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 |
|---|---|---|---|---|---|---|---|---|---|---|
FCDiscriminator | import torch
from torch import nn
import torch.utils.data
import torch.distributed
import torch.backends.cudnn
import torch.utils
import torch.backends
class FCDiscriminator(nn.Module):
def __init__(self, num_classes, ndf=64):
super(FCDiscriminator, self).__init__()
self.conv1 = nn.Conv2d(num_cla... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.utils.data
import torch.distributed
import tor... | BinhuiXie/SPCL | FCDiscriminator | false | 17,025 | [
"MIT"
] | 6 | 9e5bab7b5d38fde847f9e8f85ca64498baaf86be | https://github.com/BinhuiXie/SPCL/tree/9e5bab7b5d38fde847f9e8f85ca64498baaf86be |
PaddedMaxPool2d | import torch
import torch.nn as nn
import torch.nn.functional as F
class PaddedMaxPool2d(nn.Module):
""" Maxpool layer with a replicating padding.
Args:
kernel_size (int or tuple): Kernel size for maxpooling
stride (int or tuple, optional): The stride of the window; Default ``kernel_size``
... | 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... | hilman-dayo/ObjectDetection-OneStageDet | PaddedMaxPool2d | false | 15,516 | [
"MIT"
] | 331 | 44054ad335e24e99a98fdad0d18b9bf3a80c941c | https://github.com/hilman-dayo/ObjectDetection-OneStageDet/tree/44054ad335e24e99a98fdad0d18b9bf3a80c941c |
SeparableBlock | # 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.nn import Module
from torch.nn import Linear
assert_size_stride = tor... | Kiberchaika/hyperstyle | SeparableBlock | false | 11,693 | [
"MIT"
] | 0 | b67e5ca9c67dfdfa18f1d6cda6e8eff5da07db7b | https://github.com/Kiberchaika/hyperstyle/tree/b67e5ca9c67dfdfa18f1d6cda6e8eff5da07db7b |
L1CompositionLoss | import functools
import torch
import torch.nn as nn
from torch.nn import functional as F
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Returns:
Tensor: Reduced lo... | 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 functools
impor... | rivergold/mmediting | L1CompositionLoss | false | 7,565 | [
"Apache-2.0"
] | 1 | fd972635c48bb065db29d1b5090592a87c7263d2 | https://github.com/rivergold/mmediting/tree/fd972635c48bb065db29d1b5090592a87c7263d2 |
Sigmoid | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | jiwidi/lightning-tutorials | Sigmoid | false | 15,700 | [
"Apache-2.0"
] | 114 | 70ba437447f345d4d6ba089d5b30fd1da2cbc04b | https://github.com/jiwidi/lightning-tutorials/tree/70ba437447f345d4d6ba089d5b30fd1da2cbc04b |
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_... | stjordanis/ml-cheatsheet | MLP | false | 16,508 | [
"MIT"
] | 1,031 | d34e096032b7ae826868be8808aee01699cec491 | https://github.com/stjordanis/ml-cheatsheet/tree/d34e096032b7ae826868be8808aee01699cec491 |
Quantize | import torch
import torch.nn as nn
import torch.nn.functional as F
class Quantize(nn.Module):
def __init__(self, emb_dim, emb_size, decay=0.99, eps=1e-05, ema_flag=
False, bdt_flag=False):
super().__init__()
self.emb_dim = emb_dim
self.emb_size = emb_size
self.ema_flag = e... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.functional as F
assert_size_stride = torch... | unilight/crank | Quantize | false | 4,482 | [
"MIT"
] | 0 | 0dc5d9df17f3186155b1c9583ab604ff218ad9a6 | https://github.com/unilight/crank/tree/0dc5d9df17f3186155b1c9583ab604ff218ad9a6 |
CoxPHLoss | # 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, split_scan_grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from ... | bseewald/pycox | CoxPHLoss | false | 9,909 | [
"BSD-2-Clause"
] | 0 | 366348d51ecd902a01ab830b2f0a4cf1694d9ae2 | https://github.com/bseewald/pycox/tree/366348d51ecd902a01ab830b2f0a4cf1694d9ae2 |
PartitionLoss | import torch
import torch.nn as nn
class PartitionLoss(nn.Module):
def __init__(self):
super(PartitionLoss, self).__init__()
def forward(self, x):
num_head = x.size(1)
if num_head > 1:
var = x.var(dim=1).mean()
loss = torch.log(1 + num_head / var)
else... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | orena1/DAN | PartitionLoss | false | 16,203 | [
"MIT"
] | 50 | 49247ad0cad2a67057d184fa92d15fe2e7bb2cb6 | https://github.com/orena1/DAN/tree/49247ad0cad2a67057d184fa92d15fe2e7bb2cb6 |
XSigmoidLoss | import torch
class XSigmoidLoss(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, y_t, y_prime_t):
ey_t = y_t - y_prime_t
return torch.mean(2 * ey_t * torch.sigmoid(ey_t) - ey_t)
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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | tuantle/regression-losses-pytorch | XSigmoidLoss | false | 16,628 | [
"MIT"
] | 82 | 2893f4439ada5df239e3afd0ec7e781dd61403e9 | https://github.com/tuantle/regression-losses-pytorch/tree/2893f4439ada5df239e3afd0ec7e781dd61403e9 |
DownsampleA | import torch
import torch.nn as nn
class DownsampleA(nn.Module):
def __init__(self, nIn, nOut, stride):
super(DownsampleA, self).__init__()
assert stride == 2
self.avg = nn.AvgPool2d(kernel_size=1, stride=stride)
def forward(self, x):
x = self.avg(x)
return torch.cat(... | 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... | Danden1/DER-ClassIL.pytorch | DownsampleA | false | 13,563 | [
"MIT"
] | 79 | 66ccdb45890d3da335f4dcb841160cbea8719c15 | https://github.com/Danden1/DER-ClassIL.pytorch/tree/66ccdb45890d3da335f4dcb841160cbea8719c15 |
AdaptiveInstanceNorm | import torch
import torch.nn as nn
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 GANs for Improved Quality, Stability, and Variation
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | arkel23/mmgeneration | AdaptiveInstanceNorm | false | 9,948 | [
"Apache-2.0"
] | 0 | 41a30e2972f2037f6aac60ed761bed3fe47bfe4d | https://github.com/arkel23/mmgeneration/tree/41a30e2972f2037f6aac60ed761bed3fe47bfe4d |
AsymmetricMultiLabelLoss | import torch
import torch.nn as nn
import torch.nn.parallel
import torch._utils
import torch.optim
class AsymmetricMultiLabelLoss(nn.Module):
def __init__(self, gamma_neg=4, gamma_pos=1, clip=0.05, eps=1e-08,
disable_torch_grad_focal_loss=False):
super(AsymmetricMultiLabelLoss, self).__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... | Alicegaz/torchok | AsymmetricMultiLabelLoss | false | 16,922 | [
"Apache-2.0"
] | 8 | 7b8f95df466a25b1ad8ee93bed1a3c7516440cf4 | https://github.com/Alicegaz/torchok/tree/7b8f95df466a25b1ad8ee93bed1a3c7516440cf4 |
SineLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import numpy ... | Juju-botu/diffeqml-research | SineLayer | false | 13,952 | [
"Apache-2.0"
] | 49 | aa796c87447e5299ec4f25a07fc4d032afb1f63e | https://github.com/Juju-botu/diffeqml-research/tree/aa796c87447e5299ec4f25a07fc4d032afb1f63e |
GroupNorm32 | import torch
import torch.nn as nn
import torch.nn.functional as F
class GroupNorm32(nn.GroupNorm):
def __init__(self, num_groups, num_channels, swish, eps=1e-05):
super().__init__(num_groups=num_groups, num_channels=num_channels,
eps=eps)
self.swish = swish
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.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | ShivanshuPurohit/Diffusion | GroupNorm32 | false | 1,063 | [
"MIT"
] | 0 | 9a190d9aa4ed9767cf223e4ef57d0c31690f92cc | https://github.com/ShivanshuPurohit/Diffusion/tree/9a190d9aa4ed9767cf223e4ef57d0c31690f92cc |
EqualLinearActModule | # 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 copy import deepcopy
from functools import partial
fr... | bladesaber/mmgeneration | EqualLinearActModule | false | 1,585 | [
"Apache-2.0"
] | 0 | 158b49f7efd8028f231f6e9ca758ae0e20dd72ae | https://github.com/bladesaber/mmgeneration/tree/158b49f7efd8028f231f6e9ca758ae0e20dd72ae |
ReQUNet | import torch
import torch.nn as nn
def MyReQU(x):
x[x < 0] = 0
z = x * x
return z
class ReQUNet(nn.Module):
def __init__(self):
super(ReQUNet, self).__init__()
n_in, n_h, n_out = 4, 64, 3
self.fc1 = nn.Linear(n_in, n_h, True)
self.fc2 = nn.Linear(n_h, n_out, True)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | RoyHirsch/DeepLearningCourse | ReQUNet | false | 1,002 | [
"MIT"
] | 0 | 9036c0fdbb08b610524d7be991f8e4b490a82c6c | https://github.com/RoyHirsch/DeepLearningCourse/tree/9036c0fdbb08b610524d7be991f8e4b490a82c6c |
PSN | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cu... | Chay16/PortfolioOptimization | PSN | false | 2,096 | [
"Apache-2.0"
] | 0 | d8a6e7215d64038766beaf1c9325abc46ef05ffc | https://github.com/Chay16/PortfolioOptimization/tree/d8a6e7215d64038766beaf1c9325abc46ef05ffc |
QuadricLinearLoss | import torch
import torch.nn as nn
class QuadricLinearLoss(nn.Module):
def __init__(self, clip_delta):
super(QuadricLinearLoss, self).__init__()
self.clip_delta = clip_delta
def forward(self, y_pred, y_true, weights):
td_error = y_true - y_pred
td_error_abs = torch.abs(td_err... | 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
... | Shmuma/Run-Skeleton-Run | QuadricLinearLoss | false | 14,407 | [
"MIT"
] | 92 | a953e6c524a444b6a99a54ef5b2886a57de0d185 | https://github.com/Shmuma/Run-Skeleton-Run/tree/a953e6c524a444b6a99a54ef5b2886a57de0d185 |
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.... | lin-bo/RL_back2depot_VRP | MultiHeadAttention | false | 7,099 | [
"MIT"
] | 1 | 2a159d1df221ff314d98d79b8fde2b739a454ff7 | https://github.com/lin-bo/RL_back2depot_VRP/tree/2a159d1df221ff314d98d79b8fde2b739a454ff7 |
ContextGate | import torch
import torch.multiprocessing
from torch import nn
import torch.utils.data
class ContextGate(nn.Module):
def __init__(self, vector_dim, topic_dim):
super().__init__()
assert vector_dim == topic_dim
self.fusion_linear = nn.Linear(vector_dim + topic_dim, vector_dim)
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.triton_helpers import libdevice
import torch.multip... | WuDiDaBinGe/TAKG | ContextGate | false | 1,227 | [
"MIT"
] | 0 | 83e608e677a4ee74722d18cb5ef430f4f6c6ad31 | https://github.com/WuDiDaBinGe/TAKG/tree/83e608e677a4ee74722d18cb5ef430f4f6c6ad31 |
AsymmetricLossOptimized | # 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... | ChangeTheWorld20191008/query2labels | AsymmetricLossOptimized | false | 2,144 | [
"MIT"
] | 0 | cdca1f3519f75cc91ef2aa166c2534691016f04f | https://github.com/ChangeTheWorld20191008/query2labels/tree/cdca1f3519f75cc91ef2aa166c2534691016f04f |
Accuracy | # 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... | IC-hub/ProteinLM | Accuracy | false | 13,820 | [
"Apache-2.0"
] | 59 | 58fbf1f674569cf814becf32f71dd0d8f0c592fa | https://github.com/IC-hub/ProteinLM/tree/58fbf1f674569cf814becf32f71dd0d8f0c592fa |
MultiSampleDropout | import torch
import torch.nn as nn
class MultiSampleDropout(nn.Module):
"""
# multisample dropout (wut): https://arxiv.org/abs/1905.09788
"""
def __init__(self, hidden_size, num_labels, K=5, p=0.5):
super().__init__()
self.K = K
self.dropout = nn.Dropout(p)
self.classi... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | lonePatient/TorchBlocks | MultiSampleDropout | false | 15,957 | [
"MIT"
] | 82 | 4a65d746cc8a396cb7df73ed4644d97ddf843e29 | https://github.com/lonePatient/TorchBlocks/tree/4a65d746cc8a396cb7df73ed4644d97ddf843e29 |
DiceLoss | import torch
import torch.nn as nn
class DiceLoss(nn.Module):
def __init__(self):
super(DiceLoss, self).__init__()
self.smooth = 1.0
def forward(self, y_pred, y_true):
assert y_pred.size() == y_true.size()
y_pred = y_pred[:, 0].contiguous().view(-1)
y_true = y_true[:,... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | anudeepsekhar/Lane-Detection-Pytorch | DiceLoss | false | 6,216 | [
"MIT"
] | 1 | cfddda8a0768cf83afd87e29d605fd58aa89df59 | https://github.com/anudeepsekhar/Lane-Detection-Pytorch/tree/cfddda8a0768cf83afd87e29d605fd58aa89df59 |
scaleCompositor | # 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_... | qbhan/pathembed | scaleCompositor | false | 7,533 | [
"MIT"
] | 1 | c21823529840593bf606e10696f5879e5adb51b2 | https://github.com/qbhan/pathembed/tree/c21823529840593bf606e10696f5879e5adb51b2 |
Dice | import torch
import torch.nn as nn
class Dice(nn.Module):
"""
This class implements the dice score for validation. No gradients supported.
"""
def __init__(self, threshold: 'float'=0.5) ->None:
"""
Constructor method
:param threshold: (float) Threshold to be applied
""... | 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/Cell-DETR | Dice | false | 13,489 | [
"MIT"
] | 55 | 4d0c3a2d3ffd19184c8443e5b3a6dccc053c77ea | https://github.com/ChristophReich1996/Cell-DETR/tree/4d0c3a2d3ffd19184c8443e5b3a6dccc053c77ea |
HardMGUCell | # 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... | RuokaiYin/UnarySim | HardMGUCell | false | 5,787 | [
"MIT"
] | 1 | 343ff9abf356a63d526b1df8eb946ad528690a27 | https://github.com/RuokaiYin/UnarySim/tree/343ff9abf356a63d526b1df8eb946ad528690a27 |
CRF | import torch
import torch.nn as nn
class CRF(nn.Module):
"""
Implements Conditional Random Fields that can be trained via
backpropagation.
"""
def __init__(self, num_tags):
super(CRF, self).__init__()
self.num_tags = num_tags
self.transitions = nn.Parameter(torch.Tensor(n... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | yezhengli-Mr9/torchnlp | CRF | false | 13,148 | [
"Apache-2.0"
] | 0 | 0f2ad6d149a413da9f03c6f6694c429746de6551 | https://github.com/yezhengli-Mr9/torchnlp/tree/0f2ad6d149a413da9f03c6f6694c429746de6551 |
ToRGB | import math
import torch
import torch.utils.data
import torch
from torch import nn
import torch.nn.functional as F
def make_kernel(k):
k = torch.tensor(k, dtype=torch.float32)
if len(k.shape) == 1:
k = k[None, :] * k[:, None]
k /= k.sum()
return k
def upfirdn2d_native(input, kernel, up_x, up... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
import torch.utils.data
import torch
from torch import nn
import tor... | guyii54/Contrastive-I2I | ToRGB | false | 6,775 | [
"BSD-3-Clause"
] | 1 | e73daa0f9d3770c2280a304c39678d5b22440647 | https://github.com/guyii54/Contrastive-I2I/tree/e73daa0f9d3770c2280a304c39678d5b22440647 |
DenseModel | import torch
import torch.nn as nn
class DenseModel(nn.Module):
def __init__(self, input_dim, num_classes=2):
super(DenseModel, self).__init__()
self.fc1 = nn.Linear(input_dim, 400)
self.relu1 = nn.ReLU(inplace=True)
self.fc2 = nn.Linear(400, 400)
self.relu2 = nn.ReLU(inpl... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | chawins/adv-exp | DenseModel | false | 6,429 | [
"MIT"
] | 1 | 5423e135c5599e4ec2bf90372916d8d05c89f285 | https://github.com/chawins/adv-exp/tree/5423e135c5599e4ec2bf90372916d8d05c89f285 |
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... | Maxi-0902/DRAN | RewardCriterion | false | 835 | [
"MIT"
] | 0 | c3dbfcbc018446544150dc4e151442d6a9fcd4d9 | https://github.com/Maxi-0902/DRAN/tree/c3dbfcbc018446544150dc4e151442d6a9fcd4d9 |
InceptionB | # 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 | InceptionB | false | 17,396 | [
"WTFPL"
] | 10 | 11077704cc0bc9a42fc4b94da60b57d31ff0f65c | https://github.com/Hiroaki-Ozaki/modelib-classification/tree/11077704cc0bc9a42fc4b94da60b57d31ff0f65c |
PixelNorm | # 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.utils.cpp_extension
assert_size_stride = tor... | STomoya/animeface | PixelNorm | false | 15,276 | [
"MIT"
] | 61 | 37b3cd26097d7874559d4c152e41e5712b7a1a42 | https://github.com/STomoya/animeface/tree/37b3cd26097d7874559d4c152e41e5712b7a1a42 |
QREmbeddingBag | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import numpy as np
from torch import nn
from torch.nn.parameter import Paramete... | divyanshugit/EnvisEdge | QREmbeddingBag | false | 3,480 | [
"Apache-2.0"
] | 0 | 26b21fd0eb665fa23a8b8a825c9bf460994d6714 | https://github.com/divyanshugit/EnvisEdge/tree/26b21fd0eb665fa23a8b8a825c9bf460994d6714 |
LinearPotential | import torch
from torch import nn
from torch.nn import Parameter
class LinearPotential(torch.nn.Module):
def __init__(self, n_features, n_states, init_idx=None, eos_idx=None):
super(LinearPotential, self).__init__()
self.transition = Parameter(torch.zeros((n_states, n_states)))
self.init_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
from torch.nn import Parameter
assert_size_stride = torch._... | minhnhat93/didyprog | LinearPotential | false | 16,091 | [
"MIT"
] | 57 | 78886ed939d269b9b2bcb192bf849aa34082880c | https://github.com/minhnhat93/didyprog/tree/78886ed939d269b9b2bcb192bf849aa34082880c |
Interpolate | # 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.fft
import torch.nn as nn
import torch.utils.cpp_extension
assert_size_strid... | CeciLyu/projected_gan | Interpolate | false | 11,580 | [
"MIT"
] | 0 | 5e86ee0c88d47164c30ede37448e7ba7f010fa7b | https://github.com/CeciLyu/projected_gan/tree/5e86ee0c88d47164c30ede37448e7ba7f010fa7b |
SpatialTokenGen | import torch
import torch.nn as nn
class SpatialTokenGen(nn.Module):
def __init__(self, d_ffn, seq_len):
super(SpatialTokenGen, self).__init__()
self.layer_norm = nn.LayerNorm(d_ffn)
self.squeeze_layer_i = nn.Linear(d_ffn, 1)
self.squeeze_layer_ii = nn.Conv1d(seq_len, 1, 1)
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.triton_helpers import libdevice
import torch.nn as ... | SeungoneKim/sgMLP_Implementation | SpatialTokenGen | false | 9,474 | [
"Apache-2.0"
] | 0 | 5c5e623577a7ada3b200d99e77dc707a10cb1195 | https://github.com/SeungoneKim/sgMLP_Implementation/tree/5c5e623577a7ada3b200d99e77dc707a10cb1195 |
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.... | davidhtf/drlnd | Actor | false | 6,530 | [
"MIT"
] | 1 | 221601f38659055824763ce41c6d9edd3d476fd4 | https://github.com/davidhtf/drlnd/tree/221601f38659055824763ce41c6d9edd3d476fd4 |
MergeBlok | # 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... | robtu328/TextBPN | MergeBlok | false | 16,336 | [
"MIT"
] | 49 | 225844770e0107817be9fb86d53f873fa3eb07ae | https://github.com/robtu328/TextBPN/tree/225844770e0107817be9fb86d53f873fa3eb07ae |
VectorQuantizer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | OmeGaNo1/PyTorch-VAE | VectorQuantizer | false | 9,446 | [
"Apache-2.0"
] | 0 | e7b6aad70682b574c947947733794b4246a48838 | https://github.com/OmeGaNo1/PyTorch-VAE/tree/e7b6aad70682b574c947947733794b4246a48838 |
OutlookAttention | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
class OutlookAttention(nn.Module):
"""
Implementation of outlook attention
--dim: hidden dim
--num_heads: number of heads
--kernel_size: kernel size in each window for outlook attention
retu... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Inch-Z/volo | OutlookAttention | false | 11,515 | [
"Apache-2.0"
] | 0 | 8bbb40838f5cc889ccae26b97438ea73cb1b4e07 | https://github.com/Inch-Z/volo/tree/8bbb40838f5cc889ccae26b97438ea73cb1b4e07 |
TestNet | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | dizzyvn/torch-tcav | TestNet | false | 1,846 | [
"Apache-2.0"
] | 0 | c9795e817d1104923ef7422f5575607e6b835abc | https://github.com/dizzyvn/torch-tcav/tree/c9795e817d1104923ef7422f5575607e6b835abc |
GeneratorLon | # 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.... | PhilippeW83440/conv-social-pooling | GeneratorLon | false | 17,824 | [
"MIT"
] | 4 | 93d3a08af8678c3309d75a9bfb37df500da5cc46 | https://github.com/PhilippeW83440/conv-social-pooling/tree/93d3a08af8678c3309d75a9bfb37df500da5cc46 |
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
import torch.nn as nn
import torch.nn.parallel
assert_size_stride = torch._C._dy... | Inch-Z/volo | Downsample | false | 11,510 | [
"Apache-2.0"
] | 0 | 8bbb40838f5cc889ccae26b97438ea73cb1b4e07 | https://github.com/Inch-Z/volo/tree/8bbb40838f5cc889ccae26b97438ea73cb1b4e07 |
FFN | import torch
import torch.nn as nn
import torch.nn.functional as F
class FFN(nn.Module):
"""
Feed-Forward Network
"""
def __init__(self, d_inner_hid, d_model, dropout_rate):
super(FFN, self).__init__()
self.dropout_rate = dropout_rate
self.fc1 = torch.nn.Linear(in_features=d_m... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | BHD233/PaddleOCR2Pytorch | FFN | false | 13,359 | [
"Apache-2.0"
] | 364 | f114069b3e2669c6adf0adf9596756205f184c9c | https://github.com/BHD233/PaddleOCR2Pytorch/tree/f114069b3e2669c6adf0adf9596756205f184c9c |
Patch2Image | import torch
from torch import nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
class Patch2Image(nn.Module):
""" take in patch and copy n_up times to form the full image"""
def __init__(self, patch_sz, n_up):
super(Patch2Image, self).__init__()
self.patch_sz = patch_sz
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert... | nudro/counterfactual_generative_networks | Patch2Image | false | 10,766 | [
"MIT"
] | 0 | 0d000903ad9da4eab0f4d397395a769c9c7bff5d | https://github.com/nudro/counterfactual_generative_networks/tree/0d000903ad9da4eab0f4d397395a769c9c7bff5d |
HuberLoss | # 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... | cvpr22sub7201/SpeechDrivenTongueAnimation | HuberLoss | false | 6,502 | [
"MIT"
] | 1 | 82caf9d7f4331e039e3b2f0d31df6393d24ccb1c | https://github.com/cvpr22sub7201/SpeechDrivenTongueAnimation/tree/82caf9d7f4331e039e3b2f0d31df6393d24ccb1c |
ps_FNNDenoiser | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch.nn import Module
f... | ddcas/singing-language-identification | ps_FNNDenoiser | false | 1,817 | [
"MIT"
] | 0 | d104419b196d56d4de37cff47c32e88e28c58690 | https://github.com/ddcas/singing-language-identification/tree/d104419b196d56d4de37cff47c32e88e28c58690 |
MSELoss | import functools
import torch
import torch.nn as nn
import torch.nn.functional as F
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Return:
Tensor: Reduced loss ten... | 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 functools
import torch.nn as nn
import torch.nn.functional as F
assert_size_stride... | Andrew-Zhu/DyFPN | MSELoss | false | 7,716 | [
"Apache-2.0"
] | 32 | a74463b59c4ce28253c2449a07c0f6692a0147a1 | https://github.com/Andrew-Zhu/DyFPN/tree/a74463b59c4ce28253c2449a07c0f6692a0147a1 |
FusedLeakyReLU | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | ShinoharaHare/stylegan2-pytorch | FusedLeakyReLU | false | 2,828 | [
"MIT",
"BSD-2-Clause",
"Apache-2.0"
] | 0 | 5a4b1c4e9753681bc1694195f3b2391527c1b525 | https://github.com/ShinoharaHare/stylegan2-pytorch/tree/5a4b1c4e9753681bc1694195f3b2391527c1b525 |
PatchEmbed3D | # 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... | Viditagarwal7479/Video-Swin-Transformer | PatchEmbed3D | false | 18,060 | [
"Apache-2.0"
] | 9 | 37910ef3141c7b2eef76544f9ec8bdf26ec94c7d | https://github.com/Viditagarwal7479/Video-Swin-Transformer/tree/37910ef3141c7b2eef76544f9ec8bdf26ec94c7d |
DiceLoss | import torch
from torch import nn
class DiceLoss(nn.Module):
"""
Implementation of the dice loss proposed in:
https://arxiv.org/abs/1707.03237
"""
def __init__(self, smooth: 'float'=1.0) ->None:
"""
Constructor method
:param smooth: (float) Smoothness factor used in comput... | 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... | ChristophReich1996/3D_Baggage_Segmentation | DiceLoss | false | 5,004 | [
"MIT"
] | 1 | 00392cb0fde22d3180b6baf81e404d0fcf4e2ebf | https://github.com/ChristophReich1996/3D_Baggage_Segmentation/tree/00392cb0fde22d3180b6baf81e404d0fcf4e2ebf |
PoolingF | import torch
import torch.utils.data
import torch
import torch.nn as nn
class Normalize(nn.Module):
def __init__(self, power=2):
super(Normalize, self).__init__()
self.power = power
def forward(self, x):
norm = x.pow(self.power).sum(1, keepdim=True).pow(1.0 / self.power)
out ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.data
impo... | bronemos/contrastive-unpaired-translation-focal | PoolingF | false | 3,242 | [
"BSD-3-Clause"
] | 0 | 50b9008d08a86439ede081a910d02df5da8e32df | https://github.com/bronemos/contrastive-unpaired-translation-focal/tree/50b9008d08a86439ede081a910d02df5da8e32df |
ITN3D | # 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_... | swaroopkml96/istn | ITN3D | false | 16,546 | [
"Apache-2.0"
] | 91 | 600543e071aa56907509aa090697295cdc69a6b1 | https://github.com/swaroopkml96/istn/tree/600543e071aa56907509aa090697295cdc69a6b1 |
BertLayerNormNoVar | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | KaidiXu/LiRPA_Verify | BertLayerNormNoVar | false | 8,401 | [
"BSD-2-Clause"
] | 14 | 71f5327a8abf136bcfb3e1ec07604628abf8126e | https://github.com/KaidiXu/LiRPA_Verify/tree/71f5327a8abf136bcfb3e1ec07604628abf8126e |
FeedForward | import math
import torch
import torch.nn as nn
def activation(act_type='swish'):
if act_type == 'swish':
act = swish()
return act
else:
act = nn.ReLU(inplace=True)
return act
class swish(nn.Module):
def __init__(self):
super(swish, self).__init__()
def forwa... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
import ... | Sense-GVT/BigPretrain | FeedForward | false | 17,917 | [
"Apache-2.0"
] | 8 | d8d9b43d94dd1364c18c1e5ba21b85a31cdbba9e | https://github.com/Sense-GVT/BigPretrain/tree/d8d9b43d94dd1364c18c1e5ba21b85a31cdbba9e |
GHMC | # 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
... | AllenPeng0209/SaccadeNet | GHMC | false | 7,648 | [
"Apache-2.0"
] | 30 | 0fce4266cbffc9a2c5f70335efa636da849ce70c | https://github.com/AllenPeng0209/SaccadeNet/tree/0fce4266cbffc9a2c5f70335efa636da849ce70c |
LearnMaskedDefault | import torch
import torch.nn as nn
import torch.utils.data
import torch.nn
class LearnMaskedDefault(nn.Module):
"""
Learns default values to fill invalid entries within input tensors. The
invalid entries are represented by a mask which is passed into forward alongside
the input tensor. Note the defaul... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.utils.data
import torch.nn
assert_size_stride = torch.... | TheShadow29/pytorchvideo | LearnMaskedDefault | false | 9,699 | [
"Apache-2.0"
] | 0 | 39a3e34e33fb0e1ec142288df08f6e8c3585961a | https://github.com/TheShadow29/pytorchvideo/tree/39a3e34e33fb0e1ec142288df08f6e8c3585961a |
LongCNN | import torch
from torch import nn
class LongCNN(nn.Module):
def __init__(self, num_channels, input_shape, name, conv_sizes=[64, 128,
128, 256], lin_size=512):
super(LongCNN, self).__init__()
self.name = name
self.relu = nn.ReLU(inplace=True)
self.do1 = nn.Dropout(p=0.25)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | Csaba591/LHYP | LongCNN | false | 8,973 | [
"MIT"
] | 0 | d1b07381b9dc39210d338b60908acfa64c476b8e | https://github.com/Csaba591/LHYP/tree/d1b07381b9dc39210d338b60908acfa64c476b8e |
GraphConvolution | # 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.nn import Module
import torch.nn as nn
from torch.nn.modules.module i... | wangzefan666/pygcn | GraphConvolution | false | 13,090 | [
"MIT"
] | 0 | 2a5e4f299e3c9d3eafe3014622e8ec3742ba365c | https://github.com/wangzefan666/pygcn/tree/2a5e4f299e3c9d3eafe3014622e8ec3742ba365c |
Quantization | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.data
impo... | qwopqwop200/Fast-Invertible-Rescaling-Net | Quantization | false | 7,514 | [
"MIT"
] | 1 | 871733f2eee7929d6b37c4d1d6a27347b39b67a9 | https://github.com/qwopqwop200/Fast-Invertible-Rescaling-Net/tree/871733f2eee7929d6b37c4d1d6a27347b39b67a9 |
IReLU | import math
import torch
class IReLU(torch.nn.Module):
__constants__ = ['negative_slope', 'positive_slope']
negative_slope: 'float'
positive_slope: 'float'
def __init__(self, negative_slope=math.tan(math.pi / 8), positive_slope
=math.tan(3 * math.pi / 8)):
super(IReLU, self).__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
import math
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided... | rupumped/DFL | IReLU | false | 4,208 | [
"BSD-3-Clause"
] | 0 | a4e4d96b7ce7522cf7fee3c2cfdbb54eb7a473f2 | https://github.com/rupumped/DFL/tree/a4e4d96b7ce7522cf7fee3c2cfdbb54eb7a473f2 |
CSAM | import torch
import torch.nn as nn
class CSAM(nn.Module):
"""
Compact Spatial Attention Module
"""
def __init__(self, channels):
super(CSAM, self).__init__()
mid_channels = 4
self.relu1 = nn.ReLU()
self.conv1 = nn.Conv2d(channels, mid_channels, kernel_size=1, padding=0... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | arkel23/mmgeneration | CSAM | false | 9,946 | [
"Apache-2.0"
] | 0 | 41a30e2972f2037f6aac60ed761bed3fe47bfe4d | https://github.com/arkel23/mmgeneration/tree/41a30e2972f2037f6aac60ed761bed3fe47bfe4d |
GatedLinear | import torch
import torch.nn as nn
class GatedLinear(nn.Module):
def __init__(self, input_size, output_size):
super(GatedLinear, self).__init__()
self.linear = nn.Linear(input_size, output_size * 2)
self.glu = nn.GLU(dim=-1)
def forward(self, x, y=None, x_mask=None, y_mask=None, rel_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | ParadoxZW/mmnas | GatedLinear | false | 8,643 | [
"Apache-2.0"
] | 23 | 186ef8648e71b5fc4433faf80431a0f8bc9261a0 | https://github.com/ParadoxZW/mmnas/tree/186ef8648e71b5fc4433faf80431a0f8bc9261a0 |
InnerProductDecoder | import torch
import torch.utils.data
class InnerProductDecoder(torch.nn.Module):
"""The inner product decoder from the `"Variational Graph Auto-Encoders"
<https://arxiv.org/abs/1611.07308>`_ paper
.. math::
\\sigma(\\mathbf{Z}\\mathbf{Z}^{\\top})
where :math:`\\mathbf{Z} \\in \\mathbb{R}^{N ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_... | MINATILO/pytroch-geometric | InnerProductDecoder | false | 9,391 | [
"MIT"
] | 0 | 706aba3b4a6477a83a1fb73eb3cf0ee9661b70e4 | https://github.com/MINATILO/pytroch-geometric/tree/706aba3b4a6477a83a1fb73eb3cf0ee9661b70e4 |
CrossEntropyLossLabelSmoothing | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.utils.dat... | litvinich/detectron2 | CrossEntropyLossLabelSmoothing | false | 12,724 | [
"Apache-2.0"
] | 0 | ac622e22eb0f13c9b5838a1e45b046212f22f814 | https://github.com/litvinich/detectron2/tree/ac622e22eb0f13c9b5838a1e45b046212f22f814 |
MetaLayerNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import re
import warnings
import torch.nn as nn
from collections import Ordered... | SDivakarBhat/pytorch-meta | MetaLayerNorm | false | 11,822 | [
"MIT"
] | 0 | 74cbc8ae625d85c6b954aad159ccb26b523b2240 | https://github.com/SDivakarBhat/pytorch-meta/tree/74cbc8ae625d85c6b954aad159ccb26b523b2240 |
Quadratic | # 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... | craigxchen/Reinforcement-Learning-Function-Approximation | Quadratic | false | 6,485 | [
"MIT"
] | 1 | 09c4df1dd44c6a76a3f574bebc959a19b141f3fe | https://github.com/craigxchen/Reinforcement-Learning-Function-Approximation/tree/09c4df1dd44c6a76a3f574bebc959a19b141f3fe |
AdaptiveInstanceNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import n... | hologerry/style-based-gan-pytorch | AdaptiveInstanceNorm | false | 3,620 | [
"MIT"
] | 0 | 1a694fb3ea0288f1aaaa43aa67a570d908d9dc27 | https://github.com/hologerry/style-based-gan-pytorch/tree/1a694fb3ea0288f1aaaa43aa67a570d908d9dc27 |
ZeroConv2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch im... | XeniaLLL/glow-pytorch | ZeroConv2d | false | 11,967 | [
"MIT"
] | 0 | 66d434e57853de1aaafaa5a5533d21705dc92e10 | https://github.com/XeniaLLL/glow-pytorch/tree/66d434e57853de1aaafaa5a5533d21705dc92e10 |
FloorDivConst | import torch
class FloorDivConst(torch.nn.Module):
def __init__(self):
super(FloorDivConst, self).__init__()
def forward(self, x):
return x // 2.0
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_c... | PogChamper/torch2trt | FloorDivConst | false | 14,202 | [
"MIT"
] | 3,363 | 43b12627ec0de4d212efb6d02b07570205085ccc | https://github.com/PogChamper/torch2trt/tree/43b12627ec0de4d212efb6d02b07570205085ccc |
DivideMax | import torch
from torch import nn
class DivideMax(nn.Module):
def __init__(self, dim):
super().__init__()
self.dim = dim
def forward(self, x):
maxes = x.amax(dim=self.dim, keepdim=True).detach()
return x / maxes
def get_inputs():
return [torch.rand([4, 4, 4, 4, 4])]
d... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | Gitsamshi/DALLE-pytorch | DivideMax | false | 13,719 | [
"MIT"
] | 4,025 | 6cfc43158a4615865e97c839133290afcf289824 | https://github.com/Gitsamshi/DALLE-pytorch/tree/6cfc43158a4615865e97c839133290afcf289824 |
Head | import torch
import torch.nn as nn
import torch.utils.data
class Conv(nn.Module):
def __init__(self, filters0, filters1, kernel_size, bn, bias=True):
super().__init__()
if bn:
bias = False
self.conv = nn.Conv2d(filters0, filters1, kernel_size, stride=1,
padding=ker... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | Weiyuhong-1998/DI-engine | Head | false | 14,577 | [
"Apache-2.0"
] | 464 | 88658ea358298c6e61e95a454284b8853a3e9484 | https://github.com/Weiyuhong-1998/DI-engine/tree/88658ea358298c6e61e95a454284b8853a3e9484 |
EdgeGateFree | import torch
import torch.nn as nn
from torch.nn import Parameter
class EdgeGateFree(nn.Module):
"""
Calculate gates for each edge in message passing.
The gates are free parameters.
Note:
This will make the parameters depend on the number of edges, which will limit the model
to work on... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from torch.nn import Parameter
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = to... | AnchoretY/botnet-detection | EdgeGateFree | false | 11,188 | [
"MIT"
] | 0 | e2066ff314f1ea2ccbf4c10ddff819f344a2b715 | https://github.com/AnchoretY/botnet-detection/tree/e2066ff314f1ea2ccbf4c10ddff819f344a2b715 |
Accuracy | # 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... | StephanHeijl/tape | Accuracy | false | 2,865 | [
"BSD-3-Clause"
] | 0 | ec631ca53217686605477cf31af4fb8846ff660f | https://github.com/StephanHeijl/tape/tree/ec631ca53217686605477cf31af4fb8846ff660f |
Baseline | import torch
import torch.nn as nn
class Baseline(nn.Module):
"""Baseline
"""
def __init__(self, hid_dim, x_dim, binary_dim, inp_dim):
super(Baseline, self).__init__()
self.x_dim = x_dim
self.binary_dim = binary_dim
self.inp_dim = inp_dim
self.hid_dim = hid_dim
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | nyu-dl/MultimodalGame | Baseline | false | 16,199 | [
"BSD-3-Clause"
] | 54 | 0782a7bf3cf5125cd7c35a243e97f0e9e016fca3 | https://github.com/nyu-dl/MultimodalGame/tree/0782a7bf3cf5125cd7c35a243e97f0e9e016fca3 |
OneHiddenLayer | # 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.... | MichaelArbel/MMD-gradient-flow | OneHiddenLayer | false | 17,705 | [
"BSD-3-Clause"
] | 5 | aa7be78c53c1995ae156fb04b6f1b4fcf02dd039 | https://github.com/MichaelArbel/MMD-gradient-flow/tree/aa7be78c53c1995ae156fb04b6f1b4fcf02dd039 |
Greedy | import torch
import torch.nn as nn
from matplotlib.font_manager import *
class Greedy(nn.Module):
def __init__(self):
super().__init__()
def forward(self, log_p):
return torch.argmax(log_p, dim=1).long()
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
r... | 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 matplotlib.font_manager import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cu... | zifeiyu0531/TSP_DRL_PtrNet | Greedy | false | 4,685 | [
"MIT"
] | 0 | c62fab73347556173d301c1561edf927e6fbe1d7 | https://github.com/zifeiyu0531/TSP_DRL_PtrNet/tree/c62fab73347556173d301c1561edf927e6fbe1d7 |
Classifier | # 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.distributed
import torch
import torch.nn as nn
assert_size_stride =... | dat821168/PreSumm | Classifier | false | 1,798 | [
"MIT"
] | 0 | 3c84fc97f50a193a865ccef2300adf5683397539 | https://github.com/dat821168/PreSumm/tree/3c84fc97f50a193a865ccef2300adf5683397539 |
TripletMarginCosineLoss | from torch.nn import Module
import torch
from torch.nn.functional import cosine_similarity
def triplet_margin_cosine_loss(anchor, positive, negative, margin=1.0, eps=
1e-08, sum_loss=False):
'Creates a criterion that measures the triplet cosine loss given input\n tensors x1, x2, x3 and a margin with a valu... | 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.nn import Module
... | monkeyhjy/aspect_summarization | TripletMarginCosineLoss | false | 10,572 | [
"MIT"
] | 0 | 3018815cd0aeccb752e9f51a4d49453c4f441650 | https://github.com/monkeyhjy/aspect_summarization/tree/3018815cd0aeccb752e9f51a4d49453c4f441650 |
Ranking | # 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
assert_size_stride = torch._... | alexcapstick/minder_utils | Ranking | false | 3,084 | [
"MIT"
] | 0 | 3bb9380b7796b5dd5b995ce1839ea6a94321021d | https://github.com/alexcapstick/minder_utils/tree/3bb9380b7796b5dd5b995ce1839ea6a94321021d |
SingleHiddenLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
assert_size_stride = torch._C... | athon-millane/NeuralCDE | SingleHiddenLayer | false | 12,124 | [
"Apache-2.0"
] | 0 | 4196890fe5bf7a69925a12ff35e86f212963be71 | https://github.com/athon-millane/NeuralCDE/tree/4196890fe5bf7a69925a12ff35e86f212963be71 |
VGAE | from torch.nn import Module
import torch
import torch.nn.functional as F
from torch.nn.modules.module import Module
import torch.nn as nn
import torch.nn.modules.loss
import torch.utils.data
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... | WanyuGroup/CVPR2022-OrphicX | VGAE | false | 1,213 | [
"MIT"
] | 0 | 98d8d8259439c45661573e575cf956331df16abc | https://github.com/WanyuGroup/CVPR2022-OrphicX/tree/98d8d8259439c45661573e575cf956331df16abc |
ExponentialClass | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.cuda
import torch.distributed
from torch.cuda.amp import aut... | MikyasDesta/NeMo | ExponentialClass | false | 2,647 | [
"Apache-2.0"
] | 0 | 4995477e6ce49de55b123723e42021c9eff8e2c0 | https://github.com/MikyasDesta/NeMo/tree/4995477e6ce49de55b123723e42021c9eff8e2c0 |
GeM | import torch
import torch.nn.functional as F
from torch import nn
from torch.nn import Parameter
def gem(x, p=3, eps=1e-06):
return F.avg_pool2d(x.clamp(min=eps).pow(p), (x.size(-2), x.size(-1))).pow(
1.0 / p)
class GeM(nn.Module):
def __init__(self, p=3, eps=1e-06):
super(GeM, self).__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
import torch.nn.functional a... | LightnessOfBeing/kaggle-bengali-classification | GeM | false | 17,625 | [
"MIT"
] | 5 | 342bc2a9bf57f9f03fa25f5271cb178ab8f7b4ff | https://github.com/LightnessOfBeing/kaggle-bengali-classification/tree/342bc2a9bf57f9f03fa25f5271cb178ab8f7b4ff |
XOR | # 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.utils.... | csh-tech/horovod | XOR | false | 6,488 | [
"Apache-2.0"
] | 1 | 2a3f43f35c840d7e8cfa9674a051ffa53be9918d | https://github.com/csh-tech/horovod/tree/2a3f43f35c840d7e8cfa9674a051ffa53be9918d |
LayerNorm | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.data
import torch.nn as nn
assert_size_stride = torch._C._dy... | E18301194/DepthAwareCNN | LayerNorm | false | 13,595 | [
"MIT"
] | 278 | 8ae98f7f18b69f79e7df03397dec2543d3d0c8eb | https://github.com/E18301194/DepthAwareCNN/tree/8ae98f7f18b69f79e7df03397dec2543d3d0c8eb |
backWarp | # 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 numpy as np
import to... | CM-BF/FeatureFlow | backWarp | false | 13,450 | [
"MIT"
] | 161 | 06642697922f17211e5faa353e24b1a0946885b1 | https://github.com/CM-BF/FeatureFlow/tree/06642697922f17211e5faa353e24b1a0946885b1 |
net_nvidia_pytorch | # 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 ... | YuShen0118/SAAP_Auto-driving_Platform | net_nvidia_pytorch | false | 18,170 | [
"MIT"
] | 4 | 785f899fb3b3ad92075318f9fcb69b8e09597202 | https://github.com/YuShen0118/SAAP_Auto-driving_Platform/tree/785f899fb3b3ad92075318f9fcb69b8e09597202 |
h_sigmoid | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
from itertools import product as product
import torch.nn.parallel
i... | DefTruth/PIPNet | h_sigmoid | false | 13,578 | [
"MIT"
] | 162 | a1fb1e229319dac0069e37eb8fb4278d454edbb0 | https://github.com/DefTruth/PIPNet/tree/a1fb1e229319dac0069e37eb8fb4278d454edbb0 |
SqueezeExcite | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn.functional as... | Capetian/FaceX-Zoo | SqueezeExcite | false | 4,988 | [
"Apache-2.0"
] | 1 | 029786c40d8aba15d891d33973de25fcd7e5399a | https://github.com/Capetian/FaceX-Zoo/tree/029786c40d8aba15d891d33973de25fcd7e5399a |
HorizontalMaxPool2d | import torch
import torch.nn as nn
class HorizontalMaxPool2d(nn.Module):
def __init__(self):
super(HorizontalMaxPool2d, self).__init__()
def forward(self, x):
inp_size = x.size()
return nn.functional.max_pool2d(input=x, kernel_size=(1, inp_size[3]))
def get_inputs():
return [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 import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | LT1st/ReID_Alined_beginer | HorizontalMaxPool2d | false | 13,980 | [
"MIT"
] | 370 | 1a12403a32d99900451ac05cd3623a9b770f6d24 | https://github.com/LT1st/ReID_Alined_beginer/tree/1a12403a32d99900451ac05cd3623a9b770f6d24 |
ModuloMapIDList | import abc
import torch
import torch.nn
import torch.optim
class MapIDList(torch.nn.Module):
@abc.abstractmethod
def forward(self, raw_values: 'torch.Tensor') ->torch.Tensor:
pass
class ModuloMapIDList(MapIDList):
def __init__(self, modulo: 'int'):
super().__init__()
self.modul... | 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 abc
import torch.nn
import torch.optim
assert_size_stride = torch._C._dy... | mcx/ReAgent | ModuloMapIDList | false | 4,102 | [
"BSD-3-Clause"
] | 0 | 57b58a8b3a6b74bb87a197b73a6cd108ddad895e | https://github.com/mcx/ReAgent/tree/57b58a8b3a6b74bb87a197b73a6cd108ddad895e |
TimeEncoding | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | GuyTevet/MotionCLIP | TimeEncoding | false | 8,156 | [
"MIT"
] | 45 | c2b9f40b0e721e42981f3e8b58133a1c51fde715 | https://github.com/GuyTevet/MotionCLIP/tree/c2b9f40b0e721e42981f3e8b58133a1c51fde715 |
tLNv2 | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
from torch.autograd import Variable
assert_size_stride = ... | rbodo/pytorch-OpCounter | tLNv2 | false | 7,546 | [
"MIT"
] | 1 | 1857cbb5f9e53343fb349af84efdfde2554a2691 | https://github.com/rbodo/pytorch-OpCounter/tree/1857cbb5f9e53343fb349af84efdfde2554a2691 |
L2Norm | import torch
import torch.nn.functional as F
import torch.nn as nn
class L2Norm(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
assert x.dim(
) == 2, 'the input tensor of L2Norm must be the shape of [B, C]'
return F.normalize(x, p=2, dim=-1)
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._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | deokhk/Proxy-Anchor-CVPR2020 | L2Norm | false | 12,261 | [
"MIT"
] | 0 | acb3a16c3ebc8b8777542898ec83de32aa8ba64e | https://github.com/deokhk/Proxy-Anchor-CVPR2020/tree/acb3a16c3ebc8b8777542898ec83de32aa8ba64e |
L1 | # 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
... | Khoronus/MonoDepth-FPN-PyTorch | L1 | false | 730 | [
"MIT"
] | 0 | 6e41e297723d1490c537e04afff905c61d6f0ff8 | https://github.com/Khoronus/MonoDepth-FPN-PyTorch/tree/6e41e297723d1490c537e04afff905c61d6f0ff8 |
DeepNN_v3 | import torch
import torch.nn as nn
class DeepNN_v3(nn.Module):
def __init__(self, X_dim, i_dropout_rate, h_dropout_rate):
super().__init__()
self.v3_layer1 = nn.Linear(X_dim, 128, bias=True)
self.v3_layer2 = nn.Linear(128, 128, bias=True)
self.v3_layer3 = nn.Linear(128, 128, bias=... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | SBIlab/NetBio | DeepNN_v3 | false | 11,831 | [
"MIT"
] | 0 | 7abd24b8989cea381147d912f76a72676750b9d2 | https://github.com/SBIlab/NetBio/tree/7abd24b8989cea381147d912f76a72676750b9d2 |
SDNE_layer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.... | ckhui/cogdl | SDNE_layer | false | 12,666 | [
"MIT"
] | 0 | 93bea17c2dc7084857cd0a4af8178c174965127c | https://github.com/ckhui/cogdl/tree/93bea17c2dc7084857cd0a4af8178c174965127c |
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