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Code Issues Actions 6 Packages Projects Releases Wiki Activity

fix typos (#4176)

Browse Source 
* fix typos

* fix
This commit is contained in:
co63oc
2025-09-22 14:27:17 +08:00
committed by GitHub
parent 0b62648924
commit c4830ef24c
10 changed files with 34 additions and 50 deletions
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2
benchmarks/benchmark_mtp.py
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@@ -98,7 +98,7 @@ def main(args):
raise ValueError("--max_concurrency should be same length as --s_itl_base_model")
for max_concurrency, s_itl in zip(args.max_concurrency, args.s_itl_base_model):
# Wramup
# Warmup
print("Starting warmup...")
with open(os.devnull, "w") as f:
with contextlib.redirect_stdout(f):

2
custom_ops/gpu_ops/custom_all_reduce/all_reduce.cuh
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@@ -303,7 +303,7 @@ class CustomAllreduce {
bool full_nvlink_;
RankSignals sg_;
// Stores an map from a pointer to its peer pointters from all ranks.
// Stores an map from a pointer to its peer pointers from all ranks.
std::unordered_map<void*, RankData*> buffers_;
Signal* self_sg_;

2
docs/get_started/installation/nvidia_gpu.md
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@@ -10,7 +10,7 @@ The following installation methods are available when your environment meets the
## 1. Pre-built Docker Installation (Recommended)
**Notice**: The pre-built image only supports SM80/90 GPU(e.g. H800/A800)，if you are deploying on SM86/89GPU(L40/4090/L20), please reinstall ```fastdpeloy-gpu``` after you create the container.
**Notice**: The pre-built image only supports SM80/90 GPU(e.g. H800/A800)，if you are deploying on SM86/89GPU(L40/4090/L20), please reinstall ```fastdeploy-gpu``` after you create the container.
```shell
docker pull ccr-2vdh3abv-pub.cnc.bj.baidubce.com/paddlepaddle/fastdeploy-cuda-12.6:2.2.0

2
docs/usage/code_overview.md
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@@ -20,6 +20,6 @@ Below is an overview of the FastDeploy code structure and functionality organize
- ```platforms```: Platform-specific modules for underlying hardware support.
- ```scheduler```: Request scheduling module for large models.
- ```metrics```: Core component for collecting, managing, and exporting Prometheus metrics, tracking key runtime performance data (e.g., request latency, resource utilization, successful request counts).
- ```splitwise```: Modules related to PD disaggragation deployment.
- ```splitwise```: Modules related to PD disaggregation deployment.
- ```scripts```/```tools```: Utility scripts for FastDeploy operations (e.g., compilation, unit testing, code style fixes).
- ```test```: Code for unit testing and validation.

2
docs/usage/log.md
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@@ -30,7 +30,7 @@ By default, logs are stored in the `log` directory under the execution path. To
* `cache_transfer_manager.log` : Logs startup parameters and received request information.
* `launch_cache_manager.log` : Records cache transfer startup parameters and error messages.
## PD Disaggragation Logs
## PD Disaggregation Logs
* `cache_messager.log` : Logs transmission protocols and messages used by the P instance.
* `splitwise_connector.log` : Records data received from P/D instances and connection establishment details.

8
mkdocs.yml
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@@ -1,4 +1,4 @@
site_name: 'FastDeploy : Large Language Model Deployement'
site_name: 'FastDeploy : Large Language Model Deployment'
repo_url: https://github.com/PaddlePaddle/FastDeploy
repo_name: FastDeploy
@@ -36,7 +36,7 @@ plugins:
- locale: en
default: true
name: English
site_name: 'FastDeploy: Large Language Model Deployement'
site_name: 'FastDeploy: Large Language Model Deployment'
build: true
link: /FastDeploy/
- locale: zh
@@ -59,7 +59,7 @@ plugins:
ERNIE-4.5-VL-424B-A47B: ERNIE-4.5-VL-424B-A47B快速部署
Quick Deployment For QWEN: Qwen3-0.6b快速部署
Online Serving: 在线服务
OpenAI-Compitable API Server: 兼容 OpenAI 协议的服务化部署
OpenAI-Compatible API Server: 兼容 OpenAI 协议的服务化部署
Monitor Metrics: 监控Metrics
Scheduler: 调度器
Graceful Shutdown: 服务优雅关闭
@@ -114,7 +114,7 @@ nav:
- ERNIE-4.5-VL-424B-A47B: get_started/ernie-4.5-vl.md
- Quick Deployment For QWEN: get_started/quick_start_qwen.md
- Online Serving:
- OpenAI-Compitable API Server: online_serving/README.md
- OpenAI-Compatible API Server: online_serving/README.md
- Monitor Metrics: online_serving/metrics.md
- Scheduler: online_serving/scheduler.md
- Graceful Shutdown: online_serving/graceful_shutdown_service.md

50
tests/ce/server/test_return_token_ids.py
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@@ -9,12 +9,7 @@ Checking for /v1/completions parameters
import json
from core import (
TEMPLATE,
URL,
build_request_payload,
send_request,
)
from core import TEMPLATE, URL, build_request_payload, send_request
COMPLETIONS_URL = URL.replace("/v1/chat/completions", "/v1/completions")
@@ -29,9 +24,9 @@ def test_completion_stream_text_after_process_raw_prediction():
"stream": True,
"stream_options": {"include_usage": True, "continuous_usage_stats": True},
"max_tokens": 50,
"return_token_ids": True
"return_token_ids": True,
}
payload = build_request_payload(TEMPLATE, data)
resp = send_request(COMPLETIONS_URL, payload, stream=True)
for line in resp.iter_lines(decode_unicode=True):
@@ -39,7 +34,7 @@ def test_completion_stream_text_after_process_raw_prediction():
break
if line.strip() == "" or not line.startswith("data: "):
continue
line = line[len("data: "):]
line = line[len("data: ") :]
response_data = json.loads(line)
choice = response_data["choices"][0]
@@ -51,21 +46,16 @@ def test_completion_stream_text_after_process_raw_prediction():
reasoning_content = choice["reasoning_content"]
text = choice["text"]
assert reasoning_content or text in raw_prediction, "raw_prediction取值结果不正确"
if "finish_reason" in line.strip() :
if "finish_reason" in line.strip():
break
def test_completion_text_after_process_raw_predictio_return_tokrn_ids():
def test_completion_text_after_process_raw_predictio_return_token_ids():
"""
/v1/completions接口,非流式接口
返回属性"text_after_process"和"reasoning_content"
"""
data = {
"stream": False,
"prompt": "你是谁",
"max_tokens": 50,
"return_token_ids": True
}
data = {"stream": False, "prompt": "你是谁", "max_tokens": 50, "return_token_ids": True}
payload = build_request_payload(TEMPLATE, data)
resp = send_request(COMPLETIONS_URL, payload).json()
@@ -80,14 +70,10 @@ def test_completion_text_after_process_raw_predictio_return_tokrn_ids():
def test_completion_text_after_process_raw_prediction():
"""
/v1/completions接口,无return_tokrn_ids参数
/v1/completions接口,无return_token_ids参数
非流式接口中,无return token ids 属性"text_after_process"和"reasoning_content"值为null
"""
data = {
"stream": False,
"prompt": "你是谁",
"max_tokens": 50
}
data = {"stream": False, "prompt": "你是谁", "max_tokens": 50}
payload = build_request_payload(TEMPLATE, data)
resp = send_request(COMPLETIONS_URL, payload).json()
@@ -108,17 +94,17 @@ def test_stream_text_after_process_raw_prediction():
"stream": True,
"stream_options": {"include_usage": True, "continuous_usage_stats": True},
"max_tokens": 50,
"return_token_ids": True
"return_token_ids": True,
}
payload = build_request_payload(TEMPLATE, data)
resp = send_request(URL, payload, stream=True)
for line in resp.iter_lines(decode_unicode=True):
if line.strip() == "data: [DONE]" :
if line.strip() == "data: [DONE]":
break
if line.strip() == "" or not line.startswith("data: "):
continue
line = line[len("data: "):]
line = line[len("data: ") :]
response_data = json.loads(line)
choice = response_data["choices"][0]
@@ -130,11 +116,11 @@ def test_stream_text_after_process_raw_prediction():
reasoning_content = choice["delta"]["reasoning_content"]
content = choice["delta"]["content"]
assert reasoning_content or content in raw_prediction, "raw_prediction取值结果不正确"
if "finish_reason" in line.strip() :
if "finish_reason" in line.strip():
break
def test_text_after_process_raw_prediction_return_tokrn_ids():
def test_text_after_process_raw_prediction_return_token_ids():
"""
/v1/chat/completions接口,非流式接口
返回属性"text_after_process"和"reasoning_content"
@@ -161,7 +147,7 @@ def test_text_after_process_raw_prediction_return_tokrn_ids():
def test_text_after_process_raw_prediction():
"""
/v1/chat/completions接口,无return_tokrn_ids参数
/v1/chat/completions接口,无return_token_ids参数
无return token ids 属性"text_after_process"和"reasoning_content"值为null
"""
data = {
@@ -179,5 +165,3 @@ def test_text_after_process_raw_prediction():
raw_prediction = resp["choices"][0]["message"]["raw_prediction"]
assert raw_prediction is None, "raw_prediction取值结果不正确"

4
tests/ci_use/EB_Lite_with_adapter/zmq_client.py
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@@ -50,7 +50,7 @@ class LLMReqClient:
if self.need_exit:
break
except Exception as e:
print(f"zmq client occured error {e} type: {type(e)} frames: {frames}")
print(f"zmq client occurred error {e} type: {type(e)} frames: {frames}")
def start(self, result_queue):
threading.Thread(target=self.consume_results, args=(result_queue,), daemon=True).start()
@@ -118,4 +118,4 @@ class LLMControlClient:
self.result[task_id] = result["result"]
self.task_event[task_id].set()
except Exception as e:
print(f"zmq client occured error {e} type: {type(e)} frames: {frames}")
print(f"zmq client occurred error {e} type: {type(e)} frames: {frames}")

10
tests/distributed/custom_all_reduce.py
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@@ -54,14 +54,14 @@ class Test(unittest.TestCase):
fa = CustomAllreduce(model_parallel_group)
for m, n in mns:
data_cusom_ar = paddle.rand([m, n], dtype="bfloat16")
data_paddle = data_cusom_ar.clone()
if fa.should_custom_ar(data_cusom_ar):
fa.custom_all_reduce(data_cusom_ar)
data_custom_ar = paddle.rand([m, n], dtype="bfloat16")
data_paddle = data_custom_ar.clone()
if fa.should_custom_ar(data_custom_ar):
fa.custom_all_reduce(data_custom_ar)
dist.all_reduce(data_paddle)
if dist.get_rank() == 0:
np.testing.assert_allclose(
data_cusom_ar.numpy(),
data_custom_ar.numpy(),
data_paddle.numpy(),
rtol=1e-04,
atol=1e-04,

2
tools/deep_gemm_pre-compile/pre_compile.py
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@@ -158,7 +158,7 @@ def pre_compile_from_config(config_file: str, num_threads: int, expert_parallel:
pbar.close()
logger.info(f"Total compliation time: {time() - start_time:.2f} seconds")
logger.info(f"Total compilation time: {time() - start_time:.2f} seconds")
def main(args):