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 |
|---|---|---|---|---|---|---|---|---|---|---|
NormalAttention_dot | import torch
import torch.nn as nn
class NormalAttention_dot(nn.Module):
def __init__(self, input_channel_num, k=4):
super(NormalAttention_dot, self).__init__()
self.c_in = input_channel_num
self.query_conv = nn.Conv2d(in_channels=self.c_in, out_channels=
self.c_in // k, kerne... | 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 ... | Schwartz-Zha/My-invertible-resnet | NormalAttention_dot | false | 1,037 | [
"MIT"
] | 0 | 5415975bb0d640f3bf3ef4a7b986563e84109270 | https://github.com/Schwartz-Zha/My-invertible-resnet/tree/5415975bb0d640f3bf3ef4a7b986563e84109270 |
MnistClassifier | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class MnistClassifier(nn.Module):
def __init__(self, config):
super(MnistClassifier, self).__init__()
self.config = config
self.h = self.config['image_h']
self.w = self.config['image_w']
self.... | 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
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | DanielKalicki/homomorphic_mnist | MnistClassifier | false | 3,493 | [
"BSD-3-Clause"
] | 0 | 954e9df2123527bfd266757f3b96897e405e5356 | https://github.com/DanielKalicki/homomorphic_mnist/tree/954e9df2123527bfd266757f3b96897e405e5356 |
MyCustom | import torch
import torch.nn as nn
class Sigmoid(torch.nn.Module):
def __init__(self, a=1, max=10):
super().__init__()
self.a = a
self.max = max
def forward(self, v):
sig = nn.Sigmoid()
act = sig(self.a * v) * self.max
return act
class TanH(torch.nn.Module):... | 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_... | ElliotHYLee/MyPyTorchAPI | MyCustom | false | 11,392 | [
"MIT"
] | 0 | edb25b724372367e96e3bd2f420c023c4efbfcd7 | https://github.com/ElliotHYLee/MyPyTorchAPI/tree/edb25b724372367e96e3bd2f420c023c4efbfcd7 |
TransformerNet | # 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.... | Bartolo1024/ignite | TransformerNet | false | 5,023 | [
"BSD-3-Clause"
] | 1 | b087fef0bc5f97cda415c1c56f1cd589383c54be | https://github.com/Bartolo1024/ignite/tree/b087fef0bc5f97cda415c1c56f1cd589383c54be |
Swish | import torch
import torch.nn as nn
class Swish(nn.Module):
def forward(self, x):
return x.mul_(torch.sigmoid(x))
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| 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
@triton.jit
def triton_poi_fused_mul_sigmoid_0(in_pt... | Nigel233/Different-Backbones-for-YOLO-v3 | Swish | false | 9,353 | [
"MIT"
] | 0 | 030e7860e966b079afc9b53a320a41f3eb7950be | https://github.com/Nigel233/Different-Backbones-for-YOLO-v3/tree/030e7860e966b079afc9b53a320a41f3eb7950be |
SimpleFmodModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleFmodModule(torch.nn.Module):
def __init__(self):
super(SimpleFmodModule, self).__init__()
def forward(self, a, b):
if b.size() == torch.Size([]):
c = a.fmod(b.item())
else:
c = a.fmod(... | 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 | SimpleFmodModule | false | 14,659 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
DownConv | import torch
import torch.nn as nn
import torch.nn.functional as F
def conv3x3(in_channels, out_channels, stride=1, padding=1, bias=True, groups=1
):
return nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=
stride, padding=padding, bias=bias, groups=groups)
class DownConv(nn.Module):
"... | 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_... | ForrestPi/SegDL | DownConv | false | 5,171 | [
"MIT"
] | 1 | 56f2ff229dfa7540704d6de50292c724693aac75 | https://github.com/ForrestPi/SegDL/tree/56f2ff229dfa7540704d6de50292c724693aac75 |
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
from torch.nn import Parameter
assert_size_stride = torch... | LenKerr/Semantic-Colorization-GAN | LayerNorm | false | 5,504 | [
"MIT"
] | 1 | 2ce52406ca6fc92e69692b451b1c9ae66ba3b76f | https://github.com/LenKerr/Semantic-Colorization-GAN/tree/2ce52406ca6fc92e69692b451b1c9ae66ba3b76f |
TextureFinder | # 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 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 |
DecoderBlock | # 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.utils.data
impor... | Iceofsky/Roofpedia | DecoderBlock | false | 8,284 | [
"MIT"
] | 16 | 933dd3ff6e77ace78be6d2a23ac6692281475073 | https://github.com/Iceofsky/Roofpedia/tree/933dd3ff6e77ace78be6d2a23ac6692281475073 |
RSubInt | import torch
class RSubInt(torch.nn.Module):
def __init__(self):
super(RSubInt, self).__init__()
def forward(self, x):
return 1 - x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| 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 | RSubInt | false | 10,534 | [
"MIT"
] | 0 | 953d60039e0c81e90eea467c3df2e6e3f7040242 | https://github.com/NVIDIA-AI-IOT-private/torch2trt/tree/953d60039e0c81e90eea467c3df2e6e3f7040242 |
ReOrgLayer | # 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... | AlexRogalskiy/smart-social-distancing | ReOrgLayer | false | 13,248 | [
"Apache-2.0"
] | 113 | 2def6738038035e67ac79fc9b72ba072e190321f | https://github.com/AlexRogalskiy/smart-social-distancing/tree/2def6738038035e67ac79fc9b72ba072e190321f |
CosLoss | import torch
from torch.nn.modules.loss import _Loss
class CosLoss(_Loss):
def __init__(self, eps=1e-05):
super(CosLoss, self).__init__(True)
self.eps = eps
def forward(self, pred_ofsts, kp_targ_ofst, labels, normalize=True):
"""
:param pred_ofsts: [bs, n_kpts, n_pts, c]... | 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.modules.loss import _Loss
assert_size_stride = torch._C._dynamo.g... | ezxzeng/FFB6D | CosLoss | false | 15,328 | [
"MIT"
] | 145 | fd0ea6471532ab1dc68f9a58b52d9a63f8fb76f2 | https://github.com/ezxzeng/FFB6D/tree/fd0ea6471532ab1dc68f9a58b52d9a63f8fb76f2 |
Normalize | # 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... | dariocazzani/vo-id | Normalize | false | 1,783 | [
"MIT"
] | 0 | 41d0f2779e7909cfa15afcb6c8222c48a5855eb8 | https://github.com/dariocazzani/vo-id/tree/41d0f2779e7909cfa15afcb6c8222c48a5855eb8 |
SingleBlock | # 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.... | TranTony/DFAF-for-VQA.pytorch | SingleBlock | false | 12,053 | [
"MIT"
] | 0 | eba1a893e8e5d3d8bf85078611b0bcf4d56eea86 | https://github.com/TranTony/DFAF-for-VQA.pytorch/tree/eba1a893e8e5d3d8bf85078611b0bcf4d56eea86 |
ToTensor | # 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.nn import Module
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._em... | Yu-Zhewen/finn | ToTensor | false | 12,002 | [
"BSD-3-Clause"
] | 0 | 5c1be584d47edfe4b43976a32a5c537f4037b017 | https://github.com/Yu-Zhewen/finn/tree/5c1be584d47edfe4b43976a32a5c537f4037b017 |
A2CNetwork | # 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 ... | benvoe/mushroom-rl-benchmark | A2CNetwork | false | 1,541 | [
"MIT"
] | 0 | 217d8c077bf6f3febaed92821a2cf183c83f703b | https://github.com/benvoe/mushroom-rl-benchmark/tree/217d8c077bf6f3febaed92821a2cf183c83f703b |
DisAlignCosineFastRCNNOutputLayers | import math
import torch
import numpy as np
import torch.nn as nn
import torch.utils.data
from itertools import product as product
from math import sqrt as sqrt
import torch.nn
def cat(tensors, dim=0):
"""
Efficient version of torch.cat that avoids a copy if there is only a single element in a list
"""
... | 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.... | tonysy/cvpods | DisAlignCosineFastRCNNOutputLayers | false | 16,615 | [
"Apache-2.0"
] | 548 | e322d7842ca0e34b1ef6237ea6d350633efc793a | https://github.com/tonysy/cvpods/tree/e322d7842ca0e34b1ef6237ea6d350633efc793a |
BayesConv1d | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import init
def calculate_kl(mu_p, sig_p, mu_q, sig_q):
"""
Calculates the Kullback-Leibler divergence between two univariate Gaussians (p and q)
Args:
mu_p: mean of the Gaussian p
sig_p: standard... | 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... | chapmanbe/uncertainty | BayesConv1d | false | 6,423 | [
"Apache-2.0"
] | 1 | d4eec00e937c76043d57a13ffcc9618b1e08d967 | https://github.com/chapmanbe/uncertainty/tree/d4eec00e937c76043d57a13ffcc9618b1e08d967 |
Spatial_Attention | import torch
import torch.nn as nn
class Spatial_Attention(nn.Module):
def __init__(self, input_dim):
super(Spatial_Attention, self).__init__()
self.att_conv1 = nn.Conv2d(input_dim, 1, kernel_size=(1, 1),
padding=0, stride=1, bias=False)
self.att_act2 = nn.Softplus(beta=1, thr... | 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... | MCC-WH/Token | Spatial_Attention | false | 8,504 | [
"MIT"
] | 30 | eadc301f2df9e1851633be1b63c273659af0da49 | https://github.com/MCC-WH/Token/tree/eadc301f2df9e1851633be1b63c273659af0da49 |
h_tanh | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
class h_tanh(nn.Module):
def __init__(self, inplace=True, h_max=1):
super(h_tanh, self).__init__()
self.relu = nn.ReLU6(inplace=inplace)
self.h_max = ... | 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.nn.parallel
import torch.optim
import torch.utils.data... | PINTO0309/micronet | h_tanh | false | 14,133 | [
"MIT"
] | 221 | 97ff01d0ea9a42f0a3f0a93ac67660df26411f28 | https://github.com/PINTO0309/micronet/tree/97ff01d0ea9a42f0a3f0a93ac67660df26411f28 |
SurfaceLoss | import torch
import torch.nn as nn
class SurfaceLoss(nn.Module):
def __init__(self, epsilon=1e-05, softmax=True):
super(SurfaceLoss, self).__init__()
self.weight_map = []
def forward(self, x, distmap):
x = torch.softmax(x, dim=1)
self.weight_map = distmap
score = x.fl... | 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
... | KamranBinaee/RGnet | SurfaceLoss | false | 9,246 | [
"MIT"
] | 0 | 85861ab47a94018c8f8fa01fb7e64d8eec7fdc43 | https://github.com/KamranBinaee/RGnet/tree/85861ab47a94018c8f8fa01fb7e64d8eec7fdc43 |
TAM | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
class SEModule(nn.Module):
def __init__(self, channels, dw_conv):
super().__init__()
ks = 1
pad = (ks - 1) // 2
... | 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 ... | peter-yys-yoon/traditional-dance-recognition | TAM | false | 12,960 | [
"Apache-2.0"
] | 0 | be4939d53b838624a04dba0826532c65421d1325 | https://github.com/peter-yys-yoon/traditional-dance-recognition/tree/be4939d53b838624a04dba0826532c65421d1325 |
DCCWeightedELoss | import torch
import numpy as np
import torch.nn as nn
class DCCWeightedELoss(nn.Module):
def __init__(self, size_average=True):
super(DCCWeightedELoss, self).__init__()
self.size_average = size_average
def forward(self, inputs, outputs, weights):
out = (inputs - outputs).view(len(inp... | 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
assert... | lbasora/DCC | DCCWeightedELoss | false | 12,741 | [
"MIT"
] | 0 | c9abcd7d697cc9e50e874286f1edfb3be93ce6d9 | https://github.com/lbasora/DCC/tree/c9abcd7d697cc9e50e874286f1edfb3be93ce6d9 |
EncoderImagePrecomp | import torch
import numpy as np
from collections import OrderedDict
import torch.nn as nn
import torch.nn.init
def l2norm(X):
"""L2-normalize columns of X
"""
norm = torch.pow(X, 2).sum(dim=1, keepdim=True).sqrt()
X = torch.div(X, norm)
return X
class EncoderImagePrecomp(nn.Module):
def __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.triton_helpers import libdevice
import numpy as np
... | ascott02/vsepp | EncoderImagePrecomp | false | 9,736 | [
"Apache-2.0"
] | 0 | c09abd2be5f1fec237ccfe3d7f41bfdea2acfde2 | https://github.com/ascott02/vsepp/tree/c09abd2be5f1fec237ccfe3d7f41bfdea2acfde2 |
make_dense | import torch
import torch.nn as nn
import torch.utils.model_zoo
class make_dense(nn.Module):
def __init__(self, channels_in, channels_out, kernel_size=3):
super(make_dense, self).__init__()
self.leaky_relu = nn.LeakyReLU(0.1, inplace=True)
self.conv = nn.Conv2d(channels_in, channels_out, ... | 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.model_zoo
assert_size_stride = torch._C... | SeleSchaefer/super_resolution | make_dense | false | 17,946 | [
"MIT"
] | 5 | bf28a959fb150ceeadbd9f0bcfc12f3025cf82f4 | https://github.com/SeleSchaefer/super_resolution/tree/bf28a959fb150ceeadbd9f0bcfc12f3025cf82f4 |
ShakeResNeXt | # 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 math
from torch import... | AustinCai/gmaxup-augmentation | ShakeResNeXt | false | 94 | [
"MIT"
] | 0 | a64ca0a76eb333e5ce6b217c301d27ca04d73bce | https://github.com/AustinCai/gmaxup-augmentation/tree/a64ca0a76eb333e5ce6b217c301d27ca04d73bce |
TestMul | import torch
import torch.nn as nn
class TestMul(nn.Module):
"""Module for Element-wise multiplication conversion testing
"""
def __init__(self, inp=10, out=16, kernel_size=3, bias=True):
super(TestMul, self).__init__()
self.conv2d_1 = nn.Conv2d(inp, out, stride=inp % 3 + 1, kernel_size
... | 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
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | AliaksandrSiarohin/pytorch2keras | TestMul | false | 8,893 | [
"MIT"
] | 0 | 9c8ee213cff43ade152b1de78fa76fd05ec8b40a | https://github.com/AliaksandrSiarohin/pytorch2keras/tree/9c8ee213cff43ade152b1de78fa76fd05ec8b40a |
Conv2dSame | # 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
from torchvis... | COMHTVM/lensless | Conv2dSame | false | 17,313 | [
"MIT"
] | 6 | 0d67a310bab08551d7422fa792f3422a7ee7d9cb | https://github.com/COMHTVM/lensless/tree/0d67a310bab08551d7422fa792f3422a7ee7d9cb |
LinearBlock | # 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.... | joemzhao/learn2learn | LinearBlock | false | 12,769 | [
"MIT"
] | 0 | e161e0a9e0de513d64315c4ceaf2d8608e4cef4d | https://github.com/joemzhao/learn2learn/tree/e161e0a9e0de513d64315c4ceaf2d8608e4cef4d |
UNet | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.functional import F
from torch.nn import functional as F
class down(nn.Module):
"""
A class for creating neural network blocks containing layers:
Average Pooling --> Convlution + Leaky ReLU --> Convolution + Leaky ReLU
Thi... | 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 ... | CM-BF/FeatureFlow | UNet | false | 13,711 | [
"MIT"
] | 161 | 06642697922f17211e5faa353e24b1a0946885b1 | https://github.com/CM-BF/FeatureFlow/tree/06642697922f17211e5faa353e24b1a0946885b1 |
OneHotEncode | # 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.optim
import torch.nn as nn
import torch.nn.utils
import torch.autograd
assert_size_stride = torch._C._dynamo.guards.assert_siz... | galatolofederico/vel | OneHotEncode | false | 15,399 | [
"MIT"
] | 273 | 0473648cffb3f34fb784d12dbb25844ab58ffc3c | https://github.com/galatolofederico/vel/tree/0473648cffb3f34fb784d12dbb25844ab58ffc3c |
LxmertCrossAttentionLayer | from _paritybench_helpers import _mock_config
import math
import torch
from torch import nn
import torch.utils.checkpoint
class LxmertAttention(nn.Module):
def __init__(self, config, ctx_dim=None):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0:
raise Value... | 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.... | Clemens123/transformers | LxmertCrossAttentionLayer | false | 13,217 | [
"Apache-2.0"
] | 0 | 22abe7bbc587c16ec30f9d1aa549dcbeba6e9e26 | https://github.com/Clemens123/transformers/tree/22abe7bbc587c16ec30f9d1aa549dcbeba6e9e26 |
Policy | # 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.... | PaulPan00/donkey_wrapper | Policy | false | 17,808 | [
"MIT"
] | 6 | a03cf0f42f65625fbce792b06c98acd153c5d6c8 | https://github.com/PaulPan00/donkey_wrapper/tree/a03cf0f42f65625fbce792b06c98acd153c5d6c8 |
MagnitudeTestModel | # 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 import nn
from torchvision import models as models
import torch.nn.pa... | JinYAnGHe/openvino_training_extensions | MagnitudeTestModel | false | 2,977 | [
"Apache-2.0"
] | 0 | a0b4456a3c9fe6c1b7eabc9d5eb4e74d01453dee | https://github.com/JinYAnGHe/openvino_training_extensions/tree/a0b4456a3c9fe6c1b7eabc9d5eb4e74d01453dee |
Conv | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Numb523/FastSpeech2_emotion | Conv | false | 911 | [
"MIT"
] | 0 | a541ce89ddf66625ee57c0a294d0bec1ae701f0c | https://github.com/Numb523/FastSpeech2_emotion/tree/a541ce89ddf66625ee57c0a294d0bec1ae701f0c |
SurfaceClassifier | import torch
import torch.nn as nn
import torch.nn.functional as F
class SurfaceClassifier(nn.Module):
def __init__(self, filter_channels, num_views=1, no_residual=True,
last_op=None):
super(SurfaceClassifier, self).__init__()
self.filters = []
self.num_views = num_views
s... | 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
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | KORguy/PIFu_Part | SurfaceClassifier | false | 9,299 | [
"MIT"
] | 0 | bd199d439a94f8bc8b4036898b0f1ec01e56ab9e | https://github.com/KORguy/PIFu_Part/tree/bd199d439a94f8bc8b4036898b0f1ec01e56ab9e |
AsymmetricLoss | # 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... | daniil-lyakhov/deep-object-reid | AsymmetricLoss | false | 1,779 | [
"Apache-2.0"
] | 0 | b0f7d6a2d4cff8c417a66d82c09d16788d81ec67 | https://github.com/daniil-lyakhov/deep-object-reid/tree/b0f7d6a2d4cff8c417a66d82c09d16788d81ec67 |
Conv1D | import torch
import torch.nn.parallel
import torch.nn as nn
import torch.utils.data
import torch.backends.cudnn
class Conv1D(nn.Module):
def __init__(self, in_dim, out_dim, kernel_size=1, stride=1, padding=0,
bias=True):
super(Conv1D, self).__init__()
self.conv1d = nn.Conv1d(in_channels=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
import torch.nn.parallel
import torch.nn as nn
import torch.utils.data
import to... | EGO4D/episodic-memory | Conv1D | false | 8,059 | [
"MIT"
] | 27 | 2a3464882cd4f665c358c1b05a6397339e33c2e1 | https://github.com/EGO4D/episodic-memory/tree/2a3464882cd4f665c358c1b05a6397339e33c2e1 |
GramMatrix | import torch
import torch.nn as nn
class GramMatrix(nn.Module):
def forward(self, input):
a, b, c, d = input.size()
features = input.view(a * b, c * d)
G = torch.mm(features, features.t())
return G.div(a * b * c * d)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def ... | 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
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | invoker4zoo/pytorch_model | GramMatrix | false | 12,536 | [
"MIT"
] | 0 | b74f005ba1be5e66fafaa2745fc7d1815979e91f | https://github.com/invoker4zoo/pytorch_model/tree/b74f005ba1be5e66fafaa2745fc7d1815979e91f |
DownConvBlock | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
from torch.cuda import *
def conv3x3(in_channels, out_channels):
return nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1,
padding=1, bias=True)
def maxpool2x2():
return nn.MaxPool2d(kernel_size=2, ... | 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.... | jabae/detectEM | DownConvBlock | false | 6,925 | [
"MIT"
] | 1 | 2d1a5116164d0bed0a8ea767a227d05a8970a448 | https://github.com/jabae/detectEM/tree/2d1a5116164d0bed0a8ea767a227d05a8970a448 |
AddReadout | # 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... | HrithikNambiar/vformer | AddReadout | false | 541 | [
"MIT"
] | 0 | 5bd902a45e5cae70ab001ca6c217f12f923561f1 | https://github.com/HrithikNambiar/vformer/tree/5bd902a45e5cae70ab001ca6c217f12f923561f1 |
CPAMDec | from torch.nn import Module
import torch
from torch.nn import Conv2d
from torch.nn import Parameter
from torch.nn import Softmax
from torch.nn import Linear
from torch.nn.parameter import Parameter
class CPAMDec(Module):
"""
CPAM decoding module
"""
def __init__(self, in_channels):
super(CPAM... | 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.... | bfjei2825401/siamban | CPAMDec | false | 10,410 | [
"Apache-2.0"
] | 0 | c41d58742b146dfc8960053453227c6e9fec1bac | https://github.com/bfjei2825401/siamban/tree/c41d58742b146dfc8960053453227c6e9fec1bac |
Model | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | triper1022/s3prl | Model | false | 13,045 | [
"MIT"
] | 0 | d48e9e1d062d6cb14b66048eb56193fb50c60c24 | https://github.com/triper1022/s3prl/tree/d48e9e1d062d6cb14b66048eb56193fb50c60c24 |
_Logit | import torch
class _Logit(torch.nn.Module):
""" Simple logistic regression model.
"""
def __init__(self, din, dout=1):
""" Model parameter constructor.
Args:
din Number of input dimensions
dout Number of output dimensions
"""
super().__init__()
self._din = din
... | 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cu... | IKACE/DifferentialByzantine-1 | _Logit | false | 5,313 | [
"MIT"
] | 1 | 809fd6e070fedeb87a6dbff6f883e93e3c5c8e09 | https://github.com/IKACE/DifferentialByzantine-1/tree/809fd6e070fedeb87a6dbff6f883e93e3c5c8e09 |
GLU | import torch
import torch.nn.functional as F
import torch.nn as nn
class GLU(nn.Module):
def __init__(self, dim):
super(GLU, self).__init__()
self.dim = dim
def forward(self, x):
return F.glu(x, self.dim)
def get_inputs():
return [torch.rand([4, 4, 4, 4, 4])]
def get_init_inp... | 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... | ishine/tfm-tts | GLU | false | 3,675 | [
"MIT"
] | 0 | a964736467851ddec8f8e8933b9550cbe7d7d7eb | https://github.com/ishine/tfm-tts/tree/a964736467851ddec8f8e8933b9550cbe7d7d7eb |
MSE | # 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... | KMU-AELAB/Active_Learning | MSE | false | 2,437 | [
"MIT"
] | 0 | bc569c16b5f12b58989a8f3db59b7eb4e35cce1b | https://github.com/KMU-AELAB/Active_Learning/tree/bc569c16b5f12b58989a8f3db59b7eb4e35cce1b |
MaxPool2d | import torch
import numpy as np
import torch.nn as nn
from numbers import Number
def keep_variance_fn(x):
return x + 0.001
def normcdf(value, mu=0.0, stddev=1.0):
sinv = 1.0 / stddev if isinstance(stddev, Number) else stddev.reciprocal()
return 0.5 * (1.0 + torch.erf((value - mu) * sinv / np.sqrt(2.0)))... | 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, math as tl_math
import numpy as np
import torch.nn as nn
from numbers import N... | collector-m/LiDAR-MOS | MaxPool2d | false | 15,070 | [
"MIT"
] | 268 | 7ccbb63b4ee7c40195b35dd0dddd71473fae25b1 | https://github.com/collector-m/LiDAR-MOS/tree/7ccbb63b4ee7c40195b35dd0dddd71473fae25b1 |
EQ | # 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... | bunderhi/torch2trt | EQ | false | 1,593 | [
"MIT"
] | 0 | fa5e31e742a0f0c9a9ee38909a6fa56bb07ba96d | https://github.com/bunderhi/torch2trt/tree/fa5e31e742a0f0c9a9ee38909a6fa56bb07ba96d |
GramMatrix | # 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | czczup/URST | GramMatrix | false | 15,093 | [
"Apache-2.0"
] | 119 | 000ec9f7728f12ffad989ec1d07b1dd579514133 | https://github.com/czczup/URST/tree/000ec9f7728f12ffad989ec1d07b1dd579514133 |
PerceptronTanh | import torch
import torch.nn as nn
import torch.nn.functional as F
class PerceptronTanh(nn.Module):
"""Implements a 1-layer perceptron with Tanh activaton."""
def __init__(self, input_dimension, hidden_dimension, output_dimension):
super(PerceptronTanh, self).__init__()
self._layer1 = nn.Line... | 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.... | Bhaskers-Blu-Org2/PDP-Solver | PerceptronTanh | false | 7,792 | [
"MIT"
] | 28 | 1fca34d81f36268288f46416fb6956e5b36df69e | https://github.com/Bhaskers-Blu-Org2/PDP-Solver/tree/1fca34d81f36268288f46416fb6956e5b36df69e |
ChannelWiseDivergence | import torch
from torch import nn
import torch.nn.functional as F
class ChannelWiseDivergence(nn.Module):
"""PyTorch version of `Channel-wise Distillation for Semantic Segmentation.
<https://arxiv.org/abs/2011.13256>`_.
Args:
tau (float): Temperature coefficient. Defaults to 1.0.
loss_we... | 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... | HIT-cwh/mmrazor | ChannelWiseDivergence | false | 13,784 | [
"Apache-2.0"
] | 553 | 2dad24044d7f1dad88f20221f8fc071dd40fdd4f | https://github.com/HIT-cwh/mmrazor/tree/2dad24044d7f1dad88f20221f8fc071dd40fdd4f |
ProposalNet | # 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 ... | Liuhongzhi2018/ClassNet | ProposalNet | false | 11,839 | [
"MIT"
] | 0 | 7d427dc9b8c38abf0a4eedfdeb75c09c59aa7185 | https://github.com/Liuhongzhi2018/ClassNet/tree/7d427dc9b8c38abf0a4eedfdeb75c09c59aa7185 |
FeedForward | # 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_... | MadanMl/PyTorch-Transformer-for-RUL-Prediction | FeedForward | false | 8,497 | [
"Apache-2.0"
] | 25 | 5bf0a4739abdecbbc88118ea413393997bdc1e24 | https://github.com/MadanMl/PyTorch-Transformer-for-RUL-Prediction/tree/5bf0a4739abdecbbc88118ea413393997bdc1e24 |
AsymmetricLossOptimized | # 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
from torchv... | jasonnoy/COMP5329 | AsymmetricLossOptimized | false | 10,323 | [
"MIT"
] | 0 | fc17c80b1ac41d788cc0a92d3a033dbe2f9b8b81 | https://github.com/jasonnoy/COMP5329/tree/fc17c80b1ac41d788cc0a92d3a033dbe2f9b8b81 |
fusion | # 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 torch.nn import Linear
assert_size_stride = torch._C.... | funnyzhou/REFERS | fusion | false | 15,373 | [
"MIT"
] | 46 | 392eddf13cbf3c3a7dc0bf8bfffd108ca4a65a19 | https://github.com/funnyzhou/REFERS/tree/392eddf13cbf3c3a7dc0bf8bfffd108ca4a65a19 |
RewardCriterion | # 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 import nn
import torch.nn.init
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dyn... | KunpengLi1994/VSRN | RewardCriterion | false | 13,957 | [
"Apache-2.0"
] | 238 | 777ae74326fdb6abe69dbd3911d0e545322520d1 | https://github.com/KunpengLi1994/VSRN/tree/777ae74326fdb6abe69dbd3911d0e545322520d1 |
Actor | # 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.... | brabeem/deep-reinforcement-learning | Actor | false | 12,188 | [
"MIT"
] | 0 | aff919545a1b6d9d44f5aaaa13b9981c888e7169 | https://github.com/brabeem/deep-reinforcement-learning/tree/aff919545a1b6d9d44f5aaaa13b9981c888e7169 |
Contraster | # 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.distributions
import torch.utils.data
assert_size_stride = torch._C._dynamo.... | AlexMeinke/Provable-OOD-Detection | Contraster | false | 7,685 | [
"MIT"
] | 21 | 9a132aec994ff718c96b81885736ab866df60d87 | https://github.com/AlexMeinke/Provable-OOD-Detection/tree/9a132aec994ff718c96b81885736ab866df60d87 |
ConvRelu | # 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 import nn
import t... | kevin-14/intel-extension-for-pytorch | ConvRelu | false | 7,020 | [
"Apache-2.0"
] | 1 | f0cdcc602658340a957a964447d8e76bf413f66a | https://github.com/kevin-14/intel-extension-for-pytorch/tree/f0cdcc602658340a957a964447d8e76bf413f66a |
Normalize | # 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
assert... | uripatish/torchup | Normalize | false | 4,490 | [
"MIT"
] | 0 | 0b7bee031fc99e536342331ba567c523a790d742 | https://github.com/uripatish/torchup/tree/0b7bee031fc99e536342331ba567c523a790d742 |
FeatureL2Norm | import torch
import torch.nn as nn
import torch.nn
class FeatureL2Norm(nn.Module):
"""
Implementation by Ignacio Rocco
paper: https://arxiv.org/abs/1703.05593
project: https://github.com/ignacio-rocco/cnngeometric_pytorch
"""
def __init__(self):
super(FeatureL2Norm, self).__init__()
... | 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.nn
assert_size_stride = torch._C._dynamo.gua... | JiwonCocoder/-Joint-Learning-of-Feature-Extraction-and-Cost-Aggregation-for-Semantic-Matching | FeatureL2Norm | false | 5,405 | [
"MIT"
] | 1 | b79e0e20fd5a1a9ddc0ffa9d7a92e0ebd21018b9 | https://github.com/JiwonCocoder/-Joint-Learning-of-Feature-Extraction-and-Cost-Aggregation-for-Semantic-Matching/tree/b79e0e20fd5a1a9ddc0ffa9d7a92e0ebd21018b9 |
ConvBlock | import torch
import torch.nn as nn
import torch.nn.functional as F
def conv3x3(in_planes, out_planes, strd=1, padding=1, bias=False):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=strd,
padding=padding, bias=bias)
class ConvBlock(nn.Module):
de... | 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.... | hhj1897/fan_training | ConvBlock | false | 6,822 | [
"MIT"
] | 1 | 5882f9edf2f1a07c80a6d1f3341a7cf1d348e217 | https://github.com/hhj1897/fan_training/tree/5882f9edf2f1a07c80a6d1f3341a7cf1d348e217 |
Normalize | import torch
from torchvision.datasets import *
import torch.nn.functional as F
import torch.nn as nn
from torchvision.transforms import *
class Normalize(nn.Module):
"""Performs :math:`L_p` normalization of inputs over specified dimension.
Does:
.. math::
v = \\frac{v}{\\max(\\lVert v \\rVert_p... | 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 torchvision.datasets im... | Womcos/SCARF | Normalize | false | 5,979 | [
"MIT"
] | 1 | b90251bc23410cb810a7082ca75147a7aae21dec | https://github.com/Womcos/SCARF/tree/b90251bc23410cb810a7082ca75147a7aae21dec |
LovaszHingeLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class LovaszHingeLoss(nn.Module):
"""
This class implements the lovasz hinge loss which is the continuous of the IoU for binary segmentation.
Source: https://github.com/bermanmaxim/LovaszSoftmax
"""
def __init__(self) ->None:
... | 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... | ChristophReich1996/Cell-DETR | LovaszHingeLoss | false | 13,607 | [
"MIT"
] | 55 | 4d0c3a2d3ffd19184c8443e5b3a6dccc053c77ea | https://github.com/ChristophReich1996/Cell-DETR/tree/4d0c3a2d3ffd19184c8443e5b3a6dccc053c77ea |
PositionEmbs | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | longxianlei/UtilsTools | PositionEmbs | false | 10,488 | [
"MIT"
] | 0 | f45c648eb679ed59bb512b61a1af52938e326ac3 | https://github.com/longxianlei/UtilsTools/tree/f45c648eb679ed59bb512b61a1af52938e326ac3 |
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... | Sardhendu/mmediting | DiscShiftLoss | false | 9,878 | [
"Apache-2.0"
] | 0 | 623b59ac758d856abc9fab7e845beeab61074d8f | https://github.com/Sardhendu/mmediting/tree/623b59ac758d856abc9fab7e845beeab61074d8f |
group | # 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 ... | CFengFeng/face-nn | group | false | 4,935 | [
"MIT"
] | 1 | a76a689774b5101959d3c5b8a04898ae82c7bfc2 | https://github.com/CFengFeng/face-nn/tree/a76a689774b5101959d3c5b8a04898ae82c7bfc2 |
ScaleLayer | import torch
import torch.nn as nn
class ScaleLayer(nn.Module):
def __init__(self, init_value=0.001):
super(ScaleLayer, self).__init__()
self.scale = nn.Parameter(torch.FloatTensor([init_value]))
def forward(self, input):
None
return input * self.scale
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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | Anurag14/STar-framework | ScaleLayer | false | 16,956 | [
"MIT"
] | 4 | 6670499c681fce8d76aae1d1910bc849ec5f56ea | https://github.com/Anurag14/STar-framework/tree/6670499c681fce8d76aae1d1910bc849ec5f56ea |
NN1 | import torch
import torch.nn as nn
class NN1(nn.Module):
def __init__(self, input_dimension):
super(NN1, self).__init__()
self.linear1 = nn.Linear(input_dimension, 60)
self.linear2 = nn.Linear(60, 25)
self.linear3 = nn.Linear(25, 1)
self.sig = nn.Sigmoid()
self.rel... | 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_... | kirtanp/MAMO-fair | NN1 | false | 7,039 | [
"Apache-2.0"
] | 1 | fd0fc39383f11a9e1ec401233b89c2399860fb94 | https://github.com/kirtanp/MAMO-fair/tree/fd0fc39383f11a9e1ec401233b89c2399860fb94 |
AutoEncoder | import torch
import torch.nn as nn
import torch.utils.data
class AutoEncoder(nn.Module):
def __init__(self, num_question, k=100):
""" Initialize a class AutoEncoder.
:param num_question: int
:param k: int
"""
super(AutoEncoder, self).__init__()
self.g = 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 ... | zuoyuwang/ML-Correctness-prediction | AutoEncoder | false | 13,183 | [
"MIT"
] | 0 | 15180b73567e61cc7a5dd61b0202a42eca808734 | https://github.com/zuoyuwang/ML-Correctness-prediction/tree/15180b73567e61cc7a5dd61b0202a42eca808734 |
Critic | # 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 numpy as np
import tor... | david-varela/collaboration_and_competition | Critic | false | 12,250 | [
"MIT"
] | 0 | a170cc02eb3917af19d6aafa8b37f6089b83c35f | https://github.com/david-varela/collaboration_and_competition/tree/a170cc02eb3917af19d6aafa8b37f6089b83c35f |
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 import triton_helpers
from torch._inductor.runtime.... | kongziyue1234/mooc | Attention | false | 3,855 | [
"MIT"
] | 0 | 3b0c822dd55c1066cbc829137e6c424dcda5067e | https://github.com/kongziyue1234/mooc/tree/3b0c822dd55c1066cbc829137e6c424dcda5067e |
ShuffleCatChunk | # 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... | rbli-john/yolact_edge | ShuffleCatChunk | false | 12,936 | [
"MIT"
] | 0 | 48305b45baf2154c336884aeb8a98cfc2c0a8cee | https://github.com/rbli-john/yolact_edge/tree/48305b45baf2154c336884aeb8a98cfc2c0a8cee |
LeakyReLU | import torch
import numpy as np
import torch.nn as nn
from numbers import Number
def normcdf(value, mu=0.0, stddev=1.0):
sinv = 1.0 / stddev if isinstance(stddev, Number) else stddev.reciprocal()
return 0.5 * (1.0 + torch.erf((value - mu) * sinv / np.sqrt(2.0)))
def _normal_log_pdf(value, mu, stddev):
v... | 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, math as tl_math
import numpy as np
import torch.nn as nn
from numbers import N... | DoggyLiu0116/MamboNet | LeakyReLU | false | 5,074 | [
"MIT"
] | 1 | 3b708091422491f660c4bd5eb12b06ce3b8a5f79 | https://github.com/DoggyLiu0116/MamboNet/tree/3b708091422491f660c4bd5eb12b06ce3b8a5f79 |
Mish | # 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, math as tl_math
from torch import nn
assert_size_stride = torch._C._dynamo.gua... | Benjamin-Etheredge/lightning-bolts | Mish | false | 133 | [
"Apache-2.0"
] | 0 | 1971d6a924729940b98793aa7751bdf769350aca | https://github.com/Benjamin-Etheredge/lightning-bolts/tree/1971d6a924729940b98793aa7751bdf769350aca |
GumbelSigmoid | import torch
import torch.nn as nn
def gumbel(x, eps=1e-20):
return -torch.log(-torch.log(torch.rand_like(x) + eps) + eps)
class GumbelSigmoid(nn.Module):
def __init__(self, scale=1.0):
super().__init__()
self.scale = float(scale)
def forward(self, logits):
y = logits + gumbel(... | 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... | daniil-lyakhov/deep-object-reid | GumbelSigmoid | false | 1,781 | [
"Apache-2.0"
] | 0 | b0f7d6a2d4cff8c417a66d82c09d16788d81ec67 | https://github.com/daniil-lyakhov/deep-object-reid/tree/b0f7d6a2d4cff8c417a66d82c09d16788d81ec67 |
CumulativeMagSpectralNorm | import torch
import torch.nn as nn
class CumulativeMagSpectralNorm(nn.Module):
def __init__(self, cumulative=False, use_mid_freq_mu=False):
"""
Args:
cumulative: 是否采用累积的方式计算 mu
use_mid_freq_mu: 仅采用中心频率的 mu 来代替全局 mu
Notes:
先算均值再累加 等同于 先累加再算均值
... | 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... | ShkarupaDC/FullSubNet | CumulativeMagSpectralNorm | false | 14,410 | [
"MIT"
] | 219 | 2aef8b656376a42fbf519e0020636a893b56c4f8 | https://github.com/ShkarupaDC/FullSubNet/tree/2aef8b656376a42fbf519e0020636a893b56c4f8 |
velocity_adding_neuron | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_... | AgamChopra/simulation-in-a-box | velocity_adding_neuron | false | 11,149 | [
"MIT"
] | 0 | 2a346a2fc83d79e542b64f1bd45c338d27a1934d | https://github.com/AgamChopra/simulation-in-a-box/tree/2a346a2fc83d79e542b64f1bd45c338d27a1934d |
DummyAttention | # 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.... | aaalgo/health_outcome_challenge | DummyAttention | false | 3,022 | [
"BSD-3-Clause"
] | 0 | 6d0cb660123ac6fb4939c1d50a8f1914e8e3e165 | https://github.com/aaalgo/health_outcome_challenge/tree/6d0cb660123ac6fb4939c1d50a8f1914e8e3e165 |
Simplenet | # 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.optim.lr_scheduler... | aam12/distiller | Simplenet | false | 12,042 | [
"Apache-2.0"
] | 0 | fd06fcba028d023e430cd37d1531bc2ac5202ea6 | https://github.com/aam12/distiller/tree/fd06fcba028d023e430cd37d1531bc2ac5202ea6 |
BertLayerNorm | # 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.cuda
import torch.onnx.utils
import torch.ra... | nict-wisdom/rannc | BertLayerNorm | false | 16,294 | [
"MIT"
] | 45 | a1708807e053e2d58b7f6d6ed925f03aa8504416 | https://github.com/nict-wisdom/rannc/tree/a1708807e053e2d58b7f6d6ed925f03aa8504416 |
FeatureResizer | import torch
import torch.utils.data
import torch
import torch.nn
import torch.optim
import torch.utils
from torch import nn
import torch.distributed
class FeatureResizer(nn.Module):
"""
This class takes as input a set of embeddings of dimension C1 and outputs a set of
embedding of dimension C2, after a l... | 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.utils.... | ShoufaChen/mdetr-1 | FeatureResizer | false | 11,878 | [
"Apache-2.0"
] | 0 | 3d9e40891ffdd39d6a5bf56730d468ace142752f | https://github.com/ShoufaChen/mdetr-1/tree/3d9e40891ffdd39d6a5bf56730d468ace142752f |
RollLayer | import torch
class RollLayer(torch.nn.Module):
"""
Layer which shifts the dimensions for performing the coupling permutations
on different dimensions
"""
def __init__(self, shift):
super(RollLayer, self).__init__()
self.shift = shift
def forward(self, x):
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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | NGoetz/NF | RollLayer | false | 5,620 | [
"MIT"
] | 1 | 935886db48f4675db1a2c42f7c264b12d5014ed8 | https://github.com/NGoetz/NF/tree/935886db48f4675db1a2c42f7c264b12d5014ed8 |
CAModel | import torch
import torch.nn as nn
import torch.nn.functional as F
class CAModel(nn.Module):
def __init__(self, env_d):
super(CAModel, self).__init__()
self.conv1 = nn.Conv2d(env_d * 3, 144, 1)
self.conv2 = nn.Conv2d(144, env_d, 1)
nn.init.zeros_(self.conv2.weight)
nn.init... | 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_... | anishau/Growing-Neural-Cellular-Automata-Pytorch | CAModel | false | 14,922 | [
"Apache-2.0"
] | 47 | 0e99815060ea4977597059fac5b556fe24e80dff | https://github.com/anishau/Growing-Neural-Cellular-Automata-Pytorch/tree/0e99815060ea4977597059fac5b556fe24e80dff |
Gaussian | # 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 math as tl_math
import torch.utils.tensorboard
import torch.utils.data
assert_size_stride... | yangyinuo823/torchani | Gaussian | false | 16,848 | [
"MIT"
] | 305 | b0cd62eda59829d197b3c37f2215ba1af64f1c8d | https://github.com/yangyinuo823/torchani/tree/b0cd62eda59829d197b3c37f2215ba1af64f1c8d |
Biaffine | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | LindgeW/BiaffineParser | Biaffine | false | 17,583 | [
"Apache-2.0"
] | 4 | 3671f9f5d4fdbcad67d90ecfdafbeb316e4378db | https://github.com/LindgeW/BiaffineParser/tree/3671f9f5d4fdbcad67d90ecfdafbeb316e4378db |
BasicBlock | import torch
import torch.nn as nn
import torch.utils.data
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, dim):
super(BasicBlock, self).__init__()
self.conv1 = nn.Conv2d(dim, dim, kernel_size=3, padding=1, bias=False)
self.bn1 = nn.GroupNorm(2, dim, eps=0.0001)
... | 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.... | ClaraBing/ffjord | BasicBlock | false | 13,522 | [
"MIT"
] | 518 | a97c34ff546a063316828f53bd041555e663428d | https://github.com/ClaraBing/ffjord/tree/a97c34ff546a063316828f53bd041555e663428d |
DownConv | # 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 ... | HenryOsborne/SemanticSegmentation | DownConv | false | 9,218 | [
"MIT"
] | 0 | d41549c3fd22731d7a12cdb1b438f730b0ebfcbc | https://github.com/HenryOsborne/SemanticSegmentation/tree/d41549c3fd22731d7a12cdb1b438f730b0ebfcbc |
AsymmetricLoss | # 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... | colinski/mmclassification | AsymmetricLoss | false | 6,465 | [
"Apache-2.0"
] | 1 | 447c8291bc2e2abda6f3eafe2e6d0f13d65843cb | https://github.com/colinski/mmclassification/tree/447c8291bc2e2abda6f3eafe2e6d0f13d65843cb |
AllocatingLayer | # 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.nn import Module
from torch.nn.modules.module import Module
assert_si... | avranasa/DRL_Scheduling_Communications | AllocatingLayer | false | 6,284 | [
"MIT"
] | 1 | 2e6cb3a9599e43b73547f4281d82b1e5999271b7 | https://github.com/avranasa/DRL_Scheduling_Communications/tree/2e6cb3a9599e43b73547f4281d82b1e5999271b7 |
spectral_model | import torch
import torch.nn as nn
import torch.nn.functional as F
class spectral_model(nn.Module):
def __init__(self, num_classes):
super(spectral_model, self).__init__()
self.mlp1 = nn.Conv1d(6, 64, 1)
self.mlp2 = nn.Conv1d(64, 128, 1)
self.mlp3 = nn.Conv1d(128, 256, 1)
... | 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_... | berkbilir/point-cloud-classification | spectral_model | false | 3,210 | [
"MIT"
] | 0 | 4188b317acc8efccb694831b26a3a8564dee5530 | https://github.com/berkbilir/point-cloud-classification/tree/4188b317acc8efccb694831b26a3a8564dee5530 |
extractNet_connected | import torch
import torch.nn as nn
import torch.nn.functional as F
class extractNet_connected(nn.Module):
def __init__(self):
super(extractNet_connected, self).__init__()
self.conv1 = nn.Conv2d(3, 16, 3, stride=2, padding=1)
self.conv2 = nn.Conv2d(16, 32, 3, stride=2, padding=1)
s... | 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_... | MNRKhan/aps360-project | extractNet_connected | false | 17,674 | [
"MIT"
] | 3 | 1d91a4262c95cd6b5610aae16e1a30f2749a4373 | https://github.com/MNRKhan/aps360-project/tree/1d91a4262c95cd6b5610aae16e1a30f2749a4373 |
Word2Vec | import torch
from torch import nn
class Word2Vec(nn.Module):
def __init__(self, features, embedding_size):
super().__init__()
0.5 / embedding_size
self.fc1 = nn.Linear(features, embedding_size)
self.fc2 = nn.Linear(embedding_size, features)
def forward(self, one_hot):
... | 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.... | salmanedhi/NNTI-WS2021-NLP-Project | Word2Vec | false | 4,253 | [
"MIT"
] | 0 | 5b0a8f1258ef4e835a6e647082a8286078a0bdd6 | https://github.com/salmanedhi/NNTI-WS2021-NLP-Project/tree/5b0a8f1258ef4e835a6e647082a8286078a0bdd6 |
LT | import torch
class LT(torch.nn.Module):
def __init__(self):
super(LT, self).__init__()
def forward(self, x, y):
return x < y
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| 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... | Ilyabasharov/torch2trt | LT | false | 2,515 | [
"MIT"
] | 0 | 76bf298b3da408509665e23e2494922b131afb10 | https://github.com/Ilyabasharov/torch2trt/tree/76bf298b3da408509665e23e2494922b131afb10 |
CFReLU | # 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 import triton_helpers
import torch.nn as nn
import torch.nn.functional as F
assert_size_stride = torch._C._dyna... | RosarioAndolina/psychXRF | CFReLU | false | 1,000 | [
"MIT"
] | 0 | e2adadbd17664d7f74c10304f84b3751c571226e | https://github.com/RosarioAndolina/psychXRF/tree/e2adadbd17664d7f74c10304f84b3751c571226e |
MultimodalFusionModule | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | bentrevett/task-oriented-language-grounding | MultimodalFusionModule | false | 6,335 | [
"MIT"
] | 1 | 812a7bc21ee622030eb0594c576c7d60dc630148 | https://github.com/bentrevett/task-oriented-language-grounding/tree/812a7bc21ee622030eb0594c576c7d60dc630148 |
BasicModel6_MultiTensor | # 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... | LMdeLiangMi/captum | BasicModel6_MultiTensor | false | 5,478 | [
"BSD-3-Clause"
] | 1 | 8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 | https://github.com/LMdeLiangMi/captum/tree/8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 |
DistillLoss | # 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
from torch ... | mrtunguyen/knowledge_distillation | DistillLoss | false | 7,292 | [
"MIT"
] | 1 | dd114e980dbebda6cc247f658eb801ab948ee6ba | https://github.com/mrtunguyen/knowledge_distillation/tree/dd114e980dbebda6cc247f658eb801ab948ee6ba |
_Transition | # 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.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
from torchvisi... | HelenR6/robustness-1 | _Transition | false | 11,142 | [
"Apache-2.0"
] | 0 | 5527250df02195dff37628a9d76ae7d76c3c51d1 | https://github.com/HelenR6/robustness-1/tree/5527250df02195dff37628a9d76ae7d76c3c51d1 |
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