use split activations when possible (#12293)

* use ggml_*_split activations when possible

* forward qkv

ml/backend.go

@@ -430,12 +430,13 @@ type Tensor interface {
 	Sin(ctx Context) Tensor
 	Cos(ctx Context) Tensor
 	Tanh(ctx Context) Tensor
-	GELU(ctx Context) Tensor
-	QuickGELU(ctx Context) Tensor
-	SILU(ctx Context) Tensor
-	RELU(ctx Context) Tensor
+	GELU(ctx Context, up ...Tensor) Tensor
+	SILU(ctx Context, up ...Tensor) Tensor
+	RELU(ctx Context, up ...Tensor) Tensor
 	Sigmoid(ctx Context) Tensor
-	SwiGLU(ctx Context, up Tensor, alpha, limit float32) Tensor
+
+	// AlphaLimitSILU is a variant of SILU that clamps the input to the range [-limit, limit]
+	SILUAlphaLimit(ctx Context, up Tensor, alpha, limit float32) Tensor
 
 	Reshape(ctx Context, shape ...int) Tensor
 	View(ctx Context, offset int, shape ...int) Tensor

ml/backend/ggml/ggml.go

@@ -1431,35 +1431,46 @@ func (t *Tensor) IM2Col(ctx ml.Context, t2 ml.Tensor, s0, s1, p0, p1, d0, d1 int
 	}
 }
 
-func (t *Tensor) GELU(ctx ml.Context) ml.Tensor {
+func (t *Tensor) GELU(ctx ml.Context, t2 ...ml.Tensor) ml.Tensor {
+	if len(t2) > 0 {
+		return &Tensor{
+			b: t.b,
+			t: C.ggml_geglu_split(ctx.(*Context).ctx, t.t, t2[0].(*Tensor).t),
+		}
+	}
 	return &Tensor{
 		b: t.b,
 		t: C.ggml_gelu_inplace(ctx.(*Context).ctx, t.t),
 	}
 }
 
-func (t *Tensor) QuickGELU(ctx ml.Context) ml.Tensor {
-	return &Tensor{
-		b: t.b,
-		t: C.ggml_gelu_quick_inplace(ctx.(*Context).ctx, t.t),
+func (t *Tensor) SILU(ctx ml.Context, t2 ...ml.Tensor) ml.Tensor {
+	if len(t2) > 0 {
+		return &Tensor{
+			b: t.b,
+			t: C.ggml_swiglu_split(ctx.(*Context).ctx, t.t, t2[0].(*Tensor).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) RELU(ctx ml.Context) ml.Tensor {
+func (t *Tensor) RELU(ctx ml.Context, t2 ...ml.Tensor) ml.Tensor {
+	if len(t2) > 0 {
+		return &Tensor{
+			b: t.b,
+			t: C.ggml_reglu_split(ctx.(*Context).ctx, t.t, t2[0].(*Tensor).t),
+		}
+	}
 	return &Tensor{
 		b: t.b,
 		t: C.ggml_relu_inplace(ctx.(*Context).ctx, t.t),
 	}
 }
 
-func (t *Tensor) SwiGLU(ctx ml.Context, up ml.Tensor, alpha, limit float32) ml.Tensor {
+func (t *Tensor) SILUAlphaLimit(ctx ml.Context, up ml.Tensor, alpha, limit float32) ml.Tensor {
 	return &Tensor{
 		b: t.b,
 		t: C.ggml_swiglu_oai(ctx.(*Context).ctx, t.t, up.(*Tensor).t, C.float(alpha), C.float(limit)),

ml/nn/attention.go

@@ -26,6 +26,7 @@ func Attention(ctx ml.Context, query, key, value ml.Tensor, scale float64, cache
 }
 
 func AttentionWithSinks(ctx ml.Context, query, key, value, sinks ml.Tensor, scale float64, cache kvcache.Cache) ml.Tensor {
+	ctx.Forward(query)
 	if key != nil && value != nil {
 		if query.Dim(0) != key.Dim(0) {
 			panic(fmt.Errorf("d_k in attention operation does not match between query(%v) and key(%v)", query.Dim(0), key.Dim(0)))
@@ -39,6 +40,7 @@ func AttentionWithSinks(ctx ml.Context, query, key, value, sinks ml.Tensor, scal
 			panic(fmt.Errorf("seq_len_k in attention operation does not match between key(%v) and value(%v)", key.Dim(2), value.Dim(2)))
 		}
 
+		ctx.Forward(key, value)
 		if cache != nil {
 			cache.Put(ctx, key, value)
 		}

model/models/gemma2/model.go

@@ -138,7 +138,7 @@ type MLP struct {
 }
 
 func (mlp *MLP) Forward(ctx ml.Context, hiddenState ml.Tensor, opts *Options) ml.Tensor {
-	hiddenState = mlp.Gate.Forward(ctx, hiddenState).GELU(ctx).Mul(ctx, mlp.Up.Forward(ctx, hiddenState))
+	hiddenState = mlp.Gate.Forward(ctx, hiddenState).GELU(ctx, mlp.Up.Forward(ctx, hiddenState))
 	return mlp.Down.Forward(ctx, hiddenState)
 }
 

model/models/gemma3/model_text.go

@@ -123,7 +123,7 @@ type TextMLP struct {
 }
 
 func (mlp *TextMLP) Forward(ctx ml.Context, hiddenState ml.Tensor, opts *TextConfig) ml.Tensor {
-	hiddenState = mlp.Gate.Forward(ctx, hiddenState).GELU(ctx).Mul(ctx, mlp.Up.Forward(ctx, hiddenState))
+	hiddenState = mlp.Gate.Forward(ctx, hiddenState).GELU(ctx, mlp.Up.Forward(ctx, hiddenState))
 	return mlp.Down.Forward(ctx, hiddenState)
 }
 

model/models/gemma3n/model_text.go

@@ -170,8 +170,7 @@ func (d TextLayer) Forward(ctx ml.Context, hiddenStates, perLayerInput, position
 	}
 
 	active = d.PerLayerInputGate.Forward(ctx, active)
-	active = active.GELU(ctx)
-	active = active.Mul(ctx, perLayerInput)
+	active = active.GELU(ctx, perLayerInput)
 
 	active = d.PerLayerProjection.Forward(ctx, active)
 	active = d.PostPerLayerNorm.Forward(ctx, active, opts.eps)
@@ -292,7 +291,7 @@ func (mlp TextMLP) Forward(ctx ml.Context, hiddenStates ml.Tensor, activationSpa
 		hiddenStates = hiddenStates.Sub(ctx, cutoff).RELU(ctx)
 	}
 
-	hiddenStates = hiddenStates.GELU(ctx).Mul(ctx, upStates)
+	hiddenStates = hiddenStates.GELU(ctx, upStates)
 	hiddenStates = mlp.Down.Forward(ctx, hiddenStates)
 	return hiddenStates
 }

model/models/gptoss/model.go

@@ -210,7 +210,7 @@ func (mlp *MLPBlock) Forward(ctx ml.Context, hiddenStates, one ml.Tensor, opts *
 		up = mlp.Up.Forward(ctx, hiddenStates, selectedExperts)
 	}
 
-	hiddenStates = gate.SwiGLU(ctx, up, 1.702, 7)
+	hiddenStates = gate.SILUAlphaLimit(ctx, up, 1.702, 7)
 
 	experts := mlp.Down.Forward(ctx, hiddenStates, selectedExperts)
 	experts = experts.Mul(ctx, routingWeights)

model/models/llama/model.go

@@ -118,7 +118,7 @@ type MLP struct {
 }
 
 func (mlp *MLP) Forward(ctx ml.Context, hiddenState ml.Tensor, opts *Options) ml.Tensor {
-	hiddenState = mlp.Gate.Forward(ctx, hiddenState).SILU(ctx).Mul(ctx, mlp.Up.Forward(ctx, hiddenState))
+	hiddenState = mlp.Gate.Forward(ctx, hiddenState).SILU(ctx, mlp.Up.Forward(ctx, hiddenState))
 	return mlp.Down.Forward(ctx, hiddenState)
 }
 

model/models/llama4/model_text.go

@@ -58,14 +58,14 @@ type TextMLP struct {
 }
 
 func (mlp *TextMLP) Forward(ctx ml.Context, hiddenStates ml.Tensor, opts *TextOptions) ml.Tensor {
-	hiddenStates = mlp.Gate.Forward(ctx, hiddenStates).SILU(ctx).Mul(ctx, mlp.Up.Forward(ctx, hiddenStates))
+	hiddenStates = mlp.Gate.Forward(ctx, hiddenStates).SILU(ctx, mlp.Up.Forward(ctx, hiddenStates))
 	return mlp.Down.Forward(ctx, hiddenStates)
 }
 
 type TextExperts struct {
-	Gate *nn.Linear `gguf:"ffn_gate_exps"`
-	Up   *nn.Linear `gguf:"ffn_up_exps"`
-	Down *nn.Linear `gguf:"ffn_down_exps"`
+	Gate *nn.LinearBatch `gguf:"ffn_gate_exps"`
+	Up   *nn.LinearBatch `gguf:"ffn_up_exps"`
+	Down *nn.LinearBatch `gguf:"ffn_down_exps"`
 }
 
 func (e *TextExperts) Forward(ctx ml.Context, hiddenStates, routerLogits ml.Tensor, opts *TextOptions) ml.Tensor {
@@ -76,9 +76,9 @@ func (e *TextExperts) Forward(ctx ml.Context, hiddenStates, routerLogits ml.Tens
 	hiddenStates = hiddenStates.Repeat(ctx, 1, opts.numExpertsUsed)
 	hiddenStates = hiddenStates.Mul(ctx, scores)
 
-	upStates := e.Up.Weight.MulmatID(ctx, hiddenStates, experts)
-	gateStates := e.Gate.Weight.MulmatID(ctx, hiddenStates, experts)
-	downStates := e.Down.Weight.MulmatID(ctx, upStates.Mul(ctx, gateStates.SILU(ctx)), experts)
+	upStates := e.Up.Forward(ctx, hiddenStates, experts)
+	gateStates := e.Gate.Forward(ctx, hiddenStates, experts)
+	downStates := e.Down.Forward(ctx, upStates.Mul(ctx, gateStates.SILU(ctx)), experts)
 
 	nextStates := downStates.View(ctx, 0, hiddenStates.Dim(0), downStates.Stride(2), hiddenStates.Dim(2))
 	for i := 1; i < opts.numExpertsUsed; i++ {
@@ -96,7 +96,7 @@ type TextSharedExpert struct {
 }
 
 func (mlp *TextSharedExpert) Forward(ctx ml.Context, hiddenStates ml.Tensor, opts *TextOptions) ml.Tensor {
-	hiddenStates = mlp.Gate.Forward(ctx, hiddenStates).SILU(ctx).Mul(ctx, mlp.Up.Forward(ctx, hiddenStates))
+	hiddenStates = mlp.Gate.Forward(ctx, hiddenStates).SILU(ctx, mlp.Up.Forward(ctx, hiddenStates))
 	return mlp.Down.Forward(ctx, hiddenStates)
 }
 

model/models/mistral3/model_text.go

@@ -65,7 +65,7 @@ type MLP struct {
 }
 
 func (mlp *MLP) Forward(ctx ml.Context, hiddenState ml.Tensor, opts *TextOptions) ml.Tensor {
-	hiddenState = mlp.Gate.Forward(ctx, hiddenState).SILU(ctx).Mul(ctx, mlp.Up.Forward(ctx, hiddenState))
+	hiddenState = mlp.Gate.Forward(ctx, hiddenState).SILU(ctx, mlp.Up.Forward(ctx, hiddenState))
 	return mlp.Down.Forward(ctx, hiddenState)
 }
 

model/models/mistral3/model_vision.go

@@ -51,7 +51,7 @@ type VisionMLP struct {
 }
 
 func (mlp *VisionMLP) Forward(ctx ml.Context, hiddenStates ml.Tensor, opts *VisionModelOptions) ml.Tensor {
-	hiddenStates = mlp.Gate.Forward(ctx, hiddenStates).SILU(ctx).Mul(ctx, mlp.Up.Forward(ctx, hiddenStates))
+	hiddenStates = mlp.Gate.Forward(ctx, hiddenStates).SILU(ctx, mlp.Up.Forward(ctx, hiddenStates))
 	return mlp.Down.Forward(ctx, hiddenStates)
 }
 

model/models/mllama/model_text.go

@@ -58,7 +58,7 @@ type TextMLP struct {
 }
 
 func (mlp *TextMLP) Forward(ctx ml.Context, hiddenState ml.Tensor, opts *TextModelOptions) ml.Tensor {
-	hiddenState = mlp.Gate.Forward(ctx, hiddenState).SILU(ctx).Mul(ctx, mlp.Up.Forward(ctx, hiddenState))
+	hiddenState = mlp.Gate.Forward(ctx, hiddenState).SILU(ctx, mlp.Up.Forward(ctx, hiddenState))
 	return mlp.Down.Forward(ctx, hiddenState)
 }
 

model/models/qwen2/model.go

@@ -59,7 +59,7 @@ type MLP struct {
 }
 
 func (mlp MLP) Forward(ctx ml.Context, hiddenStates ml.Tensor) ml.Tensor {
-	hiddenStates = mlp.Gate.Forward(ctx, hiddenStates).SILU(ctx).Mul(ctx, mlp.Up.Forward(ctx, hiddenStates))
+	hiddenStates = mlp.Gate.Forward(ctx, hiddenStates).SILU(ctx, mlp.Up.Forward(ctx, hiddenStates))
 	return mlp.Down.Forward(ctx, hiddenStates)
 }
 

model/models/qwen25vl/model_text.go

@@ -90,7 +90,7 @@ type MLP struct {
 
 func (mlp *MLP) Forward(ctx ml.Context, hiddenState ml.Tensor, opts *TextOptions) ml.Tensor {
 	// Apply SwiGLU activation gating
-	hiddenState = mlp.Gate.Forward(ctx, hiddenState).SILU(ctx).Mul(ctx, mlp.Up.Forward(ctx, hiddenState))
+	hiddenState = mlp.Gate.Forward(ctx, hiddenState).SILU(ctx, mlp.Up.Forward(ctx, hiddenState))
 	// Project back to hidden dimension
 	return mlp.Down.Forward(ctx, hiddenState)
 }

model/models/qwen25vl/model_vision.go

@@ -100,8 +100,7 @@ type VisionMLP struct {
 func (mlp *VisionMLP) Forward(ctx ml.Context, hiddenStates ml.Tensor, opts *VisionModelOptions) ml.Tensor {
 	// Using activation as specified in config (likely GELU or SiLU/Swish)
 	gateOutput := mlp.Gate.Forward(ctx, hiddenStates)
-	upOutput := mlp.Up.Forward(ctx, hiddenStates)
-	hiddenStates = gateOutput.SILU(ctx).Mul(ctx, upOutput)
+	hiddenStates = gateOutput.SILU(ctx, mlp.Up.Forward(ctx, hiddenStates))
 
 	return mlp.Down.Forward(ctx, hiddenStates)
 }

model/models/qwen3/model.go

@@ -30,10 +30,10 @@ func (o Options) headDim() int {
 }
 
 type Attention struct {
-	QueryNorm *nn.RMSNorm `gguf:"attn_q_norm"`
 	Query     *nn.Linear  `gguf:"attn_q"`
-	KeyNorm   *nn.RMSNorm `gguf:"attn_k_norm"`
+	QueryNorm *nn.RMSNorm `gguf:"attn_q_norm"`
 	Key       *nn.Linear  `gguf:"attn_k"`
+	KeyNorm   *nn.RMSNorm `gguf:"attn_k_norm"`
 	Value     *nn.Linear  `gguf:"attn_v"`
 	Output    *nn.Linear  `gguf:"attn_output"`
 }
@@ -65,10 +65,10 @@ type MLP interface {
 }
 
 type sparse struct {
-	Router *nn.Linear `gguf:"ffn_gate_inp"`
-	Gate   *nn.Linear `gguf:"ffn_gate_exps"`
-	Up     *nn.Linear `gguf:"ffn_up_exps"`
-	Down   *nn.Linear `gguf:"ffn_down_exps"`
+	Router *nn.Linear      `gguf:"ffn_gate_inp"`
+	Gate   *nn.LinearBatch `gguf:"ffn_gate_exps"`
+	Up     *nn.LinearBatch `gguf:"ffn_up_exps"`
+	Down   *nn.LinearBatch `gguf:"ffn_down_exps"`
 }
 
 func (mlp *sparse) Forward(ctx ml.Context, hiddenStates ml.Tensor, opts *Options) ml.Tensor {
@@ -87,13 +87,9 @@ func (mlp *sparse) Forward(ctx ml.Context, hiddenStates ml.Tensor, opts *Options
 
 	hiddenStates = hiddenStates.Reshape(ctx, hiddenStates.Dim(0), 1, hiddenStates.Dim(1))
 
-	upStates := mlp.Up.Weight.MulmatID(ctx, hiddenStates, selectedExperts)
+	hiddenStates = mlp.Gate.Forward(ctx, hiddenStates, selectedExperts).SILU(ctx, mlp.Up.Forward(ctx, hiddenStates, selectedExperts))
 
-	hiddenStates = mlp.Gate.Weight.MulmatID(ctx, hiddenStates, selectedExperts)
-	hiddenStates = hiddenStates.SILU(ctx)
-	hiddenStates = hiddenStates.Mul(ctx, upStates)
-
-	experts := mlp.Down.Weight.MulmatID(ctx, hiddenStates, selectedExperts)
+	experts := mlp.Down.Forward(ctx, hiddenStates, selectedExperts)
 	experts = experts.Mul(ctx, routingWeights)
 
 	nextStates := experts.View(ctx, 0, experts.Dim(0), experts.Stride(2), experts.Dim(2))
@@ -111,7 +107,8 @@ type dense struct {
 }
 
 func (mlp *dense) Forward(ctx ml.Context, hiddenStates ml.Tensor, _ *Options) ml.Tensor {
-	hiddenStates = mlp.Gate.Forward(ctx, hiddenStates).SILU(ctx).Mul(ctx, mlp.Up.Forward(ctx, hiddenStates))
+	hiddenStates = mlp.Gate.Forward(ctx, hiddenStates).
+		SILU(ctx, mlp.Up.Forward(ctx, hiddenStates))
 	return mlp.Down.Forward(ctx, hiddenStates)
 }