[feat] zerobubble support moehybridplugin;

colossalai/amp/naive_amp/mixed_precision_mixin/base.py

@@ -43,7 +43,7 @@ class MixedPrecisionMixin(ABC):
     dtype: torch.dtype
 
     @abstractmethod
-    def pre_backward(self, loss: Tensor) -> Tensor:
+    def pre_backward(self, loss: Tensor, *args, **kwargs) -> Tensor:
         """Called before backward.
 
         Args:

colossalai/amp/naive_amp/mixed_precision_optimizer.py

@@ -85,13 +85,18 @@ class MixedPrecisionOptimizer(OptimizerWrapper):
                     master_params.append(master_p)
             group["params"] = master_params
 
-    def backward(self, loss: Tensor, *args, **kwargs):
+    def backward(self, loss: Tensor, inputs=None, retain_graph=False, **kwargs):
         loss = self.mixed_precision.pre_backward(loss)
-        loss.backward(*args, **kwargs)
+        loss.backward(inputs=inputs, retain_graph=retain_graph, **kwargs)
 
-    def backward_by_grad(self, tensor: Tensor, grad: Tensor):
+    def backward_by_grad(self, tensor: Tensor, grad: Tensor, inputs: Tensor = None, retain_graph: bool = False):
         grad = self.mixed_precision.pre_backward_by_grad(tensor, grad)
-        tensor.backward(grad)
+        torch.autograd.backward(
+            tensors=tensor,
+            grad_tensors=grad,
+            inputs=inputs,
+            retain_graph=retain_graph,
+        )
 
     def zero_grad(self, *args, **kwargs):
         for p in self.working_to_master_map.keys():

colossalai/booster/mixed_precision/fp16_torch.py

@@ -46,9 +46,9 @@ class TorchAMPOptimizer(OptimizerWrapper):
             growth_interval=growth_interval,
         )
 
-    def backward(self, loss: Tensor, *args, **kwargs) -> None:
+    def backward(self, loss: Tensor, inputs=None, retain_graph=False, **kwargs) -> None:
         scaled_loss = self.scale_loss(loss)
-        scaled_loss.backward(*args, **kwargs)
+        scaled_loss.backward(inputs=inputs, retain_graph=retain_graph, **kwargs)
 
     def step(self, *args, **kwargs) -> Optional[float]:
         out = self.scaler.step(self.optim, *args, **kwargs)

colossalai/booster/plugin/hybrid_parallel_plugin.py

@@ -28,7 +28,7 @@ from colossalai.interface import AMPModelMixin, ModelWrapper, OptimizerWrapper
 from colossalai.interface.optimizer import DistributedOptim
 from colossalai.logging import get_dist_logger
 from colossalai.nn.optimizer import DistGaloreAwamW, cast_to_distributed
-from colossalai.pipeline.schedule import InterleavedSchedule, OneForwardOneBackwardSchedule
+from colossalai.pipeline.schedule import InterleavedSchedule, OneForwardOneBackwardSchedule, ZeroBubbleVPipeScheduler
 from colossalai.pipeline.stage_manager import PipelineStageManager
 from colossalai.quantization import BnbQuantizationConfig, quantize_model
 from colossalai.shardformer import GradientCheckpointConfig, ShardConfig, ShardFormer
@@ -288,7 +288,7 @@ class HybridParallelNaiveOptimizer(OptimizerWrapper):
         self.pp_size = get_world_size(self.pp_pg) if self.pp_pg is not None else 1
         super().__init__(optim)
 
-    def backward(self, loss: Tensor, *args, **kwargs):
+    def backward(self, loss: Tensor, inputs=None, retain_graph=False, **kwargs):
         r"""
         Backpropagate gradients through the model and optionally synchronize sequence parallelism gradients.
 
@@ -306,7 +306,7 @@ class HybridParallelNaiveOptimizer(OptimizerWrapper):
         """
 
         # Call the superclass backward method to compute gradients.
-        super().backward(loss, *args, **kwargs)
+        super().backward(loss, inputs=inputs, retain_graph=retain_graph, **kwargs)
 
         if self.model.require_grad_sync:
             # If gradient synchronization is required, sync sequence parallelism gradients.
@@ -315,7 +315,7 @@ class HybridParallelNaiveOptimizer(OptimizerWrapper):
             # If gradient synchronization is is not required, return.
             return
 
-    def backward_by_grad(self, tensor: Tensor, grad: Tensor):
+    def backward_by_grad(self, tensor: Tensor, grad: Tensor, inputs: Tensor = None, retain_graph: bool = False):
         """
         Backpropagate gradients through the model using a precomputed gradient and optionally synchronize sequence parallelism gradients.
 
@@ -332,7 +332,7 @@ class HybridParallelNaiveOptimizer(OptimizerWrapper):
         """
 
         # Call the superclass backward method to compute gradients.
-        super().backward_by_grad(tensor, grad)
+        super().backward_by_grad(tensor, grad, inputs=inputs, retain_graph=retain_graph)
 
         if self.model.require_grad_sync:
             # If gradient synchronization is required, sync sequence parallelism gradients.
@@ -512,7 +512,7 @@ class HybridParallelAMPOptimizer(MixedPrecisionOptimizer):
             max_norm=max_norm,
         )
 
-    def backward(self, loss: Tensor, *args, **kwargs):
+    def backward(self, loss: Tensor, inputs=None, retain_graph=False, **kwargs):
         r"""
         Backpropagate gradients through the model and optionally synchronize sequence parallelism gradients.
 
@@ -529,7 +529,7 @@ class HybridParallelAMPOptimizer(MixedPrecisionOptimizer):
             None
         """
         # Call the superclass backward method to compute gradients.
-        super().backward(loss, *args, **kwargs)
+        super().backward(loss, inputs=inputs, retain_graph=retain_graph, **kwargs)
 
         if self.model.require_grad_sync:
             # If gradient synchronization is required, sync sequence parallelism gradients.
@@ -538,7 +538,7 @@ class HybridParallelAMPOptimizer(MixedPrecisionOptimizer):
             # If gradient synchronization is is not required, return.
             return
 
-    def backward_by_grad(self, tensor: Tensor, grad: Tensor):
+    def backward_by_grad(self, tensor: Tensor, grad: Tensor, inputs: Tensor = None, retain_graph: bool = False):
         """
         Backpropagate gradients through the model using a precomputed gradient and optionally synchronize sequence parallelism gradients.
 
@@ -554,7 +554,7 @@ class HybridParallelAMPOptimizer(MixedPrecisionOptimizer):
             None
         """
         # Call the superclass backward method to compute gradients.
-        super().backward_by_grad(tensor, grad)
+        super().backward_by_grad(tensor, grad, inputs=inputs, retain_graph=retain_graph)
 
         if self.model.require_grad_sync:
             # If gradient synchronization is required, sync sequence parallelism gradients.
@@ -768,7 +768,7 @@ class HybridParallelZeroOptimizer(LowLevelZeroOptimizer):
         else:
             return
 
-    def backward(self, loss, retain_graph=False):
+    def backward(self, loss, inputs=None, retain_graph=False):
         """
         Backpropagate gradients through the model and optionally synchronize sequence parallelism gradients.
 
@@ -784,7 +784,7 @@ class HybridParallelZeroOptimizer(LowLevelZeroOptimizer):
             None
         """
         # Call the superclass backward method to compute gradients.
-        super().backward(loss, retain_graph)
+        super().backward(loss, inputs=inputs, retain_graph=retain_graph)
 
         if self.require_grad_sync and self.model.shard_config.enable_sequence_parallelism:
             # If gradient synchronization is required, sync sequence parallelism gradients.
@@ -793,7 +793,7 @@ class HybridParallelZeroOptimizer(LowLevelZeroOptimizer):
             # If gradient synchronization is is not required, return.
             return
 
-    def backward_by_grad(self, tensor, grad):
+    def backward_by_grad(self, tensor, grad, inputs: Tensor = None, retain_graph: bool = False):
         """
         Backpropagate gradients through the model using a precomputed gradient and optionally synchronize sequence parallelism gradients.
 
@@ -809,7 +809,7 @@ class HybridParallelZeroOptimizer(LowLevelZeroOptimizer):
             None
         """
         # Call the superclass backward_by_grad method to compute gradients.
-        super().backward_by_grad(tensor, grad)
+        super().backward_by_grad(tensor, grad, inputs=inputs, retain_graph=retain_graph)
 
         if self.require_grad_sync and self.model.shard_config.enable_sequence_parallelism:
             # If gradient synchronization is required, sync sequence parallelism gradients.
@@ -1013,6 +1013,7 @@ class HybridParallelPlugin(PipelinePluginBase):
         custom_policy: Policy = None,
         pp_style: str = "1f1b",
         num_model_chunks: int = 1,
+        scheduler_nodes: List = None,
         num_layers_per_stage: Optional[List[int]] = None,
         gradient_checkpoint_config: Optional[GradientCheckpointConfig] = None,
         enable_metadata_cache: bool = True,
@@ -1029,6 +1030,9 @@ class HybridParallelPlugin(PipelinePluginBase):
             dist.get_world_size() % (tp_size * pp_size) == 0
         ), f"World size {dist.get_world_size()} is not divisible by tp_size {tp_size} * pp_size {pp_size}"
 
+        assert (
+            not pp_style == "zbv" or scheduler_nodes is not None
+        ), f"scheduler_nodes must not be None when using zero bubble pipeline."
         if enable_sequence_parallelism:
             self.sequence_parallelism_mode = (
                 sequence_parallelism_mode if sequence_parallelism_mode is not None else "all_to_all"
@@ -1088,29 +1092,39 @@ class HybridParallelPlugin(PipelinePluginBase):
                 self.pg_mesh = ProcessGroupMesh(self.pp_size, self.dp_size, self.tp_size, self.sp_size)
 
         self.stage_manager = None
-        self.schedule = None
+        self.scheduler = None
         self.custom_policy = custom_policy
         assert zero_stage in (0, 1, 2)
         if self.pp_size > 1:
-            assert pp_style in ["1f1b", "interleaved"], "Unsupported pipeline parallelism style"
+            assert pp_style in ["1f1b", "interleaved", "zbv"], "Unsupported pipeline parallelism style"
-            assert pp_style == "interleaved" or num_model_chunks == 1, "num_model_chunks must be 1 when using 1f1b"
+            assert (
+                pp_style in ["interleaved", "zbv"] or num_model_chunks == 1
+            ), "num_model_chunks must be 1 when using 1f1b"
+            assert (
+                pp_style in ["1f1b", "interleaved"] or num_model_chunks == 2
+            ), "num_model_chunks must be 2 when using zero bubble pipeline"
             assert (
                 num_microbatches is not None or microbatch_size is not None
             ), "num_microbatches or microbatch_size must be specified when using pipeline parallelism"
             assert (
                 self.zero_stage <= 1
             ), "To avoid prohibitive gradient synchronization costs, zero stage must be 0 or 1 when using pipeline parallelism"
+            if pp_style == "zbv":
+                self.logger.warning(
+                    """the enable_gradient_checkpointing function must set the use_reentrant to False, such as  model.gradient_checkpointing_enable(gradient_checkpointing_kwargs={'use_reentrant':False})"""
+                )
             self.stage_manager = PipelineStageManager(
                 self.pg_mesh,
                 pipeline_axis=self.pp_axis,
-                enable_interleave=(pp_style == "interleaved"),
+                enable_interleave=(pp_style == "interleaved" or pp_style == "zbv"),
+                use_zbv=(pp_style == "zbv"),
                 num_model_chunks=num_model_chunks,
                 num_layers_per_stage=num_layers_per_stage,
             )
 
             if pp_style == "interleaved":
                 assert num_model_chunks > 1, "number of model chunks must be > 1 when using interleaved"
-                self.schedule = InterleavedSchedule(
+                self.scheduler = InterleavedSchedule(
                     stage_manager=self.stage_manager,
                     num_model_chunks=num_model_chunks,
                     num_microbatch=num_microbatches,
@@ -1119,12 +1133,20 @@ class HybridParallelPlugin(PipelinePluginBase):
                     overlap_p2p=overlap_p2p,
                 )
             elif pp_style == "1f1b":
-                self.schedule = OneForwardOneBackwardSchedule(
+                self.scheduler = OneForwardOneBackwardSchedule(
                     stage_manager=self.stage_manager,
                     num_microbatches=num_microbatches,
                     microbatch_size=microbatch_size,
                     enable_metadata_cache=enable_metadata_cache,
                 )
+            elif pp_style == "zbv":
+                self.scheduler = ZeroBubbleVPipeScheduler(
+                    stage_manager=self.stage_manager,
+                    schedule=scheduler_nodes,
+                    num_model_chunks=num_model_chunks,
+                    num_microbatch=num_microbatches,
+                    microbatch_size=microbatch_size,
+                )
             else:
                 raise NotImplementedError()
         if sequence_parallelism_mode == "ring_attn":
@@ -1236,7 +1258,6 @@ class HybridParallelPlugin(PipelinePluginBase):
 
         # Replace with distributed implementation if exists
         optimizer = cast_to_distributed(optimizer)
-
         if isinstance(optimizer, DistGaloreAwamW) and zero_stage > 0 and self.dp_size > 0:
             self.logger.warning(
                 "Galore is only supported for Tensor Parallel and vanilla Data Parallel yet. Disabling ZeRO.",
@@ -1352,7 +1373,7 @@ class HybridParallelPlugin(PipelinePluginBase):
         ctx = optimizer.no_sync() if isinstance(optimizer, HybridParallelZeroOptimizer) else model.no_sync()
 
         with ctx, model._wait_all_gather():
-            outputs = self.schedule.forward_backward_step(
+            outputs = self.scheduler.forward_backward_step(
                 model, data_iter, criterion, optimizer, return_loss, return_outputs
             )
 

colossalai/booster/plugin/moe_hybrid_parallel_plugin.py

@@ -280,14 +280,17 @@ class MoeHybridParallelPlugin(HybridParallelPlugin):
             self.pg_mesh = ProcessGroupMesh(self.pp_size, self.moe_dp_size, self.ep_size, self.tp_size, self.sp_size)
 
         self.stage_manager = None
-        self.schedule = None
+        self.scheduler = None
         self.custom_policy = custom_policy
         assert zero_stage in (0, 1, 2)
         if self.pp_size > 1:
             assert pp_style in ["1f1b", "interleaved", "zbv"], "Unsupported pipeline parallelism style"
             assert (
-                pp_style == "interleaved" or pp_style == "zbv"
+                pp_style in ["interleaved", "zbv"] or num_model_chunks == 1
-            ) or num_model_chunks == 1, "num_model_chunks must be 1 when using 1f1b"
+            ), "num_model_chunks must be 1 when using 1f1b"
+            assert (
+                pp_style in ["1f1b", "interleaved"] or num_model_chunks == 2
+            ), "num_model_chunks must be 2 when using zero bubble pipeline"
             assert (
                 num_microbatches is not None or microbatch_size is not None
             ), "num_microbatches or microbatch_size must be specified when using pipeline parallelism"
@@ -300,11 +303,12 @@ class MoeHybridParallelPlugin(HybridParallelPlugin):
                 enable_interleave=(pp_style == "interleaved" or pp_style == "zbv"),
                 num_model_chunks=num_model_chunks,
                 num_layers_per_stage=num_layers_per_stage,
+                use_zbv=(pp_style == "zbv"),
             )
 
             if pp_style == "interleaved":
                 assert num_model_chunks > 1, "number of model chunks must be > 1 when using interleaved"
-                self.schedule = InterleavedSchedule(
+                self.scheduler = InterleavedSchedule(
                     stage_manager=self.stage_manager,
                     num_model_chunks=num_model_chunks,
                     num_microbatch=num_microbatches,
@@ -313,14 +317,15 @@ class MoeHybridParallelPlugin(HybridParallelPlugin):
                     overlap_p2p=overlap_p2p,
                 )
             elif pp_style == "1f1b":
-                self.schedule = OneForwardOneBackwardSchedule(
+                self.scheduler = OneForwardOneBackwardSchedule(
                     stage_manager=self.stage_manager,
                     num_microbatches=num_microbatches,
                     microbatch_size=microbatch_size,
                     enable_metadata_cache=enable_metadata_cache,
                 )
             elif pp_style == "zbv":
-                self.schedule = ZeroBubbleVPipeScheduler(
+                assert num_model_chunks > 1, "number of model chunks must be > 1 when using ZerbubbleV"
+                self.scheduler = ZeroBubbleVPipeScheduler(
                     schedule=scheduler_nodes,
                     stage_manager=self.stage_manager,
                     num_model_chunks=num_model_chunks,

colossalai/pipeline/stage_manager.py

@@ -136,7 +136,11 @@ class PipelineStageManager:
         if not self.is_interleave or ignore_chunk:
             return self.stage == self.num_stages - 1
         else:
-            return self.stage == self.num_stages - 1 and self.model_chunk_id == self.num_model_chunks - 1
+            # use zero bubble pipeline
+            if self.use_zbv:
+                return self.stage == 0 and self.model_chunk_id == self.num_model_chunks - 1
+            else:
+                return self.stage == self.num_stages - 1 and self.model_chunk_id == self.num_model_chunks - 1
 
     @property
     def num_stages(self) -> int:

colossalai/shardformer/policies/mixtral.py

@@ -234,14 +234,28 @@ class MixtralPolicy(Policy):
         stage_manager = self.pipeline_stage_manager
 
         held_layers = []
-        layers_per_stage = stage_manager.distribute_layers(len(module.layers))
-        if stage_manager.is_first_stage():
-            held_layers.append(module.embed_tokens)
-        start_idx, end_idx = stage_manager.get_stage_index(layers_per_stage)
-        held_layers.extend(module.layers[start_idx:end_idx])
-        if stage_manager.is_last_stage():
-            held_layers.append(module.norm)
 
+        if stage_manager.is_interleave:
+            assert stage_manager.num_model_chunks is not None
+            layers_per_stage = stage_manager.distribute_layers(len(module.layers))
+            stage_indices = stage_manager.get_stage_index(layers_per_stage)
+            stage_manager.stage_indices = stage_indices
+            if stage_manager.is_first_stage(ignore_chunk=True):
+                held_layers.append(module.embed_tokens)
+            for start_idx, end_idx in stage_indices:
+                held_layers.extend(module.layers[start_idx:end_idx])
+            if stage_manager.use_zbv and stage_manager.is_first_stage(ignore_chunk=True):
+                held_layers.append(module.norm)
+            elif stage_manager.is_last_stage(ignore_chunk=True):
+                held_layers.append(module.norm)
+        else:
+            layers_per_stage = stage_manager.distribute_layers(len(module.layers))
+            if stage_manager.is_first_stage():
+                held_layers.append(module.embed_tokens)
+            start_idx, end_idx = stage_manager.get_stage_index(layers_per_stage)
+            held_layers.extend(module.layers[start_idx:end_idx])
+            if stage_manager.is_last_stage():
+                held_layers.append(module.norm)
         return held_layers
 
 

tests/test_pipeline/test_schedule/test_zerobubble_pp.py

@@ -7,17 +7,28 @@ import torch
 import torch.distributed as dist
 import torch.nn as nn
 from torch.testing import assert_close
+from transformers.models.mixtral.configuration_mixtral import MixtralConfig
+from transformers.models.mixtral.modeling_mixtral import MixtralModel
 
 import colossalai
+from colossalai.booster.booster import Booster
+from colossalai.booster.plugin.moe_hybrid_parallel_plugin import MoeHybridParallelPlugin
 from colossalai.cluster import ProcessGroupMesh
 from colossalai.interface import OptimizerWrapper
 from colossalai.logging import disable_existing_loggers
 from colossalai.pipeline.schedule.v_schedule import PipelineGraph, ScheduledNode
 from colossalai.pipeline.schedule.zero_bubble_pp import ZeroBubbleVPipeScheduler
 from colossalai.pipeline.stage_manager import PipelineStageManager
-from colossalai.tensor.d_tensor.api import clear_layout_converter
 from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn
-from tests.kit.model_zoo import model_zoo
+from colossalai.testing.random import seed_all
+from tests.test_moe.moe_utils import assert_loose_close
+
+NUM_BATCH = 8
+NUM_TOK_PER_BATCH, NUM_EXPERTS = 4, 4
+NUM_LAYERS = 8
+HIDDEN_SIZE_PER_HEAD = 4
+NUM_HEADS = 4
+TOP_K = 1
 
 
 class MlpModel(nn.Module):
@@ -730,127 +741,165 @@ def run_fwd_bwd_vschedule_with_optim(test_config):
     assert_optim_param_groups(optim_base_param_groups, optim_pp_param_groups)
 
 
-# TODO:4) support Hybrid base 3)
+# TODO:3) support booster & Hybrid base 2)
 def run_with_hybridplugin(test_config):
     pass
 
 
-# TODO:5) support MoEHybrid base 3)
+# TODO:4) support booster & MoEHybrid base 2)
 @parameterize(
-    "test_config",
+    "config",
     [
-        {
+        (0, 1, 4, 1, 1),
-            "pp_style": "zbv",
+        # (0, 2, 2, 1, 1),
-            "tp_size": 1,
+        # (0, 2, 1, 2, 1),
-            "ep_size": 1,
+        # (0, 2, 1, 1, 2),
-            "pp_size": 4,
-            "num_microbatches": 4,
-            "zero_stage": 1,
-            "precision": "bf16",
-            "num_model_chunks": 2,
-        },
     ],
 )
-def run_with_moehybridplugin(test_config):
+def run_with_booster_moehybridplugin(config: Tuple[int, ...]):
-    sub_model_zoo = model_zoo.get_sub_registry("transformers_bert")
+    stage, ep_size, pp_size, tp_size, sp_size = config
-    # test_config["use_lazy_init"] = False
+    num_microbatches = pp_size
-    test_config["initial_scale"] = 2**16
+    dist.get_world_size()
-    model_list = [
+    rank = dist.get_rank()
-        "transformers_bert",
+    dtype, precision = torch.float16, "fp16"
-    ]
+    torch.cuda.set_device(dist.get_rank())
-    clear_layout_converter()
 
-    for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items():
+    ########
-        if name in model_list:
+    # init base model
-            # base param
+    ########
-            model = model_fn()
+    assert pp_size <= NUM_LAYERS, "pp_size should be less than or equal to NUM_LAYERS"
-            data = data_gen_fn()
+    config = MixtralConfig(
-            print(f"data {data}")
+        hidden_size=HIDDEN_SIZE_PER_HEAD * NUM_HEADS,
-            criterion = loss_fn
+        intermediate_size=HIDDEN_SIZE_PER_HEAD * NUM_HEADS * 2,
-            optimizer = torch.optim.SGD(model.parameters(), momentum=0.1, lr=1e-5)
+        num_hidden_layers=NUM_LAYERS,
+        num_attention_heads=NUM_HEADS,
+        num_key_value_heads=NUM_HEADS,
+        num_local_experts=NUM_EXPERTS,
+        num_experts_per_tok=TOP_K,
+        attn_implementation="flash_attention_2",
+    )
 
-            output = model(**data)
+    # init model with the same seed
-            loss = criterion(output)
+    seed_all(10086)
-            loss.backward()
-            optimizer.step()
-            print(f"output {output}")
 
-            # # pp param
+    torch_model = MixtralModel(config).to(dtype).cuda()
-            # model_pp = deepcopy(model)
+    torch_optimizer = torch.optim.SGD(torch_model.parameters(), lr=1)
-            # data_pp = deepcopy(data)
+    # init schedule
-            # optimizer_pp = OptimizerWrapper(torch.optim.SGD(model_pp.parameters(), momentum=0.1, lr=1e-5))
+    h, a, s = config.hidden_size, config.num_attention_heads, 1024
+    mem_f = 34 * h + 5 * a * s
+    mem_w = -32 * h
+    mem_b = -mem_w - mem_f
+    graph = PipelineGraph(
+        n_stage=pp_size,
+        n_micro=num_microbatches,
+        f_cost=1,
+        b_cost=1,
+        w_cost=1,
+        c_cost=1,
+        f_mem=mem_f,
+        b_mem=mem_b,
+        w_mem=mem_w,
+        # max_mem=mem_f * (p * 2 + m_offset),
+    )
 
-            # # init pipeline graph
+    zbv_schedule = graph.get_v_schedule()
-            # h, a, s = model.config.hidden_size, model.config.num_attention_heads, 1024
-            # mem_f = 34 * h + 5 * a * s
-            # mem_w = -32 * h
-            # mem_b = -mem_w - mem_f
-            # graph = PipelineGraph(
-            #     n_stage=test_config["pp_size"],
-            #     n_micro=test_config["num_microbatches"],
-            #     f_cost=1,
-            #     b_cost=1,
-            #     w_cost=1,
-            #     c_cost=1,
-            #     f_mem=mem_f,
-            #     b_mem=mem_b,
-            #     w_mem=mem_w,
-            #     # max_mem=mem_f * (p * 2 + m_offset),
-            # )
 
-            # zbv_schedule = graph.get_v_schedule()
+    # init MoeHybridPlugin
+    plugin = MoeHybridParallelPlugin(
+        pp_size=pp_size,
+        num_microbatches=pp_size,
+        tp_size=tp_size,
+        sp_size=sp_size,
+        ep_size=ep_size,
+        zero_stage=stage,
+        enable_sequence_parallelism=sp_size > 1,
+        sequence_parallelism_mode="all_to_all" if sp_size > 1 else None,
+        overlap_communication=False,
+        initial_scale=1,
+        precision=precision,
+        find_unused_parameters=True,
+        pp_style="zbv",
+        scheduler_nodes=zbv_schedule,
+        num_model_chunks=2,
+    )
 
-            # test_config["scheduler_nodes"] = zbv_schedule
+    dp_size = plugin.dp_size
-            # plugin = MoeHybridParallelPlugin(
-            #     **test_config
-            # )
-            # model_pp, optimizer_pp, criterion, data_pp, _ = plugin.configure(
-            #     model = model_pp,
-            #     optimizer = optimizer_pp,
-            #     criterion = criterion,
-            #     dataloader = data_pp,
-            # )
 
-            # output_pp = plugin.execute_pipeline(
+    booster = Booster(plugin=plugin)
-            #     data_iter=iter(data),
-            #     model=model,
-            #     criterion=criterion,
-            #     optimizer=optimizer,
-            #     return_loss = True,
-            #     return_outputs = True,
-            # )
 
+    ########
+    # init pp model
+    ########
 
-# TODO:6) support booster & Hybrid base 4)
+    parallel_model = deepcopy(torch_model)
+    parallel_optimizer = torch.optim.SGD(parallel_model.parameters(), lr=1)
+    parallel_model, parallel_optimizer, _, _, _ = booster.boost(parallel_model, parallel_optimizer)
+    # create different input along dp axis
+    seed_all(1453 + rank)
 
+    torch_model.train()
+    parallel_model.train()
+    for _ in range(2):
+        # gen random input
+        input_embeddings = torch.rand(
+            NUM_BATCH, NUM_TOK_PER_BATCH, HIDDEN_SIZE_PER_HEAD * NUM_HEADS, requires_grad=True
+        ).cuda()
+        dist.all_reduce(
+            input_embeddings, group=plugin.pp_group
+        )  # pp inputs except the first stage doesn't matter, but need to be replicate for torch model check
 
-# TODO:7) support booster & MoEHybrid base 4)
+        dist.all_reduce(input_embeddings, group=plugin.tp_group)  # tp group duplicate input
-@parameterize(
+        dist.all_reduce(input_embeddings, group=plugin.sp_group)  # sp group duplicate input
-    "test_config",
+
-    [
+        # run the model with hybrid parallel
-        {
+        if booster.plugin.stage_manager is not None:
-            "pp_style": "zbv",
+            # for test with pp
-            "tp_size": 1,
+            data_iter = iter([{"inputs_embeds": input_embeddings}])
-            "ep_size": 1,
+            sharded_output = booster.execute_pipeline(
-            "pp_size": 4,
+                data_iter,
-            "num_microbatches": 4,
+                parallel_model,
-            "zero_stage": 1,
+                lambda x, y: x.last_hidden_state.mean(),
-            "precision": "bf16",
+                parallel_optimizer,
-            "num_model_chunks": 2,
+                return_loss=True,
-        },
+                return_outputs=True,
-    ],
+            )
-)
+            # stage 0 chunk 0
-def run_with_booster_moehybridplugin(test_config):
+            parallel_output = None
-    pass
+            if rank == dist.get_process_group_ranks(plugin.pp_group)[0]:
+                parallel_output = sharded_output["loss"]
+
+        else:
+            # for test without pp
+            parallel_output = parallel_model(inputs_embeds=input_embeddings.to(dtype)).last_hidden_state.mean()
+            parallel_optimizer.backward(parallel_output)
+        parallel_optimizer.step()
+        parallel_optimizer.zero_grad()
+        # dist.all_reduce(parallel_output, group=plugin.dp_group)
+
+        # ===================================================================================
+        # run normal model with all dp(different) inputs
+        all_inputs = [torch.empty_like(input_embeddings) for _ in range(dp_size)]
+        dist.all_gather(all_inputs, input_embeddings, group=plugin.dp_group)
+        torch_output_sum = 0
+        for input_data_ in all_inputs:
+            torch_output = torch_model(inputs_embeds=input_data_.to(dtype)).last_hidden_state.mean()
+            torch_output.backward()
+            torch_output_sum += torch_output.detach()
+        # avg dp grads follows zero optimizer
+        for p in torch_model.parameters():
+            if p.grad is not None:
+                p.grad /= dp_size
+        torch_optimizer.step()
+        torch_optimizer.zero_grad()
+        if rank == dist.get_process_group_ranks(plugin.pp_group)[0]:
+            assert_loose_close(parallel_output, torch_output_sum, dtype=dtype)
 
 
 def run_dist(rank, world_size, port):
     disable_existing_loggers()
     colossalai.launch(rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
-    # run_fwd_bwd_iter_input()
+    # run_fwd_bwd_vschedule_with_optim()
-    run_fwd_bwd_vschedule_with_optim()
+    run_with_booster_moehybridplugin()
-    # run_with_moehybridplugin()
-    # run_with_booster_moehybridplugin()
 
 
 @pytest.mark.dist