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https://github.com/ikawrakow/ik_llama.cpp.git
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Split mode "graph" for Ernie-4.5-MoE (#1121)
* Ernie-4.5-MoE split mode graph * Cleanup --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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Kawrakow
@@ -8178,122 +8178,42 @@ ggml_cgraph * llm_build_context::build_ernie4_5_moe() {
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GGML_ASSERT(hparams.n_moe_layer_step > 0 && "Ernie 4.5 MoE requires n_moe_layer_step > 0");
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for (int il = 0; il < n_layer; ++il) {
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ggml_tensor * inpSA = inpL;
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// norm
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// Pre-attention norm
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cur = llm_build_norm(ctx0, inpL, hparams, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, cb, il);
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cb(cur, "attn_norm", il);
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// self-attention
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{
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// Q, K, V projections
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ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
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cb(Qcur, "Qcur", il);
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if (model.layers[il].bq) {
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Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
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cb(Qcur, "Qcur", il);
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}
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ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
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cb(Kcur, "Kcur", il);
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if (model.layers[il].bk) {
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Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
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cb(Kcur, "Kcur", il);
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}
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cb(Kcur, "Kcur", il);
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ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
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cb(Vcur, "Vcur", il);
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if (model.layers[il].bv) {
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Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
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cb(Vcur, "Vcur", il);
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}
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// reshape for multi-head
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Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
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Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
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// Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
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// apply RoPE
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Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, nullptr,
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n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
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ext_factor, attn_factor, beta_fast, beta_slow);
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Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, nullptr,
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n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
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ext_factor, attn_factor, beta_fast, beta_slow);
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cb(Qcur, "Qcur", il);
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cb(Kcur, "Kcur", il);
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cb(Vcur, "Vcur", il);
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cur = llm_build_kv(ctx0, lctx, kv_self, gf,
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model.layers[il].wo, NULL,
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Kcur, Vcur, Qcur, KQ_mask,
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n_tokens, kv_head, n_kv,
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1.0f / sqrtf(float(n_embd_head)), cb, il);
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}
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cur = build_std_attention(gf, model.layers[il].attn_norm, inpL, inp_pos, nullptr, KQ_mask, nullptr, nullptr,
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1.0f/sqrtf(float(n_embd_head)), 0.0f, 0, il, true, false, true);
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if (il == n_layer - 1 && inp_out_ids) {
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cur = ggml_get_rows(ctx0, cur, inp_out_ids);
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inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
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}
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// residual connection for attention output
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ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
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cb(ffn_inp, "ffn_inp", il);
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// feed-forward network
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bool is_moe_layer = static_cast<uint32_t>(il) >= hparams.n_layer_dense_lead && (il + 1) % hparams.n_moe_layer_step == 0;
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if (!is_moe_layer) {
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cur = llm_build_ffn(ctx0, lctx, model.layers[il].ffn_norm, ffn_inp,
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model.layers[il].ffn_up, NULL, NULL,
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// dense FFN
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cur = llm_build_ffn(ctx0, lctx, model.layers[il].ffn_norm, cur,
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model.layers[il].ffn_up, NULL, NULL,
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model.layers[il].ffn_gate, NULL, NULL,
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model.layers[il].ffn_down, NULL, NULL,
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NULL,
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LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
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LLM_FFN_SILU, LLM_FFN_PAR, cb, il, gf, true);
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cb(cur, "ffn_out", il);
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}
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else {
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// MoE branch
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cur = llm_build_norm(ctx0, ffn_inp, hparams, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, cb, il);
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cb(cur, "ffn_norm", il);
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ggml_tensor * moe_out = 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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} else {
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cur = llm_build_std_moe_ffn(ctx0, lctx, model.layers[il].ffn_norm, cur,
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model.layers[il].ffn_gate_inp, model.layers[il].ffn_gate_inp_b,
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model.layers[il].ffn_up_exps, model.layers[il].ffn_up_exps_b,
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model.layers[il].ffn_gate_exps, model.layers[il].ffn_gate_exps_b,
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model.layers[il].ffn_down_exps, model.layers[il].ffn_down_exps_b,
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model.layers[il].ffn_exp_probs_b,
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model.layers[il].ffn_up_shexp, nullptr, // we don't have shared expert biases?
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model.layers[il].ffn_gate_shexp, nullptr,
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model.layers[il].ffn_down_shexp, 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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LLM_FFN_SILU, true, false, 0.0f,
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LLM_EXPERT_GATING_FUNC_SOFTMAX,
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cb, il, gf);
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cb(moe_out, "ffn_moe_out", il);
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// Shared expert (if present)
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if (hparams.n_ff_shexp > 0) {
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ggml_tensor * ffn_shexp = llm_build_ffn(ctx0, lctx, nullptr, cur,
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model.layers[il].ffn_up_shexp, NULL, NULL,
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model.layers[il].ffn_gate_shexp, NULL, NULL,
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model.layers[il].ffn_down_shexp, NULL, NULL,
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NULL,
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LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
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cb(ffn_shexp, "ffn_shexp", il);
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cur = ggml_add(ctx0, moe_out, ffn_shexp);
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}
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else {
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cur = moe_out;
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}
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cb(cur, "ffn_out", il);
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LLM_FFN_SILU, cb, il, gf, true);
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}
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cur = ggml_add(ctx0, cur, ffn_inp);
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cb(cur, "ffn_out", il);
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cur = lctx.cvec.apply_to(ctx0, cur, il);
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cb(cur, "l_out", il);
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@@ -8301,15 +8221,9 @@ ggml_cgraph * llm_build_context::build_ernie4_5_moe() {
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inpL = cur;
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}
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cur = inpL;
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cur = llm_build_norm(ctx0, cur, hparams, model.output_norm, NULL, LLM_NORM_RMS, cb, -1);
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cb(cur, "result_norm", -1);
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// lm_head
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cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
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cur = build_output(lctx, ctx0, inpL, model.output, model.output_norm, cb);
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cb(cur, "result_output", -1);
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ggml_build_forward_expand(gf, cur);
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return gf;
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}
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@@ -2490,10 +2490,9 @@ bool create_tensors_helper::create_ernie45_tensors(const LLM_TN & tn) {
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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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ggml_context* ctx_layer = ctx_for_layer(i);
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ggml_context* ctx_split = ctx_for_layer_split(i);
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layer.attn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }, 0);
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layer.attn_norm = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }, 0);
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layer.wq = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), { n_embd, n_embd_head_k * n_head }, 0);
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layer.wk = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), { n_embd, n_embd_gqa }, 0);
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@@ -2501,18 +2500,18 @@ bool create_tensors_helper::create_ernie45_tensors(const LLM_TN & tn) {
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layer.wo = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_k * n_head, n_embd }, 0);
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// optional bias tensors
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layer.bq = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), { n_embd }, llama_model_loader::TENSOR_NOT_REQUIRED);
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layer.bk = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), { n_embd_gqa }, llama_model_loader::TENSOR_NOT_REQUIRED);
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layer.bv = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), { n_embd_gqa }, llama_model_loader::TENSOR_NOT_REQUIRED);
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layer.bo = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), { n_embd }, llama_model_loader::TENSOR_NOT_REQUIRED);
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layer.bq = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "bias", i), { n_embd }, llama_model_loader::TENSOR_NOT_REQUIRED);
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layer.bk = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "bias", i), { n_embd_gqa }, llama_model_loader::TENSOR_NOT_REQUIRED);
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layer.bv = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "bias", i), { n_embd_gqa }, llama_model_loader::TENSOR_NOT_REQUIRED);
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layer.bo = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "bias", i), { n_embd }, llama_model_loader::TENSOR_NOT_REQUIRED);
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layer.ffn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), { n_embd }, 0);
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layer.ffn_norm = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_NORM, "weight", i), { n_embd }, 0);
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if (model.arch == LLM_ARCH_ERNIE4_5_MOE && static_cast<uint32_t>(i) >= hparams.n_layer_dense_lead) { // MoE layers
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int n_ff_exp = hparams.n_ff_exp;
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layer.ffn_gate_inp = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), { n_embd, n_expert }, 0);
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layer.ffn_exp_probs_b = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), { n_expert }, llama_model_loader::TENSOR_NOT_REQUIRED);
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layer.ffn_gate_inp = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), { n_embd, n_expert }, 0);
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layer.ffn_exp_probs_b = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), { n_expert }, llama_model_loader::TENSOR_NOT_REQUIRED);
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layer.ffn_gate_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert }, llama_model_loader::TENSOR_NOT_REQUIRED);
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layer.ffn_down_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff_exp, n_embd, n_expert }, 0);
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layer.ffn_up_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert }, 0);
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@@ -1735,6 +1735,7 @@ static bool is_model_split_supported(const llama_model & model) {
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LLM_ARCH_QWEN3VL,
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LLM_ARCH_HUNYUAN_MOE,
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LLM_ARCH_OPENAI_MOE,
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LLM_ARCH_ERNIE4_5_MOE,
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};
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auto it = k_supported.find(model.arch);
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return it != k_supported.end();
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