aphrodite-engine/aphrodite/modeling/layers/logits_processor.py

"""A layer that compute logits from hidden_stats."""
from typing import Optional

import torch
import torch.nn as nn

from aphrodite.distributed import tensor_model_parallel_gather
from aphrodite.modeling.sampling_metadata import SamplingMetadata


class LogitsProcessor(nn.Module):
    """Process logits and apply logits processors from sampling metadata.

    This layer does the following:
    1. Gather logits from model hidden_states.
    2. Scale logits if needed.
    3. Apply logits processors (if any).
    """

    def __init__(self,
                 vocab_size: int,
                 org_vocab_size: Optional[int] = None,
                 scale: Optional[float] = 1.0,
                 logits_as_input: bool = False) -> None:
        """
        Args:
            scale: A scaling factor to apply to the logits.
        """
        super().__init__()
        self.scale = scale
        self.vocab_size = vocab_size
        # Whether the input is logits (default is hidden states).
        self.logits_as_input = logits_as_input
        # original vocabulary size (without LoRA).
        self.org_vocab_size = org_vocab_size or vocab_size

    def forward(
        self,
        embedding: torch.Tensor,
        hidden_states: torch.Tensor,
        sampling_metadata: SamplingMetadata,
        embedding_bias: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        if self.logits_as_input:
            logits = hidden_states
        else:
            hidden_states = _prune_hidden_states(hidden_states,
                                                 sampling_metadata)

            # Get the logits for the next tokens.
            logits = self._get_logits(hidden_states, embedding, embedding_bias)

        if logits is not None:
            logits *= self.scale

            # Apply logits processors (if any).
            logits = _apply_logits_processors(logits, sampling_metadata)

        return logits

    def _get_logits(self, hidden_states: torch.Tensor, embedding: torch.Tensor,
                    embedding_bias: Optional[torch.Tensor]) -> torch.Tensor:
        # Get the logits for the next tokens.
        logits = torch.matmul(hidden_states, embedding.t())
        if embedding_bias is not None:
            logits += embedding_bias
        logits = tensor_model_parallel_gather(logits)
        # Remove paddings in vocab (if any).
        if logits is not None:
            logits = logits[:, :self.org_vocab_size]
        return logits


def _prune_hidden_states(
    hidden_states: torch.Tensor,
    sampling_metadata: SamplingMetadata,
) -> torch.Tensor:
    return hidden_states.index_select(0,
                                      sampling_metadata.selected_token_indices)


def _apply_logits_processors(
    logits: torch.Tensor,
    sampling_metadata: SamplingMetadata,
) -> torch.Tensor:
    found_logits_processors = False
    logits_processed = 0
    for seq_group in sampling_metadata.seq_groups:
        seq_ids = seq_group.seq_ids
        sampling_params = seq_group.sampling_params
        logits_processors = sampling_params.logits_processors

        if logits_processors:
            found_logits_processors = True
            for seq_id, logits_row_idx in zip(seq_ids,
                                              seq_group.sample_indices):
                logits_row = logits[logits_row_idx]
                token_ids = seq_group.seq_data[seq_id].output_token_ids
                for logits_processor in logits_processors:
                    logits_row = logits_processor(token_ids, logits_row)
                logits[logits_row_idx] = logits_row

        logits_processed += len(seq_group.sample_indices) + len(
            seq_group.prompt_logprob_indices)

    if found_logits_processors:
        # verifies that no rows in logits were missed unexpectedly
        assert logits_processed == logits.shape[0]
    return logits