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

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This commit is contained in:
Michael Yang
2025-10-28 12:08:49 -07:00
committed by GitHub
parent 15c7d30d9a
commit 1188f408dd
24 changed files with 95 additions and 76 deletions
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2
model/models/bert/embed.go
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@@ -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
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@@ -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
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@@ -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
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@@ -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
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@@ -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
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@@ -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
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@@ -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
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@@ -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
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@@ -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
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@@ -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
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@@ -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
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@@ -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
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@@ -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
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@@ -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
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@@ -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
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@@ -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
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@@ -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
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@@ -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)