ggml : group all experts in a single ggml_mul_mat_id (#6505)
* ggml : group all experts in a single ggml_mul_mat_id cuda : improve mmid row copy * cuda : fix bin bcast with non-cont src0 * test-backend-ops : only run all mul mat tests for base types * llama : disable moe offloading with SYCL --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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12 changed files with 971 additions and 821 deletions
223
llama.cpp
223
llama.cpp
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@ -4495,6 +4495,13 @@ static bool llm_load_tensors(
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auto & hparams = model.hparams;
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#ifdef GGML_USE_SYCL
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// disable MoE with SYCL until mul_mat_id is updated
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if (hparams.n_expert > 0) {
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n_gpu_layers = 0;
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}
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#endif
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model.split_mode = split_mode;
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model.main_gpu = main_gpu;
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model.n_gpu_layers = n_gpu_layers;
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@ -6099,6 +6106,100 @@ static struct ggml_tensor * llm_build_ffn(
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return cur;
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}
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static struct ggml_tensor * llm_build_moe_ffn(
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struct ggml_context * ctx,
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struct ggml_tensor * cur,
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struct ggml_tensor * gate_inp,
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struct ggml_tensor * up_exps,
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struct ggml_tensor * gate_exps,
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struct ggml_tensor * down_exps,
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int64_t n_expert,
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int64_t n_expert_used,
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llm_ffn_op_type type_op,
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bool norm_w,
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const llm_build_cb & cb,
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int il) {
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int64_t n_embd = cur->ne[0];
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int64_t n_tokens = cur->ne[1];
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ggml_tensor * logits = ggml_mul_mat(ctx, gate_inp, cur); // [n_expert, n_tokens]
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cb(logits, "ffn_moe_logits", il);
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ggml_tensor * probs = ggml_soft_max(ctx, logits); // [n_expert, n_tokens]
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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(ctx, probs, n_expert_used); // [n_expert_used, n_tokens]
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cb(selected_experts->src[0], "ffn_moe_argsort", il);
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cb(selected_experts, "ffn_moe_topk", il);
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ggml_tensor * weights = ggml_get_rows(ctx,
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ggml_reshape_3d(ctx, probs, 1, n_expert, n_tokens), selected_experts); // [1, n_expert_used, n_tokens]
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cb(weights, "ffn_moe_weights", il);
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if (norm_w) {
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weights = ggml_reshape_2d(ctx, weights, n_expert_used, n_tokens);
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ggml_tensor * weights_sum = ggml_sum_rows(ctx, weights); // [1, n_tokens]
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cb(weights_sum, "ffn_moe_weights_sum", il);
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weights = ggml_div(ctx, weights, weights_sum); // [n_expert_used, n_tokens]
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cb(weights, "ffn_moe_weights_norm", il);
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weights = ggml_reshape_3d(ctx, weights, 1, n_expert_used, n_tokens);
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}
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cur = ggml_reshape_3d(ctx, cur, n_embd, 1, n_tokens);
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ggml_tensor * up = ggml_mul_mat_id(ctx, up_exps, cur, selected_experts); // [n_ff, n_expert_used, n_tokens]
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cb(up, "ffn_moe_up", il);
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ggml_tensor * gate = ggml_mul_mat_id(ctx, gate_exps, cur, selected_experts); // [n_ff, n_expert_used, n_tokens]
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cb(gate, "ffn_moe_gate", il);
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switch (type_op) {
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case LLM_FFN_SILU:
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{
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gate = ggml_silu(ctx, gate);
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cb(gate, "ffn_moe_silu", il);
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} break;
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case LLM_FFN_GELU:
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{
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gate = ggml_gelu(ctx, gate);
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cb(gate, "ffn_moe_gelu", il);
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} break;
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default:
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GGML_ASSERT(false);
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}
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ggml_tensor * par = ggml_mul(ctx, up, gate); // [n_ff, n_expert_used, n_tokens]
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cb(par, "ffn_moe_gate_par", il);
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ggml_tensor * experts = ggml_mul_mat_id(ctx, down_exps, par, selected_experts); // [n_embd, n_expert_used, n_tokens]
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cb(experts, "ffn_moe_down", il);
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experts = ggml_mul(ctx, experts, weights);
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// aggregate experts
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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 = ggml_view_2d(ctx, experts, n_embd, n_tokens,
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experts->nb[2], i*experts->nb[1]);
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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(ctx, moe_out, cur_expert);
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}
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}
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if (n_expert_used == 1) {
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// avoid returning a non-contiguous tensor
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moe_out = ggml_cont(ctx, moe_out);
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}
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return moe_out;
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}
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// if max_alibi_bias > 0 then apply ALiBi
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static struct ggml_tensor * llm_build_kqv(
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struct ggml_context * ctx,
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@ -6642,7 +6743,15 @@ 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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cur = build_moe_ffn(cur, n_tokens, LLM_FFN_SILU, true, il);
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cur = llm_build_moe_ffn(ctx0, 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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n_expert, n_expert_used,
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LLM_FFN_SILU, true,
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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, cur, ffn_inp);
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@ -6674,80 +6783,6 @@ struct llm_build_context {
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return gf;
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}
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// REVIEW: will be replaced by https://github.com/ggerganov/llama.cpp/pull/6505
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ggml_tensor * build_moe_ffn(ggml_tensor * cur, int32_t n_tokens, llm_ffn_op_type type_op, bool norm_w, 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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if (norm_w) {
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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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}
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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 * gate = ggml_mul_mat_id(ctx0, model.layers[il].ffn_gate_exps, selected_experts, i, cur);
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cb(gate, "ffn_moe_gate", il);
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switch (type_op) {
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case LLM_FFN_SILU:
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{
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gate = ggml_silu(ctx0, gate);
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cb(gate, "ffn_moe_silu", il);
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} break;
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case LLM_FFN_GELU:
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{
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gate = ggml_gelu(ctx0, gate);
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cb(gate, "ffn_moe_gelu", il);
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} break;
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default:
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GGML_ASSERT(false);
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}
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cur_expert = ggml_mul(ctx0, cur_up, 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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@ -7195,7 +7230,15 @@ 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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cur = build_moe_ffn(cur, n_tokens, LLM_FFN_GELU, true, il);
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cur = llm_build_moe_ffn(ctx0, 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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n_expert, n_expert_used,
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LLM_FFN_GELU, true,
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cb, il);
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cb(cur, "ffn_moe_out", 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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@ -7207,7 +7250,6 @@ struct llm_build_context {
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cb(cur, "layer_out_norm", il);
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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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@ -7331,7 +7373,15 @@ struct llm_build_context {
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LLM_NORM, cb, il);
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cb(cur, "attn_out_norm", il);
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cur = build_moe_ffn(cur, n_tokens, LLM_FFN_SILU, true, il);
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cur = llm_build_moe_ffn(ctx0, 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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n_expert, n_expert_used,
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LLM_FFN_SILU, true,
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cb, il);
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cb(cur, "ffn_moe_out", il);
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cur = ggml_add(ctx0, cur, ffn_inp);
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cb(cur, "ffn_out", il);
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@ -8502,12 +8552,6 @@ struct llm_build_context {
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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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// these nodes are added to the graph together so that they are not reordered
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// by doing so, the number of splits in the graph is reduced
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ggml_build_forward_expand(gf, Qcur);
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ggml_build_forward_expand(gf, Kcur);
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ggml_build_forward_expand(gf, Vcur);
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Qcur = ggml_rope_custom(
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ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
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n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
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@ -8658,7 +8702,16 @@ 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 * moe_out = build_moe_ffn(cur, n_tokens, LLM_FFN_SILU, false, il);
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ggml_tensor * moe_out =
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llm_build_moe_ffn(ctx0, 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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n_expert, n_expert_used,
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LLM_FFN_SILU, false,
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cb, il);
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cb(cur, "ffn_moe_out", il);
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// FFN shared expert
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{
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