python_code stringlengths 0 1.02M | repo_name stringlengths 9 48 | file_path stringlengths 5 114 |
|---|---|---|
import caffe2.python.fakelowp.init_shared_libs # noqa
import numpy as np
from caffe2.python import core, workspace
from caffe2.python.onnx.onnxifi import onnxifi_caffe2_net
from hypothesis import given, strategies as st, settings
from caffe2.python.fakelowp.test_utils import print_test_debug_info
import caffe2.python.... | pytorch-master | caffe2/contrib/fakelowp/test/test_int8_ops_nnpi.py |
# Must happen before importing caffe2.python.*
import caffe2.python.fakelowp.init_shared_libs # noqa
import datetime
import numpy as np
from hypothesis import given, settings, example
from hypothesis import strategies as st
from caffe2.python import core, workspace
from caffe2.python.onnx.onnxifi import onnxifi_caffe2... | pytorch-master | caffe2/contrib/fakelowp/test/test_chunking.py |
import numpy as np
import caffe2.python.fakelowp.init_shared_libs # noqa
from caffe2.python import core, workspace
from caffe2.python.onnx.onnxifi import onnxifi_caffe2_net
from caffe2.python.fakelowp.test_utils import print_test_debug_info
import caffe2.python.serialized_test.serialized_test_util as serial
import dat... | pytorch-master | caffe2/contrib/fakelowp/test/test_deq_swish_quant_nnpi.py |
import numpy as np
import unittest
import caffe2.python.fakelowp.init_shared_libs # noqa
from hypothesis import given, settings
from hypothesis import strategies as st
from caffe2.proto import caffe2_pb2
from caffe2.python import core
from caffe2.python import workspace
from caffe2.python.onnx.onnxifi import onnxifi_... | pytorch-master | caffe2/contrib/fakelowp/test/test_fc_nnpi_fp16.py |
import unittest
# Must happen before importing caffe2.python.*
import caffe2.python.fakelowp.init_shared_libs # noqa
import datetime
import numpy as np
from hypothesis import given, settings
from hypothesis import strategies as st
from caffe2.proto import caffe2_pb2
from caffe2.python import core, workspace
from caff... | pytorch-master | caffe2/contrib/fakelowp/test/test_sls_8bit_nnpi_fp32.py |
import numpy as np
import caffe2.python.fakelowp.init_shared_libs # noqa
import datetime
from hypothesis import given, settings
from hypothesis import strategies as st
from caffe2.proto import caffe2_pb2
from caffe2.python import core
from caffe2.python import workspace
from caffe2.python.onnx.onnxifi import onnxifi_... | pytorch-master | caffe2/contrib/fakelowp/test/test_op_nnpi_fp16.py |
import unittest
from typing import Dict, Any
# Must happen before importing caffe2.python.*
import caffe2.python.fakelowp.init_shared_libs # noqa
import datetime
import numpy as np
from hypothesis import given, settings
from hypothesis import strategies as st
from caffe2.proto import caffe2_pb2
from caffe2.python imp... | pytorch-master | caffe2/contrib/fakelowp/test/test_sls_8bit_nnpi_fp16.py |
# Must happen before importing caffe2.python.*
import caffe2.python.fakelowp.init_shared_libs # noqa
import datetime
import numpy as np
from caffe2.proto import caffe2_pb2
from caffe2.python import core, workspace
from caffe2.python.onnx.onnxifi import onnxifi_caffe2_net
import caffe2.python.serialized_test.serialized... | pytorch-master | caffe2/contrib/fakelowp/test/test_int8_quant.py |
# Must happen before importing caffe2.python.*
import caffe2.python.fakelowp.init_shared_libs # noqa
import datetime
import numpy as np
from hypothesis import given, settings
from hypothesis import strategies as st
from caffe2.proto import caffe2_pb2
from caffe2.python import core, workspace
from caffe2.python.onnx.on... | pytorch-master | caffe2/contrib/fakelowp/test/test_fusions.py |
pytorch-master | caffe2/contrib/script/__init__.py | |
pytorch-master | caffe2/contrib/script/examples/__init__.py | |
import unittest
from caffe2.proto import caffe2_pb2
from caffe2.python import core, dyndep, workspace
dyndep.InitOpsLibrary("@/caffe2/caffe2/contrib/prof:cuda_profile_ops")
class CudaProfileOpsTest(unittest.TestCase):
@unittest.skipIf(workspace.NumCudaDevices() < 1, "Need at least 1 GPU")
def test_run(s... | pytorch-master | caffe2/contrib/prof/cuda_profile_ops_test.py |
pytorch-master | caffe2/contrib/prof/__init__.py | |
pytorch-master | caffe2/contrib/tensorboard/__init__.py | |
from builtins import bytes
import copy
import logging
import os
from caffe2.proto import caffe2_pb2
from caffe2.python import core, workspace
try:
# tensorboard>=1.14.0
from tensorboard.compat.proto import tensor_shape_pb2
from tensorboard.compat.proto.node_def_pb2 import NodeDef
from tensorboard... | pytorch-master | caffe2/contrib/tensorboard/tensorboard_exporter.py |
import click.testing
import numpy as np
import os
import tempfile
import unittest
from caffe2.python import brew, core, model_helper
import caffe2.contrib.tensorboard.tensorboard as tb
import caffe2.contrib.tensorboard.tensorboard_exporter as tb_exporter
try:
# tensorboard>=1.14.0
from tensorboard.compat... | pytorch-master | caffe2/contrib/tensorboard/tensorboard_test.py |
import click
import collections
import logging
import numpy as np
import os
from caffe2.proto import caffe2_pb2
from caffe2.python import core
import caffe2.contrib.tensorboard.tensorboard_exporter as tb_exporter
try:
# tensorboard>=1.14.0
from tensorboard.compat.proto.summary_pb2 import Summary, Histogr... | pytorch-master | caffe2/contrib/tensorboard/tensorboard.py |
import unittest
from caffe2.proto import caffe2_pb2
import caffe2.python.cnn as cnn
import caffe2.python.core as core
import caffe2.contrib.tensorboard.tensorboard_exporter as tb
EXPECTED = """
node {
name: "conv1/XavierFill"
op: "XavierFill"
device: "/gpu:0"
attr {
key: "_output_shapes"
value {
... | pytorch-master | caffe2/contrib/tensorboard/tensorboard_exporter_test.py |
#!/bin/env python3
# Copyright (c) 2016-present, Facebook, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appl... | pytorch-master | caffe2/contrib/aten/gen_op.py |
from caffe2.python import core
from hypothesis import given
import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
import numpy as np
class TestATen(hu.HypothesisTestCase):
@given(inputs=hu.tensors(n=2), **hu.gcs)
def test_add(self, inputs, gc, dc):
op = core.CreateOperat... | pytorch-master | caffe2/contrib/aten/aten_test.py |
pytorch-master | caffe2/contrib/aten/__init__.py | |
pytorch-master | caffe2/contrib/aten/docs/__init__.py | |
import tempfile
import numpy as np
from torch import nn
from torch.autograd import Variable, Function
import torch.onnx
import onnx
import caffe2.python.onnx.backend
class MyFunction(Function):
@staticmethod
def forward(ctx, x, y):
return x * x + y
@staticmethod
def symbolic(graph, x, y):
... | pytorch-master | caffe2/contrib/aten/docs/sample.py |
pytorch-master | caffe2/quantization/__init__.py | |
import collections
import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
import numpy as np
from caffe2.python import core, dyndep, workspace
from caffe2.quantization.server.dnnlowp_test_utils import check_quantized_results_close
from hypothesis import given
dyndep.InitOpsLibrary("//ca... | pytorch-master | caffe2/quantization/server/relu_dnnlowp_op_test.py |
# Copyright (c) 2016-present, Facebook, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed... | pytorch-master | caffe2/quantization/server/compute_equalization_scale_test.py |
import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
import numpy as np
from caffe2.python import core, dyndep, workspace
from hypothesis import given, settings
dyndep.InitOpsLibrary("//caffe2/caffe2/quantization/server:dnnlowp_ops")
workspace.GlobalInit(["caffe2", "--caffe2_omp_num_th... | pytorch-master | caffe2/quantization/server/batch_permutation_dnnlowp_op_test.py |
import collections
import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
import numpy as np
from caffe2.python import core, dyndep, workspace
from caffe2.quantization.server.dnnlowp_test_utils import check_quantized_results_close
from hypothesis import given
dyndep.InitOpsLibrary("//ca... | pytorch-master | caffe2/quantization/server/elementwise_add_dnnlowp_op_test.py |
import collections
import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
import numpy as np
from caffe2.python import core, dyndep, workspace
from caffe2.quantization.server import utils as dnnlowp_utils
from caffe2.quantization.server.dnnlowp_test_utils import (
avoid_vpmaddubsw_ove... | pytorch-master | caffe2/quantization/server/fully_connected_dnnlowp_op_test.py |
import collections
import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
import numpy as np
from caffe2.python import core, dyndep, workspace
from caffe2.quantization.server.dnnlowp_test_utils import check_quantized_results_close
from hypothesis import assume, given
dyndep.InitOpsLibra... | pytorch-master | caffe2/quantization/server/pool_dnnlowp_op_test.py |
import collections
import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
import numpy as np
from caffe2.python import core, dyndep, utils, workspace
from caffe2.quantization.server import utils as dnnlowp_utils
from caffe2.quantization.server.dnnlowp_test_utils import check_quantized_res... | pytorch-master | caffe2/quantization/server/group_norm_dnnlowp_op_test.py |
import collections
import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
import numpy as np
from caffe2.python import core, dyndep, workspace
from caffe2.quantization.server.dnnlowp_test_utils import check_quantized_results_close
from hypothesis import given
dyndep.InitOpsLibrary("//ca... | pytorch-master | caffe2/quantization/server/dequantize_dnnlowp_op_test.py |
import collections
import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
from caffe2.python import core, dyndep, workspace
from caffe2.quantization.server import utils as dnnlowp_utils
from caffe2.quantization.server.dnnlowp_test_utils import (
check_quantized_results_close,
gene... | pytorch-master | caffe2/quantization/server/conv_dnnlowp_op_test.py |
import collections
import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
import numpy as np
from caffe2.python import core, dyndep, workspace
from hypothesis import given
dyndep.InitOpsLibrary("//caffe2/caffe2/quantization/server:dnnlowp_ops")
workspace.GlobalInit(["caffe2", "--caffe2_... | pytorch-master | caffe2/quantization/server/fully_connected_fp16_test.py |
import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
import numpy as np
from caffe2.python import core, dyndep, workspace
from hypothesis import given, settings
dyndep.InitOpsLibrary("//caffe2/caffe2/quantization/server:dnnlowp_ops")
workspace.GlobalInit(["caffe2", "--caffe2_omp_num_th... | pytorch-master | caffe2/quantization/server/resize_nearest_3d_dnnlowp_op_test.py |
import collections
import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
from caffe2.python import core, dyndep, workspace
from caffe2.python.fb import hardcode_scale_zp # type: ignore[import]
from caffe2.quantization.server import utils as dnnlowp_utils
from caffe2.quantization.server.... | pytorch-master | caffe2/quantization/server/conv_groupwise_dnnlowp_op_test.py |
pytorch-master | caffe2/quantization/server/__init__.py | |
import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
import numpy as np
from caffe2.python import core, dyndep, workspace
from hypothesis import given, settings
dyndep.InitOpsLibrary("//caffe2/caffe2/quantization/server:dnnlowp_ops")
workspace.GlobalInit(["caffe2", "--caffe2_omp_num_th... | pytorch-master | caffe2/quantization/server/resize_nearest_dnnlowp_op_test.py |
import collections
import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
import numpy as np
from caffe2.python import core, dyndep, workspace
from caffe2.quantization.server.dnnlowp_test_utils import check_quantized_results_close
from hypothesis import given
dyndep.InitOpsLibrary("//ca... | pytorch-master | caffe2/quantization/server/gather_dnnlowp_op_test.py |
import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
import numpy as np
from caffe2.python import core, dyndep, utils, workspace
from hypothesis import given, settings
dyndep.InitOpsLibrary("//caffe2/caffe2/quantization/server:dnnlowp_ops")
workspace.GlobalInit(["caffe2", "--caffe2_omp... | pytorch-master | caffe2/quantization/server/channel_shuffle_dnnlowp_op_test.py |
import collections
from itertools import product
import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
import numpy as np
from caffe2.python import core, dyndep, workspace
from caffe2.quantization.server import utils as dnnlowp_utils
from caffe2.quantization.server.dnnlowp_test_utils imp... | pytorch-master | caffe2/quantization/server/batch_matmul_dnnlowp_op_test.py |
import collections
import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
import numpy as np
from caffe2.python import core, dyndep, workspace
from caffe2.quantization.server.dnnlowp_test_utils import check_quantized_results_close
from hypothesis import given
dyndep.InitOpsLibrary("//ca... | pytorch-master | caffe2/quantization/server/elementwise_sum_dnnlowp_op_test.py |
import collections
import numpy as np
from caffe2.python import utils, workspace
from caffe2.quantization.server import dnnlowp_pybind11
from hypothesis import assume
# This function asserts quantized results (output[1:]) are close enough to
# floating point results (output[0]).
# The error bound is derived based ... | pytorch-master | caffe2/quantization/server/dnnlowp_test_utils.py |
import collections
import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
import numpy as np
from caffe2.python import core, dyndep, workspace
from caffe2.quantization.server import utils as dnnlowp_utils
from caffe2.quantization.server.dnnlowp_test_utils import (
check_quantized_resu... | pytorch-master | caffe2/quantization/server/fully_connected_dnnlowp_acc16_op_test.py |
# Copyright (c) 2016-present, Facebook, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed... | pytorch-master | caffe2/quantization/server/int8_gen_quant_params_min_max_test.py |
import copy
import logging
from collections import defaultdict
import numpy as np
from caffe2.python import core, utils
from caffe2.python.fb import hardcode_scale_zp # type: ignore[import]
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)
def pairwise(iterable):
"s -> (s0,s1), (s1,s2), (s... | pytorch-master | caffe2/quantization/server/utils.py |
import collections
import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
import numpy as np
from caffe2.python import core, dyndep, workspace
from hypothesis import given, settings
dyndep.InitOpsLibrary("//caffe2/caffe2/quantization/server:dnnlowp_ops")
workspace.GlobalInit(["caffe2", ... | pytorch-master | caffe2/quantization/server/tanh_dnnlowp_op_test.py |
import collections
import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
import numpy as np
from caffe2.python import core, dyndep, workspace
from caffe2.quantization.server.dnnlowp_test_utils import check_quantized_results_close
from hypothesis import given, settings
dyndep.InitOpsLib... | pytorch-master | caffe2/quantization/server/elementwise_mul_dnnlowp_op_test.py |
import collections
import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
from caffe2.python import core, dyndep, workspace
from caffe2.quantization.server import utils as dnnlowp_utils
from caffe2.quantization.server.dnnlowp_test_utils import (
check_quantized_results_close,
gene... | pytorch-master | caffe2/quantization/server/conv_depthwise_dnnlowp_op_test.py |
# Copyright (c) 2016-present, Facebook, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed... | pytorch-master | caffe2/quantization/server/int8_quant_scheme_blob_fill_test.py |
import numpy as np
from caffe2.python import core, workspace
from caffe2.quantization.server import dnnlowp_pybind11 # type: ignore[attr-defined]
net = core.Net("test_net")
X = np.array([[1, 2], [3, 4]]).astype(np.float32)
W = np.array([[5, 6], [7, 8]]).astype(np.float32)
b = np.array([0, 1]).astype(np.float32)
... | pytorch-master | caffe2/quantization/server/observer_test.py |
import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
import numpy as np
from caffe2.python import core, dyndep, workspace
from caffe2.quantization.server import dnnlowp_pybind11
from hypothesis import given, settings
dyndep.InitOpsLibrary("//caffe2/caffe2/quantization/server:dnnlowp_op... | pytorch-master | caffe2/quantization/server/quantize_dnnlowp_op_test.py |
# Copyright (c) 2016-present, Facebook, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed... | pytorch-master | caffe2/quantization/server/int8_gen_quant_params_test.py |
import collections
import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
import numpy as np
from caffe2.python import core, dyndep, workspace
from caffe2.quantization.server import utils as dnnlowp_utils
from caffe2.quantization.server.dnnlowp_test_utils import (
avoid_vpmaddubsw_ove... | pytorch-master | caffe2/quantization/server/fully_connected_rowwise_dnnlowp_op_test.py |
import collections
import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
import numpy as np
from caffe2.python import core, dyndep, utils, workspace
from caffe2.quantization.server import utils as dnnlowp_utils
from caffe2.quantization.server.dnnlowp_test_utils import check_quantized_res... | pytorch-master | caffe2/quantization/server/spatial_batch_norm_dnnlowp_op_test.py |
import collections
import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
import numpy as np
from caffe2.python import core, dyndep, workspace
from caffe2.quantization.server.dnnlowp_test_utils import check_quantized_results_close
from hypothesis import given
dyndep.InitOpsLibrary("//ca... | pytorch-master | caffe2/quantization/server/concat_dnnlowp_op_test.py |
import collections
import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
import numpy as np
from caffe2.python import core, dyndep, utils, workspace
from caffe2.quantization.server import utils as dnnlowp_utils
from caffe2.quantization.server.dnnlowp_test_utils import (
check_quantiz... | pytorch-master | caffe2/quantization/server/conv_dnnlowp_acc16_op_test.py |
import collections
import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
import numpy as np
from caffe2.python import core, dyndep, workspace
from hypothesis import given, settings
dyndep.InitOpsLibrary("//caffe2/caffe2/quantization/server:dnnlowp_ops")
workspace.GlobalInit(["caffe2", ... | pytorch-master | caffe2/quantization/server/lstm_unit_dnnlowp_op_test.py |
import collections
import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
import numpy as np
from caffe2.python import core, dyndep, utils, workspace
from caffe2.quantization.server import utils as dnnlowp_utils
from caffe2.quantization.server.dnnlowp_test_utils import (
check_quantiz... | pytorch-master | caffe2/quantization/server/conv_groupwise_dnnlowp_acc16_op_test.py |
import collections
import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
import numpy as np
from caffe2.python import core, dyndep, workspace
from hypothesis import given
dyndep.InitOpsLibrary("//caffe2/caffe2/quantization/server:dnnlowp_ops")
workspace.GlobalInit(["caffe2", "--caffe2_... | pytorch-master | caffe2/quantization/server/sigmoid_dnnlowp_op_test.py |
import collections
import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
import numpy as np
from caffe2.python import core, dyndep, workspace
from caffe2.quantization.server.dnnlowp_test_utils import check_quantized_results_close
from hypothesis import given
dyndep.InitOpsLibrary("//ca... | pytorch-master | caffe2/quantization/server/elementwise_linear_dnnlowp_op_test.py |
"""Adds docstrings to Storage functions"""
import torch._C
from torch._C import _add_docstr as add_docstr
storage_classes = [
"StorageBase",
]
def add_docstr_all(method, docstr):
for cls_name in storage_classes:
cls = getattr(torch._C, cls_name)
try:
add_docstr(getattr(cls, meth... | pytorch-master | torch/_storage_docs.py |
from typing import List, Optional, Union
import torch
import torch._prims_common as utils
from torch import Tensor
from torch._prims_common import (
check,
corresponding_complex_dtype,
corresponding_real_dtype,
elementwise_dtypes,
ELEMENTWISE_TYPE_PROMOTION_KIND,
)
from torch._prims_common.wrapper... | pytorch-master | torch/_meta_registrations.py |
from ._ops import OpOverload
from typing import Set
import traceback
import torch
__all__ = ['Library', 'impl', 'define']
# Set containing the combination of (namespace, operator, DispatchKey) for which a new kernel has been registered
# The keys in the set are of the form `namespace + "/" + op_name + "/" + dispatch_... | pytorch-master | torch/library.py |
"""
The weak_script annotation needs to be here instead of inside torch/jit/ so it
can be used in other places in torch/ (namely torch.nn) without running into
circular dependency problems
"""
import ast
import builtins
import collections
import contextlib
import enum
import inspect
import io
import pickle
import sys
... | pytorch-master | torch/_jit_internal.py |
import torch
import inspect
__all__ = []
# error: Module has no attribute "_return_types"
return_types = torch._C._return_types # type: ignore[attr-defined]
def pytree_register_structseq(cls):
def structseq_flatten(structseq):
return list(structseq), None
def structseq_unflatten(values, context):
... | pytorch-master | torch/return_types.py |
# -*- coding: utf-8 -*-
"""Adds docstrings to functions defined in the torch._C"""
import re
import torch._C
from torch._C import _add_docstr as add_docstr
def parse_kwargs(desc):
"""Maps a description of args to a dictionary of {argname: description}.
Input:
(' weight (Tensor): a weight tensor\n... | pytorch-master | torch/_torch_docs.py |
import contextlib
import ctypes
import sys
import types
import torch._C
import torch.jit
from torch import _utils_internal
# Query `hasattr` only once.
_SET_GLOBAL_FLAGS = hasattr(sys, "getdlopenflags") and hasattr(sys, "setdlopenflags")
@contextlib.contextmanager
def dl_open_guard():
"""
Context manager t... | pytorch-master | torch/_ops.py |
import os
import sys
import tempfile
# this arbitrary-looking assortment of functionality is provided here
# to have a central place for overrideable behavior. The motivating
# use is the FB build environment, where this source file is replaced
# by an equivalent.
if sys.executable == "torch_deploy":
# __file__ ... | pytorch-master | torch/_utils_internal.py |
import torch
from typing import Optional
class SobolEngine(object):
r"""
The :class:`torch.quasirandom.SobolEngine` is an engine for generating
(scrambled) Sobol sequences. Sobol sequences are an example of low
discrepancy quasi-random sequences.
This implementation of an engine for Sobol sequenc... | pytorch-master | torch/quasirandom.py |
from typing import Any, Iterable
from .version import __version__ as internal_version
__all__ = ['TorchVersion', 'Version', 'InvalidVersion']
class _LazyImport:
"""Wraps around classes lazy imported from packaging.version
Output of the function v in following snippets are identical:
from packaging.vers... | pytorch-master | torch/torch_version.py |
"""
This makes the functions in torch._C._VariableFunctions available as
torch._VF.<funcname>
without mypy being able to find them.
A subset of those functions are mapped to ATen functions in
torch/jit/_builtins.py
See https://github.com/pytorch/pytorch/issues/21478 for the reason for
introducing torch._VF
"""
i... | pytorch-master | torch/_VF.py |
"""Adds docstrings to Tensor functions"""
import torch._C
from torch._C import _add_docstr as add_docstr
from ._torch_docs import parse_kwargs, reproducibility_notes
def add_docstr_all(method, docstr):
add_docstr(getattr(torch._C._TensorBase, method), docstr)
common_args = parse_kwargs(
"""
memory_form... | pytorch-master | torch/_tensor_docs.py |
# Copyright (c) 2010-2017 Benjamin Peterson
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publi... | pytorch-master | torch/_six.py |
r"""
The torch package contains data structures for multi-dimensional
tensors and defines mathematical operations over these tensors.
Additionally, it provides many utilities for efficient serializing of
Tensors and arbitrary types, and other useful utilities.
It has a CUDA counterpart, that enables you to run your t... | pytorch-master | torch/__init__.py |
"""
Python implementation of ``__torch_function__``
While most of the torch API and handling for ``__torch_function__`` happens
at the C++ level, some of the torch API is written in Python so we need
python-level handling for ``__torch_function__`` overrides as well. The main
developer-facing functionality in this fil... | pytorch-master | torch/overrides.py |
from collections import OrderedDict
"""
This file contains helper functions that implement experimental functionality
for named tensors in python. All of these are experimental, unstable, and
subject to change or deletion.
"""
def check_serializing_named_tensor(tensor):
if tensor.has_names():
raise Runti... | pytorch-master | torch/_namedtensor_internals.py |
import torch
from typing import Any, List, Sequence, Tuple, Union
import builtins
# Convenience aliases for common composite types that we need
# to talk about in PyTorch
_TensorOrTensors = Union[torch.Tensor, Sequence[torch.Tensor]]
# In some cases, these basic types are shadowed by corresponding
# top-level value... | pytorch-master | torch/types.py |
"""Various linear algebra utility methods for internal use.
"""
from typing import Optional, Tuple
import torch
from torch import Tensor
def is_sparse(A):
"""Check if tensor A is a sparse tensor"""
if isinstance(A, torch.Tensor):
return A.layout == torch.sparse_coo
error_str = "expected Tensor... | pytorch-master | torch/_linalg_utils.py |
import torch
def show():
"""
Return a human-readable string with descriptions of the
configuration of PyTorch.
"""
return torch._C._show_config()
# TODO: In principle, we could provide more structured version/config
# information here. For now only CXX_FLAGS is exposed, as Timer
# uses them.
def... | pytorch-master | torch/__config__.py |
import copyreg
import enum
import functools
import warnings
from collections import OrderedDict
from copy import deepcopy
from numbers import Number
from typing import Any, Dict, Optional, Tuple, Union
import torch
import torch._C as _C
import torch.utils.hooks as hooks
from torch._namedtensor_internals import (
c... | pytorch-master | torch/_tensor.py |
import contextlib
from typing import Generator
import warnings
from torch._C import default_generator
import torch
def set_rng_state(new_state: torch.Tensor) -> None:
r"""Sets the random number generator state.
.. note: This function only works for CPU. For CUDA, please use
torch.manual_seed(se... | pytorch-master | torch/random.py |
import errno
import hashlib
import json
import os
import re
import shutil
import sys
import tempfile
import torch
import warnings
import zipfile
from pathlib import Path
from typing import Callable, Dict, Optional, Union, Any
from urllib.error import HTTPError
from urllib.request import urlopen, Request
from urllib.par... | pytorch-master | torch/hub.py |
import math
from typing import Optional
import torch
from torch._six import inf
class __PrinterOptions(object):
precision: int = 4
threshold: float = 1000
edgeitems: int = 3
linewidth: int = 80
sci_mode: Optional[bool] = None
PRINT_OPTS = __PrinterOptions()
# We could use **kwargs, but this w... | pytorch-master | torch/_tensor_str.py |
import ast
import functools
import inspect
from textwrap import dedent
from typing import Any, List, NamedTuple, Optional, Tuple
from torch._C import ErrorReport
from torch._C._jit_tree_views import SourceRangeFactory
def get_source_lines_and_file(
obj: Any,
error_msg: Optional[str] = None,
) -> Tuple[List[s... | pytorch-master | torch/_sources.py |
import functools
import warnings
from typing import Any, Callable, List, Optional, Tuple, Union
import torch
from torch import Tensor
from torch.utils._pytree import _broadcast_to_and_flatten, tree_flatten, tree_unflatten
in_dims_t = Union[int, Tuple]
out_dims_t = Union[int, Tuple[int, ...]]
# Checks that all args-t... | pytorch-master | torch/_vmap_internals.py |
from typing import (
List, Tuple, Optional, Union, Any, Sequence, TYPE_CHECKING
)
import torch
from torch._C import _add_docstr
import torch.nn.functional as F
from ._lowrank import svd_lowrank, pca_lowrank
from .overrides import (
has_torch_function, has_torch_function_unary, has_torch_function_variadic,
... | pytorch-master | torch/functional.py |
"""Implement various linear algebra algorithms for low rank matrices.
"""
__all__ = ["svd_lowrank", "pca_lowrank"]
from typing import Optional, Tuple
import torch
from torch import Tensor
from . import _linalg_utils as _utils
from .overrides import handle_torch_function, has_torch_function
def get_approximate_basi... | pytorch-master | torch/_lowrank.py |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright (c) 2005-2010 ActiveState Software Inc.
# Copyright (c) 2013 Eddy Petrișor
# flake8: noqa
"""
This file is directly from
https://github.com/ActiveState/appdirs/blob/3fe6a83776843a46f20c2e5587afcffe05e03b39/appdirs.py
The license of https://github.com/ActiveS... | pytorch-master | torch/_appdirs.py |
import io
import torch
from ._utils import _type, _cuda
from torch.types import Storage
from typing import Any, TypeVar, Type, Union, cast
import copy
import collections
from functools import lru_cache
try:
import numpy as np
HAS_NUMPY = True
except ModuleNotFoundError:
np = None # type: ignore[assignment... | pytorch-master | torch/storage.py |
"""Locally Optimal Block Preconditioned Conjugate Gradient methods.
"""
# Author: Pearu Peterson
# Created: February 2020
from typing import Dict, Optional, Tuple
import torch
from torch import Tensor
from . import _linalg_utils as _utils
from .overrides import handle_torch_function, has_torch_function
__all__ = ["... | pytorch-master | torch/_lobpcg.py |
"""
This global flag controls whether to assign new tensors to the parameters
instead of changing the existing parameters in-place when converting an `nn.Module`
using the following methods:
1. `module.cuda()` / `.cpu()` (for moving `module` between devices)
2. `module.float()` / `.double()` / `.half()` (for converting... | pytorch-master | torch/__future__.py |
import io
import torch
from torch.package import Importer, OrderedImporter, PackageImporter, sys_importer
from torch.package._package_pickler import create_pickler
from torch.package._package_unpickler import PackageUnpickler
from torch.serialization import _maybe_decode_ascii
def _save_storages(importer, obj):
... | pytorch-master | torch/_deploy.py |
import re
import torch._C as C
"""
PythonDispatcher class is a thin python-binding to C++ dispatcher and it
is designed to show how dispatcher precompute works. In particular,
it shows for a certain op `foo`, what the computed dispatch table looks
like after user register their kernels to certains dispatch keys.
In... | pytorch-master | torch/_python_dispatcher.py |
import types
import torch._C
class _ClassNamespace(types.ModuleType):
def __init__(self, name):
super(_ClassNamespace, self).__init__("torch.classes" + name)
self.name = name
def __getattr__(self, attr):
proxy = torch._C._get_custom_class_python_wrapper(self.name, attr)
if pr... | pytorch-master | torch/_classes.py |
import difflib
import os
import io
import shutil
import struct
import sys
import torch
import tarfile
import tempfile
import warnings
from contextlib import closing, contextmanager
from ._utils import _import_dotted_name
from ._six import string_classes as _string_classes
from torch._sources import get_source_lines_and... | pytorch-master | torch/serialization.py |
import sys
import traceback
import warnings
from collections import defaultdict
from typing import Any, DefaultDict, List, Optional
import torch
def _type(self, dtype=None, non_blocking=False, **kwargs):
"""Returns the type if `dtype` is not provided, else casts this object to
the specified type.
If thi... | pytorch-master | torch/_utils.py |
from copy import deepcopy
from dataclasses import dataclass
from functools import lru_cache
from warnings import warn
import torch
import torch.overrides
from torch._prims_common import getnvFuserDtype, Number
from torch.fx import GraphModule
from torch.fx.passes.infra.partitioner import CapabilityBasedPartitioner
fr... | pytorch-master | torch/_prims/nvfuser_executor.py |
import contextlib
import itertools
import math
import operator
import weakref
from enum import Enum
from functools import partial, reduce
from typing import Any, Callable, List, Optional, Sequence, Tuple, Type, Union
import torch
import torch._prims_common as utils
import torch.library
from torch import Tensor, Typed... | pytorch-master | torch/_prims/__init__.py |
import functools
from contextlib import nullcontext
from typing import Any, Callable, Dict, Sequence
import torch
import torch._prims
import torch._refs
import torch._refs.nn
import torch._refs.nn.functional
import torch._refs.special
import torch.overrides
from torch._prims_common import torch_function_passthrough... | pytorch-master | torch/_prims/context.py |
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