Updated tensor.OfDataSize to write directly binary data; Updated ReadNpy and ReadNpz

example/convert-model/main.go

@@ -8,9 +8,8 @@ import (
 )
 
 func main() {
-	// filepath := "../../data/convert-model/bert/model.npz"
-	// filepath := "/home/sugarme/projects/pytorch-pretrained/bert/model.npz"
-	filepath := "/home/sugarme/rustbert/bert/model.npz"
+	// NOTE. Python script to save model to .npz can be found at https://github.com/sugarme/pytorch-pretrained/bert/bert-base-uncased-to-npz.py
+	filepath := "../../data/convert-model/bert/model.npz"
 
 	namedTensors, err := ts.ReadNpz(filepath)
 	if err != nil {
@@ -18,21 +17,9 @@ func main() {
 	}
 
 	fmt.Printf("Num of named tensor: %v\n", len(namedTensors))
-	/*
-	 *   for _, nt := range namedTensors {
-	 *     // fmt.Printf("%q\n", nt.Name)
-	 *     if nt.Name == "bert.encoder.layer.1.attention.output.LayerNorm.weight" {
-	 *       fmt.Printf("%0.3f", nt.Tensor)
-	 *     }
-	 *   }
-	 *  */
-
-	// fmt.Printf("%v", namedTensors[70].Tensor)
-
 	outputFile := "/home/sugarme/projects/transformer/data/bert/model.gt"
 	err = ts.SaveMultiNew(namedTensors, outputFile)
 	if err != nil {
 		log.Fatal(err)
 	}
-
 }

tensor/npy.go

@@ -15,7 +15,6 @@ import (
 )
 
 const (
-	// NpyMagicString string = `\x93NUMPY`
 	NpyMagicString string = "\x93NUMPY"
 	NpySuffix      string = ".npy"
 )
@@ -103,7 +102,7 @@ func (h *NpyHeader) ToString() (string, error) {
 
 	var descr string
 	switch h.descr.Kind().String() {
-	// case "float32":
+	// case "float16": // NOTE. No float16 in Go primary types. TODO. implement
 	// descr = "f2"
 	case "float32":
 		descr = "f4"
@@ -354,10 +353,6 @@ func ReadNpz(filePath string) ([]NamedTensor, error) {
 			return nil, err
 		}
 
-		if name == "bert.encoder.layer.0.attention.output.dense.weight" {
-			fmt.Printf("%4.2f", tensor)
-		}
-
 		namedTensors = append(namedTensors, NamedTensor{name, tensor})
 
 		// explicitly close before next one

tensor/tensor.go

@@ -196,7 +196,7 @@ func OfSlice(data interface{}) (*Tensor, error) {
 	return &Tensor{ctensor}, nil
 }
 
-// OfDataSize creates Tensor from input byte data and specidied shape and dtype.
+// OfDataSize creates Tensor from input byte data, shape and dtype.
 func OfDataSize(data []byte, shape []int64, dtype gotch.DType) (*Tensor, error) {
 
 	elementNum := ElementCount(shape)
@@ -215,33 +215,7 @@ func OfDataSize(data []byte, shape []int64, dtype gotch.DType) (*Tensor, error)
 	dataPtr, buff := CMalloc(nbytes)
 	defer C.free(unsafe.Pointer(dataPtr))
 
-	typ, err := gotch.ToGoType(dtype)
-	if err != nil {
-		return nil, err
-	}
-
-	var v reflect.Value
-	switch typ.Name() {
-	case "float", "float32":
-		v = reflect.ValueOf(float32(0.1))
-	case "float64":
-		v = reflect.ValueOf(float64(0.1))
-	case "int", "int32":
-		v = reflect.ValueOf(int(1))
-	case "int64":
-		v = reflect.ValueOf(int64(1))
-	case "int8":
-		v = reflect.ValueOf(int8(1))
-	case "uint8":
-		v = reflect.ValueOf(uint8(1))
-	case "bool":
-		v = reflect.ValueOf(false)
-	default:
-		err := fmt.Errorf("unsupported dtype: %v\n", dtype)
-		return nil, err
-	}
-
-	if err = EncodeTensor(buff, v, shape); err != nil {
+	if err := binary.Write(buff, nativeEndian, data); err != nil {
 		return nil, err
 	}
 
@@ -255,8 +229,6 @@ func OfDataSize(data []byte, shape []int64, dtype gotch.DType) (*Tensor, error)
 		return nil, err
 	}
 
-	buff.Reset()
-
 	return &Tensor{ctensor}, nil
 }
 
@@ -836,6 +808,7 @@ type NamedTensor struct {
 // SaveMulti saves some named tensors to a file
 //
 // The file format is the same as the one used by the PyTorch C++ API.
+// NOTE. This method is depreciated and will be replaced with `SaveMultiNew`
 func SaveMulti(namedTensors []NamedTensor, path string) error {
 	var ctensors []lib.Ctensor
 	var names []string
@@ -854,6 +827,8 @@ func SaveMulti(namedTensors []NamedTensor, path string) error {
 }
 
 // MustSaveMulti saves some named tensors to a file. It will panic if error
+//
+// NOTE. This method is depreciated and will be replaced with `MustSaveMultiNew`
 func MustSaveMulti(namedTensors []NamedTensor, path string) {
 	err := SaveMulti(namedTensors, path)
 	if err != nil {