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 |
|---|---|---|---|---|---|---|---|---|---|---|
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 math as tl_math
import torch.nn as nn
... | Raiselimit/TorchBlocks | FocalLoss | false | 5,747 | [
"MIT"
] | 1 | a5baecb9a2470ff175087475630f2b7db3f7ef51 | https://github.com/Raiselimit/TorchBlocks/tree/a5baecb9a2470ff175087475630f2b7db3f7ef51 |
NodeNetwork | # 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.... | mbrukman/machine-learning-book | NodeNetwork | false | 7,192 | [
"MIT"
] | 1 | f29a0f8aafa63a77081f3bcec68866e33dd41776 | https://github.com/mbrukman/machine-learning-book/tree/f29a0f8aafa63a77081f3bcec68866e33dd41776 |
MultiHeadAttn | # 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.... | admariner/NeMo | MultiHeadAttn | false | 1,394 | [
"Apache-2.0"
] | 0 | e542d7f9063a40afa4119a3b94de4c2c636a37bb | https://github.com/admariner/NeMo/tree/e542d7f9063a40afa4119a3b94de4c2c636a37bb |
SDNE_layer | import torch
import torch.utils.data
import torch.nn as nn
import torch.nn.functional as F
class SDNE_layer(nn.Module):
def __init__(self, num_node, hidden_size1, hidden_size2, droput, alpha,
beta, nu1, nu2):
super(SDNE_layer, self).__init__()
self.num_node = num_node
self.hidden_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | BruceW91/cogdl | SDNE_layer | false | 209 | [
"MIT"
] | 0 | 1ad524375f5ba062103698a0432fc857572a6933 | https://github.com/BruceW91/cogdl/tree/1ad524375f5ba062103698a0432fc857572a6933 |
CosineClassifier | # 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.... | SirRob1997/DomainBed | CosineClassifier | false | 5,856 | [
"MIT"
] | 1 | 7399a2b0a63df48f4b67755a3f33901223d5c8fb | https://github.com/SirRob1997/DomainBed/tree/7399a2b0a63df48f4b67755a3f33901223d5c8fb |
AnyHead | # 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... | DengpanFu/fast-reid-v0 | AnyHead | false | 9,083 | [
"Apache-2.0"
] | 0 | e444c0187ccb6ef3b8348f8c5f0c5a0814b3683e | https://github.com/DengpanFu/fast-reid-v0/tree/e444c0187ccb6ef3b8348f8c5f0c5a0814b3683e |
MultiHeadAttention | import torch
import numpy as np
from torch import nn
import torch.nn.parallel
class MultiHeadAttention(nn.Module):
def __init__(self, heads_count, d_model, dropout_prob):
super().__init__()
assert d_model % heads_count == 0, f'model dim {d_model} not divisible by {heads_count} heads'
self... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | junchen14/video_language | MultiHeadAttention | false | 3,780 | [
"Apache-2.0"
] | 0 | 1d6d304b795501d1e0d56351047a259d992fab23 | https://github.com/junchen14/video_language/tree/1d6d304b795501d1e0d56351047a259d992fab23 |
BaselineDiscreteCritic | # 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 ... | greenstar1151/pytorch-benchmark | BaselineDiscreteCritic | false | 10,444 | [
"BSD-3-Clause"
] | 0 | 8b7808d3be6b7ca1d57f1812e35fd2df5e470f8b | https://github.com/greenstar1151/pytorch-benchmark/tree/8b7808d3be6b7ca1d57f1812e35fd2df5e470f8b |
Model | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch._C
import torch.serialization
assert_size_str... | whu-pzhang/mmsegmentation | Model | false | 4,529 | [
"Apache-2.0"
] | 0 | 46326f63ce411c794d237e986dd3924590d0e75e | https://github.com/whu-pzhang/mmsegmentation/tree/46326f63ce411c794d237e986dd3924590d0e75e |
LinearConvExpansion | # 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 numpy as np
import torch.nn as nn
assert_size_stride = torch._C._dynamo.g... | Jincheng-Sun/Kylearn-pytorch | LinearConvExpansion | false | 645 | [
"MIT"
] | 0 | e72f2ab45a3f4724e843a27bec37664d3612fdca | https://github.com/Jincheng-Sun/Kylearn-pytorch/tree/e72f2ab45a3f4724e843a27bec37664d3612fdca |
Transformer | import torch
import torch as t
import torch.nn as nn
from torch.distributions.categorical import Categorical
from torch.autograd import Variable
import torch.nn.functional as F
import torch.optim as optim
class Transformer(nn.Module):
def __init__(self, input_size, num_actions, hidden_size, learning_rate=
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | LucWeber/2048-RLenv | Transformer | false | 1,014 | [
"MIT"
] | 0 | 6beff54691f0436f0fbca6bdbb9430fd37eab37d | https://github.com/LucWeber/2048-RLenv/tree/6beff54691f0436f0fbca6bdbb9430fd37eab37d |
MultiHead | import math
import torch
from torch import Tensor
from torch.nn import Linear
import torch.nn.functional as F
from torch.nn import Parameter
import torch.utils.data
def uniform(size, tensor):
bound = 1.0 / math.sqrt(size)
if tensor is not None:
tensor.data.uniform_(-bound, bound)
def kaiming_uniform... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | GrumpyZhou/pytorch_geometric | MultiHead | false | 5,263 | [
"MIT"
] | 1 | 88c54e72d3e26ad48e9ccd99e5696c7f19269d94 | https://github.com/GrumpyZhou/pytorch_geometric/tree/88c54e72d3e26ad48e9ccd99e5696c7f19269d94 |
ExpanderConv2d | # 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... | noonespecial009/resnet-variations | ExpanderConv2d | false | 12,837 | [
"MIT"
] | 0 | 11ee33d1855c292b15930a2a2c1d757d1ac85699 | https://github.com/noonespecial009/resnet-variations/tree/11ee33d1855c292b15930a2a2c1d757d1ac85699 |
AE_4D | import torch
import torch.nn as nn
import torch.utils.data
class AE_4D(nn.Module):
def __init__(self, n_features=4):
super(AE_4D, self).__init__()
self.en1 = nn.Linear(n_features, 200)
self.en2 = nn.Linear(200, 100)
self.en3 = nn.Linear(100, 50)
self.en4 = nn.Linear(50, 3)... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | Autoencoders-compression-anomaly/Various-AEs-Compression-Tensorflow | AE_4D | false | 4,888 | [
"Apache-2.0"
] | 1 | 772ba547c2b7d5d90e79382bf4d8a50e4d733210 | https://github.com/Autoencoders-compression-anomaly/Various-AEs-Compression-Tensorflow/tree/772ba547c2b7d5d90e79382bf4d8a50e4d733210 |
MaxLayer | import torch
import torch.nn as nn
class MaxLayer(nn.Module):
def __init__(self):
super(MaxLayer, self).__init__()
def forward(self, a, b):
return torch.max(a, b)
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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | DingXiaoH/Centripetal-SGD | MaxLayer | false | 7,947 | [
"Apache-2.0"
] | 35 | 992dd0fb31ee47a79cb0891f4f231707abd0c5c6 | https://github.com/DingXiaoH/Centripetal-SGD/tree/992dd0fb31ee47a79cb0891f4f231707abd0c5c6 |
CPUReverseForgetMult | import torch
class CPUReverseForgetMult(torch.nn.Module):
def __init__(self):
super(CPUReverseForgetMult, self).__init__()
def forward(self, f, x, hidden_init=None):
result = []
forgets = f.split(1, dim=0)[::-1]
inputs = (f * x).split(1, dim=0)[::-1]
prev_h = hidden_i... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret... | Smerity/pytorch-qrnn | CPUReverseForgetMult | false | 17,934 | [
"BSD-3-Clause"
] | 4 | 907c8ea53f689136fcc50996b6474de967745202 | https://github.com/Smerity/pytorch-qrnn/tree/907c8ea53f689136fcc50996b6474de967745202 |
RBF_Kernel | import torch
import numpy as np
def norm_sq(X, Y):
XX = X.matmul(X.t())
XY = X.matmul(Y.t())
YY = Y.matmul(Y.t())
return -2 * XY + XX.diag().unsqueeze(1) + YY.diag().unsqueeze(0)
class RBF_Kernel(torch.nn.Module):
"""
RBF kernel
:math:`K(x, y) = exp(||x-v||^2 / (2h))
"""
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import numpy as np
assert_size_stride = torch._C._dynamo.guards.assert_size_stri... | JeremyAlain/meta_learning_pacoh | RBF_Kernel | false | 5,385 | [
"MIT"
] | 1 | b4c2c37d9715e74542bab556ac1f5d778cc3409c | https://github.com/JeremyAlain/meta_learning_pacoh/tree/b4c2c37d9715e74542bab556ac1f5d778cc3409c |
DiffLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | Columbine21/TFR-Net | DiffLoss | false | 17,106 | [
"MIT"
] | 7 | 1da01577542e7f477fdf7323ec0696aebc632357 | https://github.com/Columbine21/TFR-Net/tree/1da01577542e7f477fdf7323ec0696aebc632357 |
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.... | RoderickGu/Pretraining_GPT | SelfAttn | false | 17,860 | [
"Apache-2.0"
] | 4 | 0a3ecd38116dc271e273f57490b9b45b660bf401 | https://github.com/RoderickGu/Pretraining_GPT/tree/0a3ecd38116dc271e273f57490b9b45b660bf401 |
FC_Layer | # 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... | loveorchids/omni_torch | FC_Layer | false | 7,119 | [
"Apache-2.0"
] | 1 | 9bd654387619c0cbc6aee9e91482ecc9200138ef | https://github.com/loveorchids/omni_torch/tree/9bd654387619c0cbc6aee9e91482ecc9200138ef |
Decoder_h | import torch
import torch.distributions as dist
import torch.nn as nn
class Decoder_h(nn.Module):
def __init__(self, B, H_dim):
super().__init__()
self.B = B
self.H_dim = H_dim
self.make_parameters()
def make_parameters(self):
self.mu = nn.Linear(self.H_dim, self.B, b... | 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.distributions as dist
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda ... | shaabhishek/pp_lvm | Decoder_h | false | 4,296 | [
"Apache-2.0"
] | 0 | 0fcceb7f004ab01da7c5508b576983b9d4af36c8 | https://github.com/shaabhishek/pp_lvm/tree/0fcceb7f004ab01da7c5508b576983b9d4af36c8 |
SimpleConv2dModule | # 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 torch.jit
import torch... | opti-mix/glow | SimpleConv2dModule | false | 7,398 | [
"Apache-2.0"
] | 1 | 4ba074df5da9822986a23a6679ab592c22660f6d | https://github.com/opti-mix/glow/tree/4ba074df5da9822986a23a6679ab592c22660f6d |
LearnedPositionalEncoding | # 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... | JamesNgo3781/vietocr | LearnedPositionalEncoding | false | 5,377 | [
"Apache-2.0"
] | 1 | 9d311bbeb18c51c8ff90022f07c0463b204407dc | https://github.com/JamesNgo3781/vietocr/tree/9d311bbeb18c51c8ff90022f07c0463b204407dc |
UpConv | import torch
from collections import OrderedDict
import torch.nn as nn
class UpConv(nn.Module):
def __init__(self, in_channels):
super().__init__()
self.up_conv = nn.Sequential(OrderedDict([('up', nn.Upsample(
scale_factor=2)), ('conv', nn.Conv2d(in_channels, in_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 collections import OrderedDict
import torch.nn as nn
assert_size_stride = t... | HCMUS-ROBOTICS/ssdf-perception | UpConv | false | 9,067 | [
"MIT"
] | 0 | c3eb426397a542da49509bb381972c8ff877597b | https://github.com/HCMUS-ROBOTICS/ssdf-perception/tree/c3eb426397a542da49509bb381972c8ff877597b |
CNN | import torch
from torch import nn
import torch.nn.functional as F
class CNN(torch.nn.Module):
"""Basic CNN architecture."""
def __init__(self, in_channels=1):
super(CNN, self).__init__()
self.conv1 = nn.Conv2d(in_channels, 64, 8, 1)
self.conv2 = nn.Conv2d(64, 128, 6, 2)
self.c... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | austereantelope/cleverhans | CNN | false | 12,143 | [
"MIT"
] | 0 | 5d68d538c89257693f9a7491994bb5586d3ec310 | https://github.com/austereantelope/cleverhans/tree/5d68d538c89257693f9a7491994bb5586d3ec310 |
CoxPHLoss | import torch
from torch import Tensor
def cox_ph_loss_sorted(log_h: 'Tensor', events: 'Tensor', eps: 'float'=1e-07
) ->Tensor:
"""Requires the input to be sorted by descending duration time.
See DatasetDurationSorted.
We calculate the negative log of $( rac{h_i}{\\sum_{j \\in R_i} h_j})^d$,
where... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_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 ... | gabrielasuchopar/pycox | CoxPHLoss | false | 3,513 | [
"BSD-2-Clause"
] | 0 | e4ea5f0ee26c6d3e3a468f164de2b7c426376e99 | https://github.com/gabrielasuchopar/pycox/tree/e4ea5f0ee26c6d3e3a468f164de2b7c426376e99 |
HuggingfaceFastGelu | import torch
import torch.nn
import torch.onnx
class HuggingfaceFastGelu(torch.nn.Module):
def forward(self, x):
return 0.5 * x * (1.0 + torch.tanh(x * 0.7978845608 * (1.0 +
0.044715 * x * x)))
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[],... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn
import torch.onnx
assert_size_stride = torch._C._dynamo.guards.... | thilow/onnxruntime | HuggingfaceFastGelu | false | 11,016 | [
"MIT"
] | 0 | 1a3ddf0714e1bdf9b807a342eee5f6e160ad1ec9 | https://github.com/thilow/onnxruntime/tree/1a3ddf0714e1bdf9b807a342eee5f6e160ad1ec9 |
CustomBatchNormAutograd | import torch
import torch.nn as nn
class CustomBatchNormAutograd(nn.Module):
"""
This nn.module implements a custom version of the batch norm operation for MLPs.
The operations called in self.forward track the history if the input tensors have the
flag requires_grad set to True.
"""
def __ini... | 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_... | RaymondKoopmanschap/DL_assignment_code | CustomBatchNormAutograd | false | 973 | [
"MIT"
] | 0 | 68b3290be9fbd6c55433a7585e2cfa18e0f35f5c | https://github.com/RaymondKoopmanschap/DL_assignment_code/tree/68b3290be9fbd6c55433a7585e2cfa18e0f35f5c |
DropConnect | # 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 import device
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
assert_size_stride = torch._C._dynamo.guards.assert_si... | KelvinYang0320/nas-without-training | DropConnect | false | 13,923 | [
"MIT"
] | 385 | 5ed77a06726a73233a5a93b8f70a7172ce570029 | https://github.com/KelvinYang0320/nas-without-training/tree/5ed77a06726a73233a5a93b8f70a7172ce570029 |
Mlp | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | Atten4Vis/DemystifyLocalViT | Mlp | false | 13,354 | [
"MIT"
] | 64 | 2e2327caec6d56ae2c8aa861b32bb62f3cdb786e | https://github.com/Atten4Vis/DemystifyLocalViT/tree/2e2327caec6d56ae2c8aa861b32bb62f3cdb786e |
BinaryActivation | import torch
import torch.nn as nn
import torch.utils
import torch.utils.data.distributed
class BinaryActivation(nn.Module):
def __init__(self):
super(BinaryActivation, self).__init__()
def forward(self, x):
out_forward = torch.sign(x)
mask1 = x < -1
mask2 = x < 0
mas... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils
import torch.utils.data.distributed
assert_size_stride = torch._C._dynamo.guards.assert_size_stride... | CQUlearningsystemgroup/LearningToBinarize | BinaryActivation | false | 4,937 | [
"MIT"
] | 1 | 1ecad897145af65ff52323bf2ec64a2154dc87d6 | https://github.com/CQUlearningsystemgroup/LearningToBinarize/tree/1ecad897145af65ff52323bf2ec64a2154dc87d6 |
PoswiseFeedForwardNet | # 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.... | alisure-fork/CONTA | PoswiseFeedForwardNet | false | 1,418 | [
"MIT"
] | 0 | dde3e5083f45598d859dde889de3ae85c7a416e9 | https://github.com/alisure-fork/CONTA/tree/dde3e5083f45598d859dde889de3ae85c7a416e9 |
ConvNCFBPRLoss | import torch
import torch.nn as nn
class ConvNCFBPRLoss(nn.Module):
""" ConvNCFBPRLoss, based on Bayesian Personalized Ranking,
Shape:
- Pos_score: (N)
- Neg_score: (N), same shape as the Pos_score
- Output: scalar.
Examples::
>>> loss = ConvNCFBPRLoss()
>>> ... | 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
... | BELIEVEfxy/LightSANs | ConvNCFBPRLoss | false | 7,769 | [
"MIT"
] | 17 | 94ce7e59d144dbc787153b8c486cad334790ec6e | https://github.com/BELIEVEfxy/LightSANs/tree/94ce7e59d144dbc787153b8c486cad334790ec6e |
VectorQuantizer | # 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.autograd import Fu... | arampacha/generative_models | VectorQuantizer | false | 1,474 | [
"Apache-2.0"
] | 0 | 34f5a2fc760bbd7f9f9a956d8d8670c9746e5152 | https://github.com/arampacha/generative_models/tree/34f5a2fc760bbd7f9f9a956d8d8670c9746e5152 |
MaskedCrossEntropyCriterion | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch.nn.modules.... | dataJSA/batch7_tomorrow_datascience | MaskedCrossEntropyCriterion | false | 9,982 | [
"MIT"
] | 0 | e2dc6bc59c456fa927e0a1f6d12024ba410f520c | https://github.com/dataJSA/batch7_tomorrow_datascience/tree/e2dc6bc59c456fa927e0a1f6d12024ba410f520c |
PSNRLoss | import torch
import torch.nn as nn
from torch.nn.functional import mse_loss
def psnr_loss(input: 'torch.Tensor', target: 'torch.Tensor', max_val: 'float'
) ->torch.Tensor:
"""Function that computes PSNR
See :class:`~kornia.losses.PSNR` for details.
"""
if not torch.is_tensor(input) or not torch.i... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
from t... | kshitij12345/kornia | PSNRLoss | false | 7,055 | [
"Apache-2.0"
] | 1 | 4fcc9a570dfa38f67ec812c8fdfabe434b3e466e | https://github.com/kshitij12345/kornia/tree/4fcc9a570dfa38f67ec812c8fdfabe434b3e466e |
Asin | import torch
import torch.onnx
import torch.nn as nn
class Asin(nn.Module):
def forward(self, x):
return torch.asin(x)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.onnx
import torch.nn as nn
assert_size_stride = torch._C._dynamo.g... | mil-tokyo/webdnn | Asin | false | 16,063 | [
"MIT"
] | 1,967 | 38a60fd3e1a4e72bc01108189a3aa51e0752aecd | https://github.com/mil-tokyo/webdnn/tree/38a60fd3e1a4e72bc01108189a3aa51e0752aecd |
CombFilter | import torch
import torch.nn as nn
import torch.nn.functional as F
class CombFilter(nn.Module):
def __init__(self, ninputs, fmaps, L):
super().__init__()
self.L = L
self.filt = nn.Conv1d(ninputs, fmaps, 2, dilation=L, bias=False)
r_init_weight = torch.ones(ninputs * fmaps, 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | mansoorcheema/segan_pytorch | CombFilter | false | 10,687 | [
"MIT"
] | 0 | 8f3b401e42cadfd1f8ad57a8ba0e89c16cc7ee65 | https://github.com/mansoorcheema/segan_pytorch/tree/8f3b401e42cadfd1f8ad57a8ba0e89c16cc7ee65 |
EncoderNO2 | import torch
import torch.nn as nn
class EncoderNO2(nn.Module):
def __init__(self, D, H, M):
super().__init__()
self.D = D
self.M = M
self.H = H
self.enc1 = nn.Linear(in_features=self.D, out_features=self.H)
self.enc2 = nn.Linear(in_features=self.H, out_features=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.... | le0x99/deep-generative-modeling | EncoderNO2 | false | 7,067 | [
"MIT"
] | 1 | 40ffd1640dc3e5a6a2b4ba16a1d767034f081475 | https://github.com/le0x99/deep-generative-modeling/tree/40ffd1640dc3e5a6a2b4ba16a1d767034f081475 |
MaxPool2dDynamicSamePadding | # 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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | AustinCai/gmaxup-augmentation | MaxPool2dDynamicSamePadding | false | 72 | [
"MIT"
] | 0 | a64ca0a76eb333e5ce6b217c301d27ca04d73bce | https://github.com/AustinCai/gmaxup-augmentation/tree/a64ca0a76eb333e5ce6b217c301d27ca04d73bce |
SparseDownSampleClose | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.asser... | Anonymous1234321/GuideFormer | SparseDownSampleClose | false | 39 | [
"MIT"
] | 0 | cccee1c5305977a1bc8d0b8df3f1b6ff66bd1736 | https://github.com/Anonymous1234321/GuideFormer/tree/cccee1c5305977a1bc8d0b8df3f1b6ff66bd1736 |
ByteCombine | # 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.... | CUMLSec/stateformer | ByteCombine | false | 7,932 | [
"MIT"
] | 41 | 87cb3c906c43fcff42b2ca820eb6e7fd918d0a1c | https://github.com/CUMLSec/stateformer/tree/87cb3c906c43fcff42b2ca820eb6e7fd918d0a1c |
GroupNorm | import torch
import torch.nn as nn
class GroupNorm(nn.Module):
def __init__(self, c_num, group_num=16, eps=1e-10):
"""
The groupnorm layer from https://arxiv.org/abs/1803.08494
Args:
c_num (int): Number of input channels
group_num (int): Number of group by which to... | 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_... | EKami/EzeeML | GroupNorm | false | 8,034 | [
"MIT"
] | 35 | 21753a0ede7cc1dc675a2dcd09b6306cea2cad56 | https://github.com/EKami/EzeeML/tree/21753a0ede7cc1dc675a2dcd09b6306cea2cad56 |
CCCLoss | # 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... | youqingxiaozhua/ABAW3 | CCCLoss | false | 11,055 | [
"Apache-2.0"
] | 0 | 51ab58ab311ecd6603a8485a45af0dcc39880e69 | https://github.com/youqingxiaozhua/ABAW3/tree/51ab58ab311ecd6603a8485a45af0dcc39880e69 |
TransposedConvModel | # 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
import torch.... | Rohan-Chaudhury/aimet | TransposedConvModel | false | 17,986 | [
"BSD-3-Clause"
] | 3 | 1c38cac8cc0fd32dca40ce5e39940805d29f7a4a | https://github.com/Rohan-Chaudhury/aimet/tree/1c38cac8cc0fd32dca40ce5e39940805d29f7a4a |
DoubleResolutionLayer | import torch
import torch.nn as nn
class DoubleResolutionLayer(nn.Module):
def forward(self, x):
x = nn.functional.interpolate(x, scale_factor=2, mode='nearest')
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | AleksiKnuutila/ganspace | DoubleResolutionLayer | false | 1,916 | [
"Apache-2.0"
] | 0 | 23471a07c8b0d693fa7f1f2dfbb8b34ce22d9d38 | https://github.com/AleksiKnuutila/ganspace/tree/23471a07c8b0d693fa7f1f2dfbb8b34ce22d9d38 |
CMul | import torch
import torch.nn
import torch.nn as nn
import torch.nn.parallel
class CMul(nn.Module):
"""
nn.CMul in Torch7.
"""
def __init__(self):
super(CMul, self).__init__()
def forward(self, x):
return x[0] * x[1]
def __repr__(self):
return self.__class__.__name__
... | 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
import torch.nn as nn
import torch.nn.parallel
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided... | nhonth/DeLF-pytorch | CMul | false | 16,162 | [
"MIT"
] | 315 | 5577a447a0330b9e976cff56a10fc91669216b8c | https://github.com/nhonth/DeLF-pytorch/tree/5577a447a0330b9e976cff56a10fc91669216b8c |
BertSelfAttention | from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
class BertSelfAttention(nn.Module):
def __init__(self, config):
super(BertSelfAttention, self).__init__()
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Worm4047/TVR | BertSelfAttention | false | 14,607 | [
"MIT"
] | 106 | 2a8ce2edbdc0966aef3b84c28872267039f01700 | https://github.com/Worm4047/TVR/tree/2a8ce2edbdc0966aef3b84c28872267039f01700 |
ToRGB | from torch.autograd import Function
import math
import torch
from torch import nn as nn
from torch.nn import functional as F
from torch.nn import init as init
from torchvision.models import vgg as vgg
import torch.utils.data
from torch.utils import data as data
from torch import autograd as autograd
def make_resample... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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 as nn
from ... | Lotayou/BasicSR | ToRGB | false | 2,594 | [
"Apache-2.0",
"MIT"
] | 0 | 6cf9a706dd680d54f7dc26e87318ff79f76c0dbf | https://github.com/Lotayou/BasicSR/tree/6cf9a706dd680d54f7dc26e87318ff79f76c0dbf |
SplitAndConcat | import torch
import torch.nn as nn
import torch.quantization.quantize_fx
import torch.utils.data
class SplitAndConcat(nn.Module):
"""Split the data from split_dim and concatenate in concat_dim.
@param split_dim from which axis the data will be chunk
@param concat_dim to which axis the data will be concat... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.quantization.quantize_fx
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size... | petoor/d2go | SplitAndConcat | false | 10,663 | [
"Apache-2.0"
] | 0 | d0a20d048738f447945d7c948a8d3019a110d2e8 | https://github.com/petoor/d2go/tree/d0a20d048738f447945d7c948a8d3019a110d2e8 |
UnStackDelta | import torch
import torch.nn as nn
class UnStackDelta(nn.Module):
"""Reverse of StackDelta"""
def __init__(self):
super().__init__()
def forward(self, x: 'torch.Tensor'):
assert x.dim() == 4
if x.requires_grad:
out = x.transpose(1, 2).contiguous()
else:
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | maxwellzh/CAT | UnStackDelta | false | 16,022 | [
"Apache-2.0"
] | 237 | b1a9c3f95e84d968593a05bf8b176b5f77b8055e | https://github.com/maxwellzh/CAT/tree/b1a9c3f95e84d968593a05bf8b176b5f77b8055e |
AddReadout | import torch
import torch.nn as nn
class AddReadout(nn.Module):
"""Handles readout operation when `readout` parameter is `add`. Removes `cls_token` or `readout_token` from tensor and adds it to the rest of tensor"""
def __init__(self, start_index=1):
super(AddReadout, self).__init__()
self.s... | 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... | aditya-agrawal-30502/vformer | AddReadout | false | 14,738 | [
"MIT"
] | 90 | e1f4950f980238442ff1dc39a8f0791e4fbc9dac | https://github.com/aditya-agrawal-30502/vformer/tree/e1f4950f980238442ff1dc39a8f0791e4fbc9dac |
SpatialCrossMapLRN | # 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.... | Tagussan/pretrained-models.pytorch | SpatialCrossMapLRN | false | 1,121 | [
"BSD-3-Clause"
] | 0 | 854e6c153c2534dd7cf76a5ec102307ea5171167 | https://github.com/Tagussan/pretrained-models.pytorch/tree/854e6c153c2534dd7cf76a5ec102307ea5171167 |
ConvWS2d | import torch
import torch.nn as nn
import torch.nn.functional as F
def conv_ws_2d(input, weight, bias=None, stride=1, padding=0, dilation=1,
groups=1, eps=1e-05):
c_in = weight.size(0)
weight_flat = weight.view(c_in, -1)
mean = weight_flat.mean(dim=1, keepdim=True).view(c_in, 1, 1, 1)
std = weight... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | Cynicsss/mmdetection | ConvWS2d | false | 8,969 | [
"Apache-2.0"
] | 0 | 89e207fc8c8a7ae3663a5cda53d77b2b94cd1ec8 | https://github.com/Cynicsss/mmdetection/tree/89e207fc8c8a7ae3663a5cda53d77b2b94cd1ec8 |
SimpleAvgPool1dModule | import torch
import torch.nn.functional as F
import torch.jit
import torch.onnx
import torch.nn
class SimpleAvgPool1dModule(torch.nn.Module):
def __init__(self, kernel_size, stride=None, padding=0):
super(SimpleAvgPool1dModule, self).__init__()
self.kernel_size = kernel_size
self.padding ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | briancoutinho/glow | SimpleAvgPool1dModule | false | 12,561 | [
"Apache-2.0"
] | 0 | 4c919d60b3c33296c4109aec8020a1733c98f5b5 | https://github.com/briancoutinho/glow/tree/4c919d60b3c33296c4109aec8020a1733c98f5b5 |
PMA | import math
import torch
import torch.nn.functional as F
import torch.nn as nn
class MAB(nn.Module):
def __init__(self, dim_Q, dim_K, dim_V, num_heads, ln=False):
super(MAB, self).__init__()
self.dim_V = dim_V
self.num_heads = num_heads
self.fc_q = nn.Linear(dim_Q, dim_V)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | AntonValk/BagGraph-Graph-MIL | PMA | false | 16,955 | [
"MIT"
] | 8 | 1447b52b32995cf6c71e731dd1261104cd66ced0 | https://github.com/AntonValk/BagGraph-Graph-MIL/tree/1447b52b32995cf6c71e731dd1261104cd66ced0 |
Net | import torch
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self):
super(Net, 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 * 94 * 94, 120)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | arefmalek/Demographics_Disenfranchisement | Net | false | 6,300 | [
"MIT"
] | 1 | f4ae8c0965cf1b1cab9b245c3f5f54d3b5fe9aba | https://github.com/arefmalek/Demographics_Disenfranchisement/tree/f4ae8c0965cf1b1cab9b245c3f5f54d3b5fe9aba |
Pad | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | icyda17/very-deep-CNNs | Pad | false | 10,219 | [
"Apache-2.0"
] | 0 | c275ef222d50dae90e508345ec3be5adfa5e33ce | https://github.com/icyda17/very-deep-CNNs/tree/c275ef222d50dae90e508345ec3be5adfa5e33ce |
ParsingRelationLoss | # 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.modules
import torch.nn as nn
assert_size_stride = torch.... | Glutamat42/Ultra-Fast-Lane-Detection | ParsingRelationLoss | false | 492 | [
"MIT"
] | 0 | 175448f39574d64a7cc6fd35ec92e3c5539c9837 | https://github.com/Glutamat42/Ultra-Fast-Lane-Detection/tree/175448f39574d64a7cc6fd35ec92e3c5539c9837 |
dce_loss | import torch
from torch import nn
class dce_loss(nn.Module):
def __init__(self, n_classes, feat_dim, init_weight=True):
super(dce_loss, self).__init__()
self.n_classes = n_classes
self.feat_dim = feat_dim
self.centers = nn.Parameter(torch.randn(self.feat_dim, self.
n_c... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | alexalex222/classification_loss | dce_loss | false | 6,174 | [
"MIT"
] | 1 | a61617e0c0d5ecf6e0ff388305dd9f3eaa5cbf94 | https://github.com/alexalex222/classification_loss/tree/a61617e0c0d5ecf6e0ff388305dd9f3eaa5cbf94 |
Intensity | # 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 import device
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guar... | alcinos/SPR | Intensity | false | 3,075 | [
"MIT"
] | 0 | dec8df83eeaa25a1d75ecff0cf4ce4bfae9cab4c | https://github.com/alcinos/SPR/tree/dec8df83eeaa25a1d75ecff0cf4ce4bfae9cab4c |
SimpleMLP | import torch
import torch.optim
import torch.jit
import torch.nn as nn
class SimpleMLP(nn.Module):
def __init__(self, num_in_features: 'int', num_out_features: 'int',
neurons_per_layer: 'int'):
super(SimpleMLP, self).__init__()
self.act = nn.ELU()
self.l_in = nn.Linear(in_features... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.optim
... | plaveczlambert/nonlinearbubbledynamics | SimpleMLP | false | 10,673 | [
"MIT"
] | 0 | 190c5170f7ff6068badeee818c01226c55aaec97 | https://github.com/plaveczlambert/nonlinearbubbledynamics/tree/190c5170f7ff6068badeee818c01226c55aaec97 |
LinearLR | import torch
import torch.nn as nn
class LinearLR(nn.Module):
"""[u * v + res] version of torch.nn.Linear"""
def __init__(self, in_features, out_features, rank_ratio=0.25, bias=
True, device=None, dtype=None):
super().__init__()
sliced_rank = int(min(in_features, out_features) * rank_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | razered/alternate | LinearLR | false | 10,703 | [
"MIT"
] | 0 | 18e876aadc76d5f675cf940549b4bcd6e80a0288 | https://github.com/razered/alternate/tree/18e876aadc76d5f675cf940549b4bcd6e80a0288 |
SpanClassifier | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from torch.nn import BCELoss
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
reinterpret_tensor = torc... | Bhaskers-Blu-Org1/superglue-mtl | SpanClassifier | false | 7,771 | [
"Apache-2.0"
] | 15 | 1eb3e581c0ef3b4c261e0256ec26116d2b657c40 | https://github.com/Bhaskers-Blu-Org1/superglue-mtl/tree/1eb3e581c0ef3b4c261e0256ec26116d2b657c40 |
ResidualBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | haonguyen1107/style_transfer | ResidualBlock | false | 3,574 | [
"MIT"
] | 0 | 8df9b20ce8ebc446cf2c0a67393001b3cf318fed | https://github.com/haonguyen1107/style_transfer/tree/8df9b20ce8ebc446cf2c0a67393001b3cf318fed |
WeightedMCEloss | import torch
import torch.nn.functional
import torch.nn as nn
import torch.nn.functional as F
def centercrop(image, w, h):
_nt, _ct, ht, wt = image.size()
padw, padh = (wt - w) // 2, (ht - h) // 2
if padw > 0 and padh > 0:
image = image[:, :, padh:-padh, padw:-padw]
return image
class Weight... | 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... | HelenGuohx/cv-ferattn-code | WeightedMCEloss | false | 5,300 | [
"MIT"
] | 1 | faa9b7850fe2a0f8c08193bb129b5fec4639d616 | https://github.com/HelenGuohx/cv-ferattn-code/tree/faa9b7850fe2a0f8c08193bb129b5fec4639d616 |
InstanceLoss | # 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
... | ZihaoWang-233/CAMP_iccv19 | InstanceLoss | false | 14,725 | [
"Apache-2.0"
] | 116 | b0ec07908f479e76f7ebddbcfb2199790305240a | https://github.com/ZihaoWang-233/CAMP_iccv19/tree/b0ec07908f479e76f7ebddbcfb2199790305240a |
ShakeResNet | import math
import torch
from torch import nn
from numpy import int64 as int64
import torch.nn.functional as F
from torch.autograd import Variable
class ShakeShake(torch.autograd.Function):
@staticmethod
def forward(ctx, x1, x2, training=True):
if training:
alpha = torch.FloatTensor(x1.si... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import math
from torch import... | Josie-Li/ZazuML-easy_AutoML | ShakeResNet | false | 2,438 | [
"MIT"
] | 0 | e4daabaab9df518c35abdba35a67607d002bee33 | https://github.com/Josie-Li/ZazuML-easy_AutoML/tree/e4daabaab9df518c35abdba35a67607d002bee33 |
hswish | # 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... | Qidian213/NAIC2019 | hswish | false | 960 | [
"MIT"
] | 0 | 23e05a8a096168ccfa4d1743467fdf78ffcaabba | https://github.com/Qidian213/NAIC2019/tree/23e05a8a096168ccfa4d1743467fdf78ffcaabba |
EuclideanDistance | import torch
from torch import Tensor
import torch.utils.data.dataloader
from torch import nn
import torch.nn
def arccosh(x):
"""Compute the arcosh, numerically stable."""
x = torch.clamp(x, min=1 + EPSILON)
a = torch.log(x)
b = torch.log1p(torch.sqrt(x * x - 1) / x)
return a + b
def mdot(x, y):... | 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.dataloader
from torch import nn
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | chen-yuxuan/flair | EuclideanDistance | false | 12,206 | [
"MIT"
] | 0 | 480d2c9afd66ab8d3bf40a676917e84dba3c4cee | https://github.com/chen-yuxuan/flair/tree/480d2c9afd66ab8d3bf40a676917e84dba3c4cee |
RegressionHead | import abc
import torch
import torch.nn as nn
from torch.nn.functional import *
import torch.utils.data.dataset
class BaseHead(nn.Module, metaclass=abc.ABCMeta):
"""Absract class for task heads"""
@abc.abstractmethod
def __init__(self):
super().__init__()
class RegressionHead(BaseHead):
de... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import abc
import t... | mfk3138/jiant | RegressionHead | false | 4,171 | [
"MIT"
] | 0 | 6e67ff1ecb1bb98533c1019a86af4ad2c04c6a64 | https://github.com/mfk3138/jiant/tree/6e67ff1ecb1bb98533c1019a86af4ad2c04c6a64 |
PowerLaw_Compressed_Loss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | taylorjdlee/VoiceSplit | PowerLaw_Compressed_Loss | false | 13,023 | [
"Apache-2.0"
] | 0 | bd914c42ae065bdda95d81a0ce0c173c29bb040f | https://github.com/taylorjdlee/VoiceSplit/tree/bd914c42ae065bdda95d81a0ce0c173c29bb040f |
MaskedL1Loss | # 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... | WeisiX/ITAS3D | MaskedL1Loss | false | 18,103 | [
"MIT"
] | 4 | fc861e0cb2d4516905bfadab5e5e880c2b021832 | https://github.com/WeisiX/ITAS3D/tree/fc861e0cb2d4516905bfadab5e5e880c2b021832 |
A2CNetwork | import torch
import torch.nn as nn
class A2CNetwork(nn.Module):
def __init__(self, input_shape, output_shape, n_features, **kwargs):
super(A2CNetwork, self).__init__()
n_input = input_shape[-1]
n_output = output_shape[0]
self._h1 = nn.Linear(n_input, n_features)
self._h2 =... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | jacarvalho/mushroom-rl-benchmark | A2CNetwork | false | 12,543 | [
"MIT"
] | 0 | 5bc2e9b1a12be33827d6edcd5c5ad49571e11275 | https://github.com/jacarvalho/mushroom-rl-benchmark/tree/5bc2e9b1a12be33827d6edcd5c5ad49571e11275 |
CNormalized_Linear | # 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 math
import ... | edgarvardanyan/CausalDiscoveryToolbox | CNormalized_Linear | false | 10,247 | [
"MIT"
] | 0 | 5497a400440b49a3af14a0c7512bcdd307c9285d | https://github.com/edgarvardanyan/CausalDiscoveryToolbox/tree/5497a400440b49a3af14a0c7512bcdd307c9285d |
GlobalAveragePool | import torch
from torch import nn
class GlobalAveragePool(nn.Module):
"""
Average pooling in an equivariant network
"""
def __init__(self):
"""
"""
super().__init__()
def forward(self, x):
"""
"""
avg = torch.mean(x, dim=[-2, -1], keepdim=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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | ElisevanderPol/symmetrizer | GlobalAveragePool | false | 8,039 | [
"MIT"
] | 16 | 8dae02bee2ba7132ae4fb07e07020767d280842c | https://github.com/ElisevanderPol/symmetrizer/tree/8dae02bee2ba7132ae4fb07e07020767d280842c |
BoundReciprocal | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
assert_size_stride = torch._C._dynamo.guards.assert_siz... | mnmueller/auto_LiRPA | BoundReciprocal | false | 7,287 | [
"BSD-3-Clause"
] | 1 | 55cb270b0b99f07b74541d55706c69fbb9daff66 | https://github.com/mnmueller/auto_LiRPA/tree/55cb270b0b99f07b74541d55706c69fbb9daff66 |
Tanh | # 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... | gndctrl2mjrtm/BNAF | Tanh | false | 12,606 | [
"MIT"
] | 0 | a8ecaa2844b5338f9091e58dd571fdc6a598e2f1 | https://github.com/gndctrl2mjrtm/BNAF/tree/a8ecaa2844b5338f9091e58dd571fdc6a598e2f1 |
MLP | from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
def gelu(x):
return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 *
torch.pow(x, 3))))
class SharedDropout(torch.nn.Module):
def __init__(self, p):
super(SharedDropout, self).__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 libdevice
import math
import ... | albertkx/GeDi | MLP | false | 12,111 | [
"BSD-3-Clause"
] | 0 | 27532eb6ac5dd42d817d25a905401504e916f9fb | https://github.com/albertkx/GeDi/tree/27532eb6ac5dd42d817d25a905401504e916f9fb |
GANFeatLoss | import functools
import torch
from torch import nn as nn
from torch.nn import functional as F
from torch.nn import init as init
from torchvision.models import vgg as vgg
import torch.utils.data
from torch.utils import data as data
from torch import autograd as autograd
def reduce_loss(loss, reduction):
"""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.triton_helpers import math as tl_math
import functools
from torch import nn as nn
from torch.nn import function... | hyunobae/BasicSR | GANFeatLoss | false | 12,524 | [
"Apache-2.0"
] | 0 | f2c2fc6cf28933658816c808f55c95fa20b16483 | https://github.com/hyunobae/BasicSR/tree/f2c2fc6cf28933658816c808f55c95fa20b16483 |
AttentionPool2d | import torch
import torch.nn.functional as F
from torch import nn
class AttentionPool2d(nn.Module):
def __init__(self, spacial_dim: 'int', embed_dim: 'int', num_heads:
'int', output_dim: 'int'=None):
super().__init__()
self.positional_embedding = nn.Parameter(torch.randn(spacial_dim **
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | graceduansu/CLIP | AttentionPool2d | false | 12,492 | [
"MIT"
] | 0 | 14605e2118f43312cc00bf549aec388f5ddf802b | https://github.com/graceduansu/CLIP/tree/14605e2118f43312cc00bf549aec388f5ddf802b |
Decoder5 | # 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.... | MingSun-Tse/Collaborative-Distillation | Decoder5 | false | 14,093 | [
"MIT"
] | 172 | 915712674af82ff91d926d922c14988cce0430f3 | https://github.com/MingSun-Tse/Collaborative-Distillation/tree/915712674af82ff91d926d922c14988cce0430f3 |
MatrixTree | import torch
import torch.nn as nn
import torch.cuda
import torch.distributed
class MatrixTree(nn.Module):
"""Implementation of the matrix-tree theorem for computing marginals
of non-projective dependency parsing. This attention layer is used
in the paper "Learning Structured Text Representations"
:ci... | 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... | ChenRocks/Distill-BERT-Textgen-ONMT | MatrixTree | false | 17,150 | [
"MIT"
] | 7 | d83dd1a95af7513cbfae4a2768f6effc2f3a589f | https://github.com/ChenRocks/Distill-BERT-Textgen-ONMT/tree/d83dd1a95af7513cbfae4a2768f6effc2f3a589f |
Encoder | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn.functional as F
from torch import nn
assert_size_stride = torch.... | GewoonMaarten/spherical-dmri-conv | Encoder | false | 2,303 | [
"MIT"
] | 0 | 6a5bbb31cf70a5f8b839f92e534f49664001ea09 | https://github.com/GewoonMaarten/spherical-dmri-conv/tree/6a5bbb31cf70a5f8b839f92e534f49664001ea09 |
SimpleGeluModule | import torch
import torch.nn.functional as F
import torch.jit
import torch.onnx
import torch.nn
class SimpleGeluModule(torch.nn.Module):
def forward(self, tensor):
return F.gelu(tensor + tensor)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._... | briancoutinho/glow | SimpleGeluModule | false | 12,574 | [
"Apache-2.0"
] | 0 | 4c919d60b3c33296c4109aec8020a1733c98f5b5 | https://github.com/briancoutinho/glow/tree/4c919d60b3c33296c4109aec8020a1733c98f5b5 |
UpBlock | # 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.cuda
import torch.nn as nn
assert_size_stride = torch._C._dynamo.gu... | HalfLemon/kaggle-dstl | UpBlock | false | 13,762 | [
"MIT"
] | 218 | b1d3a518bbbd3503bdf07400841183d2386fd158 | https://github.com/HalfLemon/kaggle-dstl/tree/b1d3a518bbbd3503bdf07400841183d2386fd158 |
VisErrorLossV13 | import torch
import torch.nn.functional as F
from torch import nn
class VisErrorLossV13(nn.Module):
def __init__(self):
super(VisErrorLossV13, self).__init__()
def compute_l1_weighted_loss(self, hm_targets, hm_preds, vismap, ohem=1.0):
"""
:param hm_targets: [batch size, keypoint num... | 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... | gathierry/FashionAI-KeyPointsDetectionOfApparel | VisErrorLossV13 | false | 15,462 | [
"Apache-2.0"
] | 174 | 2e0942b42b4a9cd974cdddc151675738dc8a8cb4 | https://github.com/gathierry/FashionAI-KeyPointsDetectionOfApparel/tree/2e0942b42b4a9cd974cdddc151675738dc8a8cb4 |
cnn_4layer_LeakyRelu | import torch
import torch.nn as nn
import torch.nn.functional as F
class cnn_4layer_LeakyRelu(nn.Module):
def __init__(self, in_ch, in_dim, width=2, linear_size=256, alpha=0.1):
super(cnn_4layer_LeakyRelu, self).__init__()
self.conv1 = nn.Conv2d(in_ch, 4 * width, 4, stride=2, padding=1)
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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | mnmueller/auto_LiRPA | cnn_4layer_LeakyRelu | false | 7,264 | [
"BSD-3-Clause"
] | 1 | 55cb270b0b99f07b74541d55706c69fbb9daff66 | https://github.com/mnmueller/auto_LiRPA/tree/55cb270b0b99f07b74541d55706c69fbb9daff66 |
ActorDDPGNonConvNetwork | # 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.... | ruyueshuo/MaskTrackRCNN | ActorDDPGNonConvNetwork | false | 7,585 | [
"Apache-2.0"
] | 1 | 3c6ada36be3c2b2df32176349ec5c0ee5b24e724 | https://github.com/ruyueshuo/MaskTrackRCNN/tree/3c6ada36be3c2b2df32176349ec5c0ee5b24e724 |
_DynamicGates | # 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... | rro2q2/transfer-learning-aaai21 | _DynamicGates | false | 10,813 | [
"BSD-3-Clause"
] | 0 | f1960540d0608ce1e4d1d64bb4abd29d953f250f | https://github.com/rro2q2/transfer-learning-aaai21/tree/f1960540d0608ce1e4d1d64bb4abd29d953f250f |
NonSaturatingLoss | import torch
import torch.nn.functional as F
def non_saturating_loss(logits, targets):
probs = logits.softmax(1)
log_prob = torch.log(1 - probs + 1e-12)
if targets.ndim == 2:
return -(targets * log_prob).sum(1).mean()
else:
return F.nll_loss(log_prob, targets)
class NonSaturatingLoss... | 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... | DensoITLab/TeachAugment | NonSaturatingLoss | false | 7,983 | [
"BSD-2-Clause"
] | 20 | 66ec099a0afab99e18531c5437182cfe17dc30c8 | https://github.com/DensoITLab/TeachAugment/tree/66ec099a0afab99e18531c5437182cfe17dc30c8 |
CausalSelfAttention | # 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.... | itsdaniele/graphtrans | CausalSelfAttention | false | 3,684 | [
"Apache-2.0"
] | 0 | 9cdf68af725b258deced4424dbcd5942a481ff8d | https://github.com/itsdaniele/graphtrans/tree/9cdf68af725b258deced4424dbcd5942a481ff8d |
Fp32LayerNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.utils.data
import torch.onnx.operators
impor... | DCMMC/chineseocr | Fp32LayerNorm | false | 9,241 | [
"MIT"
] | 0 | 0b8772615239ea7f212b1ab5bc75183e7e9f16b0 | https://github.com/DCMMC/chineseocr/tree/0b8772615239ea7f212b1ab5bc75183e7e9f16b0 |
EltwiseProdScoring | # 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... | cacosandon/speaker_follower_with_objects | EltwiseProdScoring | false | 12,194 | [
"BSD-2-Clause",
"MIT"
] | 0 | f3d454fdbd1c8129887cf4ecc4743d231c7b9555 | https://github.com/cacosandon/speaker_follower_with_objects/tree/f3d454fdbd1c8129887cf4ecc4743d231c7b9555 |
DecoderBlock | # 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.utils.data
impor... | CorentinLemaitre/robosat.pink | DecoderBlock | false | 5,031 | [
"MIT"
] | 1 | 6ec29a4dd4c0cbf953e73818d7338ee68b2451d3 | https://github.com/CorentinLemaitre/robosat.pink/tree/6ec29a4dd4c0cbf953e73818d7338ee68b2451d3 |
VAE | import torch
from torch import nn
import torch.nn.functional as F
class VAE(nn.Module):
def __init__(self, n_features):
super(VAE, self).__init__()
self.fc1 = nn.Linear(n_features, 1000)
self.fc2 = nn.Linear(1000, n_features)
def encode(self, x):
h1 = F.relu(self.fc1(x))
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
import t... | ShengquanChen/stPlus | VAE | false | 11,872 | [
"MIT"
] | 0 | b2af43a4fe78230ddf95cab75c114e25527800e1 | https://github.com/ShengquanChen/stPlus/tree/b2af43a4fe78230ddf95cab75c114e25527800e1 |
Network | import torch
import torch.nn as nn
class Network(nn.Module):
def __init__(self, input_shape, output_shape, n_features, **kwargs):
super(Network, self).__init__()
n_input = input_shape[-1]
n_output = output_shape[0]
self._h1 = nn.Linear(n_input, n_features)
self._h2 = nn.Li... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | TheCamusean/mushroom-rl | Network | false | 2,891 | [
"MIT"
] | 0 | 48585f883e546ea57224b8d446ecb9b8ba90cf73 | https://github.com/TheCamusean/mushroom-rl/tree/48585f883e546ea57224b8d446ecb9b8ba90cf73 |
DropBlock_Ske | # 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... | Levigty/AimCLR | DropBlock_Ske | false | 8,453 | [
"MIT"
] | 25 | 6cd73767f17748792508647355fa324fa63e235d | https://github.com/Levigty/AimCLR/tree/6cd73767f17748792508647355fa324fa63e235d |
SimpleMLP | import torch
import torch.optim
import torch.jit
import torch.nn as nn
class SimpleMLP(nn.Module):
def __init__(self, num_in_features: 'int', num_out_features: 'int',
neurons_per_layer: 'int'):
super(SimpleMLP, self).__init__()
self.act = nn.ELU()
self.l_in = nn.Linear(in_features... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.optim
... | plaveczlambert/deep_euler_tests | SimpleMLP | false | 7,478 | [
"MIT"
] | 1 | a3ceef98ba76bd7a00ccd3c773cd9850311b3b1a | https://github.com/plaveczlambert/deep_euler_tests/tree/a3ceef98ba76bd7a00ccd3c773cd9850311b3b1a |
GlobalAttention | import math
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
from torch.nn import Parameter
import torch.cuda
import torch.distributed
def quantize_weights(W, numbits=8):
W = W.clamp(-2 ** (numbits - 1), 2 ** (numbits - 1))
W = W.mu... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | csk7/CS550-NLP-McGill- | GlobalAttention | false | 9,968 | [
"MIT"
] | 0 | a6f295b88539015d8accdbd410357c42df7c4287 | https://github.com/csk7/CS550-NLP-McGill-/tree/a6f295b88539015d8accdbd410357c42df7c4287 |
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