[hotfix]: add pp sanity check and fix mbs arg (#5268)

* fix: fix misleading mbs arg

* feat: add pp sanity check

* fix: fix 1f1b sanity check

colossalai/pipeline/schedule/interleaved_pp.py

@@ -72,6 +72,10 @@ class InterleavedSchedule(PipelineSchedule):
             assert self.last_batch_size is None or self.last_batch_size == self.batch_size
             assert self.batch_size == self.microbatch_size * self.num_microbatch
 
+            assert (
+                self.num_microbatch % self.stage_manager.num_stages == 0
+            ), "Number of microbatch should be an integer multiple of number of pipeline parallel devices"
+
         if self.forward_only:
             self.num_microbatch = (self.batch_size - 1) // self.microbatch_size + 1
             # NOTE: disable metadata cache when batch size changes (not valid anymore)

colossalai/pipeline/schedule/one_f_one_b.py

@@ -85,6 +85,10 @@ class OneForwardOneBackwardSchedule(PipelineSchedule):
             assert self.last_batch_size is None or self.last_batch_size == self.batch_size
             assert self.batch_size == self.microbatch_size * self.num_microbatches
 
+            assert (
+                self.num_microbatches >= self.stage_manager.num_stages
+            ), "Number of microbatch should be larger than number of stages"
+
         if self.forward_only:
             self.num_microbatches = (self.batch_size - 1) // self.microbatch_size + 1
             # NOTE: disable metadata cache when batch size changes (not valid anymore)

examples/language/llama2/benchmark.py

@@ -74,8 +74,8 @@ def main():
     parser.add_argument("--tp", type=int, default=1, help="Tensor parallel size")
     parser.add_argument("--extra_dp", type=int, default=1, help="Extra data parallel size, used for Gemini")
     parser.add_argument("--pp", type=int, default=1, help="Pipeline parallel size")
-    parser.add_argument("--mbs", type=int, default=1)
-    parser.add_argument("--zero", type=int, default=0)
+    parser.add_argument("--mbs", type=int, default=1, help="Micro batch size of pipeline parallel")
+    parser.add_argument("--zero", type=int, default=0, help="Zero Stage when hybrid plugin is enabled")
     args = parser.parse_args()
 
     colossalai.launch_from_torch({})
@@ -98,7 +98,13 @@ def main():
             extra_dp_size=args.extra_dp,
         )
     elif args.plugin == "gemini_auto":
-        plugin = GeminiPlugin(placement_policy="auto", precision="bf16", warmup_non_model_data_ratio=args.warmup_ratio, tp_size=args.tp, extra_dp_size=args.extra_dp)
+        plugin = GeminiPlugin(
+            placement_policy="auto",
+            precision="bf16",
+            warmup_non_model_data_ratio=args.warmup_ratio,
+            tp_size=args.tp,
+            extra_dp_size=args.extra_dp,
+        )
     elif args.plugin == "fsdp":
         if use_empty_init:
             plugin = TorchFSDPPlugin(
@@ -137,7 +143,7 @@ def main():
             zero_stage=args.zero,
             num_model_chunks=2,
             enable_fused_normalization=torch.cuda.is_available(),
-            num_microbatches=args.mbs,
+            microbatch_size=args.mbs,
             precision="bf16",
         )
     elif args.plugin == "3d_cpu":
@@ -147,7 +153,7 @@ def main():
             zero_stage=args.zero,
             cpu_offload=True,
             enable_fused_normalization=torch.cuda.is_available(),
-            num_microbatches=args.mbs,
+            microbatch_size=args.mbs,
             initial_scale=2**8,
             precision="bf16",
         )

examples/language/llama2/scripts/benchmark_70B/3d.sh

@@ -14,4 +14,4 @@ cd ../..
 
 export OMP_NUM_THREADS=8
 
-colossalai run --nproc_per_node 8 --hostfile $HOSTFILE benchmark.py -c 70b -p 3d -g -x -b 8 --tp 4 --pp 2 --mbs 4
+colossalai run --nproc_per_node 8 --hostfile $HOSTFILE benchmark.py -c 70b -p 3d -g -x -b 8 --tp 4 --pp 2 --mbs 1

tests/test_pipeline/test_schedule/test_oneF_oneB.py

@@ -155,7 +155,7 @@ def run_dist(
 
 
 @pytest.mark.dist
-@pytest.mark.parametrize("num_microbatch", [4, 12])
+@pytest.mark.parametrize("num_microbatch", [4, 6])
 @pytest.mark.parametrize("batch_size", [12])
 @pytest.mark.parametrize("world_size", [2, 4])
 @rerun_if_address_is_in_use()