[shardformer] shardformer support t5 model (#3994)

test t5

tests/test_shardformer/test_model/test_shard_t5.py

@@ -0,0 +1,99 @@
+import copy
+import os
+import random
+
+import pytest
+import torch
+from transformers import AutoTokenizer, BertConfig, BertForMaskedLM, T5Config, T5ForConditionalGeneration, T5Tokenizer
+
+import colossalai
+from colossalai.logging import disable_existing_loggers
+from colossalai.shardformer.shard import ShardConfig, shard_model
+from colossalai.testing import rerun_if_address_is_in_use, spawn
+
+os.environ['TRANSFORMERS_NO_ADVISORY_WARNINGS'] = 'true'
+CONFIG = dict(parallel=dict(data=1, pipeline=1, tensor=dict(size=2, mode='1d')),)
+tokenizer = T5Tokenizer.from_pretrained("t5-small")
+
+
+def build_model(rank, world_size):
+    config = T5Config.from_pretrained("t5-small")
+    config.dropout_rate = 0
+    org_model = T5ForConditionalGeneration.from_pretrained("t5-small", config=config).to('cuda')
+
+    shardconfig = ShardConfig(
+        rank=rank,
+        world_size=world_size,
+        gather_output=True,
+    )
+
+    org_model_for_shard = copy.deepcopy(org_model)
+
+    sharded_model = shard_model(org_model_for_shard, shardconfig).to('cuda')
+
+    return org_model, sharded_model
+
+
+def check_forward(org_model, sharded_model):
+
+    input_ids = tokenizer("translate English to German: The house is wonderful.",
+                          return_tensors="pt").input_ids.to('cuda')
+    #orgin model
+    org_model.eval()
+    org_output = org_model.generate(input_ids)
+
+    #shard model
+    sharded_model.eval()
+    shard_output = sharded_model.generate(input_ids)
+    assert torch.allclose(
+        org_output[0], shard_output[0],
+        atol=1e-5), f"shard model output is not equal to orgin model output\n{org_out[0]}\n{shard_out[0]}"
+
+
+def check_backward(org_model, sharded_model):
+    # prepare input
+    input_ids = tokenizer("translate English to German: The house is wonderful.",
+                          return_tensors="pt").input_ids.to('cuda')
+    labels = tokenizer("Das Haus ist wunderbar.", return_tensors="pt").input_ids.to('cuda')
+
+    #orgin model
+    org_model.train()
+    org_loss = org_model(input_ids=input_ids, labels=labels).loss
+    org_loss.backward()
+    org_grad = org_model.encoder.block[0].layer[0].SelfAttention.q.weight.grad
+
+    #shard model
+    sharded_model.train()
+    shard_loss = sharded_model(input_ids=input_ids, labels=labels).loss
+    shard_loss.backward()
+    shard_grad = sharded_model.encoder.block[0].layer[0].SelfAttention.q.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{shard_grad}"
+
+
+def check_t5(rank, world_size, port):
+    disable_existing_loggers()
+    colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
+
+    org_model, sharded_model = build_model(rank, world_size)
+    check_forward(org_model, sharded_model)
+    check_backward(org_model, sharded_model)
+
+    torch.cuda.empty_cache()
+
+
+@pytest.mark.dist
+@rerun_if_address_is_in_use()
+def test_t5():
+    spawn(check_t5, 2)
+
+
+if __name__ == "__main__":
+    test_t5()