entry_point stringlengths 1 65 | original_triton_python_code stringlengths 208 619k | optimised_triton_code stringlengths 1.15k 275k | repo_name stringlengths 7 115 | module_name stringlengths 1 65 | synthetic bool 1
class | uuid int64 0 18.5k | licenses listlengths 1 6 | stars int64 0 19.8k | sha stringlengths 40 40 | repo_link stringlengths 72 180 |
|---|---|---|---|---|---|---|---|---|---|---|
GlobalAvgPool2d | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | Zhang-Jack/adversarial_yolo2 | GlobalAvgPool2d | false | 18,190 | [
"MIT"
] | 8 | 91c2a4793047f656482cebf0309984db823e8030 | https://github.com/Zhang-Jack/adversarial_yolo2/tree/91c2a4793047f656482cebf0309984db823e8030 |
FocalLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | EnochMHforever/CCF-BDCI2019-Multi-person-Face-Recognition-Competition-Baseline-master | FocalLoss | false | 11,418 | [
"MIT"
] | 0 | 5a1ac28dbfe1099f62e61975b0c1d7c43980e067 | https://github.com/EnochMHforever/CCF-BDCI2019-Multi-person-Face-Recognition-Competition-Baseline-master/tree/5a1ac28dbfe1099f62e61975b0c1d7c43980e067 |
CNNCOVID19 | from _paritybench_helpers import _mock_config
import torch
import torch.nn.functional as F
import torch.nn as nn
class CNNCOVID19(nn.Module):
def __init__(self, args):
super(CNNCOVID19, self).__init__()
self.conv1 = nn.Conv2d(in_channels=3, out_channels=8, kernel_size=3)
self.fc1 = nn.Lin... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | NaiboWang/HFL-CS6203-NaiboShiqi | CNNCOVID19 | false | 11,133 | [
"MIT"
] | 0 | 4bab35a20f1ec1229b0011c952d93c341579c402 | https://github.com/NaiboWang/HFL-CS6203-NaiboShiqi/tree/4bab35a20f1ec1229b0011c952d93c341579c402 |
CNNBlock | import torch
import torch.nn.functional as F
import torch.nn as nn
class CNNLayer(nn.Module):
"""Conv1d layer.
nn.Conv1d layer require the input shape is (batch_size, in_channels, length),
however, our input shape is (batch_size, length, in_channels), so we need to
transpose our input data into (B, C,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | WiseDoge/Text-Classification-PyTorch | CNNBlock | false | 18,069 | [
"MIT"
] | 6 | 9371eeed6bd7ecf1d529c8f2a6c997fcde67a559 | https://github.com/WiseDoge/Text-Classification-PyTorch/tree/9371eeed6bd7ecf1d529c8f2a6c997fcde67a559 |
ChannelSpatialSELayer3D | import torch
import torch.nn as nn
import torch.nn.functional as F
class ChannelSELayer3D(nn.Module):
"""
3D extension of Squeeze-and-Excitation (SE) block described in:
*Hu et al., Squeeze-and-Excitation Networks, arXiv:1709.01507*
*Zhu et al., AnatomyNet, arXiv:arXiv:1808.05238*
"""
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | EmmanuelleB985/Head-and-Neck-Tumour-Segmentation-and-Prediction-of-Patient-Survival | ChannelSpatialSELayer3D | false | 11,407 | [
"MIT"
] | 0 | 347883eb6dd5daebba091119ede7a9f5b78076d1 | https://github.com/EmmanuelleB985/Head-and-Neck-Tumour-Segmentation-and-Prediction-of-Patient-Survival/tree/347883eb6dd5daebba091119ede7a9f5b78076d1 |
L1Loss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import functools
impor... | Andrew-Zhu/DyFPN | L1Loss | false | 7,722 | [
"Apache-2.0"
] | 32 | a74463b59c4ce28253c2449a07c0f6692a0147a1 | https://github.com/Andrew-Zhu/DyFPN/tree/a74463b59c4ce28253c2449a07c0f6692a0147a1 |
cnn_7layer_alt | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Mahoumaru/auto_LiRPA | cnn_7layer_alt | false | 11,682 | [
"BSD-3-Clause"
] | 0 | b03a6c36eb1b921726778359d6d2b94e0cd7e480 | https://github.com/Mahoumaru/auto_LiRPA/tree/b03a6c36eb1b921726778359d6d2b94e0cd7e480 |
PatchEmbed3D | import torch
import torch.utils.data
from itertools import chain as chain
import torch.nn as nn
class PatchEmbed3D(nn.Module):
""" Image to Patch Embedding
"""
def __init__(self, img_size=224, temporal_resolution=4, in_chans=3,
patch_size=16, z_block_size=2, embed_dim=768, flatten=True):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
from itertools import chain as chain
import torch.nn as ... | dylan-campbell/Motionformer | PatchEmbed3D | false | 15,301 | [
"Apache-2.0"
] | 153 | 6c860614a3b252c6163971ba20e61ea3184d5291 | https://github.com/dylan-campbell/Motionformer/tree/6c860614a3b252c6163971ba20e61ea3184d5291 |
DivLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import... | XianyuanLiu/Transfer-Learning-Library | DivLoss | false | 10,140 | [
"MIT"
] | 0 | 25f83f32437032df88ca6101ecd1f63ec7a0aa2c | https://github.com/XianyuanLiu/Transfer-Learning-Library/tree/25f83f32437032df88ca6101ecd1f63ec7a0aa2c |
h_sigmoid | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guard... | anonymous2022ijcai/RGSL | h_sigmoid | false | 1,449 | [
"MIT"
] | 0 | 11c38ee50d50127c0f7c2a137bdb21ca5f7f3644 | https://github.com/anonymous2022ijcai/RGSL/tree/11c38ee50d50127c0f7c2a137bdb21ca5f7f3644 |
BinaryDivide | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import abc
import inspect
import warnings
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typ... | Johnsonms/NNI_master | BinaryDivide | false | 11,570 | [
"MIT"
] | 0 | e5e5c7aed89cf3189cffe1056464833c15eb54ff | https://github.com/Johnsonms/NNI_master/tree/e5e5c7aed89cf3189cffe1056464833c15eb54ff |
PositionalEncoding | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.optim
import torch.nn.init
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_... | NimrodShabtay/transformers-dip | PositionalEncoding | false | 8,592 | [
"MIT"
] | 25 | 61bc3008114ca950e7ea6341ae8ff317d9353f40 | https://github.com/NimrodShabtay/transformers-dip/tree/61bc3008114ca950e7ea6341ae8ff317d9353f40 |
SEModule | import torch
import torch.nn as nn
import torch.nn.functional as F
class SEModule(nn.Module):
def __init__(self, planes, compress_rate):
super(SEModule, self).__init__()
self.conv1 = nn.Conv2d(planes, planes // compress_rate, kernel_size
=1, stride=1, bias=True)
self.conv2 = n... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | AlexTintin/Face_Recognition_CV_Project | SEModule | false | 11,195 | [
"MIT"
] | 0 | 6becb159dd3d8f547d617983bd422e3f2a9fb52e | https://github.com/AlexTintin/Face_Recognition_CV_Project/tree/6becb159dd3d8f547d617983bd422e3f2a9fb52e |
CrossEntropy | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | ChenyangWang1/HRnet_Face_Parsing | CrossEntropy | false | 8,888 | [
"MIT"
] | 0 | 07ac757147865c95b0da1d15ea32608f38ca099c | https://github.com/ChenyangWang1/HRnet_Face_Parsing/tree/07ac757147865c95b0da1d15ea32608f38ca099c |
ArgMax | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.sparse
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.as... | cwerner/deadtrees | ArgMax | false | 6,506 | [
"Apache-2.0"
] | 1 | 15ddfec58c4a40f22f9c1e2424fb535df4d29b03 | https://github.com/cwerner/deadtrees/tree/15ddfec58c4a40f22f9c1e2424fb535df4d29b03 |
Normalize | import torch
import torch.nn as nn
from itertools import product as product
import torch.onnx
class Normalize(nn.Module):
def __init__(self, n_channels, scale=1.0):
super(Normalize, self).__init__()
self.n_channels = n_channels
self.scale = scale
self.eps = 1e-10
self.weig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
from itertools import product as product
import torch.onn... | Janus1984/Msnhnet | Normalize | false | 13,872 | [
"MIT"
] | 546 | 4e09f2501ba8db789f0a20441a357de3ba468f10 | https://github.com/Janus1984/Msnhnet/tree/4e09f2501ba8db789f0a20441a357de3ba468f10 |
SeqKD | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | ardasatata/SLR | SeqKD | false | 1,467 | [
"Apache-2.0"
] | 0 | a001a19775646cf7bab92f59e0d40019efb909c6 | https://github.com/ardasatata/SLR/tree/a001a19775646cf7bab92f59e0d40019efb909c6 |
ImpalaBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | abhay97ps/visual-control-ppo-procgen | ImpalaBlock | false | 1,362 | [
"MIT"
] | 0 | 765fe1ddb289d384abddc4df8eb865379c8da76a | https://github.com/abhay97ps/visual-control-ppo-procgen/tree/765fe1ddb289d384abddc4df8eb865379c8da76a |
Attention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | jomavera/DRL_HFV | Attention | false | 15,735 | [
"MIT"
] | 114 | 043e32805ec79fd35281b864659c194d7b89f5bc | https://github.com/jomavera/DRL_HFV/tree/043e32805ec79fd35281b864659c194d7b89f5bc |
BasicBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.... | RaoUmer/ISRResCNet | BasicBlock | false | 17,839 | [
"MIT"
] | 6 | 8175bb9efa5bba2cce4ad86616219209c20b7244 | https://github.com/RaoUmer/ISRResCNet/tree/8175bb9efa5bba2cce4ad86616219209c20b7244 |
EmbeddingModule | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Finspire13/Towards-Unified-Surgical-Skill-Assessment | EmbeddingModule | false | 8,115 | [
"MIT"
] | 13 | 2c398d4e93889135762e4a91fc4676bfb7706fb0 | https://github.com/Finspire13/Towards-Unified-Surgical-Skill-Assessment/tree/2c398d4e93889135762e4a91fc4676bfb7706fb0 |
TemporalPooling | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
class TemporalPooling(nn.Module):
def __init__(self, frames, kernel_size=3, stride=2, mode='avg'):
"""
Parameters
----------
frames (int): nu... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
assert_size_st... | peter-yys-yoon/traditional-dance-recognition | TemporalPooling | false | 12,958 | [
"Apache-2.0"
] | 0 | be4939d53b838624a04dba0826532c65421d1325 | https://github.com/peter-yys-yoon/traditional-dance-recognition/tree/be4939d53b838624a04dba0826532c65421d1325 |
GroupedGRUMS | import torch
from torch import Tensor
from typing import List
from typing import Tuple
from torch import nn
from functools import partial
from torch.nn.parameter import Parameter
class GroupedGRULayerMS(nn.Module):
def __init__(self, in_ch: 'int', out_ch: 'int', n_freqs: 'int',
n_groups: 'int', bias: 'bo... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import T... | Rikorose/DeepFilterNet | GroupedGRUMS | false | 14,354 | [
"ECL-2.0",
"Apache-2.0",
"MIT"
] | 54 | afe6bfb53efae70207e18df7ed372c2cfe337fee | https://github.com/Rikorose/DeepFilterNet/tree/afe6bfb53efae70207e18df7ed372c2cfe337fee |
L2Norm | import torch
from torch import nn
class L2Norm(nn.Module):
def __init__(self, n_dims, scale=20.0, eps=1e-10):
"""L2 normalization layer.
Args:
n_dims (int): Number of dimensions to be normalized
scale (float, optional): Defaults to 20..
eps (float, optional): ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | CVPR2022-911/PPH | L2Norm | false | 8,979 | [
"Apache-2.0"
] | 0 | f066933525aaeef412b8d166ef167f00170b5428 | https://github.com/CVPR2022-911/PPH/tree/f066933525aaeef412b8d166ef167f00170b5428 |
TextureFinder | import torch
import torch.nn as nn
import torch.nn.functional as F
class TextureFinder(nn.Module):
def __init__(self):
super(TextureFinder, self).__init__()
self.encoder_conv1 = nn.Conv2d(in_channels=1, out_channels=4,
kernel_size=4, stride=2, padding=1, dilation=1, groups=1, bias=Tru... | import torch
from torch import device
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from... | paucarre/staal | TextureFinder | false | 4,142 | [
"MIT"
] | 0 | 1635e514f0ed978a08c078afd258980bcb6f0cec | https://github.com/paucarre/staal/tree/1635e514f0ed978a08c078afd258980bcb6f0cec |
FilterResponseNorm | import torch
import torch.nn as nn
import torch.nn.functional as func
import torch.jit
import torch.nn
class FilterResponseNorm(nn.Module):
def __init__(self, in_size, eps=1e-16):
super().__init__()
self.eps = eps
self.in_size = in_size
self.register_parameter('scale', nn.Paramete... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import... | ankmathur96/torchsupport | FilterResponseNorm | false | 3,200 | [
"MIT"
] | 0 | 77bf4a90b8770a408665e2604428808c3ed2f979 | https://github.com/ankmathur96/torchsupport/tree/77bf4a90b8770a408665e2604428808c3ed2f979 |
Buck | import torch
import torch.nn
class Buck(torch.nn.Module):
def __init__(self, A=1.0, B=1.0, C=1.0):
super(Buck, self).__init__()
self.A = torch.nn.Parameter(torch.Tensor([A]))
self.B = torch.nn.Parameter(torch.Tensor([B]))
self.C = torch.nn.Parameter(torch.Tensor([C]))
def Buc... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | torchmd/mdgrad | Buck | false | 16,604 | [
"MIT"
] | 54 | 77bd7685b74b41acf54a9483546e1e8cb545eb01 | https://github.com/torchmd/mdgrad/tree/77bd7685b74b41acf54a9483546e1e8cb545eb01 |
Mix2Pooling | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch._C
import torch.serialization
from torch import nn
from typing import *
assert_size_stride = torch._C._dynamo.guards.assert_siz... | shuaizzZ/mmsegmentation | Mix2Pooling | false | 4,323 | [
"Apache-2.0"
] | 0 | a6c6b348dbf8c4a0a39ffbdb832a1e82309c533c | https://github.com/shuaizzZ/mmsegmentation/tree/a6c6b348dbf8c4a0a39ffbdb832a1e82309c533c |
equalized_linear | import torch
import torch.nn as nn
from torch.nn.init import normal
import torch.utils.data
def _calculate_fan_in_and_fan_out(tensor):
dimensions = tensor.ndimension()
if dimensions < 2:
raise ValueError(
'Fan in and fan out can not be computed for tensor with less than 2 dimensions'
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from torch.nn.init import normal
import torch.utils.data
a... | mingo-x/pggan-pytorch | equalized_linear | false | 7,234 | [
"MIT"
] | 1 | a1dde73cd4df52476fe7c948d81fa9caea8070a5 | https://github.com/mingo-x/pggan-pytorch/tree/a1dde73cd4df52476fe7c948d81fa9caea8070a5 |
MultiHeadedAttention | import math
import torch
from typing import Optional
import torch.nn.functional as F
from torch import nn
def attention(query, key, value, mask=None, dropout=None):
"""Compute 'Scaled Dot Product Attention'
"""
d_k = query.size(-1)
scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(d_k)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | malhotraa/transformer-experiments | MultiHeadedAttention | false | 10,463 | [
"MIT"
] | 0 | 82931b89b14d26dbd6e4ffef8d6f2fd8b7279c0f | https://github.com/malhotraa/transformer-experiments/tree/82931b89b14d26dbd6e4ffef8d6f2fd8b7279c0f |
StableLayerNorm | import torch
from torch import nn
class StableLayerNorm(nn.Module):
def __init__(self, dim):
super().__init__()
self.norm = nn.LayerNorm(dim)
def forward(self, x):
x = x / x.amax(dim=-1, keepdim=True).detach()
return self.norm(x)
def get_inputs():
return [torch.rand([4,... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_... | lucidrains/nuwa-pytorch | StableLayerNorm | false | 15,975 | [
"MIT"
] | 310 | bf1f3dc1126ba0a24a280bd7412a8082e5013b46 | https://github.com/lucidrains/nuwa-pytorch/tree/bf1f3dc1126ba0a24a280bd7412a8082e5013b46 |
ArcMarginProduct | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Lascarfo/kaggle-landmark-recognition-2020-1st-place | ArcMarginProduct | false | 2,499 | [
"MIT"
] | 0 | f9007d81e59ecd1311bdea5586a426b8973a2eb8 | https://github.com/Lascarfo/kaggle-landmark-recognition-2020-1st-place/tree/f9007d81e59ecd1311bdea5586a426b8973a2eb8 |
FPNHead | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | lePossum/DeblurGANv2 | FPNHead | false | 10,489 | [
"BSD-2-Clause"
] | 0 | b02c86de98f98604e2416a3a6121110ede7a2de9 | https://github.com/lePossum/DeblurGANv2/tree/b02c86de98f98604e2416a3a6121110ede7a2de9 |
MSBlock | import torch
import torch.nn as nn
class MSBlock(nn.Module):
def __init__(self, c_in, rate=4):
super(MSBlock, self).__init__()
self.rate = rate
self.conv = nn.Conv2d(c_in, 32, 3, stride=1, padding=1)
self.relu = nn.ReLU(inplace=True)
dilation = self.rate * 1 if self.rate >... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Ding1119/BDCN-Fiber_Detect | MSBlock | false | 9,017 | [
"MIT"
] | 0 | 7f3db5210a1a87d02c7ef8e79038ba00a8e5ef62 | https://github.com/Ding1119/BDCN-Fiber_Detect/tree/7f3db5210a1a87d02c7ef8e79038ba00a8e5ef62 |
BertLayerNormNoVar | import torch
import torch.nn as nn
class BertLayerNormNoVar(nn.Module):
def __init__(self, hidden_size, eps=1e-12):
super(BertLayerNormNoVar, self).__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.bias = nn.Parameter(torch.zeros(hidden_size))
self.variance_epsil... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | vtu81/auto_LiRPA | BertLayerNormNoVar | false | 16,689 | [
"BSD-3-Clause"
] | 161 | 294152077c0abfafb5d62fee39335e60eff087b4 | https://github.com/vtu81/auto_LiRPA/tree/294152077c0abfafb5d62fee39335e60eff087b4 |
RMSNorm | import torch
from torch import nn
class RMSNorm(nn.Module):
def __init__(self, dim, eps=1e-08):
super().__init__()
self.scale = dim ** -0.5
self.eps = eps
self.g = nn.Parameter(torch.ones(dim))
def forward(self, x):
_norm = torch.norm(x, dim=-1, keepdim=True) * self.s... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_... | antofuller/configaformers | RMSNorm | false | 14,877 | [
"Apache-2.0"
] | 51 | 293253cd35d96c8a24c4004ba3d24fc6dc85a260 | https://github.com/antofuller/configaformers/tree/293253cd35d96c8a24c4004ba3d24fc6dc85a260 |
ExponentialMSE | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
a... | dattientran/attorch | ExponentialMSE | false | 12,388 | [
"MIT"
] | 0 | 469b225846c6d8a7d833ebac19d040c7a407a0ff | https://github.com/dattientran/attorch/tree/469b225846c6d8a7d833ebac19d040c7a407a0ff |
CompositeActivation | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_c... | nicofirst1/lucent | CompositeActivation | false | 12,921 | [
"Apache-2.0"
] | 0 | 1e249918e91cc04117368826cd7a192bd8cf2046 | https://github.com/nicofirst1/lucent/tree/1e249918e91cc04117368826cd7a192bd8cf2046 |
FeedForward | import torch
import torch.nn.functional as F
import torch.nn as nn
class Linear(nn.Module):
def __init__(self, in_dim, out_dim, bias=True, w_init='linear'):
super(Linear, self).__init__()
self.linear = nn.Linear(in_dim, out_dim, bias=bias)
nn.init.xavier_uniform_(self.linear.weight, gain=... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | poria-cat/Transformer-TTS-Pytorch | FeedForward | false | 10,756 | [
"MIT"
] | 0 | 1e9e2dccc16c17372bf86ca73001f76645f53338 | https://github.com/poria-cat/Transformer-TTS-Pytorch/tree/1e9e2dccc16c17372bf86ca73001f76645f53338 |
ConvLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
im... | Gradient-PG/live-nst | ConvLayer | false | 17,320 | [
"MIT"
] | 5 | 02244172646375ff4a4a417bc8220064fadae5a9 | https://github.com/Gradient-PG/live-nst/tree/02244172646375ff4a4a417bc8220064fadae5a9 |
FilterNorm | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
from torch.nn.init import calculate_gain
assert_size_stri... | danyayay/ynet_adaptive | FilterNorm | false | 1,793 | [
"MIT"
] | 0 | f1daea6f3d5ec8a7349c2ee72bf742df83786103 | https://github.com/danyayay/ynet_adaptive/tree/f1daea6f3d5ec8a7349c2ee72bf742df83786103 |
ConvBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dyn... | kacel33/ActionAI_PC | ConvBlock | false | 15,764 | [
"MIT"
] | 1,311 | a0528f49ea61cc07d7c1e9a3cd6846e5f50cfae7 | https://github.com/kacel33/ActionAI_PC/tree/a0528f49ea61cc07d7c1e9a3cd6846e5f50cfae7 |
PreNet | import torch
import torch.nn as nn
import torch.nn.functional as F
class PreNet(nn.Module):
def __init__(self, in_dims, fc1_dims=256, fc2_dims=128, dropout=0.5):
super().__init__()
self.fc1 = nn.Linear(in_dims, fc1_dims)
self.fc2 = nn.Linear(fc1_dims, fc2_dims)
self.p = dropout
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | NarutoUA/WaveRNN | PreNet | false | 9,385 | [
"MIT"
] | 0 | ed80c3f092b9c086d42af51a7f2545727ed1610c | https://github.com/NarutoUA/WaveRNN/tree/ed80c3f092b9c086d42af51a7f2545727ed1610c |
WDV29LayerNormalization | import numbers
import torch
import torch.nn.functional as F
import torch.utils.data
from torch.nn import Parameter
import torch.onnx.operators
from torch.nn.parameter import Parameter
from torch.nn import init
import torch.optim
import torch.optim.lr_scheduler
class WDV29LayerNormalization(torch.nn.Module):
"""Ap... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import numbers
import torch.utils.data
from torch.nn import Parameter
import to... | Lollipop321/weight-distillation | WDV29LayerNormalization | false | 5,561 | [
"BSD-3-Clause"
] | 1 | cfc76ec58e3e88094dde1825287b2968f9718431 | https://github.com/Lollipop321/weight-distillation/tree/cfc76ec58e3e88094dde1825287b2968f9718431 |
StatsNet | import torch
import torch.nn as nn
class StatsNet(nn.Module):
def __init__(self):
super(StatsNet, self).__init__()
def forward(self, x):
x = x.view(x.data.shape[0], x.data.shape[1], x.data.shape[2] * x.
data.shape[3])
mean = torch.mean(x, 2)
std = torch.std(x, 2)
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | angelicagardner/ensemble-cnn-deepfakes-detection | StatsNet | false | 1,438 | [
"BSD-3-Clause"
] | 0 | 8740d2317848250249c741e0af5c4cbbe2d8af46 | https://github.com/angelicagardner/ensemble-cnn-deepfakes-detection/tree/8740d2317848250249c741e0af5c4cbbe2d8af46 |
AE_2D_v3 | import torch
import torch.nn as nn
import torch.utils.data
class AE_2D_v3(nn.Module):
def __init__(self, n_features=4):
super(AE_2D_v3, self).__init__()
self.en1 = nn.Linear(n_features, 100)
self.en2 = nn.Linear(100, 200)
self.en3 = nn.Linear(200, 100)
self.en4 = nn.Linear... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | gitter-badger/HEPAutoencoders | AE_2D_v3 | false | 12,435 | [
"Apache-2.0"
] | 0 | 43010cd66fa4335a04b30b87926148e1c8d92de9 | https://github.com/gitter-badger/HEPAutoencoders/tree/43010cd66fa4335a04b30b87926148e1c8d92de9 |
GeM | import torch
import torch.nn as nn
class GeM(nn.Module):
def __init__(self, dim=1, p=0.0, eps=1e-06):
super(GeM, self).__init__()
self.p = nn.Parameter(torch.ones(()) * p, requires_grad=True)
self.eps = eps
self.dim = dim
def forward(self, x):
return self.gem(x, p=sel... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | layumi/dgcnn | GeM | false | 10,390 | [
"MIT"
] | 0 | a7b58796ffe549f2d8bdb06a84f62aba03e1d3a1 | https://github.com/layumi/dgcnn/tree/a7b58796ffe549f2d8bdb06a84f62aba03e1d3a1 |
FbetaLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | jokingbear/DM | FbetaLoss | false | 6,978 | [
"MIT"
] | 1 | 9c4dada1756f3d866455a397511d4f3bacfadc60 | https://github.com/jokingbear/DM/tree/9c4dada1756f3d866455a397511d4f3bacfadc60 |
LandmarkHead | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from itertools import product as product
assert_size_strid... | FacePerceiver/facer | LandmarkHead | false | 8,126 | [
"MIT"
] | 12 | cbb01dc457f3713050e89af7b2c9c0d98663842c | https://github.com/FacePerceiver/facer/tree/cbb01dc457f3713050e89af7b2c9c0d98663842c |
SpatialGatherModule | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch._C
import torch.serialization
class SpatialGatherModule(nn.Module):
"""Aggregate the context features according to the initial predicted
probability distribution.
Employ the soft-weighted method to aggregate the context.
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | CarnoZhao/mmsegmentation | SpatialGatherModule | false | 7,849 | [
"Apache-2.0"
] | 18 | bdaf3d93c4d33c3f0c15f95879fdd7ab78290c1c | https://github.com/CarnoZhao/mmsegmentation/tree/bdaf3d93c4d33c3f0c15f95879fdd7ab78290c1c |
InstanceNorm | from torch.nn import Module
import torch
from torch import nn
import torch.utils.data
import torch.nn.functional
import torch.autograd
class InstanceNorm(Module):
"""
## Instance Normalization Layer
Instance normalization layer $\\text{IN}$ normalizes the input $X$ as follows:
When input $X \\in \\m... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch.nn import Module
from torch import nn
import torch.utils.data
import... | Hadryan/nn | InstanceNorm | false | 9,366 | [
"MIT"
] | 0 | b10e3dea2c7e1f6569bfdf8e1a48f8d48b5a645d | https://github.com/Hadryan/nn/tree/b10e3dea2c7e1f6569bfdf8e1a48f8d48b5a645d |
MP | import torch
import torch.nn as nn
class MP(nn.Module):
def __init__(self, k=2):
super(MP, self).__init__()
self.m = nn.MaxPool2d(kernel_size=k, stride=k)
def forward(self, x):
return self.m(x)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
ret... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | JuliannaChaykina/social-distance | MP | false | 2,427 | [
"Apache-2.0"
] | 0 | 1c8ade043254b78de49a1244d438203ddb38c586 | https://github.com/JuliannaChaykina/social-distance/tree/1c8ade043254b78de49a1244d438203ddb38c586 |
DiscriminatorLoss | from torch.nn import Module
import torch
import torch.nn.functional as F
import torch.utils.data
import torch.nn.functional
import torch.autograd
class DiscriminatorLoss(Module):
"""
## Discriminator Loss
We want to find $w$ to maximize
$$\\mathbb{E}_{x \\sim \\mathbb{P}_r} [f_w(x)]- \\mathbb{E}_{z \... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch.nn import Module
import torch.utils.data
import torch.nn.functional
import tor... | ppvalluri09/annotated_deep_learning_paper_implementations | DiscriminatorLoss | false | 11,067 | [
"MIT"
] | 0 | 387b6dfd1ef1f6d295e9394c24b5798071d9a3e4 | https://github.com/ppvalluri09/annotated_deep_learning_paper_implementations/tree/387b6dfd1ef1f6d295e9394c24b5798071d9a3e4 |
Net | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | olbedo/AAIND-Facial-Keypoints | Net | false | 10,712 | [
"MIT"
] | 0 | 3d11094665d45feb312e375ee57e09ff7f601eb9 | https://github.com/olbedo/AAIND-Facial-Keypoints/tree/3d11094665d45feb312e375ee57e09ff7f601eb9 |
ReflectionPad3d | import torch
import torch.utils.data
import torch
import torch.nn as nn
class ReflectionPad3d(nn.Module):
def __init__(self, padding):
super(ReflectionPad3d, self).__init__()
self.padding = padding
if isinstance(padding, int):
self.padding = (padding,) * 6
def forward(sel... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
import torch
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cud... | giuliabaldini/Pix2PixNIfTI | ReflectionPad3d | false | 3,542 | [
"BSD-3-Clause"
] | 0 | 59ff825760f682d2734bd5e95503a03f80d32414 | https://github.com/giuliabaldini/Pix2PixNIfTI/tree/59ff825760f682d2734bd5e95503a03f80d32414 |
MultiHeadedAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | SSussexGit/deepikachu | MultiHeadedAttention | false | 2,812 | [
"MIT"
] | 0 | 72999c4a3f1767c3e5f332fe64cba9240ef43a79 | https://github.com/SSussexGit/deepikachu/tree/72999c4a3f1767c3e5f332fe64cba9240ef43a79 |
BlendLinear | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dyn... | ClaraBing/ffjord | BlendLinear | false | 13,502 | [
"MIT"
] | 518 | a97c34ff546a063316828f53bd041555e663428d | https://github.com/ClaraBing/ffjord/tree/a97c34ff546a063316828f53bd041555e663428d |
ResidualBlockNoBN | import torch
import torch.nn as nn
from torch.nn import init as init
from torch.nn.modules.batchnorm import _BatchNorm
from torchvision.models import vgg as vgg
from torch import autograd as autograd
@torch.no_grad()
def default_init_weights(module_list, scale=1, bias_fill=0, **kwargs):
"""Initialize network weig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
from to... | cyysc1998/EDVRDarts | ResidualBlockNoBN | false | 6,548 | [
"MIT"
] | 1 | 201badbc8c6469b519647a8869c3782ebe1176cf | https://github.com/cyysc1998/EDVRDarts/tree/201badbc8c6469b519647a8869c3782ebe1176cf |
RMSEFeaturesLoss | import torch
import torch.nn as nn
import torch.utils.data
def rmseOnFeatures(feature_difference):
gt = torch.zeros_like(feature_difference)
return torch.nn.functional.mse_loss(feature_difference, gt,
size_average=False)
class RMSEFeaturesLoss(nn.Module):
def __init__(self):
super(RMSEF... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guard... | liruihui/learning3d | RMSEFeaturesLoss | false | 12,715 | [
"MIT"
] | 0 | d513fb0956926f92c185594d4e236d26ecc7e81e | https://github.com/liruihui/learning3d/tree/d513fb0956926f92c185594d4e236d26ecc7e81e |
RecognizeNet | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | ckfanzhe/Face_recognize-Pytorch- | RecognizeNet | false | 9,916 | [
"Apache-2.0"
] | 0 | 0cf0853a26a25d0166f0082d8171160daa4cf747 | https://github.com/ckfanzhe/Face_recognize-Pytorch-/tree/0cf0853a26a25d0166f0082d8171160daa4cf747 |
PSNR | import torch
import torch as th
class PSNR(th.nn.Module):
def __init__(self):
super(PSNR, self).__init__()
self.mse = th.nn.MSELoss()
def forward(self, out, ref):
mse = self.mse(out, ref)
return -10 * th.log10(mse)
def get_inputs():
return [torch.rand([4, 4, 4, 4]), tor... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch as th
assert_si... | MohamadHMousavi/demosaicnet | PSNR | false | 14,067 | [
"MIT"
] | 140 | 43f013c79395ee5bccaa0f3525cc61007808845b | https://github.com/MohamadHMousavi/demosaicnet/tree/43f013c79395ee5bccaa0f3525cc61007808845b |
TensorProba | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | Minyus/pipelinex | TensorProba | false | 14,052 | [
"Apache-2.0"
] | 188 | f35c524ec9c50751ee27d9a42d98317e16f1c544 | https://github.com/Minyus/pipelinex/tree/f35c524ec9c50751ee27d9a42d98317e16f1c544 |
MeanDistLoss | import torch
class MeanDistLoss(torch.nn.Module):
def __init__(self, p=2):
super().__init__()
self.p = p
def forward(self, x, y):
return torch.mean(torch.cdist(x, y, p=self.p))
def extra_repr(self):
return c_f.extra_repr(self, ['p'])
def get_inputs():
return [torch... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | KevinMusgrave/pytorch-adapt | MeanDistLoss | false | 13,940 | [
"MIT"
] | 131 | ff1491e1bfcc586afb8ee619712c8816ddf10358 | https://github.com/KevinMusgrave/pytorch-adapt/tree/ff1491e1bfcc586afb8ee619712c8816ddf10358 |
DiscShiftLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | Juggernaut93/mmediting | DiscShiftLoss | false | 13,915 | [
"Apache-2.0"
] | 1,884 | 8ef46ace29756dd2df1d92f2f73a33646e33e007 | https://github.com/Juggernaut93/mmediting/tree/8ef46ace29756dd2df1d92f2f73a33646e33e007 |
DPSLTMAdapter | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
from to... | madhavajay/opacus | DPSLTMAdapter | false | 10,494 | [
"Apache-2.0"
] | 0 | 7ae098764b4cf2388c66e263dd8d56bca0a290d0 | https://github.com/madhavajay/opacus/tree/7ae098764b4cf2388c66e263dd8d56bca0a290d0 |
PositionWiseFeedForward | from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
def gelu(x):
"""Implementation of the gelu activation function by Hugging Face"""
return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))
class PositionWiseFeedForward(nn.Module):
""" FeedForward Neural Networks ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
import ... | FengMingquan-sjtu/pytorchic-bert | PositionWiseFeedForward | false | 10,483 | [
"Apache-2.0"
] | 0 | 83d616fb9c7e1d5c3646f9b6267ca912e2616d65 | https://github.com/FengMingquan-sjtu/pytorchic-bert/tree/83d616fb9c7e1d5c3646f9b6267ca912e2616d65 |
Pool | import torch
import torch.nn as nn
import torch.nn.functional as F
class Pool(nn.Module):
def __init__(self):
super(Pool, self).__init__()
def forward(self, x):
return F.adaptive_avg_pool2d(x, (5, 5))
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
retu... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | borisfom/TRTorch | Pool | false | 9,792 | [
"BSD-3-Clause"
] | 0 | 1660633c6f6a480cd123d9d91cabf4eced12e8f3 | https://github.com/borisfom/TRTorch/tree/1660633c6f6a480cd123d9d91cabf4eced12e8f3 |
BinaryReg | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | pragyasingh7/pytorch_connectomics | BinaryReg | false | 4,134 | [
"MIT"
] | 0 | fdc8e1900b0a38d19ea50f78f8c81da2a4f015a9 | https://github.com/pragyasingh7/pytorch_connectomics/tree/fdc8e1900b0a38d19ea50f78f8c81da2a4f015a9 |
TorchMul | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | NVIDIA-AI-IOT-private/torch2trt | TorchMul | false | 10,547 | [
"MIT"
] | 0 | 953d60039e0c81e90eea467c3df2e6e3f7040242 | https://github.com/NVIDIA-AI-IOT-private/torch2trt/tree/953d60039e0c81e90eea467c3df2e6e3f7040242 |
Foo | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.jit
import torch... | briancoutinho/glow | Foo | false | 12,573 | [
"Apache-2.0"
] | 0 | 4c919d60b3c33296c4109aec8020a1733c98f5b5 | https://github.com/briancoutinho/glow/tree/4c919d60b3c33296c4109aec8020a1733c98f5b5 |
EncoderNO2 | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | le0x99/deep-generative-modeling | EncoderNO2 | false | 7,067 | [
"MIT"
] | 1 | 40ffd1640dc3e5a6a2b4ba16a1d767034f081475 | https://github.com/le0x99/deep-generative-modeling/tree/40ffd1640dc3e5a6a2b4ba16a1d767034f081475 |
ActNorm2D | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
import torch.utils.data
from torch.nn import Parame... | XinZhang525/fGAIL | ActNorm2D | false | 18,134 | [
"MIT"
] | 4 | 682d70286685612558e072d9a1668779b8ae325b | https://github.com/XinZhang525/fGAIL/tree/682d70286685612558e072d9a1668779b8ae325b |
GroupedChannelNorm | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.data
import torch
from torch import nn
assert_size_stride = ... | guyii54/Contrastive-I2I | GroupedChannelNorm | false | 6,766 | [
"BSD-3-Clause"
] | 1 | e73daa0f9d3770c2280a304c39678d5b22440647 | https://github.com/guyii54/Contrastive-I2I/tree/e73daa0f9d3770c2280a304c39678d5b22440647 |
Pointer | import torch
import torch.nn as nn
import torch.nn.functional as F
def mask_logits(target, mask):
mask = mask.type(torch.float32)
return target * mask + (1 - mask) * -1e+30
class Initialized_Conv1d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=1, stride=1,
padding=0, gro... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.functional as F
assert_size_stride = torch... | timgianitsos/squad | Pointer | false | 13,190 | [
"MIT"
] | 0 | 6ab502652e3528cfeeddfb8eba05221443a35294 | https://github.com/timgianitsos/squad/tree/6ab502652e3528cfeeddfb8eba05221443a35294 |
ConvBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | ArmandNM/meta-learning | ConvBlock | false | 96 | [
"MIT"
] | 0 | 173fcd4b929168e9bd7948581293020a3a932857 | https://github.com/ArmandNM/meta-learning/tree/173fcd4b929168e9bd7948581293020a3a932857 |
DQN | import torch
import torch.nn as nn
import torch.nn.functional as F
class DQN(nn.Module):
def __init__(self, inputs, outputs):
super(DQN, self).__init__()
val = int((inputs + outputs) / 2)
self.fc1 = nn.Linear(inputs, val)
self.fc2 = nn.Linear(val, val)
self.fc3 = nn.Linear... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Sai-56/Multi-Agent-DQN-Routing | DQN | false | 5,797 | [
"MIT"
] | 1 | c8e7038bd0dfb69b3bdbdeb60ff9b98bb081e95e | https://github.com/Sai-56/Multi-Agent-DQN-Routing/tree/c8e7038bd0dfb69b3bdbdeb60ff9b98bb081e95e |
BertPooler | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class BertPooler(nn.Module):
def __init__(self, config):
super(BertPooler, self).__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.activation = nn.Tanh()
def forward(self, hi... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | etherlabsio/ai-engine | BertPooler | false | 10,125 | [
"MIT"
] | 0 | e73a4419a34db42a410e2a7e7629eb946b86f2c2 | https://github.com/etherlabsio/ai-engine/tree/e73a4419a34db42a410e2a7e7629eb946b86f2c2 |
Classifier | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Develop-Packt/Solving-a-Classification-Problem-with-DNNs-Using-PyTorch | Classifier | false | 7,977 | [
"MIT"
] | 16 | d0fe33c71242da256e3727bb49417a08de39c85c | https://github.com/Develop-Packt/Solving-a-Classification-Problem-with-DNNs-Using-PyTorch/tree/d0fe33c71242da256e3727bb49417a08de39c85c |
Attention | import torch
from torch import nn
class Attention(nn.Module):
def __init__(self, in_channels):
super(Attention, self).__init__()
self.out_channels = int(in_channels / 2)
self.conv1 = nn.Conv2d(in_channels, self.out_channels, kernel_size=
3, padding=1, stride=1)
self.re... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | nivedk/SPANet | Attention | false | 10,623 | [
"BSD-3-Clause"
] | 0 | 1bd84ae67732f9885af65dcbd286075008d46e91 | https://github.com/nivedk/SPANet/tree/1bd84ae67732f9885af65dcbd286075008d46e91 |
Iter_Downsample | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
from math import sqrt as sqrt
from itertools import product as prod... | vaesl/LFIP | Iter_Downsample | false | 16,659 | [
"MIT"
] | 59 | eb9d934616c508c9a9032f170baa1d97fa792822 | https://github.com/vaesl/LFIP/tree/eb9d934616c508c9a9032f170baa1d97fa792822 |
CCX_loss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | qwopqwop200/Fast-Invertible-Rescaling-Net | CCX_loss | false | 7,538 | [
"MIT"
] | 1 | 871733f2eee7929d6b37c4d1d6a27347b39b67a9 | https://github.com/qwopqwop200/Fast-Invertible-Rescaling-Net/tree/871733f2eee7929d6b37c4d1d6a27347b39b67a9 |
MultiheadAttentionWrapper | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn.functional as F
import torch.nn as nn
from torch.nn.utils import weight_norm
from torch.optim.lr_scheduler import *
assert_s... | chunhuililili/mt_dnn | MultiheadAttentionWrapper | false | 10,209 | [
"MIT"
] | 0 | 4c6efaf21724c7b8103a05e46b5b44d7b246225e | https://github.com/chunhuililili/mt_dnn/tree/4c6efaf21724c7b8103a05e46b5b44d7b246225e |
TransformerNet | import torch
class ConvLayer(torch.nn.Module):
"""
A small wrapper around nn.Conv2d, so as to make the code cleaner and allow for experimentation with padding
"""
def __init__(self, in_channels, out_channels, kernel_size, stride):
super().__init__()
self.conv2d = torch.nn.Conv2d(i... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | gordicaleksa/pytorch-nst-feedforward | TransformerNet | false | 15,511 | [
"MIT"
] | 50 | 00c96e8e3f1b0b7fb4c14254fd0c6f1281a29598 | https://github.com/gordicaleksa/pytorch-nst-feedforward/tree/00c96e8e3f1b0b7fb4c14254fd0c6f1281a29598 |
DenseGCNConv | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | douglasrizzo/pytorch_geometric | DenseGCNConv | false | 12,292 | [
"MIT"
] | 0 | effc617c6ad6daad506038bb79e4407082e74740 | https://github.com/douglasrizzo/pytorch_geometric/tree/effc617c6ad6daad506038bb79e4407082e74740 |
Hsigmoid | import torch
import torch.nn as nn
import torch.nn.functional as F
class Hsigmoid(nn.Module):
def __init__(self, inplace=True):
super(Hsigmoid, self).__init__()
self.inplace = inplace
def forward(self, x):
return F.relu6(1.2 * x + 3.0, inplace=self.inplace) / 6.0
def get_inputs():
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | eminem171333491/PaddleOCR2Pytorch | Hsigmoid | false | 3,482 | [
"Apache-2.0"
] | 0 | ec466bb3a689eccb9290e9f80812a45301d3b030 | https://github.com/eminem171333491/PaddleOCR2Pytorch/tree/ec466bb3a689eccb9290e9f80812a45301d3b030 |
ConvAutoencoder | import torch
import torch.nn as nn
import torch.nn.functional as F
class ConvAutoencoder(nn.Module):
"""Simple convolutional autoencoder
...
Methods
-------
forward(x)
Forward pass of x
"""
def __init__(self):
super(ConvAutoencoder, self).__init__()
self.conv_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | shankal17/Autoencoders | ConvAutoencoder | false | 4,314 | [
"MIT"
] | 0 | 17aa9f1fe573008fa84694e30e9d395127684191 | https://github.com/shankal17/Autoencoders/tree/17aa9f1fe573008fa84694e30e9d395127684191 |
Self_Attn | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | hugovk/EnAET | Self_Attn | false | 15,560 | [
"MIT"
] | 87 | 596a1be95f4ebfc5fc4f372f251e66fb03e23b5a | https://github.com/hugovk/EnAET/tree/596a1be95f4ebfc5fc4f372f251e66fb03e23b5a |
ZeroLayer | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | airglow/nni | ZeroLayer | false | 12,100 | [
"MIT"
] | 0 | 751065b788f66a6b53446620293095b1fe1b1c65 | https://github.com/airglow/nni/tree/751065b788f66a6b53446620293095b1fe1b1c65 |
SimpleStackModel | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleStackModel(torch.nn.Module):
def __init__(self, dim):
super(SimpleStackModel, self).__init__()
self.dim = dim
def forward(self, a, b):
c = b + b
return torch.stack((a, c), dim=self.dim)
def get_inpu... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | opti-mix/glow | SimpleStackModel | false | 7,419 | [
"Apache-2.0"
] | 1 | 4ba074df5da9822986a23a6679ab592c22660f6d | https://github.com/opti-mix/glow/tree/4ba074df5da9822986a23a6679ab592c22660f6d |
LearnableTimeDepWeightedCost | import torch
import torch.utils.data
class LearnableTimeDepWeightedCost(torch.nn.Module):
def __init__(self, time_horizon, dim=9, weights=None):
super(LearnableTimeDepWeightedCost, self).__init__()
if weights is None:
self.weights = torch.nn.Parameter(0.01 * torch.ones([
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_... | ricklentz/LearningToLearn | LearnableTimeDepWeightedCost | false | 16,317 | [
"MIT"
] | 76 | fa32b98b40402fa15982b450ed09d9d3735ec924 | https://github.com/ricklentz/LearningToLearn/tree/fa32b98b40402fa15982b450ed09d9d3735ec924 |
Lookahead | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data.distributed
from torch import nn
assert_size_stride = to... | NikolaiBabkin/deepspeech.pytorch | Lookahead | false | 896 | [
"MIT"
] | 0 | 2b120c6b735cc46200e10f81e169c8d7b75e8495 | https://github.com/NikolaiBabkin/deepspeech.pytorch/tree/2b120c6b735cc46200e10f81e169c8d7b75e8495 |
PetarVGAT | import torch
import torch.utils.data
import torch.nn as nn
import torch.nn.functional as F
class BaseModel(nn.Module):
@staticmethod
def add_args(parser):
"""Add model-specific arguments to the parser."""
pass
@classmethod
def build_model_from_args(cls, args):
"""Build a new ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | BruceW91/cogdl | PetarVGAT | false | 260 | [
"MIT"
] | 0 | 1ad524375f5ba062103698a0432fc857572a6933 | https://github.com/BruceW91/cogdl/tree/1ad524375f5ba062103698a0432fc857572a6933 |
LayerNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.optim
assert_size_stride = torch._C._dynamo.... | awesome-archive/nmtpytorch | LayerNorm | false | 6,293 | [
"MIT"
] | 1 | 7c0ea21b29fc85a1f30ef4400d62b9d8e3d88be4 | https://github.com/awesome-archive/nmtpytorch/tree/7c0ea21b29fc85a1f30ef4400d62b9d8e3d88be4 |
StdConv2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | hrlblab/Glo-In-One | StdConv2d | false | 6,818 | [
"Apache-2.0"
] | 1 | 7daef49c557bccd6f5c956b88603357346dc78a2 | https://github.com/hrlblab/Glo-In-One/tree/7daef49c557bccd6f5c956b88603357346dc78a2 |
h_sigmoid | import torch
from torch import nn
class h_sigmoid(nn.Module):
def __init__(self, inplace=True, h_max=1):
super(h_sigmoid, self).__init__()
self.relu = nn.ReLU6(inplace=inplace)
self.h_max = h_max
def forward(self, x):
return self.relu(x + 3) * self.h_max / 6
def get_inputs(... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | shivamsnaik/dynamic-head-microsoft-fork | h_sigmoid | false | 16,428 | [
"MIT"
] | 494 | 0f337eec44d262df2517be8f5617477c0b092fcc | https://github.com/shivamsnaik/dynamic-head-microsoft-fork/tree/0f337eec44d262df2517be8f5617477c0b092fcc |
SimpleASinModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleASinModule(torch.nn.Module):
def __init__(self):
super(SimpleASinModule, self).__init__()
def forward(self, a):
return torch.asin(a + a)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._... | YaronBenAtar/glow | SimpleASinModule | false | 14,643 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
baseRNN_predict | import torch
import numpy as np
import torch.nn as nn
import torch.nn.init as init
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
weight_shape = list(m.weight.data.size())
fan_in = np.prod(weight_shape[1:4])
fan_out = np.prod(weight_shape[2:4]) *... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import numpy as np
import tor... | MLforHealth/state_representations_for_RLinHealth | baseRNN_predict | false | 8,534 | [
"MIT"
] | 24 | aa8dbb7d56caa95bf4380e3e745e134996291b66 | https://github.com/MLforHealth/state_representations_for_RLinHealth/tree/aa8dbb7d56caa95bf4380e3e745e134996291b66 |
PcamPool | import torch
from torch import nn
class PcamPool(nn.Module):
def __init__(self):
super(PcamPool, self).__init__()
def forward(self, feat_map, logit_map):
assert logit_map is not None
prob_map = torch.sigmoid(logit_map)
weight_map = prob_map / prob_map.sum(dim=2, keepdim=True)... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | alinstein/X_RAY | PcamPool | false | 18,253 | [
"MIT"
] | 4 | 35a39761d3b11ce9e47509025054f25e5f26aab9 | https://github.com/alinstein/X_RAY/tree/35a39761d3b11ce9e47509025054f25e5f26aab9 |
MaskedInstanceNorm1d | import torch
from torch import nn
import torch.utils.data
import torch.optim
class MaskedInstanceNorm1d(nn.Module):
"""Instance norm + masking."""
MAX_CNT = 100000.0
def __init__(self, d_channel: 'int', unbiased: 'bool'=True, affine:
'bool'=False):
super().__init__()
self.d_channe... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
import ... | PiotrDabkowski/NeMo | MaskedInstanceNorm1d | false | 11,789 | [
"Apache-2.0"
] | 0 | 7c251e9035b24136cf130f3caf760087e5ccf07c | https://github.com/PiotrDabkowski/NeMo/tree/7c251e9035b24136cf130f3caf760087e5ccf07c |
KeyValue | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn
import torch.utils.data.dataset
assert_size_stride = torch._C._d... | hzxie/RMNet | KeyValue | false | 15,559 | [
"MIT"
] | 66 | 32a16f9c9473463a41dd6e95f72b06dd830fc1eb | https://github.com/hzxie/RMNet/tree/32a16f9c9473463a41dd6e95f72b06dd830fc1eb |
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