[shardformer] support SAM (#4231)

* 1.support sam 2.add fused qkv for nn.Linear

* update utils support set element in list

* overtwrite SamVisionAttention foward to use DropoutForParallelInput

* remove unused code

tests/test_shardformer/test_model/test_shard_sam.py

@@ -0,0 +1,92 @@
+import pytest
+import torch
+
+import colossalai
+from colossalai.logging import disable_existing_loggers
+from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor
+from colossalai.testing import (
+    assert_hf_output_close,
+    clear_cache_before_run,
+    parameterize,
+    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, ignore_keys=['pred_masks'])
+
+    # do backward
+    org_loss.backward()
+    shard_loss.backward()
+
+    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}"
+
+    # check grad
+
+    sam = org_model
+    sharded_sam = sharded_model
+
+    # compare mask decoder grad
+
+    org_grad = sam.mask_decoder.transformer.layers[0].self_attn.q_proj.weight.grad
+    shard_grad = sharded_sam.mask_decoder.transformer.layers[0].self_attn.q_proj.weight.grad
+    shard_weight = sharded_sam.mask_decoder.transformer.layers[0].self_attn.q_proj.weight
+
+    if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight):
+        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)
+    else:
+        all_shard_grad = shard_grad
+    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}"
+
+    # compare vision_encoder grad
+    org_grad = sam.vision_encoder.layers[0].mlp.lin1.weight.grad
+    shard_grad = sharded_sam.vision_encoder.layers[0].mlp.lin1.weight.grad
+    shard_weight = sharded_sam.vision_encoder.layers[0].mlp.lin1.weight
+
+    if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight):
+        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)
+    else:
+        all_shard_grad = shard_grad
+
+    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}"
+
+
+@parameterize('enable_fused_normalization', [True, False])
+@parameterize('enable_tensor_parallelism', [True, False])
+def run_sam_test(enable_fused_normalization, enable_tensor_parallelism):
+    sub_model_zoo = model_zoo.get_sub_registry('transformers_sam')
+    for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items():
+        org_model, sharded_model = build_model(model_fn, enable_fused_normalization, enable_tensor_parallelism)
+        check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn)
+
+    torch.cuda.empty_cache()
+
+
+def check_sam(rank, world_size, port):
+    disable_existing_loggers()
+    colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
+    run_sam_test()
+
+
+@pytest.mark.dist
+@rerun_if_address_is_in_use()
+@clear_cache_before_run()
+def test_sam():
+    spawn(check_sam, 2)
+
+
+if __name__ == "__main__":
+    test_sam()