Model: Create universal HFModel class

The HFModel class serves to coalesce all config files that contain
random keys which are required for model usage.

Adding this base class allows us to expand as HuggingFace randomly
changes their JSON schemas over time, reducing the brunt that backend
devs need to feel when their next model isn't supported.

Signed-off-by: kingbri <8082010+kingbri1@users.noreply.github.com>

backends/exllamav2/model.py

@@ -4,7 +4,6 @@ import asyncio
 import gc
 import math
 import pathlib
-import traceback
 import torch
 from exllamav2 import (
     ExLlamaV2,
@@ -47,7 +46,7 @@ from common.health import HealthManager
 from common.multimodal import MultimodalEmbeddingWrapper
 from common.sampling import BaseSamplerRequest
 from common.templating import PromptTemplate, find_prompt_template
-from common.transformers_utils import GenerationConfig, TokenizerConfig
+from common.transformers_utils import HFModel
 from common.utils import calculate_rope_alpha, coalesce, unwrap
 from endpoints.core.types.model import ModelCard, ModelCardParameters
 
@@ -58,6 +57,10 @@ class ExllamaV2Container(BaseModelContainer):
     # Model directories
     model_dir: pathlib.Path = pathlib.Path("models")
     draft_model_dir: pathlib.Path = pathlib.Path("models")
+    prompt_template: Optional[PromptTemplate] = None
+
+    # HF model instance
+    hf_model: HFModel
 
     # Exl2 vars
     config: Optional[ExLlamaV2Config] = None
@@ -79,8 +82,6 @@ class ExllamaV2Container(BaseModelContainer):
     cache_mode: str = "FP16"
     draft_cache_mode: str = "FP16"
     max_batch_size: Optional[int] = None
-    generation_config: Optional[GenerationConfig] = None
-    tokenizer_config: Optional[TokenizerConfig] = None
 
     # GPU split vars
     gpu_split: List[float] = []
@@ -100,7 +101,7 @@ class ExllamaV2Container(BaseModelContainer):
     load_condition: asyncio.Condition = asyncio.Condition()
 
     @classmethod
-    async def create(cls, model_directory: pathlib.Path, **kwargs):
+    async def create(cls, model_directory: pathlib.Path, hf_model: HFModel, **kwargs):
         """
         Primary asynchronous initializer for model container.
 
@@ -114,6 +115,7 @@ class ExllamaV2Container(BaseModelContainer):
         self.config = ExLlamaV2Config()
         self.model_dir = model_directory
         self.config.model_dir = str(model_directory.resolve())
+        self.hf_model = hf_model
 
         # Make the max seq len 4096 before preparing the config
         # This is a better default than 2048
@@ -124,30 +126,6 @@ class ExllamaV2Container(BaseModelContainer):
         # Check if the model arch is compatible with various exl2 features
         self.config.arch_compat_overrides()
 
-        # Load generation config overrides
-        generation_config_path = model_directory / "generation_config.json"
-        if generation_config_path.exists():
-            try:
-                self.generation_config = await GenerationConfig.from_file(
-                    model_directory
-                )
-            except Exception:
-                logger.error(traceback.format_exc())
-                logger.warning(
-                    "Skipping generation config load because of an unexpected error."
-                )
-
-        # Load tokenizer config overrides
-        tokenizer_config_path = model_directory / "tokenizer_config.json"
-        if tokenizer_config_path.exists():
-            try:
-                self.tokenizer_config = await TokenizerConfig.from_file(model_directory)
-            except Exception:
-                logger.error(traceback.format_exc())
-                logger.warning(
-                    "Skipping tokenizer config load because of an unexpected error."
-                )
-
         # Set vision state and error if vision isn't supported on the current model
         self.use_vision = unwrap(kwargs.get("vision"), False)
         if self.use_vision and not self.config.vision_model_type:
@@ -864,7 +842,7 @@ class ExllamaV2Container(BaseModelContainer):
             self.tokenizer.encode(
                 text,
                 add_bos=unwrap(
-                    kwargs.get("add_bos_token"), self.tokenizer_config.add_bos_token
+                    kwargs.get("add_bos_token"), self.hf_model.add_bos_token()
                 ),
                 encode_special_tokens=unwrap(kwargs.get("encode_special_tokens"), True),
                 embeddings=mm_embeddings_content,
@@ -1282,16 +1260,10 @@ class ExllamaV2Container(BaseModelContainer):
         ban_eos_token = params.ban_eos_token
 
         # Set add_bos_token for generation
-        add_bos_token = unwrap(
-            params.add_bos_token, self.tokenizer_config.add_bos_token
-        )
+        add_bos_token = unwrap(params.add_bos_token, self.hf_model.add_bos_token())
 
-        # Fetch EOS tokens from generation_config if they exist
-        eos_tokens = (
-            self.generation_config.eos_tokens()
-            if self.generation_config
-            else [self.tokenizer.eos_token_id]
-        )
+        # Fetch EOS tokens from the HF model if they exist
+        eos_tokens = self.hf_model.eos_tokens() or [self.tokenizer.eos_token_id]
 
         # Ban the EOS token if specified. If not, append to stop conditions
         # as well.