ggml: Enable flash attention for vision encoders

Although the vision component of multimodal models typically already
call the optimized nn.Attention, it is converted into non-fused
operations. That is because the backend-specific fused kernels may
have requirements, such as padding, and they is performed by the
cache, which vision encoders don't use.

This implements a fallback path in the backend, softening the
requirements into optimizations. In turn, this allows flash attention
to be used for vision encoders, saving a significant amount of VRAM
and improving performance.

ml/backend.go

@@ -233,8 +233,10 @@ type Tensor interface {
 //
 // kqv := value.Mulmat(ctx, kq)
 // return kqv.Permute(ctx, 0, 2, 1, 3).Contiguous(ctx)
+//
+// cacheConfigApplied indicates whether the optimizations requested through CacheConfig have been performed
 type ScaledDotProductAttention interface {
-	ScaledDotProductAttention(ctx Context, key, value, mask, sinks Tensor, vmla Tensor, scale float64) Tensor
+	ScaledDotProductAttention(ctx Context, key, value, mask, sinks Tensor, vmla Tensor, scale float64, cacheConfigApplied bool) Tensor
 }
 
 type number interface {

ml/backend/ggml/ggml.go

@@ -1645,7 +1645,29 @@ func (t *Tensor) AvgPool2D(ctx ml.Context, k, s int, p float32) ml.Tensor {
 	}
 }
 
-func (t *Tensor) ScaledDotProductAttention(ctx ml.Context, key, value, mask, sinks ml.Tensor, vmla ml.Tensor, scale float64) ml.Tensor {
+func (t *Tensor) ScaledDotProductAttention(ctx ml.Context, key, value, mask, sinks ml.Tensor, vmla ml.Tensor, scale float64, cacheConfigApplied bool) ml.Tensor {
+	// If the cache didn't help us with required transformations, do them here
+	if !cacheConfigApplied {
+		cacheConfig := t.b.CacheConfig()
+
+		// Padding key and value to CachePadding is a performance optimization, not a requirement, so we don't do it if it wasn't done by the caller
+
+		if cacheConfig.PermutedV {
+			value = value.Permute(ctx, 1, 2, 0, 3).Contiguous(ctx)
+		}
+
+		if mask != nil {
+			padSize := int(pad(C.size_t(mask.Dim(1)), C.size_t(cacheConfig.MaskBatchPadding))) - mask.Dim(1)
+			if padSize > 0 {
+				mask = mask.Pad(ctx, 0, padSize, 0, 0)
+			}
+
+			if mask.DType() != cacheConfig.MaskDType {
+				mask = mask.Cast(ctx, cacheConfig.MaskDType)
+			}
+		}
+	}
+
 	var kqMask *C.struct_ggml_tensor
 	if mask != nil {
 		kqMask = mask.(*Tensor).t

ml/nn/attention.go

@@ -57,10 +57,9 @@ func AttentionWithVMLA(ctx ml.Context, query, key, value, sinks ml.Tensor, vmla
 		key, value, mask = cache.Get(ctx)
 	}
 
-	// Only use the fast SDPA implementation if we have a cache, since that's what
+	if sdpa, ok := query.(ml.ScaledDotProductAttention); ok {
-	// will do any expected backend-specific transformations for us
+		cacheConfigApplied := cache != nil
-	if sdpa, ok := query.(ml.ScaledDotProductAttention); ok && cache != nil {
+		return sdpa.ScaledDotProductAttention(ctx, key, value, mask, sinks, vmla, scale, cacheConfigApplied)
-		return sdpa.ScaledDotProductAttention(ctx, key, value, mask, sinks, vmla, scale)
 	} else {
 		query = query.Permute(ctx, 0, 2, 1, 3)
 		key = key.Permute(ctx, 0, 2, 1, 3)