Skip to main content

/

Docs

Nightly

v25.1

Log in

Python module

log_probabilities

compute_log_probabilities()​

max.pipelines.log_probabilities.compute_log_probabilities(get_logits_and_samples: Callable[[int, bool], tuple[numpy.ndarray, numpy.ndarray] | None], batch_top_n: list[int], batch_echo: list[bool]) → list[max.pipelines.interfaces.response.LogProbabilities | None]

Computes the log probabilities.

  • Parameters:

    • get_logits_and_samples – Callable that takes the batch index and an
    • batch. (echo bool and returns the logits and sampled tokens for that) – Args:
      • batch_index is an int between [0, batch_size)
      • echo is whether that item was requested to echo the input tokens. Returns (None if batch item is empty):
      • Logits should have shape = (n_tokens, vocab_size).
      • Sampled tokens should have shape = (n_tokens).
    • batch_top_n – Number of top log probabilities to return per input in the batch. For any element where top_n == 0, the LogProbabilities is skipped.
    • batch_echo – Whether to include input tokens in the returned log probabilities.

  • Returns:

    Computed log probabilities for each item in the batch.

log_softmax()​

max.pipelines.log_probabilities.log_softmax(x: ndarray, axis: int = -1) → ndarray

Compute the logarithm of the softmax function.

This implementation uses the identity log(softmax(x)) = x - log(sum(exp(x))) with numerical stability improvements to prevent overflow/underflow.

  • Parameters:

    • x – Input array
    • axis – Axis to compute values along

  • Returns:

    Array with same shape as x, representing log(softmax(x))