backend.go 5.62 KB
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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
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	Bool(string, ...bool) bool
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	Strings(string, ...[]string) []string
	Uints(string, ...[]uint32) []uint32
}

type Backend interface {
	Config() Config
	Get(name string) Tensor
	NewContext() Context
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	SystemInfo() string
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}

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// BackendParams controls how the backend loads and executes models
type BackendParams struct {
	// NumThreads sets the number of threads to use if running on the CPU
	NumThreads int
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	// MainGPU is the index of the primary GPU to use
	MainGPU int

	// NumGPULayers is the number of layers to offload to GPUs
	NumGPULayers int

	// TensorSplit is the fraction of the model to offload to each GPU
	TensorSplit []float32
}

var backends = make(map[string]func(*os.File, BackendParams) (Backend, error))

func RegisterBackend(name string, f func(*os.File, BackendParams) (Backend, error)) {
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	if _, ok := backends[name]; ok {
		panic("backend: backend already registered")
	}

	backends[name] = f
}

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func NewBackend(f *os.File, params BackendParams) (Backend, error) {
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	if backend, ok := backends["ggml"]; ok {
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		return backend(f, params)
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	}

	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)
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	Compute(...Tensor)
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	MaxTensors() int
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	Close()
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}

type Tensor interface {
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	Dim(n int) int
	Stride(n int) int
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	Shape() []int
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	DType() DType

	Bytes() []byte
	Floats() []float32

	Add(ctx Context, t2 Tensor) Tensor
	Mul(ctx Context, t2 Tensor) Tensor
	Mulmat(ctx Context, t2 Tensor) Tensor
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	MulmatFullPrec(ctx Context, t2 Tensor) Tensor
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	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

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	Reshape(ctx Context, shape ...int) Tensor
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	View(ctx Context, offset int, shape ...int) Tensor
	Permute(ctx Context, shape ...int) Tensor
	Contiguous(ctx Context) Tensor

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	Pad(ctx Context, shape ...int) Tensor
	Unpad(ctx Context, shape ...int) Tensor
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	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
}

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// ScaledDotProductAttention implements a fused attention
// operation equivalent to following code on a tensor named
// query:
//
// kq := key.MulmatFullPrec(ctx, query)
//
// kq = kq.Scale(ctx, scale)
//
//	if mask != nil {
//		kq = kq.Add(ctx, mask)
//	}
//
// kq = kq.Softmax(ctx)
//
// kqv := value.Mulmat(ctx, kq)
// return kqv.Permute(ctx, 0, 2, 1, 3).Contiguous(ctx)
type ScaledDotProductAttention interface {
	ScaledDotProductAttention(ctx Context, key, value, mask Tensor, scale float64) Tensor
}

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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.
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	Items int
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	// Precision is the number of decimal places to print. Applies to float32 and float64.
	Precision int
}

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func Dump(ctx Context, t Tensor, opts ...DumpOptions) string {
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	if len(opts) < 1 {
		opts = append(opts, DumpOptions{
			Items:     3,
			Precision: 4,
		})
	}

	switch t.DType() {
	case DTypeF32:
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		return dump[[]float32](ctx, t, opts[0].Items, func(f float32) string {
			return strconv.FormatFloat(float64(f), 'f', opts[0].Precision, 32)
		})
	case DTypeF16:
		f32 := ctx.Zeros(DTypeF32, t.Shape()...)
		f32 = t.Copy(ctx, f32)
		return dump[[]float32](ctx, f32, opts[0].Items, func(f float32) string {
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			return strconv.FormatFloat(float64(f), 'f', opts[0].Precision, 32)
		})
	case DTypeI32:
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		return dump[[]int32](ctx, t, opts[0].Items, func(i int32) string {
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			return strconv.FormatInt(int64(i), 10)
		})
	default:
		return "<unsupported>"
	}
}

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func dump[S ~[]E, E number](ctx Context, t Tensor, items int, fn func(E) string) string {
	if t.Bytes() == nil {
		ctx.Forward(t)
		ctx.Compute(t)
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	}

	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
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	var f func([]int, int)
	f = func(dims []int, stride int) {
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		prefix := strings.Repeat(" ", len(shape)-len(dims)+1)
		fmt.Fprint(&sb, "[")
		defer func() { fmt.Fprint(&sb, "]") }()
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		for i := 0; i < dims[0]; i++ {
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			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 (
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	DTypeOther DType = iota
	DTypeF32
	DTypeF16
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	DTypeI32
)