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 |
|---|---|---|---|---|---|---|---|---|---|---|
MeanEmbedding | import torch
from torch import nn
import torch.nn.modules.loss
from scipy.sparse import *
class MeanEmbedding(nn.Module):
"""Mean embedding class.
"""
def __init__(self):
super(MeanEmbedding, self).__init__()
def forward(self, emb, len_):
"""Compute average embeddings.
Param... | 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.modules.loss
from scipy.sparse import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride... | IBM/graph4nlp | MeanEmbedding | false | 8,348 | [
"Apache-2.0"
] | 18 | a9bf20b23fa1ec368d9bd40cc8c557f86a9f8297 | https://github.com/IBM/graph4nlp/tree/a9bf20b23fa1ec368d9bd40cc8c557f86a9f8297 |
Quantization | # 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.utils.data
impo... | AbnerVictor/HCFlow | Quantization | false | 9,093 | [
"Apache-2.0"
] | 0 | e55938ac9f58c117898e3d161ddc73b14d15289b | https://github.com/AbnerVictor/HCFlow/tree/e55938ac9f58c117898e3d161ddc73b14d15289b |
Actor | import torch
import torch.nn as nn
import torch.nn.functional as F
class Actor(nn.Module):
def __init__(self, hidden_size, num_inputs, action_space):
super(Actor, self).__init__()
self.action_space = action_space
num_outputs = action_space.shape[0]
self.linear1 = nn.Linear(num_inp... | 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.... | JieFeng-cse/power-system-rl | Actor | false | 9,191 | [
"MIT"
] | 0 | 8295d14da83a40c755b8e6a14785c53a238f9a64 | https://github.com/JieFeng-cse/power-system-rl/tree/8295d14da83a40c755b8e6a14785c53a238f9a64 |
PatchEmbedding | import torch
import torch.nn as nn
class PatchEmbedding(nn.Module):
def __init__(self, image_size, patch_size, embed_dim, channels):
super().__init__()
self.image_size = image_size
if image_size[0] % patch_size != 0 or image_size[1] % patch_size != 0:
raise ValueError(
... | 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... | avniculae/segmenter | PatchEmbedding | false | 9,769 | [
"MIT"
] | 0 | ca9683399b7dae13a8ccbadc744826306b8dbf94 | https://github.com/avniculae/segmenter/tree/ca9683399b7dae13a8ccbadc744826306b8dbf94 |
LayerNorm | import torch
from torch import nn
class LayerNorm(nn.Module):
def __init__(self, size, eps=1e-06):
super(LayerNorm, self).__init__()
self.eps = eps
self.weight = nn.Parameter(torch.ones(size, 1, 1))
self.bias = nn.Parameter(torch.zeros(size, 1, 1))
def forward(self, x):
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | ParadoxZW/CosAttention2d | LayerNorm | false | 5,708 | [
"Apache-2.0"
] | 1 | 19b3e655cf0ebc40721b806eb46a3132c488a188 | https://github.com/ParadoxZW/CosAttention2d/tree/19b3e655cf0ebc40721b806eb46a3132c488a188 |
one_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
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | Holmes-Alan/RefVAE | one_conv | false | 8,261 | [
"MIT"
] | 13 | 836b8f1168f1b0f923b609a48e202ace7806f79c | https://github.com/Holmes-Alan/RefVAE/tree/836b8f1168f1b0f923b609a48e202ace7806f79c |
DeConv | # 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
import torch.onnx
assert_size_stride = torch._C._dynamo.gua... | TriceHelix/ASMAGAN | DeConv | false | 14,512 | [
"Apache-2.0"
] | 121 | 6e2b5b587f88f641fdcc05a81cf5f0b4d6a9f3e1 | https://github.com/TriceHelix/ASMAGAN/tree/6e2b5b587f88f641fdcc05a81cf5f0b4d6a9f3e1 |
SimpleNet | import torch
import torch.cuda
import torch.backends.cudnn
import torch.backends.mkl
class SimpleNet(torch.nn.Module):
def __init__(self):
super(SimpleNet, self).__init__()
self.conv = torch.nn.Conv2d(64, 128, (3, 3), stride=(2, 2), padding
=(1, 1), bias=False)
def forward(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.cuda
import torch.backends.cudnn
import torch.backends.mkl
assert_s... | JudeDavis1/intel-extension-for-pytorch | SimpleNet | false | 2,590 | [
"Apache-2.0"
] | 0 | 364e34cb4917a709f5108c07d4005bf82f3d5067 | https://github.com/JudeDavis1/intel-extension-for-pytorch/tree/364e34cb4917a709f5108c07d4005bf82f3d5067 |
FixedSubnetConv | # 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 math
import torch.multiprocessing
import torch.nn as nn
import torch.nn.p... | adityakusupati/LLC-2.0 | FixedSubnetConv | false | 18,218 | [
"MIT"
] | 10 | 38608bbaa425b15dcf5c971000b7a1b08120fb5c | https://github.com/adityakusupati/LLC-2.0/tree/38608bbaa425b15dcf5c971000b7a1b08120fb5c |
ShearX | # 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
import torch.nn as ... | Hayoung93/UDA | ShearX | false | 966 | [
"Apache-2.0"
] | 0 | a587b01c76141d64e7cead55b62e0f3ed75890bf | https://github.com/Hayoung93/UDA/tree/a587b01c76141d64e7cead55b62e0f3ed75890bf |
Div | # 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... | PogChamper/torch2trt | Div | false | 14,188 | [
"MIT"
] | 3,363 | 43b12627ec0de4d212efb6d02b07570205085ccc | https://github.com/PogChamper/torch2trt/tree/43b12627ec0de4d212efb6d02b07570205085ccc |
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
from torch.nn import Module
i... | ZhihuaLiuEd/canetbrats | GCN | false | 18,199 | [
"MIT"
] | 7 | a23f008b2876a21026b2564588f4f51692083ae2 | https://github.com/ZhihuaLiuEd/canetbrats/tree/a23f008b2876a21026b2564588f4f51692083ae2 |
LinkClassifier | import torch
import torch.nn as nn
import torch.nn.functional as F
class LinkClassifier(nn.Module):
def __init__(self, in_features, dropout=0.2):
super(LinkClassifier, self).__init__()
self.input = nn.Linear(in_features, 32)
self.hidden1 = nn.Linear(32, 16)
self.hidden2 = nn.Linea... | 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.... | BlackReap-er/Sia | LinkClassifier | false | 7,801 | [
"MIT"
] | 13 | 70654d55caa3315187282c88a59cf9b6e0b7c52b | https://github.com/BlackReap-er/Sia/tree/70654d55caa3315187282c88a59cf9b6e0b7c52b |
SumNorm | # 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... | RosarioAndolina/psychXRF | SumNorm | false | 1,003 | [
"MIT"
] | 0 | e2adadbd17664d7f74c10304f84b3751c571226e | https://github.com/RosarioAndolina/psychXRF/tree/e2adadbd17664d7f74c10304f84b3751c571226e |
Conv2dSWD | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
import torch.nn as nn
import torch
assert_size_stride = ... | FVL2020/MSWSR | Conv2dSWD | false | 8,112 | [
"MIT"
] | 27 | 0844e78ee68fb0465efd5c4a2215ce815980526b | https://github.com/FVL2020/MSWSR/tree/0844e78ee68fb0465efd5c4a2215ce815980526b |
NormalKLLoss | import torch
from torch import distributions
from torch.nn.modules.loss import _Loss
class NormalKLLoss(_Loss):
def __init__(self, reduction='mean'):
super(NormalKLLoss, self).__init__()
assert reduction in ['none', 'sum', 'mean']
self.reduction = reduction
def forward(self, q_mu, q_... | 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.nn.modules.loss import _Loss
assert_size_stride = t... | imguozhen/proactive-chat | NormalKLLoss | false | 10,292 | [
"Apache-2.0"
] | 0 | 80d13e28cb93c26a65ace0a028c53fd0bafcdbf9 | https://github.com/imguozhen/proactive-chat/tree/80d13e28cb93c26a65ace0a028c53fd0bafcdbf9 |
ClippedReLU | import torch
import torch.nn as nn
class ClippedReLU(nn.Module):
def __init__(self):
super(ClippedReLU, self).__init__()
def forward(self, x):
return x.clamp(min=0.0, max=255.0)
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... | Bovbene/WSCCSN | ClippedReLU | false | 161 | [
"Apache-2.0"
] | 0 | 7f454050218e7f2162b0bdc1cdff938d876efc0b | https://github.com/Bovbene/WSCCSN/tree/7f454050218e7f2162b0bdc1cdff938d876efc0b |
BDiceLoss | import torch
import torch.nn as nn
def centercrop(image, w, h):
_nt, _ct, ht, wt = image.size()
padw, padh = (wt - w) // 2, (ht - h) // 2
if padw > 0 and padh > 0:
image = image[:, :, padh:-padh, padw:-padw]
return image
def flatten(x):
x_flat = x.clone()
x_flat = x_flat.view(x.shape... | 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... | CarlosPena00/pytorch-unet | BDiceLoss | false | 197 | [
"MIT"
] | 0 | 8365bace23e4b04b9c5b75cd6720807ea8cac5ab | https://github.com/CarlosPena00/pytorch-unet/tree/8365bace23e4b04b9c5b75cd6720807ea8cac5ab |
AdversarialNetwork | # 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... | pwjworks/MS-MDA | AdversarialNetwork | false | 4,148 | [
"MIT"
] | 0 | 21f921a933a318820239541adb26b9fc6feba699 | https://github.com/pwjworks/MS-MDA/tree/21f921a933a318820239541adb26b9fc6feba699 |
FocalLossV1 | import torch
import torch.nn as nn
class FocalLossV1(nn.Module):
def __init__(self, alpha=0.25, gamma=2, reduction='mean'):
super(FocalLossV1, self).__init__()
self.alpha = alpha
self.gamma = gamma
self.reduction = reduction
self.crit = nn.BCEWithLogitsLoss(reduction='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
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | imvladikon/pytorch-loss | FocalLossV1 | false | 6,875 | [
"MIT"
] | 1 | 6cfaabe1be898e1ff000b3dffb46d0ef09096f6b | https://github.com/imvladikon/pytorch-loss/tree/6cfaabe1be898e1ff000b3dffb46d0ef09096f6b |
KnowledgeDistillationLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | VitoPalmisano/MiB | KnowledgeDistillationLoss | false | 9,625 | [
"MIT"
] | 0 | 4b3d81e593471f2fb57abd852114a389ead3905c | https://github.com/VitoPalmisano/MiB/tree/4b3d81e593471f2fb57abd852114a389ead3905c |
BaselineModel | # 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 ... | Purple-PI/rlstructures | BaselineModel | false | 14,247 | [
"MIT"
] | 281 | 9b201b083715bbda2f3534b010c84e11dfc0a1c7 | https://github.com/Purple-PI/rlstructures/tree/9b201b083715bbda2f3534b010c84e11dfc0a1c7 |
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 import triton_helpers
from torch._inductor.runtime.... | RowitZou/RankAE | Generator | false | 8,727 | [
"MIT"
] | 23 | d47ab58aa4fda203c551e36cbe04edd564b76d89 | https://github.com/RowitZou/RankAE/tree/d47ab58aa4fda203c551e36cbe04edd564b76d89 |
DownBlock | import torch
import torch.utils.data
from torchvision.transforms import *
class ConvBlock(torch.nn.Module):
def __init__(self, input_size, output_size, kernel_size=3, stride=1,
padding=1, bias=True, activation='prelu', norm=None):
super(ConvBlock, self).__init__()
self.conv = torch.nn.Con... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
from torchvision.transforms import *
assert_size_stride ... | HamsterBiz/iSeeBetter | DownBlock | false | 11,669 | [
"MIT"
] | 0 | a71cee61583bdedab1f3b368e2cb7dc5ad969aed | https://github.com/HamsterBiz/iSeeBetter/tree/a71cee61583bdedab1f3b368e2cb7dc5ad969aed |
AFMS | # 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... | ishine/RawNet | AFMS | false | 15,633 | [
"MIT"
] | 199 | cddec5afa27049a4b507f3d48bb02b993ea838bb | https://github.com/ishine/RawNet/tree/cddec5afa27049a4b507f3d48bb02b993ea838bb |
SimpleAttention | import torch
import torch.nn as nn
import torch.nn.functional as F
class SimpleAttention(nn.Module):
def __init__(self, input_dim):
super(SimpleAttention, self).__init__()
self.input_dim = input_dim
self.scalar = nn.Linear(self.input_dim, 1, bias=False)
def forward(self, M, x=None):
... | 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.... | Anshul044/Project-NN | SimpleAttention | false | 60 | [
"MIT"
] | 0 | ef080846715a95b735f0381e4f60742e40791630 | https://github.com/Anshul044/Project-NN/tree/ef080846715a95b735f0381e4f60742e40791630 |
PolicyNet | import torch
import torch.nn as nn
import torch.nn.functional as F
class PolicyNet(nn.Module):
def __init__(self):
super(PolicyNet, self).__init__()
self.fc1 = nn.Linear(64, 32)
self.fc2 = nn.Linear(32, 16)
self.fc3 = nn.Linear(16, 4)
def forward(self, x):
x = torch.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_... | Jontahan/kvad | PolicyNet | false | 9,173 | [
"MIT"
] | 0 | 1b22db801048beb948b34bdd615ebe8630d13d9f | https://github.com/Jontahan/kvad/tree/1b22db801048beb948b34bdd615ebe8630d13d9f |
UpBlock | import torch
import torch.nn as nn
from torch.nn import functional as F
class UpBlock(nn.Module):
"""Upsample block for DRRG and TextSnake."""
def __init__(self, in_channels, out_channels):
super().__init__()
assert isinstance(in_channels, int)
assert isinstance(out_channels, int)
... | 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_... | jeffreykuang/mmocr-1 | UpBlock | false | 15,678 | [
"Apache-2.0"
] | 206 | b17304edeb493b0a4d7224c23d23b952350d0db5 | https://github.com/jeffreykuang/mmocr-1/tree/b17304edeb493b0a4d7224c23d23b952350d0db5 |
CausalSelfAttention | from _paritybench_helpers import _mock_config
import math
import torch
import torchvision.transforms.functional as F
import torch.nn.functional as F
import torch.nn as nn
from torchvision.transforms import functional as F
from torch.nn import functional as F
class CausalSelfAttention(nn.Module):
def __init__(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
from torch._inductor.runtime.... | DQiaole/ZITS | CausalSelfAttention | false | 8,809 | [
"Apache-2.0"
] | 40 | 5f7a060167790789d5e29a3d14d3c2ef8a34e765 | https://github.com/DQiaole/ZITS/tree/5f7a060167790789d5e29a3d14d3c2ef8a34e765 |
PlanarFlow | # 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, math as tl_math
import torch.utils.data
import torch.nn as nn
assert_size_stri... | gpoesia/variational-item-response-theory-public | PlanarFlow | false | 12,476 | [
"MIT"
] | 0 | 6a0db81068695422dddec8832ce353879c5acb82 | https://github.com/gpoesia/variational-item-response-theory-public/tree/6a0db81068695422dddec8832ce353879c5acb82 |
SimpleConv2dModule | import torch
import torch.jit
import torch.nn.functional as F
import torch.onnx
import torch.nn
class SimpleConv2dModule(torch.nn.Module):
def __init__(self, stride=1, padding=0, dilation=1, groups=1):
super(SimpleConv2dModule, self).__init__()
self.stride = stride
self.padding = padding
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.jit
import torch... | YaronBenAtar/glow | SimpleConv2dModule | false | 14,670 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
LocalNorm2d | # 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... | rdguez-mariano/affnet | LocalNorm2d | false | 16,320 | [
"MIT"
] | 211 | a3f0bb32d9001d1daf024f38d29867f37816ea78 | https://github.com/rdguez-mariano/affnet/tree/a3f0bb32d9001d1daf024f38d29867f37816ea78 |
Upsample | import torch
from torch import nn
class Upsample(nn.Module):
"""
Since the number of channels of the feature map changes after upsampling in HRNet.
we have to write a new Upsample class.
"""
def __init__(self, in_channels, out_channels, scale_factor, mode):
super(Upsample, 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 import triton_helpers
from torch._inductor.runtime.... | hjk0918/style-transfer-pytorch | Upsample | false | 3,593 | [
"MIT"
] | 0 | acbc054c734aa9c723a3a9bb36e33afb9bd7833b | https://github.com/hjk0918/style-transfer-pytorch/tree/acbc054c734aa9c723a3a9bb36e33afb9bd7833b |
MeanStdExtractor | import torch
from torch import nn
class MeanStdExtractor(nn.Module):
def __init__(self):
super().__init__()
def forward(self, feature_maps_batch):
feature_maps_batch = feature_maps_batch.view(*feature_maps_batch.
shape[:2], -1)
feature_means_batch = feature_maps_batch.mea... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | YangXuanyue/Neural-Unaligned-Phoneme-Sequence-Prediction | MeanStdExtractor | false | 18,160 | [
"BSD-3-Clause"
] | 5 | 91ef1c95478367f5b421da125f07660cfc9bed98 | https://github.com/YangXuanyue/Neural-Unaligned-Phoneme-Sequence-Prediction/tree/91ef1c95478367f5b421da125f07660cfc9bed98 |
L2Norm | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_... | AlphaLFC/mmdetection | L2Norm | false | 4,841 | [
"Apache-2.0"
] | 1 | 45619c5b8aca0ca3e6ddc211210a8946c94694d8 | https://github.com/AlphaLFC/mmdetection/tree/45619c5b8aca0ca3e6ddc211210a8946c94694d8 |
DoubleInputNet | # 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_... | cbekar/DRL_Project | DoubleInputNet | false | 9,879 | [
"MIT"
] | 0 | 90d197773c7746b253ee7d997d0526e15d05578a | https://github.com/cbekar/DRL_Project/tree/90d197773c7746b253ee7d997d0526e15d05578a |
ResidualLayer | # 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 math
from torch import Tensor
from torch.nn import Linear
from torch.nn i... | douglasrizzo/pytorch_geometric | ResidualLayer | false | 12,307 | [
"MIT"
] | 0 | effc617c6ad6daad506038bb79e4407082e74740 | https://github.com/douglasrizzo/pytorch_geometric/tree/effc617c6ad6daad506038bb79e4407082e74740 |
NNet | import torch
import torch.nn as nn
import torch.nn.functional as F
class NNet(nn.Module):
def __init__(self, input_dim, output_dim):
super(NNet, self).__init__()
self.linear1 = nn.Linear(input_dim, 64)
self.linear2 = nn.Linear(64, 256)
self.linear3 = nn.Linear(256, output_dim)
... | 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_... | gautam-sharma1/openRL | NNet | false | 6,730 | [
"MIT"
] | 1 | 14310a97a328fe5682a01ee85d83a6b5e1ae29ca | https://github.com/gautam-sharma1/openRL/tree/14310a97a328fe5682a01ee85d83a6b5e1ae29ca |
ShiftedSoftplus | import torch
import torch.nn.functional as F
import torch.utils.data
class ShiftedSoftplus(torch.nn.Module):
def __init__(self):
super(ShiftedSoftplus, self).__init__()
self.shift = torch.log(torch.tensor(2.0)).item()
def forward(self, x):
return F.softplus(x) - self.shift
def get_... | 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 torch.utils.data
assert_size_stride = torch._C._dynamo.... | beneisner/pytorch_geometric | ShiftedSoftplus | false | 6,324 | [
"MIT"
] | 1 | 53d44a96bd2de2753b1ab1d7153c026c92606a81 | https://github.com/beneisner/pytorch_geometric/tree/53d44a96bd2de2753b1ab1d7153c026c92606a81 |
HighwayNetwork | # 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_... | YoghesWaran/tacotron | HighwayNetwork | false | 18,133 | [
"MIT"
] | 10 | 0b97486da7698229bad09e2072cfa3313ae7effe | https://github.com/YoghesWaran/tacotron/tree/0b97486da7698229bad09e2072cfa3313ae7effe |
SoftmaxLoss | # 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
assert_size_stride = t... | mfredriksz/semanticGAN_code | SoftmaxLoss | false | 16,035 | [
"BSD-2-Clause",
"MIT"
] | 107 | c6e7b490086afd8a7593e2892452295555910494 | https://github.com/mfredriksz/semanticGAN_code/tree/c6e7b490086afd8a7593e2892452295555910494 |
LabelSmoothSoftmaxCEV1 | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | chizhu/pytorch-loss | LabelSmoothSoftmaxCEV1 | false | 6,443 | [
"MIT"
] | 1 | c8fbd78771f11a910b0b51ae3697c09761dd9696 | https://github.com/chizhu/pytorch-loss/tree/c8fbd78771f11a910b0b51ae3697c09761dd9696 |
BertOutput | # 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... | Stephen0808/WebQA | BertOutput | false | 11,908 | [
"Apache-2.0"
] | 0 | b9758932a9d0d75167ec837bb6ee8bc571c64681 | https://github.com/Stephen0808/WebQA/tree/b9758932a9d0d75167ec837bb6ee8bc571c64681 |
PlainRefiner | # 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_... | Jason-Khan/mmediting | PlainRefiner | false | 646 | [
"Apache-2.0"
] | 0 | d187f95a675dff3eb975a575bd9278d643b5b645 | https://github.com/Jason-Khan/mmediting/tree/d187f95a675dff3eb975a575bd9278d643b5b645 |
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/open-nre | PARALoss | false | 12,517 | [
"MIT"
] | 0 | a6e42ef074d62be4d3ceb571f412d5be8c0502d7 | https://github.com/igorvlnascimento/open-nre/tree/a6e42ef074d62be4d3ceb571f412d5be8c0502d7 |
FC | import torch
import torch.utils.data
from torch import nn
import torch.nn.functional as F
class FC(nn.Module):
def __init__(self, in_channels, out_channels, gain=2 ** 0.5, use_wscale
=False, lrmul=1.0, bias=True):
super(FC, self).__init__()
he_std = gain * in_channels ** -0.5
if u... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
from torch import nn
assert_size_stride = torch._C._dyna... | Archjbald/PoseStylizer | FC | false | 1,979 | [
"BSD-3-Clause"
] | 0 | 95aae02d1f4ac83536d91b8db5f78d12e7830f97 | https://github.com/Archjbald/PoseStylizer/tree/95aae02d1f4ac83536d91b8db5f78d12e7830f97 |
Policy | import torch
import torch.nn as nn
import torch.nn.functional as F
class Policy(nn.Module):
def __init__(self, input_size, num_actions):
super(Policy, self).__init__()
self.affines = nn.Linear(input_size, 100)
self.action_head = nn.Linear(100, num_actions)
self.saved_actions = []
... | 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.... | Dookas/Robust-Multitask-RL | Policy | false | 13,615 | [
"MIT"
] | 106 | 7970e20cbdf91703c88edcb84568d7354e2525bc | https://github.com/Dookas/Robust-Multitask-RL/tree/7970e20cbdf91703c88edcb84568d7354e2525bc |
_GatedResidualNetwork | # 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 ... | amadejkocbek/darts | _GatedResidualNetwork | false | 12,112 | [
"Apache-2.0"
] | 0 | 074be2a76eee11258da066878c564badf40834e9 | https://github.com/amadejkocbek/darts/tree/074be2a76eee11258da066878c564badf40834e9 |
TemporalFusion | import torch
import torch.nn as nn
class TemporalFusion(nn.Module):
def __init__(self, nf, n_frame):
super(TemporalFusion, self).__init__()
self.n_frame = n_frame
self.ref_conv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
self.nbr_conv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
se... | 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... | myeldib/Simple-SR | TemporalFusion | false | 12,822 | [
"MIT"
] | 0 | 583456b1f231574d9e0b45c29266cf41603d161d | https://github.com/myeldib/Simple-SR/tree/583456b1f231574d9e0b45c29266cf41603d161d |
LatentDecoder | # 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... | UKPLab/MMT-Retrieval | LatentDecoder | false | 14,524 | [
"MIT"
] | 98 | a31caaeb0da680131bf39dc855e38fdda949f38e | https://github.com/UKPLab/MMT-Retrieval/tree/a31caaeb0da680131bf39dc855e38fdda949f38e |
FocalLoss2d | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | WHU-YH-jx/bionetwork_segmentation | FocalLoss2d | false | 5,945 | [
"MIT"
] | 1 | 556c5b61a1a3784875b31eacb8c6bb418d70ee9a | https://github.com/WHU-YH-jx/bionetwork_segmentation/tree/556c5b61a1a3784875b31eacb8c6bb418d70ee9a |
ResidualSequential | # 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.init
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_... | ChongYou/robust-image-recovery | ResidualSequential | false | 7,881 | [
"MIT"
] | 13 | 5bb23142509f307d31fd435de12787a70ec3a5bc | https://github.com/ChongYou/robust-image-recovery/tree/5bb23142509f307d31fd435de12787a70ec3a5bc |
BartClassificationHead | import torch
import torch.utils.data
from torch import nn
class BartClassificationHead(nn.Module):
"""Head for sentence-level classification tasks."""
def __init__(self, input_dim, inner_dim, num_classes, pooler_dropout):
super().__init__()
self.dense = nn.Linear(input_dim, inner_dim)
... | 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.... | JuruoMP/gap-exp | BartClassificationHead | false | 9,223 | [
"Apache-2.0"
] | 0 | 2d7af8a1da2f0ff8f9d3a2c6e15cc6383c716c05 | https://github.com/JuruoMP/gap-exp/tree/2d7af8a1da2f0ff8f9d3a2c6e15cc6383c716c05 |
LogisticRegression | import torch
import torch.nn as nn
class LogisticRegression(nn.Module):
"""
A logistic regression model of the form
P(y = 1 | x) = 1 / (1 + exp(-(mx + b)))
"""
def __init__(self, init_m=1.0, init_b=1.0):
"""
Initialize a logistic regression model by defining its initial
pa... | 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... | jayelm/pytorch-project-template | LogisticRegression | false | 6,924 | [
"MIT"
] | 1 | 30306ce07b21c97c6993432764cbbe0a73092a0c | https://github.com/jayelm/pytorch-project-template/tree/30306ce07b21c97c6993432764cbbe0a73092a0c |
MeanPoolConv | import torch
import torch.nn as nn
def spectral_norm(layer, n_iters=1):
return torch.nn.utils.spectral_norm(layer, n_power_iterations=n_iters)
class MeanPoolConv(nn.Module):
def __init__(self, input_dim, output_dim, kernel_size=3, biases=True,
spec_norm=False):
super().__init__()
se... | 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... | Sriram-Ravula/ncsnv2 | MeanPoolConv | false | 2,864 | [
"MIT"
] | 0 | f610b59441a34063fae1c02aa06837b7eec95c03 | https://github.com/Sriram-Ravula/ncsnv2/tree/f610b59441a34063fae1c02aa06837b7eec95c03 |
Critic | import torch
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
def hidden_init(layer):
fan_in = layer.weight.data.size()[0]
lim = 1.0 / np.sqrt(fan_in)
return -lim, lim
class Critic(nn.Module):
"""Critic (Value) Model."""
def __init__(self, state_size, action_size, seed, ... | 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... | AnKra/deep-reinforcement-learning | Critic | false | 4,850 | [
"MIT"
] | 1 | fa906b0a3a21102b5085ce0c934185d2e50c3324 | https://github.com/AnKra/deep-reinforcement-learning/tree/fa906b0a3a21102b5085ce0c934185d2e50c3324 |
A2CActorDisc | import torch
from torch.distributions import Categorical
import torch as t
import torch.nn as nn
class A2CActorDisc(nn.Module):
def __init__(self, state_dim, action_num):
super().__init__()
self.fc1 = nn.Linear(state_dim, 16)
self.fc2 = nn.Linear(16, 16)
self.fc3 = nn.Linear(16, a... | 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.... | ikamensh/machin | A2CActorDisc | false | 6,860 | [
"MIT"
] | 1 | af7b423c47bc1412530cf6c96c11bd3af9b3e239 | https://github.com/ikamensh/machin/tree/af7b423c47bc1412530cf6c96c11bd3af9b3e239 |
ModAssign | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
@triton.jit
d... | NVIDIA-AI-IOT-private/torch2trt | ModAssign | false | 10,515 | [
"MIT"
] | 0 | 953d60039e0c81e90eea467c3df2e6e3f7040242 | https://github.com/NVIDIA-AI-IOT-private/torch2trt/tree/953d60039e0c81e90eea467c3df2e6e3f7040242 |
AttnScore | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init
def sequence_mask(lengths, max_len=None):
"""
Creates a boolean mask from sequence lengths.
"""
batch_size = lengths.numel()
max_len = max_len or lengths.max()
return torch.arange(0, max_len).type_a... | 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.... | Fengyee/ASER | AttnScore | false | 11,425 | [
"MIT"
] | 0 | c284b507ee268a8275456a969b944895cacc54b8 | https://github.com/Fengyee/ASER/tree/c284b507ee268a8275456a969b944895cacc54b8 |
KLLoss | import torch
from torch import nn
import torch.nn.functional as F
import torch.utils.checkpoint
class KLLoss(nn.Module):
"""Loss that uses a 'hinge' on the lower bound.
This means that for samples with a label value smaller than the threshold, the loss is zero if the prediction is
also smaller than that t... | 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 ... | jiazheng-xing/Swin_Multimodal | KLLoss | false | 10,320 | [
"MIT"
] | 0 | 7bc41977fe7d8d4f0091852c63a6a32a0fada0fb | https://github.com/jiazheng-xing/Swin_Multimodal/tree/7bc41977fe7d8d4f0091852c63a6a32a0fada0fb |
SpatialRescaler | import torch
from functools import partial
import torch.nn as nn
class SpatialRescaler(nn.Module):
def __init__(self, n_stages=1, method='bilinear', multiplier=0.5,
in_channels=3, out_channels=None, bias=False):
super().__init__()
self.n_stages = n_stages
assert self.n_stages >= 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 import triton_helpers
from functools import partial
import torch.nn as nn
assert_size_stride = torch._C._dynamo... | transat/latent-diffusion | SpatialRescaler | false | 10,923 | [
"MIT"
] | 0 | 1ea0d5bb3fb0fe3f7e8c42cbae91423780977f83 | https://github.com/transat/latent-diffusion/tree/1ea0d5bb3fb0fe3f7e8c42cbae91423780977f83 |
UpsamplingBilinear | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
from torch.quantization import QuantStub
from torch.quantization im... | Archermmt/tvm | UpsamplingBilinear | false | 11,193 | [
"Apache-2.0"
] | 0 | 8b900cec1a9c3cb453e159db4d497ebeb26ed289 | https://github.com/Archermmt/tvm/tree/8b900cec1a9c3cb453e159db4d497ebeb26ed289 |
lp_L1_Loss | import torch
from torch.utils.data import *
import torch.nn as nn
class lp_L1_Loss(nn.Module):
def __init__(self):
super().__init__()
self.loss = nn.L1Loss(reduction='sum')
def forward(self, x, y):
b = x.shape[0]
loss = self.loss(x, y)
return loss / b
def get_inputs... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch.utils.data ... | loveorchids/local_patch_retrieval | lp_L1_Loss | false | 3,935 | [
"Apache-2.0"
] | 0 | 52b2e8fdac965d56ef9f89a8c4de96d0b41d3981 | https://github.com/loveorchids/local_patch_retrieval/tree/52b2e8fdac965d56ef9f89a8c4de96d0b41d3981 |
SubSample | # 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.... | verages/PaddleOCR2Pytorch | SubSample | false | 4,670 | [
"Apache-2.0"
] | 0 | 201f0d5d6007f49620c49af7d222c3b220eb3e70 | https://github.com/verages/PaddleOCR2Pytorch/tree/201f0d5d6007f49620c49af7d222c3b220eb3e70 |
TransposeConv2dLayer | import torch
import torch.nn as nn
from torch.nn import functional as F
from torch.nn import Parameter
def l2normalize(v, eps=1e-12):
return v / (v.norm() + eps)
class LayerNorm(nn.Module):
def __init__(self, num_features, eps=1e-08, affine=True):
super(LayerNorm, self).__init__()
self.num_... | 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 Parameter
assert_size_stride = torch.... | piggy2303/DeepFillv2_Pytorch | TransposeConv2dLayer | false | 7,468 | [
"MIT"
] | 1 | dd35299f11704f878ed7a33e14ccd51a9d64baaf | https://github.com/piggy2303/DeepFillv2_Pytorch/tree/dd35299f11704f878ed7a33e14ccd51a9d64baaf |
MSELoss | import functools
import torch
import torch.nn as nn
import torch.cuda.comm
from torch.nn import functional as F
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
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 functools
import torch.nn as nn
import torch.cuda.comm
from torch.nn import functi... | JasonBoy1/mmhuman3d | MSELoss | false | 5,383 | [
"Apache-2.0"
] | 1 | 79b2665191115f3ed905e6afdf09990a8d484362 | https://github.com/JasonBoy1/mmhuman3d/tree/79b2665191115f3ed905e6afdf09990a8d484362 |
CriticNetwork | import torch
import torch.nn as nn
import torch.nn.functional as F
class CriticNetwork(nn.Module):
def __init__(self, input_shape, output_shape, **kwargs):
super().__init__()
n_input = input_shape[-1]
n_output = output_shape[0]
self._h = nn.Linear(n_input, n_output)
nn.ini... | 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_... | AmmarFahmy/mushroom-rl | CriticNetwork | false | 4,855 | [
"MIT"
] | 1 | 2625ee7f64d5613b3b9fba00f0b7a39fece88ca5 | https://github.com/AmmarFahmy/mushroom-rl/tree/2625ee7f64d5613b3b9fba00f0b7a39fece88ca5 |
Stack | # 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.utils
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dy... | angusl95/darts-kbc | Stack | false | 1,437 | [
"Apache-2.0"
] | 0 | 85fc6f4bdb7ba73c07d96ce47e96634599b346f9 | https://github.com/angusl95/darts-kbc/tree/85fc6f4bdb7ba73c07d96ce47e96634599b346f9 |
BertPredictionHeadTransform | from _paritybench_helpers import _mock_config
import math
import torch
from torch import nn
def gelu(x):
"""Gaussian Error Linear Unitという活性化関数です。
LeLUが0でカクっと不連続なので、そこを連続になるように滑らかにした形のLeLUです。
"""
return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))
class BertLayerNorm(nn.Module):
def __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.triton_helpers import libdevice
import math
from to... | kimihitosugiyama/text_analysis | BertPredictionHeadTransform | false | 3,840 | [
"Apache-2.0"
] | 0 | 8f51022957928c31e52af1e0fd407daca3addb40 | https://github.com/kimihitosugiyama/text_analysis/tree/8f51022957928c31e52af1e0fd407daca3addb40 |
RefModel3d | import torch
import torch.nn.functional as F
class RefModel3d(torch.nn.Module):
"""The 3D reference model."""
def __init__(self):
super().__init__()
self.l1 = torch.nn.Conv3d(2, 2, 1, bias=True)
self.l2 = torch.nn.InstanceNorm3d(2, affine=True)
self.l3 = torch.nn.ReLU()
... | 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.... | shuohan/pytorch-layers | RefModel3d | false | 4,368 | [
"MIT"
] | 0 | 020846fd02d501cf477552179c19ba4b5e9a0695 | https://github.com/shuohan/pytorch-layers/tree/020846fd02d501cf477552179c19ba4b5e9a0695 |
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
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | PacktPublishing/Hands-On-Reinforcement-Learning-for-Games | Model | false | 8,650 | [
"MIT"
] | 41 | 045b8846f2558aa8fb8ac8cef5c71ee098cb9b22 | https://github.com/PacktPublishing/Hands-On-Reinforcement-Learning-for-Games/tree/045b8846f2558aa8fb8ac8cef5c71ee098cb9b22 |
outconv | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | BloodAxe/segmentation-networks-benchmark | outconv | false | 7,862 | [
"MIT"
] | 34 | 2e3feb560102230be9369ab442b4a59cc86dff61 | https://github.com/BloodAxe/segmentation-networks-benchmark/tree/2e3feb560102230be9369ab442b4a59cc86dff61 |
SmoothL1Loss | # 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.nn as nn
import torch.cuda
import torch.distributed
import t... | krisk84/retinanet-examples | SmoothL1Loss | false | 12,690 | [
"BSD-3-Clause"
] | 0 | 174d95f3aabe1746d105c66f87aa445607f4eab8 | https://github.com/krisk84/retinanet-examples/tree/174d95f3aabe1746d105c66f87aa445607f4eab8 |
EncoderLayer | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
def attention(q, k, v, d_k, mask=None, dropout=None):
scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(d_k)
if mask is not None:
mask = mask.unsqueeze(1)
scores = F.softmax(scores, dim=-1)
if dropout is not... | 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.... | MadanMl/PyTorch-Transformer-for-RUL-Prediction | EncoderLayer | false | 8,531 | [
"Apache-2.0"
] | 25 | 5bf0a4739abdecbbc88118ea413393997bdc1e24 | https://github.com/MadanMl/PyTorch-Transformer-for-RUL-Prediction/tree/5bf0a4739abdecbbc88118ea413393997bdc1e24 |
IMul | import torch
class IMul(torch.nn.Module):
def __init__(self):
super(IMul, self).__init__()
def forward(self, x, y):
x *= y
return x
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
@triton.jit
def triton_poi_fused_mul_0(in_ptr0, in_ptr1, out_ptr1, xnumel,... | Akababa/torch2trt | IMul | false | 18,411 | [
"MIT"
] | 2 | 03063b74a7eb40f5aac88d49be6b8b5e4e4e92d7 | https://github.com/Akababa/torch2trt/tree/03063b74a7eb40f5aac88d49be6b8b5e4e4e92d7 |
LRN | import torch
import torch.nn as nn
class LRN(nn.Module):
def __init__(self, local_size=1, alpha=1.0, beta=0.75, ACROSS_CHANNELS=True
):
super(LRN, self).__init__()
self.ACROSS_CHANNELS = ACROSS_CHANNELS
if ACROSS_CHANNELS:
self.average = nn.AvgPool3d(kernel_size=(local... | 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_... | anas-awadalla/dissect | LRN | false | 12,075 | [
"MIT"
] | 0 | d74e9147731c6160274405a39ab1c98191929269 | https://github.com/anas-awadalla/dissect/tree/d74e9147731c6160274405a39ab1c98191929269 |
AdaptiveInstanceNorm | import torch
import torch.nn as nn
from torch.nn.init import _calculate_correct_fan
def equalized_lr(module, name='weight', gain=2 ** 0.5, mode='fan_in',
lr_mul=1.0):
"""Equalized Learning Rate.
This trick is proposed in:
Progressive Growing of GANs for Improved Quality, Stability, and Variation
... | 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 ... | HXWAndCL/mmgeneration | AdaptiveInstanceNorm | false | 5,248 | [
"Apache-2.0"
] | 1 | 9afb1d740bf56a4ecde5064d5bb2a4e2d777638b | https://github.com/HXWAndCL/mmgeneration/tree/9afb1d740bf56a4ecde5064d5bb2a4e2d777638b |
MedianPool2d | import torch
import torch.nn.functional as F
import torch.nn as nn
from torch.nn.modules.utils import _pair
from torch.nn.modules.utils import _quadruple
class MedianPool2d(nn.Module):
"""Median pool (usable as median filter when stride=1) module.
Args:
kernel_size: size of pooling kernel, int or 2-... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
from torch.nn.modules.utils import _pair
from torch... | LuckMonkeys/ATSPrivacy | MedianPool2d | false | 8,474 | [
"MIT"
] | 14 | 6b580942c6b98b6348d313f2bf90202ec19cefce | https://github.com/LuckMonkeys/ATSPrivacy/tree/6b580942c6b98b6348d313f2bf90202ec19cefce |
SoftDetectionModule | # 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.utils
imp... | xmlyqing00/d2-net | SoftDetectionModule | false | 4,588 | [
"BSD-3-Clause-Clear"
] | 0 | 3454a2862088682a6bdb2532ff049fd6cd82729c | https://github.com/xmlyqing00/d2-net/tree/3454a2862088682a6bdb2532ff049fd6cd82729c |
MultipleRegression | import torch
import torch.nn as nn
class MultipleRegression(nn.Module):
def __init__(self, num_features):
super(MultipleRegression, self).__init__()
self.fc1 = nn.Linear(num_features, 64)
self.fc2 = nn.Linear(64, 128)
self.output = nn.Linear(128, 1)
self.act = nn.Sigmoid()... | 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... | jiruifu-jerry0219/UpperLimbEstimator | MultipleRegression | false | 10,277 | [
"Apache-2.0"
] | 0 | d62deef93419934dcb33e43707dd0634a235fb9a | https://github.com/jiruifu-jerry0219/UpperLimbEstimator/tree/d62deef93419934dcb33e43707dd0634a235fb9a |
FermiDiracDecoder | from torch.nn import Module
import torch
from torch.nn.modules.module import Module
import torch.optim
import torch.nn.modules.loss
class FermiDiracDecoder(Module):
"""Fermi Dirac to compute edge probabilities based on distances."""
def __init__(self, r, t):
super(FermiDiracDecoder, 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 math as tl_math
from torch.nn import Module
from torch.nn.modules.module import Module
im... | RingBDStack/ACE-HGNN | FermiDiracDecoder | false | 17,836 | [
"MIT"
] | 5 | afc610dd838951dcd6c3910795b472566f0c23ca | https://github.com/RingBDStack/ACE-HGNN/tree/afc610dd838951dcd6c3910795b472566f0c23ca |
Mul | import torch
class Mul(torch.nn.Module):
def __init__(self):
super(Mul, 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... | NVIDIA-AI-IOT-private/torch2trt | Mul | false | 10,524 | [
"MIT"
] | 0 | 953d60039e0c81e90eea467c3df2e6e3f7040242 | https://github.com/NVIDIA-AI-IOT-private/torch2trt/tree/953d60039e0c81e90eea467c3df2e6e3f7040242 |
PLU | # 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... | IrisDinge/YoloV3_DOTA | PLU | false | 5,350 | [
"MIT"
] | 1 | cdfe6375a2323e9ee162e50a46478d8a66529e6c | https://github.com/IrisDinge/YoloV3_DOTA/tree/cdfe6375a2323e9ee162e50a46478d8a66529e6c |
CustomBatchNormManualModule | import torch
import torch.nn as nn
class CustomBatchNormManualFunction(torch.autograd.Function):
"""
This torch.autograd.Function implements a functional custom version of the batch norm operation for MLPs.
Using torch.autograd.Function allows you to write a custom backward function.
The function will... | 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_... | RaymondKoopmanschap/DL_assignment_code | CustomBatchNormManualModule | false | 978 | [
"MIT"
] | 0 | 68b3290be9fbd6c55433a7585e2cfa18e0f35f5c | https://github.com/RaymondKoopmanschap/DL_assignment_code/tree/68b3290be9fbd6c55433a7585e2cfa18e0f35f5c |
CapsuleLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class CapsuleLoss(nn.Module):
def __init__(self):
super(CapsuleLoss, self).__init__()
def forward(self, inputs, labels, logits, recons):
batch_size = inputs.shape[0]
left = F.relu(0.9 - logits, inplace=True) ** 2
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | ashawkey/CapsNet.pytorch | CapsuleLoss | false | 6,241 | [
"MIT"
] | 1 | 3b796b572bbabe79cc445c35913cd3584733aedf | https://github.com/ashawkey/CapsNet.pytorch/tree/3b796b572bbabe79cc445c35913cd3584733aedf |
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... | ChangeTheWorld20191008/query2labels | AsymmetricLoss | false | 2,135 | [
"MIT"
] | 0 | cdca1f3519f75cc91ef2aa166c2534691016f04f | https://github.com/ChangeTheWorld20191008/query2labels/tree/cdca1f3519f75cc91ef2aa166c2534691016f04f |
NN | import torch
import torch.nn as nn
class NN(nn.Module):
def __init__(self, input_size, num_classes):
super(NN, self).__init__()
self.fc1 = nn.Linear(in_features=input_size, out_features=50)
self.activation1 = nn.ReLU()
self.fc2 = nn.Linear(in_features=50, out_features=num_classes)... | 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_... | Dutta-SD/Python_Programs | NN | false | 5,090 | [
"MIT"
] | 1 | f002dbd49c979a6d8b156f88003a79f364ff01da | https://github.com/Dutta-SD/Python_Programs/tree/f002dbd49c979a6d8b156f88003a79f364ff01da |
TreeLSTM | import torch
import torch.nn as nn
class TreeLSTM(nn.Module):
"""
Implementation of the Tree-LSTM model:
https://arxiv.org/pdf/1503.00075.pdf
"""
def __init__(self, num_units):
super(TreeLSTM, self).__init__()
self.left = nn.Linear(num_units, 5 * num_units)
self.right = 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.triton_helpers import libdevice
import torch.nn as ... | Devin-Taylor/pytorch-dynamic-batching-benchmark | TreeLSTM | false | 2,177 | [
"Apache-2.0"
] | 0 | aaf913b13a77a2898dfdf8d92cd25b01789a548a | https://github.com/Devin-Taylor/pytorch-dynamic-batching-benchmark/tree/aaf913b13a77a2898dfdf8d92cd25b01789a548a |
My_SmoothL1Loss | # 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
assert_size_stride = t... | Jvictor97/AWR-Adaptive-Weighting-Regression | My_SmoothL1Loss | false | 690 | [
"MIT"
] | 0 | 2c29f8ac3d824edfff07465232ffed8e4d837ebf | https://github.com/Jvictor97/AWR-Adaptive-Weighting-Regression/tree/2c29f8ac3d824edfff07465232ffed8e4d837ebf |
Myloss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | cuishuhao/HDA | Myloss | false | 15,085 | [
"Apache-2.0"
] | 58 | 1733ca74eee7839b455e9ffd7a169bc54b272745 | https://github.com/cuishuhao/HDA/tree/1733ca74eee7839b455e9ffd7a169bc54b272745 |
AdaptiveFeatureNorm | # 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
import torch.utils.data
assert_size_stride = torch._C._dy... | XianyuanLiu/Transfer-Learning-Library | AdaptiveFeatureNorm | false | 10,130 | [
"MIT"
] | 0 | 25f83f32437032df88ca6101ecd1f63ec7a0aa2c | https://github.com/XianyuanLiu/Transfer-Learning-Library/tree/25f83f32437032df88ca6101ecd1f63ec7a0aa2c |
BasicModel_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
from torch._inductor.runtime.... | YNNEKUW/captum | BasicModel_ConvNet | false | 12,009 | [
"BSD-3-Clause"
] | 0 | c8b5357b21f2ddf440e5f0ce25635977292aa5d1 | https://github.com/YNNEKUW/captum/tree/c8b5357b21f2ddf440e5f0ce25635977292aa5d1 |
ScaledLeakyReLUSin | import math
import torch
from torch import nn
import torch.nn.functional as F
class ScaledLeakyReLUSin(nn.Module):
def __init__(self, negative_slope=0.2):
super().__init__()
self.negative_slope = negative_slope
def forward(self, input):
out_lr = F.leaky_relu(input[:, ::2], negative_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.triton_helpers import math as tl_math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_... | Ugness/CIPS_SR | ScaledLeakyReLUSin | false | 14,533 | [
"MIT"
] | 172 | abce872f5bc1b84afb9634a7dd1991e8c74d7616 | https://github.com/Ugness/CIPS_SR/tree/abce872f5bc1b84afb9634a7dd1991e8c74d7616 |
WeightedSoftDiceLoss | # 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.backends.cudnn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
em... | ArmenGhambaryan/kaggle_carvana_segmentation | WeightedSoftDiceLoss | false | 13,297 | [
"MIT"
] | 447 | 648a6b5c807cb69011316fe6501241dacc027db2 | https://github.com/ArmenGhambaryan/kaggle_carvana_segmentation/tree/648a6b5c807cb69011316fe6501241dacc027db2 |
PairwiseLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
import torch.nn as nn
import torch.nn.parallel
assert_size_stride... | MinesNicaicai/large-scale-pointcloud-matching | PairwiseLoss | false | 5,595 | [
"MIT"
] | 1 | cfe140f2be1110ed75b6edd27538021e513a31c9 | https://github.com/MinesNicaicai/large-scale-pointcloud-matching/tree/cfe140f2be1110ed75b6edd27538021e513a31c9 |
GAT | # 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.... | Prasath2001/commonsense-rl | GAT | false | 2,763 | [
"Apache-2.0"
] | 0 | ef3e83270d34cf211b2d2086120cccae0621477b | https://github.com/Prasath2001/commonsense-rl/tree/ef3e83270d34cf211b2d2086120cccae0621477b |
Conv2dSamePadding | import torch
from torch import nn
import torch.nn.functional as F
def conv2d_same_padding(input, weight, bias=None, stride=1, dilation=1,
groups=1):
input_rows = input.size(2)
filter_rows = weight.size(2)
effective_filter_size_rows = (filter_rows - 1) * dilation[0] + 1
out_rows = (input_rows + 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 import nn
import torch.nn.functional as F
assert_size_stride = torch.... | DaikiOnodera/pycrop-yield-prediction | Conv2dSamePadding | false | 13,544 | [
"MIT"
] | 93 | 335685d3aa6e609161737453c090f5c41b769213 | https://github.com/DaikiOnodera/pycrop-yield-prediction/tree/335685d3aa6e609161737453c090f5c41b769213 |
DenseCrossEntropy | import torch
import torch.nn.functional as F
import torch.nn as nn
class DenseCrossEntropy(nn.Module):
def __init__(self):
super().__init__()
def forward(self, logits, labels):
logits = logits.float()
labels = labels.float()
logprobs = F.log_softmax(logits, dim=-1)
lo... | 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
... | prakhar154/Cassava-Leaf-Disease-Classification | DenseCrossEntropy | false | 4,132 | [
"MIT"
] | 0 | 04824834a6a1898c77858e8134bd3767c64789f2 | https://github.com/prakhar154/Cassava-Leaf-Disease-Classification/tree/04824834a6a1898c77858e8134bd3767c64789f2 |
BinaryLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class BinaryLoss(nn.Module):
def __init__(self):
super(BinaryLoss, self).__init__()
def forward(self, pos_score, neg_score):
pos_loss = -F.log_softmax(pos_score)[:, 1]
neg_loss = -F.log_softmax(neg_score)[:, 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 import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | huanglianghua/mdnet-light | BinaryLoss | false | 12,515 | [
"MIT"
] | 0 | 955b61b8555a49fdf2e2310aa0756c68f955212c | https://github.com/huanglianghua/mdnet-light/tree/955b61b8555a49fdf2e2310aa0756c68f955212c |
Whitening2d | import torch
from torch import nn
from torch.cuda.amp import custom_fwd
from torch.nn.functional import conv2d
class Whitening2d(nn.Module):
def __init__(self, output_dim: 'int', eps: 'float'=0.0):
"""Layer that computes hard whitening for W-MSE using the Cholesky decomposition.
Args:
... | 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
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | DonkeyShot21/cassle | Whitening2d | false | 8,000 | [
"MIT"
] | 13 | d25f9c7cb5e822660dc1ef03e7fac09a33d0b1a8 | https://github.com/DonkeyShot21/cassle/tree/d25f9c7cb5e822660dc1ef03e7fac09a33d0b1a8 |
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