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Merged
merged 1 commit into from
Apr 24, 2019
Merged

Allow ArrowDataset to create an initializable iterator for input data #205

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36 changes: 23 additions & 13 deletions tensorflow_io/arrow/kernels/arrow_dataset_ops.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -19,6 +19,7 @@ limitations under the License.
#include "arrow/util/io-util.h"
#include "tensorflow_io/arrow/kernels/arrow_stream_client.h"
#include "tensorflow/core/framework/dataset.h"
#include "tensorflow/core/graph/graph.h"

#define CHECK_ARROW(arrow_status) \
do { \
Expand Down Expand Up @@ -362,8 +363,6 @@ class ArrowOpKernelBase : public DatasetOpKernel {
// memory in a Python process, or a Pandas DataFrame.
class ArrowDatasetOp : public ArrowOpKernelBase {
public:
//using DatasetOpKernel::DatasetOpKernel;

explicit ArrowDatasetOp(OpKernelConstruction* ctx) : ArrowOpKernelBase(ctx) {}

virtual void MakeArrowDataset(
Expand All @@ -374,24 +373,29 @@ class ArrowDatasetOp : public ArrowOpKernelBase {
const Tensor* batches_tensor;
OP_REQUIRES_OK(ctx, ctx->input("serialized_batches", &batches_tensor));
OP_REQUIRES(
ctx, batches_tensor->dims() <= 0,
errors::InvalidArgument("`serialized_batches` must be a scalar."));
string batches = batches_tensor->flat<string>()(0);

*output = new Dataset(ctx, batches, columns, output_types_, output_shapes_);
ctx, TensorShapeUtils::IsScalar(batches_tensor->shape()),
errors::InvalidArgument("serialized_batches must be a scalar"));
*output = new Dataset(
ctx,
*batches_tensor,
columns,
output_types_,
output_shapes_);
}

private:
class Dataset : public ArrowDatasetBase {
public:
// Construct a Dataset that consumed Arrow batches from serialized bytes
// in a string. Record batches should be serialized in Arrow File format.
Dataset(OpKernelContext* ctx, const string& serialized_batches,
Dataset(OpKernelContext* ctx,
const Tensor batches_tensor,
const std::vector<int32>& columns,
const DataTypeVector& output_types,
const std::vector<PartialTensorShape>& output_shapes)
: ArrowDatasetBase(ctx, columns, output_types, output_shapes),
batches_(serialized_batches) {}
batches_(std::move(batches_tensor)) {
}

string DebugString() const override { return "ArrowDatasetOp::Dataset"; }

Expand All @@ -400,7 +404,13 @@ class ArrowDatasetOp : public ArrowOpKernelBase {
DatasetGraphDefBuilder* b,
Node** output) const override {
Node* batches = nullptr;
TF_RETURN_IF_ERROR(b->AddScalar(batches_, &batches));
if (ctx->optimization_only()) {
TF_RETURN_IF_ERROR(b->AddPlaceholder(batches_, &batches));
DCHECK_NE(ctx->input_list(), nullptr);
ctx->input_list()->emplace_back(batches->name(), batches_);
} else {
TF_RETURN_IF_ERROR(b->AddTensor(batches_, &batches));
}
Node* columns = nullptr;
TF_RETURN_IF_ERROR(b->AddVector(columns_, &columns));
TF_RETURN_IF_ERROR(b->AddDataset(this, {batches, columns}, output));
Expand All @@ -422,8 +432,8 @@ class ArrowDatasetOp : public ArrowOpKernelBase {
private:
Status SetupStreamsLocked(Env* env)
EXCLUSIVE_LOCKS_REQUIRED(mu_) override {
std::shared_ptr<arrow::Buffer> buffer;
CHECK_ARROW(arrow::Buffer::FromString(dataset()->batches_, &buffer));
const string& batches = dataset()->batches_.scalar<string>()();
auto buffer = std::make_shared<arrow::Buffer>(batches);
auto buffer_reader = std::make_shared<arrow::io::BufferReader>(buffer);
CHECK_ARROW(
arrow::ipc::RecordBatchFileReader::Open(buffer_reader, &reader_));
Expand Down Expand Up @@ -458,7 +468,7 @@ class ArrowDatasetOp : public ArrowOpKernelBase {
int num_batches_ GUARDED_BY(mu_) = 0;
};

const string batches_;
const Tensor batches_;
};
};

Expand Down
46 changes: 33 additions & 13 deletions tensorflow_io/arrow/python/ops/arrow_dataset_ops.py
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Original file line number Diff line number Diff line change
Expand Up @@ -120,25 +120,52 @@ class ArrowDataset(ArrowBaseDataset):
"""

def __init__(self,
record_batches,
serialized_batches,
columns,
output_types,
output_shapes=None):
"""Create an ArrowDataset directly from Arrow record batches.
"""Create an ArrowDataset from a Tensor of serialized batches.
This constructor requires pyarrow to be installed.

Args:
record_batches: An Arrow record batch or sequence of record batches
serialized_batches: A string Tensor as a serialized buffer containing
Arrow record batches as Arrow file format
columns: A list of column indices to be used in the Dataset
output_types: Tensor dtypes of the output tensors
output_shapes: TensorShapes of the output tensors or None to
infer partial
"""
self._serialized_batches = serialized_batches
self._columns = columns
self._output_types = output_types
self._output_shapes = output_shapes or \
nest.map_structure(
lambda _: tensorflow.TensorShape(None), self._output_types)
super(ArrowDataset, self).__init__(columns, output_types, output_shapes)

def _as_variant_tensor(self):
return arrow_ops.arrow_dataset(
self._serialized_batches,
self._columns,
nest.flatten(self.output_types),
nest.flatten(self.output_shapes))

@classmethod
def from_record_batches(cls,
record_batches,
columns,
output_types,
output_shapes=None):
"""Create an ArrowDataset directly from Arrow record batches.
This constructor requires pyarrow to be installed.

Args:
record_batches: An Arrow record batch or sequence of record batches
columns: A list of column indices to be used in the Dataset
output_types: Tensor dtypes of the output tensors
output_shapes: TensorShapes of the output tensors or None to
infer partial
"""
import pyarrow as pa
if isinstance(record_batches, pa.RecordBatch):
record_batches = [record_batches]
Expand All @@ -148,18 +175,11 @@ def __init__(self,
for batch in record_batches:
writer.write_batch(batch)
writer.close()
self._serialized_batches = tensorflow.convert_to_tensor(
serialized_batches = tensorflow.convert_to_tensor(
buf.getvalue(),
dtype=dtypes.string,
name="serialized_batches")
super(ArrowDataset, self).__init__(columns, output_types, output_shapes)

def _as_variant_tensor(self):
return arrow_ops.arrow_dataset(
self._serialized_batches,
self._columns,
nest.flatten(self.output_types),
nest.flatten(self.output_shapes))
return cls(serialized_batches, columns, output_types, output_shapes)

@classmethod
def from_pandas(cls, df, columns=None, preserve_index=True):
Expand All @@ -179,7 +199,7 @@ def from_pandas(cls, df, columns=None, preserve_index=True):
batch = pa.RecordBatch.from_pandas(df, preserve_index=preserve_index)
columns = tuple(range(batch.num_columns))
output_types, output_shapes = arrow_schema_to_tensor_types(batch.schema)
return cls(batch, columns, output_types, output_shapes)
return cls.from_record_batches(batch, columns, output_types, output_shapes)


class ArrowFeatherDataset(ArrowBaseDataset):
Expand Down
44 changes: 39 additions & 5 deletions tests/test_arrow.py
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Original file line number Diff line number Diff line change
Expand Up @@ -19,6 +19,7 @@
from __future__ import print_function

from collections import namedtuple
import io
import os
import sys
import socket
Expand Down Expand Up @@ -175,7 +176,7 @@ def test_arrow_dataset(self):
batch = self.make_record_batch(truth_data)

# test all columns selected
dataset = arrow_io.ArrowDataset(
dataset = arrow_io.ArrowDataset.from_record_batches(
batch,
list(range(len(truth_data.output_types))),
truth_data.output_types,
Expand All @@ -184,7 +185,7 @@ def test_arrow_dataset(self):

# test column selection
columns = (1, 3, len(truth_data.output_types) - 1)
dataset = arrow_io.ArrowDataset(
dataset = arrow_io.ArrowDataset.from_record_batches(
batch,
columns,
tuple([truth_data.output_types[c] for c in columns]),
Expand Down Expand Up @@ -326,7 +327,7 @@ def test_bool_array_type(self):

batch = self.make_record_batch(truth_data)

dataset = arrow_io.ArrowDataset(
dataset = arrow_io.ArrowDataset.from_record_batches(
batch,
(0,),
truth_data.output_types,
Expand All @@ -339,7 +340,7 @@ def test_incorrect_column_type(self):
self.scalar_shapes)
batch = self.make_record_batch(truth_data)

dataset = arrow_io.ArrowDataset(
dataset = arrow_io.ArrowDataset.from_record_batches(
batch,
list(range(len(truth_data.output_types))),
tuple([dtypes.int32 for _ in truth_data.output_types]),
Expand All @@ -357,7 +358,7 @@ def test_map_and_batch(self):
(dtypes.int32,),
(tensorflow.TensorShape([]),))
batch = self.make_record_batch(truth_data)
dataset = arrow_io.ArrowDataset(
dataset = arrow_io.ArrowDataset.from_record_batches(
batch,
list(range(len(truth_data.output_types))),
truth_data.output_types,
Expand All @@ -379,6 +380,39 @@ def test_map_and_batch(self):
except tensorflow.errors.OutOfRangeError:
break

def test_feed_batches(self):
"""
Test that an ArrowDataset can initialize an iterator to feed a placeholder
"""
truth_data = TruthData(
[list(range(10)), [x * 1.1 for x in range(10)]],
(dtypes.int32, dtypes.float64),
(tensorflow.TensorShape([]), tensorflow.TensorShape([])))
batch = self.make_record_batch(truth_data)

buf = io.BytesIO()
writer = pa.RecordBatchFileWriter(buf, batch.schema)
writer.write_batch(batch)
writer.close()

buf_placeholder = tensorflow.compat.v1.placeholder(
tensorflow.dtypes.string, tensorflow.TensorShape([]))

dataset = arrow_io.ArrowDataset(
buf_placeholder,
list(range(len(truth_data.output_types))),
truth_data.output_types,
truth_data.output_shapes)
it = dataset.make_initializable_iterator()
next_element = it.get_next()

with self.test_session() as sess:
sess.run(it.initializer, feed_dict={buf_placeholder: buf.getvalue()})
for row in range(len(truth_data.data)):
value = sess.run(next_element)
self.assertEqual(value[0], truth_data.data[0][row])
self.assertAlmostEqual(value[1], truth_data.data[1][row], 4)


if __name__ == "__main__":
test.main()