[shardformer] support vision transformer (#4096)

* first v of vit shardformer

* keep vit

* update

* vit shard add vitattention vitlayer

* update num head shard para

* finish test for vit

* add new_model_class & postprocess

* add vit readme

* delete old files & fix the conflict

* fix sth

colossalai/shardformer/README.md

@@ -91,7 +91,7 @@ We will follow this roadmap to develop Shardformer:
       - [ ] GPT Neo
       - [ ] GPT-J
     - [ ] CV
-      - [ ] ViT
+      - [x] ViT
       - [ ] BEiT
       - [ ] SwinTransformer
       - [ ] SwinTransformer V2

colossalai/shardformer/policies/vit.py

+from typing import Dict, Union
+
+import torch.nn as nn
+
+from transformers.models.vit.modeling_vit import ViTModel, ViTLayer, ViTEmbeddings, ViTAttention
+
+from colossalai.shardformer.layer import Linear1D_Col, Linear1D_Row, Dropout1D
+
+from .basepolicy import ModulePolicyDescription, Policy, SubModuleReplacementDescription
+
+class ViTPolicy(Policy):
+    
+    def preprocess(self):
+        # Resize embedding
+        vocab_size = self.model.config.vocab_size
+        world_size = self.shard_config.tensor_parallel_size
+
+        if vocab_size % world_size != 0:
+            new_vocab_size = vocab_size + world_size - vocab_size % world_size
+            self.model.resize_token_embeddings(new_vocab_size)
+
+        return self.model
+    
+    def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDescription]:
+        return  {
+            ViTEmbeddings:
+                ModulePolicyDescription(
+                    attribute_replacement{},
+                    param_replacement=[],
+                    sub_module_replacement=[
+                        SubModuleReplacementDescription(
+                            suffix="dropout",
+                            target_module=Dropout1D,
+                        )
+                    ]
+                ),
+            ViTLayer:
+                ModulePolicyDescription(
+                    attribute_replacement{
+                        "attention.attention.num_attention_heads":
+                            self.model.config.num_attention_heads//self.shard_config.tensor_parallel_size,
+                        "attention.attention.all_head_size":
+                            self.model.config.hidden_size//self.shard_config.tensor_parallel_size,
+                    },
+                    param_replacement=[],
+                    sub_module_replacement=[
+                        SubModuleReplacementDescription(
+                            suffix="attention.attention.query",
+                            target_module=Linear1D_Col,
+                        ),
+                        SubModuleReplacementDescription(
+                            suffix="attention.attention.key",
+                            target_module=Linear1D_Col,
+                        ),
+                        SubModuleReplacementDescription(
+                            suffix="attention.attention.value",
+                            target_module=Linear1D_Col,
+                        ),
+                        SubModuleReplacementDescription(
+                            suffix="attention.attention.dropout",
+                            target_module=Dropout1D,
+                        ),
+                        SubModuleReplacementDescription(
+                            suffix="attention.output.dense",
+                            target_module=Linear1D_Row,
+                        ),
+                        SubModuleReplacementDescription(
+                            suffix="attention.output.dropout",
+                            target_module=Dropout1D,
+                        ),
+                        SubModuleReplacementDescription(
+                            suffix="intermediate.dense",
+                            target_module=Linear1D_Col,
+                        ),
+                        SubModuleReplacementDescription(
+                            suffix="output.dense",
+                            target_module=Linear1D_Row,
+                        ),
+                        SubModuleReplacementDescription(
+                            suffix="output.dropout",
+                            target_module=Dropout1D,
+                        ),
+                    ]
+                ),
+        }
+    
+    def new_model_class(self):
+        return None
+
+    def postprocess(self):
+        return self.model
+
+
+
+
+

tests/test_shardformer/test_model/test_shard_vit.py

+import pytest
+import torch
+
+import colossalai
+from colossalai.logging import disable_existing_loggers
+from colossalai.testing import assert_hf_output_close, clear_cache_before_run, rerun_if_address_is_in_use, spawn
+from tests.kit.model_zoo import model_zoo
+from tests.test_shardformer.test_model._utils import build_model, run_forward
+
+
+def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn):
+    # check forward
+    org_output, org_loss, shard_output, shard_loss = run_forward(org_model, sharded_model, data_gen_fn,
+                                                                 output_transform_fn, loss_fn)
+    assert_hf_output_close(org_output, shard_output)
+
+    # do backward
+    org_loss.backward()
+    shard_loss.backward()
+
+    # check grad
+    org_grad = org_model.encoder.layer[0].attention.attention.query.weight.grad
+    shard_grad = sharded_model.encoder.layer[0].attention.attention.query.weight.grad
+
+    shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)]
+    shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad)
+    all_shard_grad = torch.cat(shard_grad_list, dim=0)
+
+    assert torch.allclose(org_loss, shard_loss,
+                          atol=1e-5), f"shard model loss is not equal to orgin model loss\n{org_loss}\n{shard_loss}"
+    assert torch.allclose(org_grad, all_shard_grad,
+                          atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}"
+
+
+def check_vit(rank, world_size, port):
+    disable_existing_loggers()
+    colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
+
+    sub_model_zoo = model_zoo.get_sub_registry('transformers_vit')
+    for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items():
+        org_model, sharded_model = build_model(world_size, model_fn)
+        check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn)
+
+    torch.cuda.empty_cache()
+
+
+@pytest.mark.dist
+@rerun_if_address_is_in_use()
+@clear_cache_before_run()
+def test_vit():
+    spawn(check_vit, 4)
+
+
+if __name__ == "__main__":
+    test_vit()