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https://github.com/NVIDIA/TensorRT-LLM.git
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f7eca56161
* Update TensorRT-LLM --------- Co-authored-by: Shixiaowei02 <39303645+Shixiaowei02@users.noreply.github.com> Co-authored-by: zhang-ge-hao <842720660@qq.com>
320 lines
12 KiB
Python
320 lines
12 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from collections import defaultdict
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from typing import List
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import torch
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class Block(object):
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def __init__(self, block_idx, k_ptrs, v_ptrs):
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self.idx = block_idx
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self.k_ptrs = k_ptrs
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self.v_ptrs = v_ptrs
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self.ref_count = 0
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def add_link(self):
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self.ref_count += 1
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def remove_link(self):
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self.ref_count -= 1
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def has_link(self) -> bool:
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return self.ref_count > 0
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def get_k_ptr(self, idx) -> int:
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return self.k_ptrs[idx]
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def get_v_ptr(self, idx) -> int:
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return self.v_ptrs[idx]
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class GenerationSequence(object):
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def __init__(self, seq_idx, batch_idx):
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self.seq_idx = seq_idx
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self.batch_idx = batch_idx
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def get_batch_idx(self) -> int:
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"""
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Returns idx of sequence in batch
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"""
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return self.batch_idx
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def get_seq_idx(self) -> int:
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"""
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Returns sequence idx
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"""
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return self.seq_idx
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def __eq__(self, another):
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return hasattr(another, 'seq_idx') and self.seq_idx == another.seq_idx and \
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hasattr(another, 'batch_idx') and self.batch_idx == another.batch_idx
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def __hash__(self):
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return self.seq_idx
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class BlocksManager(object):
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_sizeof = {
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torch.float32: 4,
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torch.float16: 2,
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torch.bfloat16: 2,
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torch.int8: 1
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}
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def __init__(self,
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memory_pools: List[torch.Tensor],
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blocks: int,
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max_blocks_per_seq: int = 128,
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beam_width: int = 1):
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self.max_blocks_per_seq = max_blocks_per_seq
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self.pointer_array = None
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self.memory_pools = memory_pools
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self.blocks = blocks
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self.beam_width = beam_width
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self.elts_per_blocks = []
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for pool in memory_pools:
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# Pool consists of memory for K and V caches
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self.elts_per_blocks.append(pool.nelement() // (2 * blocks))
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self.free_blocks = []
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for bi in range(blocks):
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k_ptrs = []
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v_ptrs = []
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for pool, elts_per_block in zip(memory_pools, self.elts_per_blocks):
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k_ptrs.append(self.get_mempool_pointer(bi, pool,
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elts_per_block))
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v_ptrs.append(
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self.get_mempool_pointer(bi, pool, elts_per_block) +
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self.blocks * elts_per_block * self._sizeof[pool.dtype])
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self.free_blocks.append(Block(bi, k_ptrs, v_ptrs))
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self.allocated_blocks = defaultdict(
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lambda: [[] for _ in range(self.beam_width)])
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def has_free_block(self) -> bool:
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"""
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Returns True if we have at least 1 free block
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"""
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return len(self.free_blocks) > 0
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def allocate(self,
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owner: GenerationSequence,
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share_across_beam: bool = False):
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"""
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Add block to owner and increase ref count
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"""
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# Add blocks for whole beam width
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block = None
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for bi in range(self.beam_width):
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if not self.has_free_block():
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raise RuntimeError("Can't allocate new block for KV cache")
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# Use the same block for all seqs in beam if share_across_beam
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if block is None or share_across_beam == False:
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block = self.free_blocks.pop(0)
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# Add one reference to the block
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block.add_link()
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self.allocated_blocks[owner][bi].append(block)
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def free(self, owner: GenerationSequence):
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"""
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Unlink all blocks of given owner.
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Moves blocks with ref_count == 0 to free.
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Removes owner from allocated blocks.
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"""
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for bi in range(self.beam_width):
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for block in self.allocated_blocks[owner][bi]:
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# Move block to free if no one refers to it
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block.remove_link()
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# Move block to free if no one refers to it
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if not block.has_link():
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self.free_blocks.append(block)
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# Remove owner from allocated blocks
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self.allocated_blocks.pop(owner)
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def get_number_blocks(self, owner: GenerationSequence) -> int:
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"""
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Returns number of blocks allocated to the sequence owner
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"""
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return len(self.allocated_blocks[owner][0])
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def get_mempool_pointer(self, block_idx: int, pool: torch.Tensor,
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elts_per_block: int) -> int:
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"""
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Computes linear pointer
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"""
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return pool.data_ptr(
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) + block_idx * elts_per_block * self._sizeof[pool.dtype]
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def get_pointer_array(self, pool_idx: int, beam_width: int) -> torch.Tensor:
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"""
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Returns array of [batch size, beam_width, 2, max_blocks_per_seq] of poitners
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to the allocated blocks in memory pool
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"""
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assert (beam_width <= self.beam_width)
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def create_nested_list(dims):
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"""Recursive function to generate nested list."""
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if len(dims) == 1:
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return [0 for _ in range(dims[0])]
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return [create_nested_list(dims[1:]) for _ in range(dims[0])]
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pointer_array = create_nested_list(
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(len(self.allocated_blocks), beam_width, 2,
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self.max_blocks_per_seq))
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for owner, beams_blocks in self.allocated_blocks.items():
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for bi in range(beam_width):
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for block_linear_idx, block in enumerate(beams_blocks[bi]):
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# K cache pointers
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pointer_array[owner.get_batch_idx(
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)][bi][0][block_linear_idx] = block.get_k_ptr(pool_idx)
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# V cache pointers
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pointer_array[owner.get_batch_idx(
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)][bi][1][block_linear_idx] = block.get_v_ptr(pool_idx)
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self.pointer_array = torch.tensor(pointer_array, dtype=torch.int64)
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return self.pointer_array
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def get_continous_caches(self, pool_idx: int) -> torch.Tensor:
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"""
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Returns countinous KV caches.
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Used only for debug purposes.
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"""
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assert self.beam_width == 1
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elts_per_block = self.elts_per_blocks[pool_idx]
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pool = self.memory_pools[pool_idx].flatten()
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continous_kv_cache = torch.zeros(len(self.allocated_blocks),
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2,
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self.max_blocks_per_seq *
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elts_per_block,
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dtype=pool.dtype,
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device="cuda")
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for owner, beam_blocks in self.allocated_blocks.items():
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for bi in range(self.beam_width):
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for block_linear_idx, block in enumerate(beam_blocks[bi]):
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# The batch index.
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batch_idx = owner.get_batch_idx()
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# The first index in the sequence.
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block_offset = block_linear_idx * elts_per_block
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# The first index in the pool for K.
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k_start = block.idx * elts_per_block
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# The first index in the pool for V.
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v_start = k_start + self.blocks * elts_per_block
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continous_kv_cache[batch_idx][0][
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block_offset:block_offset +
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elts_per_block] = pool[k_start:k_start + elts_per_block]
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continous_kv_cache[batch_idx][1][
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block_offset:block_offset +
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elts_per_block] = pool[v_start:v_start + elts_per_block]
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return continous_kv_cache
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class KVCacheManager(object):
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def __init__(self,
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memory_pools: List[torch.Tensor],
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blocks: int,
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tokens_per_block: int,
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max_blocks_per_seq: int,
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max_attention_window_size: int,
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beam_width: int = 1):
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self.blocks_manager = BlocksManager(
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memory_pools=memory_pools,
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blocks=blocks,
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max_blocks_per_seq=max_blocks_per_seq,
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beam_width=beam_width)
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self.num_pools = len(memory_pools)
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self.tokens_per_block = tokens_per_block
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self.max_attention_window_size = max_attention_window_size
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self.beam_width = beam_width
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self.lens = []
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self.sequences = []
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def step(self, finished: List[bool]):
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"""
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Iterate to the next generation step.
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Add new blocks where needed and clear finished sequences.
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"""
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for seq in self.sequences:
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batch_idx = seq.get_batch_idx()
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# Enable cyclic kv cache when it exceeds the max_attention_window_size
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if self.lens[batch_idx] == self.max_attention_window_size:
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continue
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if not finished[batch_idx] and self.lens[
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batch_idx] % self.tokens_per_block == 0:
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self.blocks_manager.allocate(seq)
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self.lens[batch_idx] += 1
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# Remove finished sequences
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for fi in range(len(finished)):
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if finished[fi]:
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self.blocks_manager.free(self.sequences[fi])
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self.lens = [l for l, f in zip(self.lens, finished) if not f]
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# Remap sequence ids
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new_sequences = []
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batch_idx = 0
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for seq, finish in zip(self.sequences, finished):
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if not finish:
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seq.batch_idx = batch_idx
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new_sequences.append(seq)
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batch_idx += 1
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self.sequences = new_sequences
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def add_sequence(self, sequence: GenerationSequence, context_len: int):
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"""
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Add sequence to the manager and allocate minimum amount of blocks for context
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"""
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seq_len = min(context_len, self.max_attention_window_size)
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self.lens.append(seq_len)
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self.sequences.append(sequence)
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# With beam_width > 1 we share context blocks between beams.
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# First get number of blocks that can be shared across different beams.
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# This is only possible for complete blocks -> round down.
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context_blocks = seq_len // self.tokens_per_block
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for _ in range(context_blocks):
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# Share context stage blocks within beam
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self.blocks_manager.allocate(sequence, share_across_beam=True)
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# allocate one more block if there are tokens that can't be shared across beams.
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if seq_len % self.tokens_per_block > 0:
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self.blocks_manager.allocate(sequence, share_across_beam=False)
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def get_pointer_arrays(self, beam_width: int) -> List[torch.Tensor]:
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"""
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Returns arrays of pointers for all memory pools
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"""
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pointer_arrays = []
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for pool in range(self.num_pools):
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pointer_arrays.append(
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self.blocks_manager.get_pointer_array(
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pool, beam_width).view(dtype=torch.int64))
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return pointer_arrays
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