169 lines
6.4 KiB
Python
169 lines
6.4 KiB
Python
"""A layer that compute logits from hidden_stats."""
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import inspect
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from typing import Optional
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import torch
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import torch.nn as nn
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import vllm.envs as envs
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from vllm.config import get_current_vllm_config
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from vllm.distributed import (tensor_model_parallel_all_gather,
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tensor_model_parallel_gather)
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from vllm.model_executor.layers.vocab_parallel_embedding import (
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VocabParallelEmbedding)
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from vllm.model_executor.sampling_metadata import SamplingMetadata
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from vllm.platforms import current_platform
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class LogitsProcessor(nn.Module):
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"""Process logits and apply logits processors from sampling metadata.
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This layer does the following:
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1. Gather logits from model hidden_states.
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2. Scale logits if needed.
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3. Apply logits processors (if any).
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"""
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def __init__(self,
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vocab_size: int,
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org_vocab_size: Optional[int] = None,
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scale: float = 1.0,
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logits_as_input: bool = False,
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soft_cap: Optional[float] = None) -> None:
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"""
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Args:
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scale: A scaling factor to apply to the logits.
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"""
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super().__init__()
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self.scale = scale
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self.vocab_size = vocab_size
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# Whether the input is logits (default is hidden states).
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self.logits_as_input = logits_as_input
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# original vocabulary size (without LoRA).
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self.org_vocab_size = org_vocab_size or vocab_size
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# Soft cap the logits. Used in Gemma 2.
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self.soft_cap = soft_cap
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# Whether to use gather or all-gather to gather the logits.
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parallel_config = get_current_vllm_config().parallel_config
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self.use_all_gather = current_platform.is_tpu() \
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or envs.VLLM_USE_V1 \
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or parallel_config.distributed_executor_backend == "external_launcher" # noqa
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def forward(
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self,
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lm_head: VocabParallelEmbedding,
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hidden_states: torch.Tensor,
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sampling_metadata: Optional[SamplingMetadata] = None,
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embedding_bias: Optional[torch.Tensor] = None,
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) -> Optional[torch.Tensor]:
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if self.logits_as_input:
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logits = hidden_states
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else:
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if sampling_metadata is not None:
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hidden_states = _prune_hidden_states(hidden_states,
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sampling_metadata)
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# Get the logits for the next tokens.
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logits = self._get_logits(hidden_states, lm_head, embedding_bias)
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if logits is not None:
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if self.soft_cap is not None:
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logits = logits / self.soft_cap
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logits = torch.tanh(logits)
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logits = logits * self.soft_cap
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if self.scale != 1.0:
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logits *= self.scale
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# Apply logits processors (if any).
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if sampling_metadata is not None:
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logits = _apply_logits_processors(logits, sampling_metadata)
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return logits
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def _get_logits(
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self,
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hidden_states: torch.Tensor,
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lm_head: VocabParallelEmbedding,
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embedding_bias: Optional[torch.Tensor],
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) -> Optional[torch.Tensor]:
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# Get the logits for the next tokens.
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logits = lm_head.linear_method.apply(lm_head,
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hidden_states,
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bias=embedding_bias)
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if self.use_all_gather:
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# Gather is not supported for some devices such as TPUs.
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# Use all-gather instead.
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# NOTE(woosuk): Here, the outputs of every device should not be None
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# because XLA requires strict SPMD among all devices. Every device
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# should execute the same operations after gathering the logits.
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logits = tensor_model_parallel_all_gather(logits)
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else:
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# None may be returned for rank > 0
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logits = tensor_model_parallel_gather(logits)
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# Remove paddings in vocab (if any).
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if logits is not None:
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logits = logits[..., :self.org_vocab_size]
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return logits
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def extra_repr(self) -> str:
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s = f"vocab_size={self.vocab_size}"
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s += f", forg_vocab_size={self.org_vocab_size}"
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s += f", scale={self.scale}, logits_as_input={self.logits_as_input}"
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return s
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def _prune_hidden_states(
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hidden_states: torch.Tensor,
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sampling_metadata: SamplingMetadata,
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) -> torch.Tensor:
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# NOTE(kzawora): The if guard is needed for Gaudi - in some scenarios
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# (warmup, profile_run) we might not have selected_token_indices,
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# so we skip pruning.
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if sampling_metadata.selected_token_indices is not None:
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return hidden_states.index_select(
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0, sampling_metadata.selected_token_indices)
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else:
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return hidden_states
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def _apply_logits_processors(
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logits: torch.Tensor,
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sampling_metadata: SamplingMetadata,
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) -> torch.Tensor:
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found_logits_processors = False
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logits_processed = 0
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for seq_group in sampling_metadata.seq_groups:
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seq_ids = seq_group.seq_ids
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sampling_params = seq_group.sampling_params
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logits_processors = sampling_params.logits_processors
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if logits_processors:
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found_logits_processors = True
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for seq_id, logits_row_idx in zip(seq_ids,
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seq_group.sample_indices):
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logits_row = logits[logits_row_idx]
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past_tokens_ids = seq_group.seq_data[seq_id].output_token_ids
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prompt_tokens_ids = seq_group.seq_data[seq_id].prompt_token_ids
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for logits_processor in logits_processors:
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parameters = inspect.signature(logits_processor).parameters
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if len(parameters) == 3:
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logits_row = logits_processor(prompt_tokens_ids,
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past_tokens_ids,
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logits_row)
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else:
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logits_row = logits_processor(past_tokens_ids,
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logits_row)
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logits[logits_row_idx] = logits_row
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logits_processed += len(seq_group.sample_indices) + len(
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seq_group.prompt_logprob_indices)
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if found_logits_processors:
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# verifies that no rows in logits were missed unexpectedly
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assert logits_processed == logits.shape[0]
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return logits
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