return CodeShellModel
if model_architecture == "OrionForCausalLM":
return OrionModel
+ if model_architecture == "InternLM2ForCausalLM":
+ return InternLM2Model
return Model
def _is_model_safetensors(self) -> bool:
return gguf.MODEL_ARCH.CODESHELL
if arch == "OrionForCausalLM":
return gguf.MODEL_ARCH.ORION
+ if arch == "InternLM2ForCausalLM":
+ return gguf.MODEL_ARCH.INTERNLM2
raise NotImplementedError(f'Architecture "{arch}" not supported!')
self.gguf_writer.add_tensor("output.weight", data)
print(name, f"=> output.weight, shape = {data.shape}, {old_dtype} --> {data.dtype}")
+
+class InternLM2Model(Model):
+ def set_vocab(self):
+ # (TODO): Is there a better way?
+ # Copy from _set_vocab_sentencepiece, The only difference is that we will treat the character
+ # \x00 specially and convert it into an emoji character to prevent it from being mistakenly
+ # recognized as an empty string in C++.
+ from sentencepiece import SentencePieceProcessor
+ from sentencepiece import sentencepiece_model_pb2 as model
+
+ tokenizer_path = self.dir_model / 'tokenizer.model'
+
+ tokens: list[bytes] = []
+ scores: list[float] = []
+ toktypes: list[int] = []
+
+ if not tokenizer_path.is_file():
+ print(f'Error: Missing {tokenizer_path}', file=sys.stderr)
+ sys.exit(1)
+
+ sentencepiece_model = model.ModelProto()
+ sentencepiece_model.ParseFromString(open(tokenizer_path, "rb").read())
+ add_prefix = sentencepiece_model.normalizer_spec.add_dummy_prefix
+
+ tokenizer = SentencePieceProcessor(str(tokenizer_path))
+ vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size())
+
+ for token_id in range(vocab_size):
+ piece = tokenizer.id_to_piece(token_id)
+ text = piece.encode("utf-8")
+ score = tokenizer.get_score(token_id)
+ if text == b"\x00":
+ # (TODO): fixme
+ # Hack here and replace the \x00 characters.
+ print(f"InternLM2 convert token '{text}' to '🐉'!")
+ text = "🐉"
+
+ toktype = SentencePieceTokenTypes.NORMAL
+ if tokenizer.is_unknown(token_id):
+ toktype = SentencePieceTokenTypes.UNKNOWN
+ elif tokenizer.is_control(token_id):
+ toktype = SentencePieceTokenTypes.CONTROL
+ elif tokenizer.is_unused(token_id):
+ toktype = SentencePieceTokenTypes.UNUSED
+ elif tokenizer.is_byte(token_id):
+ toktype = SentencePieceTokenTypes.BYTE
+
+ tokens.append(text)
+ scores.append(score)
+ toktypes.append(toktype)
+
+ added_tokens_file = self.dir_model / 'added_tokens.json'
+ if added_tokens_file.is_file():
+ with open(added_tokens_file, "r", encoding="utf-8") as f:
+ added_tokens_json = json.load(f)
+
+ for key in added_tokens_json:
+ tokens.append(key.encode("utf-8"))
+ scores.append(-1000.0)
+ toktypes.append(SentencePieceTokenTypes.USER_DEFINED)
+
+ self.gguf_writer.add_tokenizer_model("llama")
+ self.gguf_writer.add_token_list(tokens)
+ self.gguf_writer.add_token_scores(scores)
+ self.gguf_writer.add_token_types(toktypes)
+ self.gguf_writer.add_add_space_prefix(add_prefix)
+
+ special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
+ special_vocab.add_to_gguf(self.gguf_writer)
+
+ def set_gguf_parameters(self):
+ self.gguf_writer.add_name("InternLM2")
+ self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"])
+ self.gguf_writer.add_block_count(self.hparams["num_hidden_layers"])
+ self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
+ self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
+ self.gguf_writer.add_rope_freq_base(self.hparams["rope_theta"])
+ self.gguf_writer.add_head_count(self.hparams["num_attention_heads"])
+ self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"])
+ self.gguf_writer.add_head_count_kv(self.hparams["num_key_value_heads"])
+
+ def post_write_tensors(self, tensor_map, name, data_torch):
+ old_dtype = data_torch.dtype
+
+ # convert any unsupported data types to float32
+ if data_torch.dtype not in (torch.float16, torch.float32):
+ data_torch = data_torch.to(torch.float32)
+
+ data = data_torch.squeeze().numpy()
+
+ # map tensor names
+ new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
+ if new_name is None:
+ print(f"Can not map tensor {name!r}")
+ sys.exit()
+
+ n_dims = len(data.shape)
+ data_dtype = data.dtype
+
+ # if f32 desired, convert any float16 to float32
+ if self.ftype == 0 and data_dtype == np.float16:
+ data = data.astype(np.float32)
+
+ # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
+ if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1:
+ data = data.astype(np.float32)
+
+ # if f16 desired, convert any float32 2-dim weight tensors to float16
+ if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
+ data = data.astype(np.float16)
+
+ print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
+ self.gguf_writer.add_tensor(new_name, data)
+
+ def write_tensors(self):
+ from einops import rearrange
+
+ num_heads = self.hparams.get("num_attention_heads")
+ num_kv_heads = self.hparams.get("num_key_value_heads")
+ hidden_size = self.hparams.get("hidden_size")
+ q_per_kv = num_heads // num_kv_heads
+ head_dim = hidden_size // num_heads
+ num_groups = num_heads // q_per_kv
+
+ block_count = self.hparams["num_hidden_layers"]
+ model_kv = dict(self.get_tensors())
+ tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
+ qkv_pattern = r"model\.layers\.(\d+)\.attention\.wqkv"
+ for name, data_torch in model_kv.items():
+ # we don't need these
+ if name.endswith(".rotary_emb.inv_freq"):
+ continue
+
+ if re.match(qkv_pattern, name):
+ bid = re.findall(qkv_pattern, name)[0]
+ qkv = data_torch
+ qkv = rearrange(qkv.T, " o (g n i) ->o g n i", g=num_groups, n=q_per_kv + 2, i=head_dim)
+ q, k, v = qkv[..., : q_per_kv, :], qkv[..., q_per_kv: q_per_kv + 1, :], qkv[..., q_per_kv + 1: q_per_kv + 2, :]
+ q = rearrange(q, " o g n i -> o (g n i)").T
+ k = rearrange(k, " o g n i -> o (g n i)").T
+ v = rearrange(v, " o g n i -> o (g n i)").T
+ self.post_write_tensors(tensor_map, f"model.layers.{bid}.attention.wq.weight", q)
+ self.post_write_tensors(tensor_map, f"model.layers.{bid}.attention.wk.weight", k)
+ self.post_write_tensors(tensor_map, f"model.layers.{bid}.attention.wv.weight", v)
+ else:
+ self.post_write_tensors(tensor_map, name, data_torch)
+
+
###### CONVERSION LOGIC ######
"language_model.embedding.word_embeddings", # persimmon
"wte", # gpt2
"transformer.embd.wte", # phi2
+ "model.tok_embeddings", # internlm2
),
# Token type embeddings
MODEL_TENSOR.OUTPUT: (
"embed_out", # gptneox
"lm_head", # gpt2 mpt falcon llama-hf baichuan qwen
- "output", # llama-pth bloom
+ "output", # llama-pth bloom internlm2
"word_embeddings_for_head", # persimmon
"lm_head.linear", # phi2
),
MODEL_TENSOR.OUTPUT_NORM: (
"gpt_neox.final_layer_norm", # gptneox
"transformer.ln_f", # gpt2 gpt-j falcon
- "model.norm", # llama-hf baichuan
+ "model.norm", # llama-hf baichuan internlm2
"norm", # llama-pth
"embeddings.LayerNorm", # bert
"transformer.norm_f", # mpt
"h.{bid}.ln_1", # gpt2
"transformer.h.{bid}.ln", # phi2
"model.layers.layers.{bid}.norm", # plamo
+ "model.layers.{bid}.attention_norm", # internlm2
),
# Attention norm 2
"encoder.layer.{bid}.attention.self.query", # bert
"transformer.h.{bid}.attn.q_proj", # gpt-j
"model.layers.layers.{bid}.self_attn.q_proj", # plamo
+ "model.layers.{bid}.attention.wq" # internlm2
),
# Attention key
"encoder.layer.{bid}.attention.self.key", # bert
"transformer.h.{bid}.attn.k_proj", # gpt-j
"model.layers.layers.{bid}.self_attn.k_proj", # plamo
+ "model.layers.{bid}.attention.wk" # internlm2
),
# Attention value
"encoder.layer.{bid}.attention.self.value", # bert
"transformer.h.{bid}.attn.v_proj", # gpt-j
"model.layers.layers.{bid}.self_attn.v_proj", # plamo
+ "model.layers.{bid}.attention.wv" # internlm2
),
# Attention output
"h.{bid}.attn.c_proj", # gpt2
"transformer.h.{bid}.mixer.out_proj", # phi2
"model.layers.layers.{bid}.self_attn.o_proj", # plamo
+ "model.layers.{bid}.attention.wo", # internlm2
),
# Rotary embeddings
"language_model.encoder.layers.{bid}.post_attention_layernorm", # persimmon
"model.layers.{bid}.ln2", # yi
"h.{bid}.ln_2", # gpt2
+ "model.layers.{bid}.ffn_norm", # internlm2
),
MODEL_TENSOR.FFN_GATE_INP: (
"transformer.h.{bid}.mlp.fc1", # phi2
"model.layers.{bid}.mlp.fc1", # phi2
"model.layers.layers.{bid}.mlp.up_proj", # plamo
+ "model.layers.{bid}.feed_forward.w3", # internlm2
),
MODEL_TENSOR.FFN_UP_EXP: (
"layers.{bid}.feed_forward.w1", # llama-pth
"transformer.h.{bid}.mlp.w2", # qwen
"model.layers.layers.{bid}.mlp.gate_proj", # plamo
+ "model.layers.{bid}.feed_forward.w1", # internlm2
),
MODEL_TENSOR.FFN_GATE_EXP: (
"transformer.h.{bid}.mlp.fc2", # phi2
"model.layers.{bid}.mlp.fc2", # phi2
"model.layers.layers.{bid}.mlp.down_proj", # plamo
+ "model.layers.{bid}.feed_forward.w2", # internlm2
),
MODEL_TENSOR.FFN_DOWN_EXP: (
LLM_ARCH_PLAMO,
LLM_ARCH_CODESHELL,
LLM_ARCH_ORION,
+ LLM_ARCH_INTERNLM2,
LLM_ARCH_UNKNOWN,
};
{ LLM_ARCH_PLAMO, "plamo" },
{ LLM_ARCH_CODESHELL, "codeshell" },
{ LLM_ARCH_ORION, "orion" },
+ { LLM_ARCH_INTERNLM2, "internlm2" },
};
enum llm_kv {
LLM_KV_TOKENIZER_PAD_ID,
LLM_KV_TOKENIZER_ADD_BOS,
LLM_KV_TOKENIZER_ADD_EOS,
+ LLM_KV_TOKENIZER_ADD_PREFIX,
LLM_KV_TOKENIZER_HF_JSON,
LLM_KV_TOKENIZER_RWKV,
};
{ LLM_KV_TOKENIZER_PAD_ID, "tokenizer.ggml.padding_token_id" },
{ LLM_KV_TOKENIZER_ADD_BOS, "tokenizer.ggml.add_bos_token" },
{ LLM_KV_TOKENIZER_ADD_EOS, "tokenizer.ggml.add_eos_token" },
+ { LLM_KV_TOKENIZER_ADD_PREFIX, "tokenizer.ggml.add_space_prefix" },
{ LLM_KV_TOKENIZER_HF_JSON, "tokenizer.huggingface.json" },
{ LLM_KV_TOKENIZER_RWKV, "tokenizer.rwkv.world" },
};
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
},
},
-
+ {
+ LLM_ARCH_INTERNLM2,
+ {
+ { LLM_TENSOR_TOKEN_EMBD, "token_embd" },
+ { LLM_TENSOR_OUTPUT_NORM, "output_norm" },
+ { LLM_TENSOR_OUTPUT, "output" },
+ { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
+ { LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
+ { LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
+ { LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
+ { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
+ { LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
+ { LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
+ { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
+ { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
+ },
+ },
{
LLM_ARCH_UNKNOWN,
{
MODEL_13B,
MODEL_14B,
MODEL_15B,
+ MODEL_20B,
MODEL_30B,
MODEL_34B,
MODEL_40B,
id special_suffix_id = 32008;
id special_eot_id = 32010;
+ bool add_space_prefix = true;
+
int find_bpe_rank(const std::string & token_left, const std::string & token_right) const {
GGML_ASSERT(token_left.find(' ') == std::string::npos);
GGML_ASSERT(token_left.find('\n') == std::string::npos);
case MODEL_13B: return "13B";
case MODEL_14B: return "14B";
case MODEL_15B: return "15B";
+ case MODEL_20B: return "20B";
case MODEL_30B: return "30B";
case MODEL_34B: return "34B";
case MODEL_40B: return "40B";
default: return "?B";
}
}
+static const char * llama_model_vocab_type_name(enum llama_vocab_type type){
+ switch (type) {
+ case LLAMA_VOCAB_TYPE_SPM: return "SPM";
+ case LLAMA_VOCAB_TYPE_BPE: return "BPE";
+ default: return "unknown";
+ }
+}
+
static void llm_load_arch(llama_model_loader & ml, llama_model & model) {
model.arch = ml.get_arch();
default: model.type = e_model::MODEL_UNKNOWN;
}
} break;
+ case LLM_ARCH_INTERNLM2:
+ {
+ ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+ switch (hparams.n_layer) {
+ case 32: model.type = e_model::MODEL_7B; break;
+ case 48: model.type = e_model::MODEL_20B; break;
+ default: model.type = e_model::MODEL_UNKNOWN;
+ }
+ } break;
default: (void)0;
}
vocab.special_unk_id = 0;
vocab.special_sep_id = -1;
vocab.special_pad_id = -1;
+
+ const int add_space_prefix_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_ADD_PREFIX).c_str());
+ if (add_space_prefix_keyidx != -1) {
+ vocab.add_space_prefix = gguf_get_val_bool(ctx, add_space_prefix_keyidx);
+ } // The default value of add_space_prefix is true.
} else if (tokenizer_name == "gpt2") {
vocab.type = LLAMA_VOCAB_TYPE_BPE;
// hparams
LLAMA_LOG_INFO("%s: format = %s\n", __func__, llama_file_version_name(ml.fver));
LLAMA_LOG_INFO("%s: arch = %s\n", __func__, LLM_ARCH_NAMES.at(model.arch).c_str());
- LLAMA_LOG_INFO("%s: vocab type = %s\n", __func__, vocab.type == LLAMA_VOCAB_TYPE_SPM ? "SPM" : "BPE"); // TODO: fix
+ LLAMA_LOG_INFO("%s: vocab type = %s\n", __func__, llama_model_vocab_type_name(vocab.type));
LLAMA_LOG_INFO("%s: n_vocab = %u\n", __func__, hparams.n_vocab);
LLAMA_LOG_INFO("%s: n_merges = %u\n", __func__, (int) vocab.bpe_ranks.size());
LLAMA_LOG_INFO("%s: n_ctx_train = %u\n", __func__, hparams.n_ctx_train);
layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
}
} break;
+ case LLM_ARCH_INTERNLM2:
+ {
+ model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+
+ // output
+ {
+ model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
+ model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab});
+ }
+ for (int i = 0; i < n_layer; ++i) {
+ ggml_context * ctx_layer = ctx_for_layer(i);
+ ggml_context * ctx_split = ctx_for_layer_split(i);
+
+ auto & layer = model.layers[i];
+ layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
+ // layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
+ layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd});
+ layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa});
+ layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
+
+ layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
+ layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
+ layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
+ layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd});
+ layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
+ }
+ } break;
default:
throw std::runtime_error("unknown architecture");
}
return gf;
}
+
+ struct ggml_cgraph * build_internlm2() {
+ struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+
+ const int64_t n_embd_head = hparams.n_embd_head_v;
+ GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+ GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+ struct ggml_tensor * cur;
+ struct ggml_tensor * inpL;
+
+ inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb);
+ cb(inpL, "inp_embd", -1);
+
+ // inp_pos - contains the positions
+ struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0);
+ cb(inp_pos, "inp_pos", -1);
+
+ // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+ 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);
+ cb(KQ_mask, "KQ_mask", -1);
+
+ // shift the entire K-cache if needed
+ if (do_rope_shift) {
+ llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, lctx.inp_K_shift, LLM_ROPE, n_ctx, freq_base, freq_scale, cb);
+ }
+
+ for (int il = 0; il < n_layer; ++il) {
+ struct ggml_tensor * inpSA = inpL;
+
+ // norm
+ cur = llm_build_norm(ctx0, inpL, hparams,
+ model.layers[il].attn_norm, NULL,
+ LLM_NORM_RMS, cb, il);
+ cb(cur, "attn_norm", il);
+
+ // self-attention
+ {
+ // compute Q and K and RoPE them
+ struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+ cb(Qcur, "Qcur", il);
+ if (model.layers[il].bq) {
+ Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
+ cb(Qcur, "Qcur", il);
+ }
+
+ struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
+ cb(Kcur, "Kcur", il);
+ if (model.layers[il].bk) {
+ Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
+ cb(Kcur, "Kcur", il);
+ }
+
+ struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
+ cb(Vcur, "Vcur", il);
+ if (model.layers[il].bv) {
+ Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
+ cb(Vcur, "Vcur", il);
+ }
+
+ Qcur = ggml_rope_custom(
+ ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
+ hparams.n_rot, 0, 0, n_orig_ctx, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow
+ );
+ cb(Qcur, "Qcur", il);
+
+ Kcur = ggml_rope_custom(
+ ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
+ hparams.n_rot, 0, 0, n_orig_ctx, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow
+ );
+ cb(Kcur, "Kcur", il);
+
+ cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
+ model.layers[il].wo, model.layers[il].bo,
+ Kcur, Vcur, Qcur, KQ_mask, n_ctx, n_tokens, kv_head, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+ cb(cur, "kqv_out", il);
+ }
+
+ struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+ cb(ffn_inp, "ffn_inp", il);
+
+ // feed-forward network
+ cur = llm_build_norm(ctx0, ffn_inp, hparams,
+ model.layers[il].ffn_norm, NULL,
+ LLM_NORM_RMS, cb, il);
+ cb(cur, "ffn_norm", il);
+
+ cur = llm_build_ffn(ctx0, cur,
+ model.layers[il].ffn_up, NULL,
+ model.layers[il].ffn_gate, NULL,
+ model.layers[il].ffn_down, NULL,
+ NULL,
+ LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
+ cb(cur, "ffn_out", il);
+
+ 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;
+ }
+
};
static struct ggml_cgraph * llama_build_graph(
{
result = llm.build_orion();
} break;
+ case LLM_ARCH_INTERNLM2:
+ {
+ result = llm.build_internlm2();
+ } break;
default:
GGML_ASSERT(false);
}
//
auto raw_text = fragment.raw_text.substr(fragment.offset, fragment.length);
if (&fragment == &fragment_buffer.front()) {
- raw_text = " " + raw_text; // prefix with space if the first token is not special
+ if (vocab.add_space_prefix) {
+ raw_text = " " + raw_text; // prefix with space if the first token is not special
+ }
}
#ifdef PRETOKENIZERDEBUG
overview / pkg / ggml / sources / llama.cpp / commitdiff