llama : add qwen2moe (#6074)
* support qwen2moe * fix-review * metal : support unary ops for nelements % 4 != 0 * metal : require contiguousness for float4 unary kernels * metal : require contiguousness for float4 unary kernels (cont) * fix-review * names : for brevity "SHARED_EXP" -> "SHEXP" * llama : reuse build_moe_ffn() * llama : add model type name --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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7 changed files with 537 additions and 101 deletions
252
llama.cpp
252
llama.cpp
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@ -105,7 +105,7 @@
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#endif
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#define LLAMA_MAX_NODES 8192
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#define LLAMA_MAX_EXPERTS 16
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#define LLAMA_MAX_EXPERTS 60
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//
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@ -209,6 +209,7 @@ enum llm_arch {
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LLM_ARCH_STABLELM,
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LLM_ARCH_QWEN,
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LLM_ARCH_QWEN2,
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LLM_ARCH_QWEN2MOE,
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LLM_ARCH_PHI2,
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LLM_ARCH_PLAMO,
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LLM_ARCH_CODESHELL,
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@ -242,6 +243,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
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{ LLM_ARCH_STABLELM, "stablelm" },
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{ LLM_ARCH_QWEN, "qwen" },
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{ LLM_ARCH_QWEN2, "qwen2" },
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{ LLM_ARCH_QWEN2MOE, "qwen2moe" },
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{ LLM_ARCH_PHI2, "phi2" },
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{ LLM_ARCH_PLAMO, "plamo" },
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{ LLM_ARCH_CODESHELL, "codeshell" },
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@ -437,6 +439,7 @@ enum llm_tensor {
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LLM_TENSOR_ATTN_OUT_NORM,
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LLM_TENSOR_ATTN_ROT_EMBD,
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LLM_TENSOR_FFN_GATE_INP,
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LLM_TENSOR_FFN_GATE_INP_SHEXP,
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LLM_TENSOR_FFN_NORM,
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LLM_TENSOR_FFN_GATE,
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LLM_TENSOR_FFN_DOWN,
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@ -448,6 +451,9 @@ enum llm_tensor {
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LLM_TENSOR_FFN_DOWN_EXPS, // merged experts
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LLM_TENSOR_FFN_GATE_EXPS,
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LLM_TENSOR_FFN_UP_EXPS,
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LLM_TENSOR_FFN_DOWN_SHEXP,
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LLM_TENSOR_FFN_GATE_SHEXP,
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LLM_TENSOR_FFN_UP_SHEXP,
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LLM_TENSOR_ATTN_Q_NORM,
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LLM_TENSOR_ATTN_K_NORM,
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LLM_TENSOR_LAYER_OUT_NORM,
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@ -745,6 +751,28 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA
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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_QWEN2MOE,
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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_ATTN_NORM, "blk.%d.attn_norm" },
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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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{ LLM_TENSOR_FFN_GATE_INP_SHEXP, "blk.%d.ffn_gate_inp_shexp" },
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{ LLM_TENSOR_FFN_GATE_SHEXP, "blk.%d.ffn_gate_shexp" },
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{ LLM_TENSOR_FFN_DOWN_SHEXP, "blk.%d.ffn_down_shexp" },
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{ LLM_TENSOR_FFN_UP_SHEXP, "blk.%d.ffn_up_shexp" },
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},
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},
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{
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LLM_ARCH_PHI2,
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{
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@ -1731,6 +1759,7 @@ enum e_model {
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MODEL_MEDIUM,
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MODEL_LARGE,
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MODEL_XL,
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MODEL_A2_7B,
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MODEL_8x7B,
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MODEL_8x22B,
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MODEL_16x12B,
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@ -1917,6 +1946,12 @@ struct llama_layer {
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struct ggml_tensor * ffn_down_exps;
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struct ggml_tensor * ffn_up_exps ;
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// ff shared expert (shexp)
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struct ggml_tensor * ffn_gate_inp_shexp;
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struct ggml_tensor * ffn_gate_shexp;
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struct ggml_tensor * ffn_down_shexp;
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struct ggml_tensor * ffn_up_shexp;
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// ff bias
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struct ggml_tensor * ffn_down_b; // b2
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struct ggml_tensor * ffn_up_b; // b3
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@ -3587,6 +3622,7 @@ static const char * llama_model_type_name(e_model type) {
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case MODEL_MEDIUM: return "0.4B";
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case MODEL_LARGE: return "0.8B";
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case MODEL_XL: return "1.5B";
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case MODEL_A2_7B: return "A2.7B";
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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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@ -3886,6 +3922,14 @@ static void llm_load_hparams(
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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_QWEN2MOE:
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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 24: model.type = e_model::MODEL_A2_7B; 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_PHI2:
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{
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ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
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@ -5156,6 +5200,54 @@ static bool llm_load_tensors(
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layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
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}
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} break;
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case LLM_ARCH_QWEN2MOE:
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{
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model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
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// output
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{
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model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
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model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab});
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}
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for (int i = 0; i < n_layer; ++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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auto & layer = model.layers[i];
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layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
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layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd});
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layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa});
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layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
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layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
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// optional bias tensors
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layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd});
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layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa});
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layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa});
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layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
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layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert});
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GGML_ASSERT(hparams.n_expert > 0);
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GGML_ASSERT(hparams.n_expert_used > 0);
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// MoE branch
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auto n_ff_exp = n_ff / hparams.n_expert_used;
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layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert});
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layer.ffn_down_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert});
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layer.ffn_up_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert});
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// Shared expert branch
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layer.ffn_gate_inp_shexp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP_SHEXP, "weight", i), {n_embd});
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layer.ffn_gate_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff});
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layer.ffn_down_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { n_ff, n_embd});
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layer.ffn_up_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff});
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}
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} break;
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case LLM_ARCH_PHI2:
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{
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model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
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@ -6532,7 +6624,7 @@ 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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cur = build_moe_ffn(cur, n_tokens, LLM_FFN_SILU, il);
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cur = build_moe_ffn(cur, n_tokens, LLM_FFN_SILU, true, il);
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}
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cur = ggml_add(ctx0, cur, ffn_inp);
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@ -6565,7 +6657,7 @@ struct llm_build_context {
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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, int il) {
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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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@ -6582,11 +6674,13 @@ struct llm_build_context {
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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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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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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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@ -7083,7 +7177,7 @@ 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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cur = build_moe_ffn(cur, n_tokens, LLM_FFN_GELU, il);
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cur = build_moe_ffn(cur, n_tokens, LLM_FFN_GELU, true, 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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@ -7219,7 +7313,7 @@ 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, il);
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cur = build_moe_ffn(cur, n_tokens, LLM_FFN_SILU, true, 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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@ -8434,6 +8528,141 @@ struct llm_build_context {
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return gf;
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}
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struct ggml_cgraph * build_qwen2moe() {
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struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
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// mutable variable, needed during the last layer of the computation to skip unused tokens
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int32_t n_tokens = this->n_tokens;
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const int64_t n_embd_head = hparams.n_embd_head_v;
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GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
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GGML_ASSERT(n_embd_head == hparams.n_rot);
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struct ggml_tensor * cur;
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struct ggml_tensor * inpL;
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inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
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// inp_pos - contains the positions
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struct ggml_tensor * inp_pos = build_inp_pos();
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// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
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struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
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for (int il = 0; il < n_layer; ++il) {
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struct ggml_tensor * inpSA = inpL;
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// norm
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cur = llm_build_norm(ctx0, inpL, hparams,
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model.layers[il].attn_norm, NULL,
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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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// compute Q and K and RoPE them
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struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
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cb(Qcur, "Qcur", il);
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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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struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
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cb(Kcur, "Kcur", il);
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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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struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
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cb(Vcur, "Vcur", il);
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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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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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ext_factor, attn_factor, beta_fast, beta_slow
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);
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cb(Qcur, "Qcur", il);
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Kcur = ggml_rope_custom(
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ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
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n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
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ext_factor, attn_factor, beta_fast, beta_slow
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);
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cb(Kcur, "Kcur", il);
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cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
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model.layers[il].wo, model.layers[il].bo,
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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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// skip computing output for unused tokens
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struct ggml_tensor * inp_out_ids = build_inp_out_ids();
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n_tokens = n_outputs;
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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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struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
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cb(ffn_inp, "ffn_inp", il);
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// MoE branch
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cur = llm_build_norm(ctx0, ffn_inp, hparams,
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model.layers[il].ffn_norm, NULL,
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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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// FFN shared expert
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{
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ggml_tensor * cur_gate_inp = ggml_mul_mat(ctx0, model.layers[il].ffn_gate_inp_shexp, cur);
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cb(cur_gate_inp, "ffn_shexp_gate_inp", il);
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// sigmoid
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ggml_tensor * cur_gate = ggml_div(ctx0, ggml_silu(ctx0, cur_gate_inp), cur_gate_inp);
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cb(cur_gate, "ffn_shexp_gate", il);
|
||||
|
||||
ggml_tensor * cur_ffn = llm_build_ffn(ctx0, cur,
|
||||
model.layers[il].ffn_up_shexp, NULL,
|
||||
model.layers[il].ffn_gate_shexp, NULL,
|
||||
model.layers[il].ffn_down_shexp, NULL,
|
||||
NULL,
|
||||
LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
|
||||
cb(cur_ffn, "ffn_shexp", il);
|
||||
|
||||
ggml_tensor * ffn_shexp_out = ggml_mul(ctx0, cur_ffn, cur_gate);
|
||||
cb(ffn_shexp_out, "ffn_shexp_out", il);
|
||||
|
||||
moe_out = ggml_add(ctx0, moe_out, ffn_shexp_out);
|
||||
cb(moe_out, "ffn_out", il);
|
||||
|
||||
cur = moe_out;
|
||||
}
|
||||
|
||||
cur = ggml_add(ctx0, cur, ffn_inp);
|
||||
cb(cur, "l_out", il);
|
||||
|
||||
// input for next layer
|
||||
inpL = cur;
|
||||
}
|
||||
|
||||
cur = inpL;
|
||||
|
||||
cur = llm_build_norm(ctx0, cur, hparams,
|
||||
model.output_norm, NULL,
|
||||
LLM_NORM_RMS, cb, -1);
|
||||
cb(cur, "result_norm", -1);
|
||||
|
||||
// lm_head
|
||||
cur = ggml_mul_mat(ctx0, model.output, cur);
|
||||
cb(cur, "result_output", -1);
|
||||
|
||||
ggml_build_forward_expand(gf, cur);
|
||||
|
||||
return gf;
|
||||
}
|
||||
|
||||
struct ggml_cgraph * build_phi2() {
|
||||
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
|
||||
|
||||
|
|
@ -9917,6 +10146,10 @@ static struct ggml_cgraph * llama_build_graph(
|
|||
{
|
||||
result = llm.build_qwen2();
|
||||
} break;
|
||||
case LLM_ARCH_QWEN2MOE:
|
||||
{
|
||||
result = llm.build_qwen2moe();
|
||||
} break;
|
||||
case LLM_ARCH_PHI2:
|
||||
{
|
||||
result = llm.build_phi2();
|
||||
|
|
@ -14834,6 +15067,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
|
|||
case LLM_ARCH_STABLELM:
|
||||
case LLM_ARCH_QWEN:
|
||||
case LLM_ARCH_QWEN2:
|
||||
case LLM_ARCH_QWEN2MOE:
|
||||
case LLM_ARCH_PHI2:
|
||||
case LLM_ARCH_GEMMA:
|
||||
case LLM_ARCH_STARCODER2:
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue