mirror of
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424810450f
* Move quantization logic to GGML via new backend This moves the model aware logic to Go code and calls GGMLs quantization code for model creation. * Remove "add model quantizations" This is no longer needed now that quantization is implemented in Go+GGML code directly.
274 lines
9.3 KiB
Go
274 lines
9.3 KiB
Go
package server
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import (
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"fmt"
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"io"
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"log/slog"
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"maps"
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"os"
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"strings"
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"unsafe"
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fsggml "github.com/ollama/ollama/fs/ggml"
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"github.com/ollama/ollama/ml/backend/ggml"
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)
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type quantizer struct {
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*os.File
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offset uint64
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from, to *fsggml.Tensor
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progressFn func(n uint64)
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}
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func (q quantizer) WriteTo(w io.Writer) (int64, error) {
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quantize := q.from.Kind != q.to.Kind
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sr := io.NewSectionReader(q, int64(q.offset), int64(q.from.Size()))
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if !quantize {
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n, err := io.Copy(w, sr)
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q.progressFn(q.from.Size())
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return n, err
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}
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data, err := io.ReadAll(sr)
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if err != nil {
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slog.Warn("file read error", "tensor", q.from.Name, "file", q.Name(), "error", err)
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return 0, fmt.Errorf("unable to read tensor %s from %s: %s", q.from.Name, q.Name(), err)
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}
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var f32s []float32
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newType := fsggml.TensorType(q.to.Kind)
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if fsggml.TensorType(q.from.Kind) == fsggml.TensorTypeF32 {
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f32s = unsafe.Slice((*float32)(unsafe.Pointer(&data[0])), q.from.Elements())
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} else {
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f32s = ggml.ConvertToF32(data, q.from.Kind, q.from.Elements())
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}
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data = ggml.Quantize(newType, f32s, q.from.Shape)
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n, err := w.Write(data)
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q.progressFn(q.from.Size())
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return int64(n), err
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}
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type quantizeState struct {
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nAttnV int // Number of attn_*v* weight tensors
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nFfnDown int // Number of ffn_down tensors
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iAttnV int // Running counter of number of attn_v tensors that have been processed
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iFfnDown int // Running counter of number of ffn_down tensors that have been processed
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hasOutput bool // used to figure out if a model shares tok_embd with the output weight
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}
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func useMoreBits(iLayer, nLayers int) bool {
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return iLayer < (nLayers/8) || iLayer >= 7*nLayers/8 || (iLayer-nLayers/8)%3 == 2
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}
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func getTensorNewType(kv fsggml.KV, qs *quantizeState, newType fsggml.TensorType, name string, shape []uint64, ftype fsggml.FileType) fsggml.TensorType {
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// Ported from llama_tensor_get_type, removed unsupported quantization types
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nExperts := max(1, kv.Uint("expert_count", 0))
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if name == "output.weight" || name == "output_norm.weight" || (!qs.hasOutput && name == "token_embd.weight") {
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nx := shape[0]
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qk_k := newType.BlockSize()
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if nx%qk_k != 0 {
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newType = fsggml.TensorTypeQ8_0
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} else if newType != fsggml.TensorTypeQ8_0 {
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newType = fsggml.TensorTypeQ6_K
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}
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} else if strings.Contains(name, "attn_v.weight") {
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if ftype == fsggml.FileTypeQ2_K {
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if kv.GQA() >= 4 {
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newType = fsggml.TensorTypeQ4_K
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} else {
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newType = fsggml.TensorTypeQ3_K
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}
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} else if ftype == fsggml.FileTypeQ2_K_S && kv.GQA() >= 4 {
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newType = fsggml.TensorTypeQ4_K
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} else if ftype == fsggml.FileTypeQ3_K_M {
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if qs.iAttnV < 2 {
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newType = fsggml.TensorTypeQ5_K
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} else {
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newType = fsggml.TensorTypeQ4_K
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}
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} else if ftype == fsggml.FileTypeQ3_K_L {
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newType = fsggml.TensorTypeQ5_K
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} else if (ftype == fsggml.FileTypeQ4_K_M || ftype == fsggml.FileTypeQ5_K_M) &&
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useMoreBits(qs.iAttnV, qs.nAttnV) {
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newType = fsggml.TensorTypeQ6_K
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} else if ftype == fsggml.FileTypeQ4_K_S && qs.iAttnV < 4 {
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newType = fsggml.TensorTypeQ5_K
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}
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// TODO
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// if (qs.model.type == LLM_TYPE_70B) {
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// // In the 70B model we have 8 heads sharing the same attn_v weights. As a result, the attn_v.weight tensor is
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// // 8x smaller compared to attn_q.weight. Hence, we can get a nice boost in quantization accuracy with
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// // nearly negligible increase in model size by quantizing this tensor with more bits:
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// if (newType == GGML_TYPE_Q3_K || newType == GGML_TYPE_Q4_K) newType = GGML_TYPE_Q5_K;
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// }
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if nExperts == 8 {
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// for the 8-expert model, bumping this to Q8_0 trades just ~128MB
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newType = fsggml.TensorTypeQ8_0
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}
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qs.iAttnV++
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} else if strings.Contains(name, "attn_k.weight") {
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if nExperts == 8 {
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// for the 8-expert model, bumping this to Q8_0 trades just ~128MB
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newType = fsggml.TensorTypeQ8_0
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}
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} else if strings.Contains(name, "ffn_down") {
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iLayer := qs.iFfnDown
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n_layer := qs.nFfnDown
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if ftype == fsggml.FileTypeQ2_K {
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newType = fsggml.TensorTypeQ3_K
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} else if ftype == fsggml.FileTypeQ2_K_S {
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if iLayer < n_layer/8 {
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newType = fsggml.TensorTypeQ4_K
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}
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} else if ftype == fsggml.FileTypeQ3_K_M {
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if iLayer < n_layer/16 {
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newType = fsggml.TensorTypeQ5_K
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} else if useMoreBits(iLayer, n_layer) {
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newType = fsggml.TensorTypeQ4_K
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} else {
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newType = fsggml.TensorTypeQ3_K
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}
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} else if ftype == fsggml.FileTypeQ3_K_L {
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newType = fsggml.TensorTypeQ5_K
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} else if ftype == fsggml.FileTypeQ4_K_M {
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if useMoreBits(iLayer, n_layer) {
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newType = fsggml.TensorTypeQ6_K
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}
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} else if ftype == fsggml.FileTypeQ5_K_M && useMoreBits(iLayer, n_layer) {
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newType = fsggml.TensorTypeQ6_K
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} else if ftype == fsggml.FileTypeQ4_K_S && iLayer < n_layer/8 {
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newType = fsggml.TensorTypeQ5_K
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}
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qs.iFfnDown++
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} else if strings.Contains(name, "attn_output.weight") {
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if nExperts == 8 {
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if ftype == fsggml.FileTypeQ2_K || ftype == fsggml.FileTypeQ3_K_S || ftype == fsggml.FileTypeQ3_K_M ||
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ftype == fsggml.FileTypeQ4_K_S || ftype == fsggml.FileTypeQ4_K_M {
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newType = fsggml.TensorTypeQ5_K
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}
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} else {
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if ftype == fsggml.FileTypeQ2_K {
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newType = fsggml.TensorTypeQ3_K
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} else if ftype == fsggml.FileTypeQ3_K_M {
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newType = fsggml.TensorTypeQ4_K
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} else if ftype == fsggml.FileTypeQ3_K_L {
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newType = fsggml.TensorTypeQ5_K
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}
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}
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} else if strings.Contains(name, "attn_qkv.weight") {
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if ftype == fsggml.FileTypeQ3_K_M || ftype == fsggml.FileTypeQ3_K_L {
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newType = fsggml.TensorTypeQ4_K
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} else if ftype == fsggml.FileTypeQ4_K_M {
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newType = fsggml.TensorTypeQ5_K
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} else if ftype == fsggml.FileTypeQ5_K_M {
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newType = fsggml.TensorTypeQ6_K
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}
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}
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if newType.IsQuantized() {
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nx := shape[0]
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ny := uint64(1)
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if len(shape) > 1 {
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ny = shape[1]
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}
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qk_k := newType.BlockSize()
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if nx%qk_k != 0 {
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slog.Warn(fmt.Sprintf("tensor cols %d x %d are not divisible by %d, required for %s. Falling back to quantization %s", nx, ny, qk_k, newType.String(), fsggml.TensorTypeF16.String()))
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newType = fsggml.TensorTypeF16
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}
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}
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return newType
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}
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func quantize(in, out *os.File, orig *fsggml.GGML, newFileType fsggml.FileType, progressFn func(n uint64)) error {
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kv := maps.Clone(orig.KV())
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kv["general.file_type"] = newFileType
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// kv["general.quantization_version"] = ggml.QuantizationVersion()
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qs := &quantizeState{}
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// Build up the quantize state so newType can adjust types
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layerCount := 0
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for k, l := range orig.Tensors().GroupLayers() {
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if strings.HasPrefix(k, "blk.") {
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layerCount++
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}
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for _, tensor := range l {
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if strings.Contains(tensor.Name, "attn_v.weight") ||
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strings.Contains(tensor.Name, "attn_qkv.weight") ||
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strings.Contains(tensor.Name, "attn_kv_b.weight") {
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qs.nAttnV++
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} else if tensor.Name == "output.weight" {
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qs.hasOutput = true
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}
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}
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}
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qs.nFfnDown = layerCount
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origTensors := orig.Tensors().Items()
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outputTensors := make([]*fsggml.Tensor, len(origTensors))
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for i, tensor := range origTensors {
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tensor := tensor
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newType := newType(tensor, kv, qs, newFileType)
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newTensor := &fsggml.Tensor{
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Name: tensor.Name,
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Shape: tensor.Shape,
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Kind: uint32(newType),
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}
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outputTensors[i] = newTensor
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outputTensors[i].WriterTo = quantizer{
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File: in,
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offset: orig.Tensors().Offset + tensor.Offset,
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from: tensor,
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to: newTensor,
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progressFn: progressFn,
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}
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}
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return fsggml.WriteGGUF(out, kv, outputTensors)
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}
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func newType(t *fsggml.Tensor, kv fsggml.KV, qs *quantizeState, ftype fsggml.FileType) fsggml.TensorType {
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defaultType := ftype.ToTensorType()
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name := t.Name
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quantize := strings.HasSuffix(name, "weight")
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// don't quantize vision stuff
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quantize = quantize && (!strings.Contains(name, "v.") || strings.Contains(name, "_v."))
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quantize = quantize && !strings.Contains(name, "mm.")
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// quantize only 2D and 3D tensors (experts)
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quantize = quantize && (len(t.Shape) >= 2)
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// do not quantize norm tensors
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quantize = quantize && !strings.Contains(name, "_norm.weight")
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// do not quantize expert gating tensors
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quantize = quantize && !strings.Contains(name, "ffn_gate_inp.weight")
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// do not quantize positional embeddings and token types (BERT)
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quantize = quantize && (name != "position_embd.weight")
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quantize = quantize && (name != "token_types.weight")
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// do not quantize Mamba's small yet 2D weights
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// NOTE: can't use LLM_TN here because the layer number is not known
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quantize = quantize && !strings.Contains(name, "ssm_conv1d.weight")
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// do not quantize RWKV's time_mix_first tensors
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quantize = quantize && !strings.Contains(name, "time_mix_first.weight")
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quantize = quantize && !strings.Contains(name, "time_mix_w1.weight")
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quantize = quantize && !strings.Contains(name, "time_mix_w2.weight")
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quantize = quantize && !strings.Contains(name, "time_mix_decay_w1.weight")
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quantize = quantize && !strings.Contains(name, "time_mix_decay_w2.weight")
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quantize = quantize && !strings.Contains(name, "time_mix_lerp_fused.weight")
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// do not quantize relative position bias (T5)
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quantize = quantize && !strings.Contains(name, "attn_rel_b.weight")
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newType := fsggml.TensorType(t.Kind)
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if quantize {
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// get more optimal quantization type based on the tensor shape, layer, etc.
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newType = getTensorNewType(kv, qs, defaultType, t.Name, t.Shape, ftype)
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if newType != defaultType {
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slog.Debug("tensor quantization adjusted for better quality", "name", t.Name, "requested", defaultType, "quantization", newType)
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}
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}
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return newType
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}
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