mirror of https://github.com/InternLM/InternLM
132 lines
4.3 KiB
Python
132 lines
4.3 KiB
Python
import typing
|
|
|
|
import torch
|
|
|
|
from internlm.core.context import ParallelMode
|
|
from internlm.core.context import global_context as gpc
|
|
from internlm.moe.experts import Experts
|
|
from internlm.moe.sharded_moe import MOELayer, TopKGate
|
|
from internlm.utils.logger import get_logger
|
|
|
|
# Copyright (c) Microsoft Corporation.
|
|
# SPDX-License-Identifier: Apache-2.0
|
|
|
|
# DeepSpeed Team
|
|
|
|
|
|
# global llm logger
|
|
logger = get_logger(__file__)
|
|
|
|
|
|
def has_moe_layers(m):
|
|
has_moe = False
|
|
num_experts = 0
|
|
|
|
for _, module in m.named_modules():
|
|
if isinstance(module, MoE):
|
|
has_moe = True
|
|
num_experts = module.num_experts
|
|
break
|
|
return has_moe, num_experts
|
|
|
|
|
|
def is_moe_param(param: torch.Tensor) -> bool:
|
|
if hasattr(param, "allreduce") and not param.allreduce:
|
|
return True
|
|
return False
|
|
|
|
|
|
class MoE(torch.nn.Module):
|
|
"""Initialize an MoE layer.
|
|
|
|
Arguments:
|
|
hidden_size (int): the hidden dimension of the model, importantly this is also the input and output dimension.
|
|
expert (torch.nn.Module): the torch module that defines the expert (e.g., MLP, torch.linear).
|
|
num_experts (int, optional): default=1, the total number of experts per layer.
|
|
ep_size (int, optional): default=1, number of ranks in the expert parallel world or group.
|
|
k (int, optional): default=1, top-k gating value, only supports k=1 or k=2.
|
|
capacity_factor (float, optional): default=1.0, the capacity of the expert at training time.
|
|
eval_capacity_factor (float, optional): default=1.0, the capacity of the expert at eval time.
|
|
min_capacity (int, optional): default=4, the minimum capacity per expert regardless of the capacity_factor.
|
|
noisy_gate_policy (str, optional): default=None, noisy gate policy, valid options are 'Jitter', 'RSample'
|
|
or 'None'.
|
|
drop_tokens (bool, optional): default=True, whether to drop tokens - (setting to False is equivalent to
|
|
infinite capacity).
|
|
use_rts (bool, optional): default=True, whether to use Random Token Selection.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
hidden_size,
|
|
expert,
|
|
num_experts=1,
|
|
ep_size=1,
|
|
k=1,
|
|
capacity_factor=1.0,
|
|
eval_capacity_factor=1.0,
|
|
min_capacity=4,
|
|
noisy_gate_policy: typing.Optional[str] = None,
|
|
drop_tokens: bool = True,
|
|
use_rts: bool = True,
|
|
using_default_moe: bool = True,
|
|
):
|
|
|
|
super().__init__()
|
|
|
|
assert (
|
|
num_experts % ep_size == 0
|
|
), f"Number of experts ({num_experts}) should be divisible by expert parallel size ({ep_size})"
|
|
self.ep_size = ep_size
|
|
self.num_experts = num_experts
|
|
self.num_local_experts = num_experts // self.ep_size
|
|
|
|
logger.info(
|
|
f"""Creating MoE layer with num_experts: {num_experts} | num_local_experts:
|
|
{self.num_local_experts} | expert_parallel_size: {self.ep_size}"""
|
|
)
|
|
|
|
assert noisy_gate_policy is None or noisy_gate_policy in ["None", "Jitter", "RSample"], (
|
|
"Unsupported noisy_gate_policy: " + noisy_gate_policy
|
|
)
|
|
|
|
experts = Experts(expert, self.num_local_experts)
|
|
|
|
if using_default_moe:
|
|
self.moe_layer = MOELayer(
|
|
TopKGate(
|
|
hidden_size,
|
|
num_experts,
|
|
k,
|
|
capacity_factor,
|
|
eval_capacity_factor,
|
|
min_capacity,
|
|
noisy_gate_policy,
|
|
drop_tokens,
|
|
use_rts,
|
|
),
|
|
experts,
|
|
gpc.get_group(ParallelMode.EXPERT),
|
|
self.ep_size,
|
|
self.num_local_experts,
|
|
)
|
|
|
|
def forward(self, hidden_states, used_token=None):
|
|
"""MoE forward
|
|
|
|
Arguments:
|
|
hidden_states (Tensor): input to the layer
|
|
used_token (Tensor, optional): default: None, mask only used tokens
|
|
|
|
Returns:
|
|
A tuple including output, gate loss, and expert count.
|
|
|
|
* output (Tensor): output of the model
|
|
|
|
* l_aux (Tensor): gate loss value
|
|
|
|
* exp_counts (int): expert count
|
|
"""
|
|
output = self.moe_layer(hidden_states, used_token)
|
|
|
|
return output, self.moe_layer.l_aux, self.moe_layer.exp_counts
|