gpt-oss: convert from hugging face format (#11907)

convert/convert_gptoss.go

@@ -16,17 +16,22 @@ import (
 type gptossModel struct {
 	ModelParameters
 	HiddenLayers          uint32  `json:"num_hidden_layers"`
+	MaxPositionEmbeddings uint32  `json:"max_position_embeddings"`
 	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"`
+	LocalExperts          uint32  `json:"num_local_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"`
+	RopeScaling           struct {
+		Factor float32 `json:"factor"`
+	} `json:"rope_scaling"`
 	SlidingWindow uint32 `json:"sliding_window"`
 }
 
@@ -36,11 +41,11 @@ 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.context_length"] = cmp.Or(m.MaxPositionEmbeddings, 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_count"] = cmp.Or(m.Experts, m.LocalExperts)
 	kv["gptoss.expert_used_count"] = m.ExpertsPerToken
 	kv["gptoss.attention.head_count"] = m.AttentionHeads
 	kv["gptoss.attention.head_count_kv"] = m.KeyValueHeads
@@ -49,7 +54,7 @@ func (m *gptossModel) KV(t *Tokenizer) ggml.KV {
 	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.factor"] = cmp.Or(m.RopeScalingFactor, m.RopeScaling.Factor)
 	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
@@ -92,6 +97,11 @@ func (m *gptossModel) Tensors(ts []Tensor) []*ggml.Tensor {
 
 	for name, mxfp4 := range mxfp4s {
 		dims := mxfp4.blocks.Shape()
+
+		if !strings.HasSuffix(name, ".weight") {
+			name += ".weight"
+		}
+
 		out = append(out, &ggml.Tensor{
 			Name:     name,
 			Kind:     uint32(ggml.TensorTypeMXFP4),
@@ -104,7 +114,27 @@ func (m *gptossModel) Tensors(ts []Tensor) []*ggml.Tensor {
 }
 
 func (m *gptossModel) Replacements() []string {
-	return []string{
+	var replacements []string
+	if m.MaxPositionEmbeddings > 0 {
+		// hf flavored model
+		replacements = []string{
+			"lm_head", "output",
+			"model.embed_tokens", "token_embd",
+			"model.layers", "blk",
+			"input_layernorm", "attn_norm",
+			"self_attn.q_proj", "attn_q",
+			"self_attn.k_proj", "attn_k",
+			"self_attn.v_proj", "attn_v",
+			"self_attn.o_proj", "attn_out",
+			"self_attn.sinks", "attn_sinks",
+			"post_attention_layernorm", "ffn_norm",
+			"mlp.router", "ffn_gate_inp",
+			"mlp.experts.gate_up_proj_", "ffn_gate_up_exps.",
+			"mlp.experts.down_proj_", "ffn_down_exps.",
+			"model.norm", "output_norm",
+		}
+	} else {
+		replacements = []string{
 			// noop replacements so other replacements will not be applied
 			".blocks", ".blocks",
 			".scales", ".scales",
@@ -123,6 +153,8 @@ func (m *gptossModel) Replacements() []string {
 			"unembedding", "output",
 			"scale", "weight",
 		}
+	}
+	return replacements
 }
 
 type mxfp4 struct {