From 25248f4bd5a1d720f6775ee1e45dfed718d6f98f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=AErekc=C3=A4H=20nitraM=E2=80=AE?= Date: Fri, 7 Mar 2025 10:26:31 +0100 Subject: [PATCH 01/42] Better WantedBy declaration The problem with default.target is that it always points to the target that is currently started. So if you boot into single user mode or the rescue mode still Ollama tries to start. I noticed this because either tried (and failed) to start all the time during a system update, where Ollama definitely is not wanted. --- docs/linux.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/linux.md b/docs/linux.md index 12581bdd..2dda87f3 100644 --- a/docs/linux.md +++ b/docs/linux.md @@ -75,7 +75,7 @@ RestartSec=3 Environment="PATH=$PATH" [Install] -WantedBy=default.target +WantedBy=multi-user.target ``` Then start the service: From a1cda80bcb0b47d493be9dc061a2dfa8a0ddd61c Mon Sep 17 00:00:00 2001 From: Jesse Gross Date: Sat, 8 Mar 2025 15:45:31 -0800 Subject: [PATCH 02/42] model: Update encoder cache to use multimodal input processing handler The encoder cache needs to know the position of images in the input stream so that it knows when to delete them. Previously images didn't have a position, so we implied one by breaking batches before an image and then assuming the image was in the first position. However, multimodal objects are now given explicit positions in the input stream, so we can use that instead. Breaking batches was also a way to simulate a cross attention mask for mllama. However, given that it only supports a single sequence and a single image, this mask doesn't serve any real purpose. Removing the batch break does not appear to affect the quality of the output. Most of this is simply moving the input data structures to a new package to avoid import cycles. --- kvcache/cache.go | 3 +- kvcache/causal.go | 13 ++--- kvcache/causal_test.go | 3 +- kvcache/encoder.go | 9 ++-- kvcache/wrapper.go | 9 ++-- model/input/input.go | 37 ++++++++++++++ model/model.go | 83 +++++++++---------------------- model/model_test.go | 3 +- model/models/llama/model.go | 3 +- model/models/mllama/model.go | 13 ++--- runner/ollamarunner/cache.go | 13 ++--- runner/ollamarunner/cache_test.go | 72 +++++++++++++-------------- runner/ollamarunner/runner.go | 56 ++++++++------------- 13 files changed, 157 insertions(+), 160 deletions(-) create mode 100644 model/input/input.go diff --git a/kvcache/cache.go b/kvcache/cache.go index 2541f7c1..d3548905 100644 --- a/kvcache/cache.go +++ b/kvcache/cache.go @@ -4,6 +4,7 @@ import ( "errors" "github.com/ollama/ollama/ml" + "github.com/ollama/ollama/model/input" ) var ( @@ -51,7 +52,7 @@ type Cache interface { // StartForward is called before the start of the model's forward pass. // For each token in the coming batch, there must be a corresponding // entry in positions and seqs. - StartForward(ctx ml.Context, positions []int32, seqs []int) error + StartForward(ctx ml.Context, opts input.Options) error // CopyPrefix copies tokens in the range [0, len) from srcSeq to dstSeq CopyPrefix(srcSeq, dstSeq int, len int32) diff --git a/kvcache/causal.go b/kvcache/causal.go index 9a79fa57..34d5337c 100644 --- a/kvcache/causal.go +++ b/kvcache/causal.go @@ -8,6 +8,7 @@ import ( "slices" "github.com/ollama/ollama/ml" + "github.com/ollama/ollama/model/input" ) type shiftFn func(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, error) @@ -140,10 +141,10 @@ func (c *Causal) Close() { } } -func (c *Causal) StartForward(ctx ml.Context, positions []int32, seqs []int) error { - c.curBatchSize = len(positions) - c.curSequences = seqs - c.curPositions = positions +func (c *Causal) StartForward(ctx ml.Context, opts input.Options) error { + c.curBatchSize = len(opts.Positions) + c.curSequences = opts.Sequences + c.curPositions = opts.Positions var err error c.curLoc, err = c.findStartLoc() @@ -156,8 +157,8 @@ func (c *Causal) StartForward(ctx ml.Context, positions []int32, seqs []int) err } c.curCellRange = newRange() - for i, pos := range positions { - seq := seqs[i] + for i, pos := range opts.Positions { + seq := opts.Sequences[i] c.cells[c.curLoc+i] = cacheCell{pos: pos, sequences: []int{seq}} diff --git a/kvcache/causal_test.go b/kvcache/causal_test.go index 412f33e3..22d8efb4 100644 --- a/kvcache/causal_test.go +++ b/kvcache/causal_test.go @@ -6,6 +6,7 @@ import ( "testing" "github.com/ollama/ollama/ml" + "github.com/ollama/ollama/model/input" ) type testCase struct { @@ -269,7 +270,7 @@ func testCache(t *testing.T, backend ml.Backend, cache Cache, tests []testCase) context := backend.NewContext() defer context.Close() - err := cache.StartForward(context, test.pos, test.seqs) + err := cache.StartForward(context, input.Options{Positions: test.pos, Sequences: test.seqs}) if err != nil { panic(err) } diff --git a/kvcache/encoder.go b/kvcache/encoder.go index 867ee37a..6a9df2ab 100644 --- a/kvcache/encoder.go +++ b/kvcache/encoder.go @@ -4,6 +4,7 @@ import ( "fmt" "github.com/ollama/ollama/ml" + "github.com/ollama/ollama/model/input" ) // Encoder cache stores K and V tensors that are position independent @@ -78,9 +79,11 @@ func (c *EncoderCache) Close() { } } -func (c *EncoderCache) StartForward(ctx ml.Context, positions []int32, seqs []int) error { - // The image is always in the first position - c.curPos = positions[0] +func (c *EncoderCache) StartForward(ctx ml.Context, opts input.Options) error { + // We work with the most recent image + if len(opts.Multimodal) > 0 { + c.curPos = opts.Positions[opts.Multimodal[len(opts.Multimodal)-1].Index] + } return nil } diff --git a/kvcache/wrapper.go b/kvcache/wrapper.go index 76956a88..aaccd166 100644 --- a/kvcache/wrapper.go +++ b/kvcache/wrapper.go @@ -4,6 +4,7 @@ import ( "math" "github.com/ollama/ollama/ml" + "github.com/ollama/ollama/model/input" ) // Wrapper cache is a container for multiple types of caches, @@ -40,14 +41,14 @@ func (c *WrapperCache) Close() { } } -func (c *WrapperCache) StartForward(ctx ml.Context, positions []int32, seqs []int) error { +func (c *WrapperCache) StartForward(ctx ml.Context, opts input.Options) error { for i, cache := range c.caches { - err := cache.StartForward(ctx, positions, seqs) + err := cache.StartForward(ctx, opts) if err != nil { // unwind on error - Remove with endIndex set to math.MaxInt32 does not fail for j := i - 1; j >= 0; j-- { - for k := range positions { - _ = c.caches[j].Remove(seqs[k], positions[k], math.MaxInt32) + for k := range opts.Positions { + _ = c.caches[j].Remove(opts.Sequences[k], opts.Positions[k], math.MaxInt32) } } return err diff --git a/model/input/input.go b/model/input/input.go new file mode 100644 index 00000000..0cb3f3f4 --- /dev/null +++ b/model/input/input.go @@ -0,0 +1,37 @@ +package input + +// Input represents one token in the input stream +type Input struct { + // Token is a single element of text. + Token int32 + + // Multimodal is opaque data representing a non-text + // element such as an image (or part of one if the image + // can be processed in pieces). It may be either together + // with Token or on its own. + Multimodal any + + // MultimodalHash is a unique representation of the data + // stored in Multimodal, used for caching and comparing + // equality. + MultimodalHash uint64 +} + +// MultimodalIndex is a multimodal element (such as an image) +// together with an index into the slice of Inputs with the +// corresponding token. Note that the index is not the same +// as the position - to find that use the index with the +// Positions slice. +type MultimodalIndex struct { + Index int + Multimodal any +} + +// Options contains the inputs for a model forward pass +type Options struct { + Inputs []int32 + Multimodal []MultimodalIndex + Positions []int32 + Sequences []int + Outputs []int32 +} diff --git a/model/model.go b/model/model.go index 75b7f639..89b6c803 100644 --- a/model/model.go +++ b/model/model.go @@ -19,66 +19,12 @@ import ( "github.com/ollama/ollama/kvcache" "github.com/ollama/ollama/ml" _ "github.com/ollama/ollama/ml/backend" + "github.com/ollama/ollama/model/input" ) -// Input represents one token in the input stream -type Input struct { - // Token is a single element of text. - Token int32 - - // Multimodal is opaque data representing a non-text - // element such as an image (or part of one if the image - // can be processed in pieces). It may be either together - // with Token or on its own. - Multimodal any - - // MultimodalHash is a unique representation of the data - // stored in Multimodal, used for caching and comparing - // equality. - MultimodalHash uint64 -} - -// MultimodalIndex is a multimodal element (such as an image) -// together with an index into the slice of Inputs with the -// corresponding token. Note that the index is not the same -// as the position - to find that use the index with the -// Positions slice. -type MultimodalIndex struct { - Index int - Multimodal any -} - -// Options contains the inputs for a model forward pass -type Options struct { - Inputs []int32 - Multimodal []MultimodalIndex - Positions []int32 - Sequences []int - Outputs []int32 -} - -type config struct { - Cache kvcache.Cache -} - -// Base implements the common fields and methods for all models -type Base struct { - b ml.Backend - config -} - -// Backend returns the underlying backend that will run the model -func (m *Base) Backend() ml.Backend { - return m.b -} - -func (m *Base) Config() config { - return m.config -} - // Model implements a specific model architecture, defining the forward pass and any model-specific configuration type Model interface { - Forward(ml.Context, Options) (ml.Tensor, error) + Forward(ml.Context, input.Options) (ml.Tensor, error) Backend() ml.Backend Config() config @@ -112,7 +58,26 @@ type MultimodalProcessor interface { // This function is also responsible for updating MultimodalHash for any Multimodal // that is modified to ensure that there is a unique hash value that accurately // represents the contents. - PostTokenize(ml.Context, []Input) ([]Input, error) + PostTokenize(ml.Context, []input.Input) ([]input.Input, error) +} + +// Base implements the common fields and methods for all models +type Base struct { + b ml.Backend + config +} + +type config struct { + Cache kvcache.Cache +} + +// Backend returns the underlying backend that will run the model +func (m *Base) Backend() ml.Backend { + return m.b +} + +func (m *Base) Config() config { + return m.config } var models = make(map[string]func(ml.Config) (Model, error)) @@ -313,7 +278,7 @@ func canNil(t reflect.Type) bool { t.Kind() == reflect.Slice } -func Forward(ctx ml.Context, m Model, opts Options) (ml.Tensor, error) { +func Forward(ctx ml.Context, m Model, opts input.Options) (ml.Tensor, error) { if len(opts.Positions) != len(opts.Sequences) { return nil, fmt.Errorf("length of positions (%v) must match length of seqs (%v)", len(opts.Positions), len(opts.Sequences)) } @@ -324,7 +289,7 @@ func Forward(ctx ml.Context, m Model, opts Options) (ml.Tensor, error) { cache := m.Config().Cache if cache != nil { - err := cache.StartForward(ctx, opts.Positions, opts.Sequences) + err := cache.StartForward(ctx, opts) if err != nil { return nil, err } diff --git a/model/model_test.go b/model/model_test.go index 8761817e..354dd1d8 100644 --- a/model/model_test.go +++ b/model/model_test.go @@ -11,6 +11,7 @@ import ( "github.com/ollama/ollama/ml" "github.com/ollama/ollama/ml/backend/ggml" "github.com/ollama/ollama/ml/nn" + "github.com/ollama/ollama/model/input" ) func TestParseTags(t *testing.T) { @@ -162,7 +163,7 @@ func TestGetTextProcessor(t *testing.T) { type notTextProcessorModel struct{} -func (notTextProcessorModel) Forward(ml.Context, Options) (ml.Tensor, error) { +func (notTextProcessorModel) Forward(ml.Context, input.Options) (ml.Tensor, error) { panic("unimplemented") } diff --git a/model/models/llama/model.go b/model/models/llama/model.go index 9ccfff61..1f27f522 100644 --- a/model/models/llama/model.go +++ b/model/models/llama/model.go @@ -9,6 +9,7 @@ import ( "github.com/ollama/ollama/ml" "github.com/ollama/ollama/ml/nn" "github.com/ollama/ollama/model" + "github.com/ollama/ollama/model/input" ) type Options struct { @@ -137,7 +138,7 @@ func (l *Layer) Forward(ctx ml.Context, hiddenState, positionIDs, outputs ml.Ten return hiddenState.Add(ctx, residual) } -func (m *Model) Forward(ctx ml.Context, opts model.Options) (ml.Tensor, error) { +func (m *Model) Forward(ctx ml.Context, opts input.Options) (ml.Tensor, error) { inputs, err := ctx.Input().FromIntSlice(opts.Inputs, len(opts.Inputs)) if err != nil { return nil, err diff --git a/model/models/mllama/model.go b/model/models/mllama/model.go index 54c63296..31ba15df 100644 --- a/model/models/mllama/model.go +++ b/model/models/mllama/model.go @@ -12,6 +12,7 @@ import ( "github.com/ollama/ollama/ml" "github.com/ollama/ollama/ml/nn" "github.com/ollama/ollama/model" + "github.com/ollama/ollama/model/input" ) type Model struct { @@ -101,8 +102,8 @@ func (m *Model) EncodeMultimodal(ctx ml.Context, multimodalData []byte) (any, er return m.Projector.Forward(ctx, crossAttentionStates), nil } -func (m *Model) PostTokenize(ctx ml.Context, inputs []model.Input) ([]model.Input, error) { - var images []model.Input +func (m *Model) PostTokenize(ctx ml.Context, inputs []input.Input) ([]input.Input, error) { + var images []input.Input fnvHash := fnv.New64a() for i := range inputs { @@ -125,15 +126,15 @@ func (m *Model) PostTokenize(ctx ml.Context, inputs []model.Input) ([]model.Inpu } } - inputs = slices.DeleteFunc(inputs, func(input model.Input) bool { return input.Token == -1 }) + inputs = slices.DeleteFunc(inputs, func(input input.Input) bool { return input.Token == -1 }) return inputs, nil } -func (m *Model) Forward(ctx ml.Context, opts model.Options) (ml.Tensor, error) { +func (m *Model) Forward(ctx ml.Context, opts input.Options) (ml.Tensor, error) { var crossAttentionStates ml.Tensor - if opts.Multimodal != nil { - crossAttentionStates = opts.Multimodal[0].Multimodal.(ml.Tensor) + if len(opts.Multimodal) > 0 { + crossAttentionStates = opts.Multimodal[len(opts.Multimodal)-1].Multimodal.(ml.Tensor) } inputs, err := ctx.Input().FromIntSlice(opts.Inputs, len(opts.Inputs)) diff --git a/runner/ollamarunner/cache.go b/runner/ollamarunner/cache.go index 3244c0b8..a411fddb 100644 --- a/runner/ollamarunner/cache.go +++ b/runner/ollamarunner/cache.go @@ -10,6 +10,7 @@ import ( "github.com/ollama/ollama/kvcache" "github.com/ollama/ollama/ml" "github.com/ollama/ollama/model" + "github.com/ollama/ollama/model/input" ) type InputCache struct { @@ -79,7 +80,7 @@ type InputCacheSlot struct { Id int // Inputs that are stored in the KV cache - Inputs []model.Input + Inputs []input.Input // is this cache actively being processed as part of a sequence? InUse bool @@ -88,7 +89,7 @@ type InputCacheSlot struct { lastUsed time.Time } -func (c *InputCache) LoadCacheSlot(prompt []model.Input, cachePrompt bool) (*InputCacheSlot, []model.Input, error) { +func (c *InputCache) LoadCacheSlot(prompt []input.Input, cachePrompt bool) (*InputCacheSlot, []input.Input, error) { var slot *InputCacheSlot var numPast int32 var err error @@ -139,7 +140,7 @@ func (c *InputCache) LoadCacheSlot(prompt []model.Input, cachePrompt bool) (*Inp return slot, prompt, nil } -func (c *InputCache) findLongestCacheSlot(prompt []model.Input) (*InputCacheSlot, int32, error) { +func (c *InputCache) findLongestCacheSlot(prompt []input.Input) (*InputCacheSlot, int32, error) { longest := int32(-1) var longestSlot *InputCacheSlot @@ -162,7 +163,7 @@ func (c *InputCache) findLongestCacheSlot(prompt []model.Input) (*InputCacheSlot return longestSlot, longest, nil } -func (c *InputCache) findBestCacheSlot(prompt []model.Input) (*InputCacheSlot, int32, error) { +func (c *InputCache) findBestCacheSlot(prompt []input.Input) (*InputCacheSlot, int32, error) { oldest := time.Now() var oldestSlot *InputCacheSlot @@ -198,7 +199,7 @@ func (c *InputCache) findBestCacheSlot(prompt []model.Input) (*InputCacheSlot, i if longest > 0 && longestSlot != oldestSlot { slog.Debug("forking cache slot", "src", longestSlot.Id, "dst", oldestSlot.Id, "inputs", longest, "total", len(longestSlot.Inputs)) - oldestSlot.Inputs = make([]model.Input, longest) + oldestSlot.Inputs = make([]input.Input, longest) copy(oldestSlot.Inputs, longestSlot.Inputs[:longest]) if c.cache != nil { c.cache.CopyPrefix(longestSlot.Id, oldestSlot.Id, longest) @@ -208,7 +209,7 @@ func (c *InputCache) findBestCacheSlot(prompt []model.Input) (*InputCacheSlot, i return oldestSlot, longest, nil } -func countCommonPrefix(a []model.Input, b []model.Input) int32 { +func countCommonPrefix(a []input.Input, b []input.Input) int32 { var count int32 for i := range a { diff --git a/runner/ollamarunner/cache_test.go b/runner/ollamarunner/cache_test.go index 9ce03b73..0a1b73f5 100644 --- a/runner/ollamarunner/cache_test.go +++ b/runner/ollamarunner/cache_test.go @@ -5,7 +5,7 @@ import ( "testing" "time" - "github.com/ollama/ollama/model" + "github.com/ollama/ollama/model/input" ) func TestCountCommon(t *testing.T) { @@ -15,50 +15,50 @@ func TestCountCommon(t *testing.T) { tests := []struct { name string - t1 []model.Input - t2 []model.Input + t1 []input.Input + t2 []input.Input expected int32 }{ { name: "Equal", - t1: []model.Input{{Token: 1}, {Token: 2}, {Token: 3}}, - t2: []model.Input{{Token: 1}, {Token: 2}, {Token: 3}}, + t1: []input.Input{{Token: 1}, {Token: 2}, {Token: 3}}, + t2: []input.Input{{Token: 1}, {Token: 2}, {Token: 3}}, expected: 3, }, { name: "Prefix", - t1: []model.Input{{Token: 1}}, - t2: []model.Input{{Token: 1}, {Token: 2}, {Token: 3}}, + t1: []input.Input{{Token: 1}}, + t2: []input.Input{{Token: 1}, {Token: 2}, {Token: 3}}, expected: 1, }, { name: "Image Prefix", - t1: []model.Input{{Multimodal: imgA, MultimodalHash: 1}}, - t2: []model.Input{{Multimodal: imgA, MultimodalHash: 1}, {Multimodal: imgB, MultimodalHash: 2}, {Multimodal: imgC, MultimodalHash: 3}}, + t1: []input.Input{{Multimodal: imgA, MultimodalHash: 1}}, + t2: []input.Input{{Multimodal: imgA, MultimodalHash: 1}, {Multimodal: imgB, MultimodalHash: 2}, {Multimodal: imgC, MultimodalHash: 3}}, expected: 1, }, { name: "Mixed", - t1: []model.Input{{Token: 1}, {Multimodal: imgA, MultimodalHash: 1}}, - t2: []model.Input{{Token: 1}, {Multimodal: imgA, MultimodalHash: 1}, {Token: 5}}, + t1: []input.Input{{Token: 1}, {Multimodal: imgA, MultimodalHash: 1}}, + t2: []input.Input{{Token: 1}, {Multimodal: imgA, MultimodalHash: 1}, {Token: 5}}, expected: 2, }, { name: "Mixed, Same Length", - t1: []model.Input{{Token: 1}, {Multimodal: imgA, MultimodalHash: 1}}, - t2: []model.Input{{Token: 1}, {Multimodal: imgB, MultimodalHash: 2}}, + t1: []input.Input{{Token: 1}, {Multimodal: imgA, MultimodalHash: 1}}, + t2: []input.Input{{Token: 1}, {Multimodal: imgB, MultimodalHash: 2}}, expected: 1, }, { name: "Empty", - t1: []model.Input{}, - t2: []model.Input{{Token: 1}, {Token: 2}, {Token: 3}}, + t1: []input.Input{}, + t2: []input.Input{{Token: 1}, {Token: 2}, {Token: 3}}, expected: 0, }, { name: "Both Empty", - t1: []model.Input{}, - t2: []model.Input{}, + t1: []input.Input{}, + t2: []input.Input{}, expected: 0, }, } @@ -82,7 +82,7 @@ func TestFindCacheSlot(t *testing.T) { tests := []struct { name string cache InputCache - prompt []model.Input + prompt []input.Input longest expected best expected }{ @@ -91,18 +91,18 @@ func TestFindCacheSlot(t *testing.T) { cache: InputCache{slots: []InputCacheSlot{ { Id: 0, - Inputs: []model.Input{}, + Inputs: []input.Input{}, InUse: false, lastUsed: time.Time{}, }, { Id: 1, - Inputs: []model.Input{}, + Inputs: []input.Input{}, InUse: false, lastUsed: time.Time{}, }, }}, - prompt: []model.Input{{Token: 1}}, + prompt: []input.Input{{Token: 1}}, longest: expected{result: 0, len: 0}, best: expected{result: 0, len: 0}, }, @@ -111,18 +111,18 @@ func TestFindCacheSlot(t *testing.T) { cache: InputCache{slots: []InputCacheSlot{ { Id: 0, - Inputs: []model.Input{{Token: 1}}, + Inputs: []input.Input{{Token: 1}}, InUse: false, lastUsed: time.Now().Add(-time.Second), }, { Id: 1, - Inputs: []model.Input{{Token: 1}, {Token: 2}}, + Inputs: []input.Input{{Token: 1}, {Token: 2}}, InUse: false, lastUsed: time.Now().Add(-2 * time.Second), }, }}, - prompt: []model.Input{{Token: 1}, {Token: 2}}, + prompt: []input.Input{{Token: 1}, {Token: 2}}, longest: expected{result: 1, len: 2}, best: expected{result: 1, len: 2}, }, @@ -131,18 +131,18 @@ func TestFindCacheSlot(t *testing.T) { cache: InputCache{slots: []InputCacheSlot{ { Id: 0, - Inputs: []model.Input{{Token: 1}, {Token: 2}}, + Inputs: []input.Input{{Token: 1}, {Token: 2}}, InUse: false, lastUsed: time.Now().Add(-time.Second), }, { Id: 1, - Inputs: []model.Input{}, + Inputs: []input.Input{}, InUse: false, lastUsed: time.Time{}, }, }}, - prompt: []model.Input{{Token: 2}}, + prompt: []input.Input{{Token: 2}}, longest: expected{result: 0, len: 0}, best: expected{result: 1, len: 0}, }, @@ -152,19 +152,19 @@ func TestFindCacheSlot(t *testing.T) { slots: []InputCacheSlot{ { Id: 0, - Inputs: []model.Input{{Token: 1}, {Token: 2}}, + Inputs: []input.Input{{Token: 1}, {Token: 2}}, InUse: false, lastUsed: time.Now().Add(-time.Second), }, { Id: 1, - Inputs: []model.Input{}, + Inputs: []input.Input{}, InUse: false, lastUsed: time.Time{}, }, }, }, - prompt: []model.Input{{Token: 1}}, + prompt: []input.Input{{Token: 1}}, longest: expected{result: 0, len: 1}, best: expected{result: 1, len: 1}, }, @@ -173,18 +173,18 @@ func TestFindCacheSlot(t *testing.T) { cache: InputCache{slots: []InputCacheSlot{ { Id: 0, - Inputs: []model.Input{{Token: 1}}, + Inputs: []input.Input{{Token: 1}}, InUse: false, lastUsed: time.Now().Add(-time.Second), }, { Id: 1, - Inputs: []model.Input{{Token: 1}, {Token: 2}}, + Inputs: []input.Input{{Token: 1}, {Token: 2}}, InUse: false, lastUsed: time.Now().Add(-2 * time.Second), }, }}, - prompt: []model.Input{{Token: 2}, {Token: 3}}, + prompt: []input.Input{{Token: 2}, {Token: 3}}, longest: expected{result: 0, len: 0}, best: expected{result: 1, len: 0}, }, @@ -193,18 +193,18 @@ func TestFindCacheSlot(t *testing.T) { cache: InputCache{slots: []InputCacheSlot{ { Id: 0, - Inputs: []model.Input{{Token: 1}, {Token: 2}}, + Inputs: []input.Input{{Token: 1}, {Token: 2}}, InUse: true, lastUsed: time.Now().Add(-time.Second), }, { Id: 1, - Inputs: []model.Input{{Token: 1}}, + Inputs: []input.Input{{Token: 1}}, InUse: false, lastUsed: time.Now().Add(-2 * time.Second), }, }}, - prompt: []model.Input{{Token: 1}, {Token: 2}}, + prompt: []input.Input{{Token: 1}, {Token: 2}}, longest: expected{result: 1, len: 1}, best: expected{result: 1, len: 2}, }, diff --git a/runner/ollamarunner/runner.go b/runner/ollamarunner/runner.go index a51b1459..c8383a5d 100644 --- a/runner/ollamarunner/runner.go +++ b/runner/ollamarunner/runner.go @@ -26,6 +26,7 @@ import ( "github.com/ollama/ollama/api" "github.com/ollama/ollama/ml" "github.com/ollama/ollama/model" + "github.com/ollama/ollama/model/input" "github.com/ollama/ollama/runner/common" "github.com/ollama/ollama/sample" @@ -41,10 +42,10 @@ type Sequence struct { iBatch int // prompt inputs left to evaluate - inputs []model.Input + inputs []input.Input // inputs that have been added to a batch but not yet submitted to Forward - pendingInputs []model.Input + pendingInputs []input.Input // tokens that have been generated but not returned yet (e.g. for stop sequences) pendingResponses []string @@ -144,8 +145,8 @@ func (s *Server) NewSequence(prompt string, images []ImageData, params NewSequen // inputs processes the prompt and images into a list of inputs // by splitting the prompt on [img-] tags, tokenizing text and // decoding images -func (s *Server) inputs(ctx ml.Context, prompt string, images []ImageData) ([]model.Input, error) { - var inputs []model.Input +func (s *Server) inputs(ctx ml.Context, prompt string, images []ImageData) ([]input.Input, error) { + var inputs []input.Input var parts []string var matches [][]string @@ -168,7 +169,7 @@ func (s *Server) inputs(ctx ml.Context, prompt string, images []ImageData) ([]mo } for _, t := range tokens { - inputs = append(inputs, model.Input{Token: t}) + inputs = append(inputs, input.Input{Token: t}) } // image - decode and store @@ -196,7 +197,7 @@ func (s *Server) inputs(ctx ml.Context, prompt string, images []ImageData) ([]mo _, _ = s.multimodalHash.Write(images[imageIndex].Data) imageHash := s.multimodalHash.Sum64() - inputs = append(inputs, model.Input{Multimodal: imageEmbeddings, MultimodalHash: imageHash}) + inputs = append(inputs, input.Input{Multimodal: imageEmbeddings, MultimodalHash: imageHash}) postTokenize = true } } @@ -250,9 +251,6 @@ type Server struct { // KV cache cache *InputCache - // next sequence for prompt processing to avoid starvation - nextSeq int - // multimodalHash generates hashes for comparing equality // of non-text data multimodalHash maphash.Hash @@ -329,29 +327,25 @@ func (s *Server) processBatch() error { } defer s.mu.Unlock() - var options model.Options - - seqIdx := s.nextSeq - 1 - for range s.seqs { - seqIdx = (seqIdx + 1) % len(s.seqs) - seq := s.seqs[seqIdx] + var options input.Options + for i, seq := range s.seqs { if seq == nil { continue } // if past the num predict limit if seq.numPredict > 0 && seq.numPredicted >= seq.numPredict { - s.removeSequence(seqIdx, "limit") + s.removeSequence(i, "limit") continue } if !s.cache.enabled { seq.inputs = append(seq.cache.Inputs, seq.inputs...) - seq.cache.Inputs = []model.Input{} + seq.cache.Inputs = []input.Input{} } - for i, input := range seq.inputs { + for j, inp := range seq.inputs { if int32(len(seq.cache.Inputs)+len(seq.pendingInputs)+1) > s.cache.numCtx { if len(seq.pendingInputs) == 0 { err := s.cache.ShiftCacheSlot(seq.cache, seq.numKeep) @@ -363,33 +357,23 @@ func (s *Server) processBatch() error { } } - if i >= s.batchSize { + if j >= s.batchSize { break } - // TODO(jessegross): This is a workaround for generating an attention mask and also providing a hint - // to the encoder cache. - // - // Break the batch when switching from text to images so that images are always at the beginning. - if input.Multimodal != nil && !(len(seq.pendingInputs) == 0 || - (len(options.Multimodal) > 0 && options.Multimodal[len(options.Multimodal)-1].Index == len(options.Inputs)-1)) { - s.nextSeq = seqIdx - break - } - - options.Inputs = append(options.Inputs, input.Token) - if input.Multimodal != nil { - options.Multimodal = append(options.Multimodal, model.MultimodalIndex{Index: len(options.Inputs) - 1, Multimodal: input.Multimodal}) + options.Inputs = append(options.Inputs, inp.Token) + if inp.Multimodal != nil { + options.Multimodal = append(options.Multimodal, input.MultimodalIndex{Index: len(options.Inputs) - 1, Multimodal: inp.Multimodal}) } options.Positions = append(options.Positions, int32(len(seq.cache.Inputs)+len(seq.pendingInputs))) options.Sequences = append(options.Sequences, seq.cache.Id) seq.iBatch = len(options.Outputs) - if i+1 == len(seq.inputs) { + if j+1 == len(seq.inputs) { options.Outputs = append(options.Outputs, int32(len(options.Inputs)-1)) } - seq.pendingInputs = append(seq.pendingInputs, input) + seq.pendingInputs = append(seq.pendingInputs, inp) } seq.inputs = seq.inputs[len(seq.pendingInputs):] @@ -417,7 +401,7 @@ func (s *Server) processBatch() error { // After calling Forward, pending inputs are now in the cache if len(seq.pendingInputs) > 0 { seq.cache.Inputs = append(seq.cache.Inputs, seq.pendingInputs...) - seq.pendingInputs = []model.Input{} + seq.pendingInputs = []input.Input{} } // don't sample prompt processing @@ -464,7 +448,7 @@ func (s *Server) processBatch() error { return err } - seq.inputs = []model.Input{{Token: token}} + seq.inputs = []input.Input{{Token: token}} seq.pendingResponses = append(seq.pendingResponses, piece) sequence := strings.Join(seq.pendingResponses, "") From e093db92c4731b0cada767c7d5877c20d5f61dcf Mon Sep 17 00:00:00 2001 From: Jeffrey Morgan Date: Mon, 10 Mar 2025 16:17:39 +0100 Subject: [PATCH 03/42] sample: temporarily use grammars for constrained generation in new engine (#9586) --- llama/llama.go | 68 +++++++++++ llama/sampling_ext.cpp | 22 ++++ llama/sampling_ext.h | 3 + llm/server.go | 39 +++--- runner/ollamarunner/runner.go | 23 +++- sample/samplers.go | 193 +++++++++++++++++++++--------- sample/samplers_benchmark_test.go | 28 ++--- sample/samplers_test.go | 94 +-------------- sample/transforms.go | 16 +-- sample/transforms_test.go | 28 ++--- 10 files changed, 301 insertions(+), 213 deletions(-) diff --git a/llama/llama.go b/llama/llama.go index bb5028bd..a026bee2 100644 --- a/llama/llama.go +++ b/llama/llama.go @@ -245,6 +245,20 @@ func LoadModelFromFile(modelPath string, params ModelParams) (*Model, error) { return &m, nil } +func LoadVocabFromFile(path string) (*Vocab, error) { + mp := C.CString(path) + defer C.free(unsafe.Pointer(mp)) + v := Vocab{c: C.llama_load_vocab_from_file(mp)} + if v.c == nil { + return nil, fmt.Errorf("unable to load vocab: %s", path) + } + return &v, nil +} + +func FreeVocab(vocab *Vocab) { + C.llama_free_vocab(vocab.c) +} + func FreeModel(model *Model) { C.llama_model_free(model.c) } @@ -293,6 +307,10 @@ func (m *Model) ApplyLoraFromFile(context *Context, loraPath string, scale float return nil } +type Vocab struct { + c *C.struct_llama_vocab +} + func (m *Model) Vocab() *C.struct_llama_vocab { return C.llama_model_get_vocab(m.c) } @@ -669,3 +687,53 @@ func SchemaToGrammar(schema []byte) []byte { } return buf[:n] } + +type Sampler struct { + c *C.struct_llama_sampler +} + +func NewGrammarSampler(vocab *Vocab, grammar string) *Sampler { + cGrammar := C.CString(grammar) + cRoot := C.CString("root") + defer C.free(unsafe.Pointer(cGrammar)) + defer C.free(unsafe.Pointer(cRoot)) + + sampler := &Sampler{c: C.llama_sampler_init_grammar(vocab.c, cGrammar, cRoot)} + + return sampler +} + +func (s *Sampler) Accept(token int32) { + C.llama_sampler_accept(s.c, C.llama_token(token)) +} + +type TokenData struct { + Id int32 + Logit float32 +} + +func (s *Sampler) Apply(tokens []TokenData) { + tds := make([]C.struct_llama_token_data, len(tokens)) + for i, token := range tokens { + tds[i] = C.struct_llama_token_data{ + id: C.int32_t(token.Id), + logit: C.float(token.Logit), + p: C.float(0.0), + } + } + tda := &C.llama_token_data_array{ + data: (*C.struct_llama_token_data)(unsafe.Pointer(&tds[0])), + size: C.size_t(len(tokens)), + selected: C.int64_t(-1), + sorted: C.bool(false), + } + + var pinner runtime.Pinner + pinner.Pin(&tds[0]) + defer pinner.Unpin() + + C.llama_sampler_apply(s.c, tda) + for i := range tokens { + tokens[i].Logit = float32(tds[i].logit) + } +} diff --git a/llama/sampling_ext.cpp b/llama/sampling_ext.cpp index 0f137dc8..b816cedd 100644 --- a/llama/sampling_ext.cpp +++ b/llama/sampling_ext.cpp @@ -2,6 +2,9 @@ #include "sampling.h" #include "sampling_ext.h" #include "json-schema-to-grammar.h" +#include "llama.h" +#include "llama-model.h" +#include "llama-model-loader.h" struct common_sampler *common_sampler_cinit(const struct llama_model *model, struct common_sampler_cparams *params) { try { @@ -64,3 +67,22 @@ int schema_to_grammar(const char *json_schema, char *grammar, size_t max_len) return 0; } } + +struct llama_vocab * llama_load_vocab_from_file(const char * fname) { + llama_vocab * vocab = new llama_vocab(); + try { + const auto kv = LLM_KV(LLM_ARCH_UNKNOWN); + std::vector splits = {}; + llama_model_loader ml(std::string(fname), splits, false, false, nullptr); + vocab->load(ml, kv); + } catch (const std::exception & err) { + LLAMA_LOG_ERROR("%s: error loading model: %s\n", __func__, err.what()); + return nullptr; + } + + return vocab; +} + +void llama_free_vocab(struct llama_vocab * vocab) { + delete vocab; +} diff --git a/llama/sampling_ext.h b/llama/sampling_ext.h index 39f499f1..9be7c100 100644 --- a/llama/sampling_ext.h +++ b/llama/sampling_ext.h @@ -35,6 +35,9 @@ extern "C" int schema_to_grammar(const char *json_schema, char *grammar, size_t max_len); + struct llama_vocab * llama_load_vocab_from_file(const char * fname); + void llama_free_vocab(struct llama_vocab * vocab); + #ifdef __cplusplus } #endif diff --git a/llm/server.go b/llm/server.go index 9553ba8f..a53306fb 100644 --- a/llm/server.go +++ b/llm/server.go @@ -729,29 +729,24 @@ func (s *llmServer) Completion(ctx context.Context, req CompletionRequest, fn fu } if len(req.Format) > 0 { - format := string(req.Format) - if format != `null` && format != `""` { - if s.textProcessor != nil { - // New engine handles this on the backend - request["format"] = req.Format - } else { - // old engine - switch format { - case `"json"`: - request["grammar"] = grammarJSON - default: - if req.Format[0] != '{' { - return fmt.Errorf("invalid format: %q; expected \"json\" or a valid JSON Schema object", req.Format) - } - - // User provided a JSON schema - g := llama.SchemaToGrammar(req.Format) - if g == nil { - return fmt.Errorf("invalid JSON schema in format") - } - request["grammar"] = string(g) - } + switch string(req.Format) { + case `null`, `""`: + // Field was set, but "missing" a value. We accept + // these as "not set". + break + case `"json"`: + request["grammar"] = grammarJSON + default: + if req.Format[0] != '{' { + return fmt.Errorf("invalid format: %q; expected \"json\" or a valid JSON Schema object", req.Format) } + + // User provided a JSON schema + g := llama.SchemaToGrammar(req.Format) + if g == nil { + return fmt.Errorf("invalid JSON schema in format") + } + request["grammar"] = string(g) } } diff --git a/runner/ollamarunner/runner.go b/runner/ollamarunner/runner.go index c8383a5d..c1475cbb 100644 --- a/runner/ollamarunner/runner.go +++ b/runner/ollamarunner/runner.go @@ -254,6 +254,12 @@ type Server struct { // multimodalHash generates hashes for comparing equality // of non-text data multimodalHash maphash.Hash + + // vocab is a llama.cpp vocab required for gammar-based + // constrained generation (json mode, structured outputs) + // TODO: this is temporary until Ollama sampling supports + // constrained generation + vocab *sample.Vocab } func (s *Server) allNil() bool { @@ -574,18 +580,25 @@ func (s *Server) completion(w http.ResponseWriter, r *http.Request) { return } + var grammar *sample.Grammar + var err error + if req.Grammar != "" { + grammar, err = sample.NewGrammar(s.vocab, req.Grammar) + if err != nil { + http.Error(w, "failed to load model vocabulary required for format", http.StatusInternalServerError) + return + } + } + sampler := sample.NewSampler( req.Temperature, req.TopK, req.TopP, req.MinP, req.Seed, + grammar, ) - if req.Grammar != "" { - panic("grammars are not yet supported") - } - seq, err := s.NewSequence(req.Prompt, req.Images, NewSequenceParams{ numPredict: req.NumPredict, stop: req.Stop, @@ -797,6 +810,8 @@ func (s *Server) loadModel( panic(err) } + s.vocab = sample.NewVocab(mpath) + // TODO(jessegross): LoRA loading if lpath.String() != "" { panic("loras are not yet implemented") diff --git a/sample/samplers.go b/sample/samplers.go index a5a0507c..a9d90692 100644 --- a/sample/samplers.go +++ b/sample/samplers.go @@ -2,43 +2,88 @@ package sample import ( "errors" + "math" "math/rand/v2" "slices" + "sync" + + "github.com/ollama/ollama/llama" ) -// Sampler is not thread-safe. Each goroutine should have its own instance -type Sampler interface { - Sample([]float32) (int32, error) -} - -// logit represents information about a single token during sampling -type logit struct { +// token represents information about a single token during sampling +type token struct { id int32 // The token's unique identifier value float32 // The raw logit or probability from the model } -type weighted struct { +type Sampler struct { rng *rand.Rand - tokens []logit topK int topP float32 minP float32 temperature float32 + grammar *Grammar } -func (s *weighted) Sample(logits []float32) (int32, error) { - if len(s.tokens) < len(logits) { - s.tokens = make([]logit, len(logits)) - } - - tokens := s.tokens[:len(logits)] - - for i, v := range logits { +func (s *Sampler) Sample(logits []float32) (int32, error) { + tokens := make([]token, len(logits)) + for i := range logits { tokens[i].id = int32(i) - tokens[i].value = v + tokens[i].value = logits[i] + } + + t, err := s.sample(tokens) + if err != nil { + return -1, err + } + + if s.grammar != nil { + // optimization: first check if the max logit is accepted by the grammar + // if the max logit is rejected, apply the grammar to all logits (slower) + top := []token{t} + s.grammar.Apply(top) + if !math.IsInf(float64(top[0].value), -1) { + s.grammar.Accept(top[0].id) + return top[0].id, nil + } + + // since .sample has side effects of modifying the tokens + // we need to reset them before applying the grammar and + // sampling again + for i := range logits { + tokens[i].id = int32(i) + tokens[i].value = logits[i] + } + s.grammar.Apply(tokens) + t, err = s.sample(tokens) + if err != nil { + return -1, err + } + s.grammar.Accept(t.id) + } + + return t.id, nil +} + +// greedy returns the highest probability token from the tokens +func greedy(tokens []token) token { + max := tokens[0] + for i := 1; i < len(tokens); i++ { + if tokens[i].value > max.value { + max = tokens[i] + } + } + + return max +} + +// sample returns the highest probability token from the tokens +// given sampler parameters. It also has side effects of modifying the tokens +func (s *Sampler) sample(tokens []token) (token, error) { + if s.temperature == 0 { + return greedy(tokens), nil } - // Tokens are sorted by logits in TopK or SortTokens if s.topK > 0 { tokens = topK(tokens, s.topK) } else { @@ -47,12 +92,14 @@ func (s *weighted) Sample(logits []float32) (int32, error) { tokens = temperature(tokens, s.temperature) tokens = softmax(tokens) - tokens = topP(tokens, s.topP) tokens = minP(tokens, s.minP) + // TODO: this should fall back to greedy sampling + // or topP, topK values etc should be such that + // there are always tokens to sample from if len(tokens) == 0 { - return -1, errors.New("no valid logits found for weighted sampling") + return token{}, errors.New("no tokens to sample from") } var r float32 @@ -70,48 +117,18 @@ func (s *weighted) Sample(logits []float32) (int32, error) { } r *= tokens[len(tokens)-1].value - idx, _ := slices.BinarySearchFunc(tokens, r, func(token logit, target float32) int { - // Compare cumulative probabilities + idx, _ := slices.BinarySearchFunc(tokens, r, func(token token, target float32) int { if token.value < target { return -1 } - // First token that exceeds target return 1 }) - if idx >= len(tokens) { - idx = len(tokens) - 1 - } - - return tokens[idx].id, nil -} - -type greedy struct{} - -// Greedy sample returns the index of the maximum value in logits. -func (s greedy) Sample(logits []float32) (int32, error) { - if len(logits) == 0 { - return -1, errors.New("no logits provided for greedy sampling") - } - - maxIdx := 0 - maxVal := logits[0] - for i := 1; i < len(logits); i++ { - if logits[i] > maxVal { - maxVal = logits[i] - maxIdx = i - } - } - - return int32(maxIdx), nil + return tokens[idx], nil } // TODO(parthsareen): update sampler interface to use json unmarshal https://github.com/ollama/ollama/issues/9278 -func NewSampler(temperature float32, topK int, topP float32, minP float32, seed int) Sampler { - if temperature == 0 { - return &greedy{} - } - +func NewSampler(temperature float32, topK int, topP float32, minP float32, seed int, grammar *Grammar) Sampler { var rng *rand.Rand if seed != -1 { // PCG requires two parameters: sequence and stream @@ -120,7 +137,9 @@ func NewSampler(temperature float32, topK int, topP float32, minP float32, seed // Use golden ratio hash to generate statistically independent seeds rng = rand.New(rand.NewPCG(sequence, sequence^0x9E3779B9)) } - temperature = max(temperature, 1) + if temperature < 0.0 { + temperature = 0.0 + } if topP < 0.0 { topP = 0.0 @@ -136,11 +155,73 @@ func NewSampler(temperature float32, topK int, topP float32, minP float32, seed minP = 1.0 } - return &weighted{ + return Sampler{ rng: rng, topK: topK, topP: topP, minP: minP, temperature: temperature, + grammar: grammar, } } + +type Grammar struct { + vocab *Vocab + grammar string + sampler *llama.Sampler +} + +func NewGrammar(vocab *Vocab, grammar string) (*Grammar, error) { + v, err := vocab.Load() + if err != nil { + return nil, err + } + + return &Grammar{ + vocab: vocab, + grammar: grammar, + sampler: llama.NewGrammarSampler(v, grammar), + }, nil +} + +func (g *Grammar) Apply(tokens []token) { + tds := make([]llama.TokenData, len(tokens)) + for i, token := range tokens { + tds[i].Id = token.id + tds[i].Logit = token.value + } + + g.sampler.Apply(tds) + + for i := range tokens { + tokens[i].value = tds[i].Logit + } +} + +func (g *Grammar) Accept(token int32) { + g.sampler.Accept(token) +} + +type Vocab struct { + once sync.Once + vocab *llama.Vocab + err error + path string +} + +func NewVocab(path string) *Vocab { + return &Vocab{path: path} +} + +// Load returns the lazily-loaded vocabulary +func (v *Vocab) Load() (*llama.Vocab, error) { + v.once.Do(func() { + vocab, err := llama.LoadVocabFromFile(v.path) + if err != nil { + v.err = err + return + } + v.vocab = vocab + }) + return v.vocab, v.err +} diff --git a/sample/samplers_benchmark_test.go b/sample/samplers_benchmark_test.go index 41c0b487..cd138014 100644 --- a/sample/samplers_benchmark_test.go +++ b/sample/samplers_benchmark_test.go @@ -16,13 +16,10 @@ func BenchmarkWeightedSampler(b *testing.B) { logits[i] = float32(rand.Float64()*10 - 5) } - sampler := NewSampler(0.8, 0, 0, 0, 42) + sampler := NewSampler(0.8, 0, 0, 0, 42, nil) b.ResetTimer() for b.Loop() { - _, err := sampler.Sample(logits) - if err != nil { - b.Fatalf("Sampling failed: %v", err) - } + sampler.Sample(logits) } }) } @@ -52,30 +49,24 @@ func BenchmarkWeightedSampler(b *testing.B) { for _, tc := range configs { b.Run("Config"+tc.name, func(b *testing.B) { - sampler := NewSampler(tc.temperature, tc.topK, tc.topP, tc.minP, tc.seed) + sampler := NewSampler(tc.temperature, tc.topK, tc.topP, tc.minP, tc.seed, nil) sampler.Sample(logits) b.ResetTimer() for b.Loop() { - _, err := sampler.Sample(logits) - if err != nil { - b.Fatalf("Sampling failed: %v", err) - } + sampler.Sample(logits) } }) } // Test with combined transforms separately - topK influences performance greatly b.Run("TransformCombined", func(b *testing.B) { - sampler := NewSampler(0.8, 50, 0.9, 0.05, 42) + sampler := NewSampler(0.8, 50, 0.9, 0.05, 42, nil) b.ResetTimer() for b.Loop() { - _, err := sampler.Sample(logits) - if err != nil { - b.Fatalf("Sampling failed: %v", err) - } + sampler.Sample(logits) } }) } @@ -90,14 +81,11 @@ func BenchmarkGreedySampler(b *testing.B) { logits[i] = float32(rand.Float64()*10 - 5) } - sampler := NewSampler(0, -1, 0, 0, -1) + sampler := NewSampler(0, -1, 0, 0, -1, nil) b.ResetTimer() for b.Loop() { - _, err := sampler.Sample(logits) - if err != nil { - b.Fatalf("Sampling failed: %v", err) - } + sampler.Sample(logits) } }) } diff --git a/sample/samplers_test.go b/sample/samplers_test.go index dbbee17b..38b9b352 100644 --- a/sample/samplers_test.go +++ b/sample/samplers_test.go @@ -7,7 +7,7 @@ import ( func TestWeighted(t *testing.T) { logits := []float32{-10, 3, -10, -10} - sampler := NewSampler(0, 0, 0, 0, 0) + sampler := NewSampler(0, 0, 0, 0, 0, nil) got, err := sampler.Sample(logits) if err != nil { t.Error(err) @@ -19,7 +19,7 @@ func TestWeighted(t *testing.T) { } logits = []float32{-100, -10, 0, 10} - sampler = NewSampler(0, 0, 0, 0, 0) + sampler = NewSampler(0, 0, 0, 0, 0, nil) got, err = sampler.Sample(logits) if err != nil { t.Error(err) @@ -31,94 +31,10 @@ func TestWeighted(t *testing.T) { } } -func TestNewSampler(t *testing.T) { - tests := []struct { - name string - temperature float32 - topK int - topP float32 - minP float32 - seed int - wantGreedy bool // Instead of wantErr, check if we get greedy sampler - }{ - { - name: "temperature", - temperature: 0.5, - wantGreedy: false, - }, - { - name: "zero temperature - greedy", - temperature: 0, - wantGreedy: true, - }, - { - name: "top k", - temperature: 0.1, - topK: 10, - wantGreedy: false, - }, - { - name: "top p", - temperature: 0.1, - topP: 0.9, - wantGreedy: false, - }, - { - name: "min p", - temperature: 0.1, - minP: 0.2, - wantGreedy: false, - }, - { - name: "seed - weighted", - temperature: 0.1, - seed: 42, - wantGreedy: false, - }, - { - name: "default values", - temperature: 0.8, - topK: 40, - topP: 0.9, - minP: 0.0, - seed: 0, - wantGreedy: false, - }, - { - name: "all zeroes - greedy", - temperature: 0.0, - topK: 0, - topP: 0.0, - minP: 0.0, - seed: 0, - wantGreedy: true, - }, - { - name: "all transforms", - temperature: 0.8, - topK: 50, - topP: 0.95, - minP: 0.1, - seed: 42, - wantGreedy: false, - }, - } - for _, tt := range tests { - t.Run(tt.name, func(t *testing.T) { - sampler := NewSampler(tt.temperature, tt.topK, tt.topP, tt.minP, tt.seed) - _, isGreedy := sampler.(*greedy) - if isGreedy != tt.wantGreedy { - t.Errorf("NewSampler() got greedy = %v, want %v", isGreedy, tt.wantGreedy) - } - }) - } -} - func BenchmarkSample(b *testing.B) { - weighted := NewSampler(0.5, 10, 0.9, 0.2, -1) samplers := map[string]Sampler{ - "Greedy": NewSampler(0, 0, 0, 0, 0), // Use NewSampler with temp=0 for greedy - "Weighted": weighted, + "Greedy": NewSampler(0, 0, 0, 0, 0, nil), // Use NewSampler with temp=0 for greedy + "Weighted": NewSampler(0.5, 10, 0.9, 0.2, -1, nil), } // Generate random logits for benchmarking @@ -132,7 +48,7 @@ func BenchmarkSample(b *testing.B) { b.ResetTimer() for b.Loop() { if _, err := s.Sample(logits); err != nil { - b.Error(err) + b.Fatalf("error sampling: %v", err) } } }) diff --git a/sample/transforms.go b/sample/transforms.go index f1f4f3b1..49625297 100644 --- a/sample/transforms.go +++ b/sample/transforms.go @@ -5,7 +5,7 @@ import ( "slices" ) -func softmax(ts []logit) []logit { +func softmax(ts []token) []token { var sum float32 for i, v := range ts { ts[i].value = float32(math.Exp(float64(v.value))) @@ -19,7 +19,7 @@ func softmax(ts []logit) []logit { return ts } -func temperature(ti []logit, t float32) []logit { +func temperature(ti []token, t float32) []token { if t == 1 { return ti } @@ -51,7 +51,7 @@ func temperature(ti []logit, t float32) []logit { // 1. Finds the smallest value between the node and its children // 2. If the node is not the smallest, swaps it with its smallest child // 3. Continues this process down the affected path until the min-heap property is restored -func siftDown(data []logit, start, end int) { +func siftDown(data []token, start, end int) { root := start for { child := 2*root + 1 @@ -73,7 +73,7 @@ func siftDown(data []logit, start, end int) { } // topK limits the number of tokens considered to the k highest logits -func topK(ts []logit, k int) []logit { +func topK(ts []token, k int) []token { if k >= len(ts) { return ts } @@ -99,7 +99,7 @@ func topK(ts []logit, k int) []logit { } // topP limits tokens to those with cumulative probability p -func topP(ts []logit, p float32) []logit { +func topP(ts []token, p float32) []token { if p == 1.0 { return ts } @@ -118,7 +118,7 @@ func topP(ts []logit, p float32) []logit { } // minP limits tokens to those with cumulative probability p -func minP(ts []logit, p float32) []logit { +func minP(ts []token, p float32) []token { if p == 1.0 { return ts } @@ -146,7 +146,7 @@ func minP(ts []logit, p float32) []logit { // TODO(parthsareen): possibly replace with simpler implementation https://github.com/ollama/ollama/issues/9584 // Conting sort implementation to sort tokens by logits -func sortLogits(tokens []logit) { +func sortLogits(tokens []token) { if len(tokens) <= 1 { return } @@ -187,7 +187,7 @@ func sortLogits(tokens []logit) { } // Second pass: place elements in correct position - output := make([]logit, len(tokens)) + output := make([]token, len(tokens)) // Track current positions countsCopy := counts diff --git a/sample/transforms_test.go b/sample/transforms_test.go index 950d79b3..1065231d 100644 --- a/sample/transforms_test.go +++ b/sample/transforms_test.go @@ -7,10 +7,10 @@ import ( ) // Helper to convert float64 slice to logit slice -func toLogits(values []float64) []logit { - tokens := make([]logit, len(values)) +func toTokens(values []float64) []token { + tokens := make([]token, len(values)) for i, v := range values { - tokens[i] = logit{ + tokens[i] = token{ id: int32(i), value: float32(v), } @@ -19,7 +19,7 @@ func toLogits(values []float64) []logit { } // Helper to compare logit slices -func compareLogits(t *testing.T, name string, want []float64, got []logit) { +func compareLogits(t *testing.T, name string, want []float64, got []token) { t.Helper() if len(want) != len(got) { t.Errorf("%s: length mismatch: want %d, got %d", name, len(want), len(got)) @@ -36,13 +36,13 @@ func TestTemperature(t *testing.T) { input := []float64{2, -1, 4, -3, 1, -2, 0} want := []float64{-4, -10, 0, -14, -6, -12, -8} // (logit - max logit) / temp - got := temperature(toLogits(input), 0.5) + got := temperature(toTokens(input), 0.5) compareLogits(t, "Temperature", want, got) } func TestSoftmax(t *testing.T) { input := []float64{-3, -2, -1, 0, 1, 2, 4} - got := softmax(toLogits(input)) + got := softmax(toTokens(input)) // Check probabilities sum to 1 var sum float32 @@ -65,7 +65,7 @@ func TestTopK(t *testing.T) { input := []float64{-3, -2, -1, 0, 1, 2, 4} // Test k=3 - got := topK(toLogits(input), 3) + got := topK(toTokens(input), 3) if len(got) != 3 { t.Errorf("topK(3): wrong length: want 3, got %d", len(got)) } @@ -74,13 +74,13 @@ func TestTopK(t *testing.T) { compareLogits(t, "topK(3)", want, got) // Test k > len - got = topK(toLogits(input), 10) + got = topK(toTokens(input), 10) compareLogits(t, "topK(10)", input, got) } func TestTopP(t *testing.T) { input := []float64{-3, -2, -1, 0, 1, 2, 4} - tokens := toLogits(input) + tokens := toTokens(input) // First apply temperature and softmax to get probabilities tokens = temperature(tokens, 1) @@ -99,7 +99,7 @@ func TestTopP(t *testing.T) { func TestMinP(t *testing.T) { input := []float64{-3, -2, -1, 0, 1, 2, 4, 3} - tokens := toLogits(input) + tokens := toTokens(input) // First apply temperature and softmax tokens = temperature(tokens, 1) @@ -116,7 +116,7 @@ func TestMinP(t *testing.T) { func TestSortLogits(t *testing.T) { input := []float64{3, 1, 4, 2, -1, 0, -2} - tokens := toLogits(input) + tokens := toTokens(input) sortLogits(tokens) @@ -133,15 +133,15 @@ func TestSortLogits(t *testing.T) { func BenchmarkTransforms(b *testing.B) { // Generate random logits - tokens := make([]logit, 1<<16) + tokens := make([]token, 1<<16) for i := range tokens { - tokens[i] = logit{ + tokens[i] = token{ id: int32(i), value: rand.Float32(), } } - tokensCopy := make([]logit, len(tokens)) + tokensCopy := make([]token, len(tokens)) b.Run("Temperature", func(b *testing.B) { b.ResetTimer() From 96ec8afd091a763762a75b57622759a1d278fa2c Mon Sep 17 00:00:00 2001 From: Sam Date: Tue, 11 Mar 2025 03:52:02 +1100 Subject: [PATCH 04/42] docs(tool): add mcp-llm (#9537) --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 1162f891..89ff82e2 100644 --- a/README.md +++ b/README.md @@ -561,6 +561,7 @@ See the [API documentation](./docs/api.md) for all endpoints. - [TextLLaMA](https://github.com/adarshM84/TextLLaMA) A Chrome Extension that helps you write emails, correct grammar, and translate into any language - [Simple-Discord-AI](https://github.com/zyphixor/simple-discord-ai) - [LLM Telegram Bot](https://github.com/innightwolfsleep/llm_telegram_bot) (telegram bot, primary for RP. Oobabooga-like buttons, [A1111](https://github.com/AUTOMATIC1111/stable-diffusion-webui) API integration e.t.c) +- [mcp-llm](https://github.com/sammcj/mcp-llm) (MCP Server to allow LLMs to call other LLMs) ### Supported backends From 757668c42f92acf249677c9b3726bbfb2c1f5593 Mon Sep 17 00:00:00 2001 From: Xiaowei Zhu <33129495+zhu-xiaowei@users.noreply.github.com> Date: Mon, 10 Mar 2025 18:01:09 +0000 Subject: [PATCH 05/42] docs: add SwiftChat (#9540) --- README.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/README.md b/README.md index 89ff82e2..96b25045 100644 --- a/README.md +++ b/README.md @@ -276,6 +276,7 @@ See the [API documentation](./docs/api.md) for all endpoints. ### Web & Desktop - [Open WebUI](https://github.com/open-webui/open-webui) +- [SwiftChat (macOS with ReactNative)](https://github.com/aws-samples/swift-chat) - [Enchanted (macOS native)](https://github.com/AugustDev/enchanted) - [Hollama](https://github.com/fmaclen/hollama) - [Lollms-Webui](https://github.com/ParisNeo/lollms-webui) @@ -433,6 +434,7 @@ See the [API documentation](./docs/api.md) for all endpoints. ### Apple Vision Pro +- [SwiftChat](https://github.com/aws-samples/swift-chat) (Cross-platform AI chat app supporting Apple Vision Pro via "Designed for iPad") - [Enchanted](https://github.com/AugustDev/enchanted) ### Database @@ -510,6 +512,7 @@ See the [API documentation](./docs/api.md) for all endpoints. ### Mobile +- [SwiftChat](https://github.com/aws-samples/swift-chat) (Lightning-fast Cross-platform AI chat app with native UI for Android, iOS and iPad) - [Enchanted](https://github.com/AugustDev/enchanted) - [Maid](https://github.com/Mobile-Artificial-Intelligence/maid) - [Ollama App](https://github.com/JHubi1/ollama-app) (Modern and easy-to-use multi-platform client for Ollama) From d8a5d96b981bf6e1c5a61fde18acaeed0fb89f7c Mon Sep 17 00:00:00 2001 From: frob Date: Mon, 10 Mar 2025 19:02:54 +0100 Subject: [PATCH 06/42] docs: Add OLLAMA_CONTEXT_LENGTH to FAQ. (#9545) --- docs/faq.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/faq.md b/docs/faq.md index 04e8433d..4aaccc2e 100644 --- a/docs/faq.md +++ b/docs/faq.md @@ -20,7 +20,7 @@ Please refer to the [GPU docs](./gpu.md). ## How can I specify the context window size? -By default, Ollama uses a context window size of 2048 tokens. +By default, Ollama uses a context window size of 2048 tokens. This can be overridden with the `OLLAMA_CONTEXT_LENGTH` environment variable. For example, to set the default context length to 8K, use: `OLLAMA_CONTEXT_LENGTH=8192 ollama serve`. To change this when using `ollama run`, use `/set parameter`: From 7e34f4fbfa192b3a2334d8fc28e24d69b83064d9 Mon Sep 17 00:00:00 2001 From: Parth Sareen Date: Mon, 10 Mar 2025 14:43:53 -0700 Subject: [PATCH 07/42] sample: add numerical stability to temperature/softmax transform (#9631) --- sample/samplers.go | 3 ++- sample/transforms.go | 40 ++++++++++++++++----------------------- sample/transforms_test.go | 27 ++++++++++---------------- 3 files changed, 28 insertions(+), 42 deletions(-) diff --git a/sample/samplers.go b/sample/samplers.go index a9d90692..aea99b3f 100644 --- a/sample/samplers.go +++ b/sample/samplers.go @@ -90,8 +90,9 @@ func (s *Sampler) sample(tokens []token) (token, error) { sortLogits(tokens) } + // token logit values are updated to probabilities tokens = temperature(tokens, s.temperature) - tokens = softmax(tokens) + tokens = topP(tokens, s.topP) tokens = minP(tokens, s.minP) diff --git a/sample/transforms.go b/sample/transforms.go index 49625297..ab62455f 100644 --- a/sample/transforms.go +++ b/sample/transforms.go @@ -5,13 +5,25 @@ import ( "slices" ) -func softmax(ts []token) []token { +// temperature applies scaling and softmax to the logits +func temperature(ts []token, temp float32) []token { + // Find max logit for numerical stability + maxLogit := float32(math.Inf(-1)) + for _, t := range ts { + if t.value > maxLogit { + maxLogit = t.value + } + } + + // Apply temperature and compute exp(x - max) + temp = max(temp, 1e-7) var sum float32 for i, v := range ts { - ts[i].value = float32(math.Exp(float64(v.value))) + ts[i].value = float32(math.Exp(float64((v.value - maxLogit) / temp))) sum += ts[i].value } + // Normalize for i := range ts { ts[i].value /= sum } @@ -19,27 +31,6 @@ func softmax(ts []token) []token { return ts } -func temperature(ti []token, t float32) []token { - if t == 1 { - return ti - } - - temp := max(t, 1e-7) - maxLogit := float32(math.Inf(-1)) - for _, token := range ti { - if token.value > maxLogit { - maxLogit = token.value - } - } - - // subtracting max logit to avoid under/overflow - for i := range ti { - ti[i].value = (ti[i].value - maxLogit) / temp - } - - return ti -} - // siftDown maintains a min-heap property by recursively moving larger elements down the heap. // // The heap is represented as an array where for any node at index i: @@ -145,7 +136,8 @@ func minP(ts []token, p float32) []token { } // TODO(parthsareen): possibly replace with simpler implementation https://github.com/ollama/ollama/issues/9584 -// Conting sort implementation to sort tokens by logits +// sortLogits sorts implementation to sort tokens by logits using counting sort +// counting sort is faster than built-in sort for this use case func sortLogits(tokens []token) { if len(tokens) <= 1 { return diff --git a/sample/transforms_test.go b/sample/transforms_test.go index 1065231d..81e8849b 100644 --- a/sample/transforms_test.go +++ b/sample/transforms_test.go @@ -32,17 +32,9 @@ func compareLogits(t *testing.T, name string, want []float64, got []token) { } } -func TestTemperature(t *testing.T) { - input := []float64{2, -1, 4, -3, 1, -2, 0} - want := []float64{-4, -10, 0, -14, -6, -12, -8} // (logit - max logit) / temp - +func TestTemperatureAndSoftmax(t *testing.T) { + input := []float64{1, 4, -2, 0} got := temperature(toTokens(input), 0.5) - compareLogits(t, "Temperature", want, got) -} - -func TestSoftmax(t *testing.T) { - input := []float64{-3, -2, -1, 0, 1, 2, 4} - got := softmax(toTokens(input)) // Check probabilities sum to 1 var sum float32 @@ -53,11 +45,14 @@ func TestSoftmax(t *testing.T) { t.Errorf("probabilities don't sum to 1: got %f", sum) } - // Check relative ordering is preserved - for i := 1; i < len(got); i++ { - if got[i].value < got[i-1].value { - t.Errorf("probability ordering not preserved at index %d", i) - } + got = temperature(toTokens(input), 1) + // Check probabilities sum to 1 + sum = 0.0 + for _, token := range got { + sum += token.value + } + if math.Abs(float64(sum)-1.0) > 1e-6 { + t.Errorf("probabilities don't sum to 1: got %f", sum) } } @@ -84,7 +79,6 @@ func TestTopP(t *testing.T) { // First apply temperature and softmax to get probabilities tokens = temperature(tokens, 1) - tokens = softmax(tokens) sortLogits(tokens) // Then apply topP @@ -103,7 +97,6 @@ func TestMinP(t *testing.T) { // First apply temperature and softmax tokens = temperature(tokens, 1) - tokens = softmax(tokens) // Then apply minP got := minP(tokens, 0.2) From 8585b7b151c404cc8044eeafe3734e676a0e572a Mon Sep 17 00:00:00 2001 From: Vincent Koc Date: Tue, 11 Mar 2025 10:15:10 +1100 Subject: [PATCH 08/42] docs: add opik to observability integrations (#9626) --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 96b25045..b4df5e2a 100644 --- a/README.md +++ b/README.md @@ -571,6 +571,7 @@ See the [API documentation](./docs/api.md) for all endpoints. - [llama.cpp](https://github.com/ggerganov/llama.cpp) project founded by Georgi Gerganov. ### Observability +- [Opik](https://www.comet.com/docs/opik/cookbook/ollama) is an open-source platform to debug, evaluate, and monitor your LLM applications, RAG systems, and agentic workflows with comprehensive tracing, automated evaluations, and production-ready dashboards. Opik supports native intergration to Ollama. - [Lunary](https://lunary.ai/docs/integrations/ollama) is the leading open-source LLM observability platform. It provides a variety of enterprise-grade features such as real-time analytics, prompt templates management, PII masking, and comprehensive agent tracing. - [OpenLIT](https://github.com/openlit/openlit) is an OpenTelemetry-native tool for monitoring Ollama Applications & GPUs using traces and metrics. - [HoneyHive](https://docs.honeyhive.ai/integrations/ollama) is an AI observability and evaluation platform for AI agents. Use HoneyHive to evaluate agent performance, interrogate failures, and monitor quality in production. From 9926eae01516fa3ab65d60df7dd0b51dceccdebf Mon Sep 17 00:00:00 2001 From: Michael Yang Date: Fri, 7 Mar 2025 18:04:16 -0800 Subject: [PATCH 09/42] fix: pad tensor item if ge zero this produces a nicer output since both positive and negative values produces the same width --- ml/backend.go | 17 ++++++++++++----- 1 file changed, 12 insertions(+), 5 deletions(-) diff --git a/ml/backend.go b/ml/backend.go index 3abacbf1..641175f0 100644 --- a/ml/backend.go +++ b/ml/backend.go @@ -5,6 +5,7 @@ import ( "encoding/binary" "fmt" "os" + "slices" "strconv" "strings" ) @@ -241,16 +242,17 @@ func dump[S ~[]E, E number](ctx Context, t Tensor, items int, fn func(E) string) } shape := t.Shape() + slices.Reverse(shape) var sb strings.Builder var f func([]int, int) f = func(dims []int, stride int) { prefix := strings.Repeat(" ", len(shape)-len(dims)+1) - fmt.Fprint(&sb, "[") - defer func() { fmt.Fprint(&sb, "]") }() + sb.WriteString("[") + defer func() { sb.WriteString("]") }() for i := 0; i < dims[0]; i++ { if i >= items && i < dims[0]-items { - fmt.Fprint(&sb, "..., ") + sb.WriteString("..., ") // skip to next printable element skip := dims[0] - 2*items if len(dims) > 1 { @@ -265,9 +267,14 @@ func dump[S ~[]E, E number](ctx Context, t Tensor, items int, fn func(E) string) fmt.Fprint(&sb, ",", strings.Repeat("\n", len(dims)-1), prefix) } } else { - fmt.Fprint(&sb, fn(s[stride+i])) + text := fn(s[stride+i]) + if len(text) > 0 && text[0] != '-' { + sb.WriteString(" ") + } + + sb.WriteString(text) if i < dims[0]-1 { - fmt.Fprint(&sb, ", ") + sb.WriteString(", ") } } } From 4dcf80167aca16c90bd2d01e0b91473e595ae936 Mon Sep 17 00:00:00 2001 From: Daniel Hiltgen Date: Tue, 11 Mar 2025 08:34:20 -0700 Subject: [PATCH 10/42] Build release for windows with local script (#9636) --- scripts/build_windows.ps1 | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/scripts/build_windows.ps1 b/scripts/build_windows.ps1 index 62930d7f..60485df8 100644 --- a/scripts/build_windows.ps1 +++ b/scripts/build_windows.ps1 @@ -80,13 +80,14 @@ function checkEnv() { function buildOllama() { + mkdir -Force -path "${script:DIST_DIR}\" if ($script:ARCH -ne "arm64") { Remove-Item -ea 0 -recurse -force -path "${script:SRC_DIR}\dist\windows-${script:ARCH}" New-Item "${script:SRC_DIR}\dist\windows-${script:ARCH}\lib\ollama\" -ItemType Directory -ea 0 & cmake --fresh --preset CPU --install-prefix $script:DIST_DIR if ($LASTEXITCODE -ne 0) { exit($LASTEXITCODE)} - & cmake --build --preset CPU --parallel $script:JOBS + & cmake --build --preset CPU --config Release --parallel $script:JOBS if ($LASTEXITCODE -ne 0) { exit($LASTEXITCODE)} & cmake --install build --component CPU --strip if ($LASTEXITCODE -ne 0) { exit($LASTEXITCODE)} @@ -101,7 +102,7 @@ function buildOllama() { # to avoid 2022 (or newer) from being used as the default & cmake --fresh --preset "CUDA 11" -G "Visual Studio 16 2019" --install-prefix $script:DIST_DIR if ($LASTEXITCODE -ne 0) { exit($LASTEXITCODE)} - & cmake --build --preset "CUDA 11" --parallel $script:JOBS + & cmake --build --preset "CUDA 11" --config Release --parallel $script:JOBS if ($LASTEXITCODE -ne 0) { exit($LASTEXITCODE)} & cmake --install build --component "CUDA" --strip if ($LASTEXITCODE -ne 0) { exit($LASTEXITCODE)} @@ -112,7 +113,7 @@ function buildOllama() { write-host "Building CUDA v12 backend libraries" & cmake --fresh --preset "CUDA 12" --install-prefix $script:DIST_DIR if ($LASTEXITCODE -ne 0) { exit($LASTEXITCODE)} - & cmake --build --preset "CUDA 12" --parallel $script:JOBS + & cmake --build --preset "CUDA 12" --config Release --parallel $script:JOBS if ($LASTEXITCODE -ne 0) { exit($LASTEXITCODE)} & cmake --install build --component "CUDA" --strip if ($LASTEXITCODE -ne 0) { exit($LASTEXITCODE)} @@ -131,7 +132,7 @@ function buildOllama() { $env:HIPCXX="" $env:HIP_PLATFORM="" $env:CMAKE_PREFIX_PATH="" - & cmake --build --preset "ROCm" --parallel $script:JOBS + & cmake --build --preset "ROCm" --config Release --parallel $script:JOBS if ($LASTEXITCODE -ne 0) { exit($LASTEXITCODE)} & cmake --install build --component "HIP" --strip if ($LASTEXITCODE -ne 0) { exit($LASTEXITCODE)} From 5f74d1fd47ec396ba40f60aee0a1a585ad0fcb4f Mon Sep 17 00:00:00 2001 From: Patrick Devine Date: Fri, 7 Feb 2025 15:58:15 -0800 Subject: [PATCH 11/42] gemma2 impl --- convert/convert.go | 12 ++ convert/convert_gemma3.go | 81 ++++++++++ convert/tokenizer_spm.go | 70 ++++++-- fs/ggml/ggml.go | 11 +- kvcache/causal_test.go | 2 +- ml/backend.go | 3 +- ml/backend/ggml/ggml.go | 11 +- model/models/gemma2/model.go | 206 ++++++++++++++++++++++++ model/models/gemma3/model.go | 74 +++++++++ model/models/gemma3/model_text.go | 193 ++++++++++++++++++++++ model/models/gemma3/process_image.go | 57 +++++++ model/models/llama/model.go | 7 +- model/models/mllama/model_text.go | 8 +- model/models/models.go | 2 + model/process_text.go | 13 +- model/process_text_spm.go | 221 ++++++++++++++++++++++++++ model/process_text_spm_test.go | 110 +++++++++++++ model/testdata/gemma2/tokenizer.model | Bin 0 -> 4241003 bytes 18 files changed, 1057 insertions(+), 24 deletions(-) create mode 100644 convert/convert_gemma3.go create mode 100644 model/models/gemma2/model.go create mode 100644 model/models/gemma3/model.go create mode 100644 model/models/gemma3/model_text.go create mode 100644 model/models/gemma3/process_image.go create mode 100644 model/process_text_spm.go create mode 100644 model/process_text_spm_test.go create mode 100644 model/testdata/gemma2/tokenizer.model diff --git a/convert/convert.go b/convert/convert.go index 015303e7..eb441715 100644 --- a/convert/convert.go +++ b/convert/convert.go @@ -15,6 +15,11 @@ import ( type ModelParameters struct { Architectures []string `json:"architectures"` VocabSize uint32 `json:"vocab_size"` + TextModel TextParameters `json:"text_config"` +} + +type TextParameters struct { + VocabSize uint32 `json:"vocab_size"` } type AdapterParameters struct { @@ -185,6 +190,8 @@ func ConvertModel(fsys fs.FS, ws io.WriteSeeker) error { conv = &gemmaModel{} case "Gemma2ForCausalLM": conv = &gemma2Model{} + case "Gemma3ForConditionalGeneration": + conv = &gemma3Model{} case "Phi3ForCausalLM": conv = &phi3Model{} case "Qwen2ForCausalLM": @@ -213,6 +220,11 @@ func ConvertModel(fsys fs.FS, ws io.WriteSeeker) error { } vocabSize := int(p.VocabSize) + if vocabSize == 0 { + tVocabSize := int(p.TextModel.VocabSize) + vocabSize = tVocabSize + } + switch { case vocabSize > len(t.Vocabulary.Tokens): slog.Warn("vocabulary is smaller than expected, padding with dummy tokens", "expect", vocabSize, "actual", len(t.Vocabulary.Tokens)) diff --git a/convert/convert_gemma3.go b/convert/convert_gemma3.go new file mode 100644 index 00000000..c2be5570 --- /dev/null +++ b/convert/convert_gemma3.go @@ -0,0 +1,81 @@ +package convert + +import "github.com/ollama/ollama/fs/ggml" + +type gemma3Model struct { + gemmaModel + TextModel gemma3TextModel `json:"text_config"` + VisionModel gemma3VisionModel `json:"vision_config"` +} + +type gemma3TextModel struct { + MaxPositionEmbeddings uint32 `json:"max_position_embeddings"` + HiddenSize uint32 `json:"hidden_size"` + HiddenLayers uint32 `json:"num_hidden_layers"` + IntermediateSize uint32 `json:"intermediate_size"` + NumAttentionHeads uint32 `json:"num_attention_heads"` + NumKeyValueHeads uint32 `json:"num_key_value_heads"` + RMSNormEPS float32 `json:"rms_norm_eps"` + HeadDim uint32 `json:"head_dim"` + SlidingWindow uint32 `json:"sliding_window"` + AttentionLogitSoftcap float32 `json:"attn_logit_softcapping"` + FinalLogitSoftcap float32 `json:"final_logit_softcapping"` + RopeLocalTheta float32 `json:"rope_local_base_freq"` + RopeGlobalTheta float32 `json:"rope_global_base_freq"` +} + +type gemma3VisionModel struct { + ImageSize uint32 `json:"image_size"` + NumChannels uint32 `json:"num_channels"` + HiddenLayers uint32 `json:"num_hidden_layers"` +} + +func (p *gemma3Model) KV(t *Tokenizer) ggml.KV { + kv := p.ModelParameters.KV(t) + kv["general.architecture"] = "gemma3" + kv["gemma3.context_length"] = p.TextModel.MaxPositionEmbeddings + kv["gemma3.embedding_length"] = p.TextModel.HiddenSize + kv["gemma3.block_count"] = p.TextModel.HiddenLayers + kv["gemma3.text.feed_forward_length"] = p.TextModel.IntermediateSize + kv["gemma3.attention.head_count"] = p.TextModel.NumAttentionHeads + kv["gemma3.attention.head_count_kv"] = p.TextModel.NumKeyValueHeads + kv["gemma3.text.attention.layer_norm_rms_epsilon"] = p.TextModel.RMSNormEPS + kv["gemma3.attention.key_length"] = p.TextModel.HeadDim + kv["gemma3.attention.value_length"] = p.TextModel.HeadDim + kv["gemma3.text.attention.sliding_window"] = p.TextModel.SlidingWindow + kv["gemma3.text.final_logit_softcapping"] = p.TextModel.FinalLogitSoftcap + kv["gemma3.text.rope.local.freq_base"] = p.TextModel.RopeLocalTheta + kv["gemma3.text.rope.global.freq_base"] = p.TextModel.RopeGlobalTheta + kv["tokenizer.ggml.bos_token_id"] = uint32(2) + kv["tokenizer.ggml.eot_token_id"] = uint32(1) + kv["gemma3.vision.image_size"] = p.VisionModel.ImageSize + kv["gemma3.vision.num_channels"] = p.VisionModel.NumChannels + kv["gemma3.vision.block_count"] = p.VisionModel.HiddenLayers + return kv +} + +func (p *gemma3Model) Replacements() []string { + return []string{ + "lm_head", "output", + "model.embed_tokens", "token_embd", + "model.norm", "output_norm", + "vision_model.vision_model", "v", + "language_model.", "", + "model.layers", "blk", + "encoder.layers", "blk", + "vision_tower.vision_model.embeddings", "v", + "input_layernorm", "attn_norm", + "self_attn.q_proj", "attn_q", + "self_attn.q_norm", "attn_q_norm", + "self_attn.k_proj", "attn_k", + "self_attn.k_norm", "attn_k_norm", + "self_attn.v_proj", "attn_v", + "self_attn.o_proj", "attn_output", + "mlp.gate_proj", "ffn_gate", + "mlp.down_proj", "ffn_down", + "mlp.up_proj", "ffn_up", + "post_attention_layernorm", "post_attention_norm", + "pre_feedforward_layernorm", "ffn_norm", + "post_feedforward_layernorm", "post_ffw_norm", + } +} diff --git a/convert/tokenizer_spm.go b/convert/tokenizer_spm.go index 5e506087..d8a012c0 100644 --- a/convert/tokenizer_spm.go +++ b/convert/tokenizer_spm.go @@ -6,7 +6,9 @@ import ( "errors" "fmt" "io/fs" + "log/slog" "os" + "reflect" "slices" "google.golang.org/protobuf/proto" @@ -15,6 +17,8 @@ import ( ) func parseSentencePiece(fsys fs.FS) (*Vocabulary, error) { + slog.Debug("using spm vocabulary") + ast, err := parseAdditionalSpecialTokens(fsys) if err != nil { return nil, err @@ -43,8 +47,11 @@ func parseSentencePiece(fsys fs.FS) (*Vocabulary, error) { v.Types = append(v.Types, int32(t)) default: tt := int32(sentencepiece.ModelProto_SentencePiece_NORMAL) - if slices.Contains(ast, piece.GetPiece()) { - tt = int32(sentencepiece.ModelProto_SentencePiece_CONTROL) + for _, t := range ast { + if t.Content == piece.GetPiece() { + tt = int32(sentencepiece.ModelProto_SentencePiece_CONTROL) + break + } } v.Types = append(v.Types, tt) @@ -78,10 +85,16 @@ func parseSentencePiece(fsys fs.FS) (*Vocabulary, error) { return cmp.Compare(i.id, j.id) }) - n := len(v.Tokens) - for i, t := range ts { - if t.id != i+n { - return nil, fmt.Errorf("invalid token id: %d", t.id) + for _, t := range ts { + if t.id < len(v.Tokens) { + if v.Tokens[t.id] == t.content { + slog.Warn("tokenizer", "duplicate token", t.content, "id", t.id) + continue + } + return nil, fmt.Errorf("token mismatch: %s != %s at pos [%d]", t.content, v.Tokens[t.id], t.id) + } + if t.id != len(v.Tokens) { + return nil, fmt.Errorf("invalid token id: [%d] as pos [%d]", t.id, len(v.Tokens)) } v.Tokens = append(v.Tokens, t.content) @@ -92,7 +105,15 @@ func parseSentencePiece(fsys fs.FS) (*Vocabulary, error) { return &v, nil } -func parseAdditionalSpecialTokens(fsys fs.FS) ([]string, error) { +type specialToken struct { + Content string `json:"content"` + Lstrip bool `json:"lstrip"` + Normalized bool `json:"normalized"` + Rstrip bool `json:"rstrip"` + SingleWord bool `json:"single_word"` +} + +func parseAdditionalSpecialTokens(fsys fs.FS) ([]specialToken, error) { f, err := fsys.Open("special_tokens_map.json") if errors.Is(err, os.ErrNotExist) { return nil, nil @@ -102,12 +123,43 @@ func parseAdditionalSpecialTokens(fsys fs.FS) ([]string, error) { defer f.Close() var m struct { - AdditionalSpecialTokens []string `json:"additional_special_tokens"` + AdditionalSpecialTokens any `json:"additional_special_tokens"` } if err := json.NewDecoder(f).Decode(&m); err != nil { return nil, err } - return m.AdditionalSpecialTokens, nil + var ast []specialToken + + switch st := m.AdditionalSpecialTokens.(type) { + case []string: + for _, s := range st { + ast = append(ast, specialToken{Content: s}) + } + case []any: + for _, s := range st { + // marshal and unmarshal the object to get the special token + tMap := s.(map[string]any) + data, err := json.Marshal(tMap) + if err != nil { + return nil, err + } + + var token specialToken + err = json.Unmarshal(data, &token) + if err != nil { + return nil, err + } + + ast = append(ast, token) + } + + default: + slog.Warn("special token", "unknown token", reflect.TypeOf(st)) + } + + slog.Debug("spm tokenizer", "additional tokens", ast) + + return ast, nil } diff --git a/fs/ggml/ggml.go b/fs/ggml/ggml.go index 8662c3b0..fe98a71b 100644 --- a/fs/ggml/ggml.go +++ b/fs/ggml/ggml.go @@ -124,6 +124,15 @@ func (kv KV) Uints(key string, defaultValue ...[]uint32) []uint32 { return s } +func (kv KV) Floats(key string, defaultValue ...[]float32) []float32 { + r := keyValue(kv, key, &array{}) + s := make([]float32, r.size) + for i := range r.size { + s[i] = float32(r.values[i].(float32)) + } + return s +} + func keyValue[T string | uint32 | uint64 | float32 | *array | bool](kv KV, key string, defaultValue ...T) T { if !strings.HasPrefix(key, "tokenizer.") && !strings.HasPrefix(key, "general.") { key = kv.Architecture() + "." + key @@ -476,7 +485,7 @@ func (f GGML) GraphSize(context, batch uint64, kvCacheType string) (kv, partialO // vocab graph 4*batch*(embedding+vocab)+embedding*vocab*105/128, ) - case "gemma", "gemma2": + case "gemma", "gemma2", "gemma3": fullOffload = max( 4*batch*(embedding+vocab), 4*batch*(2+context+context*heads+2*embedding+2*embeddingHeadsK*heads), diff --git a/kvcache/causal_test.go b/kvcache/causal_test.go index 22d8efb4..0c9e000e 100644 --- a/kvcache/causal_test.go +++ b/kvcache/causal_test.go @@ -445,7 +445,7 @@ func (t *testTensor) Conv2D(ctx ml.Context, weight ml.Tensor, s0, s1, p0, p1, d0 panic("not implemented") } -func (t *testTensor) RoPE(ctx ml.Context, positionIDs, ropeFactors ml.Tensor, dim uint32, base, scale float32) ml.Tensor { +func (t *testTensor) RoPE(ctx ml.Context, positionIDs, ropeFactors ml.Tensor, dim, ropeType uint32, base, scale float32) ml.Tensor { panic("not implemented") } diff --git a/ml/backend.go b/ml/backend.go index 641175f0..27c2d14d 100644 --- a/ml/backend.go +++ b/ml/backend.go @@ -19,6 +19,7 @@ type Config interface { Strings(string, ...[]string) []string Uints(string, ...[]uint32) []uint32 + Floats(string, ...[]float32) []float32 } type Backend interface { @@ -135,7 +136,7 @@ type Tensor interface { Scale(ctx Context, s float64) Tensor Conv2D(ctx Context, weight Tensor, s0, s1, p0, p1, d0, d1 int) Tensor - RoPE(ctx Context, positionIDs, ropeFactors Tensor, dim uint32, base, scale float32) Tensor + RoPE(ctx Context, positionIDs, ropeFactors Tensor, dim, ropeType uint32, base, scale float32) Tensor Tanh(ctx Context) Tensor GELU(ctx Context) Tensor diff --git a/ml/backend/ggml/ggml.go b/ml/backend/ggml/ggml.go index 74512f33..8843ae7c 100644 --- a/ml/backend/ggml/ggml.go +++ b/ml/backend/ggml/ggml.go @@ -893,10 +893,13 @@ func (t *Tensor) View(ctx ml.Context, offset int, shape ...int) ml.Tensor { } const ( - ropeTypeNorm C.int = iota + ropeTypeNorm C.int = 0 + ropeTypeNeox C.int = 2 + ropeTypeMrope C.int = 8 + ropeTypeVision C.int = 24 ) -func (t *Tensor) RoPE(ctx ml.Context, positionIDs, ropeFactors ml.Tensor, ropeDim uint32, ropeBase, ropeScale float32) ml.Tensor { +func (t *Tensor) RoPE(ctx ml.Context, positionIDs, ropeFactors ml.Tensor, ropeDim, ropeType uint32, ropeBase, ropeScale float32) ml.Tensor { if ropeFactors == nil { ropeFactors = &Tensor{b: t.b} } @@ -911,8 +914,8 @@ func (t *Tensor) RoPE(ctx ml.Context, positionIDs, ropeFactors ml.Tensor, ropeDi t: C.ggml_rope_ext( ctx.(*Context).ctx, dequant, positionIDs.(*Tensor).t, ropeFactors.(*Tensor).t, C.int(ropeDim), - 131072, // YaRN n_ctx_train - ropeTypeNorm, // ROPE_TYPE_NORM + C.int(ropeType), + 131072, // YaRN n_ctx_train C.float(ropeBase), C.float(ropeScale), 0., // YaRN ext_factor diff --git a/model/models/gemma2/model.go b/model/models/gemma2/model.go new file mode 100644 index 00000000..2ad9c568 --- /dev/null +++ b/model/models/gemma2/model.go @@ -0,0 +1,206 @@ +package gemma2 + +import ( + "math" + + "github.com/ollama/ollama/kvcache" + "github.com/ollama/ollama/ml" + "github.com/ollama/ollama/ml/nn" + "github.com/ollama/ollama/model" + "github.com/ollama/ollama/model/input" +) + +type Options struct { + hiddenSize, numHeads, numKVHeads int + attnKeyLen, attnValLen int + eps, ropeBase, ropeScale float32 + attnLogitSoftcap float32 + finalLogitSoftcap float32 + largeModelScaling bool +} + +type Model struct { + model.Base + model.SentencePieceModel + + TokenEmbedding *nn.Embedding `gguf:"token_embd"` + Layers []Layer `gguf:"blk"` + OutputNorm *nn.RMSNorm `gguf:"output_norm"` + Output *nn.Linear `gguf:"output,alt:token_embd"` // just set to token_embd? + + *Options +} + +const ( + gemma27BLayerCount = 46 +) + +func New(c ml.Config) (model.Model, error) { + m := Model{ + SentencePieceModel: model.NewSentencePieceModel( + 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"), + Scores: c.Floats("tokenizer.ggml.scores"), + Types: c.Uints("tokenizer.ggml.token_type"), + BOS: int32(c.Uint("tokenizer.ggml.bos_token_id")), + EOS: int32(c.Uint("tokenizer.ggml.eos_token_id")), + }, + ), + Layers: make([]Layer, c.Uint("block_count")), + Options: &Options{ + hiddenSize: int(c.Uint("embedding_length")), + numHeads: int(c.Uint("attention.head_count")), + numKVHeads: int(c.Uint("attention.head_count_kv")), + attnKeyLen: int(c.Uint("attention.key_length")), + attnValLen: int(c.Uint("attention.value_length")), + eps: c.Float("attention.layer_norm_rms_epsilon"), + ropeBase: c.Float("rope.freq_base", 10000.0), + ropeScale: c.Float("rope.freq_scale", 1.0), + attnLogitSoftcap: c.Float("attn_logit_softcapping"), + finalLogitSoftcap: c.Float("final_logit_softcapping"), + }, + } + + slidingWindowLen := int32(c.Uint("attention.sliding_window")) + m.Cache = kvcache.NewWrapperCache(kvcache.NewSWACache(slidingWindowLen, m.Shift), kvcache.NewCausalCache(m.Shift)) + + return &m, nil +} + +type SelfAttention struct { + Query *nn.Linear `gguf:"attn_q"` + Key *nn.Linear `gguf:"attn_k"` + Value *nn.Linear `gguf:"attn_v"` + Output *nn.Linear `gguf:"attn_output"` +} + +func (sa *SelfAttention) Forward(ctx ml.Context, hiddenState, positionIDs ml.Tensor, cache kvcache.Cache, opts *Options) ml.Tensor { + batchSize := hiddenState.Dim(1) + ropeType := uint32(2) + + q := sa.Query.Forward(ctx, hiddenState) + q = q.Reshape(ctx, opts.attnKeyLen, opts.numHeads, batchSize) + q = q.RoPE(ctx, positionIDs, nil, uint32(opts.attnKeyLen), ropeType, opts.ropeBase, opts.ropeScale) + + if opts.largeModelScaling { + q = q.Scale(ctx, 1.0/math.Sqrt(float64(opts.hiddenSize / opts.numHeads))) + } else { + q = q.Scale(ctx, 1.0/math.Sqrt(float64(opts.attnKeyLen))) + } + + k := sa.Key.Forward(ctx, hiddenState) + k = k.Reshape(ctx, opts.attnKeyLen, opts.numKVHeads, batchSize) + k = k.RoPE(ctx, positionIDs, nil, uint32(opts.attnKeyLen), ropeType, opts.ropeBase, opts.ropeScale) + + v := sa.Value.Forward(ctx, hiddenState) + v = v.Reshape(ctx, opts.attnValLen, opts.numKVHeads, batchSize) + + cache.Put(ctx, k, v) + k, v, mask := cache.Get(ctx) + + q = q.Permute(ctx, 0, 2, 1, 3).Contiguous(ctx) + k = k.Permute(ctx, 0, 2, 1, 3).Contiguous(ctx) + v = v.Permute(ctx, 1, 2, 0, 3).Contiguous(ctx) + + kq := k.Mulmat(ctx, q) + + // logit softcap + kq = kq.Scale(ctx, 1.0/float64(opts.attnLogitSoftcap)) + kq = kq.Tanh(ctx) + kq = kq.Scale(ctx, float64(opts.attnLogitSoftcap)) + + kq = kq.Add(ctx, mask) + kq = kq.Softmax(ctx) + + kqv := v.Mulmat(ctx, kq) + kqv = kqv.Permute(ctx, 0, 2, 1, 3).Contiguous(ctx) + kqv = kqv.Reshape(ctx, opts.attnValLen*opts.numHeads, batchSize) + + return sa.Output.Forward(ctx, kqv) +} + +func (m *Model) Shift(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, error) { + return key.RoPE(ctx, shift, nil, uint32(m.Options.attnKeyLen), uint32(2), m.Options.ropeBase, m.Options.ropeScale), nil +} + +type MLP struct { + Up *nn.Linear `gguf:"ffn_up"` + Down *nn.Linear `gguf:"ffn_down"` + Gate *nn.Linear `gguf:"ffn_gate"` +} + +func (mlp *MLP) Forward(ctx ml.Context, hiddenState ml.Tensor, opts *Options) ml.Tensor { + hiddenState = mlp.Gate.Forward(ctx, hiddenState).GELU(ctx).Mul(ctx, mlp.Up.Forward(ctx, hiddenState)) + return mlp.Down.Forward(ctx, hiddenState) +} + +type Layer struct { + AttentionNorm *nn.RMSNorm `gguf:"attn_norm"` + SelfAttention *SelfAttention + PostAttentionNorm *nn.RMSNorm `gguf:"post_attention_norm"` + MLPNorm *nn.RMSNorm `gguf:"ffn_norm"` + MLP *MLP + PostMLPNorm *nn.RMSNorm `gguf:"post_ffw_norm"` +} + +func (l *Layer) Forward(ctx ml.Context, hiddenState, positionIDs ml.Tensor, cache kvcache.Cache, opts *Options) ml.Tensor { + residual := hiddenState + + hiddenState = l.AttentionNorm.Forward(ctx, hiddenState, opts.eps) + hiddenState = l.SelfAttention.Forward(ctx, hiddenState, positionIDs, cache, opts) + hiddenState = l.PostAttentionNorm.Forward(ctx, hiddenState, opts.eps) + hiddenState = hiddenState.Add(ctx, residual) + residual = hiddenState + + hiddenState = l.MLPNorm.Forward(ctx, hiddenState, opts.eps) + hiddenState = l.MLP.Forward(ctx, hiddenState, opts) + hiddenState = l.PostMLPNorm.Forward(ctx, hiddenState, opts.eps) + return hiddenState.Add(ctx, residual) +} + +func (m *Model) Forward(ctx ml.Context, opts input.Options) (ml.Tensor, error) { + inputs, err := ctx.Input().FromIntSlice(opts.Inputs, len(opts.Inputs)) + if err != nil { + return nil, err + } + + positions, err := ctx.Input().FromIntSlice(opts.Positions, len(opts.Positions)) + if err != nil { + return nil, err + } + + hiddenState := m.TokenEmbedding.Forward(ctx, inputs) + hiddenState = hiddenState.Scale(ctx, math.Sqrt(float64(m.Options.hiddenSize))) + + if len(m.Layers) == gemma27BLayerCount { + m.Options.largeModelScaling = true + } + + for i, layer := range m.Layers { + cacheType := i % 2 + m.Cache.SetLayer(i) + wc := m.Cache.(*kvcache.WrapperCache) + wc.SetLayerType(cacheType) + hiddenState = layer.Forward(ctx, hiddenState, positions, m.Cache, m.Options) + } + + hiddenState = m.OutputNorm.Forward(ctx, hiddenState, m.eps) + hiddenState = m.Output.Forward(ctx, hiddenState) + + // final logit softcap + hiddenState = hiddenState.Scale(ctx, 1.0/float64(m.Options.finalLogitSoftcap)) + hiddenState = hiddenState.Tanh(ctx) + hiddenState = hiddenState.Scale(ctx, float64(m.Options.finalLogitSoftcap)) + + outputs, err := ctx.Output().FromIntSlice(opts.Outputs, len(opts.Outputs)) + if err != nil { + return nil, err + } + + return hiddenState.Rows(ctx, outputs), nil +} + +func init() { + model.Register("gemma2", New) +} diff --git a/model/models/gemma3/model.go b/model/models/gemma3/model.go new file mode 100644 index 00000000..0f4944a4 --- /dev/null +++ b/model/models/gemma3/model.go @@ -0,0 +1,74 @@ +package gemma3 + +import ( + "fmt" + + "github.com/ollama/ollama/kvcache" + "github.com/ollama/ollama/ml" + "github.com/ollama/ollama/model" + "github.com/ollama/ollama/model/input" +) + +type Model struct { + model.Base + model.SentencePieceModel + + //*VisionModel `gguf:"v,vision"` + *TextModel + + //Projector *nn.Linear `gguf:"mm.0"` + + ImageProcessor +} + +func New(c ml.Config) (model.Model, error) { + // Verify unified config + if c.Uint("vision.block_count") == 0 { + return nil, fmt.Errorf("non-unified vision model not supported") + } + m := Model{ + SentencePieceModel: model.NewSentencePieceModel( + 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"), + Scores: c.Floats("tokenizer.ggml.scores"), + Types: c.Uints("tokenizer.ggml.token_type"), + BOS: int32(c.Uint("tokenizer.ggml.bos_token_id")), + AddBOS: c.Bool("tokenizer.ggml.add_bos_token", true), + EOS: int32(c.Uint("tokenizer.ggml.eos_token_id")), + AddEOS: c.Bool("tokenizer.ggml.add_eos_token", false), + }, + ), + ImageProcessor: newImageProcessor(c), + //VisionModel: newVisionModel(c), + TextModel: newTextModel(c), + } + + slidingWindowLen := int32(c.Uint("text.attention.sliding_window")) + m.Cache = kvcache.NewWrapperCache(kvcache.NewSWACache(slidingWindowLen, m.Shift), kvcache.NewCausalCache(m.Shift)) + + return &m, nil +} + +func (m *Model) Forward(ctx ml.Context, opts input.Options) (ml.Tensor, error) { + inputs, err := ctx.Input().FromIntSlice(opts.Inputs, len(opts.Inputs)) + if err != nil { + return nil, err + } + + positions, err := ctx.Input().FromIntSlice(opts.Positions, len(opts.Positions)) + if err != nil { + return nil, err + } + + outputs, err := ctx.Output().FromIntSlice(opts.Outputs, len(opts.Outputs)) + if err != nil { + return nil, err + } + + return m.TextModel.Forward(ctx, inputs, positions, outputs, m.Cache), nil +} + +func init() { + model.Register("gemma3", New) +} diff --git a/model/models/gemma3/model_text.go b/model/models/gemma3/model_text.go new file mode 100644 index 00000000..051e06c5 --- /dev/null +++ b/model/models/gemma3/model_text.go @@ -0,0 +1,193 @@ +package gemma3 + +import ( + "math" + + "github.com/ollama/ollama/kvcache" + "github.com/ollama/ollama/ml" + "github.com/ollama/ollama/ml/nn" + "github.com/ollama/ollama/model" +) + +type TextOptions struct { + hiddenSize, numHeads, numKVHeads int + attnKeyLen, attnValLen int + eps, ropeScale float32 + ropeLocalBase, ropeGlobalBase float32 + finalLogitSoftcap float32 + largeModelScaling bool +} + +type TextModel struct { + model.Base + model.SentencePieceModel + + TokenEmbedding *nn.Embedding `gguf:"token_embd"` + Layers []TextLayer `gguf:"blk"` + OutputNorm *nn.RMSNorm `gguf:"output_norm"` + Output *nn.Linear `gguf:"output,alt:token_embd"` + + *TextOptions +} + +const ( + gemma27BLayerCount = 46 +) + +const ( + cacheTypeSWA = iota + cacheTypeCausal +) + +func newTextModel(c ml.Config) *TextModel { + m := TextModel{ + SentencePieceModel: model.NewSentencePieceModel( + 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"), + Scores: c.Floats("tokenizer.ggml.scores"), + Types: c.Uints("tokenizer.ggml.token_type"), + BOS: int32(c.Uint("tokenizer.ggml.bos_token_id")), + EOS: int32(c.Uint("tokenizer.ggml.eos_token_id")), + }, + ), + Layers: make([]TextLayer, c.Uint("block_count")), + TextOptions: &TextOptions{ + hiddenSize: int(c.Uint("embedding_length")), + numHeads: int(c.Uint("attention.head_count")), + numKVHeads: int(c.Uint("attention.head_count_kv")), + attnKeyLen: int(c.Uint("attention.key_length")), + attnValLen: int(c.Uint("attention.value_length")), + eps: c.Float("text.attention.layer_norm_rms_epsilon"), + ropeLocalBase: c.Float("text.rope.local.freq_base", 10000.0), + ropeGlobalBase: c.Float("text.rope.global.freq_base", 1000000.0), + ropeScale: c.Float("text.rope.freq_scale", 1.0), + finalLogitSoftcap: c.Float("text.final_logit_softcapping"), + }, + } + + slidingWindowLen := int32(c.Uint("text.attention.sliding_window")) + m.Cache = kvcache.NewWrapperCache(kvcache.NewSWACache(slidingWindowLen, m.Shift), kvcache.NewCausalCache(m.Shift)) + + return &m +} + +type TextSelfAttention struct { + Query *nn.Linear `gguf:"attn_q"` + QueryNorm *nn.RMSNorm `gguf:"attn_q_norm"` + Key *nn.Linear `gguf:"attn_k"` + KeyNorm *nn.RMSNorm `gguf:"attn_k_norm"` + Value *nn.Linear `gguf:"attn_v"` + Output *nn.Linear `gguf:"attn_output"` +} + +func (sa *TextSelfAttention) Forward(ctx ml.Context, layer int, hiddenState, positionIDs ml.Tensor, cache kvcache.Cache, opts *TextOptions) ml.Tensor { + batchSize := hiddenState.Dim(1) + ropeType := uint32(2) + + ropeBase := opts.ropeLocalBase + if (layer+1)%6 == 0 { + ropeBase = opts.ropeGlobalBase + } + + q := sa.Query.Forward(ctx, hiddenState) + q = q.Reshape(ctx, opts.attnKeyLen, opts.numHeads, batchSize) + q = sa.QueryNorm.Forward(ctx, q, opts.eps) + q = q.RoPE(ctx, positionIDs, nil, uint32(opts.attnKeyLen), ropeType, ropeBase, opts.ropeScale) + + if opts.largeModelScaling { + q = q.Scale(ctx, 1.0/math.Sqrt(float64(opts.hiddenSize/opts.numHeads))) + } else { + q = q.Scale(ctx, 1.0/math.Sqrt(float64(opts.attnKeyLen))) + } + + k := sa.Key.Forward(ctx, hiddenState) + k = k.Reshape(ctx, opts.attnKeyLen, opts.numKVHeads, batchSize) + k = sa.KeyNorm.Forward(ctx, k, opts.eps) + k = k.RoPE(ctx, positionIDs, nil, uint32(opts.attnKeyLen), ropeType, ropeBase, opts.ropeScale) + + v := sa.Value.Forward(ctx, hiddenState) + v = v.Reshape(ctx, opts.attnValLen, opts.numKVHeads, batchSize) + + scaleFactor := 1.0 + kqv := nn.Attention(ctx, q, k, v, scaleFactor, cache) + kqv = kqv.Reshape(ctx, opts.attnValLen*opts.numHeads, batchSize) + + return sa.Output.Forward(ctx, kqv) +} + +func (m *TextModel) Shift(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, error) { + ropeBase := m.TextOptions.ropeLocalBase + if (layer+1)%6 == 0 { + ropeBase = m.TextOptions.ropeGlobalBase + } + + return key.RoPE(ctx, shift, nil, uint32(m.TextOptions.attnKeyLen), uint32(2), ropeBase, m.TextOptions.ropeScale), nil +} + +type TextMLP struct { + Up *nn.Linear `gguf:"ffn_up"` + Down *nn.Linear `gguf:"ffn_down"` + Gate *nn.Linear `gguf:"ffn_gate"` +} + +func (mlp *TextMLP) Forward(ctx ml.Context, hiddenState ml.Tensor, opts *TextOptions) ml.Tensor { + hiddenState = mlp.Gate.Forward(ctx, hiddenState).GELU(ctx).Mul(ctx, mlp.Up.Forward(ctx, hiddenState)) + return mlp.Down.Forward(ctx, hiddenState) +} + +type TextLayer struct { + AttentionNorm *nn.RMSNorm `gguf:"attn_norm"` + SelfAttention *TextSelfAttention + PostAttentionNorm *nn.RMSNorm `gguf:"post_attention_norm"` + MLPNorm *nn.RMSNorm `gguf:"ffn_norm"` + MLP *TextMLP + PostMLPNorm *nn.RMSNorm `gguf:"post_ffw_norm"` +} + +func (l *TextLayer) Forward(ctx ml.Context, layer int, hiddenState, positionIDs ml.Tensor, cache kvcache.Cache, opts *TextOptions) ml.Tensor { + residual := hiddenState + + hiddenState = l.AttentionNorm.Forward(ctx, hiddenState, opts.eps) + hiddenState = l.SelfAttention.Forward(ctx, layer, hiddenState, positionIDs, cache, opts) + hiddenState = l.PostAttentionNorm.Forward(ctx, hiddenState, opts.eps) + hiddenState = hiddenState.Add(ctx, residual) + residual = hiddenState + + hiddenState = l.MLPNorm.Forward(ctx, hiddenState, opts.eps) + hiddenState = l.MLP.Forward(ctx, hiddenState, opts) + hiddenState = l.PostMLPNorm.Forward(ctx, hiddenState, opts.eps) + return hiddenState.Add(ctx, residual) +} + +func (m *TextModel) Forward(ctx ml.Context, inputs, positions, outputs ml.Tensor, cache kvcache.Cache) ml.Tensor { + hiddenState := m.TokenEmbedding.Forward(ctx, inputs) + hiddenState = hiddenState.Scale(ctx, math.Sqrt(float64(m.TextOptions.hiddenSize))) + + if len(m.Layers) == gemma27BLayerCount { + m.TextOptions.largeModelScaling = true + } + + for i, layer := range m.Layers { + // gemma alternates between the sliding window (local) and causal (global) + // kv cache every 6 layers + cacheType := cacheTypeSWA + if (i+1)%6 == 0 { + cacheType = cacheTypeCausal + } + cache.SetLayer(i) + wc := cache.(*kvcache.WrapperCache) + wc.SetLayerType(cacheType) + hiddenState = layer.Forward(ctx, i, hiddenState, positions, cache, m.TextOptions) + } + + hiddenState = m.OutputNorm.Forward(ctx, hiddenState, m.eps) + hiddenState = m.Output.Forward(ctx, hiddenState) + + // final logit softcap + hiddenState = hiddenState.Scale(ctx, 1.0/float64(m.TextOptions.finalLogitSoftcap)) + hiddenState = hiddenState.Tanh(ctx) + hiddenState = hiddenState.Scale(ctx, float64(m.TextOptions.finalLogitSoftcap)) + + return hiddenState.Rows(ctx, outputs) +} diff --git a/model/models/gemma3/process_image.go b/model/models/gemma3/process_image.go new file mode 100644 index 00000000..5cf963e8 --- /dev/null +++ b/model/models/gemma3/process_image.go @@ -0,0 +1,57 @@ +package gemma3 + +import ( + "image" + + "github.com/ollama/ollama/ml" + "github.com/ollama/ollama/model/imageproc" +) + +type ImageProcessor struct { + imageSize, numChannels int +} + +func newImageProcessor(c ml.Config) ImageProcessor { + return ImageProcessor{ + imageSize: int(c.Uint("vision.image_size")), + numChannels: int(c.Uint("vision.num_channels")), + } +} + +func (p *ImageProcessor) pack(img image.Image, mean, std [3]float32) []float32 { + var pixelVals []float32 + + bounds := img.Bounds() + var rVals, gVals, bVals []float32 + for y := bounds.Min.Y; y < bounds.Max.Y; y++ { + for x := bounds.Min.X; x < bounds.Max.X; x++ { + c := img.At(x, y) + r, g, b, _ := c.RGBA() + rVal := float32(r>>8) / 255.0 + gVal := float32(g>>8) / 255.0 + bVal := float32(b>>8) / 255.0 + + rVal = (rVal - mean[0]) / std[0] + gVal = (gVal - mean[1]) / std[1] + bVal = (bVal - mean[2]) / std[2] + + rVals = append(rVals, rVal) + gVals = append(gVals, gVal) + bVals = append(bVals, bVal) + } + } + pixelVals = append(pixelVals, rVals...) + pixelVals = append(pixelVals, gVals...) + pixelVals = append(pixelVals, bVals...) + + return pixelVals +} + +func (p ImageProcessor) ProcessImage(img image.Image) ([]float32, error) { + outputSize := image.Point{p.imageSize, p.imageSize} + newImage := imageproc.Composite(img) + newImage = imageproc.Resize(newImage, outputSize, imageproc.ResizeBilinear) + + data := p.pack(newImage, imageproc.ImageNetStandardMean, imageproc.ImageNetStandardSTD) + return data, nil +} diff --git a/model/models/llama/model.go b/model/models/llama/model.go index 1f27f522..19a2ab8c 100644 --- a/model/models/llama/model.go +++ b/model/models/llama/model.go @@ -76,14 +76,15 @@ 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) q := sa.Query.Forward(ctx, hiddenState) q = q.Reshape(ctx, headDim, opts.numHeads, batchSize) - q = q.RoPE(ctx, positionIDs, sa.RopeFactors, opts.ropeDim, opts.ropeBase, opts.ropeScale) + q = q.RoPE(ctx, positionIDs, sa.RopeFactors, opts.ropeDim, ropeType, opts.ropeBase, opts.ropeScale) k := sa.Key.Forward(ctx, hiddenState) k = k.Reshape(ctx, headDim, opts.numKVHeads, batchSize) - k = k.RoPE(ctx, positionIDs, sa.RopeFactors, opts.ropeDim, opts.ropeBase, opts.ropeScale) + k = k.RoPE(ctx, positionIDs, sa.RopeFactors, opts.ropeDim, ropeType, opts.ropeBase, opts.ropeScale) v := sa.Value.Forward(ctx, hiddenState) v = v.Reshape(ctx, headDim, opts.numKVHeads, batchSize) @@ -96,7 +97,7 @@ func (sa *SelfAttention) Forward(ctx ml.Context, hiddenState, positionIDs ml.Ten } func (m *Model) Shift(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, error) { - return key.RoPE(ctx, shift, m.Layers[layer].SelfAttention.RopeFactors, m.ropeDim, m.ropeBase, m.ropeScale), nil + return key.RoPE(ctx, shift, m.Layers[layer].SelfAttention.RopeFactors, uint32(0), m.ropeDim, m.ropeBase, m.ropeScale), nil } type MLP struct { diff --git a/model/models/mllama/model_text.go b/model/models/mllama/model_text.go index 373589f9..40c9a970 100644 --- a/model/models/mllama/model_text.go +++ b/model/models/mllama/model_text.go @@ -20,14 +20,15 @@ type TextSelfAttention struct { func (sa *TextSelfAttention) Forward(ctx ml.Context, hiddenState, positions, _ ml.Tensor, cache *kvcache.WrapperCache, opts *TextModelOptions) ml.Tensor { batchSize := hiddenState.Dim(1) headDim := opts.hiddenSize / opts.numHeads + ropeType := uint32(0) query := sa.Query.Forward(ctx, hiddenState) query = query.Reshape(ctx, headDim, opts.numHeads, batchSize) - query = query.RoPE(ctx, positions, sa.RopeFactors, opts.ropeDim, opts.ropeBase, opts.ropeScale) + query = query.RoPE(ctx, positions, sa.RopeFactors, opts.ropeDim, ropeType, opts.ropeBase, opts.ropeScale) key := sa.Key.Forward(ctx, hiddenState) key = key.Reshape(ctx, headDim, opts.numKVHeads, batchSize) - key = key.RoPE(ctx, positions, sa.RopeFactors, opts.ropeDim, opts.ropeBase, opts.ropeScale) + key = key.RoPE(ctx, positions, sa.RopeFactors, opts.ropeDim, ropeType, opts.ropeBase, opts.ropeScale) value := sa.Value.Forward(ctx, hiddenState) value = value.Reshape(ctx, headDim, opts.numKVHeads, batchSize) @@ -40,8 +41,9 @@ func (sa *TextSelfAttention) Forward(ctx ml.Context, hiddenState, positions, _ m } func (m *TextModel) Shift(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, error) { + // This will only get called for layers in the cache, which are just the self attention layers if sa, ok := m.Transformer.Layers[layer].(*TextSelfAttentionDecoderLayer); ok { - return key.RoPE(ctx, shift, sa.SelfAttention.RopeFactors, m.ropeDim, m.ropeBase, m.ropeScale), nil + return key.RoPE(ctx, shift, sa.SelfAttention.RopeFactors, m.ropeDim, uint32(0), m.ropeBase, m.ropeScale), nil } return key, nil diff --git a/model/models/models.go b/model/models/models.go index d0b68b32..ce1d2ce0 100644 --- a/model/models/models.go +++ b/model/models/models.go @@ -1,6 +1,8 @@ package models import ( + _ "github.com/ollama/ollama/model/models/gemma2" + _ "github.com/ollama/ollama/model/models/gemma3" _ "github.com/ollama/ollama/model/models/llama" _ "github.com/ollama/ollama/model/models/mllama" ) diff --git a/model/process_text.go b/model/process_text.go index 0d75a0ed..cd1deb65 100644 --- a/model/process_text.go +++ b/model/process_text.go @@ -18,6 +18,15 @@ const ( SpecialEOS ) +const ( + TOKEN_TYPE_NORMAL = iota + 1 + TOKEN_TYPE_UNKNOWN + TOKEN_TYPE_CONTROL + TOKEN_TYPE_USER_DEFINED + TOKEN_TYPE_UNUSED + TOKEN_TYPE_BYTE +) + type TextProcessor interface { Encode(s string, addSpecial bool) ([]int32, error) Decode([]int32) (string, error) @@ -27,7 +36,7 @@ type TextProcessor interface { type Vocabulary struct { Values []string Types []uint32 - Scores []uint32 + Scores []float32 Merges []string BOS, EOS int32 @@ -76,7 +85,7 @@ func (v *Vocabulary) Decode(id int32) string { func (v *Vocabulary) SpecialVocabulary() []string { v.specialOnce.Do(func() { for i := range v.Values { - if v.Types[i] == 3 { + if v.Types[i] == TOKEN_TYPE_CONTROL { v.special = append(v.special, v.Values[i]) } } diff --git a/model/process_text_spm.go b/model/process_text_spm.go new file mode 100644 index 00000000..c0bc973f --- /dev/null +++ b/model/process_text_spm.go @@ -0,0 +1,221 @@ +package model + +import ( + "iter" + "log/slog" + "strings" + + "github.com/dlclark/regexp2" + queue "github.com/emirpasic/gods/v2/queues/priorityqueue" +) + +const spmWhitespaceSep = "▁" + +func replaceWhitespaceBySeperator(s string) string { + return strings.ReplaceAll(s, " ", spmWhitespaceSep) +} + +type SentencePieceModel struct { + maxTokenLen int + pre *regexp2.Regexp + vocab *Vocabulary +} + +func NewSentencePieceModel(pre string, vocab *Vocabulary) SentencePieceModel { + slog.Debug("Tokens", "num tokens", len(vocab.Values), "vals", vocab.Values[:5], "scores", vocab.Scores[:5], "types", vocab.Types[:5]) + + counter := map[int]int{} + var maxTokenLen int + for cnt := range vocab.Types { + switch vocab.Types[cnt] { + case TOKEN_TYPE_NORMAL, TOKEN_TYPE_USER_DEFINED, TOKEN_TYPE_UNUSED: + maxTokenLen = max(maxTokenLen, len(vocab.Values[cnt])) + fallthrough + default: + counter[int(vocab.Types[cnt])] += 1 + } + } + + slog.Debug("Token counts", "normal", counter[TOKEN_TYPE_NORMAL], "unknown", counter[TOKEN_TYPE_UNKNOWN], "control", counter[TOKEN_TYPE_CONTROL], + "user defined", counter[TOKEN_TYPE_USER_DEFINED], "unused", counter[TOKEN_TYPE_UNUSED], "byte", counter[TOKEN_TYPE_BYTE], + "max token len", maxTokenLen) + + return SentencePieceModel{ + maxTokenLen: maxTokenLen, + pre: regexp2.MustCompile(pre, regexp2.Unicode|regexp2.RE2), + vocab: vocab, + } +} + +func (spm SentencePieceModel) Is(id int32, special Special) bool { + return spm.vocab.Is(id, special) +} + +func (spm *SentencePieceModel) split(s string) iter.Seq[string] { + return func(yield func(string) bool) { + for m, _ := spm.pre.FindStringMatch(s); m != nil; m, _ = spm.pre.FindNextMatch(m) { + if !yield(m.String()) { + break + } + } + } +} + +func (spm SentencePieceModel) Encode(s string) ([]int32, error) { + fragments := []fragment{{value: s}} + for _, special := range spm.vocab.SpecialVocabulary() { + // TODO: process special tokens concurrently + id := spm.vocab.Encode(special) + for i := 0; i < len(fragments); i++ { + frag := fragments[i] + if len(frag.ids) > 0 { + continue + } + + var middle []fragment + switch i := strings.Index(frag.value, special); { + case i < 0: + middle = append(middle, frag) + case i > 0: + middle = append(middle, fragment{value: frag.value[:i]}) + fallthrough + default: + middle = append(middle, fragment{value: special, ids: []int32{id}}) + if rest := frag.value[i+len(special):]; rest != "" { + middle = append(middle, fragment{value: rest}) + } + } + + fragments = append(fragments[:i], append(middle, fragments[i+1:]...)...) + } + } + slog.Debug("fragments", "frags", fragments) + + var ids []int32 + for _, frag := range fragments { + if len(frag.ids) > 0 { + ids = append(ids, frag.ids...) + continue + } + + for split := range spm.split(frag.value) { + split = replaceWhitespaceBySeperator(split) + + var sb strings.Builder + sb.Write([]byte(split)) + if id := spm.vocab.Encode(sb.String()); id >= 0 { + ids = append(ids, id) + continue + } + + runes := []rune(sb.String()) + pq := queue.NewWith(func(a, b any) int { + priA := a.(*candidate) + priB := b.(*candidate) + if priA.score > priB.score || (priA.score == priB.score && priA.a < priB.a) { + return -1 + } + return 1 + }) + + merges := make([]merge, len(runes)) + for r := range runes { + merges[r] = merge{ + p: r - 1, + n: r + 1, + runes: []rune{runes[r]}, + } + } + + slog.Debug("tokenizer", "merges", merges) + + pairwise := func(a, b int) *candidate { + if a < 0 || b >= len(runes) { + return nil + } + + left, right := string(merges[a].runes), string(merges[b].runes) + if id := spm.vocab.Encode(left + right); id >= 0 { + return &candidate{ + a: a, + b: b, + score: spm.vocab.Scores[id], + } + } + return nil + } + + for i := range len(runes) - 1 { + if pair := pairwise(i, i+1); pair != nil { + pq.Enqueue(pair) + } + } + + pqv := pq.Values() + for _, v := range pqv { + e := v.(*candidate) + slog.Debug("candidate", "candidate", e) + } + + for !pq.Empty() { + v, _ := pq.Dequeue() + pair := v.(*candidate) + left, right := merges[pair.a], merges[pair.b] + + slog.Debug("pair", "left", left, "right", right) + if len(left.runes) == 0 || len(right.runes) == 0 { + continue + } + + merges[pair.a].runes = append(left.runes, right.runes...) + merges[pair.b].runes = nil + merges[pair.a].n = right.n + if right.n < len(merges) { + merges[right.n].p = pair.a + } + + if pair := pairwise(merges[pair.a].p, pair.a); pair != nil { + pq.Enqueue(pair) + } + + if pair := pairwise(pair.a, merges[pair.a].n); pair != nil { + pq.Enqueue(pair) + } + } + + slog.Debug("merges", "merges", merges) + + for _, merge := range merges { + if len(merge.runes) > 0 { + if id := spm.vocab.Encode(string(merge.runes)); id >= 0 { + ids = append(ids, id) + } else { + slog.Debug("missing token", "token", string(merge.runes)) + } + } + } + } + } + slog.Debug("encoded", "ids", ids) + + return ids, nil +} + +type candidate struct { + a, b int + score float32 +} + +func (spm SentencePieceModel) Decode(ids []int32) (string, error) { + var sb strings.Builder + for _, id := range ids { + data := spm.vocab.Decode(id) + data = strings.ReplaceAll(data, spmWhitespaceSep, " ") + if _, err := sb.WriteString(data); err != nil { + return "", err + } + } + + slog.Debug("decoded", "ids", ids, "text", sb.String()) + return sb.String(), nil +} diff --git a/model/process_text_spm_test.go b/model/process_text_spm_test.go new file mode 100644 index 00000000..72bd629c --- /dev/null +++ b/model/process_text_spm_test.go @@ -0,0 +1,110 @@ +package model + +import ( + "log/slog" + "os" + "path/filepath" + "slices" + "testing" + + "google.golang.org/protobuf/proto" + + "github.com/ollama/ollama/convert/sentencepiece" +) + +func loadSentencePieceVocab(t *testing.T) SentencePieceModel { + t.Helper() + + bts, err := os.ReadFile(filepath.Join("testdata", "gemma2", "tokenizer.model")) + if err != nil { + t.Fatal(err) + } + + var spm sentencepiece.ModelProto + if err := proto.Unmarshal(bts, &spm); err != nil { + t.Fatal(err) + } + + 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+` + + var v Vocabulary + + for _, piece := range spm.GetPieces() { + v.Values = append(v.Values, piece.GetPiece()) + v.Scores = append(v.Scores, piece.GetScore()) + switch t := piece.GetType(); t { + case sentencepiece.ModelProto_SentencePiece_UNKNOWN, + sentencepiece.ModelProto_SentencePiece_CONTROL, + sentencepiece.ModelProto_SentencePiece_UNUSED, + sentencepiece.ModelProto_SentencePiece_BYTE: + v.Types = append(v.Types, uint32(t)) + default: + tt := uint32(sentencepiece.ModelProto_SentencePiece_NORMAL) + // todo parse the special tokens file + // - this will roundtrip correctly but the and + // tokens aren't processed + v.Types = append(v.Types, tt) + } + } + + return NewSentencePieceModel(preTokenizer, &v) +} + +func TestSentencePieceEncode(t *testing.T) { + logger := slog.New(slog.NewTextHandler(os.Stdout, &slog.HandlerOptions{Level: slog.LevelDebug})) + slog.SetDefault(logger) + + tokenizer := loadSentencePieceVocab(t) + + t.Run("basic roundtrip", func(t *testing.T) { + t.Parallel() + + cases := []string{ + "hello", + "hello ", + "hello ", + " hello", + " hello ", + " hello ", + "hello world", + "请考试我的软件！12345", + "你好", + "Hello 你好 world!", + } + + for _, want := range cases { + ids, err := tokenizer.Encode(want) + if err != nil { + t.Fatal(err) + } + + if got, err := tokenizer.Decode(ids); err != nil { + t.Fatal(err) + } else if got != want { + t.Errorf("got %q, want %q [%#v]", got, want, ids) + } + } + }) + + t.Run("special tokens", func(t *testing.T) { + type candidate struct { + token string + ids []int32 + } + + cases := []candidate{ + {"", []int32{2}}, + {"", []int32{1}}, + } + + for _, want := range cases { + ids, err := tokenizer.Encode(want.token) + if err != nil { + t.Fatal(err) + } + if !slices.Equal(ids, want.ids) { + t.Errorf("got %#v, want %#v", ids, want.ids) + } + } + }) +} diff --git a/model/testdata/gemma2/tokenizer.model b/model/testdata/gemma2/tokenizer.model new file mode 100644 index 0000000000000000000000000000000000000000..14a242262f212c6e4e55913220f22c7d768e0524 GIT binary patch literal 4241003 zcmcG%37BNpS>AuK4VbnK4cOR#!DU&tBrj5Hmu%U-Jv}|r*cwfFdSrRURlUq~X}Y`0 zz07EYm0SsLU<1v*grxxEjjg?6jF*eY5JCvl5CReqQn3gjgpdv)gb+ghzxR9ZJ>9xA z{u7pd=9%Yv&-N|n+_QXVsfjO~_}p6t-NhF@DdX=ckDd@->Tf&(&o}Va+Lze)xMgc?Yh!6~sw!xI*wqJw zU2{O#wFiVIOD85OJ+9tUWMAN(Ci?>SRM{7}r^~*; zJ!SRY9Hh}T2WfQ8 zK^k3ikVe-Wq|vnpX>{#D8eMykM%Nys(X|I@bnQVJU3-v5*B+$NwFhZ*?Lit{caTQc 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