mirror of
https://github.com/huggingface/text-generation-inference.git
synced 2025-04-19 22:02:06 +00:00
a6a0c97ed9
* wip * rollback * refactor to use prefix/postfix namming + fix all_input_ids_tensor * maybe patching vlms? * fix filter and concat * wip, no filter, no concat * current * add prepare_for_prefill * working * load tested * re-create slots * re-create slots * fix slot_filtering_indices * feedback loop * remove log * fix benchmarker * fix vlm and seq2seq * rename to cache and input lengths * fix prefill logprobs * fix launcher * fix logprobs? * idk at this point * max input length * omfg * remove debugging lines * fix tests * fix mllama * fix cargo tests * remove support chunking for paged * Fixing non blocked attentions * Fixing dtype + AMD, Ipex targets. * lint fix. * rename * Fix prefix_caching variable, remove defaults in server (confusing a lot of the times). * Add simple resolution when user specifies ATTENTION=paged. * Put back non default simple tests. * Fix env name --------- Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
68 lines
2.6 KiB
Python
68 lines
2.6 KiB
Python
# Origin: https://github.com/predibase/lorax
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# Path: lorax/server/lorax_server/utils/segments.py
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# License: Apache License Version 2.0, January 2004
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from typing import List, Tuple, Union
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import torch
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# FIXME: this should be optimized
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def find_segments(
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adapter_indices: Union[torch.Tensor, List[int]]
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) -> Tuple[List[int], List[int]]:
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segments = [0]
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segment_indices = []
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if isinstance(adapter_indices, torch.Tensor):
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# Calling .item() repeatedly on CUDA tensor is very slow, so we move it to CPU first
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adapter_indices = adapter_indices.cpu().tolist()
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start_index = 0
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for i in range(1, len(adapter_indices)):
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if adapter_indices[i] != adapter_indices[i - 1]:
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segments.append(i)
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segment_indices.append(adapter_indices[i - 1])
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start_index = i
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# Handle the last segment
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if start_index < len(adapter_indices):
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segments.append(len(adapter_indices))
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segment_indices.append(adapter_indices[-1])
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return segments, segment_indices
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class SegmentConcatBuilder:
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def __init__(self):
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self.adapter_segment_indices = []
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self.adapter_segment_tensors = []
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def concat(self, adapter_segments: torch.Tensor, segment_indices: List[int]):
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# Update adapter segments
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if self.adapter_segment_tensors:
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# Because we have already processed at least one batch, remove the 0 start index
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# from this batch denoting the beginning of the segment, then offset all segment
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# positions by the value of the last segment in the previous batch to account for
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# the concatenation.
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adapter_segments = (
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adapter_segments[1:] + self.adapter_segment_tensors[-1][-1]
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)
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if (
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self.adapter_segment_indices
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and self.adapter_segment_indices[-1] == segment_indices[0]
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):
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# If the last segment in the previous batch is the same as the first segment in this batch,
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# then we merge them together into a single segment. In effect, this means removing it from
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# the segment indices of this batch, and extending the segment span by removing the segment
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# end index from the previous batch.
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segment_indices = segment_indices[1:]
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self.adapter_segment_tensors[-1] = self.adapter_segment_tensors[-1][:-1]
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self.adapter_segment_indices.extend(segment_indices)
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self.adapter_segment_tensors.append(adapter_segments)
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def build(self) -> Tuple[torch.Tensor, List[int]]:
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return torch.concat(self.adapter_segment_tensors), self.adapter_segment_indices
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