Files
ollama/x/imagegen/client/quantize.go
Jeffrey Morgan 9667c2282f x/imagegen: add naive TeaCache and FP8 quantization support (#13683)
TeaCache:
- Timestep embedding similarity caching for diffusion models
- Polynomial rescaling with configurable thresholds
- Reduces transformer forward passes by ~30-50%

FP8 quantization:
- Support for FP8 quantized models (8-bit weights with scales)
- QuantizedMatmul on Metal, Dequantize on CUDA
- Client-side quantization via ollama create --quantize fp8

Other bug fixes:
- Fix `/api/show` API for image generation models
- Server properly returns model info (architecture, parameters, quantization)
- Memory allocation optimizations
- CLI improvements for image generation
2026-01-12 13:45:22 -08:00

121 lines
4.1 KiB
Go

//go:build mlx
package client
import (
"fmt"
"io"
"os"
"path/filepath"
"github.com/ollama/ollama/x/imagegen/mlx"
)
// quantizeTensor loads a tensor from safetensors format, quantizes it to affine int8,
// and returns safetensors data for the quantized weights, scales, and biases.
// Uses MLX's native SaveSafetensors to ensure correct dtype handling (especially uint32 for quantized weights).
func quantizeTensor(r io.Reader, name, dtype string, shape []int32) (qweightData, scalesData, qbiasData []byte, qweightShape, scalesShape, qbiasShape []int32, err error) {
tmpDir := ensureTempDir()
// Read safetensors data to a temp file (LoadSafetensorsNative needs a path)
tmpFile, err := os.CreateTemp(tmpDir, "quant-input-*.safetensors")
if err != nil {
return nil, nil, nil, nil, nil, nil, fmt.Errorf("failed to create temp file: %w", err)
}
tmpPath := tmpFile.Name()
defer os.Remove(tmpPath)
if _, err := io.Copy(tmpFile, r); err != nil {
tmpFile.Close()
return nil, nil, nil, nil, nil, nil, fmt.Errorf("failed to write temp file: %w", err)
}
tmpFile.Close()
// Load the tensor using MLX's native loader
st, err := mlx.LoadSafetensorsNative(tmpPath)
if err != nil {
return nil, nil, nil, nil, nil, nil, fmt.Errorf("failed to load safetensors: %w", err)
}
defer st.Free()
// Get the tensor (it's stored as "data" in our minimal safetensors format)
arr := st.Get("data")
if arr == nil {
return nil, nil, nil, nil, nil, nil, fmt.Errorf("tensor 'data' not found in safetensors")
}
// Convert to BFloat16 if needed (quantize expects float type)
if arr.Dtype() != mlx.DtypeBFloat16 && arr.Dtype() != mlx.DtypeFloat32 && arr.Dtype() != mlx.DtypeFloat16 {
arr = mlx.AsType(arr, mlx.DtypeBFloat16)
mlx.Eval(arr)
}
// Quantize with affine mode: group_size=32, bits=8
// Note: mxfp8 mode doesn't have matmul kernels in MLX, affine mode does
qweight, scales, qbiases := mlx.Quantize(arr, 32, 8, "affine")
// Eval and make contiguous for data access
qweight = mlx.Contiguous(qweight)
scales = mlx.Contiguous(scales)
if qbiases != nil {
qbiases = mlx.Contiguous(qbiases)
mlx.Eval(qweight, scales, qbiases)
} else {
mlx.Eval(qweight, scales)
}
// Get shapes
qweightShape = qweight.Shape()
scalesShape = scales.Shape()
// Save quantized weight using MLX's native safetensors (correctly handles uint32 dtype)
qweightPath := filepath.Join(tmpDir, "qweight.safetensors")
defer os.Remove(qweightPath)
if err := mlx.SaveSafetensors(qweightPath, map[string]*mlx.Array{"data": qweight}); err != nil {
return nil, nil, nil, nil, nil, nil, fmt.Errorf("failed to save quantized weight: %w", err)
}
qweightData, err = os.ReadFile(qweightPath)
if err != nil {
return nil, nil, nil, nil, nil, nil, fmt.Errorf("failed to read quantized weight: %w", err)
}
// Save scales using MLX's native safetensors
scalesPath := filepath.Join(tmpDir, "scales.safetensors")
defer os.Remove(scalesPath)
if err := mlx.SaveSafetensors(scalesPath, map[string]*mlx.Array{"data": scales}); err != nil {
return nil, nil, nil, nil, nil, nil, fmt.Errorf("failed to save scales: %w", err)
}
scalesData, err = os.ReadFile(scalesPath)
if err != nil {
return nil, nil, nil, nil, nil, nil, fmt.Errorf("failed to read scales: %w", err)
}
// Affine mode returns qbiases for zero-point offset
if qbiases != nil {
qbiasShape = qbiases.Shape()
qbiasPath := filepath.Join(tmpDir, "qbias.safetensors")
defer os.Remove(qbiasPath)
if err := mlx.SaveSafetensors(qbiasPath, map[string]*mlx.Array{"data": qbiases}); err != nil {
return nil, nil, nil, nil, nil, nil, fmt.Errorf("failed to save qbiases: %w", err)
}
qbiasData, err = os.ReadFile(qbiasPath)
if err != nil {
return nil, nil, nil, nil, nil, nil, fmt.Errorf("failed to read qbiases: %w", err)
}
}
return qweightData, scalesData, qbiasData, qweightShape, scalesShape, qbiasShape, nil
}
// QuantizeSupported returns true if quantization is supported (MLX build)
func QuantizeSupported() bool {
return true
}
// ensureTempDir creates the temp directory for quantization if it doesn't exist
func ensureTempDir() string {
tmpDir := filepath.Join(os.TempDir(), "ollama-quantize")
os.MkdirAll(tmpDir, 0755)
return tmpDir
}