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

model/models/qwen25vl/model_vision.go

@@ -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)

model/models/qwen3/model.go

@@ -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)
 

runner/ollamarunner/multimodal.go

@@ -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
 			}

runner/ollamarunner/runner.go

@@ -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