[pipeline]add pipeline policy and bert forward (#4130)

* add pipeline policy and bert forward to be done

* add bertmodel pipeline forward and make tests

* add Bert_Policy and test for policy

* update formatting

* update formatting

* update the code

* fix bugs

* fix name confilt

tests/test_pipeline/test_policy/test_bert_model.py

@@ -0,0 +1,112 @@
+import pytest
+import torch
+import torch.distributed as dist
+from transformers.models.bert.modeling_bert import BertModel
+
+import colossalai
+from colossalai.cluster import ProcessGroupMesh
+from colossalai.pipeline.policy.bert import BertModelPolicy, bert_model_forward
+from colossalai.pipeline.stage_manager import PipelineStageManager
+from colossalai.testing import rerun_if_address_is_in_use, spawn
+
+
+def check_bert_model_forward():
+    model = BertModel.from_pretrained('bert-base-uncased')
+    DP_DIM, PP_DIM = 0, 1
+    DP_SIZE, PP_SIZE = 2, 2
+    RANK_TO_COORDINATE = {
+        0: (0, 0),
+        1: (0, 1),
+        2: (1, 0),
+        3: (1, 1),
+    }
+    PP_RANKS_IN_GROUP = {
+        0: [0, 1],
+        1: [0, 1],
+        2: [2, 3],
+        3: [2, 3],
+    }
+    pg_mesh = ProcessGroupMesh(DP_SIZE, PP_SIZE)
+    #print(pg_mesh)
+
+    stage_manager = PipelineStageManager(pg_mesh, PP_DIM)
+    rank = dist.get_rank()
+    # print(rank)
+
+    x = torch.randint(0, 1000, (2, 3))
+    hidden_states = torch.randint(0, 1000, (2, 3, 768)).to(torch.float32)
+    if stage_manager.stage == 0:
+        attention_mask = torch.ones_like(x)
+        output = bert_model_forward(self=model, input_ids=x, attention_mask=attention_mask, stage_manager=stage_manager)
+        print(output[0].shape)
+        assert output[0].shape == (2, 3, 768)
+        print('start the training')
+    else:
+        attention_mask = torch.ones((2, 12, 3, 3))
+        output = bert_model_forward(self=model,
+                                    hidden_states=hidden_states,
+                                    attention_mask=attention_mask,
+                                    stage_manager=stage_manager)
+        print(output[0].shape)
+        assert output[0].shape == (2, 3, 768)
+        print('end the training')
+        print(output)
+
+    # assert output[1].shape == (2, 768)
+
+
+def check_bert_model_policy():
+    model = BertModel.from_pretrained('bert-base-uncased')
+    DP_DIM, PP_DIM = 0, 1
+    DP_SIZE, PP_SIZE = 2, 2
+    RANK_TO_COORDINATE = {
+        0: (0, 0),
+        1: (0, 1),
+        2: (1, 0),
+        3: (1, 1),
+    }
+    PP_RANKS_IN_GROUP = {
+        0: [0, 1],
+        1: [0, 1],
+        2: [2, 3],
+        3: [2, 3],
+    }
+    pg_mesh = ProcessGroupMesh(DP_SIZE, PP_SIZE)
+    #print(pg_mesh)
+
+    stage_manager = PipelineStageManager(pg_mesh, PP_DIM)
+    rank = dist.get_rank()
+
+    model_policy = BertModelPolicy(stage_manager, len(model.encoder.layer), 2)
+    assert model_policy.layers_per_stage == [6, 6]
+    layers = model_policy.get_hold_layers(model)
+    for layer in layers:
+        print(layer)
+
+
+def run_dist_model(rank, world_size, port):
+    colossalai.launch(config={}, rank=rank, world_size=world_size, port=port, host='localhost')
+    check_bert_model_forward()
+
+
+def run_dist_policy(rank, world_size, port):
+    colossalai.launch(config={}, rank=rank, world_size=world_size, port=port, host='localhost')
+    check_bert_model_policy()
+
+
+@pytest.mark.dist
+@rerun_if_address_is_in_use()
+def test_bert_model_forward():
+    spawn(run_dist_model, 4)
+
+
+@pytest.mark.dist
+@rerun_if_address_is_in_use()
+def test_bert_model_policy():
+    spawn(run_dist_policy, 4)
+
+
+if __name__ == "__main__":
+    """test the bert model forward and bert model policy"""
+    test_bert_model_forward()
+    test_bert_model_policy()

tests/test_pipeline/test_stage_manager.py

@@ -21,7 +21,7 @@ def check_stage_manager():
         1: [0, 1],
         2: [2, 3],
         3: [2, 3],
-    }
+    }   
     pg_mesh = ProcessGroupMesh(DP_SIZE, PP_SIZE)
     stage_manager = PipelineStageManager(pg_mesh, PP_DIM)
     rank = dist.get_rank()