flashinfer.sampling.top_k_top_p_sampling_from_probs¶

flashinfer.sampling.top_k_top_p_sampling_from_probs(probs: torch.Tensor, uniform_samples: torch.Tensor, top_k: torch.Tensor | int, top_p: torch.Tensor | float, filter_apply_order: str = 'top_k_first', deterministic: bool = True, check_nan: bool = False) → Tuple[torch.Tensor, torch.Tensor]¶

Fused GPU kernel for top-k and top-p sampling from probabilities,

this operator implements GPU-based rejection sampling without explicit sorting.

The multiple rounds of rejection sampling are implemented in a single CUDA kernel, which is more efficient than the naive implementation that launches a series of kernels.

Parameters:

probs (torch.Tensor) – Probabilities, shape (batch_size, num_classes).
uniform_samples (torch.Tensor) – The uniform samples used as needle for sampling, shape (max_top_k_rounds, batch_size,), where the first dimension is the maximum number of rounds for rejection sampling. Expected to be uniformly distributed in [0, 1).
top_k (Union[torch.Tensor, int]) – Either a scalar or a tensor of shape (batch_size,), representing the threshold for top-k sampling. If a scalar, the same threshold is used for all requests. If a tensor, each request has its own threshold.
top_p (Union[torch.Tensor, float]) – Either a scalar or a tensor of shape (batch_size,), representing the threshold for top-p sampling. If a scalar, the same threshold is used for all requests. If a tensor, each request has its own threshold.
filter_apply_order (str) – The order of applying top-k and top-p sampling, should be either "top_k_first" or "joint". If "top_k_first", we first apply top-k filter, then apply top-p sampling on the top-k results. If "joint", we apply top-k and top-p filter simultaneously in each round. Default is "top_k_first".
deterministic (bool) – Whether to use deterministic kernel implementation, default is True.
check_nan (bool) – Whether to check nan in probs, default is False.

Returns:

samples (torch.Tensor) – Sampled categories, shape (batch_size,).
success (torch.Tensor) – Whether the sampling is successful within max_top_k_rounds rounds, shape (batch_size,).

Examples

>>> import torch
>>> import flashinfer
>>> torch.manual_seed(42)
>>> batch_size = 4
>>> vocab_size = 5
>>> max_rounds = 3
>>> top_p = torch.full((batch_size,), 0.2).to(0)
>>> top_k = torch.full((batch_size,), 2).to(0)
>>> pre_norm_prob = torch.rand(batch_size, vocab_size).to(0)
>>> norm_prob = pre_norm_prob / pre_norm_prob.sum(dim=-1, keepdim=True)
>>> norm_prob
tensor([[0.2499, 0.2592, 0.1085, 0.2718, 0.1106],
        [0.2205, 0.0942, 0.2912, 0.3452, 0.0489],
        [0.2522, 0.1602, 0.2346, 0.1532, 0.2000],
        [0.1543, 0.3182, 0.2062, 0.0958, 0.2255]], device='cuda:0')
>>> uniform_samples = torch.rand(max_rounds, batch_size).to(0)
>>> samples, success = flashinfer.sampling.top_k_top_p_sampling_from_probs(norm_prob, uniform_samples, top_k, top_p)
>>> samples
tensor([3, 3, 0, 1], device='cuda:0', dtype=torch.int32)
>>> success
tensor([True, True, True, True], device='cuda:0')

Note

This function expects float32 inputs, and the output is int32. We encourage users to set max_rounds to a reasonable value, e.g., 32. The actual implementation usually use much fewer rounds for rejection sampling because of early stopping.