[fx] PoC of runtime shape consistency application (#1607)

* [fx] PoC of runtime shape consistency application

* polish code

colossalai/auto_parallel/solver/strategies_constructor.py

@@ -408,7 +408,8 @@ class StrategiesConstructor:
                     sharding_strategy_attribute = ShardingStrategy(name,
                                                                    output_sharding_spec,
                                                                    memory_cost=memory_cost,
-                                                                   resharding_costs=resharding_costs)
+                                                                   resharding_costs=resharding_costs,
+                                                                   input_shardings=tuple(input_sharding_specs))
                     strategies_vector.append(sharding_strategy_attribute)
 
             self.remove_duplicated_strategy(strategies_vector)

colossalai/fx/passes/experimental/adding_shape_consistency_pass.py

@@ -0,0 +1,111 @@
+import torch
+from typing import List
+from torch.fx import symbolic_trace
+from torch.fx.node import Node
+from colossalai.fx.passes.split_module import split_module
+from colossalai.tensor.shape_consistency import ShapeConsistencyManager
+from colossalai.device.device_mesh import DeviceMesh
+from colossalai.tensor.sharding_spec import ShardingSpec, _DimSpec
+import builtins
+import operator
+from copy import deepcopy
+
+
+def apply(*args, **kwargs):
+    shape_consistency_manager = ShapeConsistencyManager()
+    return shape_consistency_manager.apply(*args, **kwargs)
+
+
+def solution_annotatation_pass(gm: torch.fx.GraphModule, solution: List[int], device_mesh):
+    mod_graph = gm.graph
+    nodes = tuple(mod_graph.nodes)
+
+    # the dict to get origin sharding spec of node
+    origin_node_sharding_spec_dict = {}
+    for node_index, (node, strategy_index) in enumerate(zip(nodes, solution)):
+        strategies_vector = node.strategies_vector
+        setattr(node, 'best_strategy', strategies_vector[strategy_index])
+        setattr(node, 'sharding_spec', strategies_vector[strategy_index].output_sharding_spec)
+        origin_node_sharding_spec_dict[node_index] = strategies_vector[strategy_index].output_sharding_spec
+
+    # apply the sharding spec of parameters
+    for node in nodes:
+        if node.op == 'call_module':
+            target_module = node.graph.owning_module.get_submodule(node.target)
+            origin_sharding_spec = ShardingSpec(device_mesh, target_module.weight.shape, {})
+            setattr(target_module.weight, 'sharding_spec', origin_sharding_spec)
+            target_weight_sharding_spec = node.best_strategy.input_shardings[1]
+            target_module.weight.data = target_module.weight.data.permute((1, 0, 2, 3))
+            apply(target_module.weight, target_weight_sharding_spec)
+            target_module.weight.data = target_module.weight.data.permute((1, 0, 2, 3))
+
+    # the dict to get input sharding specs of user node
+    sharding_spec_convert_dict = {}
+    for index, node in enumerate(nodes):
+        target_sharding_specs = []
+        for user_node in node.strategies_vector.successor_nodes:
+            node_index = user_node.strategies_vector.predecessor_nodes.index(node)
+            target_sharding_spec = user_node.best_strategy.input_shardings[node_index]
+            target_sharding_specs.append(target_sharding_spec)
+        sharding_spec_convert_dict[index] = target_sharding_specs
+
+    # add above dicts into graph
+    for node in nodes:
+        if node.op != 'placeholder':
+            with mod_graph.inserting_before(node):
+                input_specs_node = mod_graph.create_node('placeholder', target='sharding_spec_convert_dict')
+                origin_specs_node = mod_graph.create_node('placeholder', target='origin_node_sharding_spec_dict')
+            break
+
+    return sharding_spec_convert_dict, origin_node_sharding_spec_dict
+
+
+def shape_consistency_pass(gm: torch.fx.GraphModule):
+    mod_graph = gm.graph
+    nodes = tuple(mod_graph.nodes)
+    input_dict_node = None
+    origin_dict_node = None
+
+    # mapping the node into the origin graph index
+    node_to_index_dict = {}
+    index = 0
+    for node in nodes:
+        if node.target == 'sharding_spec_convert_dict':
+            input_dict_node = node
+            continue
+        if node.target == 'origin_node_sharding_spec_dict':
+            origin_dict_node = node
+            continue
+        if not hasattr(node, 'best_strategy'):
+            continue
+        node_to_index_dict[node] = index
+        index += 1
+    assert input_dict_node is not None
+
+    # add shape consistency apply function into graph
+    for node in nodes:
+        if not hasattr(node, 'best_strategy'):
+            continue
+        with mod_graph.inserting_after(node):
+            origin_spec_node = mod_graph.create_node('call_function',
+                                                     operator.getitem,
+                                                     args=(origin_dict_node, node_to_index_dict[node]))
+        with mod_graph.inserting_after(origin_spec_node):
+            set_sharding_spec_node = mod_graph.create_node('call_function',
+                                                           builtins.setattr,
+                                                           args=(node, 'sharding_spec', origin_spec_node))
+
+        for user_node in node.strategies_vector.successor_nodes:
+            node_index = user_node.strategies_vector.predecessor_nodes.index(node)
+            with mod_graph.inserting_before(user_node):
+                input_specs_node = mod_graph.create_node('call_function',
+                                                         operator.getitem,
+                                                         args=(input_dict_node, node_to_index_dict[node]))
+            with mod_graph.inserting_before(user_node):
+                sharding_spec_node = mod_graph.create_node('call_function',
+                                                           operator.getitem,
+                                                           args=(input_specs_node, node_index))
+            with mod_graph.inserting_before(user_node):
+                shape_consistency_node = mod_graph.create_node('call_function', apply, args=(node, sharding_spec_node))
+
+    return gm

tests/test_auto_parallel/test_shape_consistency_pass.py

@@ -0,0 +1,84 @@
+from functools import partial
+import pytest
+import torch
+import torch.multiprocessing as mp
+from torch.fx import GraphModule
+import torch.nn as nn
+import pytest
+from colossalai.initialize import launch
+from colossalai.utils import free_port
+from colossalai.testing import rerun_if_address_is_in_use
+from colossalai.logging import disable_existing_loggers
+from colossalai.auto_parallel.solver.cost_graph import CostGraph
+from colossalai.auto_parallel.solver.graph_analysis import GraphAnalyser
+from colossalai.auto_parallel.solver.strategies_constructor import StrategiesConstructor
+
+from colossalai.fx.tracer.tracer import ColoTracer
+from colossalai.device.device_mesh import DeviceMesh
+from colossalai.fx.passes.experimental.adding_shape_consistency_pass import shape_consistency_pass, solution_annotatation_pass
+from colossalai.auto_parallel.solver import Solver
+from colossalai.auto_parallel.solver.options import SolverOptions
+
+
+class ConvModel(nn.Module):
+
+    def __init__(self, c_in, c_out):
+        super().__init__()
+        self.conv = nn.Conv2d(c_in, c_out, kernel_size=3, padding=1, bias=False)
+
+    def forward(self, x):
+        x = self.conv(x)
+        return x
+
+
+def check_apply(rank, world_size, port):
+    disable_existing_loggers()
+    launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
+    input = torch.rand(4, 4, 4, 4).cuda()
+    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)
+    entire_shape = torch.Size((4, 4, 8, 8))
+
+    tracer = ColoTracer()
+    model = ConvModel(4, 4).cuda()
+    origin_output = model(input)
+    input_sample = {'x': torch.rand(4, 4, 4, 4).to('meta')}
+    # graph():
+    #     %x : torch.Tensor [#users=1] = placeholder[target=x]
+    #     %conv : [#users=1] = call_module[target=conv](args = (%mul,), kwargs = {})
+    #     return conv
+    graph = tracer.trace(root=model, meta_args=input_sample)
+    gm = GraphModule(model, graph, model.__class__.__name__)
+    gm.recompile()
+    solver_options = SolverOptions(fast=True)
+    strategies_constructor = StrategiesConstructor(graph, device_mesh, solver_options)
+    strategies_constructor.build_strategies_and_cost()
+
+    cost_graph = CostGraph(strategies_constructor.leaf_strategies)
+    cost_graph.simplify_graph()
+    graph_analyser = GraphAnalyser(gm)
+    solver = Solver(gm.graph, strategies_constructor, cost_graph, graph_analyser)
+    ret = solver.call_solver_serialized_args()
+    solution = list(ret[0])
+    sharding_spec_dict, origin_spec_dict = solution_annotatation_pass(gm, solution, device_mesh)
+    shape_consistency_pass(gm)
+    nodes = [node for node in gm.graph.nodes]
+    # TODO: wrap the gm to avoid the influence of the user training code
+    output = gm(input, sharding_spec_dict, origin_spec_dict)
+    assert output.equal(origin_output)
+
+
+@pytest.mark.skip("for higher testing speed")
+@pytest.mark.dist
+@rerun_if_address_is_in_use()
+def test_apply():
+    world_size = 4
+    run_func = partial(check_apply, world_size=world_size, port=free_port())
+    mp.spawn(run_func, nprocs=world_size)
+
+
+if __name__ == '__main__':
+    test_apply()