mirror of https://github.com/hpcaitech/ColossalAI
88 lines
2.1 KiB
Python
88 lines
2.1 KiB
Python
from functools import partial
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from typing import List, Tuple
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import pytest
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import torch
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import torch.fx
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import torch.multiprocessing as mp
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try:
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from fastfold.model.nn.evoformer import EvoformerStack
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HAS_REPO = True
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except:
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HAS_REPO = False
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from test_alphafold_utils import run_test
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from colossalai.autochunk.autochunk_codegen import AUTOCHUNK_AVAILABLE
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def get_model():
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model = EvoformerStack(
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c_m=256,
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c_z=128,
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c_hidden_msa_att=32,
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c_hidden_opm=32,
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c_hidden_mul=128,
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c_hidden_pair_att=32,
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c_s=384,
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no_heads_msa=8,
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no_heads_pair=4,
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no_blocks=2, # 48
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transition_n=4,
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msa_dropout=0.15,
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pair_dropout=0.25,
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blocks_per_ckpt=None,
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inf=1000000000.0,
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eps=1e-08,
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clear_cache_between_blocks=False,
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is_multimer=False,
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).eval().cuda()
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return model
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def get_data(msa_len: int, pair_len: int) -> Tuple[List, List]:
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node = torch.randn(1, msa_len, pair_len, 256).cuda()
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node_mask = torch.randn(1, msa_len, pair_len).cuda()
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pair = torch.randn(1, pair_len, pair_len, 128).cuda()
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pair_mask = torch.randn(1, pair_len, pair_len).cuda()
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meta_args = [
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("m", node),
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("z", pair),
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("msa_mask", node_mask),
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("pair_mask", pair_mask),
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]
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concrete_args = [("chunk_size", None), ("_mask_trans", True)]
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return meta_args, concrete_args
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@pytest.mark.skipif(
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not (AUTOCHUNK_AVAILABLE and HAS_REPO),
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reason="torch version is lower than 1.12.0",
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)
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@pytest.mark.parametrize("max_memory", [None, 20, 24])
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@pytest.mark.parametrize("data_args", [(32, 64)]) # (msa_len, pair_len)
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def test_evoformer_stack(data_args, max_memory):
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run_func = partial(
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run_test,
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data_args=data_args,
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max_memory=max_memory,
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get_model=get_model,
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get_data=get_data,
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)
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mp.spawn(run_func, nprocs=1)
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if __name__ == "__main__":
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run_test(
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rank=0,
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data_args=(32, 64),
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max_memory=None,
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get_model=get_model,
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get_data=get_data,
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print_code=False,
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print_mem=False,
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print_progress=False,
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)
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