s/From*Slice/From*s/ (#12255)

kvcache/causal.go

@@ -393,7 +393,7 @@ func (c *Causal) buildMask(ctx ml.Context) ml.Tensor {
 		mask[i] = float32(math.Inf(-1))
 	}
 
-	maskTensor := ctx.Input().FromFloatSlice(mask, length, batchSize)
+	maskTensor := ctx.Input().FromFloats(mask, length, batchSize)
 
 	if c.config.MaskDType != ml.DTypeF32 {
 		maskTensor = maskTensor.Cast(ctx, c.config.MaskDType)
@@ -725,7 +725,7 @@ func (c *Causal) shift(seq int, beginIndex, offset int32) error {
 		offsets = offsets[batchFirst : batchLast+1]
 
 		ctx := c.backend.NewContext()
-		kShift := ctx.Input().FromIntSlice(offsets, len(offsets))
+		kShift := ctx.Input().FromInts(offsets, len(offsets))
 
 		for i, key := range c.keys {
 			if key == nil {

kvcache/causal_test.go

@@ -477,7 +477,7 @@ func testCache(t *testing.T, backend ml.Backend, cache Cache, tests []testCase)
 			}
 
 			cache.SetLayer(0)
-			tensor := context.FromFloatSlice(test.in, test.inShape...)
+			tensor := context.FromFloats(test.in, test.inShape...)
 			cache.Put(context, tensor, tensor)
 
 			out, _, mask := cache.Get(context)
@@ -519,7 +519,7 @@ func TestCanResume(t *testing.T) {
 	}
 
 	cache.SetLayer(0)
-	tensor := context.FromFloatSlice([]float32{1, 2, 3, 4, 5}, 1, 1, 5)
+	tensor := context.FromFloats([]float32{1, 2, 3, 4, 5}, 1, 1, 5)
 	cache.Put(context, tensor, tensor)
 
 	// with window size 4, nothing has slid out of the window yet
@@ -549,7 +549,7 @@ func TestCanResume(t *testing.T) {
 	}
 
 	cache.SetLayer(0)
-	tensor = context.FromFloatSlice([]float32{6}, 1, 1, 1)
+	tensor = context.FromFloats([]float32{6}, 1, 1, 1)
 	cache.Put(context, tensor, tensor)
 
 	// only the latest position has overlapping windows
@@ -594,7 +594,7 @@ func TestCanResumeSWAMem(t *testing.T) {
 	}
 
 	cache.SetLayer(0)
-	tensor := context.FromFloatSlice([]float32{1, 2, 3, 4, 5, 6, 7}, 1, 1, 7)
+	tensor := context.FromFloats([]float32{1, 2, 3, 4, 5, 6, 7}, 1, 1, 7)
 	cache.Put(context, tensor, tensor)
 
 	// shift window by adding position 7
@@ -607,7 +607,7 @@ func TestCanResumeSWAMem(t *testing.T) {
 	}
 
 	cache.SetLayer(0)
-	tensor = context.FromFloatSlice([]float32{8}, 1, 1, 1)
+	tensor = context.FromFloats([]float32{8}, 1, 1, 1)
 	cache.Put(context, tensor, tensor)
 
 	// only the latest position has overlapping windows
@@ -670,7 +670,7 @@ func (c *testContext) Zeros(dtype ml.DType, shape ...int) ml.Tensor {
 	return c.Empty(dtype, shape...)
 }
 
-func (c *testContext) FromFloatSlice(s []float32, shape ...int) ml.Tensor {
+func (c *testContext) FromFloats(s []float32, shape ...int) ml.Tensor {
 	t := c.Empty(ml.DTypeF32, shape...).(*testTensor)
 
 	copy(t.data, s)
@@ -678,13 +678,13 @@ func (c *testContext) FromFloatSlice(s []float32, shape ...int) ml.Tensor {
 	return t
 }
 
-func (c *testContext) FromIntSlice(s []int32, shape ...int) ml.Tensor {
+func (c *testContext) FromInts(s []int32, shape ...int) ml.Tensor {
 	f := make([]float32, len(s))
 	for i := range f {
 		f[i] = float32(s[i])
 	}
 
-	out := c.FromFloatSlice(f, shape...)
+	out := c.FromFloats(f, shape...)
 	out.(*testTensor).dtype = ml.DTypeI32
 
 	return out
@@ -696,7 +696,7 @@ func (c *testContext) Arange(start, stop, step float32, dtype ml.DType) ml.Tenso
 		s = append(s, i)
 	}
 
-	out := c.FromFloatSlice(s, len(s))
+	out := c.FromFloats(s, len(s))
 	out.(*testTensor).dtype = dtype
 	return out
 }

ml/backend.go

@@ -98,8 +98,9 @@ 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
-	FromIntSlice(s []int32, shape ...int) Tensor
+	FromBytes(dtype DType, s []byte, shape ...int) Tensor
+	FromFloats(s []float32, shape ...int) Tensor
+	FromInts(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
@@ -136,7 +137,9 @@ type Tensor interface {
 	Bytes() []byte
 	Floats() []float32
 
-	SetValueFromIntSlice(s []int32)
+	FromBytes([]byte)
+	FromFloats([]float32)
+	FromInts([]int32)
 
 	Neg(ctx Context) Tensor
 	Add(ctx Context, t2 Tensor) Tensor

ml/backend/ggml/ggml.go

@@ -12,6 +12,7 @@ import "C"
 
 import (
 	"context"
+	"encoding/binary"
 	"errors"
 	"fmt"
 	"io"
@@ -871,7 +872,7 @@ func pad(length, pad C.size_t) C.size_t {
 	return ((length + pad - 1) / pad) * pad
 }
 
-func (c *Context) newTensor(dtype ml.DType, shape []int) ml.Tensor {
+func (c *Context) newTensor(dtype ml.DType, shape []int) *Tensor {
 	if c.buft == nil {
 		panic("set Input or Layer before creating tensors")
 	}
@@ -915,7 +916,7 @@ func (c *Context) Empty(dtype ml.DType, shape ...int) ml.Tensor {
 func (c *Context) Zeros(dtype ml.DType, shape ...int) ml.Tensor {
 	t := c.newTensor(dtype, shape)
 	if c.b.allocMemory {
-		C.ggml_set_zero(t.(*Tensor).t)
+		C.ggml_set_zero(t.t)
 	}
 	return t
 }
@@ -936,25 +937,34 @@ func checkShape[S ~[]E, E any](s S, shape ...int) {
 	}
 }
 
-func (c *Context) FromFloatSlice(s []float32, shape ...int) ml.Tensor {
+func (c Context) FromBytes(dtype ml.DType, s []uint8, shape ...int) ml.Tensor {
+	// Unchecked to handle quantized types
+	t := c.newTensor(dtype, shape)
+	if c.b.allocMemory {
+		t.FromBytes(s)
+	}
+
+	return t
+}
+
+func (c *Context) FromFloats(s []float32, shape ...int) ml.Tensor {
 	checkShape(s, shape...)
 
 	t := c.newTensor(ml.DTypeF32, shape)
 
-	if c.b.allocMemory && len(s) > 0 {
-		C.ggml_backend_tensor_set(t.(*Tensor).t, unsafe.Pointer(&s[0]), 0, C.ggml_nbytes(t.(*Tensor).t))
+	if c.b.allocMemory {
+		t.FromFloats(s)
 	}
 
 	return t
 }
 
-func (c *Context) FromIntSlice(s []int32, shape ...int) ml.Tensor {
+func (c *Context) FromInts(s []int32, shape ...int) ml.Tensor {
 	checkShape(s, shape...)
 
 	t := c.newTensor(ml.DTypeI32, shape)
-
-	if c.b.allocMemory && len(s) > 0 {
-		C.ggml_backend_tensor_set(t.(*Tensor).t, unsafe.Pointer(&s[0]), 0, C.ggml_nbytes(t.(*Tensor).t))
+	if c.b.allocMemory {
+		t.FromInts(s)
 	}
 
 	return t
@@ -975,7 +985,7 @@ func (c Context) Arange(start, stop, step float32, dtype ml.DType) ml.Tensor {
 			arange = append(arange, int32(i))
 		}
 
-		return c.Input().FromIntSlice(arange, len(arange))
+		return c.Input().FromInts(arange, len(arange))
 	default:
 		panic("unsupported dtype for arange")
 	}
@@ -1045,10 +1055,26 @@ func (t *Tensor) Floats() (data []float32) {
 	return
 }
 
-func (t *Tensor) SetValueFromIntSlice(s []int32) {
-	if len(s) > 0 {
-		C.ggml_backend_tensor_set(t.t, unsafe.Pointer(&s[0]), 0, C.ggml_nbytes(t.t))
+func tensorSet[S ~[]E, E byte | float32 | int32](t *Tensor, s S) {
+	if len(s) == 0 {
+		return
+	}
+	if int(C.ggml_nbytes(t.t)) != len(s)*binary.Size(s[0]) {
+		panic("data size does not match tensor size")
 	}
+	C.ggml_backend_tensor_set(t.t, unsafe.Pointer(&s[0]), 0, C.ggml_nbytes(t.t))
+}
+
+func (t *Tensor) FromBytes(s []byte) {
+	tensorSet(t, s)
+}
+
+func (t *Tensor) FromFloats(s []float32) {
+	tensorSet(t, s)
+}
+
+func (t *Tensor) FromInts(s []int32) {
+	tensorSet(t, s)
 }
 
 func (t *Tensor) DType() ml.DType {
@@ -1622,13 +1648,3 @@ func (t *Tensor) Clamp(ctx ml.Context, min, max float32) ml.Tensor {
 		t: C.ggml_clamp(ctx.(*Context).ctx, t.t, C.float(min), C.float(max)),
 	}
 }
-
-func (c Context) FromBytes(dtype ml.DType, s []uint8, shape ...int) ml.Tensor {
-	// Unchecked to handle quantized types
-	t := c.newTensor(dtype, shape)
-	if c.b.allocMemory && len(s) > 0 {
-		C.ggml_backend_tensor_set(t.(*Tensor).t, unsafe.Pointer(&s[0]), 0, C.ggml_nbytes(t.(*Tensor).t))
-	}
-
-	return t
-}

model/models/bert/embed.go

@@ -30,7 +30,7 @@ type Model struct {
 func (m *Model) Forward(ctx ml.Context, batch input.Batch) (ml.Tensor, error) {
 	hiddenStates := m.TokenEmbedding.Forward(ctx, batch.Inputs)
 	hiddenStates = hiddenStates.Add(ctx, m.TypeEmbedding.Weight.View(ctx, 0, m.hiddenSize))
-	hiddenStates = hiddenStates.Add(ctx, m.PositionEmbedding.Forward(ctx, ctx.Input().FromIntSlice(batch.Positions, len(batch.Positions))))
+	hiddenStates = hiddenStates.Add(ctx, m.PositionEmbedding.Forward(ctx, ctx.Input().FromInts(batch.Positions, len(batch.Positions))))
 	hiddenStates = m.TokenEmbeddingNorm.Forward(ctx, hiddenStates, m.eps)
 
 	for _, layer := range m.Layers {

model/models/deepseek2/model.go

@@ -302,7 +302,7 @@ func (m Model) Shift(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor
 }
 
 func (m *Model) Forward(ctx ml.Context, batch input.Batch) (ml.Tensor, error) {
-	positions := ctx.Input().FromIntSlice(batch.Positions, len(batch.Positions))
+	positions := ctx.Input().FromInts(batch.Positions, len(batch.Positions))
 
 	hiddenStates := m.TokenEmbedding.Forward(ctx, batch.Inputs)
 

model/models/gemma2/model.go

@@ -175,7 +175,7 @@ func (l *Layer) Forward(ctx ml.Context, hiddenState, positionIDs, outputs ml.Ten
 }
 
 func (m *Model) Forward(ctx ml.Context, batch input.Batch) (ml.Tensor, error) {
-	positions := ctx.Input().FromIntSlice(batch.Positions, len(batch.Positions))
+	positions := ctx.Input().FromInts(batch.Positions, len(batch.Positions))
 
 	hiddenState := m.TokenEmbedding.Forward(ctx, batch.Inputs)
 	hiddenState = hiddenState.Scale(ctx, math.Sqrt(float64(m.Options.hiddenSize)))

model/models/gemma3/model.go

@@ -101,7 +101,7 @@ func (m *Model) EncodeMultimodal(ctx ml.Context, multimodalData []byte) ([]input
 		return nil, err
 	}
 
-	pixelValues := ctx.Input().FromFloatSlice(f32s,
+	pixelValues := ctx.Input().FromFloats(f32s,
 		m.ImageProcessor.imageSize,
 		m.ImageProcessor.imageSize,
 		m.ImageProcessor.numChannels,

model/models/gemma3/model_text.go

@@ -163,7 +163,7 @@ func (l *TextLayer) Forward(ctx ml.Context, layer int, hiddenState, positionIDs,
 }
 
 func (m *TextModel) Forward(ctx ml.Context, batch input.Batch, cache kvcache.Cache) ml.Tensor {
-	positions := ctx.Input().FromIntSlice(batch.Positions, len(batch.Positions))
+	positions := ctx.Input().FromInts(batch.Positions, len(batch.Positions))
 
 	hiddenState := m.TokenEmbedding.Forward(ctx, batch.Inputs)
 	hiddenState = hiddenState.Scale(ctx, math.Sqrt(float64(m.TextConfig.hiddenSize)))

model/models/gemma3n/model_text.go

@@ -29,9 +29,9 @@ type TextModel struct {
 }
 
 func (m *TextModel) Forward(ctx ml.Context, batch input.Batch, cache kvcache.Cache) (ml.Tensor, error) {
-	positions := ctx.Input().FromIntSlice(batch.Positions, len(batch.Positions))
+	positions := ctx.Input().FromInts(batch.Positions, len(batch.Positions))
 	// Create a tensor of a single float32 value of 1.0 to use for altup correction
-	one := ctx.Input().FromFloatSlice([]float32{1.0}, 1)
+	one := ctx.Input().FromFloats([]float32{1.0}, 1)
 
 	inputs := m.TokenEmbedding.Forward(ctx, batch.Inputs, math.Sqrt(float64(m.hiddenSize)))
 	inputsPerLayer := m.PerLayerProjector.Forward(ctx, batch, inputs, &m.TextOptions)

model/models/gptoss/model.go

@@ -30,9 +30,9 @@ type Transformer struct {
 // Forward implements model.Model.
 func (m *Transformer) Forward(ctx ml.Context, batch input.Batch) (ml.Tensor, error) {
 	hiddenStates := m.TokenEmbedding.Forward(ctx, batch.Inputs)
-	positions := ctx.Input().FromIntSlice(batch.Positions, len(batch.Positions))
+	positions := ctx.Input().FromInts(batch.Positions, len(batch.Positions))
 
-	one := ctx.Input().FromFloatSlice([]float32{1}, 1)
+	one := ctx.Input().FromFloats([]float32{1}, 1)
 	for i, block := range m.TransformerBlocks {
 		m.Cache.SetLayer(i)
 		if c, ok := m.Cache.(*kvcache.WrapperCache); ok {

model/models/llama/model.go

@@ -179,7 +179,7 @@ func (l *Layer) Forward(ctx ml.Context, hiddenState, positions, outputs ml.Tenso
 }
 
 func (m *Model) Forward(ctx ml.Context, batch input.Batch) (ml.Tensor, error) {
-	positions := ctx.Input().FromIntSlice(batch.Positions, len(batch.Positions))
+	positions := ctx.Input().FromInts(batch.Positions, len(batch.Positions))
 
 	hiddenState := m.TokenEmbedding.Forward(ctx, batch.Inputs)
 

model/models/llama4/model.go

@@ -76,7 +76,7 @@ func (m *Model) EncodeMultimodal(ctx ml.Context, multimodalData []byte) ([]input
 		return nil, err
 	}
 
-	tilesLocal := ctx.Input().FromFloatSlice(pixelsLocal, size.X, size.Y, m.numChannels)
+	tilesLocal := ctx.Input().FromFloats(pixelsLocal, size.X, size.Y, m.numChannels)
 
 	ratioW, ratioH := size.X/m.imageSize, size.Y/m.imageSize
 
@@ -87,7 +87,7 @@ func (m *Model) EncodeMultimodal(ctx ml.Context, multimodalData []byte) ([]input
 	pixelValues := tilesLocal
 
 	if len(pixelsGlobal) > 0 {
-		tilesGlobal := ctx.Input().FromFloatSlice(pixelsGlobal, m.imageSize, m.imageSize, m.numChannels)
+		tilesGlobal := ctx.Input().FromFloats(pixelsGlobal, m.imageSize, m.imageSize, m.numChannels)
 		pixelValues = pixelValues.Concat(ctx, tilesGlobal, 3)
 	}
 
@@ -174,7 +174,7 @@ func (m *Model) PostTokenize(inputs []*input.Input) ([]*input.Input, error) {
 }
 
 func (m *Model) Forward(ctx ml.Context, batch input.Batch) (ml.Tensor, error) {
-	positions := ctx.Input().FromIntSlice(batch.Positions, len(batch.Positions))
+	positions := ctx.Input().FromInts(batch.Positions, len(batch.Positions))
 	return m.TextModel.Forward(ctx, batch.Inputs, positions, batch.Outputs, batch, m.Cache), nil
 }
 

model/models/llama4/model_text.go

@@ -211,7 +211,7 @@ func (m *TextModel) Forward(ctx ml.Context, inputs, positions, outputs ml.Tensor
 			scales[i] = float32(math.Log(math.Floor(((float64(p)+1.0)/float64(m.attentionFloorScale))+1.0))*m.attentionScale + 1.0)
 		}
 
-		attentionScales = ctx.Input().FromFloatSlice(scales, 1, 1, len(scales))
+		attentionScales = ctx.Input().FromFloats(scales, 1, 1, len(scales))
 	}
 
 	for i, layer := range m.Layers {

model/models/llama4/model_vision.go

@@ -245,7 +245,7 @@ func (m *VisionModel) rotaryEmbedding(ctx ml.Context) (ml.Tensor, ml.Tensor) {
 		}
 	}
 
-	ropeFreqs := ctx.Input().FromFloatSlice(freqs, freqDim/2, numPatches, 2)
+	ropeFreqs := ctx.Input().FromFloats(freqs, freqDim/2, numPatches, 2)
 
 	ropeFreqs = ropeFreqs.Permute(ctx, 0, 2, 1, 3).Contiguous(ctx)
 	ropeFreqs = ropeFreqs.Reshape(ctx, freqDim, 1, numPatches)

model/models/mistral3/model.go

@@ -114,7 +114,7 @@ func (m *Model) EncodeMultimodal(ctx ml.Context, multimodalData []byte) ([]input
 		return nil, err
 	}
 
-	pixelValues := ctx.Input().FromFloatSlice(f32s, size.X, size.Y, m.ImageProcessor.numChannels)
+	pixelValues := ctx.Input().FromFloats(f32s, size.X, size.Y, m.ImageProcessor.numChannels)
 
 	visionOutputs := m.VisionModel.Forward(ctx, pixelValues)
 	features, size := m.MultiModalProjector.Forward(ctx, visionOutputs, size)
@@ -158,7 +158,7 @@ func (m *Model) PostTokenize(inputs []*input.Input) ([]*input.Input, error) {
 }
 
 func (m *Model) Forward(ctx ml.Context, batch input.Batch) (ml.Tensor, error) {
-	positions := ctx.Input().FromIntSlice(batch.Positions, len(batch.Positions))
+	positions := ctx.Input().FromInts(batch.Positions, len(batch.Positions))
 
 	return m.TextModel.Forward(ctx, batch.Inputs, positions, batch.Outputs, batch, m.Cache), nil
 }

model/models/mistral3/model_vision.go

@@ -110,8 +110,8 @@ func (m *VisionModel) positionalEmbedding(ctx ml.Context, positionIDs ml.Tensor)
 		}
 	}
 
-	h := ctx.Input().FromFloatSlice(frequenciesHeight, maxPatchesPerSide, frequencies/2)
-	w := ctx.Input().FromFloatSlice(frequenciesWidth, maxPatchesPerSide, frequencies/2)
+	h := ctx.Input().FromFloats(frequenciesHeight, maxPatchesPerSide, frequencies/2)
+	w := ctx.Input().FromFloats(frequenciesWidth, maxPatchesPerSide, frequencies/2)
 
 	h = h.Permute(ctx, 1, 0, 2, 3).Contiguous(ctx)
 	w = w.Permute(ctx, 1, 0, 2, 3).Contiguous(ctx)
@@ -144,7 +144,7 @@ func (m *VisionModel) Forward(ctx ml.Context, pixelValues ml.Tensor) ml.Tensor {
 		}
 	}
 
-	positionIDs := ctx.Input().FromIntSlice(positions, len(positions))
+	positionIDs := ctx.Input().FromInts(positions, len(positions))
 
 	positionEmbedding := m.positionalEmbedding(ctx, positionIDs)
 	cos, sin := positionEmbedding.Cos(ctx), positionEmbedding.Sin(ctx)

model/models/mllama/model.go

@@ -80,8 +80,8 @@ func (m *Model) EncodeMultimodal(ctx ml.Context, multimodalData []byte) ([]input
 		f32s = f32s[:m.imageSize*m.imageSize*m.numChannels*m.maxNumTiles]
 	}
 
-	pixelValues := ctx.Input().FromFloatSlice(f32s, m.imageSize, m.imageSize, m.numChannels, m.maxNumTiles)
-	aspectRatio := ctx.Input().FromIntSlice([]int32{int32(ratio.rank)}, 1)
+	pixelValues := ctx.Input().FromFloats(f32s, m.imageSize, m.imageSize, m.numChannels, m.maxNumTiles)
+	aspectRatio := ctx.Input().FromInts([]int32{int32(ratio.rank)}, 1)
 
 	positionIDs := ctx.Arange(0, 1601, 1, ml.DTypeI32)
 	crossAttentionStates := m.VisionModel.Forward(ctx, pixelValues, positionIDs, aspectRatio)
@@ -106,7 +106,7 @@ func (m *Model) Forward(ctx ml.Context, batch input.Batch) (ml.Tensor, error) {
 		crossAttentionStates = batch.Multimodal[len(batch.Multimodal)-1].Multimodal[0].Tensor
 	}
 
-	positions := ctx.Input().FromIntSlice(batch.Positions, len(batch.Positions))
+	positions := ctx.Input().FromInts(batch.Positions, len(batch.Positions))
 
 	// TODO: attention mask, cross attention mask
 	return m.TextModel.Forward(ctx, batch.Inputs, positions, batch.Outputs, crossAttentionStates, nil, m.Cache.(*kvcache.WrapperCache)), nil

model/models/qwen2/model.go

@@ -102,7 +102,7 @@ type Model struct {
 
 // Forward implements model.Model.
 func (m Model) Forward(ctx ml.Context, batch input.Batch) (ml.Tensor, error) {
-	positions := ctx.Input().FromIntSlice(batch.Positions, len(batch.Positions))
+	positions := ctx.Input().FromInts(batch.Positions, len(batch.Positions))
 
 	hiddenStates := m.TokenEmbedding.Forward(ctx, batch.Inputs)
 

model/models/qwen25vl/model.go

@@ -69,7 +69,7 @@ func (m *Model) PixelValues(ctx ml.Context, multimodalData []byte) (ml.Tensor, *
 		m.ImageProcessor.patchSize * m.ImageProcessor.patchSize
 	numPatches := grid.Temporal * grid.Height * grid.Width
 
-	pixelValues := ctx.Input().FromFloatSlice(f32s, patchDim, numPatches)
+	pixelValues := ctx.Input().FromFloats(f32s, patchDim, numPatches)
 
 	return pixelValues, grid, nil
 }
@@ -139,7 +139,7 @@ func (m *Model) PostTokenize(inputs []*input.Input) ([]*input.Input, error) {
 }
 
 func (m *Model) Forward(ctx ml.Context, batch input.Batch) (ml.Tensor, error) {
-	positions := ctx.Input().FromIntSlice(batch.Positions, len(batch.Positions))
+	positions := ctx.Input().FromInts(batch.Positions, len(batch.Positions))
 
 	return m.TextModel.Forward(ctx, batch.Inputs, positions, batch.Outputs, batch, m.Cache)
 }