tkonolige commented on a change in pull request #7442:
URL: https://github.com/apache/tvm/pull/7442#discussion_r576357085
##########
File path: python/tvm/relay/op/transform.py
##########
@@ -1322,6 +1322,20 @@ def adv_index(inputs):
return _make.adv_index(Tuple(inputs))
+def sparse_fill_empty_rows(sparse_indices, sparse_values, dense_shape,
default_value):
+ """
+ This op exactly follows the documentation here:
+ https://www.tensorflow.org/api_docs/python/tf/sparse/fill_empty_rows
Review comment:
Instead of linking to the tensorflow documentation, could you write out
what this op does. Please include parameters, return values, and examples.
##########
File path: python/tvm/relay/op/_transform.py
##########
@@ -445,6 +465,44 @@ def argwhere_shape_func(attrs, inputs, out_ndims):
_reg.register_shape_func("scatter_add", False, elemwise_shape_func)
+@script
+def _sparse_fill_empty_rows_shape_func(sparse_indices, dense_shape):
+
+ new_sparse_indices_shape = output_tensor((2,), "int64")
+ new_sparse_values_shape = output_tensor((1,), "int64")
+ empty_row_indicator_shape = output_tensor((1,), "int64")
+ num_dense_rows = int64(dense_shape[0])
+
+ if int64(sparse_indices.shape[0]) == int64(0):
Review comment:
This shape function does not support dynamic input shapes? Are you
planning on using this op after any dynamic operations?
##########
File path: src/relay/op/tensor/transform.cc
##########
@@ -1584,6 +1584,47 @@ RELAY_REGISTER_OP("repeat")
.set_attr<FTVMCompute>("FTVMCompute", RepeatCompute)
.set_attr<TOpPattern>("TOpPattern", kBroadcast);
+bool SparseFillEmptyRowsRel(const Array<Type>& types, int num_inputs, const
Attrs& attrs,
+ const TypeReporter& reporter) {
+ // types: [sparse_indices, sparse_values, dense_shape, default_value, result]
+ ICHECK_EQ(types.size(), 5);
+ std::vector<Type> fields;
+ auto sparse_indices = types[0].as<TensorTypeNode>();
+ auto ndims = sparse_indices->shape[1];
+ fields.push_back(TensorType(Array<PrimExpr>{Any(), ndims},
tvm::DataType::Int(64)));
+ fields.push_back(TensorType(Array<PrimExpr>{Any()}, tvm::DataType::Int(64)));
+ fields.push_back(TensorType(Array<PrimExpr>{Any()}, tvm::DataType::Int(64)));
+ reporter->Assign(types[types.size() - 1], TupleType(Array<Type>(fields)));
+ return true;
+}
+
+Expr MakeSparseFillEmptyRows(Expr sparse_indices, Expr sparse_values, Expr
dense_shape,
+ Expr default_value) {
+ static const Op& op = Op::Get("sparse_fill_empty_rows");
+ return Call(op, {sparse_indices, sparse_values, dense_shape, default_value},
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)
Review comment:
```suggestion
.describe(R"code(Fill empty rows of a sparse tensor with a default
value.)code" TVM_ADD_FILELINE)
```
##########
File path: src/relay/op/tensor/transform.cc
##########
@@ -1584,6 +1584,47 @@ RELAY_REGISTER_OP("repeat")
.set_attr<FTVMCompute>("FTVMCompute", RepeatCompute)
.set_attr<TOpPattern>("TOpPattern", kBroadcast);
+bool SparseFillEmptyRowsRel(const Array<Type>& types, int num_inputs, const
Attrs& attrs,
+ const TypeReporter& reporter) {
+ // types: [sparse_indices, sparse_values, dense_shape, default_value, result]
+ ICHECK_EQ(types.size(), 5);
+ std::vector<Type> fields;
+ auto sparse_indices = types[0].as<TensorTypeNode>();
+ auto ndims = sparse_indices->shape[1];
+ fields.push_back(TensorType(Array<PrimExpr>{Any(), ndims},
tvm::DataType::Int(64)));
+ fields.push_back(TensorType(Array<PrimExpr>{Any()}, tvm::DataType::Int(64)));
+ fields.push_back(TensorType(Array<PrimExpr>{Any()}, tvm::DataType::Int(64)));
+ reporter->Assign(types[types.size() - 1], TupleType(Array<Type>(fields)));
+ return true;
+}
+
+Expr MakeSparseFillEmptyRows(Expr sparse_indices, Expr sparse_values, Expr
dense_shape,
+ Expr default_value) {
+ static const Op& op = Op::Get("sparse_fill_empty_rows");
+ return Call(op, {sparse_indices, sparse_values, dense_shape, default_value},
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(4)
+ .add_argument("sparse_indices", "Tensor",
+ "A 2-D int64 tensor of shape [N, ndims], which specifies the
indices of the"
+ "elements in the sparse tensor that contain nonzero values")
+ .add_argument("sparse_values", "Tensor",
+ "A 1-D tensor[N] which supplies the values for each element
in indices")
+ .add_argument("dense_shape", "Tensor",
Review comment:
Dense shape is a bit of a misnomer. This is just the actual shape of the
sparse tensor.
##########
File path: python/tvm/topi/sparse_fill_empty_rows.py
##########
@@ -0,0 +1,117 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHnew_sparse_indices WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+# pylint: disable=no-else-return, too-many-locals, too-many-arguments,
too-many-branches
+# pylint: disable=undefined-variable, invalid-name
Review comment:
Where are you getting the undefined-variable warning? This seems like a
dangerous lint to disable.
##########
File path: python/tvm/topi/sparse_fill_empty_rows.py
##########
@@ -0,0 +1,117 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHnew_sparse_indices WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+# pylint: disable=no-else-return, too-many-locals, too-many-arguments,
too-many-branches
+# pylint: disable=undefined-variable, invalid-name
+"""SparseFillEmptyRows operator"""
+from ..te import hybrid
+
+
[email protected]
+def _sparse_fill_empty_rows(
+ sparse_indices,
+ sparse_values,
+ dense_shape,
+ default_value,
+ new_sparse_indices_shape,
+ new_sparse_values_shape,
+ empty_row_indicator_shape,
+):
+ default_value_ = int64(default_value[0])
+ new_sparse_indices = output_tensor(new_sparse_indices_shape, "int64")
+ new_sparse_values = output_tensor(new_sparse_values_shape, "int64")
+ empty_row_indicator = output_tensor(empty_row_indicator_shape, "int64")
+ idx = 0
+
+ if int64(sparse_indices.shape[0]) == int64(0):
+ for i in range(0, int64(new_sparse_indices_shape[0])):
+ new_sparse_indices[i, 0] = int64(i)
+ new_sparse_values[i] = default_value_
+ empty_row_indicator[i] = int64(1)
+ for k in range(1, int64(new_sparse_indices_shape[1])):
+ new_sparse_indices[i, k] = int64(0)
+
+ return (new_sparse_indices, new_sparse_values, empty_row_indicator)
+
+ else:
+ for i in range(0, int64(sparse_indices[0, 0])):
+ new_sparse_indices[idx, 0] = int64(i)
+ for k in range(1, int64(new_sparse_indices_shape[1])):
+ new_sparse_indices[idx, k] = int64(0)
+
+ new_sparse_values[idx] = default_value_
+ empty_row_indicator[i] = int64(1)
+ idx += 1
+
+ for i in range(0, int64(sparse_indices.shape[0])):
+ index = int64(sparse_indices[i, 0])
+ if i == 0:
+ new_sparse_indices[idx, 0] = index
Review comment:
Also, this line can be removed if you start the loop below at 0.
##########
File path: python/tvm/topi/sparse_fill_empty_rows.py
##########
@@ -0,0 +1,117 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHnew_sparse_indices WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+# pylint: disable=no-else-return, too-many-locals, too-many-arguments,
too-many-branches
+# pylint: disable=undefined-variable, invalid-name
+"""SparseFillEmptyRows operator"""
+from ..te import hybrid
+
+
[email protected]
+def _sparse_fill_empty_rows(
+ sparse_indices,
+ sparse_values,
+ dense_shape,
+ default_value,
+ new_sparse_indices_shape,
+ new_sparse_values_shape,
+ empty_row_indicator_shape,
+):
+ default_value_ = int64(default_value[0])
+ new_sparse_indices = output_tensor(new_sparse_indices_shape, "int64")
Review comment:
Shouldn't the output type match the input type?
##########
File path: src/relay/op/tensor/transform.cc
##########
@@ -1584,6 +1584,47 @@ RELAY_REGISTER_OP("repeat")
.set_attr<FTVMCompute>("FTVMCompute", RepeatCompute)
.set_attr<TOpPattern>("TOpPattern", kBroadcast);
+bool SparseFillEmptyRowsRel(const Array<Type>& types, int num_inputs, const
Attrs& attrs,
+ const TypeReporter& reporter) {
+ // types: [sparse_indices, sparse_values, dense_shape, default_value, result]
+ ICHECK_EQ(types.size(), 5);
Review comment:
Could you put an error message on this.
##########
File path: src/relay/op/tensor/transform.cc
##########
@@ -1584,6 +1584,47 @@ RELAY_REGISTER_OP("repeat")
.set_attr<FTVMCompute>("FTVMCompute", RepeatCompute)
.set_attr<TOpPattern>("TOpPattern", kBroadcast);
+bool SparseFillEmptyRowsRel(const Array<Type>& types, int num_inputs, const
Attrs& attrs,
+ const TypeReporter& reporter) {
+ // types: [sparse_indices, sparse_values, dense_shape, default_value, result]
+ ICHECK_EQ(types.size(), 5);
+ std::vector<Type> fields;
+ auto sparse_indices = types[0].as<TensorTypeNode>();
+ auto ndims = sparse_indices->shape[1];
+ fields.push_back(TensorType(Array<PrimExpr>{Any(), ndims},
tvm::DataType::Int(64)));
+ fields.push_back(TensorType(Array<PrimExpr>{Any()}, tvm::DataType::Int(64)));
+ fields.push_back(TensorType(Array<PrimExpr>{Any()}, tvm::DataType::Int(64)));
+ reporter->Assign(types[types.size() - 1], TupleType(Array<Type>(fields)));
+ return true;
+}
+
+Expr MakeSparseFillEmptyRows(Expr sparse_indices, Expr sparse_values, Expr
dense_shape,
+ Expr default_value) {
+ static const Op& op = Op::Get("sparse_fill_empty_rows");
+ return Call(op, {sparse_indices, sparse_values, dense_shape, default_value},
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(4)
+ .add_argument("sparse_indices", "Tensor",
Review comment:
Please document the format. Looking at the code, it seems as if the
format is COO?
##########
File path: python/tvm/topi/sparse_fill_empty_rows.py
##########
@@ -0,0 +1,117 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHnew_sparse_indices WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+# pylint: disable=no-else-return, too-many-locals, too-many-arguments,
too-many-branches
+# pylint: disable=undefined-variable, invalid-name
+"""SparseFillEmptyRows operator"""
+from ..te import hybrid
+
+
[email protected]
+def _sparse_fill_empty_rows(
+ sparse_indices,
+ sparse_values,
+ dense_shape,
+ default_value,
+ new_sparse_indices_shape,
+ new_sparse_values_shape,
+ empty_row_indicator_shape,
+):
+ default_value_ = int64(default_value[0])
+ new_sparse_indices = output_tensor(new_sparse_indices_shape, "int64")
+ new_sparse_values = output_tensor(new_sparse_values_shape, "int64")
+ empty_row_indicator = output_tensor(empty_row_indicator_shape, "int64")
+ idx = 0
+
+ if int64(sparse_indices.shape[0]) == int64(0):
+ for i in range(0, int64(new_sparse_indices_shape[0])):
+ new_sparse_indices[i, 0] = int64(i)
+ new_sparse_values[i] = default_value_
+ empty_row_indicator[i] = int64(1)
+ for k in range(1, int64(new_sparse_indices_shape[1])):
+ new_sparse_indices[i, k] = int64(0)
+
+ return (new_sparse_indices, new_sparse_values, empty_row_indicator)
+
+ else:
+ for i in range(0, int64(sparse_indices[0, 0])):
+ new_sparse_indices[idx, 0] = int64(i)
+ for k in range(1, int64(new_sparse_indices_shape[1])):
+ new_sparse_indices[idx, k] = int64(0)
+
+ new_sparse_values[idx] = default_value_
+ empty_row_indicator[i] = int64(1)
+ idx += 1
+
+ for i in range(0, int64(sparse_indices.shape[0])):
+ index = int64(sparse_indices[i, 0])
+ if i == 0:
+ new_sparse_indices[idx, 0] = index
+ for k in range(1, int64(new_sparse_indices_shape[1])):
+ new_sparse_indices[idx, k] = int64(sparse_indices[i, k])
+ new_sparse_values[idx] = int64(sparse_values[i])
+ empty_row_indicator[index] = int64(0)
+ idx += 1
+ else:
+ prev_index = int64(sparse_indices[i - 1, 0] + 1)
Review comment:
If you structure this loop as handling indices from
`sparse_indices[i,0]` to `sparse_indices[i+1,0]`, you can simplify the code by
removing some of these index copying blocks.
##########
File path: python/tvm/topi/sparse_fill_empty_rows.py
##########
@@ -0,0 +1,117 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHnew_sparse_indices WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+# pylint: disable=no-else-return, too-many-locals, too-many-arguments,
too-many-branches
+# pylint: disable=undefined-variable, invalid-name
+"""SparseFillEmptyRows operator"""
+from ..te import hybrid
+
+
[email protected]
+def _sparse_fill_empty_rows(
+ sparse_indices,
+ sparse_values,
+ dense_shape,
+ default_value,
+ new_sparse_indices_shape,
+ new_sparse_values_shape,
+ empty_row_indicator_shape,
+):
+ default_value_ = int64(default_value[0])
+ new_sparse_indices = output_tensor(new_sparse_indices_shape, "int64")
+ new_sparse_values = output_tensor(new_sparse_values_shape, "int64")
+ empty_row_indicator = output_tensor(empty_row_indicator_shape, "int64")
+ idx = 0
+
+ if int64(sparse_indices.shape[0]) == int64(0):
+ for i in range(0, int64(new_sparse_indices_shape[0])):
+ new_sparse_indices[i, 0] = int64(i)
+ new_sparse_values[i] = default_value_
+ empty_row_indicator[i] = int64(1)
+ for k in range(1, int64(new_sparse_indices_shape[1])):
+ new_sparse_indices[i, k] = int64(0)
+
+ return (new_sparse_indices, new_sparse_values, empty_row_indicator)
+
+ else:
+ for i in range(0, int64(sparse_indices[0, 0])):
+ new_sparse_indices[idx, 0] = int64(i)
+ for k in range(1, int64(new_sparse_indices_shape[1])):
+ new_sparse_indices[idx, k] = int64(0)
+
+ new_sparse_values[idx] = default_value_
+ empty_row_indicator[i] = int64(1)
+ idx += 1
+
+ for i in range(0, int64(sparse_indices.shape[0])):
+ index = int64(sparse_indices[i, 0])
+ if i == 0:
+ new_sparse_indices[idx, 0] = index
Review comment:
nit: you're using two variables called index (`idx` and `index`). Could
you make the names a little more specific.
##########
File path: src/relay/op/tensor/transform.cc
##########
@@ -1584,6 +1584,47 @@ RELAY_REGISTER_OP("repeat")
.set_attr<FTVMCompute>("FTVMCompute", RepeatCompute)
.set_attr<TOpPattern>("TOpPattern", kBroadcast);
+bool SparseFillEmptyRowsRel(const Array<Type>& types, int num_inputs, const
Attrs& attrs,
+ const TypeReporter& reporter) {
+ // types: [sparse_indices, sparse_values, dense_shape, default_value, result]
+ ICHECK_EQ(types.size(), 5);
+ std::vector<Type> fields;
+ auto sparse_indices = types[0].as<TensorTypeNode>();
+ auto ndims = sparse_indices->shape[1];
+ fields.push_back(TensorType(Array<PrimExpr>{Any(), ndims},
tvm::DataType::Int(64)));
+ fields.push_back(TensorType(Array<PrimExpr>{Any()}, tvm::DataType::Int(64)));
+ fields.push_back(TensorType(Array<PrimExpr>{Any()}, tvm::DataType::Int(64)));
+ reporter->Assign(types[types.size() - 1], TupleType(Array<Type>(fields)));
+ return true;
+}
+
+Expr MakeSparseFillEmptyRows(Expr sparse_indices, Expr sparse_values, Expr
dense_shape,
+ Expr default_value) {
+ static const Op& op = Op::Get("sparse_fill_empty_rows");
+ return Call(op, {sparse_indices, sparse_values, dense_shape, default_value},
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(4)
+ .add_argument("sparse_indices", "Tensor",
+ "A 2-D int64 tensor of shape [N, ndims], which specifies the
indices of the"
+ "elements in the sparse tensor that contain nonzero values")
+ .add_argument("sparse_values", "Tensor",
+ "A 1-D tensor[N] which supplies the values for each element
in indices")
+ .add_argument("dense_shape", "Tensor",
+ "A 1-D int64 tensor of shape [ndims], which specifies the
dense_shape of the"
+ "sparse tensor. Takes a list indicating the number of
elements in each dimension")
+ .add_argument("default_value", "Tensor",
Review comment:
Shouldn't this be a scalar?
##########
File path: python/tvm/topi/sparse_fill_empty_rows.py
##########
@@ -0,0 +1,117 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHnew_sparse_indices WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+# pylint: disable=no-else-return, too-many-locals, too-many-arguments,
too-many-branches
+# pylint: disable=undefined-variable, invalid-name
+"""SparseFillEmptyRows operator"""
+from ..te import hybrid
+
+
[email protected]
+def _sparse_fill_empty_rows(
+ sparse_indices,
+ sparse_values,
+ dense_shape,
+ default_value,
+ new_sparse_indices_shape,
+ new_sparse_values_shape,
+ empty_row_indicator_shape,
+):
+ default_value_ = int64(default_value[0])
+ new_sparse_indices = output_tensor(new_sparse_indices_shape, "int64")
+ new_sparse_values = output_tensor(new_sparse_values_shape, "int64")
+ empty_row_indicator = output_tensor(empty_row_indicator_shape, "int64")
+ idx = 0
+
+ if int64(sparse_indices.shape[0]) == int64(0):
+ for i in range(0, int64(new_sparse_indices_shape[0])):
+ new_sparse_indices[i, 0] = int64(i)
+ new_sparse_values[i] = default_value_
+ empty_row_indicator[i] = int64(1)
+ for k in range(1, int64(new_sparse_indices_shape[1])):
+ new_sparse_indices[i, k] = int64(0)
+
+ return (new_sparse_indices, new_sparse_values, empty_row_indicator)
+
+ else:
+ for i in range(0, int64(sparse_indices[0, 0])):
+ new_sparse_indices[idx, 0] = int64(i)
+ for k in range(1, int64(new_sparse_indices_shape[1])):
+ new_sparse_indices[idx, k] = int64(0)
+
+ new_sparse_values[idx] = default_value_
+ empty_row_indicator[i] = int64(1)
+ idx += 1
+
+ for i in range(0, int64(sparse_indices.shape[0])):
+ index = int64(sparse_indices[i, 0])
+ if i == 0:
+ new_sparse_indices[idx, 0] = index
+ for k in range(1, int64(new_sparse_indices_shape[1])):
+ new_sparse_indices[idx, k] = int64(sparse_indices[i, k])
+ new_sparse_values[idx] = int64(sparse_values[i])
+ empty_row_indicator[index] = int64(0)
+ idx += 1
+ else:
+ prev_index = int64(sparse_indices[i - 1, 0] + 1)
+ for j in range(prev_index, index):
+ new_sparse_indices[idx, 0] = int64(j)
+ for k in range(1, int64(new_sparse_indices_shape[1])):
+ new_sparse_indices[idx, k] = int64(0)
+ empty_row_indicator[prev_index] = int64(1)
+ new_sparse_values[idx] = default_value_
+ idx += 1
+
+ new_sparse_indices[idx, 0] = index
+ for k in range(1, int64(new_sparse_indices_shape[1])):
+ new_sparse_indices[idx, k] = int64(sparse_indices[i, k])
+ new_sparse_values[idx] = int64(sparse_values[i])
+ empty_row_indicator[index] = int64(0)
+ idx += 1
+
+ for i in range(
+ int64(sparse_indices[sparse_indices.shape[0] - 1, 0] + 1),
int64(dense_shape[0])
Review comment:
Why is there a `+1` in this expression? If the sparse indices have size
1 and the dense shape is size 2, then we wouldn't add any elements.
##########
File path: tests/python/frontend/tensorflow/test_forward.py
##########
@@ -1812,6 +1812,109 @@ def test_forward_sparse_dense_matmul():
)
+#######################################################################
+# SparseFillEmptyRows
+# ------------
+
+
+def _test_sparse_fill_empty_rows(indices_np, values_np, dense_shape_np,
default_value_int, use_dyn):
+ with tf.Graph().as_default():
+ if use_dyn:
+ indices = tf.placeholder(shape=(None, None),
dtype=indices_np.dtype, name="indices")
+ values = tf.placeholder(shape=(None), dtype=values_np.dtype,
name="values")
+ dense_shape = tf.placeholder(
+ shape=(None), dtype=dense_shape_np.dtype, name="dense_shape"
+ )
+ else:
+ indices = tf.placeholder(shape=indices_np.shape,
dtype=indices_np.dtype, name="indices")
+ values = tf.placeholder(shape=values_np.shape,
dtype=values_np.dtype, name="values")
+ dense_shape = tf.placeholder(
+ shape=dense_shape_np.shape, dtype=dense_shape_np.dtype,
name="dense_shape"
+ )
+
+ default_value = tf.placeholder(shape=(), dtype=values_np.dtype,
name="default_value")
+ sp_input = tf.sparse.SparseTensor(indices=indices, values=values,
dense_shape=dense_shape)
+ _ = tf.sparse.fill_empty_rows(sp_input, default_value,
name="sparse_fill_empty_rows")
+ compare_tf_with_tvm(
+ [indices_np, values_np, dense_shape_np, default_value_int],
+ [indices.name, values.name, dense_shape.name, default_value.name],
+ [
+ "sparse_fill_empty_rows/SparseFillEmptyRows:0",
+ "sparse_fill_empty_rows/SparseFillEmptyRows:1",
+ "sparse_fill_empty_rows/SparseFillEmptyRows:2",
+ ],
+ mode="vm",
+ )
+
+
[email protected](
+ "sparse_indices_np, sparse_values_np, dense_shape_np, default_value_int",
+ [
+ (
+ np.array([[1, 1], [0, 3], [0, 1], [2, 0], [3, 1]], dtype=np.int64),
Review comment:
This doesn't appear to be in the correct format. Shouldn't the first
column be in sorted order?
##########
File path: tests/python/frontend/tensorflow/test_forward.py
##########
@@ -1812,6 +1812,109 @@ def test_forward_sparse_dense_matmul():
)
+#######################################################################
+# SparseFillEmptyRows
+# ------------
+
+
+def _test_sparse_fill_empty_rows(indices_np, values_np, dense_shape_np,
default_value_int, use_dyn):
+ with tf.Graph().as_default():
+ if use_dyn:
+ indices = tf.placeholder(shape=(None, None),
dtype=indices_np.dtype, name="indices")
+ values = tf.placeholder(shape=(None), dtype=values_np.dtype,
name="values")
+ dense_shape = tf.placeholder(
+ shape=(None), dtype=dense_shape_np.dtype, name="dense_shape"
+ )
+ else:
+ indices = tf.placeholder(shape=indices_np.shape,
dtype=indices_np.dtype, name="indices")
+ values = tf.placeholder(shape=values_np.shape,
dtype=values_np.dtype, name="values")
+ dense_shape = tf.placeholder(
+ shape=dense_shape_np.shape, dtype=dense_shape_np.dtype,
name="dense_shape"
+ )
+
+ default_value = tf.placeholder(shape=(), dtype=values_np.dtype,
name="default_value")
+ sp_input = tf.sparse.SparseTensor(indices=indices, values=values,
dense_shape=dense_shape)
+ _ = tf.sparse.fill_empty_rows(sp_input, default_value,
name="sparse_fill_empty_rows")
+ compare_tf_with_tvm(
+ [indices_np, values_np, dense_shape_np, default_value_int],
+ [indices.name, values.name, dense_shape.name, default_value.name],
+ [
+ "sparse_fill_empty_rows/SparseFillEmptyRows:0",
+ "sparse_fill_empty_rows/SparseFillEmptyRows:1",
+ "sparse_fill_empty_rows/SparseFillEmptyRows:2",
+ ],
+ mode="vm",
+ )
+
+
[email protected](
+ "sparse_indices_np, sparse_values_np, dense_shape_np, default_value_int",
Review comment:
Could you add a test where the `sparse_indices_np` array is empty?
##########
File path: python/tvm/topi/sparse_fill_empty_rows.py
##########
@@ -0,0 +1,117 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHnew_sparse_indices WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+# pylint: disable=no-else-return, too-many-locals, too-many-arguments,
too-many-branches
+# pylint: disable=undefined-variable, invalid-name
+"""SparseFillEmptyRows operator"""
+from ..te import hybrid
+
+
[email protected]
+def _sparse_fill_empty_rows(
+ sparse_indices,
+ sparse_values,
+ dense_shape,
+ default_value,
+ new_sparse_indices_shape,
+ new_sparse_values_shape,
+ empty_row_indicator_shape,
+):
+ default_value_ = int64(default_value[0])
+ new_sparse_indices = output_tensor(new_sparse_indices_shape, "int64")
+ new_sparse_values = output_tensor(new_sparse_values_shape, "int64")
+ empty_row_indicator = output_tensor(empty_row_indicator_shape, "int64")
+ idx = 0
+
+ if int64(sparse_indices.shape[0]) == int64(0):
+ for i in range(0, int64(new_sparse_indices_shape[0])):
+ new_sparse_indices[i, 0] = int64(i)
+ new_sparse_values[i] = default_value_
+ empty_row_indicator[i] = int64(1)
+ for k in range(1, int64(new_sparse_indices_shape[1])):
+ new_sparse_indices[i, k] = int64(0)
+
+ return (new_sparse_indices, new_sparse_values, empty_row_indicator)
+
+ else:
+ for i in range(0, int64(sparse_indices[0, 0])):
+ new_sparse_indices[idx, 0] = int64(i)
+ for k in range(1, int64(new_sparse_indices_shape[1])):
+ new_sparse_indices[idx, k] = int64(0)
+
+ new_sparse_values[idx] = default_value_
+ empty_row_indicator[i] = int64(1)
+ idx += 1
+
+ for i in range(0, int64(sparse_indices.shape[0])):
+ index = int64(sparse_indices[i, 0])
+ if i == 0:
+ new_sparse_indices[idx, 0] = index
+ for k in range(1, int64(new_sparse_indices_shape[1])):
+ new_sparse_indices[idx, k] = int64(sparse_indices[i, k])
+ new_sparse_values[idx] = int64(sparse_values[i])
+ empty_row_indicator[index] = int64(0)
+ idx += 1
+ else:
+ prev_index = int64(sparse_indices[i - 1, 0] + 1)
Review comment:
Also, it appears that you require the indices to be in sorted order?
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