feat: use custom http_client

This commit adds the ability to pass a custom HTTP client to the
MT-Bench evaluator. This is handy when using custom certificates when
interacting with the judge model serving endpoint.

Signed-off-by: Sébastien Han <[email protected]>

Author: leseb

.spellcheck-en-custom.txt

@@ -10,6 +10,7 @@ dr
 eval
 gpt
 hoc
+http
 instructlab
 jsonl
 justfile

CHANGELOG.md

@@ -1 +1,5 @@
+## 0.4
+
+* Added ability to specify a custom http client to MT-Bench
+
 ## v0.2

requirements.txt

@@ -9,3 +9,4 @@ accelerate
 pandas
 pandas-stubs
 lm-eval>=0.4.4
+httpx

src/instructlab/eval/mt_bench.py

@@ -10,6 +10,9 @@
 import multiprocessing
 import os
 
+# Third Party
+import httpx
+
 # First Party
 from instructlab.eval import (
     mt_bench_answers,
@@ -110,6 +113,7 @@ def gen_answers(
         api_key: str | None = None,
         max_workers: int | str | None = None,
         serving_gpus: int | None = None,
+        http_client: httpx.Client | None = None,
     ) -> None:
         """
         Asks questions to model
@@ -119,6 +123,7 @@ def gen_answers(
             api_key         API token for authenticating with model server
             max_workers     Max parallel workers to run the evaluation with (int or "auto").  None indicates to use value specified in constructor.
             serving_gpus    Number of gpus allocated for serving.  Used to tune with max_workers=auto.  None indicates to use value specified in constructor.
+            http_client     Custom http client to use for requests
         """
         logger.debug(locals())
         mt_bench_answers.generate_answers(
@@ -127,6 +132,7 @@ def gen_answers(
             api_key=api_key,
             output_dir=self.output_dir,
             max_workers=self._get_effective_max_workers(max_workers, serving_gpus),
+            http_client=http_client,
         )
 
     def judge_answers(
@@ -135,6 +141,7 @@ def judge_answers(
         api_key: str | None = None,
         max_workers: int | str | None = None,
         serving_gpus: int | None = None,
+        http_client: httpx.Client | None = None,
     ) -> tuple:
         """
         Runs MT-Bench judgment
@@ -144,6 +151,7 @@ def judge_answers(
             api_key         API token for authenticating with model server
             max_workers     Max parallel workers to run the evaluation with (int or "auto").  None indicates to use value specified in constructor.
             serving_gpus    Number of gpus allocated for serving.  Used to tune with max_workers=auto.  None indicates to use value specified in constructor.
+            http_client     Custom http client to use for requests
 
         Returns:
             overall_score   MT-Bench score for the overall model evaluation
@@ -160,6 +168,7 @@ def judge_answers(
             max_workers=self._get_effective_max_workers(max_workers, serving_gpus),
             output_dir=self.output_dir,
             merge_system_user_message=self.merge_system_user_message,
+            http_client=http_client,
         )
 
 
@@ -202,6 +211,7 @@ def gen_answers(
         api_key: str | None = None,
         max_workers: int | str | None = None,
         serving_gpus: int | None = None,
+        http_client: httpx.Client | None = None,
     ) -> None:
         """
         Asks questions to model
@@ -211,6 +221,7 @@ def gen_answers(
             api_key     API token for authenticating with model server
             max_workers     Max parallel workers to run the evaluation with (int or "auto").  None indicates to use value specified in constructor.
             serving_gpus    Number of gpus allocated for serving.  Used to tune with max_workers=auto.  None indicates to use value specified in constructor.
+            http_client     Custom http client to use for requests
         """
         logger.debug(locals())
         mt_bench_branch_generator.generate(
@@ -228,6 +239,7 @@ def gen_answers(
             data_dir=self.output_dir,
             max_workers=self._get_effective_max_workers(max_workers, serving_gpus),
             bench_name="mt_bench_branch",
+            http_client=http_client,
         )
 
     def judge_answers(
@@ -236,6 +248,7 @@ def judge_answers(
         api_key: str | None = None,
         max_workers: int | str | None = None,
         serving_gpus: int | None = None,
+        http_client: httpx.Client | None = None,
     ) -> tuple:
         """
         Runs MT-Bench-Branch judgment.  Judgments can be compared across runs with consistent question_id -> qna file name.
@@ -245,6 +258,7 @@ def judge_answers(
             api_key         API token for authenticating with model server
             max_workers     Max parallel workers to run the evaluation with (int or "auto").  None indicates to use value specified in constructor.
             serving_gpus    Number of gpus allocated for serving.  Used to tune with max_workers=auto.  None indicates to use value specified in constructor.
+            http_client     Custom http client to use for requests
 
         Returns:
             overall_score   Overall score from the evaluation
@@ -263,5 +277,6 @@ def judge_answers(
             data_dir=self.output_dir,
             bench_name="mt_bench_branch",
             merge_system_user_message=self.merge_system_user_message,
+            http_client=http_client,
         )
         return overall_score, qa_pairs, error_rate

src/instructlab/eval/mt_bench_answers.py

@@ -108,11 +108,12 @@ def generate_answers(
     max_tokens=1024,
     max_workers=1,
     bench_name="mt_bench",
+    http_client=None,
 ):
     """Generate model answers to be judged"""
     logger.debug(locals())
 
-    openai_client = get_openai_client(model_api_base, api_key)
+    openai_client = get_openai_client(model_api_base, api_key, http_client)
 
     if data_dir is None:
         data_dir = os.path.join(os.path.dirname(__file__), "data")

src/instructlab/eval/mt_bench_common.py

@@ -13,6 +13,7 @@
 import time
 
 # Third Party
+import httpx
 import openai
 
 # First Party
@@ -365,8 +366,14 @@ def get_model_list(answer_file):
     return [os.path.splitext(os.path.basename(answer_file))[0]]
 
 
-def get_openai_client(model_api_base, api_key):
+def get_openai_client(
+    model_api_base,
+    api_key,
+    http_client: httpx.Client | None = None,
+):
     if api_key is None:
         api_key = "NO_API_KEY"
-    openai_client = openai.OpenAI(base_url=model_api_base, api_key=api_key)
+    openai_client = openai.OpenAI(
+        base_url=model_api_base, api_key=api_key, http_client=http_client
+    )
     return openai_client

src/instructlab/eval/mt_bench_judgment.py

@@ -286,11 +286,12 @@ def generate_judgment(
     max_workers=1,
     first_n=None,
     merge_system_user_message=False,
+    http_client=None,
 ):
     """Generate judgment with scores and qa_pairs for a model"""
     logger.debug(locals())
 
-    openai_client = get_openai_client(model_api_base, api_key)
+    openai_client = get_openai_client(model_api_base, api_key, http_client)
 
     first_n_env = os.environ.get("INSTRUCTLAB_EVAL_FIRST_N_QUESTIONS")
     if first_n_env is not None and first_n is None:

tests/test_branch_gen_answers.py

@@ -1,3 +1,6 @@
+# Third Party
+import httpx
+
 # First Party
 from instructlab.eval.mt_bench import MTBenchBranchEvaluator
 
@@ -7,4 +10,7 @@
     "../taxonomy",
     "main",
 )
-mt_bench_branch.gen_answers("http://localhost:8000/v1")
+mt_bench_branch.gen_answers(
+    "http://localhost:8000/v1",
+    http_client=httpx.Client(verify=False),
+)