llama: factorize moe graph implementation between grok, mixtral and dbrx
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Pierrick HYMBERT
parent
21fb24aa45
commit
f20c04f01f
1 changed files with 63 additions and 171 deletions
234
llama.cpp
234
llama.cpp
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@ -6457,62 +6457,7 @@ struct llm_build_context {
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LLM_NORM_RMS, cb, il);
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cb(cur, "ffn_norm", il);
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ggml_tensor * logits = ggml_mul_mat(ctx0, model.layers[il].ffn_gate_inp, cur); // [n_tokens, num_experts]
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cb(logits, "ffn_moe_logits", il);
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ggml_tensor * probs = ggml_soft_max(ctx0, logits); // [n_tokens, num_experts]
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cb(probs, "ffn_moe_probs", il);
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// select experts
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ggml_tensor * selected_experts = ggml_top_k(ctx0, probs, n_expert_used); // [n_tokens, num_experts_per_tok]
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cb(selected_experts->src[0], "ffn_moe_argsort", il);
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ggml_tensor * weights = ggml_get_rows(ctx0,
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ggml_reshape_3d(ctx0, probs, 1, n_expert, n_tokens), selected_experts);
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cb(weights, "ffn_moe_weights", il);
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weights = ggml_reshape_2d(ctx0, weights, n_expert_used, n_tokens); // [n_tokens, num_experts_per_tok]
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ggml_tensor * weights_sum = ggml_sum_rows(ctx0, weights);
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cb(weights_sum, "ffn_moe_weights_sum", il);
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weights = ggml_div(ctx0, weights, weights_sum); // [n_tokens, num_experts_per_tok]
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cb(weights, "ffn_moe_weights_norm", il);
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// compute expert outputs
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ggml_tensor * moe_out = nullptr;
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for (int i = 0; i < n_expert_used; ++i) {
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ggml_tensor * cur_expert;
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ggml_tensor * cur_up = ggml_mul_mat_id(ctx0, model.layers[il].ffn_up_exps, selected_experts, i, cur);
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cb(cur_up, "ffn_moe_up", il);
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ggml_tensor * cur_gate = ggml_mul_mat_id(ctx0, model.layers[il].ffn_gate_exps, selected_experts, i, cur);
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cb(cur_gate, "ffn_moe_gate", il);
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cur_gate = ggml_silu(ctx0, cur_gate);
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cb(cur_gate, "ffn_moe_silu", il);
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cur_expert = ggml_mul(ctx0, cur_up, cur_gate);
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cb(cur_expert, "ffn_moe_gate_par", il);
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cur_expert = ggml_mul_mat_id(ctx0, model.layers[il].ffn_down_exps, selected_experts, i, cur_expert); // [n_tokens, n_embd]
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cb(cur_expert, "ffn_moe_down", il);
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cur_expert = ggml_mul(ctx0, cur_expert,
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ggml_view_2d(ctx0, weights, 1, n_tokens, weights->nb[1], i*weights->nb[0]));
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cb(cur_expert, "ffn_moe_weighted", il);
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if (i == 0) {
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moe_out = cur_expert;
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} else {
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moe_out = ggml_add(ctx0, moe_out, cur_expert);
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cb(moe_out, "ffn_moe_out", il);
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}
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}
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cur = moe_out;
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cur = build_moe(cur, n_tokens, il);
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}
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cur = ggml_add(ctx0, cur, ffn_inp);
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@ -6544,6 +6489,65 @@ struct llm_build_context {
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return gf;
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}
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ggml_tensor * build_moe(ggml_tensor * cur, int32_t n_tokens, int il) {
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ggml_tensor * logits = ggml_mul_mat(ctx0, model.layers[il].ffn_gate_inp, cur); // [n_tokens, num_experts]
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cb(logits, "ffn_moe_logits", il);
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ggml_tensor * probs = ggml_soft_max(ctx0, logits); // [n_tokens, num_experts]
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cb(probs, "ffn_moe_probs", il);
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// select experts
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ggml_tensor * selected_experts = ggml_top_k(ctx0, probs, n_expert_used); // [n_tokens, num_experts_per_tok]
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cb(selected_experts->src[0], "ffn_moe_argsort", il);
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ggml_tensor * weights = ggml_get_rows(ctx0,
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ggml_reshape_3d(ctx0, probs, 1, n_expert, n_tokens), selected_experts);
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cb(weights, "ffn_moe_weights", il);
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weights = ggml_reshape_2d(ctx0, weights, n_expert_used, n_tokens); // [n_tokens, num_experts_per_tok]
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ggml_tensor * weights_sum = ggml_sum_rows(ctx0, weights);
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cb(weights_sum, "ffn_moe_weights_sum", il);
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weights = ggml_div(ctx0, weights, weights_sum); // [n_tokens, num_experts_per_tok]
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cb(weights, "ffn_moe_weights_norm", il);
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// compute expert outputs
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ggml_tensor * moe_out = nullptr;
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for (int i = 0; i < n_expert_used; ++i) {
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ggml_tensor * cur_expert;
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ggml_tensor * cur_up = ggml_mul_mat_id(ctx0, model.layers[il].ffn_up_exps, selected_experts, i, cur);
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cb(cur_up, "ffn_moe_up", il);
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ggml_tensor * cur_gate = ggml_mul_mat_id(ctx0, model.layers[il].ffn_gate_exps, selected_experts, i, cur);
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cb(cur_gate, "ffn_moe_gate", il);
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cur_gate = ggml_silu(ctx0, cur_gate);
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cb(cur_gate, "ffn_moe_silu", il);
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cur_expert = ggml_mul(ctx0, cur_up, cur_gate);
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cb(cur_expert, "ffn_moe_gate_par", il);
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cur_expert = ggml_mul_mat_id(ctx0, model.layers[il].ffn_down_exps, selected_experts, i, cur_expert); // [n_tokens, n_embd]
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cb(cur_expert, "ffn_moe_down", il);
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cur_expert = ggml_mul(ctx0, cur_expert,
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ggml_view_2d(ctx0, weights, 1, n_tokens, weights->nb[1], i*weights->nb[0]));
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cb(cur_expert, "ffn_moe_weighted", il);
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if (i == 0) {
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moe_out = cur_expert;
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} else {
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moe_out = ggml_add(ctx0, moe_out, cur_expert);
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cb(moe_out, "ffn_moe_out", il);
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}
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}
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return moe_out;
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}
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struct ggml_cgraph * build_baichuan() {
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struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
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@ -6991,63 +6995,7 @@ struct llm_build_context {
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LLM_NORM_RMS, cb, il);
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cb(cur, "ffn_norm", il);
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ggml_tensor * logits = ggml_mul_mat(ctx0, model.layers[il].ffn_gate_inp, cur); // [n_tokens, num_experts]
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cb(logits, "ffn_moe_logits", il);
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ggml_tensor * probs = ggml_soft_max(ctx0, logits); // [n_tokens, num_experts]
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cb(probs, "ffn_moe_probs", il);
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// select experts
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ggml_tensor * selected_experts = ggml_top_k(ctx0, probs, n_expert_used); // [n_tokens, num_experts_per_tok]
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cb(selected_experts->src[0], "ffn_moe_argsort", il);
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ggml_tensor * weights = ggml_get_rows(ctx0,
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ggml_reshape_3d(ctx0, probs, 1, n_expert, n_tokens), selected_experts);
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cb(weights, "ffn_moe_weights", il);
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weights = ggml_reshape_2d(ctx0, weights, n_expert_used, n_tokens); // [n_tokens, num_experts_per_tok]
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ggml_tensor * weights_sum = ggml_sum_rows(ctx0, weights);
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cb(weights_sum, "ffn_moe_weights_sum", il);
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weights = ggml_div(ctx0, weights, weights_sum); // [n_tokens, num_experts_per_tok]
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cb(weights, "ffn_moe_weights_norm", il);
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// compute expert outputs
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ggml_tensor * moe_out = nullptr;
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for (int i = 0; i < n_expert_used; ++i) {
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ggml_tensor * cur_expert;
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ggml_tensor * cur_up = ggml_mul_mat_id(ctx0, model.layers[il].ffn_up_exps, selected_experts, i, cur);
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cb(cur_up, "ffn_moe_up", il);
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ggml_tensor * cur_gate = ggml_mul_mat_id(ctx0, model.layers[il].ffn_gate_exps, selected_experts, i, cur);
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cb(cur_gate, "ffn_moe_gate", il);
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//GeLU
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cur_gate = ggml_gelu(ctx0, cur_gate);
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cb(cur_gate, "ffn_moe_gelu", il);
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cur_expert = ggml_mul(ctx0, cur_up, cur_gate);
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cb(cur_expert, "ffn_moe_gate_par", il);
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cur_expert = ggml_mul_mat_id(ctx0, model.layers[il].ffn_down_exps, selected_experts, i, cur_expert); // [n_tokens, n_embd]
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cb(cur_expert, "ffn_moe_down", il);
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cur_expert = ggml_mul(ctx0, cur_expert,
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ggml_view_2d(ctx0, weights, 1, n_tokens, weights->nb[1], i*weights->nb[0]));
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cb(cur_expert, "ffn_moe_weighted", il);
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if (i == 0) {
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moe_out = cur_expert;
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} else {
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moe_out = ggml_add(ctx0, moe_out, cur_expert);
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cb(moe_out, "ffn_moe_out", il);
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}
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}
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cur = moe_out;
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cur = build_moe(cur, n_tokens, il);
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// Grok
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// if layer_out_norm is present then apply it before adding the input
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@ -7163,7 +7111,6 @@ struct llm_build_context {
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cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
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model.layers[il].wo, NULL,
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Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
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}
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if (il == n_layer - 1) {
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@ -7179,64 +7126,9 @@ struct llm_build_context {
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// feed-forward network
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// MoE branch
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{
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ggml_tensor * logits = ggml_mul_mat(ctx0, model.layers[il].ffn_gate_inp, cur); // [n_tokens, num_experts]
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cb(logits, "ffn_moe_logits", il);
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ggml_tensor * probs = ggml_soft_max(ctx0, logits); // [n_tokens, num_experts]
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cb(probs, "ffn_moe_probs", il);
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// select experts
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ggml_tensor * selected_experts = ggml_top_k(ctx0, probs, n_expert_used); // [n_tokens, num_experts_per_tok]
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cb(selected_experts->src[0], "ffn_moe_argsort", il);
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ggml_tensor * weights = ggml_get_rows(ctx0,
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ggml_reshape_3d(ctx0, probs, 1, n_expert, n_tokens), selected_experts);
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cb(weights, "ffn_moe_weights", il);
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weights = ggml_reshape_2d(ctx0, weights, n_expert_used, n_tokens); // [n_tokens, num_experts_per_tok]
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ggml_tensor * weights_sum = ggml_sum_rows(ctx0, weights);
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cb(weights_sum, "ffn_moe_weights_sum", il);
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weights = ggml_div(ctx0, weights, weights_sum); // [n_tokens, num_experts_per_tok]
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cb(weights, "ffn_moe_weights_norm", il);
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// compute expert outputs
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ggml_tensor * moe_out = nullptr;
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for (int i = 0; i < n_expert_used; ++i) {
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ggml_tensor * cur_expert;
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ggml_tensor * cur_up = ggml_mul_mat_id(ctx0, model.layers[il].ffn_up_exps, selected_experts, i, cur);
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cb(cur_up, "ffn_moe_up", il);
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ggml_tensor * cur_gate = ggml_mul_mat_id(ctx0, model.layers[il].ffn_gate_exps, selected_experts, i, cur);
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cb(cur_gate, "ffn_moe_gate", il);
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//GeLU
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cur_gate = ggml_gelu(ctx0, cur_gate);
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cb(cur_gate, "ffn_moe_gelu", il);
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cur_expert = ggml_mul(ctx0, cur_up, cur_gate);
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cb(cur_expert, "ffn_moe_gate_par", il);
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cur_expert = ggml_mul_mat_id(ctx0, model.layers[il].ffn_down_exps, selected_experts, i, cur_expert); // [n_tokens, n_embd]
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cb(cur_expert, "ffn_moe_down", il);
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cur_expert = ggml_mul(ctx0, cur_expert,
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ggml_view_2d(ctx0, weights, 1, n_tokens, weights->nb[1], i * weights->nb[0]));
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cb(cur_expert, "ffn_moe_weighted", il);
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if (i == 0) {
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moe_out = cur_expert;
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} else {
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moe_out = ggml_add(ctx0, moe_out, cur_expert);
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cb(moe_out, "ffn_moe_out", il);
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}
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}
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cur = moe_out;
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}
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cur = build_moe(cur, n_tokens, il);
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// DBRX norm2
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cur = llm_build_norm(ctx0, cur, hparams,
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model.layers[il].layer_out_norm, NULL,
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LLM_NORM, cb, il);
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