attr<TOpPattern>("TOpPattern", kInjective);
+TVM_REGISTER_NODE_TYPE(SparseFillEmptyRowsAttrs);
+
+bool SparseFillEmptyRowsRel(const Array<Type>& types, int num_inputs, const
Attrs& attrs,
+ const TypeReporter& reporter) {
+ // types: [ sparse_indices, sparse_values, default_values, result]
+ ICHECK_EQ(types.size(), 4);
+ ICHECK_EQ(num_inputs, 3);
+ std::vector<Type> fields;
+ auto sparse_indices = types[0].as<TensorTypeNode>();
+ auto default_value = types[2].as<TensorTypeNode>();
+ const auto* param = attrs.as<SparseFillEmptyRowsAttrs>();
+ CHECK(param != nullptr);
+
+ Array<IndexExpr> sp_ordered_output_shape;
+ sp_ordered_output_shape.push_back(param->dense_shape[0] +
sparse_indices->shape[0]);
+ if (sparse_indices->shape.size() > 1) {
+ sp_ordered_output_shape.push_back(sparse_indices->shape[1]);
+ }
+ fields.push_back(TensorType(sp_ordered_output_shape, sparse_indices->dtype));
+ fields.push_back(TensorType(Array<PrimExpr>{param->dense_shape[0]},
tvm::DataType::Bool()));
+ fields.push_back(TensorType(Array<PrimExpr>{sp_ordered_output_shape[0]},
default_value->dtype));
+ fields.push_back(TensorType(Array<PrimExpr>{1}, tvm::DataType::Int(32)));
+ reporter->Assign(types[3], TupleType(Array<Type>(fields)));
+ return true;
+}
+
+Array<te::Tensor> SparseFillEmptyRowsCompute(const Attrs& attrs, const
Array<te::Tensor>& inputs,
+ const Type& out_type) {
+ CHECK_EQ(inputs.size(), 3);
+ const auto* param = attrs.as<SparseFillEmptyRowsAttrs>();
+ CHECK(param != nullptr);
Review comment:
```suggestion
ICHECK(param != nullptr);
```
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