[Model] Add support for xverse (#6301)
* Support xverse model convert to gguf format. * 1. Convert xverse models to gguf; 2. Add LLM_ARCH_XVERSE inference in llama.cpp; 3. Add xverse item in Supported models in README.md; * * gguf-py: remove redundant logs * llama: remove the init_mapping_prefetch custom parameter * llama.cpp: Include the changes from #6122 to exclude the unused outputs of the last layers. * - Fix format issues - Remove duplicate set kqv_out to llm_build_kv * Update llama.cpp --------- Co-authored-by: willhe <willhe@xverse.cn> Co-authored-by: willhe <hexin@xverse.cn>
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4 changed files with 329 additions and 1 deletions
165
llama.cpp
165
llama.cpp
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@ -218,6 +218,7 @@ enum llm_arch {
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LLM_ARCH_GEMMA,
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LLM_ARCH_STARCODER2,
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LLM_ARCH_MAMBA,
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LLM_ARCH_XVERSE,
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LLM_ARCH_COMMAND_R,
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LLM_ARCH_UNKNOWN,
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};
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@ -249,6 +250,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
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{ LLM_ARCH_GEMMA, "gemma" },
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{ LLM_ARCH_STARCODER2, "starcoder2" },
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{ LLM_ARCH_MAMBA, "mamba" },
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{ LLM_ARCH_XVERSE, "xverse" },
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{ LLM_ARCH_COMMAND_R, "command-r" },
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{ LLM_ARCH_UNKNOWN, "(unknown)" },
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};
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@ -878,6 +880,25 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA
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{ LLM_TENSOR_SSM_OUT, "blk.%d.ssm_out" },
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},
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},
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{
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LLM_ARCH_XVERSE,
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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_FREQS, "rope_freqs" },
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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_ATTN_ROT_EMBD, "blk.%d.attn_rot_embd" },
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{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
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{ LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
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{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
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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_COMMAND_R,
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{
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@ -3847,6 +3868,16 @@ 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_XVERSE:
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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_7B; break;
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case 40: model.type = e_model::MODEL_13B; break;
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case 80: model.type = e_model::MODEL_65B; 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_COMMAND_R:
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{
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ml.get_key(LLM_KV_LOGIT_SCALE, hparams.f_logit_scale);
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@ -5200,6 +5231,28 @@ static bool llm_load_tensors(
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layer.ssm_out = ml.create_tensor(ctx_split, tn(LLM_TENSOR_SSM_OUT, "weight", i), {d_inner, n_embd});
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}
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} break;
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case LLM_ARCH_XVERSE:
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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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{
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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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layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
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layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
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layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd});
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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_COMMAND_R:
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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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@ -5238,7 +5291,7 @@ static bool llm_load_tensors(
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ml.done_getting_tensors();
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ml.init_mappings(true, &model.mlock_mmaps);
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ml.init_mappings(true, use_mlock ? &model.mlock_mmaps : nullptr);
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model.mappings.reserve(ml.mappings.size());
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// create the backend buffers
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@ -6411,6 +6464,111 @@ struct llm_build_context {
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return gf;
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}
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struct ggml_cgraph * build_xverse() {
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struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
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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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// positions of the tokens in the KV cache
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struct ggml_tensor * KQ_pos = build_inp_KQ_pos();
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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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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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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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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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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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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, NULL,
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Kcur, Vcur, Qcur, KQ_mask, KQ_pos, 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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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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// feed-forward network
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{
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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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cur = llm_build_ffn(ctx0, cur,
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model.layers[il].ffn_up, NULL,
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model.layers[il].ffn_gate, NULL,
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model.layers[il].ffn_down, NULL,
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NULL,
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LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
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cb(cur, "ffn_out", il);
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}
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cur = ggml_add(ctx0, cur, ffn_inp);
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cb(cur, "l_out", il);
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// input for next layer
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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 = ggml_mul_mat(ctx0, model.output, cur);
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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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struct ggml_cgraph * build_falcon() {
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struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
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@ -9389,6 +9547,10 @@ static struct ggml_cgraph * llama_build_graph(
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{
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result = llm.build_mamba();
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} break;
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case LLM_ARCH_XVERSE:
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{
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result = llm.build_xverse();
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} break;
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case LLM_ARCH_COMMAND_R:
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{
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result = llm.build_command_r();
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@ -14188,6 +14350,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
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case LLM_ARCH_ORION:
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case LLM_ARCH_INTERNLM2:
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case LLM_ARCH_MINICPM:
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case LLM_ARCH_XVERSE:
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case LLM_ARCH_COMMAND_R:
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return LLAMA_ROPE_TYPE_NORM;
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