[pipeline] 1f1b schedule receive microbatch size

colossalai/booster/plugin/hybrid_parallel_plugin.py

@@ -212,6 +212,9 @@ class HybridParallelPlugin(PipelinePluginBase):
         enable_flash_attention (bool, optional): Whether to switch on flash attention. Defaults to False.
         enable_jit_fused (bool, optional): Whether to switch on JIT. Default to Falase.
         num_microbatches (int, optional): Number of microbatches when using pipeline parallelism. Defaults to None.
+        microbatch_size (int, optional): Microbatch size when using pipeline parallelism.
+            Either ``num_microbatches`` or ``microbatch_size`` should be provided if using pipeline.
+            If ``num_microbatches`` is provided, this will be ignored. Defaults to None.
         initial_scale (float, optional): The initial loss scale of AMP. Defaults to 2**16.
         min_scale (float, optional): The minimum loss scale of AMP. Defaults to 1.
         growth_factor (float, optional): The multiplication factor for increasing loss scale when using AMP. Defaults to 2.
@@ -243,6 +246,7 @@ def __init__(self,
                  enable_jit_fused: bool = False,
                  enable_sequence_parallelism: bool = False,
                  num_microbatches: Optional[int] = None,
+                 microbatch_size: Optional[int] = None,
                  initial_scale: float = 2**16,
                  min_scale: float = 1,
                  growth_factor: float = 2,
@@ -289,7 +293,9 @@ def __init__(self,
             assert num_microbatches is not None, 'num_microbatches must be specified when using pipeline parallelism'
             assert self.zero_stage <= 1, 'zero stage must be 0 or 1 when using pipeline parallelism'
             self.stage_manager = PipelineStageManager(self.pg_mesh, PP_AXIS)
-            self.schedule = OneForwardOneBackwardSchedule(num_microbatches, self.stage_manager)
+            self.schedule = OneForwardOneBackwardSchedule(self.stage_manager,
+                                                          num_microbatches=num_microbatches,
+                                                          microbatch_size=microbatch_size)
         self.tp_group = self.pg_mesh.get_group_along_axis(TP_AXIS)
         self.dp_group = self.pg_mesh.get_group_along_axis(DP_AXIS)
         self.shard_config = ShardConfig(tensor_parallel_process_group=self.tp_group,

colossalai/pipeline/schedule/one_f_one_b.py

@@ -17,14 +17,26 @@
 
 class OneForwardOneBackwardSchedule(PipelineSchedule):
 
-    def __init__(self, num_microbatches: int, stage_manager: PipelineStageManager) -> None:
+    def __init__(self,
+                 stage_manager: PipelineStageManager,
+                 num_microbatches: Optional[int] = None,
+                 microbatch_size: Optional[int] = None) -> None:
+        """1F1B pipeline schedule.
+
+        Args:
+            stage_manager (PipelineStageManager): Pipeline stage manager
+            num_microbatches (Optional[int], optional): The number of microbatches. If not provided, it will be derived from microbatch size. Defaults to None.
+            microbatch_size (Optional[int], optional): Microbatch size. If num_microbatches is provided, this will be ignored. Defaults to None.
+        """
         super().__init__(stage_manager)
+        assert num_microbatches is not None or microbatch_size is not None, \
+            "Either num_microbatches or microbatch_size should be provided"
         self.comm = PipelineP2PCommunication(stage_manager)
         self.num_microbatches = num_microbatches
+        self.microbatch_size = microbatch_size
         self.batch: Optional[Any] = None
         self.batch_size: Optional[int] = None
         self.microbatch_offset: Optional[int] = None
-        self.microbatch_size: Optional[int] = None
 
     def load_batch(self, data_iter: Iterable, device: Optional[torch.device] = None) -> None:
         """Load a batch from data iterator.
@@ -39,9 +51,14 @@ def load_batch(self, data_iter: Iterable, device: Optional[torch.device] = None)
         self.batch = batch
         self.batch_size = get_batch_size(batch)
         self.microbatch_offset = 0
-        assert self.batch_size % self.num_microbatches == 0, \
-            "Batch size should divided by the number of microbatches"
-        self.microbatch_size = self.batch_size // self.num_microbatches
+        if self.num_microbatches is not None:
+            assert self.batch_size % self.num_microbatches == 0, \
+                "Batch size should divided by the number of microbatches"
+            self.microbatch_size = self.batch_size // self.num_microbatches
+        else:
+            assert self.batch_size % self.microbatch_size == 0, \
+                "Batch size should divided by the microbatch size"
+            self.num_microbatches = self.batch_size // self.microbatch_size
 
     def load_micro_batch(self) -> Any:
         """Load a micro batch from the current batch.

tests/test_pipeline/test_schedule/test_oneF_oneB.py

@@ -61,7 +61,7 @@ def examine_pp():
     DP_DIM, PP_DIM, TP_DIM = 0, 1, 2
     pg_mesh = ProcessGroupMesh(1, world_size, 1)
     stage_manager = PipelineStageManager(pg_mesh, PP_DIM)
-    schedule = OneForwardOneBackwardSchedule(NUM_MICRO_BATCHS, stage_manager)
+    schedule = OneForwardOneBackwardSchedule(stage_manager, num_microbatches=NUM_MICRO_BATCHS)
 
     for idx, (_, sub_model) in enumerate(pp_model.named_children()):
         if idx % (world_size) == local_rank: