[autoparallel] Patch meta information for nodes that will not be handled by SPMD solver (#2823)

* [autoparallel] non spmd meta information generator

* [autoparallel] patch meta information for non spmd nodes

colossalai/auto_parallel/meta_profiler/meta_registry/__init__.py

@@ -3,6 +3,7 @@ from .binary_elementwise_ops import *
 from .conv import *
 from .embedding import *
 from .linear import *
+from .non_spmd import *
 from .norm import *
 from .pooling import *
 from .tensor import *

colossalai/auto_parallel/meta_profiler/meta_registry/non_spmd.py

+import operator
+from typing import List, Tuple
+
+import torch
+
+from colossalai.auto_parallel.tensor_shard.sharding_strategy import MemoryCost, OperationDataType, TrainCycleItem
+from colossalai.fx.profiler.memory_utils import activation_size
+from colossalai.fx.profiler.opcount import flop_mapping
+
+from ..registry import meta_register
+
+__all__ = ["non_spmd_meta_info"]
+
+
+@meta_register.register(torch.Size)
+@meta_register.register(torch.Tensor.size)
+@meta_register.register(torch.finfo)
+@meta_register.register(operator.le)
+def non_spmd_meta_info(*args, **kwargs) -> Tuple[TrainCycleItem, TrainCycleItem, List[torch.Tensor]]:
+    """Non-SPMD node meta information generator
+    Those nodes will not be handled by SPMD solver, so we just return all zero meta information for it
+
+    Returns:
+        Tuple[TrainCycleItem, TrainCycleItem, List[torch.Tensor]]: compute cost, memory cost and forward inputs
+    """
+    compute_cost = TrainCycleItem(fwd=0, bwd=0, total=0)
+    memory_cost = TrainCycleItem(fwd=MemoryCost(), bwd=MemoryCost(), total=MemoryCost())
+    fwd_in, fwd_buffer, fwd_out = [], [], []
+    return compute_cost, memory_cost, fwd_in, fwd_buffer, fwd_out