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llama : add support for Orion-14B (#5118)
* add support for Orion-14B(https://huggingface.co/OrionStarAI/Orion-14B-Chat) * flake8 support * Update llama.cpp Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update llama.cpp Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update llama.cpp Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update llama.cpp Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update llama.cpp Co-authored-by: slaren <slarengh@gmail.com> * Update llama.cpp * Update llama.cpp --------- Co-authored-by: lixiaopu <lixiaopu@cmcm.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> Co-authored-by: slaren <slarengh@gmail.com>
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@ -201,6 +201,8 @@ class Model:
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return PlamoModel
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if model_architecture == "CodeShellForCausalLM":
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return CodeShellModel
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if model_architecture == "OrionForCausalLM":
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return OrionModel
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return Model
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def _is_model_safetensors(self) -> bool:
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@ -250,6 +252,8 @@ class Model:
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return gguf.MODEL_ARCH.PLAMO
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if arch == "CodeShellForCausalLM":
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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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raise NotImplementedError(f'Architecture "{arch}" not supported!')
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@ -572,6 +576,83 @@ class MPTModel(Model):
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self.gguf_writer.add_tensor("output.weight", data)
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class OrionModel(Model):
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def set_vocab(self):
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self._set_vocab_sentencepiece()
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def set_gguf_parameters(self):
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block_count = self.hparams["num_hidden_layers"]
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head_count = self.hparams["num_attention_heads"]
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head_count_kv = self.hparams.get("num_key_value_heads", head_count)
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hf_repo = self.hparams.get("_name_or_path", "")
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ctx_length = 0
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if "max_sequence_length" in self.hparams:
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ctx_length = self.hparams["max_sequence_length"]
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elif "max_position_embeddings" in self.hparams:
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ctx_length = self.hparams["max_position_embeddings"]
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elif "model_max_length" in self.hparams:
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ctx_length = self.hparams["model_max_length"]
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else:
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print("gguf: can not find ctx length parameter.")
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sys.exit()
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self.gguf_writer.add_file_type(self.ftype)
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self.gguf_writer.add_name(self.dir_model.name)
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self.gguf_writer.add_source_hf_repo(hf_repo)
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self.gguf_writer.add_tensor_data_layout("Meta AI original pth")
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self.gguf_writer.add_context_length(ctx_length)
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self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
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self.gguf_writer.add_block_count(block_count)
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self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
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self.gguf_writer.add_head_count(head_count)
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self.gguf_writer.add_head_count_kv(head_count_kv)
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self.gguf_writer.add_layer_norm_eps(self.hparams["rms_norm_eps"])
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def write_tensors(self):
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# Collect tensors from generator object
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model_kv = dict(self.get_tensors())
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block_count = self.hparams["num_hidden_layers"]
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tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
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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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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"{name} -> {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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class BaichuanModel(Model):
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def set_vocab(self):
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self._set_vocab_sentencepiece()
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@ -101,6 +101,7 @@ class MODEL_ARCH(IntEnum):
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PHI2 = auto()
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PLAMO = auto()
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CODESHELL = auto()
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ORION = auto()
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class MODEL_TENSOR(IntEnum):
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@ -151,6 +152,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
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MODEL_ARCH.PHI2: "phi2",
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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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}
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TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
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@ -427,7 +429,23 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
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MODEL_TENSOR.FFN_NORM,
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MODEL_TENSOR.FFN_DOWN,
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MODEL_TENSOR.FFN_UP,
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]
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],
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MODEL_ARCH.ORION: [
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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.ROPE_FREQS,
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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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@ -452,6 +470,10 @@ MODEL_TENSOR_SKIP: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
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MODEL_TENSOR.ROPE_FREQS,
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MODEL_TENSOR.ATTN_ROT_EMBD,
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],
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MODEL_ARCH.ORION: [
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MODEL_TENSOR.ROPE_FREQS,
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MODEL_TENSOR.ATTN_ROT_EMBD,
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],
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}
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#
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187
llama.cpp
187
llama.cpp
@ -196,6 +196,7 @@ enum llm_arch {
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LLM_ARCH_PHI2,
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LLM_ARCH_PLAMO,
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LLM_ARCH_CODESHELL,
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LLM_ARCH_ORION,
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LLM_ARCH_UNKNOWN,
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};
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@ -217,6 +218,7 @@ static std::map<llm_arch, std::string> LLM_ARCH_NAMES = {
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{ LLM_ARCH_PHI2, "phi2" },
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{ LLM_ARCH_PLAMO, "plamo" },
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{ LLM_ARCH_CODESHELL, "codeshell" },
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{ LLM_ARCH_ORION, "orion" },
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};
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enum llm_kv {
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@ -641,6 +643,25 @@ 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_ORION,
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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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@ -1332,6 +1353,7 @@ enum e_model {
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MODEL_7B,
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MODEL_8B,
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MODEL_13B,
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MODEL_14B,
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MODEL_15B,
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MODEL_30B,
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MODEL_34B,
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@ -2683,6 +2705,7 @@ static const char * llama_model_type_name(e_model type) {
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case MODEL_7B: return "7B";
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case MODEL_8B: return "8B";
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case MODEL_13B: return "13B";
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case MODEL_14B: return "14B";
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case MODEL_15B: return "15B";
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case MODEL_30B: return "30B";
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case MODEL_34B: return "34B";
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@ -2950,7 +2973,15 @@ 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_ORION:
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{
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ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
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switch (hparams.n_layer) {
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case 40: model.type = e_model::MODEL_14B; 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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@ -3933,6 +3964,38 @@ static bool llm_load_tensors(
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layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff});
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}
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} break;
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case LLM_ARCH_ORION:
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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_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {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.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", 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_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", 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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@ -4563,6 +4626,126 @@ struct llm_build_context {
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ctx0 = nullptr;
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}
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}
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struct ggml_cgraph * build_orion() {
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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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// shift the entire K-cache if needed
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if (do_rope_shift) {
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llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, lctx.inp_K_shift, LLM_ROPE, n_ctx, freq_base, freq_scale, cb);
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}
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for (int il = 0; il < n_layer; ++il) {
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struct ggml_tensor * inpSA = inpL;
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// norm
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cur = llm_build_norm(ctx0, inpL, hparams,
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model.layers[il].attn_norm, model.layers[il].attn_norm_b,
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LLM_NORM, cb, il);
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cb(cur, "attn_norm", il);
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// self-attention
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{
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// compute Q and K and RoPE them
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struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
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cb(Qcur, "Qcur", il);
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// if (model.layers[il].bq) {
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// Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
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// cb(Qcur, "Qcur", il);
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// }
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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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// if (model.layers[il].bk) {
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// Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
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// cb(Kcur, "Kcur", il);
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// }
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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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// if (model.layers[il].bv) {
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// Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
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// cb(Vcur, "Vcur", il);
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// }
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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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hparams.n_rot, 2, 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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hparams.n_rot, 2, 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, n_ctx, n_tokens, kv_head, n_kv, -1.0f, 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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cur = llm_build_norm(ctx0, ffn_inp, hparams,
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model.layers[il].ffn_norm, model.layers[il].ffn_norm_b,
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LLM_NORM, cb, il);
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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, model.output_norm_b,
|
||||
LLM_NORM, cb, -1);
|
||||
cb(cur, "result_norm", -1);
|
||||
|
||||
// lm_head
|
||||
cur = ggml_mul_mat(ctx0, model.output, cur);
|
||||
cb(cur, "result_output", -1);
|
||||
|
||||
ggml_build_forward_expand(gf, cur);
|
||||
|
||||
return gf;
|
||||
}
|
||||
|
||||
|
||||
|
||||
struct ggml_cgraph * build_llama() {
|
||||
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
|
||||
@ -6520,6 +6703,10 @@ static struct ggml_cgraph * llama_build_graph(
|
||||
{
|
||||
result = llm.build_codeshell();
|
||||
} break;
|
||||
case LLM_ARCH_ORION:
|
||||
{
|
||||
result = llm.build_orion();
|
||||
} break;
|
||||
default:
|
||||
GGML_ASSERT(false);
|
||||
}
|
||||
|
||||
Loading…
Reference in New Issue
Block a user