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[docx] split commit for file 6400

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Signed-off-by: Ari Archer <ari.web.xyz@gmail.com>
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Ari Archer 2024-01-23 19:21:37 +02:00
parent 4e32fcb29f
commit ac0fb2a2f2
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162
twml/libtwml/src/lib/TensorRecordWriter.cpp
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#include "internal/error.h"
#include "internal/thrift.h"
#include <map>
#include <twml/ThriftWriter.h>
#include <twml/TensorRecordWriter.h>
#include <twml/io/IOError.h>
using namespace twml::io;
namespace twml {
static int32_t getRawThriftType(twml_type dtype) {
// convert twml enum to tensor.thrift enum
switch (dtype) {
case TWML_TYPE_FLOAT:
return DATA_TYPE_FLOAT;
case TWML_TYPE_DOUBLE:
return DATA_TYPE_DOUBLE;
case TWML_TYPE_INT64:
return DATA_TYPE_INT64;
case TWML_TYPE_INT32:
return DATA_TYPE_INT32;
case TWML_TYPE_UINT8:
return DATA_TYPE_UINT8;
case TWML_TYPE_STRING:
return DATA_TYPE_STRING;
case TWML_TYPE_BOOL:
return DATA_TYPE_BOOL;
default:
throw IOError(IOError::UNSUPPORTED_OUTPUT_TYPE);
}
}
void TensorRecordWriter::writeTensor(const RawTensor &tensor) {
if (tensor.getType() == TWML_TYPE_INT32) {
m_thrift_writer.writeStructFieldHeader(TTYPE_STRUCT, GT_INT32);
m_thrift_writer.writeStructFieldHeader(TTYPE_LIST, 1);
m_thrift_writer.writeListHeader(TTYPE_I32, tensor.getNumElements());
const int32_t *data = tensor.getData<int32_t>();
for (uint64_t i = 0; i < tensor.getNumElements(); i++)
m_thrift_writer.writeInt32(data[i]);
} else if (tensor.getType() == TWML_TYPE_INT64) {
m_thrift_writer.writeStructFieldHeader(TTYPE_STRUCT, GT_INT64);
m_thrift_writer.writeStructFieldHeader(TTYPE_LIST, 1);
m_thrift_writer.writeListHeader(TTYPE_I64, tensor.getNumElements());
const int64_t *data = tensor.getData<int64_t>();
for (uint64_t i = 0; i < tensor.getNumElements(); i++)
m_thrift_writer.writeInt64(data[i]);
} else if (tensor.getType() == TWML_TYPE_FLOAT) {
m_thrift_writer.writeStructFieldHeader(TTYPE_STRUCT, GT_FLOAT);
m_thrift_writer.writeStructFieldHeader(TTYPE_LIST, 1);
m_thrift_writer.writeListHeader(TTYPE_DOUBLE, tensor.getNumElements());
const float *data = tensor.getData<float>();
for (uint64_t i = 0; i < tensor.getNumElements(); i++)
m_thrift_writer.writeDouble(static_cast<double>(data[i]));
} else if (tensor.getType() == TWML_TYPE_DOUBLE) {
m_thrift_writer.writeStructFieldHeader(TTYPE_STRUCT, GT_DOUBLE);
m_thrift_writer.writeStructFieldHeader(TTYPE_LIST, 1);
m_thrift_writer.writeListHeader(TTYPE_DOUBLE, tensor.getNumElements());
const double *data = tensor.getData<double>();
for (uint64_t i = 0; i < tensor.getNumElements(); i++)
m_thrift_writer.writeDouble(data[i]);
} else if (tensor.getType() == TWML_TYPE_STRING) {
m_thrift_writer.writeStructFieldHeader(TTYPE_STRUCT, GT_STRING);
m_thrift_writer.writeStructFieldHeader(TTYPE_LIST, 1);
m_thrift_writer.writeListHeader(TTYPE_STRING, tensor.getNumElements());
const std::string *data = tensor.getData<std::string>();
for (uint64_t i = 0; i < tensor.getNumElements(); i++)
m_thrift_writer.writeString(data[i]);
} else if (tensor.getType() == TWML_TYPE_BOOL) {
m_thrift_writer.writeStructFieldHeader(TTYPE_STRUCT, GT_BOOL);
m_thrift_writer.writeStructFieldHeader(TTYPE_LIST, 1);
m_thrift_writer.writeListHeader(TTYPE_BOOL, tensor.getNumElements());
const bool *data = tensor.getData<bool>();
for (uint64_t i = 0; i < tensor.getNumElements(); i++)
m_thrift_writer.writeBool(data[i]);
} else {
throw IOError(IOError::UNSUPPORTED_OUTPUT_TYPE);
}
// write tensor shape field
m_thrift_writer.writeStructFieldHeader(TTYPE_LIST, 2);
m_thrift_writer.writeListHeader(TTYPE_I64, tensor.getNumDims());
for (uint64_t i = 0; i < tensor.getNumDims(); i++)
m_thrift_writer.writeInt64(tensor.getDim(i));
m_thrift_writer.writeStructStop();
m_thrift_writer.writeStructStop();
}
void TensorRecordWriter::writeRawTensor(const RawTensor &tensor) {
m_thrift_writer.writeStructFieldHeader(TTYPE_STRUCT, GT_RAW);
// dataType field
m_thrift_writer.writeStructFieldHeader(TTYPE_I32, 1);
m_thrift_writer.writeInt32(getRawThriftType(tensor.getType()));
// content field
uint64_t type_size = getSizeOf(tensor.getType());
m_thrift_writer.writeStructFieldHeader(TTYPE_STRING, 2);
const uint8_t *data = reinterpret_cast<const uint8_t *>(tensor.getData<void>());
m_thrift_writer.writeBinary(data, tensor.getNumElements() * type_size);
// shape field
m_thrift_writer.writeStructFieldHeader(TTYPE_LIST, 3);
m_thrift_writer.writeListHeader(TTYPE_I64, tensor.getNumDims());
for (uint64_t i = 0; i < tensor.getNumDims(); i++)
m_thrift_writer.writeInt64(tensor.getDim(i));
m_thrift_writer.writeStructStop();
m_thrift_writer.writeStructStop();
}
TWMLAPI uint32_t TensorRecordWriter::getRecordsWritten() {
return m_records_written;
}
// Caller (usually DataRecordWriter) must precede with struct header field
// like thrift_writer.writeStructFieldHeader(TTYPE_MAP, DR_GENERAL_TENSOR)
TWMLAPI uint64_t TensorRecordWriter::write(twml::TensorRecord &record) {
uint64_t bytes_written_before = m_thrift_writer.getBytesWritten();
m_thrift_writer.writeMapHeader(TTYPE_I64, TTYPE_STRUCT, record.getRawTensors().size());
for (auto id_tensor_pairs : record.getRawTensors()) {
m_thrift_writer.writeInt64(id_tensor_pairs.first);
// all tensors written as RawTensor Thrift except for StringTensors
// this avoids the overhead of converting little endian to big endian
if (id_tensor_pairs.second.getType() == TWML_TYPE_STRING)
writeTensor(id_tensor_pairs.second);
else
writeRawTensor(id_tensor_pairs.second);
}
m_records_written++;
return m_thrift_writer.getBytesWritten() - bytes_written_before;
}
} // namespace twml

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twml/libtwml/src/lib/ThriftReader.cpp
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#include "internal/endianutils.h"
#include <twml/ThriftReader.h>
#include <twml/Error.h>
#include <cstring>
namespace twml {
uint8_t ThriftReader::readByte() {
return readDirect<uint8_t>();
}
int16_t ThriftReader::readInt16() {
return betoh16(readDirect<int16_t>());
}
int32_t ThriftReader::readInt32() {
return betoh32(readDirect<int32_t>());
}
int64_t ThriftReader::readInt64() {
return betoh64(readDirect<int64_t>());
}
double ThriftReader::readDouble() {
double val;
int64_t *val_proxy = reinterpret_cast<int64_t*>(&val);
*val_proxy = readInt64();
return val;
}
} // namespace twml

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twml/libtwml/src/lib/ThriftWriter.cpp
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#include "internal/endianutils.h"
#include "internal/error.h"
#include "internal/thrift.h"
#include <twml/ThriftWriter.h>
#include <twml/Error.h>
#include <twml/io/IOError.h>
#include <cstring>
using namespace twml::io;
namespace twml {
template <typename T> inline
uint64_t ThriftWriter::write(T val) {
if (!m_dry_run) {
if (m_bytes_written + sizeof(T) > m_buffer_size)
throw IOError(IOError::DESTINATION_LARGER_THAN_CAPACITY);
memcpy(m_buffer, &val, sizeof(T));
m_buffer += sizeof(T);
}
m_bytes_written += sizeof(T);
return sizeof(T);
}
TWMLAPI uint64_t ThriftWriter::getBytesWritten() {
return m_bytes_written;
}
TWMLAPI uint64_t ThriftWriter::writeStructFieldHeader(int8_t field_type, int16_t field_id) {
return writeInt8(field_type) + writeInt16(field_id);
}
TWMLAPI uint64_t ThriftWriter::writeStructStop() {
return writeInt8(static_cast<int8_t>(TTYPE_STOP));
}
TWMLAPI uint64_t ThriftWriter::writeListHeader(int8_t element_type, int32_t num_elems) {
return writeInt8(element_type) + writeInt32(num_elems);
}
TWMLAPI uint64_t ThriftWriter::writeMapHeader(int8_t key_type, int8_t val_type, int32_t num_elems) {
return writeInt8(key_type) + writeInt8(val_type) + writeInt32(num_elems);
}
TWMLAPI uint64_t ThriftWriter::writeDouble(double val) {
int64_t bin_value;
memcpy(&bin_value, &val, sizeof(int64_t));
return writeInt64(bin_value);
}
TWMLAPI uint64_t ThriftWriter::writeInt8(int8_t val) {
return write(val);
}
TWMLAPI uint64_t ThriftWriter::writeInt16(int16_t val) {
return write(betoh16(val));
}
TWMLAPI uint64_t ThriftWriter::writeInt32(int32_t val) {
return write(betoh32(val));
}
TWMLAPI uint64_t ThriftWriter::writeInt64(int64_t val) {
return write(betoh64(val));
}
TWMLAPI uint64_t ThriftWriter::writeBinary(const uint8_t *bytes, int32_t num_bytes) {
writeInt32(num_bytes);
if (!m_dry_run) {
if (m_bytes_written + num_bytes > m_buffer_size)
throw IOError(IOError::DESTINATION_LARGER_THAN_CAPACITY);
memcpy(m_buffer, bytes, num_bytes);
m_buffer += num_bytes;
}
m_bytes_written += num_bytes;
return 4 + num_bytes;
}
TWMLAPI uint64_t ThriftWriter::writeString(std::string str) {
return writeBinary(reinterpret_cast<const uint8_t *>(str.data()), str.length());
}
TWMLAPI uint64_t ThriftWriter::writeBool(bool val) {
return write(val);
}
} // namespace twml

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twml/libtwml/src/lib/discretizer_impl.cpp
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#include "internal/interpolate.h"
#include "internal/error.h"
#include <twml/discretizer_impl.h>
#include <twml/optim.h>
namespace twml {
// it is assumed that start_compute and end_compute are valid
template<typename T>
void discretizerInfer(Tensor &output_keys,
Tensor &output_vals,
const Tensor &input_ids,
const Tensor &input_vals,
const Tensor &bin_ids,
const Tensor &bin_vals,
const Tensor &feature_offsets,
int output_bits,
const Map<int64_t, int64_t> &ID_to_index,
int64_t start_compute,
int64_t end_compute,
int64_t output_start) {
auto out_keysData = output_keys.getData<int64_t>();
auto out_valsData = output_vals.getData<T>();
uint64_t out_keysStride = output_keys.getStride(0);
uint64_t out_valsStride = output_vals.getStride(0);
auto in_idsData = input_ids.getData<int64_t>();
auto in_valsData = input_vals.getData<T>();
uint64_t in_idsStride = input_ids.getStride(0);
uint64_t in_valsStride = input_vals.getStride(0);
auto xsData = bin_vals.getData<T>();
auto ysData = bin_ids.getData<int64_t>();
uint64_t xsStride = bin_vals.getStride(0);
uint64_t ysStride = bin_ids.getStride(0);
auto offsetData = feature_offsets.getData<int64_t>();
uint64_t total_bins = bin_ids.getNumElements();
uint64_t fsize = feature_offsets.getNumElements();
uint64_t output_size = (1 << output_bits);
for (uint64_t i = start_compute; i < end_compute; i++) {
int64_t feature_ID = in_idsData[i * in_idsStride];
T val = in_valsData[i * in_valsStride];
auto iter = ID_to_index.find(feature_ID);
if (iter == ID_to_index.end()) {
// feature not calibrated
// modulo add operation for new key from feature ID
int64_t ikey = feature_ID % (output_size - total_bins) + total_bins;
out_keysData[(i + output_start - start_compute) * out_keysStride] = ikey;
out_valsData[(i + output_start - start_compute) * out_valsStride] = val;
continue;
}
int64_t ikey = iter->second;
// Perform interpolation
uint64_t offset = offsetData[ikey];
uint64_t next_offset = (ikey == (int64_t)(fsize - 1)) ? total_bins : offsetData[ikey + 1];
uint64_t mainSize = next_offset - offset;
const T *lxsData = xsData + offset;
const int64_t *lysData = ysData + offset;
int64_t okey;
okey = interpolation<T, int64_t>(lxsData, xsStride,
lysData, ysStride,
val, mainSize,
NEAREST, 0);
out_keysData[(i + output_start - start_compute) * out_keysStride] = okey;
out_valsData[(i + output_start - start_compute) * out_valsStride] = 1;
}
}
void discretizerInfer(Tensor &output_keys,
Tensor &output_vals,
const Tensor &input_ids,
const Tensor &input_vals,
const Tensor &bin_ids,
const Tensor &bin_vals,
const Tensor &feature_offsets,
int output_bits,
const Map<int64_t, int64_t> &ID_to_index,
int start_compute,
int end_compute,
int output_start) {
if (input_ids.getType() != TWML_TYPE_INT64) {
throw twml::Error(TWML_ERR_TYPE, "input_ids must be a Long Tensor");
}
if (output_keys.getType() != TWML_TYPE_INT64) {
throw twml::Error(TWML_ERR_TYPE, "output_keys must be a Long Tensor");
}
if (bin_ids.getType() != TWML_TYPE_INT64) {
throw twml::Error(TWML_ERR_TYPE, "bin_ids must be a Long Tensor");
}
if (feature_offsets.getType() != TWML_TYPE_INT64) {
throw twml::Error(TWML_ERR_TYPE, "bin_ids must be a Long Tensor");
}
if (input_vals.getType() != bin_vals.getType()) {
throw twml::Error(TWML_ERR_TYPE,
"Data type of input_vals does not match type of bin_vals");
}
if (bin_vals.getNumDims() != 1) {
throw twml::Error(TWML_ERR_SIZE,
"bin_vals must be 1 Dimensional");
}
if (bin_ids.getNumDims() != 1) {
throw twml::Error(TWML_ERR_SIZE,
"bin_ids must be 1 Dimensional");
}
if (bin_vals.getNumElements() != bin_ids.getNumElements()) {
throw twml::Error(TWML_ERR_SIZE,
"Dimensions of bin_vals and bin_ids do not match");
}
if (feature_offsets.getStride(0) != 1) {
throw twml::Error(TWML_ERR_SIZE,
"feature_offsets must be contiguous");
}
uint64_t size = input_ids.getDim(0);
if (end_compute == -1) {
end_compute = size;
}
if (start_compute < 0 || start_compute >= size) {
throw twml::Error(TWML_ERR_SIZE,
"start_compute out of range");
}
if (end_compute < -1 || end_compute > size) {
throw twml::Error(TWML_ERR_SIZE,
"end_compute out of range");
}
if (start_compute > end_compute && end_compute != -1) {
throw twml::Error(TWML_ERR_SIZE,
"must have start_compute <= end_compute, or end_compute==-1");
}
switch (input_vals.getType()) {
case TWML_TYPE_FLOAT:
twml::discretizerInfer<float>(output_keys, output_vals,
input_ids, input_vals,
bin_ids, bin_vals, feature_offsets, output_bits, ID_to_index,
start_compute, end_compute, output_start);
break;
case TWML_TYPE_DOUBLE:
twml::discretizerInfer<double>(output_keys, output_vals,
input_ids, input_vals,
bin_ids, bin_vals, feature_offsets, output_bits, ID_to_index,
start_compute, end_compute, output_start);
break;
default:
throw twml::Error(TWML_ERR_TYPE,
"Unsupported datatype for discretizerInfer");
}
}
} // namespace twml

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twml/libtwml/src/lib/functions.cpp
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#include "internal/error.h"
#include "internal/murmur_hash3.h"
#include "internal/utf_converter.h"
#include <twml/functions.h>
#include <cstring>
#include <algorithm>
namespace twml {
template<typename T>
void add1(Tensor &output, const Tensor input) {
T *odata = output.getData<T>();
const T *idata = input.getData<T>();
const uint64_t num_elements = input.getNumElements();
for (uint64_t i = 0; i < num_elements; i++) {
odata[i] = idata[i] + 1;
}
}
template<typename T>
void copy(Tensor &output, const Tensor input) {
T *odata = output.getData<T>();
const T *idata = input.getData<T>();
const uint64_t num_elements = input.getNumElements();
for (uint64_t i = 0; i < num_elements; i++) {
odata[i] = idata[i];
}
}
void add1(Tensor &output, const Tensor input) {
auto type = input.getType();
if (output.getType() != type) {
throw twml::Error(TWML_ERR_TYPE, "Output type does not match input type");
}
if (output.getNumElements() != input.getNumElements()) {
throw twml::Error(TWML_ERR_SIZE, "Output size does not match input size");
}
// TODO: Implement an easier dispatch function
switch (type) {
case TWML_TYPE_FLOAT:
twml::add1<float>(output, input);
break;
case TWML_TYPE_DOUBLE:
twml::add1<double>(output, input);
break;
default:
throw twml::Error(TWML_ERR_TYPE, "add1 only supports float and double tensors");
}
}
void copy(Tensor &output, const Tensor input) {
auto type = input.getType();
if (output.getType() != type) {
throw twml::Error(TWML_ERR_TYPE, "Output type does not match input type");
}
if (output.getNumElements() != input.getNumElements()) {
throw twml::Error(TWML_ERR_SIZE, "Output size does not match input size");
}
// TODO: Implement an easier dispatch function
switch (type) {
case TWML_TYPE_FLOAT:
twml::copy<float>(output, input);
break;
case TWML_TYPE_DOUBLE:
twml::copy<double>(output, input);
break;
default:
throw twml::Error(TWML_ERR_TYPE, "copy only supports float and double tensors");
}
}
int64_t featureId(const std::string &feature) {
const char *str = feature.c_str();
uint64_t len = feature.size();
int64_t id = 0;
TWML_CHECK(twml_get_feature_id(&id, len, str), "Error getting featureId");
return id;
}
} // namespace twml
twml_err twml_add1(twml_tensor output, const twml_tensor input) {
HANDLE_EXCEPTIONS(
auto out = twml::getTensor(output);
auto in = twml::getConstTensor(input);
twml::add1(*out, *in););
return TWML_ERR_NONE;
}
twml_err twml_copy(twml_tensor output, const twml_tensor input) {
HANDLE_EXCEPTIONS(
auto out = twml::getTensor(output);
auto in = twml::getConstTensor(input);
twml::copy(*out, *in););
return TWML_ERR_NONE;
}
inline twml_err twml_get_feature_id_internal(int64_t *result,
uint64_t out_size, uint16_t *out,
uint64_t out2_size, uint16_t *out2,
const uint64_t len, const char *str) {
uint64_t k = 0;
for (uint64_t i = 0; i < len; i++) {
if (str[i] == '#') {
k = i;
break;
}
}
uint8_t hash[16];
if (k != 0) {
ssize_t n = utf8_to_utf16((const uint8_t *) str, k, out, out_size);
if (n < 0) throw std::invalid_argument("error while converting from utf8 to utf16");
MurmurHash3_x64_128(out, n * sizeof(uint16_t), 0, out2);
n = utf8_to_utf16((const uint8_t *) (str + k + 1), len - k - 1, &out2[4], out2_size - 8);
if (n < 0) throw std::invalid_argument("error while converting from utf8 to utf16");
MurmurHash3_x64_128(out2, (n * sizeof(uint16_t)) + 8, 0, hash);
} else {
ssize_t n = utf8_to_utf16((const uint8_t *)str, len, out, out_size);
if (n < 0) throw std::invalid_argument("error while converting from utf8 to utf16");
MurmurHash3_x64_128(out, n * sizeof(uint16_t), 0, hash);
}
int64_t id;
memcpy(&id, hash, sizeof(int64_t));
*result = id;
return TWML_ERR_NONE;
}
static const int UTF16_STR_MAX_SIZE = 1024;
twml_err twml_get_feature_id(int64_t *result, const uint64_t len, const char *str) {
try {
uint16_t out[UTF16_STR_MAX_SIZE];
uint16_t out2[UTF16_STR_MAX_SIZE];
return twml_get_feature_id_internal(result,
UTF16_STR_MAX_SIZE, out,
UTF16_STR_MAX_SIZE, out2,
len, str);
} catch(const std::invalid_argument &ex) {
// If the space on the stack is not enough, try using the heap.
// len + 1 is needed because a null terminating character is added at the end.
std::vector<uint16_t> out(len + 1);
std::vector<uint16_t> out2(len + 1);
return twml_get_feature_id_internal(result,
len + 1, out.data(),
len + 1, out2.data(),
len, str);
}
}

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twml/libtwml/src/lib/hashing_discretizer_impl.cpp
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#include "internal/linear_search.h"
#include "internal/error.h"
#include <twml/hashing_discretizer_impl.h>
#include <twml/optim.h>
#include <algorithm>
namespace twml {
template<typename Tx>
static int64_t lower_bound_search(const Tx *data, const Tx val, const int64_t buf_size) {
auto index_temp = std::lower_bound(data, data + buf_size, val);
return static_cast<int64_t>(index_temp - data);
}
template<typename Tx>
static int64_t upper_bound_search(const Tx *data, const Tx val, const int64_t buf_size) {
auto index_temp = std::upper_bound(data, data + buf_size, val);
return static_cast<int64_t>(index_temp - data);
}
template<typename Tx>
using search_method = int64_t (*)(const Tx *, const Tx, const int64_t);
typedef uint64_t (*hash_signature)(uint64_t, int64_t, uint64_t);
// uint64_t integer_multiplicative_hashing()
//
// A function to hash discretized feature_ids into one of 2**output_bits buckets.
// This function hashes the feature_ids to achieve a uniform distribution of
// IDs, so the hashed IDs are with high probability far apart
// Then, bucket_indices can simply be added, resulting in unique new IDs with high probability
// We integer hash again to again spread out the new IDs
// Finally we take the upper
// Required args:
// feature_id:
// The feature id of the feature to be hashed.
// bucket_index:
// The bucket index of the discretized feature value
// output_bits:
// The number of bits of output space for the features to be hashed into.
//
// Note - feature_ids may have arbitrary distribution within int32s
// Note - 64 bit feature_ids can be processed with this, but the upper
// 32 bits have no effect on the output
// e.g. all feature ids 0 through 255 exist in movie-lens.
// this hashing constant is good for 32 LSBs. will use N=32. (can use N<32 also)
// this hashing constant is co-prime with 2**32, therefore we have that
// a != b, a and b in [0,2**32)
// implies
// f(a) != f(b) where f(x) = (hashing_constant * x) % (2**32)
// note that we are mostly ignoring the upper 32 bits, using modulo 2**32 arithmetic
uint64_t integer_multiplicative_hashing(uint64_t feature_id,
int64_t bucket_index,
uint64_t output_bits) {
// possibly use 14695981039346656037 for 64 bit unsigned??
// = 20921 * 465383 * 1509404459
// alternatively, 14695981039346656039 is prime
// We would also need to use N = 64
const uint64_t hashing_constant = 2654435761;
const uint64_t N = 32;
// hash once to prevent problems from anomalous input id distributions
feature_id *= hashing_constant;
feature_id += bucket_index;
// this hash enables the following right shift operation
// without losing the bucket information (lower bits)
feature_id *= hashing_constant;
// output size is a power of 2
feature_id >>= N - output_bits;
uint64_t mask = (1 << output_bits) - 1;
return mask & feature_id;
}
uint64_t integer64_multiplicative_hashing(uint64_t feature_id,
int64_t bucket_index,
uint64_t output_bits) {
const uint64_t hashing_constant = 14695981039346656039UL;
const uint64_t N = 64;
// hash once to prevent problems from anomalous input id distributions
feature_id *= hashing_constant;
feature_id += bucket_index;
// this hash enables the following right shift operation
// without losing the bucket information (lower bits)
feature_id *= hashing_constant;
// output size is a power of 2
feature_id >>= N - output_bits;
uint64_t mask = (1 << output_bits) - 1;
return mask & feature_id;
}
int64_t option_bits(int64_t options, int64_t high, int64_t low) {
options >>= low;
options &= (1 << (high - low + 1)) - 1;
return options;
}
// it is assumed that start_compute and end_compute are valid
template<typename T>
void hashDiscretizerInfer(Tensor &output_keys,
Tensor &output_vals,
const Tensor &input_ids,
const Tensor &input_vals,
const Tensor &bin_vals,
int output_bits,
const Map<int64_t, int64_t> &ID_to_index,
int64_t start_compute,
int64_t end_compute,
int64_t n_bin,
int64_t options) {
auto output_keys_data = output_keys.getData<int64_t>();
auto output_vals_data = output_vals.getData<T>();
auto input_ids_data = input_ids.getData<int64_t>();
auto input_vals_data = input_vals.getData<T>();
auto bin_vals_data = bin_vals.getData<T>();
// The function pointer implementation removes the option_bits
// function call (might be inlined) and corresponding branch from
// the hot loop, but it prevents inlining these functions, so
// there will be function call overhead. Uncertain which would
// be faster, testing needed. Also, code optimizers do weird things...
hash_signature hash_fn = integer_multiplicative_hashing;
switch (option_bits(options, 4, 2)) {
case 0:
hash_fn = integer_multiplicative_hashing;
break;
case 1:
hash_fn = integer64_multiplicative_hashing;
break;
default:
hash_fn = integer_multiplicative_hashing;
}
search_method<T> search_fn = lower_bound_search;
switch (option_bits(options, 1, 0)) {
case 0:
search_fn = lower_bound_search<T>;
break;
case 1:
search_fn = linear_search<T>;
break;
case 2:
search_fn = upper_bound_search<T>;
break;
default:
search_fn = lower_bound_search<T>;
}
for (uint64_t i = start_compute; i < end_compute; i++) {
int64_t id = input_ids_data[i];
T val = input_vals_data[i];
auto iter = ID_to_index.find(id);
if (iter != ID_to_index.end()) {
int64_t feature_idx = iter->second;
const T *bin_vals_start = bin_vals_data + feature_idx * n_bin;
int64_t out_bin_idx = search_fn(bin_vals_start, val, n_bin);
output_keys_data[i] = hash_fn(id, out_bin_idx, output_bits);
output_vals_data[i] = 1;
} else {
// feature not calibrated
output_keys_data[i] = id & ((1 << output_bits) - 1);
output_vals_data[i] = val;
}
}
}
void hashDiscretizerInfer(Tensor &output_keys,
Tensor &output_vals,
const Tensor &input_ids,
const Tensor &input_vals,
int n_bin,
const Tensor &bin_vals,
int output_bits,
const Map<int64_t, int64_t> &ID_to_index,
int start_compute,
int end_compute,
int64_t options) {
if (input_ids.getType() != TWML_TYPE_INT64) {
throw twml::Error(TWML_ERR_TYPE, "input_ids must be a Long Tensor");
}
if (output_keys.getType() != TWML_TYPE_INT64) {
throw twml::Error(TWML_ERR_TYPE, "output_keys must be a Long Tensor");
}
if (input_vals.getType() != bin_vals.getType()) {
throw twml::Error(TWML_ERR_TYPE,
"Data type of input_vals does not match type of bin_vals");
}
if (bin_vals.getNumDims() != 1) {
throw twml::Error(TWML_ERR_SIZE,
"bin_vals must be 1 Dimensional");
}
uint64_t size = input_ids.getDim(0);
if (end_compute == -1) {
end_compute = size;
}
if (start_compute < 0 || start_compute >= size) {
throw twml::Error(TWML_ERR_SIZE,
"start_compute out of range");
}
if (end_compute < -1 || end_compute > size) {
throw twml::Error(TWML_ERR_SIZE,
"end_compute out of range");
}
if (start_compute > end_compute && end_compute != -1) {
throw twml::Error(TWML_ERR_SIZE,
"must have start_compute <= end_compute, or end_compute==-1");
}
if (output_keys.getStride(0) != 1 || output_vals.getStride(0) != 1 ||
input_ids.getStride(0) != 1 || input_vals.getStride(0) != 1 ||
bin_vals.getStride(0) != 1) {
throw twml::Error(TWML_ERR_SIZE,
"All Strides must be 1.");
}
switch (input_vals.getType()) {
case TWML_TYPE_FLOAT:
twml::hashDiscretizerInfer<float>(output_keys, output_vals,
input_ids, input_vals,
bin_vals, output_bits, ID_to_index,
start_compute, end_compute, n_bin, options);
break;
case TWML_TYPE_DOUBLE:
twml::hashDiscretizerInfer<double>(output_keys, output_vals,
input_ids, input_vals,
bin_vals, output_bits, ID_to_index,
start_compute, end_compute, n_bin, options);
break;
default:
throw twml::Error(TWML_ERR_TYPE,
"Unsupported datatype for hashDiscretizerInfer");
}
}
} // namespace twml

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//
// endian_fix.h
// ImageCore
//
// For OSes that use glibc < 2.9 (like RHEL5)
//
#pragma once
#ifdef __APPLE__
#include <libkern/OSByteOrder.h>
#define htobe16(x) OSSwapHostToBigInt16(x)
#define htole16(x) OSSwapHostToLittleInt16(x)
#define betoh16(x) OSSwapBigToHostInt16(x)
#define letoh16(x) OSSwapLittleToHostInt16(x)
#define htobe32(x) OSSwapHostToBigInt32(x)
#define htole32(x) OSSwapHostToLittleInt32(x)
#define betoh32(x) OSSwapBigToHostInt32(x)
#define letoh32(x) OSSwapLittleToHostInt32(x)
#define htobe64(x) OSSwapHostToBigInt64(x)
#define htole64(x) OSSwapHostToLittleInt64(x)
#define betoh64(x) OSSwapBigToHostInt64(x)
#define letoh64(x) OSSwapLittleToHostInt64(x)
#else
#include <endian.h>
#ifdef __USE_BSD
/* Conversion interfaces. */
#include <byteswap.h>
#if __BYTE_ORDER == __LITTLE_ENDIAN
#ifndef htobe16
#define htobe16(x) __bswap_16(x)
#endif
#ifndef htole16
#define htole16(x) (x)
#endif
#ifndef betoh16
#define betoh16(x) __bswap_16(x)
#endif
#ifndef letoh16
#define letoh16(x) (x)
#endif
#ifndef htobe32
#define htobe32(x) __bswap_32(x)
#endif
#ifndef htole32
#define htole32(x) (x)
#endif
#ifndef betoh32
#define betoh32(x) __bswap_32(x)
#endif
#ifndef letoh32
#define letoh32(x) (x)
#endif
#ifndef htobe64
#define htobe64(x) __bswap_64(x)
#endif
#ifndef htole64
#define htole64(x) (x)
#endif
#ifndef betoh64
#define betoh64(x) __bswap_64(x)
#endif
#ifndef letoh64
#define letoh64(x) (x)
#endif
#else /* __BYTE_ORDER == __LITTLE_ENDIAN */
#ifndef htobe16
#define htobe16(x) (x)
#endif
#ifndef htole16
#define htole16(x) __bswap_16(x)
#endif
#ifndef be16toh
#define be16toh(x) (x)
#endif
#ifndef le16toh
#define le16toh(x) __bswap_16(x)
#endif
#ifndef htobe32
#define htobe32(x) (x)
#endif
#ifndef htole32
#define htole32(x) __bswap_32(x)
#endif
#ifndef betoh32
#define betoh32(x) (x)
#endif
#ifndef letoh32
#define letoh32(x) __bswap_32(x)
#endif
#ifndef htobe64
#define htobe64(x) (x)
#endif
#ifndef htole64
#define htole64(x) __bswap_64(x)
#endif
#ifndef betoh64
#define betoh64(x) (x)
#endif
#ifndef letoh64
#define letoh64(x) __bswap_64(x)
#endif
#endif /* __BYTE_ORDER == __LITTLE_ENDIAN */
#else /* __USE_BSD */
#ifndef betoh16
#define betoh16 be16toh
#endif
#ifndef betoh32
#define betoh32 be32toh
#endif
#ifndef betoh64
#define betoh64 be64toh
#endif
#ifndef letoh16
#define letoh16 le16toh
#endif
#ifndef letoh32
#define letoh32 le32toh
#endif
#ifndef letoh64
#define letoh64 le64toh
#endif
#endif /* __USE_BSD */
#endif /* __APPLE__ */

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#pragma once
#include <twml/Error.h>
#include <iostream>
#define HANDLE_EXCEPTIONS(fn) do { \
try { \
fn \
} catch(const twml::Error &e) { \
std::cerr << e.what() << std::endl; \
return e.err(); \
} catch(...) { \
std::cerr << "Unknown error\n"; \
return TWML_ERR_UNKNOWN; \
} \
} while(0)
#define TWML_CHECK(fn, msg) do { \
twml_err err = fn; \
if (err == TWML_ERR_NONE) break; \
throw twml::Error(err, msg); \
} while(0)
#define CHECK_THRIFT_TYPE(real_type, expected_type, type) do { \
int real_type_val = real_type; \
if (real_type_val != expected_type) { \
throw twml::ThriftInvalidType(real_type_val, __func__, type); \
} \
} while(0)

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#pragma once
#ifdef __cplusplus
#include <twml/optim.h>
namespace twml {
enum InterpolationMode {LINEAR, NEAREST};
template<typename Tx, typename Ty>
static Tx interpolation(const Tx *xsData, const int64_t xsStride,
const Ty *ysData, const int64_t ysStride,
const Tx val, const int64_t mainSize,
const InterpolationMode mode,
const int64_t lowest,
const bool return_local_index = false) {
int64_t left = 0;
int64_t right = mainSize-1;
if (val <= xsData[0]) {
right = 0;
} else if (val >= xsData[right*xsStride]) {
left = right;
} else {
while (left < right) {
int64_t middle = (left+right)/2;
if (middle < mainSize - 1 &&
val >= xsData[middle*xsStride] &&
val <= xsData[(middle+1)*xsStride]) {
left = middle;
right = middle + 1;
break;
} else if (val > xsData[middle*xsStride]) {
left = middle;
} else {
right = middle;
}
}
if (lowest) {
while (left > 0 &&
val >= xsData[(left - 1) * xsStride] &&
val == xsData[left * xsStride]) {
left--;
right--;
}
}
}
Ty out = 0;
if (return_local_index) {
out = left;
} else if (mode == NEAREST) {
out = ysData[left*ysStride];
} else {
int64_t leftys = left*ysStride;
int64_t rightys = right*ysStride;
int64_t leftxs = left*xsStride;
int64_t rightxs = right*xsStride;
if (right != left+1 ||
xsData[leftxs] == xsData[rightxs]) {
out = ysData[leftys];
} else {
Tx xLeft = xsData[leftxs];
Tx xRight = xsData[rightxs];
Tx yLeft = ysData[leftys];
Tx ratio = (val - xLeft) / (xRight - xLeft);
out = ratio*(ysData[rightys] - yLeft) + yLeft;
}
}
return out;
}
} // namespace twml
#endif

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/* The MIT License
Copyright (c) 2008, 2009, 2011 by Attractive Chaos <attractor@live.co.uk>
Permission is hereby granted, free of charge, to any person obtaining
a copy of this software and associated documentation files (the
"Software"), to deal in the Software without restriction, including
without limitation the rights to use, copy, modify, merge, publish,
distribute, sublicense, and/or sell copies of the Software, and to
permit persons to whom the Software is furnished to do so, subject to
the following conditions:
The above copyright notice and this permission notice shall be
included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS
BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN
ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
*/
/*
An example:
#include "khash.h"
KHASH_MAP_INIT_INT(32, char)
int main() {
int ret, is_missing;
khiter_t k;
khash_t(32) *h = kh_init(32);
k = kh_put(32, h, 5, &ret);
kh_value(h, k) = 10;
k = kh_get(32, h, 10);
is_missing = (k == kh_end(h));
k = kh_get(32, h, 5);
kh_del(32, h, k);
for (k = kh_begin(h); k != kh_end(h); ++k)
if (kh_exist(h, k)) kh_value(h, k) = 1;
kh_destroy(32, h);
return 0;
}
*/
/*
2013-05-02 (0.2.8):
* Use quadratic probing. When the capacity is power of 2, stepping function
i*(i+1)/2 guarantees to traverse each bucket. It is better than double
hashing on cache performance and is more robust than linear probing.
In theory, double hashing should be more robust than quadratic probing.
However, my implementation is probably not for large hash tables, because
the second hash function is closely tied to the first hash function,
which reduce the effectiveness of double hashing.
Reference: http://research.cs.vt.edu/AVresearch/hashing/quadratic.php
2011-12-29 (0.2.7):
* Minor code clean up; no actual effect.
2011-09-16 (0.2.6):
* The capacity is a power of 2. This seems to dramatically improve the
speed for simple keys. Thank Zilong Tan for the suggestion. Reference:
- http://code.google.com/p/ulib/
- http://nothings.org/computer/judy/
* Allow to optionally use linear probing which usually has better
performance for random input. Double hashing is still the default as it
is more robust to certain non-random input.
* Added Wang's integer hash function (not used by default). This hash
function is more robust to certain non-random input.
2011-02-14 (0.2.5):
* Allow to declare global functions.
2009-09-26 (0.2.4):
* Improve portability
2008-09-19 (0.2.3):
* Corrected the example
* Improved interfaces
2008-09-11 (0.2.2):
* Improved speed a little in kh_put()
2008-09-10 (0.2.1):
* Added kh_clear()
* Fixed a compiling error
2008-09-02 (0.2.0):
* Changed to token concatenation which increases flexibility.
2008-08-31 (0.1.2):
* Fixed a bug in kh_get(), which has not been tested previously.
2008-08-31 (0.1.1):
* Added destructor
*/
#ifndef __AC_KHASH_H
#define __AC_KHASH_H
/*!
@header
Generic hash table library.
*/
#define AC_VERSION_KHASH_H "0.2.8"
#include <stdlib.h>
#include <string.h>
#include <limits.h>
/* compiler specific configuration */
#if UINT_MAX == 0xffffffffu
typedef unsigned int khint32_t;
#elif ULONG_MAX == 0xffffffffu
typedef unsigned long khint32_t;
#endif
#if ULONG_MAX == ULLONG_MAX
typedef unsigned long khint64_t;
#else
typedef uint64_t khint64_t;
#endif
#ifndef kh_inline
#ifdef _MSC_VER
#define kh_inline __inline
#else
#define kh_inline inline
#endif
#endif /* kh_inline */
#ifndef klib_unused
#if (defined __clang__ && __clang_major__ >= 3) || (defined __GNUC__ && __GNUC__ >= 3)
#define klib_unused __attribute__ ((__unused__))
#else
#define klib_unused
#endif
#endif /* klib_unused */
typedef khint32_t khint_t;
typedef khint_t khiter_t;
#define __ac_isempty(flag, i) ((flag[i>>4]>>((i&0xfU)<<1))&2)
#define __ac_isdel(flag, i) ((flag[i>>4]>>((i&0xfU)<<1))&1)
#define __ac_iseither(flag, i) ((flag[i>>4]>>((i&0xfU)<<1))&3)
#define __ac_set_isdel_false(flag, i) (flag[i>>4]&=~(1ul<<((i&0xfU)<<1)))
#define __ac_set_isempty_false(flag, i) (flag[i>>4]&=~(2ul<<((i&0xfU)<<1)))
#define __ac_set_isboth_false(flag, i) (flag[i>>4]&=~(3ul<<((i&0xfU)<<1)))
#define __ac_set_isdel_true(flag, i) (flag[i>>4]|=1ul<<((i&0xfU)<<1))
#define __ac_fsize(m) ((m) < 16? 1 : (m)>>4)
#ifndef kroundup32
#define kroundup32(x) (--(x), (x)|=(x)>>1, (x)|=(x)>>2, (x)|=(x)>>4, (x)|=(x)>>8, (x)|=(x)>>16, ++(x))
#endif
#ifndef kcalloc
#define kcalloc(N,Z) calloc(N,Z)
#endif
#ifndef kmalloc
#define kmalloc(Z) malloc(Z)
#endif
#ifndef krealloc
#define krealloc(P,Z) realloc(P,Z)
#endif
#ifndef kfree
#define kfree(P) free(P)
#endif
static const double __ac_HASH_UPPER = 0.77;
#define __KHASH_TYPE(name, khkey_t, khval_t) \
typedef struct kh_##name##_s { \
khint_t n_buckets, size, n_occupied, upper_bound; \
khint32_t *flags; \
khkey_t *keys; \
khval_t *vals; \
} kh_##name##_t;
#define __KHASH_PROTOTYPES(name, khkey_t, khval_t) \
extern kh_##name##_t *kh_init_##name(void); \
extern void kh_destroy_##name(kh_##name##_t *h); \
extern void kh_clear_##name(kh_##name##_t *h); \
extern khint_t kh_get_##name(const kh_##name##_t *h, khkey_t key); \
extern int kh_resize_##name(kh_##name##_t *h, khint_t new_n_buckets); \
extern khint_t kh_put_##name(kh_##name##_t *h, khkey_t key, int *ret); \
extern void kh_del_##name(kh_##name##_t *h, khint_t x);
#define __KHASH_IMPL(name, SCOPE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal) \
SCOPE kh_##name##_t *kh_init_##name(void) { \
return (kh_##name##_t*)kcalloc(1, sizeof(kh_##name##_t)); \
} \
SCOPE void kh_destroy_##name(kh_##name##_t *h) \
{ \
if (h) { \
kfree((void *)h->keys); kfree(h->flags); \
kfree((void *)h->vals); \
kfree(h); \
} \
} \
SCOPE void kh_clear_##name(kh_##name##_t *h) \
{ \
if (h && h->flags) { \
memset(h->flags, 0xaa, __ac_fsize(h->n_buckets) * sizeof(khint32_t)); \
h->size = h->n_occupied = 0; \
} \
} \
SCOPE khint_t kh_get_##name(const kh_##name##_t *h, khkey_t key) \
{ \
if (h->n_buckets) { \
khint_t k, i, last, mask, step = 0; \
mask = h->n_buckets - 1; \
k = __hash_func(key); i = k & mask; \
last = i; \
while (!__ac_isempty(h->flags, i) && (__ac_isdel(h->flags, i) || !__hash_equal(h->keys[i], key))) { \
i = (i + (++step)) & mask; \
if (i == last) return h->n_buckets; \
} \
return __ac_iseither(h->flags, i)? h->n_buckets : i; \
} else return 0; \
} \
SCOPE int kh_resize_##name(kh_##name##_t *h, khint_t new_n_buckets) \
{ /* This function uses 0.25*n_buckets bytes of working space instead of [sizeof(key_t+val_t)+.25]*n_buckets. */ \
khint32_t *new_flags = 0; \
khint_t j = 1; \
{ \
kroundup32(new_n_buckets); \
if (new_n_buckets < 4) new_n_buckets = 4; \
if (h->size >= (khint_t)(new_n_buckets * __ac_HASH_UPPER + 0.5)) j = 0; /* requested size is too small */ \
else { /* hash table size to be changed (shrink or expand); rehash */ \
new_flags = (khint32_t*)kmalloc(__ac_fsize(new_n_buckets) * sizeof(khint32_t)); \
if (!new_flags) return -1; \
memset(new_flags, 0xaa, __ac_fsize(new_n_buckets) * sizeof(khint32_t)); \
if (h->n_buckets < new_n_buckets) { /* expand */ \
khkey_t *new_keys = (khkey_t*)krealloc((void *)h->keys, new_n_buckets * sizeof(khkey_t)); \
if (!new_keys) { kfree(new_flags); return -1; } \
h->keys = new_keys; \
if (kh_is_map) { \
khval_t *new_vals = (khval_t*)krealloc((void *)h->vals, new_n_buckets * sizeof(khval_t)); \
if (!new_vals) { kfree(new_flags); return -1; } \
h->vals = new_vals; \
} \
} /* otherwise shrink */ \
} \
} \
if (j) { /* rehashing is needed */ \
for (j = 0; j != h->n_buckets; ++j) { \
if (__ac_iseither(h->flags, j) == 0) { \
khkey_t key = h->keys[j]; \
khval_t val; \
khint_t new_mask; \
new_mask = new_n_buckets - 1; \
if (kh_is_map) val = h->vals[j]; \
__ac_set_isdel_true(h->flags, j); \
while (1) { /* kick-out process; sort of like in Cuckoo hashing */ \
khint_t k, i, step = 0; \
k = __hash_func(key); \
i = k & new_mask; \
while (!__ac_isempty(new_flags, i)) i = (i + (++step)) & new_mask; \
__ac_set_isempty_false(new_flags, i); \
if (i < h->n_buckets && __ac_iseither(h->flags, i) == 0) { /* kick out the existing element */ \
{ khkey_t tmp = h->keys[i]; h->keys[i] = key; key = tmp; } \
if (kh_is_map) { khval_t tmp = h->vals[i]; h->vals[i] = val; val = tmp; } \
__ac_set_isdel_true(h->flags, i); /* mark it as deleted in the old hash table */ \
} else { /* write the element and jump out of the loop */ \
h->keys[i] = key; \
if (kh_is_map) h->vals[i] = val; \
break; \
} \
} \
} \
} \
if (h->n_buckets > new_n_buckets) { /* shrink the hash table */ \
h->keys = (khkey_t*)krealloc((void *)h->keys, new_n_buckets * sizeof(khkey_t)); \
if (kh_is_map) h->vals = (khval_t*)krealloc((void *)h->vals, new_n_buckets * sizeof(khval_t)); \
} \
kfree(h->flags); /* free the working space */ \
h->flags = new_flags; \
h->n_buckets = new_n_buckets; \
h->n_occupied = h->size; \
h->upper_bound = (khint_t)(h->n_buckets * __ac_HASH_UPPER + 0.5); \
} \
return 0; \
} \
SCOPE khint_t kh_put_##name(kh_##name##_t *h, khkey_t key, int *ret) \
{ \
khint_t x; \
if (h->n_occupied >= h->upper_bound) { /* update the hash table */ \
if (h->n_buckets > (h->size<<1)) { \
if (kh_resize_##name(h, h->n_buckets - 1) < 0) { /* clear "deleted" elements */ \
*ret = -1; return h->n_buckets; \
} \
} else if (kh_resize_##name(h, h->n_buckets + 1) < 0) { /* expand the hash table */ \
*ret = -1; return h->n_buckets; \
} \
} /* TODO: to implement automatically shrinking; resize() already support shrinking */ \
{ \
khint_t k, i, site, last, mask = h->n_buckets - 1, step = 0; \
x = site = h->n_buckets; k = __hash_func(key); i = k & mask; \
if (__ac_isempty(h->flags, i)) x = i; /* for speed up */ \
else { \
last = i; \
while (!__ac_isempty(h->flags, i) && (__ac_isdel(h->flags, i) || !__hash_equal(h->keys[i], key))) { \
if (__ac_isdel(h->flags, i)) site = i; \
i = (i + (++step)) & mask; \
if (i == last) { x = site; break; } \
} \
if (x == h->n_buckets) { \
if (__ac_isempty(h->flags, i) && site != h->n_buckets) x = site; \
else x = i; \
} \
} \
} \
if (__ac_isempty(h->flags, x)) { /* not present at all */ \
h->keys[x] = key; \
__ac_set_isboth_false(h->flags, x); \
++h->size; ++h->n_occupied; \
*ret = 1; \
} else if (__ac_isdel(h->flags, x)) { /* deleted */ \
h->keys[x] = key; \
__ac_set_isboth_false(h->flags, x); \
++h->size; \
*ret = 2; \
} else *ret = 0; /* Don't touch h->keys[x] if present and not deleted */ \
return x; \
} \
SCOPE void kh_del_##name(kh_##name##_t *h, khint_t x) \
{ \
if (x != h->n_buckets && !__ac_iseither(h->flags, x)) { \
__ac_set_isdel_true(h->flags, x); \
--h->size; \
} \
}
#define KHASH_DECLARE(name, khkey_t, khval_t) \
__KHASH_TYPE(name, khkey_t, khval_t) \
__KHASH_PROTOTYPES(name, khkey_t, khval_t)
#define KHASH_INIT2(name, SCOPE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal) \
__KHASH_TYPE(name, khkey_t, khval_t) \
__KHASH_IMPL(name, SCOPE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
#define KHASH_INIT(name, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal) \
KHASH_INIT2(name, static kh_inline klib_unused, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
/* --- BEGIN OF HASH FUNCTIONS --- */
/*! @function
@abstract Integer hash function
@param key The integer [khint32_t]
@return The hash value [khint_t]
*/
#define kh_int_hash_func(key) (khint32_t)(key)
/*! @function
@abstract Integer comparison function
*/
#define kh_int_hash_equal(a, b) ((a) == (b))
/*! @function
@abstract 64-bit integer hash function
@param key The integer [khint64_t]
@return The hash value [khint_t]
*/
#define kh_int64_hash_func(key) (khint32_t)((key)>>33^(key)^(key)<<11)
/*! @function
@abstract 64-bit integer comparison function
*/
#define kh_int64_hash_equal(a, b) ((a) == (b))
/*! @function
@abstract const char* hash function
@param s Pointer to a null terminated string
@return The hash value
*/
static kh_inline khint_t __ac_X31_hash_string(const char *s)
{
khint_t h = (khint_t)*s;
if (h) for (++s ; *s; ++s) h = (h << 5) - h + (khint_t)*s;
return h;
}
/*! @function
@abstract Another interface to const char* hash function
@param key Pointer to a null terminated string [const char*]
@return The hash value [khint_t]
*/
#define kh_str_hash_func(key) __ac_X31_hash_string(key)
/*! @function
@abstract Const char* comparison function
*/
#define kh_str_hash_equal(a, b) (strcmp(a, b) == 0)
static kh_inline khint_t __ac_Wang_hash(khint_t key)
{
key += ~(key << 15);
key ^= (key >> 10);
key += (key << 3);
key ^= (key >> 6);
key += ~(key << 11);
key ^= (key >> 16);
return key;
}
#define kh_int_hash_func2(key) __ac_Wang_hash((khint_t)key)
/* --- END OF HASH FUNCTIONS --- */
/* Other convenient macros... */
/*!
@abstract Type of the hash table.
@param name Name of the hash table [symbol]
*/
#define khash_t(name) kh_##name##_t
/*! @function
@abstract Initiate a hash table.
@param name Name of the hash table [symbol]
@return Pointer to the hash table [khash_t(name)*]
*/
#define kh_init(name) kh_init_##name()
/*! @function
@abstract Destroy a hash table.
@param name Name of the hash table [symbol]
@param h Pointer to the hash table [khash_t(name)*]
*/
#define kh_destroy(name, h) kh_destroy_##name(h)
/*! @function
@abstract Reset a hash table without deallocating memory.
@param name Name of the hash table [symbol]
@param h Pointer to the hash table [khash_t(name)*]
*/
#define kh_clear(name, h) kh_clear_##name(h)
/*! @function
@abstract Resize a hash table.
@param name Name of the hash table [symbol]
@param h Pointer to the hash table [khash_t(name)*]
@param s New size [khint_t]
*/
#define kh_resize(name, h, s) kh_resize_##name(h, s)
/*! @function
@abstract Insert a key to the hash table.
@param name Name of the hash table [symbol]
@param h Pointer to the hash table [khash_t(name)*]
@param k Key [type of keys]
@param r Extra return code: -1 if the operation failed;
0 if the key is present in the hash table;
1 if the bucket is empty (never used); 2 if the element in
the bucket has been deleted [int*]
@return Iterator to the inserted element [khint_t]
*/
#define kh_put(name, h, k, r) kh_put_##name(h, k, r)
/*! @function
@abstract Retrieve a key from the hash table.
@param name Name of the hash table [symbol]
@param h Pointer to the hash table [khash_t(name)*]
@param k Key [type of keys]
@return Iterator to the found element, or kh_end(h) if the element is absent [khint_t]
*/
#define kh_get(name, h, k) kh_get_##name(h, k)
/*! @function
@abstract Remove a key from the hash table.
@param name Name of the hash table [symbol]
@param h Pointer to the hash table [khash_t(name)*]
@param k Iterator to the element to be deleted [khint_t]
*/
#define kh_del(name, h, k) kh_del_##name(h, k)
/*! @function
@abstract Test whether a bucket contains data.
@param h Pointer to the hash table [khash_t(name)*]
@param x Iterator to the bucket [khint_t]
@return 1 if containing data; 0 otherwise [int]
*/
#define kh_exist(h, x) (!__ac_iseither((h)->flags, (x)))
/*! @function
@abstract Get key given an iterator
@param h Pointer to the hash table [khash_t(name)*]
@param x Iterator to the bucket [khint_t]
@return Key [type of keys]
*/
#define kh_key(h, x) ((h)->keys[x])
/*! @function
@abstract Get value given an iterator
@param h Pointer to the hash table [khash_t(name)*]
@param x Iterator to the bucket [khint_t]
@return Value [type of values]
@discussion For hash sets, calling this results in segfault.
*/
#define kh_val(h, x) ((h)->vals[x])
/*! @function
@abstract Alias of kh_val()
*/
#define kh_value(h, x) ((h)->vals[x])
/*! @function
@abstract Get the start iterator
@param h Pointer to the hash table [khash_t(name)*]
@return The start iterator [khint_t]
*/
#define kh_begin(h) (khint_t)(0)
/*! @function
@abstract Get the end iterator
@param h Pointer to the hash table [khash_t(name)*]
@return The end iterator [khint_t]
*/
#define kh_end(h) ((h)->n_buckets)
/*! @function
@abstract Get the number of elements in the hash table
@param h Pointer to the hash table [khash_t(name)*]
@return Number of elements in the hash table [khint_t]
*/
#define kh_size(h) ((h)->size)
/*! @function
@abstract Get the number of buckets in the hash table
@param h Pointer to the hash table [khash_t(name)*]
@return Number of buckets in the hash table [khint_t]
*/
#define kh_n_buckets(h) ((h)->n_buckets)
/*! @function
@abstract Iterate over the entries in the hash table
@param h Pointer to the hash table [khash_t(name)*]
@param kvar Variable to which key will be assigned
@param vvar Variable to which value will be assigned
@param code Block of code to execute
*/
#define kh_foreach(h, kvar, vvar, code) { khint_t __i; \
for (__i = kh_begin(h); __i != kh_end(h); ++__i) { \
if (!kh_exist(h,__i)) continue; \
(kvar) = kh_key(h,__i); \
(vvar) = kh_val(h,__i); \
code; \
} }
/*! @function
@abstract Iterate over the values in the hash table
@param h Pointer to the hash table [khash_t(name)*]
@param vvar Variable to which value will be assigned
@param code Block of code to execute
*/
#define kh_foreach_value(h, vvar, code) { khint_t __i; \
for (__i = kh_begin(h); __i != kh_end(h); ++__i) { \
if (!kh_exist(h,__i)) continue; \
(vvar) = kh_val(h,__i); \
code; \
} }
/* More conenient interfaces */
/*! @function
@abstract Instantiate a hash set containing integer keys
@param name Name of the hash table [symbol]
*/
#define KHASH_SET_INIT_INT(name) \
KHASH_INIT(name, khint32_t, char, 0, kh_int_hash_func, kh_int_hash_equal)
/*! @function
@abstract Instantiate a hash map containing integer keys
@param name Name of the hash table [symbol]
@param khval_t Type of values [type]
*/
#define KHASH_MAP_INIT_INT(name, khval_t) \
KHASH_INIT(name, khint32_t, khval_t, 1, kh_int_hash_func, kh_int_hash_equal)
/*! @function
@abstract Instantiate a hash map containing 64-bit integer keys
@param name Name of the hash table [symbol]
*/
#define KHASH_SET_INIT_INT64(name) \
KHASH_INIT(name, khint64_t, char, 0, kh_int64_hash_func, kh_int64_hash_equal)
/*! @function
@abstract Instantiate a hash map containing 64-bit integer keys
@param name Name of the hash table [symbol]
@param khval_t Type of values [type]
*/
#define KHASH_MAP_INIT_INT64(name, khval_t) \
KHASH_INIT(name, khint64_t, khval_t, 1, kh_int64_hash_func, kh_int64_hash_equal)
typedef const char *kh_cstr_t;
/*! @function
@abstract Instantiate a hash map containing const char* keys
@param name Name of the hash table [symbol]
*/
#define KHASH_SET_INIT_STR(name) \
KHASH_INIT(name, kh_cstr_t, char, 0, kh_str_hash_func, kh_str_hash_equal)
/*! @function
@abstract Instantiate a hash map containing const char* keys
@param name Name of the hash table [symbol]
@param khval_t Type of values [type]
*/
#define KHASH_MAP_INIT_STR(name, khval_t) \
KHASH_INIT(name, kh_cstr_t, khval_t, 1, kh_str_hash_func, kh_str_hash_equal)
#endif /* __AC_KHASH_H */

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#pragma once
#ifdef __cplusplus
#include <twml/optim.h>
namespace twml {
template<typename Tx>
static int64_t linear_search(const Tx *xsData, const Tx val, const int64_t mainSize) {
int64_t left = 0;
int64_t right = mainSize-1;
while(left <= right && val > xsData[left])
left++;
return left;
}
} // namespace twml
#endif

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//-----------------------------------------------------------------------------
// MurmurHash3 was written by Austin Appleby, and is placed in the public
// domain. The author hereby disclaims copyright to this source code.
#ifndef _MURMURHASH3_H_
#define _MURMURHASH3_H_
//-----------------------------------------------------------------------------
// Platform-specific functions and macros
// Microsoft Visual Studio
#if defined(_MSC_VER) && (_MSC_VER < 1600)
typedef unsigned char uint8_t;
typedef unsigned int uint32_t;
typedef unsigned __int64 uint64_t;
// Other compilers
#else // defined(_MSC_VER)
#include <stdint.h>
#endif // !defined(_MSC_VER)
//-----------------------------------------------------------------------------
void MurmurHash3_x86_32 ( const void * key, int len, uint32_t seed, void * out );
void MurmurHash3_x86_128 ( const void * key, int len, uint32_t seed, void * out );
void MurmurHash3_x64_128 ( const void * key, int len, uint32_t seed, void * out );
//-----------------------------------------------------------------------------
#endif // _MURMURHASH3_H_

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// For details of how to encode and decode thrift, check
// https://github.com/apache/thrift/blob/master/doc/specs/thrift-binary-protocol.md
// Definitions of the thrift binary format
typedef enum {
TTYPE_STOP = 0,
TTYPE_VOID = 1,
TTYPE_BOOL = 2,
TTYPE_BYTE = 3,
TTYPE_DOUBLE = 4,
TTYPE_I16 = 6,
TTYPE_I32 = 8,
TTYPE_I64 = 10,
TTYPE_STRING = 11,
TTYPE_STRUCT = 12,
TTYPE_MAP = 13,
TTYPE_SET = 14,
TTYPE_LIST = 15,
TTYPE_ENUM = 16,
} TTYPES;
// Fields of a batch prediction response
typedef enum {
BPR_DUMMY ,
BPR_PREDICTIONS,
} BPR_FIELDS;
// Fields of a datarecord
typedef enum {
DR_CROSS , // fake field for crosses
DR_BINARY ,
DR_CONTINUOUS ,
DR_DISCRETE ,
DR_STRING ,
DR_SPARSE_BINARY ,
DR_SPARSE_CONTINUOUS ,
DR_BLOB ,
DR_GENERAL_TENSOR ,
DR_SPARSE_TENSOR ,
} DR_FIELDS;
// Fields for General tensor
typedef enum {
GT_DUMMY , // dummy field
GT_RAW ,
GT_STRING ,
GT_INT32 ,
GT_INT64 ,
GT_FLOAT ,
GT_DOUBLE ,
GT_BOOL ,
} GT_FIELDS;
typedef enum {
SP_DUMMY , // dummy field
SP_COO ,
} SP_FIELDS;
// Enum values from tensor.thrift
typedef enum {
DATA_TYPE_FLOAT ,
DATA_TYPE_DOUBLE ,
DATA_TYPE_INT32 ,
DATA_TYPE_INT64 ,
DATA_TYPE_UINT8 ,
DATA_TYPE_STRING ,
DATA_TYPE_BYTE ,
DATA_TYPE_BOOL ,
} DATA_TYPES;

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#ifndef _UTF_CONVERTER_H_
#define _UTF_CONVERTER_H_
#include <stddef.h>
#include <stdint.h>
#include <sys/types.h>
ssize_t utf8_to_utf16(const uint8_t *in, uint64_t in_len, uint16_t *out, uint64_t max_out);
#endif

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#include <twml/io/IOError.h>
namespace twml {
namespace io {
namespace {
std::string messageFromStatus(IOError::Status status) {
switch (status) {
case IOError::OUT_OF_RANGE:
return "failed to read enough input";
case IOError::WRONG_MAGIC:
return "wrong magic in stream";
case IOError::WRONG_HEADER:
return "wrong header in stream";
case IOError::ERROR_HEADER_CHECKSUM:
return "header checksum doesn't match";
case IOError::INVALID_METHOD:
return "using invalid method";
case IOError::USING_RESERVED:
return "using reserved flag";
case IOError::ERROR_HEADER_EXTRA_FIELD_CHECKSUM:
return "extra header field checksum doesn't match";
case IOError::CANT_FIT_OUTPUT:
return "can't fit output in the given space";
case IOError::SPLIT_FILE:
return "split files aren't supported";
case IOError::BLOCK_SIZE_TOO_LARGE:
return "block size is too large";
case IOError::SOURCE_LARGER_THAN_DESTINATION:
return "source is larger than destination";
case IOError::DESTINATION_LARGER_THAN_CAPACITY:
return "destination buffer is too small to fit uncompressed result";
case IOError::HEADER_FLAG_MISMATCH:
return "failed to match flags for compressed and decompressed data";
case IOError::NOT_ENOUGH_INPUT:
return "not enough input to proceed with decompression";
case IOError::ERROR_SOURCE_BLOCK_CHECKSUM:
return "source block checksum doesn't match";
case IOError::COMPRESSED_DATA_VIOLATION:
return "error occurred while decompressing the data";
case IOError::ERROR_DESTINATION_BLOCK_CHECKSUM:
return "destination block checksum doesn't match";
case IOError::EMPTY_RECORD:
return "can't write an empty record";
case IOError::MALFORMED_MEMORY_RECORD:
return "can't write malformed record";
case IOError::UNSUPPORTED_OUTPUT_TYPE:
return "output data type is not supported";
case IOError::OTHER_ERROR:
default:
return "unknown error occurred";
}
}
} // namespace
IOError::IOError(Status status): twml::Error(TWML_ERR_IO, "Found error while processing stream: " +
messageFromStatus(status)), m_status(status) {}
} // namespace io
} // namespace twml

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//-----------------------------------------------------------------------------
// MurmurHash3 was written by Austin Appleby, and is placed in the public
// domain. The author hereby disclaims copyright to this source code.
// Note - The x86 and x64 versions do _not_ produce the same results, as the
// algorithms are optimized for their respective platforms. You can still
// compile and run any of them on any platform, but your performance with the
// non-native version will be less than optimal.
#include "internal/murmur_hash3.h"
//-----------------------------------------------------------------------------
// Platform-specific functions and macros
// Microsoft Visual Studio
#if defined(_MSC_VER)
#define FORCE_INLINE __forceinline
#include <stdlib.h>
#define ROTL32(x,y) _rotl(x,y)
#define ROTL64(x,y) _rotl64(x,y)
#define BIG_CONSTANT(x) (x)
// Other compilers
#else // defined(_MSC_VER)
#define FORCE_INLINE inline __attribute__((always_inline))
FORCE_INLINE uint32_t rotl32 ( uint32_t x, int8_t r )
{
return (x << r) | (x >> (32 - r));
}
FORCE_INLINE uint64_t rotl64 ( uint64_t x, int8_t r )
{
return (x << r) | (x >> (64 - r));
}
#define ROTL32(x,y) rotl32(x,y)
#define ROTL64(x,y) rotl64(x,y)
#define BIG_CONSTANT(x) (x##LLU)
#endif // !defined(_MSC_VER)
//-----------------------------------------------------------------------------
// Block read - if your platform needs to do endian-swapping or can only
// handle aligned reads, do the conversion here
FORCE_INLINE uint32_t getblock32 ( const uint32_t * p, int i )
{
return p[i];
}
FORCE_INLINE uint64_t getblock64 ( const uint64_t * p, int i )
{
return p[i];
}
//-----------------------------------------------------------------------------
// Finalization mix - force all bits of a hash block to avalanche
FORCE_INLINE uint32_t fmix32 ( uint32_t h )
{
h ^= h >> 16;
h *= 0x85ebca6b;
h ^= h >> 13;
h *= 0xc2b2ae35;
h ^= h >> 16;
return h;
}
//----------
FORCE_INLINE uint64_t fmix64 ( uint64_t k )
{
k ^= k >> 33;
k *= BIG_CONSTANT(0xff51afd7ed558ccd);
k ^= k >> 33;
k *= BIG_CONSTANT(0xc4ceb9fe1a85ec53);
k ^= k >> 33;
return k;
}
//-----------------------------------------------------------------------------
void MurmurHash3_x86_32 ( const void * key, int len,
uint32_t seed, void * out )
{
const uint8_t * data = (const uint8_t*)key;
const int nblocks = len / 4;
uint32_t h1 = seed;
const uint32_t c1 = 0xcc9e2d51;
const uint32_t c2 = 0x1b873593;
//----------
// body
const uint32_t * blocks = (const uint32_t *)(data + nblocks*4);
for(int i = -nblocks; i; i++)
{
uint32_t k1 = getblock32(blocks,i);
k1 *= c1;
k1 = ROTL32(k1,15);
k1 *= c2;
h1 ^= k1;
h1 = ROTL32(h1,13);
h1 = h1*5+0xe6546b64;
}
//----------
// tail
const uint8_t * tail = (const uint8_t*)(data + nblocks*4);
uint32_t k1 = 0;
switch(len & 3)
{
case 3: k1 ^= tail[2] << 16;
case 2: k1 ^= tail[1] << 8;
case 1: k1 ^= tail[0];
k1 *= c1; k1 = ROTL32(k1,15); k1 *= c2; h1 ^= k1;
};
//----------
// finalization
h1 ^= len;
h1 = fmix32(h1);
*(uint32_t*)out = h1;
}
//-----------------------------------------------------------------------------
void MurmurHash3_x86_128 ( const void * key, const int len,
uint32_t seed, void * out )
{
const uint8_t * data = (const uint8_t*)key;
const int nblocks = len / 16;
uint32_t h1 = seed;
uint32_t h2 = seed;
uint32_t h3 = seed;
uint32_t h4 = seed;
const uint32_t c1 = 0x239b961b;
const uint32_t c2 = 0xab0e9789;
const uint32_t c3 = 0x38b34ae5;
const uint32_t c4 = 0xa1e38b93;
//----------
// body
const uint32_t * blocks = (const uint32_t *)(data + nblocks*16);
for(int i = -nblocks; i; i++)
{
uint32_t k1 = getblock32(blocks,i*4+0);
uint32_t k2 = getblock32(blocks,i*4+1);
uint32_t k3 = getblock32(blocks,i*4+2);
uint32_t k4 = getblock32(blocks,i*4+3);
k1 *= c1; k1 = ROTL32(k1,15); k1 *= c2; h1 ^= k1;
h1 = ROTL32(h1,19); h1 += h2; h1 = h1*5+0x561ccd1b;
k2 *= c2; k2 = ROTL32(k2,16); k2 *= c3; h2 ^= k2;
h2 = ROTL32(h2,17); h2 += h3; h2 = h2*5+0x0bcaa747;
k3 *= c3; k3 = ROTL32(k3,17); k3 *= c4; h3 ^= k3;
h3 = ROTL32(h3,15); h3 += h4; h3 = h3*5+0x96cd1c35;
k4 *= c4; k4 = ROTL32(k4,18); k4 *= c1; h4 ^= k4;
h4 = ROTL32(h4,13); h4 += h1; h4 = h4*5+0x32ac3b17;
}
//----------
// tail
const uint8_t * tail = (const uint8_t*)(data + nblocks*16);
uint32_t k1 = 0;
uint32_t k2 = 0;
uint32_t k3 = 0;
uint32_t k4 = 0;
switch(len & 15)
{
case 15: k4 ^= tail[14] << 16;
case 14: k4 ^= tail[13] << 8;
case 13: k4 ^= tail[12] << 0;
k4 *= c4; k4 = ROTL32(k4,18); k4 *= c1; h4 ^= k4;
case 12: k3 ^= tail[11] << 24;
case 11: k3 ^= tail[10] << 16;
case 10: k3 ^= tail[ 9] << 8;
case 9: k3 ^= tail[ 8] << 0;
k3 *= c3; k3 = ROTL32(k3,17); k3 *= c4; h3 ^= k3;
case 8: k2 ^= tail[ 7] << 24;
case 7: k2 ^= tail[ 6] << 16;
case 6: k2 ^= tail[ 5] << 8;
case 5: k2 ^= tail[ 4] << 0;
k2 *= c2; k2 = ROTL32(k2,16); k2 *= c3; h2 ^= k2;
case 4: k1 ^= tail[ 3] << 24;
case 3: k1 ^= tail[ 2] << 16;
case 2: k1 ^= tail[ 1] << 8;
case 1: k1 ^= tail[ 0] << 0;
k1 *= c1; k1 = ROTL32(k1,15); k1 *= c2; h1 ^= k1;
};
//----------
// finalization
h1 ^= len; h2 ^= len; h3 ^= len; h4 ^= len;
h1 += h2; h1 += h3; h1 += h4;
h2 += h1; h3 += h1; h4 += h1;
h1 = fmix32(h1);
h2 = fmix32(h2);
h3 = fmix32(h3);
h4 = fmix32(h4);
h1 += h2; h1 += h3; h1 += h4;
h2 += h1; h3 += h1; h4 += h1;
((uint32_t*)out)[0] = h1;
((uint32_t*)out)[1] = h2;
((uint32_t*)out)[2] = h3;
((uint32_t*)out)[3] = h4;
}
//-----------------------------------------------------------------------------
void MurmurHash3_x64_128 ( const void * key, const int len,
const uint32_t seed, void * out )
{
const uint8_t * data = (const uint8_t*)key;
const int nblocks = len / 16;
uint64_t h1 = seed;
uint64_t h2 = seed;
const uint64_t c1 = BIG_CONSTANT(0x87c37b91114253d5);
const uint64_t c2 = BIG_CONSTANT(0x4cf5ad432745937f);
//----------
// body
const uint64_t * blocks = (const uint64_t *)(data);
for(int i = 0; i < nblocks; i++)
{
uint64_t k1 = getblock64(blocks,i*2+0);
uint64_t k2 = getblock64(blocks,i*2+1);
k1 *= c1; k1 = ROTL64(k1,31); k1 *= c2; h1 ^= k1;
h1 = ROTL64(h1,27); h1 += h2; h1 = h1*5+0x52dce729;
k2 *= c2; k2 = ROTL64(k2,33); k2 *= c1; h2 ^= k2;
h2 = ROTL64(h2,31); h2 += h1; h2 = h2*5+0x38495ab5;
}
//----------
// tail
const uint8_t * tail = (const uint8_t*)(data + nblocks*16);
uint64_t k1 = 0;
uint64_t k2 = 0;
switch(len & 15)
{
case 15: k2 ^= ((uint64_t)tail[14]) << 48;
case 14: k2 ^= ((uint64_t)tail[13]) << 40;
case 13: k2 ^= ((uint64_t)tail[12]) << 32;
case 12: k2 ^= ((uint64_t)tail[11]) << 24;
case 11: k2 ^= ((uint64_t)tail[10]) << 16;
case 10: k2 ^= ((uint64_t)tail[ 9]) << 8;
case 9: k2 ^= ((uint64_t)tail[ 8]) << 0;
k2 *= c2; k2 = ROTL64(k2,33); k2 *= c1; h2 ^= k2;
case 8: k1 ^= ((uint64_t)tail[ 7]) << 56;
case 7: k1 ^= ((uint64_t)tail[ 6]) << 48;
case 6: k1 ^= ((uint64_t)tail[ 5]) << 40;
case 5: k1 ^= ((uint64_t)tail[ 4]) << 32;
case 4: k1 ^= ((uint64_t)tail[ 3]) << 24;
case 3: k1 ^= ((uint64_t)tail[ 2]) << 16;
case 2: k1 ^= ((uint64_t)tail[ 1]) << 8;
case 1: k1 ^= ((uint64_t)tail[ 0]) << 0;
k1 *= c1; k1 = ROTL64(k1,31); k1 *= c2; h1 ^= k1;
};
//----------
// finalization
h1 ^= len; h2 ^= len;
h1 += h2;
h2 += h1;
h1 = fmix64(h1);
h2 = fmix64(h2);
h1 += h2;
h2 += h1;
((uint64_t*)out)[0] = h1;
((uint64_t*)out)[1] = h2;
}
//-----------------------------------------------------------------------------

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#include "internal/interpolate.h"
#include "internal/error.h"
#include <twml/optim.h>
namespace twml {
template<typename T>
void mdlInfer(Tensor &output_keys, Tensor &output_vals,
const Tensor &input_keys, const Tensor &input_vals,
const Tensor &bin_ids,
const Tensor &bin_vals,
const Tensor &feature_offsets,
bool return_bin_indices) {
auto okeysData = output_keys.getData<int64_t>();
auto ovalsData = output_vals.getData<T>();
uint64_t okeysStride = output_keys.getStride(0);
uint64_t ovaluesStride = output_vals.getStride(0);
auto ikeysData = input_keys.getData<int64_t>();
auto ivalsData = input_vals.getData<T>();
uint64_t ikeysStride = input_keys.getStride(0);
uint64_t ivaluesStride = input_vals.getStride(0);
auto xsData = bin_vals.getData<T>();
auto ysData = bin_ids.getData<int64_t>();
uint64_t xsStride = bin_vals.getStride(0);
uint64_t ysStride = bin_ids.getStride(0);
auto offsetData = feature_offsets.getData<int64_t>();
uint64_t size = input_keys.getDim(0);
uint64_t total_bins = bin_ids.getNumElements();
uint64_t fsize = feature_offsets.getNumElements();
for (uint64_t i = 0; i < size; i++) {
int64_t ikey = ikeysData[i * ikeysStride] - TWML_INDEX_BASE;
T val = ivalsData[i * ivaluesStride];
if (ikey == -1) {
ovalsData[i * ovaluesStride] = val;
continue;
}
// Perform interpolation
uint64_t offset = offsetData[ikey];
uint64_t next_offset = (ikey == (int64_t)(fsize - 1)) ? total_bins : offsetData[ikey + 1];
uint64_t mainSize = next_offset - offset;
const T *lxsData = xsData + offset;
const int64_t *lysData = ysData + offset;
int64_t okey = interpolation<T, int64_t>(lxsData, xsStride,
lysData, ysStride,
val, mainSize, NEAREST, 0,
return_bin_indices);
okeysData[i * okeysStride] = okey + TWML_INDEX_BASE;
ovalsData[i * ovaluesStride] = 1;
}
}
void mdlInfer(Tensor &output_keys, Tensor &output_vals,
const Tensor &input_keys, const Tensor &input_vals,
const Tensor &bin_ids,
const Tensor &bin_vals,
const Tensor &feature_offsets,
bool return_bin_indices) {
if (input_keys.getType() != TWML_TYPE_INT64) {
throw twml::Error(TWML_ERR_TYPE, "input_keys must be a Long Tensor");
}
if (output_keys.getType() != TWML_TYPE_INT64) {
throw twml::Error(TWML_ERR_TYPE, "output_keys must be a Long Tensor");
}
if (bin_ids.getType() != TWML_TYPE_INT64) {
throw twml::Error(TWML_ERR_TYPE, "bin_ids must be a Long Tensor");
}
if (feature_offsets.getType() != TWML_TYPE_INT64) {
throw twml::Error(TWML_ERR_TYPE, "bin_ids must be a Long Tensor");
}
if (input_vals.getType() != bin_vals.getType()) {
throw twml::Error(TWML_ERR_TYPE,
"Data type of input_vals does not match type of bin_vals");
}
if (bin_vals.getNumDims() != 1) {
throw twml::Error(TWML_ERR_SIZE,
"bin_vals must be 1 Dimensional");
}
if (bin_ids.getNumDims() != 1) {
throw twml::Error(TWML_ERR_SIZE,
"bin_ids must be 1 Dimensional");
}
if (bin_vals.getNumElements() != bin_ids.getNumElements()) {
throw twml::Error(TWML_ERR_SIZE,
"Dimensions of bin_vals and bin_ids do not match");
}
if (feature_offsets.getStride(0) != 1) {
throw twml::Error(TWML_ERR_SIZE,
"feature_offsets must be contiguous");
}
switch (input_vals.getType()) {
case TWML_TYPE_FLOAT:
twml::mdlInfer<float>(output_keys, output_vals,
input_keys, input_vals,
bin_ids, bin_vals, feature_offsets,
return_bin_indices);
break;
case TWML_TYPE_DOUBLE:
twml::mdlInfer<double>(output_keys, output_vals,
input_keys, input_vals,
bin_ids, bin_vals, feature_offsets,
return_bin_indices);
break;
default:
throw twml::Error(TWML_ERR_TYPE,
"Unsupported datatype for mdlInfer");
}
}
const int DEFAULT_INTERPOLATION_LOWEST = 0;
/**
* @param output tensor to hold linear or nearest interpolation output.
* This function does not allocate space.
* The output tensor must have space allcoated.
* @param input input tensor; size must match output.
* input is assumed to have size [batch_size, number_of_labels].
* @param xs the bins.
* @param ys the values for the bins.
* @param mode: linear or nearest InterpolationMode.
* linear is used for isotonic calibration.
* nearest is used for MDL calibration and MDL inference.
*
* @return Returns nothing. Output is stored into the output tensor.
*
* This is used by IsotonicCalibration inference.
*/
template <typename T>
void interpolation(
Tensor output,
const Tensor input,
const Tensor xs,
const Tensor ys,
const InterpolationMode mode) {
// Sanity check: input and output should have two dims.
if (input.getNumDims() != 2 || output.getNumDims() != 2) {
throw twml::Error(TWML_ERR_TYPE,
"input and output should have 2 dimensions.");
}
// Sanity check: input and output size should match.
for (int i = 0; i < input.getNumDims(); i++) {
if (input.getDim(i) != output.getDim(i)) {
throw twml::Error(TWML_ERR_TYPE,
"input and output mismatch in size.");
}
}
// Sanity check: number of labels in input should match
// number of labels in xs / ys.
if (input.getDim(1) != xs.getDim(0)
|| input.getDim(1) != ys.getDim(0)) {
throw twml::Error(TWML_ERR_TYPE,
"input, xs, ys should have the same number of labels.");
}
const uint64_t inputStride0 = input.getStride(0);
const uint64_t inputStride1 = input.getStride(1);
const uint64_t outputStride0 = output.getStride(0);
const uint64_t outputStride1 = output.getStride(1);
const uint64_t xsStride0 = xs.getStride(0);
const uint64_t xsStride1 = xs.getStride(1);
const uint64_t ysStride0 = ys.getStride(0);
const uint64_t ysStride1 = ys.getStride(1);
const uint64_t mainSize = xs.getDim(1);
// for each value in the input matrix, compute output value by
// calling interpolation.
auto inputData = input.getData<T>();
auto outputData = output.getData<T>();
auto xsData = xs.getData<T>();
auto ysData = ys.getData<T>();
for (uint64_t i = 0; i < input.getDim(0); i++) {
for (uint64_t j = 0; j < input.getDim(1); j++) {
const T val = inputData[i * inputStride0 + j * inputStride1];
const T *lxsData = xsData + j * xsStride0;
const T *lysData = ysData + j * ysStride0;
const T res = interpolation(
lxsData, xsStride1,
lysData, ysStride1,
val,
mainSize,
mode,
DEFAULT_INTERPOLATION_LOWEST);
outputData[i * outputStride0 + j * outputStride1] = res;
}
}
}
void linearInterpolation(
Tensor output,
const Tensor input,
const Tensor xs,
const Tensor ys) {
switch (input.getType()) {
case TWML_TYPE_FLOAT:
twml::interpolation<float>(output, input, xs, ys, LINEAR);
break;
case TWML_TYPE_DOUBLE:
twml::interpolation<double>(output, input, xs, ys, LINEAR);
break;
default:
throw twml::Error(TWML_ERR_TYPE,
"Unsupported datatype for linearInterpolation.");
}
}
void nearestInterpolation(
Tensor output,
const Tensor input,
const Tensor xs,
const Tensor ys) {
switch (input.getType()) {
case TWML_TYPE_FLOAT:
twml::interpolation<float>(output, input, xs, ys, NEAREST);
break;
case TWML_TYPE_DOUBLE:
twml::interpolation<double>(output, input, xs, ys, NEAREST);
break;
default:
throw twml::Error(TWML_ERR_TYPE,
"Unsupported datatype for nearestInterpolation.");
}
}
} // namespace twml
twml_err twml_optim_mdl_infer(twml_tensor output_keys,
twml_tensor output_vals,
const twml_tensor input_keys,
const twml_tensor input_vals,
const twml_tensor bin_ids,
const twml_tensor bin_vals,
const twml_tensor feature_offsets,
bool return_bin_indices) {
HANDLE_EXCEPTIONS(
using namespace twml;
mdlInfer(*getTensor(output_keys),
*getTensor(output_vals),
*getConstTensor(input_keys),
*getConstTensor(input_vals),
*getConstTensor(bin_ids),
*getConstTensor(bin_vals),
*getConstTensor(feature_offsets),
return_bin_indices););
return TWML_ERR_NONE;
}
twml_err twml_optim_nearest_interpolation(
twml_tensor output,
const twml_tensor input,
const twml_tensor xs,
const twml_tensor ys) {
HANDLE_EXCEPTIONS(
using namespace twml;
nearestInterpolation(*getTensor(output),
*getConstTensor(input),
*getConstTensor(xs),
*getConstTensor(ys)););
return TWML_ERR_NONE;
}

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#include "internal/utf_converter.h"
ssize_t utf8_to_utf16(const uint8_t *in, uint64_t in_len, uint16_t *out, uint64_t max_out) {
uint64_t num_out = 0;
uint64_t num_in = 0;
while (num_in < in_len) {
uint32_t uni;
uint64_t todo;
uint8_t ch = in[num_in];
num_in++;
if (ch <= 0x7F) {
uni = ch;
todo = 0;
} else if (ch <= 0xBF) {
return -1;
} else if (ch <= 0xDF) {
uni = ch & 0x1F;
todo = 1;
} else if (ch <= 0xEF) {
uni = ch & 0x0F;
todo = 2;
} else if (ch <= 0xF7) {
uni = ch & 0x07;
todo = 3;
} else {
return -1;
}
for (uint64_t j = 0; j < todo; ++j) {
if (num_in == in_len) return -1;
uint8_t ch = in[num_in];
num_in++;
if (ch < 0x80 || ch > 0xBF) return -1;
uni <<= 6;
uni += ch & 0x3F;
}
if (uni >= 0xD800 && uni <= 0xDFFF) return -1;
if (uni > 0x10FFFF) return -1;
if (uni <= 0xFFFF) {
if (num_out == max_out) return -1;
out[num_out] = uni;
num_out++;
} else {
uni -= 0x10000;
if (num_out + 1 >= max_out) return -1;
out[num_out] = (uni >> 10) + 0xD800;
out[num_out + 1] = (uni & 0x3FF) + 0xDC00;
num_out += 2;
}
}
if (num_out == max_out) return -1;
out[num_out] = 0;
return num_out;
}

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set(CMAKE_MODULE_PATH ${PROJECT_SOURCE_DIR})
cmake_minimum_required(VERSION 2.8 FATAL_ERROR)
cmake_policy(VERSION 2.8)
set(CMAKE_MACOSX_RPATH 1)
file(GLOB_RECURSE sources *.cpp)
set (CMAKE_CXX_FLAGS "-Wall -std=c++11 -fno-stack-protector ${CMAKE_CXX_FLAGS}")
execute_process(
COMMAND
$ENV{LIBTWML_HOME}/src/ops/scripts/get_inc.sh
RESULT_VARIABLE
TF_RES
OUTPUT_VARIABLE
TF_INC)
if (NOT (${TF_RES} EQUAL "0"))
message(${TF_RES})
message(FATAL_ERROR "Failed to get include path for tensorflow")
endif()
execute_process(
COMMAND
$ENV{LIBTWML_HOME}/src/ops/scripts/get_lib.sh
RESULT_VARIABLE
TF_RES
OUTPUT_VARIABLE
TF_LIB)
if (NOT (${TF_RES} EQUAL "0"))
message(${TF_RES})
message(FATAL_ERROR "Failed to get lib path for tensorflow")
endif()
find_path(
TWML_INC
NAMES "twml.h"
PATHS $ENV{LIBTWML_HOME}/include)
add_library(twml_tf MODULE ${sources})
set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} "$ENV{LIBTWML_HOME}/cmake")
if (UNIX)
if (APPLE)
set (CMAKE_CXX_FLAGS "-undefined dynamic_lookup -stdlib=libc++ ${CMAKE_CXX_FLAGS}")
# -Wl,-all_load ensures symbols not used by twml_tf are also included.
# -Wl,-noall_load limits the scope of the previous flag.
set (LINK_ALL_OPTION "-Wl,-all_load")
set (NO_LINK_ALL_OPTION "-Wl,-noall_load")
set(TF_FRAMEWORK_LIB ${TF_LIB}/libtensorflow_framework.1.dylib)
else()
# -Wl,--whole-archive ensures symbols not used by twml_tf are also included.
# -Wl,--no-whole-archive limits the scope of the previous flag.
set (LINK_ALL_OPTION "-Wl,--whole-archive")
set (NO_LINK_ALL_OPTION "-Wl,--no-whole-archive")
set(TF_FRAMEWORK_LIB ${TF_LIB}/libtensorflow_framework.so.1)
endif()
endif()
target_include_directories(
twml_tf
PRIVATE
${CMAKE_CURRENT_SOURCE_DIR}
${TWML_INC}
# TF_INC needs to be the last to avoid some weird white-spacing issues with generated Makefile.
${TF_INC} # Needed because of some header files auto-generated during build time.
${TF_INC}/external/nsync/public/
)
target_link_libraries(twml_tf
PUBLIC
# Since we are using twml_tf as the "one" dynamic library,
# we want it to have the C function symbols needed for other functions as well.
${LINK_ALL_OPTION} twml ${NO_LINK_ALL_OPTION}
${TF_FRAMEWORK_LIB}
)

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#include "tensorflow/core/framework/op.h"
#include "tensorflow/core/framework/shape_inference.h"
#include "tensorflow/core/framework/op_kernel.h"
using namespace tensorflow;
REGISTER_OP("Add1")
.Attr("T: {float, double, int32}")
.Input("input1: T")
.Output("output: T")
.SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) {
c->set_output(0, c->input(0));
return Status::OK();
});
template<typename T>
class Add1 : public OpKernel {
public:
explicit Add1(OpKernelConstruction* context) : OpKernel(context) {}
void Compute(OpKernelContext* context) override {
// Grab the input tensor
const Tensor& input_tensor = context->input(0);
auto input = input_tensor.flat<T>();
// Create an output tensor
Tensor* output_tensor = nullptr;
OP_REQUIRES_OK(context, context->allocate_output(0, input_tensor.shape(),
&output_tensor));
auto output_flat = output_tensor->flat<T>();
// Add 1 to input and assign to output
const int N = input.size();
for (int i = 0; i < N; i++) {
output_flat(i) = input(i) + 1;
}
}
};
REGISTER_OP("Add1Grad")
.Attr("T: {float, double, int32}")
.Input("grad_output: T")
.Output("grad_input: T")
.SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) {
c->set_output(0, c->input(0));
return Status::OK();
});
template<typename T>
class Add1Grad : public OpKernel {
public:
explicit Add1Grad(OpKernelConstruction* context) : OpKernel(context) {}
void Compute(OpKernelContext* context) override {
// Grab the input tensor
const Tensor& grad_output_tensor = context->input(0);
auto grad_output = grad_output_tensor.flat<T>();
// Create an grad_input tensor
Tensor* grad_input_tensor = nullptr;
OP_REQUIRES_OK(context, context->allocate_output(0, grad_output_tensor.shape(),
&grad_input_tensor));
auto grad_input_flat = grad_input_tensor->flat<T>();
// Copy from grad_output to grad_input
const int N = grad_output.size();
for (int i = 0; i < N; i++) {
grad_input_flat(i) = grad_output(i);
}
}
};
#define REGISTER(Type) \
\
REGISTER_KERNEL_BUILDER( \
Name("Add1") \
.Device(DEVICE_CPU) \
.TypeConstraint<Type>("T"), \
Add1<Type>); \
\
REGISTER_KERNEL_BUILDER( \
Name("Add1Grad") \
.Device(DEVICE_CPU) \
.TypeConstraint<Type>("T"), \
Add1Grad<Type>); \
REGISTER(float);
REGISTER(double);
REGISTER(int32);

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#include "tensorflow/core/framework/op.h"
#include "tensorflow/core/framework/shape_inference.h"
#include "tensorflow/core/framework/op_kernel.h"
#include <twml.h>
#include "tensorflow_utils.h"
#include "resource_utils.h"
REGISTER_OP("DecodeAndHashBatchPredictionRequest")
.Input("input_bytes: uint8")
.Attr("keep_features: list(int)")
.Attr("keep_codes: list(int)")
.Attr("decode_mode: int = 0")
.Output("hashed_data_record_handle: resource")
.SetShapeFn(shape_inference::ScalarShape)
.Doc(R"doc(
A tensorflow OP that decodes batch prediction request and creates a handle to the batch of hashed data records.
Attr
keep_features: a list of int ids to keep.
keep_codes: their corresponding code.
decode_mode: integer, indicates which decoding method to use. Let a sparse continuous
have a feature_name and a dict of {name: value}. 0 indicates feature_ids are computed
as hash(name). 1 indicates feature_ids are computed as hash(feature_name, name)
shared_name: name used by the resource handle inside the resource manager.
container: name used by the container of the resources.
shared_name and container are required when inheriting from ResourceOpKernel.
Input
input_bytes: Input tensor containing the serialized batch of BatchPredictionRequest.
Outputs
hashed_data_record_handle: A resource handle to the HashedDataRecordResource containing batch of HashedDataRecords.
)doc");
class DecodeAndHashBatchPredictionRequest : public OpKernel {
public:
explicit DecodeAndHashBatchPredictionRequest(OpKernelConstruction* context)
: OpKernel(context) {
std::vector<int64> keep_features;
std::vector<int64> keep_codes;
OP_REQUIRES_OK(context, context->GetAttr("keep_features", &keep_features));
OP_REQUIRES_OK(context, context->GetAttr("keep_codes", &keep_codes));
OP_REQUIRES_OK(context, context->GetAttr("decode_mode", &m_decode_mode));
OP_REQUIRES(context, keep_features.size() == keep_codes.size(),
errors::InvalidArgument("keep keys and values must have same size."));
#ifdef USE_DENSE_HASH
m_keep_map.set_empty_key(0);
#endif // USE_DENSE_HASH
for (uint64_t i = 0; i < keep_features.size(); i++) {
m_keep_map[keep_features[i]] = keep_codes[i];
}
}
private:
twml::Map<int64_t, int64_t> m_keep_map;
int64 m_decode_mode;
void Compute(OpKernelContext* context) override {
try {
HashedDataRecordResource *resource = nullptr;
OP_REQUIRES_OK(context, makeResourceHandle<HashedDataRecordResource>(context, 0, &resource));
// Store the input bytes in the resource so it isnt freed before the resource.
// This is necessary because we are not copying the contents for tensors.
resource->input = context->input(0);
const uint8_t *input_bytes = resource->input.flat<uint8>().data();
twml::HashedDataRecordReader reader;
twml::HashedBatchPredictionRequest bpr;
reader.setKeepMap(&m_keep_map);
reader.setBuffer(input_bytes);
reader.setDecodeMode(m_decode_mode);
bpr.decode(reader);
resource->common = std::move(bpr.common());
resource->records = std::move(bpr.requests());
// Each datarecord has a copy of common features.
// Initialize total_size by common_size * num_records
int64 common_size = static_cast<int64>(resource->common.totalSize());
int64 num_records = static_cast<int64>(resource->records.size());
int64 total_size = common_size * num_records;
for (const auto &record : resource->records) {
total_size += static_cast<int64>(record.totalSize());
}
resource->total_size = total_size;
resource->num_labels = 0;
resource->num_weights = 0;
} catch (const std::exception &e) {
context->CtxFailureWithWarning(errors::InvalidArgument(e.what()));
}
}
};
REGISTER_KERNEL_BUILDER(
Name("DecodeAndHashBatchPredictionRequest").Device(DEVICE_CPU),
DecodeAndHashBatchPredictionRequest);
REGISTER_OP("DecodeBatchPredictionRequest")
.Input("input_bytes: uint8")
.Attr("keep_features: list(int)")
.Attr("keep_codes: list(int)")
.Output("data_record_handle: resource")
.SetShapeFn(shape_inference::ScalarShape)
.Doc(R"doc(
A tensorflow OP that decodes batch prediction request and creates a handle to the batch of data records.
Attr
keep_features: a list of int ids to keep.
keep_codes: their corresponding code.
shared_name: name used by the resource handle inside the resource manager.
container: name used by the container of the resources.
shared_name and container are required when inheriting from ResourceOpKernel.
Input
input_bytes: Input tensor containing the serialized batch of BatchPredictionRequest.
Outputs
data_record_handle: A resource handle to the DataRecordResource containing batch of DataRecords.
)doc");
class DecodeBatchPredictionRequest : public OpKernel {
public:
explicit DecodeBatchPredictionRequest(OpKernelConstruction* context)
: OpKernel(context) {
std::vector<int64> keep_features;
std::vector<int64> keep_codes;
OP_REQUIRES_OK(context, context->GetAttr("keep_features", &keep_features));
OP_REQUIRES_OK(context, context->GetAttr("keep_codes", &keep_codes));
OP_REQUIRES(context, keep_features.size() == keep_codes.size(),
errors::InvalidArgument("keep keys and values must have same size."));
#ifdef USE_DENSE_HASH
m_keep_map.set_empty_key(0);
#endif // USE_DENSE_HASH
for (uint64_t i = 0; i < keep_features.size(); i++) {
m_keep_map[keep_features[i]] = keep_codes[i];
}
}
private:
twml::Map<int64_t, int64_t> m_keep_map;
void Compute(OpKernelContext* context) override {
try {
DataRecordResource *resource = nullptr;
OP_REQUIRES_OK(context, makeResourceHandle<DataRecordResource>(context, 0, &resource));
// Store the input bytes in the resource so it isnt freed before the resource.
// This is necessary because we are not copying the contents for tensors.
resource->input = context->input(0);
const uint8_t *input_bytes = resource->input.flat<uint8>().data();
twml::DataRecordReader reader;
twml::BatchPredictionRequest bpr;
reader.setKeepMap(&m_keep_map);
reader.setBuffer(input_bytes);
bpr.decode(reader);
resource->common = std::move(bpr.common());
resource->records = std::move(bpr.requests());
resource->num_weights = 0;
resource->num_labels = 0;
resource->keep_map = &m_keep_map;
} catch (const std::exception &e) {
context->CtxFailureWithWarning(errors::InvalidArgument(e.what()));
}
}
};
REGISTER_KERNEL_BUILDER(
Name("DecodeBatchPredictionRequest").Device(DEVICE_CPU),
DecodeBatchPredictionRequest);

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#include "tensorflow/core/framework/op.h"
#include "tensorflow/core/framework/shape_inference.h"
#include "tensorflow/core/framework/op_kernel.h"
#include <cstdint>
#include <twml.h>
#include "tensorflow_utils.h"
#include "resource_utils.h"
#include <iterator>
template<typename InputType, typename RecordType>
class DecodeBatchPredictionRequestKernel : public OpKernel {
public:
explicit DecodeBatchPredictionRequestKernel(OpKernelConstruction* context)
: OpKernel(context) {
std::vector<int64> keep_features;
std::vector<int64> keep_codes;
std::vector<int64> label_features;
std::vector<int64> weight_features;
OP_REQUIRES_OK(context, context->GetAttr("keep_features", &keep_features));
OP_REQUIRES_OK(context, context->GetAttr("keep_codes", &keep_codes));
OP_REQUIRES_OK(context, context->GetAttr("label_features", &label_features));
OP_REQUIRES_OK(context, context->GetAttr("weight_features", &weight_features));
OP_REQUIRES_OK(context, context->GetAttr("decode_mode", &m_decode_mode));
OP_REQUIRES(context, keep_features.size() == keep_codes.size(),
errors::InvalidArgument("keep keys and values must have same size."));
#ifdef USE_DENSE_HASH
m_keep_map.set_empty_key(0);
m_labels_map.set_empty_key(0);
m_weights_map.set_empty_key(0);
#endif // USE_DENSE_HASH
for (uint64_t i = 0; i < keep_features.size(); i++) {
m_keep_map[keep_features[i]] = keep_codes[i];
}
for (uint64_t i = 0; i < label_features.size(); i++) {
m_labels_map[label_features[i]] = i;
}
for (uint64_t i = 0; i < weight_features.size(); i++) {
m_weights_map[weight_features[i]] = i;
}
}
protected:
twml::Map<int64_t, int64_t> m_keep_map;
twml::Map<int64_t, int64_t> m_labels_map;
twml::Map<int64_t, int64_t> m_weights_map;
int64 m_decode_mode;
template<typename ResourceType>
void Decode(OpKernelContext* context, ResourceType *resource) {
resource->input = context->input(0);
const uint8_t *input_bytes = getInputBytes<InputType>(resource->input, 0);
int num_labels = static_cast<int>(m_labels_map.size());
int num_weights = static_cast<int>(m_weights_map.size());
typename RecordType::Reader reader;
twml::GenericBatchPredictionRequest<RecordType> bpr(num_labels, num_weights);
reader.setKeepMap(&m_keep_map);
reader.setLabelsMap(&m_labels_map);
reader.setBuffer(input_bytes);
reader.setDecodeMode(m_decode_mode);
// Do not set weight map if it is empty. This will take a faster path.
if (num_weights != 0) {
reader.setWeightsMap(&m_weights_map);
}
bpr.decode(reader);
resource->common = std::move(bpr.common());
resource->records = std::move(bpr.requests());
resource->num_labels = num_labels;
resource->num_weights = num_weights;
}
};
REGISTER_OP("DecodeAndHashBatchPredictionRequestV2")
.Attr("InputType: {uint8, string}")
.Input("input_bytes: InputType")
.Attr("keep_features: list(int)")
.Attr("keep_codes: list(int)")
.Attr("label_features: list(int)")
.Attr("weight_features: list(int) = []")
.Attr("decode_mode: int = 0")
.Output("hashed_data_record_handle: resource")
.SetShapeFn(shape_inference::ScalarShape)
.Doc(R"doc(
A tensorflow OP that decodes a list/batch of data records and creates a handle to the batch of hashed data records.
Compared to DecodeAndHashBatchPredictionRequest, DecodeAndHashBatchPredictionRequestV2 is used for training instead
of serving. Thus label_features and weight_features[optional] must be passed, and labels and weights are extracted in
the output.
DecodeAndHashBatchPredictionRequestV2 controls what DataRecords we want to process together in a batch in training.
For instance, we can put all instances for a query in the same batch when training a ranking model.
Notice that this OP was added separately to make sure we would not break the API for DecodeAndHashBatchPredictionRequest.
It requires some discussions if we merge the two ops into a single .cpp file in a future API revision.
Attr
keep_features: a list of int ids to keep.
keep_codes: their corresponding code.
label_features: list of feature ids representing the labels.
weight_features: list of feature ids representing the weights. Defaults to empty list.
decode_mode: integer, indicates which decoding method to use. Let a sparse continuous
have a feature_name and a dict of {name: value}. 0 indicates feature_ids are computed
as hash(name). 1 indicates feature_ids are computed as hash(feature_name, name)
Input
input_bytes: Input tensor containing the serialized batch of BatchPredictionRequest.
Outputs
hashed_data_record_handle: A resource handle to the HashedDataRecordResource containing batch of HashedDataRecords.
)doc");
template<typename InputType>
class DecodeAndHashBatchPredictionRequestV2 :
public DecodeBatchPredictionRequestKernel<InputType, twml::HashedDataRecord> {
public:
DecodeAndHashBatchPredictionRequestV2(OpKernelConstruction *context)
: DecodeBatchPredictionRequestKernel<InputType, twml::HashedDataRecord>(context) {
}
private:
void Compute(OpKernelContext* context) override {
try {
HashedDataRecordResource *resource = nullptr;
OP_REQUIRES_OK(
context,
makeResourceHandle<HashedDataRecordResource>(context, 0, &resource));
this->Decode(context, resource);
// Each datarecord has a copy of common features.
// Initialize total_size by common_size * num_records
int64 common_size = static_cast<int64>(resource->common.totalSize());
int64 num_records = static_cast<int64>(resource->records.size());
int64 total_size = common_size * num_records;
for (const auto &record : resource->records) {
total_size += static_cast<int64>(record.totalSize());
}
resource->total_size = total_size;
} catch (const std::exception &e) {
context->CtxFailureWithWarning(errors::InvalidArgument(e.what()));
}
}
};
REGISTER_OP("DecodeBatchPredictionRequestV2")
.Attr("InputType: {uint8, string}")
.Input("input_bytes: InputType")
.Attr("keep_features: list(int)")
.Attr("keep_codes: list(int)")
.Attr("label_features: list(int)")
.Attr("weight_features: list(int) = []")
.Attr("decode_mode: int = 0")
.Output("data_record_handle: resource")
.SetShapeFn(shape_inference::ScalarShape)
.Doc(R"doc(
A tensorflow OP that decodes batch prediction request and creates a handle to the batch of data records.
Attr
keep_features: a list of int ids to keep.
keep_codes: their corresponding code.
shared_name: name used by the resource handle inside the resource manager.
label_features: list of feature ids representing the labels.
weight_features: list of feature ids representing the weights. Defaults to empty list.
decode_mode: reserved, do not use.
Input
input_bytes: Input tensor containing the serialized batch of BatchPredictionRequest.
Outputs
data_record_handle: A resource handle to the DataRecordResource containing batch of DataRecords.
)doc");
template<typename InputType>
class DecodeBatchPredictionRequestV2 :
public DecodeBatchPredictionRequestKernel<InputType, twml::DataRecord> {
public:
DecodeBatchPredictionRequestV2(OpKernelConstruction *context)
: DecodeBatchPredictionRequestKernel<InputType, twml::DataRecord>(context) {
}
private:
void Compute(OpKernelContext* context) override {
try {
DataRecordResource *resource = nullptr;
OP_REQUIRES_OK(
context,
makeResourceHandle<DataRecordResource>(context, 0, &resource));
this->Decode(context, resource);
resource->keep_map = &(this->m_keep_map);
} catch (const std::exception &e) {
context->CtxFailureWithWarning(errors::InvalidArgument(e.what()));
}
}
};
#define REGISTER_DECODE_OPS(InputType) \
REGISTER_KERNEL_BUILDER( \
Name("DecodeAndHashBatchPredictionRequestV2") \
.Device(DEVICE_CPU) \
.TypeConstraint<InputType>("InputType"), \
DecodeAndHashBatchPredictionRequestV2<InputType>); \
REGISTER_KERNEL_BUILDER( \
Name("DecodeBatchPredictionRequestV2") \
.Device(DEVICE_CPU) \
.TypeConstraint<InputType>("InputType"), \
DecodeBatchPredictionRequestV2<InputType>); \
REGISTER_DECODE_OPS(uint8)
REGISTER_DECODE_OPS(string)

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#include "tensorflow/core/framework/op.h"
#include "tensorflow/core/framework/shape_inference.h"
#include "tensorflow/core/framework/op_kernel.h"
#include <twml.h>
#include "tensorflow_utils.h"
using namespace tensorflow;
REGISTER_OP("BatchPredictionResponseWriter")
.Attr("T: {float, double}")
.Input("keys: int64")
.Input("values: T")
.Output("result: uint8")
.SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) {
return Status::OK();
}).Doc(R"doc(
A tensorflow OP that packages keys and values into a BatchPredictionResponse.
values: input feature value. (float/double)
keys: feature ids from the original BatchPredictionRequest. (int64)
Outputs
bytes: output BatchPredictionRequest serialized using Thrift into a uint8 tensor.
)doc");
template<typename T>
class BatchPredictionResponseWriter : public OpKernel {
public:
explicit BatchPredictionResponseWriter(OpKernelConstruction* context)
: OpKernel(context) {}
void Compute(OpKernelContext* context) override {
const Tensor& keys = context->input(0);
const Tensor& values = context->input(1);
try {
// Ensure the inner dimension matches.
if (values.dim_size(values.dims() - 1) != keys.dim_size(keys.dims() - 1)) {
throw std::runtime_error("The sizes of keys and values need to match");
}
// set inputs as twml::Tensor
const twml::Tensor in_keys_ = TFTensor_to_twml_tensor(keys);
const twml::Tensor in_values_ = TFTensor_to_twml_tensor(values);
// no tensors in this op
const twml::Tensor dummy_dense_keys_;
const std::vector<twml::RawTensor> dummy_dense_values_;
// call constructor BatchPredictionResponse
twml::BatchPredictionResponse tempResult(
in_keys_, in_values_, dummy_dense_keys_, dummy_dense_values_);
// determine the length of the result
int len = tempResult.encodedSize();
TensorShape result_shape = {1, len};
// Create an output tensor, the size is determined by the content of input.
Tensor* result = nullptr;
OP_REQUIRES_OK(context, context->allocate_output(0, result_shape,
&result));
twml::Tensor out_result = TFTensor_to_twml_tensor(*result);
// Call writer of BatchPredictionResponse
tempResult.write(out_result);
} catch(const std::exception &e) {
context->CtxFailureWithWarning(errors::InvalidArgument(e.what()));
}
}
};
#define REGISTER(Type) \
\
REGISTER_KERNEL_BUILDER( \
Name("BatchPredictionResponseWriter") \
.Device(DEVICE_CPU) \
.TypeConstraint<Type>("T"), \
BatchPredictionResponseWriter<Type>); \
REGISTER(float);
REGISTER(double);

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#include "tensorflow/core/framework/op.h"
#include "tensorflow/core/framework/shape_inference.h"
#include "tensorflow/core/framework/op_kernel.h"
#include <twml.h>
#include "tensorflow_utils.h"
using namespace tensorflow;
REGISTER_OP("BatchPredictionTensorResponseWriter")
.Attr("T: list({string, int32, int64, float, double})")
.Input("keys: int64")
.Input("values: T")
.Output("result: uint8")
.SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) {
return Status::OK();
}).Doc(R"doc(
A tensorflow OP that packages keys and dense tensors into a BatchPredictionResponse.
values: list of tensors
keys: feature ids from the original BatchPredictionRequest. (int64)
Outputs
bytes: output BatchPredictionRequest serialized using Thrift into a uint8 tensor.
)doc");
class BatchPredictionTensorResponseWriter : public OpKernel {
public:
explicit BatchPredictionTensorResponseWriter(OpKernelConstruction* context)
: OpKernel(context) {}
void Compute(OpKernelContext* context) override {
const Tensor& keys = context->input(0);
try {
// set keys as twml::Tensor
const twml::Tensor in_keys_ = TFTensor_to_twml_tensor(keys);
// check sizes
uint64_t num_keys = in_keys_.getNumElements();
uint64_t num_values = context->num_inputs() - 1;
OP_REQUIRES(context, num_values % num_keys == 0,
errors::InvalidArgument("Number of dense tensors not multiple of dense keys"));
// set dense tensor values
std::vector<twml::RawTensor> in_values_;
for (int i = 1; i < context->num_inputs(); i++) {
in_values_.push_back(TFTensor_to_twml_raw_tensor(context->input(i)));
}
// no continuous predictions in this op, only tensors
const twml::Tensor dummy_cont_keys_;
const twml::Tensor dummy_cont_values_;
// call constructor BatchPredictionResponse
twml::BatchPredictionResponse tempResult(
dummy_cont_keys_, dummy_cont_values_, in_keys_, in_values_);
// determine the length of the result
int len = tempResult.encodedSize();
TensorShape result_shape = {1, len};
// Create an output tensor, the size is determined by the content of input.
Tensor* result = NULL;
OP_REQUIRES_OK(context, context->allocate_output(0, result_shape,
&result));
twml::Tensor out_result = TFTensor_to_twml_tensor(*result);
// Call writer of BatchPredictionResponse
tempResult.write(out_result);
} catch(const std::exception &e) {
context->CtxFailureWithWarning(errors::InvalidArgument(e.what()));
}
}
};
REGISTER_KERNEL_BUILDER(
Name("BatchPredictionTensorResponseWriter").Device(DEVICE_CPU),
BatchPredictionTensorResponseWriter);

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/* Copyright 2015 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.
==============================================================================*/
// TWML modified to optimize binary features:
// - Sparse tensor values are assumed to be binary, so only add operation is done
// rather than mul-add;
// - In house version of vectorization is used instead of Eigen;
// - Enable sharding and multithreading.
#define EIGEN_USE_THREADS
#include "binary_sparse_dense_matmul.h"
#include "binary_sparse_dense_matmul_impl.h"
#include "tensorflow/core/framework/bounds_check.h"
#include "tensorflow/core/framework/op.h"
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/framework/common_shape_fns.h"
#include "tensorflow/core/framework/shape_inference.h"
namespace tensorflow {
namespace shape_inference {
// TODO: The `a_value` is supposed to be all ones.
// Users should not call this op directly but to use it from `sparse_op` python library.
// To make it consistent with original op, the signature remains the same currently,
// we will think a better way to contrain correct use of this op.
// CX-18174
REGISTER_OP("BinarySparseTensorDenseMatMul")
.Input("a_indices: Tindices")
.Input("a_values: T")
.Input("a_shape: int64")
.Input("b: T")
.Output("product: T")
.Attr("T: type")
.Attr("Tindices: {int32,int64} = DT_INT64")
.Attr("adjoint_a: bool = false")
.Attr("adjoint_b: bool = false")
.SetShapeFn([](InferenceContext* c) {
DimensionHandle unused_dim;
ShapeHandle unused;
ShapeHandle b;
ShapeHandle a_shape;
TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 2, &unused)); // a_indices
TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 1, &unused)); // a_values
TF_RETURN_IF_ERROR(c->MakeShapeFromShapeTensor(2, &a_shape));
TF_RETURN_IF_ERROR(c->WithRank(a_shape, 2, &a_shape));
TF_RETURN_IF_ERROR(c->WithRank(c->input(3), 2, &b));
bool adjoint_a;
bool adjoint_b;
TF_RETURN_IF_ERROR(c->GetAttr("adjoint_a", &adjoint_a));
TF_RETURN_IF_ERROR(c->GetAttr("adjoint_b", &adjoint_b));
DimensionHandle output_right = c->Dim(b, adjoint_b ? 0 : 1);
DimensionHandle output_left = c->Dim(a_shape, adjoint_a ? 1 : 0);
DimensionHandle inner_left = c->Dim(a_shape, adjoint_a ? 0 : 1);
DimensionHandle inner_right = c->Dim(b, adjoint_b ? 1 : 0);
TF_RETURN_IF_ERROR(c->Merge(inner_left, inner_right, &unused_dim));
c->set_output(0, c->Matrix(output_left, output_right));
return Status::OK();
});
} // namespace shape_inference
typedef Eigen::ThreadPoolDevice CPUDevice;
template <typename Device, typename T, typename Tindices>
class BinarySparseTensorDenseMatMulOp : public OpKernel {
public:
explicit BinarySparseTensorDenseMatMulOp(OpKernelConstruction* ctx)
: OpKernel(ctx) {
OP_REQUIRES_OK(ctx, ctx->GetAttr("adjoint_a", &adjoint_a_));
OP_REQUIRES_OK(ctx, ctx->GetAttr("adjoint_b", &adjoint_b_));
}
void Compute(OpKernelContext* ctx) override {
const Tensor* a_indices;
const Tensor* a_values;
const Tensor* a_shape;
const Tensor* b;
OP_REQUIRES_OK(ctx, ctx->input("a_indices", &a_indices));
OP_REQUIRES_OK(ctx, ctx->input("a_values", &a_values));
OP_REQUIRES_OK(ctx, ctx->input("a_shape", &a_shape));
OP_REQUIRES_OK(ctx, ctx->input("b", &b));
// Check that the dimensions of the two matrices are valid.
OP_REQUIRES(ctx, TensorShapeUtils::IsMatrix(b->shape()),
errors::InvalidArgument("Tensor 'b' is not a matrix"));
OP_REQUIRES(ctx, TensorShapeUtils::IsVector(a_shape->shape()),
errors::InvalidArgument("Tensor 'a_shape' is not a vector"));
OP_REQUIRES(
ctx, a_shape->NumElements() == 2,
errors::InvalidArgument("Tensor 'a_shape' must have 2 elements"));
OP_REQUIRES(ctx, TensorShapeUtils::IsVector(a_values->shape()),
errors::InvalidArgument("Tensor 'a_values' is not a vector"));
OP_REQUIRES(ctx, TensorShapeUtils::IsMatrix(a_indices->shape()),
errors::InvalidArgument("Tensor 'a_indices' is not a matrix"));
const int64 nnz = a_indices->shape().dim_size(0);
OP_REQUIRES(ctx, nnz == a_values->NumElements(),
errors::InvalidArgument("Number of rows of a_indices does not "
"match number of entries in a_values"));
OP_REQUIRES(
ctx, a_indices->shape().dim_size(1) == a_shape->NumElements(),
errors::InvalidArgument("Number of columns of a_indices does not match "
"number of entries in a_shape"));
auto a_shape_t = a_shape->vec<int64>();
const int64 outer_left = (adjoint_a_) ? a_shape_t(1) : a_shape_t(0);
const int64 outer_right =
(adjoint_b_) ? b->shape().dim_size(0) : b->shape().dim_size(1);
const int64 inner_left = (adjoint_a_) ? a_shape_t(0) : a_shape_t(1);
const int64 inner_right =
(adjoint_b_) ? b->shape().dim_size(1) : b->shape().dim_size(0);
OP_REQUIRES(
ctx, inner_right == inner_left,
errors::InvalidArgument(
"Cannot multiply A and B because inner dimension does not match: ",
inner_left, " vs. ", inner_right,
". Did you forget a transpose? "
"Dimensions of A: [",
a_shape_t(0), ", ", a_shape_t(1),
"). Dimensions of B: ", b->shape().DebugString()));
TensorShape out_shape({outer_left, outer_right});
Tensor* out = nullptr;
OP_REQUIRES_OK(ctx, ctx->allocate_output(0, out_shape, &out));
if (out->NumElements() == 0) {
// If a has shape [0, x] or b has shape [x, 0], the output shape
// is a 0-element matrix, so there is nothing to do.
return;
}
if (a_values->NumElements() == 0 || b->NumElements() == 0) {
// If a has shape [x, 0] and b has shape [0, y], the
// output shape is [x, y] where x and y are non-zero, so we fill
// the output with zeros.
out->flat<T>().device(ctx->eigen_device<Device>()) =
out->flat<T>().constant(T(0));
return;
}
#define MAYBE_ADJOINT(ADJ_A, ADJ_B) \
if (adjoint_a_ == ADJ_A && adjoint_b_ == ADJ_B) { \
Status functor_status = functor::SparseTensorDenseMatMulFunctor< \
Device, T, Tindices, ADJ_A, \
ADJ_B>::Compute(ctx, a_indices, a_values, a_shape, b, out); \
OP_REQUIRES_OK(ctx, functor_status); \
}
MAYBE_ADJOINT(false, false);
MAYBE_ADJOINT(false, true);
MAYBE_ADJOINT(true, false);
MAYBE_ADJOINT(true, true);
#undef MAYBE_ADJOINT
}
private:
bool adjoint_a_;
bool adjoint_b_;
};
#define REGISTER_CPU(TypeT, TypeIndex) \
REGISTER_KERNEL_BUILDER( \
Name("BinarySparseTensorDenseMatMul") \
.Device(DEVICE_CPU) \
.TypeConstraint<TypeT>("T") \
.TypeConstraint<TypeIndex>("Tindices") \
.HostMemory("a_shape"), \
BinarySparseTensorDenseMatMulOp<CPUDevice, TypeT, TypeIndex>);
#define REGISTER_KERNELS_CPU(T) \
REGISTER_CPU(T, int64); \
REGISTER_CPU(T, int32)
REGISTER_KERNELS_CPU(float);
REGISTER_KERNELS_CPU(double);
REGISTER_KERNELS_CPU(int32);
REGISTER_KERNELS_CPU(complex64);
REGISTER_KERNELS_CPU(complex128);
namespace functor {
namespace {
Status KOutOfBoundsError(int64 k, std::size_t i, int rhs_index_a,
std::size_t lhs_right) {
return errors::InvalidArgument("k (", k, ") from index[", i, ",", rhs_index_a,
"] out of bounds (>=", lhs_right, ")");
}
Status MOutOfBoundsError(int64 m, std::size_t i, int lhs_index_a,
int64 out_dim0) {
return errors::InvalidArgument("m (", m, ") from index[", i, ",", lhs_index_a,
"] out of bounds (>=", out_dim0, ")");
}
} // namespace
// The general functor just borrows the code from tf except that add is computed
// instead of mul-add.
template <typename T, typename Tindices, bool ADJ_A, bool ADJ_B>
struct SparseTensorDenseMatMulFunctor<CPUDevice, T, Tindices, ADJ_A, ADJ_B> {
// Vectorize certain operations above this size.
static const std::size_t kNumVectorize = 32;
static Status Compute(OpKernelContext* ctx,
const Tensor *a_indices,
const Tensor *a_values,
const Tensor *a_shape,
const Tensor *b,
Tensor *out) {
return EigenCompute(ctx->eigen_device<CPUDevice>(), out->matrix<T>(),
a_indices->matrix<Tindices>(), a_values->vec<T>(),
b->matrix<T>());
}
static Status EigenCompute(const CPUDevice& d, typename TTypes<T>::Matrix out,
typename TTypes<Tindices>::ConstMatrix a_indices,
typename TTypes<T>::ConstVec a_values,
typename TTypes<T>::ConstMatrix b) {
const std::size_t nnz = a_values.size();
const std::size_t rhs_right = (ADJ_B ? b.dimension(0) : b.dimension(1));
const std::size_t lhs_right = (ADJ_B ? b.dimension(1) : b.dimension(0));
const int lhs_index_a = ADJ_A ? 1 : 0;
const int rhs_index_a = ADJ_A ? 0 : 1;
out.setZero();
if (rhs_right < kNumVectorize) {
// Disable vectorization if the RHS of output is too small
auto maybe_adjoint_b = MaybeAdjoint<decltype(b), ADJ_B>(b);
for (std::size_t i = 0; i < nnz; ++i) {
const Tindices m = internal::SubtleMustCopy(a_indices(i, lhs_index_a));
const Tindices k = internal::SubtleMustCopy(a_indices(i, rhs_index_a));
if (!FastBoundsCheck(k, lhs_right)) {
return KOutOfBoundsError(k, i, rhs_index_a, lhs_right);
}
if (!FastBoundsCheck(m, out.dimension(0))) {
return MOutOfBoundsError(m, i, lhs_index_a, out.dimension(0));
}
for (std::size_t n = 0; n < rhs_right; ++n) {
const T b_value = maybe_adjoint_b(k, n);
out(m, n) += b_value;
}
}
} else {
// Vectorization via Eigen.
const int b_chip_index = ADJ_B ? 1 : 0;
#define LOOP_NNZ(b_passed) \
for (std::size_t i = 0; i < nnz; ++i) { \
const Tindices m = internal::SubtleMustCopy(a_indices(i, lhs_index_a)); \
const Tindices k = internal::SubtleMustCopy(a_indices(i, rhs_index_a)); \
if (!FastBoundsCheck(k, lhs_right)) { \
return KOutOfBoundsError(k, i, rhs_index_a, lhs_right); \
} \
if (!FastBoundsCheck(m, out.dimension(0))) { \
return MOutOfBoundsError(m, i, lhs_index_a, out.dimension(0)); \
} \
out.template chip<0>(m) += b_passed.template chip<b_chip_index>(k); \
}
if (ADJ_B) {
// Perform transpose and conjugation on B once, since we chip out B's
// columns in the nnz loop.
Eigen::array<int, 2> shuffle; // preserve dimension order
shuffle[0] = 1; shuffle[1] = 0;
Eigen::Tensor<T, 2, Eigen::ColMajor> col_major_conj_b =
b.swap_layout().shuffle(shuffle).conjugate();
LOOP_NNZ(col_major_conj_b);
} else {
LOOP_NNZ(b);
}
#undef LOOP_NNZ
}
return Status::OK();
}
};
// We have only specified and optimised the case with no matrix transpose,
// since it is the most typical usage in productions.
template <typename Tindices>
struct SparseTensorDenseMatMulFunctor<CPUDevice,
float, Tindices, false, false> {
static Status Compute(OpKernelContext* ctx,
const Tensor *a_indices,
const Tensor *a_values,
const Tensor *a_shape,
const Tensor *b,
Tensor *out) {
auto a_indices_ptr = a_indices->flat<Tindices>().data();
auto b_ptr = b->flat<float>().data();
auto out_ptr = out->flat<float>().data();
const int64 nnz = a_indices->shape().dim_size(0);
const int64 outer_left = a_shape->vec<int64>()(0);
const int64 outer_right = b->shape().dim_size(1);
ParallelLookupAndSegmentSum<Tindices>(ctx, a_indices_ptr, b_ptr, nnz,
outer_left, outer_right, out_ptr);
return Status::OK();
}
};
} // namespace functor
} // namespace tensorflow

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/* Copyright 2015 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.
==============================================================================*/
// TWML modified to optimize binary features
#ifndef TENSORFLOW_CORE_KERNELS_BINARY_SPARSE_TENSOR_DENSE_MATMUL_OP_H_
#define TENSORFLOW_CORE_KERNELS_BINARY_SPARSE_TENSOR_DENSE_MATMUL_OP_H_
#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
#include "tensorflow/core/framework/tensor_types.h"
#include "tensorflow/core/framework/types.h"
#include "tensorflow/core/lib/core/errors.h"
namespace tensorflow {
namespace functor {
template <typename Device, typename T, typename Tindices, bool ADJ_A,
bool ADJ_B>
struct SparseTensorDenseMatMulFunctor {
static EIGEN_ALWAYS_INLINE Status Compute(
const Device& d, typename TTypes<T>::Matrix out,
typename TTypes<Tindices>::ConstMatrix a_indices,
typename TTypes<T>::ConstVec a_values, typename TTypes<T>::ConstMatrix b);
};
template <typename MATRIX, bool ADJ>
class MaybeAdjoint;
template <typename MATRIX>
class MaybeAdjoint<MATRIX, false> {
public:
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE MaybeAdjoint(MATRIX m) : m_(m) {}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename MATRIX::Scalar operator()(
const typename MATRIX::Index i, const typename MATRIX::Index j) const {
return m_(i, j);
}
private:
const MATRIX m_;
};
template <typename T>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T MaybeConj(T v) {
return v;
}
template <typename MATRIX>
class MaybeAdjoint<MATRIX, true> {
public:
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE MaybeAdjoint(MATRIX m) : m_(m) {}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename MATRIX::Scalar operator()(
const typename MATRIX::Index i, const typename MATRIX::Index j) const {
return Eigen::numext::conj(m_(j, i));
}
private:
const MATRIX m_;
};
} // end namespace functor
} // end namespace tensorflow
#endif // TENSORFLOW_CORE_KERNELS_BINARY_SPARSE_TENSOR_DENSE_MATMUL_OP_H_

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#ifndef TENSORFLOW_CORE_KERNELS_BINARY_SPARSE_TENSOR_DENSE_MATMUL_IMPL_H_
#define TENSORFLOW_CORE_KERNELS_BINARY_SPARSE_TENSOR_DENSE_MATMUL_IMPL_H_
#include <atomic>
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/lib/core/blocking_counter.h"
#include "tensorflow/core/lib/core/threadpool.h"
namespace tensorflow {
namespace functor {
// `ConservativeShard` is adopted rather than `Shard` in tensorflow because the
// original `Shard` may generate number of shards more than the number of
// threads, which is not ideal for this case, as it may cause too much overhead.
static void ConservativeShard(int max_parallelism, thread::ThreadPool *workers,
int64 total, int64 cost_per_unit,
std::function<void(int64, int64)> work) {
if (total == 0) {
return;
}
max_parallelism = std::min(max_parallelism, workers->NumThreads());
if (max_parallelism <= 1) {
// Just inline the whole work since we only have 1 thread (core).
work(0, total);
return;
}
cost_per_unit = std::max(1LL, cost_per_unit);
// We shard [0, total) into "num_shards" shards.
// 1 <= num_shards <= num worker threads
//
// If total * cost_per_unit is small, it is not worth shard too
// much. Let us assume each cost unit is 1ns, kMinCostPerShard=10000
// is 10us.
static const int64 kMinCostPerShard = 10000;
const int num_shards =
std::max<int>(1, std::min(static_cast<int64>(max_parallelism),
total * cost_per_unit / kMinCostPerShard));
// Each shard contains up to "block_size" units. [0, total) is sharded
// into:
// [0, block_size), [block_size, 2*block_size), ...
// The 1st shard is done by the caller thread and the other shards
// are dispatched to the worker threads. The last shard may be smaller than
// block_size.
const int64 block_size = (total + num_shards - 1) / num_shards;
if (block_size >= total) {
work(0, total);
return;
}
const int num_shards_used = (total + block_size - 1) / block_size;
BlockingCounter counter(num_shards_used - 1);
for (int64 start = block_size; start < total; start += block_size) {
auto limit = std::min(start + block_size, total);
workers->Schedule([&work, &counter, start, limit]() {
work(start, limit); // Compute the shard.
counter.DecrementCount(); // The shard is done.
});
}
// Inline execute the 1st shard.
work(0, std::min(block_size, total));
counter.Wait();
}
static inline void VectorSum(float *a, const float *b, int n) {
for (int i = 0; i < n; ++i) {
a[i] += b[i];
}
}
// This func is to vectorize the computation of segment sum.
template<typename Tindices>
static void LookupAndSegmentSum(const Tindices *a_indices, const float *b,
int nnz, int outer_right, float *output) {
for (std::size_t i = 0; i < nnz; ++i) {
const Tindices m = a_indices[i * 2];
const Tindices k = a_indices[i * 2 + 1];
auto output_row_m = output + m * outer_right;
auto b_row_k = b + k * outer_right;
VectorSum(output_row_m, b_row_k, outer_right);
}
}
// This func enables sharding and multithreading, it comes with an overhead of
// duplicating output buffer to achieve lock free output. So there should not
// be too many threads.
template<typename Tindices>
static void ParallelLookupAndSegmentSum(OpKernelContext *ctx,
const Tindices *a_indices,
const float *b, int nnz, int outer_left,
int outer_right, float *output) {
auto worker_threads = *(ctx->device()->tensorflow_cpu_worker_threads());
int out_size = outer_left * outer_right;
if (worker_threads.num_threads <= 1) {
memset(output, 0, out_size * sizeof(float));
LookupAndSegmentSum<Tindices>(a_indices, b,
nnz, outer_right,
output);
return;
}
// this is to make buffer align with kAllocatorAlignment
int padded_out_size = (out_size + (Allocator::kAllocatorAlignment - 1)) &
~(Allocator::kAllocatorAlignment - 1);
std::size_t num_bytes =
(worker_threads.num_threads - 1) * padded_out_size * sizeof(float);
auto buffer = std::unique_ptr<float>(reinterpret_cast<float *>(
port::AlignedMalloc(num_bytes, Allocator::kAllocatorAlignment)));
float *temp_out = buffer.get();
std::atomic<int> thread_index(0);
auto task = [&](int64 start, int64 limit) {
int local_thread_index = thread_index++;
float *buf_ptr = nullptr;
if (local_thread_index == 0) {
buf_ptr = output;
} else {
buf_ptr = temp_out + (local_thread_index - 1) * padded_out_size;
}
memset(buf_ptr, 0, out_size * sizeof(float));
LookupAndSegmentSum<Tindices>(a_indices + start * 2, b,
limit - start, outer_right,
buf_ptr);
};
int cost_per_unit = outer_right;
// We don't use tensorflow shard func as tf may create more shards than
// number of threads.
ConservativeShard(worker_threads.num_threads, worker_threads.workers, nnz,
static_cast<int64>(cost_per_unit), task);
for (int i = 1; i < thread_index; ++i) {
VectorSum(output, temp_out + (i - 1) * padded_out_size, out_size);
}
}
} // namespace functor
} // namespace tensorflow
#endif // TENSORFLOW_CORE_KERNELS_BINARY_SPARSE_TENSOR_DENSE_MATMUL_IMPL_H_

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#include "block_format_reader.h"
#include "tensorflow/core/framework/dataset.h"
#include "tensorflow/core/framework/partial_tensor_shape.h"
#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/lib/io/random_inputstream.h"
#if !defined(DISABLE_ZLIB)
#include "tensorflow/core/lib/io/zlib_inputstream.h"
#endif
#include <twml.h>
#include <cstdio>
#include <algorithm>
#include <iterator>
using namespace tensorflow;
inline std::string stripPath(std::string const &file_name) {
const auto pos = file_name.find_last_of("/");
if (pos == std::string::npos) return file_name;
return file_name.substr(pos + 1);
}
inline std::string getExtension(std::string const &file_name) {
const auto stripped_file_name = stripPath(file_name);
const auto pos = stripPath(stripped_file_name).find_last_of(".");
if (pos == std::string::npos) return "";
return stripped_file_name.substr(pos + 1);
}
REGISTER_OP("BlockFormatDatasetV2")
.Input("filenames: string")
.Input("compression_type: string")
.Input("buffer_size: int64")
.Output("handle: variant")
.SetIsStateful()
.SetShapeFn(shape_inference::ScalarShape)
.Doc(R"doc(
Creates a dataset for streaming BlockFormat data in compressed (e.g. gzip), uncompressed formats.
This op also has the ability stream a dataset containing files from multiple formats mentioned above.
filenames: A scalar or vector containing the name(s) of the file(s) to be read.
compression_type: A scalar string denoting the compression type. Can be 'none', 'zlib', 'auto'.
buffer_size: A scalar denoting the buffer size to use during decompression.
Outputs
handle: A handle to the dataset. This handle is later used to create an iterator to stream the data from the dataset.
)doc");
class BlockFormatDatasetV2 : public DatasetOpKernel {
public:
using DatasetOpKernel::DatasetOpKernel;
void MakeDataset(OpKernelContext* ctx, DatasetBase **output) override {
const Tensor* filenames_tensor;
OP_REQUIRES_OK(ctx, ctx->input("filenames", &filenames_tensor));
OP_REQUIRES(
ctx, filenames_tensor->dims() <= 1,
errors::InvalidArgument("`filenames` must be a scalar or a vector."));
const auto filenames_flat = filenames_tensor->flat<string>();
const int64 num_files = filenames_tensor->NumElements();
std::vector<string> filenames;
filenames.reserve(num_files);
std::copy(filenames_flat.data(),
filenames_flat.data() + num_files,
std::back_inserter(filenames));
string compression_type;
OP_REQUIRES_OK(
ctx, tensorflow::data::ParseScalarArgument<string>(
ctx, "compression_type", &compression_type));
int64 buffer_size = -1;
OP_REQUIRES_OK(
ctx, tensorflow::data::ParseScalarArgument<int64>(
ctx, "buffer_size", &buffer_size));
OP_REQUIRES(ctx, buffer_size >= 0,
errors::InvalidArgument(
"`buffer_size` must be >= 0 (0 == no buffering)"));
OP_REQUIRES(ctx,
compression_type == "auto" ||
compression_type == "gz" ||
compression_type == "",
errors::InvalidArgument("Unknown extension: ", compression_type));
*output = new Dataset(ctx, std::move(filenames), compression_type, buffer_size);
}
private:
class Dataset : public DatasetBase {
public:
Dataset(OpKernelContext* ctx,
std::vector<string> filenames,
std::string compression_type,
int64 buffer_size)
: DatasetBase(DatasetContext(ctx)),
compression_type_(compression_type),
buffer_size_(buffer_size),
filenames_(std::move(filenames))
{}
const DataTypeVector& output_dtypes() const override {
static DataTypeVector* dtypes = new DataTypeVector({DT_STRING});
return *dtypes;
}
const std::vector<PartialTensorShape>& output_shapes() const override {
static std::vector<PartialTensorShape>* shapes =
new std::vector<PartialTensorShape>({{}});
return *shapes;
}
string DebugString() const override { return "BlockFormatDatasetV2::Dataset"; }
protected:
Status AsGraphDefInternal(SerializationContext* ctx,
DatasetGraphDefBuilder* b,
Node** output) const override {
Node* filenames = nullptr;
Node* compression_type = nullptr;
Node* buffer_size = nullptr;
TF_RETURN_IF_ERROR(b->AddVector(filenames_, &filenames));
TF_RETURN_IF_ERROR(b->AddScalar(compression_type_, &compression_type));
TF_RETURN_IF_ERROR(
b->AddScalar(buffer_size_, &buffer_size));
TF_RETURN_IF_ERROR(b->AddDataset(
this, {filenames, compression_type, buffer_size}, output));
return Status::OK();
}
private:
std::unique_ptr<IteratorBase> MakeIteratorInternal(
const string& prefix) const override {
return std::unique_ptr<IteratorBase>(
new Iterator({this, strings::StrCat(prefix, "::BlockFormat")}));
}
class Iterator : public DatasetIterator<Dataset> {
public:
explicit Iterator(const Params &params)
: DatasetIterator<Dataset>(params) {}
Status GetNextInternal(IteratorContext* ctx,
std::vector<Tensor>* out_tensors,
bool* end_of_sequence) override {
mutex_lock l(mu_);
do {
// We are currently processing a file, so try to read the next record.
if (reader_) {
Tensor result_tensor(cpu_allocator(), DT_STRING, {});
Status s = reader_->ReadNext(&result_tensor.scalar<string>()());
if (s.ok()) {
out_tensors->emplace_back(std::move(result_tensor));
*end_of_sequence = false;
return Status::OK();
} else if (!errors::IsOutOfRange(s)) {
return s;
}
// We have reached the end of the current file, so maybe
// move on to next file.
reader_.reset();
++current_file_index_;
}
// Iteration ends when there are no more files to process.
if (current_file_index_ == dataset()->filenames_.size()) {
*end_of_sequence = true;
return Status::OK();
}
// Actually move on to next file.
const string& next_filename =
dataset()->filenames_[current_file_index_];
auto compression_type = dataset()->compression_type_;
int64 buffer_size = dataset()->buffer_size_;
if (compression_type == "auto") {
compression_type = getExtension(next_filename);
}
if (compression_type != "gz" && compression_type != "") {
return errors::InvalidArgument("Unknown extension: ", compression_type);
}
tensorflow::Env* env = tensorflow::Env::Default();
TF_CHECK_OK(env->NewRandomAccessFile(next_filename, &file_));
// RandomAccessInputstream defaults the second param to "false".
// The second parameter "false" is the key issue.
// "false" assumes the ownership of the file is elsewhere.
// But making that "true" causes segfaults down the line.
// So keep the ownership of "file_" in this class and clean up properly.
file_stream_.reset(new tensorflow::io::RandomAccessInputStream(file_.get(), false));
if (compression_type == "gz") {
// unpack_stream does not take ownership of file_stream_
#if !defined(DISABLE_ZLIB)
unpack_stream_.reset(new tensorflow::io::ZlibInputStream(
file_stream_.get(),
buffer_size,
buffer_size,
tensorflow::io::ZlibCompressionOptions::GZIP()));
reader_.reset(new BlockFormatReader(unpack_stream_.get()));
#else
return errors::InvalidArgument("libtwml compiled without zlib support");
#endif
} else {
unpack_stream_.reset(nullptr);
reader_.reset(new BlockFormatReader(file_stream_.get()));
}
} while (true);
}
private:
mutex mu_;
uint64_t current_file_index_ GUARDED_BY(mu_) = 0;
std::unique_ptr<tensorflow::RandomAccessFile> file_;
std::unique_ptr<tensorflow::io::InputStreamInterface> file_stream_;
std::unique_ptr<tensorflow::io::InputStreamInterface> unpack_stream_;
std::unique_ptr<BlockFormatReader> reader_ GUARDED_BY(mu_);
};
const std::string compression_type_;
const int64 buffer_size_;
const std::vector<string> filenames_;
};
};
REGISTER_KERNEL_BUILDER(
Name("BlockFormatDatasetV2")
.Device(DEVICE_CPU),
BlockFormatDatasetV2);

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#pragma once
#include "tensorflow/core/framework/common_shape_fns.h"
#include "tensorflow/core/framework/op.h"
#include "tensorflow/core/framework/shape_inference.h"
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/platform/env.h"
#include "tensorflow/core/lib/io/random_inputstream.h"
#include <twml.h>
#include <string>
using tensorflow::int64;
using tensorflow::Status;
using std::string;
class BlockFormatReader : twml::BlockFormatReader {
public:
explicit BlockFormatReader(tensorflow::io::InputStreamInterface *stream)
: twml::BlockFormatReader() , stream_(stream) {
}
// Read the next record.
// Returns OK on success,
// Returns OUT_OF_RANGE for end of file, or something else for an error.
Status ReadNext(string* record) {
if (this->next()) {
return stream_->ReadNBytes(this->current_size(), record);
}
return tensorflow::errors::OutOfRange("eof");
}
uint64_t read_bytes(void *dest, int size, int count) {
uint64_t bytesToRead = size * count;
std::string current;
// TODO: Try to merge ReadNBytes and the memcpy below
// ReadNBytes performs a memory copy already.
Status status = stream_->ReadNBytes(bytesToRead, &current);
if (!status.ok()) {
return 0;
}
memcpy(dest, current.c_str(), bytesToRead);
return count;
}
private:
tensorflow::io::InputStreamInterface *stream_;
TF_DISALLOW_COPY_AND_ASSIGN(BlockFormatReader);
};

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#include "tensorflow/core/framework/op.h"
#include "tensorflow/core/framework/shape_inference.h"
#include "tensorflow/core/framework/op_kernel.h"
#include <algorithm> // std::fill_n
using namespace tensorflow;
REGISTER_OP("CompressSampleIds")
.Attr("T: {int32}")
.Input("input: T")
.Output("output: T")
.SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) {
c->set_output(0, c->Vector(c->kUnknownDim));
return Status::OK();
});
template<typename T>
class CompressSampleIds : public OpKernel {
public:
explicit CompressSampleIds(OpKernelConstruction* context) : OpKernel(context) {}
void Compute(OpKernelContext* context) override {
// Grab the input tensor
const Tensor& input_tensor = context->input(0);
auto input = input_tensor.flat<T>();
const int N = input.size();
// Check for improper input
bool error = (N > 0 && input(0) < 0);
for (int i = 1; !error && i < N; i++) {
error = input(i - 1) > input(i);
}
OP_REQUIRES(
context, !error,
errors::InvalidArgument(
"Error in CompressSampleIds. SampleIds must be non-negative and non-decreasing"
)
);
// choose output size, either last input element + 1, or 0
int output_size = 0;
if (N > 0) {
output_size = input(N - 1) + 1;
}
// Create an output tensor
Tensor* output_tensor = nullptr;
OP_REQUIRES_OK(
context,
context->allocate_output(0, TensorShape({output_size}), &output_tensor)
);
auto output_flat = output_tensor->flat<T>();
// Zero-initialize output
for (int i = 0; i < output_size; i++) {
output_flat(i) = 0;
}
// count how many of each input element
for (int i = 0; i < N; i++) {
output_flat(input(i)) ++;
}
}
};
REGISTER_OP("DecompressSampleIds")
.Attr("T: {int32}")
.Input("input: T")
.Output("output: T")
.SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) {
c->set_output(0, c->Vector(c->kUnknownDim));
return Status::OK();
});
template<typename T>
class DecompressSampleIds : public OpKernel {
public:
explicit DecompressSampleIds(OpKernelConstruction* context) : OpKernel(context) {}
void Compute(OpKernelContext* context) override {
// Grab the input tensor
const Tensor& input_tensor = context->input(0);
auto input = input_tensor.flat<T>();
const int N = input.size();
// Check for improper input
bool error = false;
int output_size = 0;
for (int i = 0; !error && i < N; i++) {
error = input(i) < 0;
output_size += input(i);
}
OP_REQUIRES(
context, !error,
errors::InvalidArgument(
"Error in DecompressSampleIds. Inputs must be non-negative."
)
);
// Create an output tensor
Tensor* output_tensor = nullptr;
OP_REQUIRES_OK(
context,
context->allocate_output(0, TensorShape({output_size}),&output_tensor)
);
auto output_flat = output_tensor->flat<T>();
T *output_data = output_flat.data();
for (int current_sample = 0; current_sample < N; current_sample++) {
std::fill_n(output_data, input(current_sample), current_sample);
output_data += input(current_sample);
}
}
};
#define REGISTER(Type) \
\
REGISTER_KERNEL_BUILDER( \
Name("CompressSampleIds") \
.Device(DEVICE_CPU) \
.TypeConstraint<Type>("T"), \
CompressSampleIds<Type>); \
\
REGISTER_KERNEL_BUILDER( \
Name("DecompressSampleIds") \
.Device(DEVICE_CPU) \
.TypeConstraint<Type>("T"), \
DecompressSampleIds<Type>); \
\
REGISTER(int32);

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#include "tensorflow/core/framework/op.h"
#include "tensorflow/core/framework/shape_inference.h"
#include "tensorflow/core/framework/op_kernel.h"
#include <twml.h>
#include "../tensorflow_utils.h"
#include "../resource_utils.h"
#include <string>
#include <set>
using std::string;
void join(const std::set<string>& v, char c, string& s) {
s.clear();
std::set<std::string>::iterator it = v.begin();
while (it != v.end()) {
s += *it;
it++;
if (it != v.end()) s+= c;
}
}
// cpp function that computes substrings of a given word
std::string computeSubwords(std::string word, int32_t minn, int32_t maxn) {
std::string word2 = "<" + word + ">";
std::set<string> ngrams;
std::string s;
ngrams.insert(word);
ngrams.insert(word2);
for (size_t i = 0; i < word2.size(); i++) {
if ((word2[i] & 0xC0) == 0x80) continue;
for (size_t j = minn; i+j <= word2.size() && j <= maxn; j++) {
ngrams.insert(word2.substr(i, j));
}
}
join(ngrams, ';', s);
ngrams.clear();
return s;
}
// tf-op function that computes substrings for a given tensor of words
template< typename ValueType>
void ComputeSubStringsTensor(OpKernelContext *context, int32 min_n, int32 max_n) {
try {
const Tensor& values = context->input(0);
auto values_flat = values.flat<ValueType>();
// batch_size from input_size :
const int batch_size = values_flat.size();
// define the output tensor
Tensor* substrings = nullptr;
OP_REQUIRES_OK(context, context->allocate_output(0, values.shape(), &substrings));
auto substrings_flat = substrings->flat<ValueType>();
// compute substrings for the given tensor values
for (int64 i = 0; i < batch_size; i++) {
substrings_flat(i) = computeSubwords(values_flat(i), min_n, max_n);
}
}
catch (const std::exception &err) {
context->CtxFailureWithWarning(errors::InvalidArgument(err.what()));
}
}
REGISTER_OP("GetSubstrings")
.Attr("ValueType: {string}")
.Attr("min_n: int")
.Attr("max_n: int")
.Input("values: ValueType")
.Output("substrings: ValueType")
.SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) {
c->set_output(0, c->input(0));
return Status::OK();
}).Doc(R"doc(
A tensorflow OP to convert word to substrings of length between min_n and max_n.
Attr
min_n,max_n: The size of the substrings.
Input
values: 1D input tensor containing the values.
Outputs
substrings: A string tensor where substrings are joined by ";".
)doc");
template<typename ValueType>
class GetSubstrings : public OpKernel {
public:
explicit GetSubstrings(OpKernelConstruction *context) : OpKernel(context) {
OP_REQUIRES_OK(context, context->GetAttr("min_n", &min_n));
OP_REQUIRES_OK(context, context->GetAttr("max_n", &max_n));
}
private:
int32 min_n;
int32 max_n;
void Compute(OpKernelContext *context) override {
ComputeSubStringsTensor<ValueType>(context, min_n, max_n);
}
};
#define REGISTER_SUBSTRINGS(ValueType) \
REGISTER_KERNEL_BUILDER( \
Name("GetSubstrings") \
.Device(DEVICE_CPU) \
.TypeConstraint<ValueType>("ValueType"), \
GetSubstrings<ValueType>); \
REGISTER_SUBSTRINGS(string)

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#include "tensorflow/core/framework/op.h"
#include "tensorflow/core/framework/shape_inference.h"
#include "tensorflow/core/framework/op_kernel.h"
#include <twml.h>
#include "tensorflow_utils.h"
using namespace tensorflow;
REGISTER_OP("DataRecordTensorWriter")
.Attr("T: list({string, int32, int64, float, double, bool})")
.Input("keys: int64")
.Input("values: T")
.Output("result: uint8")
.SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) {
return Status::OK();
}).Doc(R"doc(
A tensorflow OP that packages keys and dense tensors into a DataRecord.
values: list of tensors
keys: feature ids from the original DataRecord (int64)
Outputs
bytes: output DataRecord serialized using Thrift into a uint8 tensor.
)doc");
class DataRecordTensorWriter : public OpKernel {
public:
explicit DataRecordTensorWriter(OpKernelConstruction* context)
: OpKernel(context) {}
void Compute(OpKernelContext* context) override {
const Tensor& keys = context->input(0);
try {
// set keys as twml::Tensor
const twml::Tensor in_keys_ = TFTensor_to_twml_tensor(keys);
// check sizes
uint64_t num_keys = in_keys_.getNumElements();
uint64_t num_values = context->num_inputs() - 1;
OP_REQUIRES(context, num_keys == num_values,
errors::InvalidArgument("Number of dense keys and dense tensors do not match"));
// populate DataRecord object
const int64_t *keys = in_keys_.getData<int64_t>();
twml::DataRecord record = twml::DataRecord();
for (int i = 1; i < context->num_inputs(); i++) {
const twml::RawTensor& value = TFTensor_to_twml_raw_tensor(context->input(i));
record.addRawTensor(keys[i-1], value);
}
// determine the length of the encoded result (no memory is copied)
twml::ThriftWriter thrift_dry_writer = twml::ThriftWriter(nullptr, 0, true);
twml::DataRecordWriter record_dry_writer = twml::DataRecordWriter(thrift_dry_writer);
record_dry_writer.write(record);
int len = thrift_dry_writer.getBytesWritten();
TensorShape result_shape = {1, len};
// allocate output tensor
Tensor* result = NULL;
OP_REQUIRES_OK(context, context->allocate_output(0, result_shape, &result));
twml::Tensor out_result = TFTensor_to_twml_tensor(*result);
// write to output tensor
uint8_t *buffer = out_result.getData<uint8_t>();
twml::ThriftWriter thrift_writer = twml::ThriftWriter(buffer, len, false);
twml::DataRecordWriter record_writer = twml::DataRecordWriter(thrift_writer);
record_writer.write(record);
} catch(const std::exception &e) {
context->CtxFailureWithWarning(errors::InvalidArgument(e.what()));
}
}
};
REGISTER_KERNEL_BUILDER(
Name("DataRecordTensorWriter").Device(DEVICE_CPU),
DataRecordTensorWriter);

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#include "tensorflow/core/framework/op.h"
#include "tensorflow/core/framework/shape_inference.h"
#include "tensorflow/core/framework/op_kernel.h"
#include <twml.h>
#include "tensorflow_utils.h"
using namespace tensorflow;
void ComputeDiscretizers(OpKernelContext* context, const bool return_bin_indices = false) {
const Tensor& keys = context->input(0);
const Tensor& vals = context->input(1);
const Tensor& bin_ids = context->input(2);
const Tensor& bin_vals = context->input(3);
const Tensor& feature_offsets = context->input(4);
Tensor* new_keys = nullptr;
OP_REQUIRES_OK(context, context->allocate_output(0, keys.shape(),
&new_keys));
Tensor* new_vals = nullptr;
OP_REQUIRES_OK(context, context->allocate_output(1, keys.shape(),
&new_vals));
try {
twml::Tensor out_keys_ = TFTensor_to_twml_tensor(*new_keys);
twml::Tensor out_vals_ = TFTensor_to_twml_tensor(*new_vals);
const twml::Tensor in_keys_ = TFTensor_to_twml_tensor(keys);
const twml::Tensor in_vals_ = TFTensor_to_twml_tensor(vals);
const twml::Tensor bin_ids_ = TFTensor_to_twml_tensor(bin_ids);
const twml::Tensor bin_vals_ = TFTensor_to_twml_tensor(bin_vals);
const twml::Tensor feature_offsets_ = TFTensor_to_twml_tensor(feature_offsets);
twml::mdlInfer(out_keys_, out_vals_,
in_keys_, in_vals_,
bin_ids_, bin_vals_,
feature_offsets_,
return_bin_indices);
} catch (const std::exception &e) {
context->CtxFailureWithWarning(errors::InvalidArgument(e.what()));
}
}
REGISTER_OP("MDL")
.Attr("T: {float, double}")
.Input("keys: int64")
.Input("vals: T")
.Input("bin_ids: int64")
.Input("bin_vals: T")
.Input("feature_offsets: int64")
.Output("new_keys: int64")
.Output("new_vals: T")
.SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) {
// TODO: check sizes
c->set_output(0, c->input(0));
c->set_output(1, c->input(0));
return Status::OK();
}).Doc(R"doc(
This operation discretizes a tensor containing continuous features.
Input
keys: A tensor containing feature ids.
vals: A tensor containing values at corresponding feature ids.
bin_ids: A tensor containing the discretized feature id for a given bin.
bin_vals: A tensor containing the bin boundaries for value at a given feature id.
feature_offsets: Specifies the starting location of bins for a given feature id.
Expected Sizes:
keys, vals: [N].
bin_ids, bin_vals: [sum_{n=1}^{n=num_classes} num_bins(n)]
where
- N is the number of sparse features in the current batch.
- [0, num_classes) represents the range each feature id can take.
- num_bins(n) is the number of bins for a given feature id.
- If num_bins is fixed, then xs, ys are of size [num_classes * num_bins].
Expected Types:
keys, bin_ids: int64.
vals: float or double.
bin_vals: same as vals.
Before using MDL, you should use a hashmap to get the intersection of
input `keys` with the features that MDL knows about:
::
keys, vals # keys can be in range [0, 1 << 63)
mdl_keys = hashmap.find(keys) # mdl_keys are now in range [0, num_classes_from_calibration)
mdl_keys = where (mdl_keys != -1) # Ignore keys not found
Inside MDL, the following is happening:
::
start = offsets[key[i]]
end = offsets[key[i] + 1]
idx = binary_search for val[i] in [bin_vals[start], bin_vals[end]]
result_keys[i] = bin_ids[idx]
val[i] = 1 # binary feature value
Outputs
new_keys: The discretized feature ids with same shape and size as keys.
new_vals: The discretized values with the same shape and size as vals.
)doc");
template<typename T>
class MDL : public OpKernel {
public:
explicit MDL(OpKernelConstruction* context) : OpKernel(context) {
}
void Compute(OpKernelContext* context) override {
ComputeDiscretizers(context);
}
};
REGISTER_OP("PercentileDiscretizer")
.Attr("T: {float, double}")
.Input("keys: int64")
.Input("vals: T")
.Input("bin_ids: int64")
.Input("bin_vals: T")
.Input("feature_offsets: int64")
.Output("new_keys: int64")
.Output("new_vals: T")
.SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) {
// TODO: check sizes
c->set_output(0, c->input(0));
c->set_output(1, c->input(0));
return Status::OK();
}).Doc(R"doc(
This operation discretizes a tensor containing continuous features.
Input
keys: A tensor containing feature ids.
vals: A tensor containing values at corresponding feature ids.
bin_ids: A tensor containing the discretized feature id for a given bin.
bin_vals: A tensor containing the bin boundaries for value at a given feature id.
feature_offsets: Specifies the starting location of bins for a given feature id.
Expected Sizes:
keys, vals: [N].
bin_ids, bin_vals: [sum_{n=1}^{n=num_classes} num_bins(n)]
where
- N is the number of sparse features in the current batch.
- [0, num_classes) represents the range each feature id can take.
- num_bins(n) is the number of bins for a given feature id.
- If num_bins is fixed, then xs, ys are of size [num_classes * num_bins].
Expected Types:
keys, bin_ids: int64.
vals: float or double.
bin_vals: same as vals.
Before using PercentileDiscretizer, you should use a hashmap to get the intersection of
input `keys` with the features that PercentileDiscretizer knows about:
::
keys, vals # keys can be in range [0, 1 << 63)
percentile_discretizer_keys = hashmap.find(keys) # percentile_discretizer_keys are now in range [0, num_classes_from_calibration)
percentile_discretizer_keys = where (percentile_discretizer_keys != -1) # Ignore keys not found
Inside PercentileDiscretizer, the following is happening:
::
start = offsets[key[i]]
end = offsets[key[i] + 1]
idx = binary_search for val[i] in [bin_vals[start], bin_vals[end]]
result_keys[i] = bin_ids[idx]
val[i] = 1 # binary feature value
Outputs
new_keys: The discretized feature ids with same shape and size as keys.
new_vals: The discretized values with the same shape and size as vals.
)doc");
template<typename T>
class PercentileDiscretizer : public OpKernel {
public:
explicit PercentileDiscretizer(OpKernelConstruction* context) : OpKernel(context) {
}
void Compute(OpKernelContext* context) override {
ComputeDiscretizers(context);
}
};
REGISTER_OP("PercentileDiscretizerBinIndices")
.Attr("T: {float, double}")
.Input("keys: int64")
.Input("vals: T")
.Input("bin_ids: int64")
.Input("bin_vals: T")
.Input("feature_offsets: int64")
.Output("new_keys: int64")
.Output("new_vals: T")
.SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) {
// TODO: check sizes
c->set_output(0, c->input(0));
c->set_output(1, c->input(0));
return Status::OK();
}).Doc(R"doc(
This operation discretizes a tensor containing continuous features.
If the feature id and bin id of the discretized value is the same on multiple runs, they
will always be assigned to the same output key and value, regardless of the bin_id assigned during
calibration.
Input
keys: A tensor containing feature ids.
vals: A tensor containing values at corresponding feature ids.
bin_ids: A tensor containing the discretized feature id for a given bin.
bin_vals: A tensor containing the bin boundaries for value at a given feature id.
feature_offsets: Specifies the starting location of bins for a given feature id.
Expected Sizes:
keys, vals: [N].
bin_ids, bin_vals: [sum_{n=1}^{n=num_classes} num_bins(n)]
where
- N is the number of sparse features in the current batch.
- [0, num_classes) represents the range each feature id can take.
- num_bins(n) is the number of bins for a given feature id.
- If num_bins is fixed, then xs, ys are of size [num_classes * num_bins].
Expected Types:
keys, bin_ids: int64.
vals: float or double.
bin_vals: same as vals.
Before using PercentileDiscretizerBinIndices, you should use a hashmap to get the intersection of
input `keys` with the features that PercentileDiscretizerBinIndices knows about:
::
keys, vals # keys can be in range [0, 1 << 63)
percentile_discretizer_keys = hashmap.find(keys) # percentile_discretizer_keys are now in range [0, num_classes_from_calibration)
percentile_discretizer_keys = where (percentile_discretizer_keys != -1) # Ignore keys not found
Inside PercentileDiscretizerBinIndices, the following is happening:
::
start = offsets[key[i]]
end = offsets[key[i] + 1]
idx = binary_search for val[i] in [bin_vals[start], bin_vals[end]]
result_keys[i] = bin_ids[idx]
val[i] = 1 # binary feature value
Outputs
new_keys: The discretized feature ids with same shape and size as keys.
new_vals: The discretized values with the same shape and size as vals.
)doc");
template<typename T>
class PercentileDiscretizerBinIndices : public OpKernel {
public:
explicit PercentileDiscretizerBinIndices(OpKernelConstruction* context) : OpKernel(context) {
}
void Compute(OpKernelContext* context) override {
ComputeDiscretizers(context, true);
}
};
#define REGISTER(Type) \
\
REGISTER_KERNEL_BUILDER( \
Name("PercentileDiscretizerBinIndices") \
.Device(DEVICE_CPU) \
.TypeConstraint<Type>("T"), \
PercentileDiscretizerBinIndices<Type>); \
\
REGISTER_KERNEL_BUILDER( \
Name("PercentileDiscretizer") \
.Device(DEVICE_CPU) \
.TypeConstraint<Type>("T"), \
PercentileDiscretizer<Type>); \
\
REGISTER_KERNEL_BUILDER( \
Name("MDL") \
.Device(DEVICE_CPU) \
.TypeConstraint<Type>("T"), \
MDL<Type>); \
REGISTER(float);
REGISTER(double);

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#include "tensorflow/core/framework/op.h"
#include "tensorflow/core/framework/shape_inference.h"
#include "tensorflow/core/framework/op_kernel.h"
#include <twml.h>
#include "tensorflow_utils.h"
#include <map>
#include <vector>
REGISTER_OP("FeatureExtractor")
.Attr("T: {float, double} = DT_FLOAT")
.Input("mask_in: bool")
.Input("ids_in: int64")
.Input("keys_in: int64")
.Input("values_in: T")
.Input("codes_in: int64")
.Input("types_in: int8")
.Output("ids_out: int64")
.Output("keys_out: int64")
.Output("values_out: T")
.Output("codes_out: int64")
.Output("types_out: int8")
.SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) {
return Status::OK();
}).Doc(R"doc(
A tensorflow OP that extracts the desired indices of a Tensor based on a mask
Input
mask_in: boolean Tensor that determines which are the indices to be kept (bool)
ids_in: input indices Tensor (int64)
keys_in: input keys Tensor (int64)
values_in: input values Tensor (float/double)
codes_in: input codes Tensor (int64)
types_in: input types Tensor(int8)
Outputs
ids_out: output indices Tensor (int64)
keys_out: output keys Tensor (int64)
values_out: output values Tensor (float/double)
codes_out: output codes Tensor (int64)
types_out: output types Tensor(int8)
)doc");
template <typename T>
class FeatureExtractor : public OpKernel {
public:
explicit FeatureExtractor(OpKernelConstruction* context)
: OpKernel(context) {}
template <typename A, typename U>
bool allequal(const A &t, const U &u) {
return t == u;
}
template <typename A, typename U, typename... Others>
bool allequal(const A &t, const U &u, Others const &... args) {
return (t == u) && allequal(u, args...);
}
void Compute(OpKernelContext* context) override {
// Get input tensors
const Tensor& input_mask = context->input(0);
const Tensor& input_ids = context->input(1);
const Tensor& input_keys = context->input(2);
const Tensor& input_values = context->input(3);
const Tensor& input_codes = context->input(4);
const Tensor& input_types = context->input(5);
auto mask = input_mask.flat<bool>();
auto ids = input_ids.flat<int64>();
auto keys = input_keys.flat<int64>();
auto codes = input_codes.flat<int64>();
auto values = input_values.flat<T>();
auto types = input_types.flat<int8>();
// Verify that all Tensors have the same size.
OP_REQUIRES(context, allequal(mask.size(), ids.size(), keys.size(), codes.size(), values.size(), types.size()),
errors::InvalidArgument("all input vectors must be the same size."));
// Get the size of the output vectors by counting the numbers of trues.
int total_size = 0;
for (int i = 0; i < mask.size(); i++) {
if (mask(i))
total_size += 1;
}
// Shape is the number of Trues in the mask Eigen::Tensor
TensorShape shape_out = {total_size};
// Create the output tensors
Tensor* output_codes = nullptr;
Tensor* output_ids = nullptr;
Tensor* output_values = nullptr;
Tensor* output_types = nullptr;
Tensor* output_keys = nullptr;
OP_REQUIRES_OK(context, context->allocate_output(0, shape_out, &output_ids));
OP_REQUIRES_OK(context, context->allocate_output(1, shape_out, &output_keys));
OP_REQUIRES_OK(context, context->allocate_output(2, shape_out, &output_values));
OP_REQUIRES_OK(context, context->allocate_output(3, shape_out, &output_codes));
OP_REQUIRES_OK(context, context->allocate_output(4, shape_out, &output_types));
auto output_ids_ = output_ids->flat<int64>();
auto output_keys_ = output_keys->flat<int64>();
auto output_codes_ = output_codes->flat<int64>();
auto output_values_ = output_values->flat<T>();
auto output_types_ = output_types->flat<int8>();
// Iterate through the mask and set values to output Eigen::Tensors
int j = 0;
for (int i = 0; i < mask.size(); i++) {
if (mask(i)) {
output_ids_(j) = ids(i);
output_keys_(j) = keys(i);
output_values_(j) = values(i);
output_codes_(j) = codes(i);
output_types_(j) = types(i);
++j;
}
}
}
};
#define REGISTER(Type) \
\
REGISTER_KERNEL_BUILDER( \
Name("FeatureExtractor") \
.Device(DEVICE_CPU) \
.TypeConstraint<Type>("T"), \
FeatureExtractor<Type>); \
REGISTER(float);
REGISTER(double);

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twml/libtwml/src/ops/feature_id.cpp
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#include "tensorflow/core/framework/op.h"
#include "tensorflow/core/framework/shape_inference.h"
#include "tensorflow/core/framework/op_kernel.h"
#include <twml.h>
#include "tensorflow_utils.h"
using namespace tensorflow;
REGISTER_OP("FeatureId")
.Attr("feature_names: list(string)")
.Output("output: int64")
.SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) {
return Status::OK();
}).Doc(R"doc(
A tensorflow OP that hashes a list of strings into int64. This is used for feature name hashing.
Attr
feature_names: a list of string feature names (list(string)).
Outputs
ouput: hashes corresponding to the string feature names (int64).
)doc");
class FeatureId : public OpKernel {
private:
std::vector<string> input_vector;
public:
explicit FeatureId(OpKernelConstruction* context) : OpKernel(context) {
OP_REQUIRES_OK(context, context->GetAttr("feature_names", &input_vector));
}
void Compute(OpKernelContext* context) override {
// Get size of the input_vector and create TensorShape shape
const int total_size = static_cast<int>(input_vector.size());
TensorShape shape = {total_size};
// Create an output tensor
Tensor* output_tensor = nullptr;
OP_REQUIRES_OK(context, context->allocate_output(0, shape,
&output_tensor));
auto output_flat = output_tensor->flat<int64>();
// Transform the input tensor into a int64
for (int i = 0; i < total_size; i++) {
output_flat(i) = twml::featureId(input_vector[i]);
}
}
};
REGISTER_KERNEL_BUILDER(
Name("FeatureId")
.Device(DEVICE_CPU),
FeatureId);

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twml/libtwml/src/ops/feature_mask.cpp
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#include "tensorflow/core/framework/op.h"
#include "tensorflow/core/framework/shape_inference.h"
#include "tensorflow/core/framework/op_kernel.h"
#include <twml.h>
#include "tensorflow_utils.h"
#include <map>
#include <vector>
#include <set>
REGISTER_OP("FeatureMask")
.Attr("T: {int64, int8}")
.Input("keep: T")
.Attr("list_keep: list(int)")
.Output("mask: bool")
.SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) {
return Status::OK();
}).Doc(R"doc(
A tensorflow OP that creates a mask of the indices that should be kept.
Attribute
list_keep: list of values which should be kept(list(int))
Input
keep: Tensor for which we will apply the mask (int64, int8)
Outputs
mask: boolean Tensor. (bool)
)doc");
template <typename T>
class FeatureMask : public OpKernel {
private:
std::set<int64> feature_set_keep;
public:
explicit FeatureMask(OpKernelConstruction* context)
: OpKernel(context) {
std::vector<int64> feature_list_keep;
OP_REQUIRES_OK(context, context->GetAttr("list_keep", &feature_list_keep));
// create set that contains the content of the feature_list_keep, since tensorflow does not allow
// me to directly ouput the contents of list_keep to a set
feature_set_keep = std::set<int64>(feature_list_keep.begin(), feature_list_keep.end());
}
void Compute(OpKernelContext* context) override {
// Get size of the input_vector and create TensorShape shape
const Tensor& input = context->input(0);
auto keep = input.flat<T>();
// Create an output tensor
Tensor* output_mask = nullptr;
// Output shape is determined and now we can copy the contents of the vector to the output Tensor.
const int total_size_out = static_cast<int>(keep.size());
TensorShape shape_out = {total_size_out};
OP_REQUIRES_OK(context, context->allocate_output(0, shape_out, &output_mask));
auto output_mask_ = output_mask->flat<bool>();
// Check if value is in set, output is boolean
for (int j = 0; j < keep.size(); j++){
output_mask_(j) = (feature_set_keep.count(keep(j)));
}
}
};
#define REGISTER(Type) \
\
REGISTER_KERNEL_BUILDER( \
Name("FeatureMask") \
.Device(DEVICE_CPU) \
.TypeConstraint<Type>("T"), \
FeatureMask<Type>); \
REGISTER(int64);
REGISTER(int8);

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twml/libtwml/src/ops/fixed_length_tensor.cpp
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#include "tensorflow/core/framework/op.h"
#include "tensorflow/core/framework/shape_inference.h"
#include "tensorflow/core/framework/op_kernel.h"
#include <twml.h>
#include "tensorflow_utils.h"
#include "resource_utils.h"
#include <algorithm>
using std::string;
template<typename IndexType, typename ValueType, bool calc_batch_size>
void ComputeFixedLengthTensor(OpKernelContext *context, int64 max_length_) {
try {
const Tensor& segment_ids = context->input(0);
const Tensor& values = context->input(1);
const Tensor& pad_value = context->input(2);
auto indices_flat = segment_ids.flat<IndexType>();
auto values_flat = values.flat<ValueType>();
auto pad_value_scalar = pad_value.scalar<ValueType>()();
// Get maximum length from batch if user hasn't specified it.
int64 max_length = max_length_;
if (max_length < 0 && indices_flat.size() > 0) {
int64 current_id = indices_flat(0);
int64 current_length = 1;
for (int64 i = 1; i < indices_flat.size(); i++) {
if (current_id == indices_flat(i)) {
current_length++;
} else {
current_id = indices_flat(i);
max_length = std::max(max_length, current_length);
current_length = 1;
}
}
// This is needed if the last batch is the longest sequence.
max_length = std::max(max_length, current_length);
}
int64 batch_size = 0;
if (calc_batch_size) {
if (indices_flat.size() > 0) {
// The last value of segment_ids will have value batch_size 1;
batch_size = 1 + indices_flat(indices_flat.size() - 1);
} else {
batch_size = 0;
}
} else {
const Tensor& batch_size_tensor = context->input(3);
batch_size = batch_size_tensor.flat<int64>()(0);
}
TensorShape output_shape = {batch_size, max_length};
Tensor* fixed_length = nullptr;
OP_REQUIRES_OK(context, context->allocate_output(0, output_shape, &fixed_length));
auto fixed_length_flat = fixed_length->flat<ValueType>();
int64 n = 0;
int64 offset = 0;
for (int64 i = 0; i < batch_size; i++) {
for (int64 j = 0; j < max_length; j++) {
if (n < indices_flat.size() && indices_flat(n) == i) {
// Copy from variable length tensor.
fixed_length_flat(offset + j) = values_flat(n);
n++;
} else {
// Pad to fixed length.
fixed_length_flat(offset + j) = pad_value_scalar;
}
}
// Corner case: truncate to max_length if user specified max_length < current length.
while (n < indices_flat.size() && i == indices_flat(n)) n++;
// Update output pointer
offset += max_length;
}
} catch (const std::exception &err) {
context->CtxFailureWithWarning(errors::InvalidArgument(err.what()));
}
}
REGISTER_OP("FixedLengthTensor")
.Attr("IndexType: {int64, int32}")
.Attr("ValueType: {int64, int32, string}")
.Attr("max_length: int")
.Input("segment_ids: IndexType")
.Input("values: ValueType")
.Input("pad_value: ValueType")
.Output("fixed_length: ValueType")
.SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) {
return Status::OK();
}).Doc(R"doc(
A tensorflow OP to convert variable length segments into fixed length tensor.
Attr
max_length: The size of the inner most (i.e. last) dimension.
Input
segment_ids: 1D input tensor containing the sorted segment_ids.
values: 1D input tensor containing the values.
pad_value: The value used for padding the fixed length tensor.
Outputs
fixed_length: A fixed length tensor of size [batch_size, max_length].
)doc");
template<typename IndexType, typename ValueType>
class FixedLengthTensor: public OpKernel {
public:
explicit FixedLengthTensor(OpKernelConstruction *context) : OpKernel(context) {
OP_REQUIRES_OK(context, context->GetAttr("max_length", &max_length_));
}
private:
int64 max_length_;
void Compute(OpKernelContext *context) override {
ComputeFixedLengthTensor<IndexType, ValueType, true>(context, max_length_);
}
};
REGISTER_OP("FixedLengthTensorV2")
.Attr("IndexType: {int64, int32}")
.Attr("ValueType: {int64, int32, string}")
.Attr("max_length: int")
.Input("segment_ids: IndexType")
.Input("values: ValueType")
.Input("pad_value: ValueType")
.Input("batch_size: int64")
.Output("fixed_length: ValueType")
.SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) {
return Status::OK();
}).Doc(R"doc(
A tensorflow OP to convert variable length segments into fixed length tensor.
Attr
max_length: The size of the inner most (i.e. last) dimension.
Input
segment_ids: 1D input tensor containing the sorted segment_ids.
values: 1D input tensor containing the values.
pad_value: The value used for padding the fixed length tensor.
batch_size: The batch size to use.
Outputs
fixed_length: A fixed length tensor of size [batch_size, max_length].
)doc");
template<typename IndexType, typename ValueType>
class FixedLengthTensorV2: public OpKernel {
public:
explicit FixedLengthTensorV2(OpKernelConstruction *context) : OpKernel(context) {
OP_REQUIRES_OK(context, context->GetAttr("max_length", &max_length_));
}
private:
int64 max_length_;
void Compute(OpKernelContext *context) override {
ComputeFixedLengthTensor<IndexType, ValueType, false>(context, max_length_);
}
};
#define REGISTER_SPARSE_TO_FIXED_LENGTH(IndexType, ValueType) \
REGISTER_KERNEL_BUILDER( \
Name("FixedLengthTensor") \
.Device(DEVICE_CPU) \
.TypeConstraint<IndexType>("IndexType") \
.TypeConstraint<ValueType>("ValueType"), \
FixedLengthTensor<IndexType, ValueType>); \
\
REGISTER_KERNEL_BUILDER( \
Name("FixedLengthTensorV2") \
.Device(DEVICE_CPU) \
.TypeConstraint<IndexType>("IndexType") \
.TypeConstraint<ValueType>("ValueType"), \
FixedLengthTensorV2<IndexType, ValueType>); \
REGISTER_SPARSE_TO_FIXED_LENGTH(int64, int64)
REGISTER_SPARSE_TO_FIXED_LENGTH(int64, int32)
REGISTER_SPARSE_TO_FIXED_LENGTH(int64, string)
REGISTER_SPARSE_TO_FIXED_LENGTH(int32, int64)
REGISTER_SPARSE_TO_FIXED_LENGTH(int32, int32)
REGISTER_SPARSE_TO_FIXED_LENGTH(int32, string)

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twml/libtwml/src/ops/hashed_data_record.cpp
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#include "tensorflow/core/framework/op.h"
#include "tensorflow/core/framework/shape_inference.h"
#include "tensorflow/core/framework/op_kernel.h"
#include <twml.h>
#include "tensorflow_utils.h"
#include "resource_utils.h"
#include <functional>
REGISTER_OP("DecodeAndHashDataRecord")
.Attr("InputType: {uint8, string}")
.Input("input_bytes: InputType")
.Attr("keep_features: list(int)")
.Attr("keep_codes: list(int)")
.Attr("label_features: list(int)")
.Attr("weight_features: list(int) = []")
.Attr("decode_mode: int = 0")
.Output("hashed_data_record_handle: resource")
.SetShapeFn(shape_inference::ScalarShape)
.Doc(R"doc(
A tensorflow OP that creates a handle for the hashed data record.
Attr
keep_features: a list of int ids to keep.
keep_codes: their corresponding code.
label_features: list of feature ids representing the labels.
weight_features: list of feature ids representing the weights. Defaults to empty list.
decode_mode: integer, indicates which decoding method to use. Let a sparse continuous
have a feature_name and a dict of {name: value}. 0 indicates feature_ids are computed
as hash(name). 1 indicates feature_ids are computed as hash(feature_name, name)
shared_name: name used by the resource handle inside the resource manager.
container: name used by the container of the resources.
Input
input_bytes: Input tensor containing the serialized batch of HashedDataRecords.
Outputs
hashed_data_record_handle: A resource handle to batch of HashedDataRecords.
)doc");
template<typename InputType>
class DecodeAndHashDataRecord : public OpKernel {
public:
explicit DecodeAndHashDataRecord(OpKernelConstruction* context)
: OpKernel(context) {
std::vector<int64> keep_features;
std::vector<int64> keep_codes;
std::vector<int64> label_features;
std::vector<int64> weight_features;
OP_REQUIRES_OK(context, context->GetAttr("keep_features", &keep_features));
OP_REQUIRES_OK(context, context->GetAttr("keep_codes", &keep_codes));
OP_REQUIRES_OK(context, context->GetAttr("label_features", &label_features));
OP_REQUIRES_OK(context, context->GetAttr("weight_features", &weight_features));
OP_REQUIRES_OK(context, context->GetAttr("decode_mode", &m_decode_mode));
OP_REQUIRES(context, keep_features.size() == keep_codes.size(),
errors::InvalidArgument("keep keys and values must have same size."));
#ifdef USE_DENSE_HASH
m_keep_map.set_empty_key(0);
m_labels_map.set_empty_key(0);
m_weights_map.set_empty_key(0);
#endif // USE_DENSE_HASH
for (uint64_t i = 0; i < keep_features.size(); i++) {
m_keep_map[keep_features[i]] = keep_codes[i];
}
for (uint64_t i = 0; i < label_features.size(); i++) {
m_labels_map[label_features[i]] = i;
}
for (uint64_t i = 0; i < weight_features.size(); i++) {
m_weights_map[weight_features[i]] = i;
}
}
private:
twml::Map<int64_t, int64_t> m_keep_map;
twml::Map<int64_t, int64_t> m_labels_map;
twml::Map<int64_t, int64_t> m_weights_map;
int64 m_decode_mode;
void Compute(OpKernelContext* context) override {
try {
HashedDataRecordResource *resource = nullptr;
OP_REQUIRES_OK(context, makeResourceHandle<HashedDataRecordResource>(context, 0, &resource));
// Store the input bytes in the resource so it isnt freed before the resource.
// This is necessary because we are not copying the contents for tensors.
resource->input = context->input(0);
int batch_size = getBatchSize<InputType>(resource->input);
int num_labels = static_cast<int>(m_labels_map.size());
int num_weights = static_cast<int>(m_weights_map.size());
twml::HashedDataRecordReader reader;
reader.setKeepMap(&m_keep_map);
reader.setLabelsMap(&m_labels_map);
reader.setDecodeMode(m_decode_mode);
// Do not set weight map if it is empty. This will take a faster path.
if (num_weights != 0) {
reader.setWeightsMap(&m_weights_map);
}
resource->records.clear();
resource->records.reserve(batch_size);
int64 total_size = 0;
for (int id = 0; id < batch_size; id++) {
const uint8_t *input_bytes = getInputBytes<InputType>(resource->input, id);
reader.setBuffer(input_bytes);
resource->records.emplace_back(num_labels, num_weights);
resource->records[id].decode(reader);
total_size += static_cast<int64>(resource->records[id].totalSize());
}
resource->total_size = total_size;
resource->num_labels = num_labels;
resource->num_weights = num_weights;
} catch (const std::exception &e) {
context->CtxFailureWithWarning(errors::InvalidArgument(e.what()));
}
}
};
REGISTER_OP("GetIdsFromHashedDataRecord")
.Input("hashed_data_record_handle: resource")
.Output("ids: int64")
.SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) {
return Status::OK();
}).Doc(R"doc(
A tensorflow OP that returns unhashed ids from the hashed data record.
Input
hashed_data_record_handle: Resource handle to DataRecord
Outputs
ids: ids specifies the index of the records[id] in the batch (int64)
)doc");
// This Kernel is used for both training and serving once the resource is created.
class GetIdsFromHashedDataRecord : public OpKernel {
public:
explicit GetIdsFromHashedDataRecord(OpKernelConstruction* context)
: OpKernel(context) {}
void Compute(OpKernelContext* context) override {
try {
auto handle = getHandle<HashedDataRecordResource>(context, 0);
const auto &records = handle->records;
const auto &common = handle->common;
const int64 common_size = static_cast<int64>(common.totalSize());
const int64 total_size = handle->total_size;
TensorShape shape = {total_size};
Tensor *ids;
OP_REQUIRES_OK(context, context->allocate_output(0, shape, &ids));
int id = 0;
int64 offset = 0;
auto ids_flat = ids->flat<int64>();
for (const auto &record : records) {
// Since common features are added to each input, add the common_size to the current size.
// For training common_size == 0, for serving it can be a non-zero value.
int64 curr_size = static_cast<int64>(record.totalSize()) + common_size;
std::fill(ids_flat.data() + offset, ids_flat.data() + offset + curr_size, id);
offset += curr_size;
id++;
}
} catch (const std::exception &e) {
context->CtxFailureWithWarning(errors::InvalidArgument(e.what()));
}
}
};
// OutType: Output Tensor Type. FieldType: The storage type used inside HashedDatarecord.
template<typename OutType, typename FieldType>
class GetOutputFromHashedDataRecord : public OpKernel {
protected:
using Getter = std::function<const std::vector<FieldType>&(const twml::HashedDataRecord &)>;
Getter getter;
public:
explicit GetOutputFromHashedDataRecord(OpKernelConstruction* context)
: OpKernel(context) {}
void Compute(OpKernelContext* context) override {
try {
auto handle = getHandle<HashedDataRecordResource>(context, 0);
const auto &records = handle->records;
const auto &common = handle->common;
const int64 total_size = handle->total_size;
TensorShape shape = {total_size};
Tensor *output;
OP_REQUIRES_OK(context, context->allocate_output(0, shape, &output));
const auto &common_output = getter(common);
auto output_data = output->flat<OutType>().data();
for (const auto &record : records) {
// This is does not copy anything during training as common_size == 0
// It will copy the relevant common features coming from a batch prediction request.
output_data = std::copy(common_output.begin(), common_output.end(), output_data);
// Copy the current record to output.
const auto& rec_output = getter(record);
output_data = std::copy(rec_output.begin(), rec_output.end(), output_data);
}
} catch (const std::exception &e) {
context->CtxFailureWithWarning(errors::InvalidArgument(e.what()));
}
}
};
REGISTER_OP("GetUKeysFromHashedDataRecord")
.Input("hashed_data_record_handle: resource")
.Output("ukeys: int64")
.SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) {
return Status::OK();
}).Doc(R"doc(
A tensorflow OP that returns unhashed keys from the hashed data record.
Input
hashed_data_record_handle: Resource handle to DataRecord
Outputs
ukeys: unhased keys / raw feature ids from the original request.
)doc");
class GetUKeysFromHashedDataRecord : public GetOutputFromHashedDataRecord<int64, int64_t> {
public:
explicit GetUKeysFromHashedDataRecord(OpKernelConstruction* context)
: GetOutputFromHashedDataRecord<int64, int64_t>(context){
getter = [](const twml::HashedDataRecord &record) -> const std::vector<int64_t> & {
return record.keys();
};
}
};
REGISTER_OP("GetKeysFromHashedDataRecord")
.Input("hashed_data_record_handle: resource")
.Output("keys: int64")
.SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) {
return Status::OK();
}).Doc(R"doc(
A tensorflow OP that returns keys from the hashed data record.
Input
hashed_data_record_handle: Resource handle to DataRecord
Outputs
keys: keys after raw feature ids are hashed with values (int64)
)doc");
class GetKeysFromHashedDataRecord : public GetOutputFromHashedDataRecord<int64, int64_t> {
public:
explicit GetKeysFromHashedDataRecord(OpKernelConstruction* context)
: GetOutputFromHashedDataRecord<int64, int64_t>(context){
getter = [](const twml::HashedDataRecord &record) -> const std::vector<int64_t> & {
return record.transformed_keys();
};
}
};
REGISTER_OP("GetValuesFromHashedDataRecord")
.Input("hashed_data_record_handle: resource")
.Output("values: float")
.SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) {
return Status::OK();
}).Doc(R"doc(
A tensorflow OP that returns values from the hashed data record.
Input
hashed_data_record_handle: Resource handle to DataRecord
Outputs
values: feature values.
)doc");
class GetValuesFromHashedDataRecord : public GetOutputFromHashedDataRecord<float, double> {
public:
explicit GetValuesFromHashedDataRecord(OpKernelConstruction* context)
: GetOutputFromHashedDataRecord<float, double>(context){
getter = [](const twml::HashedDataRecord &record) -> const std::vector<double> & {
return record.values();
};
}
};
REGISTER_OP("GetCodesFromHashedDataRecord")
.Input("hashed_data_record_handle: resource")
.Output("codes: int64")
.SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) {
return Status::OK();
}).Doc(R"doc(
A tensorflow OP that returns codes from the hashed data record.
Input
hashed_data_record_handle: Resource handle to DataRecord
Outputs
codes: deepbird feature code, usually from A,B,C,D ... in the config.
)doc");
class GetCodesFromHashedDataRecord : public GetOutputFromHashedDataRecord<int64, int64_t> {
public:
explicit GetCodesFromHashedDataRecord(OpKernelConstruction* context)
: GetOutputFromHashedDataRecord<int64, int64_t>(context){
getter = [](const twml::HashedDataRecord &record) -> const std::vector<int64_t> & {
return record.codes();
};
}
};
REGISTER_OP("GetTypesFromHashedDataRecord")
.Input("hashed_data_record_handle: resource")
.Output("types: int8")
.SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) {
return Status::OK();
}).Doc(R"doc(
A tensorflow OP that returns types from the hashed data record.
Input
hashed_data_record_handle: Resource handle to DataRecord
Outputs
types: feature types corresponding to BINARY, DISCRETE, etc.
)doc");
class GetTypesFromHashedDataRecord : public GetOutputFromHashedDataRecord<int8, uint8_t> {
public:
explicit GetTypesFromHashedDataRecord(OpKernelConstruction* context)
: GetOutputFromHashedDataRecord<int8, uint8_t>(context){
getter = [](const twml::HashedDataRecord &record) -> const std::vector<uint8_t> & {
return record.types();
};
}
};
REGISTER_OP("GetBatchSizeFromHashedDataRecord")
.Input("hashed_data_record_handle: resource")
.Output("batch_size: int64")
.SetShapeFn(shape_inference::ScalarShape)
.Doc(R"doc(
A tensorflow OP that returns batch size from the hashed data record.
Input
hashed_data_record_handle: Resource handle to DataRecord
Outputs
batch_size: Number of records held in the handle.
)doc");
class GetBatchSizeFromHashedDataRecord : public OpKernel {
public:
explicit GetBatchSizeFromHashedDataRecord(OpKernelConstruction* context)
: OpKernel(context) {}
void Compute(OpKernelContext* context) override {
try {
auto handle = getHandle<HashedDataRecordResource>(context, 0);
Tensor *output;
OP_REQUIRES_OK(context, context->allocate_output(0, TensorShape({}), &output));
output->scalar<int64>()() = handle->records.size();
} catch (const std::exception &e) {
context->CtxFailureWithWarning(errors::InvalidArgument(e.what()));
}
}
};
REGISTER_OP("GetTotalSizeFromHashedDataRecord")
.Input("hashed_data_record_handle: resource")
.Output("total_size: int64")
.SetShapeFn(shape_inference::ScalarShape)
.Doc(R"doc(
A tensorflow OP that returns total size from the hashed data record.
Input
hashed_data_record_handle: Resource handle to DataRecord
Outputs
total_size: Total number of keys / values in the batch.
)doc");
class GetTotalSizeFromHashedDataRecord : public OpKernel {
public:
explicit GetTotalSizeFromHashedDataRecord(OpKernelConstruction* context)
: OpKernel(context) {}
void Compute(OpKernelContext* context) override {
try {
auto handle = getHandle<HashedDataRecordResource>(context, 0);
Tensor *output;
OP_REQUIRES_OK(context, context->allocate_output(0, TensorShape({}), &output));
output->scalar<int64>()() = handle->total_size;
} catch (const std::exception &e) {
context->CtxFailureWithWarning(errors::InvalidArgument(e.what()));
}
}
};
REGISTER_OP("GetLabelsFromHashedDataRecord")
.Input("hashed_data_record_handle: resource")
.Output("labels: float")
.Attr("default_label: float")
.SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) {
return Status::OK();
}).Doc(R"doc(
A tensorflow OP that returns labels from the hashed data record.
Input
hashed_data_record_handle: Resource handle to DataRecord
Outputs
labels: A 2D tensor of size [batch_size, num_labels] containing the label values.
)doc");
class GetLabelsFromHashedDataRecord : public OpKernel {
private:
float default_label;
public:
explicit GetLabelsFromHashedDataRecord(OpKernelConstruction* context)
: OpKernel(context) {
OP_REQUIRES_OK(context, context->GetAttr("default_label", &default_label));
}
void Compute(OpKernelContext* context) override {
try {
auto handle = getHandle<HashedDataRecordResource>(context, 0);
const auto &records = handle->records;
const int num_labels = static_cast<int>(handle->num_labels);
TensorShape shape = {static_cast<int64>(handle->records.size()), num_labels};
Tensor *labels;
OP_REQUIRES_OK(context, context->allocate_output(0, shape, &labels));
// The default value of label is not present in data record is std::nanf
// For continuous labels, change that to a default_label or label.
auto func = [this](float label) -> float {
return std::isnan(label) ? default_label : label;
};
auto labels_data = labels->flat<float>().data();
for (const auto &record : records) {
const auto& rec_labels = record.labels();
labels_data = std::transform(rec_labels.begin(), rec_labels.end(), labels_data, func);
}
} catch (const std::exception &e) {
context->CtxFailureWithWarning(errors::InvalidArgument(e.what()));
}
}
};
REGISTER_OP("GetWeightsFromHashedDataRecord")
.Input("hashed_data_record_handle: resource")
.Output("weights: float")
.SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) {
return Status::OK();
}).Doc(R"doc(
A tensorflow OP that returns weights from the hashed data record.
Input
hashed_data_record_handle: Resource handle to DataRecord
Outputs
weights: A 2D tensor of size [batch_size, num_weights] containing the weight values.
)doc");
class GetWeightsFromHashedDataRecord : public OpKernel {
public:
explicit GetWeightsFromHashedDataRecord(OpKernelConstruction* context)
: OpKernel(context) {}
void Compute(OpKernelContext* context) override {
try {
auto handle = getHandle<HashedDataRecordResource>(context, 0);
const auto &records = handle->records;
const int num_weights = static_cast<int>(handle->num_weights);
TensorShape shape = {static_cast<int64>(handle->records.size()), num_weights};
Tensor *weights;
OP_REQUIRES_OK(context, context->allocate_output(0, shape, &weights));
auto weights_data = weights->flat<float>().data();
for (const auto &record : records) {
const auto& rec_weights = record.weights();
weights_data = std::copy(rec_weights.begin(), rec_weights.end(), weights_data);
}
} catch (const std::exception &e) {
context->CtxFailureWithWarning(errors::InvalidArgument(e.what()));
}
}
};
#define REGISTER_DECODE_AND_HASH(InputType) \
REGISTER_KERNEL_BUILDER( \
Name("DecodeAndHashDataRecord") \
.Device(DEVICE_CPU) \
.TypeConstraint<InputType>("InputType"), \
DecodeAndHashDataRecord<InputType>); \
REGISTER_DECODE_AND_HASH(uint8)
REGISTER_DECODE_AND_HASH(string)
#define REGISTER_GETTER(FIELD) \
REGISTER_KERNEL_BUILDER( \
Name("Get" #FIELD "FromHashedDataRecord") \
.Device(DEVICE_CPU), \
Get##FIELD##FromHashedDataRecord); \
REGISTER_GETTER(Ids)
REGISTER_GETTER(UKeys)
REGISTER_GETTER(Keys)
REGISTER_GETTER(Values)
REGISTER_GETTER(Codes)
REGISTER_GETTER(Types)
REGISTER_GETTER(BatchSize)
REGISTER_GETTER(TotalSize)
REGISTER_GETTER(Labels)
REGISTER_GETTER(Weights)

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#include "tensorflow/core/framework/op.h"
#include "tensorflow/core/framework/shape_inference.h"
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/util/work_sharder.h"
#include <twml.h>
#include "tensorflow_utils.h"
using namespace tensorflow;
void ComputeHashingDiscretizer(
OpKernelContext*,
int64_t,
const twml::Map<int64_t, int64_t> &,
int64_t,
int64_t,
int64_t);
REGISTER_OP("HashingDiscretizer")
.Attr("T: {float, double}")
.Input("input_ids: int64")
.Input("input_vals: T")
.Input("bin_vals: T")
.Attr("feature_ids: tensor = { dtype: DT_INT64 }")
.Attr("n_bin: int")
.Attr("output_bits: int")
.Attr("cost_per_unit: int")
.Attr("options: int")
.Output("new_keys: int64")
.Output("new_vals: T")
.SetShapeFn(
[](::tensorflow::shape_inference::InferenceContext* c) {
c->set_output(0, c->input(0));
c->set_output(1, c->input(1));
return Status::OK();
}
)
.Doc(R"doc(
This operation discretizes a tensor containing continuous features (if calibrated).
- note - choice of float or double should be consistent among inputs/output
Input
input_ids(int64): A tensor containing input feature ids (direct from data record).
input_vals(float/double): A tensor containing input values at corresponding feature ids.
- i.e. input_ids[i] <-> input_vals[i] for each i
bin_vals(float/double): A tensor containing the bin boundaries for values of a given feature.
- float or double, matching input_vals
feature_ids(int64 attr): 1D TensorProto of feature IDs seen during calibration
-> hint: look up make_tensor_proto:
proto_init = np.array(values, dtype=np.int64)
tensor_attr = tf.make_tensor_proto(proto_init)
n_bin(int): The number of bin boundary values per feature
-> hence, n_bin + 1 buckets for each feature
output_bits(int): The maximum number of bits to use for the output IDs.
cost_per_unit(int): An estimate of the number of CPU cycles (or nanoseconds
if not CPU-bound) to complete a unit of work. Overestimating creates too
many shards and CPU time will be dominated by per-shard overhead, such as
Context creation. Underestimating may not fully make use of the specified
parallelism.
options(int): selects behavior of the op.
0x00 in bits{1:0} for std::lower_bound bucket search.
0x01 in bits{1:0} for linear bucket search
0x02 in bits{1:0} for std::upper_bound bucket search
0x00 in bits{4:2} for integer_multiplicative_hashing
0x01 in bits{4:2} for integer64_multiplicative_hashing
higher bits/other values are reserved for future extensions
Outputs
new_keys(int64): The discretized feature ids with same shape and size as keys.
new_vals(float or double): The discretized values with the same shape and size as vals.
Operation
Note that the discretization operation maps observation vectors to higher dimensional
observation vectors. Here, we describe this mapping.
Let a calibrated feature observation be given by (F,x), where F is the ID of the
feature, and x is some real value (i.e., continuous feature). This kind of
representation is useful for the representation of sparse vectors, where there
are many zeros.
For example, for a dense feature vector [1.2, 2.4, 3.6], we might have
(0, 1.2) (1, 2.4) and (2, 3.6), with feature IDs indicating the 0th, 1st, and 2nd
elements of the vector.
The disretizer performs the following operation:
(F,x) -> (map(x|F),1).
Hence, we have that map(x|F) is a new feature ID, and the value observed for that
feature is 1. We might read map(x|F) as 'the map of x for feature F'.
For each feature F, we associate a (discrete, finite) set of new feature IDs, newIDs(F).
We will then have that map(x|F) is in the set newIDs(F) for any value of x. Each
set member of newIDs(F) is associated with a 'bin', as defined by the bin
boundaries given in the bin_vals input array. For any two different feature IDs F
and G, we would ideally have that INTERSECT(newIDs(F),newIDs(G)) is the empty set.
However, this is not guaranteed for this discretizer.
In the case of this hashing discretizer, map(x|F) can actually be written as follows:
let bucket = bucket(x|F) be the the bucket index for x, according to the
calibration on F. (This is an integer value in [0,n_bin], inclusive)
F is an integer ID. Here, we have that map(x|F) = hash_fn(F,bucket). This has
the desirable property that the new ID depends only on the calibration data
supplied for feature F, and not on any other features in the dataset (e.g.,
number of other features present in the calibration data, or order of features
in the dataset). Note that PercentileDiscretizer does NOT have this property.
This comes at the expense of the possibility of output ID collisions, which
we try to minimize through the design of hash_fn.
Example - consider input vector with a single element, i.e. [x].
Let's Discretize to one of 2 values, as follows:
Let F=0 for the ID of the single feature in the vector.
Let the bin boundary of feature F=0 be BNDRY(F) = BNDRY(0) since F=0
bucket = bucket(x|F=0) = 0 if x<=BNDRY(0) else 1
Let map(x|F) = hash_fn(F=0,bucket=0) if x<=BNDRY(0) else hash_fn(F=0,bucket=1)
If we had another element y in the vector, i.e. [x, y], then we might additionally
Let F=1 for element y.
Let the bin boundary be BNDRY(F) = BNDRY(1) since F=1
bucket = bucket(x|F=1) = 0 if x<=BNDRY(1) else 1
Let map(x|F) = hash_fn(F=1,bucket=0) if x<=BNDRY(1) else hash_fn(F=1,bucket=1)
Note how the construction of map(x|F=1) does not depend on whether map(x|F=0)
was constructed.
)doc");
template<typename T>
class HashingDiscretizer : public OpKernel {
public:
explicit HashingDiscretizer(OpKernelConstruction* context) : OpKernel(context) {
OP_REQUIRES_OK(context,
context->GetAttr("n_bin", &n_bin_));
OP_REQUIRES(context,
n_bin_ > 0,
errors::InvalidArgument("Must have n_bin_ > 0."));
OP_REQUIRES_OK(context,
context->GetAttr("output_bits", &output_bits_));
OP_REQUIRES(context,
output_bits_ > 0,
errors::InvalidArgument("Must have output_bits_ > 0."));
OP_REQUIRES_OK(context,
context->GetAttr("cost_per_unit", &cost_per_unit_));
OP_REQUIRES(context,
cost_per_unit_ >= 0,
errors::InvalidArgument("Must have cost_per_unit >= 0."));
OP_REQUIRES_OK(context,
context->GetAttr("options", &options_));
// construct the ID_to_index hash map
Tensor feature_IDs;
// extract the tensors
OP_REQUIRES_OK(context,
context->GetAttr("feature_ids", &feature_IDs));
// for access to the data
// int64_t data type is set in to_layer function of the calibrator objects in Python
auto feature_IDs_flat = feature_IDs.flat<int64>();
// verify proper dimension constraints
OP_REQUIRES(context,
feature_IDs.shape().dims() == 1,
errors::InvalidArgument("feature_ids must be 1D."));
// reserve space in the hash map and fill in the values
int64_t num_features = feature_IDs.shape().dim_size(0);
#ifdef USE_DENSE_HASH
ID_to_index_.set_empty_key(0);
ID_to_index_.resize(num_features);
#else
ID_to_index_.reserve(num_features);
#endif // USE_DENSE_HASH
for (int64_t i = 0 ; i < num_features ; i++) {
ID_to_index_[feature_IDs_flat(i)] = i;
}
}
void Compute(OpKernelContext* context) override {
ComputeHashingDiscretizer(
context,
output_bits_,
ID_to_index_,
n_bin_,
cost_per_unit_,
options_);
}
private:
twml::Map<int64_t, int64_t> ID_to_index_;
int n_bin_;
int output_bits_;
int cost_per_unit_;
int options_;
};
#define REGISTER(Type) \
REGISTER_KERNEL_BUILDER( \
Name("HashingDiscretizer") \
.Device(DEVICE_CPU) \
.TypeConstraint<Type>("T"), \
HashingDiscretizer<Type>); \
REGISTER(float);
REGISTER(double);
void ComputeHashingDiscretizer(
OpKernelContext* context,
int64_t output_bits,
const twml::Map<int64_t, int64_t> &ID_to_index,
int64_t n_bin,
int64_t cost_per_unit,
int64_t options) {
const Tensor& keys = context->input(0);
const Tensor& vals = context->input(1);
const Tensor& bin_vals = context->input(2);
const int64 output_size = keys.dim_size(0);
TensorShape output_shape;
OP_REQUIRES_OK(context, TensorShapeUtils::MakeShape(&output_size, 1, &output_shape));
Tensor* new_keys = nullptr;
OP_REQUIRES_OK(context, context->allocate_output(0, output_shape, &new_keys));
Tensor* new_vals = nullptr;
OP_REQUIRES_OK(context, context->allocate_output(1, output_shape, &new_vals));
try {
twml::Tensor out_keys_ = TFTensor_to_twml_tensor(*new_keys);
twml::Tensor out_vals_ = TFTensor_to_twml_tensor(*new_vals);
const twml::Tensor in_keys_ = TFTensor_to_twml_tensor(keys);
const twml::Tensor in_vals_ = TFTensor_to_twml_tensor(vals);
const twml::Tensor bin_vals_ = TFTensor_to_twml_tensor(bin_vals);
// retrieve the thread pool from the op context
auto worker_threads = *(context->device()->tensorflow_cpu_worker_threads());
// Definition of the computation thread
auto task = [&](int64 start, int64 limit) {
twml::hashDiscretizerInfer(out_keys_, out_vals_,
in_keys_, in_vals_,
n_bin,
bin_vals_,
output_bits,
ID_to_index,
start, limit,
options);
};
// let Tensorflow split up the work as it sees fit
Shard(worker_threads.num_threads,
worker_threads.workers,
output_size,
static_cast<int64>(cost_per_unit),
task);
} catch (const std::exception &e) {
context->CtxFailureWithWarning(errors::InvalidArgument(e.what()));
}
}

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#include "tensorflow/core/framework/op.h"
#include "tensorflow/core/framework/shape_inference.h"
#include "tensorflow/core/framework/op_kernel.h"
#include <twml.h>
#include <mutex>
using namespace tensorflow;
REGISTER_OP("Hashmap")
.Input("keys: int64")
.Input("hash_keys: int64")
.Input("hash_values: int64")
.Output("values: int64")
.Output("mask: int8")
.SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) {
// TODO: check if the sizes are different in the input
c->set_output(0, c->input(0));
c->set_output(1, c->input(0));
return Status::OK();
});
class Hashmap : public OpKernel {
private:
twml::HashMap hmap;
std::once_flag flag;
public:
explicit Hashmap(OpKernelConstruction* context) : OpKernel(context) {}
void Compute(OpKernelContext* context) override {
try {
// Quick hack
const Tensor& keys = context->input(0);
std::call_once(this->flag, [this, context](){
const Tensor& hash_keys = context->input(1);
const Tensor& hash_values = context->input(2);
const auto hash_keys_flat = hash_keys.flat<int64>();
const auto hash_values_flat = hash_values.flat<int64>();
const int64 N = hash_keys_flat.size();
for (int64 i = 0; i < N; i++) {
hmap.insert(hash_keys_flat(i), hash_values_flat(i));
}
});
Tensor* values = nullptr;
OP_REQUIRES_OK(context, context->allocate_output(0, keys.shape(),
&values));
Tensor* mask = nullptr;
OP_REQUIRES_OK(context, context->allocate_output(1, keys.shape(),
&mask));
// copy the values without sharing a storage
values->flat<int64>() = keys.flat<int64>();
auto keys_flat = keys.flat<int64>();
auto values_flat = values->flat<int64>();
auto mask_flat = mask->flat<int8>();
// TODO: use twml tensor
const int64 N = keys_flat.size();
for (int64 i = 0; i < N; i++) {
// values_flat(i), keys_flat(i) return references to tensorflow::int64.
// Using them in hmap.get() was causing issues because of automatic casting.
int64_t val = values_flat(i);
int64_t key = keys_flat(i);
mask_flat(i) = hmap.get(val, key);
values_flat(i) = val;
}
} catch (const std::exception &e) {
context->CtxFailureWithWarning(errors::InvalidArgument(e.what()));
}
}
};
REGISTER_KERNEL_BUILDER(
Name("Hashmap")
.Device(DEVICE_CPU),
Hashmap);

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#include "tensorflow/core/framework/op.h"
#include "tensorflow/core/framework/shape_inference.h"
#include "tensorflow/core/framework/op_kernel.h"
#include <twml.h>
#include "tensorflow_utils.h"
using namespace tensorflow;
REGISTER_OP("IsotonicCalibration")
.Attr("T: {float, double}")
.Input("input: T")
.Input("xs: T")
.Input("ys: T")
.Output("output: T")
.SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) {
// output shape should be the same as input shape.
c->set_output(0, c->input(0));
return Status::OK();
}).Doc(R"doc(
This operation calibrates probabilities by fitting to a piece-wise non-decreasing function.
Input
input: A tensor containing uncalibrated probabilities.
xs: A tensor containing the boundaries of the bins.
ys: A tensor contianing calibrated values for the corresponding bins.
Expected Sizes:
input: [batch_size, num_labels].
xs, ys: [num_labels, num_bins].
Expected Types:
input: float or double.
xs, ys: same as input.
Outputs
output: A tensor containing calibrated probabilities with same shape and size as input.
)doc");
template<typename T>
class IsotonicCalibration : public OpKernel {
public:
explicit IsotonicCalibration(OpKernelConstruction* context)
: OpKernel(context) {}
void Compute(OpKernelContext* context) override {
const Tensor& input = context->input(0);
const Tensor& xs = context->input(1);
const Tensor& ys = context->input(2);
Tensor* output = nullptr;
OP_REQUIRES_OK(
context,
context->allocate_output(0, input.shape(), &output));
try {
const twml::Tensor twml_input = TFTensor_to_twml_tensor(input);
const twml::Tensor twml_xs = TFTensor_to_twml_tensor(xs);
const twml::Tensor twml_ys = TFTensor_to_twml_tensor(ys);
twml::Tensor twml_output = TFTensor_to_twml_tensor(*output);
twml::linearInterpolation(twml_output, twml_input, twml_xs, twml_ys);
} catch (const std::exception &e) {
context->CtxFailureWithWarning(errors::InvalidArgument(e.what()));
}
}
};
#define REGISTER(Type) \
\
REGISTER_KERNEL_BUILDER( \
Name("IsotonicCalibration") \
.Device(DEVICE_CPU) \
.TypeConstraint<Type>("T"), \
IsotonicCalibration<Type>); \
REGISTER(float);
REGISTER(double);

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#include "tensorflow/core/framework/op.h"
#include "tensorflow/core/framework/shape_inference.h"
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/framework/common_shape_fns.h"
using namespace tensorflow;
REGISTER_OP("NumIntraOpThreads")
.Input("x: float32")
.Output("num_intra_op_threads: int32")
.SetShapeFn(tensorflow::shape_inference::ScalarShape)
.Doc(R"doc(
A tensorflow OP that returns the number of threads in the intra_op_parallelism pool
This is not part of the Tensorflow API as of the date of writing this doc. Hence,
a tensorflow operation is the best resort.
Input
x: Dummy placeholder so that constant folding is not done by TF GraphOptimizer.
Please refer https://github.com/tensorflow/tensorflow/issues/22546 for more
details.
Output
num_intra_op_threads: A scalar tensor corresponding to the number of threads in
the intra_op_parallelism pool
)doc");
class NumIntraOpThreads : public OpKernel {
public:
explicit NumIntraOpThreads(OpKernelConstruction* context)
: OpKernel(context) {}
void Compute(OpKernelContext* context) override {
int num_intra_op_threads = context->device()->tensorflow_cpu_worker_threads()->num_threads;
Tensor* output_tensor = NULL;
OP_REQUIRES_OK(context, context->allocate_output(0, TensorShape({}), &output_tensor));
auto output_flat = output_tensor->flat<int32>();
output_flat(0) = num_intra_op_threads;
}
};
REGISTER_KERNEL_BUILDER(Name("NumIntraOpThreads").Device(DEVICE_CPU), NumIntraOpThreads);

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#include "tensorflow/core/framework/op.h"
#include "tensorflow/core/framework/shape_inference.h"
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/util/work_sharder.h"
#include "tensorflow/core/lib/core/threadpool.h"
#include "tensorflow/core/platform/env.h"
#include "tensorflow/core/platform/mutex.h"
#include "tensorflow/core/platform/logging.h"
#include <iostream>
#include <vector>
using namespace tensorflow;
REGISTER_OP("ParAdd")
.Input("input_a: float")
.Input("input_b: float")
.Output("a_plus_b: float")
.SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) {
c->set_output(0, c->input(0));
return Status::OK();
});
class ParAddOp : public OpKernel {
public:
explicit ParAddOp(OpKernelConstruction* context) : OpKernel(context) {
}
void Compute(OpKernelContext* context) override {
// Grab the input tensor
const Tensor& input_tensor0 = context->input(0);
auto input_flat0 = input_tensor0.flat<float>();
const Tensor& input_tensor1 = context->input(1);
auto input_flat1 = input_tensor1.flat<float>();
OP_REQUIRES(context, input_tensor0.shape() == input_tensor1.shape(),
errors::InvalidArgument("Input tensors must be identical shape."));
// Create an output tensor
Tensor* output_tensor = NULL;
OP_REQUIRES_OK(context,
context->allocate_output(0,
input_tensor0.shape(),
&output_tensor));
auto output_flat = output_tensor->flat<float>();
// PARALLEL ADD
const int N = input_flat0.size();
// retrieve the thread pool from the op context
auto worker_threads = *(context->device()->tensorflow_cpu_worker_threads());
// Definition of the computation thread
auto task = [=, &input_flat0, &input_flat1, &output_flat](int64 start, int64 limit) {
for (; start < limit; ++start) {
output_flat(start) = input_flat0(start) + input_flat1(start);
}
};
// this is a heuristic. high number is likely to be sharded into smaller pieces
int64 cost_per_unit = 1;
// let Tensorflow split up the work as it sees fit
Shard(worker_threads.num_threads,
worker_threads.workers,
N,
cost_per_unit,
task);
}
};
REGISTER_KERNEL_BUILDER(Name("ParAdd").Device(DEVICE_CPU), ParAddOp);

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#include "tensorflow/core/framework/op.h"
#include "tensorflow/core/framework/shape_inference.h"
#include "tensorflow/core/framework/op_kernel.h"
#include <twml.h>
#include "tensorflow_utils.h"
using namespace tensorflow;
REGISTER_OP("PartitionSparseTensorMod")
.Attr("T: {float, double}")
.Input("indices: int64")
.Input("values: T")
.Output("result: output_types")
.Attr("num_partitions: int")
.Attr("output_types: list({int64, float, double})")
.SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) {
return Status::OK();
}).Doc(R"doc(
A tensorflow OP that partitions an input batch represented as a sparse tensor
(indices are [ids, keys]) into separate sparse tensors to more optimally place
sparse computations in distributed training.
Inputs
indices: Indices from sparse tensor ([ids, keys] from the batch).
values: Batch values from the original features dict.
Attr
num_partitions: Number of partitions to generate.
output_types: A list of types for the output tensors like
[tf.int64, tf.float32, tf.int64, tf.float32, ...]
The length must be 2 * num_partitions (see Outputs below)
Outputs
List of dense tensors containing for each partition:
- partitioned indices tensor ([ids, keys] from partitioned batch)
- partitioned values tensor
The list lenth is 2 * num_partitions. Example:
[ [ids_1, keys_1], values_1, [ids_2, keys_2], values_2, ... ]
)doc");
template<typename T>
class PartitionSparseTensorMod : public OpKernel {
private:
int64 num_partitions;
public:
explicit PartitionSparseTensorMod(OpKernelConstruction* context) : OpKernel(context) {
OP_REQUIRES_OK(context, context->GetAttr("num_partitions", &num_partitions));
OP_REQUIRES(context, num_partitions > 0,
errors::InvalidArgument("Number of partitions must be positive"));
}
void Compute(OpKernelContext* context) override {
// grab input tensors
const Tensor& indices_tensor = context->input(0); // (ids, keys)
const Tensor& values_tensor = context->input(1);
// check sizes
int64 num_keys = indices_tensor.shape().dim_size(0);
OP_REQUIRES(context, indices_tensor.dims() == 2,
errors::InvalidArgument("Indices tensor must be 2D [ids, keys]"));
OP_REQUIRES(context, indices_tensor.shape().dim_size(1) == 2,
errors::InvalidArgument("Indices tensor must have 2 cols [ids, keys]"));
OP_REQUIRES(context, values_tensor.shape().dim_size(0) == num_keys,
errors::InvalidArgument("Number of values must match number of keys"));
// grab input vectors
auto indices = indices_tensor.flat<int64>();
auto values = values_tensor.flat<T>();
// count the number of features that fall in each partition
std::vector<int64> partition_counts(num_partitions);
for (int i = 0; i < num_keys; i++) {
int64 key = indices(2 * i + 1);
int64 partition_id = key % num_partitions;
partition_counts[partition_id]++;
}
// allocate outputs for each partition and keep references
std::vector<int64*> output_indices_partitions;
std::vector<T*> output_values_partitions;
output_indices_partitions.reserve(num_partitions);
output_values_partitions.reserve(num_partitions);
for (int i = 0; i < num_partitions; i++) {
Tensor *output_indices = nullptr, *output_values = nullptr;
TensorShape shape_indices = TensorShape({partition_counts[i], 2});
TensorShape shape_values = TensorShape({partition_counts[i]});
OP_REQUIRES_OK(context, context->allocate_output(2 * i, shape_indices, &output_indices));
OP_REQUIRES_OK(context, context->allocate_output(2 * i + 1, shape_values, &output_values));
output_indices_partitions.push_back(output_indices->flat<int64>().data());
output_values_partitions.push_back(output_values->flat<T>().data());
}
// assign a partition id to each feature
// populate tensors for each partition
std::vector<int64> partition_indices(num_partitions);
for (int i = 0; i < num_keys; i++) {
int64 key = indices(2 * i + 1);
int64 pid = key % num_partitions; // partition id
int64 idx = partition_indices[pid]++;
output_indices_partitions[pid][2 * idx] = indices(2 * i);
output_indices_partitions[pid][2 * idx + 1] = key / num_partitions;
output_values_partitions[pid][idx] = values(i);
}
}
};
#define REGISTER(Type) \
\
REGISTER_KERNEL_BUILDER( \
Name("PartitionSparseTensorMod") \
.Device(DEVICE_CPU) \
.TypeConstraint<Type>("T"), \
PartitionSparseTensorMod<Type>); \
REGISTER(float);
REGISTER(double);

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#include "tensorflow/core/framework/op.h"
#include "tensorflow/core/framework/shape_inference.h"
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/util/work_sharder.h"
#include <twml.h>
#include "tensorflow_utils.h"
using namespace tensorflow;
void CombinedComputeDiscretizers(
OpKernelContext*,
int64_t,
const twml::Map<int64_t, int64_t>&,
int64_t);
REGISTER_OP("PercentileDiscretizerV2")
.Attr("T: {float, double}")
.Input("input_ids: int64")
.Input("input_vals: T")
.Input("bin_ids: int64")
.Input("bin_vals: T")
.Input("feature_offsets: int64")
.Input("start_compute: int64")
.Input("end_compute: int64")
.Attr("output_bits: int")
.Attr("feature_ids: tensor = { dtype: DT_INT64 }")
.Attr("feature_indices: tensor = { dtype: DT_INT64 }")
.Attr("cost_per_unit: int")
.Output("new_keys: int64")
.Output("new_vals: T")
.SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) {
// TODO: check sizes
c->set_output(0, c->input(0));
c->set_output(1, c->input(0));
return Status::OK();
}).Doc(R"doc(
This operation discretizes a tensor containing continuous features (if calibrated).
- note - choice of float or double should be consistent among inputs/output
Input
input_ids(int64): A tensor containing input feature ids (direct from data record).
input_vals: A tensor containing input values at corresponding feature ids.
- i.e. input_ids[i] <-> input_vals[i] for each i
- float or double
bin_ids(int64): A tensor containing the discretized feature id for each bin.
bin_vals: A tensor containing the bin boundaries for values of a given feature.
- float or double
feature_offsets(int64): Specifies the starting location of bins for a given feature id.
start_compute(int64 scalar tensor): which index to start the computation at
end_compute(int64 scalar tensor): which index to end the computation right before
-> for example, (start_compute,end_compute)=(0,10) would compute on 0 thru 9
output_bits(int): The maximum number of bits to use for the output IDs.
-> 2**out_bits must be greater than bin_ids.size
feature_ids(int64): 1D TensorProto of feature IDs seen during calibration
feature_indices(int64): 1D TensorProto of feature indices corresponding with feature_IDs
-> hint: look up make_tensor_proto:
proto_init = np.array(values, dtype=np.int64)
tensor_attr = tf.make_tensor_proto(my_proto_init)
cost_per_unit(int): An estimate of the number of CPU cycles (or nanoseconds
if not CPU-bound) to complete a unit of work. Overestimating creates too
many shards and CPU time will be dominated by per-shard overhead, such as
Context creation. Underestimating may not fully make use of the specified
parallelism.
Outputs
new_keys(int64): The discretized feature ids with same shape and size as keys.
new_vals(float or double): The discretized values with the same shape and size as vals.
Operation
Note that the discretization operation maps observation vectors to higher dimensional
observation vectors. Here, we describe this mapping.
Let a calibrated feature observation be given by (F,x), where F is the ID of the
feature, and x is some real value (i.e., continuous feature). This kind of
representation is useful for the representation of sparse vectors, where there
are many zeros.
For example, for a dense feature vector [1.2, 2.4, 3.6], we might have
(0, 1.2) (1, 2.4) and (2, 3.6), with feature IDs indicating the 0th, 1st, and 2nd
elements of the vector
The disretizer performs the following operation:
(F,x) -> (map(x|F),1).
Hence, we have that map(x|F) is a new feature ID, and the value observed for that
feature is 1. We might read map(x|F) as 'the map of x for feature F'.
For each feature F, we associate a (discrete, finite) set of new feature IDs, newIDs(F).
We will then have that F~(x) is in the set newIDs(F) for any value of x. Each set member
of newIDs(F) is associated with a 'bin', as defined by the bin boundaries given in
the bin_vals input array. For any two different feature IDs F and G, we have that
INTERSECT(newIDs(F),newIDs(G)) is the empty set
Example - consider input vector with a single element, i.e. [x].
Let's Discretize to one of 2 values, as follows:
Let F=0 for the ID of the single feature in the vector.
Let the bin boundary of feature F=0 be BNDRY(F) = BNDRY(0) since F=0
Let newIDs(F) = newIDs(0) = {0,1}
Let map(x|F) = map(x|0) = 0 if x<=BNDRY else 1
If we had another element y in the vector, i.e. [x, y], then we might additionally
Let F=1 for element y.
Let the bin boundary be BNDRY(F) = BNDRY(1) since F=1
Let newIDs(F) = newIDs(1) = {2,3} (so as to have empty intersect with newIDs(0))
Let map(x|F) = map(x|1) = 2 if x<=BNDRY else 3
Consider vector observation [-0.1, 0.2]. We then represent this as [(0, -0.1), (1, 0.2)]
Let BNDRY(0) = BNDRY(1) = 0. When we discretize the vector, we get:
(0, -0.1) -> (map(-0.1|0), 1) = (0, 1)
(1, 0.2) -> (map( 0.2|1), 1) = (3, 1)
Our output vector is then represented sparsely as [(0, 1), (3, 1)], and the dense
representation of this could be [1, 0, 0, 1]
)doc");
template<typename T>
class PercentileDiscretizerV2 : public OpKernel {
public:
explicit PercentileDiscretizerV2(OpKernelConstruction* context) : OpKernel(context) {
// get the number of output bits
// for use with features that have not been calibrated
OP_REQUIRES_OK(context,
context->GetAttr("output_bits", &output_bits_));
OP_REQUIRES_OK(context,
context->GetAttr("cost_per_unit", &cost_per_unit_));
OP_REQUIRES(context, cost_per_unit_ >= 0,
errors::InvalidArgument("Must have cost_per_unit >= 0."));
// construct the ID_to_index hash map
Tensor feature_IDs;
Tensor feature_indices;
// extract the tensors
OP_REQUIRES_OK(context,
context->GetAttr("feature_ids", &feature_IDs));
OP_REQUIRES_OK(context,
context->GetAttr("feature_indices", &feature_indices));
// for access to the data
// int64_t data type is set in to_layer function of the calibrator objects in Python
auto feature_IDs_flat = feature_IDs.flat<int64>();
auto feature_indices_flat = feature_indices.flat<int64>();
// verify proper dimension constraints
OP_REQUIRES(context, feature_IDs.shape() == feature_indices.shape(),
errors::InvalidArgument("feature_ids and feature_indices must be identical shape."));
OP_REQUIRES(context, feature_IDs.shape().dims() == 1,
errors::InvalidArgument("feature_ids and feature_indices must be 1D."));
// reserve space in the hash map and fill in the values
int num_features = feature_IDs.shape().dim_size(0);
#ifdef USE_DENSE_HASH
ID_to_index_.set_empty_key(0);
ID_to_index_.resize(num_features);
#else
ID_to_index_.reserve(num_features);
#endif // USE_DENSE_HASH
for (int i = 0 ; i < num_features ; i++) {
ID_to_index_[feature_IDs_flat(i)] = feature_indices_flat(i);
}
}
void Compute(OpKernelContext* context) override {
CombinedComputeDiscretizers(
context,
output_bits_,
ID_to_index_,
cost_per_unit_);
}
private:
twml::Map<int64_t, int64_t> ID_to_index_;
int output_bits_;
int cost_per_unit_;
};
#define REGISTER(Type) \
REGISTER_KERNEL_BUILDER( \
Name("PercentileDiscretizerV2") \
.Device(DEVICE_CPU) \
.TypeConstraint<Type>("T"), \
PercentileDiscretizerV2<Type>); \
REGISTER(float);
REGISTER(double);
void CombinedComputeDiscretizers(
OpKernelContext* context,
int64_t output_bits,
const twml::Map<int64_t, int64_t> &ID_to_index,
int64_t cost_per_unit) {
const Tensor& keys = context->input(0);
const Tensor& vals = context->input(1);
const Tensor& bin_ids = context->input(2);
const Tensor& bin_vals = context->input(3);
const Tensor& feature_offsets = context->input(4);
uint64 full_size = keys.dim_size(0);
const int total_size = static_cast<int64>(full_size);
TensorShape output_shape = {total_size};
Tensor* new_keys = nullptr;
OP_REQUIRES_OK(context, context->allocate_output(0, output_shape, &new_keys));
Tensor* new_vals = nullptr;
OP_REQUIRES_OK(context, context->allocate_output(1, output_shape, &new_vals));
try {
twml::Tensor out_keys_ = TFTensor_to_twml_tensor(*new_keys);
twml::Tensor out_vals_ = TFTensor_to_twml_tensor(*new_vals);
const twml::Tensor in_keys_ = TFTensor_to_twml_tensor(keys);
const twml::Tensor in_vals_ = TFTensor_to_twml_tensor(vals);
const twml::Tensor bin_ids_ = TFTensor_to_twml_tensor(bin_ids);
const twml::Tensor bin_vals_ = TFTensor_to_twml_tensor(bin_vals);
const twml::Tensor feature_offsets_ = TFTensor_to_twml_tensor(feature_offsets);
// retrieve the thread pool from the op context
auto worker_threads = *(context->device()->tensorflow_cpu_worker_threads());
// Definition of the computation thread
auto task = [&](int64 start, int64 limit) {
twml::discretizerInfer(out_keys_, out_vals_,
in_keys_, in_vals_,
bin_ids_, bin_vals_,
feature_offsets_, output_bits,
ID_to_index,
start, limit,
start);
};
// let Tensorflow split up the work as it sees fit
Shard(worker_threads.num_threads,
worker_threads.workers,
full_size,
static_cast<int64>(cost_per_unit),
task);
} catch (const std::exception &e) {
context->CtxFailureWithWarning(errors::InvalidArgument(e.what()));
}
}

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#pragma once
#include <twml.h>
#include <atomic>
#include <string>
#include <vector>
// Add these to make gcc ignore the warnings from tensorflow.
#pragma GCC diagnostic push
#pragma GCC diagnostic ignored "-Wsign-compare"
#include "tensorflow/core/framework/resource_mgr.h"
#include "tensorflow/core/framework/resource_op_kernel.h"
#pragma GCC diagnostic pop
#include <memory>
#include <functional>
template<typename T>
void unrefHandle(T *handle) {
handle->Unref();
}
template <typename T>
using unique_handle = std::unique_ptr<T, std::function<void(T *)> >;
// as std::type_index is not abi compatible, we bypass the hash_code checks.
// https://github.com/tensorflow/tensorflow/commit/15275d3a14c77e2244ae1155f93243256f08e3ed
#ifdef __APPLE__
template <typename T>
Status CreateTwmlResource(OpKernelContext* ctx, const ResourceHandle& p, T* value) {
return ctx->resource_manager()->Create(p.container(), p.name(), value);
}
template <typename T>
Status LookupTwmlResource(OpKernelContext* ctx, const ResourceHandle& p,
T** value) {
return ctx->resource_manager()->Lookup(p.container(), p.name(), value);
}
#endif // __APPLE__
template<typename T>
unique_handle<T> getHandle(tensorflow::OpKernelContext* context, int input_idx) {
using namespace tensorflow;
T *ptr = nullptr;
#ifdef __APPLE__
auto s = LookupTwmlResource(context, HandleFromInput(context, input_idx), &ptr);
#else
auto s = LookupResource(context, HandleFromInput(context, input_idx), &ptr);
#endif // __APPLE__
if (!s.ok()) {
throw std::runtime_error("Failed to get resource handle");
}
return unique_handle<T>(ptr, unrefHandle<T>);
}
template<typename InputType>
const uint8_t *getInputBytes(const Tensor &input, int id) {
return reinterpret_cast<const uint8_t *>(input.flat<InputType>().data());
}
template<>
inline const uint8_t *getInputBytes<string>(const Tensor &input, int id) {
return reinterpret_cast<const uint8_t *>(input.flat<string>()(id).c_str());
}
template<typename InputType>
const int getBatchSize(const Tensor &input) {
return 1;
}
template<>
inline const int getBatchSize<string>(const Tensor &input) {
return static_cast<int>(input.NumElements());
}
class DataRecordResource : public ResourceBase {
public:
Tensor input;
int64 num_labels;
int64 num_weights;
twml::DataRecord common;
std::vector<twml::DataRecord> records;
twml::Map<int64_t, int64_t> *keep_map;
string DebugString() const override { return "DataRecords resource"; }
};
// A thin layer around batch of HashedDataRecords
class HashedDataRecordResource : public ResourceBase {
public:
Tensor input;
int64 total_size;
int64 num_labels;
int64 num_weights;
twml::HashedDataRecord common;
std::vector<twml::HashedDataRecord> records;
string DebugString() const override { return "HashedDataRecord Resource"; }
};
#define TF_CHECK_STATUS(fn) do { \
Status s = fn; \
if (!s.ok()) return s; \
} while (0)
template<typename ResourceType>
Status makeResourceHandle(OpKernelContext* context, int out_idx, ResourceType **resource_) {
static std::atomic<int64> id;
Tensor* handle_tensor;
TF_CHECK_STATUS(context->allocate_output(out_idx, TensorShape({}), &handle_tensor));
ResourceType *resource = new ResourceType();
const auto resource_name = typeid(ResourceType).name() + std::to_string(id++);
ResourceHandle handle = MakePerStepResourceHandle<ResourceType>(context, resource_name);
#ifdef __APPLE__
TF_CHECK_STATUS(CreateTwmlResource(context, handle, resource));
#else
TF_CHECK_STATUS(CreateResource(context, handle, resource));
#endif // __APPLE__
handle_tensor->scalar<ResourceHandle>()() = handle;
*resource_ = resource;
return Status::OK();
}

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"""Gets the path of headers for the current Tensorflow library"""
import tensorflow.compat.v1 as tf
print(tf.sysconfig.get_include(), end='')

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#!/bin/sh
PEX_INTERPRETER=1 "$PYTHON_ENV" "$LIBTWML_HOME"/src/ops/scripts/get_inc.py

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"""Gets the path of headers for the current Tensorflow library"""
import tensorflow.compat.v1 as tf
print(tf.sysconfig.get_lib(), end='')

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#!/bin/sh
PEX_INTERPRETER=1 "$PYTHON_ENV" "$LIBTWML_HOME"/src/ops/scripts/get_lib.py

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