model: Add support for PhiMoE arch (#11003)
* model: support phimoe * python linter * doc: minor Co-authored-by: ThiloteE <73715071+ThiloteE@users.noreply.github.com> * doc: minor Co-authored-by: ThiloteE <73715071+ThiloteE@users.noreply.github.com> * doc: add phimoe as supported model ggml-ci --------- Co-authored-by: ThiloteE <73715071+ThiloteE@users.noreply.github.com>
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Pierrick Hymbert
GitHub
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commit
f8feb4b01a
10 changed files with 208 additions and 31 deletions
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@ -27,6 +27,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
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{ LLM_ARCH_QWEN2VL, "qwen2vl" },
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{ LLM_ARCH_PHI2, "phi2" },
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{ LLM_ARCH_PHI3, "phi3" },
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{ LLM_ARCH_PHIMOE, "phimoe" },
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{ LLM_ARCH_PLAMO, "plamo" },
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{ LLM_ARCH_CODESHELL, "codeshell" },
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{ LLM_ARCH_ORION, "orion" },
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@ -584,6 +585,27 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
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{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
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},
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},
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{
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LLM_ARCH_PHIMOE,
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{
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{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
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{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
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{ LLM_TENSOR_OUTPUT, "output" },
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{ LLM_TENSOR_ROPE_FACTORS_LONG, "rope_factors_long" },
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{ LLM_TENSOR_ROPE_FACTORS_SHORT, "rope_factors_short" },
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{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
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{ LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" },
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{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
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{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
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{ LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
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{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
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{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
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{ LLM_TENSOR_FFN_GATE_INP, "blk.%d.ffn_gate_inp" },
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{ LLM_TENSOR_FFN_GATE_EXPS, "blk.%d.ffn_gate_exps" },
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{ LLM_TENSOR_FFN_DOWN_EXPS, "blk.%d.ffn_down_exps" },
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{ LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" },
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},
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},
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{
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LLM_ARCH_PLAMO,
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{
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@ -31,6 +31,7 @@ enum llm_arch {
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LLM_ARCH_QWEN2VL,
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LLM_ARCH_PHI2,
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LLM_ARCH_PHI3,
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LLM_ARCH_PHIMOE,
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LLM_ARCH_PLAMO,
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LLM_ARCH_CODESHELL,
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LLM_ARCH_ORION,
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@ -76,6 +76,7 @@ const char * llm_type_name(llm_type type) {
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case MODEL_8x7B: return "8x7B";
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case MODEL_8x22B: return "8x22B";
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case MODEL_16x12B: return "16x12B";
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case MODEL_16x3_8B: return "16x3.8B";
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case MODEL_10B_128x3_66B: return "10B+128x3.66B";
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case MODEL_57B_A14B: return "57B.A14B";
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case MODEL_27B: return "27B";
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@ -661,6 +662,15 @@ void llm_load_hparams(llama_model_loader & ml, llama_model & model) {
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throw std::runtime_error("invalid value for sliding_window");
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}
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} break;
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case LLM_ARCH_PHIMOE:
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{
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ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
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switch (hparams.n_layer) {
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case 32: model.type = e_model::MODEL_16x3_8B; break;
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default: model.type = e_model::MODEL_UNKNOWN;
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}
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} break;
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case LLM_ARCH_PLAMO:
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{
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ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
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@ -2094,6 +2104,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
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case LLM_ARCH_OLMOE:
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case LLM_ARCH_PHI2:
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case LLM_ARCH_PHI3:
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case LLM_ARCH_PHIMOE:
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case LLM_ARCH_GEMMA:
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case LLM_ARCH_GEMMA2:
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case LLM_ARCH_STARCODER2:
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@ -73,6 +73,7 @@ enum llm_type {
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MODEL_8x7B,
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MODEL_8x22B,
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MODEL_16x12B,
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MODEL_16x3_8B,
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MODEL_10B_128x3_66B,
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MODEL_57B_A14B,
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MODEL_27B,
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@ -1212,6 +1212,50 @@ static bool llm_load_tensors(
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layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd }, 0);
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layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), { n_embd, 2 * n_ff }, 0);
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layer.rope_long = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG, "weight", i), { n_embd_head/2 }, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0));
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layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), { n_embd_head/2 }, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0));
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}
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} break;
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case LLM_ARCH_PHIMOE:
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{
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const int64_t n_embd_head = n_embd / n_head;
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model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0);
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// output
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model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd }, 0);
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model.output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, 0);
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model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), { n_embd, n_vocab }, 0);
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model.output_b = create_tensor(tn(LLM_TENSOR_OUTPUT, "bias"), { n_vocab }, 0);
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for (int i = 0; i < n_layer; ++i) {
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auto & layer = model.layers[i];
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layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }, 0);
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layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i), { n_embd }, 0);
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layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), { n_embd, n_embd + 2 * n_embd_gqa }, llama_model_loader::TENSOR_NOT_REQUIRED);
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if (layer.wqkv == nullptr) {
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layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0);
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layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, 0);
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layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0);
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layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, 0);
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layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0);
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layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, 0);
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}
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layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd, n_embd }, 0);
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layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), { n_embd }, 0);
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layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), { n_embd }, 0);
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layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i), { n_embd }, 0);
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layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
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layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}, 0);
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layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff, n_embd, n_expert}, 0);
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layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff, n_expert}, 0);
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layer.rope_long = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG, "weight", i), { n_embd_head/2 }, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0));
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layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), { n_embd_head/2 }, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0));
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}
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@ -6266,7 +6310,7 @@ struct llm_build_context {
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struct ggml_tensor* attn_norm_output = llm_build_norm(ctx0, inpL, hparams,
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model.layers[il].attn_norm,
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NULL,
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model.layers[il].attn_norm_b,
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LLM_NORM_RMS, cb, il);
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cb(attn_norm_output, "attn_norm", il);
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@ -6281,8 +6325,7 @@ struct llm_build_context {
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Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0 * sizeof(float) * (n_embd)));
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Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1 * sizeof(float) * (n_embd)));
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Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1 * sizeof(float) * (n_embd + n_embd_gqa)));
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}
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else {
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} else {
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Qcur = ggml_add(ctx0, llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, attn_norm_output), model.layers[il].bq);
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Kcur = ggml_add(ctx0, llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, attn_norm_output), model.layers[il].bk);
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Vcur = ggml_add(ctx0, llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, attn_norm_output), model.layers[il].bv);
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@ -6326,14 +6369,12 @@ struct llm_build_context {
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residual = cur;
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cur = llm_build_norm(ctx0, cur, hparams,
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model.layers[il].ffn_norm, NULL,
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model.layers[il].ffn_norm, model.layers[il].ffn_norm_b,
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LLM_NORM_RMS, cb, il);
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cb(cur, "ffn_norm", il);
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// FF
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// special-case: the up and gate tensors are merged into a single tensor
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// TOOD: support into llm_build_ffn
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{
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// feed-forward network
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if (model.layers[il].ffn_gate_inp == nullptr) {
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cur = llm_build_ffn(ctx0, lctx, cur,
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model.layers[il].ffn_up, NULL, NULL,
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NULL, NULL, NULL,
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@ -6341,6 +6382,20 @@ struct llm_build_context {
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NULL,
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LLM_FFN_SWIGLU, LLM_FFN_SEQ, cb, il);
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cb(cur, "ffn_out", il);
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} else {
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// MoE branch
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cur = llm_build_moe_ffn(ctx0, lctx, cur,
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model.layers[il].ffn_gate_inp,
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model.layers[il].ffn_up_exps,
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model.layers[il].ffn_gate_exps,
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model.layers[il].ffn_down_exps,
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nullptr,
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n_expert, n_expert_used,
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LLM_FFN_SILU, true,
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false, 0.0,
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LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
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cb, il);
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cb(cur, "ffn_moe_out", il);
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}
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cur = ggml_add(ctx0, residual, cur);
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@ -6353,11 +6408,16 @@ struct llm_build_context {
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cur = llm_build_norm(ctx0, inpL, hparams,
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model.output_norm,
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NULL,
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model.output_norm_b,
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LLM_NORM_RMS, cb, -1);
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cb(cur, "result_norm", -1);
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cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
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if (model.output_b != nullptr) {
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cb(cur, "result_output_no_bias", -1);
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cur = ggml_add(ctx0, cur, model.output_b);
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}
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cb(cur, "result_output", -1);
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ggml_build_forward_expand(gf, cur);
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@ -10536,6 +10596,7 @@ static struct ggml_cgraph * llama_build_graph(
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result = llm.build_phi2();
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} break;
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case LLM_ARCH_PHI3:
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case LLM_ARCH_PHIMOE:
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{
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result = llm.build_phi3();
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} break;
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