feat: multi-step scheduling

aphrodite/common/sequence.py

@@ -9,7 +9,6 @@
                     Union, cast)
 
 import msgspec
-import numpy
 import torch
 
 from aphrodite.common.pooling_params import PoolingParams
@@ -1106,7 +1105,10 @@ class SamplerOutput(
 
     # On-device tensor containing the sampled token ids.
     sampled_token_ids: Optional[torch.Tensor] = None
-    sampled_token_ids_numpy: Optional[numpy.ndarray] = None
+    # CPU tensor containing the sampled token ids. Used during multi-step to
+    # return the sampled token ids from last rank to AsyncAphrodite to be
+    # 'broadcasted' to all other PP ranks for next step.
+    sampled_token_ids_cpu: Optional[torch.Tensor] = None
 
     # Spec decode metrics populated by workers.
     spec_decode_worker_metrics: Optional["SpecDecodeWorkerMetrics"] = None
@@ -1278,9 +1280,7 @@ def is_last_step(self) -> bool:
         assert len(self.seq_group_metadata_list) > 0
         first_seq_group = self.seq_group_metadata_list[0]
         assert first_seq_group.state is not None
-        num_steps = first_seq_group.state.num_steps
-        current_step = first_seq_group.state.current_step
-        return num_steps - current_step == 1
+        return first_seq_group.state.remaining_steps == 1
 
     @property
     def current_step(self) -> int:

aphrodite/engine/args_tools.py

@@ -983,6 +983,12 @@ def create_engine_config(self, ) -> EngineConfig:
                 "profiling phase, or result in low performance due to small "
                 "KV cache space. Consider setting --max-model-len to a "
                 "smaller value.")
+
+        if self.num_scheduler_steps > 1 and not self.use_v2_block_manager:
+            self.use_v2_block_manager = True
+            logger.warning(
+                "Enabled BlockSpaceManagerV2 because it is "
+                "required for multi-step scheduling.")
 
         speculative_config = SpeculativeConfig.maybe_create_spec_config(
             target_model_config=model_config,
@@ -1012,7 +1018,6 @@ def create_engine_config(self, ) -> EngineConfig:
         )
 
         if self.num_scheduler_steps > 1:
-            raise NotImplementedError("Multi-step is not yet supported.")
             if speculative_config is not None:
                 raise ValueError("Speculative decoding is not supported with "
                                  "multi-step (--num-scheduler-steps > 1)")

aphrodite/engine/async_aphrodite.py

@@ -1,10 +1,12 @@
 import asyncio
 import os
 import time
+from dataclasses import dataclass
 from functools import partial
 from typing import (AsyncGenerator, Callable, Dict, Iterable, List, Optional,
                     Set, Tuple, Type, Union)
 
+import torch
 from loguru import logger
 from transformers import PreTrainedTokenizer
 from typing_extensions import assert_never
@@ -15,7 +17,8 @@
 from aphrodite.common.outputs import EmbeddingRequestOutput, RequestOutput
 from aphrodite.common.pooling_params import PoolingParams
 from aphrodite.common.sampling_params import SamplingParams
-from aphrodite.common.sequence import ExecuteModelRequest, SamplerOutput
+from aphrodite.common.sequence import (ExecuteModelRequest, SamplerOutput,
+                                       SequenceGroupMetadata)
 from aphrodite.engine.aphrodite_engine import (AphroditeEngine,
                                                DecoderPromptComponents,
                                                PromptComponents)
@@ -248,9 +251,25 @@ def has_new_requests(self):
         return not self._new_requests.empty()
 
 
+@dataclass
+class SchedulerOutputState:
+    """Caches the scheduler outputs for a virtual engine. Used for Multi-Step"""
+    last_output: Optional[SamplerOutput] = None
+    seq_group_metadata_list: Optional[List[SequenceGroupMetadata]] = None
+    scheduler_outputs: Optional[SchedulerOutputs] = None
+
+
 class _AsyncAphrodite(AphroditeEngine):
     """Extension of AphroditeEngine to add async methods."""
 
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        pipeline_parallel_size = \
+            self.parallel_config.pipeline_parallel_size
+        self.cached_scheduler_outputs = [
+            SchedulerOutputState() for _ in range(pipeline_parallel_size)
+        ]
+
     async def step_async(
         self, virtual_engine: int
     ) -> List[Union[RequestOutput, EmbeddingRequestOutput]]:
@@ -263,13 +282,35 @@ async def step_async(
         and updates the scheduler with the model outputs. Finally, it decodes
         the sequences and returns the newly generated results.
         """
-        seq_group_metadata_list, scheduler_outputs = self.scheduler[
-            virtual_engine].schedule()
+        # these are cached outputs from previous iterations. None if on first
+        # iteration
+        cached_outputs = self.cached_scheduler_outputs[virtual_engine]
+        seq_group_metadata_list = cached_outputs.seq_group_metadata_list
+        scheduler_outputs = cached_outputs.scheduler_outputs
+        # skip the scheduler if there are any remaining steps in the seq groups.
+        # This ensures that the scheduler is only called again when the current
+        # batch has completed.
+        if not self._has_remaining_steps(seq_group_metadata_list):
+            seq_group_metadata_list, scheduler_outputs = self.scheduler[
+                virtual_engine].schedule()
+            if (self.scheduler_config.is_multi_step
+                    and scheduler_outputs.num_lookahead_slots > 0):
+                # cache the scheduler outputs for the next iteration if we have
+                # lookahead slots
+                self._cache_scheduler_outputs_for_multi_step(
+                    virtual_engine, seq_group_metadata_list, scheduler_outputs)
+        assert seq_group_metadata_list is not None
+        assert scheduler_outputs is not None
 
         if not scheduler_outputs.is_empty():
-            # Execute the model.
             finished_requests_ids = self.scheduler[
                 virtual_engine].get_and_reset_finished_requests_ids()
+            # Check if we have a cached last_output from the previous iteration.
+            # For supporting PP this is probably the best way to pass the
+            # sampled_token_ids, as a separate broadcast over all the PP stages
+            # will cause one virtual engine's microbatch to block the pipeline.
+            last_sampled_token_ids = \
+                self._get_last_sampled_token_ids(virtual_engine)
             execute_model_req = ExecuteModelRequest(
                 seq_group_metadata_list=seq_group_metadata_list,
                 blocks_to_swap_in=scheduler_outputs.blocks_to_swap_in,
@@ -279,20 +320,89 @@ async def step_async(
                 num_lookahead_slots=scheduler_outputs.num_lookahead_slots,
                 running_queue_size=scheduler_outputs.running_queue_size,
                 finished_requests_ids=finished_requests_ids,
-            )
+                # We use ExecuteModelRequest to pass the last sampled_token_ids
+                # to each of the non-last PP stages for in-place prepare_input.
+                last_sampled_token_ids=last_sampled_token_ids)
+            # Execute the model.
             output = await self.model_executor.execute_model_async(
                 execute_model_req)
+            # we need to do this here so that last step's sampled_token_ids can
+            # be passed to the next iteration for PP.
+            if self.scheduler_config.is_multi_step:
+                self._update_cached_scheduler_output(virtual_engine, output)
         else:
             output = []
 
-        request_outputs = self._process_model_outputs(
-            output, scheduler_outputs.scheduled_seq_groups,
-            scheduler_outputs.ignored_seq_groups, seq_group_metadata_list)
+        # Finish the current step for all the sequence groups.
+        if self.scheduler_config.is_multi_step:
+            for seq_group in seq_group_metadata_list:
+                seq_group.finish_step()
+        if not self._has_remaining_steps(seq_group_metadata_list):
+            # clear the cache if we have finished all the steps
+            if self.scheduler_config.is_multi_step:
+                self.cached_scheduler_outputs[
+                    virtual_engine] = SchedulerOutputState()
+            request_outputs = self._process_model_outputs(
+                output, scheduler_outputs.scheduled_seq_groups,
+                scheduler_outputs.ignored_seq_groups, seq_group_metadata_list)
+        else:
+            request_outputs = []
 
         # Log stats.
         self.do_log_stats(scheduler_outputs, output)
 
         return request_outputs
+
+    def _has_remaining_steps(
+        self, seq_group_metadata_list: Optional[List[SequenceGroupMetadata]]
+    ) -> bool:
+        if (not self.scheduler_config.is_multi_step
+                or not seq_group_metadata_list):
+            return False
+        # TODO: this is a sanity check for now to make sure that all the
+        # seqs are on the same steps. Eventually we will want to do some sort of
+        # dynamic scheduling when doing multi-step decoding.
+        ref_remaining_steps = seq_group_metadata_list[0].state.remaining_steps
+        if any([
+                seq_group.state.remaining_steps != ref_remaining_steps
+                for seq_group in seq_group_metadata_list[1:]
+        ]):
+            raise AssertionError(("All running sequence groups should "
+                                  "have the same remaining steps."))
+        return ref_remaining_steps > 0
+
+    def _cache_scheduler_outputs_for_multi_step(
+            self, virtual_engine: int,
+            seq_group_metadata_list: Optional[List[SequenceGroupMetadata]],
+            scheduler_outputs: SchedulerOutputs) -> None:
+        self.cached_scheduler_outputs[
+            virtual_engine].seq_group_metadata_list = seq_group_metadata_list
+        self.cached_scheduler_outputs[virtual_engine].scheduler_outputs = \
+            scheduler_outputs
+        self.cached_scheduler_outputs[virtual_engine].last_output = None
+    def _get_last_sampled_token_ids(
+            self, virtual_engine: int) -> Optional[torch.Tensor]:
+        cached_last_output = self.cached_scheduler_outputs[
+            virtual_engine].last_output
+        if (self.scheduler_config.is_multi_step
+                and self.parallel_config.pipeline_parallel_size > 1
+                and cached_last_output is not None
+                and cached_last_output.sampled_token_ids_cpu is not None):
+            return cached_last_output.sampled_token_ids_cpu
+        return None
+
+    def _update_cached_scheduler_output(
+            self, virtual_engine: int,
+            output: List[Optional[SamplerOutput]]) -> None:
+        if (self.parallel_config.pipeline_parallel_size > 1 and len(output) > 0
+                and output[0] is not None):
+            last_output = output[-1]
+            assert last_output is not None
+            assert last_output.sampled_token_ids_cpu is not None
+            assert last_output.sampled_token_ids is None
+            assert last_output.sampled_token_probs is None
+            self.cached_scheduler_outputs[
+                virtual_engine].last_output = last_output
 
     async def stop_remote_worker_execution_loop_async(self) -> None:
         """Stop the remote worker execution loop."""

aphrodite/executor/gpu_executor.py

@@ -68,14 +68,18 @@ def _get_create_worker_kwargs(
             distributed_init_method: Optional[str] = None) -> Dict:
         worker_kwargs = self._get_worker_kwargs(local_rank, rank,
                                                 distributed_init_method)
-        if self.speculative_config is None:
+        if self.scheduler_config.is_multi_step:
             worker_kwargs.update(
-                worker_module_name="aphrodite.task_handler.worker",
-                worker_class_name="Worker")
-        else:
+                worker_module_name="aphrodite.task_handler.multi_step_worker",
+                worker_class_name="MultiStepWorker")
+        elif self.speculative_config:
             worker_kwargs.update(
                 worker_module_name="aphrodite.spec_decode.spec_decode_worker",
                 worker_class_name="create_spec_worker")
+        else:
+            worker_kwargs.update(
+                worker_module_name="aphrodite.task_handler.worker",
+                worker_class_name="Worker")
         return worker_kwargs
 
     def _create_worker(self,

aphrodite/executor/ray_gpu_executor.py

@@ -104,6 +104,9 @@ def _get_worker_wrapper_args(self) -> Dict[str, Any]:
         if self.speculative_config is not None:
             worker_module_name = "aphrodite.spec_decode.spec_decode_worker"
             worker_class_name = "create_spec_worker"
+        elif self.scheduler_config.is_multi_step:
+            worker_module_name = "aphrodite.task_handler.multi_step_worker"
+            worker_class_name = "MultiStepWorker"
         else:
             worker_module_name = "aphrodite.task_handler.worker"
             worker_class_name = "Worker"

aphrodite/task_handler/model_runner_base.py

@@ -14,7 +14,7 @@
     from aphrodite.attention.backends.abstract import AttentionBackend
     from aphrodite.modeling import SamplingMetadata
 
-T = TypeVar('T', bound="ModelRunnerInputBase")
+T = TypeVar('T', bound="BroadcastableModelInput")
 
 
 def _add_attn_metadata_broadcastable_dict(
@@ -81,18 +81,24 @@ def _add_sampling_metadata_broadcastable_dict(
             sampling_metadata.selected_token_indices)
 
 
-@dataclasses.dataclass(frozen=True)
-class ModelRunnerInputBase(ABC):
-    """Local inputs to each worker's model runner. May contain
-    device-specific data. Different worker backends may have different methods
-    of converting from the global ExecuteModelRequest produced by the LLM
-    engine to the worker-local ModelRunnerInputBase objects.
-
-    Model runners that support multi-GPU execution should define a
-    ModelRunnerInputBase subclass, add their required fields, and specify how to
-    serialize/deserialize a ModelInput for broadcast between workers.
+def _init_frozen_model_input_from_tensor_dict(
+        frozen_model_input_cls: Type["ModelRunnerInputBase"],
+        tensor_dict: Dict[str, Any]) -> Dict[str, Any]:
+    """
+    Helper method to initialize a frozen ModelInput based on broadcastable
     """
+    valid_tensor_kwargs = {}
+    for field in dataclasses.fields(frozen_model_input_cls):
+        val = tensor_dict.pop(field.name, None)
+        if val is not None:
+            valid_tensor_kwargs[field.name] = val
+    frozen_model_input = frozen_model_input_cls(**valid_tensor_kwargs)
+    tensor_dict["frozen_model_input"] = frozen_model_input
+    return tensor_dict
 
+class BroadcastableModelInput(ABC):
+
+    @abstractmethod
     def as_broadcastable_tensor_dict(self) -> Dict[str, Any]:
         """
         Extract broadcastable fields. Override for fields that require some
@@ -109,11 +115,24 @@ def from_broadcasted_tensor_dict(
     ) -> T:
         """
         Pop fields from the given tensor_dict and populate a new instance of
-        ModelRunnerInputBase.
+        BroadcastableModelInput.
         """
         raise NotImplementedError
 
 
+@dataclasses.dataclass(frozen=True)
+class ModelRunnerInputBase(BroadcastableModelInput):
+    """Local inputs to each worker's model runner. May contain
+    device-specific data. Different worker backends may have different methods
+    of converting from the global ExecuteModelRequest produced by the LLM
+    engine to the worker-local ModelRunnerInputBase objects.
+    Model runners that support multi-GPU execution should define a
+    ModelRunnerInputBase subclass, add their required fields, and specify how to
+    serialize/deserialize a ModelInput for broadcast between workers.
+    """
+    pass
+
+
 class ModelRunnerInputBuilderBase(ABC, Generic[T]):
     """A builder to create ModelRunnerInputBase objects.
   """