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package tensor
import (
"github.com/pkg/errors"
"gorgonia.org/tensor/internal/execution"
"gorgonia.org/tensor/internal/storage"
"gorgonia.org/vecf64"
)
func handleFuncOptsF64(expShape Shape, o DataOrder, opts ...FuncOpt) (reuse DenseTensor, safe, toReuse, incr bool, err error) {
fo := ParseFuncOpts(opts...)
reuseT, incr := fo.IncrReuse()
safe = fo.Safe()
toReuse = reuseT != nil
if toReuse {
var ok bool
if reuse, ok = reuseT.(DenseTensor); !ok {
returnOpOpt(fo)
err = errors.Errorf("Cannot reuse a different type of Tensor in a *Dense-Scalar operation. Reuse is of %T", reuseT)
return
}
if reuse.len() != expShape.TotalSize() && !expShape.IsScalar() {
returnOpOpt(fo)
err = errors.Errorf(shapeMismatch, reuse.Shape(), expShape)
err = errors.Wrapf(err, "Cannot use reuse: shape mismatch")
return
}
if !incr && reuse != nil {
reuse.setDataOrder(o)
// err = reuse.reshape(expShape...)
}
}
returnOpOpt(fo)
return
}
func prepDataVSF64(a Tensor, b interface{}, reuse Tensor) (dataA *storage.Header, dataB float64, dataReuse *storage.Header, ait, iit Iterator, useIter bool, err error) {
// get data
dataA = a.hdr()
switch bt := b.(type) {
case float64:
dataB = bt
case *float64:
dataB = *bt
default:
err = errors.Errorf("b is not a float64: %T", b)
return
}
if reuse != nil {
dataReuse = reuse.hdr()
}
if a.RequiresIterator() || (reuse != nil && reuse.RequiresIterator()) {
ait = a.Iterator()
if reuse != nil {
iit = reuse.Iterator()
}
useIter = true
}
return
}
func (e Float64Engine) checkThree(a, b Tensor, reuse Tensor) error {
if !a.IsNativelyAccessible() {
return errors.Errorf(inaccessibleData, a)
}
if !b.IsNativelyAccessible() {
return errors.Errorf(inaccessibleData, b)
}
if reuse != nil && !reuse.IsNativelyAccessible() {
return errors.Errorf(inaccessibleData, reuse)
}
if a.Dtype() != Float64 {
return errors.Errorf("Expected a to be of Float64. Got %v instead", a.Dtype())
}
if a.Dtype() != b.Dtype() || (reuse != nil && b.Dtype() != reuse.Dtype()) {
return errors.Errorf("Expected a, b and reuse to have the same Dtype. Got %v, %v and %v instead", a.Dtype(), b.Dtype(), reuse.Dtype())
}
return nil
}
func (e Float64Engine) checkTwo(a Tensor, reuse Tensor) error {
if !a.IsNativelyAccessible() {
return errors.Errorf(inaccessibleData, a)
}
if reuse != nil && !reuse.IsNativelyAccessible() {
return errors.Errorf(inaccessibleData, reuse)
}
if a.Dtype() != Float64 {
return errors.Errorf("Expected a to be of Float64. Got %v instead", a.Dtype())
}
if reuse != nil && reuse.Dtype() != a.Dtype() {
return errors.Errorf("Expected reuse to be the same as a. Got %v instead", reuse.Dtype())
}
return nil
}
// Float64Engine is an execution engine that is optimized to only work with float64s. It assumes all data will are float64s.
//
// Use this engine only as form of optimization. You should probably be using the basic default engine for most cases.
type Float64Engine struct {
StdEng
}
// makeArray allocates a slice for the array
func (e Float64Engine) makeArray(arr *array, t Dtype, size int) {
if t != Float64 {
panic("Float64Engine only creates float64s")
}
arr.Header.Raw = make([]byte, size*8)
arr.t = t
}
func (e Float64Engine) FMA(a, x, y Tensor) (retVal Tensor, err error) {
reuse := y
if err = e.checkThree(a, x, reuse); err != nil {
return nil, errors.Wrap(err, "Failed checks")
}
var dataA, dataB, dataReuse *storage.Header
var ait, bit, iit Iterator
var useIter bool
if dataA, dataB, dataReuse, ait, bit, iit, useIter, _, err = prepDataVV(a, x, reuse); err != nil {
return nil, errors.Wrap(err, "Float64Engine.FMA")
}
if useIter {
err = execution.MulIterIncrF64(dataA.Float64s(), dataB.Float64s(), dataReuse.Float64s(), ait, bit, iit)
retVal = reuse
return
}
vecf64.IncrMul(dataA.Float64s(), dataB.Float64s(), dataReuse.Float64s())
retVal = reuse
return
}
func (e Float64Engine) FMAScalar(a Tensor, x interface{}, y Tensor) (retVal Tensor, err error) {
reuse := y
if err = e.checkTwo(a, reuse); err != nil {
return nil, errors.Wrap(err, "Failed checks")
}
var ait, iit Iterator
var dataTensor, dataReuse *storage.Header
var scalar float64
var useIter bool
if dataTensor, scalar, dataReuse, ait, iit, useIter, err = prepDataVSF64(a, x, reuse); err != nil {
return nil, errors.Wrapf(err, opFail, "Float64Engine.FMAScalar")
}
if useIter {
err = execution.MulIterIncrVSF64(dataTensor.Float64s(), scalar, dataReuse.Float64s(), ait, iit)
retVal = reuse
}
execution.MulIncrVSF64(dataTensor.Float64s(), scalar, dataReuse.Float64s())
retVal = reuse
return
}
// Add performs a + b elementwise. Both a and b must have the same shape.
// Acceptable FuncOpts are: UseUnsafe(), WithReuse(T), WithIncr(T)
func (e Float64Engine) Add(a Tensor, b Tensor, opts ...FuncOpt) (retVal Tensor, err error) {
if a.RequiresIterator() || b.RequiresIterator() {
return e.StdEng.Add(a, b, opts...)
}
var reuse DenseTensor
var safe, toReuse, incr bool
if reuse, safe, toReuse, incr, err = handleFuncOptsF64(a.Shape(), a.DataOrder(), opts...); err != nil {
return nil, errors.Wrap(err, "Unable to handle funcOpts")
}
if err = e.checkThree(a, b, reuse); err != nil {
return nil, errors.Wrap(err, "Failed checks")
}
var hdrA, hdrB, hdrReuse *storage.Header
var dataA, dataB, dataReuse []float64
if hdrA, hdrB, hdrReuse, _, _, _, _, _, err = prepDataVV(a, b, reuse); err != nil {
return nil, errors.Wrapf(err, "Float64Engine.Add")
}
dataA = hdrA.Float64s()
dataB = hdrB.Float64s()
if hdrReuse != nil {
dataReuse = hdrReuse.Float64s()
}
switch {
case incr:
vecf64.IncrAdd(dataA, dataB, dataReuse)
retVal = reuse
case toReuse:
copy(dataReuse, dataA)
vecf64.Add(dataReuse, dataB)
retVal = reuse
case !safe:
vecf64.Add(dataA, dataB)
retVal = a
default:
ret := a.Clone().(headerer)
vecf64.Add(ret.hdr().Float64s(), dataB)
retVal = ret.(Tensor)
}
return
}
func (e Float64Engine) Inner(a, b Tensor) (retVal float64, err error) {
var A, B []float64
var AD, BD *Dense
var ok bool
if AD, ok = a.(*Dense); !ok {
return 0, errors.Errorf("a is not a *Dense")
}
if BD, ok = b.(*Dense); !ok {
return 0, errors.Errorf("b is not a *Dense")
}
A = AD.Float64s()
B = BD.Float64s()
retVal = whichblas.Ddot(len(A), A, 1, B, 1)
return
}
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