[autoparallel] adapt solver with mlp (#1638)

tests/test_auto_parallel/test_solver_with_mlp.py

@@ -0,0 +1,93 @@
+import torch
+from torch.fx import GraphModule
+import torch.nn as nn
+import pytest
+
+from colossalai.fx.tracer.tracer import ColoTracer
+from colossalai.auto_parallel.solver.sharding_strategy import ShardingStrategy, StrategiesVector
+from colossalai.tensor.shape_consistency import ShapeConsistencyManager
+from colossalai.device.device_mesh import DeviceMesh
+from colossalai.auto_parallel.solver.strategies_constructor import StrategiesConstructor
+from colossalai.auto_parallel.solver.cost_graph import CostGraph
+from copy import deepcopy
+from colossalai.auto_parallel.solver import Solver
+from torchvision.models import resnet34, resnet50
+from colossalai.auto_parallel.solver.constants import *
+from colossalai.auto_parallel.solver.graph_analysis import GraphAnalyser
+from colossalai.auto_parallel.solver.options import SolverOptions
+
+
+class MLP(torch.nn.Module):
+
+    def __init__(self, dim: int):
+        super().__init__()
+        self.linear1 = torch.nn.Linear(dim, dim * 4)
+        self.linear2 = torch.nn.Linear(dim * 4, dim)
+        self.dropout = torch.nn.Dropout(0)
+        self.relu = torch.nn.ReLU()
+
+    def forward(self, x):
+        x = self.linear1(x)
+        x = self.dropout(x)
+        x = self.relu(x)
+        x = self.linear2(x)
+        return x
+
+
+@pytest.mark.skip("for higher testing speed")
+def test_cost_graph():
+    physical_mesh_id = torch.arange(0, 8)
+    mesh_shape = (2, 4)
+    device_mesh = DeviceMesh(physical_mesh_id, mesh_shape)
+    shape_consistency_manager = ShapeConsistencyManager()
+
+    tracer = ColoTracer()
+    model = MLP(32)
+
+    input_sample = {'x': torch.rand(16, 32).to('meta')}
+
+    # graph():
+    #     %x : torch.Tensor [#users=1] = placeholder[target=x]
+    #     %linear1 : [#users=1] = call_module[target=linear1](args = (%x,), kwargs = {})
+    #     %dropout : [#users=1] = call_module[target=dropout](args = (%linear1,), kwargs = {})
+    #     %relu : [#users=1] = call_module[target=relu](args = (%dropout,), kwargs = {})
+    #     %linear2 : [#users=1] = call_module[target=linear2](args = (%relu,), kwargs = {})
+    #     return linear2
+    graph = tracer.trace(root=model, meta_args=input_sample)
+
+    gm = GraphModule(model, graph, model.__class__.__name__)
+    gm.recompile()
+    graph_analyser = GraphAnalyser(gm)
+    liveness_list = graph_analyser.liveness_analysis()
+    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()
+    # # megatron mode if no memory constraints
+    # solver = Solver(gm.graph, strategies_constructor, cost_graph, graph_analyser)
+    # all sharding on out feature dim if memory budget is not sufficient for megatron mode
+    solver = Solver(gm.graph, strategies_constructor, cost_graph, graph_analyser, memory_budget=5500.0)
+
+    ret = solver.call_solver_serialized_args()
+    strategies_list = list(ret[0])
+    computation_cost = 0
+    communication_cost = 0
+    memory_cost = 0
+    for index, node in enumerate(graph.nodes):
+        print(node.name, node.strategies_vector[strategies_list[index]].name)
+        computation_cost += node.strategies_vector[strategies_list[index]].compute_cost
+        communication_cost += node.strategies_vector[strategies_list[index]].communication_cost
+        node_memory_cost = node.strategies_vector[strategies_list[index]].memory_cost
+        if isinstance(node_memory_cost, tuple):
+            node_memory_cost = node_memory_cost[0]
+        memory_cost += node_memory_cost
+
+    print(f'computation cost is {computation_cost}')
+    print(f'communication cost is {communication_cost}')
+    print(f'memory cost is {memory_cost}')
+
+
+if __name__ == '__main__':
+    test_cost_graph()