Python module
log_probabilities
compute_log_probabilities_ragged()
max.pipelines.lib.log_probabilities.compute_log_probabilities_ragged(device, model, *, input_row_offsets, logits, next_token_logits, tokens, sampled_tokens, batch_top_n, batch_echo)
Computes the log probabilities for ragged model outputs.
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Parameters:
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- device (Device) – Device on which to do the bulk of the log probabilities computation. A small amount of computation still occurs on the host regardless of this setting.
- model (Model) – A compiled version of a graph from the ‘log_probabilities_ragged_graph’ function.
- input_row_offsets (ndarray) – Token offsets into token-indexed buffers, by batch index. Should have 1 more element than there are batches (batch n is token indices [input_row_offsets[n], input_row_offsets[n+1])).
- logits (Tensor | None) – (tokens, vocab_dim) tensor full of tensor logits. Token dimension mapped to batches using input_row_offsets. May be omitted only if all ‘batch_echo’ values are False.
- next_token_logits (Tensor) – (batch_dim, vocab_dim) tensor full of tensor logits for the next token in each batch item.
- sampled_tokens (ndarray) – (batch_dim,) tensor of sampled token per batch
- batch_top_n (Sequence[int]) – 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 (Sequence[bool]) – Whether to include input tokens in the returned log probabilities.
- tokens (ndarray)
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Returns:
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Computed log probabilities for each item in the batch.
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Return type:
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list[LogProbabilities | None]
log_probabilities_ragged_graph()
max.pipelines.lib.log_probabilities.log_probabilities_ragged_graph(device, *, levels)
Create a graph to compute log probabilities over ragged inputs.
A model obtained by this graph is a required input to ‘compute_log_probabilities_ragged’.
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