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Remove deprecated scripts (#13399)

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This commit is contained in:
Liangsheng Yin
2025-11-17 16:54:39 +08:00
committed by GitHub
parent a3e4fe4b41
commit ae7698fbd5
12 changed files with 0 additions and 725 deletions
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scripts/deprecated/convert_yi_vl.py
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"""
Convert Yi-VL config into a format usable with SGLang
Usage: python3 scripts/convert_yi_vl.py --model-path <path-to-model>
"""
import argparse
import json
import os
from transformers import AutoConfig, AutoTokenizer
def add_image_token(model_path: str):
tokenizer = AutoTokenizer.from_pretrained(model_path)
tokenizer.add_tokens(["<image_placeholder>"], special_tokens=True)
print(tokenizer)
tokenizer.save_pretrained(model_path)
def edit_model_config(model_path):
config = AutoConfig.from_pretrained(model_path)
setattr(config, "architectures", ["YiVLForCausalLM"])
setattr(config, "image_token_index", 64002)
print(config)
config.save_pretrained(model_path)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--model-path", type=str)
args = parser.parse_args()
add_image_token(args.model_path)
edit_model_config(args.model_path)
scripts/deprecated/convert_yi_vl.sh
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# For 34B Model
mkdir ~/model_weights
cd ~/model_weights
git clone https://huggingface.co/01-ai/Yi-VL-34B
cp ~/model_weights/Yi-VL-34B/vit/clip-vit-H-14-laion2B-s32B-b79K-yi-vl-34B-448/preprocessor_config.json ~/model_weights/Yi-VL-34B
python3 convert_yi_vl.py --model-path ~/model_weights/Yi-VL-34B
# For 6B Model
mkdir ~/model_weights
cd ~/model_weights
git clone https://huggingface.co/01-ai/Yi-VL-6B
cp ~/model_weights/Yi-VL-6B/vit/clip-vit-H-14-laion2B-s32B-b79K-yi-vl-6B-448/preprocessor_config.json ~/model_weights/Yi-VL-6B
python3 convert_yi_vl.py --model-path ~/model_weights/Yi-VL-6B
scripts/deprecated/test_curl.sh
-9
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curl http://localhost:30000/generate \
-H "Content-Type: application/json" \
-d '{
"text": "Once upon a time,",
"sampling_params": {
"max_new_tokens": 64,
"temperature": 0
}
}'
scripts/deprecated/test_flashinfer.py
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import pytest
import torch
from flashinfer import (
BatchDecodeWithPagedKVCacheWrapper,
BatchPrefillWithPagedKVCacheWrapper,
)
from sglang.srt.layers.attention.triton_ops.decode_attention import decode_attention_fwd
from sglang.srt.layers.attention.triton_ops.extend_attention import (
extend_attention_fwd,
redundant_attention,
)
from sglang.srt.utils import should_use_tensor_core
flashinfer_prefill_wrapper = None
flashinfer_decode_wrapper = None
@pytest.mark.parametrize("batch_size", [12, 37, 67])
@pytest.mark.parametrize("kv_len", [54, 97])
@pytest.mark.parametrize("qo_len", [37, 17])
@pytest.mark.parametrize("num_kv_heads", [4])
@pytest.mark.parametrize("num_qo_heads", [32, 4])
@pytest.mark.parametrize("head_dim", [128])
def test_batch_prefill_with_paged_kv_cache(
batch_size,
kv_len,
qo_len,
num_kv_heads,
num_qo_heads,
head_dim,
):
init_flashinfer(num_qo_heads, num_kv_heads)
q = torch.randn(batch_size * qo_len, num_qo_heads, head_dim).to(0).half()
qo_indptr = torch.arange(0, batch_size + 1).to(0).int() * qo_len
total_tokens = kv_len * batch_size
kv_data = torch.randn(total_tokens, 2, num_kv_heads, head_dim).to(0).half()
kv_indptr = torch.arange(0, batch_size + 1).to(0).int() * kv_len
kv_indices = torch.arange(0, total_tokens).to(0).int()
kv_last_page_len = torch.full((batch_size,), 1, dtype=torch.int32).to(0)
# init args for triton kernel
k_extend = (
kv_data.view(batch_size, kv_len, 2, -1)[:, -qo_len:, 0]
.contiguous()
.view(-1, num_kv_heads, head_dim)
)
v_extend = (
kv_data.view(batch_size, kv_len, 2, -1)[:, -qo_len:, 1]
.contiguous()
.view(-1, num_kv_heads, head_dim)
)
o_triton = torch.empty_like(q)
k_buffer = kv_data[:, 0].view(-1, num_kv_heads, head_dim).contiguous()
v_buffer = kv_data[:, 1].view(-1, num_kv_heads, head_dim).contiguous()
req_to_token = torch.arange(0, total_tokens).to(0).int().view(batch_size, kv_len)
b_req_idx = torch.arange(0, batch_size).to(0).int()
b_seq_len = torch.full((batch_size,), kv_len, dtype=torch.int32).to(0)
b_start_loc_extend = torch.arange(0, batch_size).to(0).int() * qo_len
b_seq_len_extend = torch.full((batch_size,), qo_len, dtype=torch.int32).to(0)
max_len_in_batch = kv_len
max_len_extend = qo_len
extend_attention_fwd(
q,
k_extend,
v_extend,
o_triton,
k_buffer,
v_buffer,
req_to_token,
b_req_idx,
None, # b_start_loc = None
b_seq_len,
None, # b_seq_len_prefix = None
b_start_loc_extend,
b_seq_len_extend,
max_len_in_batch,
max_len_extend,
)
o_redundant = torch.empty_like(q)
b_start_loc = torch.zeros((batch_size,), dtype=torch.int32).to(0)
b_start_loc[1:] = torch.cumsum(b_seq_len[:-1], dim=0)
b_seq_len_prefix = b_seq_len - b_seq_len_extend
redundant_attention(
q,
k_extend,
v_extend,
o_redundant,
k_buffer,
v_buffer,
req_to_token,
b_req_idx,
b_start_loc,
b_seq_len,
b_seq_len_prefix,
max_len_in_batch,
)
print("Mean: ", torch.mean(torch.abs(o_redundant - o_triton)))
print("Max: ", torch.max(torch.abs(o_redundant - o_triton)))
assert torch.allclose(o_redundant, o_triton, rtol=1e-2, atol=1e-3)
flashinfer_prefill_wrapper.end_forward()
flashinfer_prefill_wrapper.begin_forward(
qo_indptr,
kv_indptr,
kv_indices,
kv_last_page_len,
num_qo_heads,
num_kv_heads,
head_dim,
1,
)
o = flashinfer_prefill_wrapper.forward(
q.contiguous().view(-1, num_qo_heads, head_dim), kv_data
)
print("Mean: ", torch.mean(torch.abs(o - o_triton)))
print("Max: ", torch.max(torch.abs(o - o_triton)))
assert torch.allclose(o, o_triton, rtol=1e-2, atol=1e-3)
@pytest.mark.parametrize("batch_size", [12, 17, 37])
@pytest.mark.parametrize("kv_len", [54, 127, 537])
@pytest.mark.parametrize("num_kv_heads", [32])
@pytest.mark.parametrize("num_qo_heads", [32])
@pytest.mark.parametrize("head_dim", [128])
def test_batch_decode_with_paged_kv_cache(
batch_size,
kv_len,
num_kv_heads,
num_qo_heads,
head_dim,
):
# note(lsyin): when pytest, the number of heads cannot change, because triton kernel has a cache
# to test different shape of decode, change the parameters in the __main__, and run decode only once
init_flashinfer(num_qo_heads, num_kv_heads)
q = torch.randn(batch_size, num_qo_heads, head_dim).to(0).half()
total_tokens = kv_len * batch_size
kv_data = torch.randn(total_tokens, 2, num_kv_heads, head_dim).to(0).half()
kv_indptr = torch.arange(0, batch_size + 1).to(0).int() * kv_len
kv_indices = torch.arange(0, total_tokens).to(0).int()
kv_last_page_len = torch.full((batch_size,), 1, dtype=torch.int32).to(0)
# init args for triton kernel
k_buffer = kv_data[:, 0].view(-1, num_kv_heads, head_dim).contiguous()
v_buffer = kv_data[:, 1].view(-1, num_kv_heads, head_dim).contiguous()
o_triton = torch.empty_like(q)
req_to_token = (
torch.arange(0, kv_len * batch_size).to(0).int().view(batch_size, kv_len)
)
b_req_idx = torch.arange(0, batch_size).to(0).int()
b_start_loc = torch.arange(0, batch_size).to(0).int() * kv_len
b_seq_len = torch.full((batch_size,), kv_len, dtype=torch.int32).to(0)
max_len_in_batch = kv_len
other_kv_index = 0
decode_attention_fwd(
q,
k_buffer,
v_buffer,
o_triton,
req_to_token,
b_req_idx,
b_start_loc,
b_seq_len,
max_len_in_batch,
other_kv_index,
total_tokens,
)
flashinfer_decode_wrapper.end_forward()
flashinfer_decode_wrapper.begin_forward(
kv_indptr,
kv_indices,
kv_last_page_len,
num_qo_heads,
num_kv_heads,
head_dim,
1,
pos_encoding_mode="NONE",
data_type="float16",
)
o = flashinfer_decode_wrapper.forward(
q.contiguous().view(-1, num_qo_heads, head_dim), kv_data
)
print("Mean: ", torch.mean(torch.abs(o - o_triton)))
print("Max: ", torch.max(torch.abs(o - o_triton)))
assert torch.allclose(o, o_triton, rtol=1e-2, atol=2e-3)
def init_flashinfer(num_attention_heads, num_kv_heads):
use_tensor_cores = should_use_tensor_core(
torch.half, num_attention_heads, num_kv_heads
)
workspace_buffer = torch.empty(128 * 1024 * 1024, dtype=torch.int8, device="cuda")
global flashinfer_prefill_wrapper, flashinfer_decode_wrapper
flashinfer_prefill_wrapper = BatchPrefillWithPagedKVCacheWrapper(
workspace_buffer, "NHD"
)
flashinfer_decode_wrapper = BatchDecodeWithPagedKVCacheWrapper(
workspace_buffer, "NHD", use_tensor_cores=use_tensor_cores
)
if __name__ == "__main__":
test_batch_prefill_with_paged_kv_cache(12, 54, 37, 8, 8, 128)
test_batch_prefill_with_paged_kv_cache(37, 1111, 456, 32, 32, 128)
test_batch_decode_with_paged_kv_cache(12, 54, 4, 32, 128)
scripts/deprecated/test_httpserver_concurrent.py
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"""
python3 -m sglang.launch_server --model-path TinyLlama/TinyLlama-1.1B-Chat-v0.4 --port 30000
Output:
The capital of France is Paris.\nThe capital of the United States is Washington, D.C.
The capital of the United Kindom is London.\nThe capital of the United Kingdom is London.\nThe capital of
"""
import argparse
import asyncio
import json
import time
import aiohttp
import requests
async def send_request(url, data, delay=0):
await asyncio.sleep(delay)
async with aiohttp.ClientSession() as session:
async with session.post(url, json=data) as resp:
output = await resp.json()
return output
async def main(args):
url = f"{args.host}:{args.port}"
task1 = send_request(
url + "/generate",
{
"text": "The capital of France is",
"sampling_params": {"temperature": 0, "max_new_tokens": 128},
},
delay=1,
)
task2 = send_request(
url + "/generate",
{
"text": "The capital of the United Kindom is",
"sampling_params": {"temperature": 0, "max_new_tokens": 128},
},
)
rets = await asyncio.gather(task1, task2)
print(rets)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--host", type=str, default="http://127.0.0.1")
parser.add_argument("--port", type=int, default=30000)
args = parser.parse_args()
asyncio.run(main(args))
scripts/deprecated/test_httpserver_decode.py
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"""
Usage:
python3 -m sglang.launch_server --model-path TinyLlama/TinyLlama-1.1B-Chat-v0.4 --port 30000
python3 test_httpserver_decode.py
Output:
The capital of France is Paris.\nThe capital of the United States is Washington, D.C.\nThe capital of Canada is Ottawa.\nThe capital of Japan is Tokyo
"""
import argparse
import json
import requests
def test_decode(url, return_logprob=False, top_logprobs_num=0, return_text=False, n=1):
response = requests.post(
url + "/generate",
json={
"text": "The capital of France is",
"sampling_params": {
"temperature": 0 if n == 1 else 0.5,
"max_new_tokens": 32,
"n": n,
},
"stream": False,
"return_logprob": return_logprob,
"top_logprobs_num": top_logprobs_num,
"return_text_in_logprobs": return_text,
"logprob_start_len": 0,
},
)
print(json.dumps(response.json()))
print("=" * 100)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--host", type=str, default="http://127.0.0.1")
parser.add_argument("--port", type=int, default=30000)
args = parser.parse_args()
url = f"{args.host}:{args.port}"
test_decode(url)
test_decode(url, n=3)
for top_logprobs_num in [0, 3]:
for return_text in [True, False]:
test_decode(
url,
return_logprob=True,
top_logprobs_num=top_logprobs_num,
return_text=return_text,
)
scripts/deprecated/test_httpserver_decode_stream.py
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"""
Usage:
python3 -m sglang.launch_server --model-path TinyLlama/TinyLlama-1.1B-Chat-v0.4 --port 30000
python3 test_httpserver_decode_stream.py
Output:
The capital of France is Paris.\nThe capital of the United States is Washington, D.C.\nThe capital of Canada is Ottawa.\nThe capital of Japan is Tokyo
"""
import argparse
import json
import requests
def test_decode_stream(url, return_logprob, top_logprobs_num):
response = requests.post(
url + "/generate",
json={
"text": "The capital of France is",
"sampling_params": {
"temperature": 0,
"max_new_tokens": 128,
},
"stream": True,
"return_logprob": return_logprob,
"top_logprobs_num": top_logprobs_num,
"return_text_in_logprobs": True,
"logprob_start_len": 0,
},
stream=True,
)
prev = 0
for chunk in response.iter_lines(decode_unicode=False):
chunk = chunk.decode("utf-8")
if chunk and chunk.startswith("data:"):
if chunk == "data: [DONE]":
break
data = json.loads(chunk[5:].strip("\n"))
if return_logprob:
assert data["meta_info"]["input_token_logprobs"] is not None
assert data["meta_info"]["output_token_logprobs"] is not None
for logprob, token_id, token_text in data["meta_info"][
"output_token_logprobs"
][prev:]:
print(f"{token_text:12s}\t{logprob}\t{token_id}", flush=True)
prev = len(data["meta_info"]["output_token_logprobs"])
else:
output = data["text"].strip()
print(output[prev:], end="", flush=True)
prev = len(output)
print("=" * 100)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--host", type=str, default="http://127.0.0.1")
parser.add_argument("--port", type=int, default=30000)
args = parser.parse_args()
url = f"{args.host}:{args.port}"
test_decode_stream(url, False, 0)
test_decode_stream(url, True, 0)
test_decode_stream(url, True, 3)
scripts/deprecated/test_httpserver_llava.py
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"""
Usage:
python3 -m sglang.launch_server --model-path liuhaotian/llava-v1.5-7b --tokenizer-path llava-hf/llava-1.5-7b-hf --port 30000
python3 test_httpserver_llava.py
Output:
The image features a man standing on the back of a yellow taxi cab, holding
"""
import argparse
import asyncio
import json
import aiohttp
import requests
async def send_request(url, data, delay=0):
await asyncio.sleep(delay)
async with aiohttp.ClientSession() as session:
async with session.post(url, json=data) as resp:
output = await resp.json()
return output
async def test_concurrent(args):
url = f"{args.host}:{args.port}"
response = []
for i in range(8):
response.append(
send_request(
url + "/generate",
{
"text": "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions. USER: <image>\nDescribe this picture ASSISTANT:",
"image_data": "example_image.png",
"sampling_params": {
"temperature": 0,
"max_new_tokens": 64,
},
},
)
)
rets = await asyncio.gather(*response)
for ret in rets:
print(ret["text"])
def test_streaming(args):
url = f"{args.host}:{args.port}"
response = requests.post(
url + "/generate",
json={
"text": "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions. USER: <image>\nDescribe this picture ASSISTANT:",
"image_data": "example_image.png",
"sampling_params": {
"temperature": 0,
"max_new_tokens": 128,
},
"stream": True,
},
stream=True,
)
prev = 0
for chunk in response.iter_lines(decode_unicode=False):
chunk = chunk.decode("utf-8")
if chunk and chunk.startswith("data:"):
if chunk == "data: [DONE]":
break
data = json.loads(chunk[5:].strip("\n"))
output = data["text"].strip()
print(output[prev:], end="", flush=True)
prev = len(output)
print("")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--host", type=str, default="http://127.0.0.1")
parser.add_argument("--port", type=int, default=30000)
args = parser.parse_args()
asyncio.run(test_concurrent(args))
test_streaming(args)
scripts/deprecated/test_httpserver_reuse.py
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"""
python3 -m sglang.launch_server --model-path TinyLlama/TinyLlama-1.1B-Chat-v0.4 --port 30000
Output:
The capital of France is Paris.\nThe capital of the United States is Washington, D.C.\nThe capital of Canada is Ottawa.\nThe capital of Japan is Tokyo
"""
import argparse
import requests
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--host", type=str, default="http://127.0.0.1")
parser.add_argument("--port", type=int, default=30000)
args = parser.parse_args()
url = f"{args.host}:{args.port}"
response = requests.post(
url + "/generate",
json={
"text": "The capital of France is",
"sampling_params": {
"temperature": 0,
"max_new_tokens": 32,
},
},
)
print(response.json())
response = requests.post(
url + "/generate",
json={
"text": "The capital of France is Paris.\nThe capital of the United States is",
"sampling_params": {
"temperature": 0,
"max_new_tokens": 32,
},
},
)
print(response.json())
scripts/deprecated/test_robust.py
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import argparse
import random
import string
from vllm.transformers_utils.tokenizer import get_tokenizer
import sglang as sgl
from sglang.test.test_utils import (
add_common_sglang_args_and_parse,
select_sglang_backend,
)
TOKENIZER = None
RANDOM_PREFILL_LEN = None
RANDOM_DECODE_LEN = None
def gen_prompt(token_num):
if RANDOM_PREFILL_LEN:
token_num = random.randint(1, token_num)
cha_set = string.ascii_letters + string.digits
ret = "".join(random.choices(cha_set, k=token_num))
while len(TOKENIZER(ret).input_ids) < token_num:
ret += random.choice(cha_set)
return ret
def robust_test_dfs(s, d, args, leaf_states):
if d == 0:
s += "END"
leaf_states.append(s)
return
s += gen_prompt(args.len_prefill)
forks = s.fork(args.num_fork)
for fork_s in forks:
fork_s += gen_prompt(args.len_prefill)
new_tokens = (
args.len_decode
if not RANDOM_DECODE_LEN
else random.randint(1, args.len_decode)
)
fork_s += sgl.gen(
max_tokens=new_tokens,
ignore_eos=True,
)
for fork_s in forks:
robust_test_dfs(fork_s, d - 1, args, leaf_states)
def robust_test_bfs(s, args, leaf_states):
old_forks = [s]
new_forks = []
for _ in range(args.depth):
for old_fork in old_forks:
old_fork += gen_prompt(args.len_prefill)
forks = old_fork.fork(args.num_fork)
for fork_s in forks:
fork_s += gen_prompt(args.len_prefill)
new_tokens = (
args.len_decode
if not RANDOM_DECODE_LEN
else random.randint(1, args.len_decode)
)
fork_s += sgl.gen(
max_tokens=new_tokens,
ignore_eos=True,
)
new_forks.extend(forks)
old_forks = new_forks
new_forks = []
for old_fork in old_forks:
old_fork += "END"
leaf_states.append(old_fork)
@sgl.function
def robust_test(s, args):
leaf_states = []
if args.mode == "bfs":
robust_test_bfs(s, args, leaf_states)
else:
robust_test_dfs(s, args.depth, args, leaf_states)
return leaf_states
def main(args):
backend = select_sglang_backend(args)
arguments = [{"args": args} for _ in range(args.num_req)]
states = robust_test.run_batch(
arguments, temperature=0, backend=backend, num_threads=args.parallel
)
with open(f"tmp_robust_{args.mode}.txt", "w") as f:
for state in states:
leaf_states = state.ret_value
for leaf_state in leaf_states:
assert leaf_state.text()[-3:] == "END"
f.write(leaf_state.text()[:-3] + "\n")
if __name__ == "__main__":
# fmt: off
parser = argparse.ArgumentParser()
parser.add_argument("--num-req", type=int, default=2)
parser.add_argument("--depth", type=int, default=3)
parser.add_argument("--num-fork", type=int, default=2)
parser.add_argument("--len-prefill", type=int, default=128)
parser.add_argument("--len-decode", type=int, default=128)
parser.add_argument("--random-prefill-len", action="store_true")
parser.add_argument("--random-decode-len", action="store_true")
parser.add_argument("--mode", type=str, default="bfs", choices=["dfs", "bfs"])
parser.add_argument("--tokenizer", type=str, default = "meta-llama/Llama-2-7b-chat-hf")
parser.add_argument("--trust-remote-code", action="store_true")
parser.add_argument("--seed", type=int, default=42)
args = add_common_sglang_args_and_parse(parser)
# fmt: on
RANDOM_PREFILL_LEN = args.random_prefill_len
RANDOM_DECODE_LEN = args.random_decode_len
TOKENIZER = get_tokenizer(args.tokenizer, trust_remote_code=args.trust_remote_code)
random.seed(args.seed)
main(args)
scripts/playground/launch_tgi.sh
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@@ -1,7 +0,0 @@
# Assuming the model is downdloaded at /home/ubuntu/model_weights/Llama-2-7b-chat-hf
docker run --name tgi --rm -ti --gpus all --network host \
-v /home/ubuntu/model_weights/Llama-2-7b-chat-hf:/Llama-2-7b-chat-hf \
ghcr.io/huggingface/text-generation-inference:1.1.0 \
--model-id /Llama-2-7b-chat-hf --num-shard 1 --trust-remote-code \
--max-input-length 2048 --max-total-tokens 4096 \
--port 24000
scripts/deprecated/test_jump_forward.py → test/lang/test_jump_forward.py
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