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[Benchmark] Refactor sample_requests in benchmark_throughput (#3613)

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Co-authored-by: Roger Wang <ywang@roblox.com>
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TianYu GUO 2024-04-04 17:56:22 +08:00 committed by GitHub
parent 819a309c0f
commit b7782002e1
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1 changed files with 15 additions and 16 deletions
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31
benchmarks/benchmark_throughput.py
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@ -29,22 +29,23 @@ def sample_requests(
dataset = [(data["conversations"][0]["value"], dataset = [(data["conversations"][0]["value"],
data["conversations"][1]["value"]) for data in dataset] data["conversations"][1]["value"]) for data in dataset]
# Tokenize the prompts and completions. # Shuffle the dataset.
prompts = [prompt for prompt, _ in dataset] random.shuffle(dataset)
prompt_token_ids = tokenizer(prompts).input_ids
completions = [completion for _, completion in dataset]
completion_token_ids = tokenizer(completions).input_ids
tokenized_dataset = []
for i in range(len(dataset)):
output_len = len(completion_token_ids[i])
if fixed_output_len is not None:
output_len = fixed_output_len
tokenized_dataset.append((prompts[i], prompt_token_ids[i], output_len))
# Filter out too long sequences. # Filter out sequences that are too long or too short
filtered_dataset: List[Tuple[str, int, int]] = [] filtered_dataset: List[Tuple[str, int, int]] = []
for prompt, prompt_token_ids, output_len in tokenized_dataset: 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) 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: if prompt_len < 4 or output_len < 4:
# Prune too short sequences. # Prune too short sequences.
continue continue
@ -53,9 +54,7 @@ def sample_requests(
continue continue
filtered_dataset.append((prompt, prompt_len, output_len)) filtered_dataset.append((prompt, prompt_len, output_len))
# Sample the requests. return filtered_dataset
sampled_requests = random.sample(filtered_dataset, num_requests)
return sampled_requests
def run_vllm( def run_vllm(