vllm.model_executor.layers.utils ¶

def apply_penalties(
    logits: torch.Tensor,
    prompt_tokens_tensor: torch.Tensor,
    output_tokens_tensor: torch.Tensor,
    presence_penalties: torch.Tensor,
    frequency_penalties: torch.Tensor,
    repetition_penalties: torch.Tensor,
) -> torch.Tensor:
    """
    Applies penalties in place to the logits tensor
    logits : The input logits tensor of shape [num_seqs, vocab_size]
    prompt_tokens_tensor: A tensor containing the prompt tokens. The prompts
        are padded to the maximum prompt length within the batch using
        `vocab_size` as the padding value. The value `vocab_size` is used
        for padding because it does not correspond to any valid token ID
        in the vocabulary.
    output_tokens_tensor: The output tokens tensor.
    presence_penalties: The presence penalties of shape (num_seqs, )
    frequency_penalties: The frequency penalties of shape (num_seqs, )
    repetition_penalties: The repetition penalties of shape (num_seqs, )
    """
    num_seqs, vocab_size = logits.shape
    _, prompt_mask = get_token_bin_counts_and_mask(
        prompt_tokens_tensor, vocab_size, num_seqs
    )
    output_bin_counts, output_mask = get_token_bin_counts_and_mask(
        output_tokens_tensor, vocab_size, num_seqs
    )

    # Apply repetition penalties as a custom op
    from vllm._custom_ops import apply_repetition_penalties

    apply_repetition_penalties(logits, prompt_mask, output_mask, repetition_penalties)

    # We follow the definition in OpenAI API.
    # Refer to https://platform.openai.com/docs/api-reference/parameter-details
    logits -= frequency_penalties.unsqueeze(dim=1) * output_bin_counts
    logits -= presence_penalties.unsqueeze(dim=1) * output_mask
    return logits

@functools.cache
def warmup_rocm_skinny_gemm_workspaces(device: torch.device) -> None:
    """Eagerly allocate wvSplitKrc's process-lifetime static workspaces.

    They are otherwise created lazily on the first qualifying GEMM
    (csrc/rocm/skinny_gemms.cu), which can be the first real request — after
    the KV cache backing buffer exists. If one landed in that segment's
    rounding tail, it would pin the entire segment at engine shutdown.
    """
    from vllm.platforms.rocm import on_gfx950

    if not on_gfx950():
        return
    try:
        x = torch.zeros(16, 1024, dtype=torch.bfloat16, device=device)
        weight = torch.zeros(32, 1024, dtype=torch.bfloat16, device=device)
        ops.wvSplitKrc(x, weight, num_compute_units())
    except Exception:
        logger.debug("wvSplitKrc workspace warmup failed", exc_info=True)