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