tensor.go

/*
Copyright 2016 The TensorFlow Authors. All Rights Reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
*/

package tensorflow

// #include <stdlib.h>
// #include <string.h>
// #include "tensorflow/c/c_api.h"
import "C"

import (
	"bytes"
	"encoding/binary"
	"fmt"
	"io"
	"reflect"
	"runtime"
	"unsafe"
)

// DataType holds the type for a scalar value.  E.g., one slot in a tensor.
type DataType C.TF_DataType

// Types of scalar values in the TensorFlow type system.
const (
	Float      DataType = C.TF_FLOAT
	Double     DataType = C.TF_DOUBLE
	Int32      DataType = C.TF_INT32
	Uint32     DataType = C.TF_UINT32
	Uint8      DataType = C.TF_UINT8
	Int16      DataType = C.TF_INT16
	Int8       DataType = C.TF_INT8
	String     DataType = C.TF_STRING
	Complex64  DataType = C.TF_COMPLEX64
	Complex    DataType = C.TF_COMPLEX
	Int64      DataType = C.TF_INT64
	Uint64     DataType = C.TF_UINT64
	Bool       DataType = C.TF_BOOL
	Qint8      DataType = C.TF_QINT8
	Quint8     DataType = C.TF_QUINT8
	Qint32     DataType = C.TF_QINT32
	Bfloat16   DataType = C.TF_BFLOAT16
	Qint16     DataType = C.TF_QINT16
	Quint16    DataType = C.TF_QUINT16
	Uint16     DataType = C.TF_UINT16
	Complex128 DataType = C.TF_COMPLEX128
	Half       DataType = C.TF_HALF
)

// Tensor holds a multi-dimensional array of elements of a single data type.
type Tensor struct {
	c     *C.TF_Tensor
	shape []int64
}

// NewTensor converts from a Go value to a Tensor. Valid values are scalars,
// slices, and arrays. Every element of a slice must have the same length so
// that the resulting Tensor has a valid shape.
func NewTensor(value interface{}) (*Tensor, error) {
	val := reflect.ValueOf(value)
	shape, dataType, err := shapeAndDataTypeOf(val)
	if err != nil {
		return nil, err
	}
	nflattened := numElements(shape)
	nbytes := typeOf(dataType, nil).Size() * uintptr(nflattened)
	if dataType == String {
		// TF_STRING tensors are encoded as an array of 8-byte offsets
		// followed by string data. See c_api.h.
		nbytes = uintptr(nflattened*8) + byteSizeOfEncodedStrings(value)
	}
	var shapePtr *C.int64_t
	if len(shape) > 0 {
		shapePtr = (*C.int64_t)(unsafe.Pointer(&shape[0]))
	}
	t := &Tensor{
		c:     C.TF_AllocateTensor(C.TF_DataType(dataType), shapePtr, C.int(len(shape)), C.size_t(nbytes)),
		shape: shape,
	}
	runtime.SetFinalizer(t, (*Tensor).finalize)
	raw := tensorData(t.c)
	buf := bytes.NewBuffer(raw[:0:len(raw)])
	if dataType != String {
		if err := encodeTensor(buf, val, shape); err != nil {
			return nil, err
		}
		if uintptr(buf.Len()) != nbytes {
			return nil, bug("NewTensor incorrectly calculated the size of a tensor with type %v and shape %v as %v bytes instead of %v", dataType, shape, nbytes, buf.Len())
		}
	} else {
		e := stringEncoder{offsets: buf, data: raw[nflattened*8:], status: newStatus()}
		if err := e.encode(reflect.ValueOf(value), shape); err != nil {
			return nil, err
		}
		if int64(buf.Len()) != nflattened*8 {
			return nil, bug("invalid offset encoding for TF_STRING tensor with shape %v (got %v, want %v)", shape, buf.Len(), nflattened*8)
		}
	}
	return t, nil
}

// ReadTensor constructs a Tensor with the provided type and shape from the
// serialized tensor contents in r.
//
// See also WriteContentsTo.
func ReadTensor(dataType DataType, shape []int64, r io.Reader) (*Tensor, error) {
	if err := isTensorSerializable(dataType); err != nil {
		return nil, err
	}
	nbytes := typeOf(dataType, nil).Size() * uintptr(numElements(shape))
	var shapePtr *C.int64_t
	if len(shape) > 0 {
		shapePtr = (*C.int64_t)(unsafe.Pointer(&shape[0]))
	}
	t := &Tensor{
		c:     C.TF_AllocateTensor(C.TF_DataType(dataType), shapePtr, C.int(len(shape)), C.size_t(nbytes)),
		shape: shape,
	}
	runtime.SetFinalizer(t, (*Tensor).finalize)
	raw := tensorData(t.c)
	if _, err := io.ReadFull(r, raw); err != nil {
		return nil, err
	}
	return t, nil
}

// newTensorFromC takes ownership of c and returns the owning Tensor.
func newTensorFromC(c *C.TF_Tensor) *Tensor {
	var shape []int64
	if ndims := int(C.TF_NumDims(c)); ndims > 0 {
		shape = make([]int64, ndims)
	}
	for i := range shape {
		shape[i] = int64(C.TF_Dim(c, C.int(i)))
	}
	t := &Tensor{c: c, shape: shape}
	runtime.SetFinalizer(t, (*Tensor).finalize)
	return t
}

func (t *Tensor) finalize() { C.TF_DeleteTensor(t.c) }

// DataType returns the scalar datatype of the Tensor.
func (t *Tensor) DataType() DataType { return DataType(C.TF_TensorType(t.c)) }

// Shape returns the shape of the Tensor.
func (t *Tensor) Shape() []int64 { return t.shape }

// Value converts the Tensor to a Go value. For now, not all Tensor types are
// supported, and this function may panic if it encounters an unsupported
// DataType.
//
// The type of the output depends on the Tensor type and dimensions.
// For example:
// Tensor(int64, 0): int64
// Tensor(float64, 3): [][][]float64
func (t *Tensor) Value() interface{} {
	typ := typeOf(t.DataType(), t.Shape())
	val := reflect.New(typ)
	raw := tensorData(t.c)
	if t.DataType() != String {
		if err := decodeTensor(bytes.NewReader(raw), t.Shape(), typ, val); err != nil {
			panic(bug("unable to decode Tensor of type %v and shape %v - %v", t.DataType(), t.Shape(), err))
		}
	} else {
		nflattened := numElements(t.Shape())
		d := stringDecoder{offsets: bytes.NewReader(raw[0 : 8*nflattened]), data: raw[8*nflattened:], status: newStatus()}
		if err := d.decode(val, t.Shape()); err != nil {
			panic(bug("unable to decode String tensor with shape %v - %v", t.Shape(), err))
		}
	}
	return reflect.Indirect(val).Interface()
}

// WriteContentsTo writes the serialized contents of t to w.
//
// Returns the number of bytes written. See ReadTensor for
// reconstructing a Tensor from the serialized form.
//
// WARNING: WriteContentsTo is not comprehensive and will fail
// if t.DataType() is non-numeric (e.g., String). See
// https://github.com/tensorflow/tensorflow/issues/6003.
func (t *Tensor) WriteContentsTo(w io.Writer) (int64, error) {
	if err := isTensorSerializable(t.DataType()); err != nil {
		return 0, err
	}
	return io.Copy(w, bytes.NewReader(tensorData(t.c)))
}

func tensorData(c *C.TF_Tensor) []byte {
	// See: https://github.com/golang/go/wiki/cgo#turning-c-arrays-into-go-slices
	cbytes := C.TF_TensorData(c)
	if cbytes == nil {
		return nil
	}
	length := int(C.TF_TensorByteSize(c))
	slice := (*[1 << 30]byte)(unsafe.Pointer(cbytes))[:length:length]
	return slice
}

var types = []struct {
	typ      reflect.Type
	dataType C.TF_DataType
}{
	{reflect.TypeOf(float32(0)), C.TF_FLOAT},
	{reflect.TypeOf(float64(0)), C.TF_DOUBLE},
	{reflect.TypeOf(int32(0)), C.TF_INT32},
	{reflect.TypeOf(uint32(0)), C.TF_UINT32},
	{reflect.TypeOf(uint8(0)), C.TF_UINT8},
	{reflect.TypeOf(int16(0)), C.TF_INT16},
	{reflect.TypeOf(int8(0)), C.TF_INT8},
	{reflect.TypeOf(""), C.TF_STRING},
	{reflect.TypeOf(complex(float32(0), float32(0))), C.TF_COMPLEX64},
	{reflect.TypeOf(int64(0)), C.TF_INT64},
	{reflect.TypeOf(uint64(0)), C.TF_UINT64},
	{reflect.TypeOf(false), C.TF_BOOL},
	{reflect.TypeOf(uint16(0)), C.TF_UINT16},
	{reflect.TypeOf(complex(float64(0), float64(0))), C.TF_COMPLEX128},
	// TODO(apassos): support DT_RESOURCE representation in go.
	// TODO(keveman): support DT_VARIANT representation in go.
}

// shapeAndDataTypeOf returns the data type and shape of the Tensor
// corresponding to a Go type.
func shapeAndDataTypeOf(val reflect.Value) (shape []int64, dt DataType, err error) {
	typ := val.Type()
	for typ.Kind() == reflect.Array || typ.Kind() == reflect.Slice {
		shape = append(shape, int64(val.Len()))
		if val.Len() > 0 {
			// In order to check tensor structure properly in general case we need to iterate over all slices of the tensor to check sizes match
			// Since we already going to iterate over all elements in encodeTensor() let's
			// 1) do the actual check in encodeTensor() to save some cpu cycles here
			// 2) assume the shape is represented by lengths of elements with zero index in each dimension
			val = val.Index(0)
		}
		typ = typ.Elem()
	}
	for _, t := range types {
		if typ.Kind() == t.typ.Kind() {
			return shape, DataType(t.dataType), nil
		}
	}
	return shape, dt, fmt.Errorf("unsupported type %v", typ)
}

// typeOf converts from a DataType and Shape to the equivalent Go type.
func typeOf(dt DataType, shape []int64) reflect.Type {
	var ret reflect.Type
	for _, t := range types {
		if dt == DataType(t.dataType) {
			ret = t.typ
			break
		}
	}
	if ret == nil {
		panic(bug("DataType %v is not supported (see https://www.tensorflow.org/code/tensorflow/core/framework/types.proto)", dt))
	}
	for range shape {
		ret = reflect.SliceOf(ret)
	}
	return ret
}

func numElements(shape []int64) int64 {
	n := int64(1)
	for _, d := range shape {
		n *= d
	}
	return n
}

// byteSizeOfEncodedStrings returns the size of the encoded strings in val.
// val MUST be a string, or a container (array/slice etc.) of strings.
func byteSizeOfEncodedStrings(val interface{}) uintptr {
	if s, ok := val.(string); ok {
		return uintptr(C.TF_StringEncodedSize(C.size_t(len(s))))
	}
	// Otherwise must be an array or slice.
	var size uintptr
	v := reflect.ValueOf(val)
	for i := 0; i < v.Len(); i++ {
		size += byteSizeOfEncodedStrings(v.Index(i).Interface())
	}
	return size
}

// encodeTensor writes v to the specified buffer using the format specified in
// c_api.h. Use stringEncoder for String tensors.
func encodeTensor(w *bytes.Buffer, v reflect.Value, shape []int64) error {
	switch v.Kind() {
	case reflect.Bool:
		b := byte(0)
		if v.Bool() {
			b = 1
		}
		if err := w.WriteByte(b); err != nil {
			return err
		}
	case reflect.Int8, reflect.Int16, reflect.Int32, reflect.Int64, reflect.Uint8, reflect.Uint16, reflect.Uint32, reflect.Uint64, reflect.Float32, reflect.Float64, reflect.Complex64, reflect.Complex128:
		if err := binary.Write(w, nativeEndian, v.Interface()); err != nil {
			return err
		}

	case reflect.Array, reflect.Slice:
		// If current dimension is a slice, verify that it has the expected size
		// Go's type system makes that guarantee for arrays.
		if v.Kind() == reflect.Slice {
			expected := int(shape[0])
			if v.Len() != expected {
				return fmt.Errorf("mismatched slice lengths: %d and %d", v.Len(), expected)
			}
		}

		// Optimisation: if only one dimension is left we can use binary.Write() directly for this slice
		if len(shape) == 1 && v.Len() > 0 {
			switch v.Index(0).Kind() {
			case reflect.Int8, reflect.Int16, reflect.Int32, reflect.Int64, reflect.Uint8, reflect.Uint16, reflect.Uint32, reflect.Uint64, reflect.Float32, reflect.Float64, reflect.Complex64, reflect.Complex128:
				return binary.Write(w, nativeEndian, v.Interface())
			}
		}

		subShape := shape[1:]
		for i := 0; i < v.Len(); i++ {
			err := encodeTensor(w, v.Index(i), subShape)
			if err != nil {
				return err
			}
		}

	default:
		return fmt.Errorf("unsupported type %v", v.Type())
	}
	return nil
}

// decodeTensor decodes the Tensor from the buffer to ptr using the format
// specified in c_api.h. Use stringDecoder for String tensors.
func decodeTensor(r *bytes.Reader, shape []int64, typ reflect.Type, ptr reflect.Value) error {
	switch typ.Kind() {
	case reflect.Bool:
		b, err := r.ReadByte()
		if err != nil {
			return err
		}
		ptr.Elem().SetBool(b == 1)
	case reflect.Int8, reflect.Int16, reflect.Int32, reflect.Int64, reflect.Uint8, reflect.Uint16, reflect.Uint32, reflect.Uint64, reflect.Float32, reflect.Float64, reflect.Complex64, reflect.Complex128:
		if err := binary.Read(r, nativeEndian, ptr.Interface()); err != nil {
			return err
		}

	case reflect.Slice:
		val := reflect.Indirect(ptr)
		val.Set(reflect.MakeSlice(typ, int(shape[0]), int(shape[0])))

		// Optimization: if only one dimension is left we can use binary.Read() directly for this slice
		if len(shape) == 1 && val.Len() > 0 {
			switch val.Index(0).Kind() {
			case reflect.Int8, reflect.Int16, reflect.Int32, reflect.Int64, reflect.Uint8, reflect.Uint16, reflect.Uint32, reflect.Uint64, reflect.Float32, reflect.Float64, reflect.Complex64, reflect.Complex128:
				return binary.Read(r, nativeEndian, val.Interface())
			}
		}

		for i := 0; i < val.Len(); i++ {
			if err := decodeTensor(r, shape[1:], typ.Elem(), val.Index(i).Addr()); err != nil {
				return err
			}
		}

	default:
		return fmt.Errorf("unsupported type %v", typ)
	}
	return nil
}

type stringEncoder struct {
	offsets io.Writer
	data    []byte
	offset  uint64
	status  *status
}

func (e *stringEncoder) encode(v reflect.Value, shape []int64) error {
	if v.Kind() == reflect.String {
		if err := binary.Write(e.offsets, nativeEndian, e.offset); err != nil {
			return err
		}
		var (
			s      = v.Interface().(string)
			src    = C.CString(s)
			srcLen = C.size_t(len(s))
			dst    = (*C.char)(unsafe.Pointer(&e.data[e.offset]))
			dstLen = C.size_t(uint64(len(e.data)) - e.offset)
		)
		e.offset += uint64(C.TF_StringEncode(src, srcLen, dst, dstLen, e.status.c))
		C.free(unsafe.Pointer(src))
		return e.status.Err()
	}

	if v.Kind() == reflect.Slice {
		expected := int(shape[0])
		if v.Len() != expected {
			return fmt.Errorf("mismatched slice lengths: %d and %d", v.Len(), expected)
		}
	}

	subShape := shape[1:]
	for i := 0; i < v.Len(); i++ {
		if err := e.encode(v.Index(i), subShape); err != nil {
			return err
		}
	}
	return nil
}

type stringDecoder struct {
	offsets io.Reader
	data    []byte
	status  *status
}

func (d *stringDecoder) decode(ptr reflect.Value, shape []int64) error {
	if len(shape) == 0 {
		var offset uint64
		if err := binary.Read(d.offsets, nativeEndian, &offset); err != nil {
			return err
		}
		var (
			src    = (*C.char)(unsafe.Pointer(&d.data[offset]))
			srcLen = C.size_t(len(d.data)) - C.size_t(offset)
			dst    *C.char
			dstLen C.size_t
		)
		if offset > uint64(len(d.data)) {
			return fmt.Errorf("invalid offsets in String Tensor")
		}
		C.TF_StringDecode(src, srcLen, &dst, &dstLen, d.status.c)
		if err := d.status.Err(); err != nil {
			return err
		}
		s := ptr.Interface().(*string)
		*s = C.GoStringN(dst, C.int(dstLen))
		return nil
	}
	val := reflect.Indirect(ptr)
	val.Set(reflect.MakeSlice(typeOf(String, shape), int(shape[0]), int(shape[0])))
	for i := 0; i < val.Len(); i++ {
		if err := d.decode(val.Index(i).Addr(), shape[1:]); err != nil {
			return err
		}
	}
	return nil
}

func bug(format string, args ...interface{}) error {
	return fmt.Errorf("BUG: Please report at https://github.com/tensorflow/tensorflow/issues with the note: Go TensorFlow %v: %v", Version(), fmt.Sprintf(format, args...))
}

func isTensorSerializable(dataType DataType) error {
	// For numeric types, the serialized Tensor matches the in-memory
	// representation.  See the implementation of Tensor::AsProtoContent in
	// https://www.tensorflow.org/code/tensorflow/core/framework/tensor.cc
	//
	// The more appropriate way to be in sync with Tensor::AsProtoContent
	// would be to have the TensorFlow C library export functions for
	// serialization and deserialization of Tensors.  Till then capitalize
	// on knowledge of the implementation for numeric types.
	switch dataType {
	case Float, Double, Int32, Uint8, Int16, Int8, Complex, Int64, Bool, Quint8, Qint32, Bfloat16, Qint16, Quint16, Uint16, Complex128, Half:
		return nil
	default:
		return fmt.Errorf("serialization of tensors with the DataType %d is not yet supported, see https://github.com/tensorflow/tensorflow/issues/6003", dataType)
	}
}

// nativeEndian is the byte order for the local platform. Used to send back and
// forth Tensors with the C API. We test for endianness at runtime because
// some architectures can be booted into different endian modes.
var nativeEndian binary.ByteOrder

func init() {
	buf := [2]byte{}
	*(*uint16)(unsafe.Pointer(&buf[0])) = uint16(0xABCD)

	switch buf {
	case [2]byte{0xCD, 0xAB}:
		nativeEndian = binary.LittleEndian
	case [2]byte{0xAB, 0xCD}:
		nativeEndian = binary.BigEndian
	default:
		panic("Could not determine native endianness.")
	}
}