root
/
ollama
mirror of https://github.com/ollama/ollama.git
1
0
Fork 0
Code Issues Actions 1 Packages Projects Releases Wiki Activity

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

This commit is contained in:
Michael Yang 2025-10-28 12:08:49 -07:00 committed by GitHub
parent 15c7d30d9a
commit 1188f408dd
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
24 changed files with 95 additions and 76 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

4
kvcache/causal.go
View File

@ -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 {

18
kvcache/causal_test.go
View File

@ -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
}

9
ml/backend.go
View File

@ -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

70
ml/backend/ggml/ggml.go
View File

@ -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 {
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))
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) FromIntSlice(s []int32, shape ...int) ml.Tensor {
func (c *Context) FromFloats(s []float32, shape ...int) ml.Tensor {
checkShape(s, shape...)
t := c.newTensor(ml.DTypeF32, shape)
if c.b.allocMemory {
t.FromFloats(s)
}
return t
}
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
}

2
model/models/bert/embed.go
View File

@ -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 {

2
model/models/deepseek2/model.go
View File

@ -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)

2
model/models/gemma2/model.go
View File

@ -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)))

2
model/models/gemma3/model.go
View File

@ -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,

2
model/models/gemma3/model_text.go
View File

@ -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)))

4
model/models/gemma3n/model_text.go
View File

@ -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)

4
model/models/gptoss/model.go
View File

@ -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 {

2
model/models/llama/model.go
View File

@ -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)

6
model/models/llama4/model.go
View File

@ -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
}

2
model/models/llama4/model_text.go
View File

@ -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 {

2
model/models/llama4/model_vision.go
View File

@ -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)

4
model/models/mistral3/model.go
View File

@ -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
}

6
model/models/mistral3/model_vision.go
View File

@ -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)

6
model/models/mllama/model.go
View File

@ -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

2
model/models/qwen2/model.go
View File

@ -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)

4
model/models/qwen25vl/model.go
View File

@ -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)
}

8
model/models/qwen25vl/model_vision.go
View File

@ -43,7 +43,7 @@ func blockDiagonalMask(ctx ml.Context, seqLength int, bounds []int, numHeads int
}
}
mask := ctx.Input().FromFloatSlice(flat, seqLength, seqLength)
mask := ctx.Input().FromFloats(flat, seqLength, seqLength)
// Reshape to match [seqLength, seqLength, 1] for broadcasting
mask = mask.Reshape(ctx, seqLength, seqLength, 1)
@ -299,7 +299,7 @@ func (m *VisionModel) WindowIndex(ctx ml.Context, grid *Grid) (ml.Tensor, []int)
}
}
t := ctx.Input().FromIntSlice(index, len(index))
t := ctx.Input().FromInts(index, len(index))
return t, bounds
}
@ -319,7 +319,7 @@ func (m *VisionModel) PositionalEmbedding(ctx ml.Context, grid *Grid) ml.Tensor
freqVals[i*freq+j] = float32(i) / float32(math.Pow(theta, float64(j*2)/float64(dim)))
}
}
freqs := ctx.Input().FromFloatSlice(freqVals, freq, maxGridSize)
freqs := ctx.Input().FromFloats(freqVals, freq, maxGridSize)
// Create position coordinates (y,x pairs) for the grid
// In PyTorch: Equivalent to generating position ids with torch.arange()
@ -329,7 +329,7 @@ func (m *VisionModel) PositionalEmbedding(ctx ml.Context, grid *Grid) ml.Tensor
coords = append(coords, int32(y), int32(x))
}
}
pos := ctx.Input().FromIntSlice(coords, 2, grid.Width, grid.Height)
pos := ctx.Input().FromInts(coords, 2, grid.Width, grid.Height)
// Reshape and permute positions to match spatial merging pattern
pos = pos.Reshape(ctx, 2, grid.Width, merge, grid.Height/merge)

2
model/models/qwen3/model.go
View File

@ -181,7 +181,7 @@ func (m *Model) Forward(ctx ml.Context, batch input.Batch) (ml.Tensor, error) {
// 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)

2
runner/ollamarunner/multimodal.go
View File

@ -102,7 +102,7 @@ func (m multimodalStore) getTensor(backend ml.Backend, ctx ml.Context, in ml.Ten
for i, t := range entry.mm {
if in == t.Tensor {
if !reserve {
return ctx.Input().FromFloatSlice(entry.data[i], t.Tensor.Shape()...), nil
return ctx.Input().FromFloats(entry.data[i], t.Tensor.Shape()...), nil
} else {
return ctx.Input().Empty(t.Tensor.DType(), t.Tensor.Shape()...), nil
}

6
runner/ollamarunner/runner.go
View File

@ -599,7 +599,7 @@ func (s *Server) forwardBatch(pendingBatch batchState) (nextBatch batchState, er
// Actual batchInputs values will be injected into the batch.Inputs tensor before calling Compute
batch.Inputs = nextBatch.ctx.Input().Empty(ml.DTypeI32, len(batchInputs))
batch.Outputs = nextBatch.ctx.Input().FromIntSlice(batchOutputs, len(batchOutputs))
batch.Outputs = nextBatch.ctx.Input().FromInts(batchOutputs, len(batchOutputs))
nextBatch.modelOutput, err = model.Forward(nextBatch.ctx, s.model, batch)
if err != nil {
err = fmt.Errorf("failed to build graph: %w", err)
@ -692,7 +692,7 @@ func (s *Server) computeBatch(activeBatch batchState) {
// At this point the seqs are ready for forwardBatch to move forward so unblock
s.mu.Unlock()
activeBatch.batch.Inputs.SetValueFromIntSlice(batchInputs)
activeBatch.batch.Inputs.FromInts(batchInputs)
activeBatch.ctx.ComputeWithNotify(
func() {
logutil.Trace("computeBatch: signaling computeStartedCh", "batchID", activeBatch.id)
@ -1090,7 +1090,7 @@ func (s *Server) reserveWorstCaseGraph() error {
batch.Positions[i] = int32(i)
}
batch.Inputs = ctx.Input().FromIntSlice(batchInputs, len(batchInputs))
batch.Inputs = ctx.Input().FromInts(batchInputs, len(batchInputs))
batch.Outputs = ctx.Input().Empty(ml.DTypeI32, s.parallel)
cache := s.model.Config().Cache