gpt-oss

convert/convert.go

@@ -202,6 +202,8 @@ func ConvertModel(fsys fs.FS, f *os.File) error {
 		conv = &bertModel{}
 	case "CohereForCausalLM":
 		conv = &commandrModel{}
+	case "GptOssForCausalLM":
+		conv = &gptossModel{}
 	default:
 		return fmt.Errorf("unsupported architecture %q", p.Architectures[0])
 	}

convert/convert_gptoss.go

+package convert
+
+import (
+	"cmp"
+
+	"github.com/ollama/ollama/fs/ggml"
+)
+
+type gptossModel struct {
+	ModelParameters
+	HiddenLayers         uint32  `json:"num_hidden_layers"`
+	HiddenSize           uint32  `json:"hidden_size"`
+	IntermediateSize     uint32  `json:"intermediate_size"`
+	AttentionHeads       uint32  `json:"num_attention_heads"`
+	KeyValueHeads        uint32  `json:"num_key_value_heads"`
+	HeadDim              uint32  `json:"head_dim"`
+	Experts              uint32  `json:"num_experts"`
+	ExpertsPerToken      uint32  `json:"experts_per_token"`
+	RMSNormEpsilon       float32 `json:"rms_norm_eps"`
+	InitialContextLength uint32  `json:"initial_context_length"`
+	RopeTheta            float32 `json:"rope_theta"`
+	RopeScalingFactor    float32 `json:"rope_scaling_factor"`
+	SlidingWindow        uint32  `json:"sliding_window"`
+}
+
+var _ ModelConverter = (*gptossModel)(nil)
+
+func (m *gptossModel) KV(t *Tokenizer) ggml.KV {
+	kv := m.ModelParameters.KV(t)
+	kv["general.architecture"] = "gptoss"
+	kv["general.file_type"] = uint32(4)
+	kv["gptoss.context_length"] = uint32(m.RopeScalingFactor * float32(m.InitialContextLength))
+	kv["gptoss.block_count"] = m.HiddenLayers
+	kv["gptoss.embedding_length"] = m.HiddenSize
+	kv["gptoss.feed_forward_length"] = m.IntermediateSize
+	kv["gptoss.expert_count"] = m.Experts
+	kv["gptoss.expert_used_count"] = m.ExpertsPerToken
+	kv["gptoss.attention.head_count"] = m.AttentionHeads
+	kv["gptoss.attention.head_count_kv"] = m.KeyValueHeads
+	kv["gptoss.attention.key_length"] = m.HeadDim
+	kv["gptoss.attention.value_length"] = m.HeadDim
+	kv["gptoss.attention.layer_norm_rms_epsilon"] = cmp.Or(m.RMSNormEpsilon, 1e-5)
+	kv["gptoss.attention.sliding_window"] = m.SlidingWindow
+	kv["gptoss.rope.freq_base"] = m.RopeTheta
+	kv["gptoss.rope.scaling.factor"] = m.RopeScalingFactor
+	kv["gptoss.rope.scaling.original_context_length"] = m.InitialContextLength
+	kv["tokenizer.ggml.bos_token_id"] = uint32(199998) // <|startoftext|>
+	kv["tokenizer.ggml.add_bos_token"] = false
+	kv["tokenizer.ggml.eos_token_id"] = uint32(199999) // <|endoftext|>
+	kv["tokenizer.ggml.eos_token_ids"] = []int32{
+		199999, /* <|endoftext|> */
+		200002, /* <|return|> */
+		200012, /* <|call|> */
+	}
+	kv["tokenizer.ggml.add_eos_token"] = false
+	return kv
+}
+
+func (m *gptossModel) Tensors(ts []Tensor) []*ggml.Tensor {
+	var out []*ggml.Tensor
+	for _, t := range ts {
+		out = append(out, &ggml.Tensor{
+			Name:     t.Name(),
+			Kind:     t.Kind(),
+			Shape:    t.Shape(),
+			WriterTo: t,
+		})
+	}
+
+	return out
+}
+
+func (m *gptossModel) Replacements() []string {
+	return []string{
+		"block", "blk",
+		"attn.norm", "attn_norm",
+		"attn.qkv", "attn_qkv",
+		"attn.sinks", "attn_sinks",
+		"attn.out", "attn_out",
+		"mlp.norm", "ffn_norm",
+		"mlp.gate", "ffn_gate_inp",
+		"mlp.mlp1_", "ffn_gate_up_exps.",
+		"mlp.mlp2_", "ffn_down_exps.",
+		"embedding", "token_embd",
+		"norm", "output_norm",
+		"unembedding", "output",
+		"scale", "weight",
+	}
+}

ml/backend.go

@@ -276,6 +276,7 @@ type Tensor interface {
 	Cos(ctx Context) Tensor
 	Tanh(ctx Context) Tensor
 	GELU(ctx Context) Tensor
+	QuickGELU(ctx Context) Tensor
 	SILU(ctx Context) Tensor
 	RELU(ctx Context) Tensor
 	Sigmoid(ctx Context) Tensor
@@ -283,7 +284,7 @@ type Tensor interface {
 	Reshape(ctx Context, shape ...int) Tensor
 	View(ctx Context, offset int, shape ...int) Tensor
 	Permute(ctx Context, shape ...int) Tensor
-	Contiguous(ctx Context) Tensor
+	Contiguous(ctx Context, shape ...int) Tensor
 	Set(ctx Context, t2 Tensor, offset int, strides ...int) Tensor
 
 	Pad(ctx Context, shape ...int) Tensor

ml/backend/ggml/ggml.go

@@ -958,11 +958,36 @@ func (t *Tensor) Concat(ctx ml.Context, t2 ml.Tensor, dim int) ml.Tensor {
 	}
 }
 
-func (t *Tensor) Contiguous(ctx ml.Context) ml.Tensor {
+func (t *Tensor) Contiguous(ctx ml.Context, shape ...int) ml.Tensor {
+	switch len(shape) {
+	case 0:
 		return &Tensor{
 			b: t.b,
 			t: C.ggml_cont(ctx.(*Context).ctx, t.t),
 		}
+	case 1:
+		return &Tensor{
+			b: t.b,
+			t: C.ggml_cont_1d(ctx.(*Context).ctx, t.t, C.int64_t(shape[0])),
+		}
+	case 2:
+		return &Tensor{
+			b: t.b,
+			t: C.ggml_cont_2d(ctx.(*Context).ctx, t.t, C.int64_t(shape[0]), C.int64_t(shape[1])),
+		}
+	case 3:
+		return &Tensor{
+			b: t.b,
+			t: C.ggml_cont_3d(ctx.(*Context).ctx, t.t, C.int64_t(shape[0]), C.int64_t(shape[1]), C.int64_t(shape[2])),
+		}
+	case 4:
+		return &Tensor{
+			b: t.b,
+			t: C.ggml_cont_4d(ctx.(*Context).ctx, t.t, C.int64_t(shape[0]), C.int64_t(shape[1]), C.int64_t(shape[2]), C.int64_t(shape[3])),
+		}
+	default:
+		panic("unsupported number of dimensions")
+	}
 }
 
 func (t *Tensor) Mul(ctx ml.Context, t2 ml.Tensor) ml.Tensor {
@@ -1176,11 +1201,18 @@ func (t *Tensor) View(ctx ml.Context, offset int, shape ...int) ml.Tensor {
 
 func (t *Tensor) RoPE(ctx ml.Context, positions ml.Tensor, ropeDim int, ropeBase, ropeScale float32, options ...func(*rope.Options)) ml.Tensor {
 	// Default options
-	opts := &rope.Options{OriginalContextLength: 131072, Factors: &Tensor{}}
+	opts := rope.Options{
+		Factors:               &Tensor{},
+		OriginalContextLength: 131072,
+		ExtrapolationFactor:   0.,
+		AttentionFactor:       1.,
+		BetaFast:              32.,
+		BetaSlow:              1.,
+	}
 
 	// Apply any provided options
 	for _, option := range options {
-		option(opts)
+		option(&opts)
 	}
 
 	dequant := t.t
@@ -1200,10 +1232,10 @@ func (t *Tensor) RoPE(ctx ml.Context, positions ml.Tensor, ropeDim int, ropeBase
 			C.int(opts.OriginalContextLength),
 			C.float(ropeBase),
 			C.float(ropeScale),
-			C.float(0.0),
-			C.float(1.0),
-			C.float(32.0),
-			C.float(1.0),
+			C.float(opts.ExtrapolationFactor),
+			C.float(opts.AttentionFactor),
+			C.float(opts.BetaFast),
+			C.float(opts.BetaSlow),
 		),
 	}
 }
@@ -1222,6 +1254,13 @@ func (t *Tensor) GELU(ctx ml.Context) ml.Tensor {
 	}
 }
 
+func (t *Tensor) QuickGELU(ctx ml.Context) ml.Tensor {
+	return &Tensor{
+		b: t.b,
+		t: C.ggml_gelu_quick_inplace(ctx.(*Context).ctx, t.t),
+	}
+}
+
 func (t *Tensor) SILU(ctx ml.Context) ml.Tensor {
 	return &Tensor{
 		b: t.b,

ml/nn/linear.go

@@ -15,3 +15,26 @@ func (m *Linear) Forward(ctx ml.Context, t ml.Tensor) ml.Tensor {
 
 	return t
 }
+
+type LinearBatch struct {
+	Weight ml.Tensor `gguf:"weight"`
+	Bias   ml.Tensor `gguf:"bias"`
+}
+
+func (m *LinearBatch) Forward(ctx ml.Context, t, indices ml.Tensor) ml.Tensor {
+	t = m.Weight.MulmatID(ctx, t, indices)
+	if m.Bias != nil {
+		var bias ml.Tensor
+		if len(indices.Shape()) > 1 {
+			// FIXME: Rows does not support 2D indices for a 2D input tensor so reshape indices to 1D.
+			bias = m.Bias.Rows(ctx, indices.Contiguous(ctx, indices.Dim(0)*indices.Dim(1))).
+				Duplicate(ctx).
+				Reshape(ctx, m.Bias.Dim(0), indices.Dim(0), indices.Dim(1))
+		} else {
+			bias = m.Bias.Rows(ctx, indices)
+		}
+		t = t.Add(ctx, bias)
+	}
+
+	return t
+}

ml/nn/rope/rope.go

@@ -4,9 +4,15 @@ import "github.com/ollama/ollama/ml"
 
 // Options contains optional parameters for RoPE function
 type Options struct {
-	OriginalContextLength int
 	Type                  int
 	Factors               ml.Tensor
+	OriginalContextLength int
+
+	// YaRN options
+	ExtrapolationFactor,
+	AttentionFactor,
+	BetaFast,
+	BetaSlow float32
 }
 
 // WithOriginalContextLength sets a custom context length
@@ -31,3 +37,15 @@ func WithFactors(factors ml.Tensor) func(*Options) {
 		}
 	}
 }
+
+func WithExtrapolationFactor(extrapolationFactor float32) func(*Options) {
+	return func(opts *Options) {
+		opts.ExtrapolationFactor = extrapolationFactor
+	}
+}
+
+func WithAttentionFactor(attentionFactor float32) func(*Options) {
+	return func(opts *Options) {
+		opts.AttentionFactor = attentionFactor
+	}
+}

model/models/gptoss/model.go

+package gptoss
+
+import (
+	"cmp"
+	"math"
+
+	"github.com/ollama/ollama/fs"
+	"github.com/ollama/ollama/kvcache"
+	"github.com/ollama/ollama/ml"
+	"github.com/ollama/ollama/ml/nn"
+	"github.com/ollama/ollama/ml/nn/fast"
+	"github.com/ollama/ollama/ml/nn/rope"
+	"github.com/ollama/ollama/model"
+	"github.com/ollama/ollama/model/input"
+)
+
+type Transformer struct {
+	model.Base
+	model.BytePairEncoding
+
+	TokenEmbedding    *nn.Embedding      `gguf:"token_embd"`
+	TransformerBlocks []TransformerBlock `gguf:"blk"`
+	OutputNorm        *nn.RMSNorm        `gguf:"output_norm"`
+	Output            *nn.Linear         `gguf:"output,alt:token_embd"`
+
+	Options
+}
+
+// Forward implements model.Model.
+func (m *Transformer) Forward(ctx ml.Context, batch input.Batch) (ml.Tensor, error) {
+	hiddenStates := m.TokenEmbedding.Forward(ctx, batch.Inputs)
+	positions := ctx.Input().FromIntSlice(batch.Positions, len(batch.Positions))
+
+	one := ctx.Input().FromFloatSlice([]float32{1}, 1)
+	for i, block := range m.TransformerBlocks {
+		m.Cache.SetLayer(i)
+		if c, ok := m.Cache.(*kvcache.WrapperCache); ok {
+			// Even layers are sliding window attention.
+			c.SetLayerType(i % 2)
+		}
+
+		var outputs ml.Tensor
+		if len(batch.Outputs) > 0 && i == len(m.TransformerBlocks)-1 {
+			outputs = ctx.Input().FromIntSlice(batch.Outputs, len(batch.Outputs))
+		}
+
+		hiddenStates = block.Forward(ctx, hiddenStates, positions, outputs, one, m.Cache, &m.Options)
+	}
+
+	hiddenStates = m.OutputNorm.Forward(ctx, hiddenStates, m.eps)
+	return m.Output.Forward(ctx, hiddenStates), nil
+}
+
+func (m *Transformer) Shift(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, error) {
+	return fast.RoPE(ctx, key, shift, m.headDim(), m.ropeBase, 1./m.ropeScale, m.RoPEOptions()...), nil
+}
+
+type Options struct {
+	hiddenSize,
+	numHeads,
+	numKVHeads,
+	keyLength,
+	valueLength,
+	numExperts,
+	numExpertsUsed,
+	originalContextLength int
+
+	eps,
+	ropeBase,
+	ropeScale float32
+}
+
+func (o Options) RoPEOptions() []func(*rope.Options) {
+	return []func(*rope.Options){
+		rope.WithTypeNeoX(),
+		rope.WithOriginalContextLength(o.originalContextLength),
+		rope.WithExtrapolationFactor(1.),
+		// NOTE: ggml sets this implicitly so there's no need to set it here
+		// rope.WithAttentionFactor(0.1*float32(math.Log(float64(o.ropeScale))) + 1.0),
+	}
+}
+
+func (o Options) headDim() int {
+	return cmp.Or(o.keyLength, o.valueLength, o.hiddenSize/o.numHeads)
+}
+
+type TransformerBlock struct {
+	Attention *AttentionBlock
+	MLP       *MLPBlock
+}
+
+func (d *TransformerBlock) Forward(ctx ml.Context, hiddenStates, positions, outputs, one ml.Tensor, cache kvcache.Cache, opts *Options) ml.Tensor {
+	hiddenStates = d.Attention.Forward(ctx, hiddenStates, positions, cache, opts)
+	if outputs != nil {
+		hiddenStates = hiddenStates.Rows(ctx, outputs)
+	}
+
+	hiddenStates = d.MLP.Forward(ctx, hiddenStates, one, opts)
+	return hiddenStates
+}
+
+type AttentionBlock struct {
+	Norm   *nn.RMSNorm `gguf:"attn_norm"`
+	QKV    *nn.Linear  `gguf:"attn_qkv"`
+	Output *nn.Linear  `gguf:"attn_out"`
+	Sinks  ml.Tensor   `gguf:"attn_sinks"`
+}
+
+func (attn *AttentionBlock) Forward(ctx ml.Context, hiddenStates, positions ml.Tensor, cache kvcache.Cache, opts *Options) ml.Tensor {
+	batchSize := hiddenStates.Dim(1)
+
+	residual := hiddenStates
+	hiddenStates = attn.Norm.Forward(ctx, hiddenStates, opts.eps)
+
+	qkv := attn.QKV.Forward(ctx, hiddenStates)
+
+	// query = qkv[..., : num_attention_heads * head_dim].reshape(batch_size, num_attention_heads, head_dim)
+	query := qkv.View(ctx,
+		0,
+		opts.headDim(), qkv.Stride(0)*opts.headDim(),
+		opts.numHeads, qkv.Stride(1),
+		batchSize,
+	)
+	query = fast.RoPE(ctx, query, positions, opts.headDim(), opts.ropeBase, 1./opts.ropeScale, opts.RoPEOptions()...)
+
+	// key = qkv[..., num_attention_heads * head_dim:(num_attention_heads + num_key_value_heads) * head_dim].reshape(batch_size, num_key_value_heads, head_dim)
+	key := qkv.View(ctx,
+		qkv.Stride(0)*opts.headDim()*opts.numHeads,
+		opts.headDim(), qkv.Stride(0)*opts.headDim(),
+		opts.numKVHeads, qkv.Stride(1),
+		batchSize,
+	)
+	key = fast.RoPE(ctx, key, positions, opts.headDim(), opts.ropeBase, 1./opts.ropeScale, opts.RoPEOptions()...)
+
+	// value = qkv[..., (num_attention_heads  + num_key_value_heads) * head_dim:].reshape(batch_size, num_key_value_heads, head_dim)
+	value := qkv.View(ctx,
+		qkv.Stride(0)*opts.headDim()*(opts.numHeads+opts.numKVHeads),
+		opts.headDim(), qkv.Stride(0)*opts.headDim(),
+		opts.numKVHeads, qkv.Stride(1),
+		batchSize,
+	)
+
+	cache.Put(ctx, key, value)
+	key, value, mask := cache.Get(ctx)
+
+	query = query.Permute(ctx, 0, 2, 1, 3)
+	key = key.Permute(ctx, 0, 2, 1, 3)
+	value = value.Permute(ctx, 1, 2, 0, 3).Contiguous(ctx)
+
+	scores := key.MulmatFullPrec(ctx, query)
+	scores = scores.Scale(ctx, 1./math.Sqrt(float64(opts.headDim())))
+	scores = scores.Add(ctx, mask)
+
+	scores = scores.Concat(ctx, attn.Sinks.Reshape(ctx, 1, 1, opts.numHeads, 1).Repeat(ctx, 1, batchSize), 0)
+	scores = scores.Softmax(ctx)
+	scores = scores.Pad(ctx, -1, 0, 0, 0)
+
+	attention := value.Mulmat(ctx, scores)
+	attention = attention.Permute(ctx, 0, 2, 1, 3).Contiguous(ctx)
+	attention = attention.Reshape(ctx, attention.Dim(0)*attention.Dim(1), batchSize)
+
+	return attn.Output.Forward(ctx, attention).Add(ctx, residual)
+}
+
+type MLPBlock struct {
+	Norm   *nn.RMSNorm     `gguf:"ffn_norm"`
+	Router *nn.Linear      `gguf:"ffn_gate_inp"`
+	GateUp *nn.LinearBatch `gguf:"ffn_gate_up_exps"`
+	Down   *nn.LinearBatch `gguf:"ffn_down_exps"`
+}
+
+func (mlp *MLPBlock) Forward(ctx ml.Context, hiddenStates, one ml.Tensor, opts *Options) ml.Tensor {
+	hiddenDim, sequenceLength, batchSize := hiddenStates.Dim(0), hiddenStates.Dim(1), hiddenStates.Dim(2)
+
+	residual := hiddenStates
+	hiddenStates = mlp.Norm.Forward(ctx, hiddenStates, opts.eps)
+
+	hiddenStates = hiddenStates.Reshape(ctx, hiddenDim, sequenceLength*batchSize)
+	routingWeights := mlp.Router.Forward(ctx, hiddenStates)
+
+	selectedExperts := routingWeights.TopK(ctx, opts.numExpertsUsed)
+	routingWeights = routingWeights.Reshape(ctx, 1, opts.numExperts, sequenceLength*batchSize).Rows(ctx, selectedExperts)
+	routingWeights = routingWeights.Reshape(ctx, opts.numExpertsUsed, sequenceLength*batchSize).Softmax(ctx)
+	routingWeights = routingWeights.Reshape(ctx, 1, opts.numExpertsUsed, sequenceLength*batchSize)
+
+	hiddenStates = hiddenStates.Reshape(ctx, hiddenStates.Dim(0), 1, hiddenStates.Dim(1))
+
+	hiddenStates = mlp.GateUp.Forward(ctx, hiddenStates, selectedExperts)
+	hiddenStates = hiddenStates.Reshape(ctx, 2, hiddenStates.Dim(0)/2, hiddenStates.Dim(1), hiddenStates.Dim(2))
+
+	dimStride := []int{hiddenStates.Dim(0) / 2, hiddenStates.Stride(1), hiddenStates.Dim(1), hiddenStates.Stride(2), hiddenStates.Dim(2), hiddenStates.Stride(3), hiddenStates.Dim(3)}
+	hiddenStates = hiddenStates.View(ctx, 0, dimStride...).
+		Contiguous(ctx).
+		QuickGELU(ctx).
+		Mul(ctx, hiddenStates.View(ctx, hiddenStates.Stride(0), dimStride...).Add(ctx, one))
+
+	hiddenStates = hiddenStates.Reshape(ctx, hiddenStates.Dim(0)*hiddenStates.Dim(1), hiddenStates.Dim(2), hiddenStates.Dim(3))
+
+	experts := mlp.Down.Forward(ctx, hiddenStates, selectedExperts)
+	experts = experts.Mul(ctx, routingWeights)
+
+	nextStates := experts.View(ctx, 0, experts.Dim(0), experts.Stride(2), experts.Dim(2))
+	for i := 1; i < opts.numExpertsUsed; i++ {
+		nextStates = nextStates.Add(ctx, experts.View(ctx, i*experts.Stride(1), experts.Dim(0), experts.Stride(2), experts.Dim(2)))
+	}
+
+	return nextStates.Add(ctx, residual)
+}
+
+func New(c fs.Config) (model.Model, error) {
+	m := Transformer{
+		TransformerBlocks: make([]TransformerBlock, c.Uint("block_count")),
+		BytePairEncoding: model.NewBytePairEncoding(
+			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"),
+				Types:  c.Ints("tokenizer.ggml.token_type"),
+				Merges: c.Strings("tokenizer.ggml.merges"),
+				AddBOS: c.Bool("tokenizer.ggml.add_bos_token", false),
+				BOS:    []int32{int32(c.Uint("tokenizer.ggml.bos_token_id"))},
+				AddEOS: c.Bool("tokenizer.ggml.add_eos_token", false),
+				EOS: append(
+					[]int32{int32(c.Uint("tokenizer.ggml.eos_token_id"))},
+					c.Ints("tokenizer.ggml.eos_token_ids")...,
+				),
+			},
+		),
+		Options: Options{
+			hiddenSize:            int(c.Uint("embedding_length")),
+			numHeads:              int(c.Uint("attention.head_count")),
+			numKVHeads:            int(c.Uint("attention.head_count_kv")),
+			keyLength:             int(c.Uint("attention.key_length")),
+			valueLength:           int(c.Uint("attention.value_length")),
+			numExperts:            int(c.Uint("expert_count")),
+			numExpertsUsed:        int(c.Uint("expert_used_count")),
+			eps:                   c.Float("attention.layer_norm_rms_epsilon"),
+			ropeBase:              c.Float("rope.freq_base"),
+			ropeScale:             c.Float("rope.scaling.factor", 1.),
+			originalContextLength: int(c.Uint("rope.scaling.original_context_length")),
+		},
+	}
+
+	m.Cache = kvcache.NewWrapperCache(
+		kvcache.NewSWACache(int32(c.Uint("attention.sliding_window")), m.Shift),
+		kvcache.NewCausalCache(m.Shift),
+	)
+	m.Cache.SetConfig(ml.CacheConfig{})
+	return &m, nil
+}
+
+func init() {
+	model.Register("gptoss", New)
+}

model/models/models.go

@@ -4,6 +4,7 @@ import (
 	_ "github.com/ollama/ollama/model/models/gemma2"
 	_ "github.com/ollama/ollama/model/models/gemma3"
 	_ "github.com/ollama/ollama/model/models/gemma3n"
+	_ "github.com/ollama/ollama/model/models/gptoss"
 	_ "github.com/ollama/ollama/model/models/llama"
 	_ "github.com/ollama/ollama/model/models/llama4"
 	_ "github.com/ollama/ollama/model/models/mistral3"