monitor metrics of tokens per step using cudagraph batchsizes

[youkaichao]

will be used for torch.compile

[github-actions]

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[youkaichao]

test code:

from vllm import LLM, SamplingParams

# Sample prompts.
prompts = [
    "Hello, my name is",
    "The president of the United States is",
    "The capital of France is",
    "The future of AI is",
]
# Create a sampling params object.
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)

# Create an LLM.
llm = LLM(model="facebook/opt-125m", disable_log_stats=False)
# Generate texts from the prompts. The output is a list of RequestOutput objects
# that contain the prompt, generated text, and other information.
outputs = llm.generate(prompts, sampling_params)
# Print the outputs.
for output in outputs:
    prompt = output.prompt
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

stat_logger = llm.llm_engine.stat_loggers['prometheus']
metric = stat_logger.metrics.histogram_iteration_tokens.labels(**stat_logger.labels)
values = metric._upper_bounds
counts = [x.get() for x in metric._buckets]
value_and_counts = zip(values, counts)
value_and_counts = sorted(value_and_counts, key=lambda x: x[1], reverse=True)
for x in value_and_counts:
    print(x)

bins:

(4.0, 15.0)
(1.0, 1.0)
(32.0, 1.0)
(2.0, 0.0)
(8.0, 0.0)
(16.0, 0.0)
(24.0, 0.0)
(40.0, 0.0)
(48.0, 0.0)
(56.0, 0.0)
(64.0, 0.0)
(72.0, 0.0)
(80.0, 0.0)
(88.0, 0.0)
(96.0, 0.0)
(104.0, 0.0)
(112.0, 0.0)
(120.0, 0.0)
(128.0, 0.0)
(136.0, 0.0)
(144.0, 0.0)
(152.0, 0.0)
(160.0, 0.0)
(168.0, 0.0)
(176.0, 0.0)
(184.0, 0.0)
(192.0, 0.0)
(200.0, 0.0)
(208.0, 0.0)
(216.0, 0.0)
(224.0, 0.0)
(232.0, 0.0)
(240.0, 0.0)
(248.0, 0.0)
(256.0, 0.0)
(inf, 0.0)