Merge pull request #81 from laekov/realmp

Model parallelism of single experts

fmoe/__init__.py

@@ -2,6 +2,7 @@ r"""
 The fmoe package contains MoE Layers only.
 """
 
-from .layers import FMoELinear, FMoE
+from .layers import FMoE
+from .linear import FMoELinear
 from .transformer import FMoETransformerMLP
 from .distributed import DistributedGroupedDataParallel

fmoe/distributed.py

@@ -25,11 +25,8 @@ class DistributedGroupedDataParallel(nn.Module):
     def __init__(
         self,
         module,
-        mp_group=None,
-        dp_group=None,
-        moe_group=None,
-        world_group=None,
         auto_allreduce=False,
+        **kwargs
     ):
         assert not auto_allreduce, "Automatic all-reduce is not implemented yet"
 
@@ -37,20 +34,12 @@ class DistributedGroupedDataParallel(nn.Module):
         self.module = module
 
         self.comms = dict()
-        if mp_group is not None:
-            self.comms["mp"] = mp_group
-        if dp_group is not None:
-            self.comms["dp"] = dp_group
-        else:
-            self.comms["dp"] = get_torch_default_comm()
-        if moe_group is not None:
-            self.comms["moe"] = moe_group
-        else:
-            self.comms["moe"] = get_torch_default_comm()
-        if world_group is None:
-            self.comms["world"] = get_torch_default_comm()
-        else:
-            self.comms["world"] = world_group
+        for k in kwargs:
+            if k.endswith('_group'):
+                self.comms[k[:-6]] = kwargs[k]
+        for k in ['dp', 'gate', 'moe', 'world']:
+            if k not in self.comms:
+                self.comms[k] = get_torch_default_comm()
 
         def allreduce_params(no_scale=False,
                 reduce_after=False, fp32_allreduce=False):

fmoe/functions.py

@@ -132,34 +132,6 @@ class MOEScatter(Function):
         grad_in = _local_gather(local_grad_in, pos, inp_batch_size)
         return grad_in, None, None, None, None, None
 
-
-class MOELinear(Function):
-    r"""
-    Computes linear operators within one GPU on different experts simutaneously.
-    """
-
-    @staticmethod
-    def forward(ctx, global_input_buf, fwd_expert_count, weight, bias=None):
-        global_output_buf = fmoe_cuda.linear_forward(
-            global_input_buf, fwd_expert_count, weight, bias
-        )
-        variables = (global_input_buf, fwd_expert_count, weight, bias)
-        ctx.save_for_backward(*variables)
-        return global_output_buf
-
-    @staticmethod
-    def backward(ctx, grad_out):
-        (input_buf, fwd_expert_count, weight, bias) = ctx.saved_tensors
-        grad_inp_buf, grad_weight, grad_bias = fmoe_cuda.linear_backward(
-            grad_out, input_buf, fwd_expert_count, weight, bias
-        )
-
-        if not torch.is_tensor(bias):
-            grad_bias = None
-
-        return grad_inp_buf, None, grad_weight, grad_bias
-
-
 class MOEGather(Function):
     r"""
     Gather output samples from contiguous alone experts back to [batch x

fmoe/layers.py

 r"""
-Layers that FMoE provides to users
+FMoE core layer
 """
 import torch
 import torch.nn as nn
-import math
 
 from .functions import prepare_forward, ensure_comm
-from .functions import MOEScatter, MOEGather, MOELinear
+from .functions import MOEScatter, MOEGather
 from .functions import AllGather, Slice
 from .gates import NaiveGate
 
 
-class FMoELinear(nn.Module):
-    r"""
-    A linear layer that contains multiple experts.
-    As multiple experts can be placed on the same worker, the computation can be
-    performed in parallel to increase the performance.
-    The FMoELinear module provides such function.
-    """
-
-    def __init__(
-        self,
-        num_expert: int,
-        in_feat: int,
-        out_feat: int,
-        bias: bool = True,
-        rank: int = 0,
-    ):
-        super().__init__()
-        self.num_expert = num_expert
-        self.in_feat = in_feat
-        self.out_feat = out_feat
-        self.rank = rank
-        self.weight = nn.Parameter(torch.Tensor(num_expert, out_feat, in_feat))
-        if bias:
-            self.bias = nn.Parameter(torch.zeros(num_expert, out_feat))
-        else:
-            self.register_parameter("bias", None)
-
-        self.reset_parameters()
-
-    def forward(self, inp, fwd_expert_count):
-        r"""
-        Call MOE function
-        """
-        x = MOELinear.apply(inp, fwd_expert_count, self.weight, self.bias)
-        return x
-
-    def extra_repr(self) -> str:
-        return "num_expert={}, in_features={}, \
-        out_features={}, bias={}, rank={}".format(
-            self.num_expert,
-            self.in_feat,
-            self.out_feat,
-            self.bias is not None,
-            self.rank,
-        )
-
-    def reset_parameters(self):
-        # Approach is the same as in torch.nn.Linear
-        # https://github.com/pytorch/pytorch/blob/master/torch/nn/modules/linear.py#L88
-        # bias is left to zero, similar as megatron
-
-        torch.nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5))
-
 
 def mark_module_parallel_comm(module, comm):
     r"""
@@ -121,11 +67,12 @@ class FMoE(nn.Module):
     * `num_expert` stands for the number of experts on **each** worker.
     * `world_size` stands for the total number of workers that contains
     different experts.
-    * `mp_group` can be a torch's communication group, indicating that model
-    parallel is applied across the group, which means that workers in the group
-    hold the same copy of the input feature, and demands the same copy of the
-    output. FMoE saves computation by slicing the input in the mp group and
-    performing all-gather after the MLP computation.
+    * `slice_group` can be a torch's communication group, indicating that
+    specific model parallel is applied across the group, and workers in the
+    group hold the same copy of input feature, and requires the same copy of
+    the output. For each worker, FMoE only computes the output of a certain
+    slice of the input batch, and will all-gather the outputs after
+    computation.  
     * `top_k` stands for the number of experts each token is going to.
     * `gate` is a gate class which can found in `fmoe.gates`.
     * `expert` can be specified as a module class, it is used to generate
@@ -137,7 +84,8 @@ class FMoE(nn.Module):
         num_expert=32,
         d_model=1024,
         world_size=1,
-        mp_group=None,
+        mp_group=None, # being deprecated
+        slice_group=None,
         moe_group=None,
         top_k=2,
         gate=NaiveGate,
@@ -150,13 +98,18 @@ class FMoE(nn.Module):
         self.num_expert = num_expert
         self.d_model = d_model
         self.world_size = world_size
-        self.mp_group = mp_group
-        if mp_group is None:
-            self.mp_size = 1
-            self.mp_rank = 0
+
+        self.slice_group = slice_group
+        if mp_group is not None:
+            print('[Warning] mp_group is being deprecated')
+            self.slice_group = mp_group
+        if self.slice_group is None:
+            self.slice_size = 1
+            self.slice_rank = 0
         else:
-            self.mp_size = mp_group.size()
-            self.mp_rank = mp_group.rank()
+            self.slice_size = self.slice_group.size()
+            self.slice_rank = self.slice_group.rank()
+
         self.top_k = top_k
         if type(expert) is list:
             self.experts = nn.ModuleList([e(d_model) for e in expert])
@@ -168,6 +121,7 @@ class FMoE(nn.Module):
             self.experts_fused = False
         else:
             self.experts_fused = True
+
         self.gate = gate(d_model, num_expert, world_size, top_k)
         self.gate_hook = gate_hook
         self.mask = mask
@@ -203,7 +157,7 @@ class FMoE(nn.Module):
                     mark_module_parallel_comm(e, comm)
             else:
                 mark_module_parallel_comm(self.experts, comm)
-        mark_module_parallel_comm(self.gate, "moe")
+        mark_module_parallel_comm(self.gate, "gate")
 
     def forward(self, inp):
         r"""
@@ -213,8 +167,9 @@ class FMoE(nn.Module):
         """
         if self.world_size > 1:
             ensure_comm(inp, self.moe_group)
-        if self.mp_size > 1:
-            inp = Slice.apply(inp, self.mp_rank, self.mp_size, self.mp_group)
+        if self.slice_size > 1:
+            inp = Slice.apply(inp, self.slice_rank,
+                    self.slice_size, self.slice_group)
 
         gate_top_k_idx, gate_score = self.gate(inp)
 
@@ -249,6 +204,7 @@ class FMoE(nn.Module):
         gate_score = gate_score.view(x.shape[0], 1, self.top_k)
         x = torch.bmm(gate_score, x).reshape(-1, self.d_model)
 
-        if self.mp_size > 1:
-            x = AllGather.apply(x, self.mp_rank, self.mp_size, self.mp_group)
+        if self.slice_size > 1:
+            x = AllGather.apply(x, self.slice_rank,
+                    self.slice_size, self.slice_group)
         return x

fmoe/linear.py

+r"""
+FMoE's parallel linear layer
+"""
+import torch
+import torch.nn as nn
+from torch.autograd import Function
+import math
+
+import fmoe_cuda
+
+
+class MOELinear(Function):
+    r"""
+    Computes linear operators within one GPU on different experts simutaneously.
+    """
+
+    @staticmethod
+    def forward(ctx, global_input_buf, fwd_expert_count, weight, bias=None):
+        global_output_buf = fmoe_cuda.linear_forward(
+            global_input_buf, fwd_expert_count, weight, bias
+        )
+        variables = (global_input_buf, fwd_expert_count, weight, bias)
+        ctx.save_for_backward(*variables)
+        return global_output_buf
+
+    @staticmethod
+    def backward(ctx, grad_out):
+        (input_buf, fwd_expert_count, weight, bias) = ctx.saved_tensors
+        grad_inp_buf, grad_weight, grad_bias = fmoe_cuda.linear_backward(
+            grad_out, input_buf, fwd_expert_count, weight, bias
+        )
+
+        if not torch.is_tensor(bias):
+            grad_bias = None
+
+        return grad_inp_buf, None, grad_weight, grad_bias
+
+
+
+class FMoELinear(nn.Module):
+    r"""
+    A linear layer that contains multiple experts.
+    As multiple experts can be placed on the same worker, the computation can be
+    performed in parallel to increase the performance.
+    The FMoELinear module provides such function.
+    """
+
+    def __init__(
+        self,
+        num_expert: int,
+        in_feat: int,
+        out_feat: int,
+        bias: bool = True,
+        rank: int = 0,
+    ):
+        super().__init__()
+        self.num_expert = num_expert
+        self.in_feat = in_feat
+        self.out_feat = out_feat
+        self.rank = rank
+        self.weight = nn.Parameter(torch.Tensor(num_expert, out_feat, in_feat))
+        if bias:
+            self.bias = nn.Parameter(torch.zeros(num_expert, out_feat))
+        else:
+            self.register_parameter("bias", None)
+
+        self.reset_parameters()
+
+    def forward(self, inp, fwd_expert_count):
+        r"""
+        Call MOE function
+        """
+        x = MOELinear.apply(inp, fwd_expert_count, self.weight, self.bias)
+        return x
+
+    def extra_repr(self) -> str:
+        return "num_expert={}, in_features={}, \
+        out_features={}, bias={}, rank={}".format(
+            self.num_expert,
+            self.in_feat,
+            self.out_feat,
+            self.bias is not None,
+            self.rank,
+        )
+
+    def reset_parameters(self):
+        # Approach is the same as in torch.nn.Linear
+        # https://github.com/pytorch/pytorch/blob/master/torch/nn/modules/linear.py#L88
+        # bias is left to zero, similar as megatron
+
+        torch.nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5))
+

fmoe/megatron/__init__.py

@@ -15,5 +15,6 @@ from .balance import reset_gate_hook
 from .balance import get_balance_profile
 from .balance import generate_megatron_gate_hook
 from .balance import add_balance_log
-from .balance import patch_forward_step
-from .balance import patch_model_provider
+
+from .patch import patch_forward_step
+from .patch import patch_model_provider

fmoe/megatron/balance.py

@@ -5,7 +5,6 @@ import torch
 from fmoe.balance import reset_balance_profile
 from fmoe.balance import update_balance_profile
 from fmoe.utils import get_torch_default_comm
-from .distributed import get_moe_group
 
 
 balance_dict = {}
@@ -71,63 +70,3 @@ def add_balance_log(model, writer, iteration):
                 balance_dict_tensor[idx].mean().item(),
                 iteration,
             )
-
-
-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() or not args.balance_strategy or args.balance_strategy == 'naive':
-            return output
-        loss_name = args.balance_strategy + "_loss"
-
-        while hasattr(model, 'module'):
-            model = model.module
-
-        loss_list = [l.mlp.gate.get_loss(clear=False).view(1)
-                for l in model.language_model.transformer.layers]
-        (loss, state_dict), bal_loss = (
-            output,
-            torch.cat(loss_list).mean() * args.balance_loss_weight
-        )
-
-        # avarage across moe group
-        moe_group = get_moe_group()
-        world_size = torch.distributed.get_world_size(group=moe_group)
-        averaged_bal_loss = bal_loss.clone().detach()
-        torch.distributed.all_reduce(averaged_bal_loss, group=moe_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/distributed.py

 r"""
 distributed support for Megatron
 """
+import torch
+
 from fmoe.distributed import DistributedGroupedDataParallel
 
 
-_moe_group = None
+_groups = None
+
+
+def _set_groups(**kwargs):
+    global _groups
+    _groups = kwargs
 
 
-def set_moe_group(moe_group):
-    global _moe_group
-    _moe_group = moe_group
+def _init():
+    from megatron import get_args
+    from megatron import mpu
+    args = get_args()
 
+    # Create a comm prependicular to the pipeline group as gate group
+    stage_size = args.world_size // args.pipeline_model_parallel_size
+    for i in range(0, args.world_size, stage_size):
+        ranks = range(i, i + stage_size)
+        group = torch.distributed.new_group(ranks)
+        if args.rank in ranks:
+            gate_group = group
 
-def get_moe_group():
-    return _moe_group
+    _set_groups(
+            dp_group=mpu.get_data_parallel_group(),
+            moe_group=mpu.get_data_parallel_group(),
+            gate_group=gate_group)
 
 
 class DistributedDataParallel(DistributedGroupedDataParallel):
@@ -24,14 +41,9 @@ class DistributedDataParallel(DistributedGroupedDataParallel):
     """
 
     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(),
-            moe_group=_moe_group
-        )
+        if _groups is None:
+            _init()
+        super().__init__(module, **_groups)
 
     def state_dict(self, *args, **kwargs):
         r"""

fmoe/megatron/layers.py

@@ -10,7 +10,6 @@ import torch.nn.functional as F
 from fmoe.transformer import FMoETransformerMLP
 from .balance import reset_gate_hook
 from .balance import generate_megatron_gate_hook
-from .distributed import set_moe_group
 
 
 class _FakeMegatronMLP(nn.Module):
@@ -75,33 +74,28 @@ class MegatronMLP(FMoETransformerMLP):
     communication group `group` to replace the original MLP layer in Megatron.
     """
 
-    def __init__(self, args, mp_group, moe_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"
+    def __init__(self, args, layer_idx, gate=None):
         if not args.distributed_experts:
             world_size = 1
+            moe_group = None
         else:
-            world_size = args.tensor_model_parallel_size * args.data_parallel_size
-        gate = None
+            world_size = args.data_parallel_size
+            from megatron.mpu import get_data_parallel_group
+            moe_group = get_data_parallel_group()
+
         if not args.balance_strategy or args.balance_strategy == "naive":
             from fmoe.gates import NaiveGate
-
             gate = NaiveGate
         elif args.balance_strategy == "noisy":
             from fmoe.gates import NoisyGate
-
             gate = NoisyGate
         elif args.balance_strategy == "gshard":
             from fmoe.gates import GShardGate
-
             gate = GShardGate
         elif args.balance_strategy == "switch":
             from fmoe.gates import SwitchGate
-
             gate = SwitchGate
-        else:
+        elif gate is None:
             assert False, "Undefined balance strategy {}" % (args.balance_strategy)
 
         super().__init__(
@@ -110,7 +104,6 @@ class MegatronMLP(FMoETransformerMLP):
             d_model=args.hidden_size,
             d_hidden=args.hidden_hidden_size,
             world_size=world_size,
-            mp_group=mp_group,
             moe_group=moe_group,
             expert_dp_comm="none" if args.distributed_experts else "dp",
             gate_hook=generate_megatron_gate_hook(
@@ -139,8 +132,11 @@ class MegatronMLP(FMoETransformerMLP):
         _megatron_init_method(self.experts.h4toh, rng, std)
 
     def forward(self, inp):
+        from megatron import mpu
+        x = super().forward(inp)
+        x = mpu.reduce_from_tensor_model_parallel_region(x)
         return (
-            super().forward(inp),
+            x,
             torch.zeros(self.hidden_size, dtype=inp.dtype, device=inp.device),
         )
 
@@ -151,6 +147,7 @@ def fmoefy(
     distributed_experts=True,
     hidden_hidden_size=None,
     top_k=None,
+    gate=None,
 ):
     r"""
     Replace MLP layers in a transformer-based model in Megatron by MoE.
@@ -167,47 +164,28 @@ def fmoefy(
     tensor_model_parall_comm x data_parallel_comm, which is not created.
     """
     from megatron import get_args
-    from megatron import mpu
 
     args = get_args()
+
+    # Set distributed_experts to None to use default setting in args
+    if distributed_experts is not None:
+        args.distributed_experts = distributed_experts
+
     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
+    args.hidden_hidden_size = hidden_hidden_size
 
-    if hasattr(mpu, 'get_tensor_model_parallel_group'):
-        mp_group = mpu.get_tensor_model_parallel_group()
-    else:
-        # For compatibility to older versions of Megatron-LM
-        mp_group = mpu.get_model_parallel_group()
-    if args.pipeline_model_parallel_size == 1:
-        moe_group = None
-    else:
-        # Create a comm prependicular to pipeline group
-        stage_size = args.world_size // args.pipeline_model_parallel_size
-        for i in range(0, args.world_size, stage_size):
-            ranks = range(i, i + stage_size)
-            group = torch.distributed.new_group(ranks)
-            if args.rank in ranks:
-                moe_group = group
-        set_moe_group(moe_group)
     for idx, l in enumerate(model.language_model.transformer.layers):
-        l.mlp = MegatronMLP(args, mp_group, moe_group, idx)
+        l.mlp = MegatronMLP(args, idx, gate=gate)
 
     # initialize gate hook
     num_layers = len(model.language_model.transformer.layers)

fmoe/megatron/patch.py

+r"""
+Patching some of Megatron-LM's functions to create an MoE model
+"""
+import torch
+
+
+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.mpu import get_tensor_model_parallel_group
+    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() or not args.balance_strategy or args.balance_strategy == 'naive':
+            return output
+        loss_name = args.balance_strategy + "_loss"
+
+        while hasattr(model, 'module'):
+            model = model.module
+
+        loss_list = [l.mlp.gate.get_loss(clear=False).view(1)
+                for l in model.language_model.transformer.layers]
+        (loss, state_dict), bal_loss = (
+            output,
+            torch.cat(loss_list).mean() * args.balance_loss_weight
+        )
+
+        # avarage across moe group
+        moe_group = get_tensor_model_parallel_group()
+        world_size = torch.distributed.get_world_size(group=moe_group)
+        averaged_bal_loss = bal_loss.clone().detach()
+        torch.distributed.all_reduce(averaged_bal_loss, group=moe_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, gate=None):
+    from megatron import get_args
+
+    def fmoefied_model_provider():
+        from .layers import fmoefy
+        args = get_args()
+        hhs = args.hidden_size * 4 
+        assert hhs % args.top_k == 0
+        hhs = hhs // args.top_k 
+        assert hhs % args.tensor_model_parallel_size == 0
+        hhs = hhs // args.tensor_model_parallel_size
+        return fmoefy(
+            model_provider(),
+            num_experts=args.num_experts,
+            hidden_hidden_size=hhs,
+            top_k=args.top_k,
+            gate=gate
+        )
+
+    return fmoefied_model_provider

fmoe/transformer.py

@@ -3,8 +3,8 @@ Adaption to act as the MLP layer using an MoE MLP layer in transformer.
 """
 import torch
 import torch.nn as nn
-from .gates import NaiveGate
-from .layers import FMoE, FMoELinear
+from .layers import FMoE
+from .linear import FMoELinear
 
 
 class _Expert(nn.Module):
@@ -42,31 +42,14 @@ class FMoETransformerMLP(FMoE):
         num_expert=32,
         d_model=1024,
         d_hidden=4096,
-        world_size=1,
-        mp_group=None,
-        moe_group=None,
         activation=torch.nn.GELU(),
-        gate=NaiveGate,
-        top_k=2,
         expert_dp_comm="none",
-        gate_hook=None,
-        mask=None,
-        mask_dict=None,
+        expert_rank=0,
+        **kwargs
     ):
-        super().__init__(
-            num_expert=num_expert,
-            d_model=d_model,
-            gate=gate,
-            top_k=top_k,
-            world_size=world_size,
-            mp_group=mp_group,
-            moe_group=moe_group,
-            gate_hook=gate_hook,
-            mask=mask,
-            mask_dict=mask_dict
-        )
+        super().__init__(num_expert=num_expert, d_model=d_model, **kwargs)
         self.experts = _Expert(
-            num_expert, d_model, d_hidden, activation, rank=self.mp_rank
+            num_expert, d_model, d_hidden, activation, rank=expert_rank
         )
         self.mark_parallel_comm(expert_dp_comm)
 

setup.py

@@ -36,7 +36,7 @@ else:
 if __name__ == '__main__':
     setuptools.setup(
         name='fastmoe',
-        version='0.2.1',
+        version='0.3.0',
         description='An efficient Mixture-of-Experts system for PyTorch',
         author=', '.join(authors),
         author_email='hja20@mails.tsinghua.edu.cn',

tests/test_numerical.py

@@ -342,7 +342,8 @@ def _test_fmoe_local_ddp(rank, world_size, mp_group, dp_group, world_group):
     torch.cuda.manual_seed(42 + rank)
 
     model = MyModule().cuda()
-    model_ddp = LocalDDP(deepcopy(model), mp_group, dp_group, world_group)
+    model_ddp = LocalDDP(deepcopy(model),
+            mp_group=mp_group, dp_group=dp_group, world_group=world_group)
     model.set_comm()
     model_ddp.module.set_comm()
 

tests/test_zero.py

@@ -56,8 +56,9 @@ def _test_zero_transformer(num_expert=2, batch_size=4, d_hidden=8, world_size=1)
     mask_dict = {
         1: torch.zeros(d_hidden).cuda()
     }
-    model = FMoETransformerMLP(num_expert, d_hidden, d_hidden * 4, world_size,
-            gate=ConstantGate, mask=mask, mask_dict=mask_dict).cuda()
+    model = FMoETransformerMLP(num_expert, d_hidden, d_hidden * 4,
+            world_size=world_size, gate=ConstantGate, mask=mask,
+            mask_dict=mask_dict).cuda()
     oup = model(inp)