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Add JAIS model(s) (#8118)
* Add `JAIS` model(s) * cleanup * address review comments * remove hack * un-hardcode max-alibi-bias * minor tweaks --------- Co-authored-by: fmz <quic_fzaghlou@quic.com>
This commit is contained in:
Faisal Zaghloul
GitHub
parent
023b8807e1
commit
968967376d
@ -86,6 +86,7 @@ models = [
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{"name": "poro-chat", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LumiOpen/Poro-34B-chat", },
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{"name": "jina-v2-code", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-code", },
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{"name": "viking", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LumiOpen/Viking-7B", }, # Also used for Viking 13B and 33B
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{"name": "jais", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/core42/jais-13b", },
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]
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@ -490,6 +490,9 @@ class Model:
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if chkhsh == "7fc505bd3104ca1083b150b17d088b59534ede9bde81f0dd2090967d7fe52cee":
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# ref: https://huggingface.co/LumiOpen/Viking-7B
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res = "viking"
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if chkhsh == "b53802fb28e26d645c3a310b34bfe07da813026ec7c7716883404d5e0f8b1901":
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# ref: https://huggingface.co/core42/jais-13b
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res = "jais"
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if res is None:
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logger.warning("\n")
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@ -2965,6 +2968,96 @@ class T5Model(Model):
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return [(self.map_tensor_name(name), data_torch)]
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@Model.register("JAISLMHeadModel")
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class JaisModel(Model):
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model_arch = gguf.MODEL_ARCH.JAIS
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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# SwigLU activation
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assert self.hparams["activation_function"] == "swiglu"
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# ALiBi position embedding
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assert self.hparams["position_embedding_type"] == "alibi"
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# Embeddings scale
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self.embeddings_scale = 1.0
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# note: For some JAIS flavors, output is tied to (same as) wte in original model
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self.output_is_wte = False
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if 'mup_embeddings_scale' in self.hparams:
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self.output_is_wte = True # Hack (?)
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self.embeddings_scale = self.hparams['mup_embeddings_scale']
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elif 'embeddings_scale' in self.hparams:
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self.embeddings_scale = self.hparams['embeddings_scale']
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else:
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assert False
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self.width_scale = 1.0
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if 'mup_output_alpha' in self.hparams:
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assert 'mup_width_scale' in self.hparams
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self.width_scale = self.hparams['mup_output_alpha'] * self.hparams['mup_width_scale']
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elif 'width_scale' in self.hparams:
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self.width_scale = self.hparams['width_scale']
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else:
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assert False
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self.max_alibi_bias = 8.0
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def set_vocab(self):
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self._set_vocab_gpt2()
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def set_gguf_parameters(self):
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self.gguf_writer.add_name(self.dir_model.name)
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self.gguf_writer.add_block_count(self.hparams["n_layer"])
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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(self.hparams["n_inner"])
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self.gguf_writer.add_head_count(self.hparams["n_head"])
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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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def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
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del bid # unused
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tensors: list[tuple[str, Tensor]] = []
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# we don't need these
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if name.endswith((".attn.bias")):
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return tensors
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if name.endswith(("relative_pe.slopes")):
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# Calculate max ALiBi bias (this is the inverse of the ALiBi calculation)
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# Some other models has max_alibi_bias spelled out explicitly in the hyperparams,
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# but Jais's PyTorch model simply precalculates the slope values and places them
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# in relative_pes.slopes
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n_head_closest_log2 = 2 ** math.floor(math.log2(self.hparams["n_head"]))
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first_val = float(data_torch._data[0])
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self.max_alibi_bias = -round(math.log2(first_val) * n_head_closest_log2)
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return tensors
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if name.endswith((".c_attn.weight", ".c_proj.weight", ".c_fc.weight", ".c_fc2.weight")):
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data_torch = data_torch.transpose(1, 0)
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new_name = self.map_tensor_name(name)
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if new_name == self.format_tensor_name(gguf.MODEL_TENSOR.TOKEN_EMBD):
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tensors.append((new_name, data_torch * self.embeddings_scale))
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if self.output_is_wte:
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tensors.append((self.format_tensor_name(gguf.MODEL_TENSOR.OUTPUT), data_torch * self.width_scale))
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elif new_name == self.format_tensor_name(gguf.MODEL_TENSOR.OUTPUT):
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assert not self.output_is_wte
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tensors.append((new_name, data_torch * self.width_scale))
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else:
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tensors.append((new_name, data_torch))
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return tensors
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def write_tensors(self):
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super().write_tensors()
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self.gguf_writer.add_max_alibi_bias(self.max_alibi_bias)
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###### CONVERSION LOGIC ######
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@ -164,6 +164,7 @@ class MODEL_ARCH(IntEnum):
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DEEPSEEK2 = auto()
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BITNET = auto()
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T5 = auto()
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JAIS = auto()
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class MODEL_TENSOR(IntEnum):
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@ -288,6 +289,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
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MODEL_ARCH.DEEPSEEK2: "deepseek2",
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MODEL_ARCH.BITNET: "bitnet",
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MODEL_ARCH.T5: "t5",
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MODEL_ARCH.JAIS: "jais",
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}
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TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
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@ -954,6 +956,18 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
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MODEL_TENSOR.ENC_FFN_UP,
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MODEL_TENSOR.ENC_OUTPUT_NORM,
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],
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MODEL_ARCH.JAIS: [
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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_QKV,
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MODEL_TENSOR.ATTN_OUT,
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MODEL_TENSOR.FFN_NORM,
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MODEL_TENSOR.FFN_DOWN,
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MODEL_TENSOR.FFN_GATE,
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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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@ -10,7 +10,7 @@ class TensorNameMap:
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# Token embeddings
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MODEL_TENSOR.TOKEN_EMBD: (
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"gpt_neox.embed_in", # gptneox
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"transformer.wte", # gpt2 gpt-j mpt refact qwen dbrx
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"transformer.wte", # gpt2 gpt-j mpt refact qwen dbrx jais
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"transformer.word_embeddings", # falcon
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"word_embeddings", # bloom
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"model.embed_tokens", # llama-hf
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@ -49,7 +49,7 @@ class TensorNameMap:
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# Output
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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 mamba dbrx
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"lm_head", # gpt2 mpt falcon llama-hf baichuan qwen mamba dbrx jais
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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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@ -58,7 +58,7 @@ class TensorNameMap:
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# Output norm
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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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"transformer.ln_f", # gpt2 gpt-j falcon jais
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"model.norm", # llama-hf baichuan internlm2
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"norm", # llama-pth
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"transformer.norm_f", # mpt dbrx
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@ -81,7 +81,7 @@ class TensorNameMap:
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# Attention norm
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MODEL_TENSOR.ATTN_NORM: (
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"gpt_neox.layers.{bid}.input_layernorm", # gptneox
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"transformer.h.{bid}.ln_1", # gpt2 gpt-j refact qwen
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"transformer.h.{bid}.ln_1", # gpt2 gpt-j refact qwen jais
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"transformer.blocks.{bid}.norm_1", # mpt
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"transformer.h.{bid}.input_layernorm", # falcon7b
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"h.{bid}.input_layernorm", # bloom
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@ -109,7 +109,7 @@ class TensorNameMap:
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# Attention query-key-value
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MODEL_TENSOR.ATTN_QKV: (
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"gpt_neox.layers.{bid}.attention.query_key_value", # gptneox
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"transformer.h.{bid}.attn.c_attn", # gpt2 qwen
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"transformer.h.{bid}.attn.c_attn", # gpt2 qwen jais
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"transformer.blocks.{bid}.attn.Wqkv", # mpt
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"transformer.blocks.{bid}.norm_attn_norm.attn.Wqkv", # dbrx
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"transformer.h.{bid}.self_attention.query_key_value", # falcon
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@ -160,7 +160,7 @@ class TensorNameMap:
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# Attention output
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MODEL_TENSOR.ATTN_OUT: (
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"gpt_neox.layers.{bid}.attention.dense", # gptneox
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"transformer.h.{bid}.attn.c_proj", # gpt2 refact qwen
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"transformer.h.{bid}.attn.c_proj", # gpt2 refact qwen jais
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"transformer.blocks.{bid}.attn.out_proj", # mpt
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"transformer.h.{bid}.self_attention.dense", # falcon
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"h.{bid}.self_attention.dense", # bloom
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@ -202,7 +202,7 @@ class TensorNameMap:
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# Feed-forward norm
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MODEL_TENSOR.FFN_NORM: (
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"gpt_neox.layers.{bid}.post_attention_layernorm", # gptneox
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"transformer.h.{bid}.ln_2", # gpt2 refact qwen
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"transformer.h.{bid}.ln_2", # gpt2 refact qwen jais
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"h.{bid}.post_attention_layernorm", # bloom
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"transformer.blocks.{bid}.norm_2", # mpt
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"model.layers.{bid}.post_attention_layernorm", # llama-hf
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@ -239,7 +239,7 @@ class TensorNameMap:
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# Feed-forward up
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MODEL_TENSOR.FFN_UP: (
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"gpt_neox.layers.{bid}.mlp.dense_h_to_4h", # gptneox
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"transformer.h.{bid}.mlp.c_fc", # gpt2
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"transformer.h.{bid}.mlp.c_fc", # gpt2 jais
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"transformer.blocks.{bid}.ffn.up_proj", # mpt
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"transformer.h.{bid}.mlp.dense_h_to_4h", # falcon
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"h.{bid}.mlp.dense_h_to_4h", # bloom
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@ -285,6 +285,7 @@ class TensorNameMap:
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"model.layers.{bid}.mlp.gate_proj", # llama-hf refact
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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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"transformer.h.{bid}.mlp.c_fc2", # jais
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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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"encoder.layers.{bid}.mlp.fc12", # nomic-bert
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@ -308,7 +309,7 @@ class TensorNameMap:
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# Feed-forward down
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MODEL_TENSOR.FFN_DOWN: (
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"gpt_neox.layers.{bid}.mlp.dense_4h_to_h", # gptneox
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"transformer.h.{bid}.mlp.c_proj", # gpt2 refact qwen
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"transformer.h.{bid}.mlp.c_proj", # gpt2 refact qwen jais
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"transformer.blocks.{bid}.ffn.down_proj", # mpt
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"transformer.h.{bid}.mlp.dense_4h_to_h", # falcon
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"h.{bid}.mlp.dense_4h_to_h", # bloom
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@ -89,6 +89,7 @@ extern "C" {
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LLAMA_VOCAB_PRE_TYPE_SMAUG = 14,
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LLAMA_VOCAB_PRE_TYPE_PORO = 15,
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LLAMA_VOCAB_PRE_TYPE_VIKING = 16,
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LLAMA_VOCAB_PRE_TYPE_JAIS = 17,
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};
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// note: these values should be synchronized with ggml_rope
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169
src/llama.cpp
169
src/llama.cpp
@ -228,6 +228,7 @@ enum llm_arch {
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LLM_ARCH_DEEPSEEK2,
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LLM_ARCH_BITNET,
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LLM_ARCH_T5,
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LLM_ARCH_JAIS,
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LLM_ARCH_UNKNOWN,
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};
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@ -269,6 +270,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
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{ LLM_ARCH_DEEPSEEK2, "deepseek2" },
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{ LLM_ARCH_BITNET, "bitnet" },
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{ LLM_ARCH_T5, "t5" },
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{ LLM_ARCH_JAIS, "jais" },
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{ LLM_ARCH_UNKNOWN, "(unknown)" },
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};
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@ -1236,6 +1238,21 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA
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{ LLM_TENSOR_ENC_FFN_UP, "enc.blk.%d.ffn_up" },
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},
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},
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{
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LLM_ARCH_JAIS,
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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_QKV, "blk.%d.attn_qkv" },
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{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
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{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
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{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
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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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},
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},
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{
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LLM_ARCH_UNKNOWN,
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{
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@ -2035,6 +2052,7 @@ enum e_model {
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MODEL_410M,
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MODEL_0_5B,
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MODEL_1B,
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MODEL_1_3B,
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MODEL_1_4B,
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MODEL_2B,
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MODEL_2_8B,
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@ -4276,6 +4294,7 @@ static const char * llama_model_type_name(e_model type) {
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case MODEL_410M: return "410M";
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case MODEL_0_5B: return "0.5B";
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case MODEL_1B: return "1B";
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case MODEL_1_3B: return "1.3B";
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case MODEL_1_4B: return "1.4B";
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case MODEL_2B: return "2B";
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case MODEL_2_8B: return "2.8B";
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@ -4898,6 +4917,18 @@ 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_JAIS:
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{
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ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
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ml.get_key(LLM_KV_ATTENTION_MAX_ALIBI_BIAS, hparams.f_max_alibi_bias);
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switch (hparams.n_layer) {
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case 24: model.type = e_model::MODEL_1_3B; break;
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case 40: model.type = e_model::MODEL_13B; break;
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/* TODO: add variants */
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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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@ -5129,6 +5160,9 @@ static void llm_load_vocab(
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} else if (
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tokenizer_pre == "viking") {
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vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_VIKING;
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} else if (
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tokenizer_pre == "jais") {
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vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_JAIS;
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} else {
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throw std::runtime_error(format("unknown pre-tokenizer type: '%s'", tokenizer_pre.c_str()));
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}
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@ -6962,6 +6996,44 @@ static bool llm_load_tensors(
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layer.ffn_up_scale = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "scale", i), {1});
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}
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} break;
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case LLM_ARCH_JAIS:
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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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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});
|
||||
layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa});
|
||||
|
||||
layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
|
||||
layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd});
|
||||
|
||||
layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
|
||||
layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd});
|
||||
|
||||
layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
|
||||
layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd});
|
||||
|
||||
layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
|
||||
layer.ffn_gate_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE, "bias", i), {n_ff});
|
||||
|
||||
layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
|
||||
layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff});
|
||||
}
|
||||
} break;
|
||||
default:
|
||||
throw std::runtime_error("unknown architecture");
|
||||
}
|
||||
@ -12354,6 +12426,97 @@ struct llm_build_context {
|
||||
return gf;
|
||||
}
|
||||
|
||||
struct ggml_cgraph * build_jais() {
|
||||
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
|
||||
|
||||
const int64_t n_embd_head = hparams.n_embd_head_v;
|
||||
const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
|
||||
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
|
||||
|
||||
struct ggml_tensor * cur;
|
||||
struct ggml_tensor * inpL;
|
||||
|
||||
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
|
||||
|
||||
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
|
||||
struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
|
||||
|
||||
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 * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*cur->nb[0]*(n_embd)));
|
||||
struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*cur->nb[0]*(n_embd)));
|
||||
struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*cur->nb[0]*(n_embd + n_embd_gqa)));
|
||||
|
||||
cb(Qcur, "Qcur", il);
|
||||
cb(Kcur, "Kcur", il);
|
||||
cb(Vcur, "Vcur", il);
|
||||
|
||||
Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
|
||||
|
||||
cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
|
||||
model.layers[il].wo, model.layers[il].bo,
|
||||
Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/float(n_embd_head), cb, il);
|
||||
}
|
||||
|
||||
if (il == n_layer - 1) {
|
||||
// skip computing output for unused tokens
|
||||
struct ggml_tensor * inp_out_ids = build_inp_out_ids();
|
||||
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
|
||||
inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
|
||||
}
|
||||
|
||||
// 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,
|
||||
model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, NULL,
|
||||
model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
|
||||
NULL,
|
||||
LLM_FFN_SILU, LLM_FFN_PAR, 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_defrag(llama_context & lctx, const std::vector<uint32_t> & ids) {
|
||||
@ -12585,6 +12748,10 @@ static struct ggml_cgraph * llama_build_graph(
|
||||
{
|
||||
result = llm.build_bitnet();
|
||||
} break;
|
||||
case LLM_ARCH_JAIS:
|
||||
{
|
||||
result = llm.build_jais();
|
||||
} break;
|
||||
default:
|
||||
GGML_ASSERT(false);
|
||||
}
|
||||
@ -13947,6 +14114,7 @@ struct llm_tokenizer_bpe {
|
||||
break;
|
||||
case LLAMA_VOCAB_PRE_TYPE_GPT2:
|
||||
case LLAMA_VOCAB_PRE_TYPE_OLMO:
|
||||
case LLAMA_VOCAB_PRE_TYPE_JAIS:
|
||||
regex_exprs = {
|
||||
"'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)",
|
||||
};
|
||||
@ -17826,6 +17994,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
|
||||
case LLM_ARCH_MAMBA:
|
||||
case LLM_ARCH_JINA_BERT_V2:
|
||||
case LLM_ARCH_T5:
|
||||
case LLM_ARCH_JAIS:
|
||||
return LLAMA_ROPE_TYPE_NONE;
|
||||
|
||||
// use what we call a normal RoPE, operating on pairs of consecutive head values
|
||||
|
||||
Loading…
Reference in New Issue
Block a user