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 |
|---|---|---|---|---|---|---|---|---|---|---|
GatedTanhUnit | # 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_... | Nintorac/survae_experiments | GatedTanhUnit | false | 899 | [
"MIT"
] | 0 | d68cc25e2604aab08b53617c1f3ffe4716f166c4 | https://github.com/Nintorac/survae_experiments/tree/d68cc25e2604aab08b53617c1f3ffe4716f166c4 |
ODDetector | import torch
import torch.nn as nn
class ODDetector(nn.Module):
def __init__(self, input_dim, h_size, num_classes):
super(ODDetector, self).__init__()
self.relu = nn.ReLU(True)
self.fc1 = nn.Linear(input_dim, h_size)
self.fc2 = nn.Linear(h_size, h_size)
self.classifier = 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
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | e96031413/tfvaegan | ODDetector | false | 10,103 | [
"MIT"
] | 0 | 4d0512c6ce98155b9e8ba37fbcf90d43cd5bbe90 | https://github.com/e96031413/tfvaegan/tree/4d0512c6ce98155b9e8ba37fbcf90d43cd5bbe90 |
DiceLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | bantiitnab/kaggle-TGS-salt-identification | DiceLoss | false | 1,516 | [
"MIT"
] | 0 | 8b3350278b2ee8f01ba2a0734af9514d369f3228 | https://github.com/bantiitnab/kaggle-TGS-salt-identification/tree/8b3350278b2ee8f01ba2a0734af9514d369f3228 |
EqualLinear | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.model_zoo
class EqualLinear(nn.Module):
def __init__(self, in_dim, out_dim, lr_mul=1, bias=True):
super().__init__()
self.weight = nn.Parameter(torch.randn(out_dim, in_dim))
if bias:
self.bia... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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.model_zoo
assert_size_stride = torch._C... | Aitical/ADspeech2face | EqualLinear | false | 4,806 | [
"MIT"
] | 1 | 2e811ff8cc7333729f4b77d1b1067296253e8e38 | https://github.com/Aitical/ADspeech2face/tree/2e811ff8cc7333729f4b77d1b1067296253e8e38 |
TemporalEmbedding | import math
import torch
import torch.nn as nn
class FixedEmbedding(nn.Module):
def __init__(self, c_in, d_model):
super(FixedEmbedding, self).__init__()
w = torch.zeros(c_in, d_model).float()
w.require_grad = False
position = torch.arange(0, c_in).float().unsqueeze(1)
div... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guar... | Linan2018/Informer2020 | TemporalEmbedding | false | 2,514 | [
"Apache-2.0"
] | 0 | 30e63a7d3ed9310b917b05c4d60b340d2dd0517a | https://github.com/Linan2018/Informer2020/tree/30e63a7d3ed9310b917b05c4d60b340d2dd0517a |
ResNet | # 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_... | AutumnCrocus/shadow_sim | ResNet | false | 16,959 | [
"MIT"
] | 6 | 79ad13ff9bd7131c82f269af32a3970f3e4bf2ca | https://github.com/AutumnCrocus/shadow_sim/tree/79ad13ff9bd7131c82f269af32a3970f3e4bf2ca |
ZeroConv2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch im... | AvivNavon/glow-pytorch | ZeroConv2d | false | 8,857 | [
"MIT"
] | 0 | de0fb2c1d8a4000337b2fbd1215df68530070431 | https://github.com/AvivNavon/glow-pytorch/tree/de0fb2c1d8a4000337b2fbd1215df68530070431 |
RelativeL1 | # 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... | BlueAmulet/BasicSR | RelativeL1 | false | 7,806 | [
"Apache-2.0"
] | 12 | 7040913d8659a05af4c2428feb71c260efbf1e9c | https://github.com/BlueAmulet/BasicSR/tree/7040913d8659a05af4c2428feb71c260efbf1e9c |
LocalNet | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.... | sjmoran/CURL | LocalNet | false | 16,472 | [
"BSD-3-Clause"
] | 125 | 919e519717b66e14d92ac6fa404c328ee3f254a5 | https://github.com/sjmoran/CURL/tree/919e519717b66e14d92ac6fa404c328ee3f254a5 |
MDNHead | from torch.nn import Module
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from torch.distributions import Normal
from torch.distributions import Categorical
from torch.nn.utils import vector_to_parameters
from torch.nn.utils import parameters_to_vector
def ortho_init(module, no... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch.nn... | zuoxingdong/lagom | MDNHead | false | 16,846 | [
"MIT"
] | 383 | 3b6710804dbc79c6dffb369ac87c68f4055ab6cd | https://github.com/zuoxingdong/lagom/tree/3b6710804dbc79c6dffb369ac87c68f4055ab6cd |
ClassWisePool | import sys
from torch.autograd import Function
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
class ClassWisePoolFunction(Function):
@staticmethod
def forward(ctx, input, num_maps):
batch_size, num_channels, h, w = input.size()
if num_ch... | 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 sys
from torch.autograd import Function
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
ass... | pyushkevich/wildcat.pytorch | ClassWisePool | false | 16,293 | [
"MIT"
] | 273 | 2046cde4e4a350eb1172fe60035448aa8df632d5 | https://github.com/pyushkevich/wildcat.pytorch/tree/2046cde4e4a350eb1172fe60035448aa8df632d5 |
_Residual_Block | import torch
from torch import nn
class _Residual_Block(nn.Module):
def __init__(self, num_chans=64):
super(_Residual_Block, self).__init__()
bias = True
self.conv1 = nn.Conv2d(num_chans, num_chans, kernel_size=3, stride=
1, padding=1, bias=bias)
self.relu2 = nn.PReLU(... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | khammernik/sigmanet | _Residual_Block | false | 15,884 | [
"MIT"
] | 50 | 6eb8dbd1ee350bb9baee60eb254080f7d660bbc5 | https://github.com/khammernik/sigmanet/tree/6eb8dbd1ee350bb9baee60eb254080f7d660bbc5 |
GKDLoss | # 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
... | carol007/pytorch-ImageNet-CIFAR-COCO-VOC-training | GKDLoss | false | 6,402 | [
"MIT"
] | 1 | e8b37046e6fbe914f6a68bbde1fe419c46373c1d | https://github.com/carol007/pytorch-ImageNet-CIFAR-COCO-VOC-training/tree/e8b37046e6fbe914f6a68bbde1fe419c46373c1d |
Net | # 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_... | yito0427/pytorch-basic | Net | false | 4,626 | [
"MIT"
] | 0 | 316cf460edb24da5f25dea9426c1a123912719cf | https://github.com/yito0427/pytorch-basic/tree/316cf460edb24da5f25dea9426c1a123912719cf |
ActorDownAction | # 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.... | yangfanthu/modular-rl | ActorDownAction | false | 13,140 | [
"BSD-2-Clause"
] | 0 | 25c599bab641a7e732dbaf116cd240fa2358f113 | https://github.com/yangfanthu/modular-rl/tree/25c599bab641a7e732dbaf116cd240fa2358f113 |
CategoricalPolicyTwoLayer | # 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.distributions as... | wessle/costaware | CategoricalPolicyTwoLayer | false | 10,989 | [
"MIT"
] | 0 | 151502308411528eaa703d353d138fc809e59d8e | https://github.com/wessle/costaware/tree/151502308411528eaa703d353d138fc809e59d8e |
Normalization | # 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... | asjir/adain | Normalization | false | 6,265 | [
"MIT"
] | 1 | 1d0f70f161e485ce61ea57ab619d66e8f4ccadde | https://github.com/asjir/adain/tree/1d0f70f161e485ce61ea57ab619d66e8f4ccadde |
MaxElementwise | import torch
class MaxElementwise(torch.nn.Module):
def forward(self, x, y):
return torch.max(x, y)
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | bunderhi/torch2trt | MaxElementwise | false | 1,588 | [
"MIT"
] | 0 | fa5e31e742a0f0c9a9ee38909a6fa56bb07ba96d | https://github.com/bunderhi/torch2trt/tree/fa5e31e742a0f0c9a9ee38909a6fa56bb07ba96d |
VocabGraphConvolution | import math
import torch
import torch.nn as nn
import torch.nn.init as init
class VocabGraphConvolution(nn.Module):
"""Vocabulary GCN module.
Params:
`voc_dim`: The size of vocabulary graph
`num_adj`: The number of the adjacency matrix of Vocabulary graph
`hid_dim`: The hidden dimensi... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
import torch.nn as nn
import torch.nn.init as init
assert_size_strid... | Aksh97/VGCN-BERT | VocabGraphConvolution | false | 13,244 | [
"MIT"
] | 106 | 62b5ae5a3c53f4bff555027d87a57d3a994a32bb | https://github.com/Aksh97/VGCN-BERT/tree/62b5ae5a3c53f4bff555027d87a57d3a994a32bb |
InvConvNear | import torch
from torch import nn
from torch.nn import functional as F
import torch.utils.data
class InvConvNear(nn.Module):
def __init__(self, channels, n_split=4, no_jacobian=False, **kwargs):
super().__init__()
assert n_split % 2 == 0
self.channels = channels
self.n_split = n_s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.utils.data
assert_size_stride = torch._C._dyna... | AndreHe02/glow-tts | InvConvNear | false | 2,051 | [
"MIT"
] | 0 | 683f68f17790f2f46c23e9d3eadbcac352d82e2b | https://github.com/AndreHe02/glow-tts/tree/683f68f17790f2f46c23e9d3eadbcac352d82e2b |
AttentionPooling | # 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.... | jamaalhay/Final_Proj | AttentionPooling | false | 15,668 | [
"MIT"
] | 104 | 3f524a90fee5a3cb21466ab76f630d060792045d | https://github.com/jamaalhay/Final_Proj/tree/3f524a90fee5a3cb21466ab76f630d060792045d |
SelfAttentionRE | # 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.... | chaitanya2334/lsm | SelfAttentionRE | false | 1,662 | [
"MIT"
] | 0 | 504c732238b419cd77e7e0a97af040778ee9c7dd | https://github.com/chaitanya2334/lsm/tree/504c732238b419cd77e7e0a97af040778ee9c7dd |
GlobalWeightedAvgPool2d | import torch
from torch import nn
class GlobalWeightedAvgPool2d(nn.Module):
"""
Global Weighted Average Pooling from paper "Global Weighted Average
Pooling Bridges Pixel-level Localization and Image-level Classification"
"""
def __init__(self, features: 'int', flatten=False):
super().__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.triton_helpers import math as tl_math
from torch im... | huangjiadidi/dfdc_deepfake_challenge | GlobalWeightedAvgPool2d | false | 15,549 | [
"MIT"
] | 499 | 1f78fe93a5a445ced386e43b3b0378ee567eaa77 | https://github.com/huangjiadidi/dfdc_deepfake_challenge/tree/1f78fe93a5a445ced386e43b3b0378ee567eaa77 |
SimpleLeakyReluModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleLeakyReluModule(torch.nn.Module):
def __init__(self, negative_slope=0.01, inplace=False):
super(SimpleLeakyReluModule, self).__init__()
self.negative_slope = negative_slope
self.inplace = inplace
def forward(... | 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... | mciprian13/glow | SimpleLeakyReluModule | false | 3,993 | [
"Apache-2.0"
] | 0 | 90f88205d9bf8baff8df5bbda51c9d138e3e668b | https://github.com/mciprian13/glow/tree/90f88205d9bf8baff8df5bbda51c9d138e3e668b |
InteractiveKLLoss | # 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... | Johnsonms/NNI_master | InteractiveKLLoss | false | 11,587 | [
"MIT"
] | 0 | e5e5c7aed89cf3189cffe1056464833c15eb54ff | https://github.com/Johnsonms/NNI_master/tree/e5e5c7aed89cf3189cffe1056464833c15eb54ff |
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... | naver-ai/cgl_fairness | FocalLoss | false | 7,317 | [
"MIT"
] | 1 | 00d3bec233c9b3e0f88496118abaed8321ca3159 | https://github.com/naver-ai/cgl_fairness/tree/00d3bec233c9b3e0f88496118abaed8321ca3159 |
ModulatedConv2d | from torch.autograd import Function
import math
import random
import torch
from torch import nn
from torch.nn import functional as F
def upsample(in_tens, out_H=64):
in_H = in_tens.shape[2]
scale_factor = 1.0 * out_H / in_H
return nn.Upsample(scale_factor=scale_factor, mode='bilinear',
align_corne... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | SavvaI/stylegan2-pytorch | ModulatedConv2d | false | 9,497 | [
"MIT",
"BSD-2-Clause",
"Apache-2.0"
] | 0 | b8e4b605bd951283ef2c9a784e7afa0a486975bb | https://github.com/SavvaI/stylegan2-pytorch/tree/b8e4b605bd951283ef2c9a784e7afa0a486975bb |
TVLoss | import torch
import numpy as np
import torch.nn as nn
class TVLoss(nn.Module):
def __init__(self, norm=2):
super().__init__()
self.norm = norm
def forward(self, x):
rank = len(x.shape[2:])
shift_h = torch.cat([x[:, :, 1:], x[:, :, :1]], dim=2)
shift_w = torch.cat([x[:... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | jokingbear/DM | TVLoss | false | 6,984 | [
"MIT"
] | 1 | 9c4dada1756f3d866455a397511d4f3bacfadc60 | https://github.com/jokingbear/DM/tree/9c4dada1756f3d866455a397511d4f3bacfadc60 |
ConvNet | import torch
import torch.nn as nn
import torch.nn.functional as F
class ConvNet(nn.Module):
def __init__(self):
super(ConvNet, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | AndrewAltimit/Scene-Classification-AWS-Serverless | ConvNet | false | 11,234 | [
"MIT"
] | 0 | caa4bff102987338dcfa597b9ec1638e6e5e63f5 | https://github.com/AndrewAltimit/Scene-Classification-AWS-Serverless/tree/caa4bff102987338dcfa597b9ec1638e6e5e63f5 |
Mish | # 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, math as tl_math
from torch import nn
assert_size_stride = torch._C._dynamo.gua... | khayliang/single_person_tracking | Mish | false | 3,824 | [
"MIT"
] | 0 | d93aae3742ba3c77f00b3917b182784f03b5d597 | https://github.com/khayliang/single_person_tracking/tree/d93aae3742ba3c77f00b3917b182784f03b5d597 |
MultiHeadAttention | import math
import torch
import numpy as np
from torch import nn
class MultiHeadAttention(nn.Module):
def __init__(self, n_heads, input_dim, embed_dim, val_dim=None, key_dim
=None):
super(MultiHeadAttention, self).__init__()
if val_dim is None:
val_dim = embed_dim // n_heads
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | rdjdejong/attention-learn-to-route | MultiHeadAttention | false | 16,319 | [
"MIT"
] | 540 | 3b6bbdad677a36df53eabad98b48f436be298ac8 | https://github.com/rdjdejong/attention-learn-to-route/tree/3b6bbdad677a36df53eabad98b48f436be298ac8 |
LocalResponseNormLayer | import torch
import torch.nn as nn
import torch.nn.functional as F
class LocalResponseNormLayer(nn.Module):
def forward(self, tensor, size=5, alpha=9.999999747378752e-05, beta=
0.75, k=1.0):
return F.local_response_norm(tensor, size=size, alpha=alpha, beta=
beta, k=k)
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.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | fuzhanrahmanian/lucent | LocalResponseNormLayer | false | 15,375 | [
"Apache-2.0"
] | 449 | 13b24c3c37784185275da73c7a11095b2ae809c5 | https://github.com/fuzhanrahmanian/lucent/tree/13b24c3c37784185275da73c7a11095b2ae809c5 |
CustomInverse | # 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... | natke/onnxruntime-extensions | CustomInverse | false | 4,048 | [
"MIT"
] | 0 | e7b7eb596016242a7e913044e889c4a0d7dc1000 | https://github.com/natke/onnxruntime-extensions/tree/e7b7eb596016242a7e913044e889c4a0d7dc1000 |
OutputSP | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
from torch.autograd import *
import torch.nn.functional as F
class OutputSP(nn.Module):
def __init__(self, opt):
super(OutputSP, self).__init__()
self.rnn_size = opt.rnn_size
self.att_hid_size = opt.att_hid_s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | daqingliu/CAVP | OutputSP | false | 15,126 | [
"MIT"
] | 49 | d383affde78dbc75e369095c27954dcdd79478d0 | https://github.com/daqingliu/CAVP/tree/d383affde78dbc75e369095c27954dcdd79478d0 |
BinaryMul | # 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 abc
import inspect
import warnings
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typ... | Johnsonms/NNI_master | BinaryMul | false | 11,564 | [
"MIT"
] | 0 | e5e5c7aed89cf3189cffe1056464833c15eb54ff | https://github.com/Johnsonms/NNI_master/tree/e5e5c7aed89cf3189cffe1056464833c15eb54ff |
Time2Vec | import torch
from torch import nn
class Time2Vec(nn.Module):
"""Encode time information
phi and omega has k + 1 elements per each time step
so, from input (batch_size, sample_size) will be
ouptut (batch_size, sample_size, embed_size)
Reference
* https://arxiv.org/abs/1907.05321
* https:/... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 im... | appleparan/mise.py | Time2Vec | false | 6,225 | [
"MIT"
] | 1 | a77ea51be37a739928600c66d168d69b78bc0c4b | https://github.com/appleparan/mise.py/tree/a77ea51be37a739928600c66d168d69b78bc0c4b |
DetLoss | import torch
from torch import nn
class DetLoss(nn.Module):
def __init__(self):
super().__init__()
self.hm_criterion = nn.BCEWithLogitsLoss(reduction='none')
self.ori_criterion = nn.SmoothL1Loss(reduction='none')
self.box_criterion = nn.SmoothL1Loss(reduction='none')
def forw... | 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 ... | dotchen/LAV | DetLoss | false | 15,206 | [
"Apache-2.0"
] | 122 | dc9b4cfca39abd50c7438e8749d49f6ac0fe5e4e | https://github.com/dotchen/LAV/tree/dc9b4cfca39abd50c7438e8749d49f6ac0fe5e4e |
GeneratorLoss | import torch
from torch import nn
class GeneratorLoss(nn.Module):
def __init__(self, alpha=1, beta=10, gamma=10):
super().__init__()
self.bce = nn.BCEWithLogitsLoss()
self.l1 = nn.L1Loss()
self.alpha = alpha
self.beta = beta
self.gamma = gamma
def forward(self... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch ... | akanametov/CycleGAN | GeneratorLoss | false | 6,130 | [
"MIT"
] | 1 | a61e76134cfdda43306e326e3dbba38d8cb21163 | https://github.com/akanametov/CycleGAN/tree/a61e76134cfdda43306e326e3dbba38d8cb21163 |
GCN | from torch.nn import Module
import math
import torch
from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module
import torch.nn.functional as F
import torch.nn as nn
import torch.nn.modules.loss
class GraphConvolution1(Module):
"""
Simple GCN layer, similar to https://arxiv.org/abs/16... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | thilinicooray/pygcn | GCN | false | 10,878 | [
"MIT"
] | 0 | a7d4f12f31898a3b386736215a6d5fe5cb857387 | https://github.com/thilinicooray/pygcn/tree/a7d4f12f31898a3b386736215a6d5fe5cb857387 |
ValueNetwork | # 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.... | Crawford-fang/ROS_pytorch_RL | ValueNetwork | false | 17,160 | [
"Apache-2.0"
] | 10 | 2d3476f15d51aa1f5b5ae9edc5d7f4c776e5de9f | https://github.com/Crawford-fang/ROS_pytorch_RL/tree/2d3476f15d51aa1f5b5ae9edc5d7f4c776e5de9f |
RajeevNet | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import... | carlosdcastillo/janice | RajeevNet | false | 9,829 | [
"MIT"
] | 0 | 221a94dd25ab4304d3c959a364ec89548b807509 | https://github.com/carlosdcastillo/janice/tree/221a94dd25ab4304d3c959a364ec89548b807509 |
GroupedLinearLayer | # 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.checkpoint
assert_size_stride = torch._C... | sajastu/transformers-sent-curr | GroupedLinearLayer | false | 4,239 | [
"Apache-2.0"
] | 0 | 6dc41545c4ac298a010090fbca4b454c2eaf3dbb | https://github.com/sajastu/transformers-sent-curr/tree/6dc41545c4ac298a010090fbca4b454c2eaf3dbb |
wide_basic | # 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... | FloralZhao/unlimited_labeled_data_project_strong_augmen | wide_basic | false | 509 | [
"MIT"
] | 0 | caf90d70145e5d841a38b2e2f18a710703264a28 | https://github.com/FloralZhao/unlimited_labeled_data_project_strong_augmen/tree/caf90d70145e5d841a38b2e2f18a710703264a28 |
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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | Aditya239233/MDP | Hardswish | false | 16,893 | [
"MIT"
] | 4 | 87491e1d67e547c11f4bdd5d784d120473429eae | https://github.com/Aditya239233/MDP/tree/87491e1d67e547c11f4bdd5d784d120473429eae |
ScaledDotProductAttention | import torch
import torch.nn as nn
class ScaledDotProductAttention(nn.Module):
def __init__(self, dropout: 'float'=None, scale: 'bool'=True):
super(ScaledDotProductAttention, self).__init__()
if dropout is not None:
self.dropout = nn.Dropout(p=dropout)
else:
self.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.... | JustinNeumann/pytorch-forecasting | ScaledDotProductAttention | false | 678 | [
"MIT"
] | 0 | 4f6e449cb3788b856e66c4283398a5db201aa6ff | https://github.com/JustinNeumann/pytorch-forecasting/tree/4f6e449cb3788b856e66c4283398a5db201aa6ff |
InvConvNear | # 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
import torch.optim
assert_size_stri... | JINHXu/NeMo | InvConvNear | false | 11,632 | [
"Apache-2.0"
] | 0 | 835db62e39919436824ce022fd3b3f6bac301cd6 | https://github.com/JINHXu/NeMo/tree/835db62e39919436824ce022fd3b3f6bac301cd6 |
ContrastiveLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | VictorCallejas/FB-Similarity-Challenge | ContrastiveLoss | false | 2,928 | [
"MIT"
] | 0 | 0092071f29d5d8fab055d27a1e542e2e64e9cdab | https://github.com/VictorCallejas/FB-Similarity-Challenge/tree/0092071f29d5d8fab055d27a1e542e2e64e9cdab |
ContrastiveLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | Swall0w/cougar | ContrastiveLoss | false | 5,872 | [
"MIT"
] | 1 | 9161b2b1d0c256f4bb952ec190351684f28ec1b7 | https://github.com/Swall0w/cougar/tree/9161b2b1d0c256f4bb952ec190351684f28ec1b7 |
NotNorm | import torch
import torch.nn as nn
import torch.jit
import torch.nn
class NotNorm(nn.Module):
def __init__(self, in_size):
super().__init__()
self.in_size = in_size
def forward(self, inputs):
[1] * (inputs.dim() - 2)
out = inputs.view(inputs.size(0), inputs.size(1), -1)
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.jit
import torch.nn
assert_size_stride = tor... | ankmathur96/torchsupport | NotNorm | false | 3,162 | [
"MIT"
] | 0 | 77bf4a90b8770a408665e2604428808c3ed2f979 | https://github.com/ankmathur96/torchsupport/tree/77bf4a90b8770a408665e2604428808c3ed2f979 |
outconv | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | AnonymousAuthors444/VEC_VAD | outconv | false | 13,309 | [
"MIT"
] | 67 | 0072bf857030e621e2f9c12689407b81e45ed603 | https://github.com/AnonymousAuthors444/VEC_VAD/tree/0072bf857030e621e2f9c12689407b81e45ed603 |
Linear | import math
import torch
from torch import Tensor
from torch.nn import Linear
from torch.nn import Parameter
import torch.utils.data
def uniform(size, tensor):
bound = 1.0 / math.sqrt(size)
if tensor is not None:
tensor.data.uniform_(-bound, bound)
def kaiming_uniform(tensor, fan, a):
if tensor ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
from torch import Tensor
from torch.nn import Parameter
import torch... | douglasrizzo/pytorch_geometric | Linear | false | 12,294 | [
"MIT"
] | 0 | effc617c6ad6daad506038bb79e4407082e74740 | https://github.com/douglasrizzo/pytorch_geometric/tree/effc617c6ad6daad506038bb79e4407082e74740 |
Critic | import torch
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
def hidden_init(layer):
fan_in = layer.weight.data.size()[0]
lim = 1.0 / np.sqrt(fan_in)
return -lim, lim
class Critic(nn.Module):
"""Critic (Value) Model."""
def __init__(self, state_size, action_size, seed, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import numpy as np
import tor... | AntoniaSophia/deep-reinforcement-learning | Critic | false | 8,856 | [
"MIT"
] | 0 | 1d1c77039eea22fcf6726c35c3dd2563adfcb519 | https://github.com/AntoniaSophia/deep-reinforcement-learning/tree/1d1c77039eea22fcf6726c35c3dd2563adfcb519 |
SSE | import torch
from torch import nn
class SSE(nn.Module):
"""SSE : Channel Squeeze and Spatial Excitation block.
Paper : https://arxiv.org/abs/1803.02579
Adapted from
https://www.kaggle.com/c/tgs-salt-identification-challenge/discussion/66178
"""
def __init__(self, in_channels):
"""Co... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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... | Atharva-Phatak/torchflare | SSE | false | 13,333 | [
"Apache-2.0"
] | 86 | 945f4bee73a855edd8cb19cd646731155499a27f | https://github.com/Atharva-Phatak/torchflare/tree/945f4bee73a855edd8cb19cd646731155499a27f |
MatrixTree | # 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
assert_s... | BradLin0819/kg2text | MatrixTree | false | 13,457 | [
"Apache-2.0"
] | 86 | e586eb2027c0d85db9826cbe1d9e14f2d26fc93f | https://github.com/BradLin0819/kg2text/tree/e586eb2027c0d85db9826cbe1d9e14f2d26fc93f |
GlobalAveragePool2d | # 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... | slyviacassell/Multi-taks-UNITE | GlobalAveragePool2d | false | 4,359 | [
"MIT"
] | 0 | a010a92c94c0ee0f1ffed27df6d89da58d6d34c5 | https://github.com/slyviacassell/Multi-taks-UNITE/tree/a010a92c94c0ee0f1ffed27df6d89da58d6d34c5 |
SelfAttnMatch | # 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.... | MobtgZhang/MWMLNet | SelfAttnMatch | false | 5,622 | [
"MIT"
] | 1 | 125bb39935916b6b4be505c51cb6a04eb49b96d0 | https://github.com/MobtgZhang/MWMLNet/tree/125bb39935916b6b4be505c51cb6a04eb49b96d0 |
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.... | Renovamen/Text-Classification | Attention | false | 14,279 | [
"MIT"
] | 72 | 4a4aa4001c402ed4371ebaabe1393b27794e5992 | https://github.com/Renovamen/Text-Classification/tree/4a4aa4001c402ed4371ebaabe1393b27794e5992 |
LayerNorm | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
class LayerNorm(nn.Module):
def __init__(self, num_features, eps=1e-05, affine=True):
super(LayerNorm, self).__init__()
self.num_features = num_features
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.... | ToniChopp/MIRACLE-Paper-Sharing-Album | LayerNorm | false | 18,012 | [
"MIT"
] | 7 | 72a3843101483fc8b53df2746c488da066eda2a1 | https://github.com/ToniChopp/MIRACLE-Paper-Sharing-Album/tree/72a3843101483fc8b53df2746c488da066eda2a1 |
Conv2dZeros | import torch
import torch.nn as nn
class ActNorm(nn.Module):
def __init__(self, num_channels, scale=1.0, logscale_factor=3.0,
batch_variance=False):
"""
Activation normalization layer
:param num_channels: number of channels
:type num_channels: int
:param scale: sc... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | Eladhi/VI_Glow | Conv2dZeros | false | 5,124 | [
"MIT"
] | 1 | 9c48fbf8fa10c81fc2354a07fcc2837a77d06cef | https://github.com/Eladhi/VI_Glow/tree/9c48fbf8fa10c81fc2354a07fcc2837a77d06cef |
FeatureL2Norm | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.nn
assert_size_stride = torch._C._dynamo.gua... | JiwonCocoder/-Joint-Learning-of-Feature-Extraction-and-Cost-Aggregation-for-Semantic-Matching | FeatureL2Norm | false | 5,405 | [
"MIT"
] | 1 | b79e0e20fd5a1a9ddc0ffa9d7a92e0ebd21018b9 | https://github.com/JiwonCocoder/-Joint-Learning-of-Feature-Extraction-and-Cost-Aggregation-for-Semantic-Matching/tree/b79e0e20fd5a1a9ddc0ffa9d7a92e0ebd21018b9 |
HyperpriorAnalysis | import torch
import torch.nn as nn
import torch.nn.functional as F
class HyperpriorAnalysis(nn.Module):
"""
Hyperprior 'analysis model' as proposed in [1].
[1] Ballé et. al., "Variational image compression with a scale hyperprior",
arXiv:1802.01436 (2018).
C: Number of input channels
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | ahmedfgad/high-fidelity-generative-compression | HyperpriorAnalysis | false | 6,163 | [
"Apache-2.0"
] | 1 | f3c6aa3472e3c629cbc35eefb0957119c913054a | https://github.com/ahmedfgad/high-fidelity-generative-compression/tree/f3c6aa3472e3c629cbc35eefb0957119c913054a |
InstanceNorm2d | # 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
from torch import nn as nn
from torch.nn import init as init
from torchvision.m... | Lotayou/BasicSR | InstanceNorm2d | false | 2,805 | [
"Apache-2.0",
"MIT"
] | 0 | 6cf9a706dd680d54f7dc26e87318ff79f76c0dbf | https://github.com/Lotayou/BasicSR/tree/6cf9a706dd680d54f7dc26e87318ff79f76c0dbf |
GlobalAveragePooling | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.nn.functional as F
class GlobalAveragePooling(nn.Module):
def __init__(self):
super(GlobalAveragePooling, self).__init__()
def forward(self, feat):
num_channels = feat.size(1)
return F.avg_poo... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.parallel
import torch.optim
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_stri... | kristinakupf/FeatureLearningRotNet | GlobalAveragePooling | false | 12,686 | [
"MIT"
] | 0 | d495bcfaed3e7a3ca92b7434f8ad6d7584ab173d | https://github.com/kristinakupf/FeatureLearningRotNet/tree/d495bcfaed3e7a3ca92b7434f8ad6d7584ab173d |
FusedLeakyReLU | import torch
from torch import nn
from torch.nn import functional as F
class FusedLeakyReLU(nn.Module):
def __init__(self, channel):
super().__init__()
self.bias = nn.Parameter(torch.zeros(channel))
self.scale = 1.414
def forward(self, input):
shape = 1, self.bias.shape[0], 1... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | delldu/StyleGAN2 | FusedLeakyReLU | false | 6,547 | [
"MIT",
"BSD-2-Clause",
"Apache-2.0"
] | 1 | 4bcba4673d3dc32ac3a67f6b5d5e24b490cdfbb3 | https://github.com/delldu/StyleGAN2/tree/4bcba4673d3dc32ac3a67f6b5d5e24b490cdfbb3 |
Conv2dSameExport | import torch
import torch.utils.data
import torch.utils.data.distributed
from torch import nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.optim
def _calc_same_pad(input_: 'int', kernel: 'int', stride: 'int', dilation: 'int'
):
"""calculate same padding"""
return max((-(input_ // ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
import torch.utils.data.distributed
from torch import nn... | Adlik/zen_nas | Conv2dSameExport | false | 16,901 | [
"Apache-2.0"
] | 7 | d820d5c7d5bbb6fd66a76d5f16513647d6ea7a57 | https://github.com/Adlik/zen_nas/tree/d820d5c7d5bbb6fd66a76d5f16513647d6ea7a57 |
DotProduct | # 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.... | MicroTensor-ai/episodic-memory | DotProduct | false | 11,692 | [
"MIT"
] | 0 | 295a3752ab94c7a6f45355aa2c54bffbf84b574f | https://github.com/MicroTensor-ai/episodic-memory/tree/295a3752ab94c7a6f45355aa2c54bffbf84b574f |
XCA | import torch
from torch import Tensor
from torch import nn
from torch.nn import functional as F
class XCA(nn.Module):
""" Cross-Covariance Attention (XCA) operation where the channels are updated using a weighted
sum. The weights are obtained from the (softmax normalized) Cross-covariance
matrix (Q^T K \... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | sithu31296/image_classification | XCA | false | 16,474 | [
"MIT"
] | 57 | 6b8cbce96100225621cee3166a73e852ba216cc3 | https://github.com/sithu31296/image_classification/tree/6b8cbce96100225621cee3166a73e852ba216cc3 |
HighwayLayer | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.jit
import torch.onnx.operators
class HighwayLayer(nn.Module):
def __init__(self, input_dim, transform_activation=F.relu,
gate_activation=F.softmax, gate_bias=-2):
super().__init__()
self.highway_transform_act... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Acidburn0zzz/translate-1 | HighwayLayer | false | 4,822 | [
"BSD-3-Clause"
] | 1 | 8385a3c95de397fec8ca7a032fe1c215fa4e31f9 | https://github.com/Acidburn0zzz/translate-1/tree/8385a3c95de397fec8ca7a032fe1c215fa4e31f9 |
ScalarAttention | import torch
import torch.nn as nn
import torch.utils.data
class ScalarAttention(nn.Module):
def __init__(self, in_size, hidden_size):
super(ScalarAttention, self).__init__()
self.hidden = nn.Linear(in_size, hidden_size)
nn.init.orthogonal_(self.hidden.weight.data)
self.out = nn.L... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | gchrupala/platalea | ScalarAttention | false | 6,740 | [
"Apache-2.0"
] | 1 | 65833307bb6c5ad6cbdd6b17ad8ca59cf51fcd81 | https://github.com/gchrupala/platalea/tree/65833307bb6c5ad6cbdd6b17ad8ca59cf51fcd81 |
PixBlock | import torch
import torch.nn as nn
class PixBlock(nn.Module):
def __init__(self, in_size, out_size=3, scale=2, norm=None):
super(PixBlock, self).__init__()
self.conv1 = nn.Conv2d(in_size, out_size * 2 ** scale, 1, 1)
self.up = nn.PixelShuffle(scale)
def forward(self, x):
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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | MohamadHMousavi/demo_wsi_superres | PixBlock | false | 11,844 | [
"MIT"
] | 0 | 7e846470aa228affa62ea77c38c138dde087a0de | https://github.com/MohamadHMousavi/demo_wsi_superres/tree/7e846470aa228affa62ea77c38c138dde087a0de |
CrossEntropy | import torch
import torch.nn as nn
class CrossEntropy(nn.Module):
def __init__(self):
super().__init__()
def forward(self, props, tgt):
tgt_props = props.gather(2, tgt.unsqueeze(2)).squeeze()
mask = (tgt > 0).float()
return -(tgt_props * mask).sum() / mask.sum()
def get_inp... | 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... | Gromy1211/torch-light | CrossEntropy | false | 11,450 | [
"MIT"
] | 0 | c7d7a9bc5ab1eab03d800a27d9325859516f01e6 | https://github.com/Gromy1211/torch-light/tree/c7d7a9bc5ab1eab03d800a27d9325859516f01e6 |
RegressionHead | # 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 abc
import t... | dumpmemory/jiant | RegressionHead | false | 15,269 | [
"MIT"
] | 1,108 | f9e0e7c9ecf88da0c26559c5f903aef0338c7bd9 | https://github.com/dumpmemory/jiant/tree/f9e0e7c9ecf88da0c26559c5f903aef0338c7bd9 |
BBoxTransform | import torch
import torch.nn as nn
class BBoxTransform(nn.Module):
def forward(self, anchors, regression):
"""
Args:
anchors: [batch_size, boxes, (y1, x1, y2, x2)]
regression: [batch_size, boxes, (dy, dx, dh, dw)]
"""
y_centers_a = (anchors[..., 0] + anchor... | 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... | MikoyChinese/Learn | BBoxTransform | false | 852 | [
"Apache-2.0"
] | 0 | c482b1e84496279935b5bb2cfc1e6d78e2868c63 | https://github.com/MikoyChinese/Learn/tree/c482b1e84496279935b5bb2cfc1e6d78e2868c63 |
GRUCell | import torch
import torch.nn as nn
class GRUCell(nn.Module):
def __init__(self, input_size, hidden_size):
super(GRUCell, self).__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self._W = nn.Parameter(torch.FloatTensor(input_size + hidden_size,
2 *... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | Avmb/lm-robustness | GRUCell | false | 115 | [
"BSD-3-Clause"
] | 0 | b5417d9aac01bff0d2a56b506eabed899fd718d4 | https://github.com/Avmb/lm-robustness/tree/b5417d9aac01bff0d2a56b506eabed899fd718d4 |
N_R_Align | # 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... | TMUITLab/EAFR | N_R_Align | false | 1,123 | [
"MIT"
] | 0 | dadb6485d48711ccb8aa2f03760aeb437645f1ff | https://github.com/TMUITLab/EAFR/tree/dadb6485d48711ccb8aa2f03760aeb437645f1ff |
NegPearson | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.... | Oichii/resnet3D_pulse | NegPearson | false | 17,773 | [
"MIT"
] | 4 | d123abfdb14eedc972ab1e0c4c3026fe8c4074af | https://github.com/Oichii/resnet3D_pulse/tree/d123abfdb14eedc972ab1e0c4c3026fe8c4074af |
MaxPoolStride1 | import torch
from torch import nn
import torch.nn.functional as F
import torch.utils.data
class MaxPoolStride1(nn.Module):
def __init__(self, kernel_size):
super(MaxPoolStride1, self).__init__()
self.kernel_size = kernel_size
self.pad = kernel_size - 1
def forward(self, x):
p... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards... | Dazz993/AlphaPose | MaxPoolStride1 | false | 5,049 | [
"Apache-2.0"
] | 1 | d4b9a3af5f590fa21bd033b4a19e98b5748ae683 | https://github.com/Dazz993/AlphaPose/tree/d4b9a3af5f590fa21bd033b4a19e98b5748ae683 |
LowRankDecoderLayer | # 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.... | bahducoup/factorized_training | LowRankDecoderLayer | false | 12,179 | [
"MIT"
] | 0 | 0af38f16338a9bcfcc11091b1a6b75befd67f234 | https://github.com/bahducoup/factorized_training/tree/0af38f16338a9bcfcc11091b1a6b75befd67f234 |
NaiveTorchNet | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.autograd import Variable
import torch.nn as nn
import torch.autograd
... | deo1/deo1 | NaiveTorchNet | false | 1,836 | [
"MIT"
] | 0 | 36671f12269d3bd662d746e8b9f66c22255c9df7 | https://github.com/deo1/deo1/tree/36671f12269d3bd662d746e8b9f66c22255c9df7 |
Pairer | # 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... | AxlAlm/SegNLP | Pairer | false | 4,868 | [
"Apache-2.0"
] | 1 | 89b8d077952397dfcea089376b373b117bcf6a65 | https://github.com/AxlAlm/SegNLP/tree/89b8d077952397dfcea089376b373b117bcf6a65 |
relu_constant_fraction | # 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 numpy as np
from torch import nn
from torch.nn.functional import relu
assert_size_... | Noppornying00/constant-fraction-activation | relu_constant_fraction | false | 915 | [
"Apache-2.0"
] | 0 | b25745e7339df13e3db34d8c8372d5cbaa3c13bb | https://github.com/Noppornying00/constant-fraction-activation/tree/b25745e7339df13e3db34d8c8372d5cbaa3c13bb |
Depth_Pointwise_Conv1d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | Nitin-Mane/External-Attention-pytorch | Depth_Pointwise_Conv1d | false | 14,099 | [
"MIT"
] | 4,466 | 1ceda306c41063af11c956334747763444a4d83f | https://github.com/Nitin-Mane/External-Attention-pytorch/tree/1ceda306c41063af11c956334747763444a4d83f |
ActionAttentionV2 | import torch
import numpy as np
import torch as th
import torch.nn as nn
import torch.nn.functional as F
class ActionAttentionV2(nn.Module):
def __init__(self, model_dim, n_actions):
super(ActionAttentionV2, self).__init__()
self.model_dim = model_dim
self.n_actions = n_actions
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
from torch._inductor.runtime.... | footoredo/pymarl | ActionAttentionV2 | false | 3,512 | [
"Apache-2.0"
] | 0 | 9c62dda7a7ed984e020f2cafab93601342305af2 | https://github.com/footoredo/pymarl/tree/9c62dda7a7ed984e020f2cafab93601342305af2 |
AsymmetricLossOptimized | # 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 torchv... | MinliangLin/ASL | AsymmetricLossOptimized | false | 2,655 | [
"MIT"
] | 0 | beda0989a8e30ac51a7ce9f9e247a12bbe84ec96 | https://github.com/MinliangLin/ASL/tree/beda0989a8e30ac51a7ce9f9e247a12bbe84ec96 |
MLPPolicy | import math
import torch
import numpy as np
import torch.nn as nn
from torch.nn import functional as F
def log_normal_density(x, mean, log_std, std):
"""returns guassian density given x on log scale"""
variance = std.pow(2)
log_density = -(x - mean).pow(2) / (2 * variance) - 0.5 * np.log(2 * np.pi
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | Timliang/RL-Competition | MLPPolicy | false | 9,558 | [
"MIT"
] | 0 | 638462b95a5aab0bbae46677a59ffc90ba6cd971 | https://github.com/Timliang/RL-Competition/tree/638462b95a5aab0bbae46677a59ffc90ba6cd971 |
ExpPool | # 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... | Tarandro/Chexpert | ExpPool | false | 11,931 | [
"Apache-2.0"
] | 0 | 6bc51f899a479f8dbad8a64c92f35ed4632377b3 | https://github.com/Tarandro/Chexpert/tree/6bc51f899a479f8dbad8a64c92f35ed4632377b3 |
BCE_disc_sm_v8 | import torch
import torch.nn as nn
import torch.nn.functional as F
class BCE_disc_sm_v8(nn.Module):
def __init__(self, lb_sm=0.2):
super(BCE_disc_sm_v8, self).__init__()
self.lb_sm = lb_sm
def forward(self, x, labels):
assert (x >= 0).all() and (x <= 1).all(), 'x is wrong'
as... | 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... | Sampson-Lee/SIB-Net | BCE_disc_sm_v8 | false | 2,814 | [
"MIT"
] | 0 | 650399082e9237327fa38168ccfc7d48153a1db5 | https://github.com/Sampson-Lee/SIB-Net/tree/650399082e9237327fa38168ccfc7d48153a1db5 |
ConvStem2 | # 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_... | yoookoo/cnn-transformer | ConvStem2 | false | 13,199 | [
"Apache-2.0"
] | 0 | 8ee54ea944ed752162e3098db7f8f689ec150efe | https://github.com/yoookoo/cnn-transformer/tree/8ee54ea944ed752162e3098db7f8f689ec150efe |
ViTStemPatchify | from torch.nn import Module
import torch
import torch.utils.data
import torch.nn as nn
def patchify2d(w_in, w_out, k, *, bias=True):
"""Helper for building a patchify layer as used by ViT models."""
return nn.Conv2d(w_in, w_out, k, stride=k, padding=0, bias=bias)
def patchify2d_cx(cx, w_in, w_out, k, *, bia... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.nn import Module
import torch.utils.data
import torch.nn as nn
assert... | LicharYuan/pycls | ViTStemPatchify | false | 11,643 | [
"MIT"
] | 0 | 633529425f2c9ffadd892c1a0418b37891ee2d44 | https://github.com/LicharYuan/pycls/tree/633529425f2c9ffadd892c1a0418b37891ee2d44 |
FC_Q | import torch
import torch.nn as nn
import torch.nn.functional as F
class FC_Q(nn.Module):
def __init__(self, state_dim, num_actions, num_nodes=128):
super(FC_Q, self).__init__()
self.q1 = nn.Linear(state_dim, num_nodes)
self.q2 = nn.Linear(num_nodes, num_nodes)
self.q3 = 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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | lysuk96/rl_representations | FC_Q | false | 15,989 | [
"MIT"
] | 438 | 19de69305e40c9b3a1d746a7af26d232c9fb3f6f | https://github.com/lysuk96/rl_representations/tree/19de69305e40c9b3a1d746a7af26d232c9fb3f6f |
SelfAttention | import torch
import torch.nn as nn
class MultiHeadAttention(nn.Module):
def __init__(self, num_q_channels: 'int', num_kv_channels: 'int',
num_heads: 'int', dropout: 'float'):
super().__init__()
self.attention = nn.MultiheadAttention(embed_dim=num_q_channels,
num_heads=num_head... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | krasserm/perceiver-io | SelfAttention | false | 15,855 | [
"Apache-2.0"
] | 133 | 16e1029300304b617c0b0ae8eb06129ec103c755 | https://github.com/krasserm/perceiver-io/tree/16e1029300304b617c0b0ae8eb06129ec103c755 |
CrossEn | # 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... | CryhanFang/CLIP2Video | CrossEn | false | 13,527 | [
"MIT"
] | 113 | e94131800a3a1434f6d00b89b7301d741db8ba06 | https://github.com/CryhanFang/CLIP2Video/tree/e94131800a3a1434f6d00b89b7301d741db8ba06 |
Conv2dSame | import math
import torch
from typing import List
from typing import Union
from torch import nn
import torch.nn.functional as F
from typing import Tuple
import torch.cuda
from typing import Optional
from torch.nn.common_types import _size_2_t
def get_same_padding(x: 'int', k: 'int', s: 'int', d: 'int') ->int:
"""
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
from typing import List
from typing import Union
from torch import n... | LoveEachDay/towhee | Conv2dSame | false | 11,653 | [
"Apache-2.0"
] | 0 | 513c9c2626676cadaaf0a16ac3c828d96bec91a1 | https://github.com/LoveEachDay/towhee/tree/513c9c2626676cadaaf0a16ac3c828d96bec91a1 |
SelfAttention | import math
import torch
from torch import nn
import torch.nn.functional as F
def mask_(matrices, maskval=0.0, mask_diagonal=True):
"""
Masks out all values in the given batch of matrices where i <= j holds,
i < j if mask_diagonal is false
In place operation
:param tns:
:return:
"""
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | esvhd/former | SelfAttention | false | 10,085 | [
"MIT"
] | 0 | 9aca51b8f7a6f2abe2175293b895ed4af468e890 | https://github.com/esvhd/former/tree/9aca51b8f7a6f2abe2175293b895ed4af468e890 |
ScaleToModel | import torch
import torch.nn as nn
import torch.cuda
from torch import linalg as linalg
class ScaleToModel(nn.Module):
def __init__(self, model_value_range, test_value_range):
super(ScaleToModel, self).__init__()
self.m_min, self.m_max = model_value_range
self.t_min, self.t_max = test_val... | 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.cuda
from torch import linalg as linalg
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
e... | angelvillar96/vp-suite | ScaleToModel | false | 3,102 | [
"MIT"
] | 0 | 3e7c7d852862bad09a771d754fc56a71abf0a25f | https://github.com/angelvillar96/vp-suite/tree/3e7c7d852862bad09a771d754fc56a71abf0a25f |
ResBlock | # 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_... | rawandahmad698/GFPGAN | ResBlock | false | 7,553 | [
"BSD-3-Clause"
] | 1 | 4700bf1a94ec9c36746f660db19f4f03e0eed9b0 | https://github.com/rawandahmad698/GFPGAN/tree/4700bf1a94ec9c36746f660db19f4f03e0eed9b0 |
GroupNorm32 | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Jack000/glid-3 | GroupNorm32 | false | 8,299 | [
"MIT"
] | 31 | 4a18efc2785339ebc743e149a7955e34fff436fb | https://github.com/Jack000/glid-3/tree/4a18efc2785339ebc743e149a7955e34fff436fb |
LearnedSigmoid | import torch
import torch.nn as nn
class LearnedSigmoid(nn.Module):
def __init__(self, slope=1):
super().__init__()
self.q = torch.nn.Parameter(torch.ones(slope))
self.q.requiresGrad = True
def forward(self, x):
return torch.multiply(torch.sigmoid(x), self.q)
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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | pabloguarda/NeuralTransportationNetworks | LearnedSigmoid | false | 7,441 | [
"MIT"
] | 1 | 0461c26128b09488aff237b760068b43d131f8a9 | https://github.com/pabloguarda/NeuralTransportationNetworks/tree/0461c26128b09488aff237b760068b43d131f8a9 |
RerangeLayer | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | zvict/HyperRIM | RerangeLayer | false | 16,828 | [
"Apache-2.0"
] | 92 | f3800196b59ea0f94561efa88ec2e6675e4c8b00 | https://github.com/zvict/HyperRIM/tree/f3800196b59ea0f94561efa88ec2e6675e4c8b00 |
D_DownBlock | import torch
from torchvision.transforms import *
class ConvBlock(torch.nn.Module):
def __init__(self, input_size, output_size, kernel_size=3, stride=1,
padding=1, bias=True, activation='prelu', norm=None):
super(ConvBlock, self).__init__()
self.conv = torch.nn.Conv2d(input_size, output_s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torchvision.transforms import *
assert_size_stride = torch._C._dynamo.guard... | DengZeshuai/DBPN-Pytorch | D_DownBlock | false | 2,555 | [
"MIT"
] | 0 | a90d241a1c4b07830c6d812ad8389d13e8cf05d1 | https://github.com/DengZeshuai/DBPN-Pytorch/tree/a90d241a1c4b07830c6d812ad8389d13e8cf05d1 |
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