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ml: Panic rather than return error on tensor allocation failure

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FromFloatSlice and FromIntSlice return an error if the shape doesn't
match the passed data or if memory can't be allocated. Since these
are inputs, the memory being allocated is system memory rather than VRAM.

In many cases, the caller can't really handle the error and panics.

Empty and Zeros directly panic if they can't allocate memory.

This makes things consistent by panicing for the first two cases,
removing a fair amount of error handling code. This is also consistent
with how Go typically handles these situations.
This commit is contained in:
Jesse Gross
2025-05-19 10:43:56 -07:00
committed by Jesse Gross
parent 73d6a82cce
commit 1f371ea92f
20 changed files with 68 additions and 209 deletions
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4
ml/backend.go
View File

@@ -171,8 +171,8 @@ func NewBackend(modelPath string, params BackendParams) (Backend, error) {
type Context interface {
Empty(dtype DType, shape ...int) Tensor
Zeros(dtype DType, shape ...int) Tensor
FromFloatSlice(s []float32, shape ...int) (Tensor, error)
FromIntSlice(s []int32, shape ...int) (Tensor, error)
FromFloatSlice(s []float32, shape ...int) Tensor
FromIntSlice(s []int32, shape ...int) Tensor
// Arange creates a 1D tensor with values within an interval (start, stop] increased by step.
Arange(start, stop, step float32, dtype DType) Tensor

31
ml/backend/ggml/ggml.go
View File

@@ -729,11 +729,11 @@ func (c *Context) Zeros(dtype ml.DType, shape ...int) ml.Tensor {
return t
}
func checkShape[S ~[]E, E any](s S, shape ...int) error {
func checkShape[S ~[]E, E any](s S, shape ...int) {
n := len(s)
if n == 0 {
return nil
return
}
for _, v := range shape {
@@ -741,16 +741,12 @@ func checkShape[S ~[]E, E any](s S, shape ...int) error {
}
if n != 1 {
return fmt.Errorf("invalid shape: %v", shape)
panic(fmt.Errorf("invalid shape: %v", shape))
}
return nil
}
func (c *Context) FromFloatSlice(s []float32, shape ...int) (ml.Tensor, error) {
if err := checkShape(s, shape...); err != nil {
return nil, err
}
func (c *Context) FromFloatSlice(s []float32, shape ...int) ml.Tensor {
checkShape(s, shape...)
t := c.newTensor(ml.DTypeF32, shape)
@@ -758,13 +754,11 @@ func (c *Context) FromFloatSlice(s []float32, shape ...int) (ml.Tensor, error) {
C.ggml_backend_tensor_set(t.(*Tensor).t, unsafe.Pointer(&s[0]), 0, C.ggml_nbytes(t.(*Tensor).t))
}
return t, nil
return t
}
func (c *Context) FromIntSlice(s []int32, shape ...int) (ml.Tensor, error) {
if err := checkShape(s, shape...); err != nil {
return nil, err
}
func (c *Context) FromIntSlice(s []int32, shape ...int) ml.Tensor {
checkShape(s, shape...)
t := c.newTensor(ml.DTypeI32, shape)
@@ -772,7 +766,7 @@ func (c *Context) FromIntSlice(s []int32, shape ...int) (ml.Tensor, error) {
C.ggml_backend_tensor_set(t.(*Tensor).t, unsafe.Pointer(&s[0]), 0, C.ggml_nbytes(t.(*Tensor).t))
}
return t, nil
return t
}
func (c Context) Arange(start, stop, step float32, dtype ml.DType) ml.Tensor {
@@ -790,12 +784,7 @@ func (c Context) Arange(start, stop, step float32, dtype ml.DType) ml.Tensor {
arange = append(arange, int32(i))
}
t, err := c.Input().FromIntSlice(arange, len(arange))
if err != nil {
panic(err)
}
return t
return c.Input().FromIntSlice(arange, len(arange))
default:
panic("unsupported dtype for arange")
}