[autoparallel]add bcast matmul strategies (#1605)

tests/test_auto_parallel/test_bcast_matmul.py

@@ -0,0 +1,52 @@
+import torch
+from torch.fx import GraphModule
+import torch.nn as nn
+import pytest
+
+from colossalai.auto_parallel.solver.options import SolverOptions
+from colossalai.auto_parallel.solver.strategies_constructor import StrategiesConstructor
+from colossalai.fx.tracer.tracer import ColoTracer
+from colossalai.device.device_mesh import DeviceMesh
+
+
+class MatmulModel(nn.Module):
+
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, x1, x2):
+        x = torch.matmul(x1, x2)
+
+        return x
+
+
+def test_conv_handler():
+    physical_mesh_id = torch.arange(0, 4)
+    mesh_shape = (2, 2)
+    # [[0, 1]
+    #  [2, 3]]
+    device_mesh = DeviceMesh(physical_mesh_id, mesh_shape)
+
+    tracer = ColoTracer()
+    model = MatmulModel()
+    input_sample = {'x1': torch.rand(4, 4, 8).to('meta'), 'x2': torch.rand(4, 1, 8, 4).to('meta')}
+    # graph():
+    #     %x1 : torch.Tensor [#users=1] = placeholder[target=x1]
+    #     %x2 : torch.Tensor [#users=1] = placeholder[target=x2]
+    #     %matmul : [#users=1] = call_function[target=torch.matmul](args = (%x1, %x2), kwargs = {})
+    #     return matmul
+    graph = tracer.trace(root=model, meta_args=input_sample)
+    gm = GraphModule(model, graph, model.__class__.__name__)
+    # [x1, x2, matmul, output]
+    nodes = [node for node in gm.graph.nodes]
+    solver_options = SolverOptions(fast=True)
+    strategies_constructor = StrategiesConstructor(graph, device_mesh, solver_options)
+
+    strategies_constructor.build_strategies_and_cost()
+    strategy_map = strategies_constructor.strategy_map
+    matmul_strategies = strategy_map[nodes[2]]
+    assert len(matmul_strategies) == 30
+
+
+if __name__ == '__main__':
+    test_conv_handler()