[Inference] Optimize and Refactor Inference Batching/Scheduling

colossalai/inference/config.py

@@ -109,7 +109,7 @@ def _verify_config(self) -> None:
         ), f"Expected dtype to be in {_ALLOWED_DTYPES} but found an unknown dtype: {self.dtype}"
 
         # check distributed
-        assert (
+        assert (not torch.distributed.is_initialized() and self.tp_size * self.pp_size == 1) or (
             self.tp_size * self.pp_size == dist.get_world_size()
         ), f"TP size({self.tp_size}) * PP size({self.pp_size}) should be equal to the global world size ({dist.get_world_size()})"
         # check prompt template

colossalai/inference/core/engine.py

@@ -42,7 +42,7 @@ class InferenceEngine:
     def __init__(
         self,
         model: nn.Module,
-        tokenizer: [Union[PreTrainedTokenizer, PreTrainedTokenizerFast]],
+        tokenizer: Union[PreTrainedTokenizer, PreTrainedTokenizerFast],
         inference_config: InferenceConfig,
         verbose: bool = False,
         model_policy: Policy = None,
@@ -254,20 +254,12 @@ def add_request(
             else:
                 prompt = prompts[i]
 
-            max_blocks_per_sequence = (
-                self.inference_config.max_input_len
-                + self.inference_config.max_output_len
-                + self.inference_config.block_size
-                - 1
-            ) // self.inference_config.block_size
-            block_table = torch.full([max_blocks_per_sequence], -1, device=self.device)
             sequence = Sequence(
                 request_id,
                 prompt,
                 prompts_token_ids[i],
                 block_size,
                 None,
-                block_table,
                 self.tokenizer.eos_token_id,
                 self.tokenizer.pad_token_id,
                 self.inference_config.max_output_len,

colossalai/inference/core/request_handler.py

@@ -1,15 +1,16 @@
-from typing import List
+from typing import Dict, List, Union
 
 import torch
 from transformers.configuration_utils import PretrainedConfig
 from transformers.generation import GenerationConfig
 
+from colossalai.inference.batch_bucket import BatchBucket
 from colossalai.inference.config import InferenceConfig
 from colossalai.inference.flash_decoding_utils import FDIntermTensors
 from colossalai.inference.kv_cache import KVCacheManager
 from colossalai.inference.logit_processors import logit_processor
 from colossalai.inference.sampler import *
-from colossalai.inference.struct import BatchInfo, RequestStatus, Sequence
+from colossalai.inference.struct import RequestStatus, Sequence
 from colossalai.logging import get_dist_logger
 
 __all__ = ["RunningList", "RequestHandler"]
@@ -24,45 +25,79 @@ class RunningList:
 
     Args:
         prefill_ratio: (float) A ratio for determing whether to perform prefill or not.
-        prefill: (List) List that contains default inputs, defaults to [].
+        _prefill (OrderedDict[Sequence]): Mapping of sequence uid -> Sequence.
+        _decoding (OrderedDict[Sequence]): Mapping of sequence uid -> Sequence.
     """
 
-    def __init__(self, prefill_ratio: str, prefill: List[Sequence] = None):
+    def __init__(self, prefill_ratio: int, prefill: List[Sequence] = None) -> None:
         self.prefill_ratio = prefill_ratio
-        self.decoding: List[Sequence] = []
-        self.prefill: List[Sequence] = prefill if prefill is not None else []
+        self._decoding: Dict[int, Sequence] = dict()
+        self._prefill: Dict[int, Sequence] = (
+            dict({seq.request_id: seq for seq in self._prefill}) if prefill is not None else dict()
+        )
 
-    def append(self, seq: Sequence):
-        # add seq to prefilling list first.
-        self.prefill.append(seq)
-
-    def find_seq(self, request_id):
-        for seq in self.decoding:
-            if request_id == seq.request_id:
-                return seq
-        for seq in self.prefill:
-            if request_id == seq.request_id:
-                return seq
-        return None
+    @property
+    def decoding(self):
+        return list(self._decoding.values())
+
+    @property
+    def prefill(self):
+        return list(self._prefill.values())
+
+    @property
+    def prefill_seq_num(self):
+        return len(self._prefill)
+
+    @property
+    def decoding_seq_num(self):
+        return len(self._decoding)
+
+    @property
+    def total_seq_num(self):
+        return self.prefill_seq_num + self.decoding_seq_num
 
-    def remove(self, seq: Sequence):
-        if seq in self.decoding:
-            self.decoding.remove(seq)
-        elif seq in self.prefill:
-            self.prefill.remove(seq)
+    def append(self, seq: Sequence):
+        assert (seq.request_id not in self._prefill) and (
+            seq.request_id not in self._decoding
+        ), f"Sequence uid {seq.request_id} already exists."
+        self._prefill[seq.request_id] = seq
+
+    def extend(self, seqs: List[Sequence]):
+        for seq in seqs:
+            self._prefill[seq.request_id] = seq
+
+    def find_seq(self, request_id) -> Union[Sequence, None]:
+        seq = None
+        if request_id in self._decoding:
+            seq = self._decoding[request_id]
+        elif request_id in self._prefill:
+            seq = self._prefill[request_id]
+        return seq
+
+    def remove(self, seq: Sequence) -> None:
+        if seq.request_id in self._decoding:
+            self._decoding.pop(seq.request_id)
+        elif seq.request_id in self._prefill:
+            self._prefill.pop(seq.request_id)
         else:
-            raise ValueError(f"sequence {seq.request_id} is not in running list")
+            raise ValueError(f"Sequence {seq.request_id} is not in running list")
 
     def ready_for_prefill(self):
-        if not self.decoding:
-            return len(self.prefill) > 0
-        return len(self.prefill) / len(self.decoding) >= self.prefill_ratio
+        if not self._decoding:
+            return len(self._prefill) > 0
+        return len(self._prefill) / len(self._decoding) >= self.prefill_ratio
 
     def is_empty(self):
-        return not self.decoding and not self.prefill
+        return not self._decoding and not self._prefill
 
-    def total_seq_num(self):
-        return len(self.decoding) + len(self.prefill)
+    def mark_prefill_running(self) -> None:
+        for seq_id in self._prefill:
+            self._prefill[seq_id].mark_running()
+
+    def move_prefill_to_decoding(self, seq_ids: List[int]) -> None:
+        for seq_id in seq_ids:
+            assert seq_id in self._prefill, f"Sequence {seq_id} is not in prefill list"
+            self._decoding[seq_id] = self._prefill.pop(seq_id)
 
 
 class RequestHandler:
@@ -110,25 +145,27 @@ def __init__(self, inference_config: InferenceConfig, model_config: PretrainedCo
 
         # TODO In the continuous batching scenario, the batch size may be greater than max_batch_size,
         # which may cause bugs and this issue should be fixed later.
-        self.running_batch = BatchInfo(
-            max_batch_size=self.max_batch_size,
-            kv_max_split_num=kv_max_split_num,
+        self.running_bb = BatchBucket(
             num_heads=model_config.num_attention_heads,
             head_dim=head_dim,
-            is_prompts=False,
-            device=device,
-            dtype=self.dtype,
-            fd_inter_tensor=fd_inter_tensor,
-        )
-        self.prefill_batch = BatchInfo(
             max_batch_size=self.max_batch_size,
+            max_length=inference_config.max_input_len + inference_config.max_output_len,
+            block_size=inference_config.block_size,
             kv_max_split_num=kv_max_split_num,
+            fd_interm_tensor=fd_inter_tensor,
+            dtype=self.dtype,
+            device=device,
+        )
+        self.prefill_bb = BatchBucket(
             num_heads=model_config.num_attention_heads,
             head_dim=head_dim,
-            is_prompts=True,
-            device=device,
+            max_batch_size=self.max_batch_size,
+            max_length=inference_config.max_input_len + inference_config.max_output_len,
+            block_size=inference_config.block_size,
+            kv_max_split_num=kv_max_split_num,
+            fd_interm_tensor=fd_inter_tensor,
             dtype=self.dtype,
-            fd_inter_tensor=fd_inter_tensor,
+            device=device,
         )
 
     def _init_cache(self, model_config):
@@ -159,40 +196,39 @@ def schedule(self):
                             remove_list.append(seq)
                             break
 
-                        # stop feeding new sequence into running list to assure
-                        if self.cache_manager.num_available_blocks <= self.running_list.total_seq_num():
-                            break
+                    num_seqs_to_add = min(len(lst), self.max_batch_size - self.running_list.total_seq_num)
+                    remove_list.extend(lst[:num_seqs_to_add])
+                    self.running_list.extend(lst[:num_seqs_to_add])
 
-                        # Try to allocate cache blocks for the sequence.
-                        if (
-                            self.cache_manager.check_allocation(seq)
-                            and (len(self.running_list.prefill) + len(self.running_list.decoding))
-                            < self.max_batch_size  # There some bugs in continous batching, so we disable it here.
-                        ):
-                            # If succeed, add the sequence to running list.
-                            remove_list.append(seq)
-                            self.running_list.append(seq)
-                            self.cache_manager.allocate_context_from_block_table(seq.block_table, seq.sentence_len)
                     for seq in remove_list:
                         lst.remove(seq)
 
         if self.running_list.ready_for_prefill():
-            for seq in self.running_list.prefill:
-                seq.mark_running()
-            self.prefill_batch.add_seqs(self.running_list.prefill)
-            return self.prefill_batch
+            num_seqs_to_add = min(self.running_list.prefill_seq_num, self.running_bb.available_batch_size)
 
-        if not self.running_batch.is_empty:
-            for seq in self.running_batch.sequences_set:
-                recycle = self.cache_manager.allocate_token_from_block_table(seq.block_table, seq.sentence_len)
-                if recycle:
+            for seq in self.running_list.prefill[:num_seqs_to_add]:
+                seq.mark_running()
+            # allocate blocks for the prefill batch
+            self.prefill_bb.add_seqs(
+                self.running_list.prefill[:num_seqs_to_add],
+                alloc_block_tables_fn=self.cache_manager.allocate_context_from_block_tables,
+            )
+
+            return self.prefill_bb
+
+        if not self.running_bb.is_empty:
+            seqs_ids_to_recycle = self.cache_manager.allocate_tokens_from_block_tables(
+                self.running_bb.block_tables, self.running_bb.seq_lengths, self.running_bb.current_batch_size
+            )
+            if seqs_ids_to_recycle:
+                seqs_to_recycle = self.running_bb.pop_seqs(seqs_ids_to_recycle)
+                for seq in seqs_to_recycle:
                     seq.recycle()
-                    self.running_batch.del_seq(seq)
                     self.running_list.remove(seq)
                     self.waiting_list[-1].append(seq)
                     # the recycled sequences are handled with highest priority.
 
-        return self.running_batch
+        return self.running_bb
 
     def add_sequence(self, req: Sequence):
         """
@@ -213,7 +249,7 @@ def abort_sequence(self, request_id: str):
             seq.mark_aborted()
             self.waiting_list[priority].remove(seq)
         elif seq.status.is_running():
-            self.cache_manager.free_block_table(seq.block_table)
+            self.running_bb.pop_seq_update_batch(seq.request_id, self.cache_manager.free_block_table)
             self.running_list.remove(seq)
         else:
             try:
@@ -242,7 +278,7 @@ def _sample(self, probs: torch.Tensor, logprobs: torch.Tensor, generation_config
             else:
                 sample_tokens = greedy_sample(generation_config, logprobs)
         else:
-            sample_tokens = beam_search_sample(generation_config, logprobs, is_prompt=not self.prefill_batch.is_empty)
+            sample_tokens = beam_search_sample(generation_config, logprobs, is_prompt=not self.prefill_bb.is_empty)
 
         return sample_tokens
 
@@ -273,27 +309,25 @@ def search_tokens(self, generation_config: GenerationConfig, logits):
 
         # sample the next tokens
         sample_tokens = self._sample(probs, logprobs, generation_config)
-        if not self.prefill_batch.is_empty:
-            self.prefill_batch.update_batch_tokens(sample_tokens)
+        if not self.prefill_bb.is_empty:
+            self.prefill_bb.append_batch_tokens(sample_tokens)
         else:
-            self.running_batch.update_batch_tokens(sample_tokens)
+            self.running_bb.append_batch_tokens(sample_tokens)
 
     def update(self):
         """
         Update current running list and done list
         """
-        if not self.prefill_batch.is_empty:
-            self.running_list.decoding.extend(self.running_list.prefill)
-            self.running_batch.add_seqs(self.running_list.prefill)
-            self.running_list.prefill.clear()
-            self.prefill_batch.clear_batch()
-
-        finish_seqs = self.running_batch.fliter_batch()
-
-        for seq in finish_seqs:
+        if not self.prefill_bb.is_empty:
+            self.running_list.move_prefill_to_decoding(self.prefill_bb.seqs_ids)
+            self.running_bb.merge(self.prefill_bb)
+            # clear the prefill batch without assigning a free_block_tables_fn
+            # since we want to reuse the memory recorded on the block tables
+            self.prefill_bb.clear(free_block_tables_fn=None)
+
+        finished_seqs, _ = self.running_bb.pop_finished(self.cache_manager.free_block_table)
+        for seq in finished_seqs:
             self.running_list.remove(seq)
-            self.cache_manager.free_block_table(seq.block_table)
-
-        self.done_list.extend(finish_seqs)
+        self.done_list.extend(finished_seqs)
 
-        return finish_seqs
+        return finished_seqs