[autoparallel] align the data_ptr with the old version of auto activation checkpoint pipeline

colossalai/auto_parallel/meta_profiler/constants.py

@@ -5,8 +5,11 @@
 
 from ..tensor_shard.constants import *
 
-# list of inplace operations
+# list of inplace module
 INPLACE_MODULE = [nn.ReLU]
 
+# list of inplace operations
+INPLACE_OPS = [torch.flatten]
+
 # list of operations that do not save forward activations
 NO_SAVE_ACTIVATION = [torch.add, torch.sub, operator.add, operator.sub]

colossalai/auto_parallel/meta_profiler/meta_registry/binary_elementwise_ops.py

@@ -60,7 +60,7 @@ def binary_elementwise_meta_info(*args, **kwargs) -> Tuple[TrainCycleItem, Train
     memory_cost = TrainCycleItem(fwd=fwd_mem_cost, bwd=bwd_mem_cost, total=total_mem_cost)
 
     # store fwd_in, fwd_buffer, fwd_out
-    fwd_in = [torch.zeros_like(input_op_data.data, device='meta'), torch.zeros_like(other_op_data.data, device='meta')]
+    fwd_in = []
     fwd_buffer = []
     fwd_out = [torch.zeros_like(output_op_data.data, device='meta')]
 

colossalai/auto_parallel/meta_profiler/metainfo.py

@@ -12,7 +12,7 @@
 )
 from colossalai.tensor.sharding_spec import ShardingSpec
 
-from .constants import INPLACE_MODULE, NO_SAVE_ACTIVATION
+from .constants import INPLACE_MODULE, INPLACE_OPS, NO_SAVE_ACTIVATION
 from .registry import meta_register
 
 __all__ = ['MetaInfo']
@@ -104,6 +104,8 @@ def compute_metainfo(self):
         # construct kwargs
         if self.target in INPLACE_MODULE:
             kwargs = {'inplace': self.target.inplace}
+        elif self.target in INPLACE_OPS:
+            kwargs = {'inplace': True}
         else:
             kwargs = {'inplace': False}
 

colossalai/auto_parallel/passes/constants.py

@@ -0,0 +1,8 @@
+import torch
+
+OUTPUT_SAVED_OPS = [torch.nn.functional.relu, torch.nn.functional.softmax, torch.flatten]
+
+OUTPUT_SAVED_MOD = [
+    torch.nn.ReLU,
+    torch.nn.Softmax,
+]

colossalai/auto_parallel/passes/meta_info_prop.py

@@ -8,9 +8,9 @@
 from torch.fx.node import Node
 
 from colossalai.auto_parallel.meta_profiler import MetaInfo
+from colossalai.auto_parallel.passes.constants import OUTPUT_SAVED_MOD, OUTPUT_SAVED_OPS
 from colossalai.fx._compatibility import compatibility
 from colossalai.fx.profiler import GraphInfo
-from colossalai.fx.profiler.constants import OUTPUT_SAVED_MOD, OUTPUT_SAVED_OPS
 
 
 def _normalize_tuple(x):
@@ -46,7 +46,7 @@ def _is_inplace(self, node: Node):
         """
         Check if the node is inplace operation.
         """
-        if node.op == 'call_method':
+        if node.op == 'call_module':
             return node.graph.owning_module.get_submodule(node.target).__class__ in OUTPUT_SAVED_MOD
         elif node.op == "call_function":
             return node.target in OUTPUT_SAVED_OPS
@@ -102,56 +102,51 @@ def node_handler(self, node: Node) -> None:
         meta_info: MetaInfo
 
         # set data_ptr for input_tensor in MetaInfo class
-        input_tensor: List[torch.Tensor] = meta_info.fwd_in
-        buffer_tensor: List[torch.Tensor] = meta_info.fwd_buffer
-        output_tensor: List[torch.Tensor] = meta_info.fwd_out
+        input_tensors: List[torch.Tensor] = meta_info.fwd_in
+        buffer_tensors: List[torch.Tensor] = meta_info.fwd_buffer
+        output_tensors: List[torch.Tensor] = meta_info.fwd_out
 
-        if len(input_tensor) > 0:
+        if self._is_inplace(node):
+            # inplace operation will not create new tensor, and it only has one parent node
+            # TODO: Verify this observation
+            # set data_ptr for input_tensor, buffer_tensor and output_tensor of current node
+            parent_node = list(node._input_nodes.keys())[0]
+            parent_tensor = parent_node.meta.get("fwd_out")[0]
+            parent_tensor: torch.Tensor
+            for tensor in input_tensors:
+                tensor.data_ptr = parent_tensor.data_ptr
+            for tensor in buffer_tensors:
+                tensor.data_ptr = parent_tensor.data_ptr
+            for tensor in output_tensors:
+                tensor.data_ptr = parent_tensor.data_ptr
+
+        else:
             for par in node._input_nodes:
-                if par.meta:
-                    if len(par.meta["fwd_out"]) > 0:
-                        # set data_ptr for the input_tensor of current node from the output_tensor of its parent node
-                        for tensor in par.meta["fwd_out"]:
-                            tensor: torch.Tensor
-                            target_tensor = next(
-                                (x for x in input_tensor if not x.data_ptr() and x.shape == tensor.shape), None)
-                            target_tensor.data_ptr = tensor.data_ptr
+                # set data_ptr for the input_tensor of current node from the output_tensor of its parent node
+                for tensor in par.meta.get("fwd_out", []):
+                    tensor: torch.Tensor
+                    target_input_tensor = next(
+                        (x for x in input_tensors if not x.data_ptr() and x.shape == tensor.shape), None)
+                    if target_input_tensor is not None:
+                        target_input_tensor.data_ptr = tensor.data_ptr
 
             # set data_ptr for tensor in input_tensor that is not set
-            for tensor in input_tensor:
+            for tensor in input_tensors:
                 if not tensor.data_ptr():
                     self._set_data_ptr(tensor)
 
-        # attach it to graph_info
-        graph_info.fwd_in = input_tensor
-
-        if self._is_inplace(node):
-            # inplace operation will not create new tensor
-            # set data_ptr for buffer_tensor and output_tensor of current node
-            for tensor in input_tensor:
-                tensor: torch.Tensor
-                target_buffer_tensor = next((x for x in buffer_tensor if not x.data_ptr() and x.shape == tensor.shape),
-                                            None)
-                target_output_tensor = next((x for x in output_tensor if not x.data_ptr() and x.shape == tensor.shape),
-                                            None)
-                target_buffer_tensor.data_ptr = tensor.data_ptr
-                target_output_tensor.data_ptr = tensor.data_ptr
-            # attach them to graph_info
-            graph_info.fwd_tmp = buffer_tensor
-            graph_info.fwd_out = output_tensor
-
-        else:
             # set data_ptr for buffer_tensor
-            for tensor in buffer_tensor:
+            for tensor in buffer_tensors:
                 self._set_data_ptr(tensor)
-            # attach it to graph_info
-            graph_info.fwd_tmp = buffer_tensor
 
             # set data_ptr for output_tensor
-            for tensor in output_tensor:
+            for tensor in output_tensors:
                 self._set_data_ptr(tensor)
-            # attach it to graph_info
-            graph_info.fwd_out = output_tensor
+
+        # attach them to graph_info
+        graph_info.fwd_in = input_tensors
+        graph_info.fwd_tmp = buffer_tensors
+        graph_info.fwd_out = output_tensors
 
         # fetch other memory informations
         memory_cost = meta_info.memory_cost