squall/vllm
1
0
Fork 0
Code Issues Pull Requests Actions 1 Packages Projects Releases Wiki Activity

[Frontend] add tok/s speed metric to llm class when using tqdm (#4400)

Browse Source 
Co-authored-by: Michael Goin <michael@neuralmagic.com>
This commit is contained in:
Mahmoud Ashraf 2024-05-09 08:02:31 +03:00 committed by GitHub
parent e288df0632
commit 16bc0a098f
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
1 changed files with 12 additions and 4 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

12
vllm/entrypoints/llm.py
View File

@ -238,17 +238,25 @@ class LLM:
# Initialize tqdm.
if use_tqdm:
num_requests = self.llm_engine.get_num_unfinished_requests()
pbar = tqdm(total=num_requests,
pbar = tqdm(
total=num_requests,
desc="Processed prompts",
dynamic_ncols=True)
dynamic_ncols=True,
postfix=f"Generation Speed: {0:.2f} toks/s",
)
# Run the engine.
outputs: List[RequestOutput] = []
total_toks = 0
while self.llm_engine.has_unfinished_requests():
step_outputs = self.llm_engine.step()
for output in step_outputs:
if output.finished:
outputs.append(output)
if use_tqdm:
total_toks += (sum(
len(stp.token_ids) for stp in output.outputs))
spd = total_toks / pbar.format_dict["elapsed"]
pbar.postfix = f"Generation Speed: {spd:.2f} toks/s"
pbar.update(1)
if use_tqdm:
pbar.close()