[plugin] hybrid support zero bubble pipeline (#6060)

* hybrid support zbv

* fix

fix

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* fix

* Update zero_bubble_pp.py

* fix

* fix-ci

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* [zerobubble]Support ZeroBubble Pipeline (#6034)

* [feat] add zerobubble pp (just a frame now); add POC test for dx_dw; add test for zerobubble;

* [feat] add dw test;

* [fix] fix weight not close;

* [update] update text;

* [feat] add test run_fwd_bwd automatic scheduling;

* [feat] split communication and calculation; fix pop empty send_bwd_buffer error;

* [feat] add test for p & p grad;

* [feat] add comments for ZBV func;

* [fix] rm useless assign and comments;

* [fix] fix ci test; add pytest;

* [feat] add run_fwd_bwd_with_microbatch  (replace input) & test; add p&p.grad assert close test & all pass;

* [feat] add apply v_schedule graph; p & p.grad assert err exist;

* [fix] update

* [feat] fix ci; add assert;

* [feat] fix poc format

* [feat] fix func name & ci; add comments;

* [fix] fix poc test; add comments in poc;

* [feat] add optim backward_b_by_grad

* [feat] fix optimizer bwd b & w; support return accum loss & output

* [feat] add fwd_bwd_step, run_fwd_only;

* [fix] fix optim bwd; add license for v_schedule; remove redundant attributes; fix schedule loop "while"--> "for"; add communication dict;

* [fix] fix communication_map;

* [feat] update test; rm comments;

* [fix] rm zbv in hybridplugin

* [fix] fix optim bwd;

* [fix] fix optim bwd;

* [fix] rm output.data after send fwd;

* [fix] fix bwd step if condition; remove useless comments and format info;

* [fix] fix detach output & release output;

* [fix] rm requir_grad for output;

* [fix] fix requir grad position and detach position and input&output local buffer append position;

* [feat] add memory assertation;

* [fix] fix mem check;

* [fix] mem assertation'

* [fix] fix mem assertation

* [fix] fix mem; use a new model shape; only assert mem less and equal than theo;

* [fix] fix model zoo import;

* [fix] fix redundant detach & clone; add buffer assertation in the end;

* [fix] add output_obj_grad assert None at bwd b step; replace input_obj.require_grad_ with treemap;

* [fix] update optim state dict assert (include param group & state); fix mem assert after add optim;

* [fix] add testcase with microbatch 4;

* hybrid support zbv

* fix

fix

* fix

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* Update zero_bubble_pp.py

* fix

* fix-ci

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---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: duanjunwen <935724073@qq.com>

.github/workflows/build_on_pr.yml

@@ -140,7 +140,7 @@ jobs:
 
       - name: Install Colossal-AI
         run: |
-          BUILD_EXT=1 pip install -v -e .
+          BUILD_EXT=1 pip install -v .
           pip install --no-cache-dir -r requirements/requirements-test.txt
 
       - name: Store Colossal-AI Cache

.github/workflows/build_on_schedule.yml

@@ -55,7 +55,7 @@ jobs:
         if: steps.check-avai.outputs.avai == 'true'
         run: |
           [ ! -z "$(ls -A /github/home/cuda_ext_cache/)" ] && cp -r /github/home/cuda_ext_cache/* /__w/ColossalAI/ColossalAI/
-          BUILD_EXT=1 pip install -v -e .
+          BUILD_EXT=1 pip install -v .
           cp -r /__w/ColossalAI/ColossalAI/build /github/home/cuda_ext_cache/
           pip install --no-cache-dir -r requirements/requirements-test.txt
 

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

@@ -280,7 +280,7 @@ 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:
@@ -304,7 +304,7 @@ class MoeHybridParallelPlugin(HybridParallelPlugin):
 
             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,
@@ -313,7 +313,7 @@ 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,

colossalai/interface/optimizer.py

@@ -49,11 +49,11 @@ class OptimizerWrapper:
         """
         self.optim.zero_grad(*args, **kwargs)
 
-    def backward(self, loss: Tensor, *args, **kwargs):
+    def backward(self, loss: Tensor, inputs=None, retain_graph=False, **kwargs):
         """
         Performs a backward pass on the loss.
         """
-        loss.backward(*args, **kwargs)
+        loss.backward(inputs=inputs, retain_graph=retain_graph, **kwargs)
 
     def backward_by_grad(self, tensor: Tensor, grad: Tensor, inputs: Tensor = None, retain_graph: bool = False):
         """

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/llama.py

@@ -261,7 +261,9 @@ class LlamaPolicy(Policy):
                 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.is_last_stage(ignore_chunk=True):
+            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:
@@ -351,7 +353,9 @@ class LlamaForCausalLMPolicy(LlamaPolicy):
         """Get pipeline layers for current stage."""
         stage_manager = self.pipeline_stage_manager
         held_layers = super().get_held_layers()
-        if stage_manager.is_last_stage(ignore_chunk=True):
+        if stage_manager.use_zbv and stage_manager.is_first_stage(ignore_chunk=True):
+            held_layers.append(self.model.lm_head)
+        elif stage_manager.is_last_stage(ignore_chunk=True):
             held_layers.append(self.model.lm_head)
         return held_layers
 
@@ -404,7 +408,9 @@ class LlamaForSequenceClassificationPolicy(LlamaPolicy):
         """Get pipeline layers for current stage."""
         stage_manager = self.pipeline_stage_manager
         held_layers = super().get_held_layers()
-        if stage_manager.is_last_stage(ignore_chunk=True):
+        if stage_manager.use_zbv and stage_manager.is_first_stage(ignore_chunk=True):
+            held_layers.append(self.model.score)
+        elif stage_manager.is_last_stage(ignore_chunk=True):
             held_layers.append(self.model.score)
         return held_layers
 

colossalai/zero/gemini/gemini_ddp.py

@@ -373,7 +373,7 @@ class GeminiDDP(ModelWrapper):
             loss.backward()
         self._post_backward()
 
-    def backward_by_grad(self, tensor, grad):
+    def backward_by_grad(self, tensor, grad, inputs: torch.Tensor = None, retain_graph: bool = False):
         raise RuntimeError("Gemini is not compatible with pipeline. backward_by_grad shoudn't be called in Gemini.")
 
     @staticmethod

colossalai/zero/gemini/gemini_optimizer.py

@@ -298,12 +298,14 @@ class GeminiOptimizer(OptimizerWrapper):
         loss = self.mix_precision_mixin.pre_backward(loss)
         self.module.backward(loss)
 
-    def backward_by_grad(self, tensor: torch.Tensor, grad: torch.Tensor):
+    def backward_by_grad(
+        self, tensor: torch.Tensor, grad: torch.Tensor, inputs: torch.Tensor = None, retain_graph: bool = False
+    ):
         # This function is called except the last stage of pipeline parallel
         # It receives the scaled grad from the previous rank
         # No need to scale the grad again
         # Need to unscale when optimizing
-        grad = self.mix_precision_mixin.pre_backward_by_grad(grad)
+        grad = self.mix_precision_mixin.pre_backward_by_grad(grad, inputs=inputs, retain_graph=retain_graph)
         self.module.backward_by_grad(tensor, grad)
 
     def _maybe_move_fp32_params(self):

colossalai/zero/low_level/low_level_optim.py

@@ -408,7 +408,7 @@ class LowLevelZeroOptimizer(OptimizerWrapper):
     # torch.optim.Optimizer methods
     ################################
 
-    def backward(self, loss, retain_graph=False):
+    def backward(self, loss, inputs=None, retain_graph=False):
         assert not (
             self._partition_grads and not self.require_grad_sync
         ), "ZeRO2(partition_grads) and no_sync are not compatible"
@@ -416,7 +416,7 @@ class LowLevelZeroOptimizer(OptimizerWrapper):
         if self.mixed_precision_mixin is not None:
             loss = self.mixed_precision_mixin.pre_backward(loss)
 
-        loss.backward(retain_graph=retain_graph)
+        loss.backward(inputs=inputs, retain_graph=retain_graph)
 
         if not self.require_grad_sync:
             return
@@ -427,14 +427,19 @@ class LowLevelZeroOptimizer(OptimizerWrapper):
         if self._overlap_communication:
             get_accelerator().synchronize()
 
-    def backward_by_grad(self, tensor, grad):
+    def backward_by_grad(self, tensor, grad, inputs: Tensor = None, retain_graph: bool = False):
         assert not (
             self._partition_grads and not self.require_grad_sync
         ), "ZeRO2(partition_grads) and gradient accumulation(no_sync) are not compatible"
 
         if self.mixed_precision_mixin is not None:
             grad = self.mixed_precision_mixin.pre_backward_by_grad(tensor, grad)
-        torch.autograd.backward(tensor, grad)
+        torch.autograd.backward(
+            tensor,
+            grad,
+            inputs=inputs,
+            retain_graph=retain_graph,
+        )
 
         if not self.require_grad_sync:
             return

tests/test_shardformer/test_model/_utils.py

@@ -157,7 +157,6 @@ def build_model_from_hybrid_plugin(
         sharded_optimizer = sharded_optim_class(sharded_model.parameters(), lr=1e-3)
 
     criterion = loss_fn
-
     plugin = pluggin_cls(**test_config)
     booster = Booster(plugin=plugin)
 
@@ -311,8 +310,16 @@ def check_output_hidden_state(
 ):
     org_hidden_state = org_output.last_hidden_state
 
-    if stage_manager and stage_manager.is_last_stage(ignore_chunk=True):
+    if stage_manager:
-        sharded_hidden_state = sharded_output["outputs"]["last_hidden_state"]
+        if stage_manager.use_zbv:
+            if stage_manager.is_first_stage(ignore_chunk=True):
+                sharded_hidden_state = sharded_output["outputs"]["last_hidden_state"]
+            else:
+                sharded_hidden_state = sharded_output.last_hidden_state
+        elif stage_manager.is_last_stage(ignore_chunk=True):
+            sharded_hidden_state = sharded_output["outputs"]["last_hidden_state"]
+        else:
+            sharded_hidden_state = sharded_output.last_hidden_state
     else:
         sharded_hidden_state = sharded_output.last_hidden_state
 
@@ -390,7 +397,6 @@ def get_grad_tensors_for_check(
             pass
         if verbose and dist.get_rank() == 0:
             print(f"'{suffix}' grad: {org_grad}, {shard_grad}")
-
         grad_to_check[suffix] = {
             "org_grad": org_grad.float(),
             "shard_grad": shard_grad.float(),

tests/test_shardformer/test_model/test_shard_llama.py

@@ -7,6 +7,7 @@ from torch.testing import assert_close
 
 import colossalai
 from colossalai.logging import disable_existing_loggers
+from colossalai.pipeline.schedule.v_schedule import PipelineGraph
 from colossalai.shardformer import PipelineGradientCheckpointConfig
 from colossalai.shardformer.layer.utils import Randomizer
 from colossalai.tensor.d_tensor.api import clear_layout_converter
@@ -33,7 +34,7 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn,
     )
     if enable_gradient_checkpointing:
         # org_model.gradient_checkpointing_enable()
-        sharded_model.unwrap().gradient_checkpointing_enable()
+        sharded_model.unwrap().gradient_checkpointing_enable(gradient_checkpointing_kwargs={"use_reentrant": False})
 
     org_loss, org_output, sharded_loss, sharded_output = run_forward_backward_with_hybrid_plugin(
         org_model, sharded_model, sharded_optimizer, data_gen_fn, output_transform_fn, criterion, booster
@@ -112,12 +113,20 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn,
     sharded_optimizer.step()
 
     # check last hidden state & loss
-    if stage_manager is None or stage_manager.is_last_stage(ignore_chunk=True):
+    check_flag = False
+    if stage_manager is None:
+        check_flag = True
+    else:
+        if stage_manager.use_zbv:
+            if stage_manager.is_first_stage(ignore_chunk=True):
+                check_flag = True
+        elif stage_manager.is_last_stage(ignore_chunk=True):
+            check_flag = True
+    if check_flag:
         if test_config["precision"] == "fp32":
             atol, rtol = 1e-5, 1e-3
         else:
             atol, rtol = 5e-3, 5e-3
-
         if org_model.__class__.__name__ == "LlamaModel":
             check_output_hidden_state(
                 org_output,
@@ -282,10 +291,39 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn,
             "precision": "fp16",
             "initial_scale": 1,
         },
+        {
+            "tp_size": 2,
+            "pp_size": 2,
+            "pp_style": "zbv",
+            "num_model_chunks": 2,
+            "num_microbatches": 4,
+            "enable_all_optimization": False,
+            "precision": "fp16",
+            "zero_stage": 0,
+            "initial_scale": 1,
+            "enable_gradient_checkpointing": True,
+            "parallel_output": False,
+        },
     ],
 )
 def run_llama_test(test_config):
     sub_model_zoo = model_zoo.get_sub_registry("transformers_llama")
+    if test_config.get("pp_style", None) == "zbv":
+        mem_f = 34 * 32 + 5 * 4 * 16
+        mem_w = -32 * 32
+        mem_b = -mem_w - mem_f
+        scheduler_nodes = PipelineGraph(
+            n_stage=test_config["pp_size"],
+            n_micro=test_config["num_microbatches"],
+            f_cost=1000,
+            b_cost=1000,
+            w_cost=1000,
+            c_cost=1,
+            f_mem=mem_f,
+            b_mem=mem_b,
+            w_mem=mem_w,
+        ).get_v_schedule()
+        test_config["scheduler_nodes"] = scheduler_nodes
     for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items():
         if test_config.get("sequence_parallelism_mode", None) == "ring_attn" and "causal" not in name:
             continue