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 |
|---|---|---|---|---|---|---|---|---|---|---|
MSE | import torch
import torch.nn as nn
from torch.optim import *
class MSE(nn.Module):
def __init__(self):
super().__init__()
def forward(self, outputs, target, *args):
val_pixels = (target > 0.001).float()
loss = target * val_pixels - outputs * val_pixels
return loss ** 2
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
import torch.nn as nn
from torch.optim import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._... | kakaxi314/GuideNet | MSE | false | 15,776 | [
"MIT"
] | 142 | 9f53b4086d707e94d48a47bbac7dd87aaba9fdea | https://github.com/kakaxi314/GuideNet/tree/9f53b4086d707e94d48a47bbac7dd87aaba9fdea |
NoiseInjection | import torch
import torch.nn as nn
class NoiseInjection(nn.Module):
def __init__(self):
super().__init__()
self.weight = nn.Parameter(torch.zeros(1))
def forward(self, image, noise=None):
if noise is None:
batch, _, height, width = image.shape
noise = image.ne... | 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... | AsianZeus/Diverse-Facial-Edit | NoiseInjection | false | 9,406 | [
"Apache-2.0"
] | 0 | 3d4b1b41546a08a1fa3cb164ade33e319806b12b | https://github.com/AsianZeus/Diverse-Facial-Edit/tree/3d4b1b41546a08a1fa3cb164ade33e319806b12b |
PartialConv | import math
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
def weights_init(init_type='gaussian'):
def init_fun(m):
classname = m.__class__.__name__
if (classname.find('Conv') == 0 or classname.find('Linear') == 0
) and hasattr(m, 'weight'):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
import torch.nn as nn
import torch.nn.parallel
import torch.utils.da... | labcontext/image-inpainting-oldpaper | PartialConv | false | 3,879 | [
"Apache-2.0"
] | 0 | da4683a2c58d662e443ea24ab93fd9d8fcb96bda | https://github.com/labcontext/image-inpainting-oldpaper/tree/da4683a2c58d662e443ea24ab93fd9d8fcb96bda |
InvConvNear | import torch
import torch.cuda
from torch.nn import functional as F
from torch import nn
import torch.distributed
import torch.utils.data
import torch.optim
class InvConvNear(nn.Module):
def __init__(self, channels, n_split=4, no_jacobian=False, **kwargs):
super().__init__()
assert n_split % 2 ==... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.cuda
from torch import nn
import torch.distributed
import torch.uti... | Oreoluwa1234/NeMo | InvConvNear | false | 9,725 | [
"Apache-2.0"
] | 0 | b01e3ceed34efe31fd43866685dbdd19a6b30928 | https://github.com/Oreoluwa1234/NeMo/tree/b01e3ceed34efe31fd43866685dbdd19a6b30928 |
GCN | import math
import torch
from torch import nn
from torch.nn import functional as F
class GraphConvolution(nn.Module):
"""
Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
"""
def __init__(self, in_features, out_features, bias=True):
super(GraphConvolution, 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._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Shadowalker1995/Tutorial-Resource | GCN | false | 14,396 | [
"Apache-2.0"
] | 362 | 71fe3d521cf9971f708fa9978e9c685c0dda6ba6 | https://github.com/Shadowalker1995/Tutorial-Resource/tree/71fe3d521cf9971f708fa9978e9c685c0dda6ba6 |
ChannelAttentionBlock | # 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.... | iMED-Lab/ROSE | ChannelAttentionBlock | false | 15,567 | [
"Apache-2.0"
] | 64 | 8d99a2a06fc645410b1d388193b3148404e61230 | https://github.com/iMED-Lab/ROSE/tree/8d99a2a06fc645410b1d388193b3148404e61230 |
ClassHead | # 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 itertools import product as product
assert_size_strid... | chennnnnnnnn/face_detection | ClassHead | false | 3,348 | [
"MIT"
] | 0 | 77d5a9098d9e1a65ac5093a23620ed5d99dc0723 | https://github.com/chennnnnnnnn/face_detection/tree/77d5a9098d9e1a65ac5093a23620ed5d99dc0723 |
ExgLayer | # 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... | ayyyq/T-LSTM | ExgLayer | false | 6,301 | [
"MIT"
] | 1 | 36dbc88ac710d3925851cd87c2368ecfc7061b70 | https://github.com/ayyyq/T-LSTM/tree/36dbc88ac710d3925851cd87c2368ecfc7061b70 |
GTConv_2 | import torch
import torch.nn.functional as F
from torch import nn
import torch.utils.data
import torch.onnx.operators
import torch.optim
import torch.optim.lr_scheduler
class GTConv_2(nn.Module):
def __init__(self, in_channels, out_channels):
super(GTConv_2, self).__init__()
self.in_channels = in... | 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... | verashira/TSPNet | GTConv_2 | false | 16,670 | [
"MIT"
] | 83 | ee454165dcc61cdbbff19565364e2221727ed2b8 | https://github.com/verashira/TSPNet/tree/ee454165dcc61cdbbff19565364e2221727ed2b8 |
GELU_ | import math
import torch
import torch.nn as nn
import torch.utils.data
import torch.onnx.operators
import torch.optim
import torch.optim.lr_scheduler
class GELU_(nn.Module):
def forward(self, x):
return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x +
0.044715 * torch.pow(x, 3))))
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.triton_helpers import libdevice
import torch.nn as nn
import torch.utils.data
import torch.onnx.operators
impor... | CUMLSec/stateformer | GELU_ | false | 7,911 | [
"MIT"
] | 41 | 87cb3c906c43fcff42b2ca820eb6e7fd918d0a1c | https://github.com/CUMLSec/stateformer/tree/87cb3c906c43fcff42b2ca820eb6e7fd918d0a1c |
BinaryFocalLoss | import torch
class BinaryFocalLoss(torch.nn.Module):
""" from https://github.com/qubvel/segmentation_models"""
def __init__(self, gamma=2.0, alpha=0.25, eps=1e-07):
super().__init__()
self.gamma = gamma
self.alpha = alpha
self.eps = eps
def forward(self, pr, gt):
... | 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... | kungfuai/d3m-segmentation-research | BinaryFocalLoss | false | 7,059 | [
"MIT"
] | 1 | 5bc44ddd0e8522fb2b369866ad47aa62a24a8f63 | https://github.com/kungfuai/d3m-segmentation-research/tree/5bc44ddd0e8522fb2b369866ad47aa62a24a8f63 |
SelfAttn | import torch
import torch as th
import torch.nn as nn
import torch.nn.functional as F
class SelfAttn(nn.Module):
def __init__(self, hidden_size):
super(SelfAttn, self).__init__()
self.query = nn.Linear(hidden_size, 1)
def forward(self, keys, values, attn_mask=None):
"""
:para... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Jupaoqq/Jupaoqq_LaRL | SelfAttn | false | 692 | [
"Apache-2.0"
] | 0 | ae64adda5627987d71f2948f499daa11e9f309ad | https://github.com/Jupaoqq/Jupaoqq_LaRL/tree/ae64adda5627987d71f2948f499daa11e9f309ad |
LayerNormChannel | import torch
import torch.nn as nn
class LayerNormChannel(nn.Module):
"""
LayerNorm only for Channel Dimension.
Input: tensor in shape [B, C, H, W]
"""
def __init__(self, num_channels, eps=1e-05):
super().__init__()
self.weight = nn.Parameter(torch.ones(num_channels))
self... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | TranNhiem/MVAR_SSL | LayerNormChannel | false | 5,918 | [
"MIT"
] | 1 | 339964db4d40f06a92866675ff99ef67cd968cca | https://github.com/TranNhiem/MVAR_SSL/tree/339964db4d40f06a92866675ff99ef67cd968cca |
BCEFocalLoss | # 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... | CCChenhao997/CCL2020-Humor-Computation | BCEFocalLoss | false | 17,014 | [
"MIT"
] | 7 | 700e539588904da40a9db899668437188a6b4613 | https://github.com/CCChenhao997/CCL2020-Humor-Computation/tree/700e539588904da40a9db899668437188a6b4613 |
FC_Layer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | WorksApplications/omni_torch | FC_Layer | false | 1,225 | [
"Apache-2.0"
] | 0 | 10b689d794c8f485e38c765303ef018da17bc641 | https://github.com/WorksApplications/omni_torch/tree/10b689d794c8f485e38c765303ef018da17bc641 |
GaussMembFunc | # 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 math as tl_math
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_str... | samxu0823/anfis-pytorch | GaussMembFunc | false | 4,261 | [
"MIT"
] | 0 | b4ec3f0e8259963800e9e0a2904a580d1e56cc1c | https://github.com/samxu0823/anfis-pytorch/tree/b4ec3f0e8259963800e9e0a2904a580d1e56cc1c |
Curiosity | # 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_... | VasaKiDD/TD3-deep-rl-research | Curiosity | false | 2,939 | [
"Apache-2.0"
] | 0 | f75b2f86f3b7969a82fc4b7f9ea2b62de3616217 | https://github.com/VasaKiDD/TD3-deep-rl-research/tree/f75b2f86f3b7969a82fc4b7f9ea2b62de3616217 |
AsymmetricLoss | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
class AsymmetricLoss(nn.Module):
def __init__(self, gamma_neg=4, gamma_pos=1, clip=0.05, eps=1e-08,
disable_torch_grad_focal_loss=False):
super(AsymmetricLoss... | 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 | AsymmetricLoss | false | 2,135 | [
"MIT"
] | 0 | cdca1f3519f75cc91ef2aa166c2534691016f04f | https://github.com/ChangeTheWorld20191008/query2labels/tree/cdca1f3519f75cc91ef2aa166c2534691016f04f |
NetVLAD | # 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.... | DanielPollithy/UncertainToDayGAN | NetVLAD | false | 480 | [
"BSD-2-Clause"
] | 0 | bd16fa1a34878dbdc765d548169b7058a56864ff | https://github.com/DanielPollithy/UncertainToDayGAN/tree/bd16fa1a34878dbdc765d548169b7058a56864ff |
CharbonnierLoss | import functools
import torch
import torch.utils.data
from torch.nn import functional as F
from torch.utils import data as data
import torch.nn as nn
from torch.nn import init as init
from torchvision.models import vgg as vgg
from torch import autograd as autograd
def reduce_loss(loss, reduction):
"""Reduce 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 libdevice
import functools
import torc... | BCV-Uniandes/RSR | CharbonnierLoss | false | 8,083 | [
"zlib-acknowledgement"
] | 14 | dad60eedd3560f2655e3d1ed444153ed2616af2e | https://github.com/BCV-Uniandes/RSR/tree/dad60eedd3560f2655e3d1ed444153ed2616af2e |
QuickGELU | import torch
from torch import nn
class QuickGELU(nn.Module):
def forward(self, x: 'torch.Tensor'):
return x * torch.sigmoid(1.702 * x)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | Artanic30/RentalPrediction | QuickGELU | false | 1,972 | [
"MIT"
] | 0 | 5804ab9b453d2a40bce2bb304c31efc98a803ed8 | https://github.com/Artanic30/RentalPrediction/tree/5804ab9b453d2a40bce2bb304c31efc98a803ed8 |
ResidualConvUnit | # 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
import t... | Minerva-J/Pytorch-Segmentation-multi-models | ResidualConvUnit | false | 14,014 | [
"Apache-2.0"
] | 84 | 0845b54d4fbc8d38c70f158054b7ab1be2b3ceb9 | https://github.com/Minerva-J/Pytorch-Segmentation-multi-models/tree/0845b54d4fbc8d38c70f158054b7ab1be2b3ceb9 |
ReRegualizedLinearNACLayer | # 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.util... | CUMLSec/stateformer | ReRegualizedLinearNACLayer | false | 7,919 | [
"MIT"
] | 41 | 87cb3c906c43fcff42b2ca820eb6e7fd918d0a1c | https://github.com/CUMLSec/stateformer/tree/87cb3c906c43fcff42b2ca820eb6e7fd918d0a1c |
MNIST_CNN | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class MNIST_CNN(nn.Module):
"""
Hand-tuned architecture for MNIST.
Weirdness I've noticed so far with this architecture:
- adding a linear layer after the mean-pool in features hurts
RotatedMNIST-100 gen... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | AllenPu/mbdg | MNIST_CNN | false | 7,677 | [
"MIT"
] | 27 | 243f53a57dcf4bfb6e717c0c9f64a839cff8d548 | https://github.com/AllenPu/mbdg/tree/243f53a57dcf4bfb6e717c0c9f64a839cff8d548 |
MultiHeadAttention | import torch
import torch.nn as nn
class MultiHeadAttention(nn.Module):
def __init__(self, hidden_size, attention_dropout_rate, num_heads):
super(MultiHeadAttention, self).__init__()
self.num_heads = num_heads
self.att_size = att_size = hidden_size // num_heads
self.scale = att_si... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | ChantalMP/Graphormer | MultiHeadAttention | false | 8,934 | [
"MIT"
] | 0 | 5c384d0f2840afc88ee88aeb874f4b1f41d760bf | https://github.com/ChantalMP/Graphormer/tree/5c384d0f2840afc88ee88aeb874f4b1f41d760bf |
Actor | import torch
import torch.nn as nn
import torch.nn.functional as F
class Actor(nn.Module):
def __init__(self, n_obs, output_dim, hidden_size, init_w=0.003):
super(Actor, self).__init__()
self.linear1 = nn.Linear(n_obs, hidden_size)
self.linear2 = nn.Linear(hidden_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
from torch._inductor.runtime.... | KhalilWong/Learn-RL | Actor | false | 2,452 | [
"MIT"
] | 0 | 9f63c5adafab1413362366d28d8711096ce6648c | https://github.com/KhalilWong/Learn-RL/tree/9f63c5adafab1413362366d28d8711096ce6648c |
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
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | dumpmemory/uniformer-pytorch | LayerNorm | false | 15,278 | [
"MIT"
] | 71 | 756c4edb7ab0947dc202c145f7c95571848e0594 | https://github.com/dumpmemory/uniformer-pytorch/tree/756c4edb7ab0947dc202c145f7c95571848e0594 |
SkipLastTargetChannelWrapper | import torch
from torch import nn as nn
from torch.nn import MSELoss
class SkipLastTargetChannelWrapper(nn.Module):
"""
Loss wrapper which removes additional target channel
"""
def __init__(self, loss, squeeze_channel=False):
super(SkipLastTargetChannelWrapper, self).__init__()
self.l... | 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 as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._emp... | PerceptionComputingLab/PARSE2022 | SkipLastTargetChannelWrapper | false | 9,427 | [
"Apache-2.0"
] | 0 | a34886ed9d06b424bc93953f1b2f79540ad9ebf6 | https://github.com/PerceptionComputingLab/PARSE2022/tree/a34886ed9d06b424bc93953f1b2f79540ad9ebf6 |
PolynomialEnvelope | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | Irlirion/ocp | PolynomialEnvelope | false | 13,839 | [
"MIT",
"BSD-3-Clause"
] | 242 | 6fb3e794eef31559db990300198eca20f41d8f37 | https://github.com/Irlirion/ocp/tree/6fb3e794eef31559db990300198eca20f41d8f37 |
ClassificationModel | import torch
import torch.nn as nn
import torch.utils.data
class ClassificationModel(nn.Module):
def __init__(self, num_features_in, num_anchors=9, num_classes=80,
prior=0.01, feature_size=256):
super(ClassificationModel, self).__init__()
self.num_classes = num_classes
self.num_an... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | DerekGloudemans/3D-detector-trials | ClassificationModel | false | 2,186 | [
"MIT"
] | 0 | 480274567eaa84c5c883260ef62f150c7a23ffd3 | https://github.com/DerekGloudemans/3D-detector-trials/tree/480274567eaa84c5c883260ef62f150c7a23ffd3 |
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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | MichalBusta/OpenCitiesAIC | DiceLoss | false | 17,719 | [
"MIT"
] | 7 | 2358118a782edde27a588d6adaf79941cbd90de6 | https://github.com/MichalBusta/OpenCitiesAIC/tree/2358118a782edde27a588d6adaf79941cbd90de6 |
JSDLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
a... | mirmohammad/IFT6135-TP3 | JSDLoss | false | 4,010 | [
"MIT"
] | 0 | 70453b4ea695313837ab88243b0206552eb50632 | https://github.com/mirmohammad/IFT6135-TP3/tree/70453b4ea695313837ab88243b0206552eb50632 |
BetaMish | import torch
import torch.nn as nn
class BetaMish(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
beta = 1.5
return x * torch.tanh(torch.log(torch.pow(1 + torch.exp(x), beta)))
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_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, math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.gu... | dcrmg/Efficient-Segmentation-Networks | BetaMish | false | 3,429 | [
"MIT"
] | 0 | e2f2d90d69e4e9af464678b0f02bc754c28f643d | https://github.com/dcrmg/Efficient-Segmentation-Networks/tree/e2f2d90d69e4e9af464678b0f02bc754c28f643d |
Switch | # 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 numpy as np
... | TaoMiner/eesc | Switch | false | 5,881 | [
"Apache-2.0"
] | 1 | fa0ca532333cad2262d20707899f97a6c8a99cfb | https://github.com/TaoMiner/eesc/tree/fa0ca532333cad2262d20707899f97a6c8a99cfb |
SmoothL1Loss | import torch
import torch.nn as nn
class SmoothL1Loss(nn.Module):
def __init__(self, beta=1.0, reduction='mean'):
super().__init__()
self.beta = beta
self.reduction = reduction
def forward(self, pred, target, weight=None):
assert pred.size() == target.size() and target.numel(... | 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... | liuhuaijjin/rpn_rois_proposals_layers | SmoothL1Loss | false | 7,106 | [
"MIT"
] | 1 | c5f9f09b3ae8c52e4b6fa3fda391f993cb7d42c1 | https://github.com/liuhuaijjin/rpn_rois_proposals_layers/tree/c5f9f09b3ae8c52e4b6fa3fda391f993cb7d42c1 |
RobertaClassificationHead | # 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... | INK-USC/expl-refinement | RobertaClassificationHead | false | 18,385 | [
"MIT"
] | 7 | 815a7892a8d4c42fb429856746212a44f67d2547 | https://github.com/INK-USC/expl-refinement/tree/815a7892a8d4c42fb429856746212a44f67d2547 |
GradientLoss | import torch
import torch.nn as nn
def gradient(input):
input0 = input[..., :-1, :-1]
didy = input[..., 1:, :-1] - input0
didx = input[..., :-1, 1:] - input0
return torch.cat((didy, didx), -3)
class GradientLoss(nn.Module):
def forward(self, input, target):
return torch.abs(gradient(inp... | 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
... | LongerVision/oidn | GradientLoss | false | 5,557 | [
"Apache-2.0"
] | 1 | 2f9e59f8b747b217f78c5c274f4f2bff347a03a7 | https://github.com/LongerVision/oidn/tree/2f9e59f8b747b217f78c5c274f4f2bff347a03a7 |
ConditionalBatchNorm2d | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
from torch.nn import Parameter
def l2normalize(v, eps=0.0001):
return v / (v.norm() + eps)
class SpectralNorm(nn.Module):
def __init__(self, module, name='weight', power_iterations=1):
super(SpectralNorm, self).__in... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | AnonymousGFR/wbgan.pytorch | ConditionalBatchNorm2d | false | 4,879 | [
"MIT"
] | 1 | d75cb6599852e901df0136db87520e3314f8ca71 | https://github.com/AnonymousGFR/wbgan.pytorch/tree/d75cb6599852e901df0136db87520e3314f8ca71 |
Attention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | shampooma/segmenter | Attention | false | 16,411 | [
"MIT"
] | 418 | b08fd481da6758e37d108ba28676229b62f757aa | https://github.com/shampooma/segmenter/tree/b08fd481da6758e37d108ba28676229b62f757aa |
GumbelSoftmaxLayer | # 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.distributions import RelaxedOneHotCategorical
import torch.nn.parallel
import torch.utils.data
import torch... | vengalraoguttha/EGG | GumbelSoftmaxLayer | false | 16,664 | [
"MIT"
] | 254 | e4f8412f197543ec7f1f00cf89b5a364b038dc57 | https://github.com/vengalraoguttha/EGG/tree/e4f8412f197543ec7f1f00cf89b5a364b038dc57 |
DiscrimNet | import torch
import torch.nn as nn
from torch.nn.init import kaiming_uniform_
def weight_init(m):
if m.__class__.__name__ == 'Linear':
m.weight.data.copy_(kaiming_uniform_(m.weight.data))
m.bias.data.fill_(0)
class DiscrimNet(nn.Module):
def __init__(self, ob_space, ac_space, h1=32, h2=32):... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | ven-kyoshiro/PILCO-1 | DiscrimNet | false | 10,956 | [
"MIT"
] | 0 | 61c4ef18a6bbecbeb6a10784a7925d31f46dd23b | https://github.com/ven-kyoshiro/PILCO-1/tree/61c4ef18a6bbecbeb6a10784a7925d31f46dd23b |
BoundaryDiscriminator | # 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... | JACKYLUO1991/DCBNet | BoundaryDiscriminator | false | 17,468 | [
"MIT"
] | 6 | b797584b66ad99fe984f58268befb12ec60ccfae | https://github.com/JACKYLUO1991/DCBNet/tree/b797584b66ad99fe984f58268befb12ec60ccfae |
UNet | # 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_... | frankgu968/learning-to-see-in-the-dark-pytorch | UNet | false | 3,564 | [
"MIT"
] | 0 | 6a59fc64d1f152a2410b9128a6a51687a9b179d1 | https://github.com/frankgu968/learning-to-see-in-the-dark-pytorch/tree/6a59fc64d1f152a2410b9128a6a51687a9b179d1 |
LeNet300 | # 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... | EIDOSlab/pruning-validation | LeNet300 | false | 2,176 | [
"BSD-3-Clause"
] | 0 | bd8e83cf6f564def0e193a4be0f753c768fe9e75 | https://github.com/EIDOSlab/pruning-validation/tree/bd8e83cf6f564def0e193a4be0f753c768fe9e75 |
Conv2d | # 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.autograd import Function
import numpy as np
import torch.nn as nn
imp... | chen-hao-chao/dlsm | Conv2d | false | 3,289 | [
"Apache-2.0"
] | 0 | aea88aa7e59a02fe44f25f4de9d6f2eaf044093b | https://github.com/chen-hao-chao/dlsm/tree/aea88aa7e59a02fe44f25f4de9d6f2eaf044093b |
IBertLMHead | from _paritybench_helpers import _mock_config
import math
import torch
from torch import nn
import torch.utils.checkpoint
def gelu(x):
return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 *
torch.pow(x, 3))))
class IBertLMHead(nn.Module):
"""I-BERT Head for masked language modelin... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
from to... | jxhe/unify-parameter-efficient-tuning | IBertLMHead | false | 15,767 | [
"Apache-2.0"
] | 101 | 3222ce2c0079566a28043e22380eb4ab6ad14389 | https://github.com/jxhe/unify-parameter-efficient-tuning/tree/3222ce2c0079566a28043e22380eb4ab6ad14389 |
QuickGELU | # 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... | Jinsu-L/KELIP | QuickGELU | false | 5,401 | [
"Apache-2.0"
] | 1 | d3261cbb9ba3c3ad474dd560a5add8b69ed78477 | https://github.com/Jinsu-L/KELIP/tree/d3261cbb9ba3c3ad474dd560a5add8b69ed78477 |
Delta | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from torchaudio import transforms
assert_size_stride = tor... | czlwang/s3prl | Delta | false | 12,282 | [
"Apache-2.0"
] | 0 | 81d4bb8d051cee20fa87c083b8478999e1766172 | https://github.com/czlwang/s3prl/tree/81d4bb8d051cee20fa87c083b8478999e1766172 |
MSECompositionLoss | 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
import functools
import torch.nn as nn
from torch.nn import functional as F
assert_size_s... | akimotty877/mmediting | MSECompositionLoss | false | 3,065 | [
"Apache-2.0"
] | 0 | cae872d6f3e867ba144c7c0dbc29a0ee1a29e5a6 | https://github.com/akimotty877/mmediting/tree/cae872d6f3e867ba144c7c0dbc29a0ee1a29e5a6 |
Conv | # 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
from ... | CenIII/pose-ae-train | Conv | false | 13,446 | [
"BSD-3-Clause"
] | 250 | 8780ba9f3d80ca3a724bbee7b815073adc3d3e6e | https://github.com/CenIII/pose-ae-train/tree/8780ba9f3d80ca3a724bbee7b815073adc3d3e6e |
SimpleNet | # 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 ... | P403n1x87/AI-Feynman | SimpleNet | false | 2,710 | [
"MIT"
] | 0 | 73398ad1b739d02b4cb8d9648b208e76d0a9085d | https://github.com/P403n1x87/AI-Feynman/tree/73398ad1b739d02b4cb8d9648b208e76d0a9085d |
ThreeLayerNet_tanh | import torch
class ThreeLayerNet_tanh(torch.nn.Module):
def __init__(self, D_in, H_1, H_2, D_out):
super(ThreeLayerNet_tanh, self).__init__()
self.linear1 = torch.nn.Linear(D_in, H_1)
self.tanh = torch.nn.Tanh()
self.linear2 = torch.nn.Linear(H_1, H_2)
self.linear3 = torch... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | PanosAntoniadis/pattern_recognition-ntua | ThreeLayerNet_tanh | false | 17,782 | [
"MIT"
] | 6 | 6dca44de77f0ca94221980fc789446a2e10410a4 | https://github.com/PanosAntoniadis/pattern_recognition-ntua/tree/6dca44de77f0ca94221980fc789446a2e10410a4 |
HeatmapLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_... | seeinggreen/pyslr | HeatmapLoss | false | 4,374 | [
"BSD-3-Clause"
] | 0 | 17009582f70aed09a9174ce47f9414f715173018 | https://github.com/seeinggreen/pyslr/tree/17009582f70aed09a9174ce47f9414f715173018 |
SimpleSoftmaxModel | # 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.jit
impor... | briancoutinho/glow | SimpleSoftmaxModel | false | 12,591 | [
"Apache-2.0"
] | 0 | 4c919d60b3c33296c4109aec8020a1733c98f5b5 | https://github.com/briancoutinho/glow/tree/4c919d60b3c33296c4109aec8020a1733c98f5b5 |
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
from torch import n... | seunghyukcho/vdvae | Block | false | 12,966 | [
"MIT"
] | 0 | 3a552d80351d670fdbde8302c556a6e668d33762 | https://github.com/seunghyukcho/vdvae/tree/3a552d80351d670fdbde8302c556a6e668d33762 |
Flatten | # 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... | DebugVBZ/pixel2style2pixel | Flatten | false | 8,943 | [
"MIT"
] | 0 | e884c0cf471ad9ee09b8743d7ffd532283a638e5 | https://github.com/DebugVBZ/pixel2style2pixel/tree/e884c0cf471ad9ee09b8743d7ffd532283a638e5 |
DiceLoss | import torch
import torch.nn as nn
class DiceLoss(nn.Module):
"""Sørensen–Dice coefficient loss to calculate
the mean loss over a batch of data.This loss mainly
calculates the similarity between two samples.
To know more about this loss check this link:
https://en.wikipedia.org/wiki/S%C3%B8rensen%... | 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... | g-freire/Brain-Tumor-Segmentation | DiceLoss | false | 15,379 | [
"MIT"
] | 156 | e4f258feb64c11815570e295c58bda78afd21ab9 | https://github.com/g-freire/Brain-Tumor-Segmentation/tree/e4f258feb64c11815570e295c58bda78afd21ab9 |
GramMatrix | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Holmes-Alan/Photo2Sketch | GramMatrix | false | 536 | [
"MIT"
] | 0 | 43a0ca6bb8a8e645b35a2ab23d11ed5efe117e09 | https://github.com/Holmes-Alan/Photo2Sketch/tree/43a0ca6bb8a8e645b35a2ab23d11ed5efe117e09 |
ResnetBlock | import torch
from torch import nn
import torch.nn.parallel
import torch.utils.data
import torch.utils
def actvn(x):
out = nn.functional.leaky_relu(x, 0.2)
return out
class ResnetBlock(nn.Module):
def __init__(self, fin, fout, fhidden=None, is_bias=True):
super(ResnetBlock, 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
import torch.nn.parallel
import torch.utils.data
import tor... | IdanAzuri/MixMatch-pytorch | ResnetBlock | false | 596 | [
"MIT"
] | 0 | b8de2bc30c09e1256b92e0394403487fc4f90135 | https://github.com/IdanAzuri/MixMatch-pytorch/tree/b8de2bc30c09e1256b92e0394403487fc4f90135 |
CharbonnierLoss | # 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 functools
import torc... | BCV-Uniandes/RSR | CharbonnierLoss | false | 8,083 | [
"zlib-acknowledgement"
] | 14 | dad60eedd3560f2655e3d1ed444153ed2616af2e | https://github.com/BCV-Uniandes/RSR/tree/dad60eedd3560f2655e3d1ed444153ed2616af2e |
net_nvidia_featshift_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 import triton_helpers
from torch._inductor.runtime.... | YuShen0118/SAAP_Auto-driving_Platform | net_nvidia_featshift_pytorch | false | 18,209 | [
"MIT"
] | 4 | 785f899fb3b3ad92075318f9fcb69b8e09597202 | https://github.com/YuShen0118/SAAP_Auto-driving_Platform/tree/785f899fb3b3ad92075318f9fcb69b8e09597202 |
HadamardProduct | import torch
import torch.nn as nn
import torch.distributed
import torch.optim.lr_scheduler
import torch.utils.data
class HadamardProduct(nn.Module):
def __init__(self, idim_1, idim_2, hdim):
super(HadamardProduct, self).__init__()
self.fc_1 = nn.Linear(idim_1, hdim)
self.fc_2 = nn.Linear... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | CFM-MSG/SDN | HadamardProduct | false | 193 | [
"MIT"
] | 0 | f309602dc2bb73117355003f3744f8e5450dbccc | https://github.com/CFM-MSG/SDN/tree/f309602dc2bb73117355003f3744f8e5450dbccc |
Bottleneck | import torch
from torch import nn
from collections import OrderedDict
class Bottleneck(nn.Module):
def __init__(self, in_channels, out_channels):
super(Bottleneck, self).__init__()
m = OrderedDict()
m['conv1'] = nn.Conv2d(in_channels, out_channels, kernel_size=1,
bias=False)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
from col... | nivedk/SPANet | Bottleneck | false | 10,621 | [
"BSD-3-Clause"
] | 0 | 1bd84ae67732f9885af65dcbd286075008d46e91 | https://github.com/nivedk/SPANet/tree/1bd84ae67732f9885af65dcbd286075008d46e91 |
LabelBilinear | import torch
from torch import nn
import torch.utils.data
class LabelBilinear(nn.Module):
"""helper module for Biaffine Dependency Parser predicting label
"""
def __init__(self, in1_features, in2_features, num_label, bias=True):
super(LabelBilinear, self).__init__()
self.bilinear = nn.Bil... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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
assert_size_stride = torch._C._dyna... | LindaCY/fastNLP | LabelBilinear | false | 17,618 | [
"Apache-2.0"
] | 4 | 3fa95b6cfc31211453bc21792e3eef87948858da | https://github.com/LindaCY/fastNLP/tree/3fa95b6cfc31211453bc21792e3eef87948858da |
MulMCFC | # 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.... | hoedt/stable-nalu | MulMCFC | false | 3,635 | [
"MIT"
] | 0 | 64b3d240db8bff4da857d955f213ef3c7e38e035 | https://github.com/hoedt/stable-nalu/tree/64b3d240db8bff4da857d955f213ef3c7e38e035 |
GRUCell | import torch
import numpy as np
import torch.nn.functional as F
from torch import nn
class GRUCell(nn.Module):
def __init__(self, input_size, hidden_size, bias=True):
super(GRUCell, self).__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.bias = 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 numpy as np
... | deutschmn/PM2.5-GNN | GRUCell | false | 3,413 | [
"MIT"
] | 0 | 82e3fe2f25465451cbbdd6350c91a0242ecaa1c1 | https://github.com/deutschmn/PM2.5-GNN/tree/82e3fe2f25465451cbbdd6350c91a0242ecaa1c1 |
ResidualBlockNoBN | # 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... | LasseWolter/laughter-detection | ResidualBlockNoBN | false | 9,266 | [
"MIT"
] | 0 | f0a37f8e991fc57e8bbc846695fc4dea84d60af5 | https://github.com/LasseWolter/laughter-detection/tree/f0a37f8e991fc57e8bbc846695fc4dea84d60af5 |
RobertaClassificationHead | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class RobertaClassificationHead(nn.Module):
"""Head for sentence-level classification tasks."""
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size * 2, config.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.triton_helpers import libdevice
import torch.nn as ... | Masum06/CodeXGLUE | RobertaClassificationHead | false | 4,731 | [
"CC0-1.0",
"MIT"
] | 0 | bf1ab8c8878f978bd4ef3cb5e030e52f03e92854 | https://github.com/Masum06/CodeXGLUE/tree/bf1ab8c8878f978bd4ef3cb5e030e52f03e92854 |
FirstNet | # 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_... | PacktPublishing/Designing-Models-for-Responsible-AI | FirstNet | false | 932 | [
"MIT"
] | 0 | 36b60f1e3e9db8b3d2db3ace873dbdee1b076b74 | https://github.com/PacktPublishing/Designing-Models-for-Responsible-AI/tree/36b60f1e3e9db8b3d2db3ace873dbdee1b076b74 |
HyperConv2d | import torch
import torch.nn as nn
import torch.nn.functional as F
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Linear') != -1 or classname.find('Conv') != -1:
nn.init.constant_(m.weight, 0)
nn.init.normal_(m.bias, 0, 0.01)
class HyperConv2d(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
import torch.nn as nn
import torch.nn.functional as F
assert_size_stride = torch... | D-hash-code/ffjord-rnode-finalweek-mnist | HyperConv2d | false | 2,149 | [
"MIT"
] | 0 | 4cabcbadda79c68df53ec25f1f8fe03cfeee78f9 | https://github.com/D-hash-code/ffjord-rnode-finalweek-mnist/tree/4cabcbadda79c68df53ec25f1f8fe03cfeee78f9 |
MSE | # 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... | j1a0m0e4sNTU/MachineLearning2019 | MSE | false | 3,682 | [
"MIT"
] | 0 | 44a7a3387837e53134bcf5eb8fcf95daf4dff48d | https://github.com/j1a0m0e4sNTU/MachineLearning2019/tree/44a7a3387837e53134bcf5eb8fcf95daf4dff48d |
LinearBlock | import torch
from scipy.stats import truncnorm
def truncated_normal_(tensor, mean=0.0, std=1.0):
values = truncnorm.rvs(-2, 2, size=tensor.shape)
values = mean + std * values
tensor.copy_(torch.from_numpy(values))
return tensor
def fc_init_(module):
if hasattr(module, 'weight') and module.weight... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Brikwerk/learn2learn | LinearBlock | false | 13,722 | [
"MIT"
] | 1,774 | 7997c13c26ec627d13ce77ba98427260df78ada8 | https://github.com/Brikwerk/learn2learn/tree/7997c13c26ec627d13ce77ba98427260df78ada8 |
FrobeniusNormLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | grofit/traiNNer | FrobeniusNormLoss | false | 15,470 | [
"Apache-2.0"
] | 78 | 12d006fd44ed304e4178839c53b1f3d95ca25dcb | https://github.com/grofit/traiNNer/tree/12d006fd44ed304e4178839c53b1f3d95ca25dcb |
LocalNet | import torch
import torch.nn as nn
class LocalNet(nn.Module):
def forward(self, x_in):
"""Double convolutional block
:param x_in: image features
:returns: image features
:rtype: Tensor
"""
x = self.lrelu(self.conv1(self.refpad(x_in)))
x = self.lrelu(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 math as tl_math
import torch.... | ZombaSY/DeepLPF | LocalNet | false | 1,345 | [
"BSD-3-Clause"
] | 0 | adce64ae01bc9e32f465a354cb1f6534f0d13597 | https://github.com/ZombaSY/DeepLPF/tree/adce64ae01bc9e32f465a354cb1f6534f0d13597 |
MetaAconC | # 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... | HarryPham0123/FPT_data_centric_competition | MetaAconC | false | 5,290 | [
"Apache-2.0"
] | 1 | 3fa1e0ac48fdae2649b639229d9a74f75e461878 | https://github.com/HarryPham0123/FPT_data_centric_competition/tree/3fa1e0ac48fdae2649b639229d9a74f75e461878 |
CustomReLU | # 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... | charlesmackin/tiny | CustomReLU | false | 1,655 | [
"Apache-2.0"
] | 0 | bf8afc5cfc15e12efdd3bca0d559adfdfc435981 | https://github.com/charlesmackin/tiny/tree/bf8afc5cfc15e12efdd3bca0d559adfdfc435981 |
statm_loss | import torch
import torch.nn as nn
class statm_loss(nn.Module):
def __init__(self, eps=2):
super(statm_loss, self).__init__()
self.eps = eps
def forward(self, x, y):
x = x.view(x.size(0), x.size(1), -1)
y = y.view(y.size(0), y.size(1), -1)
x_mean = x.mean(dim=2)
... | 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... | COMP6248-Reproducability-Challenge/KD_SRRL | statm_loss | false | 7,817 | [
"MIT"
] | 27 | 958c8f9fbeb7893f9bd866aff5b065b2bde87f23 | https://github.com/COMP6248-Reproducability-Challenge/KD_SRRL/tree/958c8f9fbeb7893f9bd866aff5b065b2bde87f23 |
PositionwiseFeedForward | import torch
import torch.nn as nn
class LayerNorm(nn.Module):
"""
Layer Normalization class
"""
def __init__(self, features, eps=1e-06):
super(LayerNorm, self).__init__()
self.a_2 = nn.Parameter(torch.ones(features))
self.b_2 = nn.Parameter(torch.zeros(features))
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Fengyee/ASER | PositionwiseFeedForward | false | 11,433 | [
"MIT"
] | 0 | c284b507ee268a8275456a969b944895cacc54b8 | https://github.com/Fengyee/ASER/tree/c284b507ee268a8275456a969b944895cacc54b8 |
QuantizableHSwish | import torch
import torch.nn as nn
import torch.quantization
class QuantizableHSigmoid(nn.Module):
"""Hard Sigmoid for quantization."""
def __init__(self, inplace: 'bool'=True) ->None:
"""Initialize."""
super(QuantizableHSigmoid, self).__init__()
self.relu6 = nn.ReLU6(inplace=inplace)... | 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.quantization
assert_size_stride = torch._C._dynamo.gua... | HwangJohn/model_compression | QuantizableHSwish | false | 13,834 | [
"MIT"
] | 216 | 1df40c8a531313cc9e79255f4477f39d66d9b849 | https://github.com/HwangJohn/model_compression/tree/1df40c8a531313cc9e79255f4477f39d66d9b849 |
CharbonnierLoss | # 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 functools
import torc... | Juggernaut93/mmediting | CharbonnierLoss | false | 13,898 | [
"Apache-2.0"
] | 1,884 | 8ef46ace29756dd2df1d92f2f73a33646e33e007 | https://github.com/Juggernaut93/mmediting/tree/8ef46ace29756dd2df1d92f2f73a33646e33e007 |
NeuralNetPartialNoGradModel | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn
import torch.... | thilow/onnxruntime | NeuralNetPartialNoGradModel | false | 11,022 | [
"MIT"
] | 0 | 1a3ddf0714e1bdf9b807a342eee5f6e160ad1ec9 | https://github.com/thilow/onnxruntime/tree/1a3ddf0714e1bdf9b807a342eee5f6e160ad1ec9 |
HyperpriorSynthesisDLMM | # 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 ... | ali-zafari/high-fidelity-generative-compression | HyperpriorSynthesisDLMM | false | 9,784 | [
"Apache-2.0"
] | 0 | 37ab8d6727df48f8ebf4577db0986ccd0ffe404b | https://github.com/ali-zafari/high-fidelity-generative-compression/tree/37ab8d6727df48f8ebf4577db0986ccd0ffe404b |
SubSample | import torch
import torch.nn as nn
class SubSample(nn.Module):
def __init__(self, in_channels, out_channels, types='Pool', stride=[2,
1], sub_norm='nn.LayerNorm', act=None):
super().__init__()
self.types = types
if types == 'Pool':
self.avgpool = nn.AvgPool2d(kernel_s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | verages/PaddleOCR2Pytorch | SubSample | false | 4,670 | [
"Apache-2.0"
] | 0 | 201f0d5d6007f49620c49af7d222c3b220eb3e70 | https://github.com/verages/PaddleOCR2Pytorch/tree/201f0d5d6007f49620c49af7d222c3b220eb3e70 |
TransformerLayer | import torch
import torch.nn as nn
import torch.utils.data
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.Mu... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Arui66/FPSAutomaticAiming | TransformerLayer | false | 13,320 | [
"Apache-2.0"
] | 129 | 87674385d42b065b984b38a2ff59e7f2d4f07dc9 | https://github.com/Arui66/FPSAutomaticAiming/tree/87674385d42b065b984b38a2ff59e7f2d4f07dc9 |
BayesConv2d | from torch.nn import Module
import math
import torch
from torch.nn import Parameter
import torch.nn.functional as F
from torch.nn.modules.utils import _pair
class _BayesConvNd(Module):
"""
Applies Bayesian Convolution
Arguments:
prior_mu (Float): mean of prior normal distribution.
prior_s... | 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.triton_helpers import math... | Harry24k/bayesian-neural-network-pytorch | BayesConv2d | false | 13,769 | [
"MIT"
] | 178 | d2272f09e0d08c1abe1f53ce6df56b31494d7020 | https://github.com/Harry24k/bayesian-neural-network-pytorch/tree/d2272f09e0d08c1abe1f53ce6df56b31494d7020 |
SharedAgent | # 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_... | mpgussert/fundamentalRL | SharedAgent | false | 7,280 | [
"MIT"
] | 1 | 4f45436226e0823c21cac316dec8bbf1df697467 | https://github.com/mpgussert/fundamentalRL/tree/4f45436226e0823c21cac316dec8bbf1df697467 |
Bicubic | from torch.nn import Module
import torch
import torch.nn.functional as F
class Bicubic(Module):
def __init__(self, scale_factor):
super().__init__()
self.scale_factor = scale_factor
def forward(self, x):
return F.interpolate(x, scale_factor=self.scale_factor, mode='bicubic')
def ge... | 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
... | ShivanshuPurohit/Diffusion | Bicubic | false | 1,057 | [
"MIT"
] | 0 | 9a190d9aa4ed9767cf223e4ef57d0c31690f92cc | https://github.com/ShivanshuPurohit/Diffusion/tree/9a190d9aa4ed9767cf223e4ef57d0c31690f92cc |
StyleLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | andreweskeclarke/style-transfer | StyleLoss | false | 1,451 | [
"MIT"
] | 0 | e4b18f4cdb3f473bf946f12cc39447b2f6bb15ca | https://github.com/andreweskeclarke/style-transfer/tree/e4b18f4cdb3f473bf946f12cc39447b2f6bb15ca |
ChannelAttentionModule | # 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 | ChannelAttentionModule | false | 17,596 | [
"Apache-2.0"
] | 6 | 348ec66233a7c0f95a3cb5f0f11641e2a7a9b9c3 | https://github.com/LeftAttention/Attention-Codebase/tree/348ec66233a7c0f95a3cb5f0f11641e2a7a9b9c3 |
MutiLevelEnhance | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.distributed
import torch.optim.lr_scheduler
import torch.utils.data
class MutiLevelEnhance(nn.Module):
def __init__(self, idim, odim, nhead=1, use_bias=True):
super(MutiLevelEnhance, self).__init__()
self.idim = idim
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | CFM-MSG/SDN | MutiLevelEnhance | false | 206 | [
"MIT"
] | 0 | f309602dc2bb73117355003f3744f8e5450dbccc | https://github.com/CFM-MSG/SDN/tree/f309602dc2bb73117355003f3744f8e5450dbccc |
Critic | import torch
import torch.nn as nn
import torch.nn.functional as F
class Critic(nn.Module):
def __init__(self, state_dim):
super(Critic, self).__init__()
self.fc1 = nn.Linear(state_dim, 256)
self.fc2 = nn.Linear(256, 256)
self.fc3 = nn.Linear(256, 1)
def forward(self, x):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | fengzhengyong-github/Deep-reinforcement-learning-with-pytorch | Critic | false | 6,685 | [
"MIT"
] | 1 | 3c56b601d14b0b0c8cde4b6bc6df5c1e8f298c7b | https://github.com/fengzhengyong-github/Deep-reinforcement-learning-with-pytorch/tree/3c56b601d14b0b0c8cde4b6bc6df5c1e8f298c7b |
SpatialSELayer3D | import torch
import torch.nn as nn
import torch.nn.functional as F
class SpatialSELayer3D(nn.Module):
"""
3D Re-implementation of SE block -- squeezing spatially and exciting channel-wise described in:
*Roy et al., Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks, 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | HiLab-git/PyMIC | SpatialSELayer3D | false | 13,781 | [
"Apache-2.0"
] | 147 | abf5c43de43668b85f4c049c95a8f1b7cf1d9f16 | https://github.com/HiLab-git/PyMIC/tree/abf5c43de43668b85f4c049c95a8f1b7cf1d9f16 |
softmax_with_multiuse_input | import torch
import torch.nn as nn
import torch.cuda
import torch.backends.cudnn
import torch.backends.mkl
class softmax_with_multiuse_input(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
x1 = nn.Softmax(dim=-1)(x)
x2 = x + x1
return x1, x2
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._inductor.runtime.triton_helpers import math as tl_math
import torch.cuda
impo... | JudeDavis1/intel-extension-for-pytorch | softmax_with_multiuse_input | false | 2,593 | [
"Apache-2.0"
] | 0 | 364e34cb4917a709f5108c07d4005bf82f3d5067 | https://github.com/JudeDavis1/intel-extension-for-pytorch/tree/364e34cb4917a709f5108c07d4005bf82f3d5067 |
LinearFeedforward | import torch
from torch import nn
from torch.nn import functional as F
import torch.utils.data
class Linear(nn.Linear):
def forward(self, x):
size = x.size()
return super().forward(x.contiguous().view(-1, size[-1])).view(*
size[:-1], -1)
class Feedforward(nn.Module):
def __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._inductor.runtime import triton_helpers
from torch import nn
from tor... | cristipp/decaNLP | LinearFeedforward | false | 12,230 | [
"BSD-3-Clause"
] | 0 | db64df36bf2b1b2ca6946aacf0ee7463ac80c4cb | https://github.com/cristipp/decaNLP/tree/db64df36bf2b1b2ca6946aacf0ee7463ac80c4cb |
SpatialCrossMapLRN | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.... | shubham1206agra/pretrained-models.pytorch | SpatialCrossMapLRN | false | 12,979 | [
"BSD-3-Clause"
] | 0 | a2940f79dd65656eabe5a0cd6d5d014ef1fc2523 | https://github.com/shubham1206agra/pretrained-models.pytorch/tree/a2940f79dd65656eabe5a0cd6d5d014ef1fc2523 |
ProtectedMultiheadAttention | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.utils.data
from torch.nn import Parameter
import torch.onnx.operators
import torch.optim
import torch.optim.lr_scheduler
class ProtectedMultiheadAttention(nn.Module):
"""Multi-headed attention.
See "Attention Is All You Need" for... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | laiguokun/fairseq | ProtectedMultiheadAttention | false | 7,082 | [
"MIT"
] | 1 | 6c01c91aac81eb2e3173add4463dfa45c404ffa5 | https://github.com/laiguokun/fairseq/tree/6c01c91aac81eb2e3173add4463dfa45c404ffa5 |
LocationLayer | import torch
import torch.utils.data
import torch
import torch.nn as nn
class Linear(nn.Module):
def __init__(self, in_features, out_features, bias=True, init_gain='linear'
):
super(Linear, self).__init__()
self.linear_layer = nn.Linear(in_features, out_features, bias=bias)
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
import torch.utils.data
import torch
import torch.nn as nn
assert_size_stride = ... | aidiary/tacotron-pytorch | LocationLayer | false | 3,109 | [
"MIT"
] | 0 | 8ea9b1bb61bf753a64ff611b441326ea8c001d20 | https://github.com/aidiary/tacotron-pytorch/tree/8ea9b1bb61bf753a64ff611b441326ea8c001d20 |
TdnnAffine | import torch
import torch.nn.functional as F
import torch.nn
def to_device(device_object, tensor):
"""
Select device for non-parameters tensor w.r.t model or tensor which has been specified a device.
"""
if isinstance(device_object, torch.nn.Module):
next(device_object.parameters()).device
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
... | fancyliumeng/asv-subtools | TdnnAffine | false | 6,683 | [
"Apache-2.0"
] | 1 | 56a13484472e7ae6eb00d762c00d57e581e78eb4 | https://github.com/fancyliumeng/asv-subtools/tree/56a13484472e7ae6eb00d762c00d57e581e78eb4 |
TorchGloVeLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guard... | tayfuntuna/cs224u | TorchGloVeLoss | false | 4,407 | [
"Apache-2.0"
] | 0 | 4368090c679d869f21ed2393b9ca0ef217b5c404 | https://github.com/tayfuntuna/cs224u/tree/4368090c679d869f21ed2393b9ca0ef217b5c404 |
ResBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | NJUVISION/AWnet | ResBlock | false | 8,575 | [
"MIT"
] | 16 | f47a1692819a778b513b882d36ed727f7732d37b | https://github.com/NJUVISION/AWnet/tree/f47a1692819a778b513b882d36ed727f7732d37b |
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