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Signed-off-by: nv-guomingz <137257613+nv-guomingz@users.noreply.github.com> Signed-off-by: Wangshanshan <30051912+dominicshanshan@users.noreply.github.com>
69 lines
2.6 KiB
Markdown
69 lines
2.6 KiB
Markdown
# How to Change Block Priorities
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You can change block priority by providing the optional ```kv_cache_retention_config``` argument when you submit a request to the LLM engine. Consider the quick start example found in ```examples/pytorch/quickstart.py```:
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```python
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from tensorrt_llm import LLM, SamplingParams
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def main():
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prompts = [
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"Hello, my name is",
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"The president of the United States is",
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"The capital of France is",
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"The future of AI is",
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]
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sampling_params = SamplingParams(max_tokens=32)
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llm = LLM(model='TinyLlama/TinyLlama-1.1B-Chat-v1.0')
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outputs = llm.generate(prompts, sampling_params)
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for i, output in enumerate(outputs):
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prompt = output.prompt
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generated_text = output.outputs[0].text
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print(f"[{i}] Prompt: {prompt!r}, Generated text: {generated_text!r}")
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if __name__ == '__main__':
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main()
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```
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The blocks from the prompts are stored for reuse with the default priority of 35 on a scale from 1 to 100, where 100 is highest priority and 1 is lowest priority. Assume you know that the first four tokens of each prompt represent a system prompt that should be stored with high priority (100). You can achieve this by providing a KV cache retention config object when you submit the prompts for generation:
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```python
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from tensorrt_llm import LLM, SamplingParams
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from tensorrt_llm.llmapi import KvCacheRetentionConfig
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def main():
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prompts = [
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"Hello, my name is",
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"The president of the United States is",
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"The capital of France is",
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"The future of AI is",
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]
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sampling_params = SamplingParams(max_tokens=32)
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llm = LLM(model='TinyLlama/TinyLlama-1.1B-Chat-v1.0')
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# Set priority for first 4 prompt tokens to 100. All other tokens set to default (35) priority.
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# This policy never lapses.
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tokenRangeRetentionConfig = KvCacheRetentionConfig.TokenRangeRetentionConfig(0, 4, 100, None)
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kv_cache_retention_config = KvCacheRetentionConfig(
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token_range_retention_configs=[tokenRangeRetentionConfig],
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decode_retention_priority=35, # Set generated tokens to default priority
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decode_duration_ms=None)
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outputs = llm.generate(prompts, sampling_params, kv_cache_retention_config=kv_cache_retention_config)
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for i, output in enumerate(outputs):
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prompt = output.prompt
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generated_text = output.outputs[0].text
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print(f"[{i}] Prompt: {prompt!r}, Generated text: {generated_text!r}")
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if __name__ == '__main__':
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main()
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```
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This example uses a single ```kv_cache_retention_config``` object for all the prompts. You can also provide a list that must have the same length as the list of prompts.
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