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pytorch
pytorch-main/torch/onnx/_internal/diagnostics/infra/sarif/_artifact_content.py
# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29, # with extension for dataclasses and type annotation. from __future__ import annotations import dataclasses from typing import Optional from torch.onnx._internal.diagnostics.infra.sarif import ( _multiformat_message_string, _pro...
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pytorch
pytorch-main/torch/onnx/_internal/diagnostics/infra/sarif/_special_locations.py
# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29, # with extension for dataclasses and type annotation. from __future__ import annotations import dataclasses from typing import Optional from torch.onnx._internal.diagnostics.infra.sarif import ( _artifact_location, _property_bag...
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pytorch
pytorch-main/torch/onnx/_internal/diagnostics/infra/sarif/_replacement.py
# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29, # with extension for dataclasses and type annotation. from __future__ import annotations import dataclasses from typing import Optional from torch.onnx._internal.diagnostics.infra.sarif import ( _artifact_content, _property_bag,...
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pytorch
pytorch-main/torch/onnx/_internal/diagnostics/infra/sarif/_region.py
# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29, # with extension for dataclasses and type annotation. from __future__ import annotations import dataclasses from typing import Optional from torch.onnx._internal.diagnostics.infra.sarif import ( _artifact_content, _message, ...
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pytorch-main/torch/onnx/_internal/diagnostics/infra/sarif/_message.py
# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29, # with extension for dataclasses and type annotation. from __future__ import annotations import dataclasses from typing import List, Optional from torch.onnx._internal.diagnostics.infra.sarif import _property_bag @dataclasses.dataclas...
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pytorch
pytorch-main/torch/onnx/_internal/diagnostics/infra/sarif/_run_automation_details.py
# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29, # with extension for dataclasses and type annotation. from __future__ import annotations import dataclasses from typing import Optional from torch.onnx._internal.diagnostics.infra.sarif import _message, _property_bag @dataclasses.data...
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pytorch
pytorch-main/torch/onnx/_internal/diagnostics/infra/sarif/_edge_traversal.py
# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29, # with extension for dataclasses and type annotation. from __future__ import annotations import dataclasses from typing import Any, Optional from torch.onnx._internal.diagnostics.infra.sarif import _message, _property_bag @dataclasses...
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pytorch
pytorch-main/torch/onnx/_internal/diagnostics/infra/sarif/_graph_traversal.py
# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29, # with extension for dataclasses and type annotation. from __future__ import annotations import dataclasses from typing import Any, List, Optional from torch.onnx._internal.diagnostics.infra.sarif import ( _edge_traversal, _mess...
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pytorch
pytorch-main/torch/onnx/_internal/diagnostics/infra/sarif/_external_property_file_reference.py
# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29, # with extension for dataclasses and type annotation. from __future__ import annotations import dataclasses from typing import Optional from torch.onnx._internal.diagnostics.infra.sarif import ( _artifact_location, _property_bag...
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pytorch
pytorch-main/torch/onnx/_internal/diagnostics/infra/sarif/_location_relationship.py
# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29, # with extension for dataclasses and type annotation. from __future__ import annotations import dataclasses from typing import List, Optional from torch.onnx._internal.diagnostics.infra.sarif import _message, _property_bag @dataclasse...
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pytorch
pytorch-main/torch/onnx/_internal/diagnostics/infra/sarif/_reporting_configuration.py
# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29, # with extension for dataclasses and type annotation. from __future__ import annotations import dataclasses from typing import Literal, Optional from torch.onnx._internal.diagnostics.infra.sarif import _property_bag @dataclasses.datac...
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pytorch
pytorch-main/torch/onnx/_internal/diagnostics/infra/sarif/_tool_component_reference.py
# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29, # with extension for dataclasses and type annotation. from __future__ import annotations import dataclasses from typing import Optional from torch.onnx._internal.diagnostics.infra.sarif import _property_bag @dataclasses.dataclass clas...
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pytorch-main/torch/onnx/_internal/diagnostics/infra/sarif/_thread_flow.py
# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29, # with extension for dataclasses and type annotation. from __future__ import annotations import dataclasses from typing import Any, List, Optional from torch.onnx._internal.diagnostics.infra.sarif import ( _message, _property_ba...
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pytorch
pytorch-main/torch/onnx/_internal/diagnostics/infra/sarif/_reporting_descriptor_reference.py
# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29, # with extension for dataclasses and type annotation. from __future__ import annotations import dataclasses from typing import Optional from torch.onnx._internal.diagnostics.infra.sarif import ( _property_bag, _tool_component_re...
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pytorch
pytorch-main/torch/onnx/_internal/diagnostics/infra/sarif/_invocation.py
# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29, # with extension for dataclasses and type annotation. from __future__ import annotations import dataclasses from typing import Any, List, Optional from torch.onnx._internal.diagnostics.infra.sarif import ( _artifact_location, _c...
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pytorch
pytorch-main/torch/onnx/_internal/diagnostics/infra/sarif/_node.py
# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29, # with extension for dataclasses and type annotation. from __future__ import annotations import dataclasses from typing import List, Optional from torch.onnx._internal.diagnostics.infra.sarif import ( _location, _message, _n...
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pytorch
pytorch-main/torch/onnx/_internal/diagnostics/infra/sarif/_artifact.py
# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29, # with extension for dataclasses and type annotation. from __future__ import annotations import dataclasses from typing import Any, List, Literal, Optional from torch.onnx._internal.diagnostics.infra.sarif import ( _artifact_content...
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pytorch-main/torch/onnx/_internal/diagnostics/infra/sarif/_notification.py
# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29, # with extension for dataclasses and type annotation. from __future__ import annotations import dataclasses from typing import List, Literal, Optional from torch.onnx._internal.diagnostics.infra.sarif import ( _exception, _locat...
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pytorch-main/torch/onnx/_internal/diagnostics/infra/sarif/_stack_frame.py
# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29, # with extension for dataclasses and type annotation. from __future__ import annotations import dataclasses from typing import List, Optional from torch.onnx._internal.diagnostics.infra.sarif import _location, _property_bag @dataclass...
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pytorch
pytorch-main/torch/onnx/_internal/diagnostics/infra/sarif/_run.py
# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29, # with extension for dataclasses and type annotation. from __future__ import annotations import dataclasses from typing import Any, List, Literal, Optional from torch.onnx._internal.diagnostics.infra.sarif import ( _address, _ar...
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pytorch
pytorch-main/torch/onnx/_internal/diagnostics/infra/sarif/_external_property_file_references.py
# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29, # with extension for dataclasses and type annotation. from __future__ import annotations import dataclasses from typing import List, Optional from torch.onnx._internal.diagnostics.infra.sarif import ( _external_property_file_referen...
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pytorch
pytorch-main/torch/onnx/_internal/diagnostics/infra/sarif/_tool.py
# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29, # with extension for dataclasses and type annotation. from __future__ import annotations import dataclasses from typing import List, Optional from torch.onnx._internal.diagnostics.infra.sarif import _property_bag, _tool_component @dat...
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pytorch
pytorch-main/torch/onnx/_internal/diagnostics/infra/sarif/_result_provenance.py
# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29, # with extension for dataclasses and type annotation. from __future__ import annotations import dataclasses from typing import List, Optional from torch.onnx._internal.diagnostics.infra.sarif import ( _physical_location, _proper...
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pytorch
pytorch-main/torch/onnx/_internal/diagnostics/infra/sarif/_reporting_descriptor_relationship.py
# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29, # with extension for dataclasses and type annotation. from __future__ import annotations import dataclasses from typing import List, Optional from torch.onnx._internal.diagnostics.infra.sarif import ( _message, _property_bag, ...
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pytorch-main/torch/onnx/_internal/diagnostics/infra/sarif/__init__.py
# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29, # with extension for dataclasses and type annotation. from torch.onnx._internal.diagnostics.infra.sarif._address import Address from torch.onnx._internal.diagnostics.infra.sarif._artifact import Artifact from torch.onnx._internal.diagnost...
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pytorch
pytorch-main/torch/onnx/_internal/diagnostics/infra/sarif/_stack.py
# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29, # with extension for dataclasses and type annotation. from __future__ import annotations import dataclasses from typing import List, Optional from torch.onnx._internal.diagnostics.infra.sarif import ( _message, _property_bag, ...
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pytorch-main/torch/onnx/_internal/diagnostics/infra/sarif/_graph.py
# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29, # with extension for dataclasses and type annotation. from __future__ import annotations import dataclasses from typing import List, Optional from torch.onnx._internal.diagnostics.infra.sarif import ( _edge, _message, _node,...
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pytorch
pytorch-main/torch/onnx/_internal/diagnostics/infra/sarif/_reporting_descriptor.py
# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29, # with extension for dataclasses and type annotation. from __future__ import annotations import dataclasses from typing import Any, List, Optional from torch.onnx._internal.diagnostics.infra.sarif import ( _multiformat_message_strin...
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pytorch
pytorch-main/torch/onnx/_internal/diagnostics/infra/sarif/_artifact_change.py
# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29, # with extension for dataclasses and type annotation. from __future__ import annotations import dataclasses from typing import List, Optional from torch.onnx._internal.diagnostics.infra.sarif import ( _artifact_location, _proper...
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pytorch
pytorch-main/torch/onnx/_internal/diagnostics/infra/sarif/_address.py
# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29, # with extension for dataclasses and type annotation. from __future__ import annotations import dataclasses from typing import Optional from torch.onnx._internal.diagnostics.infra.sarif import _property_bag @dataclasses.dataclass clas...
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pytorch-main/torch/onnx/_internal/diagnostics/infra/sarif/_attachment.py
# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29, # with extension for dataclasses and type annotation. from __future__ import annotations import dataclasses from typing import List, Optional from torch.onnx._internal.diagnostics.infra.sarif import ( _artifact_location, _messag...
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pytorch
pytorch-main/torch/onnx/_internal/diagnostics/infra/sarif/_thread_flow_location.py
# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29, # with extension for dataclasses and type annotation. from __future__ import annotations import dataclasses from typing import Any, List, Literal, Optional from torch.onnx._internal.diagnostics.infra.sarif import ( _location, _p...
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pytorch
pytorch-main/torch/onnx/_internal/diagnostics/infra/sarif/_web_request.py
# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29, # with extension for dataclasses and type annotation. from __future__ import annotations import dataclasses from typing import Any, Optional from torch.onnx._internal.diagnostics.infra.sarif import ( _artifact_content, _property...
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pytorch
pytorch-main/torch/onnx/_internal/diagnostics/infra/sarif/_artifact_location.py
# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29, # with extension for dataclasses and type annotation. from __future__ import annotations import dataclasses from typing import Optional from torch.onnx._internal.diagnostics.infra.sarif import _message, _property_bag @dataclasses.data...
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pytorch-main/torch/onnx/_internal/diagnostics/infra/sarif/_edge.py
# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29, # with extension for dataclasses and type annotation. from __future__ import annotations import dataclasses from typing import Optional from torch.onnx._internal.diagnostics.infra.sarif import _message, _property_bag @dataclasses.data...
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pytorch
pytorch-main/torch/onnx/_internal/fx/type_utils.py
"""Utilities for converting and operating on ONNX, JIT and torch types.""" from __future__ import annotations from typing import ( Any, Dict, List, Optional, Protocol, runtime_checkable, Set, Tuple, TYPE_CHECKING, Union, ) import torch if TYPE_CHECKING: import onnx.defs.Op...
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pytorch-main/torch/onnx/_internal/fx/dynamo_graph_extractor.py
# NOTE: This file is referenced by name at # /opt/pytorch/torch/_dynamo/eval_frame.py::DONT_WRAP_FILES. # introduced by https://github.com/pytorch/pytorch/pull/98894. # If this file is renamed, moved, etc please update the reference there! from __future__ import annotations import functools import i...
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pytorch
pytorch-main/torch/onnx/_internal/fx/fx_onnx_interpreter.py
from __future__ import annotations import inspect import operator import re import types from typing import Callable, Dict, List, Optional, Sequence, Tuple, Union import onnxscript # type: ignore[import] from onnxscript.function_libs.torch_lib import ( # type: ignore[import] graph_building as onnxscript_graph_b...
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pytorch
pytorch-main/torch/onnx/_internal/fx/op_validation.py
"""Module for handling op-level validation during exporting.""" from __future__ import annotations from typing import Any, Callable, Dict, List, Sequence, Tuple, Union import onnxscript # type: ignore[import] from onnxscript import evaluator # type: ignore[import] import torch import torch.fx from torch.onnx impo...
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pytorch-main/torch/onnx/_internal/fx/_pass.py
from __future__ import annotations import abc import contextlib import difflib import io import sys from typing import Any, Callable import torch import torch.fx from torch.onnx._internal import _beartype from torch.onnx._internal.fx import diagnostics, onnxfunction_dispatcher @contextlib.contextmanager def _pat...
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pytorch-main/torch/onnx/_internal/fx/registration.py
"""Module for handling ATen to ONNX functions registration.""" from __future__ import annotations import dataclasses from collections import defaultdict from typing import Dict, List, Optional, Set, TYPE_CHECKING, Union import torch._ops from torch.onnx._internal import _beartype # We can only import onnx from this...
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pytorch
pytorch-main/torch/onnx/_internal/fx/onnxfunction_dispatcher.py
"""Dispatcher for AtenLib functions from onnx-script.""" from __future__ import annotations import operator from typing import ( Any, Callable, Dict, List, Optional, Sequence, Set, Tuple, TYPE_CHECKING, Union, ) import torch import torch._ops import torch.fx from torch.onnx._i...
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pytorch
pytorch-main/torch/onnx/_internal/fx/fx_symbolic_graph_extractor.py
from __future__ import annotations import functools from typing import Any, Callable, Dict, Mapping, Optional, Sequence, Tuple, Union import torch import torch.fx import torch.onnx import torch.onnx._internal.fx.passes as passes from torch.onnx._internal import _beartype, exporter, io_adapter # Functions directly ...
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pytorch
pytorch-main/torch/onnx/_internal/fx/decomposition_table.py
"""Dispatcher for AtenLib functions from onnx-script.""" from __future__ import annotations from typing import Callable, Dict, Set, Union import torch import torch._ops import torch.fx from torch.onnx._internal import _beartype from torch.onnx._internal.fx import registration @_beartype.beartype def _create_onnx...
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pytorch
pytorch-main/torch/onnx/_internal/fx/diagnostics.py
from __future__ import annotations import dataclasses import functools from typing import Any, Optional import onnxscript # type: ignore[import] from onnxscript.function_libs.torch_lib import graph_building # type: ignore[import] import torch import torch.fx from torch.onnx._internal import diagnostics from torc...
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pytorch
pytorch-main/torch/onnx/_internal/fx/patcher.py
import copy import io from typing import List, Union import torch class ONNXTorchPatcher: """Context manager to temporarily patch PyTorch during FX-to-ONNX export. This class is a collection of "patches" required by FX-to-ONNX exporter. This context overrides several torch functions to support symbolic...
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pytorch
pytorch-main/torch/onnx/_internal/fx/serialization.py
from __future__ import annotations import io import os from typing import Tuple, TYPE_CHECKING, Union import torch from torch.onnx import _type_utils as jit_type_utils from torch.onnx._internal import _beartype if TYPE_CHECKING: import onnx @_beartype.beartype def _create_tensor_proto_with_external_data( t...
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pytorch-main/torch/onnx/_internal/fx/analysis/unsupported_nodes.py
from __future__ import annotations import dataclasses from typing import Dict, List import torch from torch.onnx._internal.fx import _pass, diagnostics @dataclasses.dataclass class UnsupportedFxNodesAnalysisResult(_pass.AnalysisResult): unsupported_op_to_target_mapping: Dict[str, Dict[str, None]] class Unsupp...
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pytorch
pytorch-main/torch/onnx/_internal/fx/passes/decomp.py
from __future__ import annotations from typing import Callable, Mapping, Optional import torch import torch._ops import torch.fx from torch.fx.experimental import proxy_tensor from torch.onnx._internal import _beartype from torch.onnx._internal.fx import _pass, diagnostics from torch.onnx._internal.fx.passes import ...
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pytorch
pytorch-main/torch/onnx/_internal/fx/passes/_utils.py
"""Common utility functions for FX passes. These functions should NOT be directly invoked outside of `passes` package. """ from __future__ import annotations import collections import re from typing import Callable, Dict, Optional, Tuple import torch.fx import torch.fx.traceback as fx_traceback from torch.onnx._in...
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pytorch
pytorch-main/torch/onnx/_internal/fx/passes/virtualization.py
from __future__ import annotations from typing import List, Optional, Tuple import torch import torch.fx from torch.onnx._internal import _beartype from torch.onnx._internal.fx import _pass class MovePlaceholderToFront(_pass.Transform): """This pass move all placeholder nodes to the front of the graph node lis...
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pytorch
pytorch-main/torch/onnx/_internal/fx/passes/functionalization.py
from __future__ import annotations from typing import Callable, Optional import torch import torch._ops import torch.func import torch.fx from torch.fx.experimental import proxy_tensor from torch.onnx._internal import _beartype from torch.onnx._internal.fx import _pass, diagnostics from torch.onnx._internal.fx.passe...
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pytorch-main/torch/onnx/_internal/fx/passes/readability.py
from __future__ import annotations from typing import Dict, List, Sequence, Tuple, Union import torch from torch.onnx._internal import _beartype from torch.onnx._internal.fx import _pass, diagnostics class RestoreParameterAndBufferNames(_pass.Transform): """Restore parameter and buffer names from original nn.mo...
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pytorch-main/torch/onnx/_internal/fx/passes/type_promotion.py
# Owner(s): ["module: onnx"] from __future__ import annotations import abc import dataclasses import inspect import logging from types import ModuleType from typing import Any, Callable, Mapping, Optional, Sequence, Set import torch import torch._ops import torch.fx import torch.fx.traceback as fx_traceback from t...
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pytorch-main/torch/onnx/_internal/fx/passes/shape_inference.py
from __future__ import annotations import itertools from typing import Optional import torch import torch._dynamo import torch.fx from torch._subclasses import fake_tensor from torch.fx.experimental import proxy_tensor from torch.fx.node import map_aggregate from torch.nn.utils import stateless from torch.onnx._inte...
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pytorch
pytorch-main/torch/_decomp/decompositions.py
import functools import operator import sys from enum import Enum from functools import partial, reduce from itertools import product from typing import Callable, cast, Iterable, List, Optional, Tuple, Union import torch import torch._prims as prims import torch._prims_common as utils import torch.nn.functional as F f...
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pytorch-main/torch/_decomp/decompositions_for_rng.py
import functools from collections import defaultdict from typing import Callable, Dict import torch import torch._decomp as decomp from torch._decomp import get_decompositions from torch._ops import OpOverload aten = torch.ops.aten rng_decompositions: Dict[str, Dict[OpOverload, Callable]] = defaultdict(dict) def r...
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pytorch
pytorch-main/torch/_decomp/__init__.py
import inspect from collections import defaultdict from functools import wraps from itertools import chain from typing import Callable, Dict, Sequence, Union import torch import torch.library from torch._ops import OpOverload, OpOverloadPacket from torch.utils._pytree import tree_map __all__ = [ "decomposition_ta...
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pytorch
pytorch-main/torch/_decomp/decompositions_for_jvp.py
import inspect from typing import Callable, Dict, List, Optional, Tuple import torch import torch._decomp from torch import Tensor decomposition_table = torch._decomp.decomposition_table decomposition_table_for_jvp: Dict[torch._ops.OpOverload, Callable] = {} register_decomposition = torch._decomp.register_decompositi...
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pytorch-main/torch/compiler/__init__.py
import torch from typing import List __all__ = [ "compile", "assume_constant_result", "reset", "allow_in_graph", "list_backends", "disable", ] def compile(*args, **kwargs): """ See :func:`torch.compile` for details on the arguments for this function. """ return torch.compile(*a...
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pytorch-main/torch/cpu/__init__.py
r""" This package implements abstractions found in ``torch.cuda`` to facilitate writing device-agnostic code. """ from typing import Any, Optional, Union from contextlib import AbstractContextManager from . import amp from .. import device as _device import torch __all__ = [ "is_available", "synchronize", ...
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pytorch
pytorch-main/torch/cpu/amp/autocast_mode.py
import torch from typing import Any __all__ = ["autocast"] class autocast(torch.amp.autocast_mode.autocast): r""" See :class:`torch.autocast`. ``torch.cpu.amp.autocast(args...)`` is equivalent to ``torch.autocast("cpu", args...)`` """ def __init__(self, enabled : bool = True, dtype : torch.dtype =...
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pytorch-main/torch/_prims_common/wrappers.py
import torch from torch._prims_common import ( Number, NumberType, TensorLike, TensorLikeType, ShapeType, ELEMENTWISE_TYPE_PROMOTION_KIND, ) import torch._prims_common as utils from torch.utils._pytree import tree_flatten, tree_unflatten from typing import Callable, Sequence, Tuple, NamedTuple,...
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pytorch-main/torch/_prims_common/__init__.py
from __future__ import annotations from contextlib import nullcontext from typing import Any, Union, Sequence, Optional, Tuple, List, Callable, Type, overload, cast from enum import Enum from functools import reduce, cmp_to_key import operator import sympy import weakref import warnings import torch from torch import ...
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pytorch-main/torch/utils/hooks.py
import torch from collections import OrderedDict import weakref import warnings from typing import Any, Tuple __all__ = ["RemovableHandle", "unserializable_hook", "warn_if_has_hooks", "BackwardHook"] class RemovableHandle: r""" A handle which provides the capability to remove a hook. Args: hooks_...
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pytorch-main/torch/utils/cpp_backtrace.py
from torch._C import _get_cpp_backtrace def get_cpp_backtrace(frames_to_skip=0, maximum_number_of_frames=64) -> str: r""" Returns a string containing the C++ stack trace of the current thread. Args: frames_to_skip (int): the number of frames to skip from the top of the stack maximum_number_...
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pytorch-main/torch/utils/checkpoint.py
import contextlib import uuid import warnings import weakref from collections import defaultdict from typing import ( Any, Callable, ContextManager, DefaultDict, Dict, Iterable, List, Optional, Tuple, ) from weakref import ReferenceType from torch.testing._internal.logging_tensor imp...
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pytorch-main/torch/utils/_traceback.py
from types import TracebackType import tempfile import traceback import contextlib import inspect import os.path # This file contains utilities for ensuring dynamically compile()'d # code fragments display their line numbers in backtraces. # # The constraints: # # - We don't have control over the user exception printe...
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pytorch-main/torch/utils/backend_registration.py
import torch from torch._C import _rename_privateuse1_backend, _get_privateuse1_backend_name from typing import List, Optional, Union __all__ = ["rename_privateuse1_backend", "generate_methods_for_privateuse1_backend"] # TODO: Should use `torch._C._get_privateuse1_backend_name()` to get # renamed-backend name for `pr...
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pytorch-main/torch/utils/_content_store.py
# This module provides a FAST (on GPU) content addressable store for storages # (and tensors on top of them) with VERY WEAK portability guarantees (e.g., # don't expect CPU/CUDA to address to the same hash, don't expect it to be # portable across devices) that is NOT cryptographically secure. In return, # we are able ...
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pytorch-main/torch/utils/mkldnn.py
import torch class MkldnnLinear(torch.jit.ScriptModule): def __init__(self, dense_module, dtype): super().__init__() self.register_buffer('weight', dense_module.weight.to_mkldnn(dtype)) if dense_module.bias is not None: # Bias can be fp32 or bf16 for OneDNN bf16 path, but for g...
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pytorch-main/torch/utils/bundled_inputs.py
#!/usr/bin/env python3 from typing import Any, TypeVar, Optional, Tuple, List, NamedTuple, Union, Sequence, Dict, Callable import textwrap import torch from torch._C import TupleType, ListType from torch.jit._recursive import wrap_cpp_module T = TypeVar("T") MAX_RAW_TENSOR_SIZE = 16 class InflatableArg(NamedTuple):...
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pytorch-main/torch/utils/throughput_benchmark.py
import torch._C def format_time(time_us=None, time_ms=None, time_s=None): '''Defines how to format time''' assert sum([time_us is not None, time_ms is not None, time_s is not None]) == 1 US_IN_SECOND = 1e6 US_IN_MS = 1e3 if time_us is None: if time_ms is not None: time_us = ...
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pytorch-main/torch/utils/model_zoo.py
# torchvision imports tqdm from here. from torch.hub import tqdm, load_state_dict_from_url as load_url # noqa: F401
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pytorch-main/torch/utils/_foreach_utils.py
from typing import List, Dict, Tuple, Optional import torch from torch import Tensor from torch.autograd.grad_mode import no_grad def _get_foreach_kernels_supported_devices() -> List[str]: r""" Return the device type list that supports foreach kernels. """ return ["cuda", torch._C._get_privateuse1_bac...
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pytorch-main/torch/utils/_mode_utils.py
import torch from typing import TypeVar T = TypeVar('T') # returns if all are the same mode def all_same_mode(modes): return all(tuple(mode == modes[0] for mode in modes)) no_dispatch = torch._C._DisableTorchDispatch
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pytorch-main/torch/utils/collect_env.py
# Unlike the rest of the PyTorch this file must be python2 compliant. # This script outputs relevant system environment info # Run it with `python collect_env.py`. import datetime import locale import re import subprocess import sys import os from collections import namedtuple try: import torch TORCH_AVAILAB...
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pytorch-main/torch/utils/_freeze.py
""" Freeze Python packages. Freezing makes it possible to ship arbitrary Python modules as part of a C++ library. The Python source of the module is compiled to bytecode and written to `.c` files, to be imported by Python's built-in FrozenImporter. In a normal Python installation, FrozenImporter is only used to boots...
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pytorch
pytorch-main/torch/utils/_stats.py
# NOTE! PLEASE KEEP THIS FILE *FREE* OF TORCH DEPS! IT SHOULD BE IMPORTABLE ANYWHERE. # IF YOU FEEL AN OVERWHELMING URGE TO ADD A TORCH DEP, MAKE A TRAMPOLINE FILE A LA torch._dynamo.utils # AND SCRUB AWAY TORCH NOTIONS THERE. import collections import functools from typing import OrderedDict simple_call_counter: Orde...
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pytorch
pytorch-main/torch/utils/_python_dispatch.py
import contextlib from typing import Optional import warnings import torch from torch._C import _len_torch_dispatch_stack, _get_dispatch_stack_at,\ _pop_torch_dispatch_stack, _push_on_torch_dispatch_stack, DispatchKey # TODO: Limitations and things about enable_torch_dispatch_mode we should fix before exposing i...
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pytorch-main/torch/utils/weak.py
from __future__ import annotations import weakref from weakref import ref from _weakrefset import _IterationGuard # type: ignore[attr-defined] from collections.abc import MutableMapping, Mapping from typing import Dict from torch import Tensor import collections.abc as _collections_abc WeakRef = ref __all__ = ['T...
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pytorch
pytorch-main/torch/utils/cpp_extension.py
import copy import glob import importlib import importlib.abc import os import re import shlex import shutil import setuptools import subprocess import sys import sysconfig import warnings import collections import torch import torch._appdirs from .file_baton import FileBaton from ._cpp_extension_versioner import Exte...
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pytorch-main/torch/utils/flop_counter.py
import torch import torch.nn as nn from torch.utils._pytree import tree_map from typing import List, Any, Dict, Optional, Union, NamedTuple from collections import defaultdict from torch.utils._python_dispatch import TorchDispatchMode from torch.utils.hooks import RemovableHandle from math import prod __all__ = ["Flop...
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pytorch-main/torch/utils/dlpack.py
from typing import Any import torch import enum from torch._C import _from_dlpack from torch._C import _to_dlpack as to_dlpack class DLDeviceType(enum.IntEnum): # Enums as in DLPack specification (aten/src/ATen/dlpack.h) kDLCPU = 1, kDLGPU = 2, kDLCPUPinned = 3, kDLOpenCL = 4, kDLVulkan = 7,...
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pytorch-main/torch/utils/__init__.py
import os.path as _osp import torch from .throughput_benchmark import ThroughputBenchmark from ._crash_handler import enable_minidumps, disable_minidumps, enable_minidumps_on_exceptions from .cpp_backtrace import get_cpp_backtrace from .backend_registration import rename_privateuse1_backend, generate_methods_for_priva...
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pytorch
pytorch-main/torch/utils/_crash_handler.py
import os import sys import pathlib import torch DEFAULT_MINIDUMP_DIR = "/tmp/pytorch_crashes" if sys.platform == "win32": DEFAULT_MINIDUMP_DIR = str(pathlib.Path.home() / "AppData" / "pytorch_crashes") def enable_minidumps(directory=DEFAULT_MINIDUMP_DIR): if directory == DEFAULT_MINIDUMP_DIR: pathli...
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pytorch
pytorch-main/torch/utils/_device.py
import torch from torch.overrides import TorchFunctionMode from torch.utils._contextlib import context_decorator import functools CURRENT_DEVICE = None @functools.lru_cache(1) def _device_constructors(): return { # standard ones torch.empty, torch.empty_permuted, torch.empty_stride...
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pytorch
pytorch-main/torch/utils/mobile_optimizer.py
""" This module contains utility method for mobile model optimization and lint. """ import torch from enum import Enum from torch._C import _MobileOptimizerType as MobileOptimizerType from typing import Optional, Set, List, AnyStr class LintCode(Enum): BUNDLED_INPUT = 1 REQUIRES_GRAD = 2 DROPOUT = 3 B...
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pytorch-main/torch/utils/_sympy/interp.py
""" This is a simple interpreter for Sympy expressions that dispatches to classes following the torch._inductor.virtualized calling convention. For directness, the interpreter takes the handler directly rather than consulting the TLS. It does not use most of the methods on the full handler; only those with correspondi...
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pytorch
pytorch-main/torch/utils/_sympy/value_ranges.py
import dataclasses from copy import deepcopy import itertools import sympy from sympy.logic.boolalg import BooleanAtom, Boolean as SympyBoolean import operator import math import logging import torch from typing import Union, Dict from torch._prims_common import dtype_to_type from .interp import sympy_interp log = lo...
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pytorch-main/torch/utils/model_dump/__init__.py
#!/usr/bin/env python3 """ model_dump: a one-stop shop for TorchScript model inspection. The goal of this tool is to provide a simple way to extract lots of useful information from a TorchScript model and make it easy for humans to consume. It (mostly) replaces zipinfo, common uses of show_pickle, and various ad-hoc ...
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pytorch-main/torch/utils/backcompat/__init__.py
from torch._C import _set_backcompat_broadcast_warn from torch._C import _get_backcompat_broadcast_warn from torch._C import _set_backcompat_keepdim_warn from torch._C import _get_backcompat_keepdim_warn class Warning: def __init__(self, setter, getter): self.setter = setter self.getter = getter ...
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pytorch
pytorch-main/torch/utils/bottleneck/__main__.py
import argparse import cProfile import pstats import sys import os from typing import Dict import torch from torch.autograd import profiler from torch.utils.collect_env import get_env_info def redirect_argv(new_argv): sys.argv[:] = new_argv[:] def compiled_with_cuda(sysinfo): if sysinfo.cuda_compiled_versi...
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pytorch-main/torch/utils/viz/_cycles.py
import gc import sys from typing import Any, Dict, List, NamedTuple, Optional, Tuple import types import weakref import json from tempfile import NamedTemporaryFile import torch from torch.cuda._memory_viz import _frames_fmt, _block_extra import atexit import logging logger = logging.getLogger(__name__) def observe_ga...
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pytorch-main/torch/utils/hipify/cuda_to_hip_mappings.py
import collections import os import re import subprocess from .constants import (API_BLAS, API_C10, API_CAFFE2, API_DRIVER, API_FFT, API_PYTORCH, API_RAND, API_ROCTX, API_RTC, API_RUNTIME, API_SPECIAL, API_ROCMSMI, CONV_CACHE, CONV_CONTEXT, CONV_D3D9, ...
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pytorch
pytorch-main/torch/utils/hipify/hipify_python.py
#!/usr/bin/env python3 """ The Python Hipify script. ## # Copyright (c) 2015-2016 Advanced Micro Devices, Inc. All rights reserved. # 2017-2018 Advanced Micro Devices, Inc. and # Facebook Inc. All rights reserved. # # Permission is hereby granted, free of charge, to any person obta...
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pytorch-main/torch/utils/jit/log_extract.py
from contextlib import contextmanager from typing import Any, List, Tuple, cast import random import torch import time from torch.utils.benchmark import Timer def extract_ir(filename: str) -> List[str]: BEGIN = "<GRAPH_EXPORT>" END = "</GRAPH_EXPORT>" pfx = None current = "" graphs = [] with op...
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pytorch-main/torch/utils/tensorboard/writer.py
"""Provides an API for writing protocol buffers to event files to be consumed by TensorBoard for visualization.""" import os import time import torch from tensorboard.compat import tf from tensorboard.compat.proto import event_pb2 from tensorboard.compat.proto.event_pb2 import Event, SessionLog from tensorboard.plug...
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pytorch-main/torch/utils/tensorboard/_convert_np.py
""" This module converts objects into numpy array. """ import numpy as np import torch def make_np(x): """ Args: x: An instance of torch tensor or caffe blob name Returns: numpy.array: Numpy array """ if isinstance(x, np.ndarray): return x if isinstance(x, str): # Caffe...
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pytorch-main/torch/utils/tensorboard/_caffe2_graph.py
import copy import logging import os import re from tensorboard.compat.proto.graph_pb2 import GraphDef from tensorboard.compat.proto.node_def_pb2 import NodeDef from tensorboard.compat.proto.tensor_shape_pb2 import TensorShapeProto from caffe2.proto import caffe2_pb2 from caffe2.python import core, workspace from ty...
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