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pytorch
pytorch-main/torch/_inductor/optimize_indexing.py
import math import sympy import torch from torch.utils._sympy.value_ranges import ValueRanges from .ir import LoopBody from .utils import dominated_nodes def val_expressable_in_32_bits(val): if hasattr(val, "is_Boolean") and val.is_Boolean: return True if isinstance(val, sympy.Expr): assert...
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pytorch
pytorch-main/torch/_inductor/dependencies.py
import collections import dataclasses import itertools import logging import re import typing from typing import Callable, Dict, List, Optional, Set, Tuple, Union import sympy import torch from .codegen.common import index_prevent_reordering from .utils import get_dtype_size, sympy_str, sympy_subs, sympy_symbol, Var...
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pytorch
pytorch-main/torch/_inductor/fx_passes/quantization.py
import torch from ..ir import QConv from ..pattern_matcher import Arg, CallFunction, KeywordArg, Match from .post_grad import register_lowering_pattern aten = torch.ops.aten prims = torch.ops.prims quantized_decomposed = torch.ops.quantized_decomposed dequantize_per_channel = quantized_decomposed.dequantize_per_channe...
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pytorch
pytorch-main/torch/_inductor/fx_passes/post_grad.py
import functools import itertools import logging import operator import torch import torch._inductor as inductor from .. import config, ir, pattern_matcher from ..lowering import lowerings as L from ..pattern_matcher import ( _return_true, Arg, CallFunction, filter_nodes, get_arg_value, Ignor...
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pytorch
pytorch-main/torch/_inductor/fx_passes/fuse_attention.py
import functools import logging import math import torch from ..._dynamo.utils import counters from ..pattern_matcher import ( filter_nodes, inference_graph, register_replacement, training_graph, ) log = logging.getLogger(__name__) aten = torch.ops.aten def _sfdp_pattern_1(query, key, value, inv_sca...
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pytorch
pytorch-main/torch/_inductor/fx_passes/group_batch_fusion.py
import collections import logging import operator import torch from torch._dynamo.utils import counters from .. import config from ..pattern_matcher import CallFunctionVarArgs try: # importing this will register fbgemm lowerings for inductor import deeplearning.fbgemm.fbgemm_gpu.fb.inductor_lowerings # noqa...
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pytorch
pytorch-main/torch/_inductor/fx_passes/replace_random.py
import collections import logging import torch from torch.fx.passes.shape_prop import _extract_tensor_metadata from .. import config, inductor_prims from ..pattern_matcher import ( CallFunctionVarArgs, Match, PatternMatcherPass, register_graph_pattern, ) from ..virtualized import V log = logging.getL...
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pytorch
pytorch-main/torch/_inductor/fx_passes/mkldnn_fusion.py
import functools from functools import reduce import torch from torch.fx.experimental.symbolic_shapes import free_symbols from .. import ir from ..lowering import lowerings as L from ..pattern_matcher import Arg, CallFunction, filter_nodes, get_arg_value, KeywordArg from ..virtualized import ops from .freezing_patt...
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pytorch
pytorch-main/torch/_inductor/fx_passes/pad_mm.py
import functools from itertools import chain from typing import Optional import torch from torch import Tensor from torch._inductor import utils from torch.utils._mode_utils import no_dispatch from ..pattern_matcher import inference_graph, register_replacement, training_graph aten = torch.ops.aten def fetch_fake_t...
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pytorch
pytorch-main/torch/_inductor/fx_passes/freezing_patterns.py
import functools import torch from .. import config from ..pattern_matcher import ( _return_true, inference_graph, init_once_fakemode, PatternMatcherPass, register_graph_pattern, register_replacement, stable_topological_sort, ) aten = torch.ops.aten # First pass_patterns[0] are applied, t...
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pytorch
pytorch-main/torch/_inductor/fx_passes/pre_grad.py
import copy import logging from typing import List, Optional import torch import torch.nn as nn from torch._dynamo.utils import detect_fake_mode from torch.fx.experimental.optimization import ( matches_module_pattern, replace_node_module, ) from torch.fx.passes.shape_prop import ShapeProp from torch.nn import ...
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pytorch
pytorch-main/torch/_inductor/fx_passes/split_cat.py
import logging import operator from typing import Callable, List, Sequence, Tuple, Union import numpy import torch from torch._dynamo.utils import counters from ..pattern_matcher import ( Arg, CallFunction, CallFunctionVarArgs, CallMethodVarArgs, config_flag, FailedMatch, get_arg_value, ...
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pytorch
pytorch-main/torch/_inductor/fx_passes/joint_graph.py
import logging from collections import Counter from typing import Set import torch import torch._guards from .. import config from ..pattern_matcher import ( CallFunction, init_once_fakemode, KeywordArg, Match, PatternMatcherPass, register_graph_pattern, stable_topological_sort, ) from .rep...
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pytorch
pytorch-main/torch/_inductor/codegen/triton_foreach.py
import itertools from .. import metrics from ..utils import ceildiv, sympy_product from ..virtualized import V from .common import IndentedBuffer, Kernel from .triton import TritonKernel from .triton_utils import config_of, signature_of class ForeachKernel(Kernel): MAX_NUM_ARGS = 250 # number where I would no l...
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pytorch
pytorch-main/torch/_inductor/codegen/wrapper.py
import collections import contextlib import dataclasses import functools import os import re from itertools import count from typing import Any, Dict, List, Optional, Tuple import sympy from sympy import Expr import torch from torch._dynamo.utils import counters, dynamo_timed from torch.fx.experimental.symbolic_shape...
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pytorch
pytorch-main/torch/_inductor/codegen/common.py
import contextlib import dataclasses import functools import itertools import logging import re import typing from collections import namedtuple from itertools import chain import sympy from sympy.printing.printer import Printer import torch import torch.fx from torch.utils._sympy.value_ranges import ValueRanges fro...
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pytorch
pytorch-main/torch/_inductor/codegen/triton.py
import collections import contextlib import dataclasses import functools import itertools import logging import math import operator from typing import Dict, Iterable, List, Set import sympy import torch import torch._logging from torch._prims_common import is_integer_dtype from torch.utils._sympy.functions import F...
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pytorch
pytorch-main/torch/_inductor/codegen/cpp.py
import contextlib import dataclasses import functools import itertools import logging import math import re import sys from copy import copy, deepcopy from typing import Dict, List import numpy import sympy import torch import torch.fx from torch._inductor import dependencies from torch._inductor.ir import StorageBox...
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pytorch
pytorch-main/torch/_inductor/kernel/bmm.py
import torch from ..lowering import register_lowering from ..select_algorithm import ( autotune_select_algorithm, ExternKernelChoice, TritonTemplate, ) from ..utils import ceildiv as cdiv, use_triton_template from .mm_common import addmm_epilogue, mm_args, mm_configs, mm_options aten = torch.ops.aten d...
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pytorch
pytorch-main/torch/_inductor/kernel/mm.py
import logging import torch from .. import config as inductor_config from ..lowering import register_lowering from ..select_algorithm import ( autotune_select_algorithm, ExternKernelChoice, TritonTemplate, ) from ..utils import use_triton_template from .mm_common import ( addmm_epilogue, int8_mm_co...
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pytorch
pytorch-main/torch/_inductor/kernel/mm_plus_mm.py
import functools import torch from ..lowering import lowerings from ..select_algorithm import ( autotune_select_algorithm, ExternKernelChoice, TritonTemplate, ) from ..utils import use_triton_template from ..virtualized import V from .mm_common import mm_args, mm_grid, mm_options aten = torch.ops.aten at...
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pytorch
pytorch-main/torch/_inductor/kernel/mm_common.py
import functools import logging from typing import List, Tuple import sympy import torch from torch._inductor.select_algorithm import realize_inputs from torch._inductor.virtualized import V from ..utils import ceildiv as cdiv, next_power_of_2 log = logging.getLogger(__name__) def triton_config(num_stages, num_war...
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pytorch
pytorch-main/torch/_inductor/kernel/conv.py
import functools import logging from typing import List import torch from .. import config, ir from ..ir import TensorBox from ..lowering import ( add_layout_constraint, constrain_to_fx_strides, lowerings as L, register_lowering, ) from ..select_algorithm import ( autotune_select_algorithm, Ex...
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pytorch
pytorch-main/torch/sparse/_triton_ops.py
import math import torch from torch._inductor.cuda_properties import get_device_capability def _has_triton(): if not torch.cuda.is_available(): return False try: import triton return triton is not None and get_device_capability() >= (7, 0) except ImportError: return False...
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pytorch
pytorch-main/torch/sparse/_semi_structured_conversions.py
import torch def _sparse_semi_structured_from_dense(dense): if dense.dim() != 2: raise RuntimeError( f"Expected 2-dimensional dense tensor, got {dense.dim()}-dimensional tensor" ) m, k = dense.shape device = dense.device meta_dtype = torch.int8 if dense.dtype == torch...
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pytorch
pytorch-main/torch/sparse/__init__.py
# The Tensor classes are added to this module by python_tensor.cpp from typing import Optional, Tuple, List, Union import torch from torch._C import _add_docstr, _sparse # type: ignore[attr-defined] from torch import Tensor # Semi structured sparsity support from .semi_structured import SparseSemiStructuredTensor, t...
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pytorch
pytorch-main/torch/sparse/semi_structured.py
import warnings from collections import namedtuple from typing import Any, Optional import torch __all__ = [ "SparseSemiStructuredTensor", "to_sparse_semi_structured", ] _SEMI_STRUCTURED_SPARSE_CONFIG = namedtuple( "_SEMI_STRUCTURED_SPARSE_CONFIG", "compression_factor min_rows min_cols" ) _DTYPE_TO_SEMI_...
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pytorch
pytorch-main/torch/multiprocessing/spawn.py
from typing import Optional import multiprocessing import multiprocessing.connection import signal import sys import warnings from . import _prctl_pr_set_pdeathsig # type: ignore[attr-defined] class ProcessException(Exception): __slots__ = ["error_index", "error_pid"] def __init__(self, msg: str, error_in...
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pytorch
pytorch-main/torch/multiprocessing/reductions.py
import torch import torch.utils.hooks from torch._namedtensor_internals import check_serializing_named_tensor import os import threading import multiprocessing from multiprocessing.util import register_after_fork from multiprocessing.reduction import ForkingPickler from typing import Union try: # Early load resour...
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pytorch
pytorch-main/torch/multiprocessing/__init__.py
""" torch.multiprocessing is a wrapper around the native :mod:`multiprocessing` module. It registers custom reducers, that use shared memory to provide shared views on the same data in different processes. Once the tensor/storage is moved to shared_memory (see :func:`~torch.Tensor.share_memory_`), it will be possible t...
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pytorch
pytorch-main/torch/testing/_creation.py
""" This module contains tensor creation utilities. """ import collections.abc import math import warnings from typing import cast, List, Optional, Tuple, Union import torch _INTEGRAL_TYPES = [torch.uint8, torch.int8, torch.int16, torch.int32, torch.int64] _FLOATING_TYPES = [torch.float16, torch.bfloat16, torch.floa...
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pytorch
pytorch-main/torch/testing/_comparison.py
import abc import cmath import collections.abc import contextlib import warnings from typing import ( Any, Callable, Collection, Dict, List, NoReturn, Optional, Sequence, Tuple, Type, Union, ) import torch try: import numpy as np NUMPY_AVAILABLE = True except Modul...
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pytorch
pytorch-main/torch/testing/__init__.py
from torch._C import FileCheck as FileCheck from ._comparison import assert_allclose, assert_close as assert_close from ._creation import make_tensor as make_tensor
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pytorch-main/torch/testing/_internal/logging_utils.py
import torch._dynamo.test_case import unittest.mock import os import contextlib import torch._logging import torch._logging._internal import logging @contextlib.contextmanager def preserve_log_state(): prev_state = torch._logging._internal._get_log_state() torch._logging._internal._set_log_state(torch._logging...
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pytorch
pytorch-main/torch/testing/_internal/autograd_function_db.py
import torch from functools import partial from torch.testing import make_tensor from torch.testing._internal.opinfo.core import ( OpInfo, SampleInput, ) from torch.testing._internal.common_dtype import all_types_and import numpy as np # Note: [autograd.Function db] # # This is a collection of autograd.Functio...
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pytorch
pytorch-main/torch/testing/_internal/hypothesis_utils.py
from collections import defaultdict from collections.abc import Iterable import numpy as np import torch import hypothesis from functools import reduce from hypothesis import assume from hypothesis import settings from hypothesis import strategies as st from hypothesis.extra import numpy as stnp from hypothesis.strate...
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pytorch
pytorch-main/torch/testing/_internal/common_pruning.py
# -*- coding: utf-8 -*- # Owner(s): ["module: unknown"] from torch.ao.pruning import BaseSparsifier import torch import torch.nn.functional as F from torch import nn class ImplementedSparsifier(BaseSparsifier): def __init__(self, **kwargs): super().__init__(defaults=kwargs) def update_mask(self, modu...
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pytorch
pytorch-main/torch/testing/_internal/common_nn.py
from abc import abstractmethod import tempfile import unittest from copy import deepcopy from functools import reduce, partial, wraps from itertools import product from operator import mul from math import pi import torch import torch.cuda import torch.nn as nn import torch.nn.functional as F from torch.nn import _r...
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pytorch
pytorch-main/torch/testing/_internal/common_jit.py
# Torch import torch import torch.cuda import torch.jit import torch.jit._logging import torch.jit.frontend import torch.jit.quantized # Testing utils from torch.testing._internal.common_dtype import floating_and_complex_types_and from torch.testing._internal.common_utils import TestCase, \ freeze_rng_state, Tempo...
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pytorch
pytorch-main/torch/testing/_internal/common_device_type.py
import copy import gc import inspect import runpy import sys import threading from collections import namedtuple from enum import Enum from functools import wraps, partial from typing import List, Any, ClassVar, Optional, Sequence, Tuple, Union, Dict, Set import unittest import os import torch from torch.testing._inter...
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pytorch
pytorch-main/torch/testing/_internal/jit_metaprogramming_utils.py
# Torch from torch.jit.annotations import BroadcastingList2, BroadcastingList3 # noqa: F401 import torch.nn.functional as F import torch import torch.cuda import torch.jit import torch.jit._logging import torch.jit.frontend from torch.testing._internal.common_nn import module_tests, new_module_tests from torch.testing...
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pytorch
pytorch-main/torch/testing/_internal/common_subclass.py
import torch from copy import deepcopy from torch.utils._pytree import tree_map # TODO: Move LoggingTensor here. from torch.testing._internal.logging_tensor import LoggingTensor # Base class for wrapper-style tensors. class WrapperTensor(torch.Tensor): @staticmethod def __new__(cls, *args, **kwargs): ...
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pytorch
pytorch-main/torch/testing/_internal/composite_compliance.py
import torch from torch import Tensor import itertools from torch.utils._python_dispatch import TorchDispatchMode from torch.utils._pytree import tree_map, tree_flatten, tree_unflatten from functools import partial from torch.utils._mode_utils import no_dispatch, all_same_mode import torch.autograd.forward_ad as fwAD ...
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pytorch
pytorch-main/torch/testing/_internal/dist_utils.py
import re import sys import time from functools import partial, wraps from typing import Tuple import torch.distributed as dist import torch.distributed.rpc as rpc from torch.distributed.rpc import _rref_context_get_debug_info from torch.testing._internal.common_utils import FILE_SCHEMA, TEST_WITH_TSAN if not dist.i...
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pytorch
pytorch-main/torch/testing/_internal/custom_op_db.py
import torch import functools from torch.testing import make_tensor from torch.testing._internal.opinfo.core import ( OpInfo, SampleInput, ) from torch.testing._internal.common_dtype import all_types_and import numpy as np from torch._custom_op.impl import custom_op from torch.testing._internal.autograd_functio...
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pytorch
pytorch-main/torch/testing/_internal/common_dtype.py
from typing import List import torch # Functions and classes for describing the dtypes a function supports # NOTE: these helpers should correspond to PyTorch's C++ dispatch macros # Verifies each given dtype is a torch.dtype def _validate_dtypes(*dtypes): for dtype in dtypes: assert isinstance(dtype, to...
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pytorch
pytorch-main/torch/testing/_internal/common_cuda.py
r"""This file is allowed to initialize CUDA context when imported.""" import functools import torch import torch.cuda from torch.testing._internal.common_utils import LazyVal, TEST_NUMBA, TEST_WITH_ROCM import inspect import contextlib CUDA_ALREADY_INITIALIZED_ON_IMPORT = torch.cuda.is_initialized() TEST_CUDA = to...
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pytorch
pytorch-main/torch/testing/_internal/common_utils.py
r"""Importing this file must **not** initialize CUDA context. test_distributed relies on this assumption to properly run. This means that when this is imported no CUDA calls shall be made, including torch.cuda.device_count(), etc. torch.testing._internal.common_cuda.py can freely initialize CUDA context when imported....
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pytorch
pytorch-main/torch/testing/_internal/common_quantized.py
r"""Importing this file includes common utility methods for checking quantized tensors and modules. """ import numpy as np import torch from contextlib import contextmanager from torch.testing._internal.common_utils import TEST_WITH_ASAN, TEST_WITH_TSAN, TEST_WITH_UBSAN, IS_PPC, IS_MACOS, IS_WINDOWS supported_qengines...
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pytorch
pytorch-main/torch/testing/_internal/common_modules.py
import torch import unittest from copy import deepcopy from enum import Enum from functools import wraps, partial from itertools import chain, product import itertools import math import torch.nn.functional as F from torch.nn.utils.rnn import pack_padded_sequence from torch.testing import make_tensor from torch.testing...
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pytorch
pytorch-main/torch/testing/_internal/common_methods_invocations.py
from functools import wraps, partial from itertools import product, chain, islice import itertools import functools import copy import operator import random import unittest import math import enum import torch import numpy as np from torch import inf, nan from typing import Any, Dict, List, Tuple, Union, Sequence fr...
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pytorch
pytorch-main/torch/testing/_internal/control_flow_opinfo_db.py
import torch import functools from torch.testing import make_tensor from functorch.experimental.control_flow import map from torch.testing._internal.opinfo.core import ( OpInfo, SampleInput, ) from torch.testing._internal.common_dtype import all_types_and def sample_inputs_map(opinfo, device, dtype, requires_g...
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pytorch
pytorch-main/torch/testing/_internal/quantization_torch_package_models.py
import math import torch import torch.nn as nn class LinearReluFunctionalChild(nn.Module): def __init__(self, N): super().__init__() self.w1 = nn.Parameter(torch.empty(N, N)) self.b1 = nn.Parameter(torch.zeros(N)) torch.nn.init.kaiming_uniform_(self.w1, a=math.sqrt(5)) def fo...
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pytorch
pytorch-main/torch/testing/_internal/jit_utils.py
# Torch from torch.autograd import Variable from torch.autograd.function import _nested_map from torch.jit.annotations import BroadcastingList2, BroadcastingList3 # noqa: F401 from torch.onnx import OperatorExportTypes import torch import torch.cuda import torch.jit import torch.jit._logging import torch.jit.frontend...
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pytorch
pytorch-main/torch/testing/_internal/common_distributed.py
import faulthandler import logging import multiprocessing import os import queue import subprocess import sys import tempfile import threading import time import traceback import types import unittest from contextlib import contextmanager from dataclasses import dataclass from datetime import timedelta from enum import...
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pytorch-main/torch/testing/_internal/common_dist_composable.py
# Owner(s): ["oncall: distributed"] from typing import Tuple import torch import torch.nn as nn class UnitModule(nn.Module): def __init__(self, device: torch.device): super().__init__() self.l1 = nn.Linear(100, 100, device=device) self.seq = nn.Sequential( nn.ReLU(), ...
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pytorch
pytorch-main/torch/testing/_internal/autocast_test_lists.py
import torch from torch.testing._internal.common_utils import TEST_WITH_ROCM class AutocastTestLists: def _rnn_cell_args(self, n, num_chunks, is_lstm, dev, dtype): input = (torch.randn((n, n), device=dev, dtype=torch.float32),) hx = ((torch.randn((n, n), device=dev, dtype=torch.float32), ...
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pytorch
pytorch-main/torch/testing/_internal/common_quantization.py
r"""Importing this file includes common utility methods and base clases for checking quantization api and properties of resulting modules. """ import torch import torch.nn as nn import torch.nn.functional as F import torch.ao.nn.intrinsic.quantized.dynamic as nniqd import torch.ao.nn.quantized as nnq import torch.ao.n...
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pytorch
pytorch-main/torch/testing/_internal/inductor_utils.py
from subprocess import CalledProcessError from torch._inductor.codecache import CppCodeCache from torch._inductor.utils import has_triton from torch.testing._internal.common_utils import ( LazyVal, IS_FBCODE, ) import torch def test_cpu(): try: CppCodeCache.load("") return not IS_FBCODE ...
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pytorch
pytorch-main/torch/testing/_internal/common_fsdp.py
# Owner(s): ["oncall: distributed"] import itertools import os import re import sys from abc import ABC, abstractmethod from contextlib import nullcontext from copy import deepcopy from enum import auto, Enum from typing import Any, Callable, Dict, List, Optional, Tuple, Type, Union from unittest import mock import t...
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pytorch
pytorch-main/torch/testing/_internal/logging_tensor.py
import torch from torch.utils._pytree import tree_map from typing import Iterator, List, Optional import logging import contextlib import itertools from torch.utils._python_dispatch import TorchDispatchMode from torch.utils.weak import WeakTensorKeyDictionary import functools from torch._C._profiler import gather_trace...
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pytorch
pytorch-main/torch/testing/_internal/check_kernel_launches.py
import os import re import sys from typing import List __all__ = [ "check_code_for_cuda_kernel_launches", "check_cuda_kernel_launches", ] # FILES TO EXCLUDE (match is done with suffix using `endswith`) # You wouldn't drive without a seatbelt, though, so why would you # launch a kernel without some safety? Use...
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pytorch-main/torch/testing/_internal/test_module/no_future_div.py
import torch # noqa: F401 def div_int_nofuture(): return 1 / 2 def div_float_nofuture(): return 3.14 / 0.125
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pytorch-main/torch/testing/_internal/distributed/rpc_utils.py
#!/usr/bin/env python3 import os import sys import unittest from typing import Dict, List, Type from torch.testing._internal.common_distributed import MultiProcessTestCase from torch.testing._internal.common_utils import ( TEST_WITH_DEV_DBG_ASAN, find_free_port, IS_SANDCASTLE, ) from torch.testing._interna...
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pytorch-main/torch/testing/_internal/distributed/distributed_test.py
import copy import itertools import math import os import random import sys import tempfile import time from collections import namedtuple, OrderedDict from contextlib import contextmanager, nullcontext from dataclasses import dataclass from datetime import timedelta from functools import reduce from typing import Unio...
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pytorch-main/torch/testing/_internal/distributed/ddp_under_dist_autograd_test.py
#!/usr/bin/env python3 import contextlib import enum import logging import os import threading from typing import NamedTuple import torch import torch.distributed as dist import torch.distributed.autograd as dist_autograd import torch.nn as nn from torch.distributed import rpc from torch.distributed.nn import RemoteM...
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pytorch-main/torch/testing/_internal/distributed/checkpoint_utils.py
# Copyright (c) Meta Platforms, Inc. and affiliates import shutil import tempfile from functools import wraps from typing import Any, Callable, Dict, Optional, Tuple import torch.distributed as dist def with_temp_dir( func: Optional[Callable] = None, ) -> Optional[Callable]: """ Wrapper to initialize te...
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pytorch-main/torch/testing/_internal/distributed/fake_pg.py
import torch.distributed as dist from torch._C._distributed_c10d import ( _create_work_from_future, AllgatherOptions, AllreduceOptions, BarrierOptions, ReduceScatterOptions ) from torch.futures import Future def ret_work(ret): fut = Future() fut.set_result(ret) return _create_work_fro...
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pytorch-main/torch/testing/_internal/distributed/multi_threaded_pg.py
import sys import threading from dataclasses import dataclass from typing import Dict, List, Optional, Tuple, Union from functools import partial, reduce import torch import torch.distributed as dist import weakref from torch._C._distributed_c10d import ( _create_work_from_future, AllgatherOptions, Allredu...
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pytorch-main/torch/testing/_internal/distributed/pipe_with_ddp_test.py
import torch import torch.distributed as dist from torch import nn from torch.nn.parallel import DistributedDataParallel from torch.testing._internal.dist_utils import INIT_METHOD_TEMPLATE, dist_init from torch.testing._internal.distributed.rpc.rpc_agent_test_fixture import ( RpcAgentTestFixture, ) from torch.test...
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pytorch-main/torch/testing/_internal/distributed/distributed_utils.py
from contextlib import contextmanager from datetime import timedelta from functools import ( partial, wraps, ) import torch.distributed as dist import torch.distributed.distributed_c10d as c10d class MockProcessGroup(dist.ProcessGroup): def __init__(self, rank, world): super().__init__(rank, worl...
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pytorch-main/torch/testing/_internal/distributed/nn/api/remote_module_test.py
#!/usr/bin/python3 import enum from typing import Tuple import torch import torch.distributed.rpc as rpc import torch.testing._internal.dist_utils as dist_utils from torch import Tensor, nn from torch._jit_internal import Future from torch.distributed.nn import RemoteModule from torch.distributed.nn.api.remote_module ...
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pytorch-main/torch/testing/_internal/distributed/_shard/test_common.py
import torch import torch.nn as nn from torch.distributed._shard.sharded_tensor import ShardedTensor class SimpleMegatronLM(nn.Module): def __init__(self, linear_size, rank=None, dtype=torch.float32): super().__init__() self.fc1 = nn.Linear(*linear_size[0], dtype=dtype) self.gelu = nn.GEL...
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pytorch-main/torch/testing/_internal/distributed/_shard/sharded_tensor/_test_ops_common.py
import builtins import torch from torch.distributed._shard.sharding_spec import ( ChunkShardingSpec, EnumerableShardingSpec, ShardMetadata, ) from torch.distributed._shard.sharding_spec._internals import ( get_chunked_dim_size, get_split_size, ) def generate_chunk_sharding_specs_for_test(sharding...
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pytorch-main/torch/testing/_internal/distributed/_shard/sharded_tensor/__init__.py
import sys from functools import wraps, partial import torch import torch.distributed as dist from torch.distributed import rpc from torch.testing._internal.common_distributed import ( MultiProcessTestCase, TEST_SKIPS, tp_transports, ) TEST_GPU_NUM = 4 class ShardedTensorTestBase(MultiProcessTestCase): ...
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pytorch-main/torch/testing/_internal/distributed/_shard/sharded_tensor/_test_st_common.py
import copy import random import torch from torch.distributed._shard import sharded_tensor from torch.distributed._shard.sharding_spec import ( ChunkShardingSpec, ) PLACEMENTS = [ "rank:0/cuda:0", "rank:1/cuda:1", "rank:2/cuda:2", "rank:3/cuda:3", ] DEFAULT_GPU_NUM = 4 def _chunk_sharding_specs...
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pytorch-main/torch/testing/_internal/distributed/rpc/tensorpipe_rpc_agent_test_fixture.py
import torch.distributed.rpc as rpc from torch.testing._internal.distributed.rpc.rpc_agent_test_fixture import ( RpcAgentTestFixture, ) from torch.testing._internal.common_distributed import ( tp_transports, ) class TensorPipeRpcAgentTestFixture(RpcAgentTestFixture): @property def rpc_backend(self): ...
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pytorch-main/torch/testing/_internal/distributed/rpc/dist_autograd_test.py
import sys import threading import time from enum import Enum import random import torch import torch.nn as nn from datetime import timedelta import torch.distributed as dist import torch.distributed.autograd as dist_autograd import torch.distributed.rpc as rpc import torch.testing._internal.dist_utils from torch.autog...
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pytorch-main/torch/testing/_internal/distributed/rpc/rpc_test.py
import concurrent.futures import contextlib import json import os import sys import threading import time from collections import namedtuple from functools import partial from threading import Event from threading import Lock from unittest import mock import torch import torch.nn as nn import torch.distributed as dis...
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pytorch
pytorch-main/torch/testing/_internal/distributed/rpc/faulty_rpc_agent_test_fixture.py
import torch.distributed.rpc as rpc import torch.distributed.rpc._testing # noqa: F401 from torch.testing._internal.distributed.rpc.rpc_agent_test_fixture import ( RpcAgentTestFixture, ) # The following message types are currently retried in the RREF protocol and # distributed autograd. Thus only these messages s...
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pytorch
pytorch-main/torch/testing/_internal/distributed/rpc/dist_optimizer_test.py
import threading import torch import torch.distributed.autograd as dist_autograd import torch.distributed.rpc as rpc from torch import optim from torch.distributed.optim import DistributedOptimizer from torch.testing._internal.dist_utils import dist_init from torch.testing._internal.distributed.rpc.rpc_agent_test_fix...
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pytorch-main/torch/testing/_internal/distributed/rpc/rpc_agent_test_fixture.py
import os from abc import ABC, abstractmethod import torch.testing._internal.dist_utils class RpcAgentTestFixture(ABC): @property def world_size(self) -> int: return 4 @property def init_method(self): use_tcp_init = os.environ.get("RPC_INIT_WITH_TCP", None) if use_tcp_init ==...
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pytorch-main/torch/testing/_internal/distributed/rpc/faulty_agent_rpc_test.py
import torch import time import torch.distributed.rpc as rpc from torch.distributed.rpc.api import _delete_all_user_and_unforked_owner_rrefs from torch.testing._internal.dist_utils import ( dist_init, wait_until_pending_futures_and_users_flushed, wait_until_owners_and_forks_on_rank, worker_name, ) from ...
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pytorch-main/torch/testing/_internal/distributed/rpc/examples/reinforcement_learning_rpc_test.py
# If you need to modify this file to make this test pass, please also apply same edits accordingly to # https://github.com/pytorch/examples/blob/master/distributed/rpc/rl/main.py # and https://pytorch.org/tutorials/intermediate/rpc_tutorial.html import numpy as np from itertools import count import torch import torch...
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pytorch-main/torch/testing/_internal/distributed/rpc/examples/parameter_server_test.py
# If you need to modify this file to make this test pass, please also apply same edits accordingly to # https://github.com/pytorch/examples/blob/master/distributed/rpc/batch/parameter_server.py # and https://pytorch.org/tutorials/intermediate/rpc_async_execution.html#batch-updating-parameter-server import threading fr...
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pytorch-main/torch/testing/_internal/distributed/rpc/jit/rpc_test_faulty.py
from typing import Dict, Tuple import torch import torch.distributed.rpc as rpc from torch import Tensor from torch.distributed.rpc import RRef from torch.testing._internal.dist_utils import ( dist_init, worker_name, wait_until_pending_futures_and_users_flushed ) from torch.testing._internal.distributed.rp...
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pytorch-main/torch/testing/_internal/distributed/rpc/jit/dist_autograd_test.py
from typing import Dict, Tuple import torch import torch.distributed.autograd as dist_autograd import torch.distributed.rpc as rpc from torch import Tensor from torch.distributed.rpc import rpc_async from torch.testing import FileCheck from torch.testing._internal.dist_utils import dist_init, worker_name from torch.te...
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pytorch-main/torch/testing/_internal/distributed/rpc/jit/rpc_test.py
import time import io from typing import Dict, List, Tuple, Any import torch import torch.distributed as dist import torch.distributed.rpc as rpc from torch import Tensor from torch.autograd.profiler import record_function from torch.distributed.rpc import RRef from torch.distributed.rpc.internal import RPCExecMode, _...
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pytorch-main/torch/testing/_internal/distributed/_tensor/common_dtensor.py
# Copyright (c) Meta Platforms, Inc. and affiliates from contextlib import contextmanager from dataclasses import dataclass import itertools import sys from functools import wraps from typing import ( Any, Callable, Generator, Iterator, Tuple, Dict, List, Sequence, TypeVar, cast...
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pytorch-main/torch/testing/_internal/codegen/random_topo_test.py
import torch import numpy as np import argparse from typing import Dict # debug print DEBUG_PRINT = False ################################################################################ # configuration for random tests setup ################################################################################ # maximum ...
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pytorch-main/torch/testing/_internal/data/network2.py
import torch.nn as nn class Net(nn.Module): def __init__(self): super().__init__() self.linear = nn.Linear(10, 20) self.relu = nn.ReLU()
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pytorch-main/torch/testing/_internal/data/network1.py
import torch.nn as nn class Net(nn.Module): def __init__(self): super().__init__() self.linear = nn.Linear(10, 20)
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pytorch-main/torch/testing/_internal/opinfo/core.py
import collections import collections.abc import math import operator import unittest from dataclasses import asdict, dataclass from enum import Enum from functools import partial from itertools import product from typing import Any, Callable, Iterable, List, Optional, Tuple from torchgen.utils import dataclass_repr ...
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pytorch-main/torch/testing/_internal/opinfo/utils.py
import collections import warnings from functools import partial, wraps from typing import Sequence import numpy as np import torch from torch.testing._internal.common_cuda import TEST_CUDA from torch.testing._internal.common_dtype import ( _dispatch_dtypes, all_types, all_types_and, all_types_and_com...
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pytorch-main/torch/testing/_internal/opinfo/refs.py
from torch.testing._internal.opinfo.core import ( BinaryUfuncInfo, OpInfo, ReductionOpInfo, UnaryUfuncInfo, ) # NOTE [Python References] # Python References emulate existing PyTorch operations, but can ultimately # be expressed in terms of "primitive" operations from torch._prims. # # These reference...
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pytorch-main/torch/testing/_internal/opinfo/__init__.py
import torch.testing._internal.opinfo.core import torch.testing._internal.opinfo.definitions
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pytorch-main/torch/testing/_internal/opinfo/definitions/_masked.py
import unittest from collections.abc import Sequence from functools import partial from typing import List import numpy as np import torch from torch.testing import make_tensor from torch.testing._internal.common_device_type import tol, toleranceOverride from torch.testing._internal.common_dtype import ( all_type...
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pytorch-main/torch/testing/_internal/opinfo/definitions/special.py
import unittest from functools import partial from itertools import product from typing import List import numpy as np import torch from torch.testing import make_tensor from torch.testing._internal.common_device_type import ( precisionOverride, tol, toleranceOverride, ) from torch.testing._internal.commo...
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pytorch-main/torch/testing/_internal/opinfo/definitions/fft.py
import unittest from functools import partial from typing import List import numpy as np import torch from torch.testing import make_tensor from torch.testing._internal.common_cuda import SM53OrLater from torch.testing._internal.common_device_type import precisionOverride from torch.testing._internal.common_dtype im...
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pytorch-main/torch/testing/_internal/opinfo/definitions/linalg.py
import itertools import unittest from functools import partial from itertools import chain, product from typing import Iterable, List import numpy as np from numpy import inf import torch from torch.testing import make_tensor from torch.testing._internal.common_cuda import ( _get_magma_version, _get_torch_cu...
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