[Misc][Refactor] Introduce ExecuteModelData (#4540)

tests/spec_decode/test_multi_step_worker.py

@@ -5,13 +5,12 @@ import pytest
 import torch
 
 from vllm.model_executor.utils import set_random_seed
-from vllm.sequence import SamplerOutput
+from vllm.sequence import ExecuteModelRequest, SamplerOutput
 from vllm.spec_decode.multi_step_worker import MultiStepWorker
 from vllm.spec_decode.top1_proposer import Top1Proposer
 from vllm.worker.worker import Worker
 
 from .utils import (assert_logprobs_dict_allclose, create_batch,
-                    create_execute_model_data,
                     create_seq_group_metadata_from_prompts, create_worker,
                     patch_execute_model_with_seeds, zero_kv_cache)
 
@@ -105,31 +104,32 @@ def test_same_output_for_single_step():
 
     final_prompt_lens = [len(prompt) + num_steps for prompt in prompts]
 
-    multi_step_execute_model_data = create_execute_model_data(
-        seq_group_metadata_list=create_seq_group_metadata_from_prompts(
-            prompts,
-            num_gpu_blocks,
-            block_size,
-            final_prompt_lens=final_prompt_lens))
-
-    single_step_execute_model_data = create_execute_model_data(
-        seq_group_metadata_list=create_seq_group_metadata_from_prompts(
-            prompts,
-            num_gpu_blocks,
-            block_size,
-            final_prompt_lens=final_prompt_lens))
+    multi_step_seq_group = create_seq_group_metadata_from_prompts(
+        prompts,
+        num_gpu_blocks,
+        block_size,
+        final_prompt_lens=final_prompt_lens)
 
     zero_kv_cache(multi_step_worker.cache_engine)
     set_random_seed(seed)
     actual_output, _ = multi_step_worker.sampler_output(
-        **multi_step_execute_model_data.to_dict(), sample_len=num_steps)
+        execute_model_req=ExecuteModelRequest(
+            seq_group_metadata_list=multi_step_seq_group),
+        sample_len=num_steps)
     assert len(actual_output) == num_steps
     actual_output = actual_output[0]
 
+    single_step_seq_group = create_seq_group_metadata_from_prompts(
+        prompts,
+        num_gpu_blocks,
+        block_size,
+        final_prompt_lens=final_prompt_lens)
+
     zero_kv_cache(worker.cache_engine)
     set_random_seed(seed)
     expected_output = worker.execute_model(
-        **single_step_execute_model_data.to_dict(), )[0]
+        execute_model_req=ExecuteModelRequest(
+            seq_group_metadata_list=single_step_seq_group))[0]
 
     actual_token_ids = [
         output.samples[0].output_token for output in actual_output
@@ -193,19 +193,20 @@ def test_same_output_for_multi_step():
     worker.execute_model = patch_execute_model_with_seeds(worker, rand_seeds)
 
     continuations = [[1] for _ in prompts]
-    execute_model_data = create_execute_model_data(
-        create_seq_group_metadata_from_prompts(
-            prompts,
-            num_gpu_blocks,
-            block_size,
-            continuations=continuations,
-            final_prompt_lens=final_prompt_lens), )
+    seq_group_metadata_list = create_seq_group_metadata_from_prompts(
+        prompts,
+        num_gpu_blocks,
+        block_size,
+        continuations=continuations,
+        final_prompt_lens=final_prompt_lens)
 
     # Run multi-step.
     zero_kv_cache(multi_step_worker.cache_engine)
     set_random_seed(seed)
     multi_step_output, _ = multi_step_worker.sampler_output(
-        **execute_model_data.to_dict(), sample_len=num_steps)
+        execute_model_req=ExecuteModelRequest(
+            seq_group_metadata_list=seq_group_metadata_list),
+        sample_len=num_steps)
 
     # Run single-step repeatedly.
     zero_kv_cache(worker.cache_engine)
@@ -215,16 +216,16 @@ def test_same_output_for_multi_step():
 
     for _ in multi_step_output:
 
-        execute_model_data = create_execute_model_data(
-            create_seq_group_metadata_from_prompts(
-                prompts,
-                num_gpu_blocks,
-                block_size,
-                continuations=continuations,
-                final_prompt_lens=final_prompt_lens))
+        seq_group_metadata_list = create_seq_group_metadata_from_prompts(
+            prompts,
+            num_gpu_blocks,
+            block_size,
+            continuations=continuations,
+            final_prompt_lens=final_prompt_lens)
 
         single_step_output.extend(
-            worker.execute_model(**execute_model_data.to_dict(), ))
+            worker.execute_model(execute_model_req=ExecuteModelRequest(
+                seq_group_metadata_list=seq_group_metadata_list)))
 
         # Append output tokens to new sequence data.
         for i, seq_group_output in enumerate(single_step_output[-1]):
@@ -304,12 +305,11 @@ def test_draft_proposals_full_speculation_len():
         ) for _ in range(k)
     ], True
 
-    execute_model_data, _, _ = create_batch(batch_size, k)
+    seq_group_metadata_list, _, _ = create_batch(batch_size, k)
 
-    proposals = proposer.get_proposals(
-        **execute_model_data.to_dict(),
-        proposal_len=k,
-    )
+    proposals = proposer.get_proposals(execute_model_req=ExecuteModelRequest(
+        seq_group_metadata_list=seq_group_metadata_list,
+        num_lookahead_slots=k), )
 
     assert torch.is_tensor(proposals.proposal_token_ids)
     assert torch.is_tensor(proposals.proposal_probs)
@@ -340,14 +340,13 @@ def test_draft_proposals_no_speculations():
         max_proposal_len=prompt_len + k - 1,
     )
 
-    execute_model_data, _, _ = create_batch(batch_size,
-                                            k,
-                                            prompt_len=prompt_len)
+    seq_group_metadata_list, _, _ = create_batch(batch_size,
+                                                 k,
+                                                 prompt_len=prompt_len)
 
-    proposals = proposer.get_proposals(
-        **execute_model_data.to_dict(),
-        proposal_len=k,
-    )
+    proposals = proposer.get_proposals(execute_model_req=ExecuteModelRequest(
+        seq_group_metadata_list=seq_group_metadata_list,
+        num_lookahead_slots=k), )
 
     assert torch.is_tensor(proposals.proposal_token_ids)
     assert torch.is_tensor(proposals.proposal_probs)
@@ -409,17 +408,16 @@ def test_draft_proposals_mixed_k():
         ) for _ in range(k)
     ], True
 
-    execute_model_data, _, _ = create_batch(
+    seq_group_metadata_list, _, _ = create_batch(
         batch_size,
         k,
         prompt_len=prompt_len,
         prev_output_token_len=prev_output_token_len,
     )
 
-    proposals = proposer.get_proposals(
-        **execute_model_data.to_dict(),
-        proposal_len=k,
-    )
+    proposals = proposer.get_proposals(execute_model_req=ExecuteModelRequest(
+        seq_group_metadata_list=seq_group_metadata_list,
+        num_lookahead_slots=k), )
 
     assert torch.is_tensor(proposals.proposal_token_ids)
     assert torch.is_tensor(proposals.proposal_probs)

tests/spec_decode/test_ngram_worker.py

 import torch
 
+from vllm.sequence import ExecuteModelRequest
 from vllm.spec_decode.ngram_worker import NGramWorker
 from vllm.spec_decode.top1_proposer import Top1Proposer
 
-from .utils import (create_execute_model_data,
-                    create_seq_group_metadata_from_prompts, create_worker)
+from .utils import create_seq_group_metadata_from_prompts, create_worker
 
 
 def test_ngram_algo_correctness_for_single_no_match():
@@ -44,17 +44,15 @@ def test_ngram_algo_correctness_for_single_no_match():
 
     proposal_len = 5
     final_prompt_lens = [len(prompt) + proposal_len for prompt in prompts]
-    ngram_sampler_output_data = create_execute_model_data(
-        seq_group_metadata_list=create_seq_group_metadata_from_prompts(
-            prompts,
-            num_gpu_blocks,
-            block_size,
-            final_prompt_lens=final_prompt_lens))
-
-    proposals = proposer.get_proposals(
-        **ngram_sampler_output_data.to_dict(),
-        proposal_len=proposal_len,
-    )
+    seq_group_metadata_list = create_seq_group_metadata_from_prompts(
+        prompts,
+        num_gpu_blocks,
+        block_size,
+        final_prompt_lens=final_prompt_lens)
+
+    proposals = proposer.get_proposals(execute_model_req=ExecuteModelRequest(
+        seq_group_metadata_list=seq_group_metadata_list,
+        num_lookahead_slots=proposal_len), )
 
     assert torch.is_tensor(proposals.proposal_token_ids)
     assert torch.is_tensor(proposals.proposal_probs)
@@ -113,17 +111,15 @@ def test_ngram_algo_correctness_for_batches_not_match_all():
 
     proposal_len = 5
     final_prompt_lens = [len(prompt) + proposal_len for prompt in prompts]
-    ngram_sampler_output_data = create_execute_model_data(
-        seq_group_metadata_list=create_seq_group_metadata_from_prompts(
-            prompts,
-            num_gpu_blocks,
-            block_size,
-            final_prompt_lens=final_prompt_lens))
-
-    proposals = proposer.get_proposals(
-        **ngram_sampler_output_data.to_dict(),
-        proposal_len=proposal_len,
-    )
+    seq_group_metadata_list = create_seq_group_metadata_from_prompts(
+        prompts,
+        num_gpu_blocks,
+        block_size,
+        final_prompt_lens=final_prompt_lens)
+
+    proposals = proposer.get_proposals(execute_model_req=ExecuteModelRequest(
+        seq_group_metadata_list=seq_group_metadata_list,
+        num_lookahead_slots=proposal_len), )
 
     assert torch.is_tensor(proposals.proposal_token_ids)
     assert torch.is_tensor(proposals.proposal_probs)
@@ -185,17 +181,15 @@ def test_ngram_algo_correctness_for_batches_match_all():
 
     proposal_len = 5
     final_prompt_lens = [len(prompt) + proposal_len for prompt in prompts]
-    ngram_sampler_output_data = create_execute_model_data(
-        seq_group_metadata_list=create_seq_group_metadata_from_prompts(
-            prompts,
-            num_gpu_blocks,
-            block_size,
-            final_prompt_lens=final_prompt_lens))
-
-    proposals = proposer.get_proposals(
-        **ngram_sampler_output_data.to_dict(),
-        proposal_len=proposal_len,
-    )
+    seq_group_metadata_list = create_seq_group_metadata_from_prompts(
+        prompts,
+        num_gpu_blocks,
+        block_size,
+        final_prompt_lens=final_prompt_lens)
+
+    proposals = proposer.get_proposals(execute_model_req=ExecuteModelRequest(
+        seq_group_metadata_list=seq_group_metadata_list,
+        num_lookahead_slots=proposal_len), )
 
     assert torch.is_tensor(proposals.proposal_token_ids)
     assert torch.is_tensor(proposals.proposal_probs)

tests/spec_decode/test_spec_decode_worker.py

@@ -7,7 +7,7 @@ import torch
 
 from vllm.model_executor.layers.rejection_sampler import RejectionSampler
 from vllm.model_executor.utils import set_random_seed
-from vllm.sequence import SamplerOutput
+from vllm.sequence import ExecuteModelRequest, SamplerOutput
 from vllm.spec_decode.interfaces import SpeculativeProposals
 from vllm.spec_decode.metrics import (AsyncMetricsCollector,
                                       SpecDecodeWorkerMetrics)
@@ -15,8 +15,7 @@ from vllm.spec_decode.multi_step_worker import MultiStepWorker
 from vllm.spec_decode.spec_decode_worker import (SpecDecodeWorker,
                                                  split_num_cache_blocks_evenly)
 
-from .utils import (ExecuteModelData, create_batch, create_sampler_output_list,
-                    mock_worker)
+from .utils import create_batch, create_sampler_output_list, mock_worker
 
 
 @pytest.mark.parametrize('k', [1, 2, 6])
@@ -36,24 +35,19 @@ def test_correctly_calls_draft_model(k: int, batch_size: int):
     exception_secret = 'artificial stop'
     draft_worker.get_spec_proposals.side_effect = ValueError(exception_secret)
 
-    execute_model_data, _, _ = create_batch(batch_size, k)
+    seq_group_metadata_list, _, _ = create_batch(batch_size, k)
+    execute_model_req = ExecuteModelRequest(
+        seq_group_metadata_list=seq_group_metadata_list, num_lookahead_slots=k)
 
     with pytest.raises(ValueError, match=exception_secret):
-        worker.execute_model(**execute_model_data.to_dict(),
-                             num_lookahead_slots=k)
+        worker.execute_model(execute_model_req=execute_model_req)
 
     call_args_list = draft_worker.get_spec_proposals.call_args_list
     assert len(call_args_list) == 1
 
     for args, _ in call_args_list:
-        (seq_group_metadata_list, blocks_to_swap_in, blocks_to_swap_out,
-         blocks_to_copy, actual_k) = args
-        actual_execute_model_data = ExecuteModelData(seq_group_metadata_list,
-                                                     blocks_to_swap_in,
-                                                     blocks_to_swap_out,
-                                                     blocks_to_copy)
-        assert actual_execute_model_data == execute_model_data
-        assert actual_k == k
+        actual_execute_model_data = args[0]
+        assert actual_execute_model_data == execute_model_req
 
 
 @pytest.mark.parametrize('k', [1, 2, 6])
@@ -93,7 +87,7 @@ def test_correctly_calls_target_model(k: int, batch_size: int):
     proposal_lens = torch.ones(batch_size, dtype=torch.int64,
                                device='cuda') * k
 
-    execute_model_data, prompts, prev_output_tokens = create_batch(
+    seq_group_metadata_list, prompts, prev_output_tokens = create_batch(
         batch_size, k)
 
     draft_worker.get_spec_proposals.return_value = SpeculativeProposals(
@@ -105,20 +99,20 @@ def test_correctly_calls_target_model(k: int, batch_size: int):
     target_worker.execute_model.side_effect = ValueError(exception_secret)
 
     with pytest.raises(ValueError, match=exception_secret):
-        worker.execute_model(**execute_model_data.to_dict(),
-                             num_lookahead_slots=k)
+        worker.execute_model(execute_model_req=ExecuteModelRequest(
+            seq_group_metadata_list=seq_group_metadata_list,
+            num_lookahead_slots=k))
 
     seen_contexts = []
 
     call_args_list = target_worker.execute_model.call_args_list
     assert len(call_args_list) == 1
-    for args, kwargs in call_args_list:
-        target_execute_model_data = ExecuteModelData.from_dict(kwargs)
+    for _, kwargs in call_args_list:
+        seq_group_metadata_list = kwargs[
+            "execute_model_req"].seq_group_metadata_list
 
-        assert len(target_execute_model_data.seq_group_metadata_list) == (
-            k + 1) * batch_size
-        for seq_group_metadata in (
-                target_execute_model_data.seq_group_metadata_list):
+        assert len(seq_group_metadata_list) == (k + 1) * batch_size
+        for seq_group_metadata in seq_group_metadata_list:
             for seq_data in seq_group_metadata.seq_data.values():
                 seen_contexts.append(seq_data.get_token_ids())
 
@@ -175,7 +169,7 @@ def test_correctly_calls_rejection_sampler(k: int, batch_size: int):
     proposal_lens = torch.ones(batch_size, dtype=torch.int64,
                                device='cuda') * k
 
-    execute_model_data, _, _ = create_batch(batch_size, k)
+    seq_group_metadata_list, _, _ = create_batch(batch_size, k)
 
     draft_worker.get_spec_proposals.return_value = SpeculativeProposals(
         proposal_token_ids=proposal_token_ids,
@@ -207,8 +201,9 @@ def test_correctly_calls_rejection_sampler(k: int, batch_size: int):
     rejection_sampler.side_effect = ValueError(exception_secret)
 
     with pytest.raises(ValueError, match=exception_secret):
-        worker.execute_model(**execute_model_data.to_dict(),
-                             num_lookahead_slots=k)
+        worker.execute_model(execute_model_req=ExecuteModelRequest(
+            seq_group_metadata_list=seq_group_metadata_list,
+            num_lookahead_slots=k))
 
     assert len(rejection_sampler.call_args_list) == 1
     _, kwargs = rejection_sampler.call_args_list[0]
@@ -262,7 +257,7 @@ def test_correctly_formats_output(k: int, batch_size: int):
     proposal_lens = torch.ones(batch_size, dtype=torch.int64,
                                device='cuda') * k
 
-    execute_model_data, _, _ = create_batch(batch_size, k)
+    seq_group_metadata_list, _, _ = create_batch(batch_size, k)
 
     draft_worker.get_spec_proposals.return_value = SpeculativeProposals(
         proposal_token_ids=proposal_token_ids,
@@ -302,8 +297,9 @@ def test_correctly_formats_output(k: int, batch_size: int):
 
     rejection_sampler.return_value = rejection_sampler_output
 
-    output = worker.execute_model(**execute_model_data.to_dict(),
-                                  num_lookahead_slots=k)
+    output = worker.execute_model(execute_model_req=ExecuteModelRequest(
+        seq_group_metadata_list=seq_group_metadata_list,
+        num_lookahead_slots=k))
 
     expected_output = create_sampler_output_list(
         token_ids=rejection_sampler_output.transpose(0, 1),
@@ -312,7 +308,7 @@ def test_correctly_formats_output(k: int, batch_size: int):
 
     seq_ids = [
         next(iter(seq_group_metadata.seq_data.keys()))
-        for seq_group_metadata in execute_model_data.seq_group_metadata_list
+        for seq_group_metadata in seq_group_metadata_list
     ]
     actual_output_by_seq = {seq_id: [] for seq_id in seq_ids}
     expected_output_by_seq = {seq_id: [] for seq_id in seq_ids}
@@ -383,7 +379,7 @@ def test_collects_metrics(k: int, batch_size: int, returns_metrics: bool):
     proposal_lens = torch.ones(batch_size, dtype=torch.int64,
                                device='cuda') * k
 
-    execute_model_data, _, _ = create_batch(batch_size, k)
+    seq_group_metadata_list, _, _ = create_batch(batch_size, k)
 
     draft_worker.get_spec_proposals.return_value = SpeculativeProposals(
         proposal_token_ids=proposal_token_ids,
@@ -428,8 +424,9 @@ def test_collects_metrics(k: int, batch_size: int, returns_metrics: bool):
     metrics_collector.maybe_collect_rejsample_metrics.return_value = (
         mock_rejsample_metrics)
 
-    output = worker.execute_model(**execute_model_data.to_dict(),
-                                  num_lookahead_slots=k)
+    output = worker.execute_model(execute_model_req=ExecuteModelRequest(
+        seq_group_metadata_list=seq_group_metadata_list,
+        num_lookahead_slots=k))
     assert output[0].spec_decode_worker_metrics == mock_rejsample_metrics
 
     call_args_list = (
@@ -462,21 +459,21 @@ def test_k_equals_zero(k: int, batch_size: int):
     worker = SpecDecodeWorker(draft_worker, target_worker, rejection_sampler,
                               metrics_collector)
 
-    execute_model_data, prompts, prev_output_tokens = create_batch(
-        batch_size, k, prev_output_token_len=0)
+    seq_group_metadata_list, _, _ = create_batch(batch_size,
+                                                 k,
+                                                 prev_output_token_len=0)
+    execute_model_req = ExecuteModelRequest(
+        seq_group_metadata_list=seq_group_metadata_list, num_lookahead_slots=k)
 
-    out = worker.execute_model(**execute_model_data.to_dict(),
-                               num_lookahead_slots=k)
+    out = worker.execute_model(execute_model_req=execute_model_req)
 
     assert len(out) == 1, f"expected only one token output when {k=}"
     assert out[0].probs is None, "expect gpu tensor references to be None"
     assert out[
         0].sampled_tokens is None, "expect gpu tensor references to be None"
 
-    draft_worker.execute_model.assert_called_once_with(
-        **execute_model_data.to_dict())
-    target_worker.execute_model.assert_called_once_with(
-        **execute_model_data.to_dict())
+    draft_worker.execute_model.assert_called_once_with(execute_model_req)
+    target_worker.execute_model.assert_called_once_with(execute_model_req)
 
 
 @pytest.mark.parametrize('k', [0, 5])
@@ -503,21 +500,21 @@ def test_empty_input_batch(k: int, batch_size: int):
     worker = SpecDecodeWorker(draft_worker, target_worker, rejection_sampler,
                               metrics_collector)
 
-    execute_model_data, prompts, prev_output_tokens = create_batch(
-        batch_size, k, prev_output_token_len=0)
+    seq_group_metadata_list, _, _ = create_batch(batch_size,
+                                                 k,
+                                                 prev_output_token_len=0)
+    execute_model_req = ExecuteModelRequest(
+        seq_group_metadata_list=seq_group_metadata_list, num_lookahead_slots=k)
 
-    out = worker.execute_model(**execute_model_data.to_dict(),
-                               num_lookahead_slots=k)
+    out = worker.execute_model(execute_model_req=execute_model_req)
 
     assert len(out) == 1, f"expected only one token output when {k=}"
     assert out[0].probs is None, "expect gpu tensor references to be None"
     assert out[
         0].sampled_tokens is None, "expect gpu tensor references to be None"
 
-    draft_worker.execute_model.assert_called_once_with(
-        **execute_model_data.to_dict())
-    target_worker.execute_model.assert_called_once_with(
-        **execute_model_data.to_dict())
+    draft_worker.execute_model.assert_called_once_with(execute_model_req)
+    target_worker.execute_model.assert_called_once_with(execute_model_req)
 
 
 @pytest.mark.skip_global_cleanup

tests/spec_decode/utils.py

-from dataclasses import dataclass, fields
 from itertools import count
 from typing import Dict, Iterable, List, Optional, Union
 from unittest.mock import MagicMock
@@ -16,50 +15,10 @@ from vllm.worker.cache_engine import CacheEngine
 from vllm.worker.worker import Worker
 
 
-@dataclass
-class ExecuteModelData:
-    """Helper data structure which facilitates cleaner tests.
-    """
-    seq_group_metadata_list: List[SequenceGroupMetadata]
-    blocks_to_swap_in: Dict[int, int]
-    blocks_to_swap_out: Dict[int, int]
-    blocks_to_copy: Dict[int, List[int]]
-
-    def to_dict(self):
-        return dict(
-            (field.name, getattr(self, field.name)) for field in fields(self))
-
-    @classmethod
-    def from_dict(cls, d):
-        cleaned = dict((field.name, d[field.name]) for field in fields(cls))
-        return cls(**cleaned)
-
-
 def round_up_to_next_block(seq_len: int, block_size: int) -> int:
     return (seq_len + block_size - 1) // block_size
 
 
-def create_execute_model_data(
-    seq_group_metadata_list: List[SequenceGroupMetadata],
-    blocks_to_swap_in: Optional[Dict[int, int]] = None,
-    blocks_to_swap_out: Optional[Dict[int, int]] = None,
-    blocks_to_copy: Optional[Dict[int, int]] = None,
-) -> ExecuteModelData:
-    if blocks_to_swap_in is None:
-        blocks_to_swap_in = {}
-    if blocks_to_swap_out is None:
-        blocks_to_swap_out = {}
-    if blocks_to_copy is None:
-        blocks_to_copy = {}
-
-    return ExecuteModelData(
-        seq_group_metadata_list=seq_group_metadata_list,
-        blocks_to_swap_in=blocks_to_swap_in,
-        blocks_to_swap_out=blocks_to_swap_out,
-        blocks_to_copy=blocks_to_copy,
-    )
-
-
 def mock_worker(cls=None,
                 vocab_size: int = 30_000,
                 max_model_len: int = 2048,
@@ -258,8 +217,7 @@ def create_batch(batch_size,
         for prompt, prev_output_token in zip(prompts, prev_output_tokens)
     ]
 
-    execute_model_data = create_execute_model_data(
-        create_seq_group_metadata_from_prompts(prompts, num_gpu_blocks,
-                                               block_size, final_prompt_lens,
-                                               prev_output_tokens, seq_ids), )
-    return execute_model_data, prompts, prev_output_tokens
+    seq_group_metadata_list = create_seq_group_metadata_from_prompts(
+        prompts, num_gpu_blocks, block_size, final_prompt_lens,
+        prev_output_tokens, seq_ids)
+    return seq_group_metadata_list, prompts, prev_output_tokens

tests/worker/test_swap.py

 import torch
 
 from vllm.engine.arg_utils import EngineArgs
+from vllm.sequence import ExecuteModelRequest
 from vllm.utils import get_distributed_init_method, get_ip, get_open_port
 from vllm.worker.worker import Worker
 
@@ -54,10 +55,14 @@ def test_swap() -> None:
 
     # Test swap out.
     blocks_to_swap_out = {3: 72, 56: 35, 84: 34}
-    worker.execute_model(seq_group_metadata_list=[],
-                         blocks_to_swap_in={},
-                         blocks_to_swap_out=blocks_to_swap_out,
-                         blocks_to_copy={})
+    execute_model_req = ExecuteModelRequest(
+        seq_group_metadata_list=[],
+        blocks_to_swap_in={},
+        blocks_to_swap_out=blocks_to_swap_out,
+        blocks_to_copy={},
+    )
+    worker.execute_model(execute_model_req=execute_model_req)
+
     for i in range(num_layers):
         gpu_key_cache, gpu_value_cache = gpu_cache[i]
         cpu_key_cache, cpu_value_cache = cpu_cache[i]
@@ -66,14 +71,19 @@ def test_swap() -> None:
             assert allclose(gpu_value_cache[src], cpu_value_cache[dst])
 
     # Test swap in.
-    blocks_to_swap_in = {19: 45, 67: 23, 12: 78, 40: 99, 1: 71}
-    worker.execute_model(seq_group_metadata_list=[],
-                         blocks_to_swap_in=blocks_to_swap_in,
-                         blocks_to_swap_out={},
-                         blocks_to_copy={})
+    execute_model_req.blocks_to_swap_out = {}
+    execute_model_req.blocks_to_swap_in = {
+        19: 45,
+        67: 23,
+        12: 78,
+        40: 99,
+        1: 71
+    }
+    worker.execute_model(execute_model_req=execute_model_req)
+
     for i in range(num_layers):
         gpu_key_cache, gpu_value_cache = gpu_cache[i]
         cpu_key_cache, cpu_value_cache = cpu_cache[i]
-        for src, dst in blocks_to_swap_in.items():
+        for src, dst in execute_model_req.blocks_to_swap_in.items():
             assert allclose(gpu_key_cache[dst], cpu_key_cache[src])
             assert allclose(gpu_value_cache[dst], cpu_value_cache[src])

vllm/core/scheduler.py

@@ -128,6 +128,8 @@ class SchedulerOutputs:
     ignored_seq_groups: List[SequenceGroup]
     # The number of slots for lookahead decoding.
     num_lookahead_slots: int
+    # The number of requests in the running queue
+    running_queue_size: int
 
     def __post_init__(self):
         # Swap in and swap out should never happen at the same time.
@@ -797,6 +799,7 @@ class Scheduler:
             ignored_seq_groups=prefills.ignored_seq_groups +
             swapped_in.infeasible_seq_groups,
             num_lookahead_slots=running_scheduled.num_lookahead_slots,
+            running_queue_size=len(self.running),
         )
 
     def _schedule_chunked_prefill(self):
@@ -883,6 +886,7 @@ class Scheduler:
                                        swapped_in.blocks_to_copy),
             ignored_seq_groups=prefills.ignored_seq_groups,
             num_lookahead_slots=running_scheduled.num_lookahead_slots,
+            running_queue_size=len(self.running),
         )
 
     def _schedule(self) -> SchedulerOutputs:

vllm/engine/async_llm_engine.py

@@ -16,7 +16,7 @@ from vllm.logger import init_logger
 from vllm.lora.request import LoRARequest
 from vllm.outputs import RequestOutput
 from vllm.sampling_params import SamplingParams
-from vllm.sequence import MultiModalData, SamplerOutput
+from vllm.sequence import ExecuteModelRequest, MultiModalData, SamplerOutput
 from vllm.usage.usage_lib import UsageContext
 
 logger = init_logger(__name__)
@@ -210,12 +210,16 @@ class _AsyncLLMEngine(LLMEngine):
 
         if not scheduler_outputs.is_empty():
             # Execute the model.
+            execute_model_req = ExecuteModelRequest(
+                seq_group_metadata_list=seq_group_metadata_list,
+                blocks_to_swap_in=scheduler_outputs.blocks_to_swap_in,
+                blocks_to_swap_out=scheduler_outputs.blocks_to_swap_out,
+                blocks_to_copy=scheduler_outputs.blocks_to_copy,
+                num_lookahead_slots=scheduler_outputs.num_lookahead_slots,
+                running_queue_size=scheduler_outputs.running_queue_size,
+            )
             output = await self.model_executor.execute_model_async(
-                seq_group_metadata_list,
-                scheduler_outputs.blocks_to_swap_in,
-                scheduler_outputs.blocks_to_swap_out,
-                scheduler_outputs.blocks_to_copy,
-                num_lookahead_slots=scheduler_outputs.num_lookahead_slots)
+                execute_model_req)
         else:
             output = []
 

vllm/engine/llm_engine.py

@@ -22,8 +22,8 @@ from vllm.logger import init_logger
 from vllm.lora.request import LoRARequest
 from vllm.outputs import RequestOutput
 from vllm.sampling_params import SamplingParams
-from vllm.sequence import (MultiModalData, SamplerOutput, Sequence,
-                           SequenceGroup, SequenceGroupMetadata,
+from vllm.sequence import (ExecuteModelRequest, MultiModalData, SamplerOutput,
+                           Sequence, SequenceGroup, SequenceGroupMetadata,
                            SequenceStatus)
 from vllm.transformers_utils.detokenizer import Detokenizer
 from vllm.transformers_utils.tokenizer_group import (BaseTokenizerGroup,
@@ -583,12 +583,16 @@ class LLMEngine:
         seq_group_metadata_list, scheduler_outputs = self.scheduler.schedule()
 
         if not scheduler_outputs.is_empty():
-            output = self.model_executor.execute_model(
+            execute_model_req = ExecuteModelRequest(
                 seq_group_metadata_list=seq_group_metadata_list,
                 blocks_to_swap_in=scheduler_outputs.blocks_to_swap_in,
                 blocks_to_swap_out=scheduler_outputs.blocks_to_swap_out,
                 blocks_to_copy=scheduler_outputs.blocks_to_copy,
-                num_lookahead_slots=scheduler_outputs.num_lookahead_slots)
+                num_lookahead_slots=scheduler_outputs.num_lookahead_slots,
+                running_queue_size=scheduler_outputs.running_queue_size,
+            )
+            output = self.model_executor.execute_model(
+                execute_model_req=execute_model_req)
         else:
             output = []
 

vllm/executor/cpu_executor.py

-from typing import Dict, List, Set, Tuple
+from typing import List, Set, Tuple
 
 import torch
 
@@ -7,7 +7,7 @@ from vllm.config import CacheConfig, ModelConfig, SchedulerConfig
 from vllm.executor.executor_base import ExecutorAsyncBase, ExecutorBase
 from vllm.logger import init_logger
 from vllm.lora.request import LoRARequest
-from vllm.sequence import SamplerOutput, SequenceGroupMetadata
+from vllm.sequence import ExecuteModelRequest, SamplerOutput
 from vllm.utils import (get_distributed_init_method, get_ip, get_open_port,
                         make_async)
 
@@ -72,18 +72,10 @@ class CPUExecutor(ExecutorBase):
         logger.info("# CPU blocks: %d", num_gpu_blocks)
         self.driver_worker.initialize_cache(num_gpu_blocks, num_cpu_blocks)
 
-    def execute_model(self,
-                      seq_group_metadata_list: List[SequenceGroupMetadata],
-                      blocks_to_swap_in: Dict[int, int],
-                      blocks_to_swap_out: Dict[int, int],
-                      blocks_to_copy: Dict[int, List[int]],
-                      num_lookahead_slots: int) -> List[SamplerOutput]:
-        output = self.driver_worker.execute_model(
-            seq_group_metadata_list=seq_group_metadata_list,
-            blocks_to_swap_in=blocks_to_swap_in,
-            blocks_to_swap_out=blocks_to_swap_out,
-            blocks_to_copy=blocks_to_copy,
-        )
+    def execute_model(
+            self,
+            execute_model_req: ExecuteModelRequest) -> List[SamplerOutput]:
+        output = self.driver_worker.execute_model(execute_model_req)
         return output
 
     def add_lora(self, lora_request: LoRARequest) -> bool:
@@ -104,19 +96,10 @@ class CPUExecutor(ExecutorBase):
 class CPUExecutorAsync(CPUExecutor, ExecutorAsyncBase):
 
     async def execute_model_async(
-        self,
-        seq_group_metadata_list: List[SequenceGroupMetadata],
-        blocks_to_swap_in: Dict[int, int],
-        blocks_to_swap_out: Dict[int, int],
-        blocks_to_copy: Dict[int, List[int]],
-        num_lookahead_slots: int,
-    ) -> List[SamplerOutput]:
-        output = await make_async(self.driver_worker.execute_model)(
-            seq_group_metadata_list=seq_group_metadata_list,
-            blocks_to_swap_in=blocks_to_swap_in,
-            blocks_to_swap_out=blocks_to_swap_out,
-            blocks_to_copy=blocks_to_copy,
-            num_lookahead_slots=num_lookahead_slots)
+            self,
+            execute_model_req: ExecuteModelRequest) -> List[SamplerOutput]:
+        output = await make_async(self.driver_worker.execute_model
+                                  )(execute_model_req=execute_model_req, )
         return output
 
     async def check_health_async(self) -> None:

vllm/executor/executor_base.py

 from abc import ABC, abstractmethod
-from typing import Dict, List, Optional, Set, Tuple
+from typing import List, Optional, Set, Tuple
 
 from vllm.config import (CacheConfig, DeviceConfig, LoadConfig, LoRAConfig,
                          ModelConfig, ParallelConfig, SchedulerConfig,
                          SpeculativeConfig, VisionLanguageConfig)
 from vllm.lora.request import LoRARequest
-from vllm.sequence import SamplerOutput, SequenceGroupMetadata
+from vllm.sequence import ExecuteModelRequest, SamplerOutput
 
 
 class ExecutorBase(ABC):
@@ -68,12 +68,9 @@ class ExecutorBase(ABC):
         raise NotImplementedError
 
     @abstractmethod
-    def execute_model(self,
-                      seq_group_metadata_list: List[SequenceGroupMetadata],
-                      blocks_to_swap_in: Dict[int, int],
-                      blocks_to_swap_out: Dict[int, int],
-                      blocks_to_copy: Dict[int, List[int]],
-                      num_lookahead_slots: int) -> List[SamplerOutput]:
+    def execute_model(
+            self,
+            execute_model_req: ExecuteModelRequest) -> List[SamplerOutput]:
         """Executes at least one model step on the given sequences."""
         raise NotImplementedError
 
@@ -107,13 +104,8 @@ class ExecutorAsyncBase(ExecutorBase):
 
     @abstractmethod
     async def execute_model_async(
-        self,
-        seq_group_metadata_list: List[SequenceGroupMetadata],
-        blocks_to_swap_in: Dict[int, int],
-        blocks_to_swap_out: Dict[int, int],
-        blocks_to_copy: Dict[int, List[int]],
-        num_lookahead_slots: int,
-    ) -> List[SamplerOutput]:
+            self,
+            execute_model_req: ExecuteModelRequest) -> List[SamplerOutput]:
         """Executes one model step on the given sequences."""
         raise NotImplementedError
 

vllm/executor/gpu_executor.py

@@ -3,7 +3,7 @@ from typing import Any, Dict, List, Optional, Set, Tuple
 from vllm.executor.executor_base import ExecutorAsyncBase, ExecutorBase
 from vllm.logger import init_logger
 from vllm.lora.request import LoRARequest
-from vllm.sequence import SamplerOutput, SequenceGroupMetadata
+from vllm.sequence import ExecuteModelRequest, SamplerOutput
 from vllm.utils import (get_distributed_init_method, get_ip, get_open_port,
                         make_async)
 from vllm.worker.worker_base import WorkerWrapperBase
@@ -117,20 +117,9 @@ class GPUExecutor(ExecutorBase):
         self.driver_worker.initialize_cache(num_gpu_blocks, num_cpu_blocks)
 
     def execute_model(
-        self,
-        seq_group_metadata_list: List[SequenceGroupMetadata],
-        blocks_to_swap_in: Dict[int, int],
-        blocks_to_swap_out: Dict[int, int],
-        blocks_to_copy: Dict[int, List[int]],
-        num_lookahead_slots: int,
-    ) -> List[SamplerOutput]:
-        output = self.driver_worker.execute_model(
-            seq_group_metadata_list=seq_group_metadata_list,
-            blocks_to_swap_in=blocks_to_swap_in,
-            blocks_to_swap_out=blocks_to_swap_out,
-            blocks_to_copy=blocks_to_copy,
-            num_lookahead_slots=num_lookahead_slots,
-        )
+            self,
+            execute_model_req: ExecuteModelRequest) -> List[SamplerOutput]:
+        output = self.driver_worker.execute_model(execute_model_req)
         return output
 
     def add_lora(self, lora_request: LoRARequest) -> bool:
@@ -154,16 +143,8 @@ class GPUExecutorAsync(GPUExecutor, ExecutorAsyncBase):
 
     async def execute_model_async(
         self,
-        seq_group_metadata_list: List[SequenceGroupMetadata],
-        blocks_to_swap_in: Dict[int, int],
-        blocks_to_swap_out: Dict[int, int],
-        blocks_to_copy: Dict[int, List[int]],
-        num_lookahead_slots: int,
+        execute_model_req: ExecuteModelRequest,
     ) -> List[SamplerOutput]:
-        output = await make_async(self.driver_worker.execute_model)(
-            seq_group_metadata_list=seq_group_metadata_list,
-            blocks_to_swap_in=blocks_to_swap_in,
-            blocks_to_swap_out=blocks_to_swap_out,
-            blocks_to_copy=blocks_to_copy,
-            num_lookahead_slots=num_lookahead_slots)
+        output = await make_async(self.driver_worker.execute_model
+                                  )(execute_model_req=execute_model_req, )
         return output

vllm/executor/neuron_executor.py

-from typing import Dict, List, Set, Tuple
+from typing import List, Set, Tuple
 
 from vllm.executor.executor_base import ExecutorAsyncBase, ExecutorBase
 from vllm.logger import init_logger
 from vllm.lora.request import LoRARequest
-from vllm.sequence import SamplerOutput, SequenceGroupMetadata
+from vllm.sequence import ExecuteModelRequest, SamplerOutput
 from vllm.utils import make_async
 
 logger = init_logger(__name__)
@@ -45,20 +45,18 @@ class NeuronExecutor(ExecutorBase):
         """
         self.driver_worker.initialize_cache(num_gpu_blocks, num_cpu_blocks)
 
-    def execute_model(self,
-                      seq_group_metadata_list: List[SequenceGroupMetadata],
-                      blocks_to_swap_in: Dict[int, int],
-                      blocks_to_swap_out: Dict[int, int],
-                      blocks_to_copy: Dict[int, List[int]],
-                      num_lookahead_slots: int) -> List[SamplerOutput]:
-        assert (blocks_to_swap_in == {} and blocks_to_swap_out == {}
-                and blocks_to_copy == {}), (
+    def execute_model(
+            self,
+            execute_model_req: ExecuteModelRequest) -> List[SamplerOutput]:
+        assert (execute_model_req.blocks_to_swap_in == {}
+                and execute_model_req.blocks_to_swap_out == {}
+                and execute_model_req.blocks_to_copy == {}), (
                     "Cache operations are not supported for Neuron backend.")
-        assert num_lookahead_slots == 0, (
+        assert execute_model_req.num_lookahead_slots == 0, (
             "lookahead not supported for Neuron backend.")
 
         output = self.driver_worker.execute_model(
-            seq_group_metadata_list=seq_group_metadata_list)
+            execute_model_req.seq_group_metadata_list)
         return output
 
     def add_lora(self, lora_request: LoRARequest) -> bool:
@@ -80,14 +78,11 @@ class NeuronExecutorAsync(NeuronExecutor, ExecutorAsyncBase):
 
     async def execute_model_async(
         self,
-        seq_group_metadata_list: List[SequenceGroupMetadata],
-        blocks_to_swap_in: Dict[int, int],
-        blocks_to_swap_out: Dict[int, int],
-        blocks_to_copy: Dict[int, List[int]],
-        num_lookahead_slots: int,
+        execute_model_req: ExecuteModelRequest,
     ) -> List[SamplerOutput]:
-        output = await make_async(self.driver_worker.execute_model)(
-            seq_group_metadata_list=seq_group_metadata_list, )
+        output = await make_async(
+            self.driver_worker.execute_model
+        )(seq_group_metadata_list=execute_model_req.seq_group_metadata_list, )
         return output
 
     async def check_health_async(self) -> None:

vllm/executor/ray_gpu_executor.py

@@ -10,7 +10,7 @@ from vllm.executor.distributed_gpu_executor import (  # yapf: disable
     DistributedGPUExecutor, DistributedGPUExecutorAsync)
 from vllm.executor.ray_utils import RayWorkerWrapper, ray
 from vllm.logger import init_logger
-from vllm.sequence import SamplerOutput, SequenceGroupMetadata
+from vllm.sequence import ExecuteModelRequest, SamplerOutput
 from vllm.utils import (get_distributed_init_method, get_ip, get_open_port,
                         get_vllm_instance_id, make_async)
 
@@ -166,21 +166,12 @@ class RayGPUExecutor(DistributedGPUExecutor):
                           max_concurrent_workers=self.parallel_config.
                           max_parallel_loading_workers)
 
-    def execute_model(self,
-                      seq_group_metadata_list: List[SequenceGroupMetadata],
-                      blocks_to_swap_in: Dict[int, int],
-                      blocks_to_swap_out: Dict[int, int],
-                      blocks_to_copy: Dict[int, List[int]],
-                      num_lookahead_slots: int = 0) -> List[SamplerOutput]:
+    def execute_model(
+            self,
+            execute_model_req: ExecuteModelRequest) -> List[SamplerOutput]:
         all_outputs = self._run_workers(
             "execute_model",
-            driver_kwargs={
-                "seq_group_metadata_list": seq_group_metadata_list,
-                "blocks_to_swap_in": blocks_to_swap_in,
-                "blocks_to_swap_out": blocks_to_swap_out,
-                "blocks_to_copy": blocks_to_copy,
-                "num_lookahead_slots": num_lookahead_slots,
-            },
+            driver_kwargs={"execute_model_req": execute_model_req},
             use_ray_compiled_dag=USE_RAY_COMPILED_DAG)
 
         # Only the driver worker returns the sampling results.

vllm/sequence.py

 """Sequence and its related classes."""
 import copy
 import enum
-from dataclasses import dataclass
+from dataclasses import dataclass, field
 from typing import TYPE_CHECKING, Dict, List, Optional, Union
 
 from vllm.block import LogicalTokenBlock
@@ -734,3 +734,33 @@ class SamplerOutput:
             f"sampled_token_probs={sampled_token_probs_repr}, "
             f"sampled_token_ids={sampled_token_ids_repr}, "
             f"spec_decode_worker_metrics={self.spec_decode_worker_metrics})")
+
+
+@dataclass
+class ExecuteModelRequest:
+    """The model execution request."""
+    # The sequence group metadata list.
+    seq_group_metadata_list: List[SequenceGroupMetadata]
+    # Blocks to swap in. Dict of CPU -> GPU block number.
+    blocks_to_swap_in: Dict[int, int] = field(default_factory=dict)
+    # Blocks to swap out. Dict of GPU -> CPU block number.
+    blocks_to_swap_out: Dict[int, int] = field(default_factory=dict)
+    # Blocks to copy. Source to a list of dest blocks.
+    blocks_to_copy: Dict[int, List[int]] = field(default_factory=dict)
+    # The number of slots for lookahead decoding.
+    num_lookahead_slots: int = 0
+    # The number of requests in the running queue.
+    running_queue_size: int = 0
+
+    def clone(
+        self, seq_group_metadata_list: List[SequenceGroupMetadata]
+    ) -> "ExecuteModelRequest":
+        """Clone the request with a new sequence group metadata list."""
+        return ExecuteModelRequest(
+            seq_group_metadata_list=seq_group_metadata_list,
+            blocks_to_swap_in=self.blocks_to_swap_in.copy(),
+            blocks_to_swap_out=self.blocks_to_swap_out.copy(),
+            blocks_to_copy=self.blocks_to_copy.copy(),
+            num_lookahead_slots=self.num_lookahead_slots,
+            running_queue_size=self.running_queue_size,
+        )

vllm/spec_decode/batch_expansion.py

 from itertools import chain, count
-from typing import Dict, Iterator, List, Optional, Tuple
+from typing import Iterator, List, Tuple
 
 import torch
 
-from vllm.sequence import SamplerOutput, SequenceData, SequenceGroupMetadata
+from vllm.sequence import (ExecuteModelRequest, SamplerOutput, SequenceData,
+                           SequenceGroupMetadata)
 from vllm.spec_decode.interfaces import (SpeculativeProposals,
                                          SpeculativeScorer, SpeculativeScores)
 from vllm.spec_decode.util import (get_all_seq_ids, nvtx_range,
@@ -40,11 +41,7 @@ class BatchExpansionTop1Scorer(SpeculativeScorer):
     @nvtx_range("BatchExpansionTop1Scorer.score_proposals")
     def score_proposals(
         self,
-        seq_group_metadata_list: List[SequenceGroupMetadata],
-        blocks_to_swap_in: Optional[Dict[int, int]],
-        blocks_to_swap_out: Optional[Dict[int, int]],
-        blocks_to_copy: Optional[Dict[int, List[int]]],
-        k: int,
+        execute_model_req: ExecuteModelRequest,
         proposals: SpeculativeProposals,
     ) -> SpeculativeScores:
         """Score the proposed tokens via the scorer model.
@@ -57,11 +54,7 @@ class BatchExpansionTop1Scorer(SpeculativeScorer):
         no speculation is produced for that sequence.
 
         Args:
-            seq_group_metadata_list: The input sequence group metadata.
-            blocks_to_swap_in: This is passed to the worker during scoring.
-            blocks_to_swap_out: This is passed to the worker during scoring.
-            blocks_to_copy: This is passed to the worker during scoring.
-            k: The fixed proposal length.
+            execute_model_req: The execution request.
             proposals: The speculative proposals to score.
         Returns:
             SpeculativeScores: The scores of each speculative token, along with
@@ -80,28 +73,25 @@ class BatchExpansionTop1Scorer(SpeculativeScorer):
 
         (spec_indices, non_spec_indices, target_seq_group_metadata_list,
          num_scoring_tokens) = self._expand_batch(
-             seq_group_metadata_list=seq_group_metadata_list,
+             seq_group_metadata_list=execute_model_req.seq_group_metadata_list,
              proposal_token_ids_list=proposal_token_ids_list_without_skips,
              proposal_lens_list=proposal_lens_list,
          )
 
         target_sampler_output = self._scorer_worker.execute_model(
-            seq_group_metadata_list=target_seq_group_metadata_list,
-            blocks_to_swap_in=blocks_to_swap_in,
-            blocks_to_swap_out=blocks_to_swap_out,
-            blocks_to_copy=blocks_to_copy,
-        )
+            execute_model_req=execute_model_req.clone(
+                seq_group_metadata_list=target_seq_group_metadata_list, ))
         assert len(target_sampler_output) == 1, "expected single-step output"
         target_sampler_output = target_sampler_output[0]
 
         all_tokens, all_probs, spec_logprobs = self._contract_batch(
-            contracted_bs=len(seq_group_metadata_list),
+            contracted_bs=len(execute_model_req.seq_group_metadata_list),
             target_sampler_output=target_sampler_output,
             proposals=proposals,
             num_scoring_tokens=num_scoring_tokens,
             non_spec_indices=non_spec_indices,
             spec_indices=spec_indices,
-            k=k,
+            k=execute_model_req.num_lookahead_slots,
         )
 
         return SpeculativeScores(

vllm/spec_decode/interfaces.py

 from abc import ABC, abstractmethod
 from dataclasses import dataclass
-from typing import Dict, List, Optional
 
 import torch
 
-from vllm.sequence import SequenceGroupMetadata
+from vllm.sequence import ExecuteModelRequest
 
 
 @dataclass
@@ -58,11 +57,7 @@ class SpeculativeProposer(ABC):
     @abstractmethod
     def get_proposals(
         self,
-        seq_group_metadata_list: List[SequenceGroupMetadata],
-        blocks_to_swap_in: Dict[int, int],
-        blocks_to_swap_out: Dict[int, int],
-        blocks_to_copy: Dict[int, List[int]],
-        max_proposal_len: int,
+        execute_model_req: ExecuteModelRequest,
     ) -> SpeculativeProposals:
         raise NotImplementedError
 
@@ -72,11 +67,7 @@ class SpeculativeScorer(ABC):
     @abstractmethod
     def score_proposals(
         self,
-        seq_group_metadata_list: List[SequenceGroupMetadata],
-        blocks_to_swap_in: Optional[Dict[int, int]],
-        blocks_to_swap_out: Optional[Dict[int, int]],
-        blocks_to_copy: Optional[Dict[int, List[int]]],
-        k: int,
+        execute_model_req: ExecuteModelRequest,
         proposals: SpeculativeProposals,
     ) -> SpeculativeScores:
         raise NotImplementedError

vllm/spec_decode/multi_step_worker.py

 import copy
-from typing import Dict, List, Tuple
+from typing import List, Tuple
 
 import torch
 
-from vllm.sequence import SamplerOutput, SequenceGroupMetadata
+from vllm.sequence import (ExecuteModelRequest, SamplerOutput,
+                           SequenceGroupMetadata)
 from vllm.spec_decode.interfaces import SpeculativeProposals
 from vllm.spec_decode.top1_proposer import Top1Proposer
 from vllm.worker.worker import Worker
@@ -44,10 +45,7 @@ class MultiStepWorker(Worker):
     @torch.inference_mode()
     def sampler_output(
         self,
-        seq_group_metadata_list: List[SequenceGroupMetadata],
-        blocks_to_swap_in: Dict[int, int],
-        blocks_to_swap_out: Dict[int, int],
-        blocks_to_copy: Dict[int, List[int]],
+        execute_model_req: ExecuteModelRequest,
         sample_len: int,
     ) -> Tuple[List[SamplerOutput], bool]:
         """Run the model forward pass sample_len times. Returns the list of
@@ -57,26 +55,24 @@ class MultiStepWorker(Worker):
 
         For multi step worker, this indicator shall be True.
         """
-        self._raise_if_unsupported(seq_group_metadata_list, blocks_to_swap_in,
-                                   blocks_to_swap_out, blocks_to_copy)
+        self._raise_if_unsupported(execute_model_req)
 
         # Shallow copy input data so modifications (such as appending tokens)
         # do not cause side-effects.
         copied_seq_group_metadata_list = self._shallow_copy_inputs(
-            seq_group_metadata_list)
+            execute_model_req.seq_group_metadata_list)
+        copied_execute_model_req = execute_model_req.clone(
+            copied_seq_group_metadata_list)
 
         # Assert enough KV space for sample_len tokens per sequence.
-        self._assert_enough_kv_space(seq_group_metadata_list, sample_len)
+        self._assert_enough_kv_space(execute_model_req.seq_group_metadata_list,
+                                     sample_len)
 
         # Run model sample_len times.
         model_outputs = []
         for _ in range(sample_len):
             model_output = super().execute_model(
-                seq_group_metadata_list=copied_seq_group_metadata_list,
-                blocks_to_swap_in=blocks_to_swap_in,
-                blocks_to_swap_out=blocks_to_swap_out,
-                blocks_to_copy=blocks_to_copy,
-            )
+                execute_model_req=copied_execute_model_req)
             assert (len(model_output) == 1
                     ), "composing multistep workers not supported"
             model_output = model_output[0]
@@ -89,23 +85,13 @@ class MultiStepWorker(Worker):
 
     def get_spec_proposals(
         self,
-        seq_group_metadata_list: List[SequenceGroupMetadata],
-        blocks_to_swap_in: Dict[int, int],
-        blocks_to_swap_out: Dict[int, int],
-        blocks_to_copy: Dict[int, List[int]],
-        max_proposal_len: int,
+        execute_model_req: ExecuteModelRequest,
     ) -> SpeculativeProposals:
         """Produce speculations given an input batch of sequences. The number of
         speculative tokens per sequence is determined by max_proposal_len.
         """
 
-        return self._proposer.get_proposals(
-            seq_group_metadata_list,
-            blocks_to_swap_in,
-            blocks_to_swap_out,
-            blocks_to_copy,
-            max_proposal_len,
-        )
+        return self._proposer.get_proposals(execute_model_req)
 
     def _append_new_tokens(
             self, model_output: SamplerOutput,
@@ -196,20 +182,22 @@ class MultiStepWorker(Worker):
 
     def _raise_if_unsupported(
         self,
-        seq_group_metadata_list: List[SequenceGroupMetadata],
-        blocks_to_swap_in: Dict[int, int],
-        blocks_to_swap_out: Dict[int, int],
-        blocks_to_copy: Dict[int, List[int]],
+        execute_model_req: ExecuteModelRequest,
     ) -> None:
         """MultiStepWorker does not yet implement support for cache swap
         operations or beam search.
         """
-        if any([blocks_to_swap_in, blocks_to_swap_out, blocks_to_copy]):
+        if any([
+                execute_model_req.blocks_to_swap_in,
+                execute_model_req.blocks_to_swap_out,
+                execute_model_req.blocks_to_copy
+        ]):
             raise NotImplementedError(
                 "MultiStepWorker does not support cache operations")
 
         if any(
                 len(seq_group_metadata.seq_data.keys()) != 1
-                for seq_group_metadata in seq_group_metadata_list):
+                for seq_group_metadata in
+                execute_model_req.seq_group_metadata_list):
             raise NotImplementedError(
                 "MultiStepWorker does not support beam search.")

vllm/spec_decode/ngram_worker.py

-from typing import Dict, List, Optional, Tuple
+from typing import List, Optional, Tuple
 
 import torch
 
-from vllm.sequence import SamplerOutput, SequenceGroupMetadata
+from vllm.sequence import ExecuteModelRequest, SamplerOutput
 from vllm.spec_decode.interfaces import SpeculativeProposals
 from vllm.spec_decode.top1_proposer import Top1Proposer
 from vllm.worker.worker_base import LoraNotSupportedWorkerBase
@@ -46,13 +46,7 @@ class NGramWorker(LoraNotSupportedWorkerBase):
         # NGram don't need gpu sampler
         pass
 
-    def execute_model(
-        self,
-        seq_group_metadata_list: List[SequenceGroupMetadata],
-        blocks_to_swap_in: Optional[Dict[int, int]],
-        blocks_to_swap_out: Optional[Dict[int, int]],
-        blocks_to_copy: Optional[Dict[int, List[int]]],
-    ) -> None:
+    def execute_model(self, execute_model_req: ExecuteModelRequest) -> None:
         """NGram doesn't depend on model execution, just pass this function"""
         pass
 
@@ -71,10 +65,7 @@ class NGramWorker(LoraNotSupportedWorkerBase):
 
     def sampler_output(
         self,
-        seq_group_metadata_list: List[SequenceGroupMetadata],
-        blocks_to_swap_in: Dict[int, int],
-        blocks_to_swap_out: Dict[int, int],
-        blocks_to_copy: Dict[int, List[int]],
+        execute_model_req: ExecuteModelRequest,
         sample_len: int,
     ) -> Tuple[Optional[List[SamplerOutput]], bool]:
         """NGram match algo to pick proposal candidate. Returns the list of
@@ -83,16 +74,11 @@ class NGramWorker(LoraNotSupportedWorkerBase):
         For ngram worker, we already done needed transposed internal, so the
         indicator pass to sampler_output_to_torch shall be False.
         """
-        self._raise_if_unsupported(
-            seq_group_metadata_list,
-            blocks_to_swap_in,
-            blocks_to_swap_out,
-            blocks_to_copy,
-        )
+        self._raise_if_unsupported(execute_model_req)
 
         arr = []
         has_spec_out = False
-        for seq_group_metadata in seq_group_metadata_list:
+        for seq_group_metadata in execute_model_req.seq_group_metadata_list:
             seq_data = next(iter(seq_group_metadata.seq_data.values()))
 
             input_ids = torch.as_tensor(seq_data.get_token_ids(),
@@ -135,17 +121,19 @@ class NGramWorker(LoraNotSupportedWorkerBase):
         indices = token_ids.unsqueeze(2)
 
         token_probs = torch.zeros(
-            (len(seq_group_metadata_list), sample_len, self.vocab_size),
+            (len(execute_model_req.seq_group_metadata_list), sample_len,
+             self.vocab_size),
             dtype=torch.float32,
             device=self.device,
         )
         token_probs.scatter_(2, indices, 1)
         token_logprobs = torch.zeros(
-            (len(seq_group_metadata_list), sample_len, self.vocab_size),
+            (len(execute_model_req.seq_group_metadata_list), sample_len,
+             self.vocab_size),
             dtype=torch.float32,
             device=self.device,
         )
-        for i in range(len(seq_group_metadata_list)):
+        for i in range(len(execute_model_req.seq_group_metadata_list)):
             outputs.append(
                 SamplerOutput(
                     outputs=None,
@@ -157,40 +145,32 @@ class NGramWorker(LoraNotSupportedWorkerBase):
 
     def get_spec_proposals(
         self,
-        seq_group_metadata_list: List[SequenceGroupMetadata],
-        blocks_to_swap_in: Dict[int, int],
-        blocks_to_swap_out: Dict[int, int],
-        blocks_to_copy: Dict[int, List[int]],
-        max_proposal_len: int,
+        execute_model_req: ExecuteModelRequest,
     ) -> SpeculativeProposals:
         """Produce speculations given an input batch of sequences. The number of
         speculative tokens per sequence is determined by max_proposal_len.
         """
 
-        return self._proposer.get_proposals(
-            seq_group_metadata_list,
-            blocks_to_swap_in,
-            blocks_to_swap_out,
-            blocks_to_copy,
-            max_proposal_len,
-        )
+        return self._proposer.get_proposals(execute_model_req)
 
     def _raise_if_unsupported(
         self,
-        seq_group_metadata_list: List[SequenceGroupMetadata],
-        blocks_to_swap_in: Dict[int, int],
-        blocks_to_swap_out: Dict[int, int],
-        blocks_to_copy: Dict[int, List[int]],
+        execute_model_req: ExecuteModelRequest,
     ) -> None:
         """NGramWorker does not yet implement support for cache swap
         operations or beam search.
         """
-        if any([blocks_to_swap_in, blocks_to_swap_out, blocks_to_copy]):
+        if any([
+                execute_model_req.blocks_to_swap_in,
+                execute_model_req.blocks_to_swap_out,
+                execute_model_req.blocks_to_copy
+        ]):
             raise NotImplementedError(
                 "NGramWorker does not support cache operations")
 
         if any(
                 len(seq_group_metadata.seq_data.keys()) != 1
-                for seq_group_metadata in seq_group_metadata_list):
+                for seq_group_metadata in
+                execute_model_req.seq_group_metadata_list):
             raise NotImplementedError(
                 "NGramWorker does not support beam search.")

vllm/spec_decode/spec_decode_worker.py

 from functools import cached_property
-from typing import Dict, List, Optional, Tuple
+from typing import List, Optional, Tuple
 
 import torch
 
 from vllm.logger import init_logger
 from vllm.model_executor.layers.rejection_sampler import RejectionSampler
-from vllm.sequence import SamplerOutput, SequenceGroupMetadata
+from vllm.sequence import (ExecuteModelRequest, SamplerOutput,
+                           SequenceGroupMetadata)
 from vllm.spec_decode.batch_expansion import BatchExpansionTop1Scorer
 from vllm.spec_decode.interfaces import (SpeculativeProposals,
                                          SpeculativeScorer, SpeculativeScores)
@@ -189,69 +190,37 @@ class SpecDecodeWorker(LoraNotSupportedWorkerBase):
 
     @torch.inference_mode()
     def execute_model(
-        self,
-        seq_group_metadata_list: List[SequenceGroupMetadata],
-        blocks_to_swap_in: Optional[Dict[int, int]],
-        blocks_to_swap_out: Optional[Dict[int, int]],
-        blocks_to_copy: Optional[Dict[int, List[int]]],
-        num_lookahead_slots: int,
-    ) -> List[SamplerOutput]:
+            self,
+            execute_model_req: ExecuteModelRequest) -> List[SamplerOutput]:
         """Perform speculative decoding on the input batch.
         """
 
-        assert seq_group_metadata_list is not None, (
+        assert execute_model_req.seq_group_metadata_list is not None, (
             "speculative decoding "
             "requires non-None seq_group_metadata_list")
 
-        #logger.info("spec_decode_worker.execute_model num_lookahead_slots=%d",
-        #            num_lookahead_slots)
-
         # If no spec tokens, call the proposer and scorer workers normally.
         # Used for prefill.
-        if num_lookahead_slots == 0 or len(seq_group_metadata_list) == 0:
-            return self._run_no_spec(
-                seq_group_metadata_list=seq_group_metadata_list,
-                blocks_to_swap_in=blocks_to_swap_in,
-                blocks_to_swap_out=blocks_to_swap_out,
-                blocks_to_copy=blocks_to_copy,
-            )
-
-        return self._run_speculative_decoding_step(
-            seq_group_metadata_list=seq_group_metadata_list,
-            blocks_to_swap_in=blocks_to_swap_in,
-            blocks_to_swap_out=blocks_to_swap_out,
-            blocks_to_copy=blocks_to_copy,
-            k=num_lookahead_slots,
-        )
+        if execute_model_req.num_lookahead_slots == 0 or len(
+                execute_model_req.seq_group_metadata_list) == 0:
+            return self._run_no_spec(execute_model_req)
+
+        return self._run_speculative_decoding_step(execute_model_req)
 
     @nvtx_range("spec_decode_worker._run_no_spec")
     def _run_no_spec(
-        self,
-        seq_group_metadata_list: List[SequenceGroupMetadata],
-        blocks_to_swap_in: Optional[Dict[int, int]],
-        blocks_to_swap_out: Optional[Dict[int, int]],
-        blocks_to_copy: Optional[Dict[int, List[int]]],
-    ) -> List[SamplerOutput]:
+            self,
+            execute_model_req: ExecuteModelRequest) -> List[SamplerOutput]:
         """Run a prefill step, without any speculation. The input is sent to the
         proposer and scorer model so that the KV cache is consistent between the
         two.
         """
         #logger.info("run proposer worker no spec")
 
-        self.proposer_worker.execute_model(
-            seq_group_metadata_list=seq_group_metadata_list,
-            blocks_to_swap_in=blocks_to_swap_in,
-            blocks_to_swap_out=blocks_to_swap_out,
-            blocks_to_copy=blocks_to_copy,
-        )
+        self.proposer_worker.execute_model(execute_model_req)
 
         #logger.info("run target worker no spec")
-        sampler_output = self.scorer_worker.execute_model(
-            seq_group_metadata_list=seq_group_metadata_list,
-            blocks_to_swap_in=blocks_to_swap_in,
-            blocks_to_swap_out=blocks_to_swap_out,
-            blocks_to_copy=blocks_to_copy,
-        )
+        sampler_output = self.scorer_worker.execute_model(execute_model_req)
         assert len(sampler_output) == 1
         sampler_output = sampler_output[0]
 
@@ -264,13 +233,8 @@ class SpecDecodeWorker(LoraNotSupportedWorkerBase):
 
     @nvtx_range("spec_decode_worker._run_speculative_decoding_step")
     def _run_speculative_decoding_step(
-        self,
-        seq_group_metadata_list: List[SequenceGroupMetadata],
-        blocks_to_swap_in: Optional[Dict[int, int]],
-        blocks_to_swap_out: Optional[Dict[int, int]],
-        blocks_to_copy: Optional[Dict[int, List[int]]],
-        k: int,
-    ) -> List[SamplerOutput]:
+            self,
+            execute_model_req: ExecuteModelRequest) -> List[SamplerOutput]:
         """Execute a single step of speculative decoding.
 
         This invokes the proposer worker to get k speculative tokens for each
@@ -282,33 +246,25 @@ class SpecDecodeWorker(LoraNotSupportedWorkerBase):
 
         #logger.info("get spec proposals")
         # Generate proposals using draft worker.
-        assert blocks_to_swap_in is not None
-        assert blocks_to_swap_out is not None
-        assert blocks_to_copy is not None
-        proposals = self.proposer_worker.get_spec_proposals(
-            seq_group_metadata_list, blocks_to_swap_in, blocks_to_swap_out,
-            blocks_to_copy, k)
+        proposals = self.proposer_worker.get_spec_proposals(execute_model_req)
 
         #logger.info("score proposals")
         proposal_scores = self.scorer.score_proposals(
-            seq_group_metadata_list,
-            blocks_to_swap_in,
-            blocks_to_swap_out,
-            blocks_to_copy,
-            k,
+            execute_model_req,
             proposals,
         )
 
         #logger.info("verify proposals")
         accepted_token_ids, target_logprobs = self._verify_tokens(
-            seq_group_metadata_list, proposal_scores, proposals, k)
+            execute_model_req.seq_group_metadata_list, proposal_scores,
+            proposals, execute_model_req.num_lookahead_slots)
 
         #logger.info("create output list")
         return self._create_output_sampler_list(
-            seq_group_metadata_list,
+            execute_model_req.seq_group_metadata_list,
             accepted_token_ids,
             target_logprobs=target_logprobs,
-            k=k)
+            k=execute_model_req.num_lookahead_slots)
 
     @nvtx_range("spec_decode_worker._verify_tokens")
     def _verify_tokens(

vllm/spec_decode/top1_proposer.py

-from typing import Dict, List, Optional, Tuple
+from typing import List, Optional, Tuple
 
 import torch
 
-from vllm.sequence import SamplerOutput, SequenceGroupMetadata
+from vllm.sequence import (ExecuteModelRequest, SamplerOutput,
+                           SequenceGroupMetadata)
 from vllm.spec_decode.interfaces import (SpeculativeProposals,
                                          SpeculativeProposer)
 from vllm.spec_decode.util import sampler_output_to_torch
@@ -40,17 +41,15 @@ class Top1Proposer(SpeculativeProposer):
 
     def get_proposals(
         self,
-        seq_group_metadata_list: List[SequenceGroupMetadata],
-        blocks_to_swap_in: Dict[int, int],
-        blocks_to_swap_out: Dict[int, int],
-        blocks_to_copy: Dict[int, List[int]],
-        proposal_len: int,
+        execute_model_req: ExecuteModelRequest,
     ) -> SpeculativeProposals:
         """Get speculative proposals given the input batch.
 
         Sequences which would exceed the max model length are skipped during
         speculation.
         """
+        proposal_len = execute_model_req.num_lookahead_slots
+        seq_group_metadata_list = execute_model_req.seq_group_metadata_list
 
         # Split speculative- and non-speculative- sequences.
         (
@@ -66,11 +65,12 @@ class Top1Proposer(SpeculativeProposer):
             # token_ids is like [batch] format in proposal_len size list,
             # while if it is false, the format would be [proposal_len]
             # in batch size list
-            maybe_sampler_output, transposed = self._worker.sampler_output(
+            nonzero_execute_model_req = ExecuteModelRequest(
                 seq_group_metadata_list=nonzero_proposal_len_seqs,
-                blocks_to_swap_in=blocks_to_swap_in,
-                blocks_to_swap_out=blocks_to_swap_out,
-                blocks_to_copy=blocks_to_copy,
+                num_lookahead_slots=proposal_len,
+            )
+            maybe_sampler_output, transposed = self._worker.sampler_output(
+                execute_model_req=nonzero_execute_model_req,
                 sample_len=proposal_len,
             )
         else: