mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
synced 2026-01-14 06:27:45 +08:00
103 lines
3.0 KiB
Python
103 lines
3.0 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
|
|
# SPDX-License-Identifier: Apache-2.0
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
from typing import List
|
|
|
|
|
|
class Mapping(object):
|
|
'''
|
|
A node with 8 GPUs, tp_size = 4, pp_size = 2
|
|
|
|
2 tp groups:
|
|
|
|
- [0, 1, 2, 3]
|
|
- [4, 5, 6, 7]
|
|
|
|
4 pp groups:
|
|
|
|
- [0, 4]
|
|
- [1, 5]
|
|
- [2, 6]
|
|
- [3, 7]
|
|
'''
|
|
|
|
def __init__(self,
|
|
world_size=1,
|
|
rank=0,
|
|
gpus_per_node=8,
|
|
tp_size=1,
|
|
pp_size=1):
|
|
self.tp_size = tp_size
|
|
self.pp_size = pp_size
|
|
self.world_size = world_size
|
|
self.rank = rank
|
|
self.gpus_per_node = gpus_per_node
|
|
|
|
if pp_size * tp_size != world_size:
|
|
raise ValueError("world_size must equal to pp_size * tp_size")
|
|
self.pp_groups = []
|
|
self.tp_groups = []
|
|
|
|
# init pp group
|
|
for i in range(tp_size):
|
|
ranks = range(i, world_size, tp_size)
|
|
self.pp_groups.append(list(ranks))
|
|
|
|
# init tp group
|
|
for i in range(pp_size):
|
|
ranks = range(i * tp_size, (i + 1) * tp_size)
|
|
self.tp_groups.append(list(ranks))
|
|
|
|
self.pp_rank = self.rank // self.tp_size
|
|
self.tp_rank = self.rank % self.tp_size
|
|
|
|
self.tp_group = self.tp_groups[self.pp_rank]
|
|
self.pp_group = self.pp_groups[self.tp_rank]
|
|
|
|
def has_tp(self):
|
|
return self.tp_size > 1
|
|
|
|
def is_last_pp_rank(self):
|
|
return self.pp_rank == self.pp_size - 1
|
|
|
|
def is_first_pp_rank(self):
|
|
return self.pp_rank == 0
|
|
|
|
def has_pp(self):
|
|
return self.pp_size > 1
|
|
|
|
def prev_pp_rank(self):
|
|
p = self.rank - self.tp_size
|
|
if p < 0:
|
|
p = p + self.world_size
|
|
return p
|
|
|
|
def next_pp_rank(self):
|
|
p = self.rank + self.tp_size
|
|
if p >= self.world_size:
|
|
p = p - self.world_size
|
|
return p
|
|
|
|
def pp_layers(self, num_layers: int) -> List[int]:
|
|
layers_per_pipeline_stage = num_layers // self.pp_size
|
|
layers_range = range(self.pp_rank * layers_per_pipeline_stage,
|
|
(self.pp_rank + 1) * layers_per_pipeline_stage)
|
|
return list(layers_range)
|
|
|
|
def ep_experts(self, num_experts: int) -> List[int]:
|
|
experts_per_rank = num_experts // self.tp_size
|
|
experts_range = range(self.tp_rank * experts_per_rank,
|
|
(self.tp_rank + 1) * experts_per_rank)
|
|
return list(experts_range)
|