repo stringlengths 1 99 | file stringlengths 13 215 | code stringlengths 12 59.2M | file_length int64 12 59.2M | avg_line_length float64 3.82 1.48M | max_line_length int64 12 2.51M | extension_type stringclasses 1
value |
|---|---|---|---|---|---|---|
pytorch | pytorch-main/test/bottleneck_test/test_cuda.py | # Owner(s): ["module: unknown"]
import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self):
super().__init__()
self.linear = nn.Linear(20, 20)
def forward(self, input):
out = self.linear(input[:, 10:30])
return out.sum()
def main():
data = torch.randn... | 596 | 18.9 | 62 | py |
pytorch | pytorch-main/test/mobile/test_bytecode.py | # Owner(s): ["oncall: mobile"]
import fnmatch
import io
import shutil
import tempfile
import torch
import torch.utils.show_pickle
# from torch.utils.mobile_optimizer import optimize_for_mobile
from torch.jit.mobile import (
_load_for_lite_interpreter,
_get_mobile_model_contained_types,
_get_model_bytecode_... | 13,952 | 41.410334 | 130 | py |
pytorch | pytorch-main/test/mobile/test_upgrader_codegen.py | # Owner(s): ["oncall: mobile"]
from torch.testing._internal.common_utils import TestCase, run_tests
from torchgen.operator_versions.gen_mobile_upgraders import (
sort_upgrader,
write_cpp,
)
from pathlib import Path
import tempfile
import os
from torch.jit.generate_bytecode import generate_upgraders_bytecode
... | 1,453 | 40.542857 | 115 | py |
pytorch | pytorch-main/test/mobile/test_upgraders.py | # Owner(s): ["oncall: mobile"]
import torch
import torch.utils.bundled_inputs
import io
from torch.jit.mobile import _load_for_lite_interpreter
from torch.testing._internal.common_utils import TestCase, run_tests
from pathlib import Path
from itertools import product
pytorch_test_dir = Path(__file__).resolve().paren... | 2,494 | 37.384615 | 110 | py |
pytorch | pytorch-main/test/mobile/test_lite_script_type.py | # Owner(s): ["oncall: mobile"]
import torch
import torch.utils.bundled_inputs
import io
from typing import Dict, List, NamedTuple
import unittest
from torch.jit.mobile import _load_for_lite_interpreter
from torch.testing._internal.common_utils import TestCase, run_tests
from collections import namedtuple
class Test... | 6,181 | 33.730337 | 109 | py |
pytorch | pytorch-main/test/mobile/test_lite_script_module.py | # Owner(s): ["oncall: mobile"]
import torch
import torch.utils.bundled_inputs
import io
from typing import Dict, List
import inspect
from torch.testing import FileCheck
from torch.jit.mobile import _load_for_lite_interpreter, _export_operator_list
from torch.testing._internal.common_utils import TestCase, run_tests
f... | 21,039 | 36.978339 | 128 | py |
pytorch | pytorch-main/test/mobile/test_quantize_fx_lite_script_module.py | # Owner(s): ["oncall: mobile"]
import torch
import torch.nn as nn
import torch.ao.nn.quantized as nnq
import torch.utils.bundled_inputs
from torch.ao.quantization import (
default_qconfig,
float_qparams_weight_only_qconfig,
)
# graph mode quantization based on fx
from torch.ao.quantization.quantize_fx import ... | 3,189 | 29.673077 | 82 | py |
pytorch | pytorch-main/test/mobile/custom_build/prepare_model.py | """
This is a script for end-to-end mobile custom build test purpose. It prepares
MobileNetV2 TorchScript model, and dumps root ops used by the model for custom
build script to create a tailored build which only contains these used ops.
"""
import torch
import torchvision
import yaml
# Download and trace the model.
m... | 1,554 | 37.875 | 80 | py |
pytorch | pytorch-main/test/mobile/model_test/gen_test_model.py | import io
import sys
import torch
import yaml
from android_api_module import AndroidAPIModule
from builtin_ops import (
TSBuiltinOpsModule,
TSCollectionOpsModule,
)
from math_ops import (
PointwiseOpsModule,
ReductionOpsModule,
ComparisonOpsModule,
OtherMathOpsModule,
SpectralOpsModule,
... | 8,392 | 33.257143 | 97 | py |
pytorch | pytorch-main/test/mobile/model_test/sampling_ops.py | import torch
# https://pytorch.org/docs/stable/torch.html#random-sampling
class SamplingOpsModule(torch.nn.Module):
def forward(self):
a = torch.empty(3, 3).uniform_(0.0, 1.0)
size = (1, 4)
weights = torch.tensor([0, 10, 3, 0], dtype=torch.float)
return len(
# torch.se... | 1,002 | 27.657143 | 64 | py |
pytorch | pytorch-main/test/mobile/model_test/quantization_ops.py | import torch
import torch.nn as nn
class GeneralQuantModule(torch.nn.Module):
def __init__(self):
super().__init__()
self.embedding = torch.ao.nn.quantized.Embedding(
num_embeddings=10, embedding_dim=12
)
self.embedding_input = torch.tensor([9, 6, 5, 7, 8, 8, 9, 2, 8])
... | 7,996 | 35.022523 | 121 | py |
pytorch | pytorch-main/test/mobile/model_test/torchvision_models.py | import torch
import torchvision
from torch.utils.bundled_inputs import augment_model_with_bundled_inputs
from torch.utils.mobile_optimizer import optimize_for_mobile
class MobileNetV2Module:
def getModule(self):
model = torchvision.models.mobilenet_v2(pretrained=True)
model.eval()
example ... | 689 | 30.363636 | 72 | py |
pytorch | pytorch-main/test/mobile/model_test/android_api_module.py | from typing import Dict, List, Tuple, Optional
import torch
from torch import Tensor
class AndroidAPIModule(torch.jit.ScriptModule):
@torch.jit.script_method
def forward(self, input):
return None
@torch.jit.script_method
def eqBool(self, input: bool) -> bool:
return input
@torch... | 3,488 | 26.690476 | 82 | py |
pytorch | pytorch-main/test/mobile/model_test/math_ops.py | # https://pytorch.org/docs/stable/torch.html#math-operations
import math
import torch
class PointwiseOpsModule(torch.nn.Module):
def forward(self):
return self.pointwise_ops()
def pointwise_ops(self):
a = torch.randn(4)
b = torch.randn(4)
t = torch.tensor([-1, -2, 3], dtype=... | 16,551 | 35.619469 | 110 | py |
pytorch | pytorch-main/test/mobile/model_test/update_production_ops.py | """
This is a script to aggregate production ops from xplat/pytorch_models/build/all_mobile_model_configs.yaml.
Specify the file path in the first argument. The results will be dump to model_ops.yaml
"""
import sys
import yaml
root_operators = {}
traced_operators = {}
kernel_metadata = {}
with open(sys.argv[1]) as i... | 1,489 | 40.388889 | 107 | py |
pytorch | pytorch-main/test/mobile/model_test/nn_ops.py | import torch
import torch.nn as nn
import torch.nn.functional as F
# https://pytorch.org/docs/stable/nn.html
class NNConvolutionModule(torch.nn.Module):
def __init__(self):
super().__init__()
self.input1d = torch.randn(1, 4, 36)
self.input2d = torch.randn(1, 4, 30, 10)
self.input3d ... | 13,222 | 30.558473 | 102 | py |
pytorch | pytorch-main/test/mobile/model_test/tensor_ops.py | import torch
class TensorOpsModule(torch.nn.Module):
def forward(self):
return self.tensor_general_ops()
def tensor_general_ops(self):
a = torch.randn(4)
b = torch.tensor([1.5])
x = torch.ones((2,))
c = torch.randn(4, dtype=torch.cfloat)
w = torch.rand(4, 4, 4,... | 8,635 | 31.588679 | 81 | py |
pytorch | pytorch-main/test/mobile/model_test/builtin_ops.py | import torch
# https://pytorch.org/docs/stable/jit_builtin_functions.html#builtin-functions
class TSBuiltinOpsModule(torch.nn.Module):
def forward(self):
x = torch.tensor(1)
y = torch.tensor(0.5)
b = float(1)
s = "abcde"
l = ["1", "2", "test", "a{}b"]
d = {"key": ... | 2,998 | 23.991667 | 78 | py |
pytorch | pytorch-main/test/mobile/nnc/aot_test_model.py | import torch
from torch import nn
class NeuralNetwork(nn.Module):
def forward(self, x):
return torch.add(x, 10)
model = NeuralNetwork()
script = torch.jit.script(model)
torch.jit.save(script, "aot_test_model.pt")
| 228 | 18.083333 | 43 | py |
pytorch | pytorch-main/test/mobile/lightweight_dispatch/tests_setup.py | import functools
import os
from io import BytesIO
import shutil
import sys
import torch
from torch.jit.mobile import _load_for_lite_interpreter, _export_operator_list
_OPERATORS = set()
_FILENAMES = []
_MODELS = []
def save_model(cls):
"""Save a model and dump all the ops"""
@functools.wraps(cls)
def ... | 4,331 | 29.083333 | 117 | py |
pytorch | pytorch-main/test/cpp_extensions/setup.py | import sys
import torch.cuda
import os
from setuptools import setup
from torch.utils.cpp_extension import BuildExtension, CppExtension, CUDAExtension
from torch.utils.cpp_extension import CUDA_HOME, ROCM_HOME
from torch.testing._internal.common_utils import IS_WINDOWS
if sys.platform == 'win32':
vc_version = os.ge... | 3,327 | 33.666667 | 90 | py |
pytorch | pytorch-main/test/cpp_extensions/no_python_abi_suffix_test/setup.py | from setuptools import setup
from torch.utils.cpp_extension import BuildExtension, CppExtension
setup(
name="no_python_abi_suffix_test",
ext_modules=[
CppExtension("no_python_abi_suffix_test", ["no_python_abi_suffix_test.cpp"])
],
cmdclass={"build_ext": BuildExtension.with_options(no_python_abi... | 338 | 29.818182 | 84 | py |
pytorch | pytorch-main/test/jit_hooks/model.py | import argparse
import os
import sys
import torch
# grab modules from test_jit_hooks.cpp
pytorch_test_dir = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
sys.path.append(pytorch_test_dir)
from jit.test_hooks_modules import (
create_forward_tuple_input, create_module_forward_multiple_inputs,
crea... | 2,879 | 45.451613 | 109 | py |
pytorch | pytorch-main/test/distributions/test_distributions.py | # Owner(s): ["module: distributions"]
"""
Note [Randomized statistical tests]
-----------------------------------
This note describes how to maintain tests in this file as random sources
change. This file contains two types of randomized tests:
1. The easier type of randomized test are tests that should always pass ... | 260,439 | 48.288418 | 123 | py |
pytorch | pytorch-main/test/distributions/test_constraints.py | # Owner(s): ["module: distributions"]
import pytest
import torch
from torch.distributions import biject_to, constraints, transform_to
from torch.testing._internal.common_cuda import TEST_CUDA
from torch.testing._internal.common_utils import run_tests
EXAMPLES = [
(constraints.symmetric, False, [[2., 0], [2., 2]... | 5,723 | 43.030769 | 109 | py |
pytorch | pytorch-main/test/distributions/test_transforms.py | # Owner(s): ["module: distributions"]
import io
from numbers import Number
import pytest
import torch
from torch.autograd.functional import jacobian
from torch.distributions import Dirichlet, Independent, Normal, TransformedDistribution, constraints
from torch.distributions.transforms import (AbsTransform, AffineTra... | 20,658 | 40.318 | 130 | py |
pytorch | pytorch-main/test/distributions/test_utils.py | # Owner(s): ["module: distributions"]
import pytest
import torch
from torch.distributions.utils import tril_matrix_to_vec, vec_to_tril_matrix
from torch.testing._internal.common_utils import run_tests
@pytest.mark.parametrize('shape', [
(2, 2),
(3, 3),
(2, 4, 4),
(2, 2, 4, 4),
])
def test_tril_matrix... | 638 | 22.666667 | 76 | py |
pytorch | pytorch-main/test/_nvfuser/test_torchscript.py | ../../third_party/nvfuser/python_tests/test_torchscript.py | 58 | 58 | 58 | py |
pytorch | pytorch-main/test/backends/xeon/test_launch.py | # Owner(s): ["module: intel"]
from torch.testing._internal.common_utils import TestCase, run_tests, IS_LINUX
import shutil
import subprocess
import tempfile
import unittest
@unittest.skipIf(not IS_LINUX, "Only works on linux")
class TestTorchrun(TestCase):
def setUp(self):
self._test_dir = tempfile.mkdtem... | 2,312 | 34.045455 | 110 | py |
pytorch | pytorch-main/test/lazy/test_debug_util.py | # Owner(s): ["oncall: jit"]
import os
import re
import tempfile
import torch.nn as nn
import unittest
import torch._lazy
import torch._lazy.ts_backend
from torch.testing._internal.common_utils import IS_WINDOWS, run_tests, TestCase
torch._lazy.ts_backend.init()
@unittest.skipIf(IS_WINDOWS, "To be fixed")
class Deb... | 1,444 | 31.111111 | 88 | py |
pytorch | pytorch-main/test/lazy/test_ts_opinfo.py | # Owner(s): ["oncall: jit"]
from typing import Sequence
import torch
import functools
from torch.testing._internal.common_utils import run_tests, TestCase
from torch.testing._internal.jit_utils import JitTestCase
from torch.testing._internal.common_methods_invocations import op_db
from torch.testing._internal.common_... | 11,625 | 35.791139 | 176 | py |
pytorch | pytorch-main/test/lazy/test_reuse_ir.py | # Owner(s): ["oncall: jit"]
import torch
import torch._lazy
import torch._lazy.config
import torch._lazy.ir_cache
import torch._lazy.ts_backend
import torch._lazy.metrics as metrics
from torch.testing._internal.common_utils import IS_WINDOWS, run_tests, TestCase
import os
import unittest
torch._lazy.ts_backend.init()... | 4,665 | 33.820896 | 121 | py |
pytorch | pytorch-main/test/lazy/test_bindings.py | # Owner(s): ["oncall: jit"]
import torch._lazy.metrics
def test_metrics():
names = torch._lazy.metrics.counter_names()
assert len(names) == 0, f"Expected no counter names, but got {names}"
| 199 | 24 | 73 | py |
pytorch | pytorch-main/test/lazy/test_extract_compiled_graph.py | # Owner(s): ["oncall: jit"]
import unittest
from torch._lazy.ts_backend import init as init_ts_backend
init_ts_backend()
from torch._lazy import config
from torch._lazy.extract_compiled_graph import extract_compiled_graph
import torch
from torch import nn
import dis
import inspect
from torch import fx
import re
from ... | 5,869 | 31.977528 | 132 | py |
pytorch | pytorch-main/test/lazy/test_step_closures.py | # Owner(s): ["oncall: jit"]
from threading import Event
from time import sleep
import torch._lazy
import torch._lazy.ts_backend
from torch.testing._internal.common_utils import run_tests, TestCase
torch._lazy.ts_backend.init()
class ClosuresTest(TestCase):
def test_synchronous(self):
flag = Event()
... | 2,318 | 24.206522 | 81 | py |
pytorch | pytorch-main/test/lazy/test_meta_kernel.py | # Owner(s): ["oncall: jit"]
import torch
from torch.testing._internal.common_utils import TestCase
import torch._lazy
import torch._lazy.ts_backend
torch._lazy.ts_backend.init()
class TestMetaKernel(TestCase):
def test_addmm_invalid_dtype(self):
"""Tests that the addmm meta kernel returns the correct o... | 1,189 | 33 | 79 | py |
pytorch | pytorch-main/test/cpp/api/init_baseline.py | """Script to generate baseline values from PyTorch initialization algorithms"""
import sys
import torch
HEADER = """
#include <torch/types.h>
#include <vector>
namespace expected_parameters {
"""
FOOTER = "} // namespace expected_parameters"
PARAMETERS = "inline std::vector<std::vector<torch::Tensor>> {}() {{"
I... | 2,059 | 27.219178 | 90 | py |
pytorch | pytorch-main/test/cpp/api/optim_baseline.py | """Script to generate baseline values from PyTorch optimization algorithms"""
import argparse
import math
import sys
import torch
import torch.optim
HEADER = """
#include <torch/types.h>
#include <vector>
namespace expected_parameters {
"""
FOOTER = "} // namespace expected_parameters"
PARAMETERS = "inline std:... | 4,469 | 33.921875 | 118 | py |
pytorch | pytorch-main/test/cpp/jit/tests_setup.py | import sys
import os
import torch
class Setup:
def setup(self):
raise NotImplementedError()
def shutdown(self):
raise NotImplementedError()
class FileSetup:
path = None
def shutdown(self):
if os.path.exists(self.path):
os.remove(self.path)
pass
cla... | 2,569 | 21.347826 | 92 | py |
pytorch | pytorch-main/test/cpp/aot_inductor/test.py | import shutil
import torch
import torch._dynamo
import torch._inductor
class Net(torch.nn.Module):
def __init__(self):
super().__init__()
self.fc = torch.nn.Linear(64, 10)
def forward(self, x, y):
return self.fc(torch.sin(x) + torch.cos(y))
x = torch.randn((32, 64), device="cuda")
... | 743 | 23.8 | 74 | py |
pytorch | pytorch-main/test/typing/reveal/namedtuple.py | import torch
t = torch.tensor([[3.0, 1.5], [2.0, 1.5]])
t_sort = t.sort()
t_sort[0][0, 0] == 1.5 # noqa: B015
t_sort.indices[0, 0] == 1 # noqa: B015
t_sort.values[0, 0] == 1.5 # noqa: B015
reveal_type(t_sort) # E: Tuple[{Tensor}, {Tensor}, fallback=torch.return_types.sort]
t_qr = torch.linalg.qr(t)
t_qr[0]... | 477 | 28.875 | 85 | py |
pytorch | pytorch-main/test/typing/reveal/module_list.py | import torch
# ModuleList with elements of type Module
class FooModule(torch.nn.Module):
pass
class BarModule(torch.nn.Module):
pass
ml: torch.nn.ModuleList = torch.nn.ModuleList([FooModule(), BarModule()])
ml[0].children() == [] # noqa: B015
reveal_type(ml) # E: {ModuleList}
| 290 | 21.384615 | 73 | py |
pytorch | pytorch-main/test/typing/reveal/torch_optim.py | import torch
def foo(opt: torch.optim.Optimizer) -> None:
opt.zero_grad()
opt_adagrad = torch.optim.Adagrad([torch.tensor(0.0)])
reveal_type(opt_adagrad) # E: {Adagrad}
foo(opt_adagrad)
opt_adam = torch.optim.Adam([torch.tensor(0.0)], lr=1e-2, eps=1e-6)
reveal_type(opt_adam) # E: {Adam}
foo(opt_adam)
| 312 | 21.357143 | 67 | py |
pytorch | pytorch-main/test/typing/reveal/opt_size.py | import torch
avg_pool1 = torch.nn.AdaptiveAvgPool2d((1, None))
reveal_type(avg_pool1) # E: {AdaptiveAvgPool2d}
avg_pool2 = torch.nn.AdaptiveAvgPool2d((None, 1))
reveal_type(avg_pool2) # E: {AdaptiveAvgPool2d}
max_pool1 = torch.nn.AdaptiveMaxPool2d((1, None))
reveal_type(max_pool1) # E: {AdaptiveMaxPool2d}
max_pool2... | 410 | 36.363636 | 49 | py |
pytorch | pytorch-main/test/typing/reveal/size.py | import torch
input = []
input.append(torch.tensor([1.0, 2.0, 3.0, 4.0]))
input.append(torch.tensor([[1.0, 2.0, 3.0, 4.0]]))
input.append(torch.tensor([[[1.0, 2.0, 3.0, 4.0]]]))
reveal_type(input[0].shape[0]) # E: int
reveal_type(input[1].shape[1]) # E: int
reveal_type(input[2].shape[2]) # E: int
| 300 | 32.444444 | 52 | py |
pytorch | pytorch-main/test/typing/reveal/tensor_copy.py | import torch
t = torch.randn(2, 3)
reveal_type(t) # E: {Tensor}
u = torch.randn(2, 3)
reveal_type(u) # E: {Tensor}
t.copy_(u)
reveal_type(t) # E: {Tensor}
r = (t == u).all()
reveal_type(r) # E: {Tensor}
| 209 | 16.5 | 29 | py |
pytorch | pytorch-main/test/typing/reveal/tensor_constructors.py | # flake8: noqa
import torch
from torch.testing._internal.common_utils import TEST_NUMPY
if TEST_NUMPY:
import numpy as np
# From the docs, there are quite a few ways to create a tensor:
# https://pytorch.org/docs/stable/tensors.html
# torch.tensor()
reveal_type(torch.tensor([[0.1, 1.2], [2.2, 3.1], [4.9, 5.2]])) ... | 4,408 | 37.008621 | 113 | py |
pytorch | pytorch-main/test/typing/reveal/tensor_sampling.py | # flake8: noqa
import torch
# seed
reveal_type(torch.seed()) # E: int
# manual_seed
reveal_type(torch.manual_seed(3)) # E: torch._C.Generator
# initial_seed
reveal_type(torch.initial_seed()) # E: int
# get_rng_state
reveal_type(torch.get_rng_state()) # E: {Tensor}
# bernoulli
reveal_type(torch.bernoulli(torch.... | 1,481 | 23.295082 | 77 | py |
pytorch | pytorch-main/test/typing/pass/creation_ops.py | # flake8: noqa
import torch
from torch.testing._internal.common_utils import TEST_NUMPY
if TEST_NUMPY:
import numpy as np
# From the docs, there are quite a few ways to create a tensor:
# https://pytorch.org/docs/stable/tensors.html
# torch.tensor()
torch.tensor([[0.1, 1.2], [2.2, 3.1], [4.9, 5.2]])
torch.tensor(... | 3,127 | 25.285714 | 104 | py |
pytorch | pytorch-main/test/typing/pass/math_ops.py | # flake8: noqa
import torch
import math
a = torch.randn(4)
b = torch.randn(4)
t = torch.tensor([-1, -2, 3], dtype=torch.int8)
# abs/absolute
torch.abs(torch.tensor([-1, -2, 3]))
torch.absolute(torch.tensor([-1, -2, 3]))
# acos/arccos
torch.acos(a)
torch.arccos(a)
# acosh/arccosh
torch.acosh(a.uniform_(1, 2))
# add... | 7,613 | 22.003021 | 106 | py |
pytorch | pytorch-main/test/typing/fail/creation_ops.py | # flake8: noqa
import torch
torch.tensor([3], dtype='int32') # E: expected "Optional[dtype]"
torch.ones(3, dtype='int32') # E: No overload variant of "ones" matches argument types "int", "str"
torch.zeros(3, dtype='int32') # E: No overload variant of "zeros" matches argument types "int", "str"
| 299 | 41.857143 | 102 | py |
pytorch | pytorch-main/test/typing/fail/random.py | # flake8: noqa
import torch
torch.set_rng_state([1, 2, 3]) # E: Argument 1 to "set_rng_state" has incompatible type "List[int]"; expected "Tensor"
| 149 | 29 | 119 | py |
pytorch | pytorch-main/test/typing/fail/bitwise_ops.py | # flake8: noqa
import torch
# binary ops: <<, >>, |, &, ~, ^
a = torch.ones(3, dtype=torch.float64)
i = int()
i | a # E: Unsupported operand types
| 151 | 14.2 | 38 | py |
pytorch | pytorch-main/test/autograd/test_fallback.py | # Owner(s): ["module: autograd"]
import torch
from torch.library import Library
from torch.testing._internal.common_utils import (
TestCase,
parametrize,
instantiate_parametrized_tests,
run_tests,
)
import contextlib
import numpy as np
import warnings
@contextlib.contextmanager
def autograd_fallback_m... | 13,694 | 35.715818 | 107 | py |
pytorch | pytorch-main/test/autograd/test_complex.py | # Owner(s): ["module: autograd"]
import torch
from torch.testing._internal.common_utils import TestCase, run_tests, gradcheck
class TestAutogradComplex(TestCase):
def test_view_func_for_complex_views(self):
# case 1: both parent and child have view_func
x = torch.randn(2, 2, 2, dtype=torch.doubl... | 3,157 | 28.792453 | 102 | py |
pytorch | pytorch-main/test/autograd/test_functional.py | # Owner(s): ["module: autograd"]
import types
import unittest
import warnings
import torch
import torch.autograd.functional as autogradF
from torch.testing._internal.common_cuda import TEST_CUDA
from torch.testing._internal.common_utils import (
TestCase, run_tests, subtest, gradcheck, gradgradcheck, parametrize... | 57,504 | 39.46798 | 132 | py |
pytorch | pytorch-main/test/optim/test_lrscheduler.py | # Owner(s): ["module: optimizer", "module: LrScheduler" ]
import types
import warnings
import math
import pickle
import torch
import torch.optim as optim
import torch.nn.functional as F
from torch.nn import Parameter
from torch.optim import Adam, SGD
from torch.optim.lr_scheduler import (
LambdaLR,
Multiplicat... | 83,966 | 36.77193 | 117 | py |
pytorch | pytorch-main/test/optim/test_swa_utils.py | # Owner(s): ["module: optimizer"]
import itertools
import pickle
import torch
from torch.optim.swa_utils import AveragedModel, update_bn, get_swa_multi_avg_fn, get_ema_multi_avg_fn
from torch.testing._internal.common_utils import (
TestCase,
load_tests,
parametrize,
instantiate_parametrized_tests,
)
... | 12,367 | 38.263492 | 112 | py |
pytorch | pytorch-main/test/optim/test_optim.py | # Owner(s): ["module: optimizer"]
import math
import unittest
import functools
import itertools
from copy import deepcopy
import torch
import torch.optim as optim
from torch.nn import Parameter
from torch.optim import Adam, SGD, Optimizer
from torch.optim.lr_scheduler import (
StepLR,
ConstantLR,
LinearLR... | 87,710 | 38.959453 | 130 | py |
pytorch | pytorch-main/test/scripts/cuda_memcheck_common.py | # this file contains a simple parser that parses report
# from cuda-memcheck
class ParseError(Exception):
"""Whenever the simple parser is unable to parse the report, this exception will be raised"""
pass
class Report:
"""A report is a container of errors, and a summary on how many errors are found"""
... | 4,516 | 42.432692 | 129 | py |
pytorch | pytorch-main/test/scripts/run_cuda_memcheck.py | #!/usr/bin/env python3
"""This script runs cuda-memcheck on the specified unit test. Each test case
is run in its isolated process with a timeout so that:
1) different test cases won't influence each other, and
2) in case of hang, the script would still finish in a finite amount of time.
The output will be written to ... | 6,801 | 40.730061 | 121 | py |
pytorch | pytorch-main/test/distributed/argparse_util_test.py | #!/usr/bin/env python3
# Owner(s): ["oncall: distributed"]
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import os
import unittest
from argparse import Argumen... | 5,373 | 38.226277 | 84 | py |
pytorch | pytorch-main/test/distributed/test_c10d_spawn.py | # Owner(s): ["oncall: distributed"]
import os
import sys
import tempfile
import torch
import torch.distributed as c10d
import torch.multiprocessing as mp
from torch.testing._internal.common_distributed import \
MultiProcessTestCase
from torch.testing._internal.common_utils import load_tests,\
NO_MULTIPROCESSI... | 9,355 | 35.980237 | 102 | py |
pytorch | pytorch-main/test/distributed/test_launcher.py | # Owner(s): ["oncall: distributed"]
import os
import sys
from contextlib import closing
import torch.distributed as dist
import torch.distributed.launch as launch
from torch.distributed.elastic.utils import get_socket_with_port
if not dist.is_available():
print("Distributed not available, skipping tests", file=s... | 1,403 | 23.631579 | 87 | py |
pytorch | pytorch-main/test/distributed/test_collective_utils.py | # Owner(s): ["oncall: distributed"]
from unittest import mock
import torch.distributed as c10d
from torch.distributed.collective_utils import all_gather, broadcast
from torch.testing._internal.common_distributed import MultiProcessTestCase
class TestCollectiveUtils(MultiProcessTestCase):
def setUp(self):
... | 3,666 | 32.036036 | 119 | py |
pytorch | pytorch-main/test/distributed/test_c10d_object_collectives.py | # Owner(s): ["oncall: distributed"]
import os
import sys
from functools import wraps, partial
import torch
import torch.distributed as dist
if not dist.is_available():
print("Distributed not available, skipping tests", file=sys.stderr)
sys.exit(0)
from torch.testing._internal.common_distributed import (
... | 4,989 | 29.060241 | 94 | py |
pytorch | pytorch-main/test/distributed/test_store.py | # Owner(s): ["oncall: distributed"]
import os
import socket
import sys
import tempfile
import time
from datetime import timedelta
from sys import platform
import torch
import torch.distributed as dist
import torch.distributed.rpc as rpc
from torch.testing._internal.common_distributed import MultiThreadedTestCase
from... | 26,235 | 34.890561 | 112 | py |
pytorch | pytorch-main/test/distributed/test_c10d_spawn_ucc.py | # Owner(s): ["oncall: distributed"]
import sys
import test_c10d_spawn
import torch
import torch.distributed as c10d
from test_c10d_spawn import _torch_dist_nn_available, TestDistributedNNFunctions
from torch.testing._internal.common_cuda import TEST_MULTIGPU
from torch.testing._internal.common_distributed import (
... | 4,366 | 37.991071 | 115 | py |
pytorch | pytorch-main/test/distributed/test_multi_threaded_pg.py | # Owner(s): ["oncall: distributed"]
import os
import sys
import torch
import torch.distributed as dist
from torch._C._distributed_c10d import ReduceOp
from unittest import skip, SkipTest
import operator
from functools import reduce
import threading
import torch.autograd
if not dist.is_available():
print("Distribu... | 10,459 | 36.092199 | 99 | py |
pytorch | pytorch-main/test/distributed/test_functional_api.py | # Owner(s): ["oncall: distributed"]
import os
import sys
from functools import wraps, partial
import torch
import torch.distributed as dist
import torch.distributed._functional_collectives as ft_c
import torch.distributed.distributed_c10d as c10d
import torch.distributed._tensor as dt
from torch.testing import FileC... | 14,155 | 33.781327 | 124 | py |
pytorch | pytorch-main/test/distributed/test_c10d_logger.py | # Owner(s): ["oncall: distributed"]
import json
import logging
import os
import re
import sys
import time
from functools import partial, wraps
import torch
import torch.distributed as dist
from torch.distributed.c10d_logger import _c10d_logger, _exception_logger, _time_logger
if not dist.is_available():
print("... | 5,687 | 31.135593 | 110 | py |
pytorch | pytorch-main/test/distributed/test_c10d_spawn_nccl.py | # Owner(s): ["oncall: distributed"]
import sys
import test_c10d_spawn
import torch
import torch.distributed as c10d
from test_c10d_spawn import _torch_dist_nn_available, TestDistributedNNFunctions
from torch.testing._internal.common_cuda import TEST_MULTIGPU
from torch.testing._internal.common_distributed import (
... | 9,028 | 41.389671 | 110 | py |
pytorch | pytorch-main/test/distributed/test_data_parallel.py | # Owner(s): ["oncall: distributed"]
import contextlib
import io
from copy import deepcopy
from collections import OrderedDict
from itertools import product
import functools
import torch
from torch import nn
from torch.cuda.amp import autocast
import torch.nn.parallel as dp
from torch.testing._internal.common_cuda imp... | 36,174 | 40.014739 | 118 | py |
pytorch | pytorch-main/test/distributed/test_c10d_gloo.py | # Owner(s): ["oncall: distributed"]
import copy
import logging
import math
import operator
import os
import random
import sys
import tempfile
from functools import reduce
from itertools import groupby
import torch
import torch.distributed as c10d
if not c10d.is_available() or not c10d.is_gloo_available():
print(... | 92,835 | 35.9423 | 128 | py |
pytorch | pytorch-main/test/distributed/test_nccl.py | # Owner(s): ["oncall: distributed"]
import sys
import torch
import torch.cuda.nccl as nccl
import torch.cuda
import torch.distributed as c10d
from torch.testing._internal.common_utils import (
TestCase,
run_tests,
IS_WINDOWS,
load_tests,
TEST_WITH_ROCM,
skip_but_pass_in_sandcastle_if,
NoTe... | 8,023 | 32.714286 | 88 | py |
pytorch | pytorch-main/test/distributed/test_dynamo_distributed.py | # Owner(s): ["module: dynamo"]
import copy
import functools
from io import StringIO
from typing import List
import random
import unittest
from unittest.mock import patch
import numpy as np
import torch
from torch._C import FileCheck
import torch._dynamo
from torch._dynamo.backends.distributed import DDPOptimizer
import... | 35,521 | 39.783008 | 117 | py |
pytorch | pytorch-main/test/distributed/test_c10d_spawn_gloo.py | # Owner(s): ["oncall: distributed"]
import copy
import os
import sys
import tempfile
import test_c10d_spawn
import torch
import torch.distributed as c10d
import torch.nn as nn
from test_c10d_spawn import _torch_dist_nn_available, TestDistributedNNFunctions
from torch.testing._internal.common_cuda import TEST_CUDA, TE... | 11,753 | 39.954704 | 124 | py |
pytorch | pytorch-main/test/distributed/test_distributed_spawn.py | # Owner(s): ["oncall: distributed"]
import os
import sys
import torch
import torch.distributed as dist
torch.backends.cuda.matmul.allow_tf32 = False
if not dist.is_available():
print("Distributed not available, skipping tests", file=sys.stderr)
sys.exit(0)
from torch.testing._internal.common_utils import r... | 1,206 | 27.069767 | 108 | py |
pytorch | pytorch-main/test/distributed/test_c10d_ucc.py | # Owner(s): ["oncall: distributed"]
import copy
import logging
import math
import operator
import os
import random
import sys
import tempfile
from functools import reduce
import torch
import torch.distributed as c10d
if not c10d.is_available() or not c10d.is_ucc_available():
print("c10d UCC not available, skippi... | 39,953 | 34.016652 | 128 | py |
pytorch | pytorch-main/test/distributed/test_fake_pg.py | # Owner(s): ["oncall: distributed"]
import sys
import torch
import torch.distributed as dist
import torch.nn as nn
import unittest
import torch.distributed._functional_collectives as funcol
from torch.fx.experimental.proxy_tensor import make_fx
from torch.testing._internal.distributed.fake_pg import FakeStore
from tor... | 3,446 | 30.916667 | 79 | py |
pytorch | pytorch-main/test/distributed/test_c10d_common.py | # Owner(s): ["oncall: distributed"]
import copy
import os
import pickle
import sys
import tempfile
import threading
import time
from contextlib import nullcontext
from dataclasses import dataclass
from datetime import timedelta
from itertools import product
from sys import platform
from typing import Callable, Dict, O... | 78,290 | 36.281429 | 121 | py |
pytorch | pytorch-main/test/distributed/test_pg_wrapper.py | # Owner(s): ["oncall: distributed"]
import os
import sys
from datetime import timedelta
import torch
import torch.distributed as c10d
if not c10d.is_available():
print("c10d not available, skipping tests", file=sys.stderr)
sys.exit(0)
from test_c10d_common import LOOPBACK
from torch.testing._internal.common... | 15,935 | 36.673759 | 87 | py |
pytorch | pytorch-main/test/distributed/test_inductor_collectives.py | # Owner(s): ["module: dynamo"]
import functools
import unittest
from unittest.mock import patch
import torch
from torch._C import FileCheck
# for some reason importing functional collectives after dynamo breaks collectives handling!
import torch.distributed._functional_collectives as _functional_collectives
import torc... | 26,229 | 41.035256 | 124 | py |
pytorch | pytorch-main/test/distributed/test_c10d_nccl.py | # Owner(s): ["oncall: distributed"]
import copy
import math
import os
import random
import re
import signal
import sys
import tempfile
import threading
from contextlib import contextmanager
from datetime import timedelta
from itertools import product
from unittest import mock
import torch
import torch.distributed as ... | 123,405 | 37.917061 | 126 | py |
pytorch | pytorch-main/test/distributed/test_c10d_pypg.py | # Owner(s): ["oncall: distributed"]
import os
import torch
import torch.distributed as dist
from torch.testing._internal.common_utils import (
run_tests,
)
from torch.futures import Future
import torch.nn as nn
from torch.nn.parallel import DistributedDataParallel as DDP
import test_c10d_common
import weakref
fro... | 4,309 | 26.806452 | 115 | py |
pytorch | pytorch-main/test/distributed/nn/jit/test_instantiator.py | #!/usr/bin/env python3
# Owner(s): ["oncall: distributed"]
import pathlib
import sys
from typing import Tuple
import torch
from torch import Tensor, nn
import torch.distributed as dist
if not dist.is_available():
print("Distributed not available, skipping tests", file=sys.stderr)
sys.exit(0)
from torch.dist... | 3,110 | 30.744898 | 86 | py |
pytorch | pytorch-main/test/distributed/elastic/events/lib_test.py | #!/usr/bin/env python3
# Owner(s): ["oncall: r2p"]
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.abs
import json
import logging
from dataclasses import asdict
... | 5,448 | 38.485507 | 103 | py |
pytorch | pytorch-main/test/distributed/elastic/multiprocessing/redirects_test.py | #!/usr/bin/env python3
# Owner(s): ["oncall: r2p"]
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import ctypes
import os
import shutil
import sys
import tempfi... | 4,613 | 31.723404 | 80 | py |
pytorch | pytorch-main/test/distributed/elastic/multiprocessing/tail_log_test.py | #!/usr/bin/env python3
# Owner(s): ["oncall: r2p"]
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import io
import os
import shutil
import sys
import tempfile
i... | 3,811 | 31.033613 | 86 | py |
pytorch | pytorch-main/test/distributed/elastic/multiprocessing/api_test.py | #!/usr/bin/env python3
# Owner(s): ["oncall: r2p"]
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import ctypes
import multiprocessing
import os
import shutil
i... | 32,382 | 36.350634 | 103 | py |
pytorch | pytorch-main/test/distributed/elastic/multiprocessing/errors/api_test.py | #!/usr/bin/env python3
# Owner(s): ["oncall: r2p"]
import json
import os
import shutil
import signal
import tempfile
import unittest
from unittest import mock
from torch.distributed.elastic.multiprocessing.errors import (
ChildFailedError,
ProcessFailure,
record,
)
from torch.distributed.elastic.multiproc... | 8,512 | 36.668142 | 97 | py |
pytorch | pytorch-main/test/distributed/elastic/multiprocessing/errors/error_handler_test.py | #!/usr/bin/env python3
# Owner(s): ["oncall: r2p"]
import filecmp
import json
import os
import shutil
import tempfile
import unittest
from unittest.mock import patch
from torch.distributed.elastic.multiprocessing.errors.error_handler import ErrorHandler
from torch.distributed.elastic.multiprocessing.errors.handlers i... | 4,081 | 36.796296 | 98 | py |
pytorch | pytorch-main/test/distributed/elastic/metrics/api_test.py | #!/usr/bin/env python3
# Owner(s): ["oncall: r2p"]
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.abs
import abc
import unittest.mock as mock
from torch.distrib... | 3,396 | 27.788136 | 88 | py |
pytorch | pytorch-main/test/distributed/elastic/timer/file_based_local_timer_test.py | # Owner(s): ["oncall: r2p"]
# Copyright (c) Meta Platforms, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import multiprocessing as mp
import signal
import time
import unittest
import u... | 10,154 | 37.033708 | 121 | py |
pytorch | pytorch-main/test/distributed/elastic/timer/local_timer_test.py | # Owner(s): ["oncall: r2p"]
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import multiprocessing as mp
import signal
import time
import unittest
import unittes... | 11,114 | 35.804636 | 91 | py |
pytorch | pytorch-main/test/distributed/elastic/timer/api_test.py | # Owner(s): ["oncall: r2p"]
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import unittest
import unittest.mock as mock
from torch.distributed.elastic.timer im... | 2,442 | 30.727273 | 85 | py |
pytorch | pytorch-main/test/distributed/elastic/timer/local_timer_example.py | #!/usr/bin/env python3
# Owner(s): ["oncall: r2p"]
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import logging
import multiprocessing as mp
import signal
impo... | 4,116 | 33.308333 | 94 | py |
pytorch | pytorch-main/test/distributed/elastic/utils/logging_test.py | #!/usr/bin/env python3
# Owner(s): ["oncall: r2p"]
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import torch.distributed.elastic.utils.logging as logging
from... | 1,070 | 28.75 | 71 | py |
pytorch | pytorch-main/test/distributed/elastic/utils/distributed_test.py | #!/usr/bin/env python3
# Owner(s): ["oncall: r2p"]
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import multiprocessing as mp
import os
import socket
import s... | 4,979 | 30.923077 | 117 | py |
pytorch | pytorch-main/test/distributed/elastic/utils/util_test.py | #!/usr/bin/env python3
# Owner(s): ["oncall: r2p"]
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
from unittest import mock
import torch.distributed.elastic.u... | 3,687 | 34.461538 | 87 | py |
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