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Posted to commits@tvm.apache.org by GitBox <gi...@apache.org> on 2020/12/17 22:55:03 UTC
[GitHub] [tvm] codeislife99 opened a new pull request #7126: Sparse fill empty rows op
codeislife99 opened a new pull request #7126:
URL: https://github.com/apache/tvm/pull/7126
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[GitHub] [tvm] tkonolige commented on a change in pull request #7126: Sparse fill empty rows op
Posted by GitBox <gi...@apache.org>.
tkonolige commented on a change in pull request #7126:
URL: https://github.com/apache/tvm/pull/7126#discussion_r546891906
##########
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:
Ok, I understand now. Could you add a little more documentation to that effect? I would also fill the values associated invalid indices with zeros instead of -1. That way even if someone uses the indices, the effect would be nothing.
Also, have you considered using dynamic shape instead? That would avoid the need for the negative indices.
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[GitHub] [tvm] codeislife99 commented on a change in pull request #7126: Sparse fill empty rows op
Posted by GitBox <gi...@apache.org>.
codeislife99 commented on a change in pull request #7126:
URL: https://github.com/apache/tvm/pull/7126#discussion_r547196976
##########
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:
Done
##########
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:
Done
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[GitHub] [tvm] tkonolige commented on pull request #7126: Sparse fill empty rows op
Posted by GitBox <gi...@apache.org>.
tkonolige commented on pull request #7126:
URL: https://github.com/apache/tvm/pull/7126#issuecomment-749747412
@comaniac Every time I select request changes on GitHub, it switches it "suggested changes". Maybe because I am not a committer?
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[GitHub] [tvm] codeislife99 commented on pull request #7126: Sparse fill empty rows op
Posted by GitBox <gi...@apache.org>.
codeislife99 commented on pull request #7126:
URL: https://github.com/apache/tvm/pull/7126#issuecomment-748921727
cc : @trevor-m @comaniac @zhiics
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[GitHub] [tvm] codeislife99 edited a comment on pull request #7126: Sparse fill empty rows op
Posted by GitBox <gi...@apache.org>.
codeislife99 edited a comment on pull request #7126:
URL: https://github.com/apache/tvm/pull/7126#issuecomment-748921727
cc : @trevor-m @comaniac @zhiics PTAL
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[GitHub] [tvm] codeislife99 commented on a change in pull request #7126: Sparse fill empty rows op
Posted by GitBox <gi...@apache.org>.
codeislife99 commented on a change in pull request #7126:
URL: https://github.com/apache/tvm/pull/7126#discussion_r547596393
##########
File path: include/tvm/topi/transform.h
##########
@@ -1386,6 +1386,96 @@ inline Array<Tensor> meshgrid(const Array<Tensor>& inputs, const std::string& in
return result;
}
+/*!
+ * \brief Fill Empty rows of a sparse tensor with default value
+ *
+ * \param sparse_indices Indices where values of the dense tensor exist
+ * \param sparse_values Values at the above indices respectively
+ * \param default_value Default value at to be used at empty rows
+ * \param dense_shape Dense shape of the sparse tensor
+ * \param name The name of the operation
+ * \param tag The tag to mark the operation
+ *
+ * \return A Tensor whose op member is the SparseFillEmptyRows operation
+ */
+inline Array<Tensor> SparseFillEmptyRows(const Tensor& sparse_indices, const Tensor& sparse_values,
+ const Tensor& default_value,
+ const Array<Integer>& dense_shape,
+ const std::string name = "T_sparse_fill_empty_rows",
+ std::string tag = kInjective) {
+ Array<Tensor> result;
+ Array<PrimExpr> sp_ordered_output_shape;
+ sp_ordered_output_shape.push_back(dense_shape[0] + sparse_indices->shape[0]);
+ if (sparse_indices->shape.size() > 1) {
+ sp_ordered_output_shape.push_back(sparse_indices->shape[1]);
+ }
+ auto empty_row_indicator =
+ compute(Array<PrimExpr>{dense_shape[0]}, [&](const Array<Var>& indices) {
+ PrimExpr ret = PrimExpr(Bool(1));
+ for (int i = 0; i < GetConstInt(sparse_indices->shape[0]); ++i) {
Review comment:
Yes there are 3 sparse ops that we are trying to target for a customer for a TF model explicitly.
These 3 are :
1. [sparse_reshape](https://www.tensorflow.org/api_docs/python/tf/sparse/reshape)
2. [sparse_segment_sum](https://www.tensorflow.org/api_docs/python/tf/sparse/segment_sum?hl=bn)
3. [sparse_fill_empty_rows](https://www.tensorflow.org/api_docs/python/tf/sparse/fill_empty_rows)
4. [sparse_segment_sum_sqrt_n](https://www.tensorflow.org/api_docs/python/tf/sparse/segment_sqrt_n?hl=bn)
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[GitHub] [tvm] codeislife99 commented on a change in pull request #7126: Sparse fill empty rows op
Posted by GitBox <gi...@apache.org>.
codeislife99 commented on a change in pull request #7126:
URL: https://github.com/apache/tvm/pull/7126#discussion_r547197106
##########
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:
Done.
##########
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:
Done
##########
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:
Done
##########
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:
Changed.
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[GitHub] [tvm] tkonolige commented on a change in pull request #7126: Sparse fill empty rows op
Posted by GitBox <gi...@apache.org>.
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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[GitHub] [tvm] codeislife99 commented on a change in pull request #7126: Sparse fill empty rows op
Posted by GitBox <gi...@apache.org>.
codeislife99 commented on a change in pull request #7126:
URL: https://github.com/apache/tvm/pull/7126#discussion_r547597810
##########
File path: include/tvm/topi/transform.h
##########
@@ -1386,6 +1386,96 @@ inline Array<Tensor> meshgrid(const Array<Tensor>& inputs, const std::string& in
return result;
}
+/*!
+ * \brief Fill Empty rows of a sparse tensor with default value
+ *
+ * \param sparse_indices Indices where values of the dense tensor exist
+ * \param sparse_values Values at the above indices respectively
+ * \param default_value Default value at to be used at empty rows
+ * \param dense_shape Dense shape of the sparse tensor
+ * \param name The name of the operation
+ * \param tag The tag to mark the operation
+ *
+ * \return A Tensor whose op member is the SparseFillEmptyRows operation
+ */
+inline Array<Tensor> SparseFillEmptyRows(const Tensor& sparse_indices, const Tensor& sparse_values,
+ const Tensor& default_value,
+ const Array<Integer>& dense_shape,
+ const std::string name = "T_sparse_fill_empty_rows",
+ std::string tag = kInjective) {
+ Array<Tensor> result;
+ Array<PrimExpr> sp_ordered_output_shape;
+ sp_ordered_output_shape.push_back(dense_shape[0] + sparse_indices->shape[0]);
+ if (sparse_indices->shape.size() > 1) {
+ sp_ordered_output_shape.push_back(sparse_indices->shape[1]);
+ }
+ auto empty_row_indicator =
+ compute(Array<PrimExpr>{dense_shape[0]}, [&](const Array<Var>& indices) {
+ PrimExpr ret = PrimExpr(Bool(1));
+ for (int i = 0; i < GetConstInt(sparse_indices->shape[0]); ++i) {
Review comment:
I am a beginner in op implementation, do you mind sharing some PR/code examples , on how I can achieve the same goal(loop etc.) and set the output shape in `OpRel`.
Then I will follow them to make this compatible for dynamically shaped input tensors.
There is also probably no need for the strided_slice to be outside the op implementation if I change it to support dynamic shapes.
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[GitHub] [tvm] codeislife99 commented on a change in pull request #7126: Sparse fill empty rows op
Posted by GitBox <gi...@apache.org>.
codeislife99 commented on a change in pull request #7126:
URL: https://github.com/apache/tvm/pull/7126#discussion_r546888428
##########
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:
Let me know if you have any better suggestions for implementation, else I will stick with this.
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[GitHub] [tvm] codeislife99 closed pull request #7126: Sparse fill empty rows op
Posted by GitBox <gi...@apache.org>.
codeislife99 closed pull request #7126:
URL: https://github.com/apache/tvm/pull/7126
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[GitHub] [tvm] comaniac commented on pull request #7126: Sparse fill empty rows op
Posted by GitBox <gi...@apache.org>.
comaniac commented on pull request #7126:
URL: https://github.com/apache/tvm/pull/7126#issuecomment-749724064
@tkonolige @mbrookhart PTAL and approve or request changes explicitly. Thanks.
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[GitHub] [tvm] codeislife99 commented on a change in pull request #7126: Sparse fill empty rows op
Posted by GitBox <gi...@apache.org>.
codeislife99 commented on a change in pull request #7126:
URL: https://github.com/apache/tvm/pull/7126#discussion_r550161068
##########
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:
Resolving this conversation since based on our offline discussion, we will start off with static shape
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[GitHub] [tvm] comaniac commented on pull request #7126: Sparse fill empty rows op
Posted by GitBox <gi...@apache.org>.
comaniac commented on pull request #7126:
URL: https://github.com/apache/tvm/pull/7126#issuecomment-749772392
> @comaniac Every time I select request changes on GitHub, it switches it "suggested changes". Maybe because I am not a committer?
Yes that's the case, but we'll do our best to make sure all comments are addressed before merging.
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[GitHub] [tvm] mbrookhart commented on a change in pull request #7126: Sparse fill empty rows op
Posted by GitBox <gi...@apache.org>.
mbrookhart commented on a change in pull request #7126:
URL: https://github.com/apache/tvm/pull/7126#discussion_r547556384
##########
File path: include/tvm/topi/transform.h
##########
@@ -1386,6 +1386,96 @@ inline Array<Tensor> meshgrid(const Array<Tensor>& inputs, const std::string& in
return result;
}
+/*!
+ * \brief Fill Empty rows of a sparse tensor with default value
+ *
+ * \param sparse_indices Indices where values of the dense tensor exist
+ * \param sparse_values Values at the above indices respectively
+ * \param default_value Default value at to be used at empty rows
+ * \param dense_shape Dense shape of the sparse tensor
+ * \param name The name of the operation
+ * \param tag The tag to mark the operation
+ *
+ * \return A Tensor whose op member is the SparseFillEmptyRows operation
+ */
+inline Array<Tensor> SparseFillEmptyRows(const Tensor& sparse_indices, const Tensor& sparse_values,
+ const Tensor& default_value,
+ const Array<Integer>& dense_shape,
+ const std::string name = "T_sparse_fill_empty_rows",
+ std::string tag = kInjective) {
+ Array<Tensor> result;
+ Array<PrimExpr> sp_ordered_output_shape;
+ sp_ordered_output_shape.push_back(dense_shape[0] + sparse_indices->shape[0]);
+ if (sparse_indices->shape.size() > 1) {
+ sp_ordered_output_shape.push_back(sparse_indices->shape[1]);
+ }
+ auto empty_row_indicator =
+ compute(Array<PrimExpr>{dense_shape[0]}, [&](const Array<Var>& indices) {
+ PrimExpr ret = PrimExpr(Bool(1));
+ for (int i = 0; i < GetConstInt(sparse_indices->shape[0]); ++i) {
Review comment:
I would like to see this done in a way that supports dynamic shapes, this will crash a compile time if we have a dynamic input.
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[GitHub] [tvm] codeislife99 commented on a change in pull request #7126: Sparse fill empty rows op
Posted by GitBox <gi...@apache.org>.
codeislife99 commented on a change in pull request #7126:
URL: https://github.com/apache/tvm/pull/7126#discussion_r547596393
##########
File path: include/tvm/topi/transform.h
##########
@@ -1386,6 +1386,96 @@ inline Array<Tensor> meshgrid(const Array<Tensor>& inputs, const std::string& in
return result;
}
+/*!
+ * \brief Fill Empty rows of a sparse tensor with default value
+ *
+ * \param sparse_indices Indices where values of the dense tensor exist
+ * \param sparse_values Values at the above indices respectively
+ * \param default_value Default value at to be used at empty rows
+ * \param dense_shape Dense shape of the sparse tensor
+ * \param name The name of the operation
+ * \param tag The tag to mark the operation
+ *
+ * \return A Tensor whose op member is the SparseFillEmptyRows operation
+ */
+inline Array<Tensor> SparseFillEmptyRows(const Tensor& sparse_indices, const Tensor& sparse_values,
+ const Tensor& default_value,
+ const Array<Integer>& dense_shape,
+ const std::string name = "T_sparse_fill_empty_rows",
+ std::string tag = kInjective) {
+ Array<Tensor> result;
+ Array<PrimExpr> sp_ordered_output_shape;
+ sp_ordered_output_shape.push_back(dense_shape[0] + sparse_indices->shape[0]);
+ if (sparse_indices->shape.size() > 1) {
+ sp_ordered_output_shape.push_back(sparse_indices->shape[1]);
+ }
+ auto empty_row_indicator =
+ compute(Array<PrimExpr>{dense_shape[0]}, [&](const Array<Var>& indices) {
+ PrimExpr ret = PrimExpr(Bool(1));
+ for (int i = 0; i < GetConstInt(sparse_indices->shape[0]); ++i) {
Review comment:
Yes there are 3 sparse ops that we are trying to target for a customer for a TF model explicitly.
These 3 are :
1. [sparse_reshape](https://www.tensorflow.org/api_docs/python/tf/sparse/reshape)
2. [sparse_segment_sum](https://www.tensorflow.org/api_docs/python/tf/sparse/segment_sum?hl=bn)
3. [sparse_fill_empty_rows](https://www.tensorflow.org/api_docs/python/tf/sparse/fill_empty_rows)
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[GitHub] [tvm] tkonolige commented on a change in pull request #7126: Sparse fill empty rows op
Posted by GitBox <gi...@apache.org>.
tkonolige commented on a change in pull request #7126:
URL: https://github.com/apache/tvm/pull/7126#discussion_r547480430
##########
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:
Given you are using it for a single op, I would follow NMS's example and use dynamic shape.
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[GitHub] [tvm] codeislife99 commented on a change in pull request #7126: Sparse fill empty rows op
Posted by GitBox <gi...@apache.org>.
codeislife99 commented on a change in pull request #7126:
URL: https://github.com/apache/tvm/pull/7126#discussion_r546895484
##########
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:
No I haven't considered that. I noticed that for input dependent ops like `non_maximum_suppression`, they used nms with fixed size + strided slice and I just borrowed that idea. Do you mind pointing me to an implementation similar to what you are talking about ?
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[GitHub] [tvm] codeislife99 commented on a change in pull request #7126: Sparse fill empty rows op
Posted by GitBox <gi...@apache.org>.
codeislife99 commented on a change in pull request #7126:
URL: https://github.com/apache/tvm/pull/7126#discussion_r550279440
##########
File path: python/tvm/relay/op/transform.py
##########
@@ -1320,3 +1320,83 @@ def adv_index(inputs):
Output tensor.
"""
return _make.adv_index(Tuple(inputs))
+
+
+def sparse_fill_empty_rows(sparse_indices, sparse_values, dense_shape, default_value):
+ """
+ Fill first column of the empty rows with default values for a sparse array.
+ It returns a TupleWrapper with four outputs
+
+ 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
+ -------
+ 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 irrelevant indices
+ which will be sliced in a future op discarding non-useful elements. This is done since the
+ real rows of new_sparse_indices depends on the input.
+
+ 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
Review comment:
Done.
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[GitHub] [tvm] tkonolige commented on a change in pull request #7126: Sparse fill empty rows op
Posted by GitBox <gi...@apache.org>.
tkonolige commented on a change in pull request #7126:
URL: https://github.com/apache/tvm/pull/7126#discussion_r547476827
##########
File path: python/tvm/relay/op/transform.py
##########
@@ -1320,3 +1320,83 @@ def adv_index(inputs):
Output tensor.
"""
return _make.adv_index(Tuple(inputs))
+
+
+def sparse_fill_empty_rows(sparse_indices, sparse_values, dense_shape, default_value):
+ """
+ Fill first column of the empty rows with default values for a sparse array.
+ It returns a TupleWrapper with four outputs
+
+ 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
+ -------
+ 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 irrelevant indices
+ which will be sliced in a future op discarding non-useful elements. This is done since the
+ real rows of new_sparse_indices depends on the input.
+
+ 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
Review comment:
Could you update the wording here? `to_be_discarded` refers to nothing.
##########
File path: src/relay/op/tensor/transform.cc
##########
@@ -1553,6 +1553,65 @@ 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) << "SparseFillEmptyRowsRel expects 4 arguments but provided "
+ << types.size();
+ std::vector<Type> fields;
+ auto sparse_indices = types[0].as<TensorTypeNode>();
+ auto default_value = types[2].as<TensorTypeNode>();
+ const auto* param = attrs.as<SparseFillEmptyRowsAttrs>();
+ ICHECK(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) {
+ ICHECK_EQ(inputs.size(), 3) << "SparseFillEmptyRowsCompute expects 3 arguments but provided "
+ << inputs.size();
+ const auto* param = attrs.as<SparseFillEmptyRowsAttrs>();
+ ICHECK(param != nullptr);
Review comment:
```suggestion
ICHECK_NOTNULL(param);
```
##########
File path: include/tvm/topi/transform.h
##########
@@ -1386,6 +1386,96 @@ inline Array<Tensor> meshgrid(const Array<Tensor>& inputs, const std::string& in
return result;
}
+/*!
+ * \brief Fill Empty rows of a sparse tensor with default value
Review comment:
```suggestion
* \brief Fill empty rows of a sparse tensor with default values
```
##########
File path: src/relay/op/tensor/transform.cc
##########
@@ -1553,6 +1553,65 @@ 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) << "SparseFillEmptyRowsRel expects 4 arguments but provided "
+ << types.size();
+ std::vector<Type> fields;
+ auto sparse_indices = types[0].as<TensorTypeNode>();
+ auto default_value = types[2].as<TensorTypeNode>();
+ const auto* param = attrs.as<SparseFillEmptyRowsAttrs>();
+ ICHECK(param != nullptr);
Review comment:
```suggestion
ICHECK_NOTNULL(param);
```
##########
File path: src/relay/op/tensor/transform.cc
##########
@@ -1553,6 +1553,65 @@ 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) << "SparseFillEmptyRowsRel expects 4 arguments but provided "
+ << types.size();
Review comment:
```suggestion
ICHECK_EQ(types.size(), 4) << "SparseFillEmptyRowsRel expects 4 arguments but " << types.size() << " were provided.";
```
##########
File path: src/relay/op/tensor/transform.cc
##########
@@ -1553,6 +1553,65 @@ 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) << "SparseFillEmptyRowsRel expects 4 arguments but provided "
+ << types.size();
+ std::vector<Type> fields;
+ auto sparse_indices = types[0].as<TensorTypeNode>();
+ auto default_value = types[2].as<TensorTypeNode>();
+ const auto* param = attrs.as<SparseFillEmptyRowsAttrs>();
+ ICHECK(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) {
+ ICHECK_EQ(inputs.size(), 3) << "SparseFillEmptyRowsCompute expects 3 arguments but provided "
+ << inputs.size();
Review comment:
```suggestion
ICHECK_EQ(inputs.size(), 3) << "SparseFillEmptyRowsCompute expects 3 arguments but " << inputs.size() << " were provided.";
```
##########
File path: src/relay/op/tensor/transform.cc
##########
@@ -1553,6 +1553,65 @@ 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) << "SparseFillEmptyRowsRel expects 4 arguments but provided "
+ << types.size();
+ std::vector<Type> fields;
+ auto sparse_indices = types[0].as<TensorTypeNode>();
+ auto default_value = types[2].as<TensorTypeNode>();
+ const auto* param = attrs.as<SparseFillEmptyRowsAttrs>();
+ ICHECK(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) {
+ ICHECK_EQ(inputs.size(), 3) << "SparseFillEmptyRowsCompute expects 3 arguments but provided "
+ << inputs.size();
+ const auto* param = attrs.as<SparseFillEmptyRowsAttrs>();
+ ICHECK(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("sparse_fill_empty_rows");
+ return Call(op, {sparse_indices, sparse_values, default_value}, Attrs(attrs), {});
+}
+
+TVM_REGISTER_GLOBAL("relay.op._make.sparse_fill_empty_rows")
+ .set_body_typed(MakeSparseFillEmptyRows);
+
+RELAY_REGISTER_OP("sparse_fill_empty_rows")
+ .describe(R"code(Return representation of a sparse tensor with empty rows filled with default
+ value.)code" TVM_ADD_FILELINE)
+ .set_num_inputs(3)
+ .set_attrs_type<SparseFillEmptyRowsAttrs>()
+ .add_argument("sparse_indices", "Tensor", "The first tensor")
Review comment:
Please put more useful descriptions on these arguments.
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[GitHub] [tvm] codeislife99 commented on a change in pull request #7126: Sparse fill empty rows op
Posted by GitBox <gi...@apache.org>.
codeislife99 commented on a change in pull request #7126:
URL: https://github.com/apache/tvm/pull/7126#discussion_r546961924
##########
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:
Yeah, in the short-term it will be used for TF. https://www.tensorflow.org/api_docs/python/tf/sparse/fill_empty_rows
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[GitHub] [tvm] codeislife99 commented on a change in pull request #7126: Sparse fill empty rows op
Posted by GitBox <gi...@apache.org>.
codeislife99 commented on a change in pull request #7126:
URL: https://github.com/apache/tvm/pull/7126#discussion_r546888139
##########
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:
So , if you look at the description (`N + dense_shape[0]` ) <-- this is the dim0 shape of the tensor. This is because we can't know beforehand how many rows are missing. This operation would be succeeded by strided_slice to remove with begin as 0 and end as `slice_element_index` to achieve the desired output
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[GitHub] [tvm] codeislife99 commented on pull request #7126: Sparse fill empty rows op
Posted by GitBox <gi...@apache.org>.
codeislife99 commented on pull request #7126:
URL: https://github.com/apache/tvm/pull/7126#issuecomment-778546996
Hoping back onto this stream of work after a while, was involved with something else.
Since this PR had a lot of issues, it has been completely rewritten in #7442 and addresses all the existing comments in this PR. This Op has also been tested E2E with the customer model. Please review #7442.
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[GitHub] [tvm] codeislife99 commented on a change in pull request #7126: Sparse fill empty rows op
Posted by GitBox <gi...@apache.org>.
codeislife99 commented on a change in pull request #7126:
URL: https://github.com/apache/tvm/pull/7126#discussion_r550291956
##########
File path: src/relay/op/tensor/transform.cc
##########
@@ -1553,6 +1553,65 @@ 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) << "SparseFillEmptyRowsRel expects 4 arguments but provided "
+ << types.size();
+ std::vector<Type> fields;
+ auto sparse_indices = types[0].as<TensorTypeNode>();
+ auto default_value = types[2].as<TensorTypeNode>();
+ const auto* param = attrs.as<SparseFillEmptyRowsAttrs>();
+ ICHECK(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) {
+ ICHECK_EQ(inputs.size(), 3) << "SparseFillEmptyRowsCompute expects 3 arguments but provided "
+ << inputs.size();
+ const auto* param = attrs.as<SparseFillEmptyRowsAttrs>();
+ ICHECK(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("sparse_fill_empty_rows");
+ return Call(op, {sparse_indices, sparse_values, default_value}, Attrs(attrs), {});
+}
+
+TVM_REGISTER_GLOBAL("relay.op._make.sparse_fill_empty_rows")
+ .set_body_typed(MakeSparseFillEmptyRows);
+
+RELAY_REGISTER_OP("sparse_fill_empty_rows")
+ .describe(R"code(Return representation of a sparse tensor with empty rows filled with default
+ value.)code" TVM_ADD_FILELINE)
+ .set_num_inputs(3)
+ .set_attrs_type<SparseFillEmptyRowsAttrs>()
+ .add_argument("sparse_indices", "Tensor", "The first tensor")
Review comment:
Done.
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[GitHub] [tvm] codeislife99 commented on pull request #7126: Sparse fill empty rows op
Posted by GitBox <gi...@apache.org>.
codeislife99 commented on pull request #7126:
URL: https://github.com/apache/tvm/pull/7126#issuecomment-752721781
@tkonolige @mbrookhart This PR is ready for re-review as well whenever you are back from vacation.
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[GitHub] [tvm] mbrookhart commented on a change in pull request #7126: Sparse fill empty rows op
Posted by GitBox <gi...@apache.org>.
mbrookhart commented on a change in pull request #7126:
URL: https://github.com/apache/tvm/pull/7126#discussion_r546916764
##########
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:
We have it set up that way because that's how MXNet does it (TOPI's original opset was mostly cloned from MXNet), and MXNet is fairly conservative about static shapes. From a performance perspective, if the expectation is that we'll always use this with a dynamic strided slice after, I'd think it would be better to just emit a dynamic shape. If we have another usecase, however, I think this is fine.
Do you have a framework level usecase in mind?
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[GitHub] [tvm] codeislife99 commented on a change in pull request #7126: Sparse fill empty rows op
Posted by GitBox <gi...@apache.org>.
codeislife99 commented on a change in pull request #7126:
URL: https://github.com/apache/tvm/pull/7126#discussion_r546893836
##########
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:
Changed.
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