gotch/libtch

NOTES ON WRITING WRAPPER FUNCTIONS

Function Input Arguments

tensor -> t *C_tensor

void at_print(tensor);

func AtPrint(t *C_tensor) {
	c_tensor := (C.tensor)((*t).private)
	C.at_print(c_tensor)
}

C pointer e.g int64_t * -> ptr unsafe.Pointer

In function body, cPtr := (*C.long)(ptr)

void at_shape(tensor, int64_t *);

func AtShape(t *C_tensor, ptr unsafe.Pointer) {
	c_tensor := (C.tensor)((*t).private)
	c_ptr := (*C.long)(ptr)
	C.at_shape(c_tensor, c_ptr)
}

C types e.g size_t ndims -> equivalent Go types ndims uint

In function body, c_ndims := *(*C.size_t)(unsafe.Pointer(&ndims))

tensor at_tensor_of_data(void *vs, int64_t *dims, size_t ndims, size_t element_size_in_bytes, int type);

func AtTensorOfData(vs unsafe.Pointer, dims []int64, ndims uint, elt_size_in_bytes uint, kind int) *C_tensor {

    // 1. Unsafe pointer
	c_dims := (*C.int64_t)(unsafe.Pointer(&dims[0]))
	c_ndims := *(*C.size_t)(unsafe.Pointer(&ndims))
	c_elt_size_in_bytes := *(*C.size_t)(unsafe.Pointer(&elt_size_in_bytes))
	c_kind := *(*C.int)(unsafe.Pointer(&kind))

    // 2. Call C function
	t := C.at_tensor_of_data(vs, c_dims, c_ndims, c_elt_size_in_bytes, c_kind)

    // 3. Form return value
	return &C_tensor{private: unsafe.Pointer(t)}
}

Function Return

void *CFUNC(...)

void *at_data_ptr(tensor);

func AtDataPtr(t *C_tensor) unsafe.Pointer {
	c_tensor := (C.tensor)((*t).private)
	return C.at_data_ptr(c_tensor)
}

tensor -> *C_tensor

then in the return of function body

    // Call C function
    t := C.FUNCTION_TO_CALL(...)
    // Return
	return &C_tensor{private: unsafe.Pointer(t)}

tensor *CFUNC(...)

The 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_split(tensor self, int64_t split_size, int64_t dim);

// Wrapper
func AtgSplit(self Ctensor, splitSize int64, dim int64) *Ctensor {

	csplitSize := *(*C.int64_t)(unsafe.Pointer(&splitSize))
	cdim := *(*C.int64_t)(unsafe.Pointer(&dim))

	return C.atg_split(self, csplitSize, cdim)
}

// API

// 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
}

C types e.g. C_ulong -> Go equivalent types uint64

then in the return of function body

	c_result := C.FUNCTION_CALL(...)
	return *(*uint64)(unsafe.Pointer(&c_result))

C type pointers e.g. char *FUNCTION() --> *C.char

then just return the C function call.

char *get_and_reset_last_err(); // thread-local

func GetAndResetLastErr() *C.char{
   return C.get_and_reset_last_err()
}

Multiple Tensors Created In C Land Memory

• When there are multiple Ctensor created in C land memory. A first Ctensor pointer will be created and given to the C function. It will create consecutive Ctensor(s) based on this pointer. The next pointer(s) can be calulated based on this pointer and its size.

• Example: lstm function

  • C function

      void atg_lstm(tensor *, tensor input, tensor *hx_data, int hx_len, tensor *params_data, int params_len, int has_biases, int64_t num_layers, double dropout, int train, int bidirectional, int batch_first);
  

  • Go wrapper function

      func AtgLstm(ptr *Ctensor, input Ctensor, hxData []Ctensor, hxLen int, paramsData []Ctensor, paramsLen int, hasBiases int, numLayers int64, dropout float64, train int, bidirectional int, batchFirst int) {
  
          chxDataPtr := (*Ctensor)(unsafe.Pointer(&hxData[0]))
          chxLen := *(*C.int)(unsafe.Pointer(&hxLen))
          cparamsDataPtr := (*Ctensor)(unsafe.Pointer(&paramsData[0]))
          cparamsLen := *(*C.int)(unsafe.Pointer(&paramsLen))
          chasBiases := *(*C.int)(unsafe.Pointer(&hasBiases))
          cnumLayers := *(*C.int64_t)(unsafe.Pointer(&numLayers))
          cdropout := *(*C.double)(unsafe.Pointer(&dropout))
          ctrain := *(*C.int)(unsafe.Pointer(&train))
          cbidirectional := *(*C.int)(unsafe.Pointer(&bidirectional))
          cbatchFirst := *(*C.int)(unsafe.Pointer(&batchFirst))
  
          C.atg_lstm(ptr, input, chxDataPtr, chxLen, cparamsDataPtr, cparamsLen, chasBiases, cnumLayers, cdropout, ctrain, cbidirectional, cbatchFirst)
      }
  

  • Go API function

  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)
      }
  
      chasBiases := 0
      if hasBiases {
          chasBiases = 1
      }
      ctrain := 0
      if train {
          ctrain = 1
      }
      cbidirectional := 0
      if bidirectional {
          cbidirectional = 1
      }
      cbatchFirst := 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
  
  }