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 |
|---|---|---|---|---|---|---|---|---|---|---|
Polynomial3 | import torch
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 Polynomial3(torch.nn.Module):
def __init__(self):
"""
In the constructor we instantiate four parameters and assi... | 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.quantization
import torch.onnx
import torch.nn.parallel
import torch.utils.data
import torch.fx
import torch.nn
import torch.op... | Nayef211/tutorials | Polynomial3 | false | 9,526 | [
"BSD-3-Clause"
] | 0 | faf2c476fc3be855051fbea3cce77eaf7b2a2175 | https://github.com/Nayef211/tutorials/tree/faf2c476fc3be855051fbea3cce77eaf7b2a2175 |
LayerNorm | import torch
import torch.nn as nn
from torch.nn import Parameter
class LayerNorm(nn.Module):
def __init__(self, num_features, eps=1e-08, affine=True):
super(LayerNorm, self).__init__()
self.num_features = num_features
self.affine = affine
self.eps = eps
if self.affine:
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
from torch.nn import Parameter
assert_size_stride = torch... | kangzhiq/DeepFillv2_Pytorch | LayerNorm | false | 10,429 | [
"MIT"
] | 0 | 9c7ed61b25bb995713f89108b712490737abe1b1 | https://github.com/kangzhiq/DeepFillv2_Pytorch/tree/9c7ed61b25bb995713f89108b712490737abe1b1 |
Sinkhorn | import torch
import torch.utils.data
import torch.nn as nn
from sklearn import *
class Sinkhorn(nn.Module):
"""
BiStochastic Layer turns the input matrix into a bi-stochastic matrix.
Parameter: maximum iterations max_iter
a small number for numerical stability epsilon
Input: input matri... | 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
from sklearn import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_st... | CityU-AIM-Group/SIGMA | Sinkhorn | false | 17,461 | [
"MIT"
] | 5 | 19f89777db8d42f750a9b87756d3326c7efd18f5 | https://github.com/CityU-AIM-Group/SIGMA/tree/19f89777db8d42f750a9b87756d3326c7efd18f5 |
FCN8s | import torch
import numpy as np
import torch.nn as nn
class FCN8s(nn.Module):
def __init__(self, n_class=3):
super(FCN8s, self).__init__()
self.conv1_1 = nn.Conv2d(3, 64, 3, padding=100)
self.relu1_1 = nn.ReLU(inplace=True)
self.conv1_2 = nn.Conv2d(64, 64, 3, padding=1)
se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import numpy as np
import tor... | twni2016/OrganSegRSTN_PyTorch | FCN8s | false | 16,856 | [
"MIT"
] | 100 | bf571320e718c8f138e04d48645e3b4dfe75801d | https://github.com/twni2016/OrganSegRSTN_PyTorch/tree/bf571320e718c8f138e04d48645e3b4dfe75801d |
FCLateActionSAQFunction | import torch
import numpy as np
from torch import nn
from abc import ABCMeta
from abc import abstractmethod
import torch.nn.functional as F
def init_lecun_normal(tensor, scale=1.0):
"""Initializes the tensor with LeCunNormal."""
fan_in = torch.nn.init._calculate_correct_fan(tensor, 'fan_in')
std = scale *... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import numpy as np
from torch... | KtechB/pfrl | FCLateActionSAQFunction | false | 2,465 | [
"MIT"
] | 0 | 9be4726d327b7ce32d9008c40119c98c93febad5 | https://github.com/KtechB/pfrl/tree/9be4726d327b7ce32d9008c40119c98c93febad5 |
_ASPPModule | # 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.... | yoxu515/CFBI | _ASPPModule | false | 16,847 | [
"BSD-3-Clause"
] | 312 | 0bab1e3c9fc3e3ba0629f716d60221e8f8d9d586 | https://github.com/yoxu515/CFBI/tree/0bab1e3c9fc3e3ba0629f716d60221e8f8d9d586 |
L2Norm | import torch
import torch.nn as nn
import torch._C
import torch.serialization
from torch import optim as optim
class L2Norm(nn.Module):
def __init__(self, n_dims, scale=20.0, eps=1e-10):
"""L2 normalization layer.
Args:
n_dims (int): Number of dimensions to be normalized
... | 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._C
import torch.serialization
from torch imp... | Atten4Vis/DemystifyLocalViT | L2Norm | false | 13,356 | [
"MIT"
] | 64 | 2e2327caec6d56ae2c8aa861b32bb62f3cdb786e | https://github.com/Atten4Vis/DemystifyLocalViT/tree/2e2327caec6d56ae2c8aa861b32bb62f3cdb786e |
TransformerNet | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | gordicaleksa/pytorch-nst-feedforward | TransformerNet | false | 15,511 | [
"MIT"
] | 50 | 00c96e8e3f1b0b7fb4c14254fd0c6f1281a29598 | https://github.com/gordicaleksa/pytorch-nst-feedforward/tree/00c96e8e3f1b0b7fb4c14254fd0c6f1281a29598 |
ConvBlock | import torch
from torch import nn
class ConvBlock(nn.Module):
def __init__(self, nb_in, nb_out):
super(ConvBlock, self).__init__()
self.convolution = nn.Conv2d(in_channels=nb_in, out_channels=nb_out,
kernel_size=5, stride=1, padding=2)
self.ReLU = nn.ReLU()
self.MaxPoo... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | Clement-W/PT-Activation-Map-Visualiser | ConvBlock | false | 300 | [
"MIT"
] | 0 | 6c71d5225585e5f18c3e73a4775d7816699faeea | https://github.com/Clement-W/PT-Activation-Map-Visualiser/tree/6c71d5225585e5f18c3e73a4775d7816699faeea |
SimpleNet | import torch
import torch.nn as nn
import torch.nn.functional as F
class SimpleNet(nn.Module):
def __init__(self, ni):
super().__init__()
self.linear1 = nn.Linear(ni, 128)
self.linear2 = nn.Linear(128, 128)
self.linear3 = nn.Linear(128, 64)
self.linear4 = nn.Linear(64, 64)... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | haakonrob/AI-Feynman | SimpleNet | false | 10,146 | [
"MIT"
] | 0 | 445b68e9a260dcea67a94eed6e0aeb267f25d2ef | https://github.com/haakonrob/AI-Feynman/tree/445b68e9a260dcea67a94eed6e0aeb267f25d2ef |
SigmoidFocalClassificationLoss | # 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... | brudermueller/PointRCNN | SigmoidFocalClassificationLoss | false | 3,256 | [
"MIT"
] | 0 | 430bb45d6d512ad4e3eb509d65377511361c300f | https://github.com/brudermueller/PointRCNN/tree/430bb45d6d512ad4e3eb509d65377511361c300f |
PositionwiseFeedForward | import torch
from abc import ABC
import torch.nn as nn
class PositionwiseFeedForward(nn.Module, ABC):
def __init__(self, d_in, d_hidden, dropout=0.1):
super().__init__()
self.w_1 = nn.Linear(d_in, d_hidden)
self.w_2 = nn.Linear(d_hidden, d_in)
self.layer_norm = nn.LayerNorm(d_in, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | superMC5657/transformer | PositionwiseFeedForward | false | 10,877 | [
"MIT"
] | 0 | b9d9ca3a5f307f6587330a8235e8d5a2a3650510 | https://github.com/superMC5657/transformer/tree/b9d9ca3a5f307f6587330a8235e8d5a2a3650510 |
Conv2d_fw | import torch
import torch.nn as nn
import torch.nn.functional as F
class Conv2d_fw(nn.Conv2d):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, bias=True):
super(Conv2d_fw, self).__init__(in_channels, out_channels,
kernel_size, stride=stride, padding=pad... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | RongKaiWeskerMA/INSTA | Conv2d_fw | false | 8,741 | [
"MIT"
] | 22 | 298bec0aeac3c1fde7bbcd4dece72ded1056e478 | https://github.com/RongKaiWeskerMA/INSTA/tree/298bec0aeac3c1fde7bbcd4dece72ded1056e478 |
Hardswish | # 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.utils.data
assert_size_stride = torch._C._dynamo.guard... | Arui66/FPSAutomaticAiming | Hardswish | false | 13,294 | [
"Apache-2.0"
] | 129 | 87674385d42b065b984b38a2ff59e7f2d4f07dc9 | https://github.com/Arui66/FPSAutomaticAiming/tree/87674385d42b065b984b38a2ff59e7f2d4f07dc9 |
DAImgHead | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | enpko47/DA-CenterNet | DAImgHead | false | 3,708 | [
"MIT"
] | 0 | ef0a99b8ba741fa1dbd66fa58ccae9bf8759ae86 | https://github.com/enpko47/DA-CenterNet/tree/ef0a99b8ba741fa1dbd66fa58ccae9bf8759ae86 |
ArcMarginProduct_subcenter | # 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.... | Husky95/Google-Landmark-Recognition-2020-3rd-Place-Solution | ArcMarginProduct_subcenter | false | 9,157 | [
"Apache-2.0"
] | 0 | 48806b9e09beabf74e8f96575855dcfa13a4f996 | https://github.com/Husky95/Google-Landmark-Recognition-2020-3rd-Place-Solution/tree/48806b9e09beabf74e8f96575855dcfa13a4f996 |
RNN | # 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.init
assert_size_stride = torch._C._dynamo... | OBA9k/Test_dev | RNN | false | 17,753 | [
"Apache-2.0"
] | 4 | bfdd337fb56ca160e1d09b6c310d1e6037d55fcd | https://github.com/OBA9k/Test_dev/tree/bfdd337fb56ca160e1d09b6c310d1e6037d55fcd |
PositionwiseFeedforwardLayer | # 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_... | bob80333/investigating_extrapolation | PositionwiseFeedforwardLayer | false | 6,343 | [
"MIT"
] | 1 | fc4f72baa46b8490968f7ad546897937feb8b25d | https://github.com/bob80333/investigating_extrapolation/tree/fc4f72baa46b8490968f7ad546897937feb8b25d |
GlobalAveragePooling | import torch
from torch import nn
class GlobalAveragePooling(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
return x.mean([2, 3])
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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | xh-liu-tech/CIPS-3D | GlobalAveragePooling | false | 11,106 | [
"MIT"
] | 0 | 8910dfcf19bb86aab2287d652ae4e3666806b511 | https://github.com/xh-liu-tech/CIPS-3D/tree/8910dfcf19bb86aab2287d652ae4e3666806b511 |
SimpleGate | import torch
import torch.cuda
import torch.distributed
class SimpleGate(torch.nn.Module):
def __init__(self, dim):
super(SimpleGate, self).__init__()
self.gate = torch.nn.Linear(2 * dim, dim, bias=True)
self.sig = torch.nn.Sigmoid()
def forward(self, in1, in2):
z = self.sig(... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.cuda
import torch.distributed
assert_size_stride = torch._C._dynamo... | chardmeier/OpenNMT-py | SimpleGate | false | 3,277 | [
"MIT"
] | 0 | 8ef64d10c507418102af42551c0f335270cb5b51 | https://github.com/chardmeier/OpenNMT-py/tree/8ef64d10c507418102af42551c0f335270cb5b51 |
ArcFaceLoss | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class DenseCrossEntropy(nn.Module):
def forward(self, x, target):
x = x.float()
target = target.float()
logprobs = torch.nn.functional.log_softmax(x, dim=-1)
loss = -logprobs * target
loss = los... | 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 math... | i-pan/kaggle-melanoma | ArcFaceLoss | false | 15,586 | [
"MIT"
] | 68 | caaec0d7e9cafc7b405eb86e7fdf00107d89e1d9 | https://github.com/i-pan/kaggle-melanoma/tree/caaec0d7e9cafc7b405eb86e7fdf00107d89e1d9 |
AngleSimpleLinear | import torch
from torch.nn import functional as F
from torch import nn
from torchvision import models as models
from torch.nn import Parameter
from torch.nn.parameter import Parameter
import torch.onnx
import torch.nn
class AngleSimpleLinear(nn.Module):
"""Computes cos of angles between input vectors and weights ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | krodyush/training_extensions | AngleSimpleLinear | false | 11,030 | [
"Apache-2.0"
] | 0 | 542f4004dfbc6fc62a622065367ba4f85a703dd3 | https://github.com/krodyush/training_extensions/tree/542f4004dfbc6fc62a622065367ba4f85a703dd3 |
CharbonnierLoss | import torch
import torch.nn as nn
class CharbonnierLoss(nn.Module):
"""Charbonnier Loss (L1)"""
def __init__(self, eps=1e-06):
super(CharbonnierLoss, self).__init__()
self.eps = eps
def forward(self, x, y):
diff = x - y
loss = torch.mean(torch.sqrt(diff * diff + self.eps... | 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... | CM-BF/FeatureFlow | CharbonnierLoss | false | 13,436 | [
"MIT"
] | 161 | 06642697922f17211e5faa353e24b1a0946885b1 | https://github.com/CM-BF/FeatureFlow/tree/06642697922f17211e5faa353e24b1a0946885b1 |
BertLayer | # 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.... | priyamtejaswin/minbert-assignment | BertLayer | false | 10,693 | [
"Apache-2.0"
] | 0 | fd41a54441916a6d421640bbee910f64786b303d | https://github.com/priyamtejaswin/minbert-assignment/tree/fd41a54441916a6d421640bbee910f64786b303d |
SP | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch._utils
from itertools import product as product
import torch.utils.data.distributed
class SP(nn.Module):
"""
Similarity-Preserving Knowledge Distillation
https://arxiv.org/pdf/1907.09682.pdf
"""
def __init__(self):
sup... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Capetian/FaceX-Zoo | SP | false | 4,967 | [
"Apache-2.0"
] | 1 | 029786c40d8aba15d891d33973de25fcd7e5399a | https://github.com/Capetian/FaceX-Zoo/tree/029786c40d8aba15d891d33973de25fcd7e5399a |
BertAttention | # 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.... | GingerNg/SDNet | BertAttention | false | 16,213 | [
"MIT"
] | 112 | 48ad8cc57c9a02aaad10e34d0c91a174ac68f056 | https://github.com/GingerNg/SDNet/tree/48ad8cc57c9a02aaad10e34d0c91a174ac68f056 |
L_2nd | # 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... | ZagHe568/graph_embedding | L_2nd | false | 18,169 | [
"MIT"
] | 4 | 2a6f8214ce4b30b51eb9f1904b64fe782876f010 | https://github.com/ZagHe568/graph_embedding/tree/2a6f8214ce4b30b51eb9f1904b64fe782876f010 |
SigmoidCrossEntropyLoss | import torch
from torch import nn
import torch.nn.functional as F
class SigmoidCrossEntropyLoss(nn.Module):
def __init__(self):
"""
:param num_negs: number of negative instances in bpr loss.
"""
super(SigmoidCrossEntropyLoss, self).__init__()
def forward(self, y_pred, y_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 import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch ... | byzhang/OpenMatch | SigmoidCrossEntropyLoss | false | 10,009 | [
"Apache-2.0"
] | 0 | 28b2d49a5eec2e1dc3934767c747ff0ca6c93d96 | https://github.com/byzhang/OpenMatch/tree/28b2d49a5eec2e1dc3934767c747ff0ca6c93d96 |
FocalLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class FocalLoss(nn.Module):
def __init__(self, gamma=2):
super().__init__()
self.gamma = gamma
def forward(self, logit, target):
target = target.float()
max_val = (-logit).clamp(min=0)
loss = logit - 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 libdevice, math as tl_math
import torc... | EMUNES/Auto-Subtitle-File-Generation | FocalLoss | false | 8,042 | [
"Apache-2.0"
] | 33 | 535a6351f450b1970da50bbbf4cc6d2f442ec335 | https://github.com/EMUNES/Auto-Subtitle-File-Generation/tree/535a6351f450b1970da50bbbf4cc6d2f442ec335 |
Linear_2L_KFRA | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | Neronjust2017/Bayesian-neural-networks | Linear_2L_KFRA | false | 17,762 | [
"MIT"
] | 4 | 9d7f781f5c2dfa8fadf26300b4b5b64366c939cd | https://github.com/Neronjust2017/Bayesian-neural-networks/tree/9d7f781f5c2dfa8fadf26300b4b5b64366c939cd |
Soft_Distillation_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._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | ManojKesani/Transformer-Implementations | Soft_Distillation_Loss | false | 794 | [
"MIT"
] | 0 | faca89d44523da80073790d53e53b4e80bde736f | https://github.com/ManojKesani/Transformer-Implementations/tree/faca89d44523da80073790d53e53b4e80bde736f |
conv2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from torch.autograd import Variable
assert_size_stride = t... | vandit15/Self-Supervised-Gans-Pytorch | conv2d | false | 16,654 | [
"MIT"
] | 66 | 01408fcce3e6cf4795d90c0f9d27e6906d5b59f3 | https://github.com/vandit15/Self-Supervised-Gans-Pytorch/tree/01408fcce3e6cf4795d90c0f9d27e6906d5b59f3 |
ListMLELoss | import torch
import torch.nn as nn
class ListMLELoss(nn.Module):
def __init__(self):
super(ListMLELoss, self).__init__()
return
def forward(self, y_pred, y_true, eps=1e-05, padded_value_indicator=-1):
"""
ListMLE loss introduced in "Listwise Approach to Learning to Rank - The... | import torch
from torch import device
import triton
import triton.language as tl
from 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_ma... | Pepijnnn/MasterThesis | ListMLELoss | false | 962 | [
"MIT"
] | 0 | 7ec831f5e55f5f181e0196fa78284e2846ce2e26 | https://github.com/Pepijnnn/MasterThesis/tree/7ec831f5e55f5f181e0196fa78284e2846ce2e26 |
Model4 | # 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... | TonyMTH/Resume-Ranking | Model4 | false | 9,595 | [
"MIT"
] | 0 | 6f560f7219848ddc7ee4bdbfabbd980905af4642 | https://github.com/TonyMTH/Resume-Ranking/tree/6f560f7219848ddc7ee4bdbfabbd980905af4642 |
CosineSimilarityLoss | from torch.nn import Module
import torch
import torch.nn.functional as F
class BaseLoss(Module):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
class CosineSimilarityLoss(BaseLoss):
def __init__(self, dim=1, eps=1e-08, reduction='mean', *args, **kwargs):
super().__i... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from torch.nn import Module
... | NullConvergence/torch_temp | CosineSimilarityLoss | false | 17,745 | [
"MIT"
] | 3 | 29a0d7190f0be6124f51bd85b8320cd8b3cef29a | https://github.com/NullConvergence/torch_temp/tree/29a0d7190f0be6124f51bd85b8320cd8b3cef29a |
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.... | ovechkinVT/SkipRNN | Attention | false | 7,436 | [
"MIT"
] | 1 | 7c1f37349d464b1b6bf8835520abad22b199f1ad | https://github.com/ovechkinVT/SkipRNN/tree/7c1f37349d464b1b6bf8835520abad22b199f1ad |
StochasticClassifier | import torch
import torch.nn as nn
from torch.nn import functional as F
class StochasticClassifier(nn.Module):
def __init__(self, num_features, num_classes, temp=0.05):
super().__init__()
self.mu = nn.Parameter(0.01 * torch.randn(num_classes, num_features))
self.sigma = nn.Parameter(torch... | 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... | KaiyangZhou/ssdg-benchmark | StochasticClassifier | false | 8,385 | [
"MIT"
] | 43 | aaa48be4f93b77347fbadff649be6b3e0f7a8779 | https://github.com/KaiyangZhou/ssdg-benchmark/tree/aaa48be4f93b77347fbadff649be6b3e0f7a8779 |
DiscriminatorLoss | from torch.nn import Module
import torch
import torch.nn.functional as F
import torch.utils.data
import torch.nn.functional
import torch.autograd
class DiscriminatorLoss(Module):
"""
## Discriminator Loss
We want to find $w$ to maximize
$$\\mathbb{E}_{x \\sim \\mathbb{P}_r} [f_w(x)]- \\mathbb{E}_{z \... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch.nn import Module
import torch.utils.data
import torch.nn.functional
import tor... | techthiyanes/annotated_deep_learning_paper_implementations | DiscriminatorLoss | false | 16,545 | [
"MIT"
] | 3,714 | 8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 | https://github.com/techthiyanes/annotated_deep_learning_paper_implementations/tree/8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 |
MLP | from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
def gelu(x):
return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 *
torch.pow(x, 3))))
class Conv1D(nn.Module):
def __init__(self, nf, nx):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
import ... | CaptainJa/demo-torch-gpt2 | MLP | false | 3,730 | [
"MIT"
] | 0 | 83d6074e8b321101e08c0aa5749c8eb988a5faa8 | https://github.com/CaptainJa/demo-torch-gpt2/tree/83d6074e8b321101e08c0aa5749c8eb988a5faa8 |
BiasLayer | import torch
import torch.nn as nn
class BiasLayer(nn.Module):
def __init__(self, channels, skip_dims=2):
super().__init__()
self.bias = nn.Parameter(torch.zeros(channels, *([1] * skip_dims)))
def forward(self, net):
return net + self.bias
def extra_repr(self):
return f'... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | JGU-VC/activation-pattern-analysis | BiasLayer | false | 576 | [
"MIT"
] | 0 | 14da42ad541ee4faf35d360a6e871fd44decd33d | https://github.com/JGU-VC/activation-pattern-analysis/tree/14da42ad541ee4faf35d360a6e871fd44decd33d |
LoRALayer | import torch
from torch import nn
import torch.nn.parallel
import torch.utils.data
class LoRALayer(nn.Module):
def __init__(self, n_in, n_out=None, adapter_dim=16, adapter_alpha=32):
super(LoRALayer, self).__init__()
if not n_out:
n_out = n_in
self.adapter_dim = adapter_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 import nn
import torch.nn.parallel
import torch.utils.data
assert_siz... | mojishoki/LoRA | LoRALayer | false | 7,268 | [
"MIT"
] | 1 | 556225e776b4e2c5f77d332db15f0c712c13fe0e | https://github.com/mojishoki/LoRA/tree/556225e776b4e2c5f77d332db15f0c712c13fe0e |
ChannelNorm2D | import torch
import torch.nn as nn
class ChannelNorm2D(nn.Module):
"""
Similar to default Torch instanceNorm2D but calculates
moments over channel dimension instead of spatial dims.
Expects input_dim in format (B,C,H,W)
"""
def __init__(self, input_channels, momentum=0.1, eps=0.001, affine=T... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | ali-zafari/high-fidelity-generative-compression | ChannelNorm2D | false | 9,774 | [
"Apache-2.0"
] | 0 | 37ab8d6727df48f8ebf4577db0986ccd0ffe404b | https://github.com/ali-zafari/high-fidelity-generative-compression/tree/37ab8d6727df48f8ebf4577db0986ccd0ffe404b |
PositionalEmbedding | # 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... | yafuly/PromptNMT | PositionalEmbedding | false | 10,972 | [
"BSD-3-Clause"
] | 0 | 07b1daa7c7609d6f9035b4ac71b962c3c07b2f96 | https://github.com/yafuly/PromptNMT/tree/07b1daa7c7609d6f9035b4ac71b962c3c07b2f96 |
Conv3BN | import torch
import torch.nn as nn
import torch.backends.cudnn
import torch.utils.data
def conv3x3(in_, out):
return nn.Conv2d(in_, out, 3, padding=1)
class Conv3BN(nn.Module):
def __init__(self, in_: 'int', out: 'int', bn=False):
super().__init__()
self.conv = conv3x3(in_, out)
sel... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | jayden-chua/image-mask | Conv3BN | false | 3,698 | [
"MIT"
] | 0 | ce2c6a32bf13df582e7b57e506d58518258be292 | https://github.com/jayden-chua/image-mask/tree/ce2c6a32bf13df582e7b57e506d58518258be292 |
EncoderImagePrecomp | # 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
... | jwehrmann/seamretrieval | EncoderImagePrecomp | false | 3,788 | [
"Apache-2.0"
] | 0 | ff94dccc28d56ffbbb7813832c0adbab7b7c6107 | https://github.com/jwehrmann/seamretrieval/tree/ff94dccc28d56ffbbb7813832c0adbab7b7c6107 |
BCELovaszLoss | import torch
import numpy as np
from torch import nn
import torch.nn.functional as F
from torch.autograd import Variable
def flatten_binary_scores(scores, labels, ignore=None):
"""
Flattens predictions in the batch (binary case)
Remove labels equal to 'ignore'
"""
scores = scores.view(-1)
labe... | 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 nump... | amitkumarj441/TGS_Kaggle | BCELovaszLoss | false | 6,179 | [
"MIT"
] | 1 | a4f613046cc36f3f6dbec28adb35f97a63c2a994 | https://github.com/amitkumarj441/TGS_Kaggle/tree/a4f613046cc36f3f6dbec28adb35f97a63c2a994 |
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._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | tianjiansmile/Chinese-Text-Classification-Pytorch | Encoder | false | 10,914 | [
"MIT"
] | 0 | 05cc211b161f61e6bb32ab185dadcffec2f5b5de | https://github.com/tianjiansmile/Chinese-Text-Classification-Pytorch/tree/05cc211b161f61e6bb32ab185dadcffec2f5b5de |
ModConst | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_c... | PogChamper/torch2trt | ModConst | false | 14,198 | [
"MIT"
] | 3,363 | 43b12627ec0de4d212efb6d02b07570205085ccc | https://github.com/PogChamper/torch2trt/tree/43b12627ec0de4d212efb6d02b07570205085ccc |
SimpleStackModel | # 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.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | briancoutinho/glow | SimpleStackModel | false | 12,590 | [
"Apache-2.0"
] | 0 | 4c919d60b3c33296c4109aec8020a1733c98f5b5 | https://github.com/briancoutinho/glow/tree/4c919d60b3c33296c4109aec8020a1733c98f5b5 |
TSAFusion | # 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 as nn
fr... | Lotayou/BasicSR | TSAFusion | false | 2,624 | [
"Apache-2.0",
"MIT"
] | 0 | 6cf9a706dd680d54f7dc26e87318ff79f76c0dbf | https://github.com/Lotayou/BasicSR/tree/6cf9a706dd680d54f7dc26e87318ff79f76c0dbf |
MLP | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from abc import *
from torch.... | Kyushik/JORLDY | MLP | false | 13,984 | [
"Apache-2.0"
] | 300 | 6a24a2195e5e87ade157ee53f631af2221f0a188 | https://github.com/Kyushik/JORLDY/tree/6a24a2195e5e87ade157ee53f631af2221f0a188 |
NaiveGroupNorm | from torch.nn import Module
import torch
from torch.nn import Parameter
from torch.nn import init
import torch.nn.parallel
class NaiveGroupNorm(Module):
"""NaiveGroupNorm implements Group Normalization with the high-level matrix operations in PyTorch.
It is a temporary solution to export GN by ONNX before the... | 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.nn import Parameter
from torch.nn import... | hav4ik/AdelaiDet | NaiveGroupNorm | false | 3,719 | [
"BSD-2-Clause"
] | 0 | 6ed9c1e1a25a3e25dddfa858ce0f219a30593ce2 | https://github.com/hav4ik/AdelaiDet/tree/6ed9c1e1a25a3e25dddfa858ce0f219a30593ce2 |
PositionWiseFCNetwork | import torch
from torch import nn
import torch.optim
import torch.utils.data
class PositionWiseFCNetwork(nn.Module):
"""
The Position-Wise Feed Forward Network sublayer.
"""
def __init__(self, d_model, d_inner, dropout):
"""
:param d_model: size of vectors throughout the transformer m... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | sgrvinod/a-PyTorch-Tutorial-to-Machine-Translation | PositionWiseFCNetwork | false | 16,396 | [
"MIT"
] | 59 | a4dd7bc5554d11ac80355241f603dcaa24bc70ae | https://github.com/sgrvinod/a-PyTorch-Tutorial-to-Machine-Translation/tree/a4dd7bc5554d11ac80355241f603dcaa24bc70ae |
SplAtConv2d | # 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.... | Shun14/detectron2-ResNeSt | SplAtConv2d | false | 14,445 | [
"Apache-2.0"
] | 344 | cda53a237199da3bbe7526d41c41b9d8df4c4814 | https://github.com/Shun14/detectron2-ResNeSt/tree/cda53a237199da3bbe7526d41c41b9d8df4c4814 |
ReduceMaxModule | # 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... | mirecta/nncase | ReduceMaxModule | false | 4,175 | [
"Apache-2.0"
] | 0 | d2efa59677a26f4259b3b6a5b6ec05ea16d4e40c | https://github.com/mirecta/nncase/tree/d2efa59677a26f4259b3b6a5b6ec05ea16d4e40c |
baseRNN_predict | import torch
import numpy as np
import torch.nn as nn
import torch.nn.init as init
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
weight_shape = list(m.weight.data.size())
fan_in = np.prod(weight_shape[1:4])
fan_out = np.prod(weight_shape[2:4]) *... | 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
from torch._inductor.runtime import triton_helpers
import numpy as np
import tor... | lysuk96/rl_representations | baseRNN_predict | false | 15,981 | [
"MIT"
] | 438 | 19de69305e40c9b3a1d746a7af26d232c9fb3f6f | https://github.com/lysuk96/rl_representations/tree/19de69305e40c9b3a1d746a7af26d232c9fb3f6f |
SimpleXorModule | # 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.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | YaronBenAtar/glow | SimpleXorModule | false | 14,678 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
Cos | # 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... | alartum/sngp-pytorch | Cos | false | 6,141 | [
"Apache-2.0"
] | 1 | 8d1f6c22d7ae635feeff0c0912624589e31e2e62 | https://github.com/alartum/sngp-pytorch/tree/8d1f6c22d7ae635feeff0c0912624589e31e2e62 |
Attention | import torch
from torch import nn
import torch.nn.functional as F
class Attention(nn.Module):
"""
Applies an attention mechanism on the output features from the decoder.
"""
def __init__(self, dim):
super(Attention, self).__init__()
self.dim = dim
self.linear1 = nn.Linear(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.... | JiwanChung/tapm | Attention | false | 8,377 | [
"MIT"
] | 14 | ec42b139d1c012daccc55f85e67744488d526476 | https://github.com/JiwanChung/tapm/tree/ec42b139d1c012daccc55f85e67744488d526476 |
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
import torch.nn as nn
assert_... | amogkam/learnedcardinalities | SetConv | false | 14,845 | [
"MIT"
] | 64 | 295eabcf9ede38e7e9d1a6a8bcd00f349b628bf9 | https://github.com/amogkam/learnedcardinalities/tree/295eabcf9ede38e7e9d1a6a8bcd00f349b628bf9 |
MLP | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Malta-Lab/IUPE | MLP | false | 8,541 | [
"MIT"
] | 10 | 44ddf119917538f02bb69509fec7a8314eed419f | https://github.com/Malta-Lab/IUPE/tree/44ddf119917538f02bb69509fec7a8314eed419f |
FactorizedReduce | # 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.... | evdcush/ppuda | FactorizedReduce | false | 15,335 | [
"MIT"
] | 262 | 22783ac92207da6730ee618c953af230c5c39f28 | https://github.com/evdcush/ppuda/tree/22783ac92207da6730ee618c953af230c5c39f28 |
StandardWNetDown | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | ankmathur96/torchsupport | StandardWNetDown | false | 3,176 | [
"MIT"
] | 0 | 77bf4a90b8770a408665e2604428808c3ed2f979 | https://github.com/ankmathur96/torchsupport/tree/77bf4a90b8770a408665e2604428808c3ed2f979 |
MLPLayer | import torch
import torch.nn as nn
import torch.nn.functional as F
class MLPLayer(nn.Module):
def __init__(self, in_dim, out_dim):
super().__init__()
self.in_dim = in_dim
mid_dim = in_dim // 2
self.out_dim = out_dim
self.fc1 = nn.Linear(in_dim, mid_dim)
self.fc2 = ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | luogan1234/prerequisite-prediction-co-training | MLPLayer | false | 7,132 | [
"MIT"
] | 1 | 28e3f241ada5afe75a73525375087be230735c2a | https://github.com/luogan1234/prerequisite-prediction-co-training/tree/28e3f241ada5afe75a73525375087be230735c2a |
FNormTest | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | goldbattle/onnx2keras | FNormTest | false | 12,452 | [
"MIT"
] | 0 | dcf52041299ce4216552d1132ec86eb4debd5303 | https://github.com/goldbattle/onnx2keras/tree/dcf52041299ce4216552d1132ec86eb4debd5303 |
ReconstructionLoss | import torch
import torch.nn as nn
from functools import reduce
import torch.utils.data
class BaseModule(nn.Module):
"""
Implements the basic module.
All other modules inherit from this one
"""
def load_w(self, checkpoint_path):
"""
Loads a checkpoint into the state_dict.
... | 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 functools import reduce
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
e... | ppalaupuigdevall/moments-vae | ReconstructionLoss | false | 7,488 | [
"MIT"
] | 1 | 99384094b5b7213e7669ad492f1b56216045b190 | https://github.com/ppalaupuigdevall/moments-vae/tree/99384094b5b7213e7669ad492f1b56216045b190 |
ScaledSiLU | import torch
class ScaledSiLU(torch.nn.Module):
def __init__(self):
super().__init__()
self.scale_factor = 1 / 0.6
self._activation = torch.nn.SiLU()
def forward(self, x):
return self._activation(x) * self.scale_factor
def get_inputs():
return [torch.rand([4, 4, 4, 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
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 | ScaledSiLU | false | 17,798 | [
"MIT"
] | 10 | 89948582edfb8debb736406d54db9813a5f2c88d | https://github.com/Open-Catalyst-Project/baselines/tree/89948582edfb8debb736406d54db9813a5f2c88d |
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
import ... | ChangQingAAS/Deep-Reinforcement-Learning | Critic | false | 234 | [
"MIT"
] | 0 | 3bc1381c632b1730a48e63e972aea62086c4287c | https://github.com/ChangQingAAS/Deep-Reinforcement-Learning/tree/3bc1381c632b1730a48e63e972aea62086c4287c |
Normalize | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | icyda17/very-deep-CNNs | Normalize | false | 10,218 | [
"Apache-2.0"
] | 0 | c275ef222d50dae90e508345ec3be5adfa5e33ce | https://github.com/icyda17/very-deep-CNNs/tree/c275ef222d50dae90e508345ec3be5adfa5e33ce |
PositionwiseFeedForward | import torch
import torch.nn as nn
import torch.nn.functional as F
class PositionwiseFeedForward(nn.Module):
""" A two-feed-forward-layer module """
def __init__(self, d_in, d_hid, dropout=0.1):
super(PositionwiseFeedForward, self).__init__()
self.w_1 = nn.Conv1d(d_in, d_hid, 1)
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.... | MarcosPampuch/TDNet_CARLA | PositionwiseFeedForward | false | 810 | [
"MIT"
] | 0 | efc1c872966f1cef49b82723170586a6abcfb524 | https://github.com/MarcosPampuch/TDNet_CARLA/tree/efc1c872966f1cef49b82723170586a6abcfb524 |
ResidualAttentionBlock | import torch
from torch import nn
import torch.utils.checkpoint
from collections import OrderedDict
class LayerNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-12):
"""Construct a layernorm module in the TF style (epsilon inside the square root).
"""
super(LayerNorm, self).__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
from torch._inductor.runtime.... | jiazheng-xing/Swin_Multimodal | ResidualAttentionBlock | false | 10,340 | [
"MIT"
] | 0 | 7bc41977fe7d8d4f0091852c63a6a32a0fada0fb | https://github.com/jiazheng-xing/Swin_Multimodal/tree/7bc41977fe7d8d4f0091852c63a6a32a0fada0fb |
NaiveGroupNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch.nn import Module
from torch.nn import Parameter
from torch.nn import... | XDong18/AdelaiDet | NaiveGroupNorm | false | 12,068 | [
"BSD-2-Clause"
] | 0 | 837cd1078923892fe6e84ac29fd0963f1b2c474f | https://github.com/XDong18/AdelaiDet/tree/837cd1078923892fe6e84ac29fd0963f1b2c474f |
Actor | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | asiliskender/deep-reinforcement-learning | Actor | false | 12,121 | [
"MIT"
] | 0 | dbf96d67477aa9242128b78b081474193e1e4538 | https://github.com/asiliskender/deep-reinforcement-learning/tree/dbf96d67477aa9242128b78b081474193e1e4538 |
RegressionModel | # 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_... | Hyojin021/auto_labeling | RegressionModel | false | 11,644 | [
"Apache-2.0"
] | 0 | 1ccf0cd1c5adf34692751553a988aa0fcf4efefb | https://github.com/Hyojin021/auto_labeling/tree/1ccf0cd1c5adf34692751553a988aa0fcf4efefb |
h_sigmoid | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guard... | Ghaust/SSD | h_sigmoid | false | 9,114 | [
"MIT"
] | 0 | 2bf14a48795d20ad2177f622e84d62b3ff81183f | https://github.com/Ghaust/SSD/tree/2bf14a48795d20ad2177f622e84d62b3ff81183f |
KL | # 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... | LakeAndCat/CluOReg | KL | false | 751 | [
"MIT"
] | 0 | ba50cb056061b3833050d32e532e08152bdc8de2 | https://github.com/LakeAndCat/CluOReg/tree/ba50cb056061b3833050d32e532e08152bdc8de2 |
SeasonalLayerNorm | import torch
import torch.nn as nn
import torch.fft
class SeasonalLayerNorm(nn.Module):
"""Special designed layernorm for the seasonal part."""
def __init__(self, channels):
super(SeasonalLayerNorm, self).__init__()
self.layernorm = nn.LayerNorm(channels)
def forward(self, x):
x_... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.fft
assert_size_stride = torch._C._dynamo.gu... | jianzhnie/TsFormer | SeasonalLayerNorm | false | 3,726 | [
"Apache-2.0"
] | 0 | 47e362f02445ba00d5ab8db206667767e72faca7 | https://github.com/jianzhnie/TsFormer/tree/47e362f02445ba00d5ab8db206667767e72faca7 |
FeedForwardActorNN | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
class FeedForwardActorNN(nn.Module):
def __init__(self, in_dim, out_dim, is_discrete):
super(FeedForwardActorNN, self).__init__()
self.layer1 = nn.Linear(in_dim, 64)
self.layer2 = nn.Linear(64, 64)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | britig/policy-refinement-bo | FeedForwardActorNN | false | 6,366 | [
"MIT"
] | 1 | c8a1e347d6e27c991e945afae9b5d9b482806f4b | https://github.com/britig/policy-refinement-bo/tree/c8a1e347d6e27c991e945afae9b5d9b482806f4b |
DotProductAttention | import torch
import torch.nn as nn
import torch.nn.functional as F
class DotProductAttention(nn.Module):
"""
Dot product attention.
Given a set of vector values, and a vector query, attention is a technique
to compute a weighted sum of the values, dependent on the query.
NOTE: Here we use the term... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | rupeshshrestha123/end2end-asr-pytorch | DotProductAttention | false | 16,346 | [
"MIT"
] | 250 | 8aada8f7cbe90e1d0b05d505042d9e42b8e4dd52 | https://github.com/rupeshshrestha123/end2end-asr-pytorch/tree/8aada8f7cbe90e1d0b05d505042d9e42b8e4dd52 |
DCRBranch | import torch
import torch.nn as nn
import torch.utils.data
import torch.optim
import torch.utils.data.distributed
class DCRBranch(nn.Module):
"""Branch Network for DCR"""
def __init__(self, num_classes, in_channels, mid_channels,
normalized_embeddings=False):
super().__init__()
self.n... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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
import torch.optim
import torch.ut... | gyfastas/CS7319E1G16 | DCRBranch | false | 6,770 | [
"MIT"
] | 1 | 03126af04766abcb269d0c8db481c96c856d21ef | https://github.com/gyfastas/CS7319E1G16/tree/03126af04766abcb269d0c8db481c96c856d21ef |
DiscrepancyLossWithLogits | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.utils.dat... | lizhenbang56/END-TO-END-TEMPORAL-FEATURE-AGGREGATION-FOR-SIAMESE-TRACKERS | DiscrepancyLossWithLogits | false | 12,748 | [
"MIT"
] | 0 | 132b2e28b7f66c6ba0719774e9abd9b6515dd7e2 | https://github.com/lizhenbang56/END-TO-END-TEMPORAL-FEATURE-AGGREGATION-FOR-SIAMESE-TRACKERS/tree/132b2e28b7f66c6ba0719774e9abd9b6515dd7e2 |
TokenEmbedding | # 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
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 |
MultVAE_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._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.sparse... | EricHe98/sad_final_project | MultVAE_encoder | false | 17,258 | [
"MIT"
] | 3 | 4b2b57e44f939840eede6f134493c5f8d809b1a7 | https://github.com/EricHe98/sad_final_project/tree/4b2b57e44f939840eede6f134493c5f8d809b1a7 |
FactorizedReduce | # 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.... | XuelianCheng/ppuda | FactorizedReduce | false | 6,022 | [
"MIT"
] | 1 | d5b89928e430e2d5b976f84b1ea66b4b901e6cda | https://github.com/XuelianCheng/ppuda/tree/d5b89928e430e2d5b976f84b1ea66b4b901e6cda |
RSubInt | # 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... | NVIDIA-AI-IOT-private/torch2trt | RSubInt | false | 10,534 | [
"MIT"
] | 0 | 953d60039e0c81e90eea467c3df2e6e3f7040242 | https://github.com/NVIDIA-AI-IOT-private/torch2trt/tree/953d60039e0c81e90eea467c3df2e6e3f7040242 |
NetPart1 | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | lancelee82/necklace | NetPart1 | false | 7,073 | [
"MIT"
] | 1 | 7a7cfbc05284c1a7ae0a923c8b9a3efdd0037579 | https://github.com/lancelee82/necklace/tree/7a7cfbc05284c1a7ae0a923c8b9a3efdd0037579 |
ConvolModel | import torch
import torch.nn as nn
import torch.nn.functional as F
class ConvolModel(nn.Module):
def __init__(self):
super(ConvolModel, self).__init__()
self.conv1 = nn.Conv2d(1, 5, 2)
self.conv2 = nn.Conv2d(5, 10, 2)
self.conv3 = nn.Conv2d(10, 10, 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
import torch.nn as nn
assert_... | VVKot/mlinseconds-die-hard | ConvolModel | false | 9,631 | [
"MIT"
] | 0 | dacbd448180bc992e0dab9e4b27bb594235d8c44 | https://github.com/VVKot/mlinseconds-die-hard/tree/dacbd448180bc992e0dab9e4b27bb594235d8c44 |
AdaptiveInstanceNorm | # 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... | G-arj/StyleSwin | AdaptiveInstanceNorm | false | 13,707 | [
"MIT"
] | 398 | 0c592b3334159613ebe4a33bd6c4ea042dac42d4 | https://github.com/G-arj/StyleSwin/tree/0c592b3334159613ebe4a33bd6c4ea042dac42d4 |
Policy | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | aklein1995/Pong_REINFORCE | Policy | false | 18,252 | [
"MIT"
] | 4 | 3b57777fd3ab6e98c8a1191989bd65140e19fc6e | https://github.com/aklein1995/Pong_REINFORCE/tree/3b57777fd3ab6e98c8a1191989bd65140e19fc6e |
ExtResNetBlock | # 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 ... | bounesh/pytorch-3dunet | ExtResNetBlock | false | 14,979 | [
"MIT"
] | 1,236 | 60278d01eaacc69feee731979826a0c26e223427 | https://github.com/bounesh/pytorch-3dunet/tree/60278d01eaacc69feee731979826a0c26e223427 |
HybridLoss | # 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... | hehaoming/RSI-ChangeDetection | HybridLoss | false | 6,798 | [
"MIT"
] | 1 | f24a1d79c03fb9fefc49bc91bc94b3c120992496 | https://github.com/hehaoming/RSI-ChangeDetection/tree/f24a1d79c03fb9fefc49bc91bc94b3c120992496 |
GraphResConvolution | # 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
f... | sumanmichael/Palmira_pb | GraphResConvolution | false | 4,398 | [
"MIT"
] | 0 | 8ca9f370ccd9bba694317be648ce5e4f4c55d0e7 | https://github.com/sumanmichael/Palmira_pb/tree/8ca9f370ccd9bba694317be648ce5e4f4c55d0e7 |
RecCrossEntropyLoss | # 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 import nn
a... | YuhengZhi/Attention-Net-with-MNIST- | RecCrossEntropyLoss | false | 6,019 | [
"MIT"
] | 1 | aa6805e4df777dee1056d5f4f4f9a9b1e4a5e4ff | https://github.com/YuhengZhi/Attention-Net-with-MNIST-/tree/aa6805e4df777dee1056d5f4f4f9a9b1e4a5e4ff |
SimpleExpModule | # 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.jit
import torch.onnx
import torch.nn
assert_size_stride = t... | YaronBenAtar/glow | SimpleExpModule | false | 14,662 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
MLPLayer | import torch
from torch import nn
class MLPLayer(nn.Module):
def __init__(self, input_size, output_size, non_linearity=torch.sigmoid):
super().__init__()
self.lin1 = nn.Linear(input_size, input_size // 2)
self.lin2 = nn.Linear(input_size // 2, output_size)
self.non_lin = non_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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | zoranmedic/LCR-design | MLPLayer | false | 4,674 | [
"MIT"
] | 0 | b722e4e9d00e8aaae36dd51ddc8131477ee805fd | https://github.com/zoranmedic/LCR-design/tree/b722e4e9d00e8aaae36dd51ddc8131477ee805fd |
RegressionModel | import torch
import torch.nn as nn
import torch.utils.data.distributed
class RegressionModel(nn.Module):
def __init__(self, num_features_in, num_anchors=1, feature_size=256):
super(RegressionModel, self).__init__()
self.conv1 = nn.Conv2d(num_features_in, feature_size, kernel_size=3,
p... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | YuBeomGon/pytorch_retina | RegressionModel | false | 1,278 | [
"Apache-2.0"
] | 0 | a1713ecbf99e3cf2f8f5edce3329b808b4f9dee8 | https://github.com/YuBeomGon/pytorch_retina/tree/a1713ecbf99e3cf2f8f5edce3329b808b4f9dee8 |
MultiheadAttentionWrapper | # 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
from torch.autograd import Variable
import torch.nn.functional as F
from torch.nn.utils import weight_norm
import torch... | FalconX777/CharacterBert_Multitasking | MultiheadAttentionWrapper | false | 11,508 | [
"BSD-3-Clause"
] | 0 | eab566975871fffd0ec875a05ba478f1bce9b0ab | https://github.com/FalconX777/CharacterBert_Multitasking/tree/eab566975871fffd0ec875a05ba478f1bce9b0ab |
LanguageModelCriterion | # 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 torch.autograd import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | helloMickey/self-critical.pytorch | LanguageModelCriterion | false | 10,177 | [
"MIT"
] | 0 | 3a26111012099e13daeb688136fea45186127935 | https://github.com/helloMickey/self-critical.pytorch/tree/3a26111012099e13daeb688136fea45186127935 |
LuongAttentionGeneral | import torch
import torch.nn as nn
import torch.nn.functional as F
class LuongAttentionGeneral(nn.Module):
def __init__(self, hidden_size):
super().__init__()
self.W = nn.Linear(hidden_size, hidden_size)
def forward(self, query, values):
query = torch.squeeze(query, 0)
query ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | beroguedou/nmt-pytorch | LuongAttentionGeneral | false | 6,341 | [
"MIT"
] | 1 | 8758ba33e2d5f4eca7f1ac2d04582678332bbcd5 | https://github.com/beroguedou/nmt-pytorch/tree/8758ba33e2d5f4eca7f1ac2d04582678332bbcd5 |
FocalLoss | # 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... | youqingxiaozhua/ABAW3 | FocalLoss | false | 11,062 | [
"Apache-2.0"
] | 0 | 51ab58ab311ecd6603a8485a45af0dcc39880e69 | https://github.com/youqingxiaozhua/ABAW3/tree/51ab58ab311ecd6603a8485a45af0dcc39880e69 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.