Implement fast ParseExample.

Change: 130775324

1 files changed, 761 insertions, 0 deletions

diff --git a/tensorflow/core/util/example_proto_fast_parsing.cc b/tensorflow/core/util/example_proto_fast_parsing.cc
new file mode 100644
index 0000000000..fee6b8885f
--- /dev/null
+++ b/tensorflow/core/util/example_proto_fast_parsing.cc
@@ -0,0 +1,761 @@
+/* Copyright 2016 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/util/example_proto_fast_parsing.h"
+
+#include <vector>
+
+#include "tensorflow/core/example/example.pb.h"
+#include "tensorflow/core/example/feature.pb_text.h"
+#include "tensorflow/core/framework/numeric_op.h"
+#include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/framework/register_types.h"
+#include "tensorflow/core/lib/core/blocking_counter.h"
+#include "tensorflow/core/lib/core/casts.h"
+#include "tensorflow/core/lib/core/errors.h"
+#include "tensorflow/core/lib/core/threadpool.h"
+#include "tensorflow/core/platform/logging.h"
+#include "tensorflow/core/platform/protobuf.h"
+#include "tensorflow/core/util/presized_cuckoo_map.h"
+#include "tensorflow/core/util/sparse/sparse_tensor.h"
+
+namespace tensorflow {
+namespace example {
+
+namespace {
+template <typename A>
+auto EnableAliasing(A* a) -> decltype(a->EnableAliasing(true), void()) {
+  a->EnableAliasing(true);
+}
+
+template <typename A>
+void EnableAliasing(A&& a) {}
+
+uint8 PeekTag(protobuf::io::CodedInputStream* stream) {
+  DCHECK(stream != nullptr);
+  const void* ptr;
+  int size;
+  if (!stream->GetDirectBufferPointer(&ptr, &size)) return 0;
+  return *static_cast<const uint8*>(ptr);
+}
+
+constexpr uint8 kVarintTag(uint tag) { return (tag << 3) | 0; }
+constexpr uint8 kDelimitedTag(uint tag) { return (tag << 3) | 2; }
+constexpr uint8 kFixed32Tag(uint tag) { return (tag << 3) | 5; }
+
+namespace parsed {
+
+// ParseDataType has to be called first, then appropriate ParseZzzzList.
+class Feature {
+ public:
+  Feature() {}
+  Feature(StringPiece serialized) : serialized_(serialized) {}
+
+  Status ParseDataType(DataType* dtype) {
+    DCHECK(dtype != nullptr);
+    if (serialized_.empty()) {
+      *dtype = DT_INVALID;
+      return Status::OK();
+    }
+    uint8 oneof_tag = static_cast<uint8>(*serialized_.data());
+    serialized_.remove_prefix(1);
+    switch (oneof_tag) {
+      case kDelimitedTag(1):
+        *dtype = DT_STRING;
+        break;
+      case kDelimitedTag(2):
+        *dtype = DT_FLOAT;
+        break;
+      case kDelimitedTag(3):
+        *dtype = DT_INT64;
+        break;
+      default:
+        return errors::InvalidArgument("Unsuported datatype.");
+    }
+    return Status::OK();
+  }
+
+  bool ParseBytesList(std::vector<string>* bytes_list) {
+    DCHECK(bytes_list != nullptr);
+    protobuf::io::CodedInputStream stream(
+        reinterpret_cast<const uint8*>(serialized_.data()), serialized_.size());
+
+    EnableAliasing(&stream);
+
+    uint32 length;
+    if (!stream.ReadVarint32(&length)) return false;
+    auto limit = stream.PushLimit(length);
+
+    while (!stream.ExpectAtEnd()) {
+      if (!stream.ExpectTag(kDelimitedTag(1))) return false;
+      // parse string
+      uint32 bytes_length;
+      if (!stream.ReadVarint32(&bytes_length)) return false;
+      string bytes;
+      if (!stream.ReadString(&bytes, bytes_length)) return false;
+      bytes_list->push_back(std::move(bytes));
+    }
+    stream.PopLimit(limit);
+    return true;
+  }
+
+  bool ParseFloatList(std::vector<float>* float_list) {
+    DCHECK(float_list != nullptr);
+    protobuf::io::CodedInputStream stream(
+        reinterpret_cast<const uint8*>(serialized_.data()), serialized_.size());
+    EnableAliasing(&stream);
+    uint32 length;
+    if (!stream.ReadVarint32(&length)) return false;
+    auto limit = stream.PushLimit(length);
+
+    if (!stream.ExpectAtEnd()) {
+      uint8 peek_tag = PeekTag(&stream);
+      if (peek_tag != kDelimitedTag(1) && peek_tag != kFixed32Tag(1)) {
+        return false;
+      }
+
+      if (peek_tag == kDelimitedTag(1)) {                       // packed
+        if (!stream.ExpectTag(kDelimitedTag(1))) return false;  // packed tag
+        uint32 packed_length;
+        if (!stream.ReadVarint32(&packed_length)) return false;
+        auto packed_limit = stream.PushLimit(packed_length);
+
+        while (!stream.ExpectAtEnd()) {
+          uint32 buffer32;
+          if (!stream.ReadLittleEndian32(&buffer32)) return false;
+          float_list->push_back(bit_cast<float>(buffer32));
+        }
+
+        stream.PopLimit(packed_limit);
+      } else {  // non-packed
+        while (!stream.ExpectAtEnd()) {
+          if (!stream.ExpectTag(kFixed32Tag(1))) return false;
+          uint32 buffer32;
+          if (!stream.ReadLittleEndian32(&buffer32)) return false;
+          float_list->push_back(bit_cast<float>(buffer32));
+        }
+      }
+    }
+
+    stream.PopLimit(limit);
+    return true;
+  }
+
+  bool ParseInt64List(std::vector<int64>* int64_list) {
+    DCHECK(int64_list != nullptr);
+    protobuf::io::CodedInputStream stream(
+        reinterpret_cast<const uint8*>(serialized_.data()), serialized_.size());
+    EnableAliasing(&stream);
+    uint32 length;
+    if (!stream.ReadVarint32(&length)) return false;
+    auto limit = stream.PushLimit(length);
+
+    if (!stream.ExpectAtEnd()) {
+      uint8 peek_tag = PeekTag(&stream);
+      if (peek_tag != kDelimitedTag(1) && peek_tag != kVarintTag(1)) {
+        return false;
+      }
+      if (peek_tag == kDelimitedTag(1)) {                       // packed
+        if (!stream.ExpectTag(kDelimitedTag(1))) return false;  // packed tag
+        uint32 packed_length;
+        if (!stream.ReadVarint32(&packed_length)) return false;
+        auto packed_limit = stream.PushLimit(packed_length);
+
+        while (!stream.ExpectAtEnd()) {
+          uint64 n;  // There is no API for int64
+          if (!stream.ReadVarint64(&n)) return false;
+          int64_list->push_back(n);
+        }
+
+        stream.PopLimit(packed_limit);
+      } else {  // non-packed
+        while (!stream.ExpectAtEnd()) {
+          if (!stream.ExpectTag(kVarintTag(1))) return false;
+          uint64 n;  // There is no API for int64
+          if (!stream.ReadVarint64(&n)) return false;
+          int64_list->push_back(n);
+        }
+      }
+    }
+    stream.PopLimit(limit);
+    return true;
+  }
+
+  StringPiece GetSerialized() const { return serialized_; }
+
+ private:
+  // TODO(lew): Pair of uint8* would be more natural.
+  StringPiece serialized_;
+};
+
+using FeatureMapEntry = std::pair<StringPiece, Feature>;
+using Example = std::vector<FeatureMapEntry>;
+
+}  // namespace parsed
+
+bool ParseString(protobuf::io::CodedInputStream* stream, StringPiece* result) {
+  DCHECK(stream != nullptr);
+  DCHECK(result != nullptr);
+  uint32 length;
+  if (!stream->ReadVarint32(&length)) return false;
+  if (length == 0) {
+    *result = StringPiece(nullptr, 0);
+    return true;
+  }
+  const void* stream_alias;
+  int stream_size;
+  if (!stream->GetDirectBufferPointer(&stream_alias, &stream_size)) {
+    return false;
+  }
+  if (static_cast<uint32>(stream_size) < length) return false;
+  *result = StringPiece(static_cast<const char*>(stream_alias), length);
+  stream->Skip(length);
+  return true;
+}
+
+bool ParseFeatureMapEntry(protobuf::io::CodedInputStream* stream,
+                          parsed::FeatureMapEntry* feature_map_entry) {
+  DCHECK(stream != nullptr);
+  DCHECK(feature_map_entry != nullptr);
+  uint32 length;
+  if (!stream->ReadVarint32(&length)) return false;
+  auto limit = stream->PushLimit(length);
+  if (!stream->ExpectTag(kDelimitedTag(1))) return false;
+  if (!ParseString(stream, &feature_map_entry->first)) return false;
+  if (!stream->ExpectTag(kDelimitedTag(2))) return false;
+  StringPiece feature_string_piece;
+  if (!ParseString(stream, &feature_string_piece)) return false;
+  feature_map_entry->second = parsed::Feature(feature_string_piece);
+  if (!stream->ExpectAtEnd()) return false;
+  stream->PopLimit(limit);
+  return true;
+}
+
+bool ParseFeatures(protobuf::io::CodedInputStream* stream,
+                   parsed::Example* example) {
+  DCHECK(stream != nullptr);
+  DCHECK(example != nullptr);
+  uint32 length;
+  if (!stream->ReadVarint32(&length)) return false;
+  auto limit = stream->PushLimit(length);
+  while (!stream->ExpectAtEnd()) {
+    parsed::FeatureMapEntry feature_map_entry;
+    if (!stream->ExpectTag(kDelimitedTag(1))) return false;
+    if (!ParseFeatureMapEntry(stream, &feature_map_entry)) return false;
+    example->push_back(std::move(feature_map_entry));
+  }
+  stream->PopLimit(limit);
+  return true;
+}
+
+bool ParseExample(protobuf::io::CodedInputStream* stream,
+                  parsed::Example* example) {
+  DCHECK(stream != nullptr);
+  DCHECK(example != nullptr);
+  if (stream->ExpectTag(kDelimitedTag(1))) {
+    if (!ParseFeatures(stream, example)) return false;
+  }
+  if (!stream->ExpectAtEnd()) return false;
+  return true;
+}
+
+bool ParseExample(StringPiece serialized, parsed::Example* example) {
+  DCHECK(example != nullptr);
+  protobuf::io::CodedInputStream stream(
+      reinterpret_cast<const uint8*>(serialized.data()), serialized.size());
+  EnableAliasing(&stream);
+  return ParseExample(&stream, example);
+}
+
+}  // namespace
+
+bool TestFastParse(const string& serialized, Example* example) {
+  DCHECK(example != nullptr);
+  parsed::Example parsed_example;
+  if (!ParseExample(serialized, &parsed_example)) return false;
+  auto& features = *example->mutable_features();
+  for (parsed::FeatureMapEntry& entry : parsed_example) {
+    auto& value = (*features.mutable_feature())[entry.first.ToString()];
+    DataType dtype;
+    if (!entry.second.ParseDataType(&dtype).ok()) return false;
+    switch (dtype) {
+      case DT_INVALID:
+        break;
+      case DT_STRING: {
+        std::vector<string> list;
+        if (!entry.second.ParseBytesList(&list)) return false;
+        auto* result_list = value.mutable_bytes_list();
+        for (auto& bytes : list) {
+          result_list->add_value(std::move(bytes));
+        }
+        break;
+      }
+      case DT_FLOAT: {
+        std::vector<float> list;
+        if (!entry.second.ParseFloatList(&list)) return false;
+        auto* result_list = value.mutable_float_list();
+        for (float f : list) {
+          result_list->add_value(f);
+        }
+        break;
+      }
+      case DT_INT64: {
+        std::vector<int64> list;
+        if (!entry.second.ParseInt64List(&list)) return false;
+        auto* result_list = value.mutable_int64_list();
+        for (int64 i : list) {
+          result_list->add_value(i);
+        }
+        break;
+      }
+      default:
+        CHECK(false) << "Should not happen.";
+    }
+  }
+  return true;
+}
+
+// -----------------------------------------------------------------------------
+
+namespace {
+
+using Config = FastParseExampleConfig;
+
+void ParallelFor(const std::function<void(size_t)>& f, size_t n,
+                 thread::ThreadPool* thread_pool) {
+  DCHECK(thread_pool != nullptr);
+  if (n == 0) return;
+  BlockingCounter counter(n - 1);
+  for (size_t i = 1; i < n; ++i) {
+    thread_pool->Schedule([i, &f, &counter] {
+      f(i);
+      counter.DecrementCount();
+    });
+  }
+  f(0);
+  counter.Wait();
+}
+
+enum class Type { Sparse, Dense };
+
+struct SparseBuffer {
+  // TODO(lew): Use InlinedVector.
+  // Features are in one of the 3 vectors below depending on config's dtype.
+  // Other 2 vectors remain empty.
+  std::vector<string> bytes_list;
+  std::vector<float> float_list;
+  std::vector<int64> int64_list;
+
+  // Features of example i are elements with indices
+  // from example_end_indices[i-1] to example_end_indices[i]-1 on the
+  // appropriate xxxxx_list
+  std::vector<size_t> example_end_indices;
+};
+
+struct SeededHasher {
+  uint64 operator()(StringPiece s) const {
+    return Hash64(s.data(), s.size(), seed);
+  }
+  uint64 seed{0xDECAFCAFFE};
+};
+
+Status FastParseSerializedExample(
+    const string& serialized_example, const string& example_name,
+    const size_t example_index, const Config& config,
+    const PresizedCuckooMap<std::pair<size_t, Type>>& config_index,
+    SeededHasher hasher, std::vector<Tensor>* output_dense,
+    std::vector<SparseBuffer>* output_sparse) {
+  DCHECK(output_dense != nullptr);
+  DCHECK(output_sparse != nullptr);
+  parsed::Example parsed_example;
+  if (!ParseExample(serialized_example, &parsed_example)) {
+    return errors::InvalidArgument("Could not parse example input, value: '",
+                                   serialized_example, "'");
+  }
+  constexpr size_t kMax = std::numeric_limits<size_t>::max();
+  std::vector<size_t> sparse_features_found(config.sparse.size(), kMax);
+  std::vector<size_t> dense_features_found(config.dense.size(), kMax);
+
+  // Handle features present in the example.
+  for (parsed::FeatureMapEntry& name_and_feature : parsed_example) {
+    parsed::Feature& feature = name_and_feature.second;
+    std::pair<size_t, Type> d_and_type;
+    uint64 h = hasher(name_and_feature.first);
+    if (!config_index.Find(h, &d_and_type)) continue;
+    size_t d = d_and_type.first;
+
+    auto parse_error = [&](StringPiece feature_name) {
+      return errors::InvalidArgument("Name: ", example_name, ", Key: ",
+                                     feature_name, ", Index: ", example_index,
+                                     ". Can't parse serialized Example.");
+    };
+
+    if (d_and_type.second == Type::Dense) {
+      DataType example_dtype;
+      TF_RETURN_IF_ERROR(feature.ParseDataType(&example_dtype));
+      if (example_dtype == DT_INVALID) continue;
+
+      dense_features_found[d] = example_index;
+      if (example_dtype != config.dense[d].dtype) {
+        return errors::InvalidArgument(
+            "Name: ", example_name, ", Feature: ", config.dense[d].feature_name,
+            ".  Data types don't match. ", "Data type: ",
+            DataTypeString(example_dtype), "Expected type: ",
+            DataTypeString(config.dense[d].dtype));
+      }
+      const string& feature_name = config.dense[d].feature_name;
+      const TensorShape& shape = config.dense[d].shape;
+      Tensor& out = (*output_dense)[d];
+
+      const std::size_t num_elements = shape.num_elements();
+      const std::size_t offset = example_index * num_elements;
+
+      auto shape_error = [&](size_t size, StringPiece type_str) {
+        return errors::InvalidArgument(
+            "Name: ", example_name, ", Key: ", feature_name, ", Index: ",
+            example_index, ".  Number of ", type_str,
+            " values != expected.  "
+            "Values size: ",
+            size, " but output shape: ", shape.DebugString());
+      };
+
+      switch (config.dense[d].dtype) {
+        case DT_INT64: {
+          std::vector<int64> list;
+          if (!feature.ParseInt64List(&list)) return parse_error(feature_name);
+          if (list.size() != num_elements) {
+            return shape_error(list.size(), "int64");
+          }
+          auto out_p = out.flat<int64>().data() + offset;
+          std::copy_n(list.begin(), list.size(), out_p);
+          break;
+        }
+        case DT_FLOAT: {
+          std::vector<float> list;
+          if (!feature.ParseFloatList(&list)) return parse_error(feature_name);
+          if (list.size() != num_elements) {
+            return shape_error(list.size(), "float");
+          }
+          auto out_p = out.flat<float>().data() + offset;
+          std::copy_n(list.begin(), list.size(), out_p);
+          break;
+        }
+        case DT_STRING: {
+          std::vector<string> list;
+          if (!feature.ParseBytesList(&list)) return parse_error(feature_name);
+          if (list.size() != num_elements) {
+            return shape_error(list.size(), "bytes");
+          }
+          auto out_p = out.flat<string>().data() + offset;
+          for (size_t i = 0; i < list.size(); ++i) {
+            out_p[i] = std::move(list[i]);
+          }
+          break;
+        }
+        default:
+          CHECK(false) << "Should not happen.";
+      }
+    } else {
+      // Handle sparse features.
+      sparse_features_found[d] = example_index;
+      const string& feature_name = config.sparse[d].feature_name;
+      SparseBuffer& out = (*output_sparse)[d];
+      DataType example_dtype;
+      TF_RETURN_IF_ERROR(feature.ParseDataType(&example_dtype));
+      if (example_dtype != DT_INVALID &&
+          example_dtype != config.sparse[d].dtype) {
+        return errors::InvalidArgument(
+            "Name: ", example_name, ", Feature: ",
+            config.sparse[d].feature_name, ".  Data types don't match. ",
+            "Expected type: ", DataTypeString(config.sparse[d].dtype));
+      }
+
+      switch (config.sparse[d].dtype) {
+        case DT_INT64: {
+          if (example_dtype != DT_INVALID) {
+            if (!feature.ParseInt64List(&out.int64_list)) {
+              return parse_error(feature_name);
+            }
+          }
+          out.example_end_indices.push_back(out.int64_list.size());
+          break;
+        }
+        case DT_FLOAT: {
+          if (example_dtype != DT_INVALID) {
+            if (!feature.ParseFloatList(&out.float_list)) {
+              return parse_error(feature_name);
+            }
+          }
+          out.example_end_indices.push_back(out.float_list.size());
+          break;
+        }
+        case DT_STRING: {
+          if (example_dtype != DT_INVALID) {
+            if (!feature.ParseBytesList(&out.bytes_list)) {
+              return parse_error(feature_name);
+            }
+          }
+          out.example_end_indices.push_back(out.bytes_list.size());
+          break;
+        }
+        default:
+          CHECK(false) << "Should not happen.";
+      }
+    }
+  }
+
+  // Handle missing dense features.
+  for (size_t d = 0; d < config.dense.size(); ++d) {
+    if (dense_features_found[d] == example_index) continue;
+    if (config.dense[d].default_value.NumElements() == 0) {
+      return errors::InvalidArgument("Name: ", example_name, ", Feature: ",
+                                     config.dense[d].feature_name,
+                                     " is required but could not be found.");
+    }
+
+    const Tensor& in = config.dense[d].default_value;
+    Tensor& out = (*output_dense)[d];
+    const std::size_t num_elements = in.shape().num_elements();
+    const std::size_t offset = example_index * num_elements;
+
+    switch (config.dense[d].dtype) {
+      case DT_INT64: {
+        std::copy_n(in.flat<int64>().data(), num_elements,
+                    out.flat<int64>().data() + offset);
+        break;
+      }
+      case DT_FLOAT: {
+        std::copy_n(in.flat<float>().data(), num_elements,
+                    out.flat<float>().data() + offset);
+        break;
+      }
+      case DT_STRING: {
+        std::copy_n(in.flat<string>().data(), num_elements,
+                    out.flat<string>().data() + offset);
+        break;
+      }
+      default:
+        CHECK(false) << "Should not happen.";
+    }
+  }
+
+  // Handle missing sparse features.
+  for (size_t d = 0; d < config.sparse.size(); ++d) {
+    if (sparse_features_found[d] == example_index) continue;
+    SparseBuffer& out = (*output_sparse)[d];
+    size_t prev_example_end_index =
+        out.example_end_indices.empty() ? 0 : out.example_end_indices.back();
+    out.example_end_indices.push_back(prev_example_end_index);
+  }
+
+  return Status::OK();
+}
+
+Status CheckConfigDataType(DataType dtype) {
+  switch (dtype) {
+    case DT_INT64:
+    case DT_FLOAT:
+    case DT_STRING:
+      return Status::OK();
+    default:
+      return errors::InvalidArgument("Invalid config dtype: ",
+                                     DataTypeString(dtype));
+  }
+}
+
+}  // namespace
+
+Status FastParseExample(const Config& config,
+                        gtl::ArraySlice<string> serialized,
+                        gtl::ArraySlice<string> example_names,
+                        thread::ThreadPool* thread_pool, Result* result) {
+  DCHECK(thread_pool != nullptr);
+  DCHECK(result != nullptr);
+  // Check config so we can safely CHECK(false) in switches on config.*.dtype
+  for (auto& c : config.sparse) {
+    TF_RETURN_IF_ERROR(CheckConfigDataType(c.dtype));
+  }
+  for (auto& c : config.dense) {
+    TF_RETURN_IF_ERROR(CheckConfigDataType(c.dtype));
+  }
+
+  size_t config_size = config.dense.size() + config.sparse.size();
+  SeededHasher hasher;
+  // Build config index.
+  PresizedCuckooMap<std::pair<size_t, Type>> config_index(config_size);
+  bool ok = true;
+  for (size_t i = 0; i < 1000; ++i) {
+    for (size_t d = 0; d < config.dense.size(); ++d) {
+      ok &= config_index.InsertUnique(hasher(config.dense[d].feature_name),
+                                      {d, Type::Dense});
+    }
+    for (size_t d = 0; d < config.sparse.size(); ++d) {
+      ok &= config_index.InsertUnique(hasher(config.sparse[d].feature_name),
+                                      {d, Type::Sparse});
+    }
+    if (ok) break;
+    LOG(WARNING) << "Collision found. This should happen only if you have "
+                    "around 2^32 entries in your config.";
+    hasher.seed++;
+    config_index.Clear(config_size);
+  }
+  if (!ok) {
+    return errors::Internal(
+        "Could not avoid collision. This should not happen.");
+  }
+
+  // Allocate dense output (sparse have to be buffered).
+  for (size_t d = 0; d < config.dense.size(); ++d) {
+    TensorShape out_shape;
+    out_shape.AddDim(serialized.size());
+    for (const int64 dim : config.dense[d].shape.dim_sizes()) {
+      out_shape.AddDim(dim);
+    }
+    result->dense_values.emplace_back(config.dense[d].dtype, out_shape);
+  }
+
+  // This parameter affects performance in a big and data-dependent way.
+  const size_t kMiniBatchSizeBytes = 100000;
+
+  // Split examples into mini-batches for parallel processing.
+  auto first_example_of_minibatch = [&] {
+    std::vector<size_t> result;
+    size_t minibatch_bytes = 0;
+    for (size_t i = 0; i < serialized.size(); i++) {
+      if (minibatch_bytes == 0) {  // start minibatch
+        result.push_back(i);
+      }
+      minibatch_bytes += serialized[i].size() + 1;
+      if (minibatch_bytes > kMiniBatchSizeBytes) {
+        minibatch_bytes = 0;
+      }
+    }
+    return result;
+  }();
+
+  size_t num_minibatches = first_example_of_minibatch.size();
+
+  // Do minibatches in parallel.
+  std::vector<std::vector<SparseBuffer>> sparse_buffers(num_minibatches);
+  std::vector<Status> status_of_minibatch(num_minibatches);
+
+  auto ProcessMiniBatch = [&](size_t minibatch) {
+    sparse_buffers[minibatch].resize(config.sparse.size());
+    size_t start = first_example_of_minibatch[minibatch];
+    size_t end = minibatch + 1 < num_minibatches
+                     ? first_example_of_minibatch[minibatch + 1]
+                     : serialized.size();
+    for (size_t e = start; e < end; ++e) {
+      status_of_minibatch[minibatch] = FastParseSerializedExample(
+          serialized[e],
+          (example_names.size() > 0 ? example_names[e] : "<unknown>"), e,
+          config, config_index, hasher, &result->dense_values,
+          &sparse_buffers[minibatch]);
+      if (!status_of_minibatch[minibatch].ok()) break;
+    }
+  };
+
+  ParallelFor(ProcessMiniBatch, num_minibatches, thread_pool);
+
+  for (Status& status : status_of_minibatch) {
+    TF_RETURN_IF_ERROR(status);
+  }
+
+  // Merge SparseBuffers from all minibatches for every config.sparse.
+  auto MergeMinibatches = [&](size_t d) {
+    // Loop over minibatches
+    size_t total_num_features = 0;
+    size_t max_num_features = 0;
+    for (auto& sparse_values_tmp : sparse_buffers) {
+      std::vector<size_t>& end_indices =
+          sparse_values_tmp[d].example_end_indices;
+      total_num_features += end_indices.back();
+      max_num_features = std::max(max_num_features, end_indices[0]);
+      for (size_t i = 1; i < end_indices.size(); ++i) {
+        size_t example_size = end_indices[i] - end_indices[i - 1];
+        max_num_features = std::max(max_num_features, example_size);
+      }
+    }
+
+    TensorShape indices_shape;
+    indices_shape.AddDim(total_num_features);
+    indices_shape.AddDim(2);
+    result->sparse_indices.emplace_back(DT_INT64, indices_shape);
+    Tensor* indices = &result->sparse_indices.back();
+
+    TensorShape values_shape;
+    values_shape.AddDim(total_num_features);
+    result->sparse_values.emplace_back(config.sparse[d].dtype, values_shape);
+    Tensor* values = &result->sparse_values.back();
+
+    result->sparse_shapes.emplace_back(DT_INT64, TensorShape({2}));
+    auto shapes_shape_t = result->sparse_shapes.back().vec<int64>();
+    shapes_shape_t(0) = serialized.size();
+    shapes_shape_t(1) = max_num_features;
+
+    size_t offset = 0;
+    for (size_t i = 0; i < sparse_buffers.size(); ++i) {
+      const SparseBuffer& buffer = sparse_buffers[i][d];
+
+      // Update indices.
+      int64* ix_p = &indices->matrix<int64>()(offset, 0);
+      size_t delta = 0;
+      size_t example_index = first_example_of_minibatch[i];
+      for (size_t example_end_index : buffer.example_end_indices) {
+        size_t feature_index = 0;
+        for (; delta < example_end_index; ++delta) {
+          // Column 0: example index
+          *ix_p = example_index;
+          // Column 1: the feature index buffer example
+          *(ix_p + 1) = feature_index;
+          ix_p += 2;
+          ++feature_index;
+        }
+        ++example_index;
+      }
+
+      // Copy values over.
+      switch (config.sparse[d].dtype) {
+        case DT_INT64: {
+          std::copy(buffer.int64_list.begin(), buffer.int64_list.end(),
+                    values->flat<int64>().data() + offset);
+          break;
+        }
+        case DT_FLOAT: {
+          std::copy(buffer.float_list.begin(), buffer.float_list.end(),
+                    values->flat<float>().data() + offset);
+          break;
+        }
+        case DT_STRING: {
+          std::move(buffer.bytes_list.begin(), buffer.bytes_list.end(),
+                    values->flat<string>().data() + offset);
+          break;
+        }
+        default:
+          CHECK(false) << "Should not happen.";
+      }
+
+      offset += delta;
+    }
+  };
+
+  for (size_t d = 0; d < config.sparse.size(); ++d) {
+    MergeMinibatches(d);
+  }
+
+  return Status::OK();
+}
+
+}  // namespace example
+}  // namespace tensorflow