[autoparallel] add bcast op handler (#1600)

* [autoparallel] add bcast op handler

* polish code

* add more BCAST FUNC OP

* polish code

* add exception handler

* polish

tests/test_auto_parallel/test_bcast_handler.py

@@ -0,0 +1,71 @@
+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 ConvModel(nn.Module):
+
+    def __init__(self, c_in, c_out):
+        super().__init__()
+        self.conv1 = nn.Conv2d(c_in, c_out, kernel_size=3, padding=1)
+        self.conv2 = nn.Conv2d(c_in, c_out, kernel_size=3, padding=1, stride=2)
+
+    def forward(self, x):
+        x1 = self.conv1(x)
+        x2 = x1 + 1
+        x1 = torch.reshape(x1, [1, -1, 64, 1])
+        x3 = self.conv2(x1)
+        x3 = torch.reshape(x3, [4, 1, 64, -1])
+        x = x1 + x3
+
+        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 = ConvModel(16, 32)
+    input_sample = {'x': torch.rand(4, 16, 64, 64).to('meta')}
+    # graph():
+    #     %x : torch.Tensor [#users=1] = placeholder[target=x]
+    #     %conv1 : [#users=2] = call_module[target=conv1](args = (%x,), kwargs = {})
+    #     %add : [#users=0] = call_function[target=operator.add](args = (%conv1, 1), kwargs = {})
+    #     %reshape : [#users=2] = call_function[target=torch.reshape](args = (%conv1, [1, -1, 64, 1]), kwargs = {})
+    #     %conv2 : [#users=1] = call_module[target=conv2](args = (%reshape,), kwargs = {})
+    #     %reshape_1 : [#users=1] = call_function[target=torch.reshape](args = (%conv2, [4, 1, 64, -1]), kwargs = {})
+    #     %add_1 : [#users=1] = call_function[target=operator.add](args = (%reshape, %reshape_1), kwargs = {})
+    #     return add_1
+    graph = tracer.trace(root=model, meta_args=input_sample)
+    gm = GraphModule(model, graph, model.__class__.__name__)
+    # [x, conv1, add, reshape, conv2, reshape_1, add_1, 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
+    # check a tensor add with a scalar case
+    conv1_strategies = strategy_map[nodes[1]]
+    add_strategies = strategy_map[nodes[2]]
+    add_strategies_cover_list = [strategy.input_shardings[0].sharding_sequence for strategy in add_strategies]
+    for strategy in conv1_strategies:
+        assert strategy.output_sharding_spec.sharding_sequence in add_strategies_cover_list
+
+    # check two tensors element-wise add case
+    add_1_strategies = strategy_map[nodes[6]]
+    assert len(add_1_strategies) == 25
+
+
+if __name__ == '__main__':
+    test_conv_handler()