584 lines
15 KiB
Go
584 lines
15 KiB
Go
package ts
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// #include "stdlib.h"
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import "C"
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import (
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"log"
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"unsafe"
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lib "git.andr3h3nriqu3s.com/andr3/gotch/libtch"
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)
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// NOTE. This is a temporarily patched to make it run.
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// TODO. make change at generator for []*Tensor input
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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) {
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// NOTE: `atg_lstm` will create 3 consecutive Ctensors in memory of C land. The first
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// Ctensor will have address given by `ctensorPtr1` here.
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// The next pointers can be calculated based on `ctensorPtr1`
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ctensorPtr1 := (*lib.Ctensor)(unsafe.Pointer(C.malloc(0)))
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ctensorPtr2 := (*lib.Ctensor)(unsafe.Pointer(uintptr(unsafe.Pointer(ctensorPtr1)) + unsafe.Sizeof(ctensorPtr1)))
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ctensorPtr3 := (*lib.Ctensor)(unsafe.Pointer(uintptr(unsafe.Pointer(ctensorPtr2)) + unsafe.Sizeof(ctensorPtr1)))
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var chxData []lib.Ctensor
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for _, t := range hxData {
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chxData = append(chxData, t.ctensor)
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}
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var cparamsData []lib.Ctensor
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for _, t := range paramsData {
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cparamsData = append(cparamsData, t.ctensor)
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}
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var chasBiases int32 = 0
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if hasBiases {
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chasBiases = 1
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}
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var ctrain int32 = 0
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if train {
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ctrain = 1
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}
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var cbidirectional int32 = 0
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if bidirectional {
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cbidirectional = 1
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}
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var cbatchFirst int32 = 0
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if batchFirst {
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cbatchFirst = 1
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}
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lib.AtgLstm(ctensorPtr1, ts.ctensor, chxData, len(hxData), cparamsData, len(paramsData), chasBiases, numLayers, dropout, ctrain, cbidirectional, cbatchFirst)
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err = TorchErr()
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if err != nil {
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return output, h, c, err
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}
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return newTensor(*ctensorPtr1), newTensor(*ctensorPtr2), newTensor(*ctensorPtr3), nil
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}
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func (ts *Tensor) MustLstm(hxData []*Tensor, paramsData []*Tensor, hasBiases bool, numLayers int64, dropout float64, train bool, bidirectional bool, batchFirst bool) (output, h, c *Tensor) {
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output, h, c, err := ts.Lstm(hxData, paramsData, hasBiases, numLayers, dropout, train, bidirectional, batchFirst)
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if err != nil {
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log.Fatal(err)
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}
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return output, h, c
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}
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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) {
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// NOTE: `atg_gru` will create 2 consecutive Ctensors in memory of C land.
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// The first Ctensor will have address given by `ctensorPtr1` here.
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// The next pointer can be calculated based on `ctensorPtr1`
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ctensorPtr1 := (*lib.Ctensor)(unsafe.Pointer(C.malloc(0)))
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ctensorPtr2 := (*lib.Ctensor)(unsafe.Pointer(uintptr(unsafe.Pointer(ctensorPtr1)) + unsafe.Sizeof(ctensorPtr1)))
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var cparamsData []lib.Ctensor
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for _, t := range paramsData {
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cparamsData = append(cparamsData, t.ctensor)
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}
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var chasBiases int32 = 0
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if hasBiases {
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chasBiases = 1
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}
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var ctrain int32 = 0
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if train {
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ctrain = 1
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}
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var cbidirectional int32 = 0
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if bidirectional {
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cbidirectional = 1
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}
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var cbatchFirst int32 = 0
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if batchFirst {
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cbatchFirst = 1
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}
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lib.AtgGru(ctensorPtr1, ts.ctensor, hx.ctensor, cparamsData, len(paramsData), chasBiases, numLayers, dropout, ctrain, cbidirectional, cbatchFirst)
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err = TorchErr()
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if err != nil {
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return output, h, err
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}
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return newTensor(*ctensorPtr1), newTensor(*ctensorPtr2), nil
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}
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func (ts *Tensor) MustGru(hx *Tensor, paramsData []*Tensor, hasBiases bool, numLayers int64, dropout float64, train bool, bidirectional bool, batchFirst bool) (output, h *Tensor) {
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output, h, err := ts.Gru(hx, paramsData, hasBiases, numLayers, dropout, train, bidirectional, batchFirst)
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if err != nil {
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log.Fatal(err)
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}
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return output, h
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}
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func (ts *Tensor) TopK(k int64, dim int64, largest bool, sorted bool) (ts1, ts2 *Tensor, err error) {
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// NOTE: `lib.AtgTopk` will return 2 tensors in C memory. First tensor pointer
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// is given by ctensorPtr1
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ctensorPtr1 := (*lib.Ctensor)(unsafe.Pointer(C.malloc(0)))
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ctensorPtr2 := (*lib.Ctensor)(unsafe.Pointer(uintptr(unsafe.Pointer(ctensorPtr1)) + unsafe.Sizeof(ctensorPtr1)))
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var clargest int32 = 0
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if largest {
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clargest = 1
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}
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var csorted int32 = 0
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if sorted {
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csorted = 1
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}
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lib.AtgTopk(ctensorPtr1, ts.ctensor, k, dim, clargest, csorted)
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err = TorchErr()
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if err != nil {
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return ts1, ts2, err
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}
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return newTensor(*ctensorPtr1), newTensor(*ctensorPtr2), nil
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}
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func (ts *Tensor) MustTopK(k int64, dim int64, largest bool, sorted bool) (ts1, ts2 *Tensor) {
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ts1, ts2, err := ts.TopK(k, dim, largest, sorted)
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if err != nil {
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log.Fatal(err)
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}
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return ts1, ts2
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}
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// NOTE. `NLLLoss` is a version of `NllLoss` in tensor-generated
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// with default weight, reduction and ignoreIndex
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func (ts *Tensor) NLLLoss(target *Tensor, del bool) (retVal *Tensor, err error) {
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ptr := (*lib.Ctensor)(unsafe.Pointer(C.malloc(0)))
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if del {
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defer ts.MustDrop()
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}
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reduction := int64(1) // Mean of loss
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ignoreIndex := int64(-100)
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// defer C.free(unsafe.Pointer(ptr))
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lib.AtgNllLoss(ptr, ts.ctensor, target.ctensor, nil, reduction, ignoreIndex)
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if err = TorchErr(); err != nil {
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return retVal, err
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}
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retVal = newTensor(*ptr)
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return retVal, nil
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}
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func (ts *Tensor) MustNLLLoss(target *Tensor, del bool) (retVal *Tensor) {
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retVal, err := ts.NLLLoss(target, del)
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if err != nil {
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log.Fatal(err)
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}
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return retVal
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}
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// NOTE: the following 9 APIs are missing from `tensor-generated.go` with
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// pattern of **return tensor pointer**: `tensor *atg_FUNCTION_NAME()`.
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// The returning tensor pointer actually is the FIRST element of a vector
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// of C tensor pointers. Next pointer will be calculated from the first.
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// In C land, verifying a valid pointer is to check whether it points to **NULL**.
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//
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// tensor *atg_align_tensors(tensor *tensors_data, int tensors_len);
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// tensor *atg_broadcast_tensors(tensor *tensors_data, int tensors_len);
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// tensor *atg_chunk(tensor self, int64_t chunks, int64_t dim);
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// tensor *atg_meshgrid(tensor *tensors_data, int tensors_len);
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// tensor *atg_nonzero_numpy(tensor self);
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// tensor *atg_split(tensor self, int64_t split_size, int64_t dim);
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// tensor *atg_split_with_sizes(tensor self, int64_t *split_sizes_data, int split_sizes_len, int64_t dim);
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// tensor *atg_unbind(tensor self, int64_t dim);
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// tensor *atg_where(tensor condition);
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// tensor *atg_align_tensors(tensor *tensors_data, int tensors_len);
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func AlignTensors(tensors []*Tensor) (retVal []*Tensor, err error) {
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var ctensors []lib.Ctensor
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for _, t := range tensors {
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ctensors = append(ctensors, t.ctensor)
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}
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ctensorsPtr := lib.AtgAlignTensors(ctensors, len(ctensors))
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if err = TorchErr(); err != nil {
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return retVal, err
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}
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currentPtr := ctensorsPtr
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retVal = append(retVal, newTensor(*currentPtr))
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for {
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nextPtr := (*lib.Ctensor)(unsafe.Add(unsafe.Pointer(currentPtr), unsafe.Sizeof(currentPtr)))
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if *nextPtr == nil {
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break
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}
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retVal = append(retVal, newTensor(*nextPtr))
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currentPtr = nextPtr
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}
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return retVal, nil
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}
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func MustAlignTensors(tensors []*Tensor, del bool) (retVal []*Tensor) {
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if del {
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for _, t := range tensors {
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defer t.MustDrop()
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}
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}
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retVal, err := AlignTensors(tensors)
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if err != nil {
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log.Fatal(err)
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}
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return retVal
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}
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// tensor *atg_broadcast_tensors(tensor *tensors_data, int tensors_len);
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func BroadcastTensors(tensors []*Tensor) (retVal []*Tensor, err error) {
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var ctensors []lib.Ctensor
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for _, t := range tensors {
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ctensors = append(ctensors, t.ctensor)
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}
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ctensorsPtr := lib.AtgBroadcastTensors(ctensors, len(ctensors))
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if err = TorchErr(); err != nil {
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return retVal, err
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}
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currentPtr := ctensorsPtr
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retVal = append(retVal, newTensor(*currentPtr))
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for {
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nextPtr := (*lib.Ctensor)(unsafe.Add(unsafe.Pointer(currentPtr), unsafe.Sizeof(currentPtr)))
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if *nextPtr == nil {
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break
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}
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retVal = append(retVal, newTensor(*nextPtr))
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currentPtr = nextPtr
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}
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return retVal, nil
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}
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func MustBroadcastTensors(tensors []*Tensor, del bool) (retVal []*Tensor) {
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if del {
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for _, t := range tensors {
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defer t.MustDrop()
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}
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}
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retVal, err := BroadcastTensors(tensors)
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if err != nil {
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log.Fatal(err)
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}
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return retVal
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}
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// tensor *atg_chunk(tensor self, int64_t chunks, int64_t dim);
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func (ts *Tensor) Chunk(chunks int64, dim int64) (retVal []*Tensor, err error) {
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ctensorsPtr := lib.AtgChunk(ts.ctensor, chunks, dim)
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if err = TorchErr(); err != nil {
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return retVal, err
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}
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currentPtr := ctensorsPtr
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retVal = append(retVal, newTensor(*currentPtr))
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for {
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nextPtr := (*lib.Ctensor)(unsafe.Add(unsafe.Pointer(currentPtr), unsafe.Sizeof(currentPtr)))
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if *nextPtr == nil {
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break
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}
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retVal = append(retVal, newTensor(*nextPtr))
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currentPtr = nextPtr
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}
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return retVal, nil
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}
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func (ts *Tensor) MustChunk(chunks int64, dim int64, del bool) (retVal []*Tensor) {
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if del {
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defer ts.MustDrop()
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}
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retVal, err := ts.Chunk(chunks, dim)
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if err != nil {
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log.Fatal(err)
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}
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return retVal
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}
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// tensor *atg_meshgrid(tensor *tensors_data, int tensors_len);
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func Meshgrid(tensors []*Tensor) (retVal []*Tensor, err error) {
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var ctensors []lib.Ctensor
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for _, t := range tensors {
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ctensors = append(ctensors, t.ctensor)
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}
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ctensorsPtr := lib.AtgMeshgrid(ctensors, len(ctensors))
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if err = TorchErr(); err != nil {
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return retVal, err
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}
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currentPtr := ctensorsPtr
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retVal = append(retVal, newTensor(*currentPtr))
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for {
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nextPtr := (*lib.Ctensor)(unsafe.Add(unsafe.Pointer(currentPtr), unsafe.Sizeof(currentPtr)))
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if *nextPtr == nil {
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break
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}
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retVal = append(retVal, newTensor(*nextPtr))
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currentPtr = nextPtr
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}
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return retVal, nil
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}
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func MustMeshgrid(tensors []*Tensor) (retVal []*Tensor) {
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retVal, err := Meshgrid(tensors)
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if err != nil {
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log.Fatal(err)
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}
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return retVal
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}
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// tensor *atg_nonzero_numpy(tensor self);
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func (ts *Tensor) NonzeroNumpy() (retVal []*Tensor, err error) {
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ctensorsPtr := lib.AtgNonzeroNumpy(ts.ctensor)
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if err = TorchErr(); err != nil {
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return retVal, err
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}
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currentPtr := ctensorsPtr
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retVal = append(retVal, newTensor(*currentPtr))
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for {
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nextPtr := (*lib.Ctensor)(unsafe.Add(unsafe.Pointer(currentPtr), unsafe.Sizeof(currentPtr)))
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if *nextPtr == nil {
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break
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}
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retVal = append(retVal, newTensor(*nextPtr))
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currentPtr = nextPtr
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}
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return retVal, nil
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}
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func (ts *Tensor) MustNonzeroNumpy(del bool) (retVal []*Tensor) {
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if del {
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defer ts.MustDrop()
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}
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retVal, err := ts.NonzeroNumpy()
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if err != nil {
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log.Fatal(err)
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}
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return retVal
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}
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// Split splits tensor into chunks
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//
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// Parameters:
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// - splitSize – size of a single chunk
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// - dim – dimension along which to split the tensor.
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//
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// Ref. https://pytorch.org/docs/stable/generated/torch.split.html
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func (ts *Tensor) Split(splitSize, dim int64) (retVal []*Tensor, err error) {
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ctensorsPtr := lib.AtgSplit(ts.ctensor, splitSize, dim)
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if err = TorchErr(); err != nil {
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return retVal, err
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}
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// NOTE: ctensorsPtr is a c-pointer to a vector of tensors. The first
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// C tensor is the `ctensorsPtr` value. The next pointer will be
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// calculated from there. The vector of tensors will end if the calculated
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// pointer value is `null`.
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currentPtr := ctensorsPtr
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retVal = append(retVal, newTensor(*currentPtr))
|
||
for {
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nextPtr := (*lib.Ctensor)(unsafe.Add(unsafe.Pointer(currentPtr), unsafe.Sizeof(currentPtr)))
|
||
if *nextPtr == nil {
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break
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||
}
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||
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retVal = append(retVal, newTensor(*nextPtr))
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||
currentPtr = nextPtr
|
||
}
|
||
|
||
return retVal, nil
|
||
}
|
||
|
||
func (ts *Tensor) MustSplit(splitSize, dim int64, del bool) (retVal []*Tensor) {
|
||
if del {
|
||
defer ts.MustDrop()
|
||
}
|
||
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||
retVal, err := ts.Split(splitSize, dim)
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||
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
|
||
// }
|