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 |
|---|---|---|---|---|---|---|---|---|---|---|
GymDqn | from _paritybench_helpers import _mock_config
import torch
from torch.nn import functional as F
from torch import nn
class GymDqn(nn.Module):
def __init__(self, args, action_space):
super(GymDqn, self).__init__()
self.atoms = args.atoms
self.action_space = action_space
self.input_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch.nn import function... | xssstory/Rainbow | GymDqn | false | 4,608 | [
"MIT"
] | 0 | 919a48f5fd67b6860906188b02c1b4dbe729033e | https://github.com/xssstory/Rainbow/tree/919a48f5fd67b6860906188b02c1b4dbe729033e |
Clamp | import torch
import torch.nn as nn
class Clamp(nn.Module):
def __init__(self, min, max):
super(Clamp, self).__init__()
self.min = min
self.max = max
def forward(self, x):
return torch.clamp(x, min=self.min, max=self.max)
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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | AsyaPes/light-reid-master | Clamp | false | 8,963 | [
"MIT"
] | 0 | acb4bdd973cdf3832294d8e42442305ab52014f5 | https://github.com/AsyaPes/light-reid-master/tree/acb4bdd973cdf3832294d8e42442305ab52014f5 |
Disc | # 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
from torch.nn import functional as F
assert_size_stride = t... | bigdata-ustc/DisenQNet | Disc | false | 6,367 | [
"MIT"
] | 1 | 908fadeb9b8d278450213deff70205703bd91da6 | https://github.com/bigdata-ustc/DisenQNet/tree/908fadeb9b8d278450213deff70205703bd91da6 |
F1_Loss | import torch
import torch.nn as nn
class F1_Loss(nn.Module):
"""Calculate F1 score. Can work with gpu tensors
The original implmentation is written by Michal Haltuf on Kaggle.
Returns
-------
torch.Tensor
`ndim` == 1. epsilon <= val <= 1
Reference
---------
- htt... | 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... | Darkgaja/edGNN | F1_Loss | false | 7,942 | [
"MIT"
] | 44 | a7d6bce2f84fccdc2e09b642afe584aa0fb96d81 | https://github.com/Darkgaja/edGNN/tree/a7d6bce2f84fccdc2e09b642afe584aa0fb96d81 |
Sinh | # 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.onnx
import torch.nn as nn
assert_size_stride = torch._C._dynamo.g... | mil-tokyo/webdnn | Sinh | false | 16,110 | [
"MIT"
] | 1,967 | 38a60fd3e1a4e72bc01108189a3aa51e0752aecd | https://github.com/mil-tokyo/webdnn/tree/38a60fd3e1a4e72bc01108189a3aa51e0752aecd |
FlexibleRNN | import torch
import numpy as np
from torch import nn
def create_diag_(A, diag):
""" This code comes is extracted from https://github.com/Lezcano/expRNN, we just repeat it as it is needed by our experiment"""
n = A.size(0)
diag_z = torch.zeros(n - 1)
diag_z[::2] = diag
A_init = torch.diag(diag_z, d... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | EMassart/OrthCDforRNNs | FlexibleRNN | false | 11,420 | [
"MIT"
] | 0 | 487102a4e249ccfbca3062a613011e6cec09ba3a | https://github.com/EMassart/OrthCDforRNNs/tree/487102a4e249ccfbca3062a613011e6cec09ba3a |
SpatialGatherModule | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | CharlesPikachu/mcibi | SpatialGatherModule | false | 7,889 | [
"MIT"
] | 41 | 6ce453504741c2eed1d290306055258a377a4094 | https://github.com/CharlesPikachu/mcibi/tree/6ce453504741c2eed1d290306055258a377a4094 |
NeuralNetMultiplePositionalArgumentsVarKeyword | import torch
import torch.nn
import torch.onnx
class NeuralNetMultiplePositionalArgumentsVarKeyword(torch.nn.Module):
def __init__(self, input_size, hidden_size, num_classes):
super(NeuralNetMultiplePositionalArgumentsVarKeyword, self).__init__()
self.fc1 = torch.nn.Linear(input_size, hidden_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
import torch.nn
import torch.... | RyanUnderhill/onnxruntime | NeuralNetMultiplePositionalArgumentsVarKeyword | false | 11,828 | [
"MIT"
] | 0 | 6df4e293ffbb47d739d2dedfbb87fa6234b8c37c | https://github.com/RyanUnderhill/onnxruntime/tree/6df4e293ffbb47d739d2dedfbb87fa6234b8c37c |
VertexDirectEmbedder | # 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 libdevice
import torch.utils.data
from... | av777x/detectron2 | VertexDirectEmbedder | false | 3,151 | [
"Apache-2.0"
] | 0 | c1794881d6d2fac6af0b3206937d32628677469c | https://github.com/av777x/detectron2/tree/c1794881d6d2fac6af0b3206937d32628677469c |
AdditiveAttention | import torch
import torch.nn as nn
import torch.nn.functional as F
class AdditiveAttention(torch.nn.Module):
"""
A general additive attention module.
Originally for NAML.
"""
def __init__(self, query_vector_dim, candidate_vector_dim, writer=None,
tag=None, names=None):
super(Addit... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Janelovelzy/NewsRecommendation | AdditiveAttention | false | 640 | [
"MIT"
] | 0 | bd2d70e828ffa66ea4cbbd3c6ac09f14e7f0179b | https://github.com/Janelovelzy/NewsRecommendation/tree/bd2d70e828ffa66ea4cbbd3c6ac09f14e7f0179b |
BarlowTwinsLoss | import torch
import torch.nn as nn
class BarlowTwinsLoss(nn.Module):
def __init__(self, batch_size, lambda_coeff=0.005, z_dim=128):
super().__init__()
self.z_dim = z_dim
self.batch_size = batch_size
self.lambda_coeff = lambda_coeff
def off_diagonal_ele(self, x):
n, m ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | ashutoshml/lightning-tutorials | BarlowTwinsLoss | false | 6,262 | [
"Apache-2.0"
] | 1 | 898b8b6f9852c0b80f034a3187bc1cd34dd521ce | https://github.com/ashutoshml/lightning-tutorials/tree/898b8b6f9852c0b80f034a3187bc1cd34dd521ce |
GCN | # 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 ... | Yottaxx/T-LSTM | GCN | false | 18,158 | [
"MIT"
] | 9 | 92618d8c3ee2418b194a2e1592512548da955b77 | https://github.com/Yottaxx/T-LSTM/tree/92618d8c3ee2418b194a2e1592512548da955b77 |
Net | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch._utils
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5, stride=(2, 2))
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5, stride=(2, 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
import torch.nn as nn
import ... | zyouc518/crow | Net | false | 4,740 | [
"Apache-2.0"
] | 0 | e3fe92e329649fb82b3fef6c0ab5b732f1918900 | https://github.com/zyouc518/crow/tree/e3fe92e329649fb82b3fef6c0ab5b732f1918900 |
MaxOut | # 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... | YuxiXie/Semantic-Graphs-for-Generating-Deep-Questions | MaxOut | false | 14,698 | [
"MIT"
] | 62 | 6e5ef241c64b5b30a6ff54ddad31e610013b8388 | https://github.com/YuxiXie/Semantic-Graphs-for-Generating-Deep-Questions/tree/6e5ef241c64b5b30a6ff54ddad31e610013b8388 |
GramMatrix | # 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 as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | invoker4zoo/pytorch_model | GramMatrix | false | 12,536 | [
"MIT"
] | 0 | b74f005ba1be5e66fafaa2745fc7d1815979e91f | https://github.com/invoker4zoo/pytorch_model/tree/b74f005ba1be5e66fafaa2745fc7d1815979e91f |
BiaffineScorer | # 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... | CopticScriptorium/stanza | BiaffineScorer | false | 11,366 | [
"Apache-2.0"
] | 0 | a16b152fce3d2cc325b7d67e03952bd00c878fe3 | https://github.com/CopticScriptorium/stanza/tree/a16b152fce3d2cc325b7d67e03952bd00c878fe3 |
LanguageModelCriterion | import torch
import torch.nn as nn
from torch.autograd import *
def to_contiguous(tensor):
if tensor.is_contiguous():
return tensor
else:
return tensor.contiguous()
class LanguageModelCriterion(nn.Module):
def __init__(self):
super(LanguageModelCriterion, self).__init__()
d... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from torch.autograd import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | curlG0/videotime | LanguageModelCriterion | false | 9,934 | [
"MIT"
] | 0 | 4eba44d148ba2d11f9bf2e9ba3ea9a3ecac70721 | https://github.com/curlG0/videotime/tree/4eba44d148ba2d11f9bf2e9ba3ea9a3ecac70721 |
SIMSE | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.utils.checkpoint
assert_size_stride = torch._C._dynamo... | byamao1/MMSA | SIMSE | false | 14,986 | [
"MIT"
] | 198 | 1a894d042144c9ac75b3465d38871ce8c2987251 | https://github.com/byamao1/MMSA/tree/1a894d042144c9ac75b3465d38871ce8c2987251 |
_MLP_C | import torch
import torch.nn as nn
class _MLP_C(nn.Module):
"""MLP that use DPMs from fcn and age, gender and MMSE"""
def __init__(self, in_size, drop_rate, fil_num):
super(_MLP_C, self).__init__()
self.fc1 = nn.Linear(in_size, fil_num)
self.fc2 = nn.Linear(fil_num, 2)
self.do... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | colorfulbrain/brain2020 | _MLP_C | false | 15,075 | [
"MIT"
] | 91 | 1dde5d34fd2ba1f38bcc38f2c973d167c8c3a168 | https://github.com/colorfulbrain/brain2020/tree/1dde5d34fd2ba1f38bcc38f2c973d167c8c3a168 |
SigmoidFocalClassificationLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | Chuxwa/OpenPCDet | SigmoidFocalClassificationLoss | false | 5,007 | [
"Apache-2.0"
] | 1 | be064eafee68cb23f4bbe7decf2286ef13a94ebb | https://github.com/Chuxwa/OpenPCDet/tree/be064eafee68cb23f4bbe7decf2286ef13a94ebb |
FloorDiv | import torch
class FloorDiv(torch.nn.Module):
def __init__(self):
super(FloorDiv, 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
from torch._inductor.runtime.triton_helpers import libdevice
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_c... | Ilyabasharov/torch2trt | FloorDiv | false | 2,527 | [
"MIT"
] | 0 | 76bf298b3da408509665e23e2494922b131afb10 | https://github.com/Ilyabasharov/torch2trt/tree/76bf298b3da408509665e23e2494922b131afb10 |
Decoder | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | gaozhihan/torchdiffeq | Decoder | false | 6,719 | [
"MIT"
] | 1 | 414781617d595ba01cc3f23382e25ab890f4ca66 | https://github.com/gaozhihan/torchdiffeq/tree/414781617d595ba01cc3f23382e25ab890f4ca66 |
FocalLoss2d | import torch
import torch.nn as nn
class FocalLoss2d(nn.Module):
def __init__(self, alpha=0.25, gamma=2, ignore_index=None, reduction=
'mean', **kwargs):
super(FocalLoss2d, self).__init__()
self.alpha = alpha
self.gamma = gamma
self.smooth = 1e-06
self.ignore_index... | 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
... | WHU-YH-jx/bionetwork_segmentation | FocalLoss2d | false | 5,945 | [
"MIT"
] | 1 | 556c5b61a1a3784875b31eacb8c6bb418d70ee9a | https://github.com/WHU-YH-jx/bionetwork_segmentation/tree/556c5b61a1a3784875b31eacb8c6bb418d70ee9a |
AdaptiveInstanceNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | ankmathur96/torchsupport | AdaptiveInstanceNorm | false | 3,163 | [
"MIT"
] | 0 | 77bf4a90b8770a408665e2604428808c3ed2f979 | https://github.com/ankmathur96/torchsupport/tree/77bf4a90b8770a408665e2604428808c3ed2f979 |
StyledResBlock | import math
import torch
from torch import nn
from torch.nn import functional as F
def make_kernel(k):
k = torch.tensor(k, dtype=torch.float32)
if k.ndim == 1:
k = k[None, :] * k[:, None]
k /= k.sum()
return k
def upfirdn2d_native(input, kernel, up_x, up_y, down_x, down_y, pad_x0,
pad_x1... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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
from to... | ishine/GANsNRoses | StyledResBlock | false | 15,629 | [
"MIT"
] | 969 | 414e9e77c3df47d4ecf7941b5dcfdffec67403ee | https://github.com/ishine/GANsNRoses/tree/414e9e77c3df47d4ecf7941b5dcfdffec67403ee |
Sub | # 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... | Akababa/torch2trt | Sub | false | 18,437 | [
"MIT"
] | 2 | 03063b74a7eb40f5aac88d49be6b8b5e4e4e92d7 | https://github.com/Akababa/torch2trt/tree/03063b74a7eb40f5aac88d49be6b8b5e4e4e92d7 |
Gaussian_Distance | import torch
import torch.nn as nn
class Gaussian_Distance(nn.Module):
def __init__(self, kern=1):
super(Gaussian_Distance, self).__init__()
self.kern = kern
self.avgpool = nn.AvgPool2d(kernel_size=kern, stride=kern)
def forward(self, mu_a, logvar_a, mu_b, logvar_b):
mu_a = 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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | LOUEY233/Toward-Mutual-Information | Gaussian_Distance | false | 9,230 | [
"MIT"
] | 0 | cde9ce5c9920bbc9c6e39dafb61ff1dd0c97772f | https://github.com/LOUEY233/Toward-Mutual-Information/tree/cde9ce5c9920bbc9c6e39dafb61ff1dd0c97772f |
AttendedTextEncoding | # 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.... | CFM-MSG/Code_LEORN | AttendedTextEncoding | false | 4,943 | [
"MIT"
] | 1 | fabea1e1ded973a4db692e51e2df442bde55f626 | https://github.com/CFM-MSG/Code_LEORN/tree/fabea1e1ded973a4db692e51e2df442bde55f626 |
Bottleneck | import torch
import torch.nn as nn
import torch.nn.functional as F
class Bottleneck(nn.Module):
def __init__(self, inplanes, planes, droprate=0.2, attention=None):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, inplanes, kernel_size=1, stride=1,
bias=False)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Galaxies99/alpha-protein | Bottleneck | false | 17,321 | [
"MIT"
] | 4 | db4b77ab48d5905ade5d4a66004f8387773718fa | https://github.com/Galaxies99/alpha-protein/tree/db4b77ab48d5905ade5d4a66004f8387773718fa |
TransposedUpsample | import torch
import torch.nn as nn
class TransposedUpsample(nn.Module):
"""Learned 2x upsampling without padding"""
def __init__(self, channels, out_channels=None, ks=5):
super().__init__()
self.channels = channels
self.out_channels = out_channels or channels
self.up = nn.Conv... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | samedii/latent-diffusion | TransposedUpsample | false | 16,360 | [
"MIT"
] | 563 | f13bf9bf463d95b5a16aeadd2b02abde31f769f8 | https://github.com/samedii/latent-diffusion/tree/f13bf9bf463d95b5a16aeadd2b02abde31f769f8 |
OpenPoseLoss | # 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... | makotovnjp/Talent5OpenPose | OpenPoseLoss | false | 3,973 | [
"Apache-2.0"
] | 0 | 1ebbbd4f226b6839d7d1627d6c33edd416c137fc | https://github.com/makotovnjp/Talent5OpenPose/tree/1ebbbd4f226b6839d7d1627d6c33edd416c137fc |
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
from torch.quantization import QuantStub
from torch.quantization im... | Leslie-Fang/incubator-tvm | Hswish | false | 9,291 | [
"Apache-2.0"
] | 0 | aa035f4650926f5e714b02cbab6d974f0a17352f | https://github.com/Leslie-Fang/incubator-tvm/tree/aa035f4650926f5e714b02cbab6d974f0a17352f |
ScModel | # 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 libdevice, math as tl_math
import torc... | denizcangi/stereoscope | ScModel | false | 9,979 | [
"MIT"
] | 0 | cfe70e5d1e174dedd2d1a0c4a86ae0131e8e4218 | https://github.com/denizcangi/stereoscope/tree/cfe70e5d1e174dedd2d1a0c4a86ae0131e8e4218 |
NeuralNetMultiplePositionalArgumentsMultiOutputsWithDependency | import torch
import torch.nn
import torch.onnx
class NeuralNetMultiplePositionalArgumentsMultiOutputsWithDependency(torch.
nn.Module):
def __init__(self, input_size, hidden_size, num_classes):
super(NeuralNetMultiplePositionalArgumentsMultiOutputsWithDependency,
self).__init__()
s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn
import torch.... | thilow/onnxruntime | NeuralNetMultiplePositionalArgumentsMultiOutputsWithDependency | false | 11,025 | [
"MIT"
] | 0 | 1a3ddf0714e1bdf9b807a342eee5f6e160ad1ec9 | https://github.com/thilow/onnxruntime/tree/1a3ddf0714e1bdf9b807a342eee5f6e160ad1ec9 |
CoughNet | # 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
assert_size_stride = torch._C... | DerWaldi/COVID-19-Cough-Classification | CoughNet | false | 17,243 | [
"MIT"
] | 7 | 40f85133b0b8973c088dc2730c592af1b89b29b7 | https://github.com/DerWaldi/COVID-19-Cough-Classification/tree/40f85133b0b8973c088dc2730c592af1b89b29b7 |
ActorSigmoid | import torch
import torch.nn as nn
class ActorSigmoid(nn.Module):
def __init__(self, state_dim, action_dim, max_action):
super(ActorSigmoid, self).__init__()
self.l3 = nn.Linear(state_dim, action_dim)
self.max_action = max_action
def forward(self, x):
x = self.max_action * to... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | KuangenZhang/StructuredRL | ActorSigmoid | false | 5,461 | [
"MIT"
] | 1 | 9b05e5034ff0e045aabf83786efb0859f08e989a | https://github.com/KuangenZhang/StructuredRL/tree/9b05e5034ff0e045aabf83786efb0859f08e989a |
NegativeCosineSimilarity | import torch
import torch.nn.functional as F
class NegativeCosineSimilarity(torch.nn.Module):
"""Implementation of the Negative Cosine Simililarity used in the
SimSiam[0] paper.
[0] SimSiam, 2020, https://arxiv.org/abs/2011.10566
Examples:
>>> # initialize loss function
>>> loss_fn ... | 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
assert_size_stride = torch._... | jianzhnie/self_supervised | NegativeCosineSimilarity | false | 6,941 | [
"Apache-2.0"
] | 1 | d1e0f31ab032150ab0ad007c1e19773135a5fb79 | https://github.com/jianzhnie/self_supervised/tree/d1e0f31ab032150ab0ad007c1e19773135a5fb79 |
L2Norm | # 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
from math import sqrt as sqrt
from it... | BingzheWu/ssd-pytorch | L2Norm | false | 17,006 | [
"MIT"
] | 7 | bc3f1f5473170082e3b01adb1f4e5d4fb7e0077e | https://github.com/BingzheWu/ssd-pytorch/tree/bc3f1f5473170082e3b01adb1f4e5d4fb7e0077e |
StyleMod | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.functional as F
assert_size_stride = torch... | justinpinkney/ganspace | StyleMod | false | 10,462 | [
"Apache-2.0"
] | 0 | 7dc76d1d2ddad21d946a7ceb375efe5d5316fb3f | https://github.com/justinpinkney/ganspace/tree/7dc76d1d2ddad21d946a7ceb375efe5d5316fb3f |
ConcatBlock | # 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... | HiLab-git/WSL4MIS | ConcatBlock | false | 8,276 | [
"MIT"
] | 29 | 9683e7c7409b95c0ac2169fe7964f6ca04c80d9a | https://github.com/HiLab-git/WSL4MIS/tree/9683e7c7409b95c0ac2169fe7964f6ca04c80d9a |
SoftQNetwork | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Crawford-fang/ROS_pytorch_RL | SoftQNetwork | false | 17,175 | [
"Apache-2.0"
] | 10 | 2d3476f15d51aa1f5b5ae9edc5d7f4c776e5de9f | https://github.com/Crawford-fang/ROS_pytorch_RL/tree/2d3476f15d51aa1f5b5ae9edc5d7f4c776e5de9f |
ActorNetwork | # 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.... | HatemSelim94/RL-MADDPG | ActorNetwork | false | 2,339 | [
"MIT"
] | 0 | 037a722f59e2e461fe6615685b434365fc5540b1 | https://github.com/HatemSelim94/RL-MADDPG/tree/037a722f59e2e461fe6615685b434365fc5540b1 |
UnpackLayerConv2d | # 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 ... | Fatmangh/VIDEO-ACTION-CLASSIFICATION-USING-PRETAINED-SELF-SUPERVISED-DEPTH-AWARE-DENSE-PREDICTIVE-CODING- | UnpackLayerConv2d | false | 2,252 | [
"MIT"
] | 0 | 13fac05601efed16ae8b29989aad487e04cd90a7 | https://github.com/Fatmangh/VIDEO-ACTION-CLASSIFICATION-USING-PRETAINED-SELF-SUPERVISED-DEPTH-AWARE-DENSE-PREDICTIVE-CODING-/tree/13fac05601efed16ae8b29989aad487e04cd90a7 |
VAE_Kl_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.triton_helpers import math as tl_math
import torch.nn as nn
import torch.nn.functional
import torch.nn.parallel... | TPCD/LifelongReID | VAE_Kl_Loss | false | 14,456 | [
"MIT"
] | 63 | cb33f9c29fe398e7546db345fab1c338dda8252f | https://github.com/TPCD/LifelongReID/tree/cb33f9c29fe398e7546db345fab1c338dda8252f |
BentIdentity | import torch
import torch.nn as nn
class BentIdentity(nn.Module):
def forward(self, x, alpha=1.0):
return x + (torch.sqrt(1.0 + x * x) - 1.0) / 2.0
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 as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | awlange/pysurvival | BentIdentity | false | 14,923 | [
"Apache-2.0"
] | 242 | 841b9bc6ce700ba8898d2a1488aa9cd25ee7a8e6 | https://github.com/awlange/pysurvival/tree/841b9bc6ce700ba8898d2a1488aa9cd25ee7a8e6 |
BMNLoss | import torch
import torch.nn.functional as F
import torch.nn as nn
def binary_logistic_regression_loss(reg_score, label, threshold=0.5,
ratio_range=(1.05, 21), eps=1e-05):
"""Binary Logistic Regression Loss."""
label = label.view(-1)
reg_score = reg_score.contiguous().view(-1)
pmask = (label > thr... | import torch
from torch import device
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_ma... | scenarios/dev | BMNLoss | false | 4,471 | [
"Apache-2.0"
] | 0 | 9f91ebc142cea1c31231d233571ad59460ab6fba | https://github.com/scenarios/dev/tree/9f91ebc142cea1c31231d233571ad59460ab6fba |
DICELossMultiClass | # 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... | Rehan-Ahmar/UNet-Zoo | DICELossMultiClass | false | 14,299 | [
"MIT"
] | 345 | 630f9290d487fda828e7118a3d953575b27a2686 | https://github.com/Rehan-Ahmar/UNet-Zoo/tree/630f9290d487fda828e7118a3d953575b27a2686 |
baseRNN_predict | # 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 numpy as np
import tor... | MLforHealth/state_representations_for_RLinHealth | baseRNN_predict | false | 8,534 | [
"MIT"
] | 24 | aa8dbb7d56caa95bf4380e3e745e134996291b66 | https://github.com/MLforHealth/state_representations_for_RLinHealth/tree/aa8dbb7d56caa95bf4380e3e745e134996291b66 |
LayerNorm | import torch
from typing import Callable
from typing import Tuple
import torch.utils.data
from typing import Union
import torch.nn
import torch.cuda
import torch.backends.cudnn
def batch_elementwise(input: 'torch.Tensor', param: 'torch.Tensor', op:
'Callable[[torch.Tensor, torch.Tensor], torch.Tensor]', input_bat... | 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 typing import Callable
from typing import Tuple
import torch.utils.data
fr... | RobertCsordas/modules | LayerNorm | false | 8,706 | [
"BSD-3-Clause"
] | 22 | efdb8790b074862581e035c9ab5bf889440a8023 | https://github.com/RobertCsordas/modules/tree/efdb8790b074862581e035c9ab5bf889440a8023 |
SqueezeExcite | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
from to... | Erfun76/insightface | SqueezeExcite | false | 9,279 | [
"MIT"
] | 0 | 148cef36a43a055f68d2b6a475f4aa38625ad8b4 | https://github.com/Erfun76/insightface/tree/148cef36a43a055f68d2b6a475f4aa38625ad8b4 |
MLP_G | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Linear') != -1:
m.weight.data.normal_(0.0, 0.02)
m.bias.data.fill_(0)
elif classname.find('BatchNorm') != -1:
m.weight.data.norm... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | Huihui-z/CE-GZSL | MLP_G | false | 15,033 | [
"MIT"
] | 58 | 7bf5358ac4727ea1dc2dc9dec2f453b014500bd8 | https://github.com/Huihui-z/CE-GZSL/tree/7bf5358ac4727ea1dc2dc9dec2f453b014500bd8 |
MCRMSE | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | mohsinkhn/standford-covid-vaccine-kaggle | MCRMSE | false | 12,789 | [
"MIT"
] | 0 | fc1e160a6ee67d1ca21dfec3da4dc4863e6bbdba | https://github.com/mohsinkhn/standford-covid-vaccine-kaggle/tree/fc1e160a6ee67d1ca21dfec3da4dc4863e6bbdba |
deconv2d | import torch
import torch.nn as nn
from torch.autograd import Variable
def spectral_norm(module, name='weight'):
SpectralNorm.apply(module, name)
return module
class SpectralNorm:
def __init__(self, name):
self.name = name
def compute_weight(self, module):
weight = getattr(module, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from torch.autograd import Variable
assert_size_stride = t... | vandit15/Self-Supervised-Gans-Pytorch | deconv2d | false | 16,656 | [
"MIT"
] | 66 | 01408fcce3e6cf4795d90c0f9d27e6906d5b59f3 | https://github.com/vandit15/Self-Supervised-Gans-Pytorch/tree/01408fcce3e6cf4795d90c0f9d27e6906d5b59f3 |
TLU | # 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 import nn
from torch.nn import Parameter
from torch.nn.parameter import Parame... | DengpanFu/fast-reid-v0 | TLU | false | 9,081 | [
"Apache-2.0"
] | 0 | e444c0187ccb6ef3b8348f8c5f0c5a0814b3683e | https://github.com/DengpanFu/fast-reid-v0/tree/e444c0187ccb6ef3b8348f8c5f0c5a0814b3683e |
Decoder | import torch
import torch.nn as nn
class Decoder(nn.Module):
def __init__(self, latent_size, out_size):
super().__init__()
self.linear1 = nn.Linear(latent_size, int(out_size / 4))
self.linear2 = nn.Linear(int(out_size / 4), int(out_size / 2))
self.linear3 = nn.Linear(int(out_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
import torch.nn as nn
assert_... | DuneeshaFernando/usad | Decoder | false | 11,388 | [
"BSD-3-Clause"
] | 0 | 22653a96deefe57013b1df57bb6dc316ef423c95 | https://github.com/DuneeshaFernando/usad/tree/22653a96deefe57013b1df57bb6dc316ef423c95 |
BERTLhuc | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
class BERTLhuc(nn.Module):
def __init__(self, config):
super(BERTLhuc, self).__init__()
self.lhuc = Parameter(torch.zeros(config.hidden_size))
def forward(self, hidden_st... | 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.parameter import Parameter
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided... | DAQuestionAnswering/Bert-n-Pals | BERTLhuc | false | 7,035 | [
"MIT"
] | 1 | d5a288b9ac62259e70c249635108ba3906e19f00 | https://github.com/DAQuestionAnswering/Bert-n-Pals/tree/d5a288b9ac62259e70c249635108ba3906e19f00 |
PositionwiseFeedForwardNet | import torch
import torch.nn as nn
class PositionwiseFeedForwardNet(nn.Module):
"""
It's position-wise because this feed forward net will be independently applied to every token's representation.
Representations batch is of the shape (batch size, max token sequence length, model dimension).
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | ozzieba/pytorch-original-transformer | PositionwiseFeedForwardNet | false | 16,212 | [
"MIT"
] | 654 | 4c1e17a701fae050e362e962284fb99547636f75 | https://github.com/ozzieba/pytorch-original-transformer/tree/4c1e17a701fae050e362e962284fb99547636f75 |
ConvNet64 | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | GloryyrolG/normalized-autoencoders | ConvNet64 | false | 554 | [
"MIT"
] | 0 | 27ccb74bb725768f9ba9ea6fa03a7a40867eebb1 | https://github.com/GloryyrolG/normalized-autoencoders/tree/27ccb74bb725768f9ba9ea6fa03a7a40867eebb1 |
loss_shape_exp | # 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
... | Tsinghua-gongjing/StructureImpute | loss_shape_exp | false | 18,049 | [
"MIT"
] | 9 | 59e33e913998a8841c2cb552828f0f0cc19ebc21 | https://github.com/Tsinghua-gongjing/StructureImpute/tree/59e33e913998a8841c2cb552828f0f0cc19ebc21 |
AngleError | import torch
import torch.optim
def _angular_error(gt: 'torch.Tensor', pred: 'torch.Tensor', radians: 'bool'):
relative = gt @ torch.transpose(pred, -2, -1)
trace = relative[:, 0, 0] + relative[:, 1, 1] + relative[:, 2, 2]
trace = torch.clamp(trace, -1.0, 3.0)
phi = 0.5 * (trace - 1.0)
return phi.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | ai-in-motion/moai | AngleError | false | 18,328 | [
"Apache-2.0"
] | 10 | e38cac046c059d2e2331ef4883bbabc5a500a5cf | https://github.com/ai-in-motion/moai/tree/e38cac046c059d2e2331ef4883bbabc5a500a5cf |
ConvNorm | import torch
import torch.utils.data
class ConvNorm(torch.nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=1, stride=1,
padding=None, dilation=1, bias=True, w_init_gain='linear'):
super(ConvNorm, self).__init__()
if padding is None:
assert kernel_size % 2 ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size... | FadyKhalaf/tacotron2 | ConvNorm | false | 439 | [
"BSD-3-Clause"
] | 0 | d9bf28a6d286aab42bce46df9f26a9a3d7c2f01f | https://github.com/FadyKhalaf/tacotron2/tree/d9bf28a6d286aab42bce46df9f26a9a3d7c2f01f |
ShuffleCatAlt | import torch
import torch.nn as nn
class ShuffleCatAlt(nn.Module):
def forward(self, a, b):
assert a.size() == b.size()
n, c, h, w = a.size()
x = torch.zeros(n, c * 2, h, w, dtype=a.dtype, device=a.device)
x[:, ::2] = a
x[:, 1::2] = b
return x
def get_inputs():
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | akaneko1019/yolact_edge | ShuffleCatAlt | false | 14,768 | [
"MIT"
] | 1,036 | a9a00281b33b3ac90253a4939773308a8f95e21d | https://github.com/akaneko1019/yolact_edge/tree/a9a00281b33b3ac90253a4939773308a8f95e21d |
Conv2dZeros | import torch
import torch.nn as nn
class ActNorm(nn.Module):
def __init__(self, num_channels, scale=1.0, logscale_factor=3.0,
batch_variance=False):
"""
Activation normalization layer
:param num_channels: number of channels
:type num_channels: int
:param scale: sc... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.... | NirDiamant/pytorch-glow | Conv2dZeros | false | 908 | [
"MIT"
] | 0 | 2ab11f3a8486b86a279fe4fa64f25aa91226ee8a | https://github.com/NirDiamant/pytorch-glow/tree/2ab11f3a8486b86a279fe4fa64f25aa91226ee8a |
Cartesian | # 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
from torch import nn
import torch.utils.data
import torch.utils.data.dist... | Gaskell-1206/fastMRI | Cartesian | false | 13,697 | [
"MIT"
] | 815 | 1b6d1f9020bc9209afa65ef9b9f2f3fa3348901c | https://github.com/Gaskell-1206/fastMRI/tree/1b6d1f9020bc9209afa65ef9b9f2f3fa3348901c |
Conv3dMaxPool | import torch
from torch import nn
class Conv3dMaxPool(nn.Module):
def __init__(self, out_channels: 'int', in_channels: 'int'):
super().__init__()
self.sat_conv3d = nn.Conv3d(in_channels=in_channels, out_channels=
out_channels, kernel_size=(3, 3, 3), padding=(1, 1, 1))
self.sat... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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... | openclimatefix/predict_pv_yield | Conv3dMaxPool | false | 16,201 | [
"MIT"
] | 47 | 83f27bd392190f1771221e92bfebb879bf562f5d | https://github.com/openclimatefix/predict_pv_yield/tree/83f27bd392190f1771221e92bfebb879bf562f5d |
rbbox_corners_aligned | # 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... | liuhuaijjin/rpn_rois_proposals_layers | rbbox_corners_aligned | false | 7,111 | [
"MIT"
] | 1 | c5f9f09b3ae8c52e4b6fa3fda391f993cb7d42c1 | https://github.com/liuhuaijjin/rpn_rois_proposals_layers/tree/c5f9f09b3ae8c52e4b6fa3fda391f993cb7d42c1 |
BinaryCrossEntropyLoss | # 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... | RndmVariableQ/deep-person-reid | BinaryCrossEntropyLoss | false | 11,858 | [
"MIT"
] | 0 | 9ab8343b2fc2ac130aeca5bc2bd1ae808e9ce1b9 | https://github.com/RndmVariableQ/deep-person-reid/tree/9ab8343b2fc2ac130aeca5bc2bd1ae808e9ce1b9 |
DiceBCELoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class DiceBCELoss(nn.Module):
"""
This loss combines Dice loss with the standard binary cross-entropy (BCE) loss that is generally the default for segmentation models.
Combining the two methods allows for some diversity in the loss, while... | 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... | Exdenta/torchsat | DiceBCELoss | false | 13,648 | [
"MIT"
] | 316 | 70ea3db758757104fb3ba618ddf7997f0f3a75b4 | https://github.com/Exdenta/torchsat/tree/70ea3db758757104fb3ba618ddf7997f0f3a75b4 |
HardAttn | import torch
from torch.nn import functional as F
import torch.nn as nn
from math import *
class HardAttn(nn.Module):
"""Hard Attention (Sec. 3.1.II)"""
def __init__(self, in_channels):
super(HardAttn, self).__init__()
self.fc = nn.Linear(in_channels, 4 * 2)
self.init_params()
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 torch.nn as ... | Helicopt/torchreid-preprocess | HardAttn | false | 537 | [
"MIT"
] | 0 | 2597e502eef079705a5f8a9115a9a1980a9d080d | https://github.com/Helicopt/torchreid-preprocess/tree/2597e502eef079705a5f8a9115a9a1980a9d080d |
SchedulerTestNet | # 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
assert_size_stride = torch._C... | Luab/pytorch-lightning-bolts | SchedulerTestNet | false | 11,714 | [
"Apache-2.0"
] | 0 | b8ac85154465956b06fd1005b21b071af5493f11 | https://github.com/Luab/pytorch-lightning-bolts/tree/b8ac85154465956b06fd1005b21b071af5493f11 |
ExponentialEnvelope | import torch
class ExponentialEnvelope(torch.nn.Module):
"""
Exponential envelope function that ensures a smooth cutoff,
as proposed in Unke, Chmiela, Gastegger, Schütt, Sauceda, Müller 2021.
SpookyNet: Learning Force Fields with Electronic Degrees of Freedom
and Nonlocal Effects
"""
def ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_str... | RolnickLab/ocp | ExponentialEnvelope | false | 2,785 | [
"MIT"
] | 0 | e120c3b90203a46f5fc7626f0b5c8979e4944765 | https://github.com/RolnickLab/ocp/tree/e120c3b90203a46f5fc7626f0b5c8979e4944765 |
SimpleMatmulModule | # 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.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C... | YaronBenAtar/glow | SimpleMatmulModule | false | 14,667 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
ResBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Teemo341/BDNN | ResBlock | false | 2,880 | [
"Apache-2.0"
] | 0 | d53d4634a7a43d038faa049d7dfd10b3578ae267 | https://github.com/Teemo341/BDNN/tree/d53d4634a7a43d038faa049d7dfd10b3578ae267 |
GlobalAvgPool1d | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from abc import abstractmethod
from torch.nn import functional
from typing import *
class AvgPool(nn.Module):
"""
AvgPool Module.
"""
def __init__(self):
super().__init__()
@abstractmet... | 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
from abc import abstractmethod
from typing import ... | Johnsonms/NNI_master | GlobalAvgPool1d | false | 11,588 | [
"MIT"
] | 0 | e5e5c7aed89cf3189cffe1056464833c15eb54ff | https://github.com/Johnsonms/NNI_master/tree/e5e5c7aed89cf3189cffe1056464833c15eb54ff |
SlideNet | import torch
import torch.nn.functional as F
import torch.nn as nn
class SlideNet(nn.Module):
"""
Slided window network
"""
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(in_channels=1, out_channels=10, kernel_size=6)
self.conv2 = nn.Conv2d(in_channels=10, out_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
import torch.nn as nn
assert_... | JHorcasitas/cnn_document_binarization | SlideNet | false | 17,474 | [
"MIT"
] | 9 | 075f76aed375ca14a53011f4dfeb12379debb5b3 | https://github.com/JHorcasitas/cnn_document_binarization/tree/075f76aed375ca14a53011f4dfeb12379debb5b3 |
RpowInt | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_c... | bunderhi/torch2trt | RpowInt | false | 1,650 | [
"MIT"
] | 0 | fa5e31e742a0f0c9a9ee38909a6fa56bb07ba96d | https://github.com/bunderhi/torch2trt/tree/fa5e31e742a0f0c9a9ee38909a6fa56bb07ba96d |
MultiheadAttention | import torch
import torch.nn as nn
import torch._C
import torch.serialization
from torch import optim as optim
class MultiheadAttention(nn.Module):
"""A warpper for torch.nn.MultiheadAttention.
This module implements MultiheadAttention with residual connection,
and positional encoding used in DETR is als... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Atten4Vis/DemystifyLocalViT | MultiheadAttention | false | 13,379 | [
"MIT"
] | 64 | 2e2327caec6d56ae2c8aa861b32bb62f3cdb786e | https://github.com/Atten4Vis/DemystifyLocalViT/tree/2e2327caec6d56ae2c8aa861b32bb62f3cdb786e |
LayerNorm | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_c... | netdrones/ml-agents | LayerNorm | false | 7,331 | [
"Apache-2.0"
] | 1 | 7d7d6f149c92ea2067d7cea364d92c8c3b8db3f4 | https://github.com/netdrones/ml-agents/tree/7d7d6f149c92ea2067d7cea364d92c8c3b8db3f4 |
IDiv | import torch
class IDiv(torch.nn.Module):
def __init__(self):
super(IDiv, self).__init__()
def forward(self, x, y):
x /= y
return x
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
@triton.jit
def triton_poi_fused_div_0(in_ptr0, in_ptr1, out_ptr1, xnumel,... | bunderhi/torch2trt | IDiv | false | 1,591 | [
"MIT"
] | 0 | fa5e31e742a0f0c9a9ee38909a6fa56bb07ba96d | https://github.com/bunderhi/torch2trt/tree/fa5e31e742a0f0c9a9ee38909a6fa56bb07ba96d |
TransposedConv3d | import torch
import torch.nn as nn
import torch.nn.functional as F
class TransposedConv3d(nn.Module):
def __init__(self, in_channels, output_channels, kernel_shape=(3, 3, 3),
stride=(2, 1, 1), padding=(1, 1, 1), output_padding=(1, 0, 0),
activation_fn=F.relu, use_batch_norm=False, use_bias=True):... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | TencentYoutuResearch/ActionDetection-AFSD | TransposedConv3d | false | 14,471 | [
"BSD-3-Clause"
] | 112 | ed86a0df91e58baa7d78c796ed29cff82b1f3fa6 | https://github.com/TencentYoutuResearch/ActionDetection-AFSD/tree/ed86a0df91e58baa7d78c796ed29cff82b1f3fa6 |
LWS | import torch
import torch.nn as nn
class LWS(nn.Module):
def __init__(self, num_features, num_classes, bias=True):
super(LWS, self).__init__()
self.fc = nn.Linear(num_features, num_classes, bias=bias)
self.scales = nn.Parameter(torch.ones(num_classes))
for param_name, param in 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | zhangyongshun/BagofTricks-LT | LWS | false | 16,809 | [
"MIT"
] | 115 | aec4d9a552236c32231374b7b00fa5bf4208dae3 | https://github.com/zhangyongshun/BagofTricks-LT/tree/aec4d9a552236c32231374b7b00fa5bf4208dae3 |
ContrastiveLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.data
asse... | guruprasaad123/all_dl_projects | ContrastiveLoss | false | 6,757 | [
"Apache-2.0"
] | 1 | 04c869f7f001ef94c467740260663d91a34815e0 | https://github.com/guruprasaad123/all_dl_projects/tree/04c869f7f001ef94c467740260663d91a34815e0 |
PredictionLayer | import torch
import torch.nn as nn
from sklearn.metrics import *
class PredictionLayer(nn.Module):
"""
Arguments
- **task**: str, ``"binary"`` for binary logloss or ``"regression"`` for regression loss
- **use_bias**: bool.Whether add bias term or not.
"""
def __init__(self, tas... | 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 sklearn.metrics import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = tor... | chenkkkk/DeepCTR-PyTorch | PredictionLayer | false | 6,430 | [
"Apache-2.0"
] | 1 | a10a3ace4ad79171e7fb182407b3e4d22bf753e7 | https://github.com/chenkkkk/DeepCTR-PyTorch/tree/a10a3ace4ad79171e7fb182407b3e4d22bf753e7 |
InceptionE | import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicConv2d(nn.Module):
def __init__(self, in_channels, out_channels, **kwargs):
super(BasicConv2d, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, bias=True, **kwargs)
def forward(self, x):
x ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | Galaxies99/inception-cuda | InceptionE | false | 11,476 | [
"MIT"
] | 0 | ed8fdbe3caef415e60b52e671273be90e9423e44 | https://github.com/Galaxies99/inception-cuda/tree/ed8fdbe3caef415e60b52e671273be90e9423e44 |
complex_relu_layer | import torch
import torch.nn as nn
class complex_relu_layer(nn.Module):
def __init__(self):
super(complex_relu_layer, self).__init__()
def complex_relu(self, real, img):
mask = 1.0 * (real >= 0)
return mask * real, mask * img
def forward(self, real, img=None):
if img is ... | 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... | XitongZhang1994/SimpleMagNet | complex_relu_layer | false | 5,995 | [
"MIT"
] | 1 | d3df7a2f528474214b7d396ea9831db3aa280090 | https://github.com/XitongZhang1994/SimpleMagNet/tree/d3df7a2f528474214b7d396ea9831db3aa280090 |
ZeroLayer | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
class ZeroLayer(nn.Module):
def __init__(self, stride):
super(ZeroLayer, self).__init__()
self.stride = stride
def forward(self, x):
"""n, c, h, w = x.size()
h //= self.stri... | 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... | HarshCasper/nni | ZeroLayer | false | 5,279 | [
"MIT"
] | 1 | 291bbbba9f296382015a77b2c88eb5db5b44bf94 | https://github.com/HarshCasper/nni/tree/291bbbba9f296382015a77b2c88eb5db5b44bf94 |
GlobalAvgPool2d | # 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._utils
from itertools import product as product
import torch.utils.data.distributed
assert_size_stride = ... | Capetian/FaceX-Zoo | GlobalAvgPool2d | false | 4,957 | [
"Apache-2.0"
] | 1 | 029786c40d8aba15d891d33973de25fcd7e5399a | https://github.com/Capetian/FaceX-Zoo/tree/029786c40d8aba15d891d33973de25fcd7e5399a |
FixedSubnetConv | import math
import torch
import torch.multiprocessing
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torch.nn.functional as F
class FixedSubnetConv(nn.Conv2d):
def __init__(self, *args, **kwargs):
super().__init__(*args... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
import torch.multiprocessing
import torch.nn as nn
import torch.nn.p... | ZeroEi8ht/hidden-networks | FixedSubnetConv | false | 14,721 | [
"Apache-2.0"
] | 132 | ebe13e71d2f60356ee473cd3cff3e14b69d13d70 | https://github.com/ZeroEi8ht/hidden-networks/tree/ebe13e71d2f60356ee473cd3cff3e14b69d13d70 |
LaplacianPyramidLayer | # 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 as nn
from torch.nn import functional as F
assert_size_stride = ... | masanorihirano/pytorch_extra_mhirano | LaplacianPyramidLayer | false | 7,174 | [
"MIT"
] | 1 | d19e07445567c069793b7ca1a22a846d7cbce58d | https://github.com/masanorihirano/pytorch_extra_mhirano/tree/d19e07445567c069793b7ca1a22a846d7cbce58d |
BinaryFocalLossWithLogits | # 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... | shiyangc-intusurg/kornia | BinaryFocalLossWithLogits | false | 16,427 | [
"ECL-2.0",
"Apache-2.0"
] | 4,894 | 2e2512f8f20d300d8732e5873e16336b5a01f3bd | https://github.com/shiyangc-intusurg/kornia/tree/2e2512f8f20d300d8732e5873e16336b5a01f3bd |
NLKDifferenceOffset | import torch
from torch import nn
import torch.nn.functional as F
class NLKDifferenceOffset(nn.Module):
def __init__(self, dim, hidden_dim):
super(NLKDifferenceOffset, self).__init__()
self.dim = dim
self.hidden_dim = hidden_dim
self.layer1 = nn.Linear(self.dim, self.hidden_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.... | HKUST-KnowComp/EFO-1-QA-benchmark | NLKDifferenceOffset | false | 17,364 | [
"MIT"
] | 9 | 600fb02c76ab631f93ee362ceb789216ec085790 | https://github.com/HKUST-KnowComp/EFO-1-QA-benchmark/tree/600fb02c76ab631f93ee362ceb789216ec085790 |
Net16 | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | gautam-sharma1/openRL | Net16 | false | 6,727 | [
"MIT"
] | 1 | 14310a97a328fe5682a01ee85d83a6b5e1ae29ca | https://github.com/gautam-sharma1/openRL/tree/14310a97a328fe5682a01ee85d83a6b5e1ae29ca |
MixtureSoftmax | import torch
import torch.nn as nn
def project_simplex(x):
"""
Project an arbitary vector onto the simplex.
See [Wang & Carreira-Perpin 2013] for a description and references.
"""
n = x.size()[0]
mu = torch.sort(x, 0, descending=True)[0]
sm = 0
for j in xrange(1, n + 1):
sm += ... | 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... | anuar12/deep_game_theory | MixtureSoftmax | false | 6,217 | [
"MIT"
] | 1 | 1debe5a498fe5f017f2791965a5e529b0dfb0529 | https://github.com/anuar12/deep_game_theory/tree/1debe5a498fe5f017f2791965a5e529b0dfb0529 |
DenseBlock | import torch
from torch import nn
from torch.nn import functional as F
class CausalConv1d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=2, dilation=2):
super(CausalConv1d, self).__init__()
self.padding = dilation
self.causal_conv = nn.Conv1d(in_channels, out_channe... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import n... | gaotianyu1350/new_fewrel_bertpair | DenseBlock | false | 15,401 | [
"MIT"
] | 180 | 27184050d476fc93576948fb26680d508a2824bb | https://github.com/gaotianyu1350/new_fewrel_bertpair/tree/27184050d476fc93576948fb26680d508a2824bb |
Attention | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | HeegyuKim/RecSys-MovieLens100k | Attention | false | 2,337 | [
"MIT"
] | 0 | aa3a272e6045d8230ecbabbf94a6f68170a26c9e | https://github.com/HeegyuKim/RecSys-MovieLens100k/tree/aa3a272e6045d8230ecbabbf94a6f68170a26c9e |
ResidualBlock_noBN | import torch
import torch.utils.data
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init
def initialize_weights(net_l, scale=1):
if not isinstance(net_l, list):
net_l = [net_l]
for net in net_l:
for m in net.modules():
if isinstance(m, nn.Conv2d):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | GuoShi28/GCP-Net | ResidualBlock_noBN | false | 8,191 | [
"Apache-2.0"
] | 24 | cef7513fa242343055af64e612429e4384d3c1d7 | https://github.com/GuoShi28/GCP-Net/tree/cef7513fa242343055af64e612429e4384d3c1d7 |
Attention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | HeGuanyuan/ABSA-PyTorch | Attention | false | 2,352 | [
"MIT"
] | 0 | 8244aeb39007a2714ccbfd54629ddbbb013ea87e | https://github.com/HeGuanyuan/ABSA-PyTorch/tree/8244aeb39007a2714ccbfd54629ddbbb013ea87e |
deconv2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from torch.autograd import Variable
assert_size_stride = t... | vandit15/Self-Supervised-Gans-Pytorch | deconv2d | false | 16,656 | [
"MIT"
] | 66 | 01408fcce3e6cf4795d90c0f9d27e6906d5b59f3 | https://github.com/vandit15/Self-Supervised-Gans-Pytorch/tree/01408fcce3e6cf4795d90c0f9d27e6906d5b59f3 |
EqualizedLinear | # 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
import torch.nn as nn
import torch.utils.cpp_extension
assert_size_s... | STomoya/animeface | EqualizedLinear | false | 14,378 | [
"MIT"
] | 61 | 37b3cd26097d7874559d4c152e41e5712b7a1a42 | https://github.com/STomoya/animeface/tree/37b3cd26097d7874559d4c152e41e5712b7a1a42 |
FusionMul | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._... | Singingkettle/SAF-FCOS | FusionMul | false | 18,388 | [
"BSD-2-Clause"
] | 10 | 5d00b83d659552940025923460d02bb2db7d29e8 | https://github.com/Singingkettle/SAF-FCOS/tree/5d00b83d659552940025923460d02bb2db7d29e8 |
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