mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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258c7540c0
Co-authored-by: Kaiyu Xie <26294424+kaiyux@users.noreply.github.com> open source f8c0381a2bc50ee2739c3d8c2be481b31e5f00bd (#2736) Co-authored-by: Kaiyu Xie <26294424+kaiyux@users.noreply.github.com> Add note for blackwell (#2742) Update the docs to workaround the extra-index-url issue (#2744) update README.md (#2751) Fix github io pages (#2761) Update
354 lines
9.8 KiB
Python
354 lines
9.8 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2022-2024 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 typing import List
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class Mapping(object):
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'''
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A node with 8 GPUs, tp_size = 4, cp_size = 1, pp_size = 2
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2 tp groups:
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- [0, 1, 2, 3]
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- [4, 5, 6, 7]
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4 pp groups:
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- [0, 4]
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- [1, 5]
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- [2, 6]
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- [3, 7]
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A node with 8 GPUs, tp_size = 4, cp_size = 2, pp_size = 1
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2 tp groups:
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- [0, 1, 2, 3]
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- [4, 5, 6, 7]
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4 cp groups:
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- [0, 4]
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- [1, 5]
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- [2, 6]
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- [3, 7]
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A node with 8 GPUs, moe_tp_size = 2, moe_ep_size = 4
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4 moe_tp groups:
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- [0, 4]
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- [1, 5]
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- [2, 6]
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- [3, 7]
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2 moe_ep groups:
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- [0, 1, 2, 3]
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- [4, 5, 6, 7]
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2 nodes with 16 GPUs, moe_tp_size = 2, moe_ep_size = 4, pp_size = 2
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8 moe_tp groups:
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- [0 4]
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- [1 5]
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- [2 6]
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- [3 7]
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- [8 12]
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- [9 13]
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- [10 14]
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- [11 15]
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4 moe_ep groups:
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- [0, 1, 2, 3]
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- [4, 5, 6, 7]
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- [8, 9, 10, 11]
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- [12, 13, 14, 15]
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8 pp groups:
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- [0 8]
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- [1 9]
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- [2 10]
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- [3 11]
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- [4 12]
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- [5 13]
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- [6 14]
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- [7 15]
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2 nodes with 8 GPUs, tp_size 2, pp_size 2, cp_size 2
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4 tp groups:
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- [0, 1]
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- [2, 3]
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- [4, 5]
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- [6, 7]
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4 pp groups:
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- [0, 4]
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- [1, 5]
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- [2, 6]
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- [3, 7]
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4 cp groups:
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- [0, 2]
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- [1, 3]
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- [4, 6]
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- [5, 7]
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'''
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def __init__(
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self,
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world_size=1,
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rank=0,
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gpus_per_node=8,
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*,
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cp_size=1,
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cp_config=None,
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tp_size=1,
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pp_size=1,
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moe_tp_size=-1, # -1 means no moe
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moe_ep_size=-1, # -1 means no moe
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auto_parallel=False):
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# set default values for non-moe cases
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if moe_tp_size == -1:
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moe_tp_size = tp_size
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moe_ep_size = 1
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if auto_parallel:
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if tp_size != 1 or pp_size != 1 or tp_size != 1:
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raise ValueError(
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f"When auto parallel is enabled, tp_size, pp_size, cp_size must be 1, but got {tp_size}, {pp_size}, {cp_size}."
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)
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else:
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if tp_size * pp_size * cp_size != world_size:
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raise ValueError(
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f"world_size must equal to tp_size * pp_size * cp_size, but got {world_size} != {tp_size} * {pp_size} * {cp_size}."
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)
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moe_tp_ep_size = moe_tp_size * moe_ep_size
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if moe_tp_ep_size != tp_size:
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raise ValueError(
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f"tp_size must equal to moe_tp_size * moe_ep_size, but got {tp_size} != {moe_tp_size} * {moe_ep_size}"
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)
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if moe_ep_size != 1 and cp_size > 1:
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raise NotImplementedError("CP don't support MoE tp/ep yet")
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self.tp_size = tp_size
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self.cp_size = cp_size
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self.cp_config = cp_config if cp_config is not None else {}
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self.pp_size = pp_size
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self.moe_tp_size = moe_tp_size
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self.moe_ep_size = moe_ep_size
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self.auto_parallel = auto_parallel
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self.world_size = world_size
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self.rank = rank
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self.gpus_per_node = gpus_per_node
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self.pp_groups = []
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self.cp_groups = []
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self.tp_groups = []
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self.moe_tp_groups = []
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self.moe_ep_groups = []
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# init pp group
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for i in range(tp_size * cp_size):
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ranks = range(i, world_size, tp_size * cp_size)
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self.pp_groups.append(list(ranks))
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# init cp group
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for i in range(pp_size):
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for j in range(tp_size):
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ranks = range(i * tp_size * cp_size + j,
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(i + 1) * tp_size * cp_size + j, tp_size)
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self.cp_groups.append(list(ranks))
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# init tp group
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for i in range(pp_size):
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for j in range(cp_size):
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ranks = range(i * tp_size * cp_size + j * tp_size,
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i * tp_size * cp_size + (j + 1) * tp_size)
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self.tp_groups.append(list(ranks))
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# init moe tp group
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for i in range(pp_size):
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for j in range(moe_ep_size):
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ranks = range(i * moe_tp_ep_size + j, (i + 1) * moe_tp_ep_size,
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moe_ep_size)
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self.moe_tp_groups.append(list(ranks))
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# init moe ep group
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for i in range(pp_size):
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for j in range(moe_tp_size):
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ranks = range(i * moe_tp_ep_size + j * moe_ep_size,
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i * moe_tp_ep_size + (j + 1) * moe_ep_size)
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self.moe_ep_groups.append(list(ranks))
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def __eq__(self, other):
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if not isinstance(other, Mapping):
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return NotImplemented
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return (self.world_size == other.world_size and self.rank == other.rank
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and self.gpus_per_node == other.gpus_per_node
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and self.cp_size == other.cp_size
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and self.tp_size == other.tp_size
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and self.pp_size == other.pp_size
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and self.moe_tp_size == other.moe_tp_size
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and self.moe_ep_size == other.moe_ep_size
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and self.auto_parallel == other.auto_parallel)
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def __hash__(self):
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return (hash(self.world_size) ^ hash(self.rank)
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^ hash(self.gpus_per_node) ^ hash(self.cp_size)
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^ hash(self.tp_size) ^ hash(self.pp_size)
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^ hash(self.moe_tp_size) ^ hash(self.moe_ep_size)
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^ hash(self.auto_parallel))
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@property
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def rank(self):
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return self._rank
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@rank.setter
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def rank(self, rank: int):
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if not isinstance(rank, int) or rank < 0 or rank >= self.world_size:
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raise ValueError(
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f"Rank should be an integer between 0 and {self.world_size-1}, but got {rank}."
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)
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self._rank = rank
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@property
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def tp_rank(self):
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return 0 if self.auto_parallel else self.rank % self.tp_size
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@property
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def pp_rank(self):
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return 0 if self.auto_parallel else self.rank // (self.tp_size *
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self.cp_size)
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@property
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def cp_rank(self):
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return 0 if self.auto_parallel else self.rank % (
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self.tp_size * self.cp_size) // self.tp_size
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@property
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def moe_tp_rank(self):
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return self.tp_rank // self.moe_ep_size
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@property
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def moe_ep_rank(self):
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return self.tp_rank % self.moe_ep_size
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@property
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def tp_group(self):
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return self.tp_groups[self.pp_rank * self.cp_size + self.cp_rank]
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@property
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def pp_group(self):
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return self.pp_groups[self.cp_rank * self.tp_size + self.tp_rank]
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@property
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def cp_group(self):
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return self.cp_groups[self.pp_rank * self.tp_size + self.tp_rank]
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@property
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def moe_tp_group(self):
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return self.moe_tp_groups[self.pp_rank * self.moe_ep_size +
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self.moe_ep_rank]
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@property
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def moe_ep_group(self):
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return self.moe_ep_groups[self.pp_rank * self.moe_tp_size +
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self.moe_tp_rank]
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@property
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def node_rank(self):
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return self.rank // self.gpus_per_node
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@property
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def local_rank(self):
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return self.rank % self.gpus_per_node
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def has_cp(self):
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return self.cp_size > 1
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def get_node_rank(self, rank: int):
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return rank // self.gpus_per_node
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def get_local_rank(self, rank: int):
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return rank % self.gpus_per_node
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def has_tp(self):
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return self.tp_size > 1
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def is_last_pp_rank(self):
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return self.pp_rank == self.pp_size - 1
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def is_first_pp_rank(self):
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return self.pp_rank == 0
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def has_pp(self):
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return self.pp_size > 1
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def prev_pp_rank(self):
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p = self.rank - self.tp_size * self.cp_size
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if p < 0:
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p = p + self.world_size
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return p
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def next_pp_rank(self):
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p = self.rank + self.tp_size * self.cp_size
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if p >= self.world_size:
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p = p - self.world_size
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return p
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def has_moe_tp(self):
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return self.moe_tp_size > 1
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def has_moe_ep(self):
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return self.moe_ep_size > 1
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def pp_layers(self, num_layers: int) -> List[int]:
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layers_per_pipeline_stage = num_layers // self.pp_size
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layers_range = range(self.pp_rank * layers_per_pipeline_stage,
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(self.pp_rank + 1) * layers_per_pipeline_stage)
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return list(layers_range)
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def ep_experts(self, num_experts: int) -> List[int]:
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assert self.cp_size == 1
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experts_per_rank = num_experts // self.moe_ep_size
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experts_range = range(self.moe_ep_rank * experts_per_rank,
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(self.moe_ep_rank + 1) * experts_per_rank)
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return list(experts_range)
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@classmethod
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def from_dict(cls, mapping: dict):
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return cls(**mapping)
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def to_dict(self):
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return {
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'world_size': self.world_size,
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'rank': self.rank,
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'gpus_per_node': self.gpus_per_node,
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'cp_size': self.cp_size,
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'tp_size': self.tp_size,
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'pp_size': self.pp_size,
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'moe_tp_size': self.moe_tp_size,
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'moe_ep_size': self.moe_ep_size,
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'auto_parallel': self.auto_parallel,
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}
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