[Model] Initial Support for Chameleon (#5770)

vllm/transformers_utils/configs/__init__.py

@@ -1,3 +1,4 @@
+from vllm.transformers_utils.configs.chameleon import ChameleonConfig
 from vllm.transformers_utils.configs.chatglm import ChatGLMConfig
 from vllm.transformers_utils.configs.dbrx import DbrxConfig
 # RWConfig is for the original tiiuae/falcon-40b(-instruct) and
@@ -10,6 +11,7 @@ from vllm.transformers_utils.configs.mlp_speculator import MLPSpeculatorConfig
 from vllm.transformers_utils.configs.mpt import MPTConfig
 
 __all__ = [
+    "ChameleonConfig",
     "ChatGLMConfig",
     "DbrxConfig",
     "MPTConfig",

vllm/transformers_utils/configs/chameleon.py

@@ -0,0 +1,101 @@
+from transformers import PretrainedConfig
+
+
+#TODO (ywang96): Remove this file and import it from
+# transformers once the new release with Chameleon support
+# is available.
+class ChameleonConfig(PretrainedConfig):
+
+    model_type = "chameleon"
+    is_composition = True
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=65536,
+        hidden_size=4096,
+        intermediate_size=11008,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=32,
+        hidden_act="silu",
+        max_position_embeddings=4096,
+        initializer_range=0.02,
+        rms_norm_eps=1e-05,
+        use_cache=True,
+        pad_token_id=None,
+        bos_token_id=1,
+        eos_token_id=2,
+        tie_word_embeddings=False,
+        rope_theta=10000.0,
+        rope_scaling=None,
+        attention_bias=False,
+        attention_dropout=0.0,
+        qk_layernorm=False,
+        swin_norm=False,
+        vq_config=None,
+        vocabulary_map=None,
+        mlp_bias=False,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.mlp_bias = mlp_bias
+
+        # for backward compatibility
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self._rope_scaling_validation()
+        self.attention_bias = attention_bias
+        self.attention_dropout = attention_dropout
+        self.qk_layernorm = qk_layernorm
+        self.swin_norm = swin_norm
+        # vq config is currently ignored
+        # self.vq_config = ChameleonVQConfig(**vq_config)
+        self.vocabulary_map = vocabulary_map
+
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+
+    def _rope_scaling_validation(self):
+        """
+        Validate the `rope_scaling` configuration.
+        """
+        if self.rope_scaling is None:
+            return
+
+        if not isinstance(self.rope_scaling,
+                          dict) or len(self.rope_scaling) != 2:
+            raise ValueError(
+                "`rope_scaling` must be a dictionary with with two fields, "
+                f"`type` and `factor`, got {self.rope_scaling}")
+        rope_scaling_type = self.rope_scaling.get("type", None)
+        rope_scaling_factor = self.rope_scaling.get("factor", None)
+        if rope_scaling_type is None or rope_scaling_type not in [
+                "linear", "dynamic"
+        ]:
+            raise ValueError(
+                "`rope_scaling`'s type field must be one of ['linear', "
+                f"'dynamic'], got {rope_scaling_type}")
+        if rope_scaling_factor is None or not isinstance(
+                rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
+            raise ValueError(
+                "`rope_scaling`'s factor field must be a float > 1, "
+                f"got {rope_scaling_factor}")