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Hkv code draft
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@Lifann
Lifann authored and oppenheimli committed Jun 16, 2023
1 parent 4020aa9 commit 929920e
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2 changes: 2 additions & 0 deletions tensorflow_recommenders_addons/dynamic_embedding/core/BUILD
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Expand Up @@ -97,3 +97,5 @@ custom_op_library(
"kernels/sparse_reshape_op.cu.cc",
],
)

# TODO: Add hkv targets.
311 changes: 311 additions & 0 deletions tensorflow_recommenders_addons/dynamic_embedding/core/hkv_hashtable_ops.cc
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@@ -0,0 +1,311 @@
/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
Licensed 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,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/

#include "tensorflow/core/framework/common_shape_fns.h"
#include "tensorflow/core/framework/op.h"
#include "tensorflow/core/framework/op_def_builder.h"
#include "tensorflow/core/framework/shape_inference.h"
#include "tensorflow_recommenders_addons/dynamic_embedding/core/utils/utils.h"

namespace tensorflow {

using shape_inference::DimensionHandle;
using shape_inference::InferenceContext;
using shape_inference::ShapeAndType;
using shape_inference::ShapeHandle;

namespace {

Status ScalarAndTwoElementVectorInputsAndScalarOutputs(InferenceContext* c) {
ShapeHandle handle;
DimensionHandle unused_handle;
TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &handle));
for (int i = 1; i < c->num_inputs(); ++i) {
TF_RETURN_IF_ERROR(c->WithRank(c->input(i), 1, &handle));
TF_RETURN_IF_ERROR(c->WithValue(c->Dim(handle, 0), 2, &unused_handle));
}
for (int i = 0; i < c->num_outputs(); ++i) {
c->set_output(i, c->Scalar());
}
return Status::OK();
}

} // namespace

Status ValidateTableResourceHandle(InferenceContext* c, ShapeHandle keys,
const string& key_dtype_attr,
const string& value_dtype_attr,
bool is_lookup,
ShapeAndType* output_shape_and_type) {
auto* handle_data = c->input_handle_shapes_and_types(0);
if (handle_data == nullptr || handle_data->size() != 2) {
output_shape_and_type->shape = c->UnknownShape();
output_shape_and_type->dtype = DT_INVALID;
} else {
const ShapeAndType& key_shape_and_type = (*handle_data)[0];
const ShapeAndType& value_shape_and_type = (*handle_data)[1];
DataType key_dtype;
TF_RETURN_IF_ERROR(c->GetAttr(key_dtype_attr, &key_dtype));
if (key_shape_and_type.dtype != key_dtype) {
return errors::InvalidArgument(
"Trying to read value with wrong dtype. "
"Expected ",
DataTypeString(key_shape_and_type.dtype), " got ",
DataTypeString(key_dtype));
}
DataType value_dtype;
TF_RETURN_IF_ERROR(c->GetAttr(value_dtype_attr, &value_dtype));
if (value_shape_and_type.dtype != value_dtype) {
return errors::InvalidArgument(
"Trying to read value with wrong dtype. "
"Expected ",
DataTypeString(value_shape_and_type.dtype), " got ",
DataTypeString(value_dtype));
}
output_shape_and_type->dtype = value_shape_and_type.dtype;

if (is_lookup) {
if (c->RankKnown(key_shape_and_type.shape) && c->RankKnown(keys)) {
int keys_rank = c->Rank(keys);
int key_suffix_rank = c->Rank(key_shape_and_type.shape);
if (keys_rank < key_suffix_rank) {
return errors::InvalidArgument(
"Expected keys to have suffix ",
c->DebugString(key_shape_and_type.shape),
" but saw shape: ", c->DebugString(keys));
}
for (int d = 0; d < key_suffix_rank; d++) {
// Ensure the suffix of keys match what's in the Table.
DimensionHandle dim = c->Dim(key_shape_and_type.shape, d);
TF_RETURN_IF_ERROR(
c->ReplaceDim(keys, keys_rank - key_suffix_rank + d, dim, &keys));
}
std::vector<DimensionHandle> keys_prefix_vec;
keys_prefix_vec.reserve(keys_rank - key_suffix_rank);
for (int d = 0; d < keys_rank - key_suffix_rank; ++d) {
keys_prefix_vec.push_back(c->Dim(keys, d));
}
ShapeHandle keys_prefix = c->MakeShape(keys_prefix_vec);
TF_RETURN_IF_ERROR(c->Concatenate(keys_prefix,
value_shape_and_type.shape,
&output_shape_and_type->shape));
} else {
output_shape_and_type->shape = c->UnknownShape();
}
} else {
TF_RETURN_IF_ERROR(c->Concatenate(keys, value_shape_and_type.shape,
&output_shape_and_type->shape));
}
}
return Status::OK();
}

Status HkvHashTableShape(InferenceContext* c, const ShapeHandle& key,
const ShapeHandle& value) {
c->set_output(0, c->Scalar());

ShapeHandle key_s;
TF_RETURN_IF_ERROR(c->WithRankAtMost(key, 1, &key_s));

DataType key_t;
TF_RETURN_IF_ERROR(c->GetAttr("key_dtype", &key_t));

DataType value_t;
TF_RETURN_IF_ERROR(c->GetAttr("value_dtype", &value_t));

c->set_output_handle_shapes_and_types(
0, std::vector<ShapeAndType>{{key_s, key_t}, {value, value_t}});

return Status::OK();
}

REGISTER_OP("TfraHkvHashTableFind")
.Input("table_handle: resource")
.Input("keys: Tin")
.Input("default_value: Tout")
.Output("values: Tout")
.Attr("Tin: type")
.Attr("Tout: type")
.SetShapeFn([](InferenceContext* c) {
ShapeHandle handle;
TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &handle));

ShapeAndType value_shape_and_type;
TF_RETURN_IF_ERROR(ValidateTableResourceHandle(
c,
/*keys=*/c->input(1),
/*key_dtype_attr=*/"Tin",
/*value_dtype_attr=*/"Tout",
/*is_lookup=*/true, &value_shape_and_type));
c->set_output(0, value_shape_and_type.shape);

return Status::OK();
});

REGISTER_OP("TfraHkvHashTableFindWithExists")
.Input("table_handle: resource")
.Input("keys: Tin")
.Input("default_value: Tout")
.Output("values: Tout")
.Output("exists: bool")
.Attr("Tin: type")
.Attr("Tout: type")
.SetShapeFn([](InferenceContext* c) {
ShapeHandle handle;
TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &handle));

ShapeHandle keys = c->UnknownShapeOfRank(1);
ShapeAndType value_shape_and_type;
TF_RETURN_IF_ERROR(ValidateTableResourceHandle(
c,
/*keys=*/c->input(1),
/*key_dtype_attr=*/"Tin",
/*value_dtype_attr=*/"Tout",
/*is_lookup=*/true, &value_shape_and_type));
c->set_output(0, value_shape_and_type.shape);
c->set_output(1, keys);

return Status::OK();
});

REGISTER_OP("TfraHkvHashTableInsert")
.Input("table_handle: resource")
.Input("keys: Tin")
.Input("values: Tout")
.Attr("Tin: type")
.Attr("Tout: type")
.SetShapeFn([](InferenceContext* c) {
ShapeHandle handle;
TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &handle));

// TODO: Validate keys and values shape.
return Status::OK();
});

REGISTER_OP("TfraHkvHashTableAccum")
.Input("table_handle: resource")
.Input("keys: key_dtype")
.Input("values_or_deltas: value_dtype")
.Input("exists: bool")
.Attr("key_dtype: type")
.Attr("value_dtype: type")
.SetShapeFn([](InferenceContext* c) {
ShapeHandle handle;
TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &handle));

// TODO: Validate keys and values shape.
return Status::OK();
});

REGISTER_OP("TfraHkvHashTableRemove")
.Input("table_handle: resource")
.Input("keys: Tin")
.Attr("Tin: type")
.SetShapeFn([](InferenceContext* c) {
ShapeHandle handle;
TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &handle));
TF_RETURN_IF_ERROR(c->WithRankAtLeast(c->input(1), 1, &handle));

// TODO(turboale): Validate keys shape.
return Status::OK();
});

REGISTER_OP("TfraHkvHashTableClear")
.Input("table_handle: resource")
.Attr("key_dtype: type")
.Attr("value_dtype: type");

REGISTER_OP("TfraHkvHashTableSize")
.Input("table_handle: resource")
.Output("size: int64")
.SetShapeFn(ScalarAndTwoElementVectorInputsAndScalarOutputs);

REGISTER_OP("TfraHkvHashTableExport")
.Input("table_handle: resource")
.Output("keys: Tkeys")
.Output("values: Tvalues")
.Attr("Tkeys: type")
.Attr("Tvalues: type")
.SetShapeFn([](InferenceContext* c) {
ShapeHandle handle;
TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &handle));
ShapeHandle keys = c->UnknownShapeOfRank(1);
ShapeAndType value_shape_and_type;
TF_RETURN_IF_ERROR(ValidateTableResourceHandle(
c,
/*keys=*/keys,
/*key_dtype_attr=*/"Tkeys",
/*value_dtype_attr=*/"Tvalues",
/*is_lookup=*/false, &value_shape_and_type));
c->set_output(0, keys);
c->set_output(1, value_shape_and_type.shape);
return Status::OK();
});

REGISTER_OP("TfraHkvHashTableSaveToFileSystem")
.Input("table_handle: resource")
.Input("dirpath: string")
.Input("file_name: string")
.Attr("key_dtype: type")
.Attr("value_dtype: type")
.Attr("dirpath_env: string")
.Attr("append_to_file: bool")
.Attr("buffer_size: int >= 1");

REGISTER_OP("TfraHkvHashTableImport")
.Input("table_handle: resource")
.Input("keys: Tin")
.Input("values: Tout")
.Attr("Tin: type")
.Attr("Tout: type")
.SetShapeFn([](InferenceContext* c) {
ShapeHandle handle;
TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &handle));

ShapeHandle keys;
TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 1, &keys));
TF_RETURN_IF_ERROR(c->Merge(keys, c->input(2), &keys));
return Status::OK();
});

REGISTER_OP("TfraHkvHashTableLoadFromFileSystem")
.Input("table_handle: resource")
.Input("dirpath: string")
.Input("file_name: string")
.Attr("key_dtype: type")
.Attr("value_dtype: type")
.Attr("dirpath_env: string")
.Attr("load_entire_dir: bool")
.Attr("buffer_size: int >= 1");

REGISTER_OP("TfraHkvHashTableOfTensors")
.Output("table_handle: resource")
.Attr("container: string = ''")
.Attr("shared_name: string = ''")
.Attr("use_node_name_sharing: bool = false")
.Attr("key_dtype: type")
.Attr("value_dtype: type")
.Attr("value_shape: shape = {}")
.Attr("init_capacity: int = 0")
.Attr("max_capacity: int = 0")
.SetIsStateful()
.SetShapeFn([](InferenceContext* c) {
PartialTensorShape value_p;
TF_RETURN_IF_ERROR(c->GetAttr("value_shape", &value_p));
ShapeHandle value_s;
TF_RETURN_IF_ERROR(c->MakeShapeFromPartialTensorShape(value_p, &value_s));
return HkvHashTableShape(c, /*key=*/c->Scalar(), /*value=*/value_s);
});
} // namespace tensorflow
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