[autoparallel] support origin activation ckpt on autoprallel system

colossalai/auto_parallel/passes/runtime_apply_pass.py

@@ -128,6 +128,8 @@ def _shape_consistency_apply(gm: torch.fx.GraphModule):
                                                                    runtime_apply,
                                                                    args=(node, origin_dict_node, input_dict_node,
                                                                          node_to_index_dict[node], user_node_index))
+            if 'activation_checkpoint' in user_node.meta:
+                shape_consistency_node.meta['activation_checkpoint'] = user_node.meta['activation_checkpoint']
 
             new_args = list(user_node.args)
             new_kwargs = dict(user_node.kwargs)
@@ -208,6 +210,37 @@ def _comm_spec_apply(gm: torch.fx.GraphModule):
                         # substitute the origin node with comm_spec_apply_node
                         new_kwargs[str(node)] = comm_spec_apply_node
                         user.kwargs = new_kwargs
+
+            if 'activation_checkpoint' in node.meta:
+                comm_spec_apply_node.meta['activation_checkpoint'] = node.meta['activation_checkpoint']
+
+    return gm
+
+
+def _act_annotataion_pass(gm: torch.fx.GraphModule):
+    """
+    This pass is used to add the act annotation to the new inserted nodes.
+    """
+    mod_graph = gm.graph
+    nodes = tuple(mod_graph.nodes)
+
+    for node in nodes:
+        if not hasattr(node.meta, 'activation_checkpoint'):
+            from .runtime_preparation_pass import size_processing
+
+            user_act_annotation = -1
+            input_act_annotation = -1
+            for user_node in node.users.keys():
+                if 'activation_checkpoint' in user_node.meta:
+                    user_act_annotation = user_node.meta['activation_checkpoint']
+                    break
+            for input_node in node._input_nodes.keys():
+                if 'activation_checkpoint' in input_node.meta:
+                    input_act_annotation = input_node.meta['activation_checkpoint']
+                    break
+            if user_act_annotation == input_act_annotation and user_act_annotation != -1:
+                node.meta['activation_checkpoint'] = user_act_annotation
+
     return gm
 
 

colossalai/auto_parallel/passes/runtime_preparation_pass.py

@@ -179,6 +179,8 @@ def _size_value_converting(gm: torch.fx.GraphModule, device_mesh: DeviceMesh):
                 # It will be used to replace the original node with processing node in slice object
                 node_pairs[node] = size_processing_node
                 size_processing_node._meta_data = node._meta_data
+                if 'activation_checkpoint' in node.meta:
+                    size_processing_node.meta['activation_checkpoint'] = node.meta['activation_checkpoint']
 
             user_list = list(node.users.keys())
             for user in user_list:

colossalai/auto_parallel/tensor_shard/initialize.py

@@ -18,6 +18,7 @@
 )
 from colossalai.device.alpha_beta_profiler import AlphaBetaProfiler
 from colossalai.device.device_mesh import DeviceMesh
+from colossalai.fx.graph_module import ColoGraphModule
 from colossalai.fx.tracer import ColoTracer
 from colossalai.tensor.sharding_spec import ShardingSpec
 
@@ -28,7 +29,7 @@ class ModuleWrapper(nn.Module):
     into the forward function.
     '''
 
-    def __init__(self, module: GraphModule, sharding_spec_dict: Dict[int, List[ShardingSpec]],
+    def __init__(self, module: ColoGraphModule, sharding_spec_dict: Dict[int, List[ShardingSpec]],
                  origin_spec_dict: Dict[int, ShardingSpec], comm_actions_dict: Dict[int, Dict[str, CommAction]]):
         '''
         Args:
@@ -81,7 +82,7 @@ def build_strategy_constructor(graph: Graph, device_mesh: DeviceMesh):
     return strategies_constructor
 
 
-def solve_solution(gm: GraphModule, strategy_constructor: StrategiesConstructor, memory_budget: float = -1.0):
+def solve_solution(gm: ColoGraphModule, strategy_constructor: StrategiesConstructor, memory_budget: float = -1.0):
     '''
     This method is used to solve the best solution for the given graph.
     The solution is a list of integers, each integer represents the best strategy index of the corresponding node.
@@ -97,7 +98,7 @@ def solve_solution(gm: GraphModule, strategy_constructor: StrategiesConstructor,
     return solution
 
 
-def transform_to_sharded_model(gm: GraphModule, solution: List[int], device_mesh: DeviceMesh,
+def transform_to_sharded_model(gm: ColoGraphModule, solution: List[int], device_mesh: DeviceMesh,
                                strategies_constructor: StrategiesConstructor):
     '''
     This method is used to transform the original graph to the sharded graph.
@@ -197,10 +198,10 @@ def initialize_model(model: nn.Module,
             solution will be used to debug or help to analyze the sharding result. Therefore, we will not just
             return a series of integers, but return the best strategies.
     '''
-    tracer = ColoTracer()
+    tracer = ColoTracer(trace_act_ckpt=True)
 
     graph = tracer.trace(root=model, meta_args=meta_args)
-    gm = GraphModule(model, graph, model.__class__.__name__)
+    gm = ColoGraphModule(model, graph, model.__class__.__name__)
     gm.recompile()
     strategies_constructor = build_strategy_constructor(graph, device_mesh)
     if load_solver_solution:

tests/test_auto_parallel/test_tensor_shard/test_checkpoint.py

@@ -0,0 +1,70 @@
+from functools import partial
+from typing import Optional, Tuple, Union
+
+import pytest
+import torch
+import torch.multiprocessing as mp
+import torch.nn as nn
+from torch.utils.checkpoint import checkpoint
+from transformers.pytorch_utils import Conv1D
+
+from colossalai.auto_parallel.tensor_shard.initialize import initialize_model
+from colossalai.device.device_mesh import DeviceMesh
+from colossalai.fx.graph_module import ColoGraphModule
+from colossalai.fx.tracer import ColoTracer
+from colossalai.initialize import launch
+from colossalai.logging import disable_existing_loggers
+from colossalai.tensor.shape_consistency import ShapeConsistencyManager
+from colossalai.testing import rerun_if_address_is_in_use
+from colossalai.testing.pytest_wrapper import run_on_environment_flag
+from colossalai.utils import free_port
+
+HIDDEN_SIZE = 16
+
+
+class GPT2MLPWithCkpt(nn.Module):
+
+    def __init__(self, intermediate_size, hidden_size):
+        super().__init__()
+        embed_dim = hidden_size
+        self.c_fc = Conv1D(intermediate_size, embed_dim)
+        self.c_proj = Conv1D(embed_dim, intermediate_size)
+        self.act = torch.nn.ReLU()
+
+    def forward(self, hidden_states: Optional[Tuple[torch.FloatTensor]]) -> torch.FloatTensor:
+        hidden_states = self.c_fc(hidden_states)
+        hidden_states = checkpoint(self.c_proj, hidden_states)
+        hidden_states = self.act(hidden_states)
+
+        return hidden_states
+
+
+def check_act_ckpt(rank, world_size, port):
+    disable_existing_loggers()
+    launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
+    model = GPT2MLPWithCkpt(intermediate_size=4 * HIDDEN_SIZE, hidden_size=HIDDEN_SIZE)
+    input_sample = {
+        'hidden_states': torch.rand(1, 64, HIDDEN_SIZE).to('meta'),
+    }
+    physical_mesh_id = torch.arange(0, 4)
+    mesh_shape = (2, 2)
+    # [[0, 1]
+    #  [2, 3]]
+    device_mesh = DeviceMesh(physical_mesh_id, mesh_shape, init_process_group=True)
+    gm = initialize_model(model, input_sample, device_mesh)
+    code = gm.module.graph.python_code('self').src
+    assert "runtime_comm_spec_apply_1 = colossalai_auto_parallel_passes_runtime_apply_pass_runtime_comm_spec_apply(linear_1, comm_actions_dict, 12, 'linear_1')" in code
+    assert "view_3 = colossalai.utils.activation_checkpoint.checkpoint(self.checkpoint_0, False, view_1, comm_actions_dict, use_reentrant=True)" in code
+
+
+@run_on_environment_flag(name='AUTO_PARALLEL')
+@pytest.mark.dist
+@rerun_if_address_is_in_use()
+def test_mlp_layer():
+    world_size = 4
+    run_func = partial(check_act_ckpt, world_size=world_size, port=free_port())
+    mp.spawn(run_func, nprocs=world_size)
+
+
+if __name__ == '__main__':
+    test_mlp_layer()