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;
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willhe
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3 changed files with 192 additions and 15 deletions
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@ -110,6 +110,7 @@ Typically finetunes of the base models below are supported as well.
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- [x] [CodeShell](https://github.com/WisdomShell/codeshell)
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- [x] [Gemma](https://ai.google.dev/gemma)
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- [x] [Mamba](https://github.com/state-spaces/mamba)
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- [x] [Xverse](https://huggingface.co/models?search=xverse)
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**Multimodal models:**
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@ -772,6 +772,7 @@ class XverseModel(Model):
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assert (self.dir_model / "tokenizer.json").is_file()
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dir_model = self.dir_model
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hparams = self.hparams
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tokens: list[bytearray] = []
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toktypes: list[int] = []
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@ -783,26 +784,30 @@ class XverseModel(Model):
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reverse_vocab = {id_: encoded_tok for encoded_tok, id_ in tokenizer.vocab.items()}
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added_vocab = tokenizer.get_added_vocab()
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for i in range(vocab_size):
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if i not in reverse_vocab:
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pad_token = f"[PAD{i}]".encode('utf-8')
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tokens.append(bytearray(pad_token))
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toktypes.append(gguf.TokenType.USER_DEFINED)
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elif reverse_vocab[i] in added_vocab:
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tokens.append(reverse_vocab[i])
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if tokenizer.added_tokens_decoder[i].special:
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toktypes.append(gguf.TokenType.CONTROL)
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for token_id in range(vocab_size):
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token_text = reverse_vocab[token_id].encode('utf-8')
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# replace "\x00" to string with length > 0
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if token_text == b"\x00":
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toktype = gguf.TokenType.BYTE # special
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token_text = f"<{token_text}>".encode('utf-8')
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elif re.fullmatch(br"<0x[0-9A-Fa-f]{2}>", token_text):
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toktype = gguf.TokenType.BYTE # special
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elif reverse_vocab[token_id] in added_vocab:
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if tokenizer.added_tokens_decoder[token_id].special:
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toktype = gguf.TokenType.CONTROL
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else:
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toktypes.append(gguf.TokenType.USER_DEFINED)
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toktype = gguf.TokenType.USER_DEFINED
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else:
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tokens.append(reverse_vocab[i])
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toktypes.append(gguf.TokenType.NORMAL)
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toktype = gguf.TokenType.NORMAL
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self.gguf_writer.add_tokenizer_model("xverse")
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tokens.append(token_text)
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toktypes.append(toktype)
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self.gguf_writer.add_tokenizer_model("llama")
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self.gguf_writer.add_token_list(tokens)
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self.gguf_writer.add_token_types(toktypes)
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special_vocab = gguf.SpecialVocab(dir_model, load_merges=True)
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special_vocab = gguf.SpecialVocab(dir_model, n_vocab=len(tokens))
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special_vocab.add_to_gguf(self.gguf_writer)
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def set_gguf_parameters(self):
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173
llama.cpp
173
llama.cpp
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@ -214,6 +214,7 @@ 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_UNKNOWN,
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};
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@ -243,6 +244,7 @@ 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_UNKNOWN, "(unknown)" },
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};
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@ -836,6 +838,25 @@ 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_UNKNOWN,
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{
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@ -3628,6 +3649,16 @@ 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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default: (void)0;
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}
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@ -4106,6 +4137,7 @@ static bool llm_load_tensors(
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LLAMA_LOG_INFO("%s: ggml ctx size = %7.2f MiB\n", __func__, model.ctxs.size()*ctx_size/1024.0/1024.0);
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bool init_mapping_prefetch = true;
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// create tensors for the weights
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{
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const int64_t n_embd = hparams.n_embd;
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@ -4910,6 +4942,35 @@ 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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init_mapping_prefetch = false;
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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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default:
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throw std::runtime_error("unknown architecture");
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}
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@ -4917,7 +4978,7 @@ static bool llm_load_tensors(
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ml.done_getting_tensors();
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ml.init_mapping(true, use_mlock ? &model.mlock_mmap : nullptr);
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ml.init_mapping(init_mapping_prefetch, use_mlock ? &model.mlock_mmap : nullptr);
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// create the backend buffers
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std::vector<std::pair<ggml_context *, ggml_backend_buffer_t>> ctx_bufs;
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@ -5910,6 +5971,111 @@ 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, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb);
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cb(inpL, "inp_embd", -1);
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// inp_pos - contains the positions
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struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0);
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cb(inp_pos, "inp_pos", -1);
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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 = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0);
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cb(KQ_mask, "KQ_mask", -1);
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// positions of the tokens in the KV cache
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struct ggml_tensor * KQ_pos = ggml_view_1d(ctx0, lctx.inp_KQ_pos, n_kv, 0);
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cb(KQ_pos, "KQ_pos", -1);
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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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cb(cur, "kqv_out", il);
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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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@ -8473,6 +8639,10 @@ 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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default:
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GGML_ASSERT(false);
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}
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@ -13053,6 +13223,7 @@ 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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return LLAMA_ROPE_TYPE_NORM;
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// the pairs of head values are offset by n_rot/2
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