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 |
|---|---|---|---|---|---|---|---|---|---|---|
NN | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | Jie-Yuan/Deeps | NN | false | 17,478 | [
"MIT"
] | 4 | b4acbb8e16b8ff5d181e70c3b549df0d818d0d76 | https://github.com/Jie-Yuan/Deeps/tree/b4acbb8e16b8ff5d181e70c3b549df0d818d0d76 |
AttentionLayer | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import *
class AttentionLayer(nn.Module):
def __init__(self, hidden_dim_en, hidden_dim_de, projected_size):
super(AttentionLayer, self).__init__()
self.linear1 = nn.Linear(hidden_dim_en, projected_size)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | minhdo3000/visual_storytelling | AttentionLayer | false | 4,008 | [
"MIT"
] | 0 | 451c5194564fb1bb02929f57eac8f026662637b1 | https://github.com/minhdo3000/visual_storytelling/tree/451c5194564fb1bb02929f57eac8f026662637b1 |
Hsigmoid | import torch
import torch.nn as nn
from torch.quantization import QuantStub
from torch.quantization import DeQuantStub
class Hsigmoid(nn.Module):
def __init__(self, inplace=True, add_stub=False):
super().__init__()
self.float_op = nn.quantized.FloatFunctional()
self.relu6 = nn.ReLU6(inpla... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
from torch.quantization import QuantStub
from torch.quantization im... | akosik-anyvision/incubator-tvm | Hsigmoid | false | 18,241 | [
"Apache-2.0"
] | 9 | e1b11712ac09c32614483d24a4c7e0245ee4cb4b | https://github.com/akosik-anyvision/incubator-tvm/tree/e1b11712ac09c32614483d24a4c7e0245ee4cb4b |
FirstOctaveConv | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data.distributed
class FirstOctaveConv(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, alpha=0.5,
stride=1, padding=1, dilation=1, groups=1, bias=False):
super(FirstOctaveConv, 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
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data.distribut... | SSusantAchary/OctaveConv_pytorch | FirstOctaveConv | false | 14,362 | [
"MIT"
] | 633 | 079f7da29d55c2eeed8985d33f0b2f765d7a469e | https://github.com/SSusantAchary/OctaveConv_pytorch/tree/079f7da29d55c2eeed8985d33f0b2f765d7a469e |
Conv3x3 | import torch
import torch.nn as nn
class Conv3x3(nn.Module):
"""Layer to pad and convolve input
"""
def __init__(self, in_channels, out_channels, use_refl=True):
super(Conv3x3, self).__init__()
if use_refl:
self.pad = nn.ReflectionPad2d(1)
else:
self.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
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.... | Morbotu/drone-PWS | Conv3x3 | false | 11,719 | [
"MIT"
] | 0 | face9cbf30a55783592cce8af59c1c70da982b6a | https://github.com/Morbotu/drone-PWS/tree/face9cbf30a55783592cce8af59c1c70da982b6a |
Solver_GAP_OneFClayers | import torch
import torch.nn as nn
import torch.nn.functional as F
class Solver_GAP_OneFClayers(nn.Module):
""" GAP + fc1 """
def __init__(self, input_nc, input_width, input_height, dropout_prob=
0.0, reduction_rate=2, **kwargs):
super(Solver_GAP_OneFClayers, self).__init__()
self.dro... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | yulinfeng000/AdaptiveNeuralTrees | Solver_GAP_OneFClayers | false | 13,159 | [
"MIT"
] | 0 | bbcb381b9cb0c91ae1af33ce43b43f352055041c | https://github.com/yulinfeng000/AdaptiveNeuralTrees/tree/bbcb381b9cb0c91ae1af33ce43b43f352055041c |
Expand | # 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... | Lalihoo/yolov5-detect | Expand | false | 9,606 | [
"MIT"
] | 0 | 265c3137ea3586d913541501a1562488fbe59e9e | https://github.com/Lalihoo/yolov5-detect/tree/265c3137ea3586d913541501a1562488fbe59e9e |
NormalDivLoss | import torch
import torch.nn as nn
def fuzzyDist(x, a=0.1, b=2):
return 1 / (1 + (x / a).abs().pow(2 * b))
class SoftHist(nn.Module):
def __init__(self, bins, dist):
super(SoftHist, self).__init__()
bins[1] - bins[0]
self.bins = nn.Parameter(bins.unsqueeze(1))
self.dist = di... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.gu... | JWHan717/CS492I-Project | NormalDivLoss | false | 8,315 | [
"MIT"
] | 23 | 5da80bc41425ee90711a3de89c5501b5f7acd4b7 | https://github.com/JWHan717/CS492I-Project/tree/5da80bc41425ee90711a3de89c5501b5f7acd4b7 |
ChannelSpatialSELayer3D | import torch
import torch.nn as nn
import torch.nn.functional as F
class ChannelSELayer3D(nn.Module):
"""
3D extension of Squeeze-and-Excitation (SE) block described in:
*Hu et al., Squeeze-and-Excitation Networks, arXiv:1709.01507*
*Zhu et al., AnatomyNet, arXiv:arXiv:1808.05238*
"""
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | Jianrong-Lu/Head-and-Neck-Tumour-Segmentation-and-Prediction-of-Patient-Survival | ChannelSpatialSELayer3D | false | 666 | [
"MIT"
] | 0 | 257cf17ce6d405166dd8449f3b34e305cb5103b2 | https://github.com/Jianrong-Lu/Head-and-Neck-Tumour-Segmentation-and-Prediction-of-Patient-Survival/tree/257cf17ce6d405166dd8449f3b34e305cb5103b2 |
Encoder1 | import torch
import torch.nn as nn
class Encoder1(nn.Module):
def __init__(self, model=None, fixed=False):
super(Encoder1, self).__init__()
self.fixed = fixed
self.conv0 = nn.Conv2d(3, 3, 1, 1, 0)
self.conv11 = nn.Conv2d(3, 64, 3, 1, 0, dilation=1)
self.relu = nn.ReLU(inpl... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | EndyWon/Texture-Reformer | Encoder1 | false | 8,144 | [
"MIT"
] | 11 | f84f95accb3574c7b759a7f03c0b0b4e150314b5 | https://github.com/EndyWon/Texture-Reformer/tree/f84f95accb3574c7b759a7f03c0b0b4e150314b5 |
ModelWithDuplicates | # 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.... | Donfa1con/distiller | ModelWithDuplicates | false | 11,525 | [
"Apache-2.0"
] | 0 | 645ee41bfebc463523b228ff087e41619607d8b2 | https://github.com/Donfa1con/distiller/tree/645ee41bfebc463523b228ff087e41619607d8b2 |
GlobalAveragePooling | import torch
import torch.nn as nn
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_pool2d(feat, (feat.size(2), feat.size(3))).view... | 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... | MrChenFeng/Project-Template | GlobalAveragePooling | false | 850 | [
"MIT"
] | 0 | 42a335c6abb710bbae6407cbb0ca461533bc12f9 | https://github.com/MrChenFeng/Project-Template/tree/42a335c6abb710bbae6407cbb0ca461533bc12f9 |
MLP | import torch
from abc import *
import torch.nn.functional as F
from torch.optim import *
def orthogonal_init(layer, nonlinearity='relu'):
if isinstance(nonlinearity, str):
if nonlinearity == 'policy':
gain = 0.01
else:
gain = torch.nn.init.calculate_gain(nonlinearity)
e... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | ramanuzan/JORLDY | MLP | false | 12,939 | [
"Apache-2.0"
] | 0 | be371ad0607e5dba5d5082101c38c6a9f2c96767 | https://github.com/ramanuzan/JORLDY/tree/be371ad0607e5dba5d5082101c38c6a9f2c96767 |
L_1st | import torch
import torch.nn as nn
class L_1st(nn.Module):
def __init__(self, alpha):
super(L_1st, self).__init__()
self.alpha = alpha
def forward(self, y_pred, y_true):
Y = y_pred
L = y_true
batch_size = Y.shape[0]
return 2 * self.alpha * torch.trace(torch.mm... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | ZagHe568/graph_embedding | L_1st | false | 18,166 | [
"MIT"
] | 4 | 2a6f8214ce4b30b51eb9f1904b64fe782876f010 | https://github.com/ZagHe568/graph_embedding/tree/2a6f8214ce4b30b51eb9f1904b64fe782876f010 |
SAGEAggregator | # 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... | EdisonLeeeee/Graphgallery | SAGEAggregator | false | 5,109 | [
"MIT"
] | 1 | 8ae9ef57d44f073d0ceaf3f33a3a998546f960a8 | https://github.com/EdisonLeeeee/Graphgallery/tree/8ae9ef57d44f073d0ceaf3f33a3a998546f960a8 |
Discriminator | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | usherbob/dgcnn.pytorch | Discriminator | false | 10,954 | [
"MIT"
] | 0 | fdf5f7a470123b292ac7642f65fd4f693d9b010d | https://github.com/usherbob/dgcnn.pytorch/tree/fdf5f7a470123b292ac7642f65fd4f693d9b010d |
Parseval_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
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | cadurosar/laplacian_networks | Parseval_Conv2d | false | 6,371 | [
"MIT"
] | 1 | 27f6f2d7145426b38f578e9c1beecae3e7392f1b | https://github.com/cadurosar/laplacian_networks/tree/27f6f2d7145426b38f578e9c1beecae3e7392f1b |
LayerNormalization | import torch
import torch.nn as nn
class LayerNormalization(nn.Module):
def __init__(self, hidden_size, eps=1e-05):
super(LayerNormalization, self).__init__()
self.eps = eps
self.hidden_size = hidden_size
self.a2 = nn.Parameter(torch.ones(hidden_size), requires_grad=True)
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | analvikingur/RGAN | LayerNormalization | false | 18,310 | [
"MIT"
] | 8 | b1893c2f53d11c9173c7a30f63f6d93d72232493 | https://github.com/analvikingur/RGAN/tree/b1893c2f53d11c9173c7a30f63f6d93d72232493 |
Sum | # 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... | Alex-Beh/hand_tracking | Sum | false | 11,150 | [
"Apache-2.0"
] | 0 | 40ac39e10ed5815d9400d6a87149015ad6ab9686 | https://github.com/Alex-Beh/hand_tracking/tree/40ac39e10ed5815d9400d6a87149015ad6ab9686 |
ApplyHardAttention | # 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... | ishine/NISQA | ApplyHardAttention | false | 15,620 | [
"MIT"
] | 223 | 2c8917f30c4e4bbca3a48e9852301f1e2480a741 | https://github.com/ishine/NISQA/tree/2c8917f30c4e4bbca3a48e9852301f1e2480a741 |
GCN | from torch.nn import Module
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module
class GraphConvolution(Module):
"""
Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
"""
def __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.... | yutaoming/Rare-Category-Detection | GCN | false | 4,693 | [
"MIT"
] | 0 | 76cf023dff44eef3ecc17f0ebf2b11a08cd63a73 | https://github.com/yutaoming/Rare-Category-Detection/tree/76cf023dff44eef3ecc17f0ebf2b11a08cd63a73 |
SpeakNet | import math
import torch
import torch.nn as nn
import torch.optim
def xavier_init(module):
"""
Xavier initializer for module parameters.
"""
for parameter in module.parameters():
if len(parameter.data.shape) == 1:
parameter.data.fill_(0)
else:
fan_in = parameter... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | aaronmueller/discourse-hw3 | SpeakNet | false | 1,350 | [
"MIT"
] | 0 | 93313a2ce83fde9480914384be52ec9160e967af | https://github.com/aaronmueller/discourse-hw3/tree/93313a2ce83fde9480914384be52ec9160e967af |
ScoreCap | import torch
from torch import nn
import torch.nn
import torch.optim
class ScoreCap(nn.Module):
def __init__(self, cap: 'float'):
super().__init__()
self.cap = cap
def forward(self, input):
return torch.clip(input, max=self.cap)
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
from torch._inductor.runtime import triton_helpers
from torch import nn
import torch.nn
import torch.optim
assert_size_stride = torch._C._dy... | mikaylagawarecki/ReAgent | ScoreCap | false | 10,696 | [
"BSD-3-Clause"
] | 0 | b1a306a9d3641c8adeb03ac272e5774a0009fa88 | https://github.com/mikaylagawarecki/ReAgent/tree/b1a306a9d3641c8adeb03ac272e5774a0009fa88 |
BertLayerNormNoVar | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | jiachens/auto_LiRPA | BertLayerNormNoVar | false | 10,286 | [
"BSD-3-Clause"
] | 0 | cc1ff18e8fbc938953b20ae6a030a25761cb0b78 | https://github.com/jiachens/auto_LiRPA/tree/cc1ff18e8fbc938953b20ae6a030a25761cb0b78 |
TransformerEncoderLayer | # 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.... | OneDirection9/Persona-Dialogue-Generation | TransformerEncoderLayer | false | 11,792 | [
"MIT"
] | 0 | 9696659efe668177bb775dc4192b4b6dd41a9ce1 | https://github.com/OneDirection9/Persona-Dialogue-Generation/tree/9696659efe668177bb775dc4192b4b6dd41a9ce1 |
FusedLeakyReLU | import torch
import torch.utils.data
import torch
import torch.nn as nn
import torch.nn.functional as F
def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5):
return F.leaky_relu(input + bias, negative_slope) * scale
class FusedLeakyReLU(nn.Module):
def __init__(self, channel, negative_slop... | 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
import torch.nn as nn
import torch.nn.functional as F
assert_size_stride = torch._C._dynamo.guards.asse... | a11isonliu/contrastive-unpaired-translation | FusedLeakyReLU | false | 9,842 | [
"BSD-3-Clause"
] | 0 | 67651ed9877cae121d9398f46094ce8dbc678802 | https://github.com/a11isonliu/contrastive-unpaired-translation/tree/67651ed9877cae121d9398f46094ce8dbc678802 |
Zeros | import torch
import torch.optim
class Zeros(torch.nn.Module):
def __init__(self):
super(Zeros, self).__init__()
def forward(self, tensor: 'torch.Tensor') ->torch.Tensor:
return torch.zeros(1, *tensor.shape[1:], dtype=tensor.dtype, device
=tensor.device).expand_as(tensor)
def ge... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.optim
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strid... | ai-in-motion/moai | Zeros | false | 18,344 | [
"Apache-2.0"
] | 10 | e38cac046c059d2e2331ef4883bbabc5a500a5cf | https://github.com/ai-in-motion/moai/tree/e38cac046c059d2e2331ef4883bbabc5a500a5cf |
ChannelNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | czlwang/s3prl | ChannelNorm | false | 12,279 | [
"Apache-2.0"
] | 0 | 81d4bb8d051cee20fa87c083b8478999e1766172 | https://github.com/czlwang/s3prl/tree/81d4bb8d051cee20fa87c083b8478999e1766172 |
SelfAttn | # 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.... | DreamerDeo/gazp | SelfAttn | false | 7,998 | [
"MIT"
] | 18 | 5f823a447ffdf5176023a01516d2be7c383294d9 | https://github.com/DreamerDeo/gazp/tree/5f823a447ffdf5176023a01516d2be7c383294d9 |
MulticlassDiceLoss | import torch
import torch.nn as nn
class DiceLoss(nn.Module):
def __init__(self):
super(DiceLoss, self).__init__()
def forward(self, input, target, logits=True):
if logits:
input = nn.Sigmoid()(input)
N = target.size(0)
smooth = 1
input_flat = input.view(N... | 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... | LanXiangExcavator/challenge2021_submission_4 | MulticlassDiceLoss | false | 784 | [
"BSD-2-Clause"
] | 0 | ca0d4d4dd219119f7dc46464c92062ecdb7f9c49 | https://github.com/LanXiangExcavator/challenge2021_submission_4/tree/ca0d4d4dd219119f7dc46464c92062ecdb7f9c49 |
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.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | ashutoshml/lightning-tutorials | Sigmoid | false | 6,243 | [
"Apache-2.0"
] | 1 | 898b8b6f9852c0b80f034a3187bc1cd34dd521ce | https://github.com/ashutoshml/lightning-tutorials/tree/898b8b6f9852c0b80f034a3187bc1cd34dd521ce |
_AddNorm | import torch
import torch.nn as nn
import torch.nn.functional as F
class _TimeDistributedInterpolation(nn.Module):
def __init__(self, output_size: 'int', batch_first: 'bool'=False,
trainable: 'bool'=False):
super().__init__()
self.output_size = output_size
self.batch_first = batch... | 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.functional as F
assert_size_stride = torc... | Gian-Wiher/darts | _AddNorm | false | 5,206 | [
"Apache-2.0"
] | 1 | 0d267e08643e2e3f88163a5d955b8be75840c2f6 | https://github.com/Gian-Wiher/darts/tree/0d267e08643e2e3f88163a5d955b8be75840c2f6 |
SmallMnistNoDropout | # 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.... | quic-akhobare/aimet | SmallMnistNoDropout | false | 11,114 | [
"BSD-3-Clause"
] | 0 | 1811a0ef58a75d103e173731b436876ee5dc4c49 | https://github.com/quic-akhobare/aimet/tree/1811a0ef58a75d103e173731b436876ee5dc4c49 |
Actor | import torch
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
def hidden_init(layer):
fan_in = layer.weight.data.size()[0]
lim = 1.0 / np.sqrt(fan_in)
return -lim, lim
class Actor(nn.Module):
"""Actor (Policy) Model."""
def __init__(self, state_size, action_size, seed, f... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | adriaciurana/udacity-project-3 | Actor | false | 9,677 | [
"MIT"
] | 0 | 806f78e35a6699eeb0a3272e326d0edc199d16be | https://github.com/adriaciurana/udacity-project-3/tree/806f78e35a6699eeb0a3272e326d0edc199d16be |
EqualConv2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.as... | BinahHu/stylegan2-pytorch | EqualConv2d | false | 166 | [
"MIT",
"BSD-2-Clause",
"Apache-2.0"
] | 0 | 9975707ffd93872fce02f7e3654eb588a09e23e4 | https://github.com/BinahHu/stylegan2-pytorch/tree/9975707ffd93872fce02f7e3654eb588a09e23e4 |
TensorCumsum | # 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... | Minyus/kedex | TensorCumsum | false | 9,676 | [
"Apache-2.0"
] | 0 | 92f952eed3cb6109bc783f449051f2bd13579d2a | https://github.com/Minyus/kedex/tree/92f952eed3cb6109bc783f449051f2bd13579d2a |
DQN | import torch
import torch.nn.functional as F
import torch.nn as nn
class DQN(nn.Module):
"""A simple deep Q network implementation.
Computes Q values for each (action, object) tuple given an input state vector
"""
def __init__(self, state_dim, action_dim, object_dim, hidden_size=100):
super(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
import torch.nn as nn
assert_... | stepinski/machinelearning | DQN | false | 12,998 | [
"MIT"
] | 0 | 1f84883a25616da4cd76bb4655267efd3421e561 | https://github.com/stepinski/machinelearning/tree/1f84883a25616da4cd76bb4655267efd3421e561 |
DownBlock | import torch
import torch.utils.data
from torchvision.transforms import *
class ConvBlock(torch.nn.Module):
def __init__(self, input_size, output_size, kernel_size=3, stride=1,
padding=1, bias=True, activation='prelu', norm=None):
super(ConvBlock, self).__init__()
self.conv = torch.nn.Con... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
from torchvision.transforms import *
assert_size_stride ... | RyanMoussouni/iSeeBetter | DownBlock | false | 14,360 | [
"MIT"
] | 327 | af193ae0852f8e477fcd6875dce874eb5092a24a | https://github.com/RyanMoussouni/iSeeBetter/tree/af193ae0852f8e477fcd6875dce874eb5092a24a |
ResidualBlock | import torch
import numpy as np
import torch.nn as nn
class ConvLayer(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride):
super(ConvLayer, self).__init__()
reflection_padding = int(np.floor(kernel_size / 2))
self.reflection_pad = nn.ReflectionPad2d(reflection_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
from torch._inductor.runtime.... | IvoryCandy/neural-style | ResidualBlock | false | 5,397 | [
"Apache-2.0"
] | 1 | d9d73676479e36c1cbd6c9af36d857f80099504b | https://github.com/IvoryCandy/neural-style/tree/d9d73676479e36c1cbd6c9af36d857f80099504b |
SoftmaxLoss | # 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.... | B06901052/s3prl | SoftmaxLoss | false | 119 | [
"MIT"
] | 0 | 5f63d2df043d2d7c81580cd042fa2cea34746f48 | https://github.com/B06901052/s3prl/tree/5f63d2df043d2d7c81580cd042fa2cea34746f48 |
Combinator | # 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 import nn
import torch.autograd
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dy... | Goschjann/ssltsc | Combinator | false | 17,314 | [
"MIT"
] | 5 | 08d6b1bf711bb1c8f19f9bfb66a98d4e423e932e | https://github.com/Goschjann/ssltsc/tree/08d6b1bf711bb1c8f19f9bfb66a98d4e423e932e |
BareLoss | import torch
import torch.nn as nn
class BareLoss(nn.Module):
def __init__(self, loss_weight=1.0):
super().__init__()
self.loss_weight = loss_weight
def forward(self, pre_loss):
loss = self.loss_weight * pre_loss.mean()
return loss
def get_inputs():
return [torch.rand([... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | JDAI-CV/LIO | BareLoss | false | 13,847 | [
"Apache-2.0"
] | 105 | 7bcd4d5e2990db5c8a7ec6ecc76a23c2e913e523 | https://github.com/JDAI-CV/LIO/tree/7bcd4d5e2990db5c8a7ec6ecc76a23c2e913e523 |
InnerProductLoss | import torch
import numpy as np
import torch.nn as nn
import torch.utils.data
import torch.autograd
class InnerProductLoss(nn.Module):
def __init__(self, code_weights: 'list'=None):
super(InnerProductLoss, self).__init__()
if code_weights is not None:
self.code_weights = np.array(code... | 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 numpy as np
import torch.nn as nn
import torch.utils.data
import torch.a... | LaudateCorpus1/LIGA-Stereo | InnerProductLoss | false | 13,985 | [
"Apache-2.0"
] | 56 | aee3731a24a0ab1667e633e520cc89be2f135272 | https://github.com/LaudateCorpus1/LIGA-Stereo/tree/aee3731a24a0ab1667e633e520cc89be2f135272 |
SplitChannels | import torch
class SplitChannels(torch.nn.Module):
def __init__(self, split_location):
super(SplitChannels, self).__init__()
self.split_location = split_location
def forward(self, x):
a, b = x[:, :self.split_location], x[:, self.split_location:]
a, b = a.clone(), b.clone()
... | 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... | Myyyr/segmentation | SplitChannels | false | 859 | [
"MIT"
] | 0 | 6b9423e327cff1eb23599404031b7fb8e9ecf75d | https://github.com/Myyyr/segmentation/tree/6b9423e327cff1eb23599404031b7fb8e9ecf75d |
BNMLoss | import torch
class BNMLoss(torch.nn.Module):
"""
Implementation of the loss in
[Towards Discriminability and Diversity:
Batch Nuclear-norm Maximization
under Label Insufficient Situations](https://arxiv.org/abs/2003.12237).
"""
def forward(self, x):
""""""
x = torch.nn.fun... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
assert_size_stride = t... | KevinMusgrave/pytorch-adapt | BNMLoss | false | 13,950 | [
"MIT"
] | 131 | ff1491e1bfcc586afb8ee619712c8816ddf10358 | https://github.com/KevinMusgrave/pytorch-adapt/tree/ff1491e1bfcc586afb8ee619712c8816ddf10358 |
BlendConv2d | # 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.utils.data
assert_size_stride = torch._C._dyn... | D-hash-code/ffjord | BlendConv2d | false | 11,375 | [
"MIT"
] | 0 | 3647ab35537a8bac3b4dc1e45a593819ac8e2c18 | https://github.com/D-hash-code/ffjord/tree/3647ab35537a8bac3b4dc1e45a593819ac8e2c18 |
Linear_QNet | import torch
import torch.nn as nn
class Linear_QNet(nn.Module):
def __init__(self, input_size, hidden_size_1, hidden_size_2, output_size):
super().__init__()
self.linear1 = nn.Linear(input_size, hidden_size_1)
self.leakyrelu = nn.LeakyReLU()
self.linear2 = nn.Linear(hidden_size_1... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | cheapmouse94/Machine-Learning-tank1990-python | Linear_QNet | false | 3,281 | [
"MIT"
] | 0 | 8b75983289c7bc0831827561cec12d4ad2addee2 | https://github.com/cheapmouse94/Machine-Learning-tank1990-python/tree/8b75983289c7bc0831827561cec12d4ad2addee2 |
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
import torch.nn as nn
import ... | Hao-Kailong/DisFeb | Encoder | false | 522 | [
"MIT"
] | 0 | 2877edd587556e127d6648ee211ed22838c8d015 | https://github.com/Hao-Kailong/DisFeb/tree/2877edd587556e127d6648ee211ed22838c8d015 |
CNNLayer | # 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... | WiseDoge/Text-Classification-PyTorch | CNNLayer | false | 18,095 | [
"MIT"
] | 6 | 9371eeed6bd7ecf1d529c8f2a6c997fcde67a559 | https://github.com/WiseDoge/Text-Classification-PyTorch/tree/9371eeed6bd7ecf1d529c8f2a6c997fcde67a559 |
Encoding | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | SeHwanJoo/mmsegmentation_body | Encoding | false | 1,044 | [
"Apache-2.0"
] | 0 | 31c4bf27c3dc0a84bfbb06a0c017c5908c17f0ac | https://github.com/SeHwanJoo/mmsegmentation_body/tree/31c4bf27c3dc0a84bfbb06a0c017c5908c17f0ac |
AdaptiveInstanceNorm | import torch
import torch.nn as nn
class AdaptiveInstanceNorm(nn.Module):
def __init__(self, style_dim, in_channel):
super(AdaptiveInstanceNorm, self).__init__()
self.norm = nn.InstanceNorm2d(in_channel)
self.style = nn.Linear(style_dim, in_channel * 2)
self.style.bias.data[:in_ch... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | williamyang1991/DeepPS | AdaptiveInstanceNorm | false | 11,098 | [
"MIT"
] | 0 | f3eb6ba4b0f2ef068361a4bbbd3d6c2c2f6726b4 | https://github.com/williamyang1991/DeepPS/tree/f3eb6ba4b0f2ef068361a4bbbd3d6c2c2f6726b4 |
SelfAttention | import torch
import torch.nn as nn
from scipy.sparse import *
class SelfAttention(nn.Module):
def __init__(self, input_size, hidden_size):
super(SelfAttention, self).__init__()
self.W1 = torch.Tensor(input_size, hidden_size)
self.W1 = nn.Parameter(nn.init.xavier_uniform_(self.W1))
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | talha1503/RL-based-Graph2Seq-for-NQG | SelfAttention | false | 16,522 | [
"Apache-2.0"
] | 100 | 1039e0b6231ae7029ea6e4073b1e55df5ad2e928 | https://github.com/talha1503/RL-based-Graph2Seq-for-NQG/tree/1039e0b6231ae7029ea6e4073b1e55df5ad2e928 |
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... | YaronBenAtar/glow | SimpleStackModel | false | 14,688 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
KLDivLoss | import torch
class KLDivLoss(torch.nn.KLDivLoss):
def __init__(self, reduction='none'):
super().__init__(reduction=reduction)
def forward(self, preds, targets):
"""
Applies ``log_softmax`` to ``pred`` and ``softmax`` to ``targets``
prior to computing KL-Divergence loss. These... | 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
assert_size... | Thesys-lab/learned-coded-computation | KLDivLoss | false | 17,997 | [
"Apache-2.0"
] | 8 | c5c32bcfb7cc4a9f52079f648373e6972c19eff9 | https://github.com/Thesys-lab/learned-coded-computation/tree/c5c32bcfb7cc4a9f52079f648373e6972c19eff9 |
ILN | import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
class ILN(nn.Module):
def __init__(self, num_features, eps=1e-05, momentum=0.9,
using_moving_average=True, using_bn=False):
super(ILN, self).__init__()
self.eps = eps
self.momentum = momentum
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
import torc... | Elvinky/IEGAN | ILN | false | 8,063 | [
"MIT"
] | 29 | db072e38fb022b367da24d3210c59136fbad224e | https://github.com/Elvinky/IEGAN/tree/db072e38fb022b367da24d3210c59136fbad224e |
PADB | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
import torch.nn as nn
assert_size_stride = torch._C._dyn... | qwopqwop200/Fast-Invertible-Rescaling-Net | PADB | false | 7,545 | [
"MIT"
] | 1 | 871733f2eee7929d6b37c4d1d6a27347b39b67a9 | https://github.com/qwopqwop200/Fast-Invertible-Rescaling-Net/tree/871733f2eee7929d6b37c4d1d6a27347b39b67a9 |
MyDilateBlur | # 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
import math
import torch.nn a... | williamyang1991/DeepPS | MyDilateBlur | false | 11,104 | [
"MIT"
] | 0 | f3eb6ba4b0f2ef068361a4bbbd3d6c2c2f6726b4 | https://github.com/williamyang1991/DeepPS/tree/f3eb6ba4b0f2ef068361a4bbbd3d6c2c2f6726b4 |
MyMaxPool1dPadSame | import torch
import torch.nn as nn
import torch.nn.functional as F
class MyMaxPool1dPadSame(nn.Module):
"""
extend nn.MaxPool1d to support SAME padding
"""
def __init__(self, kernel_size):
super(MyMaxPool1dPadSame, self).__init__()
self.kernel_size = kernel_size
self.stride = ... | 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... | huynhnhathao/hum_to_find | MyMaxPool1dPadSame | false | 6,833 | [
"MIT"
] | 1 | a0d7ec4bab1a7e2f7175956ff2721e23e2448840 | https://github.com/huynhnhathao/hum_to_find/tree/a0d7ec4bab1a7e2f7175956ff2721e23e2448840 |
VGGBase | import torch
import torchvision
from torch import nn
import torch.nn.functional as F
from itertools import product as product
import torch.optim
import torch.utils.data
def decimate(tensor, m):
"""
Decimate a tensor by a factor 'm', i.e. downsample by keeping every 'm'th value.
This is used when we conve... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 torchvision
from torch... | aarashfeizi/a-PyTorch-Tutorial-to-Object-Detection | VGGBase | false | 1,906 | [
"MIT"
] | 0 | a9e1f3092d4b8c094bff5cd0897e0e3c1e0bc9c2 | https://github.com/aarashfeizi/a-PyTorch-Tutorial-to-Object-Detection/tree/a9e1f3092d4b8c094bff5cd0897e0e3c1e0bc9c2 |
SEBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
import t... | gentlebreeze1/dbnet | SEBlock | false | 3,538 | [
"Apache-2.0"
] | 0 | be28a7ae835af7d6f8b7c2b636b875adc9fc187c | https://github.com/gentlebreeze1/dbnet/tree/be28a7ae835af7d6f8b7c2b636b875adc9fc187c |
TorchClampMin | # 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... | bunderhi/torch2trt | TorchClampMin | false | 1,611 | [
"MIT"
] | 0 | fa5e31e742a0f0c9a9ee38909a6fa56bb07ba96d | https://github.com/bunderhi/torch2trt/tree/fa5e31e742a0f0c9a9ee38909a6fa56bb07ba96d |
SelfAttention | # 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.... | Gan-Tu/ganutils | SelfAttention | false | 5,193 | [
"MIT"
] | 1 | 203c703cbba0345f9cfe23b03e1e3981f03e43db | https://github.com/Gan-Tu/ganutils/tree/203c703cbba0345f9cfe23b03e1e3981f03e43db |
l1normalization | import torch
import torch.nn as nn
class l1normalization(nn.Module):
def __init__(self, scale):
super(l1normalization, self).__init__()
self.scale = scale
def forward(self, x, dim=1):
return self.scale * x * x.pow(1).sum(dim).clamp(min=1e-12).rsqrt(
).expand_as(x)
def g... | 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... | kensakurada/SceneChangeDet | l1normalization | false | 15,792 | [
"MIT"
] | 199 | 0530e0162863fec0c5296188526f0d27e0109814 | https://github.com/kensakurada/SceneChangeDet/tree/0530e0162863fec0c5296188526f0d27e0109814 |
BertAttention | from _paritybench_helpers import _mock_config
import math
import torch
from torch import nn
class BertLayerNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-05):
"""Construct a layernorm module in the TF style (epsilon inside the square root).
"""
super(BertLayerNorm, 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.... | caldoe/BERT-NL2SPARQL | BertAttention | false | 6,386 | [
"MIT"
] | 1 | 2e09c1aeffc855bc7f1dc8c182e21153b2bc73a8 | https://github.com/caldoe/BERT-NL2SPARQL/tree/2e09c1aeffc855bc7f1dc8c182e21153b2bc73a8 |
PatchEmbeddings | from torch.nn import Module
import torch
from torch import nn
import torch.utils.data
import torch.nn.functional
import torch.autograd
class PatchEmbeddings(Module):
"""
<a id="PatchEmbeddings"></a>
## Get patch embeddings
The paper splits the image into patches of equal size and do a linear transfo... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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
from torch import nn
import torch.utils.data
import ... | techthiyanes/annotated_deep_learning_paper_implementations | PatchEmbeddings | false | 16,573 | [
"MIT"
] | 3,714 | 8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 | https://github.com/techthiyanes/annotated_deep_learning_paper_implementations/tree/8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 |
LayerNorm | import torch
from torch import nn
from torch.nn import Parameter
from torch.nn.parameter import Parameter
import torch.nn.utils
from torch.optim.lr_scheduler import *
class LayerNorm(nn.Module):
def __init__(self, hidden_size, eps=0.0001):
super(LayerNorm, self).__init__()
self.alpha = Parameter(... | 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
from torch.nn import Parameter
from torch.nn.parameter imp... | FalconX777/CharacterBert_Multitasking | LayerNorm | false | 11,509 | [
"BSD-3-Clause"
] | 0 | eab566975871fffd0ec875a05ba478f1bce9b0ab | https://github.com/FalconX777/CharacterBert_Multitasking/tree/eab566975871fffd0ec875a05ba478f1bce9b0ab |
SamePadConv3d | # 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... | AshBT/VideoGPT | SamePadConv3d | false | 13,313 | [
"MIT"
] | 396 | a823bc734af3387129f3bd624caad3db270707f2 | https://github.com/AshBT/VideoGPT/tree/a823bc734af3387129f3bd624caad3db270707f2 |
LogCoshLoss | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_strid... | leoauri/auraloss | LogCoshLoss | false | 15,904 | [
"Apache-2.0"
] | 272 | 0e3362674ae1b53aa61c6a631fb4e6970c5683c1 | https://github.com/leoauri/auraloss/tree/0e3362674ae1b53aa61c6a631fb4e6970c5683c1 |
ASC | import torch
import torch.optim
import torch.nn as nn
import torch.nn.init
class ASC(nn.Module):
def __init__(self, a=3.5):
super().__init__()
self.a = a
def forward(self, input):
return torch.div(torch.exp(self.a * input), torch.sum(torch.exp(
self.a * input), dim=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 math as tl_math
import torch.optim
import torch.nn as nn
import torch.nn.init
assert_size... | RichardScottOZ/UnDIP | ASC | false | 8,714 | [
"Apache-2.0"
] | 10 | 8e4a39801142495e785cfbae0744872729fa3fac | https://github.com/RichardScottOZ/UnDIP/tree/8e4a39801142495e785cfbae0744872729fa3fac |
SobelConv | # 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.... | SeVEnMY/hyper-reconstruction | SobelConv | false | 5,807 | [
"MIT"
] | 1 | 018c34aaf6884650c36a73bd7f4635f927a79da3 | https://github.com/SeVEnMY/hyper-reconstruction/tree/018c34aaf6884650c36a73bd7f4635f927a79da3 |
NegSamplingLoss | # 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... | MIracleyin/RecBole-notebook | NegSamplingLoss | false | 9,560 | [
"MIT"
] | 0 | ef32b3e57a297ff4889dec1f63c7984f8f901a23 | https://github.com/MIracleyin/RecBole-notebook/tree/ef32b3e57a297ff4889dec1f63c7984f8f901a23 |
PositionwiseFeedForward | import torch
import torch.nn as nn
class LayerNormalization(nn.Module):
""" Layer normalization module """
def __init__(self, d_hid, eps=0.001):
super(LayerNormalization, self).__init__()
self.eps = eps
self.a_2 = nn.Parameter(torch.ones(d_hid), requires_grad=True)
self.b_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 import triton_helpers
from torch._inductor.runtime.... | awesome-archive/attention-is-all-you-need-pytorch | PositionwiseFeedForward | false | 9,847 | [
"MIT"
] | 0 | d1fb26fafaf7170a7c3a45968cd555f3c6aeb3bc | https://github.com/awesome-archive/attention-is-all-you-need-pytorch/tree/d1fb26fafaf7170a7c3a45968cd555f3c6aeb3bc |
AlexNet | import torch
import torch.nn as nn
class LRN(nn.Module):
"""
Local Response Normalization
"""
def __init__(self, kernel_size, alpha, beta):
super(LRN, self).__init__()
self.avg_pool = nn.AvgPool2d(kernel_size=kernel_size, stride=1,
padding=int(kernel_size / 2))
sel... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | jjeamin/obJDetection | AlexNet | false | 7,088 | [
"MIT"
] | 1 | eb7fbc410beb00fad1a6477e827e9ce2d8efbac5 | https://github.com/jjeamin/obJDetection/tree/eb7fbc410beb00fad1a6477e827e9ce2d8efbac5 |
Gaussian | # 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... | awlange/pysurvival | Gaussian | false | 14,925 | [
"Apache-2.0"
] | 242 | 841b9bc6ce700ba8898d2a1488aa9cd25ee7a8e6 | https://github.com/awlange/pysurvival/tree/841b9bc6ce700ba8898d2a1488aa9cd25ee7a8e6 |
_ASPPModule | import torch
import torch.nn as nn
class GCT(nn.Module):
def __init__(self, num_channels, epsilon=1e-05, mode='l2', after_relu=False
):
super(GCT, self).__init__()
self.alpha = nn.Parameter(torch.ones(1, num_channels, 1, 1))
self.gamma = nn.Parameter(torch.zeros(1, num_channels, 1... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | huanglf714/COMatchNet | _ASPPModule | false | 6,832 | [
"Apache-2.0"
] | 1 | 79023f5be65d354eb9bdac026d7e0d73110bc4aa | https://github.com/huanglf714/COMatchNet/tree/79023f5be65d354eb9bdac026d7e0d73110bc4aa |
MagnitudeTestModel | import torch
from torch import nn
from torchvision import models as models
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
from torchvision.transforms import *
import torch.onnx
def fill_bias(module, value):
module.bias.data.fill_(value)
def fill_conv_weig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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 torchvision import models as models
import torch.nn.pa... | aalborov/openvino_training_extensions | MagnitudeTestModel | false | 6,038 | [
"Apache-2.0"
] | 1 | a0bb39424151a98e1ca80c4aa5c865636d401785 | https://github.com/aalborov/openvino_training_extensions/tree/a0bb39424151a98e1ca80c4aa5c865636d401785 |
ConvEncoder | import torch
import numpy as np
import torch.nn as nn
from typing import Tuple
def to_sate_tensor(s, device):
""" converts a numpy array to a Tensor suitable for passing through DQNs """
return torch.from_numpy(s)
class ConvEncoder(nn.Module):
def __init__(self, state_shape: 'Tuple', device=None):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
from ty... | jondeaton/AgarAI | ConvEncoder | false | 6,986 | [
"MIT"
] | 1 | 0c60896465a969ba6832a4b417cf6199715799a1 | https://github.com/jondeaton/AgarAI/tree/0c60896465a969ba6832a4b417cf6199715799a1 |
DIAYNBaselineModel | import torch
import torch.nn as nn
class DIAYNBaselineModel(nn.Module):
"""The model that computes V(s)"""
def __init__(self, n_observations, n_hidden, n_policies):
super().__init__()
self.linear = nn.Linear(n_observations, n_hidden)
self.linear2 = nn.Linear(n_hidden, n_policies)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | Purple-PI/rlstructures | DIAYNBaselineModel | false | 14,252 | [
"MIT"
] | 281 | 9b201b083715bbda2f3534b010c84e11dfc0a1c7 | https://github.com/Purple-PI/rlstructures/tree/9b201b083715bbda2f3534b010c84e11dfc0a1c7 |
Discriminator | # 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.... | tan-huaiyu/Network_science-and-Evolutionary_dynamics | Discriminator | false | 13,024 | [
"Apache-2.0"
] | 0 | 4bdaaed18c6f230213fd69a31144db8e97eb0c7b | https://github.com/tan-huaiyu/Network_science-and-Evolutionary_dynamics/tree/4bdaaed18c6f230213fd69a31144db8e97eb0c7b |
SoftmaxLoss | import torch
import torch.utils.cpp_extension
class SoftmaxLoss(torch.nn.Module):
def __init__(self, tau=1.0):
super().__init__()
self.tau = tau
self.ce_loss = torch.nn.CrossEntropyLoss()
def forward(self, pred, true):
logits = pred / self.tau
l = self.ce_loss(logits,... | 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.cpp... | crobbins327/semanticGAN_WSI | SoftmaxLoss | false | 1,755 | [
"BSD-2-Clause",
"MIT"
] | 0 | 4046ddc822f463e03952402247f79d540bf7be95 | https://github.com/crobbins327/semanticGAN_WSI/tree/4046ddc822f463e03952402247f79d540bf7be95 |
PerOutputClassifierHead | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch.nn import Module
i... | SpyrosMouselinos/DeltaFormers | PerOutputClassifierHead | false | 5,882 | [
"Apache-2.0"
] | 1 | 38508fa9b85f2c50aa0031b67e7e8feff1a75b27 | https://github.com/SpyrosMouselinos/DeltaFormers/tree/38508fa9b85f2c50aa0031b67e7e8feff1a75b27 |
ToRGB | # 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 Function
import math
from torch import nn
from torch.... | BinahHu/stylegan2-pytorch | ToRGB | false | 183 | [
"MIT",
"BSD-2-Clause",
"Apache-2.0"
] | 0 | 9975707ffd93872fce02f7e3654eb588a09e23e4 | https://github.com/BinahHu/stylegan2-pytorch/tree/9975707ffd93872fce02f7e3654eb588a09e23e4 |
ResidualSequential | import torch
import torch.optim
import torch.nn as nn
import torch
import torch.nn.init
class ResidualSequential(nn.Sequential):
def __init__(self, *args):
super(ResidualSequential, self).__init__(*args)
def forward(self, x):
out = super(ResidualSequential, self).forward(x)
x_ = None... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.optim
import torch.nn as nn
import torch
import torch.nn.init
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
e... | Jay-Lewis/phase_retrieval | ResidualSequential | false | 17,446 | [
"MIT"
] | 4 | 799cef92852c53e62e2a548f605652923e979456 | https://github.com/Jay-Lewis/phase_retrieval/tree/799cef92852c53e62e2a548f605652923e979456 |
ExponentialUpdate | # 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.jit import Final
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch.... | Rikorose/clc-dns-challenge-2020 | ExponentialUpdate | false | 8,694 | [
"Apache-2.0"
] | 12 | 4f1c078691327a75b3a338fe372ba356b450a6da | https://github.com/Rikorose/clc-dns-challenge-2020/tree/4f1c078691327a75b3a338fe372ba356b450a6da |
L2Norm | import torch
import torch.nn as nn
import torch.nn.init
class L2Norm(nn.Module):
def __init__(self):
super(L2Norm, self).__init__()
self.eps = 1e-10
def forward(self, x):
norm = torch.sqrt(torch.sum(x * x, dim=1) + self.eps)
x = x / norm.unsqueeze(-1).expand_as(x)
ret... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.nn.init
assert_size_stride = torch._C._dynam... | keeeeenw/image-matching-benchmark-baselines | L2Norm | false | 15,785 | [
"Apache-2.0"
] | 103 | 1a11bedbe3c57f477ab9de302591811115ada37a | https://github.com/keeeeenw/image-matching-benchmark-baselines/tree/1a11bedbe3c57f477ab9de302591811115ada37a |
TOP1Loss | # 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... | Ethan-Yys/GRU4REC-pytorch-master | TOP1Loss | false | 2,218 | [
"Apache-2.0"
] | 0 | 175ccb851f881d3506680c459491e76f50aa9898 | https://github.com/Ethan-Yys/GRU4REC-pytorch-master/tree/175ccb851f881d3506680c459491e76f50aa9898 |
NsKlCriterion | # 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.functi... | johnson7788/mt-dnn | NsKlCriterion | false | 3,899 | [
"MIT"
] | 0 | 26e5c4a5bfdbf1a1dd1c903e606db1c070568237 | https://github.com/johnson7788/mt-dnn/tree/26e5c4a5bfdbf1a1dd1c903e606db1c070568237 |
DepthGTLoss | # 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.... | simon-donne/defusr | DepthGTLoss | false | 16,469 | [
"MIT"
] | 65 | fa4275070af4024eea128e99d7c6df2358d129a5 | https://github.com/simon-donne/defusr/tree/fa4275070af4024eea128e99d7c6df2358d129a5 |
SimpleSumModule | # 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.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.... | andreas-hommel/glow | SimpleSumModule | false | 3,354 | [
"Apache-2.0"
] | 0 | 2bbbf8188a2a941e85677c83f2146bbd076a262e | https://github.com/andreas-hommel/glow/tree/2bbbf8188a2a941e85677c83f2146bbd076a262e |
MLP3_hardsig | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
class MLP3_hardsig(nn.Module):
def __init__(self, width=512, p=0.5):
super(MLP3_hardsig, self).__init__()
self.fc1 = 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.... | RuokaiYin/UnarySim | MLP3_hardsig | false | 5,784 | [
"MIT"
] | 1 | 343ff9abf356a63d526b1df8eb946ad528690a27 | https://github.com/RuokaiYin/UnarySim/tree/343ff9abf356a63d526b1df8eb946ad528690a27 |
MetricLoss | import torch
import torch.nn as nn
import torch.jit
import torch.nn
class MetricLoss(nn.Module):
"""Loss designed to train a true metric, as opposed to a
sigmoid classifier.
"""
def __init__(self):
super(MetricLoss, self).__init__()
def forward(self, input, target):
weight = 1.0 - ta... | 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.jit
import torch.nn
assert_size_stride = torch._C._dyn... | ankmathur96/torchsupport | MetricLoss | false | 3,160 | [
"MIT"
] | 0 | 77bf4a90b8770a408665e2604428808c3ed2f979 | https://github.com/ankmathur96/torchsupport/tree/77bf4a90b8770a408665e2604428808c3ed2f979 |
SwiGLU | # 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... | Actis92/pytorch_tabular | SwiGLU | false | 4,798 | [
"MIT"
] | 1 | 78dabf5e7b97d8ff24db4bc83d9d0a2273941bbe | https://github.com/Actis92/pytorch_tabular/tree/78dabf5e7b97d8ff24db4bc83d9d0a2273941bbe |
ReuseLayerNet | # 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.functional
assert_size_stride = torch._C._dynamo.guards.assert_s... | elad-c/model_optimization | ReuseLayerNet | false | 10,655 | [
"Apache-2.0"
] | 0 | b0ecf41c3f9434008d57d7fe724ff8585e19d4cc | https://github.com/elad-c/model_optimization/tree/b0ecf41c3f9434008d57d7fe724ff8585e19d4cc |
GraphLearner | # 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.... | Originofamonia/vqa-project | GraphLearner | false | 925 | [
"Apache-2.0"
] | 0 | cf67b62ddf5732881dfb4278478accfd15c4df6d | https://github.com/Originofamonia/vqa-project/tree/cf67b62ddf5732881dfb4278478accfd15c4df6d |
BinaryCrossEntropyLabelSmooth | import torch
class BinaryCrossEntropyLabelSmooth(torch.nn.BCEWithLogitsLoss):
def __init__(self, num_classes, epsilon=0.1, weight=None, size_average=
None, reduce=None, reduction='mean', pos_weight=None):
super(BinaryCrossEntropyLabelSmooth, self).__init__(weight,
size_average, reduce... | 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
assert_size... | NehzUx/autodl | BinaryCrossEntropyLabelSmooth | false | 8,580 | [
"Apache-2.0"
] | 25 | c80fdc4b297ed1ec2b9e6911d313f1fe31d83cb9 | https://github.com/NehzUx/autodl/tree/c80fdc4b297ed1ec2b9e6911d313f1fe31d83cb9 |
_AddNorm | import torch
import torch.nn as nn
import torch.nn.functional as F
class _TimeDistributedInterpolation(nn.Module):
def __init__(self, output_size: 'int', batch_first: 'bool'=False,
trainable: 'bool'=False):
super().__init__()
self.output_size = output_size
self.batch_first = batch... | 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.functional as F
assert_size_stride = torc... | amadejkocbek/darts | _AddNorm | false | 12,105 | [
"Apache-2.0"
] | 0 | 074be2a76eee11258da066878c564badf40834e9 | https://github.com/amadejkocbek/darts/tree/074be2a76eee11258da066878c564badf40834e9 |
ConcatenatedAttention | import torch
import torch.optim
import torch.utils.data
from torch import nn
class ConcatenatedAttention(nn.Module):
"""
ConcatenatedAttention module which uses concatenation of encoder and decoder
attention vectors instead of summing them up
"""
def __init__(self, encoder_dim, decoder_dim, atten... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | enesmsahin/ShowAttendTell | ConcatenatedAttention | false | 3,472 | [
"MIT"
] | 0 | ae94b9a61c3b7e6f2302b9fd4477b6a3e14a33fe | https://github.com/enesmsahin/ShowAttendTell/tree/ae94b9a61c3b7e6f2302b9fd4477b6a3e14a33fe |
Rectifier | # 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.optim
assert_size_stride = torch._C._dynamo.guards.ass... | ovechkinVT/SkipRNN | Rectifier | false | 7,433 | [
"MIT"
] | 1 | 7c1f37349d464b1b6bf8835520abad22b199f1ad | https://github.com/ovechkinVT/SkipRNN/tree/7c1f37349d464b1b6bf8835520abad22b199f1ad |
EQ | import torch
class EQ(torch.nn.Module):
def __init__(self):
super(EQ, self).__init__()
def forward(self, x, y):
return x == y
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | PogChamper/torch2trt | EQ | false | 14,180 | [
"MIT"
] | 3,363 | 43b12627ec0de4d212efb6d02b07570205085ccc | https://github.com/PogChamper/torch2trt/tree/43b12627ec0de4d212efb6d02b07570205085ccc |
angularLoss | # 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... | MingzheWu418/plastering | angularLoss | false | 9,322 | [
"MIT"
] | 0 | 322531e934c3acf2ecc8f520b37a6d255b9959c2 | https://github.com/MingzheWu418/plastering/tree/322531e934c3acf2ecc8f520b37a6d255b9959c2 |
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