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[shardformer] adapted llama to the new API (#4036)

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Frank Lee
2023-06-19 13:53:17 +08:00
parent 74d176c8d8
commit c1d5453e9f
9 changed files with 238 additions and 201 deletions
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45
tests/test_shardformer/test_model/test_shard_llama.py
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@@ -4,31 +4,28 @@ import random
import pytest
import torch
from transformers import AutoTokenizer, LlamaConfig, LlamaForCausalLM, LlamaModel, LlamaTokenizerFast
from transformers import LlamaConfig, LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizerFast
import colossalai
from colossalai.logging import disable_existing_loggers
from colossalai.shardformer.shard import ShardConfig, shard_model
from colossalai.shardformer import ShardConfig, ShardFormer
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=4, mode='1d')),)
tokenizer = LlamaTokenizerFast.from_pretrained("hf-internal-testing/llama-tokenizer")
def build_model(rank, world_size):
cfg = LlamaConfig(num_hidden_layers=16)
org_model = LlamaForCausalLM(cfg)
def build_model(world_size, model_fn):
# create new model
config = LlamaConfig(num_hidden_layers=8)
org_model = model_fn(config).cuda()
shardconfig = ShardConfig(
rank=rank,
world_size=world_size,
gather_output=True,
)
org_model = org_model.to('cuda')
org_model_forshard = copy.deepcopy(org_model)
sharded_model = shard_model(org_model_forshard, shardconfig).to('cuda')
# shard model
shard_config = ShardConfig(tensor_parallel_size=world_size)
model_copy = copy.deepcopy(org_model)
shard_former = ShardFormer(shard_config=shard_config)
shard_former.init_distributed()
sharded_model = shard_former.shard_model(model_copy)
return org_model, sharded_model
@@ -38,6 +35,7 @@ def check_forward(org_model, sharded_model):
inputs = tokenizer(input, return_tensors='pt').to('cuda')
del inputs["token_type_ids"]
del inputs["attention_mask"]
#orgin model
org_model.eval()
org_out = org_model(**inputs)
@@ -87,11 +85,20 @@ def check_backward(org_model, sharded_model):
def check_llama(rank, world_size, port):
disable_existing_loggers()
colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
colossalai.launch(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)
model_list = [
LlamaForCausalLM,
# TODO: do not work yet
# LlamaModel,
# LlamaForSequenceClassification
]
for model_fn in model_list:
org_model, sharded_model = build_model(world_size, model_fn)
check_forward(org_model, sharded_model)
check_backward(org_model, sharded_model)
torch.cuda.empty_cache()