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tensor.go
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tensor.go
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// Licensed to the Apache Software Foundation (ASF) under one
// or more contributor license agreements. See the NOTICE file
// distributed with this work for additional information
// regarding copyright ownership. The ASF licenses this file
// to you under the Apache License, Version 2.0 (the
// "License"); you may not use this file except in compliance
// with the License. You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
// Package tensor provides types that implement n-dimensional arrays.
package tensor
import (
"fmt"
"sync/atomic"
"github.com/apache/arrow/go/arrow"
"github.com/apache/arrow/go/arrow/array"
"github.com/apache/arrow/go/arrow/internal/debug"
)
// Interface represents an n-dimensional array of numerical data.
type Interface interface {
// Retain increases the reference count by 1.
// Retain may be called simultaneously from multiple goroutines.
Retain()
// Release decreases the reference count by 1.
// Release may be called simultaneously from multiple goroutines.
// When the reference count goes to zero, the memory is freed.
Release()
// Len returns the number of elements in the tensor.
Len() int
// Shape returns the size - in each dimension - of the tensor.
Shape() []int64
// Strides returns the number of bytes to step in each dimension when traversing the tensor.
Strides() []int64
// NumDims returns the number of dimensions of the tensor.
NumDims() int
// DimName returns the name of the i-th dimension.
DimName(i int) string
// DimNames returns the names for all dimensions
DimNames() []string
DataType() arrow.DataType
Data() *array.Data
// IsMutable returns whether the underlying data buffer is mutable.
IsMutable() bool
IsContiguous() bool
IsRowMajor() bool
IsColMajor() bool
}
type tensorBase struct {
refCount int64
dtype arrow.DataType
bw int64 // bytes width
data *array.Data
shape []int64
strides []int64
names []string
}
// Retain increases the reference count by 1.
// Retain may be called simultaneously from multiple goroutines.
func (tb *tensorBase) Retain() {
atomic.AddInt64(&tb.refCount, 1)
}
// Release decreases the reference count by 1.
// Release may be called simultaneously from multiple goroutines.
// When the reference count goes to zero, the memory is freed.
func (tb *tensorBase) Release() {
debug.Assert(atomic.LoadInt64(&tb.refCount) > 0, "too many releases")
if atomic.AddInt64(&tb.refCount, -1) == 0 {
tb.data.Release()
tb.data = nil
}
}
func (tb *tensorBase) Len() int {
o := int64(1)
for _, v := range tb.shape {
o *= v
}
return int(o)
}
func (tb *tensorBase) Shape() []int64 { return tb.shape }
func (tb *tensorBase) Strides() []int64 { return tb.strides }
func (tb *tensorBase) NumDims() int { return len(tb.shape) }
func (tb *tensorBase) DimName(i int) string { return tb.names[i] }
func (tb *tensorBase) DataType() arrow.DataType { return tb.dtype }
func (tb *tensorBase) Data() *array.Data { return tb.data }
func (tb *tensorBase) DimNames() []string { return tb.names }
// IsMutable returns whether the underlying data buffer is mutable.
func (tb *tensorBase) IsMutable() bool { return false } // FIXME(sbinet): implement it at the array.Data level
func (tb *tensorBase) IsContiguous() bool {
return tb.IsRowMajor() || tb.IsColMajor()
}
func (tb *tensorBase) IsRowMajor() bool {
strides := rowMajorStrides(tb.dtype, tb.shape)
return equalInt64s(strides, tb.strides)
}
func (tb *tensorBase) IsColMajor() bool {
strides := colMajorStrides(tb.dtype, tb.shape)
return equalInt64s(strides, tb.strides)
}
func (tb *tensorBase) offset(index []int64) int64 {
var offset int64
for i, v := range index {
offset += v * tb.strides[i]
}
return offset / tb.bw
}
// New returns a new n-dim array from the provided backing data and the shape and strides.
// If strides is nil, row-major strides will be inferred.
// If names is nil, a slice of empty strings will be created.
//
// New panics if the backing data is not a numerical type.
func New(data *array.Data, shape, strides []int64, names []string) Interface {
dt := data.DataType()
switch dt.ID() {
case arrow.INT8:
return NewInt8(data, shape, strides, names)
case arrow.INT16:
return NewInt16(data, shape, strides, names)
case arrow.INT32:
return NewInt32(data, shape, strides, names)
case arrow.INT64:
return NewInt64(data, shape, strides, names)
case arrow.UINT8:
return NewUint8(data, shape, strides, names)
case arrow.UINT16:
return NewUint16(data, shape, strides, names)
case arrow.UINT32:
return NewUint32(data, shape, strides, names)
case arrow.UINT64:
return NewUint64(data, shape, strides, names)
case arrow.FLOAT32:
return NewFloat32(data, shape, strides, names)
case arrow.FLOAT64:
return NewFloat64(data, shape, strides, names)
case arrow.DATE32:
return NewDate32(data, shape, strides, names)
case arrow.DATE64:
return NewDate64(data, shape, strides, names)
default:
panic(fmt.Errorf("arrow/tensor: invalid data type %s", dt.Name()))
}
}
func newTensor(dtype arrow.DataType, data *array.Data, shape, strides []int64, names []string) *tensorBase {
tb := tensorBase{
refCount: 1,
dtype: dtype,
bw: int64(dtype.(arrow.FixedWidthDataType).BitWidth()) / 8,
data: data,
shape: shape,
strides: strides,
names: names,
}
tb.data.Retain()
if len(tb.shape) > 0 && len(tb.strides) == 0 {
tb.strides = rowMajorStrides(dtype, shape)
}
return &tb
}
func rowMajorStrides(dtype arrow.DataType, shape []int64) []int64 {
dt := dtype.(arrow.FixedWidthDataType)
rem := int64(dt.BitWidth() / 8)
for _, v := range shape {
rem *= v
}
if rem == 0 {
strides := make([]int64, len(shape))
rem := int64(dt.BitWidth() / 8)
for i := range strides {
strides[i] = rem
}
return strides
}
var strides []int64
for _, v := range shape {
rem /= v
strides = append(strides, rem)
}
return strides
}
func colMajorStrides(dtype arrow.DataType, shape []int64) []int64 {
dt := dtype.(arrow.FixedWidthDataType)
total := int64(dt.BitWidth() / 8)
for _, v := range shape {
if v == 0 {
strides := make([]int64, len(shape))
for i := range strides {
strides[i] = total
}
return strides
}
}
var strides []int64
for _, v := range shape {
strides = append(strides, total)
total *= v
}
return strides
}
func equalInt64s(a, b []int64) bool {
if len(a) != len(b) {
return false
}
for i := range a {
if a[i] != b[i] {
return false
}
}
return true
}