python_code stringlengths 0 1.02M | repo_name stringlengths 9 48 | file_path stringlengths 5 114 |
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# Owner(s): ["oncall: distributed"]
from typing import Any, Callable
import torch
from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
from torch.distributed.fsdp._symbolic_trace import TracingConfig
from torch.distributed.fsdp.fully_sharded_data_parallel import ShardingStrategy
from torch.distributed.... | pytorch-master | test/distributed/fsdp/test_fsdp_param_exec_order_wrap.py |
# Owner(s): ["oncall: distributed"]
import itertools
import sys
from contextlib import suppress
from copy import deepcopy
from functools import partial
from typing import Any, Dict
import torch
import torch.nn as nn
from torch import distributed as dist
from torch.distributed.algorithms._checkpoint.checkpoint_wrapper... | pytorch-master | test/distributed/fsdp/test_fsdp_state_dict.py |
# Owner(s): ["oncall: distributed"]
import sys
import time
from statistics import mean
from unittest.mock import patch
import torch
import torch.nn as nn
from torch import distributed as dist
from torch.cuda import Event
from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
from torch.testing._internal.... | pytorch-master | test/distributed/fsdp/test_fsdp_overlap.py |
# Owner(s): ["oncall: distributed"]
import contextlib
from copy import deepcopy
from functools import partial
import torch
import torch.nn as nn
from torch.distributed.fsdp.fully_sharded_data_parallel import (
FullyShardedDataParallel as FSDP,
CPUOffload,
)
from torch.distributed.algorithms._checkpoint.checkp... | pytorch-master | test/distributed/fsdp/test_fsdp_checkpoint.py |
# Owner(s): ["oncall: distributed"]
import functools
import os
import tempfile
import unittest
from enum import Enum, auto
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.distributed.fsdp.fully_sharded_data_parallel import (
BackwardPrefetch,
CPUOffload,
)
from torch.distributed.... | pytorch-master | test/distributed/fsdp/test_wrap.py |
# Owner(s): ["oncall: distributed"]
import sys
from typing import Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import distributed as dist
from torch.distributed.algorithms._comm_hooks import default_hooks
from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
fro... | pytorch-master | test/distributed/fsdp/test_fsdp_comm_hooks.py |
# Owner(s): ["oncall: distributed"]
import sys
import torch
import torch.distributed as dist
from torch.distributed._shard.partial_tensor import (
_PartialTensor,
)
from torch.distributed._shard.sharding_spec import (
ChunkShardingSpec,
EnumerableShardingSpec,
ShardMetadata,
)
from torch.testing._inte... | pytorch-master | test/distributed/_shard/test_partial_tensor.py |
# Owner(s): ["oncall: distributed"]
import io
import torch
import torch.distributed._shard.sharded_tensor as sharded_tensor
import torch.distributed as dist
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.distributed._shard import _shard_tensor
from torch.distributed._shard.replicated_tensor ... | pytorch-master | test/distributed/_shard/test_replicated_tensor.py |
# Owner(s): ["oncall: distributed"]
import sys
import copy
import torch
import torch.nn as nn
from torch.testing._internal.common_distributed import (
requires_nccl,
skip_if_lt_x_gpu,
)
from torch.distributed._shard import shard_module
from torch.distributed._shard.sharding_plan import ShardingPlan
from torch... | pytorch-master | test/distributed/_shard/test_sharder.py |
# Owner(s): ["oncall: distributed"]
import sys
import torch
from torch.distributed._shard.sharded_tensor import (
Shard,
ShardMetadata,
ShardedTensor,
ShardedTensorMetadata,
)
from torch.distributed._shard.sharded_tensor.metadata import TensorProperties
from torch.testing._internal.common_utils impo... | pytorch-master | test/distributed/_shard/checkpoint/test_utils.py |
# Owner(s): ["oncall: distributed"]
import sys
import os
import shutil
import tempfile
from typing import Dict
import torch
import torch.distributed as dist
from torch.distributed._shard import sharded_tensor
from torch.distributed._shard.sharded_tensor import ShardedTensor, state_dict_hook
from torch.distributed._sh... | pytorch-master | test/distributed/_shard/checkpoint/test_file_system_checkpoint_cpu.py |
# Owner(s): ["oncall: distributed"]
import sys
import os
import shutil
import tempfile
from typing import Dict
import torch
import torch.distributed as dist
from torch.distributed._shard import sharded_tensor
from torch.distributed._shard.sharded_tensor import ShardedTensor, state_dict_hook
from torch.distributed._sh... | pytorch-master | test/distributed/_shard/checkpoint/test_file_system_checkpoint.py |
# Owner(s): ["oncall: distributed"]
import random
import sys
from typing import Optional, List, Union, cast
from torch.distributed._shard.checkpoint import (
StorageReader,
StorageWriter,
CheckpointException,
load_state_dict,
save_state_dict,
)
import torch
import torch.distributed as dist
import ... | pytorch-master | test/distributed/_shard/checkpoint/test_checkpoint.py |
# Owner(s): ["oncall: distributed"]
import sys
from itertools import product
import torch
from torch.distributed._shard import (
sharded_tensor,
_shard_tensor,
)
from torch.distributed._shard.sharding_spec import (
EnumerableShardingSpec,
ShardMetadata,
)
from torch.testing._internal.common_distribute... | pytorch-master | test/distributed/_shard/sharded_tensor/test_sharded_tensor_reshard.py |
# Owner(s): ["oncall: distributed"]
import copy
import math
import io
import itertools
import pickle
import sys
import torch
import torch.distributed as dist
from torch.distributed import rpc
from torch.distributed import distributed_c10d
from torch.distributed._shard import sharded_tensor
from torch.distributed._shar... | pytorch-master | test/distributed/_shard/sharded_tensor/test_sharded_tensor.py |
# Owner(s): ["oncall: distributed"]
import copy
import sys
import torch
import torch.distributed as dist
from torch.distributed._shard.sharded_optim import (
ShardedOptimizer,
)
from torch.distributed._shard.api import (
shard_parameter,
_reshard_output,
_collect_local_shard
)
from torch.testing._inte... | pytorch-master | test/distributed/_shard/sharded_tensor/test_megatron_prototype.py |
# Owner(s): ["oncall: distributed"]
import sys
import torch
from torch.distributed._shard import sharded_tensor, _shard_tensor
from torch.testing._internal.common_distributed import (
requires_nccl,
skip_if_lt_x_gpu,
)
from torch.testing._internal.common_utils import (
TEST_WITH_DEV_DBG_ASAN,
run_test... | pytorch-master | test/distributed/_shard/sharded_tensor/ops/test_elementwise_ops.py |
# Owner(s): ["oncall: distributed"]
import copy
import itertools
import sys
import torch
from torch.distributed._shard import sharded_tensor, _shard_tensor
from torch.testing._internal.common_distributed import (
requires_nccl,
skip_if_lt_x_gpu,
)
from torch.testing._internal.common_utils import (
TEST_WI... | pytorch-master | test/distributed/_shard/sharded_tensor/ops/test_matrix_ops.py |
# Owner(s): ["oncall: distributed"]
import sys
import torch
import torch.distributed as dist
from torch.distributed._shard import (
shard_parameter,
)
from torch.testing._internal.common_distributed import (
requires_nccl,
skip_if_lt_x_gpu,
)
from torch.testing._internal.common_utils import (
TEST_WIT... | pytorch-master | test/distributed/_shard/sharded_tensor/ops/test_embedding_bag.py |
# Owner(s): ["oncall: distributed"]
import copy
import torch
import torch.distributed._shard.sharded_tensor as sharded_tensor
from torch.distributed._shard.sharding_spec import (
ChunkShardingSpec,
)
from torch.testing._internal.common_distributed import (
requires_nccl,
skip_if_lt_x_gpu,
)
from torch.t... | pytorch-master | test/distributed/_shard/sharded_tensor/ops/test_tensor_ops.py |
# Owner(s): ["oncall: distributed"]
import sys
import torch
import torch.distributed as dist
from torch.distributed._shard import (
shard_parameter,
)
from torch.testing._internal.common_distributed import (
requires_nccl,
skip_if_lt_x_gpu,
)
from torch.testing._internal.common_utils import (
TEST_WIT... | pytorch-master | test/distributed/_shard/sharded_tensor/ops/test_embedding.py |
# Owner(s): ["oncall: distributed"]
import sys
import torch
import torch.distributed as dist
from torch.distributed._shard import sharded_tensor
from torch.distributed.distributed_c10d import _get_default_group
from torch.testing._internal.common_distributed import (
requires_nccl,
skip_if_lt_x_gpu,
)
from t... | pytorch-master | test/distributed/_shard/sharded_tensor/ops/test_binary_cmp.py |
# Owner(s): ["oncall: distributed"]
import copy
import sys
import torch
import torch.distributed as dist
from torch.distributed._shard.api import (
shard_parameter,
_collect_local_shard,
_reshard_output,
)
from torch.distributed._shard.sharded_optim import (
ShardedOptimizer,
)
from torch.distributed.... | pytorch-master | test/distributed/_shard/sharded_tensor/ops/test_linear.py |
# Owner(s): ["oncall: distributed"]
import sys
import torch
from torch.testing._internal.common_utils import (
TEST_WITH_DEV_DBG_ASAN,
run_tests,
)
from torch.testing._internal.distributed._shard.sharded_tensor import (
TEST_GPU_NUM,
ShardedTensorTestBase,
with_comms,
)
from torch.testing._internal... | pytorch-master | test/distributed/_shard/sharded_tensor/ops/test_softmax.py |
# Owner(s): ["oncall: distributed"]
import sys
import torch
from torch.distributed._shard import sharded_tensor
from torch.distributed._shard.sharding_spec import (
ChunkShardingSpec,
)
from torch.testing._internal.common_distributed import (
requires_nccl,
skip_if_lt_x_gpu,
)
from torch.testing._internal... | pytorch-master | test/distributed/_shard/sharded_tensor/ops/test_init.py |
# Owner(s): ["oncall: distributed"]
import torch
from torch.distributed._shard import _shard_tensor
import torch.distributed._shard.sharded_tensor as sharded_tensor
import torch.distributed as dist
from torch.distributed._shard.sharding_spec import (
ChunkShardingSpec,
EnumerableShardingSpec,
ShardMetadat... | pytorch-master | test/distributed/_shard/sharded_tensor/ops/test_math_ops.py |
# Owner(s): ["oncall: distributed"]
import sys
import torch
from torch.distributed._shard import sharded_tensor, _shard_tensor
from torch.testing._internal.common_distributed import (
requires_nccl,
skip_if_lt_x_gpu,
)
from torch.testing._internal.common_utils import (
TEST_WITH_DEV_DBG_ASAN,
run_test... | pytorch-master | test/distributed/_shard/sharded_tensor/ops/test_chunk.py |
# Owner(s): ["oncall: distributed"]
import sys
import copy
import torch
import torch.nn as nn
import torch.distributed as dist
from torch.distributed._shard.sharded_optim import (
ShardedOptimizer,
)
from torch.testing._internal.common_distributed import (
requires_nccl,
skip_if_lt_x_gpu,
)
from torch.dis... | pytorch-master | test/distributed/_shard/sharding_plan/test_sharding_plan.py |
# Owner(s): ["oncall: distributed"]
from typing import List, Union
from dataclasses import dataclass
import copy
import torch
from torch.testing._internal.common_utils import TestCase
from torch.testing._internal.common_distributed import (
requires_nccl,
skip_if_lt_x_gpu,
)
from torch.distributed._shard impor... | pytorch-master | test/distributed/_shard/sharding_spec/test_sharding_spec.py |
# Owner(s): ["oncall: distributed"]
import torch
import torch.optim as optim
from torch.distributed._shard import (
sharded_tensor,
shard_parameter
)
from copy import deepcopy
from torch.distributed._shard.sharding_spec import (
ChunkShardingSpec,
)
from torch.distributed._shard.sharded_optim import (
... | pytorch-master | test/distributed/_shard/sharded_optim/test_sharded_optim.py |
#!/usr/bin/env python3
# Owner(s): ["oncall: distributed"]
import sys
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_utils import IS_CI, run_tests
from torch.testing... | pytorch-master | test/distributed/rpc/test_tensorpipe_agent.py |
#!/usr/bin/env python3
# Owner(s): ["oncall: distributed"]
import torch
import torch.distributed as dist
if not dist.is_available():
print("Distributed not available, skipping tests", file=sys.stderr)
sys.exit(0)
import copyreg
import os
import contextlib
from torch import multiprocessing
import torch.multi... | pytorch-master | test/distributed/rpc/test_share_memory.py |
#!/usr/bin/env python3
# Owner(s): ["oncall: distributed"]
import sys
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_utils import IS_CI, run_tests
from torch.testing... | pytorch-master | test/distributed/rpc/test_faulty_agent.py |
#!/usr/bin/env python3
# Owner(s): ["oncall: distributed"]
import sys
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_utils import run_tests
from torch.testing._internal.distribut... | pytorch-master | test/distributed/rpc/cuda/test_tensorpipe_agent.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... | pytorch-master | test/autograd/test_functional.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... | pytorch-master | test/autograd/test_complex.py |
# Owner(s): ["oncall: fx"]
import torch
from torch.testing._internal.common_utils import (
TestCase, run_tests)
from torch.fx.experimental.proxy_tensor import make_fx
from torch.fx.passes.dialect.common.cse_pass import CSEPass, get_CSE_banned_ops
from torch.fx import symbolic_trace
import random
banned_ops = g... | pytorch-master | test/fx/test_cse_pass.py |
# Owner(s): ["oncall: fx"]
import torch
from torch.testing._internal.common_utils import (
TestCase, parametrize, instantiate_parametrized_tests, run_tests)
from torch.fx.experimental.proxy_tensor import make_fx
from torch.fx.passes.dialect.common.cse_pass import CSEPass
from torch.fx.graph_module import GraphMod... | pytorch-master | test/fx/test_common_passes.py |
# Owner(s): ["module: fx"]
import operator
import unittest
from torch.fx import GraphModule, symbolic_trace
from torch.fx.experimental.meta_tracer import symbolic_trace as meta_symbolic_trace
from torch.fx.experimental.migrate_gradual_types.constraint import BinConstraintT, DVar, TVar, T
from torch.fx.experimental.migr... | pytorch-master | test/fx/test_z3_gradual_types.py |
# Owner(s): ["module: fx"]
import os
import sys
import torch
from torch.fx import symbolic_trace, subgraph_rewriter
from torch.fx.annotate import annotate
# Make the helper files in test/ importable
from torch.fx.experimental.rewriter import RewritingTracer
pytorch_test_dir = os.path.dirname(os.path.dirname(os.path.... | pytorch-master | test/fx/test_subgraph_rewriter.py |
# Owner(s): ["module: fx"]
from __future__ import annotations # type: ignore[attr-defined]
import torch
import typing
from torch.fx import symbolic_trace
class A:
def __call__(self, x: torch.Tensor):
return torch.add(x, x)
# No forward references
class M1(torch.nn.Module):
def forward(self, x: tor... | pytorch-master | test/fx/test_future.py |
r'''
**This file is EXPERIMENTAL and is mostly used for testing purposes! Do not
rely on it for anything!**
'''
from torch.fx import Graph, GraphModule
from torch.fx.graph import map_arg
from torch.fx.proxy import Proxy
import sys
import torch
from torch.nn.utils import fuse_conv_bn_weights
import operator
# can be a
... | pytorch-master | test/fx/quantization.py |
# Owner(s): ["module: fx"]
import unittest
import torch
from torch.fx import symbolic_trace
from torch.fx.experimental.unify_refinements import infer_symbolic_types
from torch.fx.experimental.refinement_types import Equality
from torch.fx.tensor_type import TensorType, Dyn, is_consistent, is_more_precise
from torch.fx... | pytorch-master | test/fx/test_gradual_type.py |
from typing import NamedTuple
import torch
class MyNamedTup(NamedTuple):
i : torch.Tensor
f : torch.Tensor
| pytorch-master | test/fx/named_tup.py |
# Owner(s): ["module: fx"]
import unittest
import torch
import torch.fx
from torch.testing._internal.common_utils import TestCase
class MyModuleBase(torch.nn.Module):
def forward(self, x):
matrx = self.get_mul_matrix()
if self.no_relu():
return torch.mm(x, matrx)
else:
... | pytorch-master | test/fx/test_fx_param_shape_control_flow.py |
# Owner(s): ["module: fx"]
from typing import Set, Type
import torch
import torch.fx
from torch.testing._internal.common_utils import TestCase
class TestDCE(TestCase):
def _has_nodes_without_users(self, m: torch.fx.GraphModule):
for node in m.graph.nodes:
if node.is_impure():
... | pytorch-master | test/fx/test_dce_pass.py |
# Owner(s): ["module: fx"]
import operator
import torch
import torch.fx
from torch.fx.experimental import const_fold
from torch.fx.passes.shape_prop import _extract_tensor_metadata, ShapeProp
from torch.testing._internal.common_utils import TestCase
class TestConstFold(TestCase):
def _get_attr(self, node):
... | pytorch-master | test/fx/test_fx_const_fold.py |
# Owner(s): ["module: fx"]
import torch
import torch.fx as fx
from torch.testing._internal.common_utils import TestCase
from torch.fx.passes.infra.pass_base import PassResult
from torch.fx.passes.infra.pass_manager import (
PassManager,
this_before_that_pass_constraint,
_topological_sort_passes,
)
def re... | pytorch-master | test/fx/test_pass_infra.py |
import argparse
import torch
def dump(filename):
schemas = torch._C._jit_get_all_schemas()
schemas += torch._C._jit_get_custom_class_schemas()
with open(filename, 'w') as f:
for s in schemas:
f.write(str(s))
f.write('\n')
if __name__ == '__main__':
parser = argparse.... | pytorch-master | test/forward_backward_compatibility/dump_all_function_schemas.py |
import argparse
import datetime
import re
import sys
import warnings
from collections import defaultdict
import torch
from torch._C import parse_schema
# How to run this test locally:
# 1 Have two virtual environments (eg conda env), one without PyTorch installed (venv_nightly)
# one with your local changes (venv_... | pytorch-master | test/forward_backward_compatibility/check_forward_backward_compatibility.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... | pytorch-master | test/backends/xeon/test_launch.py |
# Owner(s): ["module: unknown"]
import collections
import json
import os
import re
import textwrap
import timeit
from typing import Any, List, Tuple
import unittest
import torch
import torch.utils.benchmark as benchmark_utils
from torch.testing._internal.common_utils import TestCase, run_tests, IS_SANDCASTLE, IS_WIND... | pytorch-master | test/benchmark_utils/test_benchmark_utils.py |
# Owner(s): ["module: unknown"]
import os
import tempfile
import torch
from backend import Model, to_custom_backend, get_custom_backend_library_path
from torch.testing._internal.common_utils import TestCase, run_tests
class TestCustomBackend(TestCase):
def setUp(self):
# Load the library containing the ... | pytorch-master | test/custom_backend/test_custom_backend.py |
import argparse
import os.path
import sys
import torch
def get_custom_backend_library_path():
"""
Get the path to the library containing the custom backend.
Return:
The path to the custom backend object, customized by platform.
"""
if sys.platform.startswith("win32"):
library_file... | pytorch-master | test/custom_backend/backend.py |
pytorch-master | test/quantization/__init__.py | |
# Owner(s): ["oncall: quantization"]
import torch
import math
from typing import Tuple
from torch.ao.quantization import (
FakeQuantize,
MovingAverageMinMaxObserver,
default_observer,
default_fixed_qparams_range_0to1_fake_quant,
)
from torch.ao.quantization._learnable_fake_quantize import _LearnableFa... | pytorch-master | test/quantization/core/test_workflow_ops.py |
# Owner(s): ["oncall: quantization"]
import torch
import torch.nn.intrinsic as nni
import torch.nn.qat as nnqat
import torch.nn.quantized._reference as nnqr
from torch.testing._internal.common_quantization import QuantizationTestCase
from torch.ao.quantization.backend_config import (
BackendConfig,
BackendPat... | pytorch-master | test/quantization/core/test_backend_config.py |
# Owner(s): ["oncall: quantization"]
import numpy as np
import math
import torch
import io
import unittest
from copy import deepcopy
from hypothesis import given
from hypothesis import strategies as st
from torch.testing._internal.common_utils import TemporaryFileName
from torch.testing._internal.common_cuda import TE... | pytorch-master | test/quantization/core/test_quantized_tensor.py |
# Owner(s): ["oncall: quantization"]
import torch
from torch.testing._internal.common_utils import TestCase
from torch.ao.quantization.utils import get_fqn_to_example_inputs
class TestUtils(TestCase):
def _test_get_fqn_to_example_inputs(self, M, example_inputs, expected_fqn_to_dim):
m = M().eval()
... | pytorch-master | test/quantization/core/test_utils.py |
# Owner(s): ["oncall: quantization"]
import torch
import torch.nn as nn
import torch.nn.intrinsic as nni
import torch.nn.intrinsic.quantized as nniq
import torch.nn.quantized as nnq
import torch.nn.quantized.dynamic as nnqd
import torch.nn.quantized._reference as nnqr
import torch.ao.quantization
from torch.ao.quanti... | pytorch-master | test/quantization/core/test_quantized_module.py |
# Owner(s): ["oncall: quantization"]
import re
import contextlib
from pathlib import Path
import torch
# import torch.nn.quantized as nnq
from torch.testing._internal.common_quantization import (
QuantizationTestCase,
SingleLayerLinearModel,
)
from torch.testing._internal.common_quantized import override_qua... | pytorch-master | test/quantization/core/test_docs.py |
pytorch-master | test/quantization/core/__init__.py | |
# Owner(s): ["oncall: quantization"]
from builtins import round
import copy
import itertools
import numpy as np
import unittest
import operator
import random
import torch
from torch import _VF
import torch.jit
import torch.nn.functional as F
from torch.nn.modules.utils import _single, _pair
from hypothesis import s... | pytorch-master | test/quantization/core/test_quantized_op.py |
# Owner(s): ["oncall: quantization"]
# Torch
import torch
import torch.nn.functional as F
import torch.nn.quantized.functional as qF
# Standard library
import numpy as np
# Testing utils
from hypothesis import assume, given
from hypothesis import strategies as st
from torch.testing._internal.common_quantization impo... | pytorch-master | test/quantization/core/test_quantized_functional.py |
# Owner(s): ["oncall: quantization"]
# Torch
import torch
from torch.ao.quantization import (
MinMaxObserver,
PerChannelMinMaxObserver,
MovingAverageMinMaxObserver,
MovingAveragePerChannelMinMaxObserver,
HistogramObserver,
RecordingObserver,
PlaceholderObserver,
NoopObserver,
FakeQu... | pytorch-master | test/quantization/core/test_workflow_module.py |
# Owner(s): ["oncall: quantization"]
from torch.ao.quantization.experimental.observer import APoTObserver
import unittest
import torch
class TestNonUniformObserver(unittest.TestCase):
"""
Test case 1: calculate_qparams
Test that error is thrown when k == 0
"""
def test_calculate_qparams_in... | pytorch-master | test/quantization/core/experimental/test_nonuniform_observer.py |
# Owner(s): ["oncall: quantization"]
import torch
import unittest
from torch.ao.quantization.experimental.observer import APoTObserver
from torch.ao.quantization.experimental.quantizer import quantize_APoT
class TestQuantizedTensor(unittest.TestCase):
r""" Tests int_repr on APoTQuantizer with random tensor2quanti... | pytorch-master | test/quantization/core/experimental/test_quantized_tensor.py |
import torch
import torchvision
import torchvision.transforms.transforms as transforms
import os
import torch.quantization
from torchvision.models.quantization.resnet import resnet18
from torch.autograd import Variable
# Setup warnings
import warnings
warnings.filterwarnings(
action='ignore',
category=Deprecat... | pytorch-master | test/quantization/core/experimental/quantization_util.py |
# Owner(s): ["oncall: quantization"]
import torch
import unittest
from torch.ao.quantization.experimental.observer import APoTObserver
from torch.ao.quantization.experimental.quantizer import quantize_APoT, dequantize_APoT
from torch.ao.quantization.experimental.fake_quantize import APoTFakeQuantize
from torch.ao.quan... | pytorch-master | test/quantization/core/experimental/test_fake_quantize.py |
# Owner(s): ["oncall: quantization"]
import torch
from torch import quantize_per_tensor
from torch.ao.quantization.observer import MinMaxObserver
from torch.ao.quantization.experimental.observer import APoTObserver
from torch.ao.quantization.experimental.quantizer import APoTQuantizer, quantize_APoT, dequantize_APoT
i... | pytorch-master | test/quantization/core/experimental/test_quantizer.py |
# Owner(s): ["oncall: quantization"]
import torch
from torch.ao.quantization.experimental.linear import LinearAPoT
from torch.nn.modules.linear import Linear
import unittest
class TestNonUniformObserver(unittest.TestCase):
"""
Test linear_APoT_fn by comparing to uniform linear
for 2d tensors with ... | pytorch-master | test/quantization/core/experimental/test_linear.py |
from torchvision.models.quantization.resnet import resnet18
from torch.ao.quantization.experimental.quantization_helper import (
evaluate,
prepare_data_loaders,
training_loop
)
# training and validation dataset: full ImageNet dataset
data_path = '~/my_imagenet/'
train_batch_size = 30
eval_batch_size = 50
... | pytorch-master | test/quantization/core/experimental/apot_fx_graph_mode_qat.py |
import torch
import torch.nn as nn
import torch.quantization
from torchvision.models.quantization.resnet import resnet18
from torch.ao.quantization.experimental.quantization_helper import (
evaluate,
prepare_data_loaders
)
# validation dataset: full ImageNet dataset
data_path = '~/my_imagenet/'
data_loader, d... | pytorch-master | test/quantization/core/experimental/apot_fx_graph_mode_ptq.py |
# -*- coding: utf-8 -*-
# Owner(s): ["oncall: quantization"]
import sys
import os
import unittest
from typing import Set
# torch
import torch
import torch.nn as nn
import torch.nn.quantized as nnq
import torch.nn.quantized.dynamic as nnqd
import torch.nn.intrinsic.quantized as nniq
from torch.fx import GraphModule
#... | pytorch-master | test/quantization/bc/test_backward_compatibility.py |
pytorch-master | test/quantization/bc/__init__.py | |
# Owner(s): ["oncall: quantization"]
# Copied from pytorch/test/fx/test_subgraph_rewriter.py
import os
import sys
import torch
from torch.fx import symbolic_trace, subgraph_rewriter
from torch.fx.annotate import annotate
# Make the helper files in test/ importable
from torch.fx.experimental.rewriter import RewritingT... | pytorch-master | test/quantization/fx/test_subgraph_rewriter.py |
pytorch-master | test/quantization/fx/__init__.py | |
# Owner(s): ["oncall: quantization"]
import copy
import math
import operator
import unittest
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.ao.quantization import default_dynamic_qconfig
import torch.nn.quantized as nnq
toq = torch.ops.quantized
from torch.ao.quantization.quantize_fx im... | pytorch-master | test/quantization/fx/test_numeric_suite_fx.py |
# Owner(s): ["oncall: quantization"]
from collections import OrderedDict
import os
import contextlib
import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.nn.quantized as nnq
import torch.nn.quantized._reference as nnqr
import torch.nn.quantized.dynamic as nnqd
import torch.nn.intrinsic as nn... | pytorch-master | test/quantization/fx/test_quantize_fx.py |
# -*- coding: utf-8 -*-
# Owner(s): ["oncall: quantization"]
import torch
import torch.nn as nn
import torch.ao.quantization.quantize_fx as quantize_fx
import torch.nn.functional as F
from torch.ao.quantization import QConfig, QConfigMapping
from torch.ao.quantization.fx._model_report.detector import (
DynamicStat... | pytorch-master | test/quantization/fx/test_model_report_fx.py |
# Owner(s): ["oncall: quantization"]
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.intrinsic.quantized as nniq
import torch.nn.quantized as nnq
from torch.ao.quantization import default_qconfig
from torch.ao.quantization.observer import MinMaxObserver, PerChannelMinMaxObserver
from... | pytorch-master | test/quantization/fx/test_equalize_fx.py |
pytorch-master | test/quantization/dbr/__init__.py | |
# Owner(s): ["oncall: quantization"]
import collections
import copy
import math
import tempfile
import unittest
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.intrinsic as nni
import torch.nn.quantized as nnq
toq = torch.ops.quantized
from torch.testing._internal.common_quantizatio... | pytorch-master | test/quantization/dbr/test_quantize_dbr.py |
# Owner(s): ["oncall: quantization"]
from .common import AOMigrationTestCase
class TestAOMigrationQuantizationFx(AOMigrationTestCase):
def test_package_import_quantize_fx(self):
self._test_package_import('quantize_fx')
def test_function_import_quantize_fx(self):
function_list = [
... | pytorch-master | test/quantization/ao_migration/test_quantization_fx.py |
pytorch-master | test/quantization/ao_migration/__init__.py | |
# Owner(s): ["oncall: quantization"]
from .common import AOMigrationTestCase
class TestAOMigrationQuantization(AOMigrationTestCase):
def test_package_import_quantize(self):
self._test_package_import('quantize')
def test_function_import_quantize(self):
function_list = [
'_convert'... | pytorch-master | test/quantization/ao_migration/test_ao_migration.py |
# Owner(s): ["oncall: quantization"]
from .common import AOMigrationTestCase
class TestAOMigrationQuantization(AOMigrationTestCase):
r"""Modules and functions related to the
`torch/quantization` migration to `torch/ao/quantization`.
"""
def test_package_import_quantize(self):
self._test_packa... | pytorch-master | test/quantization/ao_migration/test_quantization.py |
from torch.testing._internal.common_utils import TestCase
import importlib
from typing import List, Optional
class AOMigrationTestCase(TestCase):
def _test_package_import(self, package_name: str, base: Optional[str] = None):
r"""Tests the module import by making sure that all the internals match
(... | pytorch-master | test/quantization/ao_migration/common.py |
# Owner(s): ["oncall: quantization"]
import torch
from torch.testing._internal.common_quantization import (
skipIfNoFBGEMM
)
from torch.testing._internal.common_utils import suppress_warnings
from torch.testing._internal.jit_utils import JitTestCase
from typing import Tuple
import copy
class TestDeprecatedJitQua... | pytorch-master | test/quantization/jit/test_deprecated_jit_quant.py |
# -*- coding: utf-8 -*-
# Owner(s): ["oncall: quantization"]
# torch
import torch
from torch.testing import FileCheck
from torch.testing._internal.common_quantization import QuantizationTestCase
class TestFusionPasses(QuantizationTestCase):
def test_quantized_add_relu_fusion(self):
class MAdd(torch.nn.Mod... | pytorch-master | test/quantization/jit/test_fusion_passes.py |
# -*- coding: utf-8 -*-
# Owner(s): ["oncall: quantization"]
# torch
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.jit
import torch.jit.quantized
# torch.ao.quantization
from torch.ao.quantization import (
QConfig,
default_dynamic_qconfig,
float16_dynamic_qconfig,
def... | pytorch-master | test/quantization/jit/test_quantize_jit.py |
pytorch-master | test/quantization/jit/__init__.py | |
# Owner(s): ["oncall: quantization"]
import unittest
import torch
import torch.nn as nn
import torch.nn.quantized as nnq
from torch.ao.quantization import (
DeQuantStub,
QuantStub,
convert,
default_qconfig,
prepare,
quantize,
quantize_dynamic,
)
from torch.ao.ns._numeric_suite import (
... | pytorch-master | test/quantization/eager/test_numeric_suite_eager.py |
# Owner(s): ["oncall: quantization"]
import copy
import math
import torch
import torch.nn as nn
import torch.backends.mkldnn
from torch.nn import Conv2d, BatchNorm2d, ReLU, init
from torch.nn.intrinsic.qat import ConvBn2d, ConvBnReLU2d
from torch.nn.modules.utils import _pair
import torch.nn.quantized as nnq
import to... | pytorch-master | test/quantization/eager/test_quantize_eager_qat.py |
# Owner(s): ["oncall: quantization"]
import torch
import torch.nn as nn
import torch.nn.quantized as nnq
from torch.nn.utils.rnn import PackedSequence
from torch.ao.quantization import (
quantize,
prepare,
convert,
prepare_qat,
quantize_dynamic,
QuantWrapper,
QuantStub,
DeQuantStub,
... | pytorch-master | test/quantization/eager/test_quantize_eager_ptq.py |
# Owner(s): ["oncall: quantization"]
import torch
import torch.nn as nn
from torch.testing._internal.common_quantization import QuantizationTestCase
from torch.ao.quantization.fuse_modules import fuse_modules
import torch.ao.quantization._equalize as _equalize
import copy
class TestEqualizeEager(QuantizationTestCa... | pytorch-master | test/quantization/eager/test_equalize_eager.py |
pytorch-master | test/quantization/eager/__init__.py | |
# Owner(s): ["oncall: quantization"]
import torch
from torch.testing._internal.common_quantization import (
QuantizationTestCase,
ModelMultipleOps,
ModelMultipleOpsNoAvgPool,
)
from torch.testing._internal.common_quantized import (
override_quantized_engine,
supported_qengines,
)
class TestModelN... | pytorch-master | test/quantization/eager/test_model_numerics.py |
# Owner(s): ["oncall: quantization"]
import torch
import torch.nn as nn
from torch.testing._internal.common_quantization import QuantizationTestCase
from torch.testing._internal.common_quantization import skipIfNoFBGEMM
from torch.ao.quantization import default_qconfig
from torch.ao.quantization import QuantWrapper
i... | pytorch-master | test/quantization/eager/test_bias_correction_eager.py |
# Owner(s): ["oncall: quantization"]
import copy
import torch
import torch.nn as nn
import torch.nn.quantized as nnq
import torch.nn.intrinsic as nni
import torch.nn.intrinsic.quantized as nniq
import torch.nn.intrinsic.qat as nniqat
from torch.ao.quantization import (
quantize,
prepare,
convert,
prep... | pytorch-master | test/quantization/eager/test_fuse_eager.py |
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