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 |
|---|---|---|---|---|---|---|---|---|---|---|
BhattacharyyaDistance | import torch
import torch.nn as nn
class BhattacharyyaDistance(nn.Module):
def __init__(self):
super(BhattacharyyaDistance, self).__init__()
def forward(self, hist1, hist2):
bh_dist = torch.sqrt(hist1 * hist2).sum()
return bh_dist
def get_inputs():
return [torch.rand([4, 4, 4, ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | kensakurada/SceneChangeDet | BhattacharyyaDistance | false | 15,803 | [
"MIT"
] | 199 | 0530e0162863fec0c5296188526f0d27e0109814 | https://github.com/kensakurada/SceneChangeDet/tree/0530e0162863fec0c5296188526f0d27e0109814 |
DenseSAGEConv | import math
import torch
import torch.nn.functional as F
import torch.utils.data
from torch.nn import Parameter
def uniform(size, tensor):
stdv = 1.0 / math.sqrt(size)
if tensor is not None:
tensor.data.uniform_(-stdv, stdv)
class DenseSAGEConv(torch.nn.Module):
"""See :class:`torch_geometric.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.... | Cyanogenoid/fspool | DenseSAGEConv | false | 7,964 | [
"MIT"
] | 41 | 7525cb17992ec7a1bb7f92996c2b31a65aa8eba2 | https://github.com/Cyanogenoid/fspool/tree/7525cb17992ec7a1bb7f92996c2b31a65aa8eba2 |
GaussionConvD | # 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.... | EdisonLeeeee/GraphGallery | GaussionConvD | false | 13,636 | [
"MIT"
] | 300 | 4eec9c5136bda14809bd22584b26cc346cdb633b | https://github.com/EdisonLeeeee/GraphGallery/tree/4eec9c5136bda14809bd22584b26cc346cdb633b |
ContrastiveEmbeddingLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
from t... | asmekal/catalyst | ContrastiveEmbeddingLoss | false | 12,123 | [
"MIT"
] | 0 | e11365c0a9812649ceaef14e53061cd5117d8684 | https://github.com/asmekal/catalyst/tree/e11365c0a9812649ceaef14e53061cd5117d8684 |
AffineGridGen | # 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.nn import Module
import torch.nn
from torch.nn.modules.module import Module
assert_size_stride = torch._C._dynamo.guards.assert_s... | sebastian-echeverria/ncnet | AffineGridGen | false | 10,840 | [
"MIT"
] | 0 | c7249fe8f908813bab6443ebfa4590bd362a0dc2 | https://github.com/sebastian-echeverria/ncnet/tree/c7249fe8f908813bab6443ebfa4590bd362a0dc2 |
SqueezeEmbedding | # 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... | minionssso/PyABSA | SqueezeEmbedding | false | 4,001 | [
"MIT"
] | 0 | fd9a9a6fd55552a60329fd04b6830e1bb144d50f | https://github.com/minionssso/PyABSA/tree/fd9a9a6fd55552a60329fd04b6830e1bb144d50f |
Transition | import torch
import torch.nn as nn
import torch.nn.functional as F
class Transition(nn.Module):
def __init__(self, in_planes, out_planes):
super(Transition, self).__init__()
self.conv = nn.Conv2d(in_planes, out_planes, kernel_size=1, bias=True)
def forward(self, x):
out = self.conv(F... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | mnmueller/auto_LiRPA | Transition | false | 7,256 | [
"BSD-3-Clause"
] | 1 | 55cb270b0b99f07b74541d55706c69fbb9daff66 | https://github.com/mnmueller/auto_LiRPA/tree/55cb270b0b99f07b74541d55706c69fbb9daff66 |
WrapPad2d | import torch
import torch.nn as nn
import torch.nn.init
import torch.optim
class WrapPad2d(nn.Module):
"""Create a padding layer that wraps the data
Arguments:
padding (int): the size of the padding
"""
def __init__(self, padding):
super(WrapPad2d, self).__init__()
self.paddi... | 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.init
import torch.optim
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_... | NREL/deep-image-prior-cfd | WrapPad2d | false | 17,716 | [
"Apache-2.0"
] | 5 | 85a86ac10bef070b1a973d2a6569849583e08d79 | https://github.com/NREL/deep-image-prior-cfd/tree/85a86ac10bef070b1a973d2a6569849583e08d79 |
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... | hassony2/frankmocap | ConvertPointsFromHomogeneous | false | 6,787 | [
"BSD-3-Clause"
] | 1 | 50aae41d9b41d2f344ae1709bbf1b25974209fa9 | https://github.com/hassony2/frankmocap/tree/50aae41d9b41d2f344ae1709bbf1b25974209fa9 |
MSE | import torch
import torch.nn as nn
class MSE(nn.Module):
def __init__(self):
super().__init__()
self.loss = nn.MSELoss(reduction='mean')
def forward(self, recon, target):
return self.loss(recon, target)
def get_inputs():
return [torch.rand([4, 4, 4, 4]), 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... | KMU-AELAB/Active_Learning | MSE | false | 2,437 | [
"MIT"
] | 0 | bc569c16b5f12b58989a8f3db59b7eb4e35cce1b | https://github.com/KMU-AELAB/Active_Learning/tree/bc569c16b5f12b58989a8f3db59b7eb4e35cce1b |
ConcatELU | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.nn.functional as F
assert_size_stride = torc... | Nintorac/survae_experiments | ConcatELU | false | 900 | [
"MIT"
] | 0 | d68cc25e2604aab08b53617c1f3ffe4716f166c4 | https://github.com/Nintorac/survae_experiments/tree/d68cc25e2604aab08b53617c1f3ffe4716f166c4 |
Critic | # 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
from torch... | ori-goals/lfd-min-human-effort | Critic | false | 7,429 | [
"MIT"
] | 1 | f9fd70cdeb661151e5f81ac538ceb865531146b9 | https://github.com/ori-goals/lfd-min-human-effort/tree/f9fd70cdeb661151e5f81ac538ceb865531146b9 |
GreedyTop1 | # 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 as pt
import torch.distributed
import torch.distributed.elastic.multiprocessing.errors
assert_size_stride = torch._C._dynamo.gu... | blchu/sockeye | GreedyTop1 | false | 1,555 | [
"Apache-2.0"
] | 0 | 28044a44ee409c9b3df1711c0b16bdebdd463b2e | https://github.com/blchu/sockeye/tree/28044a44ee409c9b3df1711c0b16bdebdd463b2e |
SpatialGatherModule | import torch
from torch.nn import functional as F
import torch.nn as nn
class SpatialGatherModule(nn.Module):
"""Aggregate the context features according to the initial predicted
probability distribution.
Employ the soft-weighted method to aggregate the context.
"""
def __init__(self, scale):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | Jason-Khan/mmediting | SpatialGatherModule | false | 622 | [
"Apache-2.0"
] | 0 | d187f95a675dff3eb975a575bd9278d643b5b645 | https://github.com/Jason-Khan/mmediting/tree/d187f95a675dff3eb975a575bd9278d643b5b645 |
CoordConv | import torch
import torch.nn as nn
from torch.nn.utils import spectral_norm
def mk_conv2d(*args, sn=False, **kwargs):
m = nn.Conv2d(*args, **kwargs)
if sn:
m = spectral_norm(m)
return m
class AddCoords(nn.Module):
"""
Source: https://github.com/mkocabas/CoordConv-pytorch/blob/master/Coor... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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.utils import spectral_norm
assert_size_strid... | HexagonPrime/pixel-nerf | CoordConv | false | 2,416 | [
"BSD-2-Clause"
] | 0 | 298aa7a3451c01e6f19f73f0c756672d3de54bf9 | https://github.com/HexagonPrime/pixel-nerf/tree/298aa7a3451c01e6f19f73f0c756672d3de54bf9 |
StackTime | import torch
from torchvision.models.quantization import *
class StackTime(torch.nn.Module):
__constants__ = ['factor']
def __init__(self, factor):
super().__init__()
self.factor = int(factor)
def forward(self, x, x_lens):
seq = [x]
for i in range(1, self.factor):
... | 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.models.quantization import *
assert_size_stride = torch._C._dy... | CaoZhongZ/inference | StackTime | false | 13,850 | [
"Apache-2.0"
] | 388 | 58025f8fde679ea864d34f96ecc9f14bf70ece53 | https://github.com/CaoZhongZ/inference/tree/58025f8fde679ea864d34f96ecc9f14bf70ece53 |
LinearNetwork | # 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 typing import List
import torch.nn as nn
assert_size_stride = torch._C._dyn... | redvinaa/multiagent-path-finding-continuous | LinearNetwork | false | 10,713 | [
"MIT"
] | 0 | 2d4ba3388f9b951c443ba72a33bd7af4f461275f | https://github.com/redvinaa/multiagent-path-finding-continuous/tree/2d4ba3388f9b951c443ba72a33bd7af4f461275f |
MaskL1Loss | import torch
from torch import nn
class MaskL1Loss(nn.Module):
def __init__(self, eps=1e-06):
super(MaskL1Loss, self).__init__()
self.eps = eps
def forward(self, pred: 'torch.Tensor', gt, mask):
loss = (torch.abs(pred - gt) * mask).sum() / (mask.sum() + self.eps)
return loss
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
a... | gentlebreeze1/dbnet | MaskL1Loss | false | 3,533 | [
"Apache-2.0"
] | 0 | be28a7ae835af7d6f8b7c2b636b875adc9fc187c | https://github.com/gentlebreeze1/dbnet/tree/be28a7ae835af7d6f8b7c2b636b875adc9fc187c |
LinReLU | import torch
from torch import nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
class LinReLU(torch.nn.Module):
__constants__ = ['bias']
def __init__(self, in_features: 'int', out_features: 'int') ->None:
super(LinReLU, self).__init__()
self.in_features = in_feature... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 tor... | mrahman93/nam | LinReLU | false | 4,036 | [
"MIT"
] | 0 | 1a2f286a87ffa024040e3330088b4a375700c1c6 | https://github.com/mrahman93/nam/tree/1a2f286a87ffa024040e3330088b4a375700c1c6 |
ConcatSquashConv2d | import torch
import torch.nn as nn
import torch.utils.data
class ConcatSquashConv2d(nn.Module):
def __init__(self, dim_in, dim_out, ksize=3, stride=1, padding=0,
dilation=1, groups=1, bias=True, transpose=False):
super(ConcatSquashConv2d, self).__init__()
module = nn.ConvTranspose2d if tr... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dyn... | musyoku/ffjord | ConcatSquashConv2d | false | 7,301 | [
"MIT"
] | 1 | 9e431e122e59fa9a71f3f301dec8fdd3db51e0ce | https://github.com/musyoku/ffjord/tree/9e431e122e59fa9a71f3f301dec8fdd3db51e0ce |
MuSigmaEncoder | import torch
import torch.nn as nn
class MuSigmaEncoder(nn.Module):
"""
Maps a representation r to mu and sigma which will define the normal
distribution from which we sample the latent variable z.
Parameters
----------
r_dim : int
Dimension of output representation r.
z_dim : 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
import torch.nn as nn
assert_... | TheBonheurs/neural-processes | MuSigmaEncoder | false | 9,556 | [
"MIT"
] | 0 | 5834bc65f406456e53c363ade1cb0f2a5f23a033 | https://github.com/TheBonheurs/neural-processes/tree/5834bc65f406456e53c363ade1cb0f2a5f23a033 |
BertAttention | # 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.... | hongyuntw/Col-KBERT | BertAttention | false | 6,828 | [
"MIT"
] | 1 | e77ce2585d228a783bf83cc1de53583aff70f7b4 | https://github.com/hongyuntw/Col-KBERT/tree/e77ce2585d228a783bf83cc1de53583aff70f7b4 |
Block | # 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 ... | sayakpaul/ConvNeXt-TF | Block | false | 16,371 | [
"Apache-2.0"
] | 68 | bf610810558b4248cd969aa7db42fadff1fdf57a | https://github.com/sayakpaul/ConvNeXt-TF/tree/bf610810558b4248cd969aa7db42fadff1fdf57a |
DurationPredictorLoss | # 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
assert_size_stride = t... | carankt/FastSpeech2-1 | DurationPredictorLoss | false | 6,378 | [
"Apache-2.0"
] | 1 | 42c06e4fbdf741a0719154d1cb4617b7d3f15a5c | https://github.com/carankt/FastSpeech2-1/tree/42c06e4fbdf741a0719154d1cb4617b7d3f15a5c |
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.nn.parallel
import torch.optim
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.asser... | Alin1102/Yolov3_Dartnet2Caffe | GlobalAvgPool2d | false | 7,639 | [
"MIT"
] | 21 | b4284b080f53c1ac73c1930b1b1c4e07dcd97559 | https://github.com/Alin1102/Yolov3_Dartnet2Caffe/tree/b4284b080f53c1ac73c1930b1b1c4e07dcd97559 |
Critic | # 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 ... | amy12xx/lets-do-irl | Critic | false | 14,832 | [
"MIT"
] | 408 | fd469e9fb7426e41b07c83ce4b87962ac3543b1e | https://github.com/amy12xx/lets-do-irl/tree/fd469e9fb7426e41b07c83ce4b87962ac3543b1e |
FixupResUnit | import torch
import torch.nn.functional as F
import torch.nn as nn
class FixupResUnit(nn.Module):
def __init__(self, in_channels, out_channels, stride=1):
super().__init__()
self.bias1a = nn.Parameter(torch.zeros(1))
self.conv1 = nn.Conv2d(in_channels, out_channels, 3, padding=1,
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | OpenXAIProject/dac | FixupResUnit | false | 8,640 | [
"MIT"
] | 17 | 652776e21b56dcb68839363bb077d5c5ea28d81e | https://github.com/OpenXAIProject/dac/tree/652776e21b56dcb68839363bb077d5c5ea28d81e |
rec_attention | from _paritybench_helpers import _mock_config
import torch
import torch.utils.data
import torch.nn as nn
def batch_product(iput, mat2):
result = None
for i in range(iput.size()[0]):
op = torch.mm(iput[i], mat2)
op = op.unsqueeze(0)
if result is None:
result = op
els... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | gzerveas/TransformChrome | rec_attention | false | 6,784 | [
"MIT"
] | 1 | ab1046009aff2ec863aa65223dcfcd750d41ab86 | https://github.com/gzerveas/TransformChrome/tree/ab1046009aff2ec863aa65223dcfcd750d41ab86 |
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.... | LiuXiang199x/DRL_Navigation | Actor | false | 788 | [
"MIT"
] | 0 | 336e847bde8261d429fd2de8111b3d24c0ab4bae | https://github.com/LiuXiang199x/DRL_Navigation/tree/336e847bde8261d429fd2de8111b3d24c0ab4bae |
eSEModule | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
impor... | FluteXu/DW-Research | eSEModule | false | 13,704 | [
"Apache-2.0"
] | 780 | 6b559d2d1d440c07e5936a65cd74a3bc657962dc | https://github.com/FluteXu/DW-Research/tree/6b559d2d1d440c07e5936a65cd74a3bc657962dc |
CDEFunc | import torch
class CDEFunc(torch.nn.Module):
def __init__(self, input_channels, hidden_channels):
super(CDEFunc, self).__init__()
self.input_channels = input_channels
self.hidden_channels = hidden_channels
self.linear1 = torch.nn.Linear(hidden_channels, 128)
self.linear2 =... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | athon-millane/NeuralCDE | CDEFunc | false | 12,127 | [
"Apache-2.0"
] | 0 | 4196890fe5bf7a69925a12ff35e86f212963be71 | https://github.com/athon-millane/NeuralCDE/tree/4196890fe5bf7a69925a12ff35e86f212963be71 |
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.utils.data
impor... | FabianSchuetze/world-models | Decoder | false | 13,691 | [
"MIT"
] | 440 | d6abd9ce97409734a766eb67ccf0d1967ba9bf0c | https://github.com/FabianSchuetze/world-models/tree/d6abd9ce97409734a766eb67ccf0d1967ba9bf0c |
DiagGaussianActor | import torch
import torch.nn as nn
class DiagGaussianActor(nn.Module):
"""torch.distributions implementation of an diagonal Gaussian policy."""
def __init__(self, log_std_bounds=[-5, 2]):
super().__init__()
self.log_std_bounds = log_std_bounds
def forward(self, mu, log_std):
log_... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.gu... | Purple-PI/rlstructures | DiagGaussianActor | false | 14,241 | [
"MIT"
] | 281 | 9b201b083715bbda2f3534b010c84e11dfc0a1c7 | https://github.com/Purple-PI/rlstructures/tree/9b201b083715bbda2f3534b010c84e11dfc0a1c7 |
CaffeNormalize | import torch
import torch.utils.data
import torch.nn as nn
class CaffeNormalize(nn.Module):
def __init__(self, features, eps=1e-07):
super(CaffeNormalize, self).__init__()
self.scale = nn.Parameter(10.0 * torch.ones(features))
self.eps = eps
def forward(self, x):
x_size = x.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.triton_helpers import libdevice
import torch.utils.data
import torch.nn as nn
assert_size_stride = torch._C._dy... | cynthiamao98/DepthAwareCNN | CaffeNormalize | false | 12,234 | [
"MIT"
] | 0 | 824cffaa4159e3dc7cc251a4a659e35c437bb92c | https://github.com/cynthiamao98/DepthAwareCNN/tree/824cffaa4159e3dc7cc251a4a659e35c437bb92c |
AngularPenaltySMLoss | # 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.... | dayoungMM/Angular-Penalty-Softmax-Losses-Pytorch | AngularPenaltySMLoss | false | 1,812 | [
"MIT"
] | 0 | 5599f2e280b2af8d40e53727290eb797d18e7239 | https://github.com/dayoungMM/Angular-Penalty-Softmax-Losses-Pytorch/tree/5599f2e280b2af8d40e53727290eb797d18e7239 |
InvDepth | import torch
import torch.nn as nn
class InvDepth(nn.Module):
def __init__(self, height, width, min_depth=0.5, max_depth=25.0):
super(InvDepth, self).__init__()
self._min_range = 1.0 / max_depth
self._max_range = 1.0 / min_depth
self.w = nn.Parameter(self._init_weights(height, wid... | 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... | ChristophReich1996/kornia | InvDepth | false | 291 | [
"ECL-2.0",
"Apache-2.0"
] | 0 | 35f955b46e8015da1cb9faa28c6943ec2b09cc2a | https://github.com/ChristophReich1996/kornia/tree/35f955b46e8015da1cb9faa28c6943ec2b09cc2a |
BottleNeck | # 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... | etienne87/pytorch-cifar | BottleNeck | false | 10,087 | [
"MIT"
] | 0 | d9164df8ba0cb9259daf857e006db3fecb762af7 | https://github.com/etienne87/pytorch-cifar/tree/d9164df8ba0cb9259daf857e006db3fecb762af7 |
UpSampleAndHalveChannels | import torch
from torch import Tensor
import torch.nn as nn
class UpSampleAndHalveChannels(nn.Module):
"""
Doubles the spatial dimensions (H,W) but halves the number of channels.
Inverse of the DownSample function in blocks.py
From Diakogiannis et al.
doi: 10.1016/j.isprsjprs.2020.01.013
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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_... | danielnflam/GAN-Tests | UpSampleAndHalveChannels | false | 3,438 | [
"BSD-3-Clause"
] | 0 | f112e27b802d717f64a8f2cfa79b9898667da14c | https://github.com/danielnflam/GAN-Tests/tree/f112e27b802d717f64a8f2cfa79b9898667da14c |
TVLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.parallel
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C... | Blatts01/VckImageRestoration | TVLoss | false | 2,022 | [
"MIT"
] | 0 | ae4e2221d9d4e236a08722cb92ac5cc88947e311 | https://github.com/Blatts01/VckImageRestoration/tree/ae4e2221d9d4e236a08722cb92ac5cc88947e311 |
RegLoss | import torch
from torch import nn
import torch.onnx
from torch.nn.parallel.scatter_gather import gather
import torch.utils.data
def _gather_feat(feat, ind, mask=None, trt=False):
dim = feat.size(2)
ind = ind.unsqueeze(2).expand(ind.size(0), ind.size(1), dim)
if trt:
feat = gather(feat, 1, ind)
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
i... | kuanhungchen/CenterNet-HarDNet | RegLoss | false | 15,864 | [
"MIT"
] | 164 | 050d55a532706d989105982c5bc10f1c89edc8d2 | https://github.com/kuanhungchen/CenterNet-HarDNet/tree/050d55a532706d989105982c5bc10f1c89edc8d2 |
GumbelSoftMax | import torch
import torch.nn as nn
import torch.nn.functional as F
from math import sqrt as sqrt
from itertools import product as product
class _GumbelSoftMax(torch.autograd.Function):
"""
implementing the MixedOp, but carried out in a different way as DARTS
DARTS adds all operations together, then selec... | 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... | TayaPenskaya/hair_classifier | GumbelSoftMax | false | 2,883 | [
"MIT"
] | 0 | 669f42b705a4dd0bdde9d330db5214a3213db5a5 | https://github.com/TayaPenskaya/hair_classifier/tree/669f42b705a4dd0bdde9d330db5214a3213db5a5 |
EuclideanDistLoss | import torch
from torch import nn
class EuclideanDistLoss(nn.Module):
def __init__(self):
super(EuclideanDistLoss, self).__init__()
def forward(self, inputs, inputs_rot):
dist = torch.dist(inputs, inputs_rot, p=2.0)
return dist
def get_inputs():
return [torch.rand([4, 4, 4, 4])... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_... | DeVriesMatt/cellshape-voxel | EuclideanDistLoss | false | 5,060 | [
"BSD-3-Clause"
] | 1 | 64c2c57cc8b8ebe7f6ba1934caaaa3aaa1d6a0c1 | https://github.com/DeVriesMatt/cellshape-voxel/tree/64c2c57cc8b8ebe7f6ba1934caaaa3aaa1d6a0c1 |
HorizontalMaxPool2d | # 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... | existentmember7/TEMP_monitor | HorizontalMaxPool2d | false | 6,667 | [
"MIT"
] | 1 | b8116f4c134793c4caa22eda78f90dd24d0cad30 | https://github.com/existentmember7/TEMP_monitor/tree/b8116f4c134793c4caa22eda78f90dd24d0cad30 |
CMVN | import torch
import torch.nn as nn
class CMVN(nn.Module):
__constants__ = ['mode', 'dim', 'eps']
def __init__(self, mode='global', dim=2, eps=1e-10):
super(CMVN, self).__init__()
if mode != 'global':
raise NotImplementedError(
'Only support global mean variance nor... | 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_... | hhhaaahhhaa/s3prl | CMVN | false | 15,506 | [
"Apache-2.0"
] | 856 | a469787f05c42196c4d989555082f5fd9dcbe8a6 | https://github.com/hhhaaahhhaa/s3prl/tree/a469787f05c42196c4d989555082f5fd9dcbe8a6 |
PReLU | # 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
import torch.nn as nn
from torch.nn.parameter import Parameter
import torch.utils.data
im... | Flamexmt/LMA | PReLU | false | 13,684 | [
"MIT"
] | 321 | f6fdec2d17a2d7a7733dd5a5745312bad392cdf3 | https://github.com/Flamexmt/LMA/tree/f6fdec2d17a2d7a7733dd5a5745312bad392cdf3 |
MinimaxDiscriminatorLoss | # 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... | kayuksel/torchgan | MinimaxDiscriminatorLoss | false | 10,553 | [
"MIT"
] | 0 | 739d97cef4c49fb80155de84e609471efafab107 | https://github.com/kayuksel/torchgan/tree/739d97cef4c49fb80155de84e609471efafab107 |
GELU | import torch
import numpy as np
from torch import nn
import torch.nn.functional as F
class GELU(nn.Module):
def __init__(self):
super(GELU, self).__init__()
def forward(self, x):
return 0.5 * x * (1 + F.tanh(np.sqrt(2 / np.pi) * (x + 0.044715 *
torch.pow(x, 3))))
def get_inputs... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | au55555/classification-pytorch | GELU | false | 6,277 | [
"MIT"
] | 1 | 1937599ae6e688ed7af7470f69964fb6f97241c4 | https://github.com/au55555/classification-pytorch/tree/1937599ae6e688ed7af7470f69964fb6f97241c4 |
JointsMSELoss | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
class JointsMSELoss(nn.Module):
def __init__(self, use_target_weight):
super(JointsMSELoss, self).__init__()
self.criterion = nn.MSELoss(reduction='mean')
... | 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
import torch.utils.data.distributed
assert_size_st... | HowieMa/TransFusion-Pose | JointsMSELoss | false | 8,233 | [
"MIT"
] | 17 | b66ee5bafdc12a971088f9d54233408249e067db | https://github.com/HowieMa/TransFusion-Pose/tree/b66ee5bafdc12a971088f9d54233408249e067db |
CRF | # 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... | mingrui/NCRF | CRF | false | 16,105 | [
"Apache-2.0"
] | 734 | d3dcb50739a9eb8621d42ea98b7d0496afe430ca | https://github.com/mingrui/NCRF/tree/d3dcb50739a9eb8621d42ea98b7d0496afe430ca |
DeconvBlock | # 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, math as tl_math
im... | ArminMasoumian/GCNDepth | DeconvBlock | false | 7,723 | [
"MIT"
] | 32 | 9fa77812fa944c2701a45f09acf988815ca50aee | https://github.com/ArminMasoumian/GCNDepth/tree/9fa77812fa944c2701a45f09acf988815ca50aee |
LayerNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
import torch.utils.data
import torch.optim
assert_size_str... | Oktai15/NeMo | LayerNorm | false | 5,676 | [
"Apache-2.0"
] | 1 | 5b6dd3850129898be47cf0d65587897ec45a5b59 | https://github.com/Oktai15/NeMo/tree/5b6dd3850129898be47cf0d65587897ec45a5b59 |
SigM | import torch
import torch.utils.data
import torch.nn as nn
class h_sigmoid(nn.Module):
def __init__(self, inplace=True, h_max=1):
super(h_sigmoid, self).__init__()
self.relu = nn.ReLU6(inplace=inplace)
self.h_max = h_max
def forward(self, x):
return self.relu(x + 3) * self.h_... | 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.utils.data
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guard... | anonymous2022ijcai/RGSL | SigM | false | 1,450 | [
"MIT"
] | 0 | 11c38ee50d50127c0f7c2a137bdb21ca5f7f3644 | https://github.com/anonymous2022ijcai/RGSL/tree/11c38ee50d50127c0f7c2a137bdb21ca5f7f3644 |
NeuralNetMultiplePositionalArguments | import torch
import torch.nn
import torch.onnx
class NeuralNetMultiplePositionalArguments(torch.nn.Module):
def __init__(self, input_size, hidden_size, num_classes):
super(NeuralNetMultiplePositionalArguments, self).__init__()
self.fc1 = torch.nn.Linear(input_size, hidden_size)
self.relu ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 | NeuralNetMultiplePositionalArguments | false | 11,019 | [
"MIT"
] | 0 | 1a3ddf0714e1bdf9b807a342eee5f6e160ad1ec9 | https://github.com/thilow/onnxruntime/tree/1a3ddf0714e1bdf9b807a342eee5f6e160ad1ec9 |
RandomShiftsAug | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch import device
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._d... | nsortur/drqv2 | RandomShiftsAug | false | 12,853 | [
"MIT"
] | 0 | 2443f93feeb5cace855d16bfa31152d63a2d66aa | https://github.com/nsortur/drqv2/tree/2443f93feeb5cace855d16bfa31152d63a2d66aa |
attention | import torch
import torch.utils
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.nn.functional as F
class attention(nn.Module):
def __init__(self, input_channels, map_size):
super(attention, self).__init__()
self.pool = nn.AvgPool... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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
import tor... | yaowenlong/clique | attention | false | 11,048 | [
"MIT"
] | 0 | a9814ef643f7dac6080cebf76ab804d942c9cd8e | https://github.com/yaowenlong/clique/tree/a9814ef643f7dac6080cebf76ab804d942c9cd8e |
MultiHeadAttention | import math
import torch
import torch.cuda
from torch import nn
import torch.distributed
import torch.utils.data
import torch.optim
class MultiHeadAttention(nn.Module):
"""
Multi-head scaled dot-product attention layer.
Args:
hidden_size: size of the embeddings in the model, also known as d_model... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | hamjam/NeMo | MultiHeadAttention | false | 15,502 | [
"Apache-2.0"
] | 4,145 | b3484d32e1317666151f931bfa39867d88ed8658 | https://github.com/hamjam/NeMo/tree/b3484d32e1317666151f931bfa39867d88ed8658 |
DummyLayer | import torch
class DummyLayer(torch.nn.Module):
def __init__(self):
super().__init__()
self.dummy = torch.nn.Parameter(torch.ones(1, dtype=torch.float32))
def forward(self, x):
return x + self.dummy - self.dummy
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | RICE-EIC/Early-Bird-GCN | DummyLayer | false | 964 | [
"Apache-2.0"
] | 0 | 25a80b23f2ecfc46ffe00b1cf0e06052b32aad0f | https://github.com/RICE-EIC/Early-Bird-GCN/tree/25a80b23f2ecfc46ffe00b1cf0e06052b32aad0f |
MyMaxPool1dPadSame | # 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... | huynhnhathao/hum_to_find | MyMaxPool1dPadSame | false | 6,833 | [
"MIT"
] | 1 | a0d7ec4bab1a7e2f7175956ff2721e23e2448840 | https://github.com/huynhnhathao/hum_to_find/tree/a0d7ec4bab1a7e2f7175956ff2721e23e2448840 |
CNN_Model | import torch
import torch.nn as nn
import torch.nn.functional as F
class CNN_Model(nn.Module):
def __init__(self):
super(CNN_Model, self).__init__()
self.conv1 = nn.Conv2d(3, 32, 3, padding=1)
self.conv2 = nn.Conv2d(32, 64, 3, padding=1)
self.conv3 = nn.Conv2d(64, 64, 3, padding=1... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | CaFeCoKe/Leaf_Disease_Classification | CNN_Model | false | 11,317 | [
"MIT"
] | 0 | 113a69cc896f91c878eb391b3650fb4bfe1975c3 | https://github.com/CaFeCoKe/Leaf_Disease_Classification/tree/113a69cc896f91c878eb391b3650fb4bfe1975c3 |
ConvNet | import torch
import torch.nn as nn
class ConvNet(nn.Module):
"""
A network with a single convolution layer. This is used for testing flop
count for convolution layers.
"""
def __init__(self, conv_dim: 'int', input_dim: 'int', output_dim: 'int',
kernel_size: 'int', spatial_dim: 'int', stri... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | wangg12/fvcore | ConvNet | false | 13,076 | [
"Apache-2.0"
] | 0 | aca6e95b3319144ec3c66385ff348c1557a2147f | https://github.com/wangg12/fvcore/tree/aca6e95b3319144ec3c66385ff348c1557a2147f |
MSE_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
import torch.nn as nn
import torch.utils.data
import torch.optim
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strid... | alopezgit/project-adapt | MSE_loss | false | 18,303 | [
"MIT"
] | 8 | e93ab350344a5504f76f4e460002e0163996f88a | https://github.com/alopezgit/project-adapt/tree/e93ab350344a5504f76f4e460002e0163996f88a |
TransformerLayer | import torch
import torch.nn as nn
class TransformerLayer(nn.Module):
def __init__(self, c, num_heads):
super().__init__()
self.q = nn.Linear(c, c, bias=False)
self.k = nn.Linear(c, c, bias=False)
self.v = nn.Linear(c, c, bias=False)
self.ma = nn.MultiheadAttention(embed_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.... | Aditya239233/MDP | TransformerLayer | false | 16,920 | [
"MIT"
] | 4 | 87491e1d67e547c11f4bdd5d784d120473429eae | https://github.com/Aditya239233/MDP/tree/87491e1d67e547c11f4bdd5d784d120473429eae |
MLP | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | imxian/FlexTensor | MLP | false | 15,610 | [
"MIT"
] | 135 | 311af3362856ea1b0073404fffad42c54585c205 | https://github.com/imxian/FlexTensor/tree/311af3362856ea1b0073404fffad42c54585c205 |
HingeMarginLoss | # 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... | Aurelius84/SPWE | HingeMarginLoss | false | 16,936 | [
"MIT"
] | 9 | 5f9fc5495e879b5272c118271a69c5adad4ba260 | https://github.com/Aurelius84/SPWE/tree/5f9fc5495e879b5272c118271a69c5adad4ba260 |
tripletLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class tripletLoss(nn.Module):
def __init__(self, margin):
super(tripletLoss, self).__init__()
self.margin = margin
def forward(self, anchor, pos, neg):
distance_pos = (anchor - pos).pow(2).sum(1)
distance_neg ... | 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... | MingzheWu418/plastering | tripletLoss | false | 9,326 | [
"MIT"
] | 0 | 322531e934c3acf2ecc8f520b37a6d255b9959c2 | https://github.com/MingzheWu418/plastering/tree/322531e934c3acf2ecc8f520b37a6d255b9959c2 |
SeE_Block | import torch
import torch.nn as nn
import torch.nn.functional as F
class SeE_Block(nn.Module):
def __init__(self, channel):
super(SeE_Block, self).__init__()
self.channel = channel
self.relu = nn.ReLU()
self.sigmoid = nn.Sigmoid()
self.fc1 = nn.Conv2d(self.channel, self.ch... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Mhaiyang/TCSVT2021_DCENet | SeE_Block | false | 17,710 | [
"BSD-3-Clause"
] | 4 | aae8c7643402c15847836c0ce4934b743e11fd8a | https://github.com/Mhaiyang/TCSVT2021_DCENet/tree/aae8c7643402c15847836c0ce4934b743e11fd8a |
ConvPredictor | # 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... | TomScheffers/Residual-Prediction-Networks-using-Pytorch | ConvPredictor | false | 5,908 | [
"MIT"
] | 1 | c0e8b60c188414d71c389a0fd034f50017c24a93 | https://github.com/TomScheffers/Residual-Prediction-Networks-using-Pytorch/tree/c0e8b60c188414d71c389a0fd034f50017c24a93 |
FeedForward | import torch
import torch.nn as nn
import torch.cuda
class FeedForward(nn.Module):
def __init__(self, hidden_size, inner_size, dropout):
super(FeedForward, self).__init__()
self.linear_in = nn.Linear(hidden_size, inner_size, bias=False)
self.linear_out = nn.Linear(inner_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 as nn
import ... | XL2248/VHM | FeedForward | false | 18,094 | [
"MIT"
] | 8 | d6c21938f7cf095590b35e6ae7e0ef2b27d430f8 | https://github.com/XL2248/VHM/tree/d6c21938f7cf095590b35e6ae7e0ef2b27d430f8 |
Attention | import torch
import torch.nn as nn
import torch.nn.functional as F
class Attention(nn.Module):
"""
Applies an attention mechanism on the output features from the decoder.
.. math::
\\begin{array}{ll}
x = context*output \\\\
attn = exp(x_i) / sum_j exp(x_j) \\\\
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | EVASHINJI/Seq2Seq-PyTorch | Attention | false | 17,233 | [
"Apache-2.0"
] | 4 | d53f8d7c240bd7c16ebd1475384774bd064b4b03 | https://github.com/EVASHINJI/Seq2Seq-PyTorch/tree/d53f8d7c240bd7c16ebd1475384774bd064b4b03 |
cnn_4layer_LeakyRelu | # 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... | Mahoumaru/auto_LiRPA | cnn_4layer_LeakyRelu | false | 11,686 | [
"BSD-3-Clause"
] | 0 | b03a6c36eb1b921726778359d6d2b94e0cd7e480 | https://github.com/Mahoumaru/auto_LiRPA/tree/b03a6c36eb1b921726778359d6d2b94e0cd7e480 |
GNN_Valuator | from torch.nn import Module
import math
import torch
from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module
import torch.nn as nn
import torch.nn.functional as F
class GraphConvolution(Module):
"""
Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
"""
def __in... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch.nn import Module
i... | Zhen-Tan-dmml/GFCIL | GNN_Valuator | false | 18,197 | [
"MIT"
] | 7 | 9b78210418711a795280c588f55aef63f7df5b3b | https://github.com/Zhen-Tan-dmml/GFCIL/tree/9b78210418711a795280c588f55aef63f7df5b3b |
NIN | # 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 string
import numpy as np
import torch.utils.data
import torch
import tor... | ayulockin/Image-Super-Resolution-via-Iterative-Refinement | NIN | false | 14,939 | [
"Apache-2.0"
] | 1,764 | 8a75df33d9ed1a2cc0da22f36f576abfc9482913 | https://github.com/ayulockin/Image-Super-Resolution-via-Iterative-Refinement/tree/8a75df33d9ed1a2cc0da22f36f576abfc9482913 |
GatedDense | import torch
import torch.nn as nn
class GatedDense(nn.Module):
def __init__(self, input_size, output_size, activation=torch.relu):
super(GatedDense, self).__init__()
self.activation = activation
self.sigmoid = nn.Sigmoid()
self.h = nn.Linear(input_size, output_size)
self.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | dendisuhubdy/flow_synthesizer | GatedDense | false | 15,172 | [
"MIT"
] | 93 | 1561e8ce2520258acb3d228beebbb626a8abc04f | https://github.com/dendisuhubdy/flow_synthesizer/tree/1561e8ce2520258acb3d228beebbb626a8abc04f |
resnet_block | # 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... | luixiao1223/BSP-NET-pytorch | resnet_block | false | 3,940 | [
"MIT"
] | 0 | f871c8ce6a9d52ac922e110702c47cd1c89d0a73 | https://github.com/luixiao1223/BSP-NET-pytorch/tree/f871c8ce6a9d52ac922e110702c47cd1c89d0a73 |
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... | liuhuaijjin/rpn_rois_proposals_layers | SigmoidFocalClassificationLoss | false | 7,103 | [
"MIT"
] | 1 | c5f9f09b3ae8c52e4b6fa3fda391f993cb7d42c1 | https://github.com/liuhuaijjin/rpn_rois_proposals_layers/tree/c5f9f09b3ae8c52e4b6fa3fda391f993cb7d42c1 |
ChannelSpatialSELayer3D | # 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 ... | Jianrong-Lu/Head-and-Neck-Tumour-Segmentation-and-Prediction-of-Patient-Survival | ChannelSpatialSELayer3D | false | 666 | [
"MIT"
] | 0 | 257cf17ce6d405166dd8449f3b34e305cb5103b2 | https://github.com/Jianrong-Lu/Head-and-Neck-Tumour-Segmentation-and-Prediction-of-Patient-Survival/tree/257cf17ce6d405166dd8449f3b34e305cb5103b2 |
BaselineDiscreteCritic | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.functional import F
from torch.nn import functional as F
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torch.distributions
class BaselineDiscreteCritic(nn.Module):
def __... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | greenstar1151/pytorch-benchmark | BaselineDiscreteCritic | false | 10,444 | [
"BSD-3-Clause"
] | 0 | 8b7808d3be6b7ca1d57f1812e35fd2df5e470f8b | https://github.com/greenstar1151/pytorch-benchmark/tree/8b7808d3be6b7ca1d57f1812e35fd2df5e470f8b |
GroupNorm | import torch
import torch.nn as nn
class GroupNorm(nn.Module):
def __init__(self, c_num, group_num=16, eps=1e-10):
"""
The groupnorm layer from https://arxiv.org/abs/1803.08494
Args:
c_num (int): Number of input channels
group_num (int): Number of group by which to... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | generall/Torchlite | GroupNorm | false | 6,735 | [
"MIT"
] | 1 | 2eb3e2a20b7619bd58b0b0fca120e2aefca0e79a | https://github.com/generall/Torchlite/tree/2eb3e2a20b7619bd58b0b0fca120e2aefca0e79a |
NPairLoss | import torch
class NPairLoss(torch.nn.Module):
def __init__(self, l2=0.05):
"""
Basic N-Pair Loss as proposed in 'Improved Deep Metric Learning with Multi-class N-pair Loss Objective'
Args:
l2: float, weighting parameter for weight penality due to embeddings not being normaliz... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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_s... | bm2-lab/scPrivacy | NPairLoss | false | 6,431 | [
"MIT"
] | 1 | 444c8f3a5e7b890c299cd823359e5414f73d6205 | https://github.com/bm2-lab/scPrivacy/tree/444c8f3a5e7b890c299cd823359e5414f73d6205 |
downsampleLayer | import torch
import torch.nn as nn
class downsampleLayer(nn.Module):
"""
A downsample layer of UNet. LeakyReLU is used as the activation func.
"""
def __init__(self, infeature, outfeature, kernelSize, strides=2,
paddings=1, bn=False):
super(downsampleLayer, 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | GentleDell/DEBOR | downsampleLayer | false | 17,294 | [
"BSD-3-Clause"
] | 4 | cd566f173599fe7419e7baf312f63830c28d5de2 | https://github.com/GentleDell/DEBOR/tree/cd566f173599fe7419e7baf312f63830c28d5de2 |
DoubleAttention | # 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.... | LeftAttention/Attention-Codebase | DoubleAttention | false | 17,590 | [
"Apache-2.0"
] | 6 | 348ec66233a7c0f95a3cb5f0f11641e2a7a9b9c3 | https://github.com/LeftAttention/Attention-Codebase/tree/348ec66233a7c0f95a3cb5f0f11641e2a7a9b9c3 |
SmallMnist | import torch
import torch.nn as nn
import torch.nn
import torch.utils.data
import torch.utils.tensorboard._pytorch_graph
import torch.onnx.symbolic_caffe2
class SmallMnist(nn.Module):
def __init__(self):
super(SmallMnist, self).__init__()
self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
self.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Rohan-Chaudhury/aimet | SmallMnist | false | 17,953 | [
"BSD-3-Clause"
] | 3 | 1c38cac8cc0fd32dca40ce5e39940805d29f7a4a | https://github.com/Rohan-Chaudhury/aimet/tree/1c38cac8cc0fd32dca40ce5e39940805d29f7a4a |
DiceLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | DeVriesMatt/cellshape-voxel | DiceLoss | false | 5,067 | [
"BSD-3-Clause"
] | 1 | 64c2c57cc8b8ebe7f6ba1934caaaa3aaa1d6a0c1 | https://github.com/DeVriesMatt/cellshape-voxel/tree/64c2c57cc8b8ebe7f6ba1934caaaa3aaa1d6a0c1 |
FSub | # 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... | dawnclaude/onnx2keras | FSub | false | 15,144 | [
"MIT"
] | 115 | 3d2a47c0a228b91fd434232274e216e491da36e3 | https://github.com/dawnclaude/onnx2keras/tree/3d2a47c0a228b91fd434232274e216e491da36e3 |
RBFExpansion | import torch
import numpy as np
import torch.nn as nn
class RBFExpansion(nn.Module):
"""Expand distances between nodes by radial basis functions.
.. math::
\\exp(- \\gamma * ||d - \\mu||^2)
where :math:`d` is the distance between two nodes and :math:`\\mu` helps centralizes
the distances. We... | 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 numpy as np
import torch.nn as nn
assert_size_stride = torch._C._d... | VoVAllen/dgl-lifesci | RBFExpansion | false | 11,954 | [
"Apache-2.0"
] | 0 | 96895f2bddf255ad326f0bc4e8064bc3ed5c3044 | https://github.com/VoVAllen/dgl-lifesci/tree/96895f2bddf255ad326f0bc4e8064bc3ed5c3044 |
TransformerEncoderLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Chertushkin/efficient-dl-systems | TransformerEncoderLayer | false | 9,035 | [
"MIT"
] | 0 | 9541dbbbc92f8cf58d0f14c646562e068089aad0 | https://github.com/Chertushkin/efficient-dl-systems/tree/9541dbbbc92f8cf58d0f14c646562e068089aad0 |
VAE | import torch
from torch import nn
from torch.nn import functional as F
class Encoder(nn.Module):
def __init__(self, latent_size):
super().__init__()
self.latent_size = latent_size
self.conv1 = nn.Conv2d(3, 32, 4, stride=2)
self.conv2 = nn.Conv2d(32, 64, 4, stride=2)
self.c... | import torch
from torch import device
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | jinyeom/ga-plastic-models | VAE | false | 3,784 | [
"MIT"
] | 0 | e38b245ae51c35a5f32679cc9f215463a3d58f1a | https://github.com/jinyeom/ga-plastic-models/tree/e38b245ae51c35a5f32679cc9f215463a3d58f1a |
TransformerEncoderLayer | import math
import torch
import warnings
from torch import Tensor
import torch.nn as nn
from torch.nn.parameter import Parameter
from torch.nn.init import xavier_uniform_
from torch.nn.init import constant_
import torch.nn.functional as F
from typing import Optional
from typing import Tuple
from typing import List
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 import triton_helpers
from torch._inductor.runtime.... | Treedy2020/TransNet | TransformerEncoderLayer | false | 18,051 | [
"MIT"
] | 4 | dd0e43e1931153baea4e5fe8cb31dc5ff0cb7b09 | https://github.com/Treedy2020/TransNet/tree/dd0e43e1931153baea4e5fe8cb31dc5ff0cb7b09 |
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
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | a3ahmad/DDPM | Upsample | false | 1,341 | [
"MIT"
] | 0 | 180440740cb82c2b4e7e0b06a0d8e662b5aa3f05 | https://github.com/a3ahmad/DDPM/tree/180440740cb82c2b4e7e0b06a0d8e662b5aa3f05 |
upsample | import torch
import torch.nn as nn
class upsample(nn.Module):
def __init__(self, scale_factor):
super(upsample, self).__init__()
self.scale_factor = scale_factor
def forward(self, x):
return nn.functional.interpolate(x, scale_factor=self.scale_factor)
def get_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... | FMsunyh/CornerNet-Lite | upsample | false | 2,244 | [
"BSD-3-Clause"
] | 0 | 85770fa6682646d572a5bd2277a0075d6dd22b93 | https://github.com/FMsunyh/CornerNet-Lite/tree/85770fa6682646d572a5bd2277a0075d6dd22b93 |
WrapperKLDiv | import torch
from torch import Tensor
from torch import nn
class WrapperKLDiv(nn.Module):
"""Wrapper for KL-Divergence for easy argument passing."""
def __init__(self, reduction: 'str'='mean') ->None:
"""Constructor.
Args:
reduction (str, optional): One of 'none','batchmean','sum... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch ... | PaccMann/paccmann_datasets | WrapperKLDiv | false | 8,623 | [
"MIT"
] | 14 | 0cb0cee349ffab8e227f09f7df0a8bca6a71f22e | https://github.com/PaccMann/paccmann_datasets/tree/0cb0cee349ffab8e227f09f7df0a8bca6a71f22e |
SegmentationHead | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data.dataloader
assert_size_stride = to... | jamt9000/DVE | SegmentationHead | false | 15,669 | [
"MIT"
] | 72 | 208514419dd1eb0d27ce60876ca836d1ab8c4f4a | https://github.com/jamt9000/DVE/tree/208514419dd1eb0d27ce60876ca836d1ab8c4f4a |
HyperpriorSynthesis | import torch
import torch.nn as nn
import torch.nn.functional as F
class HyperpriorSynthesis(nn.Module):
"""
Hyperprior 'synthesis model' as proposed in [1]. Outputs
distribution parameters of input latents.
[1] Ballé et. al., "Variational image compression with a scale hyperprior",
arXiv:1... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | sedrickkeh/high-fidelity-dual-image | HyperpriorSynthesis | false | 16,419 | [
"Apache-2.0"
] | 266 | 9cefd378467826b91596653df38666e469bb23e0 | https://github.com/sedrickkeh/high-fidelity-dual-image/tree/9cefd378467826b91596653df38666e469bb23e0 |
SpaceToDepth | # 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.optim
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strid... | LeikvollE/pytorch-superpoint | SpaceToDepth | false | 11,634 | [
"MIT"
] | 0 | 52144a760e0cc46259e57397a5a55f0585fe6d0b | https://github.com/LeikvollE/pytorch-superpoint/tree/52144a760e0cc46259e57397a5a55f0585fe6d0b |
ConvTemporalGraphical | # 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... | Alexis-Fab/mmaction2 | ConvTemporalGraphical | false | 11,221 | [
"Apache-2.0"
] | 0 | 6f76bb465a7164f907318cf58f77fc3d613f8f0f | https://github.com/Alexis-Fab/mmaction2/tree/6f76bb465a7164f907318cf58f77fc3d613f8f0f |
BCE_disc_sm_v3 | import torch
import torch.nn as nn
import torch.nn.functional as F
class BCE_disc_sm_v3(nn.Module):
def __init__(self, weight_list=None, lb_sm=0.2):
super(BCE_disc_sm_v3, self).__init__()
self.weight_list = weight_list
self.lb_sm = lb_sm
def forward(self, x, labels):
assert (... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | Sampson-Lee/SIB-Net | BCE_disc_sm_v3 | false | 2,816 | [
"MIT"
] | 0 | 650399082e9237327fa38168ccfc7d48153a1db5 | https://github.com/Sampson-Lee/SIB-Net/tree/650399082e9237327fa38168ccfc7d48153a1db5 |
HGNN_embedding | # 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
from torch import... | DCMMC/HGNN | HGNN_embedding | false | 13,545 | [
"MIT"
] | 124 | 4315f27faaffb8f2cf1463049a4dc596694e44e1 | https://github.com/DCMMC/HGNN/tree/4315f27faaffb8f2cf1463049a4dc596694e44e1 |
AttentionPooling | import torch
import torch.utils.data
import torch.nn.functional as F
def masked_softmax(x, m=None, dim=-1):
"""
Softmax with mask
:param x:
:param m:
:param dim:
:return:
"""
if m is not None:
m = m.float()
x = x * m
e_x = torch.exp(x - torch.max(x, dim=dim, keepdim... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | xdong73S/Match_LSTM_v2.0 | AttentionPooling | false | 4,576 | [
"MIT"
] | 0 | dfb8cfbc2a5dafc6655eecf151a7dbcf808cd729 | https://github.com/xdong73S/Match_LSTM_v2.0/tree/dfb8cfbc2a5dafc6655eecf151a7dbcf808cd729 |
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.triton_helpers import libdevice, math as tl_math
im... | entn-at/espresso | BahdanauAttention | false | 10,047 | [
"MIT"
] | 0 | 754b69a316429446a5602e13e644142310b7980b | https://github.com/entn-at/espresso/tree/754b69a316429446a5602e13e644142310b7980b |
GSympNet | from torch.nn import Module
import torch
import torch.nn as nn
class Module(torch.nn.Module):
"""Standard module format.
"""
def __init__(self):
super(Module, self).__init__()
self.activation = None
self.initializer = None
self.__device = None
self.__dtype = None
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.nn import Module
import torch.nn as nn
assert_size_stride = torch._C.... | shushu-qin/deeponet | GSympNet | false | 16,448 | [
"Apache-2.0"
] | 140 | 5bbe066279bba055ad80e04c364140363c87634a | https://github.com/shushu-qin/deeponet/tree/5bbe066279bba055ad80e04c364140363c87634a |
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