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ggml-backend: Store parent backend as part of tensor

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It can be important for a tensor to know what backend it came from -
for example, to know if flash attention is enabled.
This commit is contained in:
Jesse Gross 2025-02-27 14:52:39 -08:00 committed by Jesse Gross
parent 854a9195f3
commit 55e5776c44
1 changed files with 35 additions and 7 deletions
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42
ml/backend/ggml/ggml.go
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@ -219,7 +219,7 @@ func (b *Backend) Get(name string) ml.Tensor {
for _, c := range append(b.gpus, b.cpus...) {
if t := C.ggml_get_tensor(c.ctx, cname); t != nil {
return &Tensor{t: t}
return &Tensor{b: b, t: t}
}
}
@ -330,7 +330,7 @@ func (c Context) Zeros(dtype ml.DType, shape ...int) ml.Tensor {
b := C.ggml_backend_alloc_buffer(c.backend, C.ggml_nbytes(t))
C.ggml_backend_tensor_alloc(b, t, C.ggml_backend_buffer_get_base(b))
C.ggml_set_zero(t)
return &Tensor{t: t}
return &Tensor{b: c.b, t: t}
}
func fromSlice[S ~[]E, E float32 | int32](ctx Context, s S, shape []int, dtype uint32) (ml.Tensor, error) {
@ -339,7 +339,7 @@ func fromSlice[S ~[]E, E float32 | int32](ctx Context, s S, shape []int, dtype u
if n == 0 {
var shape C.int64_t = 0
t := C.ggml_new_tensor(ctx.ctx, dtype, 1, &shape)
return &Tensor{t: t}, nil
return &Tensor{b: ctx.b, t: t}, nil
}
for _, v := range shape {
@ -354,7 +354,7 @@ func fromSlice[S ~[]E, E float32 | int32](ctx Context, s S, shape []int, dtype u
b := C.ggml_backend_alloc_buffer(ctx.backend, C.ggml_nbytes(t))
C.ggml_backend_tensor_alloc(b, t, C.ggml_backend_buffer_get_base(b))
C.ggml_backend_tensor_set(t, unsafe.Pointer(&s[0]), 0, C.ggml_nbytes(t))
return &Tensor{t: t}, nil
return &Tensor{b: ctx.b, t: t}, nil
}
func (c Context) FromFloatSlice(s []float32, shape ...int) (ml.Tensor, error) {
@ -372,6 +372,7 @@ func (c *Context) Close() {
}
type Tensor struct {
b *Backend
t *C.struct_ggml_tensor
sync func()
}
@ -438,6 +439,7 @@ func (t *Tensor) DType() ml.DType {
func (t *Tensor) Add(ctx ml.Context, t2 ml.Tensor) ml.Tensor {
return &Tensor{
b: t.b,
t: C.ggml_add(ctx.(*Context).ctx, t.t, t2.(*Tensor).t),
}
}
@ -452,24 +454,28 @@ func (t *Tensor) Stack(ctx ml.Context, dim int, s ...ml.Tensor) ml.Tensor {
func (t *Tensor) Concat(ctx ml.Context, t2 ml.Tensor, dim int) ml.Tensor {
return &Tensor{
b: t.b,
t: C.ggml_concat(ctx.(*Context).ctx, t.t, t2.(*Tensor).t, C.int(dim)),
}
}
func (t *Tensor) Contiguous(ctx ml.Context) ml.Tensor {
return &Tensor{
b: t.b,
t: C.ggml_cont(ctx.(*Context).ctx, t.t),
}
}
func (t *Tensor) Mul(ctx ml.Context, t2 ml.Tensor) ml.Tensor {
return &Tensor{
b: t.b,
t: C.ggml_mul(ctx.(*Context).ctx, t.t, t2.(*Tensor).t),
}
}
func (t *Tensor) Mulmat(ctx ml.Context, t2 ml.Tensor) ml.Tensor {
return &Tensor{
b: t.b,
t: C.ggml_mul_mat(ctx.(*Context).ctx, t.t, t2.(*Tensor).t),
}
}
@ -479,12 +485,13 @@ func (t *Tensor) MulmatFullPrec(ctx ml.Context, t2 ml.Tensor) ml.Tensor {
C.ggml_mul_mat_set_prec(mul, C.GGML_PREC_F32)
return &Tensor{
b: t.b,
t: mul,
}
}
func (t *Tensor) LayerNorm(ctx ml.Context, w, b ml.Tensor, eps float32) ml.Tensor {
tt := (&Tensor{t: C.ggml_norm(ctx.(*Context).ctx, t.t, C.float(eps))}).Mul(ctx, w)
tt := (&Tensor{b: t.b, t: C.ggml_norm(ctx.(*Context).ctx, t.t, C.float(eps))}).Mul(ctx, w)
if b != nil {
tt = tt.Add(ctx, b)
}
@ -493,7 +500,7 @@ func (t *Tensor) LayerNorm(ctx ml.Context, w, b ml.Tensor, eps float32) ml.Tenso
}
func (t *Tensor) RMSNorm(ctx ml.Context, w ml.Tensor, eps float32) ml.Tensor {
return (&Tensor{t: C.ggml_rms_norm(ctx.(*Context).ctx, t.t, C.float(eps))}).Mul(ctx, w)
return (&Tensor{b: t.b, t: C.ggml_rms_norm(ctx.(*Context).ctx, t.t, C.float(eps))}).Mul(ctx, w)
}
func (t *Tensor) Pad(ctx ml.Context, shape ...int) ml.Tensor {
@ -502,6 +509,7 @@ func (t *Tensor) Pad(ctx ml.Context, shape ...int) ml.Tensor {
}
return &Tensor{
b: t.b,
t: C.ggml_pad(ctx.(*Context).ctx, t.t, C.int(shape[0]), C.int(shape[1]), C.int(shape[2]), C.int(shape[3])),
}
}
@ -512,18 +520,21 @@ func (t *Tensor) Permute(ctx ml.Context, shape ...int) ml.Tensor {
}
return &Tensor{
b: t.b,
t: C.ggml_permute(ctx.(*Context).ctx, t.t, C.int(shape[0]), C.int(shape[1]), C.int(shape[2]), C.int(shape[3])),
}
}
func (t *Tensor) Rows(ctx ml.Context, t2 ml.Tensor) ml.Tensor {
return &Tensor{
b: t.b,
t: C.ggml_get_rows(ctx.(*Context).ctx, t.t, t2.(*Tensor).t),
}
}
func (t *Tensor) Copy(ctx ml.Context, t2 ml.Tensor) ml.Tensor {
return &Tensor{
b: t.b,
t: C.ggml_cpy(ctx.(*Context).ctx, t.t, t2.(*Tensor).t),
}
}
@ -532,18 +543,22 @@ func (t *Tensor) Reshape(ctx ml.Context, shape ...int) ml.Tensor {
switch len(shape) {
case 1:
return &Tensor{
b: t.b,
t: C.ggml_reshape_1d(ctx.(*Context).ctx, t.t, C.int64_t(shape[0])),
}
case 2:
return &Tensor{
b: t.b,
t: C.ggml_reshape_2d(ctx.(*Context).ctx, t.t, C.int64_t(shape[0]), C.int64_t(shape[1])),
}
case 3:
return &Tensor{
b: t.b,
t: C.ggml_reshape_3d(ctx.(*Context).ctx, t.t, C.int64_t(shape[0]), C.int64_t(shape[1]), C.int64_t(shape[2])),
}
case 4:
return &Tensor{
b: t.b,
t: C.ggml_reshape_4d(ctx.(*Context).ctx, t.t, C.int64_t(shape[0]), C.int64_t(shape[1]), C.int64_t(shape[2]), C.int64_t(shape[3])),
}
default:
@ -553,18 +568,21 @@ func (t *Tensor) Reshape(ctx ml.Context, shape ...int) ml.Tensor {
func (t *Tensor) Scale(ctx ml.Context, s float64) ml.Tensor {
return &Tensor{
b: t.b,
t: C.ggml_scale(ctx.(*Context).ctx, t.t, (C.float)(s)),
}
}
func (t *Tensor) Softmax(ctx ml.Context) ml.Tensor {
return &Tensor{
b: t.b,
t: C.ggml_soft_max(ctx.(*Context).ctx, t.t),
}
}
func (t *Tensor) Tanh(ctx ml.Context) ml.Tensor {
return &Tensor{
b: t.b,
t: C.ggml_tanh_inplace(ctx.(*Context).ctx, t.t),
}
}
@ -575,6 +593,7 @@ func (t *Tensor) Unpad(ctx ml.Context, shape ...int) ml.Tensor {
}
return &Tensor{
b: t.b,
t: C.ggml_unpad(ctx.(*Context).ctx, t.t, C.int(shape[0]), C.int(shape[1]), C.int(shape[2]), C.int(shape[3])),
}
}
@ -583,10 +602,12 @@ func (t *Tensor) View(ctx ml.Context, offset int, shape ...int) ml.Tensor {
switch len(shape) {
case 1:
return &Tensor{
b: t.b,
t: C.ggml_view_1d(ctx.(*Context).ctx, t.t, C.int64_t(shape[0]), C.size_t(offset)),
}
case 3:
return &Tensor{
b: t.b,
t: C.ggml_view_2d(ctx.(*Context).ctx, t.t,
C.int64_t(shape[0]), C.int64_t(shape[2]),
C.size_t(shape[1]),
@ -594,6 +615,7 @@ func (t *Tensor) View(ctx ml.Context, offset int, shape ...int) ml.Tensor {
}
case 5:
return &Tensor{
b: t.b,
t: C.ggml_view_3d(ctx.(*Context).ctx, t.t,
C.int64_t(shape[0]), C.int64_t(shape[2]), C.int64_t(shape[4]),
C.size_t(shape[1]), C.size_t(shape[3]),
@ -601,6 +623,7 @@ func (t *Tensor) View(ctx ml.Context, offset int, shape ...int) ml.Tensor {
}
case 7:
return &Tensor{
b: t.b,
t: C.ggml_view_4d(ctx.(*Context).ctx, t.t,
C.int64_t(shape[0]), C.int64_t(shape[2]), C.int64_t(shape[4]), C.int64_t(shape[6]),
C.size_t(shape[1]), C.size_t(shape[3]), C.size_t(shape[5]),
@ -617,7 +640,7 @@ const (
func (t *Tensor) RoPE(ctx ml.Context, positionIDs, ropeFactors ml.Tensor, ropeDim uint32, ropeBase, ropeScale float32) ml.Tensor {
if ropeFactors == nil {
ropeFactors = &Tensor{}
ropeFactors = &Tensor{b: t.b}
}
dequant := t.t
@ -626,6 +649,7 @@ func (t *Tensor) RoPE(ctx ml.Context, positionIDs, ropeFactors ml.Tensor, ropeDi
}
return &Tensor{
b: t.b,
t: C.ggml_rope_ext(
ctx.(*Context).ctx, dequant, positionIDs.(*Tensor).t, ropeFactors.(*Tensor).t,
C.int(ropeDim),
@ -643,18 +667,21 @@ func (t *Tensor) RoPE(ctx ml.Context, positionIDs, ropeFactors ml.Tensor, ropeDi
func (t *Tensor) GELU(ctx ml.Context) ml.Tensor {
return &Tensor{
b: t.b,
t: C.ggml_gelu_inplace(ctx.(*Context).ctx, t.t),
}
}
func (t *Tensor) SILU(ctx ml.Context) ml.Tensor {
return &Tensor{
b: t.b,
t: C.ggml_silu_inplace(ctx.(*Context).ctx, t.t),
}
}
func (t *Tensor) Conv2D(ctx ml.Context, t2 ml.Tensor, s0, s1, p0, p1, d0, d1 int) ml.Tensor {
return &Tensor{
b: t.b,
t: C.ggml_conv_2d(ctx.(*Context).ctx, t.t, t2.(*Tensor).t, C.int(s0), C.int(s1), C.int(p0), C.int(p1), C.int(d0), C.int(d1)),
}
}
@ -670,6 +697,7 @@ func (t *Tensor) ScaledDotProductAttention(ctx ml.Context, key, value, mask ml.T
kq := key.MulmatFullPrec(ctx, query)
kq = &Tensor{
b: t.b,
t: C.ggml_soft_max_ext(ctx.(*Context).ctx, kq.(*Tensor).t, kqMask, C.float(scale), 0),
}