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 |
|---|---|---|---|---|---|---|---|---|---|---|
InverseSoftplus | import torch
import torch.utils.data
def inverseSoftplus(y, beta=1, threshold=20):
"""
inverse of y=torch.nn.functional.softplus(x, beta, threshold)
:param y: the output of the softplus
:param beta: the smoothness of the step
:param threshold: the threshold after which a linear function is used
: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 math as tl_math
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.asse... | khoehlein/fV-SRN | InverseSoftplus | false | 3,832 | [
"MIT"
] | 0 | 601f3e952b090df92e875c233c2c9ca646523948 | https://github.com/khoehlein/fV-SRN/tree/601f3e952b090df92e875c233c2c9ca646523948 |
PSNRLoss | import torch
from torch import nn
class PSNRLoss(nn.Module):
def __init__(self):
super(PSNRLoss, self).__init__()
self.criterion = nn.MSELoss(size_average=True)
def __repr__(self):
return 'PSNR'
def forward(self, output, target):
mse = self.criterion(output, target)
... | 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_... | nthuy190991/geoseg | PSNRLoss | false | 7,356 | [
"MIT"
] | 1 | b679af5dc558720df36dddc7abfd4e6ecb46d7de | https://github.com/nthuy190991/geoseg/tree/b679af5dc558720df36dddc7abfd4e6ecb46d7de |
SigmoidDeepLiftModel | import torch
import torch.nn as nn
class SigmoidDeepLiftModel(nn.Module):
"""
Model architecture from:
https://medium.com/coinmonks/create-a-neural-network-in
-pytorch-and-make-your-life-simpler-ec5367895199
"""
def __init__(self, num_in, num_hidden, num_out):
super().... | 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_... | ngduduong/captum | SigmoidDeepLiftModel | false | 4,079 | [
"BSD-3-Clause"
] | 0 | 6fe5f0f23ea975e73e0c0dee79bdc01b4223d283 | https://github.com/ngduduong/captum/tree/6fe5f0f23ea975e73e0c0dee79bdc01b4223d283 |
Upsample | # 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... | AranKomat/Diff-DALLE | Upsample | false | 13,284 | [
"MIT"
] | 53 | 9418e98e97b599c5c65f16ee168fedf76a29095f | https://github.com/AranKomat/Diff-DALLE/tree/9418e98e97b599c5c65f16ee168fedf76a29095f |
Discriminator2 | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | XrosLiang/GraphCL | Discriminator2 | false | 5,996 | [
"MIT"
] | 1 | fdf9fabcdaddbc17e5c8b7ac9e9d2bdfe4acc56c | https://github.com/XrosLiang/GraphCL/tree/fdf9fabcdaddbc17e5c8b7ac9e9d2bdfe4acc56c |
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.triton_helpers import libdevice, math as tl_math
im... | yanjiajia-september/Pytorch-DPPO | Model | false | 16,759 | [
"MIT"
] | 179 | 5e1a75b6dfc6a170270253a35d10109718240e97 | https://github.com/yanjiajia-september/Pytorch-DPPO/tree/5e1a75b6dfc6a170270253a35d10109718240e97 |
MyModel | # 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_... | Yaphetsf75/slimevolleygym | MyModel | false | 1,269 | [
"Apache-2.0"
] | 0 | 39882c2c8c86c974c9b1083e8d93b2b0fdeecb56 | https://github.com/Yaphetsf75/slimevolleygym/tree/39882c2c8c86c974c9b1083e8d93b2b0fdeecb56 |
ModifiedSmoothedL1 | # 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.utils.data
assert_size_stride = torch.... | AIpakchoi/visualDet3D | ModifiedSmoothedL1 | false | 4,759 | [
"Apache-2.0"
] | 1 | 920f6f8ea44eac4c1896b7d157c015e039ac39f9 | https://github.com/AIpakchoi/visualDet3D/tree/920f6f8ea44eac4c1896b7d157c015e039ac39f9 |
BasicBlock | import torch
import torch.nn as nn
import torch.utils.data
def conv1x1(in_planes, out_planes, stride=1):
"""1x1 convolution"""
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride,
bias=False)
def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
"""3x3 convolution ... | 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.... | ferodia/MichiGAN | BasicBlock | false | 15,351 | [
"MIT"
] | 235 | a49acb49f9659d7538e62faa3ed08e46afb0ddae | https://github.com/ferodia/MichiGAN/tree/a49acb49f9659d7538e62faa3ed08e46afb0ddae |
PolicyNetwork | import torch
import torch.nn as nn
import torch.nn.functional as F
class PolicyNetwork(nn.Module):
def __init__(self):
super(PolicyNetwork, self).__init__()
self.fc1 = nn.Linear(4, 256)
self.fc2 = nn.Linear(256, 256)
self.fc3 = nn.Linear(256, 2)
def forward(self, x):
... | 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.... | DensoITLab/spinningup_in_pytorch | PolicyNetwork | false | 7,955 | [
"MIT"
] | 11 | 612d8c4c6593c8c5ecb5a939bf43085daac9e552 | https://github.com/DensoITLab/spinningup_in_pytorch/tree/612d8c4c6593c8c5ecb5a939bf43085daac9e552 |
SimpleMatmulModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleMatmulModule(torch.nn.Module):
def __init__(self):
super(SimpleMatmulModule, self).__init__()
def forward(self, a, b):
return a.matmul(b + b)
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C... | briancoutinho/glow | SimpleMatmulModule | false | 12,577 | [
"Apache-2.0"
] | 0 | 4c919d60b3c33296c4109aec8020a1733c98f5b5 | https://github.com/briancoutinho/glow/tree/4c919d60b3c33296c4109aec8020a1733c98f5b5 |
MaximumLikelihoodLoss | # 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
from torch.nn import Module
import torch.utils.data
import torch.nn.funct... | mcx/annotated_deep_learning_paper_implementations | MaximumLikelihoodLoss | false | 7,200 | [
"MIT"
] | 1 | f169f3a71dd2d36eb28ad31062d3475efa367b88 | https://github.com/mcx/annotated_deep_learning_paper_implementations/tree/f169f3a71dd2d36eb28ad31062d3475efa367b88 |
StyledConv | from torch.autograd import Function
import math
import torch
from torch import nn
import torch.nn.functional as F
def make_kernel(k):
k = torch.tensor(k, dtype=torch.float32)
if k.ndim == 1:
k = k[None, :] * k[:, None]
k /= k.sum()
return k
def upfirdn2d(input, kernel, up=1, down=1, pad=(0, ... | 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... | Ugness/CIPS_SR | StyledConv | false | 14,549 | [
"MIT"
] | 172 | abce872f5bc1b84afb9634a7dd1991e8c74d7616 | https://github.com/Ugness/CIPS_SR/tree/abce872f5bc1b84afb9634a7dd1991e8c74d7616 |
PrecomputedNorm | # 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... | AyushExel/s3prl | PrecomputedNorm | false | 1,984 | [
"MIT"
] | 0 | 6531904e9621a778978b9cfef3ba9f582e56639a | https://github.com/AyushExel/s3prl/tree/6531904e9621a778978b9cfef3ba9f582e56639a |
PositionalEncoding | # 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 math
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda... | Nemexur/nonauto-lm | PositionalEncoding | false | 17,750 | [
"Apache-2.0"
] | 3 | 6f237e4fc2b3b679cd92126ea5facd58d3cf6e75 | https://github.com/Nemexur/nonauto-lm/tree/6f237e4fc2b3b679cd92126ea5facd58d3cf6e75 |
FeatureNorm | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
class FeatureNorm(nn.Module):
def __init__(self, eps=1e-06):
super(FeatureNorm, self).__init__()
self.eps = eps
def forward(self, feature):
norm_feat = torch.sum(torch.pow(feature, ... | 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.parallel
import torch.optim
import torch.... | Dogacel/mmfashion | FeatureNorm | false | 11,410 | [
"Apache-2.0"
] | 0 | e49613245c8501042edd7aeeaa8fb93e5ea13238 | https://github.com/Dogacel/mmfashion/tree/e49613245c8501042edd7aeeaa8fb93e5ea13238 |
MotionModel | import torch
import torch.nn.functional as F
import torch.nn as nn
class MotionModel(nn.Module):
def __init__(self, n):
super(MotionModel, self).__init__()
self.rotation_scale = 0.01
self.fc1 = nn.Linear(n, n)
self.fc2 = nn.Linear(n, n)
self.fc3 = nn.Linear(n, n)
s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | apurvtwr/Jarvis | MotionModel | false | 3,127 | [
"Apache-2.0"
] | 0 | bdd25e059826a0403c6282a1ee206f9f4c3e9355 | https://github.com/apurvtwr/Jarvis/tree/bdd25e059826a0403c6282a1ee206f9f4c3e9355 |
Discrete | import torch
import torch.nn as nn
class Discrete(nn.Module):
def __init__(self, num_outputs):
super(Discrete, self).__init__()
def forward(self, x):
probs = nn.functional.softmax(x, dim=0)
dist = torch.distributions.Categorical(probs=probs)
return dist.entropy()
def get_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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | rsomani95/client | Discrete | false | 10,701 | [
"MIT"
] | 0 | 772c6de325b30323397cfb98ab7e126910c5912b | https://github.com/rsomani95/client/tree/772c6de325b30323397cfb98ab7e126910c5912b |
BiInteractionPooling | # 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
from sklearn.metrics import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = tor... | Sunmyunghan/Final_Project | BiInteractionPooling | false | 1,201 | [
"MIT"
] | 0 | 28cde293dc6d07521b2e1c5613b20444aea91d21 | https://github.com/Sunmyunghan/Final_Project/tree/28cde293dc6d07521b2e1c5613b20444aea91d21 |
DECLoss | import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
class DECLoss(nn.Module):
def __init__(self):
super(DECLoss, self).__init__()
def target_distribution(self, q):
weigh... | 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.autograd import Variable
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.u... | Crazy-Jack/SpatialExpGeneCluster | DECLoss | false | 368 | [
"MIT"
] | 0 | 9e57c308d1c577a936a2358d0641c65b8130034f | https://github.com/Crazy-Jack/SpatialExpGeneCluster/tree/9e57c308d1c577a936a2358d0641c65b8130034f |
StyleLayer | # 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... | HappyBelief/ContraD | StyleLayer | false | 13,780 | [
"MIT"
] | 168 | abb72562ddac8d8ab37fe9af6ac4c44c61e8ea0f | https://github.com/HappyBelief/ContraD/tree/abb72562ddac8d8ab37fe9af6ac4c44c61e8ea0f |
IOUloss | import torch
import torch.nn as nn
class IOUloss(nn.Module):
def __init__(self, reduction='none', loss_type='iou'):
super(IOUloss, self).__init__()
self.reduction = reduction
self.loss_type = loss_type
def forward(self, pred, target):
assert pred.shape[0] == target.shape[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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | Arui66/YOLOX | IOUloss | false | 7,762 | [
"Apache-2.0"
] | 16 | 7ee17936db849600817d7de05269bfdfb1a0eb48 | https://github.com/Arui66/YOLOX/tree/7ee17936db849600817d7de05269bfdfb1a0eb48 |
MixerBlock | import torch
import torch.nn.functional as F
from torch import nn
class FeedForward(nn.Module):
def __init__(self, num_features, expansion_factor, dropout):
super().__init__()
num_hidden = expansion_factor * num_features
self.fc1 = nn.Linear(num_features, num_hidden)
self.fc2 = 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.fun... | Misuzu-Kurenai/mlp-singer | MixerBlock | false | 866 | [
"MIT"
] | 0 | 416451045bb9b3965aaf496e84a8b45332a6ba59 | https://github.com/Misuzu-Kurenai/mlp-singer/tree/416451045bb9b3965aaf496e84a8b45332a6ba59 |
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._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | DarkoBomer/VCANet | DiceLoss | false | 2,125 | [
"MIT"
] | 0 | 1c76deb195a2dcb8aa4b40856d49eb6796de12bc | https://github.com/DarkoBomer/VCANet/tree/1c76deb195a2dcb8aa4b40856d49eb6796de12bc |
MultiHeadAttentionBlock | # 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.... | MicroTensor-ai/episodic-memory | MultiHeadAttentionBlock | false | 11,709 | [
"MIT"
] | 0 | 295a3752ab94c7a6f45355aa2c54bffbf84b574f | https://github.com/MicroTensor-ai/episodic-memory/tree/295a3752ab94c7a6f45355aa2c54bffbf84b574f |
EntMinLoss | # 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
... | leoandeol/ldir | EntMinLoss | false | 3,887 | [
"MIT"
] | 0 | f90408c5fb16a52c6c5a76fff1c46b9062343ad5 | https://github.com/leoandeol/ldir/tree/f90408c5fb16a52c6c5a76fff1c46b9062343ad5 |
WeightL1Loss | import torch
import torch.nn as nn
class WeightL1Loss(nn.Module):
def __init__(self):
super(WeightL1Loss, self).__init__()
def forward(self, pred_loc, label_loc, loss_weight):
b, _, sh, sw = pred_loc.size()
pred_loc = pred_loc.view(b, 4, -1, sh, sw)
diff = (pred_loc - label_l... | 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
... | mshmoon/siamrpn-lightweight | WeightL1Loss | false | 7,288 | [
"MIT"
] | 1 | f6527e34c9eaaeb45817b12babd78ee73b1c7525 | https://github.com/mshmoon/siamrpn-lightweight/tree/f6527e34c9eaaeb45817b12babd78ee73b1c7525 |
DDPGCritic | # 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_... | iffiX/machin | DDPGCritic | false | 15,603 | [
"MIT"
] | 287 | 7fa986b1bafdefff117d6ff73d14644a5488de9d | https://github.com/iffiX/machin/tree/7fa986b1bafdefff117d6ff73d14644a5488de9d |
Upsampler | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
import torch.utils.data
from torchvision.transforms import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | HamsterBiz/iSeeBetter | Upsampler | false | 11,656 | [
"MIT"
] | 0 | a71cee61583bdedab1f3b368e2cb7dc5ad969aed | https://github.com/HamsterBiz/iSeeBetter/tree/a71cee61583bdedab1f3b368e2cb7dc5ad969aed |
TensorClampOptionMaxMin | # 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 | TensorClampOptionMaxMin | false | 10,521 | [
"MIT"
] | 0 | 953d60039e0c81e90eea467c3df2e6e3f7040242 | https://github.com/NVIDIA-AI-IOT-private/torch2trt/tree/953d60039e0c81e90eea467c3df2e6e3f7040242 |
AutoEncoder | # 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 ... | zuoyuwang/ML-Correctness-prediction | AutoEncoder | false | 13,183 | [
"MIT"
] | 0 | 15180b73567e61cc7a5dd61b0202a42eca808734 | https://github.com/zuoyuwang/ML-Correctness-prediction/tree/15180b73567e61cc7a5dd61b0202a42eca808734 |
MeanEncoder | # 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... | ktodorov/uva-semantics-19 | MeanEncoder | false | 3,851 | [
"MIT"
] | 0 | c20e4f1d00f6693a8a46dd1d5576cfd3adced896 | https://github.com/ktodorov/uva-semantics-19/tree/c20e4f1d00f6693a8a46dd1d5576cfd3adced896 |
PReLU | import torch
import torch.nn as nn
class PReLU(nn.Module):
def __init__(self):
super(PReLU, self).__init__()
self.layer = nn.PReLU()
def forward(self, x):
x = self.layer(x)
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | yifanpu001/PytorchToCaffe | PReLU | false | 4,715 | [
"MIT"
] | 0 | 37c1ebfc3547e93b1c174721036d03c831c60e48 | https://github.com/yifanpu001/PytorchToCaffe/tree/37c1ebfc3547e93b1c174721036d03c831c60e48 |
RAddFloat | # 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 | RAddFloat | false | 14,245 | [
"MIT"
] | 3,363 | 43b12627ec0de4d212efb6d02b07570205085ccc | https://github.com/PogChamper/torch2trt/tree/43b12627ec0de4d212efb6d02b07570205085ccc |
Actor | 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 Actor(nn.Module):
"""Actor (Policy) Model."""
def __init__(self, state_size, action_size, seed, 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.... | BruceChanJianLe/drlnd-tennis-project3 | Actor | false | 11,257 | [
"MIT"
] | 0 | cb2b880c55eedb6eef3775ed19e90aeec60174d8 | https://github.com/BruceChanJianLe/drlnd-tennis-project3/tree/cb2b880c55eedb6eef3775ed19e90aeec60174d8 |
Scale | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from torch.nn.parameter import Parameter
from itertools import product as product
import torch.onnx
assert_size_stride... | Janus1984/Msnhnet | Scale | false | 13,874 | [
"MIT"
] | 546 | 4e09f2501ba8db789f0a20441a357de3ba468f10 | https://github.com/Janus1984/Msnhnet/tree/4e09f2501ba8db789f0a20441a357de3ba468f10 |
Actor | 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 Actor(nn.Module):
"""Actor (Policy) Model."""
def __init__(self, state_size, action_size, seed, 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.... | BenKang34/deep-reinforcement-learning-nanodegree | Actor | false | 145 | [
"MIT"
] | 0 | 17c9007f757dfb1217c869fdee51798c4a21ba92 | https://github.com/BenKang34/deep-reinforcement-learning-nanodegree/tree/17c9007f757dfb1217c869fdee51798c4a21ba92 |
DenseSAGEConv | import math
import torch
import torch.nn.functional as F
from torch.nn import Parameter
import torch.utils.data
def uniform(size, tensor):
bound = 1.0 / math.sqrt(size)
if tensor is not None:
tensor.data.uniform_(-bound, bound)
class DenseSAGEConv(torch.nn.Module):
"""See :class:`torch_geometric... | 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.nn imp... | douglasrizzo/pytorch_geometric | DenseSAGEConv | false | 12,295 | [
"MIT"
] | 0 | effc617c6ad6daad506038bb79e4407082e74740 | https://github.com/douglasrizzo/pytorch_geometric/tree/effc617c6ad6daad506038bb79e4407082e74740 |
AttentiveNet | import torch
from torch import nn
import torch.nn.functional as F
class AttentiveNet(nn.Module):
def __init__(self, input_size, hidden_size) ->None:
super().__init__()
self.cov2 = nn.Conv1d(hidden_size, hidden_size, kernel_size=3,
padding=1)
self.cov1 = nn.Conv1d(input_size, h... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | ISYSLAB-HUST/DeepNeuropePred | AttentiveNet | false | 5,344 | [
"MIT"
] | 1 | f87f36fdbbc966f727eb063a0f9984850294ba37 | https://github.com/ISYSLAB-HUST/DeepNeuropePred/tree/f87f36fdbbc966f727eb063a0f9984850294ba37 |
sum_squared_error | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch.nn.modules.... | ZerojumpLine/Denoise | sum_squared_error | false | 18,164 | [
"MIT"
] | 4 | 09182b07f451d85448ce3c7a53fc69144f91384e | https://github.com/ZerojumpLine/Denoise/tree/09182b07f451d85448ce3c7a53fc69144f91384e |
PEG | import torch
from torch import nn
class Residual(nn.Module):
def __init__(self, fn):
super().__init__()
self.fn = fn
def forward(self, x, **kwargs):
return self.fn(x, **kwargs) + x
class PEG(nn.Module):
def __init__(self, dim, kernel_size=3):
super().__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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | Mohan-Zhang-u/vit-pytorch | PEG | false | 11,711 | [
"MIT"
] | 0 | 76050c812474d7c10d67db4e811f537e26c3996a | https://github.com/Mohan-Zhang-u/vit-pytorch/tree/76050c812474d7c10d67db4e811f537e26c3996a |
SmallBlock | import torch
from torch import nn
from torchvision import models as models
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torch.onnx
class SmallBlock(nn.Module):
def __init__(self, channels):
super(SmallBlock, self).__init__()
self.c... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
from tor... | JinYAnGHe/openvino_training_extensions | SmallBlock | false | 2,714 | [
"Apache-2.0"
] | 0 | a0b4456a3c9fe6c1b7eabc9d5eb4e74d01453dee | https://github.com/JinYAnGHe/openvino_training_extensions/tree/a0b4456a3c9fe6c1b7eabc9d5eb4e74d01453dee |
Actor | import torch
import torch.nn as nn
import torch.nn.functional as F
class Actor(nn.Module):
def __init__(self, state_dim, action_dim, max_action):
super(Actor, self).__init__()
self.l1 = nn.Linear(state_dim, 256)
self.l2 = nn.Linear(256, 256)
self.l3 = nn.Linear(256, action_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
from torch._inductor.runtime.... | Barisimre/TD3-Generative | Actor | false | 4,893 | [
"MIT"
] | 1 | 434419b020b88010f09f194c40feac1d420b2086 | https://github.com/Barisimre/TD3-Generative/tree/434419b020b88010f09f194c40feac1d420b2086 |
BahdanauAttention | # 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.... | evinaybit/100-Days-of-NLP | BahdanauAttention | false | 15,321 | [
"MIT"
] | 239 | 81e08884dd31b7b99bef27f43a179cda09ab5732 | https://github.com/evinaybit/100-Days-of-NLP/tree/81e08884dd31b7b99bef27f43a179cda09ab5732 |
EntropyLossEncap | import torch
from torch import nn
def feature_map_permute(input):
s = input.data.shape
l = len(s)
if l == 2:
x = input
elif l == 3:
x = input.permute(0, 2, 1)
elif l == 4:
x = input.permute(0, 2, 3, 1)
elif l == 5:
x = input.permute(0, 2, 3, 4, 1)
else:
... | 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_... | vartikagpt10/memae-anomaly-detection | EntropyLossEncap | false | 16,655 | [
"MIT"
] | 297 | ceece7714fb241e82ef3f3785d3d1ed86c28113e | https://github.com/vartikagpt10/memae-anomaly-detection/tree/ceece7714fb241e82ef3f3785d3d1ed86c28113e |
SeqAttnMatch | import torch
import torch.nn as nn
import torch.nn.functional as F
class SeqAttnMatch(nn.Module):
"""
Given sequences X and Y, match sequence Y to each element in X.
* o_i = sum(alpha_j * y_j) for i in X
* alpha_j = softmax(y_j * x_i)
"""
def __init__(self, embed_dim, identity=False):
... | 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.... | GMDennis/claf | SeqAttnMatch | false | 8,153 | [
"MIT"
] | 10 | d1e064e593127e5d654f000f5506c5ae1caab5ce | https://github.com/GMDennis/claf/tree/d1e064e593127e5d654f000f5506c5ae1caab5ce |
ThreeLayerCNN | import torch
import torch.utils.data
class ThreeLayerCNN(torch.nn.Module):
"""
Input: 128x128 face image (eye aligned).
Output: 1-D tensor with 2 elements. Used for binary classification.
Parameters:
Number of conv layers: 3
Number of fully connected layers: 2
"""
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 import triton_helpers
import torch.utils.data
asser... | aleb/pipelines | ThreeLayerCNN | false | 6,178 | [
"Apache-2.0"
] | 1 | 2181b2fb8bdd6cd93e7d677b9840ed1b58a83a85 | https://github.com/aleb/pipelines/tree/2181b2fb8bdd6cd93e7d677b9840ed1b58a83a85 |
PartitionLoss | # 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
assert_size_stride = torch._C._dynamo.guards.assert... | orena1/DAN | PartitionLoss | false | 16,203 | [
"MIT"
] | 50 | 49247ad0cad2a67057d184fa92d15fe2e7bb2cb6 | https://github.com/orena1/DAN/tree/49247ad0cad2a67057d184fa92d15fe2e7bb2cb6 |
LRN | # 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.distributed
assert_size_stride = ... | HKBU-HPML/gtopkssgd | LRN | false | 8,201 | [
"Apache-2.0"
] | 33 | 6f57343f3749939b0345d36fcb2c24470942aefd | https://github.com/HKBU-HPML/gtopkssgd/tree/6f57343f3749939b0345d36fcb2c24470942aefd |
L1GradLoss | import torch
import torch.nn as nn
import torch.utils.data
class L1GradLoss(nn.Module):
def __init__(self, grad=False):
super(L1GradLoss, self).__init__()
self.grad = grad
def forward(self, input, target):
err = input - target
loss = err.norm(p=1).div(err.numel())
if ... | 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
... | alsgkals2/SRResCGAN | L1GradLoss | false | 14,814 | [
"MIT"
] | 81 | a71201a93e1819045f9c7711743812546d3a1f31 | https://github.com/alsgkals2/SRResCGAN/tree/a71201a93e1819045f9c7711743812546d3a1f31 |
ODEfunc_double_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._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | shuj1234/Hopfield-ODE | ODEfunc_double_conv | false | 10,834 | [
"MIT"
] | 0 | 2b770c0141082174f394b189df725088308d8bdd | https://github.com/shuj1234/Hopfield-ODE/tree/2b770c0141082174f394b189df725088308d8bdd |
DWT | import torch
import torch.nn.parallel
import torch.utils.data
from torch import nn
import torch.fft
class LossyYCbCr(nn.Module):
def forward(self, rgb: 'torch.Tensor'):
return torch.cat([0.299 * rgb[:, 0:1] + 0.587 * rgb[:, 1:2] + 0.114 *
rgb[:, 2:3], -0.16875 * rgb[:, 0:1] - 0.33126 * rgb[:,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.... | KazutakaYamanouchi/bachelor-study | DWT | false | 2,653 | [
"Apache-2.0"
] | 0 | a5b8392459e7649cb8a35d09e65bd269d13b5297 | https://github.com/KazutakaYamanouchi/bachelor-study/tree/a5b8392459e7649cb8a35d09e65bd269d13b5297 |
QNetwork | import torch
import torch.nn as nn
import torch.nn.functional as F
class QNetwork(nn.Module):
"""Actor (Policy) Model."""
def __init__(self, state_size, action_size, seed):
"""Initialize parameters and build model.
Parameters:
==========
state_size (int): Dimension of each... | 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_... | andreaspts/DRL_CartPole | QNetwork | false | 9,733 | [
"MIT"
] | 0 | e4f018ab4adaeeaac2902c541e14933b56957e22 | https://github.com/andreaspts/DRL_CartPole/tree/e4f018ab4adaeeaac2902c541e14933b56957e22 |
convblock | import torch
import torch.nn as nn
import torch.nn.functional as F
class AdaptiveInstanceNorm2d(nn.Module):
def __init__(self, num_features, eps=1e-05, momentum=0.1):
super(AdaptiveInstanceNorm2d, self).__init__()
self.num_features = num_features
self.eps = eps
self.momentum = mom... | 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 ... | BradyFU/DVG-Face | convblock | false | 7,824 | [
"MIT"
] | 33 | 16d51fe7da6e4a52d144e938afb3072eb8e4e8de | https://github.com/BradyFU/DVG-Face/tree/16d51fe7da6e4a52d144e938afb3072eb8e4e8de |
AdMSoftmaxLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class AdMSoftmaxLoss(nn.Module):
def __init__(self, in_features, out_features, s=30.0, m=0.4):
"""
AM Softmax Loss
"""
super(AdMSoftmaxLoss, self).__init__()
self.s = s
self.m = m
self.in_fe... | 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.... | B06901052/s3prl | AdMSoftmaxLoss | false | 102 | [
"MIT"
] | 0 | 5f63d2df043d2d7c81580cd042fa2cea34746f48 | https://github.com/B06901052/s3prl/tree/5f63d2df043d2d7c81580cd042fa2cea34746f48 |
MetaLayerNorm | import re
import torch
import warnings
import torch.nn as nn
import torch.nn.functional as F
from collections import OrderedDict
class MetaModule(nn.Module):
"""
Base class for PyTorch meta-learning modules. These modules accept an
additional argument `params` in their `forward` method.
Notes
---... | 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 re
import warnings
import torch.nn as nn
from collections import Ordered... | Steffen-Wolf/pytorch-meta | MetaLayerNorm | false | 9,567 | [
"MIT"
] | 0 | d2dfb902cfa49574eac898045c8e9cf64ce29f96 | https://github.com/Steffen-Wolf/pytorch-meta/tree/d2dfb902cfa49574eac898045c8e9cf64ce29f96 |
ResnetBlockFC | import torch
from torch import nn
import torch.autograd.profiler as profiler
class ResnetBlockFC(nn.Module):
"""
Fully connected ResNet Block class.
Taken from DVR code.
:param size_in (int): input dimension
:param size_out (int): output dimension
:param size_h (int): hidden dimension
"""
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | alrivero/pixel-nerf | ResnetBlockFC | false | 9,798 | [
"BSD-2-Clause"
] | 0 | c054befe189602627f021cda8376adc5940c8668 | https://github.com/alrivero/pixel-nerf/tree/c054befe189602627f021cda8376adc5940c8668 |
ResidualBlock | import torch
import torch.nn as nn
class ConvLayer(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride):
super(ConvLayer, self).__init__()
padding = kernel_size // 2
self.reflection_pad = nn.ReflectionPad2d(padding)
self.conv2d = nn.Conv2d(in_channels, ou... | 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.... | naver-ai/cgl_fairness | ResidualBlock | false | 7,335 | [
"MIT"
] | 1 | 00d3bec233c9b3e0f88496118abaed8321ca3159 | https://github.com/naver-ai/cgl_fairness/tree/00d3bec233c9b3e0f88496118abaed8321ca3159 |
ContinuousEmbeddings | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
from torch import nn
import torch.nn.functional as F
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strid... | sallypannn/pytorch-widedeep | ContinuousEmbeddings | false | 7,592 | [
"MIT"
] | 1 | ab4a209a2a3bff539f543a66ac51306042ed6693 | https://github.com/sallypannn/pytorch-widedeep/tree/ab4a209a2a3bff539f543a66ac51306042ed6693 |
FFN | import torch
from torch import nn as nn
import torch.nn.functional as F
class FFN(nn.Module):
def __init__(self, d_model, hidden_size=1024):
super().__init__()
self.ln1 = nn.Linear(d_model, hidden_size)
self.ln2 = nn.Linear(hidden_size, d_model)
def reset_params(self):
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
from torch import nn as nn
as... | FFTYYY/RoR_relation_extraction | FFN | false | 8,077 | [
"MIT"
] | 25 | a099e98f3708a39debeed4dc522ff57c4f6b960d | https://github.com/FFTYYY/RoR_relation_extraction/tree/a099e98f3708a39debeed4dc522ff57c4f6b960d |
BarlowTwinsLoss | # 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 ... | jiwidi/lightning-tutorials | BarlowTwinsLoss | false | 15,704 | [
"Apache-2.0"
] | 114 | 70ba437447f345d4d6ba089d5b30fd1da2cbc04b | https://github.com/jiwidi/lightning-tutorials/tree/70ba437447f345d4d6ba089d5b30fd1da2cbc04b |
FRN_self | # 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.nn as nn
assert_size_stride = torch._C._dynamo.gu... | EkdeepSLubana/BeyondBatchNorm | FRN_self | false | 17,244 | [
"MIT"
] | 10 | 2ab1626a1ebfdfe55f0a4bc6ac24c8bbdd4e0196 | https://github.com/EkdeepSLubana/BeyondBatchNorm/tree/2ab1626a1ebfdfe55f0a4bc6ac24c8bbdd4e0196 |
BoundReciprocal | from _paritybench_helpers import _mock_config
import math
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import MSELoss
def isnan(x):
if isinstance(x, Patches):
return False
return torch.isnan(x).any()
class Perturbation:
def __init__(self):
... | 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 math
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
assert_size_stride = torch._C._dynamo.guards.assert_siz... | mnmueller/auto_LiRPA | BoundReciprocal | false | 7,287 | [
"BSD-3-Clause"
] | 1 | 55cb270b0b99f07b74541d55706c69fbb9daff66 | https://github.com/mnmueller/auto_LiRPA/tree/55cb270b0b99f07b74541d55706c69fbb9daff66 |
Conv2d | import torch
import torch.nn as nn
import torch.utils.data
class Conv2d(nn.Conv2d):
"""
:param in_channels: Scalar
:param out_channels: Scalar
:param kernel_size: Scalar
:param activation_fn: activation function
:param drop_rate: Scalar. dropout rate
:param stride: Scalar
:param paddin... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dyn... | CookiePPP/mellotron | Conv2d | false | 9,058 | [
"BSD-3-Clause"
] | 0 | 488425981c19cd0eddddea13d1348da4bfef8d26 | https://github.com/CookiePPP/mellotron/tree/488425981c19cd0eddddea13d1348da4bfef8d26 |
GaussActivation | import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
class GaussActivation(nn.Module):
def __init__(self, a, mu, sigma1, sigma2):
super(GaussActivation, self).__init__()
self.a = Parameter(torch.tensor(a, dtype=torch.float32))
self.mu = Parameter(torch.tensor(mu, dt... | 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
... | Vious/LBAM_Pytorch | GaussActivation | false | 14,572 | [
"MIT"
] | 112 | b9292440e7a7559c027f48d6fd061dcabc41a6bf | https://github.com/Vious/LBAM_Pytorch/tree/b9292440e7a7559c027f48d6fd061dcabc41a6bf |
MLP | import torch
from torch import Tensor
from torch import nn
class GELU(nn.Module):
"""Quick GELU"""
def forward(self, x: 'Tensor') ->Tensor:
return x * torch.sigmoid(1.702 * x)
class MLP(nn.Module):
def __init__(self, c1, ch, c2=None):
super().__init__()
self.c_fc = nn.Linear(c1... | 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 Tensor
from torch import nn
assert_size_stride = torch._C._dyn... | sithu31296/multimodal | MLP | false | 4,340 | [
"MIT"
] | 0 | 78f57956cc84273579eb9e2e2be2a58fa1f38814 | https://github.com/sithu31296/multimodal/tree/78f57956cc84273579eb9e2e2be2a58fa1f38814 |
Upsampling | # 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 M
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | SuperbTUM/RAW-image-denoising | Upsampling | false | 17,978 | [
"MIT"
] | 4 | 9f81be8da6a576f641022707d98b8c37f5c599ab | https://github.com/SuperbTUM/RAW-image-denoising/tree/9f81be8da6a576f641022707d98b8c37f5c599ab |
VishalNet | import torch
import torch.nn as nn
class VishalNet(nn.Module):
def __init__(self):
super(VishalNet, self).__init__()
self.cnn1 = nn.Conv1d(1, 60, 81, 1, 40)
self.cnn2 = nn.Conv1d(60, 1, 301, 1, 150)
def forward(self, input):
out1 = nn.functional.relu(self.cnn1(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_... | olivesgatech/Geophysics-2021-Joint-learning-for-spatial-context-based-inversion | VishalNet | false | 7,364 | [
"MIT"
] | 1 | 56f506dfe62ac3557febb4c8e3c62542b1624a1b | https://github.com/olivesgatech/Geophysics-2021-Joint-learning-for-spatial-context-based-inversion/tree/56f506dfe62ac3557febb4c8e3c62542b1624a1b |
TorchDense | # 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 numpy as np
... | Hadjubuntu/sweet-rl | TorchDense | false | 17,338 | [
"MIT"
] | 3 | f0dedadf8a7187e9b9b70436f05c637960fd72a7 | https://github.com/Hadjubuntu/sweet-rl/tree/f0dedadf8a7187e9b9b70436f05c637960fd72a7 |
Caps_Conv | import math
import torch
from torch import nn
class Caps_Conv(nn.Module):
def __init__(self, in_C, in_D, out_C, out_D, kernel_size, stride=1,
padding=0, dilation=1, bias=False):
super(Caps_Conv, self).__init__()
self.in_C = in_C
self.in_D = in_D
self.out_C = out_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
import math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.as... | WdBlink/AugMix-3DOCUNet-Brats2019 | Caps_Conv | false | 5,960 | [
"MIT"
] | 1 | 125c6c8682b51a550eeac9173d13d0a211576abc | https://github.com/WdBlink/AugMix-3DOCUNet-Brats2019/tree/125c6c8682b51a550eeac9173d13d0a211576abc |
injective_pad | # 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... | david-klindt/invertible-resnet | injective_pad | false | 3,389 | [
"MIT"
] | 0 | ac6756a7ba5d0dbcb6b4cec43f8b86079318fd89 | https://github.com/david-klindt/invertible-resnet/tree/ac6756a7ba5d0dbcb6b4cec43f8b86079318fd89 |
Upsampling | import torch
import torch.nn as M
class Upsampling(M.Module):
def __init__(self, in_channels, out_channels, kernel_size=2):
super().__init__()
self.upsample = M.ConvTranspose2d(in_channels, out_channels,
kernel_size=kernel_size, stride=2)
def forward(self, x):
return self... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as M
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | SuperbTUM/RAW-image-denoising | Upsampling | false | 17,978 | [
"MIT"
] | 4 | 9f81be8da6a576f641022707d98b8c37f5c599ab | https://github.com/SuperbTUM/RAW-image-denoising/tree/9f81be8da6a576f641022707d98b8c37f5c599ab |
SoftDiceLossSquared | # 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 numpy as np
from torch import nn
import torch.jit
import torch.nn.functional
assert_size_stride = torch._C._dynamo.guards.assert_size... | CamilaGL/nnUNet | SoftDiceLossSquared | false | 221 | [
"Apache-2.0"
] | 0 | 471ab73a6e4f67fc72d476183b5344be4cccf7ca | https://github.com/CamilaGL/nnUNet/tree/471ab73a6e4f67fc72d476183b5344be4cccf7ca |
ResidualBlock | # 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.... | Arjuna197/examples | ResidualBlock | false | 11,387 | [
"BSD-3-Clause"
] | 0 | f504ea2aafc8a8baa5effb659fc1c20a70aabdda | https://github.com/Arjuna197/examples/tree/f504ea2aafc8a8baa5effb659fc1c20a70aabdda |
lp_L2_Loss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language 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.utils.data import *
import torch.nn as nn
assert_size_stride = torch._C._dynam... | loveorchids/local_patch_retrieval | lp_L2_Loss | false | 3,937 | [
"Apache-2.0"
] | 0 | 52b2e8fdac965d56ef9f89a8c4de96d0b41d3981 | https://github.com/loveorchids/local_patch_retrieval/tree/52b2e8fdac965d56ef9f89a8c4de96d0b41d3981 |
IntrinsicsModel | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
im... | apurvtwr/Jarvis | IntrinsicsModel | false | 3,126 | [
"Apache-2.0"
] | 0 | bdd25e059826a0403c6282a1ee206f9f4c3e9355 | https://github.com/apurvtwr/Jarvis/tree/bdd25e059826a0403c6282a1ee206f9f4c3e9355 |
MyLoss | import torch
from torch import nn
import torch.utils.data
class MyLoss(nn.Module):
def __init__(self):
super(MyLoss, self).__init__()
def forward(self, pred, truth):
return torch.sum((pred - truth) ** 2)
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
de... | 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
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards... | Dora-The-Kid/culture_network | MyLoss | false | 2,159 | [
"Apache-2.0"
] | 0 | bc2bac86e821faa797eeb2670d179395724f7922 | https://github.com/Dora-The-Kid/culture_network/tree/bc2bac86e821faa797eeb2670d179395724f7922 |
CBOW | # 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... | danny-1k/autocomplete_hist | CBOW | false | 1,780 | [
"BSD-2-Clause"
] | 0 | 0a553ea59e08f2ddca60a1f35e9cf14d43370100 | https://github.com/danny-1k/autocomplete_hist/tree/0a553ea59e08f2ddca60a1f35e9cf14d43370100 |
GlobalAttentionGeneral | # 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.... | BedirYilmaz/cycle-image-gan | GlobalAttentionGeneral | false | 2,039 | [
"MIT"
] | 0 | a64da5774ec522c0322e9c21437dc9c066a50a89 | https://github.com/BedirYilmaz/cycle-image-gan/tree/a64da5774ec522c0322e9c21437dc9c066a50a89 |
ResnetBlock | import torch
from torch import nn
import torch.nn.functional as F
import torch.utils.data
import torch.utils.data.distributed
def actvn(x):
out = F.leaky_relu(x, 0.2)
return out
class ResnetBlock(nn.Module):
def __init__(self, fin, fout, fhidden=None, is_bias=True):
super().__init__()
s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.nn.functional as F
import torch.utils.data
imp... | MiaoyunZhao/GANTransferLimitedData | ResnetBlock | false | 8,539 | [
"MIT"
] | 41 | 5545bc37a1d7d4f28a9c3588aaa12a616bbddd88 | https://github.com/MiaoyunZhao/GANTransferLimitedData/tree/5545bc37a1d7d4f28a9c3588aaa12a616bbddd88 |
Attention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Francois-Aubet/AHGP | Attention | false | 8,123 | [
"MIT"
] | 19 | 3ecdd01d138f013ae8da196fbf3a71632aa2cd88 | https://github.com/Francois-Aubet/AHGP/tree/3ecdd01d138f013ae8da196fbf3a71632aa2cd88 |
Conv | import torch
import torch.nn as nn
class Conv(nn.Module):
"""
Convolution Module
"""
def __init__(self, in_channels, out_channels, kernel_size=1, stride=1,
padding=0, dilation=1, bias=True, w_init='linear'):
"""
:param in_channels: dimension of input
:param out_channel... | 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... | Ahmad1s/FastSpeech2 | Conv | false | 8,853 | [
"MIT"
] | 0 | d31802ffcd74bb2c2ca57b53e481917989ded6b9 | https://github.com/Ahmad1s/FastSpeech2/tree/d31802ffcd74bb2c2ca57b53e481917989ded6b9 |
LinearAdd | # 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.cuda
import torch.backends.cudnn
import torch... | JudeDavis1/intel-extension-for-pytorch | LinearAdd | false | 2,582 | [
"Apache-2.0"
] | 0 | 364e34cb4917a709f5108c07d4005bf82f3d5067 | https://github.com/JudeDavis1/intel-extension-for-pytorch/tree/364e34cb4917a709f5108c07d4005bf82f3d5067 |
ScalarScaleBias | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from torch.nn.parameter import Parameter
from torch.nn import init
assert_size_stride = torch._C._dynamo.guards.assert... | maltanar/logicnets-1 | ScalarScaleBias | false | 3,971 | [
"Apache-2.0"
] | 0 | 0afa2aa5b39cb484db0fcaa542e55c8cbe586119 | https://github.com/maltanar/logicnets-1/tree/0afa2aa5b39cb484db0fcaa542e55c8cbe586119 |
MultiHeadAttention | # 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.... | Ginga1892/bert-x | MultiHeadAttention | false | 2,320 | [
"MIT"
] | 0 | 903970ef0a6967aa20a82bcf56b874602e37a04d | https://github.com/Ginga1892/bert-x/tree/903970ef0a6967aa20a82bcf56b874602e37a04d |
GLU | # 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... | ishine/tfm-tts | GLU | false | 3,675 | [
"MIT"
] | 0 | a964736467851ddec8f8e8933b9550cbe7d7d7eb | https://github.com/ishine/tfm-tts/tree/a964736467851ddec8f8e8933b9550cbe7d7d7eb |
GeM | # 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
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | XiaoJake/MinkLocMultimodal | GeM | false | 14,606 | [
"MIT"
] | 49 | 683ef1aae35ab1b60f13cefccfdd0e3f9cb9ea6e | https://github.com/XiaoJake/MinkLocMultimodal/tree/683ef1aae35ab1b60f13cefccfdd0e3f9cb9ea6e |
hswish | import torch
import torch.nn as nn
import torch.nn.functional as F
class hswish(nn.Module):
def forward(self, x):
out = x * 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 | hswish | false | 960 | [
"MIT"
] | 0 | 23e05a8a096168ccfa4d1743467fdf78ffcaabba | https://github.com/Qidian213/NAIC2019/tree/23e05a8a096168ccfa4d1743467fdf78ffcaabba |
torch_fakeint8_to_float | import torch
class torch_fakeint8_to_float(torch.nn.Module):
def __init__(self):
super(torch_fakeint8_to_float, self).__init__()
def forward(self, x):
x0 = x.permute(2, 0, 1)
x0 += torch.clamp(x0, -1, 0) * -256.0
return x0.unsqueeze(0).contiguous()
def get_inputs():
ret... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | ozendelait/pytorch-semseg | torch_fakeint8_to_float | false | 7,434 | [
"MIT"
] | 1 | 200491febd653bd26befcd5b3d52c614aa832b7e | https://github.com/ozendelait/pytorch-semseg/tree/200491febd653bd26befcd5b3d52c614aa832b7e |
CombineSlices | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.utils.data
import torch.utils.data.distributed
import torch.optim
assert_size_stride = torch._C._dynamo.gu... | aslakey/fastMRI | CombineSlices | false | 1,483 | [
"MIT"
] | 0 | e94028aeccfdc70472b453c2ef2f072b40a287c7 | https://github.com/aslakey/fastMRI/tree/e94028aeccfdc70472b453c2ef2f072b40a287c7 |
ContrastiveLoss | # 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
assert_size_stride = torch._... | e-Neural/OfflineSignatureVerification | ContrastiveLoss | false | 15,283 | [
"MIT"
] | 51 | ea11009a3b2ac82c7091075466c505602a50817a | https://github.com/e-Neural/OfflineSignatureVerification/tree/ea11009a3b2ac82c7091075466c505602a50817a |
AttentionLayer | # 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.... | B0BBB/seq2seq.pytorch | AttentionLayer | false | 147 | [
"MIT"
] | 0 | 54bb0e9f3e5c7db7f257841ed652e8ff447b8ee4 | https://github.com/B0BBB/seq2seq.pytorch/tree/54bb0e9f3e5c7db7f257841ed652e8ff447b8ee4 |
DCLoss | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_str... | leoauri/auraloss | DCLoss | false | 15,896 | [
"Apache-2.0"
] | 272 | 0e3362674ae1b53aa61c6a631fb4e6970c5683c1 | https://github.com/leoauri/auraloss/tree/0e3362674ae1b53aa61c6a631fb4e6970c5683c1 |
Recon_Block | import torch
from torch import nn
class Recon_Block(nn.Module):
def __init__(self, num_chans=64):
super(Recon_Block, self).__init__()
bias = True
self.conv1 = nn.Conv2d(num_chans, num_chans, kernel_size=3, stride=
1, padding=1, bias=bias)
self.relu2 = nn.PReLU()
... | 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... | khammernik/sigmanet | Recon_Block | false | 15,848 | [
"MIT"
] | 50 | 6eb8dbd1ee350bb9baee60eb254080f7d660bbc5 | https://github.com/khammernik/sigmanet/tree/6eb8dbd1ee350bb9baee60eb254080f7d660bbc5 |
SuperLoss | # 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
from torch import nn
import torch
assert_size_stride = torch._C._... | brown-ivl/beacon | SuperLoss | false | 6,372 | [
"MIT"
] | 1 | 66a1714473b362294f787f261561e39c52f00e42 | https://github.com/brown-ivl/beacon/tree/66a1714473b362294f787f261561e39c52f00e42 |
SetConv | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | JonathanRaiman/CEB | SetConv | false | 9,209 | [
"MIT"
] | 0 | ec5338dcaa939c5df36a47ea9d0895137b1e1b5e | https://github.com/JonathanRaiman/CEB/tree/ec5338dcaa939c5df36a47ea9d0895137b1e1b5e |
MaxPoolPad | import torch
import torch.utils.data
import torch.nn as nn
import torch.backends.cudnn
class MaxPoolPad(nn.Module):
def __init__(self):
super(MaxPoolPad, self).__init__()
self.pad = nn.ZeroPad2d((1, 0, 1, 0))
self.pool = nn.MaxPool2d(3, stride=2, padding=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 import triton_helpers
import torch.utils.data
import torch.nn as nn
import torch.backends.cudnn
assert_size_str... | CalebEverett/fastai-dl2 | MaxPoolPad | false | 17,152 | [
"Apache-2.0"
] | 4 | 64d23592eddca6ca1f3647e73c319e97c8eb392b | https://github.com/CalebEverett/fastai-dl2/tree/64d23592eddca6ca1f3647e73c319e97c8eb392b |
DDPGConvBody | # 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 ... | Fieps1/p3-tennis | DDPGConvBody | false | 512 | [
"MIT"
] | 0 | 29f3dab5810d7cd7f84120416a615956d266c256 | https://github.com/Fieps1/p3-tennis/tree/29f3dab5810d7cd7f84120416a615956d266c256 |
Encoder | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import device
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from... | StefanNa/dtu_mlops | Encoder | false | 5,855 | [
"Apache-2.0"
] | 1 | 148f3427f8d090d39d127857be8a37832f800279 | https://github.com/StefanNa/dtu_mlops/tree/148f3427f8d090d39d127857be8a37832f800279 |
StyleLossBlock | # 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.nn.functional as F
assert_size_stride = torch... | Inkln/StyleTransferWithCatalyst | StyleLossBlock | false | 8,311 | [
"Apache-2.0"
] | 11 | c3181ecdfd32160907efc2d9d917a55925c25c11 | https://github.com/Inkln/StyleTransferWithCatalyst/tree/c3181ecdfd32160907efc2d9d917a55925c25c11 |
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