[autoparallel] Patch tensor related operations meta information (#2789)

* [autoparallel] tensor related meta information prototype

* [autoparallel] tensor related meta information

* [autoparallel] tensor related meta information

* [autoparallel] tensor related meta information

* [autoparallel] tensor related meta information

colossalai/auto_parallel/meta_profiler/meta_registry/__init__.py

@@ -5,3 +5,4 @@ from .embedding import *
 from .linear import *
 from .norm import *
 from .pooling import *
+from .tensor import *

colossalai/auto_parallel/meta_profiler/meta_registry/pooling.py

@@ -14,7 +14,6 @@ __all__ = ["avgpool_meta_info", "maxpool_meta_info"]
 @meta_register.register(torch.nn.AdaptiveAvgPool1d)
 @meta_register.register(torch.nn.AdaptiveAvgPool2d)
 @meta_register.register(torch.nn.AdaptiveAvgPool3d)
-@meta_register.register(torch.flatten)
 def avgpool_meta_info(*args, **kwargs) -> Tuple[TrainCycleItem, TrainCycleItem, List[torch.Tensor]]:
     """Meta info for AdaptiveAvgPool
     The aten graph of AdaptiveAvgPool is

colossalai/auto_parallel/meta_profiler/meta_registry/tensor.py

@@ -0,0 +1,79 @@
+from typing import Callable, 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__ = ["tensor_related_metainfo"]
+
+
+def tensor_related_metainfo(bwd_mem_out_factor: float = 1, bwd_mem_tmp_factor: float = 0) -> Callable:
+    """torch.Tensor related metainfo generator template
+
+    Args:
+        bwd_mem_out_factor (float, optional): backward activation memory cost factor. Defaults to 1.
+        bwd_mem_tmp_factor (float, optional): backward temp memory cost factor. Defaults to 0.
+
+    Returns:
+        Callable: torch.Tensor related metainfo generator
+    """
+
+    def meta_func(*args, **kwargs) -> Tuple[TrainCycleItem, TrainCycleItem, List[torch.Tensor]]:
+        """torch.Tensor related metainfo generator
+
+        Returns:
+            Tuple[TrainCycleItem, TrainCycleItem, List[torch.Tensor]]: compute cost, memory cost and forward inputs
+        """
+        outputs = next(filter(lambda x: x.type == OperationDataType.OUTPUT, args)).data
+
+        # compute costs are all zero
+        compute_cost = TrainCycleItem(fwd=0, bwd=0, total=0)
+
+        # memory costs
+        # NOTE: currently in SPMD solver we always believe that there will be a new tensor created in forward
+        fwd_mem_cost = MemoryCost(activation=activation_size(outputs) * 2, parameter=0, temp=0, buffer=0)
+
+        bwd_mem_cost = MemoryCost(activation=activation_size(outputs) * bwd_mem_out_factor,
+                                  parameter=0,
+                                  temp=activation_size(outputs) * bwd_mem_tmp_factor,
+                                  buffer=0)
+
+        total_mem_cost = MemoryCost(activation=fwd_mem_cost.activation + bwd_mem_cost.activation,
+                                    parameter=fwd_mem_cost.parameter + bwd_mem_cost.parameter,
+                                    temp=fwd_mem_cost.temp + bwd_mem_cost.temp,
+                                    buffer=fwd_mem_cost.buffer + bwd_mem_cost.buffer)
+
+        memory_cost = TrainCycleItem(fwd=fwd_mem_cost, bwd=bwd_mem_cost, total=total_mem_cost)
+
+        # store fwd_in, fwd_buffer, fwd_out
+        fwd_in = []
+        fwd_buffer = []
+        if isinstance(outputs, tuple) or isinstance(outputs, list) or isinstance(outputs, dict):
+            # tuple of tensors
+            fwd_out = [torch.zeros_like(tensor) for tensor in outputs]
+        else:
+            # enaged_tensors is a single tensor
+            fwd_out = [torch.zeros_like(outputs)]
+
+        return compute_cost, memory_cost, fwd_in, fwd_buffer, fwd_out
+
+    return meta_func
+
+
+# register torch.Tensor related metainfo
+# (0, 0)
+meta_register.register([torch.tensor, torch.Tensor.to, torch.Tensor.unsqueeze, torch.unsqueeze,
+                        torch.arange])(tensor_related_metainfo(0, 0))
+
+# (1, 0)
+meta_register.register([
+    torch.Tensor.flatten, torch.flatten, torch.Tensor.transpose, torch.transpose, torch.Tensor.permute, torch.permute,
+    torch.Tensor.split, torch.split, torch.Tensor.view
+])(tensor_related_metainfo(1, 0))
+
+# (1, 1)
+meta_register.register([torch.Tensor.type, torch.Tensor.contiguous])(tensor_related_metainfo(1, 1))