ctxLen -> origCtxLen

Add support for model-specific RoPE configuration parameters by:

1. Creating a new `RopeConfig` struct to encapsulate all RoPE parameters
2. Adding `RopeType` enum to specify different RoPE variants (Standard/NeoX)
3. Extracting original context length from model config
4. Refactoring `RoPE()` interface to use the new config struct
5. Updating llama and mllama models to use new RoPE configuration

This change allows models to specify their RoPE implementation type and
original context length, which is important for proper position embedding
calculation and model compatibility.

.github/workflows/release.yaml

@@ -329,9 +329,7 @@ jobs:
           done
         working-directory: dist/${{ matrix.os }}-${{ matrix.arch }}
       - run: |
-          for ARCHIVE in dist/${{ matrix.os }}-${{ matrix.arch }}/*.tar.in; do
-            tar c -C dist/${{ matrix.os }}-${{ matrix.arch }} -T $ARCHIVE --owner 0 --group 0 | pigz -9vc >$(basename ${ARCHIVE//.*/}.tgz);
-          done
+          for ARCHIVE in dist/${{ matrix.os }}-${{ matrix.arch }}/*.tar.in; do tar c -C dist/${{ matrix.os }}-${{ matrix.arch }} -T $ARCHIVE | pigz -9vc >$(basename ${ARCHIVE//.*/}.tgz); done
       - uses: actions/upload-artifact@v4
         with:
           name: dist-${{ matrix.os }}-${{ matrix.arch }}-${{ matrix.target }}

README.md

@@ -381,7 +381,6 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [ChibiChat](https://github.com/CosmicEventHorizon/ChibiChat) (Kotlin-based Android app to chat with Ollama and Koboldcpp API endpoints)
 - [LocalLLM](https://github.com/qusaismael/localllm) (Minimal Web-App to run ollama models on it with a GUI)
 - [Ollamazing](https://github.com/buiducnhat/ollamazing) (Web extension to run Ollama models)
-- [OpenDeepResearcher-via-searxng](https://github.com/benhaotang/OpenDeepResearcher-via-searxng) (A Deep Research equivent endpoint with Ollama support for running locally)
 
 ### Cloud
 
@@ -549,7 +548,6 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [Alfred Ollama](https://github.com/zeitlings/alfred-ollama) (Alfred Workflow)
 - [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)
 
 ### Supported backends
 

docs/windows.md

@@ -55,7 +55,7 @@ Here's a quick example showing API access from `powershell`
 ## Troubleshooting
 
 Ollama on Windows stores files in a few different locations.  You can view them in
-the explorer window by hitting `<Ctrl>+R` and type in:
+the explorer window by hitting `<cmd>+R` and type in:
 - `explorer %LOCALAPPDATA%\Ollama` contains logs, and downloaded updates
     - *app.log* contains most resent logs from the GUI application
     - *server.log* contains the most recent server logs

format/format_test.go

@@ -12,9 +12,6 @@ func TestHumanNumber(t *testing.T) {
 
 	testCases := []testCase{
 		{0, "0"},
-		{999, "999"},
-		{1000, "1K"},
-		{1001, "1K"},
 		{1000000, "1M"},
 		{125000000, "125M"},
 		{500500000, "500.50M"},

fs/ggml/ggml.go

@@ -120,15 +120,6 @@ 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](kv KV, key string, defaultValue ...T) T {
 	if !strings.HasPrefix(key, "tokenizer.") && !strings.HasPrefix(key, "general.") {
 		key = kv.Architecture() + "." + key

go.mod

@@ -18,7 +18,6 @@ require (
 	github.com/agnivade/levenshtein v1.1.1
 	github.com/d4l3k/go-bfloat16 v0.0.0-20211005043715-690c3bdd05f1
 	github.com/dlclark/regexp2 v1.11.4
-	github.com/emirpasic/gods v1.18.1
 	github.com/emirpasic/gods/v2 v2.0.0-alpha
 	github.com/google/go-cmp v0.6.0
 	github.com/mattn/go-runewidth v0.0.14

go.sum

@@ -44,8 +44,6 @@ github.com/dgryski/trifles v0.0.0-20200323201526-dd97f9abfb48 h1:fRzb/w+pyskVMQ+
 github.com/dgryski/trifles v0.0.0-20200323201526-dd97f9abfb48/go.mod h1:if7Fbed8SFyPtHLHbg49SI7NAdJiC5WIA09pe59rfAA=
 github.com/dlclark/regexp2 v1.11.4 h1:rPYF9/LECdNymJufQKmri9gV604RvvABwgOA8un7yAo=
 github.com/dlclark/regexp2 v1.11.4/go.mod h1:DHkYz0B9wPfa6wondMfaivmHpzrQ3v9q8cnmRbL6yW8=
-github.com/emirpasic/gods v1.18.1 h1:FXtiHYKDGKCW2KzwZKx0iC0PQmdlorYgdFG9jPXJ1Bc=
-github.com/emirpasic/gods v1.18.1/go.mod h1:8tpGGwCnJ5H4r6BWwaV6OrWmMoPhUl5jm/FMNAnJvWQ=
 github.com/emirpasic/gods/v2 v2.0.0-alpha h1:dwFlh8pBg1VMOXWGipNMRt8v96dKAIvBehtCt6OtunU=
 github.com/emirpasic/gods/v2 v2.0.0-alpha/go.mod h1:W0y4M2dtBB9U5z3YlghmpuUhiaZT2h6yoeE+C1sCp6A=
 github.com/envoyproxy/go-control-plane v0.9.0/go.mod h1:YTl/9mNaCwkRvm6d1a2C3ymFceY/DCBVvsKhRF0iEA4=

kvcache/causal_test.go

@@ -305,10 +305,6 @@ func (b *testBackend) NewContext() ml.Context {
 	return &testContext{}
 }
 
-func (b *testBackend) SystemInfo() string {
-	return "not implemented"
-}
-
 type testContext struct{}
 
 func (c *testContext) Zeros(dtype ml.DType, shape ...int) ml.Tensor {
@@ -434,7 +430,7 @@ 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, ropeType uint32, base, scale float32) ml.Tensor {
+func (t *testTensor) RoPE(ctx ml.Context, rc ml.RopeConfig) ml.Tensor {
 	panic("not implemented")
 }
 

ml/backend.go

@@ -17,14 +17,12 @@ type Config interface {
 
 	Strings(string, ...[]string) []string
 	Uints(string, ...[]uint32) []uint32
-	Floats(string, ...[]float32) []float32
 }
 
 type Backend interface {
 	Config() Config
 	Get(name string) Tensor
 	NewContext() Context
-	SystemInfo() string
 }
 
 var backends = make(map[string]func(*os.File) (Backend, error))
@@ -45,6 +43,42 @@ func NewBackend(f *os.File) (Backend, error) {
 	return nil, fmt.Errorf("unsupported backend")
 }
 
+// RopeType specifies the type of RoPE (Rotary Position Embedding) to use, these types are implemented in the backend
+type RopeType int
+
+const (
+	RopeTypeStandard RopeType = iota
+	_                         // not yet used
+	RopeTypeNeoX
+)
+
+// RopeConfig contains all configuration for the RoPE (Rotary Position Embedding) operation
+type RopeConfig struct {
+	// PositionIDs contains the position indices for each token in the sequence
+	// These indices are used to calculate the rotary embeddings
+	PositionIDs Tensor
+
+	// RopeFactors is an optional tensor containing pre-computed rotation factors
+	RopeFactors Tensor
+
+	// RopeDim specifies the dimension size for the rotary embeddings
+	RopeDim uint32
+
+	// RopeType indicates which RoPE variant to use (e.g. normal or neox)
+	RopeType RopeType
+
+	// OrigCtxLen stores the original context length the model was trained with
+	OrigCtxLen int
+
+	// RopeBase is the base value used in the frequency calculation
+	RopeBase float32
+
+	// RopeScale is a scaling factor applied to position indices
+	RopeScale float32
+
+	// YaRN parameters can be added here if they need to be configurable
+}
+
 type Context interface {
 	Zeros(dtype DType, shape ...int) Tensor
 	FromFloatSlice(s []float32, shape ...int) (Tensor, error)
@@ -77,7 +111,7 @@ 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, ropeType uint32, base, scale float32) Tensor
+	RoPE(ctx Context, rc RopeConfig) Tensor
 
 	Tanh(ctx Context) Tensor
 	GELU(ctx Context) Tensor

ml/backend/ggml/ggml.go

@@ -1,27 +1,11 @@
 package ggml
 
-/*
-#cgo CPPFLAGS: -I${SRCDIR}/ggml/include
-#include <stdlib.h>
-#include <stdint.h>
-#include "ggml.h"
-#include "ggml-cpu.h"
-#include "ggml-backend.h"
-static struct ggml_backend_feature * getBackendFeatures(void *fp, ggml_backend_reg_t reg) {return ((ggml_backend_get_features_t)(fp))(reg);}
-static struct ggml_backend_feature * getNextBackendFeatures(struct ggml_backend_feature * feature) { return &feature[1];}
-
-typedef enum {COMP_UNKNOWN,COMP_GCC,COMP_CLANG} COMPILER;
-COMPILER inline get_compiler() {
-#if defined(__clang__)
-	return COMP_CLANG;
-#elif defined(__GNUC__)
-	return COMP_GCC;
-#else
-	return UNKNOWN_COMPILER;
-#endif
-}
-
-*/
+// #cgo CPPFLAGS: -I${SRCDIR}/ggml/include
+// #include <stdlib.h>
+// #include <stdint.h>
+// #include "ggml.h"
+// #include "ggml-cpu.h"
+// #include "ggml-backend.h"
 import "C"
 
 import (
@@ -595,16 +579,9 @@ func (t *Tensor) View(ctx ml.Context, offset int, shape ...int) ml.Tensor {
 	}
 }
 
-const (
-	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, ropeType uint32, ropeBase, ropeScale float32) ml.Tensor {
-	if ropeFactors == nil {
-		ropeFactors = &Tensor{}
+func (t *Tensor) RoPE(ctx ml.Context, rc ml.RopeConfig) ml.Tensor {
+	if rc.RopeFactors == nil {
+		rc.RopeFactors = &Tensor{}
 	}
 
 	dequant := t.t
@@ -614,12 +591,15 @@ func (t *Tensor) RoPE(ctx ml.Context, positionIDs, ropeFactors ml.Tensor, ropeDi
 
 	return &Tensor{
 		t: C.ggml_rope_ext(
-			ctx.(*Context).ctx, dequant, positionIDs.(*Tensor).t, ropeFactors.(*Tensor).t,
-			C.int(ropeDim),
-			C.int(ropeType),
-			131072, // YaRN n_ctx_train
-			C.float(ropeBase),
-			C.float(ropeScale),
+			ctx.(*Context).ctx,
+			dequant,
+			rc.PositionIDs.(*Tensor).t,
+			rc.RopeFactors.(*Tensor).t,
+			C.int(rc.RopeDim),
+			C.int(rc.RopeType),
+			C.int(rc.OrigCtxLen),
+			C.float(rc.RopeBase),
+			C.float(rc.RopeScale),
 			0.,  // YaRN ext_factor
 			1.,  // YaRN attn_factor
 			32., // YaRN beta_fast
@@ -645,34 +625,3 @@ func (t *Tensor) Conv2D(ctx ml.Context, t2 ml.Tensor, s0, s1, p0, p1, d0, d1 int
 		t: C.ggml_conv_2d(ctx.(*Context).ctx, t.t, t2.(*Tensor).t, C.int(s0), C.int(s1), C.int(p0), C.int(p1), C.int(d0), C.int(d1)),
 	}
 }
-
-func (b *Backend) SystemInfo() string {
-	var compiler string
-	switch C.get_compiler() {
-	case C.COMP_UNKNOWN:
-		compiler = "cgo(unknown_compiler)"
-	case C.COMP_GCC:
-		compiler = "cgo(gcc)"
-	case C.COMP_CLANG:
-		compiler = "cgo(clang)"
-	}
-
-	var s string
-	for i := range C.ggml_backend_reg_count() {
-		reg := C.ggml_backend_reg_get(i)
-		fName := C.CString("ggml_backend_get_features")
-		defer C.free(unsafe.Pointer(fName))
-		get_features_fn := C.ggml_backend_reg_get_proc_address(reg, fName)
-		if get_features_fn != nil {
-			s += C.GoString(C.ggml_backend_reg_name(reg))
-			s += " : "
-			for features := C.getBackendFeatures(get_features_fn, reg); features.name != nil; features = C.getNextBackendFeatures(features) {
-				s += C.GoString(features.name)
-				s += " = "
-				s += C.GoString(features.value)
-				s += " | "
-			}
-		}
-	}
-	return s + compiler
-}

model/model.go

@@ -21,7 +21,6 @@ import (
 	_ "github.com/ollama/ollama/ml/backend"
 )
 
-// Options contains the inputs for a model forward pass
 type Options struct {
 	Inputs    []int32
 	Positions []int32
@@ -35,13 +34,11 @@ 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
 }
@@ -50,7 +47,6 @@ 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)
 
@@ -60,7 +56,6 @@ type Model interface {
 
 var models = make(map[string]func(ml.Config) (Model, error))
 
-// Register registers a model constructor for the given architecture
 func Register(name string, f func(ml.Config) (Model, error)) {
 	if _, ok := models[name]; ok {
 		panic("model: model already registered")
@@ -69,9 +64,8 @@ func Register(name string, f func(ml.Config) (Model, error)) {
 	models[name] = f
 }
 
-// New initializes a new model instance with the provided configuration based on the metadata in the model file
-func New(modelPath string) (Model, error) {
-	r, err := os.Open(modelPath)
+func New(s string) (Model, error) {
+	r, err := os.Open(s)
 	if err != nil {
 		return nil, err
 	}

model/models/gemma2/model.go

@@ -1,193 +0,0 @@
-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"
-)
-
-type Options struct {
-	hiddenSize, numHeads, numKVHeads int
-	attnKeyLen, attnValLen           int
-	eps, ropeBase, ropeScale         float32
-	attnLogitSoftcap                 float32
-	finalLogitSoftcap                float32
-}
-
-type Model struct {
-	model.Base
-	model.SentencePieceModel
-
-	TokenEmbedding *nn.Embedding `gguf:"token_embd"`
-	Layers         []Layer       `gguf:"blk"`
-	OutputNorm     *nn.RMSNorm   `gguf:"output_norm"`           // is this supposed to be root means square?
-	Output         *nn.Linear    `gguf:"output,alt:token_embd"` // just set to token_embd?
-
-	*Options
-}
-
-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)
-
-	// todo: this should be 1.0/math.Sqrt(float64(headDim)) for 27B models
-	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 model.Options) (ml.Tensor, error) {
-	inputs, err := ctx.FromIntSlice(opts.Inputs, len(opts.Inputs))
-	if err != nil {
-		return nil, err
-	}
-
-	positions, err := ctx.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)))
-
-	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.FromIntSlice(opts.Outputs, len(opts.Outputs))
-	if err != nil {
-		return nil, err
-	}
-
-	return hiddenState.Rows(ctx, outputs), nil
-}
-
-func init() {
-	model.Register("gemma2", New)
-}

model/models/llama/model.go

@@ -10,10 +10,10 @@ import (
 )
 
 type Options struct {
-	RopeFactors                      ml.Tensor `gguf:"rope_freqs.weight"`
-	hiddenSize, numHeads, numKVHeads int
-	eps, ropeBase, ropeScale         float32
-	ropeDim                          uint32
+	RopeFactors                                  ml.Tensor `gguf:"rope_freqs.weight"`
+	origCtxLen, hiddenSize, numHeads, numKVHeads int
+	eps, ropeBase, ropeScale                     float32
+	ropeDim                                      uint32
 }
 
 type Model struct {
@@ -46,6 +46,7 @@ func New(c ml.Config) (model.Model, error) {
 			numHeads:   int(c.Uint("attention.head_count")),
 			numKVHeads: int(c.Uint("attention.head_count_kv")),
 			eps:        c.Float("attention.layer_norm_rms_epsilon"),
+			origCtxLen: int(c.Uint("context_length")),
 			ropeBase:   c.Float("rope.freq_base"),
 			ropeScale:  c.Float("rope.freq_scale", 1),
 			ropeDim:    c.Uint("rope.dimension_count"),
@@ -67,15 +68,23 @@ 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)
+	rc := ml.RopeConfig{
+		PositionIDs: positionIDs,
+		RopeFactors: opts.RopeFactors,
+		RopeDim:     opts.ropeDim,
+		RopeType:    ml.RopeTypeStandard,
+		OrigCtxLen:  opts.origCtxLen,
+		RopeBase:    opts.ropeBase,
+		RopeScale:   opts.ropeScale,
+	}
 
 	q := sa.Query.Forward(ctx, hiddenState)
 	q = q.Reshape(ctx, headDim, opts.numHeads, batchSize)
-	q = q.RoPE(ctx, positionIDs, opts.RopeFactors, opts.ropeDim, ropeType, opts.ropeBase, opts.ropeScale)
+	q = q.RoPE(ctx, rc)
 
 	k := sa.Key.Forward(ctx, hiddenState)
 	k = k.Reshape(ctx, headDim, opts.numKVHeads, batchSize)
-	k = k.RoPE(ctx, positionIDs, opts.RopeFactors, opts.ropeDim, ropeType, opts.ropeBase, opts.ropeScale)
+	k = k.RoPE(ctx, rc)
 
 	v := sa.Value.Forward(ctx, hiddenState)
 	v = v.Reshape(ctx, headDim, opts.numKVHeads, batchSize)
@@ -100,7 +109,18 @@ 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.Options.RopeFactors, m.Options.ropeDim, uint32(0), m.Options.ropeBase, m.Options.ropeScale), nil
+	return key.RoPE(
+		ctx,
+		ml.RopeConfig{
+			PositionIDs: shift,
+			RopeFactors: m.Options.RopeFactors,
+			RopeDim:     m.Options.ropeDim,
+			RopeType:    ml.RopeTypeStandard,
+			OrigCtxLen:  m.Options.origCtxLen,
+			RopeBase:    m.Options.ropeBase,
+			RopeScale:   m.Options.ropeScale,
+		},
+	), nil
 }
 
 type MLP struct {

model/models/mllama/model_text.go

@@ -19,15 +19,23 @@ 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)
+	rc := ml.RopeConfig{
+		PositionIDs: positions,
+		RopeFactors: opts.RopeFactors,
+		RopeDim:     opts.ropeDim,
+		RopeType:    ml.RopeTypeStandard,
+		OrigCtxLen:  opts.ctxLen,
+		RopeBase:    opts.ropeBase,
+		RopeScale:   opts.ropeScale,
+	}
 
 	query := sa.Query.Forward(ctx, hiddenState)
 	query = query.Reshape(ctx, headDim, opts.numHeads, batchSize)
-	query = query.RoPE(ctx, positions, opts.RopeFactors, opts.ropeDim, ropeType, opts.ropeBase, opts.ropeScale)
+	query = query.RoPE(ctx, rc)
 
 	key := sa.Key.Forward(ctx, hiddenState)
 	key = key.Reshape(ctx, headDim, opts.numKVHeads, batchSize)
-	key = key.RoPE(ctx, positions, opts.RopeFactors, opts.ropeDim, ropeType, opts.ropeBase, opts.ropeScale)
+	key = key.RoPE(ctx, rc)
 
 	value := sa.Value.Forward(ctx, hiddenState)
 	value = value.Reshape(ctx, headDim, opts.numKVHeads, batchSize)
@@ -53,7 +61,18 @@ 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
-	return key.RoPE(ctx, shift, m.RopeFactors, m.ropeDim, uint32(0), m.ropeBase, m.ropeScale), nil
+	return key.RoPE(
+		ctx,
+		ml.RopeConfig{
+			PositionIDs: shift,
+			RopeFactors: m.RopeFactors,
+			RopeDim:     m.ropeDim,
+			RopeType:    ml.RopeTypeStandard,
+			OrigCtxLen:  m.ctxLen,
+			RopeBase:    m.ropeBase,
+			RopeScale:   m.ropeScale,
+		},
+	), nil
 }
 
 type TextMLP struct {
@@ -190,9 +209,9 @@ func (d *TextDecoder) Forward(ctx ml.Context, hiddenState, positionIDs, mask, cr
 type TextModelOptions struct {
 	RopeFactors ml.Tensor `gguf:"rope_freqs.weight"`
 
-	hiddenSize, numHeads, numKVHeads int
-	eps, ropeBase, ropeScale         float32
-	ropeDim                          uint32
+	ctxLen, hiddenSize, numHeads, numKVHeads int
+	eps, ropeBase, ropeScale                 float32
+	ropeDim                                  uint32
 
 	crossAttentionLayers []uint32
 }

model/models/models.go

@@ -1,7 +1,6 @@
 package models
 
 import (
-	_ "github.com/ollama/ollama/model/models/gemma2"
 	_ "github.com/ollama/ollama/model/models/llama"
 	_ "github.com/ollama/ollama/model/models/mllama"
 )

model/process_text.go

@@ -18,15 +18,6 @@ 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(string) ([]int32, error)
 	Decode([]int32) (string, error)
@@ -36,7 +27,7 @@ type TextProcessor interface {
 type Vocabulary struct {
 	Values []string
 	Types  []uint32
-	Scores []float32
+	Scores []uint32
 	Merges []string
 
 	BOS, EOS int32
@@ -84,7 +75,7 @@ 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] == TOKEN_TYPE_CONTROL {
+			if v.Types[i] == 3 {
 				v.special = append(v.special, v.Values[i])
 			}
 		}

model/process_text_spm.go

@@ -1,220 +0,0 @@
-package model
-
-import (
-	"iter"
-	"log/slog"
-	"strings"
-
-	"github.com/dlclark/regexp2"
-	queue "github.com/emirpasic/gods/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[:3], "scores", vocab.Scores[:3], "types", vocab.Types[:3])
-
-	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]},
-				}
-			}
-
-			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,
-						length: len(left + " " + right),
-						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]
-
-				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
-	length int
-}
-
-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
-}

runner/ollamarunner/runner.go

@@ -813,8 +813,6 @@ func (s *Server) loadModel(
 		panic(err)
 	}
 
-	slog.Info("system", "info", s.model.Backend().SystemInfo() /* "threads", *threads */)
-
 	// TODO(jessegross): LoRA loading
 	if lpath.String() != "" {
 		panic("loras are not yet implemented")
@@ -883,6 +881,7 @@ func Execute(args []string) error {
 	})
 	slog.SetDefault(slog.New(handler))
 	slog.Info("starting ollama engine")
+	// TODO(jessegross): Some system info would be useful
 
 	server := &Server{
 		batchSize: *batchSize,