mirror of https://github.com/hpcaitech/ColossalAI
[Inference] Fix request handler and add recycle logic (#5260)
* fix request handler * fix commentpull/5264/head
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c597678da4
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d8db500efc
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@ -57,6 +57,9 @@ class RunningList:
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def is_empty(self):
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return not self.decoding and not self.prefill
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def total_seq_num(self):
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return len(self.decoding) + len(self.prefill)
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class RequestHandler:
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"""
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@ -105,6 +108,11 @@ class RequestHandler:
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)
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self.abort_sequence(seq.request_id)
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break
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# stop feeding new sequence into running list to assure
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if self.cache_manager.num_available_blocks <= self.running_list.total_seq_num:
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break
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# Try to allocate cache blocks for the sequence.
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if self.cache_manager.check_allocation(seq):
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# If succeed, add the sequence to running list.
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@ -113,6 +121,7 @@ class RequestHandler:
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self.cache_manager.allocate_context_from_block_table(seq.block_table, seq.input_len)
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for seq in remove_list:
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lst.remove(seq)
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if self.running_list.ready_for_prefill():
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for seq in self.running_list.prefill:
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seq.mark_running()
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@ -121,7 +130,12 @@ class RequestHandler:
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if not self.running_batch.is_empty:
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for seq in self.running_batch.sequences_set:
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self.cache_manager.allocate_token_from_block_table(seq.block_table, seq.sentence_len)
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recycle = self.cache_manager.allocate_token_from_block_table(seq.block_table, seq.sentence_len)
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if recycle:
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seq.recycle()
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self.running_batch.remove(seq)
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self.waiting_list[-1].append(seq)
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# the recycled sequences are handled with highest priority.
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return self.running_batch
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@ -227,4 +241,4 @@ class RequestHandler:
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self.done_list.extend(finish_seqs)
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return finish_seqs
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return finish_seqs
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@ -208,9 +208,9 @@ class KVCacheManager:
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# The last allocated block may be either partially or fully occupied.
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# `alloc_local_block_idx` is the index of block to be allocated on provided block table.
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alloc_local_block_idx = context_len // self.block_size
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self.allocate_single_block(block_table, alloc_local_block_idx, 1)
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return self.allocate_single_block(block_table, alloc_local_block_idx)
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def allocate_single_block(self, block_table: torch.Tensor, block_local_idx: int, space_asked: int) -> int:
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def allocate_single_block(self, block_table: torch.Tensor, block_local_idx: int) -> int:
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"""Allocate space asked on a single block in the block table, specified by the provided position id,
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and updates the provided block table with the allocated block.
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@ -221,11 +221,14 @@ class KVCacheManager:
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Returns:
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The remaining space required to be allocated (in other blocks).
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"""
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assert block_table.dim() == 1
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space_asked = 1
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block_global_id = block_table[block_local_idx].item()
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if block_global_id < 0:
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# Allocate a new block if the current position is not assigned a block yet
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assert self._available_blocks > 0, "No available blocks to allocate."
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if self._available_blocks <= 0:
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# No available blocks to allocate, we free current sequence and return it to
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self.free_block_table(block_table)
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return True
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free_block_id = torch.nonzero(self._block_states == 1).view(-1)[0]
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block: CacheBlock = self._cache_blocks[free_block_id]
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block.add_ref()
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@ -235,6 +238,7 @@ class KVCacheManager:
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block_table[block_local_idx] = block_global_id
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block: CacheBlock = self._cache_blocks[block_global_id]
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return self._allocate_on_block(block, space_asked)
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# only when space asked if fully satisfied, the return value will be zero.
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def free_block_table(self, block_table: torch.Tensor) -> None:
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"""Free the logical cache blocks for **a single sequence**."""
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@ -269,7 +273,9 @@ class KVCacheManager:
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Returns:
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The remaining space required to be allocated (in other blocks).
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"""
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assert block.available_space > 0, "No available space on block to allocate."
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assert (
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block.available_space > 0
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), "Tried to allocate some space but found no available space left in chosen block."
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space_to_allocate = min(block.available_space, space_asked)
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block.allocate(space_to_allocate)
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return space_asked - space_to_allocate
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@ -134,6 +134,16 @@ class Sequence:
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"""
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self.status = RequestStatus.ABORTED
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def recycle(self) -> None:
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"""
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Recycle a running sequnce to waiitting list
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"""
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assert (
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not self.status.is_finished and not self.status == RequestStatus.ABORTED
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), "The running sequence \
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is already done but it still in running list"
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self.status = RequestStatus.WAITING
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def __repr__(self) -> str:
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return (
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f"(request_id={self.request_id}, "
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