attention

package
v0.2.0 Latest Latest
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 Go to latest Published: Aug 22, 2025 License: Apache-2.0 Imports: 11 Imported by: 1

Documentation

Overview

Package attention provides attention mechanisms for neural networks.

Package attention provides attention mechanisms for neural networks.

Index

Constants

This section is empty.

Variables

This section is empty.

Functions

func BuildGroupQueryAttention added in v0.2.0 

func BuildGroupQueryAttention[T tensor.Numeric](
	engine compute.Engine[T],
	_ numeric.Arithmetic[T],
	_ string,
	_ map[string]*graph.Parameter[T],
	_ map[string]interface{},
) (graph.Node[T], error)

func QKNorm added in v0.2.0 

func QKNorm[T tensor.Numeric](ctx context.Context, engine compute.Engine[T], q, k *tensor.TensorNumeric[T], epsilon float64) (*tensor.TensorNumeric[T], *tensor.TensorNumeric[T], error)

QKNorm applies a form of normalization to Query (Q) and Key (K) tensors to stabilize attention score scales, similar to RMSNorm. It normalizes Q and K independently by their respective RMS values.

Types

type AttentionHead added in v0.2.0 

type AttentionHead[T tensor.Numeric] struct {
	// contains filtered or unexported fields
}

AttentionHead implements a single attention head, including linear projections for Query, Key, and Value, followed by scaled dot-product attention.

func NewAttentionHead added in v0.2.0 

func NewAttentionHead[T tensor.Numeric](engine compute.Engine[T], inputDim, headDim int, opts ...AttentionHeadOption[T]) *AttentionHead[T]

NewAttentionHead creates a new AttentionHead instance. inputDim is the dimension of the input features. headDim is the dimension of the query, key, and value vectors for this head.

func (*AttentionHead[T]) Backward added in v0.2.0 

func (ah *AttentionHead[T]) Backward(ctx context.Context, dOut *tensor.TensorNumeric[T], inputs ...*tensor.TensorNumeric[T]) ([]*tensor.TensorNumeric[T], error)

Backward computes the gradients for the AttentionHead.

func (*AttentionHead[T]) Forward added in v0.2.0 

func (ah *AttentionHead[T]) Forward(ctx context.Context, input *tensor.TensorNumeric[T]) (*tensor.TensorNumeric[T], error)

Forward computes the output of the attention head. input is expected to be a 3D tensor (batch_size, seq_len, input_dim).

func (*AttentionHead[T]) OutputShape added in v0.2.0 

func (ah *AttentionHead[T]) OutputShape() []int

OutputShape returns the output shape of the AttentionHead. It assumes the input shape is (batch_size, seq_len, input_dim). The output shape will be (batch_size, seq_len, head_dim).

func (*AttentionHead[T]) Parameters added in v0.2.0 

func (ah *AttentionHead[T]) Parameters() []graph.Parameter[T]

Parameters returns all trainable parameters of the AttentionHead.

type AttentionHeadOption added in v0.2.0 

type AttentionHeadOption[T tensor.Numeric] func(*AttentionHeadOptions[T])

AttentionHeadOption is a function that applies an option to AttentionHeadOptions.

type AttentionHeadOptions added in v0.2.0 

type AttentionHeadOptions[T tensor.Numeric] struct {
}

NewAttentionHead creates a new AttentionHead instance. inputDim is the dimension of the input features. headDim is the dimension of the query, key, and value vectors for this head. AttentionHeadOptions holds configuration options for AttentionHead.

type GQAOption added in v0.2.0 

type GQAOption[T tensor.Numeric] func(*GQAOptions[T])

GQAOption is a function that applies an option to GQAOptions.

func WithMaxSeqLen added in v0.2.0 

func WithMaxSeqLen[T tensor.Numeric](maxSeqLen int) GQAOption[T]

WithMaxSeqLen sets the maximum sequence length for Rotary Positional Embeddings.

func WithRopeBase added in v0.2.0 

func WithRopeBase[T tensor.Numeric](base float64) GQAOption[T]

WithRopeBase sets the base for Rotary Positional Embeddings.

type GQAOptions added in v0.2.0 

type GQAOptions[T tensor.Numeric] struct {
	Base      float64
	MaxSeqLen int
}

GQAOptions holds configuration options for the GroupedQueryAttention layer.

type GlobalAttention added in v0.2.0 

type GlobalAttention[T tensor.Numeric] struct {
	// contains filtered or unexported fields
}

GlobalAttention wraps GroupedQueryAttention to provide a global attention interface.

func NewGlobalAttention added in v0.2.0 

func NewGlobalAttention[T tensor.Numeric](
	engine compute.Engine[T],
	ops numeric.Arithmetic[T],
	modelDim, numQueryHeads, numKeyValueHeads int,
	options ...GlobalAttentionOption,
) (*GlobalAttention[T], error)

NewGlobalAttention creates a new GlobalAttention layer.

Parameters: - engine: compute engine for tensor operations - ops: arithmetic operations for the numeric type - modelDim: model dimension - numQueryHeads: number of query heads - numKeyValueHeads: number of key/value heads - options: functional options for configuration

Default values: - base: 10000.0 - maxSeqLen: 2048

func NewGlobalAttentionFromParams added in v0.2.0 

func NewGlobalAttentionFromParams[T tensor.Numeric](gqa *GroupedQueryAttention[T]) *GlobalAttention[T]

NewGlobalAttentionFromParams creates a new GlobalAttention layer from an existing GroupedQueryAttention layer.

func (*GlobalAttention[T]) Attributes added in v0.2.0 

func (ga *GlobalAttention[T]) Attributes() map[string]interface{}

Attributes returns the attributes.

func (*GlobalAttention[T]) Backward added in v0.2.0 

func (ga *GlobalAttention[T]) Backward(ctx context.Context, dOut *tensor.TensorNumeric[T], inputs ...*tensor.TensorNumeric[T]) ([]*tensor.TensorNumeric[T], error)

Backward is not implemented

func (*GlobalAttention[T]) Forward added in v0.2.0 

func (ga *GlobalAttention[T]) Forward(ctx context.Context, inputs ...*tensor.TensorNumeric[T]) (*tensor.TensorNumeric[T], error)

Forward computes the forward pass of the GlobalAttention layer.

func (*GlobalAttention[T]) OpType added in v0.2.0 

func (ga *GlobalAttention[T]) OpType() string

OpType returns the operation type.

func (*GlobalAttention[T]) OutputShape added in v0.2.0 

func (ga *GlobalAttention[T]) OutputShape() []int

func (*GlobalAttention[T]) Parameters added in v0.2.0 

func (ga *GlobalAttention[T]) Parameters() []*graph.Parameter[T]

Parameters returns the parameters of the GlobalAttention layer.

func (*GlobalAttention[T]) ScaleRope added in v0.2.0 

func (ga *GlobalAttention[T]) ScaleRope(ctx context.Context, factor float64) error

ScaleRope scales the rotary positional embeddings.

type GlobalAttentionOption added in v0.2.0 

type GlobalAttentionOption func(*GlobalAttentionOptions)

GlobalAttentionOption is a function that configures GlobalAttentionOptions.

func WithGlobalAttentionBase added in v0.2.0 

func WithGlobalAttentionBase(base float64) GlobalAttentionOption

WithGlobalAttentionBase sets the base (theta) parameter for rotary positional embeddings.

func WithGlobalAttentionMaxSeqLen added in v0.2.0 

func WithGlobalAttentionMaxSeqLen(maxSeqLen int) GlobalAttentionOption

WithGlobalAttentionMaxSeqLen sets the maximum sequence length.

type GlobalAttentionOptions added in v0.2.0 

type GlobalAttentionOptions struct {
	Base      float64
	MaxSeqLen int
}

GlobalAttentionOptions holds configuration options for GlobalAttention layer.

type GroupQueryAttention added in v0.2.0 

type GroupQueryAttention[T tensor.Numeric] struct {
	// contains filtered or unexported fields
}

GroupQueryAttention implements grouped query attention mechanism.

func NewGroupQueryAttention added in v0.2.0 

func NewGroupQueryAttention[T tensor.Numeric](engine compute.Engine[T]) *GroupQueryAttention[T]

NewGroupQueryAttention creates a new GroupQueryAttention layer.

func (*GroupQueryAttention[T]) Attributes added in v0.2.0 

func (g *GroupQueryAttention[T]) Attributes() map[string]interface{}

Attributes returns nil for the GroupQueryAttention layer.

func (*GroupQueryAttention[T]) Backward added in v0.2.0 

func (g *GroupQueryAttention[T]) Backward(ctx context.Context, outputGradient *tensor.TensorNumeric[T], inputs ...*tensor.TensorNumeric[T]) ([]*tensor.TensorNumeric[T], error)

Backward computes the gradients for the GroupQueryAttention layer.

func (*GroupQueryAttention[T]) Forward added in v0.2.0 

func (g *GroupQueryAttention[T]) Forward(ctx context.Context, inputs ...*tensor.TensorNumeric[T]) (*tensor.TensorNumeric[T], error)

Forward applies group query attention to the inputs. For now, this is a simplified implementation that returns the first input unchanged.

func (*GroupQueryAttention[T]) Name added in v0.2.0 

func (g *GroupQueryAttention[T]) Name() string

Name returns the name of the layer.

func (*GroupQueryAttention[T]) OpType added in v0.2.0 

func (g *GroupQueryAttention[T]) OpType() string

OpType returns the operation type of the GroupQueryAttention layer.

func (*GroupQueryAttention[T]) OutputShape added in v0.2.0 

func (g *GroupQueryAttention[T]) OutputShape() []int

OutputShape returns the output shape of the layer.

func (*GroupQueryAttention[T]) Parameters added in v0.2.0 

func (g *GroupQueryAttention[T]) Parameters() []*graph.Parameter[T]

Parameters returns the parameters of the layer (none for GroupQueryAttention).

func (*GroupQueryAttention[T]) SetName added in v0.2.0 

func (g *GroupQueryAttention[T]) SetName(name string)

SetName sets the name of the layer.

type GroupedQueryAttention 

type GroupedQueryAttention[T tensor.Numeric] struct {
	// contains filtered or unexported fields
}

GroupedQueryAttention implements grouped query attention mechanism.

func NewGroupedQueryAttention 

func NewGroupedQueryAttention[T tensor.Numeric](
	engine compute.Engine[T],
	ops numeric.Arithmetic[T],
	modelDim, numQueryHeads, numKeyValueHeads int,
	opts ...GQAOption[T],
) (*GroupedQueryAttention[T], error)

NewGroupedQueryAttention creates a new GroupedQueryAttention layer. modelDim: The dimension of the input and output of the block (d_model). numQueryHeads: The number of query heads. numKeyValueHeads: The number of key/value heads.

func NewGroupedQueryAttentionFromParams added in v0.2.0 

func NewGroupedQueryAttentionFromParams[T tensor.Numeric](
	engine compute.Engine[T],
	ops numeric.Arithmetic[T],
	modelDim, numQueryHeads, numKeyValueHeads int,
	wq, wk, wv, wo *core.Dense[T],
	rope *embeddings.RotaryPositionalEmbedding[T],
) (*GroupedQueryAttention[T], error)

NewGroupedQueryAttentionFromParams creates a new GroupedQueryAttention layer from existing parameters.

func (*GroupedQueryAttention[T]) Attributes added in v0.2.0 

func (gqa *GroupedQueryAttention[T]) Attributes() map[string]interface{}

Attributes returns the attributes.

func (*GroupedQueryAttention[T]) Backward 

func (gqa *GroupedQueryAttention[T]) Backward(ctx context.Context, dOut *tensor.TensorNumeric[T], inputs ...*tensor.TensorNumeric[T]) ([]*tensor.TensorNumeric[T], error)

Backward computes the gradients for GroupedQueryAttention.

func (*GroupedQueryAttention[T]) Forward 

func (gqa *GroupedQueryAttention[T]) Forward(ctx context.Context, inputs ...*tensor.TensorNumeric[T]) (*tensor.TensorNumeric[T], error)

Forward computes the grouped query attention.

func (*GroupedQueryAttention[T]) OpType added in v0.2.0 

func (gqa *GroupedQueryAttention[T]) OpType() string

OpType returns the operation type.

func (*GroupedQueryAttention[T]) OutputShape 

func (gqa *GroupedQueryAttention[T]) OutputShape() []int

OutputShape returns the output shape of the GroupedQueryAttention.

func (*GroupedQueryAttention[T]) Parameters 

func (gqa *GroupedQueryAttention[T]) Parameters() []*graph.Parameter[T]

Parameters returns the parameters of the GroupedQueryAttention layer.

func (*GroupedQueryAttention[T]) ScaleRope added in v0.2.0 

func (gqa *GroupedQueryAttention[T]) ScaleRope(ctx context.Context, factor float64) error

ScaleRope scales the rotary positional embeddings.

type LocalAttention added in v0.2.0 

type LocalAttention[T tensor.Numeric] struct {
	// contains filtered or unexported fields
}

LocalAttention implements a local, sliding-window self-attention mechanism.

func NewLocalAttention added in v0.2.0 

func NewLocalAttention[T tensor.Numeric](
	engine compute.Engine[T],
	ops numeric.Arithmetic[T],
	modelDim, numQueryHeads, numKeyValueHeads, windowSize int,
	opts ...LocalAttentionOption[T],
) (*LocalAttention[T], error)

NewLocalAttention creates a new LocalAttention layer.

func (*LocalAttention[T]) Backward added in v0.2.0 

func (la *LocalAttention[T]) Backward(ctx context.Context, dOut *tensor.TensorNumeric[T], inputs ...*tensor.TensorNumeric[T]) ([]*tensor.TensorNumeric[T], error)

Backward is not implemented

func (*LocalAttention[T]) Forward added in v0.2.0 

func (la *LocalAttention[T]) Forward(ctx context.Context, inputs ...*tensor.TensorNumeric[T]) (*tensor.TensorNumeric[T], error)

Forward computes the forward pass of the LocalAttention layer.

func (*LocalAttention[T]) OutputShape added in v0.2.0 

func (la *LocalAttention[T]) OutputShape() []int

func (*LocalAttention[T]) Parameters added in v0.2.0 

func (la *LocalAttention[T]) Parameters() []*graph.Parameter[T]

Parameters returns the parameters of the LocalAttention layer.

type LocalAttentionOption added in v0.2.0 

type LocalAttentionOption[T tensor.Numeric] func(*LocalAttentionOptions[T])

LocalAttentionOption is a function that applies an option to LocalAttentionOptions.

func WithLocalMaxSeqLen added in v0.2.0 

func WithLocalMaxSeqLen[T tensor.Numeric](maxSeqLen int) LocalAttentionOption[T]

WithMaxSeqLen sets the maximum sequence length for Rotary Positional Embeddings.

func WithLocalRopeBase added in v0.2.0 

func WithLocalRopeBase[T tensor.Numeric](base float64) LocalAttentionOption[T]

WithRopeBase sets the base for Rotary Positional Embeddings.

type LocalAttentionOptions added in v0.2.0 

type LocalAttentionOptions[T tensor.Numeric] struct {
	Base      float64
	MaxSeqLen int
}

LocalAttentionOptions holds configuration options for the LocalAttention layer.

type RopeScaler added in v0.2.0 

type RopeScaler[T tensor.Numeric] interface {
	ScaleRope(ctx context.Context, factor float64) error
}

RopeScaler is an interface for layers that support scaling of RoPE.

type ScaledDotProductAttention 

type ScaledDotProductAttention[T tensor.Numeric] struct {
	// contains filtered or unexported fields
}

ScaledDotProductAttention implements the scaled dot-product attention mechanism.

func NewScaledDotProductAttention 

func NewScaledDotProductAttention[T tensor.Numeric](engine compute.Engine[T], headDim int, opts ...ScaledDotProductAttentionOption[T]) *ScaledDotProductAttention[T]

NewScaledDotProductAttention creates a new ScaledDotProductAttention layer.

func (*ScaledDotProductAttention[T]) Backward 

func (sdpa *ScaledDotProductAttention[T]) Backward(ctx context.Context, dOut *tensor.TensorNumeric[T], _, _, _ *tensor.TensorNumeric[T]) ([]*tensor.TensorNumeric[T], error)

Backward computes the gradients for ScaledDotProductAttention. dOut is the gradient from the subsequent layer.

func (*ScaledDotProductAttention[T]) Forward 

func (sdpa *ScaledDotProductAttention[T]) Forward(ctx context.Context, q, k, v *tensor.TensorNumeric[T], mask *tensor.TensorNumeric[T]) (*tensor.TensorNumeric[T], error)

Forward computes the scaled dot-product attention. Q, K, V are expected to be 3D tensors (batch_size, seq_len, head_dim). mask is an optional 4D tensor (batch_size, num_heads, seq_len_q, seq_len_k).

type ScaledDotProductAttentionOption added in v0.2.0 

type ScaledDotProductAttentionOption[T tensor.Numeric] func(*ScaledDotProductAttentionOptions[T])

ScaledDotProductAttentionOption is a function that applies an option to ScaledDotProductAttentionOptions.

type ScaledDotProductAttentionOptions added in v0.2.0 

type ScaledDotProductAttentionOptions[T tensor.Numeric] struct {
}

NewScaledDotProductAttention creates a new ScaledDotProductAttention layer. ScaledDotProductAttentionOptions holds configuration options for ScaledDotProductAttention.

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