llama : support InternLM2 (#5184)
* support InternLM2 inference * add add_space_prefix KV pair
This commit is contained in:
Guoteng
GitHub
parent
1cfb5372cf
commit
ce32060198
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@ -203,6 +203,8 @@ class Model:
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return CodeShellModel
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if model_architecture == "OrionForCausalLM":
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return OrionModel
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if model_architecture == "InternLM2ForCausalLM":
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return InternLM2Model
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return Model
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def _is_model_safetensors(self) -> bool:
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@ -254,6 +256,8 @@ class Model:
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return gguf.MODEL_ARCH.CODESHELL
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if arch == "OrionForCausalLM":
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return gguf.MODEL_ARCH.ORION
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if arch == "InternLM2ForCausalLM":
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return gguf.MODEL_ARCH.INTERNLM2
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raise NotImplementedError(f'Architecture "{arch}" not supported!')
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@ -1344,6 +1348,154 @@ class CodeShellModel(Model):
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self.gguf_writer.add_tensor("output.weight", data)
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print(name, f"=> output.weight, shape = {data.shape}, {old_dtype} --> {data.dtype}")
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class InternLM2Model(Model):
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def set_vocab(self):
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# (TODO): Is there a better way?
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# Copy from _set_vocab_sentencepiece, The only difference is that we will treat the character
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# \x00 specially and convert it into an emoji character to prevent it from being mistakenly
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# recognized as an empty string in C++.
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from sentencepiece import SentencePieceProcessor
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from sentencepiece import sentencepiece_model_pb2 as model
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tokenizer_path = self.dir_model / 'tokenizer.model'
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tokens: list[bytes] = []
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scores: list[float] = []
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toktypes: list[int] = []
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if not tokenizer_path.is_file():
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print(f'Error: Missing {tokenizer_path}', file=sys.stderr)
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sys.exit(1)
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sentencepiece_model = model.ModelProto()
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sentencepiece_model.ParseFromString(open(tokenizer_path, "rb").read())
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add_prefix = sentencepiece_model.normalizer_spec.add_dummy_prefix
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tokenizer = SentencePieceProcessor(str(tokenizer_path))
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vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size())
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for token_id in range(vocab_size):
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piece = tokenizer.id_to_piece(token_id)
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text = piece.encode("utf-8")
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score = tokenizer.get_score(token_id)
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if text == b"\x00":
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# (TODO): fixme
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# Hack here and replace the \x00 characters.
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print(f"InternLM2 convert token '{text}' to '🐉'!")
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text = "🐉"
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toktype = SentencePieceTokenTypes.NORMAL
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if tokenizer.is_unknown(token_id):
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toktype = SentencePieceTokenTypes.UNKNOWN
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elif tokenizer.is_control(token_id):
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toktype = SentencePieceTokenTypes.CONTROL
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elif tokenizer.is_unused(token_id):
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toktype = SentencePieceTokenTypes.UNUSED
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elif tokenizer.is_byte(token_id):
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toktype = SentencePieceTokenTypes.BYTE
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tokens.append(text)
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scores.append(score)
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toktypes.append(toktype)
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added_tokens_file = self.dir_model / 'added_tokens.json'
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if added_tokens_file.is_file():
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with open(added_tokens_file, "r", encoding="utf-8") as f:
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added_tokens_json = json.load(f)
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for key in added_tokens_json:
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tokens.append(key.encode("utf-8"))
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scores.append(-1000.0)
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toktypes.append(SentencePieceTokenTypes.USER_DEFINED)
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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_scores(scores)
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self.gguf_writer.add_token_types(toktypes)
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self.gguf_writer.add_add_space_prefix(add_prefix)
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special_vocab = gguf.SpecialVocab(self.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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self.gguf_writer.add_name("InternLM2")
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self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"])
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self.gguf_writer.add_block_count(self.hparams["num_hidden_layers"])
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self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
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self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
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self.gguf_writer.add_rope_freq_base(self.hparams["rope_theta"])
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self.gguf_writer.add_head_count(self.hparams["num_attention_heads"])
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self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"])
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self.gguf_writer.add_head_count_kv(self.hparams["num_key_value_heads"])
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def post_write_tensors(self, tensor_map, name, data_torch):
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old_dtype = data_torch.dtype
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# convert any unsupported data types to float32
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if data_torch.dtype not in (torch.float16, torch.float32):
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data_torch = data_torch.to(torch.float32)
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data = data_torch.squeeze().numpy()
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# map tensor names
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new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
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if new_name is None:
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print(f"Can not map tensor {name!r}")
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sys.exit()
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n_dims = len(data.shape)
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data_dtype = data.dtype
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# if f32 desired, convert any float16 to float32
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if self.ftype == 0 and data_dtype == np.float16:
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data = data.astype(np.float32)
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# TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
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if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1:
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data = data.astype(np.float32)
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# if f16 desired, convert any float32 2-dim weight tensors to float16
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if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
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data = data.astype(np.float16)
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print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
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self.gguf_writer.add_tensor(new_name, data)
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def write_tensors(self):
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from einops import rearrange
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num_heads = self.hparams.get("num_attention_heads")
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num_kv_heads = self.hparams.get("num_key_value_heads")
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hidden_size = self.hparams.get("hidden_size")
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q_per_kv = num_heads // num_kv_heads
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head_dim = hidden_size // num_heads
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num_groups = num_heads // q_per_kv
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block_count = self.hparams["num_hidden_layers"]
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model_kv = dict(self.get_tensors())
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tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
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qkv_pattern = r"model\.layers\.(\d+)\.attention\.wqkv"
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for name, data_torch in model_kv.items():
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# we don't need these
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if name.endswith(".rotary_emb.inv_freq"):
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continue
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if re.match(qkv_pattern, name):
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bid = re.findall(qkv_pattern, name)[0]
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qkv = data_torch
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qkv = rearrange(qkv.T, " o (g n i) ->o g n i", g=num_groups, n=q_per_kv + 2, i=head_dim)
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q, k, v = qkv[..., : q_per_kv, :], qkv[..., q_per_kv: q_per_kv + 1, :], qkv[..., q_per_kv + 1: q_per_kv + 2, :]
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q = rearrange(q, " o g n i -> o (g n i)").T
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k = rearrange(k, " o g n i -> o (g n i)").T
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v = rearrange(v, " o g n i -> o (g n i)").T
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self.post_write_tensors(tensor_map, f"model.layers.{bid}.attention.wq.weight", q)
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self.post_write_tensors(tensor_map, f"model.layers.{bid}.attention.wk.weight", k)
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self.post_write_tensors(tensor_map, f"model.layers.{bid}.attention.wv.weight", v)
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else:
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self.post_write_tensors(tensor_map, name, data_torch)
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###### CONVERSION LOGIC ######
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@ -72,6 +72,7 @@ class Keys:
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PAD_ID = "tokenizer.ggml.padding_token_id"
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ADD_BOS = "tokenizer.ggml.add_bos_token"
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ADD_EOS = "tokenizer.ggml.add_eos_token"
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ADD_PREFIX = "tokenizer.ggml.add_space_prefix"
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HF_JSON = "tokenizer.huggingface.json"
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RWKV = "tokenizer.rwkv.world"
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CHAT_TEMPLATE = "tokenizer.chat_template"
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@ -102,6 +103,7 @@ class MODEL_ARCH(IntEnum):
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PLAMO = auto()
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CODESHELL = auto()
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ORION = auto()
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INTERNLM2 = auto()
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class MODEL_TENSOR(IntEnum):
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@ -153,6 +155,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
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MODEL_ARCH.PLAMO: "plamo",
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MODEL_ARCH.CODESHELL: "codeshell",
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MODEL_ARCH.ORION: "orion",
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MODEL_ARCH.INTERNLM2: "internlm2",
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}
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TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
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@ -446,6 +449,21 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
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MODEL_TENSOR.FFN_DOWN,
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MODEL_TENSOR.FFN_UP,
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],
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MODEL_ARCH.INTERNLM2: [
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MODEL_TENSOR.TOKEN_EMBD,
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MODEL_TENSOR.OUTPUT_NORM,
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MODEL_TENSOR.OUTPUT,
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MODEL_TENSOR.ATTN_NORM,
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MODEL_TENSOR.ATTN_Q,
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MODEL_TENSOR.ATTN_K,
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MODEL_TENSOR.ATTN_V,
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MODEL_TENSOR.ATTN_OUT,
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MODEL_TENSOR.ATTN_ROT_EMBD,
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MODEL_TENSOR.FFN_NORM,
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MODEL_TENSOR.FFN_GATE,
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MODEL_TENSOR.FFN_DOWN,
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MODEL_TENSOR.FFN_UP,
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],
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# TODO
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}
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@ -411,6 +411,9 @@ class GGUFWriter:
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def add_add_eos_token(self, value: bool) -> None:
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self.add_bool(Keys.Tokenizer.ADD_EOS, value)
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def add_add_space_prefix(self, value: bool) -> None:
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self.add_bool(Keys.Tokenizer.ADD_PREFIX, value)
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def add_chat_template(self, value: str) -> None:
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self.add_string(Keys.Tokenizer.CHAT_TEMPLATE, value)
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@ -19,6 +19,7 @@ class TensorNameMap:
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"language_model.embedding.word_embeddings", # persimmon
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"wte", # gpt2
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"transformer.embd.wte", # phi2
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"model.tok_embeddings", # internlm2
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),
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# Token type embeddings
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@ -42,7 +43,7 @@ class TensorNameMap:
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MODEL_TENSOR.OUTPUT: (
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"embed_out", # gptneox
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"lm_head", # gpt2 mpt falcon llama-hf baichuan qwen
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"output", # llama-pth bloom
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"output", # llama-pth bloom internlm2
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"word_embeddings_for_head", # persimmon
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"lm_head.linear", # phi2
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),
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@ -51,7 +52,7 @@ class TensorNameMap:
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MODEL_TENSOR.OUTPUT_NORM: (
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"gpt_neox.final_layer_norm", # gptneox
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"transformer.ln_f", # gpt2 gpt-j falcon
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"model.norm", # llama-hf baichuan
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"model.norm", # llama-hf baichuan internlm2
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"norm", # llama-pth
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"embeddings.LayerNorm", # bert
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"transformer.norm_f", # mpt
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@ -84,6 +85,7 @@ class TensorNameMap:
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"h.{bid}.ln_1", # gpt2
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"transformer.h.{bid}.ln", # phi2
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"model.layers.layers.{bid}.norm", # plamo
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"model.layers.{bid}.attention_norm", # internlm2
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),
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# Attention norm 2
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@ -111,6 +113,7 @@ class TensorNameMap:
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"encoder.layer.{bid}.attention.self.query", # bert
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"transformer.h.{bid}.attn.q_proj", # gpt-j
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"model.layers.layers.{bid}.self_attn.q_proj", # plamo
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"model.layers.{bid}.attention.wq" # internlm2
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),
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# Attention key
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@ -120,6 +123,7 @@ class TensorNameMap:
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"encoder.layer.{bid}.attention.self.key", # bert
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"transformer.h.{bid}.attn.k_proj", # gpt-j
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"model.layers.layers.{bid}.self_attn.k_proj", # plamo
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"model.layers.{bid}.attention.wk" # internlm2
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),
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# Attention value
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@ -129,6 +133,7 @@ class TensorNameMap:
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"encoder.layer.{bid}.attention.self.value", # bert
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"transformer.h.{bid}.attn.v_proj", # gpt-j
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"model.layers.layers.{bid}.self_attn.v_proj", # plamo
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"model.layers.{bid}.attention.wv" # internlm2
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),
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# Attention output
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@ -147,6 +152,7 @@ class TensorNameMap:
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"h.{bid}.attn.c_proj", # gpt2
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"transformer.h.{bid}.mixer.out_proj", # phi2
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"model.layers.layers.{bid}.self_attn.o_proj", # plamo
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"model.layers.{bid}.attention.wo", # internlm2
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),
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# Rotary embeddings
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@ -169,6 +175,7 @@ class TensorNameMap:
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"language_model.encoder.layers.{bid}.post_attention_layernorm", # persimmon
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"model.layers.{bid}.ln2", # yi
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"h.{bid}.ln_2", # gpt2
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"model.layers.{bid}.ffn_norm", # internlm2
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),
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MODEL_TENSOR.FFN_GATE_INP: (
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@ -194,6 +201,7 @@ class TensorNameMap:
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"transformer.h.{bid}.mlp.fc1", # phi2
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"model.layers.{bid}.mlp.fc1", # phi2
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"model.layers.layers.{bid}.mlp.up_proj", # plamo
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"model.layers.{bid}.feed_forward.w3", # internlm2
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),
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MODEL_TENSOR.FFN_UP_EXP: (
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@ -212,6 +220,7 @@ class TensorNameMap:
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"layers.{bid}.feed_forward.w1", # llama-pth
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"transformer.h.{bid}.mlp.w2", # qwen
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"model.layers.layers.{bid}.mlp.gate_proj", # plamo
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"model.layers.{bid}.feed_forward.w1", # internlm2
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),
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MODEL_TENSOR.FFN_GATE_EXP: (
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@ -236,6 +245,7 @@ class TensorNameMap:
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"transformer.h.{bid}.mlp.fc2", # phi2
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"model.layers.{bid}.mlp.fc2", # phi2
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"model.layers.layers.{bid}.mlp.down_proj", # plamo
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"model.layers.{bid}.feed_forward.w2", # internlm2
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),
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MODEL_TENSOR.FFN_DOWN_EXP: (
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205
llama.cpp
205
llama.cpp
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@ -204,6 +204,7 @@ enum llm_arch {
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LLM_ARCH_PLAMO,
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LLM_ARCH_CODESHELL,
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LLM_ARCH_ORION,
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LLM_ARCH_INTERNLM2,
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LLM_ARCH_UNKNOWN,
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};
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@ -226,6 +227,7 @@ static std::map<llm_arch, std::string> LLM_ARCH_NAMES = {
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{ LLM_ARCH_PLAMO, "plamo" },
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{ LLM_ARCH_CODESHELL, "codeshell" },
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{ LLM_ARCH_ORION, "orion" },
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{ LLM_ARCH_INTERNLM2, "internlm2" },
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};
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enum llm_kv {
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@ -278,6 +280,7 @@ enum llm_kv {
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LLM_KV_TOKENIZER_PAD_ID,
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LLM_KV_TOKENIZER_ADD_BOS,
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LLM_KV_TOKENIZER_ADD_EOS,
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LLM_KV_TOKENIZER_ADD_PREFIX,
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LLM_KV_TOKENIZER_HF_JSON,
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LLM_KV_TOKENIZER_RWKV,
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};
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@ -332,6 +335,7 @@ static std::map<llm_kv, std::string> LLM_KV_NAMES = {
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{ LLM_KV_TOKENIZER_PAD_ID, "tokenizer.ggml.padding_token_id" },
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{ LLM_KV_TOKENIZER_ADD_BOS, "tokenizer.ggml.add_bos_token" },
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{ LLM_KV_TOKENIZER_ADD_EOS, "tokenizer.ggml.add_eos_token" },
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{ LLM_KV_TOKENIZER_ADD_PREFIX, "tokenizer.ggml.add_space_prefix" },
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{ LLM_KV_TOKENIZER_HF_JSON, "tokenizer.huggingface.json" },
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{ LLM_KV_TOKENIZER_RWKV, "tokenizer.rwkv.world" },
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};
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@ -669,7 +673,23 @@ static std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES =
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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_INTERNLM2,
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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" },
|
||||
{ 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,
|
||||
{
|
||||
|
|
@ -1377,6 +1397,7 @@ enum e_model {
|
|||
MODEL_13B,
|
||||
MODEL_14B,
|
||||
MODEL_15B,
|
||||
MODEL_20B,
|
||||
MODEL_30B,
|
||||
MODEL_34B,
|
||||
MODEL_40B,
|
||||
|
|
@ -1618,6 +1639,8 @@ struct llama_vocab {
|
|||
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);
|
||||
|
|
@ -2731,6 +2754,7 @@ static const char * llama_model_type_name(e_model type) {
|
|||
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";
|
||||
|
|
@ -2743,6 +2767,14 @@ static const char * llama_model_type_name(e_model type) {
|
|||
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();
|
||||
|
|
@ -3006,6 +3038,15 @@ static void llm_load_hparams(
|
|||
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;
|
||||
}
|
||||
|
||||
|
|
@ -3057,6 +3098,11 @@ static void llm_load_vocab(
|
|||
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;
|
||||
|
||||
|
|
@ -3269,7 +3315,7 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) {
|
|||
// 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);
|
||||
|
|
@ -4018,8 +4064,35 @@ static bool llm_load_tensors(
|
|||
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");
|
||||
}
|
||||
|
|
@ -6588,6 +6661,126 @@ struct llm_build_context {
|
|||
|
||||
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(
|
||||
|
|
@ -6746,6 +6939,10 @@ 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);
|
||||
}
|
||||
|
|
@ -7688,7 +7885,9 @@ static std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab &
|
|||
//
|
||||
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
|
||||
|
|
|
|||
Loading…
Reference in a new issue