github/ColossalAI
1
0
Fork 0
mirror of https://github.com/hpcaitech/ColossalAI.git synced 2025-09-05 11:02:05 +00:00
Code Issues Packages Projects Releases Wiki Activity

[misc] update pre-commit and run all files (#4752)

Browse Source 
* [misc] update pre-commit

* [misc] run pre-commit

* [misc] remove useless configuration files

* [misc] ignore cuda for clang-format
This commit is contained in:
Hongxin Liu
2023-09-19 14:20:26 +08:00
committed by GitHub
parent 3c6b831c26
commit 079bf3cb26
1268 changed files with 50037 additions and 38444 deletions
Download Patch File Download Diff File Expand all files Collapse all files

44
colossalai/shardformer/examples/performance_benchmark.py
View File

@@ -20,35 +20,35 @@ def data_gen_for_sequence_classification(batch_size, seq_length):
# LM data gen
# the `labels` of LM is the token of the output, cause no padding, use `input_ids` as `labels`
data = data_gen(batch_size, seq_length)
data['labels'] = torch.ones((batch_size), dtype=torch.long)
data["labels"] = torch.ones((batch_size), dtype=torch.long)
return data
MODEL_CONFIG = transformers.LlamaConfig(num_hidden_layers=4,
hidden_size=128,
intermediate_size=256,
num_attention_heads=4,
max_position_embeddings=128,
num_labels=16,
pad_token_id=2)
MODEL_CONFIG = transformers.LlamaConfig(
num_hidden_layers=4,
hidden_size=128,
intermediate_size=256,
num_attention_heads=4,
max_position_embeddings=128,
num_labels=16,
pad_token_id=2,
)
BATCH, N_HEADS, N_CTX, D_HEAD = 4, 8, 4096, 64
model_func = lambda: transformers.LlamaForSequenceClassification(MODEL_CONFIG)
# vary seq length for fixed head and batch=4
configs = [
triton.testing.Benchmark(x_names=['N_CTX'],
x_vals=[2**i for i in range(8, 13)],
line_arg='provider',
line_vals=['org_model', 'shard_model'],
line_names=['org_model', 'shard_model'],
styles=[('red', '-'), ('blue', '-')],
ylabel='ms',
plot_name=f'lama_for_sequence_classification-batch-{BATCH}',
args={
'BATCH': BATCH,
'dtype': torch.float16,
'model_func': model_func
})
triton.testing.Benchmark(
x_names=["N_CTX"],
x_vals=[2**i for i in range(8, 13)],
line_arg="provider",
line_vals=["org_model", "shard_model"],
line_names=["org_model", "shard_model"],
styles=[("red", "-"), ("blue", "-")],
ylabel="ms",
plot_name=f"lama_for_sequence_classification-batch-{BATCH}",
args={"BATCH": BATCH, "dtype": torch.float16, "model_func": model_func},
)
]
@@ -85,4 +85,4 @@ def bench_shardformer(BATCH, N_CTX, provider, model_func, dtype=torch.float32, d
# torchrun --standalone --nproc_per_node=2 performance_benchmark.py
if __name__ == "__main__":
colossalai.launch_from_torch({})
bench_shardformer.run(save_path='.', print_data=dist.get_rank() == 0)
bench_shardformer.run(save_path=".", print_data=dist.get_rank() == 0)