TensorRT-LLMs/docs/source/features/sampling.md

Sampling

The PyTorch backend supports a wide variety of features, listed below:

Forward Pass	Sampling Strategies	Sampling Features
No drafting	Greedy	Guided Decoding
Draft target model	TopP	Plugging Logits Post-Processor
Eagle 3	TopK	Temperature
Ngram	TopK + TopP	MinP
	Beam Search	Embedding / Logits Bias
	Best of / n (composable)	Stop criteria
	Rejection sampling (composable)	Return Logits
		Return LogProbs
		TopK LogProbs

General usage

There are two sampling backends available.

• Torch Sampler
• TRTLLM Sampler

Torch Sampler currently supports a superset of features of TRTLLM Sampler, and is intended as the long-term solution. One can specify which sampler to use explicitly with:

from tensorrt_llm import LLM

# Chooses TorchSampler explicitly
llm = LLM(model='nvidia/Llama-3.1-8B-Instruct-FP8',
          sampler_type="TorchSampler")

# Chooses TRTLLMSampler explicitly
llm = LLM(model='nvidia/Llama-3.1-8B-Instruct-FP8',
          sampler_type="TRTLLMSampler")

By default, the sampling backend is chosen to be auto. This will use:

• TRTLLM Sampler when using Beam Search.
• Torch Sampler otherwise.

Here is an example to run a model with basic usage of sampling parameters. This example prepares two identical prompts which will give different results due to the sampling parameters chosen:

from tensorrt_llm import LLM, SamplingParams
llm = LLM(model='nvidia/Llama-3.1-8B-Instruct-FP8')
sampling_params = SamplingParams(
        temperature=1.0,
        top_k=8,
        top_p=0.5,
    )
llm.generate(["Hello, my name is",
            "Hello, my name is"], sampling_params)

It is also possible to specify different sampling parameters on a per-prompt basis:

from tensorrt_llm import LLM, SamplingParams
llm = LLM(model='nvidia/Llama-3.1-8B-Instruct-FP8')
sampling_params_0 = SamplingParams(
        temperature=1.0,
        top_k=8,
        top_p=0.5,
    )
sampling_params_1 = SamplingParams(
        top_k=4,
    )
llm.generate(["Hello, my name is",
            "Hello, my name is"],
            [sampling_params_0,
            sampling_params_1])

LLM API sampling behavior when using Torch Sampler

• The sampling is controlled via SamplingParams.

• By default (temperature = top_p = top_k = None), greedy sampling is used.

• If either temperature = 0, top_p = 0, and/or top_k = 1, is specified, sampling is greedy, irrespective of the values of the remaining parameters.

• Otherwise, sampling proceeds according to the specified sampling parameter values and any unspecified parameters default to top_k = 0, top_p = 1, temperature = 1.0:

  • The logits are scaled by 1/temperature before applying softmax to compute probabilities. Sampling is performed according to these probabilities.

  • If top_k = 0 (or top_k = vocab_size) and top_p = 1, the output tokens are sampled from the entire vocabulary.

  • If 1 < top_k < vocab_size is specified, the sampling is restricted to the top_k highest-probability tokens.

  • If 0 < top_p < 1.0 is specified, the sampling is further restricted to a minimal subset of highest-probability tokens with total probability greater than top_p ("nucleus sampling"). In particular, the probability of the lowest-probability token in the selected subset is greater or equal than the probability of any not selected token. When combined with top_k, the probabilities of the tokens selected by top_k are rescaled such that they sum to one before top_p is applied.

  • The implementation does not guarantee any particular treatment of tied probabilities.

Performance

The Torch Sampler leverages the optimized sampling kernels provided by FlashInfer. The sampler also uses the sorting-free implementations whenever possible. This optimization does not compute the complete set of token sampling probabilities (after top-k / top-p masking etc.), which typically can be omitted unless requested by the user or required for speculative decoding (rejection sampling). In case of unexpected problems, the use of FlashInfer in Torch Sampler can be disabled via the disable_flashinfer_sampling config option (note that this option is likely to be removed in a future TensorRT LLM release).

Moreover, Torch Sampler internally batches requests with compatible sampling parameters. This can greatly reduce the overall latency of the sampling step when request batches are comprised of requests with very heterogeneous sampling strategies (e.g. a mix of requests using greedy and top-p-after-top-k sampling).

Beam search

Beam search is a decoding strategy that maintains multiple candidate sequences (beams) during text generation, exploring different possible continuations to find higher quality outputs. Unlike greedy decoding or sampling, beam search considers multiple hypotheses simultaneously.

To enable beam search, you must:

1. Enable the use_beam_search option in the SamplingParams object
2. Set the max_beam_width parameter in the LLM class to match the best_of parameter in SamplingParams

Parameter Configuration:

• best_of: Controls the number of beams processed during generation (beam width)
• n: Controls the number of output sequences returned (can be less than best_of)
• If best_of is omitted, the number of beams processed defaults to n
• max_beam_width in the LLM class must equal best_of in SamplingParams

The following example demonstrates beam search with a beam width of 4, returning the top 3 sequences:

from tensorrt_llm import LLM, SamplingParams
llm = LLM(model='nvidia/Llama-3.1-8B-Instruct-FP8',
          max_beam_width=4,   # must equal SamplingParams.best_of
    )
sampling_params = SamplingParams(
        best_of=4,   # must equal LLM.max_beam_width
        use_beam_search=True,
        n=3,         # return top 3 sequences
    )
llm.generate(["Hello, my name is",
            "Hello, my name is"], sampling_params)

Logits processor

Logits processors allow you to modify the logits produced by the network before sampling, enabling custom generation behavior and constraints.

To use a custom logits processor:

1. Create a custom class that inherits from LogitsProcessor and implements the __call__ method
2. Pass an instance of this class to the logits_processor parameter of SamplingParams

The following example demonstrates logits processing:

import torch
from typing import List, Optional

from tensorrt_llm import LLM, SamplingParams
from tensorrt_llm.sampling_params import LogitsProcessor

class MyCustomLogitsProcessor(LogitsProcessor):
    def __call__(self,
        req_id: int,
        logits: torch.Tensor,
        token_ids: List[List[int]],
        stream_ptr: Optional[int],
        client_id: Optional[int]
    ) -> None:
        # Implement your custom inplace logits processing logic
        logits *= logits

llm = LLM(model='nvidia/Llama-3.1-8B-Instruct-FP8')
sampling_params = SamplingParams(
        logits_processor=MyCustomLogitsProcessor()
    )
llm.generate(["Hello, my name is"], sampling_params)

You can find a more detailed example on logits processors here.