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 |
|---|---|---|---|---|---|---|---|---|---|---|
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... | MartinHahner/OpenPCDet | SigmoidFocalClassificationLoss | false | 14,006 | [
"Apache-2.0"
] | 1,984 | 9375908d30ee5023355ebdd77041d7f2cbfd7ec8 | https://github.com/MartinHahner/OpenPCDet/tree/9375908d30ee5023355ebdd77041d7f2cbfd7ec8 |
Downsample | # 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.... | BCV-Uniandes/SAMA | Downsample | false | 116 | [
"BSD-3-Clause"
] | 0 | 4c732c71486af17efed17480e363298cb65c851f | https://github.com/BCV-Uniandes/SAMA/tree/4c732c71486af17efed17480e363298cb65c851f |
EqualLinearActModule | import torch
import torch.nn as nn
from copy import deepcopy
from functools import partial
from torch.nn.init import _calculate_correct_fan
def equalized_lr(module, name='weight', gain=2 ** 0.5, mode='fan_in',
lr_mul=1.0):
"""Equalized Learning Rate.
This trick is proposed in:
Progressive Growing of ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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 copy import deepcopy
from functools import partial
fr... | akimotty877/mmediting | EqualLinearActModule | false | 3,068 | [
"Apache-2.0"
] | 0 | cae872d6f3e867ba144c7c0dbc29a0ee1a29e5a6 | https://github.com/akimotty877/mmediting/tree/cae872d6f3e867ba144c7c0dbc29a0ee1a29e5a6 |
MockModule | import torch
from torch import nn
class MockModule(nn.Module):
def __init__(self, k: 'int'):
super().__init__()
self.p = nn.Parameter(torch.tensor(k, dtype=torch.float))
def forward(self, x, y=None):
assert len(x.shape) == 2
out = x + self.p
if y is not None:
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | HiroakiMikami/mlprogram | MockModule | false | 17,365 | [
"MIT"
] | 9 | 573e94c567064705fa65267dd83946bf183197de | https://github.com/HiroakiMikami/mlprogram/tree/573e94c567064705fa65267dd83946bf183197de |
Network | # 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_... | HyperScypion/KMS_Neural_Networks | Network | false | 17,409 | [
"MIT"
] | 6 | 71d0e9c6ee02ea7978ac8ab1b899290743afac7d | https://github.com/HyperScypion/KMS_Neural_Networks/tree/71d0e9c6ee02ea7978ac8ab1b899290743afac7d |
KeypointRCNNPredictor | import torch
import torch.nn as nn
from torch.autograd import *
import torch.utils.data
class KeypointRCNNPredictor(nn.Module):
def __init__(self, in_channels, num_keypoints):
super(KeypointRCNNPredictor, self).__init__()
input_features = in_channels
deconv_kernel = 4
self.kps_sco... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | HeartFu/NeuralBabyTalk | KeypointRCNNPredictor | false | 5,296 | [
"MIT"
] | 1 | acd9f927d3b977c69ff8286bc45f9fb073dd1b6b | https://github.com/HeartFu/NeuralBabyTalk/tree/acd9f927d3b977c69ff8286bc45f9fb073dd1b6b |
Acosh | # 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 | Acosh | false | 16,058 | [
"MIT"
] | 1,967 | 38a60fd3e1a4e72bc01108189a3aa51e0752aecd | https://github.com/mil-tokyo/webdnn/tree/38a60fd3e1a4e72bc01108189a3aa51e0752aecd |
BinaryNLLEntropy | import torch
import torch.nn.functional as F
import torch.utils.data
import torch.nn.init
from torch.nn.modules.loss import _Loss
class BinaryNLLEntropy(_Loss):
def __init__(self, size_average=True):
super(BinaryNLLEntropy, self).__init__()
self.size_average = size_average
def forward(self, ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | ChrisGeishauser/ConvLab-2 | BinaryNLLEntropy | false | 2,210 | [
"Apache-2.0"
] | 0 | 8f55d033c6e2453fdc092c4f504be3973a55e7ea | https://github.com/ChrisGeishauser/ConvLab-2/tree/8f55d033c6e2453fdc092c4f504be3973a55e7ea |
Network | # 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 ... | tae-yeop/Udacity_DRLND_navigation | Network | false | 10,839 | [
"MIT"
] | 0 | dd4a4609c5fe3e00cb4deea3ebd9922dd0772447 | https://github.com/tae-yeop/Udacity_DRLND_navigation/tree/dd4a4609c5fe3e00cb4deea3ebd9922dd0772447 |
Cosine | from _paritybench_helpers import _mock_config
import torch
from torch.optim.lr_scheduler import *
class Cosine(torch.nn.Module):
def __init__(self, config):
super().__init__()
def forward(self, src, tgt):
src = src.float()
tgt = tgt.float()
return (torch.matmul(src, tgt.trans... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.optim.lr... | johnson7788/mt-dnn | Cosine | false | 3,897 | [
"MIT"
] | 0 | 26e5c4a5bfdbf1a1dd1c903e606db1c070568237 | https://github.com/johnson7788/mt-dnn/tree/26e5c4a5bfdbf1a1dd1c903e606db1c070568237 |
MaskedDirectMultiheadAttention | # 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.... | wukevin/RoseTTAFold | MaskedDirectMultiheadAttention | false | 4,563 | [
"MIT"
] | 0 | e3c15dbf4bc1e4f8726e26c63aca1625188da803 | https://github.com/wukevin/RoseTTAFold/tree/e3c15dbf4bc1e4f8726e26c63aca1625188da803 |
Attention | import torch
from torch import nn
from torch.nn import functional as F
from torch.nn import Parameter
from torch import FloatTensor
def new_parameter(*size):
out = Parameter(FloatTensor(*size))
torch.nn.init.xavier_normal(out)
return out
class Attention(nn.Module):
def __init__(self, attention_size... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Yucao42/DeepLearning2019 | Attention | false | 12,010 | [
"MIT"
] | 0 | 90421a85686655e969bc473c60dfafc3558b6f33 | https://github.com/Yucao42/DeepLearning2019/tree/90421a85686655e969bc473c60dfafc3558b6f33 |
SCRM | # 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.... | Tiamat-Tech/ZAQ-code | SCRM | false | 14,538 | [
"MIT"
] | 55 | e7e9f55791e36c6784d58c356d3ced76a7583369 | https://github.com/Tiamat-Tech/ZAQ-code/tree/e7e9f55791e36c6784d58c356d3ced76a7583369 |
AuxiliaryConvolutions | import torch
from torch import nn
import torch.nn.functional as F
from itertools import product as product
import torch.optim
import torch.utils.data
class AuxiliaryConvolutions(nn.Module):
"""
Additional convolutions to produce higher-level feature maps.
"""
def __init__(self):
super(Auxilia... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ite... | gigajet/ICDAR-2019-SROIE | AuxiliaryConvolutions | false | 12,612 | [
"MIT"
] | 0 | 62dd3ecc90600c0bdf8ceece796fc4e555d3bd16 | https://github.com/gigajet/ICDAR-2019-SROIE/tree/62dd3ecc90600c0bdf8ceece796fc4e555d3bd16 |
EncoderLayer | # 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.... | AbubakrHassan/attention-is-all-you-need-pytorch | EncoderLayer | false | 11,182 | [
"MIT"
] | 0 | 2bf9a477dea6271b082556069f3665ffed2745cd | https://github.com/AbubakrHassan/attention-is-all-you-need-pytorch/tree/2bf9a477dea6271b082556069f3665ffed2745cd |
RewardCriterion | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from torch.autograd import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | VISLANG-Lab/MGCL | RewardCriterion | false | 1,171 | [
"MIT"
] | 0 | 22da06ffa7410d9632bfda8eefb1b79e4f660de0 | https://github.com/VISLANG-Lab/MGCL/tree/22da06ffa7410d9632bfda8eefb1b79e4f660de0 |
Actor | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | JieFeng-cse/power-system-rl | Actor | false | 9,191 | [
"MIT"
] | 0 | 8295d14da83a40c755b8e6a14785c53a238f9a64 | https://github.com/JieFeng-cse/power-system-rl/tree/8295d14da83a40c755b8e6a14785c53a238f9a64 |
ShuffleCatAlt | # 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... | rbli-john/yolact_edge | ShuffleCatAlt | false | 12,926 | [
"MIT"
] | 0 | 48305b45baf2154c336884aeb8a98cfc2c0a8cee | https://github.com/rbli-john/yolact_edge/tree/48305b45baf2154c336884aeb8a98cfc2c0a8cee |
MCCLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class MCCLoss(nn.Module):
def __init__(self, eps=1e-06):
super(MCCLoss, self).__init__()
self.eps = eps
def forward(self, y_pred, y_true, w=None):
y_pred = F.softmax(y_pred, dim=1)
y_true_mean = torch.mean(y_t... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | CarlosPena00/pytorch-unet | MCCLoss | false | 215 | [
"MIT"
] | 0 | 8365bace23e4b04b9c5b75cd6720807ea8cac5ab | https://github.com/CarlosPena00/pytorch-unet/tree/8365bace23e4b04b9c5b75cd6720807ea8cac5ab |
ActLog | import torch
import torch.nn as nn
class ActLog(nn.Module):
def __init__(self, eps=1e-06):
super(ActLog, self).__init__()
self.eps = eps
def forward(self, x):
return torch.log(torch.clamp(x, min=self.eps))
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_input... | 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
... | High-East/BCI-ToolBox | ActLog | false | 17,372 | [
"MIT"
] | 10 | 57015ae5fd008e8636889b9afba49c64c3a35ff3 | https://github.com/High-East/BCI-ToolBox/tree/57015ae5fd008e8636889b9afba49c64c3a35ff3 |
ActNorm2D | import torch
import torch.nn as nn
from torch.nn import Parameter
from torch.nn.parameter import Parameter
class ActNorm2D(nn.Module):
def __init__(self, num_channels, eps=1e-05):
super(ActNorm2D, self).__init__()
self.eps = eps
self.num_channels = num_channels
self._log_scale = P... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
from torch.nn import Parameter
from torch.nn.parame... | david-klindt/invertible-resnet | ActNorm2D | false | 3,432 | [
"MIT"
] | 0 | ac6756a7ba5d0dbcb6b4cec43f8b86079318fd89 | https://github.com/david-klindt/invertible-resnet/tree/ac6756a7ba5d0dbcb6b4cec43f8b86079318fd89 |
PositionwiseFeedForward | import torch
import torch.nn as nn
import torch.nn.functional as F
class PositionwiseFeedForward(nn.Module):
""" A two-feed-forward-layer module """
def __init__(self, d_in, d_hid, dropout=0.1):
super().__init__()
self.w_1 = nn.Conv1d(d_in, d_hid, 1)
self.w_2 = nn.Conv1d(d_hid, d_in, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | CSLT-THU/Vivi_3.0 | PositionwiseFeedForward | false | 17,029 | [
"Apache-2.0"
] | 3 | 86996d99d662cd33100755501a971c41ce30ca70 | https://github.com/CSLT-THU/Vivi_3.0/tree/86996d99d662cd33100755501a971c41ce30ca70 |
PositionWiseFFN | # 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.... | ruifan831/NLP-Tutorials | PositionWiseFFN | false | 12,949 | [
"MIT"
] | 0 | d1fe27b2891156be4d8054022b762f758e9113a9 | https://github.com/ruifan831/NLP-Tutorials/tree/d1fe27b2891156be4d8054022b762f758e9113a9 |
Attention | import math
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
class Attention(nn.Module):
def __init__(self, num_heads, model_dim, k_dim=None, v_dim=None,
out_dim=None, temperature=None, dropout=0, score_function=
'scaled_dot_product'):
super(Attention,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | ZhengZixiang/OpenTC | Attention | false | 18,189 | [
"MIT"
] | 5 | 00306c4736d50f8f53c21c1dd0559144a8fcafa9 | https://github.com/ZhengZixiang/OpenTC/tree/00306c4736d50f8f53c21c1dd0559144a8fcafa9 |
TransformerNet | import torch
from torch.nn import functional as F
class ConvLayer(torch.nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride):
super(ConvLayer, self).__init__()
reflection_padding = kernel_size // 2
self.reflection_pad = torch.nn.ReflectionPad2d(reflection_padding... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | JEF1056/Reconstruction-Style | TransformerNet | false | 17,541 | [
"MIT"
] | 6 | 3430d9e9f05c6980ae251cf15b619148a2c899d6 | https://github.com/JEF1056/Reconstruction-Style/tree/3430d9e9f05c6980ae251cf15b619148a2c899d6 |
VAE | import torch
import torch.nn as nn
import torch.utils.data
from math import *
class VAE(nn.Module):
def __init__(self):
super(VAE, self).__init__()
self.fc1 = nn.Linear(784, 400)
self.fc2 = nn.Linear(400, 20)
self.fc3 = nn.Linear(20, 2)
self.fc4 = nn.Linear(2, 20)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | niujinshuchong/stochastic_processes | VAE | false | 4,099 | [
"MIT"
] | 0 | ea2538d2f09c39bec1834df5addd37e0699a88bf | https://github.com/niujinshuchong/stochastic_processes/tree/ea2538d2f09c39bec1834df5addd37e0699a88bf |
ShiftSoftplus | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch.nn import Softplus
assert_size_stride = torch._C._d... | jeah-z/BDE-FGCN-DFT | ShiftSoftplus | false | 3,709 | [
"MIT"
] | 0 | 5542544079642a371f08c8c1f356fa235d895194 | https://github.com/jeah-z/BDE-FGCN-DFT/tree/5542544079642a371f08c8c1f356fa235d895194 |
DWConv | # 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... | LSH9832/MyPythonModules | DWConv | false | 837 | [
"MIT"
] | 0 | 442566a0fbd6ebe2bc20b6914686a1e2663d10c0 | https://github.com/LSH9832/MyPythonModules/tree/442566a0fbd6ebe2bc20b6914686a1e2663d10c0 |
PrimaryCapsule | import torch
import torch.nn as nn
def squash(inputs, axis=-1):
"""
The non-linear activation used in Capsule. It drives the length of a large vector to near 1 and small vector to 0
:param inputs: vectors to be squashed
:param axis: the axis to squash
:return: a Tensor with same size as inputs
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | Arno3165229/Corner_Traffic_Light | PrimaryCapsule | false | 8,881 | [
"BSD-3-Clause"
] | 0 | 91eead49318a3b1e3a9c2295cbe5661cb1074b69 | https://github.com/Arno3165229/Corner_Traffic_Light/tree/91eead49318a3b1e3a9c2295cbe5661cb1074b69 |
SelfAttentionFuseLayer | # 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.... | nju-websoft/Jeeves | SelfAttentionFuseLayer | false | 12,834 | [
"Apache-2.0"
] | 0 | 6c817ed9e9c36a27c1c10a0a3c863ca0e5fdb5c1 | https://github.com/nju-websoft/Jeeves/tree/6c817ed9e9c36a27c1c10a0a3c863ca0e5fdb5c1 |
EncoderLayer | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from functools import partial
def exists(val):
return val is not None
def default(val, d):
return val if exists(val) else d
def orthogonal_matrix_chunk(cols, qr_uniform_q=False, device=None):
unstructured_block = torch.rand... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | wukevin/RoseTTAFold | EncoderLayer | false | 4,573 | [
"MIT"
] | 0 | e3c15dbf4bc1e4f8726e26c63aca1625188da803 | https://github.com/wukevin/RoseTTAFold/tree/e3c15dbf4bc1e4f8726e26c63aca1625188da803 |
NegativeLpSimilarity | import torch
from torch import nn
from abc import abstractmethod
import torch.utils.data
class Similarity(nn.Module):
"""Base class for similarity functions."""
@abstractmethod
def forward(self, x: 'torch.Tensor', y: 'torch.Tensor') ->torch.Tensor:
"""
Compute pair-wise similarities.
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
from abc import abstractmethod
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
... | DimitrisAlivas/StarQE | NegativeLpSimilarity | false | 7,976 | [
"MIT"
] | 11 | c17676e5f1e3f19c0c4c117a50abe2ce22ffef28 | https://github.com/DimitrisAlivas/StarQE/tree/c17676e5f1e3f19c0c4c117a50abe2ce22ffef28 |
l2normalization | import torch
import torch.nn as nn
class l2normalization(nn.Module):
def __init__(self, scale):
super(l2normalization, self).__init__()
self.scale = scale
def forward(self, x, dim=1):
"""out = scale * x / sqrt(\\sum x_i^2)"""
return self.scale * x * x.pow(2).sum(dim).clamp(mi... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | kensakurada/SceneChangeDet | l2normalization | false | 15,800 | [
"MIT"
] | 199 | 0530e0162863fec0c5296188526f0d27e0109814 | https://github.com/kensakurada/SceneChangeDet/tree/0530e0162863fec0c5296188526f0d27e0109814 |
NeuralNet | import torch
import torch.nn as nn
class NeuralNet(nn.Module):
def __init__(self, input_size, hidden_size, num_classes):
super(NeuralNet, self).__init__()
self.l1 = nn.Linear(input_size, hidden_size)
self.l2 = nn.Linear(hidden_size, hidden_size)
self.l3 = nn.Linear(hidden_size, nu... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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_... | Chris01e/Minh-V- | NeuralNet | false | 11,304 | [
"MIT"
] | 0 | 87e080f8583c0658f683e5a82cfa9ba2d116901e | https://github.com/Chris01e/Minh-V-/tree/87e080f8583c0658f683e5a82cfa9ba2d116901e |
NumericalFeaturesEmbedding | # 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.tensorboard
import torch.utils.data
import torch.distributed
assert_size_stride = torch._C._dynamo.guards.assert_size_str... | JoeLambourne/SketchGraphs | NumericalFeaturesEmbedding | false | 1,312 | [
"MIT"
] | 0 | 183c65f82d71d82c62b253092e9b7fa65846a3e6 | https://github.com/JoeLambourne/SketchGraphs/tree/183c65f82d71d82c62b253092e9b7fa65846a3e6 |
MultiHeadQKVAttention | # 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.... | karayanni/torch-scae | MultiHeadQKVAttention | false | 10,423 | [
"Apache-2.0"
] | 0 | e044662d8942d8d1923d13d071f375144cf4a1e8 | https://github.com/karayanni/torch-scae/tree/e044662d8942d8d1923d13d071f375144cf4a1e8 |
ScaledDotProductAttention | import torch
from typing import Optional
import torch.nn.functional as F
import torch.nn as nn
class ScaledDotProductAttention(nn.Module):
"""
Overview:
Implementation of dot product attentionn with scaling.
"""
def __init__(self, d_k: 'int', dropout: 'float'=0.0) ->None:
super(Scaled... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | PaParaZz1/DI-engine | ScaledDotProductAttention | false | 11,840 | [
"Apache-2.0"
] | 0 | b38144117c1ebc6eb860d8637ec8866dfbcdf2de | https://github.com/PaParaZz1/DI-engine/tree/b38144117c1ebc6eb860d8637ec8866dfbcdf2de |
ConvertPointsFromHomogeneous | # 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... | Wizaron/torchgeometry | ConvertPointsFromHomogeneous | false | 5,975 | [
"Apache-2.0"
] | 1 | 59a8d25dd811ded6a139d5c0c2442b06f43dc775 | https://github.com/Wizaron/torchgeometry/tree/59a8d25dd811ded6a139d5c0c2442b06f43dc775 |
Cartesian | import torch
from torch import nn
import torch.utils.data
import torch.utils.data.distributed
import torch.optim
class Cartesian(nn.Module):
def forward(self, x):
r, phi = x[..., 0], x[..., 1]
return torch.stack((r * torch.cos(phi), r * torch.sin(phi)), dim=-1)
def get_inputs():
return [tor... | 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... | aslakey/fastMRI | Cartesian | false | 1,484 | [
"MIT"
] | 0 | e94028aeccfdc70472b453c2ef2f072b40a287c7 | https://github.com/aslakey/fastMRI/tree/e94028aeccfdc70472b453c2ef2f072b40a287c7 |
FeedForward | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import n... | GimmeSpoon/mlp-singer | FeedForward | false | 5,210 | [
"MIT"
] | 1 | 36d10a23c46fa7400994ccd063de79ff089efd5e | https://github.com/GimmeSpoon/mlp-singer/tree/36d10a23c46fa7400994ccd063de79ff089efd5e |
FCNet | import torch
import torch.utils.data
import torch.nn as nn
from torch.nn.utils import weight_norm
import torch.nn.modules.module
class FCNet(nn.Module):
def __init__(self, in_size, out_size, activate=None, drop=0.0):
super(FCNet, self).__init__()
self.lin = weight_norm(nn.Linear(in_size, 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.triton_helpers import libdevice
import torch.utils.... | ChCh1999/RTPB | FCNet | false | 17,088 | [
"MIT"
] | 8 | 1066a3bfe4fe1b41eff74fd152936880302a60a2 | https://github.com/ChCh1999/RTPB/tree/1066a3bfe4fe1b41eff74fd152936880302a60a2 |
DeiTEmbeddings | # 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 collections
from torch import nn
import torch.utils.checkpoint
import col... | ncoop57/transformers | DeiTEmbeddings | false | 4,059 | [
"Apache-2.0"
] | 0 | d7e156bd1ae2467e9ea1dbc44f31da0ed2296aee | https://github.com/ncoop57/transformers/tree/d7e156bd1ae2467e9ea1dbc44f31da0ed2296aee |
ThresholdedRelu | import torch
from torch import nn
import torch.onnx
class ThresholdedRelu(nn.Module):
def __init__(self, alpha=1.0):
self.alpha = alpha
super().__init__()
def forward(self, X: 'torch.Tensor'):
Y = torch.clamp(X, min=self.alpha)
Y[Y == self.alpha] = 0.0
return Y
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 import triton_helpers
from torch import nn
import torch.onnx
assert_size_stride = torch._C._dynamo.guards.asser... | Piteryo/onnx2pytorch | ThresholdedRelu | false | 9,444 | [
"Apache-2.0"
] | 0 | c25b3a5289ee7073d644d280a112c15382b7f690 | https://github.com/Piteryo/onnx2pytorch/tree/c25b3a5289ee7073d644d280a112c15382b7f690 |
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 import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import... | rotorliu/DALI | L2Norm | false | 7,578 | [
"ECL-2.0",
"Apache-2.0"
] | 1 | 4ea3529fc9b35cbdf09b260ec95197cfd52c0395 | https://github.com/rotorliu/DALI/tree/4ea3529fc9b35cbdf09b260ec95197cfd52c0395 |
Critic | import torch
import torch.nn as nn
import torch.nn.functional as F
class CNN_encoder(nn.Module):
def __init__(self):
super(CNN_encoder, self).__init__()
self.net = nn.Sequential(nn.Conv2d(4, 8, kernel_size=3, padding=1,
stride=1), nn.ReLU(), nn.MaxPool2d(4, 2), nn.Conv2d(8, 8,
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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_... | Lttcc/Olympics | Critic | false | 781 | [
"MIT"
] | 0 | 97411244073d127e83e84bf61b1b0a1d6718c31c | https://github.com/Lttcc/Olympics/tree/97411244073d127e83e84bf61b1b0a1d6718c31c |
FClipTest | import torch
import numpy as np
import torch.nn as nn
class FClipTest(nn.Module):
"""
Test for nn.functional types
"""
def __init__(self):
self.low = np.random.uniform(-1, 1)
self.high = np.random.uniform(1, 2)
super(FClipTest, self).__init__()
def forward(self, x):
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import numpy as np
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.ass... | dawnclaude/onnx2keras | FClipTest | false | 15,127 | [
"MIT"
] | 115 | 3d2a47c0a228b91fd434232274e216e491da36e3 | https://github.com/dawnclaude/onnx2keras/tree/3d2a47c0a228b91fd434232274e216e491da36e3 |
UpSampling | import torch
import torch.nn as nn
class UpSampling(nn.Module):
def __init__(self, in_c):
super().__init__()
self.unpool1 = nn.Upsample(scale_factor=2)
self.conv1 = nn.Conv1d(in_c, in_c, 3, padding=1)
self.unpool2 = nn.Upsample(scale_factor=2)
self.conv2 = nn.Conv1d(in_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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | PatrickChoDev/LiDAR-ObjDetect | UpSampling | false | 2,729 | [
"MIT"
] | 0 | a839220d28a1fda045278ded0992e46f408a5442 | https://github.com/PatrickChoDev/LiDAR-ObjDetect/tree/a839220d28a1fda045278ded0992e46f408a5442 |
Mask_BN | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | XIAOYEJIAYOU/GSAN | Mask_BN | false | 18,086 | [
"MIT"
] | 6 | 8ca4fdf4c3d615af9cc10e1f9f22ceb7e27fe196 | https://github.com/XIAOYEJIAYOU/GSAN/tree/8ca4fdf4c3d615af9cc10e1f9f22ceb7e27fe196 |
GCN1 | from torch.nn import Module
import math
import torch
import torch.nn.functional as F
from torch import nn
from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module
class sparse_dropout(Module):
"""
Sparse dropout implementation
"""
def __init__(self):
super(sparse_dr... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.nn import Module
import math
import torch.nn.functional as F
from tor... | Eudialyte/SepGAT | GCN1 | false | 446 | [
"MIT"
] | 0 | 6ea77714d1b2f2f5d0857cddcc9f1f5f9c0bcf50 | https://github.com/Eudialyte/SepGAT/tree/6ea77714d1b2f2f5d0857cddcc9f1f5f9c0bcf50 |
Upsample | # 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... | Liang813/GaitGraph | Upsample | false | 13,991 | [
"MIT"
] | 57 | df8cfd8d1e7a91a738190ba68bc52a67207188e5 | https://github.com/Liang813/GaitGraph/tree/df8cfd8d1e7a91a738190ba68bc52a67207188e5 |
SoftCrossEntropyLoss | # 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.backends im... | PushparajaMurugan/dauphin | SoftCrossEntropyLoss | false | 8,677 | [
"Apache-2.0"
] | 18 | 4d9832c72288282e6b3d03be1b0ad8708282b005 | https://github.com/PushparajaMurugan/dauphin/tree/4d9832c72288282e6b3d03be1b0ad8708282b005 |
GaussianBlock | # 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 math
import torch.nn a... | bouracha/Gen_Motion | GaussianBlock | false | 9,927 | [
"MIT"
] | 0 | 873caa496d14c9a9723581cdf1464f44db4cf358 | https://github.com/bouracha/Gen_Motion/tree/873caa496d14c9a9723581cdf1464f44db4cf358 |
FeedForward | # 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.... | microsoft/Protein-Folding | FeedForward | false | 7,227 | [
"MIT"
] | 1 | f534b2dd1e3f192fbcdadf234f25828c7f458a58 | https://github.com/microsoft/Protein-Folding/tree/f534b2dd1e3f192fbcdadf234f25828c7f458a58 |
ActNorm | import torch
import torch.nn as nn
class ActNorm(nn.Module):
"""
ActNorm layer.
[Kingma and Dhariwal, 2018.]
"""
def __init__(self, dim):
super().__init__()
self.dim = dim
self.mu = nn.Parameter(torch.zeros(dim, dtype=torch.float))
self.log_sigma = nn.Parameter(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 math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | jsk389/RGB-PowerSpectra-v2 | ActNorm | false | 6,985 | [
"MIT"
] | 1 | 47ca7cae256ad09a7e5a40fe9da82d48c32ff7cc | https://github.com/jsk389/RGB-PowerSpectra-v2/tree/47ca7cae256ad09a7e5a40fe9da82d48c32ff7cc |
AttentionUnit | import torch
from torch import nn
import torch.nn.functional as F
from torch.nn import init
class AttentionUnit(nn.Module):
def __init__(self, sDim, xDim, attDim):
super(AttentionUnit, self).__init__()
self.sDim = sDim
self.xDim = xDim
self.attDim = attDim
self.sEmbed = nn... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | lohzhunyewcs/aster.pytorch | AttentionUnit | false | 10,484 | [
"MIT"
] | 0 | 9441d386135a73b1baa3ec8c505f5eba99c26905 | https://github.com/lohzhunyewcs/aster.pytorch/tree/9441d386135a73b1baa3ec8c505f5eba99c26905 |
ChannelMixer | import torch
import torch.nn.functional as F
from torch import nn
class FeedForward(nn.Module):
def __init__(self, num_features, expansion_factor, dropout):
super().__init__()
num_hidden = expansion_factor * num_features
self.fc1 = nn.Linear(num_features, num_hidden)
self.fc2 = nn... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.fun... | GimmeSpoon/mlp-singer | ChannelMixer | false | 5,211 | [
"MIT"
] | 1 | 36d10a23c46fa7400994ccd063de79ff089efd5e | https://github.com/GimmeSpoon/mlp-singer/tree/36d10a23c46fa7400994ccd063de79ff089efd5e |
KLLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim
import torch._utils
import torch.nn
class KLLoss(nn.Module):
"""
KL Divergence loss
"""
def __init__(self, norm='softmax', loss_weight=1.0):
super(KLLoss, self).__init__()
self.loss_weight = loss_wei... | 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... | ModelTC/EOD | KLLoss | false | 14,082 | [
"Apache-2.0"
] | 196 | 164bff80486e9ae6a095a97667b365c46ceabd86 | https://github.com/ModelTC/EOD/tree/164bff80486e9ae6a095a97667b365c46ceabd86 |
TransformerLayer | # 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.... | DDGRCF/YOLOX_OBB | TransformerLayer | false | 7,986 | [
"Apache-2.0"
] | 39 | 27b80953306492b8bc83b86b1353d8cee01ef9b6 | https://github.com/DDGRCF/YOLOX_OBB/tree/27b80953306492b8bc83b86b1353d8cee01ef9b6 |
_leaky_relu | # 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
import torch.optim
import torch.utils.data
assert_size_stride = torc... | ap229997/cc | _leaky_relu | false | 9,828 | [
"MIT"
] | 0 | d6f272b8270a371c877f4315047610b33a6e9f2d | https://github.com/ap229997/cc/tree/d6f272b8270a371c877f4315047610b33a6e9f2d |
Sparsify1D | import torch
import torch.nn as nn
class SparsifyBase(nn.Module):
def __init__(self, sparse_ratio=0.5):
super(SparsifyBase, self).__init__()
self.sr = sparse_ratio
self.preact = None
self.act = None
def get_activation(self):
def hook(model, input, output):
... | 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... | jmhuer/TCN | Sparsify1D | false | 10,301 | [
"MIT"
] | 0 | 8233b2ff5686ef496b113a6984f5100709a503d3 | https://github.com/jmhuer/TCN/tree/8233b2ff5686ef496b113a6984f5100709a503d3 |
VectorQuantizer | import torch
from torch import Tensor
from torch import nn
from torch.nn import functional as F
class VectorQuantizer(nn.Module):
"""
Reference:
[1] https://github.com/deepmind/sonnet/blob/v2/sonnet/src/nets/vqvae.py
"""
def __init__(self, num_embeddings: 'int', embedding_dim: 'int', beta:
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | ClaartjeBarkhof/PyTorch-VAE | VectorQuantizer | false | 2,131 | [
"Apache-2.0"
] | 0 | a1ac49015c306b1cfc0d4d797669b17044f0a1eb | https://github.com/ClaartjeBarkhof/PyTorch-VAE/tree/a1ac49015c306b1cfc0d4d797669b17044f0a1eb |
ActorNet | # 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... | mathildebadoual/RL_power_systems | ActorNet | false | 7,175 | [
"MIT"
] | 1 | 825e60bad16129e0a0229d15af5110b26e0a1577 | https://github.com/mathildebadoual/RL_power_systems/tree/825e60bad16129e0a0229d15af5110b26e0a1577 |
EqualConv2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from math import sqrt
assert_size_stride = torch._C._dynam... | KwonGihyun/DiagonalGAN | EqualConv2d | false | 8,424 | [
"MIT"
] | 13 | 9e401c00e741d700f85df2c715ee11c1e66e1d1c | https://github.com/KwonGihyun/DiagonalGAN/tree/9e401c00e741d700f85df2c715ee11c1e66e1d1c |
SoftDiceLoss | import torch
import torch.nn as nn
class SoftDiceLoss(nn.Module):
def __init__(self, weight=None, size_average=True):
super().__init__()
def forward(self, inputs, targets):
smooth = 1.0
num = targets.size(0)
m1 = inputs.view(num, -1)
m2 = targets.view(num, -1)
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | Luoxd1996/Rank2nuclearSegmentation | SoftDiceLoss | false | 17,627 | [
"MIT"
] | 5 | bd85ac13eec7ce18c286efd521a27486483da904 | https://github.com/Luoxd1996/Rank2nuclearSegmentation/tree/bd85ac13eec7ce18c286efd521a27486483da904 |
TripletLoss | # 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... | Leo-xxx/lighttrack | TripletLoss | false | 5,498 | [
"MIT"
] | 1 | bc12f53c621c42038066a1af7499838b571b0c76 | https://github.com/Leo-xxx/lighttrack/tree/bc12f53c621c42038066a1af7499838b571b0c76 |
KLCoefficient | import torch
import torch.nn as nn
from torch.nn import functional as F
class KLCoefficient(nn.Module):
def __init__(self):
super(KLCoefficient, self).__init__()
def forward(self, hist1, hist2):
kl = F.kl_div(hist1, hist2)
dist = 1.0 / 1 + kl
return dist
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
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | tommy90191/Find_Tiny_but_Important_Image_Changes | KLCoefficient | false | 4,441 | [
"MIT"
] | 0 | 429d679606f96f32db4cddf167a9cfb963d3df26 | https://github.com/tommy90191/Find_Tiny_but_Important_Image_Changes/tree/429d679606f96f32db4cddf167a9cfb963d3df26 |
InceptionC | # 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_... | Hiroaki-Ozaki/modelib-classification | InceptionC | false | 17,415 | [
"WTFPL"
] | 10 | 11077704cc0bc9a42fc4b94da60b57d31ff0f65c | https://github.com/Hiroaki-Ozaki/modelib-classification/tree/11077704cc0bc9a42fc4b94da60b57d31ff0f65c |
PMA | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
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.... | Behrouz-Babaki/NCG4CVRP | PMA | false | 4,909 | [
"MIT"
] | 1 | 87d63366c0b461f44ce8e982159a1e207af77b44 | https://github.com/Behrouz-Babaki/NCG4CVRP/tree/87d63366c0b461f44ce8e982159a1e207af77b44 |
ParallelLinear | import torch
import numpy as np
import torch.nn as nn
class ParallelLinear(nn.Module):
def __init__(self, n_parallel, in_features, out_features, act=None,
random_bias=False):
super().__init__()
self.act = act
self.weight = nn.Parameter(torch.Tensor(n_parallel, 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
import numpy as np
import torch.nn as nn
assert_size_stride = torch._C._dynamo.g... | dholzmueller/nn_inconsistency | ParallelLinear | false | 3,416 | [
"Apache-2.0"
] | 0 | 67954d71cdbbc61fda7da1f624c19985b0e51708 | https://github.com/dholzmueller/nn_inconsistency/tree/67954d71cdbbc61fda7da1f624c19985b0e51708 |
TimeStrech | # 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... | shaun95/StarGANv2-VC | TimeStrech | false | 16,400 | [
"MIT"
] | 116 | ed20538971a03d699351a349a3631767333baeb7 | https://github.com/shaun95/StarGANv2-VC/tree/ed20538971a03d699351a349a3631767333baeb7 |
DAGNNConv | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | EdisonLeeeee/Graphgallery | DAGNNConv | false | 5,115 | [
"MIT"
] | 1 | 8ae9ef57d44f073d0ceaf3f33a3a998546f960a8 | https://github.com/EdisonLeeeee/Graphgallery/tree/8ae9ef57d44f073d0ceaf3f33a3a998546f960a8 |
LayerNormConv2d | import torch
from torchvision.transforms import *
import torch.nn
import torch
import torch.nn as nn
class LayerNormConv2d(nn.Module):
def __init__(self, num_features, eps=1e-05, affine=True):
super().__init__()
self.num_features = num_features
self.affine = affine
self.eps = eps
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torchvision.transforms import *
import torch.nn
import torch
import torch.... | COMHTVM/lensless | LayerNormConv2d | false | 17,339 | [
"MIT"
] | 6 | 0d67a310bab08551d7422fa792f3422a7ee7d9cb | https://github.com/COMHTVM/lensless/tree/0d67a310bab08551d7422fa792f3422a7ee7d9cb |
WeightedBCEWithLogitsLoss | import torch
import torch.utils.data
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
class WeightedBCEWithLogitsLoss(nn.Module):
"""Weighted binary cross-entropy with logits.
"""
def __init__(self, size_average=True, reduce=True, eps=0.0):
super().__init__()
... | 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... | Atharva-Peshkar/pytorch_connectomics | WeightedBCEWithLogitsLoss | false | 13,329 | [
"MIT"
] | 99 | 8eccd9640a9a454d4df095a3529a030e58f882f5 | https://github.com/Atharva-Peshkar/pytorch_connectomics/tree/8eccd9640a9a454d4df095a3529a030e58f882f5 |
Highway | # 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... | lstsm12345/DCTTS-PyTorch | Highway | false | 3,944 | [
"MIT"
] | 0 | d44b9407b654abc2069bd2a7ef6231572ace1fa7 | https://github.com/lstsm12345/DCTTS-PyTorch/tree/d44b9407b654abc2069bd2a7ef6231572ace1fa7 |
MultiHeadAttention | # 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.... | jianqingxie/RSTNet | MultiHeadAttention | false | 15,709 | [
"BSD-3-Clause"
] | 68 | aaa7b5be08e5ec9e79e14ed3e6a04fc3d50483be | https://github.com/jianqingxie/RSTNet/tree/aaa7b5be08e5ec9e79e14ed3e6a04fc3d50483be |
RangeNorm2D | # 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.autograd
assert_size_stride = torch._C._dynamo.guards.... | LLNL/fastcam | RangeNorm2D | false | 8,427 | [
"BSD-3-Clause"
] | 25 | 99cefe37528014247319468cf05f54fef259d3bf | https://github.com/LLNL/fastcam/tree/99cefe37528014247319468cf05f54fef259d3bf |
EncoderImagePrecomp | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | davidatbu/MLVGSNL | EncoderImagePrecomp | false | 15,131 | [
"MIT"
] | 97 | 88d42424a0a7badb43e22cd3950948c9522faaa1 | https://github.com/davidatbu/MLVGSNL/tree/88d42424a0a7badb43e22cd3950948c9522faaa1 |
FloorDivAssign | import torch
class FloorDivAssign(torch.nn.Module):
def __init__(self):
super(FloorDivAssign, 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
from torch._inductor.runtime.triton_helpers import libdevice
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
@triton.jit
d... | NVIDIA-AI-IOT-private/torch2trt | FloorDivAssign | false | 10,508 | [
"MIT"
] | 0 | 953d60039e0c81e90eea467c3df2e6e3f7040242 | https://github.com/NVIDIA-AI-IOT-private/torch2trt/tree/953d60039e0c81e90eea467c3df2e6e3f7040242 |
SoftmaxAllocator | import torch
class SoftmaxAllocator(torch.nn.Module):
"""Portfolio creation by computing a softmax over the asset dimension with temperature.
Parameters
----------
temperature : None or float
If None, then needs to be provided per sample during forward pass. If ``float`` then assumed
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
assert_size_stride = t... | vishalbelsare/deepdow | SoftmaxAllocator | false | 16,672 | [
"Apache-2.0"
] | 511 | cbb99347fba9a447d4fcae64fe5137c203643e44 | https://github.com/vishalbelsare/deepdow/tree/cbb99347fba9a447d4fcae64fe5137c203643e44 |
_Enc | import torch
class _NestedEnc(torch.nn.Module):
def __init__(self, f):
super().__init__()
self.f = f
def forward(self, x):
return self.f(x)
class _Enc(torch.nn.Module):
def __init__(self):
super().__init__()
self.e1 = _NestedEnc(torch.nn.Linear(4, 2))
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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cu... | pminervini/higher | _Enc | false | 12,961 | [
"Apache-2.0"
] | 0 | c4a7697a013f7b909b3c3453fd56401d6bb91fab | https://github.com/pminervini/higher/tree/c4a7697a013f7b909b3c3453fd56401d6bb91fab |
TracedModule | import torch
import torch.quantization
import torch.onnx
import torch.nn.parallel
import torch.utils.data
import torch.fx
import torch.nn
import torch.optim
import torch.profiler
class TracedModule(torch.nn.Module):
def forward(self, x):
x = x.type(torch.float32)
return torch.floor(torch.sqrt(x) ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.quantization
import torch.onnx
import torch.nn.parallel
import tor... | Nayef211/tutorials | TracedModule | false | 9,536 | [
"BSD-3-Clause"
] | 0 | faf2c476fc3be855051fbea3cce77eaf7b2a2175 | https://github.com/Nayef211/tutorials/tree/faf2c476fc3be855051fbea3cce77eaf7b2a2175 |
FirstKernelTensorTrain | # 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... | AndresOtero/TensorDecompositionMachineLearning | FirstKernelTensorTrain | false | 16,897 | [
"MIT"
] | 3 | 455f16b405ec9d031999b0ebf9c5a68d3c20b233 | https://github.com/AndresOtero/TensorDecompositionMachineLearning/tree/455f16b405ec9d031999b0ebf9c5a68d3c20b233 |
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.... | MichiganCOG/Video-Grounding | Attention | false | 8,548 | [
"MIT"
] | 41 | 3e0ec0b69578a59be583911590354fe77d357cab | https://github.com/MichiganCOG/Video-Grounding/tree/3e0ec0b69578a59be583911590354fe77d357cab |
TripletLoss | # 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.... | chrizandr/MMT | TripletLoss | false | 12,215 | [
"MIT"
] | 0 | e2bb5984efb165e7ea1ed6080610cfe176344ac0 | https://github.com/chrizandr/MMT/tree/e2bb5984efb165e7ea1ed6080610cfe176344ac0 |
KLDivLoss | # 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
assert_size... | Thesys-lab/learned-coded-computation | KLDivLoss | false | 17,997 | [
"Apache-2.0"
] | 8 | c5c32bcfb7cc4a9f52079f648373e6972c19eff9 | https://github.com/Thesys-lab/learned-coded-computation/tree/c5c32bcfb7cc4a9f52079f648373e6972c19eff9 |
MultiplicationComposition | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
from abc import abstractmethod
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
... | DimitrisAlivas/StarQE | MultiplicationComposition | false | 7,965 | [
"MIT"
] | 11 | c17676e5f1e3f19c0c4c117a50abe2ce22ffef28 | https://github.com/DimitrisAlivas/StarQE/tree/c17676e5f1e3f19c0c4c117a50abe2ce22ffef28 |
IBLoss | # 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
... | NYCU-MLLab/Strategic-Optimization-for-Worst-case-Augmentation | IBLoss | false | 17,743 | [
"MIT"
] | 3 | fd0feab42151c0bae60712480301ea26f627a81d | https://github.com/NYCU-MLLab/Strategic-Optimization-for-Worst-case-Augmentation/tree/fd0feab42151c0bae60712480301ea26f627a81d |
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.... | uyeongkim/moca | SelfAttn | false | 10,894 | [
"MIT"
] | 0 | 8a5870898b6d59258ce1064bab440b7e8107e9b4 | https://github.com/uyeongkim/moca/tree/8a5870898b6d59258ce1064bab440b7e8107e9b4 |
Discriminator | import torch
import torch.nn as nn
class Discriminator(nn.Module):
def __init__(self):
super(Discriminator, self).__init__()
self.conv1 = nn.Conv2d(2, 16, 4, 2, 1, bias=False)
self.act1 = nn.LeakyReLU(0.2, inplace=False)
self.conv2 = nn.Conv2d(16, 32, 4, 2, 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
import torch.nn as nn
assert_... | karavik18/Federated_Learning_for_Missing_MRI_Sequence | Discriminator | false | 7,024 | [
"Apache-2.0"
] | 1 | 42924f8475f354e6b429d05867f99530aa485b96 | https://github.com/karavik18/Federated_Learning_for_Missing_MRI_Sequence/tree/42924f8475f354e6b429d05867f99530aa485b96 |
ScaledDotProductAttention | import torch
from torch import nn
from typing import Optional
class ScaledDotProductAttention(nn.Module):
"""
Scaled Dot-Product Attention
Parameters
----------
scale : float
Scale factor (sqrt(d_k))
dropout : float
Dropout
"""
def __init__(self, scale: 'float', drop... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Renovamen/Text-Classification | ScaledDotProductAttention | false | 14,298 | [
"MIT"
] | 72 | 4a4aa4001c402ed4371ebaabe1393b27794e5992 | https://github.com/Renovamen/Text-Classification/tree/4a4aa4001c402ed4371ebaabe1393b27794e5992 |
MultiHeadedAttention | import torch
from torch import nn
from torch.nn import functional as F
def same_tensor(tensor, *args):
""" Do the input tensors all point to the same underlying data """
for other in args:
if not torch.is_tensor(other):
return False
if tensor.device != other.device:
ret... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | jinga-lala/stupidNMT | MultiHeadedAttention | false | 10,280 | [
"BSD-3-Clause"
] | 0 | 2a41c072c2bc622c7edd8556f552f38556d70dae | https://github.com/jinga-lala/stupidNMT/tree/2a41c072c2bc622c7edd8556f552f38556d70dae |
BahdanauAttention | # 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.... | beroguedou/nmt-pytorch | BahdanauAttention | false | 6,331 | [
"MIT"
] | 1 | 8758ba33e2d5f4eca7f1ac2d04582678332bbcd5 | https://github.com/beroguedou/nmt-pytorch/tree/8758ba33e2d5f4eca7f1ac2d04582678332bbcd5 |
LinearAttention | import torch
import torch.nn as nn
import torch.utils.data
class LinearAttention(nn.Module):
def __init__(self, in_size):
super(LinearAttention, self).__init__()
self.out = nn.Linear(in_size, 1)
nn.init.orthogonal_(self.out.weight.data)
self.softmax = nn.Softmax(dim=1)
def fo... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | gchrupala/platalea | LinearAttention | false | 6,736 | [
"Apache-2.0"
] | 1 | 65833307bb6c5ad6cbdd6b17ad8ca59cf51fcd81 | https://github.com/gchrupala/platalea/tree/65833307bb6c5ad6cbdd6b17ad8ca59cf51fcd81 |
EdgeLoss | # 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... | Nikronic/EdgeNet | EdgeLoss | false | 8,610 | [
"MIT"
] | 12 | ec649af303bd7d5397fd3d4cbf8736bd83756abb | https://github.com/Nikronic/EdgeNet/tree/ec649af303bd7d5397fd3d4cbf8736bd83756abb |
Zero | import torch
import torch.nn as nn
class Zero(nn.Module):
def __init__(self):
super(Zero, self).__init__()
def forward(self, x):
return x * 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | BayesWatch/pytorch-prunes | Zero | false | 13,392 | [
"MIT"
] | 143 | bc85a5c52865a2daf515ad4d3c26dcab88e3d941 | https://github.com/BayesWatch/pytorch-prunes/tree/bc85a5c52865a2daf515ad4d3c26dcab88e3d941 |
HR2O_NL | import torch
import torch.nn as nn
class HR2O_NL(nn.Module):
def __init__(self, hidden_dim=512, kernel_size=3, mlp_1x1=False):
super(HR2O_NL, self).__init__()
self.hidden_dim = hidden_dim
padding = kernel_size // 2
self.conv_q = nn.Conv2d(hidden_dim, hidden_dim, kernel_size,
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | StephenStorm/ACAR | HR2O_NL | false | 1,161 | [
"Apache-2.0"
] | 0 | 21ef3eca7330bd62eccb645018c8e48d9fc52153 | https://github.com/StephenStorm/ACAR/tree/21ef3eca7330bd62eccb645018c8e48d9fc52153 |
GlobalMaxPool | import torch
from torch import nn
class GlobalMaxPool(nn.Module):
"""
Max pooling in an equivariant network
"""
def __init__(self):
"""
"""
super().__init__()
def forward(self, x):
"""
"""
mx = torch.max(torch.max(x, dim=-1, keepdim=True)[0], dim=-... | 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... | ElisevanderPol/symmetrizer | GlobalMaxPool | false | 8,056 | [
"MIT"
] | 16 | 8dae02bee2ba7132ae4fb07e07020767d280842c | https://github.com/ElisevanderPol/symmetrizer/tree/8dae02bee2ba7132ae4fb07e07020767d280842c |
DecoderLayer | # 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.... | AlbertiPot/attention-is-all-you-need-pytorch | DecoderLayer | false | 46 | [
"MIT"
] | 0 | c5ec40907db281b85b3bd7a5dd8016940291add0 | https://github.com/AlbertiPot/attention-is-all-you-need-pytorch/tree/c5ec40907db281b85b3bd7a5dd8016940291add0 |
TemporalFusion | import torch
import torch.nn as nn
class TemporalFusion(nn.Module):
def __init__(self, nf, n_frame):
super(TemporalFusion, self).__init__()
self.n_frame = n_frame
self.ref_conv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
self.nbr_conv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | juyongjiang/Simple-SR | TemporalFusion | false | 7,019 | [
"MIT"
] | 1 | 76820511abc04fbe6e4a79d23c67aee97406d563 | https://github.com/juyongjiang/Simple-SR/tree/76820511abc04fbe6e4a79d23c67aee97406d563 |
MonoLinearHyperNet | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from abc import abs... | AvivNavon/AuxiLearn | MonoLinearHyperNet | false | 13,355 | [
"MIT"
] | 58 | 2c32f5cb548714ad3efe5c804003a30d6f012e2b | https://github.com/AvivNavon/AuxiLearn/tree/2c32f5cb548714ad3efe5c804003a30d6f012e2b |
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