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Commit b690f245 authored by Rick Ho's avatar Rick Ho
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initial split

parent 66fc166e
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.pylintrc
View file @ b690f245
...@@ -179,7 +179,7 @@ score=yes ...@@ -179,7 +179,7 @@ score=yes
[REFACTORING] [REFACTORING]
# Maximum number of nested blocks for function / method body # Maximum number of nested blocks for function / method body
max-nested-blocks=5 max-nested-blocks=32
# Complete name of functions that never returns. When checking for # Complete name of functions that never returns. When checking for
# inconsistent-return-statements if a never returning function is called then # inconsistent-return-statements if a never returning function is called then
...@@ -563,10 +563,10 @@ max-attributes=32 ...@@ -563,10 +563,10 @@ max-attributes=32
max-bool-expr=5 max-bool-expr=5
# Maximum number of branch for function / method body. # Maximum number of branch for function / method body.
max-branches=12 max-branches=32
# Maximum number of locals for function / method body. # Maximum number of locals for function / method body.
max-locals=15 max-locals=32
# Maximum number of parents for a class (see R0901). # Maximum number of parents for a class (see R0901).
max-parents=7 max-parents=7
...@@ -578,7 +578,7 @@ max-public-methods=20 ...@@ -578,7 +578,7 @@ max-public-methods=20
max-returns=6 max-returns=6
# Maximum number of statements in function / method body. # Maximum number of statements in function / method body.
max-statements=50 max-statements=128
# Minimum number of public methods for a class (see R0903). # Minimum number of public methods for a class (see R0903).
min-public-methods=2 min-public-methods=2
......
fmoe/megatron/__init__.py 0 → 100644
View file @ b690f245
r"""
A set of modules to plugin into Megatron-LM with FastMoE
"""
from .utils import add_fmoe_args
from .layers import MegatronMLP
from .layers import fmoefy
from .checkpoint import save_checkpoint
from .checkpoint import load_checkpoint
from .distributed import DistributedDataParallel
fmoe/megatron/balance.py 0 → 100644
View file @ b690f245
r"""
Support for monitoring loss in Megatron
"""
import torch
from fmoe.balance import reset_balance_profile
from fmoe.balance import update_balance_profile
from fmoe.utils import get_torch_default_comm
balance_dict = {}
num_layers = 0
def reset_gate_hook(_num_layers=None):
from megatron import get_args
global balance_dict, num_layers
if _num_layers is not None:
num_layers = _num_layers
reset_balance_profile(balance_dict, num_layers, get_args().balance_strategy)
def get_balance_profile():
global balance_dict
return balance_dict
def generate_megatron_gate_hook(layer_idx, num_expert_global):
from megatron import get_args
balance_strategy = get_args().balance_strategy
def megatron_gate_hook(gate_top_k_idx, gate_score_top_k, gate_context):
global balance_dict
update_balance_profile(
balance_dict,
gate_top_k_idx,
gate_score_top_k,
gate_context,
layer_idx,
num_expert_global,
balance_strategy,
)
return megatron_gate_hook
def add_balance_log(writer, iteration):
from megatron import is_last_rank
balance_dict_tensor = torch.vstack(
[torch.tensor(item, device=item[0].device) for item in balance_dict.values()]
).detach()
world_group = get_torch_default_comm()
world_size = torch.distributed.get_world_size(group=world_group)
torch.distributed.all_reduce(balance_dict_tensor, group=world_group)
balance_dict_tensor /= world_size
if writer and is_last_rank():
for idx, metric_name in enumerate(balance_dict):
for layer_id, val in enumerate(balance_dict_tensor[idx]):
writer.add_scalar(
f"balance-{metric_name}/layer-{layer_id}", val.item(), iteration
)
writer.add_scalar(
f"balance-{metric_name}/all",
balance_dict_tensor[idx].mean().item(),
iteration,
)
reset_gate_hook()
def patch_forward_step(forward_step_func):
r"""
Patch model's forward_step_func to support balance loss
"""
from megatron.mpu import is_pipeline_last_stage
from megatron import get_args
if not get_args().balance_strategy:
return forward_step_func
def forward_step_with_balance_loss(data_iterator, model, input_tensor):
args = get_args()
output = forward_step_func(data_iterator, model, input_tensor)
if not is_pipeline_last_stage():
return output
loss_name = args.balance_strategy + "_loss"
(loss, state_dict), bal_loss = (
output,
(
torch.tensor(
balance_dict[loss_name],
device=balance_dict[loss_name][0].device,
).mean()
* args.balance_loss_weight
).float(),
)
# avarage across world group
world_group = get_torch_default_comm()
world_size = torch.distributed.get_world_size(group=world_group)
averaged_bal_loss = bal_loss.clone().detach()
torch.distributed.all_reduce(averaged_bal_loss, group=world_group)
averaged_bal_loss /= world_size
loss += bal_loss
state_dict[loss_name] = averaged_bal_loss
return loss, state_dict
return forward_step_with_balance_loss
def patch_model_provider(model_provider):
from megatron import get_args
def fmoefied_model_provider():
from .layers import fmoefy
args = get_args()
return fmoefy(
model_provider(),
num_experts=args.num_experts,
hidden_hidden_size=4 * args.hidden_size // args.top_k,
top_k=args.top_k,
)
return fmoefied_model_provider
fmoe/megatron.py → fmoe/megatron/checkpoint.py
View file @ b690f245
r""" r"""
The adaptor to seamlessly enable FastMoE in Megatron-LM v2.0 with at most two Support for Megatron to enable saving parameters of different experts on
lines of modification. different ranks.
See `examples/megatron` for usage instructions.
""" """
import os import os
import sys import sys
import math
import random import random
from collections import OrderedDict from collections import OrderedDict
import numpy as np import numpy as np
import torch import torch
import torch.nn as nn
import torch.nn.functional as F
from .transformer import FMoETransformerMLP
from .distributed import DistributedGroupedDataParallel
from .balance import update_balance_profile, reset_balance_profile
from .utils import get_torch_default_comm
class _FakeMegatronMLP(nn.Module):
r"""
A fake mlp without model parallelism for correctness testing
"""
def __init__(self, args, _):
super().__init__()
self.fc1 = nn.Linear(args.hidden_size, args.hidden_hidden_size)
self.fc2 = nn.Linear(args.hidden_hidden_size, args.hidden_size)
def forward(self, x):
r"""
Directly use GeLU
"""
x = self.fc1(x)
x = F.gelu(x)
x = self.fc2(x)
return x, torch.zeros_like(x)
def _megatron_init_method(self, rng, sigma):
r"""
Init method based on N(0, sigma).
Copied from Megatron-LM
"""
device = self.weight.device
dtype = self.weight.dtype
weight = rng.normal(loc=0.0, scale=sigma, size=tuple(self.weight.size()))
self.weight.data = torch.from_numpy(weight).to(dtype=dtype, device=device)
if self.bias is not None:
# Always initialize bias to zero.
with torch.no_grad():
self.bias.zero_()
def _random_init_weight(self, rng):
r"""
Copied from torch.nn.init.kaiming_uniform_
"""
fan = nn.init._calculate_correct_fan(self.weight[0], "fan_in")
gain = nn.init.calculate_gain("leaky_relu", math.sqrt(5))
std = gain / math.sqrt(fan)
bound = math.sqrt(3.0) * std
device = self.weight.device
dtype = self.weight.dtype
weight = rng.uniform(-bound, bound, size=tuple(self.weight.size()))
self.weight.data = torch.from_numpy(weight).to(dtype=dtype, device=device)
if self.bias is not None:
fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.weight[0])
bound = 1 / math.sqrt(fan_in)
bias = rng.uniform(-bound, bound, size=tuple(self.bias.size()))
self.bias.data = torch.from_numpy(bias).to(dtype=dtype, device=device)
balance_dict = {}
num_layers = 0
def reset_gate_hook():
from megatron import get_args
global balance_dict, num_layers
reset_balance_profile(balance_dict, num_layers, get_args().balance_strategy)
def get_balance_profile():
global balance_dict
return balance_dict
def generate_megatron_gate_hook(layer_idx, num_expert_global):
from megatron import get_args
balance_strategy = get_args().balance_strategy
def megatron_gate_hook(gate_top_k_idx, gate_score_top_k, gate_context):
global balance_dict
update_balance_profile(
balance_dict,
gate_top_k_idx,
gate_score_top_k,
gate_context,
layer_idx,
num_expert_global,
balance_strategy,
)
return megatron_gate_hook
def add_fmoe_args(parser):
group = parser.add_argument_group(title="fastmoe")
group.add_argument("--fmoefy", action="store_true")
group.add_argument("--num-experts", type=int, default=None)
group.add_argument("--top-k", type=int, default=2)
group.add_argument("--balance-loss-weight", type=float, default=1)
group.add_argument("--balance-strategy", type=str, default=None)
return parser
def add_balance_log(writer, iteration):
from megatron import is_last_rank
balance_dict_tensor = torch.vstack(
[torch.tensor(item, device=item[0].device) for item in balance_dict.values()]
).detach()
world_group = get_torch_default_comm()
world_size = torch.distributed.get_world_size(group=world_group)
torch.distributed.all_reduce(balance_dict_tensor, group=world_group)
balance_dict_tensor /= world_size
if writer and is_last_rank():
for idx, metric_name in enumerate(balance_dict):
for layer_id, val in enumerate(balance_dict_tensor[idx]):
writer.add_scalar(
f"balance-{metric_name}/layer-{layer_id}", val.item(), iteration
)
writer.add_scalar(
f"balance-{metric_name}/all",
balance_dict_tensor[idx].mean().item(),
iteration,
)
reset_gate_hook()
def patch_forward_step(forward_step_func):
r"""
Patch model's forward_step_func to support balance loss
"""
from megatron.mpu import is_pipeline_last_stage
from megatron import get_args
if not get_args().balance_strategy:
return forward_step_func
def forward_step_with_balance_loss(data_iterator, model, input_tensor):
args = get_args()
output = forward_step_func(data_iterator, model, input_tensor)
if is_pipeline_last_stage():
loss_name = args.balance_strategy + "_loss"
(loss, state_dict), bal_loss = (
output,
(
torch.tensor(
balance_dict[loss_name],
device=balance_dict[loss_name][0].device,
).mean()
* args.balance_loss_weight
).float(),
)
# avarage across world group
world_group = get_torch_default_comm()
world_size = torch.distributed.get_world_size(group=world_group)
averaged_bal_loss = bal_loss.clone().detach()
torch.distributed.all_reduce(averaged_bal_loss, group=world_group)
averaged_bal_loss /= world_size
loss += bal_loss
state_dict[loss_name] = averaged_bal_loss
return loss, state_dict
else:
return output
return forward_step_with_balance_loss
def patch_model_provider(model_provider):
from megatron import get_args
def fmoefied_model_provider():
args = get_args()
return fmoefy(
model_provider(),
num_experts=args.num_experts,
hidden_hidden_size=4 * args.hidden_size // args.top_k,
top_k=args.top_k,
)
return fmoefied_model_provider
class MegatronMLP(FMoETransformerMLP):
r"""
Make the FMoETransformerMLP layer that distributes experts across
communication group `group` to replace the original MLP layer in Megatron.
"""
def __init__(self, args, group, layer_idx):
assert (
args.seq_length * args.micro_batch_size % args.tensor_model_parallel_size
== 0
), "Batch size x sequence length should be multiple of mp size"
if not args.distributed_experts:
world_size = 1
else:
world_size = args.world_size
gate = None
if not args.balance_strategy or args.balance_strategy == "gshard":
from .gates import NaiveGate
gate = NaiveGate
elif args.balance_strategy == "noisy":
from .gates import NoisyGate
gate = NoisyGate
else:
assert False, "Undefined balance strategy {}" % (args.balance_strategy)
super().__init__(
args.num_experts,
top_k=args.top_k,
d_model=args.hidden_size,
d_hidden=args.hidden_hidden_size,
world_size=world_size,
mp_group=group,
expert_dp_comm="none" if args.distributed_experts else "dp",
gate_hook=generate_megatron_gate_hook(
layer_idx, args.num_experts * world_size
),
gate=gate,
)
self.hidden_size = args.hidden_size
if args.distributed_experts:
self.rank = args.rank
else:
self.rank = 0
self.sigma = args.init_method_std
self.num_layers = args.num_layers
self.reset_parameters()
def reset_parameters(self):
r"""
Initialize the weight as linear layers.
As megatron is using fixed random seed for some nasty stuff, an
additional numpy rng is used.
"""
rng = np.random.default_rng(np.random.randint(2048) + self.rank)
_megatron_init_method(self.experts.htoh4, rng, self.sigma)
std = self.sigma / math.sqrt(2.0 * self.num_layers)
_megatron_init_method(self.experts.h4toh, rng, std)
def forward(self, inp):
return (
super().forward(inp),
torch.zeros(self.hidden_size, dtype=inp.dtype, device=inp.device),
)
def fmoefy(
model,
num_experts=None,
distributed_experts=True,
hidden_hidden_size=None,
top_k=None,
):
r"""
Replace MLP layers in a transformer-based model in Megatron by MoE.
* `model` should be a standard Megatron model that has
`model.language_model.transformer.layers` as transformer layers, which is an
array of transformer blocks that contain an `mlp` member.
* `distributed_expert` is set to True if different experts are located in
different workers. Otherwise, the experts on the workers are identical, and
they are trained in data-parallel mode. This can be useful when testing on
small models that do not require high training throughput or large parameter
capacity.
Note that pipeline parallel is not supported yet. When distributed experts
are enabled, their communicator should be Megatron's
tensor_model_parall_comm x data_parallel_comm, which is not created.
"""
from megatron import get_args
from megatron import mpu
args = get_args()
if num_experts is not None:
args.num_experts = num_experts
assert (
"num_experts" in args
), "num_experts should be specified in arguments or fmoefy function"
if hidden_hidden_size is not None:
args.hidden_hidden_size = hidden_hidden_size
elif not hasattr(args, "hidden_hidden_size"):
args.hidden_hidden_size = args.hidden_size * 4
if top_k is not None:
args.top_k = top_k
elif not hasattr(args, "top_k"):
args.top_k = 2
# Set distributed_experts to None to use default setting in args
if distributed_experts is not None:
args.distributed_experts = distributed_experts
for idx, l in enumerate(model.language_model.transformer.layers):
l.mlp = MegatronMLP(args, mpu.get_model_parallel_group(), idx)
# initialize gate hook
global num_layers, balance_dict
num_layers = len(model.language_model.transformer.layers)
reset_gate_hook()
return model
class DistributedDataParallel(DistributedGroupedDataParallel):
r"""
A wrapper that is used to replace the DDP module provided by Megatron, which
is adapted to enable the sophiscated parallel and reduction strategies in
Fast MoE.
"""
def __init__(self, module):
from megatron import mpu
super().__init__(
module,
mp_group=mpu.get_model_parallel_group(),
dp_group=mpu.get_data_parallel_group(),
)
def state_dict(self, *args, **kwargs):
r"""
Keep consitency with Megatron
"""
return self.module.state_dict(*args, **kwargs)
def state_dict_for_save_checkpoint(self, *args, **kwargs):
r"""
Keep consitency with Megatron
"""
return self.module.state_dict_for_save_checkpoint(*args, **kwargs)
def load_state_dict(self, *args, **kwargs):
r"""
Keep consitency with Megatron
"""
return self.module.load_state_dict(*args, **kwargs)
def get_fmoe_checkpoint_name( def get_fmoe_checkpoint_name(
...@@ -424,7 +67,7 @@ def save_checkpoint(iteration, model, optimizer, lr_scheduler): ...@@ -424,7 +67,7 @@ def save_checkpoint(iteration, model, optimizer, lr_scheduler):
args = get_args() args = get_args()
# Only rank zero of the data parallel writes to the disk. # Only rank zero of the data parallel writes to the disk.
if isinstance(model, DistributedDataParallel): if hasattr(model, 'module'):
model = model.module model = model.module
print_rank_last( print_rank_last(
...@@ -527,7 +170,7 @@ def save_checkpoint(iteration, model, optimizer, lr_scheduler): ...@@ -527,7 +170,7 @@ def save_checkpoint(iteration, model, optimizer, lr_scheduler):
def merge_state_dict(state_dict_rank0, state_dict_local, fp16): def merge_state_dict(state_dict_rank0, state_dict_local, fp16):
"""merge two state dicts, one from data parallel rank 0, """merge two state dicts, one from data parallel rank 0,
another only contains expert states""" another only contains expert states"""
from megatron import print_rank_last # from megatron import print_rank_last
def merge_model(state_dict_rank0, state_dict_local): def merge_model(state_dict_rank0, state_dict_local):
for k, v in state_dict_local.items(): for k, v in state_dict_local.items():
...@@ -586,7 +229,7 @@ def load_checkpoint(model, optimizer, lr_scheduler, load_arg="load"): ...@@ -586,7 +229,7 @@ def load_checkpoint(model, optimizer, lr_scheduler, load_arg="load"):
args = get_args() args = get_args()
load_dir = getattr(args, load_arg) load_dir = getattr(args, load_arg)
if isinstance(model, DistributedDataParallel): if hasattr(model, 'module'):
model = model.module model = model.module
# Read the tracker file and set the iteration. # Read the tracker file and set the iteration.
tracker_filename = get_checkpoint_tracker_filename(load_dir) tracker_filename = get_checkpoint_tracker_filename(load_dir)
...@@ -653,9 +296,6 @@ def load_checkpoint(model, optimizer, lr_scheduler, load_arg="load"): ...@@ -653,9 +296,6 @@ def load_checkpoint(model, optimizer, lr_scheduler, load_arg="load"):
state_dict = torch.load(checkpoint_name, map_location="cpu") state_dict = torch.load(checkpoint_name, map_location="cpu")
sys.modules.pop("fp16.loss_scaler", None) sys.modules.pop("fp16.loss_scaler", None)
sys.modules.pop("megatron.fp16.loss_scaler", None) sys.modules.pop("megatron.fp16.loss_scaler", None)
except BaseException:
print_rank_last("could not load the checkpoint")
sys.exit()
return state_dict return state_dict
state_dict_rank0 = load_state_dict(checkpoint_name_rank0) state_dict_rank0 = load_state_dict(checkpoint_name_rank0)
......
fmoe/megatron/distributed.py 0 → 100644
View file @ b690f245
r"""
distributed support for Megatron
"""
from fmoe.distributed import DistributedGroupedDataParallel
class DistributedDataParallel(DistributedGroupedDataParallel):
r"""
A wrapper that is used to replace the DDP module provided by Megatron, which
is adapted to enable the sophiscated parallel and reduction strategies in
Fast MoE.
"""
def __init__(self, module):
from megatron import mpu
super().__init__(
module,
mp_group=mpu.get_model_parallel_group(),
dp_group=mpu.get_data_parallel_group(),
)
def state_dict(self, *args, **kwargs):
r"""
Keep consitency with Megatron
"""
return self.module.state_dict(*args, **kwargs)
def state_dict_for_save_checkpoint(self, *args, **kwargs):
r"""
Keep consitency with Megatron
"""
return self.module.state_dict_for_save_checkpoint(*args, **kwargs)
def load_state_dict(self, *args, **kwargs):
r"""
Keep consitency with Megatron
"""
return self.module.load_state_dict(*args, **kwargs)
fmoe/megatron/layers.py 0 → 100644
View file @ b690f245
r"""
nn modules to replace Megatron's native ones
"""
import math
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from fmoe.transformer import FMoETransformerMLP
from .balance import reset_gate_hook
from .balance import generate_megatron_gate_hook
class _FakeMegatronMLP(nn.Module):
r"""
A fake mlp without model parallelism for correctness testing
"""
def __init__(self, args, _):
super().__init__()
self.fc1 = nn.Linear(args.hidden_size, args.hidden_hidden_size)
self.fc2 = nn.Linear(args.hidden_hidden_size, args.hidden_size)
def forward(self, x):
r"""
Directly use GeLU
"""
x = self.fc1(x)
x = F.gelu(x)
x = self.fc2(x)
return x, torch.zeros_like(x)
def _megatron_init_method(self, rng, sigma):
r"""
Init method based on N(0, sigma).
Copied from Megatron-LM
"""
device = self.weight.device
dtype = self.weight.dtype
weight = rng.normal(loc=0.0, scale=sigma, size=tuple(self.weight.size()))
self.weight.data = torch.from_numpy(weight).to(dtype=dtype, device=device)
if self.bias is not None:
# Always initialize bias to zero.
with torch.no_grad():
self.bias.zero_()
def _random_init_weight(self, rng):
r"""
Copied from torch.nn.init.kaiming_uniform_
"""
fan = nn.init._calculate_correct_fan(self.weight[0], "fan_in")
gain = nn.init.calculate_gain("leaky_relu", math.sqrt(5))
std = gain / math.sqrt(fan)
bound = math.sqrt(3.0) * std
device = self.weight.device
dtype = self.weight.dtype
weight = rng.uniform(-bound, bound, size=tuple(self.weight.size()))
self.weight.data = torch.from_numpy(weight).to(dtype=dtype, device=device)
if self.bias is not None:
fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.weight[0])
bound = 1 / math.sqrt(fan_in)
bias = rng.uniform(-bound, bound, size=tuple(self.bias.size()))
self.bias.data = torch.from_numpy(bias).to(dtype=dtype, device=device)
class MegatronMLP(FMoETransformerMLP):
r"""
Make the FMoETransformerMLP layer that distributes experts across
communication group `group` to replace the original MLP layer in Megatron.
"""
def __init__(self, args, group, layer_idx):
assert (
args.seq_length * args.micro_batch_size % args.tensor_model_parallel_size
== 0
), "Batch size x sequence length should be multiple of mp size"
if not args.distributed_experts:
world_size = 1
else:
world_size = args.world_size
gate = None
if not args.balance_strategy or args.balance_strategy == "gshard":
from fmoe.gates import NaiveGate
gate = NaiveGate
elif args.balance_strategy == "noisy":
from fmoe.gates import NoisyGate
gate = NoisyGate
else:
assert False, "Undefined balance strategy {}" % (args.balance_strategy)
super().__init__(
args.num_experts,
top_k=args.top_k,
d_model=args.hidden_size,
d_hidden=args.hidden_hidden_size,
world_size=world_size,
mp_group=group,
expert_dp_comm="none" if args.distributed_experts else "dp",
gate_hook=generate_megatron_gate_hook(
layer_idx, args.num_experts * world_size
),
gate=gate,
)
self.hidden_size = args.hidden_size
if args.distributed_experts:
self.rank = args.rank
else:
self.rank = 0
self.sigma = args.init_method_std
self.num_layers = args.num_layers
self.reset_parameters()
def reset_parameters(self):
r"""
Initialize the weight as linear layers.
As megatron is using fixed random seed for some nasty stuff, an
additional numpy rng is used.
"""
rng = np.random.default_rng(np.random.randint(2048) + self.rank)
_megatron_init_method(self.experts.htoh4, rng, self.sigma)
std = self.sigma / math.sqrt(2.0 * self.num_layers)
_megatron_init_method(self.experts.h4toh, rng, std)
def forward(self, inp):
return (
super().forward(inp),
torch.zeros(self.hidden_size, dtype=inp.dtype, device=inp.device),
)
def fmoefy(
model,
num_experts=None,
distributed_experts=True,
hidden_hidden_size=None,
top_k=None,
):
r"""
Replace MLP layers in a transformer-based model in Megatron by MoE.
* `model` should be a standard Megatron model that has
`model.language_model.transformer.layers` as transformer layers, which is an
array of transformer blocks that contain an `mlp` member.
* `distributed_expert` is set to True if different experts are located in
different workers. Otherwise, the experts on the workers are identical, and
they are trained in data-parallel mode. This can be useful when testing on
small models that do not require high training throughput or large parameter
capacity.
Note that pipeline parallel is not supported yet. When distributed experts
are enabled, their communicator should be Megatron's
tensor_model_parall_comm x data_parallel_comm, which is not created.
"""
from megatron import get_args
from megatron import mpu
args = get_args()
if num_experts is not None:
args.num_experts = num_experts
assert (
"num_experts" in args
), "num_experts should be specified in arguments or fmoefy function"
if hidden_hidden_size is not None:
args.hidden_hidden_size = hidden_hidden_size
elif not hasattr(args, "hidden_hidden_size"):
args.hidden_hidden_size = args.hidden_size * 4
if top_k is not None:
args.top_k = top_k
elif not hasattr(args, "top_k"):
args.top_k = 2
# Set distributed_experts to None to use default setting in args
if distributed_experts is not None:
args.distributed_experts = distributed_experts
for idx, l in enumerate(model.language_model.transformer.layers):
l.mlp = MegatronMLP(args, mpu.get_model_parallel_group(), idx)
# initialize gate hook
num_layers = len(model.language_model.transformer.layers)
reset_gate_hook(num_layers)
return model
fmoe/megatron/utils.py 0 → 100644
View file @ b690f245
r"""
Utility in Megatron
"""
def add_fmoe_args(parser):
group = parser.add_argument_group(title="fastmoe")
group.add_argument("--fmoefy", action="store_true")
group.add_argument("--num-experts", type=int, default=None)
group.add_argument("--top-k", type=int, default=2)
group.add_argument("--balance-loss-weight", type=float, default=1)
group.add_argument("--balance-strategy", type=str, default=None)
return parser
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