LLMPerfV2 (#19)

* LLMPerfV2

The latest version of LLMPerf brings a suite of significant updates designed to provide more in-depth and customizable benchmarking capabilities for LLM inference. These updates include:

- Expanded metrics with quantile distribution (P25-99): Comprehensive data representation for deeper insights.
- Customizable benchmarking parameters: Tailor parameters to fit specific use case scenarios.
- Introduction of load test and correctness test: Assessing performance and accuracy under stress.
- Broad compatibility: Supports a range of products including [Anyscale Endpoints](https://www.anyscale.com/endpoints), [OpenAI](https://openai.com/blog/openai-api), [Anthropic](https://docs.anthropic.com/claude/reference/getting-started-with-the-api), [together.ai](http://together.ai/), [Fireworks.ai](https://app.fireworks.ai/), [Perplexity](https://www.perplexity.ai/), [Huggingface](https://huggingface.co/inference-endpoints), [Lepton AI](https://www.lepton.ai/docs/overview/model_apis), and various APIs supported by the [LiteLLM project](https://litellm.ai/)).
- Easy addition of new LLMs via the LLMClient API.

Signed-off-by: Avnish Narayan <[email protected]>

Author: avnishn

.gitignore

@@ -0,0 +1,309 @@
+import argparse
+import json
+import os
+from pathlib import Path
+import random
+import re
+import time
+from typing import Any, Dict, List, Optional, Tuple
+
+import num2words
+import ray
+from tqdm import tqdm
+
+from llmperf import common_metrics
+from llmperf.common import SUPPORTED_APIS, construct_clients
+from llmperf.models import RequestConfig
+from llmperf.requests_launcher import RequestsLauncher
+from llmperf.utils import (
+    LLMPerfResults,
+)
+
+MAX_RANDOM_NUMBER = 10000
+
+
+def llm_correctness(
+    model: str,
+    additional_sampling_params: Optional[Dict[str, Any]] = None,
+    num_concurrent_requests: int = 1,
+    max_num_completed_requests: int = 500,
+    test_timeout_s=90,
+    llm_api="chat",
+) -> Tuple[Dict[str, Any], List[Dict[str, Any]]]:
+    """Get the token throughput and latencies for the given model.
+
+    Args:
+        model: The name of the model to query.
+        additional_sampling_params: Additional sampling parameters to send with the request.
+            For more information see the LLM APIs documentation for the completions
+        num_concurrent_requests: The number of concurrent requests to make. Increase
+            this to increase the amount of load and vice versa.
+        test_timeout_s: The amount of time to run the test for before reporting results.
+        llm_api: The type of request to make. Either "chat" or "litellm".
+
+    Returns:
+        A tuple containing summary metrics and raw results from the test.
+
+    """
+
+    if not additional_sampling_params:
+        additional_sampling_params = {}
+
+    clients = construct_clients(llm_api=llm_api, num_clients=num_concurrent_requests)
+    req_launcher = RequestsLauncher(clients)
+    start_time = time.monotonic()
+
+    num_errored_requests = 0
+    num_mismatched_requests = 0
+    num_completed_requests = 0
+
+    sampling_params = {"temperature": 0.0}
+    sampling_params.update(additional_sampling_params)
+    completed_requests = []
+    iter = 0
+    pbar = tqdm(total=max_num_completed_requests)
+    while (
+        time.monotonic() - start_time < test_timeout_s
+        and num_completed_requests < max_num_completed_requests
+    ):
+        iter += 1
+        rnd_number = random.randint(0, MAX_RANDOM_NUMBER)
+        rnd_num_words = num2words.num2words(rnd_number)
+
+        prompt = f"Convert the following sequence of words into a number: {rnd_num_words}.\nPrint the number first."
+
+        request_config = RequestConfig(
+            model=model,
+            prompt=(prompt, 0),
+            sampling_params=sampling_params,
+            metadata={"rnd_number": rnd_number},
+            llm_api=llm_api,
+        )
+        req_launcher.launch_requests(request_config)
+
+        if not (iter % num_concurrent_requests):
+            completed_requests.extend(req_launcher.get_next_ready())
+        pbar.update(len(completed_requests) - num_completed_requests)
+        num_completed_requests = len(completed_requests)
+
+    pbar.close()
+    end_time = time.monotonic()
+    if end_time - start_time >= test_timeout_s:
+        print("Test timed out before all requests could be completed.")
+
+    raw_results = []
+
+    print("Mismatched and errored requests.")
+    for out in completed_requests:
+        metrics, generated_text, completed_request_config = out
+
+        raw_results.append(
+            {
+                "metrics": metrics,
+                "generated_text": generated_text,
+                "request_config": dict(completed_request_config),
+            }
+        )
+
+        # if there were no errors when making request.
+        if not metrics[common_metrics.ERROR_CODE]:
+            try:
+                commas_between_numbers_re = r"(\d+),(?=\d)"
+                gen_text_commas_removed = re.sub(
+                    commas_between_numbers_re, r"\1", generated_text
+                )
+                nums = re.findall(r"\d+", gen_text_commas_removed)
+                generated_text = gen_text_commas_removed.replace("\n", " ")
+
+                assert str(completed_request_config.metadata["rnd_number"]) in nums
+            except:
+                num_mismatched_requests += 1
+                print(
+                    f"    mismatched request: {generated_text}, expected: {completed_request_config.metadata['rnd_number']}"
+                )
+        else:
+            num_errored_requests += 1
+            print(
+                f"    The request errored: {metrics[common_metrics.ERROR_CODE]}, "
+                f"{metrics[common_metrics.ERROR_MSG]} "
+            )
+    print()
+
+    error_rate = num_errored_requests / num_completed_requests
+    mismatch_rate = num_mismatched_requests / num_completed_requests
+    num_non_errored_requests = num_completed_requests - num_errored_requests
+    summary_metrics = {}
+    summary_metrics[common_metrics.NUM_ERRORS] = num_errored_requests
+    summary_metrics["num_mismatched_requests"] = num_mismatched_requests
+    summary_metrics["error_rate"] = error_rate
+    summary_metrics["mismatch_rate"] = mismatch_rate
+    summary_metrics[common_metrics.NUM_COMPLETED_REQUESTS] = num_completed_requests
+    summary_metrics["num_non_errored_requests"] = num_non_errored_requests
+
+    # Metadata
+    summary_metrics["model"] = model
+    summary_metrics["num_concurrent_requests"] = num_concurrent_requests
+    summary_metrics["additional_sampling_params"] = additional_sampling_params
+    summary_metrics["llm_api"] = llm_api
+
+    return summary_metrics, raw_results
+
+
+def run(
+    llm_api: str,
+    model: str,
+    test_timeout_s: int,
+    max_num_completed_requests: int,
+    num_concurrent_requests: int,
+    additional_sampling_params: str,
+    results_dir: str,
+    user_metadata: Dict[str, str],
+):
+    """
+    Args:
+        llm_api: The type of request to make. Either "chat" or "litellm".
+        model: The name of the model to query.
+        max_num_completed_requests: The number of requests to complete before finishing the test.
+        test_timeout_s: The amount of time to run the test for before reporting results.
+        num_concurrent_requests: The number of concurrent requests to make. Increase
+            this to increase the amount of load and vice versa.
+        mean_input_tokens: The mean number of tokens to send in the prompt for the request.
+        stddev_input_tokens: The standard deviation of the number of tokens to send in the prompt for the request.
+        mean_output_tokens: The mean number of tokens to generate per request.
+        stddev_output_tokens: The standard deviation of the number of tokens to generate per request.
+        additional_sampling_params: Additional sampling parameters to send with the request.
+            For more information see the LLM APIs documentation for the completions.
+        results_dir: The directory to save the results to.
+
+    """
+
+    summary_metrics, raw_results = llm_correctness(
+        model=model,
+        llm_api=llm_api,
+        test_timeout_s=test_timeout_s,
+        max_num_completed_requests=max_num_completed_requests,
+        num_concurrent_requests=num_concurrent_requests,
+        additional_sampling_params=json.loads(additional_sampling_params),
+    )
+
+    time.sleep(2)
+
+    print(
+        f"Results for llm correctness test for {model} queried with the {llm_api} api."
+    )
+    print(
+        f"Errors: {summary_metrics[common_metrics.NUM_ERRORS]}, "
+        f"Error rate: {summary_metrics['error_rate']}"
+    )
+
+    print(
+        f"Mismatched: {summary_metrics['num_mismatched_requests']}, "
+        f"Mismatch rate: {summary_metrics['mismatch_rate']}"
+    )
+    print(f"Completed: {summary_metrics[common_metrics.NUM_COMPLETED_REQUESTS]}")
+    print(f"Completed without errors: {summary_metrics['num_non_errored_requests']}")
+
+    if results_dir:
+        file_name = f"{model}_correctness"
+        file_name = re.sub(r"[^\w\d-]+", "-", file_name)
+        file_name = re.sub(r"-{2,}", "-", file_name)
+        summary_file_name = f"{file_name}_summary"
+        individual_responses_filename = f"{file_name}_individual_responses"
+        summary_metrics.update(user_metadata)
+        results = LLMPerfResults(name=summary_file_name, metadata=summary_metrics)
+        results_dir = Path(results_dir)
+        if not results_dir.exists():
+            results_dir.mkdir(parents=True)
+        elif not results_dir.is_dir():
+            raise ValueError(f"{results_dir} is not a directory")
+        with open(results_dir / f"{summary_file_name}.json", "w") as f:
+            json.dump(results.to_dict(), f, indent=4)
+        with open(results_dir / f"{individual_responses_filename}.json", "w") as f:
+            json.dump(raw_results, f, indent=4)
+
+
+args = argparse.ArgumentParser(description="Run a correctness test for a given model.")
+
+args.add_argument(
+    "--model", type=str, required=True, help="The model to use for this load test."
+)
+args.add_argument(
+    "--num-concurrent-requests",
+    type=int,
+    default=10,
+    help=("The number of concurrent requests to send. (default: %(default)s)"),
+)
+args.add_argument(
+    "--timeout",
+    type=int,
+    default=90,
+    help="The amount of time to run the load test for. (default: %(default)s)",
+)
+args.add_argument(
+    "--max-num-completed-requests",
+    type=int,
+    default=50,
+    help=(
+        "The number of requests to complete before finishing the test. Note "
+        "that its possible for the test to timeout first. (default: %(default)s)"
+    ),
+)
+args.add_argument(
+    "--additional-sampling-params",
+    type=str,
+    default="{}",
+    help=(
+        "Additional sampling params to send with the each request to the LLM API. "
+        "(default: %(default)s) No additional sampling params are sent."
+    ),
+)
+args.add_argument(
+    "--results-dir",
+    type=str,
+    default="",
+    help=(
+        "The directory to save the results to. "
+        "(`default: %(default)s`) No results are saved)"
+    ),
+)
+args.add_argument(
+    "--llm-api",
+    type=str,
+    default="openai",
+    help=(
+        f"The type of request to make. The supported llm apis are {SUPPORTED_APIS} "
+        " (`default: %(default)s`)"
+    ),
+)
+args.add_argument(
+    "--metadata",
+    type=str,
+    default="",
+    help=(
+        "A comma separated list of metadata to include in the results, e.g. "
+        "name=foo,bar=1. These will be added to the metadata field of the results. "
+    ),
+)
+
+if __name__ == "__main__":
+    args = args.parse_args()
+
+    env_vars = dict(os.environ)
+    ray.init(runtime_env={"env_vars": env_vars})
+    # Parse user metadata.
+    user_metadata = {}
+    if args.metadata:
+        for item in args.metadata.split(","):
+            key, value = item.split("=")
+            user_metadata[key] = value
+
+    run(
+        llm_api=args.llm_api,
+        model=args.model,
+        test_timeout_s=args.timeout,
+        max_num_completed_requests=args.max_num_completed_requests,
+        num_concurrent_requests=args.num_concurrent_requests,
+        additional_sampling_params=args.additional_sampling_params,
+        results_dir=args.results_dir,
+        user_metadata=user_metadata,
+    )