repo_id stringclasses 400
values | commit_sha stringclasses 400
values | commit_index int32 0 951 | in_repo_split stringclasses 1
value | cross_repo_split stringclasses 1
value | test_file stringlengths 7 121 | test_function stringlengths 1 108 | assertion_type stringclasses 32
values | difficulty stringclasses 8
values | context_lines int32 3 600 | prefix large_stringlengths 44 113k | target large_stringlengths 1 498 | anchor_sha stringclasses 400
values | anchor_index int32 0 951 | qna_source stringclasses 1
value |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
microsoft/causica | f20772bf4e2d104aea043f66226b35d2527640f9 | 15 | train | train | test/metrics/test_graph_eval_metrics.py | test_graph_eval_metrics | assert | collection | 83 | import pytest
import torch
from causica.graph.evaluation_metrics import (
adjacency_f1,
adjacency_precision_recall,
orientation_f1,
orientation_precision_recall,
)
MATRICES = [
torch.tensor([[0, 1, 1], [0, 0, 0], [0, 0, 0]]),
torch.tensor([[0, 0, 0], [1, 0, 0], [1, 0, 0]]),
torch.tensor([[... | ( adj_metrics["adjacency_precision"], adj_metrics["adjacency_recall"], ) | f20772bf4e2d104aea043f66226b35d2527640f9 | 15 | v2_extractor_at_anchor |
microsoft/causica | f20772bf4e2d104aea043f66226b35d2527640f9 | 15 | train | train | test/metrics/test_graph_eval_metrics.py | test_graph_eval_metrics | assert | collection | 78 | import pytest
import torch
from causica.graph.evaluation_metrics import (
adjacency_f1,
adjacency_precision_recall,
orientation_f1,
orientation_precision_recall,
)
MATRICES = [
torch.tensor([[0, 1, 1], [0, 0, 0], [0, 0, 0]]),
torch.tensor([[0, 0, 0], [1, 0, 0], [1, 0, 0]]),
torch.tensor([[... | ( orientation_metrics["orientation_precision"], orientation_metrics["orientation_recall"], ) | f20772bf4e2d104aea043f66226b35d2527640f9 | 15 | v2_extractor_at_anchor |
microsoft/causica | f20772bf4e2d104aea043f66226b35d2527640f9 | 15 | train | train | test/nn/test_deci_embed_nn.py | test_fgnni_broadcast | assert | func_call | 30 | import pytest
import torch
from causica.nn import DECIEmbedNN
PROCESSED_DIM = 6
NODE_NUM = 4
GROUP_MASK = torch.tensor(
[
[1, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0],
[0, 0, 1, 1, 0, 0],
[0, 0, 0, 0, 1, 1],
],
dtype=torch.float32,
)
GRAPH_SHAPES = [tuple(), (5,), (2, 3)]
SAMPLE... | sample_shape + graph_shape + (PROCESSED_DIM,) | f20772bf4e2d104aea043f66226b35d2527640f9 | 15 | v2_extractor_at_anchor |
microsoft/causica | f20772bf4e2d104aea043f66226b35d2527640f9 | 15 | train | train | test/sem/test_sem.py | test_do_linear_sem_bernoulli | assert_* | complex_expr | 48 | import pytest
import torch
import torch.distributions as td
from tensordict import TensorDict
from causica.datasets.variable_types import VariableTypeEnum
from causica.distributions import JointNoiseModule, create_noise_modules
from causica.distributions.noise.joint import ContinuousNoiseDist
from causica.functional_r... | sample["x1"]) | f20772bf4e2d104aea043f66226b35d2527640f9 | 15 | v2_extractor_at_anchor |
microsoft/causica | f20772bf4e2d104aea043f66226b35d2527640f9 | 15 | train | train | test/sem/test_sem.py | test_do_linear_sem_bernoulli | assert_* | variable | 41 | import pytest
import torch
import torch.distributions as td
from tensordict import TensorDict
from causica.datasets.variable_types import VariableTypeEnum
from causica.distributions import JointNoiseModule, create_noise_modules
from causica.distributions.noise.joint import ContinuousNoiseDist
from causica.functional_r... | expected_log_probs) | f20772bf4e2d104aea043f66226b35d2527640f9 | 15 | v2_extractor_at_anchor |
microsoft/causica | f20772bf4e2d104aea043f66226b35d2527640f9 | 15 | train | train | test/sem/test_sem.py | test_do_linear_sem_bernoulli | assert_* | collection | 46 | import pytest
import torch
import torch.distributions as td
from tensordict import TensorDict
from causica.datasets.variable_types import VariableTypeEnum
from causica.distributions import JointNoiseModule, create_noise_modules
from causica.distributions.noise.joint import ContinuousNoiseDist
from causica.functional_r... | ((expected_logits + noise["x1"]) > 0).float()) | f20772bf4e2d104aea043f66226b35d2527640f9 | 15 | v2_extractor_at_anchor |
microsoft/causica | f20772bf4e2d104aea043f66226b35d2527640f9 | 15 | train | train | test/sem/test_treatment_effects.py | test_ate_ite_cf_three_node | assert_* | complex_expr | 39 | import math
import pytest
import torch
from tensordict import TensorDict
from causica.sem.structural_equation_model import ate, counterfactual, ite
from . import create_lingauss_sem
def fixture_two_variable_dict():
return {"x1": torch.Size([1]), "x2": torch.Size([2])}
def fixture_three_variable_dict():
ret... | factual_data["x1"]) | f20772bf4e2d104aea043f66226b35d2527640f9 | 15 | v2_extractor_at_anchor |
microsoft/causica | f20772bf4e2d104aea043f66226b35d2527640f9 | 15 | train | train | test/sem/test_treatment_effects.py | test_ate_ite_cf_two_node | assert_* | variable | 34 | import math
import pytest
import torch
from tensordict import TensorDict
from causica.sem.structural_equation_model import ate, counterfactual, ite
from . import create_lingauss_sem
def fixture_two_variable_dict():
return {"x1": torch.Size([1]), "x2": torch.Size([2])}
def fixture_three_variable_dict():
ret... | expected_treatment_effect) | f20772bf4e2d104aea043f66226b35d2527640f9 | 15 | v2_extractor_at_anchor |
microsoft/causica | f20772bf4e2d104aea043f66226b35d2527640f9 | 15 | train | train | test/sem/test_treatment_effects.py | test_ate_ite_cf_two_node | assert_* | func_call | 39 | import math
import pytest
import torch
from tensordict import TensorDict
from causica.sem.structural_equation_model import ate, counterfactual, ite
from . import create_lingauss_sem
def fixture_two_variable_dict():
return {"x1": torch.Size([1]), "x2": torch.Size([2])}
def fixture_three_variable_dict():
ret... | expected_mean_a.expand((sample_size, 1))) | f20772bf4e2d104aea043f66226b35d2527640f9 | 15 | v2_extractor_at_anchor |
microsoft/causica | f20772bf4e2d104aea043f66226b35d2527640f9 | 15 | train | train | test/sem/test_treatment_effects.py | test_ate_ite_cf_three_node | assert_* | func_call | 27 | import math
import pytest
import torch
from tensordict import TensorDict
from causica.sem.structural_equation_model import ate, counterfactual, ite
from . import create_lingauss_sem
def fixture_two_variable_dict():
return {"x1": torch.Size([1]), "x2": torch.Size([2])}
def fixture_three_variable_dict():
ret... | torch.zeros_like(average_treatment_effect["x1"])) | f20772bf4e2d104aea043f66226b35d2527640f9 | 15 | v2_extractor_at_anchor |
microsoft/causica | f20772bf4e2d104aea043f66226b35d2527640f9 | 15 | train | train | test/sem/test_treatment_effects.py | test_ate_ite_cf_two_node | assert_* | func_call | 38 | import math
import pytest
import torch
from tensordict import TensorDict
from causica.sem.structural_equation_model import ate, counterfactual, ite
from . import create_lingauss_sem
def fixture_two_variable_dict():
return {"x1": torch.Size([1]), "x2": torch.Size([2])}
def fixture_three_variable_dict():
ret... | expected_treatment_effect.expand((sample_size, 1))) | f20772bf4e2d104aea043f66226b35d2527640f9 | 15 | v2_extractor_at_anchor |
microsoft/causica | f20772bf4e2d104aea043f66226b35d2527640f9 | 15 | train | train | test/test_dag_constraint.py | test_calculate_dagness | assert | numeric_literal | 11 | import numpy as np
import torch
from causica.graph.dag_constraint import calculate_dagness
def test_calculate_dagness():
dag = torch.Tensor([[0, 0, 1], [0, 0, 1], [0, 0, 0]])
assert calculate_dagness(dag) == | 0 | f20772bf4e2d104aea043f66226b35d2527640f9 | 15 | v2_extractor_at_anchor |
microsoft/causica | f20772bf4e2d104aea043f66226b35d2527640f9 | 15 | train | train | test/test_dag_constraint.py | test_calculate_dagness | assert_* | func_call | 22 | import numpy as np
import torch
from causica.graph.dag_constraint import calculate_dagness
def test_calculate_dagness():
dag = torch.Tensor([[0, 0, 1], [0, 0, 1], [0, 0, 0]])
assert calculate_dagness(dag) == 0
dag_one_cycle = torch.Tensor([[0, 1, 0], [0, 0, 1], [1, 0, 0]])
dag_two_cycle = torch.Te... | 2 * np.exp(1) - 2) | f20772bf4e2d104aea043f66226b35d2527640f9 | 15 | v2_extractor_at_anchor |
microsoft/causica | f20772bf4e2d104aea043f66226b35d2527640f9 | 15 | train | train | test/test_dag_constraint.py | test_calculate_dagness | assert | func_call | 19 | import numpy as np
import torch
from causica.graph.dag_constraint import calculate_dagness
def test_calculate_dagness():
dag = torch.Tensor([[0, 0, 1], [0, 0, 1], [0, 0, 0]])
assert calculate_dagness(dag) == 0
dag_one_cycle = torch.Tensor([[0, 1, 0], [0, 0, 1], [1, 0, 0]])
dag_two_cycle = torch.Te... | calculate_dagness(dag_two_cycle) | f20772bf4e2d104aea043f66226b35d2527640f9 | 15 | v2_extractor_at_anchor |
microsoft/causica | f20772bf4e2d104aea043f66226b35d2527640f9 | 15 | train | train | test/test_triangular_transformations.py | test_unfill_fill | assert_* | variable | 16 | import pytest
import torch
from causica.triangular_transformations import fill_triangular, unfill_triangular
DIM = 5
@pytest.mark.parametrize("batch_size", [tuple(), (3,), (4, 2)])
def test_unfill_fill(batch_size):
"""Test that unfilling and filling results in the same tensor"""
vec = torch.randn(batch_size ... | vec) | f20772bf4e2d104aea043f66226b35d2527640f9 | 15 | v2_extractor_at_anchor |
microsoft/causica | f20772bf4e2d104aea043f66226b35d2527640f9 | 15 | train | train | test/test_triangular_transformations.py | test_unfill_fill | assert_* | func_call | 18 | import pytest
import torch
from causica.triangular_transformations import fill_triangular, unfill_triangular
DIM = 5
@pytest.mark.parametrize("batch_size", [tuple(), (3,), (4, 2)])
def test_unfill_fill(batch_size):
"""Test that unfilling and filling results in the same tensor"""
vec = torch.randn(batch_size ... | torch.zeros_like(vec)) | f20772bf4e2d104aea043f66226b35d2527640f9 | 15 | v2_extractor_at_anchor |
microsoft/causica | f20772bf4e2d104aea043f66226b35d2527640f9 | 15 | train | train | test/test_triangular_transformations.py | test_fill_unfill | assert_* | func_call | 17 | import pytest
import torch
from causica.triangular_transformations import fill_triangular, unfill_triangular
DIM = 5
@pytest.mark.parametrize("batch_size", [tuple(), (3,), (4, 2)])
def test_fill_unfill(batch_size):
"""Test that filling and unfilling results in the same tensor"""
matrix = torch.randn(batch_si... | fill_triangular(upper_vec, upper=True)) | f20772bf4e2d104aea043f66226b35d2527640f9 | 15 | v2_extractor_at_anchor |
microsoft/causica | f20772bf4e2d104aea043f66226b35d2527640f9 | 15 | train | train | test/test_triangular_transformations.py | test_fill_unfill | assert_* | func_call | 18 | import pytest
import torch
from causica.triangular_transformations import fill_triangular, unfill_triangular
DIM = 5
@pytest.mark.parametrize("batch_size", [tuple(), (3,), (4, 2)])
def test_fill_unfill(batch_size):
"""Test that filling and unfilling results in the same tensor"""
matrix = torch.randn(batch_si... | fill_triangular(lower_vec, upper=True)) | f20772bf4e2d104aea043f66226b35d2527640f9 | 15 | v2_extractor_at_anchor |
microsoft/causica | f20772bf4e2d104aea043f66226b35d2527640f9 | 15 | train | train | test/test_triangular_transformations.py | test_fill_unfill | assert_* | func_call | 16 | import pytest
import torch
from causica.triangular_transformations import fill_triangular, unfill_triangular
DIM = 5
@pytest.mark.parametrize("batch_size", [tuple(), (3,), (4, 2)])
def test_fill_unfill(batch_size):
"""Test that filling and unfilling results in the same tensor"""
matrix = torch.randn(batch_si... | fill_triangular(lower_vec, upper=False)) | f20772bf4e2d104aea043f66226b35d2527640f9 | 15 | v2_extractor_at_anchor |
microsoft/causica | f20772bf4e2d104aea043f66226b35d2527640f9 | 15 | train | train | test/test_triangular_transformations.py | test_fill_unfill | assert_* | func_call | 21 | import pytest
import torch
from causica.triangular_transformations import fill_triangular, unfill_triangular
DIM = 5
@pytest.mark.parametrize("batch_size", [tuple(), (3,), (4, 2)])
def test_fill_unfill(batch_size):
"""Test that filling and unfilling results in the same tensor"""
matrix = torch.randn(batch_si... | fill_triangular(upper_vec, upper=False)) | f20772bf4e2d104aea043f66226b35d2527640f9 | 15 | v2_extractor_at_anchor |
microsoft/causica | f20772bf4e2d104aea043f66226b35d2527640f9 | 15 | train | train | test/training/test_auglag.py | test_solve_auglag | assert | numeric_literal | 79 | import torch
from torch.nn import Parameter
from torch.optim import Adam
from causica.training.auglag import AugLagLossCalculator, AugLagLR, AugLagLRConfig
def _get_auglag_config(lr_init_dict: dict[str, float]):
return AugLagLRConfig(
lr_update_lag=1,
lr_update_lag_best=100,
lr_init_dict=l... | 1e-3 | f20772bf4e2d104aea043f66226b35d2527640f9 | 15 | v2_extractor_at_anchor |
microsoft/mattergen | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | train | train | mattergen/common/tests/data_utils_test.py | check_invariance | assert | numeric_literal | 164 | from collections import Counter
from itertools import product
from typing import Dict, Optional, Tuple
import numpy as np
import pytest
import torch
from pymatgen.core.structure import Structure
from pymatgen.transformations.standard_transformations import RotationTransformation
from mattergen.common.tests.testutils ... | 1 | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | v2_extractor_at_anchor |
microsoft/mattergen | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | train | train | mattergen/common/tests/data_utils_test.py | get_distances_numpy | assert | variable | 239 | from collections import Counter
from itertools import product
from typing import Dict, Optional, Tuple
import numpy as np
import pytest
import torch
from pymatgen.core.structure import Structure
from pymatgen.transformations.standard_transformations import RotationTransformation
from mattergen.common.tests.testutils ... | dtype | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | v2_extractor_at_anchor |
microsoft/mattergen | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | train | train | mattergen/common/tests/data_utils_test.py | test_pbc_graph_translation_invariant | assert | variable | 82 | from collections import Counter
from itertools import product
from typing import Dict, Optional, Tuple
import numpy as np
import pytest
import torch
from pymatgen.core.structure import Structure
from pymatgen.transformations.standard_transformations import RotationTransformation
from mattergen.common.tests.testutils ... | counter2 | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | v2_extractor_at_anchor |
microsoft/mattergen | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | train | train | mattergen/common/tests/data_utils_test.py | test_torch_nanstd | assert | func_call | 264 | from collections import Counter
from itertools import product
from typing import Dict, Optional, Tuple
import numpy as np
import pytest
import torch
from pymatgen.core.structure import Structure
from pymatgen.transformations.standard_transformations import RotationTransformation
from mattergen.common.tests.testutils ... | np.nanstd(x.numpy()) | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | v2_extractor_at_anchor |
microsoft/mattergen | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | train | train | mattergen/common/tests/gemnet_test.py | test_lattice_score_scale_invariance | assert | none_literal | 149 | from copy import deepcopy
from itertools import chain, permutations
from typing import List, Tuple
import torch
from pymatgen.core.structure import Structure
from scipy.spatial.transform import Rotation
from torch_geometric.data import Batch, Data
from mattergen.common.gemnet.gemnet import GemNetT
from mattergen.comm... | None | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | v2_extractor_at_anchor |
microsoft/mattergen | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | train | train | mattergen/common/tests/gemnet_test.py | test_nonconservative_lattice_score_translation_invariance | assert_* | complex_expr | 139 | from copy import deepcopy
from itertools import chain, permutations
from typing import List, Tuple
import torch
from pymatgen.core.structure import Structure
from scipy.spatial.transform import Rotation
from torch_geometric.data import Batch, Data
from mattergen.common.gemnet.gemnet import GemNetT
from mattergen.comm... | out_translated.stress) | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | v2_extractor_at_anchor |
microsoft/mattergen | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | train | train | mattergen/diffusion/tests/data/test_batched_data.py | test_collate_fn | assert | none_literal | 15 | import torch
from mattergen.diffusion.data.batched_data import collate_fn
def test_collate_fn():
"""Collate two pieces of data"""
data1 = {"a": torch.tensor([1, 2, 3, 4]), "b": torch.tensor([[1, 2, 3]]), "name": "data1"}
data2 = {"a": torch.tensor([10, 11]), "b": torch.tensor([[10, 11, 12]]), "name": "dat... | None | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | v2_extractor_at_anchor |
microsoft/mattergen | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | train | train | mattergen/diffusion/tests/data/test_batched_data.py | test_to_data_list | assert | string_literal | 14 | import torch
from mattergen.diffusion.data.batched_data import collate_fn
def test_to_data_list():
"""Collate and then unpack two pieces of data."""
data1 = {"a": torch.tensor([1, 2, 3, 4]), "b": torch.tensor([[1, 2, 3]]), "name": "data1"}
data2 = {"a": torch.tensor([10, 11]), "b": torch.tensor([[10, 11, ... | "data1" | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | v2_extractor_at_anchor |
microsoft/mattergen | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | train | train | mattergen/diffusion/tests/data/test_batched_data.py | test_to_data_list | assert | string_literal | 17 | import torch
from mattergen.diffusion.data.batched_data import collate_fn
def test_to_data_list():
"""Collate and then unpack two pieces of data."""
data1 = {"a": torch.tensor([1, 2, 3, 4]), "b": torch.tensor([[1, 2, 3]]), "name": "data1"}
data2 = {"a": torch.tensor([10, 11]), "b": torch.tensor([[10, 11, ... | "data2" | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | v2_extractor_at_anchor |
microsoft/mattergen | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | train | train | mattergen/diffusion/tests/data/test_batched_data.py | test_to_data_list | assert | collection | 15 | import torch
from mattergen.diffusion.data.batched_data import collate_fn
def test_to_data_list():
"""Collate and then unpack two pieces of data."""
data1 = {"a": torch.tensor([1, 2, 3, 4]), "b": torch.tensor([[1, 2, 3]]), "name": "data1"}
data2 = {"a": torch.tensor([10, 11]), "b": torch.tensor([[10, 11, ... | [10, 11] | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | v2_extractor_at_anchor |
microsoft/mattergen | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | train | train | mattergen/diffusion/tests/data/test_batched_data.py | test_to_data_list | assert | collection | 13 | import torch
from mattergen.diffusion.data.batched_data import collate_fn
def test_to_data_list():
"""Collate and then unpack two pieces of data."""
data1 = {"a": torch.tensor([1, 2, 3, 4]), "b": torch.tensor([[1, 2, 3]]), "name": "data1"}
data2 = {"a": torch.tensor([10, 11]), "b": torch.tensor([[10, 11, ... | [[1, 2, 3]] | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | v2_extractor_at_anchor |
microsoft/mattergen | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | train | train | mattergen/diffusion/tests/data/test_batched_data.py | test_to_data_list | assert | collection | 12 | import torch
from mattergen.diffusion.data.batched_data import collate_fn
def test_to_data_list():
"""Collate and then unpack two pieces of data."""
data1 = {"a": torch.tensor([1, 2, 3, 4]), "b": torch.tensor([[1, 2, 3]]), "name": "data1"}
data2 = {"a": torch.tensor([10, 11]), "b": torch.tensor([[10, 11, ... | [1, 2, 3, 4] | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | v2_extractor_at_anchor |
microsoft/mattergen | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | train | train | mattergen/diffusion/tests/data/test_batched_data.py | test_collate_fn | assert | collection | 13 | import torch
from mattergen.diffusion.data.batched_data import collate_fn
def test_collate_fn():
"""Collate two pieces of data"""
data1 = {"a": torch.tensor([1, 2, 3, 4]), "b": torch.tensor([[1, 2, 3]]), "name": "data1"}
data2 = {"a": torch.tensor([10, 11]), "b": torch.tensor([[10, 11, 12]]), "name": "dat... | ["data1", "data2"] | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | v2_extractor_at_anchor |
microsoft/mattergen | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | train | train | mattergen/diffusion/tests/data/test_batched_data.py | test_collate_fn | assert | collection | 14 | import torch
from mattergen.diffusion.data.batched_data import collate_fn
def test_collate_fn():
"""Collate two pieces of data"""
data1 = {"a": torch.tensor([1, 2, 3, 4]), "b": torch.tensor([[1, 2, 3]]), "name": "data1"}
data2 = {"a": torch.tensor([10, 11]), "b": torch.tensor([[10, 11, 12]]), "name": "dat... | [0, 0, 0, 0, 1, 1] | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | v2_extractor_at_anchor |
microsoft/mattergen | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | train | train | mattergen/diffusion/tests/data/test_batched_data.py | test_collate_fn | assert | collection | 11 | import torch
from mattergen.diffusion.data.batched_data import collate_fn
def test_collate_fn():
"""Collate two pieces of data"""
data1 = {"a": torch.tensor([1, 2, 3, 4]), "b": torch.tensor([[1, 2, 3]]), "name": "data1"}
data2 = {"a": torch.tensor([10, 11]), "b": torch.tensor([[10, 11, 12]]), "name": "dat... | [1, 2, 3, 4, 10, 11] | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | v2_extractor_at_anchor |
microsoft/mattergen | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | train | train | mattergen/diffusion/tests/test_d3pm.py | test_sample_and_posterior | assert | collection | 31 | import functools
import numpy as np
import pytest
import torch
from mattergen.diffusion.d3pm import d3pm as diffusion
def test_sample_and_posterior():
"""Tests whether the samples and posterior are as expected when providing timestep 0 for the sampling."""
schedule = diffusion.create_discrete_diffusion_sche... | (1,) | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | v2_extractor_at_anchor |
microsoft/mattergen | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | train | train | mattergen/diffusion/tests/test_d3pm.py | test_model | assert_* | numeric_literal | 35 | import functools
import numpy as np
import pytest
import torch
from mattergen.diffusion.d3pm import d3pm as diffusion
def test_model():
"""Test the Diffusion noise diffusion."""
schedule = diffusion.create_discrete_diffusion_schedule(
kind="standard",
beta_min=1e-3,
beta_max=1e-3,
... | 1.0) | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | v2_extractor_at_anchor |
microsoft/mattergen | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | train | train | mattergen/diffusion/tests/test_d3pm.py | test_loss_computation | assert | complex_expr | 45 | import functools
import numpy as np
import pytest
import torch
from mattergen.diffusion.d3pm import d3pm as diffusion
def test_loss_computation():
"""Tests whether the loss computation uses the right terms (KL / cross-entropy) and broadcasts correctly."""
torch.manual_seed(234)
num_steps = 100
num_cl... | t.shape | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | v2_extractor_at_anchor |
microsoft/mattergen | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | train | train | mattergen/diffusion/tests/test_d3pm.py | test_sample_and_posterior | assert | collection | 28 | import functools
import numpy as np
import pytest
import torch
from mattergen.diffusion.d3pm import d3pm as diffusion
def test_sample_and_posterior():
"""Tests whether the samples and posterior are as expected when providing timestep 0 for the sampling."""
schedule = diffusion.create_discrete_diffusion_sche... | (1, 100) | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | v2_extractor_at_anchor |
microsoft/mattergen | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | train | train | mattergen/diffusion/tests/test_d3pm.py | test_compute_posterior | assert | collection | 25 | import functools
import numpy as np
import pytest
import torch
from mattergen.diffusion.d3pm import d3pm as diffusion
def test_compute_posterior():
"""Tests that the forward diffusion probabilities are correct for t=0."""
schedule = diffusion.create_discrete_diffusion_schedule(
kind="linear",
... | (2, 100) | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | v2_extractor_at_anchor |
microsoft/mattergen | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | train | train | mattergen/diffusion/tests/test_d3pm.py | test_mask_diffusion_slow_and_fast | assert_* | variable | 28 | import functools
import numpy as np
import pytest
import torch
from mattergen.diffusion.d3pm import d3pm as diffusion
def test_mask_diffusion_slow_and_fast():
"""Compares fast and slow inference for mask diffusion."""
schedule = diffusion.create_discrete_diffusion_schedule(
kind="standard",
b... | qt_fast) | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | v2_extractor_at_anchor |
microsoft/mattergen | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | train | train | mattergen/diffusion/tests/test_d3pm.py | test_product_constant | assert_* | variable | 33 | import functools
import numpy as np
import pytest
import torch
from mattergen.diffusion.d3pm import d3pm as diffusion
def test_product_constant():
"""Tests, when we have a constant beta schedule (transition probabilities don't change over time),
whether the transition matrices computed via q(x_t | x_0) and q... | expected) | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | v2_extractor_at_anchor |
microsoft/mattergen | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | train | train | mattergen/diffusion/tests/test_d3pm.py | test_mask_diffusion_slow_and_fast | assert_* | variable | 43 | import functools
import numpy as np
import pytest
import torch
from mattergen.diffusion.d3pm import d3pm as diffusion
def test_mask_diffusion_slow_and_fast():
"""Compares fast and slow inference for mask diffusion."""
schedule = diffusion.create_discrete_diffusion_schedule(
kind="standard",
b... | samples_fast) | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | v2_extractor_at_anchor |
microsoft/mattergen | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | train | train | mattergen/diffusion/tests/test_d3pm.py | test_sample_and_posterior | assert_* | func_call | 32 | import functools
import numpy as np
import pytest
import torch
from mattergen.diffusion.d3pm import d3pm as diffusion
def test_sample_and_posterior():
"""Tests whether the samples and posterior are as expected when providing timestep 0 for the sampling."""
schedule = diffusion.create_discrete_diffusion_sche... | np.array([1])) | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | v2_extractor_at_anchor |
microsoft/mattergen | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | train | train | mattergen/diffusion/tests/test_d3pm.py | test_product_the_hard_way | assert_* | func_call | 26 | import functools
import numpy as np
import pytest
import torch
from mattergen.diffusion.d3pm import d3pm as diffusion
def test_product_the_hard_way():
"""Tests that the discrete transition matrices computed via q(x_t | x_0) and q(x_t|x_{t-1}) are equivalent
for t in {0, 1}. Uses the slow iterative method of ... | torch.eye(100)) | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | v2_extractor_at_anchor |
microsoft/mattergen | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | train | train | mattergen/diffusion/tests/test_data_utils.py | test_collate_fn | assert | numeric_literal | 38 | import torch
from mattergen.diffusion.data.batched_data import SimpleBatchedData, _batch_edge_index, collate_fn
def test_collate_fn():
state1 = dict(foo=torch.ones(2, 3), bar=torch.ones(5, 2))
state2 = dict(foo=torch.zeros(3, 3), bar=torch.zeros(2, 2))
batch = collate_fn([state1, state2])
field_names... | 2 | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | v2_extractor_at_anchor |
microsoft/mattergen | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | train | train | mattergen/diffusion/tests/test_data_utils.py | test_batch_edge_index | assert_* | func_call | 12 | import torch
from mattergen.diffusion.data.batched_data import SimpleBatchedData, _batch_edge_index, collate_fn
def test_batch_edge_index():
edge_index = torch.tensor(
[[0, 1], [0, 2], [1, 2], [0, 1], [0, 3], [1, 2], [2, 3], [0, 1], [1, 3]]
)
atom_batch_idx = torch.tensor([0, 0, 1, 1, 1, 1, 1, 2, ... | torch.tensor([[0, 1], [0, 2], [1, 2], [2, 3], [2, 5], [3, 4], [4, 5], [7, 8], [8, 10]])) | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | v2_extractor_at_anchor |
microsoft/mattergen | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | train | train | mattergen/diffusion/tests/test_losses.py | test_calc_loss | assert_* | variable | 67 | from functools import partial
from typing import Dict, List, Type
import pytest
import torch
from mattergen.diffusion.corruption.corruption import Corruption
from mattergen.diffusion.corruption.multi_corruption import MultiCorruption, apply
from mattergen.diffusion.corruption.sde_lib import SDE
from mattergen.diffusi... | target_loss) | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | v2_extractor_at_anchor |
microsoft/mattergen | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | train | train | mattergen/diffusion/tests/test_losses.py | test_calc_loss | assert_* | complex_expr | 86 | from functools import partial
from typing import Dict, List, Type
import pytest
import torch
from mattergen.diffusion.corruption.corruption import Corruption
from mattergen.diffusion.corruption.multi_corruption import MultiCorruption, apply
from mattergen.diffusion.corruption.sde_lib import SDE
from mattergen.diffusi... | target_loss * 4) | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | v2_extractor_at_anchor |
microsoft/mattergen | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | train | train | mattergen/diffusion/tests/test_losses.py | test_weighted_summed_field_loss | assert_* | variable | 86 | from functools import partial
from typing import Dict, List, Type
import pytest
import torch
from mattergen.diffusion.corruption.corruption import Corruption
from mattergen.diffusion.corruption.multi_corruption import MultiCorruption, apply
from mattergen.diffusion.corruption.sde_lib import SDE
from mattergen.diffusi... | unweighted_loss) | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | v2_extractor_at_anchor |
microsoft/mattergen | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | train | train | mattergen/diffusion/tests/test_losses.py | test_calc_loss | assert_* | variable | 74 | from functools import partial
from typing import Dict, List, Type
import pytest
import torch
from mattergen.diffusion.corruption.corruption import Corruption
from mattergen.diffusion.corruption.multi_corruption import MultiCorruption, apply
from mattergen.diffusion.corruption.sde_lib import SDE
from mattergen.diffusi... | loss_with_bad_bar) | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | v2_extractor_at_anchor |
microsoft/mattergen | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | train | train | mattergen/diffusion/tests/test_losses.py | test_weighted_summed_field_loss | assert_* | func_call | 82 | from functools import partial
from typing import Dict, List, Type
import pytest
import torch
from mattergen.diffusion.corruption.corruption import Corruption
from mattergen.diffusion.corruption.multi_corruption import MultiCorruption, apply
from mattergen.diffusion.corruption.sde_lib import SDE
from mattergen.diffusi... | torch.stack([weighted_loss_per_field[k] for k in weighted_loss_per_field.keys()])) | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | v2_extractor_at_anchor |
microsoft/mattergen | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | train | train | mattergen/diffusion/tests/test_losses.py | test_wrapped_normal_loss | assert_* | func_call | 77 | from functools import partial
from typing import Dict, List, Type
import pytest
import torch
from mattergen.diffusion.corruption.corruption import Corruption
from mattergen.diffusion.corruption.multi_corruption import MultiCorruption, apply
from mattergen.diffusion.corruption.sde_lib import SDE
from mattergen.diffusi... | torch.stack([non_wrapped_loss_per_field[k] for k in non_wrapped_loss_per_field.keys()])) | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | v2_extractor_at_anchor |
microsoft/mattergen | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | train | train | mattergen/diffusion/tests/test_losses.py | test_weighted_summed_field_loss | assert_* | complex_expr | 77 | from functools import partial
from typing import Dict, List, Type
import pytest
import torch
from mattergen.diffusion.corruption.corruption import Corruption
from mattergen.diffusion.corruption.multi_corruption import MultiCorruption, apply
from mattergen.diffusion.corruption.sde_lib import SDE
from mattergen.diffusi... | unweighted_loss_per_field["foo"] * weights["foo"] + unweighted_loss_per_field["bar"] * weights["bar"]) | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | v2_extractor_at_anchor |
microsoft/mattergen | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | train | train | mattergen/diffusion/tests/test_multi_corruption.py | _assert_keys | assert | collection | 20 | from typing import Any, Dict, Type
import pytest
import torch
from mattergen.diffusion.corruption.multi_corruption import MultiCorruption
from mattergen.diffusion.corruption.sde_lib import SDE
from mattergen.diffusion.tests.conftest import SDE_TYPES
def _check_keys_shapes(multi_corruption: MultiCorruption, batch, t:... | {"foo", "bar"} | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | v2_extractor_at_anchor |
microsoft/mattergen | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | train | train | mattergen/diffusion/tests/test_multi_corruption.py | _check_keys_shapes | assert | complex_expr | 16 | from typing import Any, Dict, Type
import pytest
import torch
from mattergen.diffusion.corruption.multi_corruption import MultiCorruption
from mattergen.diffusion.corruption.sde_lib import SDE
from mattergen.diffusion.tests.conftest import SDE_TYPES
def _check_keys_shapes(multi_corruption: MultiCorruption, batch, t:... | batch[k].shape | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | v2_extractor_at_anchor |
microsoft/mattergen | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | train | train | mattergen/diffusion/tests/test_multi_corruption.py | _check_keys_shapes | assert | complex_expr | 17 | from typing import Any, Dict, Type
import pytest
import torch
from mattergen.diffusion.corruption.multi_corruption import MultiCorruption
from mattergen.diffusion.corruption.sde_lib import SDE
from mattergen.diffusion.tests.conftest import SDE_TYPES
def _check_keys_shapes(multi_corruption: MultiCorruption, batch, t:... | batch[k].shape[0] | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | v2_extractor_at_anchor |
microsoft/mattergen | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | train | train | mattergen/diffusion/tests/test_reverse_sampling.py | test_wrapped_reverse_sampling | assert | numeric_literal | 98 | from argparse import Namespace
from contextlib import nullcontext
from functools import partial
from typing import List, Type
import pytest
import torch
from mattergen.diffusion.corruption.multi_corruption import MultiCorruption
from mattergen.diffusion.corruption.sde_lib import SDE
from mattergen.diffusion.data.batc... | 0.0 | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | v2_extractor_at_anchor |
microsoft/mattergen | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | train | train | mattergen/diffusion/tests/test_reverse_sampling.py | test_wrapped_reverse_sampling | assert | variable | 97 | from argparse import Namespace
from contextlib import nullcontext
from functools import partial
from typing import List, Type
import pytest
import torch
from mattergen.diffusion.corruption.multi_corruption import MultiCorruption
from mattergen.diffusion.corruption.sde_lib import SDE
from mattergen.diffusion.data.batc... | wrapping_boundary | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | v2_extractor_at_anchor |
microsoft/mattergen | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | train | train | mattergen/diffusion/tests/test_reverse_sampling.py | test_reverse_sampling | pytest.raises | variable | 75 | from argparse import Namespace
from contextlib import nullcontext
from functools import partial
from typing import List, Type
import pytest
import torch
from mattergen.diffusion.corruption.multi_corruption import MultiCorruption
from mattergen.diffusion.corruption.sde_lib import SDE
from mattergen.diffusion.data.batc... | IncompatibleSampler) | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | v2_extractor_at_anchor |
microsoft/mattergen | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | train | train | mattergen/diffusion/tests/test_sampling.py | test_predictor | assert | complex_expr | 81 | from collections import defaultdict
from contextlib import nullcontext
from typing import Callable, Dict, List, Type, Union
import pytest
import torch
from mattergen.diffusion.corruption.sde_lib import SDE, VESDE, VPSDE
from mattergen.diffusion.d3pm.d3pm_predictors_correctors import D3PMAncestralSamplingPredictor
fro... | x_mean.shape | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | v2_extractor_at_anchor |
microsoft/mattergen | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | train | train | mattergen/diffusion/tests/test_sampling.py | test_predictor | pytest.raises | variable | 62 | from collections import defaultdict
from contextlib import nullcontext
from typing import Callable, Dict, List, Type, Union
import pytest
import torch
from mattergen.diffusion.corruption.sde_lib import SDE, VESDE, VPSDE
from mattergen.diffusion.d3pm.d3pm_predictors_correctors import D3PMAncestralSamplingPredictor
fro... | IncompatibleSampler) | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | v2_extractor_at_anchor |
microsoft/mattergen | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | train | train | mattergen/diffusion/tests/test_sde_lib.py | _check_batch_shape | assert | numeric_literal | 12 | from typing import Type
import pytest
import torch
from mattergen.diffusion.corruption.sde_lib import SDE
from mattergen.diffusion.tests.conftest import SDE_TYPES
def _check_batch_shape(x: torch.Tensor, batch_size: torch.LongTensor):
"""Checks sde outputs that should be (batch_size, )"""
assert len(x.shape)... | 1 | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | v2_extractor_at_anchor |
microsoft/mattergen | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | train | train | mattergen/diffusion/tests/test_sde_lib.py | test_sde | assert | complex_expr | 45 | from typing import Type
import pytest
import torch
from mattergen.diffusion.corruption.sde_lib import SDE
from mattergen.diffusion.tests.conftest import SDE_TYPES
def _check_batch_shape(x: torch.Tensor, batch_size: torch.LongTensor):
"""Checks sde outputs that should be (batch_size, )"""
assert len(x.shape) ... | x.shape | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | v2_extractor_at_anchor |
microsoft/mattergen | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | train | train | mattergen/diffusion/tests/test_sde_lib.py | _check_batch_shape | assert | variable | 13 | from typing import Type
import pytest
import torch
from mattergen.diffusion.corruption.sde_lib import SDE
from mattergen.diffusion.tests.conftest import SDE_TYPES
def _check_batch_shape(x: torch.Tensor, batch_size: torch.LongTensor):
"""Checks sde outputs that should be (batch_size, )"""
assert len(x.shape) ... | batch_size | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | v2_extractor_at_anchor |
microsoft/mattergen | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | train | train | mattergen/diffusion/tests/test_sde_lib.py | _check_shapes | assert | complex_expr | 17 | from typing import Type
import pytest
import torch
from mattergen.diffusion.corruption.sde_lib import SDE
from mattergen.diffusion.tests.conftest import SDE_TYPES
def _check_batch_shape(x: torch.Tensor, batch_size: torch.LongTensor):
"""Checks sde outputs that should be (batch_size, )"""
assert len(x.shape) ... | x.shape[0] | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | v2_extractor_at_anchor |
microsoft/mattergen | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | train | train | mattergen/tests/test_generator.py | test_chemical_system_to_multi_hot | assert | func_call | 21 | from typing import List
import pytest
from mattergen.common.utils.globals import MAX_ATOMIC_NUM
from mattergen.property_embeddings import ChemicalSystemMultiHotEmbedding
@pytest.mark.parametrize("chemical_system", [["Li", "O"], ["Li", "O", "F"], ["C", "O", "H"]])
def test_chemical_system_to_multi_hot(chemical_system... | len(chemical_system) | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | v2_extractor_at_anchor |
microsoft/mattergen | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | train | train | mattergen/tests/test_generator.py | test_chemical_system_to_multi_hot | assert | collection | 17 | from typing import List
import pytest
from mattergen.common.utils.globals import MAX_ATOMIC_NUM
from mattergen.property_embeddings import ChemicalSystemMultiHotEmbedding
@pytest.mark.parametrize("chemical_system", [["Li", "O"], ["Li", "O", "F"], ["C", "O", "H"]])
def test_chemical_system_to_multi_hot(chemical_system... | ( 1, MAX_ATOMIC_NUM + 1, ) | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | v2_extractor_at_anchor |
microsoft/mattergen | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | train | train | mattergen/tests/test_mattergen_denoiser.py | test_mask_disallowed_elements | assert | func_call | 90 | import pytest
import torch
from torch_scatter import scatter_add
from mattergen.common.data.chemgraph import ChemGraph
from mattergen.common.data.collate import collate
from mattergen.common.data.transform import set_chemical_system_string
from mattergen.common.utils.globals import MAX_ATOMIC_NUM
from mattergen.denois... | set() | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | v2_extractor_at_anchor |
microsoft/mattergen | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | train | train | mattergen/tests/test_mattergen_denoiser.py | test_pre_corruption_fn | assert | variable | 44 | import pytest
import torch
from torch_scatter import scatter_add
from mattergen.common.data.chemgraph import ChemGraph
from mattergen.common.data.collate import collate
from mattergen.common.data.transform import set_chemical_system_string
from mattergen.common.utils.globals import MAX_ATOMIC_NUM
from mattergen.denois... | num_masked | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | v2_extractor_at_anchor |
microsoft/mattergen | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | train | train | mattergen/tests/test_mattergen_denoiser.py | test_pre_corruption_fn_multi | assert | variable | 77 | import pytest
import torch
from torch_scatter import scatter_add
from mattergen.common.data.chemgraph import ChemGraph
from mattergen.common.data.collate import collate
from mattergen.common.data.transform import set_chemical_system_string
from mattergen.common.utils.globals import MAX_ATOMIC_NUM
from mattergen.denois... | expected_number_masked | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | v2_extractor_at_anchor |
microsoft/mattergen | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | train | train | mattergen/tests/test_mattergen_denoiser.py | test_remove_conditioning_fn | assert_* | func_call | 41 | import pytest
import torch
from torch_scatter import scatter_add
from mattergen.common.data.chemgraph import ChemGraph
from mattergen.common.data.collate import collate
from mattergen.common.data.transform import set_chemical_system_string
from mattergen.common.utils.globals import MAX_ATOMIC_NUM
from mattergen.denois... | torch.ones((10, 1), dtype=torch.bool)) | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | v2_extractor_at_anchor |
microsoft/mattergen | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | train | train | mattergen/tests/test_mattergen_denoiser.py | test_keep_conditioning_fn | assert_* | func_call | 36 | import pytest
import torch
from torch_scatter import scatter_add
from mattergen.common.data.chemgraph import ChemGraph
from mattergen.common.data.collate import collate
from mattergen.common.data.transform import set_chemical_system_string
from mattergen.common.utils.globals import MAX_ATOMIC_NUM
from mattergen.denois... | torch.zeros((10, 1), dtype=torch.bool)) | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | v2_extractor_at_anchor |
microsoft/mattergen | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | train | train | mattergen/tests/test_sde_lib.py | test_expand | assert | variable | 25 | from abc import ABC, abstractmethod
from math import pi
from typing import Optional, Tuple
import pytest
import torch
from mattergen.common.data.chemgraph import ChemGraph
from mattergen.common.diffusion.corruption import (
LatticeVPSDE,
expand,
make_noise_symmetric_preserve_variance,
)
from mattergen.dif... | output_shape | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | v2_extractor_at_anchor |
microsoft/mattergen | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | train | train | mattergen/tests/test_sde_lib.py | test_LatticeVPSDE_prior_sampling | assert | collection | 34 | from abc import ABC, abstractmethod
from math import pi
from typing import Optional, Tuple
import pytest
import torch
from mattergen.common.data.chemgraph import ChemGraph
from mattergen.common.diffusion.corruption import (
LatticeVPSDE,
expand,
make_noise_symmetric_preserve_variance,
)
from mattergen.dif... | (Nbatch, 3, 3) | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | v2_extractor_at_anchor |
microsoft/mattergen | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | train | train | mattergen/tests/test_sde_lib.py | test_make_noise_symmetric_preserve_variance | pytest.raises | variable | 24 | from abc import ABC, abstractmethod
from math import pi
from typing import Optional, Tuple
import pytest
import torch
from mattergen.common.data.chemgraph import ChemGraph
from mattergen.common.diffusion.corruption import (
LatticeVPSDE,
expand,
make_noise_symmetric_preserve_variance,
)
from mattergen.dif... | AssertionError) | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | v2_extractor_at_anchor |
microsoft/mattergen | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | train | train | mattergen/tests/test_sde_lib.py | test_expand | assert | func_call | 22 | from abc import ABC, abstractmethod
from math import pi
from typing import Optional, Tuple
import pytest
import torch
from mattergen.common.data.chemgraph import ChemGraph
from mattergen.common.diffusion.corruption import (
LatticeVPSDE,
expand,
make_noise_symmetric_preserve_variance,
)
from mattergen.dif... | len(output_shape) | 11a321f568013bd732301d73bfc381a651ccc750 | 1 | v2_extractor_at_anchor |
microsoft/onnxscript | f5327f8849e3ce2a5c7c674b7dc62d67c0111ace | 621 | train | train | onnxscript/backend/onnx_backend.py | _compare_results | assert_* | variable | 147 | import os
import textwrap
from typing import Iterator
import numpy as np
import onnx
import onnx.numpy_helper
from onnx.backend.test import __file__ as backend_folder
from onnxscript.backend import onnx_export
def assert_almost_equal_string(expected, value):
"""Compares two arrays knowing they contain strings.
... | o) | f5327f8849e3ce2a5c7c674b7dc62d67c0111ace | 621 | v2_extractor_at_anchor |
microsoft/onnxscript | f5327f8849e3ce2a5c7c674b7dc62d67c0111ace | 621 | train | train | onnxscript/rewriter/llama_rule_sets_test.py | _check_model | assert_* | variable | 60 | from __future__ import annotations
import unittest
from typing import Any
import numpy as np
import onnx
import onnx.reference
import parameterized
import onnxscript
import onnxscript.onnx_types as ot
import onnxscript.rewriter.llama_rule_sets as llama_rule_sets
from onnxscript import ir
from onnxscript.onnx_opset i... | b) | f5327f8849e3ce2a5c7c674b7dc62d67c0111ace | 621 | v2_extractor_at_anchor |
microsoft/onnxscript | f5327f8849e3ce2a5c7c674b7dc62d67c0111ace | 621 | train | train | onnxscript/rewriter/ort_fusions/fused_matmul_rule_sets_test.py | _check_model | assert_* | variable | 50 | from __future__ import annotations
import unittest
from typing import Any
import numpy as np
import onnx
import onnx.reference
import onnx.reference.op_run
import onnxscript.rewriter.ort_fusions.fused_matmul_rule_sets as fused_matmul_rule_sets
from onnxscript import ir
FLOAT = onnx.TensorProto.FLOAT
class OrtRuleS... | b) | f5327f8849e3ce2a5c7c674b7dc62d67c0111ace | 621 | v2_extractor_at_anchor |
microsoft/onnxscript | f5327f8849e3ce2a5c7c674b7dc62d67c0111ace | 621 | train | train | tests/onnx_types_test.py | test_shapes_are_same_type | self.assertIs | variable | 25 | from __future__ import annotations
import unittest
from parameterized import parameterized
from onnxscript.onnx_types import DOUBLE, FLOAT, TensorType, tensor_type_registry
class TestOnnxTypes(unittest.TestCase):
@parameterized.expand(
[
(FLOAT, FLOAT),
(FLOAT[None], FLOAT[None]... | b) | f5327f8849e3ce2a5c7c674b7dc62d67c0111ace | 621 | v2_extractor_at_anchor |
microsoft/onnxscript | f5327f8849e3ce2a5c7c674b7dc62d67c0111ace | 621 | train | train | onnxscript/converter_test.py | test_opset_import | self.assertIn | variable | 134 | import ast
import inspect
import os
import pathlib
import textwrap
import types
import typing
import unittest
import warnings
import numpy as np
import onnx
import onnxruntime as ort
import pytest
from onnxruntime.capi.onnxruntime_pybind11_state import (
Fail,
InvalidArgument,
InvalidGraph,
)
import onnxs... | s16) | f5327f8849e3ce2a5c7c674b7dc62d67c0111ace | 621 | v2_extractor_at_anchor |
microsoft/onnxscript | f5327f8849e3ce2a5c7c674b7dc62d67c0111ace | 621 | train | train | onnxscript/converter_test.py | test_opset_import | self.assertIn | variable | 136 | import ast
import inspect
import os
import pathlib
import textwrap
import types
import typing
import unittest
import warnings
import numpy as np
import onnx
import onnxruntime as ort
import pytest
from onnxruntime.capi.onnxruntime_pybind11_state import (
Fail,
InvalidArgument,
InvalidGraph,
)
import onnxs... | s14) | f5327f8849e3ce2a5c7c674b7dc62d67c0111ace | 621 | v2_extractor_at_anchor |
microsoft/onnxscript | f5327f8849e3ce2a5c7c674b7dc62d67c0111ace | 621 | train | train | onnxscript/rewriter/generic_pattern_test.py | test_rotary_embedding | self.assertIn | variable | 152 | from __future__ import annotations
import contextlib
import io
import os
import unittest
import numpy as np
import onnx
import onnx.parser
import onnx.reference
import onnxruntime as ort
import parameterized
from onnxscript import ir
from onnxscript.rewriter import generic_pattern, pattern
FLOAT = onnx.TensorProto.... | out) | f5327f8849e3ce2a5c7c674b7dc62d67c0111ace | 621 | v2_extractor_at_anchor |
microsoft/onnxscript | f5327f8849e3ce2a5c7c674b7dc62d67c0111ace | 621 | train | train | onnxscript/tools/memory_peak_test.py | test_memory | self.assertIsInstance | variable | 16 | import os
import sys
import time
import unittest
import numpy as np
import torch
import onnxscript.tools.memory_peak
class TestMemoryPeak(unittest.TestCase):
@unittest.skipIf(sys.platform == "win32", reason="other test are failing")
def test_memory(self):
mem = onnxscript.tools.memory_peak.get_memory... | int) | f5327f8849e3ce2a5c7c674b7dc62d67c0111ace | 621 | v2_extractor_at_anchor |
microsoft/onnxscript | f5327f8849e3ce2a5c7c674b7dc62d67c0111ace | 621 | train | train | onnxscript/values_test.py | test_it_preserves_the_function_signature | self.assertEqual | variable | 29 | from __future__ import annotations
import inspect
import typing
import unittest
import onnxscript
from onnxscript import values
class TracedOnnxFunctionTest(unittest.TestCase):
def test_it_preserves_the_function_signature(self):
opset = values.Opset("test", 1)
def function(input1, input2, attr1... | int) | f5327f8849e3ce2a5c7c674b7dc62d67c0111ace | 621 | v2_extractor_at_anchor |
microsoft/onnxscript | f5327f8849e3ce2a5c7c674b7dc62d67c0111ace | 621 | train | train | onnxscript/values_test.py | test_it_preserves_the_function_signature | self.assertEqual | variable | 31 | from __future__ import annotations
import inspect
import typing
import unittest
import onnxscript
from onnxscript import values
class TracedOnnxFunctionTest(unittest.TestCase):
def test_it_preserves_the_function_signature(self):
opset = values.Opset("test", 1)
def function(input1, input2, attr1... | str) | f5327f8849e3ce2a5c7c674b7dc62d67c0111ace | 621 | v2_extractor_at_anchor |
microsoft/onnxscript | f5327f8849e3ce2a5c7c674b7dc62d67c0111ace | 621 | train | train | tests/if_test.py | test_no_else | assert_* | variable | 41 | import unittest
import onnxscript.testing
from onnxscript import script
from onnxscript.onnx_opset import opset15 as op
from tests.common import testutils
class IfOpTest(testutils.TestBase):
def test_no_else(self):
"""Basic test for if-then without else."""
# TODO: pass default opset as parameter... | if2) | f5327f8849e3ce2a5c7c674b7dc62d67c0111ace | 621 | v2_extractor_at_anchor |
microsoft/onnxscript | f5327f8849e3ce2a5c7c674b7dc62d67c0111ace | 621 | train | train | tests/if_test.py | test_no_else | assert_* | variable | 42 | import unittest
import onnxscript.testing
from onnxscript import script
from onnxscript.onnx_opset import opset15 as op
from tests.common import testutils
class IfOpTest(testutils.TestBase):
def test_no_else(self):
"""Basic test for if-then without else."""
# TODO: pass default opset as parameter... | if3) | f5327f8849e3ce2a5c7c674b7dc62d67c0111ace | 621 | v2_extractor_at_anchor |
microsoft/onnxscript | f5327f8849e3ce2a5c7c674b7dc62d67c0111ace | 621 | train | train | onnxscript/backend/onnx_export_test.py | test_export2python | self.assertIn | variable | 102 | from __future__ import annotations
import dataclasses
import importlib
import os
import pathlib
import re
import sys
import unittest
from typing import Pattern
import onnx
import onnxruntime as ort
import parameterized
from onnxruntime.capi import onnxruntime_pybind11_state
import onnxscript
import onnxscript.testin... | code) | f5327f8849e3ce2a5c7c674b7dc62d67c0111ace | 621 | v2_extractor_at_anchor |
microsoft/onnxscript | f5327f8849e3ce2a5c7c674b7dc62d67c0111ace | 621 | train | train | onnxscript/converter_test.py | test_opset_import | self.assertIn | variable | 138 | import ast
import inspect
import os
import pathlib
import textwrap
import types
import typing
import unittest
import warnings
import numpy as np
import onnx
import onnxruntime as ort
import pytest
from onnxruntime.capi.onnxruntime_pybind11_state import (
Fail,
InvalidArgument,
InvalidGraph,
)
import onnxs... | sdef) | f5327f8849e3ce2a5c7c674b7dc62d67c0111ace | 621 | v2_extractor_at_anchor |
microsoft/onnxscript | f5327f8849e3ce2a5c7c674b7dc62d67c0111ace | 621 | train | train | onnxscript/tools/memory_peak_test.py | test_spy | self.assertIsInstance | variable | 24 | import os
import sys
import time
import unittest
import numpy as np
import torch
import onnxscript.tools.memory_peak
class TestMemoryPeak(unittest.TestCase):
@unittest.skipIf(sys.platform == "win32", reason="other test are failing")
def test_spy(self):
p = onnxscript.tools.memory_peak.start_spying_o... | dict) | f5327f8849e3ce2a5c7c674b7dc62d67c0111ace | 621 | v2_extractor_at_anchor |
microsoft/onnxscript | f5327f8849e3ce2a5c7c674b7dc62d67c0111ace | 621 | train | train | onnxscript/tools/memory_peak_test.py | test_spy_cuda | self.assertIsInstance | variable | 25 | import os
import sys
import time
import unittest
import numpy as np
import torch
import onnxscript.tools.memory_peak
class TestMemoryPeak(unittest.TestCase):
@unittest.skipIf(not torch.cuda.is_available(), reason="CUDA not here")
def test_spy_cuda(self):
p = onnxscript.tools.memory_peak.start_spying... | list) | f5327f8849e3ce2a5c7c674b7dc62d67c0111ace | 621 | v2_extractor_at_anchor |
microsoft/onnxscript | f5327f8849e3ce2a5c7c674b7dc62d67c0111ace | 621 | train | train | onnxscript/tools/memory_peak_test.py | test_spy_cuda | self.assertIn | variable | 30 | import os
import sys
import time
import unittest
import numpy as np
import torch
import onnxscript.tools.memory_peak
class TestMemoryPeak(unittest.TestCase):
@unittest.skipIf(not torch.cuda.is_available(), reason="CUDA not here")
def test_spy_cuda(self):
p = onnxscript.tools.memory_peak.start_spying... | pres) | f5327f8849e3ce2a5c7c674b7dc62d67c0111ace | 621 | v2_extractor_at_anchor |
microsoft/onnxscript | f5327f8849e3ce2a5c7c674b7dc62d67c0111ace | 621 | train | train | onnxscript/backend/onnx_backend.py | assert_almost_equal_string | assert_* | variable | 33 | import os
import textwrap
from typing import Iterator
import numpy as np
import onnx
import onnx.numpy_helper
from onnx.backend.test import __file__ as backend_folder
from onnxscript.backend import onnx_export
def assert_almost_equal_string(expected, value):
"""Compares two arrays knowing they contain strings.
... | value) | f5327f8849e3ce2a5c7c674b7dc62d67c0111ace | 621 | v2_extractor_at_anchor |
microsoft/onnxscript | f5327f8849e3ce2a5c7c674b7dc62d67c0111ace | 621 | train | train | onnxscript/tensor_test.py | _check_values_and_shape | self.assertEqual | variable | 12 | import unittest
import numpy as np
from onnxscript import tensor
class TestTensor(unittest.TestCase):
def _check_values_and_shape(self, result, elements, shape):
values = list(result.value.flatten())
self.assertEqual(values, elements)
self.assertEqual(result.shape, | shape) | f5327f8849e3ce2a5c7c674b7dc62d67c0111ace | 621 | v2_extractor_at_anchor |
microsoft/onnxscript | f5327f8849e3ce2a5c7c674b7dc62d67c0111ace | 621 | train | train | onnxscript/values_test.py | test_init | self.assertEqual | variable | 18 | from __future__ import annotations
import inspect
import typing
import unittest
import onnxscript
from onnxscript import values
class TracedOnnxFunctionTest(unittest.TestCase):
def test_init(self):
def function(input1, input2, attr1: int, attr2: int = 1):
return input1 + input2 + attr1 + attr... | opset) | f5327f8849e3ce2a5c7c674b7dc62d67c0111ace | 621 | v2_extractor_at_anchor |
microsoft/onnxscript | f5327f8849e3ce2a5c7c674b7dc62d67c0111ace | 621 | train | train | onnxscript/values_test.py | test_it_preserves_the_function_signature | self.assertEqual | variable | 30 | from __future__ import annotations
import inspect
import typing
import unittest
import onnxscript
from onnxscript import values
class TracedOnnxFunctionTest(unittest.TestCase):
def test_it_preserves_the_function_signature(self):
opset = values.Opset("test", 1)
def function(input1, input2, attr1... | float) | f5327f8849e3ce2a5c7c674b7dc62d67c0111ace | 621 | v2_extractor_at_anchor |
microsoft/onnxscript | f5327f8849e3ce2a5c7c674b7dc62d67c0111ace | 621 | train | train | tests/ir/serde_roundtrip_test.py | test_serialization_deserialization_produces_same_model | assert_* | variable | 38 | from __future__ import annotations
import pathlib
import unittest
import onnx
import onnx.backend.test
import parameterized
import onnxscript.testing
from onnxscript import ir
model_folder_path = pathlib.Path(__file__).resolve().parent.parent.parent / "testdata"
onnx_backend_test_path = pathlib.Path(onnx.backend.te... | model) | f5327f8849e3ce2a5c7c674b7dc62d67c0111ace | 621 | v2_extractor_at_anchor |
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