feat(benchmarks): Add Prefix Caching Benchmark to Serving Benchmark (#3277)

.buildkite/run-benchmarks.sh

@@ -23,8 +23,9 @@ wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/r
 # wait for server to start, timeout after 600 seconds
 timeout 600 bash -c 'until curl localhost:8000/v1/models; do sleep 1; done' || exit 1
 python3 benchmarks/benchmark_serving.py \
-    --backend openai \
-    --dataset ./ShareGPT_V3_unfiltered_cleaned_split.json \
+    --backend vllm \
+    --dataset-name sharegpt \
+    --dataset-path ./ShareGPT_V3_unfiltered_cleaned_split.json \
     --model meta-llama/Llama-2-7b-chat-hf \
     --num-prompts 20 \
     --endpoint /v1/completions \

benchmarks/backend_request_func.py

 import json
 import os
+import sys
 import time
-from dataclasses import dataclass
-from typing import Optional
+import traceback
+from dataclasses import dataclass, field
+from typing import List, Optional
 
 import aiohttp
 from tqdm.asyncio import tqdm
@@ -26,8 +28,11 @@ class RequestFuncOutput:
     generated_text: str = ""
     success: bool = False
     latency: float = 0
-    ttft: float = 0
+    ttft: float = 0  # Time to first token
+    itl: List[float] = field(
+        default_factory=list)  # List of inter-token latencies
     prompt_len: int = 0
+    error: str = ""
 
 
 async def async_request_tgi(
@@ -55,71 +60,38 @@ async def async_request_tgi(
 
         ttft = 0
         st = time.perf_counter()
+        most_recent_timestamp = st
         try:
             async with session.post(url=api_url, json=payload) as response:
                 if response.status == 200:
-                    async for data in response.content.iter_any():
-                        if ttft == 0:
-                            ttft = time.perf_counter() - st
-                            output.ttft = ttft
-                    output.latency = time.perf_counter() - st
-
-                    body = remove_prefix(data.decode("utf-8"), "data:")
-                    output.generated_text = json.loads(body)["generated_text"]
-                    output.success = True
-                else:
-                    output.success = False
-        except (aiohttp.ClientOSError, aiohttp.ServerDisconnectedError):
-            output.success = False
-
-        if pbar:
-            pbar.update(1)
-        return output
-
-
-async def async_request_vllm(
-    request_func_input: RequestFuncInput,
-    pbar: Optional[tqdm] = None,
-) -> RequestFuncOutput:
-    api_url = request_func_input.api_url
-    assert api_url.endswith("generate")
+                    async for chunk in response.content:
+                        chunk = chunk.strip()
+                        if not chunk:
+                            continue
 
-    async with aiohttp.ClientSession(timeout=AIOHTTP_TIMEOUT) as session:
-        payload = {
-            "prompt": request_func_input.prompt,
-            "n": 1,
-            "best_of": request_func_input.best_of,
-            "use_beam_search": request_func_input.use_beam_search,
-            "temperature": 0.0 if request_func_input.use_beam_search else 1.0,
-            "top_p": 1.0,
-            "max_tokens": request_func_input.output_len,
-            "ignore_eos": True,
-            "stream": True,
-        }
-        output = RequestFuncOutput()
-        output.prompt_len = request_func_input.prompt_len
+                        chunk = remove_prefix(chunk.decode("utf-8"), "data:")
 
-        ttft = 0
-        st = time.perf_counter()
-        try:
-            async with session.post(url=api_url, json=payload) as response:
-                if response.status == 200:
-                    async for data in response.content.iter_any():
+                        data = json.loads(chunk)
+                        timestamp = time.perf_counter()
+                        # First token
                         if ttft == 0:
                             ttft = time.perf_counter() - st
                             output.ttft = ttft
-                    output.latency = time.perf_counter() - st
 
-                    # When streaming, '\0' is appended to the end of response.
-                    body = data.decode("utf-8").strip("\0")
-                    output.generated_text = json.loads(
-                        body)["text"][0][len(request_func_input.prompt):]
-                    output.success = True
+                        # Decoding phase
+                        else:
+                            output.itl.append(timestamp -
+                                              most_recent_timestamp)
 
-                else:
-                    output.success = False
-        except (aiohttp.ClientOSError, aiohttp.ServerDisconnectedError):
+                        most_recent_timestamp = timestamp
+
+                    output.latency = most_recent_timestamp - st
+                    output.success = True
+                    output.generated_text = data["generated_text"]
+        except Exception:
             output.success = False
+            exc_info = sys.exc_info()
+            output.error = "".join(traceback.format_exception(*exc_info))
 
         if pbar:
             pbar.update(1)
@@ -146,26 +118,45 @@ async def async_request_trt_llm(
         }
         output = RequestFuncOutput()
         output.prompt_len = request_func_input.prompt_len
-        ttft = 0
 
+        ttft = 0
         st = time.perf_counter()
+        most_recent_timestamp = st
         try:
-            async with session.post(url=api_url, json=payload) as resp:
-                if resp.status == 200:
-                    async for data in resp.content.iter_any():
+            async with session.post(url=api_url, json=payload) as response:
+                if response.status == 200:
+                    async for chunk in response.content:
+                        chunk = chunk.strip()
+                        if not chunk:
+                            continue
+
+                        chunk = remove_prefix(chunk.decode("utf-8"), "data:")
+
+                        data = json.loads(chunk)
+                        timestamp = time.perf_counter()
+                        # First token
                         if ttft == 0:
                             ttft = time.perf_counter() - st
                             output.ttft = ttft
-                    output.latency = time.perf_counter() - st
 
-                    body = remove_prefix(data.decode("utf-8"), "data:")
-                    output.generated_text = json.loads(body)["text_output"]
+                        # Decoding phase
+                        else:
+                            output.itl.append(timestamp -
+                                              most_recent_timestamp)
+
+                        most_recent_timestamp = timestamp
+
+                    output.latency = most_recent_timestamp - st
+                    output.generated_text = json.loads(data)["text_output"]
                     output.success = True
 
                 else:
+                    output.error = response.reason
                     output.success = False
-        except (aiohttp.ClientOSError, aiohttp.ServerDisconnectedError):
+        except Exception:
             output.success = False
+            exc_info = sys.exc_info()
+            output.error = "".join(traceback.format_exception(*exc_info))
 
         if pbar:
             pbar.update(1)
@@ -181,35 +172,35 @@ async def async_request_deepspeed_mii(
         assert not request_func_input.use_beam_search
 
         payload = {
-            "prompts": request_func_input.prompt,
-            "max_new_tokens": request_func_input.output_len,
-            "ignore_eos": True,
-            "do_sample": True,
-            "temperature":
-            0.01,  # deepspeed-mii does not accept 0.0 temperature.
+            "prompt": request_func_input.prompt,
+            "max_tokens": request_func_input.output_len,
+            "temperature": 0.01,  # deepspeed-mii does not accept 0.0 temp.
             "top_p": 1.0,
         }
         output = RequestFuncOutput()
         output.prompt_len = request_func_input.prompt_len
 
-        # DeepSpeed-MII doesn't support streaming as of Jan 28 2024,
+        # NOTE: DeepSpeed-MII doesn't support streaming as of Jan 28 2024,
         # will use 0 as placeholder.
-        # https://github.com/microsoft/DeepSpeed-MII/pull/311
+        # See https://github.com/microsoft/DeepSpeed-MII/pull/311
         output.ttft = 0
 
         st = time.perf_counter()
         try:
             async with session.post(url=request_func_input.api_url,
-                                    json=payload) as resp:
-                if resp.status == 200:
-                    parsed_resp = await resp.json()
+                                    json=payload) as response:
+                if response.status == 200:
+                    parsed_resp = await response.json()
                     output.latency = time.perf_counter() - st
-                    output.generated_text = parsed_resp[0]["generated_text"]
+                    output.generated_text = parsed_resp["text"][0]
                     output.success = True
                 else:
+                    output.error = response.reason
                     output.success = False
-        except (aiohttp.ClientOSError, aiohttp.ServerDisconnectedError):
+        except Exception:
             output.success = False
+            exc_info = sys.exc_info()
+            output.error = "".join(traceback.format_exception(*exc_info))
 
         if pbar:
             pbar.update(1)
@@ -221,7 +212,9 @@ async def async_request_openai_completions(
     pbar: Optional[tqdm] = None,
 ) -> RequestFuncOutput:
     api_url = request_func_input.api_url
-    assert api_url.endswith("v1/completions")
+    assert api_url.endswith(
+        "v1/completions"
+    ), "OpenAI Completions API URL must end with 'v1/completions'."
 
     async with aiohttp.ClientSession(timeout=AIOHTTP_TIMEOUT) as session:
         assert not request_func_input.use_beam_search
@@ -243,15 +236,12 @@ async def async_request_openai_completions(
         generated_text = ""
         ttft = 0
         st = time.perf_counter()
+        most_recent_timestamp = st
         try:
             async with session.post(url=api_url, json=payload,
                                     headers=headers) as response:
                 if response.status == 200:
                     async for chunk in response.content:
-                        if ttft == 0:
-                            ttft = time.perf_counter() - st
-                            output.ttft = ttft
-
                         chunk = chunk.strip()
                         if not chunk:
                             continue
@@ -260,16 +250,33 @@ async def async_request_openai_completions(
                         if chunk == "[DONE]":
                             latency = time.perf_counter() - st
                         else:
-                            body = json.loads(chunk)
-                            generated_text += body["choices"][0]["text"]
+                            data = json.loads(chunk)
+
+                            if data["choices"][0]["text"]:
+                                timestamp = time.perf_counter()
+                                # First token
+                                if ttft == 0:
+                                    ttft = time.perf_counter() - st
+                                    output.ttft = ttft
+
+                                # Decoding phase
+                                # NOTE: Some completion API might have a last
+                                # usage summary response without a token so we
+                                # do not want to include as inter-token-latency
+                                elif data.get("usage", None) is None:
+                                    output.itl.append(timestamp -
+                                                      most_recent_timestamp)
+
+                                most_recent_timestamp = timestamp
+                                generated_text += data["choices"][0]["text"]
 
                     output.generated_text = generated_text
                     output.success = True
                     output.latency = latency
-                else:
-                    output.success = False
-        except (aiohttp.ClientOSError, aiohttp.ServerDisconnectedError):
+        except Exception:
             output.success = False
+            exc_info = sys.exc_info()
+            output.error = "".join(traceback.format_exception(*exc_info))
 
     if pbar:
         pbar.update(1)
@@ -283,7 +290,7 @@ async def async_request_openai_chat_completions(
     api_url = request_func_input.api_url
     assert api_url.endswith(
         "v1/chat/completions"
-    ), "OpenAI Chat API URL must end with 'v1/chat/completions'."
+    ), "OpenAI Chat Completions API URL must end with 'v1/chat/completions'."
 
     async with aiohttp.ClientSession(timeout=AIOHTTP_TIMEOUT) as session:
         assert not request_func_input.use_beam_search
@@ -301,7 +308,7 @@ async def async_request_openai_chat_completions(
         }
         headers = {
             "Content-Type": "application/json",
-            "Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}"
+            "Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}",
         }
 
         output = RequestFuncOutput()
@@ -310,15 +317,12 @@ async def async_request_openai_chat_completions(
         generated_text = ""
         ttft = 0
         st = time.perf_counter()
+        most_recent_timestamp = st
         try:
             async with session.post(url=api_url, json=payload,
                                     headers=headers) as response:
                 if response.status == 200:
                     async for chunk in response.content:
-                        if ttft == 0:
-                            ttft = time.perf_counter() - st
-                            output.ttft = ttft
-
                         chunk = chunk.strip()
                         if not chunk:
                             continue
@@ -327,18 +331,35 @@ async def async_request_openai_chat_completions(
                         if chunk == "[DONE]":
                             latency = time.perf_counter() - st
                         else:
-                            body = json.loads(chunk)
-                            if "content" in body["choices"][0]["delta"]:
-                                generated_text += body["choices"][0]["delta"][
+                            timestamp = time.perf_counter()
+                            data = json.loads(chunk)
+
+                            if "content" in data["choices"][0]["delta"]:
+                                # First token
+                                if ttft == 0:
+                                    ttft = time.perf_counter() - st
+                                    output.ttft = ttft
+
+                                # Decoding phase
+                                else:
+                                    output.itl.append(timestamp -
+                                                      most_recent_timestamp)
+
+                                generated_text += data["choices"][0]["delta"][
                                     "content"]
 
+                            most_recent_timestamp = timestamp
+
                     output.generated_text = generated_text
                     output.success = True
                     output.latency = latency
                 else:
+                    output.error = response.reason
                     output.success = False
-        except (aiohttp.ClientOSError, aiohttp.ServerDisconnectedError):
+        except Exception:
             output.success = False
+            exc_info = sys.exc_info()
+            output.error = "".join(traceback.format_exception(*exc_info))
 
     if pbar:
         pbar.update(1)
@@ -355,7 +376,8 @@ def remove_prefix(text: str, prefix: str) -> str:
 
 ASYNC_REQUEST_FUNCS = {
     "tgi": async_request_tgi,
-    "vllm": async_request_vllm,
+    "vllm": async_request_openai_completions,
+    "lmdeploy": async_request_openai_completions,
     "deepspeed-mii": async_request_deepspeed_mii,
     "openai": async_request_openai_completions,
     "openai-chat": async_request_openai_chat_completions,

benchmarks/benchmark_serving.py

 """Benchmark online serving throughput.
 
 On the server side, run one of the following commands:
-    (vLLM backend)
-    python -m vllm.entrypoints.api_server \
+    vLLM OpenAI API server
+    python -m vllm.entrypoints.openai.api_server \
         --model <your_model> --swap-space 16 \
         --disable-log-requests
 
@@ -12,14 +12,19 @@ On the server side, run one of the following commands:
 On the client side, run:
     python benchmarks/benchmark_serving.py \
         --backend <backend> \
-        --model <your_model> --dataset <target_dataset> \
-        --request-rate <request_rate>
+        --model <your_model> \
+        --dataset-name sharegpt \
+        --dataset-path <path to dataset> \
+        --request-rate <request_rate> \ # By default <request_rate> is inf
+        --num-prompts <num_prompts> # By default <num_prompts> is 1000
 """
 import argparse
 import asyncio
 import json
+import os
 import random
 import time
+import warnings
 from dataclasses import dataclass
 from datetime import datetime
 from typing import AsyncGenerator, List, Tuple
@@ -49,7 +54,7 @@ class BenchmarkMetrics:
     p99_tpot_ms: float
 
 
-def sample_requests(
+def sample_sharegpt_requests(
     dataset_path: str,
     num_requests: int,
     tokenizer: PreTrainedTokenizerBase,
@@ -97,6 +102,73 @@ def sample_requests(
     return sampled_requests
 
 
+def sample_sonnet_requests(
+    dataset_path: str,
+    num_requests: int,
+    input_len: int,
+    output_len: int,
+    prefix_len: int,
+    tokenizer: PreTrainedTokenizerBase,
+) -> List[Tuple[str, str, int, int]]:
+    assert input_len > prefix_len, "input_len must be greater than prefix_len."
+
+    # Load the dataset.
+    with open(dataset_path) as f:
+        poem_lines = f.readlines()
+
+    # Tokenize the poem lines.
+    poem_token_ids = tokenizer(poem_lines).input_ids
+    average_poem_len = sum(
+        len(token_ids) for token_ids in poem_token_ids) / len(poem_token_ids)
+
+    # Base prefix for all requests.
+    base_prompt = "Pick as many lines as you can from these poem lines:\n"
+    base_message = [{
+        "role": "user",
+        "content": base_prompt,
+    }]
+    base_prompt_formatted = tokenizer.apply_chat_template(
+        base_message, add_generation_prompt=True, tokenize=False)
+    base_prompt_offset = len(tokenizer(base_prompt_formatted).input_ids)
+
+    assert (input_len > base_prompt_offset
+            ), f"Please set 'args.input-len' higher than {base_prompt_offset}."
+    num_input_lines = round(
+        (input_len - base_prompt_offset) / average_poem_len)
+
+    # First approximately `prefix_len` number of tokens in the
+    # prompt are fixed poem lines.
+    assert (
+        prefix_len > base_prompt_offset
+    ), f"Please set 'args.prefix-len' higher than {base_prompt_offset}."
+
+    num_prefix_lines = round(
+        (prefix_len - base_prompt_offset) / average_poem_len)
+    prefix_lines = poem_lines[:num_prefix_lines]
+
+    # Sample the rest of lines per request.
+    sampled_requests: List[Tuple[str, int, int]] = []
+    for _ in range(num_requests):
+        sampled_lines = "".join(
+            prefix_lines +
+            random.sample(poem_lines, num_input_lines - num_prefix_lines))
+
+        prompt = f"{base_prompt}{sampled_lines}"
+        message = [
+            {
+                "role": "user",
+                "content": prompt,
+            },
+        ]
+        prompt_formatted = tokenizer.apply_chat_template(
+            message, add_generation_prompt=True, tokenize=False)
+        prompt_len = len(tokenizer(prompt_formatted).input_ids)
+        sampled_requests.append(
+            (prompt, prompt_formatted, prompt_len, output_len))
+
+    return sampled_requests
+
+
 async def get_request(
     input_requests: List[Tuple[str, int, int]],
     request_rate: float,
@@ -119,37 +191,42 @@ def calculate_metrics(
     outputs: List[RequestFuncOutput],
     dur_s: float,
     tokenizer: PreTrainedTokenizerBase,
-) -> BenchmarkMetrics:
-    total_output = 0
+) -> Tuple[BenchmarkMetrics, List[int]]:
+    actual_output_lens = []
     total_input = 0
     completed = 0
-    per_token_latencies = []
+    tpots = []
     ttfts = []
     for i in range(len(outputs)):
         if outputs[i].success:
-            output_len = len(tokenizer.encode(outputs[i].generated_text))
-            total_output += output_len
+            output_len = len(tokenizer(outputs[i].generated_text).input_ids)
+            actual_output_lens.append(output_len)
             total_input += input_requests[i][1]
-            per_token_latencies.append(outputs[i].latency / output_len)
+            if output_len > 1:
+                tpots.append(
+                    (outputs[i].latency - outputs[i].ttft) / (output_len - 1))
             ttfts.append(outputs[i].ttft)
             completed += 1
+        else:
+            actual_output_lens.append(0)
 
     metrics = BenchmarkMetrics(
         completed=completed,
         total_input=total_input,
-        total_output=total_output,
+        total_output=sum(actual_output_lens),
         request_throughput=completed / dur_s,
         input_throughput=total_input / dur_s,
-        output_throughput=total_output / dur_s,
-        mean_ttft_ms=np.mean(ttfts) * 1000,
-        median_ttft_ms=np.median(ttfts) * 1000,
-        p99_ttft_ms=np.percentile(ttfts, 99) * 1000,
-        mean_tpot_ms=np.mean(per_token_latencies) * 1000,
-        median_tpot_ms=np.median(per_token_latencies) * 1000,
-        p99_tpot_ms=np.percentile(per_token_latencies, 99) * 1000,
+        output_throughput=sum(actual_output_lens) / dur_s,
+        mean_ttft_ms=np.mean(ttfts or 0) *
+        1000,  # ttfts is empty if streaming is not supported by backend
+        median_ttft_ms=np.median(ttfts or 0) * 1000,
+        p99_ttft_ms=np.percentile(ttfts or 0, 99) * 1000,
+        mean_tpot_ms=np.mean(tpots) * 1000,
+        median_tpot_ms=np.median(tpots) * 1000,
+        p99_tpot_ms=np.percentile(tpots, 99) * 1000,
     )
 
-    return metrics
+    return metrics, actual_output_lens
 
 
 async def benchmark(
@@ -189,40 +266,53 @@ async def benchmark(
             asyncio.create_task(
                 request_func(request_func_input=request_func_input,
                              pbar=pbar)))
-    outputs = await asyncio.gather(*tasks)
+    outputs: List[RequestFuncOutput] = await asyncio.gather(*tasks)
 
     if not disable_tqdm:
         pbar.close()
 
     benchmark_duration = time.perf_counter() - benchmark_start_time
 
-    metrics = calculate_metrics(
+    metrics, actual_output_lens = calculate_metrics(
         input_requests=input_requests,
         outputs=outputs,
         dur_s=benchmark_duration,
         tokenizer=tokenizer,
     )
 
-    print(f"Successful requests: {metrics.completed}")
-    print(f"Benchmark duration: {benchmark_duration:2f} s")
-    print(f"Total input tokens: {metrics.total_input}")
-    print(f"Total generated tokens: {metrics.total_output}")
-    print(f"Request throughput: {metrics.request_throughput:.2f} requests/s")
-    print(f"Input token throughput: {metrics.input_throughput:.2f} tokens/s")
-    print(f"Output token throughput: {metrics.output_throughput:.2f} tokens/s")
-    print(f"Mean TTFT: {metrics.mean_ttft_ms:.2f} ms")
-    print(f"Median TTFT: {metrics.median_ttft_ms:.2f} ms")
-    print(f"P99 TTFT: {metrics.p99_ttft_ms:.2f} ms")
-    print(f"Mean TPOT: {metrics.mean_tpot_ms:.2f} ms")
-    print(f"Median TPOT: {metrics.median_tpot_ms:.2f} ms")
-    print(f"P99 TPOT: {metrics.p99_tpot_ms:.2f} ms")
+    print("{s:{c}^{n}}".format(s=' Serving Benchmark Result ', n=50, 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))
+    print("{:<40} {:<10.2f}".format("Request throughput (req/s):",
+                                    metrics.request_throughput))
+    print("{:<40} {:<10.2f}".format("Input token throughput (tok/s):",
+                                    metrics.input_throughput))
+    print("{:<40} {:<10.2f}".format("Output token throughput (tok/s):",
+                                    metrics.output_throughput))
+    print("{s:{c}^{n}}".format(s='Time to First Token', n=50, c='-'))
+    print("{:<40} {:<10.2f}".format("Mean TTFT (ms):", metrics.mean_ttft_ms))
+    print("{:<40} {:<10.2f}".format("Median TTFT (ms):",
+                                    metrics.median_ttft_ms))
+    print("{:<40} {:<10.2f}".format("P99 TTFT (ms):", metrics.p99_ttft_ms))
+    print("{s:{c}^{n}}".format(s='Time per Output Token (excl. 1st token)',
+                               n=50,
+                               c='-'))
+    print("{:<40} {:<10.2f}".format("Mean TPOT (ms):", metrics.mean_tpot_ms))
+    print("{:<40} {:<10.2f}".format("Median TPOT (ms):",
+                                    metrics.median_tpot_ms))
+    print("{:<40} {:<10.2f}".format("P99 TPOT (ms):", metrics.p99_tpot_ms))
+    print("=" * 50)
 
     result = {
         "duration": benchmark_duration,
         "completed": metrics.completed,
         "total_input_tokens": metrics.total_input,
         "total_output_tokens": metrics.total_output,
-        "request_inthroughput": metrics.request_throughput,
+        "request_throughput": metrics.request_throughput,
         "input_throughput": metrics.input_throughput,
         "output_throughput": metrics.output_throughput,
         "mean_ttft_ms": metrics.mean_ttft_ms,
@@ -230,7 +320,13 @@ async def benchmark(
         "p99_ttft_ms": metrics.p99_ttft_ms,
         "mean_tpot_ms": metrics.mean_tpot_ms,
         "median_tpot_ms": metrics.median_tpot_ms,
-        "p99_tpot_ms": metrics.p99_tpot_ms
+        "p99_tpot_ms": metrics.p99_tpot_ms,
+        "input_lens": [output.prompt_len for output in outputs],
+        "output_lens": actual_output_lens,
+        "ttfts": [output.ttft for output in outputs],
+        "itls": [output.itl for output in outputs],
+        "generated_texts": [output.generated_text for output in outputs],
+        "errors": [output.error for output in outputs],
     }
     return result
 
@@ -251,7 +347,58 @@ def main(args: argparse.Namespace):
 
     tokenizer = get_tokenizer(tokenizer_id,
                               trust_remote_code=args.trust_remote_code)
-    input_requests = sample_requests(args.dataset, args.num_prompts, tokenizer)
+
+    if args.dataset is not None:
+        warnings.warn(
+            "The '--dataset' argument will be deprecated in the next "
+            "release. Please use '--dataset-name' and "
+            "'--dataset-path' in the future runs.",
+            stacklevel=2)
+        input_requests = sample_sharegpt_requests(
+            dataset_path=args.dataset,
+            num_requests=args.num_prompts,
+            tokenizer=tokenizer,
+        )
+
+    elif args.dataset_name == "sharegpt":
+        input_requests = sample_sharegpt_requests(
+            dataset_path=args.dataset_path,
+            num_requests=args.num_prompts,
+            tokenizer=tokenizer,
+        )
+
+    elif args.dataset_name == "sonnet":
+        # Do not format the prompt, pass to message directly
+        if args.backend == "openai-chat":
+            input_requests = sample_sonnet_requests(
+                dataset_path=args.dataset_path,
+                num_requests=args.num_prompts,
+                input_len=args.input_len,
+                output_len=args.output_len,
+                prefix_len=args.prefix_len,
+                tokenizer=tokenizer,
+            )
+            input_requests = [(prompt, prompt_len, output_len)
+                              for prompt, prompt_formatted, prompt_len,
+                              output_len in input_requests]
+        else:
+            assert (
+                tokenizer.chat_template or tokenizer.default_chat_template
+            ), "Tokenizer/model must have chat template for sonnet dataset."
+            input_requests = sample_sonnet_requests(
+                dataset_path=args.dataset_path,
+                num_requests=args.num_prompts,
+                input_len=args.input_len,
+                output_len=args.output_len,
+                prefix_len=args.prefix_len,
+                tokenizer=tokenizer,
+            )
+            input_requests = [(prompt_formatted, prompt_len, output_len)
+                              for prompt, prompt_formatted, prompt_len,
+                              output_len in input_requests]
+
+    else:
+        raise ValueError(f"Unknown dataset: {args.dataset_name}")
 
     benchmark_result = asyncio.run(
         benchmark(
@@ -274,13 +421,23 @@ def main(args: argparse.Namespace):
         current_dt = datetime.now().strftime("%Y%m%d-%H%M%S")
         result_json["date"] = current_dt
         result_json["backend"] = backend
-        result_json["version"] = args.version
         result_json["model_id"] = model_id
         result_json["tokenizer_id"] = tokenizer_id
         result_json["best_of"] = args.best_of
         result_json["use_beam_search"] = args.use_beam_search
         result_json["num_prompts"] = args.num_prompts
 
+        # Metadata
+        if args.metadata:
+            for item in args.metadata:
+                if "=" in item:
+                    kvstring = item.split("=")
+                    result_json[kvstring[0].strip()] = kvstring[1].strip()
+                else:
+                    raise ValueError(
+                        "Invalid metadata format. Please use KEY=VALUE format."
+                    )
+
         # Traffic
         result_json["request_rate"] = (
             args.request_rate if args.request_rate < float("inf") else "inf")
@@ -290,9 +447,9 @@ def main(args: argparse.Namespace):
 
         # Save to file
         base_model_id = model_id.split("/")[-1]
-        file_name = (
-            f"{backend}-{args.request_rate}qps-{base_model_id}-{current_dt}.json"
-        )
+        file_name = f"{backend}-{args.request_rate}qps-{base_model_id}-{current_dt}.json"  #noqa
+        if args.result_dir:
+            file_name = os.path.join(args.result_dir, file_name)
         with open(file_name, "w") as outfile:
             json.dump(result_json, outfile)
 
@@ -306,12 +463,6 @@ if __name__ == "__main__":
         default="vllm",
         choices=list(ASYNC_REQUEST_FUNCS.keys()),
     )
-    parser.add_argument(
-        "--version",
-        type=str,
-        default="N/A",
-        help="Version of the serving backend/engine.",
-    )
     parser.add_argument(
         "--base-url",
         type=str,
@@ -323,12 +474,26 @@ if __name__ == "__main__":
     parser.add_argument(
         "--endpoint",
         type=str,
-        default="/generate",
+        default="/v1/completions",
         help="API endpoint.",
     )
-    parser.add_argument("--dataset",
+    parser.add_argument(
+        "--dataset",
+        type=str,
+        default=None,
+        help="Path to the ShareGPT dataset, will be deprecated in the "
+        "next release.",
+    )
+    parser.add_argument(
+        "--dataset-name",
+        type=str,
+        default="sharegpt",
+        choices=["sharegpt", "sonnet"],
+        help="Name of the dataset to benchmark on.",
+    )
+    parser.add_argument("--dataset-path",
                         type=str,
-                        required=True,
+                        default=None,
                         help="Path to the dataset.")
     parser.add_argument(
         "--model",
@@ -356,6 +521,27 @@ if __name__ == "__main__":
         default=1000,
         help="Number of prompts to process.",
     )
+    parser.add_argument(
+        "--sonnet-input-len",
+        type=int,
+        default=550,
+        help=
+        "Number of input tokens per request, used only for sonnet dataset.",
+    )
+    parser.add_argument(
+        "--sonnet-output-len",
+        type=int,
+        default=150,
+        help=
+        "Number of output tokens per request, used only for sonnet dataset.",
+    )
+    parser.add_argument(
+        "--sonnet-prefix-len",
+        type=int,
+        default=200,
+        help=
+        "Number of prefix tokens per request, used only for sonnet dataset.",
+    )
     parser.add_argument(
         "--request-rate",
         type=float,
@@ -381,6 +567,21 @@ if __name__ == "__main__":
         action="store_true",
         help="Specify to save benchmark results to a json file",
     )
+    parser.add_argument(
+        "--metadata",
+        metavar="KEY=VALUE",
+        nargs="*",
+        help="Key-value pairs (e.g, --metadata version=0.3.3 tp=1) "
+        "for metadata of this run to be saved in the result JSON file "
+        "for record keeping purposes.",
+    )
+    parser.add_argument(
+        "--result-dir",
+        type=str,
+        default=None,
+        help="Specify directory to save benchmark json results."
+        "If not specified, results are saved in the current directory.",
+    )
 
     args = parser.parse_args()
     main(args)

pyproject.toml

@@ -50,7 +50,7 @@ exclude = "vllm/model_executor/parallel_utils/|vllm/model_executor/models/"
 
 [tool.codespell]
 ignore-words-list = "dout, te, indicies"
-skip = "./tests/prompts"
+skip = "./tests/prompts,./benchmarks/sonnet.txt"
 
 [tool.isort]
 use_parentheses = true

tests/kernels/test_prefix_prefill.py

@@ -36,8 +36,8 @@ def test_contexted_kv_attention(
         torch.cuda.manual_seed(0)
     torch.set_default_device(device)
 
-    # Need this, otherwise when we capture the graph the process for GPU 1 would
-    # run on both GPU0 and GPU1 and things would hang
+    # Need this, otherwise when we capture the graph the process
+    # for GPU 1 would run on both GPU0 and GPU1 and things would hang
     #
     # see also similar issue: https://github.com/Dao-AILab/flash-attention/issues/523
     torch.cuda.set_device(device)