Test inference endpoint model config parsing from path (#434)

* Add example model config for existing endpoint

* Test InferenceEndpointModelConfig.from_path

* Comment default main branch in example

* Fix typo

* Delete unused add_special_tokens param in endpoint example config

* Fix typo

* Implement InferenceEndpointModelConfig.from_path

* Use InferenceEndpointModelConfig.from_path

* Refactor InferenceEndpointModelConfig.from_path

* Align docs

docs/source/evaluate-the-model-on-a-server-or-container.mdx

@@ -31,7 +31,7 @@ model:
     # endpoint_name: "llama-2-7B-lighteval" # needs to be lower case without special characters
     # reuse_existing: true # defaults to false; if true, ignore all params in instance, and don't delete the endpoint after evaluation
     model_name: "meta-llama/Llama-2-7b-hf"
-    revision: "main"
+    # revision: "main" # defaults to "main"
     dtype: "float16" # can be any of "awq", "eetq", "gptq", "4bit' or "8bit" (will use bitsandbytes), "bfloat16" or "float16"
   instance:
     accelerator: "gpu"
@@ -45,8 +45,6 @@ model:
     image_url: null # Optionally specify the docker image to use when launching the endpoint model. E.g., launching models with later releases of the TGI container with support for newer models.
     env_vars:
       null # Optional environment variables to include when launching the endpoint. e.g., `MAX_INPUT_LENGTH: 2048`
-  generation:
-    add_special_tokens: true
 ```
 
 ### Text Generation Inference (TGI)

examples/model_configs/endpoint_model.yaml

@@ -4,7 +4,7 @@ model:
     # endpoint_name: "llama-2-7B-lighteval" # needs to be lower case without special characters
     # reuse_existing: true # defaults to false; if true, ignore all params in instance, and don't delete the endpoint after evaluation
     model_name: "meta-llama/Llama-2-7b-hf"
-    revision: "main"
+    # revision: "main"  # defaults to "main"
     dtype: "float16" # can be any of "awq", "eetq", "gptq", "4bit' or "8bit" (will use bitsandbytes), "bfloat16" or "float16"
   instance:
     accelerator: "gpu"
@@ -14,9 +14,7 @@ model:
     instance_size: "x1"
     framework: "pytorch"
     endpoint_type: "protected"
-    namespace: null # The namespace under which to launch the endopint. Defaults to the current user's namespace
+    namespace: null # The namespace under which to launch the endpoint. Defaults to the current user's namespace
     image_url: null # Optionally specify the docker image to use when launching the endpoint model. E.g., launching models with later releases of the TGI container with support for newer models.
     env_vars:
       null # Optional environment variables to include when launching the endpoint. e.g., `MAX_INPUT_LENGTH: 2048`
-  generation:
-    add_special_tokens: true

examples/model_configs/endpoint_model_reuse_existing.yaml

@@ -0,0 +1,5 @@
+model:
+  base_params:
+    # Pass either model_name, or endpoint_name and true reuse_existing
+     endpoint_name: "llama-2-7B-lighteval" # needs to be lower case without special characters
+     reuse_existing: true # defaults to false; if true, ignore all params in instance, and don't delete the endpoint after evaluation

src/lighteval/main_endpoint.py

@@ -198,7 +198,6 @@ def inference_endpoint(
     """
     Evaluate models using inference-endpoints as backend.
     """
-    import yaml
 
     from lighteval.logging.evaluation_tracker import EvaluationTracker
     from lighteval.models.endpoints.endpoint_model import (
@@ -220,31 +219,11 @@ def inference_endpoint(
 
     parallelism_manager = ParallelismManager.NONE  # since we're using inference endpoints in remote
 
-    with open(model_config_path, "r") as f:
-        config = yaml.safe_load(f)["model"]
-
     # Find a way to add this back
     # if config["base_params"].get("endpoint_name", None):
     #    return InferenceModelConfig(model=config["base_params"]["endpoint_name"])
-    all_params = {
-        "model_name": config["base_params"].get("model_name", None),
-        "endpoint_name": config["base_params"].get("endpoint_name", None),
-        "model_dtype": config["base_params"].get("dtype", None),
-        "revision": config["base_params"].get("revision", None) or "main",
-        "reuse_existing": config["base_params"].get("reuse_existing"),
-        "accelerator": config.get("instance", {}).get("accelerator", None),
-        "region": config.get("instance", {}).get("region", None),
-        "vendor": config.get("instance", {}).get("vendor", None),
-        "instance_size": config.get("instance", {}).get("instance_size", None),
-        "instance_type": config.get("instance", {}).get("instance_type", None),
-        "namespace": config.get("instance", {}).get("namespace", None),
-        "image_url": config.get("instance", {}).get("image_url", None),
-        "env_vars": config.get("instance", {}).get("env_vars", None),
-    }
-    model_config = InferenceEndpointModelConfig(
-        # We only initialize params which have a non default value
-        **{k: v for k, v in all_params.items() if v is not None},
-    )
+
+    model_config = InferenceEndpointModelConfig.from_path(model_config_path)
 
     pipeline_params = PipelineParameters(
         launcher_type=parallelism_manager,

src/lighteval/models/endpoints/endpoint_model.py

@@ -103,12 +103,21 @@ def __post_init__(self):
         # xor operator, one is None but not the other
         if (self.instance_size is None) ^ (self.instance_type is None):
             raise ValueError(
-                "When creating an inference endpoint, you need to specify explicitely both instance_type and instance_size, or none of them for autoscaling."
+                "When creating an inference endpoint, you need to specify explicitly both instance_type and instance_size, or none of them for autoscaling."
             )
 
         if not (self.endpoint_name is None) ^ int(self.model_name is None):
             raise ValueError("You need to set either endpoint_name or model_name (but not both).")
 
+    @classmethod
+    def from_path(cls, path: str) -> "InferenceEndpointModelConfig":
+        import yaml
+
+        with open(path, "r") as f:
+            config = yaml.safe_load(f)["model"]
+        config["base_params"]["model_dtype"] = config["base_params"].pop("dtype", None)
+        return cls(**config["base_params"], **config.get("instance", {}))
+
     def get_dtype_args(self) -> Dict[str, str]:
         if self.model_dtype is None:
             return {}

tests/models/test_endpoint_model.py

@@ -0,0 +1,85 @@
+# MIT License
+
+# Copyright (c) 2024 The HuggingFace Team
+
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+import pytest
+
+from lighteval.models.endpoints.endpoint_model import InferenceEndpointModelConfig
+
+
+# "examples/model_configs/endpoint_model.yaml"
+
+
+class TestInferenceEndpointModelConfig:
+    @pytest.mark.parametrize(
+        "config_path, expected_config",
+        [
+            (
+                "examples/model_configs/endpoint_model.yaml",
+                {
+                    "model_name": "meta-llama/Llama-2-7b-hf",
+                    "revision": "main",
+                    "model_dtype": "float16",
+                    "endpoint_name": None,
+                    "reuse_existing": False,
+                    "accelerator": "gpu",
+                    "region": "eu-west-1",
+                    "vendor": "aws",
+                    "instance_type": "nvidia-a10g",
+                    "instance_size": "x1",
+                    "framework": "pytorch",
+                    "endpoint_type": "protected",
+                    "namespace": None,
+                    "image_url": None,
+                    "env_vars": None,
+                },
+            ),
+            (
+                "examples/model_configs/endpoint_model_lite.yaml",
+                {
+                    "model_name": "meta-llama/Llama-3.1-8B-Instruct",
+                    # Defaults:
+                    "revision": "main",
+                    "model_dtype": None,
+                    "endpoint_name": None,
+                    "reuse_existing": False,
+                    "accelerator": "gpu",
+                    "region": "us-east-1",
+                    "vendor": "aws",
+                    "instance_type": None,
+                    "instance_size": None,
+                    "framework": "pytorch",
+                    "endpoint_type": "protected",
+                    "namespace": None,
+                    "image_url": None,
+                    "env_vars": None,
+                },
+            ),
+            (
+                "examples/model_configs/endpoint_model_reuse_existing.yaml",
+                {"endpoint_name": "llama-2-7B-lighteval", "reuse_existing": True},
+            ),
+        ],
+    )
+    def test_from_path(self, config_path, expected_config):
+        config = InferenceEndpointModelConfig.from_path(config_path)
+        for key, value in expected_config.items():
+            assert getattr(config, key) == value