837 lines
36 KiB
Python
837 lines
36 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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import math
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import time
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from typing import Optional
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import pytest
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from tests.v1.engine.utils import (NUM_PROMPT_LOGPROBS_UNDER_TEST,
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NUM_SAMPLE_LOGPROBS_UNDER_TEST,
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STOP_STRINGS,
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DummyOutputProcessorTestVectors,
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MockEngineCore)
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from vllm.sampling_params import RequestOutputKind, SamplingParams
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from vllm.sequence import PromptLogprobs, SampleLogprobs
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from vllm.transformers_utils.tokenizer import AnyTokenizer
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from vllm.v1.engine import EngineCoreRequest
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from vllm.v1.engine.output_processor import OutputProcessor
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from vllm.v1.metrics.stats import IterationStats
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def _ref_convert_id_to_token(
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tokenizer: AnyTokenizer,
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token_id: int,
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) -> str:
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"""Reference impl of logprobs detokenization.
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Args:
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tokenizer: tokenizer used by the model under test
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token_id: convert this token id
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Returns:
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String representation of input token id
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"""
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return tokenizer.convert_ids_to_tokens(token_id) or ""
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@pytest.mark.parametrize(
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"request_output_kind",
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[RequestOutputKind.DELTA, RequestOutputKind.FINAL_ONLY])
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def test_incremental_detokenization(request_output_kind: RequestOutputKind,
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dummy_test_vectors):
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output_processor = OutputProcessor(dummy_test_vectors.tokenizer_group,
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log_stats=False)
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engine_core = MockEngineCore(
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tokens_list=dummy_test_vectors.generation_tokens)
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# Make N requests.
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requests = [
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EngineCoreRequest(request_id=f"request-{idx}",
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prompt=prompt,
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prompt_token_ids=prompt_tokens,
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arrival_time=0,
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mm_inputs=None,
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mm_hashes=None,
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mm_placeholders=None,
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eos_token_id=None,
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lora_request=None,
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sampling_params=SamplingParams(
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skip_special_tokens=False,
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spaces_between_special_tokens=False,
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output_kind=request_output_kind,
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stop=[],
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include_stop_str_in_output=False,
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)) for idx, (prompt, prompt_tokens) in enumerate(
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zip(dummy_test_vectors.prompt_strings,
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dummy_test_vectors.prompt_tokens))
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]
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# Add requests to the detokenizer.
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for request in requests:
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output_processor.add_request(request)
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gen_strings = {}
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gen_tokens = {}
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while True:
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# Mock output from the EngineCore.
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outputs = engine_core.get_outputs()
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if len(outputs) == 0:
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break
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# Step the Detokenizer.
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processed_outputs = output_processor.process_outputs(outputs)
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request_outputs = processed_outputs.request_outputs
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requests_to_abort = processed_outputs.reqs_to_abort
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assert len(requests_to_abort) == 0
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# Update tracking.
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for request_output in request_outputs:
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request_id = request_output.request_id
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new_text = request_output.outputs[0].text
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new_tokens = request_output.outputs[0].token_ids
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if request_id not in gen_strings:
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gen_strings[request_id] = new_text
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gen_tokens[request_id] = new_tokens
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else:
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gen_strings[request_id] += new_text
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gen_tokens[request_id].extend(new_tokens)
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# Confirmed tracked values matches what we expected.
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for idx, (ref_gen_str, ref_gen_toks) in enumerate(
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zip(dummy_test_vectors.generation_strings,
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dummy_test_vectors.generation_tokens)):
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gen_str = gen_strings[f"request-{idx}"]
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gen_toks = gen_tokens[f"request-{idx}"]
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assert gen_str == ref_gen_str, f"{gen_str=}, {ref_gen_str=}"
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assert gen_toks == ref_gen_toks, f"{gen_toks=}, {ref_gen_toks=}"
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assert output_processor.get_num_unfinished_requests() == 0
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assert not output_processor.has_unfinished_requests()
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def _validate_logprobs(
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gen_tokens: dict[str, list[int]],
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gen_logprobs: dict[str, Optional[SampleLogprobs]],
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gen_prompt_logprobs: dict[str, Optional[PromptLogprobs]],
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gen_cumulative_logprob: dict[str, float],
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dtv: DummyOutputProcessorTestVectors,
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request_id_list: list[str],
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num_sample_logprobs: Optional[int],
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num_prompt_logprobs: Optional[int],
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) -> None:
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for req_idx, req_id in enumerate(request_id_list):
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new_tokens = gen_tokens[req_id]
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logprobs = gen_logprobs[req_id]
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prompt_logprobs = gen_prompt_logprobs[req_id]
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cumulative_logprob = gen_cumulative_logprob[req_id]
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prompt_token_ids = dtv.prompt_tokens[req_idx]
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ref_logprobs = dtv.generation_logprobs[req_idx]
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ref_prompt_logprobs = dtv.prompt_logprobs[req_idx]
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if num_sample_logprobs is not None:
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# Validate sample logprobs
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assert logprobs is not None, (f"Request {req_id} requires sample"
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" logprobs but sample logprobs are"
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" None.")
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# Require num sampled tokens to match num
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# sampled logprobs - especially important
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# to check since the detokenizer can cause
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# a request to finish early due to a stop
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# string being hit
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num_new_tokens = len(new_tokens)
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len_sample_logprobs = len(logprobs)
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assert num_new_tokens == len_sample_logprobs, (
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f"Request {req_id} has {num_new_tokens}"
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" completion tokens but has"
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f" {len_sample_logprobs} sample logprobs.")
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ref_cumulative_logprob = 0.0
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for idx, (sampled_token,
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pos_logprob_dict) in enumerate(zip(new_tokens,
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logprobs)):
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# Break out the reference log probability value &
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# logprob token id tensors associated with this
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# position in the completion. Also break out the
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# sampled token ranks
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(ref_pos_logprob_toks, ref_pos_logprob_vals,
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ref_sampled_token_rank) = ref_logprobs[idx]
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# For each position in the completion sequence,
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# ensure the actual sampled token is among the
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# logprobs
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assert sampled_token in pos_logprob_dict, (
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f"Sampled token {sampled_token} not"
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f" present in logprob at index {idx}")
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# Validate number of sample logprobs
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num_lp_toks = len(pos_logprob_dict)
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assert (num_lp_toks == num_sample_logprobs
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or num_lp_toks == num_sample_logprobs +
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1), ("Valid numbers of sample logprobs are"
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f" {num_sample_logprobs} or"
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f" {num_sample_logprobs+1} but"
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f" {num_lp_toks} logprobs found at"
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f" position {idx}. Logprobs dict:"
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f" {pos_logprob_dict}")
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# Validate sampled token logprob rank
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smp_lp = pos_logprob_dict[sampled_token]
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smp_lp_rank = smp_lp.rank
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assert (ref_sampled_token_rank == smp_lp_rank), (
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"Sampled token logprob rank"
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f" {smp_lp_rank} does not match"
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" correct value"
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f" {ref_sampled_token_rank}"
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f" in Logprob {smp_lp}")
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# Validate that the logprob processor yields
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# the correct log probabilities and valid
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# rankings
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rank_one_appears = False
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for jdx in range(1, len(ref_pos_logprob_toks)):
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# Iterate over the (logprob val,logprob tok id)
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# pairs expected by the test fixture at this
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# position in the completion.
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ref_lp_val = ref_pos_logprob_vals[jdx]
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ref_tok_id = ref_pos_logprob_toks[jdx]
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assert ref_tok_id in pos_logprob_dict, (
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f"Expected token {ref_tok_id} to be"
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f" in logprob dict but it is not.")
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# Extract actually-generated logprob
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# info
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lp = pos_logprob_dict[ref_tok_id]
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lp_val = lp.logprob
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lp_rank = lp.rank
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# A "top" (rank 1) logprob must be
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# present
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rank_one_appears = (True
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if lp_rank == 1 else rank_one_appears)
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# Rank must be >= 1
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assert lp_rank >= 1, (f"Logprob {lp} has invalid"
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f" rank {lp_rank} < 1."
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f" Logprob dict: {pos_logprob_dict}")
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# Validate log probability
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assert math.isclose(lp_val, ref_lp_val), (
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f"Token id {ref_tok_id} appears in logprobs dict"
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f" at position {idx} in completion with log"
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f" probability {lp_val} but {ref_lp_val} was"
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f" expected. Logprob: {lp}")
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assert rank_one_appears, (f"No Logprob has rank 1"
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" in the following Logprob"
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f" dict: {pos_logprob_dict}")
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# Validate logprobs detokenization
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for lp_tok in pos_logprob_dict:
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# Confirm that sample logprob decoded token matches
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# the logprob token id at this sequence position
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decoded_token = pos_logprob_dict[lp_tok].decoded_token
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ref_decoded_token = _ref_convert_id_to_token(
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dtv.tokenizer, lp_tok)
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assert decoded_token == ref_decoded_token, (
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f"Sampled logprob token id {lp_tok} decodes to"
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f" {ref_decoded_token} but Logprob decoded"
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f" token is {decoded_token} instead"
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f" (at position {idx})")
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ref_cumulative_logprob += pos_logprob_dict[
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sampled_token].logprob
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# Assert that cumulative logprobs are correct
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assert math.isclose(cumulative_logprob, ref_cumulative_logprob)
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else:
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# Sample logprobs disabled for this request
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assert logprobs is None
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assert cumulative_logprob is None
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if num_prompt_logprobs is not None:
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# Validate prompt logprobs
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assert prompt_logprobs is not None, (
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f"Request {req_id} requires prompt"
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" logprobs but prompt logprobs are"
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" None.")
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# Require num prompt tokens to match num
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# prompt logprobs
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num_prompt_tokens = len(prompt_token_ids)
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len_prompt_logprobs = len(prompt_logprobs)
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assert num_prompt_tokens == len_prompt_logprobs, (
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f"Request {req_id} has {num_prompt_tokens}"
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" prompt tokens but has"
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f" {len_prompt_logprobs} prompt logprobs.")
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# First prompt logprob is None
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first_plp_dict = prompt_logprobs[0]
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assert first_plp_dict is None, (
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f"Request {req_id} first prompt logprob"
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f" should be None but has following value"
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f" instead: {first_plp_dict}")
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# Break out the reference prompt log prob value &
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# logprob token id matrices for the whole prompt.
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# Also break out the prompt token rank vector
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(ref_prompt_logprob_toks, ref_prompt_logprob_vals,
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ref_prompt_token_ranks) = ref_prompt_logprobs
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for idx, (prompt_token, pos_logprob_dict) in enumerate(
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zip(prompt_token_ids[1:], prompt_logprobs[1:])):
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# Break out the reference prompt log prob value
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# vector, prompt logprob token id vector, and
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# prompt token rank at the current position.
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(ref_pos_prompt_logprob_toks, ref_pos_prompt_logprob_vals,
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ref_pos_prompt_token_rank) = (ref_prompt_logprob_toks[idx, :],
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ref_prompt_logprob_vals[idx, :],
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ref_prompt_token_ranks[idx])
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# For each position in the prompt sequence,
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# ensure the actual prompt token is among the
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# logprobs
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assert prompt_token in pos_logprob_dict, (
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f"Prompt token {prompt_token} not"
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f" present in logprob at index {idx}")
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# Validate number of prompt logprobs
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num_plp_toks = len(pos_logprob_dict)
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assert (num_plp_toks == num_prompt_logprobs
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or num_plp_toks == num_prompt_logprobs +
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1), ("Valid numbers of prompt logprobs are"
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f" {num_prompt_logprobs} or"
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f" {num_prompt_logprobs+1} but"
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f" {num_plp_toks} logprobs found at"
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f" position {idx}. Logprobs dict:"
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f" {pos_logprob_dict}")
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# Validate prompt token logprob rank
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prmpt_tok_lp = pos_logprob_dict[prompt_token]
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prmpt_tok_lp_rank = prmpt_tok_lp.rank
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ref_prmpt_tok_lp_rank = ref_pos_prompt_token_rank
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assert (ref_prmpt_tok_lp_rank == prmpt_tok_lp_rank), (
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"Prompt token logprob rank"
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f" {prmpt_tok_lp_rank} does not match"
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" correct value"
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f" {ref_prmpt_tok_lp_rank}"
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f" in Logprob {prmpt_tok_lp}")
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# Validate that the logprob processor yields
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# the correct prompt log probs and valid
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# rankings
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rank_one_appears = False
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for jdx in range(1, len(ref_pos_prompt_logprob_toks)):
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# Iterate over the (logprob val,logprob tok id)
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# pairs expected by the test fixture at this
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# position in the completion.
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ref_plp_val = float(ref_pos_prompt_logprob_vals[jdx])
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ref_tok_id = int(ref_pos_prompt_logprob_toks[jdx])
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assert ref_tok_id in pos_logprob_dict, (
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f"Expected token {ref_tok_id} to be"
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f" in logprob dict but it is not.")
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# Extract actually-generated logprob
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# info
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plp = pos_logprob_dict[ref_tok_id]
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plp_val = plp.logprob
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plp_rank = plp.rank
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# A "top" (rank 1) logprob must be
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# present
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rank_one_appears = (True
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if plp_rank == 1 else rank_one_appears)
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# Rank must be >= 1
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assert plp_rank >= 1, (
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f"Logprob {plp} has invalid"
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f" rank {plp_rank} < 1."
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f" Logprob dict: {pos_logprob_dict}")
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# Validate log probability
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assert math.isclose(plp_val, ref_plp_val), (
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f"Token id {ref_tok_id} appears in logprobs dict"
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f" at position {idx} in completion with log"
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f" probability {plp_val} but {ref_plp_val} was"
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f" expected. Logprob: {plp}")
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assert rank_one_appears, (f"No Logprob has rank 1"
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" in the following Logprob"
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f" dict: {pos_logprob_dict}")
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# Validate prompt logprob detokenization
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for plp_tok in pos_logprob_dict:
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# Confirm that prompt logprob decoded token matches
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# the logprob token id at this sequence position
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decoded_token = pos_logprob_dict[plp_tok].decoded_token
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ref_decoded_token = _ref_convert_id_to_token(
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dtv.tokenizer, plp_tok)
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assert decoded_token == ref_decoded_token, (
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f"Prompt logprob token id {plp_tok} decodes to"
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f" {ref_decoded_token} but Logprob decoded"
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f" token is {decoded_token} instead"
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f" (at position {idx})")
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else:
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# Prompt logprobs disabled for this request
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assert prompt_logprobs is None
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@pytest.mark.parametrize(
|
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"request_output_kind",
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[RequestOutputKind.DELTA, RequestOutputKind.FINAL_ONLY])
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@pytest.mark.parametrize("num_sample_logprobs",
|
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[None, NUM_SAMPLE_LOGPROBS_UNDER_TEST])
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@pytest.mark.parametrize("num_prompt_logprobs",
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[None, NUM_PROMPT_LOGPROBS_UNDER_TEST])
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def test_logprobs_processor(request_output_kind: RequestOutputKind,
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num_sample_logprobs: Optional[int],
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num_prompt_logprobs: Optional[int],
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dummy_test_vectors):
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output_processor = OutputProcessor(dummy_test_vectors.tokenizer_group,
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log_stats=False)
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engine_core = MockEngineCore(
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tokens_list=dummy_test_vectors.generation_tokens,
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generated_logprobs_raw=None if num_sample_logprobs is None else
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dummy_test_vectors.generation_logprobs,
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prompt_logprobs_raw=None
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if num_prompt_logprobs is None else dummy_test_vectors.prompt_logprobs)
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|
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# Make N requests.
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request_id_list = [
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f"request-{idx}"
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for idx in range(len(dummy_test_vectors.prompt_strings))
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]
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requests = [
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EngineCoreRequest(request_id=request_id_list[idx],
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prompt=prompt,
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prompt_token_ids=prompt_tokens,
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arrival_time=0,
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mm_inputs=None,
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mm_hashes=None,
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mm_placeholders=None,
|
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eos_token_id=None,
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lora_request=None,
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sampling_params=SamplingParams(
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skip_special_tokens=False,
|
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spaces_between_special_tokens=False,
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output_kind=request_output_kind,
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stop=[],
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include_stop_str_in_output=False,
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logprobs=num_sample_logprobs,
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prompt_logprobs=num_prompt_logprobs,
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)) for idx, (prompt, prompt_tokens) in enumerate(
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zip(dummy_test_vectors.prompt_strings,
|
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dummy_test_vectors.prompt_tokens))
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]
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|
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# Add requests to the detokenizer.
|
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for request in requests:
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output_processor.add_request(request)
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|
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gen_tokens = {}
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gen_logprobs = {}
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gen_prompt_logprobs = {}
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gen_cumulative_logprobs = {}
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while True:
|
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# Mock output from the EngineCore.
|
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outputs = engine_core.get_outputs()
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if len(outputs) == 0:
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break
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|
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# Step the logprobs processor.
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processed_outputs = output_processor.process_outputs(outputs)
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request_outputs = processed_outputs.request_outputs
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requests_to_abort = processed_outputs.reqs_to_abort
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assert len(requests_to_abort) == 0
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|
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# Update tracking.
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for request_output in request_outputs:
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request_id = request_output.request_id
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new_tokens = request_output.outputs[0].token_ids
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prompt_logprobs = request_output.prompt_logprobs
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logprobs = request_output.outputs[0].logprobs
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gen_cumulative_logprobs[request_id] = request_output.outputs[
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0].cumulative_logprob
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if request_id not in gen_logprobs:
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# Start tracking sample and prompt logprobs for this request
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gen_tokens[request_id] = new_tokens
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gen_logprobs[request_id] = logprobs
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gen_prompt_logprobs[request_id] = prompt_logprobs
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else:
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# Extend logprobs tracker
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|
gen_tokens[request_id].extend(new_tokens)
|
|
lp = gen_logprobs[request_id]
|
|
plp = gen_prompt_logprobs[request_id]
|
|
if lp:
|
|
lp.extend(logprobs)
|
|
if plp:
|
|
plp.extend(prompt_logprobs)
|
|
|
|
# Confirmed tracked logprobs match what we expect
|
|
_validate_logprobs(gen_tokens, gen_logprobs, gen_prompt_logprobs,
|
|
gen_cumulative_logprobs, dummy_test_vectors,
|
|
request_id_list, num_sample_logprobs,
|
|
num_prompt_logprobs)
|
|
|
|
assert output_processor.get_num_unfinished_requests() == 0
|
|
assert not output_processor.has_unfinished_requests()
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"include_stop_str_in_output,stop_token_type,ignore_eos,num_sample_logprobs",
|
|
[(False, "stop_token_ids", False, None),
|
|
(True, "stop_token_ids", False, None),
|
|
(False, "stop_token_ids", False, NUM_SAMPLE_LOGPROBS_UNDER_TEST),
|
|
(True, "stop_token_ids", False, NUM_SAMPLE_LOGPROBS_UNDER_TEST),
|
|
(False, "eos_token_id", False, None), (True, "eos_token_id", False, None),
|
|
(False, "eos_token_id", True, None)])
|
|
def test_stop_token(include_stop_str_in_output: bool,
|
|
num_sample_logprobs: Optional[int], stop_token_type: str,
|
|
ignore_eos: bool, dummy_test_vectors):
|
|
"""Test output processor EOS/stop token handling.
|
|
|
|
Send mock engine core request to mock engine core and pass core outputs
|
|
to output processor. Validate output processor tokens, text and
|
|
(if enabled) sample logprobs. Batch-size one.
|
|
|
|
The test emulates a scenario where a model outputs text tokens followed
|
|
by two identical control tokens:
|
|
<token><token>...<token><control><control>
|
|
|
|
If EOS is under test, the control tokens are EOS; otherwise, they are
|
|
some other token id.
|
|
|
|
Test behavior:
|
|
|
|
* If EOS is under test and `ignore_eos=True`, the detokenized string
|
|
should be <token><token>...<token><control><control> and the finish
|
|
reason should be "length" (i.e. no stop occurs)
|
|
|
|
* else, if `include_stop_str_in_output==True`, the detokenized
|
|
string should be <token><token>...<token><control> and the finish
|
|
reason should be "stop" (i.e. first control token causes stop
|
|
and is represented in output text)
|
|
|
|
* else, the detokenized string should be
|
|
<token><token>...<token> and the finish reason should be "stop"
|
|
(i.e. first control token causes stop but is not represented
|
|
in output text.)
|
|
|
|
Note: some test details are tuned for meta-llama/Llama-3.2-1B,
|
|
another model should work only if the test is modified.
|
|
|
|
Args:
|
|
include_stop_str_in_output: stop token str appears in output text
|
|
num_sample_logprobs: number of sample logprobs (`None` for no logprobs)
|
|
stop_token_type: "eos_token_id" for EOS, "stop_token_ids" for stop token
|
|
ignore_eos: if True, EOS stops are disabled
|
|
dummy_test_vectors: dummy engine core outputs and other data structures
|
|
"""
|
|
model_id = dummy_test_vectors.tokenizer.name_or_path
|
|
if model_id != 'meta-llama/Llama-3.2-1B':
|
|
raise AssertionError("Test requires meta-llama/Llama-3.2-1B but "
|
|
f"{model_id} is in use.")
|
|
do_logprobs = num_sample_logprobs is not None
|
|
# EOS under test; if False, stop_token_ids under test
|
|
is_eos_test = stop_token_type == "eos_token_id"
|
|
# EOS under test but ignore_eos enabled
|
|
is_eos_ignore_test = is_eos_test and ignore_eos
|
|
eos_token_id = (
|
|
dummy_test_vectors.tokenizer.eos_token_id if is_eos_test else None
|
|
) # '<|end_of_text|>'
|
|
stop_token_ids = [128009] if not is_eos_test else None # '<|eot_id|>'
|
|
|
|
output_processor = OutputProcessor(dummy_test_vectors.tokenizer_group,
|
|
log_stats=False)
|
|
# Dummy engine core outputs, with control tokens suffixed to test stops
|
|
suffix_token = ([eos_token_id] if is_eos_test else stop_token_ids)
|
|
assert suffix_token is not None and isinstance(suffix_token[0], int)
|
|
generation_string = dummy_test_vectors.generation_strings[0]
|
|
generation_tokens = (dummy_test_vectors.generation_tokens[0] +
|
|
2 * suffix_token)
|
|
if do_logprobs:
|
|
generation_logprobs = (
|
|
dummy_test_vectors.generation_logprobs[0] +
|
|
2 * [dummy_test_vectors.generation_logprobs[0][-1]])
|
|
prompt_string = dummy_test_vectors.prompt_strings[0]
|
|
prompt_tokens = dummy_test_vectors.prompt_tokens[0]
|
|
engine_core = MockEngineCore(
|
|
tokens_list=[generation_tokens],
|
|
generated_logprobs_raw=[generation_logprobs] if do_logprobs else None,
|
|
prompt_logprobs_raw=None,
|
|
eos_token_id=eos_token_id,
|
|
stop_token_ids=stop_token_ids,
|
|
ignore_eos=ignore_eos)
|
|
|
|
# Make request.
|
|
request_id = "request-0"
|
|
request = EngineCoreRequest(
|
|
request_id=request_id,
|
|
prompt=prompt_string,
|
|
prompt_token_ids=prompt_tokens,
|
|
arrival_time=0,
|
|
mm_inputs=None,
|
|
mm_hashes=None,
|
|
mm_placeholders=None,
|
|
eos_token_id=eos_token_id,
|
|
lora_request=None,
|
|
sampling_params=SamplingParams(
|
|
skip_special_tokens=False,
|
|
spaces_between_special_tokens=False,
|
|
output_kind=RequestOutputKind.DELTA,
|
|
stop=[],
|
|
stop_token_ids=stop_token_ids,
|
|
include_stop_str_in_output=include_stop_str_in_output,
|
|
logprobs=num_sample_logprobs,
|
|
prompt_logprobs=None,
|
|
ignore_eos=ignore_eos,
|
|
))
|
|
|
|
# Add request to the detokenizer.
|
|
output_processor.add_request(request)
|
|
|
|
# Loop over engine core steps; run output processor
|
|
gen_string = ""
|
|
gen_tokens = []
|
|
gen_logprobs = []
|
|
while True:
|
|
# Mock output from the EngineCore.
|
|
outputs = engine_core.get_outputs()
|
|
if len(outputs) == 0:
|
|
break
|
|
|
|
# Step the Detokenizer.
|
|
processed_outputs = output_processor.process_outputs(outputs)
|
|
request_outputs = processed_outputs.request_outputs
|
|
assert len(request_outputs) == 1
|
|
# Stop token does not rely on abort
|
|
assert not processed_outputs.reqs_to_abort
|
|
|
|
# Update tracking.
|
|
request_output = request_outputs[0]
|
|
if request_output.finished:
|
|
finish_reason = ("length" if is_eos_ignore_test else "stop")
|
|
assert request_output.outputs[0].finish_reason == finish_reason
|
|
|
|
gen_string += request_output.outputs[0].text
|
|
gen_tokens.extend(request_output.outputs[0].token_ids)
|
|
if do_logprobs:
|
|
gen_logprobs.extend(request_output.outputs[0].logprobs)
|
|
|
|
# Validate generated text
|
|
control_token = '<|end_of_text|>' if is_eos_test else '<|eot_id|>'
|
|
if is_eos_ignore_test:
|
|
# Length-based stop; expect full string
|
|
ref_str = generation_string + 2 * control_token
|
|
elif include_stop_str_in_output:
|
|
# Stop token triggered; include in output
|
|
ref_str = generation_string + control_token
|
|
else:
|
|
# Stop token triggered but not in output
|
|
ref_str = generation_string
|
|
assert gen_string == ref_str, (f"{gen_string=}, {ref_str=}")
|
|
|
|
if do_logprobs:
|
|
# Validate number of sample logprobs
|
|
num_tokens = len(gen_tokens)
|
|
num_logprobs = len(gen_logprobs)
|
|
assert num_tokens == num_logprobs, (
|
|
f"Token count ({num_tokens}) != logprobs count ({num_logprobs})")
|
|
|
|
# Check requests are finished
|
|
assert output_processor.get_num_unfinished_requests() == 0
|
|
assert not output_processor.has_unfinished_requests()
|
|
|
|
|
|
@pytest.mark.parametrize("include_stop_str_in_output", [True, False])
|
|
@pytest.mark.parametrize("num_sample_logprobs",
|
|
[None, NUM_SAMPLE_LOGPROBS_UNDER_TEST])
|
|
def test_stop_string(include_stop_str_in_output: bool,
|
|
num_sample_logprobs: Optional[int], dummy_test_vectors):
|
|
output_processor = OutputProcessor(dummy_test_vectors.tokenizer_group,
|
|
log_stats=False)
|
|
engine_core = MockEngineCore(
|
|
tokens_list=dummy_test_vectors.generation_tokens,
|
|
generated_logprobs_raw=dummy_test_vectors.generation_logprobs
|
|
if num_sample_logprobs else None,
|
|
prompt_logprobs_raw=None)
|
|
|
|
# Make N requests.
|
|
request_id_list = [
|
|
f"request-{idx}"
|
|
for idx in range(len(dummy_test_vectors.prompt_strings))
|
|
]
|
|
requests = [
|
|
EngineCoreRequest(
|
|
request_id=request_id_list[idx],
|
|
prompt=prompt,
|
|
prompt_token_ids=prompt_tokens,
|
|
arrival_time=0,
|
|
mm_inputs=None,
|
|
mm_hashes=None,
|
|
mm_placeholders=None,
|
|
eos_token_id=None,
|
|
lora_request=None,
|
|
sampling_params=SamplingParams(
|
|
skip_special_tokens=False,
|
|
spaces_between_special_tokens=False,
|
|
output_kind=RequestOutputKind.DELTA,
|
|
stop=STOP_STRINGS,
|
|
include_stop_str_in_output=include_stop_str_in_output,
|
|
logprobs=num_sample_logprobs,
|
|
prompt_logprobs=None,
|
|
)) for idx, (prompt, prompt_tokens) in enumerate(
|
|
zip(dummy_test_vectors.prompt_strings,
|
|
dummy_test_vectors.prompt_tokens))
|
|
]
|
|
|
|
# Add requests to the detokenizer.
|
|
for request in requests:
|
|
output_processor.add_request(request)
|
|
|
|
gen_strings = {}
|
|
gen_tokens = {}
|
|
gen_logprobs = {}
|
|
gen_prompt_logprobs = {}
|
|
gen_cumulative_logprobs = {}
|
|
aborted = []
|
|
while True:
|
|
# Mock output from the EngineCore.
|
|
outputs = engine_core.get_outputs()
|
|
if len(outputs) == 0:
|
|
break
|
|
|
|
# Step the Detokenizer.
|
|
processed_outputs = output_processor.process_outputs(outputs)
|
|
request_outputs = processed_outputs.request_outputs
|
|
requests_to_abort = processed_outputs.reqs_to_abort
|
|
for request_output in request_outputs:
|
|
# If aborted, we should not get a request output.
|
|
assert request_output.request_id not in aborted
|
|
aborted.extend(requests_to_abort)
|
|
|
|
# Update tracking.
|
|
for request_output in request_outputs:
|
|
if request_output.finished:
|
|
assert request_output.outputs[0].finish_reason == "stop"
|
|
|
|
request_id = request_output.request_id
|
|
new_text = request_output.outputs[0].text
|
|
new_tokens = request_output.outputs[0].token_ids
|
|
prompt_logprobs = request_output.prompt_logprobs
|
|
logprobs = request_output.outputs[0].logprobs
|
|
gen_cumulative_logprobs[request_id] = request_output.outputs[
|
|
0].cumulative_logprob
|
|
if request_id not in gen_strings:
|
|
gen_strings[request_id] = new_text
|
|
gen_tokens[request_id] = new_tokens
|
|
gen_logprobs[request_id] = logprobs
|
|
gen_prompt_logprobs[request_id] = prompt_logprobs
|
|
else:
|
|
gen_strings[request_id] += new_text
|
|
gen_tokens[request_id].extend(new_tokens)
|
|
lp = gen_logprobs[request_id]
|
|
plp = gen_prompt_logprobs[request_id]
|
|
if lp:
|
|
lp.extend(logprobs)
|
|
if plp:
|
|
plp.extend(prompt_logprobs)
|
|
|
|
# Confirmed tracked values matches what we expected.
|
|
for idx, (ref_gen_str, stop_str) in enumerate(
|
|
zip(dummy_test_vectors.generation_strings, STOP_STRINGS)):
|
|
|
|
# Request should be aborted.
|
|
request_id = f"request-{idx}"
|
|
assert request_id in aborted
|
|
|
|
# Collected values that were generated.
|
|
gen_str = gen_strings[request_id]
|
|
|
|
# Construct reference strings.
|
|
stop_str_idx = ref_gen_str.find(stop_str)
|
|
ref_str_exc_stop = ref_gen_str[:stop_str_idx]
|
|
ref_str_inc_stop = ref_gen_str[:stop_str_idx] + stop_str
|
|
|
|
if include_stop_str_in_output:
|
|
assert gen_str == ref_str_inc_stop, (
|
|
f"{gen_str=}, {ref_str_inc_stop=}")
|
|
else:
|
|
assert gen_str == ref_str_exc_stop, (
|
|
f"{gen_str=}, {ref_str_exc_stop=}")
|
|
|
|
# Confirmed tracked logprobs match what we expect
|
|
_validate_logprobs(gen_tokens, gen_logprobs, gen_prompt_logprobs,
|
|
gen_cumulative_logprobs, dummy_test_vectors,
|
|
request_id_list, num_sample_logprobs, None)
|
|
|
|
assert output_processor.get_num_unfinished_requests() == 0
|
|
assert not output_processor.has_unfinished_requests()
|
|
|
|
|
|
def test_iteration_stats(dummy_test_vectors):
|
|
output_processor = OutputProcessor(dummy_test_vectors.tokenizer_group,
|
|
log_stats=True)
|
|
engine_core = MockEngineCore(dummy_test_vectors.generation_tokens)
|
|
engine_core_timestamp = time.monotonic()
|
|
|
|
# Make N requests.
|
|
requests = [
|
|
EngineCoreRequest(
|
|
request_id=f"request-{idx}",
|
|
prompt=prompt,
|
|
prompt_token_ids=prompt_tokens,
|
|
arrival_time=0,
|
|
mm_inputs=None,
|
|
mm_hashes=None,
|
|
mm_placeholders=None,
|
|
eos_token_id=None,
|
|
lora_request=None,
|
|
sampling_params=SamplingParams(),
|
|
) for idx, (prompt, prompt_tokens) in enumerate(
|
|
zip(dummy_test_vectors.prompt_strings,
|
|
dummy_test_vectors.prompt_tokens))
|
|
]
|
|
|
|
# Add all requests except one to the OutputProcessor.
|
|
num_active = len(dummy_test_vectors.generation_tokens) - 1
|
|
for request in requests[:num_active]:
|
|
output_processor.add_request(request)
|
|
inactive_request = requests[num_active]
|
|
|
|
# First iteration has 2 prefills.
|
|
outputs = engine_core.get_outputs()[:num_active]
|
|
iteration_stats = IterationStats()
|
|
output_processor.process_outputs(outputs, engine_core_timestamp,
|
|
iteration_stats)
|
|
total_prompt_tokens = sum([
|
|
len(prompt_tokens)
|
|
for prompt_tokens in dummy_test_vectors.prompt_tokens[:num_active]
|
|
])
|
|
|
|
assert iteration_stats.num_prompt_tokens == total_prompt_tokens
|
|
assert iteration_stats.num_generation_tokens == num_active
|
|
|
|
# Just decodes in this step.
|
|
outputs = engine_core.get_outputs()[:num_active]
|
|
iteration_stats = IterationStats()
|
|
output_processor.process_outputs(outputs, engine_core_timestamp,
|
|
iteration_stats)
|
|
|
|
assert iteration_stats.num_prompt_tokens == 0
|
|
assert iteration_stats.num_generation_tokens == num_active
|
|
|
|
# Add a new request - prefill and 2 decodes in this step.
|
|
output_processor.add_request(inactive_request)
|
|
num_active += 1
|
|
outputs = engine_core.get_outputs()[:num_active]
|
|
iteration_stats = IterationStats()
|
|
output_processor.process_outputs(outputs, engine_core_timestamp,
|
|
iteration_stats)
|
|
total_prompt_tokens = len(dummy_test_vectors.prompt_tokens[num_active - 1])
|
|
|
|
assert iteration_stats.num_prompt_tokens == total_prompt_tokens
|
|
assert iteration_stats.num_generation_tokens == num_active
|
|
|
|
# Just decodes in this step.
|
|
outputs = engine_core.get_outputs()[:num_active]
|
|
iteration_stats = IterationStats()
|
|
output_processor.process_outputs(outputs, engine_core_timestamp,
|
|
iteration_stats)
|
|
|
|
assert iteration_stats.num_prompt_tokens == 0
|
|
assert iteration_stats.num_generation_tokens == num_active
|