vllm/tests/v1/sample/utils.py
afeldman-nm 0630d4537a
[V1] Logprobs and prompt logprobs support (#9880)
This PR is adding support for sample logprobs & prompt logprobs to vLLM v1.

New behavior:

- During model execution, model runner computes sample logprobs (if user-provided logprobs setting is not None) and prompt logprobs (if user-provided prompt_logprobs setting is not None). For both sample and prompt logprobs, the engine core returns 3 vectors: token ids, token logprob values, token ranks. Ranks reflect tokens' 1-indexed positions in the vocabulary vector after sorting the vocabulary by log probability in descending order.
- In scheduler.update_from_output(), sample and prompt logprobs are incorporated into the EngineCoreOutput data structure which is transferred to the engine client. If multiprocessing is enabled, then sample and prompt logprobs will be (de)serialized when the EngineCoreOutput data structure is (de)serialized.
- During output processing, the LogprobsProcessor transforms the triplet of token ids, token logprobs values, and token ranks into the OpenAI-compatible List[Dict[token id,Logprob]] format (for sample and prompt logprobs respectively.)
- Each Logprob instance (whether sample- or prompt-) consists of a token's log-probability, rank, and detokenized string representation. Note that logprob detokenization is handled by the LogprobsProcessor not the detokenizer.

Signed-off-by: Andrew Feldman <afeldman@neuralmagic.com>
Signed-off-by: Nick Hill <nhill@redhat.com>
Signed-off-by: rshaw@neuralmagic.com <rshaw@neuralmagic.com>


Co-authored-by: rshaw@neuralmagic.com <rshaw@neuralmagic.com>
Co-authored-by: Nick Hill <nhill@redhat.com>
2025-02-07 07:26:20 -08:00

121 lines
4.1 KiB
Python

# SPDX-License-Identifier: Apache-2.0
import re
from typing import List, Tuple
from vllm import CompletionOutput
def get_test_batch(batch_logprobs_composition: str) -> List[Tuple]:
"""Generate logprobs configs for a batch of requests
A given request's logprobs configuration is (1) num_sample_logprobs and (2)
num_prompt_logprobs. The batch logprobs configuration is the list of request
logprobs configs.
batch_logprobs_composition == "NONE" yields a batch with no sample or prompt
logprobs
batch_logprobs_composition == "SAMPLE" yields a batch with some requests
configured for sample logprobs only, and others configured for no logprobs
batch_logprobs_composition == "PROMPT" yields a batch with some requests
configured for prompt logprobs only, and others configured for no logprobs
batch_logprobs_composition == "SAMPLE_PROMPT" yields a batch with some
requests configured for sample logprobs and prompt logprobs, some configured
for only sample logprobs or only prompt logprobs, and some configured for
no logprobs
Args:
batch_logprobs_composition: types of logprobs configs to include in batch
Returns:
List of (Optional[num_sample_logprobs], Optional[num_prompt_logprobs])
tuples
"""
if batch_logprobs_composition == "NONE":
# No requests with sample or prompt logprobs
return [(None, None)]
elif batch_logprobs_composition == "SAMPLE":
# Requests requiring sample logprobs or no logprobs
return [
(None, None),
(0, None),
(5, None),
(3, None),
]
elif batch_logprobs_composition == "PROMPT":
# Requests requiring prompt logprobs or no logprobs
return [
(None, None),
(None, 0),
(None, 6),
(None, 5),
]
elif batch_logprobs_composition == "SAMPLE_PROMPT":
# Requests requiring either no logprobs, just
# sample logprobs, just prompt logprobs, or
# both sample and prompt logprobs
return [
(None, None),
(0, None),
(5, None),
(3, None),
(0, 3),
(6, 0),
(6, 3),
(None, 6),
(None, 5),
(None, 0),
]
else:
raise ValueError("Invalid logprobs batch configuration for test.")
def assert_incr_detok_str_matches_non_incr_detok_str(
incremental_detokenization_str: str,
non_incremental_detokenization_str: str,
msg: str,
) -> None:
"""Compare incrementally detok. text to non-incrementally detok. text
Fail if the strings mismatch after non-alphanumeric characters are stripped
out.
Rationale: incremental detokenization in the text generation process allows
the tokenizer to adjust the next token text output based on the token's
context in the string. However, logprobs detokenization detokenizes each
token individually, and the resultant strings may include some
non-alphanumeric placeholder characters where there could be i.e.
whitespace. So, this function compares only the alphanumeric text
between two strings and fails if there is a mismatch, which helps
with validating logprobs detokenization.
Args:
incremental_detokenization_str: incrementally-detokenized generated text
non_incremental_detokenization_str: non-incrementally-detokenized logprob
tokens
msg: error message if `assert` fails
"""
rgx = r'[^a-zA-Z0-9]+'
assert (re.sub(rgx, '', incremental_detokenization_str) == re.sub(
rgx, '', non_incremental_detokenization_str)), (msg)
def compute_correct_cumulative_logprob(
completion_output: CompletionOutput) -> float:
"""Compute known-good value for evaluating cumulative logprob
Args:
completion_output: completion output from engine
Returns:
Known-good cumulative logprob value
"""
token_ids = completion_output.token_ids
logprobs = completion_output.logprobs
assert logprobs is not None
return sum([lp[tok_id].logprob for tok_id, lp in zip(token_ids, logprobs)])