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

Sampling

The PyTorch backend supports most of the sampling features that are supported on the C++ backend, such as temperature, top-k and top-p sampling, beam search, stop words, bad words, penalty, context and generation logits, log probability and logits processors

General usage

To use the feature:

1. Enable the enable_trtllm_sampler option in the LLM class
2. Pass a SamplingParams object with the desired options to the generate() function

The following 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',
          enable_trtllm_sampler=True)
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)

Note: The enable_trtllm_sampler option is not currently supported when using speculative decoders, such as MTP or Eagle-3, so there is a smaller subset of sampling options available.

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
3. Disable overlap scheduling using the disable_overlap_scheduler parameter of the LLM class
4. Disable the usage of CUDA Graphs by passing None to the cuda_graph_config parameter of the LLM class

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',
          enable_trtllm_sampler=True,
          max_beam_width=4,   # must equal SamplingParams.best_of
          disable_overlap_scheduler=True,
          cuda_graph_config=None)
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.