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e76b79a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 | # Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
def _test_esm1b(alphabet):
import torch
batch_converter = alphabet.get_batch_converter()
data = [
("protein1", "MKTVRQG"),
("protein2 with mask", "KALTA<mask>ISQP"),
("protein3", "K A <mask> I S Q"),
]
_, _, batch_tokens = batch_converter(data)
expected_tokens = torch.tensor(
[
[0, 20, 15, 11, 7, 10, 16, 6, 2, 1, 1, 1],
[0, 15, 5, 4, 11, 5, 32, 12, 8, 16, 14, 2],
[0, 15, 5, 32, 12, 8, 16, 2, 1, 1, 1, 1],
]
)
assert torch.allclose(batch_tokens, expected_tokens)
def _test_esm1b_truncation(alphabet):
import torch
batch_converter = alphabet.get_batch_converter(truncation_seq_length=10)
data = [
("protein1", "MKTVRQGMKTVRQG"),
("protein2 with mask", "KALTA<mask>ISQPISQP"),
("protein3", "K A <mask> I S Q"),
]
_, _, batch_tokens = batch_converter(data)
expected_tokens = torch.tensor(
[
[0, 20, 15, 11, 7, 10, 16, 6, 20, 15, 11, 2],
[0, 15, 5, 4, 11, 5, 32, 12, 8, 16, 14, 2],
[0, 15, 5, 32, 12, 8, 16, 2, 1, 1, 1, 1],
]
)
assert torch.allclose(batch_tokens, expected_tokens)
def test_esm1b_alphabet():
import esm
_, alphabet = esm.pretrained.esm1b_t33_650M_UR50S()
_test_esm1b(alphabet)
_test_esm1b_truncation(alphabet)
def test_esm1v_alphabet():
import esm
_, alphabet = esm.pretrained.esm1v_t33_650M_UR90S_1()
_test_esm1b(alphabet)
_test_esm1b_truncation(alphabet)
def test_esm1_msa1b_alphabet():
import torch
import esm
# Load ESM-1b model
_, alphabet = esm.pretrained.esm_msa1b_t12_100M_UR50S()
batch_converter = alphabet.get_batch_converter()
data = [
("protein1", "MKTVRQG"),
("protein2", "KALTRAI"),
("protein3", "KAAISQQ"),
]
_, _, batch_tokens = batch_converter(data)
expected_tokens = torch.tensor(
[
[
[0, 20, 15, 11, 7, 10, 16, 6],
[0, 15, 5, 4, 11, 10, 5, 12],
[0, 15, 5, 5, 12, 8, 16, 16],
]
]
)
assert torch.allclose(batch_tokens, expected_tokens)
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