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 |
|---|---|---|---|---|---|---|---|---|---|---|
TokenEmbedding | import math
import torch
from torch import Tensor
import torch.nn as nn
import torch.quantization
import torch.onnx
import torch.nn.parallel
import torch.utils.data
import torch.fx
import torch.nn
import torch.optim
import torch.profiler
class TokenEmbedding(nn.Module):
def __init__(self, vocab_size: 'int', emb_... | 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.quantization
import torch.onnx
import torch.nn.parallel
import torch.utils.data
import torch.fx
import to... | Lezcano/tutorials | TokenEmbedding | false | 5,522 | [
"BSD-3-Clause"
] | 1 | 24946b2e6d3d825afed6b35c1c4d618a70a88be8 | https://github.com/Lezcano/tutorials/tree/24946b2e6d3d825afed6b35c1c4d618a70a88be8 |
TripletMarginLossCosine | import torch
from torch import nn
import torch.nn.functional as F
class TripletMarginLossCosine(nn.Module):
def __init__(self, margin=1.0):
super(TripletMarginLossCosine, self).__init__()
self.margin = margin
def forward(self, anchor, positive, negative):
d_p = 1 - F.cosine_similarit... | 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_... | cenkcorapci/visual-fashion-item-search | TripletMarginLossCosine | false | 6,416 | [
"MIT"
] | 1 | 47b93f97383c1b7f9ec23bb4ff66f90504db3da8 | https://github.com/cenkcorapci/visual-fashion-item-search/tree/47b93f97383c1b7f9ec23bb4ff66f90504db3da8 |
BCEBlurWithLogitsLoss | import torch
import torch.nn as nn
class BCEBlurWithLogitsLoss(nn.Module):
def __init__(self, alpha=0.05):
super(BCEBlurWithLogitsLoss, self).__init__()
self.loss_fcn = nn.BCEWithLogitsLoss(reduction='none')
self.alpha = alpha
def forward(self, pred, true):
loss = self.loss_f... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | Alex-Beh/hand_tracking | BCEBlurWithLogitsLoss | false | 11,151 | [
"Apache-2.0"
] | 0 | 40ac39e10ed5815d9400d6a87149015ad6ab9686 | https://github.com/Alex-Beh/hand_tracking/tree/40ac39e10ed5815d9400d6a87149015ad6ab9686 |
ResBlock | from _paritybench_helpers import _mock_config
import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.nn.functional
def Activation_layer(activation_cfg, inplace=True):
out = None
if activation_cfg == 'ReLU':
out = nn.ReLU(inplace=inplace)
else:
out = nn.LeakyReLU(ne... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | junyuchen245/TransMorph_Transformer_for_Medical_Image_Registration | ResBlock | false | 15,777 | [
"MIT"
] | 82 | dfa24a47a564a000aa9b4eea95a6e83a24568359 | https://github.com/junyuchen245/TransMorph_Transformer_for_Medical_Image_Registration/tree/dfa24a47a564a000aa9b4eea95a6e83a24568359 |
TransformerEncoderLayer | from torch.nn import Module
import torch
import torch.nn as nn
import torch.optim
import torch.utils.data
import torch.nn.functional as F
from torch.nn import Linear
from torch.nn import Dropout
from torch.nn import LayerNorm
from torch.nn import Identity
def drop_path(x, drop_prob: 'float'=0.0, training: 'bool'=Fals... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | alihassanijr/Compact-Transformers | TransformerEncoderLayer | false | 14,812 | [
"Apache-2.0"
] | 281 | 61b656eacdf113f92900f800410bb788bb7d9a3c | https://github.com/alihassanijr/Compact-Transformers/tree/61b656eacdf113f92900f800410bb788bb7d9a3c |
SCS_Cell | import random
import torch
import torch.nn.init
from torch import nn
from torch.autograd import Variable
import torch.utils.data
class SCS_Cell(nn.Module):
def __init__(self, input_size, input_dim, hidden_dim, kernel_size, bias,
p_TD):
super(SCS_Cell, self).__init__()
self.height, self.wi... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.ini... | BoPang1996/Semi-Coupled-Structure-for-visual-sequental-tasks | SCS_Cell | false | 7,805 | [
"Apache-2.0"
] | 13 | c6fe7c77d08928bb30cc8683123f978b0e877394 | https://github.com/BoPang1996/Semi-Coupled-Structure-for-visual-sequental-tasks/tree/c6fe7c77d08928bb30cc8683123f978b0e877394 |
BasicModel3 | # 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... | YNNEKUW/captum | BasicModel3 | false | 12,016 | [
"BSD-3-Clause"
] | 0 | c8b5357b21f2ddf440e5f0ce25635977292aa5d1 | https://github.com/YNNEKUW/captum/tree/c8b5357b21f2ddf440e5f0ce25635977292aa5d1 |
SqueezeExcite | # 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 torchvision.transforms i... | JaminFong/dali-pytorch | SqueezeExcite | false | 8,330 | [
"Apache-2.0"
] | 41 | 7bd5d2380d210a32d24c7309da69c8d2c5db8759 | https://github.com/JaminFong/dali-pytorch/tree/7bd5d2380d210a32d24c7309da69c8d2c5db8759 |
deepQ | import torch
import torch.nn as nn
import torch.nn.functional as F
class deepQ(nn.Module):
def __init__(self, action_size, obs_size, hidden_size=256):
super().__init__()
self.input_layer = nn.Linear(obs_size, hidden_size)
self.output_layer = nn.Linear(hidden_size, action_size)
def fo... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | ExilesAI/RLAgents | deepQ | false | 9,005 | [
"MIT"
] | 0 | b8159a933c4674c7a62bfe9555870336616a59f3 | https://github.com/ExilesAI/RLAgents/tree/b8159a933c4674c7a62bfe9555870336616a59f3 |
VGAE | # 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... | WanyuGroup/CVPR2022-OrphicX | VGAE | false | 1,213 | [
"MIT"
] | 0 | 98d8d8259439c45661573e575cf956331df16abc | https://github.com/WanyuGroup/CVPR2022-OrphicX/tree/98d8d8259439c45661573e575cf956331df16abc |
CharbonnierLoss | # 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 functools
import torc... | Sardhendu/mmediting | CharbonnierLoss | false | 9,888 | [
"Apache-2.0"
] | 0 | 623b59ac758d856abc9fab7e845beeab61074d8f | https://github.com/Sardhendu/mmediting/tree/623b59ac758d856abc9fab7e845beeab61074d8f |
ImpalaResidual | import torch
import torch.nn as nn
import torch.nn.functional as F
class ImpalaResidual(nn.Module):
"""
A residual block for an IMPALA CNN.
"""
def __init__(self, depth):
super().__init__()
self.conv1 = nn.Conv2d(depth, depth, 3, padding=1)
self.conv2 = nn.Conv2d(depth, depth,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 | ImpalaResidual | false | 8,670 | [
"MIT"
] | 41 | 045b8846f2558aa8fb8ac8cef5c71ee098cb9b22 | https://github.com/PacktPublishing/Hands-On-Reinforcement-Learning-for-Games/tree/045b8846f2558aa8fb8ac8cef5c71ee098cb9b22 |
SmoothL1Loss | import torch
import torch.nn as nn
import torch.nn.functional as F
class SmoothL1Loss(nn.Module):
"""SmoothL1Loss loss.
Args:
use_target_weight (bool): Option to use weighted MSE loss.
Different joint types may have different target weights.
loss_weight (float): Weight of the loss... | 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
... | ALISCIFP/mmpose | SmoothL1Loss | false | 2,044 | [
"Apache-2.0"
] | 0 | 2433e3dbcc44baa2253e2a7c748ba0216937933e | https://github.com/ALISCIFP/mmpose/tree/2433e3dbcc44baa2253e2a7c748ba0216937933e |
RWKV_ChannelMix | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
from torch.nn import functional as F
class RWKV_ChannelMix(nn.Module):
def __init__(self, config, layer_id):
super().__init__()
self.layer_id = layer_id
self.time_shift = nn.ZeroPad2d((0, 0, 1, -1))
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.triton_helpers import libdevice, math as tl_math
im... | gdtool/AI-Writer | RWKV_ChannelMix | false | 12,420 | [
"BSD-3-Clause"
] | 0 | 25582175376a1feb09aab9079f7e32bba30d0519 | https://github.com/gdtool/AI-Writer/tree/25582175376a1feb09aab9079f7e32bba30d0519 |
Upsample | import torch
import torch.nn as nn
import torch.utils.data
class Upsample(nn.Module):
def __init__(self, stride=2):
super(Upsample, self).__init__()
self.stride = stride
def forward(self, x):
stride = self.stride
assert x.data.dim() == 4
B = x.data.size(0)
C =... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | AP-EPFL/DA-segmentation-driven-pose | Upsample | false | 4,769 | [
"MIT"
] | 1 | 451b8ee3619b16db152ac37ba2b64f7ebf5e2832 | https://github.com/AP-EPFL/DA-segmentation-driven-pose/tree/451b8ee3619b16db152ac37ba2b64f7ebf5e2832 |
UpBlock | import torch
from torch import nn
import torch.nn.functional as F
def conv3d(in_channels, out_channels, kernel_size, bias, padding=1, stride=1):
return nn.Conv3d(in_channels, out_channels, kernel_size, padding=
padding, bias=bias, stride=stride)
class UpBlock(nn.Module):
"""
A module down sa... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | WdBlink/AugMix-3DOCUNet-Brats2019 | UpBlock | false | 5,970 | [
"MIT"
] | 1 | 125c6c8682b51a550eeac9173d13d0a211576abc | https://github.com/WdBlink/AugMix-3DOCUNet-Brats2019/tree/125c6c8682b51a550eeac9173d13d0a211576abc |
MaxPoolStride1 | import torch
import torch.nn as nn
import torch.nn.functional as F
class MaxPoolStride1(nn.Module):
def __init__(self):
super(MaxPoolStride1, self).__init__()
def forward(self, x):
x = F.max_pool2d(F.pad(x, (0, 1, 0, 1), mode='replicate'), 2, stride=1)
return x
def get_inputs():
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | Zhang-Jack/adversarial_yolo2 | MaxPoolStride1 | false | 18,173 | [
"MIT"
] | 8 | 91c2a4793047f656482cebf0309984db823e8030 | https://github.com/Zhang-Jack/adversarial_yolo2/tree/91c2a4793047f656482cebf0309984db823e8030 |
VanillaRNN | # 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 ... | askliar/deep_learning | VanillaRNN | false | 1,518 | [
"MIT"
] | 0 | e61b2391a3258d18719bf12d9ed1404620ce6c02 | https://github.com/askliar/deep_learning/tree/e61b2391a3258d18719bf12d9ed1404620ce6c02 |
ColorJitterLayer | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch.... | hugobloem/PyTorch-StudioGAN | ColorJitterLayer | false | 12,654 | [
"MIT"
] | 0 | 3deab27c0774adba5a94c7f452d32d4cbc3b117c | https://github.com/hugobloem/PyTorch-StudioGAN/tree/3deab27c0774adba5a94c7f452d32d4cbc3b117c |
ConvolutionTranspose | import torch
import torch.utils.data
import torch.utils.data.distributed
import torch.nn.init
import torch.nn as nn
import torch.nn.functional as F
def Normalizations(tensor_size=None, normalization=None, available=False,
**kwargs):
"""Does normalization on 4D tensor.
Args:
tensor_size: shape of ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
impor... | davidwagnerkc/TensorMONK | ConvolutionTranspose | false | 1,809 | [
"MIT"
] | 0 | 3607836d3d6bfd0994e044536b2a51bc84b35f31 | https://github.com/davidwagnerkc/TensorMONK/tree/3607836d3d6bfd0994e044536b2a51bc84b35f31 |
SelfAttn | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
import torch.nn.init
import torch as th
class SelfAttn(nn.Module):
def __init__(self, hidden_size):
super(SelfAttn, self).__init__()
self.query = nn.Linear(hidden_size, 1)
def forward(self, keys, value... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | msft-shahins/ConvLab-2 | SelfAttn | false | 12,804 | [
"Apache-2.0"
] | 0 | ad74c0e9e021916f9330af11e046ed72914b7740 | https://github.com/msft-shahins/ConvLab-2/tree/ad74c0e9e021916f9330af11e046ed72914b7740 |
NN | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
import t... | jiazhi412/Machine-Learning-Collection | NN | false | 3,737 | [
"MIT"
] | 0 | 1c30faf1e27a79eeca966c017e956df8f7f6ef17 | https://github.com/jiazhi412/Machine-Learning-Collection/tree/1c30faf1e27a79eeca966c017e956df8f7f6ef17 |
BiAttention | # 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.... | Angelinaa/KOBE | BiAttention | false | 69 | [
"MIT"
] | 0 | 4d25487051e2791a977e59297f70a25e51806466 | https://github.com/Angelinaa/KOBE/tree/4d25487051e2791a977e59297f70a25e51806466 |
ConvTransposeInstanceNorm2d | # 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 ... | Geson-anko/ReimplementCycleGAN | ConvTransposeInstanceNorm2d | false | 2,288 | [
"MIT"
] | 0 | 3bd40c519d53a7ebb284c718e935e3832326633f | https://github.com/Geson-anko/ReimplementCycleGAN/tree/3bd40c519d53a7ebb284c718e935e3832326633f |
Classifier | import torch
from torch import nn
class Classifier(nn.Module):
def __init__(self, num_inputs1, num_inputs2):
super().__init__()
self.network = nn.Bilinear(num_inputs1, num_inputs2, 1)
def forward(self, x1, x2):
return self.network(x1, x2)
def get_inputs():
return [torch.rand([4... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
reinterpret_tensor = torch._C._dynamo.guards._reinterpr... | DuaneNielsen/atari-representation-learning | Classifier | false | 13,601 | [
"MIT"
] | 175 | fe34f389768416deaa6a6ff0bdebba3d05762a55 | https://github.com/DuaneNielsen/atari-representation-learning/tree/fe34f389768416deaa6a6ff0bdebba3d05762a55 |
ConvBlock | import torch
import torch.nn as nn
from math import *
import torch.nn.functional as F
class ConvBlock(nn.Module):
def __init__(self):
super(ConvBlock, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
def forward(... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ma... | a8252525/NIID-Bench | ConvBlock | false | 3,007 | [
"MIT"
] | 0 | 33df8d3a7b941884eec3c7bd52adb8a9476eb282 | https://github.com/a8252525/NIID-Bench/tree/33df8d3a7b941884eec3c7bd52adb8a9476eb282 |
imq | import torch
import torch as tr
import torch.nn as nn
class imq(nn.Module):
def __init__(self, c=1.0):
super(imq, self).__init__()
self.c = c
def forward(self, x: 'tr.Tensor'):
return 1 / (self.c ** 2 + x ** 2)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | pierreglaser/MMD-gradient-flow | imq | false | 10,698 | [
"BSD-3-Clause"
] | 0 | 43591137e1d04bed5153887a364fae72621b01ae | https://github.com/pierreglaser/MMD-gradient-flow/tree/43591137e1d04bed5153887a364fae72621b01ae |
L1Loss | import functools
import torch
import torch.nn.functional as F
import torch.nn as nn
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Return:
Tensor: Reduced loss ten... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import functools
impor... | CvlabAssignment/AlignPS | L1Loss | false | 13,531 | [
"Apache-2.0"
] | 144 | 297f4166921d2095f9381e38e04129a103069406 | https://github.com/CvlabAssignment/AlignPS/tree/297f4166921d2095f9381e38e04129a103069406 |
Embeddings | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | CallMeSp/My_flowQA | Embeddings | false | 190 | [
"Apache-2.0"
] | 0 | 87d82551f614b089771b22a1992e2be41a2995b3 | https://github.com/CallMeSp/My_flowQA/tree/87d82551f614b089771b22a1992e2be41a2995b3 |
Classifier | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.optim.lr_scheduler
assert_size_stride = torch.... | CMU-IDS-2020/fp-index-out-of-bounds | Classifier | false | 194 | [
"BSD-3-Clause"
] | 0 | 1a9fdaac8288a980e759a0c2e46f28294d25c71f | https://github.com/CMU-IDS-2020/fp-index-out-of-bounds/tree/1a9fdaac8288a980e759a0c2e46f28294d25c71f |
MultConst | import torch
import torch.nn as nn
class MultConst(nn.Module):
def forward(self, input):
return 255 * input
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... | JonghunBok/PyTorch-Multi-Style-Transfer | MultConst | false | 9,238 | [
"MIT"
] | 0 | 0e6744eb7d9c746ba828fc406e59d619f2e60094 | https://github.com/JonghunBok/PyTorch-Multi-Style-Transfer/tree/0e6744eb7d9c746ba828fc406e59d619f2e60094 |
MHSA | import torch
from torch import nn
class MHSA(nn.Module):
def __init__(self, height, width, dim, head):
super(MHSA, self).__init__()
self.head = head
self.r_h = nn.Parameter(data=torch.randn(1, head, dim // head, 1,
height), requires_grad=True)
self.r_w = nn.Parameter(d... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | lzu-zhanghr/vision-transformer-zoo | MHSA | false | 10,475 | [
"MIT"
] | 0 | 2cc6e3551c39816acc95ade040bbf9bd115a6b03 | https://github.com/lzu-zhanghr/vision-transformer-zoo/tree/2cc6e3551c39816acc95ade040bbf9bd115a6b03 |
Value | # 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 ... | tpbarron/pytorch-ppo | Value | false | 16,608 | [
"MIT"
] | 47 | f73226865e34443f93dbec58939329c9278828e8 | https://github.com/tpbarron/pytorch-ppo/tree/f73226865e34443f93dbec58939329c9278828e8 |
My_loss_focus | import torch
import torch.nn as nn
class My_loss_focus(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x, y, batch_size):
return torch.sum(torch.pow(x - y, 4)) / batch_size
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4]), torch.rand(
... | 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... | H-Liu1997/Pytorch_Pose_Estimation_Framework | My_loss_focus | false | 5,245 | [
"MIT"
] | 1 | 06616b3459ff639f8486e6ea4f93922597788b2a | https://github.com/H-Liu1997/Pytorch_Pose_Estimation_Framework/tree/06616b3459ff639f8486e6ea4f93922597788b2a |
CosineLinear | # 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.... | JosephKJ/class-incremental-learning | CosineLinear | false | 17,517 | [
"MIT"
] | 8 | 689271b84f2e553930ca6687d036ac99bd84b311 | https://github.com/JosephKJ/class-incremental-learning/tree/689271b84f2e553930ca6687d036ac99bd84b311 |
MMTMQuad | import torch
import torch.nn as nn
from typing import Sequence
class MMTMQuad(nn.Module):
"""
quad modal fusion
"""
def __init__(self, dim_tab, dim_img, ratio=4):
"""
Parameters
----------
dim_tab: feature dimension of tabular data
dim_img: feature dimension o... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | ditannan/Multi-modal-Multi-instance-Learning | MMTMQuad | false | 6,591 | [
"Apache-2.0"
] | 1 | 06aada1ff85784d5ed50aa528c506947c892d584 | https://github.com/ditannan/Multi-modal-Multi-instance-Learning/tree/06aada1ff85784d5ed50aa528c506947c892d584 |
DenseConvBlock | import torch
from torch import nn
class DenseConvBlock(nn.Module):
def __init__(self, in_channels: 'int', out_channels: 'int'=16,
growth_channels: 'int'=16):
super(DenseConvBlock, self).__init__()
self.conv1 = nn.Conv2d(in_channels, out_channels, (3, 3), (1, 1), (
1, 1))
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | Lornatang/SRDenseNet-PyTorch | DenseConvBlock | false | 17,633 | [
"Apache-2.0"
] | 4 | d7876bda4c48195a3652aed4e207f7509ac23e4b | https://github.com/Lornatang/SRDenseNet-PyTorch/tree/d7876bda4c48195a3652aed4e207f7509ac23e4b |
SiQU | import torch
class SiQU(torch.nn.Module):
def __init__(self):
super().__init__()
self._activation = torch.nn.SiLU()
def forward(self, x):
return x * self._activation(x)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | krylea/ocp | SiQU | false | 10,493 | [
"MIT"
] | 0 | 00fc1df29731d70ff1b5cf8e9323d1d2f1f8e540 | https://github.com/krylea/ocp/tree/00fc1df29731d70ff1b5cf8e9323d1d2f1f8e540 |
MatchingNetwork | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
import torch.optim
import torch.nn.functional as F
import torch.nn.parallel
class MatchingNetwork(nn.Module):
def __init__(self, opt):
super(MatchingNetwork, self).__init__()
scale_cls = opt['scale_cls'] if 'scale_cl... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | nikran1/Few_shot | MatchingNetwork | false | 16,180 | [
"MIT"
] | 497 | 5298c98e208411e44ee7767e6f4d457006d373cb | https://github.com/nikran1/Few_shot/tree/5298c98e208411e44ee7767e6f4d457006d373cb |
Cosine | from _paritybench_helpers import _mock_config
import torch
from torch.optim.lr_scheduler import *
class Cosine(torch.nn.Module):
def __init__(self, config):
super().__init__()
def forward(self, src, tgt):
src = src.float()
tgt = tgt.float()
return (torch.matmul(src, tgt.trans... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.optim.lr... | chunhuililili/mt_dnn | Cosine | false | 10,200 | [
"MIT"
] | 0 | 4c6efaf21724c7b8103a05e46b5b44d7b246225e | https://github.com/chunhuililili/mt_dnn/tree/4c6efaf21724c7b8103a05e46b5b44d7b246225e |
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.... | salmon7ish/Video-Captioning | Attention | false | 4,378 | [
"MIT"
] | 0 | 08359b1824195a7f5eac5b58982efd19ebc6db01 | https://github.com/salmon7ish/Video-Captioning/tree/08359b1824195a7f5eac5b58982efd19ebc6db01 |
FullyCNN | import torch
from torch import nn
class FullyCNN(nn.Module):
"""UNET Without concatenation during decoding"""
def __init__(self):
super(FullyCNN, self).__init__()
self.conv1 = nn.Conv2d(in_channels=3, out_channels=6, kernel_size=3,
stride=1, padding=1, padding_mode='reflect')
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | quenting44/semantic_segmentation | FullyCNN | false | 10,812 | [
"MIT"
] | 0 | bd197ddda3c6891d69ff7e552a0c224c7ec1269a | https://github.com/quenting44/semantic_segmentation/tree/bd197ddda3c6891d69ff7e552a0c224c7ec1269a |
ToLongTensor | # 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... | NivekT/text | ToLongTensor | false | 11,751 | [
"BSD-3-Clause"
] | 0 | 4908d3c88f92296a4c23be2f064ccde13cce50ce | https://github.com/NivekT/text/tree/4908d3c88f92296a4c23be2f064ccde13cce50ce |
MHA | # 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.... | qinyiwei/MuTual | MHA | false | 4,168 | [
"MIT"
] | 0 | 3bdd13c1388d6136b8944666dfd434870760cc93 | https://github.com/qinyiwei/MuTual/tree/3bdd13c1388d6136b8944666dfd434870760cc93 |
LocationLayer | # 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
import torch.nn as nn
assert_size_stride = ... | aidiary/tacotron-pytorch | LocationLayer | false | 3,109 | [
"MIT"
] | 0 | 8ea9b1bb61bf753a64ff611b441326ea8c001d20 | https://github.com/aidiary/tacotron-pytorch/tree/8ea9b1bb61bf753a64ff611b441326ea8c001d20 |
GaussianKernel | # 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 typing import Opt... | NiteshBharadwaj/ignoringhumanpose | GaussianKernel | false | 909 | [
"MIT"
] | 0 | 1fb7a063fded9cff18f7de4e1d71845983077256 | https://github.com/NiteshBharadwaj/ignoringhumanpose/tree/1fb7a063fded9cff18f7de4e1d71845983077256 |
RelationCrossing | import torch
import torch.nn as nn
import torch.nn.functional as F
class RelationCrossing(nn.Module):
def __init__(self, in_feats: 'int', out_feats: 'int', num_heads: 'int',
dropout: 'float'=0.0, negative_slope: 'float'=0.2):
"""
Relation crossing layer
Parameters
--------... | 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
... | BUPTlfq/OpenHGNN | RelationCrossing | false | 2,010 | [
"Apache-2.0"
] | 0 | 77041e68c33a8a42a2c187c6e42d85b81cbb25d3 | https://github.com/BUPTlfq/OpenHGNN/tree/77041e68c33a8a42a2c187c6e42d85b81cbb25d3 |
Upsample2d | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import numpy as np
import torch.nn as nn
assert_size_stride = torch._C._dynamo.g... | Iceland-Leo/StyleGAN2_PyTorch | Upsample2d | false | 5,617 | [
"MIT"
] | 1 | 3621f5e4ba1c7fde7e2fae1f4700d050656a0b02 | https://github.com/Iceland-Leo/StyleGAN2_PyTorch/tree/3621f5e4ba1c7fde7e2fae1f4700d050656a0b02 |
VarifocalLoss | # 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... | Huuush/deepfashion2-det | VarifocalLoss | false | 11,483 | [
"Apache-2.0"
] | 0 | 46af0ada8d6f534de2de6a9c069580cd1bf609ec | https://github.com/Huuush/deepfashion2-det/tree/46af0ada8d6f534de2de6a9c069580cd1bf609ec |
ReconstructLoss | # 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
... | DevKiHyun/SRNTT.pytorch | ReconstructLoss | false | 5,064 | [
"MIT"
] | 1 | d7540921983cf42ea2a7eef544862a95318e6a35 | https://github.com/DevKiHyun/SRNTT.pytorch/tree/d7540921983cf42ea2a7eef544862a95318e6a35 |
Scaler | import torch
from abc import ABC
class BaseOperator(ABC):
"""
Abstract class defining the basic structure for operator implementations in Hummingbird.
"""
def __init__(self, regression=False, classification=False, transformer=
False, anomaly_detection=False, **kwargs):
super().__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 abc import ABC
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_stri... | kernc/hummingbird | Scaler | false | 12,658 | [
"MIT"
] | 0 | 8c9d5b1f19054d521b22ad7fcffa8ee10e405ac3 | https://github.com/kernc/hummingbird/tree/8c9d5b1f19054d521b22ad7fcffa8ee10e405ac3 |
Critic | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | iffiX/machin | Critic | false | 15,599 | [
"MIT"
] | 287 | 7fa986b1bafdefff117d6ff73d14644a5488de9d | https://github.com/iffiX/machin/tree/7fa986b1bafdefff117d6ff73d14644a5488de9d |
CrossModalAttention | # 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.... | QuintinPope/FASTA_Perceiver | CrossModalAttention | false | 2,752 | [
"Apache-2.0"
] | 0 | ad3a8e2333a1dec9b34ae024cb2faf38c6ea284a | https://github.com/QuintinPope/FASTA_Perceiver/tree/ad3a8e2333a1dec9b34ae024cb2faf38c6ea284a |
ScaleNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.jit
import torch.nn
assert_size_stride = tor... | ankmathur96/torchsupport | ScaleNorm | false | 3,171 | [
"MIT"
] | 0 | 77bf4a90b8770a408665e2604428808c3ed2f979 | https://github.com/ankmathur96/torchsupport/tree/77bf4a90b8770a408665e2604428808c3ed2f979 |
HighwayFC | # 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_... | avinashsai/Highway-Networks | HighwayFC | false | 3,150 | [
"MIT"
] | 0 | fe30629e47b919776f981eaa2bea7d21e648a17f | https://github.com/avinashsai/Highway-Networks/tree/fe30629e47b919776f981eaa2bea7d21e648a17f |
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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | blakecheng/CompressAI | ResidualBlock | false | 12,177 | [
"Apache-2.0"
] | 0 | 7a919e509bafacc99055dd88fc20315f3b9fc1fc | https://github.com/blakecheng/CompressAI/tree/7a919e509bafacc99055dd88fc20315f3b9fc1fc |
PreNet | # 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_... | Dacrol/WaveRNN-server | PreNet | false | 11,326 | [
"MIT"
] | 0 | 5189829cec71938ff7ec2e3eb59e73af1382430a | https://github.com/Dacrol/WaveRNN-server/tree/5189829cec71938ff7ec2e3eb59e73af1382430a |
GlobalAvgPool2d | import torch
import torch.nn as nn
class GlobalAvgPool2d(nn.Module):
def __init__(self):
"""Global average pooling over the input's spatial dimensions"""
super(GlobalAvgPool2d, self).__init__()
def forward(self, inputs):
return nn.functional.adaptive_avg_pool2d(inputs, 1).view(inputs... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | Exdenta/torchsat | GlobalAvgPool2d | false | 13,647 | [
"MIT"
] | 316 | 70ea3db758757104fb3ba618ddf7997f0f3a75b4 | https://github.com/Exdenta/torchsat/tree/70ea3db758757104fb3ba618ddf7997f0f3a75b4 |
SoftmaxAttention | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | marvosyntactical/fs2018ex3viz | SoftmaxAttention | false | 7,179 | [
"Apache-2.0"
] | 1 | 9002133a45b52c596efa91d842f691fe1f066a6c | https://github.com/marvosyntactical/fs2018ex3viz/tree/9002133a45b52c596efa91d842f691fe1f066a6c |
GaussianActorNet | # 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... | G-Flor/deeprl | GaussianActorNet | false | 5,184 | [
"Apache-2.0"
] | 1 | aeae2c5d585e5853dc638968b1f090eb60abd351 | https://github.com/G-Flor/deeprl/tree/aeae2c5d585e5853dc638968b1f090eb60abd351 |
ReconstructionBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import math
import torch.util... | adityamehta00/HIDeGAN | ReconstructionBlock | false | 3,031 | [
"BSD-3-Clause"
] | 0 | 91a0674e092ccde2784a82bf927dfefd8673eb4c | https://github.com/adityamehta00/HIDeGAN/tree/91a0674e092ccde2784a82bf927dfefd8673eb4c |
SVIGlobalMeanPool2D | # 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... | RomanShen/radial-bnn | SVIGlobalMeanPool2D | false | 989 | [
"Apache-2.0"
] | 0 | 7c8bc85397c1461a6fd5ea9adf0631f9ade27f6c | https://github.com/RomanShen/radial-bnn/tree/7c8bc85397c1461a6fd5ea9adf0631f9ade27f6c |
ItemToInterestAggregation | # 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.... | MIracleyin/RecBole-notebook | ItemToInterestAggregation | false | 9,572 | [
"MIT"
] | 0 | ef32b3e57a297ff4889dec1f63c7984f8f901a23 | https://github.com/MIracleyin/RecBole-notebook/tree/ef32b3e57a297ff4889dec1f63c7984f8f901a23 |
ResizeModule | import torch
class ResizeModule(torch.nn.Module):
def __init__(self):
super(ResizeModule, self).__init__()
def forward(self, x):
return torch.nn.functional.interpolate(x, size=(3, 4))
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | MichaelZhero/nncase | ResizeModule | false | 11,918 | [
"Apache-2.0"
] | 0 | 0fae6ce90d7adff386e1a286cd2b42422f4b850a | https://github.com/MichaelZhero/nncase/tree/0fae6ce90d7adff386e1a286cd2b42422f4b850a |
RNNMLClassification | # 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.... | hotbaby/kkb-nlp | RNNMLClassification | false | 3,631 | [
"MIT"
] | 0 | 614cd0f37aa969d21b2fbe3d9f8b2b08db1d0eb1 | https://github.com/hotbaby/kkb-nlp/tree/614cd0f37aa969d21b2fbe3d9f8b2b08db1d0eb1 |
DotProductAttention | import math
import torch
import torch.nn as nn
class DotProductAttention(nn.Module):
def __init__(self, k_dim, dropout=0.1):
super(DotProductAttention, self).__init__()
self.scale = 1.0 / math.sqrt(k_dim)
self.softmax = nn.Softmax(dim=-1)
self.dropout = nn.Dropout(dropout)
de... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | LindgeW/DomainAdaption4DependencyParsing | DotProductAttention | false | 5,527 | [
"Apache-2.0"
] | 1 | 5de136a37d8fe730e4235ed95bf923763fe21ea6 | https://github.com/LindgeW/DomainAdaption4DependencyParsing/tree/5de136a37d8fe730e4235ed95bf923763fe21ea6 |
QuantizableHSigmoid | # 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
import torch.quantization
assert_size_stride = torch._C._dynamo.gua... | dhlee347/model_compression | QuantizableHSigmoid | false | 6,567 | [
"MIT"
] | 1 | 274b85ff56d81f0b7cf6907cbc1bd10e16cdb956 | https://github.com/dhlee347/model_compression/tree/274b85ff56d81f0b7cf6907cbc1bd10e16cdb956 |
adder2d | from torch.autograd import Function
import math
import torch
import torch.nn as nn
def adder2d_function(X, W, stride=1, padding=0):
n_filters, _d_filter, h_filter, w_filter = W.size()
n_x, _d_x, h_x, w_x = X.size()
h_out = (h_x - h_filter + 2 * padding) / stride + 1
w_out = (w_x - w_filter + 2 * paddi... | 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.autograd import Function
import math
import torch.nn as nn
ass... | Xyfuture/AdderNet | adder2d | false | 11,992 | [
"BSD-3-Clause"
] | 0 | 62f567164175558622748464fb2f47d37d579b29 | https://github.com/Xyfuture/AdderNet/tree/62f567164175558622748464fb2f47d37d579b29 |
DotSelector | # 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 as th
from torch... | OkYongChoi/smac | DotSelector | false | 18,386 | [
"Apache-2.0"
] | 8 | 5b2b59e42d17a124e97feeecf9154a3a0aa9d260 | https://github.com/OkYongChoi/smac/tree/5b2b59e42d17a124e97feeecf9154a3a0aa9d260 |
RSubInt | import torch
class RSubInt(torch.nn.Module):
def __init__(self):
super(RSubInt, self).__init__()
def forward(self, x):
return 1 - x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | Akababa/torch2trt | RSubInt | false | 18,403 | [
"MIT"
] | 2 | 03063b74a7eb40f5aac88d49be6b8b5e4e4e92d7 | https://github.com/Akababa/torch2trt/tree/03063b74a7eb40f5aac88d49be6b8b5e4e4e92d7 |
ConvSwishOutplace | import torch
from torch import nn
import torch.cuda
import torch.backends.cudnn
import torch.backends.mkl
import torch.backends.cuda
import torch.backends.quantized
class ConvSwishOutplace(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, image_size):
super(ConvSwishOutplace, 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 import nn
import torch.cuda
import torch.backends.cudnn
import torch.... | Observer007/intel-extension-for-pytorch | ConvSwishOutplace | false | 5,664 | [
"Apache-2.0"
] | 1 | f8ab25c305c89d5aaf06190a4fec0727aeb4dcd7 | https://github.com/Observer007/intel-extension-for-pytorch/tree/f8ab25c305c89d5aaf06190a4fec0727aeb4dcd7 |
LAM_Gconv | # 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.... | zhaoweixi/GraFormer | LAM_Gconv | false | 16,818 | [
"BSD-2-Clause"
] | 384 | 0a0a04014cdf157c11ab8e952862efa27c6a1980 | https://github.com/zhaoweixi/GraFormer/tree/0a0a04014cdf157c11ab8e952862efa27c6a1980 |
AddLayerNorm | # 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.cuda
import torch.backends.cudnn
import torch.backends.mkl
assert_... | JudeDavis1/intel-extension-for-pytorch | AddLayerNorm | false | 2,586 | [
"Apache-2.0"
] | 0 | 364e34cb4917a709f5108c07d4005bf82f3d5067 | https://github.com/JudeDavis1/intel-extension-for-pytorch/tree/364e34cb4917a709f5108c07d4005bf82f3d5067 |
ReduceLast | # 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... | carlogrisetti/ludwig | ReduceLast | false | 1,638 | [
"Apache-2.0"
] | 0 | 5c0887f14867e1577e0ddc3806c5cf7a781fb665 | https://github.com/carlogrisetti/ludwig/tree/5c0887f14867e1577e0ddc3806c5cf7a781fb665 |
MultiHead | # 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.... | cristipp/decaNLP | MultiHead | false | 12,233 | [
"BSD-3-Clause"
] | 0 | db64df36bf2b1b2ca6946aacf0ee7463ac80c4cb | https://github.com/cristipp/decaNLP/tree/db64df36bf2b1b2ca6946aacf0ee7463ac80c4cb |
BCEDiceLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch ... | Information-Fusion-Lab-Umass/pytorch-nested-unet | BCEDiceLoss | false | 11,512 | [
"MIT"
] | 0 | 29b8704795f9d0ab17952b19bf8b4624e7aa16c0 | https://github.com/Information-Fusion-Lab-Umass/pytorch-nested-unet/tree/29b8704795f9d0ab17952b19bf8b4624e7aa16c0 |
DuelingQNetwork | # 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_... | HarryTanNguyen/flatland-railway-enviroment | DuelingQNetwork | false | 5,282 | [
"MIT"
] | 1 | 5306871a6dbedd8d2745be4ff0caf0515e4d88ac | https://github.com/HarryTanNguyen/flatland-railway-enviroment/tree/5306871a6dbedd8d2745be4ff0caf0515e4d88ac |
Net | import torch
from torch.nn import functional as F
class Net(torch.nn.Module):
def __init__(self, n_feature, n_hidden, n_output):
super(Net, self).__init__()
self.hidden = torch.nn.Linear(n_feature, n_hidden)
self.hidden_two = torch.nn.Linear(n_hidden, n_hidden)
self.hidden_3 = tor... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | Hyunmok-Park/modular-metalearning-master | Net | false | 11,501 | [
"MIT"
] | 0 | a7be61d7c48a62ec8c333b1031521977baed792b | https://github.com/Hyunmok-Park/modular-metalearning-master/tree/a7be61d7c48a62ec8c333b1031521977baed792b |
ReLUDropout | # 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.cuda
import torch.utils.checkpoint
assert_size_strid... | quanpn90/NMTGMinor | ReLUDropout | false | 16,295 | [
"MIT"
] | 75 | 0e5f989c8bc01c6c8dc3a8c1ce7c05bfd884b796 | https://github.com/quanpn90/NMTGMinor/tree/0e5f989c8bc01c6c8dc3a8c1ce7c05bfd884b796 |
SigmoidRange | import torch
from torch import nn
def sigmoid_range(x, low, high):
"""Sigmoid function with range `(low, high)`"""
return torch.sigmoid(x) * (high - low) + low
class SigmoidRange(nn.Module):
"""Sigmoid module with range `(low,x_max)`"""
def __init__(self, low, high):
super().__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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | wegroupwolves/fastai | SigmoidRange | false | 4,522 | [
"Apache-2.0"
] | 0 | df40df403e05e132411f0f7abc7ec33c86e58bb9 | https://github.com/wegroupwolves/fastai/tree/df40df403e05e132411f0f7abc7ec33c86e58bb9 |
RefModel2d2 | import torch
import torch.nn.functional as F
class RefModel2d2(torch.nn.Module):
"""The 2D reference model."""
def __init__(self):
super().__init__()
self.l1 = torch.nn.Conv2d(2, 2, 3, padding=1, stride=2,
padding_mode='circular', bias=False)
self.l2 = torch.nn.Identity()
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | shuohan/pytorch-layers | RefModel2d2 | false | 4,343 | [
"MIT"
] | 0 | 020846fd02d501cf477552179c19ba4b5e9a0695 | https://github.com/shuohan/pytorch-layers/tree/020846fd02d501cf477552179c19ba4b5e9a0695 |
Network | import torch
import torch.nn as nn
class Network(nn.Module):
def __init__(self, number_of_inputs, number_of_outputs):
super(Network, self).__init__()
self.l1 = nn.Linear(number_of_inputs, number_of_inputs)
self.l2 = nn.Linear(number_of_inputs, number_of_inputs)
self.l3 = nn.Linear... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | ajthinking/migration-analysis | Network | false | 3,047 | [
"MIT"
] | 0 | c48312c5bf513a37e23eaaf8aa245921992f8e7f | https://github.com/ajthinking/migration-analysis/tree/c48312c5bf513a37e23eaaf8aa245921992f8e7f |
PolynomialEnvelope | # 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... | Open-Catalyst-Project/baselines | PolynomialEnvelope | false | 17,797 | [
"MIT"
] | 10 | 89948582edfb8debb736406d54db9813a5f2c88d | https://github.com/Open-Catalyst-Project/baselines/tree/89948582edfb8debb736406d54db9813a5f2c88d |
GroupNorm | from torch.nn import Module
import torch
from torch import nn
import torch.utils.data
import torch.nn.functional
import torch.autograd
class GroupNorm(Module):
"""
## Group Normalization Layer
"""
def __init__(self, groups: 'int', channels: 'int', *, eps: float=1e-05,
affine: bool=True):
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch.nn import Module
from torch import nn
import torch.utils.data
import... | techthiyanes/annotated_deep_learning_paper_implementations | GroupNorm | false | 16,558 | [
"MIT"
] | 3,714 | 8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 | https://github.com/techthiyanes/annotated_deep_learning_paper_implementations/tree/8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 |
BasicBlock_IN | import torch
import torch.nn as nn
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_features = num_features
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.... | LenKerr/Semantic-Colorization-GAN | BasicBlock_IN | false | 5,544 | [
"MIT"
] | 1 | 2ce52406ca6fc92e69692b451b1c9ae66ba3b76f | https://github.com/LenKerr/Semantic-Colorization-GAN/tree/2ce52406ca6fc92e69692b451b1c9ae66ba3b76f |
EncoderImagePrecomp | import torch
import numpy as np
import torch.nn as nn
import torch.nn.init
from collections import OrderedDict
def l2norm(X):
"""L2-normalize columns of X
"""
norm = torch.pow(X, 2).sum(dim=1, keepdim=True).sqrt()
X = torch.div(X, norm)
return X
class EncoderImagePrecomp(nn.Module):
def __i... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import numpy as np
... | Harshdeep1996/jina-hub | EncoderImagePrecomp | false | 2,327 | [
"Apache-2.0"
] | 0 | 880ff719715b89969860c70219d26a931a031d10 | https://github.com/Harshdeep1996/jina-hub/tree/880ff719715b89969860c70219d26a931a031d10 |
mnistmodel_A | # 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... | layel2/layyer-lib | mnistmodel_A | false | 3,880 | [
"MIT"
] | 0 | db48b5c38098ee93d2d34693d98e5ef4d319d919 | https://github.com/layel2/layyer-lib/tree/db48b5c38098ee93d2d34693d98e5ef4d319d919 |
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... | azuredsky/retinanet-examples | SmoothL1Loss | false | 9,797 | [
"BSD-3-Clause"
] | 0 | 1b35d8e7d3360050f25fd80e09ecac3eb2654301 | https://github.com/azuredsky/retinanet-examples/tree/1b35d8e7d3360050f25fd80e09ecac3eb2654301 |
BalancedL1Loss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import functools
impor... | BUPT-PRIV/BalancedGroupSoftmax | BalancedL1Loss | false | 13,373 | [
"Apache-2.0"
] | 333 | 90e04fd8ccecd2bc61bbe6053a741ae708da2794 | https://github.com/BUPT-PRIV/BalancedGroupSoftmax/tree/90e04fd8ccecd2bc61bbe6053a741ae708da2794 |
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):
def __init__(self, state_size, action_size, seed, fc1_units=512,
fc2_units=... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | asiliskender/deep-reinforcement-learning | Actor | false | 12,121 | [
"MIT"
] | 0 | dbf96d67477aa9242128b78b081474193e1e4538 | https://github.com/asiliskender/deep-reinforcement-learning/tree/dbf96d67477aa9242128b78b081474193e1e4538 |
ScaledDotProduct | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | simonepreite/QABERT | ScaledDotProduct | false | 4,336 | [
"MIT"
] | 0 | ed3e49f6619f3ff660068291231909693cb8f5d5 | https://github.com/simonepreite/QABERT/tree/ed3e49f6619f3ff660068291231909693cb8f5d5 |
KLDLossNoReduction | # 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.... | atmacvit/meronymnet | KLDLossNoReduction | false | 6,273 | [
"MIT"
] | 1 | 47e1a7caadc0f770439bb26a93b885f790f62804 | https://github.com/atmacvit/meronymnet/tree/47e1a7caadc0f770439bb26a93b885f790f62804 |
GatedDense | # 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.utils.data
assert_size_stride = torch._C._dyna... | RobertYCXu/vae_vampprior | GatedDense | false | 9,580 | [
"MIT"
] | 0 | edcec4f5f7af673172c5b5b9aa2a22f993564fab | https://github.com/RobertYCXu/vae_vampprior/tree/edcec4f5f7af673172c5b5b9aa2a22f993564fab |
ScaleNorm | import math
import torch
import torch.nn as nn
import torch.nn.parallel
class ScaleNorm(nn.Module):
"""Apply Scale Normalization to input.
The ScaleNorm layer first computes the square root of the scale, then computes the matrix/vector norm of the input tensor.
The norm value is calculated as `sqrt(scale) / ... | 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 math
import torch.nn ... | QMrpy/deepchem | ScaleNorm | false | 2,740 | [
"MIT"
] | 0 | f38a21c71e7bc4fd1fa59601be2b79ce7d744bd6 | https://github.com/QMrpy/deepchem/tree/f38a21c71e7bc4fd1fa59601be2b79ce7d744bd6 |
ScaleReLU | import torch
import torch.nn as nn
class Scale(nn.Module):
def __init__(self, nchannels, bias=True, init_scale=1.0):
super().__init__()
self.nchannels = nchannels
self.weight = nn.Parameter(torch.Tensor(1, nchannels, 1, 1))
if bias:
self.bias = nn.Parameter(torch.Tenso... | 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 | ScaleReLU | false | 5,355 | [
"MIT"
] | 1 | cdfe6375a2323e9ee162e50a46478d8a66529e6c | https://github.com/IrisDinge/YoloV3_DOTA/tree/cdfe6375a2323e9ee162e50a46478d8a66529e6c |
Conv2d | import torch
from torch import nn
class Conv2d(nn.Module):
"""docstring for Conv2d
Attributes
----------
bn : TYPE
Description
conv : TYPE
Description
relu : TYPE
Description
"""
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | anhlt/yolo-pytorch | Conv2d | false | 18,350 | [
"MIT"
] | 4 | 6e01146a93cbad3207c070536dffb26aef1d9c0f | https://github.com/anhlt/yolo-pytorch/tree/6e01146a93cbad3207c070536dffb26aef1d9c0f |
ZeroPad1d | # 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.onnx.operators
import torch.optim
import torch.optim.lr_scheduler
assert_size_stri... | ChenDdon/AGBTcode | ZeroPad1d | false | 7,880 | [
"MIT"
] | 21 | 6c259d18b48dc8d6da1357c42a1ee088666fb7b4 | https://github.com/ChenDdon/AGBTcode/tree/6c259d18b48dc8d6da1357c42a1ee088666fb7b4 |
InputInjection | # 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._C
import torch.serialization
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strid... | CVIU-CSU/M2MRF-Lesion-Segmentation | InputInjection | false | 17,058 | [
"Apache-2.0"
] | 10 | 13af87927f4cdeca70e35d570edd1aec43b387b6 | https://github.com/CVIU-CSU/M2MRF-Lesion-Segmentation/tree/13af87927f4cdeca70e35d570edd1aec43b387b6 |
BertPooler2 | from _paritybench_helpers import _mock_config
import torch
from torch import nn
import torch.nn.parallel
import torch.optim
from torch.utils.data import *
import torch.nn.functional
class BertPooler2(nn.Module):
def __init__(self, config):
super(BertPooler2, self).__init__()
self.dense = 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.triton_helpers import libdevice
from torch import n... | samuelyu2002/PACS | BertPooler2 | false | 4,257 | [
"MIT"
] | 0 | 5010b2f0d20933b0647e3d6230d673e1830249ec | https://github.com/samuelyu2002/PACS/tree/5010b2f0d20933b0647e3d6230d673e1830249ec |
CPC | import torch
import torch.nn as nn
class CPC(nn.Module):
"""
Contrastive Predictive Coding: score computation. See https://arxiv.org/pdf/1807.03748.pdf.
Args:
x_size (int): embedding size of input modality representation x
y_size (int): embedding size of input modality rep... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | dumpmemory/Multimodal-Infomax | CPC | false | 15,255 | [
"MIT"
] | 57 | 9a6dc8f2bfa861cd447ba65c6a037cd7dd24f473 | https://github.com/dumpmemory/Multimodal-Infomax/tree/9a6dc8f2bfa861cd447ba65c6a037cd7dd24f473 |
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