Enum onednn_sys::dnnl_normalization_flags_t

#[repr(u32)]
#[non_exhaustive]pub enum dnnl_normalization_flags_t {
    dnnl_normalization_flags_none,
    dnnl_use_global_stats,
    dnnl_use_scaleshift,
    dnnl_fuse_norm_relu,
}

Flags for normalization primitives.

Variants (Non-exhaustive)

Non-exhaustive enums could have additional variants added in future. Therefore, when matching against variants of non-exhaustive enums, an extra wildcard arm must be added to account for any future variants.

dnnl_normalization_flags_none

Use no normalization flags

If specified

• on forward training propagation mean and variance are computed and stored as output
• on backward propagation compute full derivative wrt data
• on backward propagation prop_kind == #dnnl_backward_data has the same behavior as prop_kind == #dnnl_backward

dnnl_use_global_stats

Use global statistics

If specified

• on forward propagation use mean and variance provided by user (input)
• on backward propagation reduces the amount of computations, since mean and variance are considered as constants

If not specified:

• on forward propagation mean and variance are computed and stored as output
• on backward propagation compute full derivative wrt data

dnnl_use_scaleshift

Use scale and shift parameters

If specified:

• on forward propagation use scale and shift (aka scale and bias) for the batch normalization results
• on backward propagation (for prop_kind == #dnnl_backward) compute diff wrt scale and shift (hence one extra output used)

If no specified:

• on backward propagation prop_kind == #dnnl_backward_data has the same behavior as prop_kind == #dnnl_backward

dnnl_fuse_norm_relu

Fuse with ReLU

The flag implies negative slope being 0. On training this is the only configuration supported. For inference, to use non-zero negative slope consider using @ref dev_guide_attributes_post_ops.

If specified:

• on inference this option behaves the same as if the primitive were fused with ReLU using post ops API with zero negative slope.
• on training primitive requires workspace (required to be able to perform backward pass)

Trait Implementations

impl Clone for dnnl_normalization_flags_t

fn clone(&self) -> dnnl_normalization_flags_t

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Copy for dnnl_normalization_flags_t

impl Debug for dnnl_normalization_flags_t

fn fmt(&self, f: &mut Formatter) -> Result

Formats the value using the given formatter.

impl Eq for dnnl_normalization_flags_t

impl Hash for dnnl_normalization_flags_t

fn hash<__H: Hasher>(&self, state: &mut __H)

Feeds this value into the given [Hasher].

fn hash_slice<H>(data: &[Self], state: &mut H) where
    H: Hasher, 

Feeds a slice of this type into the given [Hasher].

impl PartialEq<dnnl_normalization_flags_t> for dnnl_normalization_flags_t

fn eq(&self, other: &dnnl_normalization_flags_t) -> bool

This method tests for self and other values to be equal, and is used by ==.

#[must_use]fn ne(&self, other: &Rhs) -> bool

This method tests for !=.

impl StructuralEq for dnnl_normalization_flags_t

impl StructuralPartialEq for dnnl_normalization_flags_t