gotch/ts/patch.go

package ts

// #include "stdlib.h"
import "C"

import (
	"log"
	"unsafe"

	lib "git.andr3h3nriqu3s.com/andr3/gotch/libtch"
)

// NOTE. This is a temporarily patched to make it run.
// TODO. make change at generator for []*Tensor input

func (ts *Tensor) Lstm(hxData []*Tensor, paramsData []*Tensor, hasBiases bool, numLayers int64, dropout float64, train bool, bidirectional bool, batchFirst bool) (output, h, c *Tensor, err error) {

	// NOTE: `atg_lstm` will create 3 consecutive Ctensors in memory of C land. The first
	// Ctensor will have address given by `ctensorPtr1` here.
	// The next pointers can be calculated based on `ctensorPtr1`
	ctensorPtr1 := (*lib.Ctensor)(unsafe.Pointer(C.malloc(0)))
	ctensorPtr2 := (*lib.Ctensor)(unsafe.Pointer(uintptr(unsafe.Pointer(ctensorPtr1)) + unsafe.Sizeof(ctensorPtr1)))
	ctensorPtr3 := (*lib.Ctensor)(unsafe.Pointer(uintptr(unsafe.Pointer(ctensorPtr2)) + unsafe.Sizeof(ctensorPtr1)))

	var chxData []lib.Ctensor
	for _, t := range hxData {
		chxData = append(chxData, t.ctensor)
	}

	var cparamsData []lib.Ctensor
	for _, t := range paramsData {
		cparamsData = append(cparamsData, t.ctensor)
	}

	var chasBiases int32 = 0
	if hasBiases {
		chasBiases = 1
	}
	var ctrain int32 = 0
	if train {
		ctrain = 1
	}
	var cbidirectional int32 = 0
	if bidirectional {
		cbidirectional = 1
	}
	var cbatchFirst int32 = 0
	if batchFirst {
		cbatchFirst = 1
	}

	lib.AtgLstm(ctensorPtr1, ts.ctensor, chxData, len(hxData), cparamsData, len(paramsData), chasBiases, numLayers, dropout, ctrain, cbidirectional, cbatchFirst)
	err = TorchErr()
	if err != nil {
		return output, h, c, err
	}

	return newTensor(*ctensorPtr1), newTensor(*ctensorPtr2), newTensor(*ctensorPtr3), nil

}

func (ts *Tensor) MustLstm(hxData []*Tensor, paramsData []*Tensor, hasBiases bool, numLayers int64, dropout float64, train bool, bidirectional bool, batchFirst bool) (output, h, c *Tensor) {
	output, h, c, err := ts.Lstm(hxData, paramsData, hasBiases, numLayers, dropout, train, bidirectional, batchFirst)

	if err != nil {
		log.Fatal(err)
	}

	return output, h, c
}

func (ts *Tensor) Gru(hx *Tensor, paramsData []*Tensor, hasBiases bool, numLayers int64, dropout float64, train bool, bidirectional bool, batchFirst bool) (output, h *Tensor, err error) {

	// NOTE: `atg_gru` will create 2 consecutive Ctensors in memory of C land.
	// The first Ctensor will have address given by `ctensorPtr1` here.
	// The next pointer can be calculated based on `ctensorPtr1`
	ctensorPtr1 := (*lib.Ctensor)(unsafe.Pointer(C.malloc(0)))
	ctensorPtr2 := (*lib.Ctensor)(unsafe.Pointer(uintptr(unsafe.Pointer(ctensorPtr1)) + unsafe.Sizeof(ctensorPtr1)))

	var cparamsData []lib.Ctensor
	for _, t := range paramsData {
		cparamsData = append(cparamsData, t.ctensor)
	}

	var chasBiases int32 = 0
	if hasBiases {
		chasBiases = 1
	}
	var ctrain int32 = 0
	if train {
		ctrain = 1
	}
	var cbidirectional int32 = 0
	if bidirectional {
		cbidirectional = 1
	}
	var cbatchFirst int32 = 0
	if batchFirst {
		cbatchFirst = 1
	}

	lib.AtgGru(ctensorPtr1, ts.ctensor, hx.ctensor, cparamsData, len(paramsData), chasBiases, numLayers, dropout, ctrain, cbidirectional, cbatchFirst)
	err = TorchErr()
	if err != nil {
		return output, h, err
	}

	return newTensor(*ctensorPtr1), newTensor(*ctensorPtr2), nil

}

func (ts *Tensor) MustGru(hx *Tensor, paramsData []*Tensor, hasBiases bool, numLayers int64, dropout float64, train bool, bidirectional bool, batchFirst bool) (output, h *Tensor) {
	output, h, err := ts.Gru(hx, paramsData, hasBiases, numLayers, dropout, train, bidirectional, batchFirst)
	if err != nil {
		log.Fatal(err)
	}

	return output, h
}

func (ts *Tensor) TopK(k int64, dim int64, largest bool, sorted bool) (ts1, ts2 *Tensor, err error) {

	// NOTE: `lib.AtgTopk` will return 2 tensors in C memory. First tensor pointer
	// is given by ctensorPtr1
	ctensorPtr1 := (*lib.Ctensor)(unsafe.Pointer(C.malloc(0)))
	ctensorPtr2 := (*lib.Ctensor)(unsafe.Pointer(uintptr(unsafe.Pointer(ctensorPtr1)) + unsafe.Sizeof(ctensorPtr1)))
	var clargest int32 = 0
	if largest {
		clargest = 1
	}
	var csorted int32 = 0
	if sorted {
		csorted = 1
	}

	lib.AtgTopk(ctensorPtr1, ts.ctensor, k, dim, clargest, csorted)
	err = TorchErr()
	if err != nil {
		return ts1, ts2, err
	}

	return newTensor(*ctensorPtr1), newTensor(*ctensorPtr2), nil
}

func (ts *Tensor) MustTopK(k int64, dim int64, largest bool, sorted bool) (ts1, ts2 *Tensor) {

	ts1, ts2, err := ts.TopK(k, dim, largest, sorted)
	if err != nil {
		log.Fatal(err)
	}

	return ts1, ts2
}

// NOTE. `NLLLoss` is a version of `NllLoss` in tensor-generated
// with default weight, reduction and ignoreIndex
func (ts *Tensor) NLLLoss(target *Tensor, del bool) (retVal *Tensor, err error) {
	ptr := (*lib.Ctensor)(unsafe.Pointer(C.malloc(0)))
	if del {
		defer ts.MustDrop()
	}

	reduction := int64(1) // Mean of loss
	ignoreIndex := int64(-100)
	// defer C.free(unsafe.Pointer(ptr))

	lib.AtgNllLoss(ptr, ts.ctensor, target.ctensor, nil, reduction, ignoreIndex)
	if err = TorchErr(); err != nil {
		return retVal, err
	}

	retVal = newTensor(*ptr)

	return retVal, nil
}

func (ts *Tensor) MustNLLLoss(target *Tensor, del bool) (retVal *Tensor) {
	retVal, err := ts.NLLLoss(target, del)
	if err != nil {
		log.Fatal(err)
	}

	return retVal
}

// NOTE: the following 9 APIs are missing from `tensor-generated.go` with
// pattern of **return tensor pointer**: `tensor *atg_FUNCTION_NAME()`.
// The returning tensor pointer actually is the FIRST element of a vector
// of C tensor pointers. Next pointer will be calculated from the first.
// In C land, verifying a valid pointer is to check whether it points to **NULL**.
//
// tensor *atg_align_tensors(tensor *tensors_data, int tensors_len);
// tensor *atg_broadcast_tensors(tensor *tensors_data, int tensors_len);
// tensor *atg_chunk(tensor self, int64_t chunks, int64_t dim);
// tensor *atg_meshgrid(tensor *tensors_data, int tensors_len);
// tensor *atg_nonzero_numpy(tensor self);
// tensor *atg_split(tensor self, int64_t split_size, int64_t dim);
// tensor *atg_split_with_sizes(tensor self, int64_t *split_sizes_data, int split_sizes_len, int64_t dim);
// tensor *atg_unbind(tensor self, int64_t dim);
// tensor *atg_where(tensor condition);

// tensor *atg_align_tensors(tensor *tensors_data, int tensors_len);
func AlignTensors(tensors []*Tensor) (retVal []*Tensor, err error) {

	var ctensors []lib.Ctensor
	for _, t := range tensors {
		ctensors = append(ctensors, t.ctensor)
	}

	ctensorsPtr := lib.AtgAlignTensors(ctensors, len(ctensors))
	if err = TorchErr(); err != nil {
		return retVal, err
	}

	currentPtr := ctensorsPtr
	retVal = append(retVal, newTensor(*currentPtr))
	for {
		nextPtr := (*lib.Ctensor)(unsafe.Add(unsafe.Pointer(currentPtr), unsafe.Sizeof(currentPtr)))
		if *nextPtr == nil {
			break
		}

		retVal = append(retVal, newTensor(*nextPtr))
		currentPtr = nextPtr
	}

	return retVal, nil
}

func MustAlignTensors(tensors []*Tensor, del bool) (retVal []*Tensor) {
	if del {
		for _, t := range tensors {
			defer t.MustDrop()
		}
	}
	retVal, err := AlignTensors(tensors)
	if err != nil {
		log.Fatal(err)
	}

	return retVal
}

// tensor *atg_broadcast_tensors(tensor *tensors_data, int tensors_len);
func BroadcastTensors(tensors []*Tensor) (retVal []*Tensor, err error) {

	var ctensors []lib.Ctensor
	for _, t := range tensors {
		ctensors = append(ctensors, t.ctensor)
	}

	ctensorsPtr := lib.AtgBroadcastTensors(ctensors, len(ctensors))
	if err = TorchErr(); err != nil {
		return retVal, err
	}

	currentPtr := ctensorsPtr
	retVal = append(retVal, newTensor(*currentPtr))
	for {
		nextPtr := (*lib.Ctensor)(unsafe.Add(unsafe.Pointer(currentPtr), unsafe.Sizeof(currentPtr)))
		if *nextPtr == nil {
			break
		}

		retVal = append(retVal, newTensor(*nextPtr))
		currentPtr = nextPtr
	}

	return retVal, nil
}

func MustBroadcastTensors(tensors []*Tensor, del bool) (retVal []*Tensor) {
	if del {
		for _, t := range tensors {
			defer t.MustDrop()
		}
	}

	retVal, err := BroadcastTensors(tensors)
	if err != nil {
		log.Fatal(err)
	}

	return retVal
}

// tensor *atg_chunk(tensor self, int64_t chunks, int64_t dim);
func (ts *Tensor) Chunk(chunks int64, dim int64) (retVal []*Tensor, err error) {
	ctensorsPtr := lib.AtgChunk(ts.ctensor, chunks, dim)
	if err = TorchErr(); err != nil {
		return retVal, err
	}

	currentPtr := ctensorsPtr
	retVal = append(retVal, newTensor(*currentPtr))
	for {
		nextPtr := (*lib.Ctensor)(unsafe.Add(unsafe.Pointer(currentPtr), unsafe.Sizeof(currentPtr)))
		if *nextPtr == nil {
			break
		}

		retVal = append(retVal, newTensor(*nextPtr))
		currentPtr = nextPtr
	}

	return retVal, nil
}

func (ts *Tensor) MustChunk(chunks int64, dim int64, del bool) (retVal []*Tensor) {
	if del {
		defer ts.MustDrop()
	}

	retVal, err := ts.Chunk(chunks, dim)
	if err != nil {
		log.Fatal(err)
	}

	return retVal
}

// tensor *atg_meshgrid(tensor *tensors_data, int tensors_len);
func Meshgrid(tensors []*Tensor) (retVal []*Tensor, err error) {

	var ctensors []lib.Ctensor
	for _, t := range tensors {
		ctensors = append(ctensors, t.ctensor)
	}

	ctensorsPtr := lib.AtgMeshgrid(ctensors, len(ctensors))
	if err = TorchErr(); err != nil {
		return retVal, err
	}

	currentPtr := ctensorsPtr
	retVal = append(retVal, newTensor(*currentPtr))
	for {
		nextPtr := (*lib.Ctensor)(unsafe.Add(unsafe.Pointer(currentPtr), unsafe.Sizeof(currentPtr)))
		if *nextPtr == nil {
			break
		}

		retVal = append(retVal, newTensor(*nextPtr))
		currentPtr = nextPtr
	}

	return retVal, nil
}

func MustMeshgrid(tensors []*Tensor) (retVal []*Tensor) {
	retVal, err := Meshgrid(tensors)
	if err != nil {
		log.Fatal(err)
	}

	return retVal
}

// tensor *atg_nonzero_numpy(tensor self);
func (ts *Tensor) NonzeroNumpy() (retVal []*Tensor, err error) {
	ctensorsPtr := lib.AtgNonzeroNumpy(ts.ctensor)
	if err = TorchErr(); err != nil {
		return retVal, err
	}

	currentPtr := ctensorsPtr
	retVal = append(retVal, newTensor(*currentPtr))
	for {
		nextPtr := (*lib.Ctensor)(unsafe.Add(unsafe.Pointer(currentPtr), unsafe.Sizeof(currentPtr)))
		if *nextPtr == nil {
			break
		}

		retVal = append(retVal, newTensor(*nextPtr))
		currentPtr = nextPtr
	}

	return retVal, nil
}

func (ts *Tensor) MustNonzeroNumpy(del bool) (retVal []*Tensor) {
	if del {
		defer ts.MustDrop()
	}

	retVal, err := ts.NonzeroNumpy()
	if err != nil {
		log.Fatal(err)
	}

	return retVal
}

// Split splits tensor into chunks
//
// Parameters:
//   - splitSize – size of a single chunk
//   - dim – dimension along which to split the tensor.
//
// Ref. https://pytorch.org/docs/stable/generated/torch.split.html
func (ts *Tensor) Split(splitSize, dim int64) (retVal []*Tensor, err error) {

	ctensorsPtr := lib.AtgSplit(ts.ctensor, splitSize, dim)
	if err = TorchErr(); err != nil {
		return retVal, err
	}

	// NOTE: ctensorsPtr is a c-pointer to a vector of tensors. The first
	// C tensor is the `ctensorsPtr` value. The next pointer will be
	// calculated from there. The vector of tensors will end if the calculated
	// pointer value is `null`.
	currentPtr := ctensorsPtr
	retVal = append(retVal, newTensor(*currentPtr))
	for {
		nextPtr := (*lib.Ctensor)(unsafe.Add(unsafe.Pointer(currentPtr), unsafe.Sizeof(currentPtr)))
		if *nextPtr == nil {
			break
		}

		retVal = append(retVal, newTensor(*nextPtr))
		currentPtr = nextPtr
	}

	return retVal, nil
}

func (ts *Tensor) MustSplit(splitSize, dim int64, del bool) (retVal []*Tensor) {
	if del {
		defer ts.MustDrop()
	}

	retVal, err := ts.Split(splitSize, dim)
	if err != nil {
		log.Fatal(err)
	}

	return retVal
}

// SplitWithSizes splits tensor into chunks
//
// Parameters:
//   - splitSizes – slice of sizes for each chunk
//   - dim – dimension along which to split the tensor.
//
// Ref. https://pytorch.org/docs/stable/generated/torch.split.html
func (ts *Tensor) SplitWithSizes(splitSizes []int64, dim int64) (retVal []*Tensor, err error) {

	ctensorsPtr := lib.AtgSplitWithSizes(ts.ctensor, splitSizes, len(splitSizes), dim)
	if err = TorchErr(); err != nil {
		return retVal, err
	}

	// NOTE: ctensorsPtr is a c-pointer to a vector of tensors. The first
	// C tensor is the `ctensorsPtr` value. The next pointer will be
	// calculated from there. The vector of tensors will end if the calculated
	// pointer value is `null`.
	currentPtr := ctensorsPtr
	retVal = append(retVal, newTensor(*currentPtr))
	for {
		nextPtr := (*lib.Ctensor)(unsafe.Add(unsafe.Pointer(currentPtr), unsafe.Sizeof(currentPtr)))
		if *nextPtr == nil {
			break
		}

		retVal = append(retVal, newTensor(*nextPtr))
		currentPtr = nextPtr
	}

	return retVal, nil
}

func (ts *Tensor) MustSplitWithSizes(splitSizes []int64, dim int64, del bool) (retVal []*Tensor) {
	if del {
		defer ts.MustDrop()
	}

	retVal, err := ts.SplitWithSizes(splitSizes, dim)
	if err != nil {
		log.Fatal(err)
	}

	return retVal
}

// tensor *atg_unbind(tensor self, int64_t dim);
func (ts *Tensor) Unbind(dim int64) (retVal []*Tensor, err error) {
	ctensorsPtr := lib.AtgUnbind(ts.ctensor, dim)
	if err = TorchErr(); err != nil {
		return retVal, err
	}

	currentPtr := ctensorsPtr
	retVal = append(retVal, newTensor(*currentPtr))

	for {
		nextPtr := (*lib.Ctensor)(unsafe.Add(unsafe.Pointer(currentPtr), unsafe.Sizeof(currentPtr)))
		if *nextPtr == nil {
			break
		}

		retVal = append(retVal, newTensor(*nextPtr))
		currentPtr = nextPtr
	}

	return retVal, nil
}

func (ts *Tensor) MustUnbind(dim int64, del bool) (retVal []*Tensor) {
	if del {
		defer ts.MustDrop()
	}

	retVal, err := ts.Unbind(dim)
	if err != nil {
		log.Fatal(err)
	}

	return retVal
}

// tensor *atg_where(tensor condition);
func Where(condition Tensor) (retVal []*Tensor, err error) {
	ctensorsPtr := lib.AtgWhere(condition.ctensor)
	if err = TorchErr(); err != nil {
		return retVal, err
	}

	currentPtr := ctensorsPtr
	retVal = append(retVal, newTensor(*currentPtr))
	for {
		nextPtr := (*lib.Ctensor)(unsafe.Add(unsafe.Pointer(currentPtr), unsafe.Sizeof(currentPtr)))
		if *nextPtr == nil {
			break
		}

		retVal = append(retVal, newTensor(*nextPtr))
		currentPtr = nextPtr
	}

	return retVal, nil
}

func MustWhere(condition Tensor, del bool) (retVal []*Tensor) {
	if del {
		defer condition.MustDrop()
	}

	retVal, err := Where(condition)
	if err != nil {
		log.Fatal(err)
	}

	return retVal
}

// NOTE. patches for APIs `agt_` missing in tensor/ but existing in lib
// ====================================================================

// // void atg_lstsq(tensor *, tensor self, tensor A);
// func (ts *Tensor) Lstsq(a *Tensor, del bool) (retVal *Tensor, err error) {
// if del {
// defer ts.MustDrop()
// }
// ptr := (*lib.Ctensor)(unsafe.Pointer(C.malloc(0)))
//
// lib.AtgLstsq(ptr, ts.ctensor, a.ctensor)
// if err = TorchErr(); err != nil {
// return retVal, err
// }
// retVal = &Tensor{ctensor: *ptr}
//
// return retVal, err
// }
//
// func (ts *Tensor) MustLstsq(a *Tensor, del bool) (retVal *Tensor) {
// retVal, err := ts.Lstsq(a, del)
// if err != nil {
// log.Fatal(err)
// }
//
// return retVal
// }