init

vllm/transformers_utils/config.py

@@ -0,0 +1,58 @@
+from typing import Dict, Optional
+
+from transformers import AutoConfig, PretrainedConfig
+
+from vllm.transformers_utils.configs import (ChatGLMConfig, DbrxConfig,
+                                             JAISConfig, MPTConfig, RWConfig)
+
+_CONFIG_REGISTRY: Dict[str, PretrainedConfig] = {
+    "chatglm": ChatGLMConfig,
+    "dbrx": DbrxConfig,
+    "mpt": MPTConfig,
+    "RefinedWeb": RWConfig,  # For tiiuae/falcon-40b(-instruct)
+    "RefinedWebModel": RWConfig,  # For tiiuae/falcon-7b(-instruct)
+    "jais": JAISConfig,
+}
+
+
+def get_config(model: str,
+               trust_remote_code: bool,
+               revision: Optional[str] = None,
+               code_revision: Optional[str] = None) -> PretrainedConfig:
+    try:
+        config = AutoConfig.from_pretrained(
+            model,
+            trust_remote_code=trust_remote_code,
+            revision=revision,
+            code_revision=code_revision)
+    except ValueError as e:
+        if (not trust_remote_code and
+                "requires you to execute the configuration file" in str(e)):
+            err_msg = (
+                "Failed to load the model config. If the model is a custom "
+                "model not yet available in the HuggingFace transformers "
+                "library, consider setting `trust_remote_code=True` in LLM "
+                "or using the `--trust-remote-code` flag in the CLI.")
+            raise RuntimeError(err_msg) from e
+        else:
+            raise e
+    if config.model_type in _CONFIG_REGISTRY:
+        config_class = _CONFIG_REGISTRY[config.model_type]
+        config = config_class.from_pretrained(model,
+                                              revision=revision,
+                                              code_revision=code_revision)
+    return config
+
+
+def get_hf_text_config(config: PretrainedConfig):
+    """Get the "sub" config relevant to llm for multi modal models.
+        No op for pure text models.
+    """
+    if hasattr(config, "text_config"):
+        # The code operates under the assumption that text_config should have
+        # `num_attention_heads` (among others). Assert here to fail early
+        # if transformers config doesn't align with this assumption.
+        assert hasattr(config.text_config, "num_attention_heads")
+        return config.text_config
+    else:
+        return config