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 |
|---|---|---|---|---|---|---|---|---|---|---|
VAE | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.onnx
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torch.autograd
class VAE(nn.Module):
def __init__(self):
super(VAE, self).__init__()
self.fc1 = nn.Li... | 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... | ScorpioDoctor/antares02 | VAE | false | 1,042 | [
"BSD-3-Clause"
] | 0 | 631b817d2e98f351d1173b620d15c4a5efed11da | https://github.com/ScorpioDoctor/antares02/tree/631b817d2e98f351d1173b620d15c4a5efed11da |
FeatureResizer | # 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
from torch import n... | XiaoJake/MTTR | FeatureResizer | false | 14,595 | [
"Apache-2.0"
] | 516 | c383c5b151e3c97aeb45cd2fb4bf08719016498b | https://github.com/XiaoJake/MTTR/tree/c383c5b151e3c97aeb45cd2fb4bf08719016498b |
HighwayLayer | # 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.... | vincentLiangBerkeley/translate | HighwayLayer | false | 4,497 | [
"BSD-3-Clause"
] | 0 | 734ae1ad9dfb778935e4825b5ce2687e2df559ea | https://github.com/vincentLiangBerkeley/translate/tree/734ae1ad9dfb778935e4825b5ce2687e2df559ea |
MSELead | import torch
from torch import nn
class MSELead(nn.Module):
def __init__(self):
super(MSELead, self).__init__()
self.loss_func = nn.MSELoss()
def forward(self, input, target):
loss_list = []
for i in range(input.size(1)):
loss_list.append(self.loss_func(input[:, i... | 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... | yhy489275918/Electrocardio-Panorama | MSELead | false | 4,618 | [
"MIT"
] | 0 | 1acdbb43d873ce98a0350b7912b6b190e026d3db | https://github.com/yhy489275918/Electrocardio-Panorama/tree/1acdbb43d873ce98a0350b7912b6b190e026d3db |
LinearNet | import torch
import torch.nn as nn
import torch.nn.functional as F
class LinearNet(nn.Module):
def __init__(self, n_feature, n_output):
super(LinearNet, self).__init__()
self.fc1 = nn.Linear(n_feature, 256)
self.fc2 = nn.Linear(256, 512)
self.fc3 = nn.Linear(512, 1024)
sel... | 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_... | wslerry/regresstorch | LinearNet | false | 4,553 | [
"MIT"
] | 0 | b2e3507d8ed794e5d1d75ebfe910f74bbcb9a06b | https://github.com/wslerry/regresstorch/tree/b2e3507d8ed794e5d1d75ebfe910f74bbcb9a06b |
Highway | import torch
class BaseModule(torch.nn.Module):
def __init__(self):
super(BaseModule, self).__init__()
@property
def nparams(self):
return sum(p.numel() for p in self.parameters() if p.requires_grad)
class Highway(BaseModule):
"""
Implementation as described
in https://arxi... | 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
assert_size_stride = torch._C... | aflorithmic/DurIAN | Highway | false | 14,753 | [
"BSD-3-Clause"
] | 158 | a708e9c5bb89895ddf08ca1a13bc8fd683b1e23f | https://github.com/aflorithmic/DurIAN/tree/a708e9c5bb89895ddf08ca1a13bc8fd683b1e23f |
VGG16 | import torch
import torch.nn as nn
import torch.nn.functional as F
class VGG16(nn.Module):
def __init__(self):
super(VGG16, self).__init__()
self.conv1_1 = nn.Conv2d(3, 64, 3)
self.conv1_2 = nn.Conv2d(64, 64, 3, padding=(1, 1))
self.maxpool1 = nn.MaxPool2d((2, 2), padding=(1, 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_... | Jiannan-Liu/nCoVSegNet | VGG16 | false | 17,611 | [
"MIT"
] | 5 | 7543e68edff011a7f7b694c97cf0f185d441fd6b | https://github.com/Jiannan-Liu/nCoVSegNet/tree/7543e68edff011a7f7b694c97cf0f185d441fd6b |
VDNNet | # 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... | JJBong/marl | VDNNet | false | 2,396 | [
"MIT"
] | 0 | 836ea6b478787a728506b6de3c551ce6b10f9ba4 | https://github.com/JJBong/marl/tree/836ea6b478787a728506b6de3c551ce6b10f9ba4 |
ContractingBlock | import torch
import torch.nn as nn
class ContractingBlock(nn.Module):
def __init__(self, input_channel):
super(ContractingBlock, self).__init__()
self.conv1 = nn.Conv2d(in_channels=input_channel, out_channels=
input_channel * 2, kernel_size=(3, 3))
self.conv2 = nn.Conv2d(input... | 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_... | furkannturkmen/pytorch-CNN-architecture | ContractingBlock | false | 10,124 | [
"MIT"
] | 0 | 6a864811f51409c1526224c288fe608010e0c888 | https://github.com/furkannturkmen/pytorch-CNN-architecture/tree/6a864811f51409c1526224c288fe608010e0c888 |
GCN | # 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 ... | ayyyq/T-LSTM | GCN | false | 6,299 | [
"MIT"
] | 1 | 36dbc88ac710d3925851cd87c2368ecfc7061b70 | https://github.com/ayyyq/T-LSTM/tree/36dbc88ac710d3925851cd87c2368ecfc7061b70 |
KLDLoss | import torch
import torch.nn as nn
import torch.utils.data
class KLDLoss(nn.Module):
def forward(self, mu, logvar):
return -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())
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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | kudoNCT/michigan_copy | KLDLoss | false | 12,687 | [
"MIT"
] | 0 | e857b96a65b270ef2506cb9866b7e01f117c4396 | https://github.com/kudoNCT/michigan_copy/tree/e857b96a65b270ef2506cb9866b7e01f117c4396 |
AP | # 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.... | gcambara/s3prl | AP | false | 15,411 | [
"MIT"
] | 856 | 33284ebde3a903ed8604d6dae85669d0174ae1d3 | https://github.com/gcambara/s3prl/tree/33284ebde3a903ed8604d6dae85669d0174ae1d3 |
multi_pool | # 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... | siyuhuang/crowdcount-stackedpool | multi_pool | false | 16,478 | [
"MIT"
] | 93 | bbba3d9e91a5a89642b4bd3638ae8e68801ea7bf | https://github.com/siyuhuang/crowdcount-stackedpool/tree/bbba3d9e91a5a89642b4bd3638ae8e68801ea7bf |
RefineFireModel | import torch
import numpy as np
import torch.nn as nn
class SirenLayer(nn.Module):
def __init__(self, in_f, out_f, w0=30, is_first=False, is_last=False):
super().__init__()
self.in_f = in_f
self.w0 = w0
self.linear = nn.Linear(in_f, out_f)
self.is_first = is_first
... | 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 numpy ... | BoyuanChen/neural-state-variables | RefineFireModel | false | 7,859 | [
"MIT"
] | 17 | 10483d93ac8c006f3786c434fb57d70d9ab465ec | https://github.com/BoyuanChen/neural-state-variables/tree/10483d93ac8c006f3786c434fb57d70d9ab465ec |
ContrastivePairwiseEmbeddingLoss | import torch
import torch.nn as nn
import torch.distributed
import torch.nn.functional as F
from torch.nn.modules.loss import *
from torch.nn.modules import *
from torch.optim import *
from torch.optim.lr_scheduler import *
import torch.backends
class ContrastivePairwiseEmbeddingLoss(nn.Module):
"""ContrastivePai... | 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.... | Ditwoo/catalyst | ContrastivePairwiseEmbeddingLoss | false | 5,079 | [
"Apache-2.0"
] | 1 | 3126390f9f679ebcfedbe01707b416678a2732ac | https://github.com/Ditwoo/catalyst/tree/3126390f9f679ebcfedbe01707b416678a2732ac |
Discriminator | # 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
from nu... | ducviet00/HMER | Discriminator | false | 6,627 | [
"MIT"
] | 1 | 0fa322ed35412737a24ec3955c9a3d96d1989bd4 | https://github.com/ducviet00/HMER/tree/0fa322ed35412737a24ec3955c9a3d96d1989bd4 |
PARALoss | # 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... | igorvlnascimento/redn | PARALoss | false | 15,598 | [
"MIT"
] | 100 | f40f19a0fdfbb11a7987996d520716a05bafd77b | https://github.com/igorvlnascimento/redn/tree/f40f19a0fdfbb11a7987996d520716a05bafd77b |
CQAttention | 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 CQAttention(nn.Module):
def __init__(self, d_model, dropout=0.1):
super().__init__()
w4C = torch.empty(d_mod... | 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.... | dcy2018/QANA | CQAttention | false | 3,517 | [
"MIT"
] | 0 | 69d1e4ff408a56317479e22ecc854c91fc0f420f | https://github.com/dcy2018/QANA/tree/69d1e4ff408a56317479e22ecc854c91fc0f420f |
C3D | # 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 ... | Luoyadan/MM2020_ABG | C3D | false | 17,825 | [
"MIT"
] | 8 | d74cf915deea7bb425518f5bd40e64a9a7341981 | https://github.com/Luoyadan/MM2020_ABG/tree/d74cf915deea7bb425518f5bd40e64a9a7341981 |
TensorClampMin | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | NVIDIA-AI-IOT-private/torch2trt | TensorClampMin | false | 10,538 | [
"MIT"
] | 0 | 953d60039e0c81e90eea467c3df2e6e3f7040242 | https://github.com/NVIDIA-AI-IOT-private/torch2trt/tree/953d60039e0c81e90eea467c3df2e6e3f7040242 |
FeedForward | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data.distributed
class Linear(nn.Linear):
def forward(self, x):
size = x.size()
return super().forward(x.contiguous().view(-1, size[-1])).view(*
size[:-1], -1)
class FeedForward(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
import torch.nn as nn
import ... | MichiganCOG/Video-Grounding | FeedForward | false | 8,550 | [
"MIT"
] | 41 | 3e0ec0b69578a59be583911590354fe77d357cab | https://github.com/MichiganCOG/Video-Grounding/tree/3e0ec0b69578a59be583911590354fe77d357cab |
CNNCifar | from _paritybench_helpers import _mock_config
import torch
from torch import nn
import torch.nn.functional as F
class CNNCifar(nn.Module):
def __init__(self, args):
super(CNNCifar, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.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
from torch._inductor.runtime.... | Joey61Liuyi/Federated-Learning-PyTorch | CNNCifar | false | 1,641 | [
"MIT"
] | 0 | e95f096b18c5a1bf30fc0485acd5a15c84327f2e | https://github.com/Joey61Liuyi/Federated-Learning-PyTorch/tree/e95f096b18c5a1bf30fc0485acd5a15c84327f2e |
BertOutput | from _paritybench_helpers import _mock_config
import torch
from torch import nn
import torch.onnx
class BertLayerNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-12):
"""Construct a layernorm module in the TF style (epsilon inside the square root).
"""
super(BertLayerNorm, 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
from torch._inductor.runtime.triton_helpers import libdevice
from torch import n... | Alwaysproblem/examples-1 | BertOutput | false | 1,876 | [
"MIT"
] | 0 | 9754fa63ed1931489a21ac1f5b299f945e369a5c | https://github.com/Alwaysproblem/examples-1/tree/9754fa63ed1931489a21ac1f5b299f945e369a5c |
AvgConsensus | # 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... | giahaowjx/mmaction2 | AvgConsensus | false | 10,332 | [
"Apache-2.0"
] | 0 | 4f95e9b91354acdcae768ce94e01d3821bba0154 | https://github.com/giahaowjx/mmaction2/tree/4f95e9b91354acdcae768ce94e01d3821bba0154 |
hsigmoid | import torch
import torch.nn as nn
import torch.nn.functional as F
class hsigmoid(nn.Module):
def forward(self, x):
out = F.relu6(x + 3, inplace=True) / 6
return out
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
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | Qidian213/NAIC2019 | hsigmoid | false | 949 | [
"MIT"
] | 0 | 23e05a8a096168ccfa4d1743467fdf78ffcaabba | https://github.com/Qidian213/NAIC2019/tree/23e05a8a096168ccfa4d1743467fdf78ffcaabba |
InvertibleDownsampling2D | # 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.autograd import Function
import numpy as np
from warnings import warn... | cetmann/iunets | InvertibleDownsampling2D | false | 15,021 | [
"MIT"
] | 86 | 80ed7cce0e505a0396c42359eaf27819222d71f6 | https://github.com/cetmann/iunets/tree/80ed7cce0e505a0396c42359eaf27819222d71f6 |
CosineSimilarityLoss | # 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
from torch import nn
assert_... | DunZhang/KnowledgeDistillation | CosineSimilarityLoss | false | 8,006 | [
"MIT"
] | 31 | 47a9dd0f51021001b53e3a76c9347eb3131f1f72 | https://github.com/DunZhang/KnowledgeDistillation/tree/47a9dd0f51021001b53e3a76c9347eb3131f1f72 |
D_GCN | import math
import torch
import torch.nn.functional as F
from torch import nn
class D_GCN(nn.Module):
"""
Neural network block that applies a diffusion graph convolution to sampled location
"""
def __init__(self, in_channels, out_channels, orders, activation='relu'):
"""
:param in_cha... | 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... | ZhuangDingyi/STZINB | D_GCN | false | 18,217 | [
"MIT"
] | 6 | e290ad05f76030c0c8e86b5dd78346097e1127cb | https://github.com/ZhuangDingyi/STZINB/tree/e290ad05f76030c0c8e86b5dd78346097e1127cb |
CondUpsampler | # 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... | ZhuangweiKang/pytorch-ts | CondUpsampler | false | 2,993 | [
"Apache-2.0",
"MIT"
] | 0 | 076d456358fd1bac96becba4f1ba38ec5a5fcf4d | https://github.com/ZhuangweiKang/pytorch-ts/tree/076d456358fd1bac96becba4f1ba38ec5a5fcf4d |
ConvHeadPooling | # 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... | iliasprc/Compact-Transformers | ConvHeadPooling | false | 3,664 | [
"Apache-2.0"
] | 0 | 31975a0b4469854dfb0e0cbcedd8f0698cf84a7e | https://github.com/iliasprc/Compact-Transformers/tree/31975a0b4469854dfb0e0cbcedd8f0698cf84a7e |
Encoder | import torch
import torch.nn.functional as F
from torch import nn
class Encoder(nn.Module):
def __init__(self, input_size: 'int', output_size: 'int', max_temp:
'float'=10.0, min_temp: 'float'=0.1, reg_threshold: 'float'=3.0,
reg_eps: 'float'=1e-10) ->None:
"""Feature selection encoder
... | 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.functional as F
from torch import nn
assert_size_stride = torch.... | GewoonMaarten/spherical-dmri-conv | Encoder | false | 2,303 | [
"MIT"
] | 0 | 6a5bbb31cf70a5f8b839f92e534f49664001ea09 | https://github.com/GewoonMaarten/spherical-dmri-conv/tree/6a5bbb31cf70a5f8b839f92e534f49664001ea09 |
RgbaToBgr | import torch
import torch.nn as nn
def bgr_to_rgb(image: 'torch.Tensor') ->torch.Tensor:
"""Convert a BGR image to RGB.
Args:
image (torch.Tensor): BGR Image to be converted to BGR of shape :math:`(*,3,H,W)`.
Returns:
torch.Tensor: RGB version of the image with shape of shape :math:`(*,3... | 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... | ChristophReich1996/kornia | RgbaToBgr | false | 271 | [
"ECL-2.0",
"Apache-2.0"
] | 0 | 35f955b46e8015da1cb9faa28c6943ec2b09cc2a | https://github.com/ChristophReich1996/kornia/tree/35f955b46e8015da1cb9faa28c6943ec2b09cc2a |
Autoencoder | import torch
import torch.nn as nn
class Autoencoder(nn.Module):
def __init__(self):
super(Autoencoder, self).__init__()
self.encoder = nn.Conv2d(1024, 128, kernel_size=1)
self.decoder = nn.Conv2d(128, 1024, kernel_size=1)
self.relu = nn.ReLU()
def forward(self, local_f):
... | 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_... | esha-singh/DL_project | Autoencoder | false | 3,589 | [
"MIT"
] | 0 | 11ac2874845bc3982435cc37f4e0b8896b95660e | https://github.com/esha-singh/DL_project/tree/11ac2874845bc3982435cc37f4e0b8896b95660e |
TripletMarginLoss | from torch.autograd import Function
import torch
class PairwiseDistance(Function):
def __init__(self, p):
super(PairwiseDistance, self).__init__()
self.norm = p
def forward(self, x1, x2):
assert x1.size() == x2.size()
eps = 0.0001 / x1.size(1)
diff = torch.abs(x1 - x2... | 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.autograd import Function
assert_size_stride = torch._C._dynamo.guards.assert_s... | Mikexu007/AS_CAL | TripletMarginLoss | false | 8,554 | [
"MIT"
] | 14 | 966328ae65bb16ba9b7aab153d8150c08c26c81f | https://github.com/Mikexu007/AS_CAL/tree/966328ae65bb16ba9b7aab153d8150c08c26c81f |
Conv2 | import math
import torch
import torch.nn as nn
class Conv2(nn.Module):
""" A convolution layer with the stride of 2.
Input:
x: (N, 2L+2, in_channels) numeric tensor
global_cond: (N, global_cond_channels) numeric tensor
Output:
y: (N, L, out_channels) numeric te... | 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 ... | jonojace/WaveRNN | Conv2 | false | 10,288 | [
"MIT"
] | 0 | 5ac72d5ed10262132f016f8e523bc663faa991da | https://github.com/jonojace/WaveRNN/tree/5ac72d5ed10262132f016f8e523bc663faa991da |
FocalLoss | import torch
import torch.nn as nn
from matplotlib.font_manager import *
class FocalLoss(nn.Module):
"""
Focal loss: focus more on hard samples
"""
def __init__(self, gamma=0, eps=1e-07):
"""
:param gamma:
:param eps:
"""
super(FocalLoss, 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 import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | wang-tf/RepNet-MDNet-VehicleReID | FocalLoss | false | 16,692 | [
"MIT"
] | 226 | d3d184331206ca4bdb5ea399e5b90a9ccc53b400 | https://github.com/wang-tf/RepNet-MDNet-VehicleReID/tree/d3d184331206ca4bdb5ea399e5b90a9ccc53b400 |
DiceLoss | import torch
import torch.nn as nn
class DiceLoss(nn.Module):
def __init__(self, loss_weight=1.0):
super(DiceLoss, self).__init__()
self.loss_weight = loss_weight
def forward(self, input, target, mask, reduce=True):
batch_size = input.size(0)
input = torch.sigmoid(input)
... | 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... | rigvedsah000/PAN- | DiceLoss | false | 12,937 | [
"Apache-2.0"
] | 0 | 16f8482886c5eccecf29fe072025ba54c64e4b9d | https://github.com/rigvedsah000/PAN-/tree/16f8482886c5eccecf29fe072025ba54c64e4b9d |
TransposedUpsample | import torch
import torch.nn as nn
class TransposedUpsample(nn.Module):
"""Learned 2x upsampling without padding"""
def __init__(self, channels, out_channels=None, ks=5):
super().__init__()
self.channels = channels
self.out_channels = out_channels or channels
self.up = nn.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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | transat/latent-diffusion | TransposedUpsample | false | 10,918 | [
"MIT"
] | 0 | 1ea0d5bb3fb0fe3f7e8c42cbae91423780977f83 | https://github.com/transat/latent-diffusion/tree/1ea0d5bb3fb0fe3f7e8c42cbae91423780977f83 |
GeM | import torch
from torch import nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
class GeM(nn.Module):
def __init__(self, p=3, eps=1e-06):
super(GeM, self).__init__()
self.p = Parameter(torch.ones(1) * p)
self.eps = eps
def forward(self, x):
return F... | 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
from to... | lulor/project_vg | GeM | false | 7,135 | [
"MIT"
] | 1 | 27b0c3b3038c5a666dde516a0a265ae8ddf2059f | https://github.com/lulor/project_vg/tree/27b0c3b3038c5a666dde516a0a265ae8ddf2059f |
CoAttention | import torch
import torch.nn as nn
import torch.nn.functional as F
class CoAttention(nn.Module):
"""
CoAttention encoder
in Dynamic Coattention Networks For Question Answering (https://arxiv.org/abs/1611.01604)
check the Figure 2 in paper
* Args:
embed_dim: the number of input embedd... | 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.... | srlee-ai/claf | CoAttention | false | 10,883 | [
"MIT"
] | 0 | 89b3e5c5ec0486886876ea3bac381508c6a6bf58 | https://github.com/srlee-ai/claf/tree/89b3e5c5ec0486886876ea3bac381508c6a6bf58 |
GCN | from torch.nn import Module
import math
import torch
import torch.nn.functional as F
import torch.nn as nn
class GraphConvolution(Module):
"""
A Graph Convolution Layer (GCN)
"""
def __init__(self, in_features, out_features, bias=True):
super(GraphConvolution, self).__init__()
self.in... | 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.nn import Module
i... | duzhizhai/HGNN | GCN | false | 3,519 | [
"MIT"
] | 0 | 1d219f9eb773e0d2f585295d6fc13c2eb093d908 | https://github.com/duzhizhai/HGNN/tree/1d219f9eb773e0d2f585295d6fc13c2eb093d908 |
SSE | import torch
from torch.nn.modules.loss import _Loss
class SSE(_Loss):
"""
Definition: sum_squared_error = 1/2 * nn.MSELoss(reduction = 'sum')
The backward is defined as: input-target
"""
def __init__(self, under_penalty, over_penalty):
super(SSE, self).__init__(under_penalty, over_penalt... | 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.modules.loss import _Loss
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynam... | IVRL/CCID | SSE | false | 5,316 | [
"MIT"
] | 1 | 0d57c33696da87279d24777a2efd1204f5088bc9 | https://github.com/IVRL/CCID/tree/0d57c33696da87279d24777a2efd1204f5088bc9 |
CrossEntropyLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
def mask_cross_entropy(pred, target, label):
num_rois = pred.size()[0]
inds = torch.arange(0, num_rois, dtype=torch.long, device=pred.device)
pred_slice = pred[inds, label].squeeze(1)
return F.binary_cross_entropy_with_logits(pred_slic... | 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
... | LiWentomng/boxlevelset | CrossEntropyLoss | false | 8,469 | [
"Apache-2.0"
] | 25 | 8cc40bf6ae4a343c482c676c72259cc12c29d31c | https://github.com/LiWentomng/boxlevelset/tree/8cc40bf6ae4a343c482c676c72259cc12c29d31c |
SoftDiceLoss | # 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.nn.modules.loss import _Loss
assert_size_stride = torch._C._dynamo.guards.asse... | purbayankar/pytorch-UNet | SoftDiceLoss | false | 16,288 | [
"MIT"
] | 91 | 63183199b1cf4e23a37869d30fc335e484c0c0fe | https://github.com/purbayankar/pytorch-UNet/tree/63183199b1cf4e23a37869d30fc335e484c0c0fe |
BCEFocalLoss | # 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... | earlbabson/torchflare | BCEFocalLoss | false | 6,630 | [
"Apache-2.0"
] | 1 | 15db06d313a53a3ec4640869335ba87730562b28 | https://github.com/earlbabson/torchflare/tree/15db06d313a53a3ec4640869335ba87730562b28 |
LabelSmoothing | import torch
import torch.nn as nn
class LabelSmoothing(nn.Module):
"""
NLL loss with label smoothing.
"""
def __init__(self, smoothing=0.0, n_cls=2):
"""
Constructor for the LabelSmoothing module.
:param smoothing: label smoothing factor
"""
super(LabelSmoothi... | 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
... | mrblasco/kaggle_moa_winner_hungry_for_gold | LabelSmoothing | false | 16,118 | [
"Apache-2.0"
] | 89 | 00df6d0aa4a48e526cee3e36f6e723a1534bfa08 | https://github.com/mrblasco/kaggle_moa_winner_hungry_for_gold/tree/00df6d0aa4a48e526cee3e36f6e723a1534bfa08 |
TransformerLayer | import torch
import torch.nn as nn
import torch.utils.data
class TransformerLayer(nn.Module):
def __init__(self, c, num_heads):
super().__init__()
self.q = nn.Linear(c, c, bias=False)
self.k = nn.Linear(c, c, bias=False)
self.v = nn.Linear(c, c, bias=False)
self.ma = nn.Mu... | 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.... | COEN-390/YOLOv5-Lite | TransformerLayer | false | 11,283 | [
"MIT"
] | 0 | 06a53f5d001c5d37729f55f47cbd46cc8eb63f84 | https://github.com/COEN-390/YOLOv5-Lite/tree/06a53f5d001c5d37729f55f47cbd46cc8eb63f84 |
AdditiveAttention | # 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.... | LindgeW/BiaffineNER | AdditiveAttention | false | 8,467 | [
"Apache-2.0"
] | 13 | 0ae179e9ff731362f6c8ba6d0b24485ad45e8bbf | https://github.com/LindgeW/BiaffineNER/tree/0ae179e9ff731362f6c8ba6d0b24485ad45e8bbf |
FinalTanh | # 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.... | athon-millane/NeuralCDE | FinalTanh | false | 12,125 | [
"Apache-2.0"
] | 0 | 4196890fe5bf7a69925a12ff35e86f212963be71 | https://github.com/athon-millane/NeuralCDE/tree/4196890fe5bf7a69925a12ff35e86f212963be71 |
InputTransition | # 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.... | CheerL/lancunar | InputTransition | false | 11,332 | [
"BSD-3-Clause"
] | 0 | fb00a331b5381af555fd2a7f0d03324a5355fe8c | https://github.com/CheerL/lancunar/tree/fb00a331b5381af555fd2a7f0d03324a5355fe8c |
BatchSpectralLoss | # 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... | KevinMusgrave/pytorch-adapt | BatchSpectralLoss | false | 13,946 | [
"MIT"
] | 131 | ff1491e1bfcc586afb8ee619712c8816ddf10358 | https://github.com/KevinMusgrave/pytorch-adapt/tree/ff1491e1bfcc586afb8ee619712c8816ddf10358 |
BPR | import torch
import torch.nn as nn
import torch.nn.functional as F
class BPR(nn.Module):
def __init__(self, user_size, item_size, dim, weight_decay):
super().__init__()
self.W = nn.Parameter(torch.empty(user_size, dim))
self.H = nn.Parameter(torch.empty(item_size, dim))
nn.init.xa... | 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... | georgezzzh/bpr | BPR | false | 3,556 | [
"MIT"
] | 0 | dd2f39d99f7f06ebb305b66363c89c3606a811a1 | https://github.com/georgezzzh/bpr/tree/dd2f39d99f7f06ebb305b66363c89c3606a811a1 |
Generator | # 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
from torch import n... | Dora-The-Kid/culture_network | Generator | false | 2,161 | [
"Apache-2.0"
] | 0 | bc2bac86e821faa797eeb2670d179395724f7922 | https://github.com/Dora-The-Kid/culture_network/tree/bc2bac86e821faa797eeb2670d179395724f7922 |
KLD | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torch.nn.functional as F
class KLD(nn.Module):
def forward(self, targets, inputs):
targets = F.softmax(targets, dim=1)
inputs = F.log_softmax(inputs, d... | 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... | UMBCvision/CompReSS | KLD | false | 14,526 | [
"MIT"
] | 61 | c5e57edce75da96482fd36eac484c5aca9676945 | https://github.com/UMBCvision/CompReSS/tree/c5e57edce75da96482fd36eac484c5aca9676945 |
PoseHead | # 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_... | aliyun/dro-sfm | PoseHead | false | 14,817 | [
"MIT"
] | 147 | 8707e2e0ef799d7d47418a018060f503ef449fe3 | https://github.com/aliyun/dro-sfm/tree/8707e2e0ef799d7d47418a018060f503ef449fe3 |
act_RT | # 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... | Cheeun/FDSR | act_RT | false | 4,974 | [
"MIT"
] | 1 | 28b1c3c102334c5336038d0a0f6e1fceb393659a | https://github.com/Cheeun/FDSR/tree/28b1c3c102334c5336038d0a0f6e1fceb393659a |
Binarizer | # 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
from torch.autograd... | Biaze7/lossy-image-compression | Binarizer | false | 7,779 | [
"MIT"
] | 16 | 88ca2022a306fea52d6671593b314f0de3bf6010 | https://github.com/Biaze7/lossy-image-compression/tree/88ca2022a306fea52d6671593b314f0de3bf6010 |
DilatedResidualLayer | # 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_... | MarahGamdou/sign-segmentation | DilatedResidualLayer | false | 2,620 | [
"MIT"
] | 0 | f6ef1f23b252d09b66031bfb802f18cfb4b1f4c6 | https://github.com/MarahGamdou/sign-segmentation/tree/f6ef1f23b252d09b66031bfb802f18cfb4b1f4c6 |
Conv1d2Score | # 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.optim
import torch.utils.data
assert_size_str... | BeautyOfWeb/VIN | Conv1d2Score | false | 7,761 | [
"MIT"
] | 34 | 53343d28130f5fd6e5badb58daf8079a5933fd6a | https://github.com/BeautyOfWeb/VIN/tree/53343d28130f5fd6e5badb58daf8079a5933fd6a |
FastGRNNCell | # 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 ... | ShishirPatil/EdgeML-1 | FastGRNNCell | false | 1,076 | [
"MIT"
] | 0 | cbba9f8b989e545788427c004eb8450e7e4c1a21 | https://github.com/ShishirPatil/EdgeML-1/tree/cbba9f8b989e545788427c004eb8450e7e4c1a21 |
AttCeLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class AttCeLoss(nn.Module):
def __init__(self):
super().__init__()
def forward(self, attention_S, attention_T, mask=None):
"""
Calculate the cross entropy between attention_S and attention_T.
:param logits_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 math as tl_math
import torch.nn as nn
... | lonePatient/TorchBlocks | AttCeLoss | false | 15,954 | [
"MIT"
] | 82 | 4a65d746cc8a396cb7df73ed4644d97ddf843e29 | https://github.com/lonePatient/TorchBlocks/tree/4a65d746cc8a396cb7df73ed4644d97ddf843e29 |
ConvNet | # 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_... | Guojiacheng2017/wasteNet_SH | ConvNet | false | 9,073 | [
"MIT"
] | 0 | cc02e535e52513133fe87094f76a30835dbb0010 | https://github.com/Guojiacheng2017/wasteNet_SH/tree/cc02e535e52513133fe87094f76a30835dbb0010 |
ResNNFlow | import torch
import torch.utils.data
class ResNNFlow(torch.nn.Sequential):
def __init__(self, *args, **kwargs):
super(ResNNFlow, self).__init__(*args, **kwargs)
self.gate = torch.nn.Parameter(torch.nn.init.normal_(torch.Tensor(1)))
def forward(self, inputs):
or_inputs = 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.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_... | nicola-decao/M-NAF-experiments-VAE | ResNNFlow | false | 4,086 | [
"MIT"
] | 0 | b8e127205e84d94ae50618e95734f20d259f7934 | https://github.com/nicola-decao/M-NAF-experiments-VAE/tree/b8e127205e84d94ae50618e95734f20d259f7934 |
SigmoidFocalClassificationLoss | import torch
import torch.nn as nn
class SigmoidFocalClassificationLoss(nn.Module):
"""
Sigmoid focal cross entropy loss.
"""
def __init__(self, gamma: 'float'=2.0, alpha: 'float'=0.25):
"""
Args:
gamma: Weighting parameter to balance loss for hard and easy examples.
... | 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... | Javier-DlaP/OpenPCDet | SigmoidFocalClassificationLoss | false | 620 | [
"Apache-2.0"
] | 0 | c4d308ea8052dd92948e2377b161b2519254275b | https://github.com/Javier-DlaP/OpenPCDet/tree/c4d308ea8052dd92948e2377b161b2519254275b |
SiSdr | # 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.triton_helpers import libdevice
from torch import n... | JinmingChe/DeepFilterNet | SiSdr | false | 5,400 | [
"ECL-2.0",
"Apache-2.0",
"MIT"
] | 1 | 0e35a24c33c091b4c34afb3599f2945bf5e87adf | https://github.com/JinmingChe/DeepFilterNet/tree/0e35a24c33c091b4c34afb3599f2945bf5e87adf |
Dense | import torch
import torch.nn as nn
import torch.utils.data
class Dense(nn.Module):
def __init__(self, num_channels, num_filters, filter_size, dropout_prob):
super().__init__()
self.dense_conv = nn.Conv2d(in_channels=num_channels, out_channels=
num_filters, kernel_size=filter_size, str... | 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 ... | COEN-390/YOLOv5-Lite | Dense | false | 11,289 | [
"MIT"
] | 0 | 06a53f5d001c5d37729f55f47cbd46cc8eb63f84 | https://github.com/COEN-390/YOLOv5-Lite/tree/06a53f5d001c5d37729f55f47cbd46cc8eb63f84 |
LinearCombine | # 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.nn.parallel
import torch.optim
import ... | Johnsonms/NNI_master | LinearCombine | false | 11,577 | [
"MIT"
] | 0 | e5e5c7aed89cf3189cffe1056464833c15eb54ff | https://github.com/Johnsonms/NNI_master/tree/e5e5c7aed89cf3189cffe1056464833c15eb54ff |
FCN_mse | # 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 ... | AZdet/causal-infogan | FCN_mse | false | 13,256 | [
"MIT"
] | 89 | 146b647863a27542ad4a1a01ddb033cdcab9843d | https://github.com/AZdet/causal-infogan/tree/146b647863a27542ad4a1a01ddb033cdcab9843d |
BahdanauAttention | import torch
import torch.nn as nn
import torch.nn.functional as F
class BahdanauAttention(nn.Module):
def __init__(self, units, hidden_size):
super().__init__()
self.W1 = nn.Linear(hidden_size, units)
self.W2 = nn.Linear(hidden_size, units)
self.V = nn.Linear(units, 1)
def f... | 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.... | beroguedou/nmt-pytorch | BahdanauAttention | false | 6,331 | [
"MIT"
] | 1 | 8758ba33e2d5f4eca7f1ac2d04582678332bbcd5 | https://github.com/beroguedou/nmt-pytorch/tree/8758ba33e2d5f4eca7f1ac2d04582678332bbcd5 |
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.... | pkuyym/nni | ZeroLayer | false | 10,994 | [
"MIT"
] | 0 | fe533e3bc65ea27997e16250adb503638548d500 | https://github.com/pkuyym/nni/tree/fe533e3bc65ea27997e16250adb503638548d500 |
AdMSoftmaxLoss | # 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.... | albertvillanova/s3prl | AdMSoftmaxLoss | false | 6,150 | [
"MIT"
] | 1 | b127ade4ed2f80a1027901bbd2f204b4fb1aaf03 | https://github.com/albertvillanova/s3prl/tree/b127ade4ed2f80a1027901bbd2f204b4fb1aaf03 |
MAB | # 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.... | AntonValk/BagGraph-Graph-MIL | MAB | false | 16,951 | [
"MIT"
] | 8 | 1447b52b32995cf6c71e731dd1261104cd66ced0 | https://github.com/AntonValk/BagGraph-Graph-MIL/tree/1447b52b32995cf6c71e731dd1261104cd66ced0 |
_AddNorm | # 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.nn.functional as F
assert_size_stride = torc... | Gian-Wiher/darts | _AddNorm | false | 5,206 | [
"Apache-2.0"
] | 1 | 0d267e08643e2e3f88163a5d955b8be75840c2f6 | https://github.com/Gian-Wiher/darts/tree/0d267e08643e2e3f88163a5d955b8be75840c2f6 |
MLP | # 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
assert_size_stride = torch._C... | Awannaphasch2016/tgn | MLP | false | 90 | [
"Apache-2.0"
] | 0 | a0eb4b4759cb44e053dfb6a825ccac1d54dba33f | https://github.com/Awannaphasch2016/tgn/tree/a0eb4b4759cb44e053dfb6a825ccac1d54dba33f |
Gated_Conv_1d | import torch
import torch.nn as nn
class Gated_Conv_1d(nn.Module):
def __init__(self, channels, kernel_size, stride=1, padding=0, dilation
=1, groups=1, bias=True):
super(Gated_Conv_1d, self).__init__()
self.dilation = dilation
self.channels = channels
self.conv_dil = nn.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.triton_helpers import libdevice
import torch.nn as ... | ioanvl/wavenet_classifier_torch | Gated_Conv_1d | false | 6,894 | [
"MIT"
] | 1 | de29bfce59d52ae46143f62c4d7a6158a04edf00 | https://github.com/ioanvl/wavenet_classifier_torch/tree/de29bfce59d52ae46143f62c4d7a6158a04edf00 |
MultiHeadAttn | # 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.... | Malkovsky/NeMo | MultiHeadAttn | false | 2,611 | [
"Apache-2.0"
] | 0 | 8cf9aad8ecba36f1bd7b096cf274c2bc8ac695c3 | https://github.com/Malkovsky/NeMo/tree/8cf9aad8ecba36f1bd7b096cf274c2bc8ac695c3 |
ConvDecoder | import torch
import torch.nn as nn
from torch.nn import functional as F
class ConvDecoder(nn.Module):
def __init__(self, hidden_size, state_size, embedding_size, act_fn='relu'):
super().__init__()
self.act_fn = getattr(F, act_fn)
self.embedding_size = embedding_size
self.fc_1 = nn... | 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... | alec-tschantz/planet | ConvDecoder | false | 18,250 | [
"MIT"
] | 7 | bf68722993c93129263bb9606a582d24cb4f2a58 | https://github.com/alec-tschantz/planet/tree/bf68722993c93129263bb9606a582d24cb4f2a58 |
ConvNorm | import torch
import torch.multiprocessing
class ConvNorm(torch.nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=1, stride=1,
padding=None, dilation=1, bias=True, w_init_gain='linear'):
super(ConvNorm, self).__init__()
if padding is None:
assert 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.multiprocessing
assert_size_stride = torch._C._dynamo.guards.assert... | AppleHolic/FastSpeech2 | ConvNorm | false | 16,932 | [
"MIT"
] | 8 | 8f6969edd0c86c05b1dd70a0b7841bd86505455e | https://github.com/AppleHolic/FastSpeech2/tree/8f6969edd0c86c05b1dd70a0b7841bd86505455e |
AngleSimpleLinear | # 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.... | kprokofi/ML_Decoder | AngleSimpleLinear | false | 15,852 | [
"MIT"
] | 99 | c01c50e0165e607afbebd8d615708ef9c084dd5b | https://github.com/kprokofi/ML_Decoder/tree/c01c50e0165e607afbebd8d615708ef9c084dd5b |
MulScalarNegative | import torch
import torch.nn as nn
from torch.quantization import QuantStub
from torch.quantization import DeQuantStub
class MulScalarNegative(nn.Module):
def __init__(self):
super().__init__()
self.float_op = nn.quantized.FloatFunctional()
self.quant = QuantStub()
self.dequant = ... | 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
from torch.quantization import QuantStub
from torch.quantization import DeQuantStub
assert_size_stride = torch._C._dyn... | Archermmt/tvm | MulScalarNegative | false | 11,201 | [
"Apache-2.0"
] | 0 | 8b900cec1a9c3cb453e159db4d497ebeb26ed289 | https://github.com/Archermmt/tvm/tree/8b900cec1a9c3cb453e159db4d497ebeb26ed289 |
LIN | import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
class LIN(nn.Module):
def __init__(self, num_features, eps=1e-05):
super(LIN, self).__init__()
self.eps = eps
self.rho = Parameter(torch.Tensor(1, num_features, 1, 1))
self.gamma = Parameter(torch.Tensor(1... | 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.parameter import Parameter
assert_size_stri... | Gxx-5/MyPhoto2Cartoon | LIN | false | 11,460 | [
"MIT"
] | 0 | aa05dfa8b7d6c507c33026a2e8b299d5779357be | https://github.com/Gxx-5/MyPhoto2Cartoon/tree/aa05dfa8b7d6c507c33026a2e8b299d5779357be |
NormKLLoss | import torch
import torch.nn.init
import torch as th
from torch.nn.modules.loss import _Loss
class NormKLLoss(_Loss):
def __init__(self, unit_average=False):
super(NormKLLoss, self).__init__()
self.unit_average = unit_average
def forward(self, recog_mu, recog_logvar, prior_mu, prior_logvar):... | 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.init
from torch.nn.modules.loss import _Loss
assert_size_... | haojiepan1/CrossWOZ | NormKLLoss | false | 6,790 | [
"Apache-2.0"
] | 1 | 6d7b4c4cfb73a528b76074764687906abecc90b6 | https://github.com/haojiepan1/CrossWOZ/tree/6d7b4c4cfb73a528b76074764687906abecc90b6 |
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
import torch.nn as nn
assert_... | AleksandrLiadov/fsdl-text-recognizer-2021-labs | ConvBlock | false | 13,249 | [
"MIT"
] | 402 | 9495e1457fc82ab83ff7e4141939d603565eb89b | https://github.com/AleksandrLiadov/fsdl-text-recognizer-2021-labs/tree/9495e1457fc82ab83ff7e4141939d603565eb89b |
weight_quantize_fn | # 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... | heymesut/SJTU_microe | weight_quantize_fn | false | 6,807 | [
"BSD-3-Clause"
] | 1 | 7a862d03b4d8fe4c8608173a16082f44001f3f13 | https://github.com/heymesut/SJTU_microe/tree/7a862d03b4d8fe4c8608173a16082f44001f3f13 |
WeightedMCEFocalloss | # 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.functi... | HelenGuohx/cv-ferattn-code | WeightedMCEFocalloss | false | 5,298 | [
"MIT"
] | 1 | faa9b7850fe2a0f8c08193bb129b5fec4639d616 | https://github.com/HelenGuohx/cv-ferattn-code/tree/faa9b7850fe2a0f8c08193bb129b5fec4639d616 |
DiceLoss | # 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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | kevinkwshin/kaggle-pneumothorax | DiceLoss | false | 15,821 | [
"MIT"
] | 74 | 24b91a9425097023f0cc7781a9380cb247babe22 | https://github.com/kevinkwshin/kaggle-pneumothorax/tree/24b91a9425097023f0cc7781a9380cb247babe22 |
wSummation | import torch
import torch.nn as nn
class wSummation(nn.Module):
"""
The spatial weighted summation layer.
"""
def __init__(self, input_dim):
"""
:param input_dim: input dimension [C,H,W]
"""
super(wSummation, self).__init__()
self.Q = nn.Parameter(torch.rand(in... | 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... | VincentYCYao/MVC-Net-pytorch | wSummation | false | 9,600 | [
"MIT"
] | 0 | 31f826825cdfe862fbfe0fe19edc78c04d1dec55 | https://github.com/VincentYCYao/MVC-Net-pytorch/tree/31f826825cdfe862fbfe0fe19edc78c04d1dec55 |
Envelope | # 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.fx
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynam... | HWSelf/pytorch_geometric | Envelope | false | 520 | [
"MIT"
] | 0 | c1214de674079b5e39e57c045d0f844b60caf590 | https://github.com/HWSelf/pytorch_geometric/tree/c1214de674079b5e39e57c045d0f844b60caf590 |
CosineBasisLinear | # 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 numpy ... | imatge-upc/pixelcoordEDL | CosineBasisLinear | false | 6,863 | [
"MIT"
] | 1 | 353632feed6ac8c93758c1a2a1b7a477e7ff053c | https://github.com/imatge-upc/pixelcoordEDL/tree/353632feed6ac8c93758c1a2a1b7a477e7ff053c |
Atanh | import torch
import torch.onnx
import torch.nn as nn
class Atanh(nn.Module):
def forward(self, x):
return torch.atanh(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
from torch._inductor.runtime.triton_helpers import libdevice
import torch.onnx
import torch.nn as nn
assert_size_stride = torch._C._dynamo.g... | mil-tokyo/webdnn | Atanh | false | 16,064 | [
"MIT"
] | 1,967 | 38a60fd3e1a4e72bc01108189a3aa51e0752aecd | https://github.com/mil-tokyo/webdnn/tree/38a60fd3e1a4e72bc01108189a3aa51e0752aecd |
AvgPoolPad | import torch
import torch.nn as nn
import torch.nn.init
class AvgPoolPad(nn.Module):
def __init__(self, stride=2, padding=1):
super(AvgPoolPad, self).__init__()
self.pad = nn.ZeroPad2d((1, 0, 1, 0))
self.pool = nn.AvgPool2d(3, stride=stride, padding=padding,
count_include_pad=... | 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.init
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dy... | dowhilefalse/DeOldify | AvgPoolPad | false | 12,305 | [
"MIT"
] | 0 | 08f012cdbe36e3f8482460f57e1844b361a7fb16 | https://github.com/dowhilefalse/DeOldify/tree/08f012cdbe36e3f8482460f57e1844b361a7fb16 |
BinaryDiceLoss | import torch
import torch.nn as nn
class BinaryDiceLoss(nn.Module):
def __init__(self):
super(BinaryDiceLoss, self).__init__()
def forward(self, input, targets):
N = targets.size()[0]
smooth = 1
input_flat = input.view(N, -1)
targets_flat = targets.view(N, -1)
... | 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... | chenkarl/kits19 | BinaryDiceLoss | false | 12,209 | [
"MIT"
] | 0 | 7fa912320a23c6bf649566a1509aa493656b24c1 | https://github.com/chenkarl/kits19/tree/7fa912320a23c6bf649566a1509aa493656b24c1 |
MNIST_CNN | # 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.... | Luodian/IIB | MNIST_CNN | false | 17,631 | [
"MIT"
] | 3 | a7457e56f4e389bea484e9f9cdbd01485114d6dc | https://github.com/Luodian/IIB/tree/a7457e56f4e389bea484e9f9cdbd01485114d6dc |
ModulatedConv2d | from torch.autograd import Function
import math
import torch
from torch import nn
import torch.nn.functional as F
def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5):
return FusedLeakyReLUFunction.apply(input, bias, negative_slope, scale)
def make_kernel(k):
k = torch.tensor(k, dtype=torch... | 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.autograd... | ArashVahabpour/encoder4editing | ModulatedConv2d | false | 1,990 | [
"MIT"
] | 0 | 819b90ecd7397fbe2ab7cb30eb451dab0f3149fd | https://github.com/ArashVahabpour/encoder4editing/tree/819b90ecd7397fbe2ab7cb30eb451dab0f3149fd |
DuelDQNet | # 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_... | ayjabri/DeepRL | DuelDQNet | false | 1,507 | [
"MIT"
] | 0 | 0be095e3a3d04f60b4cdc97ed330dffc17b3024a | https://github.com/ayjabri/DeepRL/tree/0be095e3a3d04f60b4cdc97ed330dffc17b3024a |
SqueezeBertLayerNorm | # 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
from torch import nn
import torch.utils.checkpoint
assert_size_stride = torch._... | Clemens123/transformers | SqueezeBertLayerNorm | false | 11,497 | [
"Apache-2.0"
] | 0 | 22abe7bbc587c16ec30f9d1aa549dcbeba6e9e26 | https://github.com/Clemens123/transformers/tree/22abe7bbc587c16ec30f9d1aa549dcbeba6e9e26 |
SigmoidFocalClassificationLoss | import torch
import torch.nn as nn
def _sigmoid_cross_entropy_with_logits(logits, labels):
loss = torch.clamp(logits, min=0) - logits * labels.type_as(logits)
loss += torch.log1p(torch.exp(-torch.abs(logits)))
return loss
class SigmoidFocalClassificationLoss(nn.Module):
"""Sigmoid focal cross entrop... | 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... | ShashwatNigam99/PointRCNN | SigmoidFocalClassificationLoss | false | 9,452 | [
"MIT"
] | 0 | eee5f90fe4215cff0156e1f8cecf485e18dce1f8 | https://github.com/ShashwatNigam99/PointRCNN/tree/eee5f90fe4215cff0156e1f8cecf485e18dce1f8 |
InteractingLayer | import torch
import torch.nn as nn
import torch.nn.functional as F
from sklearn.metrics import *
class InteractingLayer(nn.Module):
"""A Layer used in AutoInt that model the correlations between different feature fields by multi-head self-attention mechanism.
Input shape
- A 3D tensor with shape... | 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.... | chenkkkk/DeepCTR-PyTorch | InteractingLayer | false | 6,440 | [
"Apache-2.0"
] | 1 | a10a3ace4ad79171e7fb182407b3e4d22bf753e7 | https://github.com/chenkkkk/DeepCTR-PyTorch/tree/a10a3ace4ad79171e7fb182407b3e4d22bf753e7 |
fChannelAttention | # 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 math
import torch.optim
import torch.utils.data
assert_size_stride = torch._C._dyn... | dwromero/att_gconvs | fChannelAttention | false | 15,287 | [
"MIT"
] | 53 | 872259cad49763fdcfa3e96e80b6b5c331adf084 | https://github.com/dwromero/att_gconvs/tree/872259cad49763fdcfa3e96e80b6b5c331adf084 |
PositionalEncoding2D | import torch
import torch.nn as nn
import torch.utils.checkpoint
class PositionalEncoding2D(nn.Module):
def __init__(self, d_model, minpos=-32, maxpos=32, p_drop=0.1):
super(PositionalEncoding2D, self).__init__()
self.minpos = minpos
self.maxpos = maxpos
self.nbin = abs(minpos) + ... | 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.checkpoint
assert_size_stride = torch._C._dynamo... | RosettaCommons/RFDesign | PositionalEncoding2D | false | 14,338 | [
"MIT"
] | 45 | b404b8b2c57f89c047529c30259aeeb8f6012b61 | https://github.com/RosettaCommons/RFDesign/tree/b404b8b2c57f89c047529c30259aeeb8f6012b61 |
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