repo stringlengths 1 99 | file stringlengths 13 215 | code stringlengths 12 59.2M | file_length int64 12 59.2M | avg_line_length float64 3.82 1.48M | max_line_length int64 12 2.51M | extension_type stringclasses 1
value |
|---|---|---|---|---|---|---|
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tests/cpu/autocast_test_lists.py | import torch
class AutocastCPUTestLists(object):
# Supplies ops and arguments for test_autocast_* in test/test_cpu.py
def __init__(self, dev):
super().__init__()
n = 8
# Utility arguments, created as one-element tuples
pointwise0_bf16 = (torch.randn(n, dtype=torch.bfloat16, dev... | 22,097 | 42.671937 | 89 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tests/cpu/test_fpmath_mode.py | import unittest
import os
import subprocess
from torch.testing._internal.common_utils import TestCase
import intel_extension_for_pytorch as ipex
import itertools
from functools import wraps
def fpmath_mode_env(func):
@wraps(func)
def wrapTheFunction(*args):
func(*args)
# set the fp32_math_mode... | 5,133 | 40.739837 | 108 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tests/cpu/test_merged_embeddingbag.py | import torch
import torch.nn as nn
import unittest
import copy
from torch.testing._internal.common_utils import TestCase
from intel_extension_for_pytorch.nn.modules import (
MergedEmbeddingBagWithSGD as MergedEmbeddingBagWithSGD,
)
from intel_extension_for_pytorch.nn.modules import MergedEmbeddingBag
class TestMe... | 14,533 | 34.622549 | 88 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tests/cpu/test_jit_llga_fuser.py | import os
import subprocess
import unittest
import itertools
import torch
import torch.nn as nn
import torch.nn.functional as F
from test_ao_jit_llga_utils import (
JitLlgaTestCase,
LLGA_FUSION_GROUP,
llga_fp32_bf16_test_env,
get_eltwise_fn,
)
from torch.testing._internal.common_utils import run_tests, ... | 36,230 | 32.300551 | 98 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tests/cpu/test_auto_channels_last.py | import unittest
from common_utils import TestCase
import torch
import torch.nn as nn
import intel_extension_for_pytorch as ipex
from intel_extension_for_pytorch.utils.channels_last_1d import (
is_contiguous_channels_last_1d,
)
try:
import torchvision
HAS_TORCHVISION = True
except ImportError:
HAS_TORC... | 10,796 | 37.423488 | 97 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tests/cpu/test_frozen_batch_norm.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
import unittest
from common_utils import TestCase
from intel_extension_for_pytorch.nn import FrozenBatchNorm2d
try:
import torchvision # noqa: F401
from torchvision.ops.misc import FrozenBatchNorm2d as FrozenBN2d
HAS_TOR... | 3,525 | 32.903846 | 85 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tests/cpu/test_mha.py | import unittest
import torch
import torch.nn as nn
import torch.nn.functional as F
import intel_extension_for_pytorch as ipex
import math
import copy
from common_utils import TestCase
# (from Diffusers 0.12.1)
class SD_MHA_Model_v1(nn.Module):
def __init__(self, scale, num_heads, weightsize, hiddensize):
... | 36,131 | 37.561366 | 99 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tests/cpu/itensor_size1_test.py | import torch
import intel_extension_for_pytorch as ipex
# This script is called and tested by test_conv_reorder.py, and its purpose is:
# (1) This script is testing the case that conv grad tensor shape[n, 1, h ,w] stride[h*w, 1 , w, 1],
# where its stride can be considered as both default contiguous and channelslast b... | 1,528 | 48.322581 | 105 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tests/cpu/test_runtime_api.py | import unittest
import torch
import intel_extension_for_pytorch as ipex
from common_utils import TestCase
from common_ipex_conf import runtime_thread_affinity_test_env
import subprocess
import os
class SimpleNet(torch.nn.Module):
def __init__(self):
super(SimpleNet, self).__init__()
self.conv = t... | 23,649 | 33.275362 | 116 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tests/cpu/test_dyndisp.py | import unittest
import os
import subprocess
import intel_extension_for_pytorch._C as core
supported_isa_set = [
"default",
"avx2",
"avx2_vnni",
"avx512",
"avx512_vnni",
"avx512_bf16",
"amx",
]
def get_isa_val(isa_name):
if isa_name == "default":
return 0
elif isa_name == ... | 3,506 | 28.225 | 108 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tests/cpu/test_transfree_bmm.py | import unittest
import torch
import torch.nn as nn
import intel_extension_for_pytorch as ipex
from common_utils import TestCase
class TransFree_FP32_Bmm(nn.Module):
def __init__(self):
super(TransFree_FP32_Bmm, self).__init__()
def forward(self, x1, y1):
out = torch.matmul(x1, y1)
re... | 17,934 | 35.159274 | 88 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tests/cpu/test_torch_compile.py | import unittest
import itertools
import copy
import torch
import torch.nn.functional as F
from torch.optim import SGD
import intel_extension_for_pytorch as ipex
from common_utils import TestCase
conv_module = {1: torch.nn.Conv1d, 2: torch.nn.Conv2d, 3: torch.nn.Conv3d}
convtranspose_module = {
1: torch.nn.ConvTra... | 16,586 | 36.274157 | 86 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tests/cpu/test_fx_optimization.py | import unittest
import torch
import intel_extension_for_pytorch as ipex
from intel_extension_for_pytorch.nn.utils._weight_prepack import (
_IPEXLinear as _IPEXLinear,
)
from torch.testing._internal.common_utils import TestCase
from typing import List
import random
import copy
import itertools
import os
try:
i... | 8,552 | 39.535545 | 88 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tests/cpu/test_tensorexpr.py | import torch
import torch.nn as nn
from torch.testing._internal.jit_utils import JitTestCase
import unittest
import torch.nn.functional as F
import time
def get_rand_seed():
return int(time.time() * 1000000000)
conv_module = {1: torch.nn.Conv1d, 2: torch.nn.Conv2d, 3: torch.nn.Conv3d}
from typing import Dict, ... | 24,027 | 38.13355 | 88 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tests/cpu/test_tpp_ops.py | import unittest
import torch
import random
import numpy
import intel_extension_for_pytorch as ipex
try:
import transformers
except ImportError:
import sys
import subprocess
subprocess.check_call(
[sys.executable, "-m", "pip", "install", "transformers==4.11.0"]
)
import transformers
fro... | 6,620 | 34.983696 | 88 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tests/cpu/test_lru_cache.py | import unittest
import torch
import torch.nn as nn
from common_utils import TestCase
import intel_extension_for_pytorch as ipex
class Conv2d(nn.Module):
def __init__(self):
super().__init__()
self.conv = nn.Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=True)
def forward(self, x):
... | 1,575 | 24.836066 | 83 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tests/cpu/test_code_free_optimization.py | import unittest
from common_utils import TestCase
import os
import subprocess
import itertools
import logging
logging.getLogger().setLevel(logging.DEBUG)
class TestCodeFreeOptimization(TestCase):
def test_conv_bn(self):
loc = os.path.dirname(os.path.abspath(__file__))
disable_ipex_graph_modes = ... | 5,731 | 43.78125 | 98 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tests/cpu/test_ao_jit_llga_utils.py | import copy
import torch
from torch.ao.quantization import MinMaxObserver, PerChannelMinMaxObserver, QConfig
from functools import wraps
from torch.testing._internal.jit_utils import (
JitTestCase,
get_execution_plan,
)
from torch.jit._recursive import wrap_cpp_module
import intel_extension_for_pytorch as ipe... | 8,943 | 33.007605 | 88 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tests/cpu/verbose.py | import argparse
import torch
import intel_extension_for_pytorch as ipex
class Module(torch.nn.Module):
def __init__(self):
super(Module, self).__init__()
self.conv = torch.nn.Conv2d(1, 10, 5, 1)
def forward(self, x):
y = self.conv(x)
return y
def run_model(level):
m = Mo... | 667 | 22.034483 | 63 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tests/cpu/profile_ipex_op.py | import argparse
import torch
import torch.nn as nn
import intel_extension_for_pytorch as ipex
def trace_handler(prof):
print(prof.key_averages().table(sort_by="self_cpu_time_total", row_limit=-1))
class inplace_softmax(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, x... | 1,769 | 24.652174 | 83 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tests/cpu/test_softmax.py | import torch
import torch.nn as nn
import intel_extension_for_pytorch as ipex
from torch.testing._internal.jit_utils import JitTestCase
from intel_extension_for_pytorch.quantization import prepare, convert
from torch.ao.quantization import MinMaxObserver, PerChannelMinMaxObserver, QConfig
import unittest
IPEX_SOFTMAX ... | 8,197 | 35.274336 | 85 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tests/cpu/bench/custom_op_bench/interaction.py | import torch
import intel_extension_for_pytorch as ipex
from torch.utils import ThroughputBenchmark
import argparse
class Interaction(torch.nn.Module):
def __init__(self):
super(Interaction, self).__init__()
def forward(self, x):
return ipex.nn.functional.interaction(*x)
def inference_bench... | 2,028 | 29.283582 | 87 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tests/cpu/bench/custom_op_bench/merged_embeddingbag.py | import torch
import intel_extension_for_pytorch as ipex
import time
import copy
r"""
vector-size = 128
batch-size = 7168
r"""
a = torch.ones(256 * 1024 * 1024 // 4, dtype=torch.float)
b = torch.ones(256 * 1024 * 1024 // 4, dtype=torch.float)
def cache_flush():
# We assume the cache size is <= 512MB here.
# ... | 5,123 | 31.636943 | 88 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tests/cpu/bench/custom_op_bench/optimizer.py | import torch
import time
import math
a = torch.ones(256 * 1024 * 1024 // 4, dtype=torch.float)
b = torch.ones(256 * 1024 * 1024 // 4, dtype=torch.float)
def flush():
global a, b
a += b
def non_fused_sgd(
param, grad, momentum_buf, momentum, lr, weight_decay, dampening, nesterov
):
if weight_decay !... | 9,897 | 23.93199 | 85 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tests/cpu/utils/utils.py | import torch
import unittest
from torch.testing._internal import expecttest
from functools import wraps
import torch_ipex as ipex
class VerboseTestCase(expecttest.TestCase):
def __init__(self, method_name="runTest"):
super(expecttest.TestCase, self).__init__(method_name)
def is_dnnl_verbose(self, lin... | 3,062 | 30.255102 | 87 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tests/cpu/data/network2.py | """
From PyTorch:
Copyright (c) 2016- Facebook, Inc (Adam Paszke)
Copyright (c) 2014- Facebook, Inc (Soumith Chintala)
Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert)
Copyright (c) 2012-2014 Deepmind Technologies (Koray Kavukcuoglu)
Copyright (c) 2011-2012 NEC Labora... | 1,807 | 31.872727 | 106 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tests/cpu/data/network1.py | """
From PyTorch:
Copyright (c) 2016- Facebook, Inc (Adam Paszke)
Copyright (c) 2014- Facebook, Inc (Soumith Chintala)
Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert)
Copyright (c) 2012-2014 Deepmind Technologies (Koray Kavukcuoglu)
Copyright (c) 2011-2012 NEC Labora... | 1,777 | 31.925926 | 106 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/docs/conf.py | # Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If ex... | 3,441 | 32.096154 | 114 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/_meta_registrations.py | import functools
from typing import List, Optional
import torch
import torch.library
from torch._prims_common import IntLike
@functools.lru_cache(None)
def get_meta_lib():
return torch.library.Library("torch_ipex", "IMPL", "Meta")
def register_meta(op_name, overload_name="default"):
def wrapper(fn):
... | 9,941 | 24.492308 | 85 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/launcher.py | import sys
import argparse
import warnings
from functools import partial
from .cpu.launch import (
init_parser as cpu_init_parser,
run_main_with_args as cpu_run_main_with_args,
)
from .xpu.launch import (
init_parser as xpu_init_parser,
run_main_with_args as xpu_run_main_with_args,
)
def init_parser(... | 4,753 | 39.288136 | 105 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/__init__.py | # This Python file uses the following encoding: utf-8
import re
import torch
import warnings
try:
import torchvision
except ImportError:
pass # skip if torchvision is not available
import os
import sys
import glob
import ctypes
import platform
from . import cpu
from . import xpu
from . import quantization
... | 2,613 | 28.370787 | 85 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/frontend.py | # This Python file uses the following encoding: utf-8
import copy
import warnings
import torch
import torch._dynamo
import torch.fx.experimental.optimization as optimization
from enum import IntFlag, IntEnum
from .nn import utils
from .optim._optimizer_utils import (
optimizer_fusion,
IPEX_FUSED_OPTIMIZER_LIS... | 31,056 | 42.375698 | 119 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/nn/modules/weight_only_quantization.py | import torch
import torch.ao.nn.quantized as nnq
from torch.ao.nn.quantized.modules.utils import _quantize_weight
import torch.ao.nn.intrinsic as nni
from ...quantization._qconfig import get_weight_only_quant_qconfig_mapping
class IpexWoqLinear(nnq.Linear):
r"""
A weight-only quantized (WOQ) linear module wit... | 6,951 | 39.184971 | 97 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/nn/modules/merged_embeddingbag.py | import torch
from torch import Tensor, nn
from torch.autograd import Function
from typing import List, Optional, NamedTuple
from itertools import accumulate
import enum
class PoolingMode(enum.IntEnum):
SUM = 0
MEAN = 1
class SGDArgs(NamedTuple):
bf16_trail: List[Optional[torch.Tensor]]
weight_decay:... | 21,015 | 36.7307 | 123 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/nn/utils/_model_convert.py | import torch
from ._parameter_wrapper import get_shared_parameter_status, patch_state_dict
def replace_dropout_with_identity(model):
# replace dropout with identity during inference, so that aten::dropout won't be on the JIT graph.
# This optimization may provide more fusion opportunites on the graph.
if ... | 1,250 | 33.75 | 102 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/nn/utils/_parameter_wrapper.py | import torch
from typing import Set
import functools
import contextlib
import types
import warnings
from intel_extension_for_pytorch.cpu._auto_kernel_selection import _using_dnnl
from intel_extension_for_pytorch import frontend
from intel_extension_for_pytorch.nn.utils._weight_prepack import (
_IPEXLinear,
_IPE... | 23,005 | 35.229921 | 126 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/nn/utils/_weight_cast.py | import torch
import sys
from intel_extension_for_pytorch.optim import _optimizer_utils
import types
from ._parameter_wrapper import get_shared_parameter_status, patch_state_dict
def weight_dtype_convert_with_ipex(
model, optimizer, params_attr, master_weight_split, dtype=torch.bfloat16
):
assert dtype in [
... | 3,137 | 34.659091 | 85 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/nn/utils/_weight_prepack.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import logging
import os
import pkg_resources
from intel_extension_for_pytorch import optim
logger = logging.getLogger(__name__)
def may_import_deepspeed_modules():
try:
# import deepspeed in a global space will raise circular import erro... | 14,977 | 33.671296 | 128 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/nn/utils/__init__.py | from intel_extension_for_pytorch.nn.utils import _weight_prepack
from intel_extension_for_pytorch.nn.utils import _lstm_convert
from . import _model_convert, _weight_cast
| 171 | 42 | 64 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/nn/utils/_lstm_convert.py | import torch
import copy
from torch.nn.utils.rnn import PackedSequence
class _LSTM(torch.nn.LSTM):
# This is a solution to swap the lstm module with the ipex counterpart
# and will upstream this operator to PyTorch when oneDNN support
# bias and src_iter_c in bf16 in bf16 inference. Will keep this
# f... | 4,226 | 36.741071 | 94 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/nn/functional/_tensor_method.py | import warnings
import torch
from torch.overrides import has_torch_function_unary, handle_torch_function
def _numpy(x):
if x.dtype == torch.bfloat16:
warnings.warn(
"calling in ipex numpy which is not share memory with torch tensor for bfloat16 input."
)
return torch._C._Tensor... | 905 | 30.241379 | 99 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/_inductor/compile_fx.py | import builtins
import contextlib
from typing import List, Optional, Union, Dict
from unittest.mock import patch
import torch
from .decomposition import get_decompositions
from .lowering import patch_lowering
@contextlib.contextmanager
def patch_codegen():
from torch._inductor.scheduler import Scheduler
from ... | 1,575 | 28.185185 | 81 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/_inductor/lowering.py | # Custom lowerings overriding those from PyTorch
import contextlib
import functools
from torch._inductor.lowering import ELEMENTWISE_TYPE_PROMOTION_KIND
lowering_overrides = {}
def _register_lowering(
aten_fn,
decomp_fn,
broadcast=False,
type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT,
... | 1,638 | 24.609375 | 84 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/_inductor/dynamo_backends.py | from torch._dynamo import register_backend
from .compiler import compile
@register_backend
def ipex(model, inputs):
return compile(model, inputs)
| 151 | 20.714286 | 42 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/_inductor/compiler.py | import torch
from torch._subclasses import FakeTensor
from torch.utils._mode_utils import no_dispatch
import builtins
import warnings
from typing import Callable, Dict, Optional, Union, List
_compiler_backend = "torchscript"
def _get_compiler_backend():
return _compiler_backend
def _set_compiler_backend(backe... | 2,000 | 27.183099 | 81 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/_inductor/decomposition.py | import logging
import torch._decomp as decomp
log = logging.getLogger(__name__)
decomposition_overrides = {}
def register_decomposition(ops):
for op in [ops] if callable(ops) else ops:
if op in decomposition_overrides:
log.warning(f"duplicate decomp: {ops}")
return decomp.register_decompo... | 591 | 25.909091 | 72 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/_inductor/codegen/cpp.py | from torch._inductor.codegen.cpp import CppScheduling
class IpexCppScheduling(CppScheduling):
def __init__(self, scheduler):
super().__init__(scheduler)
| 167 | 23 | 53 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/xpu/single_card.py | import os
import tempfile
import torch
import torch.distributed as dist
import intel_extension_for_pytorch # noqa F401
import oneccl_bindings_for_pytorch # noqa F401
class single_card_dist:
r"""DistributedDataParallel(DDP) scaling API for XPU devices on one card.
This API wraps pytorch DDP related module, ... | 5,732 | 38.537931 | 119 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/xpu/streams.py | import ctypes
import intel_extension_for_pytorch
class Stream(intel_extension_for_pytorch._C._XPUStreamBase):
def __new__(cls, device=None, priority=0, **kwargs):
with intel_extension_for_pytorch.xpu.device(device):
return super(Stream, cls).__new__(cls, priority=priority, **kwargs)
@prop... | 3,980 | 30.595238 | 92 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/xpu/memory.py | import collections
from typing import Any, Dict, Union
import intel_extension_for_pytorch
from torch.types import Device
from torch._utils import _get_device_index
def empty_cache() -> None:
r"""Releases all unoccupied cached memory currently held by the caching
allocator so that those can be used in other G... | 13,274 | 37.367052 | 96 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/xpu/utils.py | # coding: utf-8
import torch
from .. import _C
from enum import Enum
import warnings
from .. import frontend
import intel_extension_for_pytorch # noqa
def from_usm(src, dtype, shape, stride=None, device_id: int = -1) -> torch.Tensor:
"""from_usm(src, dtype, shape, stride=None, device_d=-1) -> Tensor
Convert... | 11,158 | 26.35049 | 119 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/xpu/launch.py | import platform
import subprocess
import os
import sys
import logging
from tempfile import mkstemp
import uuid
from argparse import ArgumentParser, REMAINDER
from argparse import RawTextHelpFormatter
format_str = "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
logging.basicConfig(level=logging.INFO, format=for... | 4,854 | 28.603659 | 103 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/xpu/random.py | import torch
from typing import cast, Iterable, List, Union
from torch import Tensor
from .lazy_init import _lazy_init, _lazy_call
import contextlib
from typing import Generator
import warnings
__all__ = [
"get_rng_state",
"get_rng_state_all",
"set_rng_state",
"set_rng_state_all",
"manual_seed",
... | 8,518 | 32.671937 | 104 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/xpu/cpp_extension.py | import copy
import importlib
import os
import setuptools
import subprocess
import shutil
import re
import shlex
import sys
import sysconfig
import errno
import warnings
import torch
from torch.utils.cpp_extension import _TORCH_PATH
from torch.utils.file_baton import FileBaton
from torch.utils._cpp_extension_versioner... | 55,241 | 35.901804 | 130 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/xpu/__init__.py | r"""
This package is lazily initialized, so you can always import it.
"""
from torch import serialization
from torch.storage import _StorageBase
import sys
from typing import List, Optional, Tuple, Union, Dict
import torch
import intel_extension_for_pytorch
from .lazy_init import _lazy_init, _lazy_call
from torch impo... | 17,811 | 30.637655 | 92 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/xpu/_proxy_module.py | import torch
import intel_extension_for_pytorch._C
# utils function to define base object proxy
def _proxy_module(name: str) -> type:
def init_err(self):
class_name = self.__class__.__name__
raise RuntimeError(
"Tried to instantiate proxy base class {}.".format(class_name)
... | 1,971 | 26.774648 | 80 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/xpu/generator.py | # coding: utf-8
import torch
import intel_extension_for_pytorch
# This is a WA. We will submit a PR to stock-PyTorch and make XPU backend
# supported in torch.Generator() API.
class Generator(torch._C.Generator):
def __new__(cls, device=None):
return intel_extension_for_pytorch._C.generator_new(device)
| 318 | 28 | 73 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/xpu/overrides.py | import torch
import intel_extension_for_pytorch # noqa F401
from functools import wraps
from torch.nn.parallel.scatter_gather import _is_namedtuple
def override_tensor_totype():
r"""Override _tensor_totype to avoid triggering fp64 error when printing XPU tensor on ATS-M"""
def fp64_tensor_totype_wrapper(f):... | 12,238 | 40.208754 | 99 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/xpu/amp/autocast_mode.py | import torch
class autocast(torch.amp.autocast_mode.autocast):
r"""
See :class:`torch.autocast`.
``torch.xpu.amp.autocast(args...)`` is equivalent to ``torch.autocast("xpu", args...)``
"""
def __init__(self, enabled=True, dtype=torch.bfloat16, cache_enabled=True):
super().__init__(
... | 401 | 27.714286 | 91 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/xpu/amp/__init__.py | from .autocast_mode import autocast
import intel_extension_for_pytorch
def get_autocast_xpu_dtype():
return intel_extension_for_pytorch._C.get_autocast_xpu_dtype()
def is_autocast_xpu_enabled():
return intel_extension_for_pytorch._C.is_autocast_xpu_enabled()
def set_autocast_xpu_enabled(enabled):
retu... | 497 | 25.210526 | 75 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/xpu/intrinsic/__init__.py | import torch
from torch.nn.modules.utils import _pair
from torch import nn, Tensor
from torch.jit.annotations import BroadcastingList2
from typing import List, Union
from .modules import Interaction
import intel_extension_for_pytorch
__all__ = [
"Interaction",
"nms",
"locations_to_boxes",
"roi_align",
... | 2,239 | 26.654321 | 97 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/xpu/intrinsic/modules/intrinsic.py | import torch
import intel_extension_for_pytorch # noqa F401
from torch.autograd import Function
class InteractionFuncion(Function):
@staticmethod
def forward(ctx, input_mlp, input_emb):
return torch.ops.torch_ipex.interaction(input_mlp, input_emb)
Interaction = InteractionFuncion.apply
| 308 | 22.769231 | 69 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/optim/_lars.py | import torch
from typing import Iterable
from torch import nn
"""
We recommend using create_optimizer_lars and setting bn_bias_separately=True
instead of using class Lars directly, which helps LARS skip parameters
in BatchNormalization and bias, and has better performance in general.
Polynomial Warmup ... | 6,374 | 32.552632 | 92 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/optim/_optimizer_utils.py | import torch
import copy
import types
import warnings
from copy import deepcopy
from itertools import chain
from collections import defaultdict
from ._functional import (
sgd_step,
adagrad_step,
lamb_step,
adam_step,
adamw_step,
lars_step,
)
from ._lamb import Lamb
from ._lars import Lars
from .... | 15,121 | 37.675192 | 107 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/optim/_lamb.py | import torch
from ._functional import _lamb_impl
class Lamb(torch.optim.Optimizer):
r"""Implements Lamb algorithm.
It has been proposed in `Large Batch Optimization for Deep Learning:
Training BERT in 76 minutes`_.
Args:
params (iterable): iterable of parameters to optimize or dicts defining
... | 4,230 | 37.117117 | 88 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/optim/_functional.py | r"""Functional interface, port from torch/optim/_function.py"""
import torch
from torch import Tensor
from typing import List, Optional
def is_master_weight(param, params_attr):
if len(params_attr) == 0 or param not in params_attr:
return False
_param = params_attr[param].parameter
return (
... | 36,453 | 27.106399 | 111 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/jit/_trace.py | import torch
from functools import wraps
# For CPU, wrap torch.jit.trace to disable autocast cache when using torch.jit.trace
# within the scope of torch.cpu.amp.autocast.
# See https://github.com/pytorch/pytorch/pull/63552 for more information.
# For XPU, wrap torch.jit.trace to disable the check trace to avoid the ... | 2,504 | 31.532468 | 87 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/cpu/auto_ipex.py | import os
import platform
import glob
import logging
import sys
from argparse import ArgumentParser, REMAINDER
from argparse import RawTextHelpFormatter
from tempfile import mkstemp
import uuid
format_str = "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
logging.basicConfig(level=logging.INFO, format=format_str... | 8,482 | 35.564655 | 120 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/cpu/onednn_fusion.py | import intel_extension_for_pytorch._C as core
def enable_onednn_fusion(enabled):
r"""
Enables or disables oneDNN fusion functionality. If enabled, oneDNN
operators will be fused in runtime, when intel_extension_for_pytorch
is imported.
Args:
enabled (bool): Whether to enable oneDNN fusion... | 723 | 25.814815 | 77 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/cpu/graph_capture.py | import copy
import torch
from torch._dynamo.backends.common import fake_tensor_unsupported
from torch.jit._trace import TracerWarning
from enum import IntEnum
from typing import List
import functools
import logging
import threading
import warnings
class RunMethods(IntEnum):
JIT = 1
TorchDynamo = 2
Eager... | 5,510 | 46.921739 | 118 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/cpu/nn/linear_fuse_eltwise.py | import torch
import intel_extension_for_pytorch as ipex # noqa F401
from intel_extension_for_pytorch.nn.utils._weight_prepack import _IPEXLinear as _IPEXLinear
import enum
class EltwiseType(enum.IntEnum):
NotFused = 0
ReLU = 1
Sigmoid = 2
class IPEXLinearEltwise(torch.nn.Module):
def __init__(self,... | 998 | 28.382353 | 91 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/cpu/nn/_roi_align.py | from torch import nn, Tensor
from torch.jit.annotations import BroadcastingList2
from ...nn import functional as F
class RoIAlign(nn.Module):
"""
See :func:`roi_align`.
"""
def __init__(
self,
output_size: BroadcastingList2[int],
spatial_scale: float,
sampling_ratio: ... | 1,198 | 26.883721 | 64 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/cpu/nn/_embeddingbag.py | import torch
import warnings
import intel_extension_for_pytorch._C as core
from typing import Optional, Tuple
Tensor = torch.Tensor
def _embedding_bag_fast_path_sum(
weights: Tensor,
indices: Tensor,
offsets: Tensor,
mode: int = 0,
scale_grad_by_freq: bool = False,
per_sample_weights: Optiona... | 3,460 | 26.251969 | 100 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/cpu/nn/interaction.py | import torch
from torch.autograd import Function
def interaction(*args):
r"""
Get the interaction feature beyond different kinds of features (like gender
or hobbies), used in DLRM model.
For now, we only optimized "dot" interaction at `DLRM Github repo
<https://github.com/facebookresearch/dlrm/bl... | 1,570 | 32.425532 | 90 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/cpu/nn/frozen_batch_norm.py | import torch
from torch import nn
class FrozenBatchNorm2d(nn.Module):
r"""
BatchNorm2d where the batch statistics and the affine parameters are fixed
Args:
num_features (int): :math:`C` from an expected input of size :math:`(N, C, H, W)`
Shape
- Input: :math:`(N, C, H, W)`
- ... | 1,002 | 29.393939 | 89 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/cpu/nn/_roi_align_helper.py | # This Python file uses the following encoding: utf-8
from typing import List, Union
import torch
from torch import Tensor
from torch.nn.modules.utils import _pair
from torch.jit.annotations import BroadcastingList2
def _cat(tensors: List[Tensor], dim: int = 0) -> Tensor:
"""
Efficient version of torch.cat ... | 5,016 | 39.788618 | 117 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/cpu/launch/launcher_multi_instances.py | import sys
import subprocess
import os
import intel_extension_for_pytorch.cpu.auto_ipex as auto_ipex
from .launcher_base import Launcher
class MultiInstancesLauncher(Launcher):
"""
Launcher for single instance and multi-instance
"""
def __init__(self, logger=None, lscpu_txt=""):
super(MultiIn... | 12,435 | 36.914634 | 132 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/cpu/launch/launch.py | import platform
import os
import glob
import argparse
from argparse import SUPPRESS, OPTIONAL, ZERO_OR_MORE
import logging
from datetime import datetime
import intel_extension_for_pytorch.cpu.auto_ipex as auto_ipex
from .launcher_distributed import DistributedTrainingLauncher
from .launcher_multi_instances import Multi... | 15,263 | 32.473684 | 126 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/cpu/autocast/_autocast_mode.py | import torch
import intel_extension_for_pytorch._C as core
import warnings
from typing import Any, Optional
from torch.types import _dtype
# Expand torch.amp.autocast_mode.autocast to support both torch.bfloat16 and torch.float16 on cpu.
class _mode_autocast(torch.amp.autocast_mode.autocast):
def __init__(
... | 5,322 | 37.572464 | 114 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/cpu/autocast/_grad_scaler.py | # Copy grad scaler from PyTorch for fp16 on CPU
import torch
from collections import defaultdict, abc
from enum import Enum
from typing import Any, Dict, List, Optional, Tuple
import intel_extension_for_pytorch._C as core
class _MultiDeviceReplicator(object):
"""
Lazily serves copies of a tensor to requested... | 24,298 | 44.333955 | 119 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/cpu/runtime/multi_stream.py | import torch
import torch.nn as nn
from typing import Union
import intel_extension_for_pytorch._C as core
from .cpupool import CPUPool
from .task import Task
import copy
import warnings
class MultiStreamModuleHint(object):
r"""
MultiStreamModuleHint is a hint to MultiStreamModule about how to split the inputs... | 28,592 | 45.568404 | 116 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/cpu/runtime/cpupool.py | import functools
import warnings
import intel_extension_for_pytorch as ipex
from .runtime_utils import get_core_list_of_node_id
class CPUPool(object):
r"""
An abstraction of a pool of CPU cores used for intra-op parallelism.
Args:
core_ids (list): A list of CPU cores' ids used for intra-op parall... | 3,604 | 34 | 100 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/cpu/runtime/task.py | import torch
import intel_extension_for_pytorch as ipex
from .cpupool import CPUPool
class Task(object):
r"""
An abstraction of computation based on PyTorch module and is scheduled
asynchronously.
Args:
model (torch.jit.ScriptModule or torch.nn.Module): The input module.
cpu_pool (int... | 1,254 | 32.918919 | 84 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/cpu/tpp/fused_bert.py | import torch
from torch import nn
from .utils.blocked_layout import (
BlockedParameter,
BlockedModule,
BlockedTensor,
get_blocking_signature,
)
import pkg_resources
import warnings
from .optim import AdamW, SGD
import intel_extension_for_pytorch._C as torch_ipex_cpp
try:
from transformers.modeling_u... | 48,905 | 35.388393 | 128 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/cpu/tpp/optim.py | import math
from typing import Callable, Iterable, Tuple
import torch
from torch.optim import Optimizer
from torch.optim.optimizer import required
import intel_extension_for_pytorch._C as ipex_cpp
class SGD(Optimizer):
r"""Implements low precision stochastic gradient descent with extra state."""
def __init__... | 29,296 | 38.590541 | 111 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/cpu/tpp/utils/blocked_layout.py | import torch
# import math
# from enum import Enum
# from collections import OrderedDict
def _prod(myList):
ret = 1
for x in myList:
if x is None:
return None
ret = ret * x
return ret
def get_vnni_blocking(dtype):
if dtype == torch.float32:
return 1
elif dtyp... | 14,240 | 33.069378 | 90 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/cpu/utils/_cpu_isa.py | # This Python file uses the following encoding: utf-8
import intel_extension_for_pytorch._isa_help as isa
import sys
def check_avx2_support():
return isa._check_isa_avx2()
def check_minimal_isa_support():
err_msg = "ERROR! Intel® Extension for PyTorch* only works on machines with instruction sets equal or ... | 454 | 27.4375 | 113 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/cpu/utils/linear_bn_folding.py | import torch.nn as nn
import torch.fx as fx
import torch.fx.experimental.optimization as optimization
from torch.nn.utils.fusion import fuse_linear_bn_eval
import copy
def linear_bn_fuse(model: nn.Module, inplace=False) -> nn.Module:
# implementation follows https://github.com/pytorch/pytorch/blob/master/torch/fx... | 1,366 | 35.945946 | 117 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/cpu/utils/_custom_fx_tracer.py | import torch
import torch.fx as fx
import types
def override_is_leaf_module():
fx_tracer = fx.Tracer
orig_is_leaf_module_fn = fx_tracer.is_leaf_module
def ipex_is_leaf_module_fn(
self, m: torch.nn.Module, module_qualified_name: str
) -> bool:
is_ipex = m.__module__.startswith("intel_e... | 539 | 26 | 82 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/cpu/utils/verbose.py | import torch
import intel_extension_for_pytorch._C as core
VERBOSE_OFF = 0
VERBOSE_ON = 1
VERBOSE_ON_CREATION = 2
class verbose(object):
"""
On-demand oneDNN verbosing functionality
To make it easier to debug performance issues, oneDNN can dump verbose
messages containing information like kernel siz... | 2,400 | 30.592105 | 112 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/cpu/hypertune/conf/config.py | import copy
import os
from pathlib import Path
import ast
import re
import yaml
from schema import Schema, And, Use, Optional, Or, Hook
from .dotdict import DotDict
from ..strategy import STRATEGIES
from intel_extension_for_pytorch.cpu.launch import CPUPoolList
# ### tuning ####
tuning_default = {"strategy": "grid", "... | 8,887 | 33.184615 | 89 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/cpu/hypertune/example/resnet50.py | import torch
import torchvision.models as models
def inference(model, data):
with torch.no_grad():
# warm up
for _ in range(100):
model(data)
# measure
import time
measure_iter = 100
start = time.time()
for _ in range(measure_iter):
... | 2,247 | 27.455696 | 126 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/utils/channels_last_1d.py | import torch
# This is a work-around to convert 3d tensor to channels last format.
# Theoretically, transpose(permute/view)-contiguous(to)-transpose(permute/view)
# can convert 3d tensor to channels last. However, this formula cannot convert all
# the shapes. It is because tensor suggest_memory_format may be different... | 1,878 | 35.134615 | 85 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/quantization/_smooth_quant.py | import torch
from torch.ao.quantization import (
UniformQuantizationObserverBase,
HistogramObserver,
PerChannelMinMaxObserver,
)
import copy
class SmoothQuantActivationObserver(UniformQuantizationObserverBase):
"""
For SmoothQuant, see https://arxiv.org/pdf/2211.10438.pdf
Activation shape = T ... | 10,525 | 37 | 105 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/quantization/_utils.py | import enum
import json
from collections import OrderedDict
from typing import Callable, Optional
import inspect
import numbers
import torch
import torch.nn as nn
from torch import _VF
import torch.nn.functional as F
from torch.quantization.qconfig import QConfig
from intel_extension_for_pytorch.nn.functional import i... | 51,792 | 40.236465 | 130 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/quantization/_autotune.py | # This Python file uses the following encoding: utf-8
import sys
import subprocess
import time
def autotune(
prepared_model,
calib_dataloader,
eval_func,
sampling_sizes=None,
accuracy_criterion=None,
tuning_time=0,
):
r"""
Automatic accuracy-driven tuning helps users quickly find out ... | 3,457 | 37.853933 | 124 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/quantization/_quantize_utils.py | import os
import copy
from typing import List, Dict, Tuple, Any, Optional
import torch
from torch.fx.node import map_aggregate
from torch.ao.quantization import PlaceholderObserver
from torch.quantization.qconfig import QConfig
from torch.nn.utils.rnn import PackedSequence
from ._utils import (
get_torch_function_... | 36,581 | 45.541985 | 129 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/quantization/_qconfig.py | import torch
from torch.ao.quantization import (
PlaceholderObserver,
PerChannelMinMaxObserver,
HistogramObserver,
QConfig,
QConfigMapping,
)
from ._smooth_quant import SmoothQuantActivationObserver, SmoothQuantWeightObserver
_default_weight_observer = PerChannelMinMaxObserver.with_args(
dtype... | 3,627 | 37.189474 | 116 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/quantization/_recipe.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from intel_extension_for_pytorch.nn.functional import interaction
from ._utils import ParentNode, set_node_output_quantized
add_inplace_ops = [str(torch.Tensor.add_)]
add_ops = [str(torch.add), str(torch.Tensor.add)]
elt_wise_q_ops = [str(torch.Tensor... | 23,582 | 41.64557 | 132 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/quantization/_quantization_state.py | from typing import Callable, List, Tuple, Any, Optional, Dict
import torch
import torch.nn.functional as F
import intel_extension_for_pytorch._C as core
from ._utils import (
OpQuantizeabilityType,
is_leaf,
get_fqn_valid_for_module_dict_key,
quantized_modules_has_weights,
int8_int8_ops,
)
from ._qu... | 46,983 | 40.689441 | 126 | py |
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