make telechat2 config compatiable with Llama

Signed-off-by: Isotr0py <[email protected]>

vllm/transformers_utils/config.py

@@ -29,7 +29,8 @@
                                              MLPSpeculatorConfig, MPTConfig,
                                              NemotronConfig, NVLM_D_Config,
                                              Olmo2Config, RWConfig,
-                                             SolarConfig, UltravoxConfig)
+                                             SolarConfig, Telechat2Config,
+                                             UltravoxConfig)
 # yapf: enable
 from vllm.transformers_utils.utils import check_gguf_file
 from vllm.utils import resolve_obj_by_qualname
@@ -64,6 +65,7 @@
     "NVLM_D": NVLM_D_Config,
     "olmo2": Olmo2Config,
     "solar": SolarConfig,
+    "telechat": Telechat2Config,
     "ultravox": UltravoxConfig,
     **_CONFIG_REGISTRY_OVERRIDE_HF
 }

vllm/transformers_utils/configs/__init__.py

@@ -17,6 +17,7 @@
 from vllm.transformers_utils.configs.nvlm_d import NVLM_D_Config
 from vllm.transformers_utils.configs.olmo2 import Olmo2Config
 from vllm.transformers_utils.configs.solar import SolarConfig
+from vllm.transformers_utils.configs.telechat2 import Telechat2Config
 from vllm.transformers_utils.configs.ultravox import UltravoxConfig
 
 __all__ = [
@@ -36,5 +37,6 @@
     "NVLM_D_Config",
     "Olmo2Config",
     "SolarConfig",
+    "Telechat2Config",
     "UltravoxConfig",
 ]

vllm/transformers_utils/configs/telechat2.py

@@ -0,0 +1,76 @@
+# adapted from https://www.modelscope.cn/models/TeleAI/TeleChat2-3B/resolve/master/configuration_telechat2.py
+""" Telechat configuration compatible with LlamaConfig. """
+
+from transformers.configuration_utils import PretrainedConfig
+
+
+class Telechat2Config(PretrainedConfig):
+    """
+    Args:
+        vocab_size (`int`, *optional*, defaults to 160256): Vocabulary size of the Telechat model.
+        hidden_size (`int`, *optional*, defaults to 4096): Dimensionality of the embeddings and hidden states.
+        ffn_hidden_size (`int`, *optional*, defaults to 12288): Dimensionality of the feed-forward hidden states.
+        n_layer (`int`, *optional*, defaults to 30): Number of hidden layers in the Transformer
+        n_head (`int`, *optional*, defaults to 32): Number of attention heads for each attention layer.
+        layer_norm_epsilon (`float`, *optional*, defaults to 1e-5): The epsilon to use in the layer normalization layers.
+        initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        apply_residual_connection_post_layernorm (`bool`, *optional*, defaults to `False`): If enabled, use the layer norm of the hidden states as the residual in the transformer blocks
+        hidden_dropout (`float`, *optional*, defaults to 0.0): Dropout rate of the dropout function on the bias dropout.
+        attention_dropout (`float`, *optional*, defaults to 0.0): Dropout rate applied to the attention probs
+        use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions.
+        training_seqlen (`int`, *optional*, defaults to 8192): Sequence length during last finetuning.
+        logn (`bool`, *optional*, defaults to `True`): Whether or not to use logN during extrapolation.
+        embed_layernorm (`bool`, *optional*, defaults to `True`): Whether or not to use embedding layernorm.
+
+    """
+
+    model_type = "telechat"
+    keys_to_ignore_at_inference = ["past_key_values"]
+    attribute_map = {
+        "num_hidden_layers": "n_layer",
+        "num_attention_heads": "n_head",
+        "intermediate_size": "ffn_hidden_size",
+        "rms_norm_eps": "layer_norm_epsilon"
+    }
+
+    def __init__(
+        self,
+        vocab_size=160256,
+        hidden_size=4096,
+        n_layer=30,
+        n_head=32,
+        layer_norm_epsilon=1e-5,
+        initializer_range=0.02,
+        use_cache=True,
+        bos_token_id=1,
+        eos_token_id=2,
+        apply_residual_connection_post_layernorm=False,
+        hidden_dropout=0.0,
+        attention_dropout=0.0,
+        ffn_hidden_size=12288,
+        training_seqlen = 8192,
+        logn = True,
+        embed_layernorm = False,
+        hidden_act="silu",
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        n_embed = kwargs.pop("n_embed", None)
+        self.hidden_size = hidden_size if n_embed is None else n_embed
+        self.n_layer = n_layer
+        self.n_head = n_head
+        self.layer_norm_epsilon = layer_norm_epsilon
+        self.initializer_range = initializer_range
+        self.use_cache = use_cache
+        self.apply_residual_connection_post_layernorm = apply_residual_connection_post_layernorm
+        self.hidden_dropout = hidden_dropout
+        self.attention_dropout = attention_dropout
+        self.bos_token_id = bos_token_id
+        self.eos_token_id = eos_token_id
+        self.logn = logn
+        self.training_seqlen = training_seqlen
+        self.embed_layernorm = embed_layernorm
+        self.num_key_value_heads= kwargs.pop("num_key_value_heads", None)
+        self.ffn_hidden_size = ffn_hidden_size
+        self.hidden_act = hidden_act
+        super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)