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convert: add deepseek converter
This change adds the ability for `ollama create` to convert models that use the DeepSeek2 architecture (specifically DeepSeekV3 and DeepSeek-R1).
This commit is contained in:
Patrick Devine
@@ -206,6 +206,8 @@ func ConvertModel(fsys fs.FS, f *os.File) error {
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conv = &commandrModel{}
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case "GptOssForCausalLM":
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conv = &gptossModel{}
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case "DeepseekV3ForCausalLM":
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conv = &deepseek2Model{}
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default:
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return fmt.Errorf("unsupported architecture %q", p.Architectures[0])
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}
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173
convert/convert_deepseek2.go
Normal file
173
convert/convert_deepseek2.go
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@@ -0,0 +1,173 @@
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package convert
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import (
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"cmp"
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"fmt"
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"log/slog"
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"regexp"
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"strconv"
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"github.com/ollama/ollama/fs/ggml"
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)
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type deepseek2Model struct {
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ModelParameters // architectures, vocab_size
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MaxPositionEmbeddings uint32 `json:"max_position_embeddings"`
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HiddenSize uint32 `json:"hidden_size"`
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HiddenLayers uint32 `json:"num_hidden_layers"`
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IntermediateSize uint32 `json:"intermediate_size"`
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NumAttentionHeads uint32 `json:"num_attention_heads"`
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NumKeyValueHeads uint32 `json:"num_key_value_heads"`
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RMSNormEPS float32 `json:"rms_norm_eps"`
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RopeTheta float32 `json:"rope_theta"`
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QKNopeHeadDim uint32 `json:"qk_nope_head_dim"`
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QKRopeHeadDim uint32 `json:"qk_rope_head_dim"`
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KVLoraRank uint32 `json:"kv_lora_rank"`
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QLoraRank uint32 `json:"q_lora_rank"`
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VHeadDim uint32 `json:"v_head_dim"`
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ExpertCount uint32 `json:"n_routed_experts"`
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ExpertSharedCount uint32 `json:"n_shared_experts"`
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ExpertIntermediateSize uint32 `json:"moe_intermediate_size"`
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ExpertUsedCount uint32 `json:"num_experts_per_tok"`
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ExpertWeightsNorm bool `json:"norm_topk_prob"`
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ExpertWeightsScale float32 `json:"routed_scaling_factor"`
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ScoringFunc string `json:"scoring_func"`
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LeadingDenseBlockCount uint32 `json:"first_k_dense_replace"`
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RopeScaling struct {
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Factor float32 `json:"factor"`
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OriginalMaxPositionEmbeddings uint32 `json:"original_max_position_embeddings"`
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Type string `json:"type"`
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MScaleAllDim float32 `json:"mscale_all_dim"`
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} `json:"rope_scaling"`
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Architecture string
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}
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func (p *deepseek2Model) KV(t *Tokenizer) ggml.KV {
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kv := p.ModelParameters.KV(t)
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kv["general.architecture"] = "deepseek2"
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kv["general.type"] = "model"
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kv["deepseek2.block_count"] = p.HiddenLayers
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numHeads := p.NumAttentionHeads
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numKVHeads := p.NumKeyValueHeads
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kv["deepseek2.attention.head_count"] = numHeads
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kv["deepseek2.attention.head_count_kv"] = numKVHeads
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kv["deepseek2.attention.key_length"] = p.QKNopeHeadDim + p.QKRopeHeadDim
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kv["deepseek2.attention.kv_lora_rank"] = p.KVLoraRank
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kv["deepseek2.attention.layer_norm_rms_epsilon"] = p.RMSNormEPS
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kv["deepseek2.attention.q_lora_rank"] = p.QLoraRank
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kv["deepseek2.attention.value_length"] = p.VHeadDim
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kv["deepseek2.context_length"] = p.MaxPositionEmbeddings
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kv["deepseek2.embedding_length"] = p.HiddenSize
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kv["deepseek2.expert_count"] = p.ExpertCount
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kv["deepseek2.expert_feed_forward_length"] = p.ExpertIntermediateSize
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kv["deepseek2.expert_shared_count"] = p.ExpertSharedCount
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var scoringFunc uint32
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switch p.ScoringFunc {
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case "softmax":
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// not currently supported in the model, but needed for Deepseek-OCR
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scoringFunc = 1
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case "sigmoid":
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scoringFunc = 2
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}
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kv["deepseek2.expert_gating_func"] = scoringFunc
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kv["deepseek2.expert_used_count"] = p.ExpertUsedCount
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kv["deepseek2.expert_weights_norm"] = p.ExpertWeightsNorm
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kv["deepseek2.expert_weights_scale"] = p.ExpertWeightsScale
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kv["deepseek2.feed_forward_length"] = p.IntermediateSize
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kv["deepseek2.leading_dense_block_count"] = p.LeadingDenseBlockCount
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kv["deepseek2.rope.dimension_count"] = p.QKRopeHeadDim
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kv["deepseek2.rope.freq_base"] = cmp.Or(p.RopeTheta, 10000.0)
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kv["deepseek2.rope.scaling.factor"] = p.RopeScaling.Factor
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kv["deepseek2.rope.scaling.original_context_length"] = p.RopeScaling.OriginalMaxPositionEmbeddings
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kv["deepseek2.rope.scaling.type"] = p.RopeScaling.Type
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kv["deepseek2.rope.scaling.yarn_log_multiplier"] = 0.1 * p.RopeScaling.MScaleAllDim
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kv["tokenizer.ggml.pre"] = "deepseek-v3"
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return kv
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}
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func (p *deepseek2Model) Replacements() []string {
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return []string{
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"lm_head", "output",
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"model.embed_tokens", "token_embd",
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"model.norm", "output_norm",
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"language_model.", "",
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"model.layers", "blk",
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"input_layernorm", "attn_norm",
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"self_attn.kv_a_proj_with_mqa", "attn_kv_a_mqa",
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"self_attn.kv_a_layernorm", "attn_kv_a_norm",
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"self_attn.kv_b_proj", "attn_kv_b",
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"self_attn.q_a_proj", "attn_q_a",
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"self_attn.q_a_layernorm", "attn_q_a_norm",
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"self_attn.q_b_proj", "attn_q_b",
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"self_attn.o_proj", "attn_output",
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"post_attention_layernorm", "ffn_norm",
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"mlp.shared_experts.down_proj", "ffn_down_shexp",
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"mlp.shared_experts.gate_proj", "ffn_gate_shexp",
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"mlp.shared_experts.up_proj", "ffn_up_shexp",
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"mlp.gate_proj", "ffn_gate",
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"mlp.down_proj", "ffn_down",
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"mlp.up_proj", "ffn_up",
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"mlp.gate.e_score_correction_bias", "exp_probs_b.bias",
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"mlp.gate", "ffn_gate_inp",
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}
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}
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func (p *deepseek2Model) Tensors(s []Tensor) (out []*ggml.Tensor) {
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merges := make([]merge, p.HiddenLayers*3)
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for i := range p.HiddenLayers {
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merges[i*3+0] = merge{
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fmt.Sprintf("blk.%d.mlp.experts.*.gate_proj.weight", i),
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fmt.Sprintf("blk.%d.ffn_gate_exps.weight", i),
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}
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merges[i*3+1] = merge{
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fmt.Sprintf("blk.%d.mlp.experts.*.up_proj.weight", i),
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fmt.Sprintf("blk.%d.ffn_up_exps.weight", i),
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}
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merges[i*3+2] = merge{
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fmt.Sprintf("blk.%d.mlp.experts.*.down_proj.weight", i),
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fmt.Sprintf("blk.%d.ffn_down_exps.weight", i),
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}
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}
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skipLayer := func(n string, minValue uint32) bool {
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re := regexp.MustCompile(`^blk\.(\d+)`)
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matches := re.FindStringSubmatch(n)
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if matches == nil {
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return false
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}
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blkNum, err := strconv.Atoi(matches[1])
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if err != nil {
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return false
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}
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return uint32(blkNum) >= minValue
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}
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out, s = mergeTensors(s, merges...)
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for _, t := range s {
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// skip any additional layers (such as the Multi-Token Prediction layer)
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if skipLayer(t.Name(), p.HiddenLayers) {
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slog.Debug("skipping layer", "name", t.Name())
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continue
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}
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out = append(out, &ggml.Tensor{
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Name: t.Name(),
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Kind: t.Kind(),
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Shape: t.Shape(),
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WriterTo: t,
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})
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}
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return out
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}
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