ollama/ml/backend.go
Michael Yang 58245413f4
next ollama runner (#7913)
feat: add new Ollama engine using ggml through cgo

This change introduces a new way to run pretrained models. It introduces 3 high level interfaces and a bunch of smaller helper interfaces to facilitate this.

- `model.Model` defines the interface for a model architecture. Models such as `llama` and `mllama`, which are provided as examples, can implement the model's forward propagation in the `Forward` method. This method will be called to generate completions. This interface can be found in `model/model.go`
- `ml.Backend` defines the interface for a backend tensor library, in this case `ggml`. Among other things, a Backend is responsible for loading a pretrained model into hardware (GPU, CPU, etc) and providing an interface for Models to access loaded tensors. This interface can be found in `ml/backend.go`
- `ml.Tensor` defines the interface for a tensor and tensor operations

This is the first implementation of the new engine. Follow up PRs will implement more features:

- non-greedy sampling (#8410)
- integration with Ollama and KV caching (#8301)
- more model support (#9080) with more coming soon

Co-authored-by: Bruce MacDonald <brucewmacdonald@gmail.com>
2025-02-13 16:31:21 -08:00

196 lines
4.3 KiB
Go

package ml
import (
"bytes"
"encoding/binary"
"fmt"
"os"
"strconv"
"strings"
)
type Config interface {
Architecture() string
String(string, ...string) string
Uint(string, ...uint32) uint32
Float(string, ...float32) float32
Strings(string, ...[]string) []string
Uints(string, ...[]uint32) []uint32
}
type Backend interface {
Config() Config
Get(name string) Tensor
NewContext() Context
}
var backends = make(map[string]func(*os.File) (Backend, error))
func RegisterBackend(name string, f func(*os.File) (Backend, error)) {
if _, ok := backends[name]; ok {
panic("backend: backend already registered")
}
backends[name] = f
}
func NewBackend(f *os.File) (Backend, error) {
if backend, ok := backends["ggml"]; ok {
return backend(f)
}
return nil, fmt.Errorf("unsupported backend")
}
type Context interface {
Zeros(dtype DType, shape ...int) Tensor
FromFloatSlice(s []float32, shape ...int) (Tensor, error)
FromIntSlice(s []int32, shape ...int) (Tensor, error)
Forward(Tensor)
Compute(Tensor) Tensor
Close() error
}
type Tensor interface {
Dim(n int) int64
Stride(n int) int64
Shape() []int64
DType() DType
Bytes() []byte
Floats() []float32
Add(ctx Context, t2 Tensor) Tensor
Mul(ctx Context, t2 Tensor) Tensor
Mulmat(ctx Context, t2 Tensor) Tensor
Softmax(ctx Context) Tensor
LayerNorm(ctx Context, weight, bias Tensor, eps float32) Tensor
RMSNorm(ctx Context, weight Tensor, eps float32) Tensor
Scale(ctx Context, s float64) Tensor
Conv2D(ctx Context, weight Tensor, s0, s1, p0, p1, d0, d1 int) Tensor
RoPE(ctx Context, positionIDs, ropeFactors Tensor, dim uint32, base, scale float32) Tensor
Tanh(ctx Context) Tensor
GELU(ctx Context) Tensor
SILU(ctx Context) Tensor
Reshape(ctx Context, shape ...int64) Tensor
View(ctx Context, offset int, shape ...int) Tensor
Permute(ctx Context, shape ...int) Tensor
Contiguous(ctx Context) Tensor
Pad(ctx Context, shape ...int64) Tensor
Unpad(ctx Context, shape ...int64) Tensor
Stack(ctx Context, dim int, s ...Tensor) Tensor
Concat(ctx Context, t2 Tensor, dim int) Tensor
Rows(ctx Context, t2 Tensor) Tensor
Copy(ctx Context, t2 Tensor) Tensor
}
type number interface {
~int | ~int8 | ~int16 | ~int32 | ~int64 |
~uint | ~uint8 | ~uint16 | ~uint32 | ~uint64 |
~float32 | ~float64 |
~complex64 | ~complex128
}
func mul[T number](s ...T) T {
p := T(1)
for _, v := range s {
p *= v
}
return p
}
type DumpOptions struct {
// Items is the number of elements to print at the beginning and end of each dimension.
Items int64
// Precision is the number of decimal places to print. Applies to float32 and float64.
Precision int
}
func Dump(t Tensor, opts ...DumpOptions) string {
if len(opts) < 1 {
opts = append(opts, DumpOptions{
Items: 3,
Precision: 4,
})
}
switch t.DType() {
case DTypeF32:
return dump[[]float32](t, opts[0].Items, func(f float32) string {
return strconv.FormatFloat(float64(f), 'f', opts[0].Precision, 32)
})
case DTypeI32:
return dump[[]int32](t, opts[0].Items, func(i int32) string {
return strconv.FormatInt(int64(i), 10)
})
default:
return "<unsupported>"
}
}
func dump[S ~[]E, E number](t Tensor, items int64, fn func(E) string) string {
bts := t.Bytes()
if bts == nil {
return "<nil>"
}
s := make(S, mul(t.Shape()...))
if err := binary.Read(bytes.NewBuffer(t.Bytes()), binary.LittleEndian, &s); err != nil {
panic(err)
}
shape := t.Shape()
var sb strings.Builder
var f func([]int64, int64)
f = func(dims []int64, stride int64) {
prefix := strings.Repeat(" ", len(shape)-len(dims)+1)
fmt.Fprint(&sb, "[")
defer func() { fmt.Fprint(&sb, "]") }()
for i := int64(0); i < dims[0]; i++ {
if i >= items && i < dims[0]-items {
fmt.Fprint(&sb, "..., ")
// skip to next printable element
skip := dims[0] - 2*items
if len(dims) > 1 {
stride += mul(append(dims[1:], skip)...)
fmt.Fprint(&sb, strings.Repeat("\n", len(dims)-1), prefix)
}
i += skip - 1
} else if len(dims) > 1 {
f(dims[1:], stride)
stride += mul(dims[1:]...)
if i < dims[0]-1 {
fmt.Fprint(&sb, ",", strings.Repeat("\n", len(dims)-1), prefix)
}
} else {
fmt.Fprint(&sb, fn(s[stride+i]))
if i < dims[0]-1 {
fmt.Fprint(&sb, ", ")
}
}
}
}
f(shape, 0)
return sb.String()
}
type DType int
const (
DTypeF32 DType = iota
DTypeI32
DTypeOther
)