249 lines
7.4 KiB
Go
249 lines
7.4 KiB
Go
package tensor
|
||
|
||
// #include "stdlib.h"
|
||
import "C"
|
||
|
||
import (
|
||
"fmt"
|
||
"log"
|
||
"unsafe"
|
||
|
||
// "github.com/sugarme/gotch"
|
||
lib "github.com/sugarme/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 Tensor{ctensor: *ctensorPtr1}, Tensor{ctensor: *ctensorPtr2}, Tensor{ctensor: *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 Tensor{ctensor: *ctensorPtr1}, Tensor{ctensor: *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 Tensor, 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 Tensor{ctensor: *ctensorPtr1}, Tensor{ctensor: *ctensorPtr2}, nil
|
||
}
|
||
|
||
func (ts Tensor) MustTopK(k int64, dim int64, largest bool, sorted bool) (ts1 Tensor, 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 = Tensor{ctensor: *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);
|
||
|
||
// Split splits tensor into chunks
|
||
//
|
||
// Parameters:
|
||
// - splitSize – size of a single chunk or list 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) 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, Tensor{ctensor: *currentPtr})
|
||
for {
|
||
// calculate the next pointer value
|
||
nextPtr := (*lib.Ctensor)(unsafe.Pointer(uintptr(unsafe.Pointer(currentPtr)) + unsafe.Sizeof(currentPtr)))
|
||
if *nextPtr == nil {
|
||
break
|
||
}
|
||
|
||
retVal = append(retVal, Tensor{ctensor: *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
|
||
}
|