Merge remote-tracking branch 'happyamazonian/main' into restart

Author: HappyAmazonian

.buildkite/generate_index.py

@@ -8,3 +8,4 @@ tasks:
     value: 0.80
 limit: 250 # will run on 250 * 14 subjects = 3500 samples
 num_fewshot: 5
+rtol: 0.05

.buildkite/lm-eval-harness/configs/Meta-Llama-4-Maverick-17B-128E-Instruct-FP8.yaml

@@ -0,0 +1 @@
+Meta-Llama-4-Maverick-17B-128E-Instruct-FP8.yaml

.buildkite/lm-eval-harness/configs/models-large-rocm.txt

@@ -9,11 +9,40 @@
     --tp-size=1
 """
 
+import os
+from contextlib import contextmanager
+
 import lm_eval
 import numpy as np
 import yaml
 
-RTOL = 0.08
+DEFAULT_RTOL = 0.08
+
+
+@contextmanager
+def scoped_env_vars(new_env: dict[str, str]):
+    if not new_env:
+        # Fast path: nothing to do
+        yield
+        return
+
+    old_values = {}
+    new_keys = []
+
+    try:
+        for key, value in new_env.items():
+            if key in os.environ:
+                old_values[key] = os.environ[key]
+            else:
+                new_keys.append(key)
+            os.environ[key] = str(value)
+        yield
+    finally:
+        # Restore / clean up
+        for key, value in old_values.items():
+            os.environ[key] = value
+        for key in new_keys:
+            os.environ.pop(key, None)
 
 
 def launch_lm_eval(eval_config, tp_size):
@@ -32,23 +61,26 @@ def launch_lm_eval(eval_config, tp_size):
         f"trust_remote_code={trust_remote_code},"
         f"max_model_len={max_model_len},"
     )
-    results = lm_eval.simple_evaluate(
-        model=backend,
-        model_args=model_args,
-        tasks=[task["name"] for task in eval_config["tasks"]],
-        num_fewshot=eval_config["num_fewshot"],
-        limit=eval_config["limit"],
-        # TODO(yeq): using chat template w/ fewshot_as_multiturn is supposed help
-        # text models. however, this is regressing measured strict-match for
-        # existing text models in CI, so only apply it for mm, or explicitly set
-        apply_chat_template=eval_config.get(
-            "apply_chat_template", backend == "vllm-vlm"
-        ),
-        fewshot_as_multiturn=eval_config.get("fewshot_as_multiturn", False),
-        # Forward decoding and early-stop controls (e.g., max_gen_toks, until=...)
-        gen_kwargs=eval_config.get("gen_kwargs"),
-        batch_size=batch_size,
-    )
+
+    env_vars = eval_config.get("env_vars", None)
+    with scoped_env_vars(env_vars):
+        results = lm_eval.simple_evaluate(
+            model=backend,
+            model_args=model_args,
+            tasks=[task["name"] for task in eval_config["tasks"]],
+            num_fewshot=eval_config["num_fewshot"],
+            limit=eval_config["limit"],
+            # TODO(yeq): using chat template w/ fewshot_as_multiturn is supposed help
+            # text models. however, this is regressing measured strict-match for
+            # existing text models in CI, so only apply it for mm, or explicitly set
+            apply_chat_template=eval_config.get(
+                "apply_chat_template", backend == "vllm-vlm"
+            ),
+            fewshot_as_multiturn=eval_config.get("fewshot_as_multiturn", False),
+            # Forward decoding and early-stop controls (e.g., max_gen_toks, until=...)
+            gen_kwargs=eval_config.get("gen_kwargs"),
+            batch_size=batch_size,
+        )
     return results
 
 
@@ -57,15 +89,18 @@ def test_lm_eval_correctness_param(config_filename, tp_size):
 
     results = launch_lm_eval(eval_config, tp_size)
 
+    rtol = eval_config.get("rtol", DEFAULT_RTOL)
+
     success = True
     for task in eval_config["tasks"]:
         for metric in task["metrics"]:
             ground_truth = metric["value"]
             measured_value = results["results"][task["name"]][metric["name"]]
             print(
                 f"{task['name']} | {metric['name']}: "
-                f"ground_truth={ground_truth} | measured={measured_value}"
+                f"ground_truth={ground_truth:.3f} | "
+                f"measured={measured_value:.3f} | rtol={rtol}"
             )
-            success = success and np.isclose(ground_truth, measured_value, rtol=RTOL)
+            success = success and np.isclose(ground_truth, measured_value, rtol=rtol)
 
     assert success

.buildkite/lm-eval-harness/test_lm_eval_correctness.py

@@ -108,6 +108,65 @@ The number of this test is less stable compared to the delay and latency benchma
 
 WARNING: The benchmarking script will save json results by itself, so please do not configure `--save-results` or other results-saving-related parameters in `serving-tests.json`.
 
+#### Default Parameters Field
+
+We can specify default parameters in a JSON field with key `defaults`. Parameters defined in the field are applied globally to all serving tests, and can be overridden in test case fields. Here is an example:
+
+<details>
+<summary> An Example of default parameters field </summary>
+
+```json
+{
+  "defaults": {
+    "qps_list": [
+      "inf"
+    ],
+    "server_environment_variables": {
+      "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1
+    },
+    "server_parameters": {
+      "tensor_parallel_size": 1,
+      "dtype": "bfloat16",
+      "block_size": 128,
+      "disable_log_stats": "",
+      "load_format": "dummy"
+    },
+    "client_parameters": {
+      "backend": "vllm",
+      "dataset_name": "random",
+      "random-input-len": 128,
+      "random-output-len": 128,
+      "num_prompts": 200,
+      "ignore-eos": ""
+    }
+  },
+  "tests": [
+    {
+      "test_name": "serving_llama3B_tp2_random_128_128",
+      "server_parameters": {
+        "model": "meta-llama/Llama-3.2-3B-Instruct",
+        "tensor_parallel_size": 2,
+      },
+      "client_parameters": {
+        "model": "meta-llama/Llama-3.2-3B-Instruct",
+      }
+    },
+    {
+      "test_name": "serving_qwen3_tp4_random_128_128",
+      "server_parameters": {
+        "model": "Qwen/Qwen3-14B",
+        "tensor_parallel_size": 4,
+      },
+      "client_parameters": {
+        "model": "Qwen/Qwen3-14B",
+      }
+    },
+  ]
+}
+```
+
+</details>
+
 ### Visualizing the results
 
 The `convert-results-json-to-markdown.py` helps you put the benchmarking results inside a markdown table, by formatting [descriptions.md](performance-benchmarks-descriptions.md) with real benchmarking results.

.buildkite/performance-benchmarks/README.md

@@ -110,7 +110,8 @@ json2envs() {
 wait_for_server() {
   # wait for vllm server to start
   # return 1 if vllm server crashes
-  timeout 1200 bash -c '
+  local timeout_val="1200"
+  timeout "$timeout_val" bash -c '
     until curl -X POST localhost:8000/v1/completions; do
       sleep 1
     done' && return 0 || return 1
@@ -316,12 +317,44 @@ run_throughput_tests() {
 run_serving_tests() {
   # run serving tests using `vllm bench serve` command
   # $1: a json file specifying serving test cases
+  #
+  # Supported JSON formats:
+  # 1) Plain format: top-level array
+  #    [ { "test_name": "...", "server_parameters": {...}, ... }, ... ]
+  #
+  # 2) Default parameters field + plain format tests
+  #    {
+  #      "defaults": { ... },
+  #      "tests": [ { "test_name": "...", "server_parameters": {...}, ... }, ... ]
+  #    }
 
   local serving_test_file
   serving_test_file=$1
 
   # Iterate over serving tests
-  jq -c '.[]' "$serving_test_file" | while read -r params; do
+  jq -c '
+    if type == "array" then
+      # Plain format: test cases array
+      .[]
+    elif (type == "object" and has("tests")) then
+      # merge the default parameters into each test cases
+      . as $root
+      | ($root.defaults // {}) as $d
+      | ($root.tests // [])[]
+      # default qps / max_concurrency from defaults if missing
+      | .qps_list = (.qps_list // $d.qps_list)
+      | .max_concurrency_list = (.max_concurrency_list // $d.max_concurrency_list)
+      # merge envs / params: test overrides defaults
+      | .server_environment_variables =
+          (($d.server_environment_variables // {}) + (.server_environment_variables // {}))
+      | .server_parameters =
+          (($d.server_parameters // {}) + (.server_parameters // {}))
+      | .client_parameters =
+          (($d.client_parameters // {}) + (.client_parameters // {}))
+    else
+      error("Unsupported serving test file format: must be array or object with .tests")
+    end
+  ' "$serving_test_file" | while read -r params; do
     # get the test name, and append the GPU type back to it.
     test_name=$(echo "$params" | jq -r '.test_name')
     if [[ ! "$test_name" =~ ^serving_ ]]; then
@@ -335,20 +368,25 @@ run_serving_tests() {
       continue
     fi
 
-    # get client and server arguments
+    # get client and server arguments (after merged the default parameters)
     server_params=$(echo "$params" | jq -r '.server_parameters')
     server_envs=$(echo "$params" | jq -r '.server_environment_variables')
     client_params=$(echo "$params" | jq -r '.client_parameters')
+
     server_args=$(json2args "$server_params")
     server_envs=$(json2envs "$server_envs")
     client_args=$(json2args "$client_params")
+
+    # qps_list
     qps_list=$(echo "$params" | jq -r '.qps_list')
     qps_list=$(echo "$qps_list" | jq -r '.[] | @sh')
     echo "Running over qps list $qps_list"
+
+    # max_concurrency_list (fallback to num_prompts if missing)
     max_concurrency_list=$(echo "$params" | jq -r '.max_concurrency_list')
     if [[ -z "$max_concurrency_list" || "$max_concurrency_list" == "null" ]]; then
-        num_prompts=$(echo "$client_params" | jq -r '.num_prompts')
-        max_concurrency_list="[$num_prompts]"
+      num_prompts=$(echo "$client_params" | jq -r '.num_prompts')
+      max_concurrency_list="[$num_prompts]"
     fi
     max_concurrency_list=$(echo "$max_concurrency_list" | jq -r '.[] | @sh')
     echo "Running over max concurrency list $max_concurrency_list"