Added MoE parallel (#127)

colossalai/constants.py

@@ -15,7 +15,8 @@ INITIALIZER_MAPPING = {
     '2.5d': 'Initializer_2p5D',
     '3d': 'Initializer_3D',
     'sequence': 'Initializer_Sequence',
-    'model': 'Initializer_Model'
+    'model': 'Initializer_Model',
+    'moe': 'Initializer_Moe'
 }
 
 # 1D parallel

colossalai/context/parallel_context.py

@@ -15,6 +15,7 @@ from colossalai.registry import DIST_GROUP_INITIALIZER
 
 from .parallel_mode import ParallelMode
 from .random import add_seed, get_seeds, set_mode
+from colossalai.global_variables import moe_env
 
 
 class ParallelContext:
@@ -412,6 +413,13 @@ class ParallelContext:
             # add this config to initialize later
             pg_init.append(dict(type=INITIALIZER_MAPPING[tensor_parallel_mode.lower()], **tensor_parallel_cfg))
 
+        # initialization for moe environment
+        if parallel_config is not None and 'moe' in parallel_config:
+            param = parallel_config['moe']
+            assert 'size' in param, "Moe model parallel size should be given"
+            moe_env.setup(param['size'])
+            pg_init.append(dict(type=INITIALIZER_MAPPING['moe']))
+
         # run initialization of different process groups
         for initializer_cfg in pg_init:
             cfg = initializer_cfg.copy()

colossalai/context/parallel_mode.py

@@ -44,3 +44,7 @@ class ParallelMode(Enum):
     PARALLEL_2P5D_COL = '2p5d_col'
     PARALLEL_2P5D_DEP = '2p5d_dep'
     PARALLEL_2P5D_XZ = '2p5d_xz'
+
+    # MOE parallel
+    MOE_DATA = 'moe_data'
+    MOE_MODEL = 'moe_model'

colossalai/context/process_group_initializer/__init__.py

@@ -7,10 +7,12 @@ from .initializer_pipeline import Initializer_Pipeline
 from .initializer_sequence import Initializer_Sequence
 from .initializer_tensor import Initializer_Tensor
 from .initializer_model import Initializer_Model
+from .initializer_moe import Initializer_Moe
 from .process_group_initializer import ProcessGroupInitializer
 
 __all__ = [
     'Initializer_Tensor', 'Initializer_Sequence', 'Initializer_Pipeline',
     'Initializer_Data', 'Initializer_2p5D', 'Initializer_2D', 'Initializer_3D',
-    'Initializer_1D', 'ProcessGroupInitializer', 'Initializer_Model'
+    'Initializer_1D', 'ProcessGroupInitializer', 'Initializer_Model',
+    'Initializer_Moe'
 ]

colossalai/context/process_group_initializer/initializer_moe.py

@@ -0,0 +1,97 @@
+import torch.distributed as dist
+
+from colossalai.registry import DIST_GROUP_INITIALIZER
+from colossalai.global_variables import moe_env
+from .process_group_initializer import ProcessGroupInitializer
+from ..parallel_mode import ParallelMode
+
+
+@DIST_GROUP_INITIALIZER.register_module
+class Initializer_Moemodel(ProcessGroupInitializer):
+    """Model parallel initialization for MoE system.
+    """
+
+    def __init__(self, moe_model, moe_data, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.moe_model = moe_model
+        self.moe_data = moe_data
+
+    def init_dist_group(self):
+        """Initialize model parallel groups in moe parallel environment,
+        and assign local_ranks and groups to each gpu.
+        """
+
+        local_rank = None
+        ranks_in_group = None
+        process_group = None
+        group_world_size = None
+        mode = ParallelMode.MOE_MODEL
+
+        for i in range(self.moe_data):
+            ranks = [i * self.moe_model + j for j in range(self.moe_model)]
+            group = dist.new_group(ranks)
+
+            if self.rank in ranks:
+                local_rank = ranks.index(self.rank)
+                group_world_size = len(ranks)
+                process_group = group
+                ranks_in_group = ranks
+
+        return local_rank, group_world_size, process_group, ranks_in_group, mode
+
+
+@DIST_GROUP_INITIALIZER.register_module
+class Initializer_Moedata(ProcessGroupInitializer):
+    """Data parallel initialization for MoE system.
+    """
+
+    def __init__(self, moe_model, moe_data, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.moe_model = moe_model
+        self.moe_data = moe_data
+
+    def init_dist_group(self):
+        """Initialize data parallel groups in moe parallel environment,
+        and assign local_ranks and groups to each gpu.
+        """
+
+        local_rank = None
+        ranks_in_group = None
+        process_group = None
+        group_world_size = None
+        mode = ParallelMode.MOE_DATA
+
+        for i in range(self.moe_model):
+            ranks = [i + j * self.moe_model for j in range(self.moe_data)]
+            group = dist.new_group(ranks)
+
+            if self.rank in ranks:
+                local_rank = ranks.index(self.rank)
+                group_world_size = len(ranks)
+                process_group = group
+                ranks_in_group = ranks
+
+        return local_rank, group_world_size, process_group, ranks_in_group, mode
+
+
+@DIST_GROUP_INITIALIZER.register_module
+class Initializer_Moe(ProcessGroupInitializer):
+    """Serves as the single entry point to MoE parallel initialization.
+    """
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.moe_model = moe_env.model_parallel_size
+        self.moe_data = moe_env.data_parallel_size
+        self.model_initializer = Initializer_Moemodel(
+            self.moe_model, self.moe_data, *args, **kwargs)
+        self.data_initializer = Initializer_Moedata(
+            self.moe_model, self.moe_data, *args, **kwargs)
+
+    def init_dist_group(self):
+        """Initializes MoE parallel communication groups.
+        """
+
+        parallel_setting = [self.model_initializer.init_dist_group(),
+                            self.data_initializer.init_dist_group()]
+        return parallel_setting

colossalai/context/random/__init__.py

@@ -1,8 +1,9 @@
 from ._helper import (seed, set_mode, with_seed, add_seed,
                       get_seeds, get_states, get_current_mode,
-                      set_seed_states, sync_states)
+                      set_seed_states, sync_states, moe_set_seed)
 
 __all__ = [
     'seed', 'set_mode', 'with_seed', 'add_seed', 'get_seeds',
-    'get_states', 'get_current_mode', 'set_seed_states', 'sync_states'
+    'get_states', 'get_current_mode', 'set_seed_states', 'sync_states',
+    'moe_set_seed'
 ]

colossalai/context/random/_helper.py

@@ -49,7 +49,7 @@ def get_current_mode():
     return _SEED_MANAGER.current_mode
 
 
-def add_seed(parallel_mode: ParallelMode, seed: int):
+def add_seed(parallel_mode: ParallelMode, seed: int, overwrite: bool = False):
     """Adds a seed to the seed manager for `parallel_mode`.
 
     :param parallel_mode: The chosen parallel mode
@@ -59,7 +59,7 @@ def add_seed(parallel_mode: ParallelMode, seed: int):
     :raises AssertionError: Raises an AssertionError if `parallel_mode` is not an instance of 
         :class:`colossalai.context.ParallelMode` or the seed for `parallel_mode` has been added
     """
-    _SEED_MANAGER.add_seed(parallel_mode, seed)
+    _SEED_MANAGER.add_seed(parallel_mode, seed, overwrite)
 
 
 def set_mode(parallel_mode: ParallelMode):
@@ -142,3 +142,16 @@ def with_seed(func, parallel_mode: ParallelMode):
         return out
 
     return wrapper
+
+
+def moe_set_seed(seed):
+    if torch.cuda.is_available():
+        from colossalai.core import global_context as gpc
+        moe_mp_rank = gpc.get_local_rank(ParallelMode.MOE_MODEL)
+        moe_mp_seed = seed + moe_mp_rank
+        add_seed(ParallelMode.MOE_MODEL, moe_mp_seed)
+
+        global_rank = gpc.get_global_rank()
+        add_seed(ParallelMode.TENSOR, global_rank, True)
+        print(f"moe seed condition: {global_rank} with moe seed {moe_mp_seed}, ",
+              f"tensor seed {global_rank}", flush=True)

colossalai/context/random/seed_manager.py

@@ -54,7 +54,7 @@ class SeedManager:
         self._current_mode = parallel_mode
         torch.cuda.set_rng_state(self._seed_states[parallel_mode])
 
-    def add_seed(self, parallel_mode: ParallelMode, seed: int):
+    def add_seed(self, parallel_mode: ParallelMode, seed: int, overwrtie: bool = False):
         """Adds a seed to the seed manager for `parallel_mode`.
 
         :param parallel_mode: The chosen parallel mode
@@ -66,7 +66,11 @@ class SeedManager:
         """
         assert isinstance(
             parallel_mode, ParallelMode), 'A valid ParallelMode must be provided'
+        if overwrtie is False:
             assert parallel_mode not in self._seed_states, f'The seed for {parallel_mode} has been added'
+        elif parallel_mode in self._seed_states:
+            print(f"Warnning: {parallel_mode} seed has been overwritten.", flush=True)
+
         current_state = torch.cuda.get_rng_state()
         torch.cuda.manual_seed(seed)
         self._seed_states[parallel_mode] = torch.cuda.get_rng_state()

colossalai/engine/gradient_handler/__init__.py

@@ -2,6 +2,8 @@ from ._base_gradient_handler import BaseGradientHandler
 from ._data_parallel_gradient_handler import DataParallelGradientHandler
 from ._zero_gradient_handler import ZeROGradientHandler
 from ._pipeline_parallel_gradient_handler import PipelineSharedModuleGradientHandler
+from ._moe_gradient_handler import MoeGradientHandler
 
 __all__ = ['BaseGradientHandler', 'DataParallelGradientHandler',
-           'ZeROGradientHandler', 'PipelineSharedModuleGradientHandler']
+           'ZeROGradientHandler', 'PipelineSharedModuleGradientHandler',
+           'MoeGradientHandler']

colossalai/engine/gradient_handler/_moe_gradient_handler.py

@@ -0,0 +1,61 @@
+import torch.distributed as dist
+from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors
+from colossalai.core import global_context as gpc
+from colossalai.registry import GRADIENT_HANDLER
+from colossalai.global_variables import moe_env
+from ._base_gradient_handler import BaseGradientHandler
+from ...context.parallel_mode import ParallelMode
+
+
+@GRADIENT_HANDLER.register_module
+class MoeGradientHandler(BaseGradientHandler):
+    """A helper class to handle all-reduce operations in a data parallel group and
+    moe tensor parallel. A all-reduce collective communication will be operated in
+    :func:`handle_gradient` among a data parallel group.
+    For better performance, it bucketizes the gradients of all parameters that are
+    the same type to improve the efficiency of communication.
+    """
+
+    def handle_gradient(self):
+        """A method running an all-reduce operation in a data parallel group.
+        Then running an all-reduce operation for all parameters in experts
+        across moe tensor parallel group
+        """
+        moe_data = moe_env.data_parallel_size
+        global_data = gpc.data_parallel_size
+
+        if global_data > 1:
+            # bucketize and all-reduce
+            buckets = {}
+            # Pack the buckets.
+            for param in self._model.parameters():
+                if param.requires_grad and \
+                        param.grad is not None and \
+                        not hasattr(param, 'moe_param'):
+                    tp = param.data.type()
+                    if tp not in buckets:
+                        buckets[tp] = []
+                    buckets[tp].append(param)
+                    # param.main_grad = param.grad
+
+            # For each bucket, all-reduce and copy all-reduced grads.
+            for tp in buckets:
+                bucket = buckets[tp]
+                grads = [param.grad.data for param in bucket]
+                coalesced = _flatten_dense_tensors(grads)
+                coalesced /= gpc.get_world_size(ParallelMode.DATA)
+
+                dist.all_reduce(
+                    coalesced, group=gpc.get_group(ParallelMode.DATA))
+                for buf, synced in zip(grads, _unflatten_dense_tensors(
+                        coalesced, grads)):
+                    buf.copy_(synced)
+
+        if global_data > 1:
+            for param in self._model.parameters():
+                if not param.requires_grad or param.grad is None:
+                    continue
+                if moe_data > 1 and hasattr(param, 'moe_param'):
+                    param.grad.data /= moe_data
+                    dist.all_reduce(param.grad.data,
+                                    group=gpc.get_group(ParallelMode.MOE_DATA))

colossalai/engine/schedule/_base_schedule.py

@@ -38,8 +38,9 @@ class BaseSchedule(ABC):
         return data
 
     @staticmethod
-    def _check_sanity(data, tag):
-        assert isinstance(data, (torch.Tensor, dict)), f'{tag} must be torch.Tensor or dict'
+    def _check_sanity(data, tag: str):
+        assert isinstance(data, (torch.Tensor, dict)), \
+            f'{tag} must be torch.Tensor or dict'
 
     def load_batch(self, data_iter, to_gpu=True):
         """Loads a batch from data iterator. It returns the data and labels which are

colossalai/global_variables.py

@@ -0,0 +1,36 @@
+
+
+class MoeEnv:
+    """Moe enviroment variable.
+    """
+
+    def __init__(self):
+        self.data_parallel_size = None
+        self.model_parallel_size = None
+        self.aux_loss = None
+
+    def setup(self, moe_model_size):
+        from .core import global_context as gpc
+        if gpc.tensor_parallel_size > 1 or gpc.pipeline_parallel_size > 1:
+            raise NotImplementedError("Moe is not compatible with tensor or pipeline parallel")
+
+        assert gpc.data_parallel_size % moe_model_size == 0, \
+            "The size of data parallel needs to be divided by moe model parallel size"
+
+        self.data_parallel_size = gpc.data_parallel_size // moe_model_size
+        self.model_parallel_size = moe_model_size
+
+    def is_initialized(self):
+        return self.model_parallel_size is not None
+
+    def reset_loss(self):
+        self.aux_loss = 0
+
+    def add_loss(self, loss):
+        self.aux_loss += loss
+
+    def get_loss(self):
+        return self.aux_loss
+
+
+moe_env = MoeEnv()

colossalai/initialize.py

@@ -5,7 +5,6 @@ import argparse
 import pprint
 import os
 from colossalai.nn.optimizer.colossalai_optimizer import ColossalaiOptimizer
-import numpy as np
 import torch
 import torch.nn as nn
 
@@ -26,6 +25,7 @@ from torch.optim.lr_scheduler import _LRScheduler
 from torch.utils.data import DataLoader
 from torch.nn.modules.loss import _Loss
 from torch.nn.parallel import DistributedDataParallel as DDP
+from colossalai.global_variables import moe_env
 
 
 def get_default_parser():
@@ -224,7 +224,7 @@ def initialize(model: Union[nn.Module, List[nn.Module]],
                test_dataloader: Optional[Union[Iterable, List[Iterable]]] = None,
                lr_scheduler: _LRScheduler = None,
                verbose: bool = True
-               ) -> Tuple[Engine, DataLoader, DataLoader]:
+               ) -> Tuple[Engine, DataLoader, DataLoader, _LRScheduler]:
     ''' Core function to wrap the essential training components with our functionality based on the config which is loaded into gpc.config.
 
     :param model: your model instance
@@ -269,8 +269,13 @@ def initialize(model: Union[nn.Module, List[nn.Module]],
     # first sync model across dp ranks
     model.to(get_current_device())
     use_zero3 = hasattr(gpc.config, 'zero') and gpc.config.zero.level == 3
-    if not use_zero3:
+    if not moe_env.is_initialized() and not use_zero3:
         sync_model_param_in_dp(model)
+    else:
+        print(
+            "Warning: The parameters of models is not automatically synchronized.\n"
+            "Please make sure that all parameters are the same in data parallel group.",
+            flush=True)
 
     # check amp and zero
     fp16_cfg = gpc.config.get('fp16', None)
@@ -327,6 +332,13 @@ def initialize(model: Union[nn.Module, List[nn.Module]],
                     "Training with zero is detected, ZeROGradientHandler is automatically "
                     "added even though not specified in the configuration",
                     ranks=[0])
+        elif is_using_ddp() and moe_env.is_initialized():
+            gradient_handler_cfg = [dict(type='MoeGradientHandler')]
+            if verbose:
+                logger.info(
+                    "Data parallel training is detected with moe parallel, MoeGradientHandler is automatically "
+                    "added even though not specified in the configuration",
+                    ranks=[0])
         elif is_using_ddp() and not is_using_pp() and amp_mode != AMP_TYPE.NAIVE:
             model = DDP(model, process_group=gpc.get_group(ParallelMode.DATA))
             if verbose:

colossalai/nn/layer/moe/__init__.py

@@ -0,0 +1,8 @@
+from ._operation import AllToAll
+from .layers import Experts, MoeLayer, \
+    NormalNoiseGenerator, Top1Router, Top2Router
+
+__all__ = [
+    'AllToAll', 'Experts', 'Top1Router', 'Top2Router',
+    'MoeLayer', 'NormalNoiseGenerator'
+]

colossalai/nn/layer/moe/_operation.py

@@ -0,0 +1,29 @@
+import torch
+import torch.distributed as dist
+from torch import Tensor
+
+from colossalai.context import ParallelMode
+from colossalai.core import global_context as gpc
+from typing import Any, Tuple
+
+
+class AllToAll(torch.autograd.Function):
+    """Dispatches input tensor [e, c, h] to all experts by all_to_all_single
+    operation in torch.distributed.
+    """
+    @staticmethod
+    def forward(ctx: Any,
+                inputs: Tensor,
+                parallel_mode: ParallelMode) -> Tensor:
+        ctx.parallel_mode = parallel_mode
+        if not inputs.is_contiguous():
+            inputs = inputs.contiguous()
+
+        output = torch.empty_like(inputs)
+        dist.all_to_all_single(output, inputs,
+                               group=gpc.get_group(parallel_mode))
+        return output
+
+    @staticmethod
+    def backward(ctx: Any, *grad_outputs: Tensor) -> Tuple[Tensor, None]:
+        return AllToAll.apply(*grad_outputs, ctx.parallel_mode), None

colossalai/nn/layer/moe/layers.py

@@ -0,0 +1,242 @@
+import math
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.cuda.amp import autocast
+from colossalai.global_variables import moe_env
+from colossalai.context import ParallelMode, seed
+from colossalai.utils import get_current_device
+from ._operation import AllToAll
+
+
+class NormalNoiseGenerator:
+    """Generates a random noisy mask for logtis tensor.
+    All noise is generated from a normal distribution (0, 1 / E^2), where
+    E = the number of experts.
+    """
+
+    def __init__(self, num_experts: int):
+        self.normal = torch.distributions.normal.Normal(
+            loc=torch.tensor(0.0, device=get_current_device()),
+            scale=torch.tensor(1.0 / num_experts ** 2, device=get_current_device())
+        ).rsample
+
+    def __call__(self, inputs: torch.Tensor):
+        noisy = self.normal(inputs.shape)
+        return inputs + noisy
+
+
+class Experts(nn.Module):
+    """A wrapper class to create experts. It will create E experts across the
+    moe model parallel group, where E is the number of experts. Every expert
+    is a instence of the class, 'expert' in initialization parameters.
+    """
+
+    def __init__(self, expert, num_experts, **expert_args):
+        super().__init__()
+
+        assert num_experts % moe_env.model_parallel_size == 0, \
+            "The number of experts should be divied by moe model size"
+
+        num_local_experts = num_experts // moe_env.model_parallel_size
+        with seed(ParallelMode.MOE_MODEL):
+            self.experts = nn.ModuleList([
+                expert(**expert_args) for _ in range(num_local_experts)])
+        self.num_local_experts = num_local_experts
+        for exp in self.experts:
+            for param in exp.parameters():
+                param.__setattr__('moe_param', 1)
+
+    def forward(self, inputs):
+        expert_input = torch.chunk(inputs, self.num_local_experts, dim=0)
+        expert_output = []
+
+        for i in range(self.num_local_experts):
+            expert_output.append(self.experts[i](expert_input[i]))
+
+        output = torch.cat(expert_output, dim=0)
+        return output
+
+
+class Top1Router(nn.Module):
+    """Top1 router that returns the dispatch mask [s, e, c] and combine weight [s, e, c]
+    for routing usage. More deailted function can be found in the paper about Switch Transformer
+    of Google.
+    """
+
+    def __init__(self,
+                 capacity_factor: float,
+                 min_capacity: int,
+                 noisy_func=None):
+        super().__init__()
+        self.capacity_factor = capacity_factor
+        self.min_capacity = min_capacity
+        self.noisy_func = noisy_func
+        self.uniform = torch.distributions.uniform.Uniform(
+            low=torch.tensor(0.0, device=get_current_device()),
+            high=torch.tensor(1.0, device=get_current_device())).rsample
+
+    def get_capacity(self, logits_shape):
+        capacity = math.ceil(self.capacity_factor *
+                             logits_shape[0] / logits_shape[1])
+        if capacity < self.min_capacity:
+            capacity = self.min_capacity
+        return capacity
+
+    def forward(self, inputs):
+
+        if self.noisy_func is not None:
+            inputs_noisy = self.noisy_func(inputs)
+        else:
+            inputs_noisy = inputs
+
+        logits = F.softmax(inputs, dim=1)
+
+        num_experts = logits.shape[1]
+        capacity = self.get_capacity(logits.shape)
+
+        expert_idx = torch.argmax(inputs_noisy, dim=1)
+        expert_mask = F.one_hot(expert_idx, num_classes=num_experts)
+        expert_mask_f = expert_mask.float()
+
+        exp_counts = torch.sum(expert_mask, dim=0).detach().to('cpu')
+
+        me = torch.mean(logits, dim=0)
+        ce = torch.mean(expert_mask_f, dim=0)
+        l_aux = torch.sum(me * ce) * num_experts
+        moe_env.add_loss(l_aux)
+
+        rand_mask = expert_mask * self.uniform(logits.shape)
+        _, dispatch_idx = torch.topk(rand_mask, k=capacity, dim=0)
+
+        dispatch_mask = \
+            expert_mask * torch.zeros_like(expert_mask).scatter_(0, dispatch_idx, 1)
+
+        locations = torch.cumsum(dispatch_mask, dim=0) - 1
+        locations = torch.sum(dispatch_mask * locations, dim=1)
+        locations = F.one_hot(locations, num_classes=capacity)
+
+        logits = logits * dispatch_mask
+        combine_weights = logits.unsqueeze(2) * locations.unsqueeze(1)
+
+        sec_mask = combine_weights.bool()
+        return combine_weights, sec_mask, exp_counts
+
+
+class Top2Router(nn.Module):
+    """Top2 router that returns the dispatch mask [s, e, c] and combine weight [s, e, c]
+    for routing usage. More deailted function can be found in the paper about ViT-MoE.
+    """
+
+    def __init__(self, capacity_factor: float, noisy_func=None):
+        super().__init__()
+        self.capacity_factor = capacity_factor
+        self.noisy_func = noisy_func
+
+    def get_capacity(self, logits_shape):
+        capacity = math.ceil(2 * self.capacity_factor *
+                             logits_shape[0] / logits_shape[1])
+        return capacity
+
+    def forward(self, inputs):
+        if self.noisy_func is not None:
+            inputs = self.noisy_func(inputs)
+
+        logits = F.softmax(inputs, dim=-1)
+        num_experts = logits.size(-1)
+        capacity = self.get_capacity(logits.shape)
+
+        _, expert_idx = torch.topk(logits, k=2, dim=-1, largest=True, sorted=True)
+        top1_idx = expert_idx[:, 0]
+        top2_idx = expert_idx[:, 1]
+
+        mask1 = F.one_hot(top1_idx, num_classes=num_experts)
+        mask2 = F.one_hot(top2_idx, num_classes=num_experts)
+
+        loss_mask = (mask1 + mask2)
+        exp_counts = torch.sum(loss_mask, dim=0).detach().to('cpu')
+        me = torch.mean(logits, dim=0)
+        ce = torch.mean(loss_mask.float(), dim=0)
+        l_aux = num_experts * torch.sum(me * ce) / 2.0
+        moe_env.add_loss(l_aux)
+
+        locations1 = torch.cumsum(mask1, dim=0) - 1
+        locations2 = torch.cumsum(mask2, dim=0) - 1
+        locations2 += torch.sum(mask1, dim=0, keepdim=True)
+
+        mask1 *= torch.lt(locations1, capacity)
+        mask2 *= torch.lt(locations2, capacity)
+
+        weight1 = mask1 * logits
+        weight2 = mask2 * logits
+
+        locations1 = torch.sum(mask1 * locations1, dim=1)
+        locations2 = torch.sum(mask2 * locations2, dim=1)
+        locations1_sc = F.one_hot(locations1, num_classes=capacity)
+        locations2_sc = F.one_hot(locations2, num_classes=capacity)
+
+        combine_weights1 = weight1.unsqueeze(2) * locations1_sc.unsqueeze(1)
+        combine_weights2 = weight2.unsqueeze(2) * locations2_sc.unsqueeze(1)
+        combine_weights = combine_weights1 + combine_weights2
+        sec_mask = combine_weights.bool()
+
+        return combine_weights, sec_mask, exp_counts
+
+
+class MoeLayer(nn.Module):
+    """A MoE layer, that puts its input tensor to its gate and uses the output logits
+    to router all tokens, is mainly used to exchange all tokens for every expert across
+    the moe tensor group by all to all comunication. Then it will get the output of all
+    experts and exchange the output. At last returns the output of the moe system.
+    """
+
+    def __init__(self,
+                 dim_model: int,
+                 num_experts: int,
+                 router: nn.Module,
+                 experts: nn.Module):
+        super().__init__()
+        self.d_model = dim_model
+        self.num_experts = num_experts
+        self.gate = nn.Linear(dim_model, num_experts, device=get_current_device())
+        self.router = router
+        self.experts = experts
+
+    def _router_part(self, tokens: torch.Tensor):
+        gate_output = self.gate(tokens)
+        return self.router(gate_output)
+
+    def router_part(self, tokens: torch.Tensor):
+        autocast_context = torch.is_autocast_enabled()
+        if not autocast_context:
+            return self._router_part(tokens)
+        else:
+            with autocast(enabled=False):
+                if tokens.dtype == torch.float16:
+                    input_tokens = tokens.float()
+                else:
+                    input_tokens = tokens
+                return self._router_part(input_tokens)
+
+    def forward(self, inputs: torch.Tensor) -> torch.Tensor:
+        tokens = inputs.reshape(-1, self.d_model)
+
+        combine_weights, sec_mask, exp_counts = self.router_part(tokens)
+
+        sec_mask_f = sec_mask.type_as(inputs)
+        dispatch_data = torch.matmul(sec_mask_f.permute(1, 2, 0), tokens)
+
+        dispatch_data = AllToAll.apply(dispatch_data, ParallelMode.MOE_MODEL)
+
+        expert_output = self.experts(dispatch_data)
+
+        expert_output = AllToAll.apply(expert_output, ParallelMode.MOE_MODEL)
+
+        combine_weights = combine_weights.view(combine_weights.shape[0], -1)
+        expert_output = expert_output.view(-1, expert_output.shape[-1])
+
+        ret = torch.matmul(combine_weights, expert_output)
+        ret = ret.reshape(inputs.shape)
+
+        return ret

colossalai/nn/layer/vanilla/__init__.py

@@ -1,3 +1,5 @@
-from .layers import DropPath, VanillaClassifier, VanillaPatchEmbedding
+from .layers import DropPath, VanillaClassifier, VanillaPatchEmbedding, \
+    WrappedDropout, WrappedDropPath
 
-__all__ = ['VanillaPatchEmbedding', 'VanillaClassifier', 'DropPath']
+__all__ = ['VanillaPatchEmbedding', 'VanillaClassifier', 'DropPath',
+           'WrappedDropout', 'WrappedDropPath']

colossalai/nn/layer/vanilla/layers.py

@@ -10,6 +10,7 @@ from torch import Tensor, dtype
 from torch import nn as nn
 
 from ..utils import to_2tuple
+from colossalai.context import seed
 
 
 def drop_path(x, drop_prob: float = 0., training: bool = False):
@@ -42,6 +43,58 @@ class DropPath(nn.Module):
         return drop_path(x, self.drop_prob, self.training)
 
 
+class WrappedDropout(nn.Module):
+    """Same as torch.nn.Dropout. But it is wrapped with the context of seed manager.
+    """
+    def __init__(self, p: float = 0.5, inplace: bool = False, mode=None):
+        super().__init__()
+        if p < 0 or p > 1:
+            raise ValueError("dropout probability has to be between 0 and 1, "
+                             "but got {}".format(p))
+        self.p = p
+        self.inplace = inplace
+        if mode is None:
+            self.func = self.nonefunc
+        else:
+            self.func = self.normalfunc
+            self.mode = mode
+
+    def nonefunc(self, inputs):
+        return F.dropout(inputs, self.p, self.training, self.inplace)
+
+    def normalfunc(self, inputs):
+        with seed(self.mode):
+            return F.dropout(inputs, self.p, self.training, self.inplace)
+
+    def forward(self, inputs):
+        return self.func(inputs)
+
+
+class WrappedDropPath(nn.Module):
+    """Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks).
+    Here, it is wrapped with the context of seed manager.
+    """
+    def __init__(self, p: float = 0., mode=None):
+        super().__init__()
+        self.p = p
+        self.mode = mode
+        if self.mode is None:
+            self.func = self.nonefunc
+        else:
+            self.func = self.normalfunc
+            self.mode = mode
+
+    def nonefunc(self, inputs):
+        return drop_path(inputs, self.p, self.training)
+
+    def normalfunc(self, inputs):
+        with seed(self.mode):
+            return drop_path(inputs, self.p, self.training)
+
+    def forward(self, inputs):
+        return self.func(inputs)
+
+
 @LAYERS.register_module
 class VanillaPatchEmbedding(nn.Module):
     """ 2D Image to Patch Embedding

colossalai/nn/loss/__init__.py

@@ -6,6 +6,7 @@ from colossalai.nn.layer.utils import get_tensor_parallel_mode
 from .loss_2d import CrossEntropyLoss2D
 from .loss_2p5d import CrossEntropyLoss2p5D
 from .loss_3d import CrossEntropyLoss3D
+from .loss_moe import MoeCrossEntropyLoss, MoeLoss
 
 _parallel_cross_entropy = {
     '2d': CrossEntropyLoss2D,

colossalai/nn/loss/loss_moe.py

@@ -0,0 +1,34 @@
+import torch.nn as nn
+from colossalai.registry import LOSSES
+from torch.nn.modules.loss import _Loss
+from colossalai.global_variables import moe_env
+
+
+@LOSSES.register_module
+class MoeCrossEntropyLoss(_Loss):
+    """torch.nn.CrossEntropyLoss added with auxiliary loss.
+    """
+    def __init__(self, aux_weight: float = 0.01, *args, **kwargs):
+        super().__init__()
+        self.loss = nn.CrossEntropyLoss(*args, **kwargs)
+        self.aux_weight = aux_weight
+
+    def forward(self, *args):
+        main_loss = self.loss(*args)
+        aux_loss = moe_env.get_loss()
+        return main_loss + self.aux_weight * aux_loss
+
+
+@LOSSES.register_module
+class MoeLoss(_Loss):
+    """A wrapper class for any loss module to add with auxiliary loss.
+    """
+    def __init__(self, aux_weight: float, loss_fn, *args, **kwargs):
+        super().__init__()
+        self.loss_fn = loss_fn(*args, **kwargs)
+        self.aux_weight = aux_weight
+
+    def forward(self, *args, **kwargs):
+        main_loss = self.loss_fn(*args, **kwargs)
+        aux_loss = moe_env.get_loss()
+        return main_loss + self.aux_weight * aux_loss

colossalai/nn/lr_scheduler/cosine.py

@@ -66,7 +66,7 @@ class CosineAnnealingWarmupLR(WarmupScheduler):
     :type last_epoch: int, optional
     """
 
-    def __init__(self, optimizer, total_steps: int, warmup_steps: int = 0, eta_min: int = 0, last_epoch: int = -1):
+    def __init__(self, optimizer, total_steps: int, warmup_steps: int = 0, eta_min: float = 0., last_epoch: int = -1):
         base_scheduler = _CosineAnnealingLR(
             optimizer, total_steps - warmup_steps, eta_min=eta_min, last_epoch=last_epoch)
         super().__init__(optimizer, warmup_steps, base_scheduler)

colossalai/utils/common.py

@@ -17,6 +17,7 @@ import torch.distributed as dist
 from colossalai.constants import IS_TENSOR_PARALLEL, NUM_PARTITIONS, TENSOR_PARALLEL_ATTRIBUTES
 from colossalai.context.parallel_mode import ParallelMode
 from colossalai.core import global_context as gpc
+from colossalai.global_variables import moe_env
 
 from .multi_tensor_apply import multi_tensor_applier
 
@@ -91,6 +92,10 @@ def is_model_parallel_parameter(p):
     return hasattr(p, IS_TENSOR_PARALLEL) and getattr(p, IS_TENSOR_PARALLEL)
 
 
+def is_moe_parallel_parameter(p):
+    return hasattr(p, 'moe_param') and moe_env.data_parallel_size > 1
+
+
 def _calc_l2_norm(grads):
     norm = 0.0
     if len(grads) > 0:
@@ -165,26 +170,37 @@ def clip_grad_norm_fp32(parameters, max_norm, norm_type=2):
     else:
         tensor_parallel_grads = []
         no_tensor_parallel_grads = []
+        moe_parallel_grads = []  # used to collect moe tensor parallel gradients
         for p in params:
             if is_model_parallel_parameter(p):
                 reductor = (gpc.get_world_size(ParallelMode.TENSOR) / getattr(p, NUM_PARTITIONS)) ** (1 / norm_type)
                 tensor_parallel_grads.append(p.grad.data / reductor)
+            elif is_moe_parallel_parameter(p):
+                moe_parallel_grads.append(p.grad.data)
             else:
                 no_tensor_parallel_grads.append(p.grad.data)
+
         if norm_type == 2.0:
             tensor_parallel_norm = _calc_l2_norm(
                 tensor_parallel_grads) ** norm_type
             no_tensor_parallel_norm = _calc_l2_norm(
                 no_tensor_parallel_grads) ** norm_type
+            moe_parallel_norm = _calc_l2_norm(
+                moe_parallel_grads) ** norm_type
         else:
             tensor_parallel_norm = _calc_lp(tensor_parallel_grads, norm_type)
-            no_tensor_parallel_grads = _calc_lp(
+            no_tensor_parallel_norm = _calc_lp(
                 no_tensor_parallel_grads, norm_type)
+            moe_parallel_norm = _calc_lp(moe_parallel_grads, norm_type)
         # Sum across all model-parallel GPUs.
         if gpc.is_initialized(ParallelMode.TENSOR) and len(tensor_parallel_grads) > 0:
             dist.all_reduce(tensor_parallel_norm,
                             op=dist.ReduceOp.SUM,
                             group=gpc.get_group(ParallelMode.TENSOR))
+        # Sum across all moe-tensor-parallel GPUs
+        if len(moe_parallel_grads) > 0:
+            dist.all_reduce(moe_parallel_norm, group=gpc.get_group(ParallelMode.MOE_MODEL))
+            no_tensor_parallel_norm += moe_parallel_norm
         total_norm = tensor_parallel_norm + no_tensor_parallel_norm
         if gpc.is_initialized(ParallelMode.PIPELINE) and gpc.get_world_size(ParallelMode.PIPELINE) > 1:
             dist.all_reduce(total_norm,

model_zoo/helper.py

@@ -0,0 +1,26 @@
+import torch
+import torch.nn as nn
+from colossalai.nn.layer import WrappedDropPath as DropPath
+
+
+class TransformerLayer(nn.Module):
+    """Transformer layer builder.
+    """
+    def __init__(self,
+                 att: nn.Module,
+                 ffn: nn.Module,
+                 norm1: nn.Module,
+                 norm2: nn.Module,
+                 droppath=None,
+                 droppath_rate: float = 0):
+        super().__init__()
+        self.att = att
+        self.ffn = ffn
+        self.norm1 = norm1
+        self.norm2 = norm2
+        self.droppath = DropPath(droppath_rate) if droppath is None else droppath
+
+    def forward(self, x):
+        x = x + self.droppath(self.att(self.norm1(x)))
+        x = x + self.droppath(self.ffn(self.norm2(x)))
+        return x

model_zoo/moe/models.py

@@ -0,0 +1,146 @@
+import math
+import torch
+import torch.nn as nn
+from colossalai.context import ParallelMode
+from colossalai.nn.layer import VanillaPatchEmbedding, VanillaClassifier, \
+    WrappedDropout as Dropout, WrappedDropPath as DropPath
+from colossalai.nn.layer.moe import Experts, MoeLayer, Top2Router, NormalNoiseGenerator
+from .util import moe_sa_args, moe_mlp_args
+from ..helper import TransformerLayer
+from colossalai.global_variables import moe_env
+from colossalai.utils import get_current_device
+
+
+class VanillaSelfAttention(nn.Module):
+    """Standard ViT self attention.
+    """
+    def __init__(self,
+                 d_model: int,
+                 n_heads: int,
+                 d_kv: int,
+                 attention_drop: float = 0,
+                 drop_rate: float = 0,
+                 bias: bool = True,
+                 dropout1=None,
+                 dropout2=None):
+        super().__init__()
+        self.n_heads = n_heads
+        self.d_kv = d_kv
+        self.scale = 1.0 / math.sqrt(self.d_kv)
+
+        self.dense1 = nn.Linear(d_model, 3 * n_heads * d_kv, bias, device=get_current_device())
+        self.softmax = nn.Softmax(dim=-1)
+        self.atten_drop = nn.Dropout(attention_drop) if dropout1 is None else dropout1
+        self.dense2 = nn.Linear(n_heads * d_kv, d_model, device=get_current_device())
+        self.dropout = nn.Dropout(drop_rate) if dropout2 is None else dropout2
+
+    def forward(self, x):
+        qkv = self.dense1(x)
+        new_shape = qkv.shape[:2] + (3, self.n_heads, self.d_kv)
+        qkv = qkv.view(*new_shape)
+        qkv = qkv.permute(2, 0, 3, 1, 4)
+        q, k, v = qkv[:]
+
+        x = torch.matmul(q, k.transpose(-2, -1)) * self.scale
+        x = self.atten_drop(self.softmax(x))
+
+        x = torch.matmul(x, v)
+        x = x.transpose(1, 2)
+        new_shape = x.shape[:2] + (self.n_heads * self.d_kv,)
+        x = x.reshape(*new_shape)
+        x = self.dense2(x)
+        x = self.dropout(x)
+
+        return x
+
+
+class VanillaFFN(nn.Module):
+    """FFN composed with two linear layers, also called MLP.
+    """
+    def __init__(self,
+                 d_model: int,
+                 d_ff: int,
+                 activation=None,
+                 drop_rate: float = 0,
+                 bias: bool = True,
+                 dropout1=None,
+                 dropout2=None):
+        super().__init__()
+        dense1 = nn.Linear(d_model, d_ff, bias, device=get_current_device())
+        act = nn.GELU() if activation is None else activation
+        dense2 = nn.Linear(d_ff, d_model, bias, device=get_current_device())
+        drop1 = nn.Dropout(drop_rate) if dropout1 is None else dropout1
+        drop2 = nn.Dropout(drop_rate) if dropout2 is None else dropout2
+
+        self.ffn = nn.Sequential(
+            dense1, act, drop1,dense2, drop2)
+
+    def forward(self, x):
+        return self.ffn(x)
+
+
+class Widenet(nn.Module):
+    def __init__(self,
+                 num_experts: int,
+                 capacity_factor: float,
+                 img_size: int = 224,
+                 patch_size: int = 16,
+                 in_chans: int = 3,
+                 num_classes: int = 1000,
+                 depth: int = 12,
+                 d_model: int = 768,
+                 num_heads: int = 12,
+                 d_kv: int = 64,
+                 d_ff: int = 3072,
+                 attention_drop: float = 0.,
+                 drop_rate: float = 0.1,
+                 drop_path: float = 0.):
+        super().__init__()
+
+        embedding = VanillaPatchEmbedding(
+            img_size=img_size,
+            patch_size=patch_size,
+            in_chans=in_chans,
+            embed_size=d_model)
+        embed_dropout = Dropout(p=drop_rate, mode=ParallelMode.TENSOR)
+
+        shared_sa = VanillaSelfAttention(**moe_sa_args(
+            d_model=d_model, n_heads=num_heads, d_kv=d_kv,
+            attention_drop=attention_drop, drop_rate=drop_rate))
+
+        noisy_func = NormalNoiseGenerator(num_experts)
+        shared_router = Top2Router(capacity_factor, noisy_func=noisy_func)
+        shared_experts = Experts(expert=VanillaFFN,
+                                 num_experts=num_experts,
+                                 **moe_mlp_args(
+                                     d_model=d_model,
+                                     d_ff=d_ff,
+                                     drop_rate=drop_rate
+                                 ))
+
+        # stochastic depth decay rule
+        dpr = [x.item() for x in torch.linspace(0, drop_path, depth)]
+        blocks = [
+            TransformerLayer(
+                att=shared_sa,
+                ffn=MoeLayer(dim_model=d_model, num_experts=num_experts,
+                             router=shared_router, experts=shared_experts),
+                norm1=nn.LayerNorm(d_model, eps=1e-6),
+                norm2=nn.LayerNorm(d_model, eps=1e-6),
+                droppath=DropPath(p=dpr[i], mode=ParallelMode.TENSOR)
+            )
+            for i in range(depth)
+        ]
+        norm = nn.LayerNorm(d_model, eps=1e-6)
+        self.linear = VanillaClassifier(in_features=d_model,
+                                        num_classes=num_classes)
+        nn.init.zeros_(self.linear.weight)
+        nn.init.zeros_(self.linear.bias)
+        self.widenet = nn.Sequential(embedding, embed_dropout, *blocks, norm)
+
+    def forward(self, x):
+        moe_env.reset_loss()
+        x = self.widenet(x)
+        x = torch.mean(x, dim=1)
+        x = self.linear(x)
+        return x

model_zoo/moe/util.py

@@ -0,0 +1,41 @@
+from colossalai.context import ParallelMode
+from colossalai.nn.layer import WrappedDropout as Dropout
+
+
+def moe_sa_args(d_model: int,
+                n_heads: int,
+                d_kv: int,
+                attention_drop: float = 0,
+                drop_rate: float = 0,
+                bias: bool = True):
+    """This is an example for args in moe self attention, since lots of modules should be
+    adapted before putting them in experts.
+    """
+    dropout1 = Dropout(attention_drop, mode=ParallelMode.TENSOR)
+    dropout2 = Dropout(drop_rate, mode=ParallelMode.TENSOR)
+    return dict(
+        d_model=d_model,
+        n_heads=n_heads,
+        d_kv=d_kv,
+        bias=bias,
+        dropout1=dropout1,
+        dropout2=dropout2
+    )
+
+
+def moe_mlp_args(d_model: int,
+                 d_ff: int,
+                 drop_rate: float,
+                 bias: bool = True):
+    """This is an example for args of MLP in Experts, since lots of modules should be adapted
+    before putting them in experts.
+    """
+    dropout1 = Dropout(drop_rate, mode=ParallelMode.TENSOR)
+    dropout2 = Dropout(drop_rate, mode=ParallelMode.TENSOR)
+    return dict(
+        d_model=d_model,
+        d_ff=d_ff,
+        bias=bias,
+        dropout1=dropout1,
+        dropout2=dropout2
+    )