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feat: support multiple completions #26

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feat: support multiple completions #26

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4be107e
fix: remove vim swap file
BaiMoHan Jun 13, 2025
01cffef
feat: support multiple completions (n > 1) in OpenAIProvider
BaiMoHan Jun 13, 2025
795c7d7
fix: correct token count validation for multiple completions
BaiMoHan Jun 13, 2025
84d45cf
fix: report latency_per_char in microseconds to avoid Locust rounding
BaiMoHan Jun 17, 2025
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Binary file removed llm_bench/.load_test.py.swp
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58 changes: 41 additions & 17 deletions llm_bench/load_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -25,6 +25,8 @@


def add_custom_metric(name, value, length_value=0):
if name == "total_latency":
value = value * 1000
events.request.fire(
request_type="METRIC",
name=name,
Expand Down Expand Up @@ -273,23 +275,45 @@ def format_payload(self, prompt, max_tokens, images):
def parse_output_json(self, data, prompt):
usage = data.get("usage", None)

assert len(data["choices"]) == 1, f"Too many choices {len(data['choices'])}"
choice = data["choices"][0]
if self.parsed_options.chat:
if self.parsed_options.stream:
text = choice["delta"].get("content", "")
else:
text = choice["message"]["content"]
if self.parsed_options.n > 1:
texts = []
for choice in data["choices"]:
if self.parsed_options.chat:
if self.parsed_options.stream:
text = choice["delta"].get("content", "")
else:
text = choice["message"]["content"]
else:
text = choice["text"]
texts.append(text)

combined_text = "\n".join(texts)

logprobs = data["choices"][0].get("logprobs", None)

return ChunkMetadata(
text=combined_text,
logprob_tokens=len(logprobs["tokens"]) if logprobs else None,
usage_tokens=usage["completion_tokens"] if usage else None,
prompt_usage_tokens=usage.get("prompt_tokens", None) if usage else None,
)
else:
text = choice["text"]
choice = data["choices"][0]
if self.parsed_options.chat:
if self.parsed_options.stream:
text = choice["delta"].get("content", "")
else:
text = choice["message"]["content"]
else:
text = choice["text"]

logprobs = choice.get("logprobs", None)
return ChunkMetadata(
text=text,
logprob_tokens=len(logprobs["tokens"]) if logprobs else None,
usage_tokens=usage["completion_tokens"] if usage else None,
prompt_usage_tokens=usage.get("prompt_tokens", None) if usage else None,
)
logprobs = choice.get("logprobs", None)
return ChunkMetadata(
text=text,
logprob_tokens=len(logprobs["tokens"]) if logprobs else None,
usage_tokens=usage["completion_tokens"] if usage else None,
prompt_usage_tokens=usage.get("prompt_tokens", None) if usage else None,
)


class FireworksProvider(OpenAIProvider):
Expand Down Expand Up @@ -823,8 +847,8 @@ def generate_text(self):
add_custom_metric("time_to_first_token", dur_first_token * 1000)
add_custom_metric("total_latency", dur_total * 1000)
if num_tokens:
if num_tokens != max_tokens:
print(f"WARNING: wrong number of tokens: {num_tokens}, expected {max_tokens}")
if num_tokens != max_tokens * self.environment.parsed_options.n:
print(f"WARNING: wrong number of tokens: {num_tokens}, expected {max_tokens * self.environment.parsed_options.n} (max_tokens={max_tokens}, n={self.environment.parsed_options.n})")
add_custom_metric("num_tokens", num_tokens)
add_custom_metric("latency_per_token", dur_generation / num_tokens * 1000, num_tokens)
add_custom_metric(
Expand Down