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llama : add CodeShell support (#5016)
* llama: add codeshell support * llama.cpp: fix codeshell with NeoX rope Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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@ -197,6 +197,8 @@ class Model:
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return Phi2Model
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return Phi2Model
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if model_architecture == "PlamoForCausalLM":
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if model_architecture == "PlamoForCausalLM":
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return PlamoModel
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return PlamoModel
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if model_architecture == "CodeShellForCausalLM":
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return CodeShellModel
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return Model
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return Model
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def _is_model_safetensors(self) -> bool:
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def _is_model_safetensors(self) -> bool:
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@ -242,6 +244,8 @@ class Model:
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return gguf.MODEL_ARCH.PHI2
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return gguf.MODEL_ARCH.PHI2
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if arch == "PlamoForCausalLM":
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if arch == "PlamoForCausalLM":
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return gguf.MODEL_ARCH.PLAMO
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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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raise NotImplementedError(f'Architecture "{arch}" not supported!')
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raise NotImplementedError(f'Architecture "{arch}" not supported!')
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@ -1175,6 +1179,69 @@ class PlamoModel(Model):
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self.gguf_writer.add_tensor(new_name, data)
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self.gguf_writer.add_tensor(new_name, data)
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class CodeShellModel(Model):
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def set_gguf_parameters(self):
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block_count = self.hparams["n_layer"]
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self.gguf_writer.add_name("CodeShell")
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self.gguf_writer.add_context_length(self.hparams["n_positions"])
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self.gguf_writer.add_embedding_length(self.hparams["n_embd"])
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self.gguf_writer.add_feed_forward_length(4 * self.hparams["n_embd"])
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self.gguf_writer.add_block_count(block_count)
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self.gguf_writer.add_head_count(self.hparams["n_head"])
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self.gguf_writer.add_head_count_kv(self.hparams["num_query_groups"])
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self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"])
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self.gguf_writer.add_file_type(self.ftype)
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self.gguf_writer.add_rope_freq_base(10000.0)
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self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
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self.gguf_writer.add_rope_scaling_factor(1.0)
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def write_tensors(self):
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block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer")))
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tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
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tensors = dict(self.get_tensors())
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has_lm_head = "lm_head.weight" in tensors.keys() or "output.weight" in tensors.keys()
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for name, data_torch in tensors.items():
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# we don't need these
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if name.endswith((".attn.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"{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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if not has_lm_head and name == "transformer.wte.weight":
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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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###### CONVERSION LOGIC ######
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###### CONVERSION LOGIC ######
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@ -99,6 +99,7 @@ class MODEL_ARCH(IntEnum):
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QWEN = auto()
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QWEN = auto()
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PHI2 = auto()
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PHI2 = auto()
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PLAMO = auto()
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PLAMO = auto()
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CODESHELL = auto()
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class MODEL_TENSOR(IntEnum):
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class MODEL_TENSOR(IntEnum):
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@ -147,6 +148,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
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MODEL_ARCH.QWEN: "qwen",
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MODEL_ARCH.QWEN: "qwen",
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MODEL_ARCH.PHI2: "phi2",
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MODEL_ARCH.PHI2: "phi2",
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MODEL_ARCH.PLAMO: "plamo",
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MODEL_ARCH.PLAMO: "plamo",
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MODEL_ARCH.CODESHELL: "codeshell",
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}
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}
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TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
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TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
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@ -396,6 +398,19 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
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MODEL_TENSOR.FFN_NORM,
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MODEL_TENSOR.FFN_NORM,
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MODEL_TENSOR.FFN_DOWN,
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MODEL_TENSOR.FFN_DOWN,
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MODEL_TENSOR.FFN_UP,
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MODEL_TENSOR.FFN_UP,
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],
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MODEL_ARCH.CODESHELL: [
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MODEL_TENSOR.TOKEN_EMBD,
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MODEL_TENSOR.POS_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_QKV,
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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_DOWN,
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MODEL_TENSOR.FFN_UP,
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]
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]
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# TODO
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# TODO
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}
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}
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@ -417,6 +432,10 @@ MODEL_TENSOR_SKIP: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
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MODEL_TENSOR.ROPE_FREQS,
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MODEL_TENSOR.ROPE_FREQS,
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MODEL_TENSOR.ATTN_ROT_EMBD,
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MODEL_TENSOR.ATTN_ROT_EMBD,
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],
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],
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MODEL_ARCH.CODESHELL: [
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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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#
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#
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@ -154,6 +154,7 @@ class TensorNameMap:
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"model.layers.{bid}.self_attn.rotary_emb.inv_freq", # llama-hf
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"model.layers.{bid}.self_attn.rotary_emb.inv_freq", # llama-hf
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"layers.{bid}.attention.inner_attention.rope.freqs", # llama-pth
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"layers.{bid}.attention.inner_attention.rope.freqs", # llama-pth
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"model.layers.layers.{bid}.self_attn.rotary_emb.inv_freq", # plamo
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"model.layers.layers.{bid}.self_attn.rotary_emb.inv_freq", # plamo
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"transformer.h.{bid}.attn.rotary_emb.inv_freq", # codeshell
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),
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),
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# Feed-forward norm
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# Feed-forward norm
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181
llama.cpp
181
llama.cpp
@ -194,6 +194,7 @@ enum llm_arch {
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LLM_ARCH_QWEN,
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LLM_ARCH_QWEN,
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LLM_ARCH_PHI2,
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LLM_ARCH_PHI2,
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LLM_ARCH_PLAMO,
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LLM_ARCH_PLAMO,
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LLM_ARCH_CODESHELL,
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LLM_ARCH_UNKNOWN,
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LLM_ARCH_UNKNOWN,
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};
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};
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@ -213,6 +214,7 @@ static std::map<llm_arch, std::string> LLM_ARCH_NAMES = {
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{ LLM_ARCH_QWEN, "qwen" },
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{ LLM_ARCH_QWEN, "qwen" },
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{ LLM_ARCH_PHI2, "phi2" },
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{ LLM_ARCH_PHI2, "phi2" },
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{ LLM_ARCH_PLAMO, "plamo" },
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{ LLM_ARCH_PLAMO, "plamo" },
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{ LLM_ARCH_CODESHELL, "codeshell" },
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};
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};
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enum llm_kv {
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enum llm_kv {
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@ -600,6 +602,26 @@ 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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{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
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},
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},
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},
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},
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{
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LLM_ARCH_CODESHELL,
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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_QKV, "blk.%d.attn_qkv" },
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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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{
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LLM_ARCH_UNKNOWN,
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LLM_ARCH_UNKNOWN,
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@ -2877,6 +2899,14 @@ static void llm_load_hparams(
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default: model.type = e_model::MODEL_UNKNOWN;
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default: model.type = e_model::MODEL_UNKNOWN;
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}
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}
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} break;
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} break;
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case LLM_ARCH_CODESHELL:
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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 42: model.type = e_model::MODEL_SMALL; 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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default: (void)0;
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}
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}
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@ -3759,6 +3789,42 @@ static bool llm_load_tensors(
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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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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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}
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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.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
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layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*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.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {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_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
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layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {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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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_CODESHELL:
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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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// output
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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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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_layer = ctx_for_layer(i);
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ggml_context * ctx_split = ctx_for_layer_split(i);
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ggml_context * ctx_split = ctx_for_layer_split(i);
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@ -5965,6 +6031,117 @@ struct llm_build_context {
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return gf;
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return gf;
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}
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}
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struct ggml_cgraph * build_codeshell() {
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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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const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
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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, 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_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
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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_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
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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, LLM_ROPE, n_ctx, freq_base, freq_scale, cb);
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||||||
|
}
|
||||||
|
|
||||||
|
for (int il = 0; il < n_layer; ++il) {
|
||||||
|
cur = llm_build_norm(ctx0, inpL, hparams,
|
||||||
|
model.layers[il].attn_norm,
|
||||||
|
model.layers[il].attn_norm_b,
|
||||||
|
LLM_NORM, cb, il);
|
||||||
|
cb(cur, "attn_norm", il);
|
||||||
|
|
||||||
|
// self-attention
|
||||||
|
{
|
||||||
|
cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
|
||||||
|
cb(cur, "wqkv", il);
|
||||||
|
|
||||||
|
cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
|
||||||
|
cb(cur, "bqkv", il);
|
||||||
|
|
||||||
|
struct ggml_tensor * tmpq = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
|
||||||
|
struct ggml_tensor * tmpk = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
|
||||||
|
struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
|
||||||
|
|
||||||
|
cb(tmpq, "tmpq", il);
|
||||||
|
cb(tmpk, "tmpk", il);
|
||||||
|
cb(Vcur, "Vcur", il);
|
||||||
|
|
||||||
|
struct ggml_tensor * Qcur = ggml_rope_custom(
|
||||||
|
ctx0, ggml_reshape_3d(ctx0, tmpq, n_embd_head, n_head, n_tokens), inp_pos,
|
||||||
|
hparams.n_rot, 2, 0, n_orig_ctx, freq_base, freq_scale,
|
||||||
|
ext_factor, attn_factor, beta_fast, beta_slow
|
||||||
|
);
|
||||||
|
cb(Qcur, "Qcur", il);
|
||||||
|
|
||||||
|
struct ggml_tensor * Kcur = ggml_rope_custom(
|
||||||
|
ctx0, ggml_reshape_3d(ctx0, tmpk, n_embd_head, n_head_kv, n_tokens), inp_pos,
|
||||||
|
hparams.n_rot, 2, 0, n_orig_ctx, freq_base, freq_scale,
|
||||||
|
ext_factor, attn_factor, beta_fast, beta_slow
|
||||||
|
);
|
||||||
|
cb(Kcur, "Kcur", il);
|
||||||
|
|
||||||
|
llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);
|
||||||
|
|
||||||
|
cur = llm_build_kqv(ctx0, model, hparams, kv_self,
|
||||||
|
model.layers[il].wo, model.layers[il].bo,
|
||||||
|
Qcur, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il);
|
||||||
|
cb(cur, "kqv_out", il);
|
||||||
|
}
|
||||||
|
|
||||||
|
// add the input
|
||||||
|
struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
|
||||||
|
cb(ffn_inp, "ffn_inp", il);
|
||||||
|
|
||||||
|
// FF
|
||||||
|
{
|
||||||
|
cur = llm_build_norm(ctx0, ffn_inp, hparams,
|
||||||
|
model.layers[il].ffn_norm,
|
||||||
|
model.layers[il].ffn_norm_b,
|
||||||
|
LLM_NORM, cb, il);
|
||||||
|
cb(cur, "ffn_norm", il);
|
||||||
|
|
||||||
|
cur = llm_build_ffn(ctx0, cur,
|
||||||
|
model.layers[il].ffn_up, model.layers[il].ffn_up_b,
|
||||||
|
NULL, NULL,
|
||||||
|
model.layers[il].ffn_down, model.layers[il].ffn_down_b,
|
||||||
|
NULL,
|
||||||
|
LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
|
||||||
|
cb(cur, "ffn_out", il);
|
||||||
|
}
|
||||||
|
|
||||||
|
inpL = ggml_add(ctx0, cur, ffn_inp);
|
||||||
|
cb(inpL, "l_out", il);
|
||||||
|
}
|
||||||
|
|
||||||
|
cur = llm_build_norm(ctx0, inpL, hparams,
|
||||||
|
model.output_norm,
|
||||||
|
model.output_norm_b,
|
||||||
|
LLM_NORM, cb, -1);
|
||||||
|
cb(cur, "result_norm", -1);
|
||||||
|
|
||||||
|
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(
|
static struct ggml_cgraph * llama_build_graph(
|
||||||
@ -6159,6 +6336,10 @@ static struct ggml_cgraph * llama_build_graph(
|
|||||||
{
|
{
|
||||||
result = llm.build_gpt2();
|
result = llm.build_gpt2();
|
||||||
} break;
|
} break;
|
||||||
|
case LLM_ARCH_CODESHELL:
|
||||||
|
{
|
||||||
|
result = llm.build_codeshell();
|
||||||
|
} break;
|
||||||
default:
|
default:
|
||||||
GGML_ASSERT(false);
|
GGML_ASSERT(false);
|
||||||
}
|
}
|
||||||
|
Loading…
Reference in New Issue
Block a user