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parallel_state.py
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parallel_state.py
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# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
"""Model and data parallel groups."""
import os
import warnings
from datetime import timedelta
from typing import List, Optional
import torch
from .utils import GlobalMemoryBuffer
# Intra-layer model parallel group that the current rank belongs to.
_TENSOR_MODEL_PARALLEL_GROUP = None
# Inter-layer model parallel group that the current rank belongs to.
_PIPELINE_MODEL_PARALLEL_GROUP = None
# Model parallel group (both intra- and pipeline) that the current rank belongs to.
_MODEL_PARALLEL_GROUP = None
# Model parallel group (both intra-, pipeline, and expert) that the current rank belongs to.
_MODEL_AND_EXPERT_PARALLEL_GROUP = None
# Embedding group.
_EMBEDDING_GROUP = None
# Position embedding group.
_POSITION_EMBEDDING_GROUP = None
# Data parallel group that the current rank belongs to.
_DATA_PARALLEL_GROUP = None
_DATA_PARALLEL_GROUP_GLOO = None
# tensor model parallel group and data parallel group combined
# used for fp8 and moe training
_TENSOR_AND_DATA_PARALLEL_GROUP = None
# Expert parallel group that the current rank belongs to.
_EXPERT_MODEL_PARALLEL_GROUP = None
_TENSOR_AND_EXPERT_PARALLEL_GROUP = None
_DATA_MODULO_EXPERT_PARALLEL_GROUP = None
_DATA_MODULO_EXPERT_PARALLEL_GROUP_GLOO = None
_VIRTUAL_PIPELINE_MODEL_PARALLEL_RANK = None
_VIRTUAL_PIPELINE_MODEL_PARALLEL_WORLD_SIZE = None
_PIPELINE_MODEL_PARALLEL_SPLIT_RANK = None
# These values enable us to change the mpu sizes on the fly.
_MPU_TENSOR_MODEL_PARALLEL_WORLD_SIZE = None
_MPU_PIPELINE_MODEL_PARALLEL_WORLD_SIZE = None
_MPU_EXPERT_MODEL_PARALLEL_WORLD_SIZE = None
_MPU_TENSOR_MODEL_PARALLEL_RANK = None
_MPU_PIPELINE_MODEL_PARALLEL_RANK = None
_MPU_EXPERT_MODEL_PARALLEL_RANK = None
# A list of ranks that have a copy of the embedding.
_EMBEDDING_GLOBAL_RANKS = None
# A list of ranks that have a copy of the position embedding.
_POSITION_EMBEDDING_GLOBAL_RANKS = None
# A list of global ranks for each pipeline group to ease calculation of the source
# rank when broadcasting from the first or last pipeline stage.
_PIPELINE_GLOBAL_RANKS = None
# A list of global ranks for each data parallel group to ease calculation of the source
# rank when broadcasting weights from src to all other data parallel ranks
_DATA_PARALLEL_GLOBAL_RANKS = None
# A list of global ranks for each tensor model parallel group to ease calculation of
# the first local rank in the tensor model parallel group
_TENSOR_MODEL_PARALLEL_GLOBAL_RANKS = None
# Context parallel group that the current rank belongs to
_CONTEXT_PARALLEL_GROUP = None
# A list of global ranks for each context parallel group to ease calculation of the
# destination rank when exchanging KV/dKV between context parallel_ranks
_CONTEXT_PARALLEL_GLOBAL_RANKS = None
# Data parallel group information with context parallel combined.
_DATA_PARALLEL_GROUP_WITH_CP = None
_DATA_PARALLEL_GROUP_WITH_CP_GLOO = None
_DATA_PARALLEL_GLOBAL_RANKS_WITH_CP = None
# combined parallel group of TP, DP, and CP used for fp8
_TENSOR_AND_DATA_PARALLEL_GROUP_WITH_CP = None
# Memory buffers to avoid dynamic memory allocation
_GLOBAL_MEMORY_BUFFER = None
# MOE logging
_MOE_AUX_LOSSES_LOGGING_TRACKER = {}
def get_nccl_options(pg_name, nccl_comm_cfgs):
"""Set the NCCL process group options.
Args:
pg_name (str): process group name
nccl_comm_cfgs (dict): nccl communicator configurations
When an option (e.g., max_ctas) is not found in the config, use the NCCL default setting.
"""
if pg_name in nccl_comm_cfgs:
nccl_options = torch.distributed.ProcessGroupNCCL.Options()
nccl_options.config.cga_cluster_size = nccl_comm_cfgs[pg_name].get('cga_cluster_size', 4)
nccl_options.config.max_ctas = nccl_comm_cfgs[pg_name].get('max_ctas', 32)
nccl_options.config.min_ctas = nccl_comm_cfgs[pg_name].get('min_ctas', 1)
return nccl_options
else:
return None
def generate_masked_orthogonal_rank_groups(
world_size: int, parallel_size: List[int], mask: List[bool],
) -> List[List[int]]:
"""Generate orthogonal parallel groups based on the parallel size and mask.
Arguments:
world_size (int): world size
parallel_size (List[int]):
The parallel size of each orthogonal parallel type. For example, if
tensor_parallel_size = 2, pipeline_model_parallel_group = 3, data_parallel_size = 4,
and the parallel mapping order is tp-pp-dp, then the parallel_size = [2, 3, 4].
mask (List[bool]):
The mask controls which parallel methods the generated groups represent. If mask[i] is
True, it means the generated group contains the i-th parallelism method. For example,
if parallel_size = [tp_size, pp_size, dp_size], and mask = [True, False , True], then
the generated group is the `tp-dp` group, if the mask = [False, True, False], then the
generated group is the `pp` group.
Algorithm:
For orthogonal parallelism, such as tp/dp/pp/cp, the global_rank and
local_rank satisfy the following equation:
global_rank = tp_rank + dp_rank * tp_size + pp_rank * tp_size * dp_size (1)
tp_rank \in [0, tp_size)
dp_rank \in [0, dp_size)
pp_rank \in [0, pp_size)
If we want to get the `dp_group` (tp_size * pp_size groups of dp_size ranks each.
For example, if the gpu size is 8 and order is 'tp-pp-dp', size is '2-2-2', and the
dp_group here is [[0, 4], [1, 5], [2, 6], [3, 7]].)
The tp_rank and pp_rank will be combined to form the `dp_group_index`.
dp_group_index = tp_rank + pp_rank * tp_size (2)
So, Given that tp_rank and pp_rank satisfy equation (2), and dp_rank in
range(0, dp_size), the ranks in dp_group[dp_group_index] satisfies the
equation (1).
This function solve this math problem.
For example, if the parallel_size = [tp_size, dp_size, pp_size] = [2, 3, 4],
and the mask = [False, True, False]. Then,
dp_group_index(0) = tp_rank(0) + pp_rank(0) * 2
dp_group_index(1) = tp_rank(1) + pp_rank(0) * 2
...
dp_group_index(7) = tp_rank(1) + pp_rank(3) * 2
dp_group[0] = 0 + range(0, 3) * 2 + 0 = [0, 2, 4]
dp_group[1] = 1 + range(0, 3) * 2 + 0 = [1, 3, 5]
...
dp_group[7] = 1 + range(0, 3) * 2 + 3 * 2 * 3 = [19, 21, 23]
"""
def prefix_product(a: List[int], init=1) -> List[int]:
r = [init]
for v in a:
init = init * v
r.append(init)
return r
def inner_product(a: List[int], b: List[int]) -> int:
return sum([x * y for x, y in zip(a, b)])
def decompose(index, shape, stride=None):
'''
This function solve the math problem below:
There is an equation:
index = sum(idx[i] * stride[i])
And given the value of index, stride.
Return the idx.
This function will used to get the pp/dp/pp_rank
from group_index and rank_in_group.
'''
if stride is None:
stride = prefix_product(shape)
idx = [(index // d) % s for s, d in zip(shape, stride)]
# stride is a prefix_product result. And the value of stride[-1]
# is not used.
assert (
sum([x * y for x, y in zip(idx, stride[:-1])]) == index
), "idx {} with shape {} mismatch the return idx {}".format(index, shape, idx)
return idx
masked_shape = [s for s, m in zip(parallel_size, mask) if m]
unmasked_shape = [s for s, m in zip(parallel_size, mask) if not m]
global_stride = prefix_product(parallel_size)
masked_stride = [d for d, m in zip(global_stride, mask) if m]
unmasked_stride = [d for d, m in zip(global_stride, mask) if not m]
group_size = prefix_product(masked_shape)[-1]
num_of_group = world_size // group_size
ranks = []
for group_index in range(num_of_group):
# get indices from unmaksed for group_index.
decomposed_group_idx = decompose(group_index, unmasked_shape)
rank = []
for rank_in_group in range(group_size):
# get indices from masked for rank_in_group.
decomposed_rank_idx = decompose(rank_in_group, masked_shape)
rank.append(
inner_product(decomposed_rank_idx, masked_stride)
+ inner_product(decomposed_group_idx, unmasked_stride)
)
ranks.append(rank)
return ranks
class RankGenerator(object):
def __init__(self, tp: int, ep: int, dp: int, pp: int, cp: int, order: str) -> None:
self.tp = tp
self.ep = ep
self.dp = dp
self.pp = pp
self.cp = cp
self.world_size = tp * dp * pp * cp
self.name_to_size = {
"tp": self.tp,
"pp": self.pp,
"dp": self.dp,
"ep": self.ep,
"cp": self.cp,
}
self.order = order
order = order.lower()
if 'ep' in order:
if 'ep-dp' not in order and 'dp-ep' not in order:
raise RuntimeError(f"The ep and dp must be adjacent in order ({self.order}).")
for name in self.name_to_size.keys():
if name not in order and self.name_to_size[name] != 1:
raise RuntimeError(
f"The size of ({name}) is ({self.name_to_size[name]}), but you haven't specified the order ({self.order})."
)
elif name not in order:
order = order + '-' + name
self.order_w_ep = order
self.order_wo_ep = '-'.join([token for token in order.split('-') if token != 'ep'])
self.ordered_size_wo_ep = []
self.ordered_size_w_ep = []
for token in order.split('-'):
if token == 'dp':
self.ordered_size_w_ep.append(self.dp // self.ep)
self.ordered_size_wo_ep.append(self.dp)
elif token == 'ep':
self.ordered_size_w_ep.append(self.ep)
else:
self.ordered_size_w_ep.append(self.name_to_size[token])
self.ordered_size_wo_ep.append(self.name_to_size[token])
def get_mask(self, order: str, token: str):
ordered_token = order.split('-')
token = token.split('-')
mask = [False] * len(ordered_token)
for t in token:
mask[ordered_token.index(t)] = True
return mask
def get_ranks(self, token, independent_ep=False):
'''Get rank group by input token.
Arguments:
token (str):
Specify the ranks type that want to get. If we want
to obtain multiple parallel types, we can use a hyphen
'-' to separate them. For example, if we want to obtain
the TP_DP group, the token should be 'tp-dp'.
independent_ep (bool: True):
This flag controls whether we treat EP and DP independently.
EP shares ranks with DP, if we want to get ranks related to
EP, we should set the flag. For example, get_ranks('dp', True)
will get DP modulo EP group, and get_ranks('dp', False) will
get full DP group.
'''
if independent_ep:
parallel_size = self.ordered_size_w_ep
order = self.order_w_ep
else:
parallel_size = self.ordered_size_wo_ep
order = self.order_wo_ep
mask = self.get_mask(order, token)
ranks = generate_masked_orthogonal_rank_groups(self.world_size, parallel_size, mask)
return ranks
def initialize_model_parallel(
tensor_model_parallel_size: int = 1,
pipeline_model_parallel_size: int = 1,
virtual_pipeline_model_parallel_size: Optional[int] = None,
pipeline_model_parallel_split_rank: Optional[int] = None,
use_sharp: bool = False,
context_parallel_size: int = 1,
expert_model_parallel_size: int = 1,
nccl_communicator_config_path: Optional[str] = None,
distributed_timeout_minutes: int = 30,
order: str = "tp-cp-ep-dp-pp",
) -> None:
"""Initialize model data parallel groups.
Args:
tensor_model_parallel_size (int, default = 1):
The number of GPUs to split individual tensors across.
pipeline_model_parallel_size (int, default = 1):
The number of tensor parallel GPU groups to split the
Transformer layers across. For example, if
tensor_model_parallel_size is 4 and
pipeline_model_parallel_size is 2, the model will be split
into 2 groups of 4 GPUs.
virtual_pipeline_model_parallel_size (int, optional):
The number of stages that each pipeline group will have,
interleaving as necessary. If None, no interleaving is
performed. For example, if tensor_model_parallel_size is 1,
pipeline_model_parallel_size is 4,
virtual_pipeline_model_parallel_size is 2, and there are
16 transformer layers in the model, the model will be
split into 8 stages with two layers each and each GPU
would get 2 stages as such (layer number starting with 1):
GPU 0: [1, 2] [9, 10]
GPU 1: [3, 4] [11, 12]
GPU 2: [5, 6] [13, 14]
GPU 3: [7, 8] [15, 16]
pipeline_model_parallel_split_rank (int, optional):
For models with both an encoder and decoder, the rank in
pipeline to switch between encoder and decoder (i.e. the
first rank of the decoder). This allows the user to set
the pipeline parallel size of the encoder and decoder
independently. For example, if
pipeline_model_parallel_size is 8 and
pipeline_model_parallel_split_rank is 3, then ranks 0-2
will be the encoder and ranks 3-7 will be the decoder.
use_sharp (bool, default = False):
Set the use of SHARP for the collective communications of
data-parallel process groups. When `True`, run barrier
within each data-parallel process group, which specifies
the SHARP application target groups.
context_parallel_size (int, default = 1):
The number of tensor parallel GPU groups to split the
network input sequence length across. Compute of attention
module requires tokens of full sequence length, so GPUs
in a context parallel group need to communicate with each
other to exchange information of other sequence chunks.
Each GPU and its counterparts in other tensor parallel
groups compose a context parallel group.
For example, assume we have 8 GPUs, if tensor model parallel
size is 4 and context parallel size is 2, the network input
will be split into two sequence chunks, which are processed
by 2 different groups of 4 GPUs. One chunk is processed by
GPU0-3, the other chunk is processed by GPU4-7. Four groups
are build to do context parallel communications: [GPU0, GPU4],
[GPU1, GPU5], [GPU2, GPU6], and [GPU3, GPU7].
Context parallelism partitions sequence length, so it has no
impact on weights, which means weights are duplicated among
GPUs in a context parallel group. Hence, weight gradients
all-reduce is required in backward. For simplicity, we piggyback
GPUs of context parallelism on data parallel group for
weight gradient all-reduce.
expert_model_parallel_size (int, default = 1):
The number of Mixture of Experts parallel GPUs in each expert
parallel group.
nccl_communicator_config_path (str, default = None):
Path to the yaml file of NCCL communicator configurations.
`min_ctas`, `max_ctas`, and `cga_cluster_size` can be set
for each communicator.
distributed_timeout_minutes (int, default = 30): Timeout, in
minutes,for operations executed against distributed
process groups. See PyTorch documentation at
https://pytorch.org/docs/stable/distributed.html for
caveats.
order (str, default=tp-dp-pp):
The rank initialization order of parallelism. Now we support
tp-dp-pp and tp-pp-dp orders.
Let's say we have a total of 16 GPUs denoted by g0 ... g15 and we
use 2 GPUs to parallelize the model tensor, and 4 GPUs to parallelize
the model pipeline. The present function will
create 8 tensor model-parallel groups, 4 pipeline model-parallel groups
and 8 data-parallel groups as:
8 data_parallel groups:
[g0, g2], [g1, g3], [g4, g6], [g5, g7], [g8, g10], [g9, g11], [g12, g14], [g13, g15]
8 tensor model-parallel groups:
[g0, g1], [g2, g3], [g4, g5], [g6, g7], [g8, g9], [g10, g11], [g12, g13], [g14, g15]
4 pipeline model-parallel groups:
[g0, g4, g8, g12], [g1, g5, g9, g13], [g2, g6, g10, g14], [g3, g7, g11, g15]
Note that for efficiency, the caller should make sure adjacent ranks
are on the same DGX box. For example if we are using 2 DGX-1 boxes
with a total of 16 GPUs, rank 0 to 7 belong to the first box and
ranks 8 to 15 belong to the second box.
"""
# Get world size and rank. Ensure some consistencies.
assert torch.distributed.is_initialized()
world_size: int = torch.distributed.get_world_size()
if (
world_size
% (tensor_model_parallel_size * pipeline_model_parallel_size * context_parallel_size)
!= 0
):
raise RuntimeError(
f"world_size ({world_size}) is not divisible by tensor_model_parallel_size "
f"({tensor_model_parallel_size}) x pipeline_model_parallel_size ({pipeline_model_parallel_size}) "
f"x context_parallel_size ({context_parallel_size})"
)
data_parallel_size: int = world_size // (
tensor_model_parallel_size * pipeline_model_parallel_size * context_parallel_size
)
if data_parallel_size % expert_model_parallel_size != 0:
raise RuntimeError(
f"data_parallel_size ({data_parallel_size}) is not divisible by expert_model_parallel_size "
)
if expert_model_parallel_size > 1 and context_parallel_size > 1:
raise RuntimeError(
f"combination of expert model prallellism and context parallelism is not supported"
)
num_tensor_model_parallel_groups: int = world_size // tensor_model_parallel_size
num_pipeline_model_parallel_groups: int = world_size // pipeline_model_parallel_size
if virtual_pipeline_model_parallel_size is not None:
if not pipeline_model_parallel_size > 1:
raise RuntimeError(
"pipeline-model-parallel size should be greater than 1 with interleaved schedule"
)
global _VIRTUAL_PIPELINE_MODEL_PARALLEL_RANK
global _VIRTUAL_PIPELINE_MODEL_PARALLEL_WORLD_SIZE
_VIRTUAL_PIPELINE_MODEL_PARALLEL_RANK = 0
_VIRTUAL_PIPELINE_MODEL_PARALLEL_WORLD_SIZE = virtual_pipeline_model_parallel_size
if pipeline_model_parallel_split_rank is not None:
global _PIPELINE_MODEL_PARALLEL_SPLIT_RANK
_PIPELINE_MODEL_PARALLEL_SPLIT_RANK = pipeline_model_parallel_split_rank
rank = torch.distributed.get_rank()
nccl_comm_cfgs = {}
if nccl_communicator_config_path is not None:
try:
import yaml
except ImportError:
raise RuntimeError(
"Cannot import `yaml`. Setting custom nccl communicator configs "
"requires the yaml package."
)
with open(nccl_communicator_config_path, "r") as stream:
nccl_comm_cfgs = yaml.safe_load(stream)
rank_generator = RankGenerator(
tp=tensor_model_parallel_size,
ep=expert_model_parallel_size,
dp=data_parallel_size,
pp=pipeline_model_parallel_size,
cp=context_parallel_size,
order=order,
)
timeout = timedelta(minutes=distributed_timeout_minutes)
# Build the data-parallel groups.
global _DATA_PARALLEL_GROUP
global _DATA_PARALLEL_GROUP_GLOO
global _DATA_PARALLEL_GLOBAL_RANKS
global _DATA_PARALLEL_GROUP_WITH_CP
global _DATA_PARALLEL_GROUP_WITH_CP_GLOO
global _DATA_PARALLEL_GLOBAL_RANKS_WITH_CP
assert _DATA_PARALLEL_GROUP is None, 'data parallel group is already initialized'
for ranks in rank_generator.get_ranks('dp'):
group = torch.distributed.new_group(
ranks, timeout=timeout, pg_options=get_nccl_options('dp', nccl_comm_cfgs)
)
group_gloo = torch.distributed.new_group(ranks, timeout=timeout, backend="gloo")
if rank in ranks:
_DATA_PARALLEL_GROUP = group
_DATA_PARALLEL_GROUP_GLOO = group_gloo
_DATA_PARALLEL_GLOBAL_RANKS = ranks
for ranks_with_cp in rank_generator.get_ranks('dp-cp'):
group_with_cp = torch.distributed.new_group(
ranks_with_cp, timeout=timeout, pg_options=get_nccl_options('dp_cp', nccl_comm_cfgs)
)
group_with_cp_gloo = torch.distributed.new_group(
ranks_with_cp, timeout=timeout, backend="gloo"
)
if rank in ranks_with_cp:
_DATA_PARALLEL_GROUP_WITH_CP = group_with_cp
_DATA_PARALLEL_GROUP_WITH_CP_GLOO = group_with_cp_gloo
_DATA_PARALLEL_GLOBAL_RANKS_WITH_CP = ranks_with_cp
# Apply SHARP to DP process groups
if use_sharp:
if rank == 0:
print(
"The number of process groups to use SHARP with depends on the type "
"of the network switch. Nvidia QM1 switch supports SAHRP up to 8 "
"process groups and QM2 supports up to 256 process groups. We apply "
"SHARP to the communications of the data-parallel domain. If the "
"number of data-parallel process groups is larger than the max "
"process groups that the network switch supports, the communication "
"will fall back to non-SHARP operators. To enable SHARP, "
"`#SBATCH_NETWORK=sharp` should be set in the sbatch script."
)
torch.distributed.barrier(
group=get_data_parallel_group(with_context_parallel=True),
device_ids=[torch.cuda.current_device()],
)
# Set `NCCL_COLLNET_ENABLE=0` to restrict SHARP application to DP process groups
os.environ["NCCL_COLLNET_ENABLE"] = "0"
# Build the context-parallel groups.
global _CONTEXT_PARALLEL_GROUP
global _CONTEXT_PARALLEL_GLOBAL_RANKS
assert _CONTEXT_PARALLEL_GROUP is None, 'context parallel group is already initialized'
for ranks in rank_generator.get_ranks('cp'):
group = torch.distributed.new_group(
ranks, timeout=timeout, pg_options=get_nccl_options('cp', nccl_comm_cfgs)
)
if rank in ranks:
_CONTEXT_PARALLEL_GROUP = group
_CONTEXT_PARALLEL_GLOBAL_RANKS = ranks
# Build the model-parallel groups.
global _MODEL_PARALLEL_GROUP
assert _MODEL_PARALLEL_GROUP is None, 'model parallel group is already initialized'
for ranks in rank_generator.get_ranks('tp-pp'):
group = torch.distributed.new_group(
ranks, timeout=timeout, pg_options=get_nccl_options('mp', nccl_comm_cfgs)
)
if rank in ranks:
_MODEL_PARALLEL_GROUP = group
# Build the model-parallel groups with expert parallel
global _MODEL_AND_EXPERT_PARALLEL_GROUP
assert (
_MODEL_AND_EXPERT_PARALLEL_GROUP is None
), 'model and expert parallel group is already initialized'
for ranks in rank_generator.get_ranks('tp-ep-pp', independent_ep=True):
group = torch.distributed.new_group(
ranks, timeout=timeout, pg_options=get_nccl_options('mp_exp', nccl_comm_cfgs)
)
if rank in ranks:
_MODEL_AND_EXPERT_PARALLEL_GROUP = group
# Build the tensor model-parallel groups.
global _TENSOR_MODEL_PARALLEL_GROUP
global _TENSOR_MODEL_PARALLEL_GLOBAL_RANKS
assert (
_TENSOR_MODEL_PARALLEL_GROUP is None
), 'tensor model parallel group is already initialized'
for ranks in rank_generator.get_ranks('tp'):
group = torch.distributed.new_group(
ranks, timeout=timeout, pg_options=get_nccl_options('tp', nccl_comm_cfgs)
)
if rank in ranks:
_TENSOR_MODEL_PARALLEL_GROUP = group
_TENSOR_MODEL_PARALLEL_GLOBAL_RANKS = ranks
# Build the pipeline model-parallel groups and embedding groups
# (first and last rank in each pipeline model-parallel group).
global _PIPELINE_MODEL_PARALLEL_GROUP
global _PIPELINE_GLOBAL_RANKS
assert (
_PIPELINE_MODEL_PARALLEL_GROUP is None
), 'pipeline model parallel group is already initialized'
global _EMBEDDING_GROUP
global _EMBEDDING_GLOBAL_RANKS
assert _EMBEDDING_GROUP is None, 'embedding group is already initialized'
global _POSITION_EMBEDDING_GROUP
global _POSITION_EMBEDDING_GLOBAL_RANKS
assert _POSITION_EMBEDDING_GROUP is None, 'position embedding group is already initialized'
for ranks in rank_generator.get_ranks('pp'):
group = torch.distributed.new_group(
ranks, timeout=timeout, pg_options=get_nccl_options('pp', nccl_comm_cfgs)
)
if rank in ranks:
_PIPELINE_MODEL_PARALLEL_GROUP = group
_PIPELINE_GLOBAL_RANKS = ranks
# Setup embedding group (to exchange gradients between
# first and last stages).
if len(ranks) > 1:
embedding_ranks = [ranks[0], ranks[-1]]
position_embedding_ranks = [ranks[0]]
if pipeline_model_parallel_split_rank is not None:
if ranks[pipeline_model_parallel_split_rank] not in embedding_ranks:
embedding_ranks = [
ranks[0],
ranks[pipeline_model_parallel_split_rank],
ranks[-1],
]
if ranks[pipeline_model_parallel_split_rank] not in position_embedding_ranks:
position_embedding_ranks = [ranks[0], ranks[pipeline_model_parallel_split_rank]]
else:
embedding_ranks = ranks
position_embedding_ranks = ranks
group = torch.distributed.new_group(
embedding_ranks, timeout=timeout, pg_options=get_nccl_options('embd', nccl_comm_cfgs)
)
if rank in embedding_ranks:
_EMBEDDING_GROUP = group
if rank in ranks:
_EMBEDDING_GLOBAL_RANKS = embedding_ranks
group = torch.distributed.new_group(
position_embedding_ranks,
timeout=timeout,
pg_options=get_nccl_options('embd', nccl_comm_cfgs),
)
if rank in position_embedding_ranks:
_POSITION_EMBEDDING_GROUP = group
if rank in ranks:
_POSITION_EMBEDDING_GLOBAL_RANKS = position_embedding_ranks
# Build the tensor + data parallel groups.
global _TENSOR_AND_DATA_PARALLEL_GROUP
global _TENSOR_AND_DATA_PARALLEL_GROUP_WITH_CP
assert (
_TENSOR_AND_DATA_PARALLEL_GROUP is None
), 'Tensor + data parallel group is already initialized'
for ranks in rank_generator.get_ranks('tp-dp-cp'):
group = torch.distributed.new_group(
ranks, timeout=timeout, pg_options=get_nccl_options('tp_dp_cp', nccl_comm_cfgs)
)
if rank in ranks:
_TENSOR_AND_DATA_PARALLEL_GROUP_WITH_CP = group
for ranks in rank_generator.get_ranks('tp-dp'):
group = torch.distributed.new_group(
ranks, timeout=timeout, pg_options=get_nccl_options('tp_dp', nccl_comm_cfgs)
)
if rank in ranks:
_TENSOR_AND_DATA_PARALLEL_GROUP = group
# Build the tensor + expert parallel groups
global _EXPERT_MODEL_PARALLEL_GROUP
assert _EXPERT_MODEL_PARALLEL_GROUP is None, 'Expert parallel group is already initialized'
global _TENSOR_AND_EXPERT_PARALLEL_GROUP
assert (
_TENSOR_AND_EXPERT_PARALLEL_GROUP is None
), 'Tensor + expert parallel group is already initialized'
global _DATA_MODULO_EXPERT_PARALLEL_GROUP
assert (
_DATA_MODULO_EXPERT_PARALLEL_GROUP is None
), 'Data modulo expert group is already initialized'
global _DATA_MODULO_EXPERT_PARALLEL_GROUP_GLOO
for ranks in rank_generator.get_ranks('tp-ep', independent_ep=True):
group = torch.distributed.new_group(
ranks, timeout=timeout, pg_options=get_nccl_options('tp_exp', nccl_comm_cfgs)
)
if rank in ranks:
_TENSOR_AND_EXPERT_PARALLEL_GROUP = group
for ranks in rank_generator.get_ranks('ep', independent_ep=True):
group = torch.distributed.new_group(
ranks, pg_options=get_nccl_options('exp', nccl_comm_cfgs)
)
if rank in ranks:
_EXPERT_MODEL_PARALLEL_GROUP = group
for ranks in rank_generator.get_ranks('dp', independent_ep=True):
group = torch.distributed.new_group(
ranks, timeout=timeout, pg_options=get_nccl_options('dp_modulo_exp', nccl_comm_cfgs)
)
group_gloo = torch.distributed.new_group(ranks, backend="gloo")
if rank in ranks:
_DATA_MODULO_EXPERT_PARALLEL_GROUP = group
_DATA_MODULO_EXPERT_PARALLEL_GROUP_GLOO = group_gloo
# Initialize global memory buffer
# This isn't really "parallel state" but there isn't another good place to
# put this. If we end up with a more generic initialization of megatron-core
# we could stick it there
_set_global_memory_buffer()
def is_initialized():
"""Useful for code segments that may be accessed with or without mpu initialization"""
return _DATA_PARALLEL_GROUP is not None
def is_unitialized() -> bool:
"""Check if parallel state has been initialized
Deprecated. Use is_initialized instead.
"""
warnings.warn(
"is_unitialized is deprecated, use is_initialized instead", DeprecationWarning,
)
return not is_initialized()
def model_parallel_is_initialized():
"""Check if model and data parallel groups are initialized."""
if (
_TENSOR_MODEL_PARALLEL_GROUP is None
or _PIPELINE_MODEL_PARALLEL_GROUP is None
or _DATA_PARALLEL_GROUP is None
):
return False
return True
def get_model_parallel_group(with_expert_parallel=False):
"""Get the model parallel group the caller rank belongs to."""
if with_expert_parallel:
assert (
_MODEL_AND_EXPERT_PARALLEL_GROUP is not None
), 'model parallel group is not initialized'
return _MODEL_AND_EXPERT_PARALLEL_GROUP
assert _MODEL_PARALLEL_GROUP is not None, 'model parallel group is not initialized'
return _MODEL_PARALLEL_GROUP
def get_tensor_model_parallel_group(check_initialized=True):
"""Get the tensor model parallel group the caller rank belongs to."""
if check_initialized:
assert (
_TENSOR_MODEL_PARALLEL_GROUP is not None
), 'tensor model parallel group is not initialized'
return _TENSOR_MODEL_PARALLEL_GROUP
def get_pipeline_model_parallel_group():
"""Get the pipeline model parallel group the caller rank belongs to."""
assert (
_PIPELINE_MODEL_PARALLEL_GROUP is not None
), 'pipeline_model parallel group is not initialized'
return _PIPELINE_MODEL_PARALLEL_GROUP
def get_data_parallel_group(with_context_parallel=False):
"""Get the data parallel group the caller rank belongs to."""
if with_context_parallel:
assert (
_DATA_PARALLEL_GROUP_WITH_CP is not None
), 'data parallel group with context parallel combined is not initialized'
return _DATA_PARALLEL_GROUP_WITH_CP
else:
assert _DATA_PARALLEL_GROUP is not None, 'data parallel group is not initialized'
return _DATA_PARALLEL_GROUP
def get_data_parallel_group_gloo(with_context_parallel=False):
"""Get the data parallel group-gloo the caller rank belongs to."""
if with_context_parallel:
assert (
_DATA_PARALLEL_GROUP_WITH_CP_GLOO is not None
), 'data parallel group-gloo with context parallel combined is not initialized'
return _DATA_PARALLEL_GROUP_WITH_CP_GLOO
else:
assert _DATA_PARALLEL_GROUP_GLOO is not None, 'data parallel group-gloo is not initialized'
return _DATA_PARALLEL_GROUP_GLOO
def get_context_parallel_group(check_initialized=True):
"""Get the context parallel group the caller rank belongs to."""
if check_initialized:
assert _CONTEXT_PARALLEL_GROUP is not None, 'context parallel group is not initialized'
return _CONTEXT_PARALLEL_GROUP
def get_context_parallel_global_ranks(check_initialized=True):
"""Get all global ranks of the context parallel group that the caller rank belongs to."""
if check_initialized:
assert (
_CONTEXT_PARALLEL_GLOBAL_RANKS is not None
), 'context parallel group is not initialized'
return _CONTEXT_PARALLEL_GLOBAL_RANKS
def get_embedding_group():
"""Get the embedding group the caller rank belongs to."""
assert _EMBEDDING_GROUP is not None, 'embedding group is not initialized'
return _EMBEDDING_GROUP
def get_position_embedding_group():
"""Get the position embedding group the caller rank belongs to."""
assert _POSITION_EMBEDDING_GROUP is not None, 'position embedding group is not initialized'
return _POSITION_EMBEDDING_GROUP
def get_amax_reduction_group(with_context_parallel=False):
"""Get the FP8 amax reduction group the caller rank belongs to."""
if with_context_parallel:
assert (
_TENSOR_AND_DATA_PARALLEL_GROUP_WITH_CP is not None
), 'FP8 amax reduction group is not initialized'
return _TENSOR_AND_DATA_PARALLEL_GROUP_WITH_CP
else:
assert (
_TENSOR_AND_DATA_PARALLEL_GROUP is not None
), 'FP8 amax reduction group is not initialized'
return _TENSOR_AND_DATA_PARALLEL_GROUP
def get_tensor_and_data_parallel_group(with_context_parallel=False):
"""Get the tensor and data parallel group the caller rank belongs to."""
if with_context_parallel:
assert (
_TENSOR_AND_DATA_PARALLEL_GROUP_WITH_CP is not None
), 'tensor and data parallel group is not initialized'
return _TENSOR_AND_DATA_PARALLEL_GROUP_WITH_CP
else:
assert (
_TENSOR_AND_DATA_PARALLEL_GROUP is not None
), 'tensor and data parallel group is not initialized'
return _TENSOR_AND_DATA_PARALLEL_GROUP
def get_expert_model_parallel_group():
assert (
_EXPERT_MODEL_PARALLEL_GROUP is not None
), 'expert model parallel group is not initialized'
return _EXPERT_MODEL_PARALLEL_GROUP
def get_tensor_and_expert_parallel_group():
assert (
_TENSOR_AND_EXPERT_PARALLEL_GROUP is not None
), 'tensor and expert parallel group is not initialized'
return _TENSOR_AND_EXPERT_PARALLEL_GROUP
def get_data_modulo_expert_parallel_group():
assert (
_DATA_MODULO_EXPERT_PARALLEL_GROUP is not None
), 'data modulo expert parallel group is not initialized'
return _DATA_MODULO_EXPERT_PARALLEL_GROUP
def get_data_modulo_expert_parallel_group_gloo():
assert (
_DATA_MODULO_EXPERT_PARALLEL_GROUP_GLOO is not None
), 'data modulo expert parallel group-gloo is not initialized'
return _DATA_MODULO_EXPERT_PARALLEL_GROUP_GLOO
def set_expert_model_parallel_world_size(world_size):
global _MPU_EXPERT_MODEL_PARALLEL_WORLD_SIZE
_MPU_EXPERT_MODEL_PARALLEL_WORLD_SIZE = world_size
def set_tensor_model_parallel_world_size(world_size):
"""Set the tensor model parallel size"""
global _MPU_TENSOR_MODEL_PARALLEL_WORLD_SIZE
_MPU_TENSOR_MODEL_PARALLEL_WORLD_SIZE = world_size
def set_pipeline_model_parallel_world_size(world_size):
"""Set the pipeline model parallel size"""
global _MPU_PIPELINE_MODEL_PARALLEL_WORLD_SIZE
_MPU_PIPELINE_MODEL_PARALLEL_WORLD_SIZE = world_size
def set_virtual_pipeline_model_parallel_world_size(world_size):
"""Set the pipeline model parallel size"""
global _VIRTUAL_PIPELINE_MODEL_PARALLEL_WORLD_SIZE
_VIRTUAL_PIPELINE_MODEL_PARALLEL_WORLD_SIZE = world_size
def get_tensor_model_parallel_world_size():
"""Return world size for the tensor model parallel group."""
global _MPU_TENSOR_MODEL_PARALLEL_WORLD_SIZE
if _MPU_TENSOR_MODEL_PARALLEL_WORLD_SIZE is not None:
return _MPU_TENSOR_MODEL_PARALLEL_WORLD_SIZE
return torch.distributed.get_world_size(group=get_tensor_model_parallel_group())
def get_pipeline_model_parallel_world_size():
"""Return world size for the pipeline model parallel group."""
global _MPU_PIPELINE_MODEL_PARALLEL_WORLD_SIZE
if _MPU_PIPELINE_MODEL_PARALLEL_WORLD_SIZE is not None:
return _MPU_PIPELINE_MODEL_PARALLEL_WORLD_SIZE
return torch.distributed.get_world_size(group=get_pipeline_model_parallel_group())
def set_expert_model_parallel_rank(rank):
"""Set expert model parallel rank."""
global _MPU_EXPERT_MODEL_PARALLEL_RANK
_MPU_EXPERT_MODEL_PARALLEL_RANK = rank
def set_tensor_model_parallel_rank(rank):
"""Set tensor model parallel rank."""
global _MPU_TENSOR_MODEL_PARALLEL_RANK
_MPU_TENSOR_MODEL_PARALLEL_RANK = rank
def set_pipeline_model_parallel_rank(rank):
"""Set pipeline model parallel rank."""
global _MPU_PIPELINE_MODEL_PARALLEL_RANK
_MPU_PIPELINE_MODEL_PARALLEL_RANK = rank
def set_pipeline_model_parallel_split_rank(rank):
"""Set pipeline model parallel split rank."""
global _PIPELINE_MODEL_PARALLEL_SPLIT_RANK
_PIPELINE_MODEL_PARALLEL_SPLIT_RANK = rank
def get_tensor_model_parallel_rank():
"""Return my rank for the tensor model parallel group."""
global _MPU_TENSOR_MODEL_PARALLEL_RANK
if _MPU_TENSOR_MODEL_PARALLEL_RANK is not None:
return _MPU_TENSOR_MODEL_PARALLEL_RANK
return torch.distributed.get_rank(group=get_tensor_model_parallel_group())
def get_pipeline_model_parallel_rank():
"""Return my rank for the pipeline model parallel group."""
global _MPU_PIPELINE_MODEL_PARALLEL_RANK
if _MPU_PIPELINE_MODEL_PARALLEL_RANK is not None:
return _MPU_PIPELINE_MODEL_PARALLEL_RANK
return torch.distributed.get_rank(group=get_pipeline_model_parallel_group())
def get_pipeline_model_parallel_split_rank():
"""Return pipeline model parallel split rank."""
global _PIPELINE_MODEL_PARALLEL_SPLIT_RANK
return _PIPELINE_MODEL_PARALLEL_SPLIT_RANK
def is_pipeline_first_stage(ignore_virtual=False):
"""Return True if in the first pipeline model-parallel stage, False otherwise."""
if not ignore_virtual:
if (
get_virtual_pipeline_model_parallel_world_size() is not None
and get_virtual_pipeline_model_parallel_rank() != 0
):
return False
return get_pipeline_model_parallel_rank() == 0
def is_pipeline_last_stage(ignore_virtual=False):
"""Return True if in the last pipeline model-parallel stage, False otherwise."""
if not ignore_virtual:
virtual_pipeline_model_parallel_world_size = (
get_virtual_pipeline_model_parallel_world_size()
)
if virtual_pipeline_model_parallel_world_size is not None and get_virtual_pipeline_model_parallel_rank() != (
virtual_pipeline_model_parallel_world_size - 1
):
return False
return get_pipeline_model_parallel_rank() == (get_pipeline_model_parallel_world_size() - 1)
def is_rank_in_embedding_group(ignore_virtual=False):
"""Return true if current rank is in embedding group, False otherwise."""
rank = torch.distributed.get_rank()
global _EMBEDDING_GLOBAL_RANKS
if ignore_virtual:
return rank in _EMBEDDING_GLOBAL_RANKS
if rank in _EMBEDDING_GLOBAL_RANKS:
if rank == _EMBEDDING_GLOBAL_RANKS[0]:
return is_pipeline_first_stage(ignore_virtual=False)
elif rank == _EMBEDDING_GLOBAL_RANKS[-1]:
return is_pipeline_last_stage(ignore_virtual=False)
else:
return True
return False
def is_rank_in_position_embedding_group():
"""Return true if current rank is in position embedding group, False otherwise."""
rank = torch.distributed.get_rank()
global _POSITION_EMBEDDING_GLOBAL_RANKS
return rank in _POSITION_EMBEDDING_GLOBAL_RANKS
def is_pipeline_stage_before_split(rank=None):
"""Return True if pipeline stage executes encoder block for a model