[P/D Disagg][Benchmarking] One request at a time benchmarking for P/D

benchmarks/benchmark_one_concurrent_req.py

@@ -0,0 +1,382 @@
+# SPDX-License-Identifier: Apache-2.0
+import argparse
+import asyncio
+import logging
+import random
+import time
+from dataclasses import dataclass
+from typing import Optional
+
+import aiohttp  # Import aiohttp
+import numpy as np
+from backend_request_func import RequestFuncInput, RequestFuncOutput
+from benchmark_dataset import RandomDataset, SampleRequest
+from tqdm import tqdm
+
+try:
+    from vllm.transformers_utils.tokenizer import get_tokenizer
+except ImportError:
+    from backend_request_func import get_tokenizer
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class BenchmarkMetrics:
+    completed: int
+    total_input: int
+    total_output: int
+    mean_ttft_ms: float
+    median_ttft_ms: float
+    std_ttft_ms: float
+    percentiles_ttft_ms: list[tuple[float, float]]
+    mean_itl_ms: float
+    median_itl_ms: float
+    std_itl_ms: float
+    percentiles_itl_ms: list[tuple[float, float]]
+    mean_e2el_ms: float
+    median_e2el_ms: float
+    std_e2el_ms: float
+    percentiles_e2el_ms: list[tuple[float, float]]
+
+
+async def reset_cache(reset_url: str):
+    """Sends a POST request to reset the prefix cache."""
+    logger.debug("Resetting prefix cache at %s", reset_url)
+    try:
+        async with (aiohttp.ClientSession() as session, session.post(reset_url)
+                    as response):
+            response.raise_for_status(
+            )  # Raise an exception for bad status codes (4xx or 5xx)
+            logger.debug("Prefix cache reset successful: %s", response.status)
+    except aiohttp.ClientConnectorError as e:
+        logger.error("Failed to connect to cache reset endpoint %s: %s}",
+                     reset_url, e)
+    except aiohttp.ClientResponseError as e:
+        logger.error("Cache reset request failed with status %s: %s", e.status,
+                     e.message)
+    except Exception as e:
+        logger.error("An unexpected error occurred during cache reset: %s", e)
+
+
+async def sequential_benchmark(
+    backend: str,
+    api_url: str,
+    model_id: str,
+    tokenizer,
+    input_requests: list[SampleRequest],
+    request_func,
+    selected_percentiles: list[float],
+    cache_reset_url: Optional[str] = None,
+):
+    """
+    Benchmark that processes requests sequentially, waiting for each to complete
+    before starting the next one. Resets prefix cache between requests.
+    """
+    outputs = []
+
+    pbar = tqdm(total=len(input_requests))
+
+    # Small request to force a forward pass.
+    # Used for resetting the prefix cache.
+    dummy_req_input = RequestFuncInput(
+        model=model_id,
+        prompt="0",
+        api_url=api_url,
+        prompt_len=1,
+        output_len=1,
+    )
+
+    print("Starting initial single prompt test run...")
+    test_output = await request_func(request_func_input=dummy_req_input)
+    if not test_output.success:
+        raise ValueError(
+            "Initial test run failed - Please check your configuration. "
+            "Error: %s", test_output.error)
+    else:
+        print("Initial test run completed. Starting sequential benchmark...")
+
+    benchmark_start_time = time.perf_counter()
+
+    # Process requests sequentially
+    for request in input_requests:
+        prompt, prompt_len, output_len = (request.prompt, request.prompt_len,
+                                          request.expected_output_len)
+
+        logger.info("Sending request with len %s", request.prompt_len)
+        logger.debug("Request str: \"%s\"", request.prompt[:50])
+        request_start_time = time.perf_counter()
+
+        request_func_input = RequestFuncInput(
+            model=model_id,
+            prompt=prompt,
+            api_url=api_url,
+            prompt_len=prompt_len,
+            output_len=output_len,
+        )
+
+        output = await request_func(request_func_input=request_func_input)
+
+        request_end_time = time.perf_counter()
+        # Add timing information
+        if output.success and not hasattr(output, "latency"):
+            output.latency = request_end_time - request_start_time
+        logger.info("Finished request with latency %.4f s", output.latency)
+
+        outputs.append(output)
+        pbar.update(1)
+
+        # Reset prefix cache if configured, except after the very last request
+        if cache_reset_url:
+            await request_func(request_func_input=dummy_req_input)
+            await reset_cache(cache_reset_url)
+
+    pbar.close()
+
+    benchmark_duration = time.perf_counter() - benchmark_start_time
+
+    # Calculate metrics
+    metrics = calculate_metrics(
+        input_requests=input_requests,
+        outputs=outputs,
+        dur_s=benchmark_duration,
+        tokenizer=tokenizer,
+        selected_percentiles=selected_percentiles,
+    )
+
+    print_results(metrics, benchmark_duration)
+
+    result = {
+        "duration":
+        benchmark_duration,
+        "completed":
+        metrics.completed,
+        "total_input_tokens":
+        metrics.total_input,
+        "total_output_tokens":
+        metrics.total_output,
+        "input_lens": [request.prompt_len for request in input_requests],
+        "output_lens":
+        [output.output_tokens if output.success else 0 for output in outputs],
+        "ttfts": [output.ttft for output in outputs if output.success],
+        "itls": [output.itl for output in outputs if output.success],
+        "generated_texts":
+        [output.generated_text for output in outputs if output.success],
+        "errors": [output.error for output in outputs if not output.success],
+    }
+
+    # Add summary statistics
+    for stat_name in ["ttft", "itl", "e2el"]:
+        for metric_name in ["mean", "median", "std"]:
+            result[f"{metric_name}_{stat_name}_ms"] = getattr(
+                metrics, f"{metric_name}_{stat_name}_ms")
+
+        for p, value in getattr(metrics, f"percentiles_{stat_name}_ms"):
+            p_word = str(int(p)) if int(p) == p else str(p)
+            result[f"p{p_word}_{stat_name}_ms"] = value
+
+    return result
+
+
+def calculate_metrics(
+    input_requests: list[SampleRequest],
+    outputs: list[RequestFuncOutput],
+    dur_s: float,
+    tokenizer,
+    selected_percentiles: list[float],
+) -> BenchmarkMetrics:
+    """Calculate benchmark metrics from results."""
+    total_input = 0
+    completed = 0
+    total_output = 0
+    ttfts = []
+    itls = []
+    e2els = []
+
+    for i, output in enumerate(outputs):
+        if output.success:
+            output_len = output.output_tokens
+
+            if not output_len:
+                # Use tokenizer to count output tokens if not provided
+                output_len = len(
+                    tokenizer(output.generated_text,
+                              add_special_tokens=False).input_ids)
+
+            total_output += output_len
+            total_input += input_requests[i].prompt_len
+
+            if hasattr(output, "ttft") and output.ttft is not None:
+                ttfts.append(output.ttft)
+
+            if hasattr(output, "itl") and output.itl:
+                # Ensure itl is a list of floats
+                if isinstance(output.itl, list):
+                    itls.extend(output.itl)
+                else:
+                    logger.warning(
+                        "Expected list for ITL but got %s. Appending as is.",
+                        type(output.itl))
+                    itls.append(output.itl)
+
+            if hasattr(output, "latency") and output.latency is not None:
+                e2els.append(output.latency)
+
+            completed += 1
+
+    return BenchmarkMetrics(
+        completed=completed,
+        total_input=total_input,
+        total_output=total_output,
+        mean_ttft_ms=np.mean(ttfts or [0]) * 1000,
+        median_ttft_ms=np.median(ttfts or [0]) * 1000,
+        std_ttft_ms=np.std(ttfts or [0]) * 1000,
+        percentiles_ttft_ms=[(p, np.percentile(ttfts or [0], p) * 1000)
+                             for p in selected_percentiles],
+        mean_itl_ms=np.mean(itls or [0]) * 1000,
+        median_itl_ms=np.median(itls or [0]) * 1000,
+        std_itl_ms=np.std(itls or [0]) * 1000,
+        percentiles_itl_ms=[(p, np.percentile(itls or [0], p) * 1000)
+                            for p in selected_percentiles],
+        mean_e2el_ms=np.mean(e2els or [0]) * 1000,
+        median_e2el_ms=np.median(e2els or [0]) * 1000,
+        std_e2el_ms=np.std(e2els or [0]) * 1000,
+        percentiles_e2el_ms=[(p, np.percentile(e2els or [0], p) * 1000)
+                             for p in selected_percentiles],
+    )
+
+
+def print_results(metrics: BenchmarkMetrics, benchmark_duration: float):
+    """Print benchmark results in a formatted way."""
+    print("{s:{c}^{n}}".format(s=" Sequential Benchmark Result ", n=60, c="="))
+    print("{:<40} {:<10}".format("Successful requests:", metrics.completed))
+    print("{:<40} {:<10.2f}".format("Benchmark duration (s):",
+                                    benchmark_duration))
+    print("{:<40} {:<10}".format("Total input tokens:", metrics.total_input))
+    print("{:<40} {:<10}".format("Total generated tokens:",
+                                 metrics.total_output))
+
+    def print_metric_stats(metric_name, header):
+        print("{s:{c}^{n}}".format(s=header, n=60, c="-"))
+        print("{:<40} {:<10.2f}".format(
+            f"Mean {metric_name} (ms):",
+            getattr(metrics, f"mean_{metric_name.lower()}_ms")))
+        print("{:<40} {:<10.2f}".format(
+            f"Median {metric_name} (ms):",
+            getattr(metrics, f"median_{metric_name.lower()}_ms")))
+
+        for p, value in getattr(metrics,
+                                f"percentiles_{metric_name.lower()}_ms"):
+            p_word = str(int(p)) if int(p) == p else str(p)
+            print("{:<40} {:<10.2f}".format(f"P{p_word} {metric_name} (ms):",
+                                            value))
+
+    print_metric_stats("TTFT", "Time to First Token")
+    print_metric_stats("ITL", "Inter-token Latency")
+    print_metric_stats("E2EL", "End-to-end Latency")
+    print("=" * 60)
+
+
+async def main_async(args):
+    # Import needed functions based on your setup
+    from backend_request_func import ASYNC_REQUEST_FUNCS
+
+    backend = args.backend
+    model_id = args.model
+    tokenizer_id = args.tokenizer if args.tokenizer is not None else args.model
+
+    # Set up API URL
+    if args.base_url is not None:
+        api_url = f"{args.base_url}{args.endpoint}"
+    else:
+        api_url = f"http://{args.host}:{args.port}{args.endpoint}"
+
+    # Set up Cache Reset URL
+    cache_reset_url = f"http://{args.host}:{args.port}/reset_prefix_cache"
+    logger.info("Prefix cache reset configured at: %s", cache_reset_url)
+
+    # Get tokenizer
+    tokenizer = get_tokenizer(tokenizer_id,
+                              trust_remote_code=args.trust_remote_code)
+
+    # Get request function
+    if backend in ASYNC_REQUEST_FUNCS:
+        request_func = ASYNC_REQUEST_FUNCS[backend]
+    else:
+        raise ValueError(f"Unknown backend: {backend}")
+
+    input_requests = RandomDataset().sample(
+        tokenizer=tokenizer,
+        num_requests=args.num_requests,
+        prefix_len=0,
+        input_len=args.input_len,
+        output_len=args.output_len,
+        range_ratio=0.0,
+    )
+
+    # Run benchmark
+    result = await sequential_benchmark(
+        backend=backend,
+        api_url=api_url,
+        model_id=model_id,
+        tokenizer=tokenizer,
+        input_requests=input_requests,
+        request_func=request_func,
+        selected_percentiles=[50, 90, 95, 99],
+        cache_reset_url=cache_reset_url,
+    )
+
+    return result
+
+
+def main(args):
+    print(args)
+    random.seed(args.seed)
+    np.random.seed(args.seed)
+
+    asyncio.run(main_async(args))
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Sequential benchmark for LLM serving")
+    parser.add_argument("--backend",
+                        type=str,
+                        default="vllm",
+                        help="Backend to use for requests")
+    parser.add_argument("--base-url",
+                        type=str,
+                        default=None,
+                        help="Server base URL (overrides --host and --port)")
+    parser.add_argument("--host", type=str, default="127.0.0.1")
+    parser.add_argument("--port", type=int, default=8000)
+    parser.add_argument("--endpoint",
+                        type=str,
+                        default="/v1/completions",
+                        help="API endpoint")
+    parser.add_argument("--model",
+                        type=str,
+                        required=True,
+                        help="Name of the model")
+    parser.add_argument("--tokenizer",
+                        type=str,
+                        help="Name of the tokenizer (defaults to model name)")
+    parser.add_argument("--num-requests",
+                        type=int,
+                        default=100,
+                        help="Number of requests to process")
+    parser.add_argument("--input-len",
+                        type=int,
+                        default=128,
+                        help="Input len for generated prompts")
+    parser.add_argument("--output-len",
+                        type=int,
+                        default=None,
+                        help="Override output len for requests")
+    parser.add_argument("--seed", type=int, default=42)
+    parser.add_argument("--trust-remote-code",
+                        action="store_true",
+                        help="Trust remote code from HuggingFace")
+
+    args = parser.parse_args()
+    main(args)

tests/v1/kv_connector/run_accuracy_test.sh

@@ -3,7 +3,7 @@ set -xe
 
 # Models to run
 MODELS=(
-#    "Qwen/Qwen3-0.6B"
+    "Qwen/Qwen3-0.6B"
     "deepseek-ai/deepseek-vl2-tiny"
 )