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test_uniprot_training_data_dump_distributed.py
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# Copyright (c) 2023 University of Illinois Board of Trustees. All Rights Reserved.
# Developed at the ES|CAD group (http://dchen.ece.illinois.edu)
# This file is released under specific terms. See LICENSE.txt or go to https://opensource.org/license/mit/
import uniprot_training_data_dump_distributed
import pickle
def test_uniprot_training_data_dump_distributed():
import data_processing
import argparse
import shutil
import os
import json
import subprocess
class RandChoicesFunctor:
def __init__(self):
pass
def __call__(self, array, weights, k, uniref_id):
if uniref_id in ["ABCD0", "ABCD2a"]:
return ["train"]
elif uniref_id in ["ABCD1"]:
return ["test"]
elif uniref_id in ["ABCD3"]:
return ["val"]
else:
raise ValueError("Bad uniref_id")
class RandSampleFunctor:
def __init__(self):
pass
def __call__(self, *args, **kwargs):
return [2]
class RandIntFunctor:
def __init__(self):
pass
def __call__(self, *args, **kwargs):
return 3
tokenizer = data_processing.Tokenizer(
max_sequence_length=1024,
max_masked_segment=1024,
include_extremities=False,
randsample_functor=RandSampleFunctor(),
randint_functor=RandIntFunctor(),
)
testdir = "/tmp/test_uniprot_training_data_dump_distributed"
if os.path.exists(testdir):
shutil.rmtree(testdir)
# uniprot_training_data_dump.main(args)
thisdir = os.path.split(__file__)[0]
subprocess.run(
["torchrun", "--nproc_per_node", "2", "--nnodes", "1",
os.path.join(thisdir, "uniprot_training_data_dump_distributed.py"),
"--max_sequence_length", "12",
"--max_masked_segment", "3",
"--datadir", testdir,
"--fasta", os.path.join(thisdir, "test_fasta.fasta"),
"--test",
], check=True
)
# Train checks (ACGXXFNMBMNUACHUACPQRST, GCRXRRNMA)
full_train_sequence = "ACGXXFNMBMNUACHUACPQRST"
train_sequences = [full_train_sequence[: 12], full_train_sequence[12: ], "GCRXRRNMA"]
tokenizer_results = [tokenizer.tokenize(x) for x in train_sequences]
train_data = data_processing.CompoundDiskStorageReader(
datadir=testdir, data_type="train")
assert(len(train_data) == 3)
def compare(actual, expected):
actual_list = actual["input_ids"][actual["attention_mask"] == 1].tolist()
return actual_list == expected
def compare_helper(res):
metadata = json.loads(res.metadata)
if metadata["uniref_id"] == "ABCD0":
fragment = metadata["fragment"]
if fragment == "0":
assert(compare(res.itokens, tokenizer_results[0][0])), (res, tokenizer_results[0][0])
assert(compare(res.otokens, tokenizer_results[0][1])), (res, tokenizer_results[0][1])
elif fragment == "1":
assert(compare(res.itokens, tokenizer_results[1][0])), (res, tokenizer_results[1][0])
assert(compare(res.otokens, tokenizer_results[1][1])), (res, tokenizer_results[1][1])
else:
raise ValueError(f"Unknown fragment {fragment}")
elif metadata["uniref_id"] == "ABCD2a":
assert(metadata["fragment"] == "0"), (res)
assert(compare(res.itokens, tokenizer_results[2][0])), (res, tokenizer_results[2][0])
assert(compare(res.otokens, tokenizer_results[2][1])), (res, tokenizer_results[2][1])
else:
raise ValueError(f"Unknown uniref_id")
compare_helper(train_data[0])
compare_helper(train_data[1])
compare_helper(train_data[2])
# Validation checks (ACBDEGFHIJKL)
val_sequences = ["ACBDEGFHIJKL"]
tokenizer_results = [tokenizer.tokenize(x) for x in val_sequences]
val_data = data_processing.CompoundDiskStorageReader(
testdir, data_type="val")
res0 = val_data[0]
assert(compare(res0.itokens, tokenizer_results[0][0])), (res0, tokenizer_results[0][0])
assert(compare(res0.otokens, tokenizer_results[0][1])), (res0, tokenizer_results[0][1])
assert(json.loads(res0.metadata) == {"uniref_id": "ABCD3", "fragment": "0"})
# Test data checks (GCRXMFNMA)
test_sequences = ["GCRXMFNMA"]
tokenizer_results = [tokenizer.tokenize(x) for x in test_sequences]
test_data = data_processing.CompoundDiskStorageReader(
testdir, data_type="test")
res0 = test_data[0]
assert(compare(res0.itokens, tokenizer_results[0][0])), (res0, tokenizer_results[0][0])
assert(compare(res0.otokens, tokenizer_results[0][1])), (res0, tokenizer_results[0][1])
assert(json.loads(res0.metadata) == {"uniref_id": "ABCD1", "fragment": "0"})
print("Test test_uniprot_training_data_dump_distributed passed")
def test_uniprot_training_data_dump_distributed_uniref100():
import data_processing
import argparse
import shutil
import os
import json
import subprocess
"""
"ax" maps to 2
"av" maps to 1
"az" maps to 0
"ab" maps to 0
"""
class RandChoicesFunctor:
def __init__(self):
pass
def __call__(self, array, weights, k, uniref_id):
if uniref_id in ["ABCD0", "ABCD2a"]:
return ["train"]
elif uniref_id in ["ABCD1"]:
return ["test"]
elif uniref_id in ["ABCD3"]:
return ["val"]
else:
raise ValueError("Bad uniref_id")
class RandSampleFunctor:
def __init__(self):
pass
def __call__(self, *args, **kwargs):
return [2]
class RandIntFunctor:
def __init__(self):
pass
def __call__(self, *args, **kwargs):
return 3
tokenizer = data_processing.Tokenizer(
max_sequence_length=1024,
max_masked_segment=1024,
include_extremities=False,
randsample_functor=RandSampleFunctor(),
randint_functor=RandIntFunctor(),
)
testdir = "/tmp/test_uniprot_training_data_dump_distributed_uniref100"
clusterdir = "/tmp/test_uniprot_training_data_dump_distributed_uniref100_clusters"
if os.path.exists(testdir):
shutil.rmtree(testdir)
if os.path.exists(clusterdir):
shutil.rmtree(clusterdir)
os.makedirs(clusterdir)
with open(os.path.join(clusterdir, "cluster_0.uniref100.pkl"), "wb") as fhandle:
# ABCD0 -> val
# ABCD1 -> train
pickle.dump({"ABCD0": "av", "ABCD1": "az"}, fhandle)
with open(os.path.join(clusterdir, "cluster_1.uniref100.pkl"), "wb") as fhandle:
# ABCD2a -> test
pickle.dump({"ABCD2a": "ax"}, fhandle)
with open(os.path.join(clusterdir, "cluster_2.uniref100.pkl"), "wb") as fhandle:
# ABCD3 -> train
pickle.dump({"ABCD3": "ab"}, fhandle)
thisdir = os.path.split(__file__)[0]
subprocess.run(
["torchrun", "--nproc_per_node", "3", "--nnodes", "1",
os.path.join(thisdir, "uniprot_training_data_dump_distributed.py"),
"--max_sequence_length", "12",
"--max_masked_segment", "3",
"--datadir", testdir,
"--fasta", os.path.join(thisdir, "test_fasta.fasta"),
"--test",
"--uniref100_cluster_data", os.path.join(clusterdir, "cluster"),
"--num_sampler_buckets", str(3),
"--train_val_test_split", "0.33,0.33,0.33",
], check=True
)
# Train checks
train_sequences = ["GCRXMFNMA", "ACBDEGFHIJKL"]
tokenizer_results = [tokenizer.tokenize(x) for x in train_sequences]
train_data = data_processing.CompoundDiskStorageReader(
datadir=testdir, data_type="train")
assert(len(train_data) == 2)
def compare(actual, expected):
actual_list = actual["input_ids"][actual["attention_mask"] == 1].tolist()
return actual_list == expected
def compare_helper(res):
metadata = json.loads(res.metadata)
if metadata["uniref_id"] == "ABCD1":
fragment = metadata["fragment"]
assert(metadata["fragment"] == "0"), (res)
assert(compare(res.itokens, tokenizer_results[0][0])), (res, tokenizer_results[0][0])
assert(compare(res.otokens, tokenizer_results[0][1])), (res, tokenizer_results[0][1])
elif metadata["uniref_id"] == "ABCD3":
assert(metadata["fragment"] == "0"), (res)
assert(compare(res.itokens, tokenizer_results[1][0])), (res, tokenizer_results[1][0])
assert(compare(res.otokens, tokenizer_results[1][1])), (res, tokenizer_results[1][1])
else:
raise ValueError(f"Unknown uniref_id")
assert(len(train_data) == 2)
compare_helper(train_data[0])
compare_helper(train_data[1])
# Validation checks
full_validation_sequence = "ACGXXFNMBMNUACHUACPQRST"
val_sequences = [full_validation_sequence[:12], full_validation_sequence[12:]]
tokenizer_results = [tokenizer.tokenize(x) for x in val_sequences]
val_data = data_processing.CompoundDiskStorageReader(testdir, data_type="val")
assert(len(val_data) == 2)
for i in range(2):
res = val_data[i]
metadata = json.loads(res.metadata)
if metadata["fragment"] == "0":
assert(compare(res.itokens, tokenizer_results[0][0])), (res, tokenizer_results[0][0])
assert(compare(res.otokens, tokenizer_results[0][1])), (res, tokenizer_results[0][1])
assert(metadata == {"uniref_id": "ABCD0", "fragment": "0"}), metadata
elif metadata["fragment"] == "1":
assert(compare(res.itokens, tokenizer_results[1][0])), (res, tokenizer_results[1][0])
assert(compare(res.otokens, tokenizer_results[1][1])), (res, tokenizer_results[1][1])
assert(metadata == {"uniref_id": "ABCD0", "fragment": "1"}), metadata
else:
raise ValueError("Unknown fragment")
# Test data checks
test_sequences = ["GCRXRRNMA"]
tokenizer_results = [tokenizer.tokenize(x) for x in test_sequences]
test_data = data_processing.CompoundDiskStorageReader(
testdir, data_type="test")
res0 = test_data[0]
assert(compare(res0.itokens, tokenizer_results[0][0])), (res0, tokenizer_results[0][0])
assert(compare(res0.otokens, tokenizer_results[0][1])), (res0, tokenizer_results[0][1])
assert(json.loads(res0.metadata) == {"uniref_id": "ABCD2a", "fragment": "0"})
print("Test test_uniprot_training_data_dump_distributed_uniref100 passed")
def test_uniprot_training_data_dump_distributed_uniref50_simple_splitter():
import data_processing
import argparse
import shutil
import os
import json
import subprocess
"""
"ax" maps to 2
"av" maps to 1
"az" maps to 0
"ab" maps to 0
"""
class RandChoicesFunctor:
def __init__(self):
pass
def __call__(self, array, weights, k, uniref_id):
if uniref_id in ["ABCD0", "ABCD2a"]:
return ["train"]
elif uniref_id in ["ABCD1"]:
return ["test"]
elif uniref_id in ["ABCD3"]:
return ["val"]
else:
raise ValueError("Bad uniref_id")
class RandSampleFunctor:
def __init__(self):
pass
def __call__(self, *args, **kwargs):
return [2]
class RandIntFunctor:
def __init__(self):
pass
def __call__(self, *args, **kwargs):
return 3
tokenizer = data_processing.Tokenizer(
max_sequence_length=1024,
max_masked_segment=1024,
include_extremities=False,
randsample_functor=RandSampleFunctor(),
randint_functor=RandIntFunctor(),
)
testdir = "/tmp/test_uniprot_training_data_dump_distributed_uniref50_predefined"
clusterdir = "/tmp/test_uniprot_training_data_dump_distributed_uniref50_predefined_clusters"
if os.path.exists(testdir):
shutil.rmtree(testdir)
if os.path.exists(clusterdir):
shutil.rmtree(clusterdir)
os.makedirs(clusterdir)
with open(os.path.join(clusterdir, "cluster-0.pkl"), "wb") as fhandle:
# ABCD0 -> val
# ABCD1 -> train
# pickle.dump({"ABCD0": "av", "ABCD1": "az"}, fhandle)
pickle.dump({"ABCD0": "val", "ABCD1": "train"}, fhandle)
with open(os.path.join(clusterdir, "cluster-1.pkl"), "wb") as fhandle:
# ABCD2a -> test
# pickle.dump({"ABCD2a": "ax"}, fhandle)
pickle.dump({"ABCD2a": "test"}, fhandle)
with open(os.path.join(clusterdir, "cluster-2.pkl"), "wb") as fhandle:
# ABCD3 -> train
# pickle.dump({"ABCD3": "ab"}, fhandle)
pickle.dump({"ABCD3": "train"}, fhandle)
thisdir = os.path.split(__file__)[0]
subprocess.run(
["torchrun", "--nproc_per_node", "3", "--nnodes", "1",
os.path.join(thisdir, "uniprot_training_data_dump_distributed.py"),
"--max_sequence_length", "12",
"--max_masked_segment", "3",
"--datadir", testdir,
"--fasta", os.path.join(thisdir, "test_fasta.fasta"),
"--test",
"--uniref50_cluster_data", os.path.join(clusterdir, "cluster"),
"--num_sampler_buckets", str(3),
"--train_val_test_split", "0.33,0.33,0.33",
], check=True
)
# Train checks
train_sequences = ["GCRXMFNMA", "ACBDEGFHIJKL"]
tokenizer_results = [tokenizer.tokenize(x) for x in train_sequences]
train_data = data_processing.CompoundDiskStorageReader(
datadir=testdir, data_type="train")
assert(len(train_data) == 2)
def compare(actual, expected):
actual_list = actual["input_ids"][actual["attention_mask"] == 1].tolist()
return actual_list == expected
def compare_helper(res):
metadata = json.loads(res.metadata)
if metadata["uniref_id"] == "ABCD1":
fragment = metadata["fragment"]
assert(metadata["fragment"] == "0"), (res)
assert(compare(res.itokens, tokenizer_results[0][0])), (res, tokenizer_results[0][0])
assert(compare(res.otokens, tokenizer_results[0][1])), (res, tokenizer_results[0][1])
elif metadata["uniref_id"] == "ABCD3":
assert(metadata["fragment"] == "0"), (res)
assert(compare(res.itokens, tokenizer_results[1][0])), (res, tokenizer_results[1][0])
assert(compare(res.otokens, tokenizer_results[1][1])), (res, tokenizer_results[1][1])
else:
raise ValueError(f"Unknown uniref_id")
assert(len(train_data) == 2)
compare_helper(train_data[0])
compare_helper(train_data[1])
# Validation checks
full_validation_sequence = "ACGXXFNMBMNUACHUACPQRST"
val_sequences = [full_validation_sequence[:12], full_validation_sequence[12:]]
tokenizer_results = [tokenizer.tokenize(x) for x in val_sequences]
val_data = data_processing.CompoundDiskStorageReader(testdir, data_type="val")
assert(len(val_data) == 2)
for i in range(2):
res = val_data[i]
metadata = json.loads(res.metadata)
if metadata["fragment"] == "0":
assert(compare(res.itokens, tokenizer_results[0][0])), (res, tokenizer_results[0][0])
assert(compare(res.otokens, tokenizer_results[0][1])), (res, tokenizer_results[0][1])
assert(metadata == {"uniref_id": "ABCD0", "fragment": "0"}), metadata
elif metadata["fragment"] == "1":
assert(compare(res.itokens, tokenizer_results[1][0])), (res, tokenizer_results[1][0])
assert(compare(res.otokens, tokenizer_results[1][1])), (res, tokenizer_results[1][1])
assert(metadata == {"uniref_id": "ABCD0", "fragment": "1"}), metadata
else:
raise ValueError("Unknown fragment")
# Test data checks
test_sequences = ["GCRXRRNMA"]
tokenizer_results = [tokenizer.tokenize(x) for x in test_sequences]
test_data = data_processing.CompoundDiskStorageReader(
testdir, data_type="test")
res0 = test_data[0]
assert(compare(res0.itokens, tokenizer_results[0][0])), (res0, tokenizer_results[0][0])
assert(compare(res0.otokens, tokenizer_results[0][1])), (res0, tokenizer_results[0][1])
assert(json.loads(res0.metadata) == {"uniref_id": "ABCD2a", "fragment": "0"})
print("Test test_uniprot_training_data_dump_distributed_uniref50_simple_splitter passed")
if __name__ == "__main__":
test_uniprot_training_data_dump_distributed()
test_uniprot_training_data_dump_distributed_uniref100()
test_uniprot_training_data_dump_distributed_uniref50_simple_splitter()