[Core] Factor out common code in `SequenceData` and `Sequence` (#8675)

tests/samplers/test_sampler.py

 import itertools
 import random
-from array import array
 from typing import Dict, List, Optional, Tuple
 from unittest.mock import Mock, patch
 
@@ -12,8 +11,7 @@ import vllm.envs as envs
 from vllm.model_executor.layers.sampler import Sampler
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.model_executor.utils import set_random_seed
-from vllm.sequence import (VLLM_TOKEN_ID_ARRAY_TYPE, SamplingParams,
-                           SequenceData, SequenceGroupMetadata)
+from vllm.sequence import SamplingParams, SequenceData, SequenceGroupMetadata
 from vllm.utils import Counter, is_pin_memory_available
 
 
@@ -59,9 +57,7 @@ def _do_sample(
             SequenceGroupMetadata(
                 request_id=f"test_{i}",
                 is_prompt=True,
-                seq_data={
-                    0: SequenceData(array(VLLM_TOKEN_ID_ARRAY_TYPE, [1, 2, 3]))
-                },
+                seq_data={0: SequenceData.from_seqs([1, 2, 3])},
                 sampling_params=sampling_params,
                 block_tables={0: [1]},
             ))
@@ -205,9 +201,8 @@ def test_sampler_min_tokens_penalty(seed: int, device: str):
         return sampling_params
 
     def create_sequence_data(num_input=3, num_generated=0):
-        seq_data = SequenceData(
-            array(VLLM_TOKEN_ID_ARRAY_TYPE,
-                  random.choices(range(0, VOCAB_SIZE), k=num_input)))
+        seq_data = SequenceData.from_seqs(
+            random.choices(range(0, VOCAB_SIZE), k=num_input))
         if num_generated > 0:
             seq_data.output_token_ids = random.choices(range(0, VOCAB_SIZE),
                                                        k=num_generated)
@@ -511,9 +506,7 @@ def test_sampler_mixed(seed: int, device: str):
             SequenceGroupMetadata(
                 request_id=f"test_{i}",
                 is_prompt=True,
-                seq_data={
-                    0: SequenceData(array(VLLM_TOKEN_ID_ARRAY_TYPE, [1, 2, 3]))
-                },
+                seq_data={0: SequenceData.from_seqs([1, 2, 3])},
                 sampling_params=sampling_params,
                 block_tables={0: [1]},
             ))
@@ -613,9 +606,7 @@ def test_sampler_top_k_top_p(seed: int, device: str):
             SequenceGroupMetadata(
                 request_id=f"test_{i}",
                 is_prompt=True,
-                seq_data={
-                    0: SequenceData(array(VLLM_TOKEN_ID_ARRAY_TYPE, [1, 2, 3]))
-                },
+                seq_data={0: SequenceData.from_seqs([1, 2, 3])},
                 sampling_params=SamplingParams(
                     temperature=1,
                     top_k=top_k,
@@ -699,11 +690,7 @@ def test_sampler_repetition_penalty_mixed(device: str):
                 SequenceGroupMetadata(
                     request_id=f"test_{i}",
                     is_prompt=True,
-                    seq_data={
-                        0:
-                        SequenceData(array(VLLM_TOKEN_ID_ARRAY_TYPE,
-                                           [1, 2, 3]))
-                    },
+                    seq_data={0: SequenceData.from_seqs([1, 2, 3])},
                     sampling_params=sampling_params[i],
                     block_tables={0: [1]},
                 ))

tests/spec_decode/utils.py

-from array import array
 from itertools import count
 from typing import Callable, Dict, List, Optional
 from typing import Sequence as GenericSequence
@@ -11,8 +10,7 @@ from vllm.engine.arg_utils import EngineArgs
 from vllm.model_executor.layers.sampler import SamplerOutput
 from vllm.model_executor.utils import set_random_seed
 from vllm.sampling_params import SamplingParams
-from vllm.sequence import (VLLM_TOKEN_ID_ARRAY_TYPE,
-                           CompletionSequenceGroupOutput, Logprob,
+from vllm.sequence import (CompletionSequenceGroupOutput, Logprob,
                            SequenceData, SequenceGroupMetadata, SequenceOutput)
 from vllm.utils import get_distributed_init_method, get_ip, get_open_port
 from vllm.worker.cache_engine import CacheEngine
@@ -138,12 +136,8 @@ def create_seq_group_metadata_from_prompts(
             request_id=str(i),
             is_prompt=len(cont_token_ids) == 0,
             seq_data={
-                i:
-                SequenceData(
-                    array(VLLM_TOKEN_ID_ARRAY_TYPE, prompt_token_ids[:]),
-                    _output_token_ids=array(VLLM_TOKEN_ID_ARRAY_TYPE,
-                                            cont_token_ids[:]),
-                ),
+                i: SequenceData.from_seqs(prompt_token_ids[:],
+                                          cont_token_ids[:]),
             },
             sampling_params=SamplingParams(temperature=0.0, ),
             block_tables={i: block_allocations[i][:]},

tests/test_logits_processor.py

 import random
-from array import array
 from typing import Tuple
 from unittest.mock import patch
 
@@ -9,8 +8,7 @@ import torch
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.model_executor.utils import set_random_seed
-from vllm.sequence import (VLLM_TOKEN_ID_ARRAY_TYPE, SamplingParams,
-                           SequenceData, SequenceGroupMetadata)
+from vllm.sequence import SamplingParams, SequenceData, SequenceGroupMetadata
 from vllm.utils import is_pin_memory_available
 
 
@@ -71,9 +69,7 @@ def test_logits_processors(seed: int, device: str):
             SequenceGroupMetadata(
                 request_id=f"test_{i}",
                 is_prompt=True,
-                seq_data={
-                    0: SequenceData(array(VLLM_TOKEN_ID_ARRAY_TYPE, [1, 2, 3]))
-                },
+                seq_data={0: SequenceData.from_seqs([1, 2, 3])},
                 sampling_params=SamplingParams(temperature=0,
                                                logits_processors=[pick_ith]),
                 block_tables={0: [1]},

tests/test_sequence.py

-from array import array
-
 import pytest
 
 from vllm.model_executor.layers.sampler import SamplerOutput
-from vllm.sequence import (VLLM_TOKEN_ID_ARRAY_TYPE,
-                           CompletionSequenceGroupOutput, SequenceData,
+from vllm.sequence import (CompletionSequenceGroupOutput, SequenceData,
                            SequenceOutput)
 
 from .core.utils import create_dummy_prompt
@@ -58,7 +55,7 @@ def test_sampler_output_eq(sample_outputs):
 
 
 def test_sequence_data_prefill():
-    seq_data = SequenceData(array(VLLM_TOKEN_ID_ARRAY_TYPE, [1, 2, 3, 4]))
+    seq_data = SequenceData.from_seqs([1, 2, 3, 4])
     assert seq_data.get_num_uncomputed_tokens() == 4
     assert seq_data.get_num_computed_tokens() == 0
     # advance by 2

tests/worker/test_encoder_decoder_model_runner.py

 import itertools
-from array import array
 from typing import List
 
 import pytest
 import torch
 
 from vllm.engine.arg_utils import EngineArgs
-from vllm.sequence import (VLLM_TOKEN_ID_ARRAY_TYPE, SamplingParams,
-                           SequenceData, SequenceGroupMetadata)
+from vllm.sequence import SamplingParams, SequenceData, SequenceGroupMetadata
 from vllm.utils import is_cpu, make_tensor_with_pad
 from vllm.worker.enc_dec_model_runner import EncoderDecoderModelRunner
 from vllm.worker.model_runner import _get_graph_batch_size
@@ -119,12 +117,10 @@ def test_prepare_prompt(batch_size):
         # make sure all tokens fit into one block
         seq_len = i % (model_runner.block_size - 1) + 1
         seq_lens.append(seq_len)
-        seq_data = SequenceData(array(VLLM_TOKEN_ID_ARRAY_TYPE,
-                                      range(seq_len)))
+        seq_data = SequenceData.from_seqs(range(seq_len))
         encoder_seq_len = (i + 1) % (model_runner.block_size - 1) + 1
         encoder_seq_lens.append(encoder_seq_len)
-        encoder_seq_data = SequenceData(
-            array(VLLM_TOKEN_ID_ARRAY_TYPE, range(encoder_seq_len)))
+        encoder_seq_data = SequenceData.from_seqs(range(encoder_seq_len))
         seq_group_metadata = SequenceGroupMetadata(
             request_id=f"test_{i}",
             is_prompt=True,
@@ -317,11 +313,9 @@ def test_prepare_decode(batch_size, multiple_seqs_per_seq_group):
     for i in range(batch_size):
         # make sure all tokens fit into one block
         seq_len = i % (model_runner.block_size - 1) + 1
-        seq_data = SequenceData(
-            array(VLLM_TOKEN_ID_ARRAY_TYPE, (range(seq_len))))
+        seq_data = SequenceData.from_seqs(range(seq_len))
         encoder_seq_len = (i + 1) % (model_runner.block_size - 1) + 1
-        encoder_seq_data = SequenceData(
-            array(VLLM_TOKEN_ID_ARRAY_TYPE, (range(encoder_seq_len))))
+        encoder_seq_data = SequenceData.from_seqs(range(encoder_seq_len))
 
         seq_group_metadata = SequenceGroupMetadata(
             request_id=f"test_{i}",
@@ -523,11 +517,9 @@ def test_prepare_decode_cuda_graph(batch_size, multiple_seqs_per_seq_group):
     for i in range(batch_size):
         # make sure all tokens fit into one block
         seq_len = i % (model_runner.block_size - 1) + 1
-        seq_data = SequenceData(
-            array(VLLM_TOKEN_ID_ARRAY_TYPE, (range(seq_len))))
+        seq_data = SequenceData.from_seqs(range(seq_len))
         encoder_seq_len = (i + 1) % (model_runner.block_size - 1) + 1
-        encoder_seq_data = SequenceData(
-            array(VLLM_TOKEN_ID_ARRAY_TYPE, (range(encoder_seq_len))))
+        encoder_seq_data = SequenceData.from_seqs(range(encoder_seq_len))
         seq_group_metadata = SequenceGroupMetadata(
             request_id=f"test_{i}",
             is_prompt=False,

tests/worker/test_model_runner.py

-from array import array
 from typing import List
 
 import pytest
@@ -8,8 +7,7 @@ from vllm.distributed.parallel_state import (ensure_model_parallel_initialized,
                                              init_distributed_environment)
 from vllm.engine.arg_utils import EngineArgs
 from vllm.model_executor.sampling_metadata import SamplingMetadata
-from vllm.sequence import (VLLM_TOKEN_ID_ARRAY_TYPE, SamplingParams,
-                           SequenceData, SequenceGroupMetadata)
+from vllm.sequence import SamplingParams, SequenceData, SequenceGroupMetadata
 from vllm.utils import get_open_port
 from vllm.worker.model_runner import ModelRunner, _get_graph_batch_size
 
@@ -48,8 +46,7 @@ def test_prepare_prompt(batch_size):
         # make sure all tokens fit into one block
         seq_len = i % (model_runner.block_size - 1) + 1
         seq_lens.append(seq_len)
-        seq_data = SequenceData(array(VLLM_TOKEN_ID_ARRAY_TYPE,
-                                      range(seq_len)))
+        seq_data = SequenceData.from_seqs(range(seq_len))
         seq_group_metadata = SequenceGroupMetadata(
             request_id=f"test_{i}",
             is_prompt=True,
@@ -166,8 +163,7 @@ def test_prepare_decode_cuda_graph(batch_size):
         # make sure all tokens fit into one block
         context_len = i % (model_runner.block_size - 1) + 1
         context_lens.append(context_len)
-        seq_data = SequenceData(
-            array(VLLM_TOKEN_ID_ARRAY_TYPE, range(context_len)))
+        seq_data = SequenceData.from_seqs(range(context_len))
         seq_data.update_num_computed_tokens(context_len)
         # Append one token ID since prefill is finished.
         seq_data.append_token_id(1, 0)
@@ -326,8 +322,7 @@ def test_hybrid_batches(batch_size, enforce_eager, distributed_init):
         # make sure all tokens fit into one block
         seq_len = i % (model_runner.block_size - 1) + 1
         seq_lens.append(seq_len)
-        seq_data = SequenceData(array(VLLM_TOKEN_ID_ARRAY_TYPE,
-                                      range(seq_len)))
+        seq_data = SequenceData.from_seqs(range(seq_len))
         seq_group_metadata = SequenceGroupMetadata(
             request_id=f"test_{i}",
             is_prompt=True,
@@ -343,8 +338,7 @@ def test_hybrid_batches(batch_size, enforce_eager, distributed_init):
     for i in range(prefill_batch_size, batch_size):
         # make sure all tokens fit into one block
         context_len = i % (model_runner.block_size - 1) + 1
-        prompt_toks = array(VLLM_TOKEN_ID_ARRAY_TYPE, range(context_len))
-        seq_data = SequenceData(prompt_toks)
+        seq_data = SequenceData.from_seqs(range(context_len))
         seq_data.append_token_id(1, 0)
         seq_data.update_num_computed_tokens(context_len)
         seq_group_metadata = SequenceGroupMetadata(

vllm/inputs/registry.py

 import functools
-from array import array
 from collections import UserDict
 from dataclasses import dataclass
 from typing import (TYPE_CHECKING, Any, Callable, Dict, Mapping, Optional,
@@ -22,10 +21,6 @@ logger = init_logger(__name__)
 
 C = TypeVar("C", bound=PretrainedConfig, default=PretrainedConfig)
 
-# NOTE: This has to match with sequence.py's VLLM_TOKEN_ID_ARRAY_TYPE.
-# We cannot import it here because of circular dependencies.
-VLLM_TOKEN_ID_ARRAY_TYPE = "l"
-
 
 @dataclass(frozen=True)
 class InputContext:
@@ -130,8 +125,7 @@ class InputRegistry:
         # Avoid circular import
         from vllm.sequence import SequenceData
 
-        dummy_seq_data = SequenceData(
-            array(VLLM_TOKEN_ID_ARRAY_TYPE, [0]) * seq_len)
+        dummy_seq_data = SequenceData.from_counts({0: seq_len})
         dummy_multi_modal_data = None
 
         return dummy_seq_data, dummy_multi_modal_data

vllm/sequence.py

@@ -5,6 +5,7 @@ from abc import ABC, abstractmethod
 from array import array
 from collections import defaultdict
 from dataclasses import dataclass
+from functools import cached_property, reduce
 from typing import TYPE_CHECKING, Any, Callable, Dict, List, Mapping, Optional
 from typing import Sequence as GenericSequence
 from typing import Set, Tuple, Union, cast
@@ -169,6 +170,35 @@ class SequenceData(msgspec.Struct,
     # It is used to compute mrope_position_ids.
     _mrope_position_delta: Optional[int] = None
 
+    @staticmethod
+    def from_counts(counts_by_token: Mapping[int, int]) -> "SequenceData":
+        if len(counts_by_token) == 0:
+            return SequenceData.from_seqs([])
+
+        arrs = [
+            array(VLLM_TOKEN_ID_ARRAY_TYPE, [token_id]) * count
+            for token_id, count in counts_by_token.items()
+        ]
+
+        return SequenceData(reduce(array.__add__, arrs))
+
+    @staticmethod
+    def from_seqs(
+        prompt_token_ids: GenericSequence[int],
+        output_token_ids: Optional[GenericSequence[int]] = None,
+    ) -> "SequenceData":
+        prompt_token_ids_arr = array(VLLM_TOKEN_ID_ARRAY_TYPE,
+                                     prompt_token_ids)
+
+        if output_token_ids is None:
+            return SequenceData(prompt_token_ids_arr)
+
+        output_token_ids_arr = array(VLLM_TOKEN_ID_ARRAY_TYPE,
+                                     output_token_ids)
+
+        return SequenceData(prompt_token_ids_arr,
+                            _output_token_ids=output_token_ids_arr)
+
     def __post_init__(self) -> None:
         assert self._prompt_token_ids.typecode == "l"
         assert self._output_token_ids.typecode == "l"
@@ -370,8 +400,6 @@ class Sequence:
         self.lora_request = lora_request
         self.prompt_adapter_request = prompt_adapter_request
         self.from_decoder_prompt = from_decoder_prompt
-        self._prompt: Optional[str] = None
-        self._prompt_token_ids: Optional[List[int]] = None
 
         # For decoder-only models, a Sequence is constructed
         # from an LLMInputs instance (the `inputs` arg.)
@@ -400,8 +428,7 @@ class Sequence:
                              f"invalid input {inputs}; did you forget the "
                              "encoder input prompt fields?")
 
-        self.data = SequenceData(
-            array(VLLM_TOKEN_ID_ARRAY_TYPE, self.prompt_token_ids))
+        self.data = SequenceData.from_seqs(self.prompt_token_ids)
         self.output_logprobs: SampleLogprobs = []
         self.output_text = ""
 
@@ -422,37 +449,23 @@ class Sequence:
     def n_blocks(self) -> int:
         return (self.get_len() + self.block_size - 1) // self.block_size
 
-    @property
+    @cached_property
     def prompt(self) -> Optional[str]:
-        if self._prompt is not None:
-            # Reuse precomputed prompt string
-            return self._prompt
-
-        # Select decoder or encoder input prompt str,
-        # as appropriate
+        # Select decoder or encoder input prompt str, as appropriate
         prompt_key: str = ("prompt"
                            if self.from_decoder_prompt else "encoder_prompt")
 
-        # Cache prompt
-        self._prompt = cast(Optional[str], self.inputs.get(prompt_key))
-        return self._prompt
+        return cast(Optional[str], self.inputs.get(prompt_key))
 
-    @property
+    @cached_property
     def prompt_token_ids(self) -> List[int]:
-        if self._prompt_token_ids is not None:
-            # Reuse precomputed prompt token ids
-            return self._prompt_token_ids
-
-        # Select decoder or encoder input prompt
-        # token ids, as appropriate
+        # Select decoder or encoder input prompt token ids, as appropriate
         prompt_token_ids_key: str = ("prompt_token_ids"
                                      if self.from_decoder_prompt else
                                      "encoder_prompt_token_ids")
 
         # Cache computed prompt token ids
-        self._prompt_token_ids = cast(List[int],
-                                      self.inputs.get(prompt_token_ids_key))
-        return self._prompt_token_ids
+        return cast(List[int], self.inputs.get(prompt_token_ids_key))
 
     @property
     def multi_modal_data(self) -> "MultiModalDataDict":