kvcache-ai-ktransformers/archive/ktransformers/tests/test_client.py
Jiaqi Liao 57d14d22bc
Refactor: restructure repository to focus on kt-kernel and KT-SFT modulesq recon (#1581)
* refactor: move legacy code to archive/ directory

  - Moved ktransformers, csrc, third_party, merge_tensors to archive/
  - Moved build scripts and configurations to archive/
  - Kept kt-kernel, KT-SFT, doc, and README files in root
  - Preserved complete git history for all moved files

* refactor: restructure repository to focus on kt-kernel and KT-SFT modules

* fix README

* fix README

* fix README

* fix README

* docs: add performance benchmarks to kt-kernel section

Add comprehensive performance data for kt-kernel to match KT-SFT's presentation:
- AMX kernel optimization: 21.3 TFLOPS (3.9× faster than PyTorch)
- Prefill phase: up to 20× speedup vs baseline
- Decode phase: up to 4× speedup
- NUMA optimization: up to 63% throughput improvement
- Multi-GPU (8×L20): 227.85 tokens/s total throughput with DeepSeek-R1 FP8

Source: https://lmsys.org/blog/2025-10-22-KTransformers/

This provides users with concrete performance metrics for both core modules,
making it easier to understand the capabilities of each component.

* refactor: improve kt-kernel performance data with specific hardware and models

Replace generic performance descriptions with concrete benchmarks:
- Specify exact hardware: 8×L20 GPU + Xeon Gold 6454S, Single/Dual-socket Xeon + AMX
- Include specific models: DeepSeek-R1-0528 (FP8), DeepSeek-V3 (671B)
- Show detailed metrics: total throughput, output throughput, concurrency details
- Match KT-SFT presentation style for consistency

This provides users with actionable performance data they can use to evaluate
hardware requirements and expected performance for their use cases.

* fix README

* docs: clean up performance table and improve formatting

* add pic for README

* refactor: simplify .gitmodules and backup legacy submodules

- Remove 7 legacy submodules from root .gitmodules (archive/third_party/*)
- Keep only 2 active submodules for kt-kernel (llama.cpp, pybind11)
- Backup complete .gitmodules to archive/.gitmodules
- Add documentation in archive/README.md for researchers who need legacy submodules

This reduces initial clone size by ~500MB and avoids downloading unused dependencies.

* refactor: move doc/ back to root directory

Keep documentation in root for easier access and maintenance.

* refactor: consolidate all images to doc/assets/

- Move kt-kernel/assets/heterogeneous_computing.png to doc/assets/
- Remove KT-SFT/assets/ (images already in doc/assets/)
- Update KT-SFT/README.md image references to ../doc/assets/
- Eliminates ~7.9MB image duplication
- Centralizes all documentation assets in one location

* fix pic path for README
2025-11-10 17:42:26 +08:00

105 lines
4.1 KiB
Python

import asyncio
import json
import sys
import aiohttp
import argparse
prompt_list = [
'Please elaborate on modern world history.',
'Please introduce Harry Potter.',
'I want to learn Python. Please give me some advice.',
'Please tell me a joke '
]
async def fetch_event_stream(session, payload, request_id, stream):
try:
headers = {
'accept': 'application/json',
'Content-Type': 'application/json'
}
async with session.post(SERVER_URL, json=payload, headers=headers, timeout=50000) as response:
print(f"Request {request_id}: Connected, status {response.status}")
if response.status != 200:
print(f"Request {request_id}: Error, status {response.status}")
return
output_text = ""
if stream:
async for line in response.content:
try:
decoded_line = line.decode("utf-8").strip()
if not decoded_line or not decoded_line.startswith("data: "):
continue
decoded_line = decoded_line[6:].strip()
if not decoded_line:
continue
response_data = json.loads(decoded_line)
choices = response_data.get("choices", [])
if not choices:
continue
delta = choices[0].get("delta", {})
token = delta.get("content", "")
if token:
output_text += token
sys.stdout.write(token)
sys.stdout.flush()
finish_reason = choices[0].get("finish_reason", None)
if finish_reason:
break
except json.JSONDecodeError as e:
print(f"\nRequest {request_id}: JSON Decode Error - {e}")
except IndexError:
print(f"\nRequest {request_id}: List Index Error - choices is empty")
except Exception as e:
print(f"\nRequest {request_id}: Error parsing stream - {e}")
else:
# 非 stream 模式下,一次性接收完整 json
response_data = await response.json()
choices = response_data.get("choices", [])
if choices:
content = choices[0].get("message", {}).get("content", "")
print(f"Request {request_id} Output:\n{content}")
output_text += content
except Exception as e:
print(f"\nRequest {request_id}: Exception - {e}")
async def main(prompt_id, model, stream, max_tokens, temperature, top_p):
async with aiohttp.ClientSession() as session:
payload = {
"messages": [
{"role": "system", "content": ""},
{"role": "user", "content": prompt_list[prompt_id]}
],
"model": model,
"stream": stream,
"max_tokens": max_tokens,
"temperature": temperature,
"top_p": top_p
}
tasks = [fetch_event_stream(session, payload, prompt_id, stream)]
await asyncio.gather(*tasks)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Event Stream Request Tester")
parser.add_argument("--question_id", type=int, default=0)
parser.add_argument("--model", type=str, default="DeepSeek-V3")
parser.add_argument("--stream", type=bool, default=True)
parser.add_argument("--max_tokens", type=int, default=500)
parser.add_argument("--temperature", type=float, default=0.8)
parser.add_argument("--top_p", type=float, default=1)
parser.add_argument("--api_url", type=str, default="http://localhost:10002/v1/chat/completions", help="API URL")
args = parser.parse_args()
SERVER_URL = args.api_url
asyncio.run(main(args.question_id, args.model, args.stream, args.max_tokens, args.temperature, args.top_p))