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<!--
* @Author: zhuww
* @email: zhuww@sugon.com
* @Date: 2024-05-24 14:15:07
* @LastEditTime: 2024-09-30 08:30:01
-->
# Qwen1.5
## 论文
无
## 模型结构
Qwen1.5是阿里云开源大型语言模型系列，是Qwen2.0的beta版本。相较于以往版本，本次更新着重提升了Chat模型与人类偏好的对齐程度，并且显著增强了模型的多语言处理能力。在序列长度方面，所有规模模型均已实现 32768 个tokens的上下文长度范围支持。同时，预训练 Base 模型的质量也有关键优化，有望在微调过程中带来更佳体验。
<div align=center>
<img src="./doc/qwen1.5.jpg"/>
</div>
## 算法原理
和Qwen一样，Qwen1.5仍然是一个decoder-only的transformer模型，使用SwiGLU激活函数、RoPE、多头注意力机制等。
<div align=center>
<img src="./doc/qwen1.5.png"/>
</div>
## 环境配置
### Docker（方法一）
提供[光源](https://www.sourcefind.cn/#/image/dcu/custom)拉取推理的docker镜像：
```
docker pull image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.1.0-ubuntu20.04-dtk24.04.2-py3.10
# <Image ID>用上面拉取docker镜像的ID替换
# <Host Path>主机端路径
# <Container Path>容器映射路径
docker run -it --name qwen1.5_vllm --privileged --shm-size=64G --device=/dev/kfd --device=/dev/dri/ --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --ulimit memlock=-1:-1 --ipc=host --network host --group-add video -v /opt/hyhal:/opt/hyhal -v <Host Path>:<Container Path> <Image ID> /bin/bash
```
`Tips：若在K100/Z100L上使用，使用定制镜像docker pull image.sourcefind.cn:5000/dcu/admin/base/custom:vllm0.5.0-dtk24.04.1-ubuntu20.04-py310-zk-v1,K100/Z100L不支持awq量化`
### Dockerfile（方法二）
```
# <Host Path>主机端路径
# <Container Path>容器映射路径
docker build -t qwen1.5:latest .
docker run -it --name qwen1.5_vllm --privileged --shm-size=64G --device=/dev/kfd --device=/dev/dri/ --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --ulimit memlock=-1:-1 --ipc=host --network host --group-add video -v /opt/hyhal:/opt/hyhal:ro -v <Host Path>:<Container Path> qwen1.5:latest /bin/bash
```
### Anaconda（方法三）
```
conda create -n qwen1.5_vllm python=3.10
```
关于本项目DCU显卡所需的特殊深度学习库可从[光合](https://developer.hpccube.com/tool/)开发者社区下载安装。
* DTK驱动：dtk24.04.2
* Pytorch: 2.1.0
* triton:2.1.0
* lmslim: 0.1.0
* xformers: 0.0.25
* flash_attn: 2.0.4
* vllm: 0.5.0
* python: python3.10
`Tips：需先安装相关依赖，最后安装vllm包`
## 数据集
无
## 推理
### 模型下载
| 基座模型 | chat模型 | GPTQ模型 | AWQ模型 |
| ------- | ------- | ------- | ------- |
| [Qwen-7B](https://huggingface.co/Qwen/Qwen-7B) | [Qwen-7B-Chat](http://113.200.138.88:18080/aimodels/Qwen-7B-Chat) | [Qwen-7B-Chat-GPTQ-Int4](https://huggingface.co/Qwen/Qwen-7B-Chat-Int4) |
| [Qwen-14B](https://huggingface.co/Qwen/Qwen-14B) | [Qwen-14B-Chat](http://113.200.138.88:18080/aimodels/Qwen-14B-Chat) | [Qwen-14B-Chat-GPTQ-Int4](https://huggingface.co/Qwen/Qwen-14B-Chat-Int4) |
| [Qwen-72B](http://113.200.138.88:18080/aimodels/qwen/Qwen-72B) | [Qwen-72B-Chat](http://113.200.138.88:18080/aimodels/Qwen-72B-Chat) | [Qwen-72B-Chat-GPTQ-Int4](https://huggingface.co/Qwen/Qwen-72B-Chat-Int4) |
| [Qwen1.5-7B](https://huggingface.co/Qwen/Qwen1.5-7B) | [Qwen1.5-7B-Chat](https://huggingface.co/Qwen/Qwen1.5-7B-Chat) | [Qwen1.5-7B-Chat-GPTQ-Int4](https://huggingface.co/Qwen/Qwen1.5-7B-Chat-GPTQ-Int4) | [Qwen1.5-7B-Chat-AWQ-Int4](http://113.200.138.88:18080/aimodels/qwen/Qwen1.5-7B-Chat-AWQ) |
| [Qwen1.5-14B](https://huggingface.co/Qwen/Qwen1.5-14B) | [Qwen1.5-14B-Chat](http://113.200.138.88:18080/aimodels/qwen/Qwen1.5-14B-Chat) | [Qwen1.5-14B-Chat-GPTQ-Int4](https://huggingface.co/Qwen/Qwen1.5-14B-Chat-GPTQ-Int4) | [Qwen1.5-14B-Chat-AWQ-Int4](http://113.200.138.88:18080/aimodels/qwen/Qwen1.5-14B-Chat-AWQ) |
| [Qwen1.5-32B](http://113.200.138.88:18080/aimodels/Qwen1.5-32B) | [Qwen1.5-32B-Chat](http://113.200.138.88:18080/aimodels/Qwen1.5-32B-Chat) | [Qwen1.5-32B-Chat-GPTQ-Int4](http://113.200.138.88:18080/aimodels/Qwen1.5-32B-Chat-GPTQ-Int4) | [Qwen1.5-32B-Chat-AWQ-Int4](https://huggingface.co/Qwen/Qwen1.5-32B-Chat-AWQ) |
| [Qwen1.5-72B](http://113.200.138.88:18080/aimodels/Qwen1.5-72B) | [Qwen1.5-72B-Chat](http://113.200.138.88:18080/aimodels/Qwen1.5-72B-Chat) | [Qwen1.5-72B-Chat-GPTQ-Int4](https://huggingface.co/Qwen/Qwen1.5-72B-Chat-GPTQ-Int4) | [Qwen1.5-72B-Chat-AWQ-Int4](http://113.200.138.88:18080/aimodels/qwen/Qwen1.5-72B-Chat-AWQ) |
| [Qwen1.5-110B](http://113.200.138.88:18080/aimodels/Qwen1.5-110B) | [Qwen1.5-110B-Chat](http://113.200.138.88:18080/aimodels/Qwen1.5-110B-Chat) | [Qwen1.5-110B-Chat-GPTQ-Int4](https://huggingface.co/Qwen/Qwen1.5-110B-Chat-GPTQ-Int4) | [Qwen1.5-110B-Chat-AWQ-Int4](http://113.200.138.88:18080/aimodels/qwen/Qwen1.5-110B-Chat-AWQ) |
| [Qwen2-7B](http://113.200.138.88:18080/aimodels/Qwen2-7B) | [Qwen2-7B-Instruct](http://113.200.138.88:18080/aimodels/Qwen2-7B-Instruct) | [Qwen2-7B-Instruct-GPTQ-Int4](https://huggingface.co/Qwen/Qwen2-7B-Instruct-GPTQ-Int4) | [Qwen2-7B-Instruct-AWQ-Int4](http://113.200.138.88:18080/aimodels/qwen/Qwen2-7B-Instruct-AWQ)
| [Qwen2-72B](http://113.200.138.88:18080/aimodels/Qwen2-72B) | [Qwen2-72B-Instruct](http://113.200.138.88:18080/aimodels/Qwen2-72B-Instruct) | [Qwen2-72B-Instruct-GPTQ-Int4](https://huggingface.co/Qwen/Qwen2-72B-Instruct-GPTQ-Int4) |[Qwen2-72B-Instruct-AWQ-Int4](http://113.200.138.88:18080/aimodels/qwen/Qwen2-72B-Instruct-AWQ) |
### 离线批量推理
```bash
python examples/offline_inference.py
```
其中，`prompts`为提示词；`temperature`为控制采样随机性的值，值越小模型生成越确定，值变高模型生成更随机，0表示贪婪采样，默认为1；`max_tokens=16`为生成长度，默认为1；
`model`为模型路径；`tensor_parallel_size=1`为使用卡数，默认为1；`dtype="float16"`为推理数据类型，如果模型权重是bfloat16,需要修改为float16推理,`quantization="gptq"`为使用gptq量化进行推理,需下载以上GPTQ模型。`quantization="awq"`为使用awq量化进行推理,需下载以上AWQ模型。
### 离线批量推理性能测试
1、指定输入输出
```bash
python benchmarks/benchmark_throughput.py --num-prompts 1 --input-len 32 --output-len 128 --model Qwen/Qwen1.5-7B-Chat -tp 1 --trust-remote-code --enforce-eager --dtype float16
```
其中`--num-prompts`是batch数，`--input-len`是输入seqlen，`--output-len`是输出token长度，`--model`为模型路径，`-tp`为使用卡数，`dtype="float16"`为推理数据类型，如果模型权重是bfloat16,需要修改为float16推理。若指定`--output-len 1`即为首字延迟。`-q gptq`为使用gptq量化模型进行推理。
2、使用数据集
下载数据集：
```bash
wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
```
```bash
python benchmarks/benchmark_throughput.py --num-prompts 1 --model Qwen/Qwen1.5-7B-Chat --dataset ShareGPT_V3_unfiltered_cleaned_split.json -tp 1 --trust-remote-code --enforce-eager --dtype float16
```
其中`--num-prompts`是batch数，`--model`为模型路径，`--dataset`为使用的数据集，`-tp`为使用卡数，`dtype="float16"`为推理数据类型，如果模型权重是bfloat16,需要修改为float16推理。`-q gptq`为使用gptq量化模型进行推理。
### api服务推理性能测试
1、启动服务端：
```bash
python -m vllm.entrypoints.openai.api_server --model Qwen/Qwen1.5-7B-Chat --dtype float16 --enforce-eager -tp 1
```
2、启动客户端：
```bash
python benchmarks/benchmark_serving.py --model Qwen/Qwen1.5-7B-Chat --dataset ShareGPT_V3_unfiltered_cleaned_split.json --num-prompts 1 --trust-remote-code
```
参数同使用数据集，离线批量推理性能测试，具体参考[benchmarks/benchmark_serving.py](benchmarks/benchmark_serving.py)
### OpenAI兼容服务
启动服务：
```bash
python -m vllm.entrypoints.openai.api_server --model Qwen/Qwen1.5-7B-Chat --enforce-eager --dtype float16 --trust-remote-code
```
这里`--model`为加载模型路径，`--dtype`为数据类型：float16，默认情况使用tokenizer中的预定义聊天模板，`--chat-template`可以添加新模板覆盖默认模板,`-q gptq`为使用gptq量化模型进行推理,`-q awqq`为使用awq量化模型进行推理。
列出模型型号：
```bash
curl http://localhost:8000/v1/models
```
### OpenAI Completions API和vllm结合使用
```bash
curl http://localhost:8000/v1/completions \
-H "Content-Type: application/json" \
-d '{
"model": "Qwen/Qwen1.5-7B",
"prompt": "What is deep learning?",
"max_tokens": 7,
"temperature": 0
}'
```
或者使用[examples/openai_completion_client.py](examples/openai_completion_client.py)
### OpenAI Chat API和vllm结合使用
```bash
curl http://localhost:8000/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "Qwen/Qwen1.5-7B-Chat",
"messages": [
{"role": "system", "content": "What is deep learning?"},
{"role": "user", "content": "What is deep learning?"}
]
}'
```
或者使用[examples/openai_chatcompletion_client.py](examples/openai_chatcompletion_client.py)
## result
使用的加速卡:1张 DCU-K100_AI-64G
```
Prompt: 'What is deep learning?', Generated text: ' Deep learning is a subset of machine learning that involves the use of neural networks to model and solve complex problems. Neural networks are a network of interconnected nodes or " neurons" that are designed to recognize patterns in data, learn from examples, and make predictions or decisions.\nThe term "deep" in deep learning refers to the use of multiple layers or hidden layers in these neural networks. Each layer processes the input data in a different way, extracting increasingly abstract features as the data passes through.'
```
### 精度
无
## 应用场景
### 算法类别
对话问答
### 热点应用行业
金融,科研,教育
## 源码仓库及问题反馈
* [https://developer.hpccube.com/codes/modelzoo/qwen1.5_vllm](https://developer.hpccube.com/codes/modelzoo/qwen1.5_vllm)
## 参考资料
* [https://github.com/vllm-project/vllm](https://github.com/vllm-project/vllm)
benchmarks/README.md 0 → 100644
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# Benchmarking vLLM
## Downloading the ShareGPT dataset
You can download the dataset by running:
```bash
wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
```
benchmarks/backend_request_func.py 0 → 100644
View file @ 75b74b64
import json
import os
import sys
import time
import traceback
from dataclasses import dataclass, field
from typing import List, Optional
import aiohttp
from tqdm.asyncio import tqdm
AIOHTTP_TIMEOUT = aiohttp.ClientTimeout(total=6 * 60 * 60)
@dataclass
class RequestFuncInput:
prompt: str
api_url: str
prompt_len: int
output_len: int
model: str
best_of: int = 1
use_beam_search: bool = False
@dataclass
class RequestFuncOutput:
generated_text: str = ""
success: bool = False
latency: float = 0.0
ttft: float = 0.0 # 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(
request_func_input: RequestFuncInput,
pbar: Optional[tqdm] = None,
) -> RequestFuncOutput:
api_url = request_func_input.api_url
assert api_url.endswith("generate_stream")
async with aiohttp.ClientSession(timeout=AIOHTTP_TIMEOUT) as session:
assert not request_func_input.use_beam_search
params = {
"best_of": request_func_input.best_of,
"max_new_tokens": request_func_input.output_len,
"do_sample": True,
"temperature": 0.01, # TGI does not accept 0.0 temperature.
"top_p": 0.99, # TGI does not accept 1.0 top_p.
}
payload = {
"inputs": request_func_input.prompt,
"parameters": params,
}
output = RequestFuncOutput()
output.prompt_len = request_func_input.prompt_len
ttft = 0.0
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 chunk_bytes in response.content:
chunk_bytes = chunk_bytes.strip()
if not chunk_bytes:
continue
chunk = remove_prefix(chunk_bytes.decode("utf-8"),
"data:")
data = json.loads(chunk)
timestamp = time.perf_counter()
# First token
if ttft == 0.0:
ttft = time.perf_counter() - st
output.ttft = ttft
# Decoding phase
else:
output.itl.append(timestamp -
most_recent_timestamp)
most_recent_timestamp = timestamp
output.latency = most_recent_timestamp - st
output.success = True
output.generated_text = data["generated_text"]
else:
output.error = response.reason or ""
output.success = False
except Exception:
output.success = False
exc_info = sys.exc_info()
output.error = "".join(traceback.format_exception(*exc_info))
if pbar:
pbar.update(1)
return output
async def async_request_trt_llm(
request_func_input: RequestFuncInput,
pbar: Optional[tqdm] = None,
) -> RequestFuncOutput:
api_url = request_func_input.api_url
assert api_url.endswith("generate_stream")
async with aiohttp.ClientSession(timeout=AIOHTTP_TIMEOUT) as session:
assert not request_func_input.use_beam_search
assert request_func_input.best_of == 1
payload = {
"accumulate_tokens": True,
"text_input": request_func_input.prompt,
"temperature": 0.0,
"top_p": 1.0,
"max_tokens": request_func_input.output_len,
"stream": True,
}
output = RequestFuncOutput()
output.prompt_len = request_func_input.prompt_len
ttft = 0.0
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 chunk_bytes in response.content:
chunk_bytes = chunk_bytes.strip()
if not chunk_bytes:
continue
chunk = remove_prefix(chunk_bytes.decode("utf-8"),
"data:")
data = json.loads(chunk)
output.generated_text += data["text_output"]
timestamp = time.perf_counter()
# First token
if ttft == 0.0:
ttft = time.perf_counter() - st
output.ttft = ttft
# Decoding phase
else:
output.itl.append(timestamp -
most_recent_timestamp)
most_recent_timestamp = timestamp
output.latency = most_recent_timestamp - st
output.success = True
else:
output.error = response.reason or ""
output.success = False
except Exception:
output.success = False
exc_info = sys.exc_info()
output.error = "".join(traceback.format_exception(*exc_info))
if pbar:
pbar.update(1)
return output
async def async_request_deepspeed_mii(
request_func_input: RequestFuncInput,
pbar: Optional[tqdm] = None,
) -> RequestFuncOutput:
async with aiohttp.ClientSession(timeout=AIOHTTP_TIMEOUT) as session:
assert request_func_input.best_of == 1
assert not request_func_input.use_beam_search
payload = {
"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
# NOTE: DeepSpeed-MII doesn't support streaming as of Jan 28 2024,
# will use 0 as placeholder.
# 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 response:
if response.status == 200:
parsed_resp = await response.json()
output.latency = time.perf_counter() - st
output.generated_text = parsed_resp["text"][0]
output.success = True
else:
output.error = response.reason or ""
output.success = False
except Exception:
output.success = False
exc_info = sys.exc_info()
output.error = "".join(traceback.format_exception(*exc_info))
if pbar:
pbar.update(1)
return output
async def async_request_openai_completions(
request_func_input: RequestFuncInput,
pbar: Optional[tqdm] = None,
) -> RequestFuncOutput:
api_url = request_func_input.api_url
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
payload = {
"model": request_func_input.model,
"prompt": request_func_input.prompt,
"temperature": 0.0,
"best_of": request_func_input.best_of,
"max_tokens": request_func_input.output_len,
"stream": True,
}
headers = {
"Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}"
}
output = RequestFuncOutput()
output.prompt_len = request_func_input.prompt_len
generated_text = ""
ttft = 0.0
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_bytes in response.content:
chunk_bytes = chunk_bytes.strip()
if not chunk_bytes:
continue
chunk = remove_prefix(chunk_bytes.decode("utf-8"),
"data: ")
if chunk == "[DONE]":
latency = time.perf_counter() - st
else:
data = json.loads(chunk)
if data["choices"][0]["text"]:
timestamp = time.perf_counter()
# First token
if ttft == 0.0:
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.error = response.reason or ""
output.success = False
except Exception:
output.success = False
exc_info = sys.exc_info()
output.error = "".join(traceback.format_exception(*exc_info))
if pbar:
pbar.update(1)
return output
async def async_request_openai_chat_completions(
request_func_input: RequestFuncInput,
pbar: Optional[tqdm] = None,
) -> RequestFuncOutput:
api_url = request_func_input.api_url
assert api_url.endswith(
"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
payload = {
"model": request_func_input.model,
"messages": [
{
"role": "user",
"content": request_func_input.prompt,
},
],
"temperature": 0.0,
"max_tokens": request_func_input.output_len,
"stream": True,
}
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}",
}
output = RequestFuncOutput()
output.prompt_len = request_func_input.prompt_len
generated_text = ""
ttft = 0.0
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_bytes in response.content:
chunk_bytes = chunk_bytes.strip()
if not chunk_bytes:
continue
chunk = remove_prefix(chunk_bytes.decode("utf-8"),
"data: ")
if chunk == "[DONE]":
latency = time.perf_counter() - st
else:
timestamp = time.perf_counter()
data = json.loads(chunk)
delta = data["choices"][0]["delta"]
if delta.get("content", None):
# First token
if ttft == 0.0:
ttft = time.perf_counter() - st
output.ttft = ttft
# Decoding phase
else:
output.itl.append(timestamp -
most_recent_timestamp)
generated_text += delta["content"]
most_recent_timestamp = timestamp
output.generated_text = generated_text
output.success = True
output.latency = latency
else:
output.error = response.reason or ""
output.success = False
except Exception:
output.success = False
exc_info = sys.exc_info()
output.error = "".join(traceback.format_exception(*exc_info))
if pbar:
pbar.update(1)
return output
# Since vllm must support Python 3.8, we can't use str.removeprefix(prefix)
# introduced in Python 3.9
def remove_prefix(text: str, prefix: str) -> str:
if text.startswith(prefix):
return text[len(prefix):]
return text
ASYNC_REQUEST_FUNCS = {
"tgi": async_request_tgi,
"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,
"tensorrt-llm": async_request_trt_llm,
}
benchmarks/benchmark_latency.py 0 → 100644
View file @ 75b74b64
"""Benchmark the latency of processing a single batch of requests."""
import argparse
import json
import time
from pathlib import Path
from typing import List, Optional
import numpy as np
import torch
from tqdm import tqdm
from vllm import LLM, SamplingParams
from vllm.inputs import PromptStrictInputs
from vllm.model_executor.layers.quantization import QUANTIZATION_METHODS
def main(args: argparse.Namespace):
print(args)
# NOTE(woosuk): If the request cannot be processed in a single batch,
# the engine will automatically process the request in multiple batches.
llm = LLM(model=args.model,
speculative_model=args.speculative_model,
num_speculative_tokens=args.num_speculative_tokens,
tokenizer=args.tokenizer,
quantization=args.quantization,
tensor_parallel_size=args.tensor_parallel_size,
trust_remote_code=args.trust_remote_code,
dtype=args.dtype,
enforce_eager=args.enforce_eager,
kv_cache_dtype=args.kv_cache_dtype,
quantization_param_path=args.quantization_param_path,
device=args.device,
ray_workers_use_nsight=args.ray_workers_use_nsight,
use_v2_block_manager=args.use_v2_block_manager,
enable_chunked_prefill=args.enable_chunked_prefill,
download_dir=args.download_dir,
block_size=args.block_size,
gpu_memory_utilization=args.gpu_memory_utilization,
distributed_executor_backend=args.distributed_executor_backend)
sampling_params = SamplingParams(
n=args.n,
temperature=0.0 if args.use_beam_search else 1.0,
top_p=1.0,
use_beam_search=args.use_beam_search,
ignore_eos=True,
max_tokens=args.output_len,
)
print(sampling_params)
dummy_prompt_token_ids = np.random.randint(10000,
size=(args.batch_size,
args.input_len))
dummy_inputs: List[PromptStrictInputs] = [{
"prompt_token_ids": batch
} for batch in dummy_prompt_token_ids.tolist()]
def run_to_completion(profile_dir: Optional[str] = None):
if profile_dir:
with torch.profiler.profile(
activities=[
torch.profiler.ProfilerActivity.CPU,
torch.profiler.ProfilerActivity.CUDA,
],
on_trace_ready=torch.profiler.tensorboard_trace_handler(
str(profile_dir))) as p:
llm.generate(dummy_inputs,
sampling_params=sampling_params,
use_tqdm=False)
print(p.key_averages())
else:
start_time = time.perf_counter()
llm.generate(dummy_inputs,
sampling_params=sampling_params,
use_tqdm=False)
end_time = time.perf_counter()
latency = end_time - start_time
return latency
print("Warming up...")
for _ in tqdm(range(args.num_iters_warmup), desc="Warmup iterations"):
run_to_completion(profile_dir=None)
if args.profile:
profile_dir = args.profile_result_dir
if not profile_dir:
profile_dir = Path(
"."
) / "vllm_benchmark_result" / f"latency_result_{time.time()}"
print(f"Profiling (results will be saved to '{profile_dir}')...")
run_to_completion(profile_dir=profile_dir)
return
# Benchmark.
latencies = []
for _ in tqdm(range(args.num_iters), desc="Profiling iterations"):
latencies.append(run_to_completion(profile_dir=None))
latencies = np.array(latencies)
percentages = [10, 25, 50, 75, 90]
percentiles = np.percentile(latencies, percentages)
print(f'Avg latency: {np.mean(latencies)} seconds')
for percentage, percentile in zip(percentages, percentiles):
print(f'{percentage}% percentile latency: {percentile} seconds')
# Output JSON results if specified
if args.output_json:
results = {
"avg_latency": np.mean(latencies),
"latencies": latencies.tolist(),
"percentiles": dict(zip(percentages, percentiles.tolist())),
}
with open(args.output_json, "w") as f:
json.dump(results, f, indent=4)
if __name__ == '__main__':
parser = argparse.ArgumentParser(
description='Benchmark the latency of processing a single batch of '
'requests till completion.')
parser.add_argument('--model', type=str, default='facebook/opt-125m')
parser.add_argument('--speculative-model', type=str, default=None)
parser.add_argument('--num-speculative-tokens', type=int, default=None)
parser.add_argument('--tokenizer', type=str, default=None)
parser.add_argument('--quantization',
'-q',
choices=[*QUANTIZATION_METHODS, None],
default=None)
parser.add_argument('--tensor-parallel-size', '-tp', type=int, default=1)
parser.add_argument('--input-len', type=int, default=32)
parser.add_argument('--output-len', type=int, default=128)
parser.add_argument('--batch-size', type=int, default=8)
parser.add_argument('--n',
type=int,
default=1,
help='Number of generated sequences per prompt.')
parser.add_argument('--use-beam-search', action='store_true')
parser.add_argument('--num-iters-warmup',
type=int,
default=10,
help='Number of iterations to run for warmup.')
parser.add_argument('--num-iters',
type=int,
default=30,
help='Number of iterations to run.')
parser.add_argument('--trust-remote-code',
action='store_true',
help='trust remote code from huggingface')
parser.add_argument(
'--dtype',
type=str,
default='auto',
choices=['auto', 'half', 'float16', 'bfloat16', 'float', 'float32'],
help='data type for model weights and activations. '
'The "auto" option will use FP16 precision '
'for FP32 and FP16 models, and BF16 precision '
'for BF16 models.')
parser.add_argument('--enforce-eager',
action='store_true',
help='enforce eager mode and disable CUDA graph')
parser.add_argument(
'--kv-cache-dtype',
type=str,
choices=['auto', 'fp8', 'fp8_e5m2', 'fp8_e4m3'],
default="auto",
help='Data type for kv cache storage. If "auto", will use model '
'data type. CUDA 11.8+ supports fp8 (=fp8_e4m3) and fp8_e5m2. '
'ROCm (AMD GPU) supports fp8 (=fp8_e4m3)')
parser.add_argument(
'--quantization-param-path',
type=str,
default=None,
help='Path to the JSON file containing the KV cache scaling factors. '
'This should generally be supplied, when KV cache dtype is FP8. '
'Otherwise, KV cache scaling factors default to 1.0, which may cause '
'accuracy issues. FP8_E5M2 (without scaling) is only supported on '
'cuda version greater than 11.8. On ROCm (AMD GPU), FP8_E4M3 is '
'instead supported for common inference criteria.')
parser.add_argument(
'--profile',
action='store_true',
help='profile the generation process of a single batch')
parser.add_argument(
'--profile-result-dir',
type=str,
default=None,
help=('path to save the pytorch profiler output. Can be visualized '
'with ui.perfetto.dev or Tensorboard.'))
parser.add_argument(
"--device",
type=str,
default="cuda",
choices=["cuda", "cpu"],
help='device type for vLLM execution, supporting CUDA and CPU.')
parser.add_argument('--block-size',
type=int,
default=16,
help='block size of key/value cache')
parser.add_argument(
'--enable-chunked-prefill',
action='store_true',
help='If True, the prefill requests can be chunked based on the '
'max_num_batched_tokens')
parser.add_argument('--use-v2-block-manager', action='store_true')
parser.add_argument(
"--ray-workers-use-nsight",
action='store_true',
help="If specified, use nsight to profile ray workers",
)
parser.add_argument('--download-dir',
type=str,
default=None,
help='directory to download and load the weights, '
'default to the default cache dir of huggingface')
parser.add_argument(
'--output-json',
type=str,
default=None,
help='Path to save the latency results in JSON format.')
parser.add_argument('--gpu-memory-utilization',
type=float,
default=0.9,
help='the fraction of GPU memory to be used for '
'the model executor, which can range from 0 to 1.'
'If unspecified, will use the default value of 0.9.')
parser.add_argument(
'--distributed-executor-backend',
choices=['ray', 'mp'],
default=None,
help='Backend to use for distributed serving. When more than 1 GPU '
'is used, will be automatically set to "ray" if installed '
'or "mp" (multiprocessing) otherwise.')
args = parser.parse_args()
main(args)
benchmarks/benchmark_prefix_caching.py 0 → 100644
View file @ 75b74b64
import argparse
import time
from vllm import LLM, SamplingParams
PROMPT = "You are a helpful assistant in recognizes the content of tables in markdown format. Here is a table as fellows. You need to answer my question about the table.\n# Table\n|Opening|Opening|Sl. No.|Film|Cast|Director|Music Director|Notes|\n|----|----|----|----|----|----|----|----|\n|J A N|9|1|Agni Pushpam|Jayabharathi, Kamalahasan|Jeassy|M. K. Arjunan||\n|J A N|16|2|Priyamvada|Mohan Sharma, Lakshmi, KPAC Lalitha|K. S. Sethumadhavan|V. Dakshinamoorthy||\n|J A N|23|3|Yakshagaanam|Madhu, Sheela|Sheela|M. S. Viswanathan||\n|J A N|30|4|Paalkkadal|Sheela, Sharada|T. K. Prasad|A. T. Ummer||\n|F E B|5|5|Amma|Madhu, Srividya|M. Krishnan Nair|M. K. Arjunan||\n|F E B|13|6|Appooppan|Thikkurissi Sukumaran Nair, Kamal Haasan|P. Bhaskaran|M. S. Baburaj||\n|F E B|20|7|Srishti|Chowalloor Krishnankutty, Ravi Alummoodu|K. T. Muhammad|M. S. Baburaj||\n|F E B|20|8|Vanadevatha|Prem Nazir, Madhubala|Yusufali Kechery|G. Devarajan||\n|F E B|27|9|Samasya|Madhu, Kamalahaasan|K. Thankappan|Shyam||\n|F E B|27|10|Yudhabhoomi|K. P. Ummer, Vidhubala|Crossbelt Mani|R. K. Shekhar||\n|M A R|5|11|Seemantha Puthran|Prem Nazir, Jayabharathi|A. B. Raj|M. K. Arjunan||\n|M A R|12|12|Swapnadanam|Rani Chandra, Dr. Mohandas|K. G. George|Bhaskar Chandavarkar||\n|M A R|19|13|Thulavarsham|Prem Nazir, sreedevi, Sudheer|N. Sankaran Nair|V. Dakshinamoorthy||\n|M A R|20|14|Aruthu|Kaviyoor Ponnamma, Kamalahasan|Ravi|G. Devarajan||\n|M A R|26|15|Swimming Pool|Kamal Haasan, M. G. Soman|J. Sasikumar|M. K. Arjunan||\n\n# Question\nWhat' s the content in the (1,1) cells\n" # noqa: E501
def test_prefix(llm=None, sampling_params=None, prompts=None):
start_time = time.time()
llm.generate(prompts, sampling_params=sampling_params)
end_time = time.time()
print(f"cost time {end_time - start_time}")
def main(args):
llm = LLM(model=args.model,
tokenizer_mode='auto',
trust_remote_code=True,
enforce_eager=True,
use_v2_block_manager=args.use_v2_block_manager,
tensor_parallel_size=args.tensor_parallel_size,
enable_prefix_caching=args.enable_prefix_caching)
num_prompts = 100
prompts = [PROMPT] * num_prompts
sampling_params = SamplingParams(temperature=0, max_tokens=args.output_len)
print("------warm up------")
test_prefix(
llm=llm,
prompts=prompts,
sampling_params=sampling_params,
)
print("------start generating------")
test_prefix(
llm=llm,
prompts=prompts,
sampling_params=sampling_params,
)
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description='Benchmark the performance with or without automatic '
'prefix caching.')
parser.add_argument('--model',
type=str,
default='baichuan-inc/Baichuan2-13B-Chat')
parser.add_argument('--tensor-parallel-size', '-tp', type=int, default=1)
parser.add_argument('--output-len', type=int, default=10)
parser.add_argument('--enable-prefix-caching',
action='store_true',
help='enable prefix caching')
parser.add_argument('--use-v2-block-manager',
action='store_true',
help='Use BlockSpaceMangerV2')
args = parser.parse_args()
main(args)
benchmarks/benchmark_serving.py 0 → 100644
View file @ 75b74b64
This diff is collapsed.
benchmarks/benchmark_throughput.py 0 → 100644
View file @ 75b74b64
"""Benchmark offline inference throughput."""
import argparse
import json
import random
import time
from typing import List, Optional, Tuple
import numpy as np
import torch
from tqdm import tqdm
from transformers import (AutoModelForCausalLM, AutoTokenizer,
PreTrainedTokenizerBase)
from vllm.inputs import PromptStrictInputs
from vllm.model_executor.layers.quantization import QUANTIZATION_METHODS
def sample_requests(
dataset_path: str,
num_requests: int,
tokenizer: PreTrainedTokenizerBase,
fixed_output_len: Optional[int],
) -> List[Tuple[str, int, int]]:
if fixed_output_len is not None and fixed_output_len < 4:
raise ValueError("output_len too small")
# Load the dataset.
with open(dataset_path) as f:
dataset = json.load(f)
# Filter out the conversations with less than 2 turns.
dataset = [data for data in dataset if len(data["conversations"]) >= 2]
# Only keep the first two turns of each conversation.
dataset = [(data["conversations"][0]["value"],
data["conversations"][1]["value"]) for data in dataset]
# Shuffle the dataset.
random.shuffle(dataset)
# Filter out sequences that are too long or too short
filtered_dataset: List[Tuple[str, int, int]] = []
for i in range(len(dataset)):
if len(filtered_dataset) == num_requests:
break
# Tokenize the prompts and completions.
prompt = dataset[i][0]
prompt_token_ids = tokenizer(prompt).input_ids
completion = dataset[i][1]
completion_token_ids = tokenizer(completion).input_ids
prompt_len = len(prompt_token_ids)
output_len = len(completion_token_ids
) if fixed_output_len is None else fixed_output_len
if prompt_len < 4 or output_len < 4:
# Prune too short sequences.
continue
if prompt_len > 1024 or prompt_len + output_len > 2048:
# Prune too long sequences.
continue
filtered_dataset.append((prompt, prompt_len, output_len))
return filtered_dataset
def run_vllm(
warmup_requests: List[Tuple[str, int, int]],
requests: List[Tuple[str, int, int]],
model: str,
tokenizer: str,
quantization: Optional[str],
tensor_parallel_size: int,
seed: int,
n: int,
use_beam_search: bool,
trust_remote_code: bool,
dtype: str,
max_model_len: Optional[int],
enforce_eager: bool,
kv_cache_dtype: str,
quantization_param_path: Optional[str],
device: str,
enable_prefix_caching: bool,
enable_chunked_prefill: bool,
max_num_batched_tokens: int,
distributed_executor_backend: Optional[str],
gpu_memory_utilization: float = 0.9,
download_dir: Optional[str] = None,
) -> float:
from vllm import LLM, SamplingParams
llm = LLM(
model=model,
tokenizer=tokenizer,
quantization=quantization,
tensor_parallel_size=tensor_parallel_size,
seed=seed,
trust_remote_code=trust_remote_code,
dtype=dtype,
max_model_len=max_model_len,
gpu_memory_utilization=gpu_memory_utilization,
enforce_eager=enforce_eager,
kv_cache_dtype=kv_cache_dtype,
quantization_param_path=quantization_param_path,
device=device,
enable_prefix_caching=enable_prefix_caching,
download_dir=download_dir,
enable_chunked_prefill=enable_chunked_prefill,
max_num_batched_tokens=max_num_batched_tokens,
distributed_executor_backend=distributed_executor_backend,
)
# Add the requests to the engine.
prompts = []
sampling_params = []
for prompt, _, output_len in requests:
prompts.append(prompt)
sampling_params.append(
SamplingParams(
n=n,
temperature=0.0 if use_beam_search else 1.0,
top_p=1.0,
use_beam_search=use_beam_search,
ignore_eos=True,
max_tokens=output_len,
))
# warmup
warmup_prompts = []
warmup_sampling_params = []
for prompt, _, output_len in warmup_requests:
warmup_prompts.append(prompt)
warmup_sampling_params.append(
SamplingParams(
n=n,
temperature=0.0 if use_beam_search else 1.0,
top_p=1.0,
use_beam_search=use_beam_search,
ignore_eos=True,
max_tokens=output_len,
))
print("Warming up...")
for _ in tqdm(range(args.num_iters_warmup), desc="Warmup iterations"):
llm.generate(warmup_prompts, warmup_sampling_params, use_tqdm=True)
# dummy_prompt_token_ids = np.random.randint(10000,
# size=(args.num_prompts,
# args.input_len))
# dummy_inputs: List[PromptStrictInputs] = [{
# "prompt_token_ids": batch
# } for batch in dummy_prompt_token_ids.tolist()]
# def run_to_completion():
# llm.generate(dummy_inputs,
# sampling_params=sampling_params,
# use_tqdm=False)
# print("Warming up...")
# for _ in tqdm(range(args.num_iters_warmup), desc="Warmup iterations"):
# run_to_completion()
start = time.perf_counter()
llm.generate(prompts, sampling_params, use_tqdm=True)
end = time.perf_counter()
return end - start
def run_hf(
requests: List[Tuple[str, int, int]],
model: str,
tokenizer: PreTrainedTokenizerBase,
n: int,
use_beam_search: bool,
max_batch_size: int,
trust_remote_code: bool,
) -> float:
assert not use_beam_search
llm = AutoModelForCausalLM.from_pretrained(
model, torch_dtype=torch.float16, trust_remote_code=trust_remote_code)
if llm.config.model_type == "llama":
# To enable padding in the HF backend.
tokenizer.pad_token = tokenizer.eos_token
llm = llm.cuda()
pbar = tqdm(total=len(requests))
start = time.perf_counter()
batch: List[str] = []
max_prompt_len = 0
max_output_len = 0
for i in range(len(requests)):
prompt, prompt_len, output_len = requests[i]
# Add the prompt to the batch.
batch.append(prompt)
max_prompt_len = max(max_prompt_len, prompt_len)
max_output_len = max(max_output_len, output_len)
if len(batch) < max_batch_size and i != len(requests) - 1:
# Check if we can add more requests to the batch.
_, next_prompt_len, next_output_len = requests[i + 1]
if (max(max_prompt_len, next_prompt_len) +
max(max_output_len, next_output_len)) <= 2048:
# We can add more requests to the batch.
continue
# Generate the sequences.
input_ids = tokenizer(batch, return_tensors="pt",
padding=True).input_ids
llm_outputs = llm.generate(
input_ids=input_ids.cuda(),
do_sample=not use_beam_search,
num_return_sequences=n,
temperature=1.0,
top_p=1.0,
use_cache=True,
max_new_tokens=max_output_len,
)
# Include the decoding time.
tokenizer.batch_decode(llm_outputs, skip_special_tokens=True)
pbar.update(len(batch))
# Clear the batch.
batch = []
max_prompt_len = 0
max_output_len = 0
end = time.perf_counter()
return end - start
def run_mii(
requests: List[Tuple[str, int, int]],
model: str,
tensor_parallel_size: int,
output_len: int,
) -> float:
from mii import client, serve
llm = serve(model, tensor_parallel=tensor_parallel_size)
prompts = [prompt for prompt, _, _ in requests]
start = time.perf_counter()
llm.generate(prompts, max_new_tokens=output_len)
end = time.perf_counter()
client = client(model)
client.terminate_server()
return end - start
def main(args: argparse.Namespace):
print(args)
random.seed(args.seed)
# Sample the requests.
tokenizer = AutoTokenizer.from_pretrained(
args.tokenizer, trust_remote_code=args.trust_remote_code)
warmup_prompt = "hi" * 10
warmup_requests = [(warmup_prompt, 10, 10)
for _ in range(1)]
if args.dataset is None:
# Synthesize a prompt with the given input length.
prompt = "hi" * (args.input_len - 1)
requests = [(prompt, args.input_len, args.output_len)
for _ in range(args.num_prompts)]
else:
requests = sample_requests(args.dataset, args.num_prompts, tokenizer,
args.output_len)
if args.backend == "vllm":
elapsed_time = run_vllm(
warmup_requests, requests, args.model, args.tokenizer, args.quantization,
args.tensor_parallel_size, args.seed, args.n, args.use_beam_search,
args.trust_remote_code, args.dtype, args.max_model_len,
args.enforce_eager, args.kv_cache_dtype,
args.quantization_param_path, args.device,
args.enable_prefix_caching, args.enable_chunked_prefill,
args.max_num_batched_tokens, args.distributed_executor_backend,
args.gpu_memory_utilization, args.download_dir)
elif args.backend == "hf":
assert args.tensor_parallel_size == 1
elapsed_time = run_hf(requests, args.model, tokenizer, args.n,
args.use_beam_search, args.hf_max_batch_size,
args.trust_remote_code)
elif args.backend == "mii":
elapsed_time = run_mii(requests, args.model, args.tensor_parallel_size,
args.output_len)
else:
raise ValueError(f"Unknown backend: {args.backend}")
total_num_tokens = sum(prompt_len + output_len
for _, prompt_len, output_len in requests)
if args.dataset is None:
total_out_tokens = args.output_len * args.num_prompts
else:
total_out_tokens = sum(output_len for _, _, output_len in requests)
print(f"Latency: {elapsed_time:.2f} s")
print(f"All Throughput: {len(requests) / elapsed_time:.2f} requests/s, "
f"{total_num_tokens / elapsed_time:.2f} tokens/s")
print(f"Generate Throughput: {total_out_tokens / elapsed_time:.2f} tokens/s")
# Output JSON results if specified
if args.output_json:
results = {
"elapsed_time": elapsed_time,
"num_requests": len(requests),
"total_num_tokens": total_num_tokens,
"requests_per_second": len(requests) / elapsed_time,
"tokens_per_second": total_num_tokens / elapsed_time,
}
with open(args.output_json, "w") as f:
json.dump(results, f, indent=4)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Benchmark the throughput.")
parser.add_argument("--backend",
type=str,
choices=["vllm", "hf", "mii"],
default="vllm")
parser.add_argument("--dataset",
type=str,
default=None,
help="Path to the dataset.")
parser.add_argument("--input-len",
type=int,
default=None,
help="Input prompt length for each request")
parser.add_argument("--output-len",
type=int,
default=None,
help="Output length for each request. Overrides the "
"output length from the dataset.")
parser.add_argument("--model", type=str, default="facebook/opt-125m")
parser.add_argument("--tokenizer", type=str, default=None)
parser.add_argument('--quantization',
'-q',
choices=[*QUANTIZATION_METHODS, None],
default=None)
parser.add_argument("--tensor-parallel-size", "-tp", type=int, default=1)
parser.add_argument("--n",
type=int,
default=1,
help="Number of generated sequences per prompt.")
parser.add_argument("--use-beam-search", action="store_true")
parser.add_argument('--num-iters-warmup',
type=int,
default=1,
help='Number of iterations to run for warmup.')
parser.add_argument("--num-prompts",
type=int,
default=1000,
help="Number of prompts to process.")
parser.add_argument("--seed", type=int, default=0)
parser.add_argument("--hf-max-batch-size",
type=int,
default=None,
help="Maximum batch size for HF backend.")
parser.add_argument('--trust-remote-code',
action='store_true',
help='trust remote code from huggingface')
parser.add_argument(
'--max-model-len',
type=int,
default=None,
help='Maximum length of a sequence (including prompt and output). '
'If None, will be derived from the model.')
parser.add_argument(
'--dtype',
type=str,
default='auto',
choices=['auto', 'half', 'float16', 'bfloat16', 'float', 'float32'],
help='data type for model weights and activations. '
'The "auto" option will use FP16 precision '
'for FP32 and FP16 models, and BF16 precision '
'for BF16 models.')
parser.add_argument('--gpu-memory-utilization',
type=float,
default=0.9,
help='the fraction of GPU memory to be used for '
'the model executor, which can range from 0 to 1.'
'If unspecified, will use the default value of 0.9.')
parser.add_argument("--enforce-eager",
action="store_true",
help="enforce eager execution")
parser.add_argument(
'--kv-cache-dtype',
type=str,
choices=['auto', 'fp8', 'fp8_e5m2', 'fp8_e4m3'],
default="auto",
help='Data type for kv cache storage. If "auto", will use model '
'data type. CUDA 11.8+ supports fp8 (=fp8_e4m3) and fp8_e5m2. '
'ROCm (AMD GPU) supports fp8 (=fp8_e4m3)')
parser.add_argument(
'--quantization-param-path',
type=str,
default=None,
help='Path to the JSON file containing the KV cache scaling factors. '
'This should generally be supplied, when KV cache dtype is FP8. '
'Otherwise, KV cache scaling factors default to 1.0, which may cause '
'accuracy issues. FP8_E5M2 (without scaling) is only supported on '
'cuda version greater than 11.8. On ROCm (AMD GPU), FP8_E4M3 is '
'instead supported for common inference criteria.')
parser.add_argument(
"--device",
type=str,
default="cuda",
choices=["cuda", "cpu"],
help='device type for vLLM execution, supporting CUDA and CPU.')
parser.add_argument(
"--enable-prefix-caching",
action='store_true',
help="enable automatic prefix caching for vLLM backend.")
parser.add_argument("--enable-chunked-prefill",
action='store_true',
help="enable chunked prefill for vLLM backend.")
parser.add_argument('--max-num-batched-tokens',
type=int,
default=None,
help='maximum number of batched tokens per '
'iteration')
parser.add_argument('--download-dir',
type=str,
default=None,
help='directory to download and load the weights, '
'default to the default cache dir of huggingface')
parser.add_argument(
'--output-json',
type=str,
default=None,
help='Path to save the throughput results in JSON format.')
parser.add_argument(
'--distributed-executor-backend',
choices=['ray', 'mp'],
default=None,
help='Backend to use for distributed serving. When more than 1 GPU '
'is used, will be automatically set to "ray" if installed '
'or "mp" (multiprocessing) otherwise.')
args = parser.parse_args()
if args.tokenizer is None:
args.tokenizer = args.model
if args.dataset is None:
assert args.input_len is not None
assert args.output_len is not None
else:
assert args.input_len is None
if args.backend == "vllm":
if args.hf_max_batch_size is not None:
raise ValueError("HF max batch size is only for HF backend.")
elif args.backend == "hf":
if args.hf_max_batch_size is None:
raise ValueError("HF max batch size is required for HF backend.")
if args.quantization is not None:
raise ValueError("Quantization is only for vLLM backend.")
elif args.backend == "mii":
if args.dtype != "auto":
raise ValueError("dtype must be auto for MII backend.")
if args.n != 1:
raise ValueError("n must be 1 for MII backend.")
if args.use_beam_search:
raise ValueError("Beam search is not supported for MII backend.")
if args.quantization is not None:
raise ValueError("Quantization is only for vLLM backend.")
if args.hf_max_batch_size is not None:
raise ValueError("HF max batch size is only for HF backend.")
if args.tokenizer != args.model:
raise ValueError("Tokenizer must be the same as the model for MII "
"backend.")
main(args)
benchmarks/cutlass_benchmarks/w8a8_benchmarks.py 0 → 100644
View file @ 75b74b64
import argparse
import copy
import itertools
import pickle as pkl
import time
from typing import Callable, Iterable, List, Tuple
import torch
import torch.utils.benchmark as TBenchmark
from torch.utils.benchmark import Measurement as TMeasurement
from weight_shapes import WEIGHT_SHAPES
from vllm import _custom_ops as ops
DEFAULT_MODELS = list(WEIGHT_SHAPES.keys())[1:]
DEFAULT_BATCH_SIZES = [1, 16, 32, 64, 128, 256, 512]
DEFAULT_TP_SIZES = [1]
# helpers
def to_fp8(tensor: torch.tensor) -> torch.tensor:
finfo = torch.finfo(torch.float8_e4m3fn)
return torch.round(tensor.clamp(
min=finfo.min, max=finfo.max)).to(dtype=torch.float8_e4m3fn)
def to_int8(tensor: torch.tensor) -> torch.tensor:
return torch.round(tensor.clamp(min=-128, max=127)).to(dtype=torch.int8)
def make_rand_tensors(dtype: torch.dtype, m: int, n: int,
k: int) -> Tuple[torch.tensor, torch.tensor]:
a = torch.randn((m, k), device='cuda') * 5
b = torch.randn((n, k), device='cuda').t() * 5
if dtype == torch.int8:
return to_int8(a), to_int8(b)
if dtype == torch.float8_e4m3fn:
return to_fp8(a), to_fp8(b)
raise ValueError("unsupported dtype")
# impl
def pytorch_i8_impl(a: torch.tensor, b: torch.tensor, scale_a: torch.tensor,
scale_b: torch.tensor,
out_dtype: torch.dtype) -> torch.tensor:
return torch.mm(a, b)
def pytorch_fp8_impl(a: torch.tensor, b: torch.tensor, scale_a: torch.tensor,
scale_b: torch.tensor,
out_dtype: torch.dtype) -> torch.tensor:
return torch._scaled_mm(a,
b,
scale_a=scale_a,
scale_b=scale_b,
out_dtype=out_dtype)
def pytorch_fp8_impl_fast_accum(a: torch.tensor, b: torch.tensor,
scale_a: torch.tensor, scale_b: torch.tensor,
out_dtype: torch.dtype) -> torch.tensor:
return torch._scaled_mm(a,
b,
scale_a=scale_a,
scale_b=scale_b,
out_dtype=out_dtype,
use_fast_accum=True)
def cutlass_impl(a: torch.tensor, b: torch.tensor, scale_a: torch.tensor,
scale_b: torch.tensor,
out_dtype: torch.dtype) -> torch.tensor:
return ops.cutlass_scaled_mm_dq(a,
b,
scale_a,
scale_b,
out_dtype=out_dtype)
# bench
def bench_fn(a: torch.tensor, b: torch.tensor, scale_a: torch.tensor,
scale_b: torch.tensor, out_dtype: torch.dtype, label: str,
sub_label: str, fn: Callable, description: str) -> TMeasurement:
min_run_time = 1
globals = {
"a": a,
"b": b,
"scale_a": scale_a,
"scale_b": scale_b,
"out_dtype": out_dtype,
"fn": fn,
}
return TBenchmark.Timer(
stmt="fn(a, b, scale_a, scale_b, out_dtype)",
globals=globals,
label=label,
sub_label=sub_label,
description=description,
).blocked_autorange(min_run_time=min_run_time)
def bench_int8(dtype: torch.dtype, m: int, k: int, n: int, label: str,
sub_label: str) -> Iterable[TMeasurement]:
assert dtype == torch.int8
a, b = make_rand_tensors(torch.int8, m, n, k)
scale_a = torch.tensor(1.0, device="cuda", dtype=torch.float32)
scale_b = torch.tensor(1.0, device="cuda", dtype=torch.float32)
timers = []
# pytorch impl
timers.append(
bench_fn(a.to(dtype=torch.bfloat16, device="cuda"),
b.to(dtype=torch.bfloat16, device="cuda"), scale_a, scale_b,
torch.bfloat16, label, sub_label, pytorch_i8_impl,
"pytorch_bf16_bf16_bf16_matmul-no-scales"))
# cutlass impl
timers.append(
bench_fn(a, b, scale_a.to(device="cpu"), scale_b.to(device="cpu"),
torch.bfloat16, label, sub_label, cutlass_impl,
"cutlass_i8_i8_bf16_scaled_mm"))
return timers
def bench_fp8(dtype: torch.dtype, m: int, k: int, n: int, label: str,
sub_label: str) -> Iterable[TMeasurement]:
assert dtype == torch.float8_e4m3fn
a, b = make_rand_tensors(torch.float8_e4m3fn, m, n, k)
scale_a = torch.tensor(1.0, device="cuda", dtype=torch.float32)
scale_b = torch.tensor(1.0, device="cuda", dtype=torch.float32)
timers = []
# pytorch impl: bf16 output, without fp8 fast accum
timers.append(
bench_fn(a, b, scale_a, scale_b, torch.bfloat16, label, sub_label,
pytorch_fp8_impl, "pytorch_fp8_fp8_bf16_scaled_mm"))
# pytorch impl: bf16 output, with fp8 fast accum
timers.append(
bench_fn(a, b, scale_a, scale_b, torch.bfloat16, label, sub_label,
pytorch_fp8_impl_fast_accum,
"pytorch_fp8_fp8_bf16_scaled_mm_fast_accum"))
# pytorch impl: fp16 output, without fp8 fast accum
timers.append(
bench_fn(a, b, scale_a, scale_b, torch.float16, label, sub_label,
pytorch_fp8_impl, "pytorch_fp8_fp8_fp16_scaled_mm"))
# pytorch impl: fp16 output, with fp8 fast accum
timers.append(
bench_fn(a, b, scale_a, scale_b, torch.float16, label, sub_label,
pytorch_fp8_impl_fast_accum,
"pytorch_fp8_fp8_fp16_scaled_mm_fast_accum"))
# cutlass impl: bf16 output
timers.append(
bench_fn(a, b, scale_a.to(device="cpu"), scale_b.to(device="cpu"),
torch.bfloat16, label, sub_label, cutlass_impl,
"cutlass_fp8_fp8_bf16_scaled_mm"))
# cutlass impl: fp16 output
timers.append(
bench_fn(a, b, scale_a.to(device="cpu"), scale_b.to(device="cpu"),
torch.float16, label, sub_label, cutlass_impl,
"cutlass_fp8_fp8_fp16_scaled_mm"))
return timers
def bench(dtype: torch.dtype, m: int, k: int, n: int, label: str,
sub_label: str) -> Iterable[TMeasurement]:
if dtype == torch.int8:
return bench_int8(dtype, m, k, n, label, sub_label)
if dtype == torch.float8_e4m3fn:
return bench_fp8(dtype, m, k, n, label, sub_label)
raise ValueError("unsupported type")
# runner
def print_timers(timers: Iterable[TMeasurement]):
compare = TBenchmark.Compare(timers)
compare.print()
def run(dtype: torch.dtype,
MKNs: Iterable[Tuple[int, int, int]]) -> Iterable[TMeasurement]:
results = []
for m, k, n in MKNs:
timers = bench(dtype, m, k, n, f"scaled-{dtype}-gemm",
f"MKN=({m}x{k}x{n})")
print_timers(timers)
results.extend(timers)
return results
# output makers
def make_output(data: Iterable[TMeasurement],
MKNs: Iterable[Tuple[int, int, int]],
base_description: str,
timestamp=None):
print(f"== All Results {base_description} ====")
print_timers(data)
# pickle all the results
timestamp = int(time.time()) if timestamp is None else timestamp
with open(f"{base_description}-{timestamp}.pkl", "wb") as f:
pkl.dump(data, f)
# argparse runners
def run_square_bench(args):
dim_sizes = list(
range(args.dim_start, args.dim_end + 1, args.dim_increment))
MKNs = list(zip(dim_sizes, dim_sizes, dim_sizes))
data = run(args.dtype, MKNs)
make_output(data, MKNs, f"square_bench-{args.dtype}")
def run_range_bench(args):
dim_sizes = list(range(args.dim_start, args.dim_end, args.dim_increment))
n = len(dim_sizes)
Ms = [args.m_constant] * n if args.m_constant is not None else dim_sizes
Ks = [args.k_constant] * n if args.k_constant is not None else dim_sizes
Ns = [args.n_constant] * n if args.n_constant is not None else dim_sizes
MKNs = list(zip(Ms, Ks, Ns))
data = run(args.dtype, MKNs)
make_output(data, MKNs, f"range_bench-{args.dtype}")
def run_model_bench(args):
print("Benchmarking models:")
for i, model in enumerate(args.models):
print(f"[{i}] {model}")
def model_shapes(model_name: str, tp_size: int) -> List[Tuple[int, int]]:
KNs = []
for KN, tp_split_dim in copy.deepcopy(WEIGHT_SHAPES[model_name]):
KN[tp_split_dim] = KN[tp_split_dim] // tp_size
KNs.append(KN)
return KNs
model_bench_data = []
models_tps = list(itertools.product(args.models, args.tp_sizes))
for model, tp_size in models_tps:
Ms = args.batch_sizes
KNs = model_shapes(model, tp_size)
MKNs = []
for m in Ms:
for k, n in KNs:
MKNs.append((m, k, n))
data = run(args.dtype, MKNs)
model_bench_data.append(data)
# Print all results
for data, model_tp in zip(model_bench_data, models_tps):
model, tp_size = model_tp
print(f"== Results {args.dtype} {model}-TP{tp_size} ====")
print_timers(data)
timestamp = int(time.time())
all_data = []
for d in model_bench_data:
all_data.extend(d)
# pickle all data
with open(f"model_bench-{args.dtype}-{timestamp}.pkl", "wb") as f:
pkl.dump(all_data, f)
if __name__ == '__main__':
def to_torch_dtype(dt):
if dt == "int8":
return torch.int8
if dt == "fp8":
return torch.float8_e4m3fn
raise ValueError("unsupported dtype")
parser = argparse.ArgumentParser(
description="""
Benchmark Cutlass GEMM.
To run square GEMMs:
python3 ./benchmarks/cutlass_benchmarks/w8a8_benchmarks.py --dtype fp8 square_bench --dim-start 128 --dim-end 512 --dim-increment 64
To run constant N and K and sweep M:
python3 ./benchmarks/cutlass_benchmarks/w8a8_benchmarks.py --dtype fp8 range_bench --dim-start 128 --dim-end 512 --dim-increment 64 --n-constant 16384 --k-constant 16384
To run dimensions from a model:
python3 ./benchmarks/cutlass_benchmarks/w8a8_benchmarks.py --dtype fp8 model_bench --models meta-llama/Llama-2-7b-hf --batch-sizes 16 --tp-sizes 1
Output:
- a .pkl file, that is a list of raw torch.benchmark.utils.Measurements for the pytorch and cutlass implementations for the various GEMMs.
""", # noqa: E501
formatter_class=argparse.RawTextHelpFormatter)
parser.add_argument("--dtype",
type=to_torch_dtype,
required=True,
help="Available options are ['int8', 'fp8']")
subparsers = parser.add_subparsers(dest="cmd")
square_parser = subparsers.add_parser("square_bench")
square_parser.add_argument("--dim-start", type=int, required=True)
square_parser.add_argument("--dim-end", type=int, required=True)
square_parser.add_argument("--dim-increment", type=int, required=True)
square_parser.set_defaults(func=run_square_bench)
range_parser = subparsers.add_parser("range_bench")
range_parser.add_argument("--dim-start", type=int, required=True)
range_parser.add_argument("--dim-end", type=int, required=True)
range_parser.add_argument("--dim-increment", type=int, required=True)
range_parser.add_argument("--m-constant", type=int, default=None)
range_parser.add_argument("--n-constant", type=int, default=None)
range_parser.add_argument("--k-constant", type=int, default=None)
range_parser.set_defaults(func=run_range_bench)
model_parser = subparsers.add_parser("model_bench")
model_parser.add_argument("--models",
nargs="+",
type=str,
default=DEFAULT_MODELS,
choices=WEIGHT_SHAPES.keys())
model_parser.add_argument("--tp-sizes",
nargs="+",
type=int,
default=DEFAULT_TP_SIZES)
model_parser.add_argument("--batch-sizes",
nargs="+",
type=int,
default=DEFAULT_BATCH_SIZES)
model_parser.set_defaults(func=run_model_bench)
args = parser.parse_args()
args.func(args)
benchmarks/cutlass_benchmarks/weight_shapes.py 0 → 100644
View file @ 75b74b64
# Weight Shapes are in the format
# ([K, N], TP_SPLIT_DIM)
# Example:
# A shape of ([14336, 4096], 0) indicates the following GEMM shape,
# - TP1 : K = 14336, N = 4096
# - TP2 : K = 7168, N = 4096
# A shape of ([4096, 6144], 1) indicates the following GEMM shape,
# - TP1 : K = 4096, N = 6144
# - TP4 : K = 4096, N = 1536
# TP1 shapes
WEIGHT_SHAPES = {
"mistralai/Mistral-7B-v0.1": [
([4096, 6144], 1),
([4096, 4096], 0),
([4096, 28672], 1),
([14336, 4096], 0),
],
"meta-llama/Llama-2-7b-hf": [
([4096, 12288], 1),
([4096, 4096], 0),
([4096, 22016], 1),
([11008, 4096], 0),
],
"meta-llama/Llama-2-13b-hf": [
([5120, 15360], 1),
([5120, 5120], 0),
([5120, 27648], 1),
([13824, 5120], 0),
],
"meta-llama/Llama-2-70b-hf": [
([8192, 10240], 1),
([8192, 8192], 0),
([8192, 57344], 1),
([28672, 8192], 0),
],
}
benchmarks/kernels/benchmark_aqlm.py 0 → 100644
View file @ 75b74b64
import argparse
import os
import sys
from typing import Optional
import torch
import torch.nn.functional as F
from vllm import _custom_ops as ops
from vllm.model_executor.layers.quantization.aqlm import (
dequantize_weight, generic_dequantize_gemm, get_int_dtype,
optimized_dequantize_gemm)
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
def torch_mult(
input: torch.Tensor, # [..., in_features]
weights: torch.Tensor,
scales: torch.Tensor, # [num_out_groups, 1, 1, 1]
) -> torch.Tensor:
output = F.linear(input, weights)
return output
def dequant_out_scale(
input: torch.Tensor, # [..., in_features]
codes: torch.IntTensor, # [num_out_groups, num_in_groups, num_codebooks]
codebooks: torch.
Tensor, # [num_codebooks, codebook_size, out_group_size, in_group_size]
scales: torch.Tensor, # [num_out_groups, 1, 1, 1]
output_partition_sizes: torch.IntTensor,
bias: Optional[torch.Tensor],
) -> torch.Tensor:
weights = ops.aqlm_dequant(codes, codebooks, output_partition_sizes)
if bias is None:
output = F.linear(input, weights, bias)
orig_shape = output.shape
flattened_output = output.view(-1, output.size(-1))
f_scales = scales.view(-1, scales.shape[0])
b_scales = f_scales.expand(flattened_output.shape[0], -1)
flattened_output *= b_scales
return flattened_output.view(orig_shape)
else:
b_scales = scales.view(scales.shape[:-3] + (-1, )).expand(
-1, weights.shape[1])
weights *= b_scales
return F.linear(input, weights, bias)
def dequant_weight_scale(
input: torch.Tensor, # [..., in_features]
codes: torch.IntTensor, # [num_out_groups, num_in_groups, num_codebooks]
codebooks: torch.
Tensor, # [num_codebooks, codebook_size, out_group_size, in_group_size]
scales: torch.Tensor, # [num_out_groups, 1, 1, 1]
output_partition_sizes: torch.IntTensor,
bias: Optional[torch.Tensor],
) -> torch.Tensor:
weights = ops.aqlm_dequant(codes, codebooks, output_partition_sizes)
b_scales = scales.view(scales.shape[:-3] + (-1, )).expand(
-1, weights.shape[1])
weights *= b_scales
return F.linear(input, weights, bias)
def dequant_no_scale(
input: torch.Tensor, # [..., in_features]
codes: torch.IntTensor, # [num_out_groups, num_in_groups, num_codebooks]
codebooks: torch.
Tensor, # [num_codebooks, codebook_size, out_group_size, in_group_size]
scales: torch.Tensor, # [num_out_groups, 1, 1, 1]
output_partition_sizes: torch.IntTensor,
bias: Optional[torch.Tensor],
) -> torch.Tensor:
weights = ops.aqlm_dequant(codes, codebooks, output_partition_sizes)
return F.linear(input, weights, bias)
# Compare the optimized 1x16 and 2x8 cuda decompression/dequant kernels against
# the generic pytorch version.
# Just visual comparison.
def dequant_test(k: int, parts: torch.tensor, nbooks: int, bits: int) -> None:
n = parts.sum().item()
device = torch.device('cuda:0')
code_range = (1 << bits) // 2
ingroups = 8
codes = torch.randint(-code_range,
code_range,
size=(n, k // ingroups, nbooks),
dtype=get_int_dtype(bits),
device=device)
codebooks = torch.randn(size=(parts.shape[0] * nbooks, 1 << bits, 1, 8),
dtype=torch.float16,
device=device)
count = 0
for index in range(16):
for i in range(8):
for book in range(nbooks):
codebooks[book, index, 0, i] = count * (10**book)
count += 1
print("codes shape", codes.shape)
for i in range(16):
for book in range(nbooks):
codes[0, i, book] = i
codes[0, -i, book] = i
weights = dequantize_weight(codes, codebooks, None)
weights2 = ops.aqlm_dequant(codes, codebooks, parts)
print("weights shape:", weights.shape)
print("weights2 shape:", weights2.shape)
print("weights are:", weights)
print("weights2 are:", weights2)
print("first 128 weights are", weights[0, 0:128].to(torch.int32))
print("first 128 weights2 are:", weights2[0, 0:128].to(torch.int32))
print("last 128 weights are", weights[0, -128:])
print("last 128 weights2 are:", weights2[0, -128:])
def main():
parser = argparse.ArgumentParser(description="Benchmark aqlm performance.")
# Add arguments
parser.add_argument("--nbooks",
type=int,
default=1,
help="Number of codebooks (default: 1)")
parser.add_argument("--bits",
type=int,
default=16,
help="Number of bits per code element (default: 16)")
parser.add_argument(
"--test",
type=bool,
default=False,
help="Run the decompression/dequant tester rather than benchmarking "
"(default: False)")
# Parse the arguments
args = parser.parse_args()
# Extract values
nbooks = args.nbooks
bits = args.bits
if args.test:
dequant_test(4096, torch.tensor((4096, )), nbooks, bits)
return
# Otherwise, benchmark.
methods = [
ops.aqlm_gemm,
dequant_out_scale,
generic_dequantize_gemm,
optimized_dequantize_gemm,
dequant_weight_scale,
torch_mult,
dequant_no_scale,
]
filename = f"./aqlm_benchmark_{nbooks}x{bits}.csv"
print(f"writing benchmarks to file {filename}")
with open(filename, "w") as f:
sys.stdout = f
print('m | k | n | n parts', end='')
for method in methods:
print(f" | {method.__name__.replace('_', ' ')} (µs)", end='')
print('')
# These are reasonable prefill sizes.
ksandpartions = ((4096, (4096, 4096, 4096)), (4096, (4096, )),
(4096, (11008, 11008)), (11008, (4096, )))
# reasonable ranges for m.
for m in [
1, 2, 4, 8, 10, 12, 14, 16, 24, 32, 48, 52, 56, 64, 96, 112,
128, 256, 512, 1024, 1536, 2048, 3072, 4096
]:
print(f'{m}', file=sys.__stdout__)
for ksp in ksandpartions:
run_grid(m, ksp[0], torch.tensor(ksp[1]), nbooks, bits,
methods)
sys.stdout = sys.__stdout__
def run_grid(m: int, k: int, parts: torch.tensor, nbooks: int, bits: int,
methods):
# I didn't see visible improvements from increasing these, but feel free :)
num_warmup_trials = 1
num_trials = 1
num_calls = 100
# warmup.
for method in methods:
for _ in range(num_warmup_trials):
run_timing(
num_calls=num_calls,
m=m,
k=k,
parts=parts,
nbooks=nbooks,
bits=bits,
method=method,
)
n = parts.sum().item()
print(f'{m} | {k} | {n} | {parts.tolist()}', end='')
for method in methods:
best_time_us = 1e20
for _ in range(num_trials):
kernel_dur_ms = run_timing(
num_calls=num_calls,
m=m,
k=k,
parts=parts,
nbooks=nbooks,
bits=bits,
method=method,
)
kernel_dur_us = 1000 * kernel_dur_ms
if kernel_dur_us < best_time_us:
best_time_us = kernel_dur_us
print(f' | {kernel_dur_us:.0f}', end='')
print('')
def run_timing(num_calls: int, m: int, k: int, parts: torch.tensor,
nbooks: int, bits: int, method) -> float:
n = parts.sum().item()
device = torch.device('cuda:0')
input = torch.randn((1, m, k), dtype=torch.float16, device=device)
code_range = (1 << bits) // 2
ingroups = 8
codes = torch.randint(-code_range,
code_range,
size=(n, k // ingroups, nbooks),
dtype=get_int_dtype(bits),
device=device)
codebooks = torch.randn(size=(parts.shape[0] * nbooks, 1 << bits, 1, 8),
dtype=torch.float16,
device=device)
scales = torch.randn(size=(n, 1, 1, 1), dtype=torch.float16, device=device)
# for comparison to just a pytorch mult.
weights = torch.randn((n, k), dtype=torch.float16, device=device)
start_event = torch.cuda.Event(enable_timing=True)
end_event = torch.cuda.Event(enable_timing=True)
start_event.record()
if method is torch_mult:
for i in range(num_calls):
torch_mult(input, weights, scales)
else:
for i in range(num_calls):
method(input, codes, codebooks, scales, parts, None)
end_event.record()
end_event.synchronize()
dur_ms = start_event.elapsed_time(end_event) / num_calls
return dur_ms
if __name__ == "__main__":
sys.exit(main())
benchmarks/kernels/benchmark_marlin.py 0 → 100644
View file @ 75b74b64
import argparse
import torch
import torch.utils.benchmark as benchmark
from benchmark_shapes import WEIGHT_SHAPES
from vllm import _custom_ops as ops
from vllm.model_executor.layers.quantization.gptq_marlin import (
GPTQ_MARLIN_MAX_PARALLEL, GPTQ_MARLIN_MIN_THREAD_N,
GPTQ_MARLIN_SUPPORTED_GROUP_SIZES, GPTQ_MARLIN_SUPPORTED_NUM_BITS)
from vllm.model_executor.layers.quantization.gptq_marlin_24 import (
GPTQ_MARLIN_24_MAX_PARALLEL, GPTQ_MARLIN_24_MIN_THREAD_N,
GPTQ_MARLIN_24_SUPPORTED_GROUP_SIZES, GPTQ_MARLIN_24_SUPPORTED_NUM_BITS)
from vllm.model_executor.layers.quantization.utils.marlin_utils import (
MarlinWorkspace, marlin_24_quantize, marlin_quantize)
from vllm.model_executor.layers.quantization.utils.quant_utils import (
gptq_pack, quantize_weights, sort_weights)
DEFAULT_MODELS = ["meta-llama/Llama-2-7b-hf/TP1"]
DEFAULT_BATCH_SIZES = [1, 16, 32, 64, 128, 256, 512]
ACT_ORDER_OPTS = [False, True]
K_FULL_OPTS = [False, True]
def bench_run(results, model, act_order, is_k_full, num_bits, group_size,
size_m, size_k, size_n):
label = "Quant Matmul"
sub_label = ("{}, act={} k_full={}, b={}, g={}, "
"MKN=({}x{}x{})".format(model, act_order, is_k_full, num_bits,
group_size, size_m, size_k, size_n))
print(f"Testing: {sub_label}")
a = torch.randn(size_m, size_k).to(torch.half).cuda()
b = torch.rand(size_k, size_n).to(torch.half).cuda()
a_tmp = (torch.zeros(size_m, size_k).to(torch.half).cuda())
# Marlin quant
(
marlin_w_ref,
marlin_q_w,
marlin_s,
marlin_g_idx,
marlin_sort_indices,
marlin_rand_perm,
) = marlin_quantize(b, num_bits, group_size, act_order)
# Marlin_24 quant
(marlin_24_w_ref, marlin_24_q_w_comp, marlin_24_meta,
marlin_24_s) = marlin_24_quantize(b, num_bits, group_size)
# GPTQ quant
(w_ref, q_w, s, g_idx,
rand_perm) = quantize_weights(b, num_bits, group_size, act_order)
q_w_gptq = gptq_pack(q_w, num_bits, size_k, size_n)
# For act_order, sort the "weights" and "g_idx"
# so that group ids are increasing
repack_sort_indices = torch.empty(0, dtype=torch.int, device=b.device)
if act_order:
(q_w, g_idx, repack_sort_indices) = sort_weights(q_w, g_idx)
# Prepare
marlin_workspace = MarlinWorkspace(size_n, GPTQ_MARLIN_MIN_THREAD_N,
GPTQ_MARLIN_MAX_PARALLEL)
marlin_24_workspace = MarlinWorkspace(size_n, GPTQ_MARLIN_24_MIN_THREAD_N,
GPTQ_MARLIN_24_MAX_PARALLEL)
globals = {
# Gen params
"num_bits": num_bits,
"group_size": group_size,
"size_m": size_m,
"size_n": size_n,
"size_k": size_k,
"a": a,
"a_tmp": a_tmp,
# Marlin params
"marlin_w_ref": marlin_w_ref,
"marlin_q_w": marlin_q_w,
"marlin_s": marlin_s,
"marlin_g_idx": marlin_g_idx,
"marlin_sort_indices": marlin_sort_indices,
"marlin_rand_perm": marlin_rand_perm,
"marlin_workspace": marlin_workspace,
"is_k_full": is_k_full,
# Marlin_24 params
"marlin_24_w_ref": marlin_24_w_ref,
"marlin_24_q_w_comp": marlin_24_q_w_comp,
"marlin_24_meta": marlin_24_meta,
"marlin_24_s": marlin_24_s,
"marlin_24_workspace": marlin_24_workspace,
# GPTQ params
"q_w_gptq": q_w_gptq,
"repack_sort_indices": repack_sort_indices,
# Kernels
"gptq_marlin_gemm": ops.gptq_marlin_gemm,
"gptq_marlin_24_gemm": ops.gptq_marlin_24_gemm,
"gptq_marlin_repack": ops.gptq_marlin_repack,
}
min_run_time = 1
# Warmup pytorch
for i in range(5):
torch.matmul(a, marlin_w_ref)
results.append(
benchmark.Timer(
stmt="torch.matmul(a, marlin_w_ref)",
globals=globals,
label=label,
sub_label=sub_label,
description="pytorch_gemm",
).blocked_autorange(min_run_time=min_run_time))
results.append(
benchmark.Timer(
stmt=
"output = gptq_marlin_gemm(a, marlin_q_w, marlin_s, marlin_g_idx, marlin_sort_indices, marlin_workspace.scratch, num_bits, size_m, size_n, size_k, is_k_full)", # noqa: E501
globals=globals,
label=label,
sub_label=sub_label,
description="gptq_marlin_gemm",
).blocked_autorange(min_run_time=min_run_time))
if (num_bits in GPTQ_MARLIN_24_SUPPORTED_NUM_BITS
and group_size in GPTQ_MARLIN_24_SUPPORTED_GROUP_SIZES):
results.append(
benchmark.Timer(
stmt=
"output = gptq_marlin_24_gemm(a, marlin_24_q_w_comp, marlin_24_meta, marlin_24_s, marlin_24_workspace.scratch, num_bits, size_m, size_n, size_k)", # noqa: E501
globals=globals,
label=label,
sub_label=sub_label,
description="gptq_marlin_24_gemm",
).blocked_autorange(min_run_time=min_run_time))
results.append(
benchmark.Timer(
stmt=
"q_res = gptq_marlin_repack(q_w_gptq, repack_sort_indices, size_k, size_n, num_bits)", # noqa: E501
globals=globals,
label=label,
sub_label=sub_label,
description="gptq_marlin_repack",
).blocked_autorange(min_run_time=min_run_time))
def main(args):
print("Benchmarking models:")
for i, model in enumerate(args.models):
print(f"[{i}] {model}")
results = []
for model in args.models:
for layer in WEIGHT_SHAPES[model]:
size_k = layer[0]
size_n = layer[1]
if len(args.limit_k) > 0 and size_k not in args.limit_k:
continue
if len(args.limit_n) > 0 and size_n not in args.limit_n:
continue
for act_order in ACT_ORDER_OPTS:
if len(args.limit_act_order
) > 0 and act_order not in args.limit_act_order:
continue
for is_k_full in K_FULL_OPTS:
if len(args.limit_k_full
) > 0 and is_k_full not in args.limit_k_full:
continue
for num_bits in GPTQ_MARLIN_SUPPORTED_NUM_BITS:
if len(args.limit_num_bits
) > 0 and num_bits not in args.limit_num_bits:
continue
for group_size in GPTQ_MARLIN_SUPPORTED_GROUP_SIZES:
if len(
args.limit_group_size
) > 0 and group_size not in args.limit_group_size:
continue
# For act_order, the group_size must be less than
# size_k
if act_order and (group_size == size_k
or group_size == -1):
continue
for size_m in args.batch_sizes:
bench_run(results, model, act_order, is_k_full,
num_bits, group_size, size_m, size_k,
size_n)
compare = benchmark.Compare(results)
compare.print()
# For quick benchmarking use:
# python benchmark_marlin.py --batch-sizes 1 16 32 --limit-k 4096 --limit-n 4096 --limit-group-size 128 --limit-num-bits 4 --limit-act-order 0 --limit-k-full 1 # noqa E501
#
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="Benchmark Marlin across specified models/shapes/batches")
parser.add_argument(
"--models",
nargs="+",
type=str,
default=DEFAULT_MODELS,
choices=WEIGHT_SHAPES.keys(),
)
parser.add_argument("--batch-sizes",
nargs="+",
type=int,
default=DEFAULT_BATCH_SIZES)
parser.add_argument("--limit-k", nargs="+", type=int, default=[])
parser.add_argument("--limit-n", nargs="+", type=int, default=[])
parser.add_argument("--limit-group-size", nargs="+", type=int, default=[])
parser.add_argument("--limit-num-bits", nargs="+", type=int, default=[])
parser.add_argument("--limit-act-order", nargs="+", type=int, default=[])
parser.add_argument("--limit-k-full", nargs="+", type=int, default=[])
args = parser.parse_args()
main(args)
benchmarks/kernels/benchmark_moe.py 0 → 100644
View file @ 75b74b64
import argparse
import time
from datetime import datetime
from typing import Any, Dict, List, Tuple
import ray
import torch
import triton
from ray.experimental.tqdm_ray import tqdm
from transformers import AutoConfig
from vllm.model_executor.layers.fused_moe.fused_moe import *
def benchmark_config(
config: Dict[str, int],
num_tokens: int,
num_experts: int,
shard_intermediate_size: int,
hidden_size: int,
topk: int,
dtype: torch.dtype,
use_fp8: bool,
num_iters: int = 100,
) -> float:
init_dtype = torch.float16 if use_fp8 else dtype
x = torch.randn(num_tokens, hidden_size, dtype=dtype)
w1 = torch.randn(num_experts,
shard_intermediate_size,
hidden_size,
dtype=init_dtype)
w2 = torch.randn(num_experts,
hidden_size,
shard_intermediate_size // 2,
dtype=init_dtype)
gating_output = torch.randn(num_iters,
num_tokens,
num_experts,
dtype=torch.float32)
w1_scale = None
w2_scale = None
a1_scale = None
a2_scale = None
if use_fp8:
w1_scale = torch.randn(num_experts, dtype=torch.float32)
w2_scale = torch.randn(num_experts, dtype=torch.float32)
a1_scale = torch.randn(1, dtype=torch.float32)
a2_scale = torch.randn(1, dtype=torch.float32)
w1 = w1.to(torch.float8_e4m3fn)
w2 = w2.to(torch.float8_e4m3fn)
input_gating = torch.empty(num_tokens, num_experts, dtype=torch.float32)
def prepare(i: int):
input_gating.copy_(gating_output[i])
def run():
fused_moe(
x,
w1,
w2,
input_gating,
topk,
renormalize=True,
inplace=True,
override_config=config,
use_fp8=use_fp8,
w1_scale=w1_scale,
w2_scale=w2_scale,
a1_scale=a1_scale,
a2_scale=a2_scale,
)
# JIT compilation & warmup
run()
torch.cuda.synchronize()
# Capture 10 invocations with CUDA graph
graph = torch.cuda.CUDAGraph()
with torch.cuda.graph(graph):
for _ in range(10):
run()
torch.cuda.synchronize()
# Warmup
for _ in range(5):
graph.replay()
torch.cuda.synchronize()
start_event = torch.cuda.Event(enable_timing=True)
end_event = torch.cuda.Event(enable_timing=True)
latencies = []
for i in range(num_iters):
prepare(i)
torch.cuda.synchronize()
start_event.record()
graph.replay()
end_event.record()
end_event.synchronize()
latencies.append(start_event.elapsed_time(end_event))
avg = sum(latencies) / (num_iters * 10) * 1000 # us
graph.reset()
return avg
def get_configs_compute_bound() -> List[Dict[str, int]]:
# Reduced search space for faster tuning.
# TODO(woosuk): Increase the search space and use a performance model to
# prune the search space.
configs = []
for num_stages in [2, 3, 4, 5]:
for block_m in [16, 32, 64, 128, 256]:
for block_k in [64, 128, 256]:
for block_n in [32, 64, 128, 256]:
for num_warps in [4, 8]:
for group_size in [1, 16, 32, 64]:
configs.append({
"BLOCK_SIZE_M": block_m,
"BLOCK_SIZE_N": block_n,
"BLOCK_SIZE_K": block_k,
"GROUP_SIZE_M": group_size,
"num_warps": num_warps,
"num_stages": num_stages,
})
return configs
@ray.remote(num_gpus=1)
class BenchmarkWorker:
def __init__(self, seed: int) -> None:
torch.set_default_device("cuda")
torch.cuda.manual_seed_all(seed)
self.seed = seed
def benchmark(
self,
num_tokens: int,
num_experts: int,
shard_intermediate_size: int,
hidden_size: int,
topk: int,
dtype: torch.dtype,
use_fp8: bool,
) -> Tuple[Dict[str, int], float]:
torch.cuda.manual_seed_all(self.seed)
dtype_str = "float8" if use_fp8 else None
# NOTE(woosuk): The current naming convention uses w2.shape[2], which
# is the intermediate size after silu_and_mul.
op_config = get_moe_configs(num_experts, shard_intermediate_size // 2,
dtype_str)
if op_config is None:
config = get_default_config(num_tokens, num_experts,
shard_intermediate_size, hidden_size,
topk, dtype_str)
else:
config = op_config[min(op_config.keys(),
key=lambda x: abs(x - num_tokens))]
kernel_time = benchmark_config(config, num_tokens, num_experts,
shard_intermediate_size, hidden_size,
topk, dtype, use_fp8)
return config, kernel_time
def tune(
self,
num_tokens: int,
num_experts: int,
shard_intermediate_size: int,
hidden_size: int,
topk: int,
dtype: torch.dtype,
use_fp8: bool,
search_space: List[Dict[str, int]],
) -> Dict[str, int]:
best_config = None
best_time = float("inf")
for config in tqdm(search_space):
try:
kernel_time = benchmark_config(config,
num_tokens,
num_experts,
shard_intermediate_size,
hidden_size,
topk,
dtype,
use_fp8,
num_iters=10)
except triton.runtime.autotuner.OutOfResources:
# Some configurations may be invalid and fail to compile.
continue
if kernel_time < best_time:
best_time = kernel_time
best_config = config
now = datetime.now()
print(f"{now.ctime()}] Completed tuning for batch_size={num_tokens}")
return best_config
def sort_config(config: Dict[str, int]) -> Dict[str, int]:
return {
"BLOCK_SIZE_M": config["BLOCK_SIZE_M"],
"BLOCK_SIZE_N": config["BLOCK_SIZE_N"],
"BLOCK_SIZE_K": config["BLOCK_SIZE_K"],
"GROUP_SIZE_M": config["GROUP_SIZE_M"],
"num_warps": config["num_warps"],
"num_stages": config["num_stages"],
}
def save_configs(
configs: Dict[int, Dict[str, int]],
num_experts: int,
shard_intermediate_size: int,
hidden_size: int,
topk: int,
dtype: torch.dtype,
use_fp8: bool,
) -> None:
dtype_str = "float8" if use_fp8 else None
# NOTE(woosuk): The current naming convention uses w2.shape[2], which
# is the intermediate size after silu_and_mul.
filename = get_config_file_name(num_experts, shard_intermediate_size // 2,
dtype_str)
print(f"Writing best config to {filename}...")
with open(filename, "w") as f:
json.dump(configs, f, indent=4)
f.write("\n")
def main(args: argparse.Namespace):
print(args)
config = AutoConfig.from_pretrained(args.model)
if config.architectures[0] == "DbrxForCausalLM":
E = config.ffn_config.moe_num_experts
topk = config.ffn_config.moe_top_k
intermediate_size = config.ffn_config.ffn_hidden_size
shard_intermediate_size = 2 * intermediate_size // args.tp_size
else:
# Default: Mixtral.
E = config.num_local_experts
topk = config.num_experts_per_tok
intermediate_size = config.intermediate_size
shard_intermediate_size = 2 * intermediate_size // args.tp_size
hidden_size = config.hidden_size
dtype = config.torch_dtype
use_fp8 = args.dtype == "fp8"
if args.batch_size is None:
batch_sizes = [
1, 2, 4, 8, 16, 24, 32, 48, 64, 96, 128, 256, 512, 1024, 1536,
2048, 3072, 4096
]
else:
batch_sizes = [args.batch_size]
ray.init()
num_gpus = int(ray.available_resources()["GPU"])
workers = [BenchmarkWorker.remote(args.seed) for _ in range(num_gpus)]
def _distribute(method: str, inputs: List[Any]) -> List[Any]:
outputs = []
worker_idx = 0
for input_args in inputs:
worker = workers[worker_idx]
worker_method = getattr(worker, method)
output = worker_method.remote(*input_args)
outputs.append(output)
worker_idx = (worker_idx + 1) % num_gpus
return ray.get(outputs)
if args.tune:
search_space = get_configs_compute_bound()
print(f"Start tuning over {len(search_space)} configurations...")
start = time.time()
configs = _distribute(
"tune", [(batch_size, E, shard_intermediate_size, hidden_size,
topk, dtype, use_fp8, search_space)
for batch_size in batch_sizes])
best_configs = {
M: sort_config(config)
for M, config in zip(batch_sizes, configs)
}
save_configs(best_configs, E, shard_intermediate_size, hidden_size,
topk, dtype, use_fp8)
end = time.time()
print(f"Tuning took {end - start:.2f} seconds")
else:
outputs = _distribute("benchmark",
[(batch_size, E, shard_intermediate_size,
hidden_size, topk, dtype, use_fp8)
for batch_size in batch_sizes])
for batch_size, (config, kernel_time) in zip(batch_sizes, outputs):
print(f"Batch size: {batch_size}, config: {config}")
print(f"Kernel time: {kernel_time:.2f} us")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--model",
type=str,
default="mistralai/Mixtral-8x7B-Instruct-v0.1")
parser.add_argument("--tp-size", "-tp", type=int, default=2)
parser.add_argument("--dtype",
type=str,
choices=["auto", "fp8"],
default="auto")
parser.add_argument("--seed", type=int, default=0)
parser.add_argument("--batch-size", type=int, required=False)
parser.add_argument("--tune", action="store_true")
args = parser.parse_args()
main(args)
benchmarks/kernels/benchmark_paged_attention.py 0 → 100644
View file @ 75b74b64
import argparse
import random
import time
from typing import Optional
import torch
from vllm import _custom_ops as ops
from vllm.utils import STR_DTYPE_TO_TORCH_DTYPE, create_kv_caches_with_random
NUM_BLOCKS = 1024
PARTITION_SIZE = 512
@torch.inference_mode()
def main(
version: str,
num_seqs: int,
seq_len: int,
num_query_heads: int,
num_kv_heads: int,
head_size: int,
use_alibi: bool,
block_size: int,
dtype: torch.dtype,
seed: int,
do_profile: bool,
device: str = "cuda",
kv_cache_dtype: Optional[str] = None,
) -> None:
random.seed(seed)
torch.random.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed(seed)
scale = float(1.0 / (head_size**0.5))
query = torch.empty(num_seqs,
num_query_heads,
head_size,
dtype=dtype,
device=device)
query.uniform_(-scale, scale)
assert num_query_heads % num_kv_heads == 0
alibi_slopes = None
if use_alibi:
alibi_slopes = torch.randn(num_query_heads,
dtype=torch.float,
device=device)
seq_lens = [seq_len for _ in range(num_seqs)]
max_seq_len = max(seq_lens)
seq_lens = torch.tensor(seq_lens, dtype=torch.int, device=device)
# Create the block tables.
max_num_blocks_per_seq = (max_seq_len + block_size - 1) // block_size
block_tables = []
for _ in range(num_seqs):
block_table = [
random.randint(0, NUM_BLOCKS - 1)
for _ in range(max_num_blocks_per_seq)
]
block_tables.append(block_table)
block_tables = torch.tensor(block_tables, dtype=torch.int, device=device)
# Create the KV cache.
key_caches, value_caches = create_kv_caches_with_random(NUM_BLOCKS,
block_size,
1,
num_kv_heads,
head_size,
kv_cache_dtype,
dtype,
device=device)
key_cache, value_cache = key_caches[0], value_caches[0]
# Prepare for the paged attention kernel.
output = torch.empty_like(query)
if version == "v2":
num_partitions = ((max_seq_len + PARTITION_SIZE - 1) // PARTITION_SIZE)
tmp_output = torch.empty(
size=(num_seqs, num_query_heads, num_partitions, head_size),
dtype=output.dtype,
device=output.device,
)
exp_sums = torch.empty(
size=(num_seqs, num_query_heads, num_partitions),
dtype=torch.float32,
device=output.device,
)
max_logits = torch.empty_like(exp_sums)
def run_cuda_benchmark(num_iters: int, profile: bool = False) -> float:
torch.cuda.synchronize()
if profile:
torch.cuda.cudart().cudaProfilerStart()
start_time = time.perf_counter()
# Using default kv_scale
kv_scale = 1.0
for _ in range(num_iters):
if version == "v1":
ops.paged_attention_v1(
output,
query,
key_cache,
value_cache,
num_kv_heads,
scale,
block_tables,
seq_lens,
block_size,
max_seq_len,
alibi_slopes,
kv_cache_dtype,
kv_scale,
)
elif version == "v2":
ops.paged_attention_v2(
output,
exp_sums,
max_logits,
tmp_output,
query,
key_cache,
value_cache,
num_kv_heads,
scale,
block_tables,
seq_lens,
block_size,
max_seq_len,
alibi_slopes,
kv_cache_dtype,
kv_scale,
)
else:
raise ValueError(f"Invalid version: {version}")
torch.cuda.synchronize()
end_time = time.perf_counter()
if profile:
torch.cuda.cudart().cudaProfilerStart()
return (end_time - start_time) / num_iters
# Warmup.
print("Warming up...")
run_benchmark = run_cuda_benchmark
run_benchmark(num_iters=3, profile=False)
# Benchmark.
if do_profile:
latency = run_benchmark(num_iters=1, profile=True)
else:
latency = run_benchmark(num_iters=100, profile=False)
print(f"Kernel running time: {latency * 1000000:.3f} us")
if __name__ == '__main__':
parser = argparse.ArgumentParser(
description="Benchmark the paged attention kernel.")
parser.add_argument("--version",
type=str,
choices=["v1", "v2"],
default="v2")
parser.add_argument("--batch-size", type=int, default=8)
parser.add_argument("--seq_len", type=int, default=4096)
parser.add_argument("--num-query-heads", type=int, default=64)
parser.add_argument("--num-kv-heads", type=int, default=8)
parser.add_argument("--head-size",
type=int,
choices=[64, 80, 96, 112, 128, 192, 256],
default=128)
parser.add_argument("--block-size", type=int, choices=[16, 32], default=16)
parser.add_argument("--use-alibi", action="store_true")
parser.add_argument("--dtype",
type=str,
choices=["half", "bfloat16", "float"],
default="half")
parser.add_argument("--seed", type=int, default=0)
parser.add_argument("--profile", action="store_true")
parser.add_argument(
"--kv-cache-dtype",
type=str,
choices=["auto", "fp8", "fp8_e5m2", "fp8_e4m3"],
default="auto",
help="Data type for kv cache storage. If 'auto', will use model "
"data type. CUDA 11.8+ supports fp8 (=fp8_e4m3) and fp8_e5m2. "
"ROCm (AMD GPU) supports fp8 (=fp8_e4m3)")
args = parser.parse_args()
print(args)
if args.num_query_heads % args.num_kv_heads != 0:
raise ValueError("num_query_heads must be divisible by num_kv_heads")
main(
version=args.version,
num_seqs=args.batch_size,
seq_len=args.seq_len,
num_query_heads=args.num_query_heads,
num_kv_heads=args.num_kv_heads,
head_size=args.head_size,
block_size=args.block_size,
use_alibi=args.use_alibi,
dtype=STR_DTYPE_TO_TORCH_DTYPE[args.dtype],
seed=args.seed,
do_profile=args.profile,
kv_cache_dtype=args.kv_cache_dtype,
)
benchmarks/kernels/benchmark_rope.py 0 → 100644
View file @ 75b74b64
import argparse
from itertools import accumulate
from typing import Optional
import nvtx
import torch
from vllm.model_executor.layers.rotary_embedding import get_rope
def benchmark_rope_kernels_multi_lora(
is_neox_style: bool,
batch_size: int,
seq_len: int,
num_heads: int,
head_size: int,
rotary_dim: Optional[int],
dtype: torch.dtype,
seed: int,
device: str,
max_position: int = 8192,
base: int = 10000,
) -> None:
torch.random.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed(seed)
torch.set_default_device(device)
if rotary_dim is None:
rotary_dim = head_size
# silulating serving 4 LoRAs
scaling_factors = [1, 2, 4, 8]
# batched RoPE can take multiple scaling factors
batched_rope = get_rope(head_size, rotary_dim, max_position, base,
is_neox_style, {
"type": "linear",
"factor": tuple(scaling_factors)
})
# non-batched RoPE takes only one scaling factor, we create multiple
# instances to simulate the same behavior
non_batched_ropes = []
for scaling_factor in scaling_factors:
non_batched_ropes.append(
get_rope(head_size, rotary_dim, max_position, base, is_neox_style,
{
"type": "linear",
"factor": (scaling_factor, )
}))
positions = torch.randint(0, max_position, (batch_size, seq_len))
query = torch.randn(batch_size,
seq_len,
num_heads * head_size,
dtype=dtype)
key = torch.randn_like(query)
# create query offsets for batched RoPE, we concat multiple kv cache
# together and each query needs to find the right kv cache of its type
offset_map = torch.tensor(
list(
accumulate([0] + [
max_position * scaling_factor * 2
for scaling_factor in scaling_factors[:-1]
])))
query_types = torch.randint(0,
len(scaling_factors), (batch_size, seq_len),
device=device)
# map query types to offsets
query_offsets = offset_map[query_types]
# the kernel takes flattened offsets
flatten_offsets = query_offsets.flatten()
# batched queries of the same type together for non-batched RoPE
queries = [query[query_types == i] for i in range(len(scaling_factors))]
keys = [key[query_types == i] for i in range(len(scaling_factors))]
packed_qkr = zip(queries, keys, non_batched_ropes)
# synchronize before start timing
torch.cuda.synchronize()
with nvtx.annotate("non-batched", color="yellow"):
for q, k, r in packed_qkr:
r.forward(positions, q, k)
torch.cuda.synchronize()
with nvtx.annotate("batched", color="green"):
batched_rope.forward(positions, query, key, flatten_offsets)
torch.cuda.synchronize()
if __name__ == '__main__':
parser = argparse.ArgumentParser(
description="Benchmark the rotary embedding kernels.")
parser.add_argument("--is-neox-style", type=bool, default=True)
parser.add_argument("--batch-size", type=int, default=16)
parser.add_argument("--seq-len", type=int, default=512)
parser.add_argument("--num-heads", type=int, default=8)
parser.add_argument("--head-size",
type=int,
choices=[64, 80, 96, 112, 128, 192, 256],
default=128)
parser.add_argument("--rotary-dim", type=int, choices=[16, 32], default=32)
parser.add_argument("--dtype",
type=str,
choices=["bfloat16", "float"],
default="float")
parser.add_argument("--seed", type=int, default=0)
parser.add_argument("--device",
type=str,
choices=["cuda:0", "cuda:1"],
default="cuda:0")
args = parser.parse_args()
print(args)
benchmark_rope_kernels_multi_lora(
is_neox_style=args.is_neox_style,
batch_size=args.batch_size,
seq_len=args.seq_len,
num_heads=args.num_heads,
head_size=args.head_size,
rotary_dim=args.rotary_dim,
dtype=getattr(torch, args.dtype),
seed=args.seed,
device=args.device,
)
benchmarks/kernels/benchmark_shapes.py 0 → 100644
View file @ 75b74b64
WEIGHT_SHAPES = {
"ideal": [[4 * 256 * 32, 256 * 32]],
"mistralai/Mistral-7B-v0.1/TP1": [
[4096, 6144],
[4096, 4096],
[4096, 28672],
[14336, 4096],
],
"mistralai/Mistral-7B-v0.1/TP2": [
[4096, 3072],
[2048, 4096],
[4096, 14336],
[7168, 4096],
],
"mistralai/Mistral-7B-v0.1/TP4": [
[4096, 1536],
[1024, 4096],
[4096, 7168],
[3584, 4096],
],
"meta-llama/Llama-2-7b-hf/TP1": [
[4096, 12288],
[4096, 4096],
[4096, 22016],
[11008, 4096],
],
"meta-llama/Llama-2-7b-hf/TP2": [
[4096, 6144],
[2048, 4096],
[4096, 11008],
[5504, 4096],
],
"meta-llama/Llama-2-7b-hf/TP4": [
[4096, 3072],
[1024, 4096],
[4096, 5504],
[2752, 4096],
],
"meta-llama/Llama-2-13b-hf/TP1": [
[5120, 15360],
[5120, 5120],
[5120, 27648],
[13824, 5120],
],
"meta-llama/Llama-2-13b-hf/TP2": [
[5120, 7680],
[2560, 5120],
[5120, 13824],
[6912, 5120],
],
"meta-llama/Llama-2-13b-hf/TP4": [
[5120, 3840],
[1280, 5120],
[5120, 6912],
[3456, 5120],
],
"meta-llama/Llama-2-70b-hf/TP1": [
[8192, 10240],
[8192, 8192],
[8192, 57344],
[28672, 8192],
],
"meta-llama/Llama-2-70b-hf/TP2": [
[8192, 5120],
[4096, 8192],
[8192, 28672],
[14336, 8192],
],
"meta-llama/Llama-2-70b-hf/TP4": [
[8192, 2560],
[2048, 8192],
[8192, 14336],
[7168, 8192],
],
}
benchmarks/launch_tgi_server.sh 0 → 100644
View file @ 75b74b64
#!/bin/bash
PORT=8000
MODEL=$1
TOKENS=$2
docker run -e HF_TOKEN=$HF_TOKEN --gpus all --shm-size 1g -p $PORT:80 \
-v $PWD/data:/data \
ghcr.io/huggingface/text-generation-inference:1.4.0 \
--model-id $MODEL \
--sharded false \
--max-input-length 1024 \
--max-total-tokens 2048 \
--max-best-of 5 \
--max-concurrent-requests 5000 \
--max-batch-total-tokens $TOKENS
benchmarks/overheads/benchmark_hashing.py 0 → 100644
View file @ 75b74b64
import argparse
import cProfile
import pstats
from vllm import LLM, SamplingParams
# A very long prompt, total number of tokens is about 15k.
LONG_PROMPT = ["You are an expert in large language models, aren't you?"
] * 1000
LONG_PROMPT = ' '.join(LONG_PROMPT)
def main(args):
llm = LLM(
model=args.model,
enforce_eager=True,
enable_prefix_caching=True,
tensor_parallel_size=args.tensor_parallel_size,
use_v2_block_manager=args.use_v2_block_manager,
)
sampling_params = SamplingParams(temperature=0, max_tokens=args.output_len)
profiler = cProfile.Profile()
print("------warm up------")
for i in range(3):
output = llm.generate(LONG_PROMPT, sampling_params)
print(output[0].outputs[0].text)
print("------start generating------")
for i in range(3):
profiler.runctx('llm.generate(LONG_PROMPT, sampling_params)',
globals(), locals())
# analyze the runtime of hashing function
stats = pstats.Stats(profiler)
stats.sort_stats('cumulative')
total_time = 0
total_calls = 0
for func in stats.stats:
if 'hash_of_block' in func[2]:
total_time = stats.stats[func][3]
total_calls = stats.stats[func][0]
percentage = (total_time / stats.total_tt) * 100
print(f"Hashing took {total_time:.2f} seconds,"
f"{percentage:.2f}% of the total runtime.")
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description='Benchmark the performance of hashing function in'
'automatic prefix caching.')
parser.add_argument('--model', type=str, default='lmsys/longchat-7b-16k')
parser.add_argument('--tensor-parallel-size', '-tp', type=int, default=1)
parser.add_argument('--output-len', type=int, default=10)
parser.add_argument('--enable-prefix-caching',
action='store_true',
help='enable prefix caching')
parser.add_argument('--use-v2-block-manager',
action='store_true',
help='Use BlockSpaceMangerV2')
args = parser.parse_args()
main(args)
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