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remove debugging code

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This commit is contained in:
Bruce MacDonald 2025-03-24 09:48:20 -07:00
parent 62108621d5
commit dce7cf2a1a
3 changed files with 5 additions and 26 deletions
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5
convert/convert.go
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@ -248,10 +248,5 @@ func ConvertModel(fsys fs.FS, ws io.WriteSeeker) error {
return err
}
// iterate through all ts and print the name
for _, t := range ts {
fmt.Print(t.Name(), "\n")
}
return conv.writeFile(ws, conv.KV(t), conv.Tensors(ts))
}

1
convert/convert_mistral.go
View File

@ -93,7 +93,6 @@ func (p *mistral3Model) Tensors(ts []Tensor) []ggml.Tensor {
var out []ggml.Tensor
for _, t := range ts {
fmt.Println("tensor", t.Name(), "shape", t.Shape(), "kind", t.Kind())
if strings.HasSuffix(t.Name(), "attn_q.weight") ||
strings.HasSuffix(t.Name(), "attn_k.weight") {
t.SetRepacker(p.repack)

25
model/models/llama/model.go
View File

@ -13,9 +13,9 @@ import (
)
type Options struct {
hiddenSize, numHeads, numKVHeads, headDim int
eps, ropeBase, ropeScale float32
ropeDim uint32
hiddenSize, numHeads, numKVHeads int
eps, ropeBase, ropeScale float32
ropeDim uint32
}
type Model struct {
@ -37,8 +37,6 @@ func New(c ml.Config) (model.Model, error) {
m := Model{
BytePairEncoding: model.NewBytePairEncoding(
// TODO: need to set this in the conversion for mistral:
// tokenizer.ggml.pretokenizer = [^\r\n\p{L}\p{N}]?[\p{Lu}\p{Lt}\p{Lm}\p{Lo}\p{M}]*[\p{Ll}\p{Lm}\p{Lo}\p{M}]+|[^\r\n\p{L}\p{N}]?[\p{Lu}\p{Lt}\p{Lm}\p{Lo}\p{M}]+[\p{Ll}\p{Lm}\p{Lo}\p{M}]*|\p{N}| ?[^\s\p{L}\p{N}]+[\r\n/]*|\s*[\r\n]+|\s+(?!\S)|\s+
c.String("tokenizer.ggml.pretokenizer", `(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}{1,3}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+`),
&model.Vocabulary{
Values: c.Strings("tokenizer.ggml.tokens"),
@ -55,7 +53,6 @@ func New(c ml.Config) (model.Model, error) {
hiddenSize: int(c.Uint("embedding_length")),
numHeads: int(c.Uint("attention.head_count")),
numKVHeads: int(c.Uint("attention.head_count_kv")),
headDim: int(c.Uint("attention.key_length")),
eps: c.Float("attention.layer_norm_rms_epsilon"),
ropeBase: c.Float("rope.freq_base"),
ropeScale: c.Float("rope.freq_scale", 1),
@ -78,36 +75,24 @@ type SelfAttention struct {
func (sa *SelfAttention) Forward(ctx ml.Context, hiddenState, positionIDs ml.Tensor, cache kvcache.Cache, opts *Options) ml.Tensor {
batchSize := hiddenState.Dim(1)
headDim := opts.hiddenSize / opts.numHeads
ropeType := uint32(0)
// Get head dimension - use explicit value if available, otherwise calculate
headDim := opts.headDim
if headDim == 0 {
headDim = opts.hiddenSize / opts.numHeads
}
// Query projection and reshape
q := sa.Query.Forward(ctx, hiddenState)
q = q.Reshape(ctx, headDim, opts.numHeads, batchSize)
q = q.RoPE(ctx, positionIDs, sa.RopeFactors, opts.ropeDim, ropeType, opts.ropeBase, opts.ropeScale)
// Key projection and reshape
k := sa.Key.Forward(ctx, hiddenState)
k = k.Reshape(ctx, headDim, opts.numKVHeads, batchSize)
k = k.RoPE(ctx, positionIDs, sa.RopeFactors, opts.ropeDim, ropeType, opts.ropeBase, opts.ropeScale)
// Value projection and reshape
v := sa.Value.Forward(ctx, hiddenState)
v = v.Reshape(ctx, headDim, opts.numKVHeads, batchSize)
// Attention computation
scaleFactor := 1.0 / math.Sqrt(float64(headDim))
kqv := nn.Attention(ctx, q, k, v, scaleFactor, cache)
kqv = kqv.Reshape(ctx, opts.hiddenSize, batchSize)
// Reshape attention output for final projection
outputDim := headDim * opts.numHeads
kqv = kqv.Reshape(ctx, outputDim, batchSize)
// Apply output projection
return sa.Output.Forward(ctx, kqv)
}