[GitHub] [tvm] codeislife99 commented on a change in pull request #7126: Sparse fill empty rows op
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", MeshgridCompute)
.set_attr("TOpPattern", kInjective);
+TVM_REGISTER_NODE_TYPE(SparseFillEmptyRowsAttrs);
+
+bool SparseFillEmptyRowsRel(const Array& 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 fields;
+ auto sparse_indices = types[0].as();
+ auto default_value = types[2].as();
+ const auto* param = attrs.as();
+ ICHECK(param != nullptr);
+
+ Array 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{param->dense_shape[0]},
tvm::DataType::Bool()));
+ fields.push_back(TensorType(Array{sp_ordered_output_shape[0]},
default_value->dtype));
+ fields.push_back(TensorType(Array{1}, tvm::DataType::Int(32)));
+ reporter->Assign(types[3], TupleType(Array(fields)));
+ return true;
+}
+
+Array SparseFillEmptyRowsCompute(const Attrs& attrs, const
Array& inputs,
+ const Type& out_type) {
+ ICHECK_EQ(inputs.size(), 3) << "SparseFillEmptyRowsCompute expects 3
arguments but provided "
+ << inputs.size();
+ const auto* param = attrs.as();
+ 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 dense_shape) {
+ auto attrs = make_object();
+ 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()
+.add_argument("sparse_indices", "Tensor", "The first tensor")
Review comment:
Done.
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[GitHub] [tvm] codeislife99 commented on a change in pull request #7126: Sparse fill empty rows op
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. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org
[GitHub] [tvm] codeislife99 commented on a change in pull request #7126: Sparse fill empty rows op
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 This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org
[GitHub] [tvm] codeislife99 commented on a change in pull request #7126: Sparse fill empty rows op
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 meshgrid(const Array&
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 SparseFillEmptyRows(const Tensor& sparse_indices, const
Tensor& sparse_values,
+ const Tensor& default_value,
+ const Array& dense_shape,
+ const std::string name =
"T_sparse_fill_empty_rows",
+ std::string tag = kInjective) {
+ Array result;
+ Array 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{dense_shape[0]}, [&](const Array& 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
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 meshgrid(const Array&
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 SparseFillEmptyRows(const Tensor& sparse_indices, const
Tensor& sparse_values,
+ const Tensor& default_value,
+ const Array& dense_shape,
+ const std::string name =
"T_sparse_fill_empty_rows",
+ std::string tag = kInjective) {
+ Array result;
+ Array 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{dense_shape[0]}, [&](const Array& 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
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 meshgrid(const Array&
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 SparseFillEmptyRows(const Tensor& sparse_indices, const
Tensor& sparse_values,
+ const Tensor& default_value,
+ const Array& dense_shape,
+ const std::string name =
"T_sparse_fill_empty_rows",
+ std::string tag = kInjective) {
+ Array result;
+ Array 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{dense_shape[0]}, [&](const Array& 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] codeislife99 commented on a change in pull request #7126: Sparse fill empty rows op
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", MeshgridCompute)
.set_attr("TOpPattern", kInjective);
+TVM_REGISTER_NODE_TYPE(SparseFillEmptyRowsAttrs);
+
+bool SparseFillEmptyRowsRel(const Array& 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 fields;
+ auto sparse_indices = types[0].as();
+ auto default_value = types[2].as();
+ const auto* param = attrs.as();
+ CHECK(param != nullptr);
+
+ Array 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{param->dense_shape[0]},
tvm::DataType::Bool()));
+ fields.push_back(TensorType(Array{sp_ordered_output_shape[0]},
default_value->dtype));
+ fields.push_back(TensorType(Array{1}, tvm::DataType::Int(32)));
+ reporter->Assign(types[3], TupleType(Array(fields)));
+ return true;
+}
+
+Array SparseFillEmptyRowsCompute(const Attrs& attrs, const
Array& 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", MeshgridCompute)
.set_attr("TOpPattern", kInjective);
+TVM_REGISTER_NODE_TYPE(SparseFillEmptyRowsAttrs);
+
+bool SparseFillEmptyRowsRel(const Array& 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", MeshgridCompute)
.set_attr("TOpPattern", kInjective);
+TVM_REGISTER_NODE_TYPE(SparseFillEmptyRowsAttrs);
+
+bool SparseFillEmptyRowsRel(const Array& 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 fields;
+ auto sparse_indices = types[0].as();
+ auto default_value = types[2].as();
+ const auto* param = attrs.as();
+ 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] codeislife99 commented on a change in pull request #7126: Sparse fill empty rows op
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", MeshgridCompute)
.set_attr("TOpPattern", kInjective);
+TVM_REGISTER_NODE_TYPE(SparseFillEmptyRowsAttrs);
+
+bool SparseFillEmptyRowsRel(const Array& 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 fields;
+ auto sparse_indices = types[0].as();
+ auto default_value = types[2].as();
+ const auto* param = attrs.as();
+ CHECK(param != nullptr);
+
+ Array 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{param->dense_shape[0]},
tvm::DataType::Bool()));
+ fields.push_back(TensorType(Array{sp_ordered_output_shape[0]},
default_value->dtype));
+ fields.push_back(TensorType(Array{1}, tvm::DataType::Int(32)));
+ reporter->Assign(types[3], TupleType(Array(fields)));
+ return true;
+}
+
+Array SparseFillEmptyRowsCompute(const Attrs& attrs, const
Array& inputs,
+ const Type& out_type) {
+ CHECK_EQ(inputs.size(), 3);
+ const auto* param = attrs.as();
+ 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 dense_shape) {
+ auto attrs = make_object();
+ 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", MeshgridCompute)
.set_attr("TOpPattern", kInjective);
+TVM_REGISTER_NODE_TYPE(SparseFillEmptyRowsAttrs);
+
+bool SparseFillEmptyRowsRel(const Array& 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 fields;
+ auto sparse_indices = types[0].as();
+ auto default_value = types[2].as();
+ const auto* param = attrs.as();
+ CHECK(param != nullptr);
+
+ Array 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{param->dense_shape[0]},
tvm::DataType::Bool()));
+ fields.push_back(TensorType(Array{sp_ordered_output_shape[0]},
default_value->dtype));
+ fields.push_back(TensorType(Array{1}, tvm::DataType::Int(32)));
+ reporter->Assign(types[3], TupleType(Array(fields)));
+ return true;
+}
+
+Array SparseFillEmptyRowsCompute(const Attrs& attrs, const
Array& inputs,
+ const Type& out_type) {
+ CHECK_EQ(inputs.size(), 3);
+ const auto* param = attrs.as();
+ CHECK(param != nullptr);
Review comment:
Done
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[GitHub] [tvm] codeislife99 commented on a change in pull request #7126: Sparse fill empty rows op
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 This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org
[GitHub] [tvm] codeislife99 commented on a change in pull request #7126: Sparse fill empty rows op
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 ? This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org
[GitHub] [tvm] codeislife99 commented on a change in pull request #7126: Sparse fill empty rows op
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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[GitHub] [tvm] codeislife99 commented on a change in pull request #7126: Sparse fill empty rows op
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. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org
[GitHub] [tvm] codeislife99 commented on a change in pull request #7126: Sparse fill empty rows op
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 This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org
